Authors,Author(s) ID,Title,Year,Source title,Volume,Issue,Art. No.,Page start,Page end,Page count,Cited by,DOI,Link,Affiliations,Authors with affiliations,Abstract,Author Keywords,Index Keywords,Molecular Sequence Numbers,Chemicals/CAS,Tradenames,Manufacturers,Funding Details,Funding Text 1,Funding Text 2,Funding Text 3,Funding Text 4,Funding Text 5,Funding Text 6,Funding Text 7,Funding Text 8,Funding Text 9,Funding Text 10,References,Correspondence Address,Editors,Sponsors,Publisher,Conference name,Conference date,Conference location,Conference code,ISSN,ISBN,CODEN,PubMed ID,Language of Original Document,Abbreviated Source Title,Document Type,Publication Stage,Open Access,Source,EID
"Chen L., Wu M., Pedrycz W., Hirota K.","55774663200;35231682600;57210775415;7402829702;","Introduction",2021,"Studies in Computational Intelligence","926",,,"1","13",,,"10.1007/978-3-030-61577-2_1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096223656&doi=10.1007%2f978-3-030-61577-2_1&partnerID=40&md5=ee2f8f642ff77f22f8aca1ae703b9ca2","School of Automation, China University of Geosciences, Wuhan, China; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Tokyo Institute of Technology, Yokohama, Japan","Chen, L., School of Automation, China University of Geosciences, Wuhan, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, China; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Hirota, K., Tokyo Institute of Technology, Yokohama, Japan","Human-robot interaction technology has gradually shifted from computer-centered to human-centered, as natural human-robot interaction has become a new direction of human-robot interaction technology development. © 2020, The Author(s), under exclusive license to Springer Nature Switzerland AG.",,,,,,,,,,,,,,,,,,"Schuller, D., Schuller, B.W., The age of artificial emotional intelligence (2018) Computer, 51 (9), pp. 38-46; Li, F., Feng, J., Fu, M., A natural human-robot interaction method in virtual roaming (2019) Proceedings of the 2019 15Th International Conference on Computational Intelligence and Security (CIS); Vinciarelli, A., Pantic, M., Boulard, H., Social signal processing: Survey of an emerging domain (2009) Image Vis. 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Eng, 15 (2), pp. 51-61; Saunderson, S., Nejat, G., It would make me happy if you used my guess: Comparing robot persuasive strategies in social human-robot interaction (2019) IEEE Robot. Autom. Lett., 4 (2), pp. 1707-1714; Klug, M., Zell, A., Emotion-based human-robot interaction (2013) In Proceedings of IEEE International Conference on Computational Cybernetics, pp. 365-368; Boccanfuso, L., Barney, E., Foster, C., Emotional robot to examine differences in play patterns and affective response of children with and without ASD (2016) Proceedings of ACM/IEEE International Conference on Human-Robot Interaction, pp. 19-26; Ficocelli, M., Terao, J., Nejat, G., Promoting interactions between humans and robots using robotic emotional behavior (2016) IEEE Trans. Cybern., 46 (12), pp. 2911-2923; Ding, Y., Hu, X., Xia, Z.Y., Liu, Y.J., Zhang, D., Inter-brain EEG feature extraction and analysis for continuous implicit emotion tagging during video watching IEEE Trans. Affect. Comput., , https://doi.org/10.1109/TAFFC.2018.2849758; Peng, J.C., Lina, Z., Research of wave filter method of human sphygmus signal (2016) Laser Technol, 40 (1), pp. 42-46; Chen, C.L.P., Liu, Z., Broad learning system: An effective and efficient incremental learning system without the need for deep architecture (2018) IEEE Trans. Neural Netw. Learn. Syst., 29 (1), pp. 10-24; Liu, Z.T., Dong, F.Y., Hirota, K., Wu, M., Li, D.Y., Yamazaki, Y., Emotional states based 3-D fuzzy atmosfield for casual communication between humans and robots (2011) In Proceedings of International Conference on Fuzzy Systems, Taipei, pp. 777-782; Jiang, S.Y., Lin, C.Y., Huang, K.T., Shared control design of a walking-assistant robot (2017) IEEE Trans. Control Syst. Technol., 25 (6), pp. 2143-2150; Lu, X., Bao, W., Wang, S., Three-dimensional interfacial stress decoupling method for rehabilitation therapy robot (2017) IEEE Trans. Ind. Electron., 64 (5), pp. 3970-3977; Mehrabian, A., Communication without words (1968) Psychol. Today, 2 (4), pp. 53-56; Schuller, B., Batliner, A., Steidl, S., Seppi, D., Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge (2011) Speech Commun, 53 (9), pp. 1062-1087; Chen, L., Zheng, S.K., Speech emotion recognition: Features and classification models (2012) Digit. Signal Process., 22 (6), pp. 1154-1160; Chen, L.F., Zhou, M.T., Su, W.J., Wu, M., She, J.H., Hirota, K., Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction (2018) Inf. Sci., 428, pp. 49-61; Chen, L.F., Feng, Y., Maram, M.A., Wang, Y.W., Wu, M., Hirota, K., Pedrycz, W., Multi-SVM based Dempster-Shafer theory for gesture intention understanding using sparse coding feature Appl. Soft Comput., , https://doi.org/10.1016/j.asoc.2019.105787; Chen, L.F., Su, W.J., Feng, Y., Wu, M., She, J.H., Hirota, K., Two-layer fuzzy multiple random forest for speech emotion recognition in human-robot interaction (2020) Inf. Sci., 509, pp. 150-163; Kim, Y., Provost, E.M., ISLA: Temporal segmentation and labeling for audio-visual emotion recognition IEEE Trans. Affect. Comput, , https://doi.org/10.1109/TAFFC.2017.2702653; Barros, P., Jirak, D., Weber, C., Multimodal emotional state recognition using sequence-dependent deep hierarchical features (2015) Neural Netw, 72, pp. 140-151; Soleymani, M., Asghariesfeden, S., Fu, Y., Analysis of EEG signals and facial expressions for continuous emotion detection (2016) IEEE Trans. Affect. Comput., 7 (1), pp. 17-28; Xie, Y., Liang, R., Liang, Z.L., Huang, C.W., Zou, C.R., (2019) Speech Emotion Classification Using Attention-Based LSTM, 27 (11), pp. 1675-1685; Wu, C.H., Wei, W.L., Lin, J.C., Lee, W.Y., Speaking effect removal on emotion recognition from facial expressions based on eigenface conversion (2013) IEEE Trans. Multimed., 15 (8), pp. 1732-1744; Wu, C.H., Liang, W.B., Emotion recognition of affective speech based on multiple classifiers using acoustic-prosodic information and semantic labels (2011) IEEE Trans. Affect. Comput., 2 (1), pp. 10-21; Hariharan, A., Adam, M.T.P., Blended emotion detection for decision support (2015) IEEE Trans. Hum.-Mach. Syst., 45 (4), pp. 510-517; Liu, Z.T., Wu, M., Cao, W.H., Chen, L.F., Xu, J.P., Zhang, R., Zhou, M.T., Mao, J.W., A facial expression emotion recognition based human-robot interaction system (2017) IEEE/CAA J. Autom. Sin, 4 (4), pp. 668-676; Li, Y., Zeng, J.B., Shan, S.G., Chen, X.L., Occlusion aware facial expression recognition using CNN with attention mechanism (2019) IEEE Trans. Image Process., 28 (5), pp. 2439-2450; Zhang, T., Zheng, W.M., Cui, Z., Zong, Y., Li, Y., Spatial-Temporal Recurrent Neural Network for Emotion Recognition (2019) IEEE Trans. Cybern, 49 (3), pp. 839-847; Bugnon, L.A., Calvo, R.A., Milone, D.H., Dimensional affect recognition from HRV: An approach based on supervised SOM and ELM (2020) IEEE Trans. Affect. Comput., 11 (1), pp. 32-44; Wu, M., Liu, Z.T., Chen, L.F., (2018) Affective Computing and Emotional Robot System, , Beijing Science Press, Peking; Wegner, D.M., (2002) The Illusion of Conscious Will, , MIT Press, Cambridge; Chen, L.F., Liu, Z.T., Wu, M., Ding, M., Dong, F.Y., Hirota, K., Emotion-age-gender-nationality based intention understanding in human-robot interaction using two-layer fuzzy support vector regression (2015) Int. J. Soc. Robot., 7 (5), pp. 709-729; Chen, L.F., Wu, M., Zhou, M.T., Dynamic emotion understanding in human-robot interaction based on two-layer fuzzy SVR-TS model (2017) IEEE Trans. Syst. Man Cybern. Syst. PP(99), pp. 1-12; Chen, L.F., Zhou, M.T., Wu, M., Three-layer weighted fuzzy support vector regression for emotional intention understanding in human-robot interaction (2018) IEEE Trans. Fuzzy Syst., (99), p. 1; Turner, J., Meng, Q., Schaefer, G., Distributed task rescheduling with time constraints for the optimization of total task allocations in a multi-robot system (2018) IEEE Trans. Cybern., 48 (9), pp. 2583-2597; Lui, J.H., Samani, H., Tien, K.Y., An affective mood booster robot based on emotional processing unit (2018) Proceedings of 2017 International Automatic Control Conference, pp. 1-6. , Pingtung, Taiwan, China; Klug, M., Zell, A., Emotion-based human-robot interaction (2013) Proceedings of the 9Th IEEE International Conference on Computational Cybernetics, pp. 365-368. , Tihany, Hungary; Boccanfuso, L., Barney, E., Foster, C., Emotional robot to examine differences in play patterns and affective response of children with and without ASD (2016) Proceedings of the 11Th ACM/IEEE International Conference on Human-Robot Interaction, pp. 19-26. , Christchurch, New Zealand","Chen, L.; School of Automation, China; email: chenluefeng@cug.edu.cn",,,"Springer Science and Business Media Deutschland GmbH",,,,,1860949X,,,,"English","Stud. Comput. Intell.",Book Chapter,"Final","",Scopus,2-s2.0-85096223656
"Sun Q., Guo S., Zhang L.","57215562805;55376199600;56567941600;","Kinematic dexterity analysis of human-robot interaction of an upper limb rehabilitation robot",2021,"Technology and Health Care","29","5",,"1029","1045",,,"10.3233/THC-202633c","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115631470&doi=10.3233%2fTHC-202633c&partnerID=40&md5=feace4058129acb02127dd9a4de448d8","School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China","Sun, Q., School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China; Guo, S., School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China; Zhang, L., School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China","BACKGROUND: The definition of rehabilitation training trajectory is of great significance during rehabilitation training, and the dexterity of human-robot interaction motion provides a basis for selecting the trajectory of interaction motion. OBJECTIVE: Aimed at the kinematic dexterity of human-robot interaction, a velocity manipulability ellipsoid intersection volume (VMEIV) index is proposed for analysis, and the dexterity distribution cloud map is obtained with the human-robot cooperation space. METHOD: Firstly, the motion constraint equation of human-robot interaction is established, and the Jacobian matrix is obtained based on the speed of connecting rod. Then, the Monte Carlo method and the cell body segmentation method are used to obtain the collaborative space of human-robot interaction, and the VMEIV of human-robot interaction is solved in the cooperation space. Finally, taking the upper limb rehabilitation robot as the research object, the dexterity analysis of human-robot interaction is carried out by using the index of the approximate volume of the VMEIV. RESULTS: The results of the simulation and experiment have a certain consistency, which indicates that the VMEIV index is effective as an index of human-robot interaction kinematic dexterity. CONCLUSIONS: The VMEIV index can measure the kinematic dexterity of human-robot interaction, and provide a reference for the training trajectory selection of rehabilitation robot. © 2021 -IOS Press.","cooperation space; dexterity; human-robot interaction; Rehabilitation robot; velocity manipulability ellipsoid intersection volume",,,,,,"National Natural Science Foundation of China, NSFC: 61973205; National Key Research and Development Program of China, NKRDPC: 2018YFC2001600","This work was supported by the National Key Research and Development Program of China (grant no. 2018YFC2001600) and the National Natural Science Foundation of China (grant no. 61973205).",,,,,,,,,,"Johnson, W., Onuma, O., Owolabi, M., Stroke: A global response is needed (2016) Bulletin of the World Health Organization, 94 (9), pp. 634-634A; French, B., Thomas, L.H., Leathley, M.J., Repetitive task training for improving functional ability after stroke (2007) Cochrane Database Syst Rev, 17 (4), p. 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Winter. University of Waterloo Press; Fenistein, D., Lenseigne, B., Christophe, T., A fast, fully automated cell segmentation algorithm for high-Throughput and high-content screening (2010) Cytometry Part A, 73 A (10), pp. 958-964; Corke, P., (2017) Robotics, Vision and Control: Fundamental Algorithms in Matlabr Second, Completely Revised., , Springer; Kuhlemann, I., Schweikard, A., Jauer, P., Robust inverse kinematics by configuration control for redundant manipulators with seven dof (2016) International Conference on Control.; Kang, T., He, J., Tillery, S.I.H., Determining natural arm configuration along a reaching trajectory (2005) Experimental Brain Research, 167 (3), pp. 352-361; Guo, S., Xiong, X., Zhang, L., A novel algorithm of kinematics parameters measurement for upper limb base on motion capture (2020) International Journal of Mechatronics and Applied Mechanics, 7, pp. 144-151; Kim, J.H., Kim, D., Computationally efficient cooperation dynamic range-only slam based on sum of Gaussian filter (2020) Sensors, 20 (11), p. 3306","Sun, Q.; School of Mechatronic Engineering and Automation, China; email: qingsun93@163.com",,,"IOS Press BV",,,,,09287329,,THCAE,,"English","Technol. Health Care",Article,"Final","",Scopus,2-s2.0-85115631470
"Chen L., Liu Z., Wu M., Hirota K., Pedrycz W.","55774663200;56101506800;57218664297;7402829702;57210775415;","Multimodal Emotion Recognition and Intention Understanding in Human-Robot Interaction",2021,"Studies in Systems, Decision and Control","329",,,"255","288",,,"10.1007/978-3-030-62147-6_10","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103558783&doi=10.1007%2f978-3-030-62147-6_10&partnerID=40&md5=388a213cc8557172e5558f82ffac7747","School of Automation, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Tokyo Institute of Technology, Yokohama, 226-8502, Japan; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB  TR6 2G7, Canada","Chen, L., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Liu, Z., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Hirota, K., Tokyo Institute of Technology, Yokohama, 226-8502, Japan; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB  TR6 2G7, Canada","Emotion recognition and intention understanding are important components of human-robot interaction. In multimodal emotion recognition and intent understanding, feature extraction and selection of recognition methods are related to the calculation of affective computing and the diversity of human-robot interaction. Therefore, by studying multimodal emotion recognition and intention understanding we to create an emotional and human-friendly human-robot interaction environment. This chapter introduces the characteristics of multimodal emotion recognition and intention understanding, presents different modalities emotion feature extraction methods and emotion recognition methods, proposes the intention understanding method, and finally applies them in practice to achieve human-robot interaction. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Human-robot interaction; Intention understanding; Multimodal emotion recognition",,,,,,,,,,,,,,,,,"Mehrabian, A., Communication without words (1968) Psychol. 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Fuzzy Syst., 22 (5), pp. 1074-1087; Nguyen, D.D., Ngo, L.T., Multiple kernel interval type-2 fuzzy c-means clustering (2013) IEEE International Conference on Fuzzy Systems, Hyderabad, India, pp. 1-8. , , pp; Dong, W.M., Wong, F.S., Fuzzy weighted averages and implementation of the extension principle (1987) Fuzzy Sets Syst, 21 (2), pp. 183-199","Chen, L.; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex SystemsChina; email: chenluefeng@cug.edu.cn",,,"Springer Science and Business Media Deutschland GmbH",,,,,21984182,,,,"English","Stud. Syst. Decis. Control",Book Chapter,"Final","",Scopus,2-s2.0-85103558783
"Chen L., Wu M., Pedrycz W., Hirota K.","55774663200;35231682600;57210775415;7402829702;","Experiments and Applications of Emotional Human-Robot Interaction Systems",2021,"Studies in Computational Intelligence","926",,,"223","244",,,"10.1007/978-3-030-61577-2_13","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096230408&doi=10.1007%2f978-3-030-61577-2_13&partnerID=40&md5=3ae960e15df2b3f7332ab950c6e28c95","School of Automation, China University of Geosciences, Wuhan, China; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Tokyo Institute of Technology, Yokohama, Japan","Chen, L., School of Automation, China University of Geosciences, Wuhan, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, China; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Hirota, K., Tokyo Institute of Technology, Yokohama, Japan","The ability of robots to correctly recognize emotional states, understand emotional intentions, and give emotional feedback is an important aspect and challenge in the study of emotional robot systems. In order to realize a human-robot interaction system with certain emotion recognition and intention understanding, and to establish a natural and harmonious human-robot interaction process, this section proposes a human-robot interaction system scheme based on affective computing. © 2020, The Author(s), under exclusive license to Springer Nature Switzerland AG.",,,,,,,,,,,,,,,,,,"Yamazaki, Y., Dong, F., Masuda, Y., Uehara, Y., Kormushev, P., Intent expression using eye robot for mascot robot system (2009) Comput. Sci, pp. 576-580; Hu, J., Wellman, M.P., Nash q-learning for general-sum stochastic games (2003) J. Mach. Learn. Res., 4 (4), pp. 1039-1069; Erol, B.A., Majumdar, A., Benavidez, P., Rad, P., Choo, K.R., Jamshidi, M., Toward artificial emotional intelligence for cooperative social human-machine interaction (2020) IEEE Trans. Comput. Soc. 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Adv. Comput. Intell. Intell. Inform., 17 (2), pp. 291-301; (2018) Blue Frog Robotics, Buddy: The First Emotional Companion Robot, , http://www.Bluefrogrobotics.com/en/buddy_the-emotionnal-robot/; Robotics, H., (2016) Sophia, , http://www.hansonrobotics.com/robot/sophia/; (2018) Live Science, Meet Erica, Japan’s Next Robot News Anchor, , https://www.livescience.com/61575-erica-robot-replace-japanese-news-anchor.html; Lui, J.H., Samani, H., Tien, K.Y., An affective mood booster robot based on emotional processing unit (2018) Proceedings of 2017 International Automatic Control Conference, pp. 1-6; Somchanok, T., Michiko, O., Emotion recognition using ECG signals with local pattern description methods (2016) Int. J. Affect. Eng, 15 (2), pp. 51-61; Chen, L.F., Liu, Z.T., Wu, M., Dong, F.Y., Yamazaki, Y., Hirota, K., Multi-robot behavior adaptation to local and global communication atmosphere in humans-robots interaction (2014) J. Multimodal User Interfaces, 8 (3), pp. 289-303; Klug, M., Zell, A., Emotion-based human-robot interaction (2013) Proceedings of IEEE International Conference on Computational Cybernetics, pp. 365-368; Boccanfuso, L., Barney, E., Foster, C., Emotional robot to examine differences in play patterns and affective response of children with and without ASD (2016) Proceedings of ACM/IEEE International Conference on Human-Robot Interaction, pp. 19-26; Liu, Z.T., Wu, M., Cao, W.H., Chen, L.F., Xu, J.P., Zhang, R., A facial expression emotion recognition based humans-robots interaction system (2017) IEEE/CAA J. Autom. Sin, 4 (4), pp. 668-676; Chen, L.F., Wu, M., Zhou, M.T., Liu, Z.T., She, J.H., Hirota, K., Dynamic emotion understanding in human-robot interaction based on two-layer fuzzy SVR-TS model (2020) IEEE Trans. Syst. Man Cybern.: Syst., 50 (2), pp. 490-501; Liu, Z.T., Pan, F.F., Wu, M., Cao, W.H., Chen, L.F., A multimodal emotional communication based humans-robots interaction system (2016) Proceedings of the 35Th Chinese Control Conference, 4 (4), pp. 668-676. , Chengdu","Chen, L.; School of Automation, China; email: chenluefeng@cug.edu.cn",,,"Springer Science and Business Media Deutschland GmbH",,,,,1860949X,,,,"English","Stud. Comput. Intell.",Book Chapter,"Final","",Scopus,2-s2.0-85096230408
"Mruthyunjaya V., Jankowski C.","57211644266;57212968659;","Human-Augmented Robotic Intelligence (HARI) for Human-Robot Interaction",2020,"Advances in Intelligent Systems and Computing","1070",,,"204","223",,,"10.1007/978-3-030-32523-7_14","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075669850&doi=10.1007%2f978-3-030-32523-7_14&partnerID=40&md5=b77c676d9aeafc04347a0574baf46dac","Aisera, Palo Alto, United States; Cloudminds Technology, Santa Clara, United States","Mruthyunjaya, V., Aisera, Palo Alto, United States; Jankowski, C., Cloudminds Technology, Santa Clara, United States","This paper provides a system design framework for a human-robot interaction system. The design introduces a human-augmented robotic intelligence embedded in a human-robot interaction system. The motivations behind the system design are spoken dialogue systems, Wizard-of-OZ framework, and existing HRI designs for socially intelligent robots. In this work, we explain how artificial intelligence of human-robot interaction system is enhanced by human intelligence through collaboration. The collaborative artificial intelligence enables the system to learn from demonstration. The main objective and the gradual progression from this paper is to build an iterative interactive system that is capable of achieving human-robot interaction similar to the nuances of human-human interaction. © 2020, Springer Nature Switzerland AG.","Artificial intelligence; Automatic speech; Cloud robot; Human-augmented robotic intelligence; Human-in-the-loop; Human-robot interaction; Natural language processing; Robot control unit; Social robot; Spoken dialogue systems; Virtual Backbone Network","Artificial intelligence; Intelligent robots; Machine design; Man machine systems; Natural language processing systems; Robotics; Speech processing; Systems analysis; Virtual reality; Automatic speech; Human-in-the-loop; NAtural language processing; Robot controls; Robotic intelligence; Social robots; Spoken dialogue system; Virtual backbone network; Human robot interaction",,,,,,,,,,,,,,,,"Dupont, S., Luettin, J., Audio-visual speech modeling for continuous speech recognition (2000) IEEE Trans. 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Secur., 19 (5), pp. 653-659","Jankowski, C.; Cloudminds TechnologyUnited States; email: charles.jankowski@cloudminds.com","Arai K.Bhatia R.Kapoor S.",,"Springer","4th Future Technologies Conference, FTC 2019","24 October 2019 through 25 October 2019",,233129,21945357,9783030325220,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85075669850
"Oliveira R., Arriaga P., Paiva A.","57201501871;12793958200;7102683774;","Human-robot interaction in groups: Methodological and research practices",2021,"Multimodal Technologies and Interaction","5","10","59","","",,,"10.3390/mti5100059","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116608293&doi=10.3390%2fmti5100059&partnerID=40&md5=a9118395389f1337b790c23c5a7d2295","Centro de Intervenção e Investigação Social (CIS-Iscte), Iscte-Instituto Universitário de Lisboa (CIS-IUL), Lisbon, 1100, Portugal; INESC-ID (GAIPS), Lisbon, 1100, Portugal; Instituto Superior Técnico, University of Lisbon, Lisbon, 1100, Portugal","Oliveira, R., Centro de Intervenção e Investigação Social (CIS-Iscte), Iscte-Instituto Universitário de Lisboa (CIS-IUL), Lisbon, 1100, Portugal, INESC-ID (GAIPS), Lisbon, 1100, Portugal; Arriaga, P., Centro de Intervenção e Investigação Social (CIS-Iscte), Iscte-Instituto Universitário de Lisboa (CIS-IUL), Lisbon, 1100, Portugal; Paiva, A., INESC-ID (GAIPS), Lisbon, 1100, Portugal, Instituto Superior Técnico, University of Lisbon, Lisbon, 1100, Portugal","Understanding the behavioral dynamics that underline human–robot interactions in groups remains one of the core challenges in social robotics research. However, despite a growing interest in this topic, there is still a lack of established and validated measures that allow researchers to analyze human–robot interactions in group scenarios; and very few that have been developed and tested specifically for research conducted in-the-wild. This is a problem because it hinders the development of general models of human–robot interaction, and makes the comprehension of the inner workings of the relational dynamics between humans and robots, in group contexts, significantly more difficult. In this paper, we aim to provide a reflection on the current state of research on human–robot interaction in small groups, as well as to outline directions for future research with an emphasis on methodological and transversal issues. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).","Group interactions; Human–robot interaction; Research methodology; Social robotics",,,,,,"PD/BD/150570/2020","Funding: R.O. acknowledges a PhD grant awarded by Fundação para a Ciência e Tecnologia (ref: PD/BD/150570/2020).","R.O. acknowledges a PhD grant awarded by Funda??o para a Ci?ncia e Tecnologia (ref: PD/BD/150570/2020).",,,,,,,,,"Alemi, M., Meghdari, A., Ghazisaedy, M., Employing humanoid robots for teaching English language in Iranian junior high-schools (2014) Int. J. Hum. 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"Savić S.Ž., Gnjatović M., Stefanović D., Lalić B., Maček N.","55900806500;24481099800;57198355699;35208507300;56364492700;","Automatic domain modeling for human–robot interaction",2020,"Intelligent Service Robotics","13","1",,"99","111",,1,"10.1007/s11370-019-00303-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075933023&doi=10.1007%2fs11370-019-00303-9&partnerID=40&md5=f68ea0232b287960b0dd5632d73480f1","Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, Novi Sad, 21000, Serbia; School of Electrical and Computer Engineering of Applied Studies, Vojvode Stepe 283, Belgrade, 11000, Serbia; Faculty of Computer Science, Megatrend University, Bulevar maršala Tolbuhina 8, New Belgrade, 11070, Serbia","Savić, S.Ž., Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, Novi Sad, 21000, Serbia; Gnjatović, M., Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, Novi Sad, 21000, Serbia; Stefanović, D., Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, Novi Sad, 21000, Serbia; Lalić, B., Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, Novi Sad, 21000, Serbia; Maček, N., School of Electrical and Computer Engineering of Applied Studies, Vojvode Stepe 283, Belgrade, 11000, Serbia, Faculty of Computer Science, Megatrend University, Bulevar maršala Tolbuhina 8, New Belgrade, 11070, Serbia","This paper introduces an approach to automatic domain modeling for human–robot interaction. The proposed approach is symbolic and intended for semantically unconstrained task-oriented human–robot interaction domains. At the specification level, it is cognitively inspired, addressing selected cognitive mechanisms of the human memory system (e.g., integration, semantic categorization, associative learning, etc.) that are relevant for natural language human–robot interaction. We discuss a corpus-based validation of the introduced approach and report on its particular implementation within the conversational agent integrated with a human-like robot. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.","Cognitive mechanisms; Domain modeling; Focus tree; Human–robot interaction; Robot MARKO","Semantics; Associative learning; Cognitive mechanisms; Conversational agents; Domain model; Focus trees; Human memory systems; Robot interactions; Semantic categorization; Human robot interaction",,,,,"Ministarstvo Prosvete, Nauke i TehnoloÅ¡kog Razvoja: III44008, TR32035","The presented study was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia (research Grants III44008 and TR32035), and as part of the project “Collaborative strategies of heterogeneous robot activity at solving agriculture missions controlled via intuitive human–robot interfaces” (ID 99), sponsored within the framework of the ERA.Net RUS Plus program. 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Department of Linguistics and Philosophy, Massachusetts Institute of Technology; Tasevski, J., Gnjatović, M., Borovac, B., Assessing the Children’s receptivity to the robot MARKO (2018) Acta Polytech Hung, 15 (5), pp. 47-66; Trafton, G., Hiatt, L., Harrison, A., Tamborello, F., Khemlani, S., Schultz, A., ACT-R/E: an embodied cognitive architecture for human–robot interaction (2013) J Hum Robot Interact, 2 (1), pp. 30-55; Thomson, R., Pyke, A., Trafton, J.G., Hiatt, L.M., An account of associative learning in memory recall (2015) Proceedings of the 37Th Annual Conference of the Cognitive Science Society. Cognitive Science Society. Austin, pp. 2386-2391","Gnjatović, M.; Faculty of Technical Sciences, Trg Dositeja Obradovića 6, Serbia; email: milangnjatovic@uns.ac.rs",,,"Springer",,,,,18612776,,,,"English","Intelligent Serv. Rob.",Article,"Final","",Scopus,2-s2.0-85075933023
"Tunç A.Ö.","57191197268;","Human-Robot Interaction in Organizations",2020,"Contributions to Management Science",,,,"31","45",,,"10.1007/978-3-030-29739-8_2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075067408&doi=10.1007%2f978-3-030-29739-8_2&partnerID=40&md5=5f27296ae7eea27a97fc3cb019b9bded","Department of Business, Faculty of Political Sciences, Istanbul University, Fatih, Istanbul, Turkey","Tunç, A.Ö., Department of Business, Faculty of Political Sciences, Istanbul University, Fatih, Istanbul, Turkey","As advancement in technology is on the rise in organizational setting as observed in other areas, human-robot interaction is getting more focused in both academic and practical studies. Social robots influence business life deeply and hence human life by changing organizational settings. This study aims to explore how the interaction between humans and robots affects the workplace and in what aspects we can explain the nature of sociality and collaboration with robots. It also aims to put forward advantages and disadvantages of human-robot interaction by presenting essential reference points and discussing many aspects of human-robot interaction in organizations. © 2020, Springer Nature Switzerland AG.",,,,,,,,,,,,,,,,,,"Arkin, R.C., Fujita, M., Takagi, T., Hasegawa, R., An ethological and emotional basis for human–robot interaction (2003) Robotics and Autonomous Systems, 42 (3-4), pp. 191-201; Bartneck, C., Forlizzi, J., A design-centred framework for social human-robot interaction. Proceedings of the Roman 2004 (2004) Kurashiki, pp. 591-594; Breazeal, C.L., (2004) Designing Sociable Robots. Intelligent Robotics and Autonomous Agents, , illustrated ed.). Cambridge: MIT Press; Breazeal, C., Human-robot partnership, trends and controversies: Human-inspired robots (2006) IEEE Computer Society, 21 (4), pp. 79-81; Breazeal, C., Scassellati, B., How to build robots that make friends and influence people (1999) Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No. 99CH36289, 2, pp. 858-863. , Vol., pp., IEEE; Brooks, R., Humanoid robots (2002) Communications of the ACM, 45 (3), pp. 33-38; Brynjolfsson, E., McAfee, A., The second machine age: Akıllı Teknolojiler Devrinde Çalışma, İlerleme ve Refah (2015) (Çev., , Levent Göktem). 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New York: Springer; Davenport, T.H., Kirby, J., Beyond automation: Strategies for remaining gainfully employed in an era of very smart machines (2015) Harvard Business Review, 93 (6), pp. 58-65; Ford, M., (2018) Robotların yükselişi: Yapay Zeka Ve işsiz Bir Gelecek Tehlikesi (Çev.: Cem Duran), p. 4. , Baskı. İstanbul: Kronik Yayınları; Goodrich, M.A., Schultz, A.C., Human–robot interaction: A survey (2007) Foundation and Trends in Human-Computer Interaction, 1 (3), pp. 203-275; Hinds, P.J., Roberts, T.L., Jones, H., Whose job is it anyway? A study of human-robot interaction in a collaborative task (2004) Human-Computer Interaction, 19 (1), pp. 151-181; Ishiguro, H., Asada, M., Humanoid and android science, trends and controversies: Human-inspired robots (2006) IEEE Computer Society, 21 (4), pp. 74-76; Kiesler, S., Goodrich, M.A., (2018); Kiesler, S., Hinds, P., Introduction to this special issue on human-robot interaction, human–computer (2004) Interactions, 19 (1-2), pp. 1-8; Mataric, M.J., Socially assistive robotics, trends and controversies: Human-inspired robots (2006) IEEE Computer Society, 21 (4), pp. 81-83; Mutlu, B., Forlizzi, J., (2008) Robots in Organizations: The Role of Workflow, Social, and Environmental Factors in Human-Robot Interaction, p. 36. , Human-Computer Interaction Institute, Paper; Omohundro, S.M., (2007) The Nature of Self-Improving Artificial Intelligence. 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Thompson (Eds.), (pp., London: Routledge; Robert, L.P., You, S., (2014) Human-Robot Interaction in Groups: Theory, Method, and Design for Robots in Groups; Sande, G.N., Ellard, J.H., Ross, M., Effect of arbitrarily assigned labels on self-perceptions and social perceptions: The mere position effect (1986) Journal of Personality and Social Psychology, 50, pp. 684-689; Scassellati, B., How developmental psychology and robotics complement each other (2000) MIT Artificial Intelligence Lab (Report Documentation Pages, pp. 1-8. , Lansing, MI; Shibata, T., An overview of human interactive robots for psychological enrichment (2004) Proceedings of the IEEE, 92 (11), pp. 1749-1758; Shibata, T., Irie, R., Artificial emotional creature for human-robot interaction-a new direction for intelligent systems (1997) Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics, p. 47. , (p., IEEE; Steinfeld, A., Fong, T., Kaber, D., Lewis, M., Scholtz, J., Schultz, A., Common metrics for human-robot interaction (2006) Proceedings of the 1St ACM SIGCHI/SIGART Conference on Human-Robot Interaction, pp. 33-40. , March, (pp., ACM; Steuer, J., (1995) Vividness and Source of Evaluation as Determinants of Social Responses Toward Mediated Representations of Agency. Unpublished Doctoral Dissertation, , Stanford University, California; Stock, R.M., Merkle, M., Can humanoid service robots perform better than service employees? A comparison of innovative behavior cues (2018) Proceedings of the 51St Hawaii International Conference on System Sciences; Sung, J.Y., Christensen, H.I., Grinter, R.E., Robots in the wild: Understanding long-term use (2009) HRI’09 Proceedings of the 4Th ACM/IEEE International Conference on Human Robot Interaction, pp. 45-52. , March 13, La Jolla, California, USA (pp; Tapus, A., Mataric, M.J., Emulating empathy in socially assistive robotics (2007) AAAI Spring Symposium: Multidisciplinary Collaboration for Socially Assistive Robotics, pp. 93-96. , March, (pp; Thielscher, M., Cognitive robotics, trends and controversies: Human-inspired robots (2006) IEEE Computer Society, 21 (4), pp. 77-79; van Oost, E., Reed, D., Towards a sociological understanding of robots as companions (2011) Human-Robot Personal Relationships (Vol. LNICTS 59, pp. 11-18. , M. H. Lamers & F. J. 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"She Y., Gu Z., Song S., Su H.-J., Wang J.","56545216500;57219491844;57200117296;8379272100;7701333092;","Design, modeling, and manufacturing of a variable lateral stiffness arm via shape morphing mechanisms",2021,"Journal of Mechanisms and Robotics","13","3","031020","","",,2,"10.1115/1.4050379","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107961549&doi=10.1115%2f1.4050379&partnerID=40&md5=3015e1300a9adc05d49dc7b26d9dde1c","Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH  43210, United States; Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States","She, Y., Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH  43210, United States; Gu, Z., Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH  43210, United States; Song, S., Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States; Su, H.-J., Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH  43210, United States; Wang, J., Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States","In this article, we present a continuously tunable stiffness arm for safe physical human-robot interactions. Compliant joints and compliant links are two typical solutions to address safety issues for physical human-robot interactions via introducing mechanical compliance to robotic systems. While extensive studies explore variable stiffness joints/ actuators, variable stiffness links for safe physical human-robot interactions are much less studied. This article details the design and modeling of a compliant robotic arm whose stiffness can be continuously tuned via cable-driven mechanisms actuated by a single servo motor. Specifically, a 3D-printed compliant robotic arm is prototyped and tested by static experiments, and an analytical model of the variable stiffness arm is derived and validated by testing. The results show that the lateral stiffness of the robot arm can achieve a variety of 221.26% given a morphing angle of 90 deg. The variable stiffness arm design developed in this study could be a promising approach to address safety concerns for safe physical human-robot interactions. Copyright © 2021 by ASME JUNE 2021, Vol. 13 / -1","Cable-driven mechanisms; Compliant mechanisms; Mechanism design; Robot design; Variable stiffness","3D printers; Agricultural robots; Machine design; Man machine systems; Physical addresses; Robotic arms; Robotics; Stiffness; Universal joints; Cable-driven mechanisms; Design and modeling; Lateral stiffness; Mechanical compliance; Physical human-robot interactions; Static experiments; Tunable stiffness; Variable stiffness; Social robots",,,,,"National Science Foundation, NSF: CMMI-1637656","This material is based upon work supported by the National Science Foundation (Grant No. CMMI-1637656). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.",,,,,,,,,,"Mutlu, B., Forlizzi, J., Robots in Organizations: The Role of Workflow, Social, and Environmental Factors in Human-Robot Interaction (2008) Human-Robot Interaction (HRI), 2008 3rd ACM/IEEE International Conference on, pp. 287-294. , Amsterdam, The Netherlands, Mar. 12-15, IEEE; Wolf, S., Hirzinger, G., A New Variable Stiffness Design: Matching Requirements of the Next Robot Generation (2008) IEEE International Conference on Robotics and Automation, pp. 1741-1746. , Pasadena, CA, May 19-23; Wolf, S., Eiberger, O., Hirzinger, G., The DLR FSJ: Energy Based Design of a Variable Stiffness Joint (2011) 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 5082-5089. , Shanghai, China, May 9-13; Friedl, W., Höppner, H., Petit, F., Hirzinger, G., Wrist and Forearm Rotation of the DLR Hand Arm System: Mechanical Design, Shape Analysis and Experimental Validation (2011) 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1836-1842. , San Francisco, CA, Sept. 25-30; Ménard, T., Grioli, G., Bicchi, A., A Stiffness Estimator for Agonistic-Antagonistic Variable-Stiffness-Actuator Devices (2014) IEEE Trans. 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Struct, 88 (17-18), pp. 991-1001; Derderian, J., Howell, L., Murphy, M., Lyon, S., Pack, S., Compliant Parallel-Guiding Mechanisms (1996) Proceedings of the 1996 ASME Mechanisms Conference, , Irvine, CA, Aug. 18-22, V02AT02A017; Culpepper, M. L., DiBiasio, C. M., Panas, R. M., Magleby, S., Howell, L. L., Simulation of a Carbon Nanotube-Based Compliant Parallel-Guiding Mechanism: A Nanomechanical Building Block (2006) Appl. Phys. Lett, 89 (20), p. 203111; Howell, L. L., (2001) Compliant Mechanisms, , John Wiley & Sons, Hoboken, NJ; Park, J.-J., Kim, B.-S., Song, J.-B., Kim, H.-S., Safe Link Mechanism Based on Nonlinear Stiffness for Collision Safety (2008) Mech. Mach. Theory, 43 (10), pp. 1332-1348; López-Martínez, J., Blanco-Claraco, J. L., García-Vallejo, D., Giménez-Fernández, A., Design and Analysis of a Flexible Linkage for Robot Safe Operation in Collaborative Scenarios (2015) Mech. Mach. Theory, 92, pp. 1-16; Zhang, M., Laliberté, T., Gosselin, C., Force Capabilities of Two-Degree-of-Freedom Serial Robots Equipped With Passive Isotropic Force Limiters (2016) ASME J. Mech. Rob, 8 (5), p. 051002","She, Y.; Department of Mechanical and Aerospace Engineering, United States; email: she.22@osu.edu",,,"American Society of Mechanical Engineers (ASME)",,,,,19424302,,,,"English","J. Mech. Robot.",Article,"Final","",Scopus,2-s2.0-85107961549
"She Y., Gu Z., Song S., Su H.-J., Wang J.","56545216500;57219491844;57200117296;8379272100;7701333092;","A continuously tunable stiffness arm with cable-driven mechanisms for safe physical human-robot interaction",2020,"Proceedings of the ASME Design Engineering Technical Conference","10",,"V010T10A060","","",,,"10.1115/DETC2020-22035","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096113665&doi=10.1115%2fDETC2020-22035&partnerID=40&md5=e4af94f67774880489f390140324a689","Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA  02139, United States; Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA  15213, United States; Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States; Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH  43210, United States","She, Y., Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA  02139, United States; Gu, Z., Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA  15213, United States; Song, S., Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States; Su, H.-J., Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH  43210, United States; Wang, J., Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States","In this paper, we present a continuously tunable stiffness arm for safe physical human-robot interactions. Compliant joints and compliant links are two typical solutions to address safety issues for physical human-robot interaction via introducing mechanical compliance to robotic systems. While extensive studies explore variable stiffness joints/actuators, variable stiffness links for safe physical human-robot interactions are much less studied. This paper details the design and modeling of a compliant robotic arm whose stiffness can be continuously tuned via cable-driven mechanisms actuated by a single servo motor. Specifically, a 3D printed compliant robotic arm is prototyped and tested by static experiments, and an analytical model of the variable stiffness arm is derived and validated by testing. The results show that the lateral stiffness of the robot arm can achieve a variety of 221.26 % given a morphing angle of 90◦. The study demonstrates that the compliant link design could be a promising approach to address safety concerns for safe physical human-robot interactions. Copyright © 2020 ASME",,"3D printers; Cables; Machine design; Man machine systems; Physical addresses; Robotic arms; Robotics; Safety engineering; Stiffness; Universal joints; Cable-driven mechanisms; Design and modeling; Lateral stiffness; Mechanical compliance; Physical human-robot interactions; Static experiments; Tunable stiffness; Variable stiffness; Social robots",,,,,"National Science Foundation, NSF: CMMI-1637656","This material is based upon work supported by the National Science Foundation under Grant No: CMMI-1637656. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.",,,,,,,,,,"Mutlu, B., Forlizzi, J., Robots in organizations: the role of workflow, social, and environmental factors in human-robot interaction (2008) Human-Robot Interaction (HRI), 2008 3rd ACM/IEEE International Conference on, pp. 287-294. , IEEE; Wolf, S., Hirzinger, G., A new variable stiffness design: Matching requirements of the next robot generation (2008) IEEE International Conference on Robotics and Automation, pp. 1741-1746. , 2008. ICRA; Wolf, S., Eiberger, O., Hirzinger, G., The DLR FSJ: Energy based design of a variable stiffness joint 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 5082-5089; Friedl, W., Höppner, H., Petit, F., Hirzinger, G., Wrist and forearm rotation of the DLR hand arm system: Mechanical design, shape analysis and experimental validation 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1836-1842; Ménard, T., Grioli, G., Bicchi, A., A stiffness estimator for agonistic #x2013;antagonistic variable-stiffness-actuator devices IEEE Transactions on Robotics, 30 (5), pp. 1269-1278; Garabini, M., Passaglia, A., Belo, F., Salaris, P., Bicchi, A., Optimality principles in variable stiffness control: The VSA hammer 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3770-3775; Schiavi, R., Grioli, G., Sen, S., Bicchi, A., VSA-II: a novel prototype of variable stiffness actuator for safe and performing robots interacting with humans (2008) IEEE International Conference on Robotics and Automation, pp. 2171-2176. , 2008. ICRA; Tonietti, G., Schiavi, R., Bicchi, A., Design and control of a variable stiffness actuator for safe and fast physical human/robot interaction Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 526-531; Zinn, M., Khatib, O., Roth, B., Salisbury, J. K., Playing it safe [human-friendly robots IEEE Robotics Automation Magazine, 11 (2), pp. 12-21; Migliore, S. A., Brown, E. A., DeWeerth, S. P., Biologically inspired joint stiffness control Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 4508-4513; Koganezawa, K., Mechanical stiffness control for antagonistically driven joints 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1544-1551; Pratt, G. A., Williamson, M. M., Series elastic actuators 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems 95.’Human Robot Interaction and Cooperative Robots’, Proceedings, 1, pp. 399-406. , vol.1; She, Y., Meng, D., Cui, J., Su, H.-J., On the impact force of human-robot interaction: Joint compliance vs. link compliance (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 6718-6723. , IEEE; She, Y., Song, S., Su, H.-J., Wang, J., A comparative study on the effect of mechanical compliance for a safe physical human–robot interaction (2020) Journal of Mechanical Design, 142 (6); Park, J.-J., Kim, B.-S., Song, J.-B., Kim, H.-S., Safe link mechanism based on nonlinear stiffness for collision safety Mechanism and Machine Theory, 43 (10), pp. 1332-1348; Zhang, M., Laliberté, T., Gosselin, C., Force capabilities of two-degree-of-freedom serial robots equipped with passive isotropic force limiters Journal of Mechanisms and Robotics, 8 (5), p. 051002; López-Martínez, J., Blanco-Claraco, J. L., García-Vallejo, D., Giménez-Fernández, A., Design and analysis of a flexible linkage for robot safe operation in collaborative scenarios Mechanism and Machine Theory, 92, pp. 1-16; She, Y., Su, H.-J., Hurd, C. J., Shape optimization of 2d compliant links for design of inherently safe robots (2015) ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. V05BT08A004-V05BT08A004. , American Society of Mechanical Engineers; She, Y., Su, H.-J., Meng, D., Song, S., Wang, J., Design and modeling of a compliant link for inherently safe corobots (2018) Journal of Mechanisms and Robotics, 10 (1), p. 011001; Hines, L., Arabagi, V., Sitti, M., Shape memory polymer-based flexure stiffness control in a miniature flapping-wing robot (2012) IEEE Transactions on Robotics, 28 (4), pp. 987-990; Galloway, K. C., Clark, J. E., Koditschek, D. E., Variable stiffness legs for robust, efficient, and stable dynamic running (2013) Journal of Mechanisms and Robotics, 5 (1), p. 011009; She, Y., Su, H.-J., Lai, C., Meng, D., Design and prototype of a tunable stiffness arm for safe human-robot interaction (2016) ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, , American Society of Mechanical Engineers Digital Collection; She, Y., Su, H.-J., Meng, D., Lai, C., Design and modeling of a continuously tunable stiffness arm for safe physical human–robot interaction (2020) Journal of Mechanisms and Robotics, 12 (1); Howell, L. L., Midha, A., A method for the design of compliant mechanisms with small-length flexural pivots (1994) Journal of mechanical design, 116 (1), pp. 280-290; Howell, L. L., Midha, A., Parametric deflection approximations for end-loaded, large-deflection beams in compliant mechanisms (1995) Journal of Mechanical Design, 117 (1), pp. 156-165; Howell, L. L., (2001) Compliant mechanisms, , John Wiley & Sons; Park, J.-J., Kim, B.-S., Song, J.-B., Kim, H.-S., Safe link mechanism based on nonlinear stiffness for collision safety (2008) Mechanism and Machine Theory, 43 (10), pp. 1332-1348; López-Martínez, J., Blanco-Claraco, J. L., García-Vallejo, D., Giménez-Fernández, A., Design and analysis of a flexible linkage for robot safe operation in collaborative scenarios (2015) Mechanism and Machine Theory, 92, pp. 1-16; Zhang, M., Laliberté, T., Gosselin, C., Force capabilities of two-degree-of-freedom serial robots equipped with passive isotropic force limiters (2016) Journal of Mechanisms and Robotics, 8 (5), p. 051002",,,"Computers and Information in Engineering Division;Design Engineering Division","American Society of Mechanical Engineers (ASME)","ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2020","17 August 2020 through 19 August 2020",,164730,,9780791883990,,,"English","Proc. ASME Des. Eng. Tech. Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85096113665
"Irfan B., Ramachandran A., Spaulding S., Kalkan S., Parisi G.I., Gunes H.","57194725723;56480082900;56085336700;23027462100;56028608900;7005067251;","Lifelong learning and personalization in long-term human-robot interaction (LEAP-HRI)",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444881","724","727",,,"10.1145/3434074.3444881","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102743925&doi=10.1145%2f3434074.3444881&partnerID=40&md5=10f5cc370949acc9831f97bff9ddfa89","University of Plymouth, Plymouth, United Kingdom; Van Robotics, Columbia, SC, United States; Massachusetts Institute of Technology, Cambridge, MA, United States; Middle East Technical University, Ankara, Turkey; University of Hamburg, Hamburg, Germany; University of Cambridge, Cambridge, United Kingdom","Irfan, B., University of Plymouth, Plymouth, United Kingdom; Ramachandran, A., Van Robotics, Columbia, SC, United States; Spaulding, S., Massachusetts Institute of Technology, Cambridge, MA, United States; Kalkan, S., Middle East Technical University, Ankara, Turkey; Parisi, G.I., University of Hamburg, Hamburg, Germany; Gunes, H., University of Cambridge, Cambridge, United Kingdom","While most of the research in Human-Robot Interaction (HRI) focuses on short-term interactions, long-term interactions require bolder developments and a substantial amount of resources, especially if the robots are deployed in the wild. Robots need to incrementally learn new concepts or abilities in a lifelong fashion to adapt their behaviors within new situations and personalize their interactions with users to maintain their interest and engagement. The ""Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI)""Workshop aims to take a leap from the traditional HRI approaches towards addressing the developments and challenges in these areas and create a medium for researchers to share their work in progress, present preliminary results, learn from the experience of invited researchers and discuss relevant topics. The workshop extends the topics covered in the ""Personalization in Long-Term Human-Robot Interaction (PLOT-HRI)""Workshop at the 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) and ""Lifelong Learning for Long-term Human-Robot Interaction (LL4LHRI)""Workshop at the 29th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), and focuses on studies on lifelong learning and adaptivity to users, context, environment, and tasks in long-term interactions in a variety of fields (e.g., education, rehabilitation, elderly care, collaborative tasks, customer-oriented service and companion robots). © 2021 Owner/Author.","Adaptation; Continual learning; Human-robot interaction; Lifelong learning; Long-term interaction; Long-term memory; Personalization; User modeling; User recognition","Agricultural robots; Man machine systems; Social robots; Collaborative tasks; Customer-oriented services; Human robot Interaction (HRI); Interactive communications; Life long learning; Long-term interaction; Personalizations; Work in progress; Educational robots",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/R030782/1; Bilim Akademisi","Hatice Gunes is supported by the UK Engineering and Physical Sciences Research Council (EPSRC) through the ARoEQ Project (EP/R030782/1). S. Kalkan was supported by the BAGEP Award of the Science Academy, Turkey.",,,,,,,,,,"Belpaeme, T., Baxter, P., Read, R., Wood, R., Cuayáhuitl, H., Kiefer, B., Racioppa, S., Humbert, R., Multimodal child-robot interaction: Building social bonds (2013) J. Hum.-Robot Interact., 1 (2), pp. 33-53. , Jan 2013; Chen, Z., Liu, B., Lifelong machine learning (2016) Synthesis Lectures on Artificial Intelligence and Machine Learning, 10 (3), pp. 1-145. , 2016; Churamani, N., Gunes, H., Clifer: Continual learning with imagination for facial expression recognition (2020) 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), pp. 693-699; Churamani, N., Kalkan, S., Gunes, H., Continual learning for affective robotics: Why, what and how? (2020) 29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020, pp. 425-431. , https://doi.org/10.1109/RO-MAN47096.2020.9223564, Naples, Italy, August 31-September 4, 2020; Clabaugh, C., Mahajan, K., Jain, S., Pakkar, R., Becerra, D., Shi, Z., Deng, E., Mataric, M., Long-term personalization of an in-home socially assistive robot for children with autism spectrum disorders (2019) Frontiers in Robotics and AI, 6, p. 110. , https://doi.org/10.3389/frobt.2019.00110, 2019; Dautenhahn, K., Robots we like to live with ?!-a developmental perspective on a personalized, life-long robot companion (2004) 2004 IEEE International Workshop on Robot and Human Interactive Communication, pp. 17-22; Graaf De, A.M.M., Ben Allouch, S., Klamer, T., Sharing a life with harvey: Exploring the acceptance of and relationship-building with a social robot (2015) Computers in Human Behavior, 43, pp. 1-14. , https://doi.org/10.1016/j.chb.2014.10.030, 2015; Glas, D.F., Wada, K., Shiomi, M., Kanda, T., Ishiguro, H., Hagita, N., Personal greetings: Personalizing robot utterances based on novelty of observed behavior (2017) International Journal of Social Robotics, 9 (2), pp. 181-198. , 2017; Gordon, G., Spaulding, S., Kory Westlund, J., Joo Lee, J., Plummer, L., Martinez, M., Das, M., Breazeal, C., Affective personalization of a social robot tutor for children's second language skills (2016) Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (Phoenix, Arizona) (AAAI'16), pp. 3951-3957. , AAAI Press; Irfan, B., Céspedes Gomez, N., Casas, J., Senft, E., Gutiérrez, L.F., Rincon-Roncancio, M., Munera, M., Cifuentes, C.A., Using a personalised socially assistive robot for cardiac rehabilitation: A long-term case study (2020) 29th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 124-130. , https://doi.org/10.1109/RO-MAN47096.2020.9223491; Irfan, B., Hellou, M., Mazel, A., Belpaeme, T., Challenges of a real-world hri study with non-native english speakers: Can personalisation save the day? (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (Cambridge, United Kingdom) (HRI '20), pp. 272-274. , https://doi.org/10.1145/3371382.3378278, Association for Computing Machinery, New York, NY, USA; Irfan, B., Ramachandran, A., Spaulding, S., Glas, D.F., Leite, I., Lee Koay, K., Personalization in long-term human-robot interaction (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 685-686. , https://doi.org/10.1109/HRI.2019.8673076; Jones, A., Castellano, G., Adaptive robotic tutors that support self-regulated learning: A longer-term investigation with primary school children (2018) International Journal of Social Robotics, 10, pp. 357-370. , https://doi.org/10.1007/s00371-011-0630-7, 2018; Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., Hagita, N., A communication robot in a shopping mall (2010) IEEE Transactions on Robotics, 26 (5), pp. 897-913. , https://doi.org/10.1109/TRO.2010.2062550, Oct 2010; Lee, M.K., Forlizzi, J., Kiesler, S., Rybski, P., Antanitis, J., Savetsila, S., Personalization in hri: A longitudinal field experiment (2012) 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 319-326; Leyzberg, D., Spaulding, S., Scassellati, B., Personalizing robot tutors to individuals' learning differences (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction (Bielefeld, Germany) (HRI '14), pp. 423-430. , https://doi.org/10.1145/2559636.2559671, Association for Computing Machinery, New York, NY, USA; Lomonaco, V., Maltoni, D., (2017) CORe50: A New Dataset and Benchmark for Continuous Object Recognition, , 2017 arXiv: arXiv: 1705. 03550v1; Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S., Continual lifelong learning with neural networks: A review (2019) Neural Networks, 113, pp. 54-71. , 2019; Parisi, G.I., Tani, J., Weber, C., Wermter, S., Lifelong learning of humans actions with deep neural network self-organization (2017) Neural Networks, 96, pp. 137-149. , 2017; Pentina, A., Lampert, C.H., Lifelong learning with non-iid tasks (2015) Advances in Neural Information Processing Systems, pp. 1540-1548; Ramachandran, A., Huang, C., Scassellati, B., Give me a break! personalized timing strategies to promote learning in robot-child tutoring (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (Vienna, Austria) (HRI '17), pp. 146-155. , https://doi.org/10.1145/2909824.3020209, Association for Computing Machinery, New York, NY, USA; Ring, M., (1994) Continual Learning in Reinforcement Environments, p. 78712. , Ph. D. Dissertation. University of Texas at Austin Austin, Texas; Rudovic, O., Lee, J., Dai, M., Schuller, B., Picard, R.W., Personalized machine learning for robot perception of affect and engagement in autism therapy (2018) Science Robotics, 3 (19). , https://doi.org/10.1126/scirobotics.aao6760, 2018; Saunders, J., Syrdal, D.S., Koay, K.L., Burke, N., Dautenhahn, K., Teach me-show me-end-user personalization of a smart home and companion robot (2016) IEEE Transactions on Human-Machine Systems, 46 (1), pp. 27-40. , https://doi.org/10.1109/THMS.2015.2445105, Feb 2016; Scassellati, B., Boccanfuso, L., Huang, C., Mademtzi, M., Qin, M., Salomons, N., Ventola, P., Shic, F., Improving social skills in children with asd using a long-term, in-home social robot (2018) Science Robotics, 3 (21). , https://doi.org/10.1126/scirobotics.aat7544, 2018; Sung, J., Grinter, R.E., Christensen, H.I., Pimp my roomba"": Designing for personalization (2009) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Boston, MA, USA) (CHI '09), pp. 193-196. , https://doi.org/10.1145/1518701.1518732, Association for Computing Machinery, New York, NY, USA; Tapus, A., Tapus, C., Mataric, M., Long term learning and online robot behavior adaptation for individuals with physical and cognitive impairments (2010) Field and Service Robotics, pp. 389-398. , Andrew Howard, Karl Iagnemma, and Alonzo Kelly (Eds.) Springer Berlin Heidelberg, Berlin, Heidelberg; Thrun, S., Mitchell, T.M., Lifelong robot learning (1995) Robotics and Autonomous Systems, 15 (1-2), pp. 25-46. , 1995; Tjomsland, J., Kalkan, S., Gunes, H., (2020) Mind Your Manners! A Dataset and A Continual Learning Approach for Assessing Social Appropriateness of Robot Actions, , https://arxiv.org/abs/2007.12506, CoRR abs/2007. 12506 2020",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102743925
"Trafton J.G., Raymond P., Khemlani S.","6701827368;7101815644;24332201800;","The Power of Theory",2021,"ACM Transactions on Human-Robot Interaction","10","1","11","","",,,"10.1145/3439716","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101832181&doi=10.1145%2f3439716&partnerID=40&md5=3661c12f2606848e0d915c84a4f1b80c","NRL, Code 5515, Washington, DC  20375, United States; toXcel, LLC, 7140 Heritage Village Plaza, Gainesville, VA  20155, United States","Trafton, J.G., NRL, Code 5515, Washington, DC  20375, United States; Raymond, P., toXcel, LLC, 7140 Heritage Village Plaza, Gainesville, VA  20155, United States; Khemlani, S., NRL, Code 5515, Washington, DC  20375, United States","Hoffman & Zhao's paper ""A Primer for Conducting Experiments in Human-Robot Interaction""is an excellent paper overall with many practical suggestions throughout. We expect it to be used as a source for how to run human-subject experiments in the field of human-robot interaction, especially for people not trained directly in psychology. © 2021 Copyright held by the owner/author(s).","Human robot interaction; psychology","Agricultural robots; Behavioral research; Man machine systems; Human subject experiments; Human robot interaction",,,,,"Office of Naval Research, ONR; Office of the Secretary of Defense, OSD","This work was supported by the U.S. Office of Naval Research and the Office of the Secretary of Defense to GT and SK. Authors’ addresses: G. Trafton and S. Khemlani, NRL, Code 5515, Washington, DC, 20375; emails: {greg.trafton, sunny.khemlani}@nrl.navy.mil; P. Raymond, toXcel, LLC, 7140 Heritage Village Plaza, Gainesville, Virginia 20155 USA; email: Paula.Raymond@toxcel.com.",,,,,,,,,,,,,,"Association for Computing Machinery",,,,,25739522,,,,"English","ACM Trans. Hum. Robot Interact",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85101832181
"Awais M., Saeed M.Y., Malik M.S.A., Younas M., Asif S.R.I.","57217768600;57191104285;55916938800;57213343722;57222495268;","Intention based comparative analysis of human-robot interaction",2020,"IEEE Access","8",,,"205821","205835",,1,"10.1109/ACCESS.2020.3035201","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102873314&doi=10.1109%2fACCESS.2020.3035201&partnerID=40&md5=aae036537a96823904a656ee74b8fd61","Department of Software Engineering, Government College University Faisalabad, Faisalabad, 38000, Pakistan; Department of Information Technology, Government College University Faisalabad, Faisalabad, 38000, Pakistan; Department of Computer Science, Government College University Faisalabad, Faisalabad, 38000, Pakistan","Awais, M., Department of Software Engineering, Government College University Faisalabad, Faisalabad, 38000, Pakistan; Saeed, M.Y., Department of Software Engineering, Government College University Faisalabad, Faisalabad, 38000, Pakistan; Malik, M.S.A., Department of Information Technology, Government College University Faisalabad, Faisalabad, 38000, Pakistan; Younas, M., Department of Computer Science, Government College University Faisalabad, Faisalabad, 38000, Pakistan; Asif, S.R.I., Department of Computer Science, Government College University Faisalabad, Faisalabad, 38000, Pakistan","Human-robot interaction is inevitable due to the increase of the autonomous intelligent machines in the human vicinity. The autonomous machines should synchronize with the interacting human. The key factor for synchronized Human-Robot Interaction (HRI) is a human intention. The interacting machine must have the clue about the intention of the interacting human for a useful interaction. In this review paper, recently proposed intention-based approaches for human-robot interaction are discussed. The approaches are categorized concerning different aspects, e.g., application area, specialized and generalized estimation techniques, etc. The review categorized the recently proposed approaches into five categories. The categorization is mainly based on the application areas of the intention recognition approaches. The type of approaches includes general and application-specific approaches. The application areas include synchronized physical human-assistance, human-synchronized vehicles, etc. The vehicles are synchronized with the driver and the pedestrian. The study highlighted the currently active as well as the dormant areas of the intention-based human-robot interaction and indicated new directions. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.","Intention based bionics; Intention based safe vehicles; Intention based teleoperation; Intention recognition","Man machine systems; Synchronization; Application specific; Autonomous machines; Comparative analysis; Estimation techniques; Human intentions; Human robot Interaction (HRI); Intelligent machine; Intention recognition; Human robot interaction",,,,,,"The authors would like to thank the Government College University Faisalabad (GCUF), Pakistan, to provide resources for this research.",,,,,,,,,,"Awais, M., (2013) Bayreuth University Library, , https://eref.uni-bayreuth.de/view/person/Awais=3AMuhammad=3A=3A.html; Losey, D.P., McDonald, C.G., Battaglia, E., O’Malley, M.K., A review of intent detection, arbitration, and communication aspects of shared control for physical human–robot interaction (2018) Appl. Mech. Rev., 70 (1). , Jan; Han, T.A., Pereira, L.M., State-of-the-art of intention recognition and its use in decision making (2013) AI Commun, 26 (2), pp. 237-246; Zhang, D., Yao, L., Chen, K., Wang, S., Chang, X., Liu, Y., Making sense of spatio-temporal preserving representations for EEG-based human intention recognition IEEE Trans. Cybern., 50 (7), pp. 3033-3044. , Jul. 2020; Willett, F.R., Murphy, B.A., Young, D., Memberg, W.D., Blabe, C.H., Pandarinath, C., Franco, B., Ajiboye, A.B., A comparison of intention estimation methods for decoder calibration in intracortical brain–computer interfaces (2018) IEEE Trans. Biomed. Eng., 65 (9), pp. 2066-2078. , Sep; Petković, T., Puljiz, D., Marković, I., Hein, B., Human intention estimation based on hidden Markov model motion validation for safe flexible robotized warehouses (2019) Robot. Comput.-Integr. 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"Karim B., Tan Q., Alvarez J.C.","57191078082;24529398000;57209074060;","Towards Fog-Based HiTLCPS for Human Robot Interactions in Smart Lab: Use Cases and Architecture Overview",2021,"Advances in Intelligent Systems and Computing","1268 AISC",,,"590","599",,,"10.1007/978-3-030-57802-2_57","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091312884&doi=10.1007%2f978-3-030-57802-2_57&partnerID=40&md5=3967c2dedd01f0ae5707b96f3fa77da2","University of Oviedo, Oviedo, Spain; Athabasca University, Athabasca, Canada","Karim, B., University of Oviedo, Oviedo, Spain; Tan, Q., Athabasca University, Athabasca, Canada; Alvarez, J.C., University of Oviedo, Oviedo, Spain","This paper provides use case definitions and a high-level system architecture overview for human robot interaction in a fog computing-based Human in The Loop Cyber Physical System. Our focus is to develop a practical, natural, meaningful human robot interaction framework for single and multiple avatar (CPS) robots, and this paper outlines the research road ahead of us. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.","Cloud computing; Fog computing; Human-in-the-Loop CPS; Human-Machine interaction; Robotics; Social robotics","Embedded systems; Fog computing; Man machine systems; Soft computing; High-level systems; Human-in-the-loop; Smart labs; Social robots",,,,,,,,,,,,,,,,"Tan, Q., Denojean-Mairet, M., Toward a telepresence robot empowered smart lab (2019) Smart Learn. Environ, 6, p. 5. , https://doi.org/10.1186/s40561-019-0084-3; Habib, L., Pacaux-Lemoine, M.-P., Millot, P., Adaptation of the level of automation according to the type of cooperative partner (2017) IEEE International Conference on Systems, Man, and Cybernetics, Banff, Canada, pp. 864-869. , October; Hoc, J.-M., Lemoine, M.-P., Cognitive evaluation of human-human and human-machine cooperation modes in air traffic control (1998) Int. J. Aviat. Psychol, 8 (1), pp. 1-32; Navarro, J., Mars, F., Hoc, J-M., Lateral control support for car drivers: a human-machine cooperation approach (2007) Proceedings of the 14th European Conference on Cognitive Ergonomics: Invent! Explore!, ECCE 2007, 250, pp. 249-252. , ACM; Feng, S., Quivira, F., Schirner, G., Framework for rapid development of embedded human-in-the-loop cyber-physical systems (2016) 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 208-215. , October; Truong, X.-T., Ngo, T.D., Toward socially aware robot navigation in dynamic and crowded environments: a proactive social motion (2017) IEEE Trans. Autom. Sci. Eng, 14 (4), pp. 1743-1760; Bowen, C., Alterovitz, R., Closed-loop global motion planning for reactive, collision-free execution of learned tasks (2014) 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems; Yan, W., Weber, C., Wermter, S., A neural approach for robot navigation based on cognitive map learning (2012) The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE; Tapus, A., Siegwart, R., A cognitive modeling of space using fingerprints of places for mobile robot navigation (2006) Proceedings 2006 IEEE International Conference on Robotics and Automation, ICRA 2006; Leite, I., Martinho, C., Paiva, A., Social robots for long-term interaction: a survey (2013) Int. J. Soc. Rob, 5 (2), pp. 291-308; Bonaccorsi, M., Fiorini, L., Cavallo, F., Saffiotti, A., A cloud robotics solution to improve social assistive robots for active and healthy aging (2016) Int. J. Soc. Rob, 8 (3), pp. 393-408; Park, H., Choi, Y., Jung, Y., Myaeng, S., Supporting mixed initiative human-robot interaction: a script-based cognitive architecture approach (2008) 2008 IEEE International Joint Conference on Neural Networks, pp. 4107-4113; Qu, W., Chen, Z., A new cognitive approach based on dynamic evolution of the grey hazy set (2014) 2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, pp. 572-577; Sarker, V.K., Queralta, J.P., Gia, T.N., Tenhunen, H., Westerlund, T., Offloading SLAM for indoor mobile robots with edge-fog-cloud computing (2019) 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, pp. 1-6; Cockburn, A., (2000) Writing Effective Use Cases, , Addison-Wesley Professional, Boston ISBN 0-201-70225-8","Karim, B.; University of OviedoSpain; email: behzadk0@gmail.com","Herrero A.Cambra C.Urda D.Sedano J.Quintian H.Corchado E.",,"Springer Science and Business Media Deutschland GmbH","15th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2020","16 September 2020 through 18 September 2020",,244579,21945357,9783030578015,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85091312884
"Lyu S., Cheah C.C., Yu X., Guo L.","57193015269;7007112857;51162010800;57309250600;","Human-robot Interaction Control by using a General Sensory Model",2020,"IEEE International Conference on Control and Automation, ICCA","2020-October",,"9264397","630","635",,,"10.1109/ICCA51439.2020.9264397","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098073124&doi=10.1109%2fICCA51439.2020.9264397&partnerID=40&md5=ec2117ee638c1f16432a1328b76e00b1","Hangzhou Innovation Institute of Beihang University, Hangzhou, 310052, China; Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore, 639798, Singapore; Beihang University, Beijing, 100191, China","Lyu, S., Hangzhou Innovation Institute of Beihang University, Hangzhou, 310052, China; Cheah, C.C., Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore, 639798, Singapore; Yu, X., Beihang University, Beijing, 100191, China; Guo, L., Beihang University, Beijing, 100191, China","In recent years, there has been an increasing interest in the study of human-robot interactions (HRI). In HRI tasks, the strengths of both human and robot can be utilized in task execution in a complimentary way. According to different scenarios of human-robot interaction, the interaction task requirements and the external sensory systems or configurations adopted are usually different. Majority of the existing works focus on developing the control methods for some specific applications or with specific sensors and few results has been presented to formulate different interaction task requirements and various sensory models in a general way. In this paper, a human-robot interaction task variable that is able to describe various interaction task requirements in a unified way is integrated with a general sensory model obtained from on an offline neural network based learning algorithm so that various external sensors can be directly used in the interaction control systems to provide various sensory information so as to enhance the perception capability. We present a robot controller by combining the human-robot interaction task variable and the general sensory model so as to achieve various human-robot interaction tasks based on various external sensors by simply adjusting the task parameters and training the system, without having to modify the sensory models or controller. Convergence analysis of the proposed offline neural network based learning algorithm is shown and experimental results are presented to illustrate the performance of the proposed method. © 2020 IEEE.",,"Controllers; Educational robots; Learning algorithms; Learning systems; Man machine systems; Neural networks; Sensory perception; Convergence analysis; External sensors; Human robot Interaction (HRI); Human-robot interaction control; Interaction controls; Offline neural networks; Perception capability; Sensory information; Social robots",,,,,,,,,,,,,,,,"Vukobratovic, M., Hristic, D., Stojiljkovic, Z., Development of active anthropomorphic exoskeletons (1974) Medical and Biological Engineering, 12 (1), pp. 66-80; Kazerooni, H., Human-robot interaction via the transfer of power and information signals (1990) IEEE Transactions on Systems, Man and Cybernetics, 20 (2), pp. 450-463; Kazerooni, H., Racine, J., Huang, L., Steger, R., On the control of the Berkeley lower extremity exoskeleton (bleex) (2005) IEEE International Conference on Robotics and Automation, pp. 4353-4360; Hogan, N., Impedance control: An approach to manipulation (1984) American Control Conference, pp. 304-313; Veneman, J.F., Kruidhof, R., Hekman, E.E., Ekkelenkamp, R., Van Asseldonk, E.H., Kooij Der, H.Van, Design and evaluation of the lopes exoskeleton robot for interactive gait rehabilitation (2007) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 15 (3), pp. 379-386; Kiguchi, K., Hayashi, Y., An emg-based control for an upper-limb power-assist exoskeleton robot (2012) IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42 (4), pp. 1064-1071; Zhang, J., Cheah, C.C., Passivity and stability of human-robot interaction control for upper-limb rehabilitation robots (2015) IEEE Transactions on Robotics, 31 (2), pp. 233-245; Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots (1986) Autonomous Robot Vehicles, pp. 396-404. , Springer; Lacevic, B., Rocco, P., Zanchettin, A.M., Safety assessment and control of robotic manipulators using danger field (2013) IEEE Transactions on Robotics, 29 (5), pp. 1257-1270; Cowan, N.J., Weingarten, J.D., Koditschek, D.E., Visual servoing via navigation functions (2002) IEEE Transactions on Robotics and Automation, 18 (4), pp. 521-533; Lyu, S., Cheah, C.C., Intention based control for physical humanrobot interaction (2018) 2018 IEEE International Conference on Realtime Computing and Robotics (RCAR), pp. 1-6. , Aug; Lyu, S., Cheah, C.C., Human-robot interaction control based on a general energy shaping method (2019) IEEE Transactions on Control Systems Technology, pp. 1-16; Lewis, F.L., Liu, K., Yesildirek, A., Neural net robot controller with guaranteed tracking performance (1995) IEEE Transactions on Neural Networks, 6 (3), pp. 703-715; Lewis, F.L., Yesildirek, A., Liu, K., Multilayer neural-net robot controller with guaranteed tracking performance (1996) IEEE Transactions on Neural Networks, 7 (2), pp. 388-399; Cheng, L., Hou, Z.-G., Tan, M., Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model (2009) Automatica, 45 (10), pp. 2312-2318; Zhao, Y., Cheah, C.C., Neural network control of multifingered robot hands using visual feedback (2009) IEEE Transactions on Neural Networks, 20 (5), pp. 758-767; Lyu, S., Cheah, C.C., Vision based neural network control of robot manipulators with unknown sensory jacobian matrix (2018) 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 1222-1227; Lyu, S., Cheah, C.C., Data-driven learning for robot control with unknown jacobian (2020) Automatica, 120, p. 109120; Cheah, C.C., Li, X., (2016) Task-space Sensory Feedback Control of Robot Manipulators, , Springer; Arimoto, S., (1996) Control Theory of Nonlinear Mechanical Systems, , Oxford University Press, Inc; Qu, Z., (1998) Robust Control of Nonlinear Uncertain Systems, , John Wiley & Sons, Inc; Lewis, F.L., Dawson, D.M., Abdallah, C.T., (2003) Robot Manipulator Control: Theory and Practice, , CRC Press",,,,"IEEE Computer Society","16th IEEE International Conference on Control and Automation, ICCA 2020","9 October 2020 through 11 October 2020",,165496,19483449,9781728190938,,,"English","IEEE Int. Conf. Control Autom., ICCA",Conference Paper,"Final","",Scopus,2-s2.0-85098073124
"Zong C., Ji Z., Yu H.","55413844400;8555087100;36164668500;","Dynamic stability analysis of a tracked mobile robot based on human–robot interaction",2020,"Assembly Automation","40","1",,"143","154",,3,"10.1108/AA-10-2018-0156","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063905572&doi=10.1108%2fAA-10-2018-0156&partnerID=40&md5=b45937b43db93257eec1c0ab4897f49a","Institute of Complexity Science, College of Automation, Qingdao University, Qingdao, China; College of Automation, Qingdao University, Qingdao, China","Zong, C., Institute of Complexity Science, College of Automation, Qingdao University, Qingdao, China; Ji, Z., Institute of Complexity Science, College of Automation, Qingdao University, Qingdao, China; Yu, H., College of Automation, Qingdao University, Qingdao, China","Purpose: This paper aims to provide a theoretical principle for the stability control of robot climbing stairs, autonomously based on human–robot interaction. Through this research, tracked mobile robots with human-robot interaction will be extensively used in rescue in disaster, exploration on planetary, fighting in battle, and searching for survivors in collapsed buildings. Design/methodology/approach: This paper introduces the tracked mobile robot, based on human–robot interaction, and its six moving postures. The dynamic process of climbing stairs is analyzed, and the dynamic model of the robot is proposed. The dynamic stability criterion is derived when the tracked mobile robot contacts the stairs steps in one, two and more points. A further conduction of simulation on the relationship of the traction force and bearing force vs the velocity and acceleration in the three cases was carried out. Findings: This paper explains that the tracked mobile robot, based on human–robot interaction, can stably climb stairs so long as the velocity and acceleration satisfy the dynamic stability criterion as noted above. In addition, the experiment tests the correctness of dynamic stability analysis when the tracked mobile robot contacts the stair steps in one, two or more points. Originality/value: This paper provides the mechanical structure and working principle of the tracked mobile robot based on human–robot interaction and proposes an identification method of dynamic stability criterion when the robot contacts the stairs steps in one, two and more points. © 2019, Emerald Publishing Limited.","Climbing stairs; Dynamic model; Dynamic stability; Human–robot interaction; Tracked mobile robot","Dynamic models; Machine design; Mobile robots; Stability; Stability criteria; Stairs; Collapsed buildings; Design/methodology/approach; Dynamic stability analysis; Identification method; Mechanical structures; Robot interactions; Stability control; Tracked mobile robots; Human robot interaction",,,,,"JQ201419; 201702038; National Natural Science Foundation of China, NSFC: 61374062, 61603288, 61873136","This work was supported by the National Natural Science Foundation of China (Nos. 61873136, 61374062, 61603288), Science Foundation of Shandong Province for Distinguished Young Scholars (No. JQ201419), the Special Funds of Shandong Province for Post-doctoral Innovation Projects under Grant 201702038, Post-doctoral Application Research Project of Qingdao.",,,,,,,,,,"Ben-Tzvi, P., Ito, S., Goldenberg, A., (2007) Autonomous stair climbing with reconfigurable tracked mobile robot, pp. 1-6. , IEEE International Workshop on Robotic and Sensors Environments, Ottawa; 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Rao, W., Shi, J., Wang, J., Analysis of dynamic stability for articulated-tracked robot climbing stairs (2014) Journal of Mechanical Engineering, 50 (15), pp. 60-67; Tao, W., Ou, Y., Feng, H., Research on dynamics and stability in the stairs-climbing of a tracked mobile robot (2012) Proc of the International Journal of Advanced Robotic Systems, 9 (4), pp. 467-485; Vu, Q., Kim, B., Song, J., Autonomous stair climbing algorithm for a small four-tracked robot (2008) Proceedings of the International Conference on Control, 2008, pp. 2356-2360. , Seoul; Wells, P., Deguire, D., TALON: a universal unmanned ground vehicle platform, enabling the mission to be the focus (2005) Proceedings of the SPIE International Conference on Unmmaned Ground Vehicle Technology VII, pp. 747-757. , Orlando; Wu, Z., Hu, G., Feng, L., Collision avoidance for mobile robots based on artificial potential field and obstacle envelope modelling (2016) Assembly Automation, 36 (3), pp. 318-332; Yan, F., Wang, K., Xiao, J., A bio-inspired scan matching algorithm for mobile robots in outdoor environments (2016) Assembly Automation, 36 (2), pp. 159-171; Yang, C., Jiang, Y., He, W., Na, J., Li, Z., Xu, B., Adaptive parameter estimation and control design for robot manipulators with finite-time convergence (2018) IEEE Transactions on Industrial Electronics, 65 (10), pp. 8112-8123; Yang, C., Jiang, Y., Li, Z., He, W., Su, C.-Y., Neural control of bimanual robots with guaranteed global stability and motion precision (2017) IEEE Transactions on Industrial Informatics, 13 (3), pp. 1162-1171; Yang, C., Zeng, C., Liang, P., Li, Z., Li, R., Su, C.-Y., Interface design of a physical human robot interaction system for human impedance adaptive skill transfer (2018) IEEE Transactions on Automation Science and Engineering, 15 (1), pp. 329-340; Yoneda, K., Ota, Y., Hirose, S., Development of a hi-grip stair climbing crawer with hysteresis compliant blocks (2001) Proceedings of 4th International Conference on Climbing and Walking Robots, pp. 569-576. , Karlsruhe; Yu, J.Z., Liu, J.C., Wu, Z.X., Fang, H., Depth control of a bioinspired robotic dolphin based on sliding mode fuzzy control method (2018) IEEE Transactions on Industrial Electronics, 65 (3), pp. 2429-2438; Yu, J.Z., Sun, F.H., Xu, D., Tan, M., Embedded vision guided 3-D tracking control for robotic fish (2016) IEEE Transactions on Industrial Electronics, 63 (1), pp. 355-363; Yu, J.Z., Tan, M., Chen, J., Zhang, J.W., A survey on CPG-inspired control models and system implementation (2014) IEEE Transactions on Neural Networks and Learning Systems, 25 (3), pp. 441-456; Yu, J.Z., Wang, C., Xie, G.M., Coordination of multiple robotic fish with applications to underwater robot competition (2016) IEEE Transactions on Industrial Electronics, 63 (2), pp. 1280-1288; Zong, C., Gao, X.Y.Y., Kinematics analysis for obstacle-surmounting capability of a joint double-tracked robot (2016) Journal of Beijing Institute of Technology, 25 (2), pp. 202-210; Zong, C., Jiang, S., Guo, W., Obstacle-surmounting capability analysis of a joint double-tracked robot (2014) Proceedings of IEEE International Conference on Mechatronics and Automation (ICMA) 2014, pp. 723-728. , Tianjin, China","Ji, Z.; Institute of Complexity Science, China; email: jizhijian@pku.org.cn",,,"Emerald Group Holdings Ltd.",,,,,01445154,,ASAUD,,"English","Assem. Autom.",Article,"Final","",Scopus,2-s2.0-85063905572
"Ni B., Cao J., Mao Z., Cheng L., Shen H., Chen J.","57204599982;57360186400;57360766200;56937450700;57360327700;57360618600;","Research on real-time sensing technology of aerospace interactive behavior based on infrared array force tactile",2021,"Journal of Physics: Conference Series","2078","1","012065","","",,,"10.1088/1742-6596/2078/1/012065","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120445811&doi=10.1088%2f1742-6596%2f2078%2f1%2f012065&partnerID=40&md5=398d4ff073c96e78ff6d8a555ca8ad56","Measuring and Testing Business Department, Shanghai Precision Metrology and Test Research Institute, Shanghai, 201109, China","Ni, B., Measuring and Testing Business Department, Shanghai Precision Metrology and Test Research Institute, Shanghai, 201109, China; Cao, J., Measuring and Testing Business Department, Shanghai Precision Metrology and Test Research Institute, Shanghai, 201109, China; Mao, Z., Measuring and Testing Business Department, Shanghai Precision Metrology and Test Research Institute, Shanghai, 201109, China; Cheng, L., Measuring and Testing Business Department, Shanghai Precision Metrology and Test Research Institute, Shanghai, 201109, China; Shen, H., Measuring and Testing Business Department, Shanghai Precision Metrology and Test Research Institute, Shanghai, 201109, China; Chen, J., Measuring and Testing Business Department, Shanghai Precision Metrology and Test Research Institute, Shanghai, 201109, China","Aerospace robots can replace astronauts to complete dangerous work in the expanded space. How to efficiently and reliably interact with robots in complex and harsh space environment has become an urgent problem and technical challenge. Based on the infrared array force tactile technology, an infrared point force touch detection system is developed to realize the intention sensing of aerospace human-robot interaction. Based on multipoint positioning technology combined with image method and global combined optimal tracking method, the control software is designed, and the effective human-robot interaction in the process of cooperation is realized. Finally, the touch panel test effect of the system is given. This research can provide a theoretical basis for the practical application of aerospace human-robot interaction. © 2021 Institute of Physics Publishing. All rights reserved.",,"Aerospace engineering; Man machine systems; Manned space flight; Behavior-based; Humans-robot interactions; Infrared arrays; Interactive behavior; Point force; Real time sensing; Sensing technology; Space environment; Technical challenges; Urgent problems; Human robot interaction",,,,,"2019-RGZN-01077","This work was partly supported by Artificial Intelligence Program of Shanghai (Grant No. 2019-RGZN-01077).",,,,,,,,,,"Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Real-Time Human Pose Recognition in Parts from a Single Depth Image (2011) CVPR 2011, , IEEE. Colorado Springs; Wang, A.M., Dai, J.Q., The advancement of haptic in man-machine interaction (2007) Ind. Instrumm. Autom, 2, pp. 14-18; Lu, X., Chen, X.L., Sun, H.H., Haptic rendering methods for natural human-computer interaction: A review (2017) Chinese Journal of Scientific Instrument, 10, pp. 43-51; Song, A.G., Tian, . L., Ni, D.J., Muti-mode haptic interaction technique and its application (2017) Scientia Sinica Information, 47, pp. 1183-1197; Hao, F., Lu, W., Song, A.G., Recent Developments and Applications of Tactile Information Display Technology (2011) Measurement & Control Technology, 1, pp. 6-9; Zelinsky, A., Learning OpenCV-computer vision with the OpenCV library (Bradski, G.R. et al.; 2008) (2009) IEEE Robotics & Automation Magazine, 16, pp. 100-100","Ni, B.; Measuring and Testing Business Department, China; email: tongjinibo@aliyun.com",,,"IOP Publishing Ltd","2021 3rd International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2021","10 September 2021 through 12 September 2021",,174670,17426588,,,,"English","J. Phys. Conf. Ser.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85120445811
"Li X.-Y., Zhao Z.-Q., Zhang C.-S., Lu C.-H.","19933913100;57204598264;57275550300;7404806013;","Finite element based calculation method of human⁃robot interaction force [基于有限元的人体⁃机械手交互力计算方法]",2021,"Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)","51","5",,"1612","1619",,1,"10.13229/j.cnki.jdxbgxb20200501","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115894110&doi=10.13229%2fj.cnki.jdxbgxb20200501&partnerID=40&md5=ddb3118bc3e8b38e4c4d2db32bdcf012","School of Mechanical Engineering, Shandong University, Jinan, 250061, China; Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, Shandong University, Jinan, 250061, China; National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan, 250061, China","Li, X.-Y., School of Mechanical Engineering, Shandong University, Jinan, 250061, China, Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, Shandong University, Jinan, 250061, China, National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan, 250061, China; Zhao, Z.-Q., School of Mechanical Engineering, Shandong University, Jinan, 250061, China, Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, Shandong University, Jinan, 250061, China; Zhang, C.-S., School of Mechanical Engineering, Shandong University, Jinan, 250061, China, Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, Shandong University, Jinan, 250061, China; Lu, C.-H., School of Mechanical Engineering, Shandong University, Jinan, 250061, China, Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, Shandong University, Jinan, 250061, China, National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan, 250061, China","The proposed method builds surface equations of bones, muscle of human limb and robot fingers, divides finite elements of muscle tissue of human limbs, gradually obtains finger force of rehabilitation robot hand, and finally realizes the calculation and analysis of human-robot interaction force. In simulation verification of simplified model, the effects of displacement, position of interaction, elasticity modulus of human soft tissue and Poisson's ratio on human-robot interaction force are revealed, and the average relative error between theoretical calculation and simulation data varies from 10% to 15% when the displacement is 5 mm. The simulation outcomes indicate that the proposed theory is able to effectively calculate finger force of rehabilitation robot hand, calculate and evaluate human-robot interaction force. © 2021, Jilin University Press. All right reserved.","Deformable object grasping; Finite element; Human-robot interaction force; Humanoid rehabilitation robot; Mechatronic engineering","End effectors; Human robot interaction; Man machine systems; Muscle; Robotic arms; Deformable object; Deformable object grasping; Human limbs; Human-robot interaction force; Humanoid rehabilitation robot; Humans-robot interactions; Interaction forces; Mechatronic engineering; Object grasping; Rehabilitation robot; Finite element method",,,,,,,,,,,,,,,,"Hu, J, Hou, Z G, Chen, Y X, Lower limb rehabilitation robots and interactive control methods (2014) Acta Automatica Sinica, 40 (11), pp. 2377-2390. , [J]; Basteris, A, Nijenhuis, S M, Stienen, A H A, Training modalities in robot-mediated upper limb rehabilitation in stroke: a framework for classification based on a systematic review (2014) Journal of Neuroengineering and Rehabilitation, 11 (1), pp. 1-15. , [J]; CaoEn, -guo, LiuKun, JiShuo, Mechanical structure design and optimization of weight-support STS rehabilitation training system (2019) Journal of Jilin University(Engineering and Technology Edition), 49 (5), pp. 1558-1566. , 曹恩国, 刘坤, 吉硕, 等. 减重站起康复训练系统机械结构设计与优化[J]. 吉林大学学报: 工学版, 2019, 49(5): 1558-1566. [J]; ZhaoJian, -chen, CaoEn, -guo, Control methods for standing-up rehabilitation robot based on estimation and analysis of lower limb kinetics (2015) Journal of Jilin University(Engineering and Technology Edition), 45 (3), pp. 837-844. , 刘坤, 赵建琛, 曹恩国, 等. 基于下肢动力学检测分析的站起康复训练机器人控制[J]. 吉林大学学报: 工学版, 2015, 45(3): 837-844.LiuKun, [J]; Life science robotics https://www.lifescience-robotics.com, Today Robotics. [EB/OL].[2020-05-14]; Zhao, Z, Li, X, Lu, C, Compliant manipulation method for a nursing robot based on physical structure of human limb (2020) Journal of Intelligent & Robotic Systems, 100, pp. 973-986. , [J]; Gan, H, Li, X, Lu, C, Biomechanical study of human limbs under a robot hand manipulation using finite element analysis (2018) IEEE International Conference on Mechatronics and Automation, pp. 1856-1860. , [C] Changchun,China; YuJian, -jun, AnShuo, RuanXiao, -gang, Five-finger closed grasp algorithm based on humanoid manipulator (2019) Computer Measurement and Control, 27 (5), pp. 192-198. , 于建均, 安硕, 阮晓钢, 等. 基于仿人机械手的五指力封闭抓取算法[J]. 计算机测量与控制, 2019, 27(5): 192-198. [J]; ChaoJian, -gang, LinWan, -hong, Force closure based virtual hand stable grip generation method (2019) Chinese Journal of Computational Mechanics, 36 (4), pp. 548-554. , 邹俞, 晁建刚, 林万洪. 基于力封闭的虚拟手稳定抓持力生成方法[J]. 计算力学学报, 2019, 36(4): 548-554.ZouYu, [J]; Tian, J, Jia, Y B., Modeling deformations of general parametric shells grasped by a robot hand (2009) IEEE Transactions on Robotics, 26 (5), pp. 1297-1302. , [J]; Zaidi, L, Corrales, J A, Bouzgarrou, B C, Model-based strategy for grasping deformable objects using a multi-fingered robotic hand (2017) Robotics and Autonomous Systems, 95, pp. 196-206. , [J]; Kim, D, Maeda, Y, Komiyama, S., Caging-based grasping of deformable objects for geometry-based robotic manipulation[J] (2019) Robomech Journal, 6 (3), pp. 1-13",,,,"Editorial Board of Jilin University",,,,,16715497,,JDXGA,,"Chinese","Jilin Daxue Xuebao (Gongxueban)",Article,"Final","",Scopus,2-s2.0-85115894110
"Xiao C., Fan Y., Zhang J., Zhou R.","15057300500;57222599095;57222608544;8300014600;","People Do not Automatically Take the Level-1 Visual Perspective of Humanoid Robot Avatars",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00773-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103409026&doi=10.1007%2fs12369-021-00773-x&partnerID=40&md5=343cedb4bbac38cf53fdad79a7391ffe","Department of Psychology, School of Social and Behavioral Sciences, Nanjing University, Nanjing, 210023, China","Xiao, C., Department of Psychology, School of Social and Behavioral Sciences, Nanjing University, Nanjing, 210023, China; Fan, Y., Department of Psychology, School of Social and Behavioral Sciences, Nanjing University, Nanjing, 210023, China; Zhang, J., Department of Psychology, School of Social and Behavioral Sciences, Nanjing University, Nanjing, 210023, China; Zhou, R., Department of Psychology, School of Social and Behavioral Sciences, Nanjing University, Nanjing, 210023, China","Taking the perspective of others is critical for both human–human and human–robot interactions. Previous studies using the dot perspective task have revealed that people could automatically process what other people can see. In this study, following the classical dot perspective task, we showed that Chinese participants could not automatically process humanoid robot avatars’ perspective when only judging from self-perspective (Experiment 1) or randomly judging between self and avatar’s perspectives (Experiment 2), and people’s anthropomorphism tendency was related to the efficiency but not the automaticity of perspective-taking. These results revealed that human–human and human–robot interactions might be different in the basic visual process, and suggested the anthropomorphism tendency in people as an influential factor in human–robot interaction. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Anthropomorphism; Human–robot interaction; Individual differences; Visual perspective‐taking","Agricultural robots; Social robots; Humanoid robot; Influential factors; Level-1; Perspective taking; Robot interactions; Visual process; Anthropomorphic robots",,,,,"Major Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province: 030602, 2018SJZDA020","This study was funded by the Major Projects of Philosophy and Social Science Research in Jiangsu Universities (Grant Number 2018SJZDA020), and the Fourth Pilot-research Program for Human Spaceflight of China (Grant Number 030602).",,,,,,,,,,"Broadbent, E., Interactions with robots: the truths we reveal about ourselves (2017) Annu Rev Psychol, 68, pp. 627-652; Trafton, J.G., Cassimatis, N.L., Bugajska, M.D., Brock, D.P., Mintz, F.E., Schultz, A.C., Enabling effective human–robot interaction using perspective-taking in robots (2005) IEEE Trans Syst Man Cybern Part A Syst Hum, 35, pp. 460-470; Surtees, A., Samson, D., Apperly, I., Unintentional perspective-taking calculates whether something is seen, but not how it is seen (2016) Cognition, 148, pp. 97-105; Flavell, J.H., Everett, B.A., Croft, K., Flavell, E.R., Young children’s knowledge about visual perception: further evidence for the level 1-level 2 distinction (1981) Dev Psychol, 17, pp. 99-103; Samson, D., Apperly, I.A., Braithwaite, J.J., Andrews, B.J., Scott, S.E.B., Seeing it their way: evidence for rapid and involuntary computation of what other people see (2010) J Exp Psychol Hum Percept Perform, 36, pp. 1255-1266; Nielsen, M.K., Slade, L., Levy, J.P., Holmes, A., Inclined to see it your way: do altercentric intrusion effects in visual perspective taking reflect an intrinsically social process? 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(2016) Acta Physiol (Oxf), 164, pp. 165-168; Złotowski, J., Proudfoot, D., Yogeeswaran, K., Bartneck, C., Anthropomorphism: opportunities and challenges in human–robot interaction (2015) Int J Social Robot, 7, pp. 347-360; Duffy, B.R., Anthropomorphism and the social robot (2003) Robot Auton Syst, 42, pp. 177-190; Ferguson, H.J., Brunsdon, V.E.A., Bradford, E.E.F., Age of avatar modulates the altercentric bias in a visual perspective-taking task: ERP and behavioral evidence (2018) Cogn Affect Behav Neurosci, 18 (6), pp. 1298-1319; Park, B., Knörzer, L., Plass, J.L., Brünken, R., Emotional design and positive emotions in multimedia learning: an eyetracking study on the use of anthropomorphisms (2015) Comput Educ, 86, pp. 30-42; Stárková, T., Lukavský, J., Javora, O., Brom, C., Anthropomorphisms in multimedia learning: attract attention but do not enhance learning? (2019) J Comput Assist Learn, 35, pp. 555-568; Takahashi, K., Watanabe, K., Seeing objects as faces enhances object detection (2015) i-Perception, 6, pp. 1-14; Erle, T.M., Topolinski, S., The grounded nature of psychological perspective-taking (2017) J Pers Soc Psychol, 112, pp. 683-695; Zanatto, D., Patacchiola, M., Cangelosi, A., Goslin, J., Generalisation of anthropomorphic stereotype (2020) Int J Social Robot, 12, pp. 163-172; Bartneck, C., Bleeker, T., Bun, J., Fens, P., Riet, L., The influence of robot anthropomorphism on the feelings of embarrassment when interacting with robots (2010) J Behav Robot, 1, pp. 109-115","Xiao, C.; Department of Psychology, China; email: xiaocl@nju.edu.cn
Zhou, R.; Department of Psychology, China; email: rlzhou@nju.edu.cn",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Article in Press","",Scopus,2-s2.0-85103409026
"Lang L., Peng H., Xiao J., Lu H., Zhou Z.","56121103200;57196189787;49964924600;55729801700;8974315300;","Scale force control of a robot bearing augmentation exoskeleton",2021,"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2021",,,,"219","223",,,"10.1109/ICARM52023.2021.9536125","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116201539&doi=10.1109%2fICARM52023.2021.9536125&partnerID=40&md5=2db4b9794f2fe65728a3772b19bb3ba5","The Robotics Research Center, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, China; Hunan University of Finance and Economics, Changsha, 410000, China","Lang, L., The Robotics Research Center, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, China, Hunan University of Finance and Economics, Changsha, 410000, China; Peng, H., The Robotics Research Center, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, China; Xiao, J., The Robotics Research Center, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, China; Lu, H., The Robotics Research Center, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, China; Zhou, Z., The Robotics Research Center, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, China","Human performance augmentation exoskeletons have a wide range of applications in the military, industry, construction and other environments. The control methods of augmentation exoskeletons are crucial to ensure effective assistance and the safety of the systems. We proposed a scale force control method that considers both cognitive human-robot interaction and physical human-robot interaction. Experiments on an elbow joint exoskeleton verified the feasibility of the method. © 2021 IEEE.",,"Exoskeleton (Robotics); Force control; Human robot interaction; Man machine systems; Cognitive human-robot interactions; Control methods; Elbow joints; Human performance; Military industries; Physical humanrobot interaction (phri); Military applications",,,,,"National Natural Science Foundation of China, NSFC: 61773393, 61903131, U1813205","*This work is supported by National Science Foundation of China (No. U1813205, No.U1913202, No.61773393, No.61903131).",,,,,,,,,,"Tucker, M.R., Olivier, J., Pagel, A., Bleuler, H., Bouri, M., Lambercy, O., del Millán, J.R., Gassert, R., Control strategies for active lower extremity prosthetics and orthotics: A review (2015) Journal of Neuroengineering and Rehabilitation, 12 (1), pp. 1-12; Rupal, B.S., Rafique, S., Singla, A., Singla, E., Isaksson, M., Virk, G.S., Lower-limb exoskeletons: Research trends and regulatory guidelines in medical and non-medical applications (2017) International Journal of Advanced Robotic Systems, 14 (6), pp. 172-182; Herr, H., Exoskeletons and orthoses: Classification, design challenges and future directions (2009) Journal of Neuroengineering and Rehabilitation, 6 (1), pp. 21-22; Gilbert, K., Callan, P., (1968) Hardiman I Prototype, , General Electric Company, Schenectady, NY, GE Tech. 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S-68-1081; Raab, K., Krakow, K., Tripp, F., Jung, M., Effects of training with the rewalk exoskeleton on quality of life in incomplete spinal cord injury: A single case study (2016) Spinal Cord Series and Cases, 2, pp. 150-158; Tsukahara, A., Hasegawa, Y., Eguchi, K., Sankai, Y., Restoration of gait for spinal cord injury patients using hal with intention estimator for preferable swing speed (2014) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23 (2), pp. 308-318; Baunsgaard, C.B., Nissen, U.V., Brust, A.K., Frotzler, A., Ribeill, C., Kalke, Y.-B., León, N., Antepohl, W., Gait training after spinal cord injury: Safety, feasibility and gait function following 8 weeks of training with the exoskeletons from ekso bionics (2018) Spinal Cord, 56 (2), pp. 106-116; Zoss, A.B., Kazerooni, H., Chu, A., Biomechanical design of the Berkeley lower extremity exoskeleton (bleex) (2006) IEEE/ASME Transactions on Mechatronics, 11 (2), pp. 128-138; Yang, Z., Gu, W., Zhang, J., Gui, L., (2017) Force Control Theory and Method of Human Load Carrying Exoskeleton Suit, , Springer; Fontana, M., Vertechy, R., Marcheschi, S., Salsedo, F., Bergamasco, M., The body extender: A full-body exoskeleton for the transport and handling of heavy loads (2014) IEEE Robotics & Automation Magazine, 21 (4), pp. 34-44; Cornwall, W., (2015) In Pursuit of the Perfect Power Suit; Dollar, A.M., Herr, H., Lower extremity exoskeletons and active orthoses: Challenges and state-of-the-art (2008) IEEE Transactions on Robotics, 24 (1), pp. 144-158; Pons, J.L., (2008) Wearable Robots: Biomechatronic Exoskeletons, , John Wiley & Sons; Lee, H.-D., Lee, B.-K., Kim, W.-S., Han, J.-S., Shin, K.-S., Han, C.S., Human–robot cooperation control based on a dynamic model of an upper limb exoskeleton for human power amplification (2014) Mechatronics, 24 (2), pp. 168-176; Al-Shuka, H.F., Song, R., On low-level control strategies of lower extremity exoskeletons with power augmentation (2018) 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI), pp. 63-68; Chen, S., Chen, Z., Yao, B., Zhu, X., Zhu, S., Wang, Q., Song, Y., Adaptive robust cascade force control of 1-dof hydraulic exoskeleton for human performance augmentation (2016) IEEE/ASME Transactions on Mechatronics, 22 (2), pp. 589-600; Li, Z., Su, C.-Y., Wang, L., Chen, Z., Chai, T., Nonlinear disturbance observer-based control design for a robotic exoskeleton incorporating fuzzy approximation (2015) IEEE Transactions on Industrial Electronics, 62 (9), pp. 5763-5775","Lang, L.; The Robotics Research Center, China; email: langlin850214@sina.com",,"Gaitech Robotics;Publishing House of Electronics Industry;Quanser;Sensors;Springer;Unitree Robotics","Institute of Electrical and Electronics Engineers Inc.","6th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2021","3 July 2021 through 5 July 2021",,171776,,9780738133645,,,"English","IEEE Int. Conf. Adv. Robot. Mechatronics, ICARM",Conference Paper,"Final","",Scopus,2-s2.0-85116201539
"van Maris A., Zook N., Dogramadzi S., Studley M., Winfield A., Caleb-Solly P.","57193757921;57224238437;6507900733;8922780300;7006158694;16244225300;","A new perspective on robot ethics through investigating human–robot interactions with older adults",2021,"Applied Sciences (Switzerland)","11","21","10136","","",,,"10.3390/app112110136","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118167866&doi=10.3390%2fapp112110136&partnerID=40&md5=b5c8c57fdab4fb4c6576d0ea26799ff2","Bristol Robotics Laboratory, Faculty of Environment and Technology, University of the West of England, Bristol, BS16 1QY, United Kingdom; Faculty of Health and Applied Sciences, University of the West of England, Bristol, BS16 1QY, United Kingdom; Department of Automatic Control & Systems Engineering, University of Sheffield, Sheffield, S10 2TN, United Kingdom; School of Computer Science, University of Nottingham, Nottingham, NG7 2RD, United Kingdom","van Maris, A., Bristol Robotics Laboratory, Faculty of Environment and Technology, University of the West of England, Bristol, BS16 1QY, United Kingdom; Zook, N., Faculty of Health and Applied Sciences, University of the West of England, Bristol, BS16 1QY, United Kingdom; Dogramadzi, S., Department of Automatic Control & Systems Engineering, University of Sheffield, Sheffield, S10 2TN, United Kingdom; Studley, M., Bristol Robotics Laboratory, Faculty of Environment and Technology, University of the West of England, Bristol, BS16 1QY, United Kingdom; Winfield, A., Bristol Robotics Laboratory, Faculty of Environment and Technology, University of the West of England, Bristol, BS16 1QY, United Kingdom; Caleb-Solly, P., School of Computer Science, University of Nottingham, Nottingham, NG7 2RD, United Kingdom","This work explored the use of human–robot interaction research to investigate robot ethics. A longitudinal human–robot interaction study was conducted with self-reported healthy older adults to determine whether expression of artificial emotions by a social robot could result in emotional deception and emotional attachment. The findings from this study have highlighted that currently there appears to be no adequate tools, or the means, to determine the ethical impact and concerns ensuing from long-term interactions between social robots and older adults. This raises the question whether we should continue the fundamental development of social robots if we cannot determine their potential negative impact and whether we should shift our focus to the development of human–robot interaction assessment tools that provide more objective measures of ethical impact. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Attachment; Deception; Ethics; Older adults; Social robot",,,,,,"Horizon 2020 Framework Programme, H2020: 721619","Funding: This research and the APC were funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 721619 for the SOCRATES project.",,,,,,,,,,"Roveda, L., Haghshenas, S., Caimmi, M., Pedrocchi, N., Molinari Tosatti, L., Assisting operators in heavy industrial tasks: On the design of an optimized cooperative impedance fuzzy-controller with embedded safety rules (2019) Front. Robot. AI, 6, p. 75. , [CrossRef]; Yang, G., Lv, H., Zhang, Z., Yang, L., Deng, J., You, S., Du, J., Yang, H., Keep healthcare workers safe: Application of teleoperated robot in isolation ward for COVID-19 prevention and control (2020) Chin. J. Mech. 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Geriatr, 74, pp. 15-25. , [CrossRef]; Van Maris, A., Zook, N., Caleb-Solly, P., Studley, M., Winfield, A., Dogramadzi, S., Designing ethical social robots—A longitudinal field study with older adults (2020) Front. Robot. AI, 7, p. 1. , [CrossRef]; Arkin, R.C., Ulam, P., Wagner, A.R., Moral decision making in autonomous systems: Enforcement, moral emotions, dignity, trust, and deception (2012) Proc. IEEE, 100, pp. 571-589. , [CrossRef]; Danaher, J., Robot Betrayal: A guide to the ethics of robotic deception (2020) Ethics Inf. Technol, 22, pp. 117-128. , [CrossRef]; Kirby, R., Forlizzi, J., Simmons, R., Affective social robots (2010) Robot. Auton. Syst, 58, pp. 322-332. , [CrossRef]; Coeckelbergh, M., Humans, animals, and robots: A phenomenological approach to human–robot relations (2011) Int. J. Soc. Robot, 3, pp. 197-204. , [CrossRef]; Turkle, S., (2011) Alone Together: Why We Expect More from Technology and Less from Each Other, , Basic Books: New York, NY, USA; Reeves, B., Nass, C., (1996) Media equation theory, p. 5. , Retrieved March 2009; Borenstein, J., Arkin, R., Robots, ethics, and intimacy: The need for scientific research (2019) On the Cognitive, Ethical, and Scientific Dimensions of Artificial Intelligence, pp. 299-309. , Springer: Cham, Switzerland; Feil-Seifer, D., Mataric, M.J., Socially Assistive Robotics-Ethical Issues Related to Technology (2011) IEEE Robot. Autom. Mag, 18, pp. 24-31. , [CrossRef]; Sharkey, A., Robots and human dignity: A consideration of the effects of robot care on the dignity of older people (2014) Ethics Inf. Technol, 16, pp. 63-75. , [CrossRef]; Pandey, A.K., Gelin, R., A mass-produced sociable humanoid robot: Pepper: The first machine of its kind (2018) IEEE Robot. Autom. Mag, 25, pp. 40-48. , [CrossRef]; Carros, F., Meurer, J., Löffler, D., Unbehaun, D., Matthies, S., Koch, I., Wieching, R., Wulf, V., Exploring Human-Robot Interaction with the Elderly: Results from a Ten-Week Case Study in a Care Home Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1-12. , Honolulu, HI, USA, 25–30 April 2020; Unbehaun, D., Aal, K., Carros, F., Wieching, R., Wulf, V., Creative and Cognitive Activities in Social Assistive Robots and Older Adults: Results from an Exploratory Field Study with Pepper (2019) Proceedings of the 17th European Conference on Computer-Supported Cooperative Work: The International Venue on Practice-Centred Computing and the Design of Cooperation Technologies—Demos and Posters, Reports of the European Society for Socially Embedded Technologies, pp. 1-4. , https://dl.eusset.eu/handle/20.500.12015/3291, Salzburg, Austria, 8–12 June European Society for Socially Embedded Technologies (EUSSET): France, 2019; (accessed on 30 July 2021); Bechade, L., Dubuisson-Duplessis, G., Pittaro, G., Garcia, M., Devillers, L., Towards metrics of evaluation of pepper robot as a social companion for the elderly (2019) Advanced Social Interaction with Agents, pp. 89-101. , Springer: Cham, Switzerland; Allegra, D., Alessandro, F., Santoro, C., Stanco, F., Experiences in Using the Pepper Robotic Platform for Museum Assistance Applications Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 1033-1037. , Athens, Greece, 7–10 October 2018; Niemelä, M., Heikkilä, P., Lammi, H., Oksman, V., A social robot in a shopping mall: Studies on acceptance and stakeholder expectations (2019) Social Robots: Technological, Societal and Ethical Aspects of Human-Robot Interaction, pp. 119-144. , Springer: Cham, Switzerland; Pot, E., Monceaux, J., Gelin, R., Maisonnier, B., Choregraphe: A graphical tool for humanoid robot programming Proceedings of the RO-MAN 2009—The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 46-51. , Toyama, Japan, 27 September–2 October 2009; Van Maris, A., Zook, N., Caleb-Solly, P., Studley, M., Winfield, A., Dogramadzi, S., Ethical considerations of (contextually) affective robot behaviour (2018) Hybrid Worlds: Societal and Ethical Challenges, Proceedings of the International Conference on Robot Ethics and Standards (ICRES 2018), pp. 13-19. , New York, NY, USA, 20–21 August Clawar Associate Ltd.: Buckinghamshire, UK, 2018; Rosenthal-von der Pütten, A.M., Krämer, N.C., Hoffmann, L., Sobieraj, S., Eimler, S.C., An experimental study on emotional reactions towards a robot (2013) Int. J. Soc. Robot, 5, pp. 17-34. , [CrossRef]; Heerink, M., Kröse, B., Evers, V., Wielinga, B., Assessing acceptance of assistive social agent technology by older adults: The almere model (2010) Int. J. Soc. Robot, 2, pp. 361-375. , [CrossRef]; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot, 1, pp. 71-81. , [CrossRef]; Schifferstein, H.N., Zwartkruis-Pelgrim, E.P., Consumer-product attachment: Measurement and design implications (2008) Int. J. Des, 2, pp. 1-13; Watson, D., Clark, L.A., Tellegen, A., Development and validation of brief measures of positive and negative affect: The PANAS scales (1988) J. Personal. Soc. Psychol, 54, pp. 1063-1070. , [CrossRef]; Quirin, M., Kazén, M., Kuhl, J., When nonsense sounds happy or helpless: The implicit positive and negative affect test (IPANAT) (2009) J. Personal. Soc. Psychol, 97, pp. 500-516. , [CrossRef]; Bente, G., Senokozlieva, M., Pennig, S., Al-Issa, A., Fischer, O., Deciphering the secret code: A new methodology for the cross-cultural analysis of nonverbal behavior (2008) Behav. Res. Methods, 40, pp. 269-277. , [CrossRef]; De Graaf, M.M., Allouch, S.B., van Dijk, J.A., Long-term evaluation of a social robot in real homes (2016) Interact. Stud, 17, pp. 462-491. , [CrossRef]; Frey, S., Pool, J., (1976) A New Approach to the Analysis of Visible Behavior, , Departement of Psychology, University of Bern: Bern, Switzerland; Cohen, A.S., Minor, K.S., Najolia, G.M., Hong, S.L., A laboratory-based procedure for measuring emotional expression from natural speech (2009) Behav. Res. Methods, 41, pp. 204-212. , [CrossRef] [PubMed]; Galaz, Z., Mekyska, J., Mzourek, Z., Smekal, Z., Rektorova, I., Eliasova, I., Kostalova, M., Berankova, D., Prosodic analysis of neutral, stress-modified and rhymed speech in patients with Parkinson’s disease (2016) Comput. Methods Programs Biomed, 127, pp. 301-317. , [CrossRef]; Vinciarelli, A., Pantic, M., Bourlard, H., Pentland, A., Social signal processing: State-of-the-art and future perspectives of an emerging domain Proceedings of the 16th ACM International Conference on Multimedia, pp. 1061-1070. , Vancouver, BC, Canada, 26–31 October 2008; Nikolić, M., de Vente, W., Colonnesi, C., Bögels, S.M., Autonomic arousal in children of parents with and without social anxiety disorder: A high-risk study (2016) J. Child Psychol. Psychiatry, 57, pp. 1047-1055. , [CrossRef] [PubMed]; Kühnlenz, B., Erhart, M., Kainert, M., Wang, Z.Q., Wilm, J., Kühnlenz, K., Impact of trajectory profiles on user stress in close human–robot interaction (2018) at-Automatisierungstechnik, 66, pp. 483-491. , [CrossRef]; Dehais, F., Sisbot, E.A., Alami, R., Causse, M., Physiological and subjective evaluation of a human–robot object hand-over task (2011) Appl. Ergon, 42, pp. 785-791. , [CrossRef] [PubMed]; Boersma, P., Praat, a system for doing phonetics by computer (2001) Glot. Int, 5, pp. 341-345; Baek, H.J., Cho, C.H., Cho, J., Woo, J.M., Reliability of ultra-short-term analysis as a surrogate of standard 5-min analysis of heart rate variability (2015) Telemed. e-Health, 21, pp. 404-414. , [CrossRef] [PubMed]; Esco, M.R., Flatt, A.A., Ultra-short-term heart rate variability indexes at rest and post-exercise in athletes: Evaluating the agreement with accepted recommendations (2014) J. Sport. Sci. Med, 13, pp. 535-541; Tarvainen, M.P., Niskanen, J.P., Lipponen, J.A., Ranta-Aho, P.O., Karjalainen, P.A., Kubios HRV–heart rate variability analysis software (2014) Comput. Methods Programs Biomed, 113, pp. 210-220. , [CrossRef] [PubMed]; Van Maris, A., Dogramadzi, S., Zook, N., Studley, M., Winfield, A., Caleb-Solly, P., Speech Related Accessibility Issues in Social Robots Proceedings of the Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 505-507. , Cambridge, UK, 23–26 March 2020; Kahn, P.H., Shen, S., NOC NOC, Who’s There? A New Ontological Category (NOC) for Social Robots (2017) New Perspectives on Human Development, pp. 13-142. , Cambridge University Press: Cambridge, UK; Lim, V., Rooksby, M., Cross, E.S., Social robots on a global stage: Establishing a role for culture during human–robot interaction (2021) Int. J. Soc. Robot, 13, pp. 1307-1333. , [CrossRef]; Gervasi, R., Mastrogiacomo, L., Franceschini, F., A conceptual framework to evaluate human–robot collaboration (2020) Int. J. Adv. Manuf. Technol, 108, pp. 841-865. , [CrossRef]","van Maris, A.; Bristol Robotics Laboratory, United Kingdom; email: anouk.vanmaris@brl.ac.uk",,,"MDPI",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85118167866
"Rea D.J., Schneider S., Kanda T.","55193434800;36148533200;7403134618;","""Is this all you can do? Harder!"": The effects of (Im)polite robot encouragement on exercise effort",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444660","225","233",,1,"10.1145/3434073.3444660","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102775003&doi=10.1145%2f3434073.3444660&partnerID=40&md5=6a131f023b4b82c661bbb760507a40ce","University of New Brunswick, Kyoto University, Japan; Bielefeld University, Germany; Kyoto University, Japan","Rea, D.J., University of New Brunswick, Kyoto University, Japan; Schneider, S., Bielefeld University, Germany; Kanda, T., Kyoto University, Japan","Most social robot behaviors in human-robot interaction are designed to be polite, but there is little research about how or when a robot could be impolite, and if that may ever be beneficial. We explore the potential benefits and tradeoffs of different politeness levels for human-robot interaction in an exercise context. We designed impolite and polite phrases for a robot exercise trainer and conducted a 24-person experiment where people squat in front of the robot as it uses (im)polite phrases to encourage them. We found participants exercised harder and felt competitive with the impolite robot, while the polite robot was found to be friendly, but sometimes uncompelling and disingenuous. Our work provides evidence that human-robot interaction should continue to aim for more nuanced and complex models of communication. © 2021 IEEE Computer Society. All rights reserved.","Communication; Encouragement; Feedback; Human-robot interaction; Impoliteness; Politeness; Socially assistive robot","Agricultural robots; Behavioral research; Man machine systems; Complex model; Potential benefits; Robot behavior; Social robots",,,,,"Japan Science and Technology Agency, JST: CREST JPMJCR17A2","“This research was supported by JST CREST JPMJCR17A2, Japan.”",,,,,,,,,,"Akalin, N., The influence of feedback type in robot-assisted training (2019) Multimodal Technologies and Interaction., 3 (4), p. 67. , https://doi.org/10.3390/mti3040067, Oct; Banh, A., Inspector Baxter: The Social Aspects of Integrating a Robot as a Quality Inspector in an Assembly Line (2015) Human-Agent Interaction; Borg, G., (1998) Borg's Perceived Exertion and Pain Scales., , Human Kinetics; Castro-González, A., The effects of an impolite vs. A polite robot playing rock-paper-scissors (2016) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9979 LNAI, 2016, pp. 306-316. , https://doi.org/10.1007/978-3-319-47437-3-30; Ciocirlan, S.-D., Human-robot team: Effects of communication in analyzing trust (2019) Robot and Human Interactive Communication (RO-MAN), pp. 1-7. , Oct; Culpeper, J., Towards an anatomy of impoliteness (1996) Journal of Pragmatics., 25 (3), pp. 349-367. , https://doi.org/10.1016/0378-2166(95)00014-3; Denisova, A., The convergence of player experience questionnaires (2016) Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play, pp. 33-37. , New York, NY, USA, Oct; Dynel, M., The landscape of impoliteness research (2015) Journal of Politeness Research., 11 (2), pp. 329-354. , https://doi.org/10.1515/pr-2015-0013; Edwards, C., Initial interaction expectations with robots: Testing the human-to-human interaction script (2016) Communication Studies., 67 (2), pp. 227-238. , https://doi.org/10.1080/10510974.2015.1121899; Fasola, J., Matarić, M.J., Socially assistive robot exercise coach: Motivating older adults to engage in physical exercise (2013) Experimental Robotics., pp. 463-479. , Springer; Ghazali, A.S., Pardon the rude robot: Social cues diminish reactance to high controlling language (2017) RO-MAN 2017-26th IEEE International Symposium on Robot and Human Interactive Communication. 2017-Janua, pp. 411-417. , https://doi.org/10.1109/ROMAN.2017.8172335; Goffman, E., (1967) Interaction Ritual: Essays on Face-to-face Interaction, , Aldine; Groom, V., Critic, compatriot, or chump?: Responses to robot blame attribution (2010) 5th ACM/IEEE International Conference on Human-Robot Interaction, HRI 2010, pp. 211-217. , https://doi.org/10.1145/1734454.1734545; Ham, J., Midden, C.J.H., A persuasive robot to stimulate energy conservation: The influence of positive and negative social feedback and task similarity on energy-consumption behavior (2014) International Journal of Social Robotics., 6 (2), pp. 163-171. , https://doi.org/10.1007/s12369-013-0205-z, Apr; Hattie, J., Timperley, H., The power of feedback (2007) Review of Educational Research., 77 (1), pp. 81-112. , https://doi.org/10.3102/003465430298487; Kaiser, F.G., How to make nonhumanoid mobile robots more likable: Employing kinesic courtesy cues to promote appreciation (2019) Applied Ergonomics., 78, pp. 70-75. , https://doi.org/10.1016/j.apergo.2019.02.004, October 2018; Karl, K.A., The impact of feedback and self-efficacy on performance in training (1993) Journal of Organizational Behavior., 14 (4), pp. 379-394. , https://doi.org/10.1002/job.4030140409, Jul; Kidokoro, H., Will i bother here?-A robot anticipating its influence on pedestrian walking comfort (2013) Human-Robot Interaction, pp. 259-266. , Mar; Lee, J.R., Nass, C.I., Trust in computers: The computers-are-social-actors (CASA) paradigm and trustworthiness perception in human-computer communication (2010) Trust and Technology in A Ubiquitous Modern Environment: Theoretical and Methodological Perspectives. IGI Global., pp. 1-15; Lee, K., Hey alexa! A magic spell of social glue?: Sharing a smart voice assistant speaker and its impact on users' perception of group harmony (2020) Information Systems Frontiers., 22 (3), pp. 563-583. , https://doi.org/10.1007/s10796-019-09975-1, Jun; Leite, I., Long-term interactions with empathic robots: Evaluating perceived support in children (2012) International Conference on Social Robotics, pp. 298-307; Leyzberg, D., Personalizing robot tutors to individuals' learning differences (2014) Human-robot Interaction, pp. 423-430. , New York, New York, USA; Liu, P., It's not polite to point Generating socially-appropriate deictic behaviors towards people (2013) Human-Robot Interaction, pp. 267-274. , Mar; Locher, M., Situated impoliteness: The interface between relational work and identity construction (2011) Situated Politeness. A&C Black; Max, E.J., Is silence really golden? Effect of encouragement from a partner or trainer on active video game play (2018) International Journal of Sport and Exercise Psychology., 16 (3), pp. 261-275. , https://doi.org/10.1080/1612197X.2016.1199580; Mumm, J., Mutlu, B., Human-robot proxemics: Physical and psychological distancing in human-robot interaction (2011) Proceedings of the 6th International Conference on Human-robot Interaction-HRI '11, p. 331. , New York, New York, USA; Park, E., The effects of a robot instructor's positive vs. Negative feedbacks on attraction and acceptance towards the robot in classroom (2011) International Conference on Social Robotics, pp. 135-141; Perelmutter, R., Klassika zhanra: The flamewar as a genre in the Russian blogosphere (2013) Journal of Pragmatics., 45 (1), pp. 74-89. , https://doi.org/10.1016/j.pragma.2012.10.006, Jan; Porath, C.L., Erez, A., Does rudeness really matter? The effects of rudeness on task performance and helpfulness (2007) Academy of Management Journal., 50 (5), pp. 1181-1197. , https://doi.org/10.2307/20159919; Resnick, B., Jenkins, L.S., Testing the reliability and validity of the self-efficacy for exercise scale (2000) Nursing Research., 49 (3), pp. 154-159. , https://doi.org/10.1097/00006199-200005000-00007, May; Salem, M., Effects of politeness and interaction context on perception and experience of HRI (2013) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8239 LNAI, pp. 531-541. , https://doi.org/10.1007/978-3-319-02675-6-53; Salem, M., To err is human(-like): Effects of robot gesture on perceived anthropomorphism and likability (2013) International Journal of Social Robotics, pp. 313-323; Schneider, S., Kummert, F., Comparing robot and human guided personalization: Adaptive exercise robots are perceived as more competent and trustworthy (2020) International Journal of Social Robotics, , https://doi.org/10.1007/s12369-020-00629-w; Schneider, S., Kummert, F., Motivational effects of acknowledging feedback from a socially assistive robot (2016) International Conference on Social Robotics, pp. 870-879; Schunk, D.H., Progress self-monitoring (1982) The Journal of Experimental Education., 51 (2), pp. 89-93. , https://doi.org/10.1080/00220973.1982.11011845, Dec; Seo, S.H., Investigating people's rapport building and hindering behaviors when working with a collaborative robot (2018) International Journal of Social Robotics., 10 (1), pp. 147-161. , https://doi.org/10.1007/s12369-017-0441-8; Short, E., No fair!! An interaction with a cheating robot (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 219-226. , https://doi.org/10.1109/HRI.2010.5453193; Staw, B.M., Leadership in the locker room: How the intensity of leaders' unpleasant affective displays shapes team performance (2019) Journal of Applied Psychology, , https://doi.org/10.1037/apl0000418; Strait, M., Investigating the effects of robot communication strategies in advice-giving situations based on robot appearance, interaction modality and distance (2014) ACM/IEEE International Conference on Human-Robot Interaction, pp. 479-486. , https://doi.org/10.1145/2559636.2559670; Swift-Spong, K., Effects of comparative feedback from a Socially Assistive Robot on self-efficacy in post-stroke rehabilitation (2015) IEEE International Conference on Rehabilitation Robotics. 2015, pp. 764-769. , https://doi.org/10.1109/ICORR.2015.7281294, Sept; Weinberg, R., Expectations and performance: An empirical test of bandura's self-efficacy theory (1979) Journal of Sport Psychology., 1 (4), pp. 320-331. , https://doi.org/10.1123/jsp.1.4.320, Dec; Woolum, A., Rude color glasses: The contaminating effects of witnessed morning rudeness on perceptions and behaviors throughout the workday (2017) Journal of Applied Psychology., 102 (12), pp. 1658-1672. , https://doi.org/10.1037/apl0000247; You, S., When the robot criticizes you...: Self-serving bias in human-robot interaction (2011) Human-robot Interaction, p. 295. , New York, New York, USA",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS)","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167644,21672148,9781450382892,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102775003
"Pelikan H., Robinson F.A., Keevallik L., Velonaki M., Broth M., Bown O.","57193572237;57215966842;25936365200;24073920300;23970080000;27867522200;","Sound in human-robot interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444871","706","708",,,"10.1145/3434074.3444871","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102771870&doi=10.1145%2f3434074.3444871&partnerID=40&md5=e901c85a5b9927a67c48545e6a7c5554","Linköping University, Linköping, Sweden; Creative Robotics Lab, University of New South Wales, Sydney, Australia; Interactive Media Lab, University of New South Wales, Sydney, Australia","Pelikan, H., Linköping University, Linköping, Sweden; Robinson, F.A., Creative Robotics Lab, University of New South Wales, Sydney, Australia; Keevallik, L., Linköping University, Linköping, Sweden; Velonaki, M., Creative Robotics Lab, University of New South Wales, Sydney, Australia; Broth, M., Linköping University, Linköping, Sweden; Bown, O., Interactive Media Lab, University of New South Wales, Sydney, Australia","Robot sound spans a wide continuum, from subtle motor hums, through music, bleeps and bloops, to human-inspired vocalizations, and can be an important means of communication for robotic agents. This first workshop on sound in HRI aims to bring together interdisciplinary perspectives on sound, including design, conversation analysis, (computational) linguistics, music, engineering and psychology. The goal of the workshop is to stimulate interdisciplinary exchange and to form a more coherent overview of perspectives on how sound can facilitate human-robot interaction. During the half-day workshop, we will explore (1) the diverse application opportunities of sound in human-robot interaction, (2) strategies for designing sonic human-robot interactions, and (3) methodologies for the evaluation of robot sound. Workshop outcomes will be documented on a dedicated website and are planned to be collected in a special issue. © 2021 Owner/Author.","Human-robot interaction; Non-lexical sounds; Robot sound; Robotic musicianship; Semantic-free utterances; Sonic interaction design","Agricultural robots; Man machine systems; Conversation analysis; Diverse applications; Half-day workshop; Robotic agents; Social robots",,,,,,,,,,,,,,,,"Hoffman, G., Bauman, S., Vanunu, K., Robotic experience companionship in music listening and video watching (2016) Personal and Ubiquitous Computing, 20 (1), pp. 51-63. , https://doi.org/10.1007/s00779-015-0897-1, Feb 2016; Pelikan, M.H.R., Broth, M., Keevallik, L., Are you sad, cozmo?"": How humans make sense of a home robot's emotion displays (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (Cambridge, United Kingdom) (HRI '20), pp. 461-470. , https://doi.org/10.1145/3319502.3374814, Association for Computing Machinery, New York, NY, USA; Tennent, H., Moore, D., Jung, M., Ju, W., Good vibrations: How consequential sounds affect perception of robotic arms (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN), pp. 928-935. , https://doi.org/10.1109/ROMAN.2017.8172414, IEEE, Lisbon; Weinberg, G., Bretan, M., Hoffman, G., Driscoll, S., Robotic musicianship: Embodied artificial creativity and mechatronic musical expression (2020) Springer Nature, 8; Yilmazyildiz, S., Read, R., Belpeame, T., Verhelst, W., Review of semantic-free utterances in social human-robot interaction (2016) International Journal of Human-Computer Interaction, 32 (1), pp. 63-85. , https://doi.org/10.1080/10447318.2015.1093856, Jan 2016",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102771870
"Simon O., Neuhofer B., Egger R.","57217058925;55582240100;55782025300;","Human-robot interaction: Conceptualising trust in frontline teams through LEGO® Serious Play®",2020,"Tourism Management Perspectives","35",,"100692","","",,8,"10.1016/j.tmp.2020.100692","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085968932&doi=10.1016%2fj.tmp.2020.100692&partnerID=40&md5=61e3514d0ab352655d6675099bf4f164","University of Applied Sciences, Urstein Süd 1, Puch bei, Hallein  5412, Austria","Simon, O., University of Applied Sciences, Urstein Süd 1, Puch bei, Hallein  5412, Austria; Neuhofer, B., University of Applied Sciences, Urstein Süd 1, Puch bei, Hallein  5412, Austria; Egger, R., University of Applied Sciences, Urstein Süd 1, Puch bei, Hallein  5412, Austria","Due to fast-paced developments in the technological sphere, we witness robots becoming commonplace in hospitality. While research has focused on technical and functional aspects of robots, the ways in which robots could become effective members of human service teams are less understood. By taking a human-robot interaction (HRI) lens, this study investigates the impact of integrating non-human service providers into human teams with a focus on trust. A qualitative focus group approach with the LEGO® Serious Play® method is employed to open novel avenues in tourism research, and to build, visualise and distil what human-robot interaction in hospitality frontline teams could look like. The study's contribution is a conceptual framework that reveals three dominant factors of trust and their sub-dimensions necessary for successful future human-robot interaction in frontline teams in tourism, hospitality and the wider service sector. © 2020 Elsevier Ltd","Co-creation; Collaboration; Hospitality; Human-robot interaction; LEGO® serious play®; Robotics; Trust",,,,,,,,,,,,,,,,,"Agarwal, T., Autonomous robots and their types with applications (2015), https://www.elprocus.com/different-types-of-autonomous-robots-and-real-time-applications/, (online) (Accessed 20 August 2019); Amineh, R.J., Asl, D., Review of constructivism and social constructivism (2015) Journal of Social Sciences, Literature and Languages, 1 (1), pp. 9-16; Bainbridge, W.A., Hart, J., Kim, E.S., Scassellati, B., The effect of presence on human-robot interaction (2008) Proceedings of the 17th IEEE symposium on robot and human interactive community, Munich, Germany, pp. 701-706; Baker, A., Phillips, E., Ullman, D., Keebler, J.R., Toward an understanding of trust repair in human-robot interaction (2018) Current research and future directions. 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"Lu Z., Wang H., Ma Z., Shen X., Marko P., Liu Y., Shan C., Ge L., Li B., Marjan M.","55976896200;57218315722;57209281903;57194336465;57218315685;57200703222;56856503500;57203983641;57056604400;57210775497;","Design of digital training system for cerebral palsy rehabilitation based on speech recognition [基于语音识别的脑瘫康复数字训练系统设计]",2020,"Gaojishu Tongxin/Chinese High Technology Letters","30","5",,"526","532",,1,"10.3772/j.issn.1002-0470.2020.05.012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088780319&doi=10.3772%2fj.issn.1002-0470.2020.05.012&partnerID=40&md5=ab22a102212c9c4a5469edb832c6d70f","School of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu, 215500, China; School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China; Department of Rehabilitation Medicine, Changshu No.2 People Hospital, Changshu, 215500, China; Faculty of Technical Sciences, University of Novi Sad, Novi Sad, 21000, Serbia; Management and Service Center, Changshu Science and Technology Innovation Park, Changshu, 215500, China; State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110014, China; Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, 462000, Slovenia","Lu, Z., School of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu, 215500, China; Wang, H., School of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu, 215500, China, School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China; Ma, Z., School of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu, 215500, China, School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China; Shen, X., Department of Rehabilitation Medicine, Changshu No.2 People Hospital, Changshu, 215500, China; Marko, P., Faculty of Technical Sciences, University of Novi Sad, Novi Sad, 21000, Serbia; Liu, Y., School of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu, 215500, China; Shan, C., School of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu, 215500, China; Ge, L., Management and Service Center, Changshu Science and Technology Innovation Park, Changshu, 215500, China; Li, B., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110014, China; Marjan, M., Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, 462000, Slovenia","A digital speech training system for children with cerebral palsy is designed based on speech recognition. The combination of human robot interaction (HRI) technology and bionic manipulator motion control can improve the effect of cerebral palsy (CP) rehabilitation training. In this system, the controller adopts Arduino MEGA 2560 as the main controller, and the display LCD1602 serves as the human robot interaction data display interface. The human-machine voice interaction function is realized by the LD3320 voice chip. The human robot interaction function is developed through the Labview environment, which enables real-time interactive training and evaluation of human and robot gestures and actions. The system can train the reaction ability, language expression ability and the ability of recognizing digital gestures of cerebral palsy patients, and provide a key technology for improving the rehabilitation training system of cerebral palsy. © 2020, Executive Office of the Journal. All right reserved.","Cerebral palsy (CP) rehabilitation training; Human robot interaction (HRI); Robot-assistant system; Speech recognition",,,,,,,,,,,,,,,,,", (6). , 马英杰, 陈骥, 帅杰. 基于语音识别的失语症康复治疗仪软件设计与实现[J]. 生物医学工程学杂志, 2006, 23 1343-1346; (2017), 郑义东. 言语沟通障碍儿童辅助沟通及康复训练系统的研究与实现[D]. 杭州: 浙江大学计算机科学与技术学院; (2008), 邓杏娟. 基于语音识别技术的失语症辅助诊断及康复治疗系统的研究[D]. 重庆大学生物工程学院; (2013), 肖广兵. ARM嵌入式开发实例-基于STM32的系统设计[M]. 北京: 电子工业出版社; , (11). , 陈喜春. 基于LD3320语音识别专用芯片实现的语音控制[J]. 电子技术, 2011, 38 20-21; 卢振利, 将睿萱, 马志鹏, 等. 面向机器人辅助脑瘫康复的语义训练系统设计[J]. 高技术通讯, 2019, 29(2): 183-188; , (1). , 杜希栋, 王志伟, 潘黎, 等. 基于LD3320的非特定语音控制小车设计[J]. 信息技术, 2015 53-55; , (2). , 洪家平. LD3320的嵌入式语音识别系统的应用[J]. 单片机与嵌入式系统应用, 2012, 12 47-49; , (11). , 韩宇飞. 基于LD3320的语音识别系统在家庭电路中的模拟应用[J]. 无线互联科技, 2015 66-68; , (20). , 袁嘉嵘. LabVIEW电子电路模拟仿真设计[J]. 电子技术与软件工程, 2019 105-106; 卢振利, 蒋睿萱, 马志鹏, 等. 基于表情和语音交互的脑瘫康复训练系统[J]. 高技术通讯, 2019, 29(3): 287-294; , (1). , 权爱珍, 连惠娟, 李丹. 脑瘫儿童语言认知训练对肢体功能康复的促进作用[J]. 临床医药实践, 2014, 23 6-8","Lu, Z.; School of Electrical Engineering and Automation, China; email: zhenlilu@cslg.cn",,,"Inst. of Scientific and Technical Information of China",,,,,10020470,,,,"Chinese","Gaojishu Tongxin",Article,"Final","",Scopus,2-s2.0-85088780319
"Roveda L., Maskani J., Franceschi P., Abdi A., Braghin F., Molinari Tosatti L., Pedrocchi N.","56031943800;57215031277;57207108440;57225391494;6701856637;57200040737;14016736100;","Model-Based Reinforcement Learning Variable Impedance Control for Human-Robot Collaboration",2020,"Journal of Intelligent and Robotic Systems: Theory and Applications","100","2",,"417","433",,14,"10.1007/s10846-020-01183-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081916731&doi=10.1007%2fs10846-020-01183-3&partnerID=40&md5=a15ef5afc1022f6b85bad69010a0cdde","Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA-CNR), Milano, 20133, Italy; Istituto Dalle Molle di studi sull’Intelligenza Artificiale (IDSIA), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Università della Svizzera Italiana (USI) IDSIA-SUPSI, Manno, 6928, Switzerland; School of Industrial and Information Engineering Politecnico di Milano, Milano, 20156, Italy","Roveda, L., Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA-CNR), Milano, 20133, Italy, Istituto Dalle Molle di studi sull’Intelligenza Artificiale (IDSIA), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Università della Svizzera Italiana (USI) IDSIA-SUPSI, Manno, 6928, Switzerland; Maskani, J., School of Industrial and Information Engineering Politecnico di Milano, Milano, 20156, Italy; Franceschi, P., Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA-CNR), Milano, 20133, Italy; Abdi, A., Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA-CNR), Milano, 20133, Italy; Braghin, F., School of Industrial and Information Engineering Politecnico di Milano, Milano, 20156, Italy; Molinari Tosatti, L., Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA-CNR), Milano, 20133, Italy; Pedrocchi, N., Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA-CNR), Milano, 20133, Italy","Industry 4.0 is taking human-robot collaboration at the center of the production environment. Collaborative robots enhance productivity and flexibility while reducing human’s fatigue and the risk of injuries, exploiting advanced control methodologies. However, there is a lack of real-time model-based controllers accounting for the complex human-robot interaction dynamics. With this aim, this paper proposes a Model-Based Reinforcement Learning (MBRL) variable impedance controller to assist human operators in collaborative tasks. More in details, an ensemble of Artificial Neural Networks (ANNs) is used to learn a human-robot interaction dynamic model, capturing uncertainties. Such a learned model is kept updated during collaborative tasks execution. In addition, the learned model is used by a Model Predictive Controller (MPC) with Cross-Entropy Method (CEM). The aim of the MPC+CEM is to online optimize the stiffness and damping impedance control parameters minimizing the human effort (i.e, minimizing the human-robot interaction forces). The proposed approach has been validated through an experimental procedure. A lifting task has been considered as the reference validation application (weight of the manipulated part: 10 kg unknown to the robot controller). A KUKA LBR iiwa 14 R820 has been used as a test platform. Qualitative performance (i.e, questionnaire on perceived collaboration) have been evaluated. Achieved results have been compared with previous developed offline model-free optimized controllers and with the robot manual guidance controller. The proposed MBRL variable impedance controller shows improved human-robot collaboration. The proposed controller is capable to actively assist the human in the target task, compensating for the unknown part weight. The human-robot interaction dynamic model has been trained with a few initial experiments (30 initial experiments). In addition, the possibility to keep the learning of the human-robot interaction dynamics active allows accounting for the adaptation of human motor system. © 2020, Springer Nature B.V.","Human-robot collaboration; Industry 4.0; Machine learning; Model-based reinforcement learning control; Variable impedance control","Computational electromagnetics; Controllers; Dynamic models; Industry 4.0; Learning systems; Man machine systems; Neural networks; Optimization; Predictive control systems; Reinforcement learning; Experimental procedure; Human-robot collaboration; Model predictive controllers; Model-based reinforcement learning; Optimized controllers; Production environments; Stiffness and damping; Variable impedance control; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020: 779963","This project has received funding from the European Union’s Horizon 2020 research and innovation programme, via an Open Call issued and executed under Project EUROBENCH (grant agreement N ∘ 779963).",,,,,,,,,,"Lasi, H., Fettke, P., Kemper, H.G., Industry 4.0 (2014) Business & Information Systems Engineering, 6 (4), pp. 239-242; Longo, F., Nicoletti, L., Padovano, A., Smart operators in industry 4.0: a human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context (2017) Comput. 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Fuzzy Sys., 27 (8), pp. 1587-1601","Roveda, L.; Istituto Dalle Molle di studi sull’Intelligenza Artificiale (IDSIA), Switzerland; email: loris.roveda@idsia.ch",,,"Springer Science+Business Media B.V.",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","",Scopus,2-s2.0-85081916731
"Westhoven M., Tegtmeier P.","56964308200;57200210841;","Influence of Personality Traits on Helping Behaviour in Human-Robot Interaction",2021,"Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO","2021-July",,,"78","84",,,"10.1109/ARSO51874.2021.9542832","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116469872&doi=10.1109%2fARSO51874.2021.9542832&partnerID=40&md5=db70d6bc34d87a3c0083e36af771bd09","German Federal Institute for Occupational Safety and Health BAuA, Dortmund, Germany","Westhoven, M., German Federal Institute for Occupational Safety and Health BAuA, Dortmund, Germany; Tegtmeier, P., German Federal Institute for Occupational Safety and Health BAuA, Dortmund, Germany","Trait models of personality can be used to predict prosocial behaviour. In the research field of human-robot interaction, there are many hints that interaction patterns learned from interacting with other humans also guide human behaviour regarding robots. This leads to the question, whether behaviour towards robots can be predicted by trait models of personality as well. To answer this question for the case of helping behaviour towards robots, an experiment was designed in which 50 participants answered questionnaires on personality (HEXACO-PI-R-100) and on negative attitudes towards robots (NARS), and were observed while being repeatedly asked for help by a robot. The personality dimensions of Conscientiousness and Openness influenced the observed behaviour significantly. These results show that personality also affects human-robot interaction. Nonetheless, this present work is only a first step towards a more thorough understanding of the influencing factors of successful human-robot interaction. © 2021 IEEE.",,"Agricultural robots; Behavioral research; Man machine systems; Surveys; Helping behaviours; Human behaviors; Humans-robot interactions; Interaction pattern; Personality dimensions; Personality traits; Research fields; Human robot interaction",,,,,,,,,,,,,,,,"Rosenthal, S., Veloso, M., Mobile robot planning to seek help with spatially-situated tasks (2012) Twenty-Sixth Aaai Conference on Artificial Intelligence; Cha, E., Matarić, M., Fong, T., Nonverbal signaling for nonhumanoid robots during human-robot collaboration (2016) The Eleventh ACM/IEEE International Conference on Human Robot Interaction. 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Ieee, pp. 850-855; Robert, L., Personality in the human robot interaction literature: A review and brief critique (2018) Personality in the Human Robot Interaction Literature: A Review and Brief Critique, Proceedings of the 24th Americas Conference on Information Systems, pp. 16-18. , Robert, LP (2018), Aug; Woods, S., Dautenhahn, K., Kaouri, C., Boekhorst, R., Koay, K.L., Is this robot like me? Links between human and robot personality traits (2005) 5th IEEE-RAS International Conference on Humanoid Robots, 2005. Ieee, pp. 375-380; Walters, M.L., Dautenhahn, K., Te Boekhorst, R., Koay, K.L., Kaouri, C., Woods, S., Nehaniv, C., Werry, I., The influence of subjects' personality traits on personal spatial zones in a humanrobot interaction experiment (2005) Roman 2005. Ieee International Workshop on Robot and Human Interactive Communication, 2005. Ieee, pp. 347-352; Tapus, A., Tapus, C., Matarić, M.J., User-robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy (2008) Intelligent Service Robotics, 1 (2), p. 169; Leite, I., Pereira, A., Mascarenhas, S., Martinho, C., Prada, R., Paiva, A., The influence of empathy in human-robot relations (2013) International Journal of Human-computer Studies, 71 (3), pp. 250-260; Correia, F., Mascarenhas, S.F., Gomes, S., Arriaga, P., Leite, I., Prada, R., Melo, F.S., Paiva, A., Exploring prosociality in human-robot teams (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). Ieee, pp. 143-151; Fischer, P., Jander, K., Krueger, J., (2018) Sozialpsychologie für Bachelor, , Springer; Hartshorne, H., May, M.A., (1928) Studies in the Nature of Character: Studies in Deceit, , I. Macmillan; Davis, M.H., (1980) A Multidimensional Approach to Individual Differences in Empathy; Penner, L.A., Escarraz, J., Ellis, B.B., Sociopathy and helping: Looking out for number one (1983) Academic Psychology Bulletin; Penner, L.A., Fritzsche, B.A., Craiger, J.P., Freifeld, T.R., Measuring the prosocial personality (1995) Advances in Personality Assessment, 10, pp. 147-163; Penner, L., Fritzsche, B., The measurement of individual differences in prosocial tendencies (1993) Sixth International Conference on Personality and Social Behavior, , Boca Raton, FL; Piliavin, J.A., (1981) Emergency Intervention, , Academic Pr; Gerrig, R.J., Zimbardo, P., (2014) Psychologie (20. Aufl.), , Hallbergmoos: Pearson; Habashi, M.M., Graziano, W.G., Hoover, A.E., Searching for the prosocial personality: A big five approach to linking personality and prosocial behavior (2016) Personality and Social Psychology Bulletin, 42 (9), pp. 1177-1192; Ashton, M.C., Lee, K., Empirical, theoretical, and practical advantages of the hexaco model of personality structure (2007) Personality and Social Psychology Review, 11 (2), pp. 150-166; Ashton, M.C., Lee, K., De Vries, R.E., The hexaco honestyhumility, agreeableness, and emotionality factors: A review of research and theory (2014) Personality and Social Psychology Review, 18 (2), pp. 139-152; Hilbig, B.E., Glöckner, A., Zettler, I., Personality and prosocial behavior: Linking basic traits and social value orientations (2014) Journal of Personality and Social Psychology, 107 (3), p. 529; Thalmayer, A.G., Saucier, G., Eigenhuis, A., Comparative validity of brief to medium-length big five and big six personality questionnaires (2011) Psychological Assessment, 23 (4), p. 995; Budde, V., (2018) Roboter und Prosoziales Verhalten: Untersuchung der Effekte Einer Bitte Um Hilfe Auf Die Wahrnehmung und Bewertung Einer Mensch-roboter Interaktion, , Master's thesis, Universität Duisburg-Essen, in German; Lee, K., Ashton, M.C., Psychometric properties of the hexaco-100 (2018) Assessment, 25 (5), pp. 543-556; Nomura, T., Suzuki, T., Kanda, T., Kato, K., Measurement of negative attitudes toward robots (2006) Interaction Studies, 7 (3), pp. 437-454; Morgan, T.M., Case, L.D., Conservative sample size determination for repeated measures analysis of covariance (2013) Annals of Biometrics & Biostatistics, 1 (1); Bartneck, C., Nomura, T., Kanda, T., Suzuki, T., Kato, K., Cultural differences in attitudes towards robots (2005) Aisb; Xu, R., Measuring explained variation in linear mixed effects models (2003) Statistics in Medicine, 22 (22), pp. 3527-3541; Hüttenrauch, H., Severinson-Eklundh, K., To help or not to help a service robot (2006) Interaction Studies, 7 (3), pp. 455-477; Eng, J., Sample size estimation: A glimpse beyond simple formulas (2004) Radiology, 230 (3), pp. 606-612; Westhoven, M., Grinten Der, T.Van, Mueller, S., Perceptions of a help-requesting robot-effects of eye-expressions, colored lights and politeness of speech (2019) Proceedings of Mensch und Computer 2019, pp. 43-54","Westhoven, M.; German Federal Institute for Occupational Safety and Health BAuAGermany; email: westhoven.martin@baua.bund.de",,,"IEEE Computer Society","2021 IEEE International Conference on Advanced Robotics and Its Social Impacts, ARSO 2021","8 July 2021 through 10 July 2021",,172042,21627568,9781665449533,,,"English","Proc. IEEE Workshop Adv. Rob. Soc. Impacts, ARSO",Conference Paper,"Final","",Scopus,2-s2.0-85116469872
"Ishiguro H., Kawahara T., Nakamura Y.","26643316500;7202414284;55871593000;","Autonomous dialogue technologies in symbiotic human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"650","651",,1,"10.1145/3371382.3374855","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083201002&doi=10.1145%2f3371382.3374855&partnerID=40&md5=428a68251aaf3e43857b164062c0c99d","Osaka University, Osaka and Kyoto, Japan; Kyoto University, Kyoto, Japan; Osaka University, Toyonaka, Osaka, Japan","Ishiguro, H., Osaka University, Osaka and Kyoto, Japan; Kawahara, T., Kyoto University, Kyoto, Japan; Nakamura, Y., Osaka University, Toyonaka, Osaka, Japan","The aim of this workshop is to discuss autonomous dialogue technologies in ""Symbiotic Human-Robot Interaction."" In order to inspire participants and encourage the discussion, we will introduce our research activities and have invited talks by experts in human-robot interaction and dialogue systems. We also publicly invite poster presentations to share state-of-the-art technologies in this research area. Finally, we clarify the key challenges required to develop companions living together with us through the panel discussion. © 2020 ACM.","Android; Multi-modal dialogue system","Agricultural robots; Man machine systems; Speech processing; Dialogue systems; Invited talk; Panel discussions; Poster presentations; Research activities; State-of-the-art technology; Human robot interaction",,,,,"JPM-JER1401","This work was supported by JST ERATO Grant Number JPM-JER1401, Japan.",,,,,,,,,,"Inoue, K., Lala, D., Takanashi, K., Kawahara, T., Engagement recognition by a latent character model based on multimodal listener behaviors in spoken dialogue (2018) APSIPA Transactions on Signal and Information Processing, 7, p. e9. , 2018; Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., Hagita, N., A Communication robot in a shopping mall (2010) IEEE Trans. Rob, 26 (5), pp. 897-913. , Oct. 2010; Kobayashi, T., Fujie, S., Conversational robots: An approach to conversation protocol issues that utilizes the paralinguistic information available in a robot-human setting (2013) Acoustical Science and Technology, 34 (2), pp. 64-72. , https://doi.org/10.1250/ast.34.64, 2013; Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R., (2019) ALBERT: A Lite BERT for Self-supervised Learning of Language Representations, , (Sept. 2019). ArXiv:cs.CL/1909.11942; Leviathan, Y., Matias, Y., Google Duplex: An AI System for Accomplishing Real-World Tasks over the Phone, , https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html, Accessed: 2019-12-19; MacDorman, K.F., Ishiguro, H., The uncanny advantage of using androids in cognitive and social science research (2006) Interact. Stud, 7 (3), pp. 297-337. , Jan. 2006; Sakai, K., Inago, A., Higashinaka, R., Yoshikawa, Y., Ishiguro, H., Tomita, J., Creating large-scale argumentation structures for dialogue systems Proceedings of the Eleventh International Conference on Language Resources and Evaluation. European Language Resources Association, pp. L18-1627; Shiomi, M., Shatani, K., Minato, T., Ishiguro, H., How should a robot react before people's touch: Modeling a pre-touch reaction distance for a robot's face (2018) IEEE Robotics and Automation Letters, 3 (4), pp. 3773-3780. , Oct. 2018",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083201002
"Shao S., Wang T., Li Y., Song C., Jiang Y., Yao C.","54967359700;56222883600;57300629700;23111298700;57300629800;55234317100;","Comparison Analysis of Different Time-Scale Heart Rate Variability Signals for Mental Workload Assessment in Human-Robot Interaction",2021,"Wireless Communications and Mobile Computing","2021",,"8371637","","",,,"10.1155/2021/8371637","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117371752&doi=10.1155%2f2021%2f8371637&partnerID=40&md5=42e9463d6560764f81f0f7c09d5a4301","State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Shenyang Ligong University, School of Automation and Electrical Engineering, Shenyang, 110159, China","Shao, S., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; Wang, T., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; Li, Y., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China, University of Chinese Academy of Sciences, Beijing, 100049, China; Song, C., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; Jiang, Y., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China, Shenyang Ligong University, School of Automation and Electrical Engineering, Shenyang, 110159, China; Yao, C., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China","Excessive mental workload affects human health and may lead to accidents. This study is motivated by the need to assess mental workload in the process of human-robot interaction, in particular, when the robot performs a dangerous task. In this study, the use of heart rate variability (HRV) signals with different time scales in mental workload assessment was analyzed. A humanoid dual-arm robot that can perform dangerous work was used as a human-robot interaction object. Electrocardiogram (ECG) signals of six subjects were collected in two states: during the task and in a relaxed state. Multiple time-scale (1, 3, and 5 min) HRV signals were extracted from ECG signals. Then, we extracted the same linear and nonlinear features from the HRV signals at different time scales. The performance of machine learning algorithms using the different time-scale HRV signals obtained during the human-robot interaction was evaluated. The results show that for the per-subject case with a 3 min HRV signal length, the K-nearest neighbor classifier achieved the best mental workload classification performance. For the cross-subject case with a 5 min time-scale signal length, the gentle boost classifier achieved the best mental workload classification accuracy. This study provides a novel research idea for using HRV signals to measure mental workload during human-robot interaction. © 2021 Shiliang Shao et al.",,"Electrocardiography; Heart; Human robot interaction; Machine learning; Man machine systems; Nearest neighbor search; Time measurement; Comparison analysis; Different time scale; Dual-arm robot; Electrocardiogram signal; Heart rate variability signals; Human health; Humans-robot interactions; Mental workload; Mental workload assessments; Signal length; Learning algorithms",,,,,"2020-BS-025; National Natural Science Foundation of China, NSFC: U20A20201; National Key Research and Development Program of China, NKRDPC: 2016YFE0206200; Liaoning Revitalization Talents Program: XLYC1807018","This research was funded by the National Natural Science Foundation of China (Grant number U20A20201), the Liaoning Province Doctoral Scientific Research Foundation (Grant number 2020-BS-025), the Liaoning Revitalization Talents Program (Grant number XLYC1807018), and the National Key Research and Development Program of China (Grant number 2016YFE0206200).",,,,,,,,,,"Costes, A., Danieau, F., Argelaguet, F., Guillotel, P., Lecuyer, A., Towards haptic images: A survey on touchscreen-based surface haptics (2020) Ieee Transactions on Haptics, 13 (3), pp. 530-541; Shao, S., Wang, T., Su, Y., Yao, C., Song, C., Ju, Z., Multi-IMF sample entropy features with machine learning for surface texture recognition based on robot tactile perception (2021) International Journal of Humanoid Robotics, 18 (2), p. 2150005; Zheng, W., Liu, H., Sun, F., Lifelong visual-tactile cross-modal learning for robotic material perception (2021) Ieee Transactions on Neural Networks and Learning Systems, 32 (3), pp. 1192-1203; Falco, P., Lu, S., Natale, C., Pirozzi, S., Lee, D., A transfer learning approach to cross-modal object recognition: From visual observation to robotic haptic exploration (2019) Ieee Transactions on Robotics, 35 (4), pp. 987-998. , 2-s2.0-85070506743; Jung, J., Lee, S., Myung, H., Indoor mobile robot localization and mapping based on ambient magnetic fields and aiding radio sources (2015) Ieee Transactions on Instrumentation and Measurement, 64 (7), pp. 1922-1934. , 2-s2.0-85027957500; Yuan, W., Li, Z., Su, C.-Y., Multisensor-based navigation and control of a mobile service robot (2021) Ieee Transactions on Systems, Man, and Cybernetics: Systems, 51 (4), pp. 2624-2634; Debie, E., Fernandez Rojas, R., Fidock, J., Barlow, M., Kasmarik, K., Anavatti, S., Garratt, M., Abbass, H.A., Multimodal fusion for objective assessment of cognitive workload: A review (2021) Ieee Transactions on Cybernetics, 51 (3), pp. 1542-1555; Pei, Z., Wang, H., Bezerianos, A., Li, J., EEG-based multiclass workload identification using feature fusion and selection (2021) Ieee Transactions on Instrumentation and Measurement, 704001108; Delliaux, S., Delaforge, A., Deharo, J.-C., Chaumet, G., Mental workload alters heart rate variability, lowering non-linear dynamics (2019) Frontiers in Physiology, 10565. , 2-s2.0-85068268225; Tsunoda, K., Chiba, A., Yoshida, K., Watanabe, T., Mizuno, O., Predicting changes in cognitive performance using heart rate variability (2017) Ieice Transactions on Information and Systems, 100500 (10), pp. 2411-2419. , 2-s2.0-85030218171; Shao, S., Wang, T., Song, C., Chen, X., Cui, E., Zhao, H., Obstructive sleep apnea recognition based on multi-bands spectral entropy analysis of short-time heart rate variability (2019) Entropy, 21 (8), p. 812; Shao, S.-L., Wang, T., Song, C.-H., Cui, E.N., Zhao, H., Yao, C., A novel method of heart rate variability measurement (2019) Acta Physica Sinica, 68 (17); Tiwari, A., Albuquerque, I., Parent, M., Gagnon, J.F., Lafond, D., Tremblay, S., Falk, T.H., Multi-scale heart beat entropy measures for mental workload assessment of ambulant users (2019) Entropy, 21 (8), p. 783. , 2-s2.0-85070458166; Tiwari, A., Narayanan, S., Falk, T.H., Stress and Anxiety Measurement ""in-the-Wild"" Using Quality-aware Multi-scale Hrv Features, pp. 7056-7059. , Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2019 Montreal, Canada; Gilfriche, P., Arsac, L.M., Daviaux, Y., Diaz-Pineda, J., Miard, B., Morellec, O., André, J.M., Highly sensitive index of cardiac autonomic control based on time-varying respiration derived from ECG (2018) American Journal of Physiology- Regulatory, Integrative and Comparative Physiology, 315 (3), pp. R469-R478. , 2-s2.0-85052543327; Malik, M., Bigger, J.T., Camm, A.J., Kleiger, R.E., Malliani, A., Moss, A.J., Schwartz, P.J., Heart rate variability: Standards of measurement, physiological interpretation, and clinical use (1996) European Heart Journal, 17 (3), pp. 354-381. , 2-s2.0-0029988782; Villani, V., Righi, M., Sabattini, L., Secchi, C., Wearable devices for the assessment of cognitive effort for human-robot interaction (2020) Ieee Sensors Journal, 20 (21), pp. 13047-13056; Shao, S., Wang, T., Wang, Y., Su, Y., Song, C., Yao, C., Research of HRV as a measure of mental workload in human and dual-arm robot interaction (2020) Electronics, 9 (12); Villani, V., Capelli, B., Secchi, C., Fantuzzi, C., Sabattini, L., Humans interacting with multi-robot systems: A natural affect-based approach (2020) Autonomous Robots, 44, pp. 601-616; Castaldo, R., Montesinos, L., Melillo, P., James, C., Pecchia, L., Ultra-short term HRV features as surrogates of short term HRV: A case study on mental stress detection in real life (2019) Bmc Medical Informatics and Decision Making, 19 (1). , 2-s2.0-85060153698; Pan, J., Tompkins, W.J., A real-time QRS detection algorithm (1985) Ieee Transactions on Biomedical Engineering, 32 (3), pp. 230-236. , 2-s2.0-0021892137; Chen, L., Zhang, X., Song, C., An automatic screening approach for obstructive sleep apnea diagnosis based on single-lead electrocardiogram (2015) Ieee Transactions on Automation Science and Engineering, 12 (1), pp. 106-115. , 2-s2.0-85027953078; Zhu, F., Yang, J., Xu, S., Gao, C., Ye, N., Yin, T., Incorporating neighbors' distribution knowledge into support vector machines (2017) Soft Computing, 21 (21), pp. 6407-6420. , 2-s2.0-84973171633; Zhu, F., Ning, Y., Chen, X.C., Zhao, Y., Gang, Y., On removing potential redundant constraints for SVOR learning (2021) Applied Soft Computing, 102 (4); Li, Y., Pan, W., Li, K., Jiang, Q., Liu, G., Sliding trend fuzzy approximate entropy as a novel descriptor of heart rate variability in obstructive sleep apnea (2019) Ieee Journal of Biomedical and Health Informatics, 23 (1), pp. 175-183. , 2-s2.0-85040612055; MacHetanz, K., Berelidze, L., Guggenberger, R., Gharabaghi, A., Brain-heart interaction during transcutaneous auricular vagus nerve stimulation (2021) Frontiers in Neuroscience, 15632697; Clifford, G.D., Tarassenko, L., Quantifying errors in spectral estimates of HRV due to beat replacement and resampling (2005) Ieee Transactions on Biomedical Engineering, 52 (4), pp. 630-638. , 2-s2.0-12344308577; Zheng, W., Chen, S., Fu, Z., Zhu, F., Yan, H., Yang, J., Feature selection boosted by unselected features (2021) Ieee Transactions on Neural Networks and Learning Systems, pp. 1-13","Shao, S.; State Key Laboratory of Robotics, China; email: shaoshiliang@sia.cn
Wang, T.; State Key Laboratory of Robotics, China; email: wangting@sia.cn",,,"Hindawi Limited",,,,,15308669,,,,"English","Wireless Commun. Mobile Comput.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85117371752
"Ma Q., Ma T., Lu C., Cheng B., Xie S., Gong L., Fu Z., Liu C.","57225932820;57212009944;57224535946;57224537571;57222182583;36637488200;7403346420;7409795747;","A Cloud-based Quadruped Service Robot with Multi-Scene Adaptability and various forms of Human-Robot Interaction",2020,"IFAC-PapersOnLine","53","5",,"134","139",,,"10.1016/j.ifacol.2021.04.092","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107847432&doi=10.1016%2fj.ifacol.2021.04.092&partnerID=40&md5=98fffd28e8e1d5f7cf9f72b66b21e610","Shanghai Jiao Tong University, Shanghai CO, 200240, China","Ma, Q., Shanghai Jiao Tong University, Shanghai CO, 200240, China; Ma, T., Shanghai Jiao Tong University, Shanghai CO, 200240, China; Lu, C., Shanghai Jiao Tong University, Shanghai CO, 200240, China; Cheng, B., Shanghai Jiao Tong University, Shanghai CO, 200240, China; Xie, S., Shanghai Jiao Tong University, Shanghai CO, 200240, China; Gong, L., Shanghai Jiao Tong University, Shanghai CO, 200240, China; Fu, Z., Shanghai Jiao Tong University, Shanghai CO, 200240, China; Liu, C., Shanghai Jiao Tong University, Shanghai CO, 200240, China","With the development of CPS technology, more robots are endowed with intelligence to interact and communicate with humans such as service robot AIBO and AMWELL. These intelligent pets show great human-machine interactive ability by voice and face recognition, storytelling. However, they are limited only in family scenes and fail to provide cross-scene services. Therefore, we design a quadruped service robot called Cloud Sphinx-whose motion, voice, and vision system are driven by artificial intelligent models in the cloud. Our work pushes the interaction between CPS and humans. First, an open-source 12-DoF robotic cat is rapidly prototyped with a 3D printing technique, and its versatile gestures and gaits are coordinated by advanced servo control algorithms. Second, by merging motion control, NLP, image processing deep learning and cloud computing, Cloud Sphinx can provide services as emotion classification, face recognition, and garbage classification through human-robot interaction models. Third, an RaaS (Robot as a service) architecture is developed to fulfill the incrementally learning from the human-robot interaction processes. Sufficient simulations and on-board tests verify the human-robot interaction functionality and cross-scene service performances. Applications that we designed show the A.I. performance of service robots can be effectively enhanced by model training during the human-robot interactions and knowledge accumulation in cloud database. © 2020 Elsevier B.V.. All rights reserved.","cloud architecture Cloud Sphinx; machine learning; man-machine interaction; quadruped robot","3D printers; Deep learning; Educational robots; Face recognition; Intelligent robots; Machine design; Man machine systems; Mobile robots; Visual servoing; Artificial intelligent; Emotion classification; Human-Machine Interactive; Knowledge accumulation; Model training; Service performance; Service robots; Vision systems; Social robots",,,,,"18391901000; National Natural Science Foundation of China, NSFC: 51775333; National Key Research and Development Program of China, NKRDPC: 2018YFB1305104","This work was supported by National Natural Science Foundation of China (Grant No.51775333), the Science Foundation of Shanghai Municipal Commission of Science and Technology (Grant No.18391901000) and National Key Research and Development Project (2018YFB1305104).",,,,,,,,,,"Aravind, M.A., Dinesh, N.S., Application of EMPC for precise position control of DC-motor system with Backlash (2020) Control Engineering Practice, 100, p. 104422; Cheng, X., Lu, J., Feng, J., Yuan, B., Zhou, J., Scene recognition with objectness (2018) Pattern Recognition, 74, pp. 474-487; Du, Z., Robot Cloud: Bridging the power of robotics and cloud computing (2017) Future Generation Computer Systems, 74, pp. 337-348; Yang, D., Ma, L., Liao, F., An intelligent voice interaction system based on raspberry Pi (2019) 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), pp. 237-240; Gan, M.G., Zheng, C.Y., An adaptive sliding mode observer over wide speed range for sensorless control of a brushless DC motor (2018) Control Engineering Practice, 77, pp. 52-62; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2016) Proceedings of the Ieee Conference on Com; Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., Densely connected convolutional networks (2017) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 4700-4708; Hu, L., Follow me Robot-Mind: Cloud brain based personalized robot service with migration (2020) Future Generation Computer Systems, 107, pp. 324-332; Sentiment analysis algorithms and applications: A survey (2014) Ain Shams Engineering Journal, 5 (4), pp. 1093-1113; Garciá-López, M.C., Gorrostieta-Hurtado, Kinematic analysis for trajectory generation in one leg of a hexapod robot (2012) Procedia Technology, 3, pp. 342-350; Merle, P., Mobile cloud robotics as a service with OCCIware (2017) 2017 Ieee International Congress on Internet of Things; Patil Harshali, P., Mohammad, A., CDNB: CAVIAR-Dragonfly Optimization with Naive Bayes for the Sentiment and Affect Analysis in Social Media (2020) Big Data, 8 (2), pp. 107-124; Redmon, J., Farhadi, A., (2018) Yolov3: An Incremental Improvement[J], , arXiv preprint arXiv:1804.02767; Sheba, J.K., Phuc, L.T., Salman, A.A., Development of rehabilitative multimodal interactive pet robot for elderly residents (2018) Procedia Computer Science, 133, pp. 401-408; Yi, S., Reliable gait planning and control for miniaturized quadruped robot pet (2010) Mechatronics, 20 (4), pp. 485-495; Ye, W., Gao, J., Chen, H., A voice control platform of mobile robot through ROS (2019) 38th Chinese Control Conference (CCC), pp. 4338-4341; Trot Gait Design and CPG Method for a Quadruped Robot (2014) Journal of Bionic Engineering, 11 (1), pp. 18-25","Gong, L.; Shanghai Jiao Tong UniversityChina; email: gongliang_mi@sjtu.edu.cn",,,"Elsevier B.V.","3rd IFAC Workshop on Cyber-Physical and Human Systems, CPHS 2020","3 December 2020 through 5 December 2020",,145888,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85107847432
"Wagner A.R.","7401456914;","Robot-Guided Evacuation as a Paradigm for Human-Robot Interaction Research",2021,"Frontiers in Robotics and AI","8",,"701938","","",,,"10.3389/frobt.2021.701938","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111589215&doi=10.3389%2ffrobt.2021.701938&partnerID=40&md5=5ee1bc1dc0d424bc8c04673280088cd4","Robot Ethics and Aerial Vehicles Laboratory, Department of Aerospace Engineering, The Pennsylvania State University, University Park, PA, United States","Wagner, A.R., Robot Ethics and Aerial Vehicles Laboratory, Department of Aerospace Engineering, The Pennsylvania State University, University Park, PA, United States","This paper conceptualizes the problem of emergency evacuation as a paradigm for investigating human-robot interaction. We argue that emergency evacuation offers unique and important perspectives on human-robot interaction while also demanding close attention to the ethical ramifications of the technologies developed. We present a series of approaches for developing emergency evacuation robots and detail several essential design considerations. This paper concludes with a discussion of the ethical implications of emergency evacuation robots and a roadmap for their development, implementation, and evaluation. © Copyright © 2021 Wagner.","emergency robotics; evacuation; human-robot interaction; human-robot trust; robot ethics",,,,,,"National Science Foundation, NSF: CNS-1830390, IIS-2045146","This material is based upon work partially supported by the National Science Foundation under Grant No. CNS-1830390 and No. IIS-2045146.",,,,,,,,,,"Averill, J.D., Peacock, R.D., Kuligowski, E.D., Analysis of the Evacuation of the World Trade Center Towers on September 11, 2001 (2013) Fire Technol, 49, pp. 37-63; Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., Sabanovic, S., (2020) Human-Robot Interaction: An Introduction, , Cambridge, United Kingdom, Cambridge University Press; Boukas, E., Kostavelis, I., Gasteratos, A., Sirakoulis, G., Robot Guided Crowd Evacuation (2015) IEEE Trans. Autonmation Sci. 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(2016) Is, 17 (3), pp. 347-369; Park, H.W., Gelsomini, M., Lee, J.J., Breazeal, C., Telling Stories to Robots: The Effect of Backchanneling on a Child's Storytelling (2017) Proceedings of the 2017 International Conference on Human-Robot Interaction (HRI), pp. 100-108. , IEEE; Proulx, G., Playing for Free—Understanding Human Behavior in Burning Buildings (2003) Ashrae J, 45 (7), pp. 33-35; Robinette, P., Howard, A., Emergency Evacuation Robot Design (2011) ANS EPRRSD-13th Robotics Remote Syst. Hazard. Environments 11th Emerg. Preparedness Response; Robinette, P., Howard, A.M., Trust in Emergency Evacuation Robots (2012) Proceedings of the 2012 International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 1-6. , IEEE; Robinette, P., Howard, A.M., Wagner, A.R., Effect of Robot Performance on Human-Robot Trust in Time-Critical Situations (2017) IEEE Trans. Human-mach. 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"Sejima Y., Sato Y., Watanabe T.","56036801300;55728748100;55670249000;","A body contact-driven pupil response pet-robot for enhancing affinity",2021,"Journal of Advanced Mechanical Design, Systems and Manufacturing","15","5","00085","","",,,"10.1299/JAMDSM.2021JAMDSM0061","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115951547&doi=10.1299%2fJAMDSM.2021JAMDSM0061&partnerID=40&md5=47804ff4e33a18c6c6e36b0e624f91e8","Faculty of Informatics, Kansai University, 2-1-1 Ryozenji-cho, Takatsuki-shi, OSAKA, 569-1095, Japan; Faculty of Computer Science and Systems Engineering, Okayama Prefectural University, 111 Kuboki, Soja-shi, Okayama, 719-1197, Japan","Sejima, Y., Faculty of Informatics, Kansai University, 2-1-1 Ryozenji-cho, Takatsuki-shi, OSAKA, 569-1095, Japan; Sato, Y., Faculty of Computer Science and Systems Engineering, Okayama Prefectural University, 111 Kuboki, Soja-shi, Okayama, 719-1197, Japan; Watanabe, T., Faculty of Computer Science and Systems Engineering, Okayama Prefectural University, 111 Kuboki, Soja-shi, Okayama, 719-1197, Japan","Dilated pupil response has effects on favorable impression to human. Focusing on the pupil response in human-robot interaction, we have developed a pupil response system using hemisphere displays and confirmed that the pupil response is effective for enhancing affective conveyance in human-robot interaction. In this study, for the basic research of realizing a friendly communication during embodied interaction between human and robot, we analyzed the pupil response during body contact that is closely related to affinity using a pupil measurement device. On the basis of the analysis results, we developed a pupil response pet-robot for enhancing affinity in human-robot interaction. This pet-robot generates the pupil response based on the body contact using small displays in which the 3D CG model of pupil and iris is represented, and can impress cuteness and familiarity. The effectiveness of the developed pet-robot with dilation of the pupil response at 2.0 times during body contact was demonstrated by sensory evaluations. © 2021 The Japan Society of Mechanical Engineers",": Human interface; Empathy; Human-robot interaction design; Kansei and affective engineering; Media representation; Pupil response","3D modeling; Agricultural robots; Machine design; Man machine systems; : human interface; Affective engineering; Empathy; Human Interface; Human-robot interaction design; Humans-robot interactions; Interaction design; Kansei Engineering; Media representation; Pupil response; Human robot interaction",,,,,"Japan Society for the Promotion of Science, KAKEN: 19K12890, 20H05569","This work was supported by JSPS KAKENHI Grant Numbers 19K12890, 20H05569.",,,,,,,,,,"Condon, W. S., Sander, L. 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Adv. Mech. Des. Syst. Manuf.",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85115951547
"Hosseini S.R., Taheri A., Alemi M., Meghdari A.","57204109335;56513500500;55312745000;7003828407;","One-shot Learning from Demonstration Approach Toward a Reciprocal Sign Language-based HRI",2021,"International Journal of Social Robotics",,,,"","",,2,"10.1007/s12369-021-00818-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112060374&doi=10.1007%2fs12369-021-00818-1&partnerID=40&md5=9515c7452a4303de5ce3c17ad1df7da3","Social and Cognitive Robotics Lab, Sharif University of Technology, Tehran, Iran; Faculty of Humanities, Islamic Azad University, West Tehran Branch, Tehran, Iran","Hosseini, S.R., Social and Cognitive Robotics Lab, Sharif University of Technology, Tehran, Iran; Taheri, A., Social and Cognitive Robotics Lab, Sharif University of Technology, Tehran, Iran; Alemi, M., Social and Cognitive Robotics Lab, Sharif University of Technology, Tehran, Iran, Faculty of Humanities, Islamic Azad University, West Tehran Branch, Tehran, Iran; Meghdari, A., Social and Cognitive Robotics Lab, Sharif University of Technology, Tehran, Iran","This paper addresses the lack of proper Learning from Demonstration (LfD) architectures for Sign Language-based Human–Robot Interactions to make them more extensible. The paper proposes and implements a Learning from Demonstration structure for teaching new Iranian Sign Language signs to a teacher assistant social robot, RASA. This LfD architecture utilizes one-shot learning techniques and Convolutional Neural Network to learn to recognize and imitate a sign after seeing its demonstration (using a data glove) just once. Despite using a small, low diversity data set (~ 500 signs in 16 categories), the recognition module reached a promising 4-way accuracy of 70% on the test data and showed good potential for increasing the extensibility of sign vocabulary in sign language-based human–robot interactions. The expansibility and promising results of the one-shot Learning from Demonstration technique in this study are the main achievements of conducting such machine learning algorithms in social Human–Robot Interaction. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Convolutional Neural Network (CNN); Human–Robot Interaction (HRI); One-shot Learning; Sign Language; Social Robotics","Agricultural robots; Convolutional neural networks; Demonstrations; Machine learning; Network architecture; Social robots; Statistical tests; Data glove; Data set; Learning from demonstration; One-shot learning; Proper learning; Robot interactions; Sign language; Test data; Learning algorithms",,,,,"Iran National Science Foundation, INSF: 98025100","This research was supported in part by the “Dr. Ali Akbar Siassi Memorial Research Grant Award” and the “Iranian National Science Foundation (INSF)” ( http://en.insf.org/ ) (Grant No. 98025100).",,,,,,,,,,"World Health Organization. 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J. Soc. Rob.",Article,"Article in Press","All Open Access, Bronze, Green",Scopus,2-s2.0-85112060374
"Frijns H.A., Schürer O., Koeszegi S.T.","57192806605;53875402300;8705799600;","Communication Models in Human–Robot Interaction: An Asymmetric MODel of ALterity in Human–Robot Interaction (AMODAL-HRI)",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00785-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105371803&doi=10.1007%2fs12369-021-00785-7&partnerID=40&md5=9248bda8b57da68973de2acdfe43a316","TU Wien, Institute of Management Science, Theresianumgasse 27, Vienna, Austria; TU Wien, Department of Architecture Theory, Karlsplatz 13, Vienna, Austria","Frijns, H.A., TU Wien, Institute of Management Science, Theresianumgasse 27, Vienna, Austria; Schürer, O., TU Wien, Department of Architecture Theory, Karlsplatz 13, Vienna, Austria; Koeszegi, S.T., TU Wien, Institute of Management Science, Theresianumgasse 27, Vienna, Austria","We argue for an interdisciplinary approach that connects existing models and theories in Human–Robot Interaction (HRI) to traditions in communication theory. In this article, we review existing models of interpersonal communication and interaction models that have been applied and developed in the contexts of HRI and social robotics. We argue that often, symmetric models are proposed in which the human and robot agents are depicted as having similar ways of functioning (similar capabilities, components, processes). However, we argue that models of human–robot interaction or communication should be asymmetric instead. We propose an asymmetric interaction model called AMODAL-HRI (an Asymmetric MODel of ALterity in Human–Robot Interaction). This model is based on theory on joint action, common robot architectures and cognitive architectures, and Kincaid’s model of communication. On the basis of this model, we discuss key differences between humans and robots that influence human expectations regarding interacting with robots, and identify design implications. © 2021, The Author(s).","Communication models; Communication theory; Human–Machine communication; Human–Robot interaction; Interaction design","Agricultural robots; Information theory; Machine design; Asymmetric interaction; Asymmetric models; Cognitive architectures; Communication models; Design implications; Interpersonal communication and interaction; Robot architecture; Robot interactions; Social robots",,,,,"Technische Universität Wien","This work was supported by Doctoral College TrustRobots, Technische Universität Wien. We would like to thank all anonymous reviewers, Michael Koller, and Guglielmo Papagni for their feedback on previous versions of this paper.",,,,,,,,,,"Alač, M., Social robots: things or agents? 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J. Soc. Rob.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85105371803
"Wendong W., Hanhao L., Menghan X., Yang C., Xiaoqing Y., Xing M., Bing Z.","55713621900;57215826317;57215829968;57215826574;7402202189;57215834146;57215826960;","Design and verification of a human–robot interaction system for upper limb exoskeleton rehabilitation",2020,"Medical Engineering and Physics","79",,,"19","25",,5,"10.1016/j.medengphy.2020.01.016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082002884&doi=10.1016%2fj.medengphy.2020.01.016&partnerID=40&md5=72b3e82eef2a30adf40e163bb72f2dcd","School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China; School of Mechanical Engineering, Dongguan University of Technology, China","Wendong, W., School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China; Hanhao, L., School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China; Menghan, X., School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China; Yang, C., School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China; Xiaoqing, Y., School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China; Xing, M., School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, China; Bing, Z., School of Mechanical Engineering, Dongguan University of Technology, China","This paper presents the design of a motion intent recognition system, based on an altitude signal sensor, to improve the human–robot interaction performance of upper limb exoskeleton robots during rehabilitation training. A modified adaptive Kalman filter combined with clipping filtering is proposed for the control system to mitigate the noise and time delay of the collected signal. The clipping filtering method was used to filter the accidental error and avoid the safety problem caused by a mistrigger. A modified adaptive Kalman filter was used to account for the sudden change of the motion state during rehabilitation training. The results show that the intent recognition system designed herein can accurately recognize the human–robot interaction information, and estimate the intent of human motion in time. Therefore, it can be concluded that the designed system effectively follows the predicted motion intent with the proposed method, which is a significant improvement for human–robot interaction control of upper limb extremity rehabilitation robots. © 2020 IPEM","Exoskeleton robot; Human–robot interaction; Mixed filter; Motion prediction","Adaptive control systems; Adaptive filtering; Adaptive filters; Biomedical signal processing; Delay control systems; Exoskeleton (Robotics); Kalman filters; Machine design; Man machine systems; Motion estimation; Motion sensors; Neuromuscular rehabilitation; Adaptive kalman filter; Exoskeleton robots; Mixed filter; Motion prediction; Rehabilitation robot; Rehabilitation training; Robot interaction control; Robot interactions; Human robot interaction; algorithm; Article; artificial membrane; device safety; human computer interaction; information processing; mathematical computing; mathematical model; movement (physiology); performance; priority journal; rehabilitation medicine; upper limb; devices; human; movement (physiology); physiology; procedures; rehabilitation; robotics; upper limb; Humans; Movement; Rehabilitation; Robotics; Upper Extremity",,,,,"KCYCXPT2017006; National Natural Science Foundation of China, NSFC: 51605385; Northwestern Polytechnical University, NPU: 6101030222595-202001001; Natural Science Foundation of Shaanxi Province: 2017JQ5026, 2018JM5107","This work is supported by National Natural Science Foundation of China (Grant no. 51605385 ), Natural Science Foundation of Shaanxi Province (Grant nos. 2017JQ5026 and 2018JM5107 ), the DGUT Innovation Center of Robotics and Intelligent Equipment (Grant no. KCYCXPT2017006).","This work is supported by National Natural Science Foundation of China (Grant no. 51605385), Natural Science Foundation of Shaanxi Province (Grant nos. 2017JQ5026 and 2018JM5107), the DGUT Innovation Center of Robotics and Intelligent Equipment (Grant no. KCYCXPT2017006). The study was approved by the Medical and Experimental Animal Ethics Committee of Northwestern Polytechnical University, Xi'an, China (Reference number 6101030222595-202001001), and written informed consent of the volunteer and researcher obtained.",,,,,,,,,"Liu, K., Xiong, C., He, L., Chen, W., Huang, X., Postural synergy based design of exoskeleton robot replicating human arm reaching movements (2018) Robot Auton Syst, 99, pp. 84-96; Brahmi, B., Saad, M., Luna, C.O., Archambault, P.S., Rahman, M.H., Passive and active rehabilitation control of human upper-limb exoskeleton robot with dynamic uncertainties (2018) Robotica, 36 (11), pp. 1757-1779; Buesching, I., Sehle, A., Stuerner, J., Liepert, J., Using an upper extremity exoskeleton for semi-autonomous exercise during inpatient neurological rehabilitation- a pilot study (2018) Journal of NeuroEngineering and Rehabilitation, 15 (72), pp. 1-7; Sale, P., Russo, E.F., Scarton, A., Calabrò, R., Masiero, S., Filoni, S., Training for mobility with exoskeleton robot in spinal cord injury patients: a pilot study (2018) European journal of physical and rehabilitation medicine, 54 (5), pp. 745-751; Varela, J., Saltaren, R.J., Puglisi, L., López, J., Alvarez, M., Rodríguez, J.C., Shoulder assessment methodology, clinical studies and SOFI rehabilitation exoskeleton (2017) Robotics and Autonomous Systems, 94, pp. 264-281; W, W., Zhang, P., Liang, C., Shi, Y., Design, path planning improvement and test of a portable massage robot on human back (2018) Int J Adv Robot Syst, 15 (4); Wang, W., Zhang, P., Liang, C., Shi, Y., A portable back massage robot based on traditional Chinese medicine (2018) Technol Health Care, 26 (4), pp. 709-713; Massalin, Y., Abdrakhmanova, M., Varol, H.A., User-independent intent recognition for lower limb prostheses using depth sensing (2018) IEEE Trans Bio Med Eng, 65 (8), pp. 1759-1770; Hou, Z., Zhao, X., Cheng, L., Wang, Q., Wang, W., Recent advances in rehabilitation robots and intelligent assistance systems (2016) Acta Autom Sin, 42 (0254–4156(2016), p. 42. , 12<1765:KFJQRY>2.0.TX;2-P12):1765-1779; Bogue, R., Robotic exoskeletons: a review of recent progress (2015) Ind Robot Int J, 42 (1), pp. 5-10; Ming, D., Jiang, S., Wang, Z., Qi, H., Wan, B., Review of walk assistant exoskeleton technology: human-machine interaction (2017) Acta Autom Sin, 43 (0254–4156(2017), p. 43. , 7<1089:JYRJXX>2.0.TX;2-B7):1089-1100; Minati, L., Yoshimura, N., Koike, Y., Hybrid control of a vision-guided robot arm by EOG, EMG, EEG biosignals and head movement acquired via a consumer-grade wearable device (2016) IEEE Access, 4, pp. 9528-9541; Kiguchi, K., Hayashi, Y., An EMG-based control for an upper-limb power-assist exoskeleton robot (2012) IEEE Trans Syst Man Cybern Part B Cybern, 42 (4SI), pp. 1064-1071; Leonardis, D., Barsotti, M., Loconsole, C., Solazzi, M., Troncossi, M., Mazzotti, C., An EMG-Controlled robotic hand exoskeleton for bilateral rehabilitation (2015) IEEE transactions on haptics, 8 (2), pp. 140-151; Nithya, V.S., Arun, C.S., Brain controlled wearable robotic glove for cerebral palsy patients (2017) International Conference on Circuit, Power and Computing Technologies (ICCPCT), Kollam, pp. 1-4; Sarasota-Sanz, A., Irastorza-Landa, N., Shiman, F., EMG-based multi-joint kinematics decoding for robot-aided rehabilitation therapies. 2015:229–34; Norman, S.L., Dennison, M., Wolbrecht, E., Cramer, S., Srinivasan, R., Reinkensmeyer, D., Movement anticipation and EEG: implications for BCI-Contingent robot therapy (2016) IEEE Trans Neural Syst Rehabil, 24 (8), pp. 911-919; Chen, S., Lien, W., Wang, W., Lee, G., Hsu, L., Lee, K., Assistive control system for upper-limb rehabilitation robot (2016) IEEE Trans Neural Syst Rehabil, 24 (11), pp. 1199-1209; Lee, K.H., Lee, H.J., Lee, J., Ji, S., Koo, J.C., A simple method to estimate the impedance of the human hand for physical human-robot interaction. 2017:152–4; Wang, W., Qin, L., Yuan, X., Ming, X., Sun, T., Liu, Y., Bionic control of exoskeleton robot based on motion intention for rehabilitation training (2019) Adv Robot, 33 (12), pp. 590-601; Lenzi, T., De Rossi, S.M.M., Vitiello, N., Carrozza, M.C., Intention-based EMG control for powered exoskeletons (2012) IEEE Trans Bio Med Eng, 59 (8), pp. 2180-2190; Lu, W., Zhang, J., Yang, Y., Huang, D., Parameter analyses of an adaptive amplitude limit filtering method (2012) J Tsinghua Univ Sci Technol, 52 (1000–0054(2012), p. 52. , 8<1106:KBXFLB>2.0.TX;2-28):1106-1111; Auger, F., Hilairet, M., Guerrero, J.M., Industrial applications of the Kalman filter: a review (2013) IEEE Trans Ind Electron, 60 (12), pp. 5458-5471; Li, M., Kang, R., Branson, D.T.I., Dai, J.S., Model-Free control for continuum robots based on an adaptive Kalman filter (2018) IEEE ASME Trans Mech, 23 (1), pp. 286-297; Yuan, L., Lei R.Target tracking based on amendatory Sage-Husa adaptive Kalman filtering. 2015;20:91–94; Chen, X., Shen, C., Zhang, W., Tomizuka, N., Xu, Y., Chiu, K., Novel hybrid of strong tracking Kalman filter and wavelet neural network for GPS/INS during GPS outages (2013) Measurement, 46 (10), pp. 3847-3854; Lin, C., Chang, Y., Hung, C., Tu, C., Chuang, C., Position estimation and smooth tracking with a fuzzy-logic-based adaptive strong tracking Kalman filter for capacitive touch panels (2015) IEEE Trans Ind Electron, 62 (8), pp. 5097-5108; Chai, W., Chen, C., Edwan, E., Zhang, J., Loffeld O.INS/Wi-Fi based indoor navigation using adaptive Kalman filtering and vehicle constraints. 2012:36–41","Wendong, W.; School of Mechanical Engineering, China; email: wdwang@nwpu.edu.cn",,,"Elsevier Ltd",,,,,13504533,,MEPHE,"32205023","English","Med. Eng. Phys.",Article,"Final","",Scopus,2-s2.0-85082002884
"Lin K., Li Y., Sun J., Zhou D., Zhang Q.","56431757700;57211796940;57211779729;13105639000;55624487967;","Multi-sensor fusion for body sensor network in medical human–robot interaction scenario",2020,"Information Fusion","57",,,"15","26",,16,"10.1016/j.inffus.2019.11.001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074973742&doi=10.1016%2fj.inffus.2019.11.001&partnerID=40&md5=6c641503fc7058ae389124fdadd483af","School of Computer Science and Technology, Dalian University of Technology, Dalian, China; Lanzhou Institute of Physics, China Academy of Space Technology, Lanzhou, China; Key Laboratory of Advanced Design and Intelligent Computing(Dalian University), Ministry of Education, Dalian, China","Lin, K., School of Computer Science and Technology, Dalian University of Technology, Dalian, China; Li, Y., School of Computer Science and Technology, Dalian University of Technology, Dalian, China; Sun, J., Lanzhou Institute of Physics, China Academy of Space Technology, Lanzhou, China; Zhou, D., Key Laboratory of Advanced Design and Intelligent Computing(Dalian University), Ministry of Education, Dalian, China; Zhang, Q., School of Computer Science and Technology, Dalian University of Technology, Dalian, China","With the development of sensor and communication technologies, body sensor networks(BSNs) have become an indispensable part of smart medical services by monitoring the real-time state of users. Due to introducing of smart medical robots, BSNs are not related to users, but also responsible for data acquisition and multi-sensor fusion in medical human–robot interaction scenarios. In this paper, a hybrid body sensor network architecture based on multi-sensor fusion(HBMF) is designed to support the most advanced smart medical services, which combines various sensor, communication, robot, and data processing technologies. The infrastructure and system functions are described in detail and compared with other architectures. Especially, A multi-sensor fusion method based on interpretable neural network(MFIN) for BSNs in medical human–robot interaction scenario is designed and analyzed to improve the performance of fusion decision-making. Compared with the current multi-sensor fusion methods, our design guarantees both the flexibility and reliability of the service in the medical human–robot interaction scenario. © 2019 Elsevier B.V.","Body sensor network; Fusion decision; Medical human–robot interaction; Multi-sensor fusion; Neural network","Computer architecture; Data acquisition; Data handling; Decision making; Human robot interaction; Machine design; Network architecture; Neural networks; Sensor data fusion; Body sensor networks (BSNs); Communication technologies; Data processing technologies; Fusion decision makings; Medical services; Multi-sensor fusion; Multi-sensor fusion method; Robot interactions; Body sensor networks",,,,,"Dalian Science and Technology Bureau: 2018J12GX036; Fundamental Research Funds for the Central Universities: 2017RD11; Program for Liaoning Innovative Talents in University","This work was partially supported by the Liaoning Province Higher Education Innovative Talent Support Program, and by Fundamental Research Funds for the Central Universities under grant no. DUT19JC22 . Program for the Liaoning Distinguished Professor, Program for Dalian High-level Talent Innovation Support under grant no. 2017RD11, and the Science and Technology Innovation Fund of Dalian under grant no. 2018J12GX036.",,,,,,,,,,"Zhang, Y., Gravina, R., Lu, H., Villari, M., Fortino, G., PEA: Parallel electrocardiogram-based authentication for smart healthcare systems (2018) J. Netw. Comput. Appl., 117, pp. 10-16; Fortino, G., Giannantonio, R., Gravina, R., Kuryloski, P., Jafari, R., Enabling effective programming and flexible management of efficient body sensor network applications (2012) IEEE Trans. Hum.-Mach. Syst., 43 (1), pp. 115-133; Gravina, R., Alinia, P., Ghasemzadeh, H., Fortino, G., Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges (2017) Inf. 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Syst., pp. 3856-3866; Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Wierstra, D., (2015), Continuous control with deep reinforcement learning, Continuous control with deep reinforcement learning. arXiv:; Min, C., Ping, Z., Fortino, G., Emotion communication system (2017) IEEE Access, 5 (99), pp. 326-337; Pasquale, P., Gianluca, A., Raffaele, G., Giuseppe, C., Giancarlo, F., Antonio, L., An edge-based architecture to support efficient applications for healthcare industry 4.0 (2018) IEEE Trans. Ind. Inf., , 1–1; Fortino, G., Parisi, D., Pirrone, V., Fatta, G.D., Bodycloud: a saas approach for community body sensor networks (2014) Future Gener. Comput. 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"Hruska L., Sincak P., Ferencik N., Cik I.","57217311804;6602480848;57193947712;57217306553;","Application of cloud-based social robotics in cognitive exercises for elderly people",2020,"SAMI 2020 - IEEE 18th World Symposium on Applied Machine Intelligence and Informatics, Proceedings",,,"9108737","57","61",,,"10.1109/SAMI48414.2020.9108737","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087077619&doi=10.1109%2fSAMI48414.2020.9108737&partnerID=40&md5=6639bdf999a7a83cab53d9c856cd80b7","Technical University of Košice, Department of Cybernetics and Artificial Intelligence, Košice, Slovakia","Hruska, L., Technical University of Košice, Department of Cybernetics and Artificial Intelligence, Košice, Slovakia; Sincak, P., Technical University of Košice, Department of Cybernetics and Artificial Intelligence, Košice, Slovakia; Ferencik, N., Technical University of Košice, Department of Cybernetics and Artificial Intelligence, Košice, Slovakia; Cik, I., Technical University of Košice, Department of Cybernetics and Artificial Intelligence, Košice, Slovakia","This paper demonstrates the application of cloudbased social robots in real world scenario - anti-dementia exercises for elderly people. Social robotics is a major issue in the field of rehabilitation and treatment of patients with dementia. Human-robot interaction is often helpful in expressing the patient's true feelings. We also found differences in behaviour of patients during a human-human and human-robot interaction. For better understanding of the concept, however, we first need to look at the idea behind the Wizard of Oz. © 2020 IEEE.","cloud robotics; cognitive exercises; social robotics; Wizard of Oz","Man machine systems; Neurodegenerative diseases; Patient rehabilitation; Patient treatment; Robotics; Cloud-based; Elderly people; Patients with dementia; Real-world scenario; Social robotics; Wizard of Oz; Social robots",,,,,,"This research work was supported by FEEI Grant for the year 2019 as a result of the project “Cloud-connected Assistive Robots for the Elderly”, APVV project 015-0730 “Cloud Based Human Robot Interaction”, National Research and Development Project Grant number 1/0773/16 “Cloud Based Artificial Intelligence for Intelligent Robotics” (2016-2019).",,,,,,,,,,"Young, J.E., Sung, J., Voida, A., Sharlin, E., Igarashi, T., Christensen, H.I., Grinter, R.E., Evaluating human-robot interaction (2011) International Journal of Social Robotics, 3 (1), pp. 53-67; Bartneck, C., Verbunt, M., Mubin, O., Mahmud, A., (2007) To Kill A Mockingbird Robot, 1, pp. 81-87; Magyar, G., Magyar, J., Sincak, P., Cavallo, F., (2018) Using Social Robots As Cognitive Stimulation Therapy Coaches in An Elderly Care Facility, p. 03; Riek, L.D., Wizard of oz studies in hri: A systematic review and new reporting guidelines (2012) Journal of Human-Robot Interaction, 1 (1), pp. 119-136; Magyar, G., Sinćak, P., Magyar, J., Yoshida, K., Manzi, A., Cavallo, F., Cowooz-a cloud-based teleoperation platform for social robotics (2017) 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, pp. 000049-000054; Hoffman, G., Openwoz: A runtime-configurable wizard-of-oz framework for human-robot interaction (2016) 2016 AAAI Spring Symposium Series; Toris, R., Kent, D., Chernova, S., The robot management system: A framework for conducting human-robot interaction studies through crowdsourcing (2014) Journal of Human-Robot Interaction, 3 (2), pp. 25-49; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., Ros: An open-source robot operating system ICRA Workshop on Open Source Software, 3 (2-3), p. 5. , Kobe, Japan, 2009",,,"IEEE Control Systems Chapter;IEEE Hungary Section;IEEE Joint Chapter of IES and RAS;IEEE SMC Chapter","Institute of Electrical and Electronics Engineers Inc.","18th IEEE World Symposium on Applied Machine Intelligence and Informatics, SAMI 2020","23 January 2020 through 25 January 2020",,160772,,9781728131498,,,"English","SAMI - IEEE World Symp. Appl. Mach. Intell. Informatics, Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85087077619
"Ye L., Sun C., Qiu Z.","57201975390;57198057519;57219023252;","Model-based fuzzy variable impedance sliding-mode control for trajectory tracking in physical human-robot interaction",2020,"Proceedings of 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference, ITOEC 2020",,,"9141937","871","876",,,"10.1109/ITOEC49072.2020.9141937","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091123045&doi=10.1109%2fITOEC49072.2020.9141937&partnerID=40&md5=c74b91fc5ba4226e019484921c7126ef","School of Mechanical Engineering, Nanchang University, Nanchang, China; Jiangxi Engineering Laboratory of Digital Manufacturing for Automobile Parts, Nanchang Institute of Science and Technology, Nanchang, China; Jiangxi Cigarette Factory of China Tobacco Nanchang Industrial CO.,LTD., Nanchang, China","Ye, L., School of Mechanical Engineering, Nanchang University, Nanchang, China, Jiangxi Engineering Laboratory of Digital Manufacturing for Automobile Parts, Nanchang Institute of Science and Technology, Nanchang, China; Sun, C., Jiangxi Cigarette Factory of China Tobacco Nanchang Industrial CO.,LTD., Nanchang, China; Qiu, Z., Jiangxi Engineering Laboratory of Digital Manufacturing for Automobile Parts, Nanchang Institute of Science and Technology, Nanchang, China","This paper mainly presents a nonlinear fuzzy variable impedance sliding-mode controller. The controller provides asymptotic tracking for the robot end-effector in Cartesian coordinates applicable to human-robot interaction. Firstly, the forward and analytical inverse kinematics for a 7-DOF redundant manipulator were proposed. The optimal analytical inverse kinematic solution used redundant angle is chosen according to the joint angle minimum increment. Secondly, the controller based on dynamics model of the physical human-robot interaction was designed. Finally, using simulations with Matlab software, the effectiveness of the proposed controller is investigated. © 2020 IEEE.","7-DOF robot; impedance control; physical human-robot interaction; sliding-mode control","Controllers; End effectors; Inverse kinematics; Man machine systems; MATLAB; Sliding mode control; 7-dof redundant manipulators; Analytical inverse kinematics; Asymptotic tracking; Cartesian coordinate; Physical human-robot interactions; Robot end effector; Sliding mode controller; Trajectory tracking; Human robot interaction",,,,,,"ACKNOWLEDGMENT This work was supported by the Educational hall science and technology research project of JiangXi province under Grant No.GJJ161226 and technology research for Nanchang Institute of Science and Technology under Grant No. SZZX-17-13.",,,,,,,,,,"Raibert, M.H., Craig, J.J., Hybrid position/force control of manipulators (1981) Trans. ASME J Dyn Syst Meas Control., 102, pp. 126-133. , Jun; Hou, Y.F., Mason, M.T., Robust execution of contact-rich motion plans by hybrid force-velocity control International Conference on Robotics and Automation ICRA, pp. 1933-1939. , Montreal, Canada, May. 2019; Yilmaz, N., Bazman, M., Alassi, A., Gur, B., Tumerdem, U., 6-Axis Hybrid Sensing and Estimation of Tip Forces/Torques on a Hyper-Redundant Robotic Surgical Instrument (2019) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS, pp. 2990-2997. , Macau, China, Nov; Hogan, N., Impedance Control: An approach to manipulation:Part ITheory (1985) J. Dyn. Syst. Meas. Contr., 107, pp. 1-7. , Mar; Kazerooni, H., Sheridan, T.B., Houpt, P.K., Robust compliant motion for manipulators, part I: The fundamental concepts of compliant motion (1986) IEEE Journal on Robotics and Automation, 2 (2), pp. 83-92. , Jun; Ficuciello, F., Romano, A., Villani, L., Siciliano, B., Cartesian impedance control of redundant manipulators for human-robot co-manipulation (2014) IEEE/RSJ International Conference on Intelligent Robots and Systems IROS, pp. 2120-2125. , Chicago, USA, Sep; Sharifi, M., Behzadipour, S., Vossoughi, G.R., Model reference adaptive impedance control in Cartesian coordinates for physical human-robot interaction (2014) Adv. Robotics, 28 (19), pp. 1277-1290. , Sep; Johannsmeier, L., Gerchow, M., Haddadin, S., A framework for robot manipulation: Skill formalism, meta learning and adaptive control (2019) International Conference on Robotics and Automation ICRA, pp. 5844-5850. , Montreal, Canada, May; Chien, M., Huang, A., Adaptive Impedance Control of Robot Manipulators based on Function Approximation Technique (2004) Robotica, 22 (4), pp. 395-403. , Nov; Zhu, W.H., Schutter, J.D., Experimental verifications of virtual decomposition-based motion/force control (2002) IEEE Trans. Robo. Autom., 18 (3), pp. 379-386. , Jun; Li, Y., Ganesh, G., Jarrasse, N., Haddadin, S., Albu-Schaeffer, A., Burdet, E., Force, Impedance, and trajectory learning for contact tooling and haptic identification (2018) IEEE Trans. Robot., 34 (5), pp. 1170-1182; Zhu, Y., Barth, E.J., Impedance control of a pneumatic actuator for contact tasks (2005) Proc. IEEE Int. Conf. Robotics Automat., pp. 987-992. , Barcelona, Spain, Apr; Nicolis, D., Allevi, F., Rocco, P., Robust impedance shaping of redundant teleoperators with timedelay via sliding mode control (2019) IEEE/RSJ International Conference on Intelligent Robots and Systems IROS, pp. 2740-2747. , Macau, China, Nov; Aristidou, A., Lasenby, J., Chrysanthou, Y., Shamir, A., Inverse kinematics techniques in computer graphics: A Survey (2018) Comput. Graph. Forum, 37 (6), pp. 35-58; Shimizu, M., Kakuya, H., Yoon, W.K., Kitagaki, K., Kosuge, K., Analytical inverse kinematic computation for 7dof redundant manipulators with joint limits and its application to Redundancy Resolution (2008) IEEE T. Robot., 24 (5), pp. 1131-1142. , Oct; Gong, M., Li, X., Zhang, L., Analytical inverse kinematics and self-motion application for 7dof redundant manipulator (2019) IEEE Access, 7, pp. 18662-18674. , Feb; Huo, W., (2005) Robot Dynamics and Control, , Beijing, China: Higher Education Press",,"Xu B.Mou K.","Chengdu Global Union Academy of Science and Technology;Chongqing Geeks Education Technology Co., Ltd;Chongqing Global Union Academy of Science and Technology;Global Union Academy of Science and Technology;IEEE Harbin Section","Institute of Electrical and Electronics Engineers Inc.","5th IEEE Information Technology and Mechatronics Engineering Conference, ITOEC 2020","12 June 2020 through 14 June 2020",,161902,,9781728143224,,,"English","Proc. IEEE Inf. Technol. Mechatronics Eng. Conf., ITOEC",Conference Paper,"Final","",Scopus,2-s2.0-85091123045
"Jiang Y., Peng X., Xue M., Wang C., Qi H.","8503974900;57218802103;57218799682;57201294365;55167086600;","An Underwater Human–Robot Interaction Using Hand Gestures for Fuzzy Control",2021,"International Journal of Fuzzy Systems","23","6",,"1879","1889",,,"10.1007/s40815-020-00946-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091317442&doi=10.1007%2fs40815-020-00946-2&partnerID=40&md5=196378047c9ebfcb89bf5b007afa15b9","College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China","Jiang, Y., College of Computer Science and Technology, Jilin University, Changchun, 130012, China, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; Peng, X., College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Xue, M., College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Wang, C., College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Qi, H., College of Computer Science and Technology, Jilin University, Changchun, 130012, China, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China","Autonomous underwater vehicle (AUV) plays an important role in ocean research and exploration. The underwater environment has a great influence on AUV control and human–robot interaction, since underwater environment is highly dynamic with unpredictable fluctuation of water flow, high pressure and light attenuation. The traditional control model contains a large number of parameters, which is not effective and produces errors frequently. The proposal of fuzzy control addressed this issue to a certain extent. It applies fuzzy variables to the controller, which replace the values in an interval. In addition to the controller, underwater human–robot interaction is also difficult. Divers cannot speak or show any facial expressions underwater. The buttons on the AUV also need to overcome the huge water pressure. In this paper, we proposed a method to recognize the gesture instructions and apply it to the fuzzy control of AUV. Our contribution is the gesture recognition framework for the human–robot interaction, including the gesture detection network and the algorithm for the control of AUV. The experiment result shows the efficiency of the proposed method. © 2020, Taiwan Fuzzy Systems Association.","AUV; Fuzzy control; Gesture recognition; Human–robot communication","Controllers; Flow of water; Fuzzy control; Gesture recognition; High pressure effects; Social robots; Autonomous underwater vehicles (AUV); Facial Expressions; Gesture detections; Light attenuation; Ocean research; Robot interactions; Underwater environments; Water pressures; Autonomous underwater vehicles",,,,,"National Natural Science Foundation of China, NSFC: 51679105, 51809112, 51939003","This work was supported by the National Natural Science Foundation of China under Grant 51679105, Grant 51809112, and Grant 51939003.",,,,,,,,,,"Papanikolopoulos, N.P., Khosla, P.K., Adaptive robotic visual tracking: theory and experiments (1993) IEEE Trans. Autom. Control, 38 (3), pp. 429-445; Jiang, Y., Zhao, M., Hu, C., He, L., Bai, H., Wang, J., A parallel FP-growth algorithm on World Ocean Atlas data with multi-core CPU (2019) J Supercomput, 75 (2), pp. 732-745; Qin, H., Wang, C., Jiang, Y., Deng, Z., Zhang, W., Trend prediction of the 3D thermocline’s lateral boundary based on the SVR method (2018) EURASIP J Wirel Commun Netw., 2018, p. 252; Dudek, G., A visually guided swimming robot (2005) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3604-3609; Son-Cheol, Y., Ura, T., Fujii, T., Kondo, H., Navigation of autonomous underwater vehicles based on artificial underwater landmarks (2001) Oceans IEEE, pp. 409-416; Sattar, J., Dudek, G., Robust servo-control for underwater robots using banks of visual filters (2009) IEEE International Conference on Robotics and Automation, pp. 3583-3588; Neverova, N., Wolf, C., Taylor, G.W., Nebout, F., Multi-scale deep learning for gesture detection and localization (2015) Workshop at the European Conference on Computer Vision, pp. 474-490. , Springer; Ren, S., He, K., Girshick, R., Sun, J., Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (2017) IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1137-1149; Fiala, M., ARTag, a fiducial marker system using digital techniques (2005) IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 590-596; Olson, E., AprilTag: A robust and flexible visual fiducial system (2011) IEEE International Conference on Robotics and Automation, pp. 3400-3407; Dudek, G., Sattar, J., Xu, A., A visual language for robot control and programming: A human-interface study (2007) IEEE International Conference on Robotics and Automation, pp. 2507-2513; Molchanov, P., Gupta, S., Kim, K., Kautz, J., Hand gesture recognition with 3D convolutional neural networks (2015) IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1-7; Girshick, R., Fast R-CNN (2015) IEEE International Conference on Computer Vision (ICCV), pp. 1440-1448; He, K., Gkioxari, G., Dollár, P., Girshick, R., Mask R-CNN (2017) IEEE International Conference on Computer Vision (ICCV), pp. 2980-2988; Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., Berg, A.C.S.S.D., Single Shot MultiBox Detector (2015) European Conference on Computer Vision (ECCV, p. 2016; Redmon, J., Divvala, S., Girshick, R., Farhadi, A., You only look once: unified, real-time object detection (2016) IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779-788; Jeen-Shing, W., Lee, C.S.G., Self-adaptive recurrent neuro-fuzzy control for an autonomous underwater vehicle (2002) IEEE International Conference on Robotics and Automation, pp. 1095-1100; Zhang, L., Pang, Y., Su, Y., HPSO-based fuzzy neural network control for AUV (2008) J. Control Theory Appl., 6, pp. 322-326; Huang, W., Fang, H., Liu, L., Obstacle avoiding policy of multi-AUV formation based on virtual AUV (2009) Sixth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 131-135; Li, Y., Pang, Y., Wan, L., Tang, X., A fuzzy motion control of AUV based on apery intelligence (2009) Chinese Control and Decision Conference, pp. 1316-1321; Li, Q., Shi, X.H., Kang, Z.Q., The research of Fuzzy-PID control based on grey prediction for AUV (2012) Appl. Mech. Mater., 246-247, pp. 888-892; Liang, X., Qu, X., Wan, L., Ma, Q., Three-dimensional path following of an underactuated AUV based on Fuzzy backstepping sliding mode control (2018) Int. J. Fuzzy Syst., 20, pp. 640-649; Yu, C., Xiang, X., Wilson, P.A., Guidance-Error-Based Robust Fuzzy Adaptive Control for Bottom Following of a Flight-Style AUV With Saturated Actuator Dynamics (2019) IEEE Transactions on Cybernetics, pp. 1-13; Li, H., He, B., Yin, Q., Fuzzy Optimized MFAC Based on ADRC in AUV Heading Control (2019) Electronics., 8 (6), p. 608; Gomez Chavez, A., Ranieri, A., Chiarella, D., Zereik, E., Babić, A., Birk, A., Caddy underwater stereo-vision dataset for human–robot interaction (HRI) in the context of diver activities (2019) J. Marine Sci. Eng., 7 (1), p. 16","Qi, H.; College of Computer Science and Technology, China; email: qihong@jlu.edu.cn",,,"Springer Science and Business Media Deutschland GmbH",,,,,15622479,,,,"English","Int. J. Fuzzy Syst.",Article,"Final","",Scopus,2-s2.0-85091317442
"Salek Shahrezaie R., Anima B.A., Feil-Seifer D.","57211603950;57191926631;14033850500;","Birds of a Feather Flock Together: A Study of Status Homophily in HRI",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"281","291",,,"10.1007/978-3-030-90525-5_24","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119850412&doi=10.1007%2f978-3-030-90525-5_24&partnerID=40&md5=d273d4460ecba93ea7162fc28b3ab92c","Socially Assistive Robotics Group, Department of Computer Science and Engineering University of Nevada, Reno, Reno, 89557, United States","Salek Shahrezaie, R., Socially Assistive Robotics Group, Department of Computer Science and Engineering University of Nevada, Reno, Reno, 89557, United States; Anima, B.A., Socially Assistive Robotics Group, Department of Computer Science and Engineering University of Nevada, Reno, Reno, 89557, United States; Feil-Seifer, D., Socially Assistive Robotics Group, Department of Computer Science and Engineering University of Nevada, Reno, Reno, 89557, United States","Homophily, a person’s bias for having ties with people who are similar to themselves in social ways, has a vital role in creating a social connection between people. Studying homophily in human-robot interactions can provide valuable insights for improving those interactions. In this paper, we investigate whether similar interests have a positive effect on a human-robot interaction similar to the positive impact it can have on human-human interaction. We explore whether sharing similar interests can affect trust. This experiment consisted of two NAO robots; each gave differing speeches. For each participant, their national origin was asked in the pre-questionnaire, and during the sessions, one of the robot’s topics was either personalized or not to their national origin. Since one robot shared a familiar topic, we expected to observe bonding between humans and the robot. We gathered data from a post-questionnaire and analyzed them. The results summarize the hypotheses here. We conclude that homophily plays a significant role in human-robot interaction, affecting trust in a robot partner. © 2021, Springer Nature Switzerland AG.","Homophily; HRI; Trust","Man machine systems; Surveys; Homophily; HRI; Human-human interactions; Humans-robot interactions; Robot Partners; Similar Interests; Social connection; Trust; Human robot interaction",,,,,"National Science Foundation, NSF: -1719027","Acknowledgments. The authors would also like to acknowledge the financial support of this work by the National Science Foundation (NSF, #IIS-1719027).",,,,,,,,,,"Lazarsfeld, P., Merton, R., (1954) Friendship as Social Process: A Substantive and Methodological Analysis, pp. 18-66. , pp; McPherson, M., Birds of a feather: Homophily in social networks (2001) Ann. Rev. Sociol., 27 (1), pp. 415-444; Bowman, M., Reasoning about naming systems (1993) ACM Trans. Program. Lang. Syst., 15 (5), pp. 795-825; Bernier, E.P., Scassellati, B., The similarity-attraction effect in human-robot interaction (2010) 2010 IEEE 9Th International Conference on Development and Learning, pp. 286-290. , pp; In, A.R.E., (1934) Aristotle in 23 Volumes. Rackman Translation. Cambridge, , Harvard University Press; Plato Laws Twelve Volumes, vol. 11. Bury Translator. Cambridge, Harvard University Press (1968); Jung, S., Personality and facial expressions in human-robot interaction (2012) Human-Robot Interaction (HRI), 2012 7Th ACM/IEEE International Conference On, Pp. 161–162. IEEE; Kahn, P.H., “Robovie, you’ll have to go into the closet now”: Children’s social and moral relationships with a humanoid robot (2012) Dev. Psychol., 48 (2), pp. 303-314; Aly, A., Tapus, A., A model for synthesizing a combined verbal and nonverbal behavior based on personality traits in human-robot interaction (2013) 8Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 325-332. , pp., 2013; Heerink, M., Exploring the influence of age, gender, education and computer experience on robot acceptance by older adults (2011) Proceedings of the 6Th International Conference on Human-Robot Interaction, HRI ’11 ACM, pp. 147-148. , pp., New York, NY, USA; Carlson, Z., Team-building activities for heterogeneous groups of humans and robots (2015) International Conference on Social Robotics (ICSR), pp. 113-123. , pp., Paris, France; Carlson, Z., Perceived mistreatment and emotional capability following aggressive treatment of robots and computers (2019) Int. J. Soc. Robot., 11 (5), pp. 727-739; Kühnlenz, B., Kühnlenz, K., Social bonding increases unsolicited helpfulness towards a bullied robot (2020) 2020 29Th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 833-838. , pp; Hurley, R., (2006) The Decision to Trust. 84, 156 (10), pp. 55-62; Buchan, N.R., Trust and gender: An examination of behavior and beliefs in the investment game (2008) J. Econ. Behav. Organ., 68 (3), pp. 466-476; Sutter, M., Kocher, M.G., Trust and trustworthiness across different age groups (2007) Games Econ. Behav., 59 (2), pp. 364-382; Salem, M., Would you trust a (Faulty) robot?: Effects of error, task type and personality on human-robot cooperation and trust (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI ’15. ACM, pp. 141-148. , pp., New York, NY, USA; Hancock, P.A., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527. , 22046724; Strohkorb Sebo, S., The ripple effects of vulnerability: The effects of a robot’s vulnerable behavior on trust in human-robot teams (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, HRI ’18. ACM, pp. 178-186. , pp., New York, NY, USA; Moody, J., Race, school integration, and friendship segregation in america (2001) Am. J. Sociol., 107 (3), pp. 679-716; Bureau, U.C., (2017) Race and Ethnicity; McCroskey, L.L., Analysis and improvement of the measurement of interpersonal attraction and homophily (2006) Commun. Q., 54 (1), pp. 1-31; Jian, J.-Y., (2000) Foundations for an Empirically Determined Scale of Trust in Automated Systems, 403, pp. 53-71","Salek Shahrezaie, R.; Socially Assistive Robotics Group, United States; email: rsalek@nevada.unr.edu","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119850412
"Ning Y., Liu Y., Xi F., Huang K., Li B.","57193811149;57211268565;7102360062;57221528142;55628528575;","Human-Robot Interaction Control for Robot Driven by Variable Stiffness Actuator with Force Self-Sensing",2021,"IEEE Access","9",,"9311618","6696","6705",,2,"10.1109/ACCESS.2020.3048418","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099513380&doi=10.1109%2fACCESS.2020.3048418&partnerID=40&md5=390e8da408a67f9d76cfe106a5199032","State Key Laboratory of Robotics and System (HIT), Harbin, 150001, China; School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China; Shenzhen Cansinga Technology Company Ltd., Shenzhen, 518055, China; Peng Cheng Laboratory, Shenzhen, 518055, China","Ning, Y., State Key Laboratory of Robotics and System (HIT), Harbin, 150001, China, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China; Liu, Y., State Key Laboratory of Robotics and System (HIT), Harbin, 150001, China, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China; Xi, F., School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China; Huang, K., Shenzhen Cansinga Technology Company Ltd., Shenzhen, 518055, China; Li, B., State Key Laboratory of Robotics and System (HIT), Harbin, 150001, China, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China, Peng Cheng Laboratory, Shenzhen, 518055, China","Robots driven by variable stiffness actuators (VSAs) have been an important technology as they could provide intrinsic compliance for safe human-robot interaction. The internal compliance of VSA also makes it possible for the actuator to act as a torque sensor and estimate the external force. This paper presents a physical human-robot interaction control strategy for robots driven by VSA with force self-sensing. The VSA adopted in the robotic system improves the safe performance of physical human-robot interaction. The interaction force is directly estimated by measuring the internal deformation of VSA with a stiffness region control which ensures a better resolution of force estimation and avoids transgressing the limits of deformation simultaneously. Then an online estimation method for the human motion intention is developed to generate the desired trajectory based on the estimated force. The impedance control is designed to enable the robot to actively follow the desired trajectory with compliance. Under the proposed control strategy, the robot is capable of actively tracking the 'motion intention' of human with the estimated interaction force, which is demonstrated through experiment studies. © 2013 IEEE.","Force self-sensing; motion intention estimation; physical human-robot interaction; variable stiffness actuator (VSA)","Actuators; Compliance control; Deformation; Man machine systems; Motion tracking; Stiffness; Control strategies; Desired trajectories; Human motion intention; Human-robot interaction control; Internal deformation; On-line estimation; Physical human-robot interactions; Safe human-robot interaction; Social robots",,,,,"National Key Research and Development Program of China, NKRDPC: 2017YFB1302200; Special Project for Research and Development in Key areas of Guangdong Province: 2019B090915001; Shenzhen Research and Development Program: JCYJ20200109112818703, JSGG20180504170011639","This work was supported in part by the Key-Area Research and Development Program of Guangdong Province under Grant 2019B090915001, in part by the National Key Research and Development Program of China under Grant 2017YFB1302200, and in part by the Shenzhen Research and Development Program of China under Grant JSGG20180504170011639 and Grant JCYJ20200109112818703.",,,,,,,,,,"Vanderborght, B., Variable impedance actuators: A review (2013) Robot. 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Syst., 46 (7), pp. 980-992. , Jul; Dinh, B.K., Xiloyannis, M., Antuvan, C.W., Cappello, L., Masia, L., Hierarchical cascade controller for assistance modulation in a soft wearable arm exoskeleton (2017) Ieee Robot. Autom. Lett., 2 (3), pp. 1786-1793. , Jul; Duchaine, V., St-Onge, B.M., Gao, D., Gosselin, C., Stable and intuitive control of an intelligent assist device (2012) Ieee Trans. Haptics, 5 (2), pp. 148-159. , Apr; Liang, C., Hsiao, T., Admittance control of powered exoskeletons based on joint torque estimation (2020) Ieee Access, 8, pp. 94404-94414; Li, Y., Ge, S.S., Humanfirobot collaboration based on motion intention estimation (2014) IEEE/ Asme Trans. Mechatronics, 19 (3), pp. 1007-1014. , Jun; Chen, C.-F., Du, Z.-J., He, L., Shi, Y.-J., Wang, J.-Q., Xu, G.-Q., Zhang, Y., Dong, W., Development and hybrid control of an electrically actuated lower limb exoskeleton for motion assistance (2019) Ieee Access, 7, pp. 169107-169122; Veneman, J.F., Ekkelenkamp, R., Kruidhof, R., Helm Der Van T., F.C., Kooij Der, H.Van, A series elastic-and bowden-cable-based actuation system for use as torque actuator in exoskeleton-type robots (2006) Int. J. Robot. Res., 25 (3), pp. 261-281. , Mar; Huard, B., Grossard, M., Moreau, S., Poinot, T., Sensorless force/position control of a single-acting actuator applied to compliant object interaction (2015) Ieee Trans. Ind. Electron., 62 (6), pp. 3651-3661. , Jun; Stienen, A.H.A., Hekman, E.E.G., Braak, H.T., Aalsma, A.M.M., Helm Der Van T., F.C., Kooij Der, H.Van, Design of a rotational hydroelastic actuator for a powered exoskeleton for upper limb rehabilitation (2010) Ieee Trans. Biomed. Eng., 57 (3), pp. 728-735. , Mar; Kim, B.-S., Song, J.-B., Object grasping using a 1 DOF variable stiffness gripper actuated by a hybrid variable stiffness actuator (2011) Proc. Ieee Int. Conf. Robot. Automat. (ICRA), pp. 4620-4625. , Shanghai, China, May; Ning, Y., Huang, H., Xu, W., Zhang, W., Li, B., Design and implementation of a novel variable stiffness actuator with cam-based relocation mechanism (2021) J. Mech. Robot., 13 (2). , Apr; Kim, B.-S., Song, J.-B., Design and control of a variable stiffness actuator based on adjustable moment arm (2012) Ieee Trans. Robot., 28 (5), pp. 1145-1151. , Oct; Tee, K.P., Ge, S.S., Tay, E.H., Barrier Lyapunov functions for the control of output-constrained nonlinear systems (2009) Automatica, 45 (4), pp. 918-927. , Apr; Rahman, M.M., Ikeura, R., Mizutani, K., Investigation of the impedance characteristic of human arm for development of robots to cooperate with humans (2002) Int. J. C Mech. Syst., Mach. Elements Manuf., 45 (2), pp. 510-518","Xi, F.; School of Mechanical Engineering and Automation, China; email: fengxi@ryerson.ca
Huang, K.; Shenzhen Cansinga Technology Company Ltd.China; email: k.huang@cansinga.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85099513380
"Kritskiy D., Pohudina O., Koba S., Kritskaya O., Pohudin A.","57195913632;57204907264;56311132000;57204914252;57214223045;","Automation of the Manipulator",2020,"Advances in Intelligent Systems and Computing","1113 AISC",,,"126","138",,,"10.1007/978-3-030-37618-5_12","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078411860&doi=10.1007%2f978-3-030-37618-5_12&partnerID=40&md5=9a6dbdadc7f93c99c3fcb6cd65c5e3a7","National Aerospace University, Kharkiv Aviation Institute, Kharkiv, Ukraine","Kritskiy, D., National Aerospace University, Kharkiv Aviation Institute, Kharkiv, Ukraine; Pohudina, O., National Aerospace University, Kharkiv Aviation Institute, Kharkiv, Ukraine; Koba, S., National Aerospace University, Kharkiv Aviation Institute, Kharkiv, Ukraine; Kritskaya, O., National Aerospace University, Kharkiv Aviation Institute, Kharkiv, Ukraine; Pohudin, A., National Aerospace University, Kharkiv Aviation Institute, Kharkiv, Ukraine","The subject of study in the article is the process of implementing the search for an object, its recognition (its geometric shape) and moving the manipulator to the corresponding coordinates to capture the object using a probe (ticks) and moving it to the given coordinates. The goal is to improve the quality of the robot manipulator when recognizing complex images of objects. Problems: study of human-robot interaction (HRI), gesture recognition for HRI, development of a generalized information processing algorithm, automation of collaboration between the working hand and the person, which should be guided by the movement of the hand using the camera. The models used: gesture recognition model for human-robot interaction. The following results are obtained: gesture recognition algorithm obtained during human-robot interaction, the functional structure of the system is obtained. The scientific novelty of the obtained results is as follows: improved model for recognizing objects of complex shape using the Kinect camera, proposed a special algorithm for gesture recognition in HRI, formed a series of gesture recognition tasks in HRI. © Springer Nature Switzerland AG 2020.","Gesture recognition; Human-robot interaction; Industrial robot; The algorithm of information processing","Cameras; Gesture recognition; Image enhancement; Industrial robots; Man machine systems; Manipulators; Robot applications; Complex shapes; Functional structure; Geometric shape; Gesture recognition algorithm; Human robot Interaction (HRI); Kinect cameras; Robot manipulator; Special algorithms; Human robot interaction",,,,,,,,,,,,,,,,"Krüger, J., Lien, T., Verl, A., Cooperation of human and machines in assembly lines (2009) CIRP Ann.-Manuf. Technol., 58, pp. 628-646; Green, S.A., Billinghurst, M., Chen, X., Chase, G., Human-robot collaboration: A literature view and augmented reality approach in design (2008) Int. J. Adv. Robot. Syst., 5, pp. 1-18; Vygotsky, L.S., (1980) Mind in Society: The Development of Higher Psychological Processes, , Harvard University Press, Cambridge; Cohen, P.R., Levesque, H.J., Persistence, intention, and commitment (1990) Reasoning about Actions and Plans, pp. 297-340; Breazeal, C., Brooks, A., Gray, J., Hoffman, G., Kidd, C., Lee, H., Lieberman, J., Mulanda, D., Humanoid robots as cooperative partners for people (2004) Int. J. Humanoid Robots, 1 (2004), pp. 1-34; Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots (1986) Autonomous Robot Vehicles, pp. 396-404. , Springer, New York; Saracen, M.J., Carrano, A.W., Henderson, T.D., (2012), U.S. Patent No. 8,160,205. U.S. Patent and Trademark Office, Washington, DC; Fumagalli, M., Naldi, R., Macchelli, A., Forte, F., Keemink, A.Q., Stramigioli, S., Carloni, R., Marconi, L., Developing an aerial manipulator prototype: Physical interaction with the environment (2014) IEEE Robot. Autom. Mag., 21 (3), pp. 41-50; Yamamoto, Y., Yun, X., Modeling and compensation of the dynamic interaction of a mobile manipulator (1994) Proceedings of the 1994 IEEE International Conference on Robotics and Automation, pp. 2187-2192. , IEEE, May; Kritsky, D.N., Ovsiannik, V.M., Pogudina, O.K., Shevel, V.V., Druzhinin, E.A., Model for intercepting targets by the unmanned aerial vehicle (2020) Mathematical Modeling and Simulation of Systems, MODS 2019. Advances in Intelligent Systems and Computing, 1019. , Palagin, A., Anisimov, A., Morozov, A., Shkarlet, S. (eds.), Springer, Cham; Kritskiy, D., Alexander, K., Koba, S., Druzhinin, E., Increasing the reliability of drones due to the use of quaternions in motion (2018) Proceedings of 2018 IEEE 9Th International Conference on Dependable Systems, Services and Technologies, DESSERT 2018; Kritsky, D.N., Druzhinin, E.A., Pogudina, O.K., Kritskaya, O.S., A method for assessing the impact of technical risks on the aerospace product development projects (2019) Advances in Intelligent Systems and Computing, 871, pp. 504-521. , Springer, Heidelberg","Pohudina, O.; National Aerospace University, Ukraine; email: o.pogudina@gmail.com","Nechyporuk M.Pavlikov V.Kritskiy D.",,"Springer","International Scientific and Technical Conference on Integrated Computer Technologies in Mechanical Engineering - Synergetic Engineering, ICTM 2019","28 November 2019 through 30 November 2019",,236099,21945357,9783030376178,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85078411860
"Liu Y., Ning Y., Huang H., Yang X., Li Z., Li B., Peng F., Wu A.","57211268565;57193811149;55637358900;57116729300;57226699799;55628528575;57225807171;57314670800;","Design of a Compliant Joint Based on Antagonistic-Driven Torsion Springs",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13015 LNAI",,,"622","630",,,"10.1007/978-3-030-89134-3_57","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118131986&doi=10.1007%2f978-3-030-89134-3_57&partnerID=40&md5=8c992eb2999dae16f8ee8c2216c506ee","State Key Laboratory of Robotics and System (HIT), Harbin, 150001, China; Harbin Institute of Technology, Shenzhen, 518055, China; Peng Cheng Laboratory, Shenzhen, 518055, China","Liu, Y., State Key Laboratory of Robotics and System (HIT), Harbin, 150001, China, Harbin Institute of Technology, Shenzhen, 518055, China; Ning, Y., State Key Laboratory of Robotics and System (HIT), Harbin, 150001, China, Harbin Institute of Technology, Shenzhen, 518055, China; Huang, H., State Key Laboratory of Robotics and System (HIT), Harbin, 150001, China, Harbin Institute of Technology, Shenzhen, 518055, China; Yang, X., State Key Laboratory of Robotics and System (HIT), Harbin, 150001, China, Harbin Institute of Technology, Shenzhen, 518055, China; Li, Z., State Key Laboratory of Robotics and System (HIT), Harbin, 150001, China, Harbin Institute of Technology, Shenzhen, 518055, China; Li, B., State Key Laboratory of Robotics and System (HIT), Harbin, 150001, China, Harbin Institute of Technology, Shenzhen, 518055, China, Peng Cheng Laboratory, Shenzhen, 518055, China; Peng, F., Harbin Institute of Technology, Shenzhen, 518055, China; Wu, A., Harbin Institute of Technology, Shenzhen, 518055, China","This paper proposes a novel series elastic actuator (SEA) based on torsion springs with antagonistic structure intended for human-robot interaction. Different from the traditional compliant joints constructed with torsion spring, the proposed novel SEA utilizes two torsion spring modules to construct the antagonistic structure for alleviating the empty return journey. The expression that depicts the relationship between the design parameters and stiffness of torsion spring is derived, which is conducive to designing proper torsion spring for a desired stiffness. Finally, experiments are conducted to verify the effectiveness of the proposed SEA for a friendly human-robot interaction. © 2021, Springer Nature Switzerland AG.","Antagonistic structure; Series elastic actuator; Torsion spring","Actuators; Man machine systems; Stiffness; Torsional stress; Antagonistic structure; Compliant joints; Design parameters; Humans-robot interactions; Novel series; Series elastic actuators; Torsion Springs; Human robot interaction",,,,,"Special Project for Research and Development in Key areas of Guangdong Province: 2019B090915001; Shenzhen Research and Development Program: JCYJ20200109112818703","Acknowledgement. This work was supported by the Key-Area Research and Development Program of Guangdong Province (Grant No. 2019B090915001), and the Shenzhen Research and Development Program of China (Grant No. JCYJ20200109112818703).",,,,,,,,,,"Vanderborght, B., Variable impedance actuators: A review (2013) Robot. Auton. Syst., 61 (12), pp. 1601-1614; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Trans. Robot., 31, pp. 1089-1100; Ning, Y., Xu, W., Huang, H., Li, B., Liu, F., Design methodology of a novel variable stiffness actuator based on antagonistic-driven mechanism (2019) Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci., 233 (19-20), pp. 6967-6984; Ning, Y., Huang, H., Xu, W., Zhang, W., Li, B., Design and implementation of a novel variable stiffness actuator with cam-based relocation mechanism (2021) J. Mech. Robot., 13 (2); Edwards, B.T., Jensen, B.D., Howell, L.L., A pseudo-rigid-body model for initially-curved pinned-pinned segments used in compliant mechanisms (2001) J. Mech. Des., 123 (3), pp. 464-468; Howell, L.L., Midha, A., Parametric deflection approximations for end-loaded, large-deflection beams in compliant mechanisms (1995) J. Mech. Des., 117 (1), pp. 156-165; Howell, L.L., Compliant Mechanisms (2013) 21St Century Kinematics, , Springer, London; Howell, L.L., Ashok Midha, A., Parametric deflection approximations for initially curved, large-deflection beams in compliant mechanisms (1996) ASME Design Engineering Technical Conferences, pp. 12-15. , pp","Huang, H.; State Key Laboratory of Robotics and System (HIT)China; email: huanghailin@hit.edu.cn","Liu X.Nie Z.Yu J.Xie F.Song R.",,"Springer Science and Business Media Deutschland GmbH","14th International Conference on Intelligent Robotics and Applications, ICIRA 2021","22 October 2021 through 25 October 2021",,267169,03029743,9783030891336,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85118131986
"Fronemann N., Pollmann K., Loh W.","56422460900;57128090300;57202956487;","Should my robot know what's best for me? Human–robot interaction between user experience and ethical design",2021,"AI and Society",,,,"","",,2,"10.1007/s00146-021-01210-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105397718&doi=10.1007%2fs00146-021-01210-3&partnerID=40&md5=367cd13a158ac0c800d247019dca9b68","Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, Stuttgart, 70569, Germany; Int. Center for Ethics in the Sciences and Humanities IZEW, University of Tuebingen, Wilhelmstr. 19, Tuebingen, 72074, Germany","Fronemann, N., Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, Stuttgart, 70569, Germany; Pollmann, K., Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, Stuttgart, 70569, Germany; Loh, W., Int. Center for Ethics in the Sciences and Humanities IZEW, University of Tuebingen, Wilhelmstr. 19, Tuebingen, 72074, Germany","To integrate social robots in real-life contexts, it is crucial that they are accepted by the users. Acceptance is not only related to the functionality of the robot but also strongly depends on how the user experiences the interaction. Established design principles from usability and user experience research can be applied to the realm of human–robot interaction, to design robot behavior for the comfort and well-being of the user. Focusing the design on these aspects alone, however, comes with certain ethical challenges, especially regarding the user’s privacy and autonomy. Based on an example scenario of human–robot interaction in elder care, this paper discusses how established design principles can be used in social robotic design. It then juxtaposes these with ethical considerations such as privacy and user autonomy. Combining user experience and ethical perspectives, we propose adjustments to the original design principles and canvass our own design recommendations for a positive and ethically acceptable social human–robot interaction design. In doing so, we show that positive user experience and ethical design may be sometimes at odds, but can be reconciled in many cases, if designers are willing to adjust and amend time-tested design principles. © 2021, The Author(s).","Autonomy; Elder care; Ethical design; Human–robot interaction; Privacy; User experience","Machine design; Philosophical aspects; Privacy by design; Professional aspects; Social robots; User interfaces; Design Principles; Design recommendations; Ethical considerations; Ethical designs; Ethical perspectives; Real-life contexts; Robot interactions; User experience research; User experience",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV7944, SV7941","Funded by the German Federal Ministry for Education and Research (BMBF),16SV7941, 16SV7944.",,,,,,,,,,"Alenljung, B., Lindblom, J., Andreasson, R., Ziemke, T., User experience in social human-robot interaction (2017) Int J Ambient Comput Intell, 8, pp. 12-31; Beauchamp, T.L., Childress, J.F., (1979) Principles of biomedical ethics, , Oxford Univ. 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Cambridge, Mass., MIT Press; Cavoukian, A., (2011) Privacy by Design. the 7 Foundational Principles, , http://scholar.google.de/scholar_url?url=https://www.ipc.on.ca/wp-content/uploads/Resources/pbd-implement-7found-principles.pdf&hl=de&sa=X&scisig=AAGBfm1NrsM8_Ee0B8ZoGrUt9bWYyg4Bcw&nossl=1&oi=scholarr, Accessed on 17 Oct, 2018; Chien, S.-E., Li, C., Lee, H.-H., Yang, C.-C., Lin, F.-H., Yang, P.-L., Wang, T.-M., Yeh, S.-L., Age difference in perceived ease of use, curiosity, and implicit negative attitude toward robots (2019) J. Hum.-Robot Interact., 8, pp. 1-19. , https://doi.org/https://doi.org/10.1145/3311788; European Parliament (2/16/2017): Civil law rules on robotics. EUP Resolution. 2015/2103(INL). 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"Mamedov S., Mikhel S.","57205663201;57203955171;","Practical Aspects of Model-Based Collision Detection",2020,"Frontiers in Robotics and AI","7",,"571574","","",,3,"10.3389/frobt.2020.571574","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097421170&doi=10.3389%2ffrobt.2020.571574&partnerID=40&md5=56c910b9ca1021b793b6673c54874ee5","Laboratory of Mechatronics, Control and Prototyping, Center for Technologies in Robotics Mechatronics Components, Innopolis University, Innopolis, Russian Federation; Industrial Robotics Lab, Center for Technologies in Robotics Mechatronics Components, Innopolis University, Innopolis, Russian Federation","Mamedov, S., Laboratory of Mechatronics, Control and Prototyping, Center for Technologies in Robotics Mechatronics Components, Innopolis University, Innopolis, Russian Federation; Mikhel, S., Industrial Robotics Lab, Center for Technologies in Robotics Mechatronics Components, Innopolis University, Innopolis, Russian Federation","Recently, with the increased number of robots entering numerous manufacturing fields, a considerable wealth of literature has appeared on the theme of physical human-robot interaction using data from proprioceptive sensors (motor or/and load side encoders). Most of the studies have then the accurate dynamic model of a robot for granted. In practice, however, model identification and observer design proceeds collision detection. To the best of our knowledge, no previous study has systematically investigated each aspect underlying physical human-robot interaction and the relationship between those aspects. In this paper, we bridge this gap by first reviewing the literature on model identification, disturbance estimation and collision detection, and discussing the relationship between the three, then by examining the practical sides of model-based collision detection on a case study conducted on UR10e. We show that the model identification step is critical for accurate collision detection, while the choice of the observer should be mostly based on computation time and the simplicity and flexibility of tuning. It is hoped that this study can serve as a roadmap to equip industrial robots with basic physical human-robot interaction capabilities. © Copyright © 2020 Mamedov and Mikhel.","collision detection; disturbance observer; dynamic identification; human-robot interaction; observer design; physical human-robot interaction",,,,,,"Russian Foundation for Basic Research, РФФИ: 18-38-20186","Funding. This work was supported by Russian Foundation for Basic Research (RFBR) grant 18-38-20186 and center for technologies in robotics & mechatronics components.",,,,,,,,,,"Åström, K.J., Murray, R.M., (2010) Feedback Systems: An Introduction for Scientists and Engineers, , Princeton, NJ, Princeton University Press; Anderson, B.D., Exponential convergence and persistent excitation (1982) 1982 21st IEEE Conference on Decision and Control, pp. 12-17. , Orlando, FL, IEEE; Atkeson, C.G., An, C.H., Hollerbach, J.M., Estimation of inertial parameters of manipulator loads and links (1986) Int. J. Robot. 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Eng, 15, pp. 879-886; Wensing, P.M., Kim, S., Slotine, J.J.E., Linear matrix inequalities for physically consistent inertial parameter identification: a statistical perspective on the mass distribution (2017) IEEE Robot. Autom. Lett, 3, pp. 60-67; Wu, J., Wang, J., You, Z., An overview of dynamic parameter identification of robots (2010) Robot. Comput. Integr. Manuf, 26, pp. 414-419; Wu, W., Zhu, S., Wang, X., Liu, H., Closed-loop dynamic parameter identification of robot manipulators using modified fourier series (2012) Int. J. Adv. Robot. Syst, 9, p. 29","Mamedov, S.; Laboratory of Mechatronics, Russian Federation; email: sh.mamedov@innopolis.ru",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85097421170
"Cong M., Ma H., Liu D., Zhang J.","16306181000;57219926451;57137089900;57217993373;","Design and human-robot interaction control of rehabilitation exoskeleton compliant hip joint [康复外骨骼柔性髋关节设计与人机交互控制]",2020,"Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition)","48","10",,"38","43",,,"10.13245/j.hust.201007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096116258&doi=10.13245%2fj.hust.201007&partnerID=40&md5=419d82cd4c0d3ba7938d20aceca67ce3","School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China","Cong, M., School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China; Ma, H., School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China; Liu, D., School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China; Zhang, J., School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China","To solve the problem that the auxiliary force cannot be adjusted in real-time with the parameters of the lower limbs of the human body during the rehabilitation treatment of hip joint motion,a human-robot interaction control method based on friction compensation and fuzzy impedance control was proposed.The design of the bionic mechanism was based on the biological basis of the human hip joint,and to avoid the influence of the impedance parameters of the human lower limb on the output of the compliant hip joint,the dynamic modeling of the human-robot system was carried out.Based on the EKF (extended Kalman filter),the internal friction of the motor was compensated to reduce the friction influence on the motor output position.An identification algorithm was used to estimate the impedance parameters of the human lower limbs,a closed-loop human-robot interaction controller was constructed based on fuzzy impedance control,and the contact force was adjusted in real-time to achieve gait trajectory training.The experiment verified the rationality of the design of the rehabilitation exoskeleton compliant hip joint and the effectiveness of the human-robot interaction controller method. © 2020, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.","Compliant hip joint; Friction compensation; Human-robot interaction control; Rehabilitation exoskeleton; Series elastic actuators","Bioinformatics; Controllers; Educational robots; Exoskeleton (Robotics); Extended Kalman filters; Friction; Fuzzy control; Fuzzy filters; Hip prostheses; Joints (anatomy); Machine design; Man machine systems; Motion control; EKF (extended Kalman filter); Friction compensation; Human lower limbs; Human-robot interaction control; Human-robot systems; Identification algorithms; Impedance control; Impedance parameters; Social robots",,,,,,,,,,,,,,,,", (5), p. 19. , 张雷雨,李剑锋,刘钧辉,等.上肢康复外骨骼的设计与人机相容性分析[J].机械工程学报,2018,54 -28; FARKHATDINOV, I, EBERT, J, VAN OORT, G, Assisting human balance in standing with a robotic exoskeleton[J] (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 414-421; (2017), 45 (10), p. 105. , 黄剑,余晓强,陶春静.基于无线穿戴式传感系统的智能步态检测研究[J].华中科技大学学报(自然科学版),, -110; ZHANG, T, TRAN, M, HUANG, H., Design and experimental verification of hip exoskeleton with balance capacities for walking assistance (2018) IEEE/ASME Trans- actions on Mechatronics, 23 (1), pp. 274-285. , [J]; CAPPELLO, L, XILOYANNIS, M, DINH, B K, Multistable series elastic actuators:design and control [J] (2019) Robotics and Autonomous Systems, 118, pp. 167-178; ZHAO, Y, PAINE, N, JORGENSEN, S J, Impedance control and performance measure of series elastic actuators (2018) IEEE Transactions on Industrial Electronics, 65 (3), pp. 2817-2827. , [J]; PRATT, G A, WILLISSON, P, BOLTON, C, Late motor processing in low-impedance robots:impedance control of series-elastic actuators,2004 (2004) Proc of American Control Conference, pp. 3245-3251. , [C] Boston:IEEE; VALLERY, H, VENEMAN, J, VAN ASSELDONK, E, Compliant actuation of rehabilitation robots [J] (2008) IEEE Robotics & Automation Magazine, 15 (3), pp. 60-69; WYETH, G., Demonstrating the safety and performance of a velocity sourced series elastic actuator:ICRA,2008 (2008) Proc of IEEE International Conference on Robotics and Automation, pp. 3642-3647. , [C] Pasadena:IEEE; OH, S, KONG, K., High-precision robust force control of a series elastic actuator (2017) IEEE/ASME Transactions on Mechatronics, 22 (1), pp. 71-80. , [J]; YU, H, HUANG, S, CHEN, G, Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Transactions on Robotics, 31 (5), pp. 1089-1100. , [J]; , (12), p. 1943. , 韩亚丽,许有熊,高海涛,等.基于导纳控制的膝关节外骨骼摆动控制研究[J].自动化学报,2016,42 -1950; LU, Q, GANG, T, HAO, G, Compound optimal control of harmonic drive considering hysteresis characteristic (2019) Mechanical Sciences, 10 (2), pp. 383-391. , [J]; BOVI, G, RABUFFETTI, M, MAZZOLENI, P, A multiple-task gait analysis approach:kinematic,kinetic and emg reference data for healthy young and adult subjects[J] (2010) Gait & Posture, 33 (1), pp. 6-13; LEE, H, LEE, S, SEO, K, Training for walking efficiency with a wearable hip-assist robot in patients with stroke[J] (2019) Stroke, 50 (12), pp. 3545-3552",,,,"Huazhong University of Science and Technology",,,,,16714512,,,,"Chinese","Huazhong Ligong Daxue Xuebao",Article,"Final","",Scopus,2-s2.0-85096116258
"Porfrio D., Stegner L., Cakmak M., Sauppe A., Albarghouthi A., Mutlu B.","57223994731;57219542965;22940323800;56084504000;36336887800;15060220700;","Figaro: A tabletop authoring environment for human-robot interaction",2021,"Conference on Human Factors in Computing Systems - Proceedings",,,,"","",,1,"10.1145/3411764.3446864","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106680868&doi=10.1145%2f3411764.3446864&partnerID=40&md5=f842718ed10940228da567a669f2a192","University of Washington, Seattle, WA, United States","Porfrio, D., University of Washington, Seattle, WA, United States; Stegner, L., University of Washington, Seattle, WA, United States; Cakmak, M., University of Washington, Seattle, WA, United States; Sauppe, A., University of Washington, Seattle, WA, United States; Albarghouthi, A., University of Washington, Seattle, WA, United States; Mutlu, B., University of Washington, Seattle, WA, United States","Human-robot interaction designers and developers navigate a complex design space, which creates a need for tools that support intuitive design processes and harness the programming capacity of state-of-the-art authoring environments.We introduce Figaro, an expressive tabletop authoring environment for mobile robots, inspired by shadow puppetry, that provides designers with a natural, situatedrepresentation of human-robot interactions while exploiting the intuitiveness of tabletop and tangible programming interfaces. On the tabletop, Figaro projects a representation of an environment. Users demonstrate sequences of behaviors, or scenes, of an interaction by manipulating instrumented fgurines that represent the robot and the human. During a scene, Figaro records the movement. © 2021 ACM.","Authoring environments; Human-robot interaction; Program synthesis; Shadow puppetry; Tabletop interfaces","Application programming interfaces (API); End effectors; Human engineering; Industrial manipulators; Interface states; Machine design; Man machine systems; Robot programming; Authoring environments; Complex designs; Design process; Shadow puppetries; State of the art; Tangible programming; Human robot interaction",,,,,"National Science Foundation, NSF: 1651129, 1925043","This work was supported by the National Science Foundation (NSF) awards 1651129 and 1925043 and an NSF Graduate Research Fellowship. We also thank Kevin Ponto for providing guiding insights into tabletop interface design.",,,,,,,,,,"Akgun, B., Cakmak, M., Wook Yoo, J., Lockerd Thomaz, A., Trajectories and keyframes for kinesthetic teaching: A human-robot interaction perspective (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 391-398. , https://doi.org/10.1145/2157689.2157815; Alexandrova, S., Cakmak, M., Hsiao, K., Takayama, L., Robot programming by demonstration with interactive action visualizations (2014) Robotics: Science and Systems. 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"Lin G., Yu J., Liu J.","57222714573;36026903400;57193426775;","Adaptive fuzzy finite-time command filtered impedance control for robotic manipulators",2021,"IEEE Access","9",,"9388676","50917","50925",,3,"10.1109/ACCESS.2021.3069152","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103773682&doi=10.1109%2fACCESS.2021.3069152&partnerID=40&md5=bf52a1dec8d3af38e3e5fb646cbbbd0a","School of Automation, Qingdao University, Qingdao, 266071, China; Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao, 266071, China","Lin, G., School of Automation, Qingdao University, Qingdao, 266071, China, Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao, 266071, China; Yu, J., School of Automation, Qingdao University, Qingdao, 266071, China, Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao, 266071, China; Liu, J., School of Automation, Qingdao University, Qingdao, 266071, China, Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao, 266071, China","In order to improve the security and compliance of physical human-robot interaction (pHRI), an adaptive fuzzy impedance control for robotic manipulators based on finite-time command filtered method is proposed in this paper. Firstly, robots usually encounter system uncertainties in practical applications, and the adaptive fuzzy control is introduced to approximate the system uncertainties. Secondly, the finite-time control method is used to improve the interaction performance of the system. Then, the command filtered control technique is used to deal with the 'computational complexity ' of traditional backstepping. Finally, simulations are conducted to illustrate the effectiveness of the proposed control method in physical human-robot interaction. © 2013 IEEE.","adaptive fuzzy control; command filtered control; finite-time control; impedance control; Physical human-robot interaction (pHRI)","Backstepping; Compliance control; Flexible manipulators; Fuzzy control; Human robot interaction; Industrial manipulators; Man machine systems; Robotics; Adaptive fuzzy control; Control techniques; Finite-time control; Impedance control; Physical human-robot interactions; Physical humanrobot interaction (phri); Robotic manipulators; System uncertainties; Adaptive control systems",,,,,"2017YFB1303503; 21-1-2-6-nsh; TSQN20161026; National Natural Science Foundation of China, NSFC: 61973179; China Postdoctoral Science Foundation: 2020M671995","This work was supported in part by the National Key Research and the Development Plan under Grant 2017YFB1303503, in part by the National Natural Science Foundation of China under Grant 61973179, in part by the Taishan Scholar Special Project Fund under Grant TSQN20161026, in part by the Qingdao Key Research and Development Special Project under Grant 21-1-2-6-nsh, and in part by the Project funded by the China Postdoctoral Science Foundation under Grant 2020M671995.",,,,,,,,,,"He, W., Ge, S.S., Li, Y., Chew, E., Ng, Y.S., Neural network control of a rehabilitation robot by state and output feedback (2015) J. 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Fuzzy Syst., , early access, Dec. 31; Huang, X., Lin, W., Yang, B., Global finite-time stabilization of a class of uncertain nonlinear systems (2005) Automatica, 41 (5), pp. 881-888. , May; Wang, F., Chen, B., Liu, X., Lin, C., Finite-time adaptive fuzzy tracking control design for nonlinear systems (2018) Ieee Trans. Fuzzy Syst., 26 (3), pp. 1207-1216. , Jun; Levant, A., Higher-order sliding modes, differentiation and output-feedback control (2003) Int. J. Control, 76 (9-10), pp. 924-941. , Jan; Fu, C., Wang, Q.-G., Yu, J., Lin, C., Neural network-based finite-time command filtering control for switched nonlinear systems with backlash-like hysteresis (2020) Ieee Trans. Neural Netw. Learn. Syst., , early access, Jul. 31","Yu, J.; School of Automation, China; email: yjp1109@hotmail.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85103773682
"Galin R., Meshcheryakov R., Samoshina A.","57209776926;23035794100;57217106709;","Mathematical Modelling and Simulation of Human-Robot Collaboration",2020,"Proceedings - 2020 International Russian Automation Conference, RusAutoCon 2020",,,"9208040","1058","1062",,5,"10.1109/RusAutoCon49822.2020.9208040","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093919828&doi=10.1109%2fRusAutoCon49822.2020.9208040&partnerID=40&md5=0a36e80c06ceedf37b5edd0fe9671398","V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Laboratory of Cyber-Physical Systems, Moscow, Russian Federation","Galin, R., V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Laboratory of Cyber-Physical Systems, Moscow, Russian Federation; Meshcheryakov, R., V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Laboratory of Cyber-Physical Systems, Moscow, Russian Federation; Samoshina, A., V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Laboratory of Cyber-Physical Systems, Moscow, Russian Federation","The relevance of human-robot interaction study is due to necessary to use of mathematical knowledge for building and programming robotic systems. The new stage of robotics - collaborative robotics. The paper presents a noticeable theoretical part that describes the advantages and differences of collaborative robotics from other robotic solutions. There is a mathematical representation of the kinematic characteristics of a collaborative robot is given in this paper. In particular considered the solutions to the direct and inverse problems of robot kinematics. The process of human-robot interaction was modeled in simulation environment where human and robot solve a single task in a shared workspace. © 2020 IEEE.","collaborative robot; human-robot interaction; mathematical model; simulation","Inverse problems; Kinematics; Man machine systems; Robot programming; Robotics; Collaborative robots; Direct and inverse problems; Human-robot collaboration; Kinematic characteristics; Mathematical knowledge; Mathematical modelling and simulation; Mathematical representations; Simulation environment; Social robots",,,,,"Russian Foundation for Basic Research, РФФИ: 19-08-00331","*The reported study was partially funded by RFBR according to the research project № 19-08-00331.",,,,,,,,,,"Ermishin, K., Yuschenko, A., Collaborative mobile robots-A new stage of development of service robotics (2016) Robotics and Technical Cybernetics, 12 (3), pp. 3-9; (2016) ISO TS 15066:2016, Robots and Robotic Devices-Collaborative Robots, since February 15, , http://www.iso.org; Galin, R., Meshcheryakov, R., Automation and robotics in the context of Industry 4.0: The shift to collaborative robots (2019) IOP Conf. Series: Materials Science and Engineering, 537; Robla-Gomez, S., Working Together: A review on safe humanRobot collaboration in industrial environments (2017) IEEE Access, 5, pp. 26754-26773; Galin, R., Meshcheryakov, R., Review on Human-Robot Interaction during Collaboration in a Shared Workspace Interactive Collaborative Robotics. Lecture Notes in Computer Science, , vol 11659, A. Ronzhin, G. Rigoll, and R. Meshcheryakov, Eds., Springer; ISO 10218-1-2011, Robots and Robotic Devices-Safety Requirements for Industrial Robots. Part 1: Robots, since July 1, 2011, , http://www.iso.org; ISO 10218-2-2011, Robots and Robotic Devices-Safety Requirements for Industrial Robots. Part 2: Robot Systems and Integration, since July 1, 2011, , http://www.iso.org; (2016) Universal Robots Brouchure: The Future of Collaboration. Denmark; Hartenberg, R., Denavit, J., A kinematic notation for lower pair machanisms based om matrices (1955) Journal of Applied Mechanics, pp. 215-221; Kebria, P.M., Al-Wais, S., Abdi, H., Nahavandi, S., Kinematic and dynamic modelling of UR5 manipulator (2016) IEEE Int. Conf. on Systems, Man, and Cybernetics; Khalil, W., Dombre, E., (2002) Modeling Identification and Control of Robots, , London: Hermes Penton; Hoang Dinh, K., Oguz, O.S., Elsayed, M., Wollherr, D., Adaptation and transfer of robot motion policies for close proximity humanrobot interaction (2019) Front. Robot, 6 (69); Borisov, O., Gromov, V., Pirkin, A., (2016) Methods for Managing Robotic Applications. St. Petersburg: ITMO University; Psychopop, V., Optimal trajectory for electroMechanical control of robot manipulator (2007) Journal Electromechanics, (1), pp. 51-57; Hawkins, K.P., (2013) Analytic Inverse Kinematics for the Universal Robots UR-5/UR-10 Arms. Technical Report, Georgia Institute of Technology Publications; Galin, R., Meshcheryakov, R., HumanRobot interaction efficiency and humanrobot collaboration (2020) Robotics: Industry 4.0 Issues and New Intelligent Control Paradigms. Studies in Systems, Decision and Control, 272. , A. Kravets, Ed. Springer; Tirupachuri, Y., Nava, G., Rapetti, L., Latella, C., Trajectory advancement during humanrobot collaboration (2019) 28th IEEE Int. Conf. on Robot and Human Interactive Communication; Meixin, Z., (2019) Multi-agent Robotic System in Rescue for Earthquake, , Ph.D. Dissertation; Ayupov, V., (2017) Mathematical Modeling of Technical Systems: Textbook. Perm: Procrost Publishing; Koubaa, A., Robot Operating System (ROS (2017) Studies in Computational Intelligence",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 International Russian Automation Conference, RusAutoCon 2020","6 September 2020 through 12 September 2020",,163576,,9781728161303,,,"English","Proc. - Int. Russ. Autom. Conf., RusAutoCon",Conference Paper,"Final","",Scopus,2-s2.0-85093919828
"Shiomi M., Sumioka H., Sakai K., Funayama T., Minato T.","8397042200;6505862269;55369257800;57192436219;7005390640;","SOTO: An android platform with a masculine appearance for social touch interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"447","449",,4,"10.1145/3371382.3378283","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083212300&doi=10.1145%2f3371382.3378283&partnerID=40&md5=1495b712cf85f45c9bbfd9a9e075ad5c","Interaction Science Laboratories, ATR, Soraku-gun, Kyoto, Japan; Hiroshi Ishiguro Laboratories, ATR, Soraku-gun, Kyoto, Japan","Shiomi, M., Interaction Science Laboratories, ATR, Soraku-gun, Kyoto, Japan; Sumioka, H., Interaction Science Laboratories, ATR, Soraku-gun, Kyoto, Japan; Sakai, K., Interaction Science Laboratories, ATR, Soraku-gun, Kyoto, Japan; Funayama, T., Interaction Science Laboratories, ATR, Soraku-gun, Kyoto, Japan; Minato, T., Hiroshi Ishiguro Laboratories, ATR, Soraku-gun, Kyoto, Japan","In this paper, we report an android platform with a masculine appearance. In the human-human interaction research field, several studies reported the effects of gender in the social touch context. However, in the human-robot interaction research field, gender effects are mainly focused on human genders, i.e., a robot's perceived gender is less focused. The purpose of developing the android is to investigate gender effects in social touch in the context of the human-robot interaction, comparing to existing android platforms with feminine appearances. For this purpose, we prepared a nonexistent face design in order to avoid appearance effects and fabric-based capacitance type upper-body touch sensors. © 2020 ACM.","Android platform; Human-robot interaction; Social touch","Agricultural robots; Android (operating system); Man machine systems; Social robots; Android platforms; Gender effects; Human-human interactions; Research fields; Touch interaction; Touch sensors; Upper bodies; Economic and social effects",,,,,"JPMJER1401; Japan Society for the Promotion of Science, KAKEN: JP17K00293; Japan Science and Technology Agency, JST: JPMJCR18A1","This research work was supported in part by the JST ERATO Ishiguro Symbiotic Human Robot Interaction Project (Grant Number: JPMJER1401), JST CREST Grant Number JPMJCR18A1, Japan, and JSPS KAKENHI Grant Numbers JP17K00293.",,,,,,,,,,"Watanabe, M., Ogawa, K., Ishiguro, H., At the department store-canandroids be salespeople in the real world (2018) Geminoid Studies, pp. 429-439. , Springer; Ogawa, K., Ishiguro, H., At the theater-possibilities of androids as poetry-reciting agents (2018) Geminoid Studies, pp. 455-466. , Springer; Sakamoto, D., Kanda, T., Ono, T., Ishiguro, H., Hagita, N., Android as a telecommunication medium with a human-like presence (2007) 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 193-200; Haring, K.S., Silvera-Tawil, D., Takahashi, T., Watanabe, K., Velonaki, M., How people perceive different robot types: A direct comparison of an android, humanoid, and non-biomimetic robot (2016) 2016 8th International Conference on Knowledge and Smart Technology (KST), pp. 265-270; Zheng, X., Shiomi, M., Minato, T., Ishiguro, H., What kinds of robot's touch will match expressed emotions (2019) IEEE Robotics and Automation Letters, pp. 127-134; Shiomi, M., Shatani, K., Minato, T., Ishiguro, H., Does a robot's subtle pausein reaction time to people's touch contribute to positive influences (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 364-369; Stier, D.S., Hall, J.A., Gender differences in touch: An empirical and theoretical review (1984) Journal of Personality and Social Psychology, 47 (2), p. 440; Ebesu Hubbard, A.S., Tsuji, A.A., Williams, C., Seatriz, V., Effects of touch on gratuities received in samegender and crossgender dyads (2003) Journal of Applied Social Psychology, 33 (11), pp. 2427-2438; Evans, J.A., Cautious caregivers: Gender stereotypes and the sexualization of men nurses' touch (2002) Journal of Advanced Nursing, 40 (4), pp. 441-448; Shiomi, M., Minato, T., Ishiguro, H., Subtle reaction and response time effects in human-robot touch interaction (2017) International Conference on Social Robotics, pp. 242-251; Shiomi, M., Shatani, K., Minato, T., Ishiguro, H., How should a robot react before people's touch: Modeling a pre-touch reaction distance for a robot's face (2018) IEEE Robotics and Automation Letters, 3 (4), pp. 3773-3780; Sakamoto, D., Ishiguro, H.J.K.E.I., (2009) Geminoid: Remote-controlled Android System for Studying Human Presence, 8 (1), pp. 3-9; Glas, D.F., Minato, T., Ishi, C.T., Kawahara, T., Ishiguro, H., Erica: The erato intelligent conversational android (2016) Robot and Human Interactive Communication (RO-MAN). 2016 25th IEEE International Symposium on, pp. 22-29; Perrett, D.I., Burt, D.M., Penton-Voak, I.S., Lee, K.J., Rowland, D.A., Edwards, R.J.E., Behavior, H., (1999) Symmetry and Human Facial Attractiveness, 20 (5), pp. 295-307; Glas, D., Satake, S., Kanda, T., Hagita, N., An interaction design framework for social robots (2012) Robotics: Science and Systems, p. 89",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083212300
"Jelačić Z.","57193705161;","Reinforcement Learning Based Human-Prosthetic Robot Interaction Control in Movement Therapy",2020,"Lecture Notes in Networks and Systems","128 LNNS",,,"172","181",,,"10.1007/978-3-030-46817-0_19","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085192419&doi=10.1007%2f978-3-030-46817-0_19&partnerID=40&md5=bc3bebf12af64aea8ebfb6521fdcf5da","Faculty of Mechanical Engineering, University of Sarajevo, Vilsonovošetalište 9, Sarajevo, 71000, Bosnia and Herzegovina","Jelačić, Z., Faculty of Mechanical Engineering, University of Sarajevo, Vilsonovošetalište 9, Sarajevo, 71000, Bosnia and Herzegovina","A human–robot interactive control is proposed to control a lower limb prosthetic robot for amputee patients in the movement therapy training. The developed rehabilitation prosthetic robot is driven by the hydraulic system and has two rotational degrees of freedom. An adaptive admittance model is adopted considering its suitability for human–robot interaction. The combined effects of flexibility of linear hydraulic actuators and compliance provided by the controller should contribute to the training comfort, safety, and therapeutic outcome in the gait rehabilitation. In this paper, we present the human-robot interaction admittance model where individual admittance parameters suitable for patients are obtained by reinforcement learning. © Springer Nature Switzerland AG 2020.","Admittance model; Human–robot interactive control; Movement therapy; Prosthetic robot; Reinforcement learning",,,,,,,,,,,,,,,,,"Kwakkel, G., van Peppen, R., Wagenaar, R.C., Wooddauphinee, S., Richards, C., Ashburn, A., Miller, K., Langhorne, P., Effects of augmented exercise therapy time after stroke: A meta-analysis (2004) Stroke, 35, pp. 2529-2539; Broeren, J., Claesson, L., Goude, D., Rydmark, M., Sunnerhagen, K.S., Virtual rehabilitation in an activity centre for community-dwelling persons with stroke: The possibilities of 3-dimensional computer games (2008) Cerebrovasc. Dis., 26, pp. 289-296; Sutton, R.S., Barto, A.G., (1998) Reinforcement Learning: An Introduction, , MITPress, Cambridge; Jordan, M.I., Rumelhart, D.E., Forward models: Supervised learning with a distal teacher (1992) Cogn. Sci., 16, pp. 307-354; Tin, C., Poon, C.S., Internal models in sensorimotor integration: Perspectives from adaptive control theory (2005) J. Neural Eng., 2, pp. S147-S163; Minsky, M.L., Steps toward artificial intelligence (1961) Proc. IRE, 49, pp. 8-30; Williams, R.J., Simple statistical gradient-following algorithms for connectionist reinforcement learning (1992) Mach. Learn., 8, pp. 229-256; Zaghloul, K.A., Blanco, J.A., Weidemann, C.T., McGill, K., Jaggi, J.L., Baltuch, G.H., Kahana, M.J., Human substantia nigra neuron sencode unexpected financial rewards (2009) Science, 323, pp. 1496-1499; Erev, I., Roth, A.E., Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria (1998) Am. Econ. Rev., 88, pp. 848-881; Cohen, M.X., Neurocomputational mechanisms of reinforcement-guided learning in humans: A review (2008) Cogn. Affect. Behav. Neurosci., 8, pp. 113-125","Jelačić, Z.; Faculty of Mechanical Engineering, Vilsonovošetalište 9, Bosnia and Herzegovina; email: jelacic@mef.unsa.ba","Karabegovic I.",,"Springer","International Conference on New Technologies, Development and Application, NT 2020","25 June 2020 through 27 June 2020",,239879,23673370,9783030468163,,,"English","Lect. Notes Networks Syst.",Conference Paper,"Final","",Scopus,2-s2.0-85085192419
"Yu C.-E.","57206401831;","Emotional contagion in human-robot interaction",2020,"e-Review of Tourism Research","17","5",,"793","798",,2,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078185323&partnerID=40&md5=748c598b04ae4b128092c18790e63b8d","Innovation and Management in Tourism, Salzburg University of Applied Sciences, Urstein Süd 1, Puch/Salzburg, A-5412, Austria","Yu, C.-E., Innovation and Management in Tourism, Salzburg University of Applied Sciences, Urstein Süd 1, Puch/Salzburg, A-5412, Austria","Emotional contagion is particularly important in face-to-face interaction, and hospitality industry is no exception. Given that emotion is contagious, its effects in human-robot interaction remain largely unknown. This paper takes Sophia, the first humanoid robot that can display emotions, as a reference point, and investigates the emotional effect on online users' conative reactions. The results demonstrated that the expression of fear and disgust serves as the influential factors in affecting interests and engagements. Nevertheless, emotional contagion still deserves further exploration. Overall, this research contributes to the existing body of literature related to humanlike service robot, emotional contagion, and human-robot interaction. © 2019 Texas A and M University.","Emotional contagion; Engagement; Humanoid robot; Robot service; Social media",,,,,,,,,,,,,,,,,"Choi, S., Liu, S.Q., Mattila, A.S., ""How may I help you?"" Says a robot: Examining language styles in the service encounter (2019) International Journal of Hospitality Management, 82, pp. 32-38; Ekman, P., Friesen, W.V., Hager, J.C., Facial action coding system: The manual on CD ROM (2002) A Human Face, pp. 77-254. , Salt Lake City; Goertzel, B., Mossbridge, J., Monroe, E., Hanson, D., Yu, G., (2017) Humanoid robots as agents of human consciousness expansion, , arXiv preprint; Hatfield, E., Cacioppo, J.T., Rapson, R.L., Emotional contagion (1993) Current directions in psychological science, 2 (3), pp. 96-100; Hennig-Thurau, T., Groth, M., Paul, M., Gremler, D.D., Are all smiles created equal? How emotional contagion and emotional labor affect service relationships (2006) Journal of Marketing, 70 (3), pp. 58-73; Hochschild, A.R., (2012) The managed heart: Commercialization of human feeling, , University of California Press; Houston, L., III, Grandey, A.A., Sawyer, K., Who cares if ""service with a smile"" is authentic? An expectancy-based model of customer race and differential service reactions (2018) Organizational Behavior and Human Decision Processes, 144, pp. 85-96; Huang, P.F., Dai, C.W., The impacts of emotional contagion and emotional labor perception on employee's service performance (2010) International Journal of Electronic Business Management, 8 (1), pp. 68-79; Ngan, H.F.B., Yu, C.E., To smile or not to smile-an eye-tracking study on service recovery (2018) Current Issues in Tourism, pp. 1-6; Otterbring, T., Smile for a while: the effect of employee-displayed smiling on customer affect and satisfaction (2017) Journal of Service Management, 28 (2), pp. 284-304; Schreiner, M., Fischer, T., Riedl, R., Impact of content characteristics and emotion on behavioral engagement in social media: Literature review and research agenda (2019) Electronic Commerce Research, pp. 1-17; Swani, K., Milne, G.R., Brown, B.P., Assaf, A.G., Donthu, N., What messages to post? Evaluating the popularity of social media communications in business versus consumer markets (2017) Industrial Marketing Management, 62, pp. 77-87; Tung, V.W.S., Au, N., Exploring customer experiences with robotics in hospitality (2018) International Journal of Contemporary Hospitality Management, 30 (7), pp. 2680-2697; Valentini, C., Romenti, S., Murtarelli, G., Pizzetti, M., Digital visual engagement: Influencing purchase intentions on Instagram (2018) Journal of Communication Management, 22 (4), pp. 362-381","Yu, C.-E.; Innovation and Management in Tourism, Urstein Süd 1, Austria; email: cyu.imte-m2018@fh-salzburg.ac.at",,,"Texas A and M University",,,,,19415842,,,,"English","e-Review Tour. Res.",Article,"Final","",Scopus,2-s2.0-85078185323
"Herbster S., Behrens R., Elkmann N.","57223361358;55540144500;23008393400;","A New Conversion Method to Evaluate the Hazard Potential of Collaborative Robots in Free Collisions",2021,"Springer Proceedings in Advanced Robotics","19",,,"222","232",,1,"10.1007/978-3-030-71151-1_20","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105737501&doi=10.1007%2f978-3-030-71151-1_20&partnerID=40&md5=ec12880902a72bd93dc8956ba7201982","Fraunhofer IFF, Magdeburg, Germany","Herbster, S., Fraunhofer IFF, Magdeburg, Germany; Behrens, R., Fraunhofer IFF, Magdeburg, Germany; Elkmann, N., Fraunhofer IFF, Magdeburg, Germany","In the closest form of human-robot interaction, robots and humans complete common tasks next to each other at the same time [3] © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.",,,,,,,"Horizon 2020 Framework Programme, H2020: 818087","Acknowledgement. Parts of this work have been funded by the ROSSINI-Project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 818087. The biofidelic measurement device PRMS (PILZ Robot Measurement System) used for the experiments was provided by courtesy of PILZ.",,,,,,,,,,"(2012) 7Th International Conference Safety of Industrial Automated Systems, , Montreal, Canada, October; Albu-Schäffer, A., Fischer, M., Schreiber, G., Schoeppe, F., Hirzinger, G., Soft robotics: What Cartesian stiffness can obtain with passively compliant, uncoupled joints? (2004) Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Sendai, Japan, February 2004, Vol. 4, Pp. 3295– 3301, , https://doi.org/10.1109/IROS.2004.1389925; Behrens, R., Saenz, J., Vogel, C., Elkmann, N., Upcoming technologies and fundamentals for safeguarding all forms of human-robot collaboration (2015) Proceedings of the 8Th International Conference on the Safety of Industrial Automated Systems, pp. 18-23. , , pp. , Königswinter, Germany, November; Bicchi, A., Tonietti, G.: Fast and soft-arm tactics. IEEE Rob. Autom. Mag. 11(2), 22–33 (2004). ISSN 1070-9932. https://doi.org/10.1109/MRA.2004.1310939; Falco, J., Marvel, J., Norcross, R., Collaborative robotics: Measuring blunt force impacts on humans (2012) Proceedings of the 7Th International Conference on the Safety of Industrial Automated Systems; Haddadin, S., Krieger, K., Mansfeld, N., Albu-Schäffer, A., On impact decoupling properties of elastic robots and time optimal velocity maximization on joint level (2012) Proceedings, pp. 5089-5096. , https://doi. org/10.1109/IROS.2012.6385913, , pp. , Vilamoura, Portugal, October; Huelke, H.-J., Ottersbach, M., How to approve collaborating robots-the IFA force pressure measurement system (2012) Proceedings of the 7Th International Conference on the Safety of Industrial Automated Systems SIA, pp. 5089-5096. , , pp; Khatib, O., A unified approach for motion and force control of robot manipulators: The operational space formulation (1987) IEEE J. Rob. Autom., 3 (1), pp. 43-53. , https://doi.org/10.1109/JRA.1987.1087068; Khatib, O.: Inertial properties in robotic manipulation: an object-level framework. Int. J. Rob. Res. 14(1), 19–36 (1995). ISSN: 0278-3649. https://doi.org/10.1177/027836499501400103; Oberer-Treitz, S., (2017) Abschaetzung Der Kollisionsfolgen Von Robotern Zur Bewertung Des Sicheren Einsatzes in Der Mensch-Roboter-Kooperation. Dissertation, , Univer-sitaet Stuttgart; Salisbury, J.-K., (1980) Active Stiffness Control of a Manipulator in Cartesian Coordinates; (2017) Fachbereich Holz Und Metall Der DGUV. Dguv-Information-Collaborative Robot Systems: Design of Systems with Power and Force Limiting Function","Herbster, S.; Fraunhofer IFFGermany; email: sebastian.herbster@iff.fhg.de",,,"Springer Science and Business Media B.V.",,,,,25111256,,,,"English","Springer. Proc. Adv. Robot.",Book Chapter,"Final","",Scopus,2-s2.0-85105737501
"Rodriguez-Gomez J.P., Tapia R., Eguiluz A.G., Martinez-De Dios J.R., Ollero A.","57209746441;57221553674;57220148225;8677983300;7004325905;","UAV human teleoperation using event-based and frame-based cameras",2021,"AIRPHARO 2021 - 1st AIRPHARO Workshop on Aerial Robotic Systems Physically Interacting with the Environment",,,,"","",,,"10.1109/AIRPHARO52252.2021.9571049","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119063289&doi=10.1109%2fAIRPHARO52252.2021.9571049&partnerID=40&md5=e708555cccf55227b004054e3951922f","University of Seville, GRVC Robotics Laboratory, Seville, 41092, Spain","Rodriguez-Gomez, J.P., University of Seville, GRVC Robotics Laboratory, Seville, 41092, Spain; Tapia, R., University of Seville, GRVC Robotics Laboratory, Seville, 41092, Spain; Eguiluz, A.G., University of Seville, GRVC Robotics Laboratory, Seville, 41092, Spain; Martinez-De Dios, J.R., University of Seville, GRVC Robotics Laboratory, Seville, 41092, Spain; Ollero, A., University of Seville, GRVC Robotics Laboratory, Seville, 41092, Spain","Teleoperation is a crucial aspect for human-robot interaction with unmanned aerial vehicles (UAV s) applications. Fast perception processing is required to ensure robustness, precision, and safety. Event cameras are neuromorphic sensors that provide low latency response, high dynamic range and low power consumption. Although classical image-based methods have been extensively used for human-robot interaction tasks, responsiveness is limited by their processing rates. This paper presents a human-robot teleoperation scheme for UAVs that exploits the advantages of both traditional and event cameras. The proposed scheme was tested in teleoperation missions where the pose of a multi rotor robot is controlled in real time using human gestures detected from events. © 2021 IEEE.","event-based vision; human detection; Robotic perception; teleoperation; UAS; UAV","Aircraft detection; Antennas; Cameras; Computer vision; Human robot interaction; Man machine systems; Remote control; Event frames; Event-based; Event-based vision; Frame-based; Human detection; Humans-robot interactions; Low latency; Neuromorphic; Robotic perception; Vehicle applications; Unmanned aerial vehicles (UAV)",,,,,"European Commission, EC: H2020-2019-87147; European Research Council, ERC: 788247","This work was supported by the European Research Council as part of GRIFFIN ERC Advanced Grant 2017 (Action 788247), and the European Commission as part of AERIAL-CORE project (H2020-2019-87147). The authors are with the GRVC Robotics Laboratory, University of Seville, Seville 41092, Spain email: {jrodriguezg, raultapia, ageguiluz, jdedios, aollero}@us.es",,,,,,,,,,"Ollero, A., Heredia, G., Franchi, A., Antonelli, G., Kondak, K., Sanfeliu, A., Viguria, A., Cortés, J., The aeroarms project: Aerial robots with advanced manipulation capabilities for inspection and maintenance (2018) IEEE Robotics &Automation Magazine, 25 (4), pp. 12-23; Eguiluz, G.A., Ranó, I., Coleman, S.A., McGinnity, T.M., Reliable robotic handovers through tactile sensing (2019) Autonomous Robots, 43 (7), pp. 1623-1637; Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Daniilidis, K., (2019) Eventbased Vision: A Survey; Li, R., Shi, D., Zhang, Y., Li, K., Li, R., Fa-harris: A fast and asynchronous corner detector for event cameras (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; Zhu, A.Z., Yuan, L., Chaney, K., Daniilidis, K., Ev-flownet: Selfsupervised optical flow estimation for event-based cameras (2018) Robotics: Science and Systems (RSS); Gehrig, D., R¨uegg, M., Gehrig, M., Hidalgo-Carrío, J., Scaramuzza, D., Combining events and frames using recurrent asynchronous multimodal networks for monocular depth prediction (2021) IEEE Robotics and Automation Letters, 6 (2), pp. 2822-2829; Fischer, T., Milford, M., Event-based visual place recognition with ensembles of spatio-temporal windows (2020) IEEE Robotics and Automation Letters (RA-L), 5 (4), pp. 6924-6931; Gallego, G., Scaramuzza, D., Accurate angular velocity estimation with an event camera (2017) IEEE Robotics and Automation Letters, 2 (2), pp. 632-639; Deng, Y., Li, Y., Chen, H., Amae: Adaptive motion-agnostic encoder for event-based object classification (2020) IEEE Robotics and Automation Letters, 5 (3), pp. 4596-4603; Hagenaars, J.J., Paredes-Vallés, F., Bohté, S.M., De Croon, G.C., Evolved neuromorphic control for high speed divergence-based landings of mavs (2020) IEEE Robotics and Automation Letters, 5 (4), pp. 6239-6246; Eguiluz, G.A., Rodriguez-Gómez, J., De Dios, J.M., Ollero, A., Asynchronous event-based line tracking for time-to-contact maneuvers in uas (2020) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE; Rodriguez-Gómez, J.P., Eguiluz, A.G., De Dios, J.R.M., Ollero, A., Auto-tuned event-based perception scheme for intrusion monitoring with uas (2021) IEEE Access, 9, pp. 44840-44854; Barua, S., Miyatani, Y., Veeraraghavan, A., Direct face detection and video reconstruction from event cameras (2016) 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, pp. 1-9; Amir, A., Taba, B., Berg, D., Melano, T., McKinstry, J., Di Nolfo, C., Nayak, T., Modha, D., A low power, fully event-based gesture recognition system (2017) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7388-7397; Maqueda, A.I., Loquercio, A., Gallego, G., Garcia, N., Scaramuzza, D., Event-based vision meets deep learning on steering prediction for self-driving cars (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5419-5427; Muthusamy, R., Ayyad, A., Halwani, M., Zweiri, Y., Gan, D., Seneviratne, L., Neuromorphic eye-in-hand visual servoing (2020) IEEE Access; Mitrokhin, A., Fermuller, C., Parameshwara, C., Aloimonos, Y., Event-based moving object detection and tracking (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 1-9; Hordijk, B.J.P., Scheper, K.Y., De Croon, G.C., Vertical landing for micro air vehicles using event-based optical flow (2018) Journal of Field Robotics, 35 (1), pp. 69-90; Sanket, N.J., Parameshwara, C.M., Singh, C.D., Kuruttukulam, A.V., Fermuller, C., Scaramuzza, D., Aloimonos, Y., (2019) Evdodge: Embodied Ai for High-speed Dodging on a Quadrotor Using Event Cameras; Falanga, D., Kleber, K., Scaramuzza, D., Dynamic obstacle avoidance for quadrotors with event cameras (2020) Science Robotics, 5 (40); Sun, S., Cioffi, G., De Visser, C., Scaramuzza, D., Autonomous quadrotor flight despite rotor failure with onboard vision sensors: Frames vs. events (2021) IEEE Robotics and Automation Letters, 6 (2), pp. 580-587; Alzugaray, I., Chli, M., Ace: An efficient asynchronous corner tracker for event cameras (2018) 2018 International Conference on 3D Vision (3DV). IEEE, pp. 653-661; Rodriguez-Gómez, J.P., Eguiluz, A.G., De Dios, J.M., Ollero, A., Asynchronous event-based clustering and tracking for intrusion monitoring in uas (2020) 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 8518-8524; Mueggler, E., Huber, B., Scaramuzza, D., Event-based, 6-dof pose tracking for high-speed maneuvers (2014) 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 2761-2768; Dimitrova, R.S., Gehrig, M., Brescianini, D., Scaramuzza, D., Towards low-latency high-bandwidth control of quadrotors using event cameras (2020) 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 4294-4300; Liu, H., Wang, L., Gesture recognition for human-robot collaboration: A review (2018) International Journal of Industrial Ergonomics, 68, pp. 355-367; Tapia, R., Eguiluz, G.A., De Dios, J.M., Ollero, A., ASAP: Adaptive scheme for asynchronous processing of eventbased vision algorithms (2020) IEEE Icra Workshop on Unconventional Sensors in Robotics. IEEE; Real, F., Torres-González, A., Ramón-Soria, P., Capitán, J., Ollero, A., Unmanned aerial vehicle abstraction layer: An abstraction layer to operate unmanned aerial vehicles (2020) International Journal of Advanced Robotic Systems, 17 (4), p. 1; Redmon, J., Farhadi, A., (2018) Yolov3: An Incremental Improvement",,,,"Institute of Electrical and Electronics Engineers Inc.","1st AIRPHARO Workshop on Aerial Robotic Systems Physically Interacting with the Environment, AIRPHARO 2021","4 October 2021 through 5 October 2021",,173180,,9781665433891,,,"English","AIRPHARO - AIRPHARO Workshop Aer. Robot. Syst. Phys. Interact. Environ.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85119063289
"Wang Y., An T., Chen J., Ren X., Li H., Dong B.","57219315068;57299555000;57219314458;57197630428;36067889200;57203180228;","Human Motion Intention Estimation-Based Decentralized Robust Control of Modular Robot Manipulators",2021,"Chinese Control Conference, CCC","2021-July",,,"3919","3925",,,"10.23919/CCC52363.2021.9549983","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117288795&doi=10.23919%2fCCC52363.2021.9549983&partnerID=40&md5=84642fb291bd9090c0b8350084b2a521","Changchun University of Technology, Department of Control Science and Engineering, Changchun, 130000, China; Chinese Academy of Science, Changchun Institute of Optics, Fine Mechanics and Physics, Changchun, 130033, China","Wang, Y., Changchun University of Technology, Department of Control Science and Engineering, Changchun, 130000, China; An, T., Changchun University of Technology, Department of Control Science and Engineering, Changchun, 130000, China; Chen, J., Changchun University of Technology, Department of Control Science and Engineering, Changchun, 130000, China; Ren, X., Changchun University of Technology, Department of Control Science and Engineering, Changchun, 130000, China; Li, H., Chinese Academy of Science, Changchun Institute of Optics, Fine Mechanics and Physics, Changchun, 130033, China; Dong, B., Changchun University of Technology, Department of Control Science and Engineering, Changchun, 130000, China","In this paper, a decentralized robust control method of modular robot manipulator (MRM) is proposed under the situation of physical human-robot interaction (pHRI). Different from the traditional human-robot interaction control method that relies on the precise dynamic model and centralized control scheme, the decentralized robust control method is proposed based on the human motion intention, which is estimated through radial basis function neural network (RBFNN). By only using local dynamic information of each joint, a decentralized robust controller is designed to deal with various model uncertainties and joint trajectory tracking problems. Based on Lyapunov theory, the asymptotic stability of the closed-loop robotic system is verified. Finally, the effectiveness of the proposed method is verified by experiments. © 2021 Technical Committee on Control Theory, Chinese Association of Automation.","Human Motion Intention; Modular Robot Manipulator; Neural Network; Physical Human Robot Interaction","Asymptotic stability; Flexible manipulators; Human robot interaction; Industrial robots; Man machine systems; Radial basis function networks; Robot applications; Robust control; Uncertainty analysis; Control methods; Decentralized robust controls; Human motion intention; Human-robot interaction control; Modular robot manipulator; Motion intention estimations; Neural-networks; Physical humanrobot interaction (phri); Robots manipulators; Robust control methods; Modular robots",,,,,"JJKH20200672KJ, JJKH20200673KJ, JJKH20200674KJ; National Natural Science Foundation of China, NSFC: 61703055, 61773075; Jilin Scientific and Technological Development Program: 20200801056GH","This work is supported by the National Natural Science Foundation of China (Grant no. 61773075 and 61703055), the Scientific Technological Development Plan Project in Jilin Province of China (Grant nos. 20200801056GH) and the Science and Technology project of Jilin Provincial Education Department of China during the 13th Five-Year Plan Period (JJKH20200672KJ, JJKH20200673KJ and JJKH20200674KJ).",,,,,,,,,,"Paredis, C.J.J., Brown, H.B., Khosla, P.K., A rapidly deployable manipulator system (1997) Ieee International Conference on Robotics Automation, 21 (3), pp. 289-304; Giusti, A., Althoff, M., On-The-fly control design of modular robot manipulators[J] (2017) Ieee Transactions on Control Systems Technology, pp. 1-8; Zhang, Z., Yan, Z., An adaptive fuzzy recurrent neural network for solving the nonrepetitive motion problem of redundant robot manipulators[J] (2020) Ieee Transactions on Fuzzy Systems, 28 (4), pp. 684-691; Liu, G., Abdul, S., Goldenberg, A.A., Distributed control of modular and reconfigurable robot with torque sensing (2008) Robotica, 26 (1), p. 75C84; Liu, S., Chen, J., Zhang, B., (2012) Second Order Sliding Mode Control of Pantilt Joint in Modular Manipulator, pp. 2188-2193; Dong, B., An, T., Zhou, F., Decentralized robust zerosum neuro-optimal control for modular robot manipulators in contact with uncertain environments: Theory and experimental verification[J] (2019) Nonlinear Dynamics, 97 (13); Dong, B., Liu, K., Li, Y., Decentralized control of harmonic drive based modular robot manipulator USing only position measurements: Theory and experimental verification (2017) J Intell Robot Syst, 88, pp. 3-18; Cuong, V., Yao, N., Robust adaptive trajectory tracking sliding mode control based on neural networks for cleaning and detecting robot manipulators[J] (2015) Journal of Intelligent Robotic Systems, 79 (1); Yu, H., Huang, S., Chen, G., Humancrobot interaction control of rehabilitation robots with series elastic actuators[ j] (2015) Ieee Transactions on Robotics, 31 (5), pp. 1089-1100; Luca, A.D., Mattone, R., Sensorless robot collision detection and hybrid force/motion control[C] (2005) Ieee International Conference on Robotics Automation.; Khoramshahi, M., Billard, A., A dynamical system approach to task-Adaptation in physical humancrobot interaction (2019) Auton Robot, 43, pp. 927-946; Yuan, J., Wang, S., Wan, W., Direct teaching of industrial manipulators USing current sensors[J] (2018) Assembly Automation, 38 (2), pp. 216-225; Erden, M.S., Tomiyama, T., Human-intent detection and physically interactive control of a robot without force sensors.[ j] (2010) Ieee Transactions on Robotics; Imura, J.I., Yokokohji, Y., Yoshikawa, T., Robust control of robot manipulators based on joint torque sensor information[ j] (1994) International Journal of Robotics Research, 13 (5), pp. 434-442; Li, Y., Ge, S.S., Humancrobot collaboration based on motion intention estimation[J] (2014) IEEE/ASME Transactions on Mechatronics, 19 (3), pp. 1007-1014",,"Peng C.Sun J.","Chinese Association of Automation (CAA);Shanghai University;Systems Engineering Society of China;Technical Committee on Control Theory, Chinese Association of Automation (TCCT)","IEEE Computer Society","40th Chinese Control Conference, CCC 2021","26 July 2021 through 28 July 2021",,172297,19341768,9789881563804,,,"English","Chinese Control Conf., CCC",Conference Paper,"Final","",Scopus,2-s2.0-85117288795
"Williams M.-A.","7410005152;","Designing human-robot interaction with social intelligence",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444865","iii","iv",,,"10.1145/3434073.3444865","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102776321&doi=10.1145%2f3434073.3444865&partnerID=40&md5=c1cff2f29a5d121c716a609efb825251","University of New South Wales, Australia","Williams, M.-A., University of New South Wales, Australia","Robots are a distributive technology that promotes change. Human-Robot Interaction is an emerging field that has exciting prospects for widespread transformation. Today, embedding social intelligence into human-robot interaction design is a moonshot. Moonshots can boost science and engineering by galvanizing and incentivizing research communities and industry in the pursuit of impossible challenges to accelerate progress and scale impact. Social intelligence is all about power and influence; privacy and trust; ethical decisions and happiness. What if, robots could take the initiative and purposefully persuade you to believe something new. HRI with social intelligence has the potential to create breakthrough insights and that will energize the reimagination of how humans and robots will collaborate in business and society. © 2021 IEEE Computer Society. All rights reserved.","Artificial Intelligence; Disruptive Innovation; Human-Robot Interaction; Social intelligence",,,,,,,,,,,,,,,,,"Inamura, T., Kumar Pandey, A., Kumar, S., Williams, M., Cabibihan, J.-J., Behera, L., Special issue on robot and human interactive communication 2020 (2020) Adv. Robotics, 34 (20), p. 1279; Raza, S.A., Williams, M.-A., Human feedback as action assignment in interactive reinforcement learning (2020) ACM Trans. Auton. Adapt. Syst., 14 (4), pp. 141-1424; Ojha, S., Vitale, J., Williams, M.-A., (2020) EEGS: A Transparent Model of Emotions, , CoRR abs/2011.02573; Skillicorn, D.B., Alsadhan, N., Billingsley, R., Williams, M.-A., Measuring human emotion in short documents to improve social robot and agent interactions (2019) Canadian Conference on AI, pp. 29-41; Tonkin, M., Vitale, J., Herse, S., Ali Raza, S., Madhisetty, S., Kang, L., Duc Vu, T., Williams, M.-A., (2019) Privacy First: Designing Responsible and Inclusive Social Robot Applications for in the Wild Studies, pp. 1-8; Ojha, S., Williams, M., Johnston, B., The essence of ethical reasoning in robot-emotion processing (2018) Int. J. Soc. Robotics, 10 (2), pp. 211-223; Williams, M.-A., Decision-theoretic human-robot interaction: Designing reasonable and rational robot behavior (2016) ICSR, pp. 72-82; Wang, X., Williams, M., Gärdenfors, P., Vitale, J., Abidi, S.R., Johnston, B., Kuipers, B., Huang, A., (2014) Directing Human Attention with Pointing, pp. 174-179; Novianto, R., Johnston, B., Williams, M.-A., Habituation and sensitisation learning in ASMO cognitive architecture (2013) ICSR, pp. 249-259; Williams, M.-A., Robot social intelligence (2012) ICSR, pp. 45-55","Williams, M.-A.; University of New South WalesAustralia; email: mary-anne.williams@unsw.edu.au",,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS)","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167644,21672148,9781450382892,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Editorial,"Final","",Scopus,2-s2.0-85102776321
[No author name available],[No author id available],"Culturally Sustainable Social Robotics - Proceedings of Robophilosophy 2020",2020,"Frontiers in Artificial Intelligence and Applications","335",,,"","",695,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098893166&partnerID=40&md5=cf3cbd7f2e902ae08627772d89e224e7",,"","The proceedings contain 68 papers. The topics discussed include: what robots represent… and why it matters; flourishing isn’t free: paying down our moral and social debt in robotic systems; social robots through the lens of care ethics; robots and moral revolutions; towards a middle-ground theory of agency for artificial intelligence; applying the theory of make-believe to human-robot interaction; sociomorphing, not anthropomorphizing: towards a typology of experienced sociality; of waiters, robots, and friends. functional social interactions vs. close interhuman relationships; the artificial nature of social robots: a phenomenological interpretation of two conflicting tendencies in human-robot interaction; and a robot-human handshake in space: touch and lively alterity relations in social robotics.",,,,,,,,,,,,,,,,,,,,"Norskov M.Seibt J.Quick O.S.",,"IOS Press BV","4th Conference on Robophilosophy 2020: Culturally Sustainable Social Robotics","18 August 2020 through 21 August 2020",,165874,09226389,9781643681542,,,"English","Front. Artif. Intell. Appl.",Conference Review,"Final","",Scopus,2-s2.0-85098893166
"Hassan T., Kopp S.","57193091146;35722438500;","Towards an interaction-centered and dynamically constructed episodic memory for social robots",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"233","235",,1,"10.1145/3371382.3378329","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083233064&doi=10.1145%2f3371382.3378329&partnerID=40&md5=5875031ca1754d5612b8e411a259e02a","Bielefeld University, Bielefeld, Germany","Hassan, T., Bielefeld University, Bielefeld, Germany; Kopp, S., Bielefeld University, Bielefeld, Germany","This paper outlines an interaction-centered and dynamically constructed episodic memory for social robots, in order to enable naturalistic, social human-robot interaction. The proposed model includes a record of multi-timescale events stored in the event history, a record of multi-timescale interval definitions stored as interaction episodes, and a set of links associating specific elements of the two records. The event history is constructed dynamically, depending on the occurrence of internal and external events. The interaction episodes are defined on the basis of robot-initiated and user-initiated interactions. The episodic memory is realised within a social human-robot interaction architecture, whose components generate events pertaining to the context and state of interaction. © 2020 ACM.","Episodic memory; Human-robot interaction; Social robots","Agricultural robots; Man machine systems; Episodic memory; Event history; Social human-robot interactions; Time-scales; Social robots",,,,,"Bundesministerium für Bildung und Forschung, BMBF: FKZ 16SV7959","This work was funded by the German Federal Ministry of Education and Research (BMBF) through the project ‘VIVA’ (FKZ 16SV7959). Special thanks to Hendrik Buschmeier and Sonja Stange for their feedback on the contents of this paper.",,,,,,,,,,"Dodd, W., Gutierrez, R., The role of episodic memory and emotion in a cognitive robot (2005) ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005, pp. 692-697. , https://doi.org/10.1109/ROMAN.2005.1513860; Horzyk, A., Starzyk, J.A., Graham, J., Integration of semantic and episodic memories (2017) IEEE Transactions on Neural Networks and Learning Systems, 28 (12), pp. 3084-3095. , https://doi.org/10.1109/TNNLS.2017.2728203, Dec 2017; Kasap, Z., Magnenat-Thalmann, N., Towards episodic memorybased long-term affective interaction with a human-like robot (2010) 19th International Symposium in Robot and Human Interactive Communication, pp. 452-457. , https://doi.org/10.1109/ROMAN.2010.5598644; Newell, A., (1990) Unified Theories of Cognition, , Harvard University Press; Newell, A., Card, S.K., The prospects for psychological science in human-computer interaction (1985) Hum.-Comput. Interact, 1 (3), pp. 209-242. , https://doi.org/10.1207/s15327051hci01031, Sept. 1985; Nuxoll, A.M., Laird, J.E., Extending cognitive architecture with episodic memory (2007) AAAI; Rabe, F., Wachsmuth, I., Enhancing human computer interaction with episodic memory in a virtual guide (2013) Human-Computer Interaction. Interaction Modalities and Techniques, pp. 117-125. , Masaaki Kurosu (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg; (2020) Pepper the Humanoid and Programmable Robot, , https://www.softbankrobotics.com/emea/en/pepper, SoftBank Robotics. Accessed: 2020-01-07; David, C.R., The basic systems model of autobiographical memory (2012) Understanding Autobiographical Memory: Theories and Approaches, pp. 269-289. , Dorthe Berntsen and David C. Rubin (Eds.). Cambridge University Press, Cambridge, UK; Stange, S., Buschmeier, H., Hassan, T., Ritter, C., Kopp, S., Towards self-explaining social robots: Verbal explanation strategies for a needs-based architecture (2019) Proceedings of the AAMAS 2019 Workshop on Cognitive Architectures for HRI: Embodied Models of Situated Natural Language Interactions; Tulving, E., Episodic and semantic memory (1972) Organization of Memory, pp. 381-403. , Endel Tulving and Wayne Donaldson (Eds.). Academic, New York, NY, USA",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85083233064
"Robinson F.A.","57215966842;","Audio cells: A spatial audio prototyping environment for human-robot interaction",2020,"TEI 2020 - Proceedings of the 14th International Conference on Tangible, Embedded, and Embodied Interaction",,,,"955","960",,1,"10.1145/3374920.3374999","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082449173&doi=10.1145%2f3374920.3374999&partnerID=40&md5=e702fbd5e88172bb1ed5ec3b413ae665","Creative Robotics Lab and Interactive Media Lab, University of New South Wales, Sydney, NSW, Australia","Robinson, F.A., Creative Robotics Lab and Interactive Media Lab, University of New South Wales, Sydney, NSW, Australia","Rich and elaborate communication will play an essential part in the success of social robotics. The role of non-verbal sound as a communication channel has received relatively little attention in human-robot interaction research so far. Audio Cells is a prototyping environment for spatial sonic interaction design. It is part of my ongoing work on the sonification of artificial bodies. Through a combination of modular loudspeakers and sensors, it allows the exploration of a range of spatial interactive audio effects. By bringing methods from the fields of sound design and spatial audio into the context of human-robot interaction, my research aims to enrich and refine the ways robotic agents and responsive structures communicate with humans. © 2020 Association for Computing Machinery.","Human-robot interaction; Sonic interaction design; Spatial audio","Audio acoustics; Loudspeakers; Machine design; Man machine systems; Robotics; Interactive audio; Robotic agents; Social robotics; Sonic interaction design; Sonifications; Sound designs; Spatial audio; Human robot interaction",,,,,,,,,,,,,,,,"Frid, E., Bresin, R., Alexanderson, S., Perception of mechanical sounds inherent to expressive gestures of a nao robot-implications for Movement Sonification of Humanoids (2018) Sound and Music Computing; Kulezic-Wilson, D., (2008) Sound Design Is the New Score. Music, Sound, and the Moving Image, 2 (2), pp. 127-131. , https://doi.org/10.3828/msmi.2.2.5, Dec 2008; Moore, D., Dahl, T., Varela, P., Ju, W., Tormod, Næ.S., Berget, I., Unintended Consonances: Methods to Understand Robot Motor Sound Perception (2019) Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems-CHI '19, pp. 1-12. , https://doi.org/10.1145/3290605.3300730, ACM Press, Glasgow, Scotland Uk; Moore, D., Tennent, H., Martelaro, N., Ju, W., Making noise intentional: A study of servo sound perception (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction-HRI '17. ACM Press, Vienna, Austria, pp. 12-21. , https://doi.org/10.1145/2909824.3020238; Schwenk, M., Arras, K.O., R2-D2 Reloaded: A flexible sound synthesis system for sonic human-robot interaction design (2014) The 23rd IEEE International Symposium on Robot and Human Interactive Communication, pp. 161-167. , https://doi.org/10.1109/ROMAN.2014.6926247, IEEE, Edinburgh, UK; Sidner, C.L., Kidd, C.D., Lee, C., Lesh, N., Where to look: A study of human-robot engagement (2004) Proceedings of the 9th International Conference on Intelligent User Interfaces, pp. 78-84. , ACM; Smalley, D., Spectromorphology: Explaining sound-shapes (1997) Organised Sound, 2 (2), pp. 107-126. , 1997; Tennent, H., Moore, D., Jung, M., Ju, W., Good vibrations: How consequential sounds affect perception of robotic arms (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, Lisbon, pp. 928-935. , https://doi.org/10.1109/ROMAN.2017.8172414; Trovato, G., Paredes, R., Balvin, J., Cuellar, F., Baek Thomsen, N., Bech, S., Tan, Z., The sound or silence: Investigating the influence of robot noise on proxemics (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, Nanjing, pp. 713-718. , https://doi.org/10.1109/ROMAN.2018.8525795; Yilmazyildiz, S., Read, R., Belpeame, T., Verhelst, W., Review of semantic-free utterances in social human-robot interaction (2016) International Journal of Human-Computer Interaction, 32 (1), pp. 63-85. , https://doi.org/10.1080/10447318.2015.1093856, Jan; Zhang, R., Jeon, M., Hyuk Park, C., Howard, A., Robotic sonification for promoting emotional and social interactions of children with asd (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts-HRI'15 Extended Abstracts, pp. 111-112. , https://doi.org/10.1145/2701973.2702033, ACM Press, Portland, Oregon, USA","Robinson, F.A.; Creative Robotics Lab and Interactive Media Lab, Australia; email: frederic.robinson@unsw.edu.au",,"ACM SIGCHI","Association for Computing Machinery, Inc","14th International Conference on Tangible, Embedded, and Embodied Interaction, TEI 2020","9 February 2020 through 12 February 2020",,158217,,9781450361071,,,"English","TEI - Proc. Int. Conf. Tangible, Embed., Embodied Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85082449173
"Wu W., Huang J., Zhang Z.","57211553119;56326298100;57201427541;","Human decision-making behavior modeling for human multi-robot interaction system",2020,"IFAC-PapersOnLine","53",,,"10202","10207",,,"10.1016/j.ifacol.2020.12.2749","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107761916&doi=10.1016%2fj.ifacol.2020.12.2749&partnerID=40&md5=820de5ae347560d3a8e04809d2e7ff8d","College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China","Wu, W., College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China; Huang, J., College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China; Zhang, Z., College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China","In this paper, the composition of human decision-making process in human-robot interaction is analyzed and the human decision-making behavior is modeled. The human decision-making process is divided into data-processing station and human cognitive system. By combining with the null-space-based control (NSBC) method, the traditional drift diffusion model (DDM) is applied for for human decision-making behavior modeling in human-robot interaction (HRI). In addition, HRI is studied for a platoon of autonomous robots in an unknown environment with multiple obstacles. Moreover, the human intervention task is designed to help robots achieve tasks successfully. Finally, simulation examples are given to demonstrate the satisfactory performance of the proposed method. © 2020 Elsevier B.V.. All rights reserved.","Behavioral cont; Human decision-making behavior modeling; Human drift diffusion model; Human robot interaction; Multi robot system","Behavioral research; Cognitive systems; Data handling; Decision making; Man machine systems; Drift-diffusion model; Human cognitive systems; Human decision making; Human intervention; Human multi-robot interactions; Human robot Interaction (HRI); Processing stations; Unknown environments; Social robots",,,,,,,,,,,,,,,,"Antonelli, G., Chiaverini, S., Kinematic control of platoons of autonomous vehicles (2006) IEEE Transactions on Robotics, 22 (6), pp. 1285-1292; Balch, T., Arkin, R.C., Behavior-based formation control for multirobot teams (1998) IEEE Transactions on Robotics and Automation, 14 (6), pp. 926-939; Bestick, A.M., Burden, S.A., Willits, G., Naikal, N., Sastry, S.S., Bajcsy, R., Personalized kinematics for human-robot collaborative manipulation (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1037-1044; Bogacz, R., Brown, E., Moehlis, J., Holmes, P., Cohen, J.D., The physics of optimal decision making: A formal analysis of models of performance in twoalternative forced-choice tasks (2006) Psychological Review, 113 (4), pp. 700-723; Chen, J., Gan, M., Huang, J., Dou, L., Fang, H., Formation control of multiple Euler-Lagrange systems via null-space-based behavioral control (2016) Science China Information Sciences, 59 (1), pp. 1-11; Edwards, W., Optimal strategies for seeking information: Models for statistics, choice reaction times, and human information processing (1965) Journal of Mathematical Psychology, 2 (6), pp. 312-329; Gao, J., Lee, J.D., Extending the decision field theory to model operators' reliance on automation in supervisory control situations (2006) IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 36 (5), pp. 943-959; Goodrich, M.A., Schultz, A.C., Humanrobot interaction: A survey (2008) Foundations and Trends in Human-Computer Interaction, 1 (3), pp. 203-275; Huang, J., Zhou, N., Cao, M., Adaptive fuzzy behavioral control of second-order autonomous agents with prioritized missions: Theory and experiments (2019) IEEE Transactions on Industrial Electronics, 66 (12), pp. 9612-9622; Huang, J., Wu, W., Ning, Y., Zhou, N., Xu, Z., A behavior control scheme for multi-robot systems under human intervention (2019) 2019 Chinese Control Conference, pp. 6189-6193; Hitsuda, M., Representation of Gaussian processes equivalent to Wiener process (1968) Osaka Journal of Mathematics, 5 (2), pp. 299-312; Music, S., Hirche, S., Control sharing in human-robot team interaction (2017) Annual Reviews in Control, 44 (2), pp. 342-354; Milosavljevic, M., Malmaud, J., Huth, A., Koch, C., Rangel, A., The drift diffusion model can account for the accuracy and reaction time of value-based choices under high and low time pressure (2010) Neural Computation, 5 (6), pp. 437-449; Pentland, A., Liu, A., Modeling and prediction of human behavior (1999) Neural Computation, 11 (1), pp. 229-242; Peters, J.R., Srivastava, V., Taylor, G.S., Surana, A., Eckstein, M.P., Bullo, F., Human supervisory control of robotic teams: Integrating cognitive modeling with engineering design (2015) IEEE Control Systems Magazine, 35 (6), pp. 57-80; Ren, W., Beard, R.W., (2008) Distributed Consensus in Multi-vehicle Cooperative Control., , Springer; Ratcliff, R., A theory of memory retrieval (1978) Psychological Review, 85 (2), pp. 59-82; Ratcliff, R., McKoon, G., The diffusion decision model: Theory and data for two-choice decision tasks (2008) Neural Computation, 20 (4), pp. 873-922; Ratcliff, R., Gomez, P., McKoon, G., A diffusion model account of the lexical decision task (2004) Psychological Review, 111 (1), pp. 159-182; Ratcliff, R., Rouder, J.N., Modeling response times for two-choice decisions (1998) Psychological Science, 9 (5), pp. 347-356; Sheridan, T.B., Human-robot interaction: Status and challenges (2016) Human Factors, 58 (4), pp. 525-532","Wu, W.; College of Electrical Engineering and Automation, China; email: N180127114@fzu.edu.cn",,,"Elsevier B.V.","21st IFAC World Congress 2020","12 July 2020 through 17 July 2020",,145388,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85107761916
"Strazdas D., Hintz J., Felbberg A.-M., Al-Hamadi A.","57220084480;57220088201;57220091452;57199426558;","Robots and Wizards: An Investigation into Natural Human-Robot Interaction",2020,"IEEE Access","8",,"9264629","207635","207642",,5,"10.1109/ACCESS.2020.3037724","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096826835&doi=10.1109%2fACCESS.2020.3037724&partnerID=40&md5=7df6eab5757d7cf9a6c826974236f960","Neuro-Information Technology, Otto-von-Guericke-University Magdeburg, Magdeburg, 39106, Germany; Department of Experimental Psychology, Otto-von-Guericke Universität Magdeburg, Magdeburg, 39106, Germany","Strazdas, D., Neuro-Information Technology, Otto-von-Guericke-University Magdeburg, Magdeburg, 39106, Germany; Hintz, J., Neuro-Information Technology, Otto-von-Guericke-University Magdeburg, Magdeburg, 39106, Germany; Felbberg, A.-M., Department of Experimental Psychology, Otto-von-Guericke Universität Magdeburg, Magdeburg, 39106, Germany; Al-Hamadi, A., Neuro-Information Technology, Otto-von-Guericke-University Magdeburg, Magdeburg, 39106, Germany","The goal of the study was to research different communication modalities needed for intuitive Human-Robot Interaction. This study utilizes a Wizard of Oz prototyping method to enable a restriction-free, intuitive interaction with an industrial robot. The data from 36 test subjects suggests a high preference for speech input, automatic path planning and pointing gestures. The catalogue developed during this experiment contains intrinsic gestures suggesting that the two most popular gestures per action can be sufficient to cover the majority of users. The system scored an average of 74% in different user interface experience questionnaires, while containing forced flaws. These findings allow a future development of an intuitive Human-Robot interaction system with high user acceptance. © 2013 IEEE.","Activity recognition; cooperative systems; gesture recognition; human-robot interaction; intelligent robots; interactive systems; robot control; robot learning; telerobotics","Industrial robots; Man machine systems; Surveys; User experience; User interfaces; Communication modalities; Intuitive interaction; Pointing gestures; Speech input; User acceptance; Wizard of Oz; Social robots",,,,,"03ZZ04X02B; Bundesministerium für Bildung und Forschung, BMBF: 03ZZ0448L, 03ZZ0470; Bundesministerium für Gesundheit, BMG","This work was supported in part by the Federal Ministry of Education and Research of Germany (BMBF) RoboAssist under Grant 03ZZ0448L, in part by the HuBa under Grant 03ZZ0470, and in part by the Robo-Lab within the Zwanzig20 Alliance 3Dsensation, under Grant 03ZZ04X02B.",,,,,,,,,,"Buhr, D., Soziale Innovationspolitik F-ur Die Industrie 4.0, , http://library.fes.de/pdf-les/wiso/11494.pdf, Accessed: Sep. 1, 2020; Behrens, R., Elkmann, N., Workshop: Sicherheit in der menschroboterkollaboration (2016) Proc. Robot. Kongress Hannover, pp. 1-5. , http://www.robotics-kongress.de/wp-content/uploads/sites/12/2016/11/Fraunhofer-IFF.pdf, Feb., Magdeburg, Fraunhofer; Mehrabian, A., Wiener, M., Decoding of inconsistent communications (1967) J. Pers. Social Psychol., 6 (1), p. 109; Wilson, J., Rosenberg, D., Rapid prototyping for user interface design (1988) Handbook Human-Computer Interaction. Amsterdam, the Netherlands: Elsevier, pp. 859-875; Landauer, T.K., Psychology as a mother of invention (1986) ACM SIGCHI Bull., 17 (1), pp. 333-335. , May; Kelley, J.F., An iterative design methodology for user-friendly natural language of-ce information applications (1984) ACM Trans. Inf. Syst., 2 (1), pp. 26-41. , Jan; Maulsby, D., Greenberg, S., Mander, R., Prototyping an intelligent agent through wizard of Oz (1993) Proc. Conf. Bridges between Worlds (INTERCHI), pp. 277-284; Dahlback, N., Jonsson, A., Ahrenberg, L., Wizard of Oz studies: Why and how (1993) Proc. 1st Int. Conf. Intell. User Interfaces (IUI), pp. 193-200; Hudson, S.E., Fogarty, J., Atkeson, C.G., Avrahami, D., Forlizzi, J., Kiesler, S., Lee, J.C., Yang, J., Predicting human interruptibility with sensors: A wizard of Oz feasibility study (2003) Proc. New Horizons CHI, pp. 257-264; Serrano, M., Nigay, L., A wizard of oz component-based approach for rapidly prototyping and testing input multimodal interfaces (2010) J. Multi-modal User Interface, 3 (3), pp. 215-225. , Apr; Dow, S., Macintyre, B., Lee, J., Oezbek, C., Bolter, J.D., Gandy, M., Wizard of oz support throughout an iterative design process (2005) IEEE Pervas. Comput., 4 (4), pp. 18-26. , Oct; Zaeh, M., Vogl, W., Interactive laser-projection for programming industrial robots (2006) Proc. IEEE/ACM Int. Symp. Mixed Augmented Real-ity, pp. 125-128. , Oct; Norberto Pires, J., Robot by voice: Experiments on commanding an industrial robot using the human voice (2005) Ind. Robot Int., J., 32 (6), pp. 505-511. , Dec; Reinhart, G., Vogl, W., Kresse, I., A projection-based user interface for industrial robots (2007) Proc. IEEE Symp. Virtual Environ. Hum.-Comput. Interface Meas. Syst., pp. 67-71. , Jun; Brogardh, T., Present and future robot control development-An industrial perspective (2007) Annu. Rev. Control, 31 (1), pp. 69-79. , http://www.sciencedirect.com/science/article/pii/S1367578807000077; Bischoff, R., Kazi, A., Perspectives on augmented reality based humanrobot interaction with industrial robots (2004) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), pp. 3226-3231. , Dec; Speicher, M., Nebeling, M., GestureWiz: A human-powered gesture design environment for user interface prototypes (2018) Proc. CHI Conf. Hum. Factors Comput. Syst., pp. 1-11; Hoffman, G., Openwoz: A runtime-con-gurable wizard-of-oz framework for human-robot interaction (2016) Proc. AAAI Spring Symp. Ser., pp. 1-5; Brooke, J., (2020) SUS-A Quick and Dirty Usability Scale, , www.TBIStaffTraining.info, Accessed: Sep. 1; Finstad, K., (2010) The Usability Metric for User Experience, , https://academic.oup.com/iwc/article-abstract/22/5/323/682940; Lewis, J.R., Psychometric evaluation of the PSSUQ using data from-ve years of usability studies (2002) Int. J. Hum.-Comput. Interact., 14 (3-4), pp. 463-488. , https://www.tandfonline.com/action/journalInformationjournalCode=hihc20, Sep; Lewis, J., Psychometric evaluation of an after-scenario questionnaire for computer usability studies: The ASQ (1991) SIGCHI Bull., 23, pp. 78-81. , Jan; Landis, J.R., Koch, G.G., The measurement of observer agreement for categorical data (1977) Proc. Biometrics, pp. 159-174. , Dec; Schober, P., Boer, C., Schwarte, L.A., Correlation coef-cients: Appropriate use and interpretation (2018) Anesthesia Analgesia, 126 (5), pp. 1763-1768. , May; Guilford, J.P., (1936) Psychometric Methods, , New York, NY, USA: McGrawHill; Wu, P.C., Wang, R., Kin, K., Twigg, C., Han, S., Yang, M.H., Chien, S.Y., DodecaPen: Accurate 6DoF tracking of a passive stylus (2017) Proc. 30th Annu. ACM Symp. User Interface Softw. Technol., pp. 365-374. , Oct","Strazdas, D.; Neuro-Information Technology, Germany; email: dominykas.strazdas@ovgu.de
Al-Hamadi, A.; Neuro-Information Technology, Germany; email: ayoub.al-hamadi@ovgu.de",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85096826835
"Petruck H., Nelles J., Faber M., Giese H., Geibel M., Mostert S., Mertens A., Brandl C., Nitsch V.","57191077187;56938476500;35100323900;57225798167;57203525963;57203532167;35746549300;54882881400;25929508000;","Human-Robot Cooperation in Manual Assembly &#x2013; Interaction Concepts for the Future Workplace",2020,"Advances in Intelligent Systems and Computing","962",,,"60","71",,,"10.1007/978-3-030-20467-9_6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067838673&doi=10.1007%2f978-3-030-20467-9_6&partnerID=40&md5=d91b06e7da0433488cc5f713536f9ba8","Institute of Industrial Engineering and Ergonomics of RWTH Aachen University, Bergdriesch 27, Aachen, 52062, Germany; Item Industrietechnik GmbH, Friedenstra&#x00DF;e 107-109, Solingen, 42699, Germany; ACE &#x2013; Aachen Consulting for Applied Industrial Engineering and Ergonomics UG, Im Mittelfeld 91, Aachen, 52074, Germany","Petruck, H., Institute of Industrial Engineering and Ergonomics of RWTH Aachen University, Bergdriesch 27, Aachen, 52062, Germany; Nelles, J., Institute of Industrial Engineering and Ergonomics of RWTH Aachen University, Bergdriesch 27, Aachen, 52062, Germany; Faber, M., Institute of Industrial Engineering and Ergonomics of RWTH Aachen University, Bergdriesch 27, Aachen, 52062, Germany; Giese, H., Item Industrietechnik GmbH, Friedenstra&#x00DF;e 107-109, Solingen, 42699, Germany; Geibel, M., Item Industrietechnik GmbH, Friedenstra&#x00DF;e 107-109, Solingen, 42699, Germany; Mostert, S., Item Industrietechnik GmbH, Friedenstra&#x00DF;e 107-109, Solingen, 42699, Germany; Mertens, A., Institute of Industrial Engineering and Ergonomics of RWTH Aachen University, Bergdriesch 27, Aachen, 52062, Germany, ACE &#x2013; Aachen Consulting for Applied Industrial Engineering and Ergonomics UG, Im Mittelfeld 91, Aachen, 52074, Germany; Brandl, C., Institute of Industrial Engineering and Ergonomics of RWTH Aachen University, Bergdriesch 27, Aachen, 52062, Germany, ACE &#x2013; Aachen Consulting for Applied Industrial Engineering and Ergonomics UG, Im Mittelfeld 91, Aachen, 52074, Germany; Nitsch, V., Institute of Industrial Engineering and Ergonomics of RWTH Aachen University, Bergdriesch 27, Aachen, 52062, Germany","A human-robot cooperation workstation was developed and implemented as a platform for the examination of ergonomic design approaches and human-robot interaction in manual assembly. Various control modalities are being tested for this workstation, which enable a broad range of applications for human-robot interaction and control. These modalities include computer-generated control commands, gesture-based control using Myo Armbands, force-sensitive control by guiding the robot, motion tracking of the operator, and head-based gesture control using an Inertial Measurement Unit (IMU). The focus is on human-centered and ergonomic development of interaction patterns for these control modalities. This paper presents the multimodal interaction concept with the robot and allocates the presented modalities to suitable application areas. &#x00A9; 2020, Springer Nature Switzerland AG.","Ergonomic workplace design; Human-robot interaction; Occupational safety","End effectors; Ergonomics; Human computer interaction; Machine design; Man machine systems; Occupational risks; Computer generated; Ergonomic workplace; Human-robot cooperation; Inertial measurement unit; Interaction concepts; Interaction pattern; Multi-Modal Interactions; Occupational safety; Human robot interaction",,,,,"390621612","Acknowledgements. The authors would like to thank the German Research Founda-tion DFG for the kind support within the Cluster of Excellence “Internet of Production (ID 390621612)”.",,,,,,,,,,"Masinga, P., Campbell, H., Trimble, J.A., A framework for human collaborative robots, operations in South African automotive industry (2015) IEEM 2015: 2015 IEEE International Conference on Industrial Engineering and Engineering Management: 6–9, pp. 1494-1497; Audi, A.G., (2017) Human Robot Cooperation: KLARA Facilitates Greater Diversity of Versions in Production at Audi, Ingolstadt; (2012) EN ISO 10218-2: Robots and Robotic Devices-Safety Requirements for Industrial Robots-Part 2: Robot Systems and Integration; (2012) EN ISO 10218-1: Robots and Robotic Devices-Safety Requirements for Industrial Robots-Part 1: Robots; (2017) ISO/TS 15066 Robots and Robotic Devices-Collaborative Robots; Gong, Z., Zhang, Y., Robot signaling its intentions in human-robot teaming (2018) HRI Workshop on Explainable Robotic Systems; Schmidt, L., Herrmann, R., Hegenberg, J., Evaluation einer 3-D-Gestensteuerung für einen mobilen Serviceroboter (2014) Zeitschrift für Arbeitswissenschaft, 68 (3), pp. 129-134. , https://doi.org/10.1007/BF03374438; Morais, G.D., Neves, L.C., Masiero, A.A., Application of Myo armband system to control a robot interface (2016) Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies CMOS Technology, pp. 227-231. , Bahr, A., Abu Saleh, L., Schröder, D., et al. (eds.), Technische Universität Hamburg Universitätsbib-liothek; SCITEPRESS-Science and Technology Publications Lda, Hamburg, Setúbal; Sathiyanarayanan, M., Mulling, T., Nazir, B., Controlling a robot using a wearable device (MYO) (2015) Int. J. Eng. Dev. Res.; Sanna, A., Lamberti, F., Paravati, G., A Kinect-based natural interface for quadrotor control (2013) Entertainment Comput, 4 (3), pp. 179-186. , https://doi.org/10.1016/j.entcom.2013.01.001; Stowers, J., Hayes, M., Bainbridge-Smith, A., Altitude control of a quadrotor helicopter using depth map from Microsoft Kinect sensor IEEE International Conference on Mechatronics (ICM), 2011, pp. 358-362. , Gokasan, M. (ed.), 13–15, April 2011, Istanbul, Turkey; proceedings; Biao, M., Wensheng, X., Songlin, W., A robot control system based on gesture recognition using Kinect (2013) Indonesian J. Electr. Eng. Comput. Sci., 11 (5). , https://doi.org/10.11591/telkomnika.v11i5.2493; Cheng, L., Sun, Q., Su, H., Design and implementation of human-robot interactive demonstration system based on Kinect (2012) 24Th Chinese Control and Decision Conference (CCDC), pp. 971-975; Du, G., Zhang, P., Mai, J., Markerless Kinect-based hand tracking for robot teleoperation (2012) Int. J. Adv. Rob. Syst., 9 (2), p. 36. , https://doi.org/10.5772/50093; Song, W., Guo, X., Jiang, F., Teleoperation humanoid robot control system based on Kinect sensor (2012) In: 4Th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), , 2012: 26–27, Aug. 2012, pp. 264–267. Nanchang, Jiangxi, China. IEEE, Piscataway, NJ; Li, C., Yang, C., Wan, J., Teleoperation control of Baxter robot using Kalman filter-based sensor fusion (2017) Syst. Sci. Control Eng., 5 (1), pp. 156-167. , https://doi.org/10.1080/21642583.2017.1300109; Rudigkeit, N., Gebhard, M., Gräser, A., Towards a user-friendly AHRS-based human-machine interface for a semi-autonomous robot (2014) 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Workshop on Assistive Robotics for Individuals with Disabilities: HRI Issues and Beyond; Louie, W.-Y.G., McColl, D., Nejat, G., Acceptance and attitudes toward a human-like socially assistive robot by older adults (2014) Assistive Technol, 26 (3), pp. 140-150; Nelles, J., Kohns, S., Spies, J., Analysis of stress and strain in head based control of cooperative robots through tetraplegics World Acad. Sci. Eng. Technol. Int. J. Med. Health, Biomed. Bioeng. Pharm. Eng., 11 (1), pp. 11-22; Nelles, J., Schmitz-Buhl, F., Spies, J., Altersdifferenzierte Evaluierung von Belastung und Beanspruchung bei der kopfbasierten Steuerung eines kooperierenden Roboters (2017) Frühjahrskongress Der Gesellschaft für Arbeitswissenschaft; Caccavale, R., Saveriano, M., Finzi, A., Kinesthetic teaching and attentional supervision of structured tasks in human–robot interaction (2018) Auton. Robots, 57 (5), p. 469. , https://doi.org/10.1007/s10514-018-9706-9; Louie, W.-Y.G., McColl, D., Nejat, G., Acceptance and attitudes toward a human-like socially assistive robot by older adults (2014) Assistive Technol, 26 (3), pp. 140-150; Kormushev, P., Calinon, S., Caldwell, D.G., Imitation learning of positional and force skills demonstrated via kinesthetic teaching and haptic input (2011) Adv. Robot., 25 (5), pp. 581-603. , https://doi.org/10.1163/016918611X558261; Gammieri, L., Schumann, M., Pelliccia, L., Coupling of a redundant manipulator with a virtual reality environment to enhance human-robot cooperation (2017) Procedia CIRP, 62, pp. 618-623. , https://doi.org/10.1016/j.procir.2016.06.056; Lasota, P.A., Rossano, G.F., Shah, J.A., Toward safe close-proximity human-robot interaction with standard industrial robots (2014) IEEE International Conference on Automation Science and Engineering (CASE), pp. 339-344","Petruck, H.; Institute of Industrial Engineering and Ergonomics of RWTH Aachen University, Bergdriesch 27, Germany; email: h.petruck@iaw.rwth-aachen.de","Chen J.",,"Springer Verlag","AHFE International Conference on Human Factors in Robots and Unmanned Systems, 2019","24 July 2019 through 28 July 2019",,227169,21945357,9783030204662,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85067838673
"Zörner S., Arts E., Vasiljevic B., Srivastava A., Schmalzl F., Mir G., Bhatia K., Strahl E., Peters A., Alpay T., Wermter S.","57220024969;57220038250;57218660609;57220038163;57220035839;57220040706;57220023740;55512341500;50861822600;57191225907;7003826680;","An Immersive Investment Game to Study Human-Robot Trust",2021,"Frontiers in Robotics and AI","8",,"644529","","",,1,"10.3389/frobt.2021.644529","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108070850&doi=10.3389%2ffrobt.2021.644529&partnerID=40&md5=0869203da18e4cfc5b34f012970c0377","Knowledge Technology Group, Department of Informatics, Universität Hamburg, Hamburg, Germany","Zörner, S., Knowledge Technology Group, Department of Informatics, Universität Hamburg, Hamburg, Germany; Arts, E., Knowledge Technology Group, Department of Informatics, Universität Hamburg, Hamburg, Germany; Vasiljevic, B., Knowledge Technology Group, Department of Informatics, Universität Hamburg, Hamburg, Germany; Srivastava, A., Knowledge Technology Group, Department of Informatics, Universität Hamburg, Hamburg, Germany; Schmalzl, F., Knowledge Technology Group, Department of Informatics, Universität Hamburg, Hamburg, Germany; Mir, G., Knowledge Technology Group, Department of Informatics, Universität Hamburg, Hamburg, Germany; Bhatia, K., Knowledge Technology Group, Department of Informatics, Universität Hamburg, Hamburg, Germany; Strahl, E., Knowledge Technology Group, Department of Informatics, Universität Hamburg, Hamburg, Germany; Peters, A., Knowledge Technology Group, Department of Informatics, Universität Hamburg, Hamburg, Germany; Alpay, T., Knowledge Technology Group, Department of Informatics, Universität Hamburg, Hamburg, Germany; Wermter, S., Knowledge Technology Group, Department of Informatics, Universität Hamburg, Hamburg, Germany","As robots become more advanced and capable, developing trust is an important factor of human-robot interaction and cooperation. However, as multiple environmental and social factors can influence trust, it is important to develop more elaborate scenarios and methods to measure human-robot trust. A widely used measurement of trust in social science is the investment game. In this study, we propose a scaled-up, immersive, science fiction Human-Robot Interaction (HRI) scenario for intrinsic motivation on human-robot collaboration, built upon the investment game and aimed at adapting the investment game for human-robot trust. For this purpose, we utilize two Neuro-Inspired COmpanion (NICO) - robots and a projected scenery. We investigate the applicability of our space mission experiment design to measure trust and the impact of non-verbal communication. We observe a correlation of 0.43 ((Formula presented.)) between self-assessed trust and trust measured from the game, and a positive impact of non-verbal communication on trust ((Formula presented.)) and robot perception for anthropomorphism ((Formula presented.)) and animacy ((Formula presented.)). We conclude that our scenario is an appropriate method to measure trust in human-robot interaction and also to study how non-verbal communication influences a human’s trust in robots. © Copyright © 2021 Zörner, Arts, Vasiljevic, Srivastava, Schmalzl, Mir, Bhatia, Strahl, Peters, Alpay and Wermter.","human-robot interaction; human-robot trust; investment game; non-verbal communication; science fiction",,,,,,"TRR-169; Deutsche Forschungsgemeinschaft, DFG","The authors gratefully acknowledge partial support from the German Research Foundation (DFG) under Project CML (TRR-169).",,,,,,,,,,"Ambady, N., Weisbuch, M., Nonverbal Behavior (2010) Handbook Soc. Psychol, 1, pp. 464-487; Arts, E., Zörner, S., Bhatia, K., Mir, G., Schmalzl, F., Srivastava, A., Exploring Human-Robot Trust through the Investment Game: An Immersive Space Mission Scenario (2020) Proceedings of the 8th International Conference on Human-Agent Interaction, pp. 121-130; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots (2008) Int. J. Soc. Robotics, 1, pp. 71-81; Bauer, J., Dávila-Chacón, J., Strahl, E., Wermter, S., Smoke and Mirrors — Virtual Realities for Sensor Fusion Experiments in Biomimetic Robotics (2012) 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE (IEEE), pp. 114-119; Berg, J., Dickhaut, J., McCabe, K., Trust, Reciprocity, and Social History (1995) Games Econ. 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"Schellen E., Bossi F., Wykowska A.","57206779571;56963595100;15924284200;","Robot Gaze Behavior Affects Honesty in Human-Robot Interaction",2021,"Frontiers in Artificial Intelligence","4",,"663190","","",,1,"10.3389/frai.2021.663190","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113578371&doi=10.3389%2ffrai.2021.663190&partnerID=40&md5=9b73f3755ebfad3f367bfd7f1483a6d6","Social Cognition in Human-Robot Interaction, Istituto Italiano di Tecnologia, Genoa, Italy; MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy","Schellen, E., Social Cognition in Human-Robot Interaction, Istituto Italiano di Tecnologia, Genoa, Italy; Bossi, F., Social Cognition in Human-Robot Interaction, Istituto Italiano di Tecnologia, Genoa, Italy, MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy; Wykowska, A., Social Cognition in Human-Robot Interaction, Istituto Italiano di Tecnologia, Genoa, Italy","As the use of humanoid robots proliferates, an increasing amount of people may find themselves face-to-“face” with a robot in everyday life. Although there is a plethora of information available on facial social cues and how we interpret them in the field of human-human social interaction, we cannot assume that these findings flawlessly transfer to human-robot interaction. Therefore, more research on facial cues in human-robot interaction is required. This study investigated deception in human-robot interaction context, focusing on the effect that eye contact with a robot has on honesty toward this robot. In an iterative task, participants could assist a humanoid robot by providing it with correct information, or potentially secure a reward for themselves by providing it with incorrect information. Results show that participants are increasingly honest after the robot establishes eye contact with them, but only if this is in response to deceptive behavior. Behavior is not influenced by the establishment of eye contact if the participant is actively engaging in honest behavior. These findings support the notion that humanoid robots can be perceived as, and treated like, social agents, since the herein described effect mirrors one present in human-human social interaction. © Copyright © 2021 Schellen, Bossi and Wykowska.","deception; eye contact; gaze; honesty; human-robot interaction",,,,,,"Horizon 2020 Framework Programme, H2020: ERC-2016-StG-715058; European Research Council, ERC; Horizon 2020","Funding. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant awarded to AW, titled InStance: Intentional Stance for Social Attunement. G.A. No: ERC-2016-StG-715058).","This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant awarded to AW, titled InStance: Intentional Stance for Social Attunement. G.A. 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"Altarawneh E., Jenkin M., Scott MacKenzie I.","57211239121;7005126033;57191823068;","An Extensible Cloud Based Avatar: Implementation and Evaluation",2021,"Intelligent Systems Reference Library","196",,,"503","522",,1,"10.1007/978-3-030-59608-8_27","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102897325&doi=10.1007%2f978-3-030-59608-8_27&partnerID=40&md5=b26f237ad78570ce6e66540801a9f94f","EECS, York University, 4700 Keele St., Toronto, ON, Canada","Altarawneh, E., EECS, York University, 4700 Keele St., Toronto, ON, Canada; Jenkin, M., EECS, York University, 4700 Keele St., Toronto, ON, Canada; Scott MacKenzie, I., EECS, York University, 4700 Keele St., Toronto, ON, Canada","A common issue in human-robot interaction is that a naive user expects an intelligent human-like conversational experience. Recent advances have enabled such experiences through cloud-based infrastructure; however, this is not currently possible on most mobile robots due to the need to access cloud-based (remote) AI technology. Here we describe a toolkit that supports interactive avatars using cloud-based resources for human-robot interaction. The toolkit deals with communication and rendering latency through parallelization and mechanisms that obscure delays. This technology can be used to put an interactive face on a mobile robot. But does an animated face on a robot actually make the interaction more effective or useful? To answer this question, we conducted a user study comparing human-robot interaction using text, audio, a realistic avatar, and a simplistic cartoon avatar. Although response time was longer for both avatar interfaces (due to increased computation and communication), this had no significant effect on participant satisfaction with the avatar-based interfaces. When asked about general preferences, more participants preferred the audio interface over the text interface, the avatar interfaces over the audio interface, and the realistic avatar interface over the cartoon avatar interface. This chapter includes and expands on material previously published [1, 2]. © 2021, Springer Nature Switzerland AG.","Artificial intelligence; Avatars; Cloud computing; Human-robot interaction; Intelligent agent; Parallel processing; Rendering farm; Speech-to-text; Text-to-speech",,,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC; Canada First Research Excellence Fund, CFREF","Acknowledgements The authors would like to thank NSERC, the NCRN and the CFREF VISTA project for their generous support of this work.",,,,,,,,,,"Altarawneh, E., Jenkin, M., Mackenzie, I.S., Is Putting a Face on an Interactive Robot Worthwhile? In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , submitted; Altarawneh, E., Jenkin, M.: Leveraging cloud-based tools to talk with robots. 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Available: http://www.yorku.ca/mack/HCIbook/; Hong, J., Findlater, L., Identifying speech input errors through audio-only interaction (2018) Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI), pp. 1-12. , , pp. , ACM, New York, NY, USA; Honig, S., Oron-Gilad, T., Understanding and resolving failures in human-robot interaction: Literature review and model development (2018) Front. Psychol., 9; Weinstock, A., Parmet, Y., Oron-Gilad, T., The effect of system aesthetics on trust, cooperation, satisfaction and annoyance in an imperfect automated system (2012) Work, 41, pp. 258-265. , Suppl. 1; Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K., Would you trust a (Faulty) robot?: Effects of error, task type and personality on human-robot cooperation and trust (2015) Proceedings of the 10Th Annual ACM/IEEE International Conference on Human-Robot Interaction (CRI), pp. 141-148. , , pp. , ACM, New York, NY, USA","Altarawneh, E.; EECS, 4700 Keele St., Canada; email: enas@eecs.yorku.ca",,,"Springer Science and Business Media Deutschland GmbH",,,,,18684394,,,,"English","Intell. Syst. Ref. Libr.",Book Chapter,"Final","",Scopus,2-s2.0-85102897325
"Arts E., Zörner S., Bhatia K., Mir G., Schmalzl F., Srivastava A., Vasiljevic B., Alpay T., Peters A., Strahl E., Wermter S.","57220038250;57220024969;57220023740;57220040706;57220035839;57220038163;57218660609;57191225907;50861822600;55512341500;7003826680;","Exploring Human-Robot Trust through the Investment Game: An Immersive Space Mission Scenario",2020,"HAI 2020 - Proceedings of the 8th International Conference on Human-Agent Interaction",,,,"121","130",,3,"10.1145/3406499.3415078","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096607224&doi=10.1145%2f3406499.3415078&partnerID=40&md5=5f36b0fbe779a1e1083fb78e59245e23","Universität Hamburg, Hamburg, Germany","Arts, E., Universität Hamburg, Hamburg, Germany; Zörner, S., Universität Hamburg, Hamburg, Germany; Bhatia, K., Universität Hamburg, Hamburg, Germany; Mir, G., Universität Hamburg, Hamburg, Germany; Schmalzl, F., Universität Hamburg, Hamburg, Germany; Srivastava, A., Universität Hamburg, Hamburg, Germany; Vasiljevic, B., Universität Hamburg, Hamburg, Germany; Alpay, T., Universität Hamburg, Hamburg, Germany; Peters, A., Universität Hamburg, Hamburg, Germany; Strahl, E., Universität Hamburg, Hamburg, Germany; Wermter, S., Universität Hamburg, Hamburg, Germany","As robots become more advanced and capable, developing trust is an important factor of human-robot interaction and cooperation. However, as multiple environmental and social factors can influence trust, it is important to develop more elaborate scenarios and methods to measure human-robot trust. A widely used measurement of trust in social science is the investment game. In this study, we propose a scaled-up, immersive, science fiction Human-Robot Interaction (HRI) scenario for intrinsic motivation on human-robot collaboration, built upon the investment game and aimed at adapting the investment game for human-robot trust. For this purpose, we utilise two Neuro-Inspired Companion (NICO) - robots and a projected scenery. We investigate the applicability of our space mission experiment design to measure trust and the impact of non-verbal communication. We observe a correlation of 0.43 (p=0.02)between self-assessed trust and trust measured from the game and a positive impact of non-verbal communication on trust (p=0.0008) and robot perception for anthropomorphism (p=0.007) and animacy (p=0.00002). We conclude that our scenario is an appropriate method to measure trust in human-robot interaction and also to study how non-verbal communication influences a human's trust in robots. © 2020 Owner/Author.","human-robot interaction; human-robot trust; investment game; non-verbal communication","Economic and social effects; Investments; Man machine systems; Experiment design; Human robot Interaction (HRI); Human-robot collaboration; Intrinsic motivation; Non-verbal communications; Robot perception; Science fictions; Space missions; Social robots",,,,,"TRR-169; Deutsche Forschungsgemeinschaft, DFG","This study would not have been possible without Ahmed Abdel-ghany, Connor Gäde, Vadym Gryshchuk, Matthew Ng, Shahd Sa-farani, Nilesh Vijayrania, Christoper Glenn Wulur, and Sophia Zell. 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"Zong C., Ji Z., Yu J., Yu H.","55413844400;8555087100;7405525050;36164668500;","An angle-changeable tracked robot with human-robot interaction in unstructured environments",2020,"Assembly Automation","40","4",,"565","575",,5,"10.1108/AA-11-2018-0231","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083660970&doi=10.1108%2fAA-11-2018-0231&partnerID=40&md5=577faad2a3dd2b9a10b20f5c16ac3d31","College of Automation, Institute of Complexity Science, Qingdao University, Qingdao, China; College of Automation, Institute of Complexity Science, Qingdao University, Qingdao, China; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; College of Automation, Qingdao University, Qingdao, China; College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, China","Zong, C., College of Automation, Institute of Complexity Science, Qingdao University, Qingdao, China, College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, China; Ji, Z., College of Automation, Institute of Complexity Science, Qingdao University, Qingdao, China; Yu, J., State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Yu, H., College of Automation, Qingdao University, Qingdao, China","Purpose: The purpose of this paper is to study the adaptability of the tracked robot in complex working environment. It proposes an angle-changeable tracked robot with human–robot interaction in unstructured environment. The study aims to present the mechanical structure and human–robot interaction control system of the tracked robot and analyze the static stability of the robot working in three terrains, i.e. rugged terrain, sloped terrain and stairs. Design/methodology/approach: The paper presents the mechanical structure and human–robot interaction control system of the tracked robot. To prevent the detachment of the tracks during obstacle navigation, a new type of passively adaptive device based on the relationship between the track’s variable angle and the forces is presented. Then three types of rough terrain are chosen to analyze the static stability of the tracked robot, i.e. rugged terrain, sloped terrain and stairs. Findings: This paper provides the design method of the tracked robot. Owing to its appropriate dimensions, good mass distribution and limited velocity, the tracked robot remains stable on the complex terrains. The experimental results verify the effectiveness of the design method. Originality/value: The theoretical analysis of this paper provides basic reference for the structural design of tracked robots. © 2020, Emerald Publishing Limited.","Human–robot interaction; Mechanical structure; Passively adaptive device; Tracked robot; Unstructured environments","Control systems; Landforms; Machine design; Man machine systems; Stairs; Structural design; Adaptive devices; Design/methodology/approach; Mass distribution; Mechanical structures; Robot interaction control; Robot interactions; Unstructured environments; Working environment; Social robots",,,,,"JQ201419; 201702038; National Natural Science Foundation of China, NSFC: 61374062, 61603288, 61873136","This work was supported by the National Natural Science Foundation of China (Nos. 61873136, 61374062, 61603288), Science Foundation of Shandong Province for Distinguished Young Scholars (No. JQ201419), the Special Funds of Shandong Province for Post-doctoral Innovation Projects under Grant 201702038, Post-doctoral Application Research Project of Qingdao.",,,,,,,,,,"Ben-Tzvi, P., Ito, S., Goldenberg, A., Autonomous stair climbing with reconfigurable tracked mobile robot (2007) IEEE International Workshop on Robotic and Sensors Environments, pp. 1-6. , Ottawa; 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College of Automation, China; email: jizhijian@pku.org.cn",,,"Emerald Group Holdings Ltd.",,,,,01445154,,ASAUD,,"English","Assem. Autom.",Article,"Final","",Scopus,2-s2.0-85083660970
"Hamed A., Masouleh M.T., Kalhor A.","57208001686;16550342100;24483441300;","Design & characterization of a bio-inspired 3-DOF Tactile/Force sensor and implementation on a 3-DOF decoupled parallel mechanism for human-robot interaction purposes",2020,"Mechatronics","66",,"102325","","",,6,"10.1016/j.mechatronics.2020.102325","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078134197&doi=10.1016%2fj.mechatronics.2020.102325&partnerID=40&md5=bc8b09d28ac254c7a194c0f07e53661a","School of Electrical and Computer Engineering, Human and Robot Interaction Laboratory, University of Tehran, Iran","Hamed, A., School of Electrical and Computer Engineering, Human and Robot Interaction Laboratory, University of Tehran, Iran; Masouleh, M.T., School of Electrical and Computer Engineering, Human and Robot Interaction Laboratory, University of Tehran, Iran; Kalhor, A., School of Electrical and Computer Engineering, Human and Robot Interaction Laboratory, University of Tehran, Iran","This paper presents the design and implementation of a Tactile/Force sensor which has been used on a 3-DOF decoupled parallel mechanism for Human-Robot Interaction purposes.The sensor, called HexaTactile, is a soft tactile sensor array based on six MEMS barometers, where each of them is covered by a silicone layer in the form of an incomplete pyramid. HexaTactile consists of six soft and highly sensitive tactile modules which are placed on six sides of a cube to allow simultaneous measurement of the force in the positive and negative directions along the x, y and z axes. Some of the advantages of this sensor can be regarded as its high precision, excellent linearity (coefficient of determination r2=0.99), low cost and low noise. The accuracy of the sensor is 0.01 N, within a range of 4 N and therefore HexaTactile can be suitably attached to a robot end-effector for human-robot interaction applications. Then, using the proposed force sensor some control scenarios, including fixed admittance control and active admittance control are applied for human-robot interaction purposes. 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No. 02CH37292), Vol. 1, pp. 644-650. , IEEE; Duchaine, V., Gosselin, C.M., General model of human-robot cooperation using a novel velocity based variable impedance control (2007) Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (WHC’07), pp. 446-451. , IEEE; Lecours, A., Mayer-St-Onge, B., Gosselin, C., Variable admittance control of a four-degree-of-freedom intelligent assist device (2012) 2012 IEEE International Conference on Robotics and Automation, pp. 3903-3908. , IEEE; Campeau-Lecours, A., Otis, M., Belzile, P.-L., Gosselin, C., A time-domain vibration observer and controller for physical human-robot interaction (2016) Mechatronics, 36, pp. 45-53; Sharifzadeh, M., Masouleh, M.T., Kalhor, A., Design, construction & calibration of a novel human-robot interaction 3-dof force sensor (2015) 2015 3rd RSI International Conference on Robotics and Mechatronics (ICROM), pp. 182-187. , IEEE; Sharifzadeh, M., Masouleh, M.T., Kalhor, A., On human–robot interaction of a 3-dof decoupled parallel mechanism based on the design and construction of a novel and low-cost 3-dof force sensor (2017) Meccanica, 52 (10), pp. 2471-2489; Gosselin, C., Kong, X., Cartesian parallel manipulators (2004) uS Patent 6,729,202 May 4; Hamed, A., Masouleh, M.T., Kalhor, A., Design & characterization of a bio-inspired 3-dof tactile/force sensor for human-robot interaction purposes (2018) 2018 6th RSI International Conference on Robotics and Mechatronics (IcRoM), pp. 488-493. , IEEE; Davison, B., , p. 53. , Techniques for robust touch sensing design. In: AN1334 Microchip Technology Inc","Masouleh, M.T.; School of Electrical and Computer Engineering, Iran; email: m.t.masouleh@ut.ac.ir",,,"Elsevier Ltd",,,,,09574158,,MECHE,,"English","Mechatronics",Article,"Final","",Scopus,2-s2.0-85078134197
"Chien S.-E., Yu C.-J., Lai Y.-Y., Liu J.-C., Fu L.-C., Yeh S.-L.","54415520100;57218311060;7401511121;7410102359;7401812723;7402085802;","Can Older Adults’ Acceptance Toward Robots Be Enhanced by Observational Learning?",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12192 LNCS",,,"564","576",,,"10.1007/978-3-030-49788-0_43","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088744897&doi=10.1007%2f978-3-030-49788-0_43&partnerID=40&md5=fcd12add068e098e82ef10918d9a47a5","Department of Psychology, National Taiwan University, Taipei, Taiwan; Industrial Technology Research Institute, Hsin-Chu, Taiwan; Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Center for Artificial Intelligence and Advanced Robotics, National Taiwan University, Taipei, Taiwan; Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taipei, Taiwan; Neurobiology and Cognitive Science Center, National Taiwan University, Taipei, Taiwan; Center for Advanced Study in the Behavioral Sciences, Stanford University, Stanford, CA, United States","Chien, S.-E., Department of Psychology, National Taiwan University, Taipei, Taiwan; Yu, C.-J., Department of Psychology, National Taiwan University, Taipei, Taiwan; Lai, Y.-Y., Industrial Technology Research Institute, Hsin-Chu, Taiwan; Liu, J.-C., Industrial Technology Research Institute, Hsin-Chu, Taiwan; Fu, L.-C., Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan, Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Center for Artificial Intelligence and Advanced Robotics, National Taiwan University, Taipei, Taiwan; Yeh, S.-L., Department of Psychology, National Taiwan University, Taipei, Taiwan, Center for Artificial Intelligence and Advanced Robotics, National Taiwan University, Taipei, Taiwan, Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taipei, Taiwan, Neurobiology and Cognitive Science Center, National Taiwan University, Taipei, Taiwan, Center for Advanced Study in the Behavioral Sciences, Stanford University, Stanford, CA, United States","It has been shown that older adults’ negative attitudes toward robots are stronger than those of younger adults, which presumably causes older adults to lack motivation in interacting with robots. We examined the possibility to improve older adults’ attitudes toward robots through observational learning. 40 younger and 40 older adults watched one of two video clips introducing features of an assistive robot. Within each age group, half watched a video clip containing human-robot interaction scenarios (the observational-learning group) and the other half watched a video clip without human-robot interactions (the control group). After watching the video, participants decided if they would like to interact with the actual robot. Participants’ explicit attitudes were measured by questionnaires, and implicit attitudes were measured by the implicit association test (IAT) and the name-shape association task (NSAT). Results showed that (1) for those who chose to interact with the robot, the observational-learning group reported higher perceived safety of the robot; (2) implicit negative attitudes toward the robot as measured by the IAT and the NAST was updated after watching video clips. Our results suggest that direct human-robot interaction cannot be overlooked. Furthermore, contrary to conventional assumptions, implicit attitudes toward robots can be rapidly shaped. © 2020, Springer Nature Switzerland AG.","Acceptance; Implicit attitude; Observational learning; Older adults; Robot","Human computer interaction; Surveys; User experience; Video cameras; Age groups; Assistive robots; Control groups; Implicit attitudes; Observational learning; Older adults; Video clips; Social robots",,,,,,,,,,,,,,,,"Lin, Y.-Y., Huang, C.-S., Aging in Taiwan: building a society for active aging and aging in place (2015) Gerontologist, 56, pp. 176-183. , https://doi.org/10.1093/geront/gnv107; Roy, N., (2000) In: Workshop on Interactive Robots and Entertainment, p. 184. , (WIRE); Smarr, C.-A., Domestic robots for older adults: attitudes, preferences, and potential (2014) Int. J. Social Robot, 6, pp. 229-247; Chien, S.E., Age difference in perceived ease of use, curiosity, and implicit negative attitude toward robots (2019) ACM Trans. Hum. Robot Interact, 8, pp. 1-19. , https://doi.org/10.1145/3311788; Scopelliti, M., Giuliani, M.V., Fornara, F., Robots in a domestic setting: a psychological approach (2005) Univ. Access Inf. Soc, 4, pp. 146-155; Heerink, M., Proceedings of the 6th International Conference on Human-Robot Interaction, pp. 147-148. , (ACM); Mast, M., User-centered design of a dynamic-autonomy remote interaction concept for manipulation-capable robots to assist elderly people in the home (2012) J. Hum. Robot Interact, 1, pp. 96-118. , https://doi.org/10.5898/jhri.1.1.mast; Heerink, M., Kröse, B., Evers, V., Wielinga, B., Assessing acceptance of assistive social agent technology by older adults: the almere model (2010) Int. J. Social Robot, 2, pp. 361-375; Chen, T.L., Older adults’ acceptance of a robot for partner dance-based exercise (2017) PLoS ONE, 12, p. e0182736; Barnard, Y., Bradley, M.D., Hodgson, F., Lloyd, A.D., Learning to use new technologies by older adults: Perceived difficulties, experimentation behaviour and usability (2013) Comput. Hum. Behav, 29, pp. 1715-1724; Bandura, A., Observational learning (2008) The international encyclopedia of communication; Bandura, A., Ross, D., Ross, S.A., Transmission of aggression through imitation of aggressive models (1961) J. Abnormal Soc. Psychol, 63, pp. 575-582. , https://doi.org/10.1037/h0045925; Yi, M.Y., Davis, F.D., Developing and validating an observational learning model of computer software training and skill acquisition (2003) Inf. Syst. Res, 14, pp. 146-169; Beer, J.M., Older users’ acceptance of an assistive robot: Attitudinal changes following brief exposure (2017) Gerontechnol. Int. J. Fundam. Aspects technol. Serve Ageing Soc, 16, p. 21; Dovidio, J.F., Kawakami, K., Gaertner, S.L., Implicit and explicit prejudice and interracial interaction (2002) J. Pers. Soc. Psychol, 82, p. 62; Kamin, S.T., Lang, F.R., The Subjective Technology Adaptivity Inventory (STAI): a motivational measure of technology usage in old age (2013) Gerontechnology, 12, pp. 16-25. , https://doi.org/10.4017/gt.2013.12.1.008.00; Kashdan, T.B., Rose, P., Fincham, F.D., Curiosity and exploration: facilitating positive subjective experiences and personal growth opportunities (2004) J. Pers. Assess, 82, pp. 291-305; Goldberg, L.R., The development of markers for the big-five factor structure (1992) Psychol. Assess, 4, p. 26; Sui, J., He, X., Humphreys, G.W., Perceptual effects of social salience: evidence from self-prioritization effects on perceptual matching (2012) J. Exp. Psychol. Hum. Percept. Perform, 38, p. 1105; MacDorman, K.F., Vasudevan, S.K., Ho, C.-C., Does Japan really have robot mania? Comparing attitudes by implicit and explicit measures (2008) AI Soc, 23, pp. 485-510. , https://doi.org/10.1007/s00146-008-0181-2; Ferguson, M.J., Mann, T.C., Cone, J., Shen, X., When and how implicit first impressions can be updated (2019) Curr. Dir. Psychol. Sci, 28, pp. 331-336. , https://doi.org/10.1177/0963721419835206; Lhuisset, L., Margnes, E., The influence of live-vs. video-model presentation on the early acquisition of a new complex coordination (2015) Phys. Educ. Sport Pedagogy, 20, pp. 490-502. , https://doi.org/10.1080/17408989.2014.923989; Kuhl, P.K., Tsao, F.-M., Liu, H.-M., Foreign-language experience in infancy: effects of short-term exposure and social interaction on phonetic learning (2003) Proc. Natl. Acad. Sci, 100, p. 9096. , https://doi.org/10.1073/pnas.1532872100; Li, R.-H., Chen, Y.-C., The development of a shortened version of ipip big five personality scale and the testing of its measurement invariance between middle-aged and older people (2016) J. Educ. Res. Dev, 12, pp. 87-119. , https://doi.org/10.3966/181665042016121204004; Cheng, M., Tseng, C.-H., Saliency at first sight: instant identity referential advantage toward a newly met partner (2019) Cogn. Res. Principles Implications, 4, p. 42. , https://doi.org/10.1186/s41235-019-0186-z","Chien, S.-E.; Department of Psychology, Taiwan; email: chiensungen@gmail.com","Patrick Rau P.-L.",,"Springer","12th International Conference on Cross-Cultural Design, CCD 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242309,03029743,9783030497873,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85088744897
"Mocanu I., Tudor A., Cramariuc O.","22985958700;57226602299;6507860414;","Gesture-Based Human-Robot Interaction",2021,"Proceedings - 2021 23rd International Conference on Control Systems and Computer Science Technologies, CSCS 2021",,,"9480992","151","156",,,"10.1109/CSCS52396.2021.00032","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112041227&doi=10.1109%2fCSCS52396.2021.00032&partnerID=40&md5=0122bd8c081914aa29bf1d6ab535d72a","University Politehnica of Bucharest, Computer Science Department, Bucharest, Romania; IT Center for Science and Technology, Bucharest, Romania","Mocanu, I., University Politehnica of Bucharest, Computer Science Department, Bucharest, Romania; Tudor, A., University Politehnica of Bucharest, Computer Science Department, Bucharest, Romania; Cramariuc, O., IT Center for Science and Technology, Bucharest, Romania","The paper proposes a method for human robot interaction through gestures. Even if there are different methods that perform gesture recognition, many of them are tested in ideal conditions. This paper proposes a convolutional neural network that is used for gesture recognition applied for human-robot interaction in different environmental conditions: such as: blurring, low or high illumination, low quality image or losing part of frames. We started from a pre-trained ResNeXt 101 model and we increased the accuracy by adding a preprocessing stage for improving the quality of image, applying data augmentation, using different optimisers and learning rate scheduler. The proposed model obtained an improvement of 0.9% compared to the baseline model. Online evaluation of gesture recognition was performed using the Turtlebot robot. © 2021 IEEE.","Gesture recognition; Humat-robot interaction; Turtlebot robot","Computer control systems; Convolutional neural networks; Gesture recognition; Image enhancement; Man machine systems; Baseline models; Data augmentation; Environmental conditions; Learning rates; Low qualities; On-line evaluation; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: AAL-2017-059-INCARE; European Commission, EC; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii, UEFISCDI","This work was supported by a grant of the CCDI–UEFISCDI and of the AAL Programme with co-funding from the European Union’s Horizon 2020 research and innovation programme project “INCARE – Integrated Solution for Innovative Elderly Care”, project number AAL-2017-059-INCARE.","This work was supported by a grant of the CCDI-UEFISCDI and of the AAL Programme with co-funding from the European Union's Horizon 2020 research and innovation programme project INCARE - Integrated Solution for Innovative Elderly Care, project number AAL-2017-059-INCARE",,,,,,,,,"(2021) World Population Ageing 2015, , United Nations. 2017, accessed on March; Soomro, K., Zamir, A., Shah, M., (2012) UCF101: A Dataset of 101 Human Actions Classes from Videos in the Wild, , abs/1212.0402; Lionel, P., Beyond temporal pooling: Recurrence and temporal convolutions for gesture recognition in video (2018) International Journal of Computer Vision, 126 (2-4), pp. 430-439; Limin, W., Temporal segment networks: Towards good practices for deep action recognition (2016) European Conference on Computer Vision. Springer, Cham; Shuiwang, J., 3D convolutional neural networks for human action recognition (2012) IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (1), pp. 221-231; Okan, K., Kose, N., Rigoll, G., Motion fused frames: Data level fusion strategy for hand gesture recognition (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops; Okan, K., Real-time hand gesture detection and classification using convolutional neural networks (2019) 2019 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019). IEEE; Materzynska, J., Berger, G., Bax, I., Memisevic, R., The jester dataset: A large-scale video dataset of human gestures (2019) 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 2874-2882. , Seoul, Korea (South); Nitish, S., Dropout: A simple way to prevent neural networks from overfitting (2014) The Journal of Machine Learning Research, 15 (1), pp. 1929-1958; (2021) Various Optimization Algorithms for Training Neural Network, , https://towardsdatascience.com/optimizers-for-training-neural-network-59450d71caf6, accessed on March; Sergey, I., Szegedy, C., (2015) Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift; Vinod, N., Hinton, G.E., Rectified linear units improve restricted boltzmann machines (2010) ICML; (2021) The 20BN-jester Dataset V1, , https://20bn.com/datasets/jester, accessed on March; Saining, X., Aggregated residual transformations for deep neural networks (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; (2021) Chalearn LAP RGB-D Isolated Gesture, , http://chalearnlap.cvc.uab.es/challenge/25/track/23/description/, accessed on March; Molchanov, P., Yang, X., Gupta, S., Kim, K., Tyree, S., Kautz, J., Online detection and classification of dynamic hand gestures with recurrent 3d convolutional neural networks (2016) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4207-4215. , Las Vegas, NV, USA",,,"IEEE Romania Section;IEEE Romanian Chapter of Computer Society;Romanian Academy - Science and Information Technology Section;Romanian Association for Artificial Intelligence (ARIA);Romanian Society of Control Engineering and Technical Informatics (SRAIT);The Faculty of Automatic Control and Computers (A and C) of University Politehnica of Bucharest","Institute of Electrical and Electronics Engineers Inc.","23rd International Conference on Control Systems and Computer Science Technologies, CSCS 2021","26 May 2021 through 28 May 2021",,170600,,9781665439398,,,"English","Proc. - Int. Conf. Control Syst. Comput. Sci. Technol., CSCS",Conference Paper,"Final","",Scopus,2-s2.0-85112041227
"Yan F., Namboodiri V., He H.","57205023318;8323861400;56231174200;","Common Reality: An Interface of Human-Robot Communication and Mutual Understanding",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"319","328",,,"10.1007/978-3-030-90525-5_27","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119831285&doi=10.1007%2f978-3-030-90525-5_27&partnerID=40&md5=aaa053818f9758b1a466833f58f002b8","School of Computing, Wichita State University, Wichita, KS  67260, United States","Yan, F., School of Computing, Wichita State University, Wichita, KS  67260, United States; Namboodiri, V., School of Computing, Wichita State University, Wichita, KS  67260, United States; He, H., School of Computing, Wichita State University, Wichita, KS  67260, United States","An interface that can share effective and comprehensive mutual understanding is critical for human-robot interaction. This paper designs a novel human-robot interaction interface that enables humans and robots to interact by their shared mutual understanding of the context. The interface superimposes robot-centered reality and human-centered reality on the working space to construct a mutual understanding environment. The common-reality interface enables humans to communicate with robots through speech and immersive touching. The mutual understanding is constructed by the user’s commands, localization of objects, recognition of objects, object semantics, and augmented trajectories. The user’s vocal commands are interpreted to formal logic, and finger touching is detected and represented by coordinates. Real-world experiments have been done to show the effectiveness of the proposed interface. © 2021, Springer Nature Switzerland AG.","Discourse representation structure; Human-robot collaboration; Interactive display; Speech recognition","Formal logic; Machine design; Man machine systems; Semantics; Speech recognition; Discourse representation; Discourse representation structure; Human-robot collaboration; Human-robot communication; Humans-robot interactions; Interaction interface; Interactive display; Mutual understanding; Reality interface; Working space; Human robot interaction",,,,,"National Science Foundation, NSF: CMMI 2129113","This work is partially supported by NSF CMMI 2129113.",,,,,,,,,,"Alam, M.M., Islam, M.T., Rahman, S., (2021) A Unified Learning Approach for Hand Gesture Recognition and Fingertip Detection, , UMBC Student Collection; Bauer, A., Wollherr, D., Buss, M., Human-robot collaboration: A survey (2008) Int. J. Humanoid Rob., 5 (1), pp. 47-66; Bischoff, R., Kazi, A., Seyfarth, M., The morpha style guide for icon-based programming (2002) Proceedings. 11Th IEEE International Workshop on Robot and Human Interactive Communication, Pp. 482–487. IEEE; Branavan, S.R.K., Hackman, J.E., Heckel, F.W.P., Isaksen, A., Updating natural language interfaces by processing usage data (2019) US Patent 10, 244 (19), p. 210. , Feb; Breazeal, C., Toward sociable robots (2003) Rob. Auton. Syst., 42 (3-4), pp. 167-175; Chacko, S.M., Kapila, V., An augmented reality interface for human-robot interaction in unconstrained environments (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Pp. 3222–3228. IEEE; Datteri, E., Predicting the long-term effects of human-robot interaction: A reflection on responsibility in medical robotics (2013) Sci. Eng. Ethics, 19 (1), pp. 139-160; Fang, H., Ong, S.K., Nee, A.Y., Novel AR-based interface for human-robot interaction and visualization (2014) Adv. Manuf., 2 (4), pp. 275-288; Ge, S.S., Design and development of nancy, a social robot (2011) 2011 8Th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Pp. 568–573. IEEE; He, H., Ge, S.S., Zhang, Z., A saliency-driven robotic head with bio-inspired sac-cadic behaviors for social robotics (2013) Auton. Rob., 36 (3), pp. 225-240. , https://doi.org/10.1007/s10514-013-9346-z; Kamp, H., Discourse representation theory (1988) IBM 1988. LNCS, Vol. 320, pp. 84-111. , https://doi.org/10.1007/3-540-50011-1_34, Blaser, A. (ed.), pp., Springer, Heidelberg; Katz, D., Kenney, J., Brock, O., How can robots succeed in unstructured environments (2008) In Workshop on Robot Manipulation: Intelligence in Human Environments at Robotics: Science and Systems. Citeseer; Ledbetter, S., The Chapman University Survey on American Fears, , https://blogs. chapman.edu/wilkinson/2015/10/13/americas-top-fears-2015/; Lewis, J.R., Psychometric evaluation of an after-scenario questionnaire for computer usability studies: The asq (1991) ACM Sigchi Bull, 23 (1), pp. 78-81; Lindner, K., Blosser, G., Cunigan, K., Visual versus auditory learning and memory recall performance on short-term versus long-term tests (2009) Mod. Psychol. Stud., 15 (1), p. 6; Littlewort, G., Towards social robots: Automatic evaluation of human-robot interaction by facial expression classification (2004) Advances in Neural Information Processing Systems, pp. 1563-1570. , pp; Nieto, D., Quesada-Arencibia, A., García, C.R., Moreno-Díaz, R.: A social robot in a tourist environment. In: Hervás, R., Lee, S., Nugent, C., Bravo, J. (eds.) UCAmI 2014. LNCS, vol. 8867, pp. 21–24. Springer, Cham (2014). https://doi. org/10.1007/978-3-319-13102-3_5; Ren, S., He, K., Girshick, R., Sun, J., Faster R-CNN: Towards real-time object detection with region proposal networks (2015) Advances in Neural Information Processing Systems, pp. 91-99. , pp; Scheeff, M., Pinto, J., Rahardja, K., Snibbe, S., Tow, R., Experiences with sparky, a social robot (2002) Socially Intelligent Agents. Multiagent Systems, Artificial Societies, and Simulated Organizations, 3. , https://doi.org/10.1007/0-306-47373-9_21, Dautenhahn, K., Bond, A., Cañamero, L., Edmonds, B. (eds.), vol , Springer, Boston; Sciutti, A., Mara, M., Tagliasco, V., Sandini, G., Humanizing human-robot interaction: On the importance of mutual understanding (2018) IEEE Technol. Soc. Mag., 37 (1), pp. 22-29; Thrun, S., Toward a framework for human-robot interaction (2004) Hum. Comput. Interact., 19 (1-2), pp. 9-24; Turkle, S., Breazeal, C., Dasté, O., Scassellati, B., Encounters with kismet and cog: Children respond to relational artifacts (2006) Digit. Media Transformations Hum. Commun.; Wood, L.J., Zaraki, A., Robins, B., Dautenhahn, K., Developing kaspar: A humanoid robot for children with autism (2019) Int. J. Soc. Rob., pp. 1-18; Yan, F., Tran, D.M., He, H., Robotic understanding of object semantics by referring to a dictionary (2020) Int. J. Soc. Rob., pp. 1-13","He, H.; School of Computing, United States; email: hongsheng.he@wichita.edu","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119831285
"Shi Q., Yang P., Chen C.","57219971977;55613889800;24729216100;","Preference Modeling of Spatial Description in Human-Robot Interaction",2020,"2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020",,,"9238088","","",,,"10.1109/ICNSC48988.2020.9238088","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096360301&doi=10.1109%2fICNSC48988.2020.9238088&partnerID=40&md5=9d5203e66b8ad131e2f13d56c9f0fa3c","Zijin College, Nanjing University of Science and Technology, Nanjing, China; School of Management and Engineering, Nanjing University, Nanjing, China","Shi, Q., Zijin College, Nanjing University of Science and Technology, Nanjing, China; Yang, P., School of Management and Engineering, Nanjing University, Nanjing, China; Chen, C., School of Management and Engineering, Nanjing University, Nanjing, China","Spatial description plays an important role in the design of human-robot interaction systems for intelligent robots. In this paper, we model the preference of the types of spatial description by collecting spatial constructions in two groups of tabletop task experiments, where the participants use spatial constructions to instruct the partner (human/robot) to pick up an indicated object. The preference modeling process is implemented by analyzing the probabilistic distribution of different types of spatial description (including different reference frames) of these participants in five typical scenarios regarding the partners of human and robot, respectively. The results provide a basis for the design of collaborative robots when interacting with people and will help improve the efficiency of human-centered human-robot interaction. © 2020 IEEE.","Human-robot interaction; preference modeling; reference frame; spatial cognition","Intelligent robots; Machine design; Man machine systems; Probability distributions; Collaborative robots; Preference modeling; Probabilistic distribution; Reference frame; Spatial construction; Spatial descriptions; Social robots",,,,,"SY201924; Fundamental Research Funds for the Central Universities: 011814380035; Major Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province: 2019SJ2061","ACKNOWLEDGMENT This work was supported in part by the University Philosophy and Social Science Research Project of Jiangsu Province (China) under Grant 2019SJ2061, in part by the Experimental Teaching Reform Foundation of Nanjing University under Grant SY201924, and in part by the Fundamental Research Funds for the Central Universities under Grant 011814380035.",,,,,,,,,,"Mu, X., Recent development of human-robot natural interaction in spatial cognition tasks (2016) 8th International Conference on Intelligent Human-Machine Systems and Cybernetics, pp. 446-450; Fischer, T., Demiris, Y., Markerless perspective taking for humanoid robots in unconstrained environments (2016) IEEE International Conference on Robotics and Automation, pp. 3309-3316; Li, S., Spatial references and perspective in natural language instructions for collaborative manipulation (2016) 25th International Symposium on Robot and Human Interactive Commnication, pp. 446-450; Gregory Trafton, J., Enabling effective human-robot interaction using perspective-taking in robots (2005) IEEE Transactions on Systems, Man, and Cybernetics-part A: Systems and Humans, 35 (4), pp. 460-470; Moratz, R., Fischer, K., Tenbrink, T., Congnitive modeling of spatial reference for human-robot interaction (2001) International Journal on Artificial Intelligence Tools, 10 (4), pp. 589-611; Fischer, K., The role of user's concepts of the robot in humanrobot spatial instruction (2006) International Conference Spatial Congnition, pp. 76-89; Zhao, X., Cusimano, C., Malle, B.F., Do people spotaneously take a robot's visual perspective (2016) 11th ACM/IEEE International Conference, pp. 133-134; Ros, R., Solving ambiguities with perspective-taking (2010) Proceedings of the 5th ACM/IEEE International Conference on Human-Robot Interaction, pp. 181-182; Kuman Pandey, A., Ali, M., Alami, R., Towards a task-aware proactive sociable robot based on multi-state perspective-taking (2013) Internatianl Journal of Social Robotics, 5 (2), pp. 215-236; Gunalp, P., Mossaian, T., Hegarty, M., Spatial perspective taking: Effects of social, directional, and interactuve cues (2019) Memory and Cognition, 47, pp. 1031-1043; Janczyk, M., Level 2 perspective taking entails two processes: Evidence from PRP experiments (2013) Journal of Experimental Psychology Learning Memory and Cognition, 39 (6), pp. 1878-1887; Flavell, J.H., Young children's knowledge about visual perception: Further evidence for the level 1-level 2 distinction (1981) Developmental Psychology, 17, pp. 99-103; Michelon, P., Zacks, J., Two kinds of visual perspective taking (2006) Perception & Psychophysics, 68, pp. 327-337; Surtees, A.D.R., Samson, D., Apperly, I., Unintentional perspective-taking calculates whether something is seen, but not how it is seen (2016) Cognition, 148, pp. 97-105; Costello, F.J., Proximity in context: An empirically grounded computational model of proximity for processing topological spatial expressions (2006) Proceeding of the 21th International Conference on Computational Linguistics, pp. 745-752; Kelleher, J.D., Kruijff, G.J.M., Incremental generation of spatial referring expressions in situated dialog (2006) Proceedings of the International Conference on Computational Linguistics, pp. 1041-1048; Dale, R., Reiter, E., Computational interpretations of the Gricean maxims in the generation of referring expressions (1995) Cognitive Science, 19 (2), pp. 233-263; Taylor, H.A., Tversky, B., Perspective in spatial descriptions (1996) Journal of Memory and Language, 35, pp. 371-391; Schober, M.F., Sptial prespective taking in conversation (1993) Cognition, 47, pp. 1-24; Schober, M.F., (2009) Spatial Dialogue between Partners with Mismatched Abilities, pp. 23-29. , Spatial Language and Dialogue, Oxford University Press, Editors: K.R. Coventry, T. Tenbrink, J.A. Bateman; Carlson, L., Strategies for human-driven robot comprehension of spatial descriptions by older adults in robot fetch task (2014) Topics in Cognitive Science, 6, pp. 513-533; Zlotowski, J., Anthropomorphism: Opportunities and challenges in human-robot interaction (2015) International Journal of Social Robotics, 7, pp. 347-360",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020","30 October 2020 through 2 November 2020",,164651,,9781728168531,,,"English","IEEE Int. Conf. Netw., Sens. Control, ICNSC",Conference Paper,"Final","",Scopus,2-s2.0-85096360301
"Huo W., Alouane M.A., Amirat Y., Mohammed S.","36925250000;57195559399;7003336093;7102894395;","Force Control of SEA-Based Exoskeletons for Multimode Human-Robot Interactions",2020,"IEEE Transactions on Robotics","36","2","8935391","570","577",,6,"10.1109/TRO.2019.2956341","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076853428&doi=10.1109%2fTRO.2019.2956341&partnerID=40&md5=65133081a479cda37706f1e68328979a","Department of Mechanical Engineering, Imperial College London, London, United Kingdom; LISSI Laboratory, University of Paris-Est Créteil, Vitry-sur-Seine, France","Huo, W., Department of Mechanical Engineering, Imperial College London, London, United Kingdom; Alouane, M.A., LISSI Laboratory, University of Paris-Est Créteil, Vitry-sur-Seine, France; Amirat, Y., LISSI Laboratory, University of Paris-Est Créteil, Vitry-sur-Seine, France; Mohammed, S., LISSI Laboratory, University of Paris-Est Créteil, Vitry-sur-Seine, France","In this article, a proxy-based force control method is proposed for three important human-robot interaction modes: zero-impedance mode, force assistive mode, and large force mode. A two-mass dynamic-model-based nonlinear disturbance observer is used to meet the zero-impedance output and accurate force tracking requirements with respect to disturbances from the wearer and environment. Additionally, significant force compliance can be achieved to guarantee the wearer's safety when the interaction torque is large. The proposed method is evaluated via experiments by comparison to the conventional proportional-integral-derivative and proxy-based sliding mode control methods. The results indicate that the proposed approach achieves better force tracking accuracy, robustness, and force compliance in three-mode human-robot interactions. © 2019 IEEE.","Exoskeletons; force control; nonlinear disturbance observer (NDO); series elastic actuator (SEA)","Exoskeleton (Robotics); Force control; Sliding mode control; Control methods; Force tracking; Nonlinear disturbance observer; Proxy-based sliding mode controls; Robot interactions; Series elastic actuators; Two-mass dynamic model; Zero impedance; Human robot interaction",,,,,,,,,,,,,,,,"Huo, W., Mohammed, S., Moreno, J.C., Amirat, Y., Lower limb wearable robots for assistance and rehabilitation: A state of the art (2016) IEEE Syst. J., 10 (3), pp. 1068-1081. , Sep; Tucker, M.R., Control strategies for active lower extremity prosthetics and orthotics: A review (2015) J. Neuroeng. Rehabil., 12 (1), pp. 1-29; Li, X., Pan, Y., Chen, G., Yu, H., Multi-modal control scheme for rehabilitation robotic exoskeletons (2017) Int. J. Robot. Res., 36 (5-7), pp. 759-777; Oh, S., Kong, K., High-precision robust force control of a series elastic actuator (2017) IEEE/ASME Trans. Mechatronics, 22 (1), pp. 71-80. , Feb; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Trans. Robot., 31 (5), pp. 1089-1100. , Oct; Kong, K., Bae, J., Tomizuka, M., A compact rotary series elastic actuator for human assistive systems (2012) IEEE/ASME Trans. Mechatronics, 17 (2), pp. 288-297. , Apr; Calanca, A., Fiorini, P., A rationale for acceleration feedback in force control of series elastic actuators (2018) IEEE Trans. Robot., 34 (1), pp. 48-61. , Feb; Pratt, G.A., Williamson, M.M., Series elastic actuators (1995) Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst., pp. 399-406; Rouse, E.J., Mooney, L.M., Martinez-Villalpando, E.C., Herr, H.M., Clutchable series-elastic actuator: Design of a robotic knee prosthesis for minimum energy consumption (2013) Proc. IEEE Int. Conf. Rehabil. Robot., pp. 1-6; Kaya, K.D., Çetin, L., Adaptive state feedback controller design for a rotary series elastic actuator (2017) Trans. Inst. Meas. Control, 39 (1), pp. 61-74; Losey, D.P., Erwin, A., McDonald, C.G., Sergi, F., O'Malley, M.K., A time-domain approach to control of series elastic actuators: Adaptive torque and passivity-based impedance control (2016) IEEE/ASME Trans. Mechatronics, 21 (4), pp. 2085-2096. , Aug; Farris, R.J., Quintero, H.A., Goldfarb, M., Preliminary evaluation of a powered lower limb orthosis to aid walking in paraplegic individuals (2011) IEEE Trans. Neural Syst. Rehabil. Eng., 19 (6), pp. 652-659. , Dec; Aoustin, Y., Formalskii, A., Walking of biped with passive exoskeleton: Evaluation of energy consumption (2018) Multibody Syst. Dyn., 43 (1), pp. 71-96; Huo, W., Mohammed, S., Amirat, Y., Kong, K., Fast gait mode detection and assistive torque control of an exoskeletal robotic orthosis for walking assistance (2018) IEEE Trans. Robot., 34 (4), pp. 1035-1052. , Aug; Ott, C., Albu-Schaffer, A., Kugi, A., Hirzinger, G., On the passivitybased impedance control of flexible joint robots (2008) IEEE Trans. Robot., 24 (2), pp. 416-429. , Apr; Oh, S., Woo, H., Kong, K., Frequency-shaped impedance control for safe human-robot interaction in reference tracking application (2014) IEEE/ASME Trans. Mechatronics, 19 (6), pp. 1907-1916. , Dec; Ju, M.-S., Lin, C.-C., Lin, D.-H., Hwang, I.-S., Chen, S.-M., A rehabilitation robot with force-position hybrid fuzzy controller: Hybrid fuzzy control of rehabilitation robot (2005) IEEE Trans. Neural Syst. Rehabil. Eng., 13 (3), pp. 349-358. , Sep; Simon, A.M., Gillespie, R.B., Ferris, D.P., Symmetry-based resistance as a novel means of lower limb rehabilitation (2007) J. Biomech., 40 (6), pp. 1286-1292; Li, X., Pan, Y., Chen, G., Yu, H., Adaptive human-robot interaction control for robots driven by series elastic actuators (2017) IEEE Trans. Robot., 33 (1), pp. 169-182. , Feb; Kikuuwe, R., Yasukouchi, S., Fujimoto, H., Yamamoto, M., Proxybased sliding mode control: A safer extension of PID position control (2010) IEEE Trans. Robot., 26 (4), pp. 670-683. , Aug; Van Damme, M., Vanderborght, B., Verrelst, B., Van Ham, R., Daerden, F., Lefeber, D., Proxy-based sliding mode control of a planar pneumatic manipulator (2009) Int. J. Robot. Res., 28 (2), pp. 266-284; Kashiri, N., Lee, J., Tsagarakis, N.G., Van Damme, M., Vanderborght, B., Caldwell, D.G., Proxy-based position control of manipulators with passive compliant actuators: Stability analysis and experiments (2016) Robot. Auton. Syst., 75, pp. 398-408; Yeh, T.-J., Wu, F.-K., Modeling and robust control ofworm-gear driven systems (2009) Simul. Model. Pract. Theory, 17 (5), pp. 767-777; Huo, W., Mohammed, S., Amirat, Y., Impedance reduction control of a knee joint human-exoskeleton system (2019) IEEE Trans. Control Syst. Technol., 27 (6), pp. 2541-2556. , Nov; Chen, W.-H., Disturbance observer based control for nonlinear systems (2004) IEEE/ASME Trans. Mechatronics, 9 (4), pp. 706-710. , Dec; Mohammadi, A., Tavakoli, M., Marquez, H.J., Hashemzadeh, F., Nonlinear disturbance observer design for robotic manipulators (2013) Control Eng. Pract., 21 (3), pp. 253-267; Mallat, R., Human-exoskeleton system dynamics identification using affordable sensors (2018) Proc. IEEE Int. Conf. Robot. Autom., pp. 6759-6765","Huo, W.; Department of Mechanical Engineering, United Kingdom; email: w.huo@imperial.ac.uk",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15523098,,,,"English","IEEE Trans. Rob.",Article,"Final","",Scopus,2-s2.0-85076853428
"Chen L., Wu M., Pedrycz W., Hirota K.","55774663200;35231682600;57210775415;7402829702;","AdaBoost-KNN with Direct Optimization for Dynamic Emotion Recognition",2021,"Studies in Computational Intelligence","926",,,"41","55",,,"10.1007/978-3-030-61577-2_4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096237308&doi=10.1007%2f978-3-030-61577-2_4&partnerID=40&md5=68604504cd318eff082ec16f52f2ecb1","School of Automation, China University of Geosciences, Wuhan, China; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Tokyo Institute of Technology, Yokohama, Japan","Chen, L., School of Automation, China University of Geosciences, Wuhan, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, China; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Hirota, K., Tokyo Institute of Technology, Yokohama, Japan","AdaBoost-KNN using adaptive feature selection with direct optimization is proposed for dynamic emotion recognition in human-robot interaction, where the real-time dynamic emotion is recognized based on facial expression. © 2020, The Author(s), under exclusive license to Springer Nature Switzerland AG.",,,,,,,,,,,,,,,,,,"Anderson, K., McOwan, P.W., A real-time automated system for the recognition of human facial expressions (2016) IEEE Trans. Syst. Man Cyber. Part B: Cybern, 36 (1), pp. 96-105; Wang, Z.F., Peyrodie, L., Cao, H., Slow walking model for children with multiple disabilities via an application of humanoid robot (2016) Mech. Syst. Signal Process., 68, pp. 608-619; Navarretta, C., Predicting emotions in facial expressions from the annotations in naturally occurring first encounters (2014) Knowl.-Based Syst., 71, pp. 34-40; Vezzetti, E., Marcolin, F., Fracastoro, G., 3D face recognition: An automatic strategy based on geometrical descriptors and landmarks (2014) Auton. Syst., 62 (12), pp. 1768-1776; Chen, L.F., Wu, M., Zhou, M.T., Liu, Z., She, J.H., Hirota, K., Dynamic emotion understanding in human-robot interaction based on two-layer fuzzy SVR-TS model (2020) IEEE Trans. Syst. Man Cyber.: Syst., 50 (2), pp. 490-501; Chen, L.F., Liu, Z.T., Wu, M., Ding, M., Dong, F.Y., Hirota, K., Emotion-age-gender-nationality based intention understanding in human-robot interaction using two-layer fuzzy support vector regression (2015) Int. J. Soc. Robot., 7 (5), pp. 709-729; Dong, Y.S., Wu, H., Li, X.K., Zhou, C.Q., Wu, Q.T., Multi-scale symmetric dense micro-block difference for texture classification (2018) IEEE Trans. Circuits Syst. Video Technol., 29 (12), pp. 3583-3594; Zhou, C., Wang, L., Zhang, Q., Wei, X., Face recognition based on PCA and logistic regression analysis (2014) Int. J. Light Electron Optics, 125 (20), pp. 5916-5919; Chen, L.F., Wu, M., Zhou, M.T., She, J.H., Dong, F.Y., Hirota, K., Information-driven multi-robot behavior adaptation to emotional intention in human-robot interaction (2018) IEEE Trans. Cognit. Dev. Syst., 10 (3), pp. 647-658; Smeets, D., Claes, P., Hermans, J., Vandermeulen, D., Suetens, P., A comparative study of 3-D face recognition under expression variations (2012) IEEE Trans.Syst. Man Cybern.: Part C, 42 (5), pp. 710-727; Vezzetti, E., Marcolin, F., Fracastoro, G., 3D face recognition: An automatic strategy based on geometrical descriptors and landmarks (2014) Robot. Auton. Syst., 62 (12), pp. 1768-1776; Matamoros, A.H., Bonarini, A., Hernandez, E.E., Facial expression recognition with automatic segmentation of face regions using a fuzzy based classification approach (2016) Knowl.-Based Syst., 110 (1-14); Zhang, L., Mistry, K., Neoh, S.C., Lim, C.P., Intelligent facial emotion recognition using moth-firefly optimization (2016) Knowl.-Based Syst., 111 (1), pp. 248-267; Xue, B., Zhang, M., Browne, W.N., Particle swarm optimization for feature selection in classification: A multi-objective approach (2013) IEEE Trans. Syst. Man Cybern., 43 (6), pp. 1656-1671; Zhang, Y., Zhang, L., Neoh, S.C., Mistry, K., Hossain, A., Intelligent affect regression for bodily expressions using hybrid particle swarm optimization and adaptive ensembles (2015) Expert Syst. Appl., 42 (22), pp. 8678-8697; Alweshah, M., Abdullah, S., Hybridizing firefly algorithms with a probabilistic neural network for solving classification problems (2015) Appl. Soft Comput., 35, pp. 513-524; Verma, O.P., Aggarwal, D., Patodi, T., Opposition and Dimensional based modified firefly algorithm (2016) Expert Syst. Appl., 44, pp. 168-176; Guo, Y., Liu, Y., Oerlemans, A., Deep learning for visual understanding: A review (2016) Neurocomputing, 187, pp. 27-48; Liu, J.C., Zhang, L., Chen, X., Niu, J.W., Facial landmark automatic identification from three dimensional(3D) data by using Hidden Markov Model(HMM) (2017) Int. J. Ind. 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Cybern, 47 (71), pp. 3840-3853; Chen, L.F., Wu, M., She, J.H., Dong, F.Y., Hirota, K., Three-layer weighted fuzzy SVR for emotional intention understanding in human-robot interaction (2018) IEEE Trans. Fuzzy Syst., 26 (5), pp. 2524-2538; Deng, N., Pei, Y.B., Xu, Z.G., Face recognition with single sample based on Candide-3 reconstruction model (2013) Applied Mechanics and Materials, pp. 3623-3628; Li, X.L., Lu, Q.M., Dong, Y.S., A manifold regularized set-covering method for data partitioning (2018) IEEE Trans. Neural Netw. Learn. Syst., 29 (5), pp. 1760-1773; Lu, Q.M., Li, X., Dong, Y.S., Structure preserving unsupervised feature selection (2018) Neurocomputing, 301, pp. 36-45; Maillo, J., Ramírez, F., Triguero, I., Herrera, F., K-NN-IS: An iterative spark-based design of the k-nearest neighbors classifier for big data (2017) Knowl.-Based Syst, 117, pp. 3-15; Wen, Y., He, L., Shi, P., Face recognition using difference vector plus KPCA (2012) Dig. Signal Process., 22 (1), pp. 140-146; The Japanese Female Facial Expression (JAFFE) Database, , http://www.kasrl.org/jaff.html; Demišar, J., Schuurmans, D., Statistical comparisons of classifiers over multiple data sets (2006) J. Mach. Learn. Res., 7 (1), pp. 1-30","Chen, L.; School of Automation, China; email: chenluefeng@cug.edu.cn",,,"Springer Science and Business Media Deutschland GmbH",,,,,1860949X,,,,"English","Stud. Comput. Intell.",Book Chapter,"Final","",Scopus,2-s2.0-85096237308
"Paliga M., Pollak A.","57194699368;57194705523;","Development and validation of the fluency in human-robot interaction scale. A two-wave study on three perspectives of fluency",2021,"International Journal of Human Computer Studies","155",,"102698","","",,1,"10.1016/j.ijhcs.2021.102698","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112669498&doi=10.1016%2fj.ijhcs.2021.102698&partnerID=40&md5=d611995f0b05cd8616749ce8183ccac4","Uniwersytet Śląski w Katowicach, Instytut Psychologii/ Institute of Psychology, Katowice, 40-126, Poland","Paliga, M., Uniwersytet Śląski w Katowicach, Instytut Psychologii/ Institute of Psychology, Katowice, 40-126, Poland; Pollak, A., Uniwersytet Śląski w Katowicach, Instytut Psychologii/ Institute of Psychology, Katowice, 40-126, Poland","Contemporary work offers many humans the possibility to collaborate with robots. In fact, in industrial settings, human-robot interaction has become a necessity. To improve the quality of human collaboration with their robotic counterparts, practitioners and researchers turn their attention to the concept of fluency. It is crucial for human-robot interaction yet difficult to investigate. Moreover, we argue that the notion of fluency being a unidimensional construct is insufficient to understand the complex interaction in a human-robot team. Therefore, we aimed to develop and validate a new measure to study subjective fluency from three different perspectives, i.e., the human-oriented, the robot-oriented, and the team-oriented. The scale can serve as a tool to better understand the dynamics of the relationship between the operator and the robot. In this paper, we presented the factor analyses and psychometric properties of the Fluency in Human-Robot Interaction Scale. Furthermore, we tested the validity of the scale by investigating the relationship between three perspectives of fluency and the individual performance as well as fluency change over 6 weeks. The results showed positive relationships among all perspectives of fluency and performance. However, no change over time was observed. The findings were discussed with regard to the possible use of the scale in practical and academic contexts. © 2021","Factor analysis; Human-robot fluency; Human-robot interaction; Industry 4.0; Job performance; Scale development","End effectors; Industrial robots; Man machine systems; Human-robot-team; Individual performance; Industrial settings; Psychometric properties; Two waves; Social robots",,,,,,,,,,,,,,,,"Aly, A., Griffiths, S.S., Stramandinoli, F., Metrics and benchmarks in human-robot interaction: Recent advances in cognitive robotics (2017) Cogn. Syst. Res., 43, pp. 313-323; Azevedo, J., Pereira, A., Amaral, A., Nogueira, V., Soares, M., Mota, D., Macedo, A., Perseverative thinking questionnaire: confirmatory factor analysis with two different samples (2017) Eur. Psychiatr., 41, pp. 377-378; Baraglia, J., Cakmak, M., Nagai, Y., Rao, R., Asada, M., Initiative in robot assistance during collaborative task execution (2016) 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 67-74. , ACM; Bartneck, C., Kulic, D., Croft, E.A., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2008) Int. J. Soc. Robot., 1, pp. 71-81; Baumeister, R.F., Bratslavsky, E., Muraven, M., Tice, D.M., Ego depletion: is the active self a limited resource? (1998) J. Pers. Soc. 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"Bukhari S.T.S., Qazi W.M.","57218627070;12139545500;","Perceptual and semantic processing in cognitive robots",2021,"Electronics (Switzerland)","10","18","2216","","",,,"10.3390/electronics10182216","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114639816&doi=10.3390%2felectronics10182216&partnerID=40&md5=10ea6881d5fdfde0c9607cffc4075f09","Intelligent Machines & Robotics, Department of Computer Science, COMSATS University Islamabad, Lahore, 54000, Pakistan","Bukhari, S.T.S., Intelligent Machines & Robotics, Department of Computer Science, COMSATS University Islamabad, Lahore, 54000, Pakistan; Qazi, W.M., Intelligent Machines & Robotics, Department of Computer Science, COMSATS University Islamabad, Lahore, 54000, Pakistan","The challenge in human–robot interaction is to build an agent that can act upon human implicit statements, where the agent is instructed to execute tasks without explicit utterance. Understanding what to do under such scenarios requires the agent to have the capability to process object grounding and affordance learning from acquired knowledge. Affordance has been the driving force for agents to construct relationships between objects, their effects, and actions, whereas grounding is effective in the understanding of spatial maps of objects present in the environment. The main contribution of this paper is to propose a methodology for the extension of object affordance and grounding, the Bloom-based cognitive cycle, and the formulation of perceptual semantics for the context-based human–robot interaction. In this study, we implemented YOLOv3 to formulate visual perception and LSTM to identify the level of the cognitive cycle, as cognitive processes synchronized in the cognitive cycle. In addition, we used semantic networks and conceptual graphs as a method to represent knowledge in various dimensions related to the cognitive cycle. The visual perception showed average precision of 0.78, an average recall of 0.87, and an average F1 score of 0.80, indicating an improvement in the generation of semantic networks and conceptual graphs. The similarity index used for the lingual and visual association showed promising results and improves the overall experience of human–robot interaction. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Affordance; Cognitive robots; Object grounding; Semantic memory",,,,,,,"Funding: This research was funded by Services Syndicate Private Limited for providing access to Universal Robot (UR5) for experimentations.","Acknowledgments: Authors acknowledge the support of Services Syndicate Private Limited for providing access to Universal Robot (UR5) for experimentations.",,,,,,,,,"Dubba, K.S.R., Oliveira, M.R.d., Lim, G.H., Kasaei, H., Lopes, L.S., Tome, A., Cohn, A.G., Grounding Language in Perception for Scene Conceptualization in Autonomous Robots (2014) Proceedings of the AAAI 2014 Spring Symposium, , Palo Alto, CA, USA, 24–26 March; Kotseruba, I., Tsotsos, J.K., 40 years of cognitive architectures: Core cognitive abilities and practical applications (2020) Artif. Intell. 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"Markovich T., Honig S., Oron-Gilad T.","57224123976;57201589559;16042913400;","Closing the Feedback Loop: The Relationship Between Input and Output Modalities in Human-Robot Interactions",2020,"Springer Proceedings in Advanced Robotics","12",,,"29","42",,,"10.1007/978-3-030-42026-0_3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107030799&doi=10.1007%2f978-3-030-42026-0_3&partnerID=40&md5=2caac7057f29c731bb883262d242dae1","Ben-Gurion University of the Negev, Beer-Sheva, Israel","Markovich, T., Ben-Gurion University of the Negev, Beer-Sheva, Israel; Honig, S., Ben-Gurion University of the Negev, Beer-Sheva, Israel; Oron-Gilad, T., Ben-Gurion University of the Negev, Beer-Sheva, Israel","Previous studies suggested that communication modalities used for human control and robot feedback influence human-robot interactions. However, they generally tended to focus on one part of the communication, ignoring the relationship between control and feedback modalities. We aim to understand whether the relationship between a user’s control modality and a robot’s feedback modality influences the quality of the interaction and if so, find the most compatible pairings. In a laboratory Wizard-of-Oz experiment, participants were asked to guide a robot through a maze by using either hand gestures or vocal commands. The robot provided vocal or motion feedback to the users across the experimental conditions forming different combinations of control-feedback modalities. We found that the combinations of control-feedback modalities affected the quality of human-robot interaction (subjective experience and efficiency) in different ways. Participants showed less worry and were slower when they communicated with the robot by voice and received vocal feedback, compared to gestural control and receiving vocal feedback. In addition, they felt more distress and were faster when they communicated with the robot by gestures and received motion feedback compared to vocal control and motion feedback. We also found that providing feedback improves the quality of human-robot interaction. In this paper we detail the procedure and results of this experiment. © 2020, Springer Nature Switzerland AG.","Feedback by motion cues; Feedback loop; Human-robot interaction; Navigation task; Stimulus-response compatibility",,,,,,"Leona M. and Harry B. Helmsley Charitable Trust; Ben-Gurion University of the Negev, BGU","Acknowledgments. This research was supported by the Helmsley Charitable Trust through the Agricultural, Biological and Cognitive Robotics Center at Ben-Gurion University of the Negev. The second author, SH is also supported by Ben-Gurion University of the Negev through the High-tech, Bio-tech and Chemo-tech Scholarship.",,,,,,,,,,"Dubberly, H., Pangaro, P., Haque, U., ON MODELING What is interaction?: Are there different types? (2009) Interactions, 16 (1), pp. 69-75; Mirnig, N., Riegler, S., Weiss, A., Tscheligi, M., A case study on the effect of feedback on itinerary requests in human-robot interaction (2011) 2011 IEEE RO-MAN, pp. 343-349. , , pp. , IEEE, July; Mohammad, Y., Nishida, T., Interaction between untrained users and a miniature robot in a collaborative navigation controlled experiment (2008) Int. J. Inf. Acquis., 5 (4), pp. 291-307; Redden, E.S., Carstens, C.B., Pettitt, R.A., (2010) Intuitive Speech-Based Robotic Control (No. ARL-TR-5175); Perrin, X., Chavarriaga, R., Ray, C., Siegwart, R., Millán, J.D.R., A comparative psychophysical and EEG study of different feedback modalities for HRI (2008) Proceedings of the 3Rd ACM/IEEE International Conference on Human Robot Interaction, pp. 41-48. , , pp. , ACM, March; Greenwald, A.G., A choice reaction time test of ideomotor theory (1970) J. Exp. Psychol., 86, pp. 20-25; Greenwald, A.G., Sensory feedback mechanisms in performance control: With special reference to the ideo-motor mechanism (1970) Psychol. Rev., 77 (2), p. 73; Proctor, R.W., Vu, K.P.L.: Stimulus-Response Compatibility Principles: Data, Theory, and Application. CRC Press, Boca Raton (2006); Wickens, C.D., Sandry, D.L., Vidulich, M., Compatibility and resource competition between modalities of input, central processing, and output (1983) Hum. Factors, 25 (2), pp. 227-248; Raubal, M., Egenhofer, M.J., Comparing the complexity of wayfinding tasks in built environments (1998) Environ. Plan., 25 (6), pp. 895-913; Brooke, J., SUS-a quick and dirty usability scale (1996) Usability Eval. Ind., 189 (19), pp. 4-7; Bangor, A., Kortum, P., Miller, J., Determining what individual SUS scores mean: Adding an adjective rating scale (2009) J. Usability Stud., 4 (3), pp. 114-123; Matthews, G., Campbell, S.E., Falconer, S., Joyner, L.A., Huggins, J., Gilliland, K., Grier, R., Warm, J.S., Fundamental dimensions of subjective state in performance settings: Task engagement, distress, and worry (2002) Emotion, 2 (4), p. 315; Noldus, L.P., Trienes, R.J., Hendriksen, A.H., Jansen, H., Jansen, R.G., The Observer Video-Pro: New software for the collection, management, and presentation of time-structured data from videotapes and digital media files (2000) Behav. Res. Methods Instrum. Comput., 32 (1), pp. 197-206; Bau, O., Mackay, W.E., OctoPocus: A dynamic guide for learning gesture-based command sets (2008) Proceedings of the 21St Annual ACM Symposium on User Interface Software and Technology, pp. 37-46. , , pp. , ACM, October; Matthews, G., Emo, A.K., Funke, G., Zeidner, M., Roberts, R.D., Costa, P.T., Schulze, R., Emotional intelligence, personality, and task-induced stress (2006) J. Exp. Psychol. Appl., 12 (2), p. 96; Nielsen, J., (1993) Usability Engineering, , Academic Press, Cambridge; Beringer, N., (2001) Evoking Gestures in Smartkom − Design of the Graphical User Interface","Markovich, T.; Ben-Gurion University of the NegevIsrael; email: tamarama@post.bgu.ac.il",,,"Springer Science and Business Media B.V.",,,,,25111256,,,,"English","Springer. Proc. Adv. Robot.",Book Chapter,"Final","",Scopus,2-s2.0-85107030799
"Kwon W., Jin Y., Lee S.J.","57212541649;57020309300;57203597342;","Uncertainty-aware knowledge distillation for collision identification of collaborative robots",2021,"Sensors","21","19","6674","","",,,"10.3390/s21196674","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116532540&doi=10.3390%2fs21196674&partnerID=40&md5=548096493886f8ab74564cd363e6857c","Daegu-Gyeongbuk Research Center, Electronics and Telecommunications Research Institute (ETRI), Daegu, 42994, South Korea; Division of Electronic Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju, 54896, South Korea","Kwon, W., Daegu-Gyeongbuk Research Center, Electronics and Telecommunications Research Institute (ETRI), Daegu, 42994, South Korea; Jin, Y., Daegu-Gyeongbuk Research Center, Electronics and Telecommunications Research Institute (ETRI), Daegu, 42994, South Korea; Lee, S.J., Division of Electronic Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju, 54896, South Korea","Human-robot interaction has received a lot of attention as collaborative robots became widely utilized in many industrial fields. Among techniques for human-robot interaction, collision identification is an indispensable element in collaborative robots to prevent fatal accidents. This paper proposes a deep learning method for identifying external collisions in 6-DoF articulated robots. The proposed method expands the idea of CollisionNet, which was previously proposed for collision detection, to identify the locations of external forces. The key contribution of this paper is uncertainty-aware knowledge distillation for improving the accuracy of a deep neural network. Sample-level uncertainties are estimated from a teacher network, and larger penalties are imposed for uncertain samples during the training of a student network. Experiments demonstrate that the proposed method is effective for improving the performance of collision identification. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Collaborative robot; Collision identification; Deep learning; Knowledge distillation; Uncertainty estimation","Accidents; Deep neural networks; Distillation; Human robot interaction; Man machine systems; Personnel training; Uncertainty analysis; Collaborative robots; Collision identification; Deep learning; Fatal accidents; Humans-robot interactions; Industrial fields; Knowledge distillation; Learning methods; Uncertainty; Uncertainty estimation; Collaborative robots; distillation; human; robotics; uncertainty; Distillation; Humans; Neural Networks, Computer; Robotics; Uncertainty",,,,,"Electronics and Telecommunications Research Institute, ETRI: 21ZD1130; National Research Foundation of Korea, NRF; Ministry of Science and ICT, South Korea, MSIT: 2021R1G1A1009792; Jeonbuk National University, JBNU","This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1G1A1009792). This research was supported by ?Research Base Construction Fund Support Program? funded by Jeonbuk National University in 2021, and partially supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government. (21ZD1130, Development of ICT Convergence Technology for Daegu-Gyeongbuk Regional Industry).","Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1G1A1009792). This research was supported by “Research Base Construction Fund Support Program” funded by Jeonbuk National University in 2021, and partially supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government. (21ZD1130, Development of ICT Convergence Technology for Daegu-Gyeongbuk Regional Industry).",,,,,,,,,"Goodrich, M.A., Schultz, A.C., (2008) Human-Robot Interaction: A Survey, , Now Publishers Inc.: Boston, MA, USA; Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer;, A., Kosuge, K., Khatib, O., Progress and prospects of the human–robot collaboration (2018) Auton. Rob, 42, pp. 957-975; Haidegger, T., Autonomy for surgical robots: Concepts and paradigms (2019) IEEE Trans. Med. Rob. Bionics, 1, pp. 65-76; Berezina, K., Ciftci, O., Cobanoglu, C., (2020) Robots, Artificial Intelligence, and Service Automation in Restaurants, , Emerald Group Publishing: Bingley, UK; Wilson, G., Pereyda, C., Raghunath, N., de la Cruz, G., Goel, S., Nesaei, S., Minor, B., Cook, D.J., Robot-enabled support of daily activities in smart home environments (2019) Cognitive Syst. Res, 54, pp. 258-272; Iqbal, J., Khan, Z.H., Khalid, A., Prospects of robotics in food industry (2017) Food Sci. Technol, 37, pp. 159-165; Petković, T., Puljiz, D., Marković, I., Hein, B., Human intention estimation based on hidden Markov model motion validation for safe flexible robotized warehouses (2019) Rob. Comput. Integr. Manuf, 57, pp. 182-196; Oudah, M., Al-Naji, A., Chahl, J., Hand gesture recognition based on computer vision: a review of techniques (2020) J. Imaging, 6, p. 73; Vicentini, F., Collaborative robotics: A survey (2021) J. Mech. Des, 143, p. 040802; Zhang, S., Wang, S., Jing, F., Tan, M., A sensorless hand guiding scheme based on model identification and control for industrial robot (2019) IEEE Trans. Ind. Inf, 15, pp. 5204-5213; Haddadin, S., De Luca, A., Albu-Schäffer, A., Robot collisions: A survey on detection, isolation, and identification (2017) IEEE Trans. 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"Rojas-Quintero J.A., Rodríguez-Liñán M.C.","55934334000;57193487239;","A literature review of sensor heads for humanoid robots",2021,"Robotics and Autonomous Systems","143",,"103834","","",,,"10.1016/j.robot.2021.103834","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108876061&doi=10.1016%2fj.robot.2021.103834&partnerID=40&md5=6c18e3eb04aad18d517ec6681db58dd4","CONACYT/Tecnológico Nacional de México/I.T. Ensenada, Esenada, B.C., Mexico","Rojas-Quintero, J.A., CONACYT/Tecnológico Nacional de México/I.T. Ensenada, Esenada, B.C., Mexico; Rodríguez-Liñán, M.C., CONACYT/Tecnológico Nacional de México/I.T. Ensenada, Esenada, B.C., Mexico","We conducted a literature review on sensor heads for humanoid robots. A strong case is made on topics involved in human–robot interaction. Having found that vision is the most abundant perception system among sensor heads for humanoid robots, we included a review of control techniques for humanoid active vision. We provide historical insight and inform on current robotic head design and applications. Information is chronologically organized whenever possible and exposes trends in control techniques, mechanical design, periodical advances and overall philosophy. We found that there are two main types of humanoid robot heads which we propose to classify as either non-expressive face robot heads or expressive face robot heads. We expose their respective characteristics and provide some ideas on design and vision control considerations for humanoid robot heads involved in human–robot interactions. © 2021 The Author(s)","Active vision; Control of robotic systems; Humanoid robot heads; Human–robot interaction; Review","Agricultural robots; Anthropomorphic robots; Machine design; Control techniques; Design and application; Humanoid robot; Literature reviews; Mechanical design; Perception systems; Robot interactions; Vision control; Social robots",,,,,"Secretaría de Educación Pública, SEP; Consejo Nacional de Ciencia y Tecnología, CONACYT: A1-S-29824","This work was supported by Secretaría de Educación Pública (SEP) and Consejo Nacional de Ciencia y Tecnología (CONACYT) under Grant A1-S-29824 .","This work was supported by Secretar?a de Educaci?n P?blica (SEP) and Consejo Nacional de Ciencia y Tecnolog?a (CONACYT) under Grant A1-S-29824.",,,,,,,,,"Duran, B., Thill, S., Rob's robot: Current and future challenges for humanoid robots (2012) The Future of Humanoid Robots - Research and Applications, , InTech; Saeedvand, S., Jafari, M., Aghdasi, H.S., Baltes, J., A comprehensive survey on humanoid robot development (2019) Knowl. 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"Sarthou G., Mayima A., Buisan G., Belhassein K., Clodic A.","57212393872;57216355113;57204733130;57204952523;14053492500;","The director task: A psychology-inspired task to assess cognitive and interactive robot architectures",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"770","777",,,"10.1109/RO-MAN50785.2021.9515543","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115094932&doi=10.1109%2fRO-MAN50785.2021.9515543&partnerID=40&md5=054ba12d733a5cdf05ac8caa206cd4f5","Université de Toulouse, CNRS, LAAS-CNRS, Toulouse, France; CLLE, Université de Toulouse, CNRS, !!!UT2J, Toulouse, France; Artificial and Natural Intelligence Toulouse Institute (ANITI), France","Sarthou, G., Université de Toulouse, CNRS, LAAS-CNRS, Toulouse, France; Mayima, A., Université de Toulouse, CNRS, LAAS-CNRS, Toulouse, France; Buisan, G., Université de Toulouse, CNRS, LAAS-CNRS, Toulouse, France; Belhassein, K., Université de Toulouse, CNRS, LAAS-CNRS, Toulouse, France, CLLE, Université de Toulouse, CNRS, !!!UT2J, Toulouse, France; Clodic, A., Université de Toulouse, CNRS, LAAS-CNRS, Toulouse, France, Artificial and Natural Intelligence Toulouse Institute (ANITI), France","Assessing robotic architecture for Human-Robot Interaction can be challenging due to the number of features a robot has to endow to perform an acceptable interaction. While everyday-inspired tasks are interesting as reflecting a realistic use of such robots, they often contain a lot of unknown and uncontrolled conditions and specific robot behavior can be hard to test. In this paper, we propose a new psychology-inspired task, gathering perspective-taking, planning, knowledge representation with theory of mind, manipulation, and communication. Along with a precise description of the task allowing its replication, we present a cognitive robot architecture able to perform it in its nominal cases. We finally suggest some challenges and evaluations for the Human-Robot Interaction research community, all derived from this easy-to-replicate task. © 2021 IEEE.",,"Agricultural robots; Architecture; Knowledge representation; Man machine systems; Cognitive robots; Interactive robot; Perspective taking; Research communities; Robot behavior; Robotic architectures; Theory of minds; Social robots",,,,,"ANR-16-CE33-0017","Acknowledgement: Authors want to thank Yannick Riou and Alexandre Bonneau for their work on the robotic integration. This work has been funded by the Agence Nationale de la Recherche JointAction4HRI project ANR-16-CE33-0017 and the Artificial and Natural Intelligence Toulouse Institute (ANITI).",,,,,,,,,,"Satake, S., Nakatani, K., Hayashi, K., Kanda, T., Imai, M., What should we know to develop an information robot? (2015) PeerJ Computer Science; Petrick, R.P., Foster, M.E., Isard, A., Social state recognition and knowledge-level planning for human-robot interaction in a bartender domain (2012) AAAI Workshop on Grounding Language for Physical Systems; Tellex, S., Knepper, R., Li, A., Rus, D., Roy, N., (2014) Asking for Help Using Inverse Semantics; Brawer, J., Mangin, O., Roncone, A., Widder, S., Scassellati, B., Situated human-robot collaboration: Predicting intent from grounded natural language (2018) IEEE/RSJ IROS; Milliez, G., Warnier, M., Clodic, A., Alami, R., A framework for endowing an interactive robot with reasoning capabilities about perspective-taking and belief management (2014) IEEE RO-MAN; Krauss, R.M., Glucksberg, S., Social and nonsocial speech (1977) Scientific American; Keysar, B., Barr, D.J., Balin, J.A., Brauner, J.S., Taking perspective in conversation: The role of mutual knoowledge in comprehension (2000) Psychological Science; Keysar, B., The illusory transparency of intention: Linguistic perspective taking in text (1994) Cognitive Psychology; Keysar, B., Barr, D.J., Horton, W., The egocentric basis of language use: Insights from a processing approach (1998) Current Directions in Psychological Sciences; Keysar, B., Barr, D.J., Self-anchoring in conversation: Why language users do not do what they (2002) Should"" in Heuristics and Biases, , Cambridge University Press; Keysar, B., Lin, S., Barr, D.J., Limits on theory of mind use in adults (2003) Cognition; Dumontheil, I., Apperly, I.A., Blakemore, S.-J., Online usage of theory of mind continues to develop in late adolescence (2010) Developmental Science; Santiesteban, I., White, S., Cook, J., Gilbert, S.J., Heyes, C., Bird, G., Training social cognition: From imitation to theory of mind (2012) Cognition; Happé, F.G.E., An advanced test of theory of mind: Understanding of story characters' thoughts and feelings by able autistic, mentally handicapped, and normal children and adults (1994) Journal of Autism and Developmental Disorders; Rubio-Fernández, P., The director task: A test of theory-of-mind use or selective attention? 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(1985) Cognition; Lemaignan, S., Sallami, Y., Wallhridge, C., Clodic, A., Belpaeme, T., Alami, R., Underworlds: Cascading situation assessment for robots (2018) IEEE/RSJ IROS; Fiala, M., Artag, a fiducial marker system using digital techniques (2005) IEEE (CVPR'05), 2; Buisan, G., Sarthou, G., Bit-Monnot, A., Clodic, A., Alami, R., Efficient, situated and ontology based referring expression generation for human-robot collaboration (2020) IEEE RO-MAN; Lallement, R., De Silva, L., Alami, R., Hatp: An htn planner for robotics (2014) ICAPS Workshop on Planning and Robotics; Buisan, G., Alami, R., A human-aware task planner explicitly reasoning about human and robot decision, action and reaction (2021) Companion of the ACM/IEEE HRI; Buisan, G., Sarthou, G., Alami, R., Human aware task planning using verbal communication feasibility and costs (2020) ICSR; Clodic, A., Cao, H., Alili, S., Montreuil, V., Alami, R., Chatila, R., Shary: A supervision system adapted to human-robot interaction (2009) Experimental Robotics, , O. Khatib, V. Kumar, and G. J. Pappas, Eds. Springer Berlin Heidelberg; Fiore, M., Clodic, A., Alami, R., On planning and task achievement modalities for human-robot collaboration (2014) ISER; Devin, S., Alami, R., An implemented theory of mind to improve human-robot shared plans execution (2016) ACM/IEEE HRI; Mayima, A., Clodic, A., Alami, R., Toward a robot computing an online estimation of the quality of its interaction with its human partner (2020) IEEE RO-MAN; Grice, H.P., Logic and conversation (1975) Speech Acts, , Brill",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85115094932
"Choi D., Shi W., Liang Y.S., Yeo K.H., Kim J.-J.","8883291700;57351948400;57194637692;56342233900;37007768200;","Controlling Industrial Robots with High-Level Verbal Commands",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"216","226",,,"10.1007/978-3-030-90525-5_19","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119859453&doi=10.1007%2f978-3-030-90525-5_19&partnerID=40&md5=77d2940ce180759dd5080e26441a1a25","Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore, Singapore; Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore","Choi, D., Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore, Singapore; Shi, W., Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore; Liang, Y.S., Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore, Singapore; Yeo, K.H., Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore; Kim, J.-J., Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore, Singapore","Industrial robots today are still mostly pre-programmed to perform a specific task. Despite previous research in human-robot interaction in the academia, adopting such systems in industrial settings is not trivial and has rarely been done. In this paper, we introduce a robotic system that we control with high-level verbal commands, leveraging some of the latest neural approaches to language understanding and a cognitive architecture for goal-directed but reactive execution. We show that a large-scale pre-trained language model can be effectively fine-tuned for translating verbal instructions into robot tasks, better than other semantic parsing methods, and that our system is capable of handling through dialogue a variety of exceptions that happen during human-robot interaction including unknown tasks, user interruption, and changes in the world state. © 2021, Springer Nature Switzerland AG.","Cognitive architecture; Human-robot interaction; Intention translation; Semantic parsing","High level languages; Industrial research; Industrial robots; Man machine systems; Semantics; Translation (languages); Cognitive architectures; Goal-directed; Humans-robot interactions; Industrial settings; Intention translation; Language understanding; Large-scales; Robotic systems; Semantic parsing; Specific tasks; Human robot interaction",,,,,"Agency for Science, Technology and Research, A*STAR","This research is supported by A*STAR under its Human-Robot Collaborative AI for",,,,,,,,,,"Artzi, Y., Das, D., Petrov, S., Learning compact lexicons for CCG semantic parsing (2014) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1273-1283. , pp; Cambria, E., Poria, S., Hazarika, D., Kwok, K., SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings (2018) Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1795-1802. , pp; Chen, H., Tan, H., Kuntz, A., Bansal, M., Alterovitz, R., Enabling robots to understand incomplete natural language instructions using commonsense reasoning (2020) Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1963-1969. , pp; Choi, D., Langley, P., Evolution of the ICARUS cognitive architecture (2018) Cogn. Syst. Res., 48, pp. 25-38; Devlin, J., Chang, M.W., Lee, K., Toutanova, K., BERT: Pre-training of deep bidirectional transformers for language understanding (2019) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4171-4418. , pp; Dong, L., Lapata, M., Coarse-to-fine decoding for neural semantic parsing (2018) Proceedings of the 56Th Annual Meeting of the Association for Computational Linguistics, pp. 731-742. , pp; Elgohary, A., Hosseini, S., Awadallah, A.H., Speak to your parser: Interactive text-to-SQL with natural language feedback (2020) Proceedings of the 58Th Annual Meeting of the Association for Computational Linguistics, pp. 2065-2077. , pp; Fikes, R., Nilsson, N., STRIPS: A new approach to the application of theorem proving to problem solving (1971) Artif. Intell., 2, pp. 189-208; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Comput, 9 (8), pp. 1735-1780; Horn, A., On sentences which are true of direct unions of algebras (1951) J. Symbolic Log., 16, pp. 14-21; Jia, Y., She, L., Cheng, Y., Bao, J., Chai, J.Y., Xi, N., Program robots manufacturing tasks by natural language instructions (2016) Proceedings of the IEEE International Conference on Automation Science and Engineering, pp. 633-638. , pp; Kuo, Y.L., Katz, B., Barbu, A., Deep compositional robotic planners that follow natural language commands (2020) Proceedings of the IEEE International Conference on Robotics and Automation, pp. 4906-4912. , pp; Laird, J.E., Interactive task learning (2017) IEEE Intell. Syst., 32 (4), pp. 6-21; Park, J.S., Jia, B., Bansal, M., Manocha, D., Efficient generation of motion plans from attribute-based natural language instructions using dynamic constraint mapping (2019) Proceedings of the IEEE International Conference on Robotics and Automation, pp. 6964-6971. , pp; Pennington, J., Socher, R., Manning, C.D., GloVe: Global vectors for word representation (2014) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1532-1543. , pp; Radford, A., Language models are unsupervised multitask learners (2019) Openai Blog, 1, p. 9; Raffel, C., Exploring the limits of transfer learning with a unified text-to-text transformer (2020) J. Mach. Learn. Res., 21 (140), pp. 1-67; Venkatesh, S.G., Spatial reasoning from natural language instructions for robot manipulation (2021) Proceedings of the IEEE International Conference on Robotics and Automation; Wächter, M., Integrating multi-purpose natural language understanding, robot’s memory, and symbolic planning for task execution in humanoid robots (2018) Robot. Auton. Syst., 99, pp. 148-165; Yin, P., Neubig, G., Yih, W.T., Riedel, S., TaBERT: Pretraining for joint understanding of textual and tabular data (2020) Proceedings of the 58Th Annual Meeting of the Association for Computational Linguistics, pp. 8413-8426. , pp; Zeng, J., Photon: A robust cross-domain text-to-SQL system (2020) Proceedings of the 58Th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 204-214. , pp","Choi, D.; Institute of High Performance Computing, Singapore; email: choi_dongkyu@ihpc.a-star.edu.sg","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119859453
"Zaballa K.N.H., Cameron L.D., Lugo A.S.","57226639019;57210193310;57226648881;","Human-Robot Interactions Design for Interview Process: Needs-Affordances-Features Perspective",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12783 LNCS",,,"645","655",,,"10.1007/978-3-030-77750-0_43","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112168151&doi=10.1007%2f978-3-030-77750-0_43&partnerID=40&md5=7f1028b010edf92e366f69c8452d7c63","Artificial Intelligence Lab, San Diego State University, San Diego, 92182, United States","Zaballa, K.N.H., Artificial Intelligence Lab, San Diego State University, San Diego, 92182, United States; Cameron, L.D., Artificial Intelligence Lab, San Diego State University, San Diego, 92182, United States; Lugo, A.S., Artificial Intelligence Lab, San Diego State University, San Diego, 92182, United States","Human robot interaction (HRI) offers potential in fulfilling the social needs of humans specifically, in the context of an interview setting. While human robot interaction has proven potentials to provide relatively measurable outcomes in experimentation, applications in real life are limited mainly due to primitive HRI design and implementation. Research in HRI is thus the necessary first step to the diffusion of robots and robotic technologies into social life. This study presents the results a case study on HRI design for a formal interview process. The findings are presented in a framework that elucidates the key expected robot affordances—action possibilities afforded by a humanoid robot—and their relationships with humans in the interactive interview context. This framework development has been informed by the Needs-Affordances-Features perspective. © 2021, Springer Nature Switzerland AG.","Human robot interaction; Interview; Needs-affordances-features perspective; Robots","Anthropomorphic robots; Machine design; Man machine systems; Affordances; Design and implementations; Framework development; Human robot Interaction (HRI); Humanoid robot; Robotic technologies; Social life; Social needs; Social robots",,,,,,,,,,,,,,,,"Andrist, S., Tan, X.Z., Gleicher, M., Mutlu, B., Conversational gaze aversion for humanlike robots (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction, Pp. 25–32. University of Wisconsin-Madison Department of Computer Sciences, , https://doi.org/10.1145/2559636.2559666; Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., Šabanović, S., (2019) Human-Robot Interaction: An Introduction, , Cambridge University Press, Cambridge; Deci, E., Vallerand, R., Pelletier, L., Ryan, R., Motivation and education: The self-determination perspective (1991) Educ. Psychol, 26 (3-4), pp. 325-346. , https://doi.org/10.1080/00461520.1991.9653137; Edwards, A., Omilion-Hodges, L., Edwards, C., How do patients in a medical interview perceive a robot versus human physician? (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2017, Vienna, Austria, Pp. 109–110. Companion; Elkins, A., Gupte, A., Cameron, L., Humanoid Robots as Interviewers for Automated Credibility Assessment. Artificial Intelligence Lab. San Diego State University (2018) USA; Evans, S.K., Pearce, K.E., Vitak, J., Treem, J.W., Explicating affordances: A conceptual framework for understanding affordances in communication research (2016) J. Comput.-Mediat. Commun, 22 (1), pp. 35-52; Fallon, M., An architecture for online affordance-based perception and whole-body planning (2015) J. Field Robot, 32 (2), pp. 229-254. , https://doi.org/10.21236/ada602904; Fayard, A.-L., Weeks, J., Affordances for practice. Inf (2014) Organ, 24 (4), pp. 236-249. , https://doi.org/10.1016/j.infoandorg.2014.10.001; Fox, J., Gambino, A., Relationship development with humanoid social robots: Applying interpersonal theories to human/robot interaction. Cyberpsychol. Behav. Soc (2021) Netw, pp. 1-5. , https://doi.org/10.1089/cyber.2020.0181; Ghazali, A.S., Ham, J., Barakova, E., Markopoulos, P.: Assessing the effect of persuasive robots interactive social cues on users’ psychological reactance, liking, trusting beliefs and compliance. Adv. Robot. 33(7–8), 325–337 (2019). https://doi.org/10.1080/01691864.2019. 1589570; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., de Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Hum. Factors: J. Hum. Factors Ergon. Soc, 53 (5), pp. 517-527. , https://doi.org/10.1177/0018720811417254; Harris, J., Sharlin, E., Exploring the affect of abstract motion in social human-robot interaction (2011) RO-MAN, , https://doi.org/10.1109/roman.2011.6005254; Jamone, L., Affordances in psychology, neuroscience, and robotics: A survey (2018) IEEE Trans. Cogn. Dev. Syst, pp. 4-25. , https://doi.org/10.1109/tcds.2016.2594134; Kahn, P.H., Will people keep the secret of a humanoid robot? 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Behav, 24 (5), pp. 320-334. , https://doi.org/10.1177/1059712316668238; Vallverdú, J., Trovato, G., Jamone, L., Allocentric emotional affordances in HRI: The multimodal binding. Multimodal Technol (2018) Interact, 2 (78), pp. 1-20. , https://doi.org/10.20944/preprints201808.0312.v1; Wang, S.M., Cheng, W.M.: Design thinking for developing a case-based reasoning emotion-sensing robot for interactive interview. In: Symposium on Emerging Research from Asia and on Asian Contexts and Cultures, CHI 2020, Hawaii, pp. 13–16 (2020). https://doi.org/10. 1145/3391203.3391205; Zheng, J., Jarvenpaa, S.L.: (PDF) [Internet]: Thinking Technology as Human: Affordances, Technology Features, and Egocentric Biases in Technology Anthropomorphism. ResearchGate (2020). https://www.researchgate.net/publication/347484661_Thinking_Tec hnology_as_Human_Affordances_Technology_Features_and_Egocentric_Biases_in_Tec hnology_Anthropomorphism. Accessed 21 Feb 2021","Zaballa, K.N.H.; Artificial Intelligence Lab, United States; email: nikazaballa@gmail.com","Nah F.F.Siau K.",,"Springer Science and Business Media Deutschland GmbH","8th International Conference on HCI in Business, Government and Organizations, HCIBGO 2021, Held as Part of the 23rd HCI International Conference, HCII 2021","24 July 2021 through 29 July 2021",,262219,03029743,9783030777494,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85112168151
"Stoeva D., Gelautz M.","57216345097;8610583800;","Body language in affective human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"606","608",,5,"10.1145/3371382.3377432","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083213455&doi=10.1145%2f3371382.3377432&partnerID=40&md5=a7010604b56175763410769fc8a38fe6","Institute of Visual Computing and Human-Centered Technology, TU Wien, Vienna, Austria","Stoeva, D., Institute of Visual Computing and Human-Centered Technology, TU Wien, Vienna, Austria; Gelautz, M., Institute of Visual Computing and Human-Centered Technology, TU Wien, Vienna, Austria","Social human-robot interaction is concerned with exploring the ways in which social interaction can be achieved between a human and a sociable robot. Affect has an important role in interaction as it helps interactants coordinate and indicate the success of the communication. Designing socially intelligent robots requires competence in communication, which includes exchanges of both verbal and non-verbal cues. This project will focus on non-verbal communication, more specifically body movements, postures and gestures as means of conveying socially affective information. Using the affective grounding perspective, which conceptualizes emotion as a coordination mechanism, together with honest signals as a measurement of the dynamics of the interaction, and the robot Pepper, we aim to develop a system that would be able to communicate affect, with the goal to enhance affective human-robot interaction. © 2020 ACM.","Affective computing; Body language; Human-robot interaction; Robotics; Social signals","Agricultural robots; Intelligent robots; Man machine systems; Body language; Body movements; Coordination mechanisms; Non-verbal communications; Postures and gestures; Sociable robots; Social human-robot interactions; Social interactions; Social robots",,,,,,,,,,,,,,,,"Argyle, M., (1969) Social Interaction: Process and Products, , Routledge, New York; Argyle, M., (1975) Bodily Communication, , Methuen & Co Ltd, London; Atkinson, A.P., Dittrich, W.H., Gemmell, A.J., Young, A.W., Emotion perception from dynamic and static body expressions in point-light and full-light displays (2004) Perception, 33 (6), pp. 717-746. , https://doi.org/10.1068/p5096, 2004; Bailenson, J.N., Yee, N., Digital chameleons: Automatic assimilation of nonverbal gestures in immersive virtual environments (2005) Psychological Science, 16 (10), pp. 814-819. , https://doi.org/10.1111/j.1467-9280.2005.01619.x, 2005; Beck, A., Cañamero, L., Hiolle, A., Damiano, L., Cosi, P., Tesser, F., Sommavilla, G., Interpretation of emotional body language displayed by a humanoid robot: A case study with children (2013) International Journal of Social Robotics, 5 (3), pp. 325-334. , https://doi.org/10.1007/s12369-013-0193-z, 2013; Camurri, A., Lagerlöf, I., Volpe, G., Recognizing emotion from dance movement: Comparison of spectator recognition and automated techniques (2003) International Journal of Human-Computer Studies, 59 (1-2), pp. 213-225. , https://doi.org/10.1016/S1071-5819(03)00050-8, 2003; Cao, Z., Hidalgo, G., Simon, T., Wei, S., Sheikh, Y., (2018) OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields, , arXiv:1812.08008 [cs] 2018 arXiv:cs/1812. 08008; Choi, M., Kornfield, R., Takayama, L., Mutlu, B., Movement matters: Effects of motion and mimicry on perception of similarity and closeness in robot-mediated communication (2017) 2017 CHI Conference on Human Factors in Computing Systems (CHI '17), pp. 325-335. , https://doi.org/10.1145/3025453.3025734, ACM, Denver, Colorado, USA; Chu, V., Bullard, K., Thomaz, A.L., Multimodal real-time contingency detection for hri (2014) 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3327-3332. , https://doi.org/10.1109/IROS.2014.6943025; Cohen, I., Looije, R., Neerincx, M., Child's perception of robot's emotions: Effects of platform, context and experience (2014) International Journal of Social Robotics, 6 (4), pp. 507-518. , https://doi.org/10.1007/s12369-014-0230-6, 2014; Coulson, M., Attributing emotion to static body postures: Recognition accuracy, confusions, and viewpoint dependence (2004) Journal of Nonverbal Behavior, 28 (2), pp. 117-139. , https://doi.org/10.1023/B:JONB.0000023655.25550.be, 2004; Delaherche, E., Chetouani, M., Mahdhaoui, A., Saint-Georges, C., Viaux, S., Cohen, D., Interpersonal synchrony: A survey of evaluation methods across disciplines (2012) IEEE Transactions on Affective Computing, 3 (3), pp. 349-365. , https://doi.org/10.1109/T-AFFC.2012.12, 2012; Ekman, P., An argument for basic emotions (1992) Cognition and Emotion, 6 (3-4), pp. 169-200. , https://doi.org/10.1080/02699939208411068, 1992; Suphi Erden, M., Emotional postures for the humanoid-robot nao (2013) International Journal of Social Robotics, 5 (4), pp. 441-456. , https://doi.org/10.1007/s12369-013-0200-4, 2013; Fischer, K., Jung, M.F., Jensen, L.C., Der Wieschen, A.M.V., Emotion expression in hri-When and why (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 29-38. , https://doi.org/10.1109/HRI.2019.8673078; Fuente, L.A., Ierardi, H., Pilling, M., Crook, N.T., Influence of upper body pose mirroring in human-robot interaction (2015) Social Robotics, Adriana Tapus, Elisabeth André, pp. 214-223. , Jean-Claude Martin, Fraņois Ferland, and Mehdi Ammi (Eds.). Springer International Publishing; Hale, J., Antonia, F., De Hamilton, C., Testing the relationship between mimicry, trust and rapport in virtual reality conversations (2016) Scientific Reports, 6, p. 35295. , https://doi.org/10.1038/srep35295, 2016; Häring, M., Bee, N., André, E., Creation and evaluation of emotion expression with body movement, sound and eye color for humanoid robots (2011) 2011 RO-MAN, pp. 204-209. , https://doi.org/10.1109/ROMAN.2011.6005263; Khursheed Hasnain, S., Gaussier, P., Mostafaoui, G., Synchrony as a way to choose an interacting partner (2012) 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL), pp. 1-6. , https://doi.org/10.1109/DevLrn.2012.6400882; Hasnain, S.K., Mostafaoui, G., Gaussier, P., A synchrony-based perspective for partner selection and attentional mechanism in human-robot interaction (2012) Paladyn, 3 (3), pp. 156-171. , https://doi.org/10.2478/s13230-013-0111-y, 2012; Hasumoto, R., Nakadai, K., Imai, M., Reactive chameleon: A method to mimic conversation partner's body sway for a robot (2019) International Journal of Social Robotics, , https://doi.org/10.1007/s12369-019-00557-4, 2019; Malte, F.J., Affective grounding in human-robot interaction (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction-HRI '17, pp. 263-273. , https://doi.org/10.1145/2909824.3020224; Knight, H., Simmons, R., Laban head-motions convey robot state: A call for robot body language (2016) 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, Stockholm, Sweden, pp. 2881-2888. , https://doi.org/10.1109/ICRA.2016.7487451; Kofinas, N., Orfanoudakis, E., Lagoudakis, M.G., Complete analytical forward and inverse kinematics for the nao humanoid robot (2015) Journal of Intelligent & Robotic Systems, 77 (2), pp. 251-264. , https://doi.org/10.1007/s10846-013-0015-4, 2015; Kozima, H., Nakagawa, C., Yasuda, Y., Interactive robots for communication-care: A case-study in autism therapy (2005) ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005, pp. 341-346. , https://doi.org/10.1109/ROMAN.2005.1513802; Lee, J.J., Sha, F., Breazeal, C., A Bayesian theory of mind approach to nonverbal communication (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 487-496. , https://doi.org/10.1109/HRI.2019.8673023; Li, J., Ju, W., Nass, C., Observer perception of dominance and mirroring behavior in human-robot relationships (2015) Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI '15), , https://doi.org/10.1145/2696454.2696459, ACM, Portland, Oregon, USA; Löffler, D., Schmidt, N., Tscharn, R., Multimodal expression of artificial emotion in social robots using color, motion and sound (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction-HRI '18, pp. 334-343. , https://doi.org/10.1145/3171221.3171261, ACM Press, Chicago, IL, USA; McColl, D., Nejat, G., Recognizing emotional body language displayed by a human-like social robot (2014) International Journal of Social Robotics, 6 (2), pp. 261-280. , https://doi.org/10.1007/s12369-013-0226-7, 2014; Miwa, H., Itoh, K., Matsumoto, M., Zecca, M., Takanobu, H., Rocella, S., Carrozza, M.C., Takanishi, A., Effective emotional expressions with expression humanoid robot we-4rii: Integration of humanoid robot hand rch-1 (2004) 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat No. 04CH37566), 3, pp. 2203-2208. , https://doi.org/10.1109/IROS.2004.1389736; (2008) Honest Signals: How They Shape Our World, , Alex (Sandy) Pentland The MIT Press; Rich, C., Ponsler, B., Holroyd, A., Sidner, C.L., Recognizing engagement in human-robot interaction (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 375-382. , https://doi.org/10.1109/HRI.2010.5453163; James, A.R., A circumplex model of affect (1980) Journal of Personality and Social Psychology, 39 (6), pp. 1161-1178. , https://doi.org/10.1037/h0077714, 1980; Saerbeck, M., Bartneck, C., Perception of affect elicited by robot motion (2010) Fifth ACM/IEEE International Conference on Human-Robot Interaction-HRI '10, pp. 53-60. , https://doi.org/10.1145/1734454.1734473; Senecal, S., Cuel, L., Aristidou, A., Magnenat-Thalmann, N., Continuous body emotion recognition system during theater performances: Continuous body emotion recognition (2016) Computer Animation and Virtual Worlds, 27 (3-4), pp. 311-320. , https://doi.org/10.1002/cav.1714, 2016; Simmons, R., Knight, H., Keep on dancing: Effects of expressive motion mimicry (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), , https://doi.org/10.1109/ROMAN.2017.8172382; Stoeva, D., (2018) Robot-Assisted Game Incorporating Emotional Postures for Children with ASD, , Master Thesis. Heriot Watt University, Edinburgh, UK; Stolzenwald, J., Bremner, P., Gesture mimicry in social human-robot interaction (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 430-436. , https://doi.org/10.1109/ROMAN.2017.8172338; Tsiourti, C., Weiss, A., Wac, K., Vincze, M., Designing emotionally expressive robots: A comparative study on the perception of communication modalities (2017) 2017 5th International Conference on Human Agent Interaction (HAI '17), pp. 213-222. , https://doi.org/10.1145/3125739.3125744, ACM, Bielefeld, Germany; Tuyen, V.N.T., Jeong, S., Chong, N.Y., Emotional bodily expressions for culturally competent robots through long term human-robot interaction (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2008-2013. , https://doi.org/10.1109/IROS.2018.8593974; Vicaria, I.M., Dickens, L., Meta-analyses of the intra-and interpersonal outcomes of interpersonal coordination (2016) Journal of Nonverbal Behavior, 40 (4), pp. 335-361. , https://doi.org/10.1007/s10919-016-0238-8, 2016; Zecca, M., Mizoguchi, Y., Endo, K., Iida, F., Kawabata, Y., Endo, N., Itoh, K., Takanishi, A., Whole body emotion expressions for kobian humanoid robot-Preliminary experiments with different emotional patterns (2009) RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 381-386. , https://doi.org/10.1109/ROMAN.2009.5326184",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083213455
"Ijuin K., Jokinen K.","56006019000;23397498500;","Exploring gaze behaviour and perceived personality traits",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12194 LNCS",,,"504","512",,,"10.1007/978-3-030-49570-1_35","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088506125&doi=10.1007%2f978-3-030-49570-1_35&partnerID=40&md5=33ffadf3d63f032e970c52342c520117","AI Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26 Aomi, Koto-Ku, Tokyo, Japan","Ijuin, K., AI Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26 Aomi, Koto-Ku, Tokyo, Japan; Jokinen, K., AI Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-3-26 Aomi, Koto-Ku, Tokyo, Japan","The paper discusses correlation between interlocutors’ eye-gaze behavior and their perceived personality traits in human-human and human-robot interactions. Given that personality is related to the person’s typical manners and styles of behaving, it can be assumed that such underlying characteristics are reflected in the person’s gaze patterns as well. Starting from the comparison of human-human and human-robot interaction, the participant’s gaze frequency and length in regard to the human vs. robot partner’s face and body are related to the participant’s perceived personality traits. A positive correlation is found concerning the differences in gaze patterns and the extrovert personality trait. This seems highly reasonable, considering the basic function of gaze as a means to collect situational information and the extrovert communication style as actively looking for new information. © Springer Nature Switzerland AG 2020.","Eye-gaze activity; Human-human interaction; Human-robot interaction; Personality traits","Behavioral research; Human computer interaction; Social networking (online); Basic functions; Communication styles; Eye-gaze; Gaze behaviours; Personality traits; Positive correlations; Robot Partners; Human robot interaction",,,,,"New Energy and Industrial Technology Development Organization, NEDO","This paper is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).",,,,,,,,,,"Kendon, A., Some functions of gaze–direction in social interaction (1967) Acta Physiol, 26 (1), pp. 22-63; Argyle, M., Cook, M., (1976) Gaze and Mutual Gaze, , Cambridge University Press, Cambridge; Clark, H., Wilkes-Gibbs, D., Referring as a collaborative process (1986) Cognition, 22, pp. 1-39; Jokinen, K., Furukawa, H., Nishida, M., Yamamoto, S., Gaze and turn-taking behavior in casual conversational interactions (2013) ACM Trans. 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Comput, 5 (3), pp. 273-291; Mehta, Y., Majumder, N., Gelbukh, A., Cambria, E., Recent trends in deep learning based personality detection (2019) Artif. Intell. Rev, 53 (4), pp. 2313-2339. , https://doi.org/10.1007/s10462-019-09770-z; Ponce-López, V., ChaLearn LAP 2016: first round challenge on first impressions-dataset and results (2016) ECCV 2016. LNCS, 9915, pp. 400-418. , https://doi.org/10.1007/978-3-319-49409-8_32, Hua, G., Jégou, H. (eds) Springer, Cham; Jokinen, K., The AICO multimodal corpus (2020) Proceedings of the Twelfth International Conference on Language Resources and Evaluation (LREC-2020). European Languages Resources Association (ELRA); Mori, T., Jokinen, K., Den, Y., Analysis of body behaviours in human-human and human-robot interactions (2020) Proceedings of the LREC Workshop on People in Language, Vision and the Mind; Rammstedt, B., John, O.P., Measuring personality in one minute or less: a 10-item short version of the big five inventory in English and German (2007) J. Res. Pers, 41 (1), pp. 203-212. , http://www.sciencedirect.com/science/article/pii/S0092656606000195","Ijuin, K.; AI Research Center, 2-3-26 Aomi, Koto-Ku, Japan; email: koki-ijuuin@aist.go.jp","Meiselwitz G.",,"Springer","12th International Conference on Social Computing and Social Media, SCSM 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242139,03029743,9783030495695,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85088506125
"Marvel J.A., Zimmerman M., Bagchi S.","35074587400;57208165783;57208164628;","State-of-the-art in human-robot interaction",2021,"Manufacturing In The Era Of 4th Industrial Revolution: A World Scientific Reference (In 3 Volumes)",,,,"43","86",,,"10.1142/9789811210679_0003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109605263&doi=10.1142%2f9789811210679_0003&partnerID=40&md5=b77f8b968d4cfe12f3816d35adcc4db7","U.S. National Institute of Standards and Technology (NIST), Gaithersburg, MD, United States; Johns Hopkins University, United States; National Institute of Standards and Technology, Gaithersburg, MD, United States","Marvel, J.A., U.S. National Institute of Standards and Technology (NIST), Gaithersburg, MD, United States; Zimmerman, M., Johns Hopkins University, United States; Bagchi, S., National Institute of Standards and Technology, Gaithersburg, MD, United States","The following sections are included: • Introduction • Humans and Robots in Industry • Human-Machine Interfaces • Human-Robot Interaction • Performance Metrics for HRI and HMI • Natural HRI • Conclusion • References. © 2020 by World Scientific Publishing Co. 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Psychol., 53 (1), pp. 61-86; Zimmerman, M., Marvel, J., Smart manufacturing and the promotion of artificially-intelligent human-robot collaborations in small- and medium-sized enterprises (2017) AAAI Fall Symposium Series:Symposium on Natural Communication for Human-Robot Collaboration; Norman, D., (2013) The Design of Everyday Things, , Basic Books; Lumelsky, V., On human performance in telerobotics, IEEE Transactions on Systems (1991) Man, and Cybernetics, 21 (5), pp. 971-982; Marvel, J.A., Falco, J., Marstio, I., Characterizing task-based human-robot collaboration safety in manufacturing (2015) IEEE Trans. Syst., Man, and Cybern.: Syst., 45 (2), pp. 260-275",,,,"World Scientific Publishing Co.",,,,,,9789811222795,,,"English","Manufacturing In The Era Of 4th Industrial Revolution: A World Scientific Reference (In 3 Volumes)",Book Chapter,"Final","",Scopus,2-s2.0-85109605263
"Wen K., Nguyen T.S., Harton D., Laliberte T., Gosselin C.","56067011700;57221307383;57197862171;6602468451;57210720538;","A Backdrivable Kinematically Redundant (6+3)-Degree-of-Freedom Hybrid Parallel Robot for Intuitive Sensorless Physical Human-Robot Interaction",2021,"IEEE Transactions on Robotics","37","4","9306904","1222","1238",,2,"10.1109/TRO.2020.3043723","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098774058&doi=10.1109%2fTRO.2020.3043723&partnerID=40&md5=9d447e730a0eb8d40e5c4237a7e43509","Department of Mechanical Engineering, Université Laval, Québec, QC, Canada","Wen, K., Department of Mechanical Engineering, Université Laval, Québec, QC, Canada; Nguyen, T.S., Department of Mechanical Engineering, Université Laval, Québec, QC, Canada; Harton, D., Department of Mechanical Engineering, Université Laval, Québec, QC, Canada; Laliberte, T., Department of Mechanical Engineering, Université Laval, Québec, QC, Canada; Gosselin, C., Department of Mechanical Engineering, Université Laval, Québec, QC, Canada","A novel backdrivable 3-[R(RR-RRR)SR] kinematically redundant (6+3)-degree-of-freedom (DOF) spatial hybrid parallel robot with revolute actuators is proposed for low-impedance physical human-robot interaction. The kinematic model is developed based on the constraint conditions of the robot. It is shown that the type II (parallel) singularities can be completely avoided, thereby yielding a very large translational and orientational workspace. A workspace analysis is presented in order to demonstrate the capabilities of the robot. Mechanisms are then introduced to use the redundant DOF of the robot to operate a gripper with the robot actuators, which are mounted on or close to the base, thus reducing the inertia of the moving parts. The architecture of the robot makes it possible to use direct drive motors, thereby making the robot easily backdrivable and allowing the use of a very simple and effective controller. A prototype of the robot is then designed and built and the large workspace of the robot as well as the effortless physical human-robot interaction are demonstrated. The controller of the robot is then described, including a position control mode and a control mode for physical interaction, which does not require the use of a force/torque sensor or joint torque sensors. Because of its backdrivability and low moving inertia, the robot is particularly well-suited for physical human-robot interaction, as demonstrated in the accompanying videos. © 2004-2012 IEEE.","Hybrid robot; kinematic redundancy; orientational workspace; parallel robot; physical human-robot interaction; remotely operated gripper; singularities","Actuators; Agricultural robots; Controllers; Degrees of freedom (mechanics); Kinematics; Position control; Robot applications; Constraint conditions; Degree of freedom (dof); Direct drive motor; Force/torque sensor; Joint torque sensors; Physical interactions; Position control modes; Workspace analysis; Human robot interaction",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC; Canada Research Chairs; Fonds Québécois de la Recherche sur la Nature et les Technologies, FQRNT; Fonds de recherche du Québec – Nature et technologies, FRQNT","Mr. Harton received postgraduate scholarships from the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Fonds québécois de la recherche sur la nature et les technologies (FRQNT).","Manuscript received July 7, 2020; revised October 15, 2020; accepted November 6, 2020. Date of publication December 24, 2020; date of current version August 5, 2021. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC), in part by the Canada Research Chair program, and in part by the Fonds Québécois de la Recherche sur la Nature et les Technologies (FRQNT). This article has supplementary downloadable material available at https://doi.org/10.1109/TFUZZ.2019.2933787, provided by the authors. This paper was recommended for publication by Associate Editor Q. Li and Editor E. Yoshida upon evaluation of the reviewers’ comments. (Corresponding author: Clément Gosselin.) The authors are with the Department of Mechanical Engineering, Univer-sité Laval, Québec, QC G1V 0A6, Canada (e-mail: kefei.wen.1@ulaval.ca; tan-sy.nguyen.1@ulaval.ca; david.harton.1@ulaval.ca; thierry@gmc.ulaval.ca; gosselin@gmc.ulaval.ca).",,,,,,,,,"Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P., Collaborative manufacturing with physical human-robot interaction (2016) Robot. Comput.-Integr. Manuf., 40, pp. 1-13; Gealy, D.V., Quasi-direct drive for low-cost compliant robotic manipulation (2019) Proc. IEEE Int. Conf.Robot.Autom., pp. 437-443. , May; Pratt, G.A., Williamson, M.M., Dillworth, P., Pratt, J., Wright, A., Stiffness isn't everything (1997) Experimental Robotics IV. Berlin, Germany: Springer, pp. 253-262; Albu-Schäffer, A., The DLR lightweight robot: Design and control concepts for robots in human environments (2007) Ind. Robot: An Int. J., 34 (5), pp. 376-385; Eppinger, S.D., Three dynamic problems in robot force control (1992) IEEE Trans. Robot. Autom., 8 (6), pp. 751-758. , Dec; Fauteux, P., Lauria, M., Heintz, B., Michaud, F., Dual-differential rheological actuator for high-performance physical robotic interaction (2010) IEEE Trans. Robot., 26 (4), pp. 607-618. , Aug; Sadjadian, H., Taghirad, H.D., Impedance control of the hydraulic shoulder a 3-dof parallel manipulator (2006) Proc. IEEE Int. Conf. Robot. Biomimetics., pp. 526-531; Caccavale, F., Siciliano, B., Villani, L., The tricept robot: Dynamics and impedance control (2003) IEEE/ASME Trans. Mechatronics, 8 (2), pp. 263-268. , Jun; Arabshahi, H.Z., Novinzadeh, A.B., Impedance control of the 3RPS parallel manipulator (2014) Proc. 2nd RSI/ISM Int. Conf. Robot. Mechatron., pp. 486-492; Dutta, A., Salunkhe, D.H., Kumar, S., Udai, A.D., Shah, S.V., Sensorless full body active compliance in a 6 dof parallel manipulator (2019) Robot. Comput.-Integr. Manuf., 59, pp. 278-290; Gosselin, C., Laliberté, T., Veillette, A., Singularity-free kinematically redundant planar parallel mechanisms with unlimited rotational capability (2015) IEEE Trans. Robot., 31 (2), pp. 457-467. , Apr; Schreiber, L.-T., Gosselin, C., Schönflies motion parallel robot (SPARA): A kinematically redundant parallel robot with unlimited rotation capabilities (2019) IEEE/ASME Trans. Mechatronics, 24 (5), pp. 2273-2281. , Oct; Gosselin, C., Schreiber, L.-T., Kinematically redundant spatial parallel mechanisms for singularity avoidance and large orientational workspace (2016) IEEE Trans. Robot., 32 (2), pp. 286-300. , Apr; Schreiber, L.-T., Gosselin, C., Exploiting the kinematic redundancy of a (6+3) degree-of-freedom parallel mechanism (2019) ASME J. Mechanisms Robot., 11 (2); Monsarrat, B., Gosselin, C., Singularity analysis of a three-leg sixdegree-of-freedom parallel platform mechanism based on Grassmann line geometry (2001) Int. J. Robot. Res., 20 (4), pp. 312-328. , Apr; Monsarrat, B., Gosselin, C.M., Workspace analysis and optimal design of a 3-leg 6-dof parallel platformmechanism (2003) IEEE Trans. Robot. Autom., 19 (6), pp. 954-966. , Dec; Wen, K., Harton, D., Laliberté, T., Gosselin, C., Kinematically redundant (6+3)-dof hybrid parallel robot with large orientational workspace and remotely operated gripper (2019) Proc. IEEE Int. Conf. Robot. Autom., pp. 1672-1678; Gosselin, C., Angeles, J., Singularity analysis of closed-loop kinematic chains (1990) IEEE Trans. Robot. Autom., 6 (3), pp. 281-290. , Jun; Zlatanov, D., Fenton, R., Benhabib, B., A unifying framework for classification and interpretation of mechanism singularities (1995) ASME J. Mech. Des., 117 (4), pp. 566-572; Zlatanov, D., Bonev, I.A., Gosselin, C., Constraint singularities of parallel mechanisms (2002) Proc. IEEE Int. Conf. Robot. Autom., 1, pp. 496-502; Conconi, M., Carricato, M., A new assessment of singularities of parallel kinematic chains (2009) IEEE Trans.Robot., 25 (4), pp. 757-770. , Aug; Schreiber, L.-T., Gosselin, C., Passively driven redundant spherical jointwith very large range of motion (2017) ASME J. Mechanisms Robot., 9 (3); Siciliano, B., Kinematic control of redundant robot manipulators: A tutorial (1990) J. Intell. Robot. Syst., 3 (3), pp. 201-212; Gosselin, C.M., Sefrioui, J., Richard, M.J., Solutions polynomiales au probleme de la cinématique directe des manipulateurs paralleles plans a trois degrés de liberté (1992) Mechanism Mach. Theory, 27 (2), pp. 107-119; Wen, K., Gosselin, C.M., Forward kinematic analysis of kinematically redundant hybrid parallel robots (2020) J. Mechanisms Robot., 12 (6), pp. 1-9; Bonev, I.A., Zlatanov, D., Gosselin, C.M., Advantages of the modified Euler angles in the design and control of PKMs (2002) Proc. Parallel Kinematic Mach. Int. Conf., pp. 199-215; Isaksson, M., Gosselin, C., Marlow, K., An introduction to utilising the redundancy of a kinematically redundant parallel manipulator to operate a gripper (2016) Mechanism Mach. Theory, 101, pp. 50-59; Fauteux, P., Lauria, M., Heintz, B., Michaud, F., Dual-differential rheological actuator for high-performance physical robotic interaction (2010) IEEE Trans. Robot., 26 (4), pp. 607-618. , Aug; Anderson, R.J., Spong, M.W., Hybrid impedance control of robotic manipulators (1988) IEEE J. Robot. Autom., 4 (5), pp. 549-556. , Oct","Gosselin, C.; Department of Mechanical Engineering, Canada; email: gosselin@gmc.ulaval.ca",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15523098,,,,"English","IEEE Trans. Rob.",Article,"Final","",Scopus,2-s2.0-85098774058
"Chen L., Li M., Su W., Wu M., Hirota K., Pedrycz W.","55774663200;57204930290;57196287282;57218664297;7402829702;57210775415;","Adaptive feature selection-based AdaBoost-KNN with direct optimization for dynamic emotion recognition in human-robot interaction",2021,"IEEE Transactions on Emerging Topics in Computational Intelligence","5","2","8701502","205","213",,3,"10.1109/TETCI.2019.2909930","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103359476&doi=10.1109%2fTETCI.2019.2909930&partnerID=40&md5=2e2f95b28e06755b7bca67d8a7397471","School of Automation, China University of Geosciences, Wuhan, Hubei, China; Tokyo Institute of Technology, Yokohama, Japan; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada","Chen, L., School of Automation, China University of Geosciences, Wuhan, Hubei, China; Li, M., School of Automation, China University of Geosciences, Wuhan, Hubei, China; Su, W., School of Automation, China University of Geosciences, Wuhan, Hubei, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, Hubei, China; Hirota, K., Tokyo Institute of Technology, Yokohama, Japan; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada","AdaBoost-KNN using adaptive feature selection with direct optimization is proposed for dynamic emotion recognition in human-robot interaction, where the real-time dynamic emotion is recognized based on facial expression. It can make robots capable of understanding human dynamic emotions, in such a way that human-robot interaction is realized in a smooth manner. Based on the facial key points extracted by Candide-3 model, adaptive feature selection is adopted, namely Plus-L Minus-R Selection is completed. It can determine the features that contribute the most to emotion recognition, thereby forming the basis of emotion classification. Emotion classification is based on AdaBoost-KNN, which builds a series of KNN classifiers. AdaBoost-KNN adjusts the weights of the data in an iterative manner. Moreover, global optimal parameters are approximated with direct optimization until the recognition rate reaches its maximal value. The experimental performance of the proposal is verified by a $k$-fold cross-validation. Results show that the recognition rate of the proposed approach is higher than that of the AdaBoost-KNN, adaptive feature selection-based AdaBoost-KNN, and AdaBoost-KNN with direct optimization. It is also higher than the rate achieved by other traditional recognition methods, such as AdaBoost, KNN, and SVM. In addition, preliminary application experiments are developed in our emotional social robot system, composed of two mobile robots. The experiments demonstrate the dynamic emotion understanding ability of robots in human-robot interaction. © 2017 IEEE.","AdaBoost-KNN; Dynamic emotion recognition; Human-robot interaction; Plus-L Minus-R Selection","Adaptive boosting; Classification (of information); Feature extraction; Iterative methods; Man machine systems; Speech recognition; Support vector machines; Adaptive feature selection; Direct optimization; Emotion classification; Emotion recognition; Emotion understanding; Facial Expressions; Real-time dynamics; Recognition methods; Social robots",,,,,"National Natural Science Foundation of China, NSFC: 61603356, 61733016; China University of Geosciences, Wuhan, CUG: 201839; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project: B17040; Wuhan Science and Technology Project: 2017010201010133","Manuscript received September 19, 2018; revised February 10, 2019; accepted March 25, 2019. Date of publication April 29, 2019; date of current version March 25, 2021. This work was supported in part by the National Natural Science Foundation of China under Grants 61603356 and 61733016, in part by the 111 Project under Grant B17040, in part by the Wuhan Science and Technology Project under Grant 2017010201010133, and in part by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (201839). (Corresponding author: Min Wu.) L. Chen, M. Li, W. Su, and M. Wu are with the School of Automation, China University of Geosciences, Wuhan, Hubei 430074, China, and also with the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, Hubei 430074, China (e-mail:, chenluefeng@ cug.edu.cn; limin2014@cug.edu.cn; suwanjuan@cug.edu.cn; wumin@cug. edu.cn).",,,,,,,,,,"Anderson, K., McOwan, P.W., A real-time automated system for the recognition of human facial expressions (2006) IEEE Trans. Syst. Man Cybern., Part B: Cybern., 36 (1), pp. 96-105. , Feb; Wang, Z.F., Peyrodie, L., Cao, H., Slow walking model for children with multiple disabilities via an application of humanoid robot (2016) Mech. Syst. Signal Process., 68, pp. 608-619; Navarretta, C., Predicting emotions in facial expressions from the annotations in naturally occurring first encounters (2014) Knowl.-Based Syst., 71, pp. 34-40; Vezzetti, E., Marcolin, F., Fracastoro, G., 3D face recognition: An automatic strategy based on geometrical descriptors and landmarks (2014) Robot. Auton. Syst., 62 (12), pp. 1768-1776; Chen, L.F., Wu, M., Zhou, M.T., Liu, Z., She, J.H., Hirota, K., Dynamic emotion understanding in human-robot interaction based on twolayer fuzzy SVR-TS model IEEE Trans. Syst. Man Cybern.: Syst., , to be published; Chen, L.F., Liu, Z.T., Wu, M., Ding, M., Dong, F.Y., Hirota, K., Emotionage- gender-nationality based intention understanding in human-robot interaction using two-layer fuzzy support vector regression (2015) Int. J. Social Robot., 7 (5), pp. 709-729; Dong, Y.S., Wu, H., Li, X.K., Zhou, C.Q., Wu, Q.T., Multiscale symmetric dense micro-block difference for texture classification IEEE Trans. Circuits Syst. Video Technol., , to be published; Zhou, C., Wang, L., Zhang, Q., Wei, X., Face recognition based on PCA and logistic regression analysis (2014) Int. J. Light Electron Opt., 125 (20), pp. 5916-5919; Chen, L.F., Wu, M., Zhou, M.T., She, J.H., Dong, F.Y., Hirota, K., Information-driven multi-robot behavior adaptation to emotional intention in human-robot interaction IEEE Trans. Cogn. Develop. Syst., 10 (3), pp. 647-658. , Sep. 2018; Smeets, D., Claes, P., Hermans, J., Vandermeulen, D., Suetens, P., A comparative study of 3-D face recognition under expression variations (2012) IEEE Trans. Syst. Man Cybern.: Part C, 42 (5), pp. 710-727. , Sep; Vezzetti, E., Marcolin, F., Fracastoro, G., 3D face recognition: An automatic strategy based on geometrical descriptors and landmarks (2014) Robot. Auton. Syst., 62 (12), pp. 1768-1776; Matamoros, A.H., Bonarini, A., Hernandez, E.E., Facial expression recognition with automatic segmentation of face regions using a fuzzy based classification approach (2016) Knowl.-Based Syst., 110, pp. 1-14; Zhang, L., Mistry, K., Neoh, S.C., Lim, C.P., Intelligent facial emotion recognition using moth-firefly optimization (2016) Knowl.-Based Syst., 111 (1), pp. 248-267; Xue, B., Zhang, M., Browne, W.N., Particle swarm optimization for feature selection in classification: A multi-objective approach (2013) IEEE Trans. Syst. Man Cybern., 43 (6), pp. 1656-1671. , Dec; Zhang, Y., Zhang, L., Neoh, S.C., Mistry, K., Hossain, A., Intelligent affect regression for bodily expressions using hybrid particle swarm optimization and adaptive ensembles (2015) Expert Syst. Appl., 42 (22), pp. 8678-8697; Alweshah, M., Abdullah, S., Hybridizing firefly algorithms with a probabilistic neural network for solving classification problems (2015) Appl. Soft Comput., 35, pp. 513-524; Verma, O.P., Aggarwal, D., Patodi, T., Opposition and dimensional basedmodified firefly algorithm (2016) Expert Syst. Appl., 44, pp. 168-176; Guo, Y., Liu, Y., Oerlemans, A., Deep learning for visual understanding: A review (2016) Neurocomputing, 187, pp. 27-48; Liu, J.C., Zhang, L., Chen, X., Niu, J.W., Facial landmark automatic identification from three dimensional (3D) data by using Hidden Markov Model (HMM) (2017) Int. J. Ind. Ergonom., 57, pp. 10-22; Chen, L.F., Zhou, M.T., Su, W.J., Wu, M., She, J.H., Hirota, K., Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction (2018) Inf. Sci., 428, pp. 49-61; Yu, Z.H., Li, C., Texture classification and retrieval using shearlets and linear regression (2015) IEEE Trans. Cybern., 45 (3), pp. 358-369. , Mar; Wang, J., Xiong, R., Chu, J., Facial feature points detecting based on Gaussian Mixture Models (2015) Pattern Recognit. Lett., 53, pp. 62-68; Zhang, W., Zhang, Y., Ma, L., Guan, J.W., Gong, S.J., Multimodal learning for facial expression recognition (2015) Pattern Recognit., 48 (10), pp. 3191-3202; Elaiwat, S., Bennamoun, M., Boussaid, F., A curvelet-based approach for textured 3D face recognition (2015) Pattern Recognit., 48 (4), pp. 1235-1246; Li, X.L., Cui, G.S., Dong, Y.S., Graph regularized non-negative low-rank matrix factorization for image clustering (2017) IEEE Trans. Cybern., 47 (11), pp. 3840-3853. , Nov; Chen, L.F., Wu, M., She, J.H., Dong, F.Y., Hirota, K., Three-layer weighted fuzzy SVR for emotional intention understanding in human- robot interaction IEEE Trans. Fuzzy Syst., 26 (5), pp. 2524-2538. , Oct. 2018; Deng, N., Pei, Y.B., Xu, Z.G., Face recognition with single sample based on candide-3 reconstruction model (2013) Appl. Mechanics Mater., 380-384, pp. 3623-3628; Li, X.L., Lu, Q.M., Dong, Y.S., SCE: A manifold regularized setcovering method for data partitioning (2018) IEEE Trans. Neural Netw. Learn. Syst., 29 (5), pp. 1760-1773. , May; Lu, Q.M., Li, X., Dong, Y.S., Structure preserving unsupervised feature selection (2018) Neurocomputing, 301, pp. 36-45; Maillo, J., Ramírez, F., Triguero, I., Herrera, F., K-NN-IS: An iterative Spark-based design of the k-nearest neighbors classifier for big data (2017) Knowl.-Based Syst., 117, pp. 3-15; Wen, Y., He, L., Shi, P., Face recognition using difference vector plus KPCA (2012) Digit. Signal Process., 22 (1), pp. 140-146; The Japanese Female Facial Expression (JAFFE) Database, , http://www.kasrl.org/jaff.html; Demišar, J., Schuurmans, D., Statistical comparisons of classifiers over multiple data sets (2006) J. Mach. Learn. Res., 7 (1), pp. 1-30","Wu, M.; School of Automation, China; email: wumin@cug.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,2471285X,,,,"English","IEEE Trans. Emerging Topics Comp. Intell.",Article,"Final","",Scopus,2-s2.0-85103359476
"Freddi A., Iarlori S., Longhi S., Monteriù A.","36682153900;56027864000;56911180600;16638623100;","Development and experimental validation of algorithms for human–robot interaction in simulated and real scenarios",2021,"Journal of Ambient Intelligence and Humanized Computing","12","4",,"4529","4539",,,"10.1007/s12652-019-01676-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077856023&doi=10.1007%2fs12652-019-01676-6&partnerID=40&md5=8c2b4355a1acc93c60e777360d3e4985","Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, Ancona, AN  60131, Italy","Freddi, A., Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, Ancona, AN  60131, Italy; Iarlori, S., Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, Ancona, AN  60131, Italy; Longhi, S., Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, Ancona, AN  60131, Italy; Monteriù, A., Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, Ancona, AN  60131, Italy","The development of robots, which can safely and effectively interact with people and assist them in structured environments, is an open research problem whose importance has been growing rapidly in the last years. Indeed working in shared environments with human beings, these robots require new ways to achieve human–robot interaction and cooperation. This work presents an approach for performing human–robot interaction by means of robotic manipulators. The interaction is composed by three main steps, namely the selection, the recognition and the grasping of an object. The object selection is recorded on the base of a gesture execution, realized by the user in front of a RGB-D camera and associated to each particular object. The object recognition is achieved by means of the RGB cameras mounted on the two manipulator arms, which send the workspace information to a specific classifier. With the aim of realizing the grasping step, the object position and orientation are extracted in order to correctly rotate the gripper according to the object on the desk in front of the robot. The final goal is to release the grasped object on the hand of the user standing in front of the desk. This system could support people with limited motor skills who are not able to get an object on their own, playing an important role in structured assistive and smart environments, thus promoting the human–robot interaction in activity of daily living. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.","Human–robot cooperation; Human–robot interaction; Robotic manipulators","Cameras; Classification (of information); Flexible manipulators; Object recognition; Robotics; Activity of daily livings; Experimental validations; Research problems; Robot cooperation; Robot interactions; Robotic manipulators; Smart environment; Structured environment; Social robots",,,,,,,,,,,,,,,,"Achic, F., Montero, J., Penaloza, C., Cuellar, F., Hybrid BCI system to operate an electric wheelchair and a robotic arm for navigation and manipulation tasks (2016) 2016 IEEE Workshop on Advanced Robotics and Its Social Impacts (ARSO), pp. 249-254. , https://doi.org/10.1109/ARSO.2016.7736290, pp; Bekele, E., Wade, J., Bian, D., Fan, J., Swanson, A., Warren, Z., Sarkar, N., Multimodal adaptive social interaction in virtual environment (MASI-VR) for children with autism spectrum disorders (ASD) (2016) IEEE Virtual Reality (VR), pp. 121-130. , https://doi.org/10.1109/VR.2016.7504695, pp; 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Wen, Z., Liu, D., Liu, X., Zhong, L., Lv, Y., Jia, Y., Deep learning based smart radar vision system for object recognition (2019) J Ambient Intell Humaniz Comput, 10 (3), pp. 829-839","Iarlori, S.; Department of Information Engineering, Via Brecce Bianche, Italy; email: sabrina.iarlori@gmail.com",,,"Springer Science and Business Media Deutschland GmbH",,,,,18685137,,,,"English","J. Ambient Intell. Humanized Comput.",Article,"Final","",Scopus,2-s2.0-85077856023
"Daza M., Barrios-Aranibar D., Diaz-Amado J., Cardinale Y., Vilasboas J.","57222059385;15844905100;54792894300;8963079200;57222072971;","An approach of social navigation based on proxemics for crowded environments of humans and robots",2021,"Micromachines","12","2","193","","",,1,"10.3390/mi12020193","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101246165&doi=10.3390%2fmi12020193&partnerID=40&md5=abbe291e9b4d2b4c6e988b56fbf36b21","Electrical and Electronics Engineering Department, School of Electronics and Telecommunications Engineering, Universidad Católica San Pablo, Arequipa, 04001, Peru; Instituto Federal da Bahia, Vitoria da Conquista, 45078-300, Brazil; Universidad Simón Bolívar, Caracas, 1086, Venezuela","Daza, M., Electrical and Electronics Engineering Department, School of Electronics and Telecommunications Engineering, Universidad Católica San Pablo, Arequipa, 04001, Peru; Barrios-Aranibar, D., Electrical and Electronics Engineering Department, School of Electronics and Telecommunications Engineering, Universidad Católica San Pablo, Arequipa, 04001, Peru; Diaz-Amado, J., Electrical and Electronics Engineering Department, School of Electronics and Telecommunications Engineering, Universidad Católica San Pablo, Arequipa, 04001, Peru, Instituto Federal da Bahia, Vitoria da Conquista, 45078-300, Brazil; Cardinale, Y., Electrical and Electronics Engineering Department, School of Electronics and Telecommunications Engineering, Universidad Católica San Pablo, Arequipa, 04001, Peru, Universidad Simón Bolívar, Caracas, 1086, Venezuela; Vilasboas, J., Instituto Federal da Bahia, Vitoria da Conquista, 45078-300, Brazil","Nowadays, mobile robots are playing an important role in different areas of science, industry, academia and even in everyday life. In this sense, their abilities and behaviours become increasingly complex. In particular, in indoor environments, such as hospitals, schools, banks and museums, where the robot coincides with people and other robots, its movement and navigation must be programmed and adapted to robot-robot and human-robot interactions. However, existing approaches are focused either on multi-robot navigation (robot-robot interaction) or social navigation with human presence (human-robot interaction), neglecting the integration of both approaches. Proxemic interaction is recently being used in this domain of research, to improve Human-Robot Interaction (HRI). In this context, we propose an autonomous navigation approach for mobile robots in indoor environments, based on the principles of proxemic theory, integrated with classical navigation algorithms, such as ORCA, Social Momentum, and A*. With this novel approach, the mobile robot adapts its behaviour, by analysing the proximity of people to each other, with respect to it, and with respect to other robots to decide and plan its respective navigation, while showing acceptable social behaviours in presence of humans. We describe our proposed approach and show how proxemics and the classical navigation algorithms are combined to provide an effective navigation, while respecting social human distances. To show the suitability of our approach, we simulate several situations of coexistence of robots and humans, demonstrating an effective social navigation. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Mobile robots; Proxemic interactions; Proxemics; Social momentum; Social navigation","Air navigation; Economic and social effects; Industrial robots; Man machine systems; Mobile robots; Navigation; Navigation systems; Autonomous navigation; Human robot Interaction (HRI); Indoor environment; Multi-robot navigation; Navigation algorithms; Robot interactions; Social behaviour; Social navigation; Social robots",,,,,"Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica, CONCYTEC: 01-2019-FONDECYT-BM-INC","This research was funded by FONDO NACIONAL DE DESARROLLO CIENT?FICO, TECNOL?GICO Y DE INNOVACI?N TECNOL?GICA - FONDECYT as executing entity of CONCYTEC under grant agreement no. 01-2019-FONDECYT-BM-INC.INV in the project RUTAS: Robots for Urban Tourism Centers, Autonomous and Semantic-based.",,,,,,,,,,"Vercelli, A., Rainero, I., Ciferri, L., Boido, M., Pirri, F., Robots in elderly care (2018) Digit.-Sci. J. Digit. 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"Jiang Y., Zhao M., Wang C., Wei F., Qi H.","8503974900;57201289964;57201294365;57193667386;55167086600;","A Method for Underwater Human–Robot Interaction Based on Gestures Tracking with Fuzzy Control",2021,"International Journal of Fuzzy Systems","23","7",,"2170","2181",,,"10.1007/s40815-021-01086-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108278504&doi=10.1007%2fs40815-021-01086-x&partnerID=40&md5=57a1028b47ff47d5a3e5da41c2c851cb","College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China","Jiang, Y., College of Computer Science and Technology, Jilin University, Changchun, 130012, China, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; Zhao, M., College of Computer Science and Technology, Jilin University, Changchun, 130012, China, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China; Wang, C., College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Wei, F., College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Qi, H., College of Computer Science and Technology, Jilin University, Changchun, 130012, China, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China","Working in collaboration with an Autonomous Underwater Vehicle is a new working method for divers. Using gestures to give instructions for the diver is a simple and effective mode of underwater human–robot interaction (HRI). In this paper, a gestures tracking method for under human–robot interaction based on fuzzy control is proposed. Firstly, four object recognition algorithms in terms of gesture recognition are compared. YOLO V4-tiny was an extremely high performance, as the gesture area recognition algorithm. We propose a model based on Siamese Network for gesture classification. A gesture tracking method based on fuzzy control is proposed, analyzing the image from AUV front camera to establish a 3D fuzzy rule set. This method can realize the self-regulation of AUV and keep the diver's gestures in the camera view. The experiment result shows the efficiency of the proposed method. © 2021, Taiwan Fuzzy Systems Association.","Autonomous underwater vehicle; Fuzzy control; Gesture recognition; Human–robot interaction","Autonomous underwater vehicles; Cameras; Fuzzy control; Fuzzy inference; Human robot interaction; Object recognition; Gesture classifications; Gesture tracking; Object recognition algorithm; Recognition algorithm; Robot interactions; Self regulation; Tracking method; Working methods; Gesture recognition",,,,,"20170101081JC, 20190302107GX, 20190303006SF; National Natural Science Foundation of China, NSFC: 51679105, 51809112, 51939003","This work was supported by the National Natural Science Foundation of China under Grant 51679105, Grant 51809112, and Grant 51939003. And this work is partly supported by the Science-Technology Development Plan Project of Jilin Province of China grants 20170101081JC, 20190303006SF, and 20190302107GX.",,,,,,,,,,"Kim, Y.J., Kim, H.T., Cho, Y.J., Development of a power control system for AUVs probing for underwater mineral resources (2009) J. Mar. Sci. Appl., 8 (4), p. 259; Zhang, J., Wang, D.R., Jennerjahn, T., Land–sea interactions at the east coast of Hainan Island, South China Sea: a synthesis (2013) Cont. Shelf Res., 57, pp. 132-142; Mišković, N., Pascoal, A., Bibuli, M., Caccia, M., Neasham, J., Birk, A., (2017); Chiarella, D., Bibuli, M., Bruzzone, G., Caccia, M., Ranieri, A., Zereik, E., Gesture-based language for diver-robot underwater interaction (2015) Oceans 2015-Genova, pp. 1-9; Chiarella, D., Bibuli, M., Bruzzone, G., Caccia, M., Ranieri, A., Zereik, E., A novel gestur.e-based language for underwater human–robot interaction (2018) J. Marine Sci. 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Mater., 246, pp. 888-892; Guo, Q., Feng, W., Zhou, C., Huang, R., Wan, L., Wang, S., Learning dynamic Siamese network for visual object tracking (2017) Proceedings of the IEEE International Conference on Computer Vision, pp. 1763-1771; Dong, X., Shen, J., Triplet loss in siamese network for object tracking (2018) Proceedings of the European Conference on Computer Vision (ECCV), pp. 459-474; Wang, Q., Teng, Z., Xing, J., Gao, J., Hu, W., Maybank, S., Learning attentions: Residual attentional Siamese network for high performance online visual tracking (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4854-4863; Buelow, H., Birk, A., Gesture-recognition as basis for a human robot interface (HRI) on a AUV (2011) OCEANS'11 MTS/IEEE KONA, 1–9. IEEE; Islam, M.J., Sattar, J., Mixed-domain biological motion tracking for underwater human-robot interaction (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA)., pp. 4457-4464. , IEEE; Chavez, A.G., Mueller, C.A., Birk, A., Babic, A., Miskovic, N., Stereo-vision based diver pose estimation using LSTM recurrent neural networks for AUV navigation guidance (2017) OCEANS 2017-Aberdeen, pp. 1-7; Fulton, M., Edge, C., Sattar, J., Robot communication via motion: Closing the underwater human-robot interaction loop (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 4660-4666; Sun, K., Qiu, J., Karimi, H.R., Gao, H., A novel finite-time control for Nonstrict feedback saturated nonlinear systems with tracking error constraint (2019) IEEE Transactions on Syst. Man Cyberneti. Syst.","Qi, H.; College of Computer Science and Technology, China; email: qihong@jlu.edu.cn",,,"Springer Science and Business Media Deutschland GmbH",,,,,15622479,,,,"English","Int. J. Fuzzy Syst.",Article,"Final","",Scopus,2-s2.0-85108278504
"Li Y., Sato-Shimokawara E., Yamaguchi T.","57204937045;25031564100;56501037800;","The influence of robot's unexpected behavior on individual cognitive performance",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"1103","1109",,,"10.1109/RO-MAN50785.2021.9515317","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115101148&doi=10.1109%2fRO-MAN50785.2021.9515317&partnerID=40&md5=4d2100c52e0e9b97e4c74f2e7869aadc","Tokyo Metropolitan University, Graduate School of Systems Design, Department of Computer Science, 6-6 Asahigaoka, Hino-shi, Tokyo, Japan","Li, Y., Tokyo Metropolitan University, Graduate School of Systems Design, Department of Computer Science, 6-6 Asahigaoka, Hino-shi, Tokyo, Japan; Sato-Shimokawara, E., Tokyo Metropolitan University, Graduate School of Systems Design, Department of Computer Science, 6-6 Asahigaoka, Hino-shi, Tokyo, Japan; Yamaguchi, T., Tokyo Metropolitan University, Graduate School of Systems Design, Department of Computer Science, 6-6 Asahigaoka, Hino-shi, Tokyo, Japan","Social robots have become pervasive in learning environments. The empirical understanding of how different individuals perceive and react to robot's expressions has become an urgent necessity for the sustainable deployment. In this study, we examined whether robot's unexpected actions affect individual cognitive performance. We have presented the experiment in which a robot could use unexpected visual or auditory stimuli and one's reaction time in the Simon task was recorded for the investigation of the influence from the robot. Results have verified the idea that individual differences exist both in the perception of social robot's expressions and the extent of change in the cognitive performance. This study provides insights into a richer application of human-robot interaction by taking individual differences regarding perception and response type into account, therefore constitutes a modest but significant step in the direction of adaptive human-robot interaction. © 2021 IEEE.",,"Agricultural robots; Computer aided instruction; Human reaction time; Man machine systems; Auditory stimuli; Cognitive performance; Individual Differences; Learning environments; Social robots",,,,,,,,,,,,,,,,"Jeffrey, W.E., Selective attention: Response inhibition or stimulus differentiation? A reply to lane and pearson (1982) Merrill-Palmer Quarterly, pp. 523-528. , 1982; Matthews, G., Evolution and revolution: Personality research for the coming world of robots, artificial intelligence, and autonomous systems (2021) Personality and Individual Differences, 169, p. 109969; Rheu, M., Systematic review: Trust-building factors and implications for conversational agent design (2021) International Journal of Human-Computer Interaction, 37 (1), pp. 81-96; Lemaignan, S., Fink, J., Mondada, F., Dillenbourg, P., You're doing it wrong! studying unexpected behaviors in child-robot interaction (2015) International Conference on Social Robotics, pp. 390-400. , Springer, Cham October; Short, E., Hart, J., Vu, M., Scassellati, B., No fair!! an interaction with a cheating robot (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 219-226. , IEEE, March; Mirnig, N., To err is robot: How humans assess and act toward an erroneous social robot (2017) Frontiers in Robotics and AI, 4, p. 21; Lugli, L., Emergence of the go/no-go simon effect by means of practice and mixing paradigms (2013) Acta Psychologica, 144 (1), pp. 19-24; De, Graaf, Ma, M., Ben Allouch, S., Exploring influencing variables for the acceptance of social robots (2013) Robotics and Autonomous Systems, 61 (12), pp. 1476-1486; Szalma, J.L., Taylor, G.S., Individual differences in response to automation: The five factor model of personality (2011) Journal of Experimental Psychology: Applied, 17 (2), p. 71; Cross, E.S., Hortensius, R., Wykowska, A., (2019) From Social Brains to Social Robots: Applying Neurocognitive Insights to Human-robot Interaction, p. 20180024; Hsieh, W.F., Li, Y., Sato-Shimokawara, E., Yamaguchi, T., Analyzing individual different perception of a communication robot physical attribute (2019) The 29th Symposium on Fuzzy, Artificial Intelligence, Neural Networks and Computational Intelligence, pp. 237-241. , Toyama, Japan; Bartneck, C., The influence of people's culture and prior experiences with aibo on their attitude towards robots (2007) Ai & Society, 21 (1-2), pp. 217-230; Robert, L., Personality in the human robot interaction literature: A review and brief critique (2018) Personality in the Human Robot Interaction Literature: A Review and Brief Critique, Proceedings of the 24th Americas Conference on Information Systems, pp. 16-18. , Robert, LP (2018), Aug; Harvey, P.D., Domains of cognition and their assessment (2019) Dialogues in Clinical Neuroscience, 21 (3), p. 227; Riether, N., Social facilitation with social robots? (2012) 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 41-47; Herberg, J.S., Robot watchfulness hinders learning performance (2015) 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 153-160; Spatola, N., Improved cognitive control in presence of anthropomorphized robots (2019) International Journal of Social Robotics, 11 (3), pp. 463-476; Edwards, C., Edwards, A., Spence, P.R., Westerman, D., Initial interaction expectations with robots: Testing the human-tohuman interaction script (2016) Communication Studies, 67 (2), pp. 227-238; Spatola, N., Monceau, S., Ferrand, L., Cognitive impact of social robots: How anthropomorphism boosts performances (2019) IEEE Robotics & Automation Magazine, 27 (3), pp. 73-83; Matthews, G., Personality and information processing: A cognitive-adaptive theory (2008) Handbook of Personality Theory and Assessment, 1, pp. 56-79; Hiolle, A., Lewis, M., Cañamero, L., Arousal regulation and affective adaptation to human responsiveness by a robot that explores and learns a novel environment (2014) Frontiers in Neurorobotics, 8, p. 17; Hebb, D.O., Drives and the cns (conceptual nervous system) (1955) Psychological Review, 62 (4), p. 243; Russel, J., Three dimensions of emotion (1980) J Pers Soc Psychol, 9 (39), pp. 1161-1178; Balkenius, C., Tjøstheim, T.A., Johansson, B., Arousal and awareness in a humanoid robot (2019) AAAI Spring Symposium: Towards Conscious AI Systems.; Baron, R.S., Distraction-conflict theory: Progress and problems (1986) Advances in Experimental Social Psychology, 19, pp. 1-40; Berlyne, D.E., (1954) A Theory of Human Curiosity, , Nom de l'éditeur non disponible; Berlyne, D.E., (1960) Conflict, Arousal, and Curiosity.; Manuel, A.L., Bernasconi, F., Spierer, L., Plastic modifications within inhibitory control networks induced by practicing a stop-signal task: An electrical neuroimaging study (2013) Cortex, 49 (4), pp. 1141-1147; Aron, A.R., From reactive to proactive and selective control: Developing a richer model for stopping inappropriate responses (2011) Biological Psychiatry, 69 (12), pp. 55-68; Diamond, A., Executive functions (2013) Annual Review of Psychology, 64, pp. 135-168; Verghese, A., From eyes to hands: Transfer of learning in the simon task across motor effectors (2018) Attention, Perception, & Psychophysics, 80 (1), pp. 193-210; Mavridis, N., A review of verbal and non-verbal human-robot interactive communication (2015) Robotics and Autonomous Systems, 63, pp. 22-35; Sidner, C.L., Explorations in engagement for humans and robots (2005) Artificial Intelligence, 166 (1-2), pp. 140-164; Burgoon, J.K., Guerrero, L.K., Manusov, V., (2016) Nonverbal Communication, , Routledge; Darwin, C., Prodger, P., (1998) The Expression of the Emotions in Man and Animals, , Oxford University Press, USA; Saunderson, S., Nejat, G., How robots influence humans: A survey of nonverbal communication in social human-robot interaction (2019) International Journal of Social Robotics, 11 (4), pp. 575-608; Lee, C.-S., A study on ai-FML robotic agent for student learning behavior ontology construction (2020) 2020 International Symposium on Community-centric Systems (CcS). IEEE, pp. 1-6; Pulse Sensor, , https://pulsesensor.com; Mehler, B., Impact of incremental increases in cognitive workload on physiological arousal and performance in young adult drivers (2009) Transportation Research Record, 2138 (1), pp. 6-12; (2021), https://docs.google.com/forms/, accessed March 3; Stoet, G., Psytoolkit: A software package for programming psychological experiments using linux (2010) Behavior Research Methods, 42 (4), pp. 1096-1104; Stoet, G., Psytoolkit: A novel web-based method for running online questionnaires and reaction-time experiments (2017) Teaching of Psychology, 44 (1), pp. 24-31; Desideri, L., Emotional processes in human-robot interaction during brief cognitive testing (2019) Computers in Human Behavior, 90, pp. 331-342; Murakami, H., Ohira, H., Influence of attention manipulation on emotion and autonomic responses (2007) Perceptual and Motor Skills, 105 (1), pp. 299-308; Camm, J.A., Heart rate variability: Standards of measurement, physiological interpretation and clinical use (1996) Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, pp. 1043-1065",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115101148
"Yang R., Zhou J., Song R.","57207293066;57219424421;26431599600;","Adaptive admittance control based on linear quadratic regulation optimization technique for a lower limb rehabilitation robot",2021,"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2021",,,,"396","399",,,"10.1109/ICARM52023.2021.9536058","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116269908&doi=10.1109%2fICARM52023.2021.9536058&partnerID=40&md5=c95643bcb49549b402993e9e72bee152","Department of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, 510006, China","Yang, R., Department of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, 510006, China; Zhou, J., Department of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, 510006, China; Song, R., Department of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, 510006, China","Compliant, natural and safe physical human-robot interaction is of practical significance for rehabilitation robots. In our recently developed lower limb rehabilitation robot (LLRR), an adaptive admittance control based on linear quadratic regulation (LQR) optimization technique was designed to regulate parameters synchronously with the variable impedance property of human-robot interactive system. Firstly, a computed torque PD control was designed to guarantee the accuracy and stability of trajectory tracking. Secondly, an observer was designed to estimate human-robot interaction torque (HRIT) during cooperative task. Finally, a LQR optimization technique was employed to optimize admittance model parameters and minimize tracking errors and human efforts. Simulation studies were conducted on the LLRR and the results show that the HRIT can be estimated by the observer correctly and the desired trajectory was deformed smoothly and rightly with the interaction torque. © 2021 IEEE.","Adaptive admittance control; Lower limb rehabilitation; LQR optimization; Rehabilitation robot","Adaptive control systems; Man machine systems; Neuromuscular rehabilitation; Adaptive admittance control; Admittance control; Limb rehabilitation; Linear quadratic regulation optimization; Linear quadratic regulations; Low limb rehabilitation; Lower limb; Optimisations; Optimization techniques; Rehabilitation robot; Human robot interaction",,,,,"Guangzhou Municipal Science and Technology Project: 201604020108; Science and Technology Planning Project of Guangdong Province: 2017B010110015, 2017B020210011; Science and Technology Planning Project of Shenzhen Municipality: JCYJ20170818155028942","* Research supported by the Guangdong Science and Technology Planning Project (Grant No. 2017B010110015 and 2017B020210011), Science and Technology Project of Guangzhou (Grant No. 201604020108), and Science and Technology Project of Shenzhen, JCYJ20170818155028942.",,,,,,,,,,"Wu, Q., Wang, X., Chen, B., Wu, H., Development of a minimal-intervention-based admittance control strategy for upper extremity rehabilitation exoskeleton (2018) IEEE Transactions on Systems Man Cybernetics-Systems, 48 (6), pp. 1005-1016. , Jun; Sale, P., Bovolenta, F., Agosti, M., Clerici, P., Franceschini, M., Short-term and long-term outcomes of serial robotic training for improving upper limb function in chronic stroke (2014) International Journal of Rehabilitation Research, 37 (1), pp. 67-73. , Mar; Jiang, X.Z., Huang, X.H., Xiong, C.H., Sun, R.L., Xiong, Y.L., Position control of a rehabilitation robotic joint based on neuron proportion-integral and feedforward control (2012) Journal of Computational and Nonlinear Dynamics, 7 (2). , Apr; Li, Z., Su, C.Y., Li, G., Su, H., Fuzzy approximation-based adaptive backstepping control of an exoskeleton for human upper limbs (2015) IEEE Transactions on Fuzzy Systems, 23 (3), pp. 555-566. , Jun; Zhang, M., Adaptive patient-cooperative control of a compliant ankle rehabilitation robot (CARR) with enhanced training safety (2018) IEEE Transactions on Industrial Electronics, 65 (2), pp. 1398-1407. , Feb; Zhuang, Y., Yao, S., Ma, C., Song, R., Admittance control based on EMG-driven musculoskeletal model improves the human-robot synchronization (2019) IEEE Transactions on Industrial Informatics, 15 (2), pp. 1211-1218. , Feb; Du, Z., Wang, W., Yan, Z., Dong, W., Wang, W., Variable admittance control based on fuzzy reinforcement learning for minimally invasive surgery manipulator (2017) Sensors, 17 (4). , Apr 12; Li, Z., Huang, B., Ye, Z., Deng, M., Yang, C., Physical human-robot interaction of a robo is exoskeleton by admittance control (2018) IEEE Transactions on Industrial Electronics, 65 (12), pp. 9614-9624. , Dec; Yao, B., Zhou, Z., Wang, L., Xu, W., Liu, Q., Liu, A., Sensorless and adaptive admittance control of industrial robot in physical human - Robot interaction (2018) Robotics and Computer–Integrated Manufacturing, 65, pp. 158-168; Spong, M.W., Ortega, R., On adaptive inverse dynamics control of rigid robots (1990) IEEE Trans. Autom. Control, 35 (1), pp. 92-95. , Jan; Erden, M., Tomiyama, T., Human-intent detection and physically interactive control of a robot without force sensors (2010) IEEE Trans. Robot., 26 (2), pp. 370-382. , Apr; Yang, C., Peng, G., Li, Y., Cui, R., Cheng, L., Li, Z., Neural networks enhanced adaptive admittance control of optimized robot–environment interaction (2019) IEEE Transactions on Cybernetics, 49 (7), pp. 2568-2579. , July; Suzuki, S., Furuta, K., Adaptive impedance control to enhance human skill on a haptic interface system (2012) J. Control Sci. Eng., 2012, pp. 1-10. , Jan","Song, R.; Department of Biomedical Engineering, China; email: songrong@mail.sysu.edu.cn",,"Gaitech Robotics;Publishing House of Electronics Industry;Quanser;Sensors;Springer;Unitree Robotics","Institute of Electrical and Electronics Engineers Inc.","6th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2021","3 July 2021 through 5 July 2021",,171776,,9780738133645,,,"English","IEEE Int. Conf. Adv. Robot. Mechatronics, ICARM",Conference Paper,"Final","",Scopus,2-s2.0-85116269908
"Raja K., Laohakangvalvit T., Sripian P., Sugaya M.","57226611887;56286271500;57191505459;7004276282;","Effect of Emotion Synchronization in Robot Facial Expressions",2021,"Communications in Computer and Information Science","1420",,,"181","188",,,"10.1007/978-3-030-78642-7_24","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112048783&doi=10.1007%2f978-3-030-78642-7_24&partnerID=40&md5=4667b9642baf81214a7ad02780566dfa","College of Engineering, Shibaura Institute of Technology, 3-7-5, Toyosu, Koto, Tokyo, 135-8548, Japan","Raja, K., College of Engineering, Shibaura Institute of Technology, 3-7-5, Toyosu, Koto, Tokyo, 135-8548, Japan; Laohakangvalvit, T., College of Engineering, Shibaura Institute of Technology, 3-7-5, Toyosu, Koto, Tokyo, 135-8548, Japan; Sripian, P., College of Engineering, Shibaura Institute of Technology, 3-7-5, Toyosu, Koto, Tokyo, 135-8548, Japan; Sugaya, M., College of Engineering, Shibaura Institute of Technology, 3-7-5, Toyosu, Koto, Tokyo, 135-8548, Japan","With the recent developments in fields like Artificial Intelligence and Robotics, the rise of social robots is not far away. For social robots to establish better human interaction, they must be able to interact with people casually. In this research, we introduce the idea of empathy in robots by syncing our emotions with the robot’s facial expression. The emotions are estimated and synchronized in real-time using biological signals (i.e., brainwave and heartrate). In this paper, we (1) investigate the effect that emotion synchronization has in human-robot interactions by comparing the impressions of a robot that generates synchronized facial expressions with non-synchronized ones, and (2) clarify the effectiveness of selecting stimulus according to the individual’s interest. The results suggest that emotion synchronization of facial expressions is an effective way to improve human-robot interactions. © 2021, Springer Nature Switzerland AG.","Affective interactions; Biological signals; Human-Robot interactions; Social robots","Artificial intelligence; Human computer interaction; Synchronization; Biological signals; Facial Expressions; Heart-rate; Human interactions; In-field; Real time; Social robots",,,,,,,,,,,,,,,,"Robins, B., Dautenhahn, K., Te Boekhorst, R., Robots as assistive technology-does appearance matter? (2004) Proceedings of the 2004 IEEE Int Workshop on Robot & Human Interactive Communication, pp. 277-282. , pp., IEEE, Kurashiki, Okayama, Japan; Fung, P., Towards empathetic human-robot interactions (2016) Cicling 2016. LNCS, Vol. 9624, pp. 173-193. , https://doi.org/10.1007/978-3-319-75487-1_14, Gelbukh, A. (ed.), pp., Springer, Cham; Cuff, B.M.P., Brown, S.J., Taylor, L., Howat, D.J., Empathy: A review of the concept (2016) Emot. Rev., 8 (2), pp. 144-153; Höök, K., Affective loop experiences: Designing for interactional embodiment (2009) Philos. Trans. R. Soc. Lond. Biol. Sci., 364 (1535), pp. 3585-3595; Leite, I., Castellano, G., Pereira, A., Martinho, C., Paiva, A., Long-term interactions with empathic robots: Evaluating perceived support in children (2012) ICSR 2012. LNCS, Vol. 7621, pp. 298-307. , https://doi.org/10.1007/978-3-642-34103-8_30, Ge, S.S., Khatib, O., Cabibihan, J.J., Simmons, R., Williams, M.A. (eds.), pp., Springer, Berlin, Heidelberg; Hegel, F., Spexard, T., Vogt, T., Horstmann, G., Wrede, B., Playing a different imitation game: Interaction with an empathic Android robot (2006) Proceedings of the 2006 IEEE-RAS International Conference on Humanoid Robots, pp. 56-61. , Genova, Italy, pp; Riek, L.D., Paul, P.C., Robinson, P., When my robot smiles at me: Enabling human-robot rapport via real-time head gesture mimicry (2010) J. 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Nagoya Univ., 25, pp. 233-247; Hayashi, R., Kato, S., Psychological effects of physical embodiment in artificial pet therapy (2017) Artificial Life and Robotics, 22 (1), pp. 58-63; Bradley, M.M., Lang, P.J., Measuring emotion: The self-assessment manikin and the semantic differential (1994) J. Behav. Ther. Exp. Psychiatry, 25 (1), pp. 49-59","Raja, K.; College of Engineering, 3-7-5, Toyosu, Japan; email: am20007@shibaura-it.ac.jp","Stephanidis C.Antona M.Ntoa S.",,"Springer Science and Business Media Deutschland GmbH","23rd International Conference on Human-Computer Interaction, HCII 2021","24 July 2021 through 29 July 2021",,262179,18650929,9783030786410,,,"English","Commun. Comput. Info. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85112048783
"Spezialetti M., Placidi G., Rossi S.","55421242300;7006022089;55619320887;","Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives",2020,"Frontiers in Robotics and AI","7",,"532279","","",,7,"10.3389/frobt.2020.532279","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098994284&doi=10.3389%2ffrobt.2020.532279&partnerID=40&md5=7ee74d5455ae3564abf5b4adedce43b4","PRISCA (Intelligent Robotics and Advanced Cognitive System Projects) Laboratory, Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples, Italy; Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila, Italy","Spezialetti, M., PRISCA (Intelligent Robotics and Advanced Cognitive System Projects) Laboratory, Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples, Italy, Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, L'Aquila, Italy; Placidi, G.; Rossi, S., PRISCA (Intelligent Robotics and Advanced Cognitive System Projects) Laboratory, Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples, Italy","A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities. © Copyright © 2020 Spezialetti, Placidi and Rossi.","affective computing; emotion recognition (ER); human-robot interaction; machine learning; multimodal data",,,,,,"F/190066/01-02/X44","This work has been supported by PON I&C 2014-2020 within the BRILLO research project Bartending Robot for Interactive Long Lasting Operations Prog. n. F/190066/01-02/X44.",,,,,,,,,,"Akin, M., Comparison of wavelet transform and FFT methods in the analysis of EEG signals (2002) J. Med. Syst, 26, pp. 241-247. , 12018610; Alarcao, S.M., Fonseca, M.J., Emotions recognition using EEG signals: a survey (2017) IEEE Trans. Affect. 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Vis, 63, pp. 153-161; Volkova, E., De La Rosa, S., Bülthoff, H.H., Mohler, B., The MPI emotional body expressions database for narrative scenarios (2014) PLoS ONE, 9, p. e113647. , 25461382; Wang, C.C.R., Lien, J.J.J., AdaBoost learning for human detection based on histograms of oriented gradients, (2007) Asian Conference on Computer Vision, pp. 885-895. , Berlin, Springer; Wang, K.-Y., Ho, Y.-L., Huang, Y.-D., Fang, W.-C., Design of intelligent EEG system for human emotion recognition with convolutional neural network, (2019) 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), pp. 142-145. , New York, NY, IEEE, 31606115; Wang, S., He, M., Gao, Z., He, S., Ji, Q., Emotion recognition from thermal infrared images using deep Boltzmann machine (2014) Front. Comput. Sci, 8, pp. 609-618; Wen, G., Li, H., Huang, J., Li, D., Xun, E., Random deep belief networks for recognizing emotions from speech signals (2017) Comput. Intell. Neurosci, , London, 28356908, (,) 2017; Yu, C., Tapus, A., Interactive robot learning for multimodal emotion recognition, (2019) International Conference on Social Robotics, pp. 633-642. , Berlin, Springer, 27534393; Zhang, T., Facial expression recognition based on deep learning: a survey, (2017) International Conference on Intelligent and Interactive Systems and Applications, pp. 345-352. , Cham, Springer; Zhang, Y., Zhang, L., Hossain, M.A., Adaptive 3d facial action intensity estimation and emotion recognition (2015) Expert Syst. Appl, 42, pp. 1446-1464; Zheng, W.-L., Lu, B.-L., Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks (2015) IEEE Trans. Auton. Mental Dev, 7, pp. 162-175; Zheng, W.Q., Yu, J.S., Zou, Y.X., An experimental study of speech emotion recognition based on deep convolutional neural networks, (2015) 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 827-831. , New York City, NY, Institute of Electrical and Electronics Engineers","Rossi, S.; PRISCA (Intelligent Robotics and Advanced Cognitive System Projects) Laboratory, Italy; email: silrossi@unina.it",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Review,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85098994284
"Kaburlasos V.G., Lytridis C., Bazinas C., Papakostas G.A., Naji A., Zaggaf M.H., Mansouri K., Qbadou M., Mestari M.","6701308189;8901635200;57205365418;14060879200;55819500800;57209173460;37104698500;56705618500;6507574426;","Structured Human-Head Pose Representation for Estimation Using Fuzzy Lattice Reasoning (FLR)",2020,"4th International Conference on Intelligent Computing in Data Sciences, ICDS 2020",,,"9268760","","",,,"10.1109/ICDS50568.2020.9268760","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098671194&doi=10.1109%2fICDS50568.2020.9268760&partnerID=40&md5=a551370bf1332f7711cdd980927d888f","International Hellenic University, Human-Machines Interaction Laboratory (HUMAIN-Lab), Kavala, Greece; Enset Mohammedia Hassan Ii University of Casablanca, Laboratory Ssdia, Mohammedia, Morocco","Kaburlasos, V.G., International Hellenic University, Human-Machines Interaction Laboratory (HUMAIN-Lab), Kavala, Greece; Lytridis, C., International Hellenic University, Human-Machines Interaction Laboratory (HUMAIN-Lab), Kavala, Greece; Bazinas, C., International Hellenic University, Human-Machines Interaction Laboratory (HUMAIN-Lab), Kavala, Greece; Papakostas, G.A., International Hellenic University, Human-Machines Interaction Laboratory (HUMAIN-Lab), Kavala, Greece; Naji, A., Enset Mohammedia Hassan Ii University of Casablanca, Laboratory Ssdia, Mohammedia, Morocco; Zaggaf, M.H., Enset Mohammedia Hassan Ii University of Casablanca, Laboratory Ssdia, Mohammedia, Morocco; Mansouri, K., Enset Mohammedia Hassan Ii University of Casablanca, Laboratory Ssdia, Mohammedia, Morocco; Qbadou, M., Enset Mohammedia Hassan Ii University of Casablanca, Laboratory Ssdia, Mohammedia, Morocco; Mestari, M., Enset Mohammedia Hassan Ii University of Casablanca, Laboratory Ssdia, Mohammedia, Morocco","Human-robot interaction has been a significant area of research with the widespread use of social robots. Many modalities can be used to achieve interaction, including vision. For each modality, many methodologies have been proposed, with varying degrees of effectiveness and efficiency in terms of the computational power needed. The varied nature of these algorithms makes data fusion a complex and application specific task. This paper introduces a novel Lattice Computing-based methodology to interpret visual stimuli for head pose estimation. An investigation of the various parameters involved and initial results are presented. The aim is to determine head pose in robot-assisted therapy settings and use it in decision making. This work is part of a broader effort to use the Lattice Computing (LC) paradigm as a unified methodology for sensory data interpretation in human-robot interaction. © 2020 IEEE.","data representation; head pose estimation; Lattice Computing; social robots; tree structures","Data fusion; Data Science; Decision making; Man machine systems; Social robots; Application specific; Computational power; Degrees of effectiveness; Fuzzy lattice; Head Pose Estimation; Robot-assisted therapies; Unified methodology; Visual stimulus; Intelligent computing",,,,,,,,,,,,,,,,"Lu, M., Wevers, K., Heijden Der, R.Van, Technical feasibility of advanced driver assistance systems (adas) for road traffic safety (2005) Transportation Planning and Technology, 28 (3), pp. 167-187; Wang, G., Ren, G., Wu, Z., Zhao, Y., Jiang, L., A hierarchical method for traffic sign classification with support vector machines (2013) The 2013 International Joint Conference on Neural Networks (IJCNN, pp. 1-6. , ieee; Zaklouta, F., Stanciulescu, B., Real-time traffic sign recognition in three stages (2014) Robotics and Autonomous Systems, 62 (1), pp. 16-24; Kassani, P.H., Teoh, A.B.J., A new sparse model for traffic sign classification using soft histogram of oriented gradients (2017) Applied Soft Computing, 52, pp. 231-246; Hou, Y.-L., Hao, X., Chen, H., A cognitively motivated method for classification of occluded traffic signs (2016) Ieee Transactions on Systems, Man, and Cybernetics: Systems, 47 (2), pp. 255-262; Ellahyani, A., El Ansari, M., El Jaafari, I., Traffic sign detection and recognition based on random forests (2016) Applied Soft Computing, 46, pp. 805-815; Kuang, X., Fu, W., Yang, L., Real-time detection and recognition of road traffic signs using mser and random forests (2018) International Journal of Online and Biomedical Engineering (IJOE), 14 (3), pp. 34-51; Chen, Y., Xiong, J., Xu, W., Zuo, J., A novel online incremental and decremental learning algorithm based on variable support vector machine (2019) Cluster Computing, 22 (3), pp. 7435-7445; Chen, Y., Xu, W., Zuo, J., Yang, K., The fire recognition algorithm using dynamic feature fusion and iv-svm classifier (2019) Cluster Computing, 22 (3), pp. 7665-7675; Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C., The german traffic sign recognition benchmark: A multi-class classification competition (2011) The 2011 International Joint Conference on Neural Networks, pp. 1453-1460; Cireşan, D., Meier, U., Masci, J., Schmidhuber, J., A committee of neural networks for traffic sign classification (2011) The 2011 International Joint Conference on Neural Networks, pp. 1918-1921; Sermanet, P., LeCun, Y., Traffic sign recognition with multi-scale convolutional networks (2011) The 2011 International Joint Conference on Neural Networks, pp. 2809-2813; CireşAn, D., Meier, U., Masci, J., Schmidhuber, J., Multi-column deep neural network for traffic sign classification (2012) Neural Networks, 32, pp. 333-338; Jin, J., Fu, K., Zhang, C., Traffic sign recognition with hinge loss trained convolutional neural networks (2014) Ieee Transactions on Intelligent Transportation Systems, 15 (5), pp. 1991-2000; Zhang, J., Wang, W., Lu, C., Wang, J., Sangaiah, A.K., Lightweight deep network for traffic sign classification (2019) Annals of Telecommunications, pp. 1-11; Satti, S.K., Devi, K.S., Dhar, P., Srinivasan, P., Enhancing and classifying traffic signs using computer vision and deep convolutional neural network International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, 2020, pp. 243-253. , Springer; Simonyan, K., Zisserman, A., (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition, , arXiv preprint; Yadav, G., Maheshwari, S., Agarwal, A., Contrast limited adaptive histogram equalization based enhancement for real time video system (2014) 2014 International Conference on Advances in Computing, Communications and Informatics ICACCI, pp. 2392-2397; Boureau, Y.-L., Ponce, J., LeCun, Y., A theoretical analysis of feature pooling in visual recognition (2010) Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 111-118; LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., Gradient-based learning applied to document recognition (1998) Proceedings of the Ieee, 86 (11), pp. 2278-2324; Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks (2012) Advances in Neural Information Processing Systems, pp. 1097-1105; Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., Densely connected convolutional networks (2017) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 4700-4708; Ioffe, S., Szegedy, C., Batch normalization: Accelerating deep network training by reducing internal covariate shift (2015) International Conference on Machine Learning; LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E., Jackel, L.D., Handwritten digit recognition with a back-propagation network (1990) Advances in Neural Information Processing Systems, pp. 396-404; Zhang, J., Lu, C., Li, X., Kim, H.-J., Wang, J., A full convolutional network based on densenet for remote sensing scene classification (2019) Math. Biosci. Eng, 16 (5), pp. 3345-3367",,"Oubenaalla Y.Nfaoui E.H.Joumhidi J.Loqman C.Riffi J.Kozma R.Mestari M.Alippi C.",,"Institute of Electrical and Electronics Engineers Inc.","4th International Conference on Intelligent Computing in Data Sciences, ICDS 2020","21 October 2020 through 23 October 2020",,165561,,9781728180847,,,"English","Int. Conf. Intell. Comput. Data Sci., ICDS",Conference Paper,"Final","",Scopus,2-s2.0-85098671194
"Bliek A., Bensch S., Hellstrom T.","57219854152;16315104000;55926741800;","How Can a Robot Trigger Human Backchanneling?",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223559","96","103",,1,"10.1109/RO-MAN47096.2020.9223559","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095769111&doi=10.1109%2fRO-MAN47096.2020.9223559&partnerID=40&md5=9ea23ec986b814fcaf5e684c3a5e08a3","Umeå University, Department of Computing Science, Umeå, 901 87, Sweden","Bliek, A., Umeå University, Department of Computing Science, Umeå, 901 87, Sweden; Bensch, S., Umeå University, Department of Computing Science, Umeå, 901 87, Sweden; Hellstrom, T., Umeå University, Department of Computing Science, Umeå, 901 87, Sweden","In human communication, backchanneling is an important part of the natural interaction protocol. The purpose is to signify the listener's attention, understanding, agreement, or to indicate that a speaker should go on talking. While the effects of backchanneling robots on humans have been investigated, studies of how and when humans backchannel to talking robots is poorly studied. In this paper we investigate how the robot's behavior as a speaker affects a human listener's backchanneling behavior. This is interesting in Human-Robot Interaction since backchanneling between humans has been shown to support more fluid interactions, and human-robot interaction would therefore benefit from mimicking this human communication feature. The results show that backchanneling increases when the robot exhibits backchannel-inviting cues such as pauses and gestures. Furthermore, clear differences between how a human backchannels to another human and to a robot are shown. © 2020 IEEE.",,"Agricultural robots; Man machine systems; Back channels; Fluid interactions; Human communications; Human listeners; Natural interactions; Talking robots; Social robots",,,,,,,,,,,,,,,,"Anderson, A.H., Bader, M., Gurman Bard, E., Boyle, E., Doherty, G., Garrod, S., Isard, S., Miller, J., The hcrc map task corpus (1991) Language and Speech, 34 (4), pp. 351-366; Beavin Bavelas, J., Coates, L., Johnson, T., Listeners as co-narrators (2000) Journal of Personality and Social Psychology, 79 (6), pp. 941-952; Beavin Bavelas, J., Gerwing, J., The listener as addressee in face-to-face dialogue (2011) International Journal of Listening, 25 (3), pp. 178-198; Benus, S., Gravano, A., Hirschberg, J., The prosody of backchannels in American english (2007) Proceedings of the 16th International Conference of Phonetic Sciences, pp. 1065-1068; Bertrand, R., Ferré, G., Blache, P., Espesser, R., Rauzy, S., Backchannels revisited from a multimodal perspective (2007) AVSP; Burns, M., Rapport and relationships: The basis of child care (1984) Journal of Child Care, 2 (2), pp. 47-57; Costa, S., Brunete, A., Bae, B., Mavridis, N., Emotional storytelling using virtual and robotic agents (2018) International Journal of Humanoid Robotics, 15 (3), pp. 18500061-185000631; Drolet, A.L., Morris, M.W., Rapport in conflict resolution: Accounting for how face-to-face contact fosters mutual cooperation in mixed-motive conflicts (2000) Journal of Experimental Social Psychology, 36 (1), pp. 26-50; Foot, P., The problem of abortion and the doctrine of double effect (1967) Oxford Review; Fuchs, D., Examiner familiarity effects on test performance: Implications for training and practice (1987) Topics in Early Childhood Special Education, 7 (3), pp. 90-104; Fujimoto, D.T., (2009) Listener Responses in Interaction : A Case for Abandoning the Term, Backchannel, 37, pp. 35-54. , Osaka Jogakuin University Research Repository, Osaka, Japan; Gratch, J., Wang, N., Gerten, J., Fast, E., Duffy, R., Creating rapport with virtual agents (2007) Lecture Notes in Artificial Intelligence; Proceedings of the 7th International Conference on Intelligent Virtual Agents (IVA), 4722, pp. 125-128. , Paris, France; Gravano, A., Hirschberg, J., Backchannel-inviting cues in task-oriented dialogue (2009) Proceedings of INTERSPEECH-2009, pp. 1019-1022; Heinz, B., Backchannel responses as strategic responses in bilingual speakers' conversations (2003) Journal of Pragmatics, 35 (7), pp. 1113-1142; Hjalmarsson, A., Oertel, C., Gaze direction as a backchannel inviting cue in dialogue (2012) Proceedings of the IVA 2012 Workshop on Realtime Conversational Virtual Agents, , Santa Cruz, CA, USA; Hjalmarsson, A., Oertel, C., Gaze direction as a backchannel inviting cue in dialogue (2012) IVA 2012 Workshop on Realtime Conversational Virtual Agents, 9. , Citeseer; Hussain, N., Erzin, E., Metin Sezgin, T., Yemez, Y., (2019) Speech Driven Backchannel Generation Using Deep Qnetwork for Enhancing Engagement in Human-robot Interaction, , ArXiv, abs/1908. 01618; Inden, B., Malisz, Z., Wagner, P., Wachsmuth, I., Timing and entrainment of multimodal backchanneling behavior for an embodied conversational agent (2013) Proceedings of the 15th ACM on International Conference on Multimodal Interaction, pp. 181-188; Joosse, M., Evers, V., Lost in proxemics : Spatial behavior for cross-cultural hri (2014) Proceedings of the HRI'14 Workshop on Culture Aware Robotics; Jurafsky, D., Shriberg, E., Fox, B., Curl, T., Lexical, prosodic, and syntactic cues for dialog acts (1998) Discourse Relations and Discourse Markers; Krogsager, A., Segato, N., Rehm, M., Backchannel head nods in danish first meeting encounters with a humanoid robot: The role of physical embodiment (2014) Human-Computer Interaction. Advanced Interaction Modalities and Techniques, , Springer International Publishing; Lala, D., Inoue, K., Milhorat, P., Kawahara, T., (2017) Detection of Social Signals for Recognizing Engagement in Human-robot Interaction, , ArXiv, abs/1709. 10257; Joo Lee, J., Breazeal, C., DeSteno, D., Role of speaker cues in attention inference (2017) Frontiers in Robotics and AI; Li, H.Z., Cui, Y., Wang, Z., Backchannel responses and enjoyment of the conversation: The more does not necessarily mean the better (2010) International Journal of Psychological Studies, 2 (1), p. 25; Mavridis, N., AlDhaheri, A., AlDhaheri, L., Khanii, M., Aldarmaki, N., Transforming ibnsina into an advanced multilingual interactive android robot (2011) 2011 IEEE GCC Conference and Exhibition (GCC), pp. 120-123; Mavridis, N., Katsaiti, M., Naef, S., Falasi, A., Nuaimi, A., Araifi, H., Kitbi, A., Opinions and attitudes toward humanoid robots in the middle east (2011) AI & SOCIETY, 27, pp. 517-534; Misu, T., Mizukami, E., Shiga, Y., Kawamoto, S., Kawai, H., Nakamura, S., Analysis on effects of textto-speech and avatar agent in evoking users' spontaneous listener's reactions (2011) Proceedings of the Paralinguistic Information and Its Integration in Spoken Dialogue Systems Workshop; Morency, L., De Kok, I., Gratch, J., A probabilistic multimodal approach for predicting listener backchannels (2009) J. of Autonomous Agents and Multi-Agent Systems, 20 (1), pp. 70-84; Won Park, H., Gelsomini, M., Joo Lee, J., Breazeal, C., Telling stories to robots: The effect of backchanneling on a child's storytelling (2017) 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI, pp. 100-108. , IEEE; Won Park, H., Gelsomini, M., Joo Lee, J., Breazeal, C., Telling stories to robots: The effect of backchanneling on a child's storytelling (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, HRI '17, pp. 100-108. , New York, NY, USA. Association for Computing Machinery; Scherer, S., Ozkan, D., Morency, L., Investigating the influence of pause fillers for automatic backchannel prediction (2012) Proceedings of the Interspeech Workshop on Feedback Behaviors in Dialog; Skantze, G., Hjalmarsson, A., Oertel, C., Exploring the effects of gaze and pauses in situated human-robot interaction (2013) Proc. of SIGDIAL 2013, pp. 163-172. , Metz, France, August . Association for Computational Linguistics; Stolcke, A., Coccaro, N., Bates, R., Taylor, P., Van Ess-Dykema, C., Ries, K., Shriberg, E., Meteer, M., Dialogue act modeling for automatic tagging and recognition of conversational speech (2000) Computational Linguistics, 26 (3), pp. 339-373; Tolins, J., Tree Fox, J.E., Addressee backchannels steer narrative development (2014) Journal of Pragmatics, 70, pp. 152-164; Tsui, P., Schultz, G.L., Failure of rapport: Why psychotherapeutic engagement fails in the treatment of asian clients (1985) American Journal of Orthopsychiatry, 55 (4), pp. 561-569; Ward, N., Tsukahara, W., Prosodic features which cue backchannel responses in english and Japanese (2000) Journal of Pragmatics, 32 (8), pp. 1177-1207; Wittenburg, P., Brugman, H., Russel, A., Klassmann, A., Sloetjes, H., Elan: A professional framework for multimodality research (2006) 5th International Conference on Language Resources and Evaluation (LREC 2006), pp. 1556-1559; Yngve, V.H., On getting a word in edgewise (1970) CLS-70, pp. 567-577. , University of Chicago",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095769111
"Rajavenkatanarayanan A., Kanal V., Kyrarini M., Makedon F.","57192370020;57192913571;56786567700;57207521629;","Cognitive performance assessment based on everyday activities for human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"398","400",,2,"10.1145/3371382.3378384","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083283410&doi=10.1145%2f3371382.3378384&partnerID=40&md5=52bce8cfeba8508e9d7b5c058a1208e0","Department of Computer Science, University of Texas at Arlington, United States","Rajavenkatanarayanan, A., Department of Computer Science, University of Texas at Arlington, United States; Kanal, V., Department of Computer Science, University of Texas at Arlington, United States; Kyrarini, M., Department of Computer Science, University of Texas at Arlington, United States; Makedon, F., Department of Computer Science, University of Texas at Arlington, United States","Human-Robot Interaction often involves a robot assisting or providing feedback to a human partner's performance or cooperating to complete a task. In such an interaction scenario, the robotic system requires to perceive the human teammate's cognitive state that might affect task performance. In this pilot study, the focus is on developing a framework that assesses the human's cognitive performance for human-robot synergetic task, such as an assembly task. Specifically, we explore the correlation between a person's quality of sleep and performance metric through a standard task for cognitive assessment, the N-back task. To validate our hypothesis, we conducted a study with 25 participants, and our results indicate that there is a moderate correlation between some stages of sleep cycle and performance. Additionally, we present a possible Human-Robot Interaction setup that could benefit from our results. © 2020 ACM.","Cognitive assessment; Human activity; Human-robot interaction; Wearable sensors","Agricultural robots; Man machine systems; Assembly tasks; Cognitive assessments; Cognitive performance; Cognitive state; Performance metrices; Pilot studies; Robotic systems; Task performance; Human robot interaction",,,,,"National Science Foundation, NSF; Directorate for Engineering, ENG: 1719031","This work was supported in part by National Science Foundation under award number 1719031.",,,,,,,,,,"Baxter, P., Lightbody, P., Hanheide, M., Robots providing cognitive assistance in shared workspaces (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 57-58; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Science Robotics, 3 (21), p. eaat5954. , 2018; Busch, B., Toussaint, M., Lopes, M., Planning ergonomic sequences of actions in human-robot interaction (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 1916-1923; Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P., Collaborative manufacturing with physical human-robot interaction (2016) Robotics and Computer-Integrated Manufacturing, 40, pp. 1-13. , 2016; Collins, S., Sabanovi, S., Fraune, M., Randall, N., Eldridge, L., Piatt, J.A., Bennett, C.C., Nagata, S., Sensing companions: Potential clinical uses of robot sensor data for home care of older adults with depression (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 89-90; D'Agostino, R.B., Belanger, A., D'Agostino, R.B., Jr., A suggestion for using powerful and informative tests of normality (1990) The American Statistician, 44 (4), pp. 316-321. , 1990; Georgilas, I., Dagnino, G., Dogramadzi, S., Safe human-robot interaction in medical robotics: A case study on robotic fracture surgery system (2017) Journal of Medical Robotics Research, 2 (3), p. 1740008. , 2017; Ferdinand Goldau, F., Kumar Shastha, T., Kyrarini, M., Gräser, A., Autonomous multi-sensory robotic assistant for a drinking task (2019) 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR). IEEE, pp. 210-216; Honn, K.A., Hinson, J.M., Whitney, P., Van Dongen, H., Cognitive flexibility: A distinct element of performance impairment due to sleep deprivation (2019) Accident Analysis & Prevention, 126, pp. 191-197. , 2019; Kane, M.J., Conway, A.R.A., Miura, T.K., Colflesh, G.J.H., Working memory, attention control, and the N-back task: A question of construct validity (2007) Journal of Experimental Psychology: Learning, Memory, and Cognition, 33 (3), p. 615. , 2007; Kosecki, D., (2018) REM, Light, Deep: How Much of Each Stage of Sleep Are You Getting, , https://blog.fitbit.com/sleepstages-explained/, Retrieved December 10 2019; Maurtua, I., Ibarguren, A., Kildal, J., Susperregi, L., Sierra, B., Human-robot collaboration in industrial applications: Safety, interaction and trust (2017) International Journal of Advanced Robotic Systems, 14 (4), pp. 1729881417716010. , 2017; Shafti, A., Ataka, A., Urbistondo, L.B., Shiva, A., Wurdemann, H.A., Althoefer, K., Real-time robot-assisted ergonomics (2019) 2019 International Conference on Robotics and Automation (ICRA). IEEE, pp. 1975-1981; Smith, M.E., McEvoy, L.K., Gevins, A., The impact of moderate sleep loss on neurophysiologic signals duringworking-memory task performance (2002) Sleep, 25 (7), pp. 56-66. , 2002; Tsiakas, K., Abellanoza, C., Abujelala, M., Papakostas, M., Makada, T., Makedon, F., Towards designing a socially assistive robot for adaptive and personalized cognitive training (2017) Proceedings of the Robots, 4",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083283410
"Robinson F.A., Bown O., Velonaki M.","57215966842;27867522200;24073920300;","Implicit communication through distributed sound design: Exploring a new modality in human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"597","599",,3,"10.1145/3371382.3377431","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083232681&doi=10.1145%2f3371382.3377431&partnerID=40&md5=e624fc683e52dbaee0dc30bf823f5634","Creative Robotics Lab, University of New South Wales, Sydney, Australia; Interactive Media Lab, University of New South Wales, Sydney, Australia","Robinson, F.A., Creative Robotics Lab, University of New South Wales, Sydney, Australia; Bown, O., Interactive Media Lab, University of New South Wales, Sydney, Australia; Velonaki, M., Creative Robotics Lab, University of New South Wales, Sydney, Australia","As robots find their way into homes, workplaces, and public spaces, rich and effective human-robot interaction will play an essential role in their success. While most sound-related research in the field of HRI focuses on speech and semantic-free utterances, the potential of sound as an implicit non-verbal channel of communication has only recently received attention and remains largely unexplored. This research will bring design approaches from the fields of sound design and spatial audio into the context of human-robot interaction to influence human perception of robot characteristics and refine non-verbal auditory communication. It will implement sound design systems into various physical robots and evaluate their effect through user studies. By developing design principles for the sonic augmentation of robots, we aim to provide the HRI community with new tools to enrich the way robots communicate with humans. © 2020 ACM.","Human-robot interaction; Sonic interaction design; Spatial audio","Agricultural robots; Audio acoustics; Machine design; Man machine systems; Semantics; Speech communication; Design approaches; Design Principles; Distributed sounds; Hri communities; Human perception; Implicit communications; Physical robots; Sound designs; Human robot interaction",,,,,,,,,,,,,,,,"Bethel, C.L., Murphy, R.R., Auditory and other non-verbal expressions of affect for robots (2006) AAAI Fall Symposium: Aurally Informed Performance, pp. 1-5; Bown, O., Loke, L., Ferguson, S., Reinhardt, D., (2015) Distributed Interactive Audio Devices: Creative Strategies and Audience Responses to Novel Musical Interaction Scenarios, p. 4. , 2015; Breazeal, C., Kidd, C.D., Thomaz, A.L., Hoffman, G., Berlin, M., Effects of nonverbal communication on efficiency and robustness in human-robot teamwork (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, Edmonton, Alta., Canada, pp. 708-713. , https://doi.org/10.1109/IROS.2005.1545011; Frid, E., Bresin, R., Alexanderson, S., Perception of mechanical sounds inherent to expressive gestures of a nao robot-implications for movement sonification of humanoids (2018) Sound and Music Computing; Heerink, M., Krose, B., Evers, V., Wielinga, B., Measuring acceptance of an assistive social robot: A suggested toolkit (2009) RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication. IEEE, Toyama, Japan, pp. 528-533. , https://doi.org/10.1109/ROMAN.2009.5326320; Inoue, K., Wada, K., Ito, Y., Effective application of Paro: Seal type robots for disabled people in according to ideas of occupational therapists (2008) International Conference on Computers for Handicapped Persons. Springer, pp. 1321-1324; Kulezic-Wilson, D., Sound Design is the New Score (2008) Music, Sound, and the Moving Image, 2 (2), pp. 127-131. , https://doi.org/10.3828/msmi.2.2.5, Dec. 2008; Moore, D., Dahl, T., Varela, P., Ju, W., Naes, T., Berget, I., Unintended consonances: Methods to understand robot motor sound perception (2019) Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems-CHI '19, pp. 1-12. , https://doi.org/10.1145/3290605.3300730, ACM Press, Glasgow, Scotland Uk; Moore, D., Tennent, H., Martelaro, N., Ju, W., Making noise intentional: A study of servo sound perception (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction-HRI '17, pp. 12-21. , https://doi.org/10.1145/2909824.3020238, ACM Press, Vienna, Austria; Robinson, F., Spindler, C., Böhm, V., Oña, E., Gestural control in electronic music performance: Sound design based on the 'striking' and 'bowing' movement metaphors (2015) Proceedings of the Audio Mostly 2015 on Interaction with Sound-AM '15, pp. 1-6. , https://doi.org/10.1145/2814895.2814901, ACM Press, Thessaloniki, Greece; Tennent, H., Moore, D., Jung, M., Ju, W., Good vibrations: How consequential sounds affect perception of robotic arms (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN). IEEE, Lisbon, pp. 928-935. , https://doi.org/10.1109/ROMAN.2017.8172414; Thiessen, R., Rea, D.J., Garcha, D.S., Cheng, C., Young, J.E., Infrasound for hri: A robot using low-frequency vibrations to impact how people perceive its actions (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 11-18; Trovato, G., Do, M., Terlemez, O., Mandery, C., Ishii, H., Bianchi-Berthouze, N., Asfour, T., Takanishi, A., Is hugging a robot weird Investigating the influence of robot appearance on users' perception of hugging (2016) 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids). IEEE, pp. 318-323; Trovato, G., Paredes, R., Balvin, J., Cuellar, F., Baek Thomsen, N., Bech, S., Tan, Z., The sound or silence: Investigating the influence of robot noise on proxemics (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, Nanjing, pp. 713-718. , https://doi.org/10.1109/ROMAN.2018.8525795; Tünnermann, R., Hammerschmidt, J., Hermann, T., Blended sonification-Sonification for casual information interaction (2013) Georgia Institute of Technology; Van Egmond, R., The experience of product sounds (2008) Product Experience. Elsevier, pp. 69-89; Wolford, J., Gabaldon, B., Rivas, J., Min, B., Condition-based robot audio techniques (2019) Google Patents; Yilmazyildiz, S., Read, R., Belpeame, T., Verhelst, W., Review of semantic-free utterances in social human-robot interaction (2016) International Journal of Human-Computer Interaction, 32 (1), pp. 63-85. , https://doi.org/10.1080/10447318.2015.1093856, Jan. 2016",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083232681
"Dos Santos C.W., Filho N.L.D., Espíndola D.B., Botelho S.S.C.","57224515682;36164021300;34879715800;7003517099;","Situational awareness oriented interfaces on human-robot interaction for industrial welding processes",2020,"IFAC-PapersOnLine","53",,,"10168","10173",,,"10.1016/j.ifacol.2020.12.2744","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107743975&doi=10.1016%2fj.ifacol.2020.12.2744&partnerID=40&md5=74bfec1a939c1d99e11d5a76bec45646","Center of Computational Science, Federal University of Rio Grande, Rio Grande, Brazil","Dos Santos, C.W., Center of Computational Science, Federal University of Rio Grande, Rio Grande, Brazil; Filho, N.L.D., Center of Computational Science, Federal University of Rio Grande, Rio Grande, Brazil; Espíndola, D.B., Center of Computational Science, Federal University of Rio Grande, Rio Grande, Brazil; Botelho, S.S.C., Center of Computational Science, Federal University of Rio Grande, Rio Grande, Brazil","Safety, efficiency and effectiveness are important characteristics for human-robot interaction in a factory. So, workers and operators are exposed to challenges of interacting with these systems, in particular with welding robots. Considering the complexity of the industrial context and of the welding process itself, an important factor for the operator is ""to understand what is happening""or to obtain situational awareness (SA). The SA increases decision-making capacity, reduces errors, and adds features to improve the human-robot interface (HRI). In order to integrate SA into interfaces for human-robot interaction, this work proposes the mapping of important aspects of SA for the welding process, based on a literature review and a case study to identify SA on interfaces for HRI. Finally, comments which aspects should be included in situational awareness oriented interfaces in order to provide more intelligent interfaces. Copyright © 2020 The Authors.","Human-Robot Interaction; Intelligent Interface; Situational Awareness","Decision making; Industrial robots; Man machine systems; Robot applications; Welding; Exposed to; Human-robot interfaces; Industrial context; Intelligent interface; Literature reviews; Situational awareness; Welding process; Welding robots; Human robot interaction",,,,,,,,,,,,,,,,"Endsley, M.R., (2011) Designing for Situation Awareness: An Approach to User-Centred Design, 2nd Edition., , CRC Press, Boca Raton; Argall, B.D., Aude, G.B., A survey of tactile human-robot interactions (2010) Robotics and Autonomous Systems, 58 (10), pp. 1159-1176; Kim, Y.C., Kwon, W.T., Yoon, J.C., Kim, J.C., Designing emotional and interactive behaviors for an entertainment robot (2009) International Conference on Human-Computer Interaction., pp. 321-330. , Springer, Berlin, Heidelberg; Siciliano, B., Khatib, O., (2007) Springer Handbook of Robotics, , Springer-Verlag New York; De Oliveira, A.C.M., (2016) Model-Based Design of User Interfaces to Support Situation Awareness in Maintenance, , Ph.D. Dissertation. Federal University of São Carlos, São Carlos; Garcia, E., Jimenez, M.A., De Santos, P.G., Armada, M., The evolution of robotics research (2007) IEEE Robotics & Automation Magazine, 14 (1), pp. 90-103; Scholtz, J., Evaluation methods for human-system performance of intelligent systems (2002) Proceedings of the 2002 Performance Metrics for Intelligent Systems Workshop, pp. 1-6. , Gaithersburg; Scholtz, J., Theory and evaluation of human robot interactions (2003) Proceedings of the 36th Annual Hawaii International Conference on System Sciences, pp. 1-10. , IEEE, Big Island - USA; Campana, J.R., Quaresma, M., The importance of specific usability guidelines for robot user interfaces (2017) Design, User Experience, and Usability: Designing Pleasurable Experiences. Lecture Notes in Computer Science, p. 10289. , Marcus A., Wang W. (eds), Springer, Cham; Moniz, A., Krings, B.J., Robots working with humans or humans working with robots? Searching for social dimensions in new human-robot interaction in industry (2016) Societies, 6 (3), p. 23; Bdiwi, M., Pfeifer, M., Sterzing, A., A new strategy for ensuring human safety during various levels of interaction with industrial robots (2017) CIRP Annals, 66 (1), pp. 453-456; Najmaei, N., Kermani, M.R., Prediction-based reactive control strategy for human-robot interactions (2010) The International Conferece on Robotics and Automation (ICRA), pp. 3434-3439. , IEEE, Anchorage, USA; Vasic, M., Billard, A., Safety issues in human-robot interactions (2013) The International Conference on Robotics and Automation (ICRA), pp. 197-204. , IEEE, Karlsruhe, Germany; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human- robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, (55), pp. 248-266; (1991) Welding Process. 8th Edition, , Welding Handbook., American Welding Society, Miami; Chen, S.B., Qiu, T., Lin, T., Wu, L., Tian, J.S., Lv, W.X., (2004) Intelligent Technologies for Robotic Welding. In: Lecture Notes in Control and Information Science., pp. 123-143. , Springer, Berlin; Perzanowski, D., Schultz, A.C., Adams, W., Building a multimodal human-robot interface (2001) IEEE Intelligent Systems, 16 (1), pp. 16-21; Endsley, M.R., Measurement of situation awareness in dynamic systems (1995) Human Factors, 37 (1), pp. 65-84; Endsley, M.R., Robertson, M.M., Situation awareness in aircraft maintenance teams (2000) International Journal of Industrial Ergonomics, (26), pp. 301-325; De Oliveira, A.C.M., Van Volkenburg, C.A., Jardine, A.K., De Araujo, R.B., The electronic maintenance situational awareness interface (2015) Reliability and Maintainability Symposium (RAMS), pp. 1-6. , IEEE, Palm Harbor; Parsons, K., (2014) Human Thermal Environments: The Effects of Hot, Moderate, and Cold Environments on Human Health, Comfort, and Performance. 3rd Edition., , CRC Press, Boca Raton; Endsley, M.R., (2016) Designing for Situation Awareness: An Approach to User-centered Design. 2nd Edition., , CRC Press, Boca Raton; Steffens, C., Silva Filho, S., Quaresma, B., Rozante, M., Botelho, Silvia, S.C., Rosa, V., Huttner, V., A vmb system for intelligent robotic welding (2015) Simpósio Brasileiro de Automação Inteligente - SBAI, pp. 1-6. , Proceedings of SBAI, Natal; Schott, M., (2018) Interação Humano-robô em Processos de Soldagem: Uma Proposta de Desenvolvimento e Avaliação de Interfaces Para Controle de Um Robô Baseado em Engenharia Semiótica, , Sc.M. Dissertation. Federal University of Rio Grande, Rio Grande","Dos Santos, C.W.; Center of Computational Science, Brazil; email: carolinewaschburger@gmail.com.br
Filho, N.L.D.; Center of Computational Science, Brazil; email: dmtnldf@furg.br
Espíndola, D.B.; Center of Computational Science, Brazil; email: danubiaespindola@furg.br
Botelho, S.S.C.; Center of Computational Science, Brazil; email: silviacb@furg.br",,,"Elsevier B.V.","21st IFAC World Congress 2020","12 July 2020 through 17 July 2020",,145388,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85107743975
"Pedersen J.E., Christensen K.W., Herath D., Jochum E.","57220186362;57220181144;8366358100;57074708900;","I Like the Way You Move: A Mixed-Methods Approach for Studying the Effects of Robot Motion on Collaborative Human Robot Interaction",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"73","84",,1,"10.1007/978-3-030-62056-1_7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097147271&doi=10.1007%2f978-3-030-62056-1_7&partnerID=40&md5=06ab32f13c72ba7b5fdef3264fd5d136","Aalborg University, Aalborg, Denmark; University of Canberra, Canberra, Australia","Pedersen, J.E., Aalborg University, Aalborg, Denmark; Christensen, K.W., Aalborg University, Aalborg, Denmark; Herath, D., University of Canberra, Canberra, Australia; Jochum, E., Aalborg University, Aalborg, Denmark","Human robot collaboration is an increasingly relevant area within Human Robot Interaction. As robots move into dynamic environments and engage in collaborative tasks with people, there is a need to further understand how perceptual and communication cues facilitate and support Human Robot Collaboration. Building on prior Human Robot Interaction studies, we developed a mixed-methods approach for studying the effects of expressive movements of an industrial robot arm engaged in a collaborative drawing task. The purpose was to evaluate the effects of different movement qualities on participant experience while collaborating with a robot. We present our approach and the results of the experiments. Although we did not identify any significant difference in interactions where the robot moved expressively, our study highlights the importance of in-the-wild experiments and strategies for combining qualitative and quantitative methodologies. © 2020, Springer Nature Switzerland AG.","Collaborative robots; Human-factors; Human-robot interaction; Mixed-methods","Industrial robots; Man machine systems; Robotics; Collaborative drawing; Collaborative tasks; Dynamic environments; Human-robot collaboration; Mixed method; Quantitative methodology; Robot motion; Social robots",,,,,,,,,,,,,,,,"Bauer, A., Wollherr, D., Buss, M., Human-robot collaboration: A survey (2008) Int. J. Humanoid Robot., 5 (1), pp. 47-66; Breazeal, C., Working Collaboratively with Humanoid Robots, , https://doi.org/10.1109/ICHR.2004.1442126; Breazeal, C., Kidd, C.D., Thomaz, A.L., Hoffman, G., Berlin, M., Effects of nonverbal communication on efficiency and robustness in human-robot teamwork (2005) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Pp. 383–, 388; Young, J.E., Evaluating human-robot interaction: Focusing on the holistic interaction experience (2011) Int. J. Soc. Robot., 3 (1), pp. 53-67. , https://doi.org/10.1007/s12369-010-0081-8; Seo, S.H., Griffin, K., Young, J.E., Bunt, A., Prentice, S., Loureiro-Rodríguez, V., Investigating people’s rapport building and hindering behaviors when working with a collaborative robot (2017) Int. J. Soc. Robot., 10 (1), pp. 147-161. , https://doi.org/10.1007/s12369-017-0441-8; Castro-González, Á., Admoni, H., Scassellati, B., Effects of form and motion on judgments of social robots’ animacy, likability, trustworthiness and unpleasantness (2016) Int. J. Hum. Comput. Stud., 90, pp. 27-38. , https://doi.org/10.1016/j.ijhcs.2016.02.004; Duffy, B.R., Joue, G., Bourke, J., (2002) Issues in Assessing Performance of Social Robots; Sabanovic, S., Michalowski, M.P., Simmons, R., Robots in the wild: Observing human-robot social interaction outside the lab (2006) Int. Workshop Adv. Motion Control AMC, 2006, pp. 576-581; Brooks, R., A brave, creative, and happy HRI (2018) ACM Trans. Hum.-Robot Interact, 7 (1), p. 3. , https://doi.org/10.1145/3209540; Tresset, P., Deussen, O., Artistically skilled embodied agents Proceedings of AISB2014; (2018) Issues in Science and Technology, Vol., 35 (1). , https://issues.org/sougwen-chung/; Hoffman, G., Wendy, J., Designing robots with movement in mind (2014) J. Hum.-Robot Interact., 3 (1), pp. 91-122. , https://doi.org/10.5898/JHRI.3.1.Hoffman; Riek, L.D., Wizard of oz studies in HRI: A systematic review and new reporting guide-lines (2012) J. Hum.-Robot Interact., 1 (1), pp. 119-136. , https://doi.org/10.5898/JHRI.1.1.Riek; Hinwood, D., Ireland, J., Jochum, E.A., Herath, D., A proposed wizard of OZ architecture for a human-robot collaborative drawing task (2018) ICSR 2018. LNCS (LNAI), 11357, pp. 35-44. , https://doi.org/10.1007/978-3-030-05204-14; (2018) Universal-Robots.Com.: UR10 Collaborative Industrial Robotic Arm-Payload up to 10 Kg; Salter, T., Werry, I., Michaud, F., Going into the wild in child-robot interaction studies: Issues in social robotic development (2008) Intell. Serv. Robot., 1 (2), pp. 93-108. , https://doi.org/10.1007/s11370-007-0009-9; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot., 1 (1), pp. 71-81. , https://doi.org/10.1007/s12369-008-0001-3; Lewis, J.R., Sauro, J., The factor structure of the system usability scale (2009) HCD 2009. LNCS, 5619, pp. 94-103. , https://doi.org/10.1007/978-3-642-02806-912, Kurosu, M. (ed.), Springer, Heidelberg; Bangor, A., Kortum, P., Miller, J., Determining what individual SUS scores mean: Adding an adjective rating scale (2009) J. Usability Stud., 4 (3), pp. 114-123; Pei, Y.C., Chen, J.L., Wong, A.M., Tseng, K.C., An evaluation of the design and usability of a novel robotic bilateral arm rehabilitation device for patients with stroke (2017) Front. Neurorobot., 11. , https://doi.org/10.3389/fnbot.2017.00036; Mattos, L.S., Deshpande, N., Barresi, G., Guastini, L., Peret-Ti, G., A novel computerized surgeon-machine interface for robot-assisted laser phonomi-crosurgery (2014) Laryngoscope, 124 (8), pp. 1887-1894; Herath, D.C., Jochum, E., Vlachos, E., An experimental study of embodied interaction and human perception of social presence for interactive robots in public settings (2018) IEEE Trans. Cogn. Dev. Syst., 10 (4); Jordan, B., Henderson, A., Interaction analysis: Foundations and practice (1995) J. Learn. Sci., 4, pp. 39-103. , https://doi.org/10.1207/s15327809jls0401","Herath, D.; University of CanberraAustralia; email: damith.herath@canberra.edu.au","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097147271
"Blankenbach K., Hochdorfer S., Babel F., Charrier E.","25623015000;35146014000;57204529534;57239071700;","Display evaluation for advanced human-robot interaction",2021,"Digest of Technical Papers - SID International Symposium","52","S2",,"349","353",,,"10.1002/sdtp.15119","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115856542&doi=10.1002%2fsdtp.15119&partnerID=40&md5=57beb60626145f7e285ca606ebffcda5","Pforzheim University, Display Lab, Pforzheim, Germany; ADLATUS Robotics, Ulm, Germany; Ulm University, Dep of Human Factors, Ulm, Germany","Blankenbach, K., Pforzheim University, Display Lab, Pforzheim, Germany; Hochdorfer, S., ADLATUS Robotics, Ulm, Germany; Babel, F., Ulm University, Dep of Human Factors, Ulm, Germany; Charrier, E., Pforzheim University, Display Lab, Pforzheim, Germany","Autonomous robots become increasingly widespread in public spaces. Human-robot interactions (HRI) with pedestrians is essential by visualizing trajectory information. We evaluated various display technologies for bright indoor conditions such as animated projection and LED lighting as well as displays. © 2021, John Wiley and Sons Inc. All rights reserved.","Ambient light; Autonomous robots; Human-Robot Interaction (HRI); LCD; LED; Optical measurements; Projection","Human robot interaction; Light emitting diodes; Man machine systems; Optical data processing; Ambient light; Display technologies; Human-robot interaction; Humans-robot interactions; Indoor conditions; LED lighting; Optical measurement; Projection; Public space; Trajectory information; Liquid crystal displays",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV7967","This research has been conducted within the interdisciplinary research project ‘'RobotKoop’', which is funded by the German Ministry of Education and Research (grant number 16SV7967).",,,,,,,,,,"(2020) IFR World Robotics, , https://ifr.org/worldrobotics/; Bremner, P., Trigoni, N., Brown, I., Gunes, H., Bevan, C., Danae, S., Levine, M., Being There: Humans and Robots in Public Spaces (2013) Int. Conference on Social Robotics (ICSR); Brandl, C., Mertens, A., Schlick, C.M., Human-Robot Interaction in Assisted Personal Services: Factors Influencing Distances That Humans Will Accept between Themselves and an Approaching Service Robot (2016) Hum. Factors Man, 26, pp. 713-727; Blankenbach, K., Isele, R., Ochs, D., Reichel, S., Led-Based Automotive Exterior Displays and Interior Signage for Autonomous Cars, , https://doi.org/10.1117/12.2543253, 113040U (26 February 2020); SID Information Display Measurements Standard, , https://www.sid.org/Publications/ICDM.aspx",,,,"John Wiley and Sons Inc","International Conference on Display Technology, ICDT 2021","30 May 2021 through 2 June 2021",,264989,0097966X,,,,"English","Dig. Tech. Pap. SID Int. Symp.",Conference Paper,"Final","",Scopus,2-s2.0-85115856542
"Mao Q., Li G., Zhu R.","57259613100;57259678300;7202446527;","Electronic Skin for Detections of Human-Robot Collision Force and Contact Position",2021,"21st International Conference on Solid-State Sensors, Actuators and Microsystems, TRANSDUCERS 2021",,,,"325","328",,,"10.1109/Transducers50396.2021.9495708","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114964115&doi=10.1109%2fTransducers50396.2021.9495708&partnerID=40&md5=de03a10de1e136a6e63cfe1b39415605","State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Department of Precision Instrument, Beijing, China","Mao, Q., State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Department of Precision Instrument, Beijing, China; Li, G., State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Department of Precision Instrument, Beijing, China; Zhu, R., State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Department of Precision Instrument, Beijing, China","Nowadays, robots have been widely used in industrial, medical and service fields. With more and more applications of robots, safety in human-robot interaction becomes more important. As an excellent sensing element that can be attached onto mechanical arms of robots, electronic skin (e-skin) has attracted wide interests and been recognized having promising application prospects. However, existing electronic skins relying on multi-sensor array require complex structures and conditioning circuits, as well as complicated fabrication and assembly, which limits their applications to robots. In this paper, we propose an electronic skin based on thermosensation that has a simple structure and can be easily installed on a robotic arm to measure both collision force and position of collision point simultaneously and independently in human-robot interaction. Importantly, the e-skin has a high sensitivity and a broad measuring range, which demonstrates its promising potential in practical robotic application. © 2021 IEEE.","collision force measurement; contact position measurement; Electronic skin; high sensitivity; thermosensation","Actuators; Industrial robots; Man machine systems; Medical robotics; Microsystems; Robotics; Solid-state sensors; Application prospect; Complex structure; Conditioning circuit; Measuring ranges; Multi-sensor arrays; Robotic applications; Sensing elements; Simple structures; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 51735007; Natural Science Foundation of Beijing Municipality: 3191001; National Key Research and Development Program of China, NKRDPC: 2019YFB1310204","This work is financially supported by National Key Research and Development Program of China (Grant No. 2019YFB1310204), National Natural Science Foundation of China (Grant No. 51735007) and Beijing Natural Science Foundation (Grant No. 3191001).",,,,,,,,,,"Heo, Y.J., Kim, D., Lee, W., Kim, H., Collision detection for industrial collaborative robots: A deep learning approach (2019) Ieee Robot Autom Let, 4 (2), pp. 740-746; Lee, S.D., Kim, M.C., Song, J.B., Sensorless collision detection for safe human-robot collaboration (2015) Ieee Int C Int Robot, pp. 2392-2397; Xiao, J.L., Zhang, Q.L., Hong, Y., Wang, G.D., Collision detection algorithm for collaborative robots considering joint friction (2018) Int J Adv Robot Syst, 15 (4); Maiolino, P., Maggiali, M., Cannata, G., Metta, G., A flexible and robust large scale capacitive tactile system for robots (2013) Ieee Sens J, 13 (10), pp. 3910-3917; Canavese, G., Stassi, S., Fallauto, C., Corbellini, S., Piezoresistive flexible composite for robotic tactile applications (2014) Sensor Actuat A-Phys, 208, pp. 1-9; Zhao, S., Zhu, R., Electronic skin with multifunction sensors based on thermosensation (2017) Adv Mater, 29 (15); Mohammed, A., Schmidt, B., Wang, L.H., Active collision avoidance for human-robot collaboration driven by vision sensors (2017) Int J Comput Integ M, 30 (9), pp. 970-980; Xie, M.Y., Aw, K., Edwards, B., Gao, W., Orthogonal experimental design of polydimethylsiloxane curing for the design of lowfrequency vibrational energy harvester (2014) J Intel Mat Syst Str, 25 (18), pp. 2228-2234; Zhao, S., Zhu, R., High sensitivity and broad range flexible pressure sensor using multilayered porous pdms/agnp sponge (2019) Adv Mater Technol-Us, 4 (9)",,,"MEMS and Sensors Industry Group;Semi;Transducer Research Foundation (TRF)","Institute of Electrical and Electronics Engineers Inc.","21st International Conference on Solid-State Sensors, Actuators and Microsystems, TRANSDUCERS 2021","20 June 2021 through 25 June 2021",,171245,,9781665412674,,,"English","Int. Conf. Solid-State Sensors, Actuators Microsystems, TRANSDUCERS",Conference Paper,"Final","",Scopus,2-s2.0-85114964115
"Zhou M., Wang S.","57224904825;57203504511;","A method to determine the topology of custom torsional elastic element for the lightweight rotary series elastic actuator",2021,"Journal of Physics: Conference Series","1939","1","012080","","",,,"10.1088/1742-6596/1939/1/012080","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108544808&doi=10.1088%2f1742-6596%2f1939%2f1%2f012080&partnerID=40&md5=6a8b772ac731e2c8baecf86e001b736b","School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China","Zhou, M., School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China; Wang, S., School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China","Compact and lightweight rotary series elastic actuator (SEA) is a new scheme to realize safe and friendly human-robot interaction of exoskeletons and other human-robot interaction applications. The custom monolithic torsional elastic element is a good choice for the compact design of rotary SEA, but there is a lack of guidance on selecting the topology of elastic elements in a lightweight SEA design. In this paper, with the help of the concept of specific energy, a method based on finite element simulation environment and parametric 3D CAD environment is proposed to determine the topology of the elastic element suitable for lightweight SEA by maximizing the specific energy. By adopting this method, we come to this conclusion: The topology, Archimedes-spiral-based double spiral structure without the outer ring, can be determined as a suitable topology of elastic element for a lightweight rotary SEA. © Published under licence by IOP Publishing Ltd.",,"Actuators; Computer aided design; Elasticity; Exoskeleton (Robotics); Man machine systems; Manufacture; Topology; 3-d cads; Compact designs; Elastic element; Finite element simulations; Series elastic actuators; Specific energy; Spiral structures; Human robot interaction",,,,,,,,,,,,,,,,"Carpino, G., Accoto, D., Sergi, F., A novel compact torsional spring for series elastic actuators for assistive wearable robots (2012) Journal of Mechanical Design, Transactions of the ASME, 134, pp. 1-10; Pratt, G.A., Williamson, M.M., (1995) Proceedings of the 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 399-406. , Part 3 (of 3) (Pittsburgh) Series elastic actuators; Lee, C., Oh, S., (2019) Development, Analysis, and Control of Series Elastic Actuator-Driven Robot Leg Frontiers in Neurorobotics, p. 13; Knox, B.T., Schmiedeler, J.P., A unidirectional series-elastic actuator design using a spiral torsion spring (2009) Journal of Mechanical Design, Transactions of the ASME, 131, pp. 1250011-1250015; Lagoda, C., Schouten, A.C., Stienen, A.H.A., Design of an electric series elastic actuated joint for robotic gait rehabilitation training (2010) 2010 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 21-26. , BioRob 2010; Lee, C., Kim, J.Y., Kim, S.Y., Human force observation and assistance for lower limb rehabilitation using wire-driven series elastic actuator (2018) Mechatronics, 55, pp. 13-26; Song, Z.P., Zhou, L.L., Li, Y.B., (2019) 9th IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2019 (Suzhou) Design and Control of a Series Elastic Actuator with High Compliance for Serial Manipulators, pp. 872-876; Accoto, D., Carpino, G., Sergi, F., Design and Characterization of a Novel High-Power Series Elastic Actuator for a Lower Limb Robotic Orthosis (2013) International Journal of Advanced Robotic Systems, 10; Chauhan, R.J., Ben-Tzvi, P., (2019) ASME 2019 Dynamic Systems and Control Conference, DSCC 2019 (Park City) A series elastic actuator design and control in a linkage based hand exoskeleton; Zhang, T., Xia, J., (2019) Automation and Mechatronics, CIS and RAM 2019 (Bangkok) Development of a Clutchable Series Elastic Actuator for Robotic Hip Exoskeleton, pp. 269-274; Irmscher, C., Woschke, E., May, E., Design, optimisation and testing of a compact, inexpensive elastic element for series elastic actuators (2018) Medical Engineering and Physics, 52, pp. 84-89; Stienen, A.H.A., Hekman, E.E.G., Ter Braak, H., (2008) 2nd Biennial IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2008 (Scottsdale) Design of a rotational hydro-elastic actuator for an active upper-extremity rehabilitation exoskeleton, pp. 881-888; dos Santos, W.M., Caurin, G.A.P., Siqueira, A.A.G., Design and control of an active knee orthosis driven by a rotary Series (2017) Elastic Actuator Control Engineering Practice, 58, pp. 307-318; Leal-Junior, A.G., Theodosiou, A., Min, R., Quasi-Distributed Torque and Displacement Sensing on a Series Elastic Actuator's Spring Using FBG Arrays Inscribed in CYTOP Fibers (2019) IEEE Sensors Journal, 19, pp. 4054-4061; Leal-Junior, A.G., Frizera, A., Marques, C., Polymer Optical Fiber for Angle and Torque Measurements of a Series Elastic Actuator's (2018) Spring Journal of Lightwave Technology, 36, pp. 1698-1705; Sanchez, M.R.A., Leal-Junior, A.G., Segatto, M.V., Fiber Bragg grating-based sensor for torque and angle measurement in a series elastic actuator's spring (2018) Applied Optics, 57, pp. 7883-7890; Huo, X.J., Xia, Y.F., Liu, Y.W., (2014) 2014 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2014 (Bali) Humanoid arm with the integrated serial elastic actuator, pp. 2321-2326",,,,"IOP Publishing Ltd","2021 International Conference on Mechanical Engineering, Intelligent Manufacturing and Automation Technology, MEMAT 2021","23 April 2021 through 25 April 2021",,169645,17426588,,,,"English","J. Phys. Conf. Ser.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85108544808
"Vrochidou E., Lytridis C., Bazinas C., Papakostas G.A., Wagatsuma H., Kaburlasos V.G.","53867522300;8901635200;57205365418;14060879200;6603005439;6701308189;","Brain signals classification based on fuzzy lattice reasoning",2021,"Mathematics","9","9","1063","","",,,"10.3390/math9091063","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106659780&doi=10.3390%2fmath9091063&partnerID=40&md5=63df7973e46400b6306f8d0c468be9b6","HUMAIN-Lab, International Hellenic University (IHU), Kavala, 65404, Greece; Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, 808-0135, Japan; RIKEN Center for Brain Science, Saitama, 351-0106, Japan","Vrochidou, E., HUMAIN-Lab, International Hellenic University (IHU), Kavala, 65404, Greece; Lytridis, C., HUMAIN-Lab, International Hellenic University (IHU), Kavala, 65404, Greece; Bazinas, C., HUMAIN-Lab, International Hellenic University (IHU), Kavala, 65404, Greece; Papakostas, G.A., HUMAIN-Lab, International Hellenic University (IHU), Kavala, 65404, Greece; Wagatsuma, H., Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu, 808-0135, Japan, RIKEN Center for Brain Science, Saitama, 351-0106, Japan; Kaburlasos, V.G., HUMAIN-Lab, International Hellenic University (IHU), Kavala, 65404, Greece","Cyber-Physical System (CPS) applications including human-robot interaction call for automated reasoning for rational decision-making. In the latter context, typically, audio-visual signals are employed. This work considers brain signals for emotion recognition towards an effective human-robot interaction. An ElectroEncephaloGraphy (EEG) signal here is represented by an Intervals’ Number (IN). An IN-based, optimizable parametric k Nearest Neighbor (kNN) classifier scheme for decision-making by fuzzy lattice reasoning (FLR) is proposed, where the conventional distance between two points is replaced by a fuzzy order function (σ) for reasoning-by-analogy. A main advantage of the employment of INs is that no ad hoc feature extraction is required since an IN may represent all-order data statistics, the latter are the features considered implicitly. Four different fuzzy order functions are employed in this work. Experimental results demonstrate comparably the good performance of the proposed techniques. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Cyber-Physical System (CPS); ElectroEncephaloGraphy (EEG); Emotion recognition; Fuzzy Lattice Reasoning (FLR); Human-robot interaction; Intervals’ Number (IN); KNN classifier",,,,,,"Horizon 2020 Framework Programme, H2020: 777720; Japan Society for the Promotion of Science, KAKEN: 17H06383; General Secretariat for Research and Technology, GSRT; Kyushu Institute of Technology, Kyutech","Funding: This project has received funding from (a) the General Secretariat for Research and Technology (GSRT) of Greece by the “matching funds” of the year 2019 and (b) the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 777720. This work was also supported in part by JSPS KAKENHI (17H06383) and the budget of Kyushu Institute of Technology for a strategic plan to strengthen international research collaborations.",,,,,,,,,,"Kaburlasos, V.G., Vrochidou, E., Panagiotopoulos, F., Aitsidis, C., Jaki, A., Time Series Classification in Cyber-Physical System Applications by Intervals’ Numbers Techniques Proceedings of the 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-6. , New Orleans, LA, USA, 23–26 June 2019; Kaburlasos, V.G., Vrochidou, E., Social Robots for Pedagogical Rehabilitation: Trends and Novel Modeling Principles (2019) Cyber-Physical Systems for Social Applications, pp. 1-12. , IGI Global: Hershey, PA, USA; Xu, Y., Qin, K., Ruan, D., Liu, J., Lattice-Valued Logic (2003) Studies in Fuzziness and Soft Computing, 132. , Xu, Y., Ed.; Springer: Berlin/Heidelberg, Germany, ISBN 978-3-642-07279-6; Liu, Y., Kaburlasos, V., Hatzimichailidis, A., Xu, Y., (2016) Toward a Synergy of a Lattice Implication Algebra with Fuzzy Lattice Reasoning—A Lattice Computing Approach, pp. 23-42. , Science Gate Publishing: Thrace, Greece; Pandey, P., Seeja, K.R., Emotional State Recognition with EEG Signals Using Subject Independent Approach (2019) Data Science and Big Data Analytics, pp. 117-124. , Springer: Berlin/Heidelberg, Germany; Qiu, J.-L., Zhao, W.-Y., Data Encoding Visualization Based Cognitive Emotion Recognition with AC-GAN Applied for Denoising Proceedings of the 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), pp. 222-227. , Berkeley, CA, USA, 16–18 July 2018; Sharma, R., Pachori, R.B., Sircar, P., Automated emotion recognition based on higher order statistics and deep learning algorithm (2020) Biomed. Signal Process. Control, 58, p. 101867. , [CrossRef]; Kaburlasos, V.G., Papadakis, S.E., Papakostas, G.A., Lattice Computing Extension of the FAM Neural Classifier for Human Facial Expression Recognition (2013) IEEE Trans. Neural Netw. Learn. Syst, 24, pp. 1526-1538. , [CrossRef] [PubMed]; Papadakis, S.E., Kaburlasos, V.G., Papakostas, G.A., Two Fuzzy Lattice Reasoning (FLR) Classifiers and their Application for Human Facial Expression Recognition (2014) Mult. Log. Soft Comput, 22, pp. 561-579; Spencer, C., Koc, I.A., Suga, C., Lee, A., Dhareshwar, A.M., Franzén, E., Iozzo, M., McKeown, G., Assessing the Use of Physiological Signals and Facial Behaviour to Gauge Drivers’ Emotions as a UX Metric in Automotive User Studies Proceedings of the 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 78-81. , New York, NY, USA, 21–22 September 2020; Shan, K., Guo, J., You, W., Lu, D., Bie, R., Automatic facial expression recognition based on a deep convolutional-neural-network structure Proceedings of the 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA), pp. 123-128. , London, UK, 7–9 June 2017; Majid Mehmood, R., Du, R., Lee, H.J., Optimal Feature Selection and Deep Learning Ensembles Method for Emotion Recognition From Human Brain EEG Sensors (2017) IEEE Access, 5, pp. 14797-14806. , [CrossRef]; Zeng, N., Zhang, H., Song, B., Liu, W., Li, Y., Dobaie, A.M., Facial expression recognition via learning deep sparse autoencoders (2018) Neurocomputing, 273, pp. 643-649. , [CrossRef]; Akçay, M.B., Oğuz, K., Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers (2020) Speech Commun, 116, pp. 56-76. , [CrossRef]; Semwal, N., Kumar, A., Narayanan, S., Automatic speech emotion detection system using multi-domain acoustic feature selection and classification models Proceedings of the 2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), pp. 1-6. , New Delhi, India, 23–24 February 2017; Jiang, Y., Li, W., Hossain, M.S., Chen, M., Alelaiwi, A., Al-Hammadi, M., A snapshot research and implementation of multimodal information fusion for data-driven emotion recognition (2020) Inf. Fusion, 53, pp. 209-221. , [CrossRef]; Ma, J., Tang, H., Zheng, W.-L., Lu, B.-L., Emotion Recognition using Multimodal Residual LSTM Network Proceedings of the Proceedings of the 27th ACM International Conference on Multimedia, pp. 176-183. , Nice, France, 21–25 October 2019; Zhang, J., Yin, Z., Chen, P., Nichele, S., Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review (2020) Inf. Fusion, 59, pp. 103-126. , [CrossRef]; Yee Chung, J.W., Fuk So, H.C., Tak Choi, M.M., Man Yan, V.C., Shing Wong, T.K., Artificial Intelligence in education: Using heart rate variability (HRV) as a biomarker to assess emotions objectively (2021) Comput. Educ. Artif. 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Appl, 28, pp. 1985-1990. , [CrossRef]; Saxena, A., Tripathi, K., Khanna, A., Gupta, D., Sundaram, S., Emotion Detection Through EEG Signals Using FFT and Machine Learning Techniques (2020) Advances in Intelligent Systems and Computing, pp. 543-550. , Springer: Singapore, ISBN 9789811512858; García-Martínez, B., Fernández-Caballero, A., Zunino, L., Martínez-Rodrigo, A., Recognition of Emotional States from EEG Signals with Nonlinear Regularity-and Predictability-Based Entropy Metrics (2021) Cognit. Comput, 13, pp. 403-417. , [CrossRef]; Ozel, P., Akan, A., Yilmaz, B., Synchrosqueezing transform based feature extraction from EEG signals for emotional state prediction (2019) Biomed. Signal Process. 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Mag, 10, pp. 42-51. , [CrossRef]; Lytridis, C., Lekova, A., Bazinas, C., Manios, M., Kaburlasos, V.G., WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications (2020) Mathematics, 8, p. 413. , [CrossRef]; Musha, T., Terasaki, Y., Haque, H.A., Ivamitsky, G.A., Feature extraction from EEGs associated with emotions (1997) Artif. Life Robot, 1, pp. 15-19. , [CrossRef]; Zheng, W., Multichannel EEG-Based Emotion Recognition via Group Sparse Canonical Correlation Analysis (2017) IEEE Trans. Cogn. Dev. Syst, 9, pp. 281-290. , [CrossRef]; Dabas, H., Sethi, C., Dua, C., Dalawat, M., Sethia, D., Emotion Classification Using EEG Signals Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence–CSAI ’18, pp. 380-384. , Shenzhen, China, 8–10 December 2018; Krishna, A.H., Sri, A.B., Priyanka, K.Y.V.S., Taran, S., Bajaj, V., Emotion classification using EEG signals based on tunable-Q wavelet transform (2019) IET Sci. Meas. 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Auton. Ment. Dev, 7, pp. 162-175. , [CrossRef]; Akin, M., Comparison of wavelet transform and FFT methods in the analysis of EEG signals (2002) J. Med. Syst, 26, pp. 241-247. , [CrossRef]; Uzun, S.S., Yildirim, S., Yildirim, E., Emotion primitives estimation from EEG signals using Hilbert Huang Transform Proceedings of the 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, pp. 224-227. , Hong Kong and Shenzhen, China, 2–7 January 2012; Nawaz, R., Cheah, K.H., Nisar, H., Yap, V.V., Comparison of different feature extraction methods for EEG-based emotion recognition (2020) Biocybern. Biomed. Eng, 40, pp. 910-926. , [CrossRef]; Duan, R.N., Zhu, J.Y., Lu, B.L., Differential entropy feature for EEG-based emotion classification (2013) Proceedings of the International IEEE/EMBS Conference on Neural Engineering, , San Diego, CA, USA, 6–8 November; Alarcao, S.M., Fonseca, M.J., Emotions Recognition Using EEG Signals: A Survey (2019) IEEE Trans. Affect. 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Pattern Recognit, 1, pp. 11-25; Asghar, M.A., Khan, M.J., Amin, Y., Rizwan, M., Rahman, M., Badnava, S., Mirjavadi, S.S., EEG-Based Multi-Modal Emotion Recognition using Bag of Deep Features: An Optimal Feature Selection Approach (2019) Sensors, 19, p. 5218. , [CrossRef] [PubMed]; Fu, R., Tian, Y., Bao, T., Meng, Z., Shi, P., Improvement Motor Imagery EEG Classification Based on Regularized Linear Discriminant Analysis (2019) J. Med. Syst, 43, p. 169. , [CrossRef]; Soleymani, M., Asghari-Esfeden, S., Fu, Y., Pantic, M., Analysis of EEG Signals and Facial Expressions for Continuous Emotion Detection (2016) IEEE Trans. Affect. Comput, 7, pp. 17-28. , [CrossRef]; Tang, H., Liu, W., Zheng, W.-L., Lu, B.-L., Multimodal Emotion Recognition Using Deep Neural Networks (2017) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 811-819. , Springer: Cham, Switzerland, ISBN 9783319700922; Craik, A., He, Y., Contreras-Vidal, J.L., Deep learning for electroencephalogram (EEG) classification tasks: A review (2019) J. Neural Eng, 16, p. 031001. , [CrossRef]; Özerdem, M.S., Polat, H., Emotion recognition based on EEG features in movie clips with channel selection (2017) Brain Inform, 4, pp. 241-252. , [CrossRef]; Peng, H., Wang, Y., Chao, J., Huo, X., Majoe, D., Stability study of the optimal channel selection for emotion classification from EEG Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomed. (BIBM), pp. 2031-2036. , Kansas City, MO, USA, 13–16 November 2017; A Dataset Collection for Various Purposes Using EEG Signals, , https://bcmi.sjtu.edu.cn/home/seed/seed.html, BCMI Shangai Jiao Tong University SEED Dataset. (accessed on 29 April 2021); Lin, D., Zhang, J., Li, J., Calhoun, V.D., Deng, H.W., Wang, Y.P., Group sparse canonical correlation analysis for genomic data integration (2013) BMC Bioinform, 14, pp. 1-16. , [CrossRef] [PubMed]; Zheng, W., Zhou, X., Zou, C., Zhao, L., Facial expression recognition using kernel canonical correlation analysis (KCCA) (2006) IEEE Trans. Neural Netw, 17, pp. 233-238. , [CrossRef] [PubMed]; Burges, C.J.C., A tutorial on support vector machines for pattern recognition (1998) Data Min. Knowl. Discov, 2, pp. 121-167. , [CrossRef]","Papakostas, G.A.; HUMAIN-Lab, Greece; email: gpapak@cs.ihu.gr",,,"MDPI AG",,,,,22277390,,,,"English","Mathematics",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85106659780
"Jiang J., Wang Y., Cao H., Zhu J., Zhu W., Jin L.","57193512600;56176432500;8311639900;56422710000;57215897141;57059691300;","On the control of lower extremity exoskeleton base on the interaction force of torso",2020,"Proceedings - 2020 Chinese Automation Congress, CAC 2020",,,"9327741","90","95",,,"10.1109/CAC51589.2020.9327741","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100934435&doi=10.1109%2fCAC51589.2020.9327741&partnerID=40&md5=845ecdfbc6c80c9b835ac5bdda20e35d","East China University of Science and Technology, School of Mechanical and Power Engineering, Shanghai, China; Fudan University, Academy for Engineering and Technology, Shanghai, China; Shanghai Aerospace Control Technology Institute, Shanghai, China","Jiang, J., East China University of Science and Technology, School of Mechanical and Power Engineering, Shanghai, China; Wang, Y., East China University of Science and Technology, School of Mechanical and Power Engineering, Shanghai, China; Cao, H., East China University of Science and Technology, School of Mechanical and Power Engineering, Shanghai, China; Zhu, J., Fudan University, Academy for Engineering and Technology, Shanghai, China; Zhu, W., Shanghai Aerospace Control Technology Institute, Shanghai, China; Jin, L., Shanghai Aerospace Control Technology Institute, Shanghai, China","This paper presents an ECUST Lower-extremity Exoskeleton (ELE-ROBOT) for augmenting human performance. Most exoskeletons calculate joint torques by using ground reaction forces from foot force sensors. However, this method becomes unreliable in the complex terrain environment. We propose a new control strategy by measuring the human-robot interaction forces using force sensors mounted on the wearer's torso, and placing only switches on the foot. The exoskeleton joint's torques are estimated by this measured human-robot interaction forces. The experiments were conducted to verify the feasibility of this control method, the wearer carried 35kg load, and walked on the treadmill at 1.3m/s with exoskeleton powered on or off, then the data was compared to the wearer's natural walking gait. The results show ELE-ROBOT can reduce a portion of the carried load that is exerted on the wearer significantly and trace the joints motion in real time. © 2020 IEEE.","control method; ground reaction force; human-robot interaction force; Lower extremity exoskeleton; torque estimate","Biophysics; Human robot interaction; Man machine systems; Complex terrains; Control methods; Control strategies; Ground reaction forces; Human performance; Interaction forces; Joint torques; Lower extremity exoskeletons; Exoskeleton (Robotics)",,,,,"National Natural Science Foundation of China, NSFC: 51905104, 91748110","ACKNOWLEDGMENT This work was supported by the National Natural Science Foundation of China (NO.91748110 and Grant NO.51905104).",,,,,,,,,,"Dollar, A.M., Herr, H., Lower extremity exoskeletons and active orthoses: Challenges and state-of-the-art (2008) Ieee Transactions on Robotics, 24 (1), pp. 144-158; Young, A.J., Ferris, D.P., State of the art and future directions for lower limb robotic exoskeletons (2017) Transactions on Neural Systems and Rehabilitation Engineering, 25 (2). , FEBRUARY; Zoss, A.B., Kazerooni, H., Chu, A., Biomechanical design of the Berkeley lower extremity exoskeleton (bleex) (2006) IEEE/ Asme Trans. Mechatron, 11, pp. 128-138; Kazerooni, H., Exoskeletons for human performance augmentation (2008) Handbook of Robotics, pp. 773-793. , New York, NY, USA, Springer; Gregorczyk, K.N., Hasselquist, L., Schiffman, J.M., Bensel, C.K., Obusek, J.P., Gutekunst, D.J., Effects of a lower-body exoskeleton device on metabolic cost and gait biomechanics during load carriage (2010) Ergonomics, 53, pp. 1263-1275; (2010) Raytheon Xos 2 Exoskeleton, Second-Generation Robotics Suit, , http://www.armytechnology.com/projects/raytheon-xos-2-exoskeleton-us/; Nilsson, A., Vreede, K.S., Häglund, V., Kawamoto, H., Sankai, Y., Borg, J., Gait training early after stroke with a new exoskeleton-the hybrid assistive limb: A study of safety and feasibility (2014) J. NeuroEng. Rehabil, 11, p. 92; Tsukahara, A., Kawanishi, R., Hasegawa, Y., Sankai, Y., Sit-to-stand and stand-to-sit transfer support for complete paraplegic patients with robot suit (2010) Hal Adv. Robot, 24, pp. 1615-1638; Zeilig, G., Weingarden, H., Zwecker, M., Dudkiewicz, I., Bloch, A., Esquenazi, A., Safety and tolerance of the rewalk™ exoskeleton suit for ambulation by people with complete spinal cord injury: A pilot study (2012) J. Spinal Cord Medicine, 35, pp. 101-196; Kolakowsky-Hayner, S.A., Crew, J., Moran, S., Shah, A., Safetyand feasibility of using the eksotm bionic exoskeleton to aid ambulation after spinal cord injury (2013) J. Spine; Wang, L., Wang, S., Van Asseldonk, E.H., Kooij Der, H.Van., Actively controlled lateral gait assistance in a lower limb exoskeleton, in: Intelligent robots and systems, ""iros (2013) 2013 IEEE/RSJ International Conference On, Ieee, pp. 965-970; Lim, D., Kim, W., Lee, H., Development of a lower extremity exoskeleton robot with a quasi-anthropomorphic design approach for load carriage (2015) International Conference on Intelligent Robots and Systems (IROS) Congress Center, , Hamburg Sept 28-Oct 2, Hamburg, Germany; Yan, T., Cempini, M., Maria Oddo, C., Vitiello, N., Review of assistive strategies in powered lower-limb orthoses and exoskeletons (2015) Robotics and Autonomous Systems, 64, pp. 120-136",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 Chinese Automation Congress, CAC 2020","6 November 2020 through 8 November 2020",,166835,,9781728176871,,,"English","Proc. - Chin. Autom. Congr., CAC",Conference Paper,"Final","",Scopus,2-s2.0-85100934435
"Dong M., Li J., Rong X., Fan W., Kong Y., Zhou Y.","56081285600;8322805400;57210388054;57215422407;57215417473;57221312830;","Compliant physical interaction to enhance rehabilitation training of a parallel ankle robotic system",2020,"Proceedings - 2020 Chinese Automation Congress, CAC 2020",,,"9327659","2191","2196",,2,"10.1109/CAC51589.2020.9327659","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100919868&doi=10.1109%2fCAC51589.2020.9327659&partnerID=40&md5=3ba08840e2dfbd1c3263e8b7ee9f794d","Beijing University of Technology, Faculty of Materials and Manufacturing, Beijing, China; Affiliated Hospital of Qingdao University, Department of Neurology, Qingdao, China","Dong, M., Beijing University of Technology, Faculty of Materials and Manufacturing, Beijing, China; Li, J., Beijing University of Technology, Faculty of Materials and Manufacturing, Beijing, China; Rong, X., Affiliated Hospital of Qingdao University, Department of Neurology, Qingdao, China; Fan, W., Beijing University of Technology, Faculty of Materials and Manufacturing, Beijing, China; Kong, Y., Beijing University of Technology, Faculty of Materials and Manufacturing, Beijing, China; Zhou, Y., Beijing University of Technology, Faculty of Materials and Manufacturing, Beijing, China","Isotonic exercise is an effective way to improve the ankle joint complex's (AJC) muscle strength, and a contracting muscle shortens against a constant load during the process, or in other words, the muscle maintains equal tone while shortening. In order to change the physical human-robot interaction into the reciprocating motion of the parallel ankle rehabilitation root (PARR), to improve its compliance and enhance muscle strength training, we implement the admittance-based isotonic exercise strategy on our PARR. The mechanical design of the PARR and the admittance-based isotonic exercise to change the physical human-robot interaction torque into the angle movement were described at first. Then, an adaptive law of the admittance parameters was further proposed to adaptively adjust the compliance. Experiments with neurologically intact subjects were conducted, and the experimental results showed good trajectory tracking performance and excellent practicability, which can improve muscle strength training safely. © 2020 IEEE.","adaptive law; admittance control; ankle rehabilitation; isotonic exercise; physical human-robot interaction","Educational robots; Machine design; Man machine systems; Muscle; Robotics; Admittance parameters; Ankle joint complex; Muscle strength trainings; Physical human-robot interactions; Physical interactions; Reciprocating motion; Rehabilitation training; Trajectory tracking; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 61903011; Beijing Municipal Commission of Education: KM202010005021; Natural Science Foundation of Beijing Municipality: 3204036; National Key Research and Development Program of China, NKRDPC: 2018YFB1307004, 2020YFC2004200","This work was supported in part by Beijing Natural Science Foundation under grant No. 3204036, in part by the General Program of Science and Technology Development Project of Beijing Municipal Education Commission under Grant No. KM202010005021, in part by the National Natural Science Foundation of China under Grants No. 61903011, and in part by the National Key R&D Program of China under Grant Nos. 2018YFB1307004, 2020YFC2004200. *To whom correspondence should be addressed.",,,,,,,,,,"Zhang, L., Li, J., Dong, M., Design and workspace analysis of a parallel ankle rehabilitation robot (PARR) (2019) Journal of Healthcare Engineering; Michmizos, K.P., Rossi, S., Castelli, E., Robot-aided neurorehabilitation: A pediatric robot for ankle rehabilitation (2015) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23 (6), pp. 1056-1067; Girone, M., Burdea, G., Bouzit, M., The Rutgers ankle orthopedic rehabilitation interface (1999) Proceedings of the ASME Dynamic Systems and Control Division, 67, pp. 305-312. , Nov Nashville, TN; Malosio, M., Negri, S.P., Pedrocchi, N., A spherical parallel three degrees-of-freedom robot for ankle-foot neuro-rehabilitation (2012) 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, pp. 3356-3359; Jamwal, P.K., Hussain, S., Multicriteria design optimization of a parallel ankle rehabilitation robot: Fuzzy dominated sorting evolutionary algorithm approach (2016) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46 (5), pp. 589-597; Dong, M., Kong, Y., Li, J., Kinematic calibration of a parallel 2-UPS/RRR ankle rehabilitation robot (2020) Journal of Healthcare Engineering; Li, J., Fan, W., Dong, M., Research on control strategies for ankle rehabilitation using parallel mechanism (2020) Cognitive Computation and Systems, 2 (3), pp. 105-111; Meng, W., Xie, S., Liu, Q., Robust iterative feedback tuning control of a compliant rehabilitation robot for repetitive ankle training (2017) IEEE/ASME Transactions on Mechatronics, 22 (1), pp. 173-184; Zuo, J., Meng, W., Liu, Q., Coupling disturbance compensated MIMO control of parallel ankle rehabilitation robot actuated by pneumatic muscles (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, pp. 6608-6613; Nalam, V., Lee, H., Development of a two-axis robotic platform for the characterization of two-dimensional ankle mechanics (2019) IEEE/ASME Transactions on Mechatronics, 24 (2), pp. 459-470; Jiang, J., Li, W., Lee, K., A novel pantographic exoskeleton based collocated joint design with application for early stroke rehabilitation (2020) IEEE/ASME Transactions on Mechatronics, 25 (4), pp. 1922-1932; Lenzi, T., Cempini, M., Hargrove, L.J., Design, development, and validation of a lightweight nonbackdrivable robotic ankle prosthesis (2019) IEEE/ASME Transactions on Mechatronics, 24 (2), pp. 471-482; Saglia, J.A., Tsagarakis, N.G., Dai, J.S., Control strategies for patient assisted training using the ankle rehabilitation robot (ARBOT) (2013) IEEE/ASME Transactions on Mechatronics, 18 (6), pp. 1799-1808; Zhang, M., McDaid, A., Veale, A.J., Adaptive trajectory tracking control of a parallel ankle rehabilitation robot with joint-space force distribution (2019) IEEE Access, 7, pp. 85812-85820; Wang, L., Chang, Y., Zhu, H.T., Internal model control and experimental study of ankle rehabilitation robot (2020) Robotica, 38 (5), pp. 940-956; Wang, C., Fang, Y., Guo, S., Design and kinematic analysis of redundantly actuated parallel mechanisms for ankle rehabilitation (2015) Robotica, 33 (2), pp. 366-384; Dong, M., Fan, W., Li, J., A new ankle robotic system enabling wholestage compliance rehabilitation training (2020) IEEE/ASME Transactions on Mechatronics; Ayas, M.S., Altas, I.H., Fuzzy logic based adaptive admittance control of a redundantly actuated ankle rehabilitation robot (2017) Control Engineering Practice, 59, pp. 44-54; Asl, H.J., Yamashita, M., Narikiyo, T., Field-based assist-as-needed control schemes for rehabilitation robots (2020) IEEE/ASME Transactions on Mechatronics, 25 (4), pp. 2100-2111; Perez-Ibarra, J.C., Siqueira, A.A.G., Silva-Couto, M.A., Adaptive impedance control applied to robot-aided neuro-rehabilitation of the ankle (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 185-192; Li, J., Zuo, S., Zhang, L., Mechanical design and performance analysis of a novel parallel robot for ankle rehabilitation (2020) Journal of Mechanisms and Robotics-Transactions of the ASME, 12 (5), pp. 1-17; Zuo, S., Li, J., Dong, M., Design and performance evaluation of a novel wearable parallel mechanism for ankle rehabilitation (2020) Frontiers in Neurorobotics, 14, pp. 1-14; Li, J., Fan, W., Dong, M., Implementation of passive compliance training on a parallel ankle rehabilitation robot to enhance safety (2020) Industrial Robot-The International Journal of Robotics Research and Application, 47 (5), pp. 747-755; Duan, J., Gan, Y., Chen, M., Adaptive variable impedance control for dynamic contact force tracking in uncertain environment (2018) Robotics and Autonomous Systems, 102, pp. 54-65; Ding, Q., Han, J., Zhao, X., Continuous estimation of human multi-joint angles from sEMG using a state-space model (2017) IEEE Transactions on Neural Systems & Rehabilitation Engineering, 25 (9), pp. 1518-1528; Dong, M., Yao, G., Li, J., Calibration of low cost IMUs inertial sensors for improved attitude estimation (2020) Journal of Intelligent & Robotic Systems, 100, pp. 1015-1029; Dong, M., Yao, G., Li, J., Research on attitude interpolation and tracking control based on improved orientation vector SLERP method (2020) Robotica, 38 (4), pp. 719-731","Li, J.; Beijing University of Technology, China; email: lijianfeng@bjut.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.","2020 Chinese Automation Congress, CAC 2020","6 November 2020 through 8 November 2020",,166835,,9781728176871,,,"English","Proc. - Chin. Autom. Congr., CAC",Conference Paper,"Final","",Scopus,2-s2.0-85100919868
"Stock-Homburg R., Hannig M.","56364282100;57225793828;","Is there a privacy paradox in the workplace?",2020,"International Conference on Information Systems, ICIS 2020 - Making Digital Inclusive: Blending the Local and the Global",,,,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103450110&partnerID=40&md5=a8e556ce23162593a12197da27487552","Technical University of Darmstadt, Hochschulstraße 1, Darmstadt, 64289, Germany","Stock-Homburg, R., Technical University of Darmstadt, Hochschulstraße 1, Darmstadt, 64289, Germany; Hannig, M., Technical University of Darmstadt, Hochschulstraße 1, Darmstadt, 64289, Germany","This article deals with a possible of the privacy paradox during human-robot interactions in workplace. We conducted an experimental study in a real company setting with N=210 employees, and compared privacy concerns with actual information reluctance towards service robots for different tasks. Drawing on the computer-as-social-actor (CASA) paradigm, we argue that employees might mindlessly apply social rules when disclosing information towards service robots. Based on uncanny valley theory we also tested differences between humanoid and android robots. The results show that privacy concerns in the workplace are higher towards robots than towards humans, but they do not mirror the actual information reluctance. This indicates a privacy paradox in the workplace that is especially strong for interactions with android robots. This article contributes to research by transferring the privacy paradox to human-robot interactions and providing insights for companies how to deal with the increasing privacy issues. © ICIS 2020. All rights reserved.","CASA Paradigm; Ethics; Privacy; Robots at workplace; Self-disclosure; Service robots; Task complexity; Uncanny valley","Blending; Information systems; Information use; Man machine systems; Mobile robots; Personnel; Privacy concerns; Privacy issue; Service robots; Social actors; Social rules; Uncanny valley; Human robot interaction",,,,,,,,,,,,,,,,"Acquisti, A., Brandimarte, L., Loewenstein, G., Privacy and human behavior in the age of information (2015) Science, 347 (6221), pp. 509-514; Aguinis, H., Bradley, K. J., Best Practice Recommendations for Designing and Implementing Experimental Vignette Methodology Studies (2014) Organizational Research Methods, 17 (4), pp. 351-371; Atzmüller, C., Steiner, P. 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(2006) Business Ethics Quarterly, 16 (3), pp. 323-342; Cheetham, M., Pavlovic, I., Jordan, N., Suter, P., Jancke, L., Category Processing and the human likeness dimension of the Uncanny Valley Hypothesis: Eye-Tracking Data (2013) Frontiers in Psychology, 4, p. 108. , (Article); Collins, N. L., Miller, L. C., Self-Disclosure and Liking: A Meta-Analytic Review (1994) Psychological Bulletin, 116 (3), pp. 457-475; Conti, D., Di Nuovo, S., Buono, S., Di Nuovo, A., Robots in Education and Care of Children with Developmental Disabilities: A Study on Acceptance by Experienced and Future Professionals (2017) International Journal of Social Robotics, 9 (1), pp. 51-62; Davison, D. P., Wijnen, F. M., Charisi, V., van der Meij, J., Evers, V., Reidsma, D., Working with a Social Robot in School: A Long-Term Real-World Unsupervised Deployment (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 63-72; Dienlin, T., Trepte, S., Is the privacy paradox a relic of the past? An in-depth analysis of privacy attitudes and privacy behaviors (2015) European Journal of Social Psychology, 45, pp. 285-297; Dinev, T., Bellotto, M., Hart, P., Russo, V., Serra, I., Colautti, C., Privacy calculus model in e-commerce - a study of Italy and the United States (2006) European Journal of Information Systems, (15), pp. 389-402; Dinev, T., McConnell, A. R., Smith, H. J., Research Commentary-Informing Privacy Research Through Information Systems, Psychology, and Behavioral Economics: Thinking Outside the “APCO” Box (2015) Information Systems Research, 26 (4), pp. 639-655; DiSalvo, C. F., Gemperle, F., Forlizzi, J., Kiesler, S., All robots are not created equal: The design and perception of humanoid robot heads (2002) Proceedings 4th Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques, pp. 321-326; Ferrari, F., Paladino, M. P., Jetten, J., Blurring Human-Machine Distinctions: Anthropomorphic Appearance in Social Robots as a Threat to Human Distinctiveness (2016) International Journal of Social Robotics, 8 (2), pp. 287-302; Festinger, L., (1957) A theory of cognitive dissonance, , Stanford: Stanford University Press; Floridi, L., Information ethics: On the philosophical foundation of computer ethics (1999) Ethics and Information Technology, (1), pp. 37-56; Fornell, C., Larcker, D. F., Evaluating Structural Equation Models with Unobservable Variables and Measurement Error (1981) Journal of Marketing Research, 18 (1), pp. 39-50; Gibson, K. R., Can I Tell You Something? How Disruptive Self-Disclosure Changes Who “We” Are (2018) Academy of Management Review, 43 (4), pp. 570-589; Glaser, B. G., Strauss, A. L., (1967) The Discovery of Grounded Theory: Strategies for Qualitative Research; Goetz, J., Kiesler, S., Powers, A., Matching robot appearance and behavior to tasks to improve human-robot cooperation (2003) The 12th IEEE International Workshop on Robot and Human Interactive Communication, pp. 55-60. , 2003; Gray, K., Wegner, D. M., Feeling robots and human zombies: mind perception and the uncanny valley (2012) Cognition, 125, pp. 125-130; Gulati, R., Does Familiarity Breed Trust? The Implications of Repeated Ties for Contractual Choice in Alliances (1995) The Academy of Management Journal, 38 (1), pp. 85-112; Haring, K. 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"Scholl C., Tobola A., Ludwig K., Zanca D., Eskofier B.M.","57315343900;56786073700;12769118900;57202056859;26428080900;","A smart capacitive sensor skin with embedded data quality indication for enhanced safety in human–robot interaction",2021,"Sensors","21","21","7210","","",,,"10.3390/s21217210","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118117065&doi=10.3390%2fs21217210&partnerID=40&md5=678eac87f80a2783b27ec7a51235aed4","Siemens AG, Technology, Guenther-Scharowsky-Str. 1, Erlangen, 91058, Germany; Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Carl-Thiersch-Straße 2b, Erlangen, 91052, Germany; Institute of Electronics Engineering, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Cauerstr. 9, Erlangen, 91054, Germany; Faculty of Electrical Engineering, Precision Engineering, Information Technology, Nuremberg Institute of Technology, Wassertorstraße 10, Nürnberg, 90489, Germany","Scholl, C., Siemens AG, Technology, Guenther-Scharowsky-Str. 1, Erlangen, 91058, Germany, Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Carl-Thiersch-Straße 2b, Erlangen, 91052, Germany; Tobola, A., Siemens AG, Technology, Guenther-Scharowsky-Str. 1, Erlangen, 91058, Germany, Institute of Electronics Engineering, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Cauerstr. 9, Erlangen, 91054, Germany, Faculty of Electrical Engineering, Precision Engineering, Information Technology, Nuremberg Institute of Technology, Wassertorstraße 10, Nürnberg, 90489, Germany; Ludwig, K., Siemens AG, Technology, Guenther-Scharowsky-Str. 1, Erlangen, 91058, Germany; Zanca, D., Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Carl-Thiersch-Straße 2b, Erlangen, 91052, Germany; Eskofier, B.M., Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Carl-Thiersch-Straße 2b, Erlangen, 91052, Germany","Smart sensors are an integral part of the Fourth Industrial Revolution and are widely used to add safety measures to human–robot interaction applications. With the advancement of machine learning methods in resource-constrained environments, smart sensor systems have become increasingly powerful. As more data-driven approaches are deployed on the sensors, it is of growing importance to monitor data quality at all times of system operation. We introduce a smart capacitive sensor system with an embedded data quality monitoring algorithm to enhance the safety of human– robot interaction scenarios. The smart capacitive skin sensor is capable of detecting the distance and angle of objects nearby by utilizing consumer-grade sensor electronics. To further acknowledge the safety aspect of the sensor, a dedicated layer to monitor data quality in real-time is added to the embedded software of the sensor. Two learning algorithms are used to implement the sensor functionality: (1) a fully connected neural network to infer the position and angle of objects nearby and (2) a one-class SVM to account for the data quality assessment based on out-of-distribution detection. We show that the sensor performs well under normal operating conditions within a range of 200 mm and also detects abnormal operating conditions in terms of poor data quality successfully. A mean absolute distance error of 11.6 mm was achieved without data quality indication. The overall performance of the sensor system could be further improved to 7.5 mm by monitoring the data quality, adding an additional layer of safety for human–robot interaction. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Capacitive sensor system; Data quality; Embedded AI; Human–robot interaction; Signal processing; Smart sensors","Accident prevention; Data reduction; Human robot interaction; Learning algorithms; Learning systems; Man machine systems; Monitoring; Signal processing; Smart sensors; Capacitive sensor system; Data quality; Embedded AI; Humans-robot interactions; Integral part; Machine learning methods; Safety measures; Sensor skins; Sensor systems; Signal-processing; Capacitive sensors",,,,,"Deutsche Forschungsgemeinschaft, DFG: ES 434/8-1; Bundesministerium für Wirtschaft und Energie, BMWi: 01MT20001A, 12ES0497","This work was supported in part by the Federal Ministry for Economic Affairs and Energy, Germany, within the projects ?GEMiMEG-II?Sichere und robuste kalibrierte Messsysteme f?r die digitale Transformation? (grant number 01MT20001A) and ?Verbundprojekt: Transfer-und Qualifizierungsmethodik h?chstintegrierter Elektronikkomponenten f?r die Automobil-und Automatisierungsindustrie?TRACE? (grant number 12ES0497). Bjoern Eskofier gratefully acknowledges the support of the German Research Foundation (DFG) within the framework of the Heisenberg professorship program (grant number ES 434/8-1).",,,,,,,,,,"Arents, J., Abolins, V., Judvaitis, J., Vismanis, O., Oraby, A., Ozols, K., Human–Robot Collaboration Trends and Safety Aspects: A Systematic Review (2021) J. Sens. 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"Webster M., Western D., Araiza-Illan D., Dixon C., Eder K., Fisher M., Pipe A.G.","15069696700;36814974000;55336754200;57225816972;23391989300;55741961100;7004462827;","A corroborative approach to verification and validation of human–robot teams",2020,"International Journal of Robotics Research","39","1",,"73","99",,20,"10.1177/0278364919883338","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075529404&doi=10.1177%2f0278364919883338&partnerID=40&md5=c6bde90ff09c9aa56c8720d5148c7bd7","Department of Computer Science, University of Liverpool, United Kingdom; Department of Computer Science, University of Bristol, United Kingdom; Bristol Robotics Laboratory, United Kingdom; Faculty of Environment and Technology, University of the West of England, United Kingdom","Webster, M., Department of Computer Science, University of Liverpool, United Kingdom; Western, D., Department of Computer Science, University of Bristol, United Kingdom; Araiza-Illan, D., Department of Computer Science, University of Bristol, United Kingdom; Dixon, C., Department of Computer Science, University of Liverpool, United Kingdom; Eder, K., Department of Computer Science, University of Bristol, United Kingdom, Bristol Robotics Laboratory, United Kingdom; Fisher, M., Department of Computer Science, University of Liverpool, United Kingdom; Pipe, A.G., Bristol Robotics Laboratory, United Kingdom, Faculty of Environment and Technology, University of the West of England, United Kingdom","We present an approach for the verification and validation (V&V) of robot assistants in the context of human–robot interactions, to demonstrate their trustworthiness through corroborative evidence of their safety and functional correctness. Key challenges include the complex and unpredictable nature of the real world in which assistant and service robots operate, the limitations on available V&V techniques when used individually, and the consequent lack of confidence in the V&V results. Our approach, called corroborative V&V, addresses these challenges by combining several different V&V techniques; in this paper we use formal verification (model checking), simulation-based testing, and user validation in experiments with a real robot. This combination of approaches allows V&V of the human–robot interaction task at different levels of modeling detail and thoroughness of exploration, thus overcoming the individual limitations of each technique. We demonstrate our approach through a handover task, the most critical part of a complex cooperative manufacturing scenario, for which we propose safety and liveness requirements to verify and validate. Should the resulting V&V evidence present discrepancies, an iterative process between the different V&V techniques takes place until corroboration between the V&V techniques is gained from refining and improving the assets (i.e., system and requirement models) to represent the human–robot interaction task in a more truthful manner. Therefore, corroborative V&V affords a systematic approach to “meta-V&V,” in which different V&V techniques can be used to corroborate and check one another, increasing the level of certainty in the results of V&V. © The Author(s) 2019.","Human–robot interaction; model checking; simulation; testing; validation; verification","Formal verification; Iterative methods; Model checking; Testing; Verification; Cooperative manufacturing; Functional correctness; Lack of confidences; Requirement models; Robot interactions; simulation; validation; Verification-and-validation; Human robot interaction",,,,,"EP/R026173/1; EP/R026084/1; Engineering and Physical Sciences Research Council, EPSRC: EP/J01205X/1, EP/K006193/1, EP/K006223/1, EP/K006320/1, EP/N007565/1, EP/R026092/1","The authors thank Kyriakos Georgiou for his support with data management. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the EPSRC under the FAIR-SPACE (grant number EP/R026092/1), RAIN (grant number EP/R026084/1) and ORCA (grant number EP/R026173/1) RAI Hubs, the ?Science of Sensor Systems Software? programme (grant number EP/N007565/1), the ?Trustworthy Robotic Assistants? project (grant numbers EP/K006320/1, EP/K006193/1, and EP/K006223/1), and the RIVERAS project (grant number EP/J01205X/1).","Webster Matt 1 Western David 2 Araiza-Illan Dejanira 2 Dixon Clare 1 Eder Kerstin 2 3 Fisher Michael 1 Pipe Anthony G 3 4 1 Department of Computer Science, University of Liverpool, UK 2 Department of Computer Science, University of Bristol, UK 3 Bristol Robotics Laboratory, UK 4 Faculty of Environment and Technology, University of the West of England, UK Matt Webster, Department of Computer Science, University of Liverpool, Liverpool, L69 7ZF, UK. Email: matt@liverpool.ac.uk 11 2019 0278364919883338 © The Author(s) 2019 2019 SAGE Publications This article is distributed under the terms of the Creative Commons Attribution 4.0 License ( http://www.creativecommons.org/licenses/by/4.0/ ) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage ). We present an approach for the verification and validation (V&V) of robot assistants in the context of human–robot interactions, to demonstrate their trustworthiness through corroborative evidence of their safety and functional correctness. Key challenges include the complex and unpredictable nature of the real world in which assistant and service robots operate, the limitations on available V&V techniques when used individually, and the consequent lack of confidence in the V&V results. Our approach, called corroborative V&V , addresses these challenges by combining several different V&V techniques; in this paper we use formal verification (model checking), simulation-based testing, and user validation in experiments with a real robot. This combination of approaches allows V&V of the human–robot interaction task at different levels of modeling detail and thoroughness of exploration, thus overcoming the individual limitations of each technique. We demonstrate our approach through a handover task, the most critical part of a complex cooperative manufacturing scenario, for which we propose safety and liveness requirements to verify and validate. Should the resulting V&V evidence present discrepancies, an iterative process between the different V&V techniques takes place until corroboration between the V&V techniques is gained from refining and improving the assets (i.e., system and requirement models) to represent the human–robot interaction task in a more truthful manner. Therefore, corroborative V&V affords a systematic approach to “meta-V&V,” in which different V&V techniques can be used to corroborate and check one another, increasing the level of certainty in the results of V&V. Human–robot interaction verification validation model checking simulation testing Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/J01205X/1 Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/K006193/1 Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/K006223/1 Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/K006320/1 Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/N007565/1 Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/R026084/1 Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/R026092/1 Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/R026173/1 edited-state corrected-proof The authors thank Kyriakos Georgiou for his support with data management. Funding This work was supported by the EPSRC under the FAIR-SPACE (grant number EP/R026092/1), RAIN (grant number EP/R026084/1) and ORCA (grant number EP/R026173/1) RAI Hubs, the “Science of Sensor Systems Software” programme (grant number EP/N007565/1), the “Trustworthy Robotic Assistants” project (grant numbers EP/K006320/1, EP/K006193/1, and EP/K006223/1), and the RIVERAS project (grant number EP/J01205X/1).",,,,,,,,,"Agresti, A., Coull, B.A., Approximate is better than “exact” for interval estimation of binomial proportions (1998) The American Statistician, 52 (2), pp. 119-126; Aitken, J.M., Veres, S.M., Judge, M., Adaptation of system configuration under the robot operating system (2014) IFAC Proceedings Volumes, 47 (3), pp. 4484-4492; Alami, R., Albu-Schaeffer, A., Bicchi, A., (2006) Safe and dependable physical human–robot interaction in anthropic domains: State of the art and challenges, , 2006 IEEE/RSJ international conference on intelligent robots and systems, Beijing, China, 9–15 October 2006, Piscataway, NJ, IEEE,. 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"Seibt J., Vestergaard C., Damholdt M.F.","25636250400;57000193600;38861389400;","The Complexity of Human Social Interactions Calls for Mixed Methods in HRI: Comment on ""a Primer for Conducting Experiments in Human-robot Interaction,"" by G. Hoffman and X. Zhao",2021,"ACM Transactions on Human-Robot Interaction","10","1","3439715","","",,,"10.1145/3439715","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100920990&doi=10.1145%2f3439715&partnerID=40&md5=65c62c3be7c967bf88613c627f57fa9c","School of Culture and Society, Aarhus University, Research Unit for Robophilosophy and Integrative Social Robotics, Jens Christian Skousvej 7, Aarhus, 8000, Denmark; Department of Psychology and Behavioural Sciences, Bartholins Allé 11, Aarhus C, 8000, Denmark","Seibt, J., School of Culture and Society, Aarhus University, Research Unit for Robophilosophy and Integrative Social Robotics, Jens Christian Skousvej 7, Aarhus, 8000, Denmark; Vestergaard, C., School of Culture and Society, Aarhus University, Research Unit for Robophilosophy and Integrative Social Robotics, Jens Christian Skousvej 7, Aarhus, 8000, Denmark; Damholdt, M.F., Department of Psychology and Behavioural Sciences, Bartholins Allé 11, Aarhus C, 8000, Denmark","In this research note, we offer a comment on the ""A Primer for Conducting Experiments in Human-robot Interaction,""by G. Hoffman and X. Zhao, suggesting that due to the complexity of human social reality quantitative methods should be integrated into a mixed method approach. © 2021 ACM.","Empirical research in HRI; mixed methods; responsible robotics","Agricultural robots; Man machine systems; Human social interactions; Mixed method; Quantitative method; Social reality; Social robots",,,,,"Carlsbergfondet: CF2016-0004","The research activities leading to this publication have been sponsored by the Carlsberg Foundation, Grant CF2016-0004. Authors’ addresses: J. Seibt and C. Vestergaard, School of Culture and Society, Aarhus University, Jens Christian Skousvej 7, 8000 Aarhus, Denmark; emails: filseibt@cas.au.dk, etnov@cas.au.dk; M. Damholdt, Department of Psychology and Behavioural Sciences, Bartholins Allé 11, 8000 Aarhus C, Denmark; email: malenefd@psy.au.dk. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM. 2573-9522/2021/02-ART10 https://doi.org/10.1145/3439715",,,,,,,,,,"Turkle, S., (2011) Alone Together, , Basic Books; Damholdt, M.F., Vestergaard, C., Kryvous, A., Smedegaard, C.V., Seibt, J., What is in three words? 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IOS Press, Amsterdam; Wykowska, A., Metta, G., Becchio, C., Hortensius, R., Cross, E., Cognitive and social neuroscience methods for HRI (2018) Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction., pp. 405-406; Wiese, E., Metta, G., Wykowska, A., Robots as intentional agents: Using neuroscientifc methods to make robots appear more social (2017) Front. Psychol., 8, p. 1663. , 2017; Smedegaard, C.V., Reframing the role of novelty within social HRI: From noise to information (2019) Proceedings of the 14th ACM/IEEE International Conference on Human-robot Interaction (HRI'19)., pp. 411-420; Seibt, J., Damholdt, M.F., Vestergaard, C., Integrative social robotics, value-driven design, and transdisci-plinarity (2020) Interact. Stud., 21 (1), pp. 111-144. , 2020; (2018) The Ieee Global Initiative on Ethics of Autonomous and Intelligent Systems, , https://standards.ieee.org/industry-connections/ec/autonomous-systems.html, IEEE. IEEE SA; Jost, C., Podevic, B., Grandgeorge, M., (2019) Methods in Human Robot Interaction, , Springer; Cheon, E., Su, N.M., Futuristic autobiographies: Weaving participant narratives to elicit values around robots (2018) Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI'18)., pp. 388-397",,,,"Association for Computing Machinery",,,,,25739522,,,,"English","ACM Trans. Hum. Robot Interact",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85100920990
"Bisconti P.","57205669543;","How Robots’ Unintentional Metacommunication Affects Human–Robot Interactions. A Systemic Approach",2021,"Minds and Machines","31","4",,"487","504",,,"10.1007/s11023-021-09584-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120626428&doi=10.1007%2fs11023-021-09584-5&partnerID=40&md5=939d48af38a541fb949dd8eae4c731e3","Sant’Anna School of Advanced Studies, DIRPOLIS Institute, Piazza dei Martiri della Libertà, 33, Pisa, Italy","Bisconti, P., Sant’Anna School of Advanced Studies, DIRPOLIS Institute, Piazza dei Martiri della Libertà, 33, Pisa, Italy","In this paper, we theoretically address the relevance of unintentional and inconsistent interactional elements in human–robot interactions. We argue that elements failing, or poorly succeeding, to reproduce a humanlike interaction create significant consequences in human–robot relational patterns and may affect human–human relations. When considering social interactions as systems, the absence of a precise interactional element produces a general reshaping of the interactional pattern, eventually generating new types of interactional settings. As an instance of this dynamic, we study the absence of metacommunicative abilities in social artifacts. Then, we analyze the pragmatic consequences of the aforementioned absence through the lens of Paul Watzlawick’s interactionist theory. We suggest that a fixed complementary interactional setting may be produced because of the asymmetric understanding, between robots and humans, of metacommunication. We highlight the psychological implications of this interactional asymmetry within Jessica Benjamin’s concept of “mutual recognition”. Finally, we point out the possible shift of dysfunctional interactional patterns from human–robot interactions to human–human ones. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Anthropomorphic robots; Behavioral inconsistency; HRI design; HRI social implications; Human–robot interaction; Interactionism; Nonverbal cues; Nonverbal interaction; Robotic metacommunication; Social robots","Human robot interaction; Machine design; Behavioral inconsistency; HRI design; HRI social implication; Humans-robot interactions; Interactionism; Meta-communication; Nonverbal cues; Nonverbal interaction; Nonverbals; Robotic metacommunication; Social implication; Social robots; Anthropomorphic robots",,,,,,,,,,,,,,,,"Baker, A.L., Phillips, E.K., Ullman, D., Keebler, J.R., Toward an understanding of trust repair in human-robot interaction: Current research and future directions (2018) ACM Transactions on Interactive Intelligent Systems (TiiS), 8 (4), pp. 1-30; Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., Šabanović, S., (2020) Human-robot interaction: An introduction, , Cambridge University Press; Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., Šabanović, S., Nonverbal interaction (2020) Human-Robot Interaction, pp. 81-97. , 9781108735; Benjamin, J., (2013) The bonds of love: Psychoanalysis, feminism, and the problem of domination, , Pantheon; Benjamin, J., (2017) Beyond doer and done to: Recognition theory, intersubjectivity and the third, , Routledge; Bisconti Lucidi, P., Nardi, D., Companion robots: The hallucinatory danger of human-robot interactions (2018) Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pp. 17-22. , https://doi.org/10.1145/3278721.3278741; Briggs, G., Scheutz, M., The pragmatic social robot: Toward socially-sensitive utterance generation in human-robot interactions (2016) 2016 AAAI Fall Symposium Series, , In; Brinck, I., Balkenius, C., Mutual recognition in human-robot interaction: A deflationary account (2020) Philosophy & Technology; Dautenhahn, K., Walters, M., Woods, S., Koay, K.L., Nehaniv, C.L., Sisbot, A., Alami, R., Siméon, T., How may I serve you? 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Anthropomorphism and consumer’s willingness to pay in electronic commerce (2019) Journal of Management Information Systems, 36 (2), pp. 450-477","Bisconti, P.; Sant’Anna School of Advanced Studies, Piazza dei Martiri della Libertà, 33, Italy; email: piercosma.biscontilucidi@santannapisa.it",,,"Springer Science and Business Media B.V.",,,,,09246495,,MMACE,,"English","Minds Mach",Article,"Final","",Scopus,2-s2.0-85120626428
"Xiao H., Hu Z.","56585172600;56225778100;","Feature-similarity network via soft-label training for infrared facial emotional classification in human-robot interaction",2021,"Infrared Physics and Technology","117",,"103823","","",,1,"10.1016/j.infrared.2021.103823","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110194765&doi=10.1016%2fj.infrared.2021.103823&partnerID=40&md5=fe42146e2ad842e1422a5c85c735e0c6","School of Mathematics, Physics and Optoelectronic Engineering, Hubei University of Automotive Technology, Shiyan, Hubei  442002, China","Xiao, H., School of Mathematics, Physics and Optoelectronic Engineering, Hubei University of Automotive Technology, Shiyan, Hubei  442002, China; Hu, Z., School of Mathematics, Physics and Optoelectronic Engineering, Hubei University of Automotive Technology, Shiyan, Hubei  442002, China","Facial emotional classification (FEC) is a challenging issue in human-robot interaction. In this study, we proposed a novel FEC network with feature similarity-based soft label training. The proposed model aims at addressing the problems of occlusion and label ambiguity. It argues that the mouth region is very important for FEC task. To this end, all the facial emotional images are firstly used to extract the infrared facial features by the trained ResNet networks. The facial features similarities are computed by the cosine function. The emotional labels can be constructed by the similarities values. Then, Kullback-Leibler distance is employed to measure the difference between the groundtruth label and predicted label. Extension experimental results on two public datasets demonstrate that the proposed model can obtain state-of-the-art performance compared with the existing studies. Furthermore, we have applied proposed model on the assisted driving scene and human robot interaction environment. © 2021 Elsevier B.V.","Deep neural network; Emotional understanding; Facial emotional classification; Infrared images; Robot vision","Computer vision; Cosine transforms; Deep neural networks; Human robot interaction; Man machine systems; Classification networks; Classification tasks; Emotional classification; Emotional understanding; Facial emotional classification; Facial feature; Humans-robot interactions; Neural-networks; Similarity network; Soft labels; Infrared imaging",,,,,,,,,,,,,,,,"Liu, X., Jin, L., Han, X., You, J., Mutual Information Regularized Identity-aware Facial Expression Recognition in Compressed Video (2021) Pattern Recognit., , 108105; Liu, H., Wang, X., Zhang, W., Zhang, Z., Li, Y.-F., Infrared head pose estimation with multi-scales feature fusion on the IRHP database for human attention recognition (2020) Neurocomputing, 411, pp. 510-520; Huang, Z., Fang, H., Li, Q., Li, Z., Zhang, T., Sang, N., Li, Y., Optical remote sensing image enhancement with weak structure preservation via spatially adaptive gamma correction (2018) Infrared Phys. 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"Zhou Y., Headings L.M., Dapino M.J.","57201151674;15032982500;6701926265;","Hybrid jamming variable-stiffness link for safe co-robots",2021,"2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021",,,,"1224","1229",,,"10.1109/RCAR52367.2021.9517445","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115416576&doi=10.1109%2fRCAR52367.2021.9517445&partnerID=40&md5=6c5ffd5d27b440caa97571242f615ffd","Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 510641, China; Ohio State University, Department of Mechanical and Aerospace Engineering, Columbus, OH  43210, United States","Zhou, Y., Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 510641, China; Headings, L.M., Ohio State University, Department of Mechanical and Aerospace Engineering, Columbus, OH  43210, United States; Dapino, M.J., Ohio State University, Department of Mechanical and Aerospace Engineering, Columbus, OH  43210, United States","Stiff robots lead to high injury severity when there is an impact between a robot and an operator. Introducing controllable compliance into robotic links reduces the severity of impact and enables safe human-robot interaction. Layer jamming, airtight chamber, and shape morphing are effective variable-stiffness mechanisms. This paper proposes a hybrid jamming mechanism for safe human-robot interaction that combines the above three mechanisms to further improve stiffness changing performance. The hybrid jamming mechanism is composed of two thin sheets which can be multilayered and an air bladder is placed in between the two sheets; inflating the air bladder increases the curvature of the layers and the cross-section area, hence increasing the stiffness. System stiffness is further increased by increasing the pressure since all layers are compressed firmly. The concept is validated by quasi-static cantilever bending experiments. The measurements show that a maximum stiffness ratio of 66 can be achieved. © 2021 IEEE.",,"Human robot interaction; Man machine systems; Stiffness; Effective variables; Injury severity; Multi-layered; Performance; Safe human-robot interaction; Shape morphing; Stiffness changing; Thin sheet; Variable stiffness; Variable stiffness mechanism; Jamming",,,,,"National Science Foundation, NSF: CMMI-1637656","*This research was supported by the National Science Foundation, Grant No: CMMI-1637656.",,,,,,,,,,"Wolf, S., Eiberger, O., Hirzinger, G., The dlr fsj: Energy based design of a variable stiffness joint (2011) 2011 IEEE International Conference on Robotics and Automation, pp. 5082-5089; Li, Z., Bai, S., A novel revolute joint of variable stiffness with reconfigurability (2019) Mechanism and Machine Theory, 133, pp. 720-736; Stilli, A., Wurdemann, H.A., Althoefer, K., A novel concept for safe, stiffness-controllable robot links (2017) Soft Robotics, 4 (1), pp. 16-22; Gandarias, J.M., Wang, Y., Stilli, A., García-Cerezo, A.J., Gómez-De Gabriel, J.M., Wurdemann, H.A., Open-loop position control in collaborative, modular variable-stiffnesslink (vsl) robots (2020) IEEE Robotics and Automation Letters, 5 (2), pp. 1772-1779; Aktaş, B., Narang, Y.S., Vasios, N., Bertoldi, K., Howe, R.D., A modeling framework for jamming structures (2021) Advanced Functional Materials, p. 2007554; Sebastian, F., Fu, Q., Santello, M., Polygerinos, P., Soft robotic haptic interface with variable stiffness for rehabilitation of neurologically impaired hand function (2017) Frontiers in Robotics and AI, 4, p. 69; Li, Y., Chen, Y., Yang, Y., Wei, Y., Passive particle jamming and its stiffening of soft robotic grippers (2017) IEEE Transactions on Robotics, 33 (2), pp. 446-455; Amend, J.R., Brown, E., Rodenberg, N., Jaeger, H.M., Lipson, H., A positive pressure universal gripper based on the jamming of granular material (2012) IEEE Transactions on Robotics, 28 (2), pp. 341-350; Jiang, A., Ataollahi, A., Althoefer, K., Dasgupta, P., Nanayakkara, T., A variable stiffness joint by granular jamming (2012) International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. 267-275. , American Society of Mechanical Engineers,45035; Cheng, N.G., Lobovsky, M.B., Keating, S.J., Setapen, A.M., Gero, K.I., Hosoi, A.E., Iagnemma, K.D., Design and analysis of a robust, low-cost, highly articulated manipulator enabled by jamming of granular media (2012) 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 4328-4333; Ranzani, T., Gerboni, G., Cianchetti, M., Menciassi, A., A bioinspired soft manipulator for minimally invasive surgery (2015) Bioinspiration & Biomimetics, 10 (3), p. 035008; Brancadoro, M., Manti, M., Tognarelli, S., Cianchetti, M., Fiber jamming transition as a stiffening mechanism for soft robotics (2020) Soft Robotics, 7 (6), pp. 663-674; Brancadoro, M., Manti, M., Grani, F., Tognarelli, S., Menciassi, A., Cianchetti, M., Toward a variable stiffness surgical manipulator based on fiber jamming transition (2019) Frontiers in Robotics and AI, 6, p. 12; Zeng, X., Hurd, C., Su, H.-J., Song, S., Wang, J., A parallelguided compliant mechanism with variable stiffness based on layer jamming (2020) Mechanism and Machine Theory, 148, p. 103791; Song, S., Zeng, X., She, Y., Wang, J., Su, H.-J., Modeling and control of inherently safe robots with variable stiffness links (2019) Robotics and Autonomous Systems, 120, p. 103247; Zhou, Y., Headings, L.M., Dapino, M.J., Discrete layer jamming for safe co-robots (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 6124-6129; Zhou, Y., Headings, L.M., Dapino, M.J., Discrete layer jamming for variable stiffness co-robot arms (2020) Journal of Mechanisms and Robotics, 12 (1); Wang, T., Zhang, J., Li, Y., Hong, J., Wang, M.Y., Electrostatic layer jamming variable stiffness for soft robotics (2019) IEEE/ASME Transactions on Mechatronics, 24 (2), pp. 424-433",,,"IEEE Robotics and Automation Society (RA);Shanghai Jiao Tong University;Shenzhen Institute of Advanced Technology (SIAT)","Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021","15 July 2021 through 19 July 2021",,171512,,9781665436786,,,"English","IEEE Int. Conf. Real-Time Comput. Robot., RCAR",Conference Paper,"Final","",Scopus,2-s2.0-85115416576
"Moon A., Hashmi M., Machiel Van Der Loos H.F., Croft E.A., Billard A.","54969168600;57226449544;57211078571;57222210797;7006389948;","Design of Hesitation Gestures for Nonverbal Human-Robot Negotiation of Conflicts",2021,"ACM Transactions on Human-Robot Interaction","10","3","24","","",,,"10.1145/3418302","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111558095&doi=10.1145%2f3418302&partnerID=40&md5=852a706f4baa4e7533e1bb7447dab7cc","McGill University, Montreal, Canada; University of British Columbia, Vancouver, BC, Canada; Monash University, Australia; Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland","Moon, A., McGill University, Montreal, Canada; Hashmi, M., University of British Columbia, Vancouver, BC, Canada; Machiel Van Der Loos, H.F., University of British Columbia, Vancouver, BC, Canada; Croft, E.A., Monash University, Australia; Billard, A., Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland","When the question of who should get access to a communal resource first is uncertain, people often negotiate via nonverbal communication to resolve the conflict. What should a robot be programmed to do when such conflicts arise in Human-Robot Interaction? The answer to this question varies depending on the context of the situation. Learning from how humans use hesitation gestures to negotiate a solution in such conflict situations, we present a human-inspired design of nonverbal hesitation gestures that can be used for Human-Robot Negotiation. We extracted characteristic features of such negotiative hesitations humans use, and subsequently designed a trajectory generator (Negotiative Hesitation Generator) that can re-create the features in robot responses to conflicts. Our human-subjects experiment demonstrates the efficacy of the designed robot behaviour against non-negotiative stopping behaviour of a robot. With positive results from our human-robot interaction experiment, we provide a validated trajectory generator with which one can explore the dynamics of human-robot nonverbal negotiation of resource conflicts. © 2021 ACM.","hesitation; Human-robot interaction; negotiation; nonverbal communication","Agricultural robots; Machine design; Man machine systems; Conflict situation; Human robots; Human subjects; Non-verbal communications; Non-verbal human; Resource conflict; Trajectory generator; Human robot interaction",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC; European Commission, EC: 600010","This work has been supported by Natural Sciences and Engineering Council, Canada, and the EU project AlterEgo under Grant Agreement No. 600010. Authors’ addresses: A. Moon, Dept. of Electrical and Computer Engineering, McConnell Engineering Building, 3480 University Street, Room 541, Montreal, QC, Canada H3A 0E9; email: ajung.moon@mcgill.ca; M. Hashmi and H. F. M. Van der Loos, Dept. of Mechanical Engineering, The University of British Columbia, 2054-6250 Applied Science Lane, Vancouver BC, Canada V6T 1Z4; emails: maneezhay@gmail.com, vdl@mech.ubc.ca; E. A. Croft, Faculty of Engineering, Room 107, 14 Alliance Lane, Monash University, Clayton Victoria 3800 Australia; email: elizabeth.croft@monash.edu; A. Billard, Learning Algorithms and Systems Laboratory, EPFL STI I2S LASA ME A3 393 (Bâtiment ME), Station n º9 CH-1015 Lausanne, Switzerland; email: aude.billard@epfl.ch.",,,,,,,,,,"Admoni, H., Dragan, A., Srinivasa, S.S., Scassellati, B., Deliberate delays during robotto-human handovers improve compliance with gaze communication (2014) Proceedings of the ACM/IEEE International Conference on Human-robot Interaction. ACM, pp. 49-56. , https://doi.org/10.1145/2559636.2559682; (2016) Amazon Mechanical Turk: Artificial Artificial Intelligence, , https://www.mturk.com/mturk/welcome; Argyle, M., (1991) Cooperation: The Basis of Sociability. Routledge, p. 288. , http://www.amazon.com/Cooperation-Basis-Sociability-Michael-Argyle/dp/0415035465, London/New York; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot., 1 (1), pp. 71-81. , https://doi.org/10.1007/s12369-008-0001-3, Nov. 2009; Bartneck, C., Van Der Hoek, M., Mubin, O., Mahmud, A.A., Daisy, Daisy, give me your answer do! (2007) Proceedings of the ACM/IEEE International Conference on Human-robot Interaction, p. 217. , https://doi.org/10.1145/1228716.1228746, ACM Press; Chao, C., Thomaz, A., Controlling social dynamics with a parametrized model of floor regulation (2013) J. Hum. Robot Interact., 2 (1), pp. 4-29. , https://doi.org/10.5898/jhri.2.1.chao, 2013; Corley, M., Stewart, O.W., Hesitation disfluencies in spontaneous speech: The meaning of um (2008) Lang. Linquist. Compass, 2 (4), pp. 589-602. , https://doi.org/10.1111/j.1749-818X.2008.00068.x, July 2008; Dondrup, C., Bellotto, N., Hanheide, M., Social distance augmented qualitative trajectory calculus for human-robot spatial interaction (2014) Proceedings of the 23rd IEEE International Symposium on Robot and Human Interactive Communication., pp. 519-524. , https://doi.org/10.1109/ROMAN.2014.6926305; Dondrup, C., Lichtenthäler, C., Hanheide, M., Hesitation signals in human-robot head-on encounters: A pilot study (2014) Proceedings of the 9th ACM/IEEE International Conference on Human-Robot Interaction., pp. 154-155. , https://doi.org/10.1145/2559636.2559817; Doob, L.W., (1990) Hesitation: Impulsivity and Reflection, p. 214. , http://books.google.com/books?id=Q7B9AAAAMAAJ&pgis=1http://www.questia.com/read/27488222, Greenwood Press, Westport, CT; Figueroa, N., Ureche, A.L.P., Billard, A., Learning complex sequential tasks from demonstration: A pizza dough rolling case study (2016) Proceedings of the 11th ACM/IEEE International Conference on Human-Robot Interaction. ACM/IEEE, pp. 611-612. , https://doi.org/10.1109/HRI.2016.7451881; Fischer, K., Niebuhr, O., Jensen, L.C., Bodenhagen, L., Speech melody matters-How robots profit from (2019) Trans. Hum. Robot Interact., 9 (1), pp. 1-21. , 2019; Flash, T., Hogan, N., The coordination of arm movements: Mathematical model (1985) J. Neurosci., 5 (7), pp. 1688-1703. , http://www.jneurosci.org/cgi/content/abstract/5/7/1688, 1985; Gillespie-Lynch, K., Greenfield, P.M., Feng, Y., Savage-Rumbaugh, S., Lyn, H., A cross-species study of gesture and its role in symbolic development: Implications for the gestural theory of language evolution.Front (2013) Psychol., 4, p. 160. , https://doi.org/10.3389/fpsyg.2013.00160, Jan. 2013; Givens, D.B., (2002) The Nonverbal Dictionary of Gestures, Signs & Body Language Cues, , http://www.mikolaj.info/edu/Body_Language_-_List_of_Signs_n_Gestures.pdf, Center for Nonverbal Studies Press, Spokane,Washington; Golinkoff, R.M., I beg your pardon?"": The preverbal negotiation of failed messages (1986) J. Child. Lang., 13 (3), pp. 455-476. , https://doi.org/10.1017/S0305000900006826, Sep. 1986; Golinkoff, R.M., When is communication ameeting of minds? (1993) J. Child. 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Robot., 30 (5), pp. 1-17. , https://doi.org/10.1109/TRO.2014.2316022, 2014; Leavens, D.A., Russell, J.L., Hopkins, W.D., Intentionality as measured in the persistence and elaboration of communication by chimpanzees (Pan troglodytes) (2005) Child Dev., 76 (1), pp. 291-306. , http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8624.2005.00845.x/full, 2005; Martin, J.G., Strange, W., The perception of hesitation in spontaneous speech (1968) Percept. Psychol., 3 (6), pp. 427-438. , https://doi.org/10.3758/BF03205750, 1968; Merlo, S., Barbosa, P.A., Hesitation phenomena: A dynamical perspective.Cogn (2010) Process., 11 (3), pp. 251-261. , https://doi.org/10.1007/s10339-009-0348-x, Aug. 2010; Seyed Mohammad, K.-Z., Billard, A., A dynamical system approach to realtime obstacle avoidance (2012) Auton. Robots, 32 (4), pp. 433-454. , https://doi.org/10.1007/s10514-012-9287-y, 2012; Moon, A., (2017) Negotiating with Robots: Meshing Plans and Resolving Conflicts in Human-Robot Collaboration, , https://doi.org/10.14288/1.0348225, Doctor of Philosophy-PhD thesis, University of British Columbia; Moon, A., Parker, C.A.C., Croft, E.A., Van Der Loos, H.F.M., Did you see it hesitate?-Empirically grounded design of hesitation trajectories for collaborative robots (2011) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ, pp. 1994-1999. , https://doi.org/10.1109/IROS.2011.6048228; Moon, A., Parker, C.A.C., Croft, E.A., Van Der Loos, H.F.M., Design and impact of hesitation gestures during human-robot resource conflicts (2013) J. Hum. Robot Interact., 2 (3), pp. 18-40. , https://doi.org/10.5898/JHRI.2.3.Moon, Sep. 2013; Moon, Y., Nass, C., How ""Real"" are computer personalities?: Psychological responses to personality types in human-computer interaction (1996) Commun. Res., 23 (6), pp. 651-674. , https://doi.org/10.1177/009365096023006002, Dec. 1996; Netick, A., Klapp, S.T., Hesitations in manual tracking: A single-channel limit in response programming (1994) J. Exp. Psychol. Hum., 20 (4), pp. 766-782. , https://doi.org/10.1037/0096-1523.20.4.766, 1994; Ogai, Y., Ikegami, T., Microslip as a simulated artificial mind (2008) Adapt. 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IEEE, , http://www.willowgarage.com/sites/default/files/icraoss09-ROS.pdf; Reeder, R.W., Maxion, R.A., User interface defect detection by hesitation analysis (2006) Proceedings of the International Conference on Dependable Systems and Networks., pp. 61-70. , https://doi.org/10.1109/DSN.2006.71; Rober, P., Some hypotheses about hesitations and their nonverbal expression in family therapy practice (2002) J. Fam. Ther., 24 (2), pp. 187-204. , https://doi.org/10.1111/1467-6427.00211, 2002; Sousa-Poza, J.F., Rohrberg, R., Body movement in relation to type of information (person-and nonperson-oriented) and cognitive style (field dependence) 1 (1977) Hum. Commun. Res., 4 (1), pp. 19-29. , https://doi.org/10.1111/j.1468-2958.1977.tb00592.x, Sep. 1977; Suda, C., Call, J., What does an intermediate success rate mean? An analysis of a Piagetian liquid conservation task in the great apes (2006) Cognition, 99 (1), pp. 53-71. , https://doi.org/10.1016/j.cognition.2005.01.005, Feb. 2006; Van Den Brule, R., Bijlstra, G., Dotsch, R., Haselager, P., Wigboldus, D.H.J., Warning signals for poor performance improve human-robot interaction (2016) J. Hum. Robot Interact., 5 (2), p. 69. , https://doi.org/10.5898/jhri.5.2.van_den_brule, 2016; Vinciarelli, A., Pantic, M., Bourlard, H., Social signal processing: Survey of an emerging domain (2009) Image Vis. Comput., 27 (12), pp. 1743-1759. , https://doi.org/10.1016/j.imavis.2008.11.007, Nov. 2009; Yokoi, T., Fujisaki, K., Hesitation behaviour of hoverflies Sphaerophoria spp (2009) To Avoid Ambush by Crab Spiders.Sci. Nat., 96 (2), pp. 195-200. , https://doi.org/10.1007/s00114-008-0459-8, Feb. 2009",,,,"Association for Computing Machinery",,,,,25739522,,,,"English","ACM Trans. Hum. Robot Interact",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85111558095
"Gomez Cubero C., Rehm M.","57226718958;10039896700;","Intention Recognition in Human Robot Interaction Based on Eye Tracking",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12934 LNCS",,,"428","437",,,"10.1007/978-3-030-85613-7_29","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115269762&doi=10.1007%2f978-3-030-85613-7_29&partnerID=40&md5=7a573dc68dc95a721f6de4147a18bd82","Technical Faculty of IT and Design, Aalborg University, Aalborg, Denmark","Gomez Cubero, C., Technical Faculty of IT and Design, Aalborg University, Aalborg, Denmark; Rehm, M., Technical Faculty of IT and Design, Aalborg University, Aalborg, Denmark","In human robot interaction any input that might help the robot to understand the human behaviour is valuable, and the eyes and their movement undoubtedly hold valuable information. In this paper we propose a novel algorithm for intention recognition using eye tracking in human robot collaboration. We first explore how the Cascade Effect hypothesis and a LSTM-based machine learning model perform to classify intent from gaze. Second, an algorithm is proposed, which can be used in a real time interaction to infer intention from the human user with a small uncertainty. A data collection with 30 participants was conducted in virtual reality to train and test the algorithm. The algorithm allows to detect the user intention up to two seconds before any user action with a success rate of up to 75%. These results open the possibility to study human robot interaction, where the robot can take the initiative based on the intention recognition. © 2021, IFIP International Federation for Information Processing.","Eye tracking; Human-robot interaction; Intention recognition","Behavioral research; Classification (of information); Eye movements; Eye tracking; Human computer interaction; Long short-term memory; Cascade effects; Data collection; Human behaviours; Human-robot collaboration; Intention recognition; Machine learning models; Novel algorithm; Real time interactions; Social robots",,,,,,,,,,,,,,,,"Anvaripour, M., Khoshnam, M., Menon, C., Saif, M., FMG-and RNN-based estimation of motor intention of upper-limb motion in human-robot collaboration (2020) Front. Robot. AI, 7. , https://doi.org/10.3389/frobt.2020.573096; Awais, M., Henrich, D., Human-robot collaboration by intention recognition using probabilistic state machines (2010) 19Th International Workshop on Robotics in Alpe-Adria-Danube Region, pp. 75-80. , https://doi.org/10.1109/RAAD.2010.5524605, RAAD 2010), pp; Bader, T., Vogelgesang, M., Klaus, E., Multimodal integration of natural gaze behavior for intention recognition during object manipulation (2009) Proceedings of the 2009 International Conference on Multimodal Interfaces, pp. 199-206. , https://doi.org/10.1145/1647314.1647350, ., pp., Association for Computing Machinery, New York; Bird, G.D., Lauwereyns, J., Crawford, M.T., The role of eye movements in decision making and the prospect of exposure effects (2012) Vision. Res., 60, pp. 16-21; Buerkle, A., Eaton, W., Lohse, N., Bamber, T., Ferreira, P., EEG based arm movement intention recognition towards enhanced safety in symbiotic human-robot collaboration (2021) Robot. 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Eng., pp. 1-11. , https://doi.org/10.1109/TASE.2021.3077255; Luo, R., Mai, L., Human intention inference and on-line human hand motion prediction for human-robot collaboration (2019) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5958-5964. , https://doi.org/10.1109/IROS40897.2019.8968192, pp., 2019; Pattar, S.P., Coronado, E., Ardila, L.R., Venture, G., Intention and engagement recognition for personalized human-robot interaction, an integrated and deep learning approach (2019) IEEE 4Th International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 93-98. , https://doi.org/10.1109/ICARM. 2019.8834226, pp., 2019; Russo, J.E., Leclerc, F., An eye-fixation analysis of choice processes for consumer nondurables (1994) J. Consum. 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Intell., 284; Trick, S., Koert, D., Peters, J., Rothkopf, C.A., Multimodal uncertainty reduction for intention recognition in human-robot interaction (2019) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7009-7016. , https://doi.org/10.1109/IROS40897.2019.8968171, ., pp., 2019; Vernon, D., Thill, S., Ziemke, T.: The role of intention in cognitive robotics. In: Esposito, A., Jain, L.C. (eds.) Toward Robotic Socially Believable Behaving Systems-Volume I. ISRL, vol. 105, pp. 15–27. Springer, Cham (2016). https://doi. org/10.1007/978-3-319-31056-5 3; Wang, Y., Wang, S., A new directional-intent recognition method for walking training using an omnidirectional robot (2017) J. Intell. Robot Syst., 87, pp. 231-246. , https://doi.org/10.1007/s10846-017-0503-z; Ye, L., Xiong, G., Zeng, C., Zhang, H., Trajectory tracking control of 7-DOF redundant robot based on estimation of intention in physical human-robot interaction (2020) Sci. Progr., 103 (3)","Rehm, M.; Technical Faculty of IT and Design, Denmark; email: matthias@create.aau.dk","Ardito C.Lanzilotti R.Malizia A.Malizia A.Petrie H.Piccinno A.Desolda G.Inkpen K.",,"Springer Science and Business Media Deutschland GmbH","18th IFIP TC 13 International Conference on Human-Computer Interaction, INTERACT 2021","30 August 2021 through 3 September 2021",,264499,03029743,9783030856120,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85115269762
"Formati F., Laudante E., Buono M.","57220092567;57197770710;57197769025;","Human-Centered-Design for Definition of New Collaborative Scenarios",2021,"Lecture Notes in Networks and Systems","277",,,"78","85",,,"10.1007/978-3-030-80415-2_10","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111971291&doi=10.1007%2f978-3-030-80415-2_10&partnerID=40&md5=62a597f65afe3f97ac031c30f943d627","Department of Engineering, University of Campania Luigi Vanvitelli, Aversa, 81031, Italy","Formati, F., Department of Engineering, University of Campania Luigi Vanvitelli, Aversa, 81031, Italy; Laudante, E., Department of Engineering, University of Campania Luigi Vanvitelli, Aversa, 81031, Italy; Buono, M., Department of Engineering, University of Campania Luigi Vanvitelli, Aversa, 81031, Italy","Recent technological advances have changed user-centered design criteria in industrial work contexts. The growing potential of technologies is changing industrial activity, in particular the use of collaborative robots able to involve the production operator. Starting from the study of the regulatory framework of the Robotics and the state of the art, the paper provides an overview of the different categorizations in the literature related to human-robot collaboration through the identification of illustrative industrial application associated with these categories. From a Human-Centered perspective and through the methods and approaches of the design discipline, the contribution proposes new evolutionary scenarios for the identification of “implementations” to be introduced in collaborative robotics, where human-robot interaction methods optimize the production process in terms of predictability, reliability and usability. It will be possible to focus on regulatory aspects and on the hypothesis of new regulatory tools and protocols needed to clearly and effectively define human-robot interactions. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Collaborative categories; Human-oriented; Intelligent systems; Manufacturing industry; Robotic standards",,,,,,,,,,,,,,,,,"Krüger, J., Lien, T.K., Verl, A., Cooperation of human and machines in assembly lines (2009) CIRP Ann, 58, pp. 628-646; Gualtieri, L., Monizza, G.P., Rauch, E., Vidoni, R., Matt, D.T., From design for assembly to design for collaborative assembly-product design principles for enhancing safety, ergonomics and efficiency in human-robot collaboration (2020) Procedia CIRP, 91, pp. 546-552; Cesta, A., Orlandini, A., Bernardi, G., Umbrico, A., Towards a Planning-based Framework for Symbiotic Human-Robot Collaboration (2016) IEEE 21St International Conference on Emerging Technologies and Factory Automation (ETFA), Pp. 1–, 8; Pervez, A., Ryu, J., Safe physical human robot interaction-past, present and future (2008) J. Mech. Sci. Technol., 22 (3), p. 469; Franklin, C.S., Dominguez, E.G., Fryman, J.D., Lewandowski, M.L., Collaborative robotics: New era of human–robot cooperation in the workplace (2020) J. Safety Res., 74, pp. 153-160; Vicentini, F., (2017) Sicurezza E flessibilità Delle Nuove Forme Di Collaborazione Uomo-Robot. Tecniche Nuove, p. 25. , La robotica collaborativa, p; Shi, J., Jimmerson, G., Pearson, T., Menassa, R., Levels of human and robot collaboration for automotive manufacturing (2012) Performance Metrics for Intelligent Systems (Permis) Workshop, December, pp. 95-100. , , pp; Someshwar, R., Meyer, J., Edan, Y., Models and methods for H-R synchronization (2012) IFAC Proc, 45 (6), pp. 829-834. , . Vol; Michalos, G., Makris, S., Tsarouchi, P., Guasch, T., Kontovrakis, D., Chryssolouris, G., Design considerations for safe human-robot collaborative workplaces (2015) Procedia CIRP, 37 (2), pp. 48-53; Bender, M., Braun, M., Rally, P., Scholtz, O., Lightweight robots in manual assembly – best to start simply. Examining companies’ initial experiences with lightweight robots (2016) Report. Fraunhofer Institute for Industrial Engineering IAO, , Bauer, W. (ed.); Behrens, R., Saenz, J., Vogel, C., Elkmann, N., Upcoming technologies and fundamentals for safeguarding all forms of human-robot collaboration (2015) Proceedings of the 8Th International Conference on the Safety of Industrial Automated Systems (SIAS), pp. 18-23. , Königswinter, Germany, pp; Aaltonen, I., Salmi, T., Marstio, I., Refining levels of collaboration to support the design and evaluation of human-robot interaction in the manufacturing industry (2018) Procedia CIRP, 72, pp. 93-98","Formati, F.; Department of Engineering, Italy; email: fabrizio.formati@unicampania.it","Raposo D.Martins N.Brandao D.",,"Springer Science and Business Media Deutschland GmbH","AHFE Conference on Human Dynamics for the Development of Contemporary Societies, 2021","25 July 2021 through 29 July 2021",,261889,23673370,9783030804145,,,"English","Lect. Notes Networks Syst.",Conference Paper,"Final","",Scopus,2-s2.0-85111971291
"Liu X., Ge S.S., Zhao F., Mei X.","56278821100;25924925100;57195553684;7103346480;","A Dynamic Behavior Control Framework for Physical Human-Robot Interaction",2021,"Journal of Intelligent and Robotic Systems: Theory and Applications","101","1","14","","",,,"10.1007/s10846-020-01286-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097498306&doi=10.1007%2fs10846-020-01286-x&partnerID=40&md5=9836a9717d1275391d8fff02b4a65c86","Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, China; Social Robotics Lab, National University of Singapore, Singapore, Singapore; Institute for Future, Qingdao University, Qingdao, China","Liu, X., Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, China; Ge, S.S., Social Robotics Lab, National University of Singapore, Singapore, Singapore, Institute for Future, Qingdao University, Qingdao, China; Zhao, F., Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, China; Mei, X., Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, China","The problem of dynamic behavior control for human-robot collaborative system is studied in this paper. A general dynamic behavior control framework is presented, based on which the interaction control strategies for single-point human-robot collaboration and multi-point human-robot interaction are studied. Through typical human-robot collaborative operation scenarios, the causes of role conflicts under different human-robot collaboration control strategies are analyzed, and the collaborative control algorithm integrating the active motion imitation and passive motion following is presented to reduce the conflicts during the human-robot collaboration process. For multi-point human-robot interaction, the interaction problems are classified and the interaction control strategies for single-task and multi-task scenarios are designed, both in the Cartesian space and the joint space. For multi-task interaction, a smooth task transition strategy from the collaboration control to the safety control is presented to improve the security during the task switching process. Experimental studies verify the effectiveness of the presented method. © 2020, Springer Nature B.V.","Active collaboration control; Dynamic behavior control framework; Human-robot interaction; Multi-point interaction control","Agricultural robots; Behavioral research; Man machine systems; Collaborative control; Collaborative operations; Collaborative systems; Control strategies; Human-robot collaboration; Interaction controls; Interaction problems; Physical human-robot interactions; Social robots",,,,,"National Natural Science Foundation of China, NSFC;NNSF;NNSFC: 91748208; China Scholarship Council, CSC: 201706280378; Shaanxi Provincial Science and Technology Department: 2017ZDL-G-3-1",,,,,,,,,,,"Albu-Schäffer, A., Ott, C., Hirzinger, G., A unified passivity-based control framework for position, torque and impedance control of flexible joint robots (2007) The International Journal of Robotics Research, 26 (1), pp. 23-39; Ang, M.H., Wei, L., Yong, L.S., An industrial application of control of dynamic behavior of robots-a walk-through programmed welding robot (2000) IEEE International Conference on Robotics & Automation, 3, pp. 2352-2357. , . 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Robot., 31 (4), pp. 1018-1032; Sabattini, L., Secchi, C., Fantuzzi, C., Achieving the Desired Dynamic Behavior in Multi-Robot Systems Interacting with the Environment (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 2097-2102. , IEEE; Sadeghian, H., Villani, L., Keshmiri, M., Siciliano, B., Dynamic multi-priority control in redundant robotic systems (2013) Robotica, 31 (7), pp. 1155-1167; Schiavi, R., Bicchi, A., Flacco, F., Integration of active and passive compliance control for safe human-robot coexistence (2009) IEEE International Conference on Robotics and Automation, pp. 259-264; Schöner, G., Dose, M., Engels, C., Dynamics of behavior: theory and applications for autonomous robot architectures (1995) Robotics Autonomous Syst., 16 (2-4), pp. 213-245; Slotine, S.B., A general framework for managing multiple tasks in highly redundant robotic systems (1991) Proceeding of 5Th International Conference on Advanced Robotics, 2, pp. 1211-1216; Tsuji, T., Jazidie, A., Kaneko, M., Multi-point impedance control for redundant manipulators (1996) IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26 (5), pp. 707-718; Yu, X., He, W., Li, H., Sun, J., Adaptive fuzzy full-state and output-feedback control for uncertain robots with output constraint (2020) IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-14","Liu, X.; Shaanxi Key Laboratory of Intelligent Robots, China; email: liuxing1990.ok@163.com",,,"Springer Science and Business Media B.V.",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","",Scopus,2-s2.0-85097498306
"Thunberg S., Ziemke T.","57197797180;6603792948;","User-centred design of humanoid robots' communication",2021,"Paladyn","12","1",,"58","73",,2,"10.1515/pjbr-2021-0003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096398403&doi=10.1515%2fpjbr-2021-0003&partnerID=40&md5=20b7ef1c8c64dac6f108995979b17fab","Department of Computer and Information Science, Linköping University, Linköping, 581 83, Sweden","Thunberg, S., Department of Computer and Information Science, Linköping University, Linköping, 581 83, Sweden; Ziemke, T., Department of Computer and Information Science, Linköping University, Linköping, 581 83, Sweden","Interaction between humans and robots will benefit if people have at least a rough mental model of what a robot knows about the world and what it plans to do. But how do we design human-robot interactions to facilitate this? Previous research has shown that one can change people's mental models of robots by manipulating the robots' physical appearance. However, this has mostly not been done in a user-centred way, i.e. without a focus on what users need and want. Starting from theories of how humans form and adapt mental models of others, we investigated how the participatory design method, PICTIVE, can be used to generate design ideas about how a humanoid robot could communicate. Five participants went through three phases based on eight scenarios from the state-of-the-art tasks in the RoboCup@Home social robotics competition. The results indicate that participatory design can be a suitable method to generate design concepts for robots' communication in human-robot interaction. © 2021 Sofia Thunberg and Tom Ziemke, published by De Gruyter 2021.","communication; human-robot interaction; participatory design; user-centred design",,,,,,,,,,,,,,,,,"Powers, A., Kiesler, S., The advisor robot: Tracing people's mental model from a robot's physical attributes (2006) Proceedings of Hri '06-ACM SIGCHI/SIGART Conference on Human-Robot Interaction, pp. 218-225; Nass, C., Moon, Y., Machines and mindlessness: Social responses to computers (2000) J. Soc. Issues, 56 (1), pp. 81-103; Scholl, B.J., Tremoulet, P.D., Perceptual causality and animacy (2000) Trends Cognit. Sci., 4 (8), pp. 299-309; Powers, A., Kramer, A.D.I., Lim, S., Kuo, J., Lee, S.L., Kiesler, S., Eliciting information from people with a gendered humanoid robot (2005) Ieee International Workshop on Robot and Human Interactive Communication, pp. 158-163; Powers, A., Kiesler, S., Fussell, S., Torrey, C., Comparing a computer agent with a humanoid robot (2007) ACM/IEEE International Conference on Human-Robot Interaction, pp. 145-152; Endsley, M.R., Theoretical underpinnings of situation awareness: A critical review (2000) Situation Awareness Analysis and Measurement, pp. 3-32. , M. R. Endsley and D. J. 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Res., 10 (3), pp. 317-350; Eisenberg, N., Murphy, B.C., Shepard, S., The development of empathic accuracy (1997) Empathic Accuracy, pp. 73-116. , Guilford Press: New York; Fussell, S.R., Krauss, R.M., The effects of intended audience on message production and comprehension: Reference in a common ground framework (1989) J. Exp. Soc. Psychol., 25 (3), pp. 203-219; Fussell, S.R., Krauss, R.M., Understanding friends and strangers: The affect of the audience design on message comprehension (1989) Eur. J. Soc. 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Cognition, pp. 222-233; Kiesler, S., Fostering common ground in human-robot interaction (2005) Proceedings of the Ieee International Workshop on Robot and Human Interactive Communication, 2005, pp. 729-734; Muller, M.J., Pictive-An exploration in participatory design (1991) Sigchi Conference on Human Factors in Computing Systems, pp. 225-231; Lafrenière, D., Cuta: A simple, practical, low-cost approach to task analysis (1996) Interactions, 3 (5), pp. 35-39; Mahadevan, K., Somanath, S., Sharlin, E., Communicating awareness and intent in autonomous vehicle-pedestrian interaction (2018) Chi Conference on Human Factors in Computing Systems, pp. 1-12; Hutchins, E., (1995) Cognition in the Wild, , Cambridge, MA: MIT Press; Förster, F., Weiss, A., Tscheligi, M., Anthropomorphic design for an interactive urban robot-The right design approach (2011) Hri 2011-Proceedings of the 6th ACM/IEEE International Conference on Human-Robot Interaction, pp. 137-138; Osada, J., Ohnaka, S., Sato, M., The scenario and design process of childcare robot, papero International Conference on Advances in Computer Entertainment Technology, pp. 80-86; Rossi, S., Cimmino, T., Matarese, M., Raiano, M., Coherent and incoherent robot emotional behavior for humorous and engaging recommendations 2019 28th Ieee International Conference on Robot and Human Interactive Communication, RO-MAN 2019; (2019) RoboCup@Home, , http://www.robocupathome.org, RoboCup Federation; Olson, P., (2018) Softbank's Robotics Business Prepares Scale up, , https://www.forbes.com/sites/parmyolson/2018/05/30/softbank-robotics-business-pepperboston-dynamics; Nestlé, Nestlé to Use Humanoid Robot to Sell Nescafé, , https://www.nestle.com/media/news/nestle-humanoid-robot-nescafe-japan, in Japan 29 October 2014; Foster, M., (2018) Aging Japan: Robots May Have Role in Future of Elder Care, , https://www.reuters.com/article/us-japanageing-robots-widerimage-idUSKBN1H33AB; Matamoros, M., Rascon, C., Wachsmuth, S., Moriarty, A.W., Kummert, J., Hart, J., (2019) RoboCup@Home 2019: Rules and Regulations, , http://www.robocupathome.org/rules/2019-rulebook.pdf; Alm, B., (2017) Compendium in Ethnographic Method, 2.0; Thunberg, S., Ziemke, T., Are people ready for social robots in public spaces (2020) Hri '20: ACM/IEEE International Conference on Human-Robot Interaction, pp. 482-484. , March 23-26, 2020, Cambridge, United Kingdom, ACM, New York, NY, USA; Virzi, R.A., Refining the test phase of usability evaluation: How many subjects is enough (1992) Hum. Factors, 34 (4), pp. 457-468; Nielsen, J., (2000) Why You only Need to Test with 5 Users, , www.useit.com/alertbox/2000319.html","Thunberg, S.; Department of Computer and Information Science, Sweden; email: sofia.thunberg@liu.se",,,"De Gruyter Open Ltd",,,,,20814836,,,,"English","Paladyn",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85096398403
"Zhao Z., Li X., Lu C., Zhang M., Wang Y.","57204598264;19933913100;7404806013;57217180861;55733817000;","Compliant Manipulation Method for a Nursing Robot Based on Physical Structure of Human Limb",2020,"Journal of Intelligent and Robotic Systems: Theory and Applications","100","3-4",,"973","986",,1,"10.1007/s10846-020-01221-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086672807&doi=10.1007%2fs10846-020-01221-0&partnerID=40&md5=07ce72fbb4427183883c358f476ba0e2","Key Laboratory of High Efficiency and Clean Mechanical Manufacture of MOE, National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, China; Department of Physical Medicine and Rehabilitation, Qilu Hospital, Shandong University, Jinan, 250012, China","Zhao, Z., Key Laboratory of High Efficiency and Clean Mechanical Manufacture of MOE, National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, China; Li, X., Key Laboratory of High Efficiency and Clean Mechanical Manufacture of MOE, National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, China; Lu, C., Key Laboratory of High Efficiency and Clean Mechanical Manufacture of MOE, National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, China; Zhang, M., Key Laboratory of High Efficiency and Clean Mechanical Manufacture of MOE, National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, China; Wang, Y., Department of Physical Medicine and Rehabilitation, Qilu Hospital, Shandong University, Jinan, 250012, China","Nursing robots can be a good substitute to do nursing works for severely disabled patients who have completely lost the ability of movement. In order to improve the safety and comfort of human-robot interaction, we propose a novel compliant manipulation method for a nursing robot based on physical structure of human limb, which is applicable to severely disabled patients and can reduce the high performance requirements of sensors in existing human-robot interaction systems. The method aims to obtain the optimal manipulative force in static and dynamic manipulations. In the aspect of static calculation, static equilibrium conditions are used to obtain optimal static manipulative force. And in the aspect of dynamic calculation, lagrangian method is used to obtain optimal dynamic manipulative force based on predefined movement trajectory. Adams simulations and experiments are executed by manipulating limb model to conduct rehabilitation movements. Experimental results show that the proposed method can provide compliant manipulation method for a nursing robot. © 2020, Springer Nature B.V.","Compliant manipulation; Human limb structure; Nursing robot; Optimal manipulative force","Agricultural robots; Dynamics; Lagrange multipliers; Man machine systems; Nursing; Adams simulation; Dynamic calculations; Lagrangian methods; Manipulation methods; Movement trajectories; Performance requirements; Physical structures; Static equilibrium conditions; Social robots",,,,,"2019GGX104038; Fundamental Research Fund of Shandong University: 2016JC001","This work is supported by Key R & D project of Shandong Province (Grant No.2019GGX104038) and Fundamental Research Funds of Shandong University (Grant No.2016JC001).",,,,,,,,,,"Basteris, A., Nijenhuis, S.M., Stienen, A.H., Buurke, J.H., Prange, G.B., Amirabdollahian, F., Training modalities in robot-mediated upper limb rehabilitation in stroke: a framework for classification based on a systematic review (2014) Journal of NeuroEngineering and Rehabilitation, 11, p. 111; Chinimilli, P.T., Qiao, Z., Sorkhabadi, S.M.R., Jhawar, V., Fong, I.H., Zhang, W., Automatic virtual impedance adaptation of a knee exoskeleton for personalized walking assistance (2019) Robotics and Autonomous Systems, 114, pp. 66-76; Grosu, V., Grosu, S., Vanderborght, B., Lefeber, D., Rodriguez-Guerrero, C., Multi-axis force sensor for human–robot interaction sensing in a rehabilitation robotic device (2017) Sensors, 17; Guo, J., Li, N., Guo, S., Gao, J., A labview-based human-computer interaction system for the exoskeleton hand rehabilitation robot (2017) 2017 IEEE International Conference on Mechatronics and Automation (ICMA) (571-576); Kahn, L.E., Zygman, M.L., Rymer, W.Z., Reinkensmeyer, D.J., Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: A randomized controlled pilot study (2006) Journal of Neuroengineering and Rehabilitation, 3 (12); Jones, K.B., Durney, K.M., Hung, C.T., Ateshian, G.A., The friction coefficient of shoulder joints remains remarkably low over 24 h of loading (2015) Journal of Biomechanics, 48, pp. 3945-3949. , (, -; Li, N., Yang, T., Yu, P., Chang, J., Zhao, L., Zhao, X., Elhajj, I.H., Xi, N., Bio-inspired upper limb soft exoskeleton to reduce stroke-induced complications (2018) Bioinspiration and Biomimetics, 13; Limb, D., Biomechanics of the elbow (2014) European Surgical Orthopaedics and Traumatology, pp. 1305-1316; Limb, D., Biomechanics of the shoulder (2014) European Surgical Orthopaedics and Traumatology, pp. 847-864; Long, Y., Jiang Du, Z., Dong Wang, W., He, L., Wang Mao, X., Dong, W., Physical human-robot interaction estimation based control scheme for a hydraulically actuated exoskeleton designed for power amplification (2018) Frontiers of Information Technology and Electronic Engineering, 19, pp. 1076-1085; Losey, D.P., McDonald, C.G., Battaglia, E., O’Malley, M.K., A review of intent detection, arbitration, and communication aspects of shared control for physical x GerardA.uman-robot interaction (2018) Applied Mechanics Reviews, 70; Meng, W., Liu, Q., Zhou, Z., Ai, Q., Sheng, B., Xie, S.S., Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation (2015) Mechatronics, 31, pp. 132-145. , (, -; Modares, H., Ranatunga, I., Lewis, F.L., Popa, D.O., Optimized assistive human–robot interaction using reinforcement learning (2016) IEEE Transactions on Cybernetics, 46, pp. 655-667. , (, -; Mörtl, A., Lawitzky, M., Kucukyilmaz, A., Sezgin, M., Basdogan, C., Hirche, S., The role of roles: Physical cooperation between humans and robots (2012) International Journal of Robotics Research, 31, pp. 1656-1674; Peña, G.G., Consoni, L.J., dos Santos, W.M., Siqueira, A.A., Feasibility of an optimal emg-driven adaptive impedance control applied to an active knee orthosis (2019) Robotics and Autonomous Systems, 112, pp. 98-108. , (, -; Reinkensmeyer, D.J., Kahn, L.E., Averbuch, M., McKenna-Cole, A., Schmit, B.D., Rymer, W.Z., Understanding and treating arm movement impairment after chronic brain injury: Progress with the arm guide (2000) Journal of Rehabilitation Research and Development, 37, pp. 653-662; Roemhildt, M., Gardner-Morse, M., Morgan, C., Beynnon, B., Badger, G., Calcium phosphate particulates increase friction in the rat knee joint (2014) Osteoarthritis and Cartilage, 22, pp. 706-709. , (, -; Sandoval-Gonzalez, O., Jacinto-Villegas, J., Herrera-Aguilar, I., Portillo-Rodiguez, O., Tripicchio, P., Hernandez-Ramos, M., Flores-Cuautle, A., Avizzano, C., Design and development of a hand exoskeleton robot for active and passive rehabilitation (2016) International Journal of Advanced Robotic Systems, 13; Schultz, A.E., Kuiken, T.A., Neural interfaces for control of upper limb prostheses: The state of the art and future possibilities (2011) PM and R, 3, pp. 55-67; Shao, Q., Danieln.Bassett, KurtManal, ThomasS.Buchanan: An emg-driven model to estimate muscle forces and joint moments in stroke patients (2009) Computers in Biology and Medicine, 39 (1083-1088); Sun, H., Zhang, L., Li, C., Dynamic analysis of horizontal lower limbs rehabilitative robot (2009) 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems (656-660); Valevicius, A.M., Jun, P.Y., Hebert, J.S., Vette, A.H., Use of optical motion capture for the analysis of normative upper body kinematics during functional upper limb tasks: A systematic review (2018) Journal of Electromyography and Kinesiology, 40, pp. 1-15. , (, -; Wang, D., Meng, Q., Meng, Q., Li, X., Yu, H., Design and development of a portable exoskeleton for hand rehabilitation (2018) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26, pp. 2376-2386. , (, -; Wang, W., Hou, Z.G., Tong, L., Chen, Y., Peng, L., Tan, M.: Dynamics modeling and identification of the human-robot interface based on a lower limb rehabilitation robot (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA) (6012-6017); Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Human–robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Transactions on Robotics, 31, pp. 1089-1100. , (, -; Zeiaee, A., Soltani-Zarrin, R., Langari, R., Tafreshi, R., Design and kinematic analysis of a novel upper limb exoskeleton for rehabilitation of stroke patients. 2017 IEEE International Conference on Rehabilitation (2017) Robotics, pp. 759-764; Zhang, L., Li, J., Su, P., Song, Y., Dong, M., Cao, Q., Improvement of human–machine compatibility of upper-limb rehabilitation exoskeleton using passive joints (2019) Robotics and Autonomous Systems, 112, pp. 22-31. , (, -","Li, X.; Key Laboratory of High Efficiency and Clean Mechanical Manufacture of MOE, China; email: mechsd@163.com",,,"Springer Science and Business Media B.V.",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","",Scopus,2-s2.0-85086672807
"Xia J., Huang D., Li Y., Zhong J.","57215967067;25521811100;35174564100;27268086700;","Iterative Learning Control Based on Stretch and Compression Mapping for Trajectory Tracking in Human-robot Collaboration",2020,"Proceedings - 2020 Chinese Automation Congress, CAC 2020",,,"9326665","3905","3910",,,"10.1109/CAC51589.2020.9326665","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100947996&doi=10.1109%2fCAC51589.2020.9326665&partnerID=40&md5=e22a4c981c363e9fa9477eb6e5a4b435","Southwest Jiaotong University, School of Electrical Engineering, Chengdu, China; University of Sussex, Department of Engineering and Design, Brighton, United Kingdom; Nottingham Trent University, School of Science and Technology, Nottingham, United Kingdom","Xia, J., Southwest Jiaotong University, School of Electrical Engineering, Chengdu, China; Huang, D., Southwest Jiaotong University, School of Electrical Engineering, Chengdu, China; Li, Y., University of Sussex, Department of Engineering and Design, Brighton, United Kingdom; Zhong, J., Nottingham Trent University, School of Science and Technology, Nottingham, United Kingdom","This paper presents a novel iterative learning control (ILC) scheme based on stretch and compression mapping for a robotic manipulator to learn its human partner's desired trajectory, which is a typical task in the field of human-robot interaction. The proposed scheme is used to reduce the interaction force between the robot and the human partner in repetitive learning process. Thus, the robot can track the human partner's repetitive trajectory with a small interaction force, leading to little control effort from the human. As the human is involved in the control loop, there are various uncertainties in the system, including variable iteration period in the task under study. The stretch and compression mapping is applied to this problem. In the simulation, the proposed scheme is implemented in the human-robot interaction scenario. Results confirm the effectiveness of the proposed scheme and also illustrate better performance of the proposed ILC compared with other ILC methods with variable periods. © 2020 IEEE.","Human-robot interaction; Iterative learning control; Robotic control","Industrial manipulators; Iterative methods; Learning algorithms; Man machine systems; Manipulators; Mapping; Trajectories; Two term control systems; Desired trajectories; Human-robot collaboration; Interaction forces; Iteration periods; Iterative learning control; Learning process; Robotic manipulators; Trajectory tracking; Social robots",,,,,"Sichuan Province Science and Technology Support Program: 2019YFG0345, 2019YJ0210; National Natural Science Foundation of China, NSFC: 51805025, 61733015, 61773323","*This work is supported in part by the Sichuan Science and Technology Program under Grants 2019YFG0345 and 2019YJ0210 and National Natural Science Foundation (NNSF) of China under Grants 51805025, 61773323 and 61733015. (Corresponding author : Y anan Li.)",,,,,,,,,,"Tsarouchi, P., Makris, S., Chryssolouris, G., Human-robot interaction review and challenges on task planning and programming (2016) International Journal of Computer Integrated Manufacturing, 29 (8), pp. 916-931; Brown, J.D., O'Brien, C.E., Leung, S.C., Dumon, K.R., Lee, D.I., Kuchenbecker, K.J., Using contact forces and robot arm accelerations to automatically rate surgeon skill at peg transfer (2017) IEEE Transactions on Biomedical Engineering, 64 (9), pp. 2263-2275; Jakopin, B., Mihelj, M., Munih, M., An unobtrusive measurement method for assessing physiological response in physical human-robot interaction (2017) IEEE Transactions on Human-Machine Systems, 47 (4), pp. 474-485; Yu, H., Huang, S., Chen, G., Pan, Y., Zhao, G., Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Transactions on Robotics, 31 (5), pp. 1089-1100; Cremer, S., Sumit, K.D., Wijayasinghe, I.B., Popa, D.O., Lewis, F.L., Model-free online neuroadaptive controller with intent estimation for physical humancrobot interaction (2020) IEEE Transactions on Robotics, 36 (1), pp. 240-253; Li, Y., Ge, S.S., Human-robot collaboration based on motion intention estimation (2013) IEEE/ASME Transactions on Mechatronics, 19 (3), pp. 1007-1014; Bristow, D.A., Tharayil, M., Alleyne, A.G., A survey of iterative learning control (2006) IEEE Control Systems, 26 (3), pp. 96-114; Ngo, T.Q., Wang, Y.N., Mai, T.L., Ge, J., Mai, T.L., An adaptive iterative learning control for robot manipulator in task space (2014) International Journal of Computers Communications and Control, 7 (3), pp. 518-529; Bouakrif, F., Boukhetala, D., Boudjema, F., Velocity observer-based iterative learning control for robot manipulators (2013) International Journal of Systems Science, 44 (2), pp. 214-222; Bouakrif, F., Zasadzinski, M., High order iterative learning control to solve the trajectory tracking problem for robot manipulators using lyapunov theory (2018) Transactions of the Institute of Measurement and Control, pp. 1423-1435; Ahn, H.S., Chen, Y.Q., Moore, K.L., Iterative learning control: Brief survey and categorization (2007) IEEE Transactions on Systems Man and Cybernetics Part C, 37 (6), pp. 1099-1121; Shen, D., Iterative learning control with passive incomplete information (2018) IEEE/CAA Journal of Automatica Sinica, 5 (5), pp. 885-901; Ren, B., Luo, X., Chen, J., Single leg gait tracking of lower limb exoskeleton based on adaptive iterative learning control (2019) Applied Ences, 9 (11), pp. 2251-2260; Li, X., Xu, J.-X., Huang, D., Iterative learning control for nonlinear dynamic systems with randomly varying trial lengths (2015) International Journal of Adaptive Control and Signal Processing, 29 (11), pp. 1341-1353; Wang, L., Li, X., Shen, D., Sampled-data iterative learning control for continuous-time nonlinear systems with iteration-varying lengths (2018) International Journal of Robust and Nonlinear Control, 28 (8), pp. 3073-3091; Liu, C., Shen, D., Wang, J., Adaptive learning control for general nonlinear systems with nonuniform trial lengths, initial state deviation, and unknown control direction (2019) International Journal of Robust and Nonlinear Control; Li, X., Xu, J., Huang, D., An iterative learning control approach for linear systems with randomly varying trial lengths (2014) IEEE Transactions on Automatic Control, 59 (7), pp. 1954-1960; Shen, D., Xu, J., Adaptive learning control for nonlinear systems with randomly varying iteration lengths (2019) IEEE Transactions on Neural Networks and Learning Systems, 30 (4), pp. 1119-1132; Seraji, H., Colbaugh, R., Force tracking in impedance control (1993) International Journal of Robotic Research-IJRR, 16 (2), pp. 499-506; Feldman, A.G., Once more on the equilibrium-point hypothesis ( model) for motor control (1986) Journal of Motor Behavior, 18 (1), pp. 17-54",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 Chinese Automation Congress, CAC 2020","6 November 2020 through 8 November 2020",,166835,,9781728176871,,,"English","Proc. - Chin. Autom. Congr., CAC",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85100947996
"Shi L., Copot C., Vanlanduit S.","57211018738;35301588200;7004271926;","A Deep Regression Model for Safety Control in Visual Servoing Applications",2020,"Proceedings - 4th IEEE International Conference on Robotic Computing, IRC 2020",,,"9287898","360","366",,2,"10.1109/IRC.2020.00063","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099345247&doi=10.1109%2fIRC.2020.00063&partnerID=40&md5=1aa4001d41abb6896d8a9ae3b644c439","Op3Mech, University of Antwerp, Antwerp, Belgium","Shi, L., Op3Mech, University of Antwerp, Antwerp, Belgium; Copot, C., Op3Mech, University of Antwerp, Antwerp, Belgium; Vanlanduit, S., Op3Mech, University of Antwerp, Antwerp, Belgium","In Human-Robot Interaction scenarios, a human often needs to interact or closely working with objects and/or the robot. Hence the safety aspect needs to be taken care of in the Human-Robot Interaction scenarios. In this paper, we apply a deep learning approach to learning an optimal repulsive pose. The end effector of the robot will move the optimal repulsive pose if the human hand is too close to the end effector. We use a ResNet based deep regression model to learn the weights between the input i.e. the hand position + Tool Center Point position and output i.e. the repulsive pose. We evaluate the model with different readouts and loss functions. With the Fully Connected readout, the Mean absolute Error in the x, y and z directions are between 7.4 mm and 7.7 mm. The model inference time is also smaller than the computation time of calculating the optimal repulsive pose online. © 2020 IEEE.","Deep Learning; Regression; Safety; Visual Servoing","Agricultural robots; Deep learning; End effectors; Man machine systems; Regression analysis; Robotics; Visual servoing; Computation time; Learning approach; Mean absolute error; Model inference; Regression model; Safety controls; Tool center points; Visual servoing applications; Human robot interaction",,,,,,,,,,,,,,,,"Vogel, C., Walter, C., Elkmann, N., A projection-based sensor system for safe physical human-robot collaboration (2013) International Conference on Intelligent Robots and Systems, pp. 5359-5364; Lasota, P., Fong, T., Shah, J., A survey of methods for safe human-robot interaction (2017) Foundations and Trends in Robotics, 5 (4), pp. 261-349; Scattolini, R., Architectures for distributed and hierarchical model predictive control-A review (2009) Journal of Process Control, 19 (5), pp. 723-731; Hutchinson, S., Hagerand, C., Corke, P., A tutorial on visual servo control (1996) Ieee Trans. On Robotics and Automation, 12 (5), pp. 651-670; Chaumette, F., Hutchinson, S., Visual servo control, part i: Basic approaches (2006) Ieee Robotics and Automation Magazine, 13 (4), pp. 82-90; Sadeghian, H., Villani, L., Kamranian, Z., Karami, A., Visual servoing with safe interaction using image moments (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5479-5485. , ieee; Copot, C., Lazar, C., Burlacu, A., Predictive control of nonlinear visual servoing systems using image moments (2012) Iet Control Theory and Applications, 6 (10), pp. 1486-1496; Allibert, G., Courtial, E., Chaumette, F., Visual servoing via (2010) Advan. Numer. Methods, pp. 375-393. , Springer, ch. Visual servoing via nonlinear predictive control; Burlacu, A., Copot, C., Lazar, C., Predictive control architecture for real-time image moments based servoing of robot manipulators (2013) Journal of Intelligent Manufacturing, 25 (5), pp. 1125-1134; Yan, X., Su, X., Linear regression analysis: Theory and computing (2009) World Scientific; Hoerl, A.E., Kennard, R.W., Ridge regression: Biased estimation for nonorthogonal problems (1970) Technometrics, 12 (1), pp. 55-67; Tibshirani, R., Regression shrinkage and selection via the lasso (1996) Journal of the Royal Statistical Society: Series B (Methodological), 58 (1), pp. 267-288; Zou, H., Hastie, T., Regularization and variable selection via the elastic net (2005) Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67 (2), pp. 301-320; Drucker, H., Burges, C.J., Kaufman, L., Smola, A.J., Vapnik, V., Support vector regression machines (1997) Advances in Neural Information Processing Systems, pp. 155-161; Loh, W.-Y., Classification and regression trees (2011) Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1 (1), pp. 14-23; Hornik, K., Stinchcombe, M., White, H., Multilayer feedforward networks are universal approximators (1989) Neural Networks, 2 (5), pp. 359-366; Specht, D.F., A general regression neural network (1991) Ieee Transactions on Neural Networks, 2 (6), pp. 568-576; Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks (2012) Advances in Neural Information Processing Systems, pp. 1097-1105; Simonyan, K., Zisserman, A., (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition, , arXiv preprint; Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Rabinovich, A., Going deeper with convolutions (2015) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 1-9; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2016) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 770-778; Kendall, A., Grimes, M., Cipolla, R., Posenet: A convolutional network for real-time 6-dof camera relocalization (2015) Proceedings of the Ieee International Conference on Computer Vision, pp. 2938-2946; Mahendran, S., Ali, H., Vidal, R., 3d pose regression using con-volutional neural networks (2017) Proceedings of the Ieee International Conference on Computer Vision Workshops, pp. 2174-2182; Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A., (2014) Return of the Devil in the Details: Delving Deep into Convolutional Nets, , arXiv preprint; Wan, C., Probst, T., Van Gool, L., Yao, A., Dense 3d regression for hand pose estimation (2018) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 5147-5156; Newell, A., Yang, K., Deng, J., Stacked hourglass networks for human pose estimation (2016) European Conference on Computer Vision, pp. 483-499. , Springer; Bateux, Q., Marchand, E., Leitner, J., Chaumette, F., Corke, P., Training deep neural networks for visual servoing (2018) 2018 Ieee International Conference on Robotics and Automation (ICRA), pp. 1-8; Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.-R., Jaitly, N., Senior, A., Sainath, T.N., Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups (2012) Ieee Signal Processing Magazine, 29 (6), pp. 82-97; Du, J., Xu, Y., Hierarchical deep neural network for multivariate regression (2017) Pattern Recognition, 63, pp. 149-157; Khaki, S., Wang, L., Crop yield prediction using deep neural networks (2019) Frontiers in Plant Science, 10, p. 621; Chen, D., Hu, F., Nian, G., Yang, T., Deep residual learning for nonlinear regression (2020) Entropy, 22 (2), p. 193; Tahri, O., Chaumette, F., Point-based and region-based image moments for visual servoing of planar objects (2005) Ieee Transaction on Robotics, 21 (6), pp. 1116-1127; Ioffe, S., Szegedy, C., (2015) Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, , arXiv preprint; Nair, V., Hinton, G.E., Rectified linear units improve restricted boltzmann machines (2010) Icml; Kendall, A., Cipolla, R., Geometric loss functions for camera pose regression with deep learning (2017) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 5974-5983; Kendall, A., Gal, Y., Cipolla, R., Multi-task learning using uncertainty to weigh losses for scene geometry and semantics (2018) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 7482-7491; Marchand, É., Spindler, F., Chaumette, F., Visp for visual servoing: A generic software platform with a wide class of robot control skills (2005) Ieee Robotics & Automation Magazine, 12 (4), pp. 40-52",,,,"Institute of Electrical and Electronics Engineers Inc.","4th IEEE International Conference on Robotic Computing, IRC 2020","9 November 2020 through 11 November 2020",,166083,,9781728152370,,,"English","Proc. - IEEE Int. Conf. Robot. Comput., IRC",Conference Paper,"Final","",Scopus,2-s2.0-85099345247
"Shi K., Song A., Li Y., Chen D., Li H.","57208207669;57205479536;57208216048;57188708546;36468482700;","Cable-Driven 3-DOF Wrist Rehabilitation Robot with optimized Human-Robot Interaction Performance",2020,"Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics","2020-November",,"9224316","112","117",,,"10.1109/BioRob49111.2020.9224316","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095569323&doi=10.1109%2fBioRob49111.2020.9224316&partnerID=40&md5=0b3be36087d904caf36588daf33fdab5","Southeast University, School of Instrument Science and Engineering, Nanjing, 210096, China","Shi, K., Southeast University, School of Instrument Science and Engineering, Nanjing, 210096, China; Song, A., Southeast University, School of Instrument Science and Engineering, Nanjing, 210096, China; Li, Y., Southeast University, School of Instrument Science and Engineering, Nanjing, 210096, China; Chen, D., Southeast University, School of Instrument Science and Engineering, Nanjing, 210096, China; Li, H., Southeast University, School of Instrument Science and Engineering, Nanjing, 210096, China","This paper developed a cable-driven 3-DOF (degree of freedom) wrist rehabilitation robot with optimized human-robot interaction performance. The workspace and cable tension efficiency of the robot are increased by adding the dynamic adaptive structure of cable attachment points, which improves the safety and comfort of human-robot interaction. In addition, the DASA (distributed active semi-active) system based on MRs(magnetorheological) is adopted, which reduces the effects of elastic elements (e.g., cables or Bowden cables) in the system, improves the force-feedback performance. The robot is compact and can be used independently or transplanted to any upper limb rehabilitation robot without wrist rehabilitation function. The performance of the system and control algorithm is verified by several experiments on healthy subjects. The results show that the system can meet the needs of rehabilitation training for workspace and force-feedback. This system also has the potential to be a force-feedback device for the healthy person. © 2020 IEEE.",,"Agricultural robots; Biofeedback; Cables; Degrees of freedom (mechanics); Feedback; Man machine systems; Patient rehabilitation; Robotics; Attachment points; Degree of freedom; Dynamic-adaptive; Force feedback devices; Magneto-rheological; Rehabilitation training; Upper-limb rehabilitation; Wrist Rehabilitation; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 91648206; Southeast University, SEU; National Key Research and Development Program of China, NKRDPC: 2017YFB1303201","*Research supported by the National Key Research and Development Program of China(No.2017YFB1303201), the National Natural Science Foundation of China (No.91648206), and the Key Research and Development Program of Jiangsu Province (No.BE2018004-4) Ke Shi, Aiguo Song*, Ye Li, Dapeng Chen and Huijun Li are with the School of Instrument Science and Engineering, Southeast University, Nanjing 210096 China (e-mail: a.g.song@seu.edu.cn)",,,,,,,,,,"Stroke Center, , http://www.strokecenter.org/patients/about-stroke/stroke-statistics/reference; Kwakkel, G., Kollen, B.J., Krebs, H.I., Effects of robot-assisted therapy on upper limb recovery after stroke: A systematic review (2008) Neurorehabilitation and Neural Repair, 22 (2), pp. 111-121; Su, Y.-Y., Yu, Y.-L., Lin, C.-H., Lan, C.-C., A compact wrist rehabilitation robot with accurate force/stiffness control and misalignment adaptation (2019) Int J Intell Robot Appl, 12 (1), p. 83; Gupta, A., O'Malley, M.K., Patoglu, V., Burgar, C., Design, control and performance of ricewrist: A force feedback wrist exoskeleton for rehabilitation and training (2008) The International Journal of Robotics Research, 27 (2), pp. 233-251; Bian, H., Chen, Z., Wang, H., Zhao, T., Mechanical design of efw exo II: A hybrid exoskeleton for elbow-forearm-wrist rehabilitation (2017) IEEE International Conference on Rehabilitation Robotics, pp. 689-694; Buongiorno, D., WRES: A novel 3 dof wrist exoskeleton with tendon-driven differential transmission for neuro-rehabilitation and teleoperation (2018) IEEE Robot. Autom. Lett., 3 (3), pp. 2152-2159; Alamdari, A., Krovi, V., Modeling and control of a novel home-based cable-driven parallel platform robot: Pacer (2015) IEEE International Conference on Intelligent Robots and Systems, pp. 6330-6335; Veneman, J.F., Ekkelenkamp, R., Kruidhof, R., Der Van Helm, T.F.C., Der Van Kooij, H., Design of a series elastic and bowdencable-based actuation system for use as torque-actuator in exoskeleton-type training robots (2005) International Conference on Rehabilitation Robotics, pp. 496-499. , Chicago, IL, USA; Mustafa, S.K., Yang, G., Yeo, S.H., Lin, W., Optimal design of a bio-inspired anthropocentric shoulder rehabilitator (2006) Applied Bionics and Biomechanics, 3 (3), pp. 199-208; Mao, Y., Agrawal, S.K., Design of a cable-driven arm exoskeleton (carex) for neural rehabilitation (2012) IEEE Transactions on Robotics, 28 (4), pp. 922-931; Cui, X., Chen, W., Agrawa, S.K., Wang, J., A novel customized cable-driven robot for 3-dof wrist and forearm motion training (2014) IEEE International Conference on Intelligent Robots and Systems, pp. 3579-3584; Cui, X., Chen, W., Jin, X., Agrawal, S.K., Design of a 7-dof cable-driven arm exoskeleton (carex-7) and a controller for dexterous motion training or assistance (2017) IEEE/ASME Transactions on Mechatronics, 22 (1), pp. 161-172; Mustafa, S.K., Agrawal, S.K., On the force-closure analysis of n-dof cable-driven open chains based on reciprocal screw theory (2012) IEEE Transactions on Robotics, 28 (1), pp. 22-31; Viau, J., Projected pid controller for tendon-driven manipulators actuated by magneto-rheological clutches (2015) IEEE International Conference on Intelligent Robots and Systems, pp. 5954-5959; Bryson, J.T., Jin, X., Agrawal, S.K., Optimal design of cable-driven manipulators using particle swarm optimization (2016) Journal of Mechanisms and Robotics, 8 (4); Letier, P., (2006) Bowden Cable Actuator for Torque-Feedback in Haptic Applications; Palli, M., Model and control of tendon-sheath transmission systems (2006) IEEE International Conference on Robotics & Automation IEEE; Shimoga, K.B., Finger force and touch feedback issues in dexterous telemanipulation (1992) NASA-CIRSSE International Conference of Intelligent Robotic Systems for Space Exploration",,,,"IEEE Computer Society","8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020","29 November 2020 through 1 December 2020",,164150,21551774,9781728159072,,,"English","Proc. IEEE RAS EMBS Int. Conf. Biomed. Rob. Biomechatronics, BioRob",Conference Paper,"Final","",Scopus,2-s2.0-85095569323
"Henschel A., Hortensius R., Cross E.S.","57214603084;35519731900;56497637800;","Social Cognition in the Age of Human–Robot Interaction",2020,"Trends in Neurosciences","43","6",,"373","384",,25,"10.1016/j.tins.2020.03.013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084225891&doi=10.1016%2fj.tins.2020.03.013&partnerID=40&md5=19a917203a5769c66b1497351705efec","Institute of Neuroscience and Psychology, University of Glasgow, 62 Hillhead Street, Glasgow, Scotland  G12 8QB, United Kingdom; Department of Cognitive Science, Macquarie UniversityNSW  2109, Australia","Henschel, A., Institute of Neuroscience and Psychology, University of Glasgow, 62 Hillhead Street, Glasgow, Scotland  G12 8QB, United Kingdom; Hortensius, R., Institute of Neuroscience and Psychology, University of Glasgow, 62 Hillhead Street, Glasgow, Scotland  G12 8QB, United Kingdom; Cross, E.S., Institute of Neuroscience and Psychology, University of Glasgow, 62 Hillhead Street, Glasgow, Scotland  G12 8QB, United Kingdom, Department of Cognitive Science, Macquarie UniversityNSW  2109, Australia","Artificial intelligence advances have led to robots endowed with increasingly sophisticated social abilities. These machines speak to our innate desire to perceive social cues in the environment, as well as the promise of robots enhancing our daily lives. However, a strong mismatch still exists between our expectations and the reality of social robots. We argue that careful delineation of the neurocognitive mechanisms supporting human–robot interaction will enable us to gather insights critical for optimising social encounters between humans and robots. To achieve this, the field must incorporate human neuroscience tools including mobile neuroimaging to explore long-term, embodied human–robot interaction in situ. New analytical neuroimaging approaches will enable characterisation of social cognition representations on a finer scale using sensitive and appropriate categorical comparisons (human, animal, tool, or object). The future of social robotics is undeniably exciting, and insights from human neuroscience research will bring us closer to interacting and collaborating with socially sophisticated robots. © 2020 The Author(s)","artificial intelligence; fMRI; human–robot interaction; social cognition; social robotics","artificial intelligence; controlled study; expectation; functional magnetic resonance imaging; human; human experiment; neuroimaging; neuroscience; review; robotics; social cognition; social competence; artificial intelligence; cognition; neuroscience; Artificial Intelligence; Cognition; Humans; Neuroimaging; Neurosciences; Robotics; Social Cognition",,,,,"Horizon 2020 Framework Programme, H2020; Leverhulme Trust: PLP-2018-152; European Research Council, ERC; Fundação Bial; Horizon 2020: 677270","The authors thank Michaela Kent, Te-Yi Hsieh, Laura Jastrzab, Ben Jones, Richard Ramsey, and Kohinoor Darda for comments on earlier versions of this piece. This paper originates from projects in the Social Brain in Action Lab that have received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement number 677270 to E.S.C.), the Leverhulme Trust (PLP-2018-152 to E.S.C), and the Bial Foundation (to R.H.).","The authors thank Michaela Kent, Te-Yi Hsieh, Laura Jastrzab, Ben Jones, Richard Ramsey, and Kohinoor Darda for comments on earlier versions of this piece. This paper originates from projects in the Social Brain in Action Lab that have received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement number 677270 to E.S.C.), the Leverhulme Trust ( PLP-2018-152 to E.S.C), and the Bial Foundation (to R.H.).",,,,,,,,,"Natale, S., Ballatore, A., Imagining the thinking machine (2020) Convergence Int. J. Res. New Media Technol., 26, pp. 3-18; Tulli, S., Great expectations & aborted business initiatives: The paradox of social robot between research and industry (2019) CEUR Workshop Proceedings, 2491, pp. 1-10; Campa, R., The rise of social robots: A review of the recent literature (2016) J. Evol. Technol., 26, pp. 106-113; Yang, G.Z., The grand challenges of science robotics (2018) Sci. Robot., 3; Hortensius, R., Cross, E.S., From automata to animate beings: the scope and limits of attributing socialness to artificial agents: Socialness attribution and artificial agents (2018) Ann. N. Y. Acad. Sci., 1426, pp. 93-110; Agnieszka, W., Embodied artificial agents for understanding human social cognition (2016) Philos. Trans. R. Soc. B Biol. Sci., 371, p. 20150375; Chaminade, T., Cheng, G., Social cognitive neuroscience and humanoid robotics (2009) J. Physiol. 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Springer Cham; Perez-Osorio, J., More than you expect: Priors influence on the adoption of intentional stance toward humanoid robots (2019) Social Robotics. ICSR 2019. Lecture Notes in Computer Science, 11876, pp. 119-129. , Salichs M. et al. (eds.) Springer Cham; Cameron, D., You made him be alive: Children's perceptions of animacy in a humanoid robot (2017) Biomimetic and Biohybrid Systems. Living Machines 2017. Lecture Notes in Computer Science, 10384, pp. 73-85. , Springer Cham; Gordon, E.M., Precision functional mapping of individual human brains (2017) Neuron, 95, pp. 791-807.e7; Clabaugh, C., Matarić, M., Robots for the people, by the people: Personalizing human-machine interaction (2018) Sci. Robot., 3; Chance, B., Cognition-activated low-frequency modulation of light absorption in human brain (1993) PNAS, 90, pp. 3770-3774; Pinti, P., The present and future use of functional near-infrared spectroscopy (fNIRS) for cognitive neuroscience (2018) Ann. N. Y. Acad. Sci., 34, p. 269; Solovey, E., Brainput: Enhancing interactive systems with streaming fnirs brain input (2012), pp. 2193-2202. , Proceedings of the SIGCHI Conference on Human Factors in Computing Systems New York, NY, USA; Strait, M., Scheutz, M., Measuring users’ responses to humans, robots, and human-like robots with functional near infrared spectroscopy (2014), pp. 1128-1133. , The 23rd IEEE International Symposium on Robot and Human Interactive Communication Edinburgh, UK; Kawaguchi, Y., Investigation of brain activity after interaction with seal robot measured by fNIRS (2012), pp. 571-576. , Proceedings - IEEE International Workshop on Robot and Human Interactive Communication Paris, France; Nuamah, J.K., Neural efficiency of human–robotic feedback modalities under stress differs with gender (2019) Front. Hum. Neurosci., 13; Canning, C., Scheutz, M., Functional near-infrared spectroscopy in human-robot interaction (2013) J. 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Behav., 1, p. 0021; Poldrack, R.A., Scanning the horizon: towards transparent and reproducible neuroimaging research (2017) Nat. Rev. Neurosci., 18, pp. 115-126; Hutson, M., Artificial intelligence faces reproducibility crisis (2018) Science, 359, pp. 725-726; Bethel, C.L., Murphy, R.R., Review of human studies methods in HRI and recommendations (2010) Int. J. Soc. Robot., 2, pp. 347-359; Eyssel, F., An experimental psychological perspective on social robotics (2017) Robot. Auton. Syst., 87, pp. 363-371; Irfan, B., Social psychology and human-robot interaction: An uneasy marriage (2018), pp. 13-20. , Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction Chicago, IL, USA; Button, K.S., Power failure: why small sample size undermines the reliability of neuroscience (2013) Nat. Rev. Neurosci., 14, pp. 365-376; Pinti, P., Current status and issues regarding pre-processing of fNIRS neuroimaging data: an investigation of diverse signal filtering methods within a general linear model framework (2019) Front. Hum. Neurosci., 12; Carp, J., The secret lives of experiments: Methods reporting in the fMRI literature (2012) NeuroImage, 63, pp. 289-300; Cross, E.S., From social brains to social robots: applying neurocognitive insights to human–robot interaction (2019) Philos. Trans. R. Soc. B Biol. Sci., 374, p. 20180024; Cangelosi, A., Schlesinger, M., From babies to robots: The contribution of developmental robotics to developmental psychology (2018) Child Dev. Perspect., 12, pp. 183-188; Pitcher, D., Differential selectivity for dynamic versus static information in face-selective cortical regions (2011) NeuroImage, 56, pp. 2356-2363; Gorgolewski, K.J., NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain (2015) Front. Neuroinform., 9, pp. 3743-3749","Cross, E.S.; Institute of Neuroscience and Psychology, 62 Hillhead Street, United Kingdom; email: Emily.Cross@glasgow.ac.uk",,,"Elsevier Ltd",,,,,01662236,,TNSCD,"32362399","English","Trends Neurosci.",Review,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85084225891
"Wykowska A., Perez-Osorio J., Kopp S.","15924284200;55978227200;35722438500;","Social cognition for HRI: Exploring the relationship between mindreading and social attunement in human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"665","666",,,"10.1145/3371382.3374851","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083229049&doi=10.1145%2f3371382.3374851&partnerID=40&md5=d0db09d7ba208616810d8c134d323fc4","Social Cognition in Human-Robot Interaction Unit-S4HRI, Istituto Italiano di Tecnologia, Genova, Italy; CITEC-Cognitive Interaction Technology, Social Cognitive Systems Group Faculty of Technology Bielefeld University, Germany","Wykowska, A., Social Cognition in Human-Robot Interaction Unit-S4HRI, Istituto Italiano di Tecnologia, Genova, Italy; Perez-Osorio, J., Social Cognition in Human-Robot Interaction Unit-S4HRI, Istituto Italiano di Tecnologia, Genova, Italy; Kopp, S., CITEC-Cognitive Interaction Technology, Social Cognitive Systems Group Faculty of Technology Bielefeld University, Germany","This workshop aims at bringing together various disciplines to address the relationship between mentalizing or adopting the intentional stance towards robots and social attunement in human-robot interaction. The question will be tackled from the empirical, theoretical, computational, and philosophical approaches as well as potential applications in clinical domains. We invite speakers from the areas of cognitive and social neuroscience, psychology, computational modeling, cognitive science, human-robot interaction, robotics, and philosophy of mind, who address the question of conditions and consequences of attributing mental states to robots. We will also discuss individual differences in attitudes regarding robots, including variability in the likelihood of adopting the intentional stance towards artificial agents. The concluding discussion will focus on how empirical results influence the implementation of behavior in robots, and which application contexts should promote robot design that elicits mentalizing. In the discussion, we will also address ethical aspects related to evoking socio-cognitive mechanisms (including mentalizing) towards robots. © 2020 ACM.","Computational models; Human-robot interaction; Intentional stance; Mentalizing; Mind reading; Theory of mind","Agricultural robots; Computational neuroscience; Ethical aspects; Machine design; Man machine systems; Application contexts; Artificial agents; Cognitive science; Computational model; Individual Differences; Philosophical approaches; Social cognition; Socio-cognitive; Social robots",,,,,,,,,,,,,,,,"Premack, D., Wodruff, G., Does the chimpanzee have a theory of mind (1978) Behavioral & Brain Sciences, 1, pp. 515-526; Kulms, P., Kopp, S., More human-likeness, more trust: The effect of anthropomorphism on self-reported and behavioral trust in continued and interdependent human-agent cooperation (2019) Proceedings of Mensch und Computer 2019, pp. 31-42. , ACM; Wykowska, A., Chaminade, T., Cheng, G., Embodied artificial agents for understanding human social cognition (2016) Philosophical Transaction of the Royal Society B, 371, p. 20150375. , 1693; Perez-Osorio, J., Wykowska, A., (2019) Adopting the Intentional Stance Towards Humanoid Robots, , In: Laumond JP., Danblon E., Pieters C. (eds) Wording Robotics. Springer Tracts in Advanced Robotics, vol 130. Springer, Cham",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083229049
"Schrum M.L., Johnson M., Ghuy M., Gombolay M.C.","57208165285;57216333404;57215827463;55806238000;","Four years in review: Statistical practices of likert scales in human-robot interaction studies",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"43","52",,5,"10.1145/3371382.3380739","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083160218&doi=10.1145%2f3371382.3380739&partnerID=40&md5=6cbdd405770972856b18074d018da56e","Georgia Institute of Technology, Atlanta, GA, United States","Schrum, M.L., Georgia Institute of Technology, Atlanta, GA, United States; Johnson, M., Georgia Institute of Technology, Atlanta, GA, United States; Ghuy, M., Georgia Institute of Technology, Atlanta, GA, United States; Gombolay, M.C., Georgia Institute of Technology, Atlanta, GA, United States","As robots become more prevalent, the importance of the field of human-robot interaction (HRI) grows accordingly. As such, we should endeavor to employ the best statistical practices. Likert scales are commonly used metrics in HRI to measure perceptions and attitudes. Due to misinformation or honest mistakes, most HRI researchers do not adopt best practices when analyzing Likert data. We conduct a review of psychometric literature to determine the current standard for Likert scale design and analysis. Next, we conduct a survey of four years of the International Conference on Human-Robot Interaction (2016 through 2019) and report on incorrect statistical practices and design of Likert scales. During these years, only 3 of the 110 papers applied proper statistical testing to correctly-designed Likert scales. Our analysis suggests there are areas for meaningful improvement in the design and testing of Likert scales. Lastly, we provide recommendations to improve the accuracy of conclusions drawn from Likert data. © 2020 ACM.","Likert scales; Metrics for hri; Statistical practices","Agricultural robots; Machine design; Man machine systems; Best practices; Design and analysis; Human robot Interaction (HRI); Likert data; Likert scale; Statistical testing; Human robot interaction",,,,,"National Science Foundation, NSF: 1545287; Georgia Institute of Technology, Georgia Tech","We thank Ankit Shah for his statistical insights and support. This work was supported by institute funding at the Georgia Institute of Technology and NSF ARMS Fellowship under Grant #1545287.",,,,,,,,,,"Allen, I.E., Seaman, C.A., (2007) Likert Scales and Data Analyses; Asún, R.A., Rdz-Navarro, K., Alvarado, J.M., Developing multidimensional likert scales using item factor analysis: The case of fourpoint items (2016) Sociological Methods and Research, 45 (1), pp. 109-133. , https://doi.org/10.1177/0049124114566716, 2016; Austin, P.C., Mamdani, M.M., Juurlink, D.N., Hux, J.E., Testing multiple statistical hypotheses resulted in spurious associations: A study of astrological signs and health (2006) Journal of Clinical Epidemiology, 59 (9), pp. 964-969. , https://doi.org/10.1016/j.jclinepi.2006.01.012, 2006; Balasubramanian, N., Likert technique of attitude scale construction in nursing research (2012) Cultural Anthropology Methods, 2. , 2012; Bendig, A.W., (1953) The Reliability of Self-Ratings As A Function of the Amount of Verbal Anchoring and of the Number of Categories on the Scale, 37 (1), pp. 38-41. , 1953; Bergkvist, L., Rossiter, J.R., (2007) The Predictive Validity of Multiple-Item Versus Single-Item Measures of the Same Constructs, p. 2437. , 2007; Bishop, P.A., Herron, R.L., Use and misuse of the likert item responses and other ordinal measures (2015) International Journal of Exercise Science, 8 (3), pp. 297-302. , http://www.ncbi.nlm.nih.gov/pubmed/27182418{%}0Ahttp://www.pubmedcentral.nih.gov/articlerender.fcgiartid=PMC4833473, 2015; Blair, C.R., A reaction to consequences of failure to meet assumptions underlying the fixed effects analysis of variance and covariance (1981) Review of Educational Research, 51 (4), pp. 499-507. , 1981; Brooke, J., SUS: A quick and dirty usability scale (1996) Usability Evaluation in Industry. CRC Press, pp. 189-200; Carifio, J., Perla, R., (2008) Resolving the 50-year Debate Around Using and Misusing Likert Scales, pp. 1150-1152. , https://doi.org/10.1111/j.1365-2923.2008.03172.x, 2008; Carifio, J., Perla, R.J., Ten common misunderstandings, misconceptions, persistent myths and urban legends about likert scales and likert response formats and their antidotes (2007) Journal of Social Sciences, 3 (3), pp. 106-116. , https://doi.org/10.3844/jssp.2007.106.116, 2007; Carpinella, C.M., Wyman, A.B., Perez, M.A., Stroessner, S.J., The robotic social attributes scale (rosas): Development and validation (2017) ACM/IEEE International Conference on Human-Robot Interaction Part F1271, pp. 254-262. , https://doi.org/10.1145/2909824.3020208, 2017; Chiarotti, F., Detecting assumption violations in mixed-model analysis of variance (2004) Ann Ist Super Sanità, 40 (2), pp. 165-171. , 2004; Clason, D.L., Dormody, T.J., Analyzing data measured by individual likert-type items (1994) Journal of Agricultural Education, 35 (4), pp. 31-35. , https://doi.org/10.5032/jae.1994.04031, 1994; Courtenay, B., Weidemann, C., The effects of a don't know response on palmore's facts on aging quizzes (1985) The Gerontologist, 2 (2), pp. 117-181. , 1985; Creech, J.C., Richard Johnson, D., Ordinal measures in multiple indicator models : A simulation study of categorization error author ( s ): David richard johnson and james c (2019) Creech Source : American Sociological Review, 48 (3), pp. 398-407. , 48, No. 3 ( Jun., 1983 ). pp 398-407 Published by : Amer2019; De Boer, A., Van Lanschot, P., Stalmeier, J., Is a single-item visual analogue scale as valid, reliable and responsive as multi-item scales in measuring quality of life (2004) Quality of Life Research, 13 (2), pp. 311-320. , 2004; Derrick, B., White, P., Comparing two samples from an individual Likert question (2017) International Journal of Mathematics and Statistics, , 2017; Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., Kaiser, S., (2012) Guidelines for Choosing between Multi-item and Single-item Scales for Construct Measurement : A Predictive Validity Perspective, pp. 434-449. , https://doi.org/10.1007/s11747-011-0300-3, 2012; 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Goforth, C., (2016) Using and Interpreting Cronbach's Alpha, , https://data.library.virginia.edu/using-and-interpreting-cronbachs-alpha/; Gombolay, M., Shah, A., Appraisal of statistical practices in hri vis-á-vis the t-test for likert items/scales (2016) 2016 AAAI Fall Symposium Series; Guy, R.F., Norvell, M., The neutral point on a likert scale (1997) The Journal of Psychology, 95 (2). , 1997; Penn Handwerker, W., Constructing likert scales: Testing the validity and reliability of single measures of multidimensional variables (1996) Cultural Anthropology Methods, 8. , 1996; Horan, P.M., Distefano, C., Robert, W.M., (2009) Wording Effects in Self-Esteem Scales : Methodological Artifact or Response Style, p. 5511. , https://doi.org/10.1207/S15328007SEM1003, 2009; Jamieson, S., Likert scales: How to (ab)use them (2004) Medical Education, 38 (12), pp. 1217-1218. , https://doi.org/10.1111/j.1365-2929.2004.02012.x, 2004; Johns, R., (2006) One Size Doesn ' T Fit All : Selecting Response Scales for Attitude Items, p. 7289. , https://doi.org/10.1080/13689880500178849, 2006; 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McGraw-Hil, New York, New York, USA; Preston, C.C., Colman, A.M., (2000) Optimal Number of Response Categories in Rating Scales : Reliability, Validity, Discriminating Power, and Respondent Preferences, 104, pp. 1-15. , 2000; Quilty, L.C., Oakman, J.M., Risko, E., Quilty, L.C., Oakman, J.M., Risko, E., (2009) Correlates of the Rosenberg Self-Esteem Scale Method Effects, p. 5511. , https://doi.org/10.1207/s15328007sem1301, 2009; Rossiter, J.R., (2002) The C-OAR-SE Procedure for Scale Development in Marketing, 19, pp. 305-335. , 2002; Schaefer, K.E., Measuring trust in human robot interactions: Development of the (2016) Trust Perception Scale-HRI, pp. 191-218. , https://doi.org/10.1007/978-1-4899-7668-010, Springer US, Boston, MA; Schuman, H., Presser, S., (1981) Questions and Answers in Attitude Surveys, , Academic Press, New York, New York, USA; Simms, L.J., Zelazny, K., Williams, T.F., Bernstein, L., Does the number of response options matter (2019) Psychometric Perspectives Using Personality Questionnaire Data, 31 (4), pp. 557-566. , 2019; Subedi, B.P., Using likert type data in social science research: Confusion, issues and challenges (2016) International Journal of Contemporary Applied Sciences, 3 (2), pp. 1365-2308. , www.ijcas.net, 2016; Keith, S.T., The use of cronbach's alpha when developing and reporting research instruments in science education (2018) Research in Science Education, 48 (6), pp. 1273-1296. , https://doi.org/10.1007/s11165-016-9602-2, 2018; Tavakol, M., Dennick, R., Making sense of Cronbach's alpha (2011) International Journal of Medical Education, 2, pp. 53-55. , https://doi.org/10.5116/ijme.4dfb.8dfd, 2011; Van Sonderen, E., Sanderman, R., Coyne, J.C., Ineffectiveness of reverse wording of questionnaire items: Let's learn from cows in the rain (2013) PLoS ONE, 8 (7), pp. 1-7. , https://doi.org/10.1371/journal.pone.0068967, 2013; Verhagen, T., Van Den Hooff, B., Meents, S., Toward a better use of the semantic differential in is research: An integrative framework of suggested action (2015) Journal of the Association of Information Systems, 16 (2), pp. 108-143. , 2015; Vickers, A.J., (2019) Comparison of An Ordinal and A Continuous Outcome Measure of Muscle Soreness, 4 (1999), pp. 709-716. , 2019; Warner, R., (2012) Applied Statistics from Bivariate Through Multivariate Techniquese, pp. 1-40. , Sage Publications; Willits, F., Theodori, G., Luloff, A.E., Another look at likert scales fern k (2016) Willits, pp. 126-139. , 31, August 2015 2016; Wu, H., Leung, S., Can likert scales be treated as interval scales- A simulation study (2017) Journal of Social Service Research, 43 (4), pp. 527-532. , https://doi.org/10.1080/01488376.2017.1329775, 2017; Yamaguchi, J., Positive versus negative wording (1997) Rasch Measurement Transactions, 11. , 1997; Yan, T., Tourangeau, R., (2008) Fast Times and Easy Questions : The Effects of Age, Experience and Question Complexity on Web Survey Response Times, 68, pp. 51-68. , https://doi.org/10.1002/acp, February 2007 2008",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083160218
"Connolly J., Mocz V., Salomons N., Valdez J., Tsoi N., Scassellati B., Vazquez M.","57215561944;57217952790;56896064100;57215835656;57214458789;57203072853;37017787700;","Prompting prosocial human interventions in response to robot mistreatment",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"211","220",,11,"10.1145/3319502.3374781","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082005598&doi=10.1145%2f3319502.3374781&partnerID=40&md5=5ca37572e421853f75ed741b638c5e9a","Yale University, United States","Connolly, J., Yale University, United States; Mocz, V., Yale University, United States; Salomons, N., Yale University, United States; Valdez, J., Yale University, United States; Tsoi, N., Yale University, United States; Scassellati, B., Yale University, United States; Vazquez, M., Yale University, United States","Inspired by the benefits of human prosocial behavior, we explore whether prosocial behavior can be extended to a Human-Robot Interaction (HRI) context. More specifically, we study whether robots can induce prosocial behavior in humans through a 1x2 betweensubjects user study (N = 30) in which a confederate abused a robot. Through this study, we investigated whether the emotional reactions of a group of bystander robots could motivate a human to intervene in response to robot abuse. Our results show that participants were more likely to prosocially intervene when the bystander robots expressed sadness in response to the abuse as opposed to when they ignored these events, despite participants reporting similar perception of robot mistreatment and levels of empathy for the abused robot. Our findings demonstrate possible effects of group social influence through emotional cues by robots in human-robot interaction. They reveal a need for further research regarding human prosocial behavior within HRI. © 2020 Association for Computing Machinery.","Human-robot interaction; Prosocial behavior; Robots abuse","Behavioral research; Human robot interaction; Man machine systems; Social robots; Emotional reactions; Human intervention; Human robot Interaction (HRI); Prosocial behavior; Social influence; User study; Economic and social effects",,,,,,,,,,,,,,,,"Alterovitz, R., Koenig, S., Likhachev, M., Robot planning in the real world: Research challenges and opportunities (2016) Artificial Intelligence Magazine, 37, pp. 76-84. , July 2016; Alves-Oliveira, P., Sequeira, P., Melo, F.S., Castellano, G., Paiva, A., Empathic robot for group learning: A field study (2019) ACM Trans. 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Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85082005598
"Zhang S., Zanchettin A.M., Villa R., Dai S.","57200408462;27268100300;57209134121;7203008798;","Real-time trajectory planning based on joint-decoupled optimization in human-robot interaction",2020,"Mechanism and Machine Theory","144",,"103664","","",,8,"10.1016/j.mechmachtheory.2019.103664","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073533220&doi=10.1016%2fj.mechmachtheory.2019.103664&partnerID=40&md5=5cd89224378d1e2dcf795ea0939e165d","State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China; Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy","Zhang, S., State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China; Zanchettin, A.M., Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy; Villa, R., Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy; Dai, S., State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China","In order to perform safe and natural interactions with humans, robots are required to adjust their motions quickly according to human behaviors. Performing the complex calculation and updating the trajectories in real-time is a particular challenge. In this paper, we present a real-time optimization-based trajectory planning method for serial robots. We encode the trajectory planning problem into a series of optimization problems. To solve the high-dimensional complex non-linear optimization problems in real-time, we provide a joint-decoupling method that transforms the original joint-coupled optimization problem into multiple joint-independent optimization problems, with much lower computational complexity. We implement and validate our method in a specific human-robot interaction case. Experimental results show that the computational feasibility and efficiency of optimization solution were greatly improved by the joint-decoupling transformation. Smooth, safe, and rapid motion of the robot was generated in real-time, establishing a basis for safe and reactive human-robot interactions. © 2019","Human-robot interaction; Machine learning; Non-linear optimization; Real-time trajectory planning; Serial robot","Behavioral research; Computational efficiency; Learning systems; Man machine systems; Mathematical transformations; Nonlinear programming; Robot programming; Trajectories; Computational feasibility; Decoupling transformation; Non-linear optimization; Non-linear optimization problems; Real-time optimization; Real-time trajectories; Serial robots; Trajectory planning method; Human robot interaction",,,,,,,,,,,,,,,,"Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P., Collaborative manufacturing with physical human-robot interaction (2016) Robot. Comput. Integr. Manuf., 40, pp. 1-13; Liu, H., Wang, L., Human motion prediction for human-robot collaboration (2017) J. Manuf. Syst., 44, pp. 287-294; Halme, R.-J., Lanz, M., Kämäräinen, J., Pieters, R., Latokartano, J., Hietanen, A., Review of vision-based safety systems for human-robot collaboration (2018) Procedia CIRP, 72 (1), pp. 111-116; Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human–robot collaboration (2018) Autonom. Rob., pp. 1-19; Matarić, M.J., Eriksson, J., Feil-Seifer, D.J., Winstein, C.J., Socially assistive robotics for post-stroke rehabilitation (2007) J. Neuro Eng. Rehabil., 4 (1), p. 5; Arakelian, V., Ghazaryan, S., Improvement of balancing accuracy of robotic systems: application to leg orthosis for rehabilitation devices (2008) Mech. Mach. Theory, 43 (5), pp. 565-575; Pons, J.L., Rehabilitation exoskeletal robotics (2010) IEEE Eng. Med. Biol. Mag., 29 (3), pp. 57-63; Li, X., Pan, Y., Chen, G., Yu, H., Adaptive human-robot interaction control for robots driven by series elastic actuators (2017) IEEE Trans. Robot., 33 (1), pp. 169-182; Kober, J., Glisson, M., Mistry, M., Playing catch and juggling with a humanoid robot (2012) Proceedings of the Humanoids, pp. 875-881; Ren, Z., Zhu, Q., Xiong, R., Trajectory planning of 7-DOF humanoid manipulator under rapid and continuous reaction and obstacle avoidance environment (2015) Acta Automatica Sinica, 41 (6), pp. 1131-1144; Marchal-Crespo, L., van Raai, M., Rauter, G., Wolf, P., Riener, R., The effect of haptic guidance and visual feedback on learning a complex tennis task (2013) Exper. Brain Res., 231 (3), pp. 277-291; De Santis, A., Siciliano, B., De Luca, A., Bicchi, A., An atlas of physical human–robot interaction (2008) Mech. Mach. Theory, 43 (3), pp. 253-270; von Stryk, O., Schlemmer, M., Optimal control of the industrial robot Manutec r3 (1994) Comput. Opt. Control, pp. 367-382; Chettibi, T., Lehtihet, H., Haddad, M., Hanchi, S., Minimum cost trajectory planning for industrial robots (2004) Eur. J. Mech. A Solids, 23 (4), pp. 703-715; Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., Schaal, S., Stomp: Stochastic trajectory optimization for motion planning (2011) Proceedings of the ICRA, pp. 4569-4574. , IEEE; Park, C., Pan, J., Manocha, D., Itomp: Incremental trajectory optimization for real-time replanning in dynamic environments (2012) Proceedings of the ICAPS; Zucker, M., Ratliff, N., Dragan, A.D., Pivtoraiko, M., Klingensmith, M., Dellin, C.M., Bagnell, J.A., Srinivasa, S.S., Chomp: covariant hamiltonian optimization for motion planning (2013) Int. J. Robot. Res., 32 (9-10), pp. 1164-1193; Schulman, J., Duan, Y., Ho, J., Lee, A., Awwal, I., Bradlow, H., Pan, J., Abbeel, P., Motion planning with sequential convex optimization and convex collision checking (2014) Int. J. Robot. Res., 33 (9), pp. 1251-1270; Huang, J., Hu, P., Wu, K., Zeng, M., Optimal time-jerk trajectory planning for industrial robots (2018) Mech. Mach. Theory, 121, pp. 530-544; Bäuml, B., Wimböck, T., Hirzinger, G., Kinematically optimal catching a flying ball with a hand-arm-system (2010) Proceedings of the IROS, pp. 2592-2599; Liu, C., Tomizuka, M., Algorithmic safety measures for intelligent industrial co-robots (2016) Proceedings of the ICRA, pp. 3095-3102; Cassioli, A., Di Lorenzo, D., Locatelli, M., Schoen, F., Sciandrone, M., Machine learning for global optimization (2012) Comput. Optim. Appl., 51 (1), pp. 279-303; Pan, J., Chen, Z., Abbeel, P., Predicting initialization effectiveness for trajectory optimization (2014) Proceedings of the ICRA, pp. 5183-5190; Werner, A., Trautmann, D., Lee, D., Lampariello, R., Generalization of optimal motion trajectories for bipedal walking (2015) Proc. IROS, pp. 1571-1577; Zhang, S., Dai, S., Real-time trajectory generation for haptic feedback manipulators in virtual cockpit systems (2018) J. Comput. Inf. Sci. Eng., 18 (4), p. 041015; Lampariello, R., Nguyen-Tuong, D., Castellini, C., Hirzinger, G., Peters, J., Trajectory planning for optimal robot catching in real-time (2011) Proceedings of the ICRA, pp. 3719-3726; Jetchev, N., Toussaint, M., Fast motion planning from experience: trajectory prediction for speeding up movement generation (2013) Autonom. Robots, 34 (1), pp. 111-127; Hauser, K., Learning the problem-optimum map: analysis and application to global optimization in robotics (2017) IEEE Trans. Robot., 33 (1), pp. 141-152; Kröger, T., Wahl, F.M., Online trajectory generation: basic concepts for instantaneous reactions to unforeseen events (2010) IEEE Trans. Robot., 26 (1), pp. 94-111; Tsai, C.-S., Hu, J.-S., Tomizuka, M., Ensuring safety in human-robot coexistence environment (2014) Proceedings of the IROS, pp. 4191-4196; Liu, C., Lin, C.-Y., Tomizuka, M., The convex feasible set algorithm for real time optimization in motion planning (2018) SIAM J. Control Optim., 56 (4), pp. 2712-2733; Sidobre, D., Broquère, X., Mainprice, J., Burattini, E., Finzi, A., Rossi, S., Staffa, M., Human–Robot Interaction (2012) Advanced Bimanual Manipulation, 80, pp. 123-172. , B. Siciliano Springer Tracts in Advanced Robotics; Mainprice, J., Hayne, R., Berenson, D., Goal set inverse optimal control and iterative replanning for predicting human reaching motions in shared workspaces (2016) IEEE Trans. Robot., 32 (4), pp. 897-908; Pellegrinelli, S., Orlandini, A., Pedrocchi, N., Umbrico, A., Tolio, T., Motion planning and scheduling for human and industrial-robot collaboration (2017) CIRP Annals, 66 (1), pp. 1-4; Kim, S., Shukla, A., Billard, A., Catching objects in flight (2014) IEEE Trans. Robot., 30 (5), pp. 1049-1065; Ardakani, M.M.G., Olofsson, B., Robertsson, A., Johansson, R., Real-time trajectory generation using model predictive control (2015) Proceedings of the CASE, pp. 942-948. , IEEE; Maderna, R., Casalino, A., Zanchettin, A.M., Rocco, P., Robotic handling of liquids with spilling avoidance: a constraint-based control approach (2018) Proceedings of the ICRA, pp. 7414-7420; Biagiotti, L., Melchiorri, C., Trajectory Planning for Automatic Machines and Robots (2009), Springer Berlin Heidelberg","Zhang, S.; State Key Laboratory of Virtual Reality Technology and Systems, China; email: zhangshiyu@buaa.edu.cn",,,"Elsevier Ltd",,,,,0094114X,,MHMTA,,"English","Mech Mach Theory",Article,"Final","",Scopus,2-s2.0-85073533220
"Stuhlenmiller F., Velasco-Guillen R.J., Rinderknecht S., Beckerle P.","57015622100;57219802929;6603609535;36964076200;","Fault-Tolerant Physical Human-Robot Interaction via Stiffness Adaptation of Elastic Actuators",2020,"Springer Proceedings in Advanced Robotics","12",,,"73","87",,,"10.1007/978-3-030-42026-0_6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107041920&doi=10.1007%2f978-3-030-42026-0_6&partnerID=40&md5=dcfb173cda710e4125ec2f00232ff712","Institute for Mechatronic Systems in Mechanical Engineering, Technical University Darmstadt, Darmstadt, Germany; Elastic Lightweight Robotics Group, Robotics Research Institute, Technical University Dortmund, Dortmund, Germany","Stuhlenmiller, F., Institute for Mechatronic Systems in Mechanical Engineering, Technical University Darmstadt, Darmstadt, Germany; Velasco-Guillen, R.J., Elastic Lightweight Robotics Group, Robotics Research Institute, Technical University Dortmund, Dortmund, Germany; Rinderknecht, S., Institute for Mechatronic Systems in Mechanical Engineering, Technical University Darmstadt, Darmstadt, Germany; Beckerle, P., Institute for Mechatronic Systems in Mechanical Engineering, Technical University Darmstadt, Darmstadt, Germany, Elastic Lightweight Robotics Group, Robotics Research Institute, Technical University Dortmund, Dortmund, Germany","Elastic actuators are popular in human-robot interaction as they can improve human safety and efficiency. Yet, such actuators are more complex than rigid ones and might be subject to additional technical faults, e.g., stiffness changes. This paper extends previous studies on stiffness-fault-tolerant physical human-robot interaction (pHRI) through control adaptation, introducing new methods for stiffness estimation and fault evaluation. Kalman filters with different measurement signals and system models estimating the actual stiffness value of the elastic element are compared. Faults are evaluated by analyzing the structural durability and compensated by adapting an impedance controller to provide a desired interaction stiffness. Experiments with a series elastic actuator underline the feasibility of the evaluation and compensation methods for attaining safe and reliable pHRI. Results show that stiffness estimation during pHRI is possible when the actuator friction and interaction torque is either negligible or well known, or when the torque at the spring is measured. © 2020, Springer Nature Switzerland AG.","Elastic actuator; Fault evaluation; Fault-tolerant control; Physical human-robot interaction",,,,,,"Deutsche Forschungsgemeinschaft, DFG: BE 5729/1","Acknowledgment. This work was supported by a Deutsche Forschungsgemeinschaft (DFG) Research Grant (no. BE 5729/1).",,,,,,,,,,"Haddadin, S., Albu-Schaeffer, A., de Luca, A., Hirzinger, G., Collision detection and reaction: A contribution to safe physical human-robot interaction (2008) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3356-3363. , , pp; Haddadin, S., Albu-Schäffer, A., Hirzinger, G., Safe physical human-robot interaction: Measurements, analysis and new insights (2007) ISRR, Vol. 66, pp. 395-407. , , pp. , Springer, Heidelberg; Lens, T., von Stryk, O., Investigation of safety in human-robot-interaction for a series elastic, tendon-driven robot driven robot arm (2012) IEEE/RSJ International Conference on Intelligent Robots and Systems; Vanderborght, B., van Ham, R., Lefeber, D., Sugar, T.G., Hollander, K.W., Comparison of mechanical design and energy consumption of adaptable, passive-compliant actuators (2009) Int. J. Robot. Res., 28 (1), pp. 90-103; Verstraten, T., Beckerle, P., Furnémont, R., Mathijssen, G., Vanderborght, B., Lefeber, D., Series and parallel elastic actuation: Impact of natural dynamics on power and energy consumption (2016) Mech. Mach. Theory, 102, pp. 232-246; Beckerle, P., Practical relevance of faults, diagnosis methods, and tolerance measures in elastically actuated robots (2016) Control Eng. Pract., 50, pp. 95-100; Filippini, R., Sen, S., Bicchi, A., Toward soft robots you can depend on (2008) IEEE Robot. Autom. Mag., 15 (3), pp. 31-41; Vanderborght, B., Albu-Schaeffer, A., Bicchi, A., Burdet, E., Caldwell, D., Carloni, R., Catalano, M., Wolf, S., Variable impedance actuators: A review (2013) Robot. Auton. Syst., 61 (12), pp. 1601-1614; Pratt, G.A., Williamson, M.M., Series elastic actuators (1995) IEEE/RSJ International Conference on Intelligent Robots and Systems; Schiavi, R., Grioli, G., Sen, S., Bicchi, A., VSA-II: A novel prototype of variable stiffness actuator for safe and performing robots interacting with humans (2008) IEEE International Conference on Robotics and Automation; Isermann, R., (2006) Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance, , Springer, Heidelberg; Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M., (2010) Diagnosis and Fault-Tolerant Control, , Springer, Heidelberg; Stuhlenmiller, F., Perner, G., Rinderknecht, S., Beckerle, P., (2019) A Stiffness-Fault-Tolerant Control Strategy for Reliable Physical Human-Robot Interaction. In: Human Friendly Robotics, pp. 3-14. , pp. , Springer, Heidelberg; Lendermann, M., Singh, B.R.P., Stuhlenmiller, F., Beckerle, P., Rinderknecht, S., Manivannan, P.V., Comparison of passivity based impedance controllers without torque-feedback for variable stiffness actuators (2015) IEEE/ASME International Conference on Advanced Intelligent Mechatronics; Schuy, J., Beckerle, P., Wojtusch, J., Rinderknecht, S., von Stryk, O., Conception and evaluation of a novel variable torsion stiffness for biomechanical applications (2012) IEEE/RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 713-718. , , pp; Spong, M.W., Adaptive control of flexible joint manipulators (1989) Syst. Control Lett., 13, pp. 15-21; Andersson, S., Söderberg, A., Björklund, S., Friction models for sliding dry, boundary and mixed lubricated contacts (2007) Tribol. Int., 40 (4), pp. 580-587; Lemaitre, J., Dufailly, J., Damage measurements (1987) Eng. Fract. Mech., 28 (5), pp. 643-661; Flacco, F., de Luca, A., Residual-based stiffness estimation in robots with flexible transmissions (2011) 2011 IEEE International Conference on Robotics and Automation (ICRA), Pp. 5541–5547. IEEE; Julier, S., Uhlmann, J., Unscented filtering and nonlinear estimation (2004) Proc. IEEE, 92 (3), pp. 401-422; Wan, E.A., van Der Merwe, R., The unscented Kalman filter for nonlinear estimation (2000) Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC. the IEEE2000, Pp. 153–158. IEEE; Wu, M., Smyth, A., Application of the unscented kalman filter for real-time nonlinear structural system identification (2007) Struct. Control Health Monit., 14, pp. 971-990; Schuy, J., Beckerle, P., Faber, J., Wojtusch, J., Rinderknecht, S., von Stryk, O., Dimensioning and evaluation of the elastic element in a variable torsion stiffness actuator (2013) IEEE/ASME International Conference on Advanced Intelligent Mechatronics; de Souza Neto, E.A., Perić, D., Owen, D.R.J., (2008) Computational Methods for Plasticity, , Wiley, Hoboken; Ott, C., (2008) English Cartesian Impedance Control of Redundant and Flexible-Joint Robots, , Springer, Heidelberg; Kaur, N., Kaur, A., A review on tuning of extended kalman filter using optimization techniques for state estimation (2016) Int. J. Comput. Appl., 145 (15), pp. 1-5","Velasco-Guillen, R.J.; Elastic Lightweight Robotics Group, Germany; email: rodrigo.velasco@tu-dortmund.de",,,"Springer Science and Business Media B.V.",,,,,25111256,,,,"English","Springer. Proc. Adv. Robot.",Book Chapter,"Final","All Open Access, Green",Scopus,2-s2.0-85107041920
"Jokinen K.","23397498500;","The AICO multimodal corpus - Data collection and preliminary analyses",2020,"LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings",,,,"559","564",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088507093&partnerID=40&md5=b7893c8cda010bd9fc11f5244119c360","AI Research Center, AIST Tokyo, Waterfront 2-4-7, Aomi Koto-ku Tokyo, 135-0064, Japan","Jokinen, K., AI Research Center, AIST Tokyo, Waterfront 2-4-7, Aomi Koto-ku Tokyo, 135-0064, Japan","This paper describes data collection and the first explorative research on the AICO Multimodal Corpus. The corpus contains eye-gaze, Kinect, and video recordings of human-robot and human-human interactions, and was collected to study cooperation, engagement and attention of human participants in task-based as well as in chatty type interactive situations. In particular, the goal was to enable comparison between human-human and human-robot interactions, besides studying multimodal behaviour and attention in the different dialogue activities. The robot partner was a humanoid Nao robot, and it was expected that its agent-like behaviour would render human-robot interactions similar to human-human interaction but also high-light important differences due to the robot's limited conversational capabilities. The paper reports on the preliminary studies on the corpus, concerning the participants' eye-gaze and gesturing behaviours, which were chosen as objective measures to study differences in their multimodal behaviour patterns with a human and a robot partner. © European Language Resources Association (ELRA), licensed under CC-BY-NC","Eye-tracking; Gesturing; Human-human; Human-robot dialogues; Multimodal corpus collection","Behavioral research; Data acquisition; Man machine systems; Video recording; Data collection; High lights; Human robots; Human-human interactions; Multimodal behaviour; Objective measure; Preliminary analysis; Robot Partners; Social robots",,,,,,,,,,,,,,,,"Allwood, (2004) The MUMIN Multimodal Coding Scheme, , https://www.researchgate.net/publication/228626291_The_MUMIN_multimodal_coding_scheme; Alameda-Pineda, X., Ricci, E., Sebe, N., (2019) Multimodal Behaviour Analysis in the Wild - Advances and Challenges, , (Eds) Academic Press; Ijuin, K., Jokinen, K., Kato, T., Yamamoto, S., Eye-gaze in Social Robot Interactions - Grounding of Information and Eye-gaze Patterns (2019) JSAI 2019; Ijuin, K., Jokinen, K., Utterances in Social Robot Interactions - Correlation Analyses between Robot's Fluency and Participant's Impression (2019) Procs of ACM Conference on Human-Agent Interaction (HAI '19); Ijuin, K., Jokinen, K., Exploring Gaze Behaviour and Perceived Personality Traits (2020) Proceedings of the International Conference on Human-Computer Interaction (HCII); Jokinen, K., Explorations in the Speakers' Interaction Experience and Self-assessments (2012) Procs of the COLING-2012, , Mumbai, India; Jokinen, K., Conversational Gaze Modelling in First Encounter Robot Dialogues (2018) Proceedings of the International LREC Workshop on Language and Body in Real Life (REAL-MM), , Miyazaki, Japan; Jokinen, K., Social Emotions for Social Robots - Studies on Affective Impressions in Human-Human and Human-Robot Interactions (2019) Social Robotics (ICSR 2019). Lecture Notes in Computer Science, 11876, pp. 267-277. , Springer, Cham; Jokinen, K., Furukawa, H, Nishida, M., Yamamoto, S., Gaze and Turn-taking behaviour in Casual Conversational Interactions (2013) Special Issue on Eye Gaze in Intelligent Human-Machine Interaction, ACM Trans. Interactive Intelligent Systems; Jokinen, K., Nishimura, S., Watanabe, K., Nishimura, T., Human-Robot Dialogues for Explaining Activities (2018) The 9th International Workshop on Spoken Dialogue System Technology. Lecture Notes in Electrical Engineering, 579. , Springer, Singapore; Jokinen, K., Privacy and Sensor Information in the Interactive Service Applications for Elder People (2019) Proceedings of the 7th National Conference of Serviceology, , Tokyo; Jokinen, K., Wilcock, G., Expectations and First Experience with a Social Robot (2017) Procs of ACM Conference on Human-Agent Interaction (HAI '17); Jokinen, K., Wilcock, G., Multimodal Open-domain Conversations with the Nao Robot (2014) Natural Interaction with Robots, Knowbots and Smartphones - Putting Spoken Dialog Systems into Practice, pp. 213-224. , Mariani, J. et al. (eds) pages Springer, New York; Kendon, A., (2004) Gesture: Visible Action as Utterance, , Cambridge: Cambridge University Press; Kendon, A., Some functions of gaze direction in social interaction (1967) Acta Psychologica, 26, pp. 22-63; Laohakangvalvit, T., Jokinen, K., Eye-gaze Behaviors between Human-Human and Human-Robot Interactions in Natural Scene (2019) Proceedings of the ECEM conference; Maynard, S., (1997) Japanese communication, , Honolulu: University of Hawaii Press; Meena, R., Jokinen, K., Wilcock, G., Integration of gestures and speech in human-robot interaction (2012) Proceedings of 3rd IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2012), pp. 673-678. , Kosice, Slovakia; Mori, T., Jokinen, K., Den, Y., Analysis of Body Behaviours in Human-Human and Human-Robot Interactions (2020) Procs of the International LREC Workshop peOple in laNguage, vIsiOn and the mind; Siciliano, B, Khatib, O., (2016) Springer Handbook of Robotics, , Springer-Verlag Berlin Heidelberg; Vinciarelli, A., Mohammadi, G., A survey of personality computing (2014) IEEE Transactions on Affective Computing, 5, pp. 273-291. , 03; Wittenburg, P., Brugman, H., Russel, A., Klassmann, A., Sloetjes, H., ELAN: a Professional Framework for Multimodality Research (2006) Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC)","Jokinen, K.; AI Research Center, Waterfront 2-4-7, Japan; email: Kristiina.Jokinen@aist.go.jp","Calzolari N.Bechet F.Blache P.Choukri K.Cieri C.Declerck T.Goggi S.Isahara H.Maegaard B.Mariani J.Mazo H.Moreno A.Odijk J.Piperidis S.","Amazon AWS;Bertin;Lenovo;Ontotex;Vecsys;Vocapia","European Language Resources Association (ELRA)","12th International Conference on Language Resources and Evaluation, LREC 2020","11 May 2020 through 16 May 2020",,164155,,9791095546344,,,"English","LREC - Int. Conf. Lang. Resour. Eval., Conf. Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85088507093
"Zhong L., Yang L., Rong J., Li X.","56659631600;57217524786;24462303900;57217133362;","A Complexity Analysis of User Interaction with Hotel Robots",2020,"Complexity","2020",,"4537152","","",,,"10.1155/2020/4537152","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087332248&doi=10.1155%2f2020%2f4537152&partnerID=40&md5=09cf2766768e470cc6f09489ee1a3a2e","Institute for Big Data Research in Tourism, School of Tourism Sciences, Beijing International Studies University, Chaoyang District, Beijing, China; Department of Data Science and Ai, Monash University, Melbourne, Australia","Zhong, L., Institute for Big Data Research in Tourism, School of Tourism Sciences, Beijing International Studies University, Chaoyang District, Beijing, China; Yang, L., Institute for Big Data Research in Tourism, School of Tourism Sciences, Beijing International Studies University, Chaoyang District, Beijing, China; Rong, J., Department of Data Science and Ai, Monash University, Melbourne, Australia; Li, X., Institute for Big Data Research in Tourism, School of Tourism Sciences, Beijing International Studies University, Chaoyang District, Beijing, China","Service robots have been introduced to hotel industry in the past decade and received various feedback on their performance. To provide better service, one needs to understand how the hotel customers look at the service robots. Understanding their interests, motivation, and behaviors in human-robot interaction is the key to develop high-quality services and improve robot's performance. This is the first work to study human-robot interaction in hotels in China. Frequent pattern mining and social network analysis techniques are used in this work to find out useful suggestions to both hotel management and robot manufactory. Turning on and off lights, TV, curtain, and window screens are popular services that most of the hotel customers preferred during their stays. Service robots are also found to entertain customers to carry out repeated commands for fun or to kill time. Customers also showed various motivations to stay in hotel rooms by calling different commands. © 2020 Lina Zhong et al.",,"Behavioral research; Hotels; Man machine systems; Mobile robots; Motivation; Sales; Service industry; Complexity analysis; Frequent pattern mining; High quality service; Hotel industry; Hotel managements; Service robots; User interaction; Social robots",,,,,"National Natural Science Foundation of China, NSFC: 71673015; Beijing Social Science Fund: 18JDGLB013, 19JDXCA005","This study was supported by the Beijing Social Science Foundation under grant nos. 19JDXCA005 and 18JDGLB013 and National Natural Science Foundation of China under grant no. 71673015.",,,,,,,,,,"Ivanov, S., Webster, C., Perceived appropriateness and intention to use service robots in tourism (2019) International Conference in Nicosia, pp. 237-248. , Springer, Cham, Switzerland; (2002) Statistics, Market Analysis, Forecasts, Case Studies and Profitability of Robot Investment, , International Federation of Robotics, United Nations, and Economic Commission for Europe, World Robotics, United Nations Publications, Newyork, NY, USA, 2002; Shibata, T., An overview of human interactive robots for psychological enrichment (2004) Proceedings of the Ieee, 92 (11), pp. 1749-1758; Wasko, M.M., Faraj, S., Why should I share? 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Strategies, Bourgas, Bulgaria; Kim, M., Kim, S., Park, S., Choi, M.-T., Kim, M., Gomaa, H., Service robot for the elderly (2009) Ieee Robotics & Automation Magazine, 16 (1), pp. 34-45; Lakshmi, N.K., Rajakumar, G., Kumaran, D.N.M., Dinesh, I., Caleb, V., Arshadh, H., Design and fabrication of medicine delivery robots for hospitals (2019) Proceedings of International Conference on Recent Trends in Computing, Communication & Networking Technologies (ICRTCCNT), , Chennai, Tamilnadu, India, October; Ivanov, S., Webster, C., Garenko, A., Young Russian adults' attitudes towards the potential use of robots in hotels (2018) Technology in Society, 55, pp. 24-32; Tussyadiah, I.P., Park, S., Consumer evaluation of hotel service robots (2018) Information and Communication Technologies in Tourism 2018, pp. 308-320. , Springer, Cham, Switzerland; Thrun, S., Toward a framework for human-robot interaction (2004) Human-Computer Interaction, 19 (1-2), pp. 9-24; Van Osch, M., Bera, D., Hee, K.V., Koks, Y., Zeegers, H., Tele-operated service robots: Rose (2014) Automation in Construction, 39, pp. 152-160; Berezina, S., Stainslav, H.I., Webster, C., Adoption of robots and service automation by tourism and hospitality companies (2017) Revista Turismo & Desenvolvimento, 27, pp. 1501-1517; Martins, A., Brave new world: Service robots in the frontline (2018) Journal of Service Management, 29 (5), pp. 907-931; Jeonghye, H., Jaeyeon, L., Youngjo, C., Evolutionary role model and basic emotions of service robots originated from computers (2005) Proceedings of the International Workshop on Robots and Human Interactive Communication, pp. 30-35. , IEEE, Nashville, TN, USA, August; Norman, D., Draper, S.W., (1986) User Centered Design: New Perspectives on Human-Computer Interaction, , Lawrence Erlbaurn Associates, Hillsdale, NJ, USA; Webster, C., Ivanov, S., Adoption of robots, artificial intelligence and service automation by travel, tourism and hospitality companies-a cost-benefit analysis (2017) Artificial Intelligence and Service Automation by Travel, , Emerald Group Publishing, Bingley, UK; Jeonghye, H., Miheon, J., Sungju, P., Sungho, K., The educational use of home robots for children (2005) Proceedings of the International Workshop on Robots and Human Interactive Communication, pp. 378-383. , IEEE, Nashville, TN, USA, August; 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"Bolotnikova A., Courtois S., Kheddar A.","57190802530;57204948230;56276683900;","Multi-Contact Planning on Humans for Physical Assistance by Humanoid",2020,"IEEE Robotics and Automation Letters","5","1","8873658","135","142",,5,"10.1109/LRA.2019.2947907","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075684803&doi=10.1109%2fLRA.2019.2947907&partnerID=40&md5=644e5447de6914407775387c0df13a06","SoftBank Robotics Europe, Paris, 75015, France; CNRS-University of Montpellier, Laboratoire d'Informatique de Robotique et de Microeectronique de Montpellier (LIRMM), Montpellier, 34095, France","Bolotnikova, A., SoftBank Robotics Europe, Paris, 75015, France, CNRS-University of Montpellier, Laboratoire d'Informatique de Robotique et de Microeectronique de Montpellier (LIRMM), Montpellier, 34095, France; Courtois, S., CNRS-University of Montpellier, Laboratoire d'Informatique de Robotique et de Microeectronique de Montpellier (LIRMM), Montpellier, 34095, France; Kheddar, A., SoftBank Robotics Europe, Paris, 75015, France","For robots to interact with humans in close proximity safely and efficiently, a specialized method to compute whole-body robot posture and plan contact locations is required. In our work, a humanoid robot is used as a caregiver that is performing a physical assistance task. We propose a method for formulating and initializing a non-linear optimization posture generation problem from an intuitive description of the assistance task and the result of a human point cloud processing. The proposed method allows to plan whole-body posture and contact locations on a task-specific surface of a human body, under robot equilibrium, friction cone, torque/joint limits, collision avoidance, and assistance task inherent constraints. The proposed framework can uniformly handle any arbitrary surface generated from point clouds, for autonomously planing the contact locations and interaction forces on potentially moving, movable, and deformable surfaces, which occur in direct physical human-robot interaction. We conclude the letter with examples of posture generation for physical human-robot interaction scenarios. © 2016 IEEE.","Humanoid robots; physical human-robot interaction; physically assistive devices; robot companions","Anthropomorphic robots; Location; Man machine systems; Nonlinear programming; Arbitrary surfaces; Assistive devices; Deformable surfaces; Humanoid robot; Interaction forces; Non-linear optimization; Physical human-robot interactions; Robot companion; Human robot interaction",,,,,,,,,,,,,,,,"World population ageing United Nations, Dept. Econ. Social Affairs, Population Division, Rep. ST/ESA/SER.A/390, 2015; Niemelä, M., Melkas, H., Robots as social and physical assistants in elderly care (2019) Human-Centered Digitalization Services, 19, pp. 177-197; Onishi, M., Luo, Z., Odashima, T., Hirano, S., Tahara, K., Mukai, T., Generation of human care behaviors by human-interactive robot RI-MAN (2007) Proc. IEEE Int. Conf. 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Humanoid Robots, pp. 279-286. , Madrid, Spain, Nov. 18-20; Ponton, B., Herzog, A., Schaal, S., Righetti, L., A convex model of momentum dynamics for multi-contact motion generation (2016) Proc. IEEE-RAS 16th Int. Conf. Humanoid Robots., pp. 842-849. , Nov. 15-17; Dai, H., Valenzuela, A., Tedrake, R., Whole-body motion planning with centroidal dynamics and full kinematics (2014) Proc. IEEE-RAS Int. Conf. Humanoid Robots, pp. 295-302. , Madrid, Spain, Nov. 18-20; Mordatch, I., Todorov, E., Popovíc, Z., Discovery of complex behaviors through contact-invariant optimization (2012) ACM Trans. Graph., 31 (43); Oßwald, S., Gutmann, J.-S., Hornung, A., Bennewitz, M., From 3D point clouds to climbing stairs: A comparison of plane segmentation approaches for humanoids (2011) Proc. IEEE-RAS Int. Conf. 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Vision Theory Appl, pp. 553-560. , Berlin, Germany, Mar. 11-14; Buys, K., Cagniart, C., Baksheev, A., Laet, T.D., Schutter, J.D., Pantofaru, C., An adaptable system for RGB-d based human body detection and pose estimation (2014) J. Visual Commun. Image Representation, 25 (1), pp. 39-52; Khatib, O., Yokoi, K., Brock, O., Chang, K., Casal, A., Robots in human environments: Basic autonomous capabilities (1999) Int. J. Robot. Res., 18 (7), pp. 684-696; Brossette, S., (2016) Viable Multi-Contact Posture Computation for Humanoid Robots Using Nonlinear Optimization on Manifolds, , Ph.D. dissertation, Univ. Montpellier, Montpellier, France; Piegl, L., Tiller, W., (1997) The NURBS Book, , Berlin, Germany: Springer; Dimitrov, A., Gu, R., Golparvar-Fard, M., Non-uniform b-spline surface fitting from unordered 3D point clouds for as-built modeling (2016) Comput.-Aided Civil Infrastruct. Eng., 31 (7), pp. 483-498; Flöry, S., Fitting curves and surfaces to point clouds in the presence of obstacles (2009) Comput. Aided Geometric Des., 26 (2), pp. 192-202; Flöry, S., Hofer, M., Constrained curve fitting onmanifolds (2008) Comput.-Aided Des., 40 (1), pp. 25-34; Rusu, R.B., Cousins, S., 3D is here: Point cloud library (PCL) (2011) Proc. IEEE Int. Conf. Robot, pp. 1-4. , Autom., Shanghai, China, May 9-13; Escande, A., Miossec, S., Benallegue, M., Kheddar, A., Convex hull for computing proximity distances with continuous gradients (2014) IEEE Trans. Robot., 30 (3), pp. 666-678. , Jun; Xu, W., Monoperfcap:Human performance capture from monocular video (2018) ACM Trans. Graph., 37 (2); Bouyarmane, K., Vaillant, J., Chappellet, K., Kheddar, A., Quadratic programming for multirobot and task-space force control (2019) IEEE Trans. Robot., 35 (1), pp. 64-77. , Feb; Bolotnikova, A., Courtois, S., Kheddar, A., Contact observer for humanoid robot pepper based on tracking joint position discrepancies (2018) Proc. IEEE Int. Conf. Robot Human Interactive Commun, pp. 29-34. , Nanjing, China, Aug. 27-31; Bolotnikova, A., Courtois, S., Kheddar, A., Compliant robot motion regulated via proprioceptive sensor based contact observer (2018) Proc. IEEE-RAS Int. Conf. Humanoid Robots, pp. 854-859. , Beijing, China, Nov. 6-9; López, A.M., Vaillant, J., Keith, F., Fraisse, P., Kheddar, A., Compliant control of a humanoid robot helping a person stand up from a seated position (2014) Proc IEEE-RAS Int. Conf. Humanoid Robots, pp. 817-822. , Madrid, Spain, Nov. 18-20; Dautenhahn, K., Socially intelligent robots: Dimensions of human-robot interaction (2007) Philos. Trans. Roy. Soc. B, Biol. Sci., 362 (1480), pp. 679-704","Bolotnikova, A.; SoftBank Robotics EuropeFrance; email: bolotnikova.anastasia@gmail.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85075684803
"Akhmetzyanov A., Yagfarov R., Gafurov S., Ostanin M., Klimchik A.","57210233068;57204765694;56088205500;57203783042;37016245900;","Exploration of underinvestigated indoor environment based on mobile robot and mixed reality",2020,"Advances in Intelligent Systems and Computing","1018",,,"317","322",,1,"10.1007/978-3-030-25629-6_49","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069985946&doi=10.1007%2f978-3-030-25629-6_49&partnerID=40&md5=a78092ce28a9ea3c7e1e7cf48cba0b85","Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russian Federation","Akhmetzyanov, A., Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russian Federation; Yagfarov, R., Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russian Federation; Gafurov, S., Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russian Federation; Ostanin, M., Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russian Federation; Klimchik, A., Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russian Federation","Mobile robots allow human to explore impassable or unsafe indoor locations remotely. There are a large number of scenarios where the implementation of mobile robots is the only way to successfully complete them. Herewith, human-robot interaction is the key factor that determines the efficiency of overall mission accomplishment. This paper presents a real-time approach for remote exploration of underinvestigated indoor environment conducted by human-robot interaction. The approach is based on the mobile robot and mixed reality. We have chosen a differential drive mobile robot platform called Plato and mixed reality glasses Microsoft Hololens for experimental investigation of the proposed approach. Our paper demonstrates the results of experimental investigations of the proposed approach. The results have shown that the proposed approach is helpful, intuitive and convenient for spatial understanding and improves natural interaction both with a mobile robot and remote environment. Moreover, our approach is able to significantly increase the speed of indoor environment exploration. © 2020, Springer Nature Switzerland AG.","Human-robot interaction; Mixed reality; Mobile robots","Man machine systems; Mixed reality; Mobile robots; Differential drive; Experimental investigations; Indoor environment; Indoor locations; Mobile robot platforms; Natural interactions; Remote environment; Remote explorations; Human robot interaction",,,,,"Ministry of Education and Science of the Russian Federation, Minobrnauka: 075-10-2018-017","Acknowledgments. This research was supported by the Russian Education Ministry under the Agreement No. 075-10-2018-017 on granting a subsidy from 11.26.2018 “Development of a commercial urban transport with the intellectual driver assistance system “City Pilot”. The customer is a federal target program. The unique identifier of the project RFMEFI60918X0005. Industrial partner - KAMAZ",,,,,,,,,,"Terrence, F., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) J. Robot. Auton. Syst., 42 (3-4), pp. 143-166; Goodrich, M., Schultz, A., Human–robot interaction: A survey (2008) J. Found. Trends Hum.– Comput. Interact., 1 (3), pp. 203-275; Avery, B., Piekarski, W., Thomas, B., Visualizing occluded physical objects in unfamiliar outdoor augmented reality environments (2007) The 6Th IEEE and ACM International Symposium on Mixed and Augmented Reality, , IEEE Computer Society; Grisetti, G., Stachniss, C., Burgard, W., Improved techniques for grid mapping with rao-blackwellized particle filters (2007) J. IEEE Trans. Robot., 23 (1), pp. 34-46; Yagfarov, R., Ivanou, M., Afanasyev, I., Map comparison of lidar-based 2D SLAM algorithms using precise ground truth (2018) 15Th International Conference on Control, Automation, Robotics and Vision, ICARCV; Hess, W., Kohler, D., Rapp, H., Andor, D., Real-time loop closure in 2D lidar slam (2016) IEEE International Conference on Robotics and Automation (ICRA), pp. 1271-1278. , IEEE Press; Kohlbrecher, S., von Stryk, O., Meyer, J., Klingauf, U., A flexible and scalable slam system with full 3D motion estimation (2011) IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 155-160. , IEEE Press; Presentation of the Research, , https://www.youtube.com/watch?v=DDF83pcj5gY; Besl, P.J., McKay, N.D., Method for regis-tration of 3-D shapes (1992) Sensor Fusion IV: Control Paradigms and Data Structures, 1611, pp. 586-607. , International Society for Optics and Photonics; Algorithms for Human-Robot Interaction, , https://github.com/AydarAkhmetzyanov/plato_hololens","Akhmetzyanov, A.; Center for Technologies in Robotics and Mechatronics Components, Russian Federation; email: a.akhmetzyanov@innopolis.ru","Ahram T.Taiar R.Colson S.Choplin A.",,"Springer Verlag","1st International Conference on Human Interaction and Emerging Technologies, IHIET 2019","22 August 2019 through 24 August 2019",,229139,21945357,9783030256289,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85069985946
"Buschmeier H., Foster M.E., Gkatzia D.","35483237200;23091020100;56349875100;","Second workshop on natural language generation for human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"646","647",,,"10.1145/3371382.3374853","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083247050&doi=10.1145%2f3371382.3374853&partnerID=40&md5=68fc63bf306d0137f47978010a5361fe","Bielefeld University, Bielefeld, Germany; University of Glasgow, Glasgow, United Kingdom; Edinburgh Napier University, Edinburgh, United Kingdom","Buschmeier, H., Bielefeld University, Bielefeld, Germany; Foster, M.E., University of Glasgow, Glasgow, United Kingdom; Gkatzia, D., Edinburgh Napier University, Edinburgh, United Kingdom","This workshop is the second in a series bringing together the Natural Language Generation and Human-Robot Interaction communities to discuss topics of mutual interest with the goal of developing an HRI-inspired NLG shared task. The workshop website is urlhttps: //purl.org/nlg-hri-workshop/2020. © 2020 ACM.","Human-robot interaction; Natural language generation","Agricultural robots; Man machine systems; Natural language processing systems; Natural language generation; Human robot interaction",,,,,,,,,,,,,,,,"Ellen Foster, M., Natural language generation for social robotics: Opportunities and challenges (2019) Philosophical Transactions of the Royal Society B: Biological Sciences, 374 (1771), p. 20180027. , https://doi.org/10.1098/rstb.2018.0027, 2019; Ellen Foster, M., Buschmeier, H., Gkatzia, D., Proceedings of the workshop on nlg for human-robot interaction (2018) Association for Computational Linguistics, Tilburg, the Netherlands, , https://aclweb.org/anthology/volumes/W18-69/; Gatt, A., Krahmer, E., Survey of the state of the art in Natural Language Generation: Core tasks, applications and evaluation (2018) Journal of Artificial Intelligence Research, 61, pp. 65-170. , https://doi.org/10.1613/jair.5477, 2018; Striegnitz, K., Denis, A., Gargett, A., Garoufi, K., Koller, A., Theune, M., Report on the second second challenge on generating instructions in virtual environments (give-2 5) (2011) Proceedings of the 13th European Workshop on Natural Language Generation. Nancy, France, pp. 270-279",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083247050
"Huang P., Wang H., Wang Y., Liu Z., Samuel O.W., Yu M., Li X., Chen S., Li G.","57201918519;57037447900;57216644565;56344609500;57188828713;56591026900;56647927000;35236039500;7407054228;","Identification of Upper-Limb Movements Based on Muscle Shape Change Signals for Human-Robot Interaction",2020,"Computational and Mathematical Methods in Medicine","2020",,"5694265","","",,3,"10.1155/2020/5694265","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084169941&doi=10.1155%2f2020%2f5694265&partnerID=40&md5=89cd24f858621156de0fc29247a3b29f","CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China; Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China; University of Chinese Academy of Sciences, Beijing, 100049, China","Huang, P., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China, University of Chinese Academy of Sciences, Beijing, 100049, China; Wang, H., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China; Wang, Y., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China, University of Chinese Academy of Sciences, Beijing, 100049, China; Liu, Z., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China; Samuel, O.W., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China; Yu, M., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China; Li, X., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China; Chen, S., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China; Li, G., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, 518055, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, 518055, China","Towards providing efficient human-robot interaction, surface electromyogram (EMG) signals have been widely adopted for the identification of different limb movement intentions. Since the available EMG signal sensors are highly susceptible to external interferences such as electromagnetic artifacts and muscle fatigues, the quality of EMG recordings would be mostly corrupted, which may decay the performance of EMG-based control systems. Given the fact that the muscle shape changes (MSC) would be different when doing various limb movements, the MSC signal would be nonsensitive to electromagnetic artifacts and muscle fatigues and maybe promising for movement intention recognition. In this study, a novel nanogold flexible and stretchable sensor was developed for the acquisition of MSC signals utilized for decoding multiple classes of limb movement intents. More precisely, four sensors were used to measure the MSC signals from the right forearm of each subject when they performed seven classes of movements. Also, six different features were extracted from the measured MSC signals, and a linear discriminant analysis-(LDA-) based classifier was built for movement classification tasks. The experimental results showed that using MSC signals could achieve an average recognition rate of about 96.06 ± 1.84% by properly placing the four flexible and stretchable sensors on the forearm. Additionally, when the MSC sampling rate was greater than 100 Hz and the analysis window length was greater than 20 ms, the movement recognition accuracy would be only slightly increased. These pilot results suggest that the MSC-based method should be feasible in movement identifications for human-robot interaction, and at the same time, they provide a systematic reference for the use of the flexible and stretchable sensors in human-robot interaction systems. © 2020 Pingao Huang et al.",,"Discriminant analysis; Man machine systems; Motion estimation; Muscle; Quality control; Social robots; Analysis windows; Classification tasks; EMG-based control; External interference; Linear discriminant analysis; Movement intention recognition; Movement recognition; Surface electromyogram; Biomedical signal processing; adult; Article; classifier; discriminant analysis; electromyogram; feature extraction; feature selection; female; forearm; human; human experiment; limb movement; male; multiclass classification; normal human; robotics; signal noise ratio; signal processing; upper limb; automated pattern recognition; computer interface; devices; electric conductivity; electromyography; electronic device; equipment design; movement (physiology); physiology; robotics; signal processing; skeletal muscle; upper limb; gold; Electric Conductivity; Electromyography; Equipment Design; Gold; Humans; Movement; Muscle, Skeletal; Pattern Recognition, Automated; Robotics; Signal Processing, Computer-Assisted; Upper Extremity; User-Computer Interface; Wearable Electronic Devices",,"gold, 7440-57-5; Gold",,,"National Natural Science Foundation of China, NSFC: U1613222 81927804 61803361; Dream Project of Ministry of Science and Technology of the People's Republic of China","Towards providing efficient human-robot interaction, surface electromyogram (EMG) signals have been widely adopted for the identification of different limb movement intentions. Since the available EMG signal sensors are highly susceptible to external interferences such as electromagnetic artifacts and muscle fatigues, the quality of EMG recordings would be mostly corrupted, which may decay the performance of EMG-based control systems. Given the fact that the muscle shape changes (MSC) would be different when doing various limb movements, the MSC signal would be nonsensitive to electromagnetic artifacts and muscle fatigues and maybe promising for movement intention recognition. In this study, a novel nanogold flexible and stretchable sensor was developed for the acquisition of MSC signals utilized for decoding multiple classes of limb movement intents. More precisely, four sensors were used to measure the MSC signals from the right forearm of each subject when they performed seven classes of movements. Also, six different features were extracted from the measured MSC signals, and a linear discriminant analysis- (LDA-) based classifier was built for movement classification tasks. The experimental results showed that using MSC signals could achieve an average recognition rate of about 96.06 ± 1.84% by properly placing the four flexible and stretchable sensors on the forearm. Additionally, when the MSC sampling rate was greater than 100 Hz and the analysis window length was greater than 20 ms, the movement recognition accuracy would be only slightly increased. These pilot results suggest that the MSC-based method should be feasible in movement identifications for human-robot interaction, and at the same time, they provide a systematic reference for the use of the flexible and stretchable sensors in human-robot interaction systems. National Key R&D Program of China 2018YFA0701400 National Natural Science Foundation of China U1613222 81927804 61803361 Ministry of Science and Technology of the People's Republic of China 2016YFE0124100 Shenzhen Science and Technology Plan Project JCYJ20160331174854880 Guangdong Science and Technology Project 2019B090915002 Shenzhen Institute of Artificial Intelligence and Robotics for Society",,,,,,,,,,"Patel, S., Park, H., Bonato, P., Chan, L., Rodgers, M., A review of wearable sensors and systems with application in rehabilitation (2012) Journal of NeuroEngineering and Rehabilitation, 9 (1), p. 21. , 2-s2.0-84859905662; Rodgers, P., Kirstein, T., Tröster, G., Wearable systems for health care applications (2004) Methods of Information in Medicine, 43 (43), pp. 232-238; Long, Y., Du Z.-J., Wang W.-D., Dong, W., Human motion intent learning based motion assistance control for a wearable exoskeleton (2018) Robotics and Computer-Integrated Manufacturing, 49, pp. 317-327. , 2-s2.0-85027548318; 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"Haufe F.L., Hassani R.H., Riener R., Wolf P.","57147856300;57204661824;7003404145;55627877036;","The insight crutches: Analyzing the role of arm support during robot-assisted leg movements",2020,"IEEE Robotics and Automation Magazine","27","1","8963858","103","113",,4,"10.1109/MRA.2019.2955731","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081331185&doi=10.1109%2fMRA.2019.2955731&partnerID=40&md5=361657ec32650603a2dfeca03021e50d","Sensory-Motor Systems, ETH Zurich, Zurich, Switzerland; Spinal Cord Injury Center, University of Zurich, Zurich, Switzerland; BIROMED-Lab, University of Basel, Switzerland","Haufe, F.L., Sensory-Motor Systems, ETH Zurich, Zurich, Switzerland; Hassani, R.H., Spinal Cord Injury Center, University of Zurich, Zurich, Switzerland, BIROMED-Lab, University of Basel, Switzerland; Riener, R., Sensory-Motor Systems, ETH Zurich, Zurich, Switzerland, Spinal Cord Injury Center, University of Zurich, Zurich, Switzerland; Wolf, P., Sensory-Motor Systems, ETH Zurich, Zurich, Switzerland","To complement the assistance of a wearable robot, users with leg weakness often rely on balance and body-weight support (BWS) through their arms and passive walking aids. A precise quantification of this arm support is crucial to better understand real-world robot dynamics, human-robot interaction, and human user performance. © 1994-2011 IEEE.",,"Walking aids; Arm support; Body weight supports; Human users; Leg movement; Passive walking; Real-world; Robot dynamics; Wearable robots; Human robot interaction",,,,,"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; National Centre of Competence in Research Robotics","This research was supported in part by the Swiss National Science Foundation through the National Center of Competence in Research Robotics and by the Swiss Center for Clinical Movement Analysis, Balgrist Campus, Zurich. We would like to thank Fabien Wagner and Grégoire Courtine for the much-appreciated opportunity to perform measurements at Le Centre Hospitalier Universitaire Vaudois/Ecole Polytechnique Federale de Lausanne as part of the STIMO clinical study under grant NCT02936453. We would also like to thank MyoSwiss AG for their support during experimental work with the MyoSuit wearable robot.",,,,,,,,,,"Esquenazi, A., Talaty, M., Packel, A., Saulino, M., The ReWalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury (2012) Am. J. Phys. Med. Rehabil, 91 (11), pp. 911-921; Kozlowski, A.J., Bryce, T.N., Dijkers, M.P., Time and effort required by persons with spinal cord injury to learn to use a powered exoskeleton for assisted walking (2015) Top. Spinal Cord Inj. Rehabil, 21 (2), pp. 110-121; Hartigan, C., Mobility outcomes following five training sessions with a powered exoskeleton (2015) Top. Spinal Cord Inj. Rehabil, 21 (2), pp. 93-99; Haufe, F.L., User-driven walking assistance: First experimental results using the MyoSuit (2019) Proc IEEE 16th Int. Conf. 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Mechatronics, 19 (1), pp. 141-148; Chamorro-Moriana, G., Sevillano, J.L., Ridao-Fernández, C., A compact forearm crutch based on force sensors for aided gait: Reliability and validity (2016) Sensors, 16 (6), p. 925; Lancini, M., Serpelloni, M., Pasinetti, S., Guanziroli, E., Healthcare sensor system exploiting instrumented crutches for force measurement during assisted gait of exoskeleton users (2016) IEEE Sensors J, 16 (23), pp. 8228-8237; Hurkmans, H.L., Bussmann, J.B., Selles, R.W., Benda, E., Stam, H.J., Verhaar, J.A., The difference between actual and prescribed weight bearing of total hip patients with a trochanteric osteotomy: Long-Term vertical force measurements inside and outside the hospital (2007) Arch. Phys. Med. Rehabil, 88 (2), pp. 200-206; Mignardot, J.-B., A multidirectional gravity-Assist algorithm that enhances locomotor control in patients with stroke or spinal cord injury (2017) Sci. Transl. 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"Augello A., Ciulla A., Cuzzocrea A., Gaglio S., Pilato G., Vella F.","23392168800;57219193850;23388216900;7003479605;36880684700;8889782700;","A Kinect-Based Gesture Acquisition and Reproduction System for Humanoid Robots",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12249 LNCS",,,"967","977",,1,"10.1007/978-3-030-58799-4_69","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091623676&doi=10.1007%2f978-3-030-58799-4_69&partnerID=40&md5=9e08d91a8a138e7d1d92ebb8bab9dad4","ICAR-CNR, Palermo, Italy; iDEA Lab, University of Calabria, Rende, Italy; University of Palermo, Palermo, Italy","Augello, A., ICAR-CNR, Palermo, Italy; Ciulla, A., ICAR-CNR, Palermo, Italy; Cuzzocrea, A., iDEA Lab, University of Calabria, Rende, Italy; Gaglio, S., ICAR-CNR, Palermo, Italy, University of Palermo, Palermo, Italy; Pilato, G., ICAR-CNR, Palermo, Italy; Vella, F., ICAR-CNR, Palermo, Italy","The paper illustrates a system that endows an humanoid robot with the capability to mimic the motion of a human user in real time, serving as a basis for further gesture based human-robot interactions. The described approach uses the Microsoft Kinect as a low cost alternative to expensive motion capture devices. © 2020, Springer Nature Switzerland AG.","Gestures; Humanoid robot; Kinect","Anthropomorphic robots; Cell proliferation; Costs; Human users; Humanoid robot; Low costs; Microsoft kinect; Real time; Social robots",,,,,,,,,,,,,,,,"Msrc-12 Dataset, , https://www.microsoft.com/en-us/download/details.aspx?, id=52283; Ros Kinetic, , http://wiki.ros.org/kinetic; Semaphore Flag Signalling System, , https://en.wikipedia.org/wiki/Flagsemaphore; Baron, G., Czekalski, P., Malicki, D., Tokarz, K., Remote control of the artificial arm model using 3D hand tracking (2013) 2013 International Symposium on Electro-Dynamic and Mechatronic Systems (SELM), pp. 9-10. , IEEE; Chang, C.W., He, C.J., A kinect-based gesture command control method for human action imitations of humanoid robots (2014) 2014 International Conference on Fuzzy Theory and Its Applications (Ifuzzy 2014), pp. 208-211. , IEEE; Chatzimilioudis, G., Cuzzocrea, A., Gunopulos, D., Mamoulis, N., A novel distributed framework for optimizing query routing trees in wireless sensor networks via optimal operator placement (2013) J. 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Humanoid Rob., 16 (6), pp. 1902003:1–1902003:3; Mukherjee, S., Paramkusam, D., Dwivedy, S.K., Inverse kinematics of a NAO humanoid robot using kinect to track and imitate human motion (2015) 2015 International Conference on Robotics, Automation, Control and Embedded Systems (RACE), pp. 1-7. , IEEE; Pfitscher, M., Welfer, D., Souza Leite Cuadros, M.A., Gamarra, D.F.T., Activity gesture recognition on kinect sensor using convolutional neural networks and FastDTW for the MSRC-12 dataset (2020) ISDA 2018 2018. AISC, 940, pp. 230-239. , https://doi.org/10.1007/978-3-030-16657-121, Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. 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Robot, 33 (9), pp. 454-468","Cuzzocrea, A.; iDEA Lab, Italy; email: alfredo.cuzzocrea@unical.it","Gervasi O.Murgante B.Misra S.Garau C.Blecic I.Taniar D.Apduhan B.O.Rocha A.M.A.C.Tarantino E.Torre C.M.Karaca Y.",,"Springer Science and Business Media Deutschland GmbH","20th International Conference on Computational Science and Its Applications, ICCSA 2020","1 July 2020 through 4 July 2020",,249529,03029743,9783030587987,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85091623676
"Amirova A., Rakhymbayeva N., Yadollahi E., Sandygulova A., Johal W.","57219564291;57216334119;57203384398;55193519700;55912460600;","10 Years of Human-NAO Interaction Research: A Scoping Review",2021,"Frontiers in Robotics and AI","8",,"744526","","",,,"10.3389/frobt.2021.744526","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120684276&doi=10.3389%2ffrobt.2021.744526&partnerID=40&md5=437af24d3275b0df2357d8315d52d4e9","Graduate School of Education, Nazarbayev University, Nur-Sultan, Kazakhstan; Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan; École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; University of Melbourne, Melbourne, VIC, Australia; UNSW, Sydney, NSW, Australia","Amirova, A., Graduate School of Education, Nazarbayev University, Nur-Sultan, Kazakhstan; Rakhymbayeva, N., Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan; Yadollahi, E., École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Sandygulova, A., Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan; Johal, W., University of Melbourne, Melbourne, VIC, Australia, UNSW, Sydney, NSW, Australia","The evolving field of human-robot interaction (HRI) necessitates that we better understand how social robots operate and interact with humans. This scoping review provides an overview of about 300 research works focusing on the use of the NAO robot from 2010 to 2020. This study presents one of the most extensive and inclusive pieces of evidence on the deployment of the humanoid NAO robot and its global reach. Unlike most reviews, we provide both qualitative and quantitative results regarding how NAO is being used and what has been achieved so far. We analyzed a wide range of theoretical, empirical, and technical contributions that provide multidimensional insights, such as general trends in terms of application, the robot capabilities, its input and output modalities of communication, and the human-robot interaction experiments that featured NAO (e.g. number and roles of participants, design, and the length of interaction). Lastly, we derive from the review some research gaps in current state-of-the-art and provide suggestions for the design of the next generation of social robots. Copyright © 2021 Amirova, Rakhymbayeva, Yadollahi, Sandygulova and Johal.","human-robot interaction; humanoid robot; nao; qualitative; quantitative; review; social robot; survey",,,,,,"Australian Research Council, ARC: DE210100858; Nazarbayev University, NU: 091019CRP2107","This work was supported by the Nazarbayev University Collaborative Research Program grant (091019CRP2107), the Australian Research Council (DE210100858) and the grant from Faculty of Engineering UNSW.",,,,,,,,,,"Adam, C., Johal, W., Pellier, D., Fiorino, H., Pesty, S., Social Human-Robot Interaction: A New Cognitive and Affective Interaction-Oriented Architecture (2016) Social Robotics, pp. 253-263. , Agah A., Cabibihan J.-J., Howard A.M., Salichs M.A., He H., (eds), Cham, Springer International Publishing, Editors; Ahmad, M.I., Shahid, S., Tahir, A., Towards the Applicability of NAO Robot for Children with Autism in Pakistan (2017) Human-Computer Interaction - INTERACT 2017 - 16th IFIP TC 13 International Conference of Lecture Notes in Computer Science, pp. 463-472. , Bernhaupt R., Dalvi G., Joshi A., Balkrishan D.K., O’Neill J., Winckler M., (eds), Mumbai, India, September 25–29, 2017, Springer, Editors; Ajili, I., Mallem, M., Didier, J.-Y., Gesture Recognition for Humanoid Robot Teleoperation (2017) 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) (IEEE), pp. 1115-1120. , Lisbon, Portugal, August 28–31, 2017; Alenljung, B., Andreasson, R., Lowe, R., Billing, E., Lindblom, J., Conveying Emotions by Touch to the Nao Robot: A User Experience Perspective (2018) Mti, 2, p. 82; Ali, S., Mehmood, F., Ayaz, Y., Asgher, U., Khan, M.J., Effect of Different Visual Stimuli on Joint Attention of Asd Children Using Nao Robot (2020) Advances in Neuroergonomics and Cognitive Engineering, pp. 490-499. , Ayaz H., (ed), Cham, Springer International Publishing, Editor; Aly, A.A., Dugan, J.B., Experiential Robot Learning with Deep Neural Networks (2018) 7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, pp. 356-361. , Lisbon, Portugal, September 18–21, 2017, New York City, NY, ICDL-EpiRob; Aly, A., Tapus, A., A Model for Synthesizing a Combined Verbal and Nonverbal Behavior Based on Personality Traits in Human-Robot Interaction (2013) ACM/IEEE International Conference on Human-Robot Interaction, pp. 325-332. , Tokyo, Japan, March 3–6, 2013; Anastasiou, D., Jokinen, K., Wilcock, G., Evaluation of WikiTalk - User Studies of Human-Robot Interaction (2013) Human-Computer Interaction. 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Ser, 33, pp. 137-142; Wong, C.J., Ling Tay, Y., Lew, L.W.C., Koh, H.F., Xiong, Y., Wu, Y., Advbot: Towards Understanding Human Preference in a Human-Robot Interaction Scenario (2018) 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1305-1309. , Singapore, November 18–21, 2018, IEEE; Wood, L.J., Zaraki, A., Robins, B., Dautenhahn, K., Developing Kaspar: A Humanoid Robot for Children with Autism (2019) Int. J. Soc. Robotics, 13, pp. 491-508; Wu, Y., Wang, R., Tay, Y.L., Wong, C.J., Investigation on the Roles of Human and Robot in Collaborative Storytelling (2018) Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pp. 63-68. , Kula Lumpur, Malaysia, December 12–15, 2017, New York City, NY, APSIPA ASC; Xu, J., Broekens, J., Hindriks, K., Neerincx, M.A., Mood Contagion of Robot Body Language in Human Robot Interaction (2015) Auton. Agent Multi-agent Syst, 29, pp. 1216-1248; Yadollahi, E., Johal, W., Paiva, A., Dillenbourg, P., When Deictic Gestures in a Robot Can Harm Child-Robot Collaboration (2018) Proceedings of the 17th ACM Conference on Interaction Design and Children, pp. 195-206. , Trondheim, Norway, June 17–24, 2018; Yanco, H.A., Drury, J.L., Classifying Human-Robot Interaction: an Updated Taxonomy (2004) 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), pp. 2841-2846. , The Hague, Netherlands, October 10–13, 2004; Ye, P., Wang, T., Wang, F.-Y., A Survey of Cognitive Architectures in the Past 20 Years (2018) IEEE Trans. Cybern, 48, pp. 3280-3290; Zhexenova, Z., Amirova, A., Abdikarimova, M., Kudaibergenov, K., Baimakhan, N., Tleubayev, B., A Comparison of Social Robot to Tablet and Teacher in a New Script Learning Context (2020) Front. Robot AI, 7, p. 99; Złotowski, J., Strasser, E., Bartneck, C., Dimensions of Anthropomorphism (2014) ACM/IEEE International Conference on Human-Robot Interaction, pp. 66-73. , Biefeld, Germany, March 3–6, 2014","Sandygulova, A.; Department of Robotics and Mechatronics, Kazakhstan; email: anara.sandygulova@nu.edu.kz
Johal, W.; University of MelbourneAustralia; email: wafa.johal@unimelb.edu.au",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Review,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85120684276
"Moon S.J., Kim J., Yim H., Kim Y., Choi H.R.","57240504900;57240193100;57240193200;57219162424;7404339840;","Real-time obstacle avoidance using dual-type proximity sensor for safe human-robot interaction",2021,"IEEE Robotics and Automation Letters","6","4","9508896","8021","8028",,,"10.1109/LRA.2021.3102318","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113994583&doi=10.1109%2fLRA.2021.3102318&partnerID=40&md5=0982c41667507493737e0effb0f7c2c4","School of Mechanical Engineering, Sungkyunkwan University, Seoburo, Jangan-gu, Suwon, 2066, South Korea","Moon, S.J., School of Mechanical Engineering, Sungkyunkwan University, Seoburo, Jangan-gu, Suwon, 2066, South Korea; Kim, J., School of Mechanical Engineering, Sungkyunkwan University, Seoburo, Jangan-gu, Suwon, 2066, South Korea; Yim, H., School of Mechanical Engineering, Sungkyunkwan University, Seoburo, Jangan-gu, Suwon, 2066, South Korea; Kim, Y., School of Mechanical Engineering, Sungkyunkwan University, Seoburo, Jangan-gu, Suwon, 2066, South Korea; Choi, H.R., School of Mechanical Engineering, Sungkyunkwan University, Seoburo, Jangan-gu, Suwon, 2066, South Korea","This letter introduces a dual-type proximity sensor and a control strategy for a robot manipulator to realize safe human-robot interactions (HRI) by using the sensor. Safety is an essential condition for HRI in practical scenarios. To achieve this condition, information about the relationship between an external objects and the robot is required. To obtain this information, we employ a dual-type proximity sensor, which consists of capacitive and inductive transducers and can detect the distance between a robot and external objects. Further, we propose a real-time trajectory planning method to deal with obstacles by using admittance control and distance measurements. To update the motion of the manipulator according to our control strategy, a Weight-Prioritized solution based on a QP (quadratic programming) formalism was applied. Further, the problem of self-sensing is solved via machine learning using a training dataset consisting of data corresponding to random joint positions. The proposed method was implemented on a collaborate robot (UR10). Experiments were conducted considering realistic human-robot interactions, and safety improvement was validated. © 2016 IEEE.","admittance control; human-robot interaction; Obstacle avoidance; proximity sensor","Agricultural robots; Capacitive sensors; Inductive sensors; Industrial manipulators; Man machine systems; Manipulators; Proximity sensors; Quadratic programming; Robot applications; Robot programming; Admittance control; Control strategies; Inductive transducers; Real time obstacle avoidance; Real-time trajectories; Robot manipulator; Safe human-robot interaction; Safety improvement; Human robot interaction",,,,,"Ministry of Trade, Industry and Energy, MOTIE","Manuscript received February 24, 2021; accepted July 12, 2021. Date of publication August 6, 2021; date of current version August 23, 2021. This letter was recommended for publication by Editor Gentiane Venture upon evaluation of the Associate Editor and reviewers’ comments. This work was supported by under Grant 20000944 from Robot Industry Core Technology Development Program funded by the Ministry of Trade, Industry and Energy (MI, Korea). (Corresponding author: Hyouk Ryeol Choi.) The authors are with the School of Mechanical Engineering, Sungkyunkwan University, Seoburo 2066, Jangan-gu, Suwon, South Korea (e-mail: msj19@skku.edu; sol5100@skku.edu; yimhongsik@skku.edu; ye.kim@skku.edu; choihyoukryeol@gmail.com). Digital Object Identifier 10.1109/LRA.2021.3102318",,,,,,,,,,"ISO 10218 robots and robotic devices-safety requirements for industrial robots, ISO, Geneva, CH, Tech. Rep., 2011; Reddy, A., Bright, G., Padayachee, J., A review of safety methods for human-robot collaboration and a proposed novel approach (2019) Int. Conf. On Informatics in Control, Automation, and Robotics (ICINCO), pp. 243-248; (2021), https://www.bluedanuberobotics.com/airskin, Accessed: Feb. 17; (2021), https://rtr.ai/, Accessed: Feb. 17; Tulbure, A., Khatib, O., Closing the loop: Real-time perception and control for robust collision avoidance with occluded obstacles (2020) IROS; Alagi, H., SeE, N., Aversatile and modular capacitive tactile proximity sensor (2016) Proc. IEEE Haptics Symp.; Schleg, T., Kroger, T., (2013) Virutal Whiskers-Highly Responsive Robot Collision Avoidance, IROS; (2021), https://www.comau.com/en/, Accessed: Feb. 17; (2021), https://www.fogale-robotics.com, Accessed: Feb. 17; Kim, T., Noh, J., Nguyen, T.D., Choi, H.R., Development of flexible dual-type proximity sensor with resonant frequency for robotic applications (2019) ICRA; Brandstötter, M., Muhlbacher-Karrer, S., Zangl, H., Virtual compliance control of a kinematically redundant serial manipulator with 9 DoF (2016) Adv. Robot Des. Intell. Control, Adv. Intell. Syst. Comput., p. 540; M'Colo, K.E., Luong, B., Crosnier, A., Neel, C., Fraisse, P., Obstacle avoidance using a capacitive skin for safe human-robot interaction (2019) IROS; (2020), https://www.aidinrobotics.com/, Accessed: Jul. 20; Bouyarmane, K., Khdeear, A., On weight-prioritized multitask control of humanoid robots (2018) IEEE Trans. Autom. Control, 63 (6), pp. 1632-1647; Hogan, N., Impedance control: An approach to manipulation (1984) Proc. IEEE Amer. Control Conf. 107, pp. 304-313; (2015) ISO 13849 Safety of Machinery-safety-related Parts of Control Systems, , ISO, Geneva, CH, Tech. Rep","Choi, H.R.; School of Mechanical Engineering, Seoburo, Jangan-gu, South Korea; email: choihyoukryeol@gmail.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85113994583
"Hossain Gourob J., Raxit S., Hasan A.","57271217000;57271356100;57209612034;","A robotic hand: Controlled with vision based hand gesture recognition system",2021,"2021 International Conference on Automation, Control and Mechatronics for Industry 4.0, ACMI 2021",,,,"","",,1,"10.1109/ACMI53878.2021.9528192","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115683980&doi=10.1109%2fACMI53878.2021.9528192&partnerID=40&md5=2bbf4675143e9f78f6642194fa3d9a2b","Chittagong University of Engineering and Technology, Depertment of Mechatronics and Industrial Engineering, Chittagong, Bangladesh; Chittagong University of Engineering and Technology, Department of Mechanical Engineering, Chittagong, Bangladesh","Hossain Gourob, J., Chittagong University of Engineering and Technology, Depertment of Mechatronics and Industrial Engineering, Chittagong, Bangladesh; Raxit, S., Chittagong University of Engineering and Technology, Depertment of Mechatronics and Industrial Engineering, Chittagong, Bangladesh; Hasan, A., Chittagong University of Engineering and Technology, Department of Mechanical Engineering, Chittagong, Bangladesh","Human-robot interaction (HRI) has become an important topic in today's robotic world, especially in assistive robotics. Now pen, mouse, keyboard etc. are not sufficient due to the limitations of these devices for taking command. Human now wants direct use of hands as an input device for getting more natural human-robot interactions (HRI). Vision based hand recognition system has come with a solution for these type of demands of human. In this system, human need not to operate any kind of devices, instead a camera is used for recording the movements of hands and these recorded data are used as input for performing any kind of gesture control. The elderly people, patients and disabled can be benefitted from these kind of gesture control. Besides it can be used in extreme environments where human can't go directly. Here, a vision based hand gesture system for controlling robotic hand has been developed. The main intension here, is to implement a vision based gesture recognition system that recognizes data gathered from hand movements through a camera. © 2021 IEEE.","Hand Gesture Recognition; Human Robot Interaction; Robotic Hand","Cameras; End effectors; Gesture recognition; Industry 4.0; Mammals; Man machine systems; Palmprint recognition; Robotic arms; Robotics; Assistive robotics; Elderly people; Extreme environment; Gesture control; Hand-gesture recognition; Human robot Interaction (HRI); Recognition systems; Vision-Based Gesture Recognition; Human robot interaction",,,,,,,,,,,,,,,,"Murthy, G.R.S., Jadon, R.S., A review of vision based hand gestures recognition (2009) International Journal of Information Technology and Knowledge Management, 22, pp. 405-410; Ray, D.K., Soni, M., Johri, P., Gupta, A., Hand gesture recognition using python (2018) International Journal on Future Revolution in Computer Science & Communication Engineering, 4 (6), pp. 59-62; Prabhu, R.R., Sreevidya, R., Design of robotic arm based on hand gesture control system using wireless sensor networks (2017) International Research Journal of Engineering and Technology,(IRJET), 4 (3), pp. 617-621; VanHuy, T., Simple robotic hand in motion using arduino controlled servos (2017) International Journal of Science and Research (IJSR), 6 (3), pp. 972-975; Carando, P.C., Wireless controlled robotic arm for handling hazardous chemicals (2016) 4th DLSU Innovation and Technology (DITECH) Fair, DITECH, pp. 24-25; Starner, T., Pentland, A., Real-time American sign language recognition from video using hidden markov models (1997) Motion-based Recognition, pp. 227-243. , Springer, Dordrecht; Ng, C.W., Ranganath, S., Real-time gesture recognition system and application (2002) Image and Vision Computing, 20 (13-14), pp. 993-1007; Abe, K., Saito, H., Ozawa, S., Virtual 3-D interface system via hand motion recognition from two cameras (2002) IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 32 (4), pp. 536-540; Hasanuzzaman, M., Ampornaramveth, V., Zhang, T., Bhuiyan, M.A., Shirai, Y., Ueno, H., Real-time visionbased gesture recognition for human robot interaction (2004) 2004 IEEE International Conference on Robotics and Biomimetics, pp. 413-418; Oudah, M., Al-Naji, A., Chahl, J., Hand gesture recognition based on computer vision: A review of techniques (2020) Journal of Imaging, 6 (8), p. 73; Sánchez-Nielsen, E., Hand gesture recognition for humanmachine interaction (2004) Journal of WSCG, 12; Triesch, J., Von Der Malsburg, C., A system for person-independent hand posture recognition against complex backgrounds (2001) IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 (12), pp. 1449-1453; Cui, Y., Swets, D.L., Weng, J.J., Learning-based hand sign recognition using SHOSLIF-M (1995) Proceedings of IEEE International Conference on Computer Vision; Starner, T.E., (1995) Visual Recognition of American Sign Language Using Hidden Markov Models, , Massachusetts Inst Of Tech Cambridge Dept Of Brain And Cognitive Sciences; Alon, J., Simultaneous localization and recognition of dynamic hand gestures (2005) 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05), 1-2; Ray, M.D., Soni, M., Johri, P., Gupta, A., Hand gesture recognition using python (2018) International Journal on Future Revolution in Computer Science & Communication Engineering, 4 (6), pp. 59-62. , Jun 30",,,,"Institute of Electrical and Electronics Engineers Inc.","2021 International Conference on Automation, Control and Mechatronics for Industry 4.0, ACMI 2021","8 July 2021 through 9 July 2021",,171642,,9781665438438,,,"English","Int. Conf. Autom., Control Mechatronics Ind. 4.0, ACMI",Conference Paper,"Final","",Scopus,2-s2.0-85115683980
"Chen X., Zhang K., Leng Y., Fu C.","57211101760;57193994064;55376585000;57208278421;","A multi-task learning method for human motion classification and person identification",2021,"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2021",,,,"132","137",,,"10.1109/ICARM52023.2021.9536166","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116289700&doi=10.1109%2fICARM52023.2021.9536166&partnerID=40&md5=f7368ec97ca235bce1e0761425c64c1d","Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, 518055, China; Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, 518055, China; The Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC  V6T 1Z4, Canada","Chen, X., Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, 518055, China, Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, 518055, China; Zhang, K., Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, 518055, China, Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, 518055, China, The Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC  V6T 1Z4, Canada; Leng, Y., Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, 518055, China, Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, 518055, China; Fu, C., Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, 518055, China, Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, 518055, China","Wearable robotic systems have been widely studied in recent years, but it still remains a challenge to design a user-adaptive controller for wearable robotic systems to ensure personalized and accurate human-robot interaction. Accurate human motion classification and person identification are two premises helping design user-adaptive controllers for wearable robotic systems. In this paper, we proposed a multi-task learning method for human motion classification and person identification with a single neural network, which can serve as a solution to personalized human-robot interaction, and can also serve as a benchmark for the following studies in related fields. The multi-task learning neural network was trained and tested on a public human motion data set. The proposed method was capable to classify human motions and identify the person, with 99.13% and 96.51% accuracy, respectively. We also compared the proposed method with a benchmark single task learning method for human motion classification, the results showed that the performance of the multi-task learning method is more superior. © 2021 IEEE.",,"Adaptive control systems; Benchmarking; Human robot interaction; Machine design; Man machine systems; Motion analysis; Robotics; Adaptive controllers; Classification identification; Human motions; Humans-robot interactions; Motion classification; Neural-networks; Person identification; Robotic systems; User-adaptive; Wearable robotics; Learning systems",,,,,"2020B1515120098; Massachusetts Institute of Technology, MIT; Guangdong Innovative and Entrepreneurial Research Team Program: 2016ZT06G587; Centers for Mechanical Engineering Research and Education, Massachusetts Institute of Technology; National Natural Science Foundation of China, NSFC: 51805237, U1913205; Science, Technology and Innovation Commission of Shenzhen Municipality: SGLH20180619172011638, ZDSYS20200811143601004; State Key Laboratory of Robotics; National Key Research and Development Program of China, NKRDPC: 2018YFB1305400, 2018YFC2001601","*This work was supported by the National Key R&D Program of China [Grant 2018YFB1305400 and 2018YFC2001601]; the National Natural Science Foundation of China [Grant U1913205 and 51805237]; Guangdong Basic and Applied Basic Research Foundation [Grant 2020B1515120098]; Guangdong Innovative and Entrepreneurial Research Team Program [Grant 2016ZT06G587]; the Science, Technology and Innovation Commission of Shenzhen Municipality [Grant SGLH20180619172011638 and ZDSYS20200811143601004]; Joint Fund of Science & Technology Department of Liaoning Province and State Key Laboratory of Robotics, China [Grant No.2020-KF-22-03]; and Centers for Mechanical Engineering Research and Education at MIT and SUSTech.",,,,,,,,,,"Li, Z., Su, C.Y., Wang, L., Chen, Z., Chai, T., Nonlinear disturbance observer-based control design for a robotic exoskeleton incorporating fuzzy approximation (2015) IEEE Transactions on Industrial Electronics, 62 (9), pp. 5763-5775; Huang, J., Huo, W., Xu, W., Mohammed, S., Amirat, Y., Control of upper-limb power-assist exoskeleton using a human-robot interface based on motion intention recognition (2015) IEEE Transactions on Automation Science and Engineering, 12 (4), pp. 1257-1270; Yan, Q., Huang, J., Yang, Z., Hasegawa, Y., Fukuda, T.M.F.T., Human-following control of cane-type walking-aid robot within fixed relative posture (2021) IEEE/ASME Transactions on Mechatronics; Yan, Q., Huang, J., Tao, C., Chen, X., Xu, W., Intelligent mobile walking-AIDS: Perception, control and safety (2020) Advanced Robotics, 34 (1), pp. 2-18; Hao, M., Zhang, J., Chen, K., Fu, C., Design and basic control of extra robotic legs for dynamic walking assistance (2019) 2019 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO), pp. 246-250; Wen, Y., Si, J., Brandt, A., Gao, X., Huang, H.H., Online reinforcement learning control for the personalization of a robotic knee prosthesis (2020) IEEE Transactions on Cybernetics, 50 (6), pp. 2346-2356; Leng, Y., Lin, X., Huang, G., Hao, M., Wu, J., Xiang, Y., Zhang, K., Fu, C., Wheel-legged robotic limb to assist human with load carriage: An application for environmental disinfection during covid-19 (2021) IEEE Robotics and Automation Letters, 6 (2), pp. 3695-3702; Poppe, R., Vision-based human motion analysis: An overview (2007) Computer Vision and Image Understanding, 108 (1-2), pp. 4-18; Chen, J., Zhang, X., Cheng, Y., Xi, N., Surface EMG based continuous estimation of human lower limb joint angles by using deep belief networks (2018) Biomedical Signal Processing and Control, 40, pp. 335-342; Chen, Y., Fu, C., Leung, W.S.W., Shi, L., Drift-free and self-aligned IMU-based human gait tracking system with augmented precision and robustness (2020) IEEE Robotics and Automation Letters, 5 (3), pp. 4671-4678; Hao, M., Chen, K., Fu, C., Smoother-based 3-D foot trajectory estimation using inertial sensors (2019) IEEE Transactions on Biomedical Engineering, 66 (12), pp. 3534-3542; Chen, X., Huang, J., Towards environmentally adaptive odor source localization: Fuzzy Lévy Taxis algorithm and its validation in dynamic odor plumes (2020) 2020 5th International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 282-287; Li, J., Wang, J., Peng, H., Zhang, L., Hu, Y., Su, H., Neural fuzzy approximation enhanced autonomous tracking control of the wheel-legged robot under uncertain physical interaction (2020) Neurocomputing, 410, pp. 342-353; Li, J., Wang, J., Peng, H., Hu, Y., Su, H., Fuzzy-torque approximation-enhanced sliding mode control for lateral stability of mobile robot (2021) IEEE Transactions on Systems, Man, and Cybernetics: Systems; Xiao, J., Wu, K., Yi, Y., Wang, L., Ni, L.M., FiMD: Fine-grained device-free motion detection (2012) 2012 IEEE 18th International Conference on Parallel and Distributed Systems, pp. 229-235; Chen, X., Huang, J., Odor source localization algorithms on mobile robots: A review and future outlook (2019) Robotics and Autonomous Systems, 112, pp. 123-136; Chen, X., Fu, C., Huang, J., A Deep Q-Network for robotic odor/gas source localization: Modeling, measurement and comparative study (2021) Measurement, 183, p. 109725; Libbrecht, M.W., Noble, W.S., Machine learning applications in genetics and genomics (2015) Nature Reviews Genetics, 16 (6), pp. 321-332; Caruana, R., Multitask learning (1997) Machine Learning, 28 (1), pp. 41-75; Ma, J., Zhao, Z., Yi, X., Chen, J., Hong, L., Chi, E.H., Modeling task relationships in multi-task learning with multi-gate mixture-of-experts (2018) Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1930-1939; Lang, Y., Wang, Q., Yang, Y., Hou, C., Liu, H., He, Y., Joint motion classification and person identification via multitask learning for smart homes (2019) IEEE Internet of Things Journal, 6 (6), pp. 9596-9605; Gao, C., He, X., Gan, D., Chen, X., Feng, F., Li, Y., Chua, T.-S., Jin, D., Neural multi-task recommendation from multi-behavior data (2019) 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 1554-1557; Zhang, K., Wang, J., de Silva, C.W., Fu, C., Unsupervised cross-subject adaptation for predicting human locomotion intent (2020) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28 (3), pp. 646-657; Girshick, R., Fast R-CNn (2015) Proceedings of the IEEE International Conference on Computer Vision, pp. 1440-1448","Fu, C.; Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent SystemsChina; email: fucl@sustech.edu.cn",,"Gaitech Robotics;Publishing House of Electronics Industry;Quanser;Sensors;Springer;Unitree Robotics","Institute of Electrical and Electronics Engineers Inc.","6th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2021","3 July 2021 through 5 July 2021",,171776,,9780738133645,,,"English","IEEE Int. Conf. Adv. Robot. Mechatronics, ICARM",Conference Paper,"Final","",Scopus,2-s2.0-85116289700
"Huang Y., Chen K., Zhang X., Wang K., Ota J.","54792998800;57203164000;55864283000;57200941778;7006719514;","Motion estimation of elbow joint from sEMG using continuous wavelet transform and back propagation neural networks",2021,"Biomedical Signal Processing and Control","68",,"102657","","",,1,"10.1016/j.bspc.2021.102657","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107733077&doi=10.1016%2fj.bspc.2021.102657&partnerID=40&md5=9ea88c29b040bd9760cef79217b2feb7","Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, Guangzhou, 510640, China; Research into Artifacts, Center for Engineering (RACE), School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan","Huang, Y., Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, Guangzhou, 510640, China; Chen, K., Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, Guangzhou, 510640, China; Zhang, X., Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, Guangzhou, 510640, China; Wang, K., Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, Guangzhou, 510640, China; Ota, J., Research into Artifacts, Center for Engineering (RACE), School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan","Estimation of continuous joint movement of human arm is significant in human-robot interaction. In this study, we proposed an estimating method combined continuous wavelet transform (CWT) and back propagation neural networks (BPNN) to estimate the joint angles and velocities of human arm from surface electromyography (sEMG) signals during the human-robot interaction. The proposed method was evaluated by comparing to other existing methods and the results derived by using a goniometer through experiments. Nine conditions with different motion patterns and force levels are conducted in the experiments. The root mean squared error (RMSE) and the correlation coefficient (CC) were used as the evaluation indices. Experimental results showed that the estimated angles and velocities of elbow joint were well match to the “true” values derived by the goniometer. The average RMSE of estimated angle and velocity derived by the proposed method was 8.78 degree and 9.59 degree/second, respectively. The average RMSE in both the joint angle and velocity for the proposed method was the smallest comparing to the back propagation neural networks, the higher-order polynomial, and the support vector regression. © 2021 Elsevier Ltd","Back propagation neural networks; Continuous wavelet transform; Human-robot interaction; Motion estimation; Surface electromyography","Backpropagation; Human robot interaction; Motion estimation; Neural networks; Torsional stress; Velocity; Wavelet transforms; Backpropagation neural networks; Continuous Wavelet Transform; Elbow joints; Human arm; Humans-robot interactions; Joint angle; Joint movement; Joint velocity; Root mean squared errors; Surface electromyography; Mean square error; adult; algorithm; arm; Article; back propagation neural network; continuous wavelet transform; controlled study; correlation coefficient; data analysis software; elbow; evaluation study; feature extraction; force; human; human experiment; human robot interaction; intermethod comparison; joint function; male; motion; normal human; support vector machine; surface electromyography; velocity; young adult",,,"b, Biometrics, United Kingdom; Matlab 2018","Biometrics, United Kingdom","2019A1515011154; National Natural Science Foundation of China, NSFC: 51820105007, 52075178, 91748111; Guangzhou Municipal Science and Technology Project: 202002030233","This work was supported in part by the National Natural Science Foundation of China under Grant 52075178 , Grant 91748111 , Grant 51820105007 ; in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515011154 ; in part by the Scientific and Technological Project of Guangzhou under Grant 202002030233 .",,,,,,,,,,"Totty, M.S., Wade, E., Muscle activation and inertial motion data for noninvasive classification of activities of daily living (2018) IEEE Trans. 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Biomech., 13 (2), pp. 135-163; Amsuss, S., Goebel, P.M., Jiang, N., Graimann, B., Paredes, L., Farina, D., Self-correcting pattern recognition system of surface EMG signals for upper limb prosthesis control (2014) IEEE Trans. Biomed. Eng., 61 (4), pp. 1167-1176; Ngeo, J.G., Tamei, T., Shibata, T., Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model (2014) J. Neuro Eng. Rehabil., 11 (122), pp. 1-14; Momen, K., Krishnan, S., Chau, T., Real-time classification of forearm electromyographic signals corresponding to user-selected intentional movements for multifunction prosthesis control (2007) IEEE Trans. Neural Syst. Rehabil. Eng., 15 (4), pp. 535-542; Peleg, D., Braiman, E., Yom-Tov, E., Inbar, G.F., Classification of finger activation for use in a robotic prosthesis arm (2002) IEEE Trans. Neural Syst. Rehabil. 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Syst., 26 (1), pp. 35-48; https://www.universal-robots.cn/site/products-ur5-robot, (Accessed on 1 March 2018); http://www.biometricsltd.com/goniometer.htm, (Accessed on 1 March 2018); Beale, M.H., Hagan, M.T., Demuth, H.B., Neural Network Toolbox User's Guide (2007), p. 420. , The MathWorks Inc Natick, MA, USA; Li, K., Liu, X., Zhang, J., Zhang, M., Hou, Z., Continuous motion and time-varying stiffness estimation of the human elbow joint based SEMG (2019) J. Mech. Med. Biol., 19 (5), pp. 1-24","Zhang, X.; Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, China; email: zhangxm@scut.edu.cn",,,"Elsevier Ltd",,,,,17468094,,,,"English","Biomed. Signal Process. Control",Article,"Final","",Scopus,2-s2.0-85107733077
"Abdullah M., Ahmad M., Han D.","26650323000;57210116830;56152561800;","Hierarchical Attention Approach in Multimodal Emotion Recognition for Human Robot Interaction",2021,"2021 36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021",,,"9501446","","",,,"10.1109/ITC-CSCC52171.2021.9501446","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114002304&doi=10.1109%2fITC-CSCC52171.2021.9501446&partnerID=40&md5=9948b15625d02b02febfbf0a488b8ecb","Vision and Image Processing Lab, Sejong University, Department of Computer Engineering, Seoul, South Korea","Abdullah, M., Vision and Image Processing Lab, Sejong University, Department of Computer Engineering, Seoul, South Korea; Ahmad, M., Vision and Image Processing Lab, Sejong University, Department of Computer Engineering, Seoul, South Korea; Han, D., Vision and Image Processing Lab, Sejong University, Department of Computer Engineering, Seoul, South Korea","The ability to perceive human emotions is one of the key elements that may promise a natural, genuine and more reliable human robot interaction. Though emotional perception in human robot interaction has been challenged by many difficulties, the lack of contextual understanding is can be attributed as the biggest hurdle in this regard. Most of the literature refers to the datasets developed in controlled environment to validate the performance of their systems which happens to be really good. Still those systems are very far from achieving that kind of performance in real-life scenarios. In this paper a multimodal emotion recognition strategy is presented, that uses voice features in addition to the facial expressions to determine the emotional state of the user. A hierarchical attention layer is used to for feature fusion purpose. The final system is end-to-end trainable, multimodal approach makes it more resilient to the changes in environment. The achieved 76.3% accuracy for eNterface05 video dataset, which is higher than the any single modality approach in the comparison. © 2021 IEEE.","Acoustic Emotion Recognition; Affective Computing; Emotion Recognition; Facial Expressions Recognition; HCI; HRI; Human Computer Interaction; Human Robot Interaction; Internet of Things; Multimodal","Behavioral research; Computer circuits; Man machine systems; Speech recognition; Contextual understanding; Controlled environment; Emotional state; Facial Expressions; Feature fusion; Multi-modal approach; Multimodal emotion recognition; Video dataset; Social robots",,,,,,,,,,,,,,,,"Weiser, M., The computer for the 21st century (1991) Scientific American, 265 (3), pp. 66-75; Salovey, P., Mayer, J.D., Emotional intelligence (1990) Imaginat. Cogn. Pers., 9, pp. 185-211; Picard, R.W., Affective computing for hci (1999) Proceedings of HCi International (The 8th International Conference on Human-Computer Interaction) on Human-Computer Interaction: Ergonomics and User Interfaces-Volume i, pp. 829-833. , eds H. J. Bullinger and J. Ziegler; Cavallo, F., Semeraro, F., Fiorini, L., Magyar, G., Sinák, P., Dario, P., Emotion modelling for social robotics applications: A review (2018) J. 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D. thesis) Psychology Department, The University of Western Australia, Perth; Schuller, B., Rigoll, G., Lang, M., Hidden markov model-based speech emotion recognition (2003) 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03), pp. II-1. , Hong Kong, China; Tang, H., Chu, S.M., Hasegawa-Johnson, M., Huang, T.S., Emotion recognition from speech via boosted Gaussian mixture models (2009) IEEE International Conference on Multimedia and Expo, pp. 294-297. , New York, NY, USA; Chen, S., Jin, Q., Li, X., Yang, G., Xu, J., Speech emotion classification using acoustic features (2014) The 9th International Symposium on Chinese Spoken Language Processing, pp. 579-583. , Singapore; Khalil, R.A., Jones, E., Babar, M.I., Jan, T., Zafar, M.H., Alhussain, T., Speech emotion recognition using deep learning techniques: A review (2019) IEEE Access, 7, pp. 117327-117345; Poon-Feng, K., Huang, D.-Y., Dong, M., Li, H., Acoustic emotion recognition based on fusion of multiple feature-dependent deep boltzmann machines (2014) The 9th International Symposium on Chinese Spoken Language Processing, NY, pp. 584-588; Lee, J., Tashev, I., High-level feature representation using recurrent neural network for speech emotion recognition (2015) Sixteenth Annual Conference of the International Speech Communication Association (Baixas); Wen, G., Li, H., Huang, J., Li, D., Xun, E., Random deep belief networks for recognizing emotions from speech signals (2017) Comput. Intell. Neurosci. (London); Zheng, W.Q., Yu, J.S., Zou, Y.X., An experimental study of speech emotion recognition based on deep convolutional neural networks 2015 International Conference on Affective Computing and Intelligent Interaction, NY, pp. 827-831; Spezialetti, M., Placidi, G., Rossi, S., Emotion recognition for human-robot interaction: Recent advances and future perspectives (2020) Front. Robot. AI, 7, p. 532279; Martin, O., Kotsia, I., Macq, B., Pitas, I., The enterface' 05 audio-visual emotion database (2006) 22nd International Conference on Data Engineering Workshops (ICDEW'06), pp. 8-8. , Apr; Ekman, P., An argument for basic emotions (1992) Cogn. Emot., 6, pp. 169-200; He, K., Deep residual learning for image recognition (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Li, Y., Zhao, T., Kawahara, T., Improved end-to-end speech emotion recognition using self attention mechanism and multitask learning (2019) Interspeech 2019, pp. 2803-2807; Szegedy, C., Ioffe, S., Vanhoucke, V., (2016) Inception-v4, Inception-resnet and the Impact of Residual Connections on Learning; Girdhar, R., Ramanan, D., Attentional pooling for action recognition (2017) NIPS, pp. 34-45; Islam, M.M., Iqbal, T., Multi-gat: A graphical attention-based hierarchical multimodal representation learning approach for human activity recognition (2021) IEEE Robotics and Automation Letters, 6, pp. 1729-1736. , April; Noroozi, F., Marjanovic, M., Njegus, A., Escalera, S., Anbarjafari, G., Audio-visual emotion recognition in video clips (2019) IEEE Trans. Affect. Comput., 10, pp. 60-75. , Jan; Wang, Y., Guan, L., Venetsanopoulos, A.N., Kernel cross-modal factor analysis for information fusion with application to bimodal emotion recognition (2012) IEEE Trans. Multimed., 14 (3), pp. 597-607; Zhang, S., Zhang, S., Huang, T., Gao, W., Tian, Q., Learning affective features with a hybrid deep model for audio-visual emotion recognition (2018) IEEE Trans. Circuits Syst. Video Technol., pp. 3030-3043; Mansoorizadeh, M., Moghaddam Charkari, N., Multimodal information fusion application to human emotion recognition from face and speech (2010) Multimed. Tools Appl., 49 (2), pp. 277-297. , Aug; Sahoo, S., Routray, A., Emotion recognition from audio-visual data using rule based decision level fusion (2016) IEEE Students' Technology Symposium; Zhang, S.H., Wang, X.I., Zhang, G.A., Zhao, X.I., Multimodal emotion recognition integrating affective speech with facial expression (2014) WSEAS Transactions on Signal Processing., pp. 526-537; Bejani, M., Gharavian, D., Charkari, N.M., Audiovisual emotion recognition using anova feature selection method and multi-classifier neural networks (2014) Neural Comput. Appl., 24 (2), pp. 399-412. , Feb; Zhalehpour, S., Onder, O., Akhtar, Z., Erdem, C.E., BAUM-1: A spontaneous audio-visual face database of affective and mental states (2017) IEEE Trans. Affect. Comput., 8, pp. 300-313",,,,"Institute of Electrical and Electronics Engineers Inc.","36th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2021","27 June 2021 through 30 June 2021",,171257,,9781665435536,,,"English","Int. Tech. Conf. Circuits/Syst., Comput. Commun., ITC-CSCC",Conference Paper,"Final","",Scopus,2-s2.0-85114002304
"Stancioi C.-M., Fisca M., Stan O.P., Misaros M., Clitan I., Mihai A., Muresan V., Unguresan M.-L.","57226597663;57230316600;55998341100;57229729400;56273069500;57226600233;24829543200;16176411300;","Developing an application based on the interaction between humans and the Pepper robot",2021,"2021 16th International Conference on Engineering of Modern Electric Systems, EMES 2021 - Proceedings",,,"9484141","","",,,"10.1109/EMES52337.2021.9484141","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113415691&doi=10.1109%2fEMES52337.2021.9484141&partnerID=40&md5=a353cea382b8de4979ef648bf1c025d8","Technical University of Cluj-Napoca, Automation department, Cluj-Napoca, Romania; Technical University of Cluj-Napoca, Physics and chemistry department, Cluj-Napoca, Romania","Stancioi, C.-M., Technical University of Cluj-Napoca, Automation department, Cluj-Napoca, Romania; Fisca, M., Technical University of Cluj-Napoca, Automation department, Cluj-Napoca, Romania; Stan, O.P., Technical University of Cluj-Napoca, Automation department, Cluj-Napoca, Romania; Misaros, M., Technical University of Cluj-Napoca, Automation department, Cluj-Napoca, Romania; Clitan, I., Technical University of Cluj-Napoca, Automation department, Cluj-Napoca, Romania; Mihai, A., Technical University of Cluj-Napoca, Automation department, Cluj-Napoca, Romania; Muresan, V., Technical University of Cluj-Napoca, Automation department, Cluj-Napoca, Romania; Unguresan, M.-L., Technical University of Cluj-Napoca, Physics and chemistry department, Cluj-Napoca, Romania","An increased use of robots in direct interaction with humans can be observed both in the industry and in the service sector. The specific areas of application as well as forms of interaction are diverse. Nevertheless in order to enable a structured comparison, a taxonomy is necessary, that, on one hand is generic enough to be applicable to a wide variety of human-robot interaction scenarios and on the other hand is specific enough to enable the analysis of superordinate features. This article introduces the development of a taxonomy that meets both criteria. A taxonomy of human- robot interaction was developed, which both meets the requirement of the highest possible generalizability and enables the interaction characterization of humans and robots with a high level of detail. The influence of certain design features of the robot on typical human factors aspects such as trust, acceptance, situation awareness or mental stress can be subjected to a systematic (meta) analysis. The taxonomy thus forms the basis for future recommendations for innovative and human-centered activities. © 2021 IEEE.","human-machine interaction; humanoid robots; motion; natural language processing; Pepper robot","Industrial robots; Machine design; Man machine systems; Service industry; Taxonomies; Design features; Direct interactions; Level of detail; Mental stress; Service sectors; Situation awareness; Specific areas; Social robots",,,,,,,,,,,,,,,,"Sgorbissa, A., Papadopoulos, I., Bruno, B., Koulouglioti, C., Recchiuto, C., Encoding guidelines for a culturally competent robot for elderly care (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1988-1995. , Oct; Khaliq, A.A., Kackemann, U., Pecora, F., Saffiotti, A., Bruno, B., Recchiuto, C.T., Sgorbissa, A., Chong, N.Y., Culturally aware planning and execution of robot actions (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 326-332. , Oct; Forero, L.L., Yánez, J.M., Ruiz-Del Solar, J., Integration of the ros framework in soccer robotics: The nao case (2013) Robot Soccer World Cup, pp. 664-671. , Springer; Pandey, A.K., Gelin, R., Ruocco, M., Monforte, M., Siciliano, B., When a social robot might learn to support potentially immoral behaviors in the name of privacy: The dilemma of privacy vs. ethics for a socially intelligent robot Proc. 2017 Conf. Human-Robot Interaction, pp. 1-4. , Workshop on Privacy-Sensitive Robotics; Pandey, A.K., Socially intelligent robots: The next generation of consumer robots and the challenges (2016) Advances in Intelligent Systems and Computing, 665, pp. 41-46. , G. Stojanov and A. Kulakov, Eds. Cham, Switzerland: Springer-Verlag; Rozanska, A., Podpora, M., Making eye contact with robot - exploring user experience in interacting with pepper (2018) Biomedical Engineering and Neuroscience, Proceedings of the 3rd International Scientific Conference on Brain-Computer Interfaces, Advances in Intelligent Systems and Computing Book Series, 720, pp. 172-183. , Hunek W., Paszkiel Sz.: (AISC), Springer; Foster, M.E., Alami, R., Gestranius, O., Lemon, O., Niemelä, M., Odobez, J.-M., Pandey, A.K., The MuMMER project: Engaging humanrobot interaction in real-world public spaces (2016) Proc. 8th Int. Conf. Social Robotics (ICSR), pp. 753-763; Perera, V., Pereira, T., Connell, J., Veloso, M.M., (2017) Setting up Pepper for Autonomous Navigation and Personalized Interaction with Users, , CoRR, abs/1704.04797; (2017) Aldebaran 2.5 Documentation - Pepper Robot SoftBank Robotics Webpage, , SoftBank Robotics",,,,"Institute of Electrical and Electronics Engineers Inc.","16th International Conference on Engineering of Modern Electric Systems, EMES 2021","10 June 2021 through 11 June 2021",,171173,,9781665449953,,,"English","Int. Conf. Eng. Mod. Electr. Syst., EMES - Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85113415691
"Romero-García R., Martínez-Tomás R., Pozo P., de la Paz F., Sarriá E.","57223366638;55887969100;55660810600;57223360643;22136163300;","Q-CHAT-NAO: A robotic approach to autism screening in toddlers",2021,"Journal of Biomedical Informatics","118",,"103797","","",,1,"10.1016/j.jbi.2021.103797","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105747296&doi=10.1016%2fj.jbi.2021.103797&partnerID=40&md5=47cb9977113e92316753f82790188711","Department of Artificial Intelligence, School of Computer Science, UNED, Madrid, 28040, Spain; Department of Methodology of Behavioral Sciences, Faculty of Psychology, UNED, Madrid, 28040, Spain; Joint Research Institute UNED and Health Institute Carlos III (IMIENS), Madrid, 28040, Spain","Romero-García, R., Department of Artificial Intelligence, School of Computer Science, UNED, Madrid, 28040, Spain; Martínez-Tomás, R., Department of Artificial Intelligence, School of Computer Science, UNED, Madrid, 28040, Spain, Joint Research Institute UNED and Health Institute Carlos III (IMIENS), Madrid, 28040, Spain; Pozo, P., Department of Methodology of Behavioral Sciences, Faculty of Psychology, UNED, Madrid, 28040, Spain, Joint Research Institute UNED and Health Institute Carlos III (IMIENS), Madrid, 28040, Spain; de la Paz, F., Department of Artificial Intelligence, School of Computer Science, UNED, Madrid, 28040, Spain, Joint Research Institute UNED and Health Institute Carlos III (IMIENS), Madrid, 28040, Spain; Sarriá, E., Department of Methodology of Behavioral Sciences, Faculty of Psychology, UNED, Madrid, 28040, Spain, Joint Research Institute UNED and Health Institute Carlos III (IMIENS), Madrid, 28040, Spain","The use of humanoid robots as assistants in therapy processes is not new. Several projects in the past several years have achieved promising results when combining human–robot interaction with standard techniques. Moreover, there are multiple screening systems for autism; one of the most used systems is the Quantitative Checklist for Autism in Toddlers (Q-CHAT-10), which includes ten questions to be answered by the parents or caregivers of a child. We present Q-CHAT-NAO, an observation-based autism screening system supported by a NAO robot. It includes the six questions of the Q-CHAT-10 that can be adapted to work in a robotic context; unlike the original system, it obtains information from the toddler instead of from an indirect source. The detection results obtained after applying machine learning models to the six questions in the Autistic Spectrum Disorder Screening Data for Toddlers dataset were almost equivalent to those of the original version with ten questions. These findings indicate that the Q-CHAT-NAO could be a screening option that would exploit all the benefits related to human-robot interaction. © 2021 The Authors","Autism spectrum disorder; Human-robot interaction; Machine learning","Anthropomorphic robots; Diseases; Robotics; Autistic spectrum disorders; Humanoid robot; Machine learning models; Original systems; Robot interactions; Screening data; Screening system; Used systems; Human robot interaction; Article; autism; autism assessment; Autistic Spectrum Disorder Screening Data for Toddlers; child; child health care; early diagnosis; human; information processing; intermethod comparison; machine learning; observational method; preschool child; Quantitative Checklist for Autism in Toddlers NAO; robotics; screening; toddler; mass screening; questionnaire; robot assisted surgery; 10-N-nonylacridinium orange; aminoacridine derivative; Aminoacridines; Autism Spectrum Disorder; Autistic Disorder; Child, Preschool; Humans; Mass Screening; Robotic Surgical Procedures; Robotics; Surveys and Questionnaires",,"10-N-nonylacridinium orange; Aminoacridines",,,,,,,,,,,,,,"(2013), American Psychiatric Association, Diagnostic and statistical manual of mental disorders (DSM-5), American Psychiatric Pub; (2007), pp. 12-28. , Centers for Disease Control and Prevention, et al., Prevalence of autism spectrum disorders-autism and developmental disabilities monitoring network, MMWR Surveillance Summaries 56; Maenner, M.J., Shaw, K.A., Baio, J., Prevalence of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network (2020) MMWR Surveillance Summaries, 69 (4), p. 1; Allison, C., Auyeung, B., Baron-Cohen, S., Toward brief red flags for autism screening: the short autism spectrum quotient and the short quantitative checklist in 1,000 cases and 3,000 controls (2012) Journal of the American Academy of Child & Adolescent Psychiatry, 51 (2), pp. 202-212; Mazefsky, C.A., Oswald, D.P., The discriminative ability and diagnostic utility of the ADOS-G, ADI-R, and GARS for children in a clinical setting (2006) Autism, 10 (6), pp. 533-549; Ismail, L.I., Shamsudin, S., Yussof, H., Hanapiah, F.A., Zahari, N.I., Estimation of concentration by eye contact measurement in robot–based intervention program with autistic children (2012) Procedia Engineering, 41, pp. 1548-1552; Shamsuddin, S., Yussof, H., Ismail, L.I., Mohamed, S., Hanapiah, F.A., Zahari, N.I., Humanoid robot NAO interacting with autistic children of moderately impaired intelligence to augment communication skills (2012) Procedia Engineering, 41, pp. 1533-1538; Thabtah, F., Machine learning in autistic spectrum disorder behavioral research: A review and ways forward (2018) Informatics for Health and Social Care, pp. 1-20; Tapus, A., Peca, A., Aly, A., Pop, C., Jisa, L., Pintea, S., Rusu, A.S., David, D.O., Children with autism social engagement in interaction with NAO, an imitative robot: A series of single case experiments (2012) Interaction Studies, 13 (3), pp. 315-347; Ismail, L.I., Shamsudin, S., Yussof, H., Hanapiah, F.A., Zahari, N.I., Robot-based intervention program for autistic children with humanoid robot NAO: initial response in stereotyped behavior (2012) Procedia Engineering, 41, pp. 1441-1447; Shamsuddin, S., Yussof, H., Ismail, L., Hanapiah, F.A., Mohamed, S., Piah, H.A., Zahari, N.I., Initial response of autistic children in human-robot interaction therapy with humanoid robot NAO (2012) 2012 IEEE 8th International Colloquium on Signal Processing and its Applications, IEEE, 2012, pp. 188-193; Shamsuddin, S., Yussof, H., Miskam, M.A., Hamid, A.C., Malik, N.A., Hashim, H., Humanoid robot NAO as HRI mediator to teach emotions using game-centered approach for children with autism (2013), HRI 2013 Workshop on Applications for Emotional Robots; Miskam, M.A., Hamid, M.A.C., Yussof, H., Shamsuddin, S., Malik, N.A., Basir, S.N., Study on social interaction between children with autism and humanoid robot NAO (2013), 393, pp. 573-578. , Applied Mechanics and Materials, Trans Tech Publ; Miskam, M.A., Shamsuddin, S., Samat, M.R.A., Yussof, H., Ainudin, H.A., Omar, A.R., Humanoid robot NAO as a teaching tool of emotion recognition for children with autism using the android app (2014) 2014 International Symposium on Micro-NanoMechatronics and Human Science (MHS) IEEE, pp. 1-5; Miskam, M.A., Masnin, N.F.S., Jamhuri, M.H., Shamsuddin, S., Omar, A.R., Yussof, H., Encouraging children with autism to improve social and communication skills through the game-based approach (2014) Procedia Computer Science, 42, pp. 93-98; Wall, D.P., Kosmicki, J., Deluca, T., Harstad, E., Fusaro, V.A., Use of machine learning to shorten observation-based screening and diagnosis of autism (2012) Translational Psychiatry, 2 (4), p. e100; Bone, D., Goodwin, M.S., Black, M.P., Lee, C.-C., Audhkhasi, K., Narayanan, S., Applying machine learning to facilitate autism diagnostics: pitfalls and promises (2015) J. Autism Dev. Disord., 45 (5), pp. 1121-1136; Crippa, A., Salvatore, C., Perego, P., Forti, S., Nobile, M., Molteni, M., Castiglioni, I., Use of machine learning to identify children with autism and their motor abnormalities (2015) J. Autism Dev. Disord., 45 (7), pp. 2146-2156; Thabtah, F., Autism spectrum disorder screening: machine learning adaptation and DSM-5 fulfillment (2017) Proceedings of the 1st International Conference on Medical and Health Informatics 2017, ACM, pp. 1-6; Thabtah, F., Kamalov, F., Rajab, K., A new computational intelligence approach to detect autistic features for autism screening (2018) Int. J. Med. Informatics, 117, pp. 112-124; Thabtah, F., An accessible and efficient autism screening method for behavioural data and predictive analyses (2019) Health informatics journal, 25 (4), pp. 1739-1755; Lord, C., Rutter, M., DiLavore, P.C., Risi, S., Gotham, K., Bishop, S., Autism diagnostic observation schedule, second edition, Torrance, CA: Western Psychological Services","Romero-García, R.; Department of Artificial Intelligence, Spain; email: ruromgar@dia.uned.es",,,"Academic Press Inc.",,,,,15320464,,JBIOB,"33933653","English","J. Biomed. Informatics",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85105747296
"Chesher C., Andreallo F.","26036189600;57215435340;","Eye Machines: Robot Eye, Vision and Gaze",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00777-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107393487&doi=10.1007%2fs12369-021-00777-7&partnerID=40&md5=9b39dc3b217bfd7719e21c966fbfcded","Digital Cultures, The University of Sydney, Sydney, Australia","Chesher, C., Digital Cultures, The University of Sydney, Sydney, Australia; Andreallo, F., Digital Cultures, The University of Sydney, Sydney, Australia","This article analyses three aspects of human and robotic eyes: (1) the eye as an object that evokes visceral reactions from observers when it is exposed or threatened; (2) the eye and brain, sensor and computer as mediators of vision and interpreters of visual worlds; and (3) the gaze as a mode of communication in interpersonal/human–robot interaction. Taking an interdisciplinary approach, this article draws on neuroscience, human–robot interaction, critical humanities, feminism and film studies to interrogate these three aspects of eyes as they relate to the design, theorisation and experience of social robots. We argue that some of the meanings and imaginaries associated with biological eyes are transferred and translated into robotic eyes, vision and gaze, or eye machines, following the tendency towards anthropomorphism. These imaginaries are made visible particularly in science fiction. We argue that photography, the cinematic apparatus, digital sensors and artificial intelligence are not only engineering innovations but have also contributed to transformations in the contemporary collective visual world. Our multimethod cultural studies analysis of eye machines has relevance for cultural theorists, designers and engineers. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Anthropomorphism; Biomorphism; Cinema; Cultural Studies; Eye; Gaze; Humanoid robots; Science fiction; Sensors; Social robots; Vision","Agricultural robots; Human computer interaction; Machine design; Robotics; Social robots; Cultural studies; Digital sensors; Engineering innovations; Imaginaries; Multi methods; Robot interactions; Science fictions; Visual world; Computer vision",,,,,,,,,,,,,,,,"Emery, N.J., The eyes have it: the neuroethology, function and evolution of social gaze. Neuroscience and Biobehavioral Reviews (2000) Neurosci Biobehav Rev, 24, pp. 581-604; Vacek, S., Schimmel, C., Dillmann, R., Road-marking analysis for autonomous vehicle guidance (2007) Proc Eur Conf Mob Robot, pp. 1-6; Bertozzi, M., Broggi, A., Fasciol, A., Nichele, S., Stereo Vision-based Vehicle Detection (2000) IEEE Intelligent Vehicles Symposium, pp. 39-44. , Dearbon, MI; Aryananda, L., Recognizing and remembering individuals: Online and unsupervised face recognition for humanoid robot (2002) IEEE Int Conf Intell Robot Syst, 2, pp. 1202-1207; Bartlett, M.S., Littlewort, G., Fasel, I., Movellan, J.R., Real time face detection and facial expression recognition: development and applications to human computer interaction (2003) IEEE Comput Soc Conf Comput Vis Pattern Recognit Work; Foucault, M., (1977) Discipline and Punish, , Vintage Books, New York; Shimada, M., Yoshikawa, Y., Asada, M., Effects of observing eye contact between a robot and another person (2011) Int J Soc Robot, 3, pp. 143-154; Zwickel, J., Müller, H.J., Eye movements as a means to evaluate and improve robots (2009) Int J Soc Robot, 1, pp. 357-366; Lang, C., Wachsmuth, S., Hanheide, M., Wersing, H., Facial communicative signals: valence recognition in task-oriented human-robot interaction (2012) Int J Soc Robot, 4, pp. 249-262; Pugh, A., (1983) Robot vision, , Springer, Berlin; Gunkel, D.J., Another alterity: Rethinking ethics in the face of the machine (2016) The changing face of alterity: communication, technology and other subjects, pp. 197-218. , Gunkel DJ, Marcondes Filho C, Mersch D, (eds), IOS Press, Amsterdam; Dinnen, Z., McBean, S., (2018) The Face as Technology New Form, 93, pp. 122-137; Chesher, C., Andreallo, F., Robotic faciality: The philosophy, science and art of robot faces (2020) Int J Soc Robot; Rose, G., (2016) Visual methodologies, , Sage, London; Jenks, C., (1995) Visual culture, , Taylor & Francis, Abington; Lister, M., Wells, L., Seeing beyond belief: Cultural Studies as an approach to analysing the visual (2004) The handbook of visual analysis, pp. 61-91. , Leeuwen T, Tewitt C, (eds), Sage, London; Dixon, W., (1995) It looks at you: the returned gaze of cinema, , State University of New York Press, Albany, NY; Gray, A., (2003) Research practice for cultural studies: Ethnographic methods and lived cultures, , Sage, London; Kristeva, J., (1982) Powers of horrorâ€¯: An Essay on Abjection / Julia Kristeva; Heidegger, M., Macquarrie, J., Robinson, E., (1962) Being and time, , Basil Blackwell, Oxford; Todo, T., (2018) SEER: Simulative Emotional Expression Robot, , https://doi.org/https://doi.org/10.1145/3214907.3214921; Official MDS Robot Video—First test of expressive ability (2008) [Youtube Video], , https://youtu.be/aQS2zxmrrrA; (2020) Humanoid and Programmable Robots; Hall, E.T., (1982) The hidden dimension, , Anchor, New York NY; Gibson, J.J., (1950) The perception of the visual world, , Riverside Press, Cambridge MA; Aaland, M., (2007) Shooting digital: Pro tips for taking great pictures with your digital camera, , Sybex, Indianapolis IN; Lehoux, D., Observers, objects, and the embedded eye; or, seeing and knowing in Ptolemy and Galen (2007) Isis, 98, pp. 447-467; Illich, I., The scopic past and the ethics of the gaze: A plea for the historical study of ocular perception (1995) Work Pap, (6). , Univ Park PA Sci Technol Soc Stud; Ruocco, S.R., (1987) Robot sensors and transducers, , Springer, Netherlands; Adams, M., (2012) Robotic navigation and mapping with radar, , Artech House, Norwood; Tzafestas, S.G., (2013) Introduction to mobile robot control, , Elsevier, Saint Louis; Young, A., (2019) Retinal Prosthetics: Science Fiction Or a Vision for the Future?, , Eyenews; Hassaballah, M., Awad, A.I., (2020) Computer vision: Principles and applications, , Taylor & Francis, Boca Raton; Dreyfus, H., (1995) What computers still can’t do, , MIT Press, Cambridge; Raji, I.D., Gebru, T., Mitchell, M., Saving Face: Investigating the ethical concerns of facial recognition auditing (2020) AIES 2020—Proc AAAI/ACM Conf AI, Ethics, Soc, pp. 145-151. , https://doi.org/https://doi.org/10.1145/3375627.3375820; 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Billing, E., Sciutti, A., Sandini, G., Proactive eye-gaze in human-robot interaction (2019) Anticipation and anticipatory systems: humans meet Artificial Intelligence, pp. 10-13. , Örebro, Sweden; Tamura, Y., Akashi, T., Yano, S., Osumi, H., Human visual attention model based on analysis of magic for smooth human–robot interaction (2016) Int J Soc Robot, 8, pp. 685-694; Mutlu, B., Shiwa, T., Kanda, T., (2009) Footing in Human-Robot Conversations. 2009 4Th ACM/IEEE Int Conf Human-Robot Interact 2:61, , https://doi.org/https://doi.org/10.1145/1514095.1514109; Admoni, H., Scassellati, B., Social Eye Gaze in Human-Robot Interaction: a review (2017) J Human-Robot Interact, 6, p. 25; Sartre, J.-P., (2011) Being and Nothingness: An Essay on Phenomenological Ontology; Eggers, D., (2013) The circle, , Knopf, New York; Lacan, J., (1978) The four fundamental concepts of psychoanalysis, , Norton & Co, New York; Mulvey, L., Visual pleasure and narrative cinema (1975) Screen, 16, pp. 6-18; Doane, M.A., Film and the masquerade: Theorising the female spectator (1999) Thornam S (Ed) Feminist Film Theory: A Reader, pp. 132-145; Mulvey L Afterthoughts on “Visual pleasure and narrative cinema” inspired by ‘Duel in the Sun (1946) Framework, pp. 12-15; Halberstam, J., (2005) In a queer time and place: transgender bodies, subcultural lives, , New York University Press, New York and London; (2020) The Tonight Show Starring Jimmy Fallom (2017) Tonight Showbotics: Jimmy Meets Sophia the Human-Like Robot, , https://youtu.be/Bg_tJvCA8zw.Accessed, In: [YouTube video, 5 Apr; Softbank Robotics (2018) Understanding Autonomous Life Settings., , http://doc.aldebaran.com/2-1/nao/nao_life.html, Accessed 6 May; Andreallo, F., Chesher, C., Prosthetic soul mates: sex robots as media for companionship (2019) M/C J, 22; 11 Los Angeles, F., Realistic-looking robot with soft skin, soothing voice (2017) In: [Youtube Video], , https://youtu.be/851Tx479ivM; Sparrow, R., Robots, rape, and representation (2017) Int J Soc Robot, 9, pp. 465-477; Kubes, T., (2019) New materialist perspectives on sex robots. A feminist dystopia/utopia? Soc Sci, , https://doi.org/https://doi.org/10.3390/socsci8080224","Chesher, C.; Digital Cultures, Australia; email: chris.chesher@sydney.edu.au",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Article in Press","",Scopus,2-s2.0-85107393487
"Dobrosovestnova A., Hannibal G., Reinboth T.","57199645448;56512012200;57220038583;","Service robots for affective labor: a sociology of labor perspective",2021,"AI and Society",,,,"","",,1,"10.1007/s00146-021-01208-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105398378&doi=10.1007%2fs00146-021-01208-x&partnerID=40&md5=d7651fae976970395cd791400c0a4e7a","Institute of Visual Computing and Human-Centered Technology, TU Wien, Argentinierstraße 8, 1040, Vienna, Austria; OFAI, The Austrian Research Institute for Artificial Intelligence, Vienna, Austria","Dobrosovestnova, A., Institute of Visual Computing and Human-Centered Technology, TU Wien, Argentinierstraße 8, 1040, Vienna, Austria; Hannibal, G., Institute of Visual Computing and Human-Centered Technology, TU Wien, Argentinierstraße 8, 1040, Vienna, Austria; Reinboth, T., OFAI, The Austrian Research Institute for Artificial Intelligence, Vienna, Austria","Profit-oriented service sectors such as tourism, hospitality, and entertainment are increasingly looking at how professional service robots can be integrated into the workplace to perform socio-cognitive tasks that were previously reserved for humans. This is a work in which social and labor sciences recognize the principle role of emotions. However, the models and narratives of emotions that drive research, design, and deployment of service robots in human–robot interaction differ considerably from how emotions are framed in the sociology of labor and feminist studies of service work. In this paper, we explore these tensions through the concepts of affective and emotional labor, and outline key insights these concepts offer for the design and evaluation of professional service robots. Taken together, an emphasis on interactionist approaches to emotions and on the demands of affective labor, leads us to argue that service employees are under-represented in existing studies in human–robot interaction. To address this, we outline how participatory design and value-sensitive design approaches can be applied as complimentary methodological frameworks that include service employees as vital stakeholders. © 2021, The Author(s).","Affective labor; Human–robot interaction; Service economy","Employment; Machine design; Mobile robots; Personnel; Service industry; Sociology; Design and evaluations; Methodological frameworks; Participatory design; Professional services; Robot interactions; Service employees; Under-represented; Value sensitive design; Human robot interaction",,,,,,,,,,,,,,,,"Alac, M., Social robots: things or agents? 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"Mosconi D., Nunes P.F., Ostan I., Siqueira A.A.G.","57219799033;56890685900;57219801717;7003788864;","Design and validation of a human-exoskeleton model for evaluating interaction controls applied to rehabilitation robotics",2020,"Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics","2020-November",,"9224436","629","634",,,"10.1109/BioRob49111.2020.9224436","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095613416&doi=10.1109%2fBioRob49111.2020.9224436&partnerID=40&md5=b5199c2f655bfb70a9049e8939fe93a1","University of São Paulo, Mechanical Engineering Department, School Engineering of São Carlos, São Carlos, Brazil","Mosconi, D., University of São Paulo, Mechanical Engineering Department, School Engineering of São Carlos, São Carlos, Brazil; Nunes, P.F., University of São Paulo, Mechanical Engineering Department, School Engineering of São Carlos, São Carlos, Brazil; Ostan, I., University of São Paulo, Mechanical Engineering Department, School Engineering of São Carlos, São Carlos, Brazil; Siqueira, A.A.G., University of São Paulo, Mechanical Engineering Department, School Engineering of São Carlos, São Carlos, Brazil","Robot-assisted therapy is a promising field in terms of motor rehabilitation for stroke victims. However, the interaction between the user and the robot must be done in a way to ensure patient safety and treatment effectiveness. Several human-robot interaction controls have been proposed in order to meet these requirements. However, testing and validating these controls remain a challenge, requiring physical contact between a user and a robot, which can put the user at risk as well as it can require a reasonable amount of time and resources for preparing such tests. This work proposes the development of a human-robot interaction model based on the Leg6Dof9Musc neuromusculoskeletal model from OpenSim and a simulation environment developed in MATLAB, in order to test the interaction controls developed for the rehabilitation of lower limbs. An impedance control was tested for the swing phase during gait movement and the results proved that the system is feasible and useful, with flexibility, and savings in time and resources, in addition to preventing users and robots from being put at risk during tests of interaction controls. © 2020 IEEE.",,"Agricultural robots; Exoskeleton (Robotics); Man machine systems; MATLAB; Patient treatment; Well testing; Human-robot interaction control; Interaction controls; Motor rehabilitation; Neuromusculoskeletal models; Physical contacts; Rehabilitation robotics; Robot-assisted therapies; Simulation environment; Human robot interaction",,,,,"Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP: 2013/14756-0; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES: 3457/2014","This work is supported by Pro-Rectory of Research of University of São Paulo, Coordenac¸ão de Aperfeic¸oamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, PGPTA, under grant 3457/2014, and São Paulo Research Foundation (FAPESP) under grant 2013/14756-0",,,,,,,,,,"(2011) Global Atlas on Cardiovascular Disease Prevention and Control, p. 155. , World Health Organization, Switzerland: World Health Organization; Hogan, N., Robot-aided neuro-recovery (2014) ASME Mechanical Engineering, 136 (9), pp. 3-5; (2011) World Report on Disability 2011, p. 325. , World Health Organization Switzerland: World Health Organization; Jutinico, A.L., Impedance control for robotic rehabilitation: A robust markovian approach (2017) Frontiers in Neurorobotics, 11, p. 43; Diaz, I., Gil, J.J., Sanchez, E., Lower-limb robotic rehabilitation: Literature review and challenges (2011) Journal of Robotics, pp. 1-11; Peña, G.G., (2017) Controle de Impedncia Adaptativo Dirigido Por EMG para Reabilitao Robtica, , PhD. dissertation, Dept. Mechanical Eng., University of São Paulo, São Carlos, Brazil; Xu, L., Dynamics based fuzzy adaptive impedance control for lower limb rehabilitation robot (2018) Neural Information Processing, pp. 316-326; Nunes, P.F., Santos, W.M., Siqueira, A.A.G., Control strategy based on kinetic motor primitives for lower limbs exoskeletons (2018) 10th IFAC Symposium on Biological and Medical Systems, 51, pp. 402-406. , São Paulo, Brazil: ElsevierBV; Guzman, C.H., Robust control of a hip?Joint rehabilitation robot (2017) Biomedical Signal Processing and Control, 35, pp. 100-109; Sousa, A.C.C., Freire, J.P.C.D., Bo, A.P.L., Integrating hip exosuit and fes for lower limb rehabilitation in a simulation environment (2019) IFAC-PapersOnLine, 51 (34), pp. 302-307; Peña, G.G., Feasibility of an optimal emg-driven adaptive impedance control applied to an active knee orthosis (2019) Robotics and Autonomous Systems, (112), pp. 98-108; Khamar, M., Edrisi, M., Designing a backstepping sliding mode controller for an assistant human knee exoskeleton based on nonlinear disturbance observer (2018) Mechatronics, 54, pp. 121-132; Nunes, P.F., Influncia de um exoesqueleto sobre as caractersticas cinticas e musculares durante a marcha utilizando primitivas de movimento (2018) Proceedings Do VI Encontro Nacional de Engenharia Biomecanica, 1, pp. 1-6. , Aguas de Lindoia, Brazil: ABCM; Sousa, A.C.C., A comparative study on control strategies for fes cycling using a detailed musculoskeletal model (2016) IFAC-PapersOnline, 49 (32), pp. 204-209; Handford, M.L., Srinivasan, M., Energy-optimal human walking with feedback-controlled robotic prostheses: A computational study (2018) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26 (9), pp. 1773-1782; Durandau, G., Emg-driven models of human-machine interaction in individuals wearing the h2 exoskeleton (2016) IFAC-PapersOnLine, 49, pp. 200-203; Mansouri, M., Reinbolt, J.A., A platform for dynamic simulation and control of movement based on opensim and matlab (2012) Journal of Biomechanics, 45 (8), pp. 1517-1521; Agarwal, P., Neptune, R.R., Deshpande, A.D., A simulation framework for virtual prototyping of robotic exoskeletons. journal of biomechanical engineering (2016) ASME International, 138 (6), pp. 1-15; Santos Dos, W.M., Design and evaluation of a modular lower limb exoskeleton for rehabilitation (2017) 2017 International Conference on Rehabilitation Robotics, , London, England: IEEE; Dos Santos, W.M., Caurin, G.A.P., Siqueira, A.A.G., Design and control of an active knee orthosis driven by a rotary series elastic actuator (2017) Control Engineering Practice, 58, pp. 307-318; Frey, S.H., Neurological principles and rehabilitation of action disorders: Computation (2011) Anatomy, and Physiology Model, Neurorehabilitation and Neural Repair, 25, pp. 6S-20S; Wolpert, D.M., Miall, R.C., Kawato, M., Internal models in the cerebellum (1998) Trends in Cognitive Sciences, 2 (9), pp. 338-347; Sousa, A.C.C., Freire, J.P.C.D., Freire, A.P.L., Integrating hip exosuit and fes for lower limb rehabilitation in a simulation environment (2019) IFAC-PapersOnLine, 51 (34), pp. 302-307",,,,"IEEE Computer Society","8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020","29 November 2020 through 1 December 2020",,164150,21551774,9781728159072,,,"English","Proc. IEEE RAS EMBS Int. Conf. Biomed. Rob. Biomechatronics, BioRob",Conference Paper,"Final","",Scopus,2-s2.0-85095613416
"Nishimura S., Chaichaowarat R., Krebs H.I.","56027601400;55813237300;57202890480;","Human-Robot Interaction: Controller Design and Stability",2020,"Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics","2020-November",,"9224464","1096","1101",,,"10.1109/BioRob49111.2020.9224464","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095582019&doi=10.1109%2fBioRob49111.2020.9224464&partnerID=40&md5=a3576be6f86db85b0d6bcc85a68d6a37","Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, MA, United States; Faculty of Engineering, International School of Engineering, Chulalongkorn University, Bangkok, Thailand; Department of Neurology, University of Maryland, School of Medicine, Baltimore, MD, United States; Department of Physical Medicine and Rehabilitation, Fujita Health University, Nagoya, Japan; Institute of Neuroscience, University of Newcastle, Newcastle-upon-Tyne, United Kingdom; Sogang University, Seoul, South Korea; Department of Mechanical Sciences and Bioengineering, Osaka University, Osaka, Japan; Wolfson School of Mechanical, Electrical, and Manufacturing Engineering, Loughborough University, Loughborough, United Kingdom","Nishimura, S., Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, MA, United States; Chaichaowarat, R., Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, MA, United States, Faculty of Engineering, International School of Engineering, Chulalongkorn University, Bangkok, Thailand; Krebs, H.I., Massachusetts Institute of Technology, Department of Mechanical Engineering, Cambridge, MA, United States, Department of Neurology, University of Maryland, School of Medicine, Baltimore, MD, United States, Department of Physical Medicine and Rehabilitation, Fujita Health University, Nagoya, Japan, Institute of Neuroscience, University of Newcastle, Newcastle-upon-Tyne, United Kingdom, Sogang University, Seoul, South Korea, Department of Mechanical Sciences and Bioengineering, Osaka University, Osaka, Japan, Wolfson School of Mechanical, Electrical, and Manufacturing Engineering, Loughborough University, Loughborough, United Kingdom","In this paper we derive the control parameter gains required to guarantee a stable human-robot interaction (HRI). One goal in HRI research field is to reduce the robot mechanical impedance and enable humans to easily manipulate the robot. Force feedback is an effective way to reduce robot inertia and friction, but stability is paramount especially when interacting with humans. This paper discusses gains and stability boundaries. The phase response of the open loop transfer function that represents the human robot interaction is used to derive the gains. The environment is modeled as a second-order spring-mass-damper system. The stability boundaries correspond to the region when the phase response of the transfer function becomes greater than -180° at all frequencies. This enables the system to interact stably regardless of the environment second-order system parameters. One of the interesting results is that the virtual spring used to generate the impedance field has nothing to do with the stability condition. The experimental results show the validity of our method. © 2020 IEEE.",,"Agricultural robots; Biofeedback; Machine design; Man machine systems; Robotics; Springs (components); Stability; Transfer functions; Control parameters; Human robot Interaction (HRI); Mechanical impedances; Open-loop transfer functions; Second-order systemss; Spring-mass-damper system; Stability boundaries; Stability condition; Human robot interaction",,,,,"6941127","*This work was supported by Shanghai Electric Group Co. Ltd., Shanghai, China grant #6941127.","This work was supported by Shanghai Electric Group Co. Ltd., Shanghai, China grant 6941127.",,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human-robot collaboration (2018) Autonomous Robots, 42 (5), pp. 957-975. , https://doi.org/10.1007/s10514-017-9677-2, Jun; McBean, J., Breazeal, C., Voice coil actuators for human-robot interaction (2004) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, 1, pp. 852-858. , http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1389459&isnumber=30275, Sep; John Whitney P, J.M., Chen, T., Hodgins, J.K., A hybrid hydrostatic transmission and human-safe haptic telepresence robot (2016) Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, pp. 690-695. , http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7487195&isnumber=7487087, May; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Humanrobot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Transactions on Robotics, 31 (5), pp. 1089-1100. , http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7177120&isnumber=7274528, Oct; Oh, S., Kong, K., High-precision robust force control of a series elastic actuator (2017) IEEE/ASME Transactions on Mechatronics, 22 (1), pp. 71-80. , http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7579567&isnumber=7855861, Feb; Morrell, J.B., (1996) Parallel Coupled Micro-macro Actuators, , http://hdl.handle.net/1721.1/11222, Ph. 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D. dissertation, Massachusetts Institute of Technology; Astrom, K.J., Murray, R.M., (2008) Feedback Systems: An Introduction for Scientists and Engineers, , USA: Princeton University Press; Colgate, J.E., (1988) The Control of Dynamically Interacting Systems, , http://hdl.handle.net/1721.1/14380, Ph. D. dissertation, Massachusetts Institute of Technology",,,,"IEEE Computer Society","8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020","29 November 2020 through 1 December 2020",,164150,21551774,9781728159072,,,"English","Proc. IEEE RAS EMBS Int. Conf. Biomed. Rob. Biomechatronics, BioRob",Conference Paper,"Final","",Scopus,2-s2.0-85095582019
"Hsu R.C., Su P.-C., Hsu J.-L., Wang C.-Y.","7101741590;57221607822;57221604560;57221614361;","Real-Time Interaction System of Human-Robot with Hand Gestures",2020,"2nd IEEE Eurasia Conference on IOT, Communication and Engineering 2020, ECICE 2020",,,"9301957","396","398",,,"10.1109/ECICE50847.2020.9301957","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099559615&doi=10.1109%2fECICE50847.2020.9301957&partnerID=40&md5=b47c23a0a944be6999d49d3270d562a6","National Chiayi University, Electrical Engineering Dept., Chiayi City, Taiwan","Hsu, R.C., National Chiayi University, Electrical Engineering Dept., Chiayi City, Taiwan; Su, P.-C., National Chiayi University, Electrical Engineering Dept., Chiayi City, Taiwan; Hsu, J.-L., National Chiayi University, Electrical Engineering Dept., Chiayi City, Taiwan; Wang, C.-Y., National Chiayi University, Electrical Engineering Dept., Chiayi City, Taiwan","With the declining birthrate and the aging population, the shortage of labor has become more obvious. Therefore, the teleoperation of the robot as the substitute of human labour have become popular. In this study, an application of real-time gesture-based human-robot interaction is proposed using a Kinect sensor for elderly care. By employing gesture-based teleoperation of robots, demands, and conditions of the elderly such as sudden falling and the abnormality can be quickly recognized. The robot used in this study, iRobot Create, is a popular mobile robot for indoor usage which will recognize the following gestures: right-hand raising, left-hand raising, right-hand waving, and left hand waving, in corresponding to the control the robot's moving forward, backward, right turn, and left turn, respectively. Experimental results shown that the four types of gestures can be recognized by the iRobot Create with human gestures captured by the Kinect sensor. The corresponding actions in real-time were realized through the designed gesture-based remote human-robot interaction system. The experimental results are also can be seen from the Youtube channel. © 2020 IEEE.","elderly care; gesture recognition; teleoperation of robot","Gesture recognition; Internet of things; Man machine systems; Remote control; Aging population; Elderly care; Hand gesture; Human gestures; Human robots; Kinect sensors; Left turns; Real time interactions; Social robots",,,,,,,,,,,,,,,,"IRobot Create, , https://edu.irobot.com/what-we-offer/create-robot; Kinect Sensor, , https://zh.wikipedia.org/wiki/Kinect; (2014) Arduino and IRobot Create, , https://web.ics.purdue.edu/~fwinkler/AD41700-F15/AD4170Arduino-iRobot-workshop.pdf, Prof. Fabian Winkler Spring; HC-06 Tutorial, , https://blog.cavedu.com/2017/10/18/hc05-hc06; How to Fast Develop a Kinect Project, , http://tw-hkt.blogspot.com/2012/02/kinect-for-windows-sdk-v1.html, in Chinese; How to Use Kinect in Gesture Recognition, , http://tw-hkt.blogspot.com/2012/03/kinect-for-windows-sdk-v1.html, in Chinese","Hsu, R.C.; National Chiayi University, Taiwan; email: rchsu@mail.ncyu.edu.tw","Meen T.-H.",,"Institute of Electrical and Electronics Engineers Inc.","2nd IEEE Eurasia Conference on IOT, Communication and Engineering, ECICE 2020","23 October 2020 through 25 October 2020",,166247,,9781728180601,,,"English","IEEE Eurasia Conf. IOT, Commun. Eng., ECICE",Conference Paper,"Final","",Scopus,2-s2.0-85099559615
"Alimardani M., Hiraki K.","55836794100;7102170992;","Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction",2020,"Frontiers in Robotics and AI","7",,"125","","",,4,"10.3389/frobt.2020.00125","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092925232&doi=10.3389%2ffrobt.2020.00125&partnerID=40&md5=9c1419b927d5b8660cacdde85cec975f","Department of Cognitive Science and Artificial Intelligence, School of Humanities and Digital Sciences, Tilburg University, Tilburg, Netherlands; Department of General Systems Studies, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan","Alimardani, M., Department of Cognitive Science and Artificial Intelligence, School of Humanities and Digital Sciences, Tilburg University, Tilburg, Netherlands; Hiraki, K., Department of General Systems Studies, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan","Brain-computer interfaces (BCIs) have long been seen as control interfaces that translate changes in brain activity, produced either by means of a volitional modulation or in response to an external stimulation. However, recent trends in the BCI and neurofeedback research highlight passive monitoring of a user's brain activity in order to estimate cognitive load, attention level, perceived errors and emotions. Extraction of such higher order information from brain signals is seen as a gateway for facilitation of interaction between humans and intelligent systems. Particularly in the field of robotics, passive BCIs provide a promising channel for prediction of user's cognitive and affective state for development of a user-adaptive interaction. In this paper, we first illustrate the state of the art in passive BCI technology and then provide examples of BCI employment in human-robot interaction (HRI). We finally discuss the prospects and challenges in integration of passive BCIs in socially demanding HRI settings. This work intends to inform HRI community of the opportunities offered by passive BCI systems for enhancement of human-robot interaction while recognizing potential pitfalls. © Copyright © 2020 Alimardani and Hiraki.","brain-computer interface (BCI); cognitive workload estimation; EEG; emotion recognition; error detection; human-robot interaction (HRI); passive BCIs; social robots",,,,,,"200800000003; Japan Society for the Promotion of Science, KAKEN: 15F15046; Cabinet Office, Government of Japan; Council for Science, Technology and Innovation, CSTI","This research was supported by Grant-in-Aid for JSPS Research Fellow 15F15046, ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan) and CREST-JST (No. 200800000003).",,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human–robot collaboration (2018) Auton. 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"Tausch A., Kluge A., Adolph L.","57219250178;23397227500;55116326700;","Psychological Effects of the Allocation Process in Human–Robot Interaction – A Model for Research on ad hoc Task Allocation",2020,"Frontiers in Psychology","11",,"564672","","",,2,"10.3389/fpsyg.2020.564672","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091947852&doi=10.3389%2ffpsyg.2020.564672&partnerID=40&md5=cb26f63369d5d97e85236591a7ed4ed8","Department Products and Work Systems, Federal Institute for Occupational Safety and Health, Dortmund, Germany; Chair of Work and Organisational Psychology, Ruhr University Bochum, Bochum, Germany","Tausch, A., Department Products and Work Systems, Federal Institute for Occupational Safety and Health, Dortmund, Germany; Kluge, A., Chair of Work and Organisational Psychology, Ruhr University Bochum, Bochum, Germany; Adolph, L., Department Products and Work Systems, Federal Institute for Occupational Safety and Health, Dortmund, Germany","Task allocation is immensely important when it comes to designing human–robot interaction (HRI), but although it is the shaping part of the interaction, it is merely regarded as a process with its own effects on human thinking and behavior. This study aims at linking research from different fields like psychological theory, HRI and allocation optimization to create a new process model of ad hoc task allocation in human–robot interaction. It addresses the process characteristics and psychological outcomes of a real-time allocation process that integrates the worker. To achieve this, we structured the process into steps and identified relevant psychological constructs associated with them. The model is a first step toward ergonomic research on the self-organized allocation of tasks in HRI, but may also be an inspiration for practitioners designing HRI systems. To create successful work in HRI, designing the technology is an important foundation, but a participative, thought-out process for allotting tasks could be the key to adequate autonomy, work satisfaction and successful cooperation. © Copyright © 2020 Tausch, Kluge and Adolph.","decision perception; HRI; participation; satisfaction; self-organizing sociotechnical system; theory; work psychology",,,,,,,,,,,,,,,,,"Abdallah, S., Lesser, V.R., Modeling task allocation using a decision theoretic model (2005) Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 719-726. , Pechoucek M., Steiner D., Thompson S., (eds), New York, NY, ACM; Adams, S.J., Inequity in social exchange (1965) Adv. Exp. Soc. 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Alltagshandelns, 7, pp. 30-50; Wilcox, R.J., Nikolaidis, S., Shah, J.A., Optimization of temporal dynamics for adaptive human-robot interaction in assembly manufacturing (2013) Robotics: Science and Systems VIII, , Roy N., Newman P.M., Srinivasa S., (eds), Cambridge, MA, The MIT Press; Wirtz, M.A., (2014) Dorsch - Lexikon der Psychologie [Dorsch - Encyclopedia of Psychology], , 17th Edn, Bern, Hans Huber","Tausch, A.; Department Products and Work Systems, Germany; email: tausch.alina@baua.bund.de",,,"Frontiers Media S.A.",,,,,16641078,,,,"English","Front. Psychol.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85091947852
"Matsufuji A., Sekino H., Sato-Shimokawara E., Yamaguchi T.","57202446193;57214755550;25031564100;56501037800;","A Calculation Method of the Similarity between Trained Model and New Sample by using Gaussian Distribution",2020,"Proceedings of the International Joint Conference on Neural Networks",,,"9206945","","",,,"10.1109/IJCNN48605.2020.9206945","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093845388&doi=10.1109%2fIJCNN48605.2020.9206945&partnerID=40&md5=be9c6cd19f5f0be24b3c68bbc5333605","Tokyo Metropolitan University, Department of Computer Science, Tokyo, Japan","Matsufuji, A., Tokyo Metropolitan University, Department of Computer Science, Tokyo, Japan; Sekino, H., Tokyo Metropolitan University, Department of Computer Science, Tokyo, Japan; Sato-Shimokawara, E., Tokyo Metropolitan University, Department of Computer Science, Tokyo, Japan; Yamaguchi, T., Tokyo Metropolitan University, Department of Computer Science, Tokyo, Japan","In many Human Robot Interaction scenarios, social robots are expected to communicate with human naturally. Especially, the skill of predicting human internal state is a field of great interest in many applications, including video surveillance, behavior analysis, human robot interaction, life-logging. While accurately predicting these technologies (e.g. emotion, confident for talking a topic) could have benefits for many fields, generic machine learning systems still yield low performance in some situation. We hypothesize that these sophisticated models suffer from individual differences of human's personality. Therefore, we proposed a multi characteristic model architecture which combines the personalized machine learning models and utilize each model's prediction score in the inference. This architecture formed with reference to ensemble machine learning architecture. In this research, we focus on a similarity between new user and trained user model by using the idea of applicability domain of machine learning models. In the empirical result, we confirmed that data distribution (one way of checking applicability domain) of each user model correspond to the performance of models and we estimated confidence during communication as a human internal state. © 2020 IEEE.","affective computing; human robot interaction; multi-modal learning; non-verbal; personal modeling","Architecture; Behavioral research; Forecasting; Machine learning; Man machine systems; Network architecture; Neural networks; Security systems; Behavior analysis; Data distribution; Individual Differences; Internal state; Machine learning models; Multi characteristics; User Modeling; Video surveillance; Social robots",,,,,,,,,,,,,,,,"Cherubini, A., Passama, R., Navarro, B., Sorour, M., Khelloufi, A., Mazhar, O., Fraisse, P., A collaborative robot for the factory of the future: BAZAR (2019) The International Journal of Advanced Manufacturing Technology, pp. 1-17; Choi, B., Lee, W., Park, G., Lee, Y., Min, J., Hong, S., Development and control of a military rescue robot for casualty extraction task (2019) Journal of Field Robotics, 36 (4), pp. 656-676; Walters, M.L., Syrdal, S., Dautenhan, K., Boekhorst, R., Koay, K.L., Avoiding the uncanny valley, robot appearance personality and consistency of behaivior in an attention seeking home scenario for a robot companion (2008) Autonomous Robots, 24 (2), pp. 159-178; Marinoiu, E., Zanfir, M., Olaru, V., Sminchisescu, G., 3d human sensing, action and emotion recognition in robot assisted therapy of children with autism (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2158-2167; Lakomkin, E., Zamani, M.A., Weber, C., Magg, S., Wermter, S., On the robustness of speech emotion recognition for human-robot interaction with deep neural networks (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 854-860; Merabian, A., (1971) Silent Communication, , Wadsworth, Belmont, Calfornia; Mancini, M., Castellano, G., Real-time analysis and synthesis of emotional gesture expressivity Proceedings of the Doctoral Consortium of 2nd International Conference on Affective Computing and Intelligent Interaction; Ballihi, L., Lablack, A., Amor, B.B., Biasco, I.M., Daoudi, M., Positive negative emotion detection from rgb-d upper body images (2015) Face and Facial Expression Recognition from Real World Videos, 8912, pp. 109-120. , Springer International Publishing; Mimura, A., Hagiwara, M., Understanding presumption system from facial images (2000) The Transaction of the Institute of Electrical Engineers of Japan, 120 (2), pp. 273-278; Okada, S., Matsugi, Y., Nakano, Y., Hayashi, Y., Huang, H.H., Takase, Y., Nitta, K., Estimating communication skills based on multimodal information in group discussions (2016) Journal of Japanese Society for Artificial Intelligence, 31 (6). , AI30-E112; Nagasawa, F., Ishihara, T., Okada, S., Nitta, K., A case study toward implementing adaptive interview strategy based on user's attitude recognition for a interview robot (2017) Journal of Japanese Society for Artificial Intelligence, 31. , 2H4-OS-35b-1; Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y., Multimodal deep learning (2011) Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 689-696; Dietter, T.G., Thomas, G., Ensemble methods in machine learning (2000) Multiple Classifier Systems, 1857, pp. 1-15; Quan, S., Bernhard, P., Bagging ensemble selection (2011) AI 2011: Ad Vances in Artificial Intelligence, pp. 251-260; Breiman, L., Random forests (2001) Machine Learning, 45 (1), pp. 5-32; Thomas, D.G., An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization (2000) Machine Learning, 2 (40), pp. 139-157; Kasano, E., Muramatsu, S., Matsufuji, A., Sato-Shimokawara, E., Yamaguchi, T., Estimation of speakers confidence in conversation using speech information and head motion (2019) The 16th International Conference on Ubiquitous Robots; Boersma, P., A system for doing phonetics by computer (2001) Glot International, 34 (5), pp. 9-10; Vinciarelli, A., Pantic, M., Hereve, B., Social signal processing: Survey of an emerging domain (2009) Image and Vision Computing, 27 (12), pp. 1743-1759; Giuliani, M., Mirning, N., Stollnberger, G., Stadler, S., Buchner, R., Tscheligi, M., Systematic analysis of video data from different human robot interaction studies: A categorization of social signals during error situations (2015) Frontiers in Psychology, 6, p. 931; Stiefelhagen, R., Fugen, C., Gieselmann, R., Holzapfel, H., Nickel, K., Waibel, A., Natural human-robot interaction using speech, head pose and gestures (2004) Intelligent Robots and Systems.(IROS 2004). Proceedings. 2004 IEEE/RSJ International Confidence, 3, pp. 2422-2427; Morency, L.P., Sidner, C., Lee, C., Darrel, T., Head gestures for perceptual interfaces: The role of context in improving recognition (2007) Artificial Intelligence, 171 (8-9), pp. 568-585; Allen, D.M., The relationship between variable selection and data augmentation and a method for prediction (1974) Technometrics, 16, pp. 125-127; Von Wolff, M.R., Hobert, S., Schumann, M., How may i help you-state of the art and open research questions for chatbots at the digital workplace (2019) Proceedings of the 52nd Hawaii International Conference on System Sciences; Lee, A., Oura, K., Tokuda, K., MMDAgent-A fully open-source toolkit for voice interaction systems (2013) Proceedings of the ICASSP 2013, pp. 8382-8385; Frank, E., Hall, M.A., Ian, H., The WEKA workbench: Online appendix for (2016) Data Mining: Practical Machine Learning Tools and Techniques, , Morgan Kaufmann, Fourth Edition; Irane, L.R., Gómez-Nieto, M.A., Building of robust and interpretable QSAR classification models by means of the rivality index (2019) Journal of Chemical Information and Modeling, 59 (6), pp. 2785-2804",,,"IEEE Computational Intelligence Society (CIS)","Institute of Electrical and Electronics Engineers Inc.","2020 International Joint Conference on Neural Networks, IJCNN 2020","19 July 2020 through 24 July 2020",,163566,,9781728169262,85OFA,,"English","Proc Int Jt Conf Neural Networks",Conference Paper,"Final","",Scopus,2-s2.0-85093845388
"Mou Y., Shi C., Shen T., Xu K.","36705026800;57211446262;57211441425;57193532942;","A Systematic Review of the Personality of Robot: Mapping Its Conceptualization, Operationalization, Contextualization and Effects",2020,"International Journal of Human-Computer Interaction","36","6",,"591","605",,9,"10.1080/10447318.2019.1663008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073999458&doi=10.1080%2f10447318.2019.1663008&partnerID=40&md5=fce2216fe8e9303ffbb29677a93e336b","School of Media & Communication, Shanghai Jiao Tong University, Shanghai, China; Department of Telecommunication, College of Journalism and Communications, University of Florida, Gainesville, FL, United States","Mou, Y., School of Media & Communication, Shanghai Jiao Tong University, Shanghai, China; Shi, C., School of Media & Communication, Shanghai Jiao Tong University, Shanghai, China; Shen, T., School of Media & Communication, Shanghai Jiao Tong University, Shanghai, China; Xu, K., Department of Telecommunication, College of Journalism and Communications, University of Florida, Gainesville, FL, United States","Robots are becoming prevalent as they could socially interact with humans and provide service or companionship. As people attribute personality traits to machines, the personality of robot (POR) has attracted considerable scholarly attention from researchers of human-robot interaction. However, due to the complexity of personality, the ways to design personality into robotics vary on a wide range. This systematic review attempts to map the approaches to designing the personality of robot and understand its effects on human-robot interaction. Following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, a review of 40 peer-reviewed publications was conducted. The conceptualization, operationalization, contextualization and effects of POR were summarized in the review. In general, positive POR was preferred and associated with desirable social responses. Suggestions on future design of robotics were discussed. Specifically, it is recommended that the design of POR should match users’ expectations in different social contexts. Social cues such as eye gaze, gestures, and voice should be applied at a self-explanatory level to help users efficiently predict and engage with the behaviors of social robots. © 2019, © 2019 Taylor & Francis Group, LLC.",,"Man machine systems; Robotics; Contextualization; Future designs; Meta analysis; Personality traits; Social context; Social cues; Social robots; Systematic Review; Human robot interaction",,,,,"National Office for Philosophy and Social Sciences, NPOPSS: 18BXW046","This work was supported by the National Social Science Fund of China under Grant [No. 18BXW046].",,,,,,,,,,"Aly, A., Tapus, A., A model for synthesizing a combined verbal and nonverbal behavior based on personality traits in human-robot interaction (2013) In Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction, pp. 325-332. , March, IEEE Press, &, (). 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Relationships between participant and robot personality traits in human–Robot interaction studies (2007) Interaction Studies, 8 (2), pp. 281-305; Yamashita, Y., Ishihara, H., Ikeda, T., Asada, M., Investigation of causal relationship between touch sensations of robots and personality impressions by path analysis (2018) International Journal of Social Robotics, , 11, (1), 141–150","Mou, Y.; School of Media & Communication, 800 Dongchuan Road, Minhang District, China; email: yimou@sjtu.edu.cn",,,"Taylor and Francis Inc.",,,,,10447318,,,,"English","Int. J. Hum.-Comput. Interact.",Article,"Final","",Scopus,2-s2.0-85073999458
"Lopes J., Pinheiro C., Figueiredo J., Reis L.P., Santos C.P.","57208665200;57208665955;57195993391;13907511600;57211929847;","Assist-as-needed impedance control strategy for a wearable ankle robotic orthosis",2020,"2020 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2020",,,"9096186","10","15",,3,"10.1109/ICARSC49921.2020.9096186","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085943925&doi=10.1109%2fICARSC49921.2020.9096186&partnerID=40&md5=93f0a8aa983d7d2c493eb69f7f7b5d85","University of Minho, Guimarães, Portugal; University of Porto, Porto, Portugal","Lopes, J., University of Minho, Guimarães, Portugal; Pinheiro, C., University of Minho, Guimarães, Portugal; Figueiredo, J., University of Minho, Guimarães, Portugal; Reis, L.P., University of Porto, Porto, Portugal; Santos, C.P., University of Minho, Guimarães, Portugal","The use of robots in rehabilitation attempts an effective, compliant, and time-efficient gait recovery while adapting the assistance to the user's needs. Assist-as-needed strategies (AAN), such as adaptive impedance control, have been reported as prominent strategies to enable this recovery effects. This study proposes an interaction-based assist-as-needed impedance control strategy for an ankle robotic orthosis that adapts the robotic assistance by changing the Human-Robot interaction stiffness. The adaptability of the interaction stiffness allows the real-time passage from passive assistance to an active one, approaching AAN gait training. The interaction stiffness was successfully estimated by linear regression of the Human-Robot interaction torque vs angle trajectory curve. From the validation with seven able-bodied subjects, we verified the suitability of this adaptive impedance control for a more compliant, natural, and comfortable motion than the trajectory tracking control. Moreover, the proposed strategy considers the users' motion intention and encourages them to interact closely with the robotic device while guiding their ankle trajectory according to desired trajectories. These achievements contribute to AAN gait training. © 2020 IEEE.","Human-Robot Interaction; Impedance Control; Locomotion and Actuation Systems; Robotic Rehabilitation","Agricultural robots; Man machine systems; Motion tracking; Robotics; Stiffness; Trajectories; Wearable technology; Desired trajectories; Impedance control; Impedance control strategy; Motion intention; Needed-strategies; Recovery effects; Robotic orthosis; Trajectory tracking control; Human robot interaction",,,,,"NORTE-01-0145-FEDER-030386; Programa Operacional Temático Factores de Competitividade, POFC: POCI-01-0145-FEDER-006941","This work has been supported by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from Fundação para a Ciência e Tecnologia with the project SmartOs under Grant NORTE-01-0145-FEDER-030386, and through the COMPETE 2020—Programa Operacional Competitividade e Internacionalização (POCI)—with theReference Project under Grant POCI-01-0145-FEDER-006941.",,,,,,,,,,"Meng, W., Liu, Q., Zhou, Z., Ai, Q., Sheng, B., Xie, S.S., Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation (2015) Mechatronics, 31, pp. 132-145; Johnson, W., Onuma, O., Owolabi, M., Sachdev, S., Stroke: A global response is needed (2016) Bull. World Health Organ, 94 (9), pp. 634A-635A; Awad, L.N., A soft robotic exosuit improves walking in patients after stroke (2017) Sci. Transl. Med, 9 (400); Kramer, S., Johnson, L., Bernhardt, J., Cumming, T., Energy expenditure and cost during walking after stroke: A systematic review (2016) Arch. Phys. Med. Rehabil, 97 (4), pp. 619-632. , e1; Riener, R., Lünenburger, L., Jezernik, S., Anderschitz, M., Colombo, G., Dietz, V., Patient-cooperative strategies for robot-aided treadmill training: First experimental results (2005) IEEE Trans. Neural Syst. Rehabil. Eng, 13 (3), pp. 380-394; Fleerkotte, B.M., Koopman, B., Buurke, J.H., Van Asseldonk, E.H.F., Van Der Kooij, H., Rietman, J.S., The effect of impedance-controlled robotic gait training on walking ability and quality in individuals with chronic incomplete spinal cord injury: An explorative study (2014) J. Neuroeng. 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Control, (107), pp. 1-24; Bortole, M., (2014) Robotic Exoskeleton with An Assist-as-Needed Control Strategy for Gait Rehabilitation after Stroke, , Universidad Carlos III de Madrid; Shamaei, K., Sawicki, G.S., Dollar, A.M., Estimation of quasi-stiffness and propulsive work of the human ankle in the stance phase of walking (2013) PLoS One, 8 (3); Molugaram, K., Rao, G.S., Molugaram, K., Rao, G.S., Chapter 5-curve fitting (2017) Statistical Techniques for Transportation Engineering, pp. 281-292; Figueiredo, J., Félix, P., Costa, L., Moreno, J.C., Santos, C.P., Gait event detection in controlled and real-life situations: Repeated measures from healthy subjects (2018) IEEE Trans. Neural Syst. Rehabil. Eng, 26 (10), pp. 1945-1956; Figueiredo, J., Felix, P., Santos, C.P., Moreno, J.C., Towards humanknee orthosis interaction based on adaptive impedance control through stiffness adjustment (2017) 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 406-411",,"Lau N.Silva M.F.Reis L.P.Cascalho J.","IEEE Portugal Section;IEEE Portugal Section RA Chapter;Sociedade Portuguesa de Robotica (SPR)","Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2020","15 April 2020 through 16 April 2020",,160030,,9781728170787,,,"English","IEEE Int. Conf. Auton. Robot Syst. Compet., ICARSC",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85085943925
"Leichtmann B., Nitsch V.","57191978459;25929508000;","How much distance do humans keep toward robots? Literature review, meta-analysis, and theoretical considerations on personal space in human-robot interaction",2020,"Journal of Environmental Psychology","68",,"101386","","",,11,"10.1016/j.jenvp.2019.101386","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079555414&doi=10.1016%2fj.jenvp.2019.101386&partnerID=40&md5=7001fd5e16a7d43f7cb9e0b9c28ed45a","Institute of Industrial Engineering and Ergonomics, RWTH Aachen University, Aachen, 52062, Germany","Leichtmann, B., Institute of Industrial Engineering and Ergonomics, RWTH Aachen University, Aachen, 52062, Germany; Nitsch, V., Institute of Industrial Engineering and Ergonomics, RWTH Aachen University, Aachen, 52062, Germany","With the development of mobile robots, research on interpersonal distances grew by a new body of research introducing new opportunities to environmental psychology. In this review, an overview of the literature on interpersonal distances focusing on human-robot interaction is given. Findings on the most prominent factors are summarized and discussed with respect to findings from distancing in human-human interaction and theoretical considerations. Additionally, meta-analyses were performed to gain new insights based on data of k = 27 studies including a total of N = 1299 participants. However, results did not show any meaningful effects for gender, robot appearance or whether one is active or passive in the approach. Most studies lack a theoretical foundation and have methodological weaknesses, including low statistical power or questionable research practices, leading to a high false-positive rate or overestimated effect sizes. Solutions such as pre-registration, higher sample sizes, transparency and a more careful theoretical underpinning based on models of human spacing behavior are discussed. Psychologists are encouraged to support research on human-robot interaction in this respect. © 2020 Elsevier Ltd",,,,,,,"Bayerische Forschungsstiftung","This work was supported by the FORobotics research project in support of the Bavarian Research Foundation .",,,,,,,,,,"Admoni, H., Shah, J., Srinivasa, S., Editorial: Special issue on human-robot interaction (2017) The International Journal of Robotics Research, 36 (5-7), pp. 459-460; Aiello, J.R., Human spatial behavior (1987) Handbook of environmental psychology, pp. 389-504. , D. Stokols I. Altman Wiley New York; Appel, J., von der Pütten, A., Krämer, N.C., Gratch, J., Does humanity matter? 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Churchman John Wiley & Sons Inc Hoboken, NJ, US; Stone, D.L., Rosopa, P.J., The advantages and limitations of using meta-analysis in human resource management research (2017) Human Resource Management Review, 27 (1), pp. 1-7; Sundstrom, E., An experimental study of crowding: Effects of room size, intrusion, and goal blocking on nonverbal behavior, self-disclosure, and self-reported stress (1975) Journal of Personality and Social Psychology, 32 (4), pp. 645-654; Syrdal, D.S., Dautenhahn, K., Walters, M.L., Koay, K.L., Sharing spaces with robots in a home scenario-anthropomorphic attributions and their effect on proxemic expectations and evaluations in a live HRI trial (2008) AAAI fall symposium: AI in eldercare: New solutions to old problems, pp. 116-123; Syrdal, D.S., Dautenhahn, K., Woods, S., Walters, M.L., Koay, K.L., Doing the right thing wrong' - personality and tolerance to uncomfortable robot approaches (2006) The 15th IEEE international symposium on robot and human interactive communication, pp. 183-188; Takayama, L., Pantofaru, C., Influences on proxemic behaviors in human-robot interaction (2009) IEEE/RSJ international conference on intelligent robots and systems, pp. 5495-5502; Tennis, G.H., Dabbs, J.M., Sex, setting and personal space: First grade through college (1975) Sociometry, 38 (3), pp. 385-394; Torta, E., Cuijpers, R.H., Juola, J.F., Design of a parametric model of personal space for robotic social navigation (2013) International Journal of Social Robotics, 5 (3), pp. 357-365; Trovato, G., Lopez, A., Paredes, R., Cuellar, F., Security and guidance: Two roles for a humanoid robot in an interaction experiment (2017) 2017 26th IEEE international symposium on robot and human interactive communication, pp. 230-235. , RO-MAN); Uzzell, D., Horne, N., The influence of biological sex, sexuality and gender role on interpersonal distance (2006) British Journal of Social Psychology, 45 (3), pp. 579-596; Van Oosterhout, T., Visser, A., A visual method for robot proxemics measurements (2008) Proceedings of metrics for human-robot interaction: A workshop at the third ACM/IEEE international conference on human-robot interaction, pp. 61-68; Viechtbauer, W., Conducting meta-analyses in R with the metafor package (2010) Journal of Statistical Software, 36 (3); Walters, M.L., Dautenhahn, K., Koay, K.L., Kaouri, C., Boekhorst, R.T., Nehaniv, C., Lee, D., Close encounters: Spatial distances between people and a robot of mechanistic appearance (2005) IEEE-RAS international conference on humanoid robots, pp. 450-455; Walters, M.L., Syrdal, D.S., Koay, K.L., Dautenhahn, K., te Boekhorst, R., Human approach distances to a mechanical-looking robot with different robot voice styles (2008) RO-MAN 2008 - the 17th IEEE international symposium on robot and human interactive communication, pp. 707-712; Waytz, A., Cacioppo, J., Epley, N., Who sees human? The stability and importance of individual differences in anthropomorphism (2010) Perspectives on Psychological Science, 5 (3), pp. 219-232; Waytz, A., Gray, K., Epley, N., Wegner, D.M., Causes and consequences of mind perception (2010) Trends in Cognitive Sciences, 14 (8), pp. 383-388; Webb, J.D., Weber, M.J., Influence of sensory abilities on the interpersonal distance of the elderly (2003) Environment and Behavior, 35 (5), pp. 695-711; Yasumoto, M., Kamide, H., Mae, Y., Kawabe, K., Sigemi, S., Hirose, M., Personal space of humans in relation with humanoid robots depending on the presentation method (2011) 2011 IEEE/SICE international Symposium on system integration (SII), pp. 797-801","Leichtmann, B.; Institute of Industrial Engineering and Ergonomics, Eilfschornsteinstraße 18, Germany; email: b.leichtmann@iaw.rwth-aachen.de",,,"Academic Press",,,,,02724944,,JEPSE,,"English","J. Environ. Psychol.",Review,"Final","",Scopus,2-s2.0-85079555414
"Yu C., Tapus A.","57212384467;23010519200;","Multimodal emotion recognition with thermal and rgb-d cameras for human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"532","534",,1,"10.1145/3371382.3378342","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083232127&doi=10.1145%2f3371382.3378342&partnerID=40&md5=af648dde6595526570aa65b19787a8e1","Autonomous Systems and Robotics Lab, U2IS, ENSTA Paris, Institut Polytechnique de Paris, 828 Blvd des Maréchaux, Palaiseau, 91120, France","Yu, C., Autonomous Systems and Robotics Lab, U2IS, ENSTA Paris, Institut Polytechnique de Paris, 828 Blvd des Maréchaux, Palaiseau, 91120, France; Tapus, A., Autonomous Systems and Robotics Lab, U2IS, ENSTA Paris, Institut Polytechnique de Paris, 828 Blvd des Maréchaux, Palaiseau, 91120, France","Human emotion detection is an important aspect in social robotics and in human-robot interaction (HRI). In this paper, we propose a vision-based multimodal emotion recognition method based on gait data and facial thermal images designed for social robots. Our method can detect four human emotional states (i.e., neutral, happiness, anger, and sadness). We gathered data from 25 participants in order to build-up an emotion database for training and testing our classification models. We implemented and tested several approaches such as Convolutional Neural Network (CNN), Hidden Markov Model (HMM), Support Vector Machine (SVM), and Random Forest (RF). These were trained and tested in order to compare the emotion recognition ability and to find the best approach. We designed a hybrid model with both the gait and the thermal data and the accuracy of our system shows an improvement of 10% over the other models based on our emotion database. This is a promising approach to be explored in a real-time human-robot interaction scenario. © 2020 ACM.","Gait; Human-robot interaction; Multimodal emotion recognition; Thermal face","Agricultural robots; Classification (of information); Convolutional neural networks; Decision trees; Hidden Markov models; Man machine systems; Speech recognition; Support vector machines; Classification models; Emotion recognition; Emotional state; Human robot Interaction (HRI); Multimodal emotion recognition; Social robotics; Thermal images; Training and testing; Social robots",,,,,,,,,,,,,,,,"Brody, L.R., On understanding gender differences in the expression of emotion (1993) Human Feelings: Explorations in Affect Development and Meaning, pp. 87-121. , 1993; Castellano, G., Villalba, S., Camurri, A., Recognising human emotions from body movement and gesture dynamics (2007) Affective Computing and Intelligent Interaction, pp. 71-82. , 2007; Chaplin, T.M., Gender and emotion expression: A developmental contextual perspective (2015) Emotion Review, 7 (1), pp. 14-21. , 2015; Deng, Y., Yang, M., Zhou, R., A new standardized emotional film database for Asian culture (2017) Frontiers in Psychology, 8, p. 1941. , 2017); King, D.E., Dlib-ml: A machine learning toolkit (2009) Journal of Machine Learning Research, 10, pp. 1755-1758. , Jul 2009; Nakanishi, R., Imai-Matsumura, K., Facial skin temperature decreases in infants with joyful expression (2008) Infant Behavior and Development, 31 (1), pp. 137-144. , 2008; Salazar-López, E., Domínguez, E., Ramos, V.J., De La Fuente, J., Meins, A., Iborra, O., Gálvez, G., Gómez-Milán, E., The mental and subjective skin: Emotion, empathy, feelings and thermography (2015) Consciousness and Cognition, 34, pp. 149-162. , 2015; Schaefer, A., Nils, F., Sanchez, X., Philippot, P., Assessing the effectiveness of a large database of emotion-eliciting films: A new tool for emotion researchers (2010) Cognition and Emotion, 24 (7), pp. 1153-1172. , 2010; Tanawongsuwan, R., Bobick, A., Gait recognition from timenormalized joint-angle trajectories in the walking plane (2001) Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR, 2. , IEEE, II-II 2001; Uhrig, M.K., Trautmann, N., Baumgärtner, U., Treede, R., Henrich, F., Hiller, W., Marschall, S., Emotion elicitation: A comparison of pictures and films (2016) Frontiers in Psychology, 7, p. 180. , 2016; Zhang, Y., Zhang, L., Hossain, M., Adaptive 3D facial action intensity estimation and emotion recognition (2015) Expert Systems with Applications, 42 (3), pp. 1446-1464. , 2015",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083232127
"Song Z., Yin Z., Yuan Z., Zhang C., Chi W., Ling Y., Zhang S.","57210363378;57212483138;7401477128;57216944490;55340588700;56030248400;57216951195;","Attention-oriented action recognition for real-time human-robot interaction",2020,"Proceedings - International Conference on Pattern Recognition",,,"9412346","7087","7094",,,"10.1109/ICPR48806.2021.9412346","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110458942&doi=10.1109%2fICPR48806.2021.9412346&partnerID=40&md5=9c1233ef822d3fef9db33e80f431c108","Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China; Tencent RoboticsX, Shenzhen, China","Song, Z., Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China; Yin, Z., Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China; Yuan, Z., Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, China; Zhang, C., Tencent RoboticsX, Shenzhen, China; Chi, W., Tencent RoboticsX, Shenzhen, China; Ling, Y., Tencent RoboticsX, Shenzhen, China; Zhang, S., Tencent RoboticsX, Shenzhen, China","Despite the notable progress made in action recognition tasks, not much work has been done in action recognition specifically for human-robot interaction. In this paper, we deeply explore the characteristics of the action recognition task in interaction scenes and propose an attention-oriented multi-level network framework to meet the need for real-time interaction. Specifically, a Pre-Attention network is employed to roughly focus on the interactor in the scene at low resolution firstly and then perform fine-grained pose estimation at high resolution. The other compact CNN receives the extracted skeleton sequence as input for action recognition, utilizing attention-like mechanisms to capture local spatial-temporal patterns and global semantic information effectively. To evaluate our approach, we construct a new action dataset specially for the recognition task in interaction scenes. Experimental results on our dataset and high efficiency (112 fps at 640 × 480 RGBD) on the mobile computing platform (Nvidia Jetson AGX Xavier) demonstrate excellent applicability of our method on action recognition in real-time human-robot interaction. © 2020 IEEE",,"Man machine systems; Pattern recognition; Semantics; Action recognition; High resolution; High-efficiency; Multi-level networks; Pose estimation; Real time interactions; Semantic information; Spatial temporals; Social robots",,,,,"201902, 201903; National Natural Science Foundation of China, NSFC: 61573280, 61976170, 91648121; National Key Research and Development Program of China, NKRDPC: 2016YFB1001001","This work was supported by the National Key R&D Program of China (No.2016YFB1001001), the National Natural Science Foundation of China (No.61573280, No.91648121, No.61976170), and Tencent Robotics X Lab Rhino-Bird Joint Research Program (No.201902, No.201903).",,,,,,,,,,"Ranasinghe, S., Al Machot, F., Mayr, H., A review on applications of activity recognition systems with regard to performance and evaluation (2016) International Journal of Distributed Sensor Networks, 12; Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Li, F.-F., Large-scale video classification with convolutional neural networks (2014) CVPR; Carreira, J., Zisserman, A., Quo vadis, action recognition? A new model and the kinetics dataset (2017) CVPR; Heilbron, F., Escorcia, V., Ghanem, B., Niebles, J.C., ActivityNet: A large-scale video benchmark for human activity understanding (2015) CVPR; Idrees, H., Zamir, A., Jiang, Y.-G., Gorban, A., Laptev, I., Sukthankar, R., Shah, M., The THUMOS challenge on action recognition for videos”in the wild (2016) Computer Vision and Image Understanding, 155; Chunhui, L., Yueyu, H., Yanghao, L., Sijie, S., Jiaying, L., PKU-MMD: A large scale benchmark for continuous multi-modal human action understanding (2017) ACM Multimedia Workshops; Shahroudy, A., Liu, J., Ng, T.-T., Wang, G., NTU Rgb+d: A large scale dataset for 3d human activity analysis (2016) CVPR; Elizabeth, D.S., Deep learning for human action recognition - Survey (2018) International Journal of Computer Science and Engineering, 6, pp. 323-328; Tran, D., Wang, H., Torresani, L., Feiszli, M., Video classification with channel-separated convolutional networks (2019) ICCV; Xu, H., Das, A., Saenko, K., Two-stream region convolutional 3d network for temporal activity detection (2019) IEEE Transactions on Pattern Analysis and Machine Intelligence, 41 (10), pp. 2319-2332; Liu, Y., Ma, L., Zhang, Y., Liu, W., Chang, S.-F., Multi-granularity generator for temporal action proposal (2019) CVPR; Geest, R., Gavves, E., Ghodrati, A., Li, Z., Snoek, C., Tuytelaars, T., Online action detection (2016) ECCV; Geest, R., Tuytelaars, T., Modeling temporal structure with lstm for online action detection (2018) WACV; Hoai, M., de la Torre, F., (2012) Max-Margin Early Event Detectors; Presti, L.L., la Cascia, M., 3D skeleton-based human action classification: A survey (2015) Pattern Recognition, 53; Newell, A., Yang, K., Deng, J., Stacked hourglass networks for human pose estimation (2016) ECCV; Wei, S.-E., Ramakrishna, V., Kanade, T., Sheikh, Y., Convolutional pose machines (2016) CVPR; Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A., Real-time human pose recognition in parts from single depth images (2011) CVPR; Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P., Schiele, B., Deepcut: Joint subset partition and labeling for multi person pose estimation (2016) CVPR; Cao, Z., Martinez, G., Simon, T., Wei, S.-E., Sheikh, Y., OpenPose: Realtime multi-person 2d pose estimation using part affinity fields (2019) IEEE Transactions on Pattern Analysis and Machine Intelligence; Papandreou, G., Zhu, T., Kanazawa, N., Toshev, A., Tompson, J., Bregler, C., Murphy, K., Towards accurate multi-person pose estimation in the wild (2017) CVPR; Fang, H.-S., Xie, S., Tai, Y.-W., Lu, C., RMPE: Regional multi-person pose estimation (2017) ICCV; Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J., Cascaded pyramid network for multi-person pose estimation (2018) CVPR; Yang, L., Song, Q., Wang, Z., Jiang, M., Parsing R-CNN for instance-level human analysis (2019) CVPR; Liu, J., Shahroudy, A., Xu, D., Wang, G., Spatio-temporal LSTM with trust gates for 3d human action recognition (2016) ECCV; Li, Y., Lan, C., Xing, J., Zeng, W., Yuan, C., Liu, J., Online human action detection using joint classification-regression recurrent neural networks (2016) ECCV; Wang, H., Wang, L., Beyond joints: Learning representations from primitive geometries for skeleton-based action recognition and detection (2018) IEEE Transactions on Image Processing, 27, pp. 4382-4394; Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N., View adaptive neural networks for high performance skeleton-based human action recognition (2019) IEEE Transactions on Pattern Analysis and Machine Intelligence, 41 (8), pp. 1963-1978; Li, B., Chen, H., Yucheng, C., Dai, Y., He, M., Skeleton boxes: Solving skeleton based action detection with a single deep convolutional neural network (2017) ICMEW; Ke, Q., Bennamoun, M., An, S., Sohel, F., Boussaid, F., A new representation of skeleton sequences for 3d action recognition (2017) CVPR; Caetano, C., Schwartz, W., (2019) Skeleton Image Representation for 3d Action Recognition Based on Tree Structure and Reference Joints, , arXiv preprint; Yan, S., Xiong, Y., Lin, D., Spatial temporal graph convolutional networks for skeleton-based action recognition (2018) AAAI; Shi, L., Zhang, Y., Cheng, J., Lu, H., Skeleton-based action recognition with directed graph neural networks (2019) CVPR; Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., (2019) Symbiotic Graph Neural Networks for 3d Skeleton-Based Human Action Recognition and Motion Prediction, , arXiv preprint; Gao, X., Hu, W., Tang, J., Liu, J., Guo, Z., Optimized skeleton-based action recognition via sparsified graph regression (2019) ACM; Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., Tang, X., Residual attention network for image classification (2017) CVPR; Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C., Microsoft coco: Common objects in context (2014) ECCV; Kingma, D., Ba, J., ADaM: A method for stochastic optimization (2014) ICLR","Song, Z.; Institute of Artificial Intelligence and Robotics, China
Yin, Z.; Institute of Artificial Intelligence and Robotics, China
Yuan, Z.; Institute of Artificial Intelligence and Robotics, China",,,"Institute of Electrical and Electronics Engineers Inc.","25th International Conference on Pattern Recognition, ICPR 2020","10 January 2021 through 15 January 2021",,169954,10514651,9781728188089,PICRE,,"English","Proc. Int. Conf. Pattern Recognit.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85110458942
"Steijn W.M.P., Van Oosterhout J., Willemsen J., Jansen A.","54416395400;57224214463;57224210844;57215215220;","Modelling human-robot interaction to optimize their safety, sustainability and efficiency: Identifying relevant characteristics",2020,"30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020",,,,"2571","2577",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110377688&partnerID=40&md5=c3e9e9dcd716a016d44101a8bc5951dd","Work Health and Technology, TNO, Netherlands; Sustainable Productivity and Employability, TNO, Netherlands; ZonMw, Netherlands","Steijn, W.M.P., Work Health and Technology, TNO, Netherlands; Van Oosterhout, J., Sustainable Productivity and Employability, TNO, Netherlands; Willemsen, J., Work Health and Technology, TNO, Netherlands; Jansen, A., ZonMw, Netherlands","Recent developments have introduced so-called cobots or cooperative robots, which place the machines on the work floor next to employees. A new challenge arises to keep these human robot interactions, safe and healthy, while also maintaining the sustainability and efficiency of the interaction. Here, we present a model, which can be a valuable tool to assist in future studies to systematically increase our understanding of Human-robot interaction (HRI). The model is based on the input-mediation-output (IMOI) model, which is often used in teamwork studies. Furthermore, it distinguishes between hardware, software, and mindware characteristics, to help researchers formulate more specific hypotheses for data collection in use cases. The purpose of this model is to help categorize various characteristics of humans, robots and the environment that can affect the outcome of the HRI. Ultimately, data gathered with the help of this model could help formulate design requirements for the development of future cobot applications and their implementation on the workfloor. This way we work towards inherently safe, healthy, sustainable and efficient HRI. Copyright © ESREL2020-PSAM15 Organizers.","Cobots; Efficient; Human-robot interaction; Safe; Sustainable","Efficiency; End effectors; Man machine systems; Safety engineering; Sustainable development; Cooperative robots; Data collection; Human robot Interaction (HRI); Social robots",,,,,,"This research has been made possible thanks to the support of the Ministry of Social Affairs and Employment regarding the Occupational Safety Research Program 2018. Furthermore, we would like to express our appreciation to Johan van der Vorm for his contributions to this project.",,,,,,,,,,"Adams, D.A., Nelson, R.R., Todd, P.A., Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication (1992) MIS Quarterly, 16 (2); Boenzi, F., Digiesi, S., Mossa, G., Mummolo, G., Romano, V.A., Modelling Workforce Aging in Job Rotation Problems (2015) IFAC-PapersOnLine, 48 (3), pp. 604-609; Bortot, D., Born, M., Bengler, K., Directly or on detours? How should industrial robots approximate humans? (2013) the 2013 8th ACM/IEEE International Conference on Human- Robot Interaction (HRI), , Paper presented at; Cazes, S., Hijzen, A., Saint-Martin, A., (2015) Measuring and assessing job quality: the OECD job quality framework, , Paper presented at the OECD Social, Employment and Migration; Chen, J. Y. 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F., Hancock, P.A., Chen, J.Y.C., (2014) Determinants of system transparancy and its influence on trust in and reliance on unmanned robotic systems, , Paper presented at the The Internation Society for Optical Engineering; Pervez, A., Ryu, J., Safe physical human robot interaction-past, present and future (2008) Journal of Mechanical Science and Technology, 22 (3), pp. 469-483; Rahkonen, P., Ettala, M., Loikkanen, I., Working conditions and hygiene at sanitary landfills in Finland (1987) Ann. Occup. Hyg, 31 (4A), pp. 505-513; Reason, J., Human error: models and management (2000) Bmj, 320 (7237), pp. 768-770; Rosen, M. A., Dietz, A.S., Yang, T., Priebe, C.E., Pronovost, P.J., An integrative framework for sensor-based measurement of teamwork in healthcare (2015) J Am Med Inform Assoc, 22 (1), pp. 11-18; Salas, E., Dickinson, T.L., Converse, S.A., Tannenbaum, S.I., Toward an understanding of team performance and training (1992) Teams: Their training and performance, pp. 3-29. , R. W. Swezey and E. Salas (Eds), Westport, CT, US: Ablex Publishing; Sanders, T. L., MacArthur, K., Volante, W., Hancock, G., MacGillivray, T., Shugars, W., Hancock, P.A., Trust and Prior Experience in Human-Robot Interaction (2017) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61 (1), pp. 1809-1813; Scheutz, M., DeLoach, S.A., Adams, J.A., A Framework for Developing and Using Shared Mental Models in Human-Agent Teams (2017) Journal of Cognitive Engineering and Decision Making, 11 (3), pp. 203-224; Selkowitz, A., Lakhmani, S., Chen, J.Y.C., Boyce, M., The Effects of Agent Transparency on Human Interaction with an Autonomous Robotic Agent (2016) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59 (1), pp. 806-810; Siegel, M., The sense-think-act paradigm revisited (2003) Paper presented at the 1st International Workshop on Robotic Sensing, , 2003. ROSE 03; Stolt, A., Linderoth, M., Robertsson, A., Johansson, R., Force controlled robotic assembly without a force sensor (2012) the Robotics and Automation (ICRA), 2012 IEEE International Conference on, , Paper presented at; Vasic, M., Billard, A., Safety issues in human-robot interactions (2013) the 2013 IEEE International Conference on Robotics and Automation, , Paper presented at; Wang, L., Jamieson, G.A., Hollands, J.G., The Effects of Design Features on Users' Trust in and Reliance on a Combat Identification System (2011) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 55 (1), pp. 375-379; Werkhoven, P., Kester, L., Neerincx, M., Telling autonomous systems what to do (2018) the Proceedings of the 36th European Conference on Cognitive Ergonomics - ECCE'18, , Paper presented at; Xie, B., Salvendy, G., Review and reappraisal of modelling and predicting mental workload in single- and multi-task environments (2000) Work & Stress, 14 (1), pp. 74-99; You, S., Robbert, L., Teaming up with robots: An IMOI (Inputs-Mediators-Outputs-Inputs) framework of human-robot teamwork (2017) International journal of Robotic Engineering, 2, p. 003",,"Baraldi P.Di Maio F.Zio E.",,"Research Publishing Services","30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020","1 November 2020 through 5 November 2020",,169730,,9789811485930,,,"English","Eur. Saf. Reliab. Conf., ESREL Probab. Saf. Assess. Manag. Conf., PSAM",Conference Paper,"Final","",Scopus,2-s2.0-85110377688
"Steijn W.M.P., van Oosterhout J., Willemsen J., Jansen A.","54416395400;57224214463;57224210844;57215215220;","Modelling human-robot interaction to optimize their safety, sustainability and efficiency: Identifying relevant characteristics.",2020,"Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference",,,,"2571","2577",,,"10.3850/978-981-14-8593-0_4059-cd","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107317250&doi=10.3850%2f978-981-14-8593-0_4059-cd&partnerID=40&md5=77fe99f932482e359d417e20ef773eca","Work Health and Technology, TNO, Netherlands; Sustainable Productivity and Employability, TNO, Netherlands; ZonMw, Netherlands","Steijn, W.M.P., Work Health and Technology, TNO, Netherlands; van Oosterhout, J., Sustainable Productivity and Employability, TNO, Netherlands; Willemsen, J., Work Health and Technology, TNO, Netherlands; Jansen, A., ZonMw, Netherlands","Recent developments have introduced so-called cobots or cooperative robots, which place the machines on the work floor next to employees. A new challenge arises to keep these human robot interactions, safe and healthy, while also maintaining the sustainability and efficiency of the interaction. Here, we present a model, which can be a valuable tool to assist in future studies to systematically increase our understanding of Human-robot interaction (HRI). The model is based on the input-mediation-output (IMOI) model, which is often used in teamwork studies. Furthermore, it distinguishes between hardware, software, and mindware characteristics, to help researchers formulate more specific hypotheses for data collection in use cases. The purpose of this model is to help categorize various characteristics of humans, robots and the environment that can affect the outcome of the HRI. Ultimately, data gathered with the help of this model could help formulate design requirements for the development of future cobot applications and their implementation on the workfloor. This way we work towards inherently safe, healthy, sustainable and efficient HRI. © ESREL 2020-PSAM15 Organizers. Published by Research Publishing, Singapore.","Cobots; Efficient; Human-robot interaction; Safe; Sustainable","Efficiency; End effectors; Man machine systems; Safety engineering; Sustainable development; Cooperative robots; Data collection; Human robot Interaction (HRI); Social robots",,,,,,"This research has been made possible thanks to the support of the Ministry of Social Affairs and Employment regarding the Occupational Safety Research Program 2018. Furthermore, we would like to express our appreciation to Johan van der Vorm for his contributions to this project.",,,,,,,,,,"Adams, D.A., Nelson, R.R., Todd, P.A., Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication (1992) MIS Quarterly, 16 (2); Boenzi, F., Digiesi, S., Mossa, G., Mummolo, G., Romano, V.A., Modelling Workforce Aging in Job Rotation Problems (2015) Ifac-Papersonline, 48 (3), pp. 604-609; Bortot, D., Born, M., Bengler, K., Directly or on detours? How should industrial robots approximate humans? (2013) Paper Presented at the 2013 8Th ACM/IEEE International Conference on Humanrobot Interaction (HRI); Cazes, S., Hijzen, A., Saint-Martin, A., (2015) Measuring and Assessing Job Quality: The OECD Job Quality Framework, , Paper presented at the OECD Social, Employment and Migration; Chen, J.Y.C., Barnes, M.J., Human–Agent Teaming for Multirobot Control: A Review of Human Factors Issues (2014) IEEE Transactions on Humanmachine Systems, 44 (1), pp. 13-29; de Santis, A., Siciliano, B., de Luca, A., Bicchi, A., An atlas of physical human–robot interaction (2008) Mechanism and Machine Theory, 43 (3), pp. 253-270; Dohi, M., Okada, K., Maeda, I., Fujitani, S., Fujita, T., Proposal of Collaboration Safety in a Coexistence Environment of Human and Robots (2018) Paper Presented at the 2018 IEEE International Conference on Robotics and Automation (ICRA); Douwes, M., Huysmans, M.A., Kraan, K.O., de Looze, M.P., (2017) Robotisering En De Gevolgen Voor Arbeidsbelasting En Het Arbeidsdeskundig Vak. Nijkerk: Arbeidsdeskundig Centrum (AKC), , AKC Onderzoekscahier 21; Professionalisering Arbeidsdeskundig Handelen; Endsley, M., Jones, D., (2004) Designing for Situation Awareness: An Approach to User-Centered Design, , Boca Raton: CRC Press; Gombolay, M.C., Gutierrez, R.A., Clarke, S.G., Sturla, G.F., Shah, J.A., (2015) Decision-Making Authority, Team Efficiency and Human Worker Satisfaction in Mixed human–robot Teams, , https://doi.org/10.1007/s10514-015-9457-9; Gomes, J., Lloyd, O., Norman, N., The health of the workers in a rapidly developing country: Effects of occupational exposure to noise and heat (2002) Occup. Med., 52 (3), pp. 121-128; Goodrich, M.A., Yi, D., (2013) Toward Task-Based Mental Models of Human-Robot Teaming: A Bayesian Approach, , Paper presented at the International conference on virtual, augmented and mixed reality; Green, S.A., Billinghurst, M., Chen, X., Chase, J.G., Human-Robot Collaboration: A Literature Review and Augmented Reality Approach in Design (2008) International Journal of Advanced Robotic Systems, 5 (1); Ilgen, D.R., Hollenbeck, J.R., Johnson, M., Jundt, D., Teams in organizations: From input-processoutput models to IMOI models (2005) Annu Rev Psychol, 56, pp. 517-543; Johnson, M., Bradshaw, J.M., Feltovich, P.J., Jonker, C.M., van Riemsdijk, M.B., Sierhuis, M., Coactive Design: Designing Support for Interdependence in Joint Activity (2014) Journal of Humanrobot Interaction, 3 (1); Lasota, P.A., Rossano, G.F., Shah, J.A., Toward safe close-proximity human-robot interaction with standard industrial robots (2014) Paper Presented at the 2014 IEEE International Conference on Automation Science and Engineering (CASE); Legris, P., Ingham, J., Collerette, P., Why do people use information technology? A critical review of the technology acceptance model (2003) Information and Management, 40 (3), pp. 191-204; Leonard, M., Graham, S., Bonacum, D., ; Lotter, B., Wiendahl, H.P., Changeable and Reconfigurable Assembly Systems (2009) Changeable and Reconfigurable Manufacturing Systems, pp. 127-142. , H. A. ElMaraghy (Ed.), (pp. , London: Springer London; Mattsson, S., Tarrar, M., Fast-Berglund, A., Perceived production complexity – understanding more than parts of a system (2016) International Journal of Production Research, 54 (20), pp. 6008-6016; McEwen, B.S.S., Stellar, E., Stress and the individual. Mechanisms leading to disease (1993) Archives of Internal Medicine, 153 (18), pp. 2093-2101; Michalos, G., Makris, S., Papakostas, N., Mourtzis, D., Chryssolouris, G., Automotive assembly technologies review: Challenges and outlook for a flexible and adaptive approach (2010) CIRP Journal of Manufacturing Science and Technology, 2 (2), pp. 81-91; Moniz, A., Krings, B.J., Robots Working with Humans or Humans Working with Robots? Searching for Social Dimensions in New HumanRobot Interaction in Industry (2016) Societies, 6 (3); Murashov, V., Hearl, F., Howard, J., Working safely with robot workers: Recommendations for the new workplace (2016) J Occup Environ Hyg, 13 (3), p. D6171; Nachreiner, F., Nickel, P., Meyer, I., Human factors in process control systems: The design of human–machine interfaces (2006) Safety Science, 44 (1), pp. 5-26; Niemela, R., Rautio, S., Hannula, M., Reijula, K., Work environment effects on labor productivity: An intervention study in a storage building (2002) Am J Ind Med, 42 (4), pp. 328-335; Ogorodnikova, O., Human Weaknesses and strengths in collaboration with robots (2008) Periodica Polytechnica Mechanical Engineering, 52 (1); Ososky, S.S.T.F.J., Hancock, P.A., Chen, J.Y.C., (2014) Determinants of System Transparancy and Its Influence on Trust in and Reliance on Unmanned Robotic Systems. Paper Presented at the The Internation Society for Optical Engineering; Pervez, A., Ryu, J., Safe physical human robot interaction-past, present and future (2008) Journal of Mechanical Science and Technology, 22 (3), pp. 469-483; Rahkonen, P., Ettala, M., Loikkanen, I., Working conditions and hygiene at sanitary landfills in Finland (1987) Ann. Occup. Hyg., 31 (4A), pp. 505-513. , , Vol; Reason, J., Human error: Models and management (2000) Bmj, 320 (7237), pp. 768-770; Rosen, M.A., Dietz, A.S., Yang, T., Priebe, C.E., Pronovost, P.J., An integrative framework for sensor-based measurement of teamwork in healthcare (2015) J am Med Inform Assoc, 22 (1), pp. 11-18; Salas, E., Dickinson, T.L., Converse, S.A., Tannenbaum, S.I., Toward an understanding of team performance and training (1992) Teams: Their Training and Performance, pp. 3-29. , R. W. Swezey and E. Salas (Eds.), (pp. , Westport, CT, US: Ablex Publishing; Sanders, T.L., Macarthur, K., Volante, W., Hancock, G., Macgillivray, T., Shugars, W., Hancock, P.A., Trust and Prior Experience in Human-Robot Interaction (2017) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61 (1), pp. 1809-1813; Scheutz, M., Deloach, S.A., Adams, J.A., A Framework for Developing and Using Shared Mental Models in Human-Agent Teams (2017) Journal of Cognitive Engineering and Decision Making, 11 (3), pp. 203-224; Selkowitz, A., Lakhmani, S., Chen, J.Y.C., Boyce, M., The Effects of Agent Transparency on Human Interaction with an Autonomous Robotic Agent (2016) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59 (1), pp. 806-810; Siegel, M., The sense-think-act paradigm revisited. Paper presented at the 1st International Workshop on Robotic Sensing, 2003 (2003) ROSE', p. 03; Stolt, A., Linderoth, M., Robertsson, A., Johansson, R., Force controlled robotic assembly without a force sensor. Paper presented at the Robotics and Automation (ICRA) (2012) 2012 IEEE International Conference On; Vasic, M., Billard, A., Safety issues in human-robot interactions (2013) 2013 IEEE International Conference on Robotics and Automation, , Paper presented at the; Wang, L., Jamieson, G.A., Hollands, J.G., The Effects of Design Features on Users' Trust in and Reliance on a Combat Identification System (2011) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 55 (1), pp. 375-379; Werkhoven, P., Kester, L., Neerincx, M., Telling autonomous systems what to do (2018) Proceedings of the 36Th European Conference on Cognitive Ergonomics-Ecce'18, , Paper presented at the; Xie, B., Salvendy, G., Review and reappraisal of modelling and predicting mental workload in single-and multi-task environments (2000) Work & Stress, 14 (1), pp. 74-99; You, S., Robbert, L., Teaming up with robots: An IMOI (Inputs-Mediators-Outputs-Inputs) framework of human-robot teamwork (2017) International Journal of Robotic Engineering, 2, p. 003",,"Baraldi P.Di Maio F.Zio E.",,"Research Publishing, Singapore","30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15 2020","1 November 2020 through 5 November 2020",,259839,,9789811485930,,,"English","Proc. Eur. Saf. Reliab. Conf. Probab. Saf. Assess. Manag. Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85107317250
"Stock-Homburg R., Hannig M., Lilienthal L.","56364282100;57225793828;57220182073;","Conversational Flow in Human-Robot Interactions at the Workplace: Comparing Humanoid and Android Robots",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"578","589",,,"10.1007/978-3-030-62056-1_48","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097184142&doi=10.1007%2f978-3-030-62056-1_48&partnerID=40&md5=0310700bcc9ae565a6c8efe9e9832a33","Technical University of Darmstadt, Hochschulstraße 1, Darmstadt, 64289, Germany","Stock-Homburg, R., Technical University of Darmstadt, Hochschulstraße 1, Darmstadt, 64289, Germany; Hannig, M., Technical University of Darmstadt, Hochschulstraße 1, Darmstadt, 64289, Germany; Lilienthal, L., Technical University of Darmstadt, Hochschulstraße 1, Darmstadt, 64289, Germany","This article deals with the conversational flow during human-robot interactions at the workplace. We conducted an experimental field study in a company setting. Drawing on computer-as-social-actor (CASA) paradigm, we argue that employees might mindlessly apply social rules in conversations with service robots which is reflected in the degree of conversational flow. We examined to which extent the structure of the Conversational Skills Rating Scale (CSRS) can be replicated with real-life human-robot interactions and whether there are differences in conversational flow between humanoid robots, android robots and humans. We could show that the conversational flow in such service settings at the workplace is based on the factors expressiveness, coordination and composure. The results show that android robots can evoke a higher level of expressiveness in employees than humanoid robots. To our knowledge, our study is the first to compare different aspects of conversational flow towards service robots in a real workplace setting. Since conversational flow can create social bonds and satisfy social needs, companies should use this knowledge when considering using robots in customer or employee contact. © 2020, Springer Nature Switzerland AG.","Android robots; CASA paradigm; Conversational flow; Human likeness; Human-robot interaction; Humanoid robots; Robots at workplace; Service robots","Anthropomorphic robots; Customer satisfaction; Man machine systems; Mobile robots; Personnel; Robotics; Field studies; Humanoid robot; Rating scale; Service robots; Social actors; Social needs; Social rules; Human robot interaction",,,,,,,,,,,,,,,,"Koudenburg, N., Postmes, T., Gordijn, E.H., Conversational flow and entitativity: The role of status (2014) Br. J. Soc. Psychol., 53, pp. 350-366; Koudenburg, N., Postmes, T., Gordijn, E.H., Conversational flow promotes solidarity (2013) Plos ONE, 8 (1-6); Han, J.G., Campbell, N., Jokinen, K., Wilcock, G., Investigating the use of non-verbal cues in human-robot interaction with a NAO robot (2012) 2012 IEEE 3Rd International Conference on Cognitive Infocommunications (Coginfocom); Koudenburg, N., Postmes, T., Gordijn, E.H., Disrupting the flow: How brief silences in group conversations affect social needs (2011) J. Exp. Soc. Psychol., 47, pp. 512-515; Wirtz, J., Brave new world: Service robots in the frontline (2018) J. Serv. Manag., 29, pp. 907-931; Huang, M.-H., Rust, R.T., Technology-driven service strategy (2017) J. Acad. Mark. Sci., 45, pp. 906-924; Mende, M., Scott, M.L., van Doorn, J., Grewal, D., Shanks, I., Service robots rising: How humanoid robots influence service experiences and elicit compensatory consumer responses (2019) J. Mark. Res., 56, pp. 535-556; van Doorn, J., Domo Arigato Mr. Roboto: Emergence of automated social presence in organizational frontlines and customers’ service experiences (2017) J. Serv. Res., 20, pp. 43-58; Arimoto, T., Yoshikawa, Y., Ishiguro, H., Multiple-robot conversational patterns for concealing incoherent responses (2018) Int. J. Soc. Robot., 10, pp. 583-593; Hill, J., Randolph Ford, W., Farreras, I.G., Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations (2015) Comput. Hum. Behav., 49, pp. 245-250; Spitzberg, B.H., (1995) Conversational Skills Rating Scale: An Instructional Assessment of Interpersonal Competence; Harriott, C.E., Zhang, T., Adams, J.A., Evaluating the applicability of current models of workload to peer-based human-robot teams (2011) 2011 6Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Pp. 45–52; Lu, V.N., Service robots, customers, and service employees: What can we learn from the academic literature and where are the gaps? (2020) J. Serv. Theory Pract., , forthcoming; Phillips, E., Zhao, X., Ullman, D., Malle, B.F., What is human-like?: Decomposing robots’ human-like appearance using the Anthropomorphic roBOT (ABOT) database (2018) HRI 2018, pp. 105-113. , Chicago, IL, USA, 5–8 March 2018; Ferrari, F., Paladino, M.P., Jetten, J., Blurring human-machine distinctions: Anthropomorphic appearance in social robots as a threat to human distinctiveness (2016) Int. J. Soc. Robot., 8, pp. 287-302; Mori, M., Macdorman, K., Kageki, N., The uncanny valley [from the field] (2012) IEEE Robot. Autom. Mag., 19, pp. 98-100; Macdorman, K.F., Ishiguro, H., The uncanny advantage of using androids in cognitive and social science research (2006) Interact. Stud., 7, pp. 297-337; Minato, T., Shimada, M., Itakura, S., Lee, K., Ishiguro, H., Evaluating the human likeness of an Android by comparing gaze behaviors elicited by the android and a person (2006) Adv. Robot., 20, pp. 1147-1163; Haring, K.S., Silvera-Tawil, D., Takahashi, T., Watanabe, K., Velonaki, M., How people perceive different robot types: A direct comparison of an android, humanoid, and non-biomimetic robot (2016) 8Th International Conference on Knowledge and Smart Technology (KST), pp. 265-270. , IEEE; Matsui, T., Yamada, S., Robot’s impression of appearance and their trustworthy and emotion richness (2018) Proceedings of the 27Th IEEE International Symposium on Robot and Human Interactive Communication, Nanjing, pp. 88-93. , China, 27–31 August 2018; Nass, C., Moon, Y., Machines and mindlessness: Social responses to computers (2000) J. Soc. Issues, 56, pp. 81-103; Kim, Y., Sundar, S.S., Anthropomorphism of computers: Is it mindful or mindless? (2012) Comput. Hum. Behav., 28, pp. 241-250; Nass, C., Moon, Y., Fogg, B.J., Reeves, B., Dryer, D.C., Can computer personalities be human personalities? (1995) Int. J. Hum.-Comput Stud., 43, pp. 223-239; Złotowski, J., Proudfoot, D., Yogeeswaran, K., Bartneck, C., Anthropomorphism: Opportunities and challenges in human-robot interaction (2015) Int. J. Soc. Robot., 7, pp. 347-360; Natarajan, M., Gombolay, M., Effects of anthropomorphism and accountability on trust in human robot interaction (2020) HRI 2020, Cambridge, United Kingdom, pp. 33-42. , 23–26 March 2020; Reeves, B., Nass, C., (1996) How People Treat Computers, Television, and New Media like Real People and Places, pp. 19-36. , Cambridge University Press, Cambridge; Wagner, K., Schramm-Klein, H., Alexa, are you human? Investigating the anthropomorphism of digital voice assistants – a qualitative approach (2019) Fortieth International Conference on Information Systems, , Munich; Kwon, M., Jung, M.F., Knepper, R.A., Human expectations of social robots (2016) ACM/IEEE International Conference on Human-Robot Interaction, pp. 463-464; Aguinis, H., Bradley, K.J., Best practice recommendations for designing and implementing experimental vignette methodology studies (2014) Org. Res. Methods, 17, pp. 351-371; Glaser, B.G., Strauss, A.L., (1967) The Discovery of Grounded Theory: Strategies for Qualitative Research, , Aldine Publishing Company, Chicago; Kaiser, H.F., Rice, J., Little Jiffy, Mark IV (1974) Educ. Psychol. Measur., 34, pp. 111-117; Browne, M.W., Cudeck, R., Alternative ways of assessing model fit (1992) Sociol. Methods Res., 21, pp. 230-258; Hu, L.-T., Bentler, P.M., Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives (1999) Struct. Equ. Model.: Multidisc. J., 6, pp. 1-55; Schermelleh-Engel, K., Moosbrugger, H., Müller, H., Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures (2003) Methods Psychol. Res. Online, 8, pp. 23-74; Gliem, J.A., Gliem, R.R., Calculating, interpreting, and reporting cronbach’s alpha reliability coefficient for likert-type scales (2003) 2003 Midwest Research to Practice Conference in Adult, Continuing, and Community Education, pp. 82-88; Moosbrugger, H., Kelava, A., (2012) Testtheorie Und Fragebogenkonstruktion, , https://doi.org/10.1007/978-3-642-20072-4, 2nd edn. Springer, Heidelberg; Homburg, C., Giering, A., Konzeptualisierung und Oparationalisierung komplexer Kon-strukte. Mark (1996) ZFP J. Res. Manag., 18, pp. 3-24; Churchill, G.A., Jr., A paradigm for developing better measures of marketing constructs (1979) J. Mark. Res., 16, pp. 64-73","Hannig, M.; Technical University of Darmstadt, Hochschulstraße 1, Germany; email: martin.hannig@bwl.tu-darmstadt.de","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097184142
"Rauchbauer B., Hmamouche Y., Bigi B., Prévot L., Ochs M., Chaminade T.","56607418800;57170656400;6506614361;57202367740;13608908400;8339688200;","Multimodal corpus of bidirectional conversation of human-human and human-robot interaction during fMRI scanning",2020,"LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings",,,,"668","675",,3,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096592111&partnerID=40&md5=dc7843cd98622660dc46999edb3d2e02","Aix Marseille Université, CNRS, INT, Marseille, France; Aix Marseille Université, CNRS, LPL, Aix-en-Provence, France; Aix Marseille Université, CNRS, LNC, Marseille, France; Aix Marseille Université, CNRS, LIS, Marseille, France; Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France","Rauchbauer, B., Aix Marseille Université, CNRS, INT, Marseille, France, Aix Marseille Université, CNRS, LPL, Aix-en-Provence, France, Aix Marseille Université, CNRS, LNC, Marseille, France; Hmamouche, Y., Aix Marseille Université, CNRS, LPL, Aix-en-Provence, France, Aix Marseille Université, CNRS, LIS, Marseille, France; Bigi, B., Aix Marseille Université, CNRS, LPL, Aix-en-Provence, France; Prévot, L., Aix Marseille Université, CNRS, LPL, Aix-en-Provence, France; Ochs, M., Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France; Chaminade, T., Aix Marseille Université, CNRS, INT, Marseille, France","In this paper we present investigation of real-life, bi-directional conversations. We introduce the multimodal corpus derived from these natural conversations alternating between human-human and human-robot interactions. The human-robot interactions were used as a control condition for the social nature of the human-human conversations. The experimental set up consisted of conversations between the participant in a functional magnetic resonance imaging (fMRI) scanner and a human confederate or conversational robot outside the scanner room, connected via bidirectional audio and unidirectional videoconferencing (from the outside to inside the scanner). A cover story provided a framework for natural, real-life conversations about images of an advertisement campaign. During the conversations we collected a multimodal corpus for a comprehensive characterization of bi-directional conversations. In this paper we introduce this multimodal corpus which includes neural data from functional magnetic resonance imaging (fMRI), physiological data (blood flow pulse and respiration), transcribed conversational data, as well as face and eye-tracking recordings. Thus, we present a unique corpus to study human conversations including neural, physiological and behavioral data. © European Language Resources Association (ELRA), licensed under CC-BY-NC","Artificial agents; Conversation; Physiology","Eye tracking; Functional neuroimaging; Magnetic resonance imaging; Man machine systems; Physiology; Scanning; Video conferencing; Behavioral data; Bi-directional; Blood flow; Experimental set up; Functional magnetic resonance imaging; Multi-modal; Neural data; Physiological data; Social robots",,,,,"ANR-16-CONV-0002; Aix-Marseille Université, AMU; Fondation pour la Recherche Médicale, FRM: SPF20171039127","Research supported by grants ANR-16-CONV-0002 (ILCB), ANR-11-LABX-0036 (BLRI) and AAP-ID-17-46-170301-11.1 by the Excellence Initiative of Aix-Marseille University (A*MIDEX), a French ""Investissement d'Avenir"" programme and the Institute for Language, Communication and the brain (ILCB ANR-16CONV-0002). BR is supported by the Fondation pour la Recherche M?dicale (FRM, SPF20171039127).",,,,,,,,,,"Al Moubayed, S., Beskow, J., Skantze, G., Granström, B., Furhat: a back-projected human-like robot head for multiparty human-machine interaction (2012) Cognitive behavioural systems, pp. 114-130. , Springer; Amos, B., Ludwiczuk, B., Satyanarayanan, M., Openface: A general-purpose face recognition library with mobile applications (2016) CMU School of Computer Science, 6; Ashburner, J., A fast diffeomorphic image registration algorithm (2007) Neuroimage, 38 (1), pp. 95-113; Barnes, B. K., Discourse particles in French conversation:(eh) ben, bon, and enfin (1995) French Review, pp. 813-821; Bigi, B., A multilingual text normalization approach (2011) Language and Technology Conference, pp. 515-526. , Springer; Bigi, B., SPPAS-multi-lingual approaches to the automatic annotation of speech (2015) The Phonetician-International Society of Phonetic Sciences, (111-112), pp. 54-69; Blache, P., Bertrand, R., Ferré, G., Creating and exploiting multimodal annotated corpora: the ToMA project (2009) Multimodal corpora, pp. 38-53. , Springer; Boersma, P., Praat, a system for doing phonetics by computer (2002) Glot International, 5; Britton, J. C., Taylor, S. F., Berridge, K. C., Mikels, J. A., Liberzon, I., Differential subjective and psychophysiological responses to socially and nonsocially generated emotional stimuli (2006) Emotion, 6 (1), p. 150; Chaminade, T., An experimental approach to study the physiology of natural social interactions (2017) Interaction Studies, 18 (2); Dennett, D. C., (1989) The intentional stance, , Cambridge, MA, USA: MIT press; Ellis, Y., Laughing together: Laughter as a feature of affiliation in French conversation (1997) Journal of French Language Studies, 7 (2), pp. 147-161; Frith, C. 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De, Daelemans, W., Pattern for python (2012) Journal of Machine Learning Research, 13 (Jun), pp. 2063-2067; Swerts, M., Filled pauses as markers of discourse structure (1998) Journal of Pragmatics, 30 (4), pp. 485-496; Whitfield-Gabrieli, S., Nieto-Castanon, A., Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks (2012) Brain Connectivity, 2 (3), pp. 125-141; Wood, E., Baltrusaitis, T., Zhang, X., Sugano, Y., Robinson, P., Bulling, A., Rendering of eyes for eye-shape registration and gaze estimation (2015) Proceedings of the IEEE International Conference on Computer Vision, pp. 3756-3764; Bigi, B., SPPAS - Multi-lingual Approaches to the Automatic Annotation of Speech (2015) In ""the Phonetician"" - International Society of Phonetic Sciences, pp. 54-69. , http://www.sppas.org/, ISSN 0741-6164 111-112 2015-I-II, pages retrieved from; Boersma, P., Weenink, D., (2018) Praat: doing phonetics by computer [Computer program], , http://www.praat.org/, Version 6.0.37, retrieved 14 March 2018 from; (2015) Équipe ORTOLANG, , https://www.ortolang.fr, Open Resources and Tools for LANGuage (Ortolang) 7.1.1, ORTOLANG ISSN 2417-7482; Gorgolewski, K., Esteban, O., Schaefer, G., Wandell, B., Poldrack, R., OpenNeuro-a free online platform for sharing and analysis of neuroimaging data (2017) Organization for Human Brain Mapping, 1677 (2). , https://openneuro.org/, Vancouver, Canada",,"Calzolari N.Bechet F.Blache P.Choukri K.Cieri C.Declerck T.Goggi S.Isahara H.Maegaard B.Mariani J.Mazo H.Moreno A.Odijk J.Piperidis S.","Amazon AWS;Bertin;Lenovo;Ontotex;Vecsys;Vocapia","European Language Resources Association (ELRA)","12th International Conference on Language Resources and Evaluation, LREC 2020","11 May 2020 through 16 May 2020",,164155,,9791095546344,,,"English","LREC - Int. Conf. Lang. Resour. Eval., Conf. Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85096592111
"Seiler J., Schäfer N., Latsch B., Chadda R., Hessinger M., Beckerle P., Kupnik M.","57219113421;57201306601;57219113155;57210174545;56031511500;36964076200;14060455400;","Wearable vibrotactile interface using phantom tactile sensation for human-robot interaction",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12272 LNCS",,,"380","388",,,"10.1007/978-3-030-58147-3_42","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091290104&doi=10.1007%2f978-3-030-58147-3_42&partnerID=40&md5=61469517fb70fbaf900a6b353bf00eab","Technische Universität Darmstadt, Measurement and Sensor Technology, Merckstraße 25, Darmstadt, 64283, Germany; Technische Universität Dortmund, Robotics Research Institute, Otto-Hahn-Straße 8, Dortmund, 44227, Germany; Technische Universität Darmstadt, Institute for Mechatronic Systems, Otto-Berndt-Straße 2, Darmstadt, 64287, Germany","Seiler, J., Technische Universität Darmstadt, Measurement and Sensor Technology, Merckstraße 25, Darmstadt, 64283, Germany; Schäfer, N., Technische Universität Darmstadt, Measurement and Sensor Technology, Merckstraße 25, Darmstadt, 64283, Germany; Latsch, B., Technische Universität Darmstadt, Measurement and Sensor Technology, Merckstraße 25, Darmstadt, 64283, Germany; Chadda, R., Technische Universität Darmstadt, Measurement and Sensor Technology, Merckstraße 25, Darmstadt, 64283, Germany; Hessinger, M., Technische Universität Darmstadt, Measurement and Sensor Technology, Merckstraße 25, Darmstadt, 64283, Germany; Beckerle, P., Technische Universität Dortmund, Robotics Research Institute, Otto-Hahn-Straße 8, Dortmund, 44227, Germany, Technische Universität Darmstadt, Institute for Mechatronic Systems, Otto-Berndt-Straße 2, Darmstadt, 64287, Germany; Kupnik, M., Technische Universität Darmstadt, Measurement and Sensor Technology, Merckstraße 25, Darmstadt, 64283, Germany","We present a wearable vibrotactile feedback device consisting of four linear resonant actuators (LRAs) that are able to generate virtual stimuli, known as phantom tactile sensation, for human-robot interaction. Using an energy model, we can control the location and intensity of the virtual stimuli independently. The device consists of mostly 3D-printed rigid and flexible components and uses commercially available haptic drivers for actuation. The actuators have a rated frequency of 175 Hz which is close to the highest skin sensitivity regarding vibrations (150 to 300 Hz). Our experiment was conducted with a prototype consisting of two bracelets applied to the forearm and upper arm of six participants. Eight possible circumferential angles were stimulated, of which four originated from real actuators and four were generated by virtual stimuli. The responses given by the participants showed a nearly linear relationship within ± 10 ∘ for the responded angle against the presented stimulus angle. These results show that phantom tactile sensation allows for an increase of spatial resolution to design vibrotactile interfaces for human-robot interaction with fewer actuators. © The Author(s) 2020.","Human-robot interface; Phantom tactile sensation; Vibrotactile feedback","3D printers; Actuators; Biofeedback; Machine design; Man machine systems; Phantoms; Wearable technology; Circumferential angles; Flexible components; Linear relationships; Linear resonant actuators; Spatial resolution; Tactile sensation; Vibro-tactile feedbacks; Vibro-tactile interfaces; Human robot interaction",,,,,"Deutsche Forschungsgemeinschaft, DFG: KA 417/32-1, KU 3498/3-1, SPP 2134","Acknowledgements. This research received support from the Deutsche For-schungsgemeinschaft (DFG) under grants KU 3498/3-1 and KA 417/32-1 within the priority program The Active Self (SPP 2134).",,,,,,,,,,"Beckerle, P., A human-robot interaction perspective on assistive and rehabilitation robotics (2017) Front. Neurorobot, 11, p. 24; Ajoudani, A., Progress and prospects of the human-robot collaboration (2018) Auton. Robot, 42, pp. 957-975; Elliott, L.R., Coovert, M.D., Redden, E.S., Overview of meta-analyses investigating vibrotactile versus visual display options (2009) HCI 2009. LNCS, 5611, pp. 435-443. , Jacko, J.A. (ed) Springer, Heidelberg; Burke, J.L., Comparing the effects of visual-auditory and visual-tactile feedback on user performance: a meta-analysis (2006) Proceedings of the 8th International Conference on Multimodal Interfaces-ICMI 2006, Banff, Alberta, Canada, p. 108. , ACM Press; Bimbo, J., Teleoperation in cluttered environments using wearable haptic feedback (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, pp. 3401-3408. , IEEE, September; Louison, C., Ferlay, F., Mestre, D.R., Spatialized vibrotactile feedback improves goal-directed movements in cluttered virtual environments (2018) Int. J. Hum.-Comput. Interact, 34, pp. 1015-1031; Erp, J.B.F.V., Waypoint navigation with a vibrotactile waist belt (2005) ACM Trans. Appl. Percept, 2, pp. 106-117; Bark, K., Effects of vibrotactile feedback on human learning of arm motions (2015) IEEE Trans. Neural Syst. Rehabil. Eng, 23, pp. 51-63; Pacchierotti, C., Sinclair, S., Solazzi, M., Frisoli, A., Hayward, V., Prattichizzo, D., Wearable Haptic Systems for the Fingertip and the Hand: taxonomy, review, and perspectives (2017) IEEE Trans. Haptics, 10, pp. 580-600; Pezent, E., Tasbi: multisensory squeeze and vibrotactile wrist haptics for augmented and virtual reality (2019) 2019 IEEE World Haptics Conference (WHC), Tokyo, Japan, pp. 1-6. , IEEE, July; Tsetserukou, D., Tachi, S., Efficient object exploration and object presentation in TeleTA, Teleoperation system with Tactile feedback (2009) World Haptics 2009-Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, Salt Lake City, UT, USA, pp. 97-102. , IEEE; Schaetzle, S., VibroTac: an ergonomic and versatile usable vibrotactile feedback device (2010) 19th International Symposium in Robot and Human Interactive Communication, Viareggio, Italy, pp. 670-675. , IEEE, September; Verrillo, R.T., Fraioli, A.J., Smith, R.L., Sensation magnitude of vibrotactile stimuli (1969) Percept. Psychophysics, 6, pp. 366-372; Barghout, A., Cha, J., El Saddik, A., Kammerl, J., Steinbach, E., Spatial resolution of vibrotactile perception on the human forearm when exploiting funneling illusion (2009) 2009 IEEE International Workshop on Haptic Audio visual Environments and Games, Lecco, Italy, pp. 19-23. , IEEE, November; Salazar Luces, J.V., Okabe, K., Murao, Y., Hirata, Y., A phantom-sensation based paradigm for continuous vibrotactile wrist guidance in two-dimensional space (2018) IEEE Robot. Autom. Lett, 3, pp. 163-170; Park, G., Choi, S., Tactile information transmission by 2D stationary phantom sensations (2018) Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems-CHI 2018, Montreal QC, Canada, pp. 1-12. , ACM Press; Békésy, G.V., Sensations on the skin similar to directional hearing, beats, and harmonics of the ear (1957) J. Acoust. Soc. Am, 29, pp. 489-501; Békésy, G.V., Funneling in the nervous system and its role in loudness and sensation intensity on the skin (1958) J. Acoust. Soc. Am, 30, pp. 399-412; Israr, A., Poupyrev, I., Tactile brush: drawing on skin with a tactile grid display (2011) Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems-CHI 2011, Vancouver, BC, Canada, p. 2019, , ACM Press; Choi, S., Kuchenbecker, K.J., Vibrotactile display: Perception, technology, and applications (2013) Proc. IEEE, 101, pp. 2093-2104; Bolanowski, S.J., Gescheider, G.A., Verrillo, R.T., Hairy skin: psychophysical channels and their physiological substrates (1994) Somatosens. Mot. Res, 11, pp. 279-290; Békésy, G.V., Wever, E.G., (1960) Experiments in Hearing, , McGraw-Hill, New York City","Schäfer, N.; Technische Universität Darmstadt, Merckstraße 25, Germany; email: schaefer@must.tu-darmstadt.de","Nisky I.Hartcher-O'Brien J.Wiertlewski M.Smeets J.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2020","6 September 2020 through 9 September 2020",,244859,03029743,9783030581466,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85091290104
"Val-Calvo M., Alvarez-Sanchez J.R., Ferrandez-Vicente J.M., Fernandez E.","57195318553;56786654900;56268451400;57203722548;","Affective Robot Story-Telling Human-Robot Interaction: Exploratory Real-Time Emotion Estimation Analysis Using Facial Expressions and Physiological Signals",2020,"IEEE Access","8",,"9133100","134051","134066",,4,"10.1109/ACCESS.2020.3007109","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089306806&doi=10.1109%2fACCESS.2020.3007109&partnerID=40&md5=224d52b404287276f8c4946b8f9a3f11","Departamento de Inteligencia Artificial, Universidad Nacional de Educación A Distancia (UNED), Madrid, 28040, Spain; Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Universidad Polit∅cnica de Cartagena, Cartagena, 30202, Spain; Instituto de Bioingeniería, Universidad Miguel HernÆndez, Elche, 03202, Spain; CIBER-BBN, Madrid, 28029, Spain","Val-Calvo, M., Departamento de Inteligencia Artificial, Universidad Nacional de Educación A Distancia (UNED), Madrid, 28040, Spain, Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Universidad Polit∅cnica de Cartagena, Cartagena, 30202, Spain; Alvarez-Sanchez, J.R., Departamento de Inteligencia Artificial, Universidad Nacional de Educación A Distancia (UNED), Madrid, 28040, Spain; Ferrandez-Vicente, J.M., Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Universidad Polit∅cnica de Cartagena, Cartagena, 30202, Spain; Fernandez, E., Instituto de Bioingeniería, Universidad Miguel HernÆndez, Elche, 03202, Spain, CIBER-BBN, Madrid, 28029, Spain","Affective human-robot interaction is still an active area of research in part due to the great advances in artificial intelligence. Now, the design of autonomous devices that work in real therapeutic environments has become a plausible reality. Affective human-robot interaction requires a robot to analyze the emotional state of the human interlocutor and interpret emotional responses that can be used, not merely in the interaction but, for example, to provoke desired therapeutic responses. It is, therefore, necessary to broaden experimental techniques into more realistic paradigms, where the capacity of emotion estimation can be completely explored. This exploratory paper proposes a realistic experimental paradigm in which the robot employs a dramatic story to evoke emotions in the users, and tests previously self-designed methodologies to be able to make estimates of the users' emotional state in real-time. Regardless of the multiple impediments and restrictions, and all the aspects that could still be improved, this paper can outline the feasibility of the proposed methodology in realistic scenarios. © 2013 IEEE.","Affective state; blood volume pressure; EEG; emotion estimation; face emotion recognition; galvanic skin response; human-robot interaction; real-time","Artificial intelligence; Intelligent robots; Man machine systems; Autonomous devices; Emotion estimation; Emotional response; Emotional state; Experimental techniques; Facial Expressions; Physiological signals; Realistic scenario; Social robots",,,,,"Fundación Séneca; Ministerio de Ciencia, Innovación y Universidades, MCIU: RTI2018-098969-B-100; Generalitat Valenciana, GVA: PROMETEO/2019/119","This work was supported in part by the Fundación Séneca, Agencia de Ciencia y Tecnología de la Región de Murcia under Project Programa de Ayudas a Grupos de Excelencia de la Región de Murcia, in part by the Ministerio de Ciencia, Innovación y Universidades under Project RTI2018-098969-B-100, and in part by the Generalitat Valenciana under Project PROMETEO/2019/119.",,,,,,,,,,"Picard, R.W., (2000) Affective Computing, , Cambridge MA USA: MIT Press; Vidler, V., Use puppets to reach the emotionally disturbed (1972) Instructor, 81, pp. 9-68. , May; Currant, N., The expansive educational value of puppets (1985) Acad. 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Man Cybern, pp. 407-412. , Oct; Cid, F., Manso, L.J., Núnez, P., A novel multimodal emotion recognition approach for affective human robot interaction (2015) Proc. FinE, pp. 1-9; Perez-Gaspar, L.-A., Caballero-Morales, S.-O., Trujillo-Romero, F., Multimodal emotion recognition with evolutionary computation for human-robot interaction (2016) Expert Syst. Appl., 66, pp. 42-61. , Dec; Liu, Z., Wu, M., Cao, W., Chen, L., Xu, J., Zhang, R., Zhou, M., Mao, J., A facial expression emotion recognition based human-robot interaction system (2017) IEEE/CAA J. Automatica Sinica, 4 (4), pp. 668-676; Chen, L., Zhou, M., Su, W., Wu, M., She, J., Hirota, K., Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction (2018) Inf. Sci., 428, pp. 49-61. , Feb","Val-Calvo, M.; Departamento de Inteligencia Artificial, Spain; email: mikel1982mail@gmail.com
Alvarez-Sanchez, J.R.; Departamento de Inteligencia Artificial, Spain; email: jras@dia.uned.es",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85089306806
"Bana P.R., Tsai Y.-L., Knight H.","57222467675;57222465097;26321722700;","SanitizerBot: A hand sanitizer service robot",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447274","661","",,,"10.1145/3434074.3447274","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102746744&doi=10.1145%2f3434074.3447274&partnerID=40&md5=90234ee6085e858f179fd40bfb41a04b","Oregon State University, Corvallis, OR, United States","Bana, P.R., Oregon State University, Corvallis, OR, United States; Tsai, Y.-L., Oregon State University, Corvallis, OR, United States; Knight, H., Oregon State University, Corvallis, OR, United States","This video seeks to use robot prosocial behaviors to incentivize passersby to use hand sanitizer. Teleoperated by the wizard, the robot is capable of expressing via LED lights, gestures, and speech communication modes for human-robot interaction. Future work will explore what communication modes and behaviors are the most effective in encouraging people to use hand sanitizer. © 2021 Owner/Author.","Human robot interaction; Prosocial behavior; Service robots","Agricultural robots; Man machine systems; Speech communication; Communication mode; LED lights; Service robots; Teleoperated; Social robots",,,,,,,,,,,,,,,,"Agnihotri, A., Knight, H., Persuasive chairbots: A (mostly) robot-recruited experiment (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1-7; Connolly, J., Prompting prosocial human interventions in response to robot mistreatment (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 211-220; Li, S., Clinical application of an intelligent oropharyngeal swab robot: Implication for the COVID-19 pandemic (2020) European Respiratory Journal, 56 (2). , 2020; Jagan Sathyamoorthy, A., (2020) COVID-Robot: Monitoring Social Distancing Constraints in Crowded Scenarios, , 2020",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102746744
[No author name available],[No author id available],"MuCAI 2021 - Proceedings of the 2nd ACM Multimedia Workshop on Multimodal Conversational AI, co-located with ACM MM 2021",2021,"MuCAI 2021 - Proceedings of the 2nd ACM Multimedia Workshop on Multimodal Conversational AI, co-located with ACM MM 2021",,,,"","",31,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121434375&partnerID=40&md5=f61d75d2aa0fa47c6f3f740f025b0c95",,"","The proceedings contain 4 papers. The topics discussed include: towards enriching responses with crowd-sourced knowledge for task-oriented dialogue; towards a real-time measure of the perception of anthropomorphism in human-robot interaction; the design of a trust-based game as a conversational component of interactive environment for a human-agent negotiation; and iFetch: multimodal conversational agents for the online fashion marketplace.",,,,,,,,,,,,,,,,,,,,,"ACM SIGMM","Association for Computing Machinery, Inc","2nd ACM Multimedia Workshop on Multimodal Conversational AI, MuCAI 2021, co-located with ACM MM 2021","24 October 2021",,174214,,9781450386791,,,"English","MuCAI - Proc. ACM Multimed. Workshop Multimodal Conversational AI, co-located ACM MM",Conference Review,"Final","",Scopus,2-s2.0-85121434375
"Chen L., Wu M., Pedrycz W., Hirota K.","55774663200;35231682600;57210775415;7402829702;","Dynamic Emotion Understanding Based on Two-Layer Fuzzy Fuzzy Support Vector Regression-Takagi-Sugeno Model",2021,"Studies in Computational Intelligence","926",,,"161","182",,,"10.1007/978-3-030-61577-2_10","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096235635&doi=10.1007%2f978-3-030-61577-2_10&partnerID=40&md5=5ad4dd46399f16d0635e24ce90f28ba4","School of Automation, China University of Geosciences, Wuhan, China; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Tokyo Institute of Technology, Yokohama, Japan","Chen, L., School of Automation, China University of Geosciences, Wuhan, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, China; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Hirota, K., Tokyo Institute of Technology, Yokohama, Japan","Two-layer Fuzzy SVR-TS Model is proposed for emotion understanding in human-robot interaction, where the real-time dynamic emotion is recognized according to facial expression, and emotional intention understanding is obtained mainly based on human emotions and identification information. It aims to make robots capable of recognizing and understanding human emotions, in such a way that make humanrobot interaction run smoothly. © 2020, The Author(s), under exclusive license to Springer Nature Switzerland AG.",,,,,,,,,,,,,,,,,,"Tawari, A., Trivedi, M.M., Face expression recognition by cross modal data association (2013) IEEE Trans. Multimed., 15 (7), pp. 1543-1552; Melin, P., Mendoza, O., Castillo, O., Face recognition with an improved interval type2 fuzzy logic sugeno integral and modular neural networks (2011) IEEE Trans. Syst. Man Cybern. Part a Syst. Hum., 41 (5), pp. 1001-1012; Di, W., Zhang, L., Zhang, D., Studies on hperspectral face recognition in visible spectrum with feature band selection (2010) IEEE Trans. Syst. Man Cybern. Part a Syst. Hum., 40 (6), pp. 1354-1361; Salvador, M.J., Silver, S., Mahoor, M.H., An emotion recognition comparative study of autistic and typically-developing children using the zeno robot (2015) Proceedings of IEEE International Conference on Robotics and Automation, pp. 6128-6133. , Washington, USA; Halder, A., Konar, A., Mandal, R., General and interval type-2 fuzzy face-space approach to emotion recognition (2013) IEEE Trans. Syst., Man, Cybern. Syst., 43 (3), pp. 587-605; Walter, S., Kim, J., Hrabal, D., Transsituational individual-specific biopsychological classification of emotions (2013) IEEE Trans. Syst., Man, Cybern. Syst, 43 (4), pp. 988-995; Wu, D., Fuzzy sets and systems in building closed-loop affective computing systems for Human-robot interaction: Advances and new research directions (2012) Proceedings of IEEE International Conference on Fuzzy Systems, pp. 1-8. , Niskayuna, USA; Sha, Y., Emotional intelligence and affective computing in uncertainty situation (2013) Proceedings of 3Th International Conference on Intelligent System Design and Engineering Applica-Tions(Isdea), pp. 705-708. , Hong Kong, China; Angel, F.J.M., Bonarini, A., Studying people’s emotional responses to robot’s movements in a small scene (2014) Proceedings of IEEE International Symposium on Robot and Human Interactive Communication, pp. 417-422. , Edinburgh, England; Bi, L., Tsimhoni, O., Liu, Y., Using the support vector regression approach to model human performance (2011) IEEE Trans. Syst. Man Cybern. Part a Syst. Hum., 41 (3), pp. 410-417; Jiang, C., Ni, Z., Guo, Y., He, H., Learning human-robot interaction for robot-assisted pedestrian flow optimization (2017) IEEE Trans. Syst., , https://doi.org/10.1109/TSMC.2017.2725300, Man, Cybern. Syst; Al-Arashi, W.H., Ibrahim, H., Suandi, S.A., Optimizing principal component analysis performance for face recognition using genetic algorithm (2014) Neurocomputing, 128, pp. 415-420; Zhou, C., Wang, L., Zhang, Q., Face recognition based on PCA and logistic regression analysis (2014) Optik-Int. J. Light. Electron Opt, 125 (20), pp. 5916-5919; Wang, Z., Ruan, Q.Q., An, G.Y., Facial expression recognition using sparse local Fisher discriminant analysis (2016) Neurocomputing, 174, pp. 756-766; Smeets, D., Claes, P., Hermans, J., A comparative study of 3-D face recognition under expression variations (2012) Man, Cybern., Part C, 42 (5), pp. 710-727. , IEEE Trans. Syst; Vezzetti, E., Marcolin, F., Fracastoro, G., 3D face recognition: An automatic strategy based on geometrical descriptors and landmarks (2014) Robot. Auton. Syst., 62 (12), pp. 1768-1776; Sun, F., Huang, G.B., Wu, Q.M.J., Efficient and rapid machine learning algorithms for big data and dynamic varying systems (2017) IEEE Trans. Syst., Man, Cybern.: Syst., 47 (10), pp. 2625-2626; Hsieh, C.K., Lai, S.H., Chen, Y.C., An optical flow-based approach to robust face recognition under expression variations (2010) IEEE Trans. Image Process, 19 (1), pp. 233-240; Wang, J.N., Xiong, R., Chu, J., Facial feature points detecting based on Gaussian mixture models (2015) Pattern Recognit. Lett., 53, pp. 62-68; Asteriadis, S., Nikolaidis, N., Pitas, I., Facial feature detection using distance vector fields (2009) Pattern Recogn, 42 (7), pp. 1388-1398; Cootes, T.F., Edwards, G.J., Taylor, C.J., Active appearance models (2001) IEEE Trans. Pattern Anal. Mach. 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Fuzzy Syst., 23 (1), pp. 230-233; de Souza, W.A., Teixeira, M.C.M., Cardim, R., On switched regulator design of uncertain nonlinear systems using Takagi-Sugeno fuzzy models (2014) IEEE Trans. Fuzzy Syst., 22 (6), pp. 1720-1727; Cheung, N.J., Ding, X.M., Shen, H.B., OptiFel: A convergent heterogeneous particle swarm optimization algorithm for Takagi-Sugeno fuzzy modeling (2014) IEEE Trans. Fuzzy Syst., 22 (4), pp. 919-933; Chang, P.C., Wu, J.L., Lin, J.J., A Takagi-Sugeno fuzzy model combined with a support vector regression for stock trading forecasting (2015) Appl. Soft Comput., 38, pp. 831-842; Chen, L.F., Liu, Z.T., Wu, M., Ding, M., Dong, F.Y., Hirota, K., Emotion-age-gender-nationality based intention understanding in human-robot interaction using two-layer fuzzy support vector regression (2015) Int. J. Social Robot., 7 (5), pp. 709-729; Liu, Z., Xu, S., Chen, C.L.P., Zhang, Y., Chen, X., Wang, Y., A three-domain fuzzy support vector regression for image denoising and experimental studies (2014) IEEE Trans. Cybern., 44 (4), pp. 515-526; Yang, X., Zhang, G., Lu, J., Ma, J., A kernel fuzzy c-means clustering-based fuzzy support vector machine algorithm for classification problems with outliers or noises (2011) IEEE Trans. Fuzzy Syst., 19 (1), pp. 105-115; Bottou, L., Vapnik, V., Local learning algorithms (1992) Neural Comput, 4 (6), pp. 888-900; Lin, K.P., A novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm (2014) IEEE Trans. Fuzzy Syst., 22 (5), pp. 1074-1087; Nguyen, D.D., Ngo, L.T., Multiple kernel interval type-2 fuzzy c-means clustering (2013) Proceedings of IEEE International Conference on Fuzzy Systems, pp. 1-8. , Hyderabad, India; Dong, W.M., Wong, F.S., Fuzzy weighted averages and implementation of the extension principle (1987) Fuzzy Sets Syst, 21 (2), pp. 183-199; Chen, L.F., Liu, Z.T., Dong, F.Y., Yamazaki, Y., Wu, M., Hirota, K., Adapting multi-robot behavior to communication atmosphere in humans-robots interaction using fuzzy production rule based friend-Q learning (2013) J. Adavanced Comput. Intell. Intell. Inform., 17 (2), pp. 291-301; Chen, L.F., Liu, Z.T., Wu, M., Dong, F.Y., Yamazaki, Y., Hirota, K., Multi-robot behavior adaptation to local and global communication atmosphere in humans-robots interaction (2014) J. Multimodal User Interfaces, 8 (3), pp. 289-303; Calinon, S., Kormushev, P., Caldwell, D.G., Pepper learns together with children: Development of an educational application (2015) Proceedings of IEEE-RAS 15Th International Conference on Humaniod Robots, pp. 270-275. , Seoul, Korea; Bicho, E., Erlhagen, W., Sousa, E., The power of prediction: Robots that read intentions (2012) Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (Vil-Amoura, pp. 5458-5459. , Portugal","Chen, L.; School of Automation, China; email: chenluefeng@cug.edu.cn",,,"Springer Science and Business Media Deutschland GmbH",,,,,1860949X,,,,"English","Stud. Comput. Intell.",Book Chapter,"Final","",Scopus,2-s2.0-85096235635
"Chen L., Wu M., Pedrycz W., Hirota K.","55774663200;35231682600;57210775415;7402829702;","Three-Layer Weighted Fuzzy Support Vector Regressions for Emotional Intention Understanding",2021,"Studies in Computational Intelligence","926",,,"133","159",,,"10.1007/978-3-030-61577-2_9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096234502&doi=10.1007%2f978-3-030-61577-2_9&partnerID=40&md5=2a97c30446799c0bb4c07bca3ac0ea7a","School of Automation, China University of Geosciences, Wuhan, China; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Tokyo Institute of Technology, Yokohama, Japan","Chen, L., School of Automation, China University of Geosciences, Wuhan, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, China; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Hirota, K., Tokyo Institute of Technology, Yokohama, Japan","A three-layer weighted fuzzy support vector regression (TLWFSVR) model is proposed for understanding human intention, and it is based on the emotion-identification information in human-robot interaction. TLWFSVR model consists of three layers, including adjusted weighted kernel fuzzy c-means (AWKFCM) for data clustering, fuzzy support vector regressions (FSVR) for information understanding, and weighted fusion for intention understanding. © 2020, The Author(s), under exclusive license to Springer Nature Switzerland AG.",,,,,,,,,,,,,,,,,,"Noohi, E., Zefran, M., Patton, J.L., A model for human-human collaborative object manipulation and its application to human-robot interaction (2016) IEEE Trans. Robot., 32 (4), pp. 880-896; Roudposhti, K.K., Nunes, U., Dias, J., Probabilistic social behavior analysis by exploring body motion-based patterns (2016) IEEE Trans. Pattern Anal. Mach. Intell., 38 (8), pp. 1679-1691; Zheng, K., Glas, D.F., Kanda, T., Ishiguro, H., Hagita, N., Designing and implementing a human-robot team for social interactions (2013) IEEE Trans. Syst. Man Cybern. Syst., 43 (4), pp. 843-858; Pitsch, K., Dankert, T., Gehle, R., Wrede, S., Referential practices. 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Syst, 8 (3), pp. 152-170; Hu, D., Gong, Y., Hannaford, B., Seibel, E.J., Semi-autonomous simulated brain tumor abla-tion with RAVENII surgical robot using behavior tree (2015) Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 3868-3875. , Seattle, USA; Smith, A.M.C., Yang, C., Ma, H., Culverhouse, P., Cangelosi, A., Burdet, E., Novel hybrid adaptive controller for manipulation in complex perturbation environments (2015) Plos One, 10 (6); Qiu, S., Li, Z., He, W., Zhang, L., Yang, C., Su, C.Y., Brain – machine interface and visual compressive sensing-based teleoperation control of an exoskeleton robot (2017) IEEE Trans. Fuzzy Syst., 25 (1), pp. 58-69; Chen, L.F., Liu, Z.T., Wu, M., Dong, F.Y., Yamazaki, Y., Hirota, K., Multi-robot behavior adaptation to local and global communication atmosphere in humans-robots interaction (2014) J. 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Sport. Exerc., 10 (2), pp. 226-234; Cortes, C., Vapnik, V.N., Support-vector networks (1995) Mach. Learn., 20 (3), pp. 273-297; Yu, H., Kim, J., Kim, Y., Hwang, S., Lee, Y.H., An efficient method for learning nonlinear ranking SVM functions (2012) Inf. Sci., 209, pp. 37-48; Vapnik, V.N., (2000) The Nature of Statistical Learning Theory, , Springer, New York; Song, D., Tao, D., Biologically inspired feature manifold for scene classification (2010) IEEE Trans. Image Process., 19 (1), pp. 174-184; Andreas, B., Jamie, A.W., Hans, G., Gerhard, T., Eye movement analysis for activity recognition using electrooculography (2011) IEEE Trans. Pattern Anal. Mach. Intell., 33 (4), pp. 741-753; Zhao, W., Zhang, J., Li, K., An efficient LS-SVM-based method for fuzzy system construction (2015) IEEE Trans. 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Appl., 37 (4), pp. 3088-3093; Lin, K.P., A novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm (2014) IEEE Trans. Fuzzy Syst., 22 (5), pp. 1074-1087; Nguyen, D.D., Ngo, L.T., Multiple kernel interval type-2 fuzzy c-means clustering (2013) Proceedings of IEEE International Conference on Fuzzy Systems, pp. 1-8. , Hyderabad, India; Dong, W.M., Wong, F.S., Fuzzy weighted averages and implementation of the extension principle (1987) Fuzzy Sets Syst, 21 (2), pp. 183-199; Keltner, D., Haidt, J., Social functions of emotions at four levels of analysis (1999) Cogn. Emot., 13 (5), pp. 505-521; Shafi, A., Vishanth, W., Understanding citizens’ behavioral intention in the adoption of e-government services in the state of Qatar (2009) Proceedings of European Conference on Information Systems, , Verona, Italy; Orji, R.O., Impact of gender and nationality on acceptance of a digital library: An empirical validation of nationality based UTAUT using SEM (2010) J. Emerg. Trends Comput. Inf. Sci., 1 (2), pp. 68-79; Holland, C., Hill, R., The effect of age, gender and driver status on pedestrians’ intentions to cross the road in risky situations intentions to cross the road in risky situations (2007) Accid. Anal. Prev, 39 (2), pp. 224-237; Farchi, G., Fidanza, F., Giampaoli, S., Mariotti, S., Menotti, A., Alcohol and survival in the Italian rural cohorts of the seven countries study (2000) Int. J. Epidemiol., 29, pp. 667-671; Hirota, K., Dong, F.Y., Development of mascot robot system in NEDO project (2008) Proceedings of International IEEE Conference on Intelligent Systems, pp. 38-44. , Varna, Bulgaria; Sentence Classification Data Set, , http://archive.ics.uci.edu/ml/datasets.html, UC Irvine, Irvine, CA, USA, Available; Hubert, L., Arabie, P., Comparing partitions (1985) J. Classif., 2, pp. 193-218; Chen, L.F., Wu, M., Zhou, M., She, J., Hirota, K., Dynamic emotion understanding using FCM based SVR in human-robot interaction (2016) Proceedings of the 35Th Chinese Control Conference, pp. 7064-7069. , Chengdu, China; Lafaye, J., Collette, C., Wieber, P.B., Model predictive control for tilt recovery of an omnidirec-tional wheeled humanoid robot (2015) Proceedings of IEEE International Conference on Robotics and Automation, pp. 5134-5139. , Seattle, USA; Ames, A.D., Human-inspired control of bipedal walking robots (2014) IEEE Trans. Autom. Control, 59 (5), pp. 1115-1130","Chen, L.; School of Automation, China; email: chenluefeng@cug.edu.cn",,,"Springer Science and Business Media Deutschland GmbH",,,,,1860949X,,,,"English","Stud. Comput. Intell.",Book Chapter,"Final","",Scopus,2-s2.0-85096234502
"Lindner F.","34881851900;","Towards a formalization of explanations for robots’ actions and beliefs",2020,"CEUR Workshop Proceedings","2708",,,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095583007&partnerID=40&md5=d9ef81392ba5457df5deeb05b42b778d","Institute of Artificial Intelligence, Ulm University, Germany","Lindner, F., Institute of Artificial Intelligence, Ulm University, Germany","A robot’s capacity to self-explain its behavior is a means of ensuring trust. This work presents a preliminary formal characterization of explanations embracing the distinction between explanations based on counterfactuality and those based on regularity. It also distinguishes generative and instrumental explanations. The formalization will guide future work on explanation generation, explanation sharing, and explanation understanding in human-robot interaction. Copyright © 2020 for this paper by its authors.","Explainable AI; Human-robot interaction; Ontology","Ontology; Human robot interaction",,,,,,,,,,,,,,,,"Tolmeijer, S, Weiss, A, Hanheide, M, Lindner, F, Powers, TM, Dixon, C, Tielman, ML., Taxonomy of trust-relevant failures and mitigation strategies (2020) Proceedings of HRI’20; de Graaf, M, Malle, B., How people explain action (and autonomous intelligent systems should too) (2017) AAAI Fall Symposium Series; Kunze, L, Roehm, T, Beetz, M., Towards semantic robot description languages (2011) Proceedings of 2011 IEEE International Conference on Robotics and Automation, pp. 5589-5595; Zhou, B, Khosla, A, Lapedriza, A, Oliva, A, Torralba, A., Learning deep features for discriminative localization (2016) Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2921-2929; Fox, M, Long, D, Magazzeni, D., Explainable planning (2017) IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI); Krarup, B, Krivic, S, Lindner, F, Long, D., Towards contrastive explanations for comparing the ethics of plans (2020) ICRA 2020 Workshop Against Robot Dystopias: Thinking through the ethical, legal and societal issues of robotics and automation (AGAINST-20); Broekens, J, Harbers, M, Hindriks, K, Van Den Bosch, K, Jonker, C, Meyer, JJ., Do you get it? User-evaluated explainable BDI agents (2010) German Conference on Multiagent System Technologies, pp. 28-39; Lindner, F, Möllney, K., Extracting reasons for moral judgments under various ethical principles (2019) KI 2019: Advances in Artificial Intelligence, pp. 216-229; Horridge, M., (2011) Justification based explanation in ontologies, , Ph.D. thesis, University of Manchester, UK; Sandra, W, Mittelstadt, B, Russell, C., Counterfactual explanations without opening the black box: Automated decisions and the GDPR (2018) Harvard Journal of Law & Technology, 1 (2); Ribeiro, MT, Singh, S, Guestrin, C., Anchors: High-precision model-agnostic explanations (2018) AAAI Conference on Artificial Intelligence; Tiddi, I, D’Aquin, M, Motta, E., An ontology design pattern to define explanations (2015) K-CAP 2015: Proceedings of the 8th International Conference on Knowledge Capture, pp. 1-8; Lipton, P., (1991) Inference to the best explanation, , London and New York: Routledge; Miller, T., Explanation in artificial intelligence: Insights from the social sciences (2019) Artificial Intelligence, 267; Hume, D., (1748) An Enquiry Concerning Human Understanding; Varzi, A., Mereology (2016) Stanford Encyclopedia of Philosophy","Lindner, F.; Institute of Artificial Intelligence, Germany","Hammar K.Kutz O.Dimou A.Hahmann T.Hoehndorf R.Masolo C.Vita R.",,"CEUR-WS","2020 Joint Ontology Workshops, JOWO 2020","31 August 2020 through 7 October 2020",,164284,16130073,,,,"English","CEUR Workshop Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85095583007
"Taschdjian Z.","57219593253;","Why Did the Robot Cross the Road?: A User Study of Explanation in Human-Robot Interaction",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12424 LNCS",,,"527","537",,,"10.1007/978-3-030-60117-1_39","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094173650&doi=10.1007%2f978-3-030-60117-1_39&partnerID=40&md5=80eccc65e3802fdaf48b36d7a4588a84","Georgia Institute of Technology, Atlanta, GA  30332, United States; H2O.ai, Mountain View, CA  94043, United States","Taschdjian, Z., Georgia Institute of Technology, Atlanta, GA  30332, United States, H2O.ai, Mountain View, CA  94043, United States","This work documents a pilot user study evaluating the effectiveness of contrastive, causal and example explanations in supporting human understanding of AI in a hypothetical commonplace human-robot interaction (HRI) scenario. In doing so, this work situates “explainable AI” (XAI) in the context of the social sciences and suggests that HRI explanations are improved when informed by the social sciences. © 2020, Springer Nature Switzerland AG.","Explainable AI; HCI; Human-robot interaction","Behavioral research; Human computer interaction; Human robot Interaction (HRI); Human understanding; User study; Human robot interaction",,,,,,,,,,,,,,,,"Ackermann, E., Piaget’s Constructivism (2001) Papert’s Constructionism: What’s the Difference?, Vol, p. 5; Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., Elhadad, N., Intelligible models for healthcare (2015) Predicting Pneumonia Risk and Hospital; Proceedings of the 21St ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD 2015, pp. 1721-1730. , https://doi.org/10.1145/2783258.2788613; Chapman, A., Hadfield, M., Chapman, C., Qualitative research in healthcare: An introduction to grounded theory using thematic analysis (2015) J. R. Coll. Physicians Edinb., 45 (3), pp. 201-205. , https://doi.org/10.4997/JRCPE.2015.305; Gopnik, A., The post-piaget era (1996) Psychol. Sci., 7 (4), pp. 221-225. , https://doi.org/10.1111/j.1467-9280.1996.tb00363.x; Grudin, J., AI and HCI: Two fields divided by a common focus (2009) AI Mag, 30 (4), p. 48. , https://doi.org/10.1609/aimag.v30i4.2271; Gunning, D., DARPA’s explainable artificial intelligence (XAI) program (2019) Proceedings of the 24Th International Conference on Intelligent User Interfaces (IUI 2019), , https://doi.org/10.1145/3301275.3308446, Association for Computing Machinery, New York; Halpern, J.Y., Causes and explanations: A structural-model approach. Part I: Causes (2005) Br. J. Philos. Sci., 56 (4), pp. 843-887. , https://doi.org/10.1093/bjps/axi147; Johnson-Laird, P.N., Mental models and human reasoning (2010) Proc. Natl. Acad. Sci., 107 (43). , https://doi.org/10.1073/pnas.1012933107; Jonassen, D., Operationalizing mental models: Strategies for assessing mental models to support meaningful learning and design supportive learning environments (1995) The First International Conference on Computer Support for Collaborative Learning (CSCL 1995), pp. 182-186. , https://doi.org/10.3115/222020.222166, L. Erlbaum Associates Inc., Mahwah; Kim, B., (2015) Interactive and Interpretable Machine Learning Models for Human Machine Collaboration, , MIT, Cambridge; Kozyrkov, C., (2018) Explainable AI won’t Deliver. Here’s Why, , https://hackernoon.com/explainable-ai-wont-deliver-here-swhy-6738f54216be, Accessed 11 Oct 2019; Joyner, D.A., (2018) Intelligent Evaluation and Feedback in Support of a Credit-Bearing; Penstein Rosé, C., AIED 2018 (2018) LNCS (LNAI), 10948. , https://doi.org/10.1007/978-3-319-93846-2.MOOC, Springer, Cham; Kuang, C., Can AI Be Taught to Explain Itself? The New York Times, , https://www.nytimes.com/2017/11/21/magazine/can-aibe-taught-to-explain-itself.html, 21 November 2017; Li, X., Huan, J., Constructivism learning: A learning paradigm for transparent predictive analytics (2017) Proceedings of the 23Rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD 2017, pp. 285-294. , https://doi.org/10.1145/3097983.3097994; Malinsky, D., Danks, D., Causal discovery algorithms: A practical guide (2018) Philos. Compass, 13 (1). , https://doi.org/10.1111/phc3.12470; Miller, T., (2018) Explanation in Artificial Intelligence: Insights from the Social Sciences, , http://arxiv.org/abs/1706.07269, [Cs]; Moreno, R., Mayer, R., Interactive multimodal learning environments: Special issue on interactive learning environments: Contemporary issues and trends (2007) Educ. Psychol. Rev., 19 (3), pp. 309-326. , https://doi.org/10.1007/s10648-007-9047-2; Páez, A., The pragmatic turn in explainable artificial intelligence (XAI) (2019) Minds Mach, 29 (3), pp. 441-459. , https://doi.org/10.1007/s11023-019-09502-w; Papert, S., One AI or many? (1988) Daedalus, 117 (1), pp. 1-14. , www.jstor.org/stable/20025136, Accessed 19 Feb 2020; Parusniková, Z., Popper and hume: Two great skeptics (2019) The Impact of Critical Rationalism, , Sassower, R., Laor, N. (eds.), Palgrave Macmillan, Cham; https://design.google/library/ai/; Reece, B.C., Aristotle’s four causes of action (2019) Australas. J. Philos., 97 (2), pp. 213-227. , https://doi.org/10.1080/00048402.2018.1482932; Rudin, C., (2019) Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead., , http://arxiv.org/abs/1811.10154, September; Springer, A., Hollis, V., Whittaker, S., Dice in the black box: User experiences with an inscrutable algorithm (2017) AAAI Spring Symposium-Technical Report, SS-17-01-, pp. 427-430; Vasconcelos, M., Cardonha, C., Gonçalves, B., Modeling epistemological principles for bias mitigation in ai systems: An illustration in hiring decisions (2018) Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society – AIES 2018, pp. 323-329. , https://doi.org/10.1145/3278721.3278751; Winikoff, M., Debugging agent programs with why? Questions (2017) Proceedings of the 16Th Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2017), pp. 251-259. , International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC","Taschdjian, Z.; Georgia Institute of TechnologyUnited States; email: ztaschdjian@gatech.edu","Stephanidis C.Kurosu M.Degen H.Reinerman-Jones L.",,"Springer Science and Business Media Deutschland GmbH","22nd International Conference on Human Computer Interaction,HCII 2020","19 July 2020 through 24 July 2020",,250389,03029743,9783030601164,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85094173650
"Law T., Malle B.F., Scheutz M.","57212389268;6701615560;6603548841;","A Touching Connection: How Observing Robotic Touch Can Affect Human Trust in a Robot",2021,"International Journal of Social Robotics","13","8",,"2003","2019",,,"10.1007/s12369-020-00729-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098972666&doi=10.1007%2fs12369-020-00729-7&partnerID=40&md5=a3dd2790dcd3afb2942c2568118d9cc9","HRI Lab, Tufts University, Medford, MA  02155, United States; Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI  02912, United States","Law, T., HRI Lab, Tufts University, Medford, MA  02155, United States; Malle, B.F., Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI  02912, United States; Scheutz, M., HRI Lab, Tufts University, Medford, MA  02155, United States","As robots begin to occupy our social spaces, touch will increasingly become part of human–robot interactions. This paper examines the impact of observing a robot touch a human on trust in that robot. In three online studies, observers watched short videos of human–robot interactions and provided a series of judgments about the robot, which either did or did not touch the human on the shoulder. Trust was measured using a recently introduced multi-dimensional instrument, which assesses people’s trust in a robot as being capable, reliable, sincere, and/or ethical. The first study showed that observed robot touch increased overall trust in the robot, especially for the sincere and ethical trust aspects, and led people to perceive the robot as more comforting, but also more inappropriate. A second study replicated the general pattern, even with a handshake preceding the touch; but in the context of the handshake the touch was seen as more inappropriate. A third study examined the joint impact of a handshake, touch, and information about the robot’s designed function. In the context of such information, observed touch was seen as even more inappropriate, which in turn decreased trust. © 2021, Springer Nature B.V.","Human–robot interaction; Human–robot trust; Robot touch; Social connection","Agricultural robots; Philosophical aspects; Professional aspects; Robotics; General patterns; Multi dimensional; Online studies; Robot interactions; Social spaces; Social robots",,,,,"National Science Foundation, NSF: IIS-1316809; Air Force Office of Scientific Research, AFOSR: FA9550-18-1-0465","This project was funded in part by National Science Foundation Grant IIS-1316809 and robot AFOSR Grant FA9550-18-1-0465.",,,,,,,,,,"Andersen, P.A., Leibowitz, K., The development and nature of the construct touch avoidance (1978) Environ Psychol Nonverbal Behav, 3 (2), pp. 89-106; Andreasson, R., Alenljung, B., Billing, E., Lowe, R., Affective touch in human-robot interaction: conveying emotion to the nao robot (2018) Int J Soc Robot, 10 (4), pp. 473-491; Arnold, T., Scheutz, M., Observing robot touch in context: How does touch and attitude affect perceptions of a robot’s social qualities? 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IEEE, pp. 1864-1869; Bezemer, J., Kress, G., Touch: a resource for making meaning (2014) Aust J Lang Lit, 37 (2), pp. 77-85; Blakemore, S.-J., Bristow, D., Bird, G., Frith, C., Ward, J., Somatosensory activations during the observation of touch and a case of vision-touch synaesthesia (2005) Brain J Neurol, 128, pp. 1571-1583; Bock, N., Hoffmann, L., Rosenthal-Vd Pütten, A., Your touch leaves me cold, robot (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 71-72. , ACM; (2019) What It Means When Someone Touches Your Shoulder, , https://bodylanguagecentral.com/what-it-means-when-someone-touches-your-shoulder/, Retrieved from; Burgoon, J.K., Relational message interpretations of touch, conversational distance, and posture (1991) J Nonverbal Behav, 15 (4), pp. 233-259; Chan, A.W.-Y., Baker, C.I., Seeing is not feeling: posterior parietal but not somatosensory cortex engagement during touch observation (2015) J Neurosci, 35 (4), pp. 1468-1480; Chen, T.L., King, C.-H., Thomaz, A.L., Kemp, C.C., Touched by a robot: An investigation of subjective responses to robot-initiated touch (2011) 2011 6Th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 457-464; Crowell, C., Scheutz, M., Schermerhorn, P., Villano, M., (2009) ) Gendered Voice and Robot Entities: Perceptions and Reactions of Male and Female Subjects, , St. Louis, MO; Crusco, A.H., Wetzel, C.G., The midas touch: the effects of interpersonal touch on restaurant tipping (1984) Pers Soc Psychol Bull, 10 (4), pp. 512-517; Dougherty, E.G., Scharfe, H., (2011) Initial Formation of Trust: Designing an Interaction with Geminoid-Dk to Promote a Positive Attitude for Cooperation, pp. 95-103; Erceau, D., Guéguen, N., Tactile contact and evaluation of the toucher (2007) J Soc Psychol, 147 (4), pp. 441-444; Fisher, J.D., Rytting, M., Heslin, R., Hands touching hands: affective and evaluative effects of an interpersonal touch (1976) Sociometry, 39 (4), pp. 416-421; Fukuda, H., Shiomi, M., Nakagawa, K., Ueda, K., (2012) Midas touch’ in Human-Robot Interaction: Evidence from Event-Related Potentials during the Ultimatum Game, pp. 131-132; Hall, J.A., Touch, status, and gender at professional meetings (1996) J Nonverbal Behav, 20 (1), pp. 23-44; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Hum Factors, 53 (5), pp. 517-527; Hirano, T., Shiomi, M., Iio, T., Kimoto, M., Tanev, I., Shimohara, K., Hagita, N., How do communication cues change impressions of human-robot touch interaction? (2018) Int J Soc Robot, 10 (1), pp. 21-31; Hoff, K.A., Bashir, M., Trust in automation: integrating empirical evidence on factors that influence trust (2015) Hum Factors, 57 (3), pp. 407-434; Hoffmann, L., (2017) That Robot Touch that Means So Much: On the Psychological Effects of Human-Robot Touch, , Ph.D. thesis, University of Duisburg-Essen, Germany; Hornik, J., Tactile stimulation and consumer response (1992) J Consum Res, 19 (3), pp. 449-458; Hung, L., Liu, C., Woldum, E., Au-Yeung, A., Berndt, A., Wallsworth, C., Horne, N., Chaudhury, H., The benefits of and barriers to using a social robot PARO in care settings: a scoping review (2019) BMC Geriatr, 19 (1), p. 232; (2019) JASP (Version 0.11.1, , Computer software; Jones, S.E., Yarbrough, A.E., A naturalistic study of the meanings of touch (1985) Commun Monogr, 52 (1), pp. 19-56; Jourard, S.M., An exploratory study of body-accessibility (1966) Br J Soc Clin Psychol, 5 (3), pp. 221-231; Law, T., Chita-Tegmark, M., Scheutz, M., The interplay between emotional intelligence, trust, and gender in human-robot interaction (2020) Int J Soc Robot, 86, pp. 1-13; Law, T., Scheutz, M., Trust: recent concepts and evaluations in human-robot interaction (2021) Trust in human-robot interaction: research and applications, chapter 2, , Nam CS, Lyons JB, (eds), Elsevier, San Diego; Lee, J.D., See, K.A., Trust in automation: designing for appropriate reliance (2004) Hum Factors, 46 (1), pp. 50-80; Levav, J., Argo, J.J., Physical contact and financial risk taking (2010) Psychol Sci, 21 (6), pp. 804-810; Malle, B.F., Fischer, K., Young, J.E., Moon, A., Collins, E.C., Trust and the discrepancy between expectations and actual capabilities of social robots Human-Robot Interaction: Control, Analysis, and Design, , in press, In: Zhang D, Wei B (eds), Cambridge Scholars Publishing, New York; Malle, B.F., Ullman, D., A multi-dimensional conception and measure of human-robot trust (2021) Trust in human-robot interaction: research and applications, chapter 1, pp. 3-25. , Nam CS, Lyons JB, (eds), Elsevier, San Diego; Nakagawa, K., Shiomi, M., Shinozawa, K., Matsumura, R., Ishiguro, H., Hagita, N., Effect of robot’s active touch on people’s motivation (2011) Proceedings of the 6Th International Conference on Human-Robot Interaction, pp. 465-472. , ACM; Nass, C.I., Brave, S., (2005) Wired for speech: how voice activates and advances the human-computer relationship, , MIT Press, Cambridge; Nie, J., Park, M., Marin, A.L., Sundar, S.S., Can you hold my hand? 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Investigating the Effects of Robot Communication Strategies in Advice-Giving Situations Based on Robot Appearance, Interaction Modality and Distance, pp. 479-486; Torrey, C., Fussell, S.R., Kiesler, S., How a robot should give advice (2013) 2013 8Th ACM/IEEE International Conference on Human-Robot Interaction (HRI)., pp. 275-282. , IEEE; Ullman, D., Malle, B.F., The Multidimensional Measure of Trust (MDMT), , http://research.clps.brown.edu/SocCogSci/Measures/MDMT_v1.pdf, v1. Retrieved from; Ullman, D., Malle, B.F., What does it mean to trust a robot? Steps toward a multidimensional measure of trust (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 263-264. , ACM; Ullman, D., Malle, B.F., (2019) Measuring Gains and Losses in Human-Robot Trust: Evidence for Differentiable Components of Trust, pp. 618-619; Willemse, C.J., Toet, A., van Erp, J.B., Affective and behavioral responses to robot-initiated social touch: toward understanding the opportunities and limitations of physical contact in human-robot interaction (2017) Front ICT, 4, p. 12; Willemse, C.J., van Erp, J.B., Social touch in human-robot interaction: robot-initiated touches can induce positive responses without extensive prior bonding (2019) Int J Soc Robot, 11 (2), pp. 285-304; Wilson, B.M., Harris, C.R., Wixted, J.T., Science is not a signal detection problem (2020) Proc Nat Acad Sci, 117 (11), pp. 5559-5567","Scheutz, M.; HRI Lab, United States; email: matthias.scheutz@tufts.edu",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85098972666
"Tanioka T., Locsin R.C., Betriana F., Kai Y., Osaka K., Baua E., Schoenhofer S.","7003753763;7003940978;57204071741;35966296100;8279753000;57208226904;6701588918;","Intentional observational clinical research design: Innovative design for complex clinical research using advanced technology",2021,"International Journal of Environmental Research and Public Health","18","21","11184","","",,,"10.3390/ijerph182111184","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117604088&doi=10.3390%2fijerph182111184&partnerID=40&md5=75832bf48be4d337014a9e1ecdb774ca","Department of Nursing Outcome Management, Institute of Biomedical Sciences, Tokushima University, Tokushima, 770-8509, Japan; Institute of Biomedical Sciences, Tokushima University, Tokushima, 770-8509, Japan; Christine E, Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL  33431, United States; Graduate School of Health Sciences, Tokushima University, Tokushima, 770-8503, Japan; Department of Mechanical Engineering, Tokai University, Hiratsuka, 259-1292, Japan; Department of Nursing, Nursing Course of Kochi Medical School, Kochi University, Kochi, 783-8505, Japan; Graduate School, St. Paul University Philippines, Tuguegarao, 3500, Philippines; Anne Boykin Institute, Florida Atlantic University, Boca Raton, FL  33431, United States","Tanioka, T., Department of Nursing Outcome Management, Institute of Biomedical Sciences, Tokushima University, Tokushima, 770-8509, Japan; Locsin, R.C., Institute of Biomedical Sciences, Tokushima University, Tokushima, 770-8509, Japan, Christine E, Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL  33431, United States; Betriana, F., Graduate School of Health Sciences, Tokushima University, Tokushima, 770-8503, Japan; Kai, Y., Department of Mechanical Engineering, Tokai University, Hiratsuka, 259-1292, Japan; Osaka, K., Department of Nursing, Nursing Course of Kochi Medical School, Kochi University, Kochi, 783-8505, Japan; Baua, E., Graduate School, St. Paul University Philippines, Tuguegarao, 3500, Philippines; Schoenhofer, S., Anne Boykin Institute, Florida Atlantic University, Boca Raton, FL  33431, United States","The growing use of robots in nursing and healthcare facilities has prompted increasing research on human–robot interactions. However, specific research designs that can guide researchers to conduct rigorous investigations on human–robot interactions are limited. This paper aims to discuss the development and application of a new research design—the Intentional Observational Clinical Research Design (IOCRD). Data sources to develop the IOCRD were derived from surveyed literature of the past decade, focusing on clinical nursing research and theories relating robotics to nursing and healthcare practice. The distinction between IOCRD and other research design is the simultaneous data generation collected using advanced technological devices, for example, the wireless Bonaly-light electrocardiogram (ECG) to track heart rate variability of research subjects, robot application programs on the iPad mini to control robot speech and gestures, and Natural Language Processing programs. Even though IOCRD was developed for human–robot research, there remain vast opportunities for its use in nursing practice and healthcare. With the unique feature of simultaneous data generation and analysis, an interdisciplinary collaborative research team is strongly suggested. The IOCRD is expected to contribute guidance for researchers in conducting clinical research related to robotics in nursing and healthcare. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Advanced technologies; Clinical research; Healthcare; Intentional observation; Mixed methods research; Novel research design; Nursing","data set; health care; health services; innovation; public health; technological development",,,,,"Japan Society for the Promotion of Science, KAKEN: JP17H01609","Funding: This research study was supported by JSPS KAKENHI Grant Number JP17H01609.",,,,,,,,,,"Fiorini, L., Griffiths, A., Houdmont, J., Mixed methods research in the health sciences: A review (2016) Malta J. 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"Esterwood C., Robert L.P.","57216331965;8247734500;","A Systematic Review of Human and Robot Personality in Health Care Human-Robot Interaction",2021,"Frontiers in Robotics and AI","8",,"748246","","",,,"10.3389/frobt.2021.748246","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116360922&doi=10.3389%2ffrobt.2021.748246&partnerID=40&md5=b8921e408b252b525b11336502577d43","School of Information, University of Michigan, Ann ArborMI, United States; Robotics Institute, University of Michigan, Ann ArborMI, United States","Esterwood, C., School of Information, University of Michigan, Ann ArborMI, United States; Robert, L.P., School of Information, University of Michigan, Ann ArborMI, United States, Robotics Institute, University of Michigan, Ann ArborMI, United States","Robots have become vital to the delivery of health care and their personalities are often important to understanding their effectiveness as health care providers. Despite this, there is a lack of a systematic overarching understanding of personality in health care human-robot interaction. This makes it difficult to understand what we know and do not know about the impact of personality in health care human-robot interaction (H-HRI). As a result, our understanding of personality in H-HRI has not kept pace with the deployment of robots in various health care environments. To address this, the authors conducted a literature review that identified 18 studies on personality in H-HRI. This paper expands, refines, and further explicates the systematic review done in a conference proceedings [see: Esterwood (Proceedings of the 8th International Conference on Human-Agent Interaction, 2020, 87–95)]. Review results: 1) highlight major thematic research areas, 2) derive and present major conclusions from the literature, 3) identify gaps in the literature, and 4) offer guidance for future H-HRI researchers. Overall, this paper represents a reflection on the existing literature and provides an important starting point for future research on personality in H-HRI. © Copyright © 2021 Esterwood and Robert.","big five personality; health care HRI; healthcare; healthcare robots; HRI (human robot interaction); personality; systematic review",,,,,,,,,,,,,,,,,"Andrist, S., Mutlu, B., Tapus, A., (2015) Look like Me: Matching Robot Personality via Gaze to Increase Motivation, pp. 3603-3612. , New York, NY, USA, ACM; Asch, S.E., (1961) Forming Impressions of Personality, , University of California Press; Baumert, A., Schmitt, M., Perugini, M., Johnson, W., Blum, G., Borkenau, P., Integrating Personality Structure, Personality Process, and Personality Development (2017) Eur. J. Pers, 31, pp. 503-528; Bem, S.L., (1974) Bem Sex Role Inventory [database Record], p. 10. , Retrieved from PsycTests; Bhandari, G., (2020) Robotics Industry Market Report. Tech. 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Robot. AI",Review,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85116360922
"Gonzalez-Billandon J., Belgiovine G., Tata M., Sciutti A., Sandini G., Rea F.","57207198944;57194709146;7005632914;36957816700;35280694500;54386065400;","Self-supervised learning framework for speaker localisation with a humanoid robot",2021,"IEEE International Conference on Development and Learning, ICDL 2021",,,,"","",,1,"10.1109/ICDL49984.2021.9515566","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114556238&doi=10.1109%2fICDL49984.2021.9515566&partnerID=40&md5=88baa0619c8151d1e4bf1b7fb9ab1a04","Italian Institute of Technology, Cognitive Architecture for Collaborative Technologies Lab, Genova, Italy; Italian Institute of Technology, Robotics Brain and Cognitive Science Lab, Genova, Italy; University of Lethbridge, Neuroscience Lab, Canada","Gonzalez-Billandon, J., Italian Institute of Technology, Cognitive Architecture for Collaborative Technologies Lab, Genova, Italy; Belgiovine, G., Italian Institute of Technology, Robotics Brain and Cognitive Science Lab, Genova, Italy; Tata, M., Italian Institute of Technology, Cognitive Architecture for Collaborative Technologies Lab, Genova, Italy; Sciutti, A., University of Lethbridge, Neuroscience Lab, Canada; Sandini, G., Italian Institute of Technology, Robotics Brain and Cognitive Science Lab, Genova, Italy; Rea, F., Italian Institute of Technology, Robotics Brain and Cognitive Science Lab, Genova, Italy","Locating a speaker in the space is a skill that plays an essential role in conducting smooth and natural social interactions. Equipping robots with this ability could lead to more fluid human-robot interaction, also by facilitating voice recognition in noisy environments. Most recently proposed sound localisation systems rely on model-based approaches. However, their performances depend on carefully chosen parameters, especially in the binaural and noisy settings typical of humanoids setups. The need for fine-tuning and for adaptation when facing new environments represents a considerable obstacle to the use and portability of such systems in real human-robot interaction scenarios. To overcome these limitations we propose to rely on data-driven approaches (i.e., deep learning) and exploit multi-sensory mechanisms to leverage the direct experience sensed by the robot during an interaction. Taking inspiration from how humans use vision to calibrate their auditory space representation through experiences, we enabled the robot to learn to localize a speaker in a self-supervised way. Our results show that this approach is suitable to learn to localise speakers in the challenging environments typical of human-robot collaboration. © 2021 IEEE.",,"Anthropomorphic robots; Deep learning; Man machine systems; Data-driven approach; Direct experience; Human-robot collaboration; Humanoid robot; Localisation Systems; Model based approach; Noisy environment; Social interactions; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 804388; European Research Council, ERC","A.S. is supported by a Starting Grant from the European Research Council (ERC) under the European Union s Horizon 2020 research and innovation programme. G.A. No 804388, wHiSPER.","A.S. is supported by a Starting Grant from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme. G.A. No 804388, wHiSPER.",,,,,,,,,"Sandini, G., Sciutti, A., Rea, F., Goswami, A., Vadakkepat, P., Movement-based communication for humanoid-human interaction (2017) Humanoid Robotics: A Reference, pp. 1-29; Haykin, S., Chen, Z., The cocktail party problem (2005) Neural Computation, 17 (9), pp. 1875-1902; Hirsh, I.J., The relation between localization and intelligibility (1950) The Journal of the Acoustical Society of America, 22 (2), pp. 196-200; Yost, W.A., Gourevitch, G., (1987) Directional Hearing, , Springer; Kidd, G., Jr., Mason, C.R., Best, V., Marrone, N., Stimulus factors influencing spatial release from speech-on-speech masking (2010) The Journal of the Acoustical Society of America, 128 (4), pp. 1965-1978; Licklider, J., The influence of interaural phase relations upon the masking of speech by white noise (1948) The Journal of the Acoustical Society of America, 20 (2), pp. 150-159; Arbogast, T.L., Mason, C.R., Kidd, G., Jr., The effect of spatial separation on informational masking of speech in normal-hearing and hearing-impaired listeners (2005) The Journal of the Acoustical Society of America, 117 (4), pp. 2169-2180; Ebata, M., Spatial Unmasking and Attention Related to the Cocktail Party Problem.; Schwartz, J.L., Berthommier, F., Savariaux, C., Seeing to hear better: Evidence for early audio-visual interactions in speech identification (2004) Cognition, 93 (2). , https://pubmed.ncbi.nlm.nih.gov/15147940/, sep; Sumby, W.H., Pollack, I., Visual contribution to speech intelligibility in noise (1954) The Journal of the Acoustical Society of America, 26 (2), pp. 212-215. , https://doi.org/10.1121/1.1907309; Argentieri, S., Danés, P., Souéres, P., Souéres, P.A., Argentieri, S., Danés, P., Sóu Eres, P., (2015) A Survey on Sound Source Localization in Robotics: From Binaural to Array Processing Methods, , https://hal.archives-ouvertes.fr/hal-01058575, Tech. 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Conf. Dev. Learn., ICDL",Conference Paper,"Final","",Scopus,2-s2.0-85114556238
"Shi D., Zhang W., Zhang W., Ju L., Ding X.","56978601000;24832573100;55805950200;57222658160;7401929247;","Human-centred adaptive control of lower limb rehabilitation robot based on human–robot interaction dynamic model",2021,"Mechanism and Machine Theory","162",,"104340","","",,3,"10.1016/j.mechmachtheory.2021.104340","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103613947&doi=10.1016%2fj.mechmachtheory.2021.104340&partnerID=40&md5=c017cab6d1719d051bc26487116fcac4","School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 100191, China","Shi, D., School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China; Zhang, W., School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China, Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 100191, China; Zhang, W., School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China; Ju, L., School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China; Ding, X., School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China, Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 100191, China","A rehabilitation robot can effectively alleviate the shortcomings and deficiencies of traditional treatments. The early stages of rehabilitation training use passive control based on a predefined trajectory. The goal of passive control is to control the motion of the robot, rather than the real motion of the human. However, the human and robot are connected by bands; thus, errors exist between them, and these errors affect the outcome of the rehabilitation training. This study proposes human-centred adaptive control of a lower limb rehabilitation robot based on a dynamic model of human–robot interactions. An equivalent spring model in three-dimensional space is proposed for expressing the interaction torque between the human and robot, reflecting the torque exerted by the robot on the human. A dynamic model for the human–robot system is established based on the human–robot interaction model. To overcome the uncertainty regarding the parameters of the human limbs and robots and that of the stiffness coefficient in the interaction force model, an adaptive controller is designed, and the stability of the system is analysed. The simulation results indicate the effectiveness of the controller. © 2021","Adaptive stepping control; Dynamical modelling; Human-robot interaction; Rehabilitation robot","Adaptive control systems; Controllers; Dynamic models; Neuromuscular rehabilitation; System stability; Adaptive Control; Adaptive stepping control; Dynamical model; Dynamics modelling; Humans-robot interactions; Interaction dynamics; Lower limbs rehabilitation robots; Passive control; Rehabilitation robot; Rehabilitation training; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 91748201, 91848104; National Key Research and Development Program of China, NKRDPC: 2016YFE0105000","This work was supported by the National Key R&D Program of China (Grant number 2016YFE0105000) and the National Natural Science Foundation of China (Grant number 91848104 and 91748201 ). 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"Gao Q., Liu J., Ju Z.","57214809236;7410106559;25655056800;","Hand gesture recognition using multimodal data fusion and multiscale parallel convolutional neural network for human–robot interaction",2021,"Expert Systems","38","5","e12490","","",,7,"10.1111/exsy.12490","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078064988&doi=10.1111%2fexsy.12490&partnerID=40&md5=68a33aadaf893294e532de07da9c540d","State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China; University of Chinese Academy of Sciences, Beijing, China; School of Computing, University of Portsmouth, Portsmouth, United Kingdom","Gao, Q., State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China, University of Chinese Academy of Sciences, Beijing, China; Liu, J., State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China; Ju, Z., State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China, School of Computing, University of Portsmouth, Portsmouth, United Kingdom","Hand gesture recognition plays an important role in human–robot interaction. The accuracy and reliability of hand gesture recognition are the keys to gesture-based human–robot interaction tasks. To solve this problem, a method based on multimodal data fusion and multiscale parallel convolutional neural network (CNN) is proposed in this paper to improve the accuracy and reliability of hand gesture recognition. First of all, data fusion is conducted on the sEMG signal, the RGB image, and the depth image of hand gestures. Then, the fused images are generated to two different scale images by downsampling, which are respectively input into two subnetworks of the parallel CNN to obtain two hand gesture recognition results. After that, hand gesture recognition results of the parallel CNN are combined to obtain the final hand gesture recognition result. Finally, experiments are carried out on a self-made database containing 10 common hand gestures, which verify the effectiveness and superiority of the proposed method for hand gesture recognition. In addition, the proposed method is applied to a seven-degree-of-freedom bionic manipulator to achieve robotic manipulation with hand gestures. © 2020 John Wiley & Sons, Ltd.","hand gesture recognition; multimodal data fusion; parallel CNN; sEMG signal","Convolution; Convolutional neural networks; Data fusion; Degrees of freedom (mechanics); Manipulators; Palmprint recognition; Social robots; Degree of freedom; Fused images; Hand-gesture recognition; Multimodal data fusion; parallel CNN; Robot interactions; Robotic manipulation; Semg signals; Gesture recognition",,,,,"Seventh Framework Programme, FP7; National Natural Science Foundation of China, NSFC: 51575412, 51775541; Chinese Academy of Sciences, CAS: JCTD‐2018‐11; Seventh Framework Programme, FP7: 611391; National Key Research and Development Program of China, NKRDPC: 2018YFB1304600","National Key R&D Program of China, Grant 2018YFB1304600; Natural Science Foundation of China, Grants 51775541 and 51575412; CAS Interdisciplinary Innovation Team, Grant JCTD‐2018‐11; the EU Seventh Framework Programme (FP7)‐ICT, Grant 611391.","National Key R&D Program of China, Grant 2018YFB1304600; Natural Science Foundation of China, Grants 51775541 and 51575412; CAS Interdisciplinary Innovation Team, Grant JCTD-2018-11; the EU Seventh Framework Programme (FP7)-ICT, Grant 611391.",,,,,,,,,"Benalcázar, M.E., Jaramillo, A.G., Zea, A., Páez, A., Andaluz, V.H., (2017) Hand gesture recognition using machine learning and the myo armband, pp. 1040-1044. , 2017 25th European Signal Processing Conference (EUSIPCO), IEEE; Boyali, A., Hashimoto, N., Matsumoto, O., (2015) Hand posture and gesture recognition using MYO armband and spectral collaborative representation based classification, pp. 200-201. , 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE), IEEE; Chen, C., Jafari, R., Kehtarnavaz, N., (2015) Utd-mhad: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor, pp. 168-172. , 2015 IEEE International conference on image processing (ICIP), IEEE; El-Sayed, A., Multi-biometric systems: A state of the art survey and research directions (2015) IJACSA) International Journal of Advanced Computer Science and Applications, 6; Gao, Q., Liu, J., Ju, Z., Li, Y., Zhang, T., Zhang, L., (2017) Static hand gesture recognition with parallel CNNs for space human-robot interaction, pp. 462-473. , International Conference on Intelligent Robotics and Applications, Springer; Gao, Q., Liu, J., Ju, Z., Zhang, X., Dual-hand detection for human-robot interaction by a parallel network based on hand detection and body pose estimation (2019) IEEE Transactions on Industrial Electronics; Gao, Q., Liu, J., Tian, T., Li, Y., Free-flying dynamics and control of an astronaut assistant robot based on fuzzy sliding mode algorithm (2017) Acta Astronautica, 138, pp. 462-474; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2016) Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778; Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L., Large-scale video classification with convolutional neural networks (2014) Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 1725-1732; Kopuklu, O., Kose, N., Rigoll, G., Motion fused frames: Data level fusion strategy for hand gesture recognition (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2103-2111; LeCun, Y., Bengio, Y., Hinton, G., Deep learning (2015) nature, 521 (7553), p. 436; Liu, K., Chen, C., Jafari, R., Kehtarnavaz, N., Fusion of inertial and depth sensor data for robust hand gesture recognition (2014) IEEE Sensors Journal, 14 (6), pp. 1898-1903; Liu, J., Gao, Q., Liu, Z., Li, Y., Attitude control for astronaut assisted robot in the space station (2016) International Journal of Control, Automation and Systems, 14 (4), pp. 1082-1095; Liu, J., Luo, Y., Ju, Z., An interactive astronaut-robot system with gesture control (2016) Computational intelligence and neuroscience, 2016; Malima, A.K., Özgür, E., Çetin, M., (2006) A fast algorithm for vision-based hand gesture recognition for robot control; Miao, Q., Li, Y., Ouyang, W., Ma, Z., Xu, X., Shi, W., Cao, X., Multimodal gesture recognition based on the resc3d network (2017) Proceedings of the IEEE International Conference on Computer Vision, pp. 3047-3055; Molchanov, P., Gupta, S., Kim, K., Kautz, J., Hand gesture recognition with 3D convolutional neural networks (2015) Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 1-7; Norouzi, M., Bengio, S., Jaitly, N., Schuster, M., Wu, Y., Schuurmans, D., (2016) Reward augmented maximum likelihood for neural structured prediction, pp. 1723-1731. , Advances In Neural Information Processing Systems; Raheja, J.L., Shyam, R., Kumar, U., Prasad, P., (2010) Real-time robotic hand control using hand gestures, pp. 12-16. , 2010 Second International Conference on Machine Learning and Computing, IEEE; Rautaray, S., Agrawal, A., Vision based hand gesture recognition for human computer interaction: A survey (2015) Artificial intelligence review, 43 (1), pp. 1-54; Ren, Z., Yuan, J., Meng, J., Zhang, Z., Robust part-based hand gesture recognition using kinect sensor (2013) IEEE transactions on multimedia, 15 (5), pp. 1110-1120; Simonyan, K., Zisserman, A., (2014) Two-stream convolutional networks for action recognition in videos, pp. 568-576. , Advances in neural information processing systems; Smith, A.V.W., Sutherland, A.I., Lemoine, A., Mcgrath, S., (2000) Hand gesture recognition system and method, , US 128,003; Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., Rethinking the inception architecture for computer vision (2016) Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826","Liu, J.; State Key Laboratory of Robotics, China; email: liujinguo@sia.cn",,,"John Wiley and Sons Inc",,,,,02664720,,EXSYE,,"English","Expert Syst",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85078064988
"Zhang B., Wang S., Zhou M.","57201014211;57203504511;57224904825;","A novel control for gait assistance and resistance with a robotic hip exoskeleton using compact soft force sensors",2021,"IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM","2021-July",,,"923","929",,1,"10.1109/AIM46487.2021.9517371","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114964110&doi=10.1109%2fAIM46487.2021.9517371&partnerID=40&md5=754846d0182e9bc7e5a0b243ba9cf088","Xi'An Jiaotong University, School of Mechanical Engineering, Xi'an, China","Zhang, B., Xi'An Jiaotong University, School of Mechanical Engineering, Xi'an, China; Wang, S., Xi'An Jiaotong University, School of Mechanical Engineering, Xi'an, China; Zhou, M., Xi'An Jiaotong University, School of Mechanical Engineering, Xi'an, China","Severa1 recent studies suggest that monitoring the interaction force is crucial for safe and efficient control of wearable robots. This paper presents a novel control of a hip exoskeleton for gait assistance and resistance, which directly introduces human-robot interaction force as feedback for torque control. The real-time interaction force between the human thigh and the robot is measured through a soft pneumatic force sensor system. Thus the delivered torque could be directly monitored in a lightweight, low-cost way using the proposed method, in contrast with traditional torque calculating approaches through the motor current. Furthermore, the soft force sensor-based system was preliminarily applied to control a single-joint hip exoskeleton for gait assistance, resistance, and transparent control modes during level walking. Experimental results show the feasibility of the proposed method in portable exoskeleton control, such as daily assistive or resistive gait training. The advantages of our proposed method include: the good adaptability of the soft sensor to the human body, real-time monitoring of human-robot interaction force via compact sensors, and providing a unified framework for assistive and resistive control of partially-assist exoskeletons. © 2021 IEEE.","Hip exoskeleton; Physical human-robot interaction; Soft force sensor; Wearable robots","Biofeedback; Intelligent mechatronics; Man machine systems; Social robots; Walking aids; Compact sensors; Efficient control; Force sensor systems; Interaction forces; Real time interactions; Real time monitoring; Resistive controls; Unified framework; Exoskeleton (Robotics)",,,,,,,,,,,,,,,,"Mian, O.S., Thom, J.M., Ardigo, L.P., Narici, M.V., Minetti, A.E., Metabolic cost, mechanical work, and efficiency during walking in young and older men (2006) Acta Physiologica, 186 (2), pp. 127-139. , feb; Kang, I., Hsu, H., Young, A., The effect of hip assistance levels on human energetic cost using robotic hip exoskeletons (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 430-437. , apr; 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Kazerooni, H., Steger, R., Huang, L., Hybrid control of the berkeley lower extremity exoskeleton (BLEEX) (2006) The International Journal of Robotics Research, 25 (5-6), pp. 561-573. , may; Buesing, C., Fisch, G., O'Donnell, M., Shahidi, I., Thomas, L., Mummidisetty, C.K., Williams, K.J., Jayaraman, A., Effects of a wearable exoskeleton stride management assist system (SMA®) on spatiotemporal gait characteristics in individuals after stroke: A randomized controlled trial (2015) Journal of NeuroEngineering and Rehabilitation, 12 (1). , aug; Seo, K., Lee, J., Lee, Y., Ha, T., Shim, Y., Fully autonomous hip exoskeleton saves metabolic cost of walking (2016) 2016 IEEE International Conference on Robotics and Automation (ICRA), , IEEE, may; Choi, B., Lee, Y., Lee, J., Lee, M., Lim, B., Park, Y.J., Kim, K., Shim, Y., Development of adjustable knee assist device for wearable robot based on linkage and rolling joint (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , IEEE, nov; McGibbon, C.A., Sexton, A., Jayaraman, A., Deems-Dluhy, S., Gryfe, P., Novak, A., Dutta, T., Bonato, P., Evaluation of the keeogo exoskeleton for assisting ambulatory activities in people with multiple sclerosis: An open-label, randomized, cross-over trial (2018) Journal of NeuroEngineering and Rehabilitation, 15 (1). , dec; Kim, J., Lee, G., Heimgartner, R., Revi, D.A., Karavas, N., Nathanson, D., Galiana, I., Walsh, C.J., Reducing the metabolic rate of walking and running with a versatile, portable exosuit (2019) Science, 365 (6454), pp. 668-672. , aug; Uchida, T.K., Seth, A., Pouya, S., Dembia, C.L., Hicks, J.L., Delp, S.L., Simulating ideal assistive devices to reduce the metabolic cost of running (2016) PLOS ONE, 11 (9). , sep; Bryan, G.M., Franks, P.W., Klein, S.C., Peuchen, R.J., Collins, S.H., A hip-knee-ankle exoskeleton emulator for studying gait assistance (2020) The International Journal of Robotics Research, p. 6145. , nov; Yan, T., Cempini, M., Oddo, C.M., Vitiello, N., Review of assistive strategies in powered lower-limb orthoses and exoskeletons (2015) Robotics and Autonomous Systems, 64, pp. 120-136. , feb; Young, A.J., Ferris, D.P., State of the art and future directions for lower limb robotic exoskeletons (2017) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (2), pp. 171-182. , feb; Yan, T., Parri, A., Garate, V.R., Cempini, M., Ronsse, R., Vitiello, N., An oscillator-based smooth real-time estimate of gait phase for wearable robotics (2016) Autonomous Robots, 41 (3), pp. 759-774. , may; Parri, A., Yuan, K., Marconi, D., Yan, T., Crea, S., Munih, M., Lova, R.M., Wang, Q., Real-time hybrid locomotion mode recognition for lower limb wearable robots (2017) IEEE/ASME Transactions on Mechatronics, 22 (6), pp. 2480-2491. , dec; Seo, K., Park, Y.J., Lee, J., Hyung, S., Lee, M., Kim, J., Choi, H., Shim, Y., RNN-based on-line continuous gait phase estimation from shank-mounted IMUs to control ankle exoskeletons (2019) 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), , IEEE, jun; Thatte, N., Shah, T., Geyer, H., Robust and adaptive lower limb prosthesis stance control via extended kalman filter-based gait phase estimation (2019) IEEE Robotics and Automation Letters, 4 (4), pp. 3129-3136. , oct; Rezazadeh, S., Quintero, D., Divekar, N., Reznick, E., Gray, L., Gregg, R.D., A phase variable approach for improved rhythmic and nonrhythmic control of a powered knee-ankle prosthesis (2019) IEEE Access, 7, pp. 109840-109855; Kang, I., Kunapuli, P., Young, A.J., Real-time neural networkbased gait phase estimation using a robotic hip exoskeleton (2020) IEEE Transactions on Medical Robotics and Bionics, 2 (1), pp. 28-37. , feb; Perry, J., (2010) Gait Analysis: Normal and Pathological Function (2nd Edition), , SLACK Inc; Quintero, D., Villarreal, D.J., Lambert, D.J., Kapp, S., Gregg, R.D., Continuous-phase control of a powered knee-ankle prosthesis: Amputee experiments across speeds and inclines (2018) IEEE Transactions on Robotics, 34 (3), pp. 686-701. , jun; Seo, K., Hyung, S., Choi, B.K., Lee, Y., Shim, Y., A new adaptive frequency oscillator for gait assistance (2015) 2015 IEEE International Conference on Robotics and Automation (ICRA), , IEEE, may; Zhang, B., Wang, S., Zhu, H., A user-adaptive online learning approach of real-time gait phase detection for walking assistance (2019) 2019 IEEE Symposium Series on Computational Intelligence (SSCI), , IEEE, dec; Lim, B., Jang, J., Lee, J., Choi, B., Lee, Y., Shim, Y., Delayed output feedback control for gait assistance and resistance using a robotic exoskeleton (2019) IEEE Robotics and Automation Letters, 4 (4), pp. 3521-3528. , oct; Nagarajan, U., Aguirre-Ollinger, G., Goswami, A., Integral admittance shaping: A unified framework for active exoskeleton control (2016) Robotics and Autonomous Systems, 75, pp. 310-324. , jan; Duong, M.K., Cheng, H., Tran, H.T., Jing, Q., Minimizing human-exoskeleton interaction force using compensation for dynamic uncertainty error with adaptive RBF network (2016) Journal of Intelligent & Robotic Systems, 82 (3-4), pp. 413-433. , aug; Zhang, J., Fiers, P., Witte, K.A., Jackson, R.W., Poggensee, K.L., Atkeson, C.G., Collins, S.H., Human-in-the-loop optimization of exoskeleton assistance during walking (2017) Science, 356 (6344), pp. 1280-1284. , jun; Choi, H., Seo, K., Hyung, S., Shim, Y., Lim, S.-C., Compact hip-force sensor for a gait-assistance exoskeleton system (2018) Sensors, 18 (2), p. 566. , feb; Beil, J., Ehrenberger, I., Scherer, C., Mandery, C., Asfour, T., Human motion classification based on multi-modal sensor data for lower limb exoskeletons (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , IEEE, oct; Cheng, N., Low, J.H., Ang, B.W.K., Goh, A.J.Y., Yeow, C.-H., Soft fabric-based pneumatic sensor for bending angles and contact force detection (2019) IEEE Sensors Journal, 19 (4), pp. 1269-1279. , feb; Laekeman, M., Kreutzer, R., (2009) Großer Bildatlas der Palpation., , Springer Berlin Heidelberg; Qiu, S., Guo, W., Wang, P., Chen, F., Zha, F., Wang, X., Deng, J., A unified active assistance control framework of hip exoskeleton for walking and balance assistance (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , IEEE, nov; Villarreal, D.J., Poonawala, H.A., Gregg, R.D., A robust parameterization of human gait patterns across phase-shifting perturbations (2017) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (3), pp. 265-278. , mar",,,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021","12 July 2021 through 16 July 2021",,171450,,9781665441391,,,"English","IEEE ASME Int Conf Adv Intellig Mechatron AIM",Conference Paper,"Final","",Scopus,2-s2.0-85114964110
"Wang Y., Yang Y., Zhao B., Qi X., Hu Y., Li B., Sun L., Zhang L., Meng M.Q.-H.","57205460526;57206629149;55874456100;48161630700;56201676100;55628528575;57204292698;57210475746;57221185664;","Variable admittance control based on trajectory prediction of human hand motion for physical human-robot interaction",2021,"Applied Sciences (Switzerland)","11","12","5651","","",,,"10.3390/app11125651","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108882387&doi=10.3390%2fapp11125651&partnerID=40&md5=ebf0be1c9d1e39ace82e97d6096518eb","Harbin Institute of Technology, Shenzhen, 518055, China; Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; College of Mechanical and Electrical Engineering, Soochow University, Suzhou, 215000, China; Department of Orthopaedics, Chinese PLA General Hospital, Beijing, 100853, China; Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055, China","Wang, Y., Harbin Institute of Technology, Shenzhen, 518055, China, Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Yang, Y., Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Zhao, B., Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Qi, X., Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Hu, Y., Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Li, B., Harbin Institute of Technology, Shenzhen, 518055, China; Sun, L., College of Mechanical and Electrical Engineering, Soochow University, Suzhou, 215000, China; Zhang, L., Department of Orthopaedics, Chinese PLA General Hospital, Beijing, 100853, China; Meng, M.Q.-H., Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055, China","In order to achieve effective physical human–robot interaction, human dynamic characteristics needs to be considered in admittance control. This paper proposes a variable admittance control method for physical human–robot interaction based on trajectory prediction of human hand motion. By predicting the moving direction of the robot end tool under human guidance, the admittance control parameters are adjusted to reduce the interaction force. The end tool trajectory of the robot under human guidance is used for offline training of long and short-term memory neural network to generate trajectory predictors. Then the trajectory predictors are used in variable admittance control to predict the trajectory and movement direction of the robot end tool in real time. The variable admittance controller adjusts the damping matrix to reduce the damping value in the moving di-rection. Experiment results show that, using the constant admittance method as a benchmark, the interaction force of the proposed method is reduced by 23%, the trajectory error is reduced by 51%, and the operating jerk is reduced by at least 21%, which proves that the proposed method improves the accuracy and compliance of the operation. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Compliance control; Human motion prediction; Long short-term memory network; Medical robots and systems; Physical human–robot interaction; Variable admittance control",,,,,,"JCYJ20180507182215361, JCYJ20190807170407391; National Natural Science Foundation of China, NSFC: U1713218; Guangdong Provincial Key Laboratory of Robotics and Intelligent Systems; Special Project for Research and Development in Key areas of Guangdong Province: 2019B090915001, 2020B0909020002; CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences","Funding: This research was funded by Key-Area Research and Development Program of Guangdong Province (No. 2019B090915001, No.2020B0909020002), in part by the National Natural Science Foundation of China (No. U1713218), in part by Shenzhen Fundamental Research Funds (No. JCYJ20180507182215361, JCYJ20190807170407391). This work is also supported by CAS Key Laboratory of Human–Machine Intelligence-Synergy Systems and Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology.",,,,,,,,,,"Su, H., Yang, C., Ferrigno, G., de Momi, E., Improved human-robot collaborative control of redundant robot for teleoperated minimally invasive surgery (2019) IEEE Robot. Autom. Lett, 4, pp. 1447-1453. , [CrossRef]; Losey, P.D., McDonald, G.C., Battaglia, E., O’Malley, K.M., A review of intent detection, arbitration, and communication aspects of shared control for physical human–robot interaction (2018) Appl. Mech. Rev, 70, p. 010804. , [CrossRef]; Dai, K., Liu, Y., Okui, M., Yamada, Y., Nakamura, T., Variable viscoelasticity handshake manipulator for physical human-robot interaction using artificial muscle and MR brake (2019) Smart Mater. Struct, 28, p. 064002. , [CrossRef]; Ohta, P., Valle, L., King, J., Design of a lightweight soft robotic arm using pneumatic artificial muscles and inflatable sleeves (2018) Soft Robot, 5, pp. 204-215. , [CrossRef] [PubMed]; Rahman, M.M., Ikeura, R., Mizutani, K., Investigation of the impedance characteristic of human arm for development of robots to cooperate with humans (2002) JSME Int. J. Ser. C Mech. Syst. Mach. Elem. Manuf, 45, pp. 510-518. , [CrossRef]; Tsumugiwa, T., Yokogawa, R., Hara, K., Variable impedance control based on estimation of human arm stiffness for human-robot cooperative calligraphic task (2002) Proceedings of the 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292), 1, pp. 644-650. , Washington, DC, USA, 11–15 May; Erden, M.S., Tomiyama, T., Identifying welding skills for training and assistance with robot (2009) Sci. Technol. Weld. Join, 14, pp. 523-532. , [CrossRef]; Erden, M.S., Marić, B., Assisting manual welding with robot (2011) Robot. Comput. Integr. Manuf, 27, pp. 818-828. , [CrossRef]; Erden, M.S., Billard, A., End-point impedance measurements across dominant and nondominant hands and robotic assistance with directional damping (2014) IEEE Trans. Cybern, 45, pp. 1146-1157. , [CrossRef] [PubMed]; Grafakos, S., Dimeas, F., Aspragathos, N., Variable admittance control in pHRI using EMG-based arm muscles co-activation Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1900-1905. , Budapest, Hungary, 9–12 October 2016; Gallagher, W.J., (2013) Modeling of Operator Action for Intelligent Control of Haptic Human-Robot Interfaces, , Ph.D. Thesis, Georgia Institute of Technology, Atlanta, GA, USA; Ajoudani, A., Tsagarakis, N., Bicchi, A., Tele-impedance: Teleoperation with impedance regulation using a body-machine interface (2012) Int. J. Robot. Res, 31, pp. 1642-1656. , [CrossRef]; Flash, T., Hogan, N., The coordination of arm movements: An experimentally confirmed mathematical model (1985) J. Neuroscience, 5, pp. 1688-1703. , [CrossRef]; Sands, T., Development of deterministic artificial intelligence for unmanned underwater vehicles (UUV) (2020) J. Mar. Sci. Eng, 8, p. 578. , [CrossRef]; Sands, T., Control of DC motors to guide unmanned underwater vehicles (2021) Appl. Sci, 11, p. 2144. , [CrossRef]; Baker, K., Cooper, M., Heidlauf, P., Autonomous trajectory generation for deterministic artificial intelligence (2018) Electr. Electron. Eng, 8, pp. 59-68; Sharkawy, A.N., Koustournpardis, P.N., Aspragathos, N., Variable admittance control for human-robot collaboration based on online neural network training Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1334-1339. , Madrid, Spain, 1–5 October 2018; Dimeas, F., Aspragathos, N., Reinforcement learning of variable admittance control for human-robot co-manipulation (2015) Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1011-1016. , Hamburg, Germany, 1 October; Maithani, H., Ramon, J.A.C., Lequievre, L., Exoscarne: Assistive strategies for an industrial meat cutting system based on physical human-robot interaction (2021) Appl. Sci, 11, p. 3907. , [CrossRef]; Medina, J.R., Endo, S., Hirche, S., Impedance-based gaussian processes for predicting human behavior during physical interaction Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 3055-3061. , Stockholm, Sweden, 16–21 May 2016; Lee, K.H., Baek, S.G., Lee, H.J., Enhanced transparency for physical human-robot interaction using human arm impedance compensation (2018) IEEE/ASME Trans. Mechatron, 23, pp. 2662-2670. , [CrossRef]; He, W., Xue, C., Yu, X., Li, Z., Yang, C., Admittance-based controller design for physical human–robot interaction in the constrained task space (2020) IEEE Trans. Autom. Sci. Eng, 17, pp. 1937-1949. , [CrossRef]","Yang, Y.; Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System, China; email: yy.yang1@siat.ac.cn
Li, B.; Harbin Institute of TechnologyChina; email: libing.sgs@hit.edu.cn",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85108882387
"Gaggioli A., Chirico A., Di Lernia D., Maggioni M.A., Malighetti C., Manzi F., Marchetti A., Massaro D., Rea F., Rossignoli D., Sandini G., Villani D., Wiederhold B.K., Riva G., Sciutti A.","6603138127;56755080200;57189076325;8744772600;57193443033;57201195835;36927793400;36495047000;54386065400;57140765700;35280694500;12806098100;7003634518;56962750600;36957816700;","Machines like Us and People like You: Toward Human-Robot Shared Experience",2021,"Cyberpsychology, Behavior, and Social Networking","24","5",,"357","361",,,"10.1089/cyber.2021.29216.aga","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106158296&doi=10.1089%2fcyber.2021.29216.aga&partnerID=40&md5=667017e76e874139a526a84cbc8c2c48","ExperienceLab, And Università Cattolica Del Sacro Cuore, Milan, Italy; Department of Psychology, Università Cattolica Del Sacro Cuore, Milan, Italy; ATN-P Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy; Humane Technology Lab, Università Cattolica Del Sacro Cuore, Milan, Italy; HuroLab, Università Cattolica Del Sacro Cuore, Milan, Italy; DISEIS, Department of International Economics Institutions and Development, Universitá Cattolica Del Sacro Cuore, Milano, Italy; CSCC, Cognitive Science and Communication Research Center, Universitá Cattolica Del Sacro Cuore, Milano, Italy; UniToM, Res. U. on Theor. of Mind, Dept. of Psychology, Universita Cattolica Del Sacro Cuore, Milan, Italy, Italy; Robotics, Brain and Cognitive Sciences (RBCS) Unit, Istituto Italiano di Tecnologia, Genoa, Italy; Virtual Reality Medical Center, San diego, California, United States; Virtual Reality Medical Institute, Brussels, Belgium; Cognitive Architecture for Collaborative Technologies (CONTACT) Unit, Istituto Italiano di Tecnologia, Genoa, Italy","Gaggioli, A., ExperienceLab, And Università Cattolica Del Sacro Cuore, Milan, Italy, Department of Psychology, Università Cattolica Del Sacro Cuore, Milan, Italy, ATN-P Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy, Humane Technology Lab, Università Cattolica Del Sacro Cuore, Milan, Italy; Chirico, A., ExperienceLab, And Università Cattolica Del Sacro Cuore, Milan, Italy; Di Lernia, D., Humane Technology Lab, Università Cattolica Del Sacro Cuore, Milan, Italy; Maggioni, M.A., HuroLab, Università Cattolica Del Sacro Cuore, Milan, Italy, DISEIS, Department of International Economics Institutions and Development, Universitá Cattolica Del Sacro Cuore, Milano, Italy, CSCC, Cognitive Science and Communication Research Center, Universitá Cattolica Del Sacro Cuore, Milano, Italy; Malighetti, C., Humane Technology Lab, Università Cattolica Del Sacro Cuore, Milan, Italy; Manzi, F., Department of Psychology, Università Cattolica Del Sacro Cuore, Milan, Italy, UniToM, Res. U. on Theor. of Mind, Dept. of Psychology, Universita Cattolica Del Sacro Cuore, Milan, Italy, Italy; Marchetti, A., Department of Psychology, Università Cattolica Del Sacro Cuore, Milan, Italy, Humane Technology Lab, Università Cattolica Del Sacro Cuore, Milan, Italy, UniToM, Res. U. on Theor. of Mind, Dept. of Psychology, Universita Cattolica Del Sacro Cuore, Milan, Italy, Italy; Massaro, D., Department of Psychology, Università Cattolica Del Sacro Cuore, Milan, Italy, UniToM, Res. U. on Theor. of Mind, Dept. of Psychology, Universita Cattolica Del Sacro Cuore, Milan, Italy, Italy; Rea, F., Robotics, Brain and Cognitive Sciences (RBCS) Unit, Istituto Italiano di Tecnologia, Genoa, Italy; Rossignoli, D., HuroLab, Università Cattolica Del Sacro Cuore, Milan, Italy, DISEIS, Department of International Economics Institutions and Development, Universitá Cattolica Del Sacro Cuore, Milano, Italy, CSCC, Cognitive Science and Communication Research Center, Universitá Cattolica Del Sacro Cuore, Milano, Italy; Sandini, G., Robotics, Brain and Cognitive Sciences (RBCS) Unit, Istituto Italiano di Tecnologia, Genoa, Italy; Villani, D., Department of Psychology, Università Cattolica Del Sacro Cuore, Milan, Italy; Wiederhold, B.K., Virtual Reality Medical Center, San diego, California, United States, Virtual Reality Medical Institute, Brussels, Belgium; Riva, G., Department of Psychology, Università Cattolica Del Sacro Cuore, Milan, Italy, ATN-P Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy, Humane Technology Lab, Università Cattolica Del Sacro Cuore, Milan, Italy; Sciutti, A., Cognitive Architecture for Collaborative Technologies (CONTACT) Unit, Istituto Italiano di Tecnologia, Genoa, Italy","In the past years, the field of collaborative robots has been developing fast, with applications ranging from health care to search and rescue, construction, entertainment, sports, and many others. However, current social robotics is still far from the general abilities we expect in a robot collaborator. This limitation is more evident when robots are faced with real-life contexts and activities occurring over long periods. In this article, we argue that human-robot collaboration is more than just being able to work side by side on complementary tasks: collaboration is a complex relational process that entails mutual understanding and reciprocal adaptation. Drawing on this assumption, we propose to shift the focus from ""human-robot interaction""to ""human-robot shared experience.""We hold that for enabling the emergence of such shared experiential space between humans and robots, constructs such as coadaptation, intersubjectivity, individual differences, and identity should become the central focus of modeling. Finally, we suggest that this shift in perspective would imply changing current mainstream design approaches, which are mainly focused on functional aspects of the human-robot interaction, to the development of architectural frameworks that integrate the enabling dimensions of social cognition. © Copyright 2021, Mary Ann Liebert, Inc., publishers 2021.","coadaptation; cognitive architecture; collaboration; human-robot interaction; intersubjectivity","human; procedures; robotics; Humans; Robotics",,,,,"Università Cattolica del Sacro Cuore, UCSC","This article was supported by Università Cattolica del Sacro Cuore (D3.2—2018—Human–Robot Confluence project).","This article was supported by Universit? Cattolica del Sacro Cuore (D3.2?2018?Human?Robot Confluence project).",,,,,,,,,"Roschelle, J, Teasley, SD., The construction of shared knowledge in collaborative problem solving (1995) Computer supported collaborative learning. NATO ASI Series (Series F: Computer and Systems Sciences), 128. , O'Malley C, ed. Berlin: Springer; Nikolaidis, S, Hsu, D, Srinivasa, S., Human-robot mutual adaptation in collaborative tasks: models and experiments (2017) The International Journal of Robotics Research, 36, pp. 618-634; Tanevska, A, Rea, F, Sandini, G, A socially adaptable framework for human-robot interaction (2020) Frontiers in Robotics and AI, 7, p. 121; Sandini, G, Mohan, V, Sciutti, A, Social cognition for human-robot symbiosis-challenges and building blocks (2018) Frontiers in Neurobotics, 12, p. 34; Marchetti, A, Di Dio, C, Manzi, F, Robotics in clinical and developmental psychology (2020) Reference Module in Neuroscience and Biobehavioral Psychology; Marchetti, A, Manzi, F, Itakura, S, Theory of mind and humanoid robots from a lifespan perspective (2018) Zeitschrift für Psychologie, 226, pp. 98-109; Manzi, F, Massaro, D, Di Lernia, D, Robot are not all the same: young adults' expectations, attitudes and mental attribution to two social robots Cyberpsychology, Behavior, and Social Networking 2020, , [Epub ahead of print]; Manzi, F, Peretti, G, Di Dio, C, A robot is not worth another: exploring children's mental state attribution to different humanoid robots (2020) Frontiers in Psychology, 10, p. 2011; Di Dio, C, Manzi, F, Peretti, G, Shall I trust you? From child human-robot interaction to trusting relationships (2020) Frontiers in Psychology, 11, p. 469; Stern, DM., (2004) The present moment in psychotherapy and everyday life, , New York: W. & W. Norton & Company; Vygotsky, LS., (1978) Mind in society: the development of higher psychological processes, , Cambridge, MA: Harvard University Press; Manzi, F, Ishikawa, M, Di Dio, C, The understanding of congruent and incongruent referential gaze in 17-month-old infants: an eye-tracking study comparing human and robot (2020) Scientific Reports, 10, p. 11918; Aroyo, AM, Rea, F, Sandini, G, Trust and social engineering in human robot interaction: will a robot make you disclose sensitive information, conform to its recommendations or gamble? (2018) IEEE Robotics and Automation Letters (RA-L), 3, pp. 3701-3708; Cross, ES, Hortensius, R, Wykowska, A., From social brains to social robots: applying neurocognitive insights to human-robot interaction (2019) Philosophical Transactions of the Royal Society B: Biological Sciences, 374, p. 20180024; Manzi, F, Di Dio, C, Di Lernia, D, Can you activate me? From robots to human' brain (2021) Frontiers in Robotics and AI, 8, p. 633514; Dumouchel, P, Damiano, L., (2016) Vivre avec les robots. Essai sur l'empathie artificielle: Essai sur l'empathie artificielle, , Paris, France: Le Seuil; Damiano, L, Dumouchel, P., Anthropomorphism in human-robot co-evolution (2018) Frontiers in Psychology, 9, p. 468; Fong, T, Nourbakhsh, I, Dautenhahn, K., A survey of socially interactive robots (2003) Robotics and Autonomous Systems, 42, pp. 143-166; Maggioni, MA, Rossignoli, D., (2021) If it looks like a human and speaks like a human. dialogue and cooperation in human-robot interactions, , ArXiv: 2104. 11652; Robert, L, Alahmad, R, Esterwood, C, A review of personality in human-robot interactions (2020) Foundations and Trends_ in Information Systems, 4, pp. 107-212; Pervin, LA, Cervone, D, John, OP., (2005) Personality: theory and research, , 9th ed. Hoboken, NJ: John Wiley & Sons; McCrae, RR, Costa, PT, The five-factor theory of personality (2008) Handbook of personality: theory and research, , John OP, Robins RW, Pervin LA, eds. 3rd ed. New York: Guilford; Conti, D, Commodari, E, Buono, S., Personality factors and acceptability of socially assistive robotics in teachers with and without specialized training for children with disability (2017) Life Span and Disability, 20, pp. 251-272; Robert, L., Personality in the human robot interaction literature: a review and brief critique (2018) Personality in the human robot interaction literature: a review and brief critique, Proceedings of the 24th Americas Conference on Information Systems, pp. 16-18. , Robert LP, ed. New Orleans, LA: AMCIS; Reich-Stiebert, N, Eyssel, F., Learning with educational companion robots? Toward attitudes on education robots, predictors of attitudes, and application potentials for education robots (2015) International Journal of Social Robotics, 7, pp. 875-888; Syrdal, DS, Koay, KL, Walters, ML, A personalized robot companion? The role of individual differences on spatial preferences in HRI scenarios (2007) ROMAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication, pp. 1143-1148. , Jeju, Korea: IEEE; De Ruyter, B, Saini, P, Markopoulos, P, Assessing the effects of building social intelligence in a robotic interface for the home (2005) Interacting with Computers, 17, pp. 522-541; Looije, R, Neerincx, MA, Cnossen, F., Persuasive robotic assistant for health self-management of older adults: design and evaluation of social behaviors (2010) International Journal of Human-Computer Studies, 68, pp. 386-397; Tay, B, Jung, Y, Park, T., When stereotypes meet robots: the double-edge sword of robot gender and personality in human-robot interaction (2014) Computers in Human Behavior, 38, pp. 75-84; Andriella, A, Siqueira, H, Fu, D, Do I have a personality? Endowing care robots with context-dependent personality traits (2020) International Journal of Social Robotics, pp. 1-22. , [Epub ahead of print]; Joosse, M, Lohse, M, Perez, JG, What you do is who you are: the role of task context in perceived social robot personality (2013) 2013 IEEE International Conference on Robotics and Automation, pp. 2134-2139. , Karlsruhe, Germany: IEEE; Niculescu, A, van Dijk, B, Nijholt, A, Making social robots more attractive: the effects of voice pitch, humor and empathy (2013) International Journal of Social Robotics, 5, pp. 171-191; Salam, H, Celiktutan, O, Hupont, I, Fully automatic analysis of engagement and its relationship to personality in human-robot interactions (2017) IEEE Access, 5, pp. 705-721; Nomura, T, Kanda, T, Suzuki, T, Prediction of human behavior in human-robot interaction using psychological scales for anxiety and negative attitudes toward robots (2008) IEEE Transactions on Robotics, 24, pp. 442-451; Schellen, E, Wykowska, A., Intentional mindset toward robots-open questions and methodological challenges (2019) Frontiers in Robotics and AI, 5, p. 139; Hinz, NA, Ciardo, F, Wykowska, A., Individual differences in attitude toward robots predict behavior in human-robot interaction (2019) International Conference on Social Robotics, pp. 64-73. , Cham: Springer; Manzi, F, Sorgente, A, Massaro, D, Emerging adults' expectations about next generation of robots: exploring robotic needs through a latent profile analysis (2021) Cyberpsychology, Behavior, and Social Networking, , [Epub ahead of print]; Edwards, A, Edwards, C, Westerman, D, Initial expectations, interactions, and beyond with social robots (2019) Computers in Human Behavior, 90, pp. 308-314; Sciutti, A, Mara, M, Tagliasco, V, Humanizing humanrobot interaction: on the importance of mutual understanding (2018) IEEE Technology and Society Magazine, 37, pp. 22-29; Finkel, EJ, Norton, MI, Reis, HT, When does familiarity promote versus undermine interpersonal attraction? A proposed integrative model from erstwhile adversaries (2015) Perspectives on Psychological Science, 10, pp. 3-19; Buber, M., (1970) I and thou, , (W. Kaufmann, Trans). New York: Charles Scribner's Sons. (Original work published 1923); Kotseruba, I, Tsotsos, JK., 40 years of cognitive architectures: core cognitive abilities and practical applications (2020) Artificial Intelligence Review, 53, pp. 17-94","Gaggioli, A.; Department of Psychology, Largo Gemelli 1, Italy; email: andrea.gaggioli@unicatt.ti",,,"Mary Ann Liebert Inc.",,,,,21522715,,,"34003014","English","Cyberpsychol. Behav. Soc. Networking",Review,"Final","All Open Access, Bronze",Scopus,2-s2.0-85106158296
"Hamaya M., Tanaka K., Shibata Y., Von Drigalski F., Nakashima C., Ijiri Y.","56743162600;55978878700;57215717647;57202938995;57215715842;15064202800;","Robotic learning from advisory and adversarial interactions using a soft wrist",2021,"IEEE Robotics and Automation Letters","6","2","9381639","3878","3885",,,"10.1109/LRA.2021.3067232","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103260860&doi=10.1109%2fLRA.2021.3067232&partnerID=40&md5=e9d180021c5e5dafb8251d9089bef6de","OMRON SINIC X Corporation, Hongo 5-24-5, Bunkyoku, Tokyo, 113-0033, Japan; OMRON Corporation, Konan 2-3-13, Minato-ku, Tokyo, 108-0075, Japan","Hamaya, M., OMRON SINIC X Corporation, Hongo 5-24-5, Bunkyoku, Tokyo, 113-0033, Japan; Tanaka, K., OMRON SINIC X Corporation, Hongo 5-24-5, Bunkyoku, Tokyo, 113-0033, Japan; Shibata, Y., OMRON Corporation, Konan 2-3-13, Minato-ku, Tokyo, 108-0075, Japan; Von Drigalski, F., OMRON SINIC X Corporation, Hongo 5-24-5, Bunkyoku, Tokyo, 113-0033, Japan; Nakashima, C., OMRON Corporation, Konan 2-3-13, Minato-ku, Tokyo, 108-0075, Japan; Ijiri, Y., OMRON SINIC X Corporation, Hongo 5-24-5, Bunkyoku, Tokyo, 113-0033, Japan","In this letter, we developed a novel learning framework from physical human-robot interactions. Owing to human domain knowledge, such interactions can be useful for facilitation of learning. However, applying numerous interactions for training data might place a burden on human users, particularly in real-world applications. To address this problem, we propose formulating this as a model-based reinforcement learning problem to reduce errors during training and increase robustness. Our key idea is to develop 1) an advisory and adversarial interaction strategy and 2) a human-robot interaction model to predict each behavior. In the advisory and adversarial interactions, a human guides and disturbs the robot when it moves in the wrong and correct directions, respectively. Meanwhile, the robot tries to achieve its goal in conjunction with predicting the human's behaviors using the interaction model. To verify the proposed method, we conducted peg-in-hole experiments in a simulation and real-robot environment with human participants and a robot, which has an underactuated soft wrist module. The experimental results showed that our proposed method had smaller position errors during training and a higher number of successes than the baselines without any interactions and with random interactions. © 2016 IEEE.","Physical human-robot interaction; reinforcement learning for robotic control; soft robot applications","Agricultural robots; Educational robots; Random errors; Reinforcement learning; Robotics; Action modeling; Interaction model; Interaction strategy; Learning frameworks; Model-based reinforcement learning; Physical human-robot interactions; Position errors; Robotic learning; Human robot interaction",,,,,,,,,,,,,,,,"Xu, J., Hou, Z., Liu, Z., Qiao, H., (2019) Compare Contact Model-based Control and Contact Model-free Learning:Asurvey of Robotic Peg-in-hole Assembly Strategies; Ogenyi, U.E., Liu, J., Yang, C., Ju, Z., Liu, H., Physical humanrobot collaboration: Robotic systems, learning methods, collaborative strategies, sensors, and actuators (2021) IEEE Trans. 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Robot Learn., pp. 217-226; Rus, D., Tolley, M.T., Design, fabrication and control of soft robots (2015) Nature, 521 (7553), pp. 467-475; Lee, C., Soft robot review (2017) Int. J. Control, Automat. Syst., 15 (1), pp. 3-15; Nishimura, T., Suzuki, Y., Tsuji, T., Watanabe, T., Peg-in-hole under state uncertainties via a passive wrist joint with push-activaterotation function (2017) Proc. IEEE-RAS Int. Conf. Humanoid Robot., pp. 67-74; Wang, S., Chen, G., Xu, H., Wang, Z., A robotic peg-in-hole assembly strategy based on variable compliance center (2019) IEEE Access, 7, pp. 167534-167546; Deimel, R., Brock, O., A novel type of compliant and underactuated robotic hand for dexterous grasping (2016) Int. J. Robot. Res., 35 (1-3), pp. 161-185; Homberg, B.S., Katzschmann, R.K., Dogar, M.R., Rus, D., Robust proprioceptive grasping with a soft robot hand (2019) Auton. Robots, 43 (3), pp. 681-696; Hang, K., Morgan, A.S., Dollar, A.M., Pre-grasp slidingmanipulation of thin objects using soft, compliant, or underactuated hands (2019) IEEE Robot. Automat. Lett., 4 (2), pp. 662-669. , Apr; Ansari, Y., Manti, M., Falotico, E., Cianchetti, M., Laschi, C., Multiobjective optimization for stiffness and position control in a soft robot arm module (2018) IEEE Robot. Automat. Lett., 3 (1), pp. 108-115. , Jan; Ding, Y., Kim, M., Kuindersma, S., Walsh, C.J., Human-in-The-loop optimization of hip assistance with a soft exosuit during walking (2018) Sci. Robot., 3 (15); Hamaya, M., Matsubara, T., Noda, T., Teramae, T., Morimoto, J., Learning assistive strategies for exoskeleton robots from user-robot physical interaction (2017) Pattern Recognit. Lett., 99, pp. 67-76; Hamaya, M., Learning robotic assembly tasks with lower dimensional systems by leveraging physical softness and environmental constraints (2020) Proc. IEEE Int. Conf. Robot. Automat., pp. 7747-7753; François-Lavet, V., An introduction to deep reinforcement learning (2018) Foundations Trends Mach. Learn., 11 (3-4), pp. 219-354; Nagabandi, A., Konoglie, K., Levine, S., Kumar, V., Deep dynamics models for learning dexterous manipulation (2019) Proc. Conf. Robot Learn., pp. 1101-1112; Bullet Real-time Physics Simulation, , https://pybullet.org/wordpress/; Probabilistic-model-based-Rl, , https://github.com/rlee3359/probabilistic-model-based-rl; Loram, I.D., Gawthrop, P.J., Lakie, M., The frequency of human, manual adjustments in balancing an inverted pendulum is constrained by intrinsic physiological factors (2006) J. Physiol., 577 (1), pp. 417-432","Hamaya, M.; OMRON SINIC X Corporation, Hongo 5-24-5, Japan; email: masashi.hamaya@sinicx.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85103260860
"Mazzei D., Chiarello F., Fantoni G.","24722022100;57215031142;8515018000;","Analyzing Social Robotics Research with Natural Language Processing Techniques",2021,"Cognitive Computation","13","2",,"308","321",,2,"10.1007/s12559-020-09799-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100000736&doi=10.1007%2fs12559-020-09799-1&partnerID=40&md5=fd6761c07aade088831cffa50a466718","Computer Science Department, University of Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy; Department of Energy, Process and System Engineering, University of Pisa, Pisa, Italy; Department of Civil and Industrial Engineering, University of Pisa, Pisa, Italy","Mazzei, D., Computer Science Department, University of Pisa, Largo Bruno Pontecorvo 3, Pisa, 56127, Italy; Chiarello, F., Department of Energy, Process and System Engineering, University of Pisa, Pisa, Italy; Fantoni, G., Department of Civil and Industrial Engineering, University of Pisa, Pisa, Italy","The fast growth of social robotics (SR) has not been unidirectional, but rather towards a multidisciplinary scenario, creating a need for collaboration between different fields. This divergent expansion calls for a clear analysis of the field aimed at better orienting the research, thus paving the future of social robotics. This paper aims at understanding how the SR research field evolved in the last two decades by analyzing academic publications in SR and human–robot interaction using natural language processing (NLP) techniques. The analysis spotted an overlap between SR and human–robot interaction research fields that have been disambiguated using a data-driven approach that leads to the identification of a new group of papers we clustered under the concept of “soft HRI.” This research topic has been analyzed by extracting trends and insights. Finally, another topic modelling step has been applied to identify seven sub-topics that have been discussed and analyzed picturing the current state of the art of SR. The paper reports a complete overview of the SR research field identifying various topics and sub-topics helping researchers in understanding the evolution of this field, thus supporting the strategic placing and evolution of their research activities. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.","Bibliometric analysis; Human–robot interaction; Natural language processing; Social robotics; Topic modelling","Robotics; Social robots; Academic publications; Data-driven approach; NAtural language processing; Research activities; Research fields; Research topics; Robot interactions; State of the art; Natural language processing systems",,,,,,,,,,,,,,,,"Breazeal, C.L., (2004) Designing Sociable Robots, , Cambridge, MIT Press; Breazeal, C., Dautenhahn, K., Kanda, T., (2016); Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Science Robotics, 3 (21), pp. 1-9; Johal, W., (2020) Current robotics reports, pp. 1-9; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Robot Auton Sys, 42 (3-4), p. 143; Goodrich, M.A., Schultz, A.C., HumanRobot Interaction: A Survey (2007) Foundations and Trends® in Human-Computer Interaction, 1 (3); Persson, P., Laaksolahti, J., Lonnqvist, P., Understanding socially intelligent agents-a multilayered phenomenon (2001) IEEE Trans Sys Man Cybern Sys Hum, 31 (5), pp. 349-360; Leite, I., Martinho, C., Paiva, A., Social robots for long-term interaction: a survey (2013) Int J Soc Robot, 5 (2), pp. 291-308; Paiva, A., Leite, I., Boukricha, H., Wachsmuth, I., Empathy in Virtual Agents and Robots (2017) ACM Trans Inter Intell Sys, 7 (3), pp. 1-40; Breazeal, C., Toward sociable robots (2003) Robot Auton Sys, 42 (3-4), pp. 167-175; Mejia, C., Kajikawa, Y., Bibliometric Analysis of Social Robotics Research: Identifying Research Trends and Knowledgebase (2017) Appl Sci, 7 (12), p. 1316; Fischer, K., Niebuhr, O., Studying Language Attitudes Using Robots. Companion of the 2020 ACM/IEEE international conference on human-robot (2020) Interaction, pp. 195-196; Kozima, H., Michalowski, M.P., Nakagawa, C., (2009) Keepon. Int J Soc Robot, 1 (1), pp. 3-18; Salvador, M.J., Silver, S., Mahoor, M.H., An emotion recognition comparative study of autistic and typically-developing children using the zeno robot (2015) IEEE International Conference on Robotics and Automation (ICRA). IEEE; 2015. P, pp. 6128-6133. , https://doi.org/10.1109/ICRA.2015.7140059; Mitchinson, B., Prescott, T.J.M.I.R.O., (2016) A Robot “Mammal” with a Biomimetic Brain-Based Control System. Conference on Biomimetic and Biohybrid Systems, pp. 179-191. , Berlin, Springer; Breazeal, C., (1999) Scassellati B. a Context-Dependent Attention System for a Social Robot. Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pp. 1146-1153. , San Francisco, Morgan Kaufmann Publishers; Metta, G., Sandini, G., Vernon, D., Natale, L., Nori, F., The iCub humanoid robot: An open platform for research in embodied cognition (2008) Proceedings of the 8Th Workshop on Performance Metrics for Intelligent Systems, pp. 50-56; Wainer, J., Robins, B., Amirabdollahian, F., Dautenhahn, K., Using the Humanoid Robot KASPAR to Autonomously Play Triadic Games and Facilitate Collaborative Play Among Children With Autism (2014) IEEE Trans Auton Ment Dev, 6 (3), pp. 183-199; Dautenhahn, K., Nehaniv, C.L., Walters, M.L., Robins, B., Kose-Bagci, H., Mirza, N.A., Blow, M., KASPAR–a minimally expressive humanoid robot for human–robot interaction research (2009) Appl Bionics Biomech, 6 (3-4), pp. 369-397; Mazzei, D., Billeci, L., Armato, A., Lazzeri, N., Cisternino, A., Pioggia, G., Igliozzi, R., de Rossi, D., The FACE of autism. 19th international symposium in robot and human interactive communication (2010) IEEE, pp. 791-796. , https://doi.org/10.1109/ROMAN.2010.5598683; Lazzeri, N., Mazzei, D., Greco, A., Rotesi, A., Lanatà, A., de Rossi, D.E., Can a humanoid face be expressive? A psychophysiological investigation (2015) Front Bioeng Biotechnol, 3, p. 64; Mazzei, D., Lazzeri, N., Hanson, D., de Rossi, D., Hefes: An hybrid engine for facial expressions synthesis to control human-like androids and avatars (2012) 4Th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (Biorob). IEEE; 2012. P, pp. 195-200; Nishio, S., Ishiguro, H., Hagita, N., Geminoid: Teleoperated android of an existing person (2007) Human Robot New Dev, 14, pp. 343-352; Gácsi, M., Kis, A., Faragò, T., Janiak, M., Muszyński, R., Miklósi, A., Humans attribute emotions to a robot that shows simple behavioural patterns borrowed from dog behaviour (2016) Comput Hum Behav, 59, pp. 411-419. , 10.1016/j.chb.2016.02.043. Cited By 10. DOI; Kumari, R., Jeong, J.Y., Lee, B.H., Choi, K.N., Choi, K., (2019) J Inf Sci, p. 1; Nocentini, O., Fiorini, L., Acerbi, G., Sorrentino, A., Mancioppi, G., Cavallo, F., A Survey of Behavioral Models for Social Robots (2019) Robotics, 8 (3), p. 54; Robinson, N.L., Cottier, T.V., Kavanagh, D.J., Psychosocial health interventions by social robots: systematic review of randomized controlled trials (2019) J Med Internet Res, 21 (5); Dawe, J., Sutherland, C., Barco, A., Broadbent, E., (2019) BMJ Paediat Open, 3 (1), pp. 1-16; Siddiqi, S., Sharan, A., (2015) Int J Comput Appl, 109 (2), pp. 18-23; Liu, R., Wang, Y.K., (2011); Yujian, L., Bo, L., A normalized Levenshtein distance metric (2007) IEEE Trans Patt Anal Mach Intell, 29 (6), pp. 1091-1095; Straka, M., Straková, J., Tokenizing, pos tagging, lemmatizing and parsing ud 2.0 with udpipe (2017) Proceedings of the Conll 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pp. 88-99; Chiarello, F., Melluso, N., Bonaccorsi, A., Fantoni, G.A., (2019); Trivelli, L., Apicella, A., Chiarello, F., Rana, R., (2019) Fantoni G, , Tarabella A, British Food J; Cascini, G., Fantoni, G., Montagna, F., (2013) Des Stud, 34 (5), p. 636; Blei, D.M., Ng, A.Y., Jordan, M.I., Latent Dirichlet Allocation (2003) J Mach Learn Res, 3, pp. 993-1022. , https://doi.org/10.1162/jmlr.2003.3.4-5.993; Ng, K.W., Tian, G.L., Tang, M.L., (2011) Dirichlet and Related Distributions: Theory, Methods and Applications, 888. , New York: Wiley; Amami, M., (2016) Pasi G, pp. 200-210. , Stella F, Faiz R. An lda-based approach to scientific paper recommendation. International conference on applications of natural language to information systems. Springer; Wang, C., Blei, D.M., Collaborative topic modeling for recommending scientific articles (2011) Proceedings of the 17Th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 448-456; Arun, R., Suresh, V., Veni Madhavan, C.E., Narasimha Murthy, M.N., (2010) Lecture Notes in Computer Science, pp. 391-402. , https://doi.org/10.1007/978-3-642-13657-3_43, Berlin, Springer; Cao, J., Xia, T., Li, J., Zhang, Y., Tang, S., A density-based method for adaptive LDA model selection (2009) Neurocomputing, 72 (7-9), pp. 1775-1781. , . 10.1016/J.NEUCOM.2008.06.011; Deveaud, R., Sanjuan, E., Bellot, P., Accurate and effective latent concept modeling for ad hoc information retrieval (2014) Doc Num, 17 (1), pp. 61-84. , 10.3166/dn.17.1.61-84. DOI; Griffiths, T.L., Steyvers, M., Finding Scientific Topics (2004) Proc Natl Acad Sci U S A, 101, pp. 5228-5235. , 10.1073/pnas.0307752101; Dagan, I., Lee, L., Pereira, F., (1997); Breazeal, C., Emotion and sociable humanoid robots (2003) Int J Hum Comput Stud, 59 (1-2), pp. 119-155; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int J Soc Robot, 1 (1), p. 71; Calinon, S., Guenter, F., Billard, A., On learning, representing, and generalizing a task in a humanoid robot (2007) IEEE Trans Sys Man Cybern Part B (Cybernetics), 37 (2), pp. 286-298; Dautenhahn, K., Socially intelligent robots: Dimensions of human–robot interaction (2007) Phil Trans Royal Soc B: Biol Sci, 362 (1480), pp. 679-704; Duffy, B.R., Anthropomorphism and the social robot (2003) Robot Auton Sys, 42 (3-4), p. 177; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Hum Fact, 53 (5), pp. 517-527; Pineau, J., Montemerlo, M., Pollack, M., Roy, N., Thrun, S., Towards robotic assistants in nursing homes: Challenges and results (2003) Robot Auton Sys, 42 (3-4), pp. 271-281; Laker, D.R., Powell, J.L., The differences between hard and soft skills and their relative impact on training transfer (2011) Hum Res Dev Quart, 22 (1), pp. 111-122; Schulz, B., The importance of soft skills: Education beyond academic knowledge (2008) Nawa J Commun, 2 (1), pp. 146-154; Taylor, E., Investigating the perception of stakeholders on soft skills development of students: Evidence from South Africa (2016) Inter J E-Skills Lifelong Learn, 12 (1), pp. 1-18; Cimatti, B., Definition, development, assessment of soft skills and their role for the quality of organizations and enterprises (2016) International Journal for quality research, 10 (1), pp. 97-130; Dashtipour, K., Gogate, M., Li, J., Jiang, F., Kong, B., Hussain, A., A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks (2020) Neurocomputing, 380, p. 1","Mazzei, D.; Computer Science Department, Largo Bruno Pontecorvo 3, Italy; email: daniele.mazzei@unipi.it",,,"Springer",,,,,18669956,,,,"English","Cognitive Comput.",Article,"Final","",Scopus,2-s2.0-85100000736
"Kordia A.H., Melo F.S.","57221759112;35569256100;","Compound Movement Recognition Using Dynamic Movement Primitives",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12981 LNAI",,,"456","468",,,"10.1007/978-3-030-86230-5_36","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115441084&doi=10.1007%2f978-3-030-86230-5_36&partnerID=40&md5=0e8292eb142b64908762a736927069f5","INESC-ID, Lisbon, Portugal; Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal","Kordia, A.H., INESC-ID, Lisbon, Portugal, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal; Melo, F.S., INESC-ID, Lisbon, Portugal, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal","This paper proposes a method for recognizing compound trajectories. Given a library of pre-learned movements described using dynamic movement primitives, our approach is able to break down an observed trajectory into its individual components, each of which is a segment of one of the movements in the library. We build on previous work that uses critical points for movement recognition and prediction, and extend it to handle trajectories comprising multiple segments from possibly different movements. Our approach assumes that each segment in the observed trajectory is either the initial segment of a new primitive or the continuation of the previous segment. Then, given a partial trajectory, our method is able to predict the most likely next target—i.e., the end-point of the movement currently being executed, if the latter is executed to the end. By using an effective search tree, our approach is able to run at execution time and provide an efficient way for action recognition and prediction, which has applications in human-robot interaction scenarios. We validate our approach both in simulation and in a human-robot interaction scenario involving the Baxter robot. © 2021, Springer Nature Switzerland AG.","Compound movements; Dynamic movement primitives; Movement prediction; Movement recognition","Forecasting; Human robot interaction; Man machine systems; Trajectories; Break down; Compound movement; Dynamic movement primitives; End-points; Humans-robot interactions; Individual components; Most likely; Movement prediction; Movement recognition; Partial trajectory; Motion estimation",,,,,"PTDC/CCI-COM/7203/2020, UIDB/50021/2020; Fundação para a Ciência e a Tecnologia, FCT","This work was partially supported by national funds through FCT, Funda¸cão para a Ciência e a Tecnologia, under projects UIDB/50021/2020 and PTDC/CCI-COM/7203/2020. The first author acknowledges the Ph.D. grant from the Global Platform for Syrian Students. The authors thank Miguel Vasco for useful discussions and revisions of the document.",,,,,,,,,,"Black, M.J., Jepson, A.D., A probabilistic framework for matching temporal trajectories: Condensation-based recognition of gestures and expressions (1998) ECCV 1998. LNCS, Vol. 1406, pp. 909-924. , https://doi.org/10.1007/BFb0055712, Burkhardt, H., Neumann, B. (eds.), pp. , Springer, Heidelberg; Black, M., Jepson, A., Recognizing temporal trajectories using the condensation algorithm (1998) Proceedings of the 3Rd IEEE International Conference on Automatic Face and Gesture Recognition, pp. 16-21. , , pp; Dermy, O., Paraschos, A., Ewerton, M., Peters, J., Charpillet, F., Ivaldi, S., Prediction of intention during interaction with iCub with probabilistic movement primitives (2017) Front. Robot. AI, 4 (45); Ijspeert, A., Nakanishi, J., Hoffmann, H., Pastor, P., Schaal, S., Dynamical movement primitives Learning attractor models for motor behaviors (2013) Neural Comput, 25 (2), pp. 328-373; Kelso, J., (1995) Dynamic Patterns: The Self-Organization of Brain and Behavior, , MIT Press, Cambridge; Kordia, A., Melo, F., An end-to-end approach for learning and generating complex robot motions from demonstration (2020) Proceedings of the 16Th IEEE International Conference on Control, Automation, Robotics and Vision, Pp. 1008–1014; Kordia, A., Melo, F., Movement recognition and prediction using DMPs (2021) Proceedings of the 2021 IEEE International Conference on Robotics and Automation; Kulvicius, T., Ning, K., Tamosiunaite, M., Worgötter, F., Joining movement sequences: Modified dynamic movement primitives for robotics applications exemplified on handwriting (2012) IEEE Trans. Robot., 28 (1), pp. 145-157; Lee, D., Ott, C., Nakamura, Y., Mimetic communication model with compliant physical contact in human-humanoid interaction (2010) Int. J. Robot. Res., 29 (13), pp. 1684-1704; Loshchilov, I., A computationally efficient limited memory CMA-ES for large scale optimization (2014) Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 397-404. , , pp; Ma, S., Zhang, J., Ikizler-Cinbis, N., Sclaroff, S., Action recognition and localization by hierarchical space-time segments (2013) Proceedings of the IEEE International Conference on Computer Vision, pp. 2744-2751. , , pp; Maeda, G., Neumann, G., Ewerton, M., Lioutikov, R., Kroemer, O., Peters, J., Probabilistic movement primitives for coordination of multiple human-robot collaborative tasks (2017) Auton. Robot., 41 (3), pp. 593-612; Meier, F., Theodorou, E., Schaal, S., Movement segmentation and recognition for imitation learning (2012) Proceedings of the 15Th International Conference on Artificial Intelligence and Statistics, pp. 761-769. , , pp; Mulling, K., Kober, J., Peters, J., Learning table tennis with a mixture of motor primitives (2010) Proceedings of the 2010 IEEE-RAS International Conference on Humanoid Robots, pp. 411-416. , , pp; Nemec, B., Tamosiunaite, M., Woergoetter, F., Ude, A., Task adaptation through exploration and action sequencing (2009) Proceedings of the 9Th IEEE-RAS International Conference on Humanoid Robots, pp. 610-616. , , pp; Oliver, N., Rosario, B., Pentland, A., A Bayesian computer vision system for modeling human interactions (2000) IEEE Trans. Pattern Anal. Mach. Intell., 22 (8), pp. 831-843; Stulp, F., Sigaud, O., Path integral policy improvement with covariance matrix adaptation (2012) Proceedings of the 29Th International Conference on Machine Learning, pp. 1547-1554. , , pp; Stulp, F., Sigaud, O., Robot skill learning: From reinforcement learning to evolution strategies (2013) PALADYN J. Behav. Robot., 4 (1), pp. 49-61; Tanaka, Y., Kinugawa, J., Sugahara, Y., Kosuge, K., Motion planning with worker’s trajectory prediction for assembly task partner robot (2012) Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1525-1532. , , pp; Yoon, H., Soh, J., Bae, Y., Yang, H., Hand gesture recognition using combined features of location, angle and velocity (2001) Pattern Recogn, 34 (7), pp. 1491-1501","Kordia, A.H.; Instituto Superior Técnico, Portugal; email: ali.kordia@tecnico.ulisboa.pt","Marreiros G.Melo F.S.Lau N.Lopes Cardoso H.Reis L.P.",,"Springer Science and Business Media Deutschland GmbH","20th EPIA Conference on Artificial Intelligence, EPIA 2021","7 September 2021 through 9 September 2021",,265179,03029743,9783030862299,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85115441084
"Joe H.-M.","57188687051;","Locomotion control of a humanoid robot using a biped walking pattern based on physical interaction",2021,"Journal of Institute of Control, Robotics and Systems","27","9",,"668","675",,,"10.5302/J.ICROS.2021.21.0075","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114696836&doi=10.5302%2fJ.ICROS.2021.21.0075&partnerID=40&md5=810441b1669e09f668a723ad2d1b2a71","Department of Robot & Smart System Engineering, South Korea; Department of Artificial Intelligence, Kyungpook National University, South Korea","Joe, H.-M., Department of Robot & Smart System Engineering, South Korea, Department of Artificial Intelligence, Kyungpook National University, South Korea","This paper describes a biped walking pattern for physical interaction between a human and humanoid robot. The study of the physical interaction between robots and humans is an important research field when considering a future society where humans and robots must coexist. In this paper, to move the humanoid robot in the direction intended by the human, the previous method of generating a walking pattern is extended to a method of generating a walking pattern suitable for physical human-robot interaction. When the human applies disturbance to the robot, the robot naturally move in the direction of applied disturbance to recover balance. The applied disturbance is measured by the capture point. To stabilize the perturbed capture point, the desired Zero Moment Point (ZMP) is calculated by using the capture point control. This desired ZMP is tracked by the walking pattern generator. The walking pattern generator automatically calculate the center of mass trajectory, which satisfies the ZMP constraint, by model predictive control. The proposed method was applied to the humanoid robot DRC-HUBO+ and verified that it can be used for physical human-robot interaction. © ICROS 2021.","Biped walking pattern; Capture point; Humanoid robot; Model predictive control; Zero moment point","Agricultural robots; Anthropomorphic robots; Man machine systems; Model predictive control; Applied disturbances; Biped walking pattern; Locomotion control; Physical human-robot interactions; Physical interactions; Walking pattern; Walking pattern generator; Zero moment point; Human robot interaction",,,,,,,,,,,,,,,,"Lim, J., Lee, I., Shim, I., Jung, H., Joe, H.M., Bae, H., Oh, J.H., Robot system of DRC‐HUBO+ and control strategy of team KAIST in DARPA Robotics Challenge finals (2017) Journal of Field Robotics, 34 (4), pp. 802-829; Setiawan, S.A., Yamaguchi, J.I., Hyon, S.H., Takanishi, A., Physical interaction between human and a bipedal humanoid robot-realization of human-follow walking (1999) Proc. of the 1999 IEEE International Conference on Robotics and Automation, 1; Hyon, S.-H., Hale, J.G., Cheng, G., Full-body compliant human–humanoid interaction: Balancing in the presence of unknown external forces (2007) IEEE Transactions on Robotics, 23 (5), pp. 884-898; de Luca, A., Flacco, F., Integrated control for pHRI: Collision avoidance, detection, reaction and collaboration (2012) IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, IEEE; Paolo, B., Morand, C., Robertson, N.M., A proposed gesture set for the control of industrial collaborative robots,” (2012) IEEE RO-MAN: The 21St IEEE International Symposium on Robot and Human Interactive Communication, IEEE; Niculescuandreea, I., Rafaelbanchs, E., Li, H., Why industrial robots should become more social (2014) International Conference on Social Robotics. 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Koolen, T., de Boer, T., Rebula, J., Goswami, A., Pratt, J., Capturability-based analysis and control of legged locomotion, Part 1: Theory and application to three simple gait models (2012) The International Journal of Robotics Research, 31 (9), pp. 1094-1113; Berger, E., Vogt, D., Haji-Ghassemi, N., Jung, B., Amor, H.B., Inferring guidance information in cooperative human-robot tasks (2013) 13Th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp. 124-129. , IEEE; Wieber, P.B., Trajectory free linear model predictive control for stable walking in the presence of strong perturbations (2006) 6Th IEEE-RAS International Conference on Humanoid Robots, pp. 137-142. , IEEE; Kuindersma, S., Deits, R., Fallon, M., Valenzuela, A., Dai, H., Permenter, F., Koolen, T., Tedrake, R., Optimi-zation-based locomotion planning, estimation, and control design for the atlas humanoid robot (2016) Autonomous Robots, 40 (3), pp. 429-455; Vukobratović, M., Borovac, B., Zero-moment point-thirty five years of its life (2004) International Journal of Humanoid Robotics, 1 (1), pp. 157-173; 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Hong, S., Park, G., Lee, Y., Kang, S., Design and control strategy for rescue robot to execute rescue missions in a highly unstructured environment (2020) Journal of Institute of Control, Robotics, and Systems (In Korean), 26 (8), pp. 683-695; Kajita, S., Kanehiro, F., Kaneko, K., Yokoi, K., Hirukawa, H., The 3D linear inverted pendulum mode: A simple modeling for a biped walking pattern generation,” (2001) IEEE/RSJ International Conference on Intelligent Robots and Systems, (1). , IEEE; Kajita, S., Kanehiro, F., Kaneko, K., Fujiwara, K., Harada, K., Yokoi, K., Hirukawa, H., Biped walking pattern generation by using preview control of zero-moment point (2003) IEEE International Conference on Robotics and Automation, 2, pp. 1620-1626. , IEEE; Dimitar, D., Paolillo, A., Wieber, P.-B., Walking motion generation with online foot position adaptation based on ℓ1-norm and ℓ∞-norm penalty formulations,” 2011 IEEE International Conference on Robotics and Automation (2011) IEEE; Ott, C., Roa, M.A., Hirzinger, G., Posture and balance control for biped robots based on contact force optimization (2011) 11Th IEEE-RAS International Conference on Humanoid Robots, pp. 26-33. , IEEE; Joe, H.M., Oh, J.H., A robust balance-control framework for the terrain-blind bipedal walking of a humanoid robot on unknown and uneven terrain (2019) Sensors, 19 (19), p. 4194; Jung, T., Lim, J., Bae, H., Lee, K.K., Joe, H.M., Oh, J.H., Development of the humanoid disaster response platform DRC-HUBO+ (2018) IEEE Transactions on Robotics, 34 (1), pp. 1-17","Joe, H.-M.; Department of Robot & Smart System EngineeringSouth Korea; email: hmjoe@knu.ac.kr",,,"Institute of Control, Robotics and Systems",,,,,19765622,,,,"Korean","J. Inst. Control Rob. Syst.",Article,"Final","",Scopus,2-s2.0-85114696836
"Maroto-Gomez M., Gonzalez R., Castro-Gonzalez A., Malfaz M., Salichs M.A.","57203536791;57198087356;36655483500;8613265500;6603829011;","Speeding-Up Action Learning in a Social Robot with Dyna-Q+: A Bioinspired Probabilistic Model Approach",2021,"IEEE Access","9",,"9476021","98381","98397",,,"10.1109/ACCESS.2021.3095392","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110689203&doi=10.1109%2fACCESS.2021.3095392&partnerID=40&md5=b3eb97053a436fc8e699354505b6373e","Department of Systems Engineering and Automation, University Carlos III of Madrid, Madrid, 28903, Spain","Maroto-Gomez, M., Department of Systems Engineering and Automation, University Carlos III of Madrid, Madrid, 28903, Spain; Gonzalez, R., Department of Systems Engineering and Automation, University Carlos III of Madrid, Madrid, 28903, Spain; Castro-Gonzalez, A., Department of Systems Engineering and Automation, University Carlos III of Madrid, Madrid, 28903, Spain; Malfaz, M., Department of Systems Engineering and Automation, University Carlos III of Madrid, Madrid, 28903, Spain; Salichs, M.A., Department of Systems Engineering and Automation, University Carlos III of Madrid, Madrid, 28903, Spain","Robotic systems that are developed for social and dynamic environments require adaptive mechanisms to successfully operate. Consequently, learning from rewards has provided meaningful results in applications involving human-robot interaction. In those cases where the robot's state space and the number of actions is extensive, dimensionality becomes intractable and this drastically slows down the learning process. This effect is specially notorious in one-step temporal difference methods because just one update is performed per robot-environment interaction. In this paper, we prove how the action-based learning of a social robot can be improved by combining classical temporal difference reinforcement learning methods, such as Q-learning or Q( λ), with a probabilistic model of the environment. This architecture, which we have called Dyna, allows the robot to simultaneously act and plan using the experience obtained during real human-robot interactions. Principally, Dyna improves classical algorithms in terms of convergence speed and stability, which strengthens the learning process. Hence, in this work we have embedded a Dyna architecture in our social robot, Mini, to endow it with the ability to autonomously maintain an optimal internal state while living in a dynamic environment. © 2013 IEEE.","Action learning; decision-making; human-robot interaction; probabilistic model; reinforcement learning; social robots","Learning algorithms; Man machine systems; Memory architecture; Reinforcement learning; Social robots; User experience; Adaptive mechanism; Convergence speed; Dynamic environments; Learning process; Probabilistic modeling; Robot-environment interaction; Temporal difference methods; Temporal difference reinforcement learning; Learning systems",,,,,"S2018/NMT-4331; European Commission, EC","This work was supported in part by the Robots Sociales para Estimulaci n F sica, Cognitiva y Afectiva de Mayores (ROSES), in part by the Innovaci n y Universidades and RoboCity2030-DIH-CM through the Ministerio de Ciencia, in part by the Madrid Robotics Digital Innovation Hub under Grant S2018/NMT-4331, in part by the Programas de Actividades ICD en la Comunidad de Madrid, and in part by the Structural Funds of the European Union (EU).","This work was supported in part by the Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES), in part by the Innovación y Universidades and RoboCity2030-DIH-CM through the Ministerio de Ciencia, in part by the Madrid Robotics Digital Innovation Hub under Grant S2018/NMT-4331, in part by the Programas de Actividades I+D en la Comunidad de Madrid, and in part by the Structural Funds of the European Union (EU).",,,,,,,,,"Connell, J.H., Mahadevan, S., (2012) Robot Learning, 233. , USA: Springer; Thrun, S., Mitchell, T.M., Lifelong robot learning (1995) Robot. 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Syst., 10 (2), pp. 445-454. , Jun","Maroto-Gomez, M.; Department of Systems Engineering and Automation, Spain; email: marmarot@ing.uc3m.es",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85110689203
"Benn C., Grastien A.","56191930100;8925681900;","Reducing moral ambiguity in partially observed human–robot interactions",2021,"Advanced Robotics","35","9",,"537","552",,,"10.1080/01691864.2021.1894233","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102709642&doi=10.1080%2f01691864.2021.1894233&partnerID=40&md5=239b0742b930ac43d927d770139feac4","Humanising Machine Intelligence Grand Challenge, The Australian National University, Canberra, Australia","Benn, C., Humanising Machine Intelligence Grand Challenge, The Australian National University, Canberra, Australia; Grastien, A., Humanising Machine Intelligence Grand Challenge, The Australian National University, Canberra, Australia","Human–robot interactions are increasingly taking place between a robot agent and a human observer who, unable to witness all aspects of the robot's behaviour, is uncertain as to how the robot will behave. This uncertainty has serious consequences when it concerns the normative aspects of machine behaviour: that is, whether the robot's actions are morally permissible. Bringing together distinct threads from robotic design and machine ethics, we demonstrate the importance of conveying ethical understanding and commitment in order to reduce moral ambiguity and how this can demand a behavioural demonstration. We provide a framework that structures these considerations in the form of a broad constraint on robot behaviour: roughly, to avoid behaviour, even if it is permissible, if that behaviour could appear impermissible. Thus, we formalise a model of communicative-behavioural ethics in human–robot interactions. We apply this constraint to a series of example cases demonstrating how it can be modified to incorporate different sources of information including preferences and probabilities. This reveals the complexity of less idealised cases and highlights how the constraint can be fine-tuned along a number of dimensions to take into account, amongst other thing, risk attitudes. © 2021 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","Communication; ethics; human–robot interactions; signalling; uncertainty","Agricultural robots; Behavioral research; Philosophical aspects; Human observers; Risk attitude; Robot interactions; Robotic design; Sources of informations; Human robot interaction",,,,,,,,,,,,,,,,"Floridi, L., Sanders, J.W., On the morality of artificial agents (2004) Minds Machines, 14 (3), pp. 349-379; Moor, J.H., The nature, importance, and difficulty of machine ethics (2006) IEEE Intell Syst, 21 (4), pp. 18-21; Anderson, M., Anderson, S.L., Machine ethics: creating an ethical intelligent agent (2007) Ai Magazine, 28 (4), p. 15; Wallach, W., Asaro, P., (2017) Machine ethics and robot ethics, , London: Routledge; Chen, M., Nikolaidis, S., Soh, H., et al. 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IEEE; 2013. p. 301–308","Benn, C.; Humanising Machine Intelligence Grand Challenge, Australia; email: cmabenn@gmail.com",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Final","",Scopus,2-s2.0-85102709642
"Dong W., Yang L., Gravina R., Fortino G.","56083913500;57215656715;34869586200;6602895297;","Soft wrist-worn multi-functional sensor array for real-time hand gesture recognition",2021,"IEEE Sensors Journal",,,,"","",,1,"10.1109/JSEN.2021.3050175","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099538235&doi=10.1109%2fJSEN.2021.3050175&partnerID=40&md5=4be30efb3f8e1bd80dbc0fafd28e0f33","School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330013, China.; Department of Mechanical and Electrical Engineering, Huazhong Agricultural University, Wuhan, 430070, China. (e-mail: lin.yang@hzau.edu.cn); Department of Informatics, Modeling, Electronics and Systems, University of Calabria, Rende, 87036, Italy.","Dong, W., School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330013, China.; Yang, L., Department of Mechanical and Electrical Engineering, Huazhong Agricultural University, Wuhan, 430070, China. (e-mail: lin.yang@hzau.edu.cn); Gravina, R., Department of Informatics, Modeling, Electronics and Systems, University of Calabria, Rende, 87036, Italy.; Fortino, G., Department of Informatics, Modeling, Electronics and Systems, University of Calabria, Rende, 87036, Italy.","Soft electronics have been widely applied to wearable electronics, hand gesture detection and human robot interaction (HRI). Soft wrist-worn sensor system (SWSS) with electromyography (EMG), strain/pressure sensing abilities has been created for continuous hand gesture recognition, which includes multi-source data sensing, data collection and process, and wireless communication. SWSS is conformal contact with skin surface as the softness of SWSS, and it would improve the wearability with the wrist during long-term hand gesture monitoring. Linear discriminant analysis (LDA) and support vector machines (SVM) algorithms are applied to hand gesture recognition with average accuracy 83.67%, 86.8% for Group #1 (wrist flexion (WF), wrist extension (WE), finger flexion (FF), finger extension (FE), radial deviation (RD), and ulnar deviation (UD)) and 84.71%, 88.53% for Group #2 (sign language 0-9 digits), respectively. It is found that the recognition accuracy has just a little decreased during the long-term hand gesture detection at different sessions (at initial time, one day after, three days after and seven days after). This paper demonstrated the feasibility of gesture recognition via SWSS on wrist, and it could be integrated into wrist-worn electronic system for human robot interaction. IEEE","Electromyography; Gesture recognition; hand gesture recognition; human robot interaction; intelligent algorithm; Robot sensing systems; Sensors; Skin; Soft sensor array; soft wrist-worn sensor system; Strain; wearable electronics; Wrist","Discriminant analysis; Man machine systems; Palmprint recognition; Social robots; Support vector machines; Electronic systems; Hand-gesture recognition; Human robot Interaction (HRI); Linear discriminant analysis; Multifunctional sensors; Recognition accuracy; Support vector machines algorithms; Wireless communications; Gesture recognition",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,1530437X,,,,"English","IEEE Sensors J.",Article,"Article in Press","",Scopus,2-s2.0-85099538235
"Zhang J., Wang W., Qi X., Liao Z.","57220936051;57192615133;57207460732;57210888157;","Social and robust navigation for indoor robots based on object semantic grid and topological map",2020,"Applied Sciences (Switzerland)","10","24","8991","1","20",,1,"10.3390/app10248991","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097892860&doi=10.3390%2fapp10248991&partnerID=40&md5=4ba370f1c80edafa7000ac89c9b6d819","Robotics Institute, Beihang University, Beijing, 100191, China","Zhang, J., Robotics Institute, Beihang University, Beijing, 100191, China; Wang, W., Robotics Institute, Beihang University, Beijing, 100191, China; Qi, X., Robotics Institute, Beihang University, Beijing, 100191, China; Liao, Z., Robotics Institute, Beihang University, Beijing, 100191, China","For the indoor navigation of service robots, human–robot interaction and adapting to the environment still need to be strengthened, including determining the navigation goal socially, improving the success rate of passing doors, and optimizing the path planning efficiency. This paper proposes an indoor navigation system based on object semantic grid and topological map, to optimize the above problems. First, natural language is used as a human–robot interaction form, from which the target room, object, and spatial relationship can be extracted by using speech recognition and word segmentation. Then, the robot selects the goal point from the target space by object affordance theory. To improve the navigation success rate and safety, we generate auxiliary navigation points on both sides of the door to correct the robot trajectory. Furthermore, based on the topological map and auxiliary navigation points, the global path is segmented into each topological area. The path planning algorithm is carried on respectively in every room, which significantly improves the navigation efficiency. This system has demonstrated to support autonomous navigation based on language interaction and significantly improve the safety, efficiency, and robustness of indoor robot navigation. Our system has been successfully tested in real domestic environments. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Auxiliary navigation point; Indoor robot navigation; Natural language interaction; Object sematic grid and topological map; Segmented global path planning",,,,,,"National Natural Science Foundation of China, NSFC: 91748101; National Key Research and Development Program of China, NKRDPC: 2020YFB1313600","Funding: This work was supported by the Natural Science Foundation of China under grant number 91748101 and the National Key Research and Development Program of China under grant number 2020YFB1313600.",,,,,,,,,,"Song, W.K., Song, W.J., Kim, Y., Kim, J., Usability test of KNRC self-feeding robot (2013) IEEE Int. Conf. Rehabil. 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(accessed on 15 November 2020)","Wang, W.; Robotics Institute, China; email: wangweilab@buaa.edu.cn",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85097892860
"Su D., Feng R.","57193014913;57211230117;","EISRP: Efficient infrared signal restoration processing for object tracking in human-robot interaction",2020,"Infrared Physics and Technology","111",,"103544","","",,1,"10.1016/j.infrared.2020.103544","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093964945&doi=10.1016%2fj.infrared.2020.103544&partnerID=40&md5=096881b169c28b8f0e4ba1c7bc1f1580","Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong  518055, China; Orbbec 3D Technology International Inc., Shenzhen, Guangdong  518055, China; Research Institute for Frontier Science, Beihang University, Haidian District, Beijing, 100191, China","Su, D., Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong  518055, China, Orbbec 3D Technology International Inc., Shenzhen, Guangdong  518055, China; Feng, R., Research Institute for Frontier Science, Beihang University, Haidian District, Beijing, 100191, China","Infrared (IR) imaging spectral signal can accurately distinguish different industrial metal materials in the robot vision sensing. The novel IR imaging system can assist the robot limb to gasp the object in the human-robot interaction processing. However, the low-resolution problem is often existed in the IR spectral signals. To solve this issue, we present a novel IR spectral deconvolution method via total variation regularization for robust spectral signal recognition. To reveal the properties of high-resolution IR spectrum, the histogram of spectral gradients is computed and fitted by the logarithmic function. Then, the low-resolution and high-resolution IR spectra can be distinguished by total variation-regularized prior property. To optimize the non-convex deconvolution model, alternating minimization algorithm is introduced to ensure that the instrument function and high-resolution spectral signals converge quickly. The performance of the proposed method is evaluated on the simulated IR spectrum and real spectrum. A large number of quantitative and qualitative experimental results verify that the proposed method can obtain robust results and significantly outperform the existing state-of-the-art spectral deconvolution method. The high-resolution IR spectrum promotes the development of IR spectral recognition in human-robot interaction. © 2020 Elsevier B.V.","Gaze estimation; Human-robot interaction; Infrared imaging; IR signal processing; Robot vision","Industrial robots; Infrared imaging; Man machine systems; Object tracking; Social robots; Alternating minimization algorithms; Deconvolution models; High resolution ir spectrums; Instrument functions; Logarithmic functions; Spectral deconvolution; Spectral gradients; Total variation regularization; Signal reconstruction",,,,,,,,,,,,,,,,"Liu, H., Zhou, M., Multi-order blind deconvolution algorithm with adaptive Tikhonov regularization for infrared spectroscopic data (2015) Infrared Phys. 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Spectrosc., 69, pp. 1013-1022; Sun, Y., Postprocessing and sparse blind source separation of positive and partially overlapped data (2011) Signal Process., 91, pp. 1838-1851","Feng, R.; Research Institute for Frontier Science, Haidian District, China; email: fengrui_buaa@buaa.edu.cn",,,"Elsevier B.V.",,,,,13504495,,IPTEE,,"English","Infrared Phys Technol",Article,"Final","",Scopus,2-s2.0-85093964945
"Arnold T., Scheutz M.","56895720600;6603548841;","HRI ethics and type-token ambiguity: what kind of robotic identity is most responsible?",2020,"Ethics and Information Technology","22","4",,"357","366",,,"10.1007/s10676-018-9485-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056313985&doi=10.1007%2fs10676-018-9485-1&partnerID=40&md5=a409033679ad0529f37e1fac6df9029c","Human-Robot Interaction Laboratory, Department of Computer Science, Tufts University, 200 Boston Avenue, Medford, MA  02155, United States","Arnold, T., Human-Robot Interaction Laboratory, Department of Computer Science, Tufts University, 200 Boston Avenue, Medford, MA  02155, United States; Scheutz, M., Human-Robot Interaction Laboratory, Department of Computer Science, Tufts University, 200 Boston Avenue, Medford, MA  02155, United States","This paper addresses ethical challenges posed by a robot acting as both a general type of system and a discrete, particular machine. Using the philosophical distinction between “type” and “token,” we locate type-token ambiguity within a larger field of indefinite robotic identity, which can include networked systems or multiple bodies under a single control system. The paper explores three specific areas where the type-token tension might affect human–robot interaction, including how a robot demonstrates the highly personalized recounting of information, how a robot makes moral appeals and justifies its decisions, and how the possible need for replacement of a particular robot shapes its ongoing role (including how its programming could transfer to a new body platform). We also consider how a robot might regard itself as a replaceable token of a general robotic type and take extraordinary actions on that basis. For human–robot interaction robotic type-token identity is not an ontological problem that has a single solution, but a range of possible interactions that responsible design must take into account, given how people stand to gain and lose from the shifting identities social robots will present. © 2018, Springer Nature B.V.","Artificial moral agents; Human–robot interaction; Robot ethics; Robotic design","Machine design; Man machine systems; Networked control systems; Philosophical aspects; Robot programming; Robotics; Larger fields; Moral agents; Networked systems; Ontological problems; Robot ethics; Robot interactions; Robotic design; Specific areas; Social robots",,,,,"Office of Naval Research, ONR: N00014-14-1-0144",,,,,,,,,,,"Arkin, R.C., Ulam, P., Wagner, A.R., Moral decision making in autonomous systems: Enforcement, moral emotions, dignity, trust, and deception (2012) Proceedings of the IEEE, 100 (3), pp. 571-589; Benjamin, W., Arendt, H., Zohn, H., Illuminations; edited and with an introduction by Hannah Arendt (1970) Translated by Harry Zohn, , (1st ed.). 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Washington Post, , https://www.washingtonpost.com/news/in-theory/wp/2015/11/02/were-building-superhuman-robots-will-they-be-heroes-or-villains/?utm_term=.a58657bad760; Malle, B.F., Scheutz, M., Moral competence in social robots (2014) 2014 IEEE International Symposium on Ethics in Science, Technology and Engineering, pp. 1-6. , IEEE; Moor, J., Four Kinds of Ethical Robots (2009) ”Philosophy Now, 72, pp. 12-14; Peirce, C.S., (1998) The essential Peirce: Selected philosophical writings, 2. , Indiana University Press, Bloomington; Scheutz, M., The Inherent Dangers of Unidirectional Emotional Bonds between Humans and Social Robots (2011) Robot Ethics: The Ethical and Social Implications of Robotics, p. 205. , Lin P, Abney K, Bekey GA, (eds), MIT Press, Cambridge; Scheutz, M., Teach One, Teach All’—the explosive combination of instructible robots connected via cyber systems (2014) In Proceedings of the 4Th Annual International Conference on Cyber Technology in Automation, Control and Intelligent Systems, pp. 43-48; Scheutz, M., Arnold, T., Feats without heroes: Norms, means, and ideal robotic action (2016) Frontiers in Robotics and AI, 3, p. 32; Seibt, J., Towards an ontology of simulated social interaction: Varieties of the “As If” for robots and humans (2017) Sociality and normativity for robots, pp. 11-39. , Hakli R, Seibt J, (eds), Springer International Publishing, Basel; Strait, M., Briggs, G., Scheutz, M., Some correlates of agency ascription and emotional value and their effects on decision-making (2013) Affective Computing and Intelligent Interaction (ACII), 2013 Humaine Association Conference On, pp. 505-510. , IEEE; Suchman, L., Reconfiguring human-robot relations (2006) ROMAN 2006-The 15Th IEEE International Symposium on Robot and Human Interactive Communication, pp. 652-654. , IEEE; Sung, J., Christensen, H.I., Grinter, R.E., Robots in the wild: Understanding long-term use (2009) 4Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2009, pp. 45-52. , IEEE; Sung, J.Y., Guo, L., Grinter, R.E., Christensen, H.I., My Roomba is Rambo? Intimate home appliances (2007) International Conference on Ubiquituous Computing.; Tapus, A., Mataric, M.J., Scassellati, B., Socially assistive robotics (2007) IEEE Robotics and Automation Magazine, 14 (1), p. 35; Van Wynsberghe, A., Designing robots for care: Care centered value-sensitive design (2013) Science and Engineering Ethics, 19 (2), pp. 407-433; Wetzel, L., (2009) Types and tokens, , MIT Press, Cambridge; Wetzel, L., (2014) Types and Tokens, the Stanford Encyclopedia of Philosophy, , https://plato.stanford.edu/archives/spr2014/entries/types-tokens/, (Spring 2014 Edition), E. N. Zalta (Ed.); Williams, T., Briggs, P., Pelz, N., Scheutz, M., Is robot telepathy acceptable? Investigating effects of nonverbal robot-robot communication on human-robot interaction (2014) Robot and Human Interactive Communication, 2014 RO-MAN: The 23Rd IEEE International Symposium On, pp. 886-891. , IEEE; Yohanan, S., MacLean, K.E., The role of affective touch in human-robot interaction: Human intent and expectations in touching the haptic creature (2012) International Journal of Social Robotics, 4 (2), pp. 163-180; Young, J.E., Sung, J., Voida, A., Sharlin, E., Igarashi, T., Christensen, H.I., Grinter, R.E., Evaluating human-robot interaction (2011) International Journal of Social Robotics, 3 (1), pp. 53-67","Arnold, T.; Human-Robot Interaction Laboratory, 200 Boston Avenue, United States; email: thomas.arnold@tufts.edu",,,"Springer Science and Business Media B.V.",,,,,13881957,,,,"English","Ethics Inf. Technol.",Article,"Final","",Scopus,2-s2.0-85056313985
"Hong Y., Kuang J., Xiao J., Wang Y., Zhao W., Wang J., Zhang Y., Sun Y.","57188671576;57219597651;8836539200;57219606049;55574199565;56521295900;57219606021;57192159430;","Research on the Guide Control Technology of a Cooperative Robot Based on a Handheld Navigator [基于手持导航器的协作机器人引导控制技术研究]",2020,"Tianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban)/Journal of Tianjin University Science and Technology","53","11",,"1183","1190",,,"10.11784/tdxbz201909001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094138141&doi=10.11784%2ftdxbz201909001&partnerID=40&md5=6947e4471eb682ca2d54ff9e764c45c0","School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China; Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300350, China","Hong, Y., School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China, Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300350, China; Kuang, J., School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China, Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300350, China; Xiao, J., School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China, Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300350, China; Wang, Y., School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China, Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300350, China; Zhao, W., School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China, Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300350, China; Wang, J., School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China, Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300350, China; Zhang, Y., School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China, Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300350, China; Sun, Y., School of Mechanical Engineering, Tianjin University, Tianjin, 300350, China, Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300350, China","Manufacturing has created stricter requirements for the interaction and cooperation of robots, which have been difficult for traditional robot interaction technologies to meet.In this paper, a new guide control technology is proposed that uses a handheld navigator based on force/torque recognition, and a guide control mode that realizes the conversion of fast and slow control was developed.The original data of the navigator was read and analyzed, its own coordinate system obtained, the mapping relationship with the robot established, and the quantitative relationship between the output data and the force/torque was obtained by calibration.Then, to reduce the influence of physiological jitter and noise of the operators in the process of manipulation, Kalman filter was introduced to optimize the state of operators.To realize the transformation of the multi-control mode, the variable admittance control model was established.The change rate of the interaction force was obtained using least square method, the admittance parameters in the process of the human-robot interaction were determined, and then the control mode in accordance with the operator's intention was selected.Finally, the feasibility and universality of the method in a human-robot interaction application was experimentally verified. © 2020, Editorial Board of Journal of Tianjin University(Science and Technology). All right reserved.","Cooperative robot; Guide control; Handheld navigator; Variable admittance control model","Least squares approximations; Man machine systems; Metadata; Physiological models; Admittance control; Admittance parameters; Control technologies; Cooperation of robots; Least square methods; Mapping relationships; Multi-control modes; Robot interactions; Social robots",,,,,"16ZXZNGX00140","收稿日期：2019-09-01；修回日期：2019-11-08. 作者简介：洪 鹰（1962— ），男，博士，副教授，hying1122@126.com. 通信作者：肖聚亮，tjxjl@tju.edu.cn. 基金项目：天津市智能制造重大专项资助项目(16ZXZNGX00140). Supported by the Major Projects of Tianjin Intelligent Manufacturing Science and Technology(No. 16ZXZNGX00140).",,,,,,,,,,"Li, Runwei, Research on Off-line Programming System of Industrial Robot for Complex Surface Processing (2014), 黎润伟. 面向复杂曲面加工的工业机器人离线编程系统研究[D]. 广州:华南理工大学机械与汽车工程学院, 2014. [D]. Guangzhou:School of Mechanical and Automotive Engineering, South China University of Technology, (in Chinese); Shen, Jinghao, Gesture Guided Industrial Robot Teaching Technology and Its Application (2017), 沈靖皓. 基于手势引导的工业机器人示教技术研究及应用[D]. 杭州:浙江大学机械工程学院, 2017. [D]. Hangzhou:School of Mechanical Engineering, Zhejiang University, (in Chinese); Asada, H, Izumi, H., Direct teaching and automatic program generation for the hybrid control of robot manipulators[C] (1987) 1987 IEEE International Conference on Robotics and Automation, pp. 1401-1406. , New York, USA; Fukuda, T, Nakaoka, M, Ueyama, T, Direct teaching and error recovery method for assembly task based on a transition process of a constraint condition[C] (2001) 2001 IEEE International Conference on Robotics and Automation, pp. 1518-1523. , New York, USA; Li, Yifeng, Research on Force-Sensorless Direct Teaching of Robot (2016), 黎意枫. 免力矩传感器的机器人直接示教技术研究 [D]. 哈尔滨:哈尔滨工业大学航天学院, 2016. [D]. 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[J]. -(in Chinese); Lee, S H, Li, C J, Kim, D H, The direct teaching and playback method for robotic deburring system using the adaptive force-control[C] (2009) 2009 IEEE International Symposium on Assembly and Manufacturing, pp. 235-240. , New York, USA; Lee, S, Song, C, Kim, K., Design of robot direct-teaching tools in contact with hard surface (2009) 2009 International Symposium on Assembly and Manufacturing, pp. 231-234. , [C] New York, USA; Neto, P, Pires, J N, Moreira, A P., Accelerometer-based control of an industrial robotic arm (2009) The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 1192-1197. , [C] New York, USA; Park, C, Kyung, J H, Do, M H, Development of direct teaching robot system[C] (2011) 2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence, pp. 730-732. , New York, USA; Arimoto, S, Han, H Y, Cheah, C C, Extension of impedance matching to nonlinear dynamics of robotic tasks (1999) Systems & Control Letters, 36 (2), pp. 109-119. , [J]; Zheng, Hongmei, Research on the Static and Dynamic Performance Calibration System for Robots Multi-Axis Wrist Force Sensor (2007), 郑红梅. 机器人多维腕力传感器静, 动态性能标定系统的研究[D]. 合肥:合肥工业大学机械工程学院, 2007. [D]. Hefei:School of Mechanical Engineering, Hefei University of Technology, (in Chinese); Zhang, Lijian, Hu, Ruiqin, Yi, Wangmin, Research on force sensing for the end-load of industrial robot based on a 6-axis force/torque sensor (2017) Acta Automatica Sinica, 43 (3), pp. 439-447. , 张立建, 胡瑞钦, 易旺民, 等. 基于六维力传感器的工业机器人末端负载受力感知研究[J]. 自动化学报, 2017, 43(3):439-447. [J]. -(in Chinese); Kim, M H, Lee, S, Lee, K C., Kalman predictive redundancy system for fault tolerance of safety-critical systems[J] (2010) IEEE Transactions on Industrial Informatics, 6 (1), pp. 46-53; Hogan, N., Impedance control:An approach to manipulation (1985) Journal of Dynamic Systems Measurement and Control, 107, pp. 1-24. , [J]; Ding, Keliang, Shen, Yunzhong, Ou, Jikun, Methods of line-fitting based on total least-squares (2010) Journal of Liaoning Technical University:Natural Science, 29 (1), pp. 44-47. , 丁克良, 沈云中, 欧吉坤. 整体最小二乘法直线拟合[J]. 辽宁工程技术大学学报:自然科学版, 2010, 29(1):44-47. [J]. -(in Chinese); Xiao, Juliang, Zeng, Fan, Zhang, Qiulong, Research on the forcefree control of cooperative robots based on dynamic parameters identification[J] (2019) The Industrial Robot, 46 (4), pp. 499-509","Xiao, J.; School of Mechanical Engineering, China; email: tjxjl@tju.edu.cn",,,"Tianjin University",,,,,04932137,,TCHHA,,"Chinese","Tianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban)",Article,"Final","",Scopus,2-s2.0-85094138141
"Maniadakis M., Hourdakis E., Sigalas M., Piperakis S., Koskinopoulou M., Trahanias P.","8913044900;55410147200;35410448900;56423532000;57189036916;7003421942;","Time-Aware Multi-Agent Symbiosis",2020,"Frontiers in Robotics and AI","7",,"503452","","",,,"10.3389/frobt.2020.503452","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096677753&doi=10.3389%2ffrobt.2020.503452&partnerID=40&md5=180ae438a86e2cbb8897a804d7d45cdb","Institute of Computer Science, Foundation for Research and Technology – Hellas (FORTH), Heraklion, Greece","Maniadakis, M., Institute of Computer Science, Foundation for Research and Technology – Hellas (FORTH), Heraklion, Greece; Hourdakis, E., Institute of Computer Science, Foundation for Research and Technology – Hellas (FORTH), Heraklion, Greece; Sigalas, M., Institute of Computer Science, Foundation for Research and Technology – Hellas (FORTH), Heraklion, Greece; Piperakis, S., Institute of Computer Science, Foundation for Research and Technology – Hellas (FORTH), Heraklion, Greece; Koskinopoulou, M., Institute of Computer Science, Foundation for Research and Technology – Hellas (FORTH), Heraklion, Greece; Trahanias, P., Institute of Computer Science, Foundation for Research and Technology – Hellas (FORTH), Heraklion, Greece","Contemporary research in human-machine symbiosis has mainly concentrated on enhancing relevant sensory, perceptual, and motor capacities, assuming short-term and nearly momentary interaction sessions. Still, human-machine confluence encompasses an inherent temporal dimension that is typically overlooked. The present work shifts the focus on the temporal and long-lasting aspects of symbiotic human-robot interaction (sHRI). We explore the integration of three time-aware modules, each one focusing on a diverse part of the sHRI timeline. Specifically, the Episodic Memory considers past experiences, the Generative Time Models estimate the progress of ongoing activities, and the Daisy Planner devices plans for the timely accomplishment of goals. The integrated system is employed to coordinate the activities of a multi-agent team. Accordingly, the proposed system (i) predicts human preferences based on past experience, (ii) estimates performance profile and task completion time, by monitoring human activity, and (iii) dynamically adapts multi-agent activity plans to changes in expectation and Human-Robot Interaction (HRI) performance. The system is deployed and extensively assessed in real-world and simulated environments. The obtained results suggest that building upon the unfolding and the temporal properties of team tasks can significantly enhance the fluency of sHRI. © Copyright © 2020 Maniadakis, Hourdakis, Sigalas, Piperakis, Koskinopoulou and Trahanias.","artificial time perception; autonomous systems; collaborative task execution; eterogeneous multi-agent planning; human robot interaction (HRI)",,,,,,"641100; 350/23-6/30-12-2019; European Commission, EC: 754423","The current work was partially supported by the EU FET Proactive grant (GA: 641100) TIMESTORM?Mind and Time: Investigation of the Temporal Traits of Human-Machine Convergence, the EU FET Open grant (GA: 754423) EnTiment: Industrial Exploitation and Market Uptake of a Temporal Cognition Toolbox for Commercial Robots, and the FORTH-HONDA Research Institute?JP joint research project (Ref. 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"Szabóová M., Sarnovský M., Krešňáková V.M., Machová K.","57208300075;57015679800;57217175622;17435396000;","Emotion analysis in human-robot interaction",2020,"Electronics (Switzerland)","9","11","1761","1","31",,3,"10.3390/electronics9111761","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094617369&doi=10.3390%2felectronics9111761&partnerID=40&md5=26d82129b9137f47fb0db7ef54a98ada","Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Letná 9, Košice, 040 01, Slovakia","Szabóová, M., Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Letná 9, Košice, 040 01, Slovakia; Sarnovský, M., Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Letná 9, Košice, 040 01, Slovakia; Krešňáková, V.M., Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Letná 9, Košice, 040 01, Slovakia; Machová, K., Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Letná 9, Košice, 040 01, Slovakia","This paper connects two large research areas, namely sentiment analysis and human-robot interaction. Emotion analysis, as a subfield of sentiment analysis, explores text data and, based on the characteristics of the text and generally known emotional models, evaluates what emotion is presented in it. The analysis of emotions in the human-robot interaction aims to evaluate the emotional state of the human being and on this basis to decide how the robot should adapt its behavior to the human being. There are several approaches and algorithms to detect emotions in the text data. We decided to apply a combined method of dictionary approach with machine learning algorithms. As a result of the ambiguity and subjectivity of labeling emotions, it was possible to assign more than one emotion to a sentence; thus, we were dealing with a multi-label problem. Based on the overview of the problem, we performed experiments with the Naive Bayes, Support Vector Machine and Neural Network classifiers. Results obtained from classification were subsequently used in human-robot experiments. Despise the lower accuracy of emotion classification, we proved the importance of expressing emotion gestures based on the words we speak. © 2020 by the authors.","Dictionary approach; Human-robot interaction; Machine learning approach; Sentiment analysis; Social robotics",,,,,,"Agentúra na Podporu Výskumu a Vývoja, APVV: APVV-16-0213, APVV-17-0267","Funding: The work was supported by the Slovak Research and Development Agency under the contract No. APVV-16-0213 Knowledge-based approaches for intelligent analysis of big data and No. APVV-17-0267 Automated Recognition of Antisocial Behaviour in Online Communities.",,,,,,,,,,"Chivarov, N., Chikurtev, D., Chivarov, S., Pleva, M., Ondas, S., Juhar, J., Yovchev, K., Case Study on Human-Robot Interaction of the Remote-Controlled Service Robot for Elderly and Disabled Care (2020) Comput. 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"Di Dio C., Manzi F., Itakura S., Kanda T., Ishiguro H., Massaro D., Marchetti A.","14069963000;57201195835;7006345646;7403134618;26643316500;36495047000;36927793400;","It Does Not Matter Who You Are: Fairness in Pre-schoolers Interacting with Human and Robotic Partners",2020,"International Journal of Social Robotics","12","5",,"1045","1059",,12,"10.1007/s12369-019-00528-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065134118&doi=10.1007%2fs12369-019-00528-9&partnerID=40&md5=5f8627ab63efe9df6ceb532c50be0c78","Research Unit on Theory of Mind, Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy; Human-Robot Laboratory, Università Cattolica del Sacro Cuore, Milan, Italy; Institute of Psychology and Education, University of Neuchâtel, Neuchâtel, Switzerland; Department of Psychology, Graduate School of Letters, Kyoto University, Kyoto, Japan; Human-Robot Interaction Laboratory, Graduate School of Informatics, Kyoto university, Kyoto, Japan; Advanced Telecommunications Research Institute International, IRC/HIL, Keihana Science City, Kyoto, Japan; Department of Adaptive Machine System, Osaka University, Toyonaka, Japan","Di Dio, C., Research Unit on Theory of Mind, Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy, Human-Robot Laboratory, Università Cattolica del Sacro Cuore, Milan, Italy; Manzi, F., Research Unit on Theory of Mind, Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy, Human-Robot Laboratory, Università Cattolica del Sacro Cuore, Milan, Italy, Institute of Psychology and Education, University of Neuchâtel, Neuchâtel, Switzerland; Itakura, S., Department of Psychology, Graduate School of Letters, Kyoto University, Kyoto, Japan; Kanda, T., Human-Robot Interaction Laboratory, Graduate School of Informatics, Kyoto university, Kyoto, Japan, Advanced Telecommunications Research Institute International, IRC/HIL, Keihana Science City, Kyoto, Japan; Ishiguro, H., Advanced Telecommunications Research Institute International, IRC/HIL, Keihana Science City, Kyoto, Japan, Department of Adaptive Machine System, Osaka University, Toyonaka, Japan; Massaro, D., Research Unit on Theory of Mind, Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy, Human-Robot Laboratory, Università Cattolica del Sacro Cuore, Milan, Italy; Marchetti, A., Research Unit on Theory of Mind, Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy, Human-Robot Laboratory, Università Cattolica del Sacro Cuore, Milan, Italy","The relationship between humans and robots is increasingly becoming focus of interest for many fields of research. The studies investigating the dynamics underpinning the human–robot interaction have, up to date, mainly analysed adults’ behaviour when interacting with artificial agents. In this study, we present results associated with the human–robot interaction involving children aged 5 to 6 years playing the Ultimatum Game (UG) with either another child or a humanoid robot. Assessment of children’s attribution of mental and physical properties to the interactive agents showed that children recognized the robot as a distinct entity compared to the human. Nevertheless, consistently with previous studies on adults, the results on the UG revealed very similar behavioural responses and reasoning when the children played with the other child and with the robot. Finally, by analysing children’s justifications for their behaviour at the UG, we found that children tended to consider “fair” only the divisions that were exactly equal (5–5 divisions), and to justify them either in quantitative terms (outcome) or in terms of equity. These results are discussed in terms of theory of mind, as well as in light of developmental theories underpinning children’s behaviour at the Ultimatum Game. © 2019, Springer Nature B.V.","Children; Humanoid; Human–robot interaction; Robot; Theory of mind; Ultimatum Game","Agricultural robots; Anthropomorphic robots; Robotics; Robots; Social robots; Underpinnings; Children; Humanoid; Robot interactions; Theory of minds; Ultimatum game; Game theory",,,,,"Japan Society for the Promotion of Science, JSPS: 15H01846, 16H06301; Università Cattolica del Sacro Cuore, UCSC: D1 2018",,,,,,,,,,,"Argote, L., Goodman, P.S., Schkade, D., The human side of robotics: how worker’s react to a robot (1983) International trends in manufacturing technology, pp. 19-32. , Husband TH, (ed), Springer, New York; Asada, M., MacDorman, K.F., Ishiguro, H., Kuniyoshi, Y., Cognitive developmental robotics as a new paradigm for the design of humanoid robots (2001) Rob Auton Syst, 37 (2), pp. 185-193; Bandura, A., Social cognitive theory of moral thought and action (1991) Handbook of moral behavior and development, 1, pp. 45-103. , Kurtines WM, Gewirtz J, Lamb JL, (eds), Psychology Press, London; Baron-Cohen, S., Precursors to a theory of mind: understanding attention in others (1991) Natural theories of mind: evolution, development and simulation of everyday mindreading, pp. 233-251. , Whiten A, (ed), Blackwell, Oxford; Bartneck, C., Forlizzi, J., A design-centred framework for social human–robot interaction (2004) 13Th IEEE International Workshop on Robot and Human Interactive Communication. 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J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85065134118
"Yu B., Chen C., Zhou F., Wan F., Zhuang W., Zhao Y.","57219178267;57222154404;12646806200;57201587954;57219183254;56303954400;","A bottom-up framework for construction of structured semantic 3D scene graph",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341228","8224","8230",,,"10.1109/IROS45743.2020.9341228","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102395198&doi=10.1109%2fIROS45743.2020.9341228&partnerID=40&md5=f3f99c811ace70516dca31c41d56a509","Shandong University, School of Control Science and Engineering, Jinan, 250061, China; DarkMatter AI Research, Guangzhou, 511458, China; Qilu University of Technology (Shandong Academy of Sciences), School of Electrical Engineering and Automation, Jinan, 250353, China","Yu, B., Shandong University, School of Control Science and Engineering, Jinan, 250061, China; Chen, C., DarkMatter AI Research, Guangzhou, 511458, China; Zhou, F., Shandong University, School of Control Science and Engineering, Jinan, 250061, China; Wan, F., Shandong University, School of Control Science and Engineering, Jinan, 250061, China; Zhuang, W., Shandong University, School of Control Science and Engineering, Jinan, 250061, China; Zhao, Y., Qilu University of Technology (Shandong Academy of Sciences), School of Electrical Engineering and Automation, Jinan, 250353, China","For high-level human-robot interaction tasks, 3D scene understanding is important and non-trivial for autonomous robots. However, parsing and utilizing effective environment information of the 3D scene is not trivial due to the complexity of the 3D environment and the limited ability for reasoning about our visual world. Although there have been great efforts on semantic detection and scene analysis, the existing solutions for parsing and representation of the 3D scene still fail to preserve accurate semantic information and equip sufficient applicability. This study proposes a bottomup construction framework for structured 3D scene graph generation, which efficiently describes the objects, relations and attributes of the 3D indoor environment with structured representation. In the proposed method, we adopt visual perception to capture the semantic information and inference from scene priors to calculate the optimal parse graph. Afterwards, an improved probabilistic grammar model is used to represent the scene priors. Experiment results demonstrate that the proposed framework significantly outperforms existing methods in terms of accuracy, and a demonstration is provided to verify the applicability in applying to high-level human-robot interaction tasks. The supplementary video can be accessed at the following link: https://youtu.be/vEWNxnSwmKI. © 2020 IEEE.",,"Agricultural robots; Intelligent robots; Man machine systems; Semantics; Three dimensional computer graphics; 3-D environments; Bottom-up frameworks; Environment information; Indoor environment; Probabilistic grammars; Scene analysis; Semantic information; Visual perception; Human robot interaction",,,,,,,,,,,,,,,,"Labbe, M., Michaud, F., Appearance-based loop closure detection for online large-scale and long-term operation (2013) Ieee Transactions on Robotics, 29, pp. 734-745. , jun; Hess, W., Kohler, D., Rapp, H., Andor, D., Real-time loop closure in 2D LIDAR SLAM (2016) 2016 Ieee International Conference on Robotics and Automation (ICRA), pp. 1271-1278. , IEEE, may; Yu, C., Liu, Z., Liu, X.-J., Xie, F., Yang, Y., Wei, Q., Fei, Q., Dsslam: A semantic visual slam towards dynamic environments (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1168-1174. , IEEE, oct; Grinvald, M., Furrer, F., Novkovic, T., Chung, J.J., Cadena, C., Siegwart, R., Nieto, J., Volumetric instance-aware semantic mapping and 3d object discovery (2019) Ieee Robotics and Automation Letters, 4, pp. 3037-3044. , jul; Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Fei-Fei, L., Visual genome: Connecting language and vision using crowdsourced dense image annotations (2017) International Journal of Computer Vision, 123, pp. 32-73. , may; Tang, K., Zhang, H., Wu, B., Luo, W., Liu, W., Learning to compose dynamic tree structures for visual contexts (2019) 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6612-6621. , IEEE, jun; Zellers, R., Yatskar, M., Thomson, S., Choi, Y., Neural motifs: Scene graph parsing with global context (2018) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5831-5840. , IEEE, jun; Johnson, J., Krishna, R., Stark, M., Li, L.J., Shamma, D.A., Bernstein, M.S., Li, F.F., Image retrieval using scene graphs (2015) Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3668-3678. , 07-12-June; Zhou, Y., While, Z., Kalogerakis, E., Scenegraphnet: Neural message passing for 3d indoor scene augmentation (2019) 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 7383-7391. , IEEE, oct; Redmon, J., Farhadi, A., Yolov3: An incremental improvement (2018) ArXiv E-prints, , apr; Ren, S., He, K., Girshick, R., Sun, J., Faster r-cnn: Towards real-time object detection with region proposal networks (2017) Ieee Transactions on Pattern Analysis and Machine Intelligence, 39 (6), pp. 1137-1149; Qin, T., Shen, S., Online temporal calibration for monocular visual-inertial systems (2018) Ieee International Conference on Intelligent Robots and Systems, pp. 3662-3669; Mur-Artal, R., Montiel, J.M., Tardos, J.D., Orb-slam: A versatile and accurate monocular slam system (2015) Ieee Transactions on Robotics, 31 (5), pp. 1147-1163; Kim, U.-H., Park, J.-M., Song, T.-J., Kim, J.-H., 3-d scene graph: A sparse and semantic representation of physical environments for intelligent agents (2019) Ieee Transactions on Cybernetics, pp. 1-13; Armeni, I., He, Z.-Y., Zamir, A., Gwak, J., Malik, J., Fischer, M., Savarese, S., 3d scene graph: A structure for unified semantics, 3d space, and camera (2019) 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 5663-5672. , IEEE, oct; Zhu, S.C., Mumford, D., A stochastic grammar of images (2006) Foundations and Trends in Computer Graphics and Vision, 2 (4), pp. 259-262; Liu, X., Zhao, Y., Zhu, S.-C., Single-view 3d scene parsing by attributed grammar (2014) 2014 Ieee Conference on Computer Vision and Pattern Recognition, pp. 684-691. , IEEE, jun; Kobayashi, Y.I.B.Y., Takahashi, K., (2018) Computer Vision-ECCV 2018 11211 of Lecture Notes in Computer Science, , Cham: Springer International Publishing; Qi, S., Zhu, Y., Huang, S., Jiang, C., Zhu, S.C., Human-centric indoor scene synthesis using stochastic grammar (2018) Proceedings of the Ieee Computer Society Conference on Computer Vision and Pattern Recognition, pp. 5899-5908; Wang, J., Olson, E., Apriltag 2: Efficient and robust fiducial detection (2016) Ieee International Conference on Intelligent Robots and Systems, 2016, pp. 4193-4198. , Novem; Qi, S., Huang, S., Wei, P., Zhu, S.C., Predicting human activities using stochastic grammar (2017) Proceedings of the Ieee International Conference on Computer Vision, 2017, pp. 1173-1181. , IEEE, oct-Octob",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102395198
"Finkel M., Szczuka J., Krämer N.","57220122689;57194098618;7006770164;","Does the Robot Get the Credit?: An Empirical Investigation on the Acquisition of Idiosyncrasy Credit by a Humanoid Robot in the Context of Negative Expectancy Violations",2020,"Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents, IVA 2020",,,,"","",,1,"10.1145/3383652.3423894","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096996562&doi=10.1145%2f3383652.3423894&partnerID=40&md5=28900609d2ed0ca9b778fb43fca4fbe2","Social Psychology: Media and Communication, University of Duisburg-Essen, Duisburg NRW, Germany","Finkel, M., Social Psychology: Media and Communication, University of Duisburg-Essen, Duisburg NRW, Germany; Szczuka, J., Social Psychology: Media and Communication, University of Duisburg-Essen, Duisburg NRW, Germany; Krämer, N., Social Psychology: Media and Communication, University of Duisburg-Essen, Duisburg NRW, Germany","During interactions with others, people have expectancies concerning their communication partners' behaviors. Negative violations of these expectancies are known to exert an adverse impact on people's perceptions and attitudes. Likewise, with regard to human-robot interactions, previous research on one-time interactions indicates that people's evaluations of robots can deteriorate equally. To simultaneously investigate possible compensation for this effect, the concept of idiosyncrasy credit was tested for transferability to human-robot interactions. It postulates that compensation can be achieved by an individual's consistent, beneficial behavior over time that results in a social deviation credit. During an experimental medium-term study with a 2x2 between-subjects design, 80 participants interacted with a humanoid, social robot in three time-separated sessions distributed over an average time span of ten days. The robot's acquisition of idiosyncrasy credit was manipulated as well as the occurrence of a negative expectancy violation by systematically varying the robot's polite communication style. Results of repeated measures ANOVAs support the assumption that negative expectancy violations decrease people's evaluations of a robot. However, this effect seems non-persistent over time. Furthermore, no support for the robot's acquisition of idiosyncrasy credit was gained. © 2020 ACM.","expectancy violations; human-robot interaction; idiosyncrasy credit","Anthropomorphic robots; Intelligent virtual agents; Machine design; Man machine systems; Mergers and acquisitions; Communication partners; Communication styles; Empirical investigation; Humanoid robot; Medium term; One-time; Repeated measures; Time span; Social robots",,,,,,,,,,,,,,,,"Bartneck, C., Kulic, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81. , DOI:https://doi.org/10.1007/-12369-008-0001-3, Jan. 2009; Bartneck, C., Rosalia, C., Menges, R., Deckers, I., Robot abuse: A limitation of the media equation (2005) Proceedings of the Abuse: The Darker Side of Human-Computer Interaction: An Interact 2005 Workshop, September 12, 2015, Rome, Italy., pp. 54-57; Burgoon, J.K., Hale, J.L., Nonverbal expectancy violations: Model elaboration and application to immediacy behaviors (1988) Communications Monographs, 55 (1), pp. 58-79. , DOI:https://doi.org/10.1080/03637758809376158, Mar. 1988; Correia, F., Guerra, C., Mascarenhas, S., Melo, F.S., Paiva, A., Exploring the impact of fault justification in human-robot trust (2018) Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2018), pp. 507-513. , July 10-15, 2018, Stockholm, Sweden. IFAAMAS, Richland, SC; Estrada, M., Brown, J., Lee, F., Who gets the credit? Perceptions of idiosyncrasy credit in work groups (1995) Small Group Research, 26 (1), pp. 56-76. , DOI:https://doi.org/10.1177/1046496495261003, Feb. 1995; Eyssel, F., Hegel, F., (S)he's got the look: Gender stereotyping of robots (2012) Journal of Applied Social Psychology, 42 (9), pp. 2213-2230. , DOI:https://doi.org/10.1111/j.1559-1816.2012.00937.x, Jul. 2012; Fogg, B.J., Nass, C., Silicon sycophants: The effects of computers that flatter (1997) International Journal of Human-Computer Studies, 46 (5), pp. 551-561. , DOI:https://doi.org/10.1006/ijhc.1996.0104, May 1997; Hollander, E.P., Conformity, status, and idiosyncrasy credit (1958) Psychological Review, 65 (2), pp. 117-127. , DOI:https://doi.org/10.1037/h0042501, Mar. 1958; Honig, S., Oron-Gilad, T., Understanding and resolving failures in human-robot interaction: Literature review and model development (2018) Frontiers in Psychology, 9, 21p. , DOI:https://doi.org/10.3389/fpsyg.2018.00861, Jun. 2018; Manzoni, M.E., (2012) Anthropomorphismus Als Kommunikationsinstrument im Marketing: Vermenschlichung von Objekten und Ihre Bedeutung, , Diplomica Verlag, Hamburg, DE; McCroskey, J.C., McCain, T.A., The measurement of interpersonal attraction (1974) Speech Monographs, 41 (3), pp. 261-266. , DOI:https://doi.org/10.1080/03637757409375845; McCroskey, J.C., Teven, J.J., Goodwill: A reexamination of the construct and its measurement (1999) Communication Monographs, 66 (1), pp. 90-103. , DOI:https://doi.org/10.1080/03637759909376464, March 1999; Mirnig, N., Stollnberger, G., Miksch, M., Stadler, S., Giuliani, M., Tscheligi, M., To err is robot: How humans assess and act toward an erroneous social robot (2017) Frontiers in Robotics and Ai 4, 15p. , DOI:https://doi.org/10.3389/frobt.2017.00021, May 2017; Mubin, O., Bartneck, C., Do as i say: Exploring human response to a predictable and unpredictable robot (2015) Proceedings of the 2015 British Hci Conference (British HCI'15), pp. 110-116. , https://doi.org/10.1145/2783446.2783582, July 13-17, 2015, Lincoln, United Kingdom ACM Inc., New York, NY; Nass, C., Moon, Y., Machines and mindlessness: Social responses to computers (2000) Journal of Social Issues, 56 (1), pp. 81-103. , DOI:https://doi.org/10.1111/0022-4537.00153; Packard, C., Boelk, T., Andres, J., Edwards, A., Edwards, C., Spence, P.R., The pratfall effect and interpersonal impressions of a robot that forgets and apologizes (2019) Proceedings of the 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 524-525. , https://doi.org/10.1109/HRI.2019.8673101, March 11-14, 2019, Daegu, Korea IEEE, New York, NY; Reeves, B., Nass, C., (1996) The Media Equation: How People Treat Computers, Television, and New Media like Real People and Places, , CSLI Publications, Stanford, CA; Ridgeway, C.L., Nonconformity, competence, and influence in groups: A test of two theories (1981) American Sociological Review, 46 (3), pp. 333-347. , DOI:https://doi.org/10.2307/2095064, Jun. 1981; Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K., Would you trust a (faulty) robot? Effects of error, task type and personality on human-robot cooperation and trust (2015) Proceedings of the 10th Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI '15), pp. 141-148. , https://doi.org/10.1145/2696454.2696497, March 2-5, 2015, Portland, Oregon. ACM Inc., New York, NY; Sarkar, S., Araiza-Illan, D., Eder, K., (2017) Effects of Faults, Experience, and Personality on Trust in a Robot Co-Worker, , arXiv preprint",,,"ACM Special Interest Group on Artificial Intelligence (SIGAI)","Association for Computing Machinery, Inc","20th ACM International Conference on Intelligent Virtual Agents, IVA 2020","20 October 2020 through 22 October 2020",,164271,,9781450375863,,,"English","Proc. ACM Int. Conf. Intell. Virtual Agents, IVA",Conference Paper,"Final","",Scopus,2-s2.0-85096996562
"Al G.A., Estrela P., Martinez-Hernandez U.","57202987162;14630335800;55316543200;","Towards an intuitive human-robot interaction based on hand gesture recognition and proximity sensors",2020,"IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems","2020-September",,"9235264","330","335",,1,"10.1109/MFI49285.2020.9235264","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096092462&doi=10.1109%2fMFI49285.2020.9235264&partnerID=40&md5=984a2ac5645f1f89fce761fa6a4c64c6","University of Bath, Centre for Autonomous Robotics (CENTAUR), Centre for Bioelectronics, Biosensors and Biodevices (C3Bio), Department of Electronic and Electrical Engineering, United Kingdom","Al, G.A., University of Bath, Centre for Autonomous Robotics (CENTAUR), Centre for Bioelectronics, Biosensors and Biodevices (C3Bio), Department of Electronic and Electrical Engineering, United Kingdom; Estrela, P., University of Bath, Centre for Autonomous Robotics (CENTAUR), Centre for Bioelectronics, Biosensors and Biodevices (C3Bio), Department of Electronic and Electrical Engineering, United Kingdom; Martinez-Hernandez, U., University of Bath, Centre for Autonomous Robotics (CENTAUR), Centre for Bioelectronics, Biosensors and Biodevices (C3Bio), Department of Electronic and Electrical Engineering, United Kingdom","In this paper, we present a multimodal sensor interface that is capable of recognizing hand gestures for human-robot interaction. The proposed system is composed of an array of proximity and gesture sensors, which have been mounted on a 3D printed bracelet. The gesture sensors are employed for data collection from four hand gesture movements (up, down, left and right) performed by the human at a predefined distance from the sensorised bracelet. The hand gesture movements are classified using Artificial Neural Networks. The proposed approach is validated with experiments in offline and real-time modes performed systematically. First, in offline mode, the accuracy for recognition of the four hand gesture movements achieved a mean of 97.86%. Second, the trained model was used for classification in real-time and achieved a mean recognition accuracy of 97.7%. The output from the recognised hand gesture in real-time mode was used to control the movement of a Universal Robot (UR3) arm in the CoppeliaSim simulation environment. Overall, the results from the experiments show that using multimodal sensors, together with computational intelligence methods, have the potential for the development of intuitive and safe human-robot interaction. © 2020 IEEE.",,"3D printers; Gesture recognition; Intelligent computing; Man machine systems; Neural networks; Palmprint recognition; Computational intelligence methods; Data collection; Hand-gesture recognition; Multimodal sensor; Real time modes; Recognition accuracy; Safe human-robot interaction; Simulation environment; Social robots",,,,,"Royal Society: RGS/R2/192346","*This work was supported by The Republic of Turkey Ministry of National Education, and the Royal Society Research Grants for the ‘Touching and feeling the immersive world’ project (RGS/R2/192346).",,,,,,,,,,"Pedersen, M.R., Nalpantidis, L., Andersen, R.S., Schou, C., Bøgh, S., Krüger, V., Madsen, O., Robot skills for manufacturing: From concept to industrial deployment (2016) Robotics and Computer-Integrated Manufacturing, 37, pp. 282-291; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human-robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266; Petit, M., Lallée, S., Boucher, J.-D., Pointeau, G., Cheminade, P., Ognibene, D., Chinellato, E., Martinez-Hernandez, U., The coordinating role of language in real-Time multimodal learning of cooperative tasks (2012) Ieee Transactions on Autonomous Mental Development, 5 (1), pp. 3-17; Mokaram, S., Aitken, J.M., Martinez-Hernandez, U., Eimontaite, I., Cameron, D., Rolph, J., Gwilt, I., Law, J., A rosintegrated api for the kuka lbr IIwa collaborative robot (2017) IFACPapersOnLine, 50 (1), pp. 15859-15864; Nickel, K., Stiefelhagen, R., Visual recognition of pointing gestures for human-robot interaction (2007) Image and Vision Computing, 25 (12), pp. 1875-1884; Bugmann, G., Pires, J.N., Robot-by-voice: Experiments on commanding an industrial robot using the human voice (2005) Industrial Robot: An International Journal; Weitschat, R., Ehrensperger, J., Maier, M., Aschemann, H., Safe and efficient human-robot collaboration part i: Estimation of human arm motions (2018) 2018 Ieee International Conference on Robotics and Automation (ICRA)., pp. 1993-1999; Barattini, P., Morand, C., Robertson, N.M., A proposed gesture set for the control of industrial collaborative robots (2012) 2012 Ieee ROMAN: The 21st Ieee International Symposium on Robot and Human Interactive Communication., pp. 132-137; Pedersen, M.R., Herzog, D.L., Krüger, V., Intuitive skill-level programming of industrial handling tasks on a mobile manipulator (2014) 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems., pp. 4523-4530; Elmezain, M., Al-Hamadi, A., Appenrodt, J., Michaelis, B., A hidden markov model-based continuous gesture recognition system for hand motion trajectory (2008) 2008 19th International Conference on Pattern Recognition., pp. 1-4; Maurtua, I., Fernández, I., Tellaeche, A., Kildal, J., Susperregi, L., Ibarguren, A., Sierra, B., Natural multimodal communication for human-robot collaboration (2017) International Journal of Advanced Robotic Systems, 14 (4), pp. 1729881417716043; Srimal, P.A.S., Muthugala, M.V.J., Jayasekara, A.B.P., Deictic gesture enhanced fuzzy spatial relation grounding in natural language (2017) 2017 Ieee International Conference on Fuzzy Systems (FUZZIEEE)., pp. 1-8; Fani, S., Ciotti, S., Catalano, M.G., Grioli, G., Tognetti, A., Valenza, G., Ajoudani, A., Bianchi, M., Simplifying telerobotics: Wearability and teleimpedance improves human-robot interactions in teleoperation (2018) Ieee Robotics & Automation Magazine, 25 (1), pp. 77-88; Neto, P., Norberto Pires, J., Paulo Moreira, A., High-level programming and control for industrial robotics: Using a hand-held accelerometer-based input device for gesture and posture recognition (2010) Industrial Robot: An International Journal, 37 (2), pp. 137-147; Muxfeldt, A., Haus, J.N., Cheng, J., Kubus, D., Exploring tactile surface sensors as a gesture input device for intuitive robot programming (2016) 2016 Ieee 21st International Conference on Emerging Technologies and Factory Automation (ETFA)., pp. 1-4; Saenz, J., Fritsche, M., Tactile sensors for safety and interaction with the mobile manipulator valeri (2016) Proceedings of Isr 2016: 47st International Symposium on Robotics. Vde, pp. 1-7; Hughes, D., Lammie, J., Correll, N., A robotic skin for collision avoidance and affective touch recognition (2018) Ieee Robotics and Automation Letters, 3 (3), pp. 1386-1393; Gao, Q., Liu, J., Ju, Z., Li, Y., Zhang, T., Zhang, L., Static hand gesture recognition with parallel cnns for space human-robot interaction (2017) International Conference on Intelligent Robotics and Applications., pp. 462-473. , Springer; Martinez-Hernandez, U., Damianou, A., Camilleri, D., Boorman, L.W., Lawrence, N., Prescott, T.J., An integrated probabilistic framework for robot perception, learning and memory (2016) 2016 Ieee International Conference on Robotics and Biomimetics (ROBIO)., pp. 1796-1801; Rani, P., Liu, C., Sarkar, N., Vanman, E., An empirical study of machine learning techniques for affect recognition in human-robot interaction (2006) Pattern Analysis and Applications, 9 (1), pp. 58-69",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, MFI 2020","14 September 2020 through 16 September 2020",,164537,,9781728164229,85PRA,,"English","IEEE Int Conf Multisensor Fusion Integr Intell Syst",Conference Paper,"Final","",Scopus,2-s2.0-85096092462
"Su H., Ovur S.E., Li Z., Hu Y., Li J., Knoll A., Ferrigno G., De Momi E.","57203702628;57204781654;57218308705;57188985422;57209298595;7102250639;7005615860;57203951877;","Internet of Things (IoT)-based Collaborative Control of a Redundant Manipulator for Teleoperated Minimally Invasive Surgeries",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9197321","9737","9742",,15,"10.1109/ICRA40945.2020.9197321","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091081676&doi=10.1109%2fICRA40945.2020.9197321&partnerID=40&md5=93e66c8dd83b72c2fd5e2e9ed63f5cf5","Politecnico di Milano, Department of Electronics, Milan, 20133, Italy; University of Science and Technology of China, Department of Automation, China; Technical University of Munich, Department of Informatics, Munich, 85748, Germany","Su, H., Politecnico di Milano, Department of Electronics, Milan, 20133, Italy; Ovur, S.E., Politecnico di Milano, Department of Electronics, Milan, 20133, Italy; Li, Z., University of Science and Technology of China, Department of Automation, China; Hu, Y., Technical University of Munich, Department of Informatics, Munich, 85748, Germany; Li, J., Politecnico di Milano, Department of Electronics, Milan, 20133, Italy; Knoll, A., Politecnico di Milano, Department of Electronics, Milan, 20133, Italy; Ferrigno, G., Politecnico di Milano, Department of Electronics, Milan, 20133, Italy; De Momi, E., Politecnico di Milano, Department of Electronics, Milan, 20133, Italy","In this paper, an Internet of Things-based human-robot collaborative control scheme is developed in Robot-assisted Minimally Invasive Surgery scenario. A hierarchical operational space formulation is designed to exploit the redundancies of the 7-DoFs redundant manipulator to handle multiple operational tasks based on their priority levels, such as guaranteeing a remote center of motion constraint and avoiding collision with a swivel motion without influencing the undergoing surgical operation. Furthermore, the concept of the Internet of Robotic Things is exploited to facilitate the best action of the robot in human-robot interaction. Instead of utilizing compliant swivel motion, HTC VIVE PRO controllers, used as the Internet of Things technology, is adopted to detect the collision. A virtual force is applied to the robot elbow, enabling a smooth swivel motion for human-robot interaction. The effectiveness of the proposed strategy is validated using experiments performed on a patient phantom in a lab setup environment, with a KUKA LWR4+ slave robot and a SIGMA 7 master manipulator. By comparison with previous works, the results show improved performances in terms of the accuracy of the RCM constraint and surgical tip. © 2020 IEEE.",,"Agricultural robots; Human robot interaction; Industrial manipulators; Internet of things; Man machine systems; Redundant manipulators; Robotics; Surgery; Surgical equipment; Collaborative control; Internet of Things (IOT); Internet of things technologies; Master manipulator; Minimally invasive surgery; Operational space formulation; Remote center of motions; Surgical operation; Robotic surgery",,,,,"732515, 785907","*This work was supported in part by the European Commission Horizon 2020 research and innovation program, under the project SMARTsurg, grant agreement No. 732515 and in part by the Human Brain Project SGA2, under the Specific Grant Agreement No. 785907.",,,,,,,,,,"Okamura, A.M., Simone, C., O'Leary, M.D., Force modeling for needle insertion into soft tissue (2004) IEEE Transactions on Biomedical Engineering, 51 (10), pp. 1707-1716; Su, H., Sandoval, J., Makhdoomi, M.R., Ferrigno, G., De Momi, E., Safety-enhanced human-robot interaction control of redundant robot for teleoperated minimally invasive surgery (2018) International Conference on Robotics and Automation, pp. 6611-6616; Su, H., Yang, C., Ferrigno, G., De Momi, E., Improved human-robot collaborative control of redundant robot for teleoperated minimally invasive surgery (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 1447-1453; Sandoval, J., Su, H., Vieyres, P., Poisson, G., Ferrigno, G., De Momi, E., Collaborative framework for robot-assisted minimally invasive surgery using a 7-dof anthropomorphic robot (2018) Robotics and Autonomous Systems, 106, pp. 95-106; Zanchettin, A.M., Ceriani, N.M., Rocco, P., Ding, H., Matthias, B., Safety in human-robot collaborative manufacturing environments: Metrics and control (2015) IEEE Transactions on Automation Science and Engineering, 13 (2), pp. 882-893; Sadeghian, H., Villani, L., Keshmiri, M., Siciliano, B., Task-space control of robot manipulators with null-space compliance (2014) IEEE Transactions on Robotics, 30 (2), pp. 493-506; Su, H., Li, S., Manivannan, J., Bascetta, L., Ferrigno, G., Momi, E.D., Manipulability optimization control of a serial redundant robot for robot-assisted minimally invasive surgery (2019) 2019 International Conference on Robotics and Automation (ICRA, pp. 1323-1328. , May; Aghakhani, N., Geravand, M., Shahriari, N., Vendittelli, M., Oriolo, G., Task control with remote center of motion constraint for minimally invasive robotic surgery (2013) Robotics and Automation (ICRA), 2013 IEEE International Conference On. IEEE, pp. 5807-5812; Dietrich, A., Ott, C., Park, J., The hierarchical operational space formulation: Stability analysis for the regulation case (2018) IEEE Robotics and Automation Letters, 3 (2), pp. 1120-1127; Bussmann, K., Dietrich, A., Ott, C., Whole-body impedance control for a planetary rover with robotic arm: Theory, control design, and experimental validation (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA, pp. 910-917. , https://ieeexplore.ieee.org/abstract/document/8460533, May; Olaković, A.C., Hadzialic, M., Internet of things (iot): A review of enabling technologies, challenges, and open research issues (2018) Computer Networks, 144, p. 07; Batth, R., Nayyar, A., Nagpal, A., (2018) Internet of Robotic Things: Driving Intelligent Robotics of Future-Concept, Architecture, Applications and Technologies, 8, pp. 151-160; Akimana, B.-T., Bonnaerens, M., Van Wilder, J., Vuylsteker, B., (2016) A Survey of Human-Robot Interaction in the Internet of Things, 12; Ajoudani, A., Tsagarakis, N., Bicchi, A., Tele-impedance: Teleoperation with impedance regulation using a body-machine interface (2012) The International Journal of Robotics Research, 31 (13), pp. 1642-1656; Simoens, P., Dragone, M., Saflotti, A., The internet of robotic things: A review of concept, added value and applications (2018) International Journal of Advanced Robotic Systems, 15 (2), p. 5942; Uddin, M.S., Gianni, M., Lab, A., Long range robot teleoperation system based on internet of things (2017) 2017 2nd International Conference on Computer and Communication Systems (ICCCS, pp. 163-167. , July; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) IEEE Transactions on Robotics, 31 (4), pp. 850-863; Safeea, M., Neto, P., Minimum distance calculation using laser scanner and imus for safe human-robot interaction (2019) Robotics and Computer-Integrated Manufacturing, 58, pp. 33-42; Safeea, M., Neto, P., Bearee, R., On-line collision avoidance for collaborative robot manipulators by adjusting off-line generated paths: An industrial use case (2019) Robotics and Autonomous Systems, 119, pp. 278-288; Su, H., Sandoval, J., Vieyres, P., Poisson, G., Ferrigno, G., De Momi, E., Safety-enhanced collaborative framework for teleoperated minimally invasive surgery using a 7-dof torque-controlled robot (2018) International Journal of Control, Automation and Systems, 16 (6), pp. 2915-2923; Horn, B., Closed-form solution of absolute orientation using unit quaternions (1987) Journal of the Optical Society A, 4 (4), pp. 629-642; Groves, L.A., Carnahan, P., Allen, D.R., Adam, R., Peters, T., Chen, E.C.S., Accuracy assessment for the co-registration between optical and vive head-mounted display tracking (2019) International Journal of Computer Assisted Radiology and Surgery, 14, p. 05; Shimizu, M., Kakuya, H., Yoon, W.-K., Kitagaki, K., Kosuge, K., Analytical inverse kinematic computation for 7-dof redundant manipulators with joint limits and its application to redundancy resolution (2008) IEEE Transactions on Robotics, 24 (5), pp. 1131-1142; Zhang, L., Li, Z., Yang, C., Adaptive neural network based variable stiffness control of uncertain robotic systems using disturbance observer (2017) IEEE Transactions on Industrial Electronics, 64 (3), pp. 2236-2245. , https://ieeexplore.ieee.org/document/7731156, March; Schreiber, G., Stemmer, A., Bischo, R., The fast research interface for the kuka lightweight robot (2010) IEEE Workshop on Innovative Robot Control Architectures for Demanding (Research) Applications How to Modify and Enhance Commercial Controllers (ICRA 2010, pp. 15-21; Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F.J., Marín-Jiḿenez, M.J., Automatic generation and detection of highly reliable fiducial markers under occlusion (2014) Pattern Recognition, 47 (6), pp. 2280-2292; Su, H., Qi, W., Yang, C., Sandoval, J., Ferrigno, G., De Momi, E., Deep neural network approach in robot tool dynamics identification for bilateral teleoperation (2020) IEEE Robotics and Automation Letters, p. 1",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","",Scopus,2-s2.0-85091081676
"She Y., Su H.-J., Meng D., Lai C.","56545216500;8379272100;55827505800;57192718439;","Design and modeling of a continuously tunable stiffness arm for safe physical human-robot interaction",2020,"Journal of Mechanisms and Robotics","12","1","011006","","",,7,"10.1115/1.4044840","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106493202&doi=10.1115%2f1.4044840&partnerID=40&md5=3ca9154d3cbe48218beaf922601e76bc","Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA  02139, United States; Department of Automation, Graduate School at Shenzhen, Tsinghua University, Shenzhen, Guangdong, 518055, China; Department of Mechanical Engineering, Texas A and M University, College Station, TX  77840, United States","She, Y., Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA  02139, United States; Su, H.-J., Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States; Meng, D., Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States, Department of Automation, Graduate School at Shenzhen, Tsinghua University, Shenzhen, Guangdong, 518055, China; Lai, C., Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States, Department of Mechanical Engineering, Texas A and M University, College Station, TX  77840, United States","To reduce injury in physical human-robot interactions (pHRIs), a common practice is to introduce compliance to joints or arm of a robotic manipulator. In this paper, we present a robotic arm made of parallel guided beams whose stiffness can be continuously tuned by morphing the shape of the cross section through two four-bar linkages actuated by servo motors. An analytical lateral stiffness model is derived based on the pseudo-rigid-body model and validated by experiments. A physical prototype of a three-armed manipulator is built. Extensive stiffness and impact tests are conducted, and the results show that the stiffness of the robotic arm can be changed up to 3.6 times at a morphing angle of 37 deg. At an impact velocity of 2.2 m/s, the peak acceleration has a decrease of 19.4% and a 28.57% reduction of head injury criteria (HIC) when the arm is tuned from the high stiffness mode to the low stiffness mode. These preliminary results demonstrate the feasibility to reduce impact injury by introducing compliance into the robotic link and that the compliant link solution could be an alternative approach for addressing safety concerns of physical human-robot interactions. © 2020 American Society of Mechanical Engineers (ASME). All rights reserved.","Compliant mechanisms; mechanism design; physical human-robot interactions; robot design; tunable stiffness arm","Agricultural robots; Industrial manipulators; Machine design; Man machine systems; Manipulators; Robotic arms; Robotics; Stiffness; Design and modeling; Head injury criterion (HIC); Impact velocities; Peak acceleration; Physical human-robot interactions; Pseudo-rigid body models; Robotic manipulators; Tunable stiffness; Human robot interaction",,,,,"National Science Foundation, NSF: 1637656, CMMI-1637656","This material is based upon work supported by National Science Foundation (Grant No. CMMI-1637656; Funder ID: 10.13039/ 501100008982). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.",,,,,,,,,,"Colgate, E., Wannasuphoprasit, W., Peshkin, M. 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Theory, 46 (12), pp. 1970-1979; Wu, Y.-S., Lan, C.-C., Linear Variable-Stiffness Mechanisms Based on Preloaded Curved Beams (2014) J. Mech. Design, 136 (12), p. 122302; Ayoubi, Y., Laribi, M. A., Courrèges, F., Zeghloul, S., Arsicault, M., A Complete Methodology to Design a Safety Mechanism for Prismatic Joint Implementation (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 304-309. , Daejeon, South Korea, Oct. 9-14; Ayoubi, Y., Laribi, M. A., Courrèges, F., Zeghloul, S., Arsicault, M., Complete Design Methodology of Biomimetic Safety Device for Cobots Prismatic Joints (2018) Rob. Auton. Syst, 102, pp. 44-53; López-Martínez, J., Blanco-Claraco, J. L., Garciá-Vallejo, D., Giménez-Fernández, A., Design and Analysis of a Flexible Linkage for Robot Safe Operation in Collaborative Scenarios (2015) Mech. Mach. Theory, 92, pp. 1-16; Park, J.-J., Kim, B.-S., Song, J.-B., Kim, H.-S., Safe Link Mechanism Based on Nonlinear Stiffness for Collision Safety (2008) Mech. Mach. Theory, 43 (10), pp. 1332-1348; Zhang, M., Laliberté, T., Gosselin, C., Force Capabilities of Two-Degree-of-Freedom Serial Robots Equipped With Passive Isotropic Force Limiters (2016) ASME J. Mech. Robot, 8 (5), p. 051002; She, Y., Su, H.-J., Hurd, C. J., Shape Optimization of 2D Compliant Links for Design of Inherently Safe Robots (2015) Proceedings of IDETC/CIE, , Boston, MA, Aug. 2-5, V05BT08A004, ASME Paper No. DETC2015-46622; She, Y., Su, H.-J., Meng, D., Song, S., Wang, J., Design and Modeling of a Compliant Link for Inherently Safe Corobots (2018) J. Mech. Robot, 10 (1), p. 011001; Galloway, K. C., Clark, J. E., Koditschek, D. E., Variable Stiffness Legs for Robust, Efficient, and Stable Dynamic Running (2013) J. Mech. Robot, 5 (1), p. 011009; Kim, Y.-J., Cheng, S., Kim, S., Iagnemma, K., A Novel Layer Jamming Mechanism With Tunable Stiffness Capability for Minimally Invasive Surgery (2013) IEEE Trans. Rob, 29 (4), pp. 1031-1042; Hines, L., Arabagi, V., Sitti, M., Shape Memory Polymer-Based Flexure Stiffness Control in a Miniature Flapping-Wing Robot (2012) IEEE Trans. Rob, 28 (4), pp. 987-990; Stilli, A., Wurdemann, H. A., Althoefer, K., A Novel Concept for Safe, Stiffness-Controllable Robot Links (2017) Soft Robot, 4 (1), pp. 16-22; She, Y., (2018) Compliant Robotic Arms for Inherently Safe Physical Human-Robot Interaction, , Ph.D. Thesis, The Ohio State University, Columbus, OH; Song, S., Zeng, X., She, Y., Wang, J., Su, H.-J., Modeling and Control of Inherently Safe Robots With Variable Stiffness Links (2019) Rob. Auton. Syst, 120, p. 103247; She, Y., Su, H.-J., Lai, C., Meng, D., Design and Prototype of a Tunable Stiffness Arm for Safe Human-Robot Interaction (2016) Proceedings of IDETC/CIE, , Charlotte, NC, Aug. 21-24, V05BT07A063, ASME Paper No. IDETC2016-59523; Haddadin, S., Albu-Schäffer, A., Hirzinger, G., Safety Evaluation of Physical Human-Robot Interaction via Crash-Testing (2007) Robotics: Science and Systems Conference Workshop, 3, pp. 217-224. , Atlanta, GA, June 27-30; Haddadin, S., Albu-Schaffer, A., Hirzinger, G., The Role of the Robot Mass and Velocity in Physical Human-Robot Interaction-Part I: Non-Constrained Blunt Impacts (2008) 2008 IEEE International Conference on Robotics and Automation (ICRA), pp. 1331-1338. , Pasadena, CA, May 19-23; Gao, D., Wampler, C., Head Injury Criterion (2009) IEEE Rob. Autom. Mag, 16 (4), pp. 71-74; Howell, L. L., Midha, A., A Method for the Design of Compliant Mechanisms With Small-Length Flexural Pivots (1994) J. Mech. Design, 116 (1), pp. 280-290; Howell, L. L., Midha, A., Parametric Deflection Approximations for End-Loaded, Large-Deflection Beams in Compliant Mechanisms (1995) J. Mech. Design, 117 (1), pp. 156-165; Howell, L. L., (2001) Compliant Mechanisms, , Wiley-Interscience, New York, NY; Waldron, K. J., Kinzel, G. L., Agrawal, S. K., (2016) Kinematics, Dynamics, and Design of Machinery, , John Wiley & Sons, New York, NY","She, Y.; Department of Mechanical and Aerospace Engineering, United States; email: she.22@osu.edu",,,"American Society of Mechanical Engineers (ASME)",,,,,19424302,,,,"English","J. Mech. Robot.",Article,"Final","",Scopus,2-s2.0-85106493202
"Bhalaji R.K.A., Bathrinath S., Ponnambalam S.G., Saravanasankar S.","57206469714;54779403600;7005929538;23975020200;","Analyze the factors influencing human-robot interaction using MCDM method",2020,"Materials Today: Proceedings","39",,,"100","104",,,"10.1016/j.matpr.2020.06.316","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102449574&doi=10.1016%2fj.matpr.2020.06.316&partnerID=40&md5=9534d30e8ef594bc5f850a5c6f535a91","Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankovil, 626126, India; Universiti Malaysia, Pekan, Pahang, 26000, Malaysia","Bhalaji, R.K.A., Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankovil, 626126, India; Bathrinath, S., Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankovil, 626126, India; Ponnambalam, S.G., Universiti Malaysia, Pekan, Pahang, 26000, Malaysia; Saravanasankar, S., Department of Mechanical Engineering, Kalasalingam Academy of Research and Education, Krishnankovil, 626126, India","Robots play a key role in medical equipment manufacturing industry by safeguarding human workers from hazardous environment and risky jobs. Human robot interaction (HRI) is one of the robotic features that are enhanced in industrial robots. They mimic human behavior while arriving at a decision, contributing to the proficiency of the product. Tasks involving human cognitive skills and flexibility in the workers are combined with robots to obtain high-level accuracy, repeatability, and speed. Further, more challenges are to be met for achieving an effective human-robot interaction. In this paper, risk factors affecting the interaction between both robot and humans are discussed, and a contextual case is performed in a top south Indian medical equipment manufacturing industry. Industrial experts' inputs and relevant literature are considered to recognize the risk factors. Multi-Criteria decision-making method (MCDM) like DEMATEL (Decision Making Trial and Evaluation Laboratory) is used to analyze the risk factors influencing HRI in the assembly section. The paper's findings show that automation level and reliability of the robot are the most influential factor in the assembly section and need more attention to control and reduce the risk factor for the effective assembly. © 2020 The Authors. Published by Elsevier Ltd.","DEMATEL; HRI; India; Medical equipments; Multi-criteria decision making",,,,,,,,,,,,,,,,,"Erden, M.S., Jonkman, J.A., Physical human-robot interaction by observing actuator currents (2012) Int. J. Rob. Autom., 27 (3), p. 233; Zheng, M., Liu, P.X., Max, Q.H.M., Interpretation of human and robot emblematic gestures: How do they differ? (2019) Int. J. Rob. Autom., 34 (1), pp. 55-70; Robinette, P., Howard, A.M., Wagner, A.R., Effect of robot performance on human–robot trust in time-critical situations (2017) IEEE Trans. Hum.-Mach. Syst., 47 (4), pp. 425-436; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Hum. Factors, 53 (5), pp. 517-527; Hoff, K.A., Bashir, M., Trust in automation: Integrating empirical evidence on factors that influence trust (2015) Hum. Factors, 57 (3), pp. 407-434; Johannsmeier, L., Haddadin, S., A hierarchical human-robot interaction-planning framework for task allocation in collaborative industrial assembly processes (2016) IEEE Rob. Autom. Lett., 2 (1), pp. 41-48; Tsarouchi, P., Makris, S., Chryssolouris, G., Human–robot interaction review and challenges on task planning and programming (2016) Int. J. Comput. Integr. Manuf., 29 (8), pp. 916-931; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266; Bathrinath, S., Charan, V.S., Ponnambalam, S.G., Saravanasankar, S., Identification and evaluation of criteria of agile manufacturing using dematel: a case from an indian metal fabrication industry (2019) J. Modern Manuf. Syst. Technol., 2 (1), pp. 61-74; Bhalaji, R.K.A., Bathrinath, S., Ponnambalam, S.G., Saravanasankar, S., A fuzzy decision-making trial and evaluation laboratory approach to analyse risk factors related to environmental health and safety aspects in the healthcare industry (2019) Sādhanā, 44 (3), p. 55; Alkan, A., Canbay, K., Akman, G., Aladağ, Z., Researching usage of globe culture dimensions in organizational management by using dematel method (2019) Sakarya Univ. J. Sci., 23 (2), pp. 282-290; Xu, H., Deng, Y., Dependent evidence combination based on decision-making trial and evaluation laboratory method (2019) Int. J. Intell. Syst., 34 (7), pp. 1555-1571; Qarnain, S.S., Muthuvel, S., Bathrinath, S., Analyzing factors necessitating conservation of energy in residential buildings of Indian subcontinent: A DEMATEL approach (2020) Mater. Today: Proc.; Bhalaji, R.K.A., Bathrinath, S., Ponnambalam, S.G., Saravanasankar, S., A soft computing methodology to analyze sustainable risks in surgical cotton manufacturing companies (2020) Sādhanā, 45 (1), pp. 1-22; Karuppiah, K., Sankaranarayanan, B., Ali, S.M., Chowdhury, P., Paul, S.K., An integrated approach to modeling the barriers in implementing green manufacturing practices in SMEs (2020) J. Cleaner Prod., 121737","Bhalaji, R.K.A.; Department of Mechanical Engineering, India; email: bathri@gmail.com","Devaraju A.Palanisamy D.",,"Elsevier Ltd","3rd International Conference on Advanced Materials and Modern Manufacturing, ICAMMM 2020","3 April 2020 through 4 April 2020",,167571,22147853,,,,"English","Mater. Today Proc.",Conference Paper,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85102449574
"Nguyen Van T., Yi S.-Y., Bui Khoi P.","56811211200;7201404755;57201260426;","Hedge algebras-based admittance controller for safe natural human–robot interaction",2020,"Advanced Robotics","34","24",,"1546","1558",,1,"10.1080/01691864.2020.1852958","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097415163&doi=10.1080%2f01691864.2020.1852958&partnerID=40&md5=69f4cfc9e35967836cf0a3d59bd7c37f","Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul, South Korea; School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Viet Nam","Nguyen Van, T., Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul, South Korea; Yi, S.-Y., Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul, South Korea; Bui Khoi, P., School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, Viet Nam","In physical human–robot interaction (pHRI), the identification of inertia and damping matrices in the dynamic admittance model is still an open problem. Besides, the natural interaction is rarely considered in previous studies while it is crucial to obtain the effective cooperation. To this end, a fuzzy-based admittance controller is presented, in which the end-effector's velocity is adaptively adjusted via the external wrench and transmitted power without the identification of inertia and damping matrices. Besides, this fuzzy-based admittance controller also guarantees the natural cooperation between human and robot. Unfortunately, there is no formulated linkage of the fuzzy sets with the natural linguistic term semantics. As a consequence, human experts must utilize the order relationships between the terms of interest when formulating the fuzzy rule-based knowledge of the fuzzy controller. This paper presents an alternative admittance controller for pHRI based on an algebraic approach to linguistic hedges in fuzzy logic to overcome the existing shortcomings of previous admittance controllers. In addition, this paper also considers end-effector's full degree of freedom to guarantee the natural human–robot interaction. The proposed admittance controller is experimentally evaluated on a teaching task set-up using 6-DOF manipulator. © 2020 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","admittance control; fuzzy control; Hedge algebras; natural linguistic semantics; physical human–robot interaction","Agricultural robots; Controllers; Damping; Degrees of freedom (mechanics); End effectors; Fuzzy inference; Fuzzy logic; Matrix algebra; Semantics; Wooden fences; Algebraic approaches; Degree of freedom; Fuzzy controllers; Fuzzy rule based; Linguistic hedges; Natural interactions; Robot interactions; Transmitted power; Social robots",,,,,"National Research Foundation of Korea, NRF: NRF-2018R1D1A1B07044841","This research was partly supported by the research program funded by the National Research Foundation of Korea [grant number NRF-2018R1D1A1B07044841].",,,,,,,,,,"(2011) Robot for industrial environments–safety requirements–part 1: robot, technical report, , Geneva, Switzerland: International Organization for Standardization; 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2015. p. 5622–5627; Dimeas, F., Mouolianitis, V., Papakonstantinou, C., Manipulator performance constraints in Cartesian admittance control for human-robot cooperation 2016 IEEE International Conference on Robotics and Automation (ICRA), , Stockholm, Sweden; 2016. p. 3049–3054; Takagi, T., Sugeno, M., Fuzzy identification of systems and its applications to modeling and control (1985) IEEE Trans Syst Man Cybern, SMC-15 (1), pp. 116-132; Mamdani, E., Twenty years of fuzzy control: experiences gained and lessons learnt IEEE International Conference on Fuzzy Systems, , San Francisco, CA, USA; 1993. p. 339–344; Wang, L., Stable adaptive fuzzy control of nonlinear systems (1993) IEEE Trans Fuzzy Syst, 1 (2), pp. 146-155; Wang, L., (1997) A course in fuzzy systems and control, , Upper Saddle River (NJ: Prentice-Hall; Su, C., Stepanenko, Y., Adaptive control of a class of nonlinear systems with fuzzy logic (1994) IEEE Trans Fuzzy Syst, 2 (4), pp. 285-294; Yang, Y.S., Jia, X.L., Zhou, C.J., Robust adaptive fuzzy control for a class of uncertain nonlinear systems 4th International Conference of Intelligent Techniques Soft Computing Nuclear Science Engineering, , Bruges, Belgium; 2000. p. 303–311; Dimeas, F., Aspragathos, N., Fuzzy learning variable admittance control for human-robot cooperation 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, , Chicago (IL), USA; 2014. p. 4770–4775; Jamil, M., Jalani, J., Ahmad, A., A new approach of active compliance control via fuzzy logic control for multifingered robot hand First International Conference on Intelligent Robots and Systems, , 2014. p. 4770–4775; Khoi, P., Toan, N., Application of fuzzy logic for controlling mechanism of relative manipulation robot (MRM robot) (2016) J Sci Technol, 54 (3), pp. 385-401; Toan, N., Khoi, P., A control solution for closed-form mechanisms of relative manipulation based on fuzzy approach (2019) Int J Adv Rob Syst, 16 (2), pp. 1-11; Toan, N., Kim, J., Kim, K., Application of fuzzy logic to damping controller for safe human-robot interaction 14th International Conference on Ubiquitous Robots and Ambient intelligence, , Jeju, South Korea; 2017. p. 109–113; Toan, N., Khoi, P., Fuzzy-based admittance controller for safe natural human-robot interaction (2019) Adv Robot, 33 (15-16), pp. 815-823; Ho, N., Wechler, W., Hedge algebras: An algebraic approach to structure of sets of linguistic truth values (1990) Fuzzy Sets Syst, 35 (3), pp. 281-293; Ho, N., Wechler, W., Extended hedge algebras and their application to fuzzy logic (1992) Fuzzy Sets Syst, 52 (3), pp. 259-281; Ho, N., Nam, H., An algebraic approach to linguistic hedges in Zadeh’s fuzzy logic (2002) Fuzzy Sets Syst, 129 (2), pp. 229-254; Ho, N., Tran, T., Pham, D., Modeling of a semantics core of linguistic terms based on an extension of hedge algebra semantics and its application (2014) Knowl Based Syst, 67, pp. 244-262; Khoi, P., Toan, N., Hedge-algebras-based controller for mechanisms of relative manipulation (2018) Int J Prec Eng Manuf, 19 (3), pp. 377-385; Phong, P., Dong, D., Khang, T., Hedge algebra based type-2 fuzzy logic system and its application to predict survival time of myeloma patients International Conference on Knowledge and Systems Engineering, , 2009. p. 13–18; Bui, H., Tran, D., Vu, N., Optimal fuzzy control of an inverted pendulum (2012) J Vib Control, 18 (14), pp. 2097-2110; Bui, H., Nguyen, C., Bui, V., Vibration control of uncertain structures with actuator saturation using hedge-algebras-based fuzzy controller (2015) J Vib Control, 23 (12), pp. 1-19; Duc, N., Vu, N., Tran, D., A study on the application of hedge algebras to active fuzzy control of a seism-excited structure (2012) J Vib Control, 18 (14), pp. 2186-2200; Nguyen, C., Pedrycz, W., Duong, T., A genetic design of linguistic terms for fuzzy rule based classifiers (2013) Int J Approx Reason, 54 (1), pp. 1-21; Vukadinović, D., Bašić, M., Nguyen, C., Hedge-algebra-based voltage controller for a self-excited induction generator (2014) Control Eng Pract, 30, pp. 78-90; Ali, O., Ali, A., Sumait, B., Comparison between the effects of different types of membership functions on fuzzy logic controller performance (2015) Int J Emerging Eng Res Technol, 3 (3), pp. 76-83; Naaz, S., Alam, A., Biswas, R., Effect of different defuzzification methods in a fuzzy based load balancing application (2011) Int J Comput Sci Issues, 8 (1), pp. 261-267; Buss, S., (2003) 3D computer graphics: A mathematical introduction with OpenGL, , New York (NY: Cambridge University Press; Tsumugiwa, T., Yokogawa, R., Yoshida, K., Stability analysis for impedance control of robot for human-robot cooperative task system 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, , Sendai, Japan; 2004. p. 3883–3888; Mussa-Ivaldi, F., Hogan, N., Bizzi, E., Neural, mechanical, and geometric factors subserving arm posture in humans (1985) J Neurosci, 5 (10), pp. 2732-2743; Phan, K.B., Ha, H.T., Hoang, S.V., Eliminating the effect of uncertainties of cutting forces by fuzzy controller for robots in millings process (2020) Appl Sci, 10 (5), p. 1685; Cat, H.N., Nhu, L.V., Le Xuan, V., An Interpolative reasoning method based on hedge algebras and its application to a problem of fuzzy control Proceedings of the 10th WSEAS International Conference on Computers, Vouliagmeni, , Athens, Greece, July 13–15, 2006, 526–534; Ho, N.C., Lan, V.N., Viet, L.X., Optimal hedge-algebras-based controller: Design and application (2008) Fuzzy Sets Syst, 159, pp. 968-989","Bui Khoi, P.; School of Mechanical Engineering, Viet Nam; email: khoi.phanbui@hust.edu.vn",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Final","",Scopus,2-s2.0-85097415163
"Rossi A., Dautenhahn K., Koay K.L., Walters M.L., Holthaus P.","57189499587;7003305185;9732723100;56214015200;36699613400;","Evaluating People’s Perceptions of Trust in a Robot in a Repeated Interactions Study",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"453","465",,4,"10.1007/978-3-030-62056-1_38","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097202293&doi=10.1007%2f978-3-030-62056-1_38&partnerID=40&md5=d770346aa0e2101faba4d8f75a9f78e5","School of Engineering and Computer Science, University of Hertfordshire, Hatfield, United Kingdom; Departments of Electrical and Computer Engineering/Systems Design Engineering, University of Waterloo, Waterloo, Canada","Rossi, A., School of Engineering and Computer Science, University of Hertfordshire, Hatfield, United Kingdom; Dautenhahn, K., Departments of Electrical and Computer Engineering/Systems Design Engineering, University of Waterloo, Waterloo, Canada; Koay, K.L., School of Engineering and Computer Science, University of Hertfordshire, Hatfield, United Kingdom; Walters, M.L., School of Engineering and Computer Science, University of Hertfordshire, Hatfield, United Kingdom; Holthaus, P., School of Engineering and Computer Science, University of Hertfordshire, Hatfield, United Kingdom","Trust has been established to be a key factor in fostering human-robot interactions. However, trust can change overtime according to different factors, including a breach of trust due to a robot’s error. In this exploratory study, we observed people’s interactions with a companion robot in a real house, adapted for human-robot interaction experimentation, over three weeks. The interactions happened in six scenarios in which a robot performed different tasks under two different conditions. Each condition included fourteen tasks performed by the robot, either correctly, or with errors with severe consequences on the first or last day of interaction. At the end of each experimental condition, participants were presented with an emergency scenario to evaluate their trust in the robot. We evaluated participants’ trust in the robot by observing their decision to trust the robot during the emergency scenario, and by collecting their views through questionnaires. We concluded that there is a correlation between the timing of an error with severe consequences performed by the robot and the corresponding loss of trust of the human in the robot. In particular, people’s trust is subjected to the initial mental formation. © 2020, Springer Nature Switzerland AG.","Human-robot interaction; Long-term interaction; Social robots; Trust","Errors; Man machine systems; Robotics; Surveys; Companion robot; Emergency scenario; Experimental conditions; Exploratory studies; Key factors; Perceptions of trust; Social robots",,,,,"642667; Horizon 2020 Framework Programme, H2020; Canada Research Chairs","Acknowledgment. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642667 (Safety Enables Cooperation in Uncertain Robotic Environments - SECURE). KD acknowledges funding from the Canada 150 Research Chairs Program.",,,,,,,,,,"Ambady, N., Bernieri, F.J., Richeson, J.A., Toward a histology of social behavior: Judgmental accuracy from thin slices of the behavioral stream (2000) Adv. Exp. Soc. Psychol., 32, pp. 201-271. , https://doi.org/10.1016/S0065-2601(00)80006-4; Booth, S., Tompkin, J., Pfister, H., Waldo, J., Gajos, K., Nagpal, R., Piggybacking robots: Human-robot overtrust in university dormitory security (2017) 12Th ACM/IEEE International Conference on Human-Robot Interaction, pp. 426-434. , ACM; Chekroun, P., Brauer, M., The bystander effect and social control behavior: The effect of the presence of others on people’s reactions to norm violations (2002) Eur. J. Soc. 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Rev., 20, pp. 709-734; McKnight, D.H., Choudhury, V., Kacmar, C., Developing and validating trust measures for e-commerce: An integrative typology (2001) Inf. Syst. Res., 13 (3), pp. 334-359; Nomura, T., Suzuki, T., Kanda, T., Kato, K., Measurement of negative attitudes toward robots (2006) Interact. Stud., 7 (3), pp. 437-454. , https://doi.org/10.1075/is.7.3.14nom; Paetzel, M., Perugia, G., Castellano, G., The persistence of first impressions: The effect of repeated interactions on the perception of a social robot (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2020, pp. 73-82. , https://doi.org/10.1145/3319502.3374786, Association for Computing Machinery, New York; Rossi, A., Dautenhahn, K., Koay, K.L., Walters, M.L., How the timing and magnitude of robot errors influence peoples’ trust of robots in an emergency scenario (2017) Social Robotics – ICSR 2017. 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Relat., 61 (2), pp. 271-295. , https://doi.org/10.1177/0018726707087787","Rossi, A.; School of Engineering and Computer Science, United Kingdom; email: a.rossi@herts.ac.uk","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85097202293
"Khavas Z.R., Ahmadzadeh S.R., Robinette P.","57202646373;13807679300;25823528900;","Modeling Trust in Human-Robot Interaction: A Survey",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"529","541",,6,"10.1007/978-3-030-62056-1_44","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097174866&doi=10.1007%2f978-3-030-62056-1_44&partnerID=40&md5=573b34f532218d613a041a1b7d4d956d","Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA  01854, United States; Computer Science, University of Massachusetts Lowell, Lowell, MA  01854, United States","Khavas, Z.R., Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA  01854, United States; Ahmadzadeh, S.R., Computer Science, University of Massachusetts Lowell, Lowell, MA  01854, United States; Robinette, P., Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA  01854, United States","As the autonomy and capabilities of robotic systems increase, they are expected to play the role of teammates rather than tools and interact with human collaborators in a more realistic manner, creating a more human-like relationship. Given the impact of trust observed in human-robot interaction (HRI), appropriate trust in robotic collaborators is one of the leading factors influencing the performance of human-robot interaction. Team performance can be diminished if people do not trust robots appropriately by disusing or misusing them based on limited experience. Therefore, trust in HRI needs to be calibrated properly, rather than maximized, to let the formation of an appropriate level of trust in human collaborators. For trust calibration in HRI, trust needs to be modeled first. There are many reviews on factors affecting trust in HRI[22], however, as there are no reviews concentrated on different trust models, in this paper, we review different techniques and methods for trust modeling in HRI. We also present a list of potential directions for further research and some challenges that need to be addressed in future work on human-robot trust modeling. © 2020, Springer Nature Switzerland AG.","Human-robot interaction; Human-robot trust; Modeling trust in HRI; Trust calibration in HRI; Trust measurement","End effectors; Man machine systems; Robotics; Human robot Interaction (HRI); Human robots; Model trusts; Robotic collaborators; Robotic systems; Team performance; Trust modeling; Trust models; Social robots",,,,,,,,,,,,,,,,"Ajenaghughrure, I.B., Sousa, S.C., Kosunen, I.J., Lamas, D., Predictive model to assess user trust: A psycho-physiological approach (2019) Proceedings of the 10Th Indian Conference on Human-Computer Interaction, pp. 1-10; Atoyan, H., Duquet, J.R., Robert, J.M., Trust in new decision aid systems (2006) Proceedings of the 18Th Conference on l’Interaction Homme-Machine, pp. 115-122; Bainbridge, W.A., Hart, J.W., Kim, E.S., Scassellati, B., The benefits of interactions with physically present robots over video-displayed agents (2011) Int. 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(eds.) , Springer, Cham; Yagoda, R.E., Gillan, D.J., You want me to trust a robot? The development of a human-robot interaction trust scale (2012) Int. J. Soc. Robot., 4 (3), pp. 235-248. , https://doi.org/10.1007/s12369-012-0144-0; Yang, X.J., Unhelkar, V.V., Li, K., Shah, J.A., Evaluating effects of user experience and system transparency on trust in automation (2017) 2017 12Th ACM/IEEE International Conference on HRI, pp. 408-416. , IEEE; You, S., Robert, L.P., Jr., Human-robot similarity and willingness to work with a robotic co-worker (2018) Proceedings of the 2018 ACM/IEEE International Conference on HRI, pp. 251-260","Khavas, Z.R.; Electrical and Computer Engineering, United States; email: Zahra_RezaeiKhavas@student.uml.edu","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85097174866
"Diab J., Fonte A., Poisson G., Novales C.","57218835619;24721080700;8891049700;56139433700;","PHRI safety control using a virtual flexible joint approach",2020,"ICINCO 2020 - Proceedings of the 17th International Conference on Informatics in Control, Automation and Robotics",,,,"262","271",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090395942&partnerID=40&md5=8c7540937da763dc297c4fdf88aaecef","Laboratoire PRISMES, Université d'Orléans, 8 rue Léonard de Vinci-45072, Orléans, France","Diab, J., Laboratoire PRISMES, Université d'Orléans, 8 rue Léonard de Vinci-45072, Orléans, France; Fonte, A., Laboratoire PRISMES, Université d'Orléans, 8 rue Léonard de Vinci-45072, Orléans, France; Poisson, G., Laboratoire PRISMES, Université d'Orléans, 8 rue Léonard de Vinci-45072, Orléans, France; Novales, C., Laboratoire PRISMES, Université d'Orléans, 8 rue Léonard de Vinci-45072, Orléans, France","Physical Human-Robot Interaction (PHRI) emphasize on human safety. In literature, two techniques were presented to improving this critical factor concerning moving devices; the first solution is purely mechanical, while the second one is based on the control. In this paper, we describe a new approach combining the two previous solutions. Our proposed paper explores a control scheme involving the use of a virtual component with an adjustable stiffness supposed to be placed between the motor shaft and the robot link. This scheme proposes a Variable Impedance Actuator (VIA) robot control methodology based on the integration of a virtual component, reflecting the behaviour of a real intrinsic Series Elastic Actuator (SEA). This novel method is potentially beneficial in reducing injuries in human/robot interaction by combining a mechanical operating principle and a control approach in order to reduce the collision forces in collaborative applications. This proposed approach was simulated and validated using a UR3 robot model, showing great capacities in reducing collision’s peak forces. This paper begins with particular attention to the robot dynamics, then the articulation flexibility and force estimation have been tackled and finally ending the control architecture. Copyright © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.","Articulation flexibility control; Physical human-robot interaction; Virtual adjustable stiffness","Actuators; Agricultural robots; Robotics; Adjustable stiffness; Collaborative application; Control architecture; Operating principles; Physical humanrobot interaction (phri); Series elastic actuators; Variable impedance; Virtual components; Human robot interaction",,,,,,,,,,,,,,,,"Ansarieshlaghi, Fatemeh, Eberhard, Peter, Hybrid Force/Position Control of a Very Flexible Parallel Robot Manipulator in Contact with an Environment (2019) ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics, 2, pp. 59-67. , (Icinco); Bissell, C C., (2009) Springer Handbook of Automation Springer Handbook of Automation, , http://link.springer.com/10.1007/978-3-540-78831-7, ed. Shimon Y. Nof. Berlin, Heidelberg: Springer Berlin Heidelberg; Bruno, Siciliano, Khatib, (2013) 46 Choice Reviews Online Springer Handbook of Robotics, , http://link.springer.com/10.1007/978-3-540-30301-5, eds. Bruno Siciliano and Oussama Khatib. 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W., Modeling and Control of Elastic Joint Robots (1987) Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, 109 (4), pp. 310-319; Tonietti, Giovanni, Schiavi, Riccardo, Bicchi, Antonio, Design and Control of a Variable Stiffness Actuator for Safe and Fast Physical Human/Robot Interaction (2005) Proceedings - IEEE International Conference on Robotics and Automation, 2005, pp. 526-531. , (April); (2012) Ur Parameters for Kinematics and Dynamics Calculations, , https://www.universalrobots.com/how-tos-and-faqs/faq/ur-faq/parametersfor-calculations-of-kinematics-and-dynamics-45257/, Universal-Robots; Vanderborght, B., Variable Impedance Actuators: A Review (2013) Robotics and Autonomous Systems, 61 (12), pp. 1601-1614. , http://dx.doi.org/10.1016/j.robot.2013.06.009; Whitney, Daniel E., Historical Perspective and State of the Art in Robot Force Control (1985) Proceedings - IEEE International Conference on Robotics and Automation, pp. 262-268; Zeng, Ganwen, Hemami, Ahmad, An Overview of Robot Force Control (1997) Robotica, 15 (5), pp. 473-482",,"Gusikhin O.Madani K.Zaytoon J.","Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","SciTePress","17th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2020","7 July 2020 through 9 July 2020",,162154,,9789897584428,,,"English","ICINCO - Proc. Int. Conf. Inf. Control, Autom. Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85090395942
"Björling E.A., Rose E., Davidson A., Ren R., Wong D.","55301519700;8851758600;57192296130;57201554498;57211588145;","Can We Keep Him Forever? Teens’ Engagement and Desire for Emotional Connection with a Social Robot",2020,"International Journal of Social Robotics","12","1",,"65","77",,10,"10.1007/s12369-019-00539-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073940979&doi=10.1007%2fs12369-019-00539-6&partnerID=40&md5=6893094de1483961efa70df46c9fcf87","University of Washington, Seattle, United States; University of Washington Tacoma, Tacoma, United States","Björling, E.A., University of Washington, Seattle, United States; Rose, E., University of Washington Tacoma, Tacoma, United States; Davidson, A., University of Washington, Seattle, United States; Ren, R., University of Washington, Seattle, United States; Wong, D., University of Washington, Seattle, United States","Today’s teens will most likely be the first generation to spend a lifetime living and interacting with both mechanical and social robots. Although human–robot interaction has been explored in children, adults, and seniors, examination of teen–robot interaction has been limited. In this paper, we provide evidence that teen–robot interaction is a unique area of inquiry and designing for teens is categorically different from other types of human–robot interaction. Using human-centered design, our team is developing a social robot to gather stress and mood data from teens in a public high school. To better understand teen–robot interaction, we conducted an interaction study in the wild to explore and capture teens’ interactions with a low-fidelity social robot prototype. Then, through group interviews we gathered data regarding their perceptions about social robots. Although we anticipated minimal engagement due to the low fidelity of our prototype, teens showed strong engagement and lengthy interactions. Additionally, teens expressed thoughtful articulations of how a social robot could be emotionally supportive. We conclude the paper by discussing future areas for consideration when designing for teen–robot interaction. © 2019, Springer Nature B.V.","Engagement; Human-centered design; Prototype; Teen–robot interaction","Agricultural robots; Machine design; Emotional connections; Engagement; Human-centered designs; Interaction studies; Low fidelities; Prototype; Robot interactions; Robot prototypes; Social robots",,,,,"National Science Foundation, NSF: 1734100","This study was funded in part by the National Science Foundation under Grant No. NRI-1734100.",,,,,,,,,,"Davies, C., Eynon, R., (2013) Teenagers and Technology (Adolescence and Society), , Taylor and Francis; Lenhart, A., (2015) Teens, Social Media & Technology Overview 2015, , http://www.pewinternet.org/2015/04/09/teens-social-media-technology-2015/, Pew Research Center’s Internet & American Life Project, 2015, Accessed 10 Sept 2017; Spies Shapiro, L.A., Margolin, G., Growing up wired: social networking sites and adolescent psychosocial development (2014) Clin Child Fam Psychol Rev, 17 (1), pp. 1-18; Fitton, D., Little, L., Bell, B.T., Introduction: HCI reaches adolescence (2016) Perspectives on HCI research with teens, pp. 1-9. , Little L, FittoBell B, Toth N, (eds), Springer, Berlin; Kanda, T., Sato, R., Saiwaki, N., Ishiguro, H., A two-month field trial in an elementary school for long-term human–robot interaction (2007) IEEE Trans Robot, 23 (5), pp. 962-971; Kim, E.S., Social robots as embedded reinforcers of social behavior in children with autism (2013) J Autism Dev Disord, 43 (5), pp. 1038-1049; Robins, B., Dautenhahn, K., Te Boekhorst, R., Billard, A., Robotic assistants in therapy and education of children with autism: can a small humanoid robot help encourage social interaction skills? 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(2010) Proceedings of the 9Th International Conference on Interaction Design and children—IDC’10, p. 39; Gockley, R., Designing robots for long-term social interaction (2005) 2005 Ieee/Rsj International Conference on Intelligent Robots and Systems, pp. 2199-2204; Tanaka, F., Suzuki, H., September) Dance interaction with QRIO: A case study for non-boring interaction by using an entrainment ensemble model (2004) RO-MAN 2004. 13Th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No. 04TH8759). IEEE, pp. 419-424","Björling, E.A.; University of WashingtonUnited States; email: bjorling@uw.edu",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85073940979
"Song J., Zhu A., Tu Y., Wu X., Zhang Y., Zhou X.","57203911021;8876295500;57210522898;57219625510;55972397900;57360377300;","Prediction of Expected Angle of Joints of Lower Limb Assisted Exoskeleton with Human-Robot Interaction [人机交互力对下肢助力外骨骼关节期望角度的预测]",2021,"Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University","55","12",,"55","63 and 69",,,"10.7652/xjtuxb202112007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120482443&doi=10.7652%2fxjtuxb202112007&partnerID=40&md5=e2197df2e135ed0f296654cc624897e5","Institute of Robotics & Intelligent Systems, Xi'an Jiaotong University, Xi'an, 710049, China; Shaanxi Key Laboratory of Intelligent Robots, Xi'an Jiaotong University, Xi'an, 710049, China; Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, 710049, China; Taiyuan Research Institute of China Coal Technology & Engineering Group Co. Ltd., Taiyuan, 030006, China","Song, J., Institute of Robotics & Intelligent Systems, Xi'an Jiaotong University, Xi'an, 710049, China, Shaanxi Key Laboratory of Intelligent Robots, Xi'an Jiaotong University, Xi'an, 710049, China; Zhu, A., Institute of Robotics & Intelligent Systems, Xi'an Jiaotong University, Xi'an, 710049, China, Shaanxi Key Laboratory of Intelligent Robots, Xi'an Jiaotong University, Xi'an, 710049, China; Tu, Y., Institute of Robotics & Intelligent Systems, Xi'an Jiaotong University, Xi'an, 710049, China, Shaanxi Key Laboratory of Intelligent Robots, Xi'an Jiaotong University, Xi'an, 710049, China; Wu, X., Institute of Robotics & Intelligent Systems, Xi'an Jiaotong University, Xi'an, 710049, China, Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, 710049, China; Zhang, Y., Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi'an Jiaotong University, Xi'an, 710049, China; Zhou, X., Taiyuan Research Institute of China Coal Technology & Engineering Group Co. Ltd., Taiyuan, 030006, China","The mapping relationship between the joint angular velocity of the external skeleton and the joint torque generated by the interaction force of the legs is studied to solve the problem of poor human-robot interaction flexibility and discomfort caused by excessive human-robot interaction force in the use of lower limb assisted exoskeleton. A model to simplify the human lower limb swing phase leg into a plane two-bar model with the hip joint as the fulcrum is proposed, and a dynamic model of swing leg of human lower limb is established to obtain the gravity compensation item of human body. A force sensing unit is used to obtain the interaction force between the human body and the exoskeleton, and the leg interaction force between the exoskeleton and the human body is converted into the desired angle of the human hip and knee joint movement at the next moment. A non-linear mapping relationship between the swing phase joint angular velocity of human body and the leg interaction torque is established by means of the S-curve mapping and the admittance control model respectively. Experimental results show that both the expected angle prediction algorithm based on S-curve mapping and the expected angle prediction algorithm based on the admittance control model realize the continuous tracking of human motion intention from the exoskeleton. The expected angle prediction algorithm based on S-curve mapping makes the peak value of the thigh and leg interaction force less than 25 N and the peak value of shank and leg interaction force less than 20 N in the process of the exoskeleton following the human body movement; while the expected angle prediction algorithm based on the admittance control model makes the peak value of thigh-leg interaction force less than 15 N and the peak value of calf-leg interaction force less than 15 N in the process of the exoskeleton following human movement, and the joint angle curve is smooth, which can better identify human movement intention and improve the flexibility of human-robot interaction. © 2021, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.","Admittance control; Human motion intention; Human-robot interaction force; Lower limb exoskeleton","Angular velocity; Exoskeleton (Robotics); Forecasting; Gravitation; Human robot interaction; Joints (anatomy); Man machine systems; Admittance control; Human bodies; Human motion intention; Human-robot interaction force; Humans-robot interactions; Interaction forces; Low limb exoskeleton; Lower limb; Peak values; Prediction algorithms; Mapping",,,,,,,,,,,,,,,,"ZHAO, Xingang, TAN, Xiaowei, ZHANG, Bi, Development of soft lower extremity exoskeleton and its key technologies: a survey (2020) Robot, 42 (3), pp. 365-384. , 赵新刚, 谈晓伟, 张弼. 柔性下肢外骨骼机器人研究进展及关键技术分析[J]. 机器人, 2020, 42(3): 365-384. [J]; MOUNIR ALAOUI, O, EXPERT, F, MOREL, G, Using generic upper-body movement strategies in a free walking setting to detect gait initiation intention in a lower-limb exoskeleton (2020) IEEE Transactions on Medical Robotics and Bionics, 2 (2), pp. 236-247. , [J]; QIU, Shiyin, GUO, Wei, CALDWELL, D, Exoskeleton online learning and estimation of human walking intention based on dynamical movement primitives (2021) IEEE Transactions on Cognitive and Developmental Systems, 13 (1), pp. 67-79. , [J]; TSUKAHARA, A, HASEGAWA, Y, SANKAI, Y., Standing-up motion support for paraplegic patient with robot suit HAL (2009) 2009 IEEE International Conference on Rehabilitation Robotics, pp. 211-217. , [C] Piscataway, NJ, USA: IEEE; KAWABATA, T, SATOH, H, SANKAI, Y., Working posture control of robot suit HAL for reducing structural stress (2009) 2009 IEEE International Conference on Robotics and Biomimetics, pp. 2013-2018. , [C] Piscataway, NJ, USA: IEEE; YOSHIMITSU, T, YAMAMOTO, K., Development of a power assist suit for nursing work (2004) SICE 2004 Annual Conference, pp. 577-580. , [C] Piscataway, NJ, USA: IEEE; WANG, Letian, WANG, Shiqian, VAN ASSELDONK, E H F, Actively controlled lateral gait assistance in a lower limb exoskeleton (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 965-970. , [C] Piscataway, NJ, USA: IEEE; TALATY, M, ESQUENAZI, A, BRICEÑO, J E., Differentiating ability in users of the ReWalkTM powered exoskeleton: an analysis of walking kinematics (2013) 2013 IEEE 13th International Conference on Rehabilitation Robotics, pp. 1-5. , [C] Piscataway, NJ, USA: IEEE; STRAUSSER, K A, KAZEROONI, H., The development and testing of a human machine interface for a mobile medical exoskeleton (2011) 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4911-4916. , [C] Piscataway, NJ, USA: IEEE; KAZEROONI, H, STEGER, R, HUANG, Lihua, Hybrid control of the Berkeley lower extremity exoskeleton (BLEEX) (2006) The International Journal of Robotics Research, 25 (5), pp. 561-573. , [J]. (/6); GHAN, J, STEGER, R, KAZEROONI, H., Control and system identification for the Berkeley lower extremity exoskeleton (BLEEX) (2006) Advanced Robotics, 20 (9), pp. 989-1014. , [J]; STEGER, R, KIM, S H, KAZEROONI, H., Control scheme and networked control architecture for the Berkeley lower extremity exoskeleton (BLEEX) (2006) Proceedings 2006 IEEE International Conference on Robotics and Automation, pp. 3469-3476. , [C] Piscataway, NJ, USA: IEEE; ZHANG, Lufeng, MA, Yue, WANG, Can, A method for arm motions classification and a lower-limb exoskeleton control based on sEMG signals (2019) 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics, pp. 118-123. , [C] Piscataway, NJ, USA: IEEE; CHEN, Qiming, HUANG, Rui, Intention recognition for lower-limb exoskeleton (2018) Journal of University of Electronic Science and Technology of China, 47 (3), pp. 330-336. , 陈启明, 黄瑞. 下肢外骨骼机器人意图识别算法研究[J]. 电子科技大学学报, 2018, 47(3): 330-336. [J]; CHEN, Jiangcheng, ZHANG, Xiaodong, Surface electromyography decoding for continuous movement of human lower limb during walking (2016) Journal of Xi'an Jiaotong University, 50 (6), pp. 61-67. , 陈江城, 张小栋. 人体下肢行走关节连续运动表面肌电解码方法[J]. 西安交通大学学报, 2016, 50(6): 61-67. [J]; LI, Hanzhe, ZHANG, Xiaodong, LI, Rui, A preperception method for voluntary movement intention of lower limb using readiness potential (2019) Journal of Xi'an Jiaotong University, 53 (10), pp. 16-23. , 李瀚哲, 张小栋, 李睿, 等. 利用运动准备电位的人体下肢自主运动意图预先感知方法[J]. 西安交通大学学报, 2019, 53(10): 16-23. [J]; JIA, Shan, WANG, Xingsong, LU, Xinliang, Control of the exoskeleton's swing leg based on the human-machine posture error at ankle joint (2015) Ro-bot, 37 (4), pp. 403-414. , 贾山, 王兴松, 路新亮, 等. 基于踝关节处人机位姿误差的外骨骼摆动腿控制[J]. 机器人, 2015, 37(4): 403-414. [J]; SONG, Jiyuan, ZHU, Aibin, TU, Yao, Human body mixed motion pattern recognition method based on multi-source feature parameter fusion (2020) Sensors, 20 (2), p. 537. , [J]; SONG, Jiyuan, ZHU, Aibin, TU, Yao, Effects of different feature parameters of sEMG on human motion pattern recognition using multilayer perceptrons and LSTM neural networks (2020) Applied Sciences, 10 (10), p. 3358. , [J]; TU, Yao, ZHU, Aibin, SONG, Jiyuan, Adaptive admittance control of man-robot interaction force for lower limb exoskeleton rehabilitation robot (2019) Journal of Xi'an Jiaotong University, 53 (6), pp. 9-16. , 屠尧, 朱爱斌, 宋纪元, 等. 下肢外骨骼康复机器人人机交互力自适应导纳控制[J]. 西安交通大学学报, 2019, 53(6): 9-16. [J]; (2015), pp. 15-27. , 武晓虎. 基于S曲线的数控系统自适应样条插补算法研究[D]. 杭州: 浙江理工大学","Zhu, A.; Institute of Robotics & Intelligent Systems, China
Zhu, A.; Shaanxi Key Laboratory of Intelligent Robots, China",,,"Xi'an Jiaotong University",,,,,0253987X,,HCTPD,,"Chinese","Hsi An Chiao Tung Ta Hsueh",Article,"Final","",Scopus,2-s2.0-85120482443
"Pollak A., Paliga M., Kozusznik B.","57194705523;57194699368;7004263979;","The Impact of New Technologies on Work Design - Case Study of the Industrial Robot Controllers from One Organization",2021,"Proceedings - 2021 IEEE International Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2021",,,"9475924","156","160",,,"10.1109/CogSIMA51574.2021.9475924","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112863943&doi=10.1109%2fCogSIMA51574.2021.9475924&partnerID=40&md5=0d726d1c5cb8047d116fdea4ddc14c2c","University of Silesia, Institute of Psychology, Katowice, Poland","Pollak, A., University of Silesia, Institute of Psychology, Katowice, Poland; Paliga, M., University of Silesia, Institute of Psychology, Katowice, Poland; Kozusznik, B., University of Silesia, Institute of Psychology, Katowice, Poland","Work design theory research has a long and rich tradition in the industrial and organisational psychology. Work design identifies tasks, activities, relationships, and responsibilities in a specific job. It provides a comprehensive job description useful for examining and creating optimal working conditions, also by developing empowering workers or by restructuring their responsibilities. Human-robot interaction is among the domains with a high demand for work design. It is related to the widespread human fear of being replaced by machines at work, as well as the still small number of publications in this field, mostly devoted to human-robot interaction analysis conditions. Scholars have identified an elaborated set of tasks by a human during working with a robot. From work design perspective, besides expanding what tasks are considered, it is crucial to consider its meaning for creating critical psychological states, which are important for individual and organisational results. Thus, the paper preparation's impetus describes and analyses the tasks carried out by robot operators in a more precise manner, by using psychological work analysis from the work design perspective. The research group consists of 32 robot operators from a large international manufacturing company. The Work Design Questionnaire (WDQ) by Morgeson and Humphrey (2006) was used to describe 21 of work characteristics. Results show that Task Variety, Information Processing, Task Identity, Specialisation, Skill Variety, Problem-solving, Task Significance, Received Interdependence, Feedback From Others, and Feedback from Job are the highest work characteristics of robot operators. It suggests that robot operators distinguish non-routine, cognitive, and interpersonal tasks as more substantial than other tasks. Alike, according to the work design theory, the work characteristics indicated by respondents are recognised as important job-specific factors for workers' critical psychological states i.e., experiencing meaning, feeling responsible for outcomes, and understanding the results of their efforts, and helping shape the context of the work. It is imperative that further and more extensive research is conducted to bring more attention to how work design can support individuals' cognitive, social, and identity development in human-robot interaction. © 2021 IEEE.","human-robot interaction; task-based approach; work characteristics; Work Design","Biofeedback; Computation theory; Economic and social effects; Employment; Feedback; Industrial research; Industrial robots; Job analysis; Man machine systems; Social robots; Job description; Manufacturing companies; Organisational; Psychological state; Research groups; Robot controller; Robot operators; Specialisation; Machine design",,,,,,,,,,,,,,,,"Grant, A.M., Fried, Y., Juillerat, T., Work matters: Job design in classic and contemporary perspectives (2011) Apa Handbook of Industrial and Organizational Psychology, Vol 1: Building and Developing the Organization, pp. 417-453; Torraco, R.J., Work design theory: A review and critique with implications for human resource development (2005) Human Resource Development Quarterly, 16 (1), pp. 85-109; Parker, S.K., Beyond motivation: Job and work design for development, health, ambidexterity, and more (2014) Annu. Rev. Psychol, 65, pp. 661-691; Hackman, J.R., Oldham, G.R., Development of the job diagnostic survey (1975) J Appl Psychol, 60, pp. 159-170; Parker, S., Wall, T., (1998) Job and Work Design: Organizing Work to Promote Well-being and Effectiveness, , London: Sage; Campion, M.A., McCelland, C.L., Follow-up and extension of the interdisciplinary costs and benefits of enlarged jobs (1993) J Appl Psychol, 78, pp. 339-351; Humphrey, S.E., Nahrgang, J.D., Morgeson, F.P., Integrating motivational, social, and contextual work design features: A meta-analytic summary and theoretical extension of the work design literature (2007) J Appl Psychol, 92 (5), pp. 1332-1356; Walsh, S.M., Strano, M.S., Robotic systems and autonomous platforms (2018) Advances in Materials and Manufacturing, , Eds., . Cambridge: Woodhead Publishing; Gnambs, T., Appel, M., Are robots becoming unpopular? Changes in attitudes towards autonomous robotic systems in Europe (2019) Comput Human Behav, 93, pp. 53-61. , (Apr), 2019; Wisskirchen, G., Biacabe, B.T., Bormann, U., Muntz, A., Niehaus, G., Soler, G.J., Brauchitsch, B.V., Artificial intelligence and robotics and their impact on the workplace (2017) Iba Global Employment Institute, , www.ibanet.org, London, England, [Online] Available:, [Accessed Feb. 17, 2021]; Schwab, K., The fourth industrial revolution (2015) Foreign Affairs, World Economic Forum, Geneva, , https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolutionwhat-it-means-and-how-to-respond/, [Online] Available:, [Accessed Feb. 17, 2021]; Arntz, M., Gregory, T., Zierahn, U., The risk of automation for jobs in OECD countries: A comparative analysis (2016) Oecd Social, Employment and Migration Working Papers, No. 189, , http://dx.doi.org/10.1787/5jlz9h56dvq7-en, OECD Publishing, Paris, [Online] Available:, [Accessed Feb. 17, 2021]; Acemoglu, D., Restrepo, P., The race between man and machine: Implications of technology for growth, factor shares, and employment (2018) American Economic Review, 108 (6), pp. 1488-1542; Parker, S.K., Andrei, D.M., Van Der Broeck, A., Poor work design begets poor work design: Capacity and willingness antecedents of individual work design behavior (2019) J Appl Psychol, 104 (7), pp. 907-928; Parker, S.K., Grote, G., Automation, algorithms, and beyond: Why work design matters more than ever in digital world (2019) Applied Psychology: An International Review; Grant, A.M., Parker, S.K., 7 redesigning work design theories: The rise of relational and proactive perspectives (2009) Acad, 3 (1), pp. 317-375; Frey, C.B., Osborne, M.A., The future of employment: How susceptible are jobs to computerisation? (2017) Technological Forecasting and Social Change, 114, pp. 254-280; Autor, D.H., Why are there still so many jobs? The History and future of workplace automation (2015) Jep, 29 (3), pp. 3-30; Pollak, A., Paliga, M., Pulopulos, M.M., Kozusznik, B., Kozusznik, M.W., Stress in manual and autonomous modes of collaboration with a cobot (2020) Comput Human Behav, 112, p. 106469. , 2020; Royakkers, L., Van Est, R., A literature review on new robotics: Automation from love to war (2015) International Journal of Social Robotics, 7 (5), pp. 549-570; Welfare, K.S., Hallowell, M.R., Shah, J.A., Riek, L.D., Consider the human work experience when integrating robotics in the workplace (2019) Proceedings of the 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 75-84. , Daegu, Korea South; Kildal, J., Tellaeche, A., Fernández, I., Maurtua, I., Potential users' key concerns and expectations for the adoption of cobots (2018) Procedia Cirp, 72, pp. 21-26. , 2018; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y.C., Visser, E.J.D., Parasuraman, R., Meta-analysis of factors affecting trust in human- robot interaction (2011) Hum Factors, 16, pp. 517-527. , (Sep, ), 2011; Autor, D.H., Levy, F., Murnane, R.J., The skill content of recent technological change: An empirical investigation (2003) Quarterly Journal of Economics, 118 (3), pp. 1279-1333. , Nov; Acemoglu, D., Autor, D., Skills, tasks and technologies: Implications for employment and earnings (2011) Handbook of Labor Economics, 4, pp. 1043-1171. , Elsevier; Frank, K., Frenette, M., Are new technologies changing the nature of work? The evidence so far (2021) Irpp Study, 81. , https://onirpp.org/3phLRP8, Montreal: Institute for Research on Public Policy, [Online] Available:, [Accessed Feb. 17, 2021; Spitz-Oener, A., Technical change, job tasks, and rising educational demands: Looking outside the wage structure (2006) J Labor Econ, 24 (2), pp. 235-270; Morgeson, F.P., Humphrey, S.E., The work design questionnaire (WDQ): Developing and validating a comprehensive measure for assessing job design and the nature of work (2006) J Appl Psychol, 91 (6), pp. 1321-1339; Sims, H.P., Szilagyi, A.D., Keller, R.T., The measurement of job characteristics (1976) Academy of Management Journal, 19, pp. 195-212; Campion, M.A., Interdisciplinary approaches to job design: A constructive replication with extensions (1988) J Appl Psychol, 73, pp. 467-481; Stone, E.F., Gueutal, H.G., An empirical derivation of the dimensions along which characteristics of jobs are perceived (1985) Academy of Management Journal, 28, pp. 376-396; Eisinga, R., Grotenhuis, M., Pelzer, B., The reliability of a two-item scale: Pearson, cronbach, or spearman-brown? (2012) Int J Publ Health, 23, pp. 637-642. , Oct; Hyde, S., Chow, W., How robots will transform the C-suite (2019) Harvard Business Review, , Spring; Shneiderman, B., Human-centered artificial intelligence: Three fresh ideas (2020) Ais Transaction on Human Computer Interaction, 12 (3), pp. 109-124; Frey, C.B., Osborne, M.A., The future of employment: How susceptible are jobs to computerization? (2017) Technological Forecasting and Social Change, 114, pp. 254-280; Clegg, C., Shepherd, C., The biggest computer programme in the world ever!: Time for a change in mindset (2007) J. Inf. Technol, 22 (3), pp. 212-221",,"McGeorge N.Ruvinsky A.Teichmann M.Motus L.Freiman M.",,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2021","14 May 2021 through 22 May 2021",,170822,,9781728176987,,,"English","Proc. - IEEE Int. Conf. Cogn. Comput. Asp. Situat. Manag., CogSIMA",Conference Paper,"Final","",Scopus,2-s2.0-85112863943
"Xiao J., Wang P., Lu H., Zhang H.","49964924600;57213429804;55729801700;56979541200;","A three-dimensional mapping and virtual reality-based human–robot interaction for collaborative space exploration",2020,"International Journal of Advanced Robotic Systems","17","3",,"","",,2,"10.1177/1729881420925293","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086001420&doi=10.1177%2f1729881420925293&partnerID=40&md5=bbcc562bb685db7cf8ccb83bf391aa3c","Department of Automation, National University of Defense Technology, Changsha, China; School of Computer Science, University of Lincoln, Lincoln, United Kingdom; China Aerodynamics Research and Development Center, Changsha, China","Xiao, J., Department of Automation, National University of Defense Technology, Changsha, China, School of Computer Science, University of Lincoln, Lincoln, United Kingdom; Wang, P., China Aerodynamics Research and Development Center, Changsha, China; Lu, H., Department of Automation, National University of Defense Technology, Changsha, China; Zhang, H., Department of Automation, National University of Defense Technology, Changsha, China","Human–robot interaction is a vital part of human–robot collaborative space exploration, which bridges the high-level decision and path planning intelligence of human and the accurate sensing and modelling ability of the robot. However, most conventional human–robot interaction approaches rely on video streams for the operator to understand the robot’s surrounding, which lacks situational awareness and force the operator to be stressed and fatigued. This research aims to improve efficiency and promote the natural level of interaction for human–robot collaboration. We present a human–robot interaction method based on real-time mapping and online virtual reality visualization, which is implemented and verified for rescue robotics. At the robot side, a dense point cloud map is built in real-time by LiDAR-IMU tightly fusion; the resulting map is further transformed into three-dimensional normal distributions transform representation. Wireless communication is employed to transmit the three-dimensional normal distributions transform map to the remote control station in an incremental manner. At the remote control station, the received map is rendered in virtual reality using parameterized ellipsoid cells. The operator controls the robot with three modes. In complex areas, the operator can use interactive devices to give low-level motion commands. In the less unstructured region, the operator can specify a path or even a target point. Afterwards, the robot follows the path or navigates to the target point autonomously. In other words, these two modes rely more on the robot’s autonomy. By virtue of virtual reality visualization, the operator can have a more comprehensive understanding of the space to be explored. In this case, the high-level decision and path planning intelligence of human and the accurate sensing and modelling ability of the robot can be well integrated as a whole. Although the method is proposed for rescue robots, it can also be used in other out-of-sight teleoperation-based human–robot collaboration systems, including but not limited to manufacturing, space, undersea, surgery, agriculture and military operations. © The Author(s) 2020.","3D mapping; human–robot interaction; Human–robot space exploration; rescue robotics; virtual reality","Agricultural robots; Intelligent robots; Mapping; Military operations; Normal distribution; Remote control; Robot programming; Space research; Virtual reality; Visualization; Collaboration systems; Collaborative spaces; Reality visualization; Remote-control stations; Robot interactions; Situational awareness; Three dimensional mapping; Wireless communications; Social robots",,,,,"National Natural Science Foundation of China, NSFC: 61773393, U1813205; China Scholarship Council, CSC; National Key Research and Development Program of China, NKRDPC: 2017YFC0803300","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by National Key R&D Program of China (no. 2017YFC0803300) and National Science Foundation of China (nos. U1813205 and 61773393).","The support provided by China Scholarship Council (CSC) during a visit of Junhao Xiao to University of Lincoln is acknowledged.","The support provided by China Scholarship Council (CSC) during a visit of Junhao Xiao to University of Lincoln is acknowledged. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is supported by National Key R&D Program of China (no. 2017YFC0803300) and National Science Foundation of China (nos. U1813205 and 61773393).",,,,,,,,"Kitano, H., Asada, M., Kuniyoshi, Y., Robocup: the robot world cup initiative Proceedings of the first international conference on autonomous agents, pp. 340-347. , New York, ACM, In; Kitano, H., Tadokoro, S., Noda, I., Robocup rescue: search and rescue in large-scale disasters as a domain for autonomous agents research, pp. 739-743. , IEEE international conference on systems, man, and cybernetics, Tokyo, 12–15 October 1999, In; Spenko, M., Buerger, S., Iagnemma, K., Editorial, special issue: the Darpa robotics challenge finals (2017) J Fiel Robot, 34 (2), pp. 227-228; Murphy, R.R., Tadokoro, S., Kleiner, A., Disaster robotics (2016) Springer handbook of robotics, pp. 1577-1604. , Berlin, Springer, In; Doroodgar, B., Ficocelli, M., Mobedi, B., The search for survivors: cooperative human–robot interaction in search and rescue environments using semi-autonomous robots, pp. 2858-2863. , IEEE international conference on robotics and automation, Anchorage, 3–7 May 2010, In; Wang, P., Xiao, J., Lu, H., A novel human-robot interaction system based on 3d mapping and virtual reality, pp. 5888-5894. , 2017 Chinese automation congress (CAC), IEEE, Jinan, 20–22 October 2017, In; Murphy, R.R., Human–robot interaction in rescue robotics (2004) IEEE Trans Syst, Man, Cybernet Part C (Applications and Reviews), 34 (2), pp. 138-153; Nomura, T., Kanda, T., Suzuki, T., Experimental investigation into influence of negative attitudes toward robots on human–robot interaction (2006) Ai Society, 20 (2), pp. 138-150; Goodrich, M.A., Schultz, A.C., Human–robot interaction: a survey (2007) Found Trends Human-Comput Interac, 1 (3), pp. 203-275; Amor, H.B., Neumann, G., Kamthe, S., Interaction primitives for human–robot cooperation tasks, pp. 2831-2837. , IEEE international conference on robotics and automation, Hong Kong, 31 May–7 June 2014, IEEE, In; Valero, A., Randelli, G., Botta, F., Operator performance in exploration robotics (2011) J Intellig Robot Syst, 64 (3-4), pp. 365-385; Sheridan, T.B., Human–robot interaction: status and challenges (2016) Human Fact, 58 (4), pp. 525-532; Wang, Z., Liu, S., Zhang, J., A spatio-temporal CRF for human interaction understanding (2016) IEEE Transactions on Circu Syst Video Technol, 27 (8), pp. 1647-1660; Birk, A., Schwertfeger, S., Pathak, K., A networking framework for teleoperation in safety, security, and rescue robotics (2009) IEEE Wirel Commun, 16 (1), pp. 6-13; Yanco, H.A., Drury, J.L., Rescuing interfaces: a multi-year study of human–robot interaction at the AAAI robot rescue competition (2007) Auto Robot, 22 (4), pp. 333-352; Yanco, H.A., Norton, A., Ober, W., Analysis of human–robot interaction at the Darpa robotics challenge trials (2015) J Fiel Robot, 32 (3), pp. 420-444; Norton, A., Ober, W., Baraniecki, L., Analysis of human–robot interaction at the Darpa robotics challenge finals (2017) Int J Robot Res, 36 (5-7), pp. 483-513; Zhang, J., Singh, S., LOAM: lidar odometry and mapping in real-time, 2, pp. 1-9. , Robotics: science and systems, Berkeley, CA, July 2014, In; Stoyanov, T., Magnusson, M., Almqvist, H., On the accuracy of the 3D normal distributions transform as a tool for spatial representation, pp. 4080-4085. , Robotics and automation (ICRA), 2011 IEEE international conference on IEEE, Shanghai, 9-13 May 2011, In; Quigley, M., Conley, K., Gerkey, B., ROS: an open-source robot operating system, pp. 5-10. , Workshop on open source software, IEEE international conference on robotics and automation, Kobe, Japan, 12 May 2009, In; Durrant-Whyte, H., Bailey, T., Simultaneous localization and mapping: part I (2006) IEEE Robot Auto Mag, 13 (2), pp. 99-110; Bailey, T., Durrant-Whyte, H., Simultaneous localization and mapping (slam): part II (2006) IEEE Robot Auto Mag, 13 (3), pp. 108-117; Fuentes-Pacheco, J., Ruiz-Ascencio, J., Rendón-Mancha, J.M., Visual simultaneous localization and mapping: a survey (2015) Artif Intellig Rev, 43 (1), pp. 55-81; Zhang, J., Gui, M., Wang, Q., Hierarchical topic model based object association for semantic slam (2019) IEEE Tran Visual Comput Graph, 25 (11), pp. 3052-3062; Xiao, J., Xiong, D., Yu, Q., A real-time sliding window based visual-inertial odometry for MAVs (2020) IEEE Tran Indust Inf, 16, pp. 4049-4058; Xiao, J., Adler, B., Zhang, J., Planar segment based three-dimensional point cloud registration in outdoor environments (2013) J Field Robot, 30 (4), pp. 552-582; Pomerleau, F., Colas, F., Siegwart, R., A review of point cloud registration algorithms for mobile robotics (2015) Found Tren Robot, 4 (1), pp. 1-104; Hornung, A., Wurm, K.M., Bennewitz, M., Octomap: an efficient probabilistic 3D mapping framework based on octrees (2013) Auto Robot, 34 (3), pp. 189-206; Magnusson, M., (2009) The three-dimensional normal-distributions transform: an efficient representation for registration, surface analysis, and loop detection, , Örebro, PhD Thesis, Örebro universitet; Bray, T., (2017) The javascript object notation (JSON) data interchange format, , https://www.json.org/","Xiao, J.; Department of Automation, China; email: junhao.xiao@ieee.org",,,"SAGE Publications Inc.",,,,,17298806,,,,"English","Int. J. Adv. Rob. Syst.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85086001420
"Tran N.","57209796697;","Adapting mixed reality robot communication to mental workload",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"609","611",,,"10.1145/3371382.3377438","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083220859&doi=10.1145%2f3371382.3377438&partnerID=40&md5=38ad3957654454be8cf92cfebbf55a98","Colorado School of Mines, Golden, CO, United States","Tran, N., Colorado School of Mines, Golden, CO, United States","This paper describes early work in the intersection of Mixed Reality for Human-Robot Interaction and Brain-Computer Interface fields. Our research seeks to answer these two questions: (1) How do different types of mental workload impact the effectiveness of different robot communication modalities? (2) How can a robot select the effective communication modality given information regarding its human teammate's level and type of mental workload? © 2020 ACM.","Augmented reality; Brain-computer interface; Extended reality; Human-robot interaction; Mixed reality","Agricultural robots; Brain computer interface; Human computer interaction; Man machine systems; Mixed reality; Effective communication; Mental workload; Robot communication; Human robot interaction",,,,,"National Science Foundation, NSF: IIS-1909864","This research was funded in part by NSF grant IIS-1909864.",,,,,,,,,,"Beck, D.M., Lavie, N., Look here but ignore what you see: Effects of distractors at fixation (2005) Journal of Experimental Psychology: Human Perception and Performance, 31 (3), p. 592. , 2005; Chakraborti, T., Sreedharan, S., Kulkarni, A., Kambhampati, S., (2017) Alternative Modes of Interaction in Proximal Human-in-the-loop Operation of Robots, , 2017 arXiv preprint; Hirshfield, L., Williams, T., Sommer, N., Grant, T., Velipasalar, G.S., Workload-driven modulation of mixed-reality robot-human communication (2018) Proceedings of the Workshop on Modeling Cognitive Processes from Multimodal Data. ACM, 3; Lavie, N., Perceptual load as a necessary condition for selective attention (1995) Journal of Experimental Psychology: Human Perception and Performance, 21 (3), p. 451. , 1995; Lavie, N., The role of perceptual load in visual awareness (2006) Brain Research, 1080 (1), pp. 91-100. , 2006; Muhl-Richardson, A., Cornes, K., Godwin, H.J., Garner, M., Hadwin, J.A., Liversedge, S.P., Donnelly, N., Searching for two categories of target in dynamic visual displays impairs monitoring ability (2018) Applied Cognitive Psychology, 32 (4), pp. 440-449. , 2018; Scheutz, M., Williams, T., Krause, E., Oosterveld, B., Sarathy, V., Frasca, T., An overview of the distributed integrated cognition affect and reflection diarc architecture (2019) Cognitive Architectures. Springer, pp. 165-193; Serwadda, A., Phoha, V.V., Poudel, S., Hirshfield, L.M., Bandara, D., Bratt, S.E., Costa, M.R., FNIRS: A new modality for brain activity-based biometric authentication (2015) 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE, pp. 1-7; Treacy, S.E., Girouard, A., Chauncey, K., Hirshfield, L.M., Sassaroli, A., Zheng, F., Fantini, S., Jacob, R.J.K., Using fNIRS brain sensing in realistic HCI settings: Experiments and guidelines (2009) Proceedings of the 22nd Annual ACM Symposium on User Interface Software and Technology. ACM, pp. 157-166; Szafir, D., Mediating human-robot interactions with virtual, augmented, and mixed reality (2019) International Conference on Human-Computer Interaction. Springer, pp. 124-149; Wickens, C.D., Multiple resources and performance prediction (2002) Theoretical Issues in Ergonomics Science, 3 (2), pp. 159-177. , 2002; Williams, T., A framework for robot-generated mixed-reality deixis (2018) Proceedings of the 1st International Workshop on Virtual, Augmented, and Mixed Reality for HRI (VAM-HRI); Williams, T., Szafir, D., Chakraborti, T., Ben Amor, H., Virtual, augmented, and mixed reality for human-robot interaction (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 403-404; Williams, T., Tran, N., Rands, J., Dantam, N.T., Augmented, mixed, and virtual reality enabling of robot deixis (2018) International Conference on Virtual, Augmented and Mixed Reality. Springer, pp. 257-275","Tran, N.; Colorado School of MinesUnited States; email: nttran@mines.edu",,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083220859
"Somani N., Zhen L.L., Lakshminarayanan S., Hettiarachchi R., Wee-Ching P., Lee G.S.G., Campolo D.","55917561000;57352401000;57211169233;57352099900;57352553000;56593983300;15622892900;","Simplified Robot Programming Framework for a Gearbox Assembly Application",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"181","190",,,"10.1007/978-3-030-90525-5_16","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119868191&doi=10.1007%2f978-3-030-90525-5_16&partnerID=40&md5=7d1e2e4f9ea7440dd2b0389563e9b3b4","Advanced Remanufacturing and Technology Centre, A*STAR Cleantech Loop, #01/01 CleanTech Two, Singapore, 637143, Singapore; School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore","Somani, N., Advanced Remanufacturing and Technology Centre, A*STAR Cleantech Loop, #01/01 CleanTech Two, Singapore, 637143, Singapore; Zhen, L.L., Advanced Remanufacturing and Technology Centre, A*STAR Cleantech Loop, #01/01 CleanTech Two, Singapore, 637143, Singapore; Lakshminarayanan, S., School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore; Hettiarachchi, R., Advanced Remanufacturing and Technology Centre, A*STAR Cleantech Loop, #01/01 CleanTech Two, Singapore, 637143, Singapore; Wee-Ching, P., School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore; Lee, G.S.G., School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore; Campolo, D., School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore","In this paper, we are presenting a framework for multi-modal human-robot interaction (HRI), where complex robotic tasks can be programmed using a skill-based approach and intuitive HRI modalities. This approach is demonstrated using a gearbox assembly application in a realistic industrial environment. Our system includes mobile and static robots for actuation, 2D and 3D cameras for sensing, and GUIs, spatial- and see-through- Augmented Reality for HRI. © 2021, Springer Nature Switzerland AG.","Human-robot interaction; Robotic assembly; Simplified programming","Augmented reality; Cameras; Gears; Man machine systems; Robot programming; Robotic assembly; Robotics; 3D camera; Gearbox assembly; Humans-robot interactions; Industrial environments; Multi-modal; Programming framework; Robotic tasks; Simplified programming; Human robot interaction",,,,,"Agency for Science, Technology and Research, A*STAR; Science and Engineering Research Council, SERC: 1623a00035","This research is partially supported by the Agency for Science, Technology and Research (A*STAR) under its SERC Grant (Project #A1623a00035).",,,,,,,,,,"Cai, C., Liang, Y.S., Somani, N., Yan, W., Inferring the geometric nullspace of robot skills from human demonstrations (2020) IEEE International Conference on Robotics and Automation (ICRA), pp. 7668-7675. , pp; Castro, A., Souza, J.P., Rocha, L., Silva, M.F., AdaptPack studio: Automatic offline robot programming framework for factory environments (2019) IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 1-6. , pp., 2019; Coronado, E., Mastrogiovanni, F., Venture, G.: Design of a human-centered robot framework for end-user programming and applications. In: Arakelian, V., Wenger, P. (eds.) ROMANSY 22 – Robot Design, Dynamics and Control. CICMS, vol. 584, pp. 450–457. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-78963-7 56; Danielsson, O., Syberfeldt, A., Brewster, R., Wang, L., Assessing instructions in augmented reality for human-robot collaborative assembly by using demonstrators (2017) Procedia CIRP, 63, pp. 89-94; Elsdon, J., Demiris, Y.: Augmented reality for feedback in a shared control spraying task. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1939–1946. https://doi.org/10.1109/ICRA.2018.8461179, ISSN: 2577-087X; (2019) APMS 2019. IAICT, 566. , https://doi.org/10.1007/978-3-030-30000-5, Ameri, F., Stecke, K.E., von Cieminski, G., Kiritsis, D. (eds.), vol., Springer, Cham; Karaman, S., Frazzoli, E., Sampling-based algorithms for optimal motion planning (2011) Int. J. Robot. Res., 30 (7), pp. 846-894; Kraft, M., Rickert, M., How to teach your robot in 5 minutes: Applying UX paradigms to human-robot-interaction (2017) 2017 26Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Pp. 942–949; Kuffner, J., Lavalle, S., RRT-connect: An efficient approach to single-query path planning (2000) IEEE International Conference on Robotics and Automation, 2, pp. 995-1001. , vol., pp; Lakshminarayanan, S., Kana, S., Mohan, D.M., Manyar, O.M., Then, D., Campolo, D., An adaptive framework for robotic polishing based on impedance control (2021) Int. J. Adv. Manuf. Technol., 112 (1), pp. 401-417; Makris, S., Karagiannis, P., Koukas, S., Matthaiakis, A.S., Augmented reality system for operator support in human-robot collaborative assembly (2016) CIRP Ann, 65 (1), pp. 61-64; Perzylo, A., et al.: SMErobotics: smart robots for flexible manufacturing. IEEE Robot. Autom. Mag. 26(1), 78–90 (2019). https://doi.org/10.1109/MRA.2018. 2879747; Ren, S., He, K., Girshick, R., Sun, J., Faster R-CNN: Towards real-time object detection with region proposal networks (2015) Advances in Neural Information Processing Systems, 28. , vol., Curran Associates, Inc; Roitberg, A., Somani, N., Perzylo, A., Rickert, M., Knoll, A., Multimodal human activity recognition for industrial manufacturing processes in robotic workcells (2015) Proceedings of the ACM International Conference on Multimodal Interaction, pp. 259-266. , pp; Schou, C., Andersen, R.S., Chrysostomou, D., Bøgh, S., Madsen, O., Skill-based instruction of collaborative robots in industrial settings (2018) Robot. Comput.-Integr. Manuf., 53, pp. 72-80; Steinmetz, F., Wollschläger, A., Weitschat, R., RAZER-a HRI for visual task-level programming and intuitive skill parameterization (2018) IEEE Robot. Autom. Lett., 3 (3), pp. 1362-1369. , https://doi.org/10.1109/LRA.2018.2798300; Vogel, C., Poggendorf, M., Walter, C., Elkmann, N.: Towards safe physical human-robot collaboration: a projection-based safety system. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3355–3360 (2011). ISSN: 2153-0866; Wojtynek, M., Oestreich, H., Beyer, O., Wrede, S., Collaborative and robot-based plug produce for rapid reconfiguration of modular production systems (2017) 2017 IEEE/SICE International Symposium on System Integration (SII), pp. 1067-1073. , pp","Somani, N.; Advanced Remanufacturing and Technology Centre, #01/01 CleanTech Two, Singapore; email: somanin@artc.a-star.edu.sg","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119868191
"Xue C., Qiao Y., Murray N.","57220024635;22036417400;7201513152;","Enabling Human-Robot-Interaction for Remote Robotic Operation via Augmented Reality",2020,"Proceedings - 21st IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2020",,,"9217702","194","196",,2,"10.1109/WoWMoM49955.2020.00046","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096586417&doi=10.1109%2fWoWMoM49955.2020.00046&partnerID=40&md5=ab4759faa1f5a48342707cbe21af813e","Computer and Software Engineering, Faculty of Engineering and Informatics, Athlone Institute of Technology, Athlone, Ireland; Software Research Institute, Faculty of Engineering and Informatics, Athlone Institute of Technology, Athlone, Ireland","Xue, C., Computer and Software Engineering, Faculty of Engineering and Informatics, Athlone Institute of Technology, Athlone, Ireland; Qiao, Y., Software Research Institute, Faculty of Engineering and Informatics, Athlone Institute of Technology, Athlone, Ireland; Murray, N., Computer and Software Engineering, Faculty of Engineering and Informatics, Athlone Institute of Technology, Athlone, Ireland","Human-Robot Interaction (HRI) will be a crucial component of smart factories of the future (FoF). This demo presents a reliable and cost-effective HRI system based on Augmented Reality (AR). In this demonstration, we offer concepts and methods currently being developed to enable high-level human-robot collaboration and interaction. The user interaction and instructions are captured via AR and communicated to a Robot Operating System (ROS) powered robotic arm. © 2020 IEEE.","Augmented Reality; Human-Robot Interaction; Robot Operating System; Unity3D","Augmented reality; Cost effectiveness; Man machine systems; Robotics; Cost effective; Factories of the futures; Human robot Interaction (HRI); Human-robot collaboration; Robot operating systems (ROS); User interaction; Social robots",,,,,"Science Foundation Ireland, SFI: SFI/16/RC/3918","ACKNOWLEDGEMENT This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under grant number SFI/16/RC/3918, co-funded by CONFIRM Smart Manufacturing and Robotics & Drives Ireland",,,,,,,,,,"Feil-Seifer, D., Mataric, M., Human-robot interaction (2019) Encyclopedia of Complexity and Systems Science, pp. 4643-4659; Goodrich, M.A., Human-robot interaction: A survey: Foundations and trends in human-computer interaction: Vol 1, no 3 (2020) Dl.acm.org, , https://dl.acm.org/doi/10.1561/1100000005; Green, S., Billinghurst, M., Chen, X., Chase, J., Human robot collaboration: An augmented reality approach a literature review and analysis (2007) Proceeding of Mesa 07:3RD International Conference on Mechatronics and Embedded System and Applications, , Nevada,USA; De Melo, M.S.P., Da Silva, N.J.G., Da Silva, P.J.L., Teixeira, J.M.X.N., Teichrieb, V., Analysis and comparison of robotics 3d simulators (2019) 2019 21st Symposium on Virtual and Augmented Reality (SVR), pp. 242-251. , Rio de Janeiro, Brazil; (2020) Ros.org, , https://www.ros.org/about-ros/, ROS.org | About ROS; Lasota, P.A., Song, T., Shah, J.A., A survey of methods for safe human-robot interaction (2017) A Survey of Methods for Safe Human-Robot Interaction; Chacko, S.M., Kapila, V., An augmented reality interface for human-robot interaction in unconstrained environments (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3222-3228. , Macau, China; Quintero, C.P., Li, S., Pan, M.K., Chan, W.P., Der Van Loos, M.H.F., Croft, E., Robot programming through augmented trajectories in augmented reality (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1838-1844. , Madrid; (2020) Docs.microsoft.com, , https://docs.microsoft.com/en-us/windows/mixed-reality/mrtk-gettingstarted, Getting started with MRTK version 2-Mixed Reality; (2020) GitHub, , https://github.com/siemens/ros-sharp/wiki, siemens/ros-sharp; Aristidou, A., Lasenby, J., FABRIK: A fast iterative solver for the Inverse Kinematics problem (2011) Graphical Models, 73 (5), pp. 243-260; Assetstore.unity.com, 2020, , https://assetstore.unity.com/packages/tools/animation/final-ik-14290, Final IK | Animation Tools | Unity Asset Store; (2019) Tarang Shah-Blog, , https://tarangshah.com/blog/2017-04-01/rosbasics-1-nodes-topics-services-actions, ROS Basics 1-Nodes, Topics, Services &Actions; (2020) Docs.ros.org, , http://docs.ros.org/melodic/api/moveit_tutorials/html/index.html, MoveIt Tutorials - moveit tutorials Kinetic documentation; Rodrigues, T.B., An evaluation of a 3d multimodal marker-less motion analysis system | proceedings of the 10th acm multimedia systems conference (2020) Dl.acm.org, , https://dl.acm.org/doi/10.1145/3304109.3306236; Keighrey, C., Comparing user QoE via physiological and interaction measurements of immersive ar and vr speech and language therapy applications | proceedings of the on thematic workshops of acm multimedia 2017 (2019) Dl.acm.org, , https://dl.acm.org/doi/10.1145/3126686.3126747; Hynes, E., Flynn, R., Lee, B., Murray, N., A quality of experience evaluation comparing augmented reality and paper based instruction for complex task assistance (2019) 2019 Ieee 21st International Workshop on Multimedia Signal Processing (MMSP), pp. 1-6. , Kuala Lumpur, Malaysia",,,"IEEE;IEEE Computer Society;Missouri University of Science and Technology","Institute of Electrical and Electronics Engineers Inc.","21st IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2020","31 August 2020 through 3 September 2020",,163852,,9781728173740,,,"English","Proc. - IEEE Int. Symp. World Wirel., Mob. Multimed. Networks, WoWMoM",Conference Paper,"Final","",Scopus,2-s2.0-85096586417
"Petrov P., Georgieva V., Kralov I., Nikolov S.","26665747200;45561158100;36992295500;57211156406;","An adaptive control scheme for human following behavior of mobile robots",2020,"11th National Conference with International Participation, ELECTRONICA 2020 - Proceedings",,,"9305108","","",,2,"10.1109/ELECTRONICA50406.2020.9305108","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099878528&doi=10.1109%2fELECTRONICA50406.2020.9305108&partnerID=40&md5=682858c1077f65dec0209fff0fe9d33e","Faculty of Mechanical Engineering, Technical University of Sofia, Sofia, 1000, Bulgaria; Faculty of Telecommunications, Technical University of Sofia, Sofia, 1000, Bulgaria; Technical University of Sofia, Department of Mechanics, Sofia, 1000, Bulgaria","Petrov, P., Faculty of Mechanical Engineering, Technical University of Sofia, Sofia, 1000, Bulgaria; Georgieva, V., Faculty of Telecommunications, Technical University of Sofia, Sofia, 1000, Bulgaria; Kralov, I., Technical University of Sofia, Department of Mechanics, Sofia, 1000, Bulgaria; Nikolov, S., Faculty of Mechanical Engineering, Technical University of Sofia, Sofia, 1000, Bulgaria","This paper presents an adaptive mobile robot control scheme for autonomous human following in which the robot keeps a desired distance from the person. A human-robot interaction model in error coordinates suitable for robot following applications is proposed. An adaptive control law is presented to tackle the problem of the unknown human velocity. Simulation and experimental results are presented to illustrate the effectiveness of the proposed control scheme. © 2020 IEEE.","Adaptive control; Human following; Human-robot interaction; Mobile robots","Behavioral research; Mobile robots; Adaptive control law; Adaptive control schemes; Control schemes; Human following; Mobile robot control; Human robot interaction",,,,,,,,,,,,,,,,"Rubio, F., Valero, F., Liopes-Albert, C., A review of mobile robots: Concepts, methods, theoretical framework, and applications (2019) Int. J. Adv. Rob. Systems, pp. 1-22. , March-April; Petrov, P., Nashashibi, F., Planning and nonlinear adaptive control for an automated overtaking maneuver (2011) IEEE Conf. Intel. Transp. Systems, pp. 662-667; Petrov, P., Nashashibi, F., Saturated feedback control for an automated parallel parking assist system (2014) Int. Conf. Contr. Automat. Robotics and Vision, ICARCV 2014, pp. 577-582; Jevtic, A., Doisy, G., Parmet, Y., Edan, Y., Comparison of interaction modalities for mobile indoor robot guidance: Direct physical interaction, person following, and pointing Control (2015) IEEE Trans. Hum.-Mach. Systems, 45 (6), pp. 653-663; Honig, S., Gilad, T., Oron-Gilad, T., Zaichyk, H., Fleischmann, S.-V., Toward socially aware person-following robots (2018) IEEE Trans. Cogn. Develop. Systems, 10 (4), pp. 936-954; Wu, Z., Payandeh, S., Toward design of a drip-stand patient follower robot (2020) Journal of Robotics, pp. 1-16. , Article ID 9080642; Islam, M., Hong, J., Sattar, J., Person following by autonomous robots: A categorical overview (2019) Int. J. Rob. Research; Gockley, R., Forlizzi, J., Simmons, R., Natural person following behavior for social robots (2007) ACM/IEEE Int. Conf. Hum.-Rob. Interaction; Petrov, P., A mathematical model for control of an autonomous vehicle convoy (2008) WSEAS Transactions on Systems and Control, 3 (9), pp. 835-848; Petrov, P., Kralov, I., A look-ahead approach to mobile robot path tracking based on distance-only measurements (2019) AIP Conference Proceedings, 2172, p. 110005. , https://doi.org/10.1063/1.5133608; https://www.youtube.com/watch?v=0sF0Hf4G07c; https://www.youtube.com/watch?v=2I-sLl8xmcE",,,,"Institute of Electrical and Electronics Engineers Inc.","11th National Conference with International Participation, ELECTRONICA 2020","23 July 2020 through 24 July 2020",,166415,,9781728175317,,,"English","Natl. Conf. Int. Particip., ELECTRONICA - Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85099878528
"Chen X., Williams A.","57117174400;7405838909;","Improving engagement by letting social robots learn and call your name",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"160","162",,1,"10.1145/3371382.3378355","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083202689&doi=10.1145%2f3371382.3378355&partnerID=40&md5=0bbfae94aafc3224a7b32b05b5eb599e","University of Kansas, Lawrence, KS, United States","Chen, X., University of Kansas, Lawrence, KS, United States; Williams, A., University of Kansas, Lawrence, KS, United States","This paper aims to improve the engagement in human-robot interactions by enabling social robots to call people by their name during a social interaction. To this end, we propose to integrate computer vision, online learning using a convolutional neural network, and speech technologies and concepts to learn names to facilitate and improve engagement. Our experiments show that human-robot engagement shows significant improvement through the proposed approach. © 2020 ACM.","Engagement; Face recognition; Scaffolding; Social robots; Speech recognition","Agricultural robots; Convolutional neural networks; Man machine systems; Human robots; Online learning; Social interactions; Speech technology; Social robots",,,,,,,,,,,,,,,,"Carmody, D.P., Lewis, M., Brain activation when hearing one's own and others' names (2006) Brain Research, 1116 (1), pp. 153-158. , https://doi.org/10.1016/j.brainres.2006.07.121, 2006; Kim, Y., Kwak, S.S., Suk Kim, M., Am i acceptable to you Effect of a robot's verbal language forms on people's social distance from robots (2013) Computers in Human Behavior, 29 (3), pp. 1091-1101. , https://doi.org/10.1016/j.chb.2012.10.001, 2013; Macharet, D.G., Florencio, D.A., Learning how to increase the chance of human-robot engagement (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 2173-2179; Mascolo, M., Change processes in development: The concept of coactive scaffolding (2005) New Ideas in Psychology, 23, pp. 185-196. , https://doi.org/10.1016/j.newideapsych.2006.05.002, 12 2005; Rich, C., Ponsler, B., Holroyd, A., Sidner, C.L., Recognizing engagement in human-robot interaction (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 375-382; Shiomi, M., Kanda, T., Ishiguro, H., Hagita, N., Interactive humanoid robots for a science museum (2006) Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction (HRI '06), pp. 305-312. , https://doi.org/10.1145/1121241.1121293, Association for Computing Machinery, New York, NY, USA; Zhang, Y., Hornfeck, K., Lee, K., Adaptive face recognition for low-cost, embedded human-robot interaction (2013) Intelligent Autonomous Systems 12. Springer, pp. 863-872",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083202689
"Bering Christiansen M., Jørgensen J.","57216348043;57199227765;","Augmenting soft robotics with sound",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"133","135",,1,"10.1145/3371382.3378328","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083175799&doi=10.1145%2f3371382.3378328&partnerID=40&md5=e3f8500b31be670894e1815c480c3dd6","University of Southern Denmark, Odense, Denmark","Bering Christiansen, M., University of Southern Denmark, Odense, Denmark; Jørgensen, J., University of Southern Denmark, Odense, Denmark","During the past decade soft robotics has emerged as a growing field of research. In this paper we present exploratory research on sound design for soft robotics with potential applications within the human-robot interaction domain. We conducted an analysis of the sounds made by imaginary soft creatures in movies. Drawing inspiration from the analysis, we designed a soft robotic prototype that features real-time sound generation based on FM synthesis. © 2020 ACM.","Human-robot interaction; Media art; Robotic art; Soft robotics; Sound design","Agricultural robots; Machine design; Man machine systems; Robotics; Exploratory research; Real time; Soft robotics; Sound designs; Sound generation; Human robot interaction",,,,,,,,,,,,,,,,"Arnold, T., Scheutz, M., The tactile ethics of soft robotics: Designing wisely for human-robot interaction (2017) Soft Robotics, 4 (2), pp. 81-87. , https://doi.org/10.1089/soro.2017.0032, May 2017; Boer, L., Bewley, H., Reconfiguring the appearance and expression of social robots by acknowledging their otherness (2018) Proceedings of the 2018 Designing Interactive Systems Conference (DIS '18). ACM, pp. 667-677. , https://doi.org/10.1145/3196709.3196743; Breazeal, C., (2002) Designing Sociable Robots, , MIT Press; Carlile, S., (2011) Psychoacoustics. The Sonification Handbook, , Thomas Hermann, Andy Hunt, and John G.Editors Neuhoff (Eds.). Logos Verlag Berlin, Berlin, Germany; Cha, E., Kim, Y., Fong, T., Mataric, M.J., A survey of nonverbal signaling methods for non-humanoid robots (2018) Foundations and TrendsR in Robotics, 6 (4), pp. 211-323. , https://doi.org/10.1561/2300000057, 2018; Brian, R.D., Anthropomorphism and the social robot (2003) Robotics and Autonomous Systems, 42 (3), pp. 177-190. , https://doi.org/10.1016/S0921-8890(02)00374-3, Mar 2003; Jee, E., Cheong, Y., Hui Kim, C., Kwon, D., Kobayashi, H., Sound production for the emotional expression of socially interactive robots (2009) Advances in Human-Robot Interaction, , https://doi.org/10.1007/3-540-27437-58, Vladimir A. Kulyukin (Ed.). IntechOpen; Jee, E., Jeong, Y., Hui Kim, C., Kobayashi, H., Sound design for emotion and intention expression of socially interactive robots (2010) Intelligent Service Robotics, 3 (3), pp. 199-206. , https://doi.org/10.1007/s11370-010-0070-7, Jul 2010; Jekosch, U., Assigning meaning to sounds-Semiotics in the context of product-sound design (2005) Communication Acoustics, pp. 193-221. , https://doi.org/10.1007/3-540-27437-58, Jens Blauert (Ed.). Springer, Heidelberg, Berlin; Jeong, E., Hyun Kwon, G., So, J., Exploring the taxonomie and associative link between emotion and function for robot sound design (2017) 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 641-643. , https://doi.org/10.1109/URAI.2017.7992692; Jørgensen, J., Prolegomena for a transdisciplinary investigation into the materialities of soft systems (2017) ISEA 2017 Manizales BIO-CREATIon and PEACE: Proceedings of the 23rd International Symposium on Electronic Arts, pp. 153-160. , Department of Visual Design, Universidad de Caldas, and ISEA International; Jørgensen, J., Appeal and perceived naturalness of a soft robotic tentacle (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (HRI '18). ACM, pp. 139-140. , https://doi.org/10.1145/3173386.3176985; Jørgensen, J., (2019) Constructing Soft Robot Aesthetics: Art, Sensation, and Materiality in Practice, , IT University of Copenhagen. PhD Thesis; Langeveld, L., Van, R., Jansen, R., Ozcan, E., Product sound design: Intentional and consequential sounds (2013) Advances in Industrial Design Engineering, , https://doi.org/10.5772/55274, Denis A. Coelho (Ed.). IntechOpen; Laschi, C., Mazzolai, B., Cianchetti, M., Soft robotics: Technologies and systems pushing the boundaries of robot abilities (2016) Science Robotics, 1 (1). , https://doi.org/10.1126/scirobotics.aah3690, Dec 2016; Dagmar, B.M.A., Bjerre, H.A., Jung, J.J., Goul, J.L., Annochum, E., Löchtefeld, M., The helpless soft robot-Stimulating human collaboration through robotic movement (2019) Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (CHI EA '19). ACM, , https://doi.org/10.1145/3290607.3312807; Moore, D., Tennent, H., Martelaro, N., Ju, W., Making noise intentional: A study of servo sound perception (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (HRI '17). ACM, pp. 12-21. , https://doi.org/10.1145/2909824.3020238; Rus, D., Tolley, M.T., Design, fabrication and control of soft robots (2015) Nature, 521 (7553), pp. 467-475. , https://doi.org/10.1038/nature14543, May 2015; Schwenk, M., Arras, K.O., R2-D2 Reloaded: A flexible sound synthesis system for sonic human-robot interaction design (2014) The 23rd IEEE International Symposium on Robot and Human Interactive Communication, pp. 161-167. , https://doi.org/10.1109/ROMAN.2014.6926247; Shutterly, A., Menguc, Y., Knight, H., Contextual collision: The social consequences of touch and collision in human robot interaction (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (HRI '18). ACM, pp. 323-324. , https://doi.org/10.1145/3173386.3176922; Yan Zheng, C., Affective touch with soft robotic actuators-A design toolkit for personalised affective communication (2018) Workshop: Reshaping Touch Communication: An Interdisciplinary Research Agenda, p. 4. , 2018; Zheng, C.Y., Walker, K., Soft grippers not only grasp fruits: From affective to psychotropic HRI (2019) 2019 Convention of Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB), , http://aisb2019.machinemovementlab.net/MTSB2019ZhengWalker.pdf, 2019",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85083175799
"Baumgartner M., Kopp T., Kinkel S.","57218326101;57201388519;6701428946;","Industrial human-robot-collaboration in smes – smes underestimate the potential of human-robot-collaboration [Kmu unterschätzen das potenzial der mensch-roboter-interaktion industrielle menschroboter-interaktion in kmu]",2020,"WT Werkstattstechnik","110","3",,"146","150",,2,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088837235&partnerID=40&md5=8f1f54518abfa1142157291bbc2b94d6","Institut für Lernen und Innovation in Netzwerken (ILIN), Hochschule Karlsruhe – Technik und Wirtschaft, Moltkestr. 30, Karlsruhe, 76133, Germany","Baumgartner, M., Institut für Lernen und Innovation in Netzwerken (ILIN), Hochschule Karlsruhe – Technik und Wirtschaft, Moltkestr. 30, Karlsruhe, 76133, Germany; Kopp, T., Institut für Lernen und Innovation in Netzwerken (ILIN), Hochschule Karlsruhe – Technik und Wirtschaft, Moltkestr. 30, Karlsruhe, 76133, Germany; Kinkel, S., Institut für Lernen und Innovation in Netzwerken (ILIN), Hochschule Karlsruhe – Technik und Wirtschaft, Moltkestr. 30, Karlsruhe, 76133, Germany","According to experts, industrial human-robot interaction (HRI) is particularly suitable for the specific production conditions of small and medium-sized enterprises (SMEs). Nevertheless, HRI solutions are currently mainly found in large companies. An empirical survey of 81 representatives of German industrial companies suggests that this is not just due to barriers in implementing collaborative robots. On the contrary, SMEs seem to systematically underestimate the potential of HRI solutions. © 2020, VDI Fachmedien GmBbH & Co.. All rights reserved.",,,,,,,,,,,,,,,,,,"Europäische Union: Empfehlung der Kommission vom 6. Mai 2003 betreffend die Definition der Kleinstunternehmen sowie der kleinen und mittleren Unternehmen. Amtsblatt der Europäischen Union, Brüssel, 2003; Bauer, W., Bender, M., Braun, M., Leichtbauroboter in der manuellen Montage – einfach einfach anfangen. Fraunhofer-Institut für Arbeitswirtschaft und Organisation (IAO) (2016) Fraunhofer-Institut für Arbeitswirtschaft Und Organisation; Gorle, P., Clive, A., (2013) Positive Impact of Industrial Robots on Employment, International Federation of Robotics (IFR), , London: Metra Martech; Faber, M., Bützler, J., Schlick, C.M., Human-robot Cooperation in Future Production Systems: Analysis of Requirements for Designing an Ergonomic Work System (2015) Procedia Manufacturing, 3, pp. 510-517; Brooks, R., Robots at Work: Toward a Smarter Factory (2013) The Futurist, 47 (3), pp. 24-27; Scholer, M., Branchenspezifische Applikationen (2019) Handbuch Mensch-Roboter-Kollaboration, pp. 361-428. , Müller, R.; Franke, J.; Henrich, D. et al., München: Hanser; Selevsek, N., Köhler, C., Angepasste Planungssystematik für MRK-Systeme (2018) ZWF Zeitschrift für Wirtschaftlichen Fabrikbetrieb, 113 (1-2), pp. 55-58; Bauer, W., Rally, P., Scholtz, O., Schnelle Ermittlung sinnvoller MRK-Anwendungen (2018) ZWF Zeitschrift für Wirtschaftlichen Fabrikbetrieb, 113 (9), pp. 554-559; Bischoff, J., Erschließen der Potenziale der Anwendung von,Industrie 4.0‘ im Mittelstand (2015) Studie Im Auftrag Des Bundes-Ministeriums für Wirtschaft Und Energie (Bmwi), , (Hg.); Schou, C., Hansson, M., Madsen, O., Assisted Hardware Selection for Industrial Collaborative Robots (2017) Procedia Manufacturing, 11, pp. 174-184; Cencen, A., Verlinden, J.C., Geraedts, J.M.P., Design Methodology to Improve Human-Robot Coproduction in Small-and Medium-Sized Enterprises (2018) IEEE/ASME Transactions on Mechatronics, 23 (3), pp. 1092-1102; Becker, W., Ulrich, P., Botzkowski, T., (2017) Industrie 4.0 Im Mittelstand. Best Practices Und Implikationen für KMU, , Wiesbaden: Springer Gabler","Baumgartner, M.; Institut für Lernen und Innovation in Netzwerken (ILIN), Moltkestr. 30, Germany; email: marco.baumgartner@ilin.eu",,,"VDI Fachmedien GmBbH & Co.",,,,,14364980,,,,"German","WT Werkstattstech.",Article,"Final","",Scopus,2-s2.0-85088837235
"Ruiz A.Y.R., Chandrasekaranxs B.","57208120529;56740438200;","Implementation of a Sensor Fusion Based Robotic System Architecture for Motion Control using Human-Robot Interaction",2020,"Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020",,,"9026276","405","409",,1,"10.1109/SII46433.2020.9026276","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082608849&doi=10.1109%2fSII46433.2020.9026276&partnerID=40&md5=326ea919aba81cdfa77a67b3cf3db297","Florida Polytechnic University, Department of Electrical Computer Engineering, Lakeland, FL, United States","Ruiz, A.Y.R., Florida Polytechnic University, Department of Electrical Computer Engineering, Lakeland, FL, United States; Chandrasekaranxs, B., Florida Polytechnic University, Department of Electrical Computer Engineering, Lakeland, FL, United States","In this paper an initial implementation of a sensor fusion based robotic system architecture for motion control is presented. The architecture also consists of a human robot interaction interface to help the robotic system during the application. This is a work in progress and the paper presents the initial efforts covering the architectural implementation of the system that includes the experimental setup and the block diagram of the architecture. © 2020 IEEE.","Control System Architecture; Human-Robot Interaction; Motion Control; Robotics System Toolbox; ROS; Sensor Fusion; Simulink","Computer architecture; Man machine systems; Motion control; Robotics; Block diagrams; Control system architecture; Robotic system architectures; Robotic systems; Robotics systems; Sensor fusion; Simulink; Work in progress; Human robot interaction",,,,,,,,,,,,,,,,"Ilbeygi, M., Reza Kangavari, M., Comprehensive architecture for intelligent adaptive interface in the field of single-human multiple-robot interaction (2018) ETRI Journal, 40 (4), pp. 483-498; Doroodgar, B., Ficocelli, M., Mobedi, B., Nejat, G., The search for survivors: Cooperative human-robot interaction in search and rescue environments using semi-autonomous robots (2010) 2010 IEEE International Conference on Robotics and Automation, , Anchorage, Alaska, USA, May; Kruijff, G.J.M., Experience in system design for human-robot teaming in urban search and rescue (2014) Field and Service Robotics, 92. , Yoshida K., Tadokoro S. (eds) . Springer Tracts in Advanced Robotics. Springer, Berlin, Heidelberg; Li, X., Pan, Y., Chen, G., Yu, H., Adaptive human-robot interaction control for robots driven by series elastic actuators (2017) IEEE Transactions on Robotics, 33 (1). , February; Ragaglia, M., Maria Zanchettin, A., Rocco, P., Safety-aware trajectory scaling for Human-Robot Collaboration with Prediction of Human Occupancy (2015) 2015 International Conference on Advanced Robotics (ICAR), , Istanbul, Turkey, July; Sierra, S.D., Development of an interface for human-robot interaction on a robotic platform for gait assistance: Agora smart walker (2018) 2018 Ieee Andescon; Sierra, S.D., Development of an interface for human-robot interaction on a robotic platform for gait assistance: Agora smart walker (2018) 2018 Ieee Andescon; Ikemoto, S., Ben Amor, H., Minato, T., Jung, B., Ishiguro, H., Physical human-robot interaction mutual learning and adaptation (2012) IEEE Robotics & Automation Magazine, 19 (4). , December; Dieterle, T., Particke, F., Patino-Studencki, L., Thielecke, J., Sensor data fusion of LIDAR with stereo RGB-D camera for object tracking (2017) 2017 Ieee Sensors; Jian, S., Lou, Y., Application of motion control system for delta parallel robot (2017) 2017 IEEE International Conference on Information and Automation (ICIA); Rodriguez, I., Nottensteiner, K., Leidner, D., Kabecker, M., Stulp, F., Albu-Schaffer, A., Iteratively refined feasibility checks in robotic assembly sequence planning (2019) IEEE Robotics and Automation Letters, p. 1; Ruiz, R., Ariel, Y., Rivera Figueroa, L.J., Chandrasekaran, B., A sensor fusion based robotic system architecture using human interaction for motion control (2019) 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC); Powering the World's Robots, , http://www.ros.org/, ROS. org. [Accessed: 8-Nov-2019]; Robotics system toolbox Reconstructing An Image from Projection Data-MATLAB & Simulink Example, , https://www.mathworks.com/products/robotics.html, [Accessed: 8-Nov-2019]; Automated driving system toolbox Reconstructing An Image from Projection Data-MATLAB & Simulink Example, , www.mathworks.com/products/automated-driving.html, [Accessed: 8-Nov-2019]; Path following with obstacle avoidance in simulink Reconstructing An Image from Projection Data-MATLAB & Simulink Example, , www.mathworks.com/help/robotics/examples/path-following-withobstacle-avoidance-in-simulink.html, [Accessed: 8-Nov-2019]",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/SICE International Symposium on System Integration, SII 2020","12 January 2020 through 15 January 2020",,158395,,9781728166674,,,"English","Proc. IEEE/SICE Int. Symp. Syst. Integr., SII",Conference Paper,"Final","",Scopus,2-s2.0-85082608849
"Zhang S., Wang S., Zheng Y., Yang R., Dong E., Lu L., Xuan S., Gong X.","57193765660;57209185996;57262119500;57220031100;16443984400;57241427200;55352816900;55628578322;","Coaxial 3D-Printed and kirigami-inspired deployable wearable electronics for complex body surfaces",2021,"Composites Science and Technology","216",,"109041","","",,,"10.1016/j.compscitech.2021.109041","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115114011&doi=10.1016%2fj.compscitech.2021.109041&partnerID=40&md5=7385f302c8b7a3a354b141b54a649893","CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics, University of Science and Technology of China (USTC), Hefei, Anhui  230027, China; Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China (USTC), Hefei, Anhui  230027, China; The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui  230036, China","Zhang, S., CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics, University of Science and Technology of China (USTC), Hefei, Anhui  230027, China; Wang, S., CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics, University of Science and Technology of China (USTC), Hefei, Anhui  230027, China; Zheng, Y., Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China (USTC), Hefei, Anhui  230027, China; Yang, R., Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China (USTC), Hefei, Anhui  230027, China; Dong, E., Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China (USTC), Hefei, Anhui  230027, China; Lu, L., The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui  230036, China; Xuan, S., CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics, University of Science and Technology of China (USTC), Hefei, Anhui  230027, China; Gong, X., CAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics, University of Science and Technology of China (USTC), Hefei, Anhui  230027, China","Wearable electronics used to capture biological signals are substantially important in human–robot interactions, health monitoring, and clinical treatment. However, for curved or irregular body surfaces, intimate interfacing with the skin, which is essential for robust signal recording, is challenging. In this study, flexible core-sheath fiber sensors and adaptive devices were developed using a coaxial 3D printing technique and kirigami-inspired patterns. The printed core-sheath fiber, which was successfully applied in human–robot interaction, exhibited excellent electromechanical properties with a sensing strain range of 700%, and had high accuracy of approximately 3 mN and high electromechanical durability. In addition, the viscoelastic nature of the core material (shear-stiffening gel) provided the fiber array with fine energy dissipation performance against external harm by buffering the impact force by 51% while simultaneously capturing the dynamic impact in 4 ms. Moreover, the introduction of kirigami-inspired deformability to planar electronics facilitated conformable attachment of sensing devices with substantial adjustability to 3D curved surfaces; they can be adapted to shoe pads of different sizes without compromising their sensitivity. The 3D printing technique and kirigami-inspired pattern designs for creating adaptive and flexible wearable electronics hold great potential for advanced health monitoring of diverse and complex epidermal surfaces. © 2021 Elsevier Ltd","Core-sheath fiber; Deployable devices; Human-robot interaction; Kirigami; Shear stiffening","3D printers; Coremaking; Energy dissipation; Fibers; Flexible electronics; Wearable technology; 3-D printing; 3D-printing; Body surface; Core/sheath fibers; Deployable devices; Health monitoring; Humans-robot interactions; Kirigami; Printing techniques; Shear stiffening; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 11772320, 11802303, 11822209, 11972032; Chinese Academy of Sciences, CAS: XDB22040502; University of Science and Technology of China, USTC; Fundamental Research Funds for the Central Universities: WK2480000007; National Synchrotron Radiation Laboratory, NSRL: KY2090000055","Financial support from the National Natural Science Foundation of China (Grant No. 11822209, 11802303, 11772320, 11972032), the Fundamental Research Funds for the Central Universities (WK2480000007), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB22040502), and the Joint Fund of USTC-National Synchrotron Radiation Laboratory (KY2090000055) are gratefully acknowledged.","Financial support from the National Natural Science Foundation of China (Grant No. 11822209 , 11802303 , 11772320 , 11972032 ), the Fundamental Research Funds for the Central Universities ( WK2480000007 ), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB22040502 ), and the Joint Fund of USTC- National Synchrotron Radiation Laboratory ( KY2090000055 ) are gratefully acknowledged.",,,,,,,,,"Lukas, H., Xu, C., Yu, Y., Gao, W., Emerging telemedicine tools for remote COVID-19 diagnosis, monitoring, and management (2020) ACS Nano, 14 (12), pp. 16180-16193; Ma, Y.J., Zhang, Y.C., Cai, S.S., Han, Z.Y., Liu, X., Wang, F.L., Flexible hybrid electronics for digital healthcare (2020) Adv. 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Xuan, S.; CAS Key Laboratory of Mechanical Behavior and Design of Materials, China; email: xuansh@ustc.edu.cn",,,"Elsevier Ltd",,,,,02663538,,CSTCE,,"English","Compos. Sci. Technol.",Article,"Final","",Scopus,2-s2.0-85115114011
"Kim J., Kim S., Kim S., Lee E., Heo Y., Hwang C.-Y., Choi Y.-Y., Kong H.-J., Ryu H., Lee H.","57209737019;8557758200;57338419900;9842361300;57338565200;57226651149;56799405400;16637352200;57195625698;57197736696;","Companion robots for older adults: Rodgers’ evolutionary concept analysis approach",2021,"Intelligent Service Robotics","14","5",,"729","739",,,"10.1007/s11370-021-00394-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119053734&doi=10.1007%2fs11370-021-00394-3&partnerID=40&md5=38e3b37d556796ff580da81a3499d635","Research Institute of Nursing Science, Seoul National University, Seoul, South Korea; Department of Reconstructive Plastic Surgery, Medical Big Data Research Center, Seoul National University College of Medicine, Seoul, South Korea; College of Fine Arts, Seoul National University, Seoul, South Korea; Korea Advanced Institute of Science and Technology, Daejeon, South Korea; College of Music, Seoul National University, Seoul, South Korea; College of Veterinary Medicine, Seoul National University, Seoul, South Korea; College of Humanities, Seoul National University, Seoul, South Korea; Transdisciplinary Department of Medicine and Advanced Technology (TDMAT), Seoul National University Hospital, Seoul, South Korea; Seoul National University Hospital, Seoul, South Korea; College of Nursing, Gachon University, Seoul, South Korea","Kim, J., Research Institute of Nursing Science, Seoul National University, Seoul, South Korea; Kim, S., Department of Reconstructive Plastic Surgery, Medical Big Data Research Center, Seoul National University College of Medicine, Seoul, South Korea; Kim, S., College of Fine Arts, Seoul National University, Seoul, South Korea; Lee, E., Korea Advanced Institute of Science and Technology, Daejeon, South Korea; Heo, Y., College of Music, Seoul National University, Seoul, South Korea; Hwang, C.-Y., College of Veterinary Medicine, Seoul National University, Seoul, South Korea; Choi, Y.-Y., College of Humanities, Seoul National University, Seoul, South Korea; Kong, H.-J., Transdisciplinary Department of Medicine and Advanced Technology (TDMAT), Seoul National University Hospital, Seoul, South Korea; Ryu, H., Seoul National University Hospital, Seoul, South Korea; Lee, H., College of Nursing, Gachon University, Seoul, South Korea","This study aims to analyze the concept of companion robots for older adults from the perspective of nursing. This study employed a concept analysis. The literature from July 2011 to June 2021 was sought from databases using specific keywords. Any quantitative or qualitative study published in English or Korean focusing on companion robots for older adults was included in the study. Rodgers’ evolutionary concept analysis was used to clarify the antecedents, attributes, and consequences. Seventy-five eligible articles were studied. The findings were categorized into antecedents, attributes, and consequences. Companion robot antecedents were classified into individual factors, attitude toward robots, and caregiver and social factors. The defining attributes included human–robot interaction, function, features, structure, cost, and management of the robot being a companion. Consequences were categorized into user, caregiver, and health related. Companion robots are designed to enhance well-being, quality of life, and independence by providing service and companionship and assisting daily life. This mainly includes cognitive and social support, mobility support, relaxation, health monitoring, and self-care support through human–robot interaction. The attributes, antecedents, and consequences of companion robots identified in this study can inform future decision making and interventions by caregivers for aging in place. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.","Aging in place; Companion robot; Concept analysis; Nursing care; Older adults","Decision making; Nursing; Aging in place; Analysis approach; Classifieds; Companion robot; Concept analysis; Humans-robot interactions; Nursing care; Older adults; Qualitative study; Quantitative study; Human robot interaction",,,,,"College of Medicine, Seoul National University, SNUCM; Seoul National University, SNU; Gachon University: GCU-2020-02130001; Gil Medical Center, Gachon University; Advanced Biometric Research Center, Seoul National University, ABRC; Institute of Construction and Environmental Engineering, Seoul National University, ICEE, SNU; College of Engineering, Seoul National University; Institute of Engineering Research, Seoul National University, IOER; Institute of Advanced Aerospace Technology, Seoul National University, IAAT SNU, IAAT; Department of Computer Science and Engineering, Seoul National University, CSE, SNU; College of Veterinary Medicine, Seoul National University, CVM SNU, CVM, SNU-VET, SNU CVM","This work was supported by Seoul National University Research Grant in 2019. This work was supported by the Gachon University Research Fund of 2020 (GCU-2020-02130001).",,,,,,,,,,"Harrington, L., Heidkamp, M., The aging workforce: Challenges for the health care industry workforce (2013) Aging, 2020, p. 115; Shishehgar, M., Kerr, D., Blake, J., The effectiveness of various robotic technologies in assisting older adults (2019) J Health Inform, 25, pp. 892-918; Isabet, B., Pino, M., Lewis, M., Social telepresence robots: A narrative review of experiments involving older adults before and during the COVID-19 pandemic (2021) Int J Environ Res Pub He, 18 (7), p. 3597; Truxillo, D.M., Cadiz, D.M., Hammer, L.B., Supporting the aging workforce: a review and recommendations for workplace intervention research (2015) Annu Rev Organ Psychol Organ Behav, 2, pp. 251-381; Zsiga, K., Tóth, A., Pilissy, T., Evaluation of a companion robot based on field tests with single older adults in their homes (2018) Assist Technol, 30 (5), pp. 259-266; Abbott, R., Orr, N., McGill, P., How do “robopets” impact the health and well-being of residents in care homes? 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(2014) Nurs Adm Q, 38 (1), pp. 5-12; Chen, N., Song, J., Li, B., Providing aging adults social robots' companionship in home-based elder care (2019) J Healthc Eng, 2019, p. 2726837; Hirano, M., Ogura, K., Sakamoto, D., Robotic utterance style to promote conversation with older people in Japan (2019) Gerontechnology, 18 (2), pp. 89-96; Pearce, A.J., Adair, B., Miller, K., Robotics to enable older adults to remain living at home (2012) J Aging Res, 2012, p. 538169; Mois, G., Beer, J.M., The role of healthcare robotics in providing support to older adults: a socio-ecological perspective (2020) Curr Geriatr Rep, 9 (2), pp. 82-89; Keiko, H., Osamu, M., Development of a risk assessment assistance tool for robotic care devices (2017) Stud Health Technol Inform, 242, pp. 551-557","Lee, H.; College of Nursing, South Korea; email: leehyeongsuk@gachon.ac.kr",,,"Springer Science and Business Media Deutschland GmbH",,,,,18612776,,,,"English","Intelligent Serv. Rob.",Article,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85119053734
"Jing X., Qian K., Xu X., Bai J., Zhou B.","57216936905;35243175300;56383137300;57228199700;57227289000;","Domain adversarial transfer for cross-domain and task-constrained grasp pose detection",2021,"Robotics and Autonomous Systems","145",,"103872","","",,,"10.1016/j.robot.2021.103872","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113316154&doi=10.1016%2fj.robot.2021.103872&partnerID=40&md5=36705274245ae7b72eafa23030702650","School of Automation, Southeast University, Nanjing, 210096, China; The Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University, Nanjing, 210096, China","Jing, X., School of Automation, Southeast University, Nanjing, 210096, China, The Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University, Nanjing, 210096, China; Qian, K., School of Automation, Southeast University, Nanjing, 210096, China, The Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University, Nanjing, 210096, China; Xu, X., School of Automation, Southeast University, Nanjing, 210096, China, The Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University, Nanjing, 210096, China; Bai, J., School of Automation, Southeast University, Nanjing, 210096, China, The Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University, Nanjing, 210096, China; Zhou, B., School of Automation, Southeast University, Nanjing, 210096, China, The Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University, Nanjing, 210096, China","Transferring the grasping skills learned from simulated environments to the real world is favorable for many robotic applications, in which the collecting and labeling processes of real-world visual grasping datasets are often expensive or even impractical. However, the models purely trained on simulated data are often difficult to generalize well to the unseen real world due to the domain gap between the training and testing data. In this paper, we propose a novel domain adversarial transfer network to narrow the domain gap for cross-domain and task-constrained grasp pose detection. Generative adversarial training is exploited to constrain the generator to produce simulation-like data for extracting the shared features with the joint distribution. We also propose to improve the backbone by extracting task-constrained grasp candidates and constructing the grasp candidate evaluator with a lightweight structure and an embedded recalibration technique. To validate the effectiveness and superiority of our proposed method, grasping performance evaluation and task-oriented human–robot interaction experiments were investigated. The experiment results indicate that the proposed method achieves state-of-the-art performance in these experimental settings. An average task-constrained grasping success rate of 83.3% without using any real-world labels for the task-oriented human–robot interaction experiment was achieved especially. © 2021 Elsevier B.V.","Adversarial transfer learning; Domain adaptation; Grasp pose detection; Human–robot interaction","Agricultural robots; Human robot interaction; Well testing; Joint distributions; Pose detection; Robot interactions; Robotic applications; Simulated environment; State-of-the-art performance; Training and testing; Transfer network; Gesture recognition",,,,,"National Natural Science Foundation of China, NNSFC: 61573101, 62073075; Natural Science Foundation of Jiangsu Province: BK20201264; Fundamental Research Funds for the Central Universities","This work was supported by the Jiangsu Province Natural Science Foundation, China (Grant number BK20201264 ); the National Natural Science Foundation of China (Grant numbers 61573101 , 62073075 ); and the Fundamental Research Funds for the Central Universities, China .",,,,,,,,,,"Kokic, M., Stork, J.A., Haustein, J.A., Kragic, D., Affordance detection for task-specific grasping using deep learning (2017), pp. 91-98. , IEEE-RAS International Conference on Humanoid Robotics (Humanoids); Nguyen, A., Kanoulas, D., Caldwell, D.G., Tsagarakis, N.G., Object-based affordances detection with convolutional neural networks and dense conditional random fields (2017), pp. 5908-5915. , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Lakani, S.R., Rodríguez-Sánchez, A.J., Piater, J., Towards affordance detection for robot manipulation using affordance for parts and parts for affordance (2019) Auton. 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"Filippini C., Perpetuini D., Cardone D., Merla A.","57205533040;57196390915;54788843200;24169413000;","Improving human–robot interaction by enhancing nao robot awareness of human facial expression",2021,"Sensors","21","19","6438","","",,,"10.3390/s21196438","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115757400&doi=10.3390%2fs21196438&partnerID=40&md5=5d799c68ae8cf9d677b4fa60dddd159f","Department of Neurosciences, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara 9, Chieti, 66100, Italy","Filippini, C., Department of Neurosciences, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara 9, Chieti, 66100, Italy; Perpetuini, D., Department of Neurosciences, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara 9, Chieti, 66100, Italy; Cardone, D., Department of Neurosciences, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara 9, Chieti, 66100, Italy; Merla, A., Department of Neurosciences, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara 9, Chieti, 66100, Italy","An intriguing challenge in the human–robot interaction field is the prospect of endowing robots with emotional intelligence to make the interaction more genuine, intuitive, and natural. A crucial aspect in achieving this goal is the robot’s capability to infer and interpret human emotions. Thanks to its design and open programming platform, the NAO humanoid robot is one of the most widely used agents for human interaction. As with person-to-person communication, facial expressions are the privileged channel for recognizing the interlocutor’s emotional expressions. Although NAO is equipped with a facial expression recognition module, specific use cases may require additional features and affective computing capabilities that are not currently available. This study proposes a highly accurate convolutional-neural-network-based facial expression recognition model that is able to further enhance the NAO robot’ awareness of human facial expressions and provide the robot with an interlocutor’s arousal level detection capability. Indeed, the model tested during human–robot interactions was 91% and 90% accurate in recognizing happy and sad facial expressions, respectively; 75% accurate in recognizing surprised and scared expressions; and less accurate in recognizing neutral and angry expressions. Finally, the model was successfully integrated into the NAO SDK, thus allowing for high-performing facial expression classification with an inference time of 0.34 ± 0.04 s. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Affective computing; Emotion recognition; Facial expression recognition; Human–robot interaction; Machine learning","Agricultural robots; Anthropomorphic robots; Convolutional neural networks; Face recognition; Human robot interaction; Intelligent robots; Machine design; Robot programming; Affective Computing; Emotion recognition; Emotional intelligence; Facial expression recognition; Facial Expressions; Human emotion; Human facial expressions; Humanoid robot; Humans-robot interactions; Interaction fields; Machine learning; 10-N-nonylacridinium orange; aminoacridine derivative; emotion; facial expression; facial recognition; human; robotics; Aminoacridines; Emotions; Facial Expression; Facial Recognition; Humans; Robotics",,"10-N-nonylacridinium orange; Aminoacridines",,,,"Funding: This research was funded by the following grants: PON FESR MIUR R&I 2014-2020-ADAS+, grant number ARS01_00459, and PON MIUR SI-ROBOTICS, grant number ARS01_01120.",,,,,,,,,,"Scassellati, B., Admoni, H., Matarić, M., Robots for Use in Autism Research (2012) Annu. 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Image Underst, 140, pp. 93-114. , [CrossRef]; Liu, K., Zhang, M., Pan, Z., Facial Expression Recognition with CNN Ensemble Proceedings of the 2016 International Conference on Cyberworlds (CW), pp. 163-166. , Chongqing, China, 28–30 September 2016; Zahara, L., Musa, P., Wibowo, E.P., Karim, I., Musa, S.B., The Facial Emotion Recognition (FER-2013) Dataset for Prediction System of Micro-Expressions Face Using the Convolutional Neural Network (CNN) Algorithm Based Raspberry Pi Proceedings of the 2020 5th International Conference on Informatics and Computing (ICIC), pp. 1-9. , Shenzhen, China, 12–15 August 2020; Knyazev, B., Shvetsov, R., Efremova, N., Kuharenko, A., (2017) Convolutional Neural Networks Pretrained on Large Face Recognition Datasets for Emotion Classification from Video, , arXiv arXiv:1711.04598; Melinte, D.O., Vladareanu, L., Facial Expressions Recognition for Human–Robot Interaction Using Deep Convolutional Neural Networks with Rectified Adam Optimizer (2020) Sensors, 20, p. 2393. , [CrossRef] [PubMed]","Merla, A.; Department of Neurosciences, Italy; email: arcangelo.merla@unich.it",,,"MDPI",,,,,14248220,,,"34640758","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85115757400
"McKenzie-Mcharg L., Kulic D., Tian L.","57261618700;10244652900;56151869000;","Discrepancies between designs of robot communicative styles and their perceived assertiveness",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"1045","1052",,,"10.1109/RO-MAN50785.2021.9515536","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115078576&doi=10.1109%2fRO-MAN50785.2021.9515536&partnerID=40&md5=25f51603e3a53993cd0ff767c815987f","Telstra, Melbourne, Australia; Monash University, Faculty of Engineering, Melbourne, Australia; Monash University, Faculty of Engineering and the Faculty of IT, Melbourne, Australia","McKenzie-Mcharg, L., Telstra, Melbourne, Australia; Kulic, D., Monash University, Faculty of Engineering, Melbourne, Australia; Tian, L., Monash University, Faculty of Engineering and the Faculty of IT, Melbourne, Australia","A robot's perceived assertiveness can influence how people assess its credibility and their willingness to comply with its suggestions during human-robot interaction. This study proposes a novel measurement of the perceived assertiveness of a robot using both objective assessment of a person's compliance to a robot's suggestions in a math quiz task and subjective assessments of a person's perception and expectation of a robot in different hypothetical scenarios. The proposed measurement evaluates perceived assertiveness of a robot in three dimensions inspired by social behavioral studies on human-human interaction, namely social assertiveness, directiveness, and independence. We conducted an exploratory study using crowdsourcing to test the efficacy of this proposed measurement of perceived assertiveness. In particular, participants were exposed to robots that differed in terms of their anthropomorphism and their communicative styles. The communicative styles are designed following human studies of behaviors that are commonly associated with high or low assertiveness. Our results demonstrated the validity of the proposed measurement of perceived assertiveness. The observed discrepancy between the objective and subjective measurements highlights the necessity of evaluating human perception through multiple approaches. Moreover, gaps between intended and perceived robot assertiveness indicate a potential gap between human communication theories and their applicability to human-robot interaction. © 2021 IEEE.",,"Agricultural robots; Behavioral research; Information theory; Machine design; Man machine systems; Behavioral studies; Exploratory studies; Human perception; Human-human interactions; Objective and subjective measurements; Objective assessment; Subjective assessments; Three dimensions; Social robots",,,,,"Monash University, MU","We would like to thank Dr Pamela Carreno-Medrano for her help with recording the Fetch videos. This work was partly funded by the Faculty of IT, Monash University.",,,,,,,,,,"Niemelä, M., Heikkilä, P., Lammi, H., A social service robot in a shopping mall: Expectations of the management, retailers and consumers (2017) HRI'17, pp. 227-228; Carros, F., Meurer, J., Löfer, D., Unbehaun, D., Matthies, S., Koch, I., Wieching, R., Wulf, V., Exploring human-robot interaction with the elderly: Results from a ten-week case study in a care home (2020) CHI'20, pp. 1-12; Inam, R., Raizer, K., Hata, A., Souza, R., Forsman, E., Cao, E., Wang, S., Risk assessment for human-robot collaboration in an automated warehouse scenario (2018) ETFA'18, 1, pp. 743-751; Tian, L., Oviatt, S., A taxonomy of social errors in human-robot interaction (2021) THRI, 10 (2), pp. 1-32; Rich, A.R., Schroeder, H.E., Research issues in assertiveness training (1976) Psychological Bulletin, 83 (6), p. 1081; Hall, P., Interprofessional teamwork: Professional cultures as barriers (2005) Journal of Interprofessional Care, 19, pp. 188-196; Vagos, P., Pereira, A., Towards a cognitive-behavioral understanding of assertiveness: Effects of cognition and distress on different expressions of assertive behavior (2019) Journal of Rational-Emotive & Cognitive-Behavior Therapy, 37 (2), pp. 133-148; Furnham, A., Henderson, M., Assessing assertiveness: A content and correlational analysis of five assertiveness inventories (1984) Behavioral Assessment; Furnham, A., Assertiveness in three cultures: Multidimensionality and cultural differences (1979) Journal of Clinical Psychology, 35 (3), pp. 522-527; Wolpe, J., Lazarus, A.A., (1966) Behavior Therapy Techniques: A Guide to the Treatment of Neuroses; Rathus, S.A., A 30-item schedule for assessing assertive behavior (1973) Behavior Therapy, 4 (3), pp. 398-406; Galassi, J.P., DeLo, J.S., Galassi, M.D., Bastien, S., The college self-expression scale: A measure of assertiveness (1974) Behavior Therapy, 5 (2), pp. 165-171; Gambrill, E.D., Richey, C.A., An assertion inventory for use in assessment and research (1975) Behavior Therapy, 6 (4), pp. 550-561; 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Chidambaram, V., Chiang, Y.-H., Mutlu, B., Designing persuasive robots: How robots might persuade people using vocal and nonverbal cues (2012) HRI'12, pp. 293-300; Rotter, J.B., A new scale for the measurement of interpersonal trust (1967) Journal of Personality; Tolmeijer, S., Weiss, A., Hanheide, M., Lindner, F., Powers, T.M., Dixon, C., Tielman, M.L., Taxonomy of trust-relevant failures and mitigation strategies (2020) HRI'20, pp. 3-12; Khavas, Z.R., Ahmadzadeh, S.R., Robinette, P., Modeling trust in human-robot interaction: A survey (2020) International Conference on Social Robotics, pp. 529-541. , Springer; Baker, A.L., Phillips, E.K., Ullman, D., Keebler, J.R., Toward an understanding of trust repair in human-robot interaction: Current research and future directions (2018) TiiS, 8 (4), pp. 1-30; Kazdin, A.E., Effects of covert modeling and model reinforcement on assertive behavior (1974) Journal of Abnormal Psychology, 83 (3), p. 240; House, D., Perceiving question intonation: The role of pre-focal pause and delayed focal peak (2003) Proc 15th ICPhS, pp. 755-758. , Citeseer; 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Robinson, N.L., Hicks, T.-N., Suddrey, G., Kavanagh, D.J., The robot self-efficacy scale: Robot self-efficacy, likability and willingness to interact increases after a robot-delivered tutorial (2020) RO-MAN'20. IEEE, pp. 272-277; Bartneck, C., Kulíc, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) IJSR, 1 (1), pp. 71-81; Faul, F., Erdfelder, E., Lang, A.-G., Buchner, A., G power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences (2007) Behavior Research Methods, 39 (2), pp. 175-191; Bainbridge, W.A., Hart, J.W., Kim, E.S., Scassellati, B., The benefits of interactions with physically present robots over videodisplayed agents (2011) IJSR, 3 (1), pp. 41-52; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K., Would you trust a (faulty) robot? effects of error, task type and personality on human-robot cooperation and trust (2015) HRI'15. IEEE, pp. 1-8; Freedy, A., DeVisser, E., Weltman, G., Coeyman, N., Measurement of trust in human-robot collaboration (2007) CTS'07. IEEE, pp. 106-114; Irfan, B., Kennedy, J., Lemaignan, S., Papadopoulos, F., Senft, E., Belpaeme, T., Social psychology and human-robot interaction: An uneasy marriage (2018) HRI'18, pp. 13-20",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115078576
"Raptopoulou A., Komnidis A., Bamidis P.D., Astaras A.","57223639683;36537172500;6603398831;6507977235;","Human–robot interaction for social skill development in children with ASD: A literature review",2021,"Healthcare Technology Letters","8","4",,"90","96",,,"10.1049/htl2.12013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105923742&doi=10.1049%2fhtl2.12013&partnerID=40&md5=7f80782c2da06775dea6eb4cb132c160","Lab of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece; Computer Science, American College of Thessaloniki, Thessaloniki, Greece","Raptopoulou, A., Lab of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece; Komnidis, A., Lab of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece; Bamidis, P.D., Lab of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece; Astaras, A., Lab of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece, Computer Science, American College of Thessaloniki, Thessaloniki, Greece","Human–robot interaction has been demonstrated to be a promising methodology for developing socio-communicational skills of children and adolescents with autism spectrum disorder (ASD). This paper systematically reviews studies that report experimental results on this topic published in scientific journals between the years 2010 and2018. A total of 1805 articles from various literature were filtered based on relevance and transparency. In the first set of criteria, article titles are screened and in the second both titles and abstracts. The final number of articles which were subsequently thoroughly reviewed was 32 (N = 32). The findings suggest that there are benefits in using human–robot interaction to assist with the development of social skills for children with ASD. Specifically, it was found that the majority of studies used humanoid robots, 64% relied on a small number of participants and sessions, while few of the studies included a control group or follow-up sessions. Based on these findings, this paper tried to identify areas that have not been extensively addressed to propose several directions for future improvements for studies in this field, such as control groups with typical developmental children, minimum number of sessions and participants, as well as standardization of criteria for assessing the level of functionality for ASD children. © 2021 The Authors. Healthcare Technology Letters published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology",,"Anthropomorphic robots; Economic and social effects; Autism spectrum disorders; Children and adolescents; Control groups; Future improvements; Humanoid robot; Literature reviews; Robot interactions; Scientific journals; Social robots",,,,,"FWF-P12945","T. Ungar is grateful to the Hungarian Science Foundations for OTKA T029701 and FKFP 0116/1997. Thanks are also due to J. Gubicza and P. Hanák for their kind assistance in the x-ray diffraction work. M. Zehetbauer thanks the Austrian Science Foundation for financial support under project FWF-P12945.",,,,,,,,,,"Connor, M., Children on the autistic spectrum: guidelines for mainstream practice (1999) Support Learn, 14 (2), pp. 80-886; Kirkovski, M., A review of the role of female gender in autism spectrum disorders (2013) J. Autism Dev. Disord., 43 (11), pp. 2584-2603; Frith, U., Autism: Explaining the Enigma, (2015), p. 249. , 2nd ed., p., Blackwell,, Malden, MA; Dominick, K.C., (2007) Atypical behaviors in children with autism and children with a history of language impairment, 28 (2), pp. 145-162. , Res. Dev. Disabilities; Mundy, P., Defining the social deficits of autism: the contribution of non-verbal communication measures (1986) J. Child Psychol. Psychiatry, 27 (5), pp. 657-669; Gibson, J., Social communication disorder outside autism? A diagnostic classification approach to delineating pragmatic language impairment, high functioning autism and specific language impairment (2013) J. Child Psychol. Psychiatry, 54 (11), pp. 1186-1197; Frith, C., Role of facial expressions in social interactions (2009) Philos. Trans. R. Soc. B: Biol. Sci., 364 (1535), pp. 3453-3458; Rogers, S.J., Ozonoff, S., Annotation: what do we know about sensory dysfunction in autism? A critical review of the empirical evidence (2005) J Child Psychol. Psychiatry, 46 (12), pp. 1255-1268; McConnell, S.R., Interventions to facilitate social interaction for young children with autism: review of available research and recommendations for educational intervention and future research (2002) J. Autism and Dev. Disord., 32 (5), pp. 351-372; Owens, G., LEGO ® therapy and the social use of language programme: an evaluation of two social skills interventions for children with high functioning autism and asperger syndrome (2008) J. Autism Dev. Disord., 38 (10), pp. 1944-1957; Giullian, N., Detailed requirements for robots in autism therapy 2010 IEEE International Conference on Systems, Man and Cybernetics – SMC, , Istanbul, Turkey, 10–13 Oct. 2010; Warren, Z.E., Can robotic interaction improve joint attention skills? (2015) J. Autism Dev. Disord., 45 (11), pp. 3726-3734; Shamsuddin, S., Humanoid robot NAO interacting with autistic children of moderately impaired intelligence to augment communication skills (2012) Proc. Eng., 41, pp. 1533-1538; Shamsuddin, S., Initial response in HRI: a case study on evaluation of child with autism spectrum disorders interacting with a (2012) Hum. Robot NAO. Procedia Eng., 41, pp. 1448-1455; Valadão, C.T., Analysis of the use of a robot to improve social skills in children with autism spectrum disorder (2016) Res. Biomed. Eng., 32 (2), pp. 161-175; Shimaya, J., Advantages of indirect conversation via a desktop humanoid robot: case study on daily life guidance for adolescents with autism spectrum disorders (2016) 2016 25th IEEE International Symposium onRobot and Human Interactive Communication (RO-MAN), , New York, NY, USA, 26-31 Aug; Hinds, P.J., Whose job is it anyway? a study of human-robot interaction in a collaborative task (2004) Hum.-Comput. Interact., 19 (1), pp. 151-181; Tapus, A., Children with autism social engagement in interaction with Nao, an imitative robot: a series of single case experiments (2012) Interact. Stud., 13 (3), pp. 315-347; Wainer, J., The effectiveness of using a robotics class to foster collaboration among groups of children with autism in an exploratory study (2010) Pers. Ubiquitous Comput., 14 (5), pp. 445-455; Robins, B., Dautenhahn, K., Tactile interactions with a humanoid robot: novel play scenario implementations with children with autism (2014) Int. J. Soc. Rob., 6 (3), pp. 397-415; Jordan, K., Feasibility of using a humanoid robot for enhancing attention and social skills in adolescents with autism spectrum disorder (2013) Int. J. Rehabil. Res., 36 (3), pp. 221-227; Costescu, C.A., Reversal learning task in children with autism spectrum disorder: a robot-based approach (2015) J. Autism Dev. Disord., 45 (11), pp. 3715-3725; Boccanfuso, L., A low-cost socially assistive robot and robot-assisted intervention for children with autism spectrum disorder: field trials and lessons learned (2017) Auton. R., 41 (3), pp. 637-655; Kim, E.S., Social robots as embedded reinforcers of social behavior in children with autism (2013) J. Autism Dev. Disord, 43 (5), pp. 1038-1049; Van Straten, C.L., Effects of robots’ intonation and bodily appearance on robot-mediated communicative treatment outcomes for children with autism spectrum disorder (2017) Pers. Ubiquitous Comp.; Mengoni, S.E., Feasibility study of a randomised controlled trial to investigate the effectiveness of using a humanoid robot to improve the social skills of children with autism spectrum disorder (Kaspar RCT): a study protocol (2017) BMJ Open, 7 (6); Huskens, B., Improving collaborative play between children with autism spectrum disorders and their siblings: the effectiveness of a robot-mediated intervention based on Lego(R) therapy (2015) J. Autism Dev. Disord., 45 (11), pp. 3746-3755; Kaboski, J.R., Brief report: a pilot summer robotics camp to reduce social anxiety and improve social/vocational skills in adolescents with ASD (2015) J. Autism Dev. Disord., 45 (12), pp. 3862-3869; Warren, Z., Brief report: development of a robotic intervention platform for young children with ASD (2015) J. Autism Dev. Disord., 45 (12), pp. 3870-3876; Srinivasan, S.M., A comparison of the effects of rhythm and robotic interventions on repetitive behaviors and affective states of children with autism spectrum disorder (ASD) (2015) Res. Autism Spectr. Disord., 18, pp. 51-63; Bekele, E., Pilot clinical application of an adaptive robotic system for young children with autism (2014) Autism, 18 (5), pp. 598-608; Wainer, J., A pilot study with a novel setup for collaborative play of the humanoid robot KASPAR with children with autism (2014) Int. J. Soc. Rob., 6 (1), pp. 45-65; Pop, C.A., Enhancing play skills, engagement and social skills in a play task in ASD children by using robot-based interventions. A pilot study (2014) Interact. Stud., 15 (2), pp. 292-320; Simut, R.E., Children with autism spectrum disorders make a fruit salad with probo, the social robot: an interaction study (2016) J. Autism Dev. Disord., 46 (1), pp. 113-126; Giannopulu, I., Minimalistic toy robot to analyze a scenery of speaker-listener condition in autism (2016) Cogn. Process., 17 (2), pp. 195-203; Anzalone, S.M., Quantifying patterns of joint attention during human-robot interactions: an application for autism spectrum disorder assessment (2018) Pattern Recog. Lett.; Saadatzi, M.N., Small-group technology-assisted instruction: virtual teacher and robot peer for individuals with autism spectrum disorder (2018) J. Autism Dev. Disord., 48, pp. 3816-3830; Taheri, A., Human–robot interaction in autism treatment: a case study on three pairs of autistic children as twins, siblings, and classmates (2018) Int. J. Soc. Rob., 10 (1), pp. 93-113; Pour, A.G., Human–robot facial expression reciprocal interaction platform: case studies on children with autism (2018) Int. J. Soc. Rob., 10 (2), pp. 179-198; So, W.-C., Robot-based intervention may reduce delay in the production of intransitive gestures in Chinese-speaking preschoolers with autism spectrum disorder (2018) Mol. Autism, 9 (1). , 34; Di Nuovo, A., Deep learning systems for estimating visual attention in robot-assisted therapy of children with autism and intellectual disability (2018) Robotics, 7 (2), p. 25; Desideri, L., Using a humanoid robot as a complement to interventions for children with autism spectrum disorder: a pilot study (2018) Adv. Neurodev. Disord., 2 (3), pp. 273-285; Feng, Y., Jia, Q., Wei, W., A control architecture of robot-assisted intervention for children with autism spectrum disorders (2018) J. Rob., 2018; David, D.O., Developing joint attention for children with autism in robot-enhanced therapy (2018) Int. J. Soc. Rob., 10 (5), pp. 595-605; Yun, S.-S., Social skills training for children with autism spectrum disorder using a robotic behavioral intervention system (2017) Autism Res., 10 (7), pp. 1306-1323; Huskens, B., Promoting question-asking in school-aged children with autism spectrum disorders: effectiveness of a robot intervention compared to a human-trainer intervention (2013) Dev. Neurorehab., 16 (5), pp. 345-356; Barakova, E.I., Long-term LEGO therapy with humanoid robot for children with ASD (2015) Expert Syst., 32 (6), pp. 698-709; Boccanfuso, L., O 'Kane, J.M., CHARLIE: an adaptive robot design with hand and face tracking for use in autism therapy (2011) Int. J. Soc. Rob., 3 (4), pp. 337-347","Bamidis, P.D.; Lab of Medical Physics, Greece; email: bamidis@med.auth.gr",,,"John Wiley and Sons Inc",,,,,20533713,,,,"English","Healthc. Technol. Lett.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85105923742
"Webb H., Dumitru M., van Maris A., Winkle K., Jirotka M., Winfield A.","57189389446;57226003940;57193757921;57201504964;8850301900;7006158694;","Role-Play as Responsible Robotics: The Virtual Witness Testimony Role-Play Interview for Investigating Hazardous Human-Robot Interactions",2021,"Frontiers in Robotics and AI","8",,"644336","","",,,"10.3389/frobt.2021.644336","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110129864&doi=10.3389%2ffrobt.2021.644336&partnerID=40&md5=f4b1c642dda294d35f75ea8e46ca3b2d","Department of Computer Science, University of Oxford, Oxford, United Kingdom; Bristol Robotics Lab, University of West of England, Bristol, United Kingdom; Royal Institute of Technology, Stockholm, Sweden","Webb, H., Department of Computer Science, University of Oxford, Oxford, United Kingdom; Dumitru, M., Department of Computer Science, University of Oxford, Oxford, United Kingdom; van Maris, A., Bristol Robotics Lab, University of West of England, Bristol, United Kingdom; Winkle, K., Royal Institute of Technology, Stockholm, Sweden; Jirotka, M., Department of Computer Science, University of Oxford, Oxford, United Kingdom; Winfield, A., Bristol Robotics Lab, University of West of England, Bristol, United Kingdom","The development of responsible robotics requires paying attention to responsibility within the research process in addition to responsibility as the outcome of research. This paper describes the preparation and application of a novel method to explore hazardous human-robot interactions. The Virtual Witness Testimony role-play interview is an approach that enables participants to engage with scenarios in which a human being comes to physical harm whilst a robot is present and may have had a malfunction. Participants decide what actions they would take in the scenario and are encouraged to provide their observations and speculations on what happened. Data collection takes place online, a format that provides convenience as well as a safe space for participants to role play a hazardous encounter with minimal risk of suffering discomfort or distress. We provide a detailed account of how our initial set of Virtual Witness Testimony role-play interviews were conducted and describe the ways in which it proved to be an efficient approach that generated useful findings, and upheld our project commitments to Responsible Research and Innovation. We argue that the Virtual Witness Testimony role-play interview is a flexible and fruitful method that can be adapted to benefit research in human robot interaction and advance responsibility in robotics. © Copyright © 2021 Webb, Dumitru, van Maris, Winkle, Jirotka and Winfield.","human-robot interaction; interview; methods; responsible research and innovation; role-play",,,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/S005099/1","RoboTIPS: Developing Responsible Robots for the Digital Economy is an EPSRC Established Career Fellowship awarded to Professor Marina Jirotka. Award reference EP/S005099/1.",,,,,,,,,,"Benford, S., Greenhalgh, C., Anderson, B., Jacobs, R., Golembewski, M., Jirotka, M., The Ethical Implications of HCI's Turn to the Cultural (2015) ACM Trans. Comput.-Hum. Interact, 22 (5), pp. 1-37; Benford, S., Greenhalgh, C., Giannachi, G., Walker, B., Marshall, J., Rodden, T., (2012) “Uncomfortable Interactions” Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’12), pp. 2005-2013. , New York, NY, USA, Association for Computing Machinery; Bokhove, C., Downey, C., Automated Generation of 'good Enough' Transcripts as a First Step to Transcription of Audio-Recorded Data (2018) Methodological Innov, 11. , 205979911879074; Coulton, P., Burnett, D., Gradinar, A., Games as Speculative Design: Allowing Players to Consider Alternate Presents and Plausible Features (2016) DRS Conference Papers, , https://dl.designresearchsociety.org/drs-conference-papers/drs2016/researchpapers/2/, in; Green, A., Topp, E.A., Hüttenrauch, H., Measuring up as an Intelligent Robot – on the Use of High-Fidelity Simulations for human-robot interaction Research (2006) Performance Metrics for Intelligent Systems workshop, pp. 21-23. , Gaithersburg, Maryland USA, Gaithersburg, National Institute of Standards and Technology; Joyner, B., Young, L., Teaching Medical Students Using Role Play: Twelve Tips for Successful Role Plays (2006) Med. Teach, 28 (3), pp. 225-229; Koay, K.L., Syrdal, D.S., Dautenhahn, K., Walters, M.L., A Narrative Approach to Human-Robot Interaction Prototyping for Companion Robots (2020) Paladyn, J. Behav. Robotics, 11 (1), pp. 66-85; Ladousse, G., (1995) Role Play, , New York, Oxford University Press; Lewis-Beck, M., Bryman, A., Futing Liao, T., (2004) The SAGE Encyclopedia of Social Science Research Methods, , Thousand Oaks, CA, Sage Publications, Inc; Liu, P., Glas, D.F., Kanda, T., Ishiguro, H., Data-Driven HRI: Learning Social Behaviors by Example From Human–Human Interaction IEEE Transactions on Robotics, 32 (4), pp. 988-1008. , IEEE Xplore; Mariani, I., Other Worlds. When Worldbuilding and Roleplay Feed Speculation (2020) Design, User Experience, and Usability. Design for Contemporary Interactive Environments, pp. 482-495. , Marcus A., Rosenzweig E., (eds), Springer International Publishing, Editors; Meneweger, T., Daniela, W., Verena, F., Manfred, T., Working Together with Industrial Robots: Experiencing Robots in a Production Environment (2015) 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 833-838. , IEEE Xplore; Nielsen, L., Acting as Someone like Me - Personas in Participatory Innovation (2012) Participatory Innovation Conference 2012, , https://pure.itu.dk/ws/files/38903074/PIN_C_Acting_as_me_final.pdf, Melbourne Australia,, in; Orne, M.T., Whitehouse, W.G., Demand Characteristics (2000) Encyclopedia of Psychology, pp. 469-470. , Kazdin A.E., (ed), Washington, D.C., American Psychological Association and Oxford Press, Editor; Ortiz, P., Harrell, D.F., Enabling Critical Self-Reflection through Roleplay with Chimeria (2018) “, pp. 353-364. , Proceedings of the 2018 Annual Symposium on Computer-Human Interaction Play, Association for Computing Machinery, New York, ACM Digital Library; Owen, R., Stilgoe, J., Macnaghten, P., Gorman, M., Fisher, E., Guston, D., A Framework for Responsible Innovation (2013) Responsible innovation: managing responsible emergence Sci. innovation Soc, 31, pp. 27-50; Prasolova-Forland, E., Fominykh, M., Darisiro, R., Morch, A.I., Training Cultural Awareness in Military Operations in a Virtual Afghan Village: A Methodology for Scenario Development (2013) 46th Hawaii International Conference on System Sciences, 2013, pp. 903-912; (2014) Rome Declaration on Responsible Research and Innovation in Europe, , https://ec.europa.eu/research/swafs/pdf/rome_declaration_RRI_final_21_November.pdf, Brussels, European Commission; Schomberg, R.V., Hankins, J., (2019) International Handbook on Responsible, , Cheltenham, Innovation Edward Elgar Publishing; Stilgoe, J., Owen, R., Macnaghten, P., Developing a Framework for Responsible Innovation (2013) Res. Pol, 42 (9), pp. 1568-1580; Stokoe, E., The Conversation Analytic Role-Play Method (CARM): A Method for Training Communication Skills as an Alternative to Simulated Role-Play (2014) Res. Lang. Soc. Interaction, 47 (3), pp. 255-265; Tonkin, M., Vitale, J., Herse, S., Williams, M.-A., Judge, W., Wang, X., Design Methodology for the UX of HRI (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 407-415. , New York, Association for Computing Machinery; Vallès-Peris, N., Angulo, C., Domènech, M., Children's Imaginaries of Human-Robot Interaction in Healthcare (2018) Ijerph, 15 (5), p. 970; Villamor, G.B., Badmos, B.K., Grazing Game: A Learning Tool for Adaptive Management in Response to Climate Variability in Semiarid Areas of Ghana (2016) Ecol. Soc, 21 (1). , https://www.jstor.org/stable/26270341; Webb, H., Jirotka, M., F.T. Winfield, A., Winkle, K., Human-robot Relationships and the Development of Responsible Social Robots (2019) ACM Proc. Halfway Future Symp, pp. 1-7; Weiss, A., Huber, A., (2016) User Experience of a Smart Factory Robot: Assembly Line Workers Demand Adaptive Robots, , 1606.03846; Weiss, A., Huber, A., Minichberger, J., Ikeda, M., First Application of Robot Teaching in an Existing Industry 4.0 Environment: Does It Really Work? (2016) Societies, 6 (3), p. 20; Wurhofer, D., Meneweger, T., Fuchsberger, V., Tscheligi, M., Deploying Robots in a Production Environment: A Study on Temporal Transitions of Workers’ Experiences (2015) Human-Computer Interaction – INTERACT 2015, pp. 203-220. , Julio A., (ed), Springer International Publishing, Editors; Winfield, A.F.T., Jirotka, M., The Case for an Ethical Black Box (2017) Lecture Notes in Computer Science, 10454. , Gao Y., Fallah S., Jin Y., Lekakou C., (eds), Cham, Springer, Editors; Winfield, A.F.T., Winkle, K., Webb, H., Lyngs, U., Jirotka, M., MacraeC Jirotka, M., Robot Accident Investigation: a Case Study in Responsible Robotics (2021) Software Engineering for Robotics, , Cham, Springer, :, Editors","Jirotka, M.; Department of Computer Science, United Kingdom; email: marina.jirotka@cs.ox.ac.uk",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85110129864
"Silpani D.C., Suematsu K., Yoshida K.","57237671400;57237122700;35321688600;","A Feasibility Study on Hand Gesture Recognition in Natural Conversation",2021,"2021 5th IEEE International Conference on Cybernetics, CYBCONF 2021",,,"9464141","85","90",,,"10.1109/CYBCONF51991.2021.9464141","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113788192&doi=10.1109%2fCYBCONF51991.2021.9464141&partnerID=40&md5=470222285e218afcc2620dec1a006e51","Kyushu Institute of Technology, Japan Graduate School of Life Science and Systems Engineering, Kitakyushu city, Fukuoka, 808-0196, Japan","Silpani, D.C., Kyushu Institute of Technology, Japan Graduate School of Life Science and Systems Engineering, Kitakyushu city, Fukuoka, 808-0196, Japan; Suematsu, K., Kyushu Institute of Technology, Japan Graduate School of Life Science and Systems Engineering, Kitakyushu city, Fukuoka, 808-0196, Japan; Yoshida, K., Kyushu Institute of Technology, Japan Graduate School of Life Science and Systems Engineering, Kitakyushu city, Fukuoka, 808-0196, Japan","This paper proposes to approach natural hand gestures that occur in natural conversations. Hand gesture recognition in Human-Robot Interaction is considered a preliminary study for the next step, based on the vision system of a humanoid robot. We experimented as a feasibility study on hand gestures recognition in natural conversation. The conversations between an experimental assistant (the first author) and two different subjects (both female) were recorded in the experiment. Each conversation includes two people, one of whom is the subject of the hand gesture detection that appears during the conversation. We manually captured the hand gestures that appear naturally during the session and analyzed their duration and frequency of occurrence from each video. As a result of conversations with two subjects, it was clear that hand gestures significantly impact people's conversations. We observed that hand gestures with the same word or similar meaning appeared several times with the same intention, such as pointing at something and emphasizing a sentence or word. The topic of natural hand-gesture recognition by a robot will be a challenge. Cultural diversity makes the scope of natural hand-gesture broad, but this can be considered a topic in developing the knowledge of human-robot interaction in the future. © 2021 IEEE.","hand gestures; Human-Robot Interaction; video recorded natural conversation","Anthropomorphic robots; Man machine systems; Palmprint recognition; Planning; Social robots; Cultural diversity; Feasibility studies; Hand gesture; Hand-gesture recognition; Humanoid robot; Vision systems; Gesture recognition",,,,,,,,,,,,,,,,"Infantes, G., Ghallab, M., Ingrand, F., (2011) Learning the Behavior Model of A Robot, 30, pp. 157-177. , Springer. Auton Robot; Augello, A., Daniela, L., Gentile, M., Ifenthaler, D., Pilato, G., Editorial: Robot assisted learning and education (2020) Front. Robot. AI, 7, p. 591319. , November; Cabibihan, J.-J., So, W.C., Pramanik, S., Human-recognizable robotic gestures, autonomous mental development (2012) IEEE Transactions, 4 (4), pp. 305-314; Lapusan, C., Rad, C.R., Besoiu, S., Plesa, A., Design of a humanoid robot head for studying human-robot interaction Bucharest, ROMÂ NIA, 2015, ECAI 2015-International Conference-7th Edition, Electronics, Computers and Artificial Intelligence; Tolgyessy, M., Dekan, M., Duchon, F., Rodina, J., Hubinsky, P., Chovanec, L., Foundations of visual linear human-robot interaction via pointing gesture navigation (2017) Int J of Soc Robotics, 9, pp. 509-523; Fakhrurroja, H., Riyanto, Purwarianti, A., Prihatmanto, A.S., Machbub, C., Integration of Indonesian speech and hand gesture recognition for controlling humanoid robot (2018) 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), , Singapore; Trigueiros, P., Ribeiro, F., Reis, L.P., Hand gesture recognition system based in computer vision and machine learning (2013) Computational Vision and Medical Image Processing IV: VIPIMAGE, pp. 137-142; Tanevska, A., Rea, F., Sandini, G., Canamero, L., Sciutti, A., A socially adaptable framework for human-robot interaction (2020) Front. Robot. AI, 7, p. 121. , October; Pedro, L.M., Cauri, G.A.P., Belini, V.L., Pechoneri, R.D., Gonzaga, A., Neto, I.M., Nazareno, F., Stucheli, M., Hand gesture recognition for robot hand teleoperation (2012) ABCM Symposium Series in Mechatronics, 5, p. 1065. , Section VII-Robotics; Khan, S., Ali, M.E., Das, S.S., Rahman, M.M., Real time hand gesture recognition by skin color detection for American sign language (2019) 4th International Conference on Electrical Information and Communication Technology (EICT), , Khulna, Bangladesh, 20-22 December; Meena, R., Jokinen, K., Wilcock, G., Integration of gestures and speech in human-robot interaction (2012) IEEE 3rd International Conference on Cognitive Infocommunications (CogInfoCom); Chella, A., Cangelosi, A., Metta, G., Bringsjord, S., (2019) Editorial : Consciousness in Humanoid Robot, Front. Robot. AI, 6, p. 17. , March; Reggia, J.A., Katz, G.A., Davis, G.P., Humanoid cognitive robots that learn by imitating: Implications for consciousness studies (2018) Front. Robot. AI, 5, p. 1. , January; Cominelli, L., Mazzei, D., De Rossi, D.E., SEAI : Social emotional artificial intelligence based on Damasio?s theory of mind (2018) Front. Robot. AI, 5, p. 6. , February; Kendon, A., (2004) Gesture: Visible Action As Utterance, , Cambridge: Cambridge University Press",,,"IEEE Systems, Man, and Cybernetics Society (SMCS)","Institute of Electrical and Electronics Engineers Inc.","5th IEEE International Conference on Cybernetics, CYBCONF 2021","8 June 2021 through 10 June 2021",,171036,,9781665403207,,,"English","IEEE Int. Conf. Cybern., CYBCONF",Conference Paper,"Final","",Scopus,2-s2.0-85113788192
"Alarcon G.M., Gibson A.M., Jessup S.A., Capiola A.","35101277300;57209638343;57194107331;55155617300;","Exploring the differential effects of trust violations in human-human and human-robot interactions",2021,"Applied Ergonomics","93",,"103350","","",,1,"10.1016/j.apergo.2020.103350","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100020047&doi=10.1016%2fj.apergo.2020.103350&partnerID=40&md5=45f461bd6f560ba3a379a6a03c8107af","Air Force Research Laboratory, 2210 Eighth Street Bldg. 146, Wright Patterson Air Force Base, OH  45433, United States; Consortium of Universities, 1020 19th St NW #500, Washington, DC, 20036, United States","Alarcon, G.M., Air Force Research Laboratory, 2210 Eighth Street Bldg. 146, Wright Patterson Air Force Base, OH  45433, United States; Gibson, A.M., Consortium of Universities, 1020 19th St NW #500, Washington, DC, 20036, United States; Jessup, S.A., Air Force Research Laboratory, 2210 Eighth Street Bldg. 146, Wright Patterson Air Force Base, OH  45433, United States; Capiola, A., Air Force Research Laboratory, 2210 Eighth Street Bldg. 146, Wright Patterson Air Force Base, OH  45433, United States","There is sparse research directly investigating the effects of trust manipulations in human-human and human-robot interactions. Moreover, studies on human-human versus human-robot trust have leveraged unusual or low vulnerability contexts to investigate such effects and have focused mostly on robot performance. In the present research, we seek to remedy these limitations and compare trust in human-human versus human-robot collaborations in an augmented and adapted version of the Trust Game. We used a mixed factorial design to examine the effects of trust and trust violations on human-human and human-robot interactions over time with an emphasis on anthropomorphic robots in a social context. We found consistent and significant effects of partner behavior. Specifically, partner distrust behaviors led to participants’ lower levels of trustworthiness perceptions, trust intentions, and trust behaviors over time compared to partner trust behaviors. We found no significant effect of partnering with a human versus an anthropomorphic robot over time across the three dependent variables, supporting the computers as social actors (CASA; Nass and Moon, 2000) paradigm. This study demonstrated that there may be instances where the effects of trust violations from an anthropomorphized robot partner are not meaningfully different from those of a human partner in a social context. © 2021","Automation bias; Human-robot interaction; Trust violations","Anthropomorphic robots; Economic and social effects; Machine design; Man machine systems; Dependent variables; Differential effect; Human-robot collaboration; Mixed factorial designs; Robot Partners; Robot performance; Social actors; Social context; Social robots; Article; behavior; behavior assessment; human; human relation; human robot interaction; linear regression analysis; partner distrust behavior; partner trust behavior; perception; probability; social environment; trust; human relation; robotics; trust; Humans; Intention; Interpersonal Relations; Robotics; Social Environment; Trust",,,,,"Air Force Office of Scientific Research, AFOSR: 18RHCR002","Distribuition A. Approved for public release: 88ABW Cleared 10/25/19 88ABW-2019-5214. This work was supported by Air Force Office of Scientific Research, United States , Grant: 18RHCR002 .",,,,,,,,,,"Alarcon, G.M., Lyons, J.B., Christensen, J.C., Klosterman, S.L., Bowers, M.A., Ryan, T.J., Jessup, S.J., Wynne, K.T., The effect of propensity to trust and perceptions of trustworthiness on trust behaviors in dyads (2018) Behav. Res. Methods, 50 (5), pp. 1906-1920; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: a review (2018) Sci. Robot., 3 (21). , Article eaat5954; Bemelmans, R., Gelderblom, G.J., Jonker, P., De Witte, L., Socially assistive robots in elderly care: a systematic review into effects and effectiveness (2012) J. Am. Med. Dir. Assoc., 13 (2), pp. 114-120; Berg, J., Dickhaut, J., McCabe, K., Trust, reciprocity, and social history (1995) Game. Econ. Behav., 10 (1), pp. 122-142; Bonnini, S., Corain, L., Marozzi, M., Salmaso, L., Nonparametric Hypothesis Testing: Rank and Permutation Methods with Applications in R. Wiley (2014); Broadbent, E., Kumar, V., Li, X., 3rd, J.S., Stafford, R.Q., MacDonald, B.A., Wegner, D.M., Robots with display screens: a robot with a more humanlike face display is perceived to have more mind and a better personality (2013) PloS One, 8 (8). , e72589; Calhoun, C.S., Bobko, P., Gallimore, J.J., Lyons, J.B., Linking precursors of interpersonal trust to human-automation trust: an expanded typology and exploratory experiment (2019) J. Trust Resear., 9 (1), pp. 28-46; Chidambaram, V., Chiang, Y.H., Mutlu, B., (2012) Designing Persuasive Robots: How Robots Might Persuade People Using Vocal and Nonverbal Cues, pp. 293-300. , Proceedings of the International Conference on Human-Robot Interaction USA; Chang, W.H., Kim, Y.-H., Robot-assisted therapy in stroke rehabilitation (2013) J. 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"Xiong J., Chen J., Lee P.S.","57195104603;57221654039;7406117427;","Functional Fibers and Fabrics for Soft Robotics, Wearables, and Human–Robot Interface",2021,"Advanced Materials","33","19","2002640","","",,38,"10.1002/adma.202002640","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092075207&doi=10.1002%2fadma.202002640&partnerID=40&md5=8006a358457098ed4b9affeb6cb9d639","School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore","Xiong, J., School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore; Chen, J., School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore; Lee, P.S., School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore","Soft robotics inspired by the movement of living organisms, with excellent adaptability and accuracy for accomplishing tasks, are highly desirable for efficient operations and safe interactions with human. With the emerging wearable electronics, higher tactility and skin affinity are pursued for safe and user-friendly human–robot interactions. Fabrics interlocked by fibers perform traditional static functions such as warming, protection, and fashion. Recently, dynamic fibers and fabrics are favorable to deliver active stimulus responses such as sensing and actuating abilities for soft-robots and wearables. First, the responsive mechanisms of fiber/fabric actuators and their performances under various external stimuli are reviewed. Fiber/yarn-based artificial muscles for soft-robots manipulation and assistance in human motion are discussed, as well as smart clothes for improving human perception. Second, the geometric designs, fabrications, mechanisms, and functions of fibers/fabrics for sensing and energy harvesting from the human body and environments are summarized. Effective integration between the electronic components with garments, human skin, and living organisms is illustrated, presenting multifunctional platforms with self-powered potential for human–robot interactions and biomedicine. Lastly, the relationships between robotic/wearable fibers/fabrics and the external stimuli, together with the challenges and possible routes for revolutionizing the robotic fibers/fabrics and wearables in this new era are proposed. © 2020 The Authors. Published by Wiley-VCH GmbH","actuators; fibers/fabrics; power sources; sensors; soft robotics","Biology; Clothes; Energy harvesting; Fibers; Robotics; Wearable technology; Artificial muscle; Electronic component; External stimulus; Functional fibers; Multifunctional platforms; Robot interactions; Sensing and actuating; Stimulus response; Human robot interaction",,,,,"National Research Foundation Singapore, NRF: NRF‐CRP13‐2014‐02, NRF‐NRFI201605","J.X., J.C., and P.S.L. contributed equally to this work. This work was supported by the National Research Foundation Investigatorship (NRF‐NRFI201605) and the Competitive Research Programme (NRF‐CRP13‐2014‐02) under the National Research Foundation, Prime Minister's Office, Singapore.",,,,,,,,,,"Gries, T., Raina, M., Quadflieg, T., Stolyarov, O., (2016) Textile Fibre Composites in Civil Engineering, p. 3. , Elsevier, Amsterdam, The Netherlands, p; Vigo, T.L., (2013) Textile Processing and Properties: Preparation, Dyeing, Finishing and Performance, 11. , Elsevier, Amsterdam, The Netherlands; Zeng, W., Shu, L., Li, Q., Chen, S., Wang, F., Tao, X.M., (2014) Adv. Mater., 26, p. 5310; Weng, W., Chen, P., He, S., Sun, X., Peng, H., (2016) Angew. Chem., Int. Ed., 55, p. 6140; Jost, K., Dion, G., Gogotsi, Y., (2014) J. Mater. Chem. A, 2; Rus, D., Tolley, M.T., (2015) Nature, 521, p. 467; Rich, S.I., Wood, R.J., Majidi, C., (2018) Nat. Electron., 1, p. 102; Wallin, T.J., Pikul, J., Shepherd, R.F., (2018) Nat. Rev. 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"Gemeinboeck P.","10043705000;","The Aesthetics of Encounter: A Relational-Performative Design Approach to Human-Robot Interaction",2021,"Frontiers in Robotics and AI","7",,"577900","","",,3,"10.3389/frobt.2020.577900","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103832305&doi=10.3389%2ffrobt.2020.577900&partnerID=40&md5=c7e9d1481e65171f2e5458350049cf2d","Department of Media Theory, University of Applied Arts Vienna, Vienna, Austria; Centre for Transformative Media Technologies, School of Arts, Social Sciences and Humanities, Swinburne University of Technology, Melbourne, VIC, Australia","Gemeinboeck, P., Department of Media Theory, University of Applied Arts Vienna, Vienna, Austria, Centre for Transformative Media Technologies, School of Arts, Social Sciences and Humanities, Swinburne University of Technology, Melbourne, VIC, Australia","This article lays out the framework for relational-performative aesthetics in human-robot interaction, comprising a theoretical lens and design approach for critical practice-based inquiries into embodied meaning-making in human-robot interaction. I explore the centrality of aesthetics as a practice of embodied meaning-making by drawing on my arts-led, performance-based approach to human-robot encounters, as well as other artistic practices. Understanding social agency and meaning as being enacted through the situated dynamics of the interaction, I bring into focus a process of bodying-thinging; entangling and transforming subjects and objects in the encounter and rendering elastic boundaries in-between. Rather than serving to make the strange look more familiar, aesthetics here is about rendering the differences between humans and robots more relational. My notion of a relational-performative design approach—designing with bodying-thinging—proposes that we engage with human-robot encounters from the earliest stages of the robot design. This is where we begin to manifest boundaries that shape meaning-making and the potential for emergence, transformation, and connections arising from intra-bodily resonances (bodying-thinging). I argue that this relational-performative approach opens up new possibilities for how we design robots and how they socially participate in the encounter. © Copyright © 2021 Gemeinboeck.","aesthetics; agency; design; human-robot interaction design; movement; performativity",,,,,,"Falmouth University; Seventh Framework Programme, FP7: 621403; Australian Research Council, ARC: DP160104706; Universiteit Leiden; Austrian Science Fund, FWF: AR545; Technische Universiteit Eindhoven, TU/e","I would like to thank my collaborators for their contributions to the ongoing research: Rob Saunders (Leiden University, Leiden, Netherlands), Marie-Claude Poulin (kondition pluriel, Montreal, Canada; University of Applied Arts Vienna, Vienna, Austria), Maaike Bleeker (Utrecht University, Utrecht, Netherlands), Roos van Berkel (Eindhoven University of Technology, Eindhoven, Netherlands), Rochelle Haley (University of New South Wales, Sydney, Australia), and Audrey Rochette (kondition pluriel, Montreal, Canada). I would also like to thank members of De Quincey Co. (Sydney, Australia) for their contributions to the project in its earlier stages, in particular, director and choreographer Tess de Quincey, and performers Linda Luke and Kirsten Packham. Finally, I would like to thank the performers Sarah Levinsky and Katrina Brown from Falmouth University (Falmouth, United Kingdom) for their contributions to the workshops in 2018.","The research presented in this article has been partly supported by several funding bodies: the Australian Government through the Australian Research Council (DP160104706); the EU Framework Program (FP7) European Research Area Chairs Scheme project (621403); and the Austrian Government through the Austrian Science Fund (FWF, AR545).",,,,,,,,,"Airenti, G., The cognitive bases of anthropomorphism: from relatedness to empathy (2015) Int. J. Soc. Robot, 7, pp. 117-127; Alač, M., Social robots: things or agents? 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Syst. Res, 3, pp. 271-274; Ziemke, T., Sharkey, N., A stroll through the worlds of robots and animals (2001) Semiotica, 13 (1–4), pp. 701-746; Ziemke, T., The body of knowledge: on the role of the living body in grounding embodied cognition (2016) Biosystems, 148, pp. 4-11; Zivanovic, A., Elegant, natural motion of robots: lessons from an artist” in Gemeinboeck (2019) Proceedings of the AISB 2019 Symposium on Movement that shapes behaviour (MTSB’19), , http://aisb2019.machinemovementlab.net/MTSB2019_Proceedings.pdf, Gemeinboeck P., Saunders R., (eds), :, Accessed, June, 21, 2020, Editors","Gemeinboeck, P.; Department of Media Theory, Austria; email: petra.gemeinboeck@uni-ak.ac.at
Gemeinboeck, P.; Centre for Transformative Media Technologies, Australia; email: petra.gemeinboeck@uni-ak.ac.at",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85103832305
"Saheb Jam G., Rhim J., Lim A.","57222470882;55619493200;36801929900;","Developing a data-driven categorical taxonomy of emotional expressions in real world human robot interactions",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447218","479","483",,1,"10.1145/3434074.3447218","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102773751&doi=10.1145%2f3434074.3447218&partnerID=40&md5=ace723a641f6e074e089dfa391ed803d","School of Computing Science, Simon Fraser University, Burnaby, BC, Canada","Saheb Jam, G., School of Computing Science, Simon Fraser University, Burnaby, BC, Canada; Rhim, J., School of Computing Science, Simon Fraser University, Burnaby, BC, Canada; Lim, A., School of Computing Science, Simon Fraser University, Burnaby, BC, Canada","Emotions are reactions that can be expressed through a variety of social signals. For example, anger can be expressed through a scowl, narrowed eyes, a long stare, or many other expressions. This complexity is problematic when attempting to recognize a human's expression in a human-robot interaction: categorical emotion models used in HRI typically use only a few prototypical classes, and do not cover the wide array of expressions in the wild. We propose a data-driven method towards increasing the number of known emotion classes present in human-robot interactions, to 28 classes or more. The method includes the use of automatic segmentation of video streams into short (<10s) videos, and annotation using the large set of widely-understood emojis as categories. In this work, we showcase our initial results using a large in-the-wild HRI dataset (UE-HRI), with 61 clips randomly sampled from the dataset, labeled with 28 different emojis. In particular, our results showed that the ""skeptical""emoji was a common expression in our dataset, which is not often considered in typical emotion taxonomies. This is the first step in developing a rich taxonomy of emotional expressions that can be used in the future as labels for training machine learning models, towards more accurate perception of humans by robots. © 2021 ACM.","Emojis; Emotion categories; Emotion-recognition; In-the-wild; Social signals","Agricultural robots; Behavioral research; Educational robots; Large dataset; Man machine systems; Taxonomies; Automatic segmentations; Data driven; Data-driven methods; Emotion models; Emotional expressions; Real-world; Social signals; Training machines; Human robot interaction",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC","This work was supported by the Natural Sciences and Engineering Research Council of Canada Discovery Grants program.",,,,,,,,,,"Admoni, H., Nonverbal communication for human-robot interaction (2014) ACM Transactions on Embedded Computing Systems, 10 (10), pp. 1-3. , 2014; Al-Halah, Z., Aitken, A., Shi, W., Caballero, J., Smile, be happy:) emoji embedding for visual sentiment analysis (2019) IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 4491-4500; 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Liu, Z., Wu, M., Cao, W., Chen, L., Xu, J., Zhang, R., Zhou, M., Mao, J., A facial expression emotion recognition based human-robot interaction system (2017) IEEE/CAA Journal of Automatica Sinica, 4 (4), pp. 668-676. , 2017; Luo, R.C., Lin, P.H., Wu, Y.C., Huang, C.Y., Dynamic face recognition system in recognizing facial expressions for service robotics (2012) IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 879-884; McColl, D., Hong, A., Hatakeyama, N., Nejat, G., Benhabib, B., A survey of autonomous human affect detection methods for social robots engaged in natural hri (2016) Journal of Intelligent & Robotic Systems, 82 (1), pp. 101-133. , 2016; McColl, D., Nejat, G., Recognizing emotional body language displayed by a human-like social robot (2014) International Journal of Social Robotics, 6 (2), pp. 261-280. , 2014; Mehrabian, A., Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament (1996) Current Psychology, 14 (4), pp. 261-292. , 1996; Mollahosseini, A., Hasani, B., Mahoor, M.H., Affectnet: A database for facial expression, valence, and arousal computing in the wild (2017) IEEE Transactions on Affective Computing, 10 (1), pp. 18-31. , 2017; Montáns, F.J., Chinesta, F., Gómez-Bombarelli, R., Nathan Kutz, J., Data-driven modeling and learning in science and engineering (2019) Comptes Rendus Mécanique, 347 (11), pp. 845-855. , 2019; Ed Plutchik, R., Conte, H.R., (1997) Circumplex Models of Personality and Emotions, , American Psychological Association; Querci, T., Duan, Y., Ince, R.A.A., Chen, C., Peussa, L.K., Garrod, O.G.B., Schyns, P.G., Jack, R.E., Distinct face movements boost recognition of facial expressions of emotions (2020) Journal of Vision, 20 (11), p. 1039. , 2020; Russell, J.A., A circumplex model of affect (1980) Journal of Personality and Social Psychology, 39 (6), p. 1161. , 1980; Saunderson, S., Nejat, G., How robots influence humans: A survey of nonverbal communication in social human-robot interaction (2019) International Journal of Social Robotics, 11 (4), pp. 575-608. , 2019; Sharma, G., Dhall, A., A survey on automatic multimodal emotion recognition in the wild (2020) Advances in Data Science: Methodologies and Applications, pp. 35-64. , Springer; Vaufreydaz, D., Johal, W., Combe, C., Starting engagement detection towards a companion robot using multimodal features (2016) Robotics and Autonomous Systems, 75, pp. 4-16. , 2016; Viera, A.J., Garrett, J.M., Understanding interobserver agreement: The kappa statistic (2005) Fam Med, 37 (5), pp. 360-363. , 2005; Watson, S.G., Judgment of emotion from facial and contextual cue combinations (1972) Journal of Personality and Social Psychology, 24 (3), p. 334. , 1972; Wimmer, M., MacDonald, B.A., Jayamuni, D., Arpit, Yadav., Facial expression recognition for human-robot interaction-a prototype (2008) International Workshop on Robot Vision, pp. 139-152; Yu, C., Tapus, A., Interactive robot learning for multimodal emotion recognition (2019) International Conference on Social Robotics, pp. 633-642",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102773751
"Lavit Nicora M., Ambrosetti R., Wiens G.J., Fassi I.","57220203573;57222104656;7003372495;6506998602;","Human-robot collaboration in smart manufacturing: Robot reactive behavior intelligence",2021,"Journal of Manufacturing Science and Engineering, Transactions of the ASME","143","3","031009","","",,1,"10.1115/1.4048950","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101987934&doi=10.1115%2f1.4048950&partnerID=40&md5=106ebc60d919357a8f80806e3a2135d4","Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Previati 1/E, Lecco, 23900, Italy; Department of Mechanical Engineering, Politecnico di Milano, Milano, 20156, Italy; Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL  32611-6250, United States; Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Alfonso Corti 12, Milano, 20133, Italy","Lavit Nicora, M., Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Previati 1/E, Lecco, 23900, Italy; Ambrosetti, R., Department of Mechanical Engineering, Politecnico di Milano, Milano, 20156, Italy; Wiens, G.J., Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL  32611-6250, United States; Fassi, I., Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Alfonso Corti 12, Milano, 20133, Italy","To enable safe and effective human-robot collaboration (HRC) in smart manufacturing, seamless integration of sensing, cognition, and prediction into the robot controller is critical for real-time awareness, response, and communication inside a heterogeneous environment (robots, humans, and equipment). The specific research objective is to provide the robot Proactive Adaptive Collaboration Intelligence (PACI) and switching logic within its control architecture in order to give the robot the ability to optimally and dynamically adapt its motions, given a priori knowledge and predefined execution plans for its assigned tasks. The challenge lies in augmenting the robot's decision-making process to have greater situation awareness and to yield smart robot behaviors/reactions when subject to different levels of human-robot interaction, while maintaining safety and production efficiency. Robot reactive behaviors were achieved via cost function-based switching logic activating the best suited high-level controller. The PACI's underlying segmentation and switching logic framework is demonstrated to yield a high degree of modularity and flexibility. The performance of the developed control structure subjected to different levels of human-robot interactions was validated in a simulated environment. Open-loop commands were sent to the physical e.DO robot to demonstrate how the proposed framework would behave in a real application. Copyright © 2020 by ASME","Cobot; Control and automation; Flexible tooling; Human-robot collaboration; Robotics; Smart factory; Task segmentation","Computer circuits; Controllers; Cost functions; Decision making; Flow control; Intelligent robots; Man machine systems; Manufacture; Robot programming; Adaptive collaboration; Control architecture; Decision making process; Heterogeneous environments; High level controllers; Human-robot collaboration; Production efficiency; Simulated environment; Social robots",,,,,,,,,,,,,,,,"IFR Press Release (2019) IFR Press Conference, , https://ifr.org/ifr-press-releases/news/robot-investment-reaches-record-16.5-billion-usd, International Federation of Robotics, Shanghai, China, Sept. 18, Accessed October 18, 2019; Fenech, Celine, Perkins, Ben, Made-to-Order: The Rise of Mass Personalization (2015), pp. 1-24. , 11th ed., The Deloitte Consumer Review, London, UK; Streitmatter, G. L., Wiens, G. J., Multi-Objective Approach to HRC Manufacturing Environment (2019) ASME Manufacturing Science and Engineering Conference, , MSEC2019-2935, Erie, PA, June 10-14 (poster); Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A., ROS: An Open-Source Robot Operating System (2009) ICRA Workshop on Open Source Software, 3 (2-3), p. 5; Chitta, S., Sucan, I., Cousins, S., Moveit! (2012) IEEE Robot. Autom. Mag, 19 (1), pp. 18-19; (2018) Comau e.DO Robot Specifications, , https://edo.cloud/edo-robot/, Accessed October 18, 2019; Kragic, D., Gustafson, J., Karaoguz, H., Jensfelt, P., Krug, R., Interactive, Collaborative Robots: Challenges and Opportunities (2018) International Joint Conference on Artificial Intelligence, pp. 18-25. , Stockholm, Sweden, July 13-19; Colledanchise, M., Ogren, P., How Behavior Trees Modularize Hybrid Control Systems and Generalize Sequential Behavior Compositions, the Subsumption Architecture and Decision Trees (2017) IEEE Trans. Rob, 33 (2), pp. 372-389; Roveda, L., Vicentini, F., Molinari Tosatti, L., Deformation-Tracking Impedance Control in Interaction With Uncertain Environments (2013) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1992-1997. , Tokyo, Japan, Nov. 3-7; Liu, C., Tomizuka, M., Algorithmic Safety Measures for Intelligent Industrial Co-Robots (2016) IEEE International Conference on Robotics and Automation, pp. 3095-3102. , Stockholm, Sweden, May 16-21; Pedrocchi, N., Vicentini, F., Malosio, M., Molinari Tosatti, L., Safe Human-Robot Cooperation in Industrial Environment (2013) Int. J. Adv. Rob. Syst, 10 (1), pp. 1-13; Sadrfaridpour, B., Wang, Y., Collaborative Assembly in Hybrid Manufacturing Cells: An Integrated Framework for Human-Robot Interaction (2018) IEEE Trans. Autom. Sci. Eng, 15 (3), pp. 1178-1192; Pellegrinelli, S., Moro, F. L., Pedrocchi, N., Molinari Tosatti, L., Tolio, T., A Probabilistic Approach to Workspace Sharing for Human-Robot Cooperation in Assembly Tasks (2016) Robotics and Computer-Integrated Manufacturing, CIRP Annals, 65 (1), pp. 57-60; Ferraguti, F., Talignani Landi, C., Secchi, C., Fantuzzi, C., Nolli, M., Pesamosca, M., Walk-Through Programming for Industrial Applications (2017) Procedia Manufacturing, 11, pp. 31-38; Billard, A. G., Calinon, S., Dillmann, R., Learning From Humans (2016) Springer Handbook of Robotics, pp. 1995-2014. , Springer Handbooks, B. Siciliano, and O. Khatib, eds., Springer, Cham, ISBN: 978-3-319-32550-7; Neto, P., Mendes, N., Direct Off-Line Robot Programming via a Common CAD Package (2013) Rob. Auton. Syst, 61 (8), pp. 896-910; Michalos, G., Karagiannis, P., Makris, S., Tokçalar, Ö, Chryssolouris, G., Augmented Reality (AR) Applications for Supporting Human-Robot Interactive Cooperation (2016) Procedia CIRP, 41, pp. 370-375","Wiens, G.J.; Department of Mechanical and Aerospace Engineering, United States; email: gwiens@ufl.edu",,,"American Society of Mechanical Engineers (ASME)",,,,,10871357,,,,"English","J Manuf Sci Eng Trans ASME",Article,"Final","",Scopus,2-s2.0-85101987934
"Fu L., Zhao J.","57195510891;57203289935;","Maxwell-Model-Based Compliance Control for Human-Robot Friendly Interaction",2021,"IEEE Transactions on Cognitive and Developmental Systems","13","1","9086886","118","131",,,"10.1109/TCDS.2020.2992538","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084471941&doi=10.1109%2fTCDS.2020.2992538&partnerID=40&md5=51540a256ff2cd62b8021c75b53f88ca","State Key Laboratory of Robotics and System, School of Mechanical Engineering, Harbin Institute of Technology, Harbin, China","Fu, L., State Key Laboratory of Robotics and System, School of Mechanical Engineering, Harbin Institute of Technology, Harbin, China; Zhao, J., State Key Laboratory of Robotics and System, School of Mechanical Engineering, Harbin Institute of Technology, Harbin, China","This article explores an alternative design philosophy of compliance control for safe and friendly human-robot interaction. Conventional approaches are based on the Voigt model with spring and damper connected in parallel. The theoretical and experimental results of disciplines other than robotics (e.g., mechanics of materials and elastoplastic mechanics) show that the Maxwell model has the merit of removing the elastic return force and reducing the effect of collisions with respect to the Voigt model. Therefore, the authors are motivated to exploit the advantages of the Maxwell model in reactions to human interactions and develop a series of Maxwell model-based compliance control methodologies, which only require kinematic control interfaces. First, a Maxwell model-based Cartesian admittance control scheme is presented to produce a novel plastic compliance of robot end effector. Moreover, we present a Maxwell model-based null space admittance control approach for manipulators with redundancy to achieve a novel plastic whole body compliance. We have evaluated the effectiveness and generalizability of the proposed methods by extensive comparative experiments on human-robot cooperative tasks in various sectors, including domestic, part assembly, and robotic puncture surgery. Our approaches can be viewed as a new and comfortable interface of physical human-robot interaction and collaboration. © 2016 IEEE.","Admittance control; compliance control; Maxwell model; null space; plastic deformation","End effectors; Machine design; Man machine systems; Robotic assembly; Robotic surgery; Robotics; Social robots; Alternative designs; Comparative experiments; Control methodology; Conventional approach; Friendly interaction; Human interactions; Mechanics of materials; Physical human-robot interactions; Compliance control",,,,,"Major Research Plan: 91648201; National Natural Science Foundation of China, NSFC: 51521003","Manuscript received October 30, 2019; revised February 13, 2020 and April 10, 2020; accepted May 1, 2020. Date of publication May 6, 2020; date of current version March 11, 2021. This work was supported in part by the Major Research Plan under Grant 91648201, and in part by the Foundation for Innovative Research Groups through the National Natural Science Foundation of China under Grant 51521003. (Corresponding author: Jie Zhao.) The authors are with the State Key Laboratory of Robotics and System, School of Mechanical Engineering, Harbin Institute of Technology, Harbin 150080, China (e-mail: 14b308007@hit.edu.cn; jzhao@hit.edu.cn).",,,,,,,,,,"Hirzinger, G., DLR?s torque-controlled light weight robot III-Are we reaching the technological limits now?"" in (2002) Proc Ieee Int. Conf. Robot. Autom, 2, pp. 1710-1716; Zinn, M., Khatib, O., Roth, B., Salisbury, J.K., Playing it safe [human-friendly robots] (2004) Ieee Robot. Autom. 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(ICRA, pp. 15-21; Chawda, V., Niemeyer, G., Toward torque control of a KUKA LBR IIWA for physical human-robot interaction (2017) Proc IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS, pp. 6387-6392; Sciavicco, L., Siciliano, B., A solution algorithm to the inverse kinematic problem for redundant manipulators (1988) Ieee J. Robot. Autom, 4 (4), pp. 403-410. , Apr","Zhao, J.; State Key Laboratory of Robotics and System, China; email: jzhao@hit.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23798920,,,,"English","IEEE Trans. Cogn. Dev. Syst.",Article,"Final","",Scopus,2-s2.0-85084471941
"Ghonasgi K., Yousaf S.N., Esmatloo P., Deshpande A.D.","57190752341;57212314873;57195415034;7202168327;","A modular design for distributed measurement of human–robot interaction forces in wearable devices",2021,"Sensors","21","4","1445","1","17",,2,"10.3390/s21041445","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100940696&doi=10.3390%2fs21041445&partnerID=40&md5=25b9471455dd1a4190f13c675a8b83a8","Rehabilitation and Neuromuscular Robotics Lab, The University of Texas at Austin, Austin, TX  78712, United States","Ghonasgi, K., Rehabilitation and Neuromuscular Robotics Lab, The University of Texas at Austin, Austin, TX  78712, United States; Yousaf, S.N., Rehabilitation and Neuromuscular Robotics Lab, The University of Texas at Austin, Austin, TX  78712, United States; Esmatloo, P., Rehabilitation and Neuromuscular Robotics Lab, The University of Texas at Austin, Austin, TX  78712, United States; Deshpande, A.D., Rehabilitation and Neuromuscular Robotics Lab, The University of Texas at Austin, Austin, TX  78712, United States","Measurement of interaction forces distributed across the attachment interface in wearable devices is critical for understanding ergonomic physical human–robot interaction (pHRI). The main challenges in sensorization of pHRI interfaces are (i) capturing the fine nature of force transmission from compliant human tissue onto rigid surfaces in the wearable device and (ii) utilizing a low-cost and easily implementable design that can be adapted for a variety of human interfaces. This paper addresses both challenges and presents a modular sensing panel that uses force-sensing resistors (FSRs) combined with robust electrical and mechanical integration principles that result in a reliable solution for distributed load measurement. The design is demonstrated through an upper-arm cuff, which uses 24 sensing panels, in conjunction with the Harmony exoskeleton. Validation of the design with controlled loading of the sensorized cuff proves the viability of FSRs in an interface sensing solution. Preliminary experiments with a human subject highlight the value of distributed interface force measurement in recognizing the factors that influence ergonomic pHRI and elucidating their effects. The modular design and low cost of the sensing panel lend themselves to extension of this approach for studying ergonomics in a variety of wearable applications with the goal of achieving safe, comfortable, and effective human–robot interaction. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Exoskeleton attachment design; Force sensing; Physical human robot interaction","Costs; Ergonomics; Exoskeleton (Robotics); Machine design; Wearable technology; Distributed interfaces; Distributed measurements; Force sensing resistor; Force transmission; Interaction forces; Mechanical integration; Robot interactions; Wearable applications; Human robot interaction; electronic device; ergonomics; exoskeleton (rehabilitation); human; robotics; Ergonomics; Exoskeleton Device; Humans; Robotics; Wearable Electronic Devices",,,,,"National Science Foundation, NSF: 1941260, 2019704, DGE-1610403","Funding: This research was funded, in part, by the National Science Foundation (NSF) grant numbers 2019704 and 1941260, as well as the NSF Graduate Research Fellowship Program (GRFP) grant number DGE-1610403.",,,,,,,,,,"Viteckova, S., Kutilek, P., Jirina, M., Wearable lower limb robotics: A review (2013) Biocybern. Biomed. Eng, 33, pp. 96-105. , [CrossRef]; Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Troy, A., Leonhardt, S., A survey on robotic devices for upper limb rehabilitation (2014) J. Neuroeng. Rehabil, 11, p. 3. , [CrossRef]; Dollar, A.M., Herr, H., Lower extremity exoskeletons and active orthoses: Challenges and state-of-the-art (2008) IEEE Trans. Rob, 24, pp. 144-158. , [CrossRef]; Rocon, E., Ruiz, A., Raya, R., Schiele, A., Pons, J.L., Belda-Lois, J., Poveda, R., Moreno, J., Human-robot physical interaction (2008) Wearable Robots: Biomechatronic Exoskeletons, pp. 127-163. , Wiley: Hoboken, NJ, USA; De Santis, A., Siciliano, B., De Luca, A., Bicchi, A., An atlas of physical human–robot interaction (2008) Mech. Mach. Theory, 43, pp. 253-270. , [CrossRef]; Kim, B., Deshpande, A.D., An upper-body rehabilitation exoskeleton Harmony with an anatomical shoulder mechanism: Design, modeling, control, and performance evaluation (2017) Int. J. Rob. 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(accessed on 23 November 2020); Pliance System, , https://www.novelusa.com/pliance, (accessed on 7 December 2020); Pezent, E., Fani, S., Clark, J., Bianchi, M., O’Malley, M.K., Spatially separating haptic guidance from task dynamics through wearable devices (2019) IEEE Trans. Haptic, 12, pp. 581-593. , [CrossRef] [PubMed]; Sengeh, D.M., Herr, H., A variable-impedance prosthetic socket for a transtibial amputee designed from magnetic resonance imaging data (2013) JPO, 25, pp. 129-137. , [CrossRef]","Deshpande, A.D.; Rehabilitation and Neuromuscular Robotics Lab, United States; email: ashish@austin.utexas.edu",,,"MDPI AG",,,,,14248220,,,"33669615","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85100940696
"Romeo M., Hernández García D., Han T., Cangelosi A., Jokinen K.","57204472530;56520258500;57217385990;6701387796;23397498500;","Predicting apparent personality from body language: benchmarking deep learning architectures for adaptive social human–robot interaction",2021,"Advanced Robotics","35","19",,"1167","1179",,,"10.1080/01691864.2021.1974941","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118275214&doi=10.1080%2f01691864.2021.1974941&partnerID=40&md5=afb2069bea278c3d855c78891c52b086","Department of Computer Science, The University of Manchester, Manchester, United Kingdom; Artificial Intelligence Research Centre (AIRC), AIST Tokyo, Tokyo, Japan; School of Mathematical and Computer Sciences, Herriot-Watt University, Edinburgh, United Kingdom","Romeo, M., Department of Computer Science, The University of Manchester, Manchester, United Kingdom, Artificial Intelligence Research Centre (AIRC), AIST Tokyo, Tokyo, Japan; Hernández García, D., School of Mathematical and Computer Sciences, Herriot-Watt University, Edinburgh, United Kingdom; Han, T., Artificial Intelligence Research Centre (AIRC), AIST Tokyo, Tokyo, Japan; Cangelosi, A., Department of Computer Science, The University of Manchester, Manchester, United Kingdom, Artificial Intelligence Research Centre (AIRC), AIST Tokyo, Tokyo, Japan; Jokinen, K., Artificial Intelligence Research Centre (AIRC), AIST Tokyo, Tokyo, Japan","First impressions of personality traits can be inferred by non-verbal behaviours such as head pose, body postures, and hand gestures. Enabling social robots to infer the apparent personalities of their users based on such non-verbal cues will allow robots to gain the ability of adapting to their users, constituting a further step towards the personalisation of human–robot interactions. Deep learning architectures such as residual networks, 3D convolutional networks, and long-short time memory networks have been applied to classify human activities and actions in computer vision tasks. These same architectures are beginning to be applied to study human emotions and personality by focusing mainly on facial features in video recordings. In this work, we exploit body language cues to predict apparent personality traits for human–robot interactions. We customised four state-of-the-art neural network architectures to the task, and benchmarked them on a dataset of short side-view videos of dyadic interactions. Our results show the potential for deep learning architectures to predict apparent personality traits from body language cues. While the performance varied between models and personality traits, our results show that these models could still be able to predict sole personality traits, as exemplified by the results on the conscientiousness trait. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","adaptive robotics; deep learning; Personality computing; video classification","Deep learning; Human robot interaction; Network architecture; Robotics; Video recording; Adaptive robotic; Body language; Deep learning; First impressions; Humans-robot interactions; Learning architectures; Personality computing; Personality traits; Social human-robot interactions; Video classification; Forecasting",,,,,"Horizon 2020 Framework Programme, H2020; University of Manchester; European Research Council, ERC; New Energy and Industrial Technology Development Organization, NEDO","This work was partially supported by a grant of AIST-AIRC (Japan) for the collaboration with the University of Manchester. The study is based on the results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO). 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"Huang H., Rau P.-L.P., Ma L.","57189994466;7003915521;55721002600;","Will you listen to a robot? Effects of robot ability, task complexity, and risk on human decision-making",2021,"Advanced Robotics","35","19",,"1156","1166",,,"10.1080/01691864.2021.1974940","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115156246&doi=10.1080%2f01691864.2021.1974940&partnerID=40&md5=73da2a5bacbfc227f025fda7f7cef0d7","School of Economics and Management, Fuzhou University, Fuzhou, China; Department of Industrial Engineering, Tsinghua University, Beijing, China","Huang, H., School of Economics and Management, Fuzhou University, Fuzhou, China, Department of Industrial Engineering, Tsinghua University, Beijing, China; Rau, P.-L.P., Department of Industrial Engineering, Tsinghua University, Beijing, China; Ma, L., Department of Industrial Engineering, Tsinghua University, Beijing, China","With the rapid development of new technology, robots are increasingly being introduced into decision-making such that people can receive advice from them. Decision-making involves many variables, which should be investigated together. In the current study, we explored human-robot interaction in decision-making from the perspectives of confidence, trust, decision change, and reciprocity. We also investigated the effects of robot ability, task complexity, and risk on human-robot interaction in decision-making. We conducted an experiment where participants received advice from robots in a length judgment task, for which the participants had to select the longest line among eight lines. The results showed that participants had higher trust and confidence when receiving advice from high-ability robots. However, the participants had less confidence, reduced trust in the robots, a lower tendency to change their decision, and shared more losses with robots during complex tasks. They placed less trust in the robots and shared fewer benefits with them in a high-risk condition. Additionally, the participants’ self-construal was significantly correlated with trust and gender was significantly correlated with confidence, benefits shared with the robot, and decision change. These findings can aid researchers and designers in the development of service robots. © 2021 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","Human-robot interaction; risk; robot ability; task complexity; trust","Agricultural robots; Behavioral research; Decision making; Man machine systems; Complex task; Human decision making; Service robots; Task complexity; Social robots",,,,,"National Natural Science Foundation of China, NSFC: 71942005","This work was supported by the National Natural Science Foundation of China under [Grant number 71942005].",,,,,,,,,,"Wang, B., Rau, P.-L.P., Influence of embodiment and substrate of social robots on users’ decision-making and attitude (2019) Int J Soc Robot, 11 (3), pp. 411-421; Nomura, T., Sasa, M., Investigation of differences on impressions of and behaviors toward real and virtual robots between elder people and university students (2009) 2009 IEEE International Conference on Rehabilitation Robotics, p. 939. , Kyoto, Japan; Fasola, J., Mataric, M., A socially assistive robot exercise coach for the elderly (2013) J Hum-Robot Interact, 2; Komatsu, T., Yamada, S., Kobayashi, K., Effects of different types of artifacts on interpretations of artificial subtle expressions (ASEs) (2011) CHI ‘11 extended abstracts on Human Factors in Computing systems, pp. 1249-1254. , Association for Computing Machinery, Vancouver, BC, Canada; Shinozawa, K., Naya, F., Yamato, J., Differences in effect of robot and screen agent recommendations on human decision-making (2005) Int J Hum-Comput Stud, 62 (2), pp. 267-279; Ju, W., Sirkin, D., Animate objects: How physical motion encourages public interaction (2010) Persuasive technology, pp. 40-51. , Berlin, Heidelberg; Turchenko, A., https://payspacemagazine.com/banks/top-5-robots-working-in-banks/, Best employees: Top 5 robots that work banks., PaySpace Magazine, May 20, 2020. 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"Roda-Sanchez L., Olivares T., Garrido-Hidalgo C., De La Vara J.L., Fernandez-Caballero A.","57202112308;6602909884;57193328014;24400984100;6602230534;","Human-robot interaction in Industry 4.0 based on an Internet of Things real-time gesture control system",2021,"Integrated Computer-Aided Engineering","28","2",,"159","175",,,"10.3233/ICA-200637","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102170520&doi=10.3233%2fICA-200637&partnerID=40&md5=8d6200fa1484006a1b9cda830646c3fb","Instituto de Investigacion en Informatica de Albacete (I3A), Albacete, 02071, Spain; Departamento de Sistemas Informaticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain","Roda-Sanchez, L., Instituto de Investigacion en Informatica de Albacete (I3A), Albacete, 02071, Spain; Olivares, T., Instituto de Investigacion en Informatica de Albacete (I3A), Albacete, 02071, Spain, Departamento de Sistemas Informaticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Garrido-Hidalgo, C., Instituto de Investigacion en Informatica de Albacete (I3A), Albacete, 02071, Spain; De La Vara, J.L., Instituto de Investigacion en Informatica de Albacete (I3A), Albacete, 02071, Spain, Departamento de Sistemas Informaticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Fernandez-Caballero, A., Instituto de Investigacion en Informatica de Albacete (I3A), Albacete, 02071, Spain, Departamento de Sistemas Informaticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain","In the context of fast-growing digitization of industrial environments, Industry 4.0 aims to improve key elements to achieve more efficient processes, flexibility in customizing products and reduction in energy consumption, among other objectives. This paper presents a system that exploits the Internet of Things (IoT), massive data computation, and human-robot collaboration to reach these goals. The described system combines technological and human-centered aspects to enhance human-robot interaction. In fact, the human factor cannot be left aside when technological advances affecting society are foreseen. The proposal has been tested on a gesture control system that enables a natural interaction with a robotic arm through the use of IoT-oriented inertial measurement unit devices. These devices capture the movements of both human's arms. Experiments of a technical nature have been run to measure accuracy and latency. In addition, human-centered tests have been conducted with real users to determine the level of intuitiveness and acceptance of the proposed gesture control. The results obtained demonstrate that the proposal meets the demands in terms of real-time, success rate, flexibility and scalability, which are fundamental requirements in Industry 4.0. The usability results have enabled drawing useful conclusions on the use of such human-robot interaction systems. © 2021 - IOS Press. All rights reserved.","gesture control; human-robot interaction; Industry 4.0; Internet of Things; wearables; wireless communications","Acceptance tests; Control systems; Energy efficiency; Energy utilization; Industrial robots; Industry 4.0; Internet of things; Man machine systems; Efficient process; Human-robot collaboration; Industrial environments; Inertial measurement unit; Internet of thing (IOT); Natural interactions; Reduction in energy consumption; Technological advances; Social robots",,,,,"RYC-2017-1053 22836; Universidad de Castilla-La Mancha, UCLM; Horizon 2020: DIN2018-010177, PCI2020-112001; European Regional Development Fund, FEDER; Agencia Estatal de Investigación, AEI; Electronic Components and Systems for European Leadership, ECSEL: 876659, 876852; University of the East, UE: PID2020-115220RBC21, RTI2018-098156-B-C52","This work was partially supported by Spanish Ministerio de Ciencia e Innovacion, Agencia Estatal 1045 de Investigacion (AEI)/European Regional Development Fund (FEDER, UE) under PID2020-115220RBC21 and RTI2018-098156-B-C52 projects. The work leading to this paper has also received funding from iRel40 and VALU3S projects. iRel40 and VALU3S are European co-funded innovation projects that have been granted by the ECSEL Joint Undertaking (JU) under grant agreements No 876659 and 876852, respectively. The funding of the projects comes from the Horizon 2020 research programme and participating countries. National funding is provided by Germany, including the Free States of Saxony and Thuringia, Austria, Belgium, Finland, France, Italy, the Netherlands, Slovakia, Spain, Sweden, and Turkey. Therefore, this work has received Spanish co-funding under PCI2020-112240 and PCI2020-112001 grants, respectively. Luis Roda-Sanchez is pursuing his Industrial PhD under DIN2018-010177 fellowship from Ministerio de Ciencia e Innovacion. Jose Luis de la Vara holds a ?Ramon y Cajal? fellowship (RYC-2017-1053 22836) from Ministerio de Ciencia e Innovacion. 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"Hinz N.-A., Ciardo F., Wykowska A.","57205877645;57164931800;15924284200;","ERP markers of action planning and outcome monitoring in human – robot interaction",2021,"Acta Psychologica","212",,"103216","","",,4,"10.1016/j.actpsy.2020.103216","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097430846&doi=10.1016%2fj.actpsy.2020.103216&partnerID=40&md5=a7a51f21e20d82a2368f377e610fe928","Istituto Italiano di Tecnologia, Via Enrico Melen 83, Genova, 16152, Italy; Department of Psychology and Educational Sciences, Ludwig-Maximilians-University, Geschwister-Scholl-Platz 1, München, 80539, Germany","Hinz, N.-A., Istituto Italiano di Tecnologia, Via Enrico Melen 83, Genova, 16152, Italy, Department of Psychology and Educational Sciences, Ludwig-Maximilians-University, Geschwister-Scholl-Platz 1, München, 80539, Germany; Ciardo, F., Istituto Italiano di Tecnologia, Via Enrico Melen 83, Genova, 16152, Italy; Wykowska, A., Istituto Italiano di Tecnologia, Via Enrico Melen 83, Genova, 16152, Italy","The present study aimed to examine event-related potentials (ERPs) of action planning and outcome monitoring in human-robot interaction. To this end, participants were instructed to perform costly actions (i.e. losing points) to stop a balloon from inflating and to prevent its explosion. They performed the task alone (individual condition) or with a robot (joint condition). Similar to findings from human-human interactions, results showed that action planning was affected by the presence of another agent, robot in this case. Specifically, the early readiness potential (eRP) amplitude was larger in the joint, than in the individual, condition. The presence of the robot affected also outcome perception and monitoring. Our results showed that the P1/N1 complex was suppressed in the joint, compared to the individual condition when the worst outcome was expected, suggesting that the presence of the robot affects attention allocation to negative outcomes of one's own actions. Similarly, results also showed that larger losses elicited smaller feedback-related negativity (FRN) in the joint than in the individual condition. Taken together, our results indicate that the social presence of a robot may influence the way we plan our actions and also the way we monitor their consequences. Implications of the study for the human-robot interaction field are discussed. © 2020 The Author(s)","Feedback-related negativity (FRN); Human-robot interaction; Joint action; P1/N1 complex; Readiness potential (RP)","attention; electroencephalography; evoked response; human; robotics; Attention; Electroencephalography; Evoked Potentials; Humans; Robotics",,,,,"Horizon 2020 Framework Programme, H2020: 715058; European Commission, EC; European Research Council, ERC; Horizon 2020","This work received support by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant awarded to AW, titled ?InStance: Intentional Stance for Social Attunement.? G.A. No: ERC-2016-StG-715058). The content of this paper is the sole responsibility of the authors. 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"Qin W., Zhang X., Hu J., Lou P.","57224085962;55715410700;57194417584;36899486300;","Real-time virtual UR5 robot imitation of human motion based on 3D camera",2020,"Proceedings - 2020 5th International Conference on Mechanical, Control and Computer Engineering, ICMCCE 2020",,,"9421649","387","391",,,"10.1109/ICMCCE51767.2020.00092","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106871308&doi=10.1109%2fICMCCE51767.2020.00092&partnerID=40&md5=2f0b365287480e255f8079cb869bda73","School of Information Engineering, Wuhan University of Technology, Wuhan, China","Qin, W., School of Information Engineering, Wuhan University of Technology, Wuhan, China; Zhang, X., School of Information Engineering, Wuhan University of Technology, Wuhan, China; Hu, J., School of Information Engineering, Wuhan University of Technology, Wuhan, China; Lou, P., School of Information Engineering, Wuhan University of Technology, Wuhan, China","Human robot interaction is playing significant roles in many industries, in which humans are allowed to corporate with robots or control robots to complete some tasks. Achieving real-time control of robots by demonstrating human arm motion is one of the effective methods to improve the quality of human robot interaction. In this paper, a new method to accomplish human skeleton key points extraction is proposed and a system for controlling a virtual UR5 robot (which is modeling in Unity 3D) is developed to imitate human motions. A sequential architecture composed of convolutional networks is used to identify the 2D coordinates; the depth information from Kinect v2 sensor is used to obtain the actual 3D coordinates and the result of the proposed method is used for joint angles calculation. The appropriate mapping relationships between human arm and UR5 robots are built by transforming the arm angel to UR5's degrees of freedom. The performance of this method is measured by the degree of real-time simulation of human hand trajectory. Further, the comparison between the method in this paper and other method is implemented. The experimental results indicate that the developed system and proposed method are reliable and efficient to accomplish human motion imitation. © 2020 IEEE.","Convolutional neural network; Human robot interaction; Microsoft kinect; Real-time control; Virtual robot","3D modeling; Cameras; Convolutional neural networks; Degrees of freedom (mechanics); Industrial robots; Man machine systems; Quality control; Real time control; Virtual reality; 2D coordinates; Control robots; Convolutional networks; Depth information; Human arm motion; Mapping relationships; Real time simulations; Robot imitations; Social robots",,,,,"2020010601012176; National Natural Science Foundation of China, NSFC: 52075404","ACKNOWLEDGMENT This project is supported by National Natural Science Fou ndation of China (Grant No.52075404) and Application found ation frontier special project of Wuhan Science and Technolog y Bureau (No.2020010601012176).","This project is supported by National Natural Science Foundation of China (Grant No.52075404) and Application foundation frontier special project of Wuhan Science and Technology Bureau (No.2020010601012176).",,,,,,,,,"Toshev, A., Szegedy, C., Deeppose: Human pose estimation via deep neural networks [C] (2014) 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2, pp. 68-73. , 1653-1660. 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Elissa, Title of paper if known,"" unpublished; Oberweger, M., Wohlhart, P., Lepetit, V., (2015) Hands Deep in Deep Learning for Hand Pose Estimation[J]; Tompson, J., Goroshin, R., Jain, A., LeCun, Y., Bregler, C., Efficient object localization using convolutional networks (2015) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 648-656; Carreira, J., Agrawal, P., Fragkiadaki, K., Malik, J., Human pose estimation with iterative error feedback (2016) Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on; Hu, P., Ramanan, D., Bottom-up and top-down reasoning with hierarchical rectified gaussians (2016) Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on; Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P., Schiele, B., Deepcut: Joint subset partition and labeling for multi person pose estimation (2015) Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on; Fan, X., Zheng, K., Lin, Y., Wang, S., Combining local appearance and holistic view: Dual-source deep neural networks for human pose estimation (2015) 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1347-1355; Chen, X., Yuille, A., Articulated pose estimation by a graphical model with image dependent pairwise relations (2014) Advances in Neural Information Processing Systems (NIPS); Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y., Convolutional pose machines (2016) Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on",,,"AEIC Academic Exchange Information Center;Xijing University","Institute of Electrical and Electronics Engineers Inc.","5th International Conference on Mechanical, Control and Computer Engineering, ICMCCE 2020","25 December 2020 through 27 December 2020",,168952,,9780738105208,,,"English","Proc. - Int. Conf. Mech., Control Comput. Eng., ICMCCE",Conference Paper,"Final","",Scopus,2-s2.0-85106871308
"Chen S.-L., Huang L.-W., Huang C.-C., Lee F.-C., Huang H.-C., Chen C.-Y.","7410253186;57196687876;35107414000;55695938100;57222741236;57222746651;","Implementation of a Human-Robot Collaboration System Based On Smart Hand Gesture and Speech Recognition [基於智慧手勢和語音辨識實現之人機協作系統]",2020,"Journal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'eng Hsuebo Pao","41","6",,"755","762",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103947010&partnerID=40&md5=255dfeabc05878741b68a56d3aa9eeab","Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, 70101, Taiwan; Department of Automation and Control Engineering, Far East University, Tainan, 74448, Taiwan; Department of Mechatronics Control, Industrial Technology Research Institute, Hsinchu, 310, Taiwan; Department of Intelligent Commerce, National Kaohsiung University of Applied Sciences, Kaohsiung, 824, Taiwan; Computer Science and Information Engineering, National University of Tainan, Tainan, 70005, Taiwan","Chen, S.-L., Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, 70101, Taiwan; Huang, L.-W., Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan, 70101, Taiwan; Huang, C.-C., Department of Automation and Control Engineering, Far East University, Tainan, 74448, Taiwan; Lee, F.-C., Department of Mechatronics Control, Industrial Technology Research Institute, Hsinchu, 310, Taiwan; Huang, H.-C., Department of Intelligent Commerce, National Kaohsiung University of Applied Sciences, Kaohsiung, 824, Taiwan; Chen, C.-Y., Computer Science and Information Engineering, National University of Tainan, Tainan, 70005, Taiwan","With the development of artificial intelligence technology, the robot arm will be applied in more fields, undertake more work, and become an important assistant of humans. Especially in the manufacturing industry, if a perfect human-robot cooperation (HRC) model can be developed, in which the engineer is responsible for complex operations and monitoring the production process and the robot arm is responsible for highly repetitive and tiring work, the flexibility of the manufacturing process can be improved, the risk of personnel injury can be reduced, and the production line productivity can be improved. To realize the human-robot collaboration system that can be applied in various fields, the technologies of robot arm control, human-robot interaction (HRI), Radio Frequency Identification, machine-to-machine communication, and automatic speech recognition (ASR), etc. are combined in this research. Besides, the HRC in intelligent manufacturing is taken as the demonstration result of this research. In this application example, the human-robot interaction function is realized through smart hand gesture and speech recognition technology, which enables the user to communicate commands to the robot arm most intuitively and easily. The robot arm can execute specific actions, provide processing prompts, or read corresponding motion control command macros according to different commands, to realize various processing contents required by users. © 2020, Chinese Mechanical Engineering Society. All right reserved.","ASR; Deep learning; Hand gesture recognition; HRC; RFID","Accident prevention; Artificial intelligence; Gesture recognition; Industrial research; Industrial robots; Machine-to-machine communication; Man machine systems; Manufacture; Palmprint recognition; Radio frequency identification (RFID); Robotic arms; Speech communication; Speech recognition; Artificial intelligence technologies; Automatic speech recognition; Human robot Interaction (HRI); Human-robot collaboration; Human-robot cooperation; Intelligent Manufacturing; Manufacturing industries; Speech recognition technology; Social robots",,,,,"Industrial Technology Research Institute, ITRI: B109-F007; Ministry of Science and Technology, Taiwan, MOST: 107-2221-E-006-230-MY2","The authors would like to extend our sincerest thanks for the support by Industrial Technology Research Institute under the contract B109-F007 and Ministry of Science and Technology under the contract MOST 107-2221-E-006-230-MY2.",,,,,,,,,,"Adolph, Lars, Anlahr, T., Bedenbender, H., (2016) German standardization roadmap: Industry 4.0, , Version 2. Berlin: DIN eV; Bdiwi, Mohamad, Pfeifer, Marko, Sterzing, Andreas, A new strategy for ensuring human safety during various levels of interaction with industrial robots (2017) CIRP Annals, 66 (1), pp. 453-456; Ghazivakili, Mohammad, Demartini, Claudio, Zunino, Claudio, Industrial data-collector by enabling OPC-UA standard for Industry 4.0 (2018) 2018 14th IEEE International Workshop on Factory Communication Systems (WFCS), , IEEE; Gomez, R., Nakamura, K., Mizumoto, T., Nakadai, K., Compensating changes in speaker position for improved voice-based human-robot communication (2015) 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 977-982. , (November) IEEE; (2011) 10218-2: 2011: Robots and robotic devices-Safety requirements for industrial robots-Part 2: Robot systems and integration, , ISO, ISO. Geneva, Switzerland: International Organization for Standardization 3; Jain, M., Object Detection and Gesture Control of Four-Wheel Mobile Robot (2019) 2019 International Conference on Communication and Electronics Systems (ICCES), , IEEE; Liu, Hongyi, Wang, Lihui, Gesture recognition for human-robot collaboration: A review (2018) International Journal of Industrial Ergonomics, 68, pp. 355-367; Meng, Zhaozong, Wu, Zhipeng, Gray, John, RFID-based object-centric data management framework for smart manufacturing applications (2018) IEEE Internet of Things Journal, 6 (2), pp. 2706-2716; Liu, Peter X., Chan, A.D.C., Chen, R., Wang, K., Zhu, Y., Voice Based Robot Control (2005) Proceedings of the 2005 IEEE International Conference on Information Acquisition, , June 27-July 3; Rasal, Subhash P., Voice Controlled Robotic Vehicle (2014) International Journal of New Trends in Electronics and Communication, 2 (1), pp. 28-30. , ISSN 2347-7334","Chen, S.-L.; Institute of Manufacturing Information and Systems, Taiwan",,,"Chinese Mechanical Engineering Society",,,,,02579731,,,,"English","J Chin Soc Mech Eng Trans Chin Inst Eng Ser C",Article,"Final","",Scopus,2-s2.0-85103947010
"Zhang B., Yao J., Zhao X.-G., Tan X.-W.","57218441703;57221419488;16551339800;57201974714;","An adaptive human-robot interaction control method based on electromyography signals [一种基于肌电信号的自适应人机交互控制方法]",2020,"Kongzhi Lilun Yu Yingyong/Control Theory and Applications","37","12",,"2560","2570",,,"10.7641/CTA.2020.90316","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099087185&doi=10.7641%2fCTA.2020.90316&partnerID=40&md5=81bfdfaed9f76993afc5d77aadc0b85f","Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; University of Chinese Academy of Sciences, Beijing, 100049, China","Zhang, B., Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; Yao, J., Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; Zhao, X.-G., Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; Tan, X.-W., Shenyang Institute of Automation, Chinese Academy of Sciences, State Key Laboratory of Robotics, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China, University of Chinese Academy of Sciences, Beijing, 100049, China","Aiming at the issues in human-robot interaction of ankle rehabilitation robot's movement, this paper proposes a new surface electromyography (sEMG) signals based robust adaptive control strategy. Considering that sEMG signals of stroke patients are weak and it is difficult for them to maintain some certain actions, a new joint angle estimation method is proposed. The antagonistic relationship between the tibialis anterior muscle and gastrocnemius muscle during ankle joint movement is fully used in this method, and the motion type of the ankle joint is correlated with the contraction of a single muscle group. After the recognition of the motion intention and the estimation of the movement angle are completed by using the normalized characteristic value, the continuous and smooth angle estimation curve is obtained. To ensure human's safety when the interaction torque suddenly increases, this paper designs an adaptive control law, in which the stiffness parameter and the damping parameter are adaptively tuned. By using the interactive torque, the moving angle and speed of the robot end are adjusted in real time, so that the external flexibilities have been achieved. The experimental results demonstrate that the proposed human-robot interaction control method is effective and has potentiol for practical application. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.","Adaptive control; Ankle rehabilitation robot; EMG signals; Impedance control; Stability","Control theory; Man machine systems; Muscle; Adaptive control law; Ankle rehabilitation robots; Electromyography signals; Gastrocnemius muscle; Human-robot interaction control; Robust-adaptive control; Stiffness parameters; Surface electromyography; Human robot interaction",,,,,"20180540131; National Natural Science Foundation of China, NSFC: U1813214","本文责任编委: 詹宜巨. 国家自然科学基金深圳联合基金项目(U1813214), 辽宁省博士启动基金项目(20180540131)资助. Supported by the National Natural Science Foundation of China (U1813214) and the Liaoning Province PHD Foundation of China (20180540131).",,,,,,,,,,"ZHANG, Tong, Guidance for stroke rehabilitation in China (2012) Chinese Journal of the Frontiers of Medical Science (Electronic Version), 4 (6), pp. 55-56. , (张通. 中国脑卒中康复治疗指南(2011年完全版). 中国医学前沿杂志(电子版), 2012, 4(6): 55-56); BERNHARDT, M, FREY, M, COLOMBO, G, Hybrid forceposition control yields cooperative behaviour of the rehabilitation robot LOKOMAT (2005) Proceedings of IEEE International Conference on Rehabilitation Robotics, pp. 536-539. , Chicago: IEEE; SUN, Huabao, (2017) Research and design on training system of ankle joint rehabilitation robot, , Shenyang: Northeastern University, (孙华宝. 踝关节康复机器人训练系统的设计与研究. 沈阳: 东北大学, 2017); REN, Y, XU, T, WANG, L, Develop a wearable ankle robot for in-bed acute stroke rehabilitation (2011) Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 7483-7486. , Boston: IEEE; JAMWAL, P K, HUSSAIN, S, GHAYESHMH, Impedance control of an intrinsically compliant parallel ankle rehabilitation robot (2016) IEEE Transactions on Industrial Electronics, 63 (6), pp. 3638-3647; ZHANG, Yu, (2010) Development and research of ankle-foot rehabilitation exoskeleton orthosis, , Hangzhou: Zhejiang University, (张煜. 踝关节康复医疗外骨骼系统开发研究. 杭州: 浙江大学, 2010); ZHANG, M M, XIE, S, LI, X L, Adaptive patient-cooperative control of a compliant ankle rehabilitation robot (CARR) with enhanced training safety (2018) IEEE Transactions on Industrial Electronics, 65 (2), pp. 1398-1407; YU, H Y, HUANG, S N, CHEN, G, Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Transactions on Robotics, 31 (5), pp. 1089-1100; FARJADIAN, A B, NABIAN, M, MAVROIDIS, C, Implementation of a task-dependent anisotropic impedance controller into a 2-DOF platform-based ankle rehabilitation robot (2015) IEEE International Conference on Robotics and Automation (ICRA), pp. 5590-5595. , Seattle: IEEE; MENG, W, XIE, S Q, LIU, Q, Robust iterative feedback tuning control of a compliant rehabilitation robot for repetitive ankle training (2017) IEEE/ASME Transactions on Mechatronics, 22 (1), pp. 173-184; AYAS, M S, ALTAS, I H., Fuzzy logic based adaptive admittance control of a redundantly actuated ankle rehabilitation robot (2017) Control Engineering Practice, 59 (1), pp. 44-54; DING, Qichuan, ZHAO, Xingang, HAN, Jianda, EMG-based estimation for multi-joint continuous movement of human upper limb (2014) Robot, 36 (4), pp. 469-476. , (丁其川, 赵新刚, 韩建达. 基于肌电信号的上肢多关节连续运动估计. 机器人, 2014, 36(4): 469-476); DUAN, F, DAI, L, CHANG, W, sEMG-based identification of hand motion commands using wavelet neural network combined with discrete wavelet transform (2016) IEEE Transactions on Industrial Electronics, 63 (3), pp. 1923-1934; XING, K, YANG, P, HUANG, J, A real-time EMG pattern recognition method for virtual myoelectric hand control (2014) Neurocomputing, 136 (1), pp. 345-355; TSAI, A C, HSIEH, T H, LUH, J J, A comparison of upperlimb motion pattern recognition using EMG signals during dynamic and isometric muscle contractions (2014) Biomedical Signal Processing and Control, 11 (1), pp. 17-26; KIGUCHI, K, HAYASHI, Y., An EMG-based control for an upper-limb power-assist exoskeleton robot (2012) IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42 (4), pp. 1064-1071; YIN, Y H, FAN, Y J, XU, L D., EMG and EPP-integrated humanmachine interface between the paralyzed and rehabilitation exoskeleton (2012) IEEE Transactions on Information Technology in Biomedicine, 16 (4), pp. 542-549; AL-QURAISHI, M, ISHAK, A, AHMAD, S, Impact of feature extraction techniques on classification accuracy for EMG based ankle joint movements (2015) Proceedings of Asian Control Conference(ASCC), pp. 1-5. , Kota Kinabalu: IEEE; AO, D, SONG, R, GAO, J., Movement performance of human-robot cooperation control based on EMG-driven hill-type and proportional models for an ankle power-assist exoskeleton robot (2017) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (8), pp. 1125-1134; YAO, S W, ZHUANG, Y, LI, Z J., Adaptive admittance control for an ankle exoskeleton using an EMG-driven musculoskeletal model (2018) Frontiers in Neurorobotics, 12 (16), pp. 1-12; DU, Yihao, QIU, Shi, XIE, Ping, Adaptive interaction control for lower limb rehabilitation robots (2018) Acta Automatica Sinica, 44 (4), pp. 743-750. , (杜义浩, 邱石, 谢平, 等. 下肢康复机器人的自适应人机交互控制策略. 自动化学报, 2018, 44(4): 743-750); ZHOU, Z, ZHOU, Y, WANG, N, On the design of a robot-assisted rehabilitation system for ankle joint with contracture and/or spasticity based on proprioceptive neuromuscular facilitation (2014) IEEE International Conference on Robotics and Automation (ICRA), pp. 736-741. , Hong Kong: IEEE; HAN, J, DING, Q, XIONG, A, A state-space EMG model for the estimation of continuous joint movements (2015) IEEE Transactions on Industrial Electronics, 62 (7), pp. 4267-4275; ZHANG, B, ZHAO, X G, LI, X G, Robust indirect adaptive control for a class of nonlinear systems and its application to shape memory alloy actuators (2018) IEEE Access, 16, pp. 35809-35823; DING, Qichuan, XIONG, Anbin, ZHAO, Xingang, A review on researches and applications of sEMG-based motion intent recognition methods (2016) Acta Automatica Sinica, 42 (1), pp. 13-25. , (丁其川, 熊安斌, 赵新刚, 等. 基于表面肌电的运动意图识别方法研究及应用综述. 自动化学报, 2016, 42(1): 13-25)","Zhao, X.-G.; Shenyang Institute of Automation, China; email: zhaoxingang@sia.cn",,,"South China University of Technology",,,,,10008152,,KLYYE,,"Chinese","Kong Zhi Li Lun Yu Ying Yong",Article,"Final","",Scopus,2-s2.0-85099087185
"Kawasaki Y., Yorozu A., Takahashi M., Pagello E.","57205406502;51261534300;7406846479;7003342218;","A Multimodal Path Planning Approach to Human Robot Interaction Based on Integrating Action Modeling",2020,"Journal of Intelligent and Robotic Systems: Theory and Applications","100","3-4",,"955","972",,1,"10.1007/s10846-020-01244-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090143317&doi=10.1007%2fs10846-020-01244-7&partnerID=40&md5=9ad3ac3d592943f1cbc831b396b8b14b","The Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan; The Department of System Design Engineering, Keio University, 3-14-1, Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan; Intelligent Autonomous System Lab, Department of Information Engineering, University of Padova, Padua, Italy, and IT+Robotics Srl, Vicenza, Italy","Kawasaki, Y., The Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan; Yorozu, A., The Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan; Takahashi, M., The Department of System Design Engineering, Keio University, 3-14-1, Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan; Pagello, E., Intelligent Autonomous System Lab, Department of Information Engineering, University of Padova, Padua, Italy, and IT+Robotics Srl, Vicenza, Italy","To complete a task consisting of a series of actions that involve human-robot interaction, it is necessary to plan a motion that considers each action individually as well as in relation to the following action. We then focus on the specific action of “approaching a group of people” in order to accurately obtain human data that is used to make the performance of tasks involving interactions with multiple people more smooth. The movement depends on the characteristics of the important sensors used for the task and on the placement of people at and around the destination. Considering the multiple tasks and placement of people, the pre-calculation of the destinations and paths is difficult. This paper thus presents a system of navigation that can accurately obtain human data based on sensor characteristics, task content, and real-time sensor data for processes involving human-robot interaction (HRI); this method does not navigate specifically toward a previously determined static point. Our goal was achieved by using a multimodal path planning based on integration of action modeling by considering both voice and image sensing of interacting people as well as obstacle avoidance. We experimentally verified our method by using a robot in a coffee shop environment. © 2020, Springer Nature B.V.","Action modeling; Human-robot interaction; Multimodal path planning; Robot navigation","Agricultural robots; Man machine systems; Social robots; Action modeling; Coffee shops; Human robot Interaction (HRI); Multiple people; Multiple tasks; Real time sensors; Sensor characteristics; Voice and images; Robot programming",,,,,"Japan Science and Technology Agency, JST: JPMJCR14E3; Core Research for Evolutional Science and Technology, CREST","This study was supported by “A Framework PRINTEPS to Develop Practical Artificial Intelligence” of the Core Research for Evolutional Science and Technology (CREST) of the Japan Science and Technology Agency (JST) under Grant Number JPMJCR14E3.",,,,,,,,,,"McColl, D., Hong, A., Hatakeyama, N., Nejat, G., Benhabib, B., A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI (2016) J. Intell. Robot. Syst.; Dantam, N.T., Chaudhuri, S., Kavraki, L.E., The Task-Motion Kit: An Open Source, General-Purpose Task and Motion-Planning Framework (2018) IEEE Robot. Autom. Mag.; Park, Y.S., Cho, H.S., Task Oriented Optimum Positioning of a Mobile Manipulator Base in a Cluttered Environment (1997) J. Intell. Robot. Syst; Cognetti, M., Mohammadi, P., Oriolo, G., Vendittelli, M., (2014) Task-Oriented Whole-Body Planning for Humanoids Based on Hybrid Motion Generation, pp. 4071-4076; Lagriffoul, F., Dimitrov, D., Bidot, J., Saffiotti, A., Karlsson, L., Efficiently combining task and motion planning using geometric constraints (2014) Int. J. Robot. Res; Long, X., Wonsick, M., Dimitrov, V., Padir, T., (2017) Anytime Multi-Task Motion Planning for Humanoid Robots; Sisbot, E.A., Sisbot, L.F., Alami, T., A Human Aware Mobile Robot Motion Planner (2007) IEEE T. Robot; Rios-Martinez, J., Spalanzani, A., Laugier, C., Understanding human interaction for probabilistic autonomous navigation using risk-RRT approach (2011) IEEE Int. C. Int. Robot; Batista, M.R., Socially Acceptable Navigation of People with Multi-robot Teams (2019) J. Intell. Robot. Syst; Kruse, T., Pandey, A.K., Alami, R., Kirsch, A., Human-aware robot navigation: A survey (2013) Robot. Auton. Syst.; Lu, D.V., Hershberger, D., Smart, W.D., Layered costmaps for context-sensitive navigation (2014) IEEE Int. C. Int. Robot; Kollmitz, M., Hsiao, K., Gaa, J., Burgard, W., Time dependent planning on a layered social cost map for human-aware robot navigation (2015) European Conference on Mobile Robots; Charalampous, K., Kostavelis, I., Gasteratos, A., Recent trends in social aware robot navigation: A survey (2017) Robot. Auton. Syst; Rios-Martinez, J., Escobedo, A., Intention Driven Human Aware Navigation for Assisted Mobility (2012) Workshop on Assistance and Service Robotics in a Human Environment at International Conference on Intelligent Robots and Systems; Hirakawa, T., Yamashita, T., Fujiyoshi, H., Scene Context-aware Rapidly-exploring Random Trees for Global Path Planning (2019) IEEE International Conference on Pervasive Computing and Communications Workshops; Dautenhahn, K., Walters, M., Woods, S., Koay, K.L., Nehaniv, C.L., Sisbot, E.A., Alami, R., Siméon, T., How may I serve you? A robot companion approaching a seated person in a helping context (2006) ACMIEEE Int. Conf. Hum, pp. 172-179; Koay, K.L., Sisbot, E.A., Syrdal, D.S., Walters, M.L., Dautenhahn, K., Alami, R., Exploratory study of a robot approaching a person in the context of handing over an object (2007) AAAI Spring Symposium: Multidisciplinary Collaboration for Socially Assistive Robotics, pp. 18-24; Satake, S., Kanda, T., Glas, D.F., Imai, M., Ishiguro, H., Hagita, N., How to approach humans?-Strategies for social robots to initiate interaction (2008) ACMIEEE Int. Conf. Hum.; Kato, Y., Kanda, T., Ishiguro, H., May i help you?: Design of Human-like Polite Approaching Behavior (2015) ACMIEEE Int. Conf. Hum; Mizumaru, K., Satake, S., Kanda, T., Ono, T., Stop Doing it! Approaching Strategy for a Robot to Admonish Pedestrians (2019) ACMIEEE Int. Conf. Hum.; Mead, R., Matarić, M.J., Autonomous human robot proxemics: Socially aware navigation based on interaction potential (2017) Auton. Robot; Cao, Z., Simon, T., Wei, S., Sheikh, Y., (2016) Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields, pp. 7291-7299; (2017) Cmu-Perceptual-Computing-Lab: Openpose, , https://github.com/CMU-Perceptual-Computing-Lab/openpose, Accessed 11 December 2019; Khoshelham, K., Elberink, S.O., Accuracy and resolution of kinect depth data for indoor mapping applications (2012) SENSORS, 12 (2), pp. 1437-1454; Ishiguro, H., Miyashita, T., Kanda, T., (2005) Science of knowledge Communication robot, , Ohmsha; Lewis, J., (2011) Microphone specifications explained, , Application Note 1112, Analog Devices; Tsuzaki, R., Yoshida, K., Motion Control Based on Fuzzy Potential Method for Autonomous Mobile Robot with Omnidirectional Vision (2003) Journal of the Robotics Society of Japan, 6, pp. 676-682; Suzuki, T., Takahashi, M., Obstacle Avoidance for Autonomous Mobile Robots based on Position Prediction using Fuzzy Inference (2009) ICINCO-RA, pp. 299-304; Takahashi, M., Suzuki, T., Cinquegrani, F., Sorbello, R., Pagello, E., A mobile robot for transport applications in hospital domain with safe human detection algorithm (2009) IEEE Robio, pp. 1543-1548; (2019), http://wiki.ros.org/amcl, amcl, Accessed 11 December 2019; (2019), http://wiki.ros.org/gmapping, ROS.org: gmapping, Accessed 11 December 2019 (,)","Kawasaki, Y.; The Graduate School of Science and Technology, 3-14-1 Hiyoshi, Kohoku-ku, Japan; email: y-kawasaki@keio.jp",,,"Springer Science and Business Media B.V.",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","",Scopus,2-s2.0-85090143317
"Fuentetaja R., García-Olaya A., García J., González J.C., Fernández F.","15044256600;37096880000;57196717269;56727174200;7401589140;","An automated planning model for hri: Use cases on social assistive robotics",2020,"Sensors (Switzerland)","20","22","6520","1","19",,,"10.3390/s20226520","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096032575&doi=10.3390%2fs20226520&partnerID=40&md5=9ed885ea2a1cc00a6b9195dfa4c29b92","Computer Science Department, Universidad Carlos III de Madrid, Leganés, 28911, Spain","Fuentetaja, R., Computer Science Department, Universidad Carlos III de Madrid, Leganés, 28911, Spain; García-Olaya, A., Computer Science Department, Universidad Carlos III de Madrid, Leganés, 28911, Spain; García, J., Computer Science Department, Universidad Carlos III de Madrid, Leganés, 28911, Spain; González, J.C., Computer Science Department, Universidad Carlos III de Madrid, Leganés, 28911, Spain; Fernández, F., Computer Science Department, Universidad Carlos III de Madrid, Leganés, 28911, Spain","Using Automated Planning for the high level control of robotic architectures is becoming very popular thanks mainly to its capability to define the tasks to perform in a declarative way. However, classical planning tasks, even in its basic standard Planning Domain Definition Language (PDDL) format, are still very hard to formalize for non expert engineers when the use case to model is complex. Human Robot Interaction (HRI) is one of those complex environments. This manuscript describes the rationale followed to design a planning model able to control social autonomous robots interacting with humans. It is the result of the authors’ experience in modeling use cases for Social Assistive Robotics (SAR) in two areas related to healthcare: Comprehensive Geriatric Assessment (CGA) and non-contact rehabilitation therapies for patients with physical impairments. In this work a general definition of these two use cases in a unique planning domain is proposed, which favors the management and integration with the software robotic architecture, as well as the addition of new use cases. Results show that the model is able to capture all the relevant aspects of the Human-Robot interaction in those scenarios, allowing the robot to autonomously perform the tasks by using a standard planning-execution architecture. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Automated Planning; Human-Robot Interaction; Knowledge representation; Social Assistive Robotics","Machine design; Man machine systems; Patient rehabilitation; Robot programming; Robotics; Automated planning; Complex environments; Geriatric assessments; Human robot Interaction (HRI); Physical impairments; Planning domain definition language; Rehabilitation therapy; Robotic architectures; Social robots; aged; geriatric assessment; human; rehabilitation; robotics; self help device; software; Aged; Geriatric Assessment; Humans; Rehabilitation; Robotics; Self-Help Devices; Software",,,,,"National Institutes of Health, NIH: E07DA001712; Comunidad de Madrid: 2016-T2/TIC-1712; Fondo Nacional de Ciencia Tecnología e Innovación, FONACIT; Agencia Estatal de Investigación, AEI","This work has been partially funded by the European Union ECHORD++ project (FP7-ICT-601116), and grants TIN2017-88476-C2-2-R and RTI2018-099522-B-C43 of FEDER/Ministerio de Ciencia e Innovaci?n? Ministerio de Universidades?Agencia Estatal de Investigaci?n. Javier Garc?a is partially supported by the Comunidad de Madrid funds under the project 2016-T2/TIC-1712.","Funding: This work has been partially funded by the European Union ECHORD++ project (FP7-ICT-601116), and grants TIN2017-88476-C2-2-R and RTI2018-099522-B-C43 of FEDER/Ministerio de Ciencia e Innovación— Ministerio de Universidades—Agencia Estatal de Investigación. Javier García is partially supported by the Comunidad de Madrid funds under the project 2016-T2/TIC-1712.",,,,,,,,,"Leite, I., Martinho, C., Paiva, A., Social Robots for Long-Term Interaction: A Survey (2013) Int. J. Soc. Robot, 5, pp. 291-308; Fasola, J., Mataric, M., A Socially Assistive Robot Exercise Coach for the Elderly (2013) J. Hum. Robot. Interact, 2, pp. 3-32; Rodríguez-Lera, F.J., Matellán-Olivera, V., Conde-González, M.Á., Martín-Rico, F., HiMoP: A three-component architecture to create more human-acceptable social-assistive robots (2018) Cogn. Process, pp. 1-12; Hong, A., Lunscher, N., Hu, T., Tsuboi, Y., Zhang, X., Alves, d. R., Nejat, G., Benhabib, B., A Multimodal Emotional Human-Robot Interaction Architecture for Social Robots Engaged in Bidirectional Communication (2020) IEEE Trans. Cybern, pp. 1-15. , S.F; Zlatintsi, A., Dometios, A., Kardaris, N., Rodomagoulakis, I., Koutras, P., Papageorgiou, X., Maragos, P., Hauer, K., I-Support: A robotic platform of an assistive bathing robot for the elderly population (2020) Robot. Auton. Syst, 126, p. 103451; Varrasi, S., Di Nuovo, S., Conti, D., Di Nuovo, A., Social Robots as Psychometric Tools for Cognitive Assessment: A Pilot Test (2019) Human Friendly Robotics, pp. 99-112. , Ficuciello, F., Ruggiero, F., Finzi, A., Eds.; Springer International Publishing: Cham, Switzerland; Fikes, R., Nilsson, N.J., STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving (1971) Artif. Intell, 2, pp. 189-208; Jónsson, A.K., Morris, P.H., Muscettola, N., Rajan, K., Smith, B.D., Planning in Interplanetary Space: Theory and Practice (2000) Proceedings of the Fifth International Conference on Artificial Intelligence Planning Systems, pp. 177-186. , Breckenridge, CO, USA, 14–17 April Chien, S.A., Kambhampati, S., Knoblock, C.A., Eds.; AAAI Press: Palo Alto, CA, USA, 2000; Barreiro, J., Boyce, M., Do, M., Frank, J., Iatauro, M., Kichkaylo, T., Morris, P., Smith, D., EUROPA: A platform for AI planning, scheduling, constraint programming, and optimization. Technical report (2012) Proceedings of the 4th International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS), , Sao Paulo, Brazil, 25–29 June; McDermott, D., Ghallab, M., Howe, A., Knoblock, C., Ram, A., Veloso, M., Weld, D., Wilkins, D., (1998) PDDL—The Planning Domain Definition Language, , Technical Report CVC TR-98-003/DCS TR-1165; Yale Center for Computational Vision and Control. New Haven: CT, USA; Ai-Chang, M., Bresina, J., Charest, L., Chase, A., Hsu, J.C.-J., Jonsson, A., Kanefsky, B., Yglesias, J., MAPGEN: Mixed-initiative planning and scheduling for the Mars Exploration Rover mission (2004) IEEE Intell. Syst, 19, pp. 8-12; Maza, I., Kondak, K., Bernard, M., Ollero, A., Multi-UAV Cooperation and Control for Load Transportation and Deployment (2009) J. Intell. Robot. Syst, 57, pp. 417-449; Rajan, K., Py, F., T-REX: Partitioned inference for AUV mission control (2012) Further Advances in Unmanned Marine Vehicles, pp. 171-199. , The Institution of Engineering and Technology: London, UK; Umbrico, A., Cesta, A., Cortellessa, G., Orlandini, A., A Holistic Approach to Behavior Adaptation for Socially Assistive Robots (2020) Int. J. Soc. Robot, pp. 1-21; Bernardini, S., Fox, M., Long, D., Combining temporal planning with probabilistic reasoning for autonomous surveillance missions (2017) Auton. Robot, 41, pp. 181-203; González, J.C., Pulido, J.C., Fernández, F., A three-layer planning architecture for the autonomous control of rehabilitation therapies based on social robots (2017) Cogn. Syst. Res, 43, pp. 232-249; Quintero, E., García-Olaya, Á., Borrajo, D., Fernández, F., Control of Autonomous Mobile Robots with Automated Planning (2011) J. Phys. Agents, 5, pp. 3-13; Rocco, M.D., Sathyakeerthy, S., Grosinger, J., Pecora, F., Saffiotti, A., Bonaccorsi, M., Cavallo, F., Teti, G., A Planner for Ambient Assisted Living: From High-Level Reasoning to Low-Level Robot Execution and Back (2014) AAAI Spring Symposium on Qualitative Representations for Robots, pp. 10-17. , Palo Alto, CA, 24-26 March AAAI Press: Palo Alto, CA, USA; Tran, T.T., Vaquero, T., Nejat, G., Beck, J.C., Robots in retirement homes: Applying off-the-shelf planning and scheduling to a team of assistive robots (2017) J. Artif. Intell. Res, 58, pp. 523-590; Perrault, C.R., Allen, J.F., A plan-based analysis of indirect speech acts (1980) Comput. Linguist, 6, pp. 167-182; Brenner, M., Kruijff-Korbayová, I., A continual multiagent planning approach to situated dialogue (2008) Proceedings of the 12th Workshop on Semantics and Pragmatics of Dialogue (LONDIAL), , London, UK, 2–4 June; Steedman, M., Petrick, R.P.A., Planning Dialog Actions (2007) Proceedings of the 8th Workshop on Discourse and Dialogue (SIGDIAL), pp. 265-272. , Antwerp, Belgium, 1–2 September; Petrick, R.P.A., Foster, M.E., Planning for Social Interaction in a Robot Bartender Domain Proceedings of the Twenty-Third International Conference on Automated Planning and Scheduling, pp. 389-397. , Rome, Italy, 10–14 June 2013; Garoufi, K., Koller, A., Automated planning for situated natural language generation Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 1573-1582. , Uppsala, Sweden, 11–16 July 2010; Cashmore, M., Fox, M., Long, D., Magazzeni, D., Ridder, B., Carreraa, A., Palomeras, N., Carrerasa, M., ROSPlan: Planning in the Robot Operating System Proceedings of the Twenty-Fifth International Conference on International Conference on Automated Planning and Scheduling, pp. 333-341. , Jerusalem, Israel, 7–11 June 2015; Fox, M., Long, D., PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains (2003) J. Artif. Intell. Res, 20, pp. 61-124; García-Olaya, A., Fuentetaja, R., García-Polo, J., González, J.C., Fernández, F., Challenges on the Application of Automated Planning for Comprehensive Geriatric Assessment Using an Autonomous Social Robot (2019) Advances in Intelligent Systems and Computing, 855, pp. 179-194. , Springer International Publishing: New York, NY, USA; Bandera, A., Bandera, J.P., Bustos, P., Calderita, L.V., Duenas, A., Fernández, F., Fuentetaja, R., González, J.C., CLARC: A Robotic Architecture for Comprehensive Geriatric Assessment Proceedings of the 17th Workshop of Physical Agents (WAF), pp. 1-8. , Malaga, Spain, 16–17 June 2016; Voilmy, D., Suarez, C., Romero-Garcés, A., Reuther, C., Pulido, J., Marfil, R., Manso, L., González, J., CLARC: A Cognitive Robot for Helping Geriatric Doctors in Real Scenarios. ROBOT (1) (2017) Advances in Intelligent Systems and Computing, 693, pp. 403-414. , Springer International Publishing: New York, NY, USA; Matarić, M.J., Scassellati, B., Socially Assistive Robotics (2016) Springer Handbook of Robotics, pp. 1973-1994. , Siciliano, B., Khatib, O., Eds.; Springer International Publishing: New York, NY, USA; Ingrand, F., Ghallab, M., Deliberation for autonomous robots: A survey (2017) Artif. Intell, 247, pp. 10-44; Alcázar, V., Guzmán, C., Prior, D., Borrajo, D., Castillo, L., Onaindia, E., PELEA: Planning, Learning and Execution Architecture Proceedings of the 28th Workshop of the UK Planning and Scheduling Special Interest Group (PlanSIG), pp. 1-8. , Brescia, Italy, 1–3 December 2010; Ellis, G., Langhorne, P., Comprehensive geriatric assessment for older hospital patients (2005) Br. Med. Bull, 71, pp. 45-59; Mahoney, F., Barthel, D., Functional evaluation: The Barthel index (1965) Md. State Med. J, 14, pp. 56-61; Folstein, S.F.M., McHugh, P., Mini-mental state: A practical method for grading the cognitive state of patients for the clinician (1975) J. Psychiatr. Res, 12, pp. 189-198; Mathias, S., Nayak, U.S., Isaacs, B., Balance in elderly patients: The get-up and go (1986) Arch. Phys. Med. Rehabil, 67, pp. 387-389; Pulido, J.C., Suarez-Mejias, C., González, J.C., Dueñas-Ruiz, A., Ferri, P.F., Sahuquillo, M.E.M., Vargas, C.E.R.D., Fernández, F., A Socially Assistive Robotic Platform for Upper-Limb Rehabilitation: A Longitudinal Study With Pediatric Patients (2019) IEEE Robot. Automat. Mag, 26, pp. 24-39; Searle, J.R., (1969) Speech Acts: An Essay in the Philosophy of Language, 626. , Cambridge University Press: Cambridge, UK; Austin, J.L., (1975) How to Do Things with Words, 88. , Oxford University Press: Oxford, UK; Searle, J.R., A taxonomy of illocutionary acts (1975) Language, Mind and Knowledge, Minnesota Studies in the Philosophy of Science, 7, pp. 344-369. , University of Minnesota Press: Minneapolis, MN, USA; Hoffmann, J., The Metric-FF Planning System: Translating “Ignoring Delete Lists” to Numeric State Variables (2003) J. Artif. Intell. Res, 20, pp. 291-341; Canal, G., Cashmore, M., Krivić, S., Alenyà, G., Magazzeni, D., Torras, C., Probabilistic Planning for Robotics with ROSPlan (2019) Towards Autonomous Robotic Systems, pp. 236-250. , Althoefer, K., Konstantinova, J., Zhang, K., Eds.; Springer International Publishing: Cham, Switzerland; Gragera, A., García, A.M., Fernández, F., A Modelling and Formalisation Tool for Use Case Design in Social Autonomous Robotics (2019) Proceedings of the Fourth Iberian Robotics Conference (Robot 2019) —Advances in Robotics, 1093, pp. 656-667. , 2, Porto, Portugal, 20–22 November Silva, M.F., Lima, J.L., Reis, L.P., Sanfeliu, A., Tardioli, D., Eds.; Springer International Publishing: New York, NY, USA, 2019; Konidaris, G., Kaelbling, L.P., Lozano-Pérez, T., Constructing Symbolic Representations for High-Level Planning (2014) AAAI Conf. Artif. Intell, pp. 1932-1940","Fuentetaja, R.; Computer Science Department, Spain; email: rfuentet@inf.uc3m.es",,,"MDPI AG",,,,,14248220,,,"33202674","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85096032575
"Rajendran V., Carreno-Medrano P., Fisher W., Werner A., Kulic D.","57222562988;56336138700;57211956135;55556945800;10244652900;","A framework for human-robot interaction user studies",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341286","6215","6222",,1,"10.1109/IROS45743.2020.9341286","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102404791&doi=10.1109%2fIROS45743.2020.9341286&partnerID=40&md5=bcfafd1fe3484b224f425803f7e5caf9","University of Waterloo, Faculty of Engineering, Department of Electrical and Computer EngineeringON, Canada; Monash University, Faculty of Engineering, Melbourne, Australia","Rajendran, V., University of Waterloo, Faculty of Engineering, Department of Electrical and Computer EngineeringON, Canada; Carreno-Medrano, P., Monash University, Faculty of Engineering, Melbourne, Australia; Fisher, W., University of Waterloo, Faculty of Engineering, Department of Electrical and Computer EngineeringON, Canada; Werner, A., University of Waterloo, Faculty of Engineering, Department of Electrical and Computer EngineeringON, Canada; Kulic, D., University of Waterloo, Faculty of Engineering, Department of Electrical and Computer EngineeringON, Canada, Monash University, Faculty of Engineering, Melbourne, Australia","Human-Robot Interaction (HRI) user studies are challenging to evaluate and compare due to a lack of standardization and the infrastructure required to implement each study. The lack of experimental infrastructure also makes it difficult to systematically evaluate the impact of individual components (e.g., the quality of perception software) on overall system performance. This work proposes a framework to ease the implementation and reproducibility of human-robot interaction user studies. The framework utilizes ROS middleware and is implemented with four modules: perception, decision, action, and metrics. The perception module aggregates sensor data to be used by the decision and action modules. The decision module is the task-level executive and can be designed by the HRI researcher for their specific task. The action module takes subtask requests from the decision module and breaks them down into motion primitives for execution on the robot. The metrics module tracks and generates quantitative metrics for the study. The framework is implemented with modular interfaces to allow for alternate implementations within each module and can be generalized for a variety of tasks and human/robot roles. The framework is illustrated through an example scenario involving a human and a Franka Emika Panda arm collaboratively assembling a toolbox together. © 2020 IEEE.",,"Agricultural robots; Intelligent robots; Man machine systems; Middleware; Quality control; Decision modules; Human robot Interaction (HRI); Individual components; Motion primitives; Quality of perception; Quantitative metrics; Reproducibilities; Specific tasks; Human robot interaction",,,,,,,,,,,,,,,,"Ajoudani, A., Progress and prospects of the human.robot collaboration (2018) Autonomous Robots, 42 (5), pp. 957-975; Khan, A., Robots in healthcare: A survey (2019) Science and Information Conference. Springer, pp. 280-292; Zeylikman, S., The HRC model set for human-robot collaboration research (2017) CoRR, , abs/1710.11211; Murali, A., Pyrobot: An open-source robotics framework for research and benchmarking (2019) CoRR, , abs/1906.08236; Steinfeld, A., Common metrics for human-robot interaction (2006) Proceedings of the 1st Acm SIGCHI/SIGART Conference on Human-robot Interaction, pp. 33-40. , ser. HRI '06. New York, NY, USA: ACM; Hoffman, G., Evaluating fluency in human.robot collaboration (2019) Ieee Transactions on Human-Machine Systems, 49 (3), pp. 209-218. , June; Lindblom, J., Current challenges for ux evaluation of humanrobot interaction (2016) Advances in Ergonomics of Manufacturing: Managing the Enterprise of the Future. Springer, pp. 267-277; Belhassein, K., Towards methodological principles for user studies in human-robot interaction (2019) Test Methods and Metrics for Effective Hri in Collaborative Human-Robot Teams Workshop, , ACM/IEEE International Conference on Human-Robot Interaction; Quigley, M., Ros: An open-source robot operating system (2009) Icra Workshop on Open Source Software; Malik, A.A., (2018) Digital Twins of Human Robot Collaboration in a Production Setting, ., 17. , 06; Tsarouchi, P., Ros based coordination of human robot cooperative assembly tasks-an industrial case study (2015) Procedia Cirp, 37, pp. 254-259; Lane, I., HRItk: The human-robot interaction ToolKit rapid development of speech-centric interactive systems in ROS (2012) NAACL-HLT Workshop on Future Directions and Needs in the Spoken Dialog Community: Tools and Data (SDCTD 2012). Montréal, pp. 41-44. , Canada: Association for Computational Linguistics, June; Clodic, A., Shary: A supervision system adapted to human-robot interaction (2009) Experimental Robotics. Springer, pp. 229-238; Paxton, C., CoSTAR: Instructing collaborative robots with behavior trees and vision (2017) Robotics and Automation (ICRA), 2017 Ieee International Conference on; Fischer, T., Icub-hri: A software framework for complex human-robot interaction scenarios on the icub humanoid robot (2018) Frontiers in Robotics and Ai, 5 (22), pp. 1-9; Fong, T., The human-robot interaction operating system (2006) Proceedings of the 1st Acm SIGCHI/SIGART Conference on Human-robot Interaction, pp. 41-48. , ser. HRI '06. New York, NY, USA: ACM; Missri, S., (2019) Tealsense Gazebo Plugin, , https://github.com/SyrianSpock/realsensegazeboplugin; Sucan, I.A., MoveIt, , https://moveit.ros.org/about/; Marco, E., Easy Handeye: Tf Nvisp Hand-Eye Calibration, , https://github.com/IFL-CAMP/easyhandeye; Toussaint, M., Relational activity processes for modeling concurrent cooperation (2016) (ICRA 2016); Cao, Z., Openpose: Realtime multi-person 2d pose estimation using part affinity fields (2018) CoRR, , http://arxiv.org/abs/1812.08008, abs/1812.08008; Goebel, P., Pi Trees, , https://github.com/pirobot/pitrees,2019",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102404791
"Xu C., Yu X., Wang Z., Ou L.","57221160189;15764009800;57220876392;13404303100;","Multi-View Human Pose Estimation in Human-Robot Interaction",2020,"IECON Proceedings (Industrial Electronics Conference)","2020-October",,"9255211","4769","4775",,,"10.1109/IECON43393.2020.9255211","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097777591&doi=10.1109%2fIECON43393.2020.9255211&partnerID=40&md5=1700cbfcd2d0ed8d277054fc85d6e6d8","Zhejiang University of Technology Hang Zhou, College of Information Engineering, Hangzhou, China","Xu, C., Zhejiang University of Technology Hang Zhou, College of Information Engineering, Hangzhou, China; Yu, X., Zhejiang University of Technology Hang Zhou, College of Information Engineering, Hangzhou, China; Wang, Z., Zhejiang University of Technology Hang Zhou, College of Information Engineering, Hangzhou, China; Ou, L., Zhejiang University of Technology Hang Zhou, College of Information Engineering, Hangzhou, China","This paper contributes a real-time human-robot interaction system based on multi-human poses capture. First, we propose an iterative method for multi-human 3D poses estimation from multi-view. There are three mainly steps: (1) all independent 2D poses under each view are obtained by using the pose detector; (2) 2D poses from different perspectives are associated based on a greedy algorithm; (3) 3D skeletons are generated with the 2D poses that have been correctly associated. In the process of each iteration, the poses with the highest correspondence are always paired priority to ensure the accuracy of the results. Due to the occlusion in each view, previous methods that rely on the appearance of the human body and wearable devices are not suitable for multi-human 3D poses estimation, especially in crowded scenes. The proposed method does not required any markers or devices, which has good robustness even under occlusion or crowded. In addition, we also designed three human-robot interaction modes, including action following, target specification and dynamic obstacle avoidance. For each mode, target generation and correction methods are present. Combining human body pose estimation and robot motion, design experiments to verify the smoothness of the robot control method for dynamic target tracking and the effectiveness in different interaction modes. © 2020 IEEE.","3D skeletons; human-robot interaction; iteration; multi-human; target","Industrial electronics; Iterative methods; Machine design; Man machine systems; Motion control; Motion tracking; Target tracking; Correction method; Design experiments; Dynamic obstacle avoidance; Greedy algorithms; Human pose estimations; Interaction modes; Target specifications; Wearable devices; Social robots",,,,,"2018YF-B1308400","VII. ACKNOWLEDGEMENTS This paper was supported by National Key Research and Development Plan Intelligent Robot Key Project (2018YF-B1308400).",,,,,,,,,,"Mehta, D., Sridhar, S., Sotnychenko, O., Rhodin, H., Shafiei, M., Seidel, H.-P., Xu, W., Theobalt, C., Vnect: Real-time 3d human pose estimation with a single rgb camera (2017) ACM Transactions on Graphics (TOG), pp. 1-14; Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kipman, A., Efficient human pose estimation from single depth images (2012) IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 2821-2840; Dong, J., Jiang, W., Huang, Q., Bao, H., Zhou, X., Fast and robust multi-person 3d pose estimation from multiple views (2019) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7792-7801; Bridgeman, L., Volino, M., Guillemaut, J.-Y., Hilton, A., Multiperson 3d pose estimation and tracking in sports (2019) 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); Zhang, Y., An, L., Yu, T., Li, X., Li, K., Liu, Y., (2020) 4d Association Graph for Realtime Multi-person Motion Capture Using Multiple Video Cameras; Ershadi-Nasab, S., Noury, E., Kasaei, S., Sanaei, E., Multiple human 3d pose estimation from multiview images (2018) Multimedia Tools and Applications, pp. 15573-15601; Joo, H., Simon, T., Li, X., Liu, H., Tan, L., Gui, L., Banerjee, S., Matthews, I., Panoptic studio: A massively multiview system for social interaction capture (2017) IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 190-204; He, K., Gkioxari, G., Dollár, P., Girshick, R., Mask r-cnn (2017) Proceedings of the IEEE International Conference on Computer Vision, pp. 2961-2969; Fang, H.-S., Xie, S., Tai, Y.-W., Lu, C., Rmpe: Regional multi-person pose estimation (2017) ICCV; Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P.V., Schiele, B., Deepcut: Joint subset partition and labeling for multi person pose estimation (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4929-4937; Newell, A., Huang, Z., Deng, J., Associative embedding: End-toend learning for joint detection and grouping (2017) Advances in Neural Information Processing Systems, pp. 2277-2287; Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y., Realtime multi-person 2d pose estimation using part affinity fields (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7291-7299; Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., Sheikh, Y., (2018) Openpose: Realtime Multi-person 2d Pose Estimation Using Part Affinity Fields; Rakprayoon, P., Ruchanurucks, M., Coundoul, A., Kinect-based obstacle detection for manipulator (2011) IEEE/SICE International Symposium on System Integration",,,"IEEE Industrial Electronics Society (IES);SPECS - Smart Grid + Power Electronics Consortium Singapore;The Institute of Electrical and Electronics Engineers (IEEE)","IEEE Computer Society","46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020","19 October 2020 through 21 October 2020",,165032,,9781728154145,IEPRE,,"English","IECON Proc",Conference Paper,"Final","",Scopus,2-s2.0-85097777591
"Han J., Ajaykumar G., Li Z., Huang C.-M.","57219858579;57215834400;57219859443;55193383300;","Structuring Human-Robot Interactions via Interaction Conventions",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223468","341","348",,1,"10.1109/RO-MAN47096.2020.9223468","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095787678&doi=10.1109%2fRO-MAN47096.2020.9223468&partnerID=40&md5=cc2a2861fb08b788982378ae282154fb","Johns Hopkins University, Baltimore, MD  21218, United States; Tsinghua University, Beijing, 100084, China","Han, J., Johns Hopkins University, Baltimore, MD  21218, United States; Ajaykumar, G., Johns Hopkins University, Baltimore, MD  21218, United States; Li, Z., Tsinghua University, Beijing, 100084, China; Huang, C.-M., Johns Hopkins University, Baltimore, MD  21218, United States","Interaction conventions (e.g., using pinch gestures to zoom in and out) are designed to structure how users effectively work with an interactive technology. We contend in this paper that successful human-robot interactions may be achieved through an appropriate use of interaction conventions. We present a simple, natural interaction convention - Put That Here - for instructing a robot partner to perform pick-and-place tasks. This convention allows people to use common gestures and verbal commands to select objects of interest and to specify their intended location of placement. We implement an autonomous robot system capable of parsing and operating through this convention. Through a user study, we show that participants were easily able to adopt and use the convention to provide task specifications. Our results show that participants using this convention were able to complete tasks faster and experienced significantly lower cognitive load than when using only verbal commands to give instructions. Furthermore, when asked to give natural pick-and-place instructions to a human collaborator, the participants intuitively used task specification methods that paralleled our convention, incorporating both gestures and verbal commands to provide precise task-relevant information. We discuss the potential of interaction conventions in enabling productive human-robot interactions. © 2020 IEEE.",,"Agricultural robots; Man machine systems; Specifications; Cognitive loads; Interactive technology; Natural interactions; Pick and place; Robot Partners; Task relevant; Task specifications; User study; Social robots",,,,,"National Science Foundation, NSF: DGE-1746891; Johns Hopkins University, JHU: 1840088","We thank Yuxiang Gao, Jaimie Patterson, Eden Metzger, Anthonia Duru, and Erica Hwang for their help with this work. This work was partially supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1746891, the Nursing/Engineering joint fellowship from the Johns Hopkins University, and the National Science Foundation award #1840088.",,,,,,,,,,"Krämer, N.C., Von Der Pütten, A., Eimler, S., Humanagent and human-robot interaction theory: Similarities to and differences from human-human interaction (2012) Human-computer Interaction: The Agency Perspective, pp. 215-240. , Springer; Aylett, R.S., Castellano, G., Raducanu, B., Paiva, A., Hanheide, M., Long-term socially perceptive and interactive robot companions: Challenges and future perspectives (2011) Proceedings of the 13th International Conference on Multimodal Interfaces, pp. 323-326; Norman, D.A., Affordance, conventions, and design (1999) Interactions, 6 (3), pp. 38-43; Raskin, J., Intuitive equals familiar (1994) Communications of the ACM, 37 (9), pp. 17-19; Lozano-Pérez, T., Jones, J.L., Mazer, E., O'Donnell, P.A., Task-level planning of pick-and-place robot motions (1989) Computer, 22 (3), pp. 21-29; Bolt, R.A., (1980) Put-that-there: Voice and Gesture at the Graphics Interface, 14. , ACM; Wiedenbeck, S., Davis, S., The influence of interaction style and experience on user perceptions of software packages (1997) International Journal of Human-Computer Studies, 46 (5), pp. 563-588; Shyam Sundar, S., Bellur, S., Oh, J., Xu, Q., Jia, H., User experience of on-screen interaction techniques: An experimental investigation of clicking, sliding, zooming, hovering, dragging, and flipping (2014) Human-Computer Interaction, 29 (2), pp. 109-152; Rauterberg, M., Fjeld, M., Krueger, H., Bichsel, M., Leonhardt, U., Meier, M., Build-it: A computer vision-based interaction technique for a planning tool (1997) People and Computers XII, pp. 303-314. , Springer; Schmidt, A., Implicit human computer interaction through context (2000) Personal Technologies, 4 (2-3), pp. 191-199; Oviatt, S., DeAngeli, A., Kuhn, K., Integration and synchronization of input modes during multimodal human-computer interaction (1997) Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems, pp. 415-422; Surakka, V., Illi, M., Isokoski, P., Gazing and frowning as a new human-computer interaction technique (2004) ACM Transactions on Applied Perception (TAP), 1 (1), pp. 40-56; Jacob, R.J.K., The use of eye movements in human-computer interaction techniques: What you look at is what you get (1991) ACM Transactions on Information Systems (TOIS), 9 (2), pp. 152-169; Jacob, R.J.K., Eye movement-based human-computer interaction techniques: Toward non-command interfaces (1993) Advances in Humancomputer Interaction, 4, pp. 151-190; Rempel, D., Camilleri, M.J., Lee, D.L., The design of hand gestures for human-computer interaction: Lessons from sign language interpreters (2014) International Journal of Human-computer Studies, 72 (10-11), pp. 728-735; Kendon, A., Do gestures communicate? 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Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095787678
"Ye L., Xiong G., Zeng C., Zhang H.","57201975390;34769433000;57218991943;57192485618;","Trajectory tracking control of 7-DOF redundant robot based on estimation of intention in physical human-robot interaction",2020,"Science Progress","103","3",,"","",,2,"10.1177/0036850420953642","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090986206&doi=10.1177%2f0036850420953642&partnerID=40&md5=4d06933f081a9a132e2627960dac489c","School of Mechanical Engineering, Nanchang University, Nanchang, China; School of Mechatronic Engineering, Shanghai University of Engineering Science, Shanghai, China; School of mechanical and vehicle engineering, Nanchang Institute of Science and Technology, Nanchang, China","Ye, L., School of Mechanical Engineering, Nanchang University, Nanchang, China, School of mechanical and vehicle engineering, Nanchang Institute of Science and Technology, Nanchang, China; Xiong, G., School of Mechanical Engineering, Nanchang University, Nanchang, China; Zeng, C., School of Mechanical Engineering, Nanchang University, Nanchang, China; Zhang, H., School of Mechanical Engineering, Nanchang University, Nanchang, China, School of Mechatronic Engineering, Shanghai University of Engineering Science, Shanghai, China","Collaborative robot has been widespread application prospect, such as homes, manufacturing, and health-care etc. In physical human-robot interaction, the external force appears inevitably in contact with environment or human, especially the interactive tasks such as trajectory tracking requirements and force compliance control. In this article, a method based on interaction intention estimation, which solve the problem of trajectory tracking accuracy and force compliance control in the same direction for the 7-DOF robot, is proposed. The increased virtual force depended on the manipuility performance index and inverse kinematic solution used the kinematic decoupling method based on the redundant angle avoid the singularity of redundant robot. Then, based on interactive intention estimation, a control strategy of variable impedance sliding mode theory in the presence of virtual force and contact force is proposed to achieve the trajectory tracking. We adopted hyperbolic tangent function to alleviate the chattering problem caused by switch function and validated the control system stability by Lyapunov theorem. Finally, Matlab simulations exhibit a 97.8% of high tracking accuracy amid the external force is 43% less than variable impedance parameters. It is therefore proved that the proposed method can achieve asymptotic tracking and the compliant behavior in physical human-robot interaction. © The Author(s) 2020.","7-DOF redundant robot; Physical human-robot interaction; sliding mode control; trajectory tracking control; variable impedance control","article; control strategy; control system; human; impedance; robotics; simulation; theoretical study",,,,,"National Natural Science Foundation of China, NSFC: 61763030; National Key Research and Development Program of China, NKRDPC: 2018YFB1305300","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financially supported in part by the National Key Research and Development Program of China under Grant 2018YFB1305300, and in part by the National Natural Science Foundation of China under Grant 61763030.",,,,,,,,,,"Aristidou, A., Lasenby, J., Chrysanthou, Y., Inverse kinematics techniques in computer graphics: a survey (2018) Comput Graph Forum, 37 (6), pp. 35-58; Yoon, J.G., Jung, M.G., Lee, M.C., Inverse kinematic analysis of 5 DOF manipulator using numerical method, , 2017 Changwon International Conference on Intelligent Robot and Convergence Industry (CICIRO 2017), Changwon, Korea, 30 November–1 December 2017, In; Shimizu, M., Kakuya, H., Yoon, W.K., Analytical inverse kinematic computation for 7-DOF redundant manipulators with joint limits and its application to redundancy resolution (2008) IEEE Trans Robot, 24 (5), pp. 1131-1142; Wan, Y., Kou, Y., Liang, X., (2017) Closed-loop inverse kinematic analysis of redundant manipulators with joint limits, pp. 1241-1255. , International Conference on Mechanical Design (ICMD): Advances Mechanical Design, Singapore, Springer Nature Singapore Pte Ltd, In; Zhou, D., Ji, L., Zhang, Q., Practical analytical inverse kinematic approach for 7-DOF space manipulators with joint and attitude limits (2015) Intell Serv Robot, 8 (4), pp. 215-224; Gong, M., Li, X., Zhang, L., Analytical inverse kinematics and self-motion application for 7-DOF redundant manipulator (2019) IEEE Access, 7, pp. 18662-18674; Ficuciello, F., Villani, L., Siciliano, B., Redundancy resolution in human-robot co-manipulation with cartesian impedance control (2016) Experimental robotics, volume 109 of the series springer tracts in advanced robotics, pp. 165-176. , Switzerland, Springer International Publishing, In; Ficuciello, F., Romano, A., Villani, L., Cartesian impedance control of redundant manipulators for human-robot co-manipulation, pp. 2120-2125. , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Chicago, IL, USA, 14–18 September 2014, In; Nageshrao, S., Lopes, G., Jeltsema, D., Passivity based reinforcement learning control of a 2-DOF manipulator arm (2014) Mechatronics, 24 (8), pp. 1001-1007; Tang, L., Liu, Y., Tong, S., Adaptive neural control using reinforcement learning for a class of robot manipulator (2014) Neural Comput Appl, 25 (1), pp. 135-141; Zhu, Y., Barth, E.J., Impedance control of a pneumatic actuator for contact tasks, pp. 987-992. , Proceedings of the 2005 IEEE international conference on robotics and automation, Barcelona, Spain, April 2005, In; Saleh, M., Fairouz, T., A novel robust adaptive second-order sliding mode tracking control technique for uncertain dynamical systems with matched and unmatched disturbances (2017) Int J Control Automat Syst, 15 (3), pp. 1097-1106; Yu, F., Chen, Y., Trajectory tracking control of an amphibian robot with operational capability (2019) Int J Adv Robot Syst, 16 (4). , 172988141986542; Dong, B., An, T., Zhou, F., Model-free optimal decentralized sliding mode control for modular and reconfigurable robots based on adaptive dynamic programming (2019) Adv Mech Eng, 11 (12). , 168781401989692; Liu, X., Jiang, W., Dong, X.-C., Nonlinear adaptive control for dynamic and dead-zone uncertainties in robotic systems (2017) Int J Control Autom Syst, 15 (2), pp. 875-882; Wang, F., Chao, Z., Huang, L., Trajectory tracking control of robot manipulator based on RBF neural network and fuzzy sliding mode (2019) Cluster Comput, 22, pp. 5799-5809. , (S3; Nicolis, D., Allevi, F., Rocco, P., Robust impedance shaping of redundant teleoperators with time-delay via sliding mode control, pp. 2740-2747. , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, November 2019, In; Jung, S., Improvement of tracking control of a sliding mode controller for robot manipulators by a neural network (2018) Int J Control Autom Syst, 16 (2), pp. 937-943; Hogan, N., Impedance control: an approach to manipulation (1985) J Dyn Syst Meas Control, 107 (1), pp. 8-16; Kazerooni, H., Sheridan, T.B., Houpt, P.K., Robust compliant motion for manipulators, part I: the fundamental concepts of compliant motion (1986) IEEE J Rob Autom, 2 (2), pp. 83-92; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) IEEE Trans Robot, 31 (4), pp. 850-863; Sharifi, M., Behzadipour, S., Vossoughi, G.R., Model reference adaptive impedance control in Cartesian coordinates for physical human-robot interaction (2014) Adv Robot, 28 (19), pp. 1277-1290; Xiong, G.L., Chen, H.C., Xiong, P.W., Cartesian impedance control for physical human-robot interaction using virtual decomposition control approach (2019) Iran J Sci Technol Trans Mech Eng, 43 (S1), pp. 983-994; Li, Y., Ganesh, G., Jarrasse, N., Force, impedance, and trajectory learning for contact tooling and haptic identification (2018) IEEE Trans Robot, 34 (5), pp. 1170-1182; Losey, D.P., O’Malley, M.K., Trajectory deformations from physical human-robot interaction (2018) IEEE Trans Robot, 34 (1), pp. 126-138; Craig, J.J., (2018) Introduction to robotics, mechanics and control, pp. 132-137. , 4th ed., Beijing, China, China machine Press; Mao, D., Yang, W., Du, Z., Fuzzy variable impedance control based on stiffness identification for human-robot cooperation (2017) IOP Conf Ser Earth Environ Sci, 69, p. 012090; Liu, J.K., (2008) Design of robot control system and MATLAB simulation, pp. 438-447. , Beijing, China, Tsinghua University Press","Xiong, G.; School of Mechanical Engineering, China; email: xionggenliang@ncu.edu.cn",,,"SAGE Publications Ltd",,,,,00368504,,,"32924809","English","Sci. Prog.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85090986206
"Breux Y., Druon S.","57204948177;24447666000;","Multimodal Object-Based Environment Representation for Assistive Robotics",2020,"International Journal of Social Robotics","12","3",,"807","826",,,"10.1007/s12369-019-00600-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076200921&doi=10.1007%2fs12369-019-00600-4&partnerID=40&md5=1696f0a014c50c372aea542d21f48a59","Laboratory of Informatics, Robotics and MicroElectronics, University of Montpellier, 161 rue Ada, Montpellier, 34095, France","Breux, Y., Laboratory of Informatics, Robotics and MicroElectronics, University of Montpellier, 161 rue Ada, Montpellier, 34095, France; Druon, S., Laboratory of Informatics, Robotics and MicroElectronics, University of Montpellier, 161 rue Ada, Montpellier, 34095, France","Autonomous robots are nowadays successfully used in industrial environments, where tasks follow predetermined plans and the world is a known (and closed) set of objects. The context of social robotics brings new challenges to the robot. First of all, the world is no longer closed. New objects can be introduced at any time, and it is now impossible to build an exaustive list of them nor having a precomputed set of descriptors. Moreover, natural interactions with a human being don’t follow any precomputed graph of sequences or grammar. To deal with the complexity of such an open world, a robot can no longer solely rely on its sensors data: a compact representation to comprehend its surrounding is needed. Our approach focuses on task independent environment representation where human-robot interactions are involved. We propose a global architecture bridging the gap between perception and semantic modalities through instances (physical realizations of semantic concepts). In this article, we describe a method for automatic generation of object-related ontology. Based on it, a practical formalization of the ill-defined notion of “context” is discussed. We then tackle human-robot interactions in our system through the description of user request processing. Finally, we illustrate the flow of our model on two showcases which demonstrate the validity of the approach. © 2019, Springer Nature B.V.","Assistive robotics; Human–robot interaction; Knowledge representation; Natural language processing","Agricultural robots; Man machine systems; Robotics; Semantics; Assistive robotics; Automatic Generation; Compact representation; Environment representations; Industrial environments; Natural interactions; Physical realization; Semantic concept; Social robots",,,,,,,,,,,,,,,,"Andor, D., Alberti, C., Weiss, D., Severyn, A., Presta, A., Ganchev, K., Petrov, S., Collins, M., (2016) Globally normalized transition-based neural networks; Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z., Dbpedia: A nucleus for a web of open data (2007) The Semantic Web, pp. 722-735. , Springer; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: a review (2018) Sci Robot, 3 (21), p. eaat5954; Ben Amor, H., Neumann, G., Kamthe, S., Kroemer, O., Peters, J., Interaction primitives for human–robot cooperation tasks (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 2831-2837. , https://doi.org/10.1109/ICRA.2014.6907265; Bracewell, D.B., Ren, F., Kuroiwa, S., Towards knowledge about causal agents in wordnet (2006) Proceedings of the 10Th WSEAS International Conference on Computers, World Scientific and Engineering Academy and Society (WSEAS), pp. 564-568; Brandes, U., A faster algorithm for betweenness centrality (2001) J Math Sociol, 25 (2), pp. 163-177; Brandes, U., Fleischer, D., Centrality measures based on current flow (2005) Annual Symposium on Theoretical Aspects of Computer Science, pp. 533-544. , Springer; Breux, Y., Druon, S., Zapata, R., From perception to semantics: An environment representation model based on human–robot interactions (2018) 2018 27Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 672-677. , IEEE; Buchholz, S., Marsi, E., Conll-x shared task on multilingual dependency parsing (2006) Proceedings of the Tenth Conference on Computational Natural Language Learning, pp. 149-164; Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Jr, E.R.H., Mitchell, T.M., Toward an architecture for never-ending language learning (2010) Proceedings of the Twenty-Fourth Conference on Artificial Intelligence (AAAI); Darwiche, A., Human-level intelligence or animal-like abilities ? 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"Siebert F.W., Klein J., Rötting M., Roesler E.","42962296200;57217216758;6506942608;57217215081;","The Influence of Distance and Lateral Offset of Follow Me Robots on User Perception",2020,"Frontiers in Robotics and AI","7",,"74","","",,3,"10.3389/frobt.2020.00074","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086771302&doi=10.3389%2ffrobt.2020.00074&partnerID=40&md5=c25d9d150b3793fd3358665926b88b85","Department of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany","Siebert, F.W., Department of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany; Klein, J., Department of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany; Rötting, M., Department of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany; Roesler, E., Department of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany","Robots that are designed to work in close proximity to humans are required to move and act in a way that ensures social acceptance by their users. Hence, a robot's proximal behavior toward a human is a main concern, especially in human-robot interaction that relies on relatively close proximity. This study investigated how the distance and lateral offset of “Follow Me” robots influences how they are perceived by humans. To this end, a Follow Me robot was built and tested in a user study for a number of subjective variables. A total of 18 participants interacted with the robot, with the robot's lateral offset and distance varied in a within-subject design. After each interaction, participants were asked to rate the movement of the robot on the dimensions of comfort, expectancy conformity, human likeness, safety, trust, and unobtrusiveness. Results show that users generally prefer robot following distances in the social space, without a lateral offset. However, we found a main influence of affinity for technology, as those participants with a high affinity for technology preferred closer following distances than participants with low affinity for technology. The results of this study show the importance of user-adaptiveness in human-robot-interaction. © Copyright © 2020 Siebert, Klein, Rötting and Roesler.","affinity for technology; human following robots; human robot interaction; proxemics; robot movement conventions",,,,,,,,,,,,,,,,,"Costa, M., Interpersonal distances in group walking (2010) J. Nonverb. 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Factors, 58, pp. 525-532. , 27098262; Siebert, F., Klein, J., Roetting, M., Roesler, E., (2020) The Influence of Distance and Lateral Offset of Follow Me Robots on User Perception, , Open Science Framework; Takayama, L., Pantofaru, C., Influences on proxemic behaviors in human-robot interaction (2009) 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5495-5502. , St. Louis, IEEE; Walters, M.L., Dautenhahn, K., Koay, K.L., Kaouri, C., Boekhorst, R.T., Nehaniv, C., Close encounters: Spatial distances between people and a robot of mechanistic appearance (2005) 5th IEEE-RAS International Conference on Humanoid Robots, 2005, pp. 450-455. , a, Tsukuba, IEEE; Walters, M.L., Dautenhahn, K., Te Boekhorst, R., Koay, K.L., Kaouri, C., Woods, S., The influence of subjects' personality traits on personal spatial zones in a human-robot interaction experiment (2005) ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, 2005, pp. 347-352. , b, Nashville, IEEE; Walters, M.L., Oskoei, M.A., Syrdal, D.S., Dautenhahn, K., A long-term human-robot proxemic study (2011) 2011 RO-MAN, pp. 137-142. , Atlanta, GA, IEEE","Siebert, F.W.; Department of Psychology and Ergonomics, Germany; email: felix.siebert@tu-berlin.de",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85086771302
"Coronado E., Venture G.","57188864138;15623574000;","Towards IoT-aided human–robot interaction using NEP and ROS: A platform-independent, accessible and distributed approach",2020,"Sensors (Switzerland)","20","5","1500","","",,5,"10.3390/s20051500","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081206205&doi=10.3390%2fs20051500&partnerID=40&md5=a6f419d40b010bb13f2198c4d68d3da1","Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, 184-0012, Japan","Coronado, E., Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, 184-0012, Japan; Venture, G., Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, 184-0012, Japan","This article presents the novel Python, C# and JavaScript libraries of Node Primitives (NEP), a high-level, open, distributed, and component-based framework designed to enable easy development of cross-platform software architectures. NEP is built on top of low-level, high-performance and robust sockets libraries (ZeroMQ and Nanomsg) and robot middlewares (ROS 1 and ROS 2). This enables platform-independent development of Human–Robot Interaction (HRI) software architectures. We show minimal code examples for enabling Publish/Subscribe communication between Internet of Things (IoT) and Robotics modules. Two user cases performed outside laboratories are briefly described in order to prove the technological feasibility of NEP for developing real-world applications. The first user case briefly shows the potential of using NEP for enabling the creation of End-User Development (EUD) interfaces for IoT-aided Human–Robot Interaction. The second user case briefly describes a software architecture integrating state-of-art sensory devices, deep learning perceptual modules, and a ROS-based humanoid robot to enable IoT-aided HRI in a public space. Finally, a comparative study showed better latency results of NEP over a popular state-of-art tool (ROS using rosbridge) for connecting different nodes executed in local-host and local area network (LAN). © 2019 by the authors. Licensee MDPI, Basel, Switzerland.","Artificial intelligence and robotics; Computer interaction; Human; Human; Internet of robot things; Robot interaction","Anthropomorphic robots; Arts computing; Computer software; Deep learning; High level languages; Human computer interaction; Human robot interaction; Intelligent robots; Libraries; Local area networks; Network architecture; Robotics; Component-based framework; Computer interaction; End user development(EUD); Human; Internet of Things (IOT); Local area networks (LAN); Robot interactions; Technological feasibility; Internet of things; adult; artificial intelligence; child; computer interface; human; procedures; robotics; software; Adult; Artificial Intelligence; Child; Humans; Internet of Things; Robotics; Software; User-Computer Interface",,,,,"Japan Society for the Promotion of Science, KAKEN: 19K12167",,,,,,,,,,,"Coronado, E., Indurkhya, X., Venture, G., Robots Meet Children, Development of Semi-Autonomous Control Systems for Children-Robot Interaction in the Wild Proceedings of the 2019 IEEE 4Th International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 360-365. , Toyonaka, Japan, 3–5 July 2019; Coronado, E., Mastrogiovanni, F., Venture, G., Design of a Human-Centered Robot Framework for End-User Programming and Applications (2019) ROMANSY 22–Robot Design, Dynamics and Control, pp. 450-457. , Springer: Cham, Switzerland; Simoens, P., Dragone, M., Saffiotti, A., The Internet of Robotic Things: A review of concept, added value and applications (2018) Int. 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Autom. Syst., 61, pp. 704-713; (2019) IREX International Robot Exhibition, , https://biz.nikkan.co.jp/eve/irex/english/, Available online:, accessed on 15 December 2019; Maruyama, Y., Kato, S., Azumi, T., Exploring the performance of ROS2 (2016) Proceedings of the 13Th International Conference on Embedded Software, pp. 1-10. , Pittsburgh, PA, USA, 2–7 October; https://enriquecoronadozu.github.io/NEP/, accessed on 15 December 2019","Coronado, E.; Department of Mechanical Systems Engineering, Japan; email: enriquecoronadozu@gmail.com",,,"MDPI AG",,,,,14248220,,,"32182906","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85081206205
"El Hafi L., Isobe S., Tabuchi Y., Katsumata Y., Nakamura H., Fukui T., Matsuo T., Garcia Ricardez G.A., Yamamoto M., Taniguchi A., Hagiwara Y., Taniguchi T.","57188836029;57197875878;57212147622;57208401562;57212144620;57212140619;26424278200;55779197600;7405302568;57189575562;26648361300;35585520600;","System for augmented human–robot interaction through mixed reality and robot training by non-experts in customer service environments",2020,"Advanced Robotics","34","3-4",,"157","172",,12,"10.1080/01691864.2019.1694068","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076035415&doi=10.1080%2f01691864.2019.1694068&partnerID=40&md5=b64f705dbfe5ac2ee7bf3ef70c016206","College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Division of Information Science, Nara Institute of Science and Technology, Ikoma, Japan; Business Innovation Division, Panasonic Corporation, Osaka, Japan","El Hafi, L., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Isobe, S., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Tabuchi, Y., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Katsumata, Y., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Nakamura, H., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Fukui, T., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Matsuo, T., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Garcia Ricardez, G.A., Division of Information Science, Nara Institute of Science and Technology, Ikoma, Japan; Yamamoto, M., Business Innovation Division, Panasonic Corporation, Osaka, Japan; Taniguchi, A., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Hagiwara, Y., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan; Taniguchi, T., College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan","Human–robot interaction during general service tasks in home or retail environment has been proven challenging, partly because (1) robots lack high-level context-based cognition and (2) humans cannot intuit the perception state of robots as they can for other humans. To solve these two problems, we present a complete robot system that has been given the highest evaluation score at the Customer Interaction Task of the Future Convenience Store Challenge at the World Robot Summit 2018, which implements several key technologies: (1) a hierarchical spatial concepts formation for general robot task planning and (2) a mixed reality interface to enable users to intuitively visualize the current state of the robot perception and naturally interact with it. The results obtained during the competition indicate that the proposed system allows both non-expert operators and end users to achieve human–robot interactions in customer service environments. Furthermore, we describe a detailed scenario including employee operation and customer interaction which serves as a set of requirements for service robots and a road map for development. The system integration and task scenario described in this paper should be helpful for groups facing customer interaction challenges and looking for a successfully deployed base to build on. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","human–robot interaction; mixed reality interface; Service robotics competition; spatial concepts formation; unsupervised multimodal learning","Interface states; Mixed reality; Personnel training; Robot learning; Robot programming; Sales; Mixed reality interfaces; Multi-modal learning; Robot interactions; Service robotics; Spatial concepts; Human robot interaction",,,,,"Australian Institute of Physics, AIP; New Energy and Industrial Technology Development Organization, NEDO; Japan Science and Technology Agency, JST; Ministry of Economy, Trade and Industry, METI; Core Research for Evolutional Science and Technology, CREST: JPMJCR15E3","We gratefully acknowledge the Ministry of Economy, Trade and Industry (METI) and the New Energy and Industrial Technology Development Organization (NEDO) of Japan for organizing WRS and providing us with the necessary financial support to participate. This project was also partially funded by the Japan Science and Technology Agency (JST) Core Research for Evolutionary Science and Technology (CREST) research programme (grant number JPMJCR15E3), including the Center for Advanced Intelligence Project (AIP) challenge programme. We thank all the members of Team NAIST-RITS-Panasonic for their valuable contributions and high commitment during the FCSC at WRS 2018.",,,,,,,,,,"Kishida, N., Nishiura, H., Demographic supply-demand imbalance in industrial structure in the super-aged nation Japan (2018) Theor Biol Med Model, 15 (1), p. 19; Wada, K., New robot technology challenge for convenience store. 2017 IEEE/SICE International Symposium on System Integration (SII 2017); 2017 Dec; Taipei. 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Available from; Zhao, H., Shi, J., Qi, X., Pyramid scene parsing network. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017); 2017 Jul; Honolulu, HI. p. 6230–6239, et al; Mur-Artal, R., Montiel, J.M.M., Tardos, J.D., ORB-SLAM: a versatile and accurate monocular SLAM system (2015) IEEE Trans Robot, 31 (5), pp. 1147-1163; Mur-Artal, R., Tardos, J.D., ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras (2017) IEEE Trans Robot, 33 (5), pp. 1255-1262","El Hafi, L.; College of Information Science and Engineering, Japan; email: lotfi.elhafi@em.ci.ritsumei.ac.jp",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Final","",Scopus,2-s2.0-85076035415
"Bevins A., McPhaul N., Duncan B.A.","57200625900;57220178829;36025235500;","Content Is King: Impact of Task Design for Eliciting Participant Agreement in Crowdsourcing for HRI",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"640","651",,1,"10.1007/978-3-030-62056-1_53","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097139396&doi=10.1007%2f978-3-030-62056-1_53&partnerID=40&md5=58b2369ca90094868aeb0f7c23a645f8","University of Nebraska-Lincoln, Lincoln, NE  68588, United States; Howard University, Washington, DC  20059, United States","Bevins, A., University of Nebraska-Lincoln, Lincoln, NE  68588, United States; McPhaul, N., Howard University, Washington, DC  20059, United States; Duncan, B.A., University of Nebraska-Lincoln, Lincoln, NE  68588, United States","This work investigates how the design of crowdsourced tasks can influence responses. As a formative line of inquiry, this study sought to understand how users would respond either through movement, response, or shift of focus to varying flight paths from a drone. When designing an experiment, running several proto-studies can help with generating a dataset that is actionable, but it has been unclear how differences in things such as phrasing or pre- and post-surveys can impact the results. Leveraging methods from psychology, computer-supported cooperative work, and the human-robot interaction communities this work explored the best practices and lessons learned for crowdsourcing to reduce time to actionable data for defining new communication paradigms. The lessons learned in this work will be applicable broadly within the human-robot interaction community, even outside those who are interested in defining flight paths, because they provide a scaffold on which to build future experiments seeking to communicate using non-anthropomorphic robots. Important results and recommendations include: increased negative affect with increased question quantity, completion time being relatively consistent based on total number of responses rather than number of videos, responses being more related to the video than the question, and necessity of varying question lengths to maintain engagement. © 2020, Springer Nature Switzerland AG.","Aerial vehicle; Crowdsourced; Gesture","Anthropomorphic robots; Cooperative communication; Crowdsourcing; Flight paths; Human computer interaction; Man machine systems; Robotics; Scaffolds; Best practices; Communication paradigm; Completion time; Designing an experiment; Negative affects; Reduce time; Task design; Social robots",,,,,"National Science Foundation, NSF: 1638099, 1750750, 1757908, 1925368",,,,,,,,,,,"Bartneck, C., Kanda, T., Mubin, O., Al Mahmud, A., Does the design of a robot influence its animacy and perceived intelligence? (2009) Int. J. Social Robot., 1 (2), pp. 195-204; Brosnan, K., Babakhani, N., Dolnicar, S., I know what you’re going to ask me” why respondents don’t read survey questions (2019) Int. J. Market Res., 147; Cañamero, L., Fredslund, J., I show you how i like you-can you read it in my face? [robotics] (2001) IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum., 31 (5), pp. 454-459; Casler, K., Separate but Equal? A Comparison of Participants and Data Gathered via Amazon’s Mturk, Social Media, and Face-To-Face Behavioral Testing, , https://doi.org/10.1016/j.chb.2013.05.009; Chaminade, T., Brain response to a humanoid robot in areas implicated in the perception of human emotional gestures (2010) Plos ONE, 5 (7); Christensen, L.B., Johnson, B., Turner, L.A., Christensen, L.B., Research methods, design (2011) And Analysis; Duncan, B.A., Beachly, E., Bevins, A., Elbaum, S., Detweiler, C., Investigation of communicative flight paths for small unmanned aerial systems* this work was supported by NSF NRI 1638099 (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 602-609. , IEEE; Firestone, J.W., Quiñones, R., Duncan, B.A., Learning from users: An elicita-tion study and taxonomy for communicating small unmanned aerial system states through gestures (2019) 2019 14Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 163-171. , IEEE; Gadiraju, U., Kawase, R., Dietze, S., Demartini, G., Understanding malicious behavior in crowdsourcing platforms: The case of online surveys (2015) Proceedings of the 33Rd Annual ACM Conference on Human Factors in Computing Systems, pp. 1631-1640. , ACM; Gadiraju, U., Yang, J., Bozzon, A., Clarity is a worthwhile quality: On the role of task clarity in microtask crowdsourcing (2017) Proceedings of the 28Th ACM Conference on Hypertext and Social Media, pp. 5-14. , ACM; Landis, J.R., Koch, G.G., The measurement of observer agreement for categorical data (1977) Biometrics, pp. 159-174; Riek, L.D., Rabinowitch, T.C., Bremner, P., Pipe, A.G., Fraser, M., Robinson, P., Cooperative gestures: Effective signaling for humanoid robots (2010) Proceedings of the 5Th ACM/IEEE International Conference on Human-Robot Interaction, pp. 61-68. , IEEE Press; Sorokin, A., Berenson, D., Srinivasa, S.S., Hebert, M., People helping robots helping people: Crowdsourcing for grasping novel objects (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2117-2122. , https://doi.org/10.1109/IROS.2010.5650464, October; Takayama, L., Dooley, D., Ju, W., Expressing thought: Improving robot readability with animation principles (2011) 2011 6Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Pp. 69–76. IEEE; Tellex, S., Understanding Natural Language Commands for Robotic Navigation and Mobile Manipulation, p. 8; Toris, R., Kent, D., Chernova, S., The robot management system: A framework for conducting human-robot interaction studies through crowdsourcing (2014) J, Hum.-Robot Interact., 3 (2), p. 25","Duncan, B.A.; University of Nebraska-LincolnUnited States; email: bduncan@cse.unl.edu","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097139396
"Setchi R., Dehkordi M.B., Khan J.S.","6603499046;55510123800;57216273029;","Explainable robotics in human-robot interactions",2020,"Procedia Computer Science","176",,,"3057","3066",,4,"10.1016/j.procs.2020.09.198","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093362632&doi=10.1016%2fj.procs.2020.09.198&partnerID=40&md5=0250699a8285a172630b17414cca0093","Research Centre in AI, Robotics and Human-Machine Systems (IROHMS), Cardiff University, Cardiff, CF24 3AA, United Kingdom; Centre for Advanced Studies, Dr. A.P.J.Abdul Kalam Technical University, Lucknow, India","Setchi, R., Research Centre in AI, Robotics and Human-Machine Systems (IROHMS), Cardiff University, Cardiff, CF24 3AA, United Kingdom; Dehkordi, M.B., Research Centre in AI, Robotics and Human-Machine Systems (IROHMS), Cardiff University, Cardiff, CF24 3AA, United Kingdom; Khan, J.S., Centre for Advanced Studies, Dr. A.P.J.Abdul Kalam Technical University, Lucknow, India","This paper introduces a new research area called Explainable Robotics, which studies explainability in the context of human-robot interactions. The focus is on developing novel computational models, methods and algorithms for generating explanations that allow robots to operate at different levels of autonomy and communicate with humans in a trustworthy and human-friendly way. Individuals may need explanations during human-robot interactions for different reasons, which depend heavily on the context and human users involved. Therefore, the research challenge is identifying what needs to be explained at each level of autonomy and how these issues should be explained to different individuals. The paper presents the case for Explainable Robotics using a scenario involving the provision of medical health care to elderly patients with dementia with the help of technology. The paper highlights the main research challenges of Explainable Robotics. The first challenge is the need for new algorithms for generating explanations that use past experiences, analogies and real-time data to adapt to particular audiences and purposes. The second research challenge is developing novel computational models of situational and learned trust and new algorithms for the real-time sensing of trust. Finally, more research is needed to understand whether trust can be used as a control variable in Explainable Robotics. © 2020 The Authors. Published by Elsevier B.V.","AI; Explainable AI; Explainable Robotics; Explanation; Reasoning; Robotics","Computation theory; Computational methods; Knowledge based systems; Man machine systems; Medical robotics; Robotics; Computational model; Control variable; Level of autonomies; Medical health; Patients with dementia; Real time sensing; Real-time data; Research challenges; Social robots",,,,,"C82092; Llywodraeth Cymru; European Regional Development Fund, ERDF","The authors would like to acknowledge the support of the Centre for Artificial Intelligence, Robotics and Human-Machine Systems (IROHMS) operation C82092, partially funded by the European Regional Development Fund (ERDF) through the Welsh Government.",,,,,,,,,,"(2019) Explainable AI: The Basics, , https://royalsociety.org/-/media/policy/projects/explainable-ai/AIand-interpretability-policy-briefing.pdf; Robinette, P., Li, W., Allen, R., Howard, A.M., Wagner, A.R., Overtrust of robots in emergency evacuation scenarios (2016) 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 101-108. , Christchurch, New Zealand, 7-10 March 2016; Edmonds, M., Gao, F., Liu, H., Xie, X., Qi, S., Rothrock, B., Zhu, Y., A tale of two explanations: Enhancing human trust by explaining robot behavior (2019) Science Robotics, 4 (37); Core, M.G., Lane, H.C., van Lent, M., Gamboc, D., (2006) Building Explainable Artificial Intelligence Systems, pp. 1766-1773. , MIT Press, AAAI; Holzinger, A., Langs, G., Denk, H., Zatloukal, K., Müller, H., Causability and explainability of artificial intelligence in medicine (2019) Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9 (4); Kiesenberg, P., Weippl, E.W., Holzinger, A., Trust for the doctor-in-the-loop (2016) European Research Consortium for Informatics and Mathematics (ERCIM) News: Tackling Big Data in the Life Sciences, 104 (1), pp. 32-33; Hamburg, M.A., Collins, F., The path to personalized medicine (2010) New England Journal of Medicine, 363 (4), pp. 301-304; Hudec, M., Bednarova, E., Holzinger, A., Augmenting statistical data dissemination by short quantified sentences of natural language (2018) Journal of Official Statistics (JOS), 34, pp. 981-1010; Skirpan, M., Yeh, T., Designing a moral compass for the future of computer vision using speculative analysis (2017) Proc. Of IEEE Computer Vision and Pattern Recognition, pp. 64-73; Montavon, G., Samek, W., Muller, K.-R., Methods for interpreting and understanding deep neural networks (2018) Digital Signal Processing, 73, pp. 1-15; Lundberg, S.M., Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz, R., Lee, S.-I., From local explanations to global understanding with explainable AI for trees (2020) Nature Machine Intelligence, 2 (1), pp. 2522-5839; Schoenborn, J.M., Althoff, K.-D., Recent trends in XAI: A broad overview on current approaches, methodologies and interactions (2019) Case-Based Reasoning for the Explanation of Intelligent Systems (XCBR) Workshop, pp. 1-10; Poulin, B., Eisner, R., Szafron, D., Lu, P., Greiner, R., Wishart, D.S., Fyshe, A., Anvik, J., Visual explanation of evidence with additive classifiers (2006) National Conference on Artificial Intelligence, AAAI, pp. 1822-1829; Szafron, D., Lu, P., Greiner, R., Wishart, D.S., Poulin, B., Eisner, R., Lu, Z., Meeuwis, D., Proteome analyst: Custom predictions with explanations in a web-based tool for high-throughput proteome annotations (2004) Nucleic Acids Research, 32 (S2), pp. W365-W371; Veruggio, G., Operto, F., Roboethics: A bottom-up interdisciplinary discourse in the field of ap-plied ethics in robotics (2006) International Review of Information Ethics, 6; Deng, B., The robot's dilemma (2015) Nature, 523. , 2 July 2015; Körtner, T., Ethical challenges in the use of social service robots for elderly people (2016) Zeitschrift Für Gerontologie und Geriatrie, 49, pp. 303-307; Sharkey, N., Sharkey, A., Robotic surgery and ethical challenges (2012) Medical Robotics- Minimally Invasive Surgery, pp. 276-291; Hameed, I.A., Tan, Z.-H., Thomsen, N., Duan, X., User acceptance of social robots (2016) ACHI 2016: The Ninth International Conference on Advances in Computer-Human Interactions, pp. 274-279; Goodrich, M.A., Schultz, A.C., Human-robot interaction: A survey (2007) Foundations and Trends in Human-Computer Interaction, 1 (3), pp. 203-275; Sheridan, T.B., (2002) Humans and Automation: System Design and Research Issues, , John Wiley and Sons; Schilling, M., Kopp, S., Wachsmuth, S., Wrede, B., Ritter, H., Brox, T., Nebel, B., Burgard, W., Towards a multidimensional perspective on shared autonomy (2016) 2016 AAAI Fall Symposium Series; Nikolaidis, S., Zhu, Y.X., Hsu, D., Srinivasa, S., Human-robot mutual adaptation in shared autonomy (2017) Proc ACM SIGCHI, pp. 294-302. , Mar; 2017; Beer, J.A., Toward a framework for levels of robot autonomy in human-robot interaction (2014) J Hum Robot Interact, 3 (2), pp. 74-99; Scholtz, J., Theory and evaluation of human robot interactions (2003) Proc. Hawaii International Conference on System Science, 36; Lemaignan, S., Warnier, M., Sisbot, E.A., Clodic, A., Alami, R., Artificial cognition for social human-robot interaction: An implementation (2017) Artif. Intell., 247, pp. 45-69; Lombrozo, T., The structure and function of explanations (2006) TRENDS in Cognitive Sciences, 10 (10), pp. 464-470; Pennington, N., Hastie, R., Reasoning in explanation-based decision making (1993) Cognition, 49, pp. 123-163; Falcon, A., Aristotle on Causality (2019) The Stanford Encyclopedia of Philosophy, , Edward N. Zalta (ed.), Stanford University, USA; Wilkinson, S., Levels and kinds of explanation: Lessons from neuropsychiatry (2014) Frontiers of Philosophy, 5. , Article","Setchi, R.; Research Centre in AI, United Kingdom; email: setchi@cf.ac.uk","Cristani M.Toro C.Zanni-Merk C.Howlett R.J.Jain L.C.Jain L.C.",,"Elsevier B.V.","24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020","16 September 2020 through 18 September 2020",,163593,18770509,,,,"English",,Conference Paper,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85093362632
"Nicora M.L., Ambrosetti R., Wiens G.J., Fassi I.","57220203573;57222104656;7003372495;6506998602;","Human-robot collaboration in smart manufacturing: Robot reactive behavior intelligence",2020,"ASME 2020 15th International Manufacturing Science and Engineering Conference, MSEC 2020","2",,"V002T07A011","","",,2,"10.1115/MSEC2020-8402","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092664786&doi=10.1115%2fMSEC2020-8402&partnerID=40&md5=26a7f73c0b3f3ecf36bf35c4f1a691ae","Politecnico di Milano, Milan, Italy; University of Florida, Gainesville, FL, United States; STIIMA, CNR, Milan, Italy","Nicora, M.L., Politecnico di Milano, Milan, Italy; Ambrosetti, R., Politecnico di Milano, Milan, Italy; Wiens, G.J., University of Florida, Gainesville, FL, United States; Fassi, I., STIIMA, CNR, Milan, Italy","To enable safe and effective human-robot collaboration (HRC) in smart manufacturing, seamless integration of sensing, cognition and prediction into the robot controller is critical for real-time awareness, response and communication inside a heterogeneous environment (robots, humans, equipment). The specific research objective is to provide the robot Proactive Adaptive Collaboration Intelligence (PACI) and switching logic within its control architecture in order to give the robot the ability to optimally and dynamically adapt its motions, given a priori knowledge and predefined execution plans for its assigned tasks. The challenge lies in augmenting the robot's decision-making process to have greater situation awareness and to yield smart robot behaviors/reactions when subject to different levels of human-robot interaction, while maintaining safety and production efficiency. Robot reactive behaviors were achieved via cost function-based switching logic activating the best suited high-level controller. The PACI's underlying segmentation and switching logic framework is demonstrated to yield a high degree of modularity and flexibility. The performance of the developed control structure subjected to different levels of human-robot interactions was validated in a simulated environment. Open-loop commands were sent to the physical e.DO robot to demonstrate how the proposed framework would behave in a real application. Copyright © 2020 ASME","Cobot; HRC; Smart factory; Task segmentation","Computer circuits; Controllers; Cost functions; Decision making; Flow control; Intelligent robots; Man machine systems; Manufacture; Robot programming; Adaptive collaboration; Control architecture; Decision making process; Heterogeneous environments; High level controllers; Human-robot collaboration; Production efficiency; Simulated environment; Social robots",,,,,"National Science Foundation, NSF; Niroo Research Institute, NRI; Politecnico di Milano","This research was conducted under the University of Florida J-1 program and in collaboration with STIIMA-CNR of Italy. Funding was provided by Politecnico di Milano scholarships “Thesis Abroad – Borsa di Studio Tesi all’Estero” for both Matteo Lavit Nicora and Roberto Ambrosetti and by the NSF/NRI: INT: COLLAB: Manufacturing USA: Intelligent Human-Robot Collaboration for Smart Factory (Award I.D. #:1830383). Any opinions, findings and conclusions or recommendations expressed are those of the researchers and do not necessarily reflect the views of the National Science Foundation.",,,,,,,,,,"(2018), https://ifr.org/ifr-press-releases/news/robot-investment-reaches-record-16.5-billion-usd, International Federation of Robotics, (Retrieved October 18, 2019, from); (2015) Made-to-order: The rise of mass personalization, , The Deloitte Consumer Review, The Deloitte LLP, London, UK; Streitmatter, G.L., Wiens, G.J., Multi-Objective Approach to HRC Manufacturing Environment (2019) ASME Manufacturing Science and Engineering Conference, , MSEC2019-2935, June 10-14, Erie, PA, poster; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., Ros: an open-source robot operating system (2009) ICRA workshop on open source software, p. 3; Chitta, S., Sucan, I., Cousins, S., Moveit! (2012) Robotics & Automation Magazine, IEEE, 19 (1), pp. 18-19; (2018) Comau e.DO Robot, , https://edo.cloud/edo-robot/, (Retrieved October 18, 2019, from); Kragic, D., Gustafson, J., Karaoguz, H., Jensfelt, P, Krug, R, Interactive, Collaborative Robots: Challenges and Opportunities (2018) International Joint Conference on Artificial Intelligence, pp. 18-25. , Stockholm, SE; Colledanchise, M., Ogren, P., How Behavior Trees Modularize Hybrid Control Systems and Generalize Sequential Behavior Compositions, the Subsumption Architecture and Decision Trees (2016) IEEE Transactions on Robotics, 33 (2), pp. 372-389; Roveda, L., Vicentini, F., Molinari Tosatti, L., Deformation-tracking impedance control in interaction with uncertain environments (2013) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1992-1997; Liu, C., Tomizuka, M., Algorithmic safety measures for intelligent industrial co-robots 2016 IEEE International Conference on Robotics and Automation, pp. 3095-3102. , Stockholm, SE; Pedrocchi, N., Vicentini, F., Malosio, M., Molinari Tosatti, L., Safe Human-Robot Cooperation in Industrial Environment (2013) International Journal of Advanced Robotic Systems, 10, pp. 1-13; Sadrfaridpour, B., Wang, Y., Collaborative Assembly in Hybrid Manufacturing Cells: An Integrated Framework for Human-Robot Interaction IEEE Transactions on Automation Science and Engineering, 15 (3), pp. 1178-1192; Pellegrinelli, S, Moro, F.L., Pedrocchi, N., Molinari Tosatti, L., Tolio, T., A probabilistic approach to workspace sharing for human-robot cooperation in assembly tasks (2016) Robotics and Computer-Integrated Manufacturing, 48, pp. 243-253; Ferraguti, F., Talignani Landi, C., Secchi, C., Fantuzzi, C., Nolli, M., Pesamosca, M., Walk-through programming for industrial applications (2017) Procedia Manufacturing, 11, pp. 31-38; Billard, A.G., Calinon, S., Dillmann, R., Learning from Humans (2016) Springer Handbook of Robotics, , and and Siciliano, B. and Khatib, O., Springer Handbooks, Springer; Neto, P., Mendes, N., Direct off-line robot programming via a common CAD package (2013) Robotics and Autonomous Systems, 61, pp. 896-910; Michalos, G, Karagiannis, P., Makris, S., Tokçalar, Ö., Chryssolouris, G., Augmented Reality (AR) Applications for Supporting Human-robot Interactive Cooperation (2016) Procedia CIRP, 41, pp. 370-375","Wiens, G.J.; University of FloridaUnited States; email: gwiens@ufl.edu",,"Manufacturing Engineering Division","American Society of Mechanical Engineers","ASME 2020 15th International Manufacturing Science and Engineering Conference, MSEC 2020","3 September 2020",,166527,,9780791884263,,,"English","ASME Int. Manuf. Sci. Eng. Conf., MSEC",Conference Paper,"Final","",Scopus,2-s2.0-85092664786
"Rodríguez E., Gutiérrez C., Ochoa C., Trávez F., Escobar L., Loza D.","57207839664;57212899090;57215926850;57215930249;57203333629;55597229100;","Construction of a Computer Vision Test Platform: VISART for Facial Recognition in Social Robotics",2020,"Communications in Computer and Information Science","1194 CCIS",,,"637","651",,,"10.1007/978-3-030-42520-3_50","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082400576&doi=10.1007%2f978-3-030-42520-3_50&partnerID=40&md5=00757b4e572f84db0250cde1ffef54a3","Department of Energy and Mechanics, Universidad de las Fuerzas Armadas - ESPE, 170121 Sangolquí, Pichincha, Ecuador","Rodríguez, E., Department of Energy and Mechanics, Universidad de las Fuerzas Armadas - ESPE, 170121 Sangolquí, Pichincha, Ecuador; Gutiérrez, C., Department of Energy and Mechanics, Universidad de las Fuerzas Armadas - ESPE, 170121 Sangolquí, Pichincha, Ecuador; Ochoa, C., Department of Energy and Mechanics, Universidad de las Fuerzas Armadas - ESPE, 170121 Sangolquí, Pichincha, Ecuador; Trávez, F., Department of Energy and Mechanics, Universidad de las Fuerzas Armadas - ESPE, 170121 Sangolquí, Pichincha, Ecuador; Escobar, L., Department of Energy and Mechanics, Universidad de las Fuerzas Armadas - ESPE, 170121 Sangolquí, Pichincha, Ecuador; Loza, D., Department of Energy and Mechanics, Universidad de las Fuerzas Armadas - ESPE, 170121 Sangolquí, Pichincha, Ecuador","Robotics has undoubtedly found its way deeper into every day human tasks up to the point where now they even share workspaces with people. In this context, social robotics has increased its field of action, one important part is the advances in aspects that make up the Human-Machine Interaction. This article reports the advance that an investigation team at Universidad de las Fuerzas Armadas ESPE has done to create a test platform for Computer Vision algorithms. This approach consists of a 3-DOF articulated robotic head with anthropomorphic characteristics, considering the guidelines established by different authors for social robotics, besides it provides affordable, flexible and scalable hardware resources to facilitate the implementation, testing, analysis and development of different stereo artificial vision algorithms. The system, called Visart, is intended to work in more natural situations than independent cameras, therefore, work deeply in Human-Robot Interaction. As the first report of the Visart prototype, some basic tests with the platform were performed, they consist of face detection, facial and expression recognition, object tracking, and object distance calculation. The results obtained were consistent, opening a door for further research focusing on the comparison of more computer vision algorithms to examine their performance in real scenarios and evaluate them more naturally with our prototype for Human-Robot Interaction. © 2020, Springer Nature Switzerland AG.","Anthropomorphic robot; Computer Vision; HRI; Human-Robot Interaction; Social robotics","Anthropomorphic robots; Economic and social effects; Face recognition; Human computer interaction; Human robot interaction; Man machine systems; Object tracking; Robotics; Social computing; Social robots; Stereo image processing; Stereo vision; Computer vision algorithms; Expression recognition; Facial recognition; Human machine interaction; Object distance; Scalable hardware; Social robotics; Test platforms; Computer vision",,,,,,,,,,,,,,,,"Armea, A., (2017) Calculating a Depth Map from a Stereo Camera with Opencv, , https://albertarmea.com/post/opencv-stereo-camera/; Azevedo, H., Belo, J.P.R., Romero, R.A.F., Cognitive and robotic systems: Speeding up integration and results (2017) 2017 Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR), Curitiba, , https://doi.org/10.1109/SBR-LARS-R.2017.8215337; Budynas, R.G., Keith, N., (2008) Diseño En ingeniería mecánica De Shigley, , 8th edn. McGrawHill, Mexico; Duffy, B.R., (2003), pp. 177-190; Faust, R.A., (2007); Fernández, L., Sotomayor, L., (2016) Análisis cinemático Inverso Y Directo Del Robot Par-Alelo; Fong, T., Thorpe, C., Baur, C., https://doi.org/10.1007/3-540-36460-917; (2016), https://doi.org/10.13140/rg.2.1.3267.2881; Goodrich, M.A., Schultz, A.C., Human-robot interaction: A survey (2007) Found. Trends Hum. Comput. Interact., 1 (3), pp. 203-275. , https://doi.org/10.1561/1100000005; Hayashi, K., Shiomi, M., Kanda, T., Hagita, N., Who is appropriate? A robot, human and mascot perform three troublesome tasks (2010) 19Th International Symposium in Robot and Human Interactive Communication, Viareggio, Italy, , https://doi.org/10.1109/ROMAN.2010.5598661; Hockstein, N.G., Gourin, C.G., Faust, R.A., Terris, D.J., A history of robots: From science fiction to surgical robotics (2007) J. Robot. Surg., 1 (2), pp. 113-118. , https://doi.org/10.1007/s11701-007-0021-2; (2015) IFR: World Robotics Survey: Industrial Robots are Conquering the World, , https://ifr.org/news/world-robotics-survey-industrial-robots-are-conquering-the-world-/; Kehoe, B., Patil, S., Abbeel, P., Goldberg, K., A survey of research on cloud robotics and automation (2015) IEEE Trans. Autom. Sci. Eng., 12 (2), pp. 398-409. , https://doi.org/10.1109/TASE.2014.2376492; Krishnan, A., Hs, A., Design and implementation of robotic vision for face recognition (2017) 2017 International Conference on Circuits, Controls, and Communications (CCUBE), Bangalore, , https://doi.org/10.1109/CCUBE.2017.8394148; Leite, I., Martinho, C., Paiva, A., Social robots for long-term interaction: A survey (2013) Int. J. Social Robot., 5 (2), pp. 291-308. , https://doi.org/10.1007/s12369-013-0178-y; Lindblom, J., Andreasson, R., Current challenges for UX evaluation of human-robot interaction (2016) Advances in Ergonomics of Manufacturing: Managing the Enterprise of the Future. Advances in Intelligent Systems and Computing, 490, pp. 267-277. , https://doi.org/10.1007/978-3-319-41697-724, Schlick, C., Trzcieliński, S. (eds.), pp., Springer, Cham; Littlewort, G., Towards social robots: Automatic evaluation of human-robot interaction by facial expression classification (2004) Advances in Neural Information Processing Systems, 16, pp. 1563-1570. , Thrun, S., Saul, L.K., Schölkopf, B. (eds.), pp., MIT Press, Cambridge; Lohse, M., Hegel, F., Wrede, B., Domestic applications for social robots: An online survey on the influence of appearance and capabilities (2008) J. Phys. Agents (Jopha), 2 (2), pp. 21-32. , https://doi.org/10.14198/JoPha.2008.2.2.04; Martinez, B., Valstar, M.F., Jiang, B., Pantic, M., https://ieeexplore.ieee.org/document/7990582/; McGinn, C., Why do robots need a head? The role of social interfaces on service robots (2019) Int. J. Social Robot., , https://doi.org/10.1007/s12369-019-00564-5; Nagai, K., Sakabe, H., Ohka, M., Finger direction recognition toward human-and-robot cooperative tasks (2017) 2017 International Symposium on Micro-Nanomechatronics and Human Science (MHS), Nagoya, , https://doi.org/10.1109/MHS.2017.8305242; Ochoa, C., Trávez, F., (2018) Diseno Y construcción De Una Plataforma robótica Para Pruebas técnicas Sobre visión Artificial Que Permita El Aprendizaje Y Desarrollo Del Software En Robots Interactivos, , Ph.D. thesis, Universidad de las Fuerzas Armadas ESPE, Sangolquí, Ecuador; Pinillos, R., Marcos, S., Feliz, R., Zalama, E., Gómez-García-Bermejo, J.: Long-term assessment of a service robot in a hotel environment (2016) Robot. Auton. Syst, 79, pp. 40-57. , https://doi.org/10.1016/j.robot.2016.01.014; Pinto Sánchez-Matamoros, L.F., Carrasco, J.A.P., (2015) Análisis De La aplicación De Algoritmos De K-Means Y Continuous Max-Flow a La segmentación De imágenes En Color, p. 80. , Dep. Teoría de la Señal y Comunicaciones Escuela Técnica Superior de Ingeniería Universidad de Sevilla, p; Preston, S.D., de Waal, F.B.M., Empathy: Its ultimate and proximate bases (2002) Behav. Brain Sci., 25 (1), pp. 1-20. , https://doi.org/10.1017/S0140525X02000018; Reyes Fernández, B., Empathy and moral development: Implications for caring and justice (2011) Actualidades En Psicología, 20 (107), p. 141. , https://doi.org/10.15517/ap.v20i107.40; Riek, L.D., Robinson, P., When my robot smiles at me: Enabling human-robot rapport via real-time head gesture mimicry (2010) J. Multimodal User Interfaces, 3 (1-2), pp. 99-108; Robie, A.A., Seagraves, K.M., Egnor, S.E.R., Branson, K., (2017); Stewart, M., Gwen, B., Ian, L., Javier, F., Movellan, R., (2003) Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction, p. 53. , Madison, Wisconsin, USA, p., IEEE; Tapus, A., Maja, M., Scassellatti, B., The grand challenges in socially assistive robotics (2007) IEEE Robot. Autom. Mag., 14 (1); Tontini, G., Integrating the Kano model and QFD for designing new products (2007) Total Qual. Manag. Bus. Excell., 18 (6), pp. 599-612. , https://doi.org/10.1080/14783360701349351; Wiese, E., Metta, G., Wykowska, A., Robots as intentional agents: Using neuro-scientific methods to make robots appear more social (2017) Front. Psychol, 8, p. 1663. , http://journal.frontiersin.org/article/10.3389/fpsyg.2017.01663/full; Yi, D., Liu, R., Chu, R.F., Lei, Z., Li, S.Z., Face matching between near infrared and visible light images (2007) ICB 2007. LNCS, 4642, pp. 523-530. , https://doi.org/10.1007/978-3-540-74549-555, Lee, S.-W., Li, S.Z. (eds.), pp., Springer, Heidelberg; Yii Lim, M., Aylett, R., (2011) Human-Like Memory Retrieval Mechanisms for Social Companions, , AAMAS, Taipei","Escobar, L.; Department of Energy and Mechanics, 170121 Sangolquí, Ecuador; email: lfescobar@espe.edu.ec","Botto-Tobar M.Zambrano Vizuete M.Torres-Carrion P.Montes Leon S.Pizarro Vasquez G.Durakovic B.",,"Springer","1st International Conference on Applied Technologies, ICAT 2019","3 December 2019 through 5 December 2019",,238249,18650929,9783030425197,,,"English","Commun. Comput. Info. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85082400576
"Popov V., Gorbenko A.","8988704500;46861096400;","Deceptive actions and demonstrations of intentions for robot collision avoidance",2020,"AIP Conference Proceedings","2293",,"420032","","",,,"10.1063/5.0027660","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097978147&doi=10.1063%2f5.0027660&partnerID=40&md5=2f16b7cee9b75fad5a52f82f2b642ede","Department of Intelligent Systems and Robotics, Institute of Natural Sciences and Mathematics, Ural Federal University, Lenin st, 51, Ekaterinburg, 620083, Russian Federation","Popov, V., Department of Intelligent Systems and Robotics, Institute of Natural Sciences and Mathematics, Ural Federal University, Lenin st, 51, Ekaterinburg, 620083, Russian Federation; Gorbenko, A., Department of Intelligent Systems and Robotics, Institute of Natural Sciences and Mathematics, Ural Federal University, Lenin st, 51, Ekaterinburg, 620083, Russian Federation","Efficiency and effectiveness of many robotics tasks depend crucially on the quality of the solution of the problem of collision avoidance. In many cases, traditional path planning and obstacle avoiding approaches and algorithms can not guarantee a sufficiently high quality of the solution of the problem of collision avoidance for multi-robot and human-robot interactions. In this paper, we consider demonstrations of true intentions and demonstrations of deceptions for the problem of collision avoidance. © 2020 American Institute of Physics Inc.. All rights reserved.",,,,,,,"Ministry of Education and Science of the Russian Federation, Minobrnauka","This work is partially supported by the Ministry of Education and Science of the Russian Federation project ""Combinatorial models in computer science and their applications"". The work was supported by Act 211 Government of the Russian Federation, contract N 02. A03.21.0006.",,,,,,,,,,"Almasri, M.M., Alajlan, A.M., Elleithy, K.M., (2016) IEEE Sensors J, 16, pp. 5021-5028; Zeng, L., Bone, G.M., (2013) Int. J. Adv. Robot. Syst., 10, pp. 1-14; Liu, F., Narayanan, A., (2013) LNCS, 8291, pp. 181-196; Agarwal, M., Agrawal, N., Sharma, S., Vig, L., Kumar, N., (2015) Expert Syst. Appl., 42, pp. 7797-7811; Li, Y., Chen, L., Tee, K.P., (2015) Neurocomputing, 170, pp. 168-175; Oh, H., Shirazi, A.R., Sun, C., Jin, Y., (2017) Robot. Auton. Syst., 91, pp. 83-100; Burgard, W., Cremers, A.B., Fox, D., Hahnel, D., Lakemeyer, G., Schulz, D., Steiner, W., Thrun, S., (1999) Artif. Intell., 114, pp. 3-55; Thrun, S., Bennewitz, M., Burgard, W., Cremers, A.B., Dellaert, F., Fox, D., Hahnel, D., Schulz, D., Minerva: A second-generation museum tour-guide robot (1999) Proceedings of the 1999 IEEE International Conference on Robotics and Automation, 3, pp. 1999-2005. , IEEE Press, Piscataway; Nourbakhsh, I.R., Kunz, C., Willeke, T., The mobot museum robot installations: A five year experiment (2003) IEEE/RJS International Conference on Intelligent Robots and Systems, pp. 3636-3641. , IEEE Press, Piscataway; Lacevic, B., Rocco, P., Kinetostatic danger field- A novel safety assessment for human-robot interaction (2010) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2169-2174. , IEEE Press, Piscataway; Polverini, M.P., Zanchettin, A.M., Rocco, P., (2017) Robot. Comput. Integr. Manuf., 46, pp. 25-37; Trautman, P., Ma, J., Murray, R.M., Krause, A., (2015) Int. J. Rob. Res., 34, pp. 335-356; Lo, S., Cheng, C., Huang, H., (2016) J. Intell. Robot. Syst., 82, pp. 3-19; Dragan, A., Holladay, R., Srinivasa, S., (2015) Auton. Robots, 39, pp. 331-345; Gerwehr, S., Glenn, R.W., (2000) The Art of Darkness: Deception and Urban Operations, , Rand Corporation, Santa Monica; Wagner, A.R., Arkin, R.C., (2011) Int. J. Of Soc. Robotics, 3, pp. 5-26; Gorbenko, A., Popov, V., Deceptive actions and robot collision avoidance (2020) Biologically Inspired Cognitive Architectures 2019, Advances in Intelligent Systems and Computing, 948, pp. 105-109. , edited by A. Samsonovich Springer, Cham, Switzerland; Yang, S.X., Meng, M., (2000) Neural Netw, 138, pp. 143-148; Althoff, M., Lutz, S., Automatic generation of safety-critical test scenarios for collision avoidance of road vehicles (2018) 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 1-8. , IEEE Press, Piscataway; Vassallo, C., Olivier, A., Soueres, P., Cretual, A., Stasse, O., Pettre, J., (2017) Gait & Posture, 51, pp. 97-103","Popov, V.; Department of Intelligent Systems and Robotics, Lenin st, 51, Russian Federation; email: popovvw@gmail.com","Simos T.E.Simos T.E.Simos T.E.Simos T.E.Simos T.E.Tsitouras C.",,"American Institute of Physics Inc.","International Conference on Numerical Analysis and Applied Mathematics 2019, ICNAAM 2019","23 September 2019 through 28 September 2019",,165330,0094243X,9780735440258,,,"English","AIP Conf. Proc.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85097978147
"Zehfroosh A., Tanner H.G.","55993914700;7007108972;","A new sample-efficient PAC reinforcement learning algorithm",2020,"2020 28th Mediterranean Conference on Control and Automation, MED 2020",,,"9182985","788","793",,,"10.1109/MED48518.2020.9182985","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092171751&doi=10.1109%2fMED48518.2020.9182985&partnerID=40&md5=81465254a24e7604e508b6e6d9831196","University of Delaware, Department of Mechanical Engineering, United States","Zehfroosh, A., University of Delaware, Department of Mechanical Engineering, United States; Tanner, H.G., University of Delaware, Department of Mechanical Engineering, United States","This paper introduces a new hybrid PAC RL algorithm for MDPS, which intelligently maintains favorable features of its parents. The DDQ algorithm, integrates model-free and model-based learning approaches, preserving some advantages from both. A PAC analysis of the DDQ algorithm is presented and its sample complexity is explicitly bounded. Numerical results from a small-scale example motivated by work on human-robot interaction models corroborates the theoretical predictions on sample complexity. © 2020 IEEE.",,"Human robot interaction; Reinforcement learning; Model free; Modeling-based learning; Numerical results; Sample complexity; Small scale; Learning algorithms",,,,,"National Institutes of Health, NIH: R01HD87133","Ashkan Zehfroosh, and Bert Tanner are with the Department of Mechanical Engineering, University of Delaware. {ashkanz, btanner}@udel.edu This work was supported by NIH under grant # R01HD87133.",,,,,,,,,,"Kearns, M., Singh, S., Near-optimal reinforcement learning in polynomial time (2002) Machine Learning, 49 (2-3), pp. 209-232; Brafman, R.I., Tennenholtz, M., R-max a general polynomial time algorithm for near-optimal reinforcement learning (2002) Journal of Machine Learning Research, 3, pp. 213-231. , Oct; Strehl, A.L., Littman, M.L., A theoretical analysis of model-based interval estimation (2005) Proceedings of the 22nd International Conference on Machine Learning, pp. 856-863. , ACM; Strehl, A.L., Li, L., Littman, M.L., (2012) Incremental Model-based Learners with Formal Learning-time Guarantees; Strehl, A.L., Li, L., Wiewiora, E., Langford, J., Littman, M.L., PAC model-free reinforcement learning (2006) Proceedings of the 23rd International Conference on Machine Learning, pp. 881-888. , ACM; Zehfroosh, A., Kokkoni, E., Tanner, H.G., Heinz, J., Learning models of human-robot interaction from small data (2017) 2017 25th Ieee Mediterranean Conference on Control and Automation, pp. 223-228. , July; Broz, F., Nourbakhsh, I., Simmons, R., Planning for human-robot interaction in socially situated tasks: The impact of representing time and intention (2013) International Journal of Social Robotics, 5 (2), pp. 193-214; Bandyopadhyay, T., Won, K.S., Frazzoli, E., Hsu, D., Lee, W.S., Rus, D., (2013) Intention-Aware Motion Planning, 86, pp. 475-491. , Springer-Verlag In E. Frazzoli et al., editor, Algorithmic Foundations of Robotics X; McGhan, C.L.R., Nasir, A., Atkins, E.M., Human intent prediction using markov decision processes (2015) Journal of Aerospace Information Systems, 5 (12), pp. 393-397; Zehfroosh, A., Tanner, H.G., Heinz, J., Learning option mdps from small data (2018) 2018 Ieee American Control Conference, pp. 252-257; Zehfroosh, A., Tanner, H.G., Reactive motion planning for temporal logic tasks without workspace discretization (2019) 2019 American Control Conference (ACC), pp. 4872-4877. , IEEE; Pong, V., Gu, S., Dalal, M., Levine, S., (2018) Temporal Difference Models: Model-free Deep Rl for Model-based Control; Nagabandi, A., Kahn, G., Fearing, R.S., Levine, S., Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning (2018) 2018 Ieee International Conference on Robotics and Automation, pp. 7559-7566; Strehl, A.L., Li, L., Littman, M.L., Reinforcement learning in finite MDPs: PAC analysis (2009) Journal of Machine Learning Research, pp. 2413-2444. , 10(Nov); Machandranath Kakade, S., On the sample complexity of reinforcement learning (2003) PhD Thesis, , University of London London, England; Lee, S.W., Shimojo, S., O'Doherty, J.P., Neural computations underlying arbitration between model-based and modelfree learning (2014) Neuron, 81 (3), pp. 687-699; Sutton, R.S., Dyna, an integrated architecture for learning, planning, and reacting (1991) Acm Sigart Bulletin, 2 (4), pp. 160-163; Heess, N., Wayne, G., Silver, D., Lillicrap, T., Erez, T., Tassa, Y., Learning continuous control policies by stochastic value gradients (2015) Advances in Neural Information Processing Systems, pp. 2944-2952; Parr, R., Li, L., Taylor, G., Painter-Wakefield, C., Littman, M.L., An analysis of linear models, linear valuefunction approximation, and feature selection for reinforcement learning (2008) Proceedings of the 25th International Conference on Machine Learning, pp. 752-759. , ACM; Sutton, R.S., Modayil, J., Delp, M., Degris, T., Pilarski, P.M., White, A., Precup, D., Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction (2011) 10th International Conference on Autonomous Agents and Multiagent Systems, 2, pp. 761-768. , International Foundation for Autonomous Agents and Multiagent Systems; Schaul, T., Horgan, D., Gregor, K., Silver, D., Universal value function approximators (2015) International Conference on Machine Learning, pp. 1312-1320; Andrychowicz, M., Wolski, F., Ray, A., Schneider, J., Fong, R., Welinder, P., McGrew, B., Zaremba, W., Hindsight experience replay (2017) Advances in Neural Information Processing Systems, pp. 5048-5058. , OpenAI; Chebotar, Y., Hausman, K., Zhang, M., Sukhatme, G., Schaal, S., Levine, S., Combining model-based and modelfree updates for trajectory-centric reinforcement learning (2017) Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 703-711. , JMLR. Org",,,,"Institute of Electrical and Electronics Engineers Inc.","28th Mediterranean Conference on Control and Automation, MED 2020","15 September 2020 through 18 September 2020",,162791,,9781728157429,,,"English","Mediterr. Conf. Control Autom., MED",Conference Paper,"Final","",Scopus,2-s2.0-85092171751
"Krummheuer A.L., Rehm M., Rodil K.","7801324145;10039896700;52063699200;","Triadic human-robot interaction. distributed agency and memory in robot assisted interactions",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"317","319",,1,"10.1145/3371382.3378269","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083282765&doi=10.1145%2f3371382.3378269&partnerID=40&md5=f44bc122bd5c1ccd9dabc932826c5577","Faculty of Humanities, Aalborg University, Denmark; Technical Faculty of IT and Design, Aalborg University, Denmark","Krummheuer, A.L., Faculty of Humanities, Aalborg University, Denmark; Rehm, M., Technical Faculty of IT and Design, Aalborg University, Denmark; Rodil, K., Technical Faculty of IT and Design, Aalborg University, Denmark","We argue that the field of human-robot interaction needs a distributed and socially situated understanding of reminding and scheduling practices in the design of robots to meet the needs of people with cognitive disabilities. The results are based on an interaction analysis of video recorded workshop interactions during a co-creation process in which the participants tested a reminder-robot prototype that was designed for and with people with acquired brain injury. © 2020 ACM.","Brain injury; Ethnomethodology; Memory aid; Multimodal interaction analysis; Reminder robot","Agricultural robots; Machine design; Man machine systems; Acquired brain injuries; Co-creation; Cognitive disability; Distributed agencies; Interaction analysis; Robot prototypes; Human robot interaction",,,,,,,,,,,,,,,,"Amrhein, A., Cyra, K., Pitsch, K., Processes of reminding and requesting in supporting people with special needs: Human practices as basis for modeling a virtual assistant (2016) Proceedings of the 1st Workshop on Ethics in the Design of Intelligent Agents, 30, pp. 14-19. , http://ceur-ws.org/Vol-1668/paper3.pdf, The Hague, The Netherlands, August 2016; Barnes, S., Planning' talk and traumatic brain injury: An exploratory application of conversation analysis (2012) Journal of Interactional Research in Communication Disorders, 3 (2), pp. 115-140. , https://doi.org/, 2012; Brockmeier, J., From memory as archive to remembering as conversation (2017) Handbook of Culture and Memory, pp. 41-64. , BradyWagoner (Ed.). Oxford University Press, Oxford; Crabtree, A., Rouncefield, M., Tolmie, P., (2012) Doing Design Ethnography, , Springer, London; Frennert, S., Ostlund, B., Review: Seven matters of concern of social robots and older people (2014) International Journal of Social Robotics, 6 (2), pp. 299-310. , https://doi.org/10.1007/s12369-013-0225-8, April 2014; Gross, H., Schroeter, C., Mueller, S., Volkhardt, M., Einhorn, E., Bley, A., Martin, C., Merten, M., Progress in developing a socially assistive mobile home robot companion for the elderly with mild cognitive impairment (2011) 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2430-2437. , https://doi.org/10.1109/IROS.2011.6094770; Heath, C., Hindmarsh, J., Luff, P., (2010) Video in Qualitative Research : Analysing Social Interaction in Everyday Life, , Sage, London; Hughes, J., King, V., Rodden, T., Andersen, H., Moving out from the Control Room: Ethnography in System Design (1994) Proceedings of the 1994 ACM Conference on Computer Supported CooperativeWork (CSCW '94), pp. 429-439. , https://doi.org/10.1145/192844.193065, ACM, New York, NY, USA; Hydén, L., Cutting brussels sprouts: Collaboration involving persons with dementia (2014) Journal of Aging Studies, 29, pp. 115-123. , https://doi.org/10.1016/j.jaging.2014.02.004, April 2014; Jayawardena, C., Kuo, I.H., Unger, U., Igic, A., Wong, R., Watson, C.I., Stafford, R.Q., MacDonald, B.A., Deployment of a service robot to help older people (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5990-5995. , https://doi.org/10.1109/IROS.2010.5649910; Kopp, S., Brandt, M., Buschmeier, H., Cyra, K., Freigang, F., Krämer, N.C., Kummert, F., Yaghoubzadeh, R., Conversational assistants for elderly users-The importance of socially cooperative dialogue (2018) Proceedings of the AAMAS Workshop on Intelligent Conversation Agents in Home and Geriatric Care Applications Co-located with the Federated AI Meeting (FAIM 2018), 15, pp. 10-17. , http://ceur-ws.org/Vol-2338/paper1.pdf, Stockholm, Sweden, July2018; Krummheuer, A., Conversation analysis, video recordings, and humancomputer interchanges (2009) Video Interaction Analysis. Methods and Methodology, pp. 59-83. , Ulrike Tikvah Kissmann (Ed.). Peter Lang, Frankfurt am Main; Lina Krummheuer, A., Rehm, M., Lund, M.K.L., Nielsen, K.N., The chance for sociability-An institutional corridor as a place for learning for adolescents with brain injury (2019) ID&A Interaction Design & Architecture(s), 39, pp. 78-95. , 2019; Majlesi, A.R., Ekström, A., Baking together- The coordination of actions in activities involving people with dementia (2016) Journal of Aging Studies, 38, pp. 37-46. , https://doi.org/10.1016/j.jaging.2016.04.004, Aug. 2016; Matari, M., On relevance: Balancing theory and practice in hri (2018) ACM Trans. Hum.-Robot Interact, 7 (1), p. 2. , https://doi.org/10.1145/3209770, May 2018; Mataric, M., Tapus, A., Winstein, C., Eriksson, J., Advanced technologies in rehabilitation (2009) IOS Press, Chapter Socially Assistive Robotics for Stroke and Mild TBI Rehabilitation, pp. 249-262; Montemerlo, M., Pineau, J., Roy, N., Thrun, S., Verma, V., Experiences with a mobile robotic guide for the elderly (2002) Eighteenth National Conference on Artificial Intelligence. American Association for Artificial Intelligence, pp. 587-592; Rehm, M., Lina Krummheuer, A., Rodil, K., Developing a new brand of culturally-aware personal robots based on local cultural practices in the danish health care system (2018) Proceedings of the International Conference on Intelligent Robots and Systems (IROS). IEEE, United States, , https://doi.org/10.1109/IROS.2018.8594478; Rehm, M., Lina Krummheuer, A., Rodil, K., Nguyen, M., Thorlacius, B., From social practices to social robots: User-driven robot development in elder care (2016) Social Robotics. Springer, Germany, pp. 692-701. , https://doi.org/10.1007/978-3-319-47437-368; Rodil, K., Rehm, M., Lina Krummheuer, A., Co-designing social robots with cognitively impaired citizens (2018) Proceedings of the 10th Nordic Conference on Human-Computer Interaction (NordiCHI '18), pp. 686-690. , https://doi.org/10.1145/3240167.3240253, ACM, New York, NY, USA; Sander, A.M., Van Veldhoven, L.M., Rehabilitation of memory problems associated with traumatic brain injury (2014) Handbook on the Neuropsychology of Traumatic Brain Injury, pp. 173-190. , https://doi.org/10.1007/978-1-4939-0784-79, Mark Sherer and Angelle M. Sander (Eds.). Springer New York, New York, NY; Suchman, L., (2007) Human and Machine Reconfigurations : Plans and Situated Actions, , 2nd ed ). Cambridge University Press, Cambridge; Tekin, B.S., Reeves, S., Ways of spectating: Unravelling spectator participation in kinect play (2017) Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17), pp. 1558-1570. , https://doi.org/10.1145/3025453.3025813, ACM, New York, NY, USA; Van Der Machiel, L.H.F., Reinkensmeyer, D.J., Guglielmelli, E., Rehabilitation and health care robotics (2016) Springer Handbook of Robotics, pp. 1685-1728. , https://doi.org/10.1007/978-3-319-32552-164, Bruno Siciliano and Oussama Khatib (Eds.). Springer International Publishing, Cham; Wilson, B.A., Memory rehabilitation (2009) Integrating Theory and Practice. The Guilford Press",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083282765
"Niemelä M., Ahtinen A., Turunen M.","23397605400;56091305500;8680277100;","Social human-robot interaction in the wild: A workshop proposal for academic mindtrek 2020",2020,"PervasiveHealth: Pervasive Computing Technologies for Healthcare",,,,"168","169",,,"10.1145/3377290.3377320","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080870105&doi=10.1145%2f3377290.3377320&partnerID=40&md5=a76d0001803ed5604fcf4cd48ad95dbb","VTT Technical Research Centre of Finland, Tampere, Finland; Human-Centered Technology, Tampere University, Tampere, Finland","Niemelä, M., VTT Technical Research Centre of Finland, Tampere, Finland; Ahtinen, A., Human-Centered Technology, Tampere University, Tampere, Finland; Turunen, M., VTT Technical Research Centre of Finland, Tampere, Finland","This workshop will collect experts and stakeholders from all fields of human-robot interaction: both social and industrial applications and uses of robotics are of interest as far as they have human in the loop. The workshop will present recent and fully new research work in social HRI, including first results of a 3.5 month field trial and mixed-method study of a social robot Pepper in a shopping mall in Finland. © 2020 Authors.","collaborative robots; human-robot interaction; social robots","Human robot interaction; Man machine systems; Collaborative robots; Field trial; Finland; Human-in-the-loop; Mixed method; Social hri; Social human-robot interactions; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 688147; Academy of Finland; Strategic Research Council, SRC: 292980, 314180","The workshop is supported by the project MultiModal Mall Entertainment Robot (http://www.mummer-project.eu) that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688147, and the project Robots and the Future of Welfare Services (http://roseproject.aalto.fi/en/) that is funded by the Academy of Finland, the Strategic Research Council (decision numbers: 292980 and 314180).",,,,,,,,,,"De Graaf, A.M.M., Ben Allouch, S., Klamer, T., Sharing a life with harvey: Exploring the acceptance of and relationship-building with a social robot (2015) Computers in Human Behavior 43 (February), pp. 1-14. , https://doi.org/10.1016/j.chb.2014.10.030; Vandemeulebroucke, T., De Casterlé, B.D., Gastmans, C., (2017) How Do Older Adults Experience and Perceive Socially Assistive Robots in Aged Care: A Systematic Review of Qualitative Evidence, pp. 1-19. , https://doi.org/10.1080/13607863.2017.1286455, Aging & Mental Health 7863 (February; Ellen Foster, M., Alami, R., Gestranius, O., Lemon, O., Niemelä, M., Jean-Marc Odobez, J.-M., Kumar, A., Pandey, A.K., The mummer project: Engaging human-robot interaction in real-world public spaces (2016) Lecture Notes in Computer Science, 9979, pp. 753-763. , https://doi.org/10.1007/978-3-319-47437-3_74, edited by A. Agah, JJ. Cabibihan, A. Howard, M. Salichs, and H. He, 9979 LNAI, Springer, Cham; Niemelä, M., Heikkilä, P., Lammi, H., Oksman, V., A social robot in a shopping mall: Studies on acceptance and stakeholder expectations (in publication) (2019) Book Social Robots: Technological, Societal and Ethical Aspects of Human-Robot Interaction, Edited by Oliver Korn, , Springer; Kaipainen, K., Ahtinen, A., Hiltunen, A., Nice surprise, more present than a machine: Experiences evoked by a social robot for guidance and edutainment at a city service point (2018) Proceedings of the 22nd International Academic Mindtrek Conference (Mindtrek '18) ACM, New York, NY, USA, pp. 163-171. , https://doi.org/10.1145/3275116.3275137",,,,"ICST","23rd International Academic Mindtrek Conference, AcademicMindtrek 2020","29 January 2020 through 30 January 2020",,157484,21531633,9781450377744,,,"English","PervasiveHealth: Pervasive Computing Technol Healthc.",Conference Paper,"Final","",Scopus,2-s2.0-85080870105
"Beschi M., Faroni M., Copot C., Pedrocchi N.","49962916100;57192065300;35301588200;14016736100;","How motion planning affects human factors in human-robot collaboration",2020,"IFAC-PapersOnLine","53","5",,"744","749",,,"10.1016/j.ifacol.2021.04.167","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107837678&doi=10.1016%2fj.ifacol.2021.04.167&partnerID=40&md5=0297e4d107fb168a075474e4f08cc5c2","University of Brescia, Department of Mechanical and Industrial Engineering, Via Branze 38, Brescia, 25123, Italy; National Research Council of Italy, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Via Alfonso Corti 12, Milan, 20133, Italy; Antwerp University, Department of Electromechanics, Op3Mech, University of Antwerp, Groenenborgerlaan 171, Antwerp, 2020, Belgium","Beschi, M., University of Brescia, Department of Mechanical and Industrial Engineering, Via Branze 38, Brescia, 25123, Italy; Faroni, M., National Research Council of Italy, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Via Alfonso Corti 12, Milan, 20133, Italy; Copot, C., Antwerp University, Department of Electromechanics, Op3Mech, University of Antwerp, Groenenborgerlaan 171, Antwerp, 2020, Belgium; Pedrocchi, N., National Research Council of Italy, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Via Alfonso Corti 12, Milan, 20133, Italy","Dependability of robot co-workers plays an important role in increasing the effectiveness of human-robot interaction in manufacturing. Our goal is to understand the role of motion planning parameters in human-robot collaboration and to provide guidelines for the selection of the most suitable motion planner. The human factors analysis provided in this paper highlights that repeatability of the motion and predictability of the robot timing affect the quality of human-robot collaboration. © 2020 Elsevier B.V.. All rights reserved.","ergonomics; human factors; human-aware motion planning; human-robot interaction","Human engineering; Motion planning; Robot programming; Factors analysis; Human-robot collaboration; Motion planners; Social robots",,,,,"780488","The researflh leading to these results was partially funded by the European Union H2020-ICT-2017-1 – Piflkplafle: Flexible, safe and dependable robotifl part handling in industrial environment (grant agreement: 780488).",,,,,,,,,,"Aaltonen, I., Salmi, T., Marstio, I., Refining levels of collaboration to support the design and evaluation of human-robot interaction in the manufacturing industry (2018) Procedia Cirp, , doi:10.1016/j.procir.2018.03.214; Adiyatov, O., Varol, H.A., A novel RRT&z.ast;-based algorithm for motion planning in Dynamic environments (2017) 2017 Ieee International Conference on Mechatronics and Automation, Icma 2017, , doi:10.1109/ICMA.2017.8016024; Behrens, R., Saenz, J., Vogel, C., Elkmann, N., Upcoming technologies and fundamentals for safeguarding all forms of human-robot collaboration (2015) The 8th International Conference on the Safety of Industrial Automated Systems (SIAS); Bolmsjö, G., Supporting Tools for Operator in Robot Collaborative Mode (2015) Procedia Manufacturing, , 101016/j.promfg.2015.07.190; Brusaferri, A., Leo, E., Nicolosi, L., Ramin, D., Spinelli, S., Integrated automation system with PSO based scheduling for PCB reman-ufacturing plants (2019) Ieee International Conference on Industrial Informatics (INDIN), , doi:10.1109/INDIN41052.2019.8972118; Charalambous, G., Fletcher, S., Webb, P., Identifying the key organisational human factors for introducing human-robot collaboration in industry: An exploratory study (2015) International Journal of Advanced Manufacturing Technology, , 101007/s00170-015-7335-4; Choudhury, S., Gammell, J.D., Barfoot, T.D., Srini-Vasa, S.S., Scherer, S., Regionally accelerated batch informed trees (RABIT&z.ast;): A framework to integrate local information into optimal path planning (2016) Proceedings-IEEE International Conference on Robotics and Automation, pp. 4207-4214. , doi:10.1109/ICRA.2016.7487615, 2016-June; Devaurs, D., Simeon, T., Cortes, J., Enhancing the transition-based RRT to deal with complex cost spaces (2013) 2013 Ieee International Conference on Robotics and Automation, pp. 4120-4125. , http://ieeexplore.ieee.org/document/6631158/, IEEE. doi:10.1109/ICRA.2013.6631158. URL; Freedy, A., DeVisser, E., Weltman, G., Coeyman, N., Measurement of trust in human-robot collaboration (2007) Proceedings of the 2007 International Symposium on Collaborative Technologies and Systems, Cts, , doi:10.1109/CTS.2007.4621745; Gammell, J.D., Srinivasa, S.S., Barfoot, T.D., Informed RRT&z.ast;: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic (2014) Ieee International Conference on Intelligent Robots and Systems, , doi:10.1109/IROS.2014.6942976; Hoffman, G., Evaluating Fluency in Human-Robot Collaboration (2019) Ieee Transactions on Human-Machine Systems, , 101109/THMS.2019.2904558; Jaillet, L., Cortés, J., Simeón, T., Sampling-Based Path Planning on Configuration-Space Costmaps (2010) Ieee Transactions on Robotics, 26 (4), pp. 635-646. , 101109/TRO.2010.2049527; Karpas, E., Magazzeni, D., Automated planning for robotics (2020) Annual Review of Control, Robotics, and Autonomous Systems, p. 3; Koppenborg, M., Nickel, P., Naber, B., Lungfiel, A., Huelke, M., Effects of movement speed and predictability in human-robot collaboration (2017) Human Factors and Ergonomics in Manufacturing, , 101002/hfm.20703; Kuffner, J.J., La Valle, S.M., RRT-connect: An efficient approach to single-query path planning (2000) Proceedings-IEEE International Conference on Robotics and Automation, , doi:10.1109/robot.2000.844730; Lasota, P.A., Shah, J.A., Analyzing the effects of human-aware motion planning on close-proximity human-robot collaboration (2015) Human Factors, 57 (1), pp. 21-33. , 101177/0018720814565188; LaValle, S.M., (1998) Rapidly-exploring Random Trees: A New Tool for Path Planning, , doi:10.1.1.35.1853, In; LaValle, S.M., (2006) Planning Algorithms, , Cambridge University Press Cambridge doi: 10.1017/CBO9780511546877; Luo, J., Hauser, K., An empirical study of optimal motion planning (2014) Ieee International Conference on Intelligent Robots and Systems, , doi:10.1109/IROS.2014.6942793; Nagamachi, M., Human factors of industrial robots and robot safety management in Japan (1986) Applied Ergonomics, 17 (1), pp. 9-18. , 101016/0003-6870(86)90187-0; Oliff, H., Liu, Y., Kumar, M., Williams, M., A framework of integrating knowledge of human factors to facilitate hmi and collaboration in intelligent manufacturing (2018) Procedia Cirp, , doi:10.1016/j.procir.2018.03.047; Panny, M., Consequences of industrial robots in the field of work organization (1983) Ifac Proceedings Volumes, 16 (22), pp. 115-119. , doi:10.1016/S1474-6670(17)61565-9; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., ROS: An open-source Robot Operating System (2009) Icra Workshop on Open Source Software; Robla-Gomez, S., Becerra, V.M., Llata, J.R., Gonzalez-Sarabia, E., Torre-Ferrero, C., Perez-Oria, J., (2017) Working Together: A Review on Safe Human-Robot Collaboration in Industrial Environments, , doi:10.1109/ACCESS.2017.2773127; Saenz, J., Elkmann, N., Gibaru, O., Neto, P., Survey of methods for design of collaborative robotics applications-Why safety is a barrier to more widespread robotics uptake (2018) Proceedings of the 2018 4th International Conference on Mechatronics and Robotics Engineering-ICMRE 2018, pp. 95-101. , doi:10.1145/3191477.3191507, ACM Press, New York, New York, USA",,,,"Elsevier B.V.","3rd IFAC Workshop on Cyber-Physical and Human Systems, CPHS 2020","3 December 2020 through 5 December 2020",,145888,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85107837678
"Brelstaff G., Brelstaff A.","6603066582;57219696068;","Affective HRI: A potential impedance mismatch",2020,"CEUR Workshop Proceedings","2696",,,"33","34",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094821550&partnerID=40&md5=977e4749dc1748eccae16a2b244ba3de","CRS4 - Sardinia, Pula CA, 09050, Italy; University of Cagliari, Cagliari, Sardinia, Italy","Brelstaff, G., CRS4 - Sardinia, Pula CA, 09050, Italy; Brelstaff, A., University of Cagliari, Cagliari, Sardinia, Italy","This position paper explores algorithmic considerations underlying affective Human Robot Interaction (HRI) with attention given to the case of individuals unable to communicate rationally. First we follows Dennett's algorithmic conception of Mind before elaborating an alternative by which Affect gains the role of co-founding constituent. This raises the prospect of an impedance mismatch for Affective HRI where non-algorithmic qualities get pursued on a purely algorithmic basis. Possible consequences are weighed. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).","Affective Interaction; Algorithms; HRI; User Illusion","Social robots; Human robot Interaction (HRI); Impedance mismatch; Position papers; Impedance matching (electric)",,,,,,,,,,,,,,,,"Breazeal, Cynthia, Affective Interaction between Humans and Robots (2001) Advances in Artificial Life. ECAL 2001. Lecture Notes in Computer Science, 2159. , https://doi.org/10.1007/3-540-44811-X_66, Kelemen J., Sosík P. (eds) Springer, Berlin, Heidelberg; Patočka, Jan, (1938) The Natural World as a Philosophical Problem, , Northwestern University Press 2016; Sellars, Wilfrid, (1962) Science, Perception, and Reality, , London: Routledge and Paul; Dennett, Daniel C., (2017) From Bacteria To Bach and Back, , Norton, New York NY; Huang, Yanping, Rao, Rajesh P. N., Predictive coding (2011) WIREs Cogn Sci, 2, pp. 580-593. , https://doi.org/10.1002/wcs.142; Piaget, Jean, Part I: Cognitive development in children: Piaget development and learning (1964) Journal of Research in Science Teaching; Clark, Andy, Whatever next? Predictive brains, situated agents, and the future of cognitive science (2013) Behavioral and Brain Sciences, 36 (3), pp. 181-204. , https://doi.org/10.1017/S0140525X12000477, 196; Brelstaff, Gavin, Brelstaff, Alexandra, (2019) A Touch of Grounding, Luminous Workshop: Studying, Measuring and Altering Consciousness, pp. 8-11; O'Brien, Dan, (2017) An Introduction to the Theory of Knowledge, , Polity Press Cambridge, UK,. Ch.7 Sec 3.1; Damasio, Antonio, (2010) Self Comes to Mind: Constructing the Conscious Brain Pantheon, p. 116. , New York NY; Halina, Marta, Mechanistic Explanation and its Limits (2018) The Routledge Handbook of mechanisms and mechanical philosophy, pp. 213-237. , (ed. St Glennan &P. Illari) Routledge: New York, NY; Atee, M., Hoti, K., Hughes, J.D., Psychometric Evaluation of the Electronic Pain Assessment Tool: An Innovative Instrument for Individuals with Moderate-to-Severe Dementia (2017) Dement Geriatr Cogn Disord, 44, pp. 256-267. , https://doi.org/10.1159/000485377","Brelstaff, G.; CRS4 - SardiniaItaly; email: gjb@crs4.it","De Carolis B.Gena C.Lieto A.Rossi S.Sciutti A.",,"CEUR-WS","2020 Workshop on Adapted Interaction with Social Robots, cAESAR 2020","17 March 2020",,163832,16130073,,,,"English","CEUR Workshop Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85094821550
"Shiomi M., Sumioka H., Ishiguro H.","8397042200;6505862269;26643316500;","Survey of social touch interaction between humans and robots",2020,"Journal of Robotics and Mechatronics","32","1",,"128","135",,3,"10.20965/jrm.2020.p0128","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079857837&doi=10.20965%2fjrm.2020.p0128&partnerID=40&md5=23e82fa8b6bc30f09b4f2056a3270f4d","Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan; Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Osaka, Toyonaka, 560-8531, Japan","Shiomi, M., Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan; Sumioka, H., Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan; Ishiguro, H., Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Osaka, Toyonaka, 560-8531, Japan","In human-human interaction, social touch provides several merits, from both physical and mental perspectives. The physical existence of robots helps them reproduce human-like social touch, during their interaction with people. Such social touch shows positive effects, similar to those observed in human-human interaction. Therefore, social touch is a growing research topic in the field of human-robot interaction. This survey provides an overview of the work conducted so far on this topic. © 2020, Fuji Technology Press. All rights reserved.","Human-robot interaction; Social touch","Human robot interaction; Man machine systems; Surveys; Human like; Human-human interactions; Research topics; Social touch; Touch interaction; Social robots",,,,,"JPMJCR18A1; JP-MJER1401","This research work was partially supported by the JST CREST Grant Number JPMJCR18A1, Japan, and JST ERATO Ishiguro Symbiotic Human Robot Interaction Project (Grant Number: JP-MJER1401).",,,,,,,,,,"Li, J., The benefit of being physically present: A survey of experimental works comparing copresent robots, telepresent robots and virtual agents (2015) Int. J. of Human-Computer Studies, 77, pp. 23-37; Bainbridge, W.A., Hart, J., Kim, E.S., Scassellati, B., The effect of presence on human-robot interaction (2008) Proc. of the 17Th IEEE Int. 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Conf. on Robotics and Automation, pp. 3966-3971; Sumioka, H., Nakae, A., Kanai, R., Ishiguro, H., Huggable communication medium decreases cortisol levels (2013) Scientific Reports, 3; Shi, C., Shiomi, M., Smith, C., Kanda, T., Ishiguro, H., A Model of Distributional Handing Interaction for a Mobile Robot (2013) Proc. of Robotics: Science and Systems, pp. 24-28; Satake, S., Hayashi, K., Nakatani, K., Kanda, T., Field trial of an information-providing robot in a shopping mall (2015) Proc. of 2015 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 1832-1839",,,,"Fuji Technology Press",,,,,09153942,,,,"English","J. Rob. Mechatronics",Review,"Final","",Scopus,2-s2.0-85079857837
"Keijsers M., Kazmi H., Eyssel F., Bartneck C.","57201505732;57189214664;9846264800;6602256988;","Teaching Robots a Lesson: Determinants of Robot Punishment",2021,"International Journal of Social Robotics","13","1",,"41","54",,5,"10.1007/s12369-019-00608-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076205242&doi=10.1007%2fs12369-019-00608-w&partnerID=40&md5=59a2361046ae4cab602120a4bde50642","Human Interface Technology lab NZ, University of Canterbury, Christchurch, New Zealand; Center of Excellence Cognitive Interaction Technology, Bielefeld University, Bielefeld, Germany; Department of Electrical Engineering, KU Leuven, Leuven, Belgium","Keijsers, M., Human Interface Technology lab NZ, University of Canterbury, Christchurch, New Zealand; Kazmi, H., Department of Electrical Engineering, KU Leuven, Leuven, Belgium; Eyssel, F., Center of Excellence Cognitive Interaction Technology, Bielefeld University, Bielefeld, Germany; Bartneck, C., Human Interface Technology lab NZ, University of Canterbury, Christchurch, New Zealand","There have been multiple incidents where humans attacked robots in a public environment (Brscić et al., in: Proceedings of the international conference on human–robot interaction, ACM/IEEE, Portland, 2015, https://doi.org/10.1145/2696454.2696468); Vincent, in: A drunk man was arrested for knocking over Silicon Valley’s crime-fighting robot, 2017, https://www.theverge.com/2017/4/26/15432280/security-robot-knocked-over-drunk-man-knightscope-k5-mountain-view; Mosbergen, in: Good job, America. You killed hitchBOT. Huffpost, 2015, https://www.huffpost.com/entry/hitchbot-destroyed-philadelphia_n_55bf24cde4b0b23e3ce32a67; Mutlu and Forlizzi, in: Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction, ACM, 2008, https://doi.org/10.1145/2696454.2696468; Rehm and Krogsager, in: 2013 Proceedings of IEEE RO-MAN, IEEE, 2013, https://doi.org/10.1145/2696454.2696468; (Salvini et al., in: 19th International symposium in robot and human interactive communication, 2010). Although the form of aggression suggests that this behaviour might be motivated by the aggressor’s desire for social recognition rather than an urge for vandalism (Salvini et al. 2010; Keijsers and Bartneck, in: Proceedings of the international conference on human–robot interaction, ACM/IEEE, New York, 2018, https://doi.org/10.1145/2696454.2696468), very little is known about the underlying psychological mechanisms. Therefore, extending previous research, the current study investigated if human aggression towards a robot would be influenced by the aggressor’s feelings of power, the perception of the threat that robots in general might pose, mind attribution to the robot, and the robot’s embodiment. First, threat and power were manipulated. Subsequently, participants played a learning task with either a virtual or an embodied robot. Mind attribution was measured afterwards. Participants were asked to restrict the robot’s energy supply after each wrong answer, which was taken as a measure of aggression. Results indicated that an embodied robot was punished less harshly than a virtual one, except for when people had been primed with power and threat. Being primed with power diminished the influence of mind attribution. Mind attribution increased aggression in the threat condition but was related to decreased aggression when people had not been reminded of threat. © 2019, Springer Nature B.V.","Human–Robot interaction; Mind attribution; Robot aggression; Robot embodiment; Robot threat","Agricultural robots; HTTP; Energy supplies; Interactive communications; Mind attribution; Public environment; Robot interactions; Silicon valley; Social recognition; Teaching robots; Social robots",,,,,"University of Canterbury, UC","This research was funded by the University of Canterbury, New Zealand.",,,,,,,,,,"(2017) Adobe After Effects CC for MacOS (14.2.1), , Computer software; (2014) Choregraphe for Macos (2.1.4) [Computer Software]; (1995) Textedit (Version 1.12 (329)) [Computer Software], Voice “Junior”; Bain, P., Park, J., Kwok, C., Haslam, N., Attributing human uniqueness and human nature to cultural groups: Distinct forms of subtle dehumanization (2009) Group Process Intergroup Relat, 12 (6), pp. 789-805. , 10.1177/1368430209340415 DOI; Bartneck, C., Hu, J., Exploring the abuse of robots (2008) Interact Stud, 9 (3), pp. 415-433. , . 10.1075/is.9.3.04bar DOI; Bartneck, C., Rosalia, C., Menges, R., Deckers, I., Robot abuse—a limitation of the media equation (2005) Proceedings of the Interact 2005 Workshop on Agent Abuse, Designed Intelligence, , https://doi.org/10.17605/OSF.IO/4FXQ6, Rome, Italy; Bartneck, C., van Der Hoek, M., Mubin, O., Al Mahmud, A., Daisy, Daisy, give me your answer do! 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Autonomous robots threaten human identity, uniqueness, safety, and resources (2017) Int J Hum Comput Stud, 100","Keijsers, M.; Human Interface Technology lab NZ, New Zealand; email: merel.keijsers@pg.canterbury.ac.nz",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85076205242
"Küster D., Putze F., St-Onge D., Fortin P.E., Urrestilla N., Schultz T.","15064259400;22036416700;53880511100;56439075200;57221469479;7201770266;","3rd Workshop on Modeling Socio-Emotional and Cognitive Processes from Multimodal Data in the Wild",2021,"ICMI 2021 - Proceedings of the 2021 International Conference on Multimodal Interaction",,,,"860","861",,,"10.1145/3462244.3480978","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118973720&doi=10.1145%2f3462244.3480978&partnerID=40&md5=89e57282d81a88df22df43ae8c4c0fed","University of Bremen, Bremen, Germany; École de Technologie Supérieure, Montréal, Canada; McGill University, Montréal, Canada","Küster, D., University of Bremen, Bremen, Germany; Putze, F., University of Bremen, Bremen, Germany; St-Onge, D., École de Technologie Supérieure, Montréal, Canada; Fortin, P.E., McGill University, Montréal, Canada; Urrestilla, N., École de Technologie Supérieure, Montréal, Canada; Schultz, T., University of Bremen, Bremen, Germany","Modeling with multimodal data in the wild poses similar challenges in human-computer and human-robot interaction (HCI, HRI). This workshop series thus blends HCI and HRI to jointly address a broad range of current topics in multimodal modeling aimed at designing intelligent systems in the wild. From addressing data scarcity in multimodal user state recognition to emotion prediction from EEG while listening to music, our third workshop in this series aims to further stimulate this important multidisciplinary exchange. © 2021 Owner/Author.","attention; emotion recognition; engagement; field studies; human-robot-interaction (HRI); multimodal modeling","Intelligent systems; Man machine systems; 'current; Attention; Cognitive process; Emotion recognition; Engagement; Field studies; Human-robot-interaction; Humans-robot interactions; Multi-modal data; Multimodal models; Human robot interaction",,,,,,,,,,,,,,,,"Alves-Oliveira, P., Küster, D., Kappas, A., Paiva, A., Psychological science in hri: Striving for a more integrated field of research (2016) 2016 Aaai Fall Symposium Series; Belpaeme, T., The wizard is dead, long live data: Towards autonomous social behaviour using data-driven methods (2020) Companion Publication of the 2020 International Conference on Multimodal Interaction, p. 411. , https://doi.org/10.1145/3395035.3425204, (Virtual Event, Netherlands) (ICMI '20 Companion). Association for Computing Machinery, New York, NY, USA; Cacioppo, J.T., Tassinary, L.G., (1990) Principles of Psychophysiology: Physical, Social, and Inferential Elements, , Cambridge University Press; Chatterjee, S., Scheck, K., Küster, D., Putze, F., Moturu, H., Schering, J., Gómez, J.M., Schultz, T., Smart helm: Towards multimodal detection of attention in an outdoor augmented reality biking scenario (2020) Companion Publication of the 2020 International Conference on Multimodal Interaction, pp. 426-432. , https://doi.org/10.1145/3395035.3425207, (Virtual Event, Netherlands) (ICMI '20 Companion). Association for Computing Machinery, New York, NY, USA; Foltyn, A., Deuschel, J., Towards reliable multimodal stress detection under distribution shift (2021) Proceedings of the 2021 International Conference on Multimodal Interaction (ICMI '21), , October 18-22, 2021, Montreal, Canada. Association for Computing Machinery, New York, NY, USA; Gümüsl, E., Barkana, D.E., Köse, H., Emotion recognition using EEG and physiological data for robot-assisted rehabilitation systems (2020) Companion Publication of the 2020 International Conference on Multimodal Interaction, pp. 379-387. , https://doi.org/10.1145/3395035.3425199, (Virtual Event, Netherlands) (ICMI '20 Companion). Association for Computing Machinery, New York, NY, USA; Küster, D., Putze, F., Alves-Oliveira, P., Paetzel, M., Schultz, T., Modeling socio-emotional and cognitive processes from multimodal data in the wild (2020) Proceedings of the 2020 International Conference on Multimodal Interaction, pp. 883-885. , https://doi.org/10.1145/3382507.3420053, (Virtual Event, Netherlands) (ICMI '20). Association for Computing Machinery, New York, NY, USA; Küster, D., Kappas, A., What could a body tell a social robot that it does not know? (2014) Proceedings of the International Conference on Physiological Computing Systems, pp. 358-367. , https://doi.org/10.5220/0004892503580367, (Lisbon, Portugal). SCITEPRESS - Science and and Technology Publications; Lechappe, A., Chollet, M., Rigaud, J., Cao, C.G.L., Assessment of situation awareness during robotic surgery using multimodal data (2020) Companion Publication of the 2020 International Conference on Multimodal Interaction, pp. 412-416. , https://doi.org/10.1145/3395035.3425205, (Virtual Event, Netherlands) (ICMI '20 Companion). Association for Computing Machinery, New York, NY, USA; Miyamoto, K., Tanaka, H., Nakamura, S., Meta-learning for emotion prediction from eeg while listening to music (2021) Proceedings of the 2021 International Conference on Multimodal Interaction (ICMI '21), , October 18-22, 2021, Montreal, Canada. Association for Computing Machinery, New York, NY, USA; Neal, T., Zanna, K., Canavan, S., Clustering of physiological signals by emotional state, race, and sex (2021) Proceedings of the 2021 International Conference on Multimodal Interaction (ICMI '21), , October 18-22, 2021, Montreal, Canada. Association for Computing Machinery, New York, NY, USA; Parthasarathy, S., Sundaram, S., Training strategies to handle missing modalities for audio-visual expression recognition (2020) Companion Publication of the 2020 International Conference on Multimodal Interaction, pp. 400-404. , https://doi.org/10.1145/3395035.3425202, (Virtual Event, Netherlands) (ICMI '20 Companion). Association for Computing Machinery, New York, NY, USA; Putze, F., Burri, M., Vortmann, L.-M., Schultz, T., Model- based prediction of exogeneous and endogeneous attention shifts during an everyday activity (2020) Companion Publication of the 2020 International Conference on Multimodal Interaction, pp. 417-425. , https://doi.org/10.1145/3395035.3425206, (Virtual Event, Netherlands) (ICMI '20 Companion). Association for Computing Machinery, New York, NY, USA; Putze, F., Hild, J., Sano, A., Kasneci, E., Solovey, E., Schultz, T., Modeling cognitive processes from multimodal signals (2018) Proceedings of the 20th Acm International Conference on Multimodal Interaction, p. 663. , https://doi.org/10.1145/3242969.3265861, (Boulder, CO, USA) (ICMI '18). Association for Computing Machinery, New York, NY, USA; St-Onge, D., Kaufmann, M., Panerati, J., Ramtoula, B., Cao, Y., Coffey, E.B.J., Beltrame, G., Planetary exploration with robot teams: Implementing higher autonomy with swarm intelligence (2020) Ieee Robotics Automation Magazine, 27 (2), pp. 159-168. , https://doi.org/10.1109/MRA.2019.2940413, 2020; Urrestilla, N., St-Onge, D., Measuring cognitive load: Heart-rate variability and pupillometry assessment (2020) Companion Publication of the 2020 International Conference on Multimodal Interaction, pp. 405-410. , https://doi.org/10.1145/3395035.3425203, (Virtual Event, Netherlands) (ICMI '20 Companion). Association for Computing Machinery, New York, NY, USA; Voss, H., Wersing, H., Kopp, S., Addressing data scarcity in multimodal user state recognition by combining semi-supervised and supervised learning (2021) Proceedings of the 2021 International Conference on Multimodal Interaction (ICMI '21), , October 18-22, 2021, Montreal, Canada. Association for Computing Machinery, New York, NY, USA",,,"ACM SIGCHI","Association for Computing Machinery, Inc","23rd ACM International Conference on Multimodal Interaction, ICMI 2021","18 October 2021 through 22 October 2021",,172814,,9781450384810,,,"English","ICMI - Proc. Int. Conf. Multimodal Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85118973720
"Adhau S.P., Kadwane S.G., Rajguru V.S., Telrandhe S.","35932091600;16319099000;36959819700;57217134036;","Pattern Recognition Classification for Myoelectric based Control of the Robotic Arm using Neural Network",2021,"2021 International Conference on Intelligent Technologies, CONIT 2021",,,,"","",,,"10.1109/CONIT51480.2021.9498500","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114888115&doi=10.1109%2fCONIT51480.2021.9498500&partnerID=40&md5=8544e61bf96c3c13defac3f01d0086d8","Yeshwantrao Chavan College of Engineering, Nagpur, India; College of Engineering, Department of Electrical Engineering, Pune, India; Datta Meghe Institute of Medical Sciences, Jawaharlal Nehru Medical College, Sawangi, Wardha, India","Adhau, S.P., Yeshwantrao Chavan College of Engineering, Nagpur, India; Kadwane, S.G., Yeshwantrao Chavan College of Engineering, Nagpur, India; Rajguru, V.S., College of Engineering, Department of Electrical Engineering, Pune, India; Telrandhe, S., Datta Meghe Institute of Medical Sciences, Jawaharlal Nehru Medical College, Sawangi, Wardha, India","Because of its relevance to society, human-robot interaction has long been a research subject for academics. The creation of a robust human-interactive robot that takes commands from electromyogram (EMG) signals is currently being investigated. This paper explores the movements of a system that enables signals to be recorded directly from a human body and then used to operate a small robotic arm. Pattern classification is done with neural network and used for gestures of the artificialarm. © 2021 IEEE.","gesture recognition; myoelectric patterns; neural network","Biomedical signal processing; Pattern recognition; Robotic arms; Robotics; Social robots; Electromyogram; Human bodies; Human interactive robots; Research subjects; Neural networks",,,,,,,,,,,,,,,,"Saravanan, N., Kazi, M.M., (2008) Biosignal Based Human-Machine Interface for Robotic Arm, , Madras Institute of Technology; Akazawa, K., Okuno, R., Yoshida, M., Biomimetic EMG-prosthesis-hand (1996) Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 535-536; Barrero, V., Grisales, V., Rosas, F., Sanchez, C., León, J., Design and implementation of an intelligent interface for myoelectric controlled prosthesis (2001) 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 4, pp. 3352-3355. , October. IEEE; Hudgins, B., Parker, P., Scott, R.N., A new strategy for multifunction myoelectric control (1993) IEEE Transactions on Biomedical Engineering, 40 (1), pp. 82-94; Andrade, A.O., Nasuto, S., Kyberd, P., Sweeney-Reed, C.M., van Kanijn, F.R., EMG signal filtering based on empirical mode decomposition (2006) Biomedical Signal Processing and Control, 1 (1), pp. 44-55; Crawford, B., Miller, K., Shenoy, P., Rao, R., Real-time classification of electromyographic signals for robotic control (2005) AAAI, 5, pp. 523-528. , July; Reaz, M.B.I., Hussain, M.S., Mohd-Yasin, F., Techniques of EMG signal analysis: Detection, processing, classification and applications (2006) Biological Procedures Online, 8 (1), pp. 11-35; Su, Y., Fisher, M.H., Wolczowski, A., Bell, G.D., Burn, D.J., Gao, R.X., Towards an EMG-controlled prosthetic hand using a 3-D electromagnetic positioning system (2007) IEEE Transactions on Instrumentation and Measurement, 56 (1), pp. 178-186; Fukuda, O., Tsuji, T., Shigeyoshi, H., Kaneko, M., An EMG controlled human supporting robot using neural network (1999) Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients, 3, pp. 1586-1591. , October. Cat.99CH36289) . IEEE; Elbasioun, S.M., EI-Bialy, A.M., Taher, M.F., Kandil, A.H., Rasmy, M.E., “A Myoelectric Prosthesis Controller” Department of Biomedical Engineering, , Helwan University, cario.Egypt; Manal, K., Gonzalez, R.V., Lloyd, D.G., Buchanan, T.S., A real-time EMG-driven virtual arm (2002) Computers in Biology and Medicine, 32 (1), pp. 25-36; Monzon, J., Moore, J., Song, S., The Emg Controlled Robotic Arm, , Bioelectronics project laboratory; Mulas, M., Folgheraiter, M., Gini, G., An EMG-controlled exoskeleton for hand rehabilitation (2005) 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005, pp. 371-374. , June. IEEE; Choi, C., Development and Performance Evaluation of a Neural Signal-based Assistive Computer Interface (2007) RO-MAN 2007- The 16th IEEE International Symposium on Robot and Human Interactive Communication, pp. 944-949; Jeong, H., Choi, J., An EMG-controlled graphic interface considering wearability (2003) INTERACT; Karlik, B., Differentiating type of muscle movement via AR modeling and neural network classification (2000) Turkish Journal of Electrical Engineering & Computer Sciences, 7 (1-3), pp. 45-52; Zecca, M., Micera, S., Carrozza, M.C., Dario, P., Control of multifunctional prosthetic hands by processing the electromyographic signal Biomedical Engineering; Rani, P., Sarkar, M.S., Emg-based high level human- Robot interaction system for people with disability 2005 IEEE International Workshop on Robots and Human Interactive Communication; Jones, D.W., Control of stepping motors (2001) Handbook of Small Electric Motors, , McGraw-Hill; Franke, D.R., (2005) Design and Application of A PC Interfaced Cosmic Ray Detection System for Secondary School Physics Programs, , University of Missouri-Saint Louis; User’S Guide, , Neural Network tool box. For use with MATLAB. The Math Works; Jain, G., Noorani, N., Badole, V., Design and Implementation of a GPS Receiver using 8051 Microcontroller (2013) International Journal of Research, 1, p. 5; Singh, D., Singh, G.K., Srivastava, S., Carpal tunnel syndrome among occupational workers in India (2013) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 57 (1), pp. 883-886. , September. Sage CA: Los Angeles, CA: SAGE Publications; He, J., Joshi, M.V., Chang, J., Jiang, N., Efficient correction of amband rotation for myoelectric-based gesture control interface (2020) Journal of Neural Engineering; Jaber, H.A., Rashid, M.T., Fortuna, L., Adaptive myoelectric pattern recognition based on hybrid spatial features of HD-SEMG signals (2020) Iranian Journal of Science and Technology, Transactions of Electrical Engineering, pp. 1-12",,,,"Institute of Electrical and Electronics Engineers Inc.","2021 International Conference on Intelligent Technologies, CONIT 2021","25 June 2021 through 27 June 2021",,171211,,9781728185835,,,"English","Int. Conf. Intell. Technol., CONIT",Conference Paper,"Final","",Scopus,2-s2.0-85114888115
"Yim Y., Tanaka F.","57222466741;8640493100;","Development of an inflatable haptic device for pain reduction by social touch",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447134","86","88",,,"10.1145/3434074.3447134","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102748967&doi=10.1145%2f3434074.3447134&partnerID=40&md5=0779d8765030ebd205e7874b1edf4086","University of Tsukuba, Tsukuba, Japan","Yim, Y., University of Tsukuba, Tsukuba, Japan; Tanaka, F., University of Tsukuba, Tsukuba, Japan","Previous Human-Robot Interaction studies have shown that social touching can be established under human-robot touch conditions. To provide the sensation of a robot grasping human hand when he/she grasps the robot, we develop a soft robot that is consisted of airbags and a force sensor. We expect that this mutual touching can take a positive role in alleviating human pains. This paper presents the development of our two prototypes of inflatable haptic devices consisting of airbags. © 2021 ACM.","Haptic device; Pain reduction; Relief; Social touch; Soft robotics","Agricultural robots; Man machine systems; Force sensor; Haptic devices; Human hands; Human robots; Robot grasping; Soft robot; Social robots",,,,,"Japan Society for the Promotion of Science, KAKEN: 20K21800","This work is supported by JSPS KAKENHI 20K21800.",,,,,,,,,,"Geva, N., Uzefovsky, F., Levy-Tzedek, S., Touching the social robot paro reduces pain perception and salivary oxytocin levels (2020) Scientific Reports, 10 (9814). , 2020; Goldstein, P., Shamay-Tsoory, S.G., Yellinek, S., Weissman-Fogel, I., Empathy predicts an experimental pain reduction during touch (2016) The Journal of Pain, 17 (10), pp. 1049-1057. , 2016; Goldstein, P., Weissman-Fogel, I., Dumas, G., Shamay-Tsoory, S.G., Brain-to-brain coupling during handholding is associated with pain reduction (2018) Proceedings of the National Academy of Sciences, 115 (11), pp. E2528-E2537. , 2018; Goldstein, P., Weissman-Fogel, I., Shamay-Tsoory, S.G., The role of touch in regulating inter-partner physiological coupling during empathy for pain (2017) Scientific Reports, 7 (3252). , 2017; López-Solà, M., Geuter, S., Koban, L., Coan, J.A., Wager, T.D., Brain mechanisms of social touch-induced analgesia in females (2019) PAIN, 160 (9), pp. 2072-2085. , 2019; Mancini, F., Beaumont, A., Hu, L., Haggard, P., Domenico, I.D.G., Touch inhibits subcortical and cortical nociceptive responses (2015) PAIN, 156 (10), pp. 1936-1944. , 2015; Mancini, F., Nash, T., Iannetti Domenico, G., Haggard, P., Pain relief by touch: A quantitative approach (2014) PAIN, 155 (3), pp. 635-642. , 2014; Ou, J., Skouras, M., Vlavianos, N., Heibeck, F., Cheng, C., Peters, J., Ishii, H., Aeromorph-heat-sealing inflatable shape-change materials for interaction design (2016) Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST '16), pp. 121-132. , Association for Computing Machinery; Willemse, M.A.C.J., Erp Van, F.J.B., Social touch in human-robot interaction: Robot-initiated touches can induce positive responses without extensive prior bonding (2019) International Journal of Social Robotics, 11 (2), pp. 285-304. , 2019",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102748967
"Moore R., Williams A.B.","57208161221;7405838909;","Towards a learning architecture to support social scaffolding for an artificially intelligent disability assistant",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447215","467","469",,,"10.1145/3434074.3447215","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102736881&doi=10.1145%2f3434074.3447215&partnerID=40&md5=2280ee580c571fecd955948f0d1ea3e4","Emory University, Atlanta, GA, United States; University of Kansas, Lawrence, KS, United States","Moore, R., Emory University, Atlanta, GA, United States; Williams, A.B., University of Kansas, Lawrence, KS, United States","Humanoid workers that can improve the joy and achievement of workers with intellectual and developmental disabilities (IDD) hold promise in light manufacturing settings. In this paper, we provide details of an architecture to support social scaffolding for workers with IDD and efforts to adapt it to learn. This architecture is developed for human-robot interaction using the Pepper robot and will support future improvements using machine learning. Additional recommendations based on past experimentation are given for future work. © 2021 ACM.","Collaborative robotics; Intellectual and developmental disability; Machine learning; Social robotics; Social scaffolding","Agricultural robots; Architecture; Man machine systems; Scaffolds; Developmental disability; Future improvements; Learning architectures; Human robot interaction",,,,,,,,,,,,,,,,"Breazeal, C., Thomaz, A.L., Learning from human teachers with socially guided exploration (2008) 2008 IEEE International Conference on Robotics and Automation, pp. 3539-3544. , https://doi.org/10.1109/ROBOT.2008.4543752; Chen, S., Li, X., Jin, Q., Zhang, S., Qin, Y., Video emotion recognition in the wild based on fusion of multimodal features (2016) Proceedings of the 18th ACM International Conference on Multimodal Interaction (ICMI '16), pp. 494-500. , https://doi.org/10.1145/2993148.2997629, Association for Computing Machinery, New York, NY, USA; Mascolo, M.F., Change processes in development: The concept of coactive scaffolding (2005) New Ideas in Psychology, 23 (3), pp. 185-196. , https://doi.org/10.1016/j.newideapsych.2006.05.002Revisitingscaffolding:Whatdowethinkweknowandwhatdowestillneedtofigureout?, 2005; Moore, R., Williams, A.B., Aida: Using social scaffolding to assist workers with intellectual and developmental disabilities (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI '20), pp. 366-368. , https://doi.org/10.1145/3371382.3378385, Association for Computing Machinery, New York, NY, USA; Williams, A.B., Williams, R.M., Moore, R.E., McFarlane, M., Aida: A social co-robot to upliftworkers with intellectual and developmental disabilities (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 584-585. , https://doi.org/10.1109/HRI.2019.8673272",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102736881
"Chen L., Wu M., Pedrycz W., Hirota K.","55774663200;35231682600;57210775415;7402829702;","Emotion-Age-Gender-Nationality Based Intention Understanding Using Two-Layer Fuzzy Support Vector Regression",2021,"Studies in Computational Intelligence","926",,,"183","214",,,"10.1007/978-3-030-61577-2_11","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096207038&doi=10.1007%2f978-3-030-61577-2_11&partnerID=40&md5=9033849dddc0d75d4a9d9c8d781c7857","School of Automation, China University of Geosciences, Wuhan, China; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Tokyo Institute of Technology, Yokohama, Japan","Chen, L., School of Automation, China University of Geosciences, Wuhan, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, China; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Hirota, K., Tokyo Institute of Technology, Yokohama, Japan","An intention understanding model based on two-layer fuzzy support vector regression (TLFSVR) is proposed in human-robot interaction, where Fuzzy C-Means clustering is used to classify the input data, and intention understanding is mainly obtained by emotion, with identification information such as age, gender, and nationality. It aims to realize the transparent communication by understanding customers’ order intentions at a bar, in such a way that the social relationship between bar staffs and customers becomes smooth. © 2020, The Author(s), under exclusive license to Springer Nature Switzerland AG.",,,,,,,,,,,,,,,,,,"Kirby, R., Forlizzi, J., Simmons, R., Affective Social Robots. Robot. Automous Syst., 58, pp. 322-332; Murphy, R.R., Nomura, T., Billard, A., Burke, J.L., Human-robot interaction IEEE Robot. Autom. Mag, 17 (2), pp. 85-89; Young, J.E., Sung, J., Voida, A., Sharlin, E., Igarashi, T., Christensen, H.I., Grinter, R.E., Evaluating human-robot interaction Int. J. Soc. Robot., 3 (1), pp. 53-67; Mitssunaga, N., Miyashita, T., Ishiguro, H., Kogure, K., Hagita, N., Robovie-IV: A communication robot interacting with people daily in an office Proceedings of International Conference on Intelligent Robots and System, pp. 5066-5072. , Beijing, China; Fasola, J., Mataric, M., Using socially assistive human-robot interaction to motivate physical exercise for older adults Proceed. IEEE, 100 (8), pp. 2512-2526; Mitsunaga, N., Smith, C., Kanda, T., Ishiguro, H., Hagita, N., Adapting robot behavior for human-robot interaction IEEE Trans. Robot., 24 (4), pp. 911-916; Calinon, S., Sardellitti, I., Caldwell, D.G., Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 249-254. , Taibei, Taiwan; Broz, F., Nourbakhsh, I., Simmons, R., Planning for human-robot interaction in socially situated tasks Int. J. Soc. Robot., 5 (2), pp. 193-241; Liu, Z.T., Chen, L.F., Dong, F.Y., Yamazaki, Y., Wu, M., Hirota, K., Adapting multi-robot behavior to communication atmosphere in humans-robots interaction using fuzzy production rule based friend-Q learning J. Adv. Comput. Intell. Intell. Inf., 17 (2), pp. 291-301; Liu, Z.T., Chen, L.F., Dong, F.Y., Yamazaki, Y., Wu, M., Hirota, K., Multi-robot behavior adaptation to local and global communication atmosphere in humans-robots interaction J. Multimodal User Inter., 8 (3), pp. 289-303; Drenner, A., Janssen, M., Kottas, A., Coordination and longevity in multi-robot teams involving miniature robots J. Intell. & Robot. Syst, 72 (2), pp. 263-284; Geiger, A., Lauer, M., Urtasun, R., A generative model for 3D urban scene understanding from movable platforms Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1945-1952. , Colorado, USA; Roberson, M.J., Bohg, J., Skantze, G.L., Gustafson, J., Carlson, R., Rasolzadeh, B., Kragic, D., Enhanced visual scene understanding through human-robot dialog Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3342-3348. , San Francisco, USA; Bergh, M.V., Carton, D., Nijs, R.D., Mitsou, N., Landsiedel, C., Kuehnlenz, K., Wollherr, D., Buss, M., Real-time 3D hand gesture interaction with a robot for understanding directions from humans Proceedings of IEEE International Symposium on Robot and Human Interactive Communication, pp. 357-362. , Atlanta, USA; Mohiyeddini, C., Pauli, R., Bauer, S., The role of emotion in bridging the intention behaviour gap: The case of sports participation Psychol. Sport Exercise, 10 (2), pp. 226-234; Olsson, A., Ochsner, K.N., The role of social cognition in emotion Cognit.-Emot. Inter., 12 (2), pp. 65-71; Vapnik, V.N., The Nature of Statistical Learning Theory, , Springer, New York; Bottou, L., Vapnik, V.N., Local learning algorithms Neural Comput, 4 (6), pp. 888-900; Shafi, A., Vishanth, W., Understanding citizens’ behavioural intention in the adoption of e-government services in the state of Qatar Proceedings of European Conference on Information Systems, , Verona, Italy; Orji, R.O., Impact of gender and nationality on acceptance of a digital library: An empirical validation of nationality based UTAUT using SEM J. Emer. Trends Comput. Inf. Sci, 1 (2), pp. 68-79; Holland, C., Hill, R., The effect of age, gender and driver status on pedestrians’ intentions to cross the road in risky situations intentions to cross the road in risky situations Accid. Anal. Prevent., 39 (2), pp. 224-237; Keysar, B., The illusory transparency of intention: Linguistic perspective taking in tex Cognit. Psychol, 26 (2), pp. 165-208; Hanheide, M., Lohse, M., Zender, H., Expectations, intentions, and actions in human-robot interaction Int. J. Soc. Robot., 4 (2), pp. 107-108; Hirota, K., Dong, F., Development of mascot robot system in NEDO project Proceedings of International IEEE Conference on Intelligent Systems, 1, pp. 38-44. , Varna, Bulgaria; Cortes, C., Vapnik, V.N., Support-vector networks Mach. Learn., 20 (3), pp. 273-297; Yu, H., Kim, J., Kim, Y., Hwang, S., Lee, Y.H., An efficient method for learning nonlinear ranking SVM functions Inf. Sci., 209, pp. 37-48; Tarabalka, Y., Fauvel, M., Chanussot, J., Benediktsson, J.A., SVM-and MRF-based method for accurate classification of hyperspectral images IEEE Geosci. Remote Sens. Lett, 7 (4), pp. 736-740; Moavenian, M., Khorrami, H., A qualitative comparison of artificial neural networks and support vector machines in ECG arrhythmias classification Expert Syst. Appl., 37 (4), pp. 3088-3093; Suykens, J.A.K., Vandewalle, J., Moor, B.D., Optimal control by least squares support vector machines Neural Netw, 14 (1), pp. 23-25; Ismail, S., Shabri, A., Samsudin, R., A hybrid model of self-organizing maps (SOM) and least square support vector machine (LSSVM) for time-series forecasting Expert Syst. Appl., 38 (8), pp. 10574-10578; Huang, C., A reduced support vector machine approach for interval regression analysis Inf. Sci., 217, pp. 56-64; Ferreira, J.P., Crisostomo, M.M., Coimbra, A.P., Adaptive PD controller modeled via support vector regression for a biped robot IEEE Trans. Control Syst. Technol., 21 (3), pp. 941-949; Dunn, J.C., A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters J. Cybern., 3 (3), pp. 32-57; Bezdek, J.C., A convergence theorem for the fuzzy ISODATA clustering algorithms IEEE Trans. Pattern Anal. Mach. Intell. PAMI, 2 (1), p. 18; Dong, W.M., Wong, F.S., Fuzzy weighted averages and implementation of the extension principle Fuzzy Sets Syst, 21 (2), pp. 183-199; Keltner, D., Haidt, J., Social functions of emotions at four levels of analysis Cognit. Emotion, 13 (5), pp. 505-521; Spoor, J.R., Kelly, J.R., The evolutionary significance of affect in groups: Communication and group bonding Group Process. Intergroup Rel, 7 (4), pp. 398-412; Klauer, K.C., Musch, J., Affective priming: Findings and theories Proceedings of Psychology of Evaluation. Affective Processes in Cognition and Emotion, pp. 7-49. , Lawrence Erlbaum, USA; Jain, A.K., Dubes, R.C., Algorithms for Clustering Data, , Prentice Hall, Englewood Cliffs; Liu, Z.T., Mu, Z., Chen, L.F., Emotion recognition of violin music based on strings music theory for mascot robot system 9Th International Conference on Informatics in Control, Automation and Robotics, pp. 5-14. , Rome, Italy; Farchi, G., Fidanza, F., Giampaoli, S., Mariotti, S., Menotti, A., Alcohol and survival in the Italian rural cohorts of the seven countries study Int. J. Epidemiol., 29, pp. 667-671; Tang, K., Dong, F.Y., Yuhki, M., Yamazaki, Y., Shibata, T., Hirota, K., Deep level situation understanding and its application to casual communication between robots and humans Proceedings of International Conference on Informatics in Control, Automation and Robotics, pp. 292-299. , Reykjavik, Iceland; Liu, Z.T., Wu, M., Li, D.Y., Chen, L.F., Dong, F.Y., Yamazaki, Y., Hirota, K., Communication atmosphere in humans-robots interaction based on the concept of fuzzy atmosphere generated by emotional states of humans and robots J. Autom. Mobile Robot. Intell. Syst., 7 (2), pp. 52-63; Yamazaki, Y., Hatakeyama, Y., Dong, F.Y., Nomoto, K., Hirota, K., Fuzzy inference based mentality expression for eye robot in affinity pleasure-arousal space J. Adv. Comput. Intell. Intell. Inf., 12 (3), pp. 304-313; Kelley, R., Tavakkoli, A., King, C., Nicolescu, M., Nicolescu, M., Bebis, G., Understanding human intentions via hidden markov models in autonomous mobile robots Proceedings of the 3Rd ACM/IEEE International Conference on Human Robot Interaction, pp. 367-374. , Amsterdam, Netherlands; Bicho, E., Erlhagen, W., Sousa, E., The power of prediction: Robots that read intentions Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5458-5459. , Vilamoura, Portugal","Chen, L.; School of Automation, China; email: chenluefeng@cug.edu.cn",,,"Springer Science and Business Media Deutschland GmbH",,,,,1860949X,,,,"English","Stud. Comput. Intell.",Book Chapter,"Final","",Scopus,2-s2.0-85096207038
"Frijns H.A., Schürer O.","57192806605;53875402300;","Context-Awareness for Social Robots",2020,"Frontiers in Artificial Intelligence and Applications","335",,,"520","524",,,"10.3233/FAIA200951","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098883193&doi=10.3233%2fFAIA200951&partnerID=40&md5=2d4c454929919e9e82dc4c326f6f77d7","Trust Robots, Tu Wien, Austria; Department for Architecture Theory and Philosophy of Technics, Tu Wien, Austria","Frijns, H.A., Trust Robots, Tu Wien, Austria; Schürer, O., Department for Architecture Theory and Philosophy of Technics, Tu Wien, Austria","In the present work, we provide a short literature review of three different ways of approaching the topics of context and context-awareness and relate these to developments in Human-Robot Interaction (HRI) and social robotics. We distinguish an engineering approach to context-awareness, the study of social context in human-centred design, and a view of context as a cognitive component in interaction. We propose a revised definition of context to capture these three views and discuss implications. © 2020 The authors and IOS Press. All rights reserved.","context; context-awareness; human-robot interaction; Social robotics","Robotics; Cognitive components; Context- awareness; Human centred design; Human robot Interaction (HRI); Literature reviews; Social context; Social robotics; Three views; Social robots",,,,,,,,,,,,,,,,"Bloisi, D.D., Nardi, D., Riccio, F., Trapani, F., Context in robotics and information fusion (2016) Context-Enhanced Information Fusion, pp. 675-699. , Snidaro L, Garciá J, Llinas J, Blasch E. editors. Springer; Dey, A.K., Abowd, G.D., Towards a better understanding of context and context-awareness (2000) Proceedings of the Chi 2000 Workshop on the What, , Who, Where, When and How of Context-Awareness; Chen, J.Y.C., Boyce, M., Wright, J., Barnes, M., Procci, K., Garcia, A., (2014) Situation Awareness-Based Agent Transparency, , Army Research Laboratory, Aberdeen Proving Ground MD; Endsley, M.R., Toward a theory of situation awareness in dynamic systems (1995) Human Factors: The Journal of the Human Factors and Ergonomics Society, 37 (1), pp. 32-64; Akman, V., Rethinking context as a social construct (2000) Journal of Pragmatics, 32 (6), pp. 743-759; Dourish, P., What we talk about when we talk about context (2004) Personal and Ubiquitous Computing, 8 (1), pp. 19-30; Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., Riboni, D., A survey of context modelling and reasoning techniques (2010) Pervasive and Mobile Computing, 6 (2), pp. 161-180; Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D., Context aware computing for the internet of things: A survey (2014) Ieee Communications Surveys & Tutorials, 16 (1), pp. 414-454; Grieco, L.A., Rizzo, A., Colucci, S., Sicari, S., Piro, G., Di, P.D., Boggia, G., IoT-aided robotics applications: Technological implications, target domains and open issues (2014) Elsevier Computer Communications, 54 (C), pp. 32-47; Buchner, R., Weiss, A., Tscheligi, M., Development of a context model based on video analysis (2011) Proceedings of the 6th International Conference on Human-robot Interaction, pp. 117-118; Afyouni, I., Ray, C., Claramunt, C., Spatial models for context-aware indoor navigation systems: A survey (2012) Journal of Spatial Information Science, 4 (4), pp. 85-123; Rios-Martinez, J., Spalanzani, A., Laugier, C., From proxemics theory to socially-aware navigation: A survey (2015) International Journal of Social, Robotics, 7 (2), pp. 137-153; Lazzeri, N., Mazzei, D., Cominelli, L., Cisternino, A., De, R.D.E., Designing the mind of a social robot (2018) Applied Sciences, 8 (2), p. 302. , Feb; Xu, Q., Ng, J., Cheong, Y.L., Tan, O., Wong, J.B., Tay, T.C., The role of social context in human-robot interaction (2012) 2012 Southeast Asian Network of Ergonomics Societies Conference (SEANES), pp. 1-5; Schürer, O., Stangl, B., Müller, C., Hubatschke, C., A first prototype of a space model of cultural meaning by natural-language human-robot interaction (2017) Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 339-345. , Internet]. New York, NY, USA: Association for Computing Machinery, [cited 2020 Oct 15; Young, J.E., Sung, J., Voida, A., Sharlin, E., Igarashi, T., Christensen, H.I., Grinter, R.E., Evaluating human-robot interaction: Focusing on the holistic interaction experience (2011) International Journal of Social Robotics, 3 (1), pp. 53-67; Forlizzi, J., The product ecology: Understanding social product use and supporting design culture (2008) International Journal of Design, 2 (1), pp. 11-20; Sung, J.Y., Grinter, R.E., Christensen, H.I., Domestic robot ecology: An initial framework to unpack long-term acceptance of robots at home (2010) International Journal of Social Robotics, 2 (4), pp. 417-429; Vieweg, S., Hodges, A., Rethinking context: Leveraging human and machine computation in disaster response Computer, 47 (4), pp. 22-27; Clark, H.H., Common ground (1996) Using Language, pp. 92-121. , Cambridge University Press; Goffman, E., Primary frameworks (1974) Frame Analysis-An Essay on the Organization of Experience, pp. 21-39. , Boston: Northeastern University Press; Schürer, O., Stangl, B., Müller, C., Hubatschke, C., A first prototype of a space model of cultural meaning by natural-language human-robot interaction (2017) Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 339-345. , Internet]. New York, NY, USA: Association for Computing Machinery; Alegre, U., Augusto, J.C., Clark, T., Engineering context-aware systems and applications: A survey (2016) Journal of Systems and Software, 117, pp. 55-83; Mutlu, B., Roy, N., Sabanovic, S., (2016) Cognitive Human-Robot Interaction, pp. 1907-1933. , Springer Handbook of Robotics. 2nd ed. Springer; Hellström, T., Bensch, S., Understandable robots-What, why, and how (2018) Paladyn, Journal of Behavioral Robotics, 9 (1), pp. 110-123; Turner, R.M., Context-Mediated behavior for intelligent agents (1998) International Journal of Human-Computer, Studies, 48, pp. 307-330","Frijns, H.A.; Institute of Management Science, Theresianumgasse 27, Austria; email: Helena.Frijns@tuwien.ac.at","Norskov M.Seibt J.Quick O.S.",,"IOS Press BV","4th Conference on Robophilosophy 2020: Culturally Sustainable Social Robotics","18 August 2020 through 21 August 2020",,165874,09226389,9781643681542,,,"English","Front. Artif. Intell. Appl.",Conference Paper,"Final","",Scopus,2-s2.0-85098883193
"Matulík M., Vavrecka M., Vidovicová L.","57219627545;33568452100;24067964900;","Edutainment software for the pepper robot",2020,"PervasiveHealth: Pervasive Computing Technologies for Healthcare",,,,"","",,,"10.1145/3440084.3441194","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117539700&doi=10.1145%2f3440084.3441194&partnerID=40&md5=0d3a7df1cd6bdd345d1bd8431cc9550a","CTU, Prague, Czech Republic; FSS, MUNI, Brno, Czech Republic","Matulík, M., CTU, Prague, Czech Republic; Vavrecka, M., CTU, Prague, Czech Republic; Vidovicová, L., FSS, MUNI, Brno, Czech Republic","We present our software for the Pepper robot that is suitable for human-robot interaction in the area of edutainment. The combination of education and entertainment is achieved by modifying a state-of-the-art conversational artificial intelligence system, developing several interactive quiz applications for the robot and implementing these into Pepper, a humanoid robot. In the paper, we describe the technical details of the chatbot implementation and also the description of the software for the edutainment. © 2020 ACM.","Chatbot; Edutainment; HRI; Humanoid Robots; Social Robots","Anthropomorphic robots; Artificial intelligence; Computer software; Social robots; Artificial intelligence systems; Chatbot; Edutainment; Humanoid robot; Interactive quizzes; State of the art; Technical details; Educational robots",,,,,"Technologická Agentura České Republiky: CZ.02.1.01/0.0/0.0/16.019/, TL02000362","The paper was supported by the Technological Agency of the Czech Republic grant No. TL02000362 “HUMR - The Use of Humanoid Robot in Promoting Active Ageing in Older Men and Women” and Inafym project CZ.02.1.01/0.0/0.0/16.019/",,,,,,,,,,"Wang, M., Kogkas, A.A., Darzi, A., Mylonas, G.P., Free-view 3d gaze-guided, assistive robotic system for activities of daily living (2018) CoRR, , abs/1807.05452; Vandemeulebroucke, T., De Casterlé, B.D., Gastmans, C., How do older adults experience and perceive socially assistive robots in aged care: A systematic review of qualitative evidence (2018) Aging & Mental Health, 22 (2), pp. 149-167. , PMID: 28282732; McLaughlin, A., Gandy, M., Allaire, J., Whitlock, L., Putting fun into video games for older adults (2012) Ergonomics in Design, 20 (2), pp. 13-22. , https://doi.org/10.1177/1064804611435654, April; Rapeepisarn, K., Wong, K., Che Fung, C., Depickere, A., Similarities and differences between ""learn through play"" and ""edutainment"" (2006) Proceedings of the 3rd Australasian Conference on Interactive Entertainment, pp. 28-32. , 4-6 December 2006, Perth, W.A; Smith, A., Chilcott, M., An analysis of the potential to utilize virtual worlds to enhance edutainment and improve the wellbeing of the ageing population (2013) Transactions on Edutainment IX, pp. 65-80. , Ed. Z. Pan, A. D. Cheok, W. Müller, F. Liarokapis. Springer Berlin Heidelberg; Wu, Y., Damnée, S., Kerhervé, H., Ware, C., Rigaud, A., Bridging the digital divide in older adults: A study from an initiative to inform older adults about new technologies (2015) Clinical Interventions in Aging, 10, pp. 193-201. , 01; Pu, L., Moyle, W., Jones, C., Todorovic, M., The effectiveness of social robots for older adults: A systematic review and meta-analysis of randomized controlled studies (2018) The Gerontologist, 59 (1), pp. e37-e51. , 06; Pedersen, I., Reid, S., Aspevig, K., Developing social robots for aging populations: A literature review of recent academic sources (2018) Sociology Compass, p. 12585. , 04; Shishehgar, M., Kerr, D., Blake, J., A systematic review of research into how robotic technology can help older people (2018) Smart Health, 7-8, pp. 1-18; Seah, E., Wee, T., Kaufman, D., Sauvé, L., Zhang, F., Play, learn, connect: Older adults' experience with a multiplayer, educational, digital bingo game (2018) Journal of Educational Computing Research, 56 (5), pp. 675-700; Coughlin, J., Five perceptions on misperceptions of technology aging and business innovation (2017) LinkedIn., , https://www.linkedin.com/pulse/five-perceptionsmisperceptions-technology-aging-joseph-f-coughlin/; Dinet, J., Vivian, R., Expolatory investigation of attitudes towards assistive robots for future users (2014) Le Travail Humain, 77 (2), pp. 105-126; Kumar Pandey, A., Gelin, R., A mass-produced sociable humanoid robot: Pepper: The first ma-chine of its kind (2018) IEEE Robotics& Automation Magazine, p. 1. , 07; Pichl, J., Marek, P., Konrád, J., Matulík, M., Šedivý, J., Alquist 2.0: Alexa prize socialbot based on sub- dialogue models (2018) 2ndProceedings of Alexa Prize; Khatri, C., Venkatesh, A., Hedayatnia, B., Ram, A., Gabriel, R., Alexa prize - State of the Art in Conversational AI (2018) AI Magazine, pp. 40-55. , Fall; Štepánová, K., Mapping language to vision in a real-world robotic scenario (2018) IEEE Transactions on Cognitive and Developmental Systems, 10 (3), pp. 784-794; Vavrecka, M., Farkaš, I., Unsupervised grounding of spatial relations (2011) Proceedings of European Conference on Cognitive Science, , Sofia, Bulgaria; Gabriela, S., Tesar, M., Vavrecka, M., Compositional models for VQA: Can neural module networks really count? (2018) Procedia Computer Science, 145, pp. 481-487; Michal, V., Farkaš, I., Lhotská, L., Bioinspired model of spatial cognition (2011) International Conference on Neural Information Processing., , Springer, Berlin, Heidelberg; Williams, J.D., Asadi, K., Zweig, G., Hybrid code networks: Practical and efficient end-to-end dialog control with supervised and reinforcement learning (2017) CoRR, , abs/1702.03274; Joulin, A., Grave, E., Bojanowski, P., Mikolov, T., Bag of tricks for efficient text classification (2016) CoRR, , abs/1607.01759; Straková, J., Straka, M., Hajic, J., Open-source tools for morphology, lemmatization, pos tagging and named entity recognition (2014) Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 13-18. , Baltimore, Maryland, June, Association for Computational Linguistics; Caic, M., Avelino, J., Mahr, D., Robotic versus human coaches for active aging: An automated social presence perspective (2020) Int J of Soc Robotics, 12, pp. 867-882. , https://doi.org/10.1007/s12369-018-0507-2",,,"Newcastle University","Association for Computing Machinery","4th International Symposium on Computer Science and Intelligent Control, ISCSIC 2020","17 November 2020 through 19 November 2020",,167082,21531633,9781450388894,,,"English","PervasiveHealth: Pervasive Computing Technol Healthc.",Conference Paper,"Final","",Scopus,2-s2.0-85117539700
[No author name available],[No author id available],"Proceedings of the 1st Workshop on Agents and Robots for Reliable Engineered Autonomy, AREA 2020",2020,"Electronic Proceedings in Theoretical Computer Science, EPTCS","319",,,"","",145,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094665305&partnerID=40&md5=af6da52e16a1aefd1e5bf0b24ba1b251",,"","The proceedings contain 10 papers. The topics discussed include: co-simulation of human-robot collaboration: from temporal logic to 3D simulation; statistical model checking of human-robot interaction scenarios; establishing reliable robot behavior using capability analysis tables; improving competence for reliable autonomy; exploratory experiments on programming autonomous robots in JadeScript; engineering reliable interactions in the reality-artificiality continuum; semi-supervised learning from demonstration through program synthesis: an inspection robot case study; and testing the robustness of AutoML systems.",,,,,,,,,,,,,,,,,,,,"Cardoso R.C.Ferrando A.Briola D.Menghi C.Ahlbrecht T.",,"Open Publishing Association","1st Workshop on Agents and Robots for Reliable Engineered Autonomy, AREA 2020","4 September 2020",,161993,20752180,,,,"English","Electron. Proc. Theor. Comput. Sci., EPTCS",Conference Review,"Final","",Scopus,2-s2.0-85094665305
"Papageorgiou X.S., Tsampounaris G., Karavasili A., Efthimiou E., Fotinea S.-E., Vacalopoulou A., Karioris P., Koureta F., Alexopoulou D., Dimou D.","8532478500;57193558157;57188706311;22333400500;6507953358;56202059900;56237726100;57193715552;57188695070;24480691300;","User-centered Implementation of Rehabilitation Exercising on an Assistive Robotic Platform",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12426 LNCS",,,"689","698",,,"10.1007/978-3-030-60149-2_52","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092197596&doi=10.1007%2f978-3-030-60149-2_52&partnerID=40&md5=eb4b57ca942cd4be83397049e31a0f8b","Institute for Language and Speech Processing/ATHENA RC, Athens, Greece; DIAPLASIS Rehabilitation Center, Kalamata, Greece","Papageorgiou, X.S., Institute for Language and Speech Processing/ATHENA RC, Athens, Greece; Tsampounaris, G., Institute for Language and Speech Processing/ATHENA RC, Athens, Greece; Karavasili, A., DIAPLASIS Rehabilitation Center, Kalamata, Greece; Efthimiou, E., Institute for Language and Speech Processing/ATHENA RC, Athens, Greece; Fotinea, S.-E., Institute for Language and Speech Processing/ATHENA RC, Athens, Greece; Vacalopoulou, A., Institute for Language and Speech Processing/ATHENA RC, Athens, Greece; Karioris, P., Institute for Language and Speech Processing/ATHENA RC, Athens, Greece; Koureta, F., DIAPLASIS Rehabilitation Center, Kalamata, Greece; Alexopoulou, D., DIAPLASIS Rehabilitation Center, Kalamata, Greece; Dimou, D., DIAPLASIS Rehabilitation Center, Kalamata, Greece","The paper focuses on the method and steps implementing a suite of rehabilitation exercises on an assistive robotic platform. The suite is based on extensive user needs identification procedures and consultation with medical and rehabilitation experts. For the design of the human-robot interaction (HRI) component of the platform, the user centered approach was adopted, which in this case employed a range of multimodal interaction facilities including a free user-robot dialogue, visual and speech signals. © 2020, Springer Nature Switzerland AG.","Assistive HRI; Human-robot interaction; Multimodal HRI design; Rehabilitation strategy; User group recruitment methodology; User-centered design","Human computer interaction; Machine design; Robotics; Assistive robotics; Human robot Interaction (HRI); Identification procedure; Multi-Modal Interactions; Rehabilitation exercise; Speech signals; User-centered; User-centered approach; Human robot interaction",,,,,"5030856; 448306","Acknowledgements. This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T1EDK-01248/MIS: 5030856), as well as POLYTROPON project (KRIPIS-GSRT, MIS: 448306).",,,,,,,,,,"(2019) Health at a Glance: Europe 2018: State of Health in the EU Cycle, , https://doi.org/10.1787/health_glance:eur-2018-en, OECD/EU. OECD Publishing, Paris; Disability statistics. Statistics Explained, , https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Disability_statistics, EUROSTAT. Online publication, ISSN 2443-8219. Accessed Sep 2019; Hirvensalo, M., Rantanen, T., Heikkinen, M., Mobility difficulties and physical activity as predictors of mortality and loss of independence in the community-living older population (2005) J. Am. Geriatr. Soc, 48, pp. 493-498. , https://www.ncbi.nlm.nih.gov/pubmed/10811541; Chuy, O.J., Hirata, Y., Wang, Z., Kosuge, K., Approach in assisting a sit-to-stand movement using robotic walking support system (2006) 2006 IEEE/RSJ Int’l Conference on Intelligent Robots and Systems, pp. 4343-4348. , https://doi.org/10.1109/iros.2006.282007, Beijing; Chugo, D., Asawa, T., Kitamura, T., Jia, S., Takase, K., A rehabilitation walker with standing and walking assistance (2008) 2008 IEE/RSJ Int’l Conf. on Intelligent Robots and Systems, Nice, pp. 260-265. , https://doi.org/10.1109/iros.2008.4650845; Hirata, Y., Komatsuda, S., Kosuge, K., Fall prevention control of passive intelligent walker based on human model (2008) 2008 IEE/RSJ Int’l Conference on Intelligent Robots and Systems, Nice, pp. 1222-1228. , https://doi.org/10.1109/iros.2008.4651173; Morris, A., Donamukkala, R., A robotic walker that provides guidance (2003) ICRA 2003; Dubowsky, S., Pamm-a robotic aid to the elderly for mobility assistance and monitoring: a ‘helping-hand’ for the elderly (2000) IEEE Int’l Conf. on Robotics and Automation, pp. 570-576; Graf, B., Hans, M., Schraft, R.D., Mobile robot assistants (2004) IEEE Rob. 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Rehabil, 95 (10), pp. 1954-1961. , https://doi.org/10.1016/j.apmr.2014.06.008; Efthimiou, E., Fotinea, S.-E., Vacalopoulou, A., Papageorgiou, X., Karavasili, A., Goulas, T., User centered design in practice: adapting HRI to real user needs (2019) PETRA 2019 Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments, pp. 425-429. , https://doi.org/10.1145/3316782.3322778, Rhodes, Greece—05–07 June 2019, ACM, New York; Kötteritzsch, A., Weyers, B., Assistive technologies for older adults in urban areas: a literature review (2016) Cogn. Comput, 8 (2), pp. 299-317. , https://doi.org/10.1007/s12559-015-9355-7; Stuck, R.E., Rogers, W.A., Understanding older adult’s perceptions of factors that support trust in human and robot care providers (2017) 10th Int’l Conference on PErvasive Technologies Related to Assistive Environments (PETRA 2017), pp. 372-377. , https://doi.org/10.1145/3056540.3076186, Rhodes, 21–23 June ACM New York (2017). ISBN: 978-1-4503-5227-7","Papageorgiou, X.S.; Institute for Language and Speech Processing/ATHENA RCGreece; email: xpapag@athenarc.gr","Stephanidis C.Antona M.Gao Q.Zhou J.",,"Springer Science and Business Media Deutschland GmbH","22nd International Conference on Human Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,249359,03029743,9783030601485,,,"English",,Conference Paper,"Final","",Scopus,2-s2.0-85092197596
"Augello A., Ciulla A., Cuzzocrea A., Gaglio S., Pilato G., Vella F.","23392168800;57219193850;23388216900;7003479605;36880684700;8889782700;","Towards an intelligent system for supporting gesture acquisition and reproduction in humanoid robots",2020,"DMSVIVA 2020 - Proceedings of the 26th International DMS Conference on Visualization and Visual Languages",,,,"82","86",,,"10.18293/DMSVIVA2020-017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091661890&doi=10.18293%2fDMSVIVA2020-017&partnerID=40&md5=951b4f6eba6af53383f183d3e01bd3f6","ICAR-CNR, Palermo, Italy; iDEA Lab, University of Calabria, Rende, Italy; University of Palermo, ICAR-CNR, Palermo, Italy","Augello, A., ICAR-CNR, Palermo, Italy; Ciulla, A., ICAR-CNR, Palermo, Italy; Cuzzocrea, A., iDEA Lab, University of Calabria, Rende, Italy; Gaglio, S., University of Palermo, ICAR-CNR, Palermo, Italy; Pilato, G., ICAR-CNR, Palermo, Italy; Vella, F., ICAR-CNR, Palermo, Italy","In this paper, an intelligent system for supporting gesture acquisition and reproduction in humanoid robots, which is based on the well-known Microsoft Kinect framework, is introduced and discussed in this paper. The idea that has inspired the paper is represented by endowing an humanoid robot with the capability to mimic the motion of a human user in real time. As a further extension, the latter amenity may serve as a basis for further gesture based human-robot interactions. © 2020 DMSVIVA 2020 - Proceedings of the 26th International DMS Conference on Visualization and Visual Languages. All rights reserved.",,"Anthropomorphic robots; Cell proliferation; Intelligent robots; Intelligent systems; Visual languages; Visualization; Human users; Humanoid robot; Microsoft kinect; Real time; Social robots",,,,,,,,,,,,,,,,"Msrc-12 dataset, , https://www.microsoft.com/en-us/download/details.aspx?id=52283; http://wiki.ros.org/kinetic, Ros kinetic; Semaphore flag signalling system, , https://en.wikipedia.org/wiki/Flag\_semaphore; Augello, A., Ciulla, A., Cuzzocrea, A., Gaglio, S., Pilato, G., Vella, F., A kinect-based gesture acquisition and reproduction system for humanoid robots Computational Science and Its Applications - ICCSA 2020 - 20th International Conference, , Cagliari, Italy, July 1-4, 2020, Proceedings, 2020; Baron, G., Czekalski, P., Malicki, D., Tokarz, K., Remote control of the artificial arm model using 3d hand tracking (2013) 2013 International Symposium on Electrodynamic and Mechatronic Systems (SELM), pp. 9-10. , IEEE; Chang, C.-w., He, C.-j., A kinect-based gesture command control method for human action imitations of humanoid robots (2014) 2014 International Conference on Fuzzy Theory and Its Applications (iFUZZY2014), pp. 208-211. , IEEE; Chatzimilioudis, G., Cuzzocrea, A., Gunopulos, D., Mamoulis, N., A novel distributed framework for optimizing query routing trees in wireless sensor networks via optimal operator placement (2013) J. 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A., de la Casa Díaz, S. M., Modeling and control of humanoid robots (2019) Int. J. Humanoid Robotics, 16 (6), p. 1902003. , 1-1902003:3; Mukherjee, S., Paramkusam, D., Dwivedy, S. K., Inverse kinematics of a nao humanoid robot using kinect to track and imitate human motion (2015) 2015 International Conference on Robotics, Automation, Control and Embedded Systems (RACE), pp. 1-7. , IEEE; Pfitscher, M., Welfer, D., Cuadros, M. A. d. S. L., Gamarra, D. F. T., Activity gesture recognition on kinect sensor using convolutional neural networks and fastdtw for the msrc-12 dataset (2018) International Conference on Intelligent Systems Design and Applications, pp. 230-239. , Springer; Regier, P., Milioto, A., Karkowski, P., Stachniss, C., Bennewitz, M., Classifying obstacles and exploiting knowledge about classes for efficient humanoid navigation (2018) 18th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2018, pp. 820-826. , Beijing, China, November 6-9, pages 2018; Saeedvand, S., Aghdasi, H. S., Baltes, J., Robust multi-objective multi-humanoid robots task allocation based on novel hybrid meta-heuristic algorithm (2019) Appl. Intell, 49 (12), pp. 4097-4127; Zabala, U., Rodriguez, I., Martínez-Otzeta, J. M., Lazkano, E., (2019) Learning to gesticulate by observation using a deep generative approach, , arXiv preprint arXiv:1909.01768; Zhang, A., Ramirez-Alpizar, I. G., Giraud-Esclasse, K., Stasse, O., Harada, K., Humanoid walking pattern generation based on model predictive control approximated with basis functions Adv. Robotics, 33 (9), pp. 454-468",,,"KSI Research Inc. and Knowledge Systems Institute, USA","Knowledge Systems Institute Graduate School, KSI Research Inc.","26th International DMS Conference on Visualization and Visual Languages, DMSVIVA 2020","7 July 2020 through 8 July 2020",,162436,,1891706519; 9781891706516,,,"English","DMSVIVA - Proc. Int. DMS Conf. Vis. Vis. Lang.",Conference Paper,"Final","",Scopus,2-s2.0-85091661890
"Gaifullin R., Ivanou M., Gazizov R.","57210234843;57205614485;57210232892;","Natural human-robot interaction toolkit",2020,"Advances in Intelligent Systems and Computing","1018",,,"196","200",,1,"10.1007/978-3-030-25629-6_31","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070011084&doi=10.1007%2f978-3-030-25629-6_31&partnerID=40&md5=059ab49414e9c429f7f9396861ce9c87","Innopolis University, Universitetskaya. 1, Innopolis, 420500, Russian Federation","Gaifullin, R., Innopolis University, Universitetskaya. 1, Innopolis, 420500, Russian Federation; Ivanou, M., Innopolis University, Universitetskaya. 1, Innopolis, 420500, Russian Federation; Gazizov, R., Innopolis University, Universitetskaya. 1, Innopolis, 420500, Russian Federation","Conventional human-robot interaction is usually limited to a set of commands a professional human operator is supposed to use, and/or teleoperation capabilities. The paper explores capabilities a robot could use to communicate more naturally with a human, support for a range of hardware configurations is provided. The capabilities developed for interaction toolkit include speech and face recognition. Finally, a use case for such a toolkit and performance with respect to different hardware configurations are discussed. © 2020, Springer Nature Switzerland AG.","Human-centered design; Human-robot interaction; Interaction infrastructure; Robotics","Face recognition; Machine design; Man machine systems; Robotics; Speech recognition; Hardware configurations; Human operator; Human-centered designs; Interaction infrastructure; Human robot interaction",,,,,"Ministry of Science and Higher Education of the Russian Federation: 075-10-2018-010, 14.606.21.0007, RFMEFI60617X0007","Acknowledgements. This work has been supported by the Ministry of Science and Higher Education of the Russian Federation with the project “Development of anthropomorphic robotic complexes with variable stiffness actuators for movement on the flat and the rugged terrains” (agreement: № 075-10-2018-010 (№ 14.606.21.0007), ID: RFMEFI60617X0007).",,,,,,,,,,"Fong, T., Nourbakhsh, I., Kunz, C., Fluckiger, L., Schreiner, J., Ambrose, R., Burridge, R., Scholtz, J., The peer-to-peer human-robot interaction project (2005) Space 2005, , American Institute of Aeronautics and Astronautics; Abadi, M., Agarwal, A., Barham, P., (2015) Tensorflow: Large-Scale Machine Learning on Heterogeneous Systems, , arXiv e-prints; Openvino Deep Learning Computer Vision Toolkit, , https://software.intel.com/en-us/openvino-toolkit; Povey, D., The Kaldi speech recognition toolkit (2011) Proc. ASRU, pp. 1-4. , Hawaii, US, pp; Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., Li, S.Z., FaceBoxes: A CPU real-time face detector with high accuracy (2017) 2017 IEEE International Joint Conference on Biometrics (IJCB), , IEEE; Schroff, F., Kalenichenko, D., Philbin, J., FaceNet: A unified embedding for face recognition and clustering (2015) In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; Gsteamer Multimedia Framework, , https://gstreamer.freedesktop.org/; Tanel, A., Full-duplex speech-to-text system for Estonian (2014) FAIA, 268, pp. 3-10; Introducing the Intel Neural Compute Stick 2, , https://software.intel.com/en-us/neural-compute-stick; Steinfeld, A., Fong, T., Kaber, D., Lewis, M., Scholtz, J., Schultz, A., Goodrich, M., Common metrics for human-robot interaction (2006) Proceeding of the 1St ACM SIGCHI/SIGART Conference on Human-Robot Interaction-Hri ’06, , ACM Press","Gaifullin, R.; Innopolis University, Universitetskaya. 1, Russian Federation; email: r.gayfullin@innopolis.ru","Ahram T.Taiar R.Colson S.Choplin A.",,"Springer Verlag","1st International Conference on Human Interaction and Emerging Technologies, IHIET 2019","22 August 2019 through 24 August 2019",,229139,21945357,9783030256289,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85070011084
"Ye H., Jeong H., Zhong W., Bhatt S., Izzetoglu K., Ayaz H., Suri R.","57202888529;57209418986;57209420778;57196045718;6603128234;6507639765;7103225263;","The effect of anthropomorphization and gender of a robot on human-robot interactions",2020,"Advances in Intelligent Systems and Computing","953",,,"357","362",,4,"10.1007/978-3-030-20473-0_34","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067658410&doi=10.1007%2f978-3-030-20473-0_34&partnerID=40&md5=a33c027cea039a33b96b2a6a6510eac4","Drexel University, 3141 Chestnut Street, Philadelphia, PA  19104, United States; Drexel Business Solutions Institute, 3220 Market Street, Philadelphia, PA  19104, United States","Ye, H., Drexel University, 3141 Chestnut Street, Philadelphia, PA  19104, United States; Jeong, H., Drexel University, 3141 Chestnut Street, Philadelphia, PA  19104, United States; Zhong, W., Drexel University, 3141 Chestnut Street, Philadelphia, PA  19104, United States; Bhatt, S., Drexel University, 3141 Chestnut Street, Philadelphia, PA  19104, United States; Izzetoglu, K., Drexel University, 3141 Chestnut Street, Philadelphia, PA  19104, United States; Ayaz, H., Drexel University, 3141 Chestnut Street, Philadelphia, PA  19104, United States, Drexel Business Solutions Institute, 3220 Market Street, Philadelphia, PA  19104, United States; Suri, R., Drexel University, 3141 Chestnut Street, Philadelphia, PA  19104, United States, Drexel Business Solutions Institute, 3220 Market Street, Philadelphia, PA  19104, United States","The popularity of assistant robots has increased in the recent past. Past research has looked at the effect of anthropomorphization of robots and considered the consumers’ gender as an important factor. However, research has not examined the interaction between a robot’s gender and its level of anthropomorphization on human-robot interactions. Our results indicate that males and females perceive a service failure differently depending upon the level of anthropomorphization of the robots involved in the failure. © Springer Nature Switzerland AG 2020.","Automation; Consumer behavior; Gender; Human-robot interaction; Service failure","Automation; Consumer behavior; Ergonomics; Human computer interaction; Man machine systems; Safety engineering; Assistant robot; Gender; Service failure; Human robot interaction",,,,,,,,,,,,,,,,"Epley, N., Waytz, A., Cacioppo, J.T., On seeing human: A three-factor theory of anthropomorphism (2007) Psychol. Rev., 114 (4), p. 864; Hinds, P.J., Roberts, T.L., Jones, H., Whose job is it anyway? A study of human-robot interaction in a collaborative task (2004) Hum. Comput. Interact., 19 (1), pp. 151-181; Złotowski, J., Proudfoot, D., Yogeeswaran, K., Bartneck, C., Anthropomorphism: Opportunities and challenges in human–robot interaction (2015) Int. J. Social Robot., 7 (3), pp. 347-360; de Visser, E.J., Monfort, S.S., Goodyear, K., Lu, L., O’Hara, M., Lee, M.R., Parasuraman, R., Krueger, F., A little anthropomorphism goes a long way: Effects of oxytocin on trust, compliance, and team performance with automated agents (2017) Hum. Factors, 59 (1), pp. 116-133; Mori, M., The Uncanny Valley (1970) Energy, 7 (4), pp. 33-35; Saygin, A.P., Chaminade, T., Ishiguro, H., Driver, J., Frith, C., The thing that should not be: Predictive coding and the Uncanny Valley in perceiving human and humanoid robot actions (2011) Soc. Cogn. Affect. Neurosci., 7 (4), pp. 413-422; Walters, M.L., Syrdal, D.S., Dautenhahn, K., Te Boekhorst, R., Koay, K.L., Avoiding the Uncanny Valley: Robot appearance, personality and consistency of behavior in an attention-seeking home scenario for a robot companion (2008) Auton. Robots, 24 (2), pp. 159-178; Tay, B., Jung, Y., Park, T., When stereotypes meet robots: The double-edge sword of robot gender and personality in human–robot interaction (2014) Comput. Hum. Behav., 38, pp. 75-84; Parasuraman, A., Zeithaml, V.A., Berry, L.L., Servqual: A multiple-item scale for measuring consumer perc (1988) J. Retail., 64 (1), p. 12; Hur, J.D., Koo, M., Hofmann, W., When temptations come alive: How anthropomorphism undermines self-control (2015) J. Consum. Res., 42 (2), pp. 340-358; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots: Concepts, design and applications (2002) Technical Report CMU-RI-TR-02-, 29; Thrun, S., Toward a framework for human-robot interaction (2004) Hum. Comput. Interact., 19 (1), pp. 9-24; Ayaz, H., Onaral, B., Izzetoglu, K., Shewokis, P.A., McKendrick, R., Parasuraman, R., Continuous monitoring of brain dynamics with functional near infrared spectroscopy as a tool for neuroergonomic research: Empirical examples and a technological development (2013) Front. Hum. Neuros., 7, p. 871","Ye, H.; Drexel University, 3141 Chestnut Street, United States; email: hy368@drexel.edu","Ayaz H.",,"Springer Verlag","AHFE International Conference on Neuroergonomics and Cognitive Engineering, 2019 and the AHFE International Conference on Industrial Cognitive Ergonomics and Engineering Psychology, 2019","24 July 2019 through 28 July 2019",,227099,21945357,9783030204723,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85067658410
"Lim V., Rooksby M., Cross E.S.","57219908339;16646936300;56497637800;","Social Robots on a Global Stage: Establishing a Role for Culture During Human–Robot Interaction",2021,"International Journal of Social Robotics","13","6",,"1307","1333",,6,"10.1007/s12369-020-00710-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095988448&doi=10.1007%2fs12369-020-00710-4&partnerID=40&md5=9c8365a1c5ddb6ad22552963596d7adf","School of Management, University College London, London, United Kingdom; Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom; Department of Cognitive Science, Macquarie University, Sydney, Australia","Lim, V., School of Management, University College London, London, United Kingdom; Rooksby, M., Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom; Cross, E.S., Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom, Department of Cognitive Science, Macquarie University, Sydney, Australia","Robotic agents designed to assist people across a variety of social and service settings are becoming increasingly prevalent across the world. Here we synthesise two decades of empirical evidence from human–robot interaction (HRI) research to focus on cultural influences on expectations towards and responses to social robots, as well as the utility of robots displaying culturally specific social cues for improving human engagement. Findings suggest complex and intricate relationships between culture and human cognition in the context of HRI. The studies reviewed here transcend the often-studied and prototypical east–west dichotomy of cultures, and explore how people’s perceptions of robots are informed by their national culture as well as their experiences with robots. Many of the findings presented in this review raise intriguing questions concerning future directions for robotics designers and cultural psychologists, in terms of conceptualising and delivering culturally sensitive robots. We point out that such development is currently limited by heterogenous methods and low statistical power, which contribute to a concerning lack of generalisability. We also propose several avenues through which future work may begin to address these shortcomings. In sum, we highlight the critical role of culture in mediating efforts to develop robots aligned with human users’ cultural backgrounds, and argue for further research into the role of culturally-informed robotic development in facilitating human–robot interaction. © 2020, The Author(s).","Culture; Embodied social interaction; Human cognition; Human–robot interaction; Individualism–collectivism; Social robotics","Agricultural robots; Robotics; Cultural backgrounds; Cultural influences; Human cognition; Intriguing questions; National cultures; Robot interactions; Robotic agents; Statistical power; Social robots",,,,,"Leverhulme Trust: PLP-2018-152; European Research Council, ERC; Horizon 2020: 677270","M.R. and E.S.C.’s contributions to this piece were supported by funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement number 677270 to E.S.C.). E.S.C. is further supported by the Leverhulme Trust (PLP-2018-152). Acknowledgements",,,,,,,,,,"Frith, C.D., Frith, U., Social cognition in humans (2007) Curr Biol, 17, pp. R724-R732; Gates, B., (2007) A Robot in Every Home. 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"Savery R., Rogel A., Weinberg G.","57190783994;57261588400;8601346400;","Emotion musical prosody for robotic groups and entitativity",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"440","446",,,"10.1109/RO-MAN50785.2021.9515314","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115101730&doi=10.1109%2fRO-MAN50785.2021.9515314&partnerID=40&md5=82cff6f96c01cb2642cd2b7e1828a5ad","Georgia Tech Center for Music Technology, Atlanta, GA, United States","Savery, R., Georgia Tech Center for Music Technology, Atlanta, GA, United States; Rogel, A., Georgia Tech Center for Music Technology, Atlanta, GA, United States; Weinberg, G., Georgia Tech Center for Music Technology, Atlanta, GA, United States","Research in human-robot interaction has focused on the relationship between a single robot and a single human participant. Only limited research has addressed the contrasting dynamic when humans interact with a group of robots. This dynamic adds additional human-robot interaction considerations, such as the level of entitativity, which is the identification of a group as a single entity as opposed to a collection of individuals. This paper proposes that emotional music prosody can play a key role in improving the interaction between humans and groups of robots by modifying the level of entitativity. Musical prosody refers to the use of pitch, rhythm and timbre features derived from language, but used without semantic meaning.We conducted a between-group experiment, presenting to subjects a group of industrial robotic arms performing a task either without sound, with the same emotional musical prosody voice for each robot, or with contrasting voices for different robots. We were able to show with significant results that the use of musical prosody improved likeability and trust over soundless gestures for groups of robots. We also demonstrate that, through subtle variations, prosody is able to alter the level of entitativity perceived by external observers. Finally, our results indicate a complex relationship between entitativity and common HRI metrics with higher levels of entitativity leading to improved performance, contradicting past literature. © 2021 IEEE.",,"Agricultural robots; Industrial robots; Man machine systems; Robotics; Semantics; Complex relationships; External observer; Industrial robotics; Likeability; Single robots; Social robots",,,,,"National Science Foundation, NSF: 1925178","*This material is based upon work supported by the National Science Foundation under Grant No. 1925178 1Georgia Tech Center for Music Technology, Atlanta, GA, USA,",,,,,,,,,,"Fraune, M.R., Sherrin, S., Sabanovíc, S., Smith, E.R., Is humanrobot interaction more competitive between groups than between individuals? (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). 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Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115101730
"Schoffmann C., Ubezio B., Bohm C., Muhlbacher-Karrer S., Zangl H.","57222569554;57212347685;57219722857;35797484900;6603046362;","Virtual Radar: Real-Time Millimeter-Wave Radar Sensor Simulation for Perception-Driven Robotics",2021,"IEEE Robotics and Automation Letters","6","3","9387149","4704","4711",,,"10.1109/LRA.2021.3068916","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103270087&doi=10.1109%2fLRA.2021.3068916&partnerID=40&md5=b427da36084871df3b6d0a517abe3cc1","Institute of Smart System Technologies, University of Klagenfurt, Austria","Schoffmann, C., Institute of Smart System Technologies, University of Klagenfurt, Austria; Ubezio, B., Institute of Smart System Technologies, University of Klagenfurt, Austria; Bohm, C., Institute of Smart System Technologies, University of Klagenfurt, Austria; Muhlbacher-Karrer, S., Institute of Smart System Technologies, University of Klagenfurt, Austria; Zangl, H., Institute of Smart System Technologies, University of Klagenfurt, Austria","This article presents ViRa11Available online: https://vira.aau.at/., a real-time open-source millimeter-wave radar simulation framework for perception-driven robotic applications. ViRa provides (i) raw data of radar sensors in real-time, simulation of (ii) multi-antenna configurations for spatial estimation of objects, (iii) wave penetration of non-conductive objects to infer information in occluded situations, (iv) different radar beam patterns, and, (v) configurations of radar sensors as given by real-world radars. By using ViRa, researchers can simulate radar sensors in different robotic scenarios and investigate radars prior to the installation. This allows an acceleration in the development of radar sensors for robotic applications without the need of real hardware. Contrary to simple model abstractions, which only output loose features, ViRa generates raw radar data using computer graphics techniques on graphics processing unit (GPU) level embedded inside a game engine environment. ViRa allows to feed data directly into machine learning frameworks, which enables further improvement in novel research directions, such as safe human-robot interaction or agile drone flights in obstacle-rich environments. The proposed simulation framework is validated with data from different scenarios in robotics such as human tracking for human-robot interaction. The obtained results are compared with a reference simulation framework and show significantly higher correlation when compared to real-world measurement data. © 2016 IEEE.","Range sensing; simulation and animation","Agricultural robots; Antennas; Computer games; Computer graphics; Computer graphics equipment; Directional patterns (antenna); Graphics processing unit; Image coding; Man machine systems; Millimeter waves; Program processors; Radar; Radar equipment; Robotics; Measurement data; Millimeter wave radar; Robotic applications; Safe human-robot interaction; Simple modeling; Simulation framework; Spatial estimation; Wave penetration; Human robot interaction",,,,,"KWF Kankerbestrijding, DCS; European Regional Development Fund, ERDF: 26616/30969/44253","Manuscript received October 15, 2020; accepted February 13, 2021. Date of publication March 25, 2021; date of current version April 13, 2021. This letter was recommended for publication by Associate Editor J. Rosell and Editor N. Amato upon evaluation of the reviewers’ comments. This work was supported by the “Kärntner Wirtschaftsförderung Fonds” (KWF), and the “European Regional Development Fund” (EFRE) within the CapSize Project 26616/30969/44253. (Corresponding author: Christian Schffmann.) Christian Schöffmann, Christoph Böhm, and Hubert Zangl are with the Institute of Smart System Technologies, University of Klagenfurt, Austria (e-mail: christian.stetco@aau.at; christoph.boehm@aau.at; hubert.zangl@aau.at).",,,,,,,,,,"Park, Y.S., Shin, Y.S., Kim, A., Pharao: Direct radar odometry using phase correlation (2020) Proc. IEEE Int. Conf. Robot. Automat., pp. 2617-2623; Barnes, D., Posner, I., Under the radar: Learning to predict robust keypoints for odometry estimation and metric localisation in radar (2020) Proc. IEEE Int. Conf. Robot. 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Theory Techn., 66 (8), pp. 3683-3689; (2020) Mathworks Phased Array System Toolbox, , https://de.mathworks.com/products/phased-array.html; (2019) Leap Motion Controller, , https://www.ultraleap.com/product/leapmotion-controller/; (2013) Technical data, , Natural Point Inc. Optitrack","Schoffmann, C.; Institute of Smart System Technologies, Austria; email: christian.stetco@aau.at",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85103270087
"Mohapatra S.","26427741300;","Human and computer interaction in information system design for managing business",2021,"Information Systems and e-Business Management","19","1",,"","",,3,"10.1007/s10257-020-00475-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089384728&doi=10.1007%2fs10257-020-00475-3&partnerID=40&md5=783cf676065cb1ac5014b71751f07ccb","Information Systems, Xavier Institute of Management, Bhubaneswar, 751005, India","Mohapatra, S., Information Systems, Xavier Institute of Management, Bhubaneswar, 751005, India","Artificial intelligence seems to be all pervasive and slowly has become an integral part of commercial entities. However, there needs to be an approach that can integrate human and machine interaction and make it a sustainable implementation. Using framework developed by Mohapatra (2019), we find that such parameters that can improve physical interactions with humans and domain processes. This article is an attempt to characterise these challenges and to exhibit a set of key decisional issues that need to be addressed for a cognitive robot to successfully share space and tasks with a human in retial services in a banking domain (Yang et al. in Inf Syst e-Bus Manag 17(1):1–25, 2019). Here, we identify first the needed individual and collaborative cognitive skills from the published framework and then apply them to a case study in retail banking domain. The article discusses each of these abilities, presents working implementations, and shows how they combine in a coherent and original deliberative architecture for human–robot interaction, which can be used for servicing customers (Tobias and Sebastian in Inf Syst e-Bus Manag 16(3):493–546, 2018). Supported by experimental results, we eventually show how explicit knowledge management, both symbolic and geometric, proves to be instrumental to richer and more natural human–robot interactions by pushing for pervasive, human-level semantics within the robot’s deliberative system. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.","Artificial intelligence; Banking services; Case study; Framework; Framework validation",,,,,,,,,,,,,,,,,"Abidoye, R.B., Chan, E., Achieving property valuation accuracy in developing countries: the implication of data source (2016) J Hous Markets Anal, 13 (3), pp. 37-72; Beetz, M.S., Lemaignan, R., Ros, L., Mösenlechner, R., Alami, M., ORO, a knowledge management platform for cognitive architectures in robotics (2010) IEEE/RSJ International Conference on Intelligent Robots and Systems; Ge, X.J., Runeson, G., Modeling property prices using neural network model for Hong Kong (2004) Int Real Estate Rev, 7 (1), pp. 121-138; https://www.mckinsey.com/~/media/McKinsey/Industries/Retail/Our%20Insights/The%20State%20of%20Fashion%202019%20A%20year%20of%20awakening/The-State-of-Fashion-2019-final.ashx; Mora-Esperanza, J.G., Artificial intelligence applied to real estate valuation. An example for the appraisal of Madrid (2004) Proceedings of CATASTRO, 2004; McDowell, M., (2019) Analytics are Reshaping fashion’s Old-School Instincts., , https://www.voguebusiness.com/technology/data-trend-forecasting-google-tracking-tools, Retrieved December 1, 2019, from; Mohapatra, S., Using integrated information system for patient benefits: a case study in India (2015) Int J Healthc Manag, 8 (4), pp. 262-271; Mohapatra, S., An empirical study for finding factors that would optimise productivity and quality in IT business (2017) Int J Prod Qual Manag, 20 (2), pp. 169-196; Mohapatra, S., Critical review of literature and development of a framework for application of artificial intelligence in business (2019) Int J Enterp Netw Manag, 10 (2), pp. 176-185; Mohapatra, S., Mohanty, R., Adopting MOOCs for afforable quality education (2017) Educ Inf Technol, 22 (5), pp. 2027-2053; Mohapatra, S., Mohanty, S., Assessing the overall value of an online writing community (2017) Educ Inf Technol, 22 (3), pp. 985-1003; Mohapatra, S., Mohanty, S., Improving operational efficiency in utility sector through technology intervention (2017) Int J Enterp Netw Manag, 8 (4), pp. 291-326; Mohapatra, S., Mohanty, R., Making online courses free for everyone: an empirical study to determine the feasibility (2018) Int J Bus Innov Res, 15 (4), pp. 403-430; Mohapatra, S., Murarka, S., Improving patient care in hospital in India by monitoring influential parameters (2016) Int J Healthc Manag, 9 (2), pp. 83-101; Mohapatra, S., Patra, D., User acceptance of web-based talent assessment platform (TAP) (2017) Int J Bus Excell, 12 (4), pp. 450-468; Mohapatra, S., Roy, R., Moving to products: entrepreneurial IT firms in India (2012) Int J Project Organ Manag, 4 (1), pp. 86-97; Mohapatra, S., Sahu, K., Empirical research on the adoption and diffusion of e-commerce portals (2018) Int J Bus Innov Res, 15 (2), pp. 137-151; Mohapatra, S., Sabharwal, L., Sabharwal, A., Bridge: the missing link (2013) Asian Case Res J, 17 (1), pp. 181-198; Mohapatra, S., Choudhury, A., Ganesh, K., Framework for supporting business process reengineering’-based business models (2017) Int J Bus Innov Res, 13 (4), pp. 451-474; Mooya, M.M., The education and professional practice of valuers in South Africa: a critical review (2015) Property Manag, 33 (3), pp. 245-274; Sarkar, S., Mohapatra, S., Sundarakrishnan, J., Assessing impact of technology based digital equalizer programme on improving student learning outcomes (2017) Educ Inf Technol, 22 (1), pp. 195-213; Standish, J., Ganapathy, V., Think tank: How AI can power the future of fashion (2018) WWD., , https://wwd.com/business-news/business-features/jill-standish-think-tank-1202941433/, Accessed 6 Dec 2019; (2017) Can Data Predict Fashion Trends? (2017), , https://www.economist.com/business/2017/07/27/can-data-predict-fashion-trends, Retrieved 1 Dec 2019; Yalpir, S., Forecasting residential real estate values with AHP method and integrated GIS (2014) Conference Proceedings of People, Buildings and Environment 2014, an International Scientific Conference, pp. 694-706. , Kroměříž, Czech Republic; Yazan, B., Three approaches to case study methods in education: yin, merriam, and stake (2015) Qual Rep, 20 (2), pp. 134-152","Mohapatra, S.; Information Systems, India; email: sanjay_mohapatra@yahoo.com",,,"Springer Science and Business Media Deutschland GmbH",,,,,16179846,,,,"English","Inf. Syst. e-Bus. Manage.",Article,"Final","",Scopus,2-s2.0-85089384728
"Pasquali D., Gonzalez-Billandon J., Aroyo A.M., Sandini G., Sciutti A., Rea F.","57216357588;57207198944;57192384790;35280694500;36957816700;54386065400;","Detecting Lies is a Child (Robot)’s Play: Gaze-Based Lie Detection in HRI",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00822-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119083560&doi=10.1007%2fs12369-021-00822-5&partnerID=40&md5=a75ded6080c59dabc131616c6192cf9e","Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS), Università di Genova, Opera Pia 13, Genova, 16145, Italy; Robotics Brain and Cognitive Sciences (RBCS), Istituto Italiano di Tecnologia, Enrico Melen 83, Bldg B, Genova, 16152, Italy; COgNiTive Architecture for Collaborative Technologies (CONTACT), Istituto Italiano di Tecnologia, Enrico Melen 83, Bldg B, Genova, 16152, Italy; Information and Communication Technologies Directorate (ICT), Istituto Italiano di Tecnologia, Enrico Melen 83, Bldg B, Genova, 16152, Italy; Social and Intelligent Robotics Research Laboratory (SIRRL), Department of Electrical and Computer Engineering, University of Waterloo, Engineering 7, 200 University Ave W, Waterloo, ON N2L 3G5, Canada","Pasquali, D., Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS), Università di Genova, Opera Pia 13, Genova, 16145, Italy, Robotics Brain and Cognitive Sciences (RBCS), Istituto Italiano di Tecnologia, Enrico Melen 83, Bldg B, Genova, 16152, Italy, COgNiTive Architecture for Collaborative Technologies (CONTACT), Istituto Italiano di Tecnologia, Enrico Melen 83, Bldg B, Genova, 16152, Italy; Gonzalez-Billandon, J., COgNiTive Architecture for Collaborative Technologies (CONTACT), Istituto Italiano di Tecnologia, Enrico Melen 83, Bldg B, Genova, 16152, Italy; Aroyo, A.M., Social and Intelligent Robotics Research Laboratory (SIRRL), Department of Electrical and Computer Engineering, University of Waterloo, Engineering 7, 200 University Ave W, Waterloo, ON N2L 3G5, Canada; Sandini, G., Robotics Brain and Cognitive Sciences (RBCS), Istituto Italiano di Tecnologia, Enrico Melen 83, Bldg B, Genova, 16152, Italy; Sciutti, A., Information and Communication Technologies Directorate (ICT), Istituto Italiano di Tecnologia, Enrico Melen 83, Bldg B, Genova, 16152, Italy; Rea, F., Robotics Brain and Cognitive Sciences (RBCS), Istituto Italiano di Tecnologia, Enrico Melen 83, Bldg B, Genova, 16152, Italy","Robots destined to tasks like teaching or caregiving have to build a long-lasting social rapport with their human partners. This requires, from the robot side, to be capable of assessing whether the partner is trustworthy. To this aim a robot should be able to assess whether someone is lying or not, while preserving the pleasantness of the social interaction. We present an approach to promptly detect lies based on the pupil dilation, as intrinsic marker of the lie-associated cognitive load that can be applied in an ecological human–robot interaction, autonomously led by a robot. We demonstrated the validity of the approach with an experiment, in which the iCub humanoid robot engages the human partner by playing the role of a magician in a card game and detects in real-time the partner deceptive behavior. On top of that, we show how the robot can leverage on the gained knowledge about the deceptive behavior of each human partner, to better detect subsequent lies of that individual. Also, we explore whether machine learning models could improve lie detection performances for both known individuals (within-participants) over multiple interaction with the same partner, and with novel partners (between-participant). The proposed setup, interaction and models enable iCub to understand when its partners are lying, which is a fundamental skill for evaluating their trustworthiness and hence improving social human–robot interaction. © 2021, The Author(s).","Adaptation; Entertainment; Human-robot interaction; Lie detection; Machine learning","Anthropomorphic robots; Behavioral research; Human robot interaction; Knowledge management; Adaptation; Card games; Cognitive loads; Deceptive behaviors; Humanoid robot; Humans-robot interactions; Lie detection; Long lasting; Pupil dilation; Social interactions; Machine learning",,,,,"Horizon 2020 Framework Programme, H2020: 804388; European Research Council, ERC; Istituto Italiano di Tecnologia, IIT","Open access funding provided by Istituto Italiano di Tecnologia within the CRUI-CARE Agreement. This work has been supported by a Starting Grant from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme. G.A. No 804388, wHiSPER. Researchers of the SIRRL lab at University of Waterloo are supported, in part, thanks to funding from the Canada 150 Research Chairs Program.",,,,,,,,,,"Mccornack, S.A., Parks, M.R., Deception detection and relationship development: the other side of trust (1986) Ann Int Commun Assoc, 9 (1), pp. 377-389; Lucas, G., Lieblich, S., Gratch, J., Trust Meâ€¯: Multimodal Signals of Trustworthiness (2016) ICMI ’16: Proceedings of the 18Th ACM International Conference on Multimodal Interaction, pp. 5-12. , https://doi.org/10.1145/2993148.2993178; Rueben, M., Aroyo, A.M., Lutz, C., Schmolz, J., Cleynenbreugel, P., Corti, A., Agrawal, S., Smart, W., (2018) Themes and Research Directions in Privacy-Sensitive Robotics, , EEE Work. onAdvanced Robot. its Soc. 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J. Soc. Rob.",Article,"Article in Press","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85119083560
"Vasylkovskyi V., Guerreiro S., Sequeira J.S.","56784404400;24536769600;7006523955;","Designing and Validating a Blockchain-based Architecture to Enforce Privacy in Human Robot Interaction",2021,"Proceedings of the Annual Hawaii International Conference on System Sciences","2020-January",,,"566","575",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108353955&partnerID=40&md5=851dc69d311e5e2fcc21f5c614a1001e","INESC-ID, Instituto Superior Técnico, University of Lisbon, Portugal; Institute for Systems and Robotics, Instituto Superior Técnico, University of Lisbon, Portugal","Vasylkovskyi, V., INESC-ID, Instituto Superior Técnico, University of Lisbon, Portugal; Guerreiro, S., INESC-ID, Instituto Superior Técnico, University of Lisbon, Portugal; Sequeira, J.S., Institute for Systems and Robotics, Instituto Superior Técnico, University of Lisbon, Portugal","Social Robots (SR) can record large streams of raw data in the form of images, audio, Radio Frequency Identification (RFID), among other sensory sources, which could be instrumental in enforcing Human-robot interactions (HRI). However, the emotional bonds between humans and SR can raise an SR accessing/inferring profoundly private information, e.g., emotional states. Therefore, in what concerns privacy, SR may be a liability. Clarifying the ownership of data collected by robots is an issue of concern in the European Union (EU) General Data Protection Regulation (GDPR), which requires'privacy-by-design'. The paper presents BlockRobot - a Blockchain (BC)-based software design model for developing privacy-by-design application in Human-Robot Interactions (HRI). Based on this design model, users can access the private data generated in their HRI. This paper's contributions are the design and validation of a model to develop privacy-by-design applications for HRI. This model integrates BC technology with robots to audit robotic events such as Red, Green, Blue, Depth (RGB-D) images, and RFID tags. Smart contracts and crypto-wallets accomplish identity management. To demonstrate the utility of the initial implementation of a Decentralized Application (dApp), we implement a prototype, based on the private permissioned Evolution of Scalability (EOS) Blockchain integrated with the social robot moving in a non-laboratory environment. © 2021 IEEE Computer Society. All rights reserved.",,"Application programs; Blockchain; Data streams; Machine design; Man machine systems; Privacy by design; Radio frequency identification (RFID); Software design; Design applications; Emotional state; General data protection regulations; Human robot Interaction (HRI); Identity management; Laboratory environment; Private information; Software design model; Social robots",,,,,"822404; 2020-2023; Fundação para a Ciência e a Tecnologia, FCT: UIDB/50021/2020","The first and second authors state that this work was supported by the European Commission program H2020 under the grant agreement 822404 (project QualiChain) and by national funds through Fundac¸ão para a Ciência e a Tecnologia (FCT) with reference UIDB/50021/2020 (INESC-ID). The third author state that this work was supported by LARSyS – FCT Plurianual funding 2020-2023.","The first and second authors state that this work was supported by the European Commission program H2020 under the grant agreement 822404 (project QualiChain) and by national funds through Funda??o para a Ci?ncia e a Tecnologia (FCT) with reference UIDB/50021/2020 (INESC-ID). The third author state that this work was supported by LARSyS - FCT Plurianual funding 2020-2023.",,,,,,,,,"Cucco, E., Fisher, M., Dennis, L., Dixon, C., Webster, M., Broecker, B., Williams, R., Tuyls, K., (2017) Towards Robots for Social Engagement, , Department of Computer Science, University of Liverpool, UK; Lutz, C., Tamò, A., Privacy and healthcare robots-an ant analysis (2016) We Robot 2016: The Fifth Annual Conference on Legal and Policy Issues Relating to Robotics; Dziergwa, M., Kaczmarek, P., Kedzierski, J., Rgb-d sensors in social robotics (2015) Journal of Automation Mobile Robotics and Intelligent Systems, 9; Hegel, F., Lohse, M., Swadzba, A., Wachsmuth, S., Rohlfing, K., Wrede, B., Classes of applications for social robots: A user study (2007) RO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication, pp. 938-943; Alonso-Martín, F., Salichs, M.A., Integration of a voice recognition system in a social robot (2011) Cybernetics and Systems: An International Journal, 42 (4), pp. 215-245; Sequeira, J., Gameiro, D., A probabilistic approach to RFID-based localization for human-robot interaction in social robotics (2017) Electronics, 6 (2), p. 32; Vitale, J., Tonkin, M., Herse, S., Ojha, S., Clark, J., Williams, M.-A., Wang, X., Judge, W., (2018) Be More Transparent and Users Will like You:A Robot Privacy and User Experience Design Experiment; Richert, A., Müller, S., Schröder, S., Jeschke, S., Anthropomorphism in social robotics: Empirical results on human-robot interaction in hybrid production workplaces (2018) AI & SOCIETY, 33 (3), pp. 413-424; Finck, M., Blockchains and data protection in the european union (2018) Eur. 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Rev., 4, p. 17; Finck, M., (2019) Blockchain and the General Data Protection Regulation: Can Distributed Ledgers Be Squared with European Data Protection Law?: Study, , European Parliament; Jay, R., Malcolm, W., Parry, E., Townsend, L., Bapat, A., (2017) Guide to the General Data Protection Regulation (GDPR), , Sweet & Maxwell; van der Aalst, W., Data science in action (2016) Process Mining, pp. 3-23. , Springer; Laurence, T., (2019) Blockchain for Dummies, , John Wiley & Sons; Zyskind, G., Nathan, O., Decentralizing privacy: Using blockchain to protect personal data (2015) 2015 IEEE Security and Privacy Workshops, pp. 180-184; Truong, N.B., Sun, K., Lee, G.M., Guo, Y., Gdpr-compliant personal data management: A blockchain-based solution (2019) IEEE Transactions on Information Forensics and Security, 15, pp. 1746-1761; Faber, B., Michelet, G.C., Weidmann, N., Mukkamala, R.R., Vatrapu, R., BPDims: A blockchain-based personal data and identity management system (2019) Proceedings of the 52nd Hawaii International Conference on System Sciences; O'Kane, J.M., (2014) A Gentle Introduction to Ros; Ferrer, E.C., Rudovic, O., Hardjono, T., Pentland, A., (2018) Robochain: A Secure Data-Sharing Framework for Human-Robot Interaction, , arXiv preprint; Degardin, B., (2018) Blockchain for Robotic Event Recognition, , Covilh: Universidade da Beira Interior; Fernandes, M., Alexandre, L.A., Robotchain: Using tezos technology for robot event management (2019) Ledger; Brown, A., Rise of the machines? Amazons army of more than 100,000 warehouse robots still cant replace humans because they lack common sense (2018) Daily Mail, , accessed 9 March 2019; Benet, J., Ipfs-content addressed (2014) Versioned, P2P File System, 2; Izaguirre, J., Furgeson, J., Ma, Q., (2000) Proceedings of Cse 331, Data Structures, Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana, pp. 63-67; Grigg, I., (2017) Eos-An Introduction, , https://whitepaperdatabase.com/eos-whitepaper, White paper; Sequeira, J., Lima, P., Saffiotti, A., Gonzalez-Pacheco, V., Salichs, M.A., Monarch: Multi-robot cognitive systems operating in hospitals (2013) ICRA 2013 Workshop on Many Robot Systems; Ault, I.S.M., Why new off-chain storage is required for blockchains (2019) NIST National Institute of Standards and Technology; (2018) Aitheon Whitepaper, , Aitheon Online; Belchior, R., Vasconcelos, A., Guerreiro, S., Correia, M., (2020) A Survey on Blockchain Interoperability: Past, Present, and Future Trends, , arXiv preprint",,"Bui T.X.","NSA","IEEE Computer Society","54th Annual Hawaii International Conference on System Sciences, HICSS 2021","4 January 2021 through 8 January 2021",,169537,15301605,9780998133140,,,"English","Proc. Annu. Hawaii Int. Conf. Syst. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85108353955
"Gulletta G., Silva E.C.E., Erlhagen W., Meulenbroek R., Costa M.F.P., Bicho E.","56790459300;57221379490;6602126228;7003314452;57220736334;6602434613;","A Human-like Upper-limb Motion Planner: Generating naturalistic movements for humanoid robots",2021,"International Journal of Advanced Robotic Systems","18","2",,"","",,2,"10.1177/1729881421998585","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103554780&doi=10.1177%2f1729881421998585&partnerID=40&md5=ad8d6559a42d90589f1f197fd1162c37","Centre Algoritmi, Department of Industrial Electronics, University of Minho, Braga, Portugal; CIICESI, ESTG, Polytechnic of Porto and Centre Algoritmi, Porto, Portugal; Centre of Mathematics, Department of Mathematics and Applications, University of Minho, Braga, Portugal; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands","Gulletta, G., Centre Algoritmi, Department of Industrial Electronics, University of Minho, Braga, Portugal; Silva, E.C.E., CIICESI, ESTG, Polytechnic of Porto and Centre Algoritmi, Porto, Portugal; Erlhagen, W., Centre of Mathematics, Department of Mathematics and Applications, University of Minho, Braga, Portugal; Meulenbroek, R., Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands; Costa, M.F.P., Centre of Mathematics, Department of Mathematics and Applications, University of Minho, Braga, Portugal; Bicho, E., Centre Algoritmi, Department of Industrial Electronics, University of Minho, Braga, Portugal","As robots are starting to become part of our daily lives, they must be able to cooperate in a natural and efficient manner with humans to be socially accepted. Human-like morphology and motion are often considered key features for intuitive human–robot interactions because they allow human peers to easily predict the final intention of a robotic movement. Here, we present a novel motion planning algorithm, the Human-like Upper-limb Motion Planner, for the upper limb of anthropomorphic robots, that generates collision-free trajectories with human-like characteristics. Mainly inspired from established theories of human motor control, the planning process takes into account a task-dependent hierarchy of spatial and postural constraints modelled as cost functions. For experimental validation, we generate arm-hand trajectories in a series of tasks including simple point-to-point reaching movements and sequential object-manipulation paradigms. Being a major contribution to the current literature, specific focus is on the kinematics of naturalistic arm movements during the avoidance of obstacles. To evaluate human-likeness, we observe kinematic regularities and adopt smoothness measures that are applied in human motor control studies to distinguish between well-coordinated and impaired movements. The results of this study show that the proposed algorithm is capable of planning arm-hand movements with human-like kinematic features at a computational cost that allows fluent and efficient human–robot interactions. © The Author(s) 2021.","cognitive systems; human-like motion planning; Humanoids and human-like robotics; human–robot interaction; naturalistic obstacles-avoidance","Agricultural robots; Anthropomorphic robots; Biomechanics; Computation theory; Computational efficiency; Cost functions; Kinematics; Social robots; Collision-free trajectory; Computational costs; Experimental validations; Human motor control; Motion planning algorithms; Object manipulation; Postural constraints; Robot interactions; Robot programming",,,,,"Seventh Framework Programme, FP7: 289146; European Commission, EC; Fundação para a Ciência e a Tecnologia, FCT: SFRH/BD/114923/2016, UID/MAT/00013/2013, UIDB/00319/2020","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the EU Project FP7 Marie Curie NETTNeural Engineering and Transformative Technologies (ID 289146), the FCT PhD grant (ref. SFRH/BD/114923/2016), the FCT Project UID/MAT/00013/2013, and the FCT within the R&D Units Project Scope: UIDB/00319/2020.",,,,,,,,,,"Garcia, E., Jimenez, M.A., De Santos, P.G., The evolution of robotics research (2007) IEEE Robot Autom Mag, 14, pp. 90-103; Schaal, S., The new robotics: towards human-centered machines (2007) HFSP J, 1, pp. 115-126; Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Progress and prospects of the human-robot collaboration (2018) Autonom Robot, 42, pp. 957-975; Sebanz, N., Bekkering, H., Knoblich, G., Joint action: bodies and minds moving together (2006) Trends Cogn Sci, 10, pp. 70-76; Rizzolatti, G., Fogassi, L., Gallese, V., Neurophysiological mechanisms underlying the understanding and imitation of action (2001) Nat Rev Neurosci, 2, pp. 661-670; Erlhagen, W., Mukovskiy, A., Bicho, E., Goal-directed imitation for robots: a bio-inspired approach to action understanding and skill learning (2006) Robot Autonom Syst, 54, pp. 353-360; Bicho, E., Erlhagen, W., Hipolito, N., A dynamic field approach to goal inference, error detection and anticipatory action selection in human-robot collaboration (2011) New frontiers in human robots interaction, pp. 135-164. , Kerstin D., Joe S., (eds), Amsterdam, John Benjamins Publishing Company, In:, (eds; Bicho, E., Erlhagen, W., Louro, L., Neuro-cognitive mechanisms of decision making in joint action: a human-robot interaction study (2011) Hum Move Sci, 30, pp. 846-868; Bisio, A., Sciutti, A., Nori, F., Motor contagion during human-human and human-robot interaction (2014) PLoS ONE, 9, p. e106172; Kupferberg, A., Huber, M., Helfer, B., Moving just like you: motor interference depends on similar motility of agent and observer (2012) PLoS ONE, 7, pp. 1-8; Gladden, M.E., Who will be the members of society 5.0? 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Conference - 8th Portuguese Conference on Automatic Control, UTAD, Vila Real, Portugal, 21–23 July 2008, In; Drillis, R., Contini, R., Bluestein, M., (1966) Body segment parameters, , Technical Report, New York; Craig, J.J., (2005) Introduction to robotics, , 3rd ed, London, Pearson Education Inc; Flanagan, J.R., Bowman, M.C., Johansson, R.S., Control strategies in object manipulation tasks (2006) Curr Opin Neurobiol, 16, pp. 650-659; Napier, J.R., The prehensile movements of the human hand (1956) J Bone Jt Surg Br Volume, 38B, pp. 902-913; Costa e Silva, E., Costa, F., Bicho, E., Nonlinear optimization for human-like movements of a high degree of freedom robotics arm-hand system (2011) Computational Science and Its Applications – ICCSA 2011, 6784, pp. 327-342. , Murgante B., (ed), Berlin, Heidelberg, Springer, In:, (ed; Rosenbaum, D.A., Loukopoulos, L.D., Meulenbroek, R.G.J., Planning reaches by evaluating stored postures (1995) Psychol Rev, 102, pp. 28-63; Beeson, P., Ames, B., TRAC-IK: an open-source library for improved solving of generic inverse kinematics 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 928-935. , Seoul, Korea (South), 3–5 November 2015, IEEE, In; Rosenbaum, D.A., Meulenbroek, R.G.J., Vaughan, J., Coordination of reaching and grasping by capitalizing on obstacle avoidance and other constraints (1999) Exp Brain Res, 128, pp. 92-100; Meulenbroek, R., Rosenbaum, D., Jansen, C., Multijoint grasping movements (2001) Exp Brain Res, 138, pp. 219-234; Guiard, Y., On Fitts’s and Hooke’s laws: simple harmonic movement in upper-limb cyclical aiming (1993) Acta Psychol, 82, pp. 139-159; Vaughan, J., Rosenbaum, D.A., Meulenbroek, R.G.J., Planning reaching and grasping movements: the problem of obstacle avoidance (2001) Motor control, pp. 116-135. , Champaign, Human Kinetics Publishers, In; Stulp, F., Theodorou, E.A., Schaal, S., Reinforcement learning with sequences of motion primitives for robust manipulation (2012) IEEE Trans Robot, 28, pp. 1360-1370; Castiello, U., The neuroscience of grasping (2005) Nat Rev Neurosci, 6, pp. 726-736; Breteler, M.D.K., Meulenbroek, R.G.J., Modeling 3D object manipulation: Synchronous single-axis joint rotations? (2006) Exp Brain Res, 168, pp. 395-409; Fourer, R., Gay, D.M., Kernighan, B.W., (1989) AMPL: a mathematical programming language, pp. 150-151. , Algorithms and model formulations mathematical programming, Berlin, Heidelberg, Springer-Verlag, (., –; Wachter, A., Biegler, L.T., On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming (2005) Math Program, 106, pp. 25-57; Quigley, M., Conley, K., Gerkey, B., ROS: an open-source robot operating system ICRA Workshop on Open Source Software, 3, p. 5. , Kobe, Japan, 12–17 May 2009, In:,., p; Rohmer, E., Singh, S.P.N., Freese, M., V-REP: a versatile and scalable robot simulation framework, pp. 1321-1326. , Proceedings 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan, 3–7 November 2013, IEEE, In; Chitta, S., Sucan, I., Cousins, S.M., (2012) IEEE Robotics & Automation Magazine, 19 (1), pp. 18-19. , [ROS Topics]. In:; Sucan, I.A., Moll, M., Kavraki, L.E., The open motion planning library (2012) IEEE Robot Autom Mag, 19, pp. 72-82; De Momi, E., Kranendonk, L., Valenti, M., A neural network-based approach for trajectory planning in robot-human handover tasks (2016) Front Robot AI, 3, pp. 1-10; Sousa, E., Erlhagen, W., Ferreira, F., Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning (2015) Neural Netw, 72, pp. 123-139; Smits, R., KDL: kinematics and dynamics library, , http://wwworocosorg/kdl, accessed 26 September 2016; Yamaguchi, A., Atkeson, C.G., Niekum, S., Learning pouring skills from demonstration and practice, pp. 908-915. , 2014 IEEE-RAS International Conference on Humanoid Robots, Madrid, Spain, 18–20 November 2014, IEEE, In; Lommertzen, J., Costa e Silva, E., Cuijpers, R.H., Collision-avoidance characteristics of grasping (2009) Anticipatory behavior in adaptive learning systems, pp. 188-208. , Berlin, Springer, In; Grimme, B., Lipinski, J., Schöner, G., Naturalistic arm movements during obstacle avoidance in 3D and the identification of movement primitives (2012) Exp Brain Res, 222, pp. 185-200; Dragan, A.D., Lee, K.C.T., Srinivasa, S.S., Legibility and predictability of robot motion, pp. 301-308. , 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Tokyo, Japan, 3–6 March 2013, IEEE, In; Costa e Silva, E., Costa, M.F., Erlhagen, W., Global vs. local nonlinear optimization techniques for human-like movement of an anthropomorphic robot AIP Conference Proceedings, 1648, p. 140004. , 10 March 2015, In:,., p; Liarokapis, M., Bechlioulis, C.P., Artemiadis, P.K., Deriving humanlike arm hand system poses (2017) J Mech Robot, 9, pp. 1-11; Gielniak, M., Liu, K., Thomaz, A., Generating human-like motion for robots (2013) Int J Robot Res, 32, pp. 1275-1301; Zanchettin, A.M., Rocco, P., Bascetta, L., Kinematic analysis and synthesis of the human arm motion during a manipulation task, pp. 2692-2697. , 2011 IEEE International Conference on Robotics and Automation, Shanghai, China, 9–13 May 2011, IEEE, In; Chaminade, T., Franklin, D.W., Oztop, E., Motor interference between humans and humanoid robots: effect of biological and artificial motion, 2005, pp. 96-101. , Proceedings of the 4th International Conference on Development and Learning, Osaka, Japan, 19–21 July 2005, IEEE, In","Gulletta, G.; Centre Algoritmi, Portugal; email: estela.bicho@dei.uminho.pt
Bicho, E.; Centre Algoritmi, Portugal; email: estela.bicho@dei.uminho.pt",,,"SAGE Publications Inc.",,,,,17298806,,,,"English","Int. J. Adv. Rob. Syst.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85103554780
"Jørgensen J., Bojesen K.B., Jochum E.","57199227765;57200601656;57074708900;","Is a Soft Robot More “Natural”? Exploring the Perception of Soft Robotics in Human–Robot Interaction",2021,"International Journal of Social Robotics",,,,"","",,2,"10.1007/s12369-021-00761-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103100092&doi=10.1007%2fs12369-021-00761-1&partnerID=40&md5=e46191f6b05fb33e0617747332a77ba4","Center for Soft Robotics, SDU Biorobotics, University of Southern Denmark, Campusvej 55, Odense M, 5230, Denmark; Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Nordstjernevej 29-67, Glostrup, 2600, Denmark; Research Laboratory for Art and Technology, Department of Communication and Psychology, Aalborg University, Rendsburggade 14, Aalborg, 9000, Denmark","Jørgensen, J., Center for Soft Robotics, SDU Biorobotics, University of Southern Denmark, Campusvej 55, Odense M, 5230, Denmark; Bojesen, K.B., Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Centre Glostrup, University of Copenhagen, Nordstjernevej 29-67, Glostrup, 2600, Denmark; Jochum, E., Research Laboratory for Art and Technology, Department of Communication and Psychology, Aalborg University, Rendsburggade 14, Aalborg, 9000, Denmark","Soft robotics technology has been proposed for a number of applications that involve human–robot interaction. It is commonly presumed that soft robots are perceived as more natural, and thus more appealing, than rigid robots, an assumption that has not hitherto been tested or validated. This study investigates human perception of and physical interaction with soft robots as compared with rigid robots. Using a mixed-methods approach, we conducted an observational study to explore whether soft robots are perceived as more natural, and what types of interactions soft robots encourage. In a between-subjects study, participants interacted with a soft robotic tentacle or a rigid robot of a similar shape. The interactions were video recorded, and data was also obtained from questionnaires (Nvideo = 123, Nquest = 94). Despite their drastically different appearances and materials, we found no significant differences in how appealing or natural the robots were rated to be. Appeal was positively associated with perceived naturalness in all cases, however we observed a wide variation in how participants define “natural”. Although participants showed no clear preference, qualitative analysis of video data indicated that soft robots and rigid robots elicit different interaction patterns and behaviors. The findings highlight the key role of physical embodiment and materiality in human–robot interaction, and challenge existing assumptions about what makes robots appear natural. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Embodiment; Haptic interaction; Human factors; Human–robot interaction; Naturalness; Soft robotics","Agricultural robots; Social robots; Surveys; Human perception; Interaction pattern; Mixed method; Observational study; Physical interactions; Qualitative analysis; Robot interactions; Soft robotics; Robotics",,,,,,,,,,,,,,,,"Pfeifer, R., Lungarella, M., Iida, F., The challenges ahead for bio-inspired “soft” robotics (2012) Commun ACM, 55, pp. 76-87; Majidi, C., Soft robotics: a perspective—current trends and prospects for the future (2013) Soft Robot, 1, pp. 5-11; Rus, D., Tolley, M.T., Design, fabrication and control of soft robots (2015) Nature, 521, pp. 467-475; Verl, A., Albu-Schäffer, A., Brock, O., Raatz, A., (2015) Soft robotics: transferring theory to application, , Springer, Berlin; Soft Robotics Inc. (N.D.) Customers Using Soft Robotics, , https://www.softroboticsinc.com/in-use, Soft robot, Accessed 11 Mar 2019; von Zitzewitz, J., Boesch, P.M., Wolf, P., Riener, R., Quantifying the human likeness of a humanoid robot (2013) Int J Soc Robot, 5, pp. 263-276; Rossiter, J., Hauser, H., Soft robotics—the next industrial revolution? [industrial activities] (2016) IEEE Robot Autom Mag, 23, pp. 17-20; Laschi, C., Mazzolai, B., Cianchetti, M., Soft robotics: Technologies and systems pushing the boundaries of robot abilities (2016) Sci Robot, 1, p. eaah3690; Lipson, H., Challenges and opportunities for design, simulation, and fabrication of soft robots (2013) Soft Robot, 1, pp. 21-27; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Robot Auton Syst, 42, pp. 143-166; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int J Soc Robot, 1, pp. 71-81; Li, H., Cabibihan, J.-J., Tan, Y.K., Towards an effective design of social robots (2011) Int J Soc Robot, 3, pp. 333-335; Cha, E., Kim, Y., Fong, T., Mataric, M.J., A survey of nonverbal signaling methods for non-humanoid robots (2018) Found Trends® Robot, 6, pp. 211-323; Suchman, L., (2007) Human–machine reconfigurations: plans and situated actions, , Cambridge University Press, Cambridge; Weiss, A., Tscheligi, M., Special issue on robots for future societies: evaluating social acceptance and societal impact of robots (2010) Int J Soc Robot, 2, pp. 345-346; Kroos, C., Herath, D.C., Evoking agency: attention model and behavior control in a robotic art installation (2012) Leonardo, 45, pp. 401-407; Alač, M., Social robots: things or agents? (2015) AI Soc; Goetz, J., Kiesler, S., Powers, A., Matching robot appearance and behavior to tasks to improve human–robot cooperation (2003) In: The 12Th IEEE International Workshop on Robot and Human Interactive Communication, 2003. Proceedings. ROMAN 2003. IEEE, pp. 55-60; Bartneck, C., Forlizzi, J., A design-centred framework for social human–robot interaction (2004) RO-MAN 2004. 13Th IEEE International Workshop on Robot and Human Interactive Communication, pp. 591-594. , IEEE Catalog No. 04TH8759, IEEE; Walters, M.L., Koay, K.L., Syrdal, D.S., Dautenhahn, K., Te Boekhorst, R., Preferences and perceptions of robot appearance and embodiment in human–robot interaction trials (2009) Proceedings of New Frontiers in human–robot Interaction. SSAISB, pp. 136-143; Boer, L., Bewley, H., Reconfiguring the appearance and expression of social robots by acknowledging their otherness (2018) Proceedings of the 2018 Designing Interactive Systems Conference, pp. 667-677. , ACM, New York; Jørgensen, J., Appeal and perceived naturalness of a soft robotic tentacle (2018) Companion of the 2018 ACM/IEEE International Conference on human–robot Interaction. ACM, pp. 139-140; Milthers, A.D.B., Bjerre Hammer, A., Jung Johansen, J., Jensen, L.G., Jochum, E.A., Löchtefeld, M., The helpless soft robot—stimulating human collaboration through robotic movement (2019) In: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, New York, pp. LBW1-LBW2421; Vlachos, E., Jochum, E., Demers, L.-P., The effects of exposure to different social robots on attitudes toward preferences (2016) Interact Stud, 17, pp. 390-404; Portney, L.G., Watkins, M.P., (2009) Foundations of clinical research: applications to practice, , 3, Prentice Hall, Upper Saddle River; Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., Šabanović, S., (2020) Human–robot interaction: an introduction, , 1, Cambridge University Press, Cambridge; Papageorgiou, X.S., Tzafestas, C.S., Vartholomeos, P.P., Laschi, C., Lopez, R., (2015) ICT-supported bath robots: Design concepts.; Borgatti, M., (2013) A little background|Silicone Robo-Tentacle|Adafruit Learning System, , Adafruit.com.https://learn.adafruit.com/silicone-robo-tentacle/a-little-background?embeds=allow, In:, Accessed 11 Mar 2019; Estevez, D., (2013), Thingiverse.com, REPY-2.0 Module by DEF, In:, Accessed 11 Mar 2019; Sabanovic, S., Michalowski, M.P., Simmons, R., Robots in the wild: Observing human–robot social interaction outside the lab (2006) In: 9Th IEEE International Workshop on Advanced Motion Control, 2006. IEEE, pp. 596-601; Dautenhahn, K., Some brief thoughts on the past and future of human–robot interaction (2018) ACM Trans Hum-Robot Interact, 7, pp. 4:1-4:3; Kiesler, S., Goodrich, M.A., The science of human–robot interaction (2018) ACM Trans Hum-Robot Interact, 7, pp. 9:1-9:3; Krummheuer, A.L., Users, bystanders and agents: Participation roles in human–agent interaction (2015) Human–computer Interaction. 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Department of Visual Design, pp. 153-160. , Universidad de Caldas, and ISEA International, University of Caldas, Manizales, Colombia; Jørgensen, J., Interaction with soft robotic tentacles (2018) Companion of the 2018 ACM/IEEE International Conference on human–robot Interaction. ACM, New York, pp. 38-38; Briggs, S.R., Cheek, J.M., The role of factor analysis in the development and evaluation of personality scales (1986) J Pers, 54, pp. 106-148; The Uncanny Valley: The original essay by Masahiro Mori—IEEE Spectrum IEEE Spectr. Technol. Eng. Sci. News., , https://spectrum.ieee.org/automaton/robotics/humanoids/the-uncanny-valley, Accessed 7 Aug 2020; Heider, F., Simmel, M., An experimental study of apparent behavior (1944) Am J Psychol, 57, pp. 243-259; Hoffman, G., Ju, W., Designing robots with movement in mind (2014) J Hum-Robot Interact, 3, pp. 89-122; Reeves, B., Nass, C.I., (1996) The media equation: how people treat computers, television, and new media like real people and places, , Cambridge University Press, Cambridge; Ghedini, F., Bergamasco, M., Robotic creatures: Anthropomorphism and interaction in contemporary art (2010) 19Th International Symposium in Robot and Human Interactive Communication. IEEE, pp. 731-736; Barthes, R., (2009) Mythologies, , revised, Vintage Classics, London; Foucault, M., (2001) The order of things: archaeology of the human sciences, , Routledge, London; Sandry, E., Re-evaluating the form and communication of social robots (2015) Int J Soc Robot, 7, pp. 335-346; Hansen, L.K., Dalsgaard, P., Note to self: Stop calling interfaces “natural (2015) Proceedings of the Fifth Decennial Aarhus Conference on Critical Alternatives, pp. 65-68. , Aarhus University Press; Arnold, T., Scheutz, M., the tactile ethics of soft robotics: designing wisely for human–robot interaction (2017) Soft Robot, 4, pp. 81-87; Lee, K.M., Presence, explicated (2004) Commun Theory, 14, pp. 27-50; Nguyen, T., Heslin, R., Nguyen, M.L., The meanings of touch: sex differences (1975) J Commun, 25, pp. 92-103; Willemse, C.J.A.M., Toet, A., van Erp, J.B.F., Affective and behavioral responses to robot-initiated social touch: toward understanding the opportunities and limitations of physical contact in human–robot interaction (2017) Front ICT; Willemse, C.J.A.M., van Erp, J.B.F., Social touch in human–robot interaction: robot-initiated touches can induce positive responses without extensive prior bonding (2019) Int J Soc Robot, 11, pp. 285-304; Harlow, H.F., Zimmermann, R.R., Affectional responses in the infant monkey (1959) Science, 130, pp. 421-432; Aragón, O.R., Clark, M.S., Dyer, R.L., Bargh, J.A., Dimorphous expressions of positive emotion: displays of both care and aggression in response to cute stimuli (2015) Psychol Sci, 26, pp. 259-273; Block, A.E., Kuchenbecker, K.J., Softness, warmth, and responsiveness improve robot hugs (2018) Int J Soc Robot; Eggink, W., Snippert, J., Future Aesthetics of Technology; context specific theories from design and philosophy of technology (2017) Des J, 20, pp. S196-S208","Jørgensen, J.; Center for Soft Robotics, Campusvej 55, Denmark; email: jonj@sdu.dk",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Article in Press","",Scopus,2-s2.0-85103100092
"Wang R., Wang J., Liao Y., Wang J.","57223912004;57223917847;57223911082;57223917846;","Supervised machine learning chatbots for perinatal mental healthcare",2020,"Proceedings - 2020 International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2020",,,"9424857","378","383",,,"10.1109/ICHCI51889.2020.00086","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106426137&doi=10.1109%2fICHCI51889.2020.00086&partnerID=40&md5=9963570c8c4c0b56aa55a12a8d0d2c78","University of Sheffield, Department of Electronic and Electrical Engineering, United Kingdom; Shenzhen Sunline Tech Co.Ltd, Department of AI Research and Development, Shenzhen, China; Haibin Engineering University, Department of Economics and Management, China","Wang, R., University of Sheffield, Department of Electronic and Electrical Engineering, United Kingdom; Wang, J., Shenzhen Sunline Tech Co.Ltd, Department of AI Research and Development, Shenzhen, China; Liao, Y., Haibin Engineering University, Department of Economics and Management, China; Wang, J., University of Sheffield, Department of Electronic and Electrical Engineering, United Kingdom","Perinatal mental health (PMH) problems are types of mood disorders which arise during pregnancy and within 24 months after the birth of a child, which affects pregnant women, newborns and family relationships. These problems may occur at any stage of maternal women. PMH is mainly diagnosed through behavioral observation, self-reporting, and behavioral scale testing. Chatbot is an effective technology. Through human-robot interaction, it can monitor the mental health status of perinatal women in real time while collecting user health data. The application of human-robot interaction in mental health services has attracted widespread attention. Compared with traditional methods, robot intervention in mental health care can help reduce the obstacles for subjects to seek help for mental health, and can collect more comprehensive and detailed data of patients, which helps users recognize their own mental health level, and can also help clinicians make diagnoses more accurately and in a timely manner. In this article, the author proposes a chatbot to monitor and assess the mental state of perinatal women. This article uses supervised machine learning to analyze the 31 characteristics of 223 samples, and trains a model to determine the anxiety, depression and hypomania index of perinatal women. Meanwhile, psychological test scales are used to assist in evaluation and make treatment suggestions to help users improve their mental health. © 2020 IEEE.","Chatbots; Perinatal mental health; Supervised machine learning","Diagnosis; Human computer interaction; Intelligent computing; Learning systems; Patient treatment; Supervised learning; Testing; Behavioral observation; Mental health; Mental health services; Mood disorders; Pregnant woman; Psychological tests; Self-reporting; Supervised machine learning; Human robot interaction",,,,,,,,,,,,,,,,"Baba, S., Impact of intention and feeling toward being pregnant on postpartum depression: The Japan environment and children's study (jecs) (2020) Archives of Women's Mental Health, 23 (1), pp. 131-137; Bowen, K.-L., (2019) Measuring the Postnatal Health Outcomes for Mothers and Fathers during the First Twelve Months after the Birth of a Baby, , PhD thesis, University of Sheffield; Fitzpatrick, K.K., Darcy, A., Vierhile, M., Delivering cognitive behavior therapy to Young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial (2017) Jmir Mental Health, 4 (2), p. e19; Frieder, A., Fersh, M., Hainline, R., Deligiannidis, K.M., Pharmacotherapy of postpartum depression: Current approaches and novel drug development (2019) Cns Drugs, 33 (3), pp. 265-282; Lu, L., Duan, Z., Wang, Y., Wilson, A., Yang, Y., Zhu, L., Guo, Y., Chen, R., Mental health outcomes among Chinese prenatal and postpartum women after the implementation of universal two-child policy (2020) Journal of Affective Disorders, 264, pp. 187-192; Medeiros, L., Gerritsen, C., Bosse, T., Towards humanlike chatbots helping users cope with stressful situations (2019) International Conference on Computational Collective Intelligence, pp. 232-243. , Springer, Cham; Nadarzynski, T., Miles, O., Cowie, A., Ridge, D., Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study (2019) Digital Health, 5, pp. 1-12; Schwank, S., Andersson, E., Wickberg, B., Fu, S.-C., Ding, Y., Lindgren, H., Care-seeking behavior and disclosure on mental health among women in urban Shanghai, China (2019) Health Psychology Open; Schwank, S., Lindgren, H., Wickberg, B., Fu, S.-C., Yan, D., Andersson, E., When a new mother becomes mentally unhealthy (2020) It Is Everyone' S Problem: Shanghai Women' S Perceptions of Perinatal Mental Health Problems, Women's Reproductive Health, 7 (3), pp. 190-204; The British Journal of Psychiatry. 1987; 150(6):782-786. BC Reproductive Mental Health Program and Perinatal Services BC. (2014), Best Practice Guidelines for Mental Health Disorders in the Perinatal Period; Ullas, M.T.R., A machine learning approach to detect depression and anxiety using supervised learning (2019) Diss. Brac University; Wu, Y., Perinatal depressive and anxiety symptoms of pregnant women during the coronavirus disease 2019 outbreak in China (2020) American Journal of Obstetrics and Gynecology, 223 (2), pp. 240e1-240e9. , 383",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2020","4 December 2020 through 6 December 2020",,168867,,9781665423168,,,"English","Proc. - Int. Conf. Intell. Comput. Hum.-Comput. Interact., ICHCI",Conference Paper,"Final","",Scopus,2-s2.0-85106426137
"Manjunatha H., Jujjavarapu S.S., Esfahani E.T.","57192710332;57197767379;16424554200;","Classification of Motor Control Difficulty using EMG in Physical Human-Robot Interaction",2020,"Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics","2020-October",,"9283016","2708","2713",,1,"10.1109/SMC42975.2020.9283016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098876762&doi=10.1109%2fSMC42975.2020.9283016&partnerID=40&md5=548f35b5f4426ec68093235c7e9d4a10","State University of New York at Buffalo, Department of Mechanical and Aerospace Engineering, Buffalo, NY  14260, United States","Manjunatha, H., State University of New York at Buffalo, Department of Mechanical and Aerospace Engineering, Buffalo, NY  14260, United States; Jujjavarapu, S.S., State University of New York at Buffalo, Department of Mechanical and Aerospace Engineering, Buffalo, NY  14260, United States; Esfahani, E.T., State University of New York at Buffalo, Department of Mechanical and Aerospace Engineering, Buffalo, NY  14260, United States","In physical human-robot interaction, a variable admittance/impedance controller is desired to adjust its controller parameters to enhance the collaboration by minimizing the human effort and maximizing the stability. In this paper, we propose a physiological monitoring approach based on electroencephalogram activities to classify the motor control difficulty and use that information for adjusting an admittance controller. We designed a physical human-robot interaction experiment where the human guides the robot's end-effector across four tasks with varying motor control difficulty. Each task is a combination of high/low damping and fine/gross motor control. During the experiments, we measure the muscle activation information in terms of surface electromyogram from eight channels. Two sets of features based on Riemann geometry and time domain (Hudgins' features) are extracted every 500 ms from the EMG data. A support vector machine classifier is trained on these features to estimate whether the existing admittance parameters are comfortable for the user else an increase/decrease of the damping is suggested. Riemann geometry-based features yielded higher accuracy (85.7%) than the Hudgins' features (69.1%) across 21 participants; however, the performance of these classifiers on the new sessions degraded to 63.1% and 54.5% respectively. To address this issue, we implemented a transfer learning approach using Riemannian features that improved the inter-session detection rate to 73.95%. © 2020 IEEE.",,"Controllers; Damping; End effectors; Man machine systems; Patient monitoring; Social robots; Support vector machines; Transfer learning; Admittance parameters; Controller parameter; Muscle activation; Physical human-robot interactions; Physiological monitoring; Robot's end effectors; Support vector machine classifiers; Surface electromyogram; Classification (of information)",,,,,,,,,,,,,,,,"Calanca, A., Muradore, R., Fiorini, P., A review of algorithms for compliant control of stiff and fixed-compliance robots (2015) IEEE/ASME Transactions on Mechatronics, 21 (2), pp. 613-624; Gopinathan, S., Ötting, S.K., Steil, J.J., A user study on personalized stiffness control and task specificity in physical human-robot interaction (2017) Frontiers in Robotics and AI, 4, p. 58; Grafakos, S., Dimeas, F., Aspragathos, N., Variable admittance control in pHRI using EMG-based arm muscles co-activation (2016) 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC, pp. 001900-001905. , IEEE; Kosuge, K., Kazamura, N., Control of a robot handling an object in cooperation with a human (1997) Proceedings 6th IEEE International Workshop on Robot and Human Communication. RO-MAN'97 SENDAI, pp. 142-147; Memar, A.H., Esfahani, E.T., Objective assessment of human workload in physical human-robot cooperation using brain monitoring (2019) ACM Transactions on Human-Robot Interaction, 9, pp. 1-21. , Dec; Peternel, L., Tsagarakis, N., Ajoudani, A., A human-robot comanipulation approach based on human sensorimotor information (2017) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (7), pp. 811-822; Memar, A.H., Esfahani, E.T., EEG correlates of motor control difficulty in physical human-robot interaction: A frequency domain analysis (2018) 2018 IEEE Haptics Symposium (HAPTICS, pp. 229-234. , IEEE; Novak, D., Beyeler, B., Omlin, X., Riener, R., Workload estimation in physical human-robot interaction using physiological measurements (2014) Interacting with Computers, 27 (6), pp. 616-629; Manjunatha, H., Esfahani, E.T., (2020) Application of Reinforcement and Deep Learning Techniques in Brain-Machine Interfaces, pp. 1-14. , Cham: Springer International Publishing; Rodrigues, P.L.C., Jutten, C., Congedo, M., Riemannian procrustes analysis: Transfer learning for brain-computer interfaces (2019) IEEE Transactions on Biomedical Engineering, 66 (8), pp. 2390-2401; Phinyomark, A., Phukpattaranont, P., Limsakul, C., Feature reduction and selection for EMG signal classification (2012) Expert Systems with Applications, 39 (8), pp. 7420-7431; Atzori, M., Gijsberts, A., Castellini, C., Caputo, B., Hager, A.-G.M., Elsig, S., Giatsidis, G., Müller, H., Electromyography data for non-invasive naturally-controlled robotic hand prostheses (2014) Scientific Data, 1, p. 140053; Tuzel, O., Porikli, F., Meer, P., Pedestrian detection via classification on riemannian manifolds (2008) IEEE Transactions on Pattern Analysis and Machine Intelligence, 30 (10), pp. 1713-1727; Barachant, A., Bonnet, S., Congedo, M., Jutten, C., Multiclass brain-computer interface classification by riemannian geometry (2011) IEEE Transactions on Biomedical Engineering, 59 (4), pp. 920-928; Pan, L., Zhang, D., Jiang, N., Sheng, X., Zhu, X., Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns (2015) Journal of Neuroengineering and Rehabilitation, 12 (1), p. 110; Congedo, M., Barachant, A., Bhatia, R., Riemannian geometry for EEG-based brain-computer interfaces; A primer and a review (2017) Brain-Computer Interfaces, 4 (3), pp. 155-174; Moakher, M., A differential geometric approach to the geometric mean of symmetric positive-definite matrices (2005) SIAM Journal on Matrix Analysis and Applications, 26 (3), pp. 735-747; Cohen, M.X., (2014) Analyzing Neural Time Series Data: Theory and Practice., , MIT press","Esfahani, E.T.; State University of New York at Buffalo, United States; email: ehsanesf@buffalo.edu",,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020","11 October 2020 through 14 October 2020",,165855,1062922X,9781728185262,PICYE,,"English","Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.",Conference Paper,"Final","",Scopus,2-s2.0-85098876762
"Ranieri C.M., Vargas P.A., Romero R.A.F.","55662436600;55146997400;24465478300;","Uncovering Human Multimodal Activity Recognition with a Deep Learning Approach",2020,"Proceedings of the International Joint Conference on Neural Networks",,,"9207255","","",,2,"10.1109/IJCNN48605.2020.9207255","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093857978&doi=10.1109%2fIJCNN48605.2020.9207255&partnerID=40&md5=f05b01818fac32541b0df11ac966e02a","ICMC University of São Paulo (USP), Brazil; Heriot-Watt University (HWU), Edinburgh Centre for Robotics (ECR), Edinburgh, United Kingdom","Ranieri, C.M., ICMC University of São Paulo (USP), Brazil; Vargas, P.A., Heriot-Watt University (HWU), Edinburgh Centre for Robotics (ECR), Edinburgh, United Kingdom; Romero, R.A.F., ICMC University of São Paulo (USP), Brazil","Recent breakthroughs on deep learning and computer vision have encouraged the use of multimodal human activity recognition aiming at applications in human-robot-interaction. The wide availability of videos at online platforms has made this modality one of the most promising for this task, whereas some researchers have tried to enhance the video data with wearable sensors attached to human subjects. However, temporal information on both video and inertial sensors are still under investigation. Most of the current work focusing on daily activities do not present comparative studies considering different temporal approaches. In this paper, we are proposing a new model build upon a Two-Stream ConvNet for action recognition, enhanced with Long Short-Term Memory (LSTM) and a Temporal Convolution Networks (TCN) to investigate the temporal information on videos and inertial sensors. A feature-level fusion approach prior to temporal modelling is also proposed and evaluated. Experiments have been conducted on the egocentric multimodal dataset and on the UTD-MHAD. LSTM and TCN showed competitive results, with the TCN performing slightly better for most applications. The feature-level fusion approach also performed well on the UTD-MHAD with some overfitting on the egocentric multimodal dataset. Overall the proposed model presented promising results on both datasets compatible with the state-of-the-art, providing insights on the use of deep learning for human-robot-interaction applications. © 2020 IEEE.","CNN; Deep learning; human activity recognition; human-robot-interaction; LSTM; RNN; TCN","Convolutional neural networks; Human computer interaction; Inertial navigation systems; Long short-term memory; Man machine systems; Pattern recognition; Social robots; Wearable sensors; Action recognition; Activity recognition; Comparative studies; Feature level fusion; Human activity recognition; Learning approach; Multi-modal dataset; Temporal information; Deep learning",,,,,"Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP: 2017/01687-0, 2017/02377-5, 2018/25902-0; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq: 2013/07375-0, 306151/2018-9","São Paulo Research Foundation (FAPESP), grants 2017/02377-5, 2018/25902-0 and 2017/01687-0, and Brazilian National Council for Scientific and Technological Development (CNPq), grant 306151/2018-9. This research was carried out using the computational resources of the Center for Mathematical Sciences Applied to Industry (CeMEAI) funded by FAPESP, grant 2013/07375-0. Additional resources were provided by the Nvidia Grants program.",,,,,,,,,,"Song, S., Chandrasekhar, V., Mandal, B., Li, L., Lim, J.-H., Babu, G.S., San, P.P., Cheung, N.-M., Multimodal multi-stream deep learning for egocentric activity recognition (2016) IEEE CVPRW; Browne, J.T., Wizard of oz prototyping for machine learning experiences (2019) 2019 Conference on Human Factors in Computing Systems, pp. 1-6; Song, S., Cheung, N.-M., Chandrasekhar, V., Mandal, B., Liri, J., Egocentric activity recognition with multimodal fisher vector (2016) IEEE ICASSP. IEEE, pp. 2717-2721; Feichtenhofer, C., Pinz, A., Wildes, R.P., Zisserman, A., Deep insights into convolutional networks for video recognition (2019) International Journal of Computer Vision, pp. 1-18; Ordóñez, F., Roggen, D., Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition (2016) Sensors, 16 (1), p. 115. , 1; Chen, C., Jafari, R., Kehtarnavaz, N., A survey of depth and inertial sensor fusion for human action recognition (2017) Multimedia Tools and Applications, 76 (3), pp. 4405-4425. , 2; Chen, C., Jafari, R., Kehtarnavaz, N., Utd-mhad: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor (2015) 2015 IEEE ICIP, pp. 168-172; Bai, S., Zico Kolter, J., Koltun, V., Convolutional sequence modeling revisited (2018) 6th ICLR. OpenReview. Net; Gan, C., Sun, C., Duan, L., Gong, B., Webly-supervised video recognition by mutually voting for relevant web images and web video frames (2016) ECCV., pp. 849-866. , Springer, Cham; Chavarriaga, R., Sagha, H., Calatroni, A., Digumarti, S.T., Tröster, G., Millán, J.D.R., Roggen, D., The opportunity challenge: A benchmark database for on-body sensor-based activity recognition (2013) Pattern Recognition Letters, 34 (15), pp. 2033-2042; Soomro, K., Zamir, A.R., Shah, M., Ucf101: A dataset of 101 human actions classes from videos in the wild (2012) CVPR; Kay, W., Carreira, J., Simonyan, K., Zhang, B., Hillier, C., Vijayanarasimhan, S., Viola, F., Zisserman, A., (2017) The Kinetics Human Action Video Dataset; Herath, S., Harandi, M., Porikli, F., Going deeper into action recognition: A survey (2017) Image and Vision Computing, 60; Simonyan, K., Zisserman, A., Two-stream convolutional networks for action recognition in videos (2014) NIPS, pp. 568-576; Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., Van Gool, L., Temporal segment networks: Towards good practices for deep action recognition (2016) ECCV., pp. 20-36. , Springer, Cham; Donahue, J., Hendricks, L.A., Rohrbach, M., Venugopalan, S., Guadarrama, S., Saenko, K., Darrell, T., Long-term recurrent convolutional networks for visual recognition and description (2017) IEEE Trans. on Patt. 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IEEE, pp. 523-528; Xu, C., Chai, D., He, J., Zhang, X., Duan, S., Innohar: A deep neural network for complex human activity recognition (2019) IEEE Access, 7, pp. 9893-9902; Sousa Lima, W., Souto, E., El-Khatib, K., Jalali, R., Gama, J., Human activity recognition using inertial sensors in a smartphone: An overview (2019) Sensors, 19 (14), p. 3213; Stein, S., Mckenna, S.J., Combining embedded accelerometers with computer vision for recognizing food preparation activities (2013) UbiComp., , Zurich, Switzerland: ACM; Chen, C., Jafari, R., Kehtarnavaz, N., A real-time human action recognition system using depth and inertial sensor fusion (2016) IEEE Sensors Journal, 16 (3), pp. 773-781. , 2; Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., Rethinking the inception architecture for computer vision (2016) IEEE CVPR., pp. 2818-2826. , Las Vegas, NV, USA: IEEE; Wu, Z., Wang, X., Jiang, Y.-G., Ye, H., Xue, X., Modeling spatialtemporal clues in a hybrid deep learning framework for video classification (2015) 23rd ICM. ACM, pp. 461-470; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Computation, 9 (8), pp. 1735-1780. , 11; Sundermeyer, M., Ney, H., Schluter, R., From feedforward torrecurrent lstm neural networks for language modeling (2015) IEEE/ACM Trans. on Audio, Speech, and Lang. Proc, 23 (3), pp. 517-529; Chung, J., Gulcehre, C., Cho, K., Bengio, Y., Empirical evaluation of gated recurrent neural networks on sequence modeling (2014) NIPS; Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Fei-Fei, L., Imagenet large scale visual recognition challenge (2015) International Journal of Computer Vision, 115 (3), pp. 211-252. , 12; Feichtenhofer, C., Pinz, A., Zisserman, A., Convolutional two-stream network fusion for video action recognition (2016) CVPR IEEE, 6, pp. 1933-1941; Zach, C., Pock, T., Bischof, H., A duality based approach for realtime tv-l 1 optical flow (2007) Patt. Recog. Symp, pp. 214-223; Farnebäck, G., Two-frame motion estimation based on polynomial expansion (2003) Scandinavian Conference on Image Analysis (SCIA)., pp. 363-370. , Halmstad, Sweden: Springer, Berlin, Heidelberg; Mukkamala, M.C., Hein, M., Variants of rmsprop and adagrad with logarithmic regret bounds (2017) Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 2545-2553. , Sydney, NSW, Australia: ACM; Li, K., Zhao, X., Bian, J., Tan, M., Sequential learning for multimodal 3d human activity recognition with long short-term memory (2017) 2017 IEEE International Conference on Mechatronics and Automation (ICMA)., 8, pp. 1556-1561. , Takamatsu, Japan: IEEE; Vargas, P., Fernaeus, Y., Lim, M., Enz, S., Ho, W., Jacobson, M., Aylett, R., Advocating an ethical memory model for artificial companions from a human-centred perspective (2011) AI Society, 26, pp. 329-337; Ferreira, B.V., Carvalho, E., Ferreira, M.R., Vargas, P.A., Ueyama, J., Pessin, G., Exploiting the use of convolutional neural networks for localization in indoor environments (2017) Applied Artificial Intelligence, 31 (3), pp. 279-287; Enz, S., Diruf, M., Spielhagen, C., Zoll, C., Vargas, P.A., The social role of robots in the future-explorative measurement of hopes and fears (2011) Int J of Soc Robotics, 3 (263); Rizzi, C., Johnson, C.G., Fabris, F., Vargas, P.A., A situation-aware fear learning (safel) model for robots (2017) Neurocomputing, 221, pp. 32-47",,,"IEEE Computational Intelligence Society (CIS)","Institute of Electrical and Electronics Engineers Inc.","2020 International Joint Conference on Neural Networks, IJCNN 2020","19 July 2020 through 24 July 2020",,163566,,9781728169262,85OFA,,"English","Proc Int Jt Conf Neural Networks",Conference Paper,"Final","",Scopus,2-s2.0-85093857978
"Chen L., Su W., Wu M., Pedrycz W., Hirota K.","55774663200;57196287282;35231682600;57210775415;7402829702;","A Fuzzy Deep Neural Network with Sparse Autoencoder for Emotional Intention Understanding in Human-Robot Interaction",2020,"IEEE Transactions on Fuzzy Systems","28","7","8957487","1252","1264",,7,"10.1109/TFUZZ.2020.2966167","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087843300&doi=10.1109%2fTFUZZ.2020.2966167&partnerID=40&md5=54c3e7ff26bb2034fff574856643442f","School of Automation, China University of Geosciences, Wuhan, 430074, China; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB  T6R 2G7, Canada; Tokyo Institute of Technology, Yokohama, 226-8502, Japan","Chen, L., School of Automation, China University of Geosciences, Wuhan, 430074, China; Su, W., School of Automation, China University of Geosciences, Wuhan, 430074, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, 430074, China; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB  T6R 2G7, Canada; Hirota, K., Tokyo Institute of Technology, Yokohama, 226-8502, Japan","A fuzzy deep neural network with sparse autoencoder (FDNNSA) is proposed for intention understanding based on human emotions and identification information (i.e., age, gender, and region), in which the fuzzy C-means (FCM) is used to cluster the input data, and deep neural network with sparse autoencoder (DNNSA) is designed for emotional intention understanding in human-robot interaction. It aims to make robots capable of recognizing human emotions and understanding related emotional intention, the FCM is suitable for gathering similar information so that the calculations of dimensionality of DNNSA will be reduced, and the sparse autoencoder of DNNSA can make the neuron of DNNSA sparse to reduce the complexity of the network in such a way human-robot interaction is running smoothly. To validate the proposal, simulation experiments based on benchmark databases such as facial expression database of CK+, and speech emotion corpus of CASIA were completed. The experimental results show that the proposal outperforms the baseline algorithms of Softmax regression (SR), DNNSA, FCM-based SR (FSR), Softplus, Gath Geva-based DNNSA (GDNNSA), and ensemble DNNSA (EDNNSA). Preliminary application experiments are performed in the development of emotional social robot system, where volunteers experience the scenario of 'drinking at the bar'. The obtained results indicate that the proposed FDNNSA can promote robot understanding of emotional intention of human. © 1993-2012 IEEE.","Candide3 model; deep learning; fuzzy C-means; human-robot interaction; intention understanding","Deep neural networks; Economic and social effects; Fuzzy inference; Fuzzy logic; Fuzzy neural networks; Learning systems; Man machine systems; Auto encoders; Benchmark database; Facial Expressions; Human emotion; Intention understanding; Recognizing Human Emotion; Softmax regressions; Speech emotions; Human robot interaction",,,,,"201839; National Natural Science Foundation of China, NSFC: 61603356, 61733016, 61973286; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project: B17040","Manuscript received November 29, 2018; revised March 2, 2019 and August 30, 2019; accepted December 9, 2019. Date of publication January 13, 2020; date of current version July 1, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61973286, Grant 61603356, and Grant 61733016, in part by the 111 project under Grant B17040, and in part by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) under Grant No. 201839. (Corresponding author: Min Wu.) L. Chen, W. Su, and M. Wu are with the School of Automation, China University of Geosciences, Wuhan 430074, China, and also with the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China (e-mail: chenluefeng@cug.edu.cn; suwanjuan@cug.edu.cn; wumin@cug.edu.cn).",,,,,,,,,,"Angel, F.J.M., Bonarini, A., Studying people's emotional responses to robot's movements in a small scene (2014) Proc. 23th Ieee Int. Symp. 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Syst., 50 (2), pp. 490-501. , Feb; Chen, L.F., Three-layer weighted fuzzy support vector regression for emotional intention understanding in human-robot interaction (2018) Ieee Trans. Fuzzy Syst., 26 (5), pp. 2524-2538. , Oct; (2000) Cohn-Kanade (CK and CK+) Database Download Site., , http://www.consortium.ri.cmu.edu/data/ck, [Online]; Viola, P., Jones, M.J., Robust real-time face detection (2004) Int. J. Comput. Vision, 57 (2), pp. 137-154; Seghouane, A.K.K., Shokouhi, N., Two-dimensional whitening of face images for improved PCA performance (2018) Ieee Signal Process. 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Humaniod Robots, pp. 270-275","Wu, M.; School of Automation, China; email: wumin@cug.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10636706,,IEFSE,,"English","IEEE Trans Fuzzy Syst",Article,"Final","",Scopus,2-s2.0-85087843300
"Bütepage J., Ghadirzadeh A., Öztimur Karadaǧ Ö., Björkman M., Kragic D.","57190122296;57199671469;35173174100;7005103070;6701873248;","Imitating by Generating: Deep Generative Models for Imitation of Interactive Tasks",2020,"Frontiers in Robotics and AI","7",,"47","","",,3,"10.3389/frobt.2020.00047","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084053889&doi=10.3389%2ffrobt.2020.00047&partnerID=40&md5=2104dbfa688ef2846fc50976e171fea8","Robotics, Perception and Learning, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden; Intelligent Robotics Research Group, Aalto University, Espoo, Finland; Department of Computer Engineering, Alanya Alaaddin Keykubat University, Antalya, Turkey","Bütepage, J., Robotics, Perception and Learning, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden; Ghadirzadeh, A., Robotics, Perception and Learning, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden, Intelligent Robotics Research Group, Aalto University, Espoo, Finland; Öztimur Karadaǧ, Ö., Robotics, Perception and Learning, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden, Department of Computer Engineering, Alanya Alaaddin Keykubat University, Antalya, Turkey; Björkman, M., Robotics, Perception and Learning, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden; Kragic, D., Robotics, Perception and Learning, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden","To coordinate actions with an interaction partner requires a constant exchange of sensorimotor signals. Humans acquire these skills in infancy and early childhood mostly by imitation learning and active engagement with a skilled partner. They require the ability to predict and adapt to one's partner during an interaction. In this work we want to explore these ideas in a human-robot interaction setting in which a robot is required to learn interactive tasks from a combination of observational and kinesthetic learning. To this end, we propose a deep learning framework consisting of a number of components for (1) human and robot motion embedding, (2) motion prediction of the human partner, and (3) generation of robot joint trajectories matching the human motion. As long-term motion prediction methods often suffer from the problem of regression to the mean, our technical contribution here is a novel probabilistic latent variable model which does not predict in joint space but in latent space. To test the proposed method, we collect human-human interaction data and human-robot interaction data of four interactive tasks “hand-shake,” “hand-wave,” “parachute fist-bump,” and “rocket fist-bump.” We demonstrate experimentally the importance of predictive and adaptive components as well as low-level abstractions to successfully learn to imitate human behavior in interactive social tasks. © Copyright © 2020 Bütepage, Ghadirzadeh, Öztimur Karadaǧ, Björkman and Kragic.","deep learning; generative models; human-robot interaction; imitation learning; sensorimotor coordination; variational autoencoders",,,,,,"H2020-FETPROACT-824160; Horizon 2020 Framework Programme, H2020: 641321; European Commission, EC: H2020-FETPROACT-2014; Knut och Alice Wallenbergs Stiftelse","Funding. This work was supported by the EU through the project socSMCs (H2020-FETPROACT-2014) and the Swedish Foundation for Strategic Research and EnTimeMent (H2020-FETPROACT-824160), and the Knut and Alice Wallenberg Foundation.",,,,,,,,,,"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., (2015) TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, , Available online at: tensorflow.org; Alissandrakis, A., Nehaniv, C.L., Dautenhahn, K., Correspondence mapping induced state and action metrics for robotic imitation (2007) IEEE Trans. Syst. Man Cybernet. Part B, 37, pp. 299-307. , 17416158; Argall, B.D., Chernova, S., Veloso, M., Browning, B., A survey of robot learning from demonstration (2009) Robot. Auton. Syst, 57, pp. 469-483; Billard, A., Calinon, S., Dillmann, R., Schaal, S., “Robot programming by demonstration,” (2008) Springer Handbook of Robotics, pp. 1371-1394. , Secaucus, NJ, Springer, eds B. Siciliano and O. Khatib; Brownell, C.A., Early developments in joint action (2011) Rev. Philos. Psychol, 2, pp. 193-211. , 23087769; Bütepage, J., Kjellström, H., Kragic, D., “Anticipating many futures: online human motion prediction and generation for human-robot interaction,” (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1-9. , a, Brisbane, QLD, IEEE; Bütepage, J., Kjellström, H., Kragic, D., Classify, predict, detect, anticipate and synthesize: hierarchical recurrent latent variable models for human activity modeling (2018) arXiv[Preprint]. arXiv:1809.08875, , b; Calinon, S., D'halluin, F., Sauser, E.L., Caldwell, D.G., Billard, A.G., Learning and reproduction of gestures by imitation (2010) IEEE Robot. Autom. Mag, 17, pp. 44-54; Dautenhahn, K., Socially intelligent robots: dimensions of human–robot interaction (2007) Philos. Trans. R. Soc. B Biol. 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Psychol, 7, p. 2039. , 28101077; Zhang, C., Butepage, J., Kjellstrom, H., Mandt, S., Advances in variational inference (2018) IEEE Trans. Pattern Anal. Mach. Intell, 41, pp. 2008-2026. , 30596568; Zhang, T., McCarthy, Z., Jow, O., Lee, D., Chen, X., Goldberg, K., “Deep imitation learning for complex manipulation tasks from virtual reality teleoperation,” (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1-8. , IEEE","Bütepage, J.; Robotics, Sweden; email: butepage@kth.se",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85084053889
"Patompak P., Jeong S., Nilkhamhang I., Chong N.Y.","36185797700;23100090400;12795795800;7007012034;","Learning Proxemics for Personalized Human–Robot Social Interaction",2020,"International Journal of Social Robotics","12","1",,"267","280",,12,"10.1007/s12369-019-00560-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066288063&doi=10.1007%2fs12369-019-00560-9&partnerID=40&md5=663d4987b1824cfd47328d9a7983dae4","School of Information Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, Japan; Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Phahonyothin Rd., Tambon Khlong Nung, Amphoe Khlong Luang, Pathum Thani, Thailand; Center for Artificial Intelligence in Medicine, Bio-medical Research Institute, Kyungpook National University Hospital, Daegu, South Korea; School of Medicine, Kyungpook National University, Daegu, South Korea","Patompak, P., School of Information Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, Japan, Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Phahonyothin Rd., Tambon Khlong Nung, Amphoe Khlong Luang, Pathum Thani, Thailand; Jeong, S., Center for Artificial Intelligence in Medicine, Bio-medical Research Institute, Kyungpook National University Hospital, Daegu, South Korea, School of Medicine, Kyungpook National University, Daegu, South Korea; Nilkhamhang, I., Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Phahonyothin Rd., Tambon Khlong Nung, Amphoe Khlong Luang, Pathum Thani, Thailand; Chong, N.Y., School of Information Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, Japan","Each person has their personal area which they do not want to share with others during social interactions. The size of this area usually depends on various factors such as their culture, personal traits, and acquaintanceship. The same applies to the case of human–robot interaction, especially when the robot is required to exhibit a certain level of social competence. Here, we propose a new robot navigation strategy to socially interact with people reflecting upon the social relationship between the robot and each person. To this end, we need a clear definition of interaction areas: (1) quality interaction area where people can be engaged in high-quality interactions with robots, and (2) private area not to be interfered with by the robot speech or action. A technical challenge in enhancing social human–robot interactions is how to enable robots to delineate the boundary of the two areas of each person. Specifically, the social force model (SFM) is designed by a fuzzy inference system, where the membership functions are optimized to give the robot the ability to navigate autonomously in the quality interaction area using a reinforcement learning algorithm. Finally, the proposed model was verified through simulations and experiments with a real robot that can generate a suitable SFM of each person, allowing the robot to maintain the quality of interaction with each person while keeping their private personal distance. © 2019, Springer Nature B.V.","Fuzzy inference system; Proxemics; Reinforcement learning; Social force model; Social interaction","Agricultural robots; Fuzzy inference; Fuzzy systems; Inference engines; Learning algorithms; Learning systems; Membership functions; Reinforcement learning; Fuzzy inference systems; Proxemics; Quality of interaction; Robot interactions; Social force models; Social interactions; Social relationships; Technical challenges; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 737858; Ministry of Internal Affairs and Communications, MIC","This work was supported by the EU-Japan coordinated R&D project on ?Culture Aware Robots and Environmental Sensor Systems for Elderly Support? commissioned by the Ministry of Internal Affairs and Communications of Japan and EC Horizon 2020.",,,,,,,,,,"Butler, J.T., Agah, A., Psychological effects of behavior patterns of a mobile personal robot (2001) Auton Robot, 10 (2), pp. 185-202; Edward, H., (1969) The hidden dimension: man’s use of space in public and in private, , The Bodley Head Ltd, London; (2006), Embodied social interaction for service robots in hallway environments. 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Automation and Systems (ICCAS), Gyeongju, Korea, pp. 1298-1303; Patompak, P., Jeong, S., Chong, N.Y., Nilkhamhang, I., Learning social relations for culture aware interaction (2017) 14Th Ubiquitous Robots and Ambient Intelligence (URAI), pp. 1298-1303. , Jeju, Korea; Rios-Martinez, J., (2013) Socially-Aware Robot Navigation: Combining Risk Assessment and Social Conventions, , Dissertation INRIA Sophia-Antipolis, France; Rueben, M., Smart, W.D., Privacy in human–robot interaction survey and future work (2016) We Robot 2016: The Fifth Annual Conference on Legal and Policy Issues Relating to Robotics, , University of Miami School of Law; Sisbot, E.A., Marin-Urias, L.F., Alami, R., Simeon, T., A human aware mobile robot motion planner (2007) IEEE Trans Robot, 23 (5), pp. 874-883; Svenstrup, M., Bak, T., Andersen, H.J., Trajectory planning for robots in dynamic human environments (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4293-4298. , Taipei; Takayama, L., Pantofaru, C., Influences on proxemic behaviors in human–robot interaction (2009) International Conference on IEEE/RSJ Intelligent Robots and Systems, IROS, 2009, pp. 5495-5502; Tomari, R., Kobayashi, Y., Kuno, Y., Empirical framework for autonomous wheelchair systems in human-shared environments (2012) 2012 IEEE International Conference on Mechatronics and Automation, pp. 493-498. , Chengdu; Tora, E., Cuijpers, R.H., Juolia, J.F., Van Der Pol, D., Modelling and testing proxemic behavior for humanoid robots (2012) Int J Humanoid Robot, 9 (4), p. 1250,028; Tutsoy, O., Brown, M., Reinforcement learning analysis for a minimum time balance problem (2016) Trans Inst Meas Control, 38 (10), pp. 1186-1200; Westin, A., (1970) Privacy and freedom, , Bodley Head, London; Zeeger, S.K., Readdick, C.A., Hansen-Gandy, S., Daycare children’s establishment of territory to experience privacy (1994) Child Environ, 11, pp. 265-271","Patompak, P.; School of Information Science, 1-1 Asahidai, Japan; email: p.patompak@gmail.com",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85066288063
"Yazdani A., Sabbagh Novin R.","57208065928;56513986100;","Posture estimation and optimization in ergonomically intelligent teleoperation systems",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3446350","604","606",,3,"10.1145/3434074.3446350","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102732838&doi=10.1145%2f3434074.3446350&partnerID=40&md5=55de857c71718fd015920392c5af6580","Utah Robotics Center, University of UtahUT, United States","Yazdani, A., Utah Robotics Center, University of UtahUT, United States; Sabbagh Novin, R., Utah Robotics Center, University of UtahUT, United States","Ergonomics and human comfort are essential concerns in physical human-robot interaction∼(p-HRI) applications such as teleoperation. We introduce a novel framework for posture estimation and optimization for ergonomically intelligent teleoperation systems that estimates the human operator's posture solely from the leader robot's trajectory and provides online postural correction and offline initial posture correction according to the type of teleoperation task. Although our framework is in teleoperation, it can be extended to other p-HRI applications with minimal modifications. © 2021 Owner/Author.","Ergonomics; Postural optimization; Posture estimation; Teleoperation","Agricultural robots; Ergonomics; Man machine systems; Online systems; Remote control; Human comforts; Human operator; Leader robots; Offline; Physical human-robot interactions; Posture estimation; Teleoperation systems; Social robots",,,,,,,,,,,,,,,,"Alvarez, M.T., Torricelli, D., Del Ama, A.J., Pinto, D., Gonzalez-Vargas, J., Moreno, J.C., Gil-Agudo, A., Pons, J.L., Simultaneous estimation of human and exoskeleton motion: A simplified protocol (2017) IEEE Int. Conf. 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On Biomedical Robotics and Biomechatronics, pp. 1144-1150. , https://doi.org/10.1109/BIOROB.2018.8487858; Yazdani, A., Sabbagh Novin, R., Merryweather, A., Hermans, T., (2020) Is the Leader Robot An Adequate Sensor for Posture Estimation and Ergonomic Assessment of A Human Teleoperator?; Yu, S., Lee, J., Park, B., Kim, K., Cho, I., Ergonomic analysis of a telemanipulation technique for a pyroprocess demonstration facility (2014) Nuclear Engineering and Technology, 46 (4), pp. 489-500. , 2014; Zhang, D., Liu, J., Gao, A., Yang, G., An ergonomic shared workspace analysis framework for the optimal placement of a compact master control console (2020) IEEE Robotics and Automation Letters, 5 (2), pp. 2995-3002. , 2020",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102732838
"Ahlberg S., Axelsson A., Yu P., Cortez W.S., Gao Y., Ghadirzadeh A., Castellano G., Kragic D., Skantze G., Dimarogonas D.V.","57211204477;57292133300;57292363800;55848824000;56666237700;57199671469;23007528900;6701873248;6505943187;6506281602;","Co-adaptive human–robot cooperation: Summary and challenges",2021,"Unmanned Systems",,,,"1","17",,,"10.1142/S230138502250011X","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116890059&doi=10.1142%2fS230138502250011X&partnerID=40&md5=58d4f0d85d666c9f46b30243b7a24c20","Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden; Department of Information Technology, Uppsala University, Uppsala, Sweden; Center for Intelligent Robots, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China","Ahlberg, S., Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden; Axelsson, A., Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden; Yu, P., Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden; Cortez, W.S., Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden; Gao, Y., Department of Information Technology, Uppsala University, Uppsala, Sweden, Center for Intelligent Robots, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China; Ghadirzadeh, A., Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden; Castellano, G., Department of Information Technology, Uppsala University, Uppsala, Sweden; Kragic, D., Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden; Skantze, G., Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden; Dimarogonas, D.V., Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden","The work presented here is a culmination of developments within the Swedish project COIN: Co-adaptive human-robot interactive systems, funded by the Swedish Foundation for Strategic Research (SSF), which addresses a unified framework for co-adaptive methodologies in human–robot co-existence. We investigate co-adaptation in the context of safe planning/control, trust, and multi-modal human–robot interactions, and present novel methods that allow humans and robots to adapt to one another and discuss directions for future work. © World Scientific Publishing Company.","Co-adaptive systems; Human-in-the-loop systems; Human–robot interaction","Robot programming; Co-adaptive system; Human robots; Human-in-the-loop; Human-in-the-loop system; Human-robot-cooperation; Humans-robot interactions; Interactive system; Loop systems; Strategic research; Swedishs; Human robot interaction",,,,,,,,,,,,,,,,"Tadele, T. S., de Vries, T., Stramigioli, S., The safety of domestic robotics: A survey of various safety-related publications (2014) IEEE Robot. Autom. 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D., Sedivy, J., (2020) Alquist 3.0: Alexa prize bot using conversational knowledge graph, , preprint arXiv:2011.03261","Ahlberg, S.; Department of Intelligent Systems, Sweden; email: sofa@kth.se
Axelsson, A.; Department of Intelligent Systems, Sweden; email: agnaxe@kth.se
Yu, P.; Department of Intelligent Systems, Sweden; email: kpiany@kth.se
Cortez, W.S.; Department of Intelligent Systems, Sweden; email: wencsc@kth.se
Ghadirzadeh, A.; Department of Intelligent Systems, Sweden; email: algh@kth.se
Kragic, D.; Department of Intelligent Systems, Sweden; email: dani@kth.se
Skantze, G.; Department of Intelligent Systems, Sweden; email: kkgabriel@speech.kth.se
Dimarogonas, D.V.; Department of Intelligent Systems, Sweden; email: dimos@kth.se",,,"World Scientific",,,,,23013850,,,,"English","Unmanned Syst.",Article,"Article in Press","",Scopus,2-s2.0-85116890059
"Ananto R.A., Geiskkovitch D.Y., Young J.E.","57220030143;56896186200;20434970900;","Domestic Robots for Individuals Living with Loneliness: A Long-Term In-Home Interaction Study Design",2020,"HAI 2020 - Proceedings of the 8th International Conference on Human-Agent Interaction",,,,"263","265",,1,"10.1145/3406499.3418749","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096615676&doi=10.1145%2f3406499.3418749&partnerID=40&md5=2268dfd36a72a0d4a6327d62c23373b9","University of Manitoba, Winnipeg, MB, Canada","Ananto, R.A., University of Manitoba, Winnipeg, MB, Canada; Geiskkovitch, D.Y., University of Manitoba, Winnipeg, MB, Canada; Young, J.E., University of Manitoba, Winnipeg, MB, Canada","A growing area of human-robot interaction explores how robots, for example as companions, can be used to help people manage loneliness. However, we do not yet have research results indicating if people are ready to accept companion robots in their daily lives, and thus if companion robots can actually be successful broadly in society. We present a novel long-term in-home interaction study design that will explore how people accept these robots in their homes and how the robots impact loneliness. © 2020 Owner/Author.","companion robot; loneliness; social human-robot interaction","Machine design; Companion robot; Daily lives; Domestic robots; Interaction studies; Research results; Human robot interaction",,,,,,,,,,,,,,,,"Stickley, A., Loneliness: Its correlates and association with health behaviours and outcomes in nine countries of the former Soviet Union PLoS One, 8 (7). , 2013; Chopik, W.J., The benefits of social technology use among older adults are mediated by reduced loneliness Cyberpsychology, Behav. Soc. Netw., 19 (9), pp. 551-556. , 2016; Brandtzaeg, P.B., Følstad, A., Why people use chatbots (2017) Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), pp. 377-392. , vol. 10673 LNCS; Pransky, J., AIBO - The no. 1 selling service robot (2001) Ind. Rob., 28 (1), pp. 24-26; Breazeal, C., Toward social robots (2003) Robotics and Autonomous Systems, 42, pp. 167-175; Seo, S.H., Geiskkovitch, D., Nakane, M., King, C., Young, J.E., Poor thing! would you feel sorry for a simulated robot?: A comparison of empathy toward a physical and a simulated robot (2015) ACM/IEEE Int. Conf. Human-Robot Interact., 2015, pp. 125-132. , March; Geiskkovitch, D.Y., Cormier, D., Seo, S.H., Young, J.E., Please continue, We need more data: An exploration of obedience to robots (2015) J. Human-Robot Interact., 5 (1), p. 82; Strohkorb Sebo, S., Traeger, M., Jung, M., Scassellati, B., The ripple effects of vulnerability: The effects of a robot's vulnerable behavior on trust in human- robot teams (2018) ACM/IEEE Int. Conf. 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Notes Bioinformatics), 5615, pp. 39-48; Davis, F.D., Perceived usefulness, perceived ease of use, and user acceptance of information technology (1989) MIS Q. Manag. Inf. Syst., 13 (3), pp. 319-339; Dewar, R.D., Dutton, J.E., The adoption of radical and incremental innovations: An empirical analysis (1986) Manage. Sci., 32 (11), pp. 1422-1433; Sung, J.Y., Grinter, R.E., Christensen, H.I., Domestic robot ecology: An initial framework to unpack long-term acceptance of robots at home (2010) Int. J. Soc. Robot., 2 (4), pp. 417-429; McGlynn, S., Snook, B., Kemple, S., Mitzner, T.L., Rogers, W.A., (2014) Therapeutic Robots for Older Adults, pp. 246-247; Broekens, J., Heerink, M., Rosendal, H., Assistive social robots in elderly care: A review (2009) Gerontechnology, 8 (2); Marti, P., Bacigalupo, M., Giusti, L., Mennecozzi, C., Shibata, T., Socially assistive robotics in the treatment of behavioural and psychological symptoms of dementia (2006) Proc. First IEEE/RAS-EMBS Int. Conf. Biomed. Robot. Biomechatronics, 2006, pp. 483-488. , BioRob 2006, no. February, 2006; Okita, S.Y., Self-other's perspective taking: The use of therapeutic robot companions as social agents for reducing pain and anxiety in pediatric patients (2013) Cyberpsychology, Behav. Soc. Netw., 16 (6), pp. 436-441; Wada, K., Shibatal, T., Musha, T., Kimura, S., Effects of robot therapy for demented patients evaluated by EEG (2005) 2005 IEEE/RSJ Int. Conf. Intell. Robot. Syst. IROS, pp. 2205-2210; Wada, K., Shibata, T., Living with seal robots - its sociopsychological and physiological influences on the elderly at a care house (2007) IEEE Trans. Robot., 23 (5), pp. 972-980; Bemelmans, R., Gelderblom, G.J., Jonker, P., De Witte, L., Socially assistive robots in elderly care: A systematic review into effects and effectiveness (2012) J. Am. Med. Dir. Assoc., 13 (2), pp. 114-120; Eyssel, F., Reich, N., Loneliness makes the heart grow fonder (of robots) - On the effects of loneliness on psychological anthropomorphism (2013) ACM/IEEE Int. Conf. Human-Robot Interact., pp. 121-122; Lee, K.M., Jung, Y., Kim, J., Kim, S.R., Are physically embodied social agents better than disembodied social agents?: The effects of physical embodiment, tactile interaction, and people's loneliness in human-robot interaction (2006) Int. J. Hum. Comput. Stud., 64 (10), pp. 962-973; Hutson, S., Lim, S.L., Bentley, P.J., Bianchi-Berthouze, N., Bowling, A., Investigating the suitability of social robots for the wellbeing of the elderly (2011) Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 6974, pp. 578-587; Hoorn, J.F., From lonely to resilient through humanoid robots: Building a new framework of resilience (2018) J. Robot., 2018; De Graaf, M.M.A., Ben Allouch, S., Klamer, T., Sharing a life with harvey: Exploring the acceptance of and relationship-building with a social robot (2015) Comput. Human Behav., 43, pp. 1-14; Leite, I., Martinho, C., Paiva, A., Social robots for long-term interaction: A survey (2013) Int. J. Soc. Robot., 5 (2), pp. 291-308; Fernaeus, Y., Håkansson, M., Jacobsson, M., Ljungblad, S., (2010) How Do You Play with A Robotic Toy Animal?; De Graaf, M., Ben Allouch, S., Van Dijk, J., Why Do they refuse to use My robot?: Reasons for non-use derived from a long-term home study (2017) ACM/IEEE Int. Conf. Human-Robot Interact., pp. 224-233. , vol. Part F1271, doi: 10.1145/1810543.1810549; De Graaf, M., Ben Allouch, S., Van Dijk, J., Why Do they refuse to use My robot?: Reasons for non-use derived from a long-term home study (2017) ACM/IEEE Int. Conf. Human-Robot Interact., pp. 224-233. , vol. Part F1271; De Graaf, M.M.A., Ben Allouch, S., Van Dijk, J.A.G.M., Why would i use this in My home? A model of domestic social robot acceptance (2019) Human-Computer Interact., 34 (2), pp. 115-173; Sung, J.-Y., Guo, L., Grinter, R.E., Christensen, H.I., 'My roomba is rambo': Intimate home appliances UbiComp 2007: Ubiquitous Computing, pp. 145-162. , Berlin, Heidelberg: Springer Berlin Heidelberg; Forlizzi, J., How robotic products become social products: An ethnographic study of cleaning in the home (2007) HRI 2007 - Proc. 2007 ACM/IEEE Conf. Human-Robot Interact. - Robot As Team Memb., pp. 129-136; De Graaf, M.M.A., Ben Allouch, S., Van Dijk, J.A.G.M., Long-term acceptance of social robots in domestic environments: Insights from a user's perspective (2016) AAAI Spring Symp. - Tech. Rep., (2004), pp. 96-103. , vol. SS-16-01; Russell, D., Ucla loneliness scale version 3 (description of measure) (1996) J. Pers. Soc. Psychol., 39, pp. 3-4; Wenzel, A., State-trait anxiety inventory (2017) SAGE Encycl. Abnorm. Clin. Psychol., pp. 3-4; Brdar, I., Positive and negative affect schedule (PANAS) (2014) Encycl. Qual. Life Well- Being Res., pp. 4918-4920",,,"Cyber Agent;frontiers in Robotics and AI","Association for Computing Machinery, Inc","8th International Conference on Human-Agent Interaction, HAI 2020","10 November 2020 through 13 November 2020",,164494,,9781450380546,,,"English","HAI - Proc. Int. Conf. Hum.-Agent Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85096615676
"Lee A., Toombs A.L.","55662789800;39062263300;","Robots on campus: Understanding public perception of robots using social media",2020,"Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW",,,,"305","309",,,"10.1145/3406865.3418321","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095129265&doi=10.1145%2f3406865.3418321&partnerID=40&md5=2eb9ce58dbc1d5ea60b250f2dd28bf2d","Purdue University, West Lafayette, IN, United States","Lee, A., Purdue University, West Lafayette, IN, United States; Toombs, A.L., Purdue University, West Lafayette, IN, United States","As robots engage more in society in various forms, it is important to understand the public perception of robots. In this poster, we focus on a campus-centric subreddit to explore online discourse about delivery robots on university campus. We specifically identify how people share their experiences with robots and how people perceive robots in society by analyzing Reddit posts. In so doing, we raise existing concerns about the robots which give insights into acceptance and sociability in human-robot interaction. © 2020 Owner/Author.","Delivery robots; Human-robot interaction; Social analysis of robotics","Groupware; Interactive computer systems; Social networking (online); Public perception; Social media; University campus; Social robots",,,,,,,,,,,,,,,,"Braun, V., Clarke, V., (2013) Successful Qualitative Research: A Practical Guide for Beginners; De Graaf, M.M., Ben Allouch, S., Van Dijk, J.A., Why would i use this in my home? A model of domestic social robot acceptance (2019) Human-Computer Interaction, 34 (2), pp. 115-173. , 2019; Eslami, M., Karahalios, K., Sandvig, C., Vaccaro, K., Rickman, A., Hamilton, K., Kirlik, A., First i ""like"" it, then i hide it: Folk theories of social feeds (2016) Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, California, USA) (CHI '16), pp. 2371-2382. , https://doi.org/10.1145/2858036.2858494, Association for Computing Machinery, New York, NY, USA; Jung, M., Hinds, P., Robots in the wild: A time for more robust theories of human-robot interaction (2018) J. Hum.-Robot Interact, 7 (1), p. 5. , https://doi.org/10.1145/3208975, May 2018; Neuendorf, K.A., Measurement and validity (2016) The Content Analysis Guidebook; Šabanovic, S., Robots in society, society in robots (2010) International Journal of Social Robotics, 2 (4), pp. 439-450. , https://doi.org/10.1007/s12369-010-0066-7, 2010; Simon, M., (2018) It's Time to Talk about Robot Gender Stereotypes, , https://www.wired.com/story/robot-gender-stereotypes/; Tay, B., Jung, Y., Park, T., When stereotypes meet robots: The double-edge sword of robot gender and personality in human-robot interaction (2014) Computers in Human Behavior, 38, pp. 75-84. , https://doi.org/10.1016/j.chb.2014.05.014, 2014; Zeller, F., Harris Smith, D., Au Duong, J., Mager, A., Social media in human-robot interaction (2019) International Journal of Social Robotics, pp. 1-14. , https://doi.org/10.1007/s12369-019-00573-4, 2019",,,"ACM SIGCHI","Association for Computing Machinery","3rd ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2020","17 October 2020 through 21 October 2020",,164204,,9781450380591,,,"English","Proc. ACM Conf. Comput. Support. Coop. Work CSCW",Conference Paper,"Final","",Scopus,2-s2.0-85095129265
"Craig M.J., Edwards C., Edwards A.","57208160215;55467888500;17433903500;","""but they're my avatar"": Examining character attachment to android avatars in quantic dream's detroit: Become human",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"169","170",,1,"10.1145/3371382.3378298","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083234526&doi=10.1145%2f3371382.3378298&partnerID=40&md5=939316f404558de1b731acc440bddcee","Communication and Social Robotics Labs, Western Michigan University, Kalamazoo, MI  49008, United States","Craig, M.J., Communication and Social Robotics Labs, Western Michigan University, Kalamazoo, MI  49008, United States; Edwards, C., Communication and Social Robotics Labs, Western Michigan University, Kalamazoo, MI  49008, United States; Edwards, A., Communication and Social Robotics Labs, Western Michigan University, Kalamazoo, MI  49008, United States","This study examines how players describe their rationale behind decisions made during gameplay of Detroit: Become Human and how responses may coincide with character attachment (CA). Semi-structured interviews were conducted to examine the presence of character attachment and how it may lead to the player's understanding of their gameplay choices. Both the emotions (or lack of) of the avatar and strategizing about gameplay emerged as two central themes. Results are discussed in light of human-robot interaction based on popular media. © 2020 ACM.","Androids; Attachment; Human-robot interaction; Video games","Agricultural robots; Interactive computer graphics; Man machine systems; Detroit; Gameplay; Semi structured interviews; Human robot interaction",,,,,,,,,,,,,,,,"Banks, J., David Bowman, N., Avatars are (sometimes) people too: Linguistic indicators of parasocial and social ties in player-avatar relationships (2016) New Media & Society, 18 (7), pp. 1257-1276. , https://doi.org/10.1177/1461444814554898, 2016; Banks, J., David Bowman, N., Emotion, anthropomorphism, realism, control: Validation of a merged metric for player-avatar interaction (PAX) (2016) Computers in Human Behavior, 54, pp. 215-223. , https://doi.org/10.1016/j.chb.2015.07.030, 2016; Edwards, A., Edwards, C., Westerman, D., Spence, P.R., Initial expectations, interactions, and beyond with social robots (2019) Computers in Human Behavior, 90, pp. 308-314. , https://doi.org/10.1016/j.chb.2018.08.042, 2019; Horton, D., Richard Wohl, R., Mass communication and para-social interaction: Observations on intimacy at a distance (1956) Psychiatry, 19 (3), pp. 215-229. , https://doi.org/10.1080/00332747.1956.11023049, 1956; Lewis, M.L., Weber, R., David Bowman, N., They may be pixels, but they're MY pixels. Developing a metric of character attachment in role-playing video games (2008) CyberPsychology & Behavior, 11 (4), pp. 515-518. , https://doi.org/10.1089/cpb.2007.0137, 2008; Little, G., An avatar manifesto (1999) Intertexts, 3 (2). , 1999; Stephen Meadows, M., I, avatar: The culture and consequences of having a second life (2007) New Riders; Saldaña, J., (2009) The Coding Manual for Qualitative Researchers, 3, pp. 45-145. , Sage Publications Ltd, Thousand Oaks, CA, Chapter; Sarah, J.T., (2012) Qualitative Research Methods: Collecting Evidence, Crafting Analysis, Communicating Impact, , John Wiley & Sons, Hoboken, NJ",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083234526
"Hussain I., Prattichizzo D.","56730038800;7004048807;","Introduction",2020,"Biosystems and Biorobotics","26",,,"1","7",,,"10.1007/978-3-030-52002-1_1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089502802&doi=10.1007%2f978-3-030-52002-1_1&partnerID=40&md5=168afc909470140e9c16694fd3baefd3","Department of Mechanical Engineering, Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Department of Information Engineering, Università degli Studi di Siena, Siena, Italy","Hussain, I., Department of Mechanical Engineering, Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Prattichizzo, D., Department of Information Engineering, Università degli Studi di Siena, Siena, Italy","Over many centuries the human has been extending their physical, cognitive, and social abilities in all activities. Every major technological breakthrough in the history made the human more capable, which changed the life style and the society. These changes have been many orders of magnitude faster than evolutional changes. Robotic applications have rapidly grown from classical industrial applications with repetitive tasks to applications with close human-robot interaction. In particular, assistive robotics has gained an increasing attention in the last decades, see [7]. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.",,,,,,,,,,,,,,,,,,,"Hussain, I.; Department of Mechanical Engineering, United Arab Emirates; email: irfan.hussain@ku.ac.ae",,,"Springer",,,,,21953562,,,,"English","Biosyst. Biorobotics",Editorial,"Final","",Scopus,2-s2.0-85089502802
"Uzunovic T., Sabanovic A.","36462655600;35555658100;","Universal Motion Controller",2021,"IEEE International Symposium on Industrial Electronics","2021-June",,,"","",,,"10.1109/ISIE45552.2021.9576463","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118767112&doi=10.1109%2fISIE45552.2021.9576463&partnerID=40&md5=834293ae41a69e61e63fe05c39d36d63","University of Sarajevo, Faculty of Electrical Engineering, Sarajevo, Bosnia and Herzegovina; International University of Sarajevo, Faculty of Engineering and Natural Sciences, Sarajevo, Bosnia and Herzegovina","Uzunovic, T., University of Sarajevo, Faculty of Electrical Engineering, Sarajevo, Bosnia and Herzegovina; Sabanovic, A., International University of Sarajevo, Faculty of Engineering and Natural Sciences, Sarajevo, Bosnia and Herzegovina","The paper presents a concept of universal motion controller. The controller merges both position and force control into a single control structure. Therefore, it gives a possibility to use the same control algorithm, both for position tracking tasks as well as for the interaction force control. The universal motion controller can be used not only to make the interaction force track its reference, but also for the limiting of the interaction force, so that safety is ensured. This makes it very useful for human-robot interaction applications. © 2021 IEEE.","motion control; position/force control; universal motion controller","Controllers; Force control; Human robot interaction; Position control; Control structure; Humans-robot interactions; Interaction forces; Motion controller; Position and force control; Position/force control; Universal motion controller; Motion control",,,,,,"The authors would like to thank the Ministry of Civil Affairs of Bosnia and Herzegovina and Ministry for Science, High Education and Youth of Sarajevo Canton for the financial support provided for this study.",,,,,,,,,,"Sabanovíc, A., Ohnishi, K., (2011) Motion Control Systems, , John Wiley & Sons, Singapore; Ohnishi, K., Shibata, M., Murakami, T., Motion control for advanced mechatronics (1996) IEEE/ASME Trans. Mechatronics, 1 (1), pp. 56-67. , Mar; Khatib, O., A unified approach for motion and force control of robot manipulators: The operational space formulation (1987) IEEE J. Robot. Autom., 3 (1), pp. 43-53. , Feb; Shimada, N., Ohishi, K., Yoshioka, T., Miyazaki, T., Quick and reliable contact detection for sensorless force control of industrial robots for human support (2012) Proc. 2012 IEEE International Symposium on Industrial Electronics, pp. 1674-1679; Uzunovic, T., Sabanovic, A., Yokoyama, M., Shimono, T., Novel algorithm for effective position/force control (2019) IEEJ Journal of Industry Applications, 8 (6), pp. 960-966. , Nov; Uzunovic, T., Sabanovic, A., Yokoyama, M., Shimono, T., Novel algorithm for position/force control of multi-dof robotic systems (2020) Proceedings of the 2020 IEEE 16th International Workshop on Advanced Motion Control (AMC), pp. 273-278; Ohishi, K., Ohnishi, K., Miyachi, K., Torque-speed regulation of dc motor based on load torque estimation (1983) Proceedings of the IEEJ International Power Electronics Conference, 2, pp. 1209-1216; Murakami, T., Ohnishi, K., Observer-based motion controlapplication to robust control and parameter identification (1993) Proc. Asia-Pacific Workshop on Advances in Motion Control, pp. 1-6; Uzunovíc, T., Sabanovíc, A., (2020) Motion Control of Functionally Related Systems, , CRC Press; Spong, M.W., Vidyasagar, M., (1989) Robot Dynamics and Control, , John Wiley & Sons, USA",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Symposium on Industrial Electronics, ISIE 2021","20 June 2021 through 23 June 2021",,173407,,9781728190235,85PTA,,"English","IEEE Int Symp Ind Electron",Conference Paper,"Final","",Scopus,2-s2.0-85118767112
"Zedda E.","57215870693;","Robot personality for cognitive training",2021,"CEUR Workshop Proceedings","2892",,,"78","82",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109782581&partnerID=40&md5=bcb0b6e9078a5bd694de84b949b4c5c1","ISTI-CNR Pisa, Via Giuseppe Moruzzi, Pisa PI, 56124, Italy; University of Pisa, Largo Bruno Pontecorvo, 3, Pisa PI, 56127, Italy","Zedda, E., ISTI-CNR Pisa, Via Giuseppe Moruzzi, Pisa PI, 56124, Italy, University of Pisa, Largo Bruno Pontecorvo, 3, Pisa PI, 56127, Italy","Robots are becoming more present in our daily activities and in particular in the health context. To improve the human-robot interaction in a training session it is important to design and develop social behavior and personality in robots. Recent studies found that personality is an essential feature for creating socially assistive robots. For this purpose, I want to investigate if the robot personality (extrovert or introvert) can improve the user’s cognitive performances in elders with Mild Cognitive Impairments during one-o-one cognitive training. © 2021 Copyright for this paper by its authors.","Human-robot interaction; Personality; Socially assistive robots","Machine design; Social robots; Cognitive performance; Cognitive training; Essential features; Mild cognitive impairments; Robot personalities; Social behavior; Socially assistive robots; Training sessions; Educational robots",,,,,,,,,,,,,,,,"(2015) World report on ageing and health, , World Health Organization; Bishop, N. A., Lu, T., Yankner, B. A., Neural mechanisms of ageing and cognitive decline (2010) Nature, 464, pp. 529-535; Tanaka, M., Ishii, A., Yamano, E., Ogikubo, H., Okazaki, M., Kamimura, K., Konishi, Y., Watanabe, Y., Effect of a human-type communication robot on cognitive function in elderly women living alone (2012) Medical science monitor: international medical journal of experimental and clinical research, 18, p. CR550; Pino, O., Palestra, G., Trevino, R., De Carolis, B., The humanoid robot nao as trainer in a memory program for elderly people with mild cognitive impairment (2020) International Journal of Social Robotics, 12, pp. 21-33; Tapus, A., Tapus, C., Mataric, M. J., User—robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy (2008) Intelligent Service Robotics, 1, pp. 169-183; Manca, M., Paternò, F., Santoro, C., Zedda, E., Braschi, C., Franco, R., Sale, A., The impact of serious games with humanoid robots on mild cognitive impairment older adults (2021) International Journal of Human-Computer Studies, 145, p. 102509; Costa, J., Paul, T., of personality theories: Theoretical contexts for the five-factor model (1996) The five-factor model of personality: Theoretical perspectives, 51; O’Brien, H. L., Cairns, P., Hall, M., A practical approach to measuring user engagement with the refined user engagement scale (ues) and new ues short form (2018) International Journal of Human-Computer Studies, 112, pp. 28-39; Rammstedt, B., John, O. P., Measuring personality in one minute or less: A 10-item short version of the big five inventory in english and german (2007) Journal of research in Personality, 41, pp. 203-212; Weiss, A., Bartneck, C., Meta analysis of the usage of the godspeed questionnaire series (2015) 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 381-388. , IEEE","Zedda, E.; ISTI-CNR Pisa, Via Giuseppe Moruzzi, Italy; email: eleonora.zedda@phd.unipi.it","Menendez-Blanco M.Yavuz S.U.Schubert J.Fogli D.Paterno F.",,"CEUR-WS","2021 Joint Interactive Experiences and Doctoral Consortium at CHItaly, CHItaly-JP 2021","11 July 2021 through 13 July 2021",,169870,16130073,,,,"English","CEUR Workshop Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85109782581
"Lee T.H., Liang W., de Silva C.W., Tan K.K.","24780933300;8572802900;7007032904;7403999687;","Robust Impedance Control of Constrained Piezoelectric Actuator-Based End-Effector",2021,"Advances in Industrial Control",,,,"157","178",,,"10.1007/978-3-030-52693-1_7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097646609&doi=10.1007%2f978-3-030-52693-1_7&partnerID=40&md5=00651cc7677a0c6e2e37b3bd24905867","Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada","Lee, T.H., Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; Liang, W., Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; de Silva, C.W., Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada; Tan, K.K., Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore","Impedance control is one of the common force and position control methods. It regulates the relationship between motion and force which is able to deal with the tasks where the control subject has certain level of uncertainty. It is often used in the applications of robot–environment interaction or human–robot interaction where the force–position relation is of concern. In this chapter, a robust impedance control is designed for a semi-automated surgical device based on the dynamic coordination of force and position control. © 2021, Springer Nature Switzerland AG.",,"Human robot interaction; Piezoelectric actuators; Control subject; Dynamic coordination; Impedance control; Robot interactions; Robust impedance control; Surgical devices; Position control",,,,,,,,,,,,,,,,"Uchino, K., Piezoelectric Actuators and Ultrasonic Motors, vol. 1 (Springer, Science & Business (1997) Media; Henderson, D., Mechanism comprised of ultrasonic lead screw motor (2008) US Patent; Huang, C.-H., Zou, J., A novel two-axis micromechanical scanning transducer using water-immersible electromagnetic actuators for handheld 3d ultrasound imaging (2015) Sens. Actuators A: Phys., 236, pp. 281-288; Payne, C.J., Yang, G.-Z., Hand-held medical robots (2014) Ann. Biomed. Eng., 42 (8), pp. 1594-1605; Catarino, S., Silva, L.R., Mendes, P., Miranda, J., Lanceros-Mendez, S., Minas, G., Piezoelectric actuators for acoustic mixing in microfluidic devices-numerical prediction and experimental validation of heat and mass transport (2014) Sens. Actuators B Chem., 205, pp. 206-214; J. Zhang, J. Tian, N. Ta, X. Huang, Z. Rao, Numerical evaluation of implantable hearing devices using a finite element model of human ear considering viscoelastic properties, in Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine (2016), p. 0954411916652923; Yang, S., Maclachlan, R.A., Riviere, C.N., Manipulator design and operation of a six-degree-of-freedom handheld tremor-canceling microsurgical instrument (2015) IEEE/ASME Trans. Mechatron., 20 (2), pp. 761-772; Xu, Q., (2018) Micromachines for Biological Micromanipulation, , Springer; Zhao, C., Ultrasonic Motors: Technologies and Applications (Springer (2011) Science & Business Media; Gu, G.-Y., Zhu, L.-M., Su, C.-Y., Ding, H., Motion control of piezoelectric positioning stages: Modeling, controller design, and experimental evaluation (2013) IEEE/ASME Trans. Mechatron., 18 (5), pp. 1459-1471; Ghafarirad, H., Rezaei, S.M., Zareinejad, M., Sarhan, A.A., Disturbance rejection-based robust control for micropositioning of piezoelectric actuators (2014) Comptes Rendus Mécanique, 342 (1), pp. 32-45; Wen, C.-M., Cheng, M.-Y., Development of a recurrent fuzzy CMAC with adjustable input space quantization and self-tuning learning rate for control of a dual-axis piezoelectric actuated micromotion stage (2013) IEEE Trans. Ind. Electron., 60 (11), pp. 5105-5115; Minase, J., Lu, T.-F., Cazzolato, B., Grainger, S., A review, supported by experimental results, of voltage, charge and capacitor insertion method for driving piezoelectric actuators (2010) Precis. Eng., 34 (4), pp. 692-700; Ohnishi, K., Shibata, M., Murakami, T., Motion control for advanced mechatronics (1996) IEEE/ASME Trans. Mechatron., 1 (1), pp. 56-67; Sariyildiz, E., Ohnishi, K., Stability and robustness of disturbance-observer-based motion control systems (2015) IEEE Trans. Ind. Electron., 62 (1), pp. 414-422; Kim, K.-S., Rew, K.-H., Kim, S., Disturbance observer for estimating higher order disturbances in time series expansion (2010) IEEE Trans. Autom. Control, 55 (8), pp. 1905-1911; Ohishi, K., Realization of fine motion control based on disturbance observer (2008) 10Th IEEE International Workshop on Advanced Motion Control (AMC’08, pp. 1-8. , ), pp; Yamada, K., Murakami, I., Ando, Y., Hagiwara, T., Imai, Y., Zhi, G.D., Kobayashi, M., (2009) The Parametrization of All Disturbance Observers for Plants with Input Disturbance, in 4Th IEEE Conference on Industrial Electronics and Applications (ICIEA, 2009, pp. 41-46. , , pp; Xu, Q., Precision motion control of piezoelectric nanopositioning stage with chattering-free adaptive sliding mode control (2017) IEEE Trans. Autom. Sci. Eng., 14 (1), pp. 238-248; Shtessel, Y., Edwards, C., Fridman, L., Levant, A., (2014) Sliding Mode Control and Observation, , Springer; Besnard, L., Shtessel, Y.B., Landrum, B., Quadrotor vehicle control via sliding mode controller driven by sliding mode disturbance observer (2012) J. Frankl. Inst., 349 (2), pp. 658-684; Lu, Y.-S., Sliding-mode disturbance observer with switching-gain adaptation and its application to optical disk drives (2009) IEEE Trans. Ind. Electron., 56 (9), pp. 3743-3750; Kun, X., Mou, C., Terminal sliding mode control with disturbance observer for autonomous mobile robots 2015 IEEE 34Th Chinese Control Conference (CCC2015) (2015), pp. 765-770. , , pp; Sakaino, S., Sato, T., Ohnishi, K., Precise position/force hybrid control with modal mass decoupling and bilateral communication between different structures (2011) IEEE Trans. Ind. Inf., 7 (2), pp. 266-276; Gierlak, P., Hybrid position/force control in robotised machining (2014) Solid State Phenom, 210, pp. 192-199. , Trans Tech Publ; Xu, Q., Precision position/force interaction control of a piezoelectric multimorph microgripper for microassembly (2013) IEEE Trans. Autom. Sci. 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"Orabona A., Palazzi A., Graziosi S., Ferrise F., Bordegoni M.","57226070500;57226067456;24468093300;23466900300;6602375436;","DESIGN of A SIMPLIFIED 3D-PRINTED ARTIFICIAL UNDERACTUATED HAND",2020,"Proceedings of the Design Society: DESIGN Conference","1",,,"1027","1036",,1,"10.1017/dsd.2020.311","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110285136&doi=10.1017%2fdsd.2020.311&partnerID=40&md5=ac4eda1fdcef2ca7cf2f424a94ffb570","Politecnico di Milano, Italy","Orabona, A., Politecnico di Milano, Italy; Palazzi, A., Politecnico di Milano, Italy; Graziosi, S., Politecnico di Milano, Italy; Ferrise, F., Politecnico di Milano, Italy; Bordegoni, M., Politecnico di Milano, Italy","The recent interest in human-robot interaction requires the development of new gripping solutions, compared to those already available and widely used. One of the most advanced solutions in nature is that of the human hand, and several research contributions try to replicate its functionality. Technological advances in manufacturing technologies and design tools are opening possibilities in the design of new solutions. The paper reports the results of the design of an underactuated artificial robotic hand, designed by exploiting the benefits offered by additive manufacturing technologies. © The Author(s), 2020. Published by Cambridge University Press.","3D printing; additive manufacturing; design for additive manufacturing; mechatronics","Design; Human robot interaction; Additive manufacturing technology; Design tool; Human hands; Manufacturing technologies; New solutions; Technological advances; Underactuated; Underactuated hand; 3D printers",,,,,,,,,,,,,,,,"Agarwal, A., Effects of material properties on soft gripper grasping forces (2018) 2018 IEEE International Conference on Soft Robotics (RoboSoft), pp. 437-442. , https://doi.org/10.1109/ROBOSOFT.2018.8405365; Bhatia, A., Johnson, A.M., Mason, M.T., Direct drive hands: Force-motion transparency in gripper design (2019) Proceedings of the Robotics: Science and Systems, pp. 22-26. , https://doi.org/10.15607/RSS.2019.XV.053, Freiburg, Germany; Catalano, M.G., Adaptive synergies for the design and control of the pisa/iit softhand (2014) The International Journal of Robotics Research, 33 (5), pp. 768-782. , https://doi.org/10.1177%2F0278364913518998; 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Holland, D.P., The soft robotics toolkit: Shared resources for research and design (2014) Soft Robotics, 1 (3), pp. 224-230. , https://doi.org/10.1089/soro.2014.0010; Kappassov, Z., Semi-anthropomorphic 3d printed multigrasp hand for industrial and service robots (2013) 2013 IEEE International Conference on Mechatronics and Automation, pp. 1697-1702. , https://doi.org/10.1109/ICMA.2013.6618171; Kaur, M., Kim, W.S., Toward a smart compliant robotic gripper equipped with 3d-designed cellular fingers (2019) Advanced Intelligent Systems, 1 (3), p. 1900019. , https://doi.org/10.1002/aisy.201900019; Malvezzi, M., Syngrasp: A matlab toolbox for underactuated and compliant hands (2015) IEEE Robotics & Automation Magazine, 22 (4), pp. 52-68. , https://doi.org/10.1109/MRA.2015.2408772; Matsas, E., Vosniakos, G.C., Batras, D., Prototyping proactive and adaptive techniques for human-robot collaboration in manufacturing using virtual reality (2018) Robotics and Computer-Integrated Manufacturing, 50, pp. 168-180. , https://doi.org/10.1016/j.rcim.2017.09.005; Muth, J.T., Embedded 3d printing of strain sensors within highly stretchable elastomers (2014) Advanced Materials, 26 (36), pp. 6307-6312. , https://doi.org/10.1002/adma.201400334; Mutlu, R., 3d printed flexure hinges for soft monolithic prosthetic fingers (2016) Soft Robotics, 3 (3), pp. 120-133. , https://doi.org/10.1089/soro.2016.0026; Patel, D.K., Highly stretchable and uv curable elastomers for digital light processing based 3d printing (2017) Advanced Materials, 29 (15), p. 1606000. , https://doi.org/10.1002/adma.201606000; Piazza, C., A century of robotic hands (2019) Annual Review of Control Robotics, and Autonomous Systems, 2, pp. 1-32. , https://doi.org/10.1146/annurev-control-060117-105003; Renaud, C., The yeoh model applied to the modeling of large deformation contact/impact problems (2009) International Journal of Impact Engineering, 36 (5), pp. 659-666. , https://doi.org/10.1016/j.ijimpeng.2008.09.008; Santello, M., Flanders, M., Soechting, J.F., Postural hand synergies for tool use (1998) Journal of Neuroscience, 18 (23), pp. 10105-10115. , https://doi.org/10.1523/JNEUROSCI.18-23-10105.1998; 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Xu, Z., Kumar, V., Todorov, E., A low-cost and modular, 20-dof anthropomorphic robotic hand: Design, actuation and modeling (2013) 2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp. 368-375. , https://doi.org/10.1109/HUMANOIDS.2013.7030001; (2019), https://ultimaker.com/3d-printers/ultimaker-3, Ultimaker 3, accessed 06.11. 2019; (2019), https://ultimaker.com/en/resources/49914-cpe, Ultimaker CPE, accessed 06.11. 2019; (2019), https://ultimaker.com/en/resources/49919-pv, Ultimaker PVA, accessed 06.11. 2019; (2019), https://ultimaker.com/en/resources/49917-tpu-95, Ultimaker TPU95A, accessed 06.11. 2019",,,,"Cambridge University Press","16th International Design Conference, DESIGN 2020","26 October 2020 through 29 October 2020",,260269,26337762,,,,"English","Proc. Design Soc. Design Conf.",Conference Paper,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85110285136
"Corrales-Paredes A., Malfaz M., Egido-García V., Salichs M.Á.","17433438000;8613265500;57219970132;6603829011;","Waymarking in social robots: Environment signaling using human–robot interaction",2021,"Sensors","21","23","8145","","",,,"10.3390/s21238145","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120622897&doi=10.3390%2fs21238145&partnerID=40&md5=42211fe28f319155bad0911422a54634","Science, Computation and Technology Department, School of Architecture, Engineering and Design, Universidad Europea de Madrid, Villaviciosa de Odón, 28670, Spain; Robotics Lab, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés, Madrid, 28911, Spain; School of Architecture, Engineering and Design, Universidad Europea de Madrid, Villaviciosa de Odón, 28670, Spain","Corrales-Paredes, A., Science, Computation and Technology Department, School of Architecture, Engineering and Design, Universidad Europea de Madrid, Villaviciosa de Odón, 28670, Spain; Malfaz, M., Robotics Lab, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés, Madrid, 28911, Spain; Egido-García, V., School of Architecture, Engineering and Design, Universidad Europea de Madrid, Villaviciosa de Odón, 28670, Spain; Salichs, M.Á., Robotics Lab, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés, Madrid, 28911, Spain","Travellers use the term waymarking to define the action of posting signs, or waymarks, along a route. These marks are intended to be points of reference during navigation for the environ-ment. In this research, we will define waymarking as the skill of a robot to signal the environment or generate information to facilitate localization and navigation, both for its own use and for other robots as well. We present an automated environment signaling system using human–robot interaction and radio frequency identification (RFID) technology. The goal is for the robot, through human–robot interaction, to obtain information from the environment and use this information to carry out the signaling or waymarking process. HRI will play a key role in the signaling process since this type of communication makes it possible to exchange more specific and enriching information. The robot uses common phrases such as “Where am I?” and “Where can I go?”, just as we humans do when we ask other people for information about the environment. It is also possible to guide the robot and “show” it the environment to carry out the task of writing the signs. The robot will use the information received to create, update, or improve the navigation data in the RFID signals. In this paper, the signaling process will be described, how the robot acquires the information for signals, writing and updating process and finally, the implementation and integration in a real social robot in a real indoor environment. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Human–robot interaction; Indoor; Navigation; Radio frequency identification; RFID; Signaling; Social robots; Topological; Wayfinding; Waymarking","Human robot interaction; Indoor positioning systems; Information use; Radio frequency identification (RFID); Radio waves; Humans-robot interactions; Indoor; Radio-frequency-identification; Robot environment; Signaling process; Social robots; Topological; Way finding; Waymarking; Signaling",,,,,"CIPI/18/069; Ministerio de Ciencia, Innovación y Universidades, MCIU; Agencia Estatal de Investigación, AEI","This research was (partially) supported by project ?Robots sociales para estimulaci?n f?sica, cognitiva y afectiva de mayores (ROSES)? RTI2018-096338-B-I00 funded by Agencia Estatal de Investigaci?n (AEI), Ministerio de Ciencia, Innovaci?n y Universidades (Spain). 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"Shen Z., Elibol A., Chong N.Y.","57200151005;24733903500;7007012034;","Multi-modal feature fusion for better understanding of human personality traits in social human–robot interaction",2021,"Robotics and Autonomous Systems","146",,"103874","","",,,"10.1016/j.robot.2021.103874","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113553122&doi=10.1016%2fj.robot.2021.103874&partnerID=40&md5=0e50194dedd8832beb4cb5de473b53ab","School of Information Science, Japan Advanced Institute of Science and Technology, Japan","Shen, Z., School of Information Science, Japan Advanced Institute of Science and Technology, Japan; Elibol, A., School of Information Science, Japan Advanced Institute of Science and Technology, Japan; Chong, N.Y., School of Information Science, Japan Advanced Institute of Science and Technology, Japan","Since the dynamic nature of human–robot interaction becomes increasingly prevalent in our daily life, there is a great demand for enabling the robot to better understand human personality traits and inspiring humans to be more engaged in the interaction with the robot. Therefore, in this work, as we design the paradigm of human–robot interaction as close to the real situation as possible, the following three main problems are addressed: (1) fusion of visual and audio features of human interaction modalities, (2) integration of variable length feature vectors, and (3) compensation of shaky camera motion caused by movements of the robot's communicative gesture. Specifically, the three most important visual features of humans including head motion, gaze, and body motion were extracted from a camera mounted on the robot performing verbal and body gestures during the interaction. Then, our system was geared to fuse the aforementioned visual features and different types of vocal features, such as voice pitch, voice energy, and Mel-Frequency Cepstral Coefficient, dealing with variable length multiple feature vectors. Lastly, considering unknown patterns and sequential characteristics of human communicative behavior, we proposed a multi-layer Hidden Markov Model that improved the classification accuracy of personality traits and offered notable advantages of fusing the multiple features. The results were thoroughly analyzed and supported by psychological studies. The proposed multi-modal fusion approach is expected to deepen the communicative competence of social robots interacting with humans from different cultures and backgrounds. © 2021 Elsevier B.V.","Human personality traits; Human–robot interaction; Machine learning; Multi-modal feature fusion","Agricultural robots; Behavioral research; Cameras; Hidden Markov models; Machine design; Classification accuracy; Communicative competences; Communicative gestures; Human interactions; Mel frequency cepstral co-efficient; Multi-modal fusion; Personality traits; Robot interactions; Social robots",,,,,"Air Force Office of Scientific Research, AFOSR: AFOSR-AOARD / FA2386-19-1-4015","The authors are grateful for financial support from the Air Force Office of Scientific Research, United State under AFOSR-AOARD / FA2386-19-1-4015 and the Shibuya Science, Culture, and Sports Foundation 2019 Grant Program, Japan .",,,,,,,,,,"Allport, G.W., Personality: A Psychological Interpretation (1937), Holt; Vinciarelli, A., Mohammadi, G., A survey of personality computing (2014); Hurtz, G.M., Donovan, J.J., Personality and job performance: The big five revisited (2000); Mõttus, R., McNeill, G., Jia, X., Craig, L.C., Starr, J.M., Deary, I.J., The associations between personality, diet and body mass index in older people (2013) Health Psychol., 32 (4), pp. 353-360. , http://www.ncbi.nlm.nih.gov/pubmed/21928903, URL; Ozer, D.J., Benet-Martínez, V., Personality and the prediction of consequential outcomes (2006) Annu. 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"Lübbert A., Göschl F., Krause H., Schneider T.R., Maye A., Engel A.K.","57286084300;56116797800;55354410400;23994400600;18536954100;57202521319;","Socializing Sensorimotor Contingencies",2021,"Frontiers in Human Neuroscience","15",,"624610","","",,1,"10.3389/fnhum.2021.624610","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116500228&doi=10.3389%2ffnhum.2021.624610&partnerID=40&md5=1206985988735fb9dc79fd16ea77d640","Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany","Lübbert, A., Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Göschl, F., Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Krause, H., Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Schneider, T.R., Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Maye, A., Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Engel, A.K., Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany","The aim of this review is to highlight the idea of grounding social cognition in sensorimotor interactions shared across agents. We discuss an action-oriented account that emerges from a broader interpretation of the concept of sensorimotor contingencies. We suggest that dynamic informational and sensorimotor coupling across agents can mediate the deployment of action-effect contingencies in social contexts. We propose this concept of socializing sensorimotor contingencies (socSMCs) as a shared framework of analysis for processes within and across brains and bodies, and their physical and social environments. In doing so, we integrate insights from different fields, including neuroscience, psychology, and research on human–robot interaction. We review studies on dynamic embodied interaction and highlight empirical findings that suggest an important role of sensorimotor and informational entrainment in social contexts. Furthermore, we discuss links to closely related concepts, such as enactivism, models of coordination dynamics and others, and clarify differences to approaches that focus on mentalizing and high-level cognitive representations. Moreover, we consider conceptual implications of rethinking cognition as social sensorimotor coupling. The insight that social cognitive phenomena like joint attention, mutual trust or empathy rely heavily on the informational and sensorimotor coupling between agents may provide novel remedies for people with disturbed social cognition and for situations of disturbed social interaction. Furthermore, our proposal has potential applications in the field of human–robot interaction where socSMCs principles might lead to more natural and intuitive interfaces for human users. © Copyright © 2021 Lübbert, Göschl, Krause, Schneider, Maye and Engel.","autism spectrum disorder; coordination dynamics; coupling; human–robot interaction; joint action; prediction; sensorimotor contingencies","Article; attention; behavior; cognitive defect; conceptual framework; empathy; extrinsic neural coupling mode; human; human relation; human robot interaction; nerve cell network; sensorimotor network; social behavior; social cognition; social coordination dynamics; social coupling impairment; social interaction; socializing sensorimotor contingency; togetherness; trust",,,,,"European Commission, EC: H2020-641321; Deutsche Forschungsgemeinschaft, DFG: SFB 936/A3, SFB TRR 169/B1/B4","This work was supported by grants from the EU (project ‘socSMCs,’ H2020-641321) and the DFG (SFB 936/A3 and SFB TRR 169/B1/B4).",,,,,,,,,,"Ansermin, E., Mostafaoui, G., Beaussé, N., Gaussier, P., Learning to synchronously imitate gestures using entrainment effect (2016) From Animals to Animats 14. 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"Huang Y., Chen Y., Zhang X., Zhang H., Song C., Ota J.","54792998800;57202950148;55864283000;57192156883;57227265300;57225849560;","A Novel Cable-Driven 7-DOF Anthropomorphic Manipulator",2021,"IEEE/ASME Transactions on Mechatronics","26","4","9238407","2174","2185",,,"10.1109/TMECH.2020.3033309","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113318683&doi=10.1109%2fTMECH.2020.3033309&partnerID=40&md5=feb5d0219d4423342074a79dbbe4e21d","Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, Guangzhou, 510640, China; Research into Artifacts, Center for Engineering, The University of Tokyo, Tokyo, 113-8656, Japan","Huang, Y., Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, Guangzhou, 510640, China; Chen, Y., Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, Guangzhou, 510640, China; Zhang, X., Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, Guangzhou, 510640, China; Zhang, H., Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, Guangzhou, 510640, China; Song, C., Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, Guangzhou, 510640, China; Ota, J., Research into Artifacts, Center for Engineering, The University of Tokyo, Tokyo, 113-8656, Japan","Safety design of robot arm is important for human-robot interaction. In this article, an anthropomorphic seven-DOF cable-driven manipulator, including a three-DOF shoulder joint module, a one-DOF elbow joint module, and a three-DOF wrist joint module, is proposed for human-robot interaction. These three joint modules are connected serially and the motion transmission is realized by rope. The joint stiffness and workspace of joint module are numerically analyzed and compared to that of the human arm joint. A protype of manipulator is machined and used to evaluate the joint stiffness and workspace of the joint module through experiments. The mass of the designed manipulator is approximately 4.8 kg, which is similar to that of a human arm. The stiffness of elbow joint is 185.25 N·m/rad in positive direction and 133.75 N·m/rad in negative direction, which is smaller than that of the human elbow joint. The stiffness of wrist joint is 48.25 N·m/rad. The stiffness of shoulder joint under two configurations is 138 and 124 N·m/rad. The workspace matching index (WMI) is used to evaluate the workspace of joint module by comparing to that of the human arm joint. The WMI of the developed shoulder joint is approximately 40% of the human shoulder joint due to the structure constraint. While the workspaces of the developed elbow joint and wrist joint are the same with that of human joint. © 1996-2012 IEEE.","Anthropomorphic manipulator; cable-driven; human-robot interaction","Anthropomorphic robots; Cables; Industrial manipulators; Machine design; Man machine systems; Manipulators; Stiffness; Anthropomorphic Manipulators; Cable-driven manipulators; Human shoulder; Joint stiffness; Motion transmission; Shoulder joints; Structure constraints; Workspace matching; Human robot interaction",,,,,"2019A1515011154; National Natural Science Foundation of China, NNSFC: 51820105007, 52075178, 91748111; Guangzhou Municipal Science and Technology Project: 202002030233","Manuscript received November 12, 2019; revised May 31, 2020; accepted October 17, 2020. Date of publication October 23, 2020; date of current version August 13, 2021. Recommended by Technical Editor D. Oetomo and Senior Editor M. K. O’Malley. This work was supported in part by the National Natural Science Foundation of China under Grant 91748111, Grant 52075178, and Grant 51820105007; in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515011154; and in part by the Guangzhou Municipal Scientific and Technological Project under Grant 202002030233. (Corresponding author: Xianmin Zhang.) Yanjiang Huang, Yanlin Chen, Xianmin Zhang, Hongchuan Zhang, and Chunyu Song are with the Guangdong Provincial Key Laboratory of Precision Equipment and Manufacturing Technology, South China University of Technology, Guangzhou 510640, China (e-mail: mehuangyj@ scut.edu.cn; 201720100340@mail.scut.edu.cn; zhangxm@scut.edu.cn; 201610100023@mail.scut.edu.cn; 460932162@qq.com).",,,,,,,,,,"Li, X., Chi, G., Vidas, S., Cheah, C., Human-guided robotic comanipulation: Two illustrative scenarios (2016) IEEE Trans. Control Syst. 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"Lv H., Yi P., Liu R., Hou Y., Zhou D., Zhang Q., Wei X.","57224510996;23993138800;56706689400;25924942100;13105639000;56383216300;7402117019;","Asymmetric Anomaly Detection for Human-Robot Interaction",2021,"Proceedings of the 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021",,,"9437868","372","377",,,"10.1109/CSCWD49262.2021.9437868","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107716284&doi=10.1109%2fCSCWD49262.2021.9437868&partnerID=40&md5=8ac024ce2305c7da1bf8aaa96cbf229f","National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University, Dalian, China; School of Information Science and Technology, Taishan University, Tai'an, China","Lv, H., National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University, Dalian, China; Yi, P., National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University, Dalian, China; Liu, R., National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University, Dalian, China; Hou, Y., School of Information Science and Technology, Taishan University, Tai'an, China; Zhou, D., National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University, Dalian, China; Zhang, Q., National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University, Dalian, China; Wei, X., School of Information Science and Technology, Taishan University, Tai'an, China","Security in human-robot interaction is the focus of research in this field. Rapid detection of abnormal events that may cause danger in the interaction process can effectively reduce the probability of occurrence of danger. In general anomaly detection methods, 2D or 3D convolutional autoencoders are widely used for anomaly detection. Among them, 2D convolutional autoencoders are with good real-time performance and lower detection accuracy, while 3D convolutional autoencoders are with higher detection accuracy and insufficient real-time performance. In order to ensure realtime performance and obtain higher accuracy, an end-to-end asymmetric convolutional autoencoder network (ACANet) using both 2D and 3D convolutions is designed. Specifically, 3D convolution is used to build the encoder to learn comprehensive information in continuous input frames, and 2D convolution is used to build the decoder to model the information fast, a dimensional alignment module is constructed to connect the encoder and the decoder while avoiding a large number of calculations in the latent space of the 3D features output by the encoder, and the skip connections module is used to obtain accurate predictions. Anomaly detection can then be completed by evaluating the differences between results predicted by the ACANet and real frames. The experimental results show that our method achieves competitive accuracy on mainstream datasets and at the same time obtains the fastest speed. Compared with mainstream methods, this method is more suitable for anomaly detection tasks in human-robot interaction. © 2021 IEEE.","anomaly detection; asymmetric autoencoder; human-robot interaction","Convolution; Convolutional neural networks; Decoding; Human robot interaction; Interactive computer systems; Learning systems; Man machine systems; Signal encoding; Accurate prediction; Anomaly detection methods; Comprehensive information; Detection accuracy; Focus of researches; Interaction process; Probability of occurrence; Real time performance; Anomaly detection",,,,,"National Natural Science Foundation of China, NSFC; Dalian Science and Technology Innovation Fund: 2020JJ25CYOO 1; Special Project of Central Government for Local Science and Technology Development of Hubei Province: 2021JH6/10500140","This work was supported in part by the Key Program of NSFC (Grant No.Ul908214), Special Project of Central Government Guiding Local Science and Technology Development (Grant No. 2021JH6/10500140), Program for the Liaoning Distinguished Professor, Program for Innovative Research Team in University of Liaoning Province and Dalian University, and in part by the Science and Technology Innovation Fund of Dalian (Grant No. 2020JJ25CYOO 1 ).",,,,,,,,,,"Vasic, M., Billard, A., Safety issues in human-robot interactions (2013) 2013 Ieee International Conference on Robotics and Automation; Wang, L., (2019) Symbiotic Human-robot Collaborative Assembly, 68 (2), pp. 701-726; Zong, B., Deep autoencoding gaussian mixture model for unsupervised anomaly detection (2018) International Conference on Ions; Sabokrou, M., Adversarially learned one-class classifier for novelty detection (2018) Computer Vision and Pattern Recognition; Chandola, V., Baneijee, A., Kumar, V., Anomaly detection: A survey (2009) Acm Comput. Surv, 41 (3). , Article 15; Hasan, M., Learning temporal regularity in video sequences (2016) Computer Vision and Pattern Recognition; Chen, B., Video imagination from a single image with transformation generation (2017) Computer Vision and Pattern Recognition; Mathieu, M., Couprie, C., LeCun, Y., (2015) Deep Multi-scale Video Prediction beyond Mean Square Error; Lu, C., Shi, J., Jia, J., Abnormal event detection at 150 fps in matlab (2013) International Conference on Computer Vision; Park, H., Noh, J., Ham, B., Learning memory-guided normality for anomaly detection (2020) Proceedings of the IEEE/ Cvf Conference on Computer Vision and Pattern Recognition; Zhao, Y., Spatio-temporal autoencoder for video anomaly detection (2017) Acm Multimedia; Fryman, J., Matthias, B., Safety of industrial robots: From conventional to collaborative applications (2012) Robotik 2012; 7th German Conference on Robotics; Ribeiro, M., Lazzaretti, A.E., Lopes, H.S., A study of deep convolutional auto-encoders for anomaly detection in videos (2018) Pattern Recognition Letters, 105, pp. 13-22; Gong, D., Memorizing normality to detect anomaly: Memoryaugmented deep autoencoder for unsupervised anomaly detection (2019) International Conference on Computer Vision; Ouali, Y., Hudelot, C., Tami, M., (2020) Semi-Supervised Semantic Segmentation with Cross-Consistency Training; Wang, L., Dual super-resolution learning for semantic segmentation (2020) 2020 IEEE/ Cvf Conference on Computer Vision and Pattern Recognition (CVPR); Liu, W., Future frame prediction for anomaly detection-a new baseline (2018) Computer Vision and Pattern Recognition; Ravanbakhsh, M., Abnormal event detection in videos using generative adversarial nets (2017) International Conference on Image Processing; Chang, Y., Clustering driven deep autoencoder for video anomaly detection (2020) European Conference on Computer Vision (ECCV); Luo, W., Liu, W., Gao, S., A revisit of sparse coding based anomaly detection in stacked rnn framework (2017) International Conference on Computer Vision; Yan, A., Pa3d: Pose-action 3d machine for video recognition (2019) Computer Vision and Pattern Recognition; Diba, A., Temporal 3d convnets: New architecture and transfer learning for video classification (2017) Computer Vision and Pattern Recognition; Sultani, W., Chen, C., Shah, M., Real-world anomaly detection in surveillance videos (2018) Computer Vision and Pattern Recognition","Zhou, D.; National and Local Joint Engineering Laboratory of Computer Aided Design, China; email: zhouds@dlu.edu.cn","Shen W.Barthes J.-P.Luo J.Shi Y.Zhang J.",,"Institute of Electrical and Electronics Engineers Inc.","24th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021","5 May 2021 through 7 May 2021",,169216,,9781728165974,,,"English","Proc. IEEE Int. Conf. Comput. Support. Coop. Work Des., CSCWD",Conference Paper,"Final","",Scopus,2-s2.0-85107716284
"Fratczak P., Goh Y.M., Kinnell P., Justham L., Soltoggio A.","57212485104;8626315800;57204794040;6506661924;15046088100;","Robot apology as a post-accident trust-recovery control strategy in industrial human-robot interaction",2021,"International Journal of Industrial Ergonomics","82",,"103078","","",,2,"10.1016/j.ergon.2020.103078","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099353931&doi=10.1016%2fj.ergon.2020.103078&partnerID=40&md5=aa1c1622d3f8812b6d4d6ce69222057f","Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, United Kingdom; Computer Science Department, Loughborough University, Loughborough, LE11 3TP, United Kingdom","Fratczak, P., Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, United Kingdom; Goh, Y.M., Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, United Kingdom; Kinnell, P., Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, United Kingdom; Justham, L., Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, LE11 3TU, United Kingdom; Soltoggio, A., Computer Science Department, Loughborough University, Loughborough, LE11 3TP, United Kingdom","Due to safety requirements for Human-Robot Interaction (HRI), industrial robots have to meet high standards of safety requirements (ISO 10218). However, even if robots are incapable of causing serious physical harm, they still may influence people's mental and emotional wellbeing, as well as their trust, behaviour and performance in close collaboration. This work uses an HTC Vive Virtual Reality headset to study the potential of using robot control strategies to positively influence human post-accident behaviour. In the designed scenario, a virtual industrial robot first makes sudden unexpected movements, after which it either does or does not attempt to apologise for them. The results show that after the robot tries to communicate with the participants, the robot is reported to be less scary, more predictable and easier to work with. Furthermore, postural analysis shows that the participants who were the most affected by the robot's sudden movement recover 74% of their postural displacement within 60 s after the event if the robot apologised, and only 34% if it did not apologise. It is concluded, that apologies, which are commonly used as a trust-recovery strategy in social robotics, can positively influence people engaged with industrial robotics as well. Relevance to industry: Findings can be used as guidelines for designing robot behaviour and trust-recovery control strategies meant to speed up human recovery after a trust-violating event in industrial Human-Robot Interaction. © 2021","Human-robot interaction; Industrial robots; Trust recovery; Virtual reality","Accident prevention; Accidents; Behavioral research; Industrial robots; Man machine systems; Recovery; Robotics; Social robots; Virtual reality; Emotional wellbeing; Human robot Interaction (HRI); Industrial robotics; Postural analysis; Recovery control; Recovery strategies; Safety requirements; Virtual-reality headsets; Economic and social effects; accidental injury; adult; Article; body position; control strategy; controlled study; experimental study; female; human; human relation; interpersonal communication; male; measurement; occupational accident; physical parameters; prediction; priority journal; questionnaire; robotics; trust; virtual reality",,,"Vive, HTC","HTC","2229424, EP/N509516/1","The authors are grateful to the Engineering and Physical Science Research Council (EPSRC) for the financial support extended to this research through the Doctoral Training Program (DTP) ( EP/N509516/1 , Project Reference number: 2229424).",,,,,,,,,,"Atkinson, D.J., Clark, M.H., Methodology for study of human-robot social interaction in dangerous situations (2014) Proceedings of the Second International Conference on Human-Agent Interaction, pp. 371-376. , October 2014, Tsukuba, Japan; Bailenson, J.N., Blascovich, J., Beall, A.C., Loomis, J.M., Interpersonal distance in immersive virtual environments (2003) Pers. 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Factors, 46 (1), pp. 50-80; Lee, M.K., Kiesler, S., Forlizzi, J., Srinivasa, S., Rybski, P., Gracefully mitigating breakdowns in robotic services (2010) 2010 5th ACM/IEEE International Conference On Human-Robot Interaction (HRI), pp. 203-210. , March 2010, Osaka, Japan; Lewicki, R.J., Bunker, B.B., Developing and maintaining trust in work relationships (1996) Trust in Organizations: Frontiers of Theory and Research, pp. 114-139. , R.M. Kramer T.M. Tyler Sage Thousand Oaks, CA; Martelaro, N., Nneji, V.C., Ju, W., Hinds, P., Tell me more designing HRI to encourage more trust, disclosure, and companionship (2016) 2016 11th ACM/IEEE International Conference On Human-Robot Interaction (HRI), pp. 181-188. , March 2016, Christchurch, New Zealand; Mayer, R.C., Davis, J.H., Schoorman, F.D., An integrative model of organizational trust (1995) Acad. Manag. Rev., 20 (3), pp. 709-734; McLaughlan, R., Virtual reality as a research method: is this the future of photo-elicitation? (2019) Vis. 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Effects of error, task type and personality on human-robot cooperation and trust (2015) 2015 10th ACM/IEEE International Conference On Human-Robot Interaction (HRI), pp. 1-8. , March 2015, Portland, OR, USA; Slater, M., Spanlang, B., Sanchez-Vives, M.V., Blanke, O., First person experience of body transfer in virtual reality (2010) PloS One, 5 (5); Stilli, A., Grattarola, L., Feldmann, H., Wurdemann, H.A., Althoefer, K., Variable Stiffness Link (VSL): toward inherently safe robotic manipulators (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), May 2017, Singapore, Singapore, pp. 4971-4976; Yu, K., Berkovsky, S., Taib, R., Conway, D., Zhou, J., Chen, F., User trust dynamics: an investigation driven by differences in system performance (2017) Proceedings of the 22nd International Conference on Intelligent User Interfaces, pp. 307-317. , March 2017, Limassol, Cyprus; Zhou, Y., Headings, L.M., Dapino, M.J., Discrete layer jamming for safe co-robots (2019) 2019 International Conference on Robotics and Automation (ICRA), May 2019, Montreal, Canada, pp. 6124-6129","Fratczak, P.; Wolfson School of Mechanical, United Kingdom; email: p.g.fratczak@lboro.ac.uk",,,"Elsevier B.V.",,,,,01698141,,IJIEE,,"English","Int. J. Ind. Ergon.",Article,"Final","",Scopus,2-s2.0-85099353931
"Li Z., Mu Y., Sun Z., Song S., Su J., Zhang J.","57200183907;57222047047;25637891100;57207760100;57045140900;57202953338;","Intention Understanding in Human–Robot Interaction Based on Visual-NLP Semantics",2021,"Frontiers in Neurorobotics","14",,"610139","","",,1,"10.3389/fnbot.2020.610139","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101079714&doi=10.3389%2ffnbot.2020.610139&partnerID=40&md5=e2a50659a2eb9dc860a94c56bd81605a","Institute of Robotics and Intelligent Manufacturing, The Chinese University of Hong Kong, Shenzhen, China; School of Statistics, Southwestern University of Finance and Economics, Chengdu, China; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China; Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China; School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University, Suzhou, China; Research Center on Special Robots, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China","Li, Z., Institute of Robotics and Intelligent Manufacturing, The Chinese University of Hong Kong, Shenzhen, China; Mu, Y., School of Statistics, Southwestern University of Finance and Economics, Chengdu, China; Sun, Z., School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China; Song, S., Department of Mathematical Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, China; Su, J., School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi'an Jiaotong-Liverpool University, Suzhou, China; Zhang, J., Institute of Robotics and Intelligent Manufacturing, The Chinese University of Hong Kong, Shenzhen, China, Research Center on Special Robots, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China","With the rapid development of robotic and AI technology in recent years, human–robot interaction has made great advancement, making practical social impact. Verbal commands are one of the most direct and frequently used means for human–robot interaction. Currently, such technology can enable robots to execute pre-defined tasks based on simple and direct and explicit language instructions, e.g., certain keywords must be used and detected. However, that is not the natural way for human to communicate. In this paper, we propose a novel task-based framework to enable the robot to comprehend human intentions using visual semantics information, such that the robot is able to satisfy human intentions based on natural language instructions (total three types, namely clear, vague, and feeling, are defined and tested). The proposed framework includes a language semantics module to extract the keywords despite the explicitly of the command instruction, a visual object recognition module to identify the objects in front of the robot, and a similarity computation algorithm to infer the intention based on the given task. The task is then translated into the commands for the robot accordingly. Experiments are performed and validated on a humanoid robot with a defined task: to pick the desired item out of multiple objects on the table, and hand over to one desired user out of multiple human participants. The results show that our algorithm can interact with different types of instructions, even with unseen sentence structures. © Copyright © 2021 Li, Mu, Sun, Song, Su and Zhang.","human–robot interaction; intention estimation; scene understanding; semantics; visual-NLP","Anthropomorphic robots; Artificial intelligence; Economic and social effects; Object recognition; Semantics; Visual languages; Intention understanding; Language semantics; Multiple objects; Natural languages; Robot interactions; Sentence structures; Similarity computation; Visual object recognition; Social robots; adult; algorithm; article; female; human; human experiment; language; male; recognition; robotics; semantics",,,,,"2019-INT020; Science, Technology and Innovation Commission of Shenzhen Municipality: JCYJ20170410172100520, JCYJ20170818104502599","This work was partially supported by the Shenzhen Science and Technology Innovation Commission (Grants Nos. JCYJ20170410172100520 and JCYJ20170818104502599). This work was also partially supported by the Shenzhen Institute of Artificial Intelligence and Robotics for Society (Grants No. 2019-INT020).",,,,,,,,,,"Alonso-Martín, F., Castro-González, A., de Gorostiza Luengo, F.J.F., Salichs, M.Á., Augmented robotics dialog system for enhancing human-robot interaction (2015) Sensors, 15, pp. 15799-15829. , 26151202; Dzifcak, J., Scheutz, M., Baral, C., Schermerhorn, P., What to do and how to do it: translating natural language directives into temporal and dynamic logic representation for goal management and action execution (2009) 2009 IEEE International Conference on Robotics and Automation, pp. 4163-4168. , Kobe, IEEE; Eppe, M., Trott, S., Feldman, J., Exploiting deep semantics and compositionality of natural language for human-robot-interaction (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 731-738. , Daejeon, IEEE; Fang, B., Sun, F., Liu, H., Liu, C., 3d human gesture capturing and recognition by the immu-based data glove (2018) Neurocomputing, 277, pp. 198-207; Fang, B., Wei, X., Sun, F., Huang, H., Yu, Y., Liu, H., Skill learning for human-robot interaction using wearable device (2019) Tsinghua Sci. 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Robot, , 33137754, 4; Mahler, J., Pokorny, F.T., Hou, B., Roderick, M., Laskey, M., Aubry, M., DEX-Net 1.0: a cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards (2016) 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1957-1964. , Stockholm, IEEE; Matuszek, C., Bo, L., Zettlemoyer, L., Fox, D., Learning from unscripted deictic gesture and language for human-robot interactions (2014) AAAI, pp. 2556-2563; Matuszek, C., Herbst, E., Zettlemoyer, L., Fox, D., Learning to parse natural language commands to a robot control system (2013) Experimental Robotics, pp. 403-415. , Springer; Mooney, R.J., Learning to connect language and perception (2008) AAAI, pp. 1598-1601. , Chicago; Paul, R., Barbu, A., Felshin, S., Katz, B., Roy, N., Temporal grounding graphs for language understanding with accrued visual-linguistic context (2018) arXiv[Preprint].arXiv: 1811.06966; Quillen, D., Jang, E., Nachum, O., Finn, C., Ibarz, J., Levine, S., Deep reinforcement learning for vision-based robotic grasping: a simulated comparative evaluation of off-policy methods (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 6284-6291. , Brisbane, QLD, IEEE; Reddy, D., Raj, D., Speech recognition by machine: a review (1976) Proc. IEEE, 64, pp. 501-531; Ren, S., He, K., Girshick, R., Sun, J., Faster r-cnn: towards real-time object detection with region proposal networks (2015) IEEE transactions on pattern analysis and machine intelligence, 39, pp. 1137-1149. , 27295650; Richards, L.E., Matuszek, C., Learning to understand non-categorical physical language for human robot interactions (2019) From the RSS Workshop on AI and its Alternatives in Assistive and Collaborative Robotics, , Messe Freiburg, in; Shridhar, M., Mittal, D., Hsu, D., INGRESS: interactive visual grounding of referring expressions (2020) Int. J. Robot. Res, 39, pp. 217-232; Trask, A., Michalak, P., Liu, J., sense2vec-a fast and accurate method for word sense disambiguation in neural word embeddings (2015) arXiv[Preprint].arXiv: 1511.06388; Uzkent, B., Yeh, C., Ermon, S., Efficient object detection in large images using deep reinforcement learning (2020) The IEEE Winter Conference on Applications of Computer Vision, pp. 1824-1833. , Snowmass Village, CO; Xie, E., Sun, P., Song, X., Wang, W., Liu, X., Liang, D., Polarmask: single shot instance segmentation with polar representation (2020) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12193-12202. , Seattle, WA","Zhang, J.; Institute of Robotics and Intelligent Manufacturing, China; email: zhangjiaming@cuhk.edu.cn
Zhang, J.; Research Center on Special Robots, China; email: zhangjiaming@cuhk.edu.cn",,,"Frontiers Media S.A.",,,,,16625218,,,,"English","Front. Neurorobotics",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85101079714
"Rosén J.","57219093157;","Expectations in Human-Robot Interaction",2021,"Lecture Notes in Networks and Systems","259",,,"98","105",,,"10.1007/978-3-030-80285-1_12","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112052093&doi=10.1007%2f978-3-030-80285-1_12&partnerID=40&md5=8f783b90cfb124679f1136831a5231f4","Interaction Lab, University of Skövde, Högskolevägen 1, Skövde, 541 28, Sweden","Rosén, J., Interaction Lab, University of Skövde, Högskolevägen 1, Skövde, 541 28, Sweden","It is acknowledged that humans expect social robots to interact in a similar way as in human-human interaction. To create successful interactions between humans and social robots, it is envisioned that the social robot should be viewed as an interaction partner rather than an inanimate thing. This implies that the robot should act autonomously, being able to ‘perceive’ and ‘anticipate’ the human’s actions as well as its own actions ‘here and now’. Two crucial aspects that affect the quality of social human-robot interaction is the social robot’s physical embodiment and its performed behaviors. In any interaction, before, during or after, there are certain expectations of what the social robot is capable of. The role of expectations is a key research topic in the field of Human-Robot Interaction (HRI); if a social robot does not meet the expectations during interaction, the human (user) may shift from viewing the robot as an interaction partner to an inanimate thing. The aim of this work is to unravel the role and relevance of humans’ expectations of social robots and why it is important area of study in HRI research. Moreover, I argue that the field of HRI can greatly benefit from incorporating approaches and methods from the field of User Experience (UX) in its efforts to gain a deeper understanding of human users’ expectations of social robots and making sure that the matching of these expectations and reality is better aligned. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Expectations; Human-robot interaction; User experience",,,,,,,,,,,,,,,,,"Dautenhahn, K., Methodology & themes of human-robot interaction (2007) Int. J. Adv. Rob. Syst., 4 (1), pp. 103-108; Duffy, B.R., Anthropomorphism and the social robot (2003) Robot. Auton. Syst., 42 (3-4), pp. 177-190; Manzi, F., Massaro, D., Di Lernia, D., Maggioni, M., Riva, G., Marchetti, A., Robots are not all the same (2020) Cyberpsychol. Behav. Soc. Network., 24, pp. 1-8; Dennett, D., (1989) The Intentional Stance, , MIT press, Cambridge; Heider, F., Simmel, M., An experimental study of apparent behavior (1944) Am. J. Psychol., 57 (2), pp. 243-259; Alač, M., Social robots: Things or agents? (2016) AI Soc, 31 (4), pp. 519-535; Hartson, R., Pyla, P.S., (2018) The UX Book, , Morgan Kaufmann, San Diego; Borg, M., Porter, L., Following the life-course of an expectation (2010) The Psychology of Expectations, pp. 1-47. , Leon, P., Tamez, N. (eds.), pp. , Nova Science Publishers; Roese, N.J., Sherman, J.W., Expectancy (2007) Social Psychology: Handbook of Basic Principles, pp. 91-115. , Kruglanski, A.W., Higgins, E.T. (eds.), pp; Hassenzahl, M., Tractinsky, N., User experience — A research agenda (2006) Behav. Inf. Technol., 25 (2), pp. 91-97; Malle, B.F., Fischer, K., Young, J.E., Moon, A., Colling, E., (2020) Trust and the Discrepancy between Expectations and Actual Capabilities of Social Robots, , Cambridge Scholars Publishing; Billing, E., Rosén, J., Lindblom, J., Expectations of robot technology in welfare (2019) The Second Workshop on Social Robots in Therapy and Care in Conjunction with the 14Th ACM/IEEE International Conference on Human-Robot Interaction (HRI 2019), , Daegu, Korea; Billing, E., The DREAM dataset (2020) PLOS ONE, 15 (8), pp. 1-15; Riek, L.D., Wizard of Oz studies in HRI (2012) J. Hum.-Robot Interact., 1 (1), pp. 119-136; Huang, W., (2015) When HCI Meets HRI. Virginia Polytechnic Institute and State University; Lindblom, J., Alenljung, B., Billing, E., Evaluating the user experience of human robot interaction (2020) Evaluation Methods Standardization for Human-Robot Interaction, pp. 231-256. , Jost, C., et al. (eds.), pp; Tonkin, M., Vitale, J., Herse, S., Williams, M.A., Judge, W., Wang, X., Design methodology for the UX of HRI (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 407-415. , , pp; Wallström, J., Lindblom, J., UX Evaluation of social robots with the USUS goals framework (2020) Evaluation Methods Standardization for Human-Robot Interaction, pp. 177-201. , Jost, C., et al. (eds.), pp. , Springer; Weiss, A., Bernhaupt, R., Schwaiger, D., Altmaninger, M., Buchner, R., Tscheligi, M., User experience evaluation with a wizard of Oz approach (2009) 2009 9Th IEEE RAS International Conference on Humanoid Robots, Pp. 303–308. IEEE; Sun, Y., Sundar, S.S., Psychological importance of human agency how self-assembly affects user experience of robots (2016) 2016 11Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Pp. 189–196. IEEE; Edwards, A., Edwards, C., Westerman, D., Spence, P.R., Initial expectations, interactions, and beyond with social robots (2019) Comput. Hum. Behav., 90, pp. 308-314; Bardzell, S., Bardzell, J., Towards a feminist HCI methodology (2011) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 675-684. , , pp","Rosén, J.; Interaction Lab, Högskolevägen 1, Sweden; email: julia.rosen@his.se","Ayaz H.Asgher U.Paletta L.",,"Springer Science and Business Media Deutschland GmbH","AHFE Conferences on Neuroergonomics and Cognitive Engineering, Industrial Cognitive Ergonomics and Engineering Psychology, and Cognitive Computing and Internet of Things, 2021","25 July 2021 through 29 July 2021",,262279,23673370,9783030802844,,,"English","Lect. Notes Networks Syst.",Conference Paper,"Final","",Scopus,2-s2.0-85112052093
"Burga-Gutierrez E., Vasquez-Chauca B., Ugarte W.","57226317529;57226310104;55440420500;","Comparative Analysis of Question Answering Models for HRI Tasks with NAO in Spanish",2021,"Communications in Computer and Information Science","1410 CCIS",,,"3","17",,,"10.1007/978-3-030-76228-5_1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111157642&doi=10.1007%2f978-3-030-76228-5_1&partnerID=40&md5=db4cabeb6862112a65dcb37673e9e708","Universidad Peruana de Ciencias Aplicadas (UPC), Lima, Peru","Burga-Gutierrez, E., Universidad Peruana de Ciencias Aplicadas (UPC), Lima, Peru; Vasquez-Chauca, B., Universidad Peruana de Ciencias Aplicadas (UPC), Lima, Peru; Ugarte, W., Universidad Peruana de Ciencias Aplicadas (UPC), Lima, Peru","Recent studies on Human Robot Interaction (HRI) have shown that different types of applications that combine metrics and techniques can help achieve a more efficient and organic interaction. This applications can be related to human care or go further and use a humanoid robot for nonverbal communication tasks. Similarly, for verbal communication, we found Question Answering, a Natural Language Processing task, that is in charge of capturing and interpret a question automatically and return a good representation of an answer. Also, recent work on creating Question Answering models, based on the Transformer architecture, have obtained state-of-the-art results. Our main goal in this project is to build a new Human Robot Interaction technique which uses a Question Answering system where we will test with college students. In the creation of the Question Answering model, we get results from state-of-the-art pre-trained models like BERT or XLNet, but also multilingual ones like m-BERT or XLM. We train them with a new Spanish dataset translated from the original SQuAD getting our best results with XLM-R, obtaining 68.1 F1 and 45.3 EM in the MLQA test dataset, and, 77.9 F1 and 58.3 EM for XQuAD test dataset. To validate the results obtained, we evaluated the project based on HRI metrics and a survey. The results demonstrate a high degree of acceptance in the students about the type of interaction that has been proposed. © 2021, Springer Nature Switzerland AG.","HRI; NAO; NLP; Question answering; SQuAD","Anthropomorphic robots; Big data; Information management; Man machine systems; Natural language processing systems; Statistical tests; Students; College students; Comparative analysis; Human robot Interaction (HRI); NAtural language processing; Non-verbal communications; Question Answering; Question answering systems; Verbal communications; Social robots",,,,,,,,,,,,,,,,"Alenljung, B., Lindblom, J., Andreasson, R., Ziemke, T., User experience in social human-robot interaction (2017) Int. J. Ambient Comput. Intell., 8 (2), pp. 12-31; Aly, A., Griffiths, S.S., Stramandinoli, F., Metrics and benchmarks in human-robot interaction: Recent advances in cognitive robotics (2017) Cogn. Syst. Res., 43, pp. 313-323; Andreasson, R., Alenljung, B., Billing, E., Lowe, R., Affective touch in human-robot interaction: Conveying emotion to the nao robot (2018) Int. J. Soc. Robot., 10, pp. 473-491. , https://doi.org/10.1007/s12369-017-0446-3; Artetxe, M., Ruder, S., Yogatama, D., On the cross-lingual transferability of monolingual representations (2020) ACL; Bertacchini, F., Bilotta, E., Pantano, P.S., Shopping with a robotic companion (2017) Comput. Hum. Behav., 77, pp. 382-395; Carrino, C.P., Costa-Jussà, M.R., Fonollosa, J.A.R., Automatic Spanish translation of the squad dataset for multilingual question answering (2019) Corr Abs/1912, p. 05200; Conneau, A., (2020) Unsupervised Cross-Lingual Representation Learning at Scale, , ACL; Conneau, A., Lample, G., (2019) Cross-Lingual Language Model Pretraining, , NeurIPS; Dautenhahn, K., Methodology and themes of human-robot interaction: A growing research field (2007) Int. J. Adv. Robot. Syst., 4 (103-108); Devlin, J., Chang, M., Lee, K., Toutanova, K., (2019) BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding, , NAACL-HLT; Gao, J., Galley, M., Li, L., Neural approaches to conversational AI (2019) Found. Trends Inf. Retr., 13, pp. 127-298; Joshi, M., Chen, D., Liu, Y., Weld, D.S., Zettlemoyer, L., Levy, O., SpanBERT: Improving pre-training by representing and predicting spans (2020) Trans. ACL, 8, pp. 64-77; Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., Soricut, R., (2020) ALBERT: A Lite BERT for Self-Supervised Learning of Language Representations, , ICLR; Lee, J., BioBERT: A pre-trained biomedical language representation model for biomedical text mining (2020) Bioinformatics, 36 (4), pp. 1234-1240; Lewis, P.S.H., Oguz, B., Rinott, R., Riedel, S., Schwenk, H., MLQA: Evaluating cross-lingual extractive question answering (2020) ACL; Lindblom, J., Andreasson, R.: Current challenges for UX evaluation of human-robot interaction. In: Schlick, C., Trzcieliński, S. (eds.) Advances in Ergonomics of Manufacturing: Managing the Enterprise of the Future. AISC, vol. 490, pp. 267–277. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41697-7 24; Liu, Y., RoBERTa: A robustly optimized BERT pretraining approach (2019) Corr Abs/1907, p. 11692; Mattioli, T., Vendittelli, M., Interaction force reconstruction for humanoid robots (2017) IEEE Robot. Autom. Lett., 2 (1), pp. 282-289; Murphy, R.R., Schreckenghost, D., Survey of metrics for human-robot interaction (2013) HRI. IEEE/ACM; Pires, T., Schlinger, E., Garrette, D., How multilingual is multilingual BERT? (2019) In: ACL; Quarteroni, S., Natural language processing for industry-ELCA’s experience (2018) Informatik Spektrum, 41, pp. 105-112. , https://doi.org/10.1007/s00287-018-1094-1; Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P., SQuAD: 100,000+ questions for machine comprehension of text (2016) EMNLP; Steinfeld, A., (2006) Common Metrics for Human-Robot Interaction, , HRI; Szűcs, V., Karolyi, G., Tatar, A., Magyar, A., Voice controlled humanoid robot based movement rehabilitation framework (2018) Coginfocom; Tiutiunnyk, S., Dyomkin, V., Context-based question-answering system for the Ukrainian language (2020) CEUR Workshop Proceedings; Ugarte, W., Boizumault, P., Loudni, S., Crémilleux, B., (2014) Computing Skypattern Cubes, , ECAI; Ugarte, W., Boizumault, P., Loudni, S., Crémilleux, B., Computing skypattern cubes using relaxation (2014) ICTAI. IEEE; Ugarte, W., Boizumault, P., Loudni, S., Crémilleux, B., Modeling and mining optimal patterns using dynamic CSP (2015) ICTAI. IEEE; Vaswani, A., Attention is all you need (2017) NIPS; Yang, Z., Dai, Z., Yang, Y., Carbonell, J.G., Salakhutdinov, R., Le, Q.V., (2019) Xlnet: Generalized Autoregressive Pretraining for Language Understanding, , NeurIPS; Zaib, M., Sheng, Q.Z., Zhang, W.E., A short survey of pre-trained language models for conversational AI-a new age in NLP (2020) ACSW. ACM; Zhang, Y., Chen, X., Ai, Q., Yang, L., Croft, W.B., Towards conversational search and recommendation: System ask, user respond (2018) CIKM. ACM","Ugarte, W.; Universidad Peruana de Ciencias Aplicadas (UPC)Peru; email: willy.ugarte@upc.pe","Lossio-Ventura J.A.Valverde-Rebaza J.C.Diaz E.Alatrista-Salas H.",,"Springer Science and Business Media Deutschland GmbH","7th Annual International Conference on Information Management and Big Data, SIMBig 2020","1 October 2020 through 3 October 2020",,260449,18650929,9783030762278,,,"English","Commun. Comput. Info. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85111157642
"Shi K., Song A., Li H.","57208207669;57205479536;36468482700;","Optimized Design for Cable-Driven Shoulder-Elbow Exoskeleton Robot",2021,"IEEE Access","9",,"9422755","68197","68207",,,"10.1109/ACCESS.2021.3077365","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107203740&doi=10.1109%2fACCESS.2021.3077365&partnerID=40&md5=f47ea81bbc11a7b586eda4f8ffcc03ef","School of Instrument Science and Engineering, Southeast University, Nanjing, China","Shi, K., School of Instrument Science and Engineering, Southeast University, Nanjing, China; Song, A., School of Instrument Science and Engineering, Southeast University, Nanjing, China; Li, H., School of Instrument Science and Engineering, Southeast University, Nanjing, China","The cable-driven exoskeletons have the advantages of low inertia and simple structure, and they are widely applied for human augmentation and rehabilitation. However, they have limited workspace and tension efficiency because of the unidirectional nature of the cables. The limited performance affects the human-robot interaction and leads to potential danger. The workspace and tension efficiency can be optimized by conventional methods, which are adjusting the cable attachment points and increasing the actuators. But the workspace still cannot cover the human joint space of activities of daily living, and the efficiency is also not high. This paper analyzed the specific application for shoulder and elbow joints rehabilitation and proposed a dynamic adaptive structure. This design can increase the workspace and improve tension efficiency greatly. The dynamic compensation control strategy is designed for the optimized system. Besides, in order to reduce the effects of compliant elements (e.g., cables or Bowden cables) between the actuators and output, and to improve the force bandwidth, we designed the distributed active semi-active system composed of the geared DC motor and magnetorheological clutches, which has low output inertia. The related experiments are carried out based on several healthy subjects. The results verified that the exoskeleton with the dynamic adaptive structure has a large workspace covering the activities of daily living joint space, high tension efficiency, and high force bandwidth, which is beneficial for the safety and comfort of human-robot interaction during rehabilitation training. © 2013 IEEE.","Cable-driven robot; distributed active semi-active systems; human-robot interaction; mechanism design; rehabilitation robot","Actuators; Bandwidth; Cables; DC motors; Educational robots; Efficiency; Exoskeleton (Robotics); Machine design; Man machine systems; Activities of Daily Living; Attachment points; Conventional methods; Dynamic compensation; Limited workspaces; Magneto-rheological; Optimized designs; Rehabilitation training; Human robot interaction",,,,,"National Key Research and Development Program of China, NKRDPC: 2017YFB1303201; Jiangsu Provincial Key Research and Development Program: BE2018004-4","This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFB1303201, and in part by the Key Research and Development Program of Jiangsu Province under Grant BE2018004-4.",,,,,,,,,,"Lum, P.S., Burgar, C.G., Shor, P.C., Majmundar, M., Loos Der, M.Van, Robot-assisted movement training compared with conventional ther-apy techniques for the rehabilitation of upper-limb motor function after stroke (2002) Arch. Phys. Med. Rehabil., 83 (7), pp. 952-959. , Jul; Ren, Y., Hoon Kang, S., Park, H.-S., Wu, Y.-N., Zhang, L.-Q., Devel-oping a multi-joint upper limb exoskeleton robot for diagnosis, therapy, and outcome evaluation in neurorehabilitation (2013) IEEE Trans. Neural Syst. Rehabil. Eng., 21 (3), pp. 490-499. , May; Perry, J.C., Rosen, J., Burns, S., Upper-limb powered exoskeleton design (2007) IEEE/ASME Trans. Mechatronics, 12 (4), pp. 408-417. , Aug; Alamdari, A., Krovi, V., Modeling and control of a novel home-based cable-driven parallel platform robot: PACER (2015) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), pp. 6330-6335. , Sep; Veneman, J.F., Ekkelenkamp, R., Kruidhof, R., Van Der Helm, F.C.T., Van Der Kooij, H., Design of a series elastic and bowden cable-based actuation system for use as torque-actuator in exoskeleton-type training robots (2005) Proc. IEEE Int. Conf. Rehab. Robot. (ICORR), pp. 496-499. , Oct; Mao, Y., Agrawal, S.K., Design of a cable-driven arm exoskeleton (CAREX) for neural rehabilitation (2012) IEEE Trans. Robot., 28 (4), pp. 922-931. , Aug; Mustafa, S.K., Yang, G., Yeo, S.H., Lin, W., Optimal design of a bio-inspired anthropocentric shoulder rehabilitator (2006) Appl. Bionics Biomech., 3 (3), pp. 199-208. , Jan; Zi, B., Wang, B., Wang, D., Design and analysis of a novel cable-actuated palletizing robot (2017) Int. J. Adv. Robot. Syst., 14 (6). , Nov; Yang, G., Mustafa, S.K., Yeo, S.H., Lin, W., Lim, W.B., Kinematic design of an anthropomimetic 7-DOF cable-driven robotic arm (2010) Frontiers Mech. Eng. China, 4, pp. 45-60. , Dec; Mustafa, S.K., Agrawal, S.K., On the force-closure analysis of n-DOF cable-driven open chains based on reciprocal screw theory (2012) IEEE Trans. Robot., 28 (1), pp. 22-31. , Feb; Murray, R.M., Li, Z., Sastry, S.S., (1994) A Mathematical Introduction to Robotic Manipulation, pp. 1-456. , Boca Raton, FL, USA: CRC Press; Mao, Y., Jin, X., Agrawal, S.K., Real-time estimation of glenohumeral joint rotation center with cable-driven arm exoskeleton (CAREX)_A cable-based arm exoskeleton (2014) J. Mech. Robot., 6 (1). , Feb; Kurbanhusen Mustafa, S., Yang, G., Huat Yeo, S., Lin, W., Chen, I.-M., Self-calibration of a biologically inspired 7 DOF cable-driven robotic arm (2008) IEEE/ASME Trans. Mechatronics, 13 (1), pp. 66-75. , Feb; Li, Z., Chen, W., Zhang, J., Bai, S., Design and control of a 4-DOF cable-driven arm rehabilitation robot (CARR-4) (2017) Proc. IEEE Int. Conf. Cybern. Intell. Syst. (CIS) IEEE Conf. Robot., Autom. Mecha-tronics (RAM), pp. 581-586. , Nov; Shao, Z.-F., Tang, X., Yi, W., Optimal design of a 3-DOF cable-driven upper arm exoskeleton (2014) Adv. Mech. Eng., 6. , Jan; Galiana, I., Hammond, F.L., Howe, R.D., Popovic, M.B., Wearable soft robotic device for post-stroke shoulder rehabilitation: Identifying misalignments (2012) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), pp. 317-322. , Oct; Gaponov, I., Popov, D., Lee, S.J., Ryu, J.-H., Auxilio: A portable cable-driven exosuit for upper extremity assistance (2017) Int. J. Control, Autom. Syst., 15 (1), pp. 73-84. , Feb; Cui, X., Chen, W., Jin, X., Agrawal, S.K., Design of a 7-DOF cable-driven arm exoskeleton (CAREX-7) and a controller for dexterous motion training or assistance (2017) IEEE/ASME Trans. Mechatronics, 22 (1), pp. 161-172. , Feb; Viau, J., Chouinard, P., Bigue, J.-P.-L., Julio, G., Michaud, F., Plante, J.-S., Tendon-driven manipulator actuated by magnetorheological clutches exhibiting both high-power and soft motion capabilities (2017) IEEE/ASME Trans. Mechatronics, 22 (1), pp. 561-571. , Feb; Shi, K., Song, A., Li, Y., Li, H., Chen, D., Zhu, L., A cable-driven three-DOF wrist rehabilitation exoskeleton with improved performance (2021) Fron-tiers Neurorobot., 15. , Apr; Shi, K., Song, A., Li, Y., Chen, D., Li, H., Cable-driven 4-DOF upper limb rehabilitation robot (2019) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), pp. 6465-6472. , Nov; Palli, G., Melchiorri, C., Model and control of tendon-sheath transmis-sion systems (2006) Proc. IEEE Int. Conf. Robot. Autom., pp. 988-993; Shimoga, K.B., Finger force and touch feedback issues in dexterous telemanipulation (1992) Proc. 4th Annu. Conf. Intell. Robot. Syst. Space Explor., pp. 159-178","Song, A.; School of Instrument Science and Engineering, China; email: a.g.song@seu.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85107203740
"Kharchenko S., Meshcheryakov R., Turovsky Y.","56938332700;23035794100;56136046200;","Influence of the number of channels when highlighting steady-state visual evoked potentials based on a multivariate synchronization index",2020,"Proceedings - International Conference on Developments in eSystems Engineering, DeSE","2020-December",,"9450761","195","199",,,"10.1109/DeSE51703.2020.9450761","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112580904&doi=10.1109%2fDeSE51703.2020.9450761&partnerID=40&md5=6cd95726a32633db8d8830bd74a7bd0f","Tomsk State University of Control Systems and Radioelectronics, Laboratory of Medical and Biological Research, Tomsk, Russian Federation; Institute of Control Sciences Academician Va Trapeznikov, Laboratory of Cyber-Physical System, Moscow, Russian Federation; Voronezh State University, Laboratory of Medical Cybernetics, Voronezh, Russian Federation","Kharchenko, S., Tomsk State University of Control Systems and Radioelectronics, Laboratory of Medical and Biological Research, Tomsk, Russian Federation; Meshcheryakov, R., Institute of Control Sciences Academician Va Trapeznikov, Laboratory of Cyber-Physical System, Moscow, Russian Federation; Turovsky, Y., Voronezh State University, Laboratory of Medical Cybernetics, Voronezh, Russian Federation","In the introduction part the authors show timeliness of the methods study for generation of the human-robot interaction systems on the basis of the extraction of steady-state visual evoked potentials (SSVEP). The main methods of the extraction of steady-state visual evoked potentials in the electroencephalograms (EEG) were observed. This article reflects researches of the EEG signals with steady-state visual evoked potentials for various frequencies of the photostimulation basing on the multivariate synchronization index (MSI). The influence of the number of channels for the analysis on the recognition rate of the signal state under study is considered. The fraction of correctly recognized states is considered as an accuracy metrics. Also, during the research, the authors inspect the influence of the channel selection for signal analysis in the single channel mode. The result of the authors' research is recommendations on the number of channels used for highlighting of steady-state visual evoked potentials within the multivariate synchronization index method and the list of channels to be used in the single channel mode of the multivariate synchronization index for SSVEP highlighting. The results obtained are of considerable practical importance because they can be used for the brain-machine interface building on the SSVEP basis and can be forth used for development of the control theory of robot systems of various purposes and for implementation of the decisions on human-robot interaction arrangement for narrow practical tasks. © 2020 IEEE.","brain-computer interface; EEG; human-robot interaction; multivariate synchronization index; SSVEP; steady-state visual evoked potentials","Bioelectric phenomena; Brain computer interface; Electroencephalography; Electrophysiology; Extraction; Man machine systems; Synchronization; Brain machine interface; Channel selection; Electro-encephalogram (EEG); Photo stimulations; Practical importance; Single channels; Steady state visual evoked potentials; Synchronization index; Social robots",,,,,"Russian Foundation for Basic Research, РФФИ: 19-08-00331, 19-29-01156","ACKNOWLEDGMENT The reported study was partially funded by RFBR according to the research projects № 19-08-00331 and № 19-29-01156.",,,,,,,,,,"Nam, C.S., Nijholt, A., Lotte, F., (2018) Brain-Computer Interfaces Handbook: Technological and Theoretical Advances, , CRC Press, January; Clerk Maxwell, J., (1892) A Treatise on Electricity and Magnetism, 2, pp. 68-73. , 3rd ed. Oxford: Clarendon; Tyagi, A., Semwal, S., Shah, G., A review of EEG sensors used for data acquisition (2012) Journal of Computer Applications (IJCA), pp. 13-17; Malmivuo, J., Plonsey, R., (1995) Bioelectromagnetism-Principles and Applications of Bioelectric and Biomagnetic Fields, , Oxford Univ. Press; Ma, W., Tran, D., Le, T., Lin, H., Zhou, S., Using EEG artifacts for bci applications (2014) 2014 International Joint Conference on Neural Networks (IJCNN), pp. 3628-3635. , Beijing; Han, J., Ji, S., Shi, C., Yu, S., Shin, J., Recent progress of non-invasive optical modality to brain computer interface: A review study (2015) The 3rd International Winter Conference on Brain-Computer Interface, pp. 1-2. , Sabuk; Gnezditsky, V.V., (1997) Evoked Brain Potentials in Clinical Practice, , [in Russian], Taganrog: Publishing house of TSURE; Maksimenko, V.A., Kuc, A.K., Pisarchik, A.N., Hramov, A.E., Mathematical modeling of visual sensory information processing using multi-channel EEG signals (2019) Information and Control Systems, (6), pp. 58-65; Stankevich, L.A., Sonkin, K.M., Nagornova, Z.V., Khomenko, J.G., Shemyakina, N.V., Classification of electroencephalographic patterns of imaginary one-hand finger movements for brain-computer interface development (2015) SPIIRAS Proceedings, 3 (40), pp. 163-182; Kurkin, S.A., Pitsik, E.M., Khramov, A.M., Classifying electrical activity of the brain during imaginary movements of untrained subjects using artificial neural networks (2020) Information and Control Systems, (6), pp. 77-84; Chueshev, A., Melekhova, O., Meshcheryakov, R., Cloud robotic platform on basis of fog computing approach (2018) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 34-43. , 11097 LNAI; Shepelenko, M., Meshcheryakov, R., Algorithm for automated calculation of a segmented electromechatronic module of robotic system motion (2017) MATEC Web of Conferences, 113. , 20 June; Zhu, Y., Zhen, R., Liu, H., Meng, S., Zhen, R., The research of rehabilitation robots bci technology based on EEG (2008) 2008 International Conference on Computer Science and Software Engineering, pp. 1107-1110; Cietto, V., Pasteris, R., Locci, S., Serra, S., Mattutino, C., Gena, C., Evaluating commercial bcis for moving robots (2019) Proceedings of the 13th Biannual Conference of the Italian SIGCHI Chapter on Designing the Next Interaction-CHItaly '19; Voznenko, T.I., Chepin, E.V., Urvanov, G.A., The control system based on extended bci for arobotic wheelchair (2018) Procedia Comput. Sci., 123, pp. 522-527; Gridnev, A.A., Voznenko, T.I., Chepin, E.V., The decision-making system for a multichannelrobotic device control (2018) Procedia Comput. Sci., 123, pp. 149-154; Kobozeva, I.M., Sidorov, G., Zimmerling, A.V., Module for dialog management in the interaction system between user and mobile robotic guide (2014) SPIIRAS Proceedings, 2 (33), pp. 186-206; Lin, Z., Zhang, C., Wu, W., Gao, X., Frequency recognition based on canonical correlation analysis for ssvep-based bcis (2006) IEEE Trans Biomed Eng, 54, pp. 1172-1176; Friman, O., Cedefamn, J., Lundberg, P., Borga, M., Knutsson, H., Detection of neural activ ity in functional mri using canonical correlation analysis (2001) Magn Reson Med, 45, pp. 323-330; Nan, W., A comparison of minimum energy combination and canonical correlation analysis for ssvep detection (2011) Proceedings of the 5th International IEEE EMBS Conference on Neural Engineering Cancun, pp. 469-472. , Mexico; Zhang, Y., Xu, P., Cheng, D.K., Multivariate synchronization index for frequency recognition of ssvep-based brain-computer interface (2014) Journal of Neuroscience Methods, 221, pp. 32-40; Purushothaman, G., Prakash, P.R., Kothari, S., Investigation of multiple frequency recognition from single-channel steady-state visual evoked potential for efficient brain-computer interfaces application (2018) IET Signal Processing, 12 (3), pp. 255-259. , 5; Zhang, Y., Guo, D., Xu, P., Robust frequency recognition for ssvep-based bci with temporally local multivariate synchronization index (2016) Cogn. Neurodyn, 10, p. 505; Turovsky, Y.A., Kharchenko, S.S., Meshcheryakov, R.V., Iskhakova, A.A., Iskhakov, A.Y., Algorithmic support of the interface of management of robot-human with the steady state visual evoked potentials based on the multivariate synchronization index (2020) IZVESTIYA SFedU. ENGINEERING SCIENCES, 1, pp. 66-78. , [in Russian]; Kharchenko, S., Turovsky, Y., Mescheryakov, R., Iskhakova, A., Restrictions of the measurement system and a patient when using visually evoked potentials (2019) 2019 12th International Conference on Developments in ESystems Engineering (DeSE), pp. 15-19. , Kazan, Russia; Oppenheim, A.V., Schafer, R.W., Yuen, C.K., Digital signal processing (1978) IEEE Transactions on Systems, Man, and Cybernetics, 8 (2), pp. 146-146. , Feb",,,,"Institute of Electrical and Electronics Engineers Inc.","13th International Conference on Developments in eSystems Engineering, DeSE 2020","13 December 2020 through 17 December 2020",,170860,21611343,9781665422383,,,"English","Proc. - Int. Conf. Dev. eSystems Eng., DeSE",Conference Paper,"Final","",Scopus,2-s2.0-85112580904
"Pinheiro C., Figueiredo J., Magalhães N., Santos C.P.","57208665955;57195993391;57219437066;57211929847;","Wearable biofeedback improves human-robot compliance during ankle-foot exoskeleton-assisted gait training: A pre-post controlled study in healthy participants",2020,"Sensors (Switzerland)","20","20","5876","1","21",,1,"10.3390/s20205876","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092700105&doi=10.3390%2fs20205876&partnerID=40&md5=0f1811aa1376f2ba50aa1b7b2795b1f2","Center for MicroElectroMechanical Systems (CMEMS), Department of Industrial Electronics, School of Engineering, University of Minho, Guimarães, 4800-058, Portugal","Pinheiro, C., Center for MicroElectroMechanical Systems (CMEMS), Department of Industrial Electronics, School of Engineering, University of Minho, Guimarães, 4800-058, Portugal; Figueiredo, J., Center for MicroElectroMechanical Systems (CMEMS), Department of Industrial Electronics, School of Engineering, University of Minho, Guimarães, 4800-058, Portugal; Magalhães, N., Center for MicroElectroMechanical Systems (CMEMS), Department of Industrial Electronics, School of Engineering, University of Minho, Guimarães, 4800-058, Portugal; Santos, C.P., Center for MicroElectroMechanical Systems (CMEMS), Department of Industrial Electronics, School of Engineering, University of Minho, Guimarães, 4800-058, Portugal","The adj unctive use of biofeedback systems with exoskeletons may accelerate post-stroke gait rehabilitation. Wearable patient-oriented human-robot interaction-based biofeedback is proposed to improve patient-exoskeleton compliance regarding the interaction torque’s direction (j oint motion strategy) and magnitude (user participation strategy) through auditory and vibrotactile cues during assisted gait training, respectively. Parallel physiotherapist-oriented strategies are also proposed such that physiotherapists can follow in real-time a patient’s motor performance towards effective involvement during training. A preliminary pre-post controlled study was conducted with eight healthy participants to conclude about the biofeedback’s efficacy during gait training driven by an ankle-foot exoskeleton and guided by a technical person. For the study group, performance related to the interaction torque’s direction increased during (p-value = 0.07) and after (p-value = 0.07) j oint motion training. Further, the performance regarding the interaction torque’s magnitude significantly increased during (p-value = 0.03) and after (p-value = 68.59×10-3 ) user participation training. The experimental group and a technical person reported promising usability of the biofeedback and highlighted the importance of the timely cues from physiotherapist-oriented strategies. Less significant improvements in patient–exoskeleton compliance were observed in the control group. The overall findings suggest that the proposed biofeedback was able to improve the participant-exoskeleton compliance by enhancing human-robot interaction; thus, it may be a powerful tool to accelerate post-stroke ankle-foot deformity recovery. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Ankle-foot exoskeleton; Biofeedback control strategies; Gait training; Human-robot interaction; User-centered design; Wearable biofeedback","Biofeedback; Compliance control; Educational robots; Exoskeleton (Robotics); Man machine systems; Wearable technology; Control groups; Experimental groups; Gait rehabilitation; Gait training; Motion strategy; Motor performance; Patient-oriented; User participation; Human robot interaction; ankle; biofeedback; electronic device; exoskeleton (rehabilitation); female; gait; human; male; neurologic gait disorder; normal human; robotics; stroke rehabilitation; Ankle; Biofeedback, Psychology; Exoskeleton Device; Female; Gait; Gait Disorders, Neurologic; Healthy Volunteers; Humans; Male; Robotics; Stroke Rehabilitation; Wearable Electronic Devices",,,,,"Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção, INCT-EN: NORTE-01-0145-FEDER-030386, UIDB/04436/2020, UIDP/04436/2020","This work has been supported by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from Funda??o para a Ci?ncia e Tecnologia with the SmartOs project under Grant NORTE-01-0145-FEDER-030386, and under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020.","Funding: This work has been supported by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from Fundação para a Ciência e Tecnologia with the SmartOs project under Grant NORTE-01-0145-FEDER-030386, and under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020.",,,,,,,,,"Mikolajczyk, T., Ciobanu, I., Badea, D.I., Iliescu, A., Pizzamiglio, S., Schauer, T., Seel, T., Berteanu, M., Advanced technology for gait rehabilitation: An overview (2018) Adv. Mech. Eng, 10, pp. 1-19; Johnson, C.O., Nguyen, M., Roth, G.A., Nichols, E., Alam, T., Abate, D., Abd-Allah, F., Abu-Rmeileh, N.M., Global, regional, and national burden of stroke, 1990–2016: A systematic analysis for the Global Burden of Disease Study (2019) Lancet Neurol, 18, pp. 439-458; Manca, M., Ferraresi, G., Cosma, M., Cavazzuti, L., Morelli, M., Benedetti, M.G., Gait Patterns in Hemiplegic Patients with Equinus Foot Deformity (2014) BioMed Res. Int, 2014, pp. 1-7; Klamroth-Marganska, V., Stroke Rehabilitation: Therapy Robots and Assistive Devices (2018) Advances in Experimental Medicine and Biology, 1065, pp. 579-587. , Springer: Berlin/Heidelberg, Germany, ISBN 9783319779324; Morone, G., Spitoni, G.F., De Bartolo, D., Ghanbari Ghooshchy, S., Di Iulio, F., Paolucci, S., Zoccolotti, P., Iosa, M., Rehabilitative devices for a top-down approach (2019) Expert Rev. Med. Devices, 16, pp. 187-195; Association for Applied Psychophysiology and Biofeedback Standards for Performing Biofeedback, , https://www.aapb.org/i4a/pages/index.cfm?pageid=3678#I, (accessed on 20 March 2020); Schwartz, M.S., Andrasik, F., (2005) Biofeedback: A Practitioner’s Guide, , 3rd ed.; Publications, G., Ed.; Guilford Publications: New York, NY, USA; Ganesh, G., Takagi, A., Osu, R., Yoshioka, T., Kawato, M., Burdet, E., Two is better than one: Physical interactions improve motor performance in humans (2015) Sci. Rep, 4, p. 3824; Tamburella, F., Moreno, J.C., Herrera Valenzuela, D.S., Pisotta, I., Iosa, M., Cincotti, F., Mattia, D., Molinari, M., Influences of the biofeedback content on robotic post-stroke gait rehabilitation: Electromyographic vs joint torque biofeedback (2019) J. Neuroeng. Rehabil, 16, p. 95; Stoller, O., Waser, M., Stammler, L., Schuster, C., Evaluation of robot-assisted gait training using integrated biofeedback in neurologic disorders (2012) Gait Posture, 35, pp. 595-600; Asín-Prieto, G., Martínez-Expósito, A., Barroso, F.O., Urendes, E.J., Gonzalez-Vargas, J., Alnajjar, F.S., González-Alted, C., Moreno, J.C., Haptic Adaptive Feedback to Promote Motor Learning With a Robotic Ankle Exoskeleton Integrated With a Video Game (2020) Front. Bioeng. Biotechnol, 8, pp. 1-15; Pinheiro, C., Lopes, J.M., Figueiredo, J., Gonçalves, L.M., Santos, C.P., Design and technical validation of a wearable biofeedback system for robotic-based gait rehabilitation Proceedings of the 20th IEEE International Conference on Autonomous Robot Systems and Competitions, , Ponta Delgada, Portugal, 15–17 April 2020; Figueiredo, J., Santos, C.P., Moreno, J.C., (2019) Smart Wearable Orthosis to Assist Impaired Human Walking, , University of Minho: Braga, Portugal; Bortole, M., Venkatakrishnan, A., Zhu, F., Moreno, J.C., Francisco, G.E., Pons, J.L., Contreras-Vidal, J.L., The H2 robotic exoskeleton for gait rehabilitation after stroke: Early findings from a clinical study (2015) J. Neuroeng. Rehabil, 12, p. 54; Winter, D.A., (2009) Biomechanics and Motor Control of Human Movement, , John Wiley & Sons, Inc.: Hoboken, NJ, USA, ISBN 9780470398180; Felix, P., Figueiredo, J., Santos, C.P., Moreno, J.C., Electronic design and validation of Powered Knee Orthosis system embedded with wearable sensors (2017) Proceedings of the 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 110-115. , Coimbra, Portugal, 26 –28 April IEEE: New York, NY, USA, 2017; Perry, J., (1992) Gait Analysis: Normal and Pathological Function, , SLACK Incorporated; SLACK Incorporated: USA, ISBN 9781556421921; Afzal, M.R., Oh, M.-K., Lee, C.-H., Park, Y.S., Yoon, J., A Portable Gait Asymmetry Rehabilitation System for Individuals with Stroke Using a Vibrotactile Feedback (2015) BioMed Res. Int, 2015, pp. 1-16; Torricelli, D., Cortés, C., Lete, N., Bertelsen, Á, Gonzalez-Vargas, J.E., Del-Ama, A.J., Dimbwadyo, I., Pons, J.L., A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait (2018) Front. Neurorobot, 12, pp. 1-11; Brooke, J., SUS: A quick and dirty usability scale (1996) Usability Eval. Ind, 189, pp. 4-7","Pinheiro, C.; Center for MicroElectroMechanical Systems (CMEMS), Portugal; email: a73727@alunos.uminho.pt",,,"MDPI AG",,,,,14248220,,,"33080845","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85092700105
"Grzeskowiak F., Babel M., Bruneau J., Pettre J.","57210640264;6701728589;34976151600;57203479876;","Toward Virtual Reality-based Evaluation of Robot Navigation among People",2020,"Proceedings - 2020 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2020",,,"9089531","766","774",,3,"10.1109/VR46266.2020.00100","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085493226&doi=10.1109%2fVR46266.2020.00100&partnerID=40&md5=6b5b715461cb189edba3585719dacc9f","Inria Rennes Bretagne Atlantique, France","Grzeskowiak, F., Inria Rennes Bretagne Atlantique, France; Babel, M., Inria Rennes Bretagne Atlantique, France; Bruneau, J., Inria Rennes Bretagne Atlantique, France; Pettre, J., Inria Rennes Bretagne Atlantique, France","This paper explores the use of Virtual Reality (VR) to study humanrobot interactions during navigation tasks by both immersing a user and a robot in a shared virtual spaces. VR combines the advantages of being safe (as robots and humans interacting by the means of VR but can physically be in remote places) and ecological (realistic environments are perceived by the robot and the human, and natural behaviors can be observed). Nevertheless, VR can introduce perceptual biases in the interaction and affect in some ways the observed behaviors, which can be problematic when used to acquire experimental data. In our case, not only human perception is concerned, but also the one of the robot which requires to be simulated to perceive the VR world. Thus, the contribution of this paper is twofold. It first provides a technical solution to perform human robot interactions in navigation tasks through VR: we describe how we combine motion tracking, VR devices, as well as robot sensors simulation algorithms to immerse together a human and a robot in a shared virtual space. We then assess a simple interaction task that we replicate in real and in virtual conditions to perform a first estimation of the importance of the biases introduced by the use of VR on both a Human and a robot. Our conclusions are in favor of using VR to study human-robot interactions, and we are developing directions for future work. © 2020 IEEE.","Human Robot Interaction; Motion Tracking; Robots simulation; Virtual Reality","Human robot interaction; Man machine systems; Motion tracking; Navigation; User interfaces; Human perception; Navigation tasks; Realistic environments; Robot navigation; Robot sensors; Simulation algorithms; Technical solutions; Virtual spaces; Virtual reality",,,,,"Horizon 2020","This work is funded by the Crowdbot Project, a European Union Horizon 2020 research and innovationproject in the area of robotics, information and communication technology.",,,,,,,,,,"Agethen, P., Sekar, V.S., Gaisbauer, F., Pfeiffer, T., Otto, M., Rukzio, E., Behavior analysis of human locomotion in the real world and virtual reality for the manufacturing industry (2018) ACM Transactions on Applied Perception (TAP), 15 (3), p. 20; Aravind, G., Darekar, A., Fung, J., Lamontagne, A., Virtual realitybased navigation task to reveal obstacle avoidance performance in individuals with visuospatial neglect (2015) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23 (2), pp. 179-188; Armbrster, C., Wolter, M., Kuhlen, T., Spijkers, W., Fimm, B., Depth perception in virtual reality: Distance estimations in peri-And extrapersonal space (2008) CyberPsychology & Behavior, 11 (1), pp. 9-15. , Feb; Bruneau, J., Olivier, A.-H., Pettré, J., Going through, going around: A study on individual avoidance of groups (2015) IEEE Transactions on Visualization and Computer Graphics, 21 (4), pp. 520-528; Bühler, M.A., Lamontagne, A., Circumvention of pedestrians while walking in virtual and physical environments (2018) IEEE Transactions on Neural Systems and Rehabilitation Engineering; Chi, W., Kono, H., Tamura, Y., Yamashita, A., Asama, H., Meng, M.Q.-H., A human-friendly robot navigation algorithm using the risk-rrt approach (2016) 2016 IEEE International Conference on Real-Time Computing and Robotics (RCAR, pp. 227-232. , IEEE, Angkor Wat, Cambodia, June; Chou, Y.-H., Wagenaar, R.C., Saltzman, E., Giphart, J.E., Young, D., Davidsdottir, R., Cronin-Golomb, A., Effects of optic flow speed and lateral flow asymmetry on locomotion in younger and older adults: A virtual reality study (2009) Journals of Gerontology: Series B, 64 (2), pp. 222-231; Cirio, G., Olivier, A.-H., Marchal, M., Pettré, J., Kinematic evaluation of virtual walking trajectories (2013) IEEE Transactions on Visualization and Computer Graphics, 19 (4), pp. 671-680; Darekar, A., Lamontagne, A., Fung, J., Dynamic clearance measure to evaluate locomotor and perceptuo-motor strategies used for obstacle circumvention in a virtual environment (2015) Human Movement Science, 40, pp. 359-371; De Santis, A., Siciliano, B., De Luca, A., Bicchi, A., An atlas of physical humanrobot interaction (2008) Mechanism and Machine Theory, 43 (3), pp. 253-270. , Mar; Devigne, L., Babel, M., Nouviale, F., Narayanan, V.K., Pasteau, F., Gallien, P., Design of an immersive simulator for assisted power wheelchair driving (2017) 2017 International Conference on Rehabilitation Robotics (ICORR, pp. 995-1000. , IEEE, London, July; http://doc.aldebaran.com/2-5/naoqi/sensors/dcm.html, P. documentation; Shi, D., Collins, E.G., Jr., Goldiez, B., Donate, A., Liu, X., Dunlap, D., Human-Aware robot motion planning with velocity constraints (2008) 2008 International Symposium on Collaborative Technologies and Systems, pp. 490-497. , IEEE, Irvine, CA, USA, May; Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V., (2017) CARLA: An Open Urban Driving Simulator, , [Cs], Nov. arXiv:1711.03938; Ducourant, T., Vieilledent, S., Kerlirzin, Y., Berthoz, A., Timing and distance characteristics of interpersonal coordination during locomotion (2005) Neuroscience Letters, 389, pp. 6-11; Fink, P.W., Foo, P.S., Warren, W.H., Obstacle avoidance during walking in real and virtual environments (2007) ACM Transactions on Applied Perception, 4 (1), p. 2es. , Jan; Fraichard, T., Motion safety with people: An open problem (2015) IEEE Int. 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Tracking System Website, , www.vicon.com; Trautman, P., Ma, J., Murray, R.M., Krause, A., Robot navigation in dense human crowds: Statistical models and experimental studies of humanrobot cooperation (2015) The International Journal of Robotics Research, 34 (3), pp. 335-356. , Mar; Varma, K., Guy, S.J., Interrante, V., (2017) Assessing the Relevance of Eye Gaze Patterns during Collision Avoidance in Virtual Reality.; Vasic, M., Billard, A., Safety issues in human-robot interactions (2013) 2013 IEEE International Conference on Robotics and Automation, pp. 197-204. , IEEE, Karlsruhe, Germany, May; Wainer, J., Feil-Seifer, D.J., Shell, D.A., Mataric, M.J., Embodiment and human-robot interaction: A task-based perspective (2007) RO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication, pp. 872-877. , Aug; Warren, W.H., Jr., Kay, B.A., Zosh, W.D., Duchon, A.P., Sahuc, S., Optic flow is used to control human walking (2001) Nature Neuroscience, 4 (2), p. 213; www.getfove.com, F.H. website; www.unity.com, U. website; www.xsens.com, X. website; Wirtz, J., Patterson, P.G., Kunz, W.H., Gruber, T., Lu, V.N., Paluch, S., Martins, A., Brave new world: Service robots in the frontline (2018) Journal of Service Management, 29 (5), pp. 907-931. , Oct",,,,"Institute of Electrical and Electronics Engineers Inc.","27th IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2020","22 March 2020 through 26 March 2020",,159690,,9781728156088,,,"English","Proc. - IEEE Conf. Virtual Real. 3D User Interfaces, VR",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85085493226
"Kim T., Lee J.-H.","55617737600;7601475407;","Reinforcement Learning-Based Path Generation Using Sequential Pattern Reduction and Self-Directed Curriculum Learning",2020,"IEEE Access","8",,"9162635","147790","147807",,3,"10.1109/ACCESS.2020.3015245","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090324374&doi=10.1109%2fACCESS.2020.3015245&partnerID=40&md5=1a13c0aecb7bc9d31a885a15ee7d6d3e","Department of Computer Software, Korea University of Science and Technology, Daejeon, 34113, South Korea; Human-Robot Interaction Research Group, Electronics and Telecommunications Research Institute, Daejeon, 34129, South Korea","Kim, T., Department of Computer Software, Korea University of Science and Technology, Daejeon, 34113, South Korea, Human-Robot Interaction Research Group, Electronics and Telecommunications Research Institute, Daejeon, 34129, South Korea; Lee, J.-H., Department of Computer Software, Korea University of Science and Technology, Daejeon, 34113, South Korea, Human-Robot Interaction Research Group, Electronics and Telecommunications Research Institute, Daejeon, 34129, South Korea","Recent advancements in robots and deep learning have led to active research in human-robot interaction. However, non-physical interaction using visual devices such as laser pointers has gained less attention than physical interaction using complex robots such as humanoids. Such vision-based interaction has high potential for use in recent human-robot collaboration environments such as assembly guidance, even with a minimum amount of configuration. In this paper, we introduce a simple robotic laser pointer device that follows an arbitrary planar path and is designed to be a visual instructional aid. We also propose an image-based automatic path generation method using reinforcement learning and a sequential pattern reduction technique. However, such vision-based human-robot interaction is generally performed in a dynamic environment, and it can frequently be necessary to calibrate the devices more than once. In this paper, we avoid the need for this re-calibration process through episodic randomization learning and improved learning efficiency. In particular, contrary to previous approaches, the agent controls the curriculum difficulty in a self-directed manner to determine the optimal curriculum. To our knowledge, this is the first study of curriculum learning that incorporates an explicit learning environment control signal initiated by the agent itself. Through quantitative and qualitative analyses, we show that the proposed self-directed curriculum learning method outperforms ordinary episodic randomization and curriculum learning. We hope that the proposed method can be extended to a general reinforcement learning framework. © 2013 IEEE.","curriculum learning; deep reinforcement learning; Path generation; robotic laser pointer","Computer aided instruction; Curricula; Deep learning; Laser applications; Learning systems; Man machine systems; Random processes; Social robots; Dynamic environments; Human-robot collaboration; Learning efficiency; Path generation methods; Physical interactions; Quantitative and qualitative analysis; Sequential patterns; Vision-based interaction; Reinforcement learning",,,,,"Institute for Information and Communications Technology Promotion, IITP; Ministry of Science and ICT, South Korea, MSIT: 2017-0-00162","This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00162, Development of Human-care Robot Technology for Aging Society).",,,,,,,,,,"Goodrich, M.A., Schultz, A.C., Human-robot interaction: A survey (2007) Found. 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Learn., pp. 41-48; Braun, S., Neil, D., Liu, S.-C., A curriculum learning method for improved noise robustness in automatic speech recognition (2017) Proc. 25th Eur. Signal Process. Conf. (EUSIPCO), pp. 548-552. , Aug; Gong, C., Tao, D., Maybank, S.J., Liu, W., Kang, G., Yang, J., Multimodal curriculum learning for semi-supervised image classification (2016) Ieee Trans. Image Process., 25 (7), pp. 3249-3260. , Jul; Shi, Y., Larson, M., Jonker, C.M., Recurrent neural network language model adaptation with curriculum learning (2015) Comput. Speech Lang., 33 (1), pp. 136-154. , Sep; Florensa, C., Held, D., Wulfmeier, M., Zhang, M., Abbeel, P., (2017) Reverse Curriculum Generation for Reinforcement Learning, , http://arxiv.org/abs/1707.05300, [Online]; Svetlik, M., Leonetti, M., Sinapov, J., Shah, R., Walker, N., Stone, P., Automatic curriculum graph generation for reinforcement learning agents (2017) Proc. 31st Aaai Conf. Artif. Intell., pp. 2590-2596; Ren, Z., Dong, D., Li, H., Chen, C., Self-paced prioritized curriculum learning with coverage penalty in deep reinforcement learning (2018) Ieee Trans. Neural Netw. Learn. Syst., 29 (6), pp. 2216-2226. , Jun; Narvekar, S., Sinapov, J., Stone, P., Autonomous task sequencing for customized curriculum design in reinforcement learning (2017) Proc. 26th Int. Joint Conf. Artif. Intell, pp. 2536-2542. , Aug; Perlin, K., Improving noise (2002) Proc. 29th Annu. Conf. Comput. Graph. Interact. Techn., pp. 681-682; Gielis, J., A generic geometric transformation that unifies a wide range of natural and abstract shapes (2003) Amer. J. Botany, 90 (3), pp. 333-338. , Mar; Gonzales, R.C., Woods, R.E., (2002) Digital Image Processing, 2nd Ed, , Boston, MA, USA: Addison-Wesley; Andrychowicz, O.M., Baker, B., Chociej, M., Jozefowicz, R., McGrew, B., Pachocki, J., Petron, A., Ray, A., Learning dexterous in-hand manipulation (2020) Int. J. Robot. Res., 39 (1), pp. 3-20; Bansal, T., Pachocki, J., Sidor, S., Sutskever, I., Mordatch, I., (2017) Emergent Complexity Via Multi-agent Competition, , http://arxiv.org/abs/1710.03748, [Online]; Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., Kavukcuoglu, K., Asynchronous methods for deep reinforcement learning (2016) Proc. Int. Conf. Mach. Learn., pp. 1928-1937; Wu, Y., Mansimov, E., Grosse, R.B., Liao, S., Ba, J., Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation (2017) Proc. Adv. Neural Inf. Process. Syst., pp. 5279-5288; Wang, J.X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J.Z., Munos, R., Blundell, C., Botvinick, M., (2016) Learning to Reinforcement Learn, , http://arxiv.org/abs/1611.05763, [Online]; Rohmer, E., Singh, S.P.N., Freese, M., V-REP: A versatile and scalable robot simulation framework (2013) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., Nov., pp. 1321-1326; Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., Wierstra, D., (2015) Continuous Control with Deep Reinforcement Learning, , http://arxiv.org/abs/1509.02971, [Online]; Schulman, J., Moritz, P., Levine, S., Jordan, M., Abbeel, P., (2015) High-dimensional Continuous Control Using Generalized Advantage Estimation, , http://arxiv.org/abs/1506.02438, [Online]; Taha, A.A., Hanbury, A., An efficient algorithm for calculating the exact Hausdorff distance (2015) Ieee Trans. Pattern Anal. Mach. Intell., 37 (11), pp. 2153-2163. , Nov","Lee, J.-H.; Department of Computer Software, South Korea; email: joohaeng@etri.re.kr",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85090324374
"Feingold-Polak R., Levy-Tzedek S.","57201547249;42061792400;","Personalized Human Robot Interaction in the Unique Context of Rehabilitation",2021,"UMAP 2021 - Adjunct Publication of the 29th ACM Conference on User Modeling, Adaptation and Personalization",,,,"126","127",,,"10.1145/3450614.3463415","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109219139&doi=10.1145%2f3450614.3463415&partnerID=40&md5=ba34aa5937b7a6b03e3705f29c145b69","Ben-Gurion University of the Negev, Israel","Feingold-Polak, R., Ben-Gurion University of the Negev, Israel; Levy-Tzedek, S., Ben-Gurion University of the Negev, Israel","Socially assistive robots (SARs) can help meet the growing need for rehabilitation assistance; We argue that personalization of human-robot interactions in the context of rehabilitation is multi-layered, and needs to be frequently updated, as opposed to a single setting that might suffice in other contexts. In rehabilitation, personalization is not only important in order to establish engagement, but it is an essential component for the recovery of motor and cognitive abilities over a long-term interaction, and is an essential part of establishing trust between the patient and the SAR. © 2021 ACM.","Care Robots; Human-Robot Interaction; Personalization; Rehabilitation; Socially Assistive Robots","Man machine systems; Social robots; Cognitive ability; Long-term interaction; Multi-layered; Personalizations; Socially assistive robots; Patient rehabilitation",,,,,,,,,,,,,,,,"Kellmeyer, P., Social robots in rehabilitation: A question of trust (2018) Science Robotics, 3, p. eaat1587; Mataric, M.J., Socially assistive robotics: Human augmentation versus automation (2017) Science Robotics, 2 (4), p. eaam5410; Clabaugh, C., Mataric, M., Robots for the people, by the people: Personalizing human-machine interaction (2018) Science Robotics, 3 (21), p. eaat7451; Bamm, E.L., Exploring client-centered care experiences in in-patient rehabilitation settings (2015) Global Qualitative Nursing Research, 2, pp. 1-11; Winstein, C., Varghese, R., Been there, done that, so what's next for arm and hand rehabilitation in stroke (2018) NeuroRehabilitation, 43 (1), pp. 3-18; Langer, A., Trust in socially assistive robots: Considerations for use in rehabilitation (2019) Neuroscience Biobehavioral Reviews, 104, pp. 231-239; Kashi, S., A machine-learning model for automatic detection of movement compensations in stroke patients (2020) IEEE Transactions on Emerging Topics in Computing; Feingold Polak, R., Novel gamified system for post-stroke upper-limb rehabilitation using a social robot: Focus groups of expert clinicians 2019 International Conference on Virtual Rehabilitation (ICVR). 2019, IEEE, pp. 1-7; Feingold Polak, R., Levy-Tzedek, S., Social robot for rehabilitation: Expert clinicians and post-stroke patients' evaluation following a long-term intervention (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction., pp. 151-160; Feingold Polak, R., Levy-Tzedek, S., A robot goes to rehab: A novel gamified system for stroke rehabilitation using a socially assistive robot: Methodology feasibility testing (2020) PREPRINT, Avilable at Research Square, p. 29",,,"ACM SIGCHI;ACM SIGWEB","Association for Computing Machinery, Inc","29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021","21 June 2020 through 25 June 2020",,169764,,9781450383677,,,"English","UMAP - Adjun. Publ. ACM Conf. User Model., Adapt. Pers.",Conference Paper,"Final","",Scopus,2-s2.0-85109219139
"Steinhaeusser S.C., Gabel J.J., Lugrin B.","57211518344;57222463242;23134700700;","Your new friend NAO vs. Robot No. 783 - Effects of personal or impersonal framing in a robotic storytelling use case",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447187","334","338",,,"10.1145/3434074.3447187","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102739846&doi=10.1145%2f3434074.3447187&partnerID=40&md5=2ac599dd084bbcde3c02ef2da23269da","Human-Computer Interaction, University of Wuerzburg, Germany","Steinhaeusser, S.C., Human-Computer Interaction, University of Wuerzburg, Germany; Gabel, J.J., Human-Computer Interaction, University of Wuerzburg, Germany; Lugrin, B., Human-Computer Interaction, University of Wuerzburg, Germany","The users' positive or negative attitude towards robots is a crucial factor in human-robot interaction (HRI). We conducted a preliminary online study comparing a storytelling scenario including personal respectively impersonal framing to investigate effects of the robot's self-introduction on the users' attitude towards and likeability of the robot, their transportation into and memory of the story told. No significant group differences were found, but transportation correlated significantly with attitude, likeability and memory. Thus, transportation might be a promising factor which can when increased positively affect HRI. © 2021 ACM.","Framing; Social robots; Storytelling","Agricultural robots; Man machine systems; Robotics; Group differences; Human robot Interaction (HRI); Investigate effects; Likeability; Online studies; Human robot interaction",,,,,,,,,,,,,,,,"Bartneck, C., Kulic, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81. , https://doi.org/10.1007/s12369-008-0001-3, 2009; Busselle, R., Bilandzic, H., Measuring narrative engagement (2009) Media Psychology, 12 (4), pp. 321-347. , https://doi.org/10.1080/15213260903287259, 2009; Costabile, K.A., Terman, A.W., Effects of film music on psychological transportation and narrative persuasion (2013) Basic and Applied Social Psychology, 35 (3), pp. 316-324. , https://doi.org/10.1080/01973533.2013.785398, 2013; Darling, K., 'who's johnny?' anthropomorphic framing in human-robot interaction, integration, and policy (2015) SSRN Electronic Journal, , https://doi.org/10.2139/ssrn.2588669, 2015; Dawes, J., Do data characteristics change according to the number of scale points used? An experiment using 5-point. 7-point and 10-point scales (2008) International Journal of Market Research, 50 (1), pp. 61-77. , 2008; Duffy, B.R., Anthropomorphism and the social robot (2003) Robotics and Autonomous Systems, 42 (3-4), pp. 177-190. , https://doi.org/10.1016/S0921-8890(02)00374-3, 2003; Duffy, B.R., Rooney, C., O'Hare, P.G.M., O'Donoghue, R., What is a social robot? (1999) 10th Irish Conference on Artificial Intelligence & Cognitive Science, , http://hdl.handle.net/10197/4412; Fink, J., Anthropomorphism and human likeness in the design of robots and human-robot interaction (2012) Social Robotics, 7621, pp. 199-208. , https://doi.org/10.1007/978-3-642-34103-8_20, David Hutchison, Takeo Kanade, Josef Kittler, Jon M. Kleinberg, Friedemann Mattern, John C. Mitchell, Moni Naor, Oscar Nierstrasz, C. Pandu Rangan, Bernhard Steffen, Madhu Sudan, Demetri Terzopoulos, Doug Tygar, Moshe Y. Vardi, Gerhard Weikum, Shuzhi Sam Ge, Oussama Khatib, John-John Cabibihan, Reid Simmons, and Mary-Anne Williams (Eds.) Lecture Notes in Computer Science. Springer Berlin Heidelberg, Berlin, Heidelberg; Green, M.C., Brock, T.C., The role of transportation in the persuasiveness of public narratives (2000) Journal of Personality and Social Psychology, 79 (5), pp. 701-721. , https://doi.org/10.1037//0022-3514.79.5.701, 2000; Heerink, M., Exploring the influence of age, gender, education and computer experience on robot acceptance by older adults (2011) Proceedings of the 6th International Conference on Human-robot Interaction-HRI '11, p. 147. , https://doi.org/10.1145/1957656.1957704, Aude Billard, Peter Kahn, Julie A. Adams, and Greg Trafton (Eds.) ACM Press, New York, New York, USA; Kuchenbrandt, D., Eyssel, F., Bobinger, S., Neufeld, M., Minimal group-maximal effect? evaluation and anthropomorphization of the humanoid robot nao (2011) Social Robotics, 7072, pp. 104-113. , https://doi.org/10.1007/978-3-642-25504-5_11, Bilge Mutlu, Christoph Bartneck, Jaap Ham, Vanessa Evers, and Takayuki Kanda (Eds.) Lecture Notes in Computer Science. Springer Berlin Heidelberg, Berlin, Heidelberg; Kuhn, M., Pollmann, K., Papadopoulos, J., I'm your partner-i'm your boss: Framing humanhuman-robot collaboration with conceptual metaphors (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 322-324. , https://doi.org/10.1145/3371382.3378250, 0323, Tony Belpaeme, James Young, Hatice Gunes, and Laurel Riek (Eds.) ACM, New York, NY, USA; Matell, M.S., Jacoby, J., Is there an optimal number of alternatives for likert scale items? (1971) Study I: Reliability and Validity. Educational and Psychological Measurement, 31, pp. 657-674. , 1971; Nomura, T., Kanda, T., Suzuki, T., Experimental investigation into influence of negative attitudes toward robots on human-robot interaction (2006) AI & SOCIETY, 20 (2), pp. 138-150. , https://doi.org/10.1007/s00146-005-0012-7, 2006; Nomura, T., Kanda, T., Suzuki, T., Kato, K., Prediction of human behavior in human-robot interaction using psychological scales for anxiety and negative attitudes toward robots (2008) IEEE Transactions on Robotics, 24 (2), pp. 442-451. , https://doi.org/10.1109/TRO.2007.914004, 2008; Nomura, T., Suzuki, T., Kanda, T., Altered attitudes of people towards robots: Investigation through the negative attitudes toward robots scale (2006) AAAI Workshop-Technical Report; Onnasch, L., Roesler, E., Anthropomorphizing robots: The effect of framing in human-robot collaboration (2019) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 63 (1), pp. 1311-1315. , https://doi.org/10.1177/1071181319631209, 2019; Poberznik, A., Merilampi, S., (2019) Older Adults' Experiences with Pepper Humanoid Robot; (2016) Aldebaran Robotics, , https://www.ald.softbankrobotics.com/en, Choregraphe [Software]; (2018) SoftBank Robotics., , https://www.softbankrobotics.com/emea/en/nao, NAO (V6) [Apparatus]; Ruf, E., Lehmann, S., Pauli, C., Misoch, S., Roboter zur unterstützung im alter (2020) HMD Praxis der Wirtschaftsinformatik, 57 (6), pp. 1251-1270. , https://doi.org/10.1365/s40702-020-00681-0, 2020; Salem, M., Eyssel, F., Rohlfing, K., Kopp, S., Joublin, F., Effects of gesture on the perception of psychological anthropomorphism: A case study with a humanoid robot (2011) Social Robotics, 7072, pp. 31-41. , https://doi.org/10.1007/978-3-642-25504-5_4, Bilge Mutlu, Christoph Bartneck, Jaap Ham, Vanessa Evers, and Takayuki Kanda (Eds.) Lecture Notes in Computer Science. Springer Berlin Heidelberg, Berlin, Heidelberg; Simoens, P., Mahieu, C., Ongenae, F., De Backere, F., De Pestel, S., Nelis, J., De Turck, F., Jacobs, A., Internet of robotic things: Context-aware and personalized interventions of assistive social robots (short paper) (2016) 2016 5th IEEE International Conference on Cloud Networking (Cloudnet), pp. 204-207. , https://doi.org/10.1109/CloudNet.2016.27, 0310. 2016-05. 10. IEEE; Simut, R., Vanderfaeillie, J., Vanderborght, B., Pop, C., Pintea, S., Rusu, A., David, D., Saldien, J., Is the social robot probo an added value for social story intervention for children with asd? 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 235-236; Striepe, H., Lugrin, B., There once was a robot storyteller: Measuring the effects of emotion and non-verbal behaviour (2017) Social Robotics, pp. 126-136. , https://doi.org/10.1007/978-3-319-70022-9_13, Abderrahmane Kheddar, Eiichi Yoshida, Shuzhi Sam Ge, Kenji Suzuki, John-John Cabibihan, Friederike Eyssel, and Hongsheng He (Eds.) Lecture Notes in Computer Science 10652. Springer International Publishing, Cham; Torta, E., Oberzaucher, J., Werner, F., Cuijpers, R.H., Juola, J.F., Attitudes towards socially assistive robots in intelligent homes: Results from laboratory studies and field trials (2013) Journal of Human-Robot Interaction, 1 (2). , https://doi.org/10.5898/JHRI.1.2.Torta, 2013; Tsui, K.M., Desai, M., Yanco, H.A., Cramer, H., Kemper, N., Using the ""negative attitude toward robots scale"" with telepresence robots (2010) Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop On-PerMIS '10, p. 243. , https://doi.org/10.1145/2377576.2377621, Elena Messina and Raj Madhavan (Eds.) ACM Press, New York, New York, USA; Van Den Berghe, R., Verhagen, J., Oudgenoeg-Paz, O., Van Der Ven, S., Leseman, P., Social robots for language learning: A review (2019) Review of Educational Research, 89 (2), pp. 259-295. , https://doi.org/10.3102/0034654318821286, 2019; Wallkötter, S., Stower, R., Kappas, A., Castellano, G., A robot by any other frame (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 609-618. , https://doi.org/10.1145/3319502.3374800, 0309, Tony Belpaeme, James Young, Hatice Gunes, and Laurel Riek (Eds.) ACM, New York, NY, USA; Kory Westlund, J., Breazeal, C., The interplay of robot language level with children's language learning during storytelling (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts-HRI'15 Extended Abstracts, pp. 65-66. , https://doi.org/10.1145/2701973.2701989, Julie A. Adams, William Smart, Bilge Mutlu, and Leila Takayama (Eds.) ACM Press, New York, New York, USA; Zheng, L., Narrative transportation in radio advertising: A study of the effects of dispositional traits on mental transportation (2014) Journal of Radio & Audio Media, 21 (1), pp. 36-50. , https://doi.org/10.1080/19376529.2014.891213, 2014",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102739846
"Fujikawa T., Sugiura R., Nishikata R., Yamada Y., Nishimoto T.","7102532161;14007993500;55959744400;35410760400;7102868497;","Marmarou-type impact tests to investigate criteria for avoiding bruises in extremities by human-robot contact",2021,"ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics",,,"9419547","297","300",,,"10.1109/ISR50024.2021.9419547","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106489865&doi=10.1109%2fISR50024.2021.9419547&partnerID=40&md5=a5aa320336f33bd06e4715cb3624aad6","Nagoya University, Department of Mechanical System Engineering, Fur-cho, Chikusa-ku Nagoya, 464-8608, Japan; College of Engineering, Nihon University, Koriyama, 963-8642, Japan; Fukushima Medical University, Department of Forensic Medicine, Fukushima, 960-1295, Japan","Fujikawa, T., Nagoya University, Department of Mechanical System Engineering, Fur-cho, Chikusa-ku Nagoya, 464-8608, Japan; Sugiura, R., College of Engineering, Nihon University, Koriyama, 963-8642, Japan; Nishikata, R., Fukushima Medical University, Department of Forensic Medicine, Fukushima, 960-1295, Japan; Yamada, Y., Nagoya University, Department of Mechanical System Engineering, Fur-cho, Chikusa-ku Nagoya, 464-8608, Japan; Nishimoto, T., College of Engineering, Nihon University, Koriyama, 963-8642, Japan","To obtain data for investigating the criteria for avoiding bruising caused by blunt impact in human-robot interactions, a Marmarou-type impact tester is introduced into experiments involving porcine extremities. The mechanical input data, i.e., the total transferred energy per unit area, and the peak mean contact pressure with and without internal bleeding, are measured. In our previous study, experiments at lower mechanical inputs required for defining the criteria could not be performed; however, this problem is solved using the Marmarou-type impact tester in this study. © 2021 IEEE.",,"Agricultural robots; Robotics; Blunt impacts; Human robots; Impact testers; Input datas; Mean contact pressure; Per unit; Human robot interaction",,,,,,,,,,,,,,,,"Suita, K., Yamada, Y., Tsuchida, N., Imai, K., Ikeda, H., Sugimoto, N., A failure-to-safety ""Kyozon"" system with simple contact detection and stop capabilities for safe human-autonomous robot coexistence (1995) Proceedings of 1995 IEEE International Conference on Robotics and Automation, 3, pp. 3089-3096; Haddadin, S., Albu-Schäffer, A., Frommberger, M., Robmann, J., Hirzinger, G., The ""DLR crash report"": Towards a standard crash-testing protocol for robot safety-Part I: Results (2009) Proc. IEEE Int. Conf. On Robotics and Automation, pp. 272-279; Robla-Gómez, S., Becerra, V.M., Llata, J.R., Gonzalez-Sarabia, E., Torre-Ferrero, C., Perez-Oria, J., Working together: A review on safe human-robot collaboration in industrial environments (2017) IEEE, (5), pp. 26754-26773. , Access; Wolf, S., Hirzinger, G., A new variable stiffness design: Matching requirements of the next robot generation (2008) Proc. 2008 IEEE International Conference on Robotics and Automation, pp. 1741-1746; Zinn, M., Khatib, O., Roth, B., Salisbury, J.K., Playing it safe (2004) IEEE Robotics & Automation Magazine, pp. 12-21. , June; Eppinger, R., Sun, E., Bandak, F., Haffner, M., Khaewpong, N., Maltese, M., Kuppa, S., Saul, R., Development of improved injury criteria for the assessment of advanced automotive restraint systems II (1999) National Highway Traffic Safety Administration; Duma, S.M., Schreiber, P.H., McMaster, J.D., Crandall, J.R., Bass, C.R., Pilkey, W.D., Dynamic injury tolerances for long bones of the female upper extremity (1999) Journal of Anatomy, 194, pp. 463-471; Takahashi, Y., Matsuoka, F., Okuyama, H., Imaizumi, I., Development of injury probability functions for the flexible pedestrian legform impactor (2012) SAE International Journal of Passenger Cars-Mechanical Systems, 5 (1), pp. 242-252; Fujikawa, T., Asano, Y., Nishimoto, T., Nishikata, R., Static fracture tolerance of human metatarsal in being run over by robot (2017) Proc. 2017 IEEE/RSJ International Conf. On Intelligent Robots and Systems (IROS2017), pp. 6935-6942; Kent, R., Stacy, S., Parenteau, C., Dynamic pinch tolerance of the phalanges and interphalangeal joints (2008) Traffic Injury Prevention, 9 (1), pp. 83-88; (2016) Uniform Provisions concerning the Approval of Vehicles with Regard to the Protection of the Occupants in the Event of A Frontal Collision, , UN Regulation No. 137; Haddadin, S., Haddadin, S., Khoury, A., Rokahr, T., Parusel, S., Bicchi, R.B.A., Lbu-Schäffer, A., On making robots understand safety: Embedding injury knowledge into control (2012) Int. J. Robotics Research; Desmoulin, G.T., Anderson, G.S., Method to investigate contusion mechanics in living humans (2011) J. Forensic Biomechanics, 2; Fujikawa, T., Sugiura, R., Nishikata, R., Nishimoto, T., Critical contact pressure and transferred energy for soft tissue injury by blunt impact in human-robot interaction (2017) Proc. 17th International Conference on Control, Automation and Systems (ICCAS 2017), pp. 867-872; Sugiura, R., Fujikawa, T., Nishikata, R., Nishimoto, T., Soft tissue bruise injury by blunt impact in human-robot interaction-Difference of tolerance between chest and extremities (2019) Proc. 19th International Conference on Control, Automation and Systems (ICCAS 2019), pp. 792-797; Marmarou, A.A., Foda, M.A.A.E., Van Den Brink, W., Campbell, J., Kita, H., Demetriadou, K., A new model of diffuse brain injury in rats Part I: Pathophysiology and biomechanics (1994) J. Neurosurgery, 80 (2), pp. 291-300; McConnell, K.G., Sang, B., Effect of mass on force= transducer sensitivity (1986) Experimental Techniques, 10 (7), pp. 18-22","Fujikawa, T.; Nagoya University, Japan; email: tatsuo.fujikawa@mae.nagoya-u-u.ac.jp",,"IEEE Robotics and Automation Society;Nagoya University","Institute of Electrical and Electronics Engineers Inc.","2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021","4 March 2021 through 6 March 2021",,168860,,9781665438629,,,"English","ISR - IEEE Int. Conf. Intell. Saf. Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85106489865
"Audet J.M., Gosselin C.","57203313801;57210720538;","Rotational low-impedance physical human-robot interaction using underactuated redundancy",2021,"Journal of Mechanisms and Robotics","13","1","014503","","",,1,"10.1115/1.4048258","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107981194&doi=10.1115%2f1.4048258&partnerID=40&md5=293e6ce863ad979b496a5c649a7a8144","Department of Mechanical Engineering, Université Laval, Québec, QC  G1V0A6, Canada","Audet, J.M., Department of Mechanical Engineering, Université Laval, Québec, QC  G1V0A6, Canada; Gosselin, C., Department of Mechanical Engineering, Université Laval, Québec, QC  G1V0A6, Canada","This paper extends the concept of underactuated redundancy for physical human-robot interaction (pHRI) in a context of industrial assembly by introducing a novel 1-dof gravity balanced rotational manipulator. The proposed architecture consists of a rotational active counterweight with a passive joint equipped with an encoder. The proposed architecture is first described, and the static equilibrium conditions are used to describe the operation of the mechanism. Then, alternative architectures are briefly introduced. Finally, an experimental validation is provided to demonstrate the viability of the concept for rotational low impedance pHRI. Copyright © 2020 by ASME","Industrial robots; Mechanical impedance; Passive-active mechanism; Physical human-robot interaction; Underactuated robot","Agricultural robots; Architecture; Industrial manipulators; Industrial robots; Man machine systems; Manipulators; Redundancy; Experimental validations; Industrial assemblies; Low impedance; Passive joints; Physical human-robot interactions; Physical humanrobot interaction (phri); Proposed architectures; Static equilibrium conditions; Human robot interaction",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC; Fonds de recherche du Québec – Nature et technologies, FRQNT","This study was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and by the Fonds de recherche du Québec – Nature et Technologies (FRQNT). The authors would like to acknowledge the help of Thierry Laliberté and Simon Foucault with the experimental validation of the prototype.",,,,,,,,,,"Krüger, J., Lien, T., Verl, A., Cooperation of Human and Machines in Assembly Lines (2009) CIRP Ann. Manuf. Technol, 58 (2), pp. 628-646; Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P., Collaborative Manufacturing with Physical Human-robot Interaction (2016) Robotics Comput.-Integrated Manuf, 40, pp. 1-13; Bonev, I., (2014) Should We Fence the Arms of Universal Robots?, , http://coro.etsmtl.ca/blog/?p=299, [Online]. Available: accessed on October 17, 2018; van der Linde, R., Lammertse, P., Hapticmaster-A Generic Force Controlled Robot for Human Interaction (2003) Industrial Robot: Int. J, 30 (6), pp. 515-524; Lecours, A., Mayer-St-Onge, B., Gosselin, C., Variable Admittance Control of a Four-Degree-of-Freedom Intelligent Assist Device (2012) IEEE International Conference on Robotics and Automation (ICRA), pp. 3903-3908. , St-Paul, MN; Newman, W. S., Zhang, Y., Stable Interaction Control and Coulomb Friction Compensation Using Natural Admittance Control (1994) J. Robotic Syst, 11 (1), pp. 3-11; Buerger, S., Hogan, N., Complementary Stability and Loop Shaping for Improved Human-Robot Interaction (2007) IEEE Trans. Robotics, 23 (2), pp. 232-244; Hogan, N., On the Stability of Manipulators Performing Contact Tasks (1988) IEEE J. Robotics Automation, 4 (6), pp. 677-686; Colgate, E., Hogan, N., An Analysis of Contact Instability in Terms of Passive Physical Equivalents (1989) IEEE International Conference on Robotics and Automation (ICRA), 1, pp. 404-409. , May Scottsdale, AZ; Lamy, X., Colledani, F., Geffard, F., Measson, Y., Morel, G., Achieving Efficient and Stable Comanipulation Through Adaptation to Changes in Human Arm Impedance (2009) IEEE International Conference on Robotics and Automation (ICRA), pp. 265-271. , May Kobe, Japan; Colgate, J. E., Hogan, N., Robust Control of Dynamically Interacting Systems (1988) Int. J. Control, 48 (1), pp. 65-88; Colgate, J. E., Coupled Stability of Multiport Systems-Theory and Experiments (1994) J. Dyn. Syst. Meas. Control, 116 (3), pp. 419-428; Labrecque, P. D., Laliberté, T., Foucault, S., Abdallah, M. E., Gosselin, C., uMan: A Low Impedance Manipulator for Human-robot Cooperation Based on Underactuated Redundancy (2017) IEEE/ASME Trans. Mechatronics, 22 (3), pp. 1401-1411; Badeau, N., Gosselin, C., Foucault, S., Laliberté, T., Abdallah, M. E., Intuitive Physical Human-Robot Interaction Using a Passive Parallel Mechanism (2018) IEEE Robot. Autom. Mag, 25 (2), pp. 28-38; Ebert-Uphoff, I., Gosselin, C. M., Laliberté, T., Static Balancing of Spatial Parallel Platform Mechanisms-Revisited (2000) ASME J. Mech. Des, 122 (1), pp. 43-51; Alamdari, A., Krovi, V. N., Design of Articulated Leg-Wheel Subsystem by Kinetostatic Optimization (2016) Mech. Mach. Theory, 100, pp. 222-234; van Dorsser, W. D., Barents, R., Wisse, B. M., Herder, J. L., Gravity-Balanced Arm Support With Energy-Free Adjustment (2007) ASME J. Med. Devices, 1 (2), pp. 151-158; Kawamura, A., Hisatsune, T., Matsusaka, K., Uemura, M., Kawamura, S., Adaptive Motion Control of a Robotic Arm With Movable Counterweights (2014) Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 882-887. , Besancon, France; Kawamura, A., Gang, B., Uemura, M., Kawamura, S., Mechanism and Control of Robotic Arm Using Rotational Counterweights (2015) Proceedings of the IEEE International Conference. on Robotics and Automation (ICRA), pp. 2716-2721. , Seatle, WA","Gosselin, C.; Department of Mechanical Engineering, Canada; email: gosselin@gmc.ulaval.ca",,,"American Society of Mechanical Engineers (ASME)",,,,,19424302,,,,"English","J. Mech. Robot.",Article,"Final","",Scopus,2-s2.0-85107981194
"Meyer zu Borgsen S., Wachsmuth S.","56202405900;6506127757;","Reactive Patterns for Human-Robot Object Handovers",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"785","790",,,"10.1007/978-3-030-90525-5_72","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119855920&doi=10.1007%2f978-3-030-90525-5_72&partnerID=40&md5=a0fd0345d954b721a628a31c8f2b9f5d","CITEC, Bielefeld University, Bielefeld, 33594, Germany","Meyer zu Borgsen, S., CITEC, Bielefeld University, Bielefeld, 33594, Germany; Wachsmuth, S., CITEC, Bielefeld University, Bielefeld, 33594, Germany","Object handovers are one of the most basic physical collaborative tasks in human-robot interaction. They are interesting because they take place in close human-robot vicinity where both’s peripersonal spaces overlap. Thus, a successful and smooth object handover requires to communicate the intention in terms of the object transition point, the timing of action, and the initiative of giving and receiving. In this paper, we model several reactive patterns extracted from human-robot handover experiments, propose an integrated robotic system implementing these strategies, and evaluate the timing, modality, and human-likeness of its implementation. © 2021, Springer Nature Switzerland AG.","Human-robot interaction; Object handovers; Social robotics","Man machine systems; Robotics; Collaborative tasks; Hand over; Human likeness; Human robots; Humans-robot interactions; Object handover; Robotic systems; Social robotics; Transition point; Human robot interaction",,,,,"DFG EXC277","This work was supported by CITEC (DFG EXC277).",,,,,,,,,,"Meyer zu Borgsen, S., Bernotat, J., Wachsmuth, S.: Hand in hand with robots: differences between experienced and Naive users in human-robot handover scenarios. In: Lecture Notes in Computer Science, pp. 587–596. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70022-9 58; Carfì, A., Foglino, F., Bruno, B., Mastrogiovanni, F., A multi-sensor dataset of human-human handover (2019) Data Brief, 22, pp. 109-117. , https://doi.org/10.1016/j.dib.2018.11.110; Mason, A.H., Mackenzie, Æ.C.L., Grip forces when passing an object to a partner (2005) Exp. Brain Res., 163, pp. 173-187. , https://doi.org/10.1007/s00221-004-2157-x; Parastegari, S., Abbasi, B., Noohi, E., Modeling human reaching phase in human-human object handover with application in robot-human handover (2017) IEEE/RSJ IROS, pp. 3597-3602. , https://doi.org/10.1109/IROS.2017.8206205, pp; Schulz, S., Meyer Zu Borgsen, S., Wachsmuth, S., See and be seen-rapid and likeable high-definition camera-eye for anthropomorphic robots (2019) IEEE ICRA, Montreal, Canada, , https://doi.org/10.1109/ICRA.2019.8794319; Schulz, S., Lier, F., Kipp, A., Wachsmuth, S., Humotion-a human inspired gaze control framework for anthropomorphic robot heads (2016) International Conference on Human Agent Interaction, pp. 207-214. , https://doi.org/10.1145/2974804. 2974827, pp; Simmering, J., Meyer zu Borgsen, S., Wachsmuth, S., Al-Hamadi, A.: Combining static and dynamic predictions of transfer points for human initiated handovers. In: Salichs, M.A., et al. (eds.) ICSR 2019. LNCS (LNAI), vol. 11876, pp. 676–686. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35888-4 63; Yamane, K., Revfi, M., Asfour, T., Synthesizing object receiving motions of humanoid robots with human motion database (2013) IEEE ICRA, Pp. 1629–1636. IEEE, , https://doi.org/10.1109/ICRA.2013.6630788","Wachsmuth, S.; CITEC, Germany; email: swachsmu@cit-ec.uni-bielefeld.de","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119855920
"Le Q.D., Kang H.-J.","57190282420;57217797183;","Sensor-Less Contact Force Estimation in Physical Human-Robot Interaction",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12837 LNCS",,,"233","244",,,"10.1007/978-3-030-84529-2_20","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115256801&doi=10.1007%2f978-3-030-84529-2_20&partnerID=40&md5=b4483fa091cf30aa4f9b7001a91c9434","Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South Korea","Le, Q.D., Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South Korea; Kang, H.-J., Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, South Korea","In this paper, the contact force estimation in physical human-robot interaction without an explicit force sensing device based on the adaptive third order sliding mode observer is proposed. Firstly, the identification parameter of a robot manipulator is required. Then an adaptive third order sliding mode observer is designed to estimate contact force. Finally, the experimental results with the immediate stop control and admittance control using the proposed estimation contact force for 3-DOF AT2-FARA robot manipulator are shown to illustrate the capability of the proposed in the real application. © 2021, Springer Nature Switzerland AG.","Admittance control sensor-less; Physical human-robot interaction; Sliding mode observer; Third order sliding mode observer","Computation theory; Flexible manipulators; Industrial manipulators; Intelligent computing; Man machine systems; Modular robots; Robot applications; Sliding mode control; Admittance control; Contact force estimations; Force sensing devices; Identification parameters; Physical human-robot interactions; Real applications; Robot manipulator; Third order sliding modes; Human robot interaction",,,,,"National Research Foundation of Korea, NRF; Kementerian Pendidikan Malaysia, KPM: NRF-2016R1D1A3B03930496","Acknowledgment. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A3B03930496).",,,,,,,,,,"Suita, K., Yamada, Y., Tsuchida, N., Imai, K., Ikeda, H., Sugimoto, N., A failure-to-safety “Kyozon” system with simple contact detection and stop capabilities for safe human-autonomous robot coexistence (1995) Proceedings of the 1995 IEEE International Conference on Robotics and Automation, pp. 3089-3096. , pp; Geravand, M., Flacco, F., de Luca, A., Human-robot physical interaction and collaboration using an industrial robot with a closed control architecture (2013) IEEE International Conference on Robotics and Automation, pp. 4000-4007. , https://doi.org/10.1109/ICRA.2013.6631141, pp., 2013; Stolt, A., Robertsson, A., Johansson, R., Robotic force estimation using dithering to decrease the low velocity friction uncertainties (2015) IEEE International Conference on Robotics and Automation (ICRA), pp. 3896-3902. , pp., 2015; Duchaine, V., Lauzier, N., Baril, M., Lacasse, M.-A., Gosselin, C., A flexible robot skin for safe physical human robot interaction (2009) IEEE International Conference on Robotics and Automation, ICRA, pp. 3676-3681. , pp., 2009; Cirillo, A., Ficuciello, F., Natale, C., Pirozzi, S., Villani, L., A conformable force/tactile skin for physical human–robot interaction (2016) IEEE Robot. Autom. 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Mech. Eng., 10; Hu, J., Xiong, R., Contact force estimation for robot manipulator using semiparametric model and disturbance Kalman filter (2018) IEEE Trans. Ind. Electron., 65, pp. 3365-3375; Khalil, H.K., Cascade high-gain observers in output feedback control (2017) Automatica, 80, pp. 110-118; Kommuri, S.K., Rath, J.J., Veluvolu, K.C., Sliding-mode-based observer-controller structure for fault-resilient control in DC servomotors (2018) IEEE Trans. Ind. Electron., 65, pp. 918-929; Fridman, L., Shtessel, Y., Edwards, C., Yan, X.-G., Higher-order sliding-mode observer for state estimation and input reconstruction in nonlinear systems (2008) Int. J. Robust Nonlinear Control IFAC Affiliated J., 18, pp. 399-412; Jung, S., Hsia, T.C., Bonitz, R.G., Force tracking impedance control of robot manipulators under unknown environment (2004) IEEE Trans. Control Syst. Technol., 12, pp. 474-483. , https://doi.org/10.1109/TCST.2004.824320; Van, M., Ge, S.S., Ren, H., Robust fault-tolerant control for a class of second-order nonlinear systems using an adaptive third-order sliding mode control (2017) IEEE Trans. Syst. Man Cybern. Syst., 47, pp. 221-228; Ortiz-Ricardez, F.A., Sánchez, T., Moreno, J.A., Smooth Lyapunov function and gain design for a second order differentiator (2015) IEEE 54Th Annual Conference on Decision and Control (CDC), pp. 5402-5407. , pp., 2015; Cruz-Zavala, E., Moreno, J.A., Lyapunov functions for continuous and discontinuous differentiators (2016) Ifac-Papersonline, 49, pp. 660-665; Bahrami, M., Naraghi, M., Zareinejad, M., Adaptive super-twisting observer for fault reconstruction in electro-hydraulic systems (2018) ISA Trans, 76, pp. 235-245; Luo, D., Xiong, X., Jin, S., Kamal, S., Adaptive gains of dual level to super-twisting algorithm for sliding mode design (2018) IET Control Theory Appl, 12, pp. 2347-2356","Kang, H.-J.; Department of Electrical, South Korea; email: hjkang@ulsan.ac.kr","Huang D.Jo K.Li J.Gribova V.Hussain A.",,"Springer Science and Business Media Deutschland GmbH","17th International Conference on Intelligent Computing, ICIC 2021","12 August 2021 through 15 August 2021",,263609,03029743,9783030845285,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85115256801
"Dos Santos Rosa N., Francelin Romero R.A., Grassi V.","57194021223;57221819735;12795251100;","A Survey on the Aspects of Human-Robot Interaction in Autonomous Vehicles",2020,"2020 Latin American Robotics Symposium, 2020 Brazilian Symposium on Robotics and 2020 Workshop on Robotics in Education, LARS-SBR-WRE 2020",,,"9307046","","",,,"10.1109/LARS/SBR/WRE51543.2020.9307046","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100306717&doi=10.1109%2fLARS%2fSBR%2fWRE51543.2020.9307046&partnerID=40&md5=92a6fa6552412fe21565757ab0c443fd","São Carlos School of Engineering, University of São Paulo, Dept. of Electrical and Computer Engineering, São Carlos, Brazil; Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, Brazil","Dos Santos Rosa, N., São Carlos School of Engineering, University of São Paulo, Dept. of Electrical and Computer Engineering, São Carlos, Brazil; Francelin Romero, R.A., Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, Brazil; Grassi, V., São Carlos School of Engineering, University of São Paulo, Dept. of Electrical and Computer Engineering, São Carlos, Brazil","As the technical challenges on intelligent vehicles have been fulfilled, researchers have begun to discuss the social and ethical aspects regarding the development of platforms that allow more interaction between passengers and autonomously driven vehicles. In this context, this paper focuses on the main topics that are currently being studied to improve aspects of this interaction and to design potential new approaches. The main contributions, discussions, concerns, and prospects in the Human-Robot Interaction field for autonomous vehicles are presented, pointing at new directions of research in this area and challenges to be overcome. © 2020 IEEE.",,"Agricultural robots; Autonomous vehicles; Educational robots; Man machine systems; Professional aspects; Robotics; New approaches; Technical challenges; Human robot interaction",,,,,"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES","This study was financed in part by the Coordenac¸ão de Aperfeic¸oamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.",,,,,,,,,,"Goodrich, M.A., Schultz, A.C., Human-robot interaction: A survey (2008) Foundations and Trends ® in Human-Computer Interaction, 1 (3), pp. 203-275; Waldrop, M.M., Autonomous vehicles: No drivers required (2015) Nature News, 518 (7537), p. 20; Litman, T., (2017) Autonomous Vehicle Implementation Predictions, , Victoria Transport Policy Institute Victoria, Canada; Kun, A.L., Boll, S., Schmidt, A., Shifting gears: User interfaces in the age of autonomous driving (2016) IEEE Pervasive Computing, 15 (1), pp. 32-38; Dabral, S., Kamath, S., Appia, V., Mody, M., Zhang, B., Batur, U., Trends in camera based automotive driver assistance systems (adas) (2014) 2014 IEEE 57th International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, pp. 1110-1115; Broy, M., Schmidt, A., Challenges in engineering cyber-physical systems (2014) Computer, 47 (2), pp. 70-72; Meschtscherjakov, A., Tscheligi, M., Szostak, D., Ratan, R., McCall, R., Politis, I., Krome, S., Experiencing autonomous vehicles: Crossing the boundaries between a drive and a ride (2015) Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, pp. 2413-2416; Meschtscherjakov, A., Tscheligi, M., Szostak, D., Krome, S., Pfleging, B., Ratan, R., Politis, I., Ju, W., Hci and autonomous vehicles: Contextual experience informs design (2016) Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 3542-3549; Meschtscherjakov, A., Tscheligi, M., Fröhlich, P., McCall, R., Riener, A., Palanque, P., Mobile interaction with and in autonomous vehicles (2017) Proceedings of the 19th Intl. Conference on Human-Computer Interaction with Mobile Devices and Services, pp. 1-6; Meschtscherjakov, A., Tscheligi, M., Pfleging, B., Sadeghian Borojeni, S., Ju, W., Palanque, P., Riener, A., Kun, A.L., Interacting with autonomous vehicles: Learning from other domains (2018) Extended Abstracts of the 2018 CHI Conf. On Human Factors in Computing Systems, pp. 1-8; Lynberg, M., (2017) Automated Vehicles for Safety, , https://www.nhtsa.gov/technology-innovation/automated-vehicles-safety, Sep; Rödel, C., Stadler, S., Meschtscherjakov, A., Tscheligi, M., Towards autonomous cars: The effect of autonomy levels on acceptance and user experience (2014) Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. ACM, pp. 1-8; Pfleging, B., Rang, M., Broy, N., Investigating user needs for nondriving-related activities during automated driving (2016) Proceedings of the 15th Intl. Conf. On Mobile and Ubiquitous Multimedia, pp. 91-99; Pettersson, I., Rydström, A., Strömberg, H., Hylving, L., Andersson, J., Klingegård, M., Karlsson, M., Living room on the move: Autonomous vehicles and social experiences (2016) Proceedings of the 9th Nordic Conference on Human-Computer Interaction, pp. 1-3; (2020) Volvo Cars Global Newsroom Media Library, , https://www.media.volvocars.com/, Volvo Car Corp; (2020) Daimler Global Media Site, , https://media.daimler.com/marsMediaSite/en/instance/ko/Start.xhtmloid=4836258, Daimler AG; Tscheligi, M., Bueker, U., Ju, W., Sendhoff, B., Panel toward 2020: Human interaction with autonomous vehicles (2014) 9th ACM/IEEE International Conference on Human-Robot Interaction; Wintersberger, P., Riener, A., Frison, A.-K., Automated driving system, male, or female driver: Who'd you prefer. comparative analysis of passengers' mental conditions, emotional states & qualitative feedback (2016) Proceedings of the 8th Intl. Conf. On Automotive User Interfaces and Interactive Vehicular Applications, pp. 51-58; Debernard, S., Chauvin, C., Pokam, R., Langlois, S., Designing human-machine interface for autonomous vehicles (2016) IFACPapersOnLine, 49 (19), pp. 609-614; Strömberg, H., Pettersson, I., Andersson, J., Rydström, A., Dey, D., Klingegård, M., Forlizzi, J., Designing for social experiences with and within autonomous vehicles-exploring methodological directions (2018) Design Science, 4; Jorge, C.C., Rossetti, R.J., On social interactions and the emergence of autonomous vehicles (2018) VEHITS, pp. 423-430; Lazányi, K., Hajdu, B., Trust in human-robot interactions (2017) 2017 IEEE 14th International Scientific Conference on Informatics. IEEE, pp. 216-220; Pedersen, B.K.M.K., Andersen, K.E., Köslich, S., Weigelin, B.C., Kuusinen, K., Simulations and self-driving cars: A study of trust and consequences (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 205-206; Ruijten, P.A., Terken, J., Chandramouli, S.N., Enhancing trust in autonomous vehicles through intelligent user interfaces that mimic human behavior (2018) Multimodal Technologies and Interaction, 2 (4), p. 62; Pieters, N., (2019) Morality in the Preference for Advanced Driver Assistance Systems: Identifying the Importance of Safety for Other Road Users in Consumers Preference for Extra Car Features; Schieben, A., Wilbrink, M., Kettwich, C., Madigan, R., Louw, T., Merat, N., Designing the interaction of automated vehicles with other traffic participants: Design considerations based on human needs and expectations (2019) Cognition, Technology & Work, 21 (1), pp. 69-85; Rasmussen, J., Information processing and human-machine interaction (1986) An Approach to Cognitive Engineering; Vicente, K.J., (1999) Cognitive Work Analysis: Toward Safe, Productive, and Healthy Computer-based Work, , CRC Press; Hoc, J.-M., From human-machine interaction to human-machine cooperation (2000) Ergonomics, 43 (7), pp. 833-843; Lyons, J.B., Being transparent about transparency: A model for human-robot interaction (2013) 2013 AAAI Spring Symposium Series; Stevens, T., How Waymo Is Defining User Interface for Selfdriving Cars, , https://www.cnet.com/roadshow/news/how-waymo-is-defining-the-ui-of-autonomy/; Waytz, A., Heafner, J., Epley, N., The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle (2014) Journal of Experimental Social Psychology, 52, pp. 113-117; Yoder, J., (2016) Vehicle-to-Vehicle Communication, , https://www.nhtsa.gov/technology-innovation/vehicle-vehicle-communication, Oct; Jafarnejad, S., Codeca, L., Bronzi, W., Frank, R., Engel, T., A car hacking experiment: When connectivity meets vulnerability (2015) 2015 IEEE Globecom Workshops (GC Wkshps). IEEE, pp. 1-6",,"Goncalves L.M.G.Drews Junior P.L.J.da Silva B.M.F.Fernandes Curvelo C.da.C.Fabro J.A.dos Santos D.H.de Melo J.C.P.","Brazilian Computer Society (SBC);IEEE Northeast Brazil RAS-CS Joint Chapter","Institute of Electrical and Electronics Engineers Inc.","17th Latin American Robotics Symposium, 12th Brazilian Symposium on Robotics and 11th Workshop on Robotics in Education, LARS-SBR-WRE 2020","9 November 2020 through 12 November 2020",,166442,,9780738111537,,,"English","Lat. American Robot. Symp., Braz. Symp. Robot. Workshop Robot. Educ., LARS-SBR-WRE",Conference Paper,"Final","",Scopus,2-s2.0-85100306717
"Richards L.E., Darvish K., Matuszek C.","57221150854;57219789008;10239806100;","Learning object attributes with category-free grounded language from deep featurization",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340824","8400","8407",,1,"10.1109/IROS45743.2020.9340824","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102410044&doi=10.1109%2fIROS45743.2020.9340824&partnerID=40&md5=1d4794728288cfea63815607d64816d0","University of Maryland, Baltimore County; Booz Allen Hamilton","Richards, L.E., University of Maryland, Baltimore County, Booz Allen Hamilton; Darvish, K., University of Maryland, Baltimore County; Matuszek, C., University of Maryland, Baltimore County","While grounded language learning, or learning the meaning of language with respect to the physical world in which a robot operates, is a major area in human-robot interaction studies, most research occurs in closed worlds or domain-constrained settings. We present a system in which language is grounded in visual percepts without using categorical constraints by combining CNN-based visual featurization with natural language labels. We demonstrate results comparable to those achieved using handcrafted features for specific traits, a step towards moving language grounding into the space of fully open world recognition. © 2020 IEEE.",,"Agricultural robots; Deep learning; Intelligent robots; Visual languages; Language grounding; Language learning; Learning objects; Natural languages; Open world; Physical world; Human robot interaction",,,,,"National Science Foundation, NSF: 1637614, 1637937, 1657469","This material is based upon work supported by the National Science Foundation under Grant Nos. 1637614, 1657469, and 1637937.",,,,,,,,,,"Al-Omari, M., Duckworth, P., Hogg, D.C., Cohn, A.G., Natural language acquisition and grounding for embodied robotic systems (2017) Proceedings of the 31st National Conference on Artificial Intelligence (AAAI), pp. 4349-4356; Chai, J.Y., Gao, Q., She, L., Yang, S., Saba-Sadiya, S., Xu, G., Language to action: Towards interactive task learning with physical agents (2018) IJCAI, pp. 2-9; Pillai, N., Matuszek, C., Unsupervised selection of negative examples for grounded language learning (2018) Proceedings of the 32nd National Conference on Artificial Intelligence (AAAI), , New Orleans, USA; Thomason, J., Padmakumar, A., Sinapov, J., Walker, N., Jiang, Y., Yedidsion, H., Hart, J., Mooney, R., Jointly improving parsing and perception for natural language commands through human-robot dialog (2020) Journal of Artificial Intelligence Research, 67, pp. 327-374; Scheirer, W.J., De Rezende Rocha, A., Sapkota, A., Boult, T.E., Toward open set recognition (2012) IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (7), pp. 1757-1772; Bendale, A., Boult, T., Towards open world recognition (2015) Computer Vision and Pattern Recognition, pp. 1893-1902; Pillai, N., Matuszek, C., Ferraro, F., Deep learning for category-free grounded language acquisition (2019) Proc. of the NAACL Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics (NAACL-SpLURoboNLP), , June, Minneapolis, MI, USA; Larochelle, H., Erhan, D., Bengio, Y., Zero-data learning of new tasks (2008) AAAI; Palatucci, M., Pomerleau, D., Hinton, G.E., Mitchell, T.M., Zero-shot learning with semantic output codes (2009) Advances in Neural Information Processing Systems, pp. 1410-1418; Carvalho, M., Cadène, R., Picard, D., Soulier, L., Thome, N., Cord, M., Crossmodal retrieval in the cooking context: Learning semantic text-image embeddings (2018) The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 35-44; Fang, H., Gupta, S., Iandola, F., Srivastava, R.K., Deng, L., Dollar, P., Gao, J., Zweig, G., From captions to visual concepts and back (2015) Computer Vision and Pattern Recognition, , June; Kim, J., Lee, S., Kwak, D., Heo, M., Kim, J., Ha, J., Zhang, B., Multimodal residual learning for visual qa (2016) Advances in Neural Information Processing Systems, 29, pp. 361-369. , D. 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ACM, 60, pp. 84-90; Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Rabinovich, A., Going deeper with convolutions (2015) Computer Vision and Pattern Recognition, pp. 1-9; Bo, L., Ren, X., Fox, D., Depth kernel descriptors for object recognition (2011) 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 821-826",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102410044
"Humayoun S.R., Faizan M., Zafar Z., Berns K.","35102152500;57219486693;56462691100;7006119626;","Space-free Gesture Interaction with Humanoid Robot",2020,"PervasiveHealth: Pervasive Computing Technologies for Healthcare",,,,"","",,,"10.1145/3399715.3399949","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117543632&doi=10.1145%2f3399715.3399949&partnerID=40&md5=4d17a9476362cbf1c1f1a86bdca75667","Department of Computer Science, San Francisco State University, San Francisco, United States","Humayoun, S.R., Department of Computer Science, San Francisco State University, San Francisco, United States; Faizan, M., Department of Computer Science, San Francisco State University, San Francisco, United States; Zafar, Z., Department of Computer Science, San Francisco State University, San Francisco, United States; Berns, K., Department of Computer Science, San Francisco State University, San Francisco, United States","In general, humanoid robots mostly use fixed-devices (e.g., camera or sensors) to detect human non-verbal communication, which have limitations in many real-life scenarios. Wearable devices could play an important role in many real-life scenarios. To address this, we propose using Myo armband for human-robot interaction using hand- and arm-based gestures. We present our end-to-end Spagti framework that is used first to train the user gestures using Myo armband and then to interact with a humanoid robot, called ROBIN, in real-time using space-free gestures. © 2020 Owner/Author.","human-robot interaction; Humanoid robot; non-verbal communication; space-free interaction","Anthropomorphic robots; End to end; Gesture interaction; Humanoid robot; Non-verbal communications; Real time; Wearable devices; Social robots",,,,,,,,,,,,,,,,"Al-Darraji, S., Zafar, Z., Berns, K., Real-time perception of non-verbal human feedback in a gaming scenario (2016) Proceedings of the 30th International BCS Human Computer Interaction Conference: Fusion (HCI '16)., , https://doi.org/10.14236/ewic/HCI2016.27, BCSLearning&DevelopmentLtd., Swindon, UK; Joseph, A., Christian, B., Abiodun, A.A., Oyawale, F., A review on humanoid robotics in health care (2018) MATECWeb Conf, 153, p. 02004. , https://doi.org/10.1051/matecconf/201815302004; Ebert, D., Makedon, F., Myolearn: Using a multimodal armband sensor for vocational safety problem identification (2017) Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA '17)., pp. 248-249. , https://doi.org/10.1145/3056540.3076212, ACM, New York, NY, USA; Experience from the operation of the pepper humanoid robots (2017) 2017 Progress in Applied Electrical Engineering (PAEE)., pp. 1-6. , https://doi.org/10.1109/PAEE.2017.8008994, Arkadiusz Gardecki Institute of Drive Systems and Robotics, Opole University of Technology, Poland; Michal Podpora; Kobayashi, Y., Kuno, Y., Gyoda, M., Yamazaki, K., Seki, Y., Tabata, T., Shibuya, M., Assisted-care robot dealing with multiple requests in multi-party settings (2011) 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI)., pp. 167-168. , https://doi.org/10.1145/1957656.1957714; Koskimäki, H., Siirtola, P., Röning, J., Myogym: Introducing an open gym data set for activity recognition collected using myo armband (2017) Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (UbiComp '17). ACM, pp. 537-546. , https://doi.org/10.1145/3123024.3124400, New York, NY, USA; Lillo, I., Carlos Niebles, J., Soto, A., Sparse composition of body poses and atomic actions for human activity recognition in rgb-d videos (2017) Image Vision Comput., 59 (C), pp. 63-75. , https://doi.org/10.1016/j.imavis.2016.11.004, March 2017; Sanfilippo, F., Ivar Hatledal, L., Zhang, H., Fago, M., Ytterstad Pettersen, K., Controlling kuka industrial robots: Flexible communication interface jopenshowvar (2015) IEEE Robotics Automation Magazine, 22 (4), pp. 96-109. , https://doi.org/10.1109/MRA.2015.2482839, Dec 2015; Yang, J.-Y., Kwon, D.-S., The effect of multiple robot interaction on human-robot interaction (2012) 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)., pp. 30-33. , https://doi.org/10.1109/URAI.2012.6462923; Yang, P.-C., Sasaki, K., Suzuki, K., Kase, K., Sugano, S., Ogata, T., Repeatable folding task by humanoid robot worker using deep learning (2017) IEEE Robotics and Automation Letters, 2 (2), pp. 397-403. , https://doi.org/10.1109/LRA.2016.2633383, April 2017",,"Tortora G.Vitiello G.Winckler M.","ACM Special Interest Group on Computer-Human Interaction (SIGCHI);ACM Special Interest Group on Hypertext, Hypermedia, and Web (SIGWEB);ACM Special Interest Group on Multimedia (SIGMM);Association for Computing Machinery (ACM)","ICST","2020 International Conference on Advanced Visual Interfaces, AVI 2020","28 September 2020 through 2 October 2020",,163613,21531633,9781450375351,,,"English","PervasiveHealth: Pervasive Computing Technol Healthc.",Conference Paper,"Final","",Scopus,2-s2.0-85117543632
"Weng Y.-H., Gulyaeva S., Winter J., Slavescu A., Hirata Y.","19337859600;57219857471;57219844783;57219857492;7402006534;","HRI for Legal Validation: On Embodiment and Data Protection",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223589","1387","1394",,,"10.1109/RO-MAN47096.2020.9223589","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095793037&doi=10.1109%2fRO-MAN47096.2020.9223589&partnerID=40&md5=522a3bb7115d1200c03597210dadc14f","Tohoku University, Frontier Research Institute for Interdisciplinary Sciences (FRIS), Sendai, 980-8579, Japan; Tu Dortmund University, Department of Applied Computer Science, Dortmund, 44227, Germany; University of Vienna, Department of Philosophy, Vienna, 1010, Austria","Weng, Y.-H., Tohoku University, Frontier Research Institute for Interdisciplinary Sciences (FRIS), Sendai, 980-8579, Japan; Gulyaeva, S., Tu Dortmund University, Department of Applied Computer Science, Dortmund, 44227, Germany; Winter, J., University of Vienna, Department of Philosophy, Vienna, 1010, Austria; Slavescu, A., Tohoku University, Frontier Research Institute for Interdisciplinary Sciences (FRIS), Sendai, 980-8579, Japan; Hirata, Y., Tohoku University, Frontier Research Institute for Interdisciplinary Sciences (FRIS), Sendai, 980-8579, Japan","This paper investigates the potential of applying the study of Human-Robot Interaction (HRI) via a Social System Design approach to legal issues. A problem when considering AI legislation is that many existing laws were written before the development of AI technologies. To address this issue, we propose examining the effectiveness of laws through the Social System Design approach. Specifically, we will use the General Data Protection Regulation (GDPR) to analyze if any legal gaps emerge when it is applied to social robots. To do so we will employ HRI experiments. © 2020 IEEE.",,"Agricultural robots; Laws and legislation; Machine design; Privacy by design; Systems analysis; AI Technologies; General data protection regulations; Human robot Interaction (HRI); Legal issues; Social system designs; Social robots",,,,,"Japan Society for the Promotion of Science, KAKEN: 19K13579","* This work was mainly supported by JSPS KAKENHI Grant Number 19K13579, and partially supported by the Stanford-Vienna Transatlantic Technology Law Forum, Stanford Law School regarding “Healthcare Robots: A Comparative EU-US Data Protection Analysis”. Special thanks to our 32 volunteers from Tohoku University who spent their valuable time in supporting this project.",,,,,,,,,,"Weng, Y.H., Chen, C.H., Sun, C.T., Toward the human-robot co-existence society: On safety intelligence for next generation robots (2009) International Journal of Social Robotics, 1 (4), pp. 267-282; Weng, Y.H., Izumo, T., Natural law and its implication to ai governance (2019) Delphi, 2 (3), pp. 122-128. , Berlin: Lexxion; Edwards, L., Schafer, B., Harbinja, E., The future's already here, it's just unevenly edited (2020) Future Law: Emerging Technology, Regulation and Ethics, , L. Edwards, B. Schafer and E. Harbinja (Eds.) , Edinburgh University Press; Fenwick, M., Kaal, W., Vermeulen, E., Regulation tomorrow: What happens when technology is faster than the law? (2017) American University Business Law Review, 3, p. 6; Weng, Y.H., Chen, C.H., Sun, C.T., Safety intelligence and legal machine language: Do we need the three laws of robotics? (2008) Y. Takahashi (Ed.), Vienna: In-Tech, June 2008, , Service Robot Applications; Weng, Y.H., Robot law 1. 0: On social system design for artificial intelligence (2018) Research Handbook on the Law of Artificial Intelligence, , W. Barfield, U. Pagallo (Eds.), Edward Elgar Publishing; Weng, Y.H., Sugahara, Y., Hashimoto, K., Takanishi, A., Intersection of tokku special zone, robots, and the law: A case study on legal impacts to humanoid robots (2015) International Journal of Social Robotics, 7 (5), pp. 841-857; Pagallo, U., From automation to autonomous systems: A legal phenomenology with problems of accountability (2017) Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17); The International Compliance Association (ICA) Official Definition to, , https://www.int-comp.org/careers/yourcareer-in-compliance/what-is-compliance/; ISO 9000:2000 Quality Management Systems - Fundamentals and Vocabulary, , https://www.iso.org/standard/29280.html, available via; CMMI-SVC, Version 1. 2, , https://resources.sei.cmu.edu/library/asset-view.cfm?assetid=9067, available via; De Conca, S., Fosch Villaronga, E., Pierce, R., De Groot, A., Felzmann, H., Robins, S., Ponce Del Castillo, A., Nothing comes between my robot and me: Privacy and human-robot interaction in robotised healthcare (2018) Data Protection and Privacy: The Internet of Bodies, p. 104. , in R. Leenes & R. van Brakel & S. Gutwirth & P. D. Hert (Eds.) , Hart Publishing; Calo, M.R., Robotics and the lessons of cyberspace (2015) California Law Review, 103 (3); Zanatto, D., Patacchiola, M., Goslin, J., Cangelosi, A., Priming anthropomorphism: Can the credibility of humanlike robots be transferred to non-humanlike robots? (2016) Proceedings of the 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), , March 7-10, 2016 Christchurch, New Zealand; Pavia, A., Leite, I., Boukricha, H., Wachsmuth, I., Empathy in virtual agents and robots: A survey (2017) ACM Transactions on Interactive Intelligent Systems (TiiS), 7 (3); Wainer, J., Feil-Seifer, D.J., Shell, D.A., Mataric, M.J., The role of physical embodiment in human-robot interaction (2006) Proceedings of the 15th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN, pp. 117-122. , September 6-8, 2006 Hatfield, Herthfordshire, UK; Tonkin, M., Vitale, J., Ojha, S., Clark, J., Pfeiffer, S., Judge, W., Wang, X., Williams, M.A., Embodiment, privacy and social robots: May i remember you? (2017) Proceedings of the 9th International Conference on Social Robotics, , Tsukuba, Japan, November 22-24; Lee, M.K., Tang, K.P., Forlizzi, J., Kiesler, S., Understanding users perception of privacy in human-robot interaction (2011) Proceedings of the 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), , March 6-9 Lausanne, Switzerland; Beer, J.M., Smarr, C.A., Fisk, A.D., Rogers, W.A., Younger and older users ? Recognition of virtual agent facial expressions (2015) International Journal of Human-Computer Studies, 1 (75), pp. 1-20. , Elsevier; Weng, Y.H., Hirata, Y., Sakura, O., Sugahara, Y., Religious impact of taoism on ethically aligned design in hri (2019) International Journal of Social Robotics, 11 (5), pp. 829-839; Wilkowska, W., Ziefle, M., Himmel, S., (2015) Perceptions of Personal Privacy in Smart Home Technologies: Do User Assessments Vary Depending on the Research Method?, pp. 592-603. , In T. Tryfonas and I. Askoxylakis (Eds.): HAS 2015 LNCS 9190. Springer 2015; Orlando, D., (2018) The Emerging Security by Design Principle in the EU Legal Framework, , (Master's thesis) University of Oslo; Weng, Y.H., Chen, C.H., Sun, C.T., The legal crisis of next generation robots: On safety intelligence (2007) Proceedings of the 11th International Conference on Artificial Intelligence and Law (ICAIL 2007), pp. 205-209. , Stanford, CA, USA; Weng, Y.H., Hirata, Y., Ethically aligned design for assistive robotics (2018) Proceedings of the 1st IEEE International Conference on Intelligence and Safety for Robots (IEEE ISR 2018), , Shengyang, China, August 24th-27th; Takeda, M., Hirata, Y., Weng, Y.H., Katayama, T., Mizuta, Y., Kojima, A., Accountable system design architecture for embodied ai: A focus on physical human support robots (2019) Advanced Robotics, 33 (23), pp. 1248-1263; Bygrave, L.A., Hardwiring privacy (2017) The Oxford Handbook of Law, Regulation and Technology, , Roger Brownsword, Eloise Scotford & Karen Yeung (eds.). Oxford University Press; Miyashita, H., (2018) General Data Protection Regulation, , Tokyo: Keiso Shobo; ISO 13482:2014 Robots and Robotic Devices - Safety Requirements for Personal Care Robots, , https://www.iso.org/standard/53820.html; Hall, E.T., (1910) The Hidden Dimension, 609. , Garden City, NY: Doubleday; Harper, C., Virk, G.S., Towards the development of international safety standards for human robot interaction (2010) International Journal of Social Robotics, 2 (3), pp. 229-234; Wilcox, H., Bhattacharya, M., A framework to mitigate social engineering through social media within the enterprise (2016) IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), , 5-7 June 2016 Hefei, China; Bygrave, L.A., (2014) Data Privacy Law: An International Perspective, , Oxford University Press",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095793037
"Srinidhi A.N., Kamath A.S., Kumaraswamy R.","57219120916;57219121525;57194600362;","Multi-Modal Humanoid",2020,"Proceedings of the 2020 IEEE International Conference on Communication and Signal Processing, ICCSP 2020",,,"9182448","799","802",,,"10.1109/ICCSP48568.2020.928271508","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091343242&doi=10.1109%2fICCSP48568.2020.928271508&partnerID=40&md5=df7248a5181a6ab2f3cffa1b6e3bc69a","Siddaganga Institute of Technology, Dept. of Electronics and Communication, Tumakuru, 572103, India","Srinidhi, A.N., Siddaganga Institute of Technology, Dept. of Electronics and Communication, Tumakuru, 572103, India; Kamath, A.S., Siddaganga Institute of Technology, Dept. of Electronics and Communication, Tumakuru, 572103, India; Kumaraswamy, R., Siddaganga Institute of Technology, Dept. of Electronics and Communication, Tumakuru, 572103, India","Human robot interaction termed as HRI deals with the interaction between humans and robots through natural means of communication. For effective communication, the robot must have its own intelligence as that of human called as artificial intelligence (AI). This paper focuses on building machines that can understand its surroundings and is able to establish HRI by mimicking the sense of speech, hearing, smell, and touch. The humanoid functions, based on the voice commands given by the user. It is able to distinguish between different odour as good or bad and respond to touch stimuli. © 2020 IEEE.","Artificial intelligence; Human Robot Interaction; Humanoid","Artificial intelligence; Audition; Intelligent robots; Signal processing; Building machines; Effective communication; Multi-modal; Natural means; Voice command; Social robots",,,,,,,,,,,,,,,,"Yuan, W., Li, Z., Development of a human-friendly robot for socially aware human robot interaction (2017) International Conference on Advanced Robotics and Mechatronics, pp. 76-81. , Tokyo; Dautenhahn, K., Robots as social actors: Aurora and the case of autism (1999) Proceedings CT99, the Third International Cognitive Technology Conference, p. 359374. , August, SanFransico,, 1999; Thoshith, S., Mulgund, S., Sindgi, P., Yogesh, N., Kumarswamy, R., Multi-modal humanoid robot (2018) Proceedings of IEEE International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), p. 14. , (201718), March 22-24, Chennai; Kumar, P., Nagapushpa, K., Kumar, V., Raj, V., Kumaraswamy, R., Multifunctional intelligent humanoid using speech processing (2017) Proc. International Conference and Signal Processing, , April 6-8, India, pp 978-1-5090-3799-5/17/$ 31.00 © 2017 IEEE; Rabiner, R., A tutorial on hidden markov models and selected applications in speech recognition (1987) Proceedings of IEEE, 77 (2), pp. 257-286; Abdel-Hamid, O., Mohamed, A., Jiang, H., Deng, L., Penn, G., Dong, Yu., Convolutional neural networks for speech recognition (2014) IEEE/ACM Transactions on Audio Speech and Language Processing, 22 (10). , October; Yang, H., Liang, D., Kuang, X., Xu, C., A vulnerability test method for speech recognition systems based on frequency signal processing (2018) Data Science in Cyberspace (DSC) 2018 IEEE Third International Conference on, pp. 943-947; Imai, A., Tazawa, N., Takagi, T., Tanaka, T., Ifukube, T., A new touchscreen application to retrieve speech information efficiently (2013) IEEE Transactions on Consumer Electronics, 59 (1). , February; Pucher, M., Schabus, D., Yamagishi, J., (2010) Synthesis of Fast Speech with Interpolation of Adapted HSMMs and Its Evaluation by Blind and Sighted Listeners, , Telecommunications Research Center Vienna (FTW), Austria, The Centre for Speech Technology Research (CSTR), University of Edinburgh, UK INTERSPEECH; Carlson, R., Sigvardson, T., Sjolander, A., (2002) Datadriven Formant Synthesis, 44. , CTT, Department of Speech, Music and Hearing, KTH, TMHQPSR-Fonetik; Cai, C., Liu, Y., Han, Y., Chen, Q., Salo, A., Xu, L., Fea based design optimization of exciting sensitivity for micromachined piezoelectric transducer (2009) Thermal Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems 2009. EuroSimE 2009. 10th International Conference on, pp. 1-5; Pi, R., Raspberry Pi Documentation, , https://Www.raspberrypi.org/documentation",,,,"Institute of Electrical and Electronics Engineers Inc.","9th IEEE International Conference on Communication and Signal Processing, ICCSP 2020","28 July 2020 through 30 July 2020",,162796,,9781728149882,,,"English","Proc. IEEE Int. Conf. Commun. Signal Process., ICCSP",Conference Paper,"Final","",Scopus,2-s2.0-85091343242
"Thellman S., Silvervarg A., Ziemke T.","57191892409;36603520900;6603792948;","Anthropocentric attribution bias in human prediction of robot behavior",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"476","478",,,"10.1145/3371382.3378347","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083200411&doi=10.1145%2f3371382.3378347&partnerID=40&md5=643d813b79c8e38077dd04897ed4456c","Computer and Information Science, Linköping University, Linköping, Sweden","Thellman, S., Computer and Information Science, Linköping University, Linköping, Sweden; Silvervarg, A., Computer and Information Science, Linköping University, Linköping, Sweden; Ziemke, T., Computer and Information Science, Linköping University, Linköping, Sweden","In many types of human-robot interactions, people must track the beliefs of robots based on uncertain estimates of robots' perceptual and cognitive capabilities. Did the robot see what happened and did it understand what it saw? In this paper, we present preliminary experimental evidence that people estimating what a humanoid robot knows or believes about the environment anthropocentrically assume it to have human-like perceptual and cognitive capabilities. However, our results also suggest that people are able to adjust their incorrect assumptions based on observations of the robot. © 2020 ACM.","Belief-tracking; Human-robot interaction; Mental state attribution","Agricultural robots; Anthropomorphic robots; Man machine systems; Cognitive capability; Experimental evidence; Human like; Humanoid robot; Robot behavior; Human robot interaction",,,,,,,,,,,,,,,,"Anjomshoae, S., Najjar, A., Calvaresi, D., Främling, K., Explainable agents and robots: Results from a systematic literature review (2019) Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems, pp. 1078-1088; Clark, H., Marshall, C., Definite knowledge and mutual knowledge (1981) Elements of Discourse Understanding, Aravind Joshi, BonnieWebber, and Ivan Sag (Eds.). Cambridge University Press, pp. 10-63; De Graaf, M., Malle, B.F., Dragan, A., Ziemke, T., Explainable robotic systems (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 387-388; Hellström, T., Bensch, S., Understandable robots-what, why, and how (2018) Paladyn, Journal of Behavioral Robotics, 9 (1), pp. 110-123. , 2018; Southgate, V., Senju, A., Csibra, G., Action anticipation through attribution of false belief by 2-year-olds (2007) Psychological Science, 18 (7), pp. 587-592. , 2007; Thellman, S., Ziemke, T., The intentional stance toward robots: Conceptual and methodological considerations (2019) The 41st Annual Conference of the Cognitive Science Society, pp. 1097-1103. , July 24-26, Montreal, Canada",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083200411
"Thunberg S., Ziemke T.","57197797180;6603792948;","Are people ready for social robots in public spaces?",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"482","484",,4,"10.1145/3371382.3378294","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083193144&doi=10.1145%2f3371382.3378294&partnerID=40&md5=32b39f651a003c56c41b835ffbe8ea4f","Department of Computer and Information Science, Linköping University, Linköping, Sweden","Thunberg, S., Department of Computer and Information Science, Linköping University, Linköping, Sweden; Ziemke, T., Department of Computer and Information Science, Linköping University, Linköping, Sweden","Recent developments in robotics are potentially changing the nature of service, and research in human-robot interaction has previously shown that humanoid robots could possibly work in public spaces. We conducted an ethnographic study with the humanoid robot Pepper at a central train station. The results indicate that people are not yet accustomed to talking to robots, and people seem to expect that the robot does not talk, that it is a queue ticket machine, or that, one should interact with it by using the tablet on the robot's chest. © 2020 ACM.","Ethnography; Human-robot interaction; Humanoid","Agricultural robots; Anthropomorphic robots; Man machine systems; Ethnographic study; Humanoid robot; Public space; Train stations; Social robots",,,,,,,,,,,,,,,,"Bartneck, C., Verbunt, M., Mubin, O., Al Mahmud, A., To kill a mockingbird robot (2007) 3rd ACM/IEEE International Conference on Human Robot Interaction, HRI 2007. ACM, pp. 81-87; Van Doorn, J., Mende, M., Noble, S.M., Hulland, J., Ostrom, A.L., Grewal, D., Petersen, Andrew, J., Domo arigato, mr. Roboto: Emergence of automated social presence in organizational frontlines and customers service experiences (2017) Journal of Service Research, 20 (1), pp. 43-58. , 2017; Peter, H.K., Jr., Kanda, T., Ishiguro, H., Freier, N.G., Severson, R.L., Gill, B.T., Ruckert, J.H., Shen, S., Robovie, you'll have to go into the closet now: Children's social and moral relationships with a humanoid robot (2012) Developmental Psychology, 48 (2), pp. 303-314. , 2012; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., ROS: An open-source robot operating system (2009) ICRA Workshop on Open Source Software, 5; Short, E., Hart, J., Vu, M., Scassellati, B., No fair!! An interaction with a cheating robot (2010) 5th ACM/IEEE International Conference on Human-Robot Interaction, HRI 2010. IEEE, pp. 219-226; Triebel, R., Arras, K., Alami, R., Beyer, L., Breuers, S., Chatila, R., Chetouani, M., Zhang, L., SPENCER: A socially aware service robot for passenger guidance and help in busy airports (2016) Springer Tracts in Advanced Robotics, 113, pp. 607-622. , 2016",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083193144
"Colle A.","57200601322;","The role of aesthetics in robotics and the rise of polymorphic robots",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"623","624",,,"10.1145/3371382.3379452","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083180890&doi=10.1145%2f3371382.3379452&partnerID=40&md5=92fcc41295539ca13676f6477bca1231","Heriot-Watt University, Edinburgh, United Kingdom","Colle, A., Heriot-Watt University, Edinburgh, United Kingdom","The general topic of this inquiry looks at the methodology of design for new types of companions robots, in the context of a domestic setting. Personalization is essential, but most of Human Robot Interaction (HRI) research focus on adaptive behaviour for social interactions using commercially available devices. These robots represent finite projects, with very little room left for meaningful physical alterations. The goal of this research is to study the impact amongst users, of a robot offering by its conception, a high range of choice for personal customisation. © 2020 ACM.","Design; Domestic; Personalization; Robot skin; Social robot","Agricultural robots; Machine design; Man machine systems; Robotics; Adaptive behaviour; Customisation; High ranges; Human robot Interaction (HRI); Personalizations; Research focus; Social interactions; Social robots",,,,,,,,,,,,,,,,"Auger, J.H., Living with robots: A speculative design approach (2014) Journal of Human-Robot Interaction, 3 (1), p. 20. , https://doi.org/10.5898/jhri.3.1.auger, 2014; Bongard, J., Why morphology matters (2014) The Horizons of Evolutionary Robotics, Phil Husbans Patricia Vargas, 6. , Ezequiel Di Paolo, Inman Hrvey (Ed.). The MIT Press c2014, Chapter; Dautenhahn, K., Socially intelligent robots: Dimensions of humanrobot interaction (2007) Philosophical Transactions of the Royal Society B: Biological Sciences, 362 (1480), pp. 679-704. , https://doi.org/10.1098/rstb.2006.2004, 2007; De Graaf, A.M.M., Benllouch, S., Exploring influencing variables for the acceptance of social robots (2013) Robotics and Autonomous Systems, 61 (12), pp. 1476-1486. , https://doi.org/10.1016/j.robot.2013.07.007, 2013; Dragone, M., Saunders, J., Dautenhahn, K., On the integration of adaptive and interactive robotic smart spaces (2015) Paladyn, Journal of Behavioral Robotics, 6 (1), pp. 165-179. , https://doi.org/10.1515/pjbr-2015-0009, jan 2015; Francalanza, E., Fenech, A., Cutajar, P., Generative design in the development of a robotic manipulator (2018) Procedia CIRP, 67, pp. 244-249. , https://doi.org/10.1016/j.procir.2017.12.207, 2018; Hegel, F., Effects of a Robot's Aesthetic Design on the attribution of social capabilities (2012) 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication. 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Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083180890
"Prasad V., Stock-Homburg R., Peters J.","57201912364;56364282100;35248912800;","Advances in Human-Robot Handshaking",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"478","489",,2,"10.1007/978-3-030-62056-1_40","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097203717&doi=10.1007%2f978-3-030-62056-1_40&partnerID=40&md5=8f0f5bedb7e522ee81944449f683c50d","Technical University of Darmstadt, Darmstadt, Germany; Max Planck Institute for Intelligent Systems, Tübingen, Germany","Prasad, V., Technical University of Darmstadt, Darmstadt, Germany; Stock-Homburg, R., Technical University of Darmstadt, Darmstadt, Germany; Peters, J., Technical University of Darmstadt, Darmstadt, Germany, Max Planck Institute for Intelligent Systems, Tübingen, Germany","The use of social, anthropomorphic robots to support humans in various industries has been on the rise. During Human-Robot Interaction (HRI), physically interactive non-verbal behaviour is key for more natural interactions. Handshaking is one such natural interaction used commonly in many social contexts. It is one of the first non-verbal interactions which takes place and should, therefore, be part of the repertoire of a social robot. In this paper, we explore the existing state of Human-Robot Handshaking and discuss possible ways forward for such physically interactive behaviours. © 2020, Springer Nature Switzerland AG.","Handshaking; Physical HRI; Social robotics","Anthropomorphic robots; End effectors; Industrial robots; Robotics; Human robot Interaction (HRI); Human robots; Natural interactions; Non-verbal behaviours; Social context; Verbal interaction; Social robots",,,,,,"Acknowledgements. The authors thank the Interdisciplinary Research Forum (Forum Interdisziplinäre Forschung) at the Technical University of Darmstadt and the Association of Supporters of Market-Oriented Management, Marketing, and Human Resource Management (Förderverein für Marktorientierte Unternehmensführung, Marketing und Personalmanagement e.V.) for funding this work.",,,,,,,,,,"Ammi, M., Haptic human-robot affective interaction in a handshaking social protocol (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 263-270; Arns, M., Laliberté, T., Gosselin, C., Design, control and experimental validation of a haptic robotic hand performing human-robot handshake with human-like agility (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4626-4633. , IEEE; Artem, M.A., Viacheslav, K.M., Volodymyr, B.P., Patrick, H., Physical human-robot interaction in the handshaking case: Learning of rhythmicity using oscillators neurons (2013) IFAC Proc, 46 (9), pp. 1055-1060; Avelino, J., The power of a hand-shake in human-robot interactions (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS, pp. 1864-1869. , IEEE; Avelino, J., Paulino, T., Cardoso, C., Moreno, P., Bernardino, A., Human-aware natural handshaking using tactile sensors for Vizzy, a social robot (2017) Interaction and Learning for Assistive Robotics at RO-MAN Workshop on Behavior Adaptation; Avelino, J., Paulino, T., Cardoso, C., Nunes, R., Moreno, P., Bernardino, A., Towards natural handshakes for social robots: Human-aware hand grasps using tactile sensors (2018) Paladyn, J. 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LNCS (LNAI), 7621, pp. 258-267. , https://doi.org/10.1007/978-3-642-34103-826, Ge, S.S., Khatib, O., Cabibihan, J.-J., Simmons, R., Williams, M.-A. (eds.) , Springer, Heidelberg","Prasad, V.; Technical University of DarmstadtGermany; email: vignesh.prasad@tu-darmstadt.de","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85097203717
"Galin R., Meshcheryakov R., Kamesheva S.","57209776926;23035794100;57217104550;","Distributing Tasks in Multi-agent Robotic System for Human-Robot Interaction Applications",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12336 LNAI",,,"99","106",,1,"10.1007/978-3-030-60337-3_10","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092934301&doi=10.1007%2f978-3-030-60337-3_10&partnerID=40&md5=9cb1cda3fc009f5ae70060e835c1bcc6","V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation","Galin, R., V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation; Meshcheryakov, R., V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation; Kamesheva, S., V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation","Human-robot interaction become a trend in robotics and happen in a wide range of situations. This research paper describes human and collaborative robots interaction behind traditional paradigms for robots in a shared workspace. This research shows that humans and robots distributing task in collaborative interaction of multi-agent robotic system. Different methods for distributing tasks on global and local levels in multi-agent robotic system analyzed as part of research work. Characteristics of tasks distribution algorithms are considered. The particle swarm algorithm for task distribution presented as an example. © 2020, Springer Nature Switzerland AG.","Collaboration; Human-robot interaction; Model; Multi-agent system; Safety-oriented behavior","Man machine systems; Multi agent systems; Robotics; Collaborative interaction; Collaborative robots; Distribution algorithms; Multi-agent robotic systems; Particle swarm algorithm; Research papers; Shared-workspace; Task distribution; Social robots",,,,,"Russian Foundation for Basic Research, РФФИ: 19-08-00331","Acknowledgements. The reported study was partially funded by RFBR according to the research project № 19-08-00331.",,,,,,,,,,"Ermishin, K., Yuschenko (2016) A: Collaborative Mobile Robots-A New Stage of Development of Service Robotics. J. Robot. Tech. 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Eng., 862. , https://doi.org/10.1088/1757-899x/862/3/032075; Nazarova, A.V., Zhai, M., Distributed solution of problems in multi agent robotic systems (2019) Smart Electromechanical Systems. SSDC, 174, pp. 107-124. , https://doi.org/10.1007/978-3-319-99759-9_9, Gorodetskiy, A.E., Tarasova, I.L. (eds.) , Springer, Cham; Liu, C., Tomizuka, M., Designing the robot behavior for safe human–robot interactions (2017) Trends in Control and Decision-Making for Human–Robot Collaboration Systems. SSDC, pp. 241-270. , https://doi.org/10.1007/978-3-319-40533-9_11, Wang, Y., Zhang, F. (eds.) , Springer, Cham; Kennedy, J., Eberhart, R., Particle swarm optimization (1995) IEEE International Conference on Neural Networks, pp. 1942-1948; Wang, Y., Zeng, J., A survey of a multi-objective particle swarm optimization algorithm (2010) CAAI Trans. Intell. Syst., 5 (5), pp. 377-384; Azzouz, R., Bechikh, S., Ben Said, L., Dynamic multi-objective optimization using evolutionary algorithms: A survey (2017) Recent Advances in Evolutionary Multi-Objective Optimization. ALO, 20, pp. 31-70. , https://doi.org/10.1007/978-3-319-42978-6_2, Bechikh, S., Datta, R., Gupta, A. (eds.) , Springer, Cham","Galin, R.; V.A. Trapeznikov Institute of Control Sciences of Russian Academy of SciencesRussian Federation; email: rinat.r.galin@yandex.ru","Ronzhin A.Rigoll G.Meshcheryakov R.",,"Springer Science and Business Media Deutschland GmbH","5th International Conference on Interactive Collaborative Robotics, ICR 2020","7 October 2020 through 9 October 2020",,249569,03029743,9783030603366,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85092934301
"Bogaerts B., Sels S., Vanlanduit S., Penne R.","57191575116;57193554563;7004271926;6701922997;","Connecting the CoppeliaSim robotics simulator to virtual reality",2020,"SoftwareX","11",,"100426","","",,2,"10.1016/j.softx.2020.100426","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080071825&doi=10.1016%2fj.softx.2020.100426&partnerID=40&md5=031766ad9c44d9d74d77dd43b781e8d1","Faculty of Applied Engineering, University of Antwerp, Antwerp, Belgium","Bogaerts, B., Faculty of Applied Engineering, University of Antwerp, Antwerp, Belgium; Sels, S., Faculty of Applied Engineering, University of Antwerp, Antwerp, Belgium; Vanlanduit, S., Faculty of Applied Engineering, University of Antwerp, Antwerp, Belgium; Penne, R., Faculty of Applied Engineering, University of Antwerp, Antwerp, Belgium","The CoppeliaSim VR Toolbox provides a set of tools to experience CoppeliaSim robot simulation software in Virtual Reality and to return user interactions. Its primary focus is to create a platform that enables the fast prototyping and verification of robotic systems. Moreover, the generality of the toolbox ensures that it can be valuable in other contexts like robotics education, human–robot interaction or reinforcement learning. The software is designed to have a low entry threshold for moderately complex use cases, but can be extended to perform very complex visualizations for more experienced users. © 2020 The Authors","Human–robot interaction; Interface; Prototyping; Robot simulation; Virtual reality","Computer software; Interfaces (materials); Reinforcement learning; Robotics; Software prototyping; Virtual reality; Fast prototyping; Robot interactions; Robot simulation software; Robot simulations; Robotic systems; Robotics education; User interaction; Human robot interaction",,,,,"Fonds Wetenschappelijk Onderzoek, FWO","B.B was funded by Fonds Wetenschappelijk Onderzoek (FWO, Research-Foundation Flanders), Belgium under Doctoral (PhD) grant strategic basic research (SB) 1S26216N .",,,,,,,,,,"Campeau-Lecours, A., Côté-Allard, U., Vu, D., Routhier, F., Gosselin, B., Gosselin, C., Intuitive adaptive orientation control for enhanced human–robot interaction (2019) IEEE Trans Robot, 35 (2), pp. 509-520; Gibson, I., Cobb, S., Eastgate, R., Virtual reality and rapid prototyping: Conflicting or complimentary? (1993), 1993 international solid freeform fabrication symposium; De Sa, A.G., Zachmann, G., Virtual reality as a tool for verification of assembly and maintenance processes (1999) Comput Graph, 23 (3), pp. 389-403; Mujber, T.S., Szecsi, T., Hashmi, M.S., Virtual reality applications in manufacturing process simulation (2004) J Mater Process Technol, 155, pp. 1834-1838; Krupke, D., Starke, S., Einig, L., Steinicke, F., Zhang, J., Prototyping of immersive HRI scenarios (2017) International conference on climbing and walking robots and the support technologies for mobile machines, pp. 537-544. , World Scientific; Bogaerts, B., Sels, S., Vanlanduit, S., Penne, R., Interactive camera network design using a virtual reality interface (2019) Sensors, 19 (5), p. 1003; Bogaerts, B., Sels, S., Vanlanduit, S., Penne, R., Enabling humans to plan inspection paths using a virtual reality interface (2019), arXiv preprint; Rohmer, E., Singh, S.P., Freese, M., V-REP: A versatile and scalable robot simulation framework (2013) 2013 IEEE/RSJ international conference on intelligent robots and systems, pp. 1321-1326. , IEEE; Hanwell, M.D., Martin, K.M., Chaudhary, A., Avila, L.S., The visualization toolkit (VTK): Rewriting the rendering code for modern graphics cards (2015) SoftwareX, 1, pp. 9-12; Schroeder, W., Martin, K., Lorensen, B., The Visualization Toolkit–An Object-Oriented Approach To 3D Graphics (2006), 4th ed. Kitware, Inc; Nogueira, L., (2014), p. 5. , Comparative analysis between gazebo and v-rep robotic simulators. In Seminario interno de cognicao artificial 2014; Livatino, S., Koffel, C., Handbook for evaluation studies in virtual reality (2007) 2007 IEEE symposium on virtual environments, human-computer interfaces and measurement systems, pp. 1-6. , IEEE","Bogaerts, B.; Faculty of Applied Engineering, Belgium; email: boris.bogaerts@uantwerpen.be",,,"Elsevier B.V.",,,,,23527110,,,,"English","SoftwareX",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85080071825
"Chew E.","23989981600;","In Love and War with Service Robots: The Passionate Deployment, Challenges and National Policy Implications",2020,"Lecture Notes in Mechanical Engineering",,,,"361","371",,1,"10.1007/978-981-13-8323-6_30","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067669665&doi=10.1007%2f978-981-13-8323-6_30&partnerID=40&md5=173237acf14a3c99c7c4a11333ea61ce","EUREKA Robotics Lab, Cardiff School of Technologies, Cardiff Metropolitan University, Western Avenue, Cardiff, CF5 2YB, United Kingdom","Chew, E., EUREKA Robotics Lab, Cardiff School of Technologies, Cardiff Metropolitan University, Western Avenue, Cardiff, CF5 2YB, United Kingdom","This paper lays a disparity discussion for the key debates of two camps in robotics and machines intelligence for industrialization 4.0. Disseminating the deployment of service robots in two national venues in Wales, the interactive model and lessons learnt can be of reference to other human-robot interaction and social intelligence researchers and practitioners. The findings of the deployment blended with the author’s contribution to the AI Select Committee’s publications at the UK Parliament, leading to the practical recommendation to the government and policymakers for a national policy implication. © Springer Nature Singapore Pte Ltd. 2020.",,,,,,,,,,,,,,,,,,"(2017) Written Evidence to UK Parliament Artificial Intelligence Select committee’s Publications, , https://www.parliament.uk/documents/lordscommittees/Artificial-Intelligence/AI-Written-Evidence-Volume.pdf; http://www.bbc.co.uk/news/technology39077618; Ding, J., (2018) Deciphering China’s AI Dream, the Context, Components, Capabilities, and Consequences of China’s Strategy to Lead the World in AI, , Faculty of Humanity Institute, University of Oxford; CBR Government, UK Government’s Biggest Investments to Date, , http://www.cbrgovernment.com/central-government/uk-governments-biggest-investmentsdate; http://ec.europa.eu/programmes/horizon2020/en/h2020section/robotics; Robotics Funding Opportunities in 2017, , https://www.eurobotics.net/sparc/upload/Newsroom/Press/2016/files/Bratislava.pdf; (2018) Written Evidence to UK Parliament Artificial Intelligence Select committee’s Publications, p. 117. , http://data.parliament.uk/writtenevidence/committeeevidence.svc/evidencedocument/artificial-intelligence-committee/artificial-intelligence/written/69650.html, BBC—Written evidence (AIC0204); Chew, E., What are the implications of artificial intelligence? (2017) Love and War, Written Evidence, , UK Parliament Artificial Intelligence Select Committee’s Publications; Innovate, U.K., (2017) Written Evidence to UK Parliament Artificial Intelligence Select Committee’s Publications, , http://data.parliament.uk/writtenevidence/committeeevidence.svc/evidencedocument/artificial-intelligence-committee/artificial-intelligence/written/70516.html, https://www.gov.uk/government/organisations/innovate-uk/about; Griffin, A., Independent News, , http://www.independent.co.uk/life-style/gadgets-andtech/news/ai-artificial-intelligence-bots-drones-danger-experts-cambridge-universityopenai-elon-musk-a8219961.html#gallery; Frey, C.N., Osborne, M., (2013) The Future of Employment: How Susceptible are Jobs to Comput-Erisation? Oxford Martin Programme, , The University of Oxford; (2018) Proceedings of the 6Th International Conference on Robot Intelligence Technology and Applications: Robotics and Machines Intelligence is the Building Blocks for Industrial, 4, p. 0. , http://2018.icrita.org; (2017) Written Evidence to UK Parliament Artificial Intelligence Select committee’s Publications, , http://data.parliament.uk/writtenevidence/committeeevidence.svc/evidencedocument/artificial-intelligence-committee/artificial-intelligence/written/69668.html; Parliament, U.K., AI in the UK: Ready, willing and able? The Authority of the House of Lords (2018) Select Committee in Artificial Intelligence; Interview with Oxford Philosopher Nick Bostrom, , https://www.theguardian.com/technology/2016/jun/12/nick-bostrom-artificial-intelligencemachine; Chen, Z., (2017) AI Takes a Look at Legal Evidence, , http://www.chinadaily.com.cn/china/2017-07/11/content_30064693.htm, China Daily; Ding, J., (2018) Deciphering China’s AI Dream: The Context, Components, Capabilities, and Consequences of China’s Strategy to Lead The World in AI, , University of Oxford; (2017) Written Evidence to UK Parliament Artificial Intelligence Select committee’s Publications, , http://data.parliament.uk/writtenevidence/committeeevidence.svc/evidencedocument/artificial-intelligence-committee/artificial-intelligence/written/70507.html; Wong, N.W.H., Chew, E., Wong, J.S.-M., A review of educational robotics and the need for real-world learning analytics (2016) 14Th International Conference on Control, Automation, Robotics and Vision (ICARCV), Phuket, Thailand, 13, , Nov; Chew, E., Lee, P.H., Dharmaratne, A., Chen, B.W., Raju, D.S., SUHAKAM—Going Digital with Monash (2016) The 3Rd Asian Symposium on Human Rights Education. Fukuoka, Japan, 4–6, , Aug; Chua, X.N., Chew, E., The next wave of learning with humanoid robot: Learning innovation design starts with “Hello NAO” (2015) Globally Connected, Digitally Enabled. Proceedings Ascilite, pp. 52-56. , Reiners T, von Konsky BR, Gibson D, Chang V, Irving L, Clarke K (eds), Perth; (2018) Cardiff Museum’ Exhibition, , https://www.artfund.org/whatson/exhibitions/2018/06/16/kizuna-japan-wales-design-exhibition; Humanoid Robots at National Museum, , https://www.cardiffmet.ac.uk/news/Pages/Cardiff-Met-humanoid-robots-greet-guests-atNational-Museum-Cardiff%E2%80%99s-new–Japan-Exhibition.aspx; Tanabata Festival, , https://museum.wales/cardiff/whatson/10102/Tanabata-Star-Festival; (2018) Wales Festival, , https://eisteddfod.wales/about-us; Meeting the Welsh Robots, , https://www.cardifflocal.tv/videos/meetingthe-welsh-robots-x6rq528/; (2018) What is a Data Trust, , https://theodi.org/article/what-is-a-data-trust/; Mastercard and IBM to Set up European Data Trust, , https://www.ft.com/content/576171dc-27ab-11e8-b27e-cc62a39d57a0; (2018) Right to Data Portability, , https://ico.org.uk/for-organisations/guide-to-thegeneral-data-protection-regulation-gdpr/individual-rights/right-to-data-portability; (2018) Art 20 GDPR, , https://gdpr-info.eu/art-20-gdpr; (2018) AI International Competition and the Balance of Power, , https://tnsr.org/2018/05/artificial-intelligence-international-competition-and-thebalance-of-power/; (2018) Global Competition Rises for AI Industrial Robotics, , https://www.techemergence.com/global-competition-rises-ai-industrial-robotics/; (2016) Executive Summary World Industrial Robotics, , https://ifr.org/img/uploads/Executive_Summary_WR_Industrial_Robots_20161.pdf; (2017) Written Evidence to UK Parliament Artificial Intelligence Select committee’s Publications, , http://data.parliament.uk/writtenevidence/committeeevidence.svc/evidencedocument/artificial-intelligence-committee/artificial-intelligence/written/69650.html; (2017) Written Evidence to UK Parliament Artificial Intelligence Select committee’s Publications, , http://data.parliament.uk/writtenevidence/committeeevidence.svc/evidencedocument/artificial-intelligence-committee/artificial-intelligence/written/70754.html; (2017) Report on Artificial Intelligence and Human Society, , http://www8.cao.go.jp/cstp/tyousakai/ai/summary/aisociety_en.pdf; Leong, T., Malaysia counts on tech to boost manufacturing (2018) The Straight Times, , http://grp.miti.gov.my/mitigrp/resources/Public%20Consultation/Industry4.0FrameworkLayout_PublicReview(9Feb)V3_pdf; (2018) National Industry 4, Policy Framework, , http://grp.miti.gov.my/mitigrp/resources/Public%20Consultation/Industry4.0FrameworkLayout_PublicReview(9Feb)V3_pdf; (2017) Written Evidence to UK Parliament Artificial Intelligence Select committee’s Publications, , https://www.parliament.uk/documents/lordscommittees/Artificial-Intelligence/AI-Written-Evidence-Volume.pdf; (2017) Principles of Robotics, , https://epsrc.ukri.org/research/ourportfolio/themes/engineering/activities/principlesofrobotics/; Bryson, J.J., The meaning of the EPSRC principles of robotics (2017) Connection Sci, 29, pp. 130-136; (2017) Malaysia Pushes Forward with Robotics Agenda, , https://oxfordbusinessgroup.com/news/malaysia-pushes-forward-robotics-agenda; Hamid, Z.A., (2017) The Robot Revolution, , https://www.nst.com.my/news/2017/04/228029/robot-revolution; (2016) Malaysian Government Encourages Manufacturing Automation, , http://www.miti.gov.my/index.php/pages/view/3526; (2018) Robotics Malaysia, , https://myras.org/robotics-malaysia/; Lin, P., Jenkins, R., Abney, K., (2017) Robot Ethics 2.0: From Autonomous Cars to Artificial Intelligence, , Oxford University Press; (2018) World Robot Summit, , https://ifr.org/ifr-press-releases/news/industrial-robotsales-increase-worldwide-by-29-percent","Chew, E.; EUREKA Robotics Lab, Western Avenue, United Kingdom; email: echew@cardiffmet.ac.uk",,,"Pleiades Publishing",,,,,21954356,,,,"English","Lect. Notes Mech. Eng.",Book Chapter,"Final","All Open Access, Green",Scopus,2-s2.0-85067669665
"Salm-Hoogstraeten S.V., Müsseler J.","57215551846;7003779205;","Human Cognition in Interaction With Robots: Taking the Robot’s Perspective Into Account",2021,"Human Factors","63","8",,"1396","1407",,4,"10.1177/0018720820933764","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087665075&doi=10.1177%2f0018720820933764&partnerID=40&md5=c485af15d0ef9b7c6f8909b45fd98924","RWTH Aachen University, Germany","Salm-Hoogstraeten, S.V., RWTH Aachen University, Germany; Müsseler, J., RWTH Aachen University, Germany","Objective: The present study investigated whether and how different human–robot interactions in a physically shared workspace influenced human stimulus–response (SR) relationships. Background: Human work is increasingly performed in interaction with advanced robots. Since human–robot interaction often takes place in physical proximity, it is crucial to investigate the effects of the robot on human cognition. Method: In two experiments, we compared conditions in which humans interacted with a robot that they either remotely controlled or monitored under otherwise comparable conditions in the same shared workspace. The cognitive extent to which the participants took the robot’s perspective served as a dependent variable and was evaluated with a SR compatibility task. Results: The results showed pronounced compatibility effects from the robot’s perspective when participants had to take the perspective of the robot during the task, but significantly reduced compatibility effects when human and robot did not interact. In both experiments, compatibility effects from the robot’s perspective resulted in statistically significant differences in response times and in error rates between compatible and incompatible conditions. Conclusion: We concluded that SR relationships from the perspective of the robot need to be considered when designing shared workspaces that require users to take the perspective of the robot. Application: The results indicate changed compatibility relationships when users share their workplace with an interacting robot and therefore have to take its perspective from time to time. The perspective-dependent processing times are expected to be accompanied by corresponding error rates, which might affect—for instance—safety and efficiency in a production process. © Copyright 2020, The Author(s).","human–robot collaboration; human–robot interaction; Simon effect; stimulus–response compatibility","Ergonomics; Human engineering; Dependent variables; Physical proximity; Production process; Robot interactions; S-r compatibilities; Safety and efficiencies; Shared-workspace; Statistically significant difference; Human robot interaction; adult; article; cognition; controlled study; dependent variable; female; human; human experiment; male; reaction time; robotics; Simon task; workplace",,,,,"Deutsche Forschungsgemeinschaft, DFG: MU 1298/11, SPP 2134","The authors declare that the study was conducted and the manuscript prepared without financial considerations or other conflicts of interest. This study was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation; project number MU 1298/11) and was associated with the DFG Priority Program “The Active Self” (DFG SPP 2134). We thank Judith Lambertz and Plamenna Koleva for running the experiments. We would also like to thank Marius Wegener for programming the robot.",,,,,,,,,,"Böffel, C., Müsseler, J., Visual perspective taking for avatars in a Simon task (2019) Attention, Perception, & Psychophysics, 81, pp. 158-172. , doi:10.3758/s13414-018-1573-0, (, a; Böffel, C., Müsseler, J., Action effect consistency and body ownership in the avatar-Simon task (2019) Plos One, 14. , doi:10.1371/journal.pone.0220817, http://www.ncbi.nlm.nih.gov/pubmed/31398233, (, b; Cavallo, A., Ansuini, C., Capozzi, F., Tversky, B., Becchio, C., When far becomes near: Perspective taking induces social remapping of spatial relations (2017) Psychological Science, 28, pp. 69-79; Cousineau, D., Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson’s method (2005) Tutorials in Quantitative Methods for Psychology, 1, pp. 42-45. , doi:10.20982/tqmp.01.1.p042; Flavell, J.H., Everett, B.A., Croft, K., Flavell, E.R., Young children’s knowledge about visual perception: Further evidence for the Level 1-Level 2 distinction (1981) Developmental Psychology, 17, pp. 99-103. , doi:10.1037/0012-1649.17.1.99; Freundlieb, M., Kovács, Á.M., Sebanz, N., When do humans spontaneously adopt another’s visuospatial perspective? (2016) Journal of Experimental Psychology: Human Perception and Performance, 42, pp. 401-412. , doi:10.1037/xhp0000153, http://www.ncbi.nlm.nih.gov/pubmed/26480249; Hoffman, G., Evaluating fluency in human–robot collaboration (2019) IEEE Transactions on Human-Machine Systems, 49, pp. 209-218. , doi:10.1109/THMS.2019.2904558; Hoffman, G., Breazeal, C., Cost-based anticipatory action selection for human–robot fluency (2007) IEEE Transactions on Robotics, 23, pp. 952-961. , doi:10.1109/TRO.2007.907483; (2016) Robots and robotic devices - Collaborative robots (ISO/TS 15066:2016(E)), , https://www.perinorm.com/document.aspx; Kirk, R.E., (1995) Experimental design, , 3rd ed., Brooks/Cole; Kleiner, M., Brainard, D.H., Pelli, D.G., What’s new in Psychtoolbox-3? (2007) Perception, 36; Kornblum, S., Hasbroucq, T., Osman, A., Dimensional overlap: cognitive basis for stimulus-response compatibility—a model and taxonomy (1990) Psychological Review, 97, pp. 253-270. , doi:10.1037/0033-295X.97.2.253, 2186425; Lasota, P.A., Fong, T., Shah, J.A., A survey of methods for safe human-robot interaction (2014) Foundations and Trends in Robotics, 5, pp. 261-349. , doi:10.1561/2300000052; Lasota, P.A., Shah, J.A., Analyzing the effects of human-aware motion planning on close-proximity human-robot collaboration (2015) Human Factors, 57, pp. 21-33. , doi:10.1177/0018720814565188, http://www.ncbi.nlm.nih.gov/pubmed/25790568; Müsseler, J., Ruhland, L., Böffel, C., Reversed effect of spatial compatibility when taking avatar’s perspective (2019) Quarterly Journal of Experimental Psychology, 72, pp. 1539-1549. , doi:10.1177/1747021818799240; Müsseler, J., Skottke, E.-M., Compatibility relationships with simple lever tools (2011) Human Factors, 53, pp. 383-390. , doi:10.1177/0018720811408599, 21901935; Onnasch, L., Maier, X., Jürgensohn, T., (2016) Mensch-Roboter-Interaktion – Eine Taxonomie für alle Anwendungsfälle. baua: Fokus, Bundesanstalt für Arbeitsschutz und Arbeitsmedizin (1. 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Reading, , Addison-Wesley; von Salm-Hoogstraeten, S., Bolzius, K., Müsseler, J., Seeing the world through the eyes of an avatar? Comparing perspective taking and referential coding (2020) Journal of Experimental Psychology: Human Perception and Performance, 46, pp. 264-273. , doi:10.1037/xhp0000711, http://www.ncbi.nlm.nih.gov/pubmed/32077741","Salm-Hoogstraeten, S.V.; RWTH Aachen UniversityGermany; email: salm@psych.rwth-aachen.de",,,"SAGE Publications Inc.",,,,,00187208,,HUFAA,,"English","Hum. Factors",Note,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85087665075
"Czeszumski A., Gert A.L., Keshava A., Ghadirzadeh A., Kalthoff T., Ehinger B.V., Tiessen M., Björkman M., Kragic D., König P.","57200248992;55016203000;57202213703;57199671469;57223348126;56644285200;57223344429;7005103070;6701873248;57193206133;","Coordinating With a Robot Partner Affects Neural Processing Related to Action Monitoring",2021,"Frontiers in Neurorobotics","15",,"686010","","",,1,"10.3389/fnbot.2021.686010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113457169&doi=10.3389%2ffnbot.2021.686010&partnerID=40&md5=543ebbfb1025b6cad790417de1e07716","Institute of Cognitive Science, Universität Osnabrück, Osnabrück, Germany; Robotics, Perception and Learning, School of Electrical Engineering and Computer Science, Kungliga Tekniska Högskolan Royal Institute of Technology, Stockholm, Sweden; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands; Stuttgart Center for Simulation Science, University of Stuttgart, Stuttgart, Germany; Institut für Neurophysiologie und Pathophysiologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany","Czeszumski, A., Institute of Cognitive Science, Universität Osnabrück, Osnabrück, Germany; Gert, A.L., Institute of Cognitive Science, Universität Osnabrück, Osnabrück, Germany; Keshava, A., Institute of Cognitive Science, Universität Osnabrück, Osnabrück, Germany; Ghadirzadeh, A., Robotics, Perception and Learning, School of Electrical Engineering and Computer Science, Kungliga Tekniska Högskolan Royal Institute of Technology, Stockholm, Sweden; Kalthoff, T., Institute of Cognitive Science, Universität Osnabrück, Osnabrück, Germany; Ehinger, B.V., Institute of Cognitive Science, Universität Osnabrück, Osnabrück, Germany, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, Stuttgart Center for Simulation Science, University of Stuttgart, Stuttgart, Germany; Tiessen, M., Institute of Cognitive Science, Universität Osnabrück, Osnabrück, Germany; Björkman, M., Robotics, Perception and Learning, School of Electrical Engineering and Computer Science, Kungliga Tekniska Högskolan Royal Institute of Technology, Stockholm, Sweden; Kragic, D., Robotics, Perception and Learning, School of Electrical Engineering and Computer Science, Kungliga Tekniska Högskolan Royal Institute of Technology, Stockholm, Sweden; König, P., Institute of Cognitive Science, Universität Osnabrück, Osnabrück, Germany, Institut für Neurophysiologie und Pathophysiologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany","Robots start to play a role in our social landscape, and they are progressively becoming responsive, both physically and socially. It begs the question of how humans react to and interact with robots in a coordinated manner and what the neural underpinnings of such behavior are. This exploratory study aims to understand the differences in human-human and human-robot interactions at a behavioral level and from a neurophysiological perspective. For this purpose, we adapted a collaborative dynamical paradigm from the literature. We asked 12 participants to hold two corners of a tablet while collaboratively guiding a ball around a circular track either with another participant or a robot. In irregular intervals, the ball was perturbed outward creating an artificial error in the behavior, which required corrective measures to return to the circular track again. Concurrently, we recorded electroencephalography (EEG). In the behavioral data, we found an increased velocity and positional error of the ball from the track in the human-human condition vs. human-robot condition. For the EEG data, we computed event-related potentials. We found a significant difference between human and robot partners driven by significant clusters at fronto-central electrodes. The amplitudes were stronger with a robot partner, suggesting a different neural processing. All in all, our exploratory study suggests that coordinating with robots affects action monitoring related processing. In the investigated paradigm, human participants treat errors during human-robot interaction differently from those made during interactions with other humans. These results can improve communication between humans and robot with the use of neural activity in real-time. © Copyright © 2021 Czeszumski, Gert, Keshava, Ghadirzadeh, Kalthoff, Ehinger, Tiessen, Björkman, Kragic and König.","action monitoring; EEG; embodied cognition; ERP; human-robot interaction; joint action; social neuroscience","Electroencephalography; Electrophysiology; Errors; Man machine systems; Neurons; Behavioral level; Corrective measures; Event related potentials; Exploratory studies; Human conditions; Neural-processing; Positional errors; Social landscapes; Social robots",,,,,"Deutsche Forschungsgemeinschaft, DFG: 390740016, GRK 2185/1; Bundesministerium für Bildung und Forschung, BMBF: ErgoVR-16SV8052","We gratefully acknowledge the support by the European Commission Horizon H2020-FETPROACT-2014 641321-socSMCs, Deutsche Forschungsgemeinschaft (DFG) funded","We would like to thank all partners in the socSMC consortium. Especially, we thank Alfred O. Effenberg and Tong-Hun Hwang for providing the sonification algorithm, and the help with implementing it. Funding. We gratefully acknowledge the support by the European Commission Horizon H2020-FETPROACT-2014 641321-socSMCs, Deutsche Forschungsgemeinschaft (DFG) funded Research Training Group Situated Cognition (GRK 2185/1), Nieders?chsischen Innovationsf?rderprogramms f?r Forschung und Entwicklung in Unternehmen (NBank)?EyeTrax, the German Federal Ministry of Education and Research funded project ErgoVR-16SV8052 and the DFG Open Access Publishing Fund of Osnabr?ck University. We acknowledge the support of Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy?EXC 2075?390740016 for BE.",,,,,,,,,"Baillet, S., Magnetoencephalography for brain electrophysiology and imaging (2017) Nat. 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"Choi S., Lee H., Lim Y., Choi J., Sung J.E.","57191965225;57226324922;24740711800;55994218300;21735141600;","Toward a conversational model for counsel robots: how different question types elicit different linguistic behaviors",2021,"Intelligent Service Robotics","14","3",,"373","385",,,"10.1007/s11370-021-00375-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111122364&doi=10.1007%2fs11370-021-00375-6&partnerID=40&md5=220caf802af7e8be8a24186525d11c7a","Department of Communication Disorders, Ewha Womans University, Education Building A-306, 52 Ewhayeodae-gil, Seodaemoon-gu, Seoul, South Korea; The Center for Intelligent and Interactive Robotics, Korea Institute of Science and Technology, Seoul, South Korea; Department of Computer Science and Engineering, Korea University, Seoul, South Korea; Department of HY-KIST Bio-Convergence, Hanyang University, Seoul, South Korea","Choi, S., Department of Communication Disorders, Ewha Womans University, Education Building A-306, 52 Ewhayeodae-gil, Seodaemoon-gu, Seoul, South Korea; Lee, H., The Center for Intelligent and Interactive Robotics, Korea Institute of Science and Technology, Seoul, South Korea, Department of Computer Science and Engineering, Korea University, Seoul, South Korea; Lim, Y., The Center for Intelligent and Interactive Robotics, Korea Institute of Science and Technology, Seoul, South Korea, Department of HY-KIST Bio-Convergence, Hanyang University, Seoul, South Korea; Choi, J., The Center for Intelligent and Interactive Robotics, Korea Institute of Science and Technology, Seoul, South Korea; Sung, J.E., Department of Communication Disorders, Ewha Womans University, Education Building A-306, 52 Ewhayeodae-gil, Seodaemoon-gu, Seoul, South Korea","In recent years, robots have been playing the role of counselor or conversational partner in everyday dialogues and interactions with humans. For successful human–robot communication, it is very important to identify the best conversational strategies that can influence the responses of the human client in human–robot interactions. The purpose of the present study is to examine linguistic behaviors in human–human conversation using chatting data to provide the best model for effective conversation in human–robot interaction. We analyzed conversational data by categorizing them into question types, namely Wh-questions and “yes” or “no” (YN) questions, and their correspondent linguistic behaviors (self-disclosure elicitation, self-disclosure, simple “yes” or “no” answers, and acknowledgment). We also compared the utterance length of clients depending on the question type. In terms of linguistic behaviors, the results reveal that the Wh-question type elicited significantly higher rates of self-disclosure elicitation and acknowledgment than YN-questions. Among the Wh-subtype, how was found to promote more linguistic behaviors such as self-disclosure elicitation, self-disclosure, and acknowledgment than other Wh-subtypes. On the other hand, YN-questions generated significantly higher rates of simple “yes” or “no” answers compared to the Wh-question. In addition, Wh-question type elicited longer utterance than the YN-question type. We suggested that the type of questions of the robot counselor must be considered to elicit various linguistic behaviors and utterances of humans. Our research is meaningful in providing efficient conversation strategies for robot utterances that conform to humans’ linguistic behaviors. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.","Conversational strategy; Human–robot dialogue model; Linguistic behavior; Question type","Agricultural robots; Behavioral research; Linguistics; Best model; Conversational model; Question type; Robot communication; Robot interactions; Self-disclosure; Social robots",,,,,"Ministry of Trade, Industry and Energy, MOTIE","This work was supported by the Technology Innovation Program-Industrialized Technology Innovation Project (10077553, Development of Social Robot Intelligence for Social Human–Robot Interaction of Service Robots) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea).","This work was supported by the Technology Innovation Program-Industrialized Technology Innovation Project (10077553, Development of Social Robot Intelligence for Social Human-Robot Interaction of Service Robots) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea).",,,,,,,,,"Uchida, T., Takahashi, H., Ban, M., Shimaya, J., Yoshikawa, Y., Ishiguro, H., A robot counseling system—what kinds of topics do we prefer to disclose to robots? 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Cengage Learning; Long, L., Long, T.J., Paradise, L.V., (1981) Questioning: Skills for the Helping Process, , Brooks/Cole; Agnew, S.E., Powell, M.B., The effect of intellectual disability on children’s recall of an event across different question types (2004) Law Hum Behav, 28 (3), pp. 273-294; Gudjonsson, G.H., Henry, L., Child and adult witnesses with intellectual disability: the importance of suggestibility (2003) Leg Criminol Psychol, 8 (2), pp. 241-252; Hershkowitz, I., Nichd-protocol investigations of individuals with intellectual disability: a descriptive analysis (2018) Psychol Public Policy Law, 24 (3), p. 393; Savage, L.B., Eliciting critical thinking skills through questioning (1998) The Clearing House, 71 (5), pp. 291-293; Kobayashi, Y., Yamamoto, D., Koga, T., Yokoyama, S., Doi, M., Design targeting voice interface robot capable of active listening (2010) 2010 5Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), IEEE, pp. 161-162; Steinfeld, A., Fong, T., Kaber, D., Lewis, M., Scholtz, J., Schultz, A., Goodrich, M., Common metrics for human-robot interaction (2006) Proceedings of the 1St ACM SIGCHI/SIGART Conference on Human-Robot Interaction, pp. 33-40; Raymond, G., Grammar and social organization: Yes/no interrogatives and the structure of responding (2003) American sociological review, pp. 939-967; Yoon, K.E., Questions and responses in korean conversation (2010) J Pragmat, 42 (10), pp. 2782-2798; Coronado, M., Iglesias, C.A., Carrera, A., Mardomingo, A., A cognitive ´ assistant for learning java featuring social dialogue (2018) Int J Hum Comput Stud, 117, pp. 55-67; Li, Y., Su, H., Shen, X., Li, W., Cao, Z., Niu, S., (2017) Dailydialog: A manually labelled multi-turn dialogue dataset, , https://arxiv.org/abs/1710.03957","Sung, J.E.; Department of Communication Disorders, Education Building A-306, 52 Ewhayeodae-gil, Seodaemoon-gu, South Korea; email: jeesung@ewha.ac.kr",,,"Springer Science and Business Media Deutschland GmbH",,,,,18612776,,,,"English","Intelligent Serv. Rob.",Article,"Final","",Scopus,2-s2.0-85111122364
"Bernotat J., Eyssel F., Sachse J.","57191710906;9846264800;57197871665;","The (Fe)male Robot: How Robot Body Shape Impacts First Impressions and Trust Towards Robots",2021,"International Journal of Social Robotics","13","3",,"477","489",,7,"10.1007/s12369-019-00562-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066286472&doi=10.1007%2fs12369-019-00562-7&partnerID=40&md5=89e310456005074b7d28f55bca367de1","Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, Inspiration 1, Bielefeld, 33619, Germany","Bernotat, J., Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, Inspiration 1, Bielefeld, 33619, Germany; Eyssel, F., Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, Inspiration 1, Bielefeld, 33619, Germany; Sachse, J., Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, Inspiration 1, Bielefeld, 33619, Germany","Humanlikeness, including robot gender, impacts people’s impression of social robots (Eyssel and Hegel in J Appl Soc Psychol 42(9):2213–2230, 2012) and actual human robot interaction (HRI) (Kuchenbrandt et al. in Int J Soc Robot 6(3):417–427, 2014; Reich-Stiebert and Eyssel in Proceedings of the 2017 ACM/IEEE international conference on human–robot interaction, ACM, pp 166–176, 2017). Although robot gender has been manipulated in various ways in previous research (Alexander et al. in Proceedings of the annual meeting of the Cognitive Science Society, vol 36, 2014; Eyssel and Hegel, 2012), robot body shape as a gender cue has been neglected in this context. Therefore, the current research investigated the effects of manipulating a robot torso’s waist-to-hip ratio and shoulder width on social judgments of a robot. As hypothesized, a robot with a female body shape was perceived as more communal, it was preferred for stereotypically female tasks, and evoked more cognitive and affective trust than a robot with a male body shape. Unexpectedly, both robot types were perceived as equally agentic and they were deemed equally suitable for stereotypically male tasks. Above and beyond, participants’ motivation to respond in a socially desirable manner, their societal beliefs about agentic and communal traits considered appropriate for men and women, sexist attitudes, gender, and technology commitment affected their impression formation about robots. We point to the risks of designing gendered robots and recommend to manipulate robot gender deliberately with regard to the effects this might have on HRI. © 2019, Springer Nature B.V.","Cognitive and affective trust in robots; Gender stereotypes; Robot body shape; Robot gender","Agricultural robots; Economic and social effects; Cognitive science; First impressions; Gender stereotypes; Human likeness; Human robot Interaction (HRI); Robot body; Robot interactions; Technology commitment; Social robots",,,,,"Deutsche Forschungsgemeinschaft, DFG; Universität Bielefeld","This research was supported by the Cluster of Excellence Cognitive Interaction Technology ?CITEC? (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG). The current research was approved by Bielefeld University?s local Ethics Committee. We report all data exclusions (if any), all manipulations, all measures in the study, and how sample sizes were calculated. The authors declare to have no conflicts of interest.",,,,,,,,,,"Abele, A.E., The dynamics of masculine-agentic and feminine-communal traits: findings from a prospective study (2003) J Pers Soc Psychol, 85 (4), p. 768; (2014) Asking for Help from a Gendered Robot, p. 36. , In, Proceedings of the annual meeting of the Cognitive Science Society, vol; Ashmore, R.D., Del Boca, F.K., Sex stereotypes and implicit personality theory: toward a cognitive-social psychological conceptualization (1979) Sex Roles, 5 (2), pp. 219-248; Asimov, I., (2011) Alle Roboter-Geschichten, 23317. , Bastei Lübbe, Cologne; Auster, C.J., Ohm, S.C., Masculinity and femininity in contemporary American society: a reevaluation using the bem sex-role inventory (2000) Sex Roles, 43 (7-8), pp. 499-528; A cross-cultural study on attitudes towards robots (2005) Proceedings of the 11Th International Conference on Human-Computer Interaction, 22–27, July 2005, Caesars Palace, Las Vegas, Nevada USA. 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In: 2018 27th IEEE international symposium on robot and human interactive communication (RO-MAN). IEEE (2018) Pp 15–22; Bernotat, J., Eyssel, F., Sachse, J., Shape it–the influence of robot body shape on gender perception in robots (2017) International Conference on Social Robotics. Springer, pp. 75-84; Cameron, D., Loh, E.J., Chua, A., Collins, E., Aitken, J.M., Law, J., (2016) Robot-Stated Limitations but Not Intentions Promote User Assistance. Arxiv, 1606, p. 02603; Capek, K., Rossum’s Universal Robots (1920) Wildside Press; Disalvo, C.F., Gemperle, F., Forlizzi, J., Kiesler, S., All robots are not created equal: The design and perception of humanoid robot heads. In: Proceedings of the 4th conference on designing interactive systems: Processes, practices, methods, and techniques. ACM (2002) Pp 321–326; Duffy, B.R., Anthropomorphism and the social robot (2003) Robot Auton Syst, 42 (3-4), pp. 177-190; Duffy, S.A., Keir, J.A., Violating stereotypes: eye movements and comprehension processes when text conflicts with world knowledge (2004) Mem Cogn, 32 (4), pp. 551-559; Eagly, A.H., Mladinic, A., Gender stereotypes and attitudes toward women and men (1989) Personal Soc Psychol Bull, 15 (4), pp. 543-558; Eckes, T., Geschlechterstereotype: Von rollen, identitäten und vorurteilen (2004) Handbuch Frauen-Und Geschlechterforschung. 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Formal and conceptual aspects of comprehending personal nouns (2007) J Psycholinguist Res, 36 (6), pp. 431-456; Irmen, L., Roßberg, N., Gender markedness of language: the impact of grammatical and nonlinguistic information on the mental representation of person information (2004) J Lang Soc Psychol, 23 (3), pp. 272-307; Johnson, D., Grayson, K., Cognitive and affective trust in service relationships (2005) J Bus Res, 58 (4), pp. 500-507; Johnson, K.L., Tassinary, L.G., Perceiving sex directly and indirectly: meaning in motion and morphology (2005) Psychol Sci, 16 (11), pp. 890-897; Jung, E.H., Waddell, T.F., Sundar, S.S., Feminizing robots: User responses to gender cues on robot body and screen (2016) Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 3107-3113. , ACM; Kuchenbrandt, D., Häring, M., Eichberg, J., Eyssel, F., André, E., Keep an eye on the task! how gender typicality of tasks influence human–robot interactions (2014) Int J Soc Robot, 6 (3), pp. 417-427; Lewis, J.D., Weigert, A., Trust as a social reality (1985) Soc Forces, 63 (4), pp. 967-985; Lippa, R., Sex typing and the perception of body outlines (1983) J Personal, 51 (4), pp. 667-682; Marti, B., Tuomilehto, J., Salomaa, V., Kartovaara, L., Korhonen, H.J., Pietinen, P., Body fat distribution in the finnish population: environmental determinants and predictive power for cardiovascular risk factor levels (1991) J Epidemiol Commun Health, 45 (2), pp. 131-137; Martin, D., Macrae, C.N., A face with a cue: exploring the inevitability of person categorization (2007) Eur J Soc Psychol, 37 (5), pp. 806-816; McAllister, D.J., Affect-and cognition-based trust as foundations for interpersonal cooperation in organizations (1995) Acad Manag J, 38 (1), pp. 24-59; Neyer, F.J., Felber, J., Gebhardt, C., Entwicklung und validierung einer kurzskala zur erfassung von technikbereitschaft (2012) Diagnostica; Pyykkönen, P., Hyönä, J., van Gompel, R.P.G., Activating gender stereotypes during online spoken language processing evidence from visual world eye tracking (2010) Exp Psychol, 57, pp. 126-133; Reich-Stiebert, N., Eyssel, F., Ir)relevance of gender? 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The stability and importance of individual differences in anthropomorphism (2010) Perspect Psychol Sci, 5 (3), pp. 219-232","Bernotat, J.; Cluster of Excellence Cognitive Interaction Technology (CITEC), Inspiration 1, Germany; email: jbernotat@cit-ec.uni-bielefeld.de",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85066286472
"Sun J., Shen Y., Rosen J.","57210645201;57203599877;7402343194;","Sensor reduction, estimation, and control of an upper-limb exoskeleton",2021,"IEEE Robotics and Automation Letters","6","2","9345464","1012","1019",,,"10.1109/LRA.2021.3056366","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100772132&doi=10.1109%2fLRA.2021.3056366&partnerID=40&md5=8910cabd9be43060a2510aed70266ae3","Department of Mechanical and Aerospace Engineering, Bionics Lab, University of California, Los Angeles, CA  90095, United States","Sun, J., Department of Mechanical and Aerospace Engineering, Bionics Lab, University of California, Los Angeles, CA  90095, United States; Shen, Y., Department of Mechanical and Aerospace Engineering, Bionics Lab, University of California, Los Angeles, CA  90095, United States; Rosen, J., Department of Mechanical and Aerospace Engineering, Bionics Lab, University of California, Los Angeles, CA  90095, United States","A multi-degree-of-freedom (multi-DOF) exoskeleton relies on an array of sensors to communicate its state (e.g., positions/orientations) and operator-exoskeleton contact interactions (e.g., forces/torques) to its control system. Although sensor redundancy is common in biological systems to cope with uncertainty and partial failure of sensors, in man-made systems, sensor redundancy increases the overall system's cost and control complexity. This study presents a sensor reduction technique for force/torque (F/T) sensors utilizing a Kalman filter-based sensor fusion system in the context of admittance control. The methodology is applied to the EXO-UL8 exoskeleton, which is a powered, redundant, dual-arm, upper-limb robotic system with (7 arm + 1 hand) DOFs incorporating three 6-axis F/T sensors in each arm. Motivated by improving wearability through minimizing human-exoskeleton contact interfaces, which reduces spurious contact forces due to joint misalignment; and reducing cost, the proposed strategy emulates the admittance controller's virtual dynamics with only a subset of sensors, resulting in the physical human-robot interaction feeling the same from the operator's perspective. Experimental results indicate that human-exoskeleton power exchange and actuation stresses of the operator's joints, with the proposed strategy on a subset of two sensors, are comparable to those in the full three-sensor case (p < 0.01). The experiments verify the proposed methodology for the EXO-UL8, and support the feasibility of operating other Kalman filter-based sensor fusion systems with fewer sensors without sacrificing transparency in physical human-robot interaction. © 2016 IEEE.","Compliance and impedance control; physical human-robot interaction; prosthetics and exoskeletons; rehabilitation robotics; wearable robotics","Agricultural robots; Degrees of freedom (mechanics); Joints (anatomy); Kalman filters; 6-axis force/torque sensors; Actuation stress; Exoskeleton systems; Kinematic redundancy; Sensing performance; Sensor fusion systems; Sensor reduction; Tuning strategy; Exoskeleton (Robotics)",,,,,,,,,,,,,,,,"Shen, Y., Sun, J., Ma, J., Rosen, J., Admittance control scheme comparison of EXO-UL8: A dual-arm exoskeleton robotic system (2019) Proc. Ieee 16th Int. Conf. Rehabil. Robot., pp. 611-617. , Jun; Shen, Y., Ma, J., Dobkin, B., Rosen, J., Asymmetric dual arm approach for post stroke recovery of motor functions utilizing the EXO-UL8 exoskeleton system: A. pilot study (2019) Proc. 40th Annu. Int. Conf. Ieee Eng. Med. Biol. Soc., pp. 1701-1707; Shen, Y., Hsiao, B.P., Ma, J., Rosen, J., Upper limb redundancy resolution under gravitational loading conditions: Arm postural stability index based on dynamicmanipulability analysis (2017) Proc. IEEE-RAS 17th Int. Conf. Humanoid Robot., pp. 332-338; Xia, L., Feng, Y., Zheng, L., Wang, C., Wu, X., Development of an adaptive iterative learning controller with sensorless force estimator for the hip-type exoskeleton (2019) Proc. Ieee Int. Conf. Robot. Biomimetics, pp. 2516-2521; Ugurlu, B., Nishimura, M., Hyodo, K., Kawanishi, M., Narikiyo, T., A framework for sensorless torque estimation and control in wearable exoskeletons (2012) Proc. 12th Ieee Int. Workshop Adv. 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Signal Process., 57, pp. 451-462. , Feb; Zare, A., Mohammadi, H., Dhingra, N.K., Georgiou, T.T., Jovanovic, M.R., Proximal algorithms for large-scale statistical modeling and sensor/actuator selection (2020) Ieee Trans. Autom. Control, 65 (8), pp. 3441-3456. , Aug; Augugliaro, F., D'Andrea, R., Admittance control for physical humanquadrocopter interaction (2013) Proc. Eur. Control Conf., pp. 1805-1810; Nagai, S., Oboe, R., Shimono, T., Kawamura, A., Fast force control without force sensor using combination of aakf and rfob for in-circuit test with probing system (2019) Ieej J. Ind. Appl., 8 (2), pp. 152-159; Akutsu, S., Sekiguchi, H., Nozaki, T., Murakami, T., Position and torque sensorless motion transmission for haptic teleoperation using two types of voltage compensation (2017) Proc. 24th Int. Conf. Mechatronics Mach. Vis. Pract., pp. 1-6; Hace, A., Franc, M., Pseudo-sensorless high-performance bilateral teleoperation by sliding-mode control and FPGA (2014) IEEE/ASME Trans. 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Psychol., 52, pp. 139-183; Lee, P., Wei, S., Zhao, J., Badler, N.I., Strength guided motion (1990) Acm Siggraph Comput. Graph., 24 (4), pp. 253-262","Sun, J.; Department of Mechanical and Aerospace Engineering, United States; email: sunjianw1@ucla.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85100772132
"Liu Y., Cui S., Sun Y.","55934204700;57195477875;56119187500;","Mechanical design and analysis of a novel variable stiffness actuator with symmetrical pivot adjustment",2021,"Frontiers of Mechanical Engineering",,,,"","",,,"10.1007/s11465-021-0647-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113921445&doi=10.1007%2fs11465-021-0647-1&partnerID=40&md5=ddafefba3767dea9685cff46e0935eda","State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150001, China","Liu, Y., State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150001, China; Cui, S., State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150001, China; Sun, Y., State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, 150001, China","The safety of human-robot interaction is an essential requirement for designing collaborative robotics. Thus, this paper aims to design a novel variable stiffness actuator (VSA) that can provide safer physical human-robot interaction for collaborative robotics. VSA follows the idea of modular design, mainly including a variable stiffness module and a drive module. The variable stiffness module transmits the motion from the drive module in a roundabout manner, making the modularization of VSA possible. As the key component of the variable stiffness module, a stiffness adjustment mechanism with a symmetrical structure is applied to change the positions of a pair of pivots in two levers linearly and simultaneously, which can eliminate the additional bending moment caused by the asymmetric structure. The design of the double-deck grooves in the lever allows the pivot to move freely in the groove, avoiding the geometric constraint between the parts. Consequently, the VSA stiffness can change from zero to infinity as the pivot moves from one end of the groove to the other. To facilitate building a manipulator in the future, an expandable electrical system with a distributed structure is also proposed. Stiffness calibration and control experiments are performed to evaluate the physical performance of the designed VSA. Experiment results show that the VSA stiffness is close to the theoretical design stiffness. Furthermore, the VSA with a proportional-derivative feedback plus feedforward controller exhibits a fast response for stiffness regulation and a good performance for position tracking.[Figure not available: see fulltext.] © 2021, The Author(s).","double-deck grooves; drive module; expandable electrical system; symmetrical structure; variable stiffness actuator; variable stiffness module",,,,,,"National Natural Science Foundation of China, NNSFC: 51805107; National Key Research and Development Program of China, NKRDPC: 2017YFB1300400","This work was supported by the National Key R&D Program of China (Grant No. 2017YFB1300400) and the National Natural Science Foundation of China (Grant No. 51805107).",,,,,,,,,,"Ren, Y., Chen, Z., Liu, Y., Adaptive hybrid position/force control of dual-arm cooperative manipulators with uncertain dynamics and closed-chain kinematics (2017) Journal of the Franklin Institute, 354 (17), pp. 7767-7793; Perrusquía, A., Yu, W., Soria, A., Position/force control of robot manipulators using reinforcement learning (2019) Industrial Robot, 46 (2), pp. 267-280; 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Stuhlenmiller, F., Clos, D., Rinderknecht, S., Impact of friction and gait parameters on the optimization of series elastic actuators for gait assistance (2019) Mechanism and Machine Theory, 133, pp. 737-749; Wang, P., Zhu, Q., Hu, X., Research on interaction safety of human-robot collision based on series elastic actuator (2018) Proceedings of the 5th International Conference on Information, Cybernetics, and Computational Social Systems, pp. 180-185. , IEEE, Hangzhou; Liu, Y., Wang, D., Yang, S., Design and experimental study of a passive power-source-free stiffness-self-adjustable mechanism (2021) Frontiers of Mechanical Engineering, 16 (1), pp. 32-45; Jiang, P., Yang, Y., Chen, M.Z., A variable stiffness gripper based on differential drive particle jamming (2019) Bioinspiration & Biomimetics, 14 (3), p. 036009; Liu, L., Leonhardt, S., Misgeld, B.J., Design and control of a mechanical rotary variable impedance actuator (2016) Mechatronics, 39, pp. 226-236; Pratt, G.A., Williamson, M., Series elastic actuators (1995) Proceedings of IEEE International Conference on Intelligent Robots and Systems, pp. 399-406. , IEEE, Pittsburgh; Cummings, J.P., Ruiken, D., Wilkinson, E.L., A compact, modular series elastic actuator (2016) Journal of Mechanisms and Robotics, 8 (4), p. 041016; Santos, W.M., Caurin, G.A., Siqueira, A.A., Design and control of an active knee orthosis driven by a rotary series elastic actuator (2017) Control Engineering Practice, 58, pp. 307-318; Braun, D., Howard, M., Vijayakumar, S., Optimal variable stiffness control: formulation and application to explosive movement tasks (2012) Autonomous Robots, 33 (3), pp. 1-17; Braun, D.J., Chalvet, V., Chong, T., Variable stiffness spring actuators for low-energy-cost human augmentation (2019) IEEE Transactions on Robotics, 35 (6), pp. 1435-1449; Vanderborght, B., Verrelst, B., Van Ham, R., Exploiting natural dynamics to reduce energy consumption by controlling the compliance of soft actuators (2006) International Journal of Robotics Research, 25 (4), pp. 343-358; Roozing, W., Li, Z., Medrano, C., Development and control of a compliant asymmetric antagonistic actuator for energy efficient mobility (2015) IEEE/ASME Transactions on Mechatronics, 21 (2), pp. 1080-1091; Visser, L.C., Carloni, R., Ünal, R., Modeling and design of energy efficient variable stiffness actuators (2010) Proceedings of IEEE International Conference on Robotics and Automation, pp. 3273-3278. , IEEE, Anchorage; Shao, Y., Zhang, W., Su, Y., Design and optimisation of loadadaptive actuator with variable stiffness for compact ankle exoskeleton (2021) Mechanism and Machine Theory, 161, p. 104323; Liu, Y., Liu, X., Yuan, Z., Design and analysis of spring parallel variable stiffness actuator based on antagonistic principle (2019) Mechanism and Machine Theory, 140, pp. 44-58; Guo, J., Tian, G., Mechanical design and analysis of the novel 6-DOF variable stiffness robot arm based on antagonistic driven joints (2016) Journal of Intelligent & Robotic Systems, 82 (2), pp. 207-235; Petit, F., Friedl, W., Höppner, H., Analysis and synthesis of the bidirectional antagonistic variable stiffness mechanism (2015) IEEE/ASME Transactions on Mechatronics, 20 (2), pp. 684-695; Friedl, W., Höppner, H., Petit, F., Wrist and forearm rotation of the DLR Hand Arm System: Mechanical design, shape analysis and experimental validation (2011) Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1836-1842. , IEEE, San Francisco; Vanderborght, B., Tsagarakis, N., Semini, C., MACCEPA 2.0: adjustable compliant actuator with stiffening characteristic for energy efficient hopping (2009) Proceedings of IEEE International Conference on Robotics and Automation, pp. 544-549. , IEEE, Kobe; Wolf, S., Hirzinger, G., A new variable stiffness design: Matching requirements of the next robot generation (2008) Proceedings of IEEE International Conference on Robotics and Automation. Pasadena, pp. 1741-1746; Wolf, S., Eiberger, O., Hirzinger, G., The DLR FSJ: energy based design of a variable stiffness joint (2011) Proceedings of IEEE International Conference on Robotics and Automation, pp. 5082-5089. , IEEE, Shanghai; Xu, Y., Guo, K., Li, J., A novel rotational actuator with variable stiffness using S-shaped springs (2021) IEEE/ASME Transactions on Mechatronics, 26 (4), pp. 2249-2260; Sun, J., Guo, Z., Zhang, Y., A novel design of serial variable stiffness actuator based on an Archimedean spiral relocation mechanism (2018) IEEE/ASME Transactions on Mechatronics, 23 (5), pp. 2121-2131; Sun, J., Zhang, Y., Zhang, C., Mechanical design of a compact serial variable stiffness actuator (SVSA) based on lever mechanism (2017) Proceedings of IEEE International Conference on Robotics and Automation, pp. 33-38. , IEEE, Singapore; Barrett, E., Fumagalli, M., Carloni, R., Elastic energy storage in leaf springs for a lever-arm based variable stiffness actuator (2016) Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 537-542. , IEEE, Daejeon; Groothuis, S., Rusticelli, G., Zucchelli, A., The variable stiffness actuator vsaUT-II: mechanical design, modeling, and identification (2014) IEEE/ASME Transactions on Mechatronics, 19 (2), pp. 589-597; Fumagalli, M., Barrett, E., Stramigioli, S., The mVSA-UT: a miniaturized differential mechanism for a continuous rotational variable stiffness actuator (2012) Proceedings of the 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 1943-1948. , IEEE, Rome; Tsagarakis, N., Sardellitti, I., Caldwell, D.G., A new variable stiffness actuator (CompAct-VSA): design and modelling (2011) Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 378-383. , IEEE, San Francisco; Jafari, A., Tsagarakis, N., Caldwell, D.G., AwAS-II: a new actuator with adjustable stiffness based on the novel principle of adaptable pivot point and variable lever ratio (2011) Proceedings of IEEE International Conference on Robotics and Automation, pp. 4638-4643. , IEEE, Shanghai; Visser, L.C., Carloni, R., Stramigioli, S., Energy-efficient variable stiffness actuators (2011) IEEE Transactions on Robotics, 27 (5), pp. 865-875; Rao, S., Carloni, R., Stramigioli, S., A novel energy-efficient rotational variable stiffness actuator (2011) Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 8175-8178. , IEEE, Boston; Visser, L.C., Carloni, R., Stramigioli, S., Variable stiffness actuators: a port-based analysis and a comparison of energy efficiency (2010) Proceedings of IEEE International Conference on Robotics and Automation, pp. 3279-3284. , IEEE, Anchorage; Sun, J., Guo, Z., Sun, D., Design, modeling and control of a novel compact, energy-efficient, and rotational serial variable stiffness actuator (SVSA-II) (2018) Mechanism and Machine Theory, 130, pp. 123-136; Wu, J., Wang, Z., Chen, W., Design and validation of a novel leaf spring-based variable stiffness joint with reconfigurability (2020) IEEE/ASME Transactions on Mechatronics, 25 (4), pp. 2045-2053; Shao, Y., Zhang, W., Ding, X., Configuration synthesis of variable stiffness mechanisms based on guide-bar mechanisms with lengthadjustable links (2021) Mechanism and Machine Theory, 156, p. 104153; Zhang, Z., Ni, F., Dong, Y., A novel absolute angular position sensor based on electromagnetism. Sensors and Actuators (2013) A, Physical, 194, pp. 196-203; Zhang, Z., Ni, F., Dong, Y., A novel absolute magnetic rotary sensor (2015) IEEE Transactions on Industrial Electronics, 62 (7), pp. 4408-4419; Liu, H., Meusel, P., Seitz, N., The modular multisensory DLRHIT-Hand (2007) Mechanism and Machine Theory, 42 (5), pp. 612-625; Santibañez, V., Kelly, R., PD control with feedforward compensation for robot manipulators: analysis and experimentation (2001) Robotica, 19 (1), pp. 11-19; Tomei, P., A simple PD controller for robots with elastic joints (1991) IEEE Transactions on Automatic Control, 36 (10), pp. 1208-1213","Liu, Y.; State Key Laboratory of Robotics and System, China; email: lyw@hit.edu.cn",,,"Higher Education Press Limited Company",,,,,20950233,,,,"English","Front. Mech. Eng.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85113921445
"Wairagkar M., Lima M.R., Bazo D., Craig R., Weissbart H., Etoundi A.C., Reichenbach T., Iyengar P., Vaswani S., James C., Barnaghi P., Melhuish C., Vaidyanathan R.","56938674800;57219193243;25627066200;24078482300;57212057221;54792993000;14060798600;57226183356;57221690720;7203017474;9337843600;55031651100;35570517600;","Emotive Response to a Hybrid-Face Robot and Translation to Consumer Social Robots",2021,"IEEE Internet of Things Journal",,,,"","",,,"10.1109/JIOT.2021.3097592","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110921086&doi=10.1109%2fJIOT.2021.3097592&partnerID=40&md5=b0b3fb9e557c7f66570560309a20991b","Dept of Mechanical Engineering, Imperial College London and UK Dementia Research Institute Care Research &#x0026; Technology Centre, London, UK. (e-mail: m.wairagkar@imperial.ac.uk); Dept of Mechanical Engineering, Imperial College London and UK Dementia Research Institute Care Research &#x0026; Technology Centre, London, UK.; Dept of Media Arts, Uni of California Santa Barbara, USA.; Frazer-Nash Consultancy, Surrey, UK.; Donders Institute, Radboud University, Nijmegen, Netherlands.; Bristol Robotics Laboratory, University of West England/University of Bristol, UK.; Dept of Artificial Intelligence in Biomedical Eng., Friedrich-Alexander-University Erlangen-N|rnberg, Erlangen, Germany.; Emotix Inc, Mumbai, India.; Dept of Biomedical Eng., University of Warwick, UK.; Dept of Brain Sciences, Imperial College London and UK Dementia Research Institute Care Research &#x0026; Technology Centre, London, UK.","Wairagkar, M., Dept of Mechanical Engineering, Imperial College London and UK Dementia Research Institute Care Research &#x0026; Technology Centre, London, UK. (e-mail: m.wairagkar@imperial.ac.uk); Lima, M.R., Dept of Mechanical Engineering, Imperial College London and UK Dementia Research Institute Care Research &#x0026; Technology Centre, London, UK.; Bazo, D., Dept of Media Arts, Uni of California Santa Barbara, USA.; Craig, R., Frazer-Nash Consultancy, Surrey, UK.; Weissbart, H., Donders Institute, Radboud University, Nijmegen, Netherlands.; Etoundi, A.C., Bristol Robotics Laboratory, University of West England/University of Bristol, UK.; Reichenbach, T., Dept of Artificial Intelligence in Biomedical Eng., Friedrich-Alexander-University Erlangen-N|rnberg, Erlangen, Germany.; Iyengar, P., Emotix Inc, Mumbai, India.; Vaswani, S., Emotix Inc, Mumbai, India.; James, C., Dept of Biomedical Eng., University of Warwick, UK.; Barnaghi, P., Dept of Brain Sciences, Imperial College London and UK Dementia Research Institute Care Research &#x0026; Technology Centre, London, UK.; Melhuish, C., Bristol Robotics Laboratory, University of West England/University of Bristol, UK.; Vaidyanathan, R., Dept of Mechanical Engineering, Imperial College London and UK Dementia Research Institute Care Research &#x0026; Technology Centre, London, UK.","We present the conceptual formulation, design, fabrication, control and commercial translation of an IoT enabled social robot as mapped through validation of human emotional response to its affective interactions. The robot design centres on a humanoid hybrid-face that integrates a rigid faceplate with a digital display to simplify conveyance of complex facial movements while providing the impression of three-dimensional depth. We map the emotions of the robot to specific facial feature parameters, characterise recognisability of archetypical facial expressions, and introduce pupil dilation as an additional degree of freedom for emotion conveyance. Human interaction experiments demonstrate the ability to effectively convey emotion from the hybrid-robot face to humans. Conveyance is quantified by studying neurophysiological electroencephalography (EEG) response to perceived emotional information as well as through qualitative interviews. Results demonstrate core hybrid-face robotic expressions can be discriminated by humans (80%+ recognition) and invoke face-sensitive neurophysiological event-related potentials such as N170 and Vertex Positive Potentials in EEG. The hybrid-face robot concept has been modified, implemented, and released by Emotix Inc in the commercial IoT robotic platform Miko (&#x2018;My Companion&#x2019;), an affective robot currently in use for human-robot interaction with children. We demonstrate that human EEG responses to Miko emotions are comparative to that of the hybrid-face robot validating design modifications implemented for large scale distribution. Finally, interviews show above 90% expression recognition rates in our commercial robot. We conclude that simplified hybrid-face abstraction conveys emotions effectively and enhances human-robot interaction. IEEE","Affective Robot; Brain-Robot Interface; Electroencephalography; Emotional Response; Event-Related Potential; Face recognition; Faces; Facial Expression; Human-robot interaction; Human-Robot Interaction.; Internet of Things; Robot sensing systems; Robots","Behavioral research; Degrees of freedom (mechanics); Electroencephalography; Electrophysiology; Internet of things; Machine design; Man machine systems; Neurophysiology; Robotics; Affective interaction; Design modifications; Emotional information; Emotional response; Event related potentials; Expression recognition; Large-scale distribution; Qualitative interviews; Social robots",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23274662,,,,"English","IEEE Internet Things J.",Article,"Article in Press","All Open Access, Green",Scopus,2-s2.0-85110921086
[No author name available],[No author id available],"International KES Conference on Human Centred Intelligent Systems, KES-HCIS 2020",2021,"Smart Innovation, Systems and Technologies","189",,,"","",448,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086319778&partnerID=40&md5=2ded416953389b211b7dda0c1adfef55",,"","The proceedings contain 35 papers. The special focus in this conference is on Human Centred Intelligent Systems. The topics include: A novel hand gesture recognition method based on illumination compensation and grayscale adjustment; architecting intelligent digital systems and services; A human-centric perspective on digital consenting: The case of GAFAM; an industrial production scenario as prerequisite for applying intelligent solutions; spectrum management of power line communications networks for industrial applications; innovations in medical apps and the integration of their data into the big data repositories of hospital information systems for improved diagnosis and treatment in healthcare; Automatic classification of rotating machinery defects using machine learning (ML) algorithms; potentials of emotionally sensitive applications using machine learning; a brief review of robotics technologies to support social interventions for older users; the human–robot interaction in robot-aided medical care; challenges of adopting human-centered intelligent systems: An organizational learning approach; experiment protocol for human–robot interaction studies with seniors with mild cognitive impairments; designing robot verbal and nonverbal interactions in socially assistive domain for quality ageing in place; ioT in smart farming analytics, big data based architecture; Review of internet of things and design of new UHF RFID folded dipole with double U slot tag; smart water distribution system based on IoT networks, a critical review; performance analysis of mobile network software testbed; Method for assessing the applicability of AI service systems; how will 5G transform industrial IoT: Latency and reliability analysis; Business process-based IS development as a natural way to human-centered digital enterprise architecture; digital architecture in startups.",,,,,,,,,,,,,,,,,,,,"Zimmermann A.Howlett R.J.Jain L.C.",,"Springer","International KES Conference on Human Centred Intelligent Systems, KES-HCIS 2020","17 June 2020 through 19 June 2020",,240529,21903018,9789811557835,,,"English","Smart Innov. Syst. Technol.",Conference Review,"Final","",Scopus,2-s2.0-85086319778
"Benos L., Bechar A., Bochtis D.","56115151500;6602528608;24484566500;","Safety and ergonomics in human-robot interactive agricultural operations",2020,"Biosystems Engineering","200",,,"55","72",,8,"10.1016/j.biosystemseng.2020.09.009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091329733&doi=10.1016%2fj.biosystemseng.2020.09.009&partnerID=40&md5=2bd69d195c16d3b10c8357e22f78d88d","Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, Thessaloniki, GR  57001, Greece; Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Bet-Dagan, Israel","Benos, L., Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, Thessaloniki, GR  57001, Greece; Bechar, A., Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Bet-Dagan, Israel; Bochtis, D., Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd, Thessaloniki, GR  57001, Greece","An emerging scientific field is the study of safety and ergonomics in the agricultural sector during human-robot interaction. Human-robotic synergetic systems are considered to be the most mature way to circumvent problems appearing due to the complex and unpredictable nature of the agricultural environment, which contrasts with the stable domain found in industrial settings. In promising working ecosystems, the distinctive cognitive human characteristics of perception, decision making and acting can be combined with the strength and repeatable accuracy of robots. However, safety must be guaranteed both in terms of avoiding accidents during unwanted physical contacts and provoking musculoskeletal disorders. The latter is a concise term for describing numerous soft tissues disorders, which have reached epidemic proportions among farmers undermining their quality of life. This investigation, by describing the fundamentals of human-robot interaction from an agriculture-oriented perspective, methodically tries to identify potential hazards that can put human safety at risk. In order to overcome these hazards, approaches for minimising the occurrence of injuries analysed along with methods for safe collaboration. The innovation of this study lies on focusing on ergonomics during agricultural human-robot interactive operations. Thus, through reviewing the basic ergonomic principles and the main risk factors, potential challenges are captured concerning human factors, technologies and policy directions. Ensuring of safety in this kind of systems should have a positive impact in technological, societal and economic aspects. For this purpose, an intensive effort and interdisciplinary collaboration are required to establish a sustainable anthropocentric human-robot interactive ecosystem. © 2020 IAgrE","Collaborative robotics; Field operations; Human factors; Human-robot interaction; Safety culture","Agricultural robots; Agriculture; Decision making; Ecosystems; Ergonomics; Hazards; Man machine systems; Agricultural environments; Agricultural operations; Ergonomic principles; Industrial settings; Interactive operations; Interdisciplinary collaborations; Musculoskeletal disorders; Repeatable accuracies; Social robots",,,,,,,,,,,,,,,,"Adamides, G., Katsanos, C., Christou, G., Xenos, M., Papadavid, G., Hadzilacos, T., User interface considerations for telerobotics: The case of an agricultural robot sprayer (2014) Second International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2014), 9229, p. 92291W. , D.G. Hadjimitsis K. Themistocleous S. Michaelides G. Papadavid; Adamides, G., Katsanos, C., Constantinou, I., Christou, G., Xenos, M., Hadzilacos, T., Design and development of a semi-autonomous agricultural vineyard sprayer: Human-robot interaction aspects (2017) Journal of Field Robotics, 34 (8), pp. 1407-1426; Adamides, G., Katsanos, C., Parmet, Y., Christou, G., Xenos, M., Hadzilacos, T., HRI usability evaluation of interaction modes for a teleoperated agricultural robotic sprayer (2017) Applied Ergonomics, 62, pp. 237-246; Akbar, S.A., Chattopadhyay, S., Elfiky, N.M., Kak, A., A novel benchmark RGBD dataset for dormant apple trees and Its application to automatic pruning (2016) IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 347-354; Akkaladevi, S.C., Heindl, C., Action recognition for human robot interaction in industrial applications (2016) 2015 IEEE International Conference on computer graphics, vision and Information Security, pp. 94-99. , CGVIS 2015; Auat Cheein, F., Steiner, G., Perez Paina, G., Carelli, R., Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection (2011) Computers and Electronics in Agriculture, 78 (2), pp. 195-207; Balan, L., Bone, G.M., Real-time 3D collision avoidance method for safe human and robot coexistence (2006) IEEE International Conference on Intelligent Robots and Systems, pp. 276-282; Barattini, P., Morand, C., Robertson, N.M., A proposed gesture set for the control of industrial collaborative robots (2012) Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, pp. 132-137; Bechar, A., Eben-Chaime, M., Hand-held computers to increase accuracy and productivity in agricultural work study (2014) International Journal of Productivity and Performance Management, 63 (2), pp. 194-208; Bechar, A., Edan, Y., Human-robot collaboration for improved target recognition of agricultural robots (2003) Industrial Robot, 30 (5), pp. 432-436; Bechar, A., Vigneault, C., Agricultural robots for field operations: Concepts and components (2016) Biosystems Engineering, 149, pp. 94-111; Bechar, A., Vigneault, C., Agricultural robots for field operations. Part 2: Operations and systems (2017) Biosystems Engineering, 153, pp. 110-128; Benos, L., Stanev, D., Spyrou, L., Moustakas, K., Tsaopoulos, D.E., A review on finite element modelling and simulation of the anterior cruciate ligament reconstruction (2020) Frontiers in Bioengineering and Biotechnology, 8, p. 967; Benos, L., Tsaopoulos, D., Bochtis, D., A review on ergonomics in agriculture. Part I: Manual operations (2020) Applied Sciences, 10 (6), p. 1905; Benos, L., Tsaopoulos, D., Bochtis, D., A review on ergonomics in agriculture. 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Eng.",Review,"Final","",Scopus,2-s2.0-85091329733
"Pinto A., Sousa S., Silva C., Coelho P.","57208733566;54883324200;55995851100;57198226876;","Adaptation and validation of the HCTM Scale into Human-robot interaction Portuguese context: A study of measuring trust in human-robot interactions",2020,"PervasiveHealth: Pervasive Computing Technologies for Healthcare",,,,"","",,,"10.1145/3419249.3420087","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117539840&doi=10.1145%2f3419249.3420087&partnerID=40&md5=97f7a92edfd8f5d03cd0ac94bf9057f2","CeBER, Centre for Business and Economics Research, University of Coimbra, Portugal; University of Trás-os-Montes e Alto Douro (UTAD), Vila Real, Portugal School of Digital Technologies, Tallinn University, Tallinn, Estonia; CEMMPRE, Centre for Mechanical Engineering, Materials and Processes, University of Coimbra, Portugal","Pinto, A., CeBER, Centre for Business and Economics Research, University of Coimbra, Portugal; Sousa, S., University of Trás-os-Montes e Alto Douro (UTAD), Vila Real, Portugal School of Digital Technologies, Tallinn University, Tallinn, Estonia; Silva, C., CeBER, Centre for Business and Economics Research, University of Coimbra, Portugal; Coelho, P., CEMMPRE, Centre for Mechanical Engineering, Materials and Processes, University of Coimbra, Portugal","As robots become increasingly common in a wide variety of domains, there is an increasing need to assess the trust humans have when interacting with robots. Specifically are two of the main objectives in this study: 1) to adapt and validate the Human-Computer Trust Model Scale (HCTM) to human-robot interaction (HRI), in a Portuguese population. This need is because trust in automation has been understood through its analogy to interpersonal trust. With the HCTM we intend to suggest an alternative to that. This measure of trust is a very recent scale, 2019, and based on the present context and research. More, this scale was subjected to robust statistical analysis tests and was tested at different scenarios of computing with successful. The final motive is that authors are not aware of a similar scale in the Portuguese context that can measure trust in HRI with COBOTS. The growth of this market is notable. For that, we used 243 undergraduate students with backgrounds on Management and Engineering and Industrial Management. Results, indicate a good measurement of the latent constructs, convergent reliability, internal consistency and discriminate validity. However, Dillon-Goldstein's rho measures for both Competency (0.696), Benevolence (0.686) and Reciprocity (0.604) constructs present scores close to 0.70. According to Hair et al [11] the composite reliability (internal consistency) should be higher than 0.7 (or >0.6 in exploratory research). This means that the model is acceptable, and the Portuguese version of the model satisfies the criteria for measure trust in human-robot interaction (HRI), however, have a poor internal consist. © 2020 Owner/Author.","Adaptation and validation; COBOTS; HRI; Trust","Human computer interaction; Students; Composite Reliability; Convergent reliability; Exploratory research; Human robot Interaction (HRI); Internal consistency; Portuguese population; Robust statistical analysis; Undergraduate students; Social robots",,,,,"825618","This project has received funding from the European Union’s Horizon 2020 research and innovation program under the NGI_TRUST grant agreement no 825618.",,,,,,,,,,"Alberdi, E., Povyakalo, A., Strigini, L., Ayton, P., Effects of incorrect computer-aided detection (CAD) output on human decision-making in mammography (2004) Academic Radiology, 11 (8), pp. 909-918; Gulati, S., Sousa, S., Lamas, D., Design, development and evaluation of a human-computer trust scale (2019) Behav. Inf. Technol, , https://doi.org/10.1080/0144929X.2019.1656779; Magalhaes, M., Hill, A., Investigaçao por questionário (2000) Lisboa; Jian, J., Bisantz, A.M., Drury, C.G., Foundations for an empirically determined scale of trust in automated systems (2000) Int. J. Cogn. Ergon, , https://doi.org/10.1207/s15327566ijce0401-04; Kelly, C., Boardman, M., Goillau, P., Jeannot, E., Guidelines for trust in future ATM systems: A literature review (2003) Eur. AIR TRAFFIC Manag. Program; Lyons, J.B., Stokes, C.K., Human-human reliance in the context of automation (2012) Human Factors, , https://doi.org/10.1177/0018720811427034; Madsen, M., Gregor, S., Measuring human-computer trust (2000) 11th Australasian Conference on Information Systems, 53, pp. 6-8. , December; Mayer, R.C., Davis, J.H., Schoorman, F.D., An integrative model of organizational trust (1995) Acad. Manag. Rev, , https://doi.org/10.5465/amr.1995.9508080335; Muir, B.M., Moray, N., Trust in automation part II. Experimental studies of trust and human intervention in a process control simulation (1996) Ergonomics, , https://doi.org/10.1080/00140139608964474; Parasuraman, R., Riley, V., Humans and automation: Use, misuse, disuse, abuse (1997) Hum. Factors, , https://doi.org/10.1518/001872097778543886; Hair, J.F., Hult, G.T.M., Ringle, C., Sarstedt, M., A primer on partial least squares structural equation modeling (PLS-SEM) (2016) Sage Publications; Hulland, J., Use of partial least squares (PLS) in strategic management research: A review of four recent studies (1999) Strategic Management Journal, 20 (2), pp. 195-204; Bagozzi, R.P., Yi, Y., On the evaluation of structural equation models (1988) Journal of the Academy of Marketing Science, 16 (1), pp. 74-94; Fornell, C., Larcker, D.F., (1981) Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics",,,,"ICST","11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, NordiCHI 2020","25 October 2020 through 29 October 2020",,164330,21531633,9781450375795,,,"English","PervasiveHealth: Pervasive Computing Technol Healthc.",Conference Paper,"Final","",Scopus,2-s2.0-85117539840
"Kim S.K., Kirchner E.A., Schloßmüller L., Kirchner F.","42761618100;23397107100;57219669704;7006011550;","Errors in Human-Robot Interactions and Their Effects on Robot Learning",2020,"Frontiers in Robotics and AI","7",,"558531","","",,1,"10.3389/frobt.2020.558531","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094680107&doi=10.3389%2ffrobt.2020.558531&partnerID=40&md5=41dfcd8d53cc44083413ebd6624a172d","Robotics Innovation Center, German Research Center for Artificial Intelligence (DFKI GmbH), Bremen, Germany; Research Group Robotics, University of Bremen, Bremen, Germany","Kim, S.K., Robotics Innovation Center, German Research Center for Artificial Intelligence (DFKI GmbH), Bremen, Germany; Kirchner, E.A., Robotics Innovation Center, German Research Center for Artificial Intelligence (DFKI GmbH), Bremen, Germany, Research Group Robotics, University of Bremen, Bremen, Germany; Schloßmüller, L., Research Group Robotics, University of Bremen, Bremen, Germany; Kirchner, F., Robotics Innovation Center, German Research Center for Artificial Intelligence (DFKI GmbH), Bremen, Germany, Research Group Robotics, University of Bremen, Bremen, Germany","During human-robot interaction, errors will occur. Hence, understanding the effects of interaction errors and especially the effect of prior knowledge on robot learning performance is relevant to develop appropriate approaches for learning under natural interaction conditions, since future robots will continue to learn based on what they have already learned. In this study, we investigated interaction errors that occurred under two learning conditions, i.e., in the case that the robot learned without prior knowledge (cold-start learning) and in the case that the robot had prior knowledge (warm-start learning). In our human-robot interaction scenario, the robot learns to assign the correct action to a current human intention (gesture). Gestures were not predefined but the robot had to learn their meaning. We used a contextual-bandit approach to maximize the expected payoff by updating (a) the current human intention (gesture) and (b) the current human intrinsic feedback after each action selection of the robot. As an intrinsic evaluation of the robot behavior we used the error-related potential (ErrP) in the human electroencephalogram as reinforcement signal. Either gesture errors (human intentions) can be misinterpreted by incorrectly captured gestures or errors in the ErrP classification (human feedback) can occur. We investigated these two types of interaction errors and their effects on the learning process. Our results show that learning and its online adaptation was successful under both learning conditions (except for one subject in cold-start learning). Furthermore, warm-start learning achieved faster convergence, while cold-start learning was less affected by online changes in the current context. © Copyright © 2020 Kim, Kirchner, Schloßmüller and Kirchner.","error-related potentials (ErrPs); human-robot interaction (HRI); learning with prior knowledge; long-term learning; reinforcement learning; robotics",,,,,,"Bundesministerium für Wirtschaft und Technologie, BMWi; Deutsches Zentrum für Luft- und Raumfahrt, DLR: FKZ 50RA1701, FKZ 50RA1703; Bundesministerium für Wirtschaft und Energie, BMWi","Funding. This work was funded by the Federal Ministry of Economics and Energy (BMWi), Project Management Agency German Aerospace Center (DLR). Funding reference number: FKZ 50RA1701 and FKZ 50RA1703.","This work was funded by the Federal Ministry of Economics and Energy (BMWi), Project Management Agency German",,,,,,,,,"Agrawal, S., Goyal, N., Thompson sampling for contextual bandits with linear payoffs (2013) International Conference on Machine Learning (ICML), pp. 127-135. , Atlanta, GA; Ahn, M., Jun, S.C., Performance variation in motor imagery brain-computer interface: a brief review (2015) J. Neurosci. Methods, 243, pp. 103-110. , 25668430; Auer, P., Cesa-Bianchi, N., Fischer, P., Finite-time analysis of the multiarmed bandit problem (2002) Mach. 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"Zhexenova Z., Amirova A., Abdikarimova M., Kudaibergenov K., Baimakhan N., Tleubayev B., Asselborn T., Johal W., Dillenbourg P., CohenMiller A., Sandygulova A.","57209799621;57219564291;57219572842;57210464213;57219562548;57209794450;57201704646;55912460600;8912010400;57193846239;55193519700;","A Comparison of Social Robot to Tablet and Teacher in a New Script Learning Context",2020,"Frontiers in Robotics and AI","7",,"99","","",,5,"10.3389/frobt.2020.00099","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093933145&doi=10.3389%2ffrobt.2020.00099&partnerID=40&md5=06170fb47ea05fb5bb37e22e5eb1f828","Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan; Graduate School of Education, Nazarbayev University, Nur-Sultan, Kazakhstan; CHILI Lab, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia","Zhexenova, Z., Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan; Amirova, A., Graduate School of Education, Nazarbayev University, Nur-Sultan, Kazakhstan; Abdikarimova, M., Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan; Kudaibergenov, K., Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan; Baimakhan, N., Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan; Tleubayev, B., Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan; Asselborn, T., CHILI Lab, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Johal, W., CHILI Lab, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia; Dillenbourg, P., CHILI Lab, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; CohenMiller, A., Graduate School of Education, Nazarbayev University, Nur-Sultan, Kazakhstan; Sandygulova, A., Department of Robotics and Mechatronics, School of Engineering and Digital Sciences, Nazarbayev University, Nur-Sultan, Kazakhstan","This research occurred in a special context where Kazakhstan's recent decision to switch from Cyrillic to the Latin-based alphabet has resulted in challenges connected to teaching literacy, addressing a rare combination of research hypotheses and technical objectives about language learning. Teachers are not necessarily trained to teach the new alphabet, and this could result in a challenge for children with learning difficulties. Prior research studies in Human-Robot Interaction (HRI) have proposed the use of a robot to teach handwriting to children (Hood et al., 2015; Lemaignan et al., 2016). Drawing on the Kazakhstani case, our study takes an interdisciplinary approach by bringing together smart solutions from robotics, computer vision areas, and educational frameworks, language, and cognitive studies that will benefit diverse groups of stakeholders. In this study, a human-robot interaction application is designed to help primary school children learn both a newly-adopted script and also its handwriting system. The setup involved an experiment with 62 children between the ages of 7–9 years old, across three conditions: a robot and a tablet, a tablet only, and a teacher. Based on the paradigm—learning by teaching—the study showed that children improved their knowledge of the Latin script by interacting with a robot. Findings reported that children gained similar knowledge of a new script in all three conditions without gender effect. In addition, children's likeability ratings and positive mood change scores demonstrate significant benefits favoring the robot over a traditional teacher and tablet only approaches. © Copyright © 2020 Zhexenova, Amirova, Abdikarimova, Kudaibergenov, Baimakhan, Tleubayev, Asselborn, Johal, Dillenbourg, CohenMiller and Sandygulova.","child learning; cognitive learning theory; human-robot interaction; interdisciplinary; language learning; learning by teaching; social robot",,,,,,"Singapore Eye Research Institute, SERI: SFG 1038; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; Staatssekretariat für Bildung, Forschung und Innovation, SBFI; National Centre of Competence in Research Robotics; Nazarbayev University, NU: 091019CRP2107","We would like to thank the children and their parents for participating in this research. The authors would also like to express great appreciation to the members of staff of the participating primary school for their time and help with the experiment. Funding. This work was supported by the Nazarbayev University Collaborative Research Program grant, CoWriting Kazakh: Learning a New Script with a Robot (Award number is 091019CRP2107). This research was also partly funded by the Swiss State Secretariat for Education, Research and Innovation (SERI) as parts of the CIS Region Seeding Grants (Project SFG 1038), and the NCCR Robotics (SNSF).","This work was supported by the Nazarbayev University Collaborative Research Program grant, CoWriting Kazakh: Learning a New Script with a Robot (Award number is 091019CRP2107). This research was also partly funded by the Swiss State Secretariat for Education, Research and Innovation (SERI) as parts of the CIS Region Seeding Grants (Project SFG 1038), and the NCCR Robotics (SNSF).",,,,,,,,,"Al-Azami, S., Kenner, C., Ruby, M., Gregory, E., Transliteration as a bridge to learning for bilingual children (2010) Int. J. Bilingual Educ. Bilingualism, 13, pp. 683-700; Alemi, M., Meghdari, A., Ghazisaedy, M., Employing humanoid robots for teaching english language in iranian junior high-schools (2014) Int. J. 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Interact, 32, pp. 493-502; (2015) Transforming Our World: The 2030 Agenda for Sustainable Development, , New York, NY, General Assembley 70 session; van den Berghe, R., Verhagen, J., Oudgenoeg-Paz, O., van der Ven, S., Leseman, P., Social robots for language learning: a review (2019) Rev. Educ. Res, 89, pp. 259-295; Vogt, P., van den Berghe, R., de Haas, M., Hoffman, L., Kanero, J., Mamus, E., Second language tutoring using social robots: a large-scale study, (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 497-505. , Daegu, IEEE; Vygotsky, L.S., (1980) Mind in Society: The Development of Higher Psychological Processes, , Cambridge, Harvard University Press, 29806712; Westlund, J.K., Dickens, L., Jeong, S., Harris, P., DeSteno, D., Breazeal, C., A comparison of children learning new words from robots, tablets, & people, (2015) Proceedings of the 1st International Conference on Social Robots in Therapy and Education, , Almere, in; Yadollahi, E., Johal, W., Paiva, A., Dillenbourg, P., When deictic gestures in a robot can harm child-robot collaboration, (2018) Proceedings of the 17th ACM Conference on Interaction Design and Children, pp. 195-206. , Trondheim; Yanikoglu, B., Gogus, A., Inal, E., Use of handwriting recognition technologies in tablet-based learning modules for first grade education (2017) Educ. Technol. Res. Dev, 65, pp. 1369-1388","Sandygulova, A.; Department of Robotics and Mechatronics, Kazakhstan; email: anara.sandygulova@nu.edu.kz",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85093933145
"Wijnen L., Bremner P., Lemaignan S., Giuliani M.","57193755961;57206776256;18234152100;23396512600;","Performing Human-Robot Interaction User Studies in Virtual Reality",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223521","794","799",,2,"10.1109/RO-MAN47096.2020.9223521","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095753006&doi=10.1109%2fRO-MAN47096.2020.9223521&partnerID=40&md5=2ff691264bfc3928dfb72c79a769cf1c","University of the West of England, Bristol Robotics Laboratory, Bristol, BS16 1QY, United Kingdom","Wijnen, L., University of the West of England, Bristol Robotics Laboratory, Bristol, BS16 1QY, United Kingdom; Bremner, P., University of the West of England, Bristol Robotics Laboratory, Bristol, BS16 1QY, United Kingdom; Lemaignan, S., University of the West of England, Bristol Robotics Laboratory, Bristol, BS16 1QY, United Kingdom; Giuliani, M., University of the West of England, Bristol Robotics Laboratory, Bristol, BS16 1QY, United Kingdom","This study investigated whether virtual reality could be used as platform for conducting human-robot interaction user studies. It was investigated whether user studies performed in virtual reality elicited realistic responses from participants. To answer this question, a real world study was replicated as closely as possible in virtual reality, where a robot tour guide asked participants to keep a secret. The experiment consisted of a virtual museum tour where the robot acted as the tour guide while displaying either social or non-social behaviour.The measurements taken in this study were the objective measurement whether the participants kept the robot's secret or not. Questionnaires were taken to investigate participants' perception of the robot and its feelings, as well as their experienced level of presence and their tendency to become immersed in the virtual environment. Results show that the participants responded differently in the virtual reality study when compared to the original real world study, where the secret was kept more often for the non-social robot, but less often for the social robot. In both the original and replicated study a strong, positive correlation was found between participants' perception of the robot as a social being and their tendency to keep the robot's secret.These inconclusive findings, some changes that were required for the virtual environment compared to the original study, and different participant demographics indicate that more work is needed to determine whether virtual reality can be used as a tool to conduct human-robot interaction experiments. © 2020 IEEE.",,"Agricultural robots; End effectors; Man machine systems; Surveys; Virtual reality; Objective measurement; Positive correlations; Real-world; Social beings; Tour guide; User study; Virtual museum tours; Social robots",,,,,"Engineering and Physical Sciences Research Council, EPSRC; University of Bristol","*This project was funded by the EPSRC and part of the FARSCOPE CDT at the Bristol Robotics Laboratory, University of the West of England and University of Bristol.",,,,,,,,,,"Kahn, P.H., Jr., Kanda, T., Ishiguro, H., Gill, B.T., Shen, S., Gary, H.E., Ruckert, J.H., Will people keep the secret of a humanoid robot?: Psychological intimacy in hri (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 173-180; Billings, D.R., Schaefer, K.E., Chen, J.Y., Hancock, P.A., Humanrobot interaction: Developing trust in robots (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 109-110; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Baier, A., Trust and antitrust (1986) Ethics, 96 (2), pp. 231-260; Abbass, H.A., Scholz, J., Reid, D.J., (2018) Foundations of Trusted Autonomy, 117. , Springer; Sherman, W.R., Craig, A.B., (2018) Understanding Virtual Reality: Interface, Application, and Design, , Morgan Kaufmann; Schuemie, M.J., Van, S.D.P., Krijn, M., Der Van Mast, C.A., Research on presence in virtual reality: A survey (2001) CyberPsychology & Behavior, 4 (2), pp. 183-201; Witmer, B.G., Singer, M.J., Measuring presence in virtual environments: A presence questionnaire (1998) Presence, 7 (3), pp. 225-240; Weistroffer, V., Paljic, A., Fuchs, P., Hugues, O., Chodacki, J.-P., Ligot, P., Morais, A., Assessing the acceptability of human-robot copresence on assembly lines: A comparison between actual situations and their virtual reality counterparts (2014) The 23rd IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 377-384; Duguleana, M., Barbuceanu, F.G., Mogan, G., Evaluating humanrobot interaction during a manipulation experiment conducted in immersive virtual reality (2011) International Conference on Virtual and Mixed Reality, pp. 164-173. , Springer; Bainbridge, W.A., Hart, J., Kim, E.S., Scassellati, B., The effect of presence on human-robot interaction (2008) RO-MAN 2008-The 17th IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 701-706; Kamide, H., Mae, Y., Takubo, T., Ohara, K., Arai, T., Direct comparison of psychological evaluation between virtual and real humanoids: Personal space and subjective impressions (2014) International Journal of Human-Computer Studies, 72 (5), pp. 451-459; Li, R., Van Almkerk, M., Van Waveren, S., Carter, E., Leite, I., Comparing human-robot proxemics between virtual reality and the real world (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 431-439",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095753006
"Canevet O., He W., Motlicek P., Odobez J.-M.","55575425100;57204212940;57203111353;57203103085;","The MuMMER Data Set for Robot Perception in Multi-party HRI Scenarios",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223340","1294","1300",,,"10.1109/RO-MAN47096.2020.9223340","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095744624&doi=10.1109%2fRO-MAN47096.2020.9223340&partnerID=40&md5=22158ff6d2c5e65fcf1f9baaf4a0861b","Idiap Research Institute, Martigny, Switzerland","Canevet, O., Idiap Research Institute, Martigny, Switzerland; He, W., Idiap Research Institute, Martigny, Switzerland; Motlicek, P., Idiap Research Institute, Martigny, Switzerland; Odobez, J.-M., Idiap Research Institute, Martigny, Switzerland","This paper presents the MuMMER data set, a data set for human-robot interaction scenarios that is available for research purposes1. It comprises 1h 29 min of multimodal recordings of people interacting with the social robot Pepper in entertainment scenarios, such as quiz, chat, and route guidance. In the 33 clips (of 1 to 4 min long) recorded from the robot point of view, the participants are interacting with the robot in an unconstrained manner.The data set exhibits interesting features and difficulties, such as people leaving the field of view, robot moving (head rotation with embedded camera in the head), different illumination conditions. The data set contains color and depth videos from a Kinect v2, an Intel D435, and the video from Pepper.All the visual faces and the identities in the data set were manually annotated, making the identities consistent across time and clips. The goal of the data set is to evaluate perception algorithms in multi-party human/robot interaction, in particular the re-identification part when a track is lost, as this ability is crucial for keeping the dialog history. The data set can easily be extended with other types of annotations.We also present a benchmark on this data set that should serve as a baseline for future comparison. The baseline system, IHPER2 (Idiap Human Perception system) is available for research and is evaluated on the MuMMER data set. We show that an identity precision and recall of ~80% and a MOTA score above 80% are obtained. © 2020 IEEE.",,"Agricultural robots; Baseline systems; Human perception systems; Illumination conditions; Precision and recall; Re identifications; Research purpose; Robot perception; Route guidance; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 688147","† Idiap Research Institute, Martigny, Switzerland ‡ École Polytechnique Fédérale de Lausanne, Switzerland. Corresponding author: odobez@idiap.ch This research was funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 688147 (MultiModal Mall Entertainment Robot, MuMMER, www.mummer-project.eu).",,,,,,,,,,"Chen, C., Yu, Y., Odobez, J.-M., Head nod detection from a full 3d model (2015) ICCV Workshop; Sheikhi, S., Odobez, J., Combining dynamic head pose and gaze mapping with the robot conversational state for attention recognition in human-robot interactions (2015) Pattern Recognition Letters, 66, pp. 81-90. , Nov; Jayagopi, D., Odobez, J.-M., Given that, should i respond? Contextual addressee estimation in multi-party human-robot interactions (2013) Int. Conf. HRI; Klotz, D., Wienke, J., Peltason, J., Wrede, B., Wrede, S., Khalidov, V., Odobez, J.-M., Engagement-based multi-party dialog with a humanoid robot (2011) SIGDIAL Conference; Jayagopi, D.B., Sheiki, S., Klotz, D., Wienke, J., Odobez, J., Wrede, S., Khalidov, V., Gatica-Perez, D., The vernissage corpus: A conversational human-robot-interaction dataset (2013) Int. Conf. on Human-Robot Interaction, pp. 149-150; Gebru, I.D., Ba, S., Li, X., Horaud, R., Audio-visual speaker diarization based on spatiotemporal Bayesian fusion (2018) IEEE Transactions on Pattern Analysis and Machine Intelligence, 40 (5), pp. 1086-1099; Celiktutan, O., Skordos, E., Gunes, H., Multimodal human-humanrobot interactions (mhhri) dataset for studying personality and engagement (2018) IEEE Transactions on Affective Computing, p. 1; Ben-Youssef, A., Clavel, C., Essid, S., Bilac, M., Chamoux, M., Lim, A., Ue-hri: A new dataset for the study of user engagement in spontaneous human-robot interactions (2017) Int. Conf. on Multimodal Interaction, pp. 464-472; Ryoo, M.S., Matthies, L., First-person activity recognition: What are they doing to me? (2013) IEEE Conference on Computer Vision and Pattern Recognition (CVPR), , Portland, OR, June; Najibi, M., Samangouei, P., Chellappa, R., Davis, L.S., Ssh: Single stage headless face detector (2017) Int. Conf. on Computer Vision, pp. 4875-4884; Odobez, J.-M., Bouthemy, P., Robust multiresolution estimation of parametric motion models (1995) Journal of Visual Communication and Image Representation, 6 (4), pp. 348-365; Amos, B., Ludwiczuk, B., Satyanarayanan, M., Openface: A general-purpose face recognition library with mobile applications (2016) CMU School of Computer Science, 6; Wang, Y., Shen, J., Petridis, S., Pantic, M., A real-time and unsupervised face re-identification system for human-robot interaction (2018) CoRR, , abs/1804. 03547; Koide, K., Menegatti, E., Carraro, M., Munaro, M., Miura, J., People tracking and re-identification by face recognition for rgb-d camera networks (2017) Eur. Conf. on Mobile Robots (ECMR); Zaraki, A., Pieroni, M., De Rossi, D., Mazzei, D., Garofalo, R., Cominelli, L., Dehkordi, M.B., Design and evaluation of a unique social perception system for human-robot interaction (2017) IEEE Transactions on Cognitive and Developmental Systems, 9 (4), pp. 341-355; Cao, Y., Canévet, O., Odobez, J., Leveraging convolutional pose machines for fast and accurate head pose estimation (2018) Int. Conf. on Intelligent Robots and Systems (IROS), pp. 1089-1094; Cao, Z., Simon, T., Wei, S., Sheikh, Y., Realtime multi-person 2d pose estimation using part affinity fields (2017) CVPR, pp. 1302-1310. , July; Khalidov, V., Odobez, J.-M., (2017) Real-time Multiple Head Tracking Using Texture and Colour Cues, Tech. Rep. Idiap-RR-02-2017, , February; Duffner, S., Odobez, J.-M., A track creation and deletion framework for long-term online multiface tracking (2013) IEEE Transactions on Image Processing, 22 (1), pp. 272-285. , Jan; He, W., Motlicek, P., Odobez, J., Deep neural networks for multiple speaker detection and localization (2018) Int. Conf. on Robotics and Automation (ICRA), pp. 74-79. , May; He, W., Motlicek, P., Odobez, J.-M., Joint localization and classification of multiple sound sources using a multi-task neural network (2018) Interspeech, pp. 312-316; Mathias, M., Benenson, R., Pedersoli, M., Van Gool, L., Face detection without bells and whistles (2014) Computer Vision-ECCV 2014, pp. 720-735. , D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds. Cham: Springer International Publishing; Milan, A., Leal-Taixé, L., Reid, I., Roth, S., Schindler, K., (2016) MOT16: A Benchmark for Multi-object Tracking, , http://arxiv.org/abs/1603.00831, [cs], Mar. 1603. 00831; Stiefelhagen, R., Bernardin, K., Bowers, R., Garofolo, J., Mostefa, D., Soundararajan, P., The clear 2006 evaluation (2007) Multimodal Technologies for Perception of Humans, pp. 1-44. , R. Stiefelhagen and J. Garofolo, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg","Odobez, J.-M.; Idiap Research InstituteSwitzerland; email: odobez@idiap.ch",,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095744624
"Leib R., Russo M., d’Avella A., Nisky I.","36195815000;56086194300;6507933379;23768361200;","A bang-bang control model predicts the triphasic muscles activity during hand reaching",2020,"Journal of Neurophysiology","124","1",,"295","304",,2,"10.1152/jn.00132.2020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088177852&doi=10.1152%2fjn.00132.2020&partnerID=40&md5=ee6f6b75214125b02d3372433e371f5b","Department of Biomedical Engineering, Ben-Gurion University of the Negev, Be’er Sheva, Israel; Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Be’er Sheva, Israel; Department of Biology, Northeastern University, Boston, MA, United States; Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy; Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Rome, Italy","Leib, R., Department of Biomedical Engineering, Ben-Gurion University of the Negev, Be’er Sheva, Israel, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Be’er Sheva, Israel; Russo, M., Department of Biology, Northeastern University, Boston, MA, United States; d’Avella, A., Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy, Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, Rome, Italy; Nisky, I., Department of Biomedical Engineering, Ben-Gurion University of the Negev, Be’er Sheva, Israel, Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Be’er Sheva, Israel","There are numerous ways to reach for an apple hanging from a tree. Yet, our motor system uses a specific muscle activity pattern that features activity bursts and silent periods. We suggest that these bursts are an evidence against the common view that the brain controls the commands to the muscles in a smooth continuous manner. Instead, we propose a model in which a motor plan is transformed into a piecewise-constant control signal that is low-pass filtered and transmitted to the muscles with different muscle-specific delays. We use a Markov chain Monte Carlo (MCMC) method to identify transitions in the state of the muscles following initial activation and show that fitting a bang-bang control model to the kinematics of movement predicts these transitions in the state of the muscles. Such a bang-bang controller suggests that the brain reduces the complexity of the problem of ballistic movements control by sending commands to the muscles at sparse times. Identifying this bang-bang controller can be useful to develop efficient controllers for neuroprostheses and other physical human-robot interaction systems. NEW & NOTEWORTHY While ballistic hand reaching movements are characterized by smooth position and velocity signals, the activity of the muscles exhibits bursts and silent periods. Here, we propose that a model based on bang-bang control provides the link between the abrupt changes in the muscle activity and the smooth reaching trajectory. Using bang-bang control instead of continuous control may simplify the design of prostheses and other physical human-robot interaction systems. © 2020 the American Physiological Society","Bang-bang control; EMG; Reaching; Triphasic pattern","acceleration; Article; bang bang control; biceps brachii muscle; deltoid muscle; electromyogram; electromyography; feedback system; hand movement; hand muscle; human; human experiment; kinematics; latissimus dorsi muscle; Markov chain Monte Carlo method; motor system; movement time; muscle contraction; pectoralis major muscle; priority journal; trapezius muscle; triceps brachii muscle; adult; biological model; electromyography; motor activity; physiology; skeletal muscle; statistical model; Adult; Electromyography; Humans; Models, Biological; Models, Statistical; Motor Activity; Muscle, Skeletal",,,,,"Leona M. and Harry B. Helmsley Charitable Trust; Israel Science Foundation, ISF: 823/15; Ministry of Science and Technology, Israel","This study was supported by Israeli Science Foundation Grant 823/15, the Israeli Ministry of Science and Technology via the Joint Israel-Italy lab on artificial somatosensation for humans and humanoids, and by the Helmsley Charitable Trust through the Agricultural, Biological and Cognitive Robotics Initiative and by the Marcus Endowment Fund both at Ben-Gurion University of the Negev.",,,,,,,,,,"Accornero, N, Berardelli, A, Argenta, M, Manfredi, M., Two joints ballistic arm movements (1984) Neurosci Lett, 46, pp. 91-95; Albert, ST, Hadjiosif, AM, Jang, J, Zimnik, AJ, Soteropoulos, DS, Baker, SN, Churchland, MM, Shadmehr, R., Postural control of arm and fingers through integration of movement commands (2020) eLife, 9, p. e52507; Andrews, B, Kirkwood, C, Barnett, R, Phillips, G, Baxendale, R., Control of hybrid FES orthosis for paraplegics (1992) Restoration of Walking for Paraplegics: Recent Advancements and Trends, pp. 235-250. , edited by Pedotti A, Ferrarin M. 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"Zacharaki A., Kostavelis I., Gasteratos A., Dokas I.","57224559553;35484641300;6701673537;6508286517;","Safety bounds in human robot interaction: A survey",2020,"Safety Science","127",,"104667","","",,30,"10.1016/j.ssci.2020.104667","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080879374&doi=10.1016%2fj.ssci.2020.104667&partnerID=40&md5=249dcb0dddbc32cfde094143169de5c8","Democritus University of Thrace, Dept. of Civil Engineering, Building A- Campus, Greece; Democritus University of Thrace, Laboratory of Robotics and Automation, Production and Management Engineering Dept., 12, Vas. Sofias Str, Xanthi, GR-671 00, Greece","Zacharaki, A., Democritus University of Thrace, Dept. of Civil Engineering, Building A- Campus, Greece; Kostavelis, I., Democritus University of Thrace, Laboratory of Robotics and Automation, Production and Management Engineering Dept., 12, Vas. Sofias Str, Xanthi, GR-671 00, Greece; Gasteratos, A., Democritus University of Thrace, Laboratory of Robotics and Automation, Production and Management Engineering Dept., 12, Vas. Sofias Str, Xanthi, GR-671 00, Greece; Dokas, I., Democritus University of Thrace, Dept. of Civil Engineering, Building A- Campus, Greece","In the era of industrialization and automation, safety is a critical factor that should be considered during the design and realization of each new system that targets operation in close collaboration with humans. Of such systems are considered personal and professional service robots which collaborate and interact with humans at diverse applications environments. In this collaboration, human safety is an important factor in the wider field of human-robot interaction (HRI) since it facilitates their harmonic coexistence. The paper at hand aims to systemize the recent literature by describing the required levels of safety during human-robot interaction, focusing on the core functions of the collaborative robots when performing specific processes. It is also oriented towards the existing methods for psychological safety during human-robot collaboration and its impact at the robot behaviour, while also discusses in depth the psychological parameters of robots incorporation in industrial and social environments. Based on the existing works on safety features that minimize the risk of HRI, a classification of the existing works into five major categories namely, Robot Perceptions for Safe HRI, Cognition-enabled robot control in HRI, Action Planning for safe navigation close to humans, Hardware safety features, and Societal and Psychological factors is also applied. Finally, the current study further discusses the existing risk assessment techniques as methods to offer additional safety in robotic systems presenting thus a holistic analysis of the safety in contemporary robots, and proposes a roadmap for safety compliance features during the development of a robotic system. © 2020 Elsevier Ltd","Human-robot interaction; Psychological safety issues; Robot safety; Safe collaboration; Safety standards; Safety techniques","Accident prevention; Human robot interaction; Man machine systems; Risk assessment; Risk perception; Robotics; Safety factor; Social robots; Psychological safety; Robot safety; Safe collaboration; Safety standard; Safety techniques; Robot programming; behavior; classification; human; human robot interaction; industry; man machine interaction; planning; priority journal; psychological aspect; psychological parameters; psychology; Review; risk assessment; risk reduction; robot behavior; robotics; safety; social environment",,,,,,,,,,,,,,,,"Ahmad, R., Plapper, P., Human-robot collaboration: twofold strategy algorithm to avoid collisions using tof sensor (2016) Int. 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Magaz., 11 (2), pp. 12-21","Zacharaki, A.; Democritus University of Thrace, Building A- Campus, Greece; email: azachara@civil.duth.gr",,,"Elsevier B.V.",,,,,09257535,,SSCIE,,"English","Saf. Sci.",Review,"Final","",Scopus,2-s2.0-85080879374
"Guo J.","56707811200;","Robust tracking control of variable stiffness joint based on feedback linearization and disturbance observer with estimation error compensation",2020,"IEEE Access","8",,,"173732","173754",,2,"10.1109/ACCESS.2020.3025489","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102795643&doi=10.1109%2fACCESS.2020.3025489&partnerID=40&md5=9f4406d4a6af9e6406a0fec8d9e14659","School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China","Guo, J., School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China","The variable stiffness joint (VSJ) has the characteristics of independent and controllable position and stiffness. The variable stiffness characteristics and inherent flexibility make the VSJ suitable to be used as the actuation joint of the physical human-robot interaction application robot, so as to improve the task adaptability of the robot and physical human-robot interaction safety. The VSJ based on equivalent lever mechanism has the advantages of low energy consumption in stiffness adjustment, so there are many researches on this type of VSJ. The tracking control of output link angular position and joint output stiffness are two basic control targets of the VSJ. For the system dynamic model of the VSJ based on equivalent lever mechanism, considering the unknown parametric perturbations, the unknown friction torques acting on the drive units, the unknown external disturbance acting on the output link and the control input saturation constraints, a robust tracking controller based on feedback linearization, disturbance observer with anti-windup measures, sliding mode control and estimation error compensator is designed to improve the tracking control accuracy of the position and stiffness of the VSJ. The simultaneous tracking control of position and stiffness of the VSJ can be achieved by the designed controller, and the simulation results show the effectiveness and robustness of the proposed controller. Moreover, the simulation results show that the proposed estimation error compensator for the disturbance observer with fixed preset observation gain can effectively reduce the system output tracking error and improve the anti-disturbance characteristics of the controller. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.","Disturbance observer; Estimation error compensator; Feedback linearization; Robust tracking control; Sliding mode control; Variable stiffness joint","Biofeedback; Energy utilization; Error compensation; Feedback linearization; Human robot interaction; Man machine systems; Sliding mode control; Stiffness; Control input saturation; Low energy consumption; Parametric perturbations; Physical human-robot interactions; Robust tracking control; Robust tracking controller; Stiffness adjustments; System dynamic models; Controllers",,,,,"81110411; Shandong Academy of Sciences, SDAS; Qilu University of Technology, QLUT","This work was supported by the Qilu University of Technology (Shandong Academy of Sciences) through the Doctoral Research and Innovation Fund Project under Grant 81110411.",,,,,,,,,,"Vanderborght, B., Albu-Schäffer, A., Bicchi, A., Burdet, E., Caldwell, D.G., Carloni, R., Catalano, M.G., Garabini, M., Variable impedance actuators: A review (2013) Robot. Auto. Syst., 61 (12), pp. 1601-1614. , Dec; Grosu, V., Rodriguez-Guerrero, C., Grosu, S., Vanderborght, B., Lefeber, D., Design of smart modular variable stiffness actuators for robotic-assistive devices (2017) IEEE/ASME Trans. Mechatronics, 22 (4), pp. 1777-1785. , Aug; Moltedo, M., Cavallo, G., Baček, T., Lataire, J., Vanderborght, B., Lefeber, D., Rodriguez-Guerrero, C., Variable stiffness ankle actuator for use in robotic-assisted walking: Control strategy and experimental characterization (2019) Mechanism Mach. Theory, 134, pp. 604-624. , Apr; Cestari, M., Sanz-Merodio, D., Arevalo, J.C., Garcia, E., An adjustable compliant joint for lower-limb exoskeletons (2015) IEEE/ASME Trans. Mechatronics, 20 (2), pp. 889-898. , Apr; Bacek, T., Moltedo, M., Rodriguez-Guerrero, C., Geeroms, J., Vanderborght, B., Lefeber, D., Design and evaluation of a torque-controllable knee joint actuator with adjustable series compliance and parallel elasticity (2018) Mechanism Mach. Theory, 130, pp. 71-85. , Dec; Li, Z., Bai, S., A novel revolute joint of variable stiffness with reconfigurability (2019) Mechanism Mach. Theory, 133, pp. 720-736. , Mar; Dežman, M., Gams, A., Rotatable cam-based variable-ratio lever compliant actuator for wearable devices (2018) Mechanism Mach. Theory, 130, pp. 508-522. , Dec; Jafari, A., Vu, H.Q., Iida, F., Determinants for stiffness adjustment mechanisms (2016) J. Intell. Robotic Syst., 82 (3-4), pp. 435-454. , nos. Jun; Wolf, S., Grioli, G., Eiberger, O., Friedl, W., Grebenstein, M., Höppner, H., Burdet, E., Albu-Schäffer, A., Variable stiffness actuators: Review on design and components (2016) IEEE/ASME Trans. Mechatronics, 21 (5), pp. 2418-2430. , Oct; Zhakatayev, A., Rubagotti, M., Varol, H.A., Energy-aware optimal control of variable stiffness actuated robots (2019) IEEE Robot. Autom. 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Mechatron., 23 (5), pp. 2121-2131. , Oct; Sun, J., Guo, Z., Sun, D., He, S., Xiao, X., Design, modeling and control of a novel compact, energy-efficient, and rotational serial variable stiffness actuator (SVSA-II) (2018) Mechanism Mach. Theory, 130, pp. 123-136. , Dec; Sardellitti, I., Medrano-Cerda, G.A., Tsagarakis, N., Jafari, A., Caldwell, D.G., Gain scheduling control for a class of variable stiffness actuators based on lever mechanisms (2013) IEEE Trans. Robot., 29 (3), pp. 791-798. , Jun; Liu, L., Leonhardt, S., Misgeld, B.J.E., Experimental validation of a torque-controlled variable stiffness actuator tuned by gain scheduling (2018) IEEE/ASME Trans. Mechatronics, 23 (5), pp. 2109-2120. , Oct; Guo, Z., Pan, Y., Sun, T., Zhang, Y., Xiao, X., Adaptive neural network control of serial variable stiffness actuators (2017) Complexity, 2017. , Nov. Art; Guo, J., Tian, G., Robust tracking control of the variable stiffness actuator based on the lever mechanism (2020) Asian J. 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Mechatronics, 19 (2), pp. 589-597. , Apr","Guo, J.; School of Electrical Engineering and Automation, China; email: guojishu_2005@163.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85102795643
"Friedman C.","57221119761;","Human-Robot Moral Relations: Human Interactants as Moral Patients of Their Own Agential Moral Actions Towards Robots",2020,"Communications in Computer and Information Science","1342",,,"3","20",,3,"10.1007/978-3-030-66151-9_1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098266870&doi=10.1007%2f978-3-030-66151-9_1&partnerID=40&md5=647da4907c21d6a0a93c56758db6264b","Department of Philosophy, University of Pretoria, Pretoria, South Africa; Centre for AI Research (CAIR), Pretoria, South Africa","Friedman, C., Department of Philosophy, University of Pretoria, Pretoria, South Africa, Centre for AI Research (CAIR), Pretoria, South Africa","This paper contributes to the debate in the ethics of social robots on how or whether to treat social robots morally by way of considering a novel perspective on the moral relations between human interactants and social robots. This perspective is significant as it allows us to circumnavigate debates about the (im)possibility of robot consciousness and moral patiency (debates which often slow down discussion on the ethics of HRI), thus allowing us to address actual and urgent current ethical issues in relation to human-robot interaction. The paper considers the different ways in which human interactants may be moral patients in the context of interaction with social robots: robots as conduits of human moral action towards human moral patients; humans as moral patients to the actions of robots; and human interactants as moral patients of their own agential moral actions towards social robots. This third perspective is the focal point of the paper. The argument is that due to perceived robot consciousness, and the possibility that the immoral treatment of social robots may morally harm human interactants, there is a unique moral relation between humans and social robots wherein human interactants are both the moral agents of their actions towards robots, as well as the actual moral patients of those agential moral actions towards robots. Robots, however, are no more than perceived moral patients. This discussion further adds to debates in the context of robot moral status, and the consideration of the moral treatment of robots in the context of human-robot interaction. © 2020, Springer Nature Switzerland AG.","Human-robot interaction; Moral patiency; Robot ethics","Artificial intelligence; Man machine systems; Patient treatment; Philosophical aspects; Ethical issues; Focal points; Human robots; Moral agents; Social robots",,,,,,,,,,,,,,,,"(2019), https://standards.ieee.org/content/dam/ieeestandards/standards/web/documents/other/ead1e.pdf; Anderson, M., Anderson, S., Machine ethics: Creating an ethical intelligent agent (2007) AI Mag, 28 (4), pp. 15-26; Arnold, T., Scheutz, M., HRI ethics and type-token ambiguity: What kind of robotic identity is most responsible? (2018) Ethics Inf. Technol.; Asaro, P., What should we want from a robot ethic? (2006) Int. Rev. Inf. Ethics, 6 (12), pp. 9-16; Barquin, R.C., (1992) Ten Commandments of Computer Ethics; Boltuć, P., (2017) Chuch-Turing Lovers, , Oxford University Press, Oxford; Bostrom, N., Yudkwosky, E., (2014) The Cambdridge Handook of Artificial Intelligence, pp. 316-334. , The ethics of artificial intelligence. In: Frankish, K., Ramsey, W. (eds.), pp., Cambridge University Press, Cambridge; Breazeal, C., (2002), Designing Sociable Robots. MIT Press, Cambridge; Breazeal, C., Towards sociable robots (2003) Robot. Auton. Syst., 42, pp. 167-175; Broadbent, E., Interactions with robots: The truths we reveal about ourselves (2017) Annu. Rev. Psychol., 68, pp. 627-652; Brundage, M., Limitations and risks of machine ethics (2014) J. Exp. Theor. Artif. Intell., 26 (3), pp. 355-372; Bryson, J., Robots should be slaves (2009) Close Engage. Artif. Companions: Key Soc. Psychol. Ethical Des. 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The future of aged care (2006) Minds Mach, 16, pp. 141-161; Sullins, J., When is a robot a moral agent? (2006) Int. Rev. Inf. Ethics, 6 (12); Sullins, J., Robots, love and sex: The ethics of building a love machine (2012) IEEE Trans. Affect. Comput., 3 (4), pp. 398-409; Torrance, S., Artificial agents and the expanding ethical circle (2013) AI Soc, 28, pp. 399-414; Turkle, S., A nascent robotics culture: New complicities for companionship (2006) AAAI Technical Report Series; Turkle, S., Authenticity in the age of digital companions (2007) Interact. Stud., 8 (3), pp. 501-517; Turner, J., (2019) Why Robot Rights? In: Robot Rules: Regulating Artificial Intelligence, pp. 145-171. , pp., Palgrave Macmillan, Cham; Vallor, S., Carebots and caregivers: Sustaining the ethical ideal of care in the twenty-first century (2011) Philos. Technol., 24, pp. 251-268; Wallach, W., Allen, C., (2009) Moral Machines: Teachinf Robots Right from Wrong, , Oxford University Press, New York; Wang, W., Siau, K., Ethical and moral issues with AI: A case study on healthcare robots (2018) Twenty-Fourth Americas Conference on Information Systems, , New Orleans","Friedman, C.; Department of Philosophy, South Africa; email: cindzfriedman@gmail.com","Gerber A.",,"Springer Science and Business Media Deutschland GmbH","1st Southern African Conference for Artificial Intelligence Research, SACAIR 2020","22 February 2021 through 26 February 2021",,253469,18650929,9783030661502,,,"English","Commun. Comput. Info. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85098266870
"Ali S., Mehmood F., Khan M.J., Ayaz Y., Ayaz Y., Asgher U., Sadia H., Sadia H., Edifor E., Nawaz R.","57209995060;57207940444;55607228600;16548915200;16548915200;55902392400;57220398481;57220398481;55414003100;52164201600;","A Preliminary Study on Effectiveness of a Standardized Multi-Robot Therapy for Improvement in Collaborative Multi-Human Interaction of Children with ASD",2020,"IEEE Access","8",,"9113320","109466","109474",,2,"10.1109/ACCESS.2020.3001365","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087404833&doi=10.1109%2fACCESS.2020.3001365&partnerID=40&md5=f63ee2872d48840385c9cf75658217ce","School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology, Islamabad, 44000, Pakistan; National Center of Artificial Intelligence (NCAI), Lahore, 54000, Pakistan; Shifa International Hospitals Limited, Islamabad, 44000, Pakistan; Autism Resource Centre, Islamabad, 44000, Pakistan; Department of Operations, Technology, Events and Hospitality Management, Manchester Metropolitan University, Manchester, M15 6BH, United Kingdom; Department of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, M15 6BH, United Kingdom","Ali, S., School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology, Islamabad, 44000, Pakistan; Mehmood, F., School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology, Islamabad, 44000, Pakistan; Khan, M.J., School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology, Islamabad, 44000, Pakistan; Ayaz, Y., School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology, Islamabad, 44000, Pakistan, National Center of Artificial Intelligence (NCAI), Lahore, 54000, Pakistan; Ayaz, Y., School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology, Islamabad, 44000, Pakistan, National Center of Artificial Intelligence (NCAI), Lahore, 54000, Pakistan; Asgher, U., School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology, Islamabad, 44000, Pakistan; Sadia, H., Shifa International Hospitals Limited, Islamabad, 44000, Pakistan, Autism Resource Centre, Islamabad, 44000, Pakistan; Sadia, H., Shifa International Hospitals Limited, Islamabad, 44000, Pakistan, Autism Resource Centre, Islamabad, 44000, Pakistan; Edifor, E., Department of Operations, Technology, Events and Hospitality Management, Manchester Metropolitan University, Manchester, M15 6BH, United Kingdom; Nawaz, R., Department of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, M15 6BH, United Kingdom","This research article presents a preliminary longitudinal study to check the improvement in multi-human communication of children with Autism Spectrum Disorder (ASD) using a standardized multi-robot therapy. The research is based on a 3 step framework: 1) Human-Human Interaction, Stage-1 (HHI-S1), 2) Human-Robot Interaction, Stage-2 (HRI-S2), and 3) Human-Human Interaction, Stage-3 (HHI-S3). All three stages of the therapy consist of two command sets: 1) Controls commands and 2) Evaluation commands (auditory commands, visual commands, and combination of both). The concept of multiple robots is introduced to help multi-human communication and discourage isolation in ASD children. The joint attention of an ASD child is improved by the robotic therapy in stage 2 considering it as a key parameter for a multi-human communication scenario. The improvement in joint attention results in better command following in a triad multi-human communication scenario in stage 3 as compared to stage 1. The proposed intervention has been tested on 8 ASD subjects with 10 sessions over a period of two and a half months (10 weeks). Each session of human-human interaction (stage 1 and 3) consisted of 14 cues whereas 18 cues were presented by each robot for human-robot interaction (stage 2). The results indicate an overall 86% improvement in the social communication skills of ASD children in case of a multi-human scenario. Validation of results and effectiveness of the therapy has been further accomplished through the use of the Childhood Autism Rating Scale (CARS) score. © 2013 IEEE.","Autism spectrum disorder (ASD); human-robot interaction; multi-robots; robotic therapy","Diseases; Industrial robots; Man machine systems; Multipurpose robots; Children with autisms; Command following; Human communications; Human interactions; Human-human interactions; Longitudinal study; Robotic therapies; Social communications; Social robots",,,,,"National University of Sciences and Technology, NUST","Funding for this article was given by National University of Sciences and Technology, Islamabad, Pakistan.",,,,,,,,,,"Tomczak, M., Wójcikowski, M., Listewnik, P., Pankiewicz, B., Majchrowicz, D., Jefidrzejewska-Szczerska, M., Support for employees with ASD in the workplace using a Bluetooth skin resistance sensorfla preliminary study (2018) Sensors, 18 (10), p. 3530. , Oct; Toth, K., Munson, J., Meltzoff, A.N., Dawson, G., Early predictors of communication development in young children with autism spectrum disorder: Joint attention, imitation, and toy play (2006) J. Autism Develop. Disorders, 36 (8), pp. 993-1005. , Dec; Barnard, J., (2000) Inclusion and Autism: Is It Working? 1,000 Examples of Inclu-sion in Education and Adult Life from the National Autistic Society's Members, , London, U.K.: National Autistic Society; Wing, L., The autistic spectrum (1997) Lancet, 350 (9093), pp. 1761-1766; Ali, S., Mehmood, F., Dancey, D., Ayaz, Y., Khan, M.J., Naseer, N., Amadeu, R.D.C., Nawaz, R., An adaptive multi-robot therapy for improving joint attention and imitation of ASD children (2019) IEEE Access, 7, pp. 81808-81825; Ali, S., Mehmood, F., Ayaz, Y., Khan, M.J., Sadia, H., Nawaz, R., Comparing the effectiveness of different reinforcement stimuli in a robotic therapy for children with ASD (2020) IEEE Access, 8, pp. 13128-13137; Diehl, J.J., Schmitt, L.M., Villano, M., Crowell, C.R., The clinical use of robots for individuals with autism spectrum disorders: A critical review (2012) Res. Autism Spectr. Disorders, 6 (1), pp. 249-262. , Jan; Fujimoto, I., Matsumoto, T., De Silva, P.R.S., Kobayashi, M., Higashi, M., Mimicking and evaluating human motion to improve the imitation skill of children with autism through a robot (2011) Int. J. Social Robot., 3 (4), pp. 349-357. , Nov; Wainer, J., Robins, B., Amirabdollahian, F., Dautenhahn, K., Using the humanoid robot KASPAR to autonomously play triadic games and facilitate collaborative play among children with autism (2014) IEEE Trans. Auton. Mental Develop., 6 (3), pp. 183-199. , Sep; Kim, E.S., Berkovits, L.D., Bernier, E.P., Leyzberg, D., Shic, F., Paul, R., Scassellati, B., Social robots as embedded reinforcers of social behavior in children with autism (2013) J. Autism Develop. Disorders, 43 (5), pp. 1038-1049. , May; Scassellati, B., Admoni, H., Mataric, M., Robots for use in autism research (2012) Annu. Rev. Biomed. Eng., 14 (1), pp. 275-294. , Aug; Goodrich, M.A., Colton, M.A., Brinton, B., Fujiki, M., A case for low-dose robotics in autism therapy (2011) Proc. 6th Int. Conf. Hum.-Robot Interact. (HRI), pp. 143-144; LeGoff, D.B., Use of LEGO as a therapeutic medium for improving social competence (2004) J. Autism Develop. Disorders, 34 (5), pp. 557-571. , Oct; Yunus, R., Arif, O., Afzal, H., Amjad, M.F., Abbas, H., Bokhari, H.N., Haider, S.T., Nawaz, R., A framework to estimate the nutritional value of food in real time using deep learning techniques (2019) IEEE Access, 7, pp. 2643-2652; Barakova, E.I., Bajracharya, P., Willemsen, M., Lourens, T., Huskens, B., Long-term LEGO therapy with humanoid robot for children with ASD (2015) Expert Syst., 32 (6), pp. 698-709. , Dec; Huskens, B., Verschuur, R., Gillesen, J., Didden, R., Barakova, E., Promoting question-asking in school-aged children with autism spectrum disorders: Effectiveness of a robot intervention compared to a human-trainer intervention (2013) Develop. Neurorehabilitation, 16 (5), pp. 345-356. , Oct; Ferraioli, S.J., Hansford, A., Harris, S.L., Benefits of including siblings in the treatment of autism spectrum disorders (2012) Cognit. Behav. Pract., 19 (3), pp. 413-422. , Aug; Baker, M.J., Incorporating the thematic ritualistic behaviors of children with autism into games: Increasing social play interactions with siblings (2000) J. Positive Behav. Intervent., 2 (2), pp. 66-84. , Apr; Tsao, L.-L., Odom, S.L., Sibling-mediated social interaction intervention for young children with autism (2006) Topics Early Childhood Special Edu., 26 (2), pp. 106-123. , Apr; Walton, K.M., Ingersoll, B.R., Evaluation of a sibling-mediated imitation intervention for young children with autism (2012) J. Positive Behav. Intervent., 14 (4), pp. 241-253. , Oct; Huskens, B., Palmen, A., Van der Werff, M., Lourens, T., Barakova, E., Improving collaborative play between children with autism spectrum disorders and their siblings: The effectiveness of a robot-mediated intervention based on Lego therapy (2015) J. Autism Develop. Disorders, 45 (11), pp. 3746-3755. , Nov; Fachantidis, N., Syriopoulou-Delli, C.K., Vezyrtzis, I., Zygopoulou, M., Beneficial effects of robot-mediated class activities on a child with ASD and his typical classmates (2020) Int. J. Dev. Disabil., 66 (3), pp. 245-253; So, W.-C., Cheng, C.-H., Lam, W.-Y., Huang, Y., Ng, K.-C., Tung, H.-C., Wong, W., A robot-based play-drama intervention may improve the joint attention and functional play behaviors of Chinese-speaking preschoolers with autism spectrum disorder: A pilot study (2020) J. Autism Develop. Disor-ders, 50 (2), pp. 467-481. , Feb","Ali, S.; School of Mechanical and Manufacturing Engineering (SMME), Pakistan; email: sarababer@smme.nust.edu.pk",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85087404833
"He W., Xue C., Yu X., Li Z., Yang C.","57218621820;57203136027;57203040999;57218308705;57204881979;","Admittance-Based Controller Design for Physical Human-Robot Interaction in the Constrained Task Space",2020,"IEEE Transactions on Automation Science and Engineering","17","4","9075439","1937","1949",,61,"10.1109/TASE.2020.2983225","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085713020&doi=10.1109%2fTASE.2020.2983225&partnerID=40&md5=b4421c532687a9f5ac3ba8332f00434a","Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China; Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Department of Automation, University of Science and Technology of China, Hefei, China; Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom","He, W., Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China, Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Xue, C., Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China, Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Yu, X., Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China, Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Li, Z., Department of Automation, University of Science and Technology of China, Hefei, China; Yang, C., Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom","In this article, an admittance-based controller for physical human-robot interaction (pHRI) is presented to perform the coordinated operation in the constrained task space. An admittance model and a soft saturation function are employed to generate a differentiable reference trajectory to ensure that the end-effector motion of the manipulator complies with the human operation and avoids collision with surroundings. Then, an adaptive neural network (NN) controller involving integral barrier Lyapunov function (IBLF) is designed to deal with tracking issues. Meanwhile, the controller can guarantee the end-effector of the manipulator limited in the constrained task space. A learning method based on the radial basis function NN (RBFNN) is involved in controller design to compensate for the dynamic uncertainties and improve tracking performance. The IBLF method is provided to prevent violations of the constrained task space. We prove that all states of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB) by utilizing the Lyapunov stability principles. At last, the effectiveness of the proposed algorithm is verified on a Baxter robot experiment platform. Note to Practitioners-This work is motivated by the neglect of safety in existing controller design in physical human-robot interaction (pHRI), which exists in industry and services, such as assembly and medical care. It is considerably required in the controller design for rigorously handling constraints. Therefore, in this article, we propose a novel admittance-based human-robot interaction controller. The developed controller has the following functionalities: 1) ensuring reference trajectory remaining in the constrained task space: A differentiable reference trajectory is shaped by the desired admittance model and a soft saturation function; 2) solving uncertainties of robotic dynamics: A learning approach based on radial basis function neural network (RBFNN) is involved in controller design; and 3) ensuring the end-effector of the manipulator remaining in the constrained task space: different from other barrier Lyapunov function (BLF), integral BLF (IBLF) is proposed to constrain system output directly rather than tracking error, which may be more convenient for controller designers. The controller can be potentially applied in many areas. First, it can be used in the rehabilitation robot to avoid injuring the patient by limiting the motion. Second, it can ensure the end-effector of the industrial manipulator in a prescribed task region. In some industrial tasks, dangerous or damageable tools are mounted on the end-effector, and it will hurt humans and bring damage to the robot when the end-effector is out of the prescribed task region. Third, it may bring a new idea to the designed controller for avoiding collisions in pHRI when collisions occur in the prescribed trajectory of end-effector. © 2004-2012 IEEE.","Adaptive neural network (NN) control; admittance control; integral barrier Lyapunov function (IBLF); motion constraint; physical human-robot interaction (pHRI)","Accident prevention; Adaptive control systems; Closed loop systems; Controllers; End effectors; Industrial manipulators; Integral equations; Learning systems; Lyapunov functions; Machine design; Man machine systems; Manipulators; Medical robotics; Navigation; Patient rehabilitation; Radial basis function networks; Service industry; Trajectories; Adaptive neural networks; Coordinated operation; Physical human-robot interactions; Physical humanrobot interaction (phri); Radial basis function neural networks; Radial basis function NN; Reference trajectories; Semi-globally uniformly ultimately bounded; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 61873298, 61933001; Ministry of Education of the People's Republic of China, MOE: 6141A02033339; University of Science and Technology Beijing, USTB; National Key Research and Development Program of China, NKRDPC: 2017YFB1302302, 2018YFC2001600","Prof. Yang has been awarded the EU Marie Curie International Incoming Fellowship, the U.K. EPSRC UKRI Innovation Fellowship, and the Best Paper Award of the IEEE TRANSACTIONS ON ROBOTICS, as well as over ten conference best paper awards.","Manuscript received December 6, 2019; revised January 31, 2020; accepted March 1, 2020. Date of publication April 21, 2020; date of current version October 6, 2020. This article was recommended for publication by Associate Editor Z. Xiong and Editor Q. Zhao upon evaluation of the reviewers’ comments. This work was supported in part by the National Natural Science Foundation of China under Grant 61933001 and Grant 61873298, in part by the Joint Funds of Equipment Pre-Research and Ministry of Education of China under Grant 6141A02033339, in part by the Beijing Top Discipline for Artificial Intelligent Science and Engineering, USTB, and in part by the National Key Research and Development Program of China under Grant 2017YFB1302302 and Grant 2018YFC2001600. (Corresponding author: Wei He.) Wei He, Chengqian Xue, and Xinbo Yu are with the Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China, and also with the Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China (e-mail: weihe@ieee.org).",,,,,,,,,"Pehlivan, A.U., Losey, D.P., Omalley, M.K., Minimal assist-Asneeded controller for upper limb robotic rehabilitation (2016) Ieee Trans. Robot, 32 (1), pp. 113-124. , Feb; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) Ieee Trans. Robot, 31 (5), pp. 1089-1100. , Oct; He, W., Ge, W., Li, Y., Liu, Y.-J., Yang, C., Sun, C., Model identification and control design for a humanoid robot (2017) Ieee Trans. Syst., Man, Cybern. 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"Korayem M.H., Azargoshasb S., Korayem A.H., Tabibian S.","6603758099;55387405500;57209727796;27467912200;","Design and Implementation of the Voice Command Recognition and the Sound Source Localization System for Human-Robot Interaction",2021,"Robotica","39","10",,"1779","1790",,,"10.1017/S0263574720001496","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102718393&doi=10.1017%2fS0263574720001496&partnerID=40&md5=b25e27619f5c877899ccd097a40d7602","Robotic Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, 1684613114, Iran; Mechanical and Mechatronics Engineering Department, University of WaterlooON  N2L 3G1, Canada; Department of Mechanical and Mechatronics Engineering, Cyberspace Research Institute, Shahid Beheshti University, Tehran, Iran","Korayem, M.H., Robotic Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, 1684613114, Iran; Azargoshasb, S., Robotic Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, 1684613114, Iran; Korayem, A.H., Mechanical and Mechatronics Engineering Department, University of WaterlooON  N2L 3G1, Canada; Tabibian, S., Department of Mechanical and Mechatronics Engineering, Cyberspace Research Institute, Shahid Beheshti University, Tehran, Iran","Human-robot interaction (HRI) is becoming more important nowadays. In this paper, a low-cost communication system for HRI is designed and implemented on the Scout robot and a robotic face. A hidden Markov model-based voice command detection system is proposed and a non-native database has been collected by Persian speakers, which contains 10 desired English commands. The experimental results confirm that the proposed system is capable to recognize the voice commands, and properly performs the task or expresses the right answer. Comparing the system with a trained system on the Julius native database shows a better true detection (about 10%). © Iran University of Science and Technology, 2021.","Hidden Markov model; Human-robot interaction; Robotic face; Social robotic; Voice command","Agricultural robots; Hidden Markov models; Machine design; Man machine systems; Speech recognition; Design and implementations; Detection system; Human robot Interaction (HRI); Robotic faces; Scout robots; Sound source localization; Voice command; Voice command recognition; Social robots",,,,,,,,,,,,,,,,"Lo, S.-Y., Huang, H.-P., Realization of sign language motion using a dual-Arm/hand humanoid robot (2016) Intell. Serv. Rob., 9 (4), pp. 333-345; Halawani, A., Ur Réhman, S., Li, H., Anani, A., Active vision for controlling an electric wheelchair (2012) Intell. Service Rob., 5 (2), pp. 89-98; Leica, P., Roberti, F., Monllor, M., Toibero, J.M., Carelli, R., Control of bidirectional physical human-robot interaction based on the human intention (2017) Intell. Serv. Rob., 10 (1), pp. 31-40; Kennedy, J., Lemaignan, S., Montassier, C., Lavalade, P., Irfan, B., Papadopoulos, F., Senft, E., Belpaeme, T., Child speech recognition in human-robot interaction: Evaluations and recommendations (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction; Ivaldi, S., Lefort, S., Peters, J., Chetouani, M., Provasi, J., Zibetti, E., Towards engagement models that consider individual factors in hri: On the relation of extroversion and negative attitude towards robots to gaze and speech during a human-robot assembly task (2017) Int. J. Soc. Rob., 9 (1), pp. 63-86; Varrasi, S., Lucas, A., Soranzo, A., McNamara, J., Di Nuovo, A., IBM cloud services enhance automatic cognitive assessment via human-robot interaction (2019) New Trends in Medical and Service Robotics, pp. 169-176. , Springer; Grigore, E.C., Pereira, A., Zhou, I., Wang, D., Scassellati, B., Talk to me: Verbal communication improves perceptions of friendship and social presence in human-robot interaction (2016) International Conference on Intelligent Virtual Agents, , Springer; Fardana, A., Jain, S., Jovancevíc, I., Suri, Y., Morand, C., Robertson, N., Controlling a mobile robot with natural commands based on voice and gesture (2013) Proceedings of the Ieee International Conference on Robotics and Automation (ICRA); Gómez, J.-B., Ceballos, A., Prieto, F., Redarce, T., Mouth gesture and voice command based robot command interface (2009) International Conference on Robotics and Automation; Mustofa, T., Implementation speech recognition for robot control using mfcc and ANFIS (2017) J. Telematics Inf. (JTI), 5 (2), pp. 47-55; Zinchenko, K., Wu, C.-Y., Song, K.-T., A study on speech recognition control for a surgical robot (2016) Ieee Trans. Ind. Inf., 13 (2), pp. 607-615; Burger, B., Ferrané, I., Lerasle, F., Infantes, G., Two-handed gesture recognition and fusion with speech to command a robot (2012) Auto. Robots., 32 (2), pp. 129-147; Abdullahi, Z.H., Muhammad, N.A., Kazaure, J.S., Amuda, F.A., Mobile robot voice recognition in control movements (2015) Int. J. Comput. Sci. Electron. Eng. (IJCSEE)., 3 (1), pp. 11-16; Abed, A.A., Jasim, A.A., Design and implementation of wireless voice controlled mobile robot (2016) Al-Qadisiyah J. Eng. Sci., 9 (2), pp. 135-147; Okuno, H.G., Nakadai, K., Takahashi, T., Takeda, R., Nakamura, K., Mizumoto, T., Yoshida, T., Itohara, T., Hark Document Version 2.0.0.(Revision: 6357; Tabibian, S., A voice command detection system for aerospace applications (2017) Int. J. Speech Technol., 20 (4), pp. 1049-1061; Yoshizawa, S., Hayasaka, N., Wada, N., Miyanaga, Y., Cepstral gain normalization for noise robust speech recognition (2004) 2004 Ieee International Conference on Acoustics, Speech, and Signal Processing; Young, S.J., Young, S., (1993) The Htk Hidden Markov Model Toolkit: Design and Philosophy, , University of Cambridge, Department of Engineering Cambridge, UK; Elahi, M.T., Korayem, A.H., Shariati, A., Meghdari, A., Alemi, M., Ahmadi, E., Taheri, A., Heidari, R., Xylotism': A tablet-based application to teach music to children with Autism (2017) International Conference on Social Robotics, , Springer; Pour, A.G., Taheri, A., Alemi, M., Meghdari, A., Human-robot facial expression reciprocal interaction platform: Case studies on children with autism (2018) Int. J. Soc. Rob., 10 (2), pp. 179-198; Taheri, A., Meghdari, A., Alemi, M., Pouretemad, H., Human-robot interaction in autism treatment: Acase study on three pairs of autistic children as twins, siblings, and classmates (2018) Int. J. Soc. Rob., 10 (1), pp. 93-113; Bennett, C., Šabanovic, S., Perceptions of affective expression in a minimalist robotic face (2013) 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI); Bennett, C.C., Šabanoviæ, S., Deriving minimal features for human-like facial expressions in robotic faces (2014) Int. J. Soc. Rob., 6 (3), pp. 367-381; Blow, M., Dautenhahn, K., Appleby, A., Nehaniv, C.L., Lee, D., The art of designing robot faces: Dimensions for human-robot interaction (2006) Proceedings of the 1st Acm SIGCHI/SIGART Conference on Human-Robot Interaction; DiSalvo, C.F., Gemperle, F., Forlizzi, J., Kiesler, S., All robots are not created equal: The design and perception of humanoid robot heads (2002) Proceedings of the 4th Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques; Korayem, A., Nekoo, S., Korayem, M., Sliding mode control design based on the state-dependent riccati equation: Theoretical and experimental implementation (2019) Int. J. Control, 92 (9), pp. 2136-2149; Korayem, A.H., Nekoo, S.R., Korayem, M.H., Optimal sliding mode control design based on the state-dependent riccati equation for cooperative manipulators to increase dynamic load carrying capacity (2019) Robotica, 37 (2), pp. 321-337; Davouddoust, A., (2018) Upgrading the Scout Mobile Robot, by Creating Social Interactions with Humans, Using Audio and Video Channels in a Dynamic Environment, , Master's Thesis Islamic Azad University of Tehran Research Sciences; Tanha, S., Dehkordi, S., Korayem, A., Control a mobile robot in social environments by considering human as a moving obstacle (2018) 2018 6th Rsi International Conference on Robotics and Mechatronics (IcRoM)","Korayem, M.H.; Robotic Research Laboratory, Iran; email: hkorayem@iust.ac.ir",,,"Cambridge University Press",,,,,02635747,,ROBOD,,"English","Robotica",Article,"Final","",Scopus,2-s2.0-85102718393
"Rao G.R.K., Sgar P.V., Mannem S.N.P., Prasad C., Cherukuri N., Talasila V.","57289033500;57292792100;57291425300;57289033600;57223128560;57289574900;","Imitation Learning with Baxter Robot using Hi-Fives",2021,"Proceedings of the 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021",,,,"90","95",,,"10.1109/ICIRCA51532.2021.9544886","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116897550&doi=10.1109%2fICIRCA51532.2021.9544886&partnerID=40&md5=a94f73a7f95cc93daab863847e4fde98","Koneru Lakshmaiah Education Foundation, Department of Cs&it, AP, Vaddeswaram, 522502, India; Gudlavalleru Engineering College, Department of Cse, AP, Gudlavalleru, 521356, India; Gandhi Institute of Technology and Management, Department of Cse, AP, Visakhapatnam, 530045, India; Koneru Lakshmaiah Education Foundation, Department of Cse, AP, Vaddeswaram, 522502, India","Rao, G.R.K., Koneru Lakshmaiah Education Foundation, Department of Cs&it, AP, Vaddeswaram, 522502, India; Sgar, P.V., Gudlavalleru Engineering College, Department of Cse, AP, Gudlavalleru, 521356, India; Mannem, S.N.P., Gudlavalleru Engineering College, Department of Cse, AP, Gudlavalleru, 521356, India; Prasad, C., Gandhi Institute of Technology and Management, Department of Cse, AP, Visakhapatnam, 530045, India; Cherukuri, N., Gandhi Institute of Technology and Management, Department of Cse, AP, Visakhapatnam, 530045, India; Talasila, V., Koneru Lakshmaiah Education Foundation, Department of Cse, AP, Vaddeswaram, 522502, India","The goal of this paper is to successfully learn hi-fives for human-robot interaction. The proposed research work has used the Imitation Learning approach by incorporating Bayesian Interaction Primitives [1]. Through expert-guided demonstrations, the robot has been trained to learn relationships between human and robot trajectories. The research study has demonstrated that, the robot is able to complete the interaction with a human and successfully issue a hi-five. Also, the Bayesian Interaction Primitives are implemented to teach a Baxter Robot to give hi-five through imitation learning. Additionally, the trajectories are compared with human biomechanics data. © 2021 IEEE.","Autonomous investigation; Autonomous specialist; DCNN; Deep support learning; operate cell robot","Human robot interaction; Autonomous investigation; Autonomous specialist; Bayesian; Cell robots; DCNN; Deep support learning; Imitation learning; Learn+; Operate cell robot; Support learning; Deep learning",,,,,,,,,,,,,,,,"Campbell, J., Hitzmann, A., Stepputtis, S., Ikemoto, S., Hosoda, K., Ben Amor, H., Learning interactive behaviors for musculoskeletal robots using Bayesian interaction primitives IEEE/RSJ International Conference on; Yuvaraj, N., Karthikeyan, T., Praghash, K., (2021) An Improved Task Allocation Scheme in Serverless Computing Using Gray Wolf Optimization (GWO) Based Reinforcement Learning (RIL) Approach, , Wireless Personal Communications; Tatineni, P., Sobhan Babu, B., Kanuri, B., Rao, G.R.K., Chitturi, P., Naresh, C., Post COVID-19 Twitter user's emotions classification using deep learning techniques in India (2021) Proceedings-International Conference on Artificial Intelligence and Smart Systems, ICAIS 2021; Cherukuri, N., Bethapudi, N.R., Thotakura, V.S.K., Chitturi, P., Basha, C.Z., Mummidi, R.M., Deep learning for lung cancer prediction using NSCLS patients CT information (2021) Proceedings-International Conference on Artificial Intelligence and Smart Systems, ICAIS 2021; Nagageetha, M., Ramesh, N.V.K., A survey of agriculture crop monitoring using iot based image processing and machine learning techniques (2021) Turkish Journal of Physiotherapy and Rehabilitation; Dandu, G., Charyulu, G.M., Kumari, L., Latha, M., Kesavakumar, P., Teaching communicative activities of learning english language speaking and its challenges for the young aspirants: A schematic view (2021) Turkish Journal of Physiotherapy and Rehabilitation; Raghavendra Sai, N., Bhargav, J., Aneesh, M., Vinay Sahit, G., Nikhil, A., Discovering network intrusion using machine learning and data analytics approach (2021) Proceedings of the 3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021; Reddy, C.R., Reddy, K.H., Goud, B.S., Pakkiraiah, B., A deep learning approach for islanding detection of integrated DG with CWT and CNN (2021) 2021 International Conference on Sustainable Energy and Future Electric Transportation, SeFet 2021; Kavitha, M., Gnaneswar, G., Dinesh, R., Sai, Y.R., Suraj, R.S., Heart disease prediction using hybrid machine learning model (2021) Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021; Kiran, C.R.S., Naveen, C., Kumar, D.A., Saiteja, T., Karthikeyan, C., Prediction of epidimic outbreak using deep learning methods (2021) Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021; Balajee, R.M., Mohapatra, H., Deepak, V., Babu, D.V., Requirements identification on automated medical care with appropriate machine learning techniques (2021) Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021; Murugudu, M.R., Reddy, L.S.S., Efficiently harvesting deep web interfaces based on adaptive learning using two-phase data crawler framework (2021) Soft Computing; Yamini Supriya, B., Prasanth, Y., An efficient learning method network intrusion detection (2021) Annals of the Romanian Society for Cell Biology; Shaik, A.A., Mareedu, V.D.P., Polurie, V.V.K., Learning multiview deep features from skeletal sign language videos for recognition 2021 Turkish Journal of Electrical Engineering and Computer Sciences; Jyothi, P., Pradeepini, G., Review on cardiac arrhythmia through segmentation approaches in deep learning (2021) Advances in Intelligent Systems and Computing; Vamseekrishna, A., Nishitha, R., Kumar, T.A., Hanuman, K., Supriya, C.G., Prediction of temperature and humidity using IoT and machine learning algorithm (2021) Advances in Intelligent Systems and Computing; Koteswari, M.J.L., Balaji, M., Sainadh, K., Sri Kavya, K.C., Reducing Covid-19 readmissions using machine learning (2021) Turkish Journal of Physiotherapy and Rehabilitation; Prasad, M.V.D., Kumar, E.K., Krishna, S.V.N.P.V., Kumar, M.S., Harsha, P.S., Ahammad, S.H., Detection of two wheelers helmet using machine learning (2021) Turkish Journal of Physiotherapy and Rehabilitation; Bharadwaj, P.K.B., Kana, G.R., Pattern recognition and machine learning (2021) EAI/Springer Innovations in Communication and Computing; Sneha, Y., Aluvala, S., Impact of experimental learning on graduates success in engineering education (2021) Journal of Engineering Education Transformations; Sekhar, C., Rao, M.S., Nayani, A.S.K., Bhattacharyya, D., Emotion recognition through human conversation using machine learning techniques (2021) Advances in Intelligent Systems and Computing; Ganesan, V., Rajarajeswari, P., Govindaraj, V., Prakash, K.B., Naren, J., Post-COVID-19 emerging challenges and predictions on people process and product by metaheuristic deep learning algorithm (2021) Advances in Intelligent Systems and Computing",,,,"Institute of Electrical and Electronics Engineers Inc.","3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021","2 September 2021 through 4 September 2021",,172137,,9780738146270,,,"English","Proc. Int. Conf. Inven. Res. Comput. Appl., ICIRCA",Conference Paper,"Final","",Scopus,2-s2.0-85116897550
"Kress-Gazit H., Eder K., Hoffman G., Admoni H., Argall B., Ehlers R., Heckman C., Jansen N., Knepper R., KÅ™etínský J., Levy-Tzedek S., Li J., Murphey T., Riek L., Sadigh D.","15762557900;23391989300;35726707500;35180933000;57265524200;57219751588;35976340800;36646082100;16549885400;56406498200;42061792400;57238171100;57265773800;27868000800;55052871500;","Formalizing and guaranteeing human-robot interaction",2021,"Communications of the ACM","64","9",,"78","84",,,"10.1145/3433637","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113859888&doi=10.1145%2f3433637&partnerID=40&md5=935683a3da12dbbe8af162cd1ecb071e","Cornell University, Ithaca, NY, United States; University of Bristol, United Kingdom; Cornell University, Ithaca, NY, United States; Carnegie Mellon University, Pittsburgh, PA, United States; Northwestern University, Evanston, IL, United States; Clausthal University of Technology in Clausthal-Zellerfeld, Germany; University of Colorado, Boulder, CO, United States; Radboud University Nijmegen, Netherlands; Technical University of Munich, Germany; Ben-Gurion University, Beersheba, Israel; Ryerson University in Toronto, Canada; Northwestern University, Evanston, IL, United States; University of California San Diego, United States; Stanford University, Stanford, CA, United States","Kress-Gazit, H., Cornell University, Ithaca, NY, United States; Eder, K., University of Bristol, United Kingdom; Hoffman, G., Cornell University, Ithaca, NY, United States; Admoni, H., Carnegie Mellon University, Pittsburgh, PA, United States; Argall, B., Northwestern University, Evanston, IL, United States; Ehlers, R., Clausthal University of Technology in Clausthal-Zellerfeld, Germany; Heckman, C., University of Colorado, Boulder, CO, United States; Jansen, N., Radboud University Nijmegen, Netherlands; Knepper, R., Cornell University, Ithaca, NY, United States; KÅ™etínský, J., Technical University of Munich, Germany; Levy-Tzedek, S., Ben-Gurion University, Beersheba, Israel; Li, J., Ryerson University in Toronto, Canada; Murphey, T., Northwestern University, Evanston, IL, United States; Riek, L., University of California San Diego, United States; Sadigh, D., Stanford University, Stanford, CA, United States","Robot capabilities are maturing across domains, from self-driving cars to bipeds to drones. The growing field of human-robot interaction (HRI) studies various aspects of this scenario, ranging from social norms to collaborative manipulation to human-robot teaming. Researchers in HRI have made great efforts in developing models, methods, and algorithms for robots acting with and around humans. Demonstrating trustworthiness in various forms of automation through formal guarantees has been the focus of validation, verification, and synthesis efforts. Formalizing HRI can enable the creation of trustworthy systems and, just as importantly, support explicit reasoning about the context of guarantees.",,"Man machine systems; Human robot Interaction (HRI); Human robots; Social norm; Trustworthy systems; Social robots",,,,,,,,,,,,,,,,"Alami, R., Verification and synthesis of human-robot interaction (Dagstuhl Seminar 19081) (2019) Dagstuhl Reports, 9 (2), pp. 91-110. , https://doi.org/10.4230/DagRep.9.2.91; Araiza-Illan, D., Coverage-driven verification-An approach to verify code for robots that directly interact with humans (2015) Hardware and Software: Verification and Testing, pp. 69-84; Araiza-Illan, D., Systematic and realistic testing in simulation of control code for robots in collaborative human-robot interactions (2016) Towards Autonomous Robotic Systems Conference, pp. 20-32; Argall, B.D., Billard, A.G., A survey of tactile human-robot interactions (2010) Robotics and Autonomous Systems, 58 (10), pp. 1159-1176. , https://doi.org/10.1016/j.robot.2010.07.002, Oct; Breazeal, C., Social robotics (2016) Springer Handbook of Robotics, Springer, pp. 1935-1972; Calinescu, R., Socio-cyber-physical systems: Models, opportunities, open challenges (2019) 2019 IEEE/ACM 5th Intern. Wkshp. Soft. Eng. For Smart Cyber-Physical Systems, pp. 2-6; Campos, T., SMT-based control and feedback for social navigation Intern. Conf. Robotics and Automation 2019, pp. 5005-5011. , Montreal, QC, Canada; Clarke, E.M., (2018) Handbook of Model Checking, , Springer; Grice, H.P., (1975) Logic and Conversation Syntax and Semantics, 3, pp. 41-58. , Speech Acts. P. Cole and J.L. Morgan, eds Academic Press; Hoare, C.A.R., An axiomatic basis for computer programming (1969) Commun Acm, 12 (10), pp. 576-580. , https://doi.org/10.1145/363235.363259, Oct; Hoffman, G., Evaluating fluency in human-robot collaboration (2019) Ieee Transactions on Human-Machine Systems, 49 (3), pp. 209-218; Iqbal, T., Riek, L.D., Human-robot teaming: Approaches from joint action and dynamical systems (2019) Humanoid Robotics: A Reference, pp. 2293-2312; Kellmeyer, P., Social robots in rehabilitation: A question of trust Science Robotics, 3 (21), p. 2018. , https://doi.org/10.1126/scirobotics.aat1587; Kress-Gazit, H., Synthesis for robots: Guarantees and feedback for robot behavior. Ann. Review of Control Robotics, and Auton. Systems, 1 (1), p. 2018. , https://doi.org/10.1146/annurev-control-060117-104838; Kretínský, J., Survey of statistical verification of linear unbounded properties: Model checking and distances (2016) Leveraging Applications of Formal Methods Verification and Validation: Foundational Techniques, pp. 27-45; Kshirsagar, A., Specifying and synthesizing human-robot handovers (2019) Proc. Of the IEEE/RSJ Intern. Conf. Of Intelligent Robots and Systems; Kwiatkowska, M., PRISM 4.0: Verification of probabilistic real-Time systems (2011) Proc. Of the 23rd Intern. Conf. Of Computer-Aided Verification (Berlin, Heidelberg, pp. 585-591; Langer, A., Trust in socially assistive robots: Considerations for use in rehabilitation Neuroscience & Biobehavioral Reviews, 104 (2019), pp. 231-239. , https://doi.org/https://doi.org/10.1016/j.neubiorev.2019.07.014; Lee, H.R., Cultural design of domestic robots: A study of user expectations in Korea and the United States (2012) 2012 Ieee RO-MAN: The 21st Ieee Intern. Symp: The Robot and Human Interactive Communication, pp. 803-808; Leucker, M., Schallhart, C., A brief account of runtime verification (2009) The J. Logic and Algebraic Programming, 78 (5), pp. 293-303. , https://doi.org/http://dx.doi.org/10.1016/j.jlap.2008.08.004; Li, W., Synthesis for human-in-The-loop control systems (2014) Tools and Algorithms for the Construction and Analysis of Systems-20th Intern. Conf, pp. 470-484; Mason, G., Assurance in reinforcement learning using quantitative verification Advances in Hybridization of Intelligent Methods: Models Systems and Applications, pp. 71-96. , I. Hatzilygeroudis and V. Palade, eds Springer International Publishing; Mataríc, M.J., Scassellati, B., Socially assistive robotics (2016) Springer Handbook of Robotics. Springer, pp. 1973-1994; Nomura, T., Cultural differences in social acceptance of robots 26th Ieee Intern. Symp. On Robot and Human Interactive Communication (RO-MAN), pp. 534-538. , Aug. 2017; Porfirio, D., Authoring and verifying human-robot interactions (2018) Proc. Of the 31st Ann Acm Symp. Of User Interface Software and Tech, pp. 75-86; Rasmussen, J., Mental models and the control of action in complex environments (1987) Selected Papers of the 6th Interdisciplinary Wkshp. On Informatics and Psychology: Mental Models and Human-Computer Interaction 1 (NLD, pp. 41-69; Riek, L.D., Healthcare robotics (2017) Commun Acm, 60 (11), pp. 68-78. , https://doi.org/10.1145/3127874; Sadigh, D., Planning for cars that coordinate with people: Leveraging effects on human actions for planning and active information gathering over human internal state Autonomous Robots (AURO, 42 (7), pp. 1405-1426. , Oct. 2018; Thomaz, A., Computational human-robot interaction (2016) Found. Trends Robotics, 4 (2-3), pp. 105-223. , https://doi.org/10.1561/2300000049, Dec; Tretmans, G.J., Test generation with inputs, outputs and repetitive quiescence Centre for Telematics and Information Technology (CTIT; Webster, M., A corroborative approach to verification and validation of human-robot teams (2020) The Intern. J. Robotics Research, 39 (1), pp. 73-99. , https://doi.org/10.1177/0278364919883338; Wilkes-Gibbs, D., Clark, H.H., Coordinating beliefs in conversation (1992) J. Memory and Language, 31 (2), pp. 183-194",,,,"Association for Computing Machinery",,,,,00010782,,CACMA,,"English","Commun ACM",Article,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85113859888
"Komagata M., Imashiro Y., Yamamoto K., Nakamura Y.","57193028646;57261737400;57261737500;57234302100;","Preferred oil and ceramics options for EHA drive systems and computed torque control of an EHA-driven robot manipulator",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"540","545",,,"10.1109/RO-MAN50785.2021.9515398","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115065586&doi=10.1109%2fRO-MAN50785.2021.9515398&partnerID=40&md5=a8056c1d9d3467c684bcf28d6a3a32db","University of Tokyo, Graduate School of Information Science and Technology, Department of Mechano-Informatics, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8656, Japan; University of Tokyo, RACE, Graduate School of Engineering, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8656, Japan","Komagata, M., University of Tokyo, Graduate School of Information Science and Technology, Department of Mechano-Informatics, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8656, Japan; Imashiro, Y., University of Tokyo, Graduate School of Information Science and Technology, Department of Mechano-Informatics, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8656, Japan; Yamamoto, K., University of Tokyo, Graduate School of Information Science and Technology, Department of Mechano-Informatics, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8656, Japan; Nakamura, Y., University of Tokyo, RACE, Graduate School of Engineering, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8656, Japan","6-DOF robot manipulator Hydracer was developed to gain high output torque and high backdrivability by adopting electro-hydrostatic actuators, however control of overall system of Hydracer is not yet conducted. To achieve flexible force control of Hydracer, we worked on the system improvements: enhancement of reliability of ceramics components, reduction of internal leakage by considering the property of hydraulic oil, and the identification of inertial parameters to improve its controllability. By using identified parameters, flexible force control of Hydracer by zero-torque control with gravity compensation was realized which reveals the potential of safe human-robot interaction. © 2021 IEEE.",,"Agricultural robots; Flexible manipulators; Force control; Industrial manipulators; Leakage (fluid); Modular robots; Robot applications; Torque control; Computed torque control; Electro-hydrostatic actuators; Gravity compensation; Identified parameter; Inertial parameters; Robot manipulator; Safe human-robot interaction; System improvements; Social robots",,,,,"University of Tokyo","This work was supported by “ Development of an Electro Hydrostatic Driven Robot System (High-Pressure Drive System/Hand /Manipulator) ” in the joint research with the University of Tokyo and NACHI-FUJIKOSHI CORP.","This work was supported by Development of an Electro Hydrostatic Driven Robot System (High-Pressure Drive System/Hand /Manipulator) in the joint research with the University of Tokyo and NACHI-FUJIKOSHI CORP",,,,,,,,,"Pratt, G.A., Williamson, M.M., Series elastic actuators (1995) Proc. of IEEE/RSJ Int'l Conf. of Intelligent Robots and Systems, pp. 339-406; Kaminaga, H., Yamamoto, T., Ono, J., Nakamura, Y., Backdrivable miniature hydrostatic transmission for actuation of anthropomorphic robot hands (2007) Proc. of 7th IEEE-RAS International Conference on Humanoid Robots, pp. 36-41; Ko, T., Kaminaga, H., Nakamura, Y., Current-pressure-position triple-loop feedback control of electro-hydrostatic actuators for humanoid robots (2018) Advanced Robotics, 32 (24), pp. 1269-1284; Bobrow, J.E., Desai, J., A high torque to weight ratio robot actuator (1995) Robotica, 13, pp. 201-208; Kaminaga, H., Ko, T., Masumura, R., Komagata, M., Sato, S., Yorita, S., Nakamura, Y., Mechanism and control of whole-body electro-hydrostatic actuator driven humanoid robot hydra (2016) The International Symposium on Experimental Robotics, pp. 656-665; Suzuki, R., Komagata, M., Ko, T., Murotani, K., Kaminaga, H., Tatano, M., Yamamoto, K., Nakamura, Y., Development of 3-DOF wrist mechanism for electro-hydrostatically driven robot arm (2020) Advanced Robotics, 34, pp. 958-973; SA701 Details, , https://www.japanfc.co.jp/en/products/cate01/cate0104/sicasa-1.html, Japan Fine Ceramics Co. Ltd; Industrial Lubricants, , https://www.cosmo-lube.co.jp/eng/product/industrial-lubricants.html, Cosmo Oil Lubricants Co., Ltd; 75W140NS GL-5 Gear Oil, , https://www.redlineoil.com/75w140-ns-gl-5-gear-oil, Red Line Synthetic Oil Corporation; Komagata, M., Ko, T., Nakamura, Y., Design and development of compact ceramics reinforced pump with low internal leakage for electro-hydrostatic actuated robots (2019) Proc. Of the 15th IFToMM World Congress on Mechanism and Machine Science, 73, pp. 2439-2448; Mayeda, H., Osuka, K., Kanagawa, A., A new identification method for serial manipulator arms (1984) Pre. IFAC 9th World Congress, pp. 74-79; Atkeson, C.G., An, C.H., Hollerbach, J.M., Estimation of inertial parameters of manipulator loads and links (1986) The International Journal of Robotics Research, 5 (3), pp. 101-119; Wensing, P.M., Kim, S., Slotine, J.-J.E., Linear matrix inequalities for physically consistent inertial parameter identification: A statistical perspective on the mass distribution (2017) IEEE Robotics and Automation Letters, 3 (1), pp. 60-67; Ayusawa, K., Venture, G., Nakamura, Y., Real-time implementation of physically consistent identification of human body segments (2011) Proc. Of IEEE International Conference on Robotics and Automation, pp. 6282-6287; Ayusawa, K., Rioux, A., Yoshida, E., Venture, G., Gautier, M., Generating persistently exciting trajectory based on condition number optimization (2017) Proc. Of IEEE International Conference on Robotics and Automation (ICRA), pp. 6518-6524; Richard, P., Modelling, trajectory calculation and servoing of a computer controlled arm (1972) STANFORD UNIV CA DEPT of COMPUTER SCIENCE; Luca, A.D., Mattone, R., Actuator failure detection and isolation using generalized momenta (2003) Proc. Of IEEE International Conference on Robotics and Automation, pp. 634-639; Tien, L.L., Albu-Schaffer, A., Luca, A.D., Friction observer and compensation for control of robots with joint torque measurement (2008) Proc. Of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3789-3795",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115065586
"Costanzo M., Pirozzi S.","57205142585;6505943410;","Optical Force/Tactile Sensors for Robotic Applications",2021,"IEEE Instrumentation and Measurement Magazine","24","5","9491003","28","35",,,"10.1109/MIM.2021.9491003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111165821&doi=10.1109%2fMIM.2021.9491003&partnerID=40&md5=bad6228ca4e1edab4c56a0ee08d3a491","University of Campania Luigi, Vanvitelli, Italy","Costanzo, M., University of Campania Luigi, Vanvitelli, Italy; Pirozzi, S., University of Campania Luigi, Vanvitelli, Italy","Nowadays, robotic systems use tactile sensing as a key enabling technology to implement complex tasks. For example, manipulation and grasping problems strongly depend on the physical and geometrical characteristics of the objects; in fact, objects may be deformable or change their shape when in contact with the robot or the environment. For this reason, often, robots end effectors are equipped with sensorized fingers which can estimate the objects' features, forces, and contact locations. This is useful in a safe and efficient physical Human-Robot Interaction (pHRI) to perform cooperation and co-manipulation tasks while limiting damage from accidental impacts. © 1998-2012 IEEE.",,"End effectors; Robotics; Contact location; Enabling technologies; Geometrical characteristics; Manipulation task; Physical humanrobot interaction (phri); Robotic applications; Robotic systems; Tactile sensing; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 731590, 870133; European Commission, EC","The first version [6] comprised only two layers: an optoelectronic layer (i.e., a Printed Circuit Board—PCB) and a deformable one. Each sensing point was realized by using a This work was supported by the European Commission within H2020 REFILLS Project (no. 731590) and H2020 REMODEL Project (no. 870133).",,,,,,,,,,"Chen, W., Khamis, H., Birznieks, I., Lepora, N.F., Redmond, S.J., Tactile sensors for friction estimation and incipient slip detection-toward dexterous robotic manipulation: A review (2018) IEEE Sensors J., 18 (22), pp. 9049-9064. , Nov; Baglio, S., Muscato Savalli N, G., Tactile measuring systems for the recognition of unknown surfaces (2002) IEEE Trans. Instrum. Meas., 51 (3), pp. 522-531. , Jun; Damilano, A., Lince, A., Appendino, S., Hayat, H.M.A., Ariano, P., Demarchi, D., Crepaldi, M., Commercial tactile sensors for hand exoskeletons: Practical considerations for ultra-low cost and verylow complexity read-out (2016) IEEE Instrum. Meas. Mag., 19 (5), pp. 49-56. , Oct; Velázquez, R., Pissaloux, E., Del-Valle-Soto, C., Lay-Ekuakille Andò B, A., Usability evaluation of foot-based interfaces for blind travelers (2020) IEEE Instrum. Meas. Mag., 23 (4), pp. 4-13. , Jun; Liu, H., Guo Sun F, D., Object recognition using tactile measurements: Kernel sparse coding methods (2016) IEEE Trans. Instrum. Meas., 65 (3), pp. 656-665. , Mar; De Maria, G., Natale, C., Pirozzi, S., Force/tactile sensor for robotic applications (2012) Sensors Actuators A: Phys., 175, pp. 60-72; Cirillo, A., Ficuciello, F., Natale, C., Pirozzi, S., Villani, L., A conformable force/tactile skin for physical human-robot interaction (2016) IEEE Robotics and Automation Letters, 1, pp. 41-48; Costanzo, M., De Maria, G., Natale, C., Pirozzi, S., Design and calibration of a force/tactile sensor for dexterous manipulation (2019) Sensors MDPI, 19 (4); Pirozzi, S., Natale, C., Tactile-based manipulation of wires for switchgear assembly (2018) IEEE/ASME Trans. Mechatronics, 23 (6), pp. 2650-2661; Costanzo, M., Stelter, S., Natale, C., Pirozzi, S., Bartels, G., Maldonado, A., Beetz, M., Manipulation planning and control for shelf replenishment (2020) IEEE Robotics and Automation Letters, 5 (2), pp. 1595-1601; Cavallo, A., Costanzo, M., De Maria, G., Natale, C., Modeling and slipping control of a planar slider (2020) Automatica, 115. , May; Goyal, S., Ruina, A., Papadopoulos, J., Planar sliding with dry friction, part 1-limit surface and moment function (1991) Wear, 143 (2), pp. 307-330; Johanastrom, K., De Canudas, W.C., Revisiting the LuGre friction model (2008) IEEE Control Syst. Mag., 28 (6), pp. 101-114; Costanzo, M., De Maria, G., Natale, C., Slipping control algorithms for object manipulation with sensorized parallel grippers (2018) IEEE Int. Conf. Robotics and Automation (ICRA), pp. 7455-7461; Costanzo, M., De Maria, G., Natale, C., Two-fingered in-hand object handling based on force/tactile feedback, IEEE Trans. Robotics (2020), 36 (1), pp. 157-173","Costanzo, M.; University of Campania LuigiItaly; email: marco.costanzo@unicampania.it",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10946969,,IIMMF,,"English","IEEE Instrum Meas Mag",Article,"Final","",Scopus,2-s2.0-85111165821
[No author name available],[No author id available],"2021 1st International Conference on Cyber Management and Engineering, CyMaEn 2021",2021,"2021 1st International Conference On Cyber Management and Engineering, CyMaEn 2021",,,,"","",189,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112472055&partnerID=40&md5=f55a89e5283a112d335bbc78a5410571",,"","The proceedings contain 35 papers. The topics discussed include: hybrid descriptor and patches optimization for face recognition; challenges facing the implementation of wind energy in Algeria: a review paper; effect of human-robot interaction on the fleet size of AIV transporters in FMS; multi-agent simulation to predict global behavior of population based on elementary local Markovian model; managing international perishable food supply chain: a literature review; a multi-objective based AMOSA approach to the dynamic location of bloodmobiles problem; optimal lockdown policies; forecasting KPIs of production systems using LSTM networks; and a major recommendation system in educational mining.",,,,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.","1st International Conference On Cyber Management and Engineering, CyMaEn 2021","26 May 2021 through 28 May 2021",,170765,,9781665432009,,,"English","Int. Conf. Cyber Manag. Eng., CyMaEn",Conference Review,"Final","",Scopus,2-s2.0-85112472055
[No author name available],[No author id available],"Proceedings - 2021 IEEE International Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2021",2021,"Proceedings - 2021 IEEE International Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2021",,,,"","",210,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113141856&partnerID=40&md5=f4083e00d360a68085226afec726c3ae",,"","The proceedings contain 26 papers. The topics discussed include: rapid establishment of data visualization tools for disaster response modeling; situation awareness, mental models and understanding; smart elevator with unsupervised learning for visitor profiling and personalized destination prediction; situational decision making using situation modeling and deep learning; emotion regulation with Markov decision process for human-robot interaction; the impact of new technologies on work design â€“ case study of the industrial robot controllers from one organization; towards an AI coach to infer team mental model alignment in healthcare; and integrated platform for sUAS operations in sensitive areas with improved pilot situation awareness.",,,,,,,,,,,,,,,,,,,,"McGeorge N.Ruvinsky A.Teichmann M.Motus L.Freiman M.",,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2021","14 May 2021 through 22 May 2021",,170822,,9781728176987,,,"English","Proc. - IEEE Int. Conf. Cogn. Comput. Asp. Situat. Manag., CogSIMA",Conference Review,"Final","",Scopus,2-s2.0-85113141856
"Lai J., Jin S., Xia P., Huang J., Yuan P.","34971520200;57222568044;57222571263;57226877304;57222574620;","Dual-Loop Iterative Learning Control of Robot Manipulator for Human-Robot cooperation",2021,"Journal of Physics: Conference Series","1846","1","012090","","",,,"10.1088/1742-6596/1846/1/012090","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103238552&doi=10.1088%2f1742-6596%2f1846%2f1%2f012090&partnerID=40&md5=ddb0381b1a74667118eded79d98ef5b7","Nuclear Power Institute of China, Chengdu, Sichuan, SC 28, China; School of Electrical Engineering and Electronic Information, XiHua University, Chengdu, Sichuan, SC 28, China","Lai, J., Nuclear Power Institute of China, Chengdu, Sichuan, SC 28, China; Jin, S., School of Electrical Engineering and Electronic Information, XiHua University, Chengdu, Sichuan, SC 28, China; Xia, P., School of Electrical Engineering and Electronic Information, XiHua University, Chengdu, Sichuan, SC 28, China; Huang, J., Nuclear Power Institute of China, Chengdu, Sichuan, SC 28, China; Yuan, P., Nuclear Power Institute of China, Chengdu, Sichuan, SC 28, China","This paper proposed a dual-loop iterative learning control strategy for robot manipulator in the situation of repetitive human-robot interaction. By utilizing the contact force between the human and the manipulator, a force-based loop is first conducted to produce the unknown desired trajectory of the manipulator. According to Lyapunov's method and using the back-stepping technique, an adaptive iterative learning controller is presented to fulfill the trajectory tracking task of the robot manipulator, in spite of the model uncertainties and the unknown disturbances. The proposed control scheme is verified by numerical simulation on a robot manipulator with two degrees-of-freedom. © Published under licence by IOP Publishing Ltd.",,"Automation; Degrees of freedom (mechanics); Flexible manipulators; Industrial manipulators; Iterative methods; Learning algorithms; Modular robots; Robot applications; Two term control systems; Uncertainty analysis; Adaptive iterative learning; Desired trajectories; Human-robot cooperation; Iterative learning control; Model uncertainties; Trajectory tracking; Two degrees of freedom; Unknown disturbance; Social robots",,,,,"Science Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China","This work is supported in part by the Science and Technology on Reactor System Design Technology Laboratory.",,,,,,,,,,"Yang, Y., Huang, D. Q., Dong, X. C., Enhanced neural network control of lower limb rehabilitation exoskeleton by add-on repetitive learning (2019) Neurocomputing, 323, pp. 256-264; Hogan, N., Impedance control-an approach to manipulation I-theory (1985) J. Dyn. Syst. Meas. Control-Trans. ASME, 107, pp. 1-7; Jung, S., Hsia, T. C., Neural network impedance force control of robot manipulator (1998) IEEE Transactions on Industrial Electronics, 45, pp. 451-461; Jung, S., Hsia, T. C., Bonitz, R. G., Force Tracking impedance control of robot manipulators under unknown environment (2004) IEEE Transactions on Control Systems Technology, 12, pp. 474-483; Huang, B., Li, Z. J., Wu, X. Y., Ajoudani, A., Bicchi, A., Liu, J. Q., Coordination control of a dual-arm exoskeleton robot using human impedance transfer skills (2019) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49, pp. 954-963; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive humancrobot physical interaction (2015) IEEE Transactions on Robotics, 31, pp. 850-863; Newman, W. S., Stability and performance limits of interaction controllers (1992) Trans. ASME, J. Dyn. Syst., Meas., Control, 114, pp. 563C-570; Song, P., Yu, Y., Zhang, X., A tutorial survey and comparison of impedance control on robotic manipulation (2019) Robotica, 37, pp. 801-836; Bristow, D. A., Tharayil, M., Alleyne, A. G., A survey of iterative learning control (2006) IEEE Control Systems Magazine, 26, pp. 96-114; Yang, Y., Dong, X. C., Liu, X., Huang, D. Q., Robust repetitive learningbased trajectory tracking control for a leg exoskeleton driven by hybrid hydraulic system (2020) IEEE Access, 8, pp. 27705-27714; Ahn, H. S., Chen, Y. Q., Moore, K. L., Iterative learning control: brief survey and categorization (2007) IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37, pp. 1099-1121; Chen, Y. Y., Chu, B., Freeman, C. T., Liu, Y. H., Generalized iterative learning control with mixed system constraints: A gantry robot based verification (2020) Control Engineering Practice, 95, pp. 1-11; Tayebi, A., Adaptive iterative learning control for robot manipulators (2004) Automatica, 40, pp. 1195-1203; Bukkems, B., Kostic, D., Jager de, B., Steinbuch, M., Learning-based identification and iterative learning control of direct-drive robots (2005) IEEE Transactions on Control Systems Technology, 13, pp. 537-549; Chen, W. J., Tomizuka, M., Dual-stage iterative learning control for mimo mismatched system with application to robots with joint elasticity (2014) IEEE Transactions on Control Systems Technology, 22, pp. 1350-1361; Aghili, F., Piedboeaeuf, J. C., Emulation of robots interacting with environment (2006) IEEE/ASME Transactions on Mechatronics, 11, pp. 35-46; Slotine, J. E., Li, W., Adaptive manipulator control: a case study (1998) IEEE Trans. Autom. Control, 33, pp. 995-1003; Li, Y. N., Ge, S. S., Human-robot collaboration based on motion intention estimation (2014) IEEE/ASME Trans. Mechatron, 19, pp. 1007-1014; Chen, Y., Huang, D. Q., Huang, T. F., Qin, N., Tracking control via iterative learning for high-speed trains with distributed input constraints (2019) IEEE Access, 7, pp. 84591-84601","Jin, S.; School of Electrical Engineering and Electronic Information, China; email: shuping201123@stu.xhu.edu.cn",,,"IOP Publishing Ltd","2021 7th International Symposium on Sensors, Mechatronics and Automation System, ISSMAS 2021","29 January 2021 through 31 January 2021",,167904,17426588,,,,"English","J. Phys. Conf. Ser.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85103238552
"Oppenheim J., Huang J., Won I., Huang C.-M.","57222471969;57222464603;57215793617;55193383300;","Mental synchronization in human task demonstration: Implications for robot teaching and learning",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447216","470","474",,1,"10.1145/3434074.3447216","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102759565&doi=10.1145%2f3434074.3447216&partnerID=40&md5=a43b8b5622867e39113ca4eab290c855","Johns Hopkins University, Baltimore, MD, United States; Tufts University, Medford, MA, United States","Oppenheim, J., Johns Hopkins University, Baltimore, MD, United States; Huang, J., Tufts University, Medford, MA, United States; Won, I., Johns Hopkins University, Baltimore, MD, United States; Huang, C.-M., Johns Hopkins University, Baltimore, MD, United States","Communication is integral to knowledge transfer in human-human interaction. To inform effective knowledge transfer in human-robot interaction, we conducted an observational study to better understand how people use gaze and other backchannel signals to ground their mutual understanding of task-oriented instruction during learning interactions. Our results highlight qualitative and quantitative differences in how people exhibit and respond to gaze, depending on motivation and instructional context. The findings of this study inform future research that seeks to improve the efficacy and naturalness of robots as they communicate with people as both learners and instructors. © 2021 ACM.","Backchanneling; Gaze; Grounding; Task demonstration","Agricultural robots; Knowledge management; Man machine systems; Human-human interactions; Instructional context; Knowledge transfer; Learning interactions; Mutual understanding; Observational study; Robot teaching; Task-oriented; Human robot interaction",,,,,,,,,,,,,,,,"Kendon, A., Some functions of gaze-direction in social interaction (1967) Acta Psychol (Amst) (1967), 26 (1), pp. 22-63; Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: A review (2017) Journal of Human-Robot Interaction, 6 (1), pp. 25-63. , 2017; Argyle, M., Cook, M., (1976) Gaze and Mutual Gaze, , 1976; Clark, H.H., Managing problems in speaking (1994) Speech Communication, 15 (3-4), pp. 243-250. , 1994; Clark, H.H., (1996) Using Language, , Cambridge university press; Digman, J.M., Personality structure: Emergence of the fve-factor model (1990) Annual Review of Psychology, 41 (1), pp. 417-440. , 1990; Fussell, S.R., Setlock, L.D., Parker, E.M., Where do helpers look? gaze targets during collaborative physical tasks (2003) CHI'03 Extended Abstracts on Human Factors in Computing Systems, pp. 768-769; Fussell, S.R., Setlock, L.D., Yang, J., Ou, J., Mauer, E., Kramer, A.D.I., Gestures over video streams to support remote collabo-ration on physical tasks (2004) Human-Computer Interaction, 19 (3), pp. 273-309. , 2004; Huang, C., Mutlu, B., Modeling and evaluating narrative gestures for humanlike robots (2013) Robotics: Science and Systems, pp. 57-64; Huang, C., Thomaz, A.L., Efects of responding to, initi-ating and ensuring joint attention in human-robot interaction (2011) 2011 RO-MAN. IEEE, pp. 65-71; Jung, M.F., Joo Lee, J., DePalma, N., Adalgeirsson, S.O., Hinds, P.J., Breazeal, C., Engaging robots: Easing complex human-robot teamwork using backchanneling (2013) Proceedings of the 2013 Conference on Computer Supported Cooperative Work, pp. 1555-1566; Ou, J., Min Oh, L., Yang, J., Fussell, S.R., Efects of task proper-ties, partner actions, and message content on eye gaze patterns in a collaborative task (2005) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 231-240; Park, H., Gelsomini, M., Joo Lee, J., Breazeal, C., Telling stories to robots: The efect of backchanneling on a child's storytelling (2017) 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI. IEEE, pp. 100-108; Cañigueral, R., Hamilton, A.F.D.C., The role of eye gaze during natural social interactions in typical and autistic people (2019) Frontiers in Psychology, , 2019; Brett, P., Aaron, H., Rich, C., Sidner, C., Rec-ognizing engagement in human-robot interaction (2010) Proceedings of ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 375-382; Sauppé, A., Mutlu, B., How social cues shape task coordination and communication (2014) Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 97-108; Shiwa, T., Kanda, T., Imai, M., Ishiguro, H., Hagita, N., How quickly should communication robots respond? (2008) 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 153-160; Sidner, C.L., Lee, C., Kidd, C., Lesh, N., Rich, C., (2005) Explorations in Engagement for Humans and Robots, , 2005; Stiber, M., Huang, C., Not all errors are created equal: Exploring human responses to robot errors with varying severity (2020) Companion Publication of the 2020 International Conference on Multimodal Interaction, pp. 97-101; Stivers, T., Stance, alignment, and afliation during storytelling: When nodding is a token of afliation (2008) Research on Language and Social Interaction, 41 (1), pp. 31-57. , 2008; Tomasello, M., Joint attention as social cognition (1995) Joint Attention: Its Origins and Role in Development, p. 103130. , 1995",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102759565
"Cai J.J., Ryu J., Vicarte E.","57222469972;57222464133;57222470301;","Alice in bookland: Robotic furnishings delivering public library services outside during the pandemic",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3446950","639","641",,1,"10.1145/3434074.3446950","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102751015&doi=10.1145%2f3434074.3446950&partnerID=40&md5=88ccc800aeebb34d4bd728f0de1dadbe","College of Engineering, Cornell University, Ithaca, NY, United States","Cai, J.J., College of Engineering, Cornell University, Ithaca, NY, United States; Ryu, J., College of Engineering, Cornell University, Ithaca, NY, United States; Vicarte, E., College of Engineering, Cornell University, Ithaca, NY, United States","This project suggests how human-robot interaction might be of service to public institutions during the pandemic crisis. Alice in Bookland aims to ensure a safe environment for children to continue their reading experiences during COVID-19. Due to the lockdown throughout the country, lots of public organizations such as libraries are having a hard time providing their resources, with limited user entrance and open hours. Here, Alice in Bookland complements the situation via providing a safe space outside the library, encouraging readers' healthy reading habits without any undue stress during the pandemic, and raising community awareness of local library resources. © 2021 Owner/Author.","Architectural robotics; Books; Covid; Enclosed space; Human robot interaction; Lcd; Led; Library; Outdoor space; Robotics","Agricultural robots; Libraries; Man machine systems; Robotics; Local libraries; Public institution; Public library; Public organizations; Human robot interaction",,,,,,,,,,,,,,,,"'do we really need it?' council leader questions library service after months of closure (2020) The Guardian, Guardian News and Media, , 9 Dec; Kozlowicz, C., The menomonee falls public library will be closed indefinitely because of staffing shortages caused by COVID-19 (2020) Milwaukee Journal Sentinel, Milwaukee Journal Sentinel, , 4 Dec",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102751015
"Thellman S., Thunberg S., Ziemke T.","57191892409;57197797180;6603792948;","Does emotional state affect how people perceive robots?",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447140","113","115",,,"10.1145/3434074.3447140","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102740994&doi=10.1145%2f3434074.3447140&partnerID=40&md5=2f7ba3f171564741f442befa0a270a38","Department of Computer and Information Science, Linköping University, Linköping, Sweden","Thellman, S., Department of Computer and Information Science, Linköping University, Linköping, Sweden; Thunberg, S., Department of Computer and Information Science, Linköping University, Linköping, Sweden; Ziemke, T., Department of Computer and Information Science, Linköping University, Linköping, Sweden","Emotions serve important regulatory roles in social interaction. Although recognition, modeling, and expression of emotion have been extensively researched in human-robot interaction and related fields, the role of human emotion in perceptions of and interactions with robots has so far received considerably less attention. We here report inconclusive results from a pilot study employing an affect induction procedure to investigate the effect of people's emotional state on their perceptions of human-likeness and mind in robots, as well as attitudes toward robots. We propose a new study design based on the findings from this study. © 2021 ACM.","Attitude; Emotion; Human-robot interaction; Mentalizing","Agricultural robots; Man machine systems; Social robots; Emotional state; Human emotion; Human likeness; New study; Pilot studies; Social interactions; Behavioral research",,,,,,,,,,,,,,,,"Arkin, R.C., Fujita, M., Takagi, T., Hasegawa, R., An ethological and emotional basis for human-robot interaction (2003) Robotics and Autonomous Systems, 42 (3-4), pp. 191-201. , 2003; Bartneck, C., Kulic, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81. , 2009; Breazeal, C., Emotion and sociable humanoid robots (2003) International Journal of Human-Computer Studies, 59 (1-2), pp. 119-155. , 2003; Bradford Cannon, W., (1915) Bodily Changes in Pain, Hunger, Fear, and Rage, , D. Appleton and company; Carpinella, C.M., Wyman, A.B., Perez, M.A., Stroessner, S.J., The robotic social attributes scale (rosas) development and validation (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 254-262; Castellano, G., Leite, I., Pereira, A., Martinho, C., Paiva, A., Mcowan, P.W., Multimodal affect modeling and recognition for empathic robot companions (2013) International Journal of Humanoid Robotics, 10 (1), p. 1350010. , 2013; Dan-Glauser, E.S., Scherer, K.R., The geneva affective picture database (gaped): A new 730-picture database focusing on valence and normative significance (2011) Behavior Research Methods, 43 (2), pp. 468-477. , https://doi.org/10.3758/s13428-011-0064-1, 2011; Dinneen, L.C., Blakesley, B.C., Algorithm as 62: A generator for the sampling distribution of the mann-whitney u statistic (1973) Journal of the Royal Statistical Society. Series C (Applied Statistics), 22 (2), pp. 269-273. , 1973; Kroenke, K., Spitzer, R.L., Williams, J.B.W., The phq-9: Validity of a brief depression severity measure (2001) Journal of General Internal Medicine, 16 (9), pp. 606-613. , 2001; Marchesi, S., Ghiglino, D., Ciardo, F., Perez-Osorio, J., Baykara, E., Wykowska, A., Do we adopt the intentional stance toward humanoid robots? (2019) Frontiers in Psychology, 10, p. 450. , 2019; Nomura, T., Suzuki, T., Kanda, T., Kato, K., Measurement of negative attitudes toward robots (2006) Interaction Studies, 7 (3), pp. 437-454. , 2006; Phillips, E., Zhao, X., Ullman, D., Malle, B.F., What is human-like? decomposing robots' human-like appearance using the anthropomorphic robot (abot) database (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 105-113; Prinz, J.J., (2004) Gut Reactions: A Perceptual Theory of Emotion, , Oxford University Press; Rani, P., Liu, C., Sarkar, N., Vanman, E., An empirical study of machine learning techniques for affect recognition in human-robot interaction (2006) Pattern Analysis and Applications, 9 (1), pp. 58-69. , 2006; Zhang, X., Yu, H.W., Barrett, L.F., How does this make you feel? A comparison of four affect induction procedures (2014) Frontiers in Psychology, 5, p. 689. , 2014",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102740994
"Fujikawa T., Yamada Y.","7102532161;35410760400;","Novel in-vivo microscopy technique for internal bleeding using transparent fish",2021,"ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics",,,"9419514","306","309",,,"10.1109/ISR50024.2021.9419514","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106496067&doi=10.1109%2fISR50024.2021.9419514&partnerID=40&md5=f5b4c609097026004958edd502202c2d","Nagoya University, Department of Mechanical System Engineering, Fur-cho, Chikusa-ku Nagoya, 464-8608, Japan","Fujikawa, T., Nagoya University, Department of Mechanical System Engineering, Fur-cho, Chikusa-ku Nagoya, 464-8608, Japan; Yamada, Y., Nagoya University, Department of Mechanical System Engineering, Fur-cho, Chikusa-ku Nagoya, 464-8608, Japan","To investigate the mechanism of internal bleeding resulting from human-robot interactions, an in-vivo microscopy technique is developed. A transparent indenter was loaded onto a subject (an anesthetized transparent glass catfish). The affected blood vessels were investigated though the intender using a microscope equipped with a video camera. The internal bleeding, changes in the arrangement of secondary vessels, and bone contusions caused by loading were observed via the proposed microscopy technique, which is expected to capture information how the vessels fracture and cause the internal bleeding in order to discuss the safety criteria of robots. © 2021 IEEE.",,"Agricultural robots; Human robot interaction; Robotics; Video cameras; In-vivo; Indenters; Microscopy technique; Safety criterion; Transparent glass; Blood vessels",,,,,,,,,,,,,,,,"Suita, K., Yamada, Y., Tsuchida, N., Imai, K., Ikeda, H., Sugimoto, N., A failure-to-safety ""Kyozon"" system with simple contact detection and stop capabilities for safe human-autonomous robot coexistence (1995) Proceedings of 1995 IEEE International Conference on Robotics and Automation, 3, pp. 3089-3096; Haddadin, S., Albu-Schäffer, A., Frommberger, M., Roßmann, J., Hirzinger, G., DLR crash report"": Towards a standard crash-testing protocol for robot safety-Part I: Results (2009) Proc. IEEE Int. Conf. On Robotics and Automation, pp. 272-279; Wolf, S., Hirzinger, G., A new variable stiffness design: Matching requirements of the next robot generation (2008) Proceedings of 2008 IEEE International Conference on Robotics and Automation, pp. 1741-1746; Zinn, M., Khatib, O., Roth, B., Salisbury, J.K., Playing it safe (2004) IEEE Robotics & Automation Magazine, 11 (2), pp. 12-21; Eppinger, R., Sun, E., Bandak, F., Haffner, M., Khaewpong, N., Maltese, M., Kuppa, S., Saul, R., Development of improved injury criteria for the assessment of advanced automotive restraint systems II (1999) National Highway Traffic Safety Administration; Duma, S.M., Schreiber, P.H., McMaster, J.D., Crandall, J.R., Bass, C.R., Pilkey, W.D., Dynamic injury tolerances for long bones of the female upper extremity (1999) Journal of Anatomy, 194, pp. 463-471; Takahashi, Y., Matsuoka, F., Okuyama, H., Imaizumi, I., Development of injury probability functions for the flexible pedestrian legform impactor (2012) SAE International Journal of Passenger Cars-Mechanical Systems, 5 (1), pp. 242-252; Fujikawa, T., Asano, Y., Nishimoto, T., Nishikata, R., Static fracture tolerance of human metatarsal in being run over by robot (2017) Proc. 2017 IEEE/RSJ International Conf. On Intelligent Robots and Systems (IROS2017), pp. 6935-6942; Kent, R., Stacy, S., Parenteau, C., Dynamic pinch tolerance of the phalanges and interphalangeal joints (2008) Traffic Injury Prevention, 9 (1), pp. 83-88; Haddadin, S., Haddadin, S., Khoury, A., Rokahr, T., Parusel, S., Burgkart Bicchi A, R., Lbu-Schäffer, A., On making robots understand safety: Embedding injury knowledge into control (2012) Int. J. Robotics Research; Desmoulin, G.T., Anderson, G.S., Method to investigate contusion mechanics in living humans (2011) J. Forensic Biomechanics, 2; Fujikawa, T., Sugiura, R., Nishikata, R., Nishimoto, T., Critical contact pressure and transferred energy for soft tissue injury by blunt impact in human-robot interaction (2017) Proceedings of 17th International Conference on Control, Automation and Systems (ICCAS 2017), pp. 867-872; Huang, L., Bakker, N., Kim, J., Marston, J., Grosse, I., Tis, J., Cullinane, D., A multi-scale Finite Element Model of bruising in soft connective tissues (2012) Journal of Forensic Biomechanics, 3; Fung, Y.C., Zweifach, B.W., Intaglietta, M., Elastic environment of the capillary bed (1966) Circulation Research, 19, pp. 441-461; Schaser, K.D., Vollmar, B., Menger, M.D., Schewior, L., Kroppenstedt, S.N., Raschke, M., Lubbe, A.S., Mittlmeier, T., In vivo analysis of microcirculation following closed soft-tissue injury (1999) Journal of Orthopaedic Research, 17 (5), pp. 678-685; Steffensen, J.F., Lomholt, J.P., Vogel, W.O.P., In vivo observation on a specialized microvasculature, the primary and secondary vessels in fishes (1986) Acta Zoologica, 67 (4), pp. 193-200; Maruvada, S., Hynynen, K., Optical monitoring of ultrasound-induced bioeffects in glass catfish (2004) Ultrasound in Medicine & Biology, 30 (1), pp. 67-74","Fujikawa, T.; Nagoya University, Japan; email: tatsuo.fujikawa@mae.nagoya-u.ac.jp",,"IEEE Robotics and Automation Society;Nagoya University","Institute of Electrical and Electronics Engineers Inc.","2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021","4 March 2021 through 6 March 2021",,168860,,9781665438629,,,"English","ISR - IEEE Int. Conf. Intell. Saf. Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85106496067
"Higuchi Y.-K., Fujikawa T., Sugiura R., Nishimoto T., Sato F.","57217171311;7102532161;14007993500;7102868497;35620890000;","Development of a porcine thigh finite element model for evaluating the soft-tissue injuries caused by blunt impacts during human-robot interactions",2021,"ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics",,,"9419540","301","305",,,"10.1109/ISR50024.2021.9419540","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106464141&doi=10.1109%2fISR50024.2021.9419540&partnerID=40&md5=24025c135ef6b08beecc397e8a4f3364","Japan Automobile Research Institute (JARI), 2530, Karima, Tsukuba, Ibaraki, 305-0822, Japan; Nagoya University, Department of Mechanical System Engineering, Fur-cho, Chikusa-ku Nagoya, 464-8608, Japan; College of Engineering, Nihon University, Koriyama, 963-8642, Japan","Higuchi, Y.-K., Japan Automobile Research Institute (JARI), 2530, Karima, Tsukuba, Ibaraki, 305-0822, Japan; Fujikawa, T., Nagoya University, Department of Mechanical System Engineering, Fur-cho, Chikusa-ku Nagoya, 464-8608, Japan; Sugiura, R., College of Engineering, Nihon University, Koriyama, 963-8642, Japan; Nishimoto, T., College of Engineering, Nihon University, Koriyama, 963-8642, Japan; Sato, F., Japan Automobile Research Institute (JARI), 2530, Karima, Tsukuba, Ibaraki, 305-0822, Japan","The data concerning the tolerance of slight injuries during human-robot interactions is essential for setting necessary safety measures for robots. In this study, to provide a tool for investigating bruise tolerance, a porcine thigh finite element (FE) model was developed based on quasi-static and dynamic compression tests with living porcine subjects. The FE model demonstrated that strain concentrations occurred in the corresponding region, where the haematoxylin-eosin staining showed blood leaks in the harvested soft-tissue specimens from the impacted porcine subjects. Based on this correlation, the maximum principal strain has potential of being a tissue-level bruise injury metric. © 2021 IEEE.",,"Agricultural robots; Compression testing; Finite element method; Man machine systems; Robotics; Tissue; Blunt impacts; Dynamic compression tests; Haematoxylin; Maximum principal strain; Safety measures; Soft-tissue injury; Strain concentration; Tissue levels; Human robot interaction",,,,,,,,,,,,,,,,"Suita, K., Yamada, Y., Tsuchida, N., Imai, K., Ikeda, H., Sugimoto, N., A failure-to-safety 'Kyozon' system with simple contact detection and stop capabilities for safe human-autonomous robot coexistence (1995) Proc. 1995 IEEE Int. Conf. Robot. Autom.; Saito, T., Ikeda, H., Measuring system and analytical method of pain tolerance to mechanical stimulus for safe design of humancollaborative robot (2005) Proc. 4th Int. Conf. Saf. Ind. Autom. Syst.; Muttray, A., Melia, M., Geibler, B., König, J., Letzel, S., (2014) Experimental Assessment of Pain Thresholds in Major Parts of the Human Body Due to Mechanical Exposure in Human-machine Interface, Final Report of Project No. FF-FP 0317, , https://www.dguv.de/ifa/forschung/projektverzeichnis/ff-fp0317.jsp; Sugiura, R., Fujikawa, T., Nishikata, R., Nishimoto, T., Soft tissue bruise injury by blunt impact in human-robot interaction-Difference of tolerance between chest and extremities (2019) Int. Conf. Control. Autom. Syst.; Fujikawa, T., Sugiura, R., Nishikata, R., Nishimoto, T., Critical contact pressure and transferred energy for soft tissue injury by blunt impact in human-robot interaction (2017) Int. Conf. Control. Autom. Syst.; Gayzik, F.S., Hoth, J.J., Stitzel, J.D., Finite element-based injury metrics for pulmonary contusion via concurrent model optimization (2011) Biomech. Model. Mechanobiol., 10, pp. 505-520; Takhounts, E.G., Craig, M.J., Moorhouse, K., McFadden, J., Hasija, V., Development of Brain Injury Criteria (BrIC) (2013) Stapp Car Crash J., 57, pp. 243-266; Ní Annaidh, A., Bruyère, K., Destrade, M., Gilchrist, M.D., Otténio, M., Characterization of the anisotropic mechanical properties of excised human skin (2012) J. Mech. Behav. Biomed. Mater., 5, pp. 139-148; Comley, K., Fleck, N., The compressive response of porcine adipose tissue from low to high strain rate (2012) Int. J. Impact Eng., 46, pp. 1-10; Alkhouli, N., The mechanical properties of human adipose tissues and their relationships to the structure and composition of the extracellular matrix (2013) Am. J. Physiol.-Endocrinol. Metab., 305, pp. 1427-1435; Van Loocke, M., Lyons, C.G., Simms, C.K., Viscoelastic properties of passive skeletal muscle in compression: Stress-relaxation behaviour and constitutive modelling (2008) J. Biomech., 41, pp. 1555-1566; Takaza, M., Moerman, K.M., Gindre, J., Lyons, G., Simms, C.K., The anisotropic mechanical behaviour of passive skeletal muscle tissue subjected to large tensile strain (2013) J. Mech. Behav. Biomed. Mater., 17, pp. 209-220; Ogden, R.W., Large deformation isotropic elasticity-on the correlation of theory and experiment for incompressible rubberlike solids (1972) Proc. R. Soc. London. A. Math. Phys. Sci., 326 (1567), pp. 565-584; Fung, Y., (2013) Biomechanics: Mechanical Properties of Living Tissues, , New York: Springer Science & Business Media; Hallquist, J.O., (2006) LS-DYNA Theory Manual. California: Livermore Software Technology Corporation; Sato, F., Hyper-viscoelastic response of perfused liver under dynamic compression and estimation of tissue strain thresholds with a liver finite element model (2013) IRCOBI Conf. 2013; Tang, K., Determining Bruise etiology in muscle tissue using finite element analysis (2014) J. Forensic Sci., 59 (2), pp. 371-374; Grosse, I.R., Huang, L., Davis, J.L., Cullinane, D., A multilevel hierarchical finite element model for capillary failure in soft tissue (2014) J. Biomech. Eng., 136 (8), p. 305",,,"IEEE Robotics and Automation Society;Nagoya University","Institute of Electrical and Electronics Engineers Inc.","2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021","4 March 2021 through 6 March 2021",,168860,,9781665438629,,,"English","ISR - IEEE Int. Conf. Intell. Saf. Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85106464141
"Murnane M., Higgins P., Saraf M., Ferraro F., Matuszek C., Engel D.","57210704318;57219786637;57211500135;53979619700;10239806100;7102518553;","A simulator for human-robot interaction in virtual reality",2021,"Proceedings - 2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021",,,"9419217","470","471",,1,"10.1109/VRW52623.2021.00117","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105967066&doi=10.1109%2fVRW52623.2021.00117&partnerID=40&md5=45fb368e39d236f2d9e7b3e295f156b6","University of Maryland, Baltimore County","Murnane, M., University of Maryland, Baltimore County; Higgins, P., University of Maryland, Baltimore County; Saraf, M., University of Maryland, Baltimore County; Ferraro, F., University of Maryland, Baltimore County; Matuszek, C., University of Maryland, Baltimore County; Engel, D., University of Maryland, Baltimore County","We present a suite of tools to model a robot, its sensors, and the surrounding environment in VR, with the goal of collecting training data for real-world robots. The virtual robot observes a rigged avatar created in our photogrammetry facility and embodying a VR user. We are particularly interested in verbal human/robot interactions, which can be combined with the robot's sensor data for grounded language learning. Because virtual scenes, tasks, and robots are easily reconfigured compared to their physical analogs, our approach proves extremely versatile in preparing a wide range of robot scenarios for an array of use cases. © 2021 IEEE.","Robotics; ROS; Unity; Virtual Reality","Abstracting; Educational robots; End effectors; User interfaces; Virtual reality; Language learning; Physical analog; Real-world; Sensor data; Surrounding environment; Training data; Virtual robots; Virtual scenes; Human robot interaction",,,,,"National Science Foundation, NSF: 1428204, 1531491, 1637614, 1637937, 1657469, 1940931","This material is based upon work supported by the National Science Foundation under Grant Nos. 1428204, 1531491, 1637614, 1657469, 1637937, and 1940931. We would like to thank Adam Berlier for extensive, helpful feedback on this manuscript, and our reviewers for their thoughtful suggestions.",,,,,,,,,,"Bansal, M., Matuszek, C., Andreas, J., Artzi, Y., Bisk, Y., Proceedings of the first workshop on language grounding for robotics (2017) Proceedings of the First Workshop on Language Grounding for Robotics; Bischoff, M., (2019) ROS#, , Dec; Hermann, K.M., Hill, F., Green, S., Wang, F., Faulkner, R., Soyer, H., Szepesvari, D., Teplyashin, D., (2017) Grounded Language Learning in A Simulated 3D World, , Technical report, arXiv preprint; Inamura, T., Mizuchi, Y., (2020) Sigverse: A Cloud-based VR Platform for Research on Social and Embodied Human-robot Interaction; Jo, E.S., Gebru, T., Lessons from archives: Strategies for collecting sociocultural data in machine learning (2020) Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 306-316; Kolve, E., Mottaghi, R., Han, W., VanderBilt, E., Weihs, L., Herrasti, A., Gordon, D., Farhadi, A., (2017) Ai2-thor: An Interactive 3D Environment for Visual Ai; Murnane, M., Breitmeyer, M., Ferraro, F., Matuszek, C., Engel, D., Learning from human-robot interactions in modeled scenes (2019) ACM SIGGRAPH 2019 Posters, SIGGRAPH '19, , Association for Computing Machinery, New York, NY, USA; Murnane, M., Breitmeyer, M., Matuszek, C., Engel, D., Virtual reality and photogrammetry for improved reproducibility of humanrobot interaction studies (2019) 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pp. 1092-1093; Robertson, A., Facebook Is Making Oculus' Worst Feature Unavoidable, , http://tiny.cc/oculus-privacy, Accessed: 22/1/2021; Shridhar, M., Thomason, J., Gordon, D., Bisk, Y., Han, W., Mottaghi, R., Zettlemoyer, L., Fox, D., Alfred: A benchmark for interpreting grounded instructions for everyday tasks (2020) The IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Robotic Operating System, , Stanford Artificial Intelligence Laboratory et al","Murnane, M.; University of Maryland, email: mark25@umbc.edu",,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2021","27 March 2021 through 3 April 2021",,168770,,9780738113678,,,"English","Proc. - IEEE Conf. Virtual Real. 3D User Interfaces Abstr. Workshops, VRW",Conference Paper,"Final","",Scopus,2-s2.0-85105967066
"Beraldo G., Tonin L., Menegatti E.","57202942006;36810606700;7007056708;","Shared Intelligence for User-Supervised Robots: From User’s Commands to Robot’s Actions",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12414 LNAI",,,"457","465",,,"10.1007/978-3-030-77091-4_27","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111365928&doi=10.1007%2f978-3-030-77091-4_27&partnerID=40&md5=b7e9846bb98b5cecaa91c535893fbb35","Department of Information Engineering, University of Padova, Padua, Italy; Institute of Cognitive Sciences and Technologies (ISTC), National Research Council, ISTC-CNR, Rome, Italy","Beraldo, G., Department of Information Engineering, University of Padova, Padua, Italy, Institute of Cognitive Sciences and Technologies (ISTC), National Research Council, ISTC-CNR, Rome, Italy; Tonin, L., Department of Information Engineering, University of Padova, Padua, Italy; Menegatti, E., Department of Information Engineering, University of Padova, Padua, Italy","This paper introduces a novel form of cooperation between the humans and user-supervised robots that we name shared intelligence. The fundamental principle at the base of shared intelligence is that the user’s commands are equally processed with the robot’s perception in order to create a successful interaction. We investigate a first shared intelligence system to mentally teleoperate a mobile robot via brain-machine interface. The preliminary results promote the introduction of shared intelligence to augment the human-robot interaction without pre-fixing specific constraints (environment-dependent) thanks to the coupling between the human and the robot. © 2021, Springer Nature Switzerland AG.","Behavior-based systems; Brain-machine interface; Neurorobotics; Telerobotics and teleoperation","Artificial intelligence; Brain computer interface; Social robots; Brain machine interface; Fundamental principles; Intelligence systems; Pre-fixing; Intelligent robots",,,,,"Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR","This research was partially supported by Fondazione Ing. Aldo Gini, by MIUR (Italian Minister for Education) under the initiative ?Departments of Excellence? (Law 232/2016) and by SI Robotics project (Invecchiamento sano e attivo attraverso SocIal ROBOTICS).","Acknowledgments. This research was partially supported by Fondazione Ing. Aldo Gini, by MIUR (Italian Minister for Education) under the initiative “Departments of Excellence” (Law 232/2016) and by SI Robotics project (Invecchiamento sano e attivo attraverso SocIal ROBOTICS).",,,,,,,,,"Beraldo, G., Tonin, L., Cesta, A., Menegatti, E., Shared-control, shared-autonomy and shared-intelligence in assistive technologies: Three forms of cooperation between user and robot (2020) Proceedings of the IEEE International Workshop Adaptive Behavioral Models of Robotic Systems Based on Brain-Inspired AI Cognitive Architectures (APHRODITE), , IEEE; Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M., Brain-computer interfaces for communication and control (2002) Clin. Neurophysiol., 113, pp. 767-791; Millán, J.R., Combining brain-computer interfaces and assistive technologies: State-of-the-art and challenges (2010) Front. Neurosci., 4, p. 161; Chaudhary, U., Birbaumer, N., Ramos-Murguialday, A., Brain-computer interfaces for communication and rehabilitation (2016) Nat. Rev. Neurol., 12 (9), pp. 513-525; Lee, K., Liu, D., Perroud, L., Chavarriaga, R., Millán, J.R., A brain-controlled exoskeleton with cascaded event-related desynchronization classifiers (2017) Robot. Auton. Syst., 90, pp. 15-23; He, J., Eguren, D., Azorín, J.M., Grossman, R.G., Luu, T.P., Brain–machine interfaces for controlling lower-limb powered robotic systems (2018) J. Neural Eng., 15 (2); Tonin, L., Leeb, R., Tavella, M., Perdikis, S., Millán, J.R., The role of shared-control in BCI-based telepresence (2010) 2010 IEEE International Conference on Systems, Man and Cybernetics, Pp. 1462–1466. IEEE; Iturrate, I., Antelis, J.M., Kubler, A., Minguez, J., A noninvasive brain-actuated wheelchair based on a P300 neurophysiological protocol and automated navigation (2009) IEEE Trans. Rob., 25 (3), pp. 614-627; Philips, J., Adaptive shared control of a brain-actuated simulated wheelchair (2007) 2007 IEEE 10Th International Conference on Rehabilitation Robotics, Pp. 408– 414. IEEE; Millán, J.R., (2008) Brain-Controlled Robots, , Technical report; Beraldo, G., Antonello, M., Cimolato, A., Menegatti, E., Tonin, L., Brain-computer interface meets ROS: A robotic approach to mentally drive telepresence robots (2018) Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1-6. , pp., IEEE; Lopes, A.C., Pires, G., Vaz, L., Nunes, U., Wheelchair navigation assisted by human-machine shared-control and a P300-based brain computer interface (2011) Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2438-2444. , pp., IEEE; Beraldo, G., Tortora, S., Menegatti, E., Towards a brain-robot interface for children (2019) 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Pp. 2799–2805. IEEE; Pfurtscheller, G., EEG event-related desynchronization (ERD) and synchronization (ERS) (1997) Electroencephalogr. Clin. Neurophysiol., 103 (1), p. 26; Levine, S.P., Bell, D.A., Jaros, L.A., Simpson, R.C., Koren, Y., Borenstein, J., The NavChair assistive wheelchair navigation system (1999) IEEE Trans. Rehabil. Eng., 7 (4), pp. 443-451; Simpson, R.C., Levine, S.P., Bell, D.A., Jaros, L.A., Koren, Y., Borenstein, J., NavChair: An assistive wheelchair navigation system with automatic adaptation (1998) Assistive Technology and Artificial Intelligence. LNCS, Vol. 1458, pp. 235-255. , https://doi.org/10.1007/BFb0055982, Mittal, V.O., Yanco, H.A., Aronis, J., Simpson, R. (eds.), pp., Springer, Heidelberg; Beraldo, G., Termine, E., Menegatti, E., Shared-autonomy navigation for mobile robots driven by a door detection module (2019) AI*IA 2019. LNCS (LNAI), 11946, pp. 511-527. , https://doi.org/10.1007/978-3-030-35166-336, In: Alviano, M., Greco, G., Scarcello, F. (eds.); Beraldo, G., ROS-Health: An open-source framework for neurorobotics (2018) Proceedings of the 2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), Pp. 174–179. IEEE","Beraldo, G.; Department of Information Engineering, Italy; email: gloria.beraldo@dei.unipd.it","Baldoni M.Bandini S.",,"Springer Science and Business Media Deutschland GmbH","19th International Conference of the Italian Association for Artiﬁcial Intelligence, AIxIA 2020","24 November 2020 through 27 November 2020",,260629,03029743,9783030770907,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85111365928
"Cacace J., Caccavale R., Finzi A.","55818757800;56085967900;8711150100;","Supervised hand-guidance during human robot collaborative task execution: A case study",2021,"CEUR Workshop Proceedings","2806",,,"1","6",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101252713&partnerID=40&md5=bbf22db899bd88ce8faa3ed126c2d1cf","DIETI, Università di Napoli Federico II, via Claudio 21, Napoli, 80125, Italy","Cacace, J., DIETI, Università di Napoli Federico II, via Claudio 21, Napoli, 80125, Italy; Caccavale, R., DIETI, Università di Napoli Federico II, via Claudio 21, Napoli, 80125, Italy; Finzi, A., DIETI, Università di Napoli Federico II, via Claudio 21, Napoli, 80125, Italy","We present and discuss a human-robot collaboration system suitable for supervising the execution of structured manipulation tasks in industrial assembly scenarios. As a case study, we consider the application domain proposed in the context of the project (PON R& I 2014-2020) ICOSAF (Integrated collaborative systems for Smart Factory) in which a human operator physically interacts with a collaborative robot (Cobot) to perform multiple item insertion tasks in a shared workspace. The proposed system combines hierarchical task orchestration and human intention recognition during human-robot interaction through hand-guidance. We provide an overview of the system discussing an initial experimental evaluation. © 2020 Copyright for this paper by its authors.","Flexible manufacturing; Hand-guidance; Human-Robot collaboration; Intention recognition","Industrial robots; Robotics; Collaborative robots; Collaborative systems; Collaborative tasks; Experimental evaluation; Human intentions; Human-robot collaboration; Industrial assemblies; Manipulation task; Social robots",,,,,,"The research leading to these results has been partially supported by the projects REFILLs (H2020-ICT-731590) and ICOSAF (PON R& I 2014-2020).",,,,,,,,,,"Corrales, J. A., Garcia Gomez, G. J., Torres, F., Perdereau, V., Cooperative tasks between humans and robots in industrial environments (2012) International Journal of Advanced Robotic Systems, 9; Romero, D., Bernus, P., Noran, O., Stahre, J., Fast-Berglund, Å., The operator 4.0: human cyber-physical systems & adaptive automation towards human-automation symbiosis work systems (2016) IFIP international conference on advances in production management systems, pp. 677-686. , Springer; Caccavale, R., Finzi, A., Flexible task execution and attentional regulations in human-robot interaction (2017) IEEE Transactions on Cognitive and Developmental Systems, 9, pp. 68-79; Caccavale, R., Saveriano, M., Finzi, A., Lee, D., Kinesthetic teaching and attentional supervision of structured tasks in human–robot interaction (2019) Autonomous Robots, 43, pp. 1291-1307; Caccavale, R., Finzi, A., Learning attentional regulations for structured tasks execution in robotic cognitive control (2019) Autonomous Robots, 43, pp. 2229-2243; Norman, D. A., Shallice, T., Attention to action (1986) Consciousness and self-regulation, pp. 1-18. , Springer; Cooper, R. P., Shallice, T., Hierarchical schemas and goals in the control of sequential behavior (2006) Psychological Review, 113, pp. 887-916; Cacace, J., Caccavale, R., Finzi, A., Lippiello, V., Interactive plan execution during human-robot cooperative manipulation (2018) IFAC-PapersOnLine, 51, pp. 500-505. , http://www.sciencedirect.com/science/article/pii/S2405896318332919, https: //doi.org 12th IFAC Symposium on Robot Control SYROCO 2018; Cacace, J., Finzi, A., Lippiello, V., Enhancing shared control via contact force classification in human-robot cooperative task execution (2019) Human Friendly Robotics, pp. 167-179. , F. Ficuciello, F. Ruggiero, A. Finzi (Eds), Springer International Publishing, Cham; Hart, S., Staveland, L., Development of nasa-tlx (task load index): Results of empirical and theoretical research, Human Mental Workload (1988) Advances in Psychology, pp. 139-183; Caccavale, R., Leone, E., Lucignano, L., Rossi, S., Staffa, M., Finzi, A., Attentional regulations in a situated human-robot dialogue (2014) The 23rd IEEE International Symposium on Robot and Human Interactive Communication, IEEE RO-MAN 2014, pp. 844-849. , Edinburgh, UK, August 25-29, 2014; Caccavale, R., Cacace, J., Fiore, M., Alami, R., Finzi, A., Attentional supervision of human-robot collaborative plans (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 867-873; Iengo, S., Rossi, S., Staffa, M., Finzi, A., Continuous gesture recognition for flexible human-robot interaction (2014) 2014 IEEE International Conference on Robotics and Automation, ICRA 2014, pp. 4863-4868. , Hong Kong, China, May 31 June 7, 2014; Cacace, J., Finzi, A., Lippiello, V., A robust multimodal fusion framework for command interpretation in human-robot cooperation (2017) 26th IEEE International Symposium on Robot and Human Interactive Communication, pp. 372-377. , RO-MAN 2017, Lisbon, Portugal, August 28 Sept. 1, 2017; Cacace, J., Caccavale, R., Finzi, A., Lippiello, V., Attentional multimodal interface for multidrone search in the alps (2016) 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016, pp. 1178-1183. , Budapest, Hungary, October 9-12, 2016",,"Finzi A.Castellini A.Buoncompagni L.Anzalone S.",,"CEUR-WS","7th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2020","26 November 2020",,166814,16130073,,,,"English","CEUR Workshop Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85101252713
"Devi M., Roy A.","57206324714;55003216700;","Angle-based feature extraction method for fingers of hand gesture recognition",2021,"Lecture Notes in Networks and Systems","137",,,"187","192",,,"10.1007/978-981-15-6198-6_17","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093882750&doi=10.1007%2f978-981-15-6198-6_17&partnerID=40&md5=9f2e1c54e43300bb7a99e282e49b0e4c","Department of Computer Science and Engineering, Tripura University, Suryamaninagar, Agartala, Tripura  799022, India; Department of Information Technology, Tripura University, Suryamaninagar, Agartala, Tripura  799022, India","Devi, M., Department of Computer Science and Engineering, Tripura University, Suryamaninagar, Agartala, Tripura  799022, India; Roy, A., Department of Information Technology, Tripura University, Suryamaninagar, Agartala, Tripura  799022, India","In this paper, two types of features ‘angle’ feature and Finger_Tips distance feature extraction methods for gestures of finger recognition are proposed. The entire image is segmented into several spatial modules and the task of feature extraction is carried out on finger of the hand images. Application of this method is extended to medical systems, sign languages for hearing-impaired people, crisis management and disaster relief, entertainment and human- -robot interaction. This method is tested on medial axis transformation (MAT) image and it does not require any gloves for recognition. This feature extraction algorithm has an advantage of very low feature dimension. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.","Classification; Feature extraction; Hand gestures recognition; MAT image",,,,,,,,,,,,,,,,,"Devi, M., Saharia, S., Bhattacharyya, D.K., A dataset of single-hand gestures of Sattriya dance (2018) Heritage Preservation, pp. 293-310. , Springer, Singapore; Devi, M., Saharia, S., A two-level classification scheme for single-hand gestures of Sattriya dance (2016) 2016 International Conference on Accessibility to Digital World (ICADW), , IEEE, New York","Roy, A.; Department of Information Technology, Suryamaninagar, India; email: alakroy@tripurauniv.in",,,"Springer Science and Business Media Deutschland GmbH",,,,,23673370,,,,"English","Lect. Notes Networks Syst.",Book Chapter,"Final","",Scopus,2-s2.0-85093882750
"Rueben M., Rothberg E., Mataric M.J.","36814315700;57216354415;7005611984;","Applying the Theory of Make-Believe to Human-Robot Interaction",2020,"Frontiers in Artificial Intelligence and Applications","335",,,"40","50",,,"10.3233/FAIA200899","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098883299&doi=10.3233%2fFAIA200899&partnerID=40&md5=7c82ac8151c667254aab3d5c6dbe7cc7","Dept. of Computer Science, University of Southern California, United States; Department of Computer Science and Engineering, Ohio State University, United States","Rueben, M., Dept. of Computer Science, University of Southern California, United States; Rothberg, E., Department of Computer Science and Engineering, Ohio State University, United States; Mataric, M.J., Dept. of Computer Science, University of Southern California, United States","People often make ascriptions that they believe to be literally false. A robot, for example, may be treated as if it were a dog, or as if it had certain intentions, emotions, or personality traits. How can one do this while also believing that robots cannot really have such traits? In this paper we explore how Kendall Walton's theory of make-believe might account for this apparent paradox. We propose several extensions to Walton's theory, some implications for how we make attributions and use mental models, and an informal account of human-robot interaction from the human's perspective. © 2020 The authors and IOS Press. All rights reserved.","anthropomorphism; fictionalism; Human-robot interaction; intentional stance; make-believe; mental state attribution; pretence theory; prop theory; theory of mind","Man machine systems; Robotics; Mental model; Personality traits; Human robot interaction",,,,,"National Science Foundation, NSF: IIS-1528121","This work was supported by an NSF NRI grant for “Socially Aware, Expressive, and Pers onalized Mobile Remote Pres ence: Co-Robots as Gateways to Acces s to K-12 In-School Education”, NSF IIS-1528121. The authors als o gratefully acknowledge Tim Buchanan for the early dis cus s ions that led to this paper, Sam Thellman and Joel Oes ch for their advice and encouragement, and Kathryn Nels on for ins piring the idea of faking or imitating participation in a make-believe. Finally, the authors would like to thank the two anonymous reviewers of this manus cript for their thoughtful and detailed comments .",,,,,,,,,,"Blake, R., Sekuler, R., (2005) Perception, , 5th ed. McGraw-Hill; Heider, F., (2013) The Psychology of Interpersonal Relations, , Psychology Press; Dennett, D.C., Précis of the intentional stance (1988) Behavioral and Brain Sciences, 11 (3), pp. 495-505; Nass, C., Moon, Y., Machines and mindlessness: Social responses to computers (2000) Journal of Social Issues, 56 (1), pp. 81-103; Zlotowski, J., Proudfoot, D., Yogeeswaran, K., Bartneck, C., Anthropomorphism: Opportunities and challenges in human-robot interaction (2015) International Journal of Social Robotics, 7 (3), pp. 347-360; Fussell, S.R., Kiesler, S., Setlock, L.D., Yew, V., How people anthropomorphize robots (2008) Proceedings of the 3rd ACM/IEEE International Conference on Human Robot Interaction, pp. 145-152. , In, New York, NY, USA: ACM; Haikonen, P.O., Consciousness and robot sentience (2012) World Scientific, 2. , Series on Machine Consciousness; Walton, K.L., Metaphor and prop oriented make-believe (2005) Fictionalism in Metaphysics, pp. 65-87; Heider, F., Simmel, M., An experimental study of apparent behavior (1944) The American Journal of Psychology, 57 (2), pp. 243-259; Malle, B.F., Korman, J., (2013) Attribution Theory, , Oxford University Press; Nass, C., Steuer, J., Tauber, E.R., Computers are social actors (1994) In: Proceedings of the Sigchi Conference on Human Factors in Computing Systems, pp. 72-78. , New York, NY, USA: ACM, CHI '94; Thellman, S., Ziemke, T., The Intentional Stance Toward Robots: Conceptual and Methodological Considerations (2019) In: The 41st Annual Conference of the Cognitive Science Society, pp. 1097-1103. , July 24-26, Montreal, Canada; Krämer, N.C., Von, D.P.A., Eimler, S., (2012) Human-agent and Human-robot Interaction Theory: Similarities to and Differences from Human-human Interaction. In: Human-computer Interaction: The Agency Perspective, pp. 215-240. , Springer; Epley, N., Waytz, A., Cacioppo, J.T., On seeing human: A three-factor theory of anthropomorphism (2007) Psychological Review, 114 (4), p. 864; Zlotowski, J., Sumioka, H., Eyssel, F., Nishio, S., Bartneck, C., Ishiguro, H., Model of dual anthropomorphism: The relationship between the media equation effect and implicit anthropomorphism (2018) International Journal of Social Robotics, 10 (5), pp. 701-714; Severson, R.L., Woodard, S.R., Imagining others' minds: The positive relation between children's role play and anthropomorphism (2018) Frontiers in Psychology, 9, p. 2140; Clark, H.H., Van Der Wege, M.M., Imagination in narratives (2015) In: The Handbook of Discourse Analysis, pp. 406-421. , 2nd ed. John Wiley and Sons, Inc; Walton, K.L., (1990) Mimesis As Make-believe: On the Foundations of the Representational Arts, , Harvard University Press; Novitz, D., Fiction, Imagination and Emotion (1980) The Journal of Aesthetics and Art Criticism, 38 (3), pp. 279-288; Gerrig, R., (2018) Experiencing Narrative Worlds, , Routledge; Byrne, R.M., Towards a model theory of imaginary thinking (1996) In: Mental Models in Cognitive Science: Essays in Honour of Phil Johnson-Laird, pp. 155-174. , Psychology Press; Walton, K.L., Appreciating fiction: Suspending disbelief or pretending belief? (1980) Dispositio, 5 (13-14), pp. 1-18; Tannen, D., What's in a frame? Surface evidence for underlying expectations (1993) Framing in Discourse, 14, p. 56; Seibt, J., Towards an ontology of simulated social interaction (2017) In: Sociality and Normativity for Robots: Philosophical Inquiries into Human-Robot Interactions, pp. 11-39. , Springer; Seibt, J., Damholdt, M.F., Vestergaard, C., Five Principles of Integrative Social Robotics (2018) Proceedings of Robophilosophy/TRANSOR 2018, pp. 28-42. , In: Coeckelbergh M, Loh J, Funk M, Seibt J, Norskov M, editors. 2018 Feb 14-17; Vienna, AT. IOS Press; Seibt, J., Integrative social robotics""-a new method paradigm to solve the description problem and the regulation problem? (2020) Proceedings of Robophilosophy/TRANSOR 2016, pp. 104-115. , In: J Seibt, M Norskov, SS Andersen, editors. 2016 Aug 19-22; Aarhus DK. IOS Press; Damholdt, M.F., Vestergaard, C., Seibt, J., Testing for 'Anthropomorphization': A Case for Mixed Methods in Human-Robot Interaction (2020) Human-Robot Interaction, pp. 203-227. , In: Jost C, Le Pévédic B, Belpaeme T, Bethel C, Chrysostomou D, Crook N, et al. editors. Springer, Cham; Clark, H.H., (1996) Using Language, , Cambridge university press; Clark, H.H., Depicting as a method of communication (2016) Psychological Review, 123 (3), pp. 324-347; Remmers, P., The artificial nature of social robots: A phenomenological interpretation of two conflicting tendencies in human-robot interaction (2020) Proceedings of the Robophilosophy 2020 Conference, , In: Norskov M, Seibt J, Quick O, editors. 2020 Aug 18-21; Aarhus, DK. IOS Press; Musial, M., Magical thinking and empathy towards robots (2016) In: What Social Robots Can and Should Do Proceedings of Robophilosophy, pp. 347-355; Johnson-Laird, P.N., (1983) Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness, , Harvard University Press; Airenti, G., The development of anthropomorphism in interaction: Intersubjectivity, imagination, and theory of mind (2018) Frontiers in Psychology, 9, p. 2136","Rueben, M.; Ronald Tutor Hall (RTH), 3710 McClintock Ave, Room 423, United States; email: mrueben@usc.edu","Norskov M.Seibt J.Quick O.S.",,"IOS Press BV","4th Conference on Robophilosophy 2020: Culturally Sustainable Social Robotics","18 August 2020 through 21 August 2020",,165874,09226389,9781643681542,,,"English","Front. Artif. Intell. Appl.",Conference Paper,"Final","",Scopus,2-s2.0-85098883299
"Grüneberg P.","55484994900;","Culturally Sustainable Social Robotics as an Empowerment Technology",2020,"Frontiers in Artificial Intelligence and Applications","335",,,"127","138",,,"10.3233/FAIA200908","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098873377&doi=10.3233%2fFAIA200908&partnerID=40&md5=f7a3b8daf38fa19fd0b729a54f5f79f0","Institute of Liberal Arts and Sciences, Kanazawa University, Japan","Grüneberg, P., Institute of Liberal Arts and Sciences, Kanazawa University, Japan","The implementation of culturally sustainable social robotics (SR) puts high requirements on the design of social human-robot interaction. This paper proposes the concept of empowerment technology (ET) as a value-driven framework for advancing the interlocking of human values and computational modeling. A capability-based model of the interactive unity of humans and robots is introduced and applied to a robotic childcare system. This case study shows that culturally sustainable SR in terms of ET is possible if SR addresses the values held by local stakeholders and ensures the support of human empowerment in terms of these values. © 2020 The authors and IOS Press. All rights reserved.","empowerment; Value-driven design; value-sensitive design","Machine design; Robotics; Computational model; Human values; Social human-robot interactions; Social robotics; Value driven; Social robots",,,,,"Japan Society for the Promotion of Science, KAKEN: JP 18K00035","December 2019 and January 2020). This paper draws on interv iews with members of Cybernics and childcare professionals. All interv iewees prov ided informed consent and were aware of the research purpose. They agreed to the anonymous citation of their statements. Interv iews lasted between 50 and 120 minutes, followed semi-structured guidelines, and were analyzed using qualitativ e content analysis [11]. The design and methodology for this study were approv ed by the Human Subject Research Ethics rev iew committee of the Institute of Liberal Arts and Science, Kanazawa Univ ersity (IRB no. 2019-10). The study and work on this paper were supported by JSPS KAKENHI Grant Number JP 18K00035.",,,,,,,,,,"Seibt, J., Damholdt, M.F., Vestergaard, C., Integrative social robotics, value-driven design, and transdisciplinarity (2020) Interact Stud, 21, pp. 111-144; Van De Poel, I., Design for values (2015) Social Responsibility and Science in Innovation Economy, pp. 115-164. , In: Kawalec P, Wierzchoslawski RP, editors. Lublin: Catholic University of Lublin; Friedman, B., Hendry, D., (2019) Value Sensitive Design: Shaping Technology with Moral Imagination, , Cambridge, Massachusetts: The MIT Press; Umbrello, S., Bellis, A.F.D., A value-sensitive design approach to intelligent agents (2018) Artificial Intelligence Safety and Security, pp. 395-410. , In: Yampolskiy R, editor. Boca Raton (FL): CRC Press; Van Wynsberghe, A., Designing robots for care: Care centered value-sensitive design (2013) Sci Eng Ethics, 19 (2), pp. 407-433. , Jun 1; Van Wynsberghe, A., Service robots, care ethics, and design (2016) Ethics Inf Technol., 18 (4), pp. 311-321. , Dec 1; Spiekermann, S., (2016) Ethical It Innovation: A Value-based System Design Approach, , Boca Raton (FL): CRC Press; Van Den Hoven, J., Value sensitive design and responsible innovation (2013) Responsible Innovation, pp. 75-83. , In: Owen R, Bessant JR, Heintz M, editors. Chichester, West Sussex, UK: Wiley; Dignum, V., Dignum, F., Vázquez-Salceda, J., Clodic, A., Gentile, M., Mascarenhas, S., Design for values for social robot architectures (2018) Front Artif Intell Appl, pp. 43-52; Grüneberg, P., Suzuki, K., An approach to subjective computing: A robot that learns from interaction with humans (2014) Ieee Trans Auton Ment Dev, 6 (1), pp. 5-18. , Mar; Gläser, J., Laudel, G., Life with and without coding: Two methods for early-stage data analysis in qualitative research aiming at causal explanations (2013) Forum Qual Sozialforschung Forum Qual Soc Res, 14 (2). , Mar 18; Grüneberg, P., Kadone, H., Suzuki, K., Voluntary initiation of movement: Multifunctional integration of subjective agency (2015) Front Psychol, 6, p. 688; Grüneberg, P., Kadone, H., Kuramoto, N., Ueno, T., Hada, Y., Yamazaki, M., Robot-assisted voluntary initiation reduces control-related difficulties of initiating joint movement: A phenomenal questionnaire study on shaping and compensation of forward gait (2018) Plos ONE., 13 (3), p. 0194214. , Mar 12; Grüneberg, P., Empowering patients in the interactive unity with machines. A study on engineers and engineering of the robotic rehabilitation system HAL (Hybrid Assistive Limb) Humans and Machines in Medical Contexts: Case Studies from Japan, , In: Brucksch S, Sasaki K, editors. London: Palgrave Macmillan; forthcoming; Sankai, Y., HAL: Hybrid assistive limb based on cybernics (2011) The 13th International Symposium Isrr, pp. 25-34. , In: Kaneko M, Nakamura Y, editors. Robotics research. Berlin: Springer; Adams, R., (2008) Empowerment, Participation and Social Work, , London: Macmillan International Higher Education; Robeyns, I., (2017) Wellbeing, Freedom and Social Justice: The Capability Approach Re-examined, , Cambridge, UK: Open Book Publishers; Belda-Lois, J.-M., Horno, S.M., Bermejo-Bosch, I., Moreno, J.C., Pons, J.L., Farina, D., Rehabilitation of gait after stroke: A review towards a top-down approach (2011) J NeuroEngineering Rehabil, 8 (1), p. 66. , Dec 13; Huber, M., Knottnerus, J.A., Green, L., Van Der Horst, H., Jadad, A.R., Kromhout, D., How should we define health? (2011) Bmj, 343, p. d4163. , Jul 26; Van Den Hoven, I., Conflicting values in design for values (2015) Handbook of Ethics, Values, and Technological Design: Sources, Theory, Values and Application Domains, pp. 89-116. , In: Van den Hoven J, Vermaas PE, van de Poel I, editors. Dordrecht: Springer Netherlands; Crumley, C.L., Heterarchy and the analysis of complex societies (2008) Archeol Pap Am Anthropol Assoc, 6 (1), pp. 1-5. , Jun 28; Günther, G., Cognition and volition. A contribution to a cybernetic theory of subjectivity (1971) 1971 Fall Conference of American Society for Cybernetics: Cybernetics Technique in Brain Research and the Educational Process, pp. 119-135. , In: American Society for Cybernetics, editor. Washington D.C; Verbeek, P.-P., (2011) Moralizing Technology. Understanding and Designing the Morality of Things, , Chicago (IL): University of Chicago Press; Robertson, J., Gendering humanoid robots: Robo-sexism in Japan (2010) Body Soc, 16 (2), pp. 1-36. , Jan 6; Lennerfors, T.T., Watsuji's ethics of technology in the container age (2019) Tetsugaku Companion to Japanese Ethics and Technology, pp. 59-74. , In: Lennerfors TT, Murata K, editors. Cham: Springer; Levy, P., Beyond kansei engineering: The emancipation of kansei design (2013) Int J Des., 7 (2), pp. 83-94; Sabanovic, S., Inventing Japan's ""robotics culture"": The repeated assembly of science, technology, and culture in social robotics (2014) Soc Stud Sci., 44 (3), pp. 342-367. , Jun","Grüneberg, P.; Institute of Liberal Arts and Sciences, Japan; email: grueneberg@staff.kanazawa-u.ac.jp","Norskov M.Seibt J.Quick O.S.",,"IOS Press BV","4th Conference on Robophilosophy 2020: Culturally Sustainable Social Robotics","18 August 2020 through 21 August 2020",,165874,09226389,9781643681542,,,"English","Front. Artif. Intell. Appl.",Conference Paper,"Final","",Scopus,2-s2.0-85098873377
"Belhassein K., Fernández Castro V., Mayima A.","57204952523;57195054016;57216355113;","A Horizontal Approach to Communication for Human-Robot Joint Action: Towards Situated and Sustainable Robotics",2020,"Frontiers in Artificial Intelligence and Applications","335",,,"204","214",,,"10.3233/FAIA200916","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098862316&doi=10.3233%2fFAIA200916&partnerID=40&md5=09acfe314765985e58eea42865b4d4b7","LAAS-CNRS, Université de Toulouse, Cnrs, France; Clle, Université de Toulouse, CNRS, UT2J, France; Institut Jean Nicod, Cnrs Umr 8129, DEC, ENS, Psl University, France","Belhassein, K., LAAS-CNRS, Université de Toulouse, Cnrs, France, Clle, Université de Toulouse, CNRS, UT2J, France; Fernández Castro, V., LAAS-CNRS, Université de Toulouse, Cnrs, France, Institut Jean Nicod, Cnrs Umr 8129, DEC, ENS, Psl University, France; Mayima, A., LAAS-CNRS, Université de Toulouse, Cnrs, France","This paper aims at presenting a horizontal approach to the design of communication for joint action in human-robot interaction. According to this approach, social robotics must focus on different parameters of the whole joint action including context, the embedded situation and human psychological profile during the design and test process. Such an approach aims at complementing the standard building-block model that represents the state-of-the-art in robotic communication. Moreover, we provide some general ideas of how the model can facilitate the use of available communicative strategies for creating more efficient culturally sustainable robots in contexts of joint action. © 2020 The authors and IOS Press. All rights reserved.","building-block model; Communication; horizontal model; joint action","Machine design; Robotics; Testing; Building-block model; Design and tests; Horizontal approach; Human robots; In contexts; Joint actions; Social robotics; State of the art; Social robots",,,,,"Agence Nationale de la Recherche, ANR: ANR-16-CE33-0017","We would like to thank Nicola Butcher, Rachid Alami and Aurélie Clodic and two anonymous referees for their valuable comments and suggestions on earlier drafts of this paper. This research was supported by the Agence Nationale de la Recherche [Grant Number ANR-16-CE33-0017].",,,,,,,,,,"Seibt, J., Integrative Social Robotics"": A new method paradigm to solve the description problem and the regulation problem? (2016) What Social Robots Can and Should Do: Proceedings of Robophilosophy 2016/TRANSOR 2016, , In: Seibt J, Norskov M, Schack Andersen S, red., Amsterdam: IOS Press; Seibt, J., Damholdt, M.F., Vestergaard, C., Five principles of integrative social robotics (2018) Envisioning Robots in Society-Power, Politics, and Public Space: Proceedings of Robophilosophy 2018, , In: Coeckelbergh M, Loh J, Funk M, Seibt J, Norskov M, editors, Amsterdam : IOS Press; Fischer, K., The role of users' preconceptions in talking to computers and robots (2006) How People Talk to Computers, Robots, and Other Artificial Communication Partner: Proceedings of the Workshop Hansewissenschaftskolleg, , In: Fisher K, editor, Delmenhorst; Bisio, A., Sciutti, A., Nori, F., Metta, G., Fadiga, L., Sandini, G., Motor contagion during human-human and human-robot interaction (2014) PLoS One., 9 (8), p. 106172; Kwak, S.S., Kim, Y., Kim, E., Shin, C., Cho, K., What makes people empathize with an emotional robot? 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(2005) In: Roman 2005 Ieee International Conference on Robot and Human Interactive Communication, pp. 7-12. , 2005. Nashville, TN, USA: IEEE; Clodic, A., Pacherie, E., Alami, R., Chatila, R., Key elements for human-robot joint action (2017) Sociality and Normativity for Robots, pp. 159-177. , In: Hakli R, Seibt J, editors. Cham: Springer International Publishing; Clodic, A., Fleury, S., Alami, R., Chatila, R., Bailly, G., Brethes, L., Rackham: An interactive robot-guide (2006) In: Roman 2006-the 15th Ieee International Symposium on Robot and Human Interactive Communication, pp. 502-509; Pacherie, E., (2012) The Phenomenology of Joint Action: Self-Agency Vs. Joint-Agency, pp. 343-389. , Seemann, A. editor. Joint Attention: New Developments, MIT Press; Clark, H., (1996) Using Language, , Cambridge: Cambridge University Press; Siposova, B., Tomasello, M., Carpenter, M., Communicative eye contact signals a commitment to cooperate for young children (2018) Cognition, 179, pp. 192-201; Noordweir, M., Van Dijk, E., Surprise: Unfolding of facial expressions (2019) Cognition and Emotion., 33 (5), pp. 915-930; Liebal, K., Behne, T., Carpenter, M., Tomasello, M., Infants use shared experience to interpret pointing gestures (2009) Dev Sci., 12 (2), pp. 264-271; Zawidzki, T., (2013) Mindshaping: A New Framework for Understanding Human Social Cognition, , Cambridge, Mass: MIT Press; Fernandez, C.V., Shaping robotic minds (2014) In: Sociable Robots and the Future of Social Relations: Proceedings of Robo-Philosophy 2014, , IOS Press; Fernandez, C.V., Mindshaping and Robotics (2017) Sociality and Normativity for Robots: Philosophical Inquiries into Human-Robot Interactions, pp. 115-135. , In: Hakli R, Seibt J, editors. Cham: Springer International Publishing; Knoblich, G., Butterfill, S., Sebanz, N., Psychological research on joint action (2011) In: Psychology of Learning and Motivation. Elsevier, pp. 59-101; Sebanz, N., Knoblich, G., Prediction in joint action: What, when, and where (2009) Top Cogn Sci., 1 (2), pp. 353-367. , Apr; Admoni, H., Dragan, A., Srinivasa, S.S., Scassellati, B., Deliberate delays during robot-to-human handovers improve compliance with gaze communication (2014) In: Proceedings of the 2014 ACM/IEEE International Conference on Human-robot Interaction-HRI '14, pp. 49-56. , Bielefeld, Germany: ACM Press; Baraka, K., Alves-Oliveira, P., Ribeiro, T., An extended framework for characterizing social robots (2020) Human-Robot Interaction, Springer Series on Bio-and Neurosystems, p. 12. , In: Jost C. et al. editors. Springer, Cham; Michael, J., Pacherie, E., On commitments and other uncertainty reduction tools in joint action (2015) J Soc Ontol., 1 (1), pp. 89-120; Malle, B., Scheutz, M., When will people regard robots as morally competent social partners? (2015) 2015 24th Ieee International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 486-491","Fernández Castro, V.; Corresponding Author: VÍctor Fernández Castro. LAAS-CNRS, 7 Avenue du Colonel Roche, France; email: vfernandezcastro@gmail.com","Norskov M.Seibt J.Quick O.S.",,"IOS Press BV","4th Conference on Robophilosophy 2020: Culturally Sustainable Social Robotics","18 August 2020 through 21 August 2020",,165874,09226389,9781643681542,,,"English","Front. Artif. Intell. Appl.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85098862316
"Jensen L.C., Melissa Langedijk R., Fischer K.","56277957900;57219848313;24491633100;","Understanding the Perception of Incremental Robot Response in Human-Robot Interaction",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223615","41","47",,2,"10.1109/RO-MAN47096.2020.9223615","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095797815&doi=10.1109%2fRO-MAN47096.2020.9223615&partnerID=40&md5=93a1bea90d10f915ec983c60d76c61e4","University of Southern Denmark, Department of Design Communication, Kolding, 6000, Denmark","Jensen, L.C., University of Southern Denmark, Department of Design Communication, Kolding, 6000, Denmark; Melissa Langedijk, R., University of Southern Denmark, Department of Design Communication, Kolding, 6000, Denmark; Fischer, K., University of Southern Denmark, Department of Design Communication, Kolding, 6000, Denmark","Incremental feedback, i.e. the timely response to human behavior while it is happening, has previously been found to potentially speed up human-robot interactions, but it is unclear how people evaluate incremental robots. In this study, we show that the evaluation of incremental robot response depends on the actual success of the incremental feedback; that is, if the feedback leads to increased efficiency, people evaluate the robot as more competent and more credible. If the robot does not use incremental feedback, no interaction between evaluation and efficiency can be found. Thus, incremental feedback draws people's attention to interaction success. © 2020 IEEE.",,"Agricultural robots; Behavioral research; Biofeedback; Efficiency; Man machine systems; Human behaviors; Speed up; Social robots",,,,,,"*This work was carried out in the framework of the Smooth Project, supported by the Danish Innovation Foundation 1Lars Christian Jensen, Kerstin Fischer and Rosalyn Melissa Langedijk are all with the Department of design & Communication, University of southern Denmark, Universitetsparken 1, 6000 Kolding, Denmark.",,,,,,,,,,"Skantze, G., Hjalmarsson, A., Towards incremental speech generation in dialogue systems (2010) Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Association for Computational Linguistics, pp. 1-8; Skantze, G., Hjalmarsson, A., Towards incremental speech generation in conversational systems (2013) Computer Speech & Language, 27 (1), pp. 243-262; Andrist, S., Tan, X.Z., Gleicher, M., Mutlu, B., Conversational gaze aversion for humanlike robots (2014) Proceedings of the 2014 ACM/IEEE Int. Conference on Human-robot Interaction, ACM, pp. 25-32; Kato, Y., Kanda, T., Ishiguro, H., May i help you?-design of human-like polite approaching behavior (2015) 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 35-42. , IEEE; Pitsch, K., Kuzuoka, H., Suzuki, Y., Sussenbach, L., Luff, P., Heath, C., The first five seconds : Contingent stepwise entry into an interaction as a means to secure sustained engagement in hri (2009) ROMAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, IEEE, pp. 985-991; Castro-González, Á., Castillo, J.C., Alonso-Martin, F., Olortegui-Ortega, O.V., González-Pacheco, V., Malfaz, M., Salichs, M.A., The effects of an impolite vs. a polite robot playing rock-paper-scissors (2016) International Conference on Social Robotics, pp. 306-316. , Springer; Chromik, M., Carlmeyer, B., Wrede, B., Ready for the next step?: Investigating the effect of incremental information presentation in an object fetching task (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, ACM, pp. 95-96; Carlmeyer, B., Schlangen, D., Wrede, B., Look at me!: Self-interruptions as attention booster? (2016) Proceedings of the Fourth International Conference on Human Agent Interaction, ACM, pp. 221-224; De Kok, I., Hough, J., Hülsmann, F., Botsch, M., Schlangen, D., Kopp, S., A multimodal system for real-time action instruction in motor skill learning (2015) Conference Paper; Kennington, C., Kousidis, S., Baumann, T., Buschmeier, H., Kopp, S., Schlangen, D., Better driving and recall when in-car information presentation uses situationally-aware incremental speech output generation (2014) Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, ACM, pp. 1-7; Ghigi, F., Eskenazi, M., Torres, M.I., Lee, S., Incremental dialog processing in a task-oriented dialog (2014) Fifteenth Annual Conference of the International Speech Communication Association; Manuvinakurike, R., Paetzel, M., Qu, C., Schlangen, D., DeVault, D., Toward incremental dialogue act segmentation in fast-paced interactive dialogue systems (2016) Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 252-262; Carlmeyer, B., Schlangen, D., Wrede, B., Towards closed feedback loops in hri: Integrating inprotk and pamini (2014) Proceedings of the 2014 Workshop on Multimodal, Multi-Party, Real-World Human-Robot Interaction, ACM, pp. 1-6; Baumann, T., Lindner, F., Incremental speech production for polite and natural personal-space intrusion (2015) International Conference on Social Robotics, pp. 72-82. , Springer; Buschmeier, H., Baumann, T., Dosch, B., Kopp, S., Schlangen, D., Combining incremental language generation and incremental speech synthesis for adaptive information presentation (2012) Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 295-303. , Association for Computational Linguistics; Tsai, V., Baumann, T., Pecune, F., Casell, J., Faster responses are better responses: Introducing incrementality into sociable virtual personal assistants (2018) Proceedings of the 2018 International Workshop on Spoken Dialog System Technology; Hough, J., De Kok, I., Schlangen, D., Kopp, S., Timing and grounding in motor skill coaching interaction: Consequences for the information state (2015) Proceedings of the 19th SemDial Workshop on the Semantics and Pragmatics of Dialogue (GoDIAL); Kobberholm, K.W., Carstens, K.S., Bøg, L.W., Santos, M.H., Ramskov, S., Mohamed, S.A., Jensen, L.C., The influence of incremental information presentation on the persuasiveness of a robot (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 302-304; Cialdini, R.B., (1987) Influence. A. Michel Port Harcourt, 3; Carpinella, C.M., Wyman, A.B., Perez, M.A., Stroessner, S.J., The robotic social attributes scale (rosas): Development and validation (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, ACM, pp. 254-262; Pajaziti, A., Slam-map building and navigation via ros (2014) International Journal of Intelligent Systems and Applications in Engineering, 2 (4), pp. 71-75; Fischer, K., Seibt, J., Rodogno, R., Rasmussen, M.K., Weiss, A., Bodenhagen, L., Juel, W.K., Krüger, N., Integrative social robotics hands-on (2020) Interaction Studies, 21 (1), pp. 145-185; (2020) R: A Language and Environment for Statistical Computing, , https://www.R-project.org/, R Core Team, Vienna, Austria: R Foundation for Statistical Computing; Sanchez, G., Trinchera, L., Russolillo, G., (2017) Plspm: Tools for Partial Least Squares Path Modeling (Pls-pm), , https://CRAN.R-project.org/package=plspm, R package version 0. 4. 9",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095797815
"Van Maris A., Sutherland A., Mazel A., Dogramadzi S., Zook N., Studley M., Winfield A., Caleb-Solly P.","57193757921;57204639879;35264397000;6507900733;57224238437;8922780300;7006158694;16244225300;","The impact of affective verbal expressions in social robots",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"508","510",,2,"10.1145/3371382.3378358","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083267955&doi=10.1145%2f3371382.3378358&partnerID=40&md5=12f95ce2e8548a05d4ae8f6197bab62b","Bristol Robotics Laboratory, Bristol, United Kingdom; Hamburg University, Hamburg, Germany; SoftBank Robotics, Paris, France; University of the West of England, Bristol, United Kingdom","Van Maris, A., Bristol Robotics Laboratory, Bristol, United Kingdom; Sutherland, A., Hamburg University, Hamburg, Germany; Mazel, A., SoftBank Robotics, Paris, France; Dogramadzi, S., Bristol Robotics Laboratory, Bristol, United Kingdom; Zook, N., University of the West of England, Bristol, United Kingdom; Studley, M., Bristol Robotics Laboratory, Bristol, United Kingdom; Winfield, A., Bristol Robotics Laboratory, Bristol, United Kingdom; Caleb-Solly, P., Bristol Robotics Laboratory, Bristol, United Kingdom","This research investigates whether there is an ethical concern in robots misrepresenting their internal state through speech. Participants were asked to discuss their food preferences with a robot, where the robot would either respond through facts or an implied personal stance. Results show that there are no significant differences in the way participants perceived the robot or accepted the interaction; nor that the interaction influenced their mood. This indicates that the use of personal opinion by a robot does not significantly impact participants' opinion of the robot and therefore may not necessarily be a concern in human-robot interactions. © 2020 ACM.","Displaying emotions; Ethics; Social robot","Agricultural robots; Man machine systems; Ethical concerns; Food preference; Internal state; Social robots",,,,,"Horizon 2020 Framework Programme, H2020; H2020 Marie Skłodowska-Curie Actions, MSCA: 721619","This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 721619 for the SOCRATES project.",,,,,,,,,,"Bishop, L., Van Maris, A., Dogramadzi, S., Zook, N., Social robots: The influence of human and robot characteristics on acceptance (2019) Paladyn Journal of Behavioral Robotics, 10 (1), pp. 346-358. , 2019; Churamani, N., Kerzel, M., Strahl, E., Barros, P., Wermter, S., Teaching emotion expressions to a human companion robot using deep neural architectures (2017) 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 627-634; Smithey Fulmer, I., Barry, B., Adam Long, D., Lying and smiling: Informational and emotional deception in negotiation (2009) Journal of Business Ethics, 88 (4), pp. 691-709. , 2009; Heerink, M., Kröse, B., Evers, V., Wielinga, B., Assessing acceptance of assistive social agent technology by older adults: The almere model (2010) International Journal of Social Robotics, 2 (4), pp. 361-375. , 2010; Kumar Pandey, A., Gelin, R., A mass-produced sociable humanoid robot: Pepper: The first machine of its kind (2018) IEEE Robotics & Automation Magazine, 25 (3), pp. 40-48. , 2018; Sharkey, A., Sharkey, N., Children, the elderly, and interactive robots (2011) IEEE Robotics & Automation Magazine, 18 (1), pp. 32-38. , 2011; Van Maris, A., Zook, N., Caleb-Solly, P., Studley, M., Winfield, A., Dogramadzi, S., Ethical considerations of (contextually) affective robot behaviour (2018) Hybrid Worlds: Societal and Ethical Challenges-Proceedings of the International Conference on Robot Ethics and Standards (ICRES 2018). CLAWAR Association Ltd, pp. 13-19; Watson, D., Anna Clark, L., Tellegen, A., Development and validation of brief measures of positive and negative affect: The PANAS scales (1988) Journal of Personality and Social Psychology, 54 (6), p. 1063. , 1988; Weiss, A., Bartneck, C., Meta analysis of the usage of the Godspeed Questionnaire Series (2015) Robot and Human Interactive Communication (RO-MAN). 2015 24th IEEE International Symposium On. IEEE, pp. 381-388",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083267955
"Schwaninger I.","57215293552;","Robots in older people's living spaces: Designing for trust in situated human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"600","602",,,"10.1145/3371382.3377449","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083260134&doi=10.1145%2f3371382.3377449&partnerID=40&md5=058a813be53663f805089a4d34ff8c89","HCI Group, TU Wien, Vienna, Austria","Schwaninger, I., HCI Group, TU Wien, Vienna, Austria","As robots are being designed to support older people in their living spaces, the use of robots in these contexts and particularly for AAL may come with issues of trust. While trust has gained increasing interest in HRI, we still have little understanding of trust when using robots in people's living spaces and taking into account their social practices. Drawing on literature, a methodological contribution and various (long-term) studies with older people and technology, the aim of this thesis is to define design guidelines for trust in situated human-robot interaction in older people's living spaces. © 2020 ACM.","Aal; Human-robot interaction; Living spaces; Situated hri; Trust","Agricultural robots; Machine design; Man machine systems; Living spaces; Methodological contributions; Older People; Situated human; Social practices; Social robots",,,,,"Technische Universitat Wien; Österreichische Forschungsförderungsgesellschaft, FFG: 856179","I greatly thank my supervisors Astrid Weiss and Geraldine Fitz-patrick for their input. This research is funded by the Trust Robots Doctoral College (TU Wien) and partly supported by the FFG (Austrian Research Promotion Agency - grant number 856179) .",,,,,,,,,,"Alves-Oliveira, P., Petisca, S., Correia, F., Maia, N., Paiva, A., Social robots for older adults: Framework of activities for aging in place with robots (2015) SpringerLink, pp. 11-20. , https://doi.org/10.1007/978-3-319-25554-52, Oct 2015; Ates, N., Aumayr, G., Drobics, M., Maria, F.K., Frauenberger, C., Garschall, M., Kofler, M., Willner, V., Assistive solutions in practice: Experiences from aal pilot regions in Austria (2017) Stud Health Technol Inform, 236, pp. 184-195. , https://www.ncbi.nlm.nih.gov/pubmed/28508795, 2017; Keith, C.B., Elicitation techniques: Getting people to talk about ideas they don't usually talk about (2015) Theory and Research in Social Education, 43 (2), pp. 179-205. , https://doi.org/10.1080/00933104.2015.1034392, May 2015; Billings, D.R., Schaefer, K.E., Chen, C.J.Y., Hancock, P.A., Human-robot interaction: Developing trust in robots (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction-HRI '12, 109. , https://doi.org/10.1145/2157689.2157709, ACM Press, Boston, Massachusetts, USA; Broadbent, E., Garrett, J., Jepsen, N., Li Ogilvie, V., Seok Ahn, H., Robinson, H., Peri, K., Pillai, A., Using robots at home to support patients with chronic obstructive pulmonary disease: Pilot randomized controlled trial (2018) Journal of Medical Internet Research, 20 (2), p. e45. , 2018; Charalambous, G., Fletcher, S., Webb, P., The development of a scale to evaluate trust in industrial human-robot collaboration (2016) Int J of Soc Robotics, 8 (2), pp. 193-209. , https://doi.org/10.1007/s12369-015-0333-8, Apr 2016; Elizabeth Clarke, R., Briggs, J., Armstrong, A., MacDonald, A., Vines, J., Flynn, E., Socio-materiality of trust: Co-design with a resource limited community organisation (2019) CoDesign, , https://doi.org/10.1080/15710882.2019.1631349, Jul 2019; Corbett, E., Le Dantec, C.A., Going the distance: Trustwork for citizen participation (2018) Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18), 312, p. 13. , https://doi.org/10.1145/3173574.3173886, ACM, New York, NY, USA, Article; Crabtree, A., Tolmie, P., Knight, W., (2017) Repacking 'Privacy' for A Networked World, , https://dl.eusset.eu/handle/20.500.12015/2865, Springer, London 2017; Deindio, F., Dearco, A., Laura, A.R., (2007) Enriching Community Networks by Supporting Deliberation, , https://dl.eusset.eu/handle/20.500.12015/2683, Springer London, Dordrecht Amsterdam 2007; Fitzpatrick, G., Huldtgren, A., Malmborg, L., Harley, D., Ijsselsteijn, W., Design for agency, adaptivity and reciprocity: Reimagining aal and telecare agendas (2015) SpringerLink, pp. 305-338. , https://doi.org/10.1007/978-1-4471-6720-413, 2015; (2019) Population Ages 65 and above (% of Total Population) | Data, , https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS, (Oct 2019) IBRD / IDA The World Bank Group[Online; accessed 25. 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Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083260134
"Li Y., Zhao T., Shen X.","57190755668;57190755191;56383239500;","Attention-based multimodal fusion for estimating human emotion in real-world HRI",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"340","342",,1,"10.1145/3371382.3378261","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083245813&doi=10.1145%2f3371382.3378261&partnerID=40&md5=068a3d02ae8cec66950df316bed84f64","Honda RandD Co., Ltd., Tokyo, Japan; Kyoto University, Kyoto, Japan; Graduate University for Advanced Studies, Tokyo, Japan","Li, Y., Honda RandD Co., Ltd., Tokyo, Japan; Zhao, T., Kyoto University, Kyoto, Japan; Shen, X., Graduate University for Advanced Studies, Tokyo, Japan","Toward empathetic and harmonious human-robot interaction (HRI), automatic estimation of human emotion has attracted increasing attention from multidisciplinary research fields. In this report, we propose an attention-based multimodal fusion approach that explores the space between traditional early and late fusion approaches, to deal with the problem of asynchronous multimodal inputs while considering their relatedness. The proposed approach enables the robot to align the human's visual and speech signals (more specifically, facial, acoustic, and lexical information) extracted by its cameras, microphones, and processing modules and is expected to achieve robust estimation performance in real-world HRI. © 2020 ACM.","Attention mechanism; Emotion estimation; Human-robot interaction (hri); Multimodal fusion","Agricultural robots; Audio signal processing; Man machine systems; Automatic estimation; Human robot Interaction (HRI); Lexical information; Multi-disciplinary research; Multi-modal fusion; Multimodal inputs; Processing modules; Robust estimation; Human robot interaction",,,,,,,,,,,,,,,,"Almaev, T.R., Valstar, M.F., Local gabor binary patterns from three orthogonal planes for automatic facial expression recognition (2013) 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. IEEE, pp. 356-361; Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y., (2014) On the Googleproperties of Neural Machine Translation: Encoder-decoder Approaches, , 2014 arXiv preprint arXiv:1409.1259; Chuang, Z., Wu, C., Multi-modal emotion recognition from speech and text (2004) International Journal of Computational Linguistics & Chinese Language Processing, 9 (2), pp. 45-62. , August 2004: Special Issue on New Trends of Speech and Language Processing; D'Mello, S., Kory, J., Consistent but modest: A metaanalysis on unimodal and multimodal affect detection accuracies from 30 studies (2012) Proceedings of the 14th ACM International Conference on Multimodal Interaction. ACM, pp. 31-38; Eyben, F., Wöllmer, M., Schuller, B., Opensmile: The munich versatile and fast open-source audio feature extractor (2010) Proceedings of the 18th ACM International Conference on Multimedia. 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ACM, pp. 365-367; Li, Y., Toshinori, I.C., Ward, N., Inoue, K., Nakamura, S., Takanashi, K., Kawahara, T., Emotion recognition by combining prosody and sentiment analysis for expressing reactive emotion by humanoid robot (2017) 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, pp. 1356-1359; Picard, R.W., (2000) Affective Computing, , MIT press; Ramirez, G.A., Baltrusaitis, T., Morency, L., Modeling latent discriminative dynamic of multi-dimensional affective signals (2011) International Conference on Affective Computing and Intelligent Interaction. Springer, pp. 396-406; Tian, L., Moore, J., Lai, C., Recognizing emotions in spoken dialogue with hierarchically fused acoustic and lexical features (2016) 2016 IEEE Spoken Language Technology Workshop (SLT). IEEE, pp. 565-572; Valstar, M., Schuller, B., Smith, K., Almaev, T., Eyben, F., Krajewski, J., Cowie, R., Pantic, M., Avec 2014: 3d dimensional affect and depression recognition challenge (2014) Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge. ACM, pp. 3-10; Wang, W., R-NET: Machine reading comprehension with self-matching networks (2017) Natural Language Computer Group, Microsoft Reserach, 5. , Asia, Beijing. Technical Report. China, Technical Report; Wu, C., Lin, J., Wei, W., Survey on audiovisual emotion recognition: Databases, features, and data fusion strategies (2014) APSIPA Transactions on Signal and Information Processing, 3. , 2014; Xie, L., Liu, Z., A coupled HMM approach to video-realistic speech animation (2007) Pattern Recognition, 40 (8), pp. 2325-2340. , 2007; Xu, H., Zhang, H., Han, K., Wang, Y., Peng, Y., Li, X., (2019) Learning Alignment for Multimodal Emotion Recognition from Speech, , arXi preprint arXiv:1909.05645 2019; Zeng, Z., Pantic, M., Roisman, G.I., Huang, T.S., A survey of affect recognition methods: Audio, visual, and spontaneous expressions (2008) IEEE Transactions on Pattern Analysis and Machine Intelligence, 31 (1), pp. 39-58. , 2008",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083245813
"Bärenholdt M., Jensen L.C., Pedersen J.E., Petersen E.","57216341456;56277957900;57216353599;57216343269;","How do situation awareness affect people's physical engagement with a robot?",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"116","118",,,"10.1145/3371382.3378368","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083238553&doi=10.1145%2f3371382.3378368&partnerID=40&md5=4bd771185b2e54e443bfff62f55ea32f","University of Southern Denmark, Kolding, Denmark","Bärenholdt, M., University of Southern Denmark, Kolding, Denmark; Jensen, L.C., University of Southern Denmark, Kolding, Denmark; Pedersen, J.E., University of Southern Denmark, Kolding, Denmark; Petersen, E., University of Southern Denmark, Kolding, Denmark","In this paper, we investigate the relationship between situation awareness and compliance in human-robot interaction. We carried out a between-subject experiment (N=30) in which a robot inter-rupts people doing solitary work to do small physical exercises. In one condition the robot displays an awareness to the activity participants are currently engaged in. In the control condition the robot displays no such awareness. Results show that participants initially ignore the robot in both condition, over time participants interacting with the 'aware' robot comply more often with the requests and as a result complete more exercises. © 2020 ACM.","Compliance; Exercise; Situation awareness; Social awareness","Agricultural robots; Man machine systems; Physical exercise; Situation awareness; Subject experiment; Human robot interaction",,,,,,,,,,,,,,,,"Baxter, P., Kennedy, J., Vollmer, A., De Greeff, J., Belpaeme, T., Tracking gaze over time in HRI as a proxy for engagement and attribution of social agency (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 126-127. , https://doi.org/10.1145/2559636.2559829; Clark, H.H., Speaking in time (2002) Speech Communication, 36 (1), pp. 5-13. , 2002; Dini, A., Murko, C., Yahyanejad, S., Augsdörfer, U., Hofbaur, M., Paletta, L., Measurement and prediction of situation awareness in human-robot interaction based on a framework of probabilistic attention (2017) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 4354-4361; Garfinkel, H., (1967) Studies in Ethnomethodology, , Englewood Cliffs, NJ, Prentice-Hall; Leite, I., Pereira, A., Fain Lehman, J., Persistent memory in repeated child-robot conversations (2017) Proceedings of the 2017 Conference on Interaction Design and Children. ACM, pp. 238-247; Lohan, K., Pitsch, K., Rohlfing, K., Fischer, K., Saunders, J., Lehmann, H., Nehaniv, C., Wrede, B., Contingency allows the robot to spot the tutor and to learn from interaction (2011) Development and Learning (ICDL). 2011 IEEE International Conference on, 2. , IEEE, 1-8; Yanco, H.A., Drury, J.L., Where Am I. Acquiring situation awareness using a remote robot platform (2004) IEEE International Conference on Systems, Man and Cybernetics, 3, pp. 2835-2840. , IEEE",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083238553
"Xiao Y., Silvera-Tawil D., Pagnucco M.","57226478379;55510328300;23397592700;","Autonomous behaviour planning for socially-assistive robots in therapy and education",2020,"Australasian Conference on Robotics and Automation, ACRA","2020-December",,,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111651605&partnerID=40&md5=c22d795e793b3f659c072344e99fcbb8","Australian e-Health Research Centre, Australia; The University of New South Wales, Australia","Xiao, Y., Australian e-Health Research Centre, Australia, The University of New South Wales, Australia; Silvera-Tawil, D., Australian e-Health Research Centre, Australia; Pagnucco, M., The University of New South Wales, Australia","Socially-assistive robots have been proposed as tools to supplement traditional therapy and education for children with a diagnosis of autism spectrum disorder. One major challenge is the substantial labour required to program robot behaviours that are appropriate for therapy and education. We have developed an autonomous system that allows robots to learn how to behave during a conversation, by ‘observing’ how people interact with each other. The work was informed by on-going research in psychology, machine learning and human-robot interaction. A user study showed comparative performance between the autonomously-generated behaviours and a set of industrial-level pre-programmed behaviours. © 2020 Australasian Robotics and Automation Association. All rights reserved.",,"Agricultural robots; End effectors; Robot programming; Robotics; Social robots; Autism spectrum disorders; Autonomous systems; Comparative performance; Program robots; Socially assistive robots; User study; Educational robots",,,,,,,,,,,,,,,,"(2018) ABC. Mathxplosion, , https://iview.abc.net.au/show/mathxplosion, [ABC, 2018] Access at; (2017) Autism Spectrum Australia. 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S., de Witte, Luc P., Matching Robot KASPAR to Autism Spectrum Disorder (ASD) Therapy and Educational Goals (2016) International Journal of Social Robotics, 8 (4), pp. 445-455. , [Huijnen et al., 2016] aug; Kelley, J. F., An empirical methodology for writing user-friendly natural language computer applications (1983) Proc. Conference on Human Factors in Computing Systems, , [Kelley, 1983]; Khamassi, M, Chalvatzaki, G, Tsitsimis, T, Velentzas, G, Tzafestas, C, A framework for robot learning during child-robot interaction with human engagement as reward signal (2018) Proc. 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Beskow J., Peters C., Castellano G., O’Sullivan C., Leite I., editor, pages Springer, Cham; Vanderborght, Bram, Simut, Ramona, Saldien, Jelle, Pop, Cristina, Rusu, Alina S, Pintea, Sebastian, Lefeber, Dirk, David, Daniel O, Using the social robot probo as a social story telling agent for children with ASD (2012) Interaction Studies, 13 (3), pp. 348-372. , [Vanderborght et al., 2012]; Wainer, Joshua, Robins, Ben, Amirabdollahian, Farshid, Dautenhahn, Kerstin, Using the Humanoid Robot KASPAR to Autonomously Play Triadic Games and Facilitate Collaborative Play Among Children With Autism (2014) IEEE Transactions on Autonomous Mental Development, 6 (3), pp. 183-199. , [Wainer et al., 2014] sep; Warren, Zachary E., Zheng, Zhi, Swanson, Amy R., Bekele, Esubalew, Zhang, Lian, Crittendon, Julie A., Weitlauf, Amy F., Sarkar, Nilanjan, Can Robotic Interaction Improve Joint Attention Skills? 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"Lin S., Qian X.","56039903000;57194871236;","Audio-visual multi-speaker tracking based on the GLMB framework",2020,"Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH","2020-October",,,"3082","3086",,1,"10.21437/Interspeech.2020-1969","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098201385&doi=10.21437%2fInterspeech.2020-1969&partnerID=40&md5=e18389e4372870fc2bdbf99702568a78","Department of Electrical and Computer Engineering, National University of Singapore, Singapore","Lin, S., Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Qian, X., Department of Electrical and Computer Engineering, National University of Singapore, Singapore","Multi-speaker tracking using both audio and video modalities is a key task in human-robot interaction and video conferencing. The complementary nature of audio and video signals improves the tracking robustness against noise and outliers compared to the uni-modal approaches. However, the online tracking of multiple speakers via audio-video fusion, especially without the target number prior, is still an open challenge. In this paper, we propose a Generalized Labelled Multi-Bernoulli (GLMB)based framework that jointly estimates the number of targets and their respective states online. Experimental results using the AV16.3 dataset demonstrate the effectiveness of the proposed method. Copyright © 2020 ISCA","3D; Audio-visual fusion; GLMB filter; Multi-speaker tracking","Audio acoustics; Human robot interaction; Speech communication; Video conferencing; Audio and video; Audio videos; Multi-Bernoulli; On-line tracking; Robustness against noise; Speaker tracking; Speech recognition",,,,,"National Research Foundation Singapore, NRF","Acknowledgements: This research work is supported by the Neuromorphic Computing project, Programmatic Grant No. A1687b0033 from the Singapore Government’s Research, Innovation and Enterprise 2020 plan (Advanced Manufacturing and Engineering domain), and Human-Robot Interaction Phase 1 (Grant No. 192 25 00054) from the National Research Foundation, Prime Minister’s Office, Singapore under the National Robotics Programme.","This research work is supported by the Neuromorphic Computing project, Programmatic Grant No. A1687b0033 from the Singapore Government's Research, Innovation and Enterprise 2020 plan (Advanced Manufacturing and Engineering domain), and Human-Robot Interaction Phase 1 (Grant No. 192 25 00054) from the National Research Foundation, Prime Minister's Office, Singapore under the National Robotics Programme.",,,,,,,,,"Qian, X., Xompero, A., Cavallaro, A., Brutti, A., Lanz, O., Omologo, M., 3D mouth tracking from a compact microphone array co-located with a camera (2018) IEEE Int. Conf. on Audio, Speech and Signal Processing, pp. 3071-3075. , Calgary, Canada, Apr; Ban, Y., Alameda-Pineda, X., Girin, L., Horaud, R., Variational Bayesian inference for audio-visual tracking of multiple speakers (2019) IEEE Trans. on Pattern Analysis and Machine Intelligence, , Nov; Shivappa, S. T., Trivedi, M. M., Rao, B. D., Audiovisual information fusion in human-computer interfaces and intelligent environments: A survey (2010) Proc. of the IEEE, 98 (10), pp. 1692-1715. , Oct; Fox, D., Adapting the sample size in particle filters through KLD-sampling (2003) The Int. Journal of Robotics Research, 22 (12), pp. 985-1003. , Dec; Kim, D. Y., Vo, B.-N., Vo, B.-T., Jeon, M., A labeled random finite set online multi-object tracker for video data (2019) Pattern Recognition, 90, pp. 377-389; Yang, H., Shao, L., Zheng, F., Wang, L., Song, Z., Recent advances and trends in visual tracking: A review (2011) Neurocomput, 74 (18), pp. 3823-3831. , Nov; Ma, W.-K., Vo, B.-N., Singh, S. S., Baddeley, A., Tracking an unknown time-varying number of speakers using TDOA measurements: a random finite set approach (2006) IEEE Trans. on Signal Processing, 54 (9), pp. 3291-3304; Lin, S., Reverberation-robust localization of speakers using distinct speech onsets and multichannel cross correlations (2018) IEEE/ACM Trans. on Audio, Speech and Language Processing, 26 (11), pp. 2098-2111; Crocco, M., Cristani, M., Trucco, A., Murino, V., Audio surveillance: a systematic review (2016) ACM Computing Surveys, 48 (4), p. 52; Jaimes, A., Sebe, N., Multimodal human computer interaction: A survey (2005) Int. Workshop on Human-Computer Interaction, pp. 1-15. , Las Vegas, Nevada, USA, Jul; Zotkin, D. N., Duraiswami, R., Davis, L. S., Joint audio-visual tracking using particle filters (2002) EURASIP Journal on Applied Signal Processing, (1), pp. 1154-1164. , Nov 2002; Taj, M., Cavallaro, A., Audio-assisted trajectory estimation in non-overlapping multi-camera networks (2009) IEEE Int. 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"Williams T., Hirshfield L., Tran N., Grant T., Woodward N.","7404172627;16303680200;57209796697;57205024437;57218272953;","Using augmented reality to better study human-robot interaction",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12190 LNCS",,,"643","654",,5,"10.1007/978-3-030-49695-1_43","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088527885&doi=10.1007%2f978-3-030-49695-1_43&partnerID=40&md5=8a3e9e28500a7c355e65f499253110ff","MIRRORLab, Colorado School of Mines, Golden, CO, United States; SHINE Lab, University of Colorado, Boulder, CO, United States","Williams, T., MIRRORLab, Colorado School of Mines, Golden, CO, United States; Hirshfield, L., SHINE Lab, University of Colorado, Boulder, CO, United States; Tran, N., MIRRORLab, Colorado School of Mines, Golden, CO, United States; Grant, T., SHINE Lab, University of Colorado, Boulder, CO, United States; Woodward, N., MIRRORLab, Colorado School of Mines, Golden, CO, United States","In the field of Human-Robot Interaction, researchers often techniques such as the Wizard-of-Oz paradigms in order to better study narrow scientific questions while carefully controlling robots’ capabilities unrelated to those questions, especially when those other capabilities are not yet easy to automate. However, those techniques often impose limitations on the type of collaborative tasks that can be used, and the perceived realism of those tasks and the task context. In this paper, we discuss how Augmented Reality can be used to address these concerns while increasing researchers’ level of experimental control, and discuss both advantages and disadvantages of this approach. © Springer Nature Switzerland AG 2020.","Augmented Reality; Human-Robot Interaction; Research Methods","Augmented reality; Human computer interaction; Mixed reality; Collaborative tasks; Experimental control; Perceived realisms; Wizard of Oz; Human robot interaction",,,,,"National Science Foundation, NSF","This work was funded in part by grant IIS-1909864 from the National Science Foundation.",,,,,,,,,,"Mavridis, N., A review of verbal and non-verbal human-robot interactive communication (2015) Robot. Auton. 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Autom. Mag, 25 (2), pp. 51-71; Chakraborti, T., Sreedharan, S., Kulkarni, A., Kambhampati, S., (2017) Alternative modes of interaction in proximal human-in-the-loop operation of robots, , arXiv preprint arXiv:1703.08930; zu Borgsen, S., Renner, P., Lier, F., Improving human-robot handover research by mixed reality techniques (2018) VAM-HRI; Walker, M., Hedayati, H., Lee, J., Szafir, D., Communicating robot motion intent with augmented reality (2018) Proceedings of the HRI, pp. 316-324. , ACM; Rosen, E., (2017) Communicating robot arm motion intent through mixed reality head-mounted displays, , arXiv preprint arXiv:1708.03655; Reardon, C., Lee, K., Fink, J., Come see this! Augmented reality to enable human-robot cooperative search (2018) International Symposium on Safety, Security, and Rescue Robotics; Amor, H.B., Ganesan, R.K., Rathore, Y., Ross, H., Intention projection for human-robot collaboration with mixed reality cues (2018) VAM-HRI","Williams, T.; MIRRORLab, United States; email: twilliams@mines.edu","Chen J.Y.C.Fragomeni G.",,"Springer","12th International Conference on Virtual, Augmented and Mixed Reality, VAMR 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242159,03029743,9783030496944,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85088527885
"Trumić M., Jovanović K., Fagiolini A.","57214797977;36137004200;15724871900;","Comparison of Model-Based Simultaneous Position and Stiffness Control Techniques for Pneumatic Soft Robots",2020,"Mechanisms and Machine Science","84",,,"218","226",,,"10.1007/978-3-030-48989-2_24","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087007933&doi=10.1007%2f978-3-030-48989-2_24&partnerID=40&md5=a5cf1493ad7acc149e2f31f9271d2ae3","School of Electrical Engineering, University of Belgrade, Belgrade, Serbia; Department of Engineering, University of Palermo, Palermo, Italy","Trumić, M., School of Electrical Engineering, University of Belgrade, Belgrade, Serbia, Department of Engineering, University of Palermo, Palermo, Italy; Jovanović, K., School of Electrical Engineering, University of Belgrade, Belgrade, Serbia; Fagiolini, A., Department of Engineering, University of Palermo, Palermo, Italy","Soft robots have been extensively studied for their ability to provide both good performance and safe human-robot interaction. In this paper, we present and compare the performance of two model-based control techniques with the common aim to independently and simultaneously control position and stiffness of a pneumatic soft robot’s joint. The dynamic system of a robot arm with flexible joints actuated by a pneumatic antagonistic pair of actuators, so-called McKibben artificial muscles, will be regarded, while its dynamic parameters will be considered imprecise. Simulation results are provided to verify the performance of the algorithms. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.","Adaptive control; Antagonistic drive; Model based control; Pneumatic actuator; Soft robots; Variable stiffness actuator","Pneumatic actuators; Pneumatics; Position control; Stiffness; Comparison of models; Dynamic parameters; Flexible joints; McKibben artificial muscles; Model based controls; Robot arms; Safe human-robot interaction; Stiffness control; Human robot interaction",,,,,,,,,,,,,,,,"Zanchettin, A.M., Ceriani, N.M., Rocco, P., Ding, H., Matthias, B., Safety in human-robot collaborative manufacturing environments: metrics and control (2016) IEEE Trans. Autom. Sci. Eng, 13 (2), pp. 882-893; Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human-robot collaboration (2018) Auton. Robots, 42 (5), pp. 957-975; Bicchi, A., Tonietti, G., (2002) Design, realization and control of soft robot arms for intrinsically safe interaction with humans, pp. 79-87; Albu-Schäffer, A., Haddadin, S., Ott, C., Stemmer, A., Wimböck, T., Hirzinger, G., The DLR lightweight robot: design and control concepts for robots in human environments (2007) Industr. Robot Int. J, 34 (5), pp. 376-385; Gavrilović, M., Marić, M., Positional servo-mechanism activated by artificial muscles (1969) Med. Biol. Eng, 7 (1), pp. 77-82; Chou, C.-P., Hannaford, B., Measurement and modeling of McKibben pneumatic artificial muscles (1996) IEEE Trans. Robot. Autom, 12 (1), pp. 90-102; Palli, G., Melchiorri, C., De Luca, A., On the feedback linearization of robots with variable joint stiffness (2008) IEEE International Conference on Robotics and Automation (ICRA), pp. 1753-1759; Jovanović, K., Lukić, B., Potkonjak, V., Feedback linearization for decoupled posi-tion/stiffness control of bidirectional antagonistic drives (2017) Facta Universitatis Ser. Electron. Energ, 31 (1), pp. 51-61; Petit, F., Daasch, A., Albu-Schäffer, A., Backstepping control of variable stiffness robots (2015) IEEE Trans. Control Syst. Technol, 23 (6), pp. 2195-2202; Ghorbel, F., Hung, J.Y., Spong, M.W., Adaptive control of flexible-joint manipulators (1989) IEEE Control Syst. Mag, 9 (7), pp. 9-13; Tonietti, G., Bicchi, A., Adaptive simultaneous position and stiffness control for a soft robot arm (2002) IEEE/RSJ International Conference on Intelligent Robots and Systems, 2, pp. 1992-1997; Trumić, M., Jovanović, K., Fagiolini, A., Decoupled nonlinear adaptive control of position and stiffness for pneumatic soft robots (2020) Int. J. Robot. Res, , https://doi.org/10.1177/0278364920903787; Fagiolini, A., Trumić, M., Jovanović, K., An input observer-based stiffness estimation approach for flexible robot joints (2020) IEEE Robot. Autom. Lett, 5 (2), pp. 1843-1850; Ménard, T., Grioli, G., Bicchi, A., A stiffness estimator for agonistic-antagonistic variable-stiffness-actuator devices (2014) IEEE Trans. Robot, 30 (5), pp. 1269-1278; Potkonjak, V., Svetozarevic, B., Jovanovic, K., Holland, O., The puller-follower control of compliant and noncompliant antagonistic tendon drives in robotic systems (2011) Int. J. Adv. Robot. Syst, 8 (5), p. 69","Fagiolini, A.; Department of Engineering, Italy; email: adriano.fagiolini@unipa.it",,,"Springer Science and Business Media B.V.",,,,,22110984,,,,"English","Mech. Mach. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85087007933
"Roesler O., Nowé A.","57208167935;6601969061;","Simultaneous action learning and grounding through reinforcement and cross-situational learning",2020,"ALA 2018 - Adaptive Learning Agents - Workshop at the Federated AI Meeting 2018",,,,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086286906&partnerID=40&md5=8839826391e18b9276cbed9904cd0443","Artificial Intelligence Lab, Vrije Universiteit Brussel Belgium Biomedical Engineering, University of Reading, United Kingdom; Artificial Intelligence Lab, Vrije Universiteit Brussel, Belgium","Roesler, O., Artificial Intelligence Lab, Vrije Universiteit Brussel Belgium Biomedical Engineering, University of Reading, United Kingdom; Nowé, A., Artificial Intelligence Lab, Vrije Universiteit Brussel, Belgium","Natural human-robot interaction requires robots to learn new tasks autonomously and link the learned actions to their corresponding words through grounding. Previous studies focused only on action learning or grounding, but not both. In this paper, we try to fill this gap by introducing a framework that uses reinforcement learning to learn actions and cross-situational learning to ground actions, object shapes and colors, and prepositions. The proposed framework is evaluated through a simulated interaction experiment between a human tutor and a robot. The results show that the employed framework can be used for simultaneous action learning and grounding. © 2018 International Foundation for Autonomous Agents and Multiagent Systems","Cross-situational learning; Human-robot interaction simulation; Reinforcement learning; Symbol grounding","Human robot interaction; Intelligent agents; Action learning; Object shape; Simulated interactions; Reinforcement learning",,,,,,,,,,,,,,,,"Abdo, N., Spinello, L., Burgard, W., Stachniss, C., Inferring what to imitate in manipulation actions by using a recommender system (2014) IEEE International Conference on Robotics and Automation (ICRA), , Hong Kong, China; Aly, A., Taniguchi, A., Taniguchi, T., A generative framework for multimodal learning of spatial concepts and object categories: An unsupervised part-of-speech tagging and 3D visual perception based approach (2017) IEEE International Conference on Development and Learning and the International Conference on Epigenetic Robotics (ICDL-EpiRob), , Lisbon, Portugal; Argall, B.D., Chernova, S., Veloso, M., Browning, B., A survey of robot learning from demonstration (2009) Robotics and Autonomous Systems, 57, pp. 469-483. , 2009; Dawson, C.R., Wright, J., Rebguns, A., Escárcega, M.V., Fried, D., Cohen, P.R., A generative probabilistic framework for learning spatial language (2013) IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL), , Osaka, Japan; Flanagan, R., Bowman, M.C., Johansson, R.S., Control strategies in object manipulation tasks (2006) Current Opinion in Neurobiology, 16, pp. 650-659. , 2006; Fontanari, J.F., Tikhanoff, V., Cangelosi, A., Ilin, R., Perlovsky, L.I., Cross-situational learning of object-word mapping using Neural Modeling Fields (2009) Neural Networks, 22 (5-6), pp. 579-585. , July-August 2009; Fontanari, J.F., Tikhanoff, V., Cangelosi, A., Perlovsky, L.I., A cross-situational algorithm for learning a lexicon using Neural modeling fields (2009) International Joint Conference on Neural Networks (IJCNN), , Atlanta, GA, USA; Gudimella, A., Story, R., Shaker, M., Kong, R., Brown, M., Shnayder, V., Campos, M., Deep reinforcement learning for dexterous manipulation with concept networks (2017) CoRR, , http://arxiv.org/abs/1709.06977, 2017; Harnad, S., The symbol grounding problem (1990) Physica D, 42, pp. 335-346. , 1990; Kemp, C.C., Edsinger, A., Torres-Jara, E., Challenges for robot manipulation in human environments (2007) IEEE Robotics & Automation Magazine, 14 (1), pp. 20-29. , March 2007; Ng, A.Y., Harada, D., Russell, S., Policy invariance under reward transformations: Theory and application to reward shaping (1999) Proceedings of the Sixteenth International Conference on Machine Learning (ICML), 99, pp. 278-287. , I. Bratko and S. Dzeroski (Eds.); (2017) World Robotics 2017 - Service Robots, , International Federation of Robotics. October 2017; Popov, I., Heess, N., Lillicrap, T., Hafner, R., Barth-Maron, G., Vecerik, M., Lampe, T., Riedmiller, M., Data-efficient deep reinforcement learning for dexterous manipulation (2017) CoRR, , http://arxiv.org/abs/1704.03073, 2017; Roesler, O., Aly, A., Taniguchi, T., Hayashi, Y., A probabilistic framework for comparing syntactic and semantic grounding of synonyms through cross-situational learning (2018) ICRA-18 Workshop on Representing a Complex World: Perception, Inference, and Learning for Joint Semantic, Geometric, and Physical Understanding, , Brisbane, Australia; Rusu, R.B., Bradski, G., Thibaux, R., Hsu, J., Fast 3D recognition and pose using the viewpoint feature histogram (2010) Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2155-2162. , Taipei, Taiwan; Siskind, J.M., A computational study of cross-situational techniques for learning word-to-meaning mappings (1996) Cognition, 61, pp. 39-91. , 1996; Smith, K., Smith, A.D.M., Blythe, R.A., Cross-situational learning: An experimental study of word-learning mechanisms (2011) Cognitive Science, 35 (3), pp. 480-498. , 2011; Stulp, F., Theodorou, E.A., Schaal, S., Reinforcement learning with sequences of motion primitives for robust manipulation (2012) IEEE Transactions on Robotics (T-RO), 28 (6), pp. 1360-1370. , December 2012; Sutton, R.S., Barto, A.G., (1998) Reinforcement Learning: An Introduction, , MIT Press, Cambridge, MA; Taniguchi, A., Taniguchi, T., Cangelosi, A., Cross-situational learning with Bayesian generative models for multimodal category and word learning in robots (2017) Frontiers in Neurorobotics, 11. , 2017; Tellex, S., Kollar, T., Dickerson, S., Walter, M.R., Banerjee, A.G., Teller, S., Roy, N., Approaching the symbol grounding problem with probabilistic graphical models (2011) AI Magazine, 32 (4), pp. 64-76. , 2011",,,,"International Foundation for Autonomous Agents and Multiagent Systems, IFAAMS","2018 Adaptive Learning Agents, ALA 2018 - Co-located Workshop at the Federated AI Meeting, FAIM 2018","14 July 2018 through 15 July 2018",,158962,,,,,"English","ALA - Adapt. Learn. Agents - Workshop Fed. AI Meet.",Conference Paper,"Final","",Scopus,2-s2.0-85086286906
"Ruiz A.Y.R., Chandrasekaran B.","57208120529;56740438200;","A Robotic Control System Using Robot Operating System and MATLAB for Sensor Fusion and Human-Robot Interaction",2020,"2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020",,,"9031184","550","555",,,"10.1109/CCWC47524.2020.9031184","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083085926&doi=10.1109%2fCCWC47524.2020.9031184&partnerID=40&md5=201c1f8892394dbb0731d92c2f602c13","Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States","Ruiz, A.Y.R., Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States; Chandrasekaran, B., Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States","Human-robot interaction is a key aspect in the area of robotics. In this paper, the implementation of a sensor fusion based robotic control system architecture using Robot Operating System (ROS) and MATLAB is presented. Also, a few test cases under different scenarios is also highlighted for a robot navigation task and using the proposed architecture. The robotic system architecture was previously proposed and the work in this paper focuses on the implementation aspect of this architecture. This paper is a work in progress and more sensors and modules will continue to be interfaced and implemented as the research progresses. © 2020 IEEE.","Control System Architecture; Human-Robot Interaction; Motion Control; ROS/MATLAB Interface; Sensor Fusion; Sensors; Simulink","Computer architecture; Control systems; Man machine systems; MATLAB; Robotics; Implementation aspects; Proposed architectures; Robot navigation; Robot operating system; Robot operating systems (ROS); Robotic control system; Robotic system architectures; Work in progress; Human robot interaction",,,,,,,,,,,,,,,,"Ruiz, A.Y.R., Rivera, L.J.F., Chandrasekaran, B., A sensor fusion based robotic system architecture using human interaction for motion contro! (2019) 20 19 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), p. 00950100. , January IEEE; Ruiz, A.Y.R., Chandrasekaran, B., Implementation of a sensor fusion based robotic system architecture for motion control using human-robot interaction 2020 IEEE/SICE International Symposium on System Integration (SII 2020; Powering the World's Robots, , http://www.ros.org/, [Accessed: II-Sept-2019]; Simulink-simulation and model-based design Simulation and Model-Based Design MATLAB, , www.mathworks.comlproducts/simulink.htm, [Accessed 30-0ct-2019]; Mahdi, L., Reza Kangavari, M., Comprehensive architecture for intelligent adaptive interface in the field of singlehuman multiple-robot interaction (2018) ETRI Journal, 50 (4), pp. 483-498; Path Following with Obstacle Avoidance in Simulink-MATLAB & Simulink, , www.mathworks.comlhelp/robotics/examples/path-following-with100bstacle-avoidance-in-simulink.htm, Path Following with Obstacle Avoidance in Simulink®[Accessed: 1O-Oct-2019]; Sensor fusion using synthetic radar and vision data in simulink matlab & simulink Driving Scenario Designer, , www.mathworks.comlhelp/driving/examples/sensor-fusion-usingsynthetic-radar-and-vision-data-in-simulink.htm, Accessed 10-Oct2019; Ros.org,wiki.ros.orgIROS/Tutorials, [Accessed 22-0ct-2019]; Welcome to VirtualBox.org!, , www.virtualbox.org/, Oracle VM VirtualBox, [Accessed: I0-Nov-20 19]; Ros.org,wiki.ros.org/stdrsimulator/Tutorials/Teleopwithteleoptwistkeyboard, [Accessed 10-Nov-20 19]; MathWorks, , www.mathworks.comlproductslmatlab.htm, [Accessed: 17-0ct-2019]; Robot Operating System (ROS) MATLAB & Simulink, MathWorks, , www.mathworks.comlhelp/robotics/gs/robot-operating-system-ros.htm, Robot Operating System (ROS)[Accessed: 10-Sept-2019]; ROS Network Setup MATLAB & Simulink, , www.mathworks.comlhelp/robotics/ug/ros-network-setup.html, [Accessed: 2-Nov-2019]; Get Started with ROS-MATLAB & Simulink, , www.mathworks.comlhelp/robotics/examples/get-started-with-ros.htm, Get Started with ROS[Accessed: 30-0ct-2019]; Creating and Executing Test Cases MATLAB & Simulink Example, , www.mathworks.comlhelp/sldv/examples/creating-and-executing-testcases.htm, Creating and Executing Test Cases[Accessed: 2-Nov-2019]",,"Chakrabarti S.Paul R.","IEEE Region 1;IEEE Region 6;IEEE USA;Institute of Engineering and Management (IEM);University of Engineering and Management (UEM);UNLV","Institute of Electrical and Electronics Engineers Inc.","10th Annual Computing and Communication Workshop and Conference, CCWC 2020","6 January 2020 through 8 January 2020",,158422,,9781728137834,,,"English","Annu. Comput. Commun. Workshop Conf., CCWC",Conference Paper,"Final","",Scopus,2-s2.0-85083085926
"Rivera L.J.F., Chandrasekaran B.","57216126752;56740438200;","Implementation of a Robotic Control System for Fault Tolerance and Safety using Human Robot Interaction",2020,"Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020",,,"9026237","1028","1032",,2,"10.1109/SII46433.2020.9026237","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082612829&doi=10.1109%2fSII46433.2020.9026237&partnerID=40&md5=ed406b7c0e715c7db14736a2e8a5eb02","Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States","Rivera, L.J.F., Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States; Chandrasekaran, B., Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States","Fault tolerance is important in a robotic system architecture because of the advancements in robotics which makes the robots much smarter and more capable on executing tasks. Thus fault tolerant and self-healing robotic control systems help prevent any cascade errors that can lead to the robot becoming inoperable or causing a safety hazard. The current paper is a work in progress describing the initial efforts of implementing a fault tolerant robotic system architecture and presents initial progress of this implementation where the robotic control system demonstrates a fault tolerance and a self-healing algorithm using human robot interaction in the process. © 2020 IEEE.","Fault Detection; Fault Tolerance; Human Robot Interaction; Motion Control; Robot Control; Self-Healing","Cascade control systems; Computer architecture; Fault detection; Fault tolerance; Man machine systems; Motion control; Robotics; Self-healing materials; Fault-tolerant; Robot controls; Robotic control system; Robotic system architectures; Safety hazards; Self-healing; Work in progress; Human robot interaction",,,,,,,,,,,,,,,,"Edwards, G., Garcia, J., Tajalli, H., Popescu, D., Medvidovic, N., Sukhatme, G., Petrus, B., Architecture-driven selfadaptation and self-management in robotics systems (2009) 2009 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems, pp. 142-151. , May. IEEE; Kirchner, D., Niemczyk, S., Geihs, K., RoSHA: A multi-robot self-healing architecture (2013) Robot Soccer World Cup, pp. 304-315. , June. Springer, Berlin, Heidelberg; Powering the World's Robots, , http://www.ros.org/, ROS. org. [Accessed: 8-Nov-2019]; Robotics system toolbox Reconstructing An Image from Projection Data-MATLAB & Simulink Example, , https://www.mathworks.com/products/robotics.html, [Accessed: 8-Nov-2019]; Rivera, L.J.F., Ruiz, A.Y.R., Chandrasekaran, B., A robotic control system for fault tolerance and safety using human robot interaction (2019) 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0435-0439. , January. IEEE",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/SICE International Symposium on System Integration, SII 2020","12 January 2020 through 15 January 2020",,158395,,9781728166674,,,"English","Proc. IEEE/SICE Int. Symp. Syst. Integr., SII",Conference Paper,"Final","",Scopus,2-s2.0-85082612829
"Dalmasso M., Garrell A., Jiménez P., Sanfeliu A.","57214778526;35589794700;7103171718;7003840083;","Human-Robot Collaborative Navigation Search Using Social Reward Sources",2020,"Advances in Intelligent Systems and Computing","1093 AISC",,,"84","95",,,"10.1007/978-3-030-36150-1_8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079097795&doi=10.1007%2f978-3-030-36150-1_8&partnerID=40&md5=2a9d54980e8a710d3ea2f1c51cb20042","Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Llorens Artigas 4-6, Barcelona, 08028, Spain","Dalmasso, M., Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Llorens Artigas 4-6, Barcelona, 08028, Spain; Garrell, A., Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Llorens Artigas 4-6, Barcelona, 08028, Spain; Jiménez, P., Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Llorens Artigas 4-6, Barcelona, 08028, Spain; Sanfeliu, A., Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Llorens Artigas 4-6, Barcelona, 08028, Spain","This paper proposes a Social Reward Sources (SRS) design for a Human-Robot Collaborative Navigation (HRCN) task: human-robot collaborative search. It is a flexible approach capable of handling the collaborative task, human-robot interaction and environment restrictions, all integrated on a common environment. We modelled task rewards based on unexplored area observability and isolation and evaluated the model through different levels of human-robot communication. The models are validated through quantitative evaluation against both agents’ individual performance and qualitative surveying of participants’ perception. After that, the three proposed communication levels are compared against each other using the previous metrics. © 2020, Springer Nature Switzerland AG.","Human-robot collaboration; Human-Robot Collaborative Navigation; Human-robot interaction; Motion planning; Social reward","Human robot interaction; Machine design; Man machine systems; Motion planning; Navigation; Robotics; Social robots; Collaborative navigation; Collaborative searches; Communication levels; Human-robot collaboration; Human-robot communication; Individual performance; Quantitative evaluation; Social reward; Robot programming",,,,,"H2020-INFRAIA-2017-1-two-stage-730994; Horizon 2020 Framework Programme, H2020: 730994; Agencia Estatal de Investigación, AEI: MDM-2016-0656","Acknowledgements. Work supported under projects ColRobTransp (DPI2016-78957-RAEI/FEDER EU), TERRINet (H2020-INFRAIA-2017-1-two-stage-730994) and by the Spanish State Research Agency through the Maria de Maeztu Seal of Excellence to IRI (MDM-2016-0656).",,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human-robot collaboration (2018) Auton. Robots, 42, pp. 957-975; Andersen, R.S., Madsen, O., Moeslund, T.B., Amor, H.B., Projecting robot intentions into human environments (2016) 2016 25Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 294-301. , IEEE; Brambilla, M., Ferrante, E., Birattari, M., Dorigo, M., Swarm robotics: A review from the swarm engineering perspective (2013) Swarm Intell, 7 (1), pp. 1-41; Bratman, M.E., Shared cooperative activity (1992) Philos. Rev., 101 (2), pp. 327-341; Carlson, T., Demiris, Y., Collaborative control for a robotic wheelchair: Evaluation of performance, attention, and workload (2012) IEEE Trans. Syst. Man Cybern. Part B (Cybern.), 42 (3), pp. 876-888; Chen, M., Nikolaidis, S., Soh, H., Hsu, D., Srinivasa, S., Planning with trust for human-robot collaboration (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 307-315. , ACM; Clark, H.H., Schreuder, R., Buttrick, S., Common ground at the understanding of demonstrative reference (1983) J. Verbal Learn. Verbal Behav., 22 (2), pp. 245-258; Clodic, A., Pacherie, E., Alami, R., Chatila, R., Key elements for human-robot joint action (2017) Sociality and Normativity for Robots, pp. 159-177. , Springer; Devin, S., Alami, R., An implemented theory of mind to improve human-robot shared plans execution (2016) 2016 11Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 319-326. , IEEE; Dragan, A.D., Bauman, S., Forlizzi, J., Srinivasa, S.S., Effects of robot motion on human-robot collaboration (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 51-58. , ACM; Ferrer, G., Zulueta, A.G., Cotarelo, F.H., Sanfeliu, A., Robot social-aware navigation framework to accompany people walking side-by-side (2017) Auton. Robots, 41 (4), pp. 775-793; Fong, T., Thorpe, C., Baur, C., Collaboration, dialogue, human-robot interaction (2003) Robotics Research, pp. 255-266. , Springer; Garrell, A., Garza-Elizondo, L., Villamizar, M., Herrero, F., Sanfeliu, A., Aerial social force model: A new framework to accompany people using autonomous flying robots (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017, pp. 7011-7017. , Vancouver, BC, Canada, 24–28 September 2017; Hoffman, G., Evaluating fluency in human-robot collaboration (2019) IEEE Trans. Hum.-Mach. Syst., 49, pp. 209-218; Hoffman, G., Breazeal, C., Collaboration in human-robot teams (2004) AIAA 1St Intelligent Systems Technical Conference, p. 6434; Hommel, B., Müsseler, J., Aschersleben, G., Prinz, W., The theory of event coding (TEC): A framework for perception and action planning (2001) Behav. Brain Sci., 24 (5), pp. 849-878; Jackson, D.E., Ratnieks, F.L., Communication in ants (2006) Curr. 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Syst., 4 (1), pp. 93-102; Peternel, L., Tsagarakis, N., Caldwell, D., Ajoudani, A., Robot adaptation to human physical fatigue in human-robot co-manipulation (2018) Auton. Robots, 42, pp. 1-11; Petit, M., Lallée, S., Boucher, J.D., Pointeau, G., Cheminade, P., Ognibene, D., Chinellato, E., Martinez-Hernandez, U., The coordinating role of language in real-time multimodal learning of cooperative tasks (2013) IEEE Trans. Auton. Ment. Dev., 5 (1), pp. 3-17; Roncone, A., Mangin, O., Scassellati, B., Transparent role assignment and task allocation in human robot collaboration (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 1014-1021. , IEEE; Susnea, I., Vasiliu, G., Filipescu, A., Radaschin, A., Virtual pheromones for real-time control of autonomous mobile robots (2009) Stud. Inform. 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Robot., 6 (3), pp. 357-366","Dalmasso, M.; Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Llorens Artigas 4-6, Spain; email: mdalmasso@iri.upc.edu","Silva M.F.Luis Lima J.Reis L.P.Sanfeliu A.Tardioli D.",,"Springer","4th Iberian Robotics Conference, ROBOT 2019","20 November 2019 through 22 November 2019",,236649,21945357,9783030361495,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85079097795
"Luo J., Liu C., Wang N., Yang C.","57198979642;56255340400;55446268900;57204881979;","A Method of Intention Estimation for Human-Robot Interaction",2020,"Advances in Intelligent Systems and Computing","1043",,,"69","80",,,"10.1007/978-3-030-29933-0_6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072866490&doi=10.1007%2f978-3-030-29933-0_6&partnerID=40&md5=78cdbfe24d07a9790564a85f62137e1b","South China University of Technology, Guangzhou, 510640, China; CNRS-University of Montpellier, UMR 5506, 161 Rue Ada, Montpellier, 34095, France; Bristol Robotics Laboratory, UWE, Bristol, BS16 1QY, United Kingdom","Luo, J., South China University of Technology, Guangzhou, 510640, China, CNRS-University of Montpellier, UMR 5506, 161 Rue Ada, Montpellier, 34095, France; Liu, C., CNRS-University of Montpellier, UMR 5506, 161 Rue Ada, Montpellier, 34095, France; Wang, N., Bristol Robotics Laboratory, UWE, Bristol, BS16 1QY, United Kingdom; Yang, C., Bristol Robotics Laboratory, UWE, Bristol, BS16 1QY, United Kingdom","Dynamics of human wrist play an important role in human-robot interaction. In this paper, we develop a novel method to classify the human wrist’s motion and to recognize its stiffness profile. In the proposed method, an integrated framework of linear discriminant analysis and extreme learning machine is developed to evaluate the intention of the wrist. Specifically, linear discriminant analysis is used to classify gestures of the wrist. Based on the result of classification, extreme learning method is use to construct a regression model of the stiffness. The experimental results are demonstrated the effectiveness of the proposed method. © 2020, Springer Nature Switzerland AG.","Extreme learning machine; Human-robot interaction; Intention estimation; Linear discriminant analysis","Discriminant analysis; Knowledge acquisition; Machine learning; Man machine systems; Regression analysis; Stiffness; Extreme learning machine; Integrated frameworks; Intention estimation; Learning methods; Linear discriminant analysis; Regression model; Stiffness profiles; Human robot interaction",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/S001913; National Natural Science Foundation of China, NSFC: 61811530281, 61861136009","information. This work was partially supported by National Nature Science Foundation (NSFC) under Grants 61861136009 and 61811530281 Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/S001913.",,,,,,,,,,"Hang, S., Improved human-robot collaborative control of redundant robot for teleoperated minimally invasive surgery (2019) IEEE Robot. Autom. Lett., 4 (2), pp. 1447-1453; Hang, S., Sandoval, J., Makhdoomi, M., Safety-enhanced human-robot interaction control of redundant robot for teleoperated minimally invasive surgery (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 6611-6616. , pp., IEEE; Luo, J., Enhanced teleoperation performance using hybrid control and virtual fixture (2019) Int. J. Syst. Sci., 50 (3), pp. 451-462; Luo, J., A task learning mechanism for the telerobots (2019) Int. J. 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Circuits Syst., 8 (2), pp. 155-164; Ajoudani, A., Tsagarakis, N., Bicchi, A., Tele-impedance: Teleoperation with impedance regulation using a body–machine interface (2012) Int. J. Robot. Res., 31 (13), pp. 1642-1656; Kiguchi, K., Hayashi, Y., An EMG-based control for an upper-limb power-assist exoskeleton robot (2012) IEEE Trans. Syst., Man, Cybern. Part B (Cybernetics), 42 (4), pp. 1064-1071; Han, J., Ding, Q., Xiong, A., Zhao, X., A state-space EMG model for the estimation of continuous joint movements (2015) IEEE Trans. Ind. Electron., 62 (7), pp. 4267-4275; Tang, Z., Yu, H., Cang, S., Impact of load variation on joint angle estimation from surface EMG signals (2016) IEEE Trans. Neural Syst. Rehabil. Eng., 24 (12), pp. 1342-1350; Yang, C., Haptics electromyography perception and learning enhanced intelligence for teleoperated robot (2018) IEEE Trans. Autom. Sci. Eng., 99, pp. 1-10; Jing, P., Heisterkamp, D.R., Dai, H.K., LDA/SVM driven nearest neighbor classification (2003) IEEE Trans. Neural Netw., 14 (4), pp. 940-942; Huang, G.B., Zhou, H., Ding, X., Zhang, R., Extreme learning machine for regression and multiclass classification (2012) IEEE Trans. Syst. Man Cybern. B Cybern., 42 (2), pp. 513-529","Yang, C.; Bristol Robotics Laboratory, United Kingdom; email: cyang@ieee.org","Ju Z.Zhou D.Gegov A.Yang L.Yang C.",,"Springer Verlag","19th Annual UK Workshop on Computational Intelligence, UKCI 2019","4 September 2019 through 6 September 2019",,231289,21945357,9783030299323,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85072866490
"Oertel C., Jonell P., Kontogiorgos D., Mora K.F., Odobez J.-M., Gustafson J.","50561763400;57015381900;57126340100;57226019871;57203103085;24480882100;","Towards an Engagement-Aware Attentive Artificial Listener for Multi-Party Interactions",2021,"Frontiers in Robotics and AI","8",,"555913","","",,,"10.3389/frobt.2021.555913","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110106028&doi=10.3389%2ffrobt.2021.555913&partnerID=40&md5=e9fae997e10e4ffc4970ccb1a80f0622","Department of Intelligent Systems, Interactive Intelligence, Delft University of Technology, Delft, Netherlands; Department of Intelligent Systems, Division of speech music and hearing, KTH Royal Institute of Technology, Stockholm, Sweden; Eyeware Tech SA, Martigny, Switzerland; Perception and Activity Understanding, Idiap Research Institute, Martigny, Switzerland","Oertel, C., Department of Intelligent Systems, Interactive Intelligence, Delft University of Technology, Delft, Netherlands; Jonell, P., Department of Intelligent Systems, Division of speech music and hearing, KTH Royal Institute of Technology, Stockholm, Sweden; Kontogiorgos, D., Department of Intelligent Systems, Division of speech music and hearing, KTH Royal Institute of Technology, Stockholm, Sweden; Mora, K.F., Eyeware Tech SA, Martigny, Switzerland; Odobez, J.-M., Perception and Activity Understanding, Idiap Research Institute, Martigny, Switzerland; Gustafson, J., Department of Intelligent Systems, Division of speech music and hearing, KTH Royal Institute of Technology, Stockholm, Sweden","Listening to one another is essential to human-human interaction. In fact, we humans spend a substantial part of our day listening to other people, in private as well as in work settings. Attentive listening serves the function to gather information for oneself, but at the same time, it also signals to the speaker that he/she is being heard. To deduce whether our interlocutor is listening to us, we are relying on reading his/her nonverbal cues, very much like how we also use non-verbal cues to signal our attention. Such signaling becomes more complex when we move from dyadic to multi-party interactions. Understanding how humans use nonverbal cues in a multi-party listening context not only increases our understanding of human-human communication but also aids the development of successful human-robot interactions. This paper aims to bring together previous analyses of listener behavior analyses in human-human multi-party interaction and provide novel insights into gaze patterns between the listeners in particular. We are investigating whether the gaze patterns and feedback behavior, as observed in the human-human dialogue, are also beneficial for the perception of a robot in multi-party human-robot interaction. To answer this question, we are implementing an attentive listening system that generates multi-modal listening behavior based on our human-human analysis. We are comparing our system to a baseline system that does not differentiate between different listener types in its behavior generation. We are evaluating it in terms of the participant’s perception of the robot, his behavior as well as the perception of third-party observers. © Copyright © 2021 Oertel, Jonell, Kontogiorgos, Mora, Odobez and Gustafson.","artificial listener; eye-gaze patterns; head gestures; human-robot interaction; multi-party interactions; non-verbal behaviors; social signal processing",,,,,,,,,,,,,,,,,"Admoni, H., Hayes, B., Feil-Seifer, D., Ullman, D., Scassellati, B., Are You Looking at Me? 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Papers from the sixth regional meeting of the chicago linguistic society. bibfieldeditorMary Ann Campbell et almbox, , Chicago, IL, USA, Chicago Linguistic Society; Yonezawa, T., Yamazoe, H., Utsumi, A., Abe, S., Gaze-communicative Behavior of Stuffed-Toy Robot with Joint Attention and Eye Contact Based on Ambient Gaze-Tracking (2007) Proceedings of the 9th international conference on Multimodal interfaces, pp. 140-145. , New York, NY, United States, Association for Computing Machinery","Oertel, C.; Department of Intelligent Systems, Netherlands; email: C.R.M.M.Oertel@tudelft.nl",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85110106028
"Basiri S., Taheri A., Meghdari A.F., Boroushaki M., Alemi M.","56032773200;56513500500;57236096900;6603178423;55312745000;","Dynamic Iranian Sign Language Recognition Using an Optimized Deep Neural Network: An Implementation via a Robotic-Based Architecture",2021,"International Journal of Social Robotics",,,,"","",,2,"10.1007/s12369-021-00819-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113793061&doi=10.1007%2fs12369-021-00819-0&partnerID=40&md5=bd5aa26aefbfe3c1b82ee61989ff5551","Social and Cognitive Robotics Lab, Sharif University of Technology, Tehran, Iran; Chancellor of Fereshtegaan International Branch, Islamic Azad University, Tehran, Iran; Department of Energy Engineering, Sharif University of Technology, Tehran, Iran; Faculty of Humanities, Islamic Azad University, West Tehran Branch, Tehran, Iran","Basiri, S., Social and Cognitive Robotics Lab, Sharif University of Technology, Tehran, Iran; Taheri, A., Social and Cognitive Robotics Lab, Sharif University of Technology, Tehran, Iran; Meghdari, A.F., Social and Cognitive Robotics Lab, Sharif University of Technology, Tehran, Iran, Chancellor of Fereshtegaan International Branch, Islamic Azad University, Tehran, Iran; Boroushaki, M., Department of Energy Engineering, Sharif University of Technology, Tehran, Iran; Alemi, M., Social and Cognitive Robotics Lab, Sharif University of Technology, Tehran, Iran, Faculty of Humanities, Islamic Azad University, West Tehran Branch, Tehran, Iran","Sign language is a non-verbal communication tool used by the deaf. A robust sign language recognition framework is needed to develop Human–Robot Interaction (HRI) platforms that are able to interact with humans via sign language. Iranian sign language (ISL) is composed of both static postures and dynamic gestures of the hand and fingers. In this paper, we present a robust framework using a Deep Neural Network (DNN) to recognize dynamic ISL gestures captured by motion capture gloves in Real-Time. To this end, first, a dataset of fifteen ISL classes was collected in time series; then, this dataset was virtually augmented and pre-processed using the “state-image” method to produce a unique collection of images, each image corresponding to a specific set of sequential data representing a class. Next, by implementing a continuous Genetic algorithm, an optimal deep neural network with the minimum number of weights (trainable parameters) and the maximum overall accuracy was found. Finally, the dataset was fed to the DNN to train the model. The results showed that the optimization process was successful at finding a DNN structure highly suitable for this application, with 99.7% accuracy on the verification (test) data. Then, after implementing the module in a robotic architecture, an HRI experiment was conducted to assess the system’s performance in real-time applications. Preliminary statistical analysis on the standard UTAUT model for eight participants showed that the system can recognize ISL signs quickly and accurately during human–robot interaction. The proposed methodology can be used for other sign languages as no specific characteristics of ISL were used in the preprocessing or training stage. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Children with a hearing problem; Deep Neural Network; Human–Robot interaction; Machine Learning; Pattern recognition; Sign language recognition","Agricultural robots; Deep neural networks; Genetic algorithms; Memory architecture; Network architecture; Robotics; Social robots; Continuous genetic algorithms; Motion capture gloves; Non-verbal communications; Overall accuracies; Real-time application; Robot interactions; Robotic architectures; Sign Language recognition; Neural networks",,,,,"Iran National Science Foundation, INSF: 98025100","Author Alireza Taheri has received research grants from the “Iranian National Science Foundation (INSF)” (Gant No. 98025100).","This research was supported by the “Iranian National Science Foundation (INSF)” ( http://en.insf.org/ ). We also appreciate the “Dr. AliAkbar Siassi Memorial Grant Award” for their complementary support of the Social & Cognitive Robotics Laboratory.",,,,,,,,,"O, W.H., (WHO). 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Ba, J., Kingma, D.P., Adam: A Method for Stochastic Optimization (2015) The 3Rd International Conference for Learning Representations, , presented at the, San Diego; Brownlee, J., ""Gentle Introduction to the Adam Optimization Algorithm for Deep Learning."" https://machinelearningmastery.com/adam-optimization-algorithm-for-deep-learning/ (accessed 2019–30–07, 2019); ""ML Cheatsheet "" https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html (accessed 2019–02–08; Mitchell, M., (1996) An Introduction to Genetic Algorithms, , MIT Press; Basiri, S., Taheri, A., Meghdari, A., Alemi, M., Design and implementation of a robotic architecture for adaptive teaching: a case study on iranian sign language (2021) J Intell Robot Syst, 102 (2), p. 48; Williams, M.D., Rana, N.P., Dwivedi, Y.K., The unified theory of acceptance and use of technology (UTAUT): a literature review (2015) J Enterp Inf Manag, 28 (3), pp. 443-488","Taheri, A.; Social and Cognitive Robotics Lab, Iran; email: artaheri@sharif.edu",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Article in Press","",Scopus,2-s2.0-85113793061
"Hussain I., Albalasie A., Awad M.I., Tamizi K., Niu Z., Seneviratne L., Gan D.","56730038800;56790100600;57192432184;57226881961;57218588255;7006006173;23060288900;","Design and Control of a Discrete Variable Stiffness Actuator with Instant Stiffness Switch for Safe Human-Robot Interaction",2021,"IEEE Access","9",,"9514838","118215","118231",,,"10.1109/ACCESS.2021.3105587","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113258589&doi=10.1109%2fACCESS.2021.3105587&partnerID=40&md5=6d8587a07965f510cb65960c7eba8c3e","Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Department of Mechanical and Mechatronics Engineering, Birzeit University, Birzeit, Palestine; Department of Mechanical Engineering, Palestine Polytechnic University, Hebron, Palestine; School of Engineering Technology, Purdue University, West Lafayette, IN, United States; Department of Mechanical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Healthcare Engineering Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates","Hussain, I., Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates, Department of Mechanical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Albalasie, A., Department of Mechanical and Mechatronics Engineering, Birzeit University, Birzeit, Palestine; Awad, M.I., Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates, Healthcare Engineering Innovation Center, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Tamizi, K., Department of Mechanical Engineering, Palestine Polytechnic University, Hebron, Palestine; Niu, Z., Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Seneviratne, L., Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Gan, D., School of Engineering Technology, Purdue University, West Lafayette, IN, United States","Variable Stiffness Actuators (VSA) have been proposed as an alternative actuation system for manipulators that are utilized for safe physical Human-Robot Interaction (pHRI). However, in the incidents of collision, the need of a fast response in stiffness tuning would rise to ensure safety. In this paper, we present a novel Discrete Variable Stiffness Actuator (DVSA) to be used in a compliant robotic manipulator for safe physical Human-Robot Interaction (pHRI). The novelty of this actuator lies in its design topology which allows the stiffness level to change swiftly among predefined levels without the need of complex stiffness tuning mechanism. Through this topology, three springs in parallel are connected serially between the motor and the link via gear train. The stiffness of the actuator is altered by adding/subtracting the number of involved springs, which can be realized through engagement/disengagement electromagnetic clutches on two of these spring's shafts. The working principle, and the detailed design of the actuator are illustrated. Moreover, the stiffness model and the dynamic model are presented and discussed thoroughly. In order to validate these mathematical models and achieve optimal control, system identification for the dynamic parameters was performed experimentally on the physical model. Furthermore, the system's ability of tracking desired trajectory was achieved through the implementation of different control techniques including PID (Proportional-Integral-Derivative), LQR (Linear Quadratic Regulator) and pole placement. The results show the high potential of utilizing the actuator in compliant manipulators. Moreover, DVSA is also characterized for safety in pHRI through Head-Injury Criterion (HIC). Finally, an application of DVSA in human augmentation task (Weight Bearing Task) is presented. © 2013 IEEE.","compliant manipulation; human-robot-interaction; Variable stiffness actuators","Actuators; Dynamics; Industrial manipulators; Machine design; Man machine systems; Manipulators; Proportional control systems; Springs (components); Stiffness; Topology; Tuning; Two term control systems; Compliant manipulators; Desired trajectories; Electromagnetic clutch; Head injury criterion; Linear quadratic regulator; Physical humanrobot interaction (phri); PID (proportionalintegral-derivative); Safe human-robot interaction; Social robots",,,,,"Harbin University of Science and Technology, HRBUST; Khalifa University of Science, Technology and Research, KU: RC1-2018-KUCARS, RC2-2018-022","This work was supported by Khalifa University of Science and Technology under Award RC1-2018-KUCARS and Award RC2-2018-022 (HEIC).",,,,,,,,,,"Tsagarakis, N.G., Sardellitti, I., Caldwell, D.G., A new variable stiffness actuator (CompAct-VSA): Design and modelling (2011) Proc. 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(ICRA), pp. 676-681. , May; Hussain, I., Albalasie, A., Awad, M.I., Seneviratne, L., Gan, D., Modeling, control, and numerical simulations of a novel binary-controlled variable stiffness actuator (BcVSA) (2018) Frontiers Robot. Ai, 5, p. 68. , Jun; Hussain, I., Albalasie, A., Awad, M.I., Gan, D., Modeling, identi-fication, and control of a discrete variable stiffness actuator (DVSA) (2019) Actuators, 8 (3), p. 50; Rao, S.S., Yap, F.F., (2010) Mechanical Vibrations, 5th Ed, , Upper Saddle River, NJ, USA: Pearson Education; Magalas, L.B., Malinowski, T., Measurement techniques of the logarithmic decrement (2003) Solid State Phenomena, 89, pp. 247-260. , http://www.scientific.net, Freienbach, Switzerland: Trans Tech Publications; Aribowo, W., Yamashita, T., Terashima, K., Masui, Y., Saeki, T., Kamigaki, T., Kawamura, H., Vibration control of semiconductor wafer transfer robot by building an integrated tool of parameter identification and input shaping (2011) Ifac Proc. 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Syst., 120. , Oct; She, Y., Su, H.-J., Meng, D., Lai, C., Design and modeling of a continuously tunable stiffness arm for safe physical human robot interaction (2020) J. Mech. Robot., 12 (1), pp. 1-11. , Feb; Bicchi, A., Tonietti, G., Fast and soft-arm' tactics [robot arm design] (2004) IEEE Robot. Autom. Mag., 11 (2), pp. 22-33. , Jul; Braun, D.J., Chalvet, V., Chong, T.-H., Apte, S.S., Hogan, N., Variable stiffness spring actuators for low-energy-cost human augmentation (2019) IEEE Trans. Robot., 35 (6), pp. 1435-1449. , Dec; De Luca, C.J., The use of surface electromyography in biomechanics (1997) J. Appl. Biomech., 13 (2), pp. 135-163. , May","Hussain, I.; Khalifa University Center for Autonomous Robotic Systems (KUCARS), United Arab Emirates; email: irfan.hussain@ku.ac.ae
Gan, D.; School of Engineering Technology, United States; email: dgan@purdue.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85113258589
"Park J., Lee S., Brotherton K., Um D., Park J.","36025591800;57194346630;57209464687;57207548495;56898183400;","Identification of speech characteristics to distinguish human personality of introversive and extroversive male groups",2020,"International Journal of Environmental Research and Public Health","17","6","2125","","",,4,"10.3390/ijerph17062125","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082380688&doi=10.3390%2fijerph17062125&partnerID=40&md5=e32de02f8fe07558a3f6bc4652158abc","Department of Engineering, Texas A&M University-Corpus Christi, Corpus Christi, TX  78412, United States; Department of English, Texas A&M University-Corpus Christi, Corpus Christi, TX  78412, United States; Department of Industrial & Management Engineering, Incheon National University, Incheon, 22012, South Korea","Park, J., Department of Engineering, Texas A&M University-Corpus Christi, Corpus Christi, TX  78412, United States; Lee, S., Department of English, Texas A&M University-Corpus Christi, Corpus Christi, TX  78412, United States; Brotherton, K., Department of Engineering, Texas A&M University-Corpus Christi, Corpus Christi, TX  78412, United States; Um, D., Department of Engineering, Texas A&M University-Corpus Christi, Corpus Christi, TX  78412, United States; Park, J., Department of Industrial & Management Engineering, Incheon National University, Incheon, 22012, South Korea","According to the similarity-attraction theory, humans respond more positively to people who are similar in personality. This observation also holds true between humans and robots, as shown by recent studies that examined human-robot interactions. Thus, it would be conducive for robots to be able to capture the user personality and adjust the interactional patterns accordingly. The present study is intended to identify significant speech characteristics such as sound and lexical features between the two different personality groups (introverts vs. extroverts), so that a robot can distinguish a user’s personality by observing specific speech characteristics. Twenty-four male participants took the Myers-Briggs Type Indicator (MBTI) test for personality screening. The speech data of those participants (identified as 12 introvertive males and 12 extroversive males through the MBTI test) were recorded while they were verbally responding to the eight Walk-in-the-Wood questions. After that, speech, sound, and lexical features were extracted. Averaged reaction time (1.200 s for introversive and 0.762 s for extroversive; p = 0.01) and total reaction time (9.39 s for introversive and 6.10 s for extroversive; p = 0.008) showed significant differences between the two groups. However, averaged pitch frequency, sound power, and lexical features did not show significant differences between the two groups. A binary logistic regression developed to classify two different personalities showed 70.8% of classification accuracy. Significant speech features between introversive and extroversive individuals have been identified, and a personality classification model has been developed. The identified features would be applicable for designing or programming a social robot to promote human-robot interaction by matching the robot’s behaviors toward a user’s personality estimated. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Human-robot interaction; Linguistics; Personality; Pitch; Reaction time; Sound pressure; Speech; Usability; User experience","accuracy assessment; classification; communication behavior; human behavior; male behavior; regression analysis; robotics; theoretical study; adult; article; clinical article; controlled study; extraversion; human; human experiment; linguistics; male; Myers-Briggs Type Indicator; pitch; reaction time; robotics; sound pressure; speech; personality test; Extraversion, Psychological; Humans; Introversion, Psychological; Male; Personality Inventory; Robotics; Speech",,,,,"Incheon National University, INU: 20190106","Acknowledgments: This work was supported by Research Grant in 2019 (Grant No.: 20190106).","This work was supported by Incheon National University (International Cooperative) Research Grant in 2019 (Grant No.: 20190106).",,,,,,,,,"Matz, S.C., Segalin, C., Stillwell, D., Müller, S.R., Bos, M.W., Predicting the personal appeal of marketing images using computational methods (2019) J. Consum. Psychol., 29, pp. 370-390; Grice, H.P., Logic and conversation (1975) In Speech Acts, Academic Press, pp. 41-58. , New York, NY, USA; Sacks, H., Schegloff, E., Jefferson, G., A simplest systematics for the organization of turn-taking in conversation (1974) Language, 50, pp. 696-735; Labov, W., (1972) Sociolinguistic Patterns, , University of Pennsylvania Press: Philadelphia, PA, USA; Bell, A., Language style as audience design (1984) Lang. Soc., 13, pp. 145-204; Giles, H., Social psychology and applied linguistics (1973) ITL Rev. Appl. Linguist., 33, pp. 27-42; Schilling-Estes, N., Constructing ethnicity in interaction (2004) J. 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Int., 5, pp. 341-345; Brotherton, K., Park, J., Um, D., Mehrubeoglu, M., Identification of the relationships between a vocal attribute and a personality (2019) Proceedings of the International Conference on Applied Human Factors and Ergonomics, pp. 163-167. , Washington, DC, USA, 24–28 July; Stahl, J., Rammsayer, T., Extroversion-related differences in speed of premotor and motor processing as revealed by lateralized readiness potentials (2008) J. Mot. Behav., 40, pp. 143-154; Brebner, J., Cooper, C., The effect of a low rate of regular signals upon the reaction times of introverts and extraverts (1974) J. Res. Pers., 8","Park, J.; Department of Industrial & Management Engineering, South Korea; email: jaehpark@inu.ac.kr",,,"MDPI AG",,,,,16617827,,,"32210035","English","Int. J. Environ. Res. Public Health",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85082380688
"Liu J., Wang M., Wang P., Hou F., Meng C., Hashimoto K., Guo S.","37034409100;57215321364;57213430555;57216506514;25723467600;57195605981;7403650479;","Cost-Efficient Flexible Supercapacitive Tactile Sensor with Superior Sensitivity and High Spatial Resolution for Human-Robot Interaction",2020,"IEEE Access","8",,"9051698","64836","64845",,,"10.1109/ACCESS.2020.2984511","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083706673&doi=10.1109%2fACCESS.2020.2984511&partnerID=40&md5=aa4905b2e9f43ea8abe210d1808f180f","State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China; Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300401, China; School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China; Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China","Liu, J., State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China, Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300401, China, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China; Wang, M., State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China, Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300401, China, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China; Wang, P., State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China, Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300401, China, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China; Hou, F., State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China, Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300401, China, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China; Meng, C., State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China, Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300401, China, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China; Hashimoto, K., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China; Guo, S., State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China, Hebei Key Laboratory of Robot Perception and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300401, China, School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300401, China, Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China","Tactile sensing is crucial for the safety, accuracy and robustness of the human-robot interactions in the fields of wearable equipment, service robots and healthcare robots. Although many efforts have been made, it still requires much work to develop functional and reliable flexible tactile sensors with superior sensitivity, wide measurement range, high spatial resolution and low cost based on simple structures and easy fabrication. Here, this paper introduces a flexible supercapacitive tactile sensor with outstanding and balanced performance. The tactile sensor contains two layers of flexible electrodes and a layer of ionic-gel coated microfiber matrix to form a supercapacitive sensing structure. The flexible electrodes and the ionic microfiber matrix are processed with scalable techniques such as screen printing and gel-coating, which guarantee the ultra-flexibility and low fabrication cost. The experimental data suggests strong linearity in the pressure-capacitance relationship and high sensitivity (135.9 nF $\cdot $ kPa $^{-1}\cdot $ cm2 or 27.11 kPa-1). Wide pressure measurement range (from 0 kPa to 1200 kPa) is also achieved by balancing structure parameters. Dynamic responses of the tactile sensors could accurately reflect the applied pressure cycles from human-finger tapping and machine pressing. The tactile sensor can map the pressure distribution with a high spatial resolution (&gt;2 points $\cdot $ mm-2) when connected with the specially designed electric circuitry. The spatial resolution from sub-mm to large area makes it promising for various sensing applications in human-robot interactions, from finger touch to body contact. The developed tactile sensor in this study owns superior applicability and universality which makes it a trustworthy candidate to benefit various applications in robotics, flexible electronics and bioengineered equipment. © 2013 IEEE.","Human-robot interaction; supercapacitive; tactile sensor","Balancing; Electrodes; Flexible electronics; Image resolution; Man machine systems; Microfibers; Screen printing; Easy fabrication; Flexible electrodes; Flexible tactile sensors; High spatial resolution; Sensing applications; Simple structures; Spatial resolution; Structure parameter; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 51605136; Natural Science Foundation of Hebei Province: E2017202248, E2017202270; National Key Research and Development Program of China, NKRDPC: 2016YFE0128700; Hebei Provincial Key Research Projects: 19211817D","This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 51605136, in part by the National Key Research and Development Plan under Grant 2016YFE0128700, in part by the Natural Science Foundation of Hebei Province under Grant E2017202248 and Grant E2017202270, and in part by the Key Research and Development Plan of Hebei Province under Grant 19211817D.",,,,,,,,,,"Dahiya, R.S., Metta, G., Valle, M., Sandini, G., Tactile sensing_ from humans to humanoids (2010) Ieee Trans. 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"Sanjeevi N.S.S., Vashista V.","57197797888;53980678300;","Stiffness modulation of a cable-driven leg exoskeleton for effective human-robot interaction",2021,"Robotica","39","12",,"2172","2192",,1,"10.1017/S0263574721000242","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105220613&doi=10.1017%2fS0263574721000242&partnerID=40&md5=fa2e1e71f7daf20df8859fdc0df9f797","Human Centered Robotics Lab, IIT Gandhinagar, Ahmedabad, India","Sanjeevi, N.S.S., Human Centered Robotics Lab, IIT Gandhinagar, Ahmedabad, India; Vashista, V., Human Centered Robotics Lab, IIT Gandhinagar, Ahmedabad, India","With the widespread development of leg exoskeletons to provide external force-based repetitive training for gait rehabilitation, the prospect of undesired movement adaptation due to applied forces and imposed constraints require adequate investigation. A cable-driven leg exoskeleton, CDLE, presents a lightweight, flexible, and redundantly actuated architecture that enables the possibility of system parameters modulation to alter human-robot interaction while applying the desired forces. In this work, multi-joint stiffness performance of CDLE is formulated to systematically analyze human-CDLE interaction. Further, potential alterations in CDLE architecture are presented to tune human-CDLE interaction that favors the desired human leg movement during a gait rehabilitation paradigm. © 2021 The Author(s). Published by Cambridge University Press.","Cable-driven robots; Exoskeleton; Gait rehabilitation; Human-Robot interaction; Stiffness modulation","Agricultural robots; Cables; Exoskeleton (Robotics); Man machine systems; Modulation; Patient rehabilitation; Stiffness; Applied forces; External force; Gait rehabilitation; Leg exoskeleton; Leg movement; Redundantly actuated; Repetitive training; Stiffness modulation; Social robots",,,,,"Department of Science and Technology, Ministry of Science and Technology, India, डीएसटी; Science and Engineering Research Board, SERB","This work is partly supported from Inspire Faculty Award, DST, India, and from Early Career Research Award, SERB, India.",,,,,,,,,,"Das, S.K., Who steps stroke surveillance system: Feasibility in India (2009) Indian J. Med. Res., 130 (4), pp. 359-361; Olney, S.J., Richards, C., Hemiparetic gait following stroke. 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Res., 19 (9), pp. 835-847","Vashista, V.; Human Centered Robotics Lab, India; email: vineet.vashista@iitgn.ac.in",,,"Cambridge University Press",,,,,02635747,,ROBOD,,"English","Robotica",Article,"Final","",Scopus,2-s2.0-85105220613
"Vyas Y., Allenspach M., Lanegger C., Siegwart R., Tognon M.","57247982900;57193098989;57216322074;57337917400;56742951400;","Modelling and Estimation of Human Walking Gait for Physical Human-Robot Interaction",2021,"AIRPHARO 2021 - 1st AIRPHARO Workshop on Aerial Robotic Systems Physically Interacting with the Environment",,,,"","",,,"10.1109/AIRPHARO52252.2021.9571032","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119087667&doi=10.1109%2fAIRPHARO52252.2021.9571032&partnerID=40&md5=c14f83f70b539c5ffd836aa91bb6fed6","ETH Zurich, Autonomous Systems Lab8092, Switzerland","Vyas, Y., ETH Zurich, Autonomous Systems Lab8092, Switzerland; Allenspach, M., ETH Zurich, Autonomous Systems Lab8092, Switzerland; Lanegger, C., ETH Zurich, Autonomous Systems Lab8092, Switzerland; Siegwart, R., ETH Zurich, Autonomous Systems Lab8092, Switzerland; Tognon, M., ETH Zurich, Autonomous Systems Lab8092, Switzerland","An approach to model and estimate human walking kinematics in real-time for Physical Human-Robot Interaction is presented. The human gait velocity along the forward and vertical direction of motion is modelled according to the Yoyo-model. We designed an Extended Kalman Filter (EKF) algorithm to estimate the frequency, bias and trigonometric state of a biased sinusoidal signal, from which the kinematic parameters of the Yoyo-model can be extracted. Quality and robustness of the estimation are improved by opportune filtering based on heuristics. The approach is successfully evaluated on a real dataset of walking humans, including complex trajectories and changing step frequency over time. © 2021 IEEE.",,"Extended Kalman filters; Human robot interaction; Man machine systems; Biased sinusoidal signals; Direction of motion; Extended Kalman filter algorithm; Frequency bias; Human gait; Human walking; Physical humanrobot interaction (phri); Real- time; Vertical direction; Walking gait; Kinematics",,,,,"Eidgenössische Technische Hochschule Zürich, ETH: SEED-04 20-2","This work was supported by ETH Career Seed Grant SEED-04 20-2 (AEROGUIDE)",,,,,,,,,,"Lasota, P.A., Fong, T., Shah, J.A., (2017) A Survey of Methods for Safe Human-robot Interaction, , Now Publishers; Losey, D.P., McDonald, C.G., Battaglia, E., O'Malley, M.K., A review of intent detection, arbitration, and communication aspects of shared control for physical human-robot interaction (2018) Applied Mechanics Reviews, 70 (1); Brescianini, D., Jung, J.-Y., Jang, I.-H., Park, H.S., Riener, R., Ins/ekf-based stride length, height and direction intent detection for walking assistance robots (2011) 2011 IEEE International Conference on Rehabilitation Robotics. IEEE, pp. 1-5; Li, Y., Ge, S.S., Human-robot collaboration based on motion intention estimation (2013) IEEE/ASME Transactions on Mechatronics, 19 (3), pp. 1007-1014; Pehlivan, A.U., Losey, D.P., O'Malley, M.K., Minimal assistas-needed controller for upper limb robotic rehabilitation (2015) IEEE Transactions on Robotics, 32 (1), pp. 113-124; Li, Z., Huang, B., Ye, Z., Deng, M., Yang, C., Physical human-robot interaction of a robotic exoskeleton by admittance control (2018) IEEE Transactions on Industrial Electronics, 65 (12), pp. 9614-9624; Acharya, U., Bevins, A., Duncan, B.A., Investigation of humanrobot comfort with a small unmanned aerial vehicle compared to a ground robot (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)., pp. 2758-2765; Augugliaro, F., D'Andrea, R., Admittance control for physical human-quadrocopter interaction (2013) 2013 European Control Conference (ECC), pp. 1805-1810; Tognon, M., Alami, R., Siciliano, B., Physical human-robot interaction with a tethered aerial vehicle: Application to a force-based human guiding problem (2021) IEEE Transactions on Robotics, 37 (3), pp. 723-734; Carpentier, J., Benallegue, M., Laumond, J.-P., On the centre of mass motion in human walking (2017) International Journal of Automation and Computing, 14 (5), pp. 542-551; Yazdanian, M., Mojiri, M., Sheikholeslam, F., An extended kalman filter for identification of biased sinusoidal signals (2012) 20th Iranian Conference on Electrical Engineering (ICEE2012)., pp. 990-993",,,,"Institute of Electrical and Electronics Engineers Inc.","1st AIRPHARO Workshop on Aerial Robotic Systems Physically Interacting with the Environment, AIRPHARO 2021","4 October 2021 through 5 October 2021",,173180,,9781665433891,,,"English","AIRPHARO - AIRPHARO Workshop Aer. Robot. Syst. Phys. Interact. Environ.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85119087667
"Qin J., Ashley J., Xie B.","53980312700;57224499052;36462894100;","Hand Gesture Recognition Based on a Nonconvex Regularization",2021,"2021 IEEE International Conference on Mechatronics and Automation, ICMA 2021",,,,"187","192",,,"10.1109/ICMA52036.2021.9512752","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115176782&doi=10.1109%2fICMA52036.2021.9512752&partnerID=40&md5=616a8c313692a5fb09e1f66d400aa70f","University of Kentucky, Department of Mathematics, Lexington, KY  40506, United States; University of Kentucky, Department of Electrical and Computer Engineering, Lexington, KY  40506, United States","Qin, J., University of Kentucky, Department of Mathematics, Lexington, KY  40506, United States; Ashley, J., University of Kentucky, Department of Electrical and Computer Engineering, Lexington, KY  40506, United States; Xie, B., University of Kentucky, Department of Electrical and Computer Engineering, Lexington, KY  40506, United States","Recognition of hand gestures is one of the most fundamental tasks in human-robot interaction. Sparse representation based methods have been widely used due to their efficiency and low demands on the training data. Recently, nonconvex regularization techniques including the l1-2 regularization have been proposed in the image processing community to promote sparsity while achieving efficient performance. In this paper, we propose a vision-based hand gesture recognition model based on the l1-2 regularization, which is solved by the alternating direction method of multipliers (ADMM). Numerical experiments on binary and gray-scale data sets have demonstrated the effectiveness of this method in identifying hand gestures. © 2021 IEEE.","alternating direction method of multipliers; Hand gesture recognition; human-robot interaction; nonconvex regularization; sparsity","Numerical methods; Palmprint recognition; Social robots; Alternating direction method of multipliers; Hand gesture; Hand-gesture recognition; Numerical experiments; Regularization technique; Sparse representation; Training data; Vision based; Gesture recognition",,,,,"National Science Foundation, NSF: DMS-1941197","ACKNOWLEDGMENTS The research of Qin is supported by the NSF grant DMS-1941197 and the research of Ashley and Xie is supported by Woodrow W. Everett, Jr. SCEEE Development Fund in cooperation with the Southeastern Association of Electrical Engineering Department Heads.",,,,,,,,,,"Aly, A., Tapus, A., A model for synthesizing a combined verbal and nonverbal behavior based on personality traits in human-robot interaction (2013) 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 325-332; Wu, M., Su, W., Chen, L., Liu, Z., Cao, W., Hirota, K., Weightadapted convolution neural network for facial expression recognition in human-robot interaction (2019) IEEE Transactions on Systems, Man, and Cybernetics: Systems; Chang, J.-Y., Tejero-De Pablos, A., Harada, T., Improved optical flow for gesture-based human-robot interaction (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 7983-7989; Patompak, P., Jeong, S., Nilkhamhang, I., Chong, N.Y., Learning proxemics for personalized human-robot social interaction (2019) Interna-tional Journal of Social Robotics, pp. 1-14; Saran, A., Majumdar, S., Short, E.S., Thomaz, A., Niekum, S., Human gaze following for human-robot interaction (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8615-8621; Barattini, P., Morand, C., Robertson, N.M., A proposed gesture set for the control of industrial collaborative robots (2012) 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication, pp. 132-137; Qiu, Q., Jiang, Z., Chellappa, R., Sparse dictionary-based representation and recognition of action attributes (2011) 2011 International Conference on Computer Vision. 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IEEE, pp. 779-783; Miao, W., Li, G., Sun, Y., Jiang, G., Kong, J., Liu, H., Gesture recognition based on sparse representation (2016) International Journal of Wireless and Mobile Computing, 11 (4), pp. 348-356; William, M.R., Freeman, T., Orientation histograms for hand gesture recognition (1994) MERL-Mitsubishi Electric Research Laboratories, , https://www.merl.com/publications/TR94-03/, Cambridge, MA 02139 Tech. Rep. TR94-03 Dec; Ojala, T., Pietikäinen, M., Harwood, D., A comparative study of texture measures with classification based on featured distributions (1996) Pattern Recognition, 29 (1), pp. 51-59; Ghorbani, M., Targhi, A.T., Dehshibi, M.M., HOG and LBP: Towards a robust face recognition system (2015) 2015 Tenth International Conference on Digital Information Management (ICDIM). IEEE, pp. 138-141; Goyal, S., (2020) Hand Gesture Recognition Database, , https://www.kaggle.com/ds/625998; Alhindi, T.J., Kalra, S., Ng, K.H., Afrin, A., Tizhoosh, H.R., Comparing LBP, HOG and deep features for classification of histopathology images (2018) 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-7; Hoang, V.T., HGM-4: A new multi-cameras dataset for hand gesture recognition (2020) Data in Brief, 30, p. 105676; Candes, E.J., Wakin, M.B., Boyd, S.P., Enhancing sparsity by reweighted ?1 minimization (2008) Journal of Fourier Analysis and Applications, 14 (5-6), pp. 877-905; Guo, W., Lou, Y., Qin, J., Yan, M., A novel regularization based on the error function for sparse recovery (2021) Journal of Scientific Computing, 87 (1), pp. 1-22",,,,"Institute of Electrical and Electronics Engineers Inc.","18th IEEE International Conference on Mechatronics and Automation, ICMA 2021","8 August 2021 through 11 August 2021",,171466,,9781665441001,,,"English","IEEE Int. Conf. Mechatronics Autom., ICMA",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85115176782
"Bennett C.C.","57221704213;","Evoking an intentional stance during human-agent social interaction: Appearances can be deceiving",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"362","368",,,"10.1109/RO-MAN50785.2021.9515420","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115110644&doi=10.1109%2fRO-MAN50785.2021.9515420&partnerID=40&md5=dc15e2489219418ca30869454b2459db","Hanyang University, Department of Intelligence Computing, Seoul, 04763, South Korea","Bennett, C.C., Hanyang University, Department of Intelligence Computing, Seoul, 04763, South Korea","A critical issue during human-agent and human-robot interaction is eliciting an intentional stance in the human interactor, whereas the human perceives the agent as a fully ""intelligent""being with full agency towards their own intentions and desires. Eliciting such a stance, however, has proven elusive, despite work in cognitive science, robotics, and human-computer interaction over the past half-century. Here, we argue for a paradigm shift in our approach to this problem, based on a synthesis of recent evidence from social robotics and digital avatars. In short, in order to trigger an intentional stance in humans, perhaps our artificial agents need to adopt one about themselves. © 2021 IEEE.",,"Agricultural robots; Human computer interaction; Robotics; Artificial agents; Cognitive science; Critical issues; Human agent; Paradigm shifts; Social robotics; Social robots",,,,,"Hanyang University, HYU: HY-2020; National Research Foundation of Korea, NRF: 04763, 2021R1G1A1003801","This work was supported by the research fund of Hanyang University (HY-2020), as well as a grant from the National Research Foundation of Korea (NRF grant# 2021R1G1A1003801). We would also like to thank our various collaborators over the years who have contributed to different aspects of this work.","*This research was supported by the research fund of Hanyang University (HY-2020), as well as a grant from the National Research Foundation of Korea (NRF grant# 2021R1G1A1003801) 1Casey C. Bennett is with the Department of Intelligence Computing, Hanyang University, Seoul Korea 04763 (email: cabennet@hanyang.ac.kr)",,,,,,,,,"Dennett, D.C., Intentional systems (1971) The Journal of Philosophy, 68 (4), pp. 87-106; Ghiglino, D., Wykowska, A., When robots (pretend to) think (2020) Artificial Intelligence: Reflections in Philosophy, Theology, and the Social Sciences, pp. 49-74. , Brill; Becker-Asano, C., Ishiguro, H., Evaluating facial displays of emotion for the android robot geminoid f (2011) IEEE Workshop on Affective Computational Intelligence (WACI), pp. 1-8; Yu, C.-E., Ngan, H.F.B., The power of head tilts: Gender and cultural differences of perceived human vs human-like robot smile in service (2019) Tourism Review, 74 (3), pp. 428-442; Faraj, Z., Selamet, M., Morales, C., Torres, P., Hossain, M., Lipson, H., Facially expressive humanoid robotic face (2020) HardwareX, p. 00117; Bennett, C.C., Sabanovíc, S., Deriving minimal features for human-like facial expressions in robotic faces (2015) International Journal of Social Robotics, 6 (3), pp. 367-381; Brinck, I., Balkenius, C., Mutual recognition in human-robot interaction: A deflationary account (2020) Philosophy & Technology, 33 (1), pp. 53-70; Rauchbauer, B., Nazarian, B., Bourhis, M., Ochs, M., Prévot, L., Chaminade, T., Brain activity during reciprocal social interaction investigated using conversational robots as control condition (2019) Philosophical Transactions of the Royal Society B, 1771, p. 20180033; Völkel, S.T., Schneegass, C., Eiband, M., Buschek, D., What is ""intelligent"" in intelligent user interfaces? 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D. dissertation, Indiana University; Terada, K., Shamoto, T., Ito, A., Human goal attribution toward behavior of artifacts (2008) 17th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN), pp. 160-165; Bennett, C.C., Sabanovic, S., Piatt, J.A., Nagata, S., Eldridge, L., Randall, N., A robot a day keeps the blues away (2017) 2017 IEEE International Conference on Healthcare Informatics (ICHI), pp. 536-540; Liang, A., Piroth, I., Robinson, H., MacDonald, B., Fisher, M., Nater, U.M., Skoluda, N., Broadbent, E., A pilot randomized trial of a companion robot for people with dementia living in the community (2017) Journal of the American Medical Directors Association, 18 (10), pp. 71-878; Sabanovic, S., Chang, W.-L., Bennett, C.C., Piatt, J.A., Hakken, D., A robot of my own: Participatory design of socially assistive robots for independently living older adults diagnosed with depression (2015) International Conference on Human Aspects of IT for the Aged Population, pp. 104-114; Randall, N., Bennett, C.C., Sabanovíc, S., Nagata, S., Eldridge, L., Collins, S., Piatt, J.A., More than just friends: In-home use and design recommendations for sensing socially assistive robots (sars) by older adults with depression (2019) Paladyn Journal of Behavioral Robotics, 10 (1), pp. 237-255; Zulueta, J., Piscitello, A., Rasic, M., Easter, R., Babu, P., Langenecker, S.A., McInnis, M., Leow, A., Predicting mood disturbance severity with mobile phone keystroke metadata: A biaffect digital phenotyping study (2018) Journal of Medical Internet Research, 20 (7), p. 241; Vesel, C., Rashidisabet, H., Zulueta, J., Stange, J.P., Duffecy, J., Hussain, F., Piscitello, A., Leow, A., Effects of mood and aging on keystroke dynamics metadata and their diurnal patterns in a large open-science sample: A biaffect ios study (2020) Journal of the American Medical Informatics Association, 27 (7), pp. 1007-1018; Intille, S., Haynes, C., Maniar, D., Ponnada, A., Manjourides, J., Uema: Microinteraction-based ecological momentary assessment (ema) using a smartwatch (2016) ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 1124-1128; Cao, B., Zheng, L., Zhang, C., Yu, P.S., Piscitello, A., Zulueta, J., Ajilore, O., Leow, A.D., Mutual recognition in humanrobot interaction: A deflationary account (2017) 23rd ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 747-755; Oh, C.S., Bailenson, J.N., Welch, G.F., A systematic review of social presence: Definition, antecedents, and implications (2018) Frontiers in Robotics and AI, 5, p. 114; Puica, M.-A., Florea, A.-M., Emotional belief-desire-intention agent model: Previous work and proposed architecture (2013) International Journal of Advanced Research in Artificial Intelligence, 2 (2), pp. 1-8; Schellen, E., Wykowska, A., Intentional mindset toward robots-open questions and methodological challenges (2019) Frontiers in Robotics and AI, 5, p. 139; Brooks, R.A., Intelligence without reason (1991) 12th International Joint Conference on Artificial Intelligence (IJCAI), pp. 569-595; Maes, P., Artificial life meets entertainment: Lifelike autonomous agents (1995) Communications of the ACM, 38 (11), pp. 108-114; Gordon, G., Infant-inspired intrinsically motivated curious robots (2020) Current Opinion in Behavioral Sciences, 35, pp. 28-34; Rolf, M., Crook, N.T., What if: Robots create novel goals? ethics based on social value systems (2016) EDIA Workshop at the European Conference on Artificial Intelligence (ECAI), pp. 20-25; Searle, J., Minds, brains and program (1971) Behavioral and Brain Science, 3, pp. 417-424; Preston, J., Bishop, M., (2002) Views into the Chinese Room: New Essays on Searle and Artificial Intelligence, , Oxford, England: Oxford University Press on Demand; Turner, R.S., (2014) Eye's Mind: Vision and the Helmholtz-Hering Controversy, , Princeton, NJ, USA: Princeton University Press; Fuchs, T., Fragmented selves: Temporality and identity in borderline personality disorder (2007) Psychopathology, 40 (6), pp. 379-387; Paret, C., Jennen-Steinmetz, C., Schmahl, C., Disadvantageous decision-making in borderline personality disorder: Partial support from a meta-analytic review (2017) Neuroscience & Biobehavioral Reviews, 72, pp. 301-309; Olier, J.S., Barakova, E., Regazzoni, C., Rauterberg, M., Reframing the characteristics of concepts and their relation to learning and cognition in artificial agents (2017) Cognitive Systems Research, 44, pp. 50-68; Dexter, J.P., Prabakaran, S., Gunawardena, J., A complex hierarchy of avoidance behaviors in a single-cell eukaryote (2019) Current Biology, 29 (24), pp. 4323-4329; Bartneck, C., Kulíc, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1, pp. 71-81; Shieber, S.M., (2004) The Turing Test: Verbal Behavior As the Hallmark of Intelligence, , Cambridge, MA, USA: Mit Press; Jesso, S.T., Kennedy, W.G., Wiese, E., Behavioral cues of humanness in complex environments: How people engage with human and artificially intelligent agents in a multiplayer videogame (2020) Frontiers in Robotics and AI, 7; Suh, J.Y., Bennett, C.C., Weiss, B., Yoon, E., Jeong, J., Chae, Y., Development of speech dialogue systems for social ai in cooperative game evironments (2021) IEEE Region 10 Symposium (TENSYMP 2021); Sabanovic, S., Bennett, C.C., Chang, W.-L., Huber, L., Paro robot affects diverse interaction modalities in group sensory therapy for older adults with dementia (2013) 13th IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 1-6; German, T.P., Johnson, S.C., Function and the origins of the design stance (2002) Journal of Cognition and Development, 3 (3), pp. 279-300; Chen, Y., Shen, C., Performance analysis of smartphone-sensor behavior for human activity recognition (2017) IEEE Access, 5, pp. 3095-3110; Ogbuabor, G., La, R., Human activity recognition for healthcare using smartphones (2018) International Conference on Machine Learning and Computing, pp. 41-46; Wiese, E., Metta, G., Wykowska, A., Robots as intentional agents: Using neuroscientific methods to make robots appear more social (2017) Frontiers in Psychology, 8, p. 1663; Shiffman, S., Stone, A.A., Hufford, M.R., Cological momentary assessment (2008) Annual Review of Clinical Psychology, 4, pp. 1-32; Liu, Y., Nie, L., Liu, L., Rosenblum, D.S., A pilot randomized trial of a companion robot for people with dementia living in the community (2016) Neurocomputing, 181, pp. 108-115; Lindsay, S., Jackson, D., Schofield, G., Olivier, P., Engaging older people using participatory design (2012) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 1199-1208; Froese, T., Gallagher, S., Getting interaction theory (it) together: Integrating developmental, phenomenological, enactive, and dynamical approaches to social interaction (2012) Interaction Studie, 13 (3), pp. 436-468; Beer, R.D., A dynamical systems perspective on agent-environment interaction (1995) Artificial Intelligence, 72 (1-2), pp. 173-215; Breazeal, C., Emotion and sociable humanoid robots (2003) International Journal on Human Computer Studies, 59 (1-2), pp. 119-155; Doering, M., Liu, P., Glas, D.F., Kanda, T., Kulíc, D., Ishiguro, H., Curiosity did not kill the robot: A curiosity-based learning system for a shopkeeper robot (2019) ACM Transactions on Human-Robot Interaction, 8 (3), pp. 1-24; Girdhar, Y., Dudek, G., Modeling curiosity in a mobile robot for long-term autonomous exploration and monitoring (2016) Autonomous Robots, 40 (7), pp. 1267-1278","Bennett, C.C.; Hanyang University, South Korea; email: cabennet@hanyang.ac.kr",,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115110644
"Du Q., Zhang T., Yang G., Chen C.-Y., Wang W., Zhang C.","57268716500;57268859400;7405752170;55597280800;35241350900;56428199200;","A Review of Powered Backdrivability of Robot Actuators for Human-Robot Interaction",2021,"Proceedings of the 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021",,,,"1115","1120",,,"10.1109/ICIEA51954.2021.9516160","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115441877&doi=10.1109%2fICIEA51954.2021.9516160&partnerID=40&md5=d43dbbafd8aaa8fa8e176f9c72026084","Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China; Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China; University of Chinese Academy of Sciences, Beijing, China","Du, Q., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China, University of Chinese Academy of Sciences, Beijing, China; Zhang, T., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China, University of Chinese Academy of Sciences, Beijing, China; Yang, G., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China; Chen, C.-Y., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China; Wang, W., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China; Zhang, C., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China","To achieve effective and safe Human-Robot Interaction (HRI), much attention is paid on the backdrivability of robot actuators. According to the realization approaches, the backdrivability of actuators are classified into two categories, i.e., powered and unpowered backdrivabilities. This paper focuses on the powered backdrivability realized through controlling actuator reversely based on the sensor feedback. A thorough literature review is conducted to obtain an in-depth understanding about the research advances on powered backdrivability of actuators. The definition, classification and evaluation methods of the actuator backdrivability are discussed firstly. Powered backdrivabilities with force sensors and encoders are reviewed. The research direction of powered backdrivabilities is proposed based on the discussion. © 2021 IEEE.","human-robot interaction; powered backdrivability; robot actuator","Human robot interaction; Man machine systems; Backdrivability; Classifieds; Humans-robot interactions; In-depth understanding; Literature reviews; Powered backdrivability; Realization approaches; Robot actuators; Safe human-robot interaction; Sensor feedback; Actuators",,,,,"National Natural Science Foundation of China, NSFC: 2018B10058, 92048201; National Key Research and Development Program of China, NKRDPC: 2018YFB1308900","This work was supported in part by National Key R&D Program of China (Grant No.2018YFB1308900), in part by the National Natural Science Foundation of China (Grant No.92048201) and in part by the Major Special Projects of the Plan “Science and Technology Innovation 2025” in Ningbo (Grant No.2018B10058)",,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schaffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human-robot collaboration (2018) Autonomous Robots, 42 (5), pp. 957-975; Robla-Gomez, S., Becerra, V.M., Llata, J.R., Gonzalez-Sarabia, E., Perez-Oria, J., Working together: A review on safe human-robot collaboration in industrial environments (2017) IEEE Access, PP (99), p. 1; Kaminaga, H., Odanaka, K., Ando, Y., Otsuki, S., Nakamura, Y., Evaluations on contribution of backdrivability and force measurement performance on force sensitivity of actuators Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference On, Conference Proceedings; Xuan, W.L., Zhou, L.L., Li, Y.B., Chai, H., Wang, H.Y., (2017) Controller Design and Experimental Validation Ofa Robot Joint with Active Compliance, Ser. 2017 Chinese Automation Congress, , New York: Ieee; Tadele, T.S., Vries T, D., Stramigioli, S., The safety of domestic robotics: A survey of various safety-related publications (2014) IEEE Robotics & Automation Magazine, 21 (3), pp. 134-142; Kaminaga, H., Amari, T., Katayama, Y., Ono, J., Shimoyama, Y., Nakamura, Y., Backdrivability analysis of electro-hydrostatic actuator and series dissipative actuation model 2010 IEEE International Conference on Robotics and Automation. 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IEEE Industrial Electronics Society. New York: Ieee, pp. 6429-6434; Kawazawa, M., Sakaino, S., Tsuji, T., High backdrivability control based on estimation of shaft torsion using load side angle sensor IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society, Conference Proceedings, pp. 5457-5462; Yamada, S., Fujimoto, H.J.I.J.O.I.A., (2020) Position-based High Back-drivable Control Using Load-side Encoder and Backlash, p. 20004583; Yamada, S., Fujimoto, H., Human-machine interactive control for geared mechatronic systems by using load-side encoder Proc. The 3rd IEEJ International Workshop on Sensing, Actuation, Motion Control, and Optimization (SAMCON), Conference Proceedings",,,,"Institute of Electrical and Electronics Engineers Inc.","16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021","1 August 2021 through 4 August 2021",,171511,,9781665422482,,,"English","Proc. IEEE Conf. Ind. Electron. Appl. ICIEA",Conference Paper,"Final","",Scopus,2-s2.0-85115441877
"Dong R., He Y., Cai D., Yamaguchi J., Kondo H., Nakagawa S., Ikuno S., Hayano S.","57195104797;57226801591;7201494376;57226796122;7402356873;35243352600;6603458244;57224984705;","Interacting with humanoid robots: Affective robot motion design with 3D squash and stretch using Japanese Jo-ha-kyu principles in Bunraku",2021,"ACM SIGGRAPH 2021 Talks, SIGGRAPH 2021",,,,"","",,,"10.1145/3450623.3464669","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112696981&doi=10.1145%2f3450623.3464669&partnerID=40&md5=4157bb122eff8a607fcdf360c6eef67a","Tokyo University of Technology, Japan; University of Tsukuba, Japan; Yamaguchi Robotics Institute, Japan; Tokyo University of Marine Science and Technology, Japan; Osaka University of Arts, Japan; Tsuru University, Japan","Dong, R., Tokyo University of Technology, Japan; He, Y., University of Tsukuba, Japan; Cai, D., University of Tsukuba, Japan; Yamaguchi, J., Yamaguchi Robotics Institute, Japan; Kondo, H., Tokyo University of Marine Science and Technology, Japan; Nakagawa, S., Osaka University of Arts, Japan; Ikuno, S., Tokyo University of Technology, Japan; Hayano, S., Tsuru University, Japan","The Bunraku puppets' affective motions are often praised as ""one of the most beautiful motions in the world""by UNESCO. We characterize 3D ""squash and stretch""motion in Bunraku puppet plays and realize them in a real life-size robot with unique mechanical structures. Our results reveal that the music tempos and the puppet movements of ""squash and stretch""follow the principle so-called ""Jo-Ha-Kyu,""which is artistic modulations in traditional Japanese performances. Our research reveals that the affective robot motion design with the 3D ""squash and stretch""and Jo-Ha-Kyu principle is one of the keys in affective human-robot interactions. © 2021 Owner/Author.","Bunraku; Human-robot interaction; Robotics; Squash and stretch","Anthropomorphic robots; Human robot interaction; Interactive computer graphics; Humanoid robot; Mechanical structures; Music tempos; Robot motion; Squash and stretches; Machine design",,,,,"Japan Society for the Promotion of Science, KAKEN: JP16H01804, JP20K12525, JP20K23352; Japan Science Society","We would like to thank YAGI Works Co., Ltd. (Hino-shi, Tokyo), Myutech35, Inc. (Hino-shi, Tokyo), and Industrial Development Division, Industry & Sports Department, Hino City (Hino-shi, Tokyo), providing robot hardware technical supports for us. This work was supported by JSPS KAKENHI Grant Number JP16H01804, JP20K12525, JP20K23352, and the Sasakawa Scientific Research Grant from The Japan Science Society.","This work was supported by JSPS KAKENHI Grant Number JP16H01804, JP20K12525, JP20K23352, and the Sasakawa Scientific Research Grant from The Japan Science Society.",,,,,,,,,"Tamba, A., (1981) The Musical Structure of No, , TOKYO: TOKAI UNIVERSITY PRESS; Yamada, C., (2017) Standards and Transformations in Narration of Tayu (Japanese), , KYOTO CITY UNIVERSITY OF THE ARTS",,,"ACM Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH)","Association for Computing Machinery, Inc","ACM SIGGRAPH 2021 Talks - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2021","9 August 2021 through 13 August 2021",,170885,,9781450383738,,,"English","ACM SIGGRAPH Talks, SIGGRAPH",Conference Paper,"Final","",Scopus,2-s2.0-85112696981
"Chuah S.H.-W., Yu J.","57192803371;57220033296;","The future of service: The power of emotion in human-robot interaction",2021,"Journal of Retailing and Consumer Services","61",,"102551","","",,9,"10.1016/j.jretconser.2021.102551","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103705475&doi=10.1016%2fj.jretconser.2021.102551&partnerID=40&md5=672de991eef721892e4c2fbfed4bd8b3","Institute of Innovation and Circular Economy & Department of Business Administration, Asia University, No. 500, Liufeng Rd., Wufeng Dist., Taichung City, 413, Taiwan; Department of Innovation and Management in Tourism, Salzburg University of Applied Sciences, Urstein Süd 1, Puch, Salzburg, A-5412, Austria","Chuah, S.H.-W., Institute of Innovation and Circular Economy & Department of Business Administration, Asia University, No. 500, Liufeng Rd., Wufeng Dist., Taichung City, 413, Taiwan; Yu, J., Department of Innovation and Management in Tourism, Salzburg University of Applied Sciences, Urstein Süd 1, Puch, Salzburg, A-5412, Austria","Astoundingly, recent technological advancements have enabled robots to display emotions. Yet, while emotional expression is valued in the field of service, understanding emotions in human-robot interaction remains underexplored. Since emotions are contagious/transmittable, this study utilised Instagram data to uncover how emotional robots influence potential consumers’ affective feelings. By employing machine learning algorithms and sentiment analysis, the findings suggest that the expressions of surprise and happiness are key to creating positive impacts on potential consumers. The cross-disciplinary nature of this study lays the groundwork for next-level social, design, and creative experiences in artificial intelligence research regarding consumer service and experience contexts. © 2021 The Authors","Consumer experience; Emotional contagion; Facial recognition; Human-robot interaction; Machine learning; Sentiment; Service robot","algorithm; artificial intelligence; automation; computer simulation; machine learning; robotics; service quality; social media",,,,,"Ministry of Science and Technology, Taiwan, MOST: 1 08-2218-E−468-003","This study was supported by Ministry of Science and Technology, Taiwan (Grant number: MOST 1 08-2218-E−468-003 ).",,,,,,,,,,"Appel, M., Izydorczyk, D., Weber, S., Mara, M., Lischetzke, T., The uncanny of mind in a machine: humanoid robots as tools, agents, and experiencers (2020) Comput. Hum. 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"Kroma A., Mazalek A.","57205078218;35581462100;","Interfacing & embodiment: ""baby tango"" dancing robot attempts to communicate",2021,"ACM International Conference Proceeding Series",,,"3466633","","",,,"10.1145/3450741.3466633","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109068842&doi=10.1145%2f3450741.3466633&partnerID=40&md5=cd3206d09a363311e63594566d250def","School of Information Technology, Carleton University, Canada; Synaesthetic Media Lab, Ryerson University, Canada","Kroma, A., School of Information Technology, Carleton University, Canada; Mazalek, A., Synaesthetic Media Lab, Ryerson University, Canada","For decades, robotic arts and dancing robots have been the focus of many research studies and artistic experiments They started in the early 1920s and began realizing some of their potential as technology advanced in the last decade. This paper presents observations on the current state of the art in dancing robots, wearable haptics, and other theoretical frameworks; then proposes a prototype ""Baby Tango""as an artistic experience, based on these observations. This experience reflects on the idea of interfacing and embodiment and their potential effects on the future of human-robot interactions by exploring new means of verbal and non-verbal communication with robots. © 2021 ACM.","dancing robots; embodiment; haptic feedback; haptic vest; haptics; interactive play; interactive robots; robotic arts; tango dance; wearables","Computer applications; Computer programming; Haptics; Non-verbal communications; Potential effects; Research studies; Robotic arts; State of the art; Theoretical framework; Social robots",,,,,,"Assem Kroma gratefully acknowledges the support of Synaesthetic Media Lab, N-CART robotics research lab and Ryerson Collabo-ratory. This project would not be possible without the exemplary guidance of Prof. Jessica Field in building the dancer robot, the technical advising of Dr. Jimmy Tran, the support and encouragement of Dr. Alex Ferworn, and the valuable feedback of the user-testers and reviewers.",,,,,,,,,,"Al-Sada, M., Jiang, K., Ranade, S., Piao, X., Höglund, T., Nakajima, T., HapticSerpent: A Wearable Haptic Feedback Robot for VR (2018) Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI EA ?18). Association for Computing Machinery, New York, NY, USA, pp. 1-6. , https://doi.org/10.1145/3170427.3188518; Blow, M., Dautenhahn, K., Appleby, A., Nehaniv, C.L., Lee, D., The Art of Designing Robot Faces: Dimensions for Human-Robot Interaction (2006) Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction (Salt Lake City, Utah, USA) (HRI ?06). 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Conf. Proc. Ser.",Conference Paper,"Final","",Scopus,2-s2.0-85109068842
"Boucher G., Laliberté T., Gosselin C.","57203313747;6602468451;57210720538;","Mechanical Design of a Low-Impedance 6-Degree-of-Freedom Displacement Sensor for Intuitive Physical Human-Robot Interaction",2021,"Journal of Mechanisms and Robotics","13","2","021002","","",,1,"10.1115/1.4049191","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099929101&doi=10.1115%2f1.4049191&partnerID=40&md5=1e77ce2537af992683594ba4a6c3f9cb","Laboratoire de Robotique, Département de Génie Mécanique, Université Laval, Québec, QC  G1V0A6, Canada","Boucher, G., Laboratoire de Robotique, Département de Génie Mécanique, Université Laval, Québec, QC  G1V0A6, Canada; Laliberté, T., Laboratoire de Robotique, Département de Génie Mécanique, Université Laval, Québec, QC  G1V0A6, Canada; Gosselin, C., Laboratoire de Robotique, Département de Génie Mécanique, Université Laval, Québec, QC  G1V0A6, Canada","This paper presents the mechanical design of a six-degree-of-freedom low-impedance displacement sensor. The sensor is mounted around a link of a serial robot and used as an interface for physical human-robot interaction. The motivation for the use of a low-impedance sensor is first discussed. The mechanical design of each of the elastic components of the sensor is then presented. The kinematic architecture of the mechanism is introduced, and the inverse and forward kinematic problems are solved. The kinematic sensitivity is then used to characterize the accuracy of the mechanism. Finally, the design of a prototype is presented and experimental results are provided. © 2021 by ASME.","mechanism design; parallel platforms; robot design","Agricultural robots; Degrees of freedom (mechanics); Inverse problems; Kinematics; Machine design; Man machine systems; Displacement sensor; Elastic components; Forward kinematics; Kinematic sensitivities; Mechanical design; Physical human-robot interactions; Serial robots; Six degree-of-freedom; Human robot interaction",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC","The authors would like to acknowledge the financial support of the Natural Sciences and Engineering Research Council of Canada (NSERC) and of the Canada Research Chair program.",,,,,,,,,,"Tadele, T. S., de Vries, T., Stramigioli, S., 1/2The Safety of Domestic Robotics: A Survey of Various Safety-Related Publications, 1/2 (2014) IEEE Robot. Autom. Mag, 21 (3), p. 134. , 1/2142; 2ger, J., Lien, T. 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J, 34 (5). , 1/2ffer, A., and; Raiola, G., Cardenas, C. A., Tadele, T. S., de Vries, T., Stramigioli, S., 1/2Development of a Safety- and Energy-Aware Impedance Controller for Collaborative Robots, 1/2 (2018) IEEE Robot. Autom. Lett, 3 (2), p. 12371. , 21244; Lecours, A., St-Onge, B. M., Gosselin, C., 1/2Variable Admittance Control of a Four-Degree-of-Freedom Intelligent Assist Device (2012) 1/2 Proceedings of the IEEE International Conference on Robotics and Automation, p. 39031. , St. Paul, MN, May 14 1/218, /23908; Eppinger, S. D., Seering, W. P., 1/2Three Dynamic Problems in Robot Force Control, 1/2 (1992) IEEE Trans. Rob. Autom, 8 (6), p. 751. , 1/2758; Fauteux, P., Lauria, M., Heintz, B., Michaud, F., 1/2Dual-Differential Rheological Actuator for High-Performance Physical Robotic Interaction, 1/2 (2010) IEEE Trans. Robot, 26 (4), p. 607. , 1/2618; Gealy, D. V., McKinley, S., Yi, B., Wu, P., Downey, P.R., Balke, G., Zhao, A., Abbeel, P., 1/2Quasi-Direct Drive for Low-Cost Compliant Robotic Manipulation (2019) 1/2 Proceedings of the IEEE International Conference on Robotics and Automation, p. 437. , Montreal, Quebec, Canada, May 20 1/224, 1/2443; Arifin, A. S., Ang, M. H., Lai, C. Y., Lim, C. W., 1/2General Framework of the Force and Compliant Motion Control for Macro Mini Manipulator (2013) 1/2 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, p. 9491. , Wollongong, Australia, July 9 1/212, IEEE, /2954; Mohammad, A. E. K., Hong, J., Wang, D., 1/2Design of a Force-Controlled End-Effector With Low-Inertia Effect for Robotic Polishing Using Macro-Mini Robot Approach, 1/2 Robot (2018) Comput. Int. Manuf, 49 (1), p. 54. , 1/265; Labrecque, P. D., Foucault, S., Abdallah, M. 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Theory, 35 (1), p. 151240; Angeles, J., (2002) Fundamentals of Robotic Mechanical Systems, , Springer, New York; Cardou, P., Bouchard, S., Gosselin, C., 1/2Kinematic-Sensitivity Indices for Dimensionally Nonhomogeneous Jacobian Matrices, 1/2 (2010) IEEE Trans. Robot, 26 (1), p. 166. , 1/2173; Labrecque, P. D., Abdallah, M., Gosselin, C., 1/2Low-Impedance Physical Human 1/2Robot Interaction Using ann Active 1/2Passive Dynamics Decoupling, 1/2 (2016) IEEE Robot. Autom. Lett, 1 (2), p. 938. , Hach 1/2, J. M., and, 1/2945","Gosselin, C.; Laboratoire de Robotique, Canada; email: clement.gosselin@gmc.ulaval.ca",,,"American Society of Mechanical Engineers (ASME)",,,,,19424302,,,,"English","J. Mech. Robot.",Article,"Final","",Scopus,2-s2.0-85099929101
"Shiomi M., Nakata A., Kanbara M., Hagita N.","8397042200;57193760265;6601908409;6701571681;","Robot Reciprocation of Hugs Increases Both Interacting Times and Self-disclosures",2021,"International Journal of Social Robotics","13","2",,"353","361",,5,"10.1007/s12369-020-00644-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082924523&doi=10.1007%2fs12369-020-00644-x&partnerID=40&md5=8dc34758c81ca93066a1f4d796b0b2b7","Interaction Science Laboratory, ATR, 2-2-2 Hikaridai, Kansai Science City, Kyoto, Japan; Nara Institute of Science and Technology, 8916-5 Takayama-Cho, Ikoma, Nara  630-0192, Japan; Norihiro Hagita Laboratories, ATR, 2-2-2 Hikaridai, Kansai Science City, Kyoto, Japan","Shiomi, M., Interaction Science Laboratory, ATR, 2-2-2 Hikaridai, Kansai Science City, Kyoto, Japan; Nakata, A., Interaction Science Laboratory, ATR, 2-2-2 Hikaridai, Kansai Science City, Kyoto, Japan, Nara Institute of Science and Technology, 8916-5 Takayama-Cho, Ikoma, Nara  630-0192, Japan; Kanbara, M., Interaction Science Laboratory, ATR, 2-2-2 Hikaridai, Kansai Science City, Kyoto, Japan, Nara Institute of Science and Technology, 8916-5 Takayama-Cho, Ikoma, Nara  630-0192, Japan; Hagita, N., Norihiro Hagita Laboratories, ATR, 2-2-2 Hikaridai, Kansai Science City, Kyoto, Japan","Physical contact like touching and hugging plays an essential role in social bonding between people by encouraging interactions and self-disclosure. However, in a human–robot interaction context, it remains unknown whether physical activity with robots provides such similar effects, even though several positive effects of touch interactions have been unveiled. Therefore, we used a hugging robot that we previously developed and experimentally investigated its physical interactions related to encouraging interactions and self-disclosure with 48 participants. Our results showed that reciprocated hugs increased the interaction times and encouraged more self-disclosure from the hugged participants than those who did not get reciprocated hugs. © 2020, Springer Nature B.V.","Hug interaction; Human–robot touch interaction; Self-disclosure","Agricultural robots; Social robots; Hug interaction; Physical activity; Physical contacts; Physical interactions; Robot interactions; Self-disclosure; Touch interaction; Human computer interaction",,,,,"Core Research for Evolutional Science and Technology, CREST: JPMJCR18A1","This work was supported by JST CREST Grant Number JPMJCR18A1, Japan.",,,,,,,,,,"Shiomi, M., Nakata, A., Kanbara, M., Hagita, N., A robot that encourages self-disclosure by hug (2017) Proceedings, 2017, pp. 324-333. , Kheddar A, Yoshida E, Ge SS, et al, Social robotics: 9th international conference, ICSR 2017, Tsukuba, Japan, November 22–24, Springer, Cham; Yun, S.-S., Kim, M., Choi, M.-T., Easy interface and control of tele-education robots (2013) Int J Social Robot, 5 (3), pp. 335-343; Shiomi, M., Kanda, T., Howley, I., Hayashi, K., Hagita, N., Can a social robot stimulate science curiosity in classrooms? 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(2019) IEEE Robot Autom Lett, 5 (1), pp. 127-134; Lucas, G.M., Gratch, J., King, A., Morency, L.-P., It’s only a computer: virtual humans increase willingness to disclose (2014) Comput Hum Behav, 37, pp. 94-100","Shiomi, M.; Interaction Science Laboratory, 2-2-2 Hikaridai, Japan; email: m-shiomi@atr.jp",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85082924523
"Luca R., Bejinariu S.-I., Costin H., Rotaru F., Petroiu G.","33867690700;7801623716;16645010800;7801419773;57223995300;","Human Activity Recognition using Inertial Data",2021,"12th International Symposium on Advanced Topics in Electrical Engineering, ATEE 2021",,,"9425112","","",,1,"10.1109/ATEE52255.2021.9425112","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106725975&doi=10.1109%2fATEE52255.2021.9425112&partnerID=40&md5=2724e36b8a1bbc186ce515d380a11c58","Institute of Computer Science, Romanian Academy Iasi Branch, Iasi, Romania; 'Gr. T. Popa' University of Medicine and Pharmacy, Faculty of Medical Bioengineering, Iasi, Romania","Luca, R., Institute of Computer Science, Romanian Academy Iasi Branch, Iasi, Romania; Bejinariu, S.-I., Institute of Computer Science, Romanian Academy Iasi Branch, Iasi, Romania; Costin, H., Institute of Computer Science, Romanian Academy Iasi Branch, Iasi, Romania; Rotaru, F., Institute of Computer Science, Romanian Academy Iasi Branch, Iasi, Romania; Petroiu, G., 'Gr. T. Popa' University of Medicine and Pharmacy, Faculty of Medical Bioengineering, Iasi, Romania","Human activity recognition is an emerging research domain due to its applications in medicine, sport, surveillance and human-robot interaction. Usually, the classification is based either on data obtained from sensors placed on the human's body or on image or video sequences analysis. In this paper is proposed a procedure based on the k-nearest neighbors, J48 and Random forests classifiers which use data acquired from the accelerometer of a wearable device. The features are extracted using the overlapping time window technique. The recognized activities are: walking, standing and going up-down stairs. The obtained results are better than those obtained in other approaches. © 2021 IEEE.","Classification; Human activity; J48; K-nearest neighbors; Random Forests","Classification (of information); Decision trees; Nearest neighbor search; Pattern recognition; Wearable technology; Human activity recognition; ITS applications; K-nearest neighbors; Research domains; Time windows; Video sequences; Wearable devices; Social robots",,,,,,,,,,,,,,,,"Bejinariu, S.-I., Costin, H., Rotaru, F., Luca, R., Lazar, C., Deep learning based human locomotion recognition in video sequences (2020) The 8th IEEE International Conference on E-Health and Bioengineering-EHB 2020, , Web Conference, Romania, October 29-30; Ji, Y., Yang, Y., Shen, F., Shen, H.T., Li, X., A survey of human action analysis in hri applications (2020) IEEE Transactions on Circuits and Systems for Video Technology, 30 (7), pp. 2114-2128; Casale, P., Pujol, O., Radeva, P., (2011) Human Activity Recognition from Accelerometer Data Using A Wearable Device, 6669, pp. 289-296. , in J. Vitri'a, J.M. Sanches, and M. Hern'andez (Eds.): IbPRIA 2011, LNCS; Casale, P., Pujol, O., Radeva, P., Personalization and user verification in wearable systems using biometric walking patterns (2012) Personal and Ubiquitous Computing, 16 (5), pp. 563-580; Bayat, A., Pomplun, M., Tran, D.A., A study on human activity recognition using accelerometer data from smartphones (2014) Procedia Computer Science, Volume, 34, pp. 450-457; Khan, R., Abbas, M., Anjum, R., Waheed, F., Ahmed, S., Bangash, F., Evaluating machine learning techniques on human activity recognition using accelerometer data (2020) 2020 International Conference on UK-China Emerging Technologies (UCET), Glasgow, UK, pp. 1-6; Li, X., Wang, Y., Zhang, B., Ma, J., PSDRNN: An efficient and effective har scheme based on feature extraction and deep learning (2020) IEEE Transactions on Industrial Informatics, 16 (10), pp. 6703-6713; Freitas, L.O., Henriques, P.R., Novais, P., Analysis of human activities and identification of uncertain situations in context-aware system (2020) International Journal of Artificial Intelligence (ISSN 0974-0635), 18, 2, pp. 135-154; Gomaa, W., Statistical and time series analysis of accelerometer signals for human activity recognition (2019) 2019 14th International Conference on Computer Engineering and Systems (ICCES), Egypt, pp. 351-356; Luca, R., Bejinariu, S.I., Classification method for human locomotion (2018) Proc. 10th Int. Conf. Electronics, Computers and Artificial Intelligence, ECAI 2018, Iasi, Romania, IEEE 2018, pp. 1-6; Luca, R., Bejinariu, S.-I., Rotaru, F., Tree-Based classifiers on human locomotion statistical parameters (2019) 7-th Edition of the International Conference on E-Health and Bioengineering, EHB 2019, pp. 21-23. , November 2019, Iasi, Romania; Cover, T., Hart, P., Nearest neighbor pattern classification (1967) IEEE Transactions on Information Theory, 13 (1), pp. 21-27; Quinlan, J.R., (1993) C4.5: Programs for Machine Learning, , Morgan Kaufmann Publishers; Breiman, L., Random forests (2001) Machine Learning, 45 (1), pp. 5-32; https://archive.ics.uci.edu/ml/datasets/Activity+Recognition+from+Single+Chest-Mounted+Accelerometer, University of California, Irvine, Machine Learning Repository, Activity Recognition from Single Chest-Mounted Accelerometer Data Setlast accessed in 1.03.2021; WEKA-The Workbench for Machine Learning, , http://www.cs.waikato.ac.nz/ml/weka/, last accesed on 1.03.2021",,,,"Institute of Electrical and Electronics Engineers Inc.","12th International Symposium on Advanced Topics in Electrical Engineering, ATEE 2021","25 March 2021 through 27 March 2021",,168910,,9781665418782,,,"English","Int. Symp. Adv. Top. Electr. Eng., ATEE",Conference Paper,"Final","",Scopus,2-s2.0-85106725975
"Ananto R.A., Young J.E.","57220030143;20434970900;","We can do better! An initial survey highlighting an opportunity for more HRI work on loneliness",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447213","457","462",,,"10.1145/3434074.3447213","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102765634&doi=10.1145%2f3434074.3447213&partnerID=40&md5=dee58abd7ee7831465727a09dbe1b9ac","University of Manitoba, Winnipeg, MB, Canada","Ananto, R.A., University of Manitoba, Winnipeg, MB, Canada; Young, J.E., University of Manitoba, Winnipeg, MB, Canada","Although research has demonstrated the potential for social robots to positively impact a person's mood and provide comfort, very little research has yet focused on social robots supporting people living with loneliness. Much of the relevant human-robot interaction work focuses on more serious situations such as living with dementia, or on related areas such as stress, anxiety, or depression, and these works generally target the older adult demographic. Loneliness, however, can affect anyone of any health and age. In this paper we present a summary review of the current research on loneliness and social robots, highlighting the gaps in research and the potential opportunity for more work in the area. © 2021 ACM.","Human-robotics interaction; Loneliness; Social robots","Agricultural robots; Man machine systems; Older adults; Social robots",,,,,,,,,,,,,,,,"Ong, A.D., Uchino, B.N., Wethington, E., Loneliness and health in older adults: A mini-review and synthesis (2016) Gerontology, 62 (4), pp. 443-449; Bin Yu, L.-J.C., Steptoe, A., Niu, K., Ku, P., (2011) Prospective Associations of Social Isolation and Loneliness with Poor Sleep Quality in Older Adults No. v, pp. 1-28. , https://link.springer.com/article/10.1007/s11136-017-1752-9; Matthews, T., Danese, A., Gregory, A.M., Caspi, A., Moffitt, T.E., Arseneault, L., Sleeping with one eye open: Loneliness and sleep quality in young adults (2017) Psychol. 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Human-Robot Interact., pp. 290-292; Prescott, T.J., Mitchinson, B., Conran, S., Miro: An animal-like companion robot with a biomimetic brain-based control system (2017) ACM/IEEE Int. Conf. Human-Robot Interact., pp. 50-51; Lee, H.R., Steps toward participatory design of social robots: Mutual learning with older adults with depression (2017) ACM/IEEE Int. Conf. Human-Robot Interact. Vol. Part F1271, pp. 244-253; Davis, F.D., Perceived usefulness, perceived ease of use, and user acceptance of information technology (1989) MIS Q. Manag. Inf. Syst., 13 (3), pp. 319-339; Hegel, F., Muhl, C., Wrede, B., Hielscher-Fastabend, M., Sagerer, G., Understanding social robots (2009) Proc. 2nd Int. Conf. Adv. Comput. Interact. ACHI 2009 No. Section II, pp. 169-174; Riek, L., Wizard of oz studies in hri: A systematic review and new reporting guidelines (2012) J. Human-Robot Interact., 1 (1), pp. 119-136",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102765634
"Ayllon D., Chou T.-S., King A., Shen Y.","37119893900;57222470879;57221307269;57203599877;","Identification and engagement of passive subjects in multiparty conversations by a humanoid robot",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447229","535","539",,,"10.1145/3434074.3447229","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102734047&doi=10.1145%2f3434074.3447229&partnerID=40&md5=6186b7a2ea8f77d8bf0df6df258b2235","UBTECH NARDC, Pasadena, CA, United States","Ayllon, D., UBTECH NARDC, Pasadena, CA, United States; Chou, T.-S., UBTECH NARDC, Pasadena, CA, United States; King, A., UBTECH NARDC, Pasadena, CA, United States; Shen, Y., UBTECH NARDC, Pasadena, CA, United States","In this work, we present a novel human-robot interaction (HRI) method to detect and engage passive subjects in multiparty conversations using a humanoid robot. Voice activity detection and speaker localization are combined with facial recognition to detect and identify non-participating subjects. Once a non-participating individual is identified, the robot addresses the subject with a fact related to the topic of the conversation, with the goal of promoting the subject to join the conversation. To prompt sentences related to the topic of the conversation, automatic speech recognition and natural language processing techniques are employed. Preliminary experiments demonstrate that the method successfully identifies and engages passive subjects in a conversation. © 2021 ACM.","Conversational engagement; Conversational participation; Natural language processing; Spoken dialogue systems","Agricultural robots; Anthropomorphic robots; Face recognition; Man machine systems; Natural language processing systems; Speech recognition; Automatic speech recognition; Facial recognition; Human robot Interaction (HRI); Humanoid robot; Multi-party conversations; NAtural language processing; Speaker localization; Voice activity detection; Human robot interaction",,,,,,,,,,,,,,,,"Bojanowski, P., Grave, E., Joulin, A., Mikolov, T., Enriching word vectors with subword information (2017) Transactions of the Association for Computational Linguistics, 5, pp. 135-146. , 2017; Breazeal, C., Toward sociable robots (2003) Robotics and Autonomous Systems, 42 (3-4), pp. 167-175. , https://doi.org/10.1016/S0921-8890(02)00373-1, 2003; Cer, D., Yang, Y., Kong, S., Hua, N., Limtiaco, N., St John, R., Constant, N., Kurzweil, R., Universal sentence encoder (2018) EMNLP 2018-Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings, pp. 169-174. , https://doi.org/10.18653/v1/d18-2029, 3 2018; Conneau, A., Kiela, D., Schwenk, H., Barrault, L., Bordes, A., (2017) Supervised Learning of Universal Sentence Representations from Natural Language Inference Data, , 2017; Devlin, J., Chang, M., Lee, K., Toutanova, K., Bert: Pre-training of deep bidirectional transformers for language understanding (2019) NAACL HLT 2019-2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies-Proceedings of the Conference, 1, pp. 4171-4186. , Mlm (2019); Godfrey, J.J., Holliman, E.C., McDaniel, J., Switchboard: Telephone speech corpus for research and development (1992) [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, pp. 517-520. , https://doi.org/10.1109/ICASSP.1992.225858; Ikari, S., Yoshikawa, Y., Ishiguro, H., Multiple-robot mediated discussion system to support group discussion (2020) 29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN, 2020, pp. 495-502. , https://doi.org/10.1109/RO-MAN47096.2020.9223444, 2020; Knapp, C., Carter, G., The generalized correlation method for estimation of time delay (1976) IEEE Transactions on Acoustics, Speech, and Signal Processing, 24 (4), pp. 320-327. , https://doi.org/10.1109/TASSP.1976.1162830, 8 1976; Matsuyama, Y., Akiba, I., Fujie, S., Kobayashi, T., Four-participant group conversation: A facilitation robot controlling engagement density as the fourth participant (2015) Computer Speech and Language, 33 (1), pp. 1-24. , https://doi.org/10.1016/j.csl.2014.12.001, 2015; Matsuyama, Y., Taniyama, H., Fujie, S., Kobayashi, T., Framework of communication activation robot participating in multiparty conversation (2010) AAAI Fall Symposium-Technical Report FS-10-05, 2017, pp. 68-73. , September 2010; Mikolov, T., Chen, K., Corrado, G., Dean, J., (2013) Efficient Estimation of Word Representations in Vector Space, , https://journal.cecyf.fr/ojs/index.php/cybin/article/view/9; Mutlu, B., Shiwa, T., Kanda, T., Ishiguro, H., Hagita, N., (2009) Footing in Human-robot Conversations., 2 (1), p. 61. , https://doi.org/10.1145/1514095.1514109, 2009; Oertel, C., Castellano, G., Chetouani, M., Nasir, J., Obaid, M., Pelachaud, C., Peters, C., Engagement in human-agent interaction: An overview (2020) Frontiers in Robotics and AI, 7, pp. 1-21. , https://doi.org/10.3389/frobt.2020.00092, August (2020); Pennington, J., Socher, R., Manning, C., Glove: Global vectors for word representation (2014) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532-1543. , https://doi.org/10.3115/v1/D14-1162, Association for Computational Linguistics, Stroudsburg, PA, USA; Sehgal, A., Kehtarnavaz, N., A convolutional neural network smartphone app for real-time voice activity detection (2018) IEEE Access, 6, pp. 9017-9026. , https://doi.org/10.1109/ACCESS.2018.2800728, 2018; Sidner, C.L., Lee, C., Kidd, C.D., Lesh, N., Rich, C., Explorations in engagement for humans and robots (2005) Artificial Intelligence, 166 (1-2), pp. 140-164. , https://doi.org/10.1016/j.artint.2005.03.005, 2005; Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C., Ng, A.Y., Potts, C., Recursive deep models for semantic compositionality over a sentiment treebank (2013) Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631-1642. , https://www.aclweb.org/anthology/D13-1170; Traeger, M.L., Strohkorb Sebo, S., Jung, M., Scassellati, B., Christakis, N.A., Vulnerable robots positively shape human conversational dynamics in a human-robot team (2020) Proceedings of the National Academy of Sciences of the United States of America, 117 (12), pp. 6370-6375. , https://doi.org/10.1073/pnas.1910402117, 2020; Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I., Attention is all you need (2017) Advances in Neural Information Processing Systems 2017-Decem, pp. 5999-6009. , Nips (2017); Vázquez, M., Carter, E.J., McDorman, B., Forlizzi, J., Steinfeld, A., Hudson, S.E., Towards robot autonomy in group conversations: Understanding the effects of body orientation and gaze (2017) ACM/IEEE International Conference on Human-Robot Interaction Part F1271, pp. 42-52. , https://doi.org/10.1145/2909824.3020207, 2017",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102734047
"Stoddard B., Fallatah A., Knight H.","57222469977;57208162008;26321722700;","A web-based user interface for HRI studies on multi-robot furniture arrangement",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447272","680","681",,1,"10.1145/3434074.3447272","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102727239&doi=10.1145%2f3434074.3447272&partnerID=40&md5=d27b601c5952c79e81b2479546c9a6f9","Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Corvallis, OR, United States; School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, United States","Stoddard, B., Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Corvallis, OR, United States; Fallatah, A., School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR, United States; Knight, H., Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Corvallis, OR, United States","This video presents a remote user interface (UI) for controlling a multi-robot furniture system intended to enable human-robot interaction studies safely during the COVID-19 pandemic. The three primary features of the system are detailed. The first, a web-based architecture, allows the operation of our chair robots (ChairBots) over the internet. Second, multiple ChairBots are simultaneously operable. Third, variable levels of autonomy allow an operator to send both high-level, with robots autonomously moving to goals, or low-level motion commands. This work presents advances in the technical capabilities of our ChairBot system, representing progress towards a viable multi-robot furniture system. © 2021 Owner/Author.","Chairbot; Multi-robot ui; Robot furniture; Teleoperation","Agricultural robots; Industrial robots; Man machine systems; Multipurpose robots; User interfaces; Websites; Low-level motions; Multirobots; Remote user interfaces; Technical capabilities; Web based; Web-based architecture; Social robots",,,,,,,,,,,,,,,,"Agnihotri, A., Shutterly, A., Fallatah, A., Layng, B., Knight, H., Chairbot café: Personality-based expressive motion (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction Companion (HRI'18 Companion), pp. 1-4; Crick, C., Jay, G., Osentoski, S., Pitzer, B., Chadwicke Jenkins, O., Rosbridge: Ros for non-ros users (2017) Robotics Research, pp. 493-504. , Springer; Fallatah, A., Chun, B., Balali, S., Knight, H., Would you please buy me a coffee? how microculturesimpact people's helpful actions toward robots (2020) Proceedings of the 2020 ACM Designing Interactive Systems Conference, pp. 939-950; Fallatah, A., Urann, J., Knight, H., The robot show must go on: Effective responses to robot failures (2019) IROS, pp. 325-332; Salama Hussen Hajjaj, S., Saleh Mohamed Sahari, K., Establishing remote networks for ros applications via port forwarding: A detailed tutorial (2017) International Journal of Advanced Robotic Systems, 14 (3). , 2017; Hauser, S., Mutlu, M., Léziart, P.-A., Khodr, H., Bernardino, A., Ijspeert Jan, A., Roombots extended: Challenges in the next generation of self-reconfigurable modular robots and their application in adaptive and assistive furniture (2020) Robotics and Autonomous Systems, 127, p. 103467. , 2020; Humphrey, C.M., Henk, C., Sewell, G., Williams, B.W., Adams, J.A., Assessing the scalability of a multiple robot interface (2007) 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 239-246; Kehoe, B., (2014) Introducing ROStful: ROS over RESTful Web Services, , https://www.ros.org/news/2014/02/introducing-rostful-ros-over-restfulweb-services.html; Knight, H., Lee, T., Hallawell, B., Ju, W., I get it already! The influence of chairbot motion gestures on bystander response (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 443-448; Rosenberg, L.B., Virtual fixtures: Perceptual tools for telerobotic manipulation (1993) Proceedings of IEEE Virtual Reality Annual International Symposium. IEEE, pp. 76-82; Sirkin, D., Mok, B., Yang, S., Ju, A., Mechanical ottoman: How robotic furniture offers and withdraws support (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 11-18; Stoddard, B., (2020) ROS Flask, , https://github.com/stoddabr/ros_flask; Suzuki, R., Hedayati, H., Zheng, C., Bohn, J.L., Szafir, D., Yi-Luen Do, E., Gross, M.D., Leithinger, D., Roomshift: Room-scale dynamic haptics for vr with furniture-moving swarm robots (2020) Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1-11; Urann, J., Fallatah, A., Knight, H., Dancing with chairbots (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), p. 364","Stoddard, B.; Collaborative Robotics and Intelligent Systems (CoRIS) InstituteUnited States; email: stoddabr@oregonstate.edu
Knight, H.; Collaborative Robotics and Intelligent Systems (CoRIS) InstituteUnited States; email: knighth@oregonstate.edu",,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102727239
"Ramadoss V., Sagar K., Ikbal M.S., Zlatanov D., Zoppi M.","57192715243;57192683963;57207710331;6602800066;35838978700;","Modeling and stiffness evaluation of tendon-driven robot for collaborative human-robot interaction",2021,"ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics",,,"9419387","233","238",,,"10.1109/ISR50024.2021.9419387","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106476640&doi=10.1109%2fISR50024.2021.9419387&partnerID=40&md5=8efbb2a34d7fa512bdaef38ec40a9882","PMAR Robotics Laboratory, University of Genoa, Genoa, 16145, Italy","Ramadoss, V., PMAR Robotics Laboratory, University of Genoa, Genoa, 16145, Italy; Sagar, K., PMAR Robotics Laboratory, University of Genoa, Genoa, 16145, Italy; Ikbal, M.S., PMAR Robotics Laboratory, University of Genoa, Genoa, 16145, Italy; Zlatanov, D., PMAR Robotics Laboratory, University of Genoa, Genoa, 16145, Italy; Zoppi, M., PMAR Robotics Laboratory, University of Genoa, Genoa, 16145, Italy","This paper presents a clear and detailed mathematical analysis of the stiffness evaluation of tendon-driven serial kinematic chains which allow single-point and multi-point routing with bundling per rigid link. The kinematic analysis of tendon driven serial chain with cable routing between links, re-routing within links and bundling is presented. Targeting a musculoskeletal two-link system with different tendon arrangements as a case study, the stiffness of each system is evaluated using a stiffness ellipse. The stiffness performance is analyzed to provide a holistic overview of passive stiffness and force production capabilities that have a pivotal role in the rehabilitation paradigm and human robot-interaction. © 2021 IEEE.",,"Agricultural robots; Kinematics; Man machine systems; Redundant manipulators; Robotics; Stiffness; Tendons; Cable routing; Kinematic Analysis; Kinematic chain; Mathematical analysis; Passive stiffness; Production capabilities; Serial chains; Tendon-driven robots; Human robot interaction",,,,,,,,,,,,,,,,"Kino, H., Kikuchi, S., Yahiro, T., Tahara, K., Basic study of biarticular muscle's effect on muscular internal force control based on physiological hypotheses (2009) 2009 IEEE International Conference on Robotics and Automation. IEEE, pp. 4195-4200; Junius, K., Moltedo, M., Cherelle, P., Rodriguez-Guerrero, C., Vanderborght, B., Lefeber, D., Biarticular elements as a contributor to energy efficiency: Biomechanical review and application in bioinspired robotics (2017) Bioinspiration & Biomimetics, 12 (6), p. 061001; Salvucci, V., Kimura, Y., Oh, S., Hori, Y., Biwi: Bi-articularly actuated and wire driven robot arm (2011) 2011 IEEE International Conference on Mechatronics. IEEE, pp. 827-832; Mitobe, K., Togashi, J., Seki, T., Capi, G., Motion generation and stability of the marionette style elastic tendon driven robot arm (2016) 2016 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS). IEEE, pp. 40-45; Inouye, J., Valero-Cuevas, F.J., Asymmetric routings with fewer tendons can offer both flexible endpoint stiffness control and high force-production capabilities in robotic fingers (2012) 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob). IEEE, pp. 1273-1280; Nakanishi, Y., Asano, Y., Kozuki, T., Mizoguchi, H., Motegi, Y., Osada, M., Shirai, T., Inaba, M., Design concept of detail musculoskeletal humanoid ""kenshiro""-toward a real human body musculoskeletal simulator (2012) 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012). IEEE, pp. 1-6; Lau, D., Oetomo, D., Halgamuge, S.K., Generalized modeling of multilink cable-driven manipulators with arbitrary routing using the cable-routing matrix (2013) IEEE Transactions on Robotics, 29 (5), pp. 1102-1113; Lau, D., Eden, J., Tan, Y., Oetomo, D., Caspr: A comprehensive cable-robot analysis and simulation platform for the research of cabledriven parallel robots (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 3004-3011; Ramadoss, V., Zlatanov, D., Zoppi, M., Kinematic and workspace analysis of minimally routed cable driven open chains (2019) IFToMM World Congress on Mechanism and Machine Science, pp. 2841-2851. , Springer; Eden, J., Song, C., Tan, Y., Oetomo, D., Lau, D., Caspr-ros: A generalised cable robot software in ros for hardware (2018) Cable-Driven Parallel Robots, pp. 50-61. , Springer; Zlatanov, D., Agrawal, S., Gosselin, C.M., Convex cones in screw spaces (2005) Mechanism and Machine Theory, 40 (6), pp. 710-727; Behzadipour, S., Sohi, M.A., Antagonistic stiffness in cabledriven mechanisms (2007) IFToMM World Congress, , Besançon, France, In; Bryson, J.T., Agrawal, S.K., Methodology to identify and analyze optimal cable configurations in the design of cable-driven serial manipulators (2013) ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, p. 69; Sanjeevi, N., Vashista, V., On the stiffness analysis of a cable driven leg exoskeleton (2017) 2017 International Conference on Rehabilitation Robotics (ICORR). IEEE, pp. 455-460; Panchal, N., Sanjeevi, N., Vashista, V., Lower limb musculoskeletal stiffness analysis during swing phase as a cable-driven serial chain system (2018) 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob). IEEE, pp. 934-939; Jeong, H., Cho, Y., Kang, B., Cheong, J., Son, Y., Stiffness characteristics of new modular type antagonistic tendon-driven joint systems (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 5972-5977; Matsutani, Y., Tahara, K., Kino, H., Ochi, H., Stiffness evaluation of a tendon-driven robot with variable joint stiffness mechanisms (2017) 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids). IEEE, pp. 213-218; Calles, J.H.L., Ramadoss, V., Zoppi, M., Cannata, G., Molfino, R., Modeling of a cable-based revolute joint using biphasic media variable stiffness actuation (2019) 2019 Third IEEE International Conference on Robotic Computing (IRC). IEEE, pp. 618-623; Ramadoss, V., Lau, D., Zlatanov, D., Zoppi, M., Analysis of planar multilink cable driven robots using internal routing scheme (2020) International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, p. 029. , American Society of Mechanical Engineers 83990; Megaro, V., Knoop, E., Spielberg, A., Levin, D.I., Matusik, W., Gross, M., Thomaszewski, B., Bacher, M., Designing cable-driven actuation networks for kinematic chains and trees (2017) Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, p. 15; Zlatanov, D., Serial kinematic chains with unilateral external force constraints (2011) IFToMM World Congress in Mechanism and Machine Science, , Guanajuato, Mexico; Inouye, J.M., Valero-Cuevas, F.J., Anthropomorphic tendondriven robotic hands can exceed human grasping capabilities following optimization (2014) The International Journal of Robotics Research, 33 (5), pp. 694-705; Alamdari, A., Haghighi, R., Krovi, V., Stiffness modulation in an elastic articulated-cable leg-orthosis emulator: Theory and experiment (2018) IEEE Transactions on Robotics, 34 (5), pp. 1266-1279; Gouttefarde, M., Gosselin, C.M., Analysis of the wrench-closure workspace of planar parallel cable-driven mechanisms (2006) IEEE Transactions on Robotics, 22 (3), pp. 434-445; Workspace Analysis of Fully Routed Two-link Tendon-driven Manipulators, , https://youtu.be/u97miAZklc; Modeling and Stiffness Evaluation of Tendon Driven Robot for Collaborative Human-robot Interaction, , https://youtu.be/bdbEHdSaCQA",,,"IEEE Robotics and Automation Society;Nagoya University","Institute of Electrical and Electronics Engineers Inc.","2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021","4 March 2021 through 6 March 2021",,168860,,9781665438629,,,"English","ISR - IEEE Int. Conf. Intell. Saf. Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85106476640
"Fischer K.","24491633100;","Effect Confirmed, Patient Dead: A Commentary on Hoffman &zhao's Primer for Conducting Experiments in HRI",2021,"ACM Transactions on Human-Robot Interaction","10","1","3439714","","",,,"10.1145/3439714","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100919222&doi=10.1145%2f3439714&partnerID=40&md5=07bef326ca0d40209c2332258c7fb236","University of Southern Denmark, Alsion, Denmark","Fischer, K., University of Southern Denmark, Alsion, Denmark","This article is a commentary on Hoffman 8 Zhao's ""A Primer for Conducting Experiments in Human-robot Interaction.""I argue that a too-narrow view of HRI methodology fails to address the dynamic systems properties of interaction. Furthermore, the focus on addressing the so-called ""replicability crisis""makes field studies next to impossible, inhibits interdisciplinarity and methodological pluralism, and draws our attention and resources away from the fact that contexts, people, cultures, expectations, and interaction itself may influence how social signals are interpreted. Therefore, in spite of its great benefits, the ""Primer""may not be taken as an instruction on ""how to carry out research in HRI""in general. © 2021 Owner/Author.","interaction; interdisciplinarity; Methodology; power analysis; replicability crisis","Agricultural robots; Field studies; Interdisciplinarity; Replicability; Social signals; Human robot interaction",,,,,,,,,,,,,,,,"André, E., Socially-sensitive technologies for HRI: Challenges and perspectives Proceedings of the FAIM/ISCA Workshop on Artifcial Intelligence for Multimodal Human Robot Interaction., pp. 1-3; Andrist, S., Tan, X.Z., Gleicher, M., Mutlu, B., Conversational gaze aversion for humanlike robots (2014) Proceedings of the 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI'14)., pp. 25-32; Butler, R., Pruitt, Z., Wiese, E., The efect of social context on the mind perception of robots (2019) Proceedings of the Human Factors and Ergonomics Society Meeting, 63 (1), pp. 230-234. , (2019), Sage Publications, Los Angeles, CA; Chang, W.L., Sabanovic, S., Interaction expands function: Social shaping of the therapeutic robot PARO in a nursing home (2015) Proceedings of the 10th ACM/IEEE International Conference on Human-robot Interaction (HRI'15)., pp. 343-350; Clark, H.H., (1996) Using Language, , Cambridge University Press, Cambridge, UK; Fischer, K., (2016) Designing Speech for a Recipient: The Roles of Partner Modeling, Alignment and Feedback in So-called ""sim-plifed Registers"", , John Benjamins Publishing Company; Gilbert, D., King, G., Pettigrew, S., Wilson, T., Comment on ""estimating the reproducibility of psychological science."" (2016) Science, 351, pp. 1037a-1037b. , 2016; Henrich, J., Heine, S., Norenzayan, A., The weirdest people in the world? (2010) Behav. Brain Sci., 33 (2-3), pp. 61-83. , 2010; Jensen, L.C., Fischer, K., Suvei, S.D., Bodenhagen, L., Timing of multimodal robot behaviors during human-robot collaboration (2017) Proceedings of the 26th Ieee International Symposium on Robot and Human Interactive Communication (RO-MAN'17)., pp. 1061-1066; Jung, M., Hinds, P., Robots in the wild: A time for more robust theories of human-robot interaction (2018) Acm Trans. Hum.-robot Interact., 7 (1). , 2018; Kuhn, T.S., (2012) The Structure of Scientifc Revolutions, , University of Chicago Press; Mutlu, B., Forlizzi, J., Robots in organizations: The role of workfow, social, and environmental factors in human-robot interaction (2008) Proceedings of International Conference on Human-robot Interaction (HRI'08); Paepcke, S., Takayama, L., Judging a bot by its cover: An experiment on expectation setting for personal robots (2010) Proceedings of the 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI'10)., pp. 45-52; Paetzel, M., Castellano, G., Let me get to know you better: Can interactions help to overcome uncanny feelings? (2019) Proceedings of the 7th International Conference on Human-agent Interaction., pp. 59-67; Sacks, H., Scheglof, E.A., Jeferson, G., A simplest systematics for the organization of turn-taking for conversation (1974) Language, 50 (4), pp. 696-735. , 1974; Strait, M., Briggs, P., Scheutz, M., Gender, more so than age, modulates positive perceptions of language-based human-robot interactions (2015) Proceedings of the 4th International Symposium on New Frontiers in Human-robot Interaction., pp. 21-22","Fischer, K.; University of Southern DenmarkDenmark; email: kerstin@sdu.dk",,,"Association for Computing Machinery",,,,,25739522,,,,"English","ACM Trans. Hum. Robot Interact",Article,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85100919222
"Han I.X., Meggers F., Parascho S.","57271212300;36731246700;56826026400;","Bridging the collectives: A review of collective human–robot construction",2021,"International Journal of Architectural Computing",,,,"","",,,"10.1177/14780771211025153","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120575816&doi=10.1177%2f14780771211025153&partnerID=40&md5=8a4f9d0d7cd745a24c408e2f057f3d71","Department of Architecture, CREATE Laboratory, Princeton University, Princeton, NJ, United States; Department of Architecture, Cooling and Heating for Architecturally Optimized Systems (CHAOS) Lab, Andlinger Center for Energy and Environment, Princeton University, Princeton, NJ, United States","Han, I.X., Department of Architecture, CREATE Laboratory, Princeton University, Princeton, NJ, United States; Meggers, F., Department of Architecture, Cooling and Heating for Architecturally Optimized Systems (CHAOS) Lab, Andlinger Center for Energy and Environment, Princeton University, Princeton, NJ, United States; Parascho, S., Department of Architecture, CREATE Laboratory, Princeton University, Princeton, NJ, United States","Advancements in multi-agent, autonomous, and intelligent robotic systems over the past decades point toward new design and fabrication possibilities. Exploring how humans and robots can create and construct collectively is essential in leveraging robotic technology in the building sector. However, only by making existing knowledge from relevant technological disciplines accessible to designers can we fully exploit current construction methods and further develop them to address the challenges in architecture. To do this, we present a review paper that bridges the gap between Collective Robotic Construction (CRC) and Human–Robot Interaction (HRI) and defines a new research domain in Collective Human–Robot Construction (CHRC) in the architectural design and fabrication context. © The Author(s) 2021.","collective human–robot construction; collective robotic construction; human–robot interaction; human–robot teams; robotic fabrication","Autonomous agents; Bridges; Fabrication; Intelligent robots; Machine design; Multi agent systems; Robotics; Collective human–robot construction; Collective robotic construction; Collective robotics; Human robots; Human-robot-team; Humans-robot interactions; Multi-agent robotic systems; Robot construction; Robotic construction; Robotic fabrications; Human robot interaction",,,,,,,,,,,,,,,,"Willmann, J., Block, P., Hutter, M., (2018) Robotic fabrication in architecture, art and design 2018: foreword by Sigrid Brell-Cokcan and Johannes Braumann, association for robots in architecture, , Berlin, Germany, Springer; 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J. Archit. Comput.",Article,"Article in Press","",Scopus,2-s2.0-85120575816
"Kawai H., Hachisu T., Hirokawa M., Suzuki K.","57352108800;36080648100;36548153500;22037104400;","A Boxed Soft Robot Conveying Emotions by Changing Apparent Stiffness of Its Lid",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"577","585",,,"10.1007/978-3-030-90525-5_50","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119880939&doi=10.1007%2f978-3-030-90525-5_50&partnerID=40&md5=ddb8136f582e15ff86a34e4f5bdb799a","Artificial intelligence laboratory, University of Tsukuba, 1-1-1 Tennodai, Tsukuba Ibaraki, 305-8573, Japan","Kawai, H., Artificial intelligence laboratory, University of Tsukuba, 1-1-1 Tennodai, Tsukuba Ibaraki, 305-8573, Japan; Hachisu, T., Artificial intelligence laboratory, University of Tsukuba, 1-1-1 Tennodai, Tsukuba Ibaraki, 305-8573, Japan; Hirokawa, M., Artificial intelligence laboratory, University of Tsukuba, 1-1-1 Tennodai, Tsukuba Ibaraki, 305-8573, Japan; Suzuki, K., Artificial intelligence laboratory, University of Tsukuba, 1-1-1 Tennodai, Tsukuba Ibaraki, 305-8573, Japan","A social robot that convey its emotions by changing its apparent stiffness is presented herein. A user interacts with a box-shaped robot by pushing its lid. The apparent stiffness of the robot is controlled by an electromagnetic brake installed on the lid based on the emotional state of the robot. To control the stiffness, we implement two approaches: (i) control the reaction force when a user presses the lid; (ii) control the temporal restoring behavior when a user releases the lid. The experimental results show a capability of the robot in providing variable apparent stiffness and a potential in eliciting the emotional impact of users through haptic human-robot interactions. © 2021, Springer Nature Switzerland AG.","Emotion; Haptic interactions; Human-robot interactions; Social robots","Behavioral research; Human robot interaction; Man machine systems; Robotics; Electromagnetic brakes; Emotion; Emotional state; Haptic interactions; Haptics; Humans-robot interactions; Reaction forces; Social robots; Soft robot; Stiffness",,,,,,,,,,,,,,,,"Alemi, M., Meghdari, A., Ghazisaedy, M., Employing humanoid robots for teaching english language in iranian junior high-schools (2014) Int. J. Human. Robot., 11 (3); Chumkamon, S., Masato, K., Hayashi, E., Facial expression of social interaction based on emotional motivation of animal robot (2015) 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 185-190. , https://doi.org/10.1109/SMC.2015.45, . pp; Daǧlarlı, E., Anbaş, E., Rehabilitation based computational models for humanoid robots (2016) Medical Technologies National Congress (TIPTEKNO), pp. 1-4. , https://doi.org/10.1109/TIPTEKNO.2016.7863124, . pp., 2016; Herdel, V., Kuzminykh, A., Hildebrandt, A., Cauchard, J.R., Drone in love: Emotional perception of facial expressions on flying robots (2021) Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems; Hertenstein, M.J., Holmes, R., McCullough, M.E., Keltner, D., The communication of emotion via touch (2009) Emotion, 9 (4), pp. 566-573; Hertenstein, M.J., Keltner, D., Gender and the communication of emotion via touch (2011) Sex Roles, 64, pp. 70-80; Hertenstein, M.J., Keltner, D., App, B., Bulleit, B.A., Jaskolka, A.R., Touch communicates distinct emotions (2006) Emotion, 6 (3), pp. 528-533; Leyzberg, D., Avrunin, E., Liu, J., Scassellati, B., Robots that express emotion elicit better human teaching (2011) 2011 6Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 347-354. , https://doi.org/10.1145/1957656.1957789, pp; Song, S., Yamada, S., Expressing emotions through color, sound, and vibration with an appearance-constrained social robot (2017) 2017 12Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Pp. 2–, 11; Terada, K., Yamauchi, A., Ito, A., Artificial emotion expression for a robot by dynamic color change (2012) 2012 IEEE RO-MAN: The 21St IEEE International Symposium on Robot and Human Interactive Communication, pp. 314-321. , https://doi.org/10.1109/ROMAN.2012.6343772, pp; Wada, K., Shibata, T., Social effects of robot therapy in a care house-change of social network of the residents for two months (2007) Proceedings 2007 IEEE International Conference on Robotics and Automation, pp. 1250-1255. , https://doi.org/10.1109/ROBOT.2007.363156, pp; Zheng, X., Shiomi, M., Minato, T., Ishiguro, H., What kinds of robot’s touch will match expressed emotions? (2020) IEEE Robot. Autom. Lett., 5 (1), pp. 127-134. , https://doi.org/10.1109/LRA.2019.2947010","Kawai, H.; Artificial intelligence laboratory, 1-1-1 Tennodai, Japan; email: hiroya@ai.iit.tsukuba.ac.jp","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119880939
"Tørresen J.","6602518300;","Undertaking Research with Humans within Artificial Intelligence and Robotics: Multimodal Elderly Care Systems",2021,"Technology Architecture and Design","5","2",,"141","145",,,"10.1080/24751448.2021.1967052","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118791881&doi=10.1080%2f24751448.2021.1967052&partnerID=40&md5=347fd54c12516f8d1505a9171becd661","University of Oslo, Norway","Tørresen, J., University of Oslo, Norway","Artificial Intelligence (AI) has become widespread in recent years and made tremendous progress thanks to automated learning and optimization. This paper outlines research methods within human-centered AI and robotics utilized in the Multimodal Elderly Care Systems (MECS 2021) research project. The project’s main research question asked how human activities and states can best be monitored by a mobile robot. A primary result is that sensors can be both effective and preferable while addressing increasing privacy concerns. Another result is that the user’s age impacts the preferred speed and distance for human/robot interaction. Research ethical considerations are also important for AI research and are presented in this paper. © 2021 Association of Collegiate Schools of Architecture.","Artificial Intelligence; Human-Robot Interaction; Machine Learning; Research Methods","Intelligent robots; Robotics; Automated learning; Elderly care; Ethical considerations; Human activities; Multi-modal; Privacy concerns; Research questions; Social robots",,,,,"247697, 259293, 261645, 309869; 262762, 288285; Norges Forskningsråd","The Research Council of Norway partially supported this work as a part of the Multimodal Elderly Care Systems (MECS) project, under grant agreement 247697, the INTROducing Mental Health through Adaptive Technology (INTROMAT) project under grant agreement 259293, the Collaboration on Intelligent Machines (COINMAC) project under grant agreement 261645 and 309869, Vulnerability in the Robot Society (VIROS) under grant agreement 288285, and the Centres of Excellence scheme, RITMO with project No. 262762. Great appreciation is also expressed to the MECS project team consisting of Diana Saplacan, Jo Herstad, Md. Zia Uddin, Rebekka Soma, Trenton Schulz, Werie Khaksar, master?s students, Kampen Omsorg+, international collaborators, and others who have contributed to the successful implementation of the project.","The Research Council of Norway partially supported this work as a part of the Multimodal Elderly Care Systems (MECS) project, under grant agreement 247697, the INTROducing Mental Health through Adaptive Technology (INTROMAT) project under grant agreement 259293, the Collaboration on Intelligent Machines (COINMAC) project under grant agreement 261645 and 309869, Vulnerability in the Robot Society (VIROS) under grant agreement 288285, and the Centres of Excellence scheme, RITMO with project No. 262762. Great appreciation is also expressed to the MECS project team consisting of Diana Saplacan, Jo Herstad, Md. Zia Uddin, Rebekka Soma, Trenton Schulz, Werie Khaksar, master’s students, Kampen Omsorg+, international collaborators, and others who have contributed to the successful implementation of the project.",,,,,,,,,"Bartneck, C., (2008) The Godspeed Questionnaire Series, , http://www.bartneck.de/2008/03/11/the-godspeed-questionnaire-series, March 11,. Christopher Bartneck, Ph.D (website; Eiben, A.E., Smith, J.E., (2015) Introduction to Evolutionary Computing, , 2nd ed, New York: Springer-Verlag Berlin Heidelberg; (2021) A European Strategy on Artificial Intelligence, , https://ec.europa.eu/digital-single-market/en/artificial-intelligence, Shaping Europe’s Digital Future, Accessed June 22, 2021; (2021) Multimodal Elderly Care Systems (MECS), , https://www.mn.uio.no/ifi/english/research/projects/mecs, University of Oslo,. Research project 2015–2021; Newaz, F.B.M., Saplacan, D., Exploring the Role of Feedback on Trust for the Robots Used in Homes of the Elderly (2018) Proceedings of the 10th Nordic Conference on Human-Computer Interaction (NordiCHI ’18), pp. 681-685. , Oslo, Norway: and, September 29–October 3; (2021) Routines for Processing Personal Data in Research Projects, , https://www.uio.no/english/for-employees/support/privacy-dataprotection/personal-data-in-research.html, University of Oslo,. Accessed June 22, 2021; Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Mastering the Game of Go without Human Knowledge (2017) Nature, 550, pp. 354-359; Tørresen, J., A Review of Future and Ethical Perspectives of Robotics and AI (2018) Frontiers in Robotics and AI, 4, pp. 1-10. , Article 75; (2021) Services for Sensitive Data (TSD), , https://www.uio.no/english/services/it/research/sensitive-data/, University of Oslo, Accessed June 22, 2021","Tørresen, J.; University of OsloNorway",,,"Routledge",,,,,24751448,,,,"English","Tech. Arch. Des.",Article,"Final","",Scopus,2-s2.0-85118791881
"Specian A., Mead R., Kim S., Mataric M., Yim M.","57076631400;23009115600;57210866341;7005611984;7005922938;","Quori: A Community-Informed Design of a Socially Interactive Humanoid Robot",2021,"IEEE Transactions on Robotics",,,,"","",,,"10.1109/TRO.2021.3111718","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118249672&doi=10.1109%2fTRO.2021.3111718&partnerID=40&md5=eacc1135b325c6f38c57e430840f730d","Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA 19104 USA (e-mail: aspecian@seas.upenn.edu).; Semio AI, Inc., Los Angeles, CA 90012 USA (e-mail: ross@semio.ai).; Weitzman School of Design, University of Pennsylvania, Philadelphia, PA 19104 USA (e-mail: simonkim@design.upenn.edu).; University of Southern California, Los Angeles, CA 90007 USA (e-mail: mataric@usc.edu).; Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA 19104 USA (e-mail: yim@grasp.upenn.edu).","Specian, A., Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA 19104 USA (e-mail: aspecian@seas.upenn.edu).; Mead, R., Semio AI, Inc., Los Angeles, CA 90012 USA (e-mail: ross@semio.ai).; Kim, S., Weitzman School of Design, University of Pennsylvania, Philadelphia, PA 19104 USA (e-mail: simonkim@design.upenn.edu).; Mataric, M., University of Southern California, Los Angeles, CA 90007 USA (e-mail: mataric@usc.edu).; Yim, M., Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA 19104 USA (e-mail: yim@grasp.upenn.edu).","Hardware platforms for socially interactive robotics can be limited by cost or lack of functionality. This article presents the overall system&#x2014;design, hardware, and software&#x2014;for Quori, a novel, affordable, socially interactive humanoid robot platform for facilitating noncontact human&#x2013;robot interaction (HRI) research. The design of the system is motivated by feedback sampled from the HRI research community. The overall design maintains a balance of affordability and functionality. Initial Quori testing and a six-month deployment are presented. Ten Quori platforms have been awarded to a diverse group of researchers from across the United States to facilitate HRI research to build a community database from a common platform. IEEE","Affordable hardware; Conferences; Costs; development and prototyping; Hardware; humanoid robots; Mobile robots; noncontact human&#x2013;robot interaction (HRI); product design; Robot sensing systems; Robots; social HRI; socially interactive robots; Software","Anthropomorphic robots; Human robot interaction; Machine design; Affordable hardware; Conference; Development and prototyping; Hardware; Humanoid robot; Interactive robot; Non-contact; Noncontact human&#x2013;; Robot interaction (HRI); Robot interactions; Robot sensing system; Social HRI; Socially interactive robot; Software; Product design",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15523098,,,,"English","IEEE Trans. Rob.",Article,"Article in Press","All Open Access, Green",Scopus,2-s2.0-85118249672
"Tamas L., Cozma A.","24475785600;57225104504;","Embedded real-time people detection and tracking with time-of-flight camera",2021,"Proceedings of SPIE - The International Society for Optical Engineering","11736",,"117360B","","",,,"10.1117/12.2586057","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109150245&doi=10.1117%2f12.2586057&partnerID=40&md5=96f53ee20ce706bd8ea3757d7e5028d2","Technical Univ. of Cluj Napoca, Romania; Analog Devices, Romania","Tamas, L., Technical Univ. of Cluj Napoca, Romania; Cozma, A., Analog Devices, Romania","People recognition is a relevant subset of the generic image based recognition task with many possible application areas such as security, surveillance, human-robot interaction or recently the social security in a pandemic context. In this work we present a light-weight recognition pipeline for time-of-flight cameras based on deep learning techniques tailored to this specific type of camera with registered infrared and depth images. By combining the maturity of the 2D image based recognition techniques with the custom depth sensing we achieved effective solutions for a number of relevant industrial applications. In particular, our focus was on automatic door-control and people counting applications. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.","Embedded computing; People recognition; ToF camera","Cameras; Deep learning; Image recognition; Optical character recognition; Social robots; Application area; Automatic doors; Effective solution; Learning techniques; People counting; People detection; People recognition; Time-of-flight cameras; Image processing",,,,,"PN-III-P2-2.1-PTE-2019-0367; Autoritatea Natională pentru Cercetare Stiintifică","The authors are thankful for the support of Analog Devices Romania, for the equipment list (cameras, embedded devices, GPUs) offered as support to this work. This work was financially supported by the Romanian National Authority for Scientific Research, CNCS-UEFISCDI, project number PN-III-P2-2.1-PTE-2019-0367.",,,,,,,,,,"Fooladgar, F., Kasaei, S., (2020) A survey on indoor RGB-D semantic segmentation : from hand-crafted features to deep convolutional neural networks, pp. 4499-4524; Xue, H., Liu, Y., Cai, D., He, X., Tracking people in rgbd videos using deep learning and motion clues (2016) Neurocomputing, 204, pp. 70-76. , Big Learning in Social Media Analytics; Zia, S., Yuksel, B., Yuret, D., Yemez, Y., Rgb-d object recognition using deep convolutional neural networks (2017) 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)], pp. 887-894; Gezawa, A. S., Zhang, Y., Wang, Q., Yunqi, L., A review on deep learning approaches for 3d data representations in retrieval and classifications (2020) IEEE Access, 8, pp. 57566-57593; Qi, C. R., Yi, L., Su, H., Guibas, L. J., PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (2017) Advances in Neural Information Processing Systems 2017-Decem, pp. 5100-5109. , (jun); Liu, Z., Tang, H., Lin, Y., Han, S., (2019) Point-Voxel CNN for Efficient 3D Deep Learning, , arXiv (jul); Jafari, O. H., Mitzel, D., Leibe, B., Real-time rgb-d based people detection and tracking for mobile robots and head-worn cameras (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA)], pp. 5636-5643; Liu, H., Luo, J., Wu, P., Xie, S., Li, H., People detection and tracking using rgb-d cameras for mobile robots (2016) International Journal of Advanced Robotic Systems, 13 (5), p. 1729881416657746; Hou, L., Wan, W., Han, K., Muhammad, R., Yang, M., Human detection and tracking over camera networks: A review (2016) 2016 International Conference on Audio, Language and Image Processing (ICALIP)], pp. 574-580; Han, X. F., Jin, J. S., Wang, M. J., Jiang, W., Gao, L., Xiao, L., A review of algorithms for filtering the 3D point cloud (2017) Signal Processing: Image Communication, 57, pp. 103-112. , (February); Rakotosaona, M. J., La Barbera, V., Guerrero, P., Mitra, N. J., Ovsjanikov, M., PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds (2020) Computer Graphics Forum, 39 (1), pp. 185-203; Rusu, R. B., Cousins, S., 3d is here: Point cloud library (pcl) (2011) 2011 IEEE International Conference on Robotics and Automation], pp. 1-4; Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C., MobileNetV2: Inverted Residuals and Linear Bottlenecks (2018) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 4510-4520. , (jan); Lee, J., Cho, J., Effective covariance tracker based on adaptive changing of tracking window (2010) ICCAS 2010], pp. 1057-1058; Tamas, L., Jensen, B., Robustness analysis of 3d feature descriptors for object recognition using a time-of-fight camera (2014) 22nd Mediterranean Conference on Control and Automation], pp. 1020-1025. , [IEEE; Gao, J., Li, P., Chen, Z., Zhang, J., A Survey on Deep Learning for Multimodal Data Fusion (2020) Neural Computation, 32, pp. 829-864. , (05); Frohlich, R., Tamas, L., Kato, Z., Absolute pose estimation of central cameras using planar regions (2019) IEEE transactions on pattern analysis and machine intelligence","Tamas, L.; Technical Univ. of Cluj NapocaRomania; email: Levente.Tamas@aut.utcluj.ro","Kehtarnavaz N.Carlsohn M.F.","The Society of Photo-Optical Instrumentation Engineers (SPIE)","SPIE","Real-Time Image Processing and Deep Learning 2021","12 April 2021 through 16 April 2021",,169833,0277786X,9781510643093,PSISD,,"English","Proc SPIE Int Soc Opt Eng",Conference Paper,"Final","",Scopus,2-s2.0-85109150245
"Taniguchi T., El Hafi L., Hagiwara Y., Taniguchi A., Shimada N., Nishiura T.","35585520600;57188836029;26648361300;57189575562;7202989705;7103179225;","Semiotically adaptive cognition: toward the realization of remotely-operated service robots for the new normal symbiotic society",2021,"Advanced Robotics","35","11",,"664","674",,1,"10.1080/01691864.2021.1928552","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106249056&doi=10.1080%2f01691864.2021.1928552&partnerID=40&md5=06610ab2628ae457fa7a9c5b8689b6ab","Department of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan; Ritsumeikan Global Innovation Research Organization, Ritsumeikan University, Kusatsu, Shiga, Japan","Taniguchi, T., Department of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan; El Hafi, L., Ritsumeikan Global Innovation Research Organization, Ritsumeikan University, Kusatsu, Shiga, Japan; Hagiwara, Y., Department of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan; Taniguchi, A., Department of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan; Shimada, N., Department of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan; Nishiura, T., Department of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan","The installation of remotely-operated service robots in the environments of our daily life (including offices, homes, and hospitals) can improve work-from-home policies and enhance the quality of the so-called new normal. However, it is evident that remotely-operated robots must have partial autonomy and the capability to learn and use local semiotic knowledge. In this paper, we argue that the development of semiotically adaptive cognitive systems is key to the installation of service robotics technologies in our service environments. To achieve this goal, we describe three challenges: the learning of local knowledge, the acceleration of onsite and online learning, and the augmentation of human–robot interactions. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","cognitive robotics; language understanding; Service robotics; symbol emergence","Adaptive systems; Agricultural robots; Cognitive systems; Mobile robots; Remote control; Daily lives; Local knowledge; Online learning; Robot interactions; Service environment; Service robotics; Service robots; Human robot interaction",,,,,"Japan Society for the Promotion of Science, KAKEN: 16H06569, 18H03308; Ministry of Education, Culture, Sports, Science and Technology, Monbusho; Japan Science and Technology Agency, JST: JPMJMS2011-44; Core Research for Evolutional Science and Technology, CREST: JPMJCR15E3, JPMJMS2011","This research was partially supported by the JSPS KAKENHI Grant-in-Aid for Scientific Research, grant numbers 18H03308 and 16H06569, funded by the Japan Ministry of Education, Culture, Sports, Science, and Technology (MEXT), and by the Core Research for Evolutionary Science and Technology (CREST), grant number JPMJCR15E3, and the Moonshot R&D Grant Number JPMJMS2011, both funded by the Japan Science and Technology Agency (JST) [JPMJMS2011-44].",,,,,,,,,,"Taniguchi, T., Ugur, E., Hoffmann, M., Symbol emergence in cognitive developmental systems: a survey (2018) IEEE Trans Cogn Dev Syst, , et al; Harnad, S., The symbol grounding problem (1990) Physica D, 42 (1), pp. 335-346; Chandler, D., (2017) Semiotics: the basics, , London: Taylor & Francis; Lake, B.M., Ullman, T.D., Tenenbaum, J.B., Building machines that learn and think like people (2017) Behav Brain Sci, 40, p. e253. , et al; LeCun, Y., Bengio, Y., Hinton, G., Deep learning (2015) Nature, 521 (7553), pp. 436-444. , https://doi.org/10.1038/nature14539, Available from; Redmon, J., Farhadi, A., Yolov3: an incremental improvement. 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In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), Macau; 2019. p. 634–639; Goodman, N., Mansinghka, V., Roy, D.M., et al. Church: a language for generative models. Preprint, 2012; Tran, D., Hoffman, M.D., Saurous, R.A., et al. Deep probabilistic programming. Preprint, 2017; Bingham, E., Chen, J.P., Jankowiak, M., Pyro: deep universal probabilistic programming (2019) J Mach Learn Res, 20 (1), pp. 973-978. , et al; Sato, T., Kameya, Y., Prism: a language for symbolic-statistical modeling. In: IJCAI; 97; Melbourne, 1997. p. 1330–1339; Mao, J., Gan, C., Kohli, P., et al. The neuro-symbolic concept learner: interpreting scenes, words, and sentences from natural supervision. Preprint, 2019; Devlin, J., Chang, M.W., Lee, K., et al. BERT: pre-training of deep bidirectional transformers for language understanding. Preprint, 2019; Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition. 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PMLR; 2020. p. 258–272; Nakamura, T., Nagai, T., Taniguchi, T., Serket: an architecture for connecting stochastic models to realize a large-scale cognitive model (2018) Front Neurorobot, 12; Taniguchi, T., Nakamura, T., Suzuki, M., Neuro-serket: development of integrative cognitive system through the composition of deep probabilistic generative models (2020) New Gener Comput, 38, pp. 1-26. , et al; Taniguchi, T., Yamakawa, H., Nagai, T., et al. Whole brain probabilistic generative model toward realizing cognitive architecture for developmental robots. Preprint,2021","Taniguchi, T.; Department of Information Science and Engineering, 1-1-1 Noji-Higashi, Japan; email: taniguchi@em.ci.ritsumei.ac.jp",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85106249056
"Mishra N., Goyal D., Sharma A.D.","57222132194;57211719867;57222119379;","Design Process and Stress Analysis of Spine in Humanoid Serving Robot to Provide Proper Balancing and Load Bearing Capabilities",2021,"Lecture Notes in Networks and Systems","166",,,"591","599",,,"10.1007/978-981-15-9689-6_64","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101526043&doi=10.1007%2f978-981-15-9689-6_64&partnerID=40&md5=8ce04193585c4d173bb072954be15a72","Centre for Cloud Infrastructure and Security, Suresh Gyan Vihar University, Jaipur, India; Gurukul Institute of Engineering and Technology, Kota, Rajasthan, India; Poornima Institute of Engineering & Technology, Jaipur, India","Mishra, N., Centre for Cloud Infrastructure and Security, Suresh Gyan Vihar University, Jaipur, India; Goyal, D., Poornima Institute of Engineering & Technology, Jaipur, India; Sharma, A.D., Gurukul Institute of Engineering and Technology, Kota, Rajasthan, India","Designing a robot that looks like human always gives the better experience in human–robot interaction. It is an important part along with the technology use in it because majorly public got attract toward robot just because of the design or in other words design is the first impression of robot. But with attraction, it is also important that design of robot body should not have any play during navigation and turns while serving. In this paper, researchers are discussing the designing process of serving robot and proposing the spine in robot. This spine basically affects two major factors in robot; they are, load bearing and balance. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.","3D printing; Humanoid robot; Inventor; Mechanical design; Serving robot; Spine",,,,,,,,,,,,,,,,,"Hosseini, S., Goher, K., Personal care robots for older adults: An overview (2017) Asian Soc Sci, 13, p. 11. , https://doi.org/10.5539/ass.v13n1p11; Portugal, D., Alvito, P., Christodoulou, E., Samaras, G., Dias, J., A study on the deployment of a service robot in an elderly care center (2018) Int J Soc Robot, 11, pp. 1-25; Asfour, T., Berns, K., Dillmann, R., Karlsruhe, F., (2004) The Humanoid Robot ARMAR: Design and Control, , https://doi.org/10.1007/978-3-7091-2498-7_32; Behnke, S., Humanoid robots—from fiction to reality? (2008) Künstliche Intelligenz—KI, 22 (4), pp. 5-9; Wang, J., Li, Y., (2009) A Survey on the Structures of Current Mobile Humanoid Robots, pp. 1826-1829. , https://doi.org/10.1109/APCCAS.2008.4746398, , pp; Moreno, P., Vizzy: A humanoid on wheels for assistive robotics (2016) Robot 2015: Second Iberian Robotics Conference. Advances in Intelligent Systems and Computing, 417. , Reis L, Moreira A, Lima P, Montano L, Muñoz-Martinez V, vol , Springer, Cham; Dsouza, R., Denny, J., D’Costa, S., Elyas, M., (2016) Humanoid robots—past. Present Future, 3, pp. 8-15; Enelson8, Online Available https://www.instructables.com/id/Autodesk-Inventor-Tutorial/","Mishra, N.; Centre for Cloud Infrastructure and Security, India; email: neel@clubfirst.org","Goyal D.Gupta A.K.Piuri V.Ganzha M.Paprzycki M.",,"Springer Science and Business Media Deutschland GmbH","2nd International Conference on Information Management and Machine Intelligence, ICIMMI 2020","24 July 2020 through 25 July 2020",,254869,23673370,9789811596889,,,"English","Lect. Notes Networks Syst.",Conference Paper,"Final","",Scopus,2-s2.0-85101526043
"Weßel M., Ellerich-Groppe N., Schweda M.","57202066186;57221336114;23669142600;","Stereotyping of Social Robots in Eldercare: An Explorative Analysis of Ethical Problems and Possible Solutions",2020,"Frontiers in Artificial Intelligence and Applications","335",,,"239","246",,,"10.3233/FAIA200920","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098875301&doi=10.3233%2fFAIA200920&partnerID=40&md5=16beafc5f0d9961d1d90d5ae27e92868","School of Medicine and Health Sciences, Carl von Ossietzky University of Oldenburg, Germany","Weßel, M., School of Medicine and Health Sciences, Carl von Ossietzky University of Oldenburg, Germany; Ellerich-Groppe, N., School of Medicine and Health Sciences, Carl von Ossietzky University of Oldenburg, Germany; Schweda, M., School of Medicine and Health Sciences, Carl von Ossietzky University of Oldenburg, Germany","Due to demographic aging, the relevance of social robots in eldercare is increasing. Studies indicate that gender stereotypes can play an important role in human-robot interaction and could be used strategically to optimize care processes and outcomes. However, as gender roles among older people become more diverse and societal recognition of different needs and preferences grows, such stereotypical notions appear increasingly problematic. Against this backdrop, our contribution provides an explorative ethical analysis of gender stereotyping in social robotics for eldercare. Starting from a principlist approach, we map potential problems and conflicts and discuss possible solutions for culturally sustainable social robots for eldercare in late-modern pluralistic societies. © 2020 The authors and IOS Press. All rights reserved.","care; ethics; gender; queer theory; robotics; Stereotypes","Philosophical aspects; Robotics; Care process; Eldercare; Ethical problems; Gender roles; Gender stereotypes; Older People; Potential problems; Social robotics; Social robots",,,,,,,,,,,,,,,,"Nass, C., Moon, Y., Machines and Mindlessness. Social responses to computers (2000) Journal of Social Issues, 56 (1), pp. 81-103; Eyssel, F., Kuchenbrandt, D., Social categorization of social robots: Anthropomorphism as a function of robot group membership (2012) British Journal of Social Psychology, 51, pp. 724-731; Kuchenbrandt, D., Eyssel, F., Bobinger, S., Neufeld, M., When Robot's Group Membership Matters. Anthropomorphization of robots as a function of social categorization (2013) International Journal of Social Robotics, 5, pp. 409-417; Häring, M., Kuchenbrandt, D., André, E., Would you like to play with me? How robots' group membership and task features influence human-robot interaction (2014) In: HRI'14. Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction, pp. 9-16. , Mar 3-6; Bielefeld, Germany. New York: ACM; 2014; Eyssel, F., Hegel, F., (S)he's got the look: Gender stereotyping of robots (2012) Journal of Applied Social Psychology, 42 (9), pp. 2213-2230; Ladwig, R.C., Ferstl, E.C., What's in a name? An online survey on gender stereotyping of humanoid social robots (2018) In: GenderIT'18: Proceedings of the 4th Conference on Gender & It, pp. 67-69. , 2018 May 14-15; Heilbronn, Germany. New York: ACM; Nomura, T., Robots and Gender (2017) Gender and the Genome, 1 (1), pp. 18-25. , See for an overview; Kraus, M., Kraus, J., Baumann, M., Minkler, W., Effects of gender stereotypes on trust and likability in spoken human-robot interaction (2018) In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), pp. 112-118. , http://www.lrec-conf.org/proceedings/lrec2018/pdf/824.pdf, May 7-12; Miyazaki, Japan. Available from:, [cited 2020 Jan 30; Marchetti-Bowick, M., Is your roomba male or female? The role of gender stereotypes and cultural norms in robot design (2009) Intersect, 2 (1). , http://ojs.stanford.edu/ojs/index.php/intersect/article/view/171/629, Available from:, [cited 2020 Jan 30; Jung, E.H., Waddell, T.F., Sundar, S.S., Feminizing robots: User responses to gender cues on robot body and screen (2016) In: Proceedings of the 2016 Chi Conference on Human Factors in Computing Systems, pp. 3107-3113. , Extended Abstracts. 2016 May 7-12; San Jose, CA. New York: ACM; Bernotat, J., Eyssel, F., Sachse, J., Shape it-the influence of robot body shape on gender perception in robots (2017) Social Robotics. 9th International Conference, Icsr 2017, pp. 75-84. , In: Kheddar A, Yoshida E, Sam Ge S, Suzuki K, Cabibihan JJ, Eyssel F, He H, editors. Tsukuba, Japan, November 22-24, 2017. Proceedings. Cham: Springer; Bernotat, J., Eyssel, F., Sachse, J., The (Fe)male robot: How robot body shape impacts first impressions and trust towards robots Int J Soc Robot, , https://doi.org/10.1007/s12369-019-00562-7, Internet; Trovato, G., Lucho, C., Paredes, R., She's electric-The influence of body proportions on perceived gender of robots across cultures (2018) Robotics, 7 (3), p. 50; Eyssel, F., Kuchenbrandt, D., Hegel, F., De Ruiter, L., Activating elicited agent knowledge: How robot and user features shape the perception of social robots (2012) In: Proceedings of 2012 Ieee RO-MAN: The 21st Ieee International Symposium on Robot and Human Interactive Communication, pp. 851-857. , https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6343858, Sep 9-13; Paris, France. Available from:, [cited 2020 Jan 30; Tay, B., Jung, Y., Park, T., When stereotypes meet robots: The double-edge sword of robot gender and personality in human-robot interaction (2014) Computers in Human Behaviour, 38, pp. 75-84; Tay, B.T.C., Park, T., Jung, Y., Tan, Y.K., Wong, A.H.Y., When stereotypes meet robots: The effect of gender stereotypes on people's acceptance of a security robot (2013) Engineering Psychology and Cognitive Ergonomics. Understanding Human Cognition. 10th International Conference on Engineering Psychology and Cognitive Ergonomics, pp. 261-270. , In: Harris D, editor., Las Vegas, NV, USA, July, Proceedings, Part I. Berlin/Heidelberg: Springer; 2013; Kuchenbrandt, D., Häring, M., Eichberg, J., Eyssel, F., André, E., Keep an eye on the task! How gender typicality of tasks influence human-robot interactions (2014) International Journal of Social Robotics, 6 (3), pp. 417-427; Rizvanoglu, K., Öztürk, O., Adiyaman, O., The impact of human likeness on the older adults' perceptions and preferences of humanoid robot appearance (2014) Design, User Experience, and Usability. User Experience Design Practice. Third International Conference, pp. 164-172. , In: Marcus A, editor., DUXU 2014. Heraklion, Crete, Greece, June 22-27, 2014, Proceedings, Part IV. Cham: Springer; Carcagnì, P., Cazzato, D., Del, C.M., Distante, C., Leo, M., Visual interaction including biometrics information for a socially assistive robotic platform (2015) Computer Vision-ECCV 2014 Workshops, pp. 391-406. , In: Agapito L, Bronstein MM, Rother C, editors. Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part III. Cham: Springer; Alesich, S., Rigby, M., Gendered robots. Implications for our humanoid future (2017) Ieee Technology and Society Magazine, 36 (2), pp. 50-59; Knox, E., Watanabe, K., Ethical considerations of gendering very humanlike androids from an interdisciplinary perspective (2017) In: Proceedings of the 2017 26th Ieee International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 539-546. , https://ieeexplore.ieee.org/document/8172355, Aug 28-Sep 1; Lisbon, Portugal. Available from:, [cited 2020 Jan 30; Robertson, J., Gendering humanoid robots: Robo-sexism in Japan (2010) Body & Society, 16 (1), pp. 1-36; Vandemeulebroucke, T., Dierckx, D.C.B., Gastmans, C., The use of care robots in aged care: A systematic review of argument-based ethics literature (2018) Archives of Gerontology and Geriatrics, 74, pp. 15-25; Körtner, T., Ethical challenges in the use of social service robots for elderly people (2016) Für Gerontologie und Geriatrie, 49 (4), pp. 303-307. , Zeitschrift; Haltaufderheide, J., Hovemann, J., Vollmann, J., (2020) Aging between Participation and Simulation-Ethical Dimensions of Socially Assistive Technologies in Elderly Care, , editors. Berlin/Boston: De Gruyter; Beauchamp, T.L., Childress, J.F., (1979) Principles of Biomedical Ethics, , New York/Oxford: Oxford University Press; 2019; Edwards, S.D., (2009) Nursing Ethics: A Principle-based Approach, , Basingstoke: Palgrave Macmillan; Collins, E.C., Vulnerable users: Deceptive robotics (2017) Connection Science, 29 (3), pp. 223-229; Borenstein, J., Arkin, R., Robotic nudges: The ethics of engineering a more socially just human being (2016) Science and Engineering Ethics, 22 (1), pp. 31-46; Danaher, J., McArthur, N., (2017) Robot Sex: Social and Ethical Implications, , editors. Cambridge (MA): MIT Press; Pereida, K., Greef, M., (2019) Diversity in Robotics: From Diverse Teams to Diverse Impact, , https://www.semanticscholar.org/paper/Diversity-in-Robotics-%3A-From-Diverse-Teams-to-Pereida-Greeff/bc7c8986ec1f247cc9cde0988e2ebde5d4a55531, Available from:, [cited 2020 Jan 30; Dufour, F., Ehrwein, N.C., Do robots need to be stereotyped? Technical characteristics as a moderator of gender stereotyping (2016) Social Sciences, 5 (3), p. 27; Butler, J., (1993) Bodies That Matter: On the Discursive Limits of ""sex, , London: Routledge; Halberstam, J., Automating gender: Postmodern feminism in the age of the intelligent machine (1991) Feminist Studies, 17 (3), pp. 439-460; Haraway, D., (1991) Simians, Cyborgs, and Women: The Reinvention of Nature, , Abingdon: Routledge; Joyce, K., Mamo, L., Graying the cyborg: New directions in feminist analyses of ageing, science, and technology (2006) Age Matters: Realigning Feminist Thinking, pp. 75-98. , In: Calasanti TM, Slevin KF, editors. New York: Routledge; Braidotti, R., (2013) The Posthuman, , Cambridge: Polity Press; Sparrow, R., Robotics has a race problem (2020) Science, Technology, & Human Values, 45 (3), pp. 538-560","Weßel, M.; Division of Ethics in Medicine, Ammerländer Heerstraße 114-118, Germany; email: merle.wessel@uni-oldenburg.de","Norskov M.Seibt J.Quick O.S.",,"IOS Press BV","4th Conference on Robophilosophy 2020: Culturally Sustainable Social Robotics","18 August 2020 through 21 August 2020",,165874,09226389,9781643681542,,,"English","Front. Artif. Intell. Appl.",Conference Paper,"Final","",Scopus,2-s2.0-85098875301
"Esterwood C., Robert L.P.","57216331965;8247734500;","Personality in Healthcare Human Robot Interaction (H-HRI): A Literature Review and Brief Critique",2020,"HAI 2020 - Proceedings of the 8th International Conference on Human-Agent Interaction",,,,"87","95",,9,"10.1145/3406499.3415075","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096539343&doi=10.1145%2f3406499.3415075&partnerID=40&md5=9d4221ac0969acc2e6d5fd5bbca7d58a","School of Information, Robotics Institute, University of Michigan, Ann Arbor, MI, United States","Esterwood, C., School of Information, Robotics Institute, University of Michigan, Ann Arbor, MI, United States; Robert, L.P., School of Information, Robotics Institute, University of Michigan, Ann Arbor, MI, United States","Robots are becoming an important way to deliver health care, and personality is vital to understanding their effectiveness. Despite this, there is a lack of a systematic overarching understanding of personality in health care human robot interaction (H-HRI). To address this, the authors conducted a review that identified 18 studies on personality in H-HRI. This paper presents the results of that systematic literature review. Insights are derived from this review regarding the methodologies, outcomes, and samples utilized. The authors of this review discuss findings across this literature while identifying several gaps worthy of attention. Overall, this paper is an important starting point in understanding personality in H-HRI. © 2020 ACM.","healthcare; healthcare human-robot interaction; hri; human robot interaction; personality; robots; systematic review","Health care; Man machine systems; Literature reviews; Systematic literature review; Social robots",,,,,,,,,,,,,,,,"Andrist, S., Mutlu, B., Tapus, A., Look like me: Matching robot personality via gaze to increase motivation (2015) Conference on Human Factors in Computing Systems - Proceedings, pp. 3603-3612. , 2015-April (2015); Blaikie, N., (2004) Encyclopedia of Social Science Research Methods; Bogue, R., Robots in healthcare (2011) Industrial Robot: An International Journal, , 2011; Broadbent, E., Kumar, V., Li, X., Sollers, J., Stafford, R.Q., MacDonald, B.A., Wegner, D.M., Robots with display screens: A robot with a more humanlike face display is perceived to have more mind and a better personality (2013) PLoS ONE, 8, p. 8. , 2013; Broadbent, E., Stafford, R., MacDonald, B., Acceptance of healthcare robots for the older population: Review and future directions (2009) International Journal of Social Robotics, 1 (4), p. 319. , 2009; Caprara, G.V., Barbaranelli, C., Borgogni, L., Secchione, M., (2007) Big Five Questionnaire-2; Conti, D., Commodari, E., Buono, S., Personality factors and acceptability of socially assistive robotics in teachers with and without specialized training for children with disability (2017) Life Span and Disability, 20 (2), pp. 251-272. , 2017; Costa, P.T., McCrae, R.R., Normal personality assessment in clinical practice: The NEO personality inventory (1992) Psychological Assessment, 4 (1), p. 5. , 1992; Cruz-Maya, A., Tapus, A., Teaching nutrition and healthy eating by using multimedia with a kompai robot: Effects of stress and user's personality (2016) IEEE-RAS International Conference on Humanoid Robots, pp. 644-649. , 2016; Damholdt, M.F., Nørskov, M., Yamazaki, R., Hakli, R., Hansen, C.V., Vestergaard, C., Seibt, J., Attitudinal change in elderly citizens toward social robots: The role of personality traits and beliefs about robot functionality (2015) Frontiers in Psychology, p. 6. , NOV (2015); Dang, T.-H.-H., Tapus, A., Stress game: The role of motivational robotic assistance in reducing user's task stress (2015) International Journal of Social Robotics, 7 (2), pp. 227-240. , 2015; Deutsch, I., Erel, H., Paz, M., Hoffman, G., Zuckerman, O., Home robotic devices for older adults: Opportunities and concerns (2019) Computers in Human Behavior, 98, pp. 122-133. , 2019; Eysenck, H.J., (1968) Eysenck Personality Inventory, , Educational and Industrial Testing Service San Diego; Gockley, R., Matarić, M.J., Encouraging physical therapy compliance with a hands-off mobile robot (2006) HRI 2006: Proceedings of the 2006 ACM Conference on Human-Robot Interaction, 2006, pp. 150-155. , 2006; Goetz, J., Kiesler, S., Cooperation with a robotic assistant (2002) Conference on Human Factors in Computing Systems - Proceedings, pp. 578-579. , 2002; Goldberg, L.R., An alternative"" description of personality"": The big-five factor structure (1990) Journal of Personality and Social Psychology, 59 (6), p. 1216. , 1990; Gosling, S.D., Rentfrow, P.J., Swann, W.B., Jr., A very brief measure of the big-five personality domains (2003) Journal of Research in Personality, 37 (6), pp. 504-528. , 2003; Harzing, A.W., (2007) Publish or Perish App, , https://harzing.com/resources/publish-or-perish; Hoffman, G., Birnbaum, G.E., Vanunu, K., Sass, O., Reis, H.T., Robot responsiveness to human disclosure affects social impression and appeal (2014) ACM/IEEE International Conference on Human-Robot Interaction, pp. 1-7. , 2014; John, O.P., Donahue, E.M., Kentle, R.L., (1991) The Big Five Inventory-versions 4a and 54; John, O.P., Srivastava, S., The big five trait taxonomy: History, measurement, and theoretical perspectives (1999) Handbook of Personality: Theory and Research, 2 (1999), pp. 102-138. , 1999; Robert, L.P., Jr., Alahmad, R., Esterwood, C., Kim, S., You, S., Zhang, Q., A review of personality in human-robot interactions (2020) Foundations and TrendsR in Information Systems, 4 (2), pp. 107-212. , 2020; Kiesler, S., Powers, A., Fussell, S.R., Torrey, C., Anthropomorphic interactions with a robot and robot-like agent (2008) Social Cognition, 26 (2), pp. 169-181. , 2008; Kimmig, R., Verheijen, R.H.M., Rudnicki, M., Robot assisted surgery during the COVID-19 pandemic, especially for gynecological cancer: A statement of the society of European robotic gynaecological surgery (SERGS) (2020) Journal of Gynecologic Oncology, 31 (3). , 2020; Kleanthous, S., Christophorou, C., Tsiourti, C., Dantas, C., Wintjens, R., Samaras, G., Christodoulou, E., Analysis of elderly users' preferences and expectations on service robot's personality, appearance and interaction (2016) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9755, pp. 35-44. , 2016; Looije, R., Neerincx, M.A., Cnossen, F., Persuasive robotic assistant for health self-management of older adults: Design and evaluation of social behaviors (2010) International Journal of Human Computer Studies, 68 (6), pp. 386-397. , 2010; McCrae, R.R., Costa, P.T., Jr., Martin, T.A., The NEO-PI- 3: A more readable revised NEO personality inventory (2005) Journal of Personality Assessment, 84 (3), pp. 261-270. , 2005; 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Rossi, S., Santangelo, G., Staffa, M., Varrasi, S., Conti, D., Di Nuovo, A., Psychometric evaluation supported by a social robot: Personality factors and technology acceptance (2018) RO-MAN 2018-27th IEEE International Symposium on Robot and Human Interactive Communication, pp. 802-807. , 2018; Sehili, M., Yang, F., Leynaert, V., Devillers, L., A corpus of social interaction between nao and elderly people (2014) 5th International Workshop on Emotion, Social Signals, Sentiment & Linked Open Data (ES3LOD2014). LREC; Sundar, S.S., Jung, E.H., Waddell, T.F., Kim, K.J., Cheery companions or serious assistants? Role and demeanor congruity as predictors of robot attraction and use intentions among senior citizens (2017) International Journal of Human Computer Studies, 97, pp. 88-97. , 2017; Tan, L.F., Seetharaman, S., Preventing the spread of COVID- 19 to nursing homes: Experience from a Singapore geriatric centre (2020) Journal of the American Geriatrics Society, , 2020; Tapus, A., Matarić, M.J., User personality matching with a hands-off robot for post-stroke rehabilitation therapy (2008) Springer Tracts in Advanced Robotics, 39, pp. 165-175. , 2008; Tapus, A., Tapus, C., Mataric, M.J., User-robot personality matching and assistive robot behavior adaptation for (2006) Journal of the Robotics Society of Japan (JRSJ), 24 (5), pp. 14-16. , 2006; Tapus, A., Ţǎpuş, C., Matarić, M.J., User-robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy (2008) Intelligent Service Robotics, 1 (2), p. 169. , 2008; Tay, B., Jung, Y., Park, T., When stereotypes meet robots: The double-edge sword of robot gender and personality in human-robot interaction (2014) Computers in Human Behavior, 38, pp. 75-84. , 2014; Van Vliet, A.J., Effecten van teamsamenstelling op het moreel van uitgezonden militairen (Effects of teamcomposition on the morale of dispatched soldiers)(Rep. No. TM-01-A0404) (2001) Soesterberg: TNO Technische Menskunde, , 2001; Weiss, A., Van Dijk, B., Evers, V., Knowing me knowing you: Exploring effects of culture and context on perception of robot personality (2012) ICIC 2012 - Proceedings of the 4th International Conference on Intercultural Collaboration, pp. 133-136. , 2012; Westgate, M.J., Revtools: An R package to support article screening for evidence synthesis (2019) Research Synthesis Methods, , 2019; Wiggins, J.S., A psychological taxonomy of trait-descriptive terms: The interpersonal domain (1979) Journal of Personality and Social Psychology, 37 (3), p. 395. , 1979; Wiggins, J.S., Broughton, R., A geometric taxonomy of personality scales (1991) European Journal of Personality, 5 (5), pp. 343-365. , 1991; Yang, G.-Z., Nelson, B.J., Murphy, R.R., Choset, H., Christensen, H., Collins, S.H., Dario, P., Jacobstein, N., (2020) Combating COVID-19-The Role of Robotics in Managing Public Health and Infectious Diseases; You, S., Robert, L., Emotional attachment, performance, and viability in teams collaborating with embodied physical action (EPA) robots (2017) Journal of the Association for Information Systems, 19 (5), pp. 377-407. , 2017; You, S., Robert, L., Teaming up with robots: An IMOI (inputsmediators- outputs-inputs) framework of human-robot teamwork (2018) Int.J Robotic Eng(IJRE), 2 (3). , 2018; You, S., Robert, L., Trusting robots in teams: Examining the impacts of trusting robots on team performance and satisfaction (2018) Proceedings of the 52th Hawaii International Conference on System Sciences, pp. 8-11; You, S., Peter Robert, L., Subgroup formation in human- robot teams (2019) International Conference on Information Systems, , 2019; You, S., Robert, L.P., Jr., Human-robot similarity and willingness to work with a robotic co-worker (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 251-260; Zeng, Z., Chen, P.-J., Lew, A.A., From high-touch to high-tech: COVID-19 drives robotics adoption (2020) Tourism Geographies, pp. 1-11. , 2020; Zhang, Q., Esterwood, C., Jessie Yang, X., Robert, L., An automated vehicle (AV) like Me? The impact of personality similarities and differences between humans and AVs (2019) 2019 AAAI Fall Symposium, , 2019",,,"Cyber Agent;frontiers in Robotics and AI","Association for Computing Machinery, Inc","8th International Conference on Human-Agent Interaction, HAI 2020","10 November 2020 through 13 November 2020",,164494,,9781450380546,,,"English","HAI - Proc. Int. Conf. Hum.-Agent Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85096539343
"Muhammad U., Sipra K.A., Waqas M., Tu S., Koubaa A.","57224873171;57221500060;57215416666;57221496402;15923354900;","POSTER: Human-Robot Interaction: A Myo Armband Using EMG and IMU Signals",2020,"Proceedings - 2020 1st International Conference of Smart Systems and Emerging Technologies, SMART-TECH 2020",,,"9283779","251","252",,,"10.1109/SMART-TECH49988.2020.00064","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099294096&doi=10.1109%2fSMART-TECH49988.2020.00064&partnerID=40&md5=7873de815a013faa81dcb87f05334d6b","Gik Institute of Engineering Sciences and Technology, Faculty of Computer Science and Engineering, Topi, 23460, Pakistan","Muhammad, U., Gik Institute of Engineering Sciences and Technology, Faculty of Computer Science and Engineering, Topi, 23460, Pakistan; Sipra, K.A., Gik Institute of Engineering Sciences and Technology, Faculty of Computer Science and Engineering, Topi, 23460, Pakistan; Waqas, M., Gik Institute of Engineering Sciences and Technology, Faculty of Computer Science and Engineering, Topi, 23460, Pakistan; Tu, S., Gik Institute of Engineering Sciences and Technology, Faculty of Computer Science and Engineering, Topi, 23460, Pakistan; Koubaa, A., Gik Institute of Engineering Sciences and Technology, Faculty of Computer Science and Engineering, Topi, 23460, Pakistan","Human-Robot Interaction (HRI) plays an important role in a wide range of applications in healthcare, entertainment, and industries. Many ways are being devised to improve the interaction between humans and machines in the field of robotics. Therefore, our goal is to mimic the human arm movements on the 6-degree of freedom (DOF) robotic arm in real-time. We practically show the interfacing of Myo armband with robots that provide tremendously low calibration time without compromising efficiency. We allow a 6-DOF using electro-Myographic (EM G) and inertial measurement unit (IMU) signals for Myo armband and extract the values of EMG and IMU sensors. © 2020 IEEE.",,"Entertainment industry; Industrial robots; Man machine systems; Robotics; Calibration time; Human arm movements; Human robot Interaction (HRI); Inertial measurement unit; Real time; Human robot interaction",,,,,,,,,,,,,,,,"De Souza, G.A.M., Da Silva, M.G.N.M., Da Gama, A.E.F., Upper limb muscle activation: An emg analysis using myo R-Armband (2019) XXVI Brazilian Congress on Biomedical Engineering. Springer, pp. 397-404; Fan, Y., Yang, C., Wu, X., Improved teleoperation of an industrial robot arm system using leap motion and myo armband (2019) 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO. IEEE, pp. 1670-1675; Said, S., Sheikh, M., Al-Rashidi, F., Lakys, Y., Beyrouthy, T., Nait-Ali, A., A customizable wearable robust 3d printed bionic arm: Muscle controlled (2019) 2019 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART. IEEE, pp. 1-6; Liu, M., Zhou, H., Pang, A., Research on motion control system of 6Dof robotic arm (2020) Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019, pp. 53-61. , Springer; Patel, A., Ramsay, J., Imtiaz, M., Lu, Y., EmgBased human machine interface control (2019) 2019 12th International Conference on Human System Interaction (HSI. IEEE, pp. 127-131; Kim, D., Jorgensen, S.J., Stone, P., Sentis, L., Dynamic behaviors on the nao robot with closedLoop whole body operational space control (2016) 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids. IEEE, pp. 1121-1128; Prasek, M., Kotyrba, M., How to program nao robot using. net (2019) AIP Conference Proceedings, 2116 (1), p. 060010. , AIP Publishing LLC; Batayneh, W., Bataineh, A., Abandeh, S., Aljarrah, M., Banisaeed, M., Alzo'Ubei, B., Using emg signals to remotely control a 3d industrial robotic arm ASME International Mechanical Engineering Congress and Exposition, 59414. American Society of Mechanical Engineers, 2019, pp. V004T05A009",,"Koubaa A.Azar A.T.Qureshi B.","College of Computer and Information Sciences (CCIS);IEEE Student, Prince Sultan University Branch;Prince Sultan University;Research and Initiatives Center (RIC);Robotics and Internet-of-Things","Institute of Electrical and Electronics Engineers Inc.","1st International Conference of Smart Systems and Emerging Technologies, SMART-TECH 2020","3 November 2020 through 5 November 2020",,165860,,9781728174075,,,"English","Proc. - Int. Conf. Smart Syst. Emerg. Technol., SMART-TECH",Conference Paper,"Final","",Scopus,2-s2.0-85099294096
"Osorio J.D.M., Abdelazim A., Allmendinger F., Zimmermann U.E.","57216687815;57222350856;54790643800;7201909731;","Unilateral constraints for torque-based whole-body control",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341241","7623","7628",,,"10.1109/IROS45743.2020.9341241","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102412123&doi=10.1109%2fIROS45743.2020.9341241&partnerID=40&md5=726a88e423992edd2742d59c9eb7ac60","Corporate Research of KUKA Germany GmbH, Augsburg, 86165, Germany; Institute of Mechatronic Systems (IMES), Leibniz University of Hannover, Hannover, 30167, Germany","Osorio, J.D.M., Corporate Research of KUKA Germany GmbH, Augsburg, 86165, Germany, Institute of Mechatronic Systems (IMES), Leibniz University of Hannover, Hannover, 30167, Germany; Abdelazim, A., Corporate Research of KUKA Germany GmbH, Augsburg, 86165, Germany; Allmendinger, F., Corporate Research of KUKA Germany GmbH, Augsburg, 86165, Germany; Zimmermann, U.E., Corporate Research of KUKA Germany GmbH, Augsburg, 86165, Germany","This work uses quadratic programming to perform torque control on an industrial collaborative robot, while keeping defined constraints. Limits for rotational and translational coordinates are considered at position, velocity and acceleration level. Although the problem of having hardware and safe limitations has been considered before. Solutions usually rely on functions that need a proper tuning. The proposed control scheme is tested to work on a real robot to avoid not only static but also dynamic obstacles without the need of any empirical tuning. The method is tested also under physical human robot interaction (pHRI) showing smooth behaviour of the robot despite of external forces. © 2020 IEEE.",,"Agricultural robots; Industrial robots; Intelligent robots; Quadratic programming; Robot programming; Torque control; Collaborative robots; Control schemes; Dynamic obstacles; Empirical tuning; External force; Physical humanrobot interaction (phri); Torque-based; Unilateral constraints; Human robot interaction",,,,,,"1 Authors are with the corporate research of KUKA Germany GmbH, 86165 Augsburg, Germany 2 Authors are with the institute of mechatronic systems (IMES), Leibniz University of Hannover, 30167 Hannover, Germany ∗ corresponding author. JuanDavid.MunozOsorio@kuka.com This work is partly supported by the European Community, within the MURAB project (Grant 688188).",,,,,,,,,,"Khatib, O., A unified approach for motion and force control of robot manipulators: The operational space formulation (1987) Ieee Journal on Robotics and Automation, 3, pp. 43-53. , February; Sentis, L., Khatib, O., Synthesis of whole-body behaviors through hierarchical control of behavioral primitives (2005) International Journal of Humanoid Robotics, 2 (4), pp. 505-518; Sentis, L., Khatib, O., A whole-body control framework for humanoids operating in human environments (2006) Proceedings 2006 Ieee International Conference on Robotics and Automation, 2006. Icra 2006., pp. 2641-2648. , May; Mansard, N., Khatib, O., Kheddar, A., A unified approach to integrate unilateral constraints in the stack of tasks (2009) Ieee Transactions on Robotics, 25, pp. 670-685. , June; Lee, J., Mansard, N., Park, J., Intermediate desired value approach for task transition of robots in kinematic control (2012) Ieee Transactions on Robotics, 28, pp. 1260-1277. , Dec; Han, H., Lee, J., Park, J., A continuous task transition algorithm for operational space control framework (2012) 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 148-152. , Nov; Han, H., Park, J., Robot control near singularity and joint limit using a continuous task transition algorithm (2013) International Journal of Advanced Robotic Systems, 10 (10), p. 346; Simetti, E., Casalino, G., A novel practical technique to integrate inequality control objectives and task transitions in priority based control (2016) Journal of Intelligent &Robotic Systems, 84, pp. 877-902. , Dec; Liu, M., Tan, Y., Padois, V., Generalized hierarchical control (2016) Autonomous Robots, 40, pp. 17-31. , Jan; Dietrich, A., Ott, C., Albu-Sch¨affer, A., An overview of null space projections for redundant, torque-controlled robots (2015) I. J. Robotics Res., 34, pp. 1385-1400; Rubrecht, S., Padois, V., Bidaud, P., De Broissia, M., Da Silva, S.M., Motion safety and constraints compatibility for multibody robots (2012) Autonomous Robots, 32 (3), pp. 333-349; Escande, A., Mansard, N., Wieber, P.-B., Hierarchical quadratic programming: Fast online humanoid-robot motion generation (2014) The International Journal of Robotics Research, 33 (7), pp. 1006-1028; Del Prete, A., Joint position and velocity bounds in discrete-time acceleration/torque control of robot manipulators (2018) Ieee Robotics and Automation Letters, 3 (1), pp. 281-288; Hoffman, E.M., Laurenzi, A., Muratore, L., Tsagarakis, N.G., Caldwell, D.G., Multi-priority cartesian impedance control based on quadratic programming optimization (2018) 2018 Ieee International Conference on Robotics and Automation (ICRA), pp. 309-315. , May; Quiroz-Omana, J.J., Adorno, B.V., Whole-body control with (self) collision avoidance using vector field inequalities (2019) Ieee Robotics and Automation Letters, 4, pp. 4048-4053. , Oct; Flacco, F., Luca, A.D., Khatib, O., Control of redundant robots under hard joint constraints: Saturation in the null space (2015) Ieee Transactions on Robotics, 31, pp. 637-654. , June; Flacco, F., De Luca, A., Khatib, O., (2012) Prioritized Multi-task Motion Control of Redundant Robots under Hard Joint Constraints, pp. 3970-3977; Osorio, J.D.M., Fiore, M.D., Allmendinger, F., Operational space framework under joint constraints (2018) Proceedings. 2018 IDETC/CIE International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, , August; Osorio, J.D.M., Allmendinger, F., Fiore, M.D., Zimmermann, U.E., Ortmaier, T., Physical human-robot interaction under joint and cartesian constraints (2019) 2019 19th International Conference on Advanced Robotics (ICAR), pp. 185-191. , Dec; Del Prete, A., Nori, F., Metta, G., Natale, L., Prioritized motion-force control of constrained fully-actuated robots: Task space inverse dynamics (2015) Robotics and Autonomous Systems, 63, pp. 150-157; Salini, J., Padois, V., Bidaud, P., Synthesis of complex humanoid whole-body behavior: A focus on sequencing and tasks transitions (2011) 2011 Ieee International Conference on Robotics and Automation, pp. 1283-1290. , May; Bouyarmane, K., Kheddar, A., Using a multi-objective controller to synthesize simulated humanoid robot motion with changing contact configurations (2011) 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4414-4419. , Sep; Kanoun, O., Lamiraux, F., Wieber, P., Kanehiro, F., Yoshida, E., Laumond, J., Prioritizing linear equality and inequality systems: Application to local motion planning for redundant robots (2009) 2009 Ieee International Conference on Robotics and Automation, pp. 2939-2944. , May; Weller, R., (2013) New Geometric Data Structures for Collision Detection and Haptics, , Springer Science &Business Media; Ferreau, H.J., Kirches, C., Potschka, A., Bock, H.G., Diehl, M., Qpoases: A parametric active-set algorithm for quadratic programming (2014) Mathematical Programming Computation, 6 (4), pp. 327-363; KUKA Deutschland GmbH KUKA Sunrise.FRI 1.16","Osorio, J.D.M.; Corporate Research of KUKA Germany GmbHGermany; email: JuanDavid.MunozOsorio@kuka.com",,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102412123
"Odoemelem H.U., Van Laerhoven K.","57215525932;8419646700;","A low-cost prototyping framework for human-robot desk interaction",2020,"UbiComp/ISWC 2020 Adjunct - Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers",,,,"191","194",,,"10.1145/3410530.3414323","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091836483&doi=10.1145%2f3410530.3414323&partnerID=40&md5=ba20eaa19f28995cf3674641c58545f0","University of Siegen, Ubiquitous Computing Lab, Siegen, Germany","Odoemelem, H.U., University of Siegen, Ubiquitous Computing Lab, Siegen, Germany; Van Laerhoven, K., University of Siegen, Ubiquitous Computing Lab, Siegen, Germany","Many current human-robot interactive systems tend to use accurate and fast - but also costly - actuators and tracking systems to establish working prototypes that are safe to use and deploy for user studies. This paper presents an embedded framework to build a desktop space for human-robot interaction, using an open-source robot arm, as well as two RGB cameras connected to a Raspberry Pi-based controller that allow a fast yet low-cost object tracking and manipulation in 3D. We show in our evaluations that this facilitates prototyping a number of systems in which user and robot arm can commonly interact with physical objects. © 2020 Owner/Author.","human-robot interaction; object tracking; tangible computing","Costs; Object tracking; Robotic arms; Ubiquitous computing; Wearable computers; Desktop space; Human robots; Interactive system; Open sources; Physical objects; RGB cameras; Robot arms; Tracking system; Social robots",,,,,,,,,,,,,,,,"Grunwald, G., Schreiber, G., Albu-Schaffer, A., Hirzinger, G., Programming by touch: The different way of human-robot interaction (2003) IEEE Transactions on Industrial Electronics, 50 (4), pp. 659-666; Kaehler, A., Bradski, G., (2016) Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library (1st Ed.)., , O'Reilly Media, Inc; Porfirio, D., Fisher, E., Sauppé, A., Albarghouthi, A., Mutlu, B., Bodystorming human-robot interactions (2019) Proceedings of ACM UIST'19, pp. 479-491. , New Orleans, LA, USA; Scholl, P.M., El Majoub, B., Santini, S., Van Laerhoven, K., Connecting wireless sensor networks to the robot operating system (2013) Procedia Computer Science, 19, pp. 1121-1128; Terashima, M., Sakane, S., A human-robot interface using an extended digital desk (1999) Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No. 99CH36288C), 4, pp. 2874-2880. , 4; Vick, A., Surdilovic, D., Krüger, J., Safe physical human-robot interaction with industrial dual-arm robots (2013) Robot Motion and Control., pp. 264-269",,,"ACM SIGCHI;ACM SIGMOBILE","Association for Computing Machinery","2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2020 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2020","12 September 2020 through 17 September 2020",,162964,,9781450380768,,,"English","UbiComp/ISWC Adjun. - Proc. ACM Int. Jt. Conf. Pervasive Ubiquitous Comput. Proc. ACM Int. Symp. Wearable Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85091836483
"Walter J.R., Saini H., Maier B., Mostashiri N., Aguayo J.L., Zarshenas H., Hinze C., Shuva S., Kohler J., Sahrmann A.S., Chang C.-M., Csiszar A., Galliani S., Cheng L.K., Rohrle O.","57220821229;35071876300;57202930268;57193695392;57219008970;56514151400;57021490800;57219005778;57219014666;57206857253;57219009444;36599906600;56893673900;7403337027;14623131100;","Comparative Study of a Biomechanical Model-based and Black-box Approach for Subject-Specific Movement Prediction",2020,"Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS","2020-July",,"9176600","4775","4778",,,"10.1109/EMBC44109.2020.9176600","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091044645&doi=10.1109%2fEMBC44109.2020.9176600&partnerID=40&md5=9a8f3cc42db568224a361bf38da4499f","University of Stuttgart, Stuttgart, Germany; University of Auckland, Auckland, New Zealand","Walter, J.R., University of Stuttgart, Stuttgart, Germany; Saini, H., University of Stuttgart, Stuttgart, Germany; Maier, B., University of Stuttgart, Stuttgart, Germany; Mostashiri, N., University of Auckland, Auckland, New Zealand; Aguayo, J.L., University of Auckland, Auckland, New Zealand; Zarshenas, H., University of Stuttgart, Stuttgart, Germany; Hinze, C., University of Stuttgart, Stuttgart, Germany; Shuva, S., University of Stuttgart, Stuttgart, Germany; Kohler, J., University of Auckland, Auckland, New Zealand; Sahrmann, A.S., University of Auckland, Auckland, New Zealand; Chang, C.-M., University of Stuttgart, Stuttgart, Germany; Csiszar, A., University of Stuttgart, Stuttgart, Germany; Galliani, S., University of Stuttgart, Stuttgart, Germany; Cheng, L.K., University of Auckland, Auckland, New Zealand; Rohrle, O., University of Stuttgart, Stuttgart, Germany","The performance and safety of human robot interaction (HRI) can be improved by using subject-specific movement prediction. Typical models include biomechanical (parametric) or black-box (non-parametric) models. The current work aims to investigate the benefits and drawbacks of these approaches by comparing elbow-joint torque predictions based on electromyography signals of the elbow flexors and extensors. To this end, a parameterized biomechanical model is compared to a non-parametric (Gaussian-process) approach. Both models showed adequate results in predicting the elbow-joint torques. While the non-parametric model requires minimal modeling effort, the parameterized biomechanical model can lead to deeper insight of the underlying subject specific musculoskeletal system. © 2020 IEEE.",,"Biomechanics; Forecasting; Human robot interaction; Musculoskeletal system; Predictive analytics; Bio-mechanical models; Black box approach; Comparative studies; Electromyography signals; Gaussian Processes; Human robot Interaction (HRI); Movement prediction; Non-parametric model; Motion estimation",,,,,,,,,,,,,,,,"Imanishi, A., Nakamura, T., Ohyama, T., Nakamura, T., 3-d finite element analysis of all-ceramic posterior crowns (2003) Journal of Oral Rehabilitation, 30 (8), pp. 818-822. , Aug; Perry, J.C., Rosen, J., Burns, S., Upper-limb powered exoskeleton design (2007) IEEE/ASME Transactions on Mechatronics, 12 (4), pp. 408-417. , Aug; Chadwick, E.K., Blana, D., Kirsch, R.F., Bogert Den Van, A.J., Real-time simulation of three-dimensional shoulder girdle and arm dynamics (2014) IEEE Transactions on Biomedical Engineering, 61 (7), pp. 1947-1956. , Jul; Cavallaro, E.E., Rosen, J., Perry, J.C., Burns, S., Real-time myoprocessors for a neural controlled powered exoskeleton arm (2006) IEEE Transactions on Biomedical Engineering, 53 (11), pp. 2387-2396. , Nov; Venture, G., Yamane, K., Nakamura, Y., Identifying musculotendon parameters of human body based on the musculo-skeletal dynamics computation and hill-stroeve muscle model (2005) 5th IEEERAS International Conference on Humanoid Robots, pp. 351-356. , Dec; Heine, R., Manal, K., Buchanan, T.S., Using hill-type muscle models and emg data in a forward dynamic analysis of joint moment: Evaluation of critical parameters (2003) Journal of Mechanics in Medicine and Biology, 3 (2), pp. 169-186; Pontonnier, C., Dumont, G., Inverse dynamics method using optimization techniques for the estimation of muscles forces involved in the elbow motion (2009) International Journal on Interactive Design and Manufacturing (IJIDeM), 3 (4), p. 227; Wang, L., Buchanan, T.S., Prediction of joint moments using a neural network model of muscle activations from emg signals (2002) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 10 (1), pp. 30-37; Song, R., Tong, K.-Y., Using recurrent artificial neural network model to estimate voluntary elbow torque in dynamic situations (2005) Medical and Biological Engineering and Computing, 43 (4), pp. 473-480; Haeufle, D., Günther, M., Bayer, A., Schmitt, S., Hill-type muscle model with serial damping and eccentric force-velocity relation (2014) Journal of Biomechanics, 47 (6), pp. 1531-1536; Delp, S.L., Anderson, F.C., Arnold, A.S., Loan, P., Habib, A., John, C.T., Guendelman, E., Thelen, D.G., Opensim: Open-source software to create and analyze dynamic simulations of movement (2007) IEEE Transactions on Biomedical Engineering, 54 (11), pp. 1940-1950; Lu, T.-W., O'Connor, J., Bone position estimation from skin marker co-ordinates using global optimisation with joint constraints (1999) Journal of Biomechanics, 32 (2), pp. 129-134; Hatze, H., A myocybernetic control model of skeletal muscle (1977) Biological Cybernetics, 25 (2), pp. 103-119. , Jun; Bayer, A., Schmitt, S., Günther, M., Haeufle, D.F., The influence of biophysical muscle properties on simulating fast human arm movements (2017) Computer Methods in Biomechanics and Biomedical Engineering, 20 (8), pp. 803-821; Kistemaker, D.A., Van Soest, A.K.J., Bobbert, M.F., Is equilibrium point control feasible for fast goal-directed single-joint movements? (2006) Journal of Neurophysiology, 95 (5), pp. 2898-2912. , pMID: 16436480; Günther, M., (1997) Computersimulationen Zur Synthetisierung des Muskulär Erzeugten Menschlichen Gehens Unter Verwendung Eines Biomechanischen Mehrkörpermodells, , Ph. D. dissertation, Eberhard-Karls-Universität zu Tübingen; Mörl, F., Siebert, T., Schmitt, S., Blickhan, R., Günther, M., Electro-mechanical delay in hill-type muscle models (2012) Journal of Mechanics in Medicine and Biology, 12 (5), p. 1250085; Van Soest, A., Bobbert, M., Iijima, T., Shimizu, K., Asanuma, N., The contribution of muscle properties in the control of explosive movements (1993) Biological Cybernetics, 69 (3), pp. 195-204; Williams, C.K., Rasmussen, C.E., (2006) Gaussian Processes for Machine Learning, 2 (3). , MIT press Cambridge, MA",,,,"Institute of Electrical and Electronics Engineers Inc.","42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020","20 July 2020 through 24 July 2020",,162693,1557170X,9781728119908,,,"English","Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS",Conference Paper,"Final","",Scopus,2-s2.0-85091044645
"Du X., Vasudevan R., Johnson-Roberson M.","56262200200;57213570482;23008918800;","Unsupervised pedestrian pose prediction: A deep predictive coding network-based approach for autonomous vehicle perception",2020,"IEEE Robotics and Automation Magazine","27","2","9042808","129","138",,1,"10.1109/MRA.2020.2976313","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083448888&doi=10.1109%2fMRA.2020.2976313&partnerID=40&md5=5ea1d9a04f5b1c58324fccb800c10a91","Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI, United States; Department of Marine Engineering, University of Michigan, Ann Arbor, MI, United States","Du, X., Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI, United States; Vasudevan, R., Department of Marine Engineering, University of Michigan, Ann Arbor, MI, United States; Johnson-Roberson, M., Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI, United States","Pedestrian pose prediction is an important topic, related closely to robotics and automation. Accurate predictions of human poses and motion can facilitate a more thorough understanding and analysis of human behavior, which benefits real-world applications such as human-robot interaction, humanoid and bipedal robot design, and safe navigation of mobile robots and autonomous vehicles. This article describes a deep predictive coding network (PredNet)-based approach for unsupervised pedestrian pose prediction from 2D camera imagery and provides experimental results of two real-world autonomous vehicle data sets. The article also discusses topics for future work in unsupervised and semisupervised pedestrian pose prediction and its potential applications in robotics and automation systems. © 1994-2011 IEEE.",,"Agricultural robots; Behavioral research; Forecasting; Human robot interaction; Machine design; Mobile robots; Network coding; Pedestrian safety; Robotics; Accurate prediction; Automation systems; Human behaviors; Network-based approach; Pose predictions; Predictive coding; Safe navigations; Semi-supervised; Autonomous vehicles",,,,,"N022884; Ford Motor Company","This work was supported by a grant from the Ford Motor Company via the Ford University of Michigan Alliance under award N022884.","This work was supported by a grant from the Ford Motor Company via the Ford–University of Michigan Alliance under award N022884.",,,,,,,,,"Burgard, W., The interactive museum tour-guide robot (1998) Proc. 15th Nat./10th Conf. Artificial Intelligence/Innovative Applications of Artificial Intelligence, pp. 11-18; Thrun, S., MINERVA: A second-generation museum tourguide robot (1999) Proc. IEEE Int. Conf. Robotics and Automation (ICRA), 3, pp. 1999-2005; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human-robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266. , Nov; Kim, W., Adaptable workstations for human-robot collaboration: A reconfigurable framework for improving worker ergonomics and productivity (2019) IEEE Robot. Autom. Mag, 26 (3), pp. 14-26; Petrov, I., Shakhuro, V., Konushin, A., Deep probabilistic human pose estimation (2018) IET Comput. Vis., 12 (5), pp. 578-585; Fragkiadaki, K., Levine, S., Felsen, P., Malik, J., Recurrent network models for human dynamics (2015) Proc. IEEE Int. Conf. Computer Vision (ICCV), pp. 4346-4354; Bütepage, J., Kjellström, H., Kragic, D., Anticipating many futures: Online human motion prediction and generation for humanrobot interaction (2018) Proc. IEEE Int. Conf. Robotics and Automation (ICRA), pp. 1-9; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Comput., 9 (8), pp. 1735-1780; Lotter, W., Kreiman, G., Cox, D., Deep predictive coding networks for video prediction and unsupervised learning (2017) Proc. Int. Conf. Learning Representations, , https://openreview.net/pdf?id=B1ewdt9xe; Geiger, A., Lenz, P., Stiller, C., Urtasun, R., Vision meets robotics: The KITTI dataset (2013) Int. J. Robot. Res., 32 (11), pp. 1231-1237; Cao, Z., Hidalgo Martinez, G., Simon, T., Wei, S., Sheikh, Y.A., OpenPose: Realtime multi-person 2D pose estimation using part affinity fields IEEE Trans. Pattern Anal. Mach. Intell, , to be published; He, K., Gkioxari, G., Dollár, P., Girshick, R., Mask R-CNN (2017) Proc. IEEE Int. Conf. Computer Vision (ICCV), pp. 2980-2988; Kotseruba, I., Rasouli, A., Tsotsos, J.K., (2016) Joint Attention in Autonomous Driving (JAAD), , https://arxiv.org/abs/1609.04741; Rasouli, A., Kotseruba, I., Tsotsos, J.K., Agreeing to cross: How drivers and pedestrians communicate (2017) Proc. IEEE Intell. Veh. Symp. (IV), pp. 264-269; Rasouli, A., Kotseruba, I., Tsotsos, J.K., Are they going to cross? A benchmark dataset and baseline for pedestrian crosswalk behavior (2017) Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pp. 206-213; Rasouli, A., Kotseruba, I., Tsotsos, J.K., Understanding pedestrian behavior in complex traffic scenes (2017) IEEE Trans. Intell. Veh., 3 (1), pp. 61-70; Kim, W., PedX: Benchmark dataset for metric 3-D pose estimation of pedestrians in complex urban intersections (2019) IEEE Robot. Autom. Lett., 4 (2), pp. 1940-1947. , http://pedx.io/, Apr; Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P., Image quality assessment: From error visibility to structural similarity (2004) IEEE Trans. Image Process., 13 (4), pp. 600-612; Du, X., Vasudevan, R., Johnson-Roberson, M., Bio-LSTM: A biomechanically inspired recurrent neural network for 3-D pedestrian pose and gait prediction (2019) IEEE Robot. Autom. Lett., 4 (2), pp. 1501-1508. , Apr; Chollet, F., Keras: The Python deep learning library (2015) Keras, , https://keras.io; Bonsignorio, F., Messina, E., Del Pobil, A.P., Hallam, J., The road ahead-final remarks (2020) Metrics of Sensory Motor Coordination and Integration in Robots and Animals: How to Measure the Success of Bioinspired Solutions with Respect to Their Natural Models, and against More 'Artificial' Solutions?, 36, pp. 181-185. , https://link.springer.com/content/pdf/10.1007/978-3-030-14126-4.pdf#page=201; (2020), https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/quick_start.md#quick-start, CMU Perceptual Computing Lab/OpenPose. Accessed on: Feb. 27; (2020), https://github.com/Superlee506/Mask_RCNN_Humanpose/releases, Mask RCNN Humanpose. Accessed on: Feb. 27",,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10709932,,IRAME,,"English","IEEE Rob Autom Mag",Article,"Final","",Scopus,2-s2.0-85083448888
"Judd E., Digumarti K.M., Rossiter J., Hauser H.","57205612644;57192678077;56230431100;35299439100;","NeatSkin: A Discrete Impedance Tomography Skin Sensor",2020,"2020 3rd IEEE International Conference on Soft Robotics, RoboSoft 2020",,,"9115979","33","38",,3,"10.1109/RoboSoft48309.2020.9115979","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088137782&doi=10.1109%2fRoboSoft48309.2020.9115979&partnerID=40&md5=5511bde3dc779082dd397592486bf50f","University of Bristol, Department of Engineering MathematicsBS8 1TH, United Kingdom","Judd, E., University of Bristol, Department of Engineering MathematicsBS8 1TH, United Kingdom; Digumarti, K.M., University of Bristol, Department of Engineering MathematicsBS8 1TH, United Kingdom; Rossiter, J., University of Bristol, Department of Engineering MathematicsBS8 1TH, United Kingdom; Hauser, H., University of Bristol, Department of Engineering MathematicsBS8 1TH, United Kingdom","In this paper we present NeatSkin, a novel artificial skin sensor based on electrical impedance tomography. The key feature is a discrete network of fluidic channels which is used to infer the location of touch. Change in resistance of the conductive fluid within these channels during deformation is used to construct sensitivity maps. We present a method to simulate touch using this unique network-based, low output dimensionality approach. The efficacy is demonstrated by fabricating a NeatSkin sensor. This paves the way for the development of more complex channel networks and a higher resolution soft skin sensor with potential applications in soft robotics, wearable devices and safe human-robot interaction. © 2020 IEEE.",,"Agricultural robots; Artificial organs; Complex networks; Electric impedance tomography; Human robot interaction; Robotics; Complex channels; Conductive fluids; Discrete networks; Electrical impedance tomography; Fluidic channels; Higher resolution; Impedance tomography; Safe human-robot interaction; Wearable sensors",,,,,"Engineering and Physical Sciences Research Council, EPSRC; Leverhulme Trust: EP/M020460/1, EP/M026388/1, RPG-2016-345; Royal Academy of Engineering, RAENG","VI. ACKNOWLEDGEMENTS This work was partially supported by the EPSRC Centre for Doctoral Training in Future Autonomous and Robotic Systems (FARSCOPE) at the Bristol Robotics Laboratory. Hauser was supported by Leverhulme Trust RPG-2016-345. Rossiter was supported by EPSRC grants EP/M020460/1 and EP/M026388/1 and by the Royal Academy of Engineering as a Chair in Emerging Technologies.",,,,,,,,,,"Trimmer, B., Soft robots and size (2015) Soft Robotics, 2 (2), pp. 49-50; Judd, E., Soter, G., Rossiter, J., Hauser, H., Sensing through the body-non-contact object localisation using morphological computation (2019) 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft), pp. 558-563. , ieee; Soter, G., Conn, A., Hauser, H., Rossiter, J., Bodily aware soft robots: Integration of proprioceptive and exteroceptive sensors (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 2448-2453. , ieee; Digumarti, K.M., Conn, A.T., Rossiter, J., Eumobot: Replicating euglenoid movement in a soft robot (2018) Journal of the Royal Society Interface, 15 (148), p. 20180301; Hauser, H., Nakajima, K., Füchslin, R., Morphological computation-the body as a computational resource (2014) E-book on Opinions and Outlooks on Morphological Computation, pp. 226-244; Lumelsky, V.J., Shur, M.S., Wagner, S., Sensitive skin (2001) IEEE Sensors Journal, 1 (1), pp. 41-51; Li, S., Zhao, H., Shepherd, R.F., Flexible and stretchable sensors for fluidic elastomer actuated soft robots (2017) MRS Bulletin, 42 (2), pp. 138-142; Yeo, J.C., Lim, C.T., Emerging flexible and wearable physical sensing platforms for healthcare and biomedical applications (2016) Microsystems & Nanoengineering, 2, p. 16043; Nittala, A.S., Withana, A., Pourjafarian, N., Steimle, J., Multi-touch skin: A thin and flexible multi-touch sensor for on-skin input (2018) Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, p. 33. , acm; Glauser, O., Panozzo, D., Hilliges, O., Sorkine-Hornung, O., Deformation capture via soft and stretchable sensor arrays (2019) ACM Transactions on Graphics (TOG), 38 (2), p. 16; Teyssier, M., Bailly, G., Pelachaud, C., Lecolinet, E., Conn, A., Roudaut, A., Skin-on interfaces: A bio-driven approach for artificial skin design to cover interactive devices The 32st Annual ACM Symposium on User Interface Software and Technology; Larson, C., Spjut, J., Knepper, R., Shepherd, R., A deformable interface for human touch recognition using stretchable carbon nanotube dielectric elastomer sensors and deep neural networks (2019) Soft Robotics; Brown, B.H., Electrical impedance tomography (eit): A review (2003) Journal of Medical Engineering & Technology, 27 (3), pp. 97-108; Kato, Y., Mukai, T., Hayakawa, T., Shibata, T., Tactile sensor without wire and sensing element in the tactile region based on eit method (2007) SENSORS 2007 IEEE, pp. 792-795; Nagakubo, A., Alirezaei, H., Kuniyoshi, Y., A deformable and deformation sensitive tactile distribution sensor (2007) 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1301-1308; Alirezaei, H., Nagakubo, A., Kuniyoshi, Y., A tactile distribution sensor which enables stable measurement under high and dynamic stretch (2009) 2009 IEEE Symposium on 3D User Interfaces, pp. 87-93; Lee, H., Kwon, D., Cho, H., Park, I., Kim, J., Soft nanocom-posite based multi-point, multi-directional strain mapping sensor using anisotropic electrical impedance tomography (2017) Scientific Reports, 7 (1), pp. 1-10; Chossat, J.-B., Shin, H.-S., Park, Y.-L., Duchaine, V., Soft tactile skin using an embedded ionic liquid and tomographic imaging (2015) Journal of Mechanisms and Robotics, 7 (2); Helps, T., Rossiter, J., Proprioceptive flexible fluidic actuators using conductive working fluids (2018) Soft Robotics, 5 (2), pp. 175-189; Cheung, Y.-N., Zhu, Y., Cheng, C.-H., Chao, C., Leung, W.W.-F., A novel fluidic strain sensor for large strain measurement (2008) Sensors and Actuators A: Physical, 147 (2), pp. 401-408; Han, S., Kim, T., Kim, D., Park, Y.-L., Jo, S., Use of deep learning for characterization of microfluidic soft sensors (2018) IEEE Robotics and Automation Letters, 3 (2), pp. 873-880; Park, Y.-L., Chen, B.-R., Wood, R.J., Design and fabrication of soft artificial skin using embedded microchannels and liquid conductors (2012) IEEE Sensors Journal, 12 (8), pp. 2711-2718; (2015) Ahkab: Open Source Electronic Circuit Simulation in Python, , https://ahkab.github.io/ahkab, G. V. et al; Garrad, M., Soter, G., Conn, A., Hauser, H., Rossiter, J., A soft matter computer for soft robots (2019) Science Robotics, 4 (33); Guliyev, N.J., Ismailov, V.E., A single hidden layer feedforward network with only one neuron in the hidden layer can approximate any univariate function (2016) Neural Computation, 28 (7), pp. 1289-1304",,,,"Institute of Electrical and Electronics Engineers Inc.","3rd IEEE International Conference on Soft Robotics, RoboSoft 2020","15 May 2020 through 15 July 2020",,161217,,9781728165707,,,"English","IEEE Int. Conf. Soft Robot., RoboSoft",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85088137782
"Bellarbi A., Mouaddib A.-I., Achour N., Ouadah N.","57193158921;6701745605;7004112248;24725147100;","Dynamic proxemia modeling approach (DPMA) for navigation and interaction with a group of persons",2020,"Proceedings of the ACM Symposium on Applied Computing",,,,"826","828",,1,"10.1145/3341105.3374109","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083036341&doi=10.1145%2f3341105.3374109&partnerID=40&md5=89fd651c7f42d30b44c16ebd79ef2d4b","Université des Sciences et de la Technologie Houari Boumediene (USTHB), Algiers, Algeria; Centre de Developpement des Technologies Avancées (CDTA), Algiers, Algeria; Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen (GREYC), Caen, France","Bellarbi, A., Université des Sciences et de la Technologie Houari Boumediene (USTHB), Algiers, Algeria, Centre de Developpement des Technologies Avancées (CDTA), Algiers, Algeria, Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen (GREYC), Caen, France; Mouaddib, A.-I., Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen (GREYC), Caen, France; Achour, N., Université des Sciences et de la Technologie Houari Boumediene (USTHB), Algiers, Algeria; Ouadah, N., Centre de Developpement des Technologies Avancées (CDTA), Algiers, Algeria","Most of approaches in Human-Robot Interaction consider a static proxemia independently from the F-Formation type of the people with which we interact and the support of communication (speech, phone, ...). In this paper, we propose a new approach by formalizing dynamic proxemia depending on both the group of people F-Formation and on the activity to accomplish. We develop ""Dynamic Proxemia modelling approach"" (DPMA) by considering different criteria such as social conventions and the activity model to produce a heat map of social interaction and navigation. We develop experiments using ROS and Stage for simulation of a guide robot in a mediation event example showing promising results of our approach. © 2020 Owner/Author.","Human-robot interaction; Proxemia; Social navigation","Air navigation; Speech communication; Activity modeling; F-formation; Guide robots; Heat maps; Model approach; New approaches; Social convention; Social interactions; Human robot interaction",,,,,,,,,,,,,,,,"Kuderer, M., Kretzschmar, H., Sprunk, C., Burgard, W., Feature-based prediction of trajectories for socially compliant navigation (2012) Robotics Science and Systems (RSS); Minguez, G.F., (2013) SOCIAL ROBOT NAVIGATION, , PhD. Thesis; Ramirez, O.A.I., Khambhaita, H., Chatila, R., Chetouani, M., Alami, R., Robots learning how and where to approach people (2016) IEEE 25th International Symposium on Robot and Human Interactive Communication (RO-MAN); Karami, A.B., (2011) ModAles DAcisionnels d'Interaction Homme-Robot, , Ph. D. Thesis report. University of Caen Basse-Normandie; Hall, E., The hidden dimension (1966) Doubleday, 6 (1); Lindner, F., A conceptual model of personal space for human-aware robot activity placement (2015) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Congress Center Hamburg; Riccio, F., Capobianco, R., Nardi, D., Learning human-robot handovers through stam: Policy improvement with spatio-temporal affordance maps (2016) IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids); Rios-Martinez, J., Spalanzani, A., Laugier, C., Understanding human interaction for probabilistic autonomous navigation using risk-rrt approach (2011) IEEE International Conference on Intelligent Robots and Systems; Kendon, A., Spacing and orientation in copresent interaction (2010) Second COST 2102 LNCS, 5967, pp. 1-15. , A. Esposito, N. Campbell, C. Vogel, A. Hussain, A. Nijholt (Eds.). Springer, Heidelberg; Yoda, M., Shiota, Y., The mobile robot which passes a man (1997) International Workshop on Robot and Human Communication. RO-MAN97; Michalowski, M.P., Sabanovic, S., Simmons, R., A spatial model of engagement for a social robot (2006) International Workshop on Advanced Motion Control; Satake, S., Kanda, T., Glas, D.F., Imai, M., Ishiguro, H., Hagita, N., How to approach humans: Strategies for social robots to initiate interaction (2009) 4th ACM/IEEE International Conference on Human-Robot Interaction; Truong, X.T., Ngo, T.D., Dynamic social zone based mobile robot navigation for human comfortable safety in social environments (2016) Int J of Soc Robot; Dijkstra, E.W., A note on two problems in connection with graphs (1959) Numerische Mathematik, 1, pp. 269-271",,,"ACM Special Interest Group on Applied Computing (SIGAPP)","Association for Computing Machinery","35th Annual ACM Symposium on Applied Computing, SAC 2020","30 March 2020 through 3 April 2020",,158685,,9781450368667,,,"English","Proc ACM Symp Appl Computing",Conference Paper,"Final","",Scopus,2-s2.0-85083036341
"Van Maris A., Dogramadzi S., Zook N., Studley M., Winfield A., Caleb-Solly P.","57193757921;6507900733;57224238437;8922780300;7006158694;16244225300;","Speech related accessibility issues in social robots",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"505","507",,1,"10.1145/3371382.3378379","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083190896&doi=10.1145%2f3371382.3378379&partnerID=40&md5=1eff115b7957e7370b1f26c43688af68","Bristol Robotics Laboratory, Bristol, United Kingdom","Van Maris, A., Bristol Robotics Laboratory, Bristol, United Kingdom; Dogramadzi, S., Bristol Robotics Laboratory, Bristol, United Kingdom; Zook, N., Bristol Robotics Laboratory, Bristol, United Kingdom; Studley, M., Bristol Robotics Laboratory, Bristol, United Kingdom; Winfield, A., Bristol Robotics Laboratory, Bristol, United Kingdom; Caleb-Solly, P., Bristol Robotics Laboratory, Bristol, United Kingdom","This work describes an incidental finding from a longitudinal Human-Robot Interaction study that was investigating whether a robot showing emotions during interactions with older adults was perceived differently than to a robot that did not display emotions during the interaction. During this study we noted that some older adults found it hard to understand what the robot was saying, regardless of the volume of speech generated by the robot. The fact that they did not have problems in understanding the researcher led us to investigating this accessibility-related issue in more depth. This paper describes the implications of this finding and recommendations on how to approach future work. © 2020 ACM.","Accessibility; Human-robot interaction; Older adults; Social robot","Agricultural robots; Behavioral research; Man machine systems; Incidental findings; Older adults; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 721619","This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 721619 for the SOCRATES project. We would also like to thank all participants for taking part in this experiment.",,,,,,,,,,"Bishop, L., Van Maris, A., Dogramadzi, S., Zook, N., Social robots: The influence of human and robot characteristics on acceptance (2019) Paladyn Journal of Behavioral Robotics, 10 (1), pp. 346-358. , 2019; Caleb-Solly, P., (2016) Person-environment Interaction, , 2016; Smithey Fulmer, I., Barry, B., Adam Long, D., Lying and smiling: Informational and emotional deception in negotiation (2009) Journal of Business Ethics, 88 (4), pp. 691-709. , 2009; Lazar, J., (2019) Web Accessibility Policy and Law, pp. 247-261. , Web Accessibility. Springer; Pandey, A.K., Gelin, R., A mass-produced sociable humanoid robot: Pepper: The first machine of its kind (2018) IEEE Robotics & Automation Magazine, 25 (3), pp. 40-48. , 2018; Stivers, T., Sidnell, J., Introduction: Multimodal interaction (2005) Semiotica 2005, 156, pp. 1-20. , 2005; Sueyoshi, A., Hardison, D.M., The role of gestures and facial cues in second language listening comprehension (2005) Language Learning, 55 (4), pp. 661-699. , 2005; Van Maris, A., Zook, N., Caleb-Solly, P., Studley, M., Winfield, A., Dogramadzi, S., Ethical considerations of (contextually) affective robot behaviour (2018) Hybrid Worlds: Societal and Ethical Challenges-Proceedings of the International Conference on Robot Ethics and Standards (ICRES 2018). CLAWAR Association Ltd, pp. 13-19",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083190896
"Leung X.Y.","37077607700;","Technology-enabled service evolution in tourism: a perspective article",2020,"Tourism Review","75","1",,"279","282",,5,"10.1108/TR-06-2019-0229","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073958077&doi=10.1108%2fTR-06-2019-0229&partnerID=40&md5=bd14d628f941568188d95786f070ccbf","Department of Hospitality and Tourism Management, University of North Texas, Denton, TX, United States","Leung, X.Y., Department of Hospitality and Tourism Management, University of North Texas, Denton, TX, United States","Purpose: Information technologies are changing the service paradigm. This paper aims to provide a concise review on technology-enabled service evolution in tourism. Design/methodology/approach: The paper is based on a review of relevant literature. Findings: The past evolution of service delivery is summarized in three stages: service, e-service and m-service. The fourth stage of service evolution is predicted to be “a-service” with three features: service automation and human–robot interaction, artificial intelligence and big data and smart travel experience. Originality/value: This paper provides a brief overview of service evolution under the impact of technology. It originally identifies the four stages of service evolution in tourism. © 2019, Emerald Publishing Limited.","a-service; e-service; m-service; Service evolution; Smart experience",,,,,,,,,,,,,,,,,"Augustyn, M., Ho, S.K., Service quality and tourism (1998) Journal of Travel Research, 37 (1), pp. 71-75; Boes, K., Buhalis, D., Inversini, A., Smart tourism destinations: ecosystems for tourism destination competitiveness (2016) International Journal of Tourism Cities, 2 (2), pp. 108-124; Buhalis, D., Amaranggana, A., Tussyadiah, I., Inversini, A., Smart tourism destinations: Enhancing tourism experience through personalisation of services (2015) Information and Communication Technologies in Tourism 2015, pp. 377-389. , and (Eds),., Springer, Heidelberg; Buhalis, D., Licata, M.C., The future of e-tourism intermediaries (2002) Tourism Management, 23 (3), pp. 207-220; Buhalis, D., O’Connor, P., Information communication technology revolutionizing tourism (2005) Tourism Recreation Research, 30 (3), pp. 7-16; Dickinson, J.E., Ghali, K., Cherrett, T., Speed, C., Davies, N., Norgate, S., Tourism and the smartphone app: capabilities, emerging practice and scope in the travel domain (2014) Current Issues in Tourism, 17 (1), pp. 84-101; Ho, C., Lee, Y., The development of an e-travel service quality scale (2007) Tourism Management, 28 (6), pp. 1434-1449; Huang, E.Y., Lin, S., Fan, Y., M-S-QUAL: mobile service quality measurement (2015) Electronic Commerce Research and Applications, 14 (2), pp. 126-142; Kuo, C., Chen, L., Tseng, C., Investigating an innovative service with hospitality robots (2017) International Journal of Contemporary Hospitality Management, 29 (5), pp. 1305-1321; Li, J., Xu, L., Tang, L., Wang, S., Big data in tourism research: a literature review (2018) Tourism Management, 68, pp. 301-323; Murphy, J., Gretzel, U., Pesonen, J., Marketing robot services in hospitality and tourism: the role of anthropomorphism (2019) Journal of Travel & Tourism Marketing, 36; Otto, J.E., Brent Ritchie, J.R., The service experience in tourism (1996) Tourism Management, 7 (3), pp. 165-174; Poon, A., The ‘new tourism’ revolution (1994) Tourism Management, 15 (2), pp. 91-92; Rushton, A.M., Carson, D.J., The marketing of services: managing the intangibles (1985) European Journal of Marketing, 19 (3), p. 19; Sigala, M., Integrating customer relationship management in hotel operations: managerial and operational implications (2005) International Journal of Hospitality Management, 24 (3), pp. 391-413; Sigala, M., Implementing social customer relationship management (2018) International Journal of Contemporary Hospitality Management, 30 (7), pp. 2698-2726; Tung, V.W.S., Law, R., The potential for tourism and hospitality experience research in human-robot interactions (2017) International Journal of Contemporary Hospitality Management, 29 (10), pp. 2498-2513","Leung, X.Y.; Department of Hospitality and Tourism Management, United States; email: xi.leung@unt.edu",,,"Emerald Group Holdings Ltd.",,,,,16605373,,,,"English","Tour. Rev.",Review,"Final","",Scopus,2-s2.0-85073958077
"Li X.","56811045500;","Human–robot interaction based on gesture and movement recognition",2020,"Signal Processing: Image Communication","81",,"115686","","",,13,"10.1016/j.image.2019.115686","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075029019&doi=10.1016%2fj.image.2019.115686&partnerID=40&md5=c39e461f8c2f5d8b14aca6a4bfc03203","State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, Liaoning  110819, China","Li, X., State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, Liaoning  110819, China","Human–robot interaction (HRI) has become a research hotspot in computer vision and robotics due to its wide application in human–computer interaction (HCI) domain. Based on the explored algorithms of gesture recognition and limb movement recognition in somatosensory interaction, an HRI model of a robotic arm is proposed for robot arm manipulation. More specifically, 3D SSD architecture is used for the location and identification of gesture and arm movement. Then, DTW template matching algorithm is adopted to trace the dynamic gestures. The interactive scenarios and interactive modes are designed for experiment and implementation. Virtual interactive experimental results have demonstrated the usefulness of our method. © 2019","3D SSD; DTW; Gesture recognition; Human–robot interaction; Movement recognition","Computer vision; Gesture recognition; Human computer interaction; Human robot interaction; Robotic arms; Robotics; Template matching; 3D SSD; Arm movements; Computer interaction; Interactive mode; Limb movements; Movement recognition; Robot interactions; Template-matching algorithms; Motion estimation",,,,,"National Natural Science Foundation of China, NSFC: 61503070, 61873054; National Natural Science Foundation of China-Yunnan Joint Fund, NSFC-Yunnan Joint Fund; Fundamental Research Funds for the Central Universities: N170804006","The authors acknowledge the National Natural Science Foundation of China (Grant: 61873054 and 61503070 ). Fundamental Research Funds for the Central Universities, China ( N170804006 ) Xing Li was born in 1982. She received the Dr. Eng.degree in Power electronics and power transmission from Northeast University. She is currently an assistant Professor with the State Key Laboratory of Synthetical Automation for Process Industries, Northeast University, Liaoning, China. A number of research results has gained the support of the National Natural Science Fund and Liaoning Natural Science Fund. She is also the member of the Big Data Committee and Process Control Committee for the Chinese Association of Automation. She is current research interests include adaptive/robust control, motion control, Intelligent robot system, etc.",,,,,,,,,,"Malima, A.K., Özgür, E., Çetin, M., A fast algorithm for vision-based hand gesture recognition for robot control (2006); Sánchez-Nielsen, E., Antón-Canalís, L., Hernández-Tejera, M., Hand gesture recognition for human-machine interaction (2004); Wang, C.C., Wang, K.C., Hand posture recognition using adaboost with sift for human robot interaction (2007) Recent Progress in Robotics: Viable Robotic Service to Human, pp. 317-329. , Springer Berlin, Heidelberg; Tang, M., Recognizing Hand Gestures with Microsoft's Kinect (2011), Department of Electrical Engineering of Stanford University: [sn] Palo Alto; Ramey, A., González-Pacheco, V., Salichs, M.A., Integration of a low-cost RGB-D sensor in a social robot for gesture recognition (2011) 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 229-230. , IEEE; Van den Bergh, M., Carton, D., De Nijs, R., Mitsou, N., Landsiedel, C., Kuehnlenz, K., Buss, ….M., Real-time 3D hand gesture interaction with a robot for understanding directions from humans (2011) 2011 Ro-Man, pp. 357-362. , IEEE; Burger, B., Ferrané, I., Lerasle, F., Multimodal interaction abilities for a robot companion (2008) International Conference on Computer Vision Systems, pp. 549-558. , Springer Berlin, Heidelberg; Gast, J., Bannat, A., Rehrl, T., Wallhoff, F., Rigoll, G., Wendt, C., Farber, ….B., Real-time framework for multimodal human–robot interaction (2009) 2009 2nd Conference on Human System Interactions, pp. 276-283. , IEEE; Kahn, P.H., Freier, N.G., Kanda, T., Ishiguro, H., Ruckert, J.H., Severson, R.L., Kane, S.K., Design patterns for sociality in human–robot interaction (2008) Proceedings of the 3rd ACM/IEEE International Conference on Human Robot Interaction, pp. 97-104. , ACM; Chen, Q., Georganas, N.D., Petriu, E.M., Hand gesture recognition using haar-like features and a stochastic context-free grammar (2008) IEEE Trans. Instrum. Meas., 57 (8), pp. 1562-1571; Rautaray, S.S., Agrawal, A., A novel human computer interface based on hand gesture recognition using computer vision techniques (2010) Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia, pp. 292-296. , ACM; Thangali, A., Nash, J.P., Sclaroff, S., Neidle, C., Exploiting phonological constraints for handshape inference in ASL video (2011) CVPR 2011, pp. 521-528. , IEEE; Cooper, H., Ong, E.J., Pugeault, N., Bowden, R., Sign language recognition using sub-units (2012) J. Mach. Learn. Res., 13 (Jul), pp. 2205-2231; Wang, X., Xia, M., Cai, H., Gao, Y., Cattani, C., Hidden-markov-models-based dynamic hand gesture recognition (2012) Math. Probl. Eng., 2012; Zou, Y., Chen, W., Wu, X., Liu, Z., Indoor localization and 3D scene reconstruction for mobile robots using the Microsoft Kinect sensor (2012) IEEE 10th International Conference on Industrial Informatics, pp. 1182-1187. , IEEE; Biswas, J., Veloso, M., Depth camera based indoor mobile robot localization and navigation (2012) 2012 IEEE International Conference on Robotics and Automation, pp. 1697-1702. , IEEE; Song, W., Guo, X., Jiang, F., Yang, S., Jiang, G., Shi, Y., Teleoperation humanoid robot control system based on kinect sensor (2012) 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, Vol. 2, pp. 264-267. , IEEE; Hartmann, J., Forouher, D., Litza, M., Kluessendorff, J.H., Maehle, E., Real-time visual slam using fastslam and the microsoft kinect camera (2012) ROBOTIK 2012; 7th German Conference on Robotics, pp. 1-6. , VDE; Siradjuddin, I., Behera, L., McGinnity, T.M., Coleman, S., A position based visual tracking system for a 7 DOF robot manipulator using a kinect camera (2012) The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1-7. , IEEE; Schröder, M., Elbrechter, C., Maycock, J., Haschke, R., Botsch, M., Ritter, H., Real-time hand tracking with a color glove for the actuation of anthropomorphic robot hands (2012) 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012), pp. 262-269. , IEEE; Jambhale, S.S., Khaparde, A., Gesture recognition using DTW & piecewise DTW (2014) 2014 International Conference on Electronics and Communication Systems (ICECS), pp. 1-5. , IEEE; Wang, X., Xia, M., Cai, H., Gao, Y., Cattani, C., Hidden-markov-models-based dynamic hand gesture recognition (2012) Math. Probl. Eng., 2012; Elmezain, M., Al-Hamadi, A., Michaelis, B., Real-time capable system for hand gesture recognition using hidden markov models in stereo color image sequences (2008); Escalera, S., Gonzàlez, J., Baró, X., Reyes, M., Lopes, O., Guyon, I., Athistos, V., H.J., E., Multi-modal gesture recognition challenge 2013: Dataset and Results (2013) ICMI",,,,"Elsevier B.V.",,,,,09235965,,SPICE,,"English","Signal Process Image Commun",Article,"Final","",Scopus,2-s2.0-85075029019
"Flanagan C., Rajaratnam D., Sammut C.","57226482762;23397871300;6701501948;","Indoor topological mapping for cognitive robots",2020,"Australasian Conference on Robotics and Automation, ACRA","2020-December",,,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111681939&partnerID=40&md5=d4d64f42bfc0cb5f6bdacf3da9a9060d","University of New South Wales, Australia","Flanagan, C., University of New South Wales, Australia; Rajaratnam, D., University of New South Wales, Australia; Sammut, C., University of New South Wales, Australia","For cognitive robots in a domestic environment, intelligent behaviour includes reasoning about the environment that they occupy at an abstract, qualitative level. Having a clear and accurate representation of the environment is essential to human-robot interaction and general cognitive functionality. A domestic environment is typically comprised of a collection of labelled rooms and corridors. In this paper we present a novel approach to segment a metric map or occupancy grid, generated using SLAM, into a collection of semantically labelled regions, while making no assumptions about the environment’s shape, size or contents. Our method achieves on average 98% and 96% precision and recall respectively on both empty and cluttered environments. © 2020 Australasian Robotics and Automation Association. All rights reserved.",,"Agricultural robots; Robotics; Cluttered environments; Cognitive robots; Domestic environments; Occupancy grids; Precision and recall; S shape; Topological mapping; Human robot interaction",,,,,,,,,,,,,,,,"Ambruş, R., Claici, S., Wendt, A., Automatic room segmentation from unstructured 3-d data of indoor environments (2017) IEEE Robotics and Automation Letters, 2 (2), pp. 749-756. , April; Beeson, P., Jong, N. K., Kuipers, B., Towards autonomous topological place detection using the extended voronoi graph (2005) Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 4373-4379; Bormann, R., Jordan, F., Li, W., Hampp, J., Hägele, M., Room segmentation: Survey, implementation, and analysis (2016) 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1019-1026. , May; Brucker, M., Durner, M., Ambruş, R., Márton, Z. C., Wendt, A., Jensfelt, P., Arras, K. O., Triebel, R., Semantic labeling of indoor environments from 3d rgb maps (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1871-1878. , May; Buschka, P., Saffiotti, A., A virtual sensor for room detection (2002) IEEE/RSJ International Conference on Intelligent Robots and Systems, 1, pp. 637-642. , (October); Choset, Howie, Burdick, J., Sensor based motion planning: The hierarchical generalized voronoi graph (1996) Workshop on Algorithmic Foundations of Robotics, , January; Choset, H., Nagatani, K., Topological simultaneous localization and mapping (slam): toward exact localization without explicit localization (2001) IEEE Transactions on Robotics and Automation, 17 (2), pp. 125-137. , April; Choset, Howie, Walker, Sean, Eiamsa-Ard, Kunnayut, Burdick, Joel, Sensor-based exploration: Incremental construction of the hierarchical generalized voronoi graph (2000) The International Journal of Robotics Research, 19 (2), pp. 126-148. , February; Cohn, A G, Qualitative spatial representation and reasoning techniques (1997) KI-97: Advances in Artificial Intelligence, pp. 1-30. , Gerhard Brewka, Christopher Habel, and Bernhard Nebel, editors, pages Berlin, Heidelberg, Springer Berlin Heidelberg; Crespo, Castillo, Oscar, Mozos, Barber, Semantic information for robot navigation: A survey (2020) Applied Sciences, 10, p. 497. , 01; Dellaert, Frank, Bruemmer, David, Semantic SLAM for Collaborative Cognitive Workspaces (2004) AAAI Fall Symposium Series 2004: Workshop on The Interaction of Cognitive Science and Robotics: From Interfaces to Intelligence; Durrant-Whyte, H., Bailey, T., Simultaneous localization and mapping: part i (2006) IEEE Robotics Automation Magazine, 13 (2), pp. 99-110. , June; Fermín-Leon, Leonardo, Neira, José, Castellanos, José A., Incremental contour-based topological segmentation for robot exploration (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 2554-2561; Friedman, Stephen, Pasula, Hanna M., Fox, Dieter, Voronoi random fields: Extracting topological structure of indoor environments via place labeling (2007) IJCAI; Galindo, C., Saffiotti, A., Coradeschi, S., Buschka, P., Fernandez-Madrigal, J. A., Gonzalez, J., Multihierarchical semantic maps for mobile robotics (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2278-2283. , Aug; Garcia-Fidalgo, Emilio, Ortiz, Alberto, Vision-based topological mapping and localization methods: A survey (2015) Robotics and Autonomous Systems, 64, pp. 1-20; Govindarajan, Vijay, Bhattacharya, Subhrajit, Kumar, Vijay, Human-robot collaborative topological exploration for search and rescue applications (2016) Distributed Autonomous Robotic Systems, pp. 17-32. , Nak-Young Chong and Young-Jo Cho, editors, pages Tokyo, Springer Japan; Hess, Wolfgang, Kohler, Damon, Rapp, Holger, Andor, Daniel, Real-time loop closure in 2d lidar slam (2016) 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1271-1278; Kalra, Nidhi, Ferguson, David, , and Anthony (Tony) Stentz. 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G., Trevor, A. J. B., Christensen, H. 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Robotics Autom., ACRA",Conference Paper,"Final","",Scopus,2-s2.0-85111681939
"Hoffman G., Zhao X.","35726707500;57189046346;","A Primer for Conducting Experiments in Human-Robot Interaction",2020,"ACM Transactions on Human-Robot Interaction","10","1","3412374","","",,3,"10.1145/3412374","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097575448&doi=10.1145%2f3412374&partnerID=40&md5=e0bc881c561433f2f288a65c8545289f","Cornell University, Ithaca, NY, United States; University of Chicago, Chicago, IL, United States","Hoffman, G., Cornell University, Ithaca, NY, United States; Zhao, X., University of Chicago, Chicago, IL, United States","We provide guidelines for planning, executing, analyzing, and reporting hypothesis-driven experiments in Human-Robot Interaction (HRI). The intended audience are researchers in the field of HRI who are not trained in empirical research but who are interested in conducting rigorous human-participant studies to support their research. Following the chronological order of research activities and grounded in updated research practices in psychological and behavioral sciences, this primer covers recommended methods and common pitfalls for defining research questions, identifying constructs and hypotheses, choosing appropriate study designs, operationalizing constructs as variables, planning and executing studies, sampling, choosing statistical tools for data analysis, and reporting results. © 2020 Owner/Author.","Experimental studies; research methods; statistical analysis","Agricultural robots; Behavioral research; Man machine systems; Robot programming; Sampling; Statistical mechanics; Behavioral science; Chronological order; Empirical research; Human robot Interaction (HRI); Research activities; Research questions; Statistical tools; Study design; Human robot interaction",,,,,,,,,,,,,,,,"Acemoglu, D., Restrepo, P., Robots and jobs: Evidence from US labormarkets (2020) Journal of Political Economy, 128 (6), pp. 2188-2244; Admoni, H., Dragan, A., Srinivasa, S.S., Scassellati, B., Deliberate delays during robot-to-human handovers improve compliance with gaze communication (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction. 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Hum. Robot Interact",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85097575448
"Guggemos J., Burkhard M., Seufert S., Sonderegger S.","57200695415;57219149785;6603394178;57209238903;","Social robots as teaching assistance system in higher education: Conceptual framework for the development of use cases",2020,"CSEDU 2020 - Proceedings of the 12th International Conference on Computer Supported Education","1",,,"125","132",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091445306&partnerID=40&md5=9809dc677cf02d87137ae625e9037354","Institute of Business Education and Educational Management, Digital Learning, University of St. Gallen, St. Jakobstrasse 21, St. Gallen, 9000, Switzerland","Guggemos, J., Institute of Business Education and Educational Management, Digital Learning, University of St. Gallen, St. Jakobstrasse 21, St. Gallen, 9000, Switzerland; Burkhard, M., Institute of Business Education and Educational Management, Digital Learning, University of St. Gallen, St. Jakobstrasse 21, St. Gallen, 9000, Switzerland; Seufert, S., Institute of Business Education and Educational Management, Digital Learning, University of St. Gallen, St. Jakobstrasse 21, St. Gallen, 9000, Switzerland; Sonderegger, S., Institute of Business Education and Educational Management, Digital Learning, University of St. Gallen, St. Jakobstrasse 21, St. Gallen, 9000, Switzerland","This paper provides an overview of the current state of research on social robots in higher education and the existing frameworks to categorize and develop social robot applications. Based on the existing work, we present our own framework to develop use cases for social robots in the education sector. Our framework is based on a heuristic and symbiotic design approach that serves as a guideline for developing use cases and views human-robot interaction as two complementary and mutually reinforcing roles. We illustrate our framework by means of a use case that we have conducted in 2019 during the initial lecture of the large-scale course 'Introduction to academic writing'. Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved","Human Robot Interaction; Humanoid Robots; Social Robots; Symbiotic Design; Teaching Assistance System","E-learning; Machine design; Robot applications; Social robots; Academic writings; Assistance system; Conceptual frameworks; Design approaches; Education sectors; Higher education; Large-scale course; State of research; Educational robots",,,,,,,,,,,,,,,,"Baker, R.S., Educational data mining: An advance for intelligent systems in education (2014) IEEE Intelligent systems, 29 (3), pp. 78-82; Baraka, K., Alves-Oliveira, P., Ribeiro, T., (2019) An extended framework for characterizing social robots, , ArXiv Preprint ArXiv:1907.09873; Belanche, D., Casaló, L. V., Flavián, C., Schepers, J., Service robot implementation: a theoretical framework and research agenda (2020) The Service Industries Journal, 40 (3-4), pp. 203-225; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: a review (2018) Science Robotics, 3 (21), pp. 1-9. , https://doi.org/10.1126/scirobotics.aat5954; Breazeal, C., Toward sociable robots (2003) Robotics and Autonomous Systems, 3-4 (42), pp. 167-175; Breazeal, C., Brooks, A., Chilongo, D., Gray, J., Hoffman, G., Kidd, C., Lee, H., Lockerd, A., Working collaboratively with humanoid robots (2004) 4th IEEE/RAS International Conference on Humanoid Robots, 2004, , Symposium conducted at the meeting of IEEE; Cooney, M., Leister, W., Using the engagement profile to design an engaging robotic teaching assistant for students (2019) Robotics, 8 (1), p. 21; Cuevas, H. M., Fiore, S. M., Caldwell, B. S., Strater, L., Augmenting team cognition in human-automation teams performing in complex operational environments (2007) Aviation, Space, and Environmental Medicine, 78 (5), pp. B63-B70; Davenport, T. H., Kirby, J., (2016) Only humans need apply: Winners and losers in the age of smart machines (First edition), , New York, NY: Harper Business; Duffy, B.R., Anthropomorphism and the social robot (2003) Robotics and Autonomous Systems, 3-4 (42), pp. 177-190; Flynn, P., Using an Autonomous Humanoid Robot as a Pedagogical Platform in the Business Classroom (2017) Journal of Social Science Studies, 4 (1), pp. 178-188; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Robotics and Autonomous Systems, 3-4 (42), pp. 143-166; Jarrahi, M. H., Artificial intelligence and the future of work: human-ai symbiosis in organizational decision making (2018) Business Horizons, 61 (4), pp. 577-586. , https://doi.org/10.1016/j.bushor.2018.03.007; King, J. L., Grudin, J., Will computers put us out of work? (2016) Computer, 49 (5), pp. 82-85. , https://doi.org/10.1109/MC.2016.126; Leister, W., Tjøstheim, I., Joryd, G., Brisis, M. de, Lauritzsen, S., Reisæter, S., An evaluation-driven design process for exhibitions (2017) Multimodal Technologies and Interaction, 1 (4), p. 25; Mubin, O., Stevens, C. J., Shahid, S., Al Mahmud, A., Dong, J. J., A review of the applicability of robots in education (2013) Journal of Technology in Education and Learning, 1 (209-15), p. 13; Nedelkoska, L., Quintini, G., (2018) Automation, skills use and training, , https://doi.org/10.1787/2e2f4eea-en, OECD Social, Employment and Migration Working Paper 202. Paris; Radanliev, P., Roure, D. de, Nicolescu, R., Huth, M., (2019) A reference architecture for integrating the Industrial Internet of Things in the Industry 4.0, , ArXiv Preprint ArXiv:1903.04369; Reeves, T., Herrington, J., Oliver, R., Design Research: A Socially Responsible Approach to Instructional Technology Research in Higher Education (2005) Journal of Computing in Higher Education, 16 (2), pp. 97-116; Thimm, C., Regier, P., Cheng, I. C., Jo, A., Lippemeier, M., Rutkosky, K, Bennewitz, M., Nehls, P., Die Maschine als Partner? Verbale und non-verbale Kommunikation mit einem humanoiden Roboter (2019) Die Maschine: Freund oder Feind: Mensch und Technologie im digitalen Zeitalter, pp. 109-134. , https://doi.org/10.1007/978-3-658-22954-2_6, C. Thimm & T. C. Bächle (Eds), Wiesbaden: Springer; Veloso, M. M., Biswas, J., Coltin, B., Rosenthal, S., CoBots: Robust Symbiotic Autonomous Mobile Service Robots (2015) IJCAI, p. 4423; Yang, J., Zhang, B., Artificial Intelligence in Intelligent Tutoring Robots: A Systematic Review and Design Guidelines (2019) Applied Sciences, 9 (10), p. 2078; You, S., Robert, L., Teaming up with robots: An IMOI (inputs-mediators-outputs-inputs) framework of human-robot teamwork (2018) International Journal of Robotic Engineering, (IJRE), 2 (3). , You, S. And Robert, LP (2017). Teaming up with Robots: An IMOI (Inputs-Mediators-Outputs-Inputs) Framework of Human-Robot Teamwork",,"Lane H.C.Zvacek S.Uhomoibhi J.","Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","SciTePress","12th International Conference on Computer Supported Education, CSEDU 2020","2 May 2020 through 4 May 2020",,160432,,9789897584176,,,"English","CSEDU - Proc. Int. Conf. Comput. Support. Educ.",Conference Paper,"Final","",Scopus,2-s2.0-85091445306
"Wang X.V., Wang L., (1), Lei M., Zhao Y.","55215531900;55080150600;57216759751;57216769625;","Closed-loop augmented reality towards accurate human-robot collaboration",2020,"CIRP Annals","69","1",,"425","428",,3,"10.1016/j.cirp.2020.03.014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084571481&doi=10.1016%2fj.cirp.2020.03.014&partnerID=40&md5=327339432b7726dcddd0e981a807a74d","Department of Production Engineering, KTH Royal Institute of Technology, Sweden","Wang, X.V., Department of Production Engineering, KTH Royal Institute of Technology, Sweden; Wang, L., (1), Department of Production Engineering, KTH Royal Institute of Technology, Sweden; Lei, M., Department of Production Engineering, KTH Royal Institute of Technology, Sweden; Zhao, Y., Department of Production Engineering, KTH Royal Institute of Technology, Sweden","Augmented Reality provides an interactive approach to combining the physical manufacturing environment with the computer-generated virtuality. However, it is still challenging to establish an efficient AR system due to the complexity of the actual manufacturing environment, and difficulties in human-robot collaboration. In this research, a feasible AR system is developed with a novel closed-loop structure. A feasible AR-based human-robot interaction method is also developed based on the HoloLens device, together with an advanced compensation mechanism towards accurate robot control. The proposed work is implemented and validated through case studies, and quantifiable performance assessment on system response time, compensation accuracy, etc. © 2020 CIRP","Augmented reality; Human-robot collaboration; Manufacturing system","Augmented reality; Manufacture; Closed-loop structures; Compensation mechanism; Computer generated; Human-robot collaboration; Interactive approach; Manufacturing environments; Performance assessment; System response time; Human robot interaction",,,,,,,,,,,,,,,,"Reinhart, G., Munzert, U., Vogl, W., A programming system for robot-based remote-laser-welding with conventional optics (2008) CIRP Ann - Manuf Technol, 57 (1), pp. 37-40; Wang, X.V., Kemény, Z., Váncza, J., Wang, L., Human-robot collaborative assembly in cyber-physical production: Classification framework and implementation (2017) CIRP Ann - Manuf Technol, 66 (4), pp. 5-8; Wang, L., Gao, R., Váncza, J., Krüger, J., Wang, X.V., Makris, S., Chryssolouris, G., Symbiotic human-robot collaborative assembly (2019) CIRP Ann - Manuf Technol, 68 (2), pp. 701-726; Wegener, K., Chen, W.H., Dietrich, F., Dröder, K., Kara, S., Robot assisted disassembly for the recycling of electric vehicle batteries (2015) Procedia CIRP, 29, pp. 716-721; Ong, S.K., Pang, Y., Nee, A.Y.C., Augmented reality aided assembly design and planning (2007) CIRP Ann - Manuf Technol, 56 (1), pp. 49-52; Ong, S.K., Shen, Y., A mixed reality environment for collaborative product design and development (2009) CIRP Ann - Manuf Technol, 58 (1), pp. 139-142; Ong, S.K., Wang, Z.B., Augmented assembly technologies based on 3D bare-hand interaction (2011) CIRP Ann - Manuf Technol, 60 (1), pp. 1-4; Nee, A.Y.C., Ong, S.-K., Virtual and augmented reality applications in manufacturing (2013) IFAC Proc Vol, 46 (9), pp. 15-26; Akan, B., Ameri, A., Cürüklü, B., Asplund, L., Intuitive industrial robot programming through incremental multimodal language and augmented reality (2011) 2011 IEEE Int. Conf. Robot. Autom. IEEE, pp. 3934-3939; Jiang, S., Nee, A.Y.C., A novel facility layout planning and optimization methodology (2013) CIRP Ann - Manuf Technol, 62 (1), pp. 483-486; Makris, S., Pintzos, G., Rentzos, L., Chryssolouris, G., Assembly support using AR technology based on automatic sequence generation (2013) CIRP Ann - Manuf Technol, 62 (1), pp. 9-12; Liu, H., Wang, L., An AR-based Worker Support System for Human-Robot Collaboration (2017) Procedia Manuf., pp. 1-9; Makris, S., Karagiannis, P., Koukas, S., Matthaiakis, A.-S., Augmented reality system for operator support in human–robot collaborative assembly (2016) CIRP Ann - Manuf Technol, 65 (1), pp. 61-64; Kousi, N., Stoubos, C., Gkournelos, C., Michalos, G., Makris, S., Enabling Human Robot Interaction in flexible robotic assembly lines: an Augmented Reality based software suite (2019) Procedia CIRP, 81, pp. 1429-1434; Erkoyuncu, J.A., del Amo, I.F., Dalle Mura, M., Roy, R., Dini, G., Improving efficiency of industrial maintenance with context aware adaptive authoring in augmented reality (2017) CIRP Ann - Manuf Technol, 66 (1), pp. 465-468; Kardos, C., Kemény, Z., Kovács, A., Pataki, B., Váncza, J., Context-dependent multimodal communication in human-robot collaboration (2018) PROCEDIA CIRP, 72, pp. 15-20; Hashimoto, S., Ishida, A., Inami, M., Igarashi, T., Touchme: An augmented reality based remote robot manipulation (2011) 21st Int. Conf. Artif. Real. Telexistence, Proc. ICAT2011. Citeseer, pp. 61-66; Lambrecht, J., Krüger, J., Spatial programming for industrial robots based on gestures and augmented reality (2012) Intell. Robot. Syst. (IROS), 2012 IEEE/RSJ Int. Conf. IEEE, pp. 466-472; Hofmann, C., Staehr, T., Cohen, S., Stricker, N., Haefner, B., Lanza, G., Augmented Go & See: An approach for improved bottleneck identification in production lines (2019) Procedia Manuf, 31, pp. 148-154; Malý, I., Sedláček, D., Leitão, P., Augmented reality experiments with industrial robot in industry 4.0 environment (2016) 2016 IEEE 14th Int. Conf. Ind. Informatics. IEEE, pp. 176-181","Wang, X.V.; Department of Production Engineering, Sweden; email: wangxi@kth.se",,,"Elsevier USA",,,,,00078506,,CIRAA,,"English","CIRP Ann",Article,"Final","",Scopus,2-s2.0-85084571481
"Galin R.R., Meshcheryakov R.V.","57209776926;23035794100;","Human-Robot Interaction Efficiency and Human-Robot Collaboration",2020,"Studies in Systems, Decision and Control","272",,,"55","63",,21,"10.1007/978-3-030-37841-7_5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078076562&doi=10.1007%2f978-3-030-37841-7_5&partnerID=40&md5=815d7db7fb6b0c635d6140107ca55551","V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation","Galin, R.R., V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation; Meshcheryakov, R.V., V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, Moscow, 117997, Russian Federation","This paper presents the question about the interaction between human and collaborative robots, their joint actions in the safety-shared workspace. Presented difference between collaborative robots and other robotic solutions. Collaborative robot technical specification reviewed and the efficiency of human-robot interaction and the HRC Index. In safety case, it is important to identify sources of potential harm, to determine which of the persons in the robot’s vicinity may be in peril and to assess the type of injuries the robot may cause to this person. One of the main tasks that we reviewed is to organize effective interaction between a human and a collaborative robot, taking into account their capabilities. © 2020, Springer Nature Switzerland AG.","Collaboration; Efficiency criterion; Human-robot collaboration; Human-robot interaction",,,,,,"Russian Foundation for Basic Research, РФФИ: 19-08-00331","Acknowledgements The reported study was partially funded by RFBR according to the research project No. 19-08-00331.",,,,,,,,,,"Alexe, B., Deselaers, T., Ferrari, V., Measuring the objectness of image windows (2012) IEEE Trans. Pattern Anal. Mach. Intell., 34, pp. 2189-2202; Asimov, I., (1942) Runaround. In: Astounding Science Fiction; Alexe, B., Deselaers, T., Ferrari, V., Measuring the objectness of image windows (2012) IEEE Trans. Pattern Anal. Mach. Intell., 34, pp. 2189-2202; ISO. ISO/TS 15066:2016-02 (E) Robots and Robotic devices—Collaborative Robots (2016); Alexe, B., Deselaers, T., Ferrari, V., Measuring the objectness of image windows (2012) IEEE Trans. Pattern Anal. Mach. Intell., 34, pp. 2189-2202; Yuschenko, A.S., Human-robot: Compatibility and cooperation (2014) J. Robot. Tech. Cybern., 1 (2), pp. 4-9. , in Russian; Alexe, B., Deselaers, T., Ferrari, V., Measuring the objectness of image windows (2012) IEEE Trans. Pattern Anal. Mach. Intell., 34, pp. 2189-2202; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266. , https://doi.org/10.1016/j.mechatronics.2018.02.009; Khalid, A., Kirisci, P., Ghrairi, Z., Thoben, K.-D., Pannek, J., Towards implementing safety and security concepts for human (2017) 39Th International MATADOR Conference on Advanced Manufacturing, pp. 0-7. , robot collaboration in the context of Industry 4.0. In, Manchester, UK), pp; Lazarte, M., Robots and humans can work together with new ISO guidance Homepage, , https://www.iso.org/news/2016/03/Ref2057.html, last accessed 2018/04/13; Yanco, H., Drury, J., Classifying human-robot interaction: An updated taxonomy (2004) Proceedings of the IEEE International Conference Systems, Man and Cybernetics (SMC), Vol. 3., Pp. 2841–2846. IEEE; Anandan, T., Robots and humans: Safety collaboration (2017) Control Eng, 6 (72), pp. 46-49; Robla-Gomez, S., Working together: A review on safe human-robot collaboration in industrial environments (2017) IEEE Access, 5, pp. 26754-26773; Chueshev, A., Melekhova, O., Meshcheryakov, R., Cloud robotic platform on basis of fog computing approach International Conference on Interactive Collaborative Robotics, pp. 34-43; Ershova, L., The factors of the effectiveness of introducing robotic complex at Russian enterprises (2018) News of the Ural State Mining University, 2 (50), pp. 130-134","Galin, R.R.; V.A. Trapeznikov Institute of Control Sciences of Russian Academy of SciencesRussian Federation; email: rinat.r.galin@yandex.ru",,,"Springer",,,,,21984182,,,,"English","Stud. Syst. Decis. Control",Book Chapter,"Final","",Scopus,2-s2.0-85078076562
"Kollegger G., Ewerton M., Wiemeyer J., Peters J.","57190123160;55976075500;6602541686;35248912800;","Visual perception of robot movements – How much information is required?",2020,"Advances in Intelligent Systems and Computing","1028 AISC",,,"201","209",,,"10.1007/978-3-030-35048-2_24","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076208957&doi=10.1007%2f978-3-030-35048-2_24&partnerID=40&md5=14dd82c0ee2864df09ab825c5ccbc9df","Institute for Sport Science, Technische Universität Darmstadt, Magdalenenstr. 27, Darmstadt, 64289, Germany; Intelligent Autonomous Systems, Computer Science Department, Technische Universität, Hochschulstr. 10, Darmstadt, 64289, Germany","Kollegger, G., Institute for Sport Science, Technische Universität Darmstadt, Magdalenenstr. 27, Darmstadt, 64289, Germany; Ewerton, M., Intelligent Autonomous Systems, Computer Science Department, Technische Universität, Hochschulstr. 10, Darmstadt, 64289, Germany; Wiemeyer, J., Institute for Sport Science, Technische Universität Darmstadt, Magdalenenstr. 27, Darmstadt, 64289, Germany; Peters, J., Intelligent Autonomous Systems, Computer Science Department, Technische Universität, Hochschulstr. 10, Darmstadt, 64289, Germany","Human-robot interactions are steadily increasing in all areas of life. In this context, a common motion learning process of human-robot dyads has not been studied so far. The observation of movement characteristics plays a crucial role in the assessment and learning of movements in human-human dyads. But what visual information of a robot movement can be perceived and predicted by humans? The following study examines the perception and prediction of robot putt movements by humans with different visual stimuli. Relevant clues could be identified for the specific movement. Ultimately, with sufficient visual information, humans are able to correctly predict the outcome of a robot putt movement. © Springer Nature Switzerland AG 2020.","Dyad-learning; Human-robot-interaction; Motor learning","Man machine systems; Sports; Dyad-learning; Motion learning; Motor learning; Movement characteristics; Robot movements; Visual information; Visual perception; Visual stimulus; Human robot interaction",,,,,,,,,,,,,,,,"Ballreich, R., Analyse und Ansteuerung sportmotorischer Techniken aus biomechanischer Sicht (1983) Motorik Und Bewegungs-Forschung, pp. 72-92. , Rieder, H., Bos, K., Mechling, H., Reischle, K. (eds.), p., Hofmann, Schorndorf; Cañal-Bruland, R., Williams, A.M., Recognizing and predicting movement effects: Identifying critical movement features (2010) Exp. Psychol., 57 (4), p. 320; Kollegger, G., Ewerton, M., Wiemeyer, J., Peters, J., BIMROB–bidirectional interaction between human and robot for the learning of movements (2018) Proceedings of the 11Th International Symposium on Computer Science in Sport (IACSS 2017), pp. 151-163. , Lames, M., Saupe, D., Wiemeyer, J. (eds.), pp., Springer, Cham; Kollegger, G., Wiemeyer, J., Ewerton, M., Peters, J., (2019) Visual Perception of Robot Movements, , Unpublished manuscript; Lens, T., Kunz, J., von Stryk, O., Trommer, C., Karguth, A., Biorob-arm: A quickly deployable and intrinsically safe, light-weight robot arm for service robotics applications. Paper presented at the Robotics (ISR) (2010) 2010 41St International Symposium on and 2010 6Th German Conference on Robotics (ROBOTIK); Lens, T., von Stryk, O., Design and dynamics model of a lightweight series elastic tendon-driven robot arm (2013) Paper Presented at the Robotics and Automation (ICRA), 2013 IEEE International Conference on Robotics and Automation (ICRA, p. 2013; Orgs, G., Bestmann, S., Schuur, F., Haggard, P., From body form to biological motion: The apparent velocity of human movement biases subjective time (2011) Psychol. Sci., 22 (6), pp. 712-717; Poolton, J.M., Maxwell, J., Masters, R., Raab, M., Benefits of an external focus of attention: Common coding or conscious processing? (2006) J. Sport. Sci., 24 (1), pp. 89-99; Runeson, S., Frykholm, G., Kinematic specification of dynamics as an informational basis for person-and-action perception: Expectation, gender recognition, and deceptive intention (1983) J. Exp. Psychol. Gen., 112 (4), pp. 585-615; Tremoulet, P.D., Feldman, J., Perception of animacy from the motion of a single object (2000) Perception, 29 (8), pp. 943-951; Schmidt, R.A., Lee, T.D., (1988) Motor Control and Learning, , Human kinetics, Champaign; Wilkins, L., Vision testing and visual training in sport (2015) University of Birmingham. Ph.D.","Kollegger, G.; Institute for Sport Science, Magdalenenstr. 27, Germany; email: kollegger@sport.tu-darmstadt.de","Lames M.Danilov A.Vassilevski Y.Timme E.",,"Springer","12th International Symposium of Computer Science in Sports, IACSS 2019","8 July 2019 through 10 July 2019",,234009,21945357,9783030350475,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85076208957
"Alonso R., Concas E., Recupero D.R.","56188189100;57212245253;57206674454;","An abstraction layer exploiting voice assistant technologies for effective human—robot interaction",2021,"Applied Sciences (Switzerland)","11","19","9165","","",,1,"10.3390/app11199165","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116318035&doi=10.3390%2fapp11199165&partnerID=40&md5=c880ea5c4eb9eb473170a3792e9b4824","ICT Division, R2M Solution s.r.l., Pavia, 27100, Italy; Department of Mathematics and Computer Science and Human-Robot Interaction Lab, University of Cagliari, Cagliari, 09124, Italy","Alonso, R., ICT Division, R2M Solution s.r.l., Pavia, 27100, Italy; Concas, E., Department of Mathematics and Computer Science and Human-Robot Interaction Lab, University of Cagliari, Cagliari, 09124, Italy; Recupero, D.R., ICT Division, R2M Solution s.r.l., Pavia, 27100, Italy, Department of Mathematics and Computer Science and Human-Robot Interaction Lab, University of Cagliari, Cagliari, 09124, Italy","A lot of people have neuromuscular problems that affect their lives leading them to lose an important degree of autonomy in their daily activities. When their disabilities do not involve speech disorders, robotic wheelchairs with voice assistant technologies may provide appropriate human–robot interaction for them. Given the wide improvement and diffusion of Google Assistant, Apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa, etc., such voice assistant technologies can be fully integrated and exploited in robotic wheelchairs to improve the quality of life of affected people. As such, in this paper, we propose an abstraction layer capable of providing appropriate human– robot interaction. It allows use of voice assistant tools that may trigger different kinds of applications for the interaction between the robot and the user. Furthermore, we propose a use case as a possible instance of the considered abstraction layer. Within the use case, we chose existing tools for each component of the proposed abstraction layer. For example, Google Assistant was employed as a voice assistant tool; its functions and APIs were leveraged for some of the applications we deployed. On top of the use case thus defined, we created several applications that we detail and discuss. The benefit of the resulting Human–Computer Interaction is therefore two-fold: on the one hand, the user may interact with any of the developed applications; on the other hand, the user can also rely on voice assistant tools to receive answers in the open domain when the statement of the user does not enable any of the applications of the robot. An evaluation of the presented instance was carried out using the Software Architecture Analysis Method, whereas the user experience was evaluated through ad-hoc questionnaires. Our proposed abstraction layer is general and can be instantiated on any robotic platform including robotic wheelchairs. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Cloud robotics; Human–robot dialogue; Language understanding; Robotic wheelchairs; Voice assistant technology",,,,,,"Horizon 2020 Framework Programme, H2020: 956573","Funding: This research was partially funded by the H2020 project STAR—Novel AI technology for dynamic and unpredictable manufacturing environments (grant number 956573).",,,,,,,,,,"Feil-Seifer, D., Mataric, M., Robot-assisted therapy for children with autism spectrum disorders Proceedings of the 7th International Conference on Interaction Design and Children, pp. 49-52. , London, UK, 17–24 June 2008; [CrossRef]; D’Onofrio, G., Sancarlo, D., Raciti, M., Recupero, D.R., Mangiacotti, A., Russo, A., Ricciardi, F., Presutti, V., MARIO Project: Experimentation in the Hospital Setting Proceedings of the Ambient Assisted Living—Italian Forum 2017, Eighth Italian on Ambient Assisted Living Forum, ForItAAL 2017, pp. 289-303. , Genoa, Italy, 14–15 June 2017; [CrossRef]; (2018) Health at a Glance: Europe 2018, p. 212. , Organisation for Economic Co-operation and Development. 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(accessed on 30 June 2021); Global-Known Robot that Helps Kids Get Smarter as They Play, , https://www.makeblock.com/steamkits/mbot/, mBot, (accessed on 30 June 2021); Recupero, D.R., Alam, M., Buscaldi, D., Grezka, A., Tavazoee, F., Frame-Based Detection of Figurative Language in Tweets [Application Notes] (2019) IEEE Comput. Intell. Mag, 14, pp. 77-88. , [CrossRef]; Discover What Google Assistant Is, , https://assistant.google.com/, (accessed on 30 June 2021); Siri Does More Than Ever. Even before You Ask, , https://www.apple.com/siri/, (accessed on 30 June 2021); Microsoft Cortana, , https://www.microsoft.com/en-in/windows/cortana, (accessed on 30 June 2021); https://developer.amazon.com/alexa, Amazon Alexa. (accessed on 30 June 2021); Build Natural Language Experiences with Wit, , https://wit.ai, (accessed on 30 June 2021); https://snips.ai/, Snips. (accessed on 30 June 2021); Erol, B.A., Wallace, C., Benavidez, P., Jamshidi, M., Voice Activation and Control to Improve Human Robot Interactions with IoT Perspectives Proceedings of the 2018 World Automation Congress (WAC), pp. 1-5. , Stevenson, WA, USA, 3–6 June 2018; [CrossRef]; Faria, B.M., Reis, L.P., Lau, N., A Survey on Intelligent Wheelchair Prototypes and Simulators (2014) New Perspectives in Information Systems and Technologies, 1, pp. 545-557. , Rocha, Á., Correia, A.M., Tan, F.B., Stroetmann, K.A., Eds.; Springer International Publishing: Cham, Switzerland; https://dialogflow.com/, Dialogflow. (accessed on 30 June 2021); Miller, D.P., Slack, M.G., Design and testing of a low-cost robotic wheelchair prototype (1995) Auton. Robot, 2, pp. 77-88. , [CrossRef]; Marchal-Crespo, L., Furumasu, J., Reinkensmeyer, D.J., A robotic wheelchair trainer: Design overview and a feasibility study (2010) J. Neuroeng. 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Manag, 57, p. 102094. , [CrossRef]; Reforgiato Recupero, D., Technology Enhanced Learning Using Humanoid Robots (2021) Future Internet, 13, p. 32. , [CrossRef]; Bass, L., Clements, P., Kazman, R., (2003) Software Architecture in Practice, , https://books.google.ro/books?hl=en&lr=&id=ZY6UZTjBnGQC&oi=fnd&pg=PA1&dq=Bass,+L.%3B+Clements,+P.%3B+Kazman,+R.+%5Cemph%7BSoftware+Architecture+in+Practice%7D%3B++%5Chl%7B2003.%7D&ots=gsptTsR_rh&sig=YhRFJCTVL4ldBqRggttYO-SIBck&redir_esc=y#v=onepage&q&f=false, (accessed on 30 June 2021); Kazman, R., Klein, M., Barbacci, M., Longstaff, T., Lipson, H., Carrière, S., The Architecture Tradeoff Analysis Method (2002) Proceedings of the Fourth IEEE International Conference on Engineering of Complex Computer Systems (Cat. No.98EX193), pp. 68-78. , Monterey, CA, USA, 6 August [CrossRef]; Kazman, R., Bass, L., Abowd, G., Webb, M., SAAM: A method for analyzing the properties of software architectures (1994) Proceedings of the 16th International Conference on Software Engineering, pp. 81-90. , Sorrento, Italy, 16–21 May; Clements, P., (2000) Active Reviews for Intermediate Designs, p. 26. , Software Engineering Institute, Carnegie Mellon University: Pittsburgh, PA, USA; Ali Babar, M., Gorton, I., Comparison of Scenario-Based Software Architecture Evaluation Methods (2004) Proceedings of the 11th Asia-Pacific Software Engineering Conference, pp. 600-607. , Busan, Korea, 30 November–3 December [CrossRef]","Recupero, D.R.; ICT Division, Italy; email: diego.reforgiato@unica.it",,,"MDPI",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85116318035
"Logacjov A., Kerzel M., Wermter S.","57212374499;41861835400;7003826680;","Learning Then, Learning Now, and Every Second in Between: Lifelong Learning With a Simulated Humanoid Robot",2021,"Frontiers in Neurorobotics","15",,"669534","","",,,"10.3389/fnbot.2021.669534","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110316863&doi=10.3389%2ffnbot.2021.669534&partnerID=40&md5=47b71b16710d975c7405b994255995cb","Department of Informatics, Research Group Knowledge Technology, Universität Hamburg, Hamburg, Germany","Logacjov, A., Department of Informatics, Research Group Knowledge Technology, Universität Hamburg, Hamburg, Germany; Kerzel, M., Department of Informatics, Research Group Knowledge Technology, Universität Hamburg, Hamburg, Germany; Wermter, S., Department of Informatics, Research Group Knowledge Technology, Universität Hamburg, Hamburg, Germany","Long-term human-robot interaction requires the continuous acquisition of knowledge. This ability is referred to as lifelong learning (LL). LL is a long-standing challenge in machine learning due to catastrophic forgetting, which states that continuously learning from novel experiences leads to a decrease in the performance of previously acquired knowledge. Two recently published LL approaches are the Growing Dual-Memory (GDM) and the Self-organizing Incremental Neural Network+ (SOINN+). Both are growing neural networks that create new neurons in response to novel sensory experiences. The latter approach shows state-of-the-art clustering performance on sequentially available data with low memory requirements regarding the number of nodes. However, classification capabilities are not investigated. Two novel contributions are made in our research paper: (I) An extended SOINN+ approach, called associative SOINN+ (A-SOINN+), is proposed. It adopts two main properties of the GDM model to facilitate classification. (II) A new LL object recognition dataset (v-NICO-World-LL) is presented. It is recorded in a nearly photorealistic virtual environment, where a virtual humanoid robot manipulates 100 different objects belonging to 10 classes. Real-world and artificially created background images, grouped into four different complexity levels, are utilized. The A-SOINN+ reaches similar state-of-the-art classification accuracy results as the best GDM architecture of this work and consists of 30 to 350 times fewer neurons, evaluated on two LL object recognition datasets, the novel v-NICO-World-LL and the well-known CORe50. Furthermore, we observe an approximately 268 times lower training time. These reduced numbers result in lower memory and computational requirements, indicating higher suitability for autonomous social robots with low computational resources to facilitate a more efficient LL during long-term human-robot interactions. © Copyright © 2021 Logacjov, Kerzel and Wermter.","growing dual-memory; lifelong learning; lifelong learning dataset; long-term human-robot interaction; self-organizing incremental neural network; simulated humanoid robot","Anthropomorphic robots; Classification (of information); Man machine systems; Object recognition; Social robots; Catastrophic forgetting; Classification accuracy; Computational requirements; Computational resources; Life long learning; Self-organizing incremental neural network; Sensory experiences; Virtual humanoids; Neural networks",,,,,"Deutsche Forschungsgemeinschaft, DFG: CML (TRR 169","We gratefully acknowledge partial support from the German Research Foundation DFG under project CML (TRR 169).",,,,,,,,,,"Adel, T., Zhao, H., Turner, R.E., Continual learning with adaptive weights (CLAW) (2020) arXiv:1911.09514 [cs, stat]; Aljundi, R., Babiloni, F., Elhoseiny, M., Rohrbach, M., Tuytelaars, T., Memory aware synapses: learning what (not) to forget (2018) arXiv:1711.09601 [cs, stat]; Belouadah, E., Popescu, A., “IL2M: class incremental learning with dual memory,” (2019) 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 583-592. , Seoul; (2020) Blender (v2.82.7), , https://www.blender.org, Available online at; Chaudhry, A., Dokania, P.K., Ajanthan, T., Torr, P.H.S., Riemannian walk for incremental learning: understanding forgetting and intransigence (2018) CoRR, , abs/1801.10112; Dautenhahn, K., (2004) Robots we like to live with?! a developmental perspective on a personalized, life-long robot companion, pp. 17-22; Douillard, A., Cord, M., Ollion, C., Robert, T., Valle, E., PODNet: pooled outputs distillation for small-tasks incremental learning (2020) arXiv:2004.13513 [cs]; Fernando, C., Banarse, D., Blundell, C., Zwols, Y., Ha, D., Rusu, A.A., (2017) Pathnet: Evolution channels gradient descent in super neural networks. 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J. Comput. Vis, 115, pp. 1-42; Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., (2016) Progressive neural networks. CoRRabs/1606.04671, , http://arxiv.org/abs/1606.04671, Available online at; Serrà, J., Surís, D., Miron, M., Karatzoglou, A., (2018) Overcoming catastrophic forgetting with hard attention to the task. CoRRabs/1801.01423, , https://arxiv.org/abs/1801.01423, Available online at; Shin, H., Lee, J.K., Kim, J., Kim, J., (2017) Continual learning with deep generative replay. CoRRabs/1705.08690, , https://arxiv.org/abs/1705.08690, Available online at; Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition (2014) arXiv 1409.1556; Stojanov, S., Mishra, S., Thai, N.A., Dhanda, N., Humayun, A., Yu, C., “Incremental object learning from contiguous views,” (2019) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), , Long Beach, CA, in; Upton, G., Cook, I., (1996) Understanding Statistics, p. 55. , Long Beach, CA, OUP Oxford; Wiwatcharakoses, C., Berrar, D., Soinn+, a self-organizing incremental neural network for unsupervised learning from noisy data streams (2019) Exp. Syst. Appl, p. 143; Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., Large Scale Incremental Learning (2019) arXiv:1905.13260 [cs], , version: 1; Yoon, J., Yang, E., Lee, J., Hwang, S.J., Lifelong learning with dynamically expandable networks (2018) arXiv:1708.01547 [cs]; Zenke, F., Poole, B., Ganguli, S., (2017) Improved multitask learning through synaptic intelligence. CoRRabs/1703.04200, , https://arxiv.org/abs/1703.04200v1, Available online at","Logacjov, A.; Department of Informatics, Germany; email: a.logacjov@gmail.com",,,"Frontiers Media S.A.",,,,,16625218,,,,"English","Front. Neurorobotics",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85110316863
"Tseng C.-H., Hung T.-F., Yeh S.-L.","7402541752;57226591310;57226597223;","Robot-Human Partnership is Unique: Partner-Advantage in a Shape-Matching Task",2021,"Communications in Computer and Information Science","1420",,,"202","206",,,"10.1007/978-3-030-78642-7_27","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112060323&doi=10.1007%2f978-3-030-78642-7_27&partnerID=40&md5=5723de60e670bad8fc51da752913a2af","Tohoku University, Sendai, Japan; National Taiwan University, Taipei, Taiwan","Tseng, C.-H., Tohoku University, Sendai, Japan; Hung, T.-F., National Taiwan University, Taipei, Taiwan; Yeh, S.-L., National Taiwan University, Taipei, Taiwan","Human-human interaction studies have shown that we prioritize psychological resources (attention, memory, etc.) to significant others (e.g., family member, friends) and to a stranger assigned to co-work with us. We examine whether the human-robot interaction shares a similar nature. We adopted a “shape-identity matching task” in which participants first learned to associate 3 shapes with 3 names, and then judged whether the shape-name association in each trial was matched or mismatched. One of the 3 names belong to a social robot with a humanoid face (ASUS Zenbo), who was introduced as a partner to co-work with our participants. The other two names belong to the participant’s best friend and to a stranger. In half of the trials, the trials were printed in green, and in the other half of the trials, the names were printed in red. Each participant was instructed to respond to the trials in their assigned color (red or green), and the other half would be completed by their robot partner (co-work on the same task). We found that a robot partner was endowed with a “partner-advantage” similar to a human partner: the trials associated with the robot’s name were responded faster and more accurately than the trials associated with a stranger’s name. This advantage toward a robot is developed quickly and requires minimum interaction between human and robot. Interestingly, the effect is further boosted when the robot’s expected role (i.e., social companion or functional robot) matches the robot’s presence during the behavioral task (i.e., presence or absence). This was never observed among human partners. This unique feature in human-robot interaction implies that we may evaluate a robot partner heavily on the alignment between the expectation and its delivered service. © 2021, Springer Nature Switzerland AG.","Functionality; Human-Robot interaction; Identify-advantage; Social robot","Human computer interaction; Human-human interactions; Robot Partners; Shape matching; Unique features; Social robots",,,,,,,,,,,,,,,,"Holzinger, A., Maurer, H., Incidental learning, motivation, and the Tamagotchi Effect: VR-friends, chances for new ways of learning with computers. In: Computer Assisted Learning (1999) CAL, 99; Wada, K., Shibata, T., Living with seal robots – its sociopsychological and physiological influences on the elderly at a care house (2007) IEEE Trans. Rob., 23 (5), pp. 972-980; Cheng, M., Tseng, C.H., Saliency at first sight: Instant identity referential advantage toward a newly met partner (2019) CRPI, 4 (1), pp. 1-18; Sui, J., He, X., Humphreys, G.W., Perceptual effects of social salience: evidence from self-prioritizeation effects on perceptual matching (2012) JEP:HPP, 38 (5), p. 1105","Tseng, C.-H.; Tohoku UniversityJapan; email: tseng@riec.tohoku.ac.jp","Stephanidis C.Antona M.Ntoa S.",,"Springer Science and Business Media Deutschland GmbH","23rd International Conference on Human-Computer Interaction, HCII 2021","24 July 2021 through 29 July 2021",,262179,18650929,9783030786410,,,"English","Commun. Comput. Info. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85112060323
"Abubshait A., McDonald C.G., Wiese E.","57195436971;12139698500;57191244776;","Examining social cognition with embodied robots: Does prior experience with a robot impact feedbackassociated learning in a gambling task?",2021,"Journal of Cognition","4","1",,"1","16",,,"10.5334/JOC.167","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108550909&doi=10.5334%2fJOC.167&partnerID=40&md5=8329ee36529a810efcbe6e8cb5097fd7","George Mason University, United States; Italian Institute of Technology, Italy; Berlin Institute of Technology, United States","Abubshait, A., George Mason University, United States, Italian Institute of Technology, Italy; McDonald, C.G., George Mason University, United States; Wiese, E., Berlin Institute of Technology, United States","Social agents rely on the ability to use feedback to learn and modify their behavior. The extent to which this happens in social contexts depends on motivational, cognitive and/or affective parameters. For instance, feedback-associated learning occurs at different rates when the outcome of an action (e.g., winning or losing in a gambling task) affects oneself (""Self"") versus another human (""Other""). Here, we examine whether similar context effects on feedback-associated learning can also be observed when the ""other"" is a social robot (here: Cozmo). We additionally examine whether a ""hybrid"" version of the gambling paradigm, where participants are free to engage in a dynamic interaction with a robot, then move to a controlled screen-based experiment can be used to examine social cognition in human-robot interaction. This hybrid method is an alternative to current designs where researchers examine the effect of the interaction on social cognition during the interaction with the robot. For that purpose, three groups of participants (n total = 60) interacted with Cozmo over different time periods (no interaction vs. a single 20 minute interaction in the lab vs. daily 20 minute interactions over five consecutive days at home) before performing the gambling task in the lab. The results indicate that prior interactions impact the degree to which participants benefit from feedback during the gambling task, with overall worse learning immediately after short-term interactions with the robot and better learning in the ""Self"" versus ""Other"" condition after repeated interactions with the robot. These results indicate that ""hybrid"" paradigms are a suitable option to investigate social cognition in human-robot interaction when a fully dynamic implementation (i.e., interaction and measurement dynamic) is not feasible. © The Author(s), 2021.","Cognitive Control; Learning; Reward processing; Social cognition",,,,,,"George Mason University, GMU","Part of this study was funded by the Human Factors and Applied Cognition Fellowship from the Human Factors and Applied Cognition program of the Psychology Department at George Mason University. The award was granted to Abdulaziz Abubshait in 2019.",,,,,,,,,,"Abubshait, A., Beatty, P. J., McDonald, C. G., Hassall, C. D., Krigolson, O. E., Wiese, E., A win-win situation: Does familiarity with a social robot modulate feedback monitoring and learning? 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"Chen L., Su W., Li M., Wu M., Pedrycz W., Hirota K.","55774663200;57196287282;57204930290;57218664297;57210775415;7402829702;","A population randomization-based multi-objective genetic algorithm for gesture adaptation in human-robot interaction",2021,"Science China Information Sciences","64","1","112208","","",,,"10.1007/s11432-019-2749-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098947115&doi=10.1007%2fs11432-019-2749-0&partnerID=40&md5=aeae1791cc147deab3a303c15132930c","School of Automation, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Department of Electrical and Computer Engineering, University of Alberta, Edmonton AB, T6R 2G7, Canada; Tokyo Institute of Technology, Yokohama, 226-8502, Japan","Chen, L., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Su, W., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Li, M., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton AB, T6R 2G7, Canada; Hirota, K., Tokyo Institute of Technology, Yokohama, 226-8502, Japan","In recent years, vision-based gesture adaptation has attracted great attention from many experts in the field of human-robot interaction, and many methods have been proposed and successfully applied, such as particle swarm optimization and genetic algorithm. However, the reduction of the error and energy consumption of a robot while paying attention to more subtle attitude changes is very important and challenging. In view of these problems, we propose a population randomization-based multi-objective genetic algorithm. The gesture signal is processed with a slight change by imitating the biological evolution mechanisms. In the proposed algorithm, a random out-of-order matrix is added in the process of population evolution synthesis to prevent the premature grouping convergence of the new population. The weights of the objective function and the elite retention strategy are adopted, and the most adaptable individuals in each generation are inherited directly in the next generation without any recombination or mutation. To verify the effectiveness of the algorithm, preliminary application experiments are performed on the gesture adaptation of a robotic arm. The results are compared with the original signal, and the comparison shows that by using the proposed method, the energy consumption is reduced, and the end error is decreased to less than 3 mm while ensuring the tracking effect of the robotic arm. These obtained results meet the communication requirements for human-robot interactions such as handshakes. Moreover, the proposed method has better performance, uses less energy, and has a smaller tracking error than the particle swarm optimization, the single-objective genetic algorithm, and the traditional multi-objective genetic algorithm. A preliminary application experiment indicates that the robotic arm can adapt to human gestures in real time. © 2020, Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature.","gesture adaptation; human-robot interaction; multi-objective genetic algorithm","Biology; Energy utilization; Errors; Man machine systems; Particle swarm optimization (PSO); Random processes; Robotic arms; Robotics; Social robots; Biological evolution; Multi-objective genetic algorithm; Objective functions; Original signal; Population evolution; Retention strategies; Single-objective genetic algorithms; Tracking effects; Genetic algorithms",,,,,"National Natural Science Foundation of China, NSFC: 61603356, 61733016, 61773353, 61973286; Natural Science Foundation of Hubei Province: 2015CFA010; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project: B17040; China University of Geosciences, CUG: 2018039; Wuhan Science and Technology Project: 2017010201010133","This work was supported by National Natural Science Foundation of China (Grant Nos. 61973286, 61603356, 61733016, 61773353), 111 Project (Grant No. B17040), Hubei Provincial Natural Science Foundation (Grant No. 2015CFA010), Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. 2018039), and Wuhan Science and Technology Project (Grant No. 2017010201010133).",,,,,,,,,,"Zhang, J.W., Wang, L., Xing, L.N., Large-scale medical examination scheduling technology based on intelligent optimization (2019) J Comb Optim, 37, pp. 385-404; 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China Inf. Sci.",Article,"Final","",Scopus,2-s2.0-85098947115
"Gonzalez J., Belgiovine G., Sciutti A., Sandini G., Francesco R.","57344917800;57194709146;36957816700;35280694500;57345381200;","Towards a cognitive framework for multimodal person recognition in multiparty hri",2021,"HAI 2021 - Proceedings of the 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction",,,,"412","416",,,"10.1145/3472307.3484675","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119325773&doi=10.1145%2f3472307.3484675&partnerID=40&md5=6274e0c2a853d57b17be7924b0e32ff1","Italian Institute of Technology, Genova, Italy; Italian Institute of Technology, University of Genova, Genova, Italy","Gonzalez, J., Italian Institute of Technology, Genova, Italy; Belgiovine, G., Italian Institute of Technology, University of Genova, Genova, Italy; Sciutti, A., Italian Institute of Technology, Genova, Italy; Sandini, G., Italian Institute of Technology, Genova, Italy; Francesco, R., Italian Institute of Technology, Genova, Italy","The ability to recognize human partners is an important social skill to build personalized and long-Term Human-Robot Interactions (HRI). However, in HRI contexts, unfolding in ever-changing and realistic environments, the identification problem presents still significant challenges. Possible solutions consist of relying on a multimodal approach and making robots learn from their first-hand sensory data. To this aim, we propose a framework to allow robots to autonomously organize their sensory experience into a structured dataset suitable for person recognition during a multiparty interaction. Our results demonstrate the effectiveness of our approach and show that it is a promising solution in the quest of making robots more autonomous in their learning process. © 2021 Owner/Author.","Multimodality; Multiparty human-robot interaction; Person recognition","Man machine systems; Changing environment; Cognitive frameworks; Humans-robot interactions; Interaction context; Multi-modal; Multi-modality; Multiparty human-robot interaction; Person recognition; Social skills; Unfoldings; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020: 804388; European Research Council, ERC; Horizon 2020","A.S. is supported by a Starting Grant from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme. G.A. No 804388, wHiSPER.","European Unions Horizon 2020 research and innovation programme. G.A. No 804388, wHiSPER.",,,,,,,,,"Belgiovine, G., Rea, F., Barros, P., Zenzeri, J., Sciutti, A., Sensing the partner: Toward effective robot tutoring in motor skill learning (2020) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 296-307. , https://doi.org/10.1007/978-3-030-62056-1-25, 12483 LNAI Springer Science and Business Media Deutschland GmbH; Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B., Simple online and realtime tracking (2016) 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464-3468. , IEEE, IEEE, New York, NY, USA; Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A., Vggface2: A dataset for recognising faces across pose and age (2018) Proceedings-13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018. Institute of Electrical and Electronics Engineers Inc, pp. 67-74. , https://doi.org/10.1109/FG.2018.00020, ew York, NY, USA, arXiv:1710.08092; Churamani, N., Anton, P., Brögger, M., Fließwasser, E., Hummel, T., Mayer, J., Mustafa, W., Wermter, S., The impact of personalisation on human-robot interaction in learning scenarios (2017) Proceedings of the 5th International Conference on Human Agent Interaction (Bielefeld, Germany) (HAI ?17), pp. 171-180. , https://doi.org/10.1145/3125739.3125756, Association for Computing Machinery, New York, NY, USA; Gonzalez-Billandon, J., Belgiovine, G., Tata, M., Sciutti, A., Sandini, G., Rea, F., Self-supervised learning framework for speaker localisation with a humanoid robot (2021) 2021 IEEE International Conference on Development and Learning (ICDL), . IEEE, pp. 1-7. , https://doi.org/10.1109/ICDL49984.2021.9515566; Gonzalez-Billandon, J., Sciutti, A., Tata, M., Sandini, G., Rea, F., Audiovisual cognitive architecture for autonomous learning of face localisation by a Humanoid Robot (2020) 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 5979-5985; Gunther, M., Cruz, S., Rudd, E.M., Boult, T.E., Toward open-set face recognition (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. IEEE, pp. 71-80; Guo, G., Zhang, N., A survey on deep learning based face recognition (2019) Computer Vision and Image Understanding, 189, p. 102805. , https://doi.org/10.1016/j.cviu.2019.102805, dec 2019; Gönther, M., Hu, P., Herrmann, C., Chan, C.H., Jiang, M., Yang, S., Dhamija, A.R., Boult, T.E., Unconstrained face detection and open-set face recognition challenge (2017) 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 697-706. , https://doi.org/10.1109/BTAS.2017.8272759, IEE, New-york, NY, USA; Irfan, B., Lyubova, N., Garcia Ortiz, M., Belpaeme, T., Multimodal open-set person identification in hri (2018) 2018 ACM/IEEE International Conference on Human-Robot Interaction Social. IEEE; Metta, G., Fitzpatrick, P., Natale, L., (2006) YARP: Yet Another Robot Platform, , https://doi.org/10.5772/5761; Metta, G., Natale, L., Nori, F., Sandini, G., Vernon, D., Fadiga, L., Von Hofsten, C., Montesano, L., The icub humanoid robot: An open-systems platform for research in cognitive development (2010) Neural Networks, 23 (8-9), pp. 1125-1134. , https://doi.org/10.1016/J.NEUNET.2010.08.010, oct 2010; Prescott, T.J., Camilleri, D., Martinez-Hernandez, U., Damianou, A., Lawrence, N.D., Memory and mental time travel in humans and social robots (2019) Philosophical Transactions of the Royal Society B 2019, 374, p. 20180025. , 1771; Ravanelli, M., Parcollet, T., Plantinga, P., Rouhe, A., Cornell, S., Lugosch, L., Subakan, C., Bengio, Y., (2021) SpeechBrain: A General-Purpose Speech Toolkit, , arXiv:2106.04624 [eess.AS] arXiva:2106.04624; Roncone, A., Pattacini, U., Metta, G., Natale, L., A cartesian 6-dof gaze controller for humanoid robots (2016) Robotics: Science and Systems 2016, , Robotics: science and systems, New-york, NY, USA; Sandini, G., Mohan, V., Sciutti, A., Morasso, P., Social cognition for human-robot symbiosis-challenges and building blocks (2018) Frontiers in Neurorobotics 2018, 12, p. 34; Schroff, F., Kalenichenko, D., Philbin, J., Facenet: A unified embedding for face recognition and clustering (2015) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815-823. , IEEE, New-york, NY, USA; Sönderhauf, N., Brock, O., Scheirer, W., Hadsell, R., Fox, D., Leitner, J., Upcroft, B., Corke, P., The limits and potentials of deep learning for robotics (2018) The International Journal of Robotics Research 2018, 37 (5), pp. 405-420. , https://doi.org/10.1177/0278364918770733; Sztahó, D., Szaszák, G., Beke, A., (2019) Deep Learning Methods in Speaker Recognition: A Review, , arXiv:1911.06615[eess.AS]; Tapus, A., Tapus, C., Mataric, M.J., User-robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy (2008) Intelligent Service Robotics 2008, 1 (2), pp. 169-183; Vignolo, A., Sciutti, A., Michael, J., Using robot adaptivity to support learning in child-robot interaction (2020) International Conference on Social Robotics, pp. 428-439. , Springer, Springer; Weinberger, K.Q., Saul, L.K., Distance metric learning for large margin nearest neighbor classification (2009) Journal of Machine Learning Research 2009, 10 (2), pp. 68-69",,,"CyberAgent","Association for Computing Machinery, Inc","9th International User Modeling, Adaptation and Personalization Human-Agent Interaction, HAI 2021","9 November 2021 through 11 November 2021",,173654,,9781450386203,,,"English","HAI - Proc. Int. User Model., Adapt. Pers. Human-Agent Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85119325773
[No author name available],[No author id available],"AIRPHARO 2021 - 1st AIRPHARO Workshop on Aerial Robotic Systems Physically Interacting with the Environment",2021,"AIRPHARO 2021 - 1st AIRPHARO Workshop on Aerial Robotic Systems Physically Interacting with the Environment",,,,"","",244,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119077396&partnerID=40&md5=b47159978a1236d6960e7f593f7d837f",,"","The proceedings contain 35 papers. The topics discussed include: development of downsized LASDRA with 2-DoF joint locking device; design and manufacture of the wing folding mechanism for a bioinspired ornithopter; an application of stereo thermal vision for preliminary inspection of electrical power lines by MAVs; robust adaptive non-linear control design for an aerial robot with in-door application in constrained corridors; an overview of hand gesture languages for autonomous UAV handling; experimental evaluation of aerial manipulation robot for the installation of clip type bird diverters: outdoor flight tests; aerodynamic interference in confined environments with tilted propellers: wall effect and corner effect; and modelling and estimation of human walking gait for physical human-robot interaction.",,,,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.","1st AIRPHARO Workshop on Aerial Robotic Systems Physically Interacting with the Environment, AIRPHARO 2021","4 October 2021 through 5 October 2021",,173180,,9781665433891,,,"English","AIRPHARO - AIRPHARO Workshop Aer. Robot. Syst. Phys. Interact. Environ.",Conference Review,"Final","",Scopus,2-s2.0-85119077396
"Honig S., Oron-Gilad T.","57201589559;16042913400;","Expect the Unexpected: Leveraging the Human-Robot Ecosystem to Handle Unexpected Robot Failures",2021,"Frontiers in Robotics and AI","8",,"656385","","",,,"10.3389/frobt.2021.656385","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112184470&doi=10.3389%2ffrobt.2021.656385&partnerID=40&md5=f8c99f84006c59aad8a0629b049a8a66","Department of Industrial Engineering and Management, Mobile Robotics Laboratory and HRI Laboratory, Ben-Gurion University of the Negev, Be’er Sheva, Israel","Honig, S., Department of Industrial Engineering and Management, Mobile Robotics Laboratory and HRI Laboratory, Ben-Gurion University of the Negev, Be’er Sheva, Israel; Oron-Gilad, T., Department of Industrial Engineering and Management, Mobile Robotics Laboratory and HRI Laboratory, Ben-Gurion University of the Negev, Be’er Sheva, Israel","Unexpected robot failures are inevitable. We propose to leverage socio-technical relations within the human-robot ecosystem to support adaptable strategies for handling unexpected failures. The Theory of Graceful Extensibility is used to understand how characteristics of the ecosystem can influence its ability to respond to unexpected events. By expanding our perspective from Human-Robot Interaction to the Human-Robot Ecosystem, adaptable failure-handling strategies are identified, alongside technical, social and organizational arrangements that are needed to support them. We argue that robotics and HRI communities should pursue more holistic approaches to failure-handling, recognizing the need to embrace the unexpected and consider socio-technical relations within the human robot ecosystem when designing failure-handling strategies. © Copyright © 2021 Honig and Oron-Gilad.","failure handling; human-robot ecosystem; non-expert user; resilience engineering; resilient robots; social robots; unexpected failures; user-centered",,,,,,"Leona M. and Harry B. Helmsley Charitable Trust; Ben-Gurion University of the Negev, BGU","The first author, SH is supported by a scholarship from The Helmsley Charitable Trust through the Agricultural, Biological, Cognitive Robotics Center; by Ben-Gurion University of the Negev through the High-tech, Bio-tech and Chemo-tech Scholarship; and by the George Shrut Chair in human performance management.",,,,,,,,,,"Adams, J.A., Human-Robot Interaction Design: Understanding User Needs and Requirements (2005) Proc. Hum. Factors Ergon. Soc. Annu. Meet, 49, pp. 447-451; Akkaladevi, S.C., Plasch, M., Pichler, A., Rinner, B., Human Robot Collaboration to Reach a Common Goal in an Assembly Process (2016) STAIRS 2016: Proceedings of the Eighth European Starting AI Researcher Symposium, pp. 3-14. , Pearce D., Pinto H.S., (eds), The Hague, the Netherlands, 23 August 2016, IOS Press, 2016, Editors; Alami, R., Warnier, M., Guitton, J., Lemaignan, S., Sisbot, E.A., When the Robot Considers the Human (2011) The 15th International Symposium on Robotics Research (ISRR), , Christensen Henrik I., Khatib Oussama, (eds), Flagstaff, Arizona, USA, December 9-12, 2011, Springer International Publishing Switzerland, Flagstaff, hal-01979210, Editors; Arboleda, S.A., Pascher, M., Lakhnati, Y., Gerken, J., Understanding Human-Robot Collaboration for People with Mobility Impairments at the Workplace, a Thematic Analysis (2020) 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 561-566. , Naples, Italy, 31 Aug.-4 Sept. 2020, New Jersey, IEEE; Arrichiello, F., Marino, A., Pierri, F., Distributed Fault-Tolerant Control for Networked Robots in the Presence of Recoverable/Unrecoverable Faults and Reactive Behaviors (2017) Front. Robot. AI, p. 2. , The Hague, the Netherlands; Avelino, J., Garcia-Marques, L., Ventura, R., Bernardino, A., Break the Ice: a Survey on Socially Aware Engagement for Human-Robot First Encounters (2021) Int. J. Soc. Rob, pp. 1-27; Azzalini, D., Castellini, A., Luperto, M., Farinelli, A., Amigoni, F., HMMs for Anomaly Detection in Autonomous Robots (2020) Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems AAMAS ’20, pp. 105-113. , An B., Yorke-Smith N., Seghrouchni A. El Fallah, Sukthankar G., (eds), Auckland, New Zealand, May 9–13, Richland, SC, International Foundation for Autonomous Agents and Multiagent Systems, Editors; Bagheri, E., Esteban, P.G., Cao, H.-L., Beir, A.D., Lefeber, D., Vanderborght, B., An Autonomous Cognitive Empathy Model Responsive to Users' Facial Emotion Expressions (2020) ACM Trans. Interact. Intell. Syst, 10, pp. 1-23; Bajones, M., Weiss, A., Vincze, M., Help, Anyone? 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Sci, 181, pp. 379-397; Zweigle, O., Keil, B., Wittlinger, M., Häussermann, K., Levi, P., Recognizing Hardware Faults on Mobile Robots Using Situation Analysis Techniques (2013) Intelligent Autonomous Systems 12: Volume 1 Proceedings of the 12th International Conference IAS-12, pp. 397-409. , Lee S., Cho H., Yoon K.-J., Lee J., (eds), Jeju Island, Korea, held June 26-29, 2012, Berlin, Heidelberg, Springer Berlin Heidelberg, Editors","Honig, S.; Department of Industrial Engineering and Management, Israel; email: shaneeh@post.bgu.ac.il",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85112184470
"Gasteiger N., Hellou M., Ahn H.S.","57204357716;57216352354;56218250500;","Optimizing human-robot interaction through personalization: An evidence-informed guide to designing social service robots",2021,"2021 18th International Conference on Ubiquitous Robots, UR 2021",,,"9494695","53","56",,,"10.1109/UR52253.2021.9494695","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112404524&doi=10.1109%2fUR52253.2021.9494695&partnerID=40&md5=5b9da5eddc8308f585f84c8700bb3018","The University of Auckland, CARES, Department of Psychological Medicine, The Department of Electrical, Computer and Software Engineering, Auckland, New Zealand; School of Health Sciences, The University of Manchester, United Kingdom; Sorbonne Université, Facultés des Sciences et Ingénieries, Département d'Informatique, Paris, France; The University of Auckland, CARES, Department of Electrical, Computer and Software Engineering, Auckland, New Zealand","Gasteiger, N., The University of Auckland, CARES, Department of Psychological Medicine, The Department of Electrical, Computer and Software Engineering, Auckland, New Zealand, School of Health Sciences, The University of Manchester, United Kingdom; Hellou, M., Sorbonne Université, Facultés des Sciences et Ingénieries, Département d'Informatique, Paris, France, The University of Auckland, CARES, Department of Electrical, Computer and Software Engineering, Auckland, New Zealand; Ahn, H.S., The University of Auckland, CARES, Department of Electrical, Computer and Software Engineering, Auckland, New Zealand","The application of social service robots is increasing in our daily lives, including in healthcare, education and public spaces (e.g., hospitality or museums). With this near-ubiquity of robots comes the requirement to interact with a diverse group of individuals; each with their own needs, preferences and expectations. This paper identifies and synthesizes previous literature on personalization and culturalization within the field of human-robot interaction. Three over-arching considerations are discussed: (1) intended purpose and actions (i.e. service and behavior), (2) interactive functions (i.e. communication, language and proxemics) and (3) physical appearance of robots. Lastly, recommendations are made for other roboticists, for designing individually responsive social service robots. © 2021 IEEE.","Design; Human-robot interaction; Literature review; Service robots; Social robots","Agricultural robots; Man machine systems; Mobile robots; Social robots; Daily lives; Interactive functions; Personalizations; Public space; Social service; Educational robots",,,,,"Ministry of Science, ICT and Future Planning, MSIP: 2020-0-00842; Institute for Information and Communications Technology Promotion, IITP","ACKNOWLEDGMENT This work was supported by the Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. 2020-0-00842, Development of Cloud Robot Intelligence for Continual Adaptation to User Reactions in Real Service Environments).",,,,,,,,,,"Clabaugh, C., Mahajan, K., Jain, S., Long-term personalization of an in-home socially assistive robot for children with autism spectrum disorders (2019) Frontiers in Robotics and AI, 6 (10); Coşar, S., Fernandez-Carmona, M., Agrigoroaie, R., ENRICHME: Perception and interaction of an assistive robot for the elderly at home (2020) International Journal of Social Robotics; Portugal, D., Santos, L., Alvito, P., SocialRobot: An interactive mobile robot for elderly home care (2015) IEEE/SICE International Symposium on System Integration; Bazzano, F., Lamberti, F., Human-robot interfaces for interactive receptionist systems and wayfinding applications (2018) Robotics, 7 (3); Shi, C., Satake, S., Kanda, T., How would store managers employ social robots? 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Psychol, 6 (204), pp. 1-11; Bartneck, C., Nomura, T., Kanda, T., A cross-cultural study on attitudes towards robots (2005) HCI International, , Las Vegas; Haring, K., Mougenot, C., Ono, F., Cultural differences in perception and attitude towards robots (2014) International Journal of Affective Engineering, 13 (3), pp. 149-157; Mavridis, N., Katsaiti, M., Naef, S., Opinions and attitudes toward humanoid robots in the Middle East (2012) AI & Soc, 27, pp. 517-534; Nomura, T., Syrdal, D., Dautenhahn, K., Differences on social acceptance of humanoid robots between Japan and the UK (2015) 4th International Symposium on New Frontiers in Human-Robot Interaction; Takayama, L., Ju, W., Nass, C., Beyond dirty, dangerous and dull: What everyday people think robots should do (2008) HRI'08, , Amsterdam: ACM; Zlotowski, J., Khalil, A., Abdallah, S., One robot doesn't fit all: Aligning social robot appearance and job suitability from a Middle Eastern perspective (2019) AI & Soc, 35, pp. 485-500; Nomura, T., Kanda, T., Suzuki, T., Implications on humanoid robots in pedagogical applications from cross-cultural analysis between Japan, korea, and the USA (2007) The 16th IEEE International Symposium on Robot and Human Interactive Communication: IEEE; Li, H., Milani, S., Krishnamoorthy, V., Perceptions of domestic robots' normative behavior across cultures (2019) The 2019 AAAI/ACM Conference; Haring, K., Silvera-Tawil, D., Matsumoto, Y., Perception of an android robot in Japan and Australia: A cross-cultural comparison (2014) ICSR'14, pp. 166-174. , Beetz M, Ed Switzerland: Springer; Butler, J., Agah, A., Psychological effects of behavior patterns of a mobile personal robot (2001) Autonomous Robots, 10, pp. 185-202; Rau, P., Li, Y., Li, D., Effects of communication style and culture on ability to accept recommendations from robots (2009) Computers in Human Behavior, 25, pp. 587-595; Salem, M., Ziadee, M., Sakr, M., Marhaba, how may i help you? Effects of politeness and culture on robot acceptance and anthropomorphization (2014) HRI'14, , Germany: ACM; Sanoubari, E., Young, J., Explicit, neutral, or implicit: A cross-cultural exploration of communication-style preferences in human robot interaction (2018) HRI '18: Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 237-238; Wang, L., Rau, P., Evers, V., When in Rome: The role of culture & context in adherence to robot recommendations (2010) 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), , Japan: IEEE; Trovato, G., Zecca, M., Sessa, S., Cross-cultural study on humanrobot greeting interaction: Acceptance and discomfort by Egyptians and Japanese (2013) Paladyn, Journal of Behavioral Robotics, 42 (2), pp. 83-93; Trovato, G., Ham, J., Hashimoto, K., Investigating the effect of relative cultural distance on the acceptance of robots (2015) ICSR'15, pp. 664-673. , In: Tapus A, ed: Springer; Hüttenrauch, H., Eklundh, K., Green, A., Investigating spatial relationships in human-robot interactions (2006) IEEE/RSJ International Conference on Intelligent Robots and Systems; Walters, M., Dautenhahn, K., Koay, K., Close encounters: Spatial distances between people and a robot of mechanistic appearance (2005) IEEE-RAS International Conference on Humanoid Robots, , Japan: IEEE; Eresha, G., Haring, M., Endrass, B., Investigating the influence of culture on proxemic behaviors for humanoid robots (2013) IEEE RO-MAN: The 22nd IEEE International Symposium on Robot and Human Interactive Communication, , Korea; Joosse, M., Poppe, R., Lohse, M., Cultural differences in how an engagement-seeking robot should approach a group of people (2014) CABS'14, , Japan; Neerincx, A., Kaptein, R., Oleari, E., Child's culture-related experiences with a social robot at diabetes camps (2016) IEEE, pp. 485-486; Syrdal, D., Koay, K., Walters, M., A personalized robot companion?-The role of individual differences on spatial preferences in HRI scenarios (2007) 16th IEEE International Conference on Robot & Human Interactive Communication, pp. 1143-1148. , Korea; Takayama, L., Pantofaru, C., Influences on proxemic behaviors in human-robot interaction (2009) Proceedings of the Intelligent Robotic Systems: IROS, pp. 5495-5502. , St Louis; Walters, M., Dautenhahn, K., Boekhorst, R., The influence of subjects' personality traits on personal spatial zones in a human-robot interaction experiment (2005) IEEE, , Japan; Syrdal, D., Dautenhahn, K., Woods, S., Doing the right thing wrong-Personality and tolerance to uncomfortable robot approaches (2006) The 15th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN06), , Hatfield, UK; Cortellessa, G., Svedberg, G., Scopelliti, M., A cross-cultural evaluation of domestic assistive robots (2008) Association for the Advancement of Artificial Intelligence; Bartneck, C., Who like androids more: Japanese or US Americans? (2008) 17th IEEE International Symposium on Robot and Human Interactive Communication, , Germany: IEEE; Agah, A., The influence of robot appearance and interactive ability in HRI: A cross-cultural study (2016) ICSR'16, , Springer; Hegel, F., Lohse, M., Wrede, B., Effects of visual appearance on the attribution of applications in social robotics (2009) 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 64-71. , Japan: IEEE; Rizvanoǧlu, K., Öztürk, Ö., Adiyaman, Ö., The impact of human likeness on the older adults' perceptions and preferences of humanoid robot appearance (2014) DUXU'14: Springer, pp. 164-172. , In: Marcus A, ed; Walters, M., Syrdal, D., Koay, K., Human approach distances to a mechanical-looking robot with different robot voice styles (2008) The 17th IEEE International Symposium on Robot and Human Interactive Communication, , Germany; Ho, Y., Sato-Shimokawara, E., Yamaguchi, T., Interaction robot system considering culture differences (2013) IEEE Workshop on Advanced Robotics and Its Social Impacts, , Japan; Makenova, R., Karsybayeva, R., Sandygulova, A., Exploring cross-cultural differences in persuasive robotics (2018) Companion of the 2018 ACM/IEEE International Conference; Lee, H., Kang, H., Kwak, S., How people perceive human-and product-like robots: Cross-cultural analysis between Japan and korea (2015) HAI '15: Proceedings of the 3rd International Conference on Human-Agent Interaction, pp. 315-318","Ahn, H.S.; The University of Auckland, New Zealand; email: hs.ahn@auckland.ac.nz",,,"Institute of Electrical and Electronics Engineers Inc.","18th International Conference on Ubiquitous Robots, UR 2021","12 July 2021 through 14 July 2021",,170755,,9781665438995,,,"English","Int. Conf. Ubiquitous Robot., UR",Conference Paper,"Final","",Scopus,2-s2.0-85112404524
"Rossi A., Rossi S.","57189499587;55619320887;","Engaged by a Bartender Robot: Recommendation and Personalisation in Human-Robot Interaction",2021,"UMAP 2021 - Adjunct Publication of the 29th ACM Conference on User Modeling, Adaptation and Personalization",,,,"115","119",,1,"10.1145/3450614.3463423","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109216352&doi=10.1145%2f3450614.3463423&partnerID=40&md5=117ae88046c7dbd5fcf39dc76c24a503","Interdepartmental Center for Advances in Robotic Surgery, University of Naples Federico Ii, Naples, Italy; Department of Electrical Engineering and Information Technologies, University of Naples Federico Ii, Naples, Italy","Rossi, A., Interdepartmental Center for Advances in Robotic Surgery, University of Naples Federico Ii, Naples, Italy; Rossi, S., Department of Electrical Engineering and Information Technologies, University of Naples Federico Ii, Naples, Italy","BRILLO (Bartending Robot for Interactive Long-Lasting Operations) project aims to deploy an autonomous robotic bartender that can naturally interact with customers in a real-world service point. In such a scenario, this work presents a multi-modal personalised recommendation method for increasing users' engagement with a bartending robot. The personalised recommendation is adopted at two levels: one for the recommendation of the service (i.e. drinks), and another for selecting the human-robot interaction (HRI) modalities. The envisioned multi-modal personalised recommendation method enables the BRILLO robot to intelligently adapt its dialogues, pose and gestures, according to the user's moods, attention behaviours, personal traits, and situational context (drink orders, group dynamics, etc.). © 2021 ACM.","human-robot interaction; personalisation; recommendation system; Service robotics","Beverages; Man machine systems; Autonomous robotics; Group dynamics; Human robot Interaction (HRI); Long lasting; Personal traits; Personalisation; Personalised recommendations; Situational context; Human robot interaction",,,,,,,,,,,,,,,,"Berezina, K., Ciftci, O., Cobanoglu, C., (2019) Robots, Artificial Intelligence, and Service Automation in Restaurants, pp. 185-219. , https://doi.org/10.1108/978-1-78756-687-320191010; Castellano, G., Pereira, A., Leite, I., Paiva, A., McOwan, P.W., Detecting user engagement with a robot companion using task and social interaction-based features (2009) Proceedings of the 2009 International Conference on Multimodal Interfaces (ICMI-MLMI '09), pp. 119-126. , https://doi.org/10.1145/1647314.1647336, Association for Computing Machinery, New York, NY, USA; Choi, J., Lee, B., Zhang, B., (2016) Human Body Orientation Estimation Using Convolutional Neural Network, , [cs. RO]; Corina, P., Irina, E., Interaction between consumers and emerging forms of artificial intelligence: A discriminant analysis studia universitatis vasile goldis (2020) Arad-Economics Series, 30 (2), pp. 1-12. , https://doi.org/10.2478/sues-2020-0008, June 2020; Gaschler, A., Jentzsch, S., Giuliani, M., Huth, K., De Ruiter, J., Knoll, A., Social behavior recognition using body posture and head pose for human-robot interaction (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2128-2133. , https://doi.org/10.1109/IROS.2012.6385460; Hidayanti, I., Edy Herman, L., Farida, N., Engaging customers through social media to improve industrial product development: The role of customer co-creation value (2018) Journal of Relationship Marketing, 17 (1), pp. 17-28. , https://doi.org/10.1080/15332667.2018.1440137, 2018; Kuhn, S., Mostert, P., Relationship intention and relationship quality as predictors of clothing retail customers' loyalty (2018) The International Review of Retail, Distribution and Consumer Research, 28 (2), pp. 206-230. , https://doi.org/10.1080/09593969.2017.1380067, 2018; Lui, J.H., Samani, H., Tien, K., An affective mood booster robot based on emotional processing unit (2017) 2017 International Automatic Control Conference (CACS), pp. 1-6. , https://doi.org/10.1109/CACS.2017.8284239; Masuda, T., Misaki, D., Development of Japanese green tea serving robot t-bartender (2005) IEEE International Conference Mechatronics and Automation, 2005, 2, pp. 1069-1074. , https://doi.org/10.1109/ICMA.2005.1626700; Moro, C., Lin, S., Nejat, G., Mihailidis, A., Social robots and seniors: A comparative study on the influence of dynamic social features on human-robot interaction (2019) International Journal of Social Robotics, 11, pp. 5-24. , 2019; Krupasindhu Panigrahi, S., Azlinna Azizan, N., Ali Khan, M., Investigating the empirical relationship between service quality, trust, satisfaction, and intention of customers purchasing life insurance products (2018) Indian Journal of Marketing, 48 (1). , 2018; Blagojevic Popovic, O., Nikic, V., Bulatovic, I., Delibasic, M., Modeling perceived quality, customer satisfaction and probability of guest returning to the destination (2018) Montenegrin Journal of Economics, 14 (1), pp. 69-78. , https://ideas.repec.org/a/mje/mjejnl/v14y2018i1p69-78.html, 2018; Rossi, A., Dautenhahn, K., Lee Koay, K., Walters, M.L., Holthaus, P., Evaluating people's perceptions of trust in a robot in a repeated interactions study (2020) Social Robotics, pp. 453-465. , Alan R. Wagner, David Feil-Seifer, Kerstin S. Haring, Silvia Rossi, Thomas Williams, Hongsheng He, and Shuzhi Sam Ge (Eds.). Springer International Publishing, Cham; Rossi, A., Dautenhahn, K., Lee Koay, K., Walters, M.L., How social robots influence people's trust in critical situations (2020) 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1020-1025. , https://doi.org/10.1109/RO-MAN47096.2020.9223471; Rossi, A., Staffa, M., Origlia, A., Di Maro, M., Rossi, S., BRILLO: A robotic architecture for personalised long-lasting interactions in a bartending domain (2021) Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (Boulder, CO, USA) (HRI '21 Companion), pp. 426-429. , https://doi.org/10.1145/3434074.3447206, Association for Computing Machinery, New York, NY, USA; Rossi, S., Dell'Aquila, E., Russo, D., Maggi, G., Increasing engagement with chameleon robots in bartending services (2020) 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 464-469. , https://doi.org/10.1109/RO-MAN47096.2020.9223488; Rossi, S., Ferland, F., Tapus, A., User profiling and behavioral adaptation for hri: A survey (2017) Pattern Recognition Letters, 99, pp. 3-12. , https://doi.org/10.1016/j.patrec.2017.06.002UserProfilingandBehaviorAdaptationforHuman-RobotInteraction, 2017; Staffa, M., Rossi, S., Recommender interfaces: The more human-like, the more humans like (2016) Social Robotics, pp. 200-210. , Arvin Agah, John-John Cabibihan, Ayanna M. Howard, Miguel A. Salichs, and Hongsheng He (Eds.). Springer International Publishing, Cham; Szabóová, M., Sarnovský, M., Maslej Kresáková, V., Machová, K., Emotion analysis in human-robot interaction (2020) Electronics, 9 (11). , https://doi.org/10.3390/electronics9111761, 2020; Tome, D., Russell, C., Agapito, L., Lifting from the deep: Convolutional 3d pose estimation from a single image (2017) The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",,,"ACM SIGCHI;ACM SIGWEB","Association for Computing Machinery, Inc","29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021","21 June 2020 through 25 June 2020",,169764,,9781450383677,,,"English","UMAP - Adjun. Publ. ACM Conf. User Model., Adapt. Pers.",Conference Paper,"Final","",Scopus,2-s2.0-85109216352
"Sasaki G., Igarashi H.","57205419482;35799952900;","Assist System Considering Cooperative Skills Improves Performance in Dyadic Interaction",2021,"IEEE International Symposium on Industrial Electronics","2021-June",,,"","",,,"10.1109/ISIE45552.2021.9576184","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118786285&doi=10.1109%2fISIE45552.2021.9576184&partnerID=40&md5=d9dca8dc68fe3147e2999106fcb43ef8","Tokyo Denki University, Graduate School of Advanced Science and Technology, Department of Electrical and Electronic System Engineering, Tokyo, 120-8551, Japan; Tokyo Denki University, School of Engineering, Department of Electronic Engineering, Tokyo, 120-8551, Japan","Sasaki, G., Tokyo Denki University, Graduate School of Advanced Science and Technology, Department of Electrical and Electronic System Engineering, Tokyo, 120-8551, Japan; Igarashi, H., Tokyo Denki University, School of Engineering, Department of Electronic Engineering, Tokyo, 120-8551, Japan","This paper focused on cooperative skills to assist a cooperative task. Human has individual skills as well as cooperative skills. In particular, cooperative skills are essential for Human-Human interaction or Human-Robot interaction. In order to reveal the Human-Human interaction and construct a cooperative assist system, this study used the 'CFO (Concern For Others),' which estimates by input operation depended on only cooperative skills. This paper proposed the active assist system in order to improve the cooperation task's performance. In the experiment, dyads performed the cooperative task with the active assist system to verify effectiveness. As a result, the active cooperative task assist system effectively improve the cooperative task's performance. © 2021 IEEE.","Assist system; Cooperation; Human interaction; Human-Human; Human-Robot; Joint Action; pHHI; Skills","Agricultural robots; Man machine systems; Assist system; Cooperation; Cooperative tasks; Human robots; Human-human; Human-human interactions; Humaninteraction; Joint actions; PHHI; Skill; Human robot interaction",,,,,"Q20D-04; Japan Society for the Promotion of Science, KAKEN: 18K11407","ACKNOWLEDGMENT This work was partially supported by JSPS KAKENHI Grant Number 18K11407 and Research Institute for Science and Technology of Tokyo Denki University Grant Number Q20D-04 / Japan.",,,,,,,,,,"Feltz, D.L., Lirgg, C.D., Perceived team and player efficacy in hockey (1998) J. Appl. Psychol., 83, pp. 557-564; Sebanz, N., Bekkering, H., Knoblich, G., Joint action: Bodies and minds moving together (2006) Trends Cogn. Sci., 10; Driver, J., Davis, G., Ricciardelli, P., Kidd, P., Maxwell, E., Baron-Cohen, S., Gaze perception triggers reflexive visuospatial orienting (1999) Visual Cognition, 6, pp. 509-540; Wahn, B., Kingstone, A., König, P., Two trackers are better than one: Information about the co-actor's actions and performance scores contribute to the collective benefit in a joint visuospatial task (2017) Front. Psychol., 8 (669); Becchio, C., Sartori, L., Castiello, U., Toward you: The social side of actions (2010) Curr. Dir. Psychol. Sci., 19, pp. 183-187; Shergill, S.S., Bays, P.M., Frith, C.D., Wolpert, D.M., Two eyes for an eye: The neuroscience of force escalation (2003) Science, 301, p. 187; Sawers, A., Bhattacharjee, T., McKay, J.L., Hackney, M.E., Kemp, C.C., Ting, L.H., Small forces that differ with prior motor experience can communicate movement goals during human-human physical interaction (2017) Journal of Neuroengineering and Rehabilitation, 14 (8); Mielke, E.A., Townsend, E.C., Killpack, M.D., (2017) Analysis of Rigid Extended Object Co-manipulation by Human Dyads: Lateral Movement Characterization, , arXiv; Reed, K.B., Peshkin, M.A., Physical collaboration of human-human and human-robot teams (2008) IEEE Transactions on Haptics, 1 (2), pp. 108-120; Sasaki, G., Igarashi, H., Teamwork assistance based on concern for others (2019) IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society, pp. 5285-5290; Furuta, K., Control of pendulum: From super mechano-system to human adaptive mechatronics (2003) IEEE International Conference on Systems, Man, and Cybernetics, pp. 1498-1507; Kusaka, T., Tanaka, T., Kaneko, S., Kajiwara, H., Skill assist and power assist for periodic motions by using semi-active assist mechanism with energy control (2011) 2011 IEEE International Conference on Robotics and Biomimetics, pp. 2187-2192; Noohi, E., Zefran, M., Patton, J.L., A model for human-human collaborative object manipulation and its application to human-robot interaction (2016) IEEE Transactions on Robotics, 32 (4); Igarashi, H., Intermediary assist for cooperative works: Quantification technique of concern for others (2012) The 21st IEEE International Symposium on Robot and Human Interactive Communication, pp. 753-758; Ohnishi, K., Shibata, M., Murakami, T., Motion control for advanced mechatronics (1996) IEEE/ASME Transactions on Mechatronics, 1 (1); Murakami, T., Fangming, Y., Ohnishi, K., Torque sensorless control in multidegree-of-freedom manipulator (1993) IEEE Transactions on Industrial Electronics, 40 (2); Sasaki, G., Igarashi, H., Teamwork evaluation by the orthogonal cfo (2019) The 5th IEEJ International Workshop on Sensing, Actuation, Motion Control, and Optimization (SAMCON2019), pp. TT9-TT1",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Symposium on Industrial Electronics, ISIE 2021","20 June 2021 through 23 June 2021",,173407,,9781728190235,85PTA,,"English","IEEE Int Symp Ind Electron",Conference Paper,"Final","",Scopus,2-s2.0-85118786285
[No author name available],[No author id available],"Proceedings - 2021 23rd International Conference on Control Systems and Computer Science Technologies, CSCS 2021",2021,"Proceedings - 2021 23rd International Conference on Control Systems and Computer Science Technologies, CSCS 2021",,,,"","",581,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112110642&partnerID=40&md5=ad5d3a04412f4a2c29b1a0ea3107aa75",,"","The proceedings contain 88 papers. The topics discussed include: reaction wheel control of an 3d printed inverted pendulum; using advanced V2X communication technologies in self-organized VANETs; stability of switched linear systems with average dwell time – a result encompassing several prior approaches; assessment of quality of health services in fractal-organized connectionist networks; top-level scene information extraction from eye-level view images; working with datasets: experience from research projects; IoT-based ambient parameters monitoring device for in-home elderly care; performance analysis of a low-cost small-scale flywheel energy storage system; context awareness in enterprise systems design; gesture-based human-robot interaction; and segmentation of brain tumors from MRI images using deep neural networks.",,,,,,,,,,,,,,,,,,,,,"IEEE Romania Section;IEEE Romanian Chapter of Computer Society;Romanian Academy - Science and Information Technology Section;Romanian Association for Artificial Intelligence (ARIA);Romanian Society of Control Engineering and Technical Informatics (SRAIT);The Faculty of Automatic Control and Computers (A and C) of University Politehnica of Bucharest","Institute of Electrical and Electronics Engineers Inc.","23rd International Conference on Control Systems and Computer Science Technologies, CSCS 2021","26 May 2021 through 28 May 2021",,170600,,9781665439398,,,"English","Proc. - Int. Conf. Control Syst. Comput. Sci. Technol., CSCS",Conference Review,"Final","",Scopus,2-s2.0-85112110642
"Glogowski P., Böhmer A., Hypki A., Kuhlenkötter B.","57195317864;57223199871;6506910986;7801348331;","Robot Speed Adaption in Multiple Trajectory Planning and Integration in a Simulation Tool for Human-Robot Interaction",2021,"Journal of Intelligent and Robotic Systems: Theory and Applications","102","1","25","","",,,"10.1007/s10846-020-01309-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105223926&doi=10.1007%2fs10846-020-01309-7&partnerID=40&md5=d55697aa2a42d4b3c0dda87fdd331fd3","Faculty of Mechanical Engineering, Chair of Production Systems, Ruhr University Bochum, Universitätsstr. 150, Bochum, 44801, Germany","Glogowski, P., Faculty of Mechanical Engineering, Chair of Production Systems, Ruhr University Bochum, Universitätsstr. 150, Bochum, 44801, Germany; Böhmer, A., Faculty of Mechanical Engineering, Chair of Production Systems, Ruhr University Bochum, Universitätsstr. 150, Bochum, 44801, Germany; Hypki, A., Faculty of Mechanical Engineering, Chair of Production Systems, Ruhr University Bochum, Universitätsstr. 150, Bochum, 44801, Germany; Kuhlenkötter, B., Faculty of Mechanical Engineering, Chair of Production Systems, Ruhr University Bochum, Universitätsstr. 150, Bochum, 44801, Germany","Speed and separation monitoring (SSM) is one of the four permissible collaborative operations in human-robot interaction (HRI). At all times, it must be ensured that the speed-dependent separation distance is maintained. To guarantee this, the robot speed or the robot path can be adapted. In this paper, the robot speed adaption for multiple trajectories is implemented in an HRI simulation tool and tested in an application example. Thereby, numerous complex process situations, such as a temporary robot stop or obstacles in the collaborative workspace, can be simulated. The simulation tool enables a comprehensive simulation, analysis and optimisation of human and robot motions within the HRI, already in the planning phase. © 2021, The Author(s).","Human-robot interaction; Multiple trajectory planning; Robot speed adaption; Speed and separation monitoring","Agricultural robots; End effectors; Man machine systems; Robot programming; Speed; Application examples; Collaborative operations; Collaborative workspace; Complex Processes; Human robot Interaction (HRI); Planning phase; Separation distances; Trajectory Planning; Human robot interaction",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 02P15A060","Open Access funding enabled and organized by Projekt DEAL. The research and development project “KoMPI” was funded by the German Federal Ministry of Education and Research (BMBF) within the Framework Concept “Research for Tomorrow’s Production” (fund number 02P15A060) and managed by the Project Management Agency Forschungszentrum Karlsruhe, Production and Manufacturing Technologies Division (PTKA-PFT).",,,,,,,,,,"(2016), ISO/TS 15066: Robots and Robotic Devices – Collaborative Robots; Glogowski, P., Lemmerz, K., Hypki, A., Kuhlenkötter, B., ROS-based Robot Simulation in Human-Robot Collaboration (2018) Developing Support Technologies: Integrating Multiple Perspectives to Create Assistance that People Really Want, pp. 237-246. , Karafillidis, A., Weidner, R; Glogowski, P., Lemmerz, K., Schulte, L., Barthelmey, A., Hypki, A., Kuhlenkötter, B., Deuse, J., Task-based Simulation Tool for Human-Robot Collaboration within Assembly Systems (2017) Tagungsband Des 2. Kongresses Montage Handhabung Industrieroboter, pp. 155-163. , Schöppstuhl, T, Franke, J., Tracht, K., Springer, Berlin; Lemmerz, K., Glogowski, P., Hypki, A., Kuhlenkötter, B., Functional Integration of a Robotics Software Framework into a Human Simulation System (2018) 50Th International Symposium on Robotics (ISR), , Munich; Lacevic, B., Rocco, P., Kinetostatic Danger Field – a Novel Safety Assessment for Human-Robot Interaction (2010) IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 2169-2174; Lacevic, B., Rocco, P., Safety-Oriented Control of Robotic Manipulators – a Kinematic Approach (2011) IFAC Proc Vol, 44 (1), pp. 11508-11513; Polverini, M.P., Zanchettin, A.M., Rocco, P., Real-time Collision Avoidance in Human-Robot Interaction based on Kinetostatic Safety Field (2014) IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 4136-4141; Marvel, J.A., Performance Metrics of Speed and Separation Monitoring in Shared Workspaces (2013) IEEE Trans. Autom. Sci. Eng., 10 (2), pp. 405-414; Marvel, J.A., Norcross, R., Implementing Speed and Separation Monitoring in Collaborative Robot Workcells (2017) Robot. Comput. Integr. Manuf., 44, pp. 144-155; Kim, E., Kirschner, R., Yamada, Y., Okamoto, S., Estimating Probability of Human Hand Intrusion for Speed and Separation Monitoring using Interference Theory (2020) Robot. Comput. Integr. Manuf., 61; Savur, C., Kumar, S., Arora, S., Hazbar, T., Sahin, F., (2019) Hrc-Sos: Human Robot Collaboration Experimentation Platform as System of Systems. Arxiv, 1905, p. 01026; Byner, C., Matthias, B., Ding, H., Dynamic Speed and Separation Monitoring for Collaborative Robot Applications – Concepts and Performance (2019) Robot. Comput. Integr. Manuf., 58, pp. 239-252; Lasota, P.A., Rossano, G.F., Shah, J.A., Toward Safe Close-Proximity Human-Robot Interaction with Standard Industrial Robots (2014) IEEE International Conference on Automation Science and Engineering (CASE), pp. 339-344; Kumar, S., Arora, S., Sahin, F., Speed and Separation Monitoring using On-Robot Time-of-Flight Laser-ranging Sensor Arrays (2019) IEEE International Conference on Automation Science and Engineering (CASE), pp. 1684-1691; Dröder, K., Bobka, P., Germann, T., Gabriel, F., Dietrich, F., A Machine Learning-Enhanced Digital Twin Approach for Human-Robot-Collaboration (2018) Procedia CIRP, 76, pp. 187-192; Glogowski, P., Lemmerz, K., Hypki, A., Kuhlenkötter, B., Menschzentrierte Simulation mit adaptiver kollisionsfreier Roboterbahnplanung in der Mensch-Roboter-Kollaboration (2018) Band Zur Dritten Transdisziplinären Konferenz “Technische Unterstützungssysteme, Die Die Menschen Wirklich wollen”, pp. 47-57. , Weidner, R., Karafillidis, A; Schmidt, B., Wang, L., Contact-less and Programming-less Human-Robot Collaboration (2013) Procedia CIRP, 7, pp. 545-550; Liu, Z., Wang, X., Cai, Y., Xu, W., Liu, Q., Zhou, Z., Pham, D.T., Dynamic Risk Assessment and Active Response Strategy for Industrial Human-Robot Collaboration (2020) Comput. Ind. Eng., 141; Vicentini, F., Giussani, M., Tosatti, L.M., Trajectory-dependent Safe Distances in Human-Robot Interaction (2014) IEEE Emerging Technology and Factory Automation (ETFA), pp. 1-4; Zanchettin, A.M., Ceriani, N.M., Rocco, P., Ding, H., Matthias, B., Safety in Human-Robot Collaborative Manufacturing Environments: Metrics and Control (2015) IEEE Trans. Autom. Sci. Eng., 13 (2), pp. 882-893; Rosenstrauch, M.J., Pannen, T.J., Krüger, J., Human Robot Collaboration – Using Kinect v2 for ISO/TS 15066 Speed and Separation Monitoring (2018) Procedia CIRP, 76, pp. 183-186; Glogowski, P., Lemmerz, K., Hypki, A., Kuhlenkötter, B., Extended Calculation of the Dynamic Separation Distance for Robot Speed Adaption in the Human-Robot Interaction (2019) International Conference on Advanced Robotics (ICAR), Belo Horizonte, Brazil; Deuflhard, P., Newton Methods for Nonlinear Problems: Affine Invariance and Adaptive Algorithms (2011) Springer Science & Business Media, Vol, p. 35; Gourdon, X., Sebah, P., (2001) Newton’s Method and High Order Iterations; Bauer, S., (2015) Prozesssprachenbasiertes System Zur Ansteuerung Digitaler Menschmodelle Als Teilkomponente Einer Software Zur Planung Und Visualisierung Menschlicher Arbeit in Der Digitalen Fabrik, , Ph.D. Thesis; Fritzsche, L., Jendrusch, R., Leidholdt, W., Bauer, S., Jäckel, T., Pirger, A., Introducing ema (Editor for Manual Work Activities) – A New Tool for Enhancing Accuracy and Efficiency of Human Simulations in Digital Production Planning (2011) International Conference on Digital Human Modeling, pp. 272-281. , Springer; Sucan, I.A., (2012) Task and Motion Planning for Mobile Manipulators, , Ph.D. Thesis; Sucan, I.A., Moll, M., Kavraki, L.E., The Open Motion Planning Library (2012) IEEE Robot. Autom. Mag., 19 (4), pp. 72-82; Kuffner, J.J., Lavalle, S.M., RRT-Connect: An Efficient Approach to Single-Query Path Planning (2000) International Conference on Robotics and Automation, Vol. 2, Pp. 995–1001. IEEE","Glogowski, P.; Faculty of Mechanical Engineering, Universitätsstr. 150, Germany; email: glogowski@lps.ruhr-uni-bochum.de",,,"Springer Science and Business Media B.V.",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85105223926
"Yang B., Xie X., Habibi G., Smith J.R.","57195419295;57222467344;24605330500;54421256200;","Competitive physical human-robot game play",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447168","242","246",,,"10.1145/3434074.3447168","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102772345&doi=10.1145%2f3434074.3447168&partnerID=40&md5=28820d231b77be6ec6c3b2a6f58cc0bd","University of Washington, Seattle, WA, United States; Massachusetts Institute of Technology, Boston, MA, United States","Yang, B., University of Washington, Seattle, WA, United States; Xie, X., University of Washington, Seattle, WA, United States; Habibi, G., Massachusetts Institute of Technology, Boston, MA, United States; Smith, J.R., University of Washington, Seattle, WA, United States","While competitive games have been studied extensively in the AI community for benchmarking purposes, there has only been limited discussion of human interaction with embodied agents under competitive settings. In this work, we aim to motivate research in competitive human-robot interaction (competitive-HRI) by discussing how human users can benefit from robot competitors. We then examine the concepts from game AI that we can adopt for competitive-HRI. Based on these discussions, we propose a robotic system that is designed to support future competitive-HRI research. A human-robot fencing game is also proposed to evaluate a robot's capability in competitive-HRI scenarios. Finally, we present the initial experimental results and discuss possible future research directions. © 2021 ACM.","Artificial intelligence for games; Competitive-HRI; Human-robot interaction; Reinforcement learning","Agricultural robots; Man machine systems; Competitive games; Embodied agent; Game plays; Human interactions; Human robots; Human users; Possible futures; Robotic systems; Human robot interaction",,,,,"National Science Foundation, NSF: CNS-1305072, EFMA-1832795","This work was supported in part by NSF awards EFMA-1832795 and CNS-1305072. This study (IRB ID: STUDY00012211) has been approved by the University of Washington Human Subjects Division.",,,,,,,,,,"Bansal, T., Pachocki, J., Sidor, S., Sutskever, I., Mordatch, I., (2017) Emergent Complexity Via Multi-agent Competition, , 2017; Bhattacharjee, T., Gordon, E.K., Scalise, R., Cabrera, M.E., Caspi, A., Cakmak, M., Srinivasa, S.S., Is more autonomy always better? exploring preferences of users with mobility impairments in robot-assisted feeding (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 181-190; Campbell, M., Hoane, A.J., Jr., Hsu, F., Deep blue (2002) Artificial Intelligence, 134 (1-2), pp. 57-83. , 2002; Chen, M., Nikolaidis, S., Soh, H., Hsu, D., Srinivasa, S., Trust-aware decision making for human-robot collaboration: Model learning and planning (2020) ACM Transactions on Human-Robot Interaction (THRI), 9 (2), pp. 1-23. , 2020; Edsinger, A., Kemp, C.C., Human-robot interaction for cooperative manipulation: Handing objects to one another (2007) RO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 1167-1172; Fasola, J., Mataric, M.J., Robot exercise instructor: A socially assistive robot system to monitor and encourage physical exercise for the elderly (2010) 19th International Symposium in Robot and Human Interactive Communication. IEEE, pp. 416-421; Feltz, D.L., Forlenza, S.T., Winn, B., Kerr, N.L., Cyber buddy is better than no buddy: A test of the köhler motivation effect in exergames (2014) GAMES for HEALTH: Research, Development, and Clinical Applications, 3 (2), pp. 98-105. , 2014; Foerster, J.N., Chen, R.Y., Al-Shedivat, M., Whiteson, S., Abbeel, P., Mordatch, I., (2017) Learning with Opponent-learning Awareness, , 2017; He, H., Boyd-Graber, J., Kwok, K., Daumé, H., III, Opponent modeling in deep reinforcement learning (2016) International Conference on Machine Learning, pp. 1804-1813; Hidi, S., Ann Renninger, K., The four-phase model of interest development (2006) Educational Psychologist, 41 (2), pp. 111-127. , 2006; Daniel Hillis, W., Co-evolving parasites improve simulated evolution as an optimization procedure (1990) Physica D: Nonlinear Phenomena, 42 (1-3), pp. 228-234. , 1990; Kshirsagar, A., Dreyfuss, B., Ishai, G., Heffetz, O., Hoffman, G., Monetary-incentive competition between humans and robots: Experimental results (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 95-103; Li, R., Van Almkerk, M., Van Waveren, S., Carter, E., Leite, I., Comparing human-robot proxemics between virtual reality and the real world (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 431-439; Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., Wierstra, D., (2015) Continuous Control with Deep Reinforcement Learning, , 2015; Lowe, R., Wu, Y.I., Tamar, A., Harb, J., Pieter Abbeel, O., Mordatch, I., Multi-agent actor-critic for mixed cooperative-competitive environments (2017) Advances in Neural Information Processing Systems, pp. 6379-6390; Luo, R., Hayne, R., Berenson, D., Unsupervised early prediction of human reaching for human-robot collaboration in shared workspaces (2018) Autonomous Robots, 42 (3), pp. 631-648. , 2018; Matsas, E., Vosniakos, G., Design of a virtual reality training system for human-robot collaboration in manufacturing tasks (2017) International Journal on Interactive Design and Manufacturing (IJIDeM), 11 (2), pp. 139-153. , 2017; Mutlu, B., Osman, S., Forlizzi, J., Hodgins, J., Kiesler, S., Perceptions of asimo: An exploration on co-operation and competition with humans and humanoid robots (2006) Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-robot Interaction, pp. 351-352; Pinto, L., Davidson, J., Sukthankar, R., Gupta, A., (2017) Robust Adversarial Reinforcement Learning, , 2017; Plass, J.L., O'Keefe, P.A., Homer, B.D., Case, J., Hayward, E.O., Stein, M., Perlin, K., The impact of individual, competitive, and collaborative mathematics game play on learning, performance, and motivation (2013) Journal of Educational Psychology, 105 (4), p. 1050. , 2013; Salen, K., Salen Tekinbas, K., Zimmerman, E., (2004) Rules of Play: Game Design Fundamentals, , MIT press; Sato, K., Watanabe, K., Mizuno, S., Manabe, M., Yano, H., Iwata, H., Development of a block machine for volleyball attack training (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 1036-1041; Schaeffer, J., Burch, N., Björnsson, Y., Kishimoto, A., Müller, M., Lake, R., Lu, P., Sutphen, S., Checkers is solved (2007) Science, 317 (5844), pp. 1518-1522. , 2007; Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O., (2017) Proximal Policy Optimization Algorithms, , 2017; Short, E., Hart, J., Vu, M., Scassellati, B., No fair!! an interaction with a cheating robot (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 219-226; Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Graepel, T., A general reinforcement learning algorithm that masters chess, shogi, and go through self-play (2018) Science, 362 (6419), pp. 1140-1144. , 2018; Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Bolton, A., Mastering the game of go without human knowledge (2017) Nature, 550 (7676), pp. 354-359. , 2017; Stanley, K.O., Miikkulainen, R., Competitive coevolution through evolutionary complexification (2004) Journal of Artificial Intelligence Research, 21, pp. 63-100. , 2004; Süssenbach, L., Riether, N., Schneider, S., Berger, I., Kummert, F., Lütkebohle, I., Pitsch, K., A robot as fitness companion: Towards an interactive action-based motivation model (2014) The 23rd IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 286-293; Tampuu, A., Matiisen, T., Kodelja, D., Kuzovkin, I., Korjus, K., Aru, J., Aru, J., Vicente, R., Multiagent cooperation and competition with deep reinforcement learning (2017) PloS One, 12 (4), p. 172395. , 2017; Viru, M., Hackney, A., Karelson, K., Janson, T., Kuus, M., Viru, A., Competition effects on physiological responses to exercise: Performance, cardiorespiratory and hormonal factors (2010) Acta Physiologica Hungarica, 97 (1), pp. 22-30. , 2010",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102772345
"Johnson B., Hernández-Cuevas B.Y., Singh U., Crawford C.S., Vikas V., Denham A.","57222467401;57209473386;57214523994;56900631800;36698441400;36967454900;","Towards web-based environments for prototyping social robot applications",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447182","308","312",,1,"10.1145/3434074.3447182","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102765393&doi=10.1145%2f3434074.3447182&partnerID=40&md5=1f2592e1f322627f1ae2c5f136a37262","Dept. of Computer Science, Univ. of Alabama; Dept. of Mechanical Engineering, Univ. of Alabama; College of Education, Univ. of Alabama","Johnson, B., Dept. of Computer Science, Univ. of Alabama; Hernández-Cuevas, B.Y., Dept. of Computer Science, Univ. of Alabama; Singh, U., Dept. of Computer Science, Univ. of Alabama; Crawford, C.S., Dept. of Computer Science, Univ. of Alabama; Vikas, V., Dept. of Mechanical Engineering, Univ. of Alabama; Denham, A., College of Education, Univ. of Alabama","Interest in web-based human-robot interaction (HRI) has grown since it was initially introduced. Similarly, interest in social signals such as voice and facial expressions continues to expand. More recently, researchers have also gained interest in the feasibility of using neurophysiological information to enhance HRI. While both social signals and web-based HRI have seen growing interest, there is limited work exploring potential advances at the intersection of these two areas. This paper describes our efforts to investigate this intersection by integrating: 1) web-based social signal interpretation, 2) hybrid block/text scripting interfaces, and 3) ROS integration via rosbridge. We further discuss potential advantages and current challenges concerning web-based platforms for prototyping social robotic applications. © 2021 ACM.","Human robot interaction; Physiological computing; Robotics","Agricultural robots; Man machine systems; Robot applications; Social robots; Facial Expressions; Human robot Interaction (HRI); Scripting interface; Social robotics; Social signals; Web based; Web based platform; Web-based environment; Websites",,,,,,,,,,,,,,,,"Bailenson, J.N., Yee, N., Digital chameleons: Automatic assimilation of nonverbal gestures in immersive virtual environments Psychological Science, 16 (10), pp. 814-819. , 2005; Beaufort, F., (2015) Communicating with Bluetooth Devices over Javascript, , https://web.dev/bluetooth/; Bethel, C.L., Salomon, K., Murphy, R.R., Burke, J.L., Survey of psychophysiology measurements applied to human-robot interaction (2007) RO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication, pp. 732-737; Blume, J., Iprogram: Intuitive programming of an industrial hri cell (2013) 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 85-86; Bohus, D., Andrist, S., Jalobeanu, M., Rapid development of multimodal interactive systems: A demonstration of platform for situated intelligence (2017) Proceedings of the 19th ACM International Conference on Multimodal Interaction, pp. 493-494; Booth, T., Stumpf, S., End-user experiences of visual and textual programming environments for arduino (2013) International Symposium on End User Development, pp. 25-39. , Springer; Brown, O., Roberts-Elliott, L., Del Duchetto, F., Hanheide, M., Baxter, P., Abstract visual programming of social robots for novice users (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 154-156; Campusano, M., Fabry, J., Live robot programming: The language, its implementation, and robot api independence (2017) Science of Computer Programming, 133, pp. 1-19; Cid, F., Prado, J.A., Bustos, P., Nunez, P., A real time and robust facial expression recognition and imitation approach for affective human-robot interaction using gabor filtering (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2188-2193; Coronado, E., Mastrogiovanni, F., Indurkhya, B., Venture, G., Visual programming environments for end-user development of intelligent and social robots, a systematic review (2020) Journal of Computer Languages, p. 100970; Crick, C., Jay, G., Osentoski, S., Jenkins, O.C., Ros and rosbridge: Roboticists out of the loop (2012) 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 493-494; Crick, C., Jay, G., Osentoski, S., Pitzer, B., Jenkins, O.C., Rosbridge: Ros for non-ros users (2017) Robotics Research, pp. 493-504. , Springer; Delgado, C., (2016) Artyom. Js, , https://sdkcarlos.github.io/sites/artyom.html; Fitter, N.T., Kuchenbecker, K.J., Designing and assessing expressive open-source faces for the baxter robot (2016) International Conference on Social Robotics, pp. 340-350. , Springer; Fraser, N., (2020), https://developers.google.com/blockly, Accessed Dec. 10; Gonsior, B., Sosnowski, S., Mayer, C., Blume, J., Radig, B., Wollherr, D., Kühnlenz, K., Improving aspects of empathy and subjective performance for hri through mirroring facial expressions (2011) 2011 RO-MAN, pp. 350-356; Huang, J., Lau, T., Cakmak, M., Design and evaluation of a rapid programming system for service robots, the eleventh acm (2016) IEEE International Conference on Human Robot Interaction; Jang, M., Park, C., Yang, H.-S., Building an automated engagement recognizer based on video analysis (2014) 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 182-183; Koitz, R., Slany, W., Empirical comparison of visual to hybrid formula manipulation in educational programming languages for teenagers (2014) Proceedings of the 5th Workshop on Evaluation and Usability of Programming Languages and Tools, pp. 21-30; Kubota, A., Peterson, E.I., Rajendren, V., Kress-Gazit, H., Riek, L.D., Jessie: Synthesizing social robot behaviors for personalized neurorehabilitation and beyond (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 121-130; Kulic, D., Croft, E., Anxiety detection during human-robot interaction (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 616-621; Lau, F., Gopinath, D., Argall, B.D., A Javascript Framework for Crowdsourced Human-robot Interaction Experiments: Remotehri; Lemaignan, S., Garcia, F., Jacq, A., Dillenbourg, P., From real-time attention assessment to ""with-me-ness"" human-robot interaction (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 157-164. , Ieee; Liu, C., Rani, P., Sarkar, N., Human-robot interaction using affective cues (2006) ROMAN 2006-The 15th IEEE International Symposium on Robot and Human Interactive Communication, pp. 285-290; Mower, E., Feil-Seifer, D.J., Mataric, M.J., Narayanan, S., Investigating implicit cues for user state estimation in human-robot interaction using physiological measurements (2007) RO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication, pp. 1125-1130; Mühler, V., (2018) Face-api. Js, , https://github.com/justadudewhohacks/face-api.js/; Pitzer, B., Osentoski, S., Jay, G., Crick, C., Jenkins, O.C., Pr2 remote lab: An environment for remote development and experimentation (2012) 2012 IEEE International Conference on Robotics and Automation, pp. 3200-3205; Price, T.W., Barnes, T., Comparing textual and block interfaces in a novice programming environment (2015) Proceedings of the Eleventh Annual International Conference on International Computing Education Research, pp. 91-99; Riek, L.D., Paul, P.C., Robinson, P., When my robot smiles at me: Enabling human-robot rapport via real-time head gesture mimicry (2010) Journal on Multimodal User Interfaces, 3 (1-2), pp. 99-108; Riek, L.D., Rabinowitch, T.-C., Bremner, P., Pipe, A.G., Fraser, M., Robinson, P., Cooperative gestures: Effective signaling for humanoid robots (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 61-68; Ritschel, H., André, E., Real-time robot personality adaptation based on reinforcement learning and social signals (2017) Proceedings of the Companion of the 2017 Acm/ieee International Conference on Human-robot Interaction, pp. 265-266; Salazar-Gomez, A.F., DelPreto, J., Gil, S., Guenther, F.H., Rus, D., Correcting robot mistakes in real time using EEG signals (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 6570-6577; Shih, V., Jangraw, D., Saproo, S., Sajda, P., Deep reinforcement learning using neurophysiological signatures of interest (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 285-286; Simmons, R., Knight, H., Keep on dancing: Effects of expressive motion mimicry (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 720-727; Skantze, G., Al Moubayed, S., Iristk: A statechart-based toolkit for multiparty face-to-face interaction (2012) Proceedings of the 14th ACM International Conference on Multimodal Interaction, pp. 69-76; Stegman, P., Crawford, C., Gray, J., Webbci: An electroencephalography toolkit built on modern web technologies (2018) International Conference on Augmented Cognition, pp. 212-221. , Springer; Stoeva, D., Gelautz, M., Body language in affective human-robot interaction (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 606-608; Suguitan, M., Hoffman, G., Blossom: A handcrafted open-source robot (2019) ACM Transactions on Human-Robot Interaction (THRI), 8 (1), pp. 1-27; Suman, A., Marvin, R., Grigore, E.C., Admoni, H., Scassellati, B., Prior behavior impacts human mimicry of robots (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 1057-1062; Szafir, D., Mutlu, B., Pay attention! designing adaptive agents that monitor and improve user engagement (2012) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 11-20; Tanevska, A., Rea, F., Sandini, G., Sciutti, A., Designing an affective cognitive architecture for human-humanoid interaction (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 253-254; Toris, R., Kammerl, J., Lu, D.V., Lee, J., Jenkins, O.C., Osentoski, S., Wills, M., Chernova, S., Robot web tools: Efficient messaging for cloud robotics (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4530-4537; Vandevelde, C., Saldien, J., An open platform for the design of social robot embodiments for face-to-face communication (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 287-294; Wagner, J., Lingenfelser, F., Baur, T., Damian, I., Kistler, F., André, E., The social signal interpretation (ssi) framework: Multimodal signal processing and recognition in real-time (2013) Proceedings of the 21st ACM International Conference on Multimedia, pp. 831-834; Weintrop, D., Wilensky, U., To block or not to block, that is the question: Students' perceptions of blocks-based programming (2015) Proceedings of the 14th International Conference on Interaction Design and Children, pp. 199-208; Weintrop, D., Wilensky, U., Between a block and a typeface: Designing and evaluating hybrid programming environments (2017) Proceedings of the 2017 Conference on Interaction Design and Children, pp. 183-192; Weintrop, D., Wilensky, U., Comparing block-based and text-based programming in high school computer science classrooms (2017) ACM Transactions on Computing Education (TOCE), 18 (1), pp. 1-25; Weintrop, D., Wilensky, U., How block-based, text-based, and hybrid block/text modalities shape novice programming practices (2018) International Journal of Child-Computer Interaction, 17, pp. 83-92",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102765393
"Tatarian K., Buyukgoz S., Chamoux M., Tabrez A., Hayes B., Chetouani M.","57203921223;57218465603;57201364987;57208160542;55638866400;8964513700;","2nd edition of solutions for socially intelligent HRI in real-world scenarios (SSIR-HRI)",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444874","694","696",,,"10.1145/3434074.3444874","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102760229&doi=10.1145%2f3434074.3444874&partnerID=40&md5=b9ac954398e00fe384280da861df1644","SoftBank Robotics Europe, Sorbonne University, Paris, France; SoftBank Robotics Europe, Paris, France; University of Colorado Boulder, Boulder, CO, United States; Sorbonne University, Paris, France","Tatarian, K., SoftBank Robotics Europe, Sorbonne University, Paris, France; Buyukgoz, S., SoftBank Robotics Europe, Sorbonne University, Paris, France; Chamoux, M., SoftBank Robotics Europe, Paris, France; Tabrez, A., University of Colorado Boulder, Boulder, CO, United States; Hayes, B., University of Colorado Boulder, Boulder, CO, United States; Chetouani, M., Sorbonne University, Paris, France","Today it seems even more evident that social robots will have a more integral role to play in the real-world scenarios and need to participate in the full richness of human society. Central to the success of robots being socially intelligent agents is insuring effective interactions between humans and robots. In order to achieve that goal, researchers and engineers from both industry and academia need to come together to share ideas, trials, failures, and successes. This workshops aims at creating the bridge between industry and academia and as such creating a community to tackle the current and future challenges of socially intelligent human-robot interaction in real-world scenarios by finding solutions for them. © 2021 Owner/Author.","Affective computing; Human-robot collaboration; Human-robot interaction; Real-world scenarios; Social intelligence","Agricultural robots; Industrial robots; Intelligent agents; Intelligent robots; Man machine systems; Effective interactions; Finding solutions; Future challenges; Human society; Real-world scenario; Socially intelligent agents; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 765955","This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 765955. (ANIMATAS)",,,,,,,,,,"Baxter, P., Kennedy, J., Senft, E., Lemaignan, S., Belpaeme, T., From characterising three years of hri to methodology and reporting recommendations (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 391-398. , https://doi.org/10.1109/HRI.2016.7451777; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Robotics and Autonomous Systems, 42 (3), pp. 143-166. , https://doi.org/10.1016/S0921-8890(02)00372-XSociallyInteractiveRobots, 2003","Tatarian, K.; SoftBank Robotics Europe, France; email: tatariankaren@gmail.com",,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102760229
"Andriella A., Rossi A., Rossi S., Van Maris A.","57202547412;57189499587;55619320887;57193757921;","The road to a successful HRI: AI, trust and ethicS - TRAITS",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444872","709","711",,,"10.1145/3434074.3444872","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102752747&doi=10.1145%2f3434074.3444872&partnerID=40&md5=71e6635418728cbbecc3104ce114eb6b","Institut de Robotica i Informatica Industrial (CSIC-UPC), Barcelona, Spain; University of Naples Federico II, Naples, Italy; University of the West of England, Bristol Robotics Laboratory, Bristol, United Kingdom","Andriella, A., Institut de Robotica i Informatica Industrial (CSIC-UPC), Barcelona, Spain; Rossi, A., University of Naples Federico II, Naples, Italy; Rossi, S., University of Naples Federico II, Naples, Italy; Van Maris, A., University of the West of England, Bristol Robotics Laboratory, Bristol, United Kingdom","The aim of this workshop is to give researchers from academia and industry the possibility to discuss the inter- and multi- disciplinary nature of the relationships between people and robots towards effective and long-lasting collaborations. This workshop will provide a forum for the HRI and robotics communities to explore successful human-robot interaction (HRI) to analyse the different aspects of HRI that impact its success. Particular focus are the AI algorithms required to implement autonomous interactions, and the factors that enhance, undermine, or recover humans' trust in robots. Finally, potential ethical and legal concerns, and how they can be addressed will be considered. Website: https://sites.google.com/view/traits-hri. © 2021 Owner/Author.","Ethics; Human-robot interaction; Mental models; Social robotics; Trust; Xai","Agricultural robots; Industrial robots; Man machine systems; Philosophical aspects; AI algorithms; Human robot Interaction (HRI); Legal concern; Long lasting; Social robots",,,,,"955778; EP/S005099/1; 741930","A. Rossi acknowledges funding from the PON I&C 2014-2020 MISE Bartending Robot for Interactive Long-Lasting Operations (BRILLO) project under the grant F/190066/01-02/X44. S. Rossi acknowledges funding from H2020 MSCA ITN PErsonalized Robotics as SErvice Oriented applications (PERSEO) project under the grant 955778. A. van Maris acknowledges funding from EPSRC Developing Responsible Robots for the Digital Economy (RoboTIPS) project under the grant EP/S005099/1. A. Andriella acknowledges funding from H2020 ERC Advance Grant Project CLOTH manIpuLation learning from DEmonstration (CLOTHILDE) project under the grant 741930.",,,,,,,,,,"Kotseruba, I., Tsotsos, J.K., 40 years of cognitive architectures: Core cognitive abilities and practical applications (2020) Artificial Intelligence Review, 53 (1), pp. 17-94. , 2020; Rossi, A., Dautenhahn, K., Lee Koay, K., Walters, M.L., How the timing and magnitude of robot errors influence peoples' trust of robots in an emergency scenario (2017) Social Robotics, pp. 42-52. , Abderrahmane Kheddar, Eiichi Yoshida, Shuzhi Sam Ge, Kenji Suzuki, John-John Cabibihan, Friederike Eyssel, and Hongsheng He (Eds.) Springer International Publishing, Cham; Rossi, A., Dautenhahn, K., Lee Koay, K., Walters, M.L., How social robots influence people's trust in critical situations (2020) 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1020-1025. , https://doi.org/10.1109/RO-MAN47096.2020.9223471; Rossi, S., Rossi, A., Dautenhahn, K., The secret life of robots: Perspectives and challenges for robot's behaviours during noninteractive tasks (2020) International Journal of Social Robotics, , https://doi.org/10.1007/s12369-020-00650-z, 2020; Salem, M., Eyssel, F., Rohlfing, K., Kopp, S., Joublin, F., To err is human(-like): Effects of robot gesture on perceived anthropomorphism and likability (2013) International Journal of Social Robotics, 5 (3), pp. 313-323. , 01 Aug 2013",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102752747
[No author name available],[No author id available],"ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics",2021,"ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics",,,,"","",435,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106520304&partnerID=40&md5=b2a3792565ceb1fce25965ebac3450da",,"","The proceedings contain 92 papers. The topics discussed include: from handcrafting to a certified and ergonomic collaborative workstation: the digital transformation process; estimation of effects of recovery step length on severity of injuries caused by the trip and fall; design for interpretability: meeting the certification challenge for surgical robots; a safety joint with passive compliant and manual override mechanisms for medical robotics; evaluating machine learning performance for safe, intelligent robots; walking motions with high margin-of-stability values; developing a framework for evaluating robotic care devices in the introduction phase; path planning algorithm based on the improved RRT-connect for home service robot arm; and modeling and stiffness evaluation of tendon-driven robot for collaborative human-robot interaction.",,,,,,,,,,,,,,,,,,,,,"IEEE Robotics and Automation Society;Nagoya University","Institute of Electrical and Electronics Engineers Inc.","2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021","4 March 2021 through 6 March 2021",,168860,,9781665438629,,,"English","ISR - IEEE Int. Conf. Intell. Saf. Robot.",Conference Review,"Final","",Scopus,2-s2.0-85106520304
[No author name available],[No author id available],"2021 IEEE/SICE International Symposium on System Integration, SII 2021",2021,"2021 IEEE/SICE International Symposium on System Integration, SII 2021",,,,"","",852,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103762904&partnerID=40&md5=1f5900fd6edb66019071aeee3e92f66a",,"","The proceedings contain 165 papers. The topics discussed include: evaluation of tomato fruit harvestability for robotic harvesting; development and demonstration of the mission control system for artificial meteor generating micro-satellites; design of class in unknown object segmentation focusing on 3D object detection in depth image; teaching system for multimodal object categorization by human-robot interaction in mixed reality; a simulator-based system for testing skill to maneuver robot remotely; cylinder detection from RGBD data based on radius estimation using number of measurement points; optimal design of rotational chuck-type hand with three parallel stick fingers for assembly tasks through grasp optimization; and primary-view-consistent operation method of master controller in multiple screen teleoperation system.",,,,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE/SICE International Symposium on System Integration, SII 2021","11 January 2021 through 14 January 2021",,168053,,9781728176581,,,"English","IEEE/SICE Int. Symp. Syst. Integr., SII",Conference Review,"Final","",Scopus,2-s2.0-85103762904
"Hu Z., Zhao Y., Zhang S., Zhou L., Liu J.","57219594892;57219600056;55585076100;57212481028;7410114768;","Crowd-Comfort Robot Navigation Among Dynamic Environment Based on Social-Stressed Deep Reinforcement Learning",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00838-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120163064&doi=10.1007%2fs12369-021-00838-x&partnerID=40&md5=149da86edb954a0931b454353ab3f9d9","Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tanjin, China; Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tanjin, China; School of Mechanical Engineering, Tianjin University of Technology, Tanjin, China","Hu, Z., Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tanjin, China, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tanjin, China; Zhao, Y., Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tanjin, China, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tanjin, China; Zhang, S., School of Mechanical Engineering, Tianjin University of Technology, Tanjin, China; Zhou, L., Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tanjin, China, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tanjin, China; Liu, J., Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tanjin, China, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tanjin, China","Robot navigation in a dynamic environment is a challenging task because not only the safety but also the comfort of surrounding pedestrians shall be necessarily considered. This paper proposes the concept of social stress based on tension space of robot and human, which is an important part of Human-Robot interaction. Especially, the proposed approach develops crowd-comfort navigation by combining social stress indexes with a deep reinforcement learning framework and the value network. A set of typical simulation experiments show that our method improves the comfort of surrounding pedestrians effectively during the process of robot navigation. In addition, the fine-tuned technology proposed in this paper has also been proven to be suitable for different scenarios. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Crowd-comfort navigation; Deep reinforcement learning; Soical stress; Tension space","Air navigation; Human robot interaction; Reinforcement learning; Crowd-comfort navigation; Dynamic environments; Humans-robot interactions; Learning frameworks; Robot navigation; Soical stress; Stress indices; Tension space; Value network; Deep learning",,,,,"National Natural Science Foundation of China, NSFC: 91848108; National Key Research and Development Program of China, NKRDPC: 2019YFB1310604","This work is supported in part by National Key Research and Development Project under Grant 2019YFB1310604, in part by National Natural Science Foundation of China under Grant 91848108.",,,,,,,,,,"Ding, H., Theory and technology of tri-co robots (2017) 2017 IEEE/SICE International Symposium on System Integration (SII). IEEE, pp. 4-4; Ding, H., Yang, X., Zheng, N., Li, M., Lai, Y., Wu, H., Tri-co robot: a Chinese robotic research initiative for enhanced robot interaction capabilities (2018) Natl Sci Rev, 5 (6), pp. 799-801; Sato, T., Oyama, E., Matsuhira, N., (2008) Development of common platform technology for next-generation robots, , INTECH Open Access Publisher, London; Dautenhahn, K., Woods, S., Kaouri, C., Walters, M.L., Koay, K.L., Werry, I., What is a robot companion-friend, assistant or butler (2005) ?. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems., pp. 1192-1197; Durrant-Whyte, H., Bailey, T., Simultaneous localization and mapping: part i (2006) IEEE Robot Autom Mag, 13 (2), pp. 99-110; Bailey, T., Durrant-Whyte, H., Simultaneous localization and mapping (slam): part ii (2006) IEEE Robot Autom Mag, 13 (3), pp. 108-117; Hart, P.E., Nilsson, N.J., Raphael, B., A formal basis for the heuristic determination of minimum cost paths (1968) IEEE Trans Syst Sci Cybern, 4 (2), pp. 100-107; Lavalle, S.M., (1998) Rapidly-Exploring Random Trees: A New Tool for Path Planning; Fiorini, P., Shiller, Z., Motion planning in dynamic environments using velocity obstacles (1998) Int J Robot Res, 17 (7), pp. 760-772; Vanden Berg, J., Lin, M., Manocha, D., Reciprocal velocity obstacles for real-time multi-agent navigation (2008) 2008 IEEE International Conference on Robotics and Automation, pp. 1928-1935. , IEEE; Van DenBerg, J., Guy, S.J., Lin, M., Manocha, D., Reciprocal n-body collision avoidance (2011) Robotics research, pp. 3-19. , Springer, New York; Fox, D., Burgard, W., Thrun, S., The dynamic window approach to collision avoidance (1997) IEEE Robot Autom Mag, 4 (1), pp. 23-33; Helbing, D., Molnar, P., Social force model for pedestrian dynamics (1995) Phys Rev E, 51 (5), p. 4282; Jiang, Y.-Q., Chen, B.-K., Wang, B.-H., Wong, W.-F., Cao, B.-Y., Extended social force model with a dynamic navigation field for bidirectional pedestrian flow (2017) Front Phys, 12 (5), p. 124502; Robicquet, A., Sadeghian, A., Alahi, A., Savarese, S., Learning social etiquette: Human trajectory understanding in crowded scenes (2016) European Conference on Computer Vision, pp. 549-565. , Springer; Trautman, P., Krause, A., Unfreezing the robot: Navigation in dense, interacting crowds (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 797-803; Trautman, P., Ma, J., Murray, R.M., Krause, A., Robot navigation in dense human crowds: statistical models and experimental studies of human-robot cooperation (2015) Int J Robot Res, 34 (3), pp. 335-356; Kim, B., Pineau, J., Socially adaptive path planning in human environments using inverse reinforcement learning (2016) Int J Soc Robot, 8 (1), pp. 51-66; Kretzschmar, H., Spies, M., Sprunk, C., Burgard, W., Socially compliant mobile robot navigation via inverse reinforcement learning (2016) Int J Robot Res, 35 (11), pp. 1289-1307; Vemula, A., Muelling, K., Oh, J., Modeling cooperative navigation in dense human crowds (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 1685-1692; Tai, L., Zhang, J., Liu, M., Burgard, W., Socially compliant navigation through raw depth inputs with generative adversarial imitation learning (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA). 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IEEE, pp. 3052-3059; Chen, Y.F., Everett, M., Liu, M., How, J.P., Socially aware motion planning with deep reinforcement learning (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1343-1350. , IEEE; Chen, C., Liu, Y., Kreiss, S., Alahi, A., Crowd-robot interaction: Crowd-aware robot navigation with attention-based deep reinforcement learning (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 6015-6022. , IEEE; Vemula, A., Muelling, K., Oh, J., Social attention: Modeling attention in human crowds (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 1-7; https://github.com/vita-epfl/CrowdNav","Liu, J.; Tianjin Key Laboratory of Intelligent Robotics, China; email: liujt@nankai.edu.cn",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Article in Press","",Scopus,2-s2.0-85120163064
"Luo J., Huang D., Li Y., Yang C.","57304842100;57218113591;35174564100;57204881979;","Trajectory Online Adaption Based on Human Motion Prediction for Teleoperation",2021,"IEEE Transactions on Automation Science and Engineering",,,,"","",,,"10.1109/TASE.2021.3111678","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115694545&doi=10.1109%2fTASE.2021.3111678&partnerID=40&md5=b30927e082f9be5ada9e84c1217b9694","College of Automation, Wuhan University of Technology, Wuhan 430070, China.; School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China.; Department of Engineering and Design, University of Sussex, Brighton BN1 9RH, U.K.; Bristol Robotics Laboratory, UWE, Bristol BS16 1QY, U.K. (e-mail: cyang@ieee.org)","Luo, J., College of Automation, Wuhan University of Technology, Wuhan 430070, China.; Huang, D., School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China.; Li, Y., Department of Engineering and Design, University of Sussex, Brighton BN1 9RH, U.K.; Yang, C., Bristol Robotics Laboratory, UWE, Bristol BS16 1QY, U.K. (e-mail: cyang@ieee.org)","In this work, a human motion intention prediction method based on an autoregressive (AR) model for teleoperation is developed. Based on this method, the robot's motion trajectory can be updated in real time through updating the parameters of the AR model. In the teleoperated robot's control loop, a virtual force model is defined to describe the interaction profile and to correct the robot's motion trajectory in real time. The proposed human motion prediction algorithm acts as a feedforward model to update the robot's motion and to revise this motion in the process of human-robot interaction (HRI). The convergence of this method is analyzed theoretically. Comparative studies demonstrate the enhanced performance of the proposed approach. IEEE","Dynamics; Haptic interfaces; Human motion estimation; Jacobian matrices; physical human-robot interaction (HRI); prediction model; Predictive models; Robots; Task analysis; teleoperation system; Trajectory; virtual force.","Forecasting; Haptic interfaces; Human computer interaction; Human robot interaction; Jacobian matrices; Job analysis; Man machine systems; Remote control; Trajectories; Virtual reality; Haptics interfaces; Human motion estimation; Physical human-robot interaction; Physical humanrobot interaction (phri); Prediction modelling; Predictive models; Task analysis; Teleoperation systems; Virtual force.; Virtual forces; Motion estimation",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15455955,,,,"English","IEEE Trans. Autom. Sci. Eng.",Article,"Article in Press","",Scopus,2-s2.0-85115694545
"Romero A., Bellas F., Duro R.J.","57202612161;57223032812;7003592275;","An autonomous drive balancing strategy for the design of purpose in open-ended learning robots",2021,"Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS","3",,,"1619","1621",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112314370&partnerID=40&md5=4d96103a965c032813e755069c636257","GII, CITIC Research Center Universidade da Coruña, Spain","Romero, A., GII, CITIC Research Center Universidade da Coruña, Spain; Bellas, F., GII, CITIC Research Center Universidade da Coruña, Spain; Duro, R.J., GII, CITIC Research Center Universidade da Coruña, Spain","This paper is concerned with designing purpose in autonomous robots for open-ended learning settings. Unconstrained human robot interaction situations and robotic systems that must operate in dynamic multi-robot scenarios are paradigmatic examples of open-endedness. An approach to the appropriate design and engineering of motivational structures to endow robots with a particular purpose is proposed and tested. This approach focuses on the drive structure and how it can be made to autonomously adapt to changing circumstances. Specifically, a simple evolutionary strategy for the autonomous regulation of multiple drives in order to optimize long-term operation is defined. The experimental results have been obtained on a Baxter robot facing changing situations in real setups. © 2021 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.","Autonomous robotics; Humanrobot interaction; Motivation; Open-ended learning","Balancing; Educational robots; Evolutionary algorithms; Machine design; Multi agent systems; Social robots; Appropriate designs; Autonomous regulation; Balancing strategy; Drive structures; Evolutionary strategies; Learning settings; Multirobots; Robotic systems; Autonomous agents",,,,,"ED431G 2019/01; Ministerio de Ciencia, Innovación y Universidades, MCIU; European Regional Development Fund, ERDF; Xunta de Galicia: ED431C 2017/12, RTI2018-101114-B-I00","This by funded by, Ministerio de Ciencia, Innovaci?n y Universidades of Spain/FEDER and Xunta de Galicia by grants RTI2018-101114-B-I00, ED431C 2017/12, CITIC research center grant ED431G 2019/01, and the FPU grant of Alejandro Romero.","This by funded by, Ministerio de Ciencia, Innovación y Universidades of Spain/FEDER and Xunta de Galicia by grants RTI2018-101114-B-I00, ED431C 2017/12, CITIC research center grant ED431G 2019/01, and the FPU grant of Alejandro Romero.",,,,,,,,,"Doncieux, S., Open-ended learning: a conceptual framework based on representational redescription (2018) Front. Neurorobot, 12; Baldassarre, G., Mirolli, M., Intrinsically motivated learning in natural and artificial systems. 2013 (2013) Intrinsically Motivated Learning Systems: an Overview, pp. 1-14. , Springer Berlin Heidelberg; Oudeyer, P., Baranes, A., Kaplan, F., Intrinsically Motivated Learning of Real-World Sensorimotor Skills with Developmental Constraints (2013) Intrinsically Motiv. Learn. Nat. Artif. Syst, pp. 303-365; Starzyk, J. A., Motivated Learning for Computational Intelligence (2011) Computational Modeling and Simulation of Intellect: Current State and Future Perspectives, pp. 265-292. , IGI Global; Singh, S. P., Lewis, R. L., Barto, A. G., Sorg, J., Intrinsically Motivated Reinforcement Learning: An Evolutionary Perspective (2010) IEEE Trans. Auton. Ment. Dev, 2 (2), pp. 70-82; Oudeyer, P., Hafner, V. V, Whyte, A., The Playground Experiment: Task- Independent Development of a Curious Robot (2005) Proc. AAAI Spring Symposium on Developmental Robotics, pp. 42-47; Nehmzow, U., Gatsoulis, Y., Kerr, E., Condell, J., Siddique, N., Mcginnity, T. M., (2013) Intrinsically Motivated Learning in Natural and Artificial Systems, , Springer Berlin Heidelberg; Ugur, E., Piater, J., Emergent structuring of interdependent affordance learning tasks using intrinsic motivation and empirical feature selection (2016) IEEE Trans. Cogn. Dev. Syst, 9 (4), pp. 328-340; Santucci, V. G., Baldassarre, G., Mirolli, M., Which is the best intrinsic motivation signal for learning multiple skills? (2013) Front. Neurorobot, 7; Wigfield, A., Eccles, J. S., Expectancy-value theory of achievement motivation (2000) Contemp. Educ. Psychol, 25 (1), pp. 68-81; Merrick, K., Value systems for developmental cognitive robotics: A survey (2017) Cogn. Syst. Res, 41, pp. 38-55; Prieto, A., Romero, A., Bellas, F., Salgado, R., Duro, R. J., Introducing Separable Utility Regions in a Motivational Engine for Cognitive Developmental Robotics (2019) Integr. Comput. Aided. Eng, 26 (1), pp. 3-20; Romero, A., Bellas, F., Becerra, J.A., Duro, R.J., Motivation as a Tool for Designing Lifelong Learning Robots (2020) Integr. Comput. Aided. Eng, 27 (4), pp. 353-372. , 2020",,,,"International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)","20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021","3 May 2021 through 7 May 2021",,170834,15488403,9781713832621,,,"English","Proc. Int. Joint Conf. Auton. Agents Multiagent Syst., AAMAS",Conference Paper,"Final","",Scopus,2-s2.0-85112314370
"Beraldo G., Tonin L., Cesta A., Menegatti E.","57202942006;36810606700;6701779736;7007056708;","Brain-Driven Telepresence Robots: A Fusion of User’s Commands with Robot’s Intelligence",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12414 LNAI",,,"235","248",,,"10.1007/978-3-030-77091-4_15","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111368705&doi=10.1007%2f978-3-030-77091-4_15&partnerID=40&md5=8c5e99bf7e582f896685eb616771d3c6","Department of Information Engineering, University of Padova, Padua, Italy; Institute of Cognitive Sciences and Technologies (ISTC), National Research Council, ISTC-CNR, Rome, Italy","Beraldo, G., Department of Information Engineering, University of Padova, Padua, Italy, Institute of Cognitive Sciences and Technologies (ISTC), National Research Council, ISTC-CNR, Rome, Italy; Tonin, L., Department of Information Engineering, University of Padova, Padua, Italy; Cesta, A., Institute of Cognitive Sciences and Technologies (ISTC), National Research Council, ISTC-CNR, Rome, Italy; Menegatti, E., Department of Information Engineering, University of Padova, Padua, Italy","This paper presents different methodologies to enhance the human-robot interaction during the control of brain-machine interface (BMI) driven telepresence robots. To overcome the limitations of BMIs, namely the low bit rate and the intrinsic uncertainty as a control channel, we hypothesize that the fusion of the user’s commands with the robot’s intelligence is essential to achieve robust and natural systems. Compared to most current neurorobotics works, we exploit the robot as an intelligent agent that contributes at different levels to choose the final action to perform. Furthermore, we present the first implementation of a BMI system inside the Robot Operating System (ROS) designed to facilitate the combination between BMI and robotics. © 2021, Springer Nature Switzerland AG.","Behavior-based systems; Brain-machine interface; Neurorobotics; Telerobotics and teleoperation","Artificial intelligence; Brain computer interface; Human robot interaction; Visual communication; Brain machine interface; Control channels; Low Bit Rate; Natural systems; Neurorobotics; Robot operating systems (ROS); Telepresence robots; Intelligent robots",,,,,"Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR","Acknowledgments. This research was partially supported by Fondazione Ing. Aldo Gini, by MIUR (Italian Minister for Education) under the initiative “Departments of Excellence” (Law 232/2016) and by SI Robotics project (Invecchiamento sano e attivo attraverso SocIal ROBOTICS) project – PON 12 Aree Call 2017.","This research was partially supported by Fondazione Ing. Aldo Gini, by MIUR (Italian Minister for Education) under the initiative ?Departments of Excellence? (Law 232/2016) and by SI Robotics project (Invecchiamento sano e attivo attraverso SocIal ROBOTICS) project ? PON 12 Aree Call 2017.",,,,,,,,,"Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M., Brain-computer interfaces for communication and control (2002) Clin. Neurophysiol., 113 (6), pp. 767-791; Del Millán, J.R., Combining brain-computer interfaces and assistive technologies: State-of-the-art and challenges (2010) Front. 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IEEE; Beraldo, G., Tortora, S., Menegatti, E., Tonin, L., ROS-Neuro: Implementation of a closed-loop BMI based on motor imagery (2020) Proceedings of The, p. 2020. , IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE; Beraldo, G., ROS-Health: An open-source framework for neurorobotics (2018) Proceedings of the 2018 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), Pp. 174–179. IEEE; Quigley, M., (2009) ROS: An Open-Source Robot Operating System","Beraldo, G.; Department of Information Engineering, Italy; email: gloria.beraldo@dei.unipd.it","Baldoni M.Bandini S.",,"Springer Science and Business Media Deutschland GmbH","19th International Conference of the Italian Association for Artiﬁcial Intelligence, AIxIA 2020","24 November 2020 through 27 November 2020",,260629,03029743,9783030770907,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85111368705
"Inamura T., Mizuchi Y., Yamada H.","7102940240;36809967300;57223666686;","VR platform enabling crowdsourcing of embodied HRI experiments–case study of online robot competition",2021,"Advanced Robotics",,,,"","",,1,"10.1080/01691864.2021.1928551","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105985158&doi=10.1080%2f01691864.2021.1928551&partnerID=40&md5=f60e5ce0220cbaa923a901de36376918","National Institute of Informatics, Chiyoda-ku, Japan; The Graduate University for Advanced Studies, Hayama, Japan","Inamura, T., National Institute of Informatics, Chiyoda-ku, Japan, The Graduate University for Advanced Studies, Hayama, Japan; Mizuchi, Y., National Institute of Informatics, Chiyoda-ku, Japan; Yamada, H., National Institute of Informatics, Chiyoda-ku, Japan","The COVID-19 pandemic has made it difficult to conduct research involving human-robot interactions (HRIs). As a result, many robot competitions have been canceled in the past year. To address this problem, we propose a cloud-based VR platform for the crowdsourcing of embodied cognition with multiple human users and a virtual robot. The feasibility of the platform is evaluated from the latency of the interactive events and the motions of avatars, based on the number of users logging in to the VR scene and the distance between the local and server environments. We also discuss the feasibility of our system when dealing with large-scale datasets in embodied HRI experiments. © 2021 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","cloud-based computation; COVID-19; human-robot interaction; robot competition; Virtual reality",,,,,,"JPMJMS2034; Japan Society for the Promotion of Science, KAKEN: 19K20379; New Energy and Industrial Technology Development Organization, NEDO; Core Research for Evolutional Science and Technology, CREST: JPMJCR15E3","This work was supported by the New Energy and Industrial Technology Development Organization (NEDO), JST CREST (Grant Number JPMJCR15E3), JST Moonshot R&D (Grant Number JPMJMS2034), and JSPS (KAKENHI) under Grant Number 19K20379.",,,,,,,,,,"Iocchi, L., Holz, D., Ruiz-Del-Solar, J., RoboCup@Home: analysis and results of evolving competitions for domestic and service robots (2015) Artif Intell, 229, pp. 258-281. , et al; Inamura, T., Mizuchi, Y., SIGVerse: a cloud-based VR platformfor research on social and embodied human-robot interaction. 2020; Okada, H., Inamura, T., Wada, K., What competitions were conducted in the service categories of the world robot summit? (2019) Adv Robot, 33 (17), pp. 900-910; Mizuchi, Y., Inamura, T., Cloud-based multimodal human-robot interaction simulator utilizing ROS and unity frameworks. In: IEEE/SICE International Symposium on System Integration, Taipei, Taiwan; 2017. p. 948–955; Inamura, T., Mizuchi, Y., Robot competition to evaluate guidance skill for general users VR environment. In: ACM/IEEE International Conference on Human-Robot Interaction, Daegu, Korea; 2019. p. 552–553; Mizuchi, Y., Inamura, T., Optimization of criterion for objective evaluation of HRI performance that approximates subjective evaluation: a case study in robot competition (2020) Adv Robot, 34 (3-4), pp. 142-156; Orkin, J., Roy, D., The restaurant game: learning social behavior and language from thousands of players online (2007) J Game Dev, 3 (1), pp. 39-60; Breazeal, C., DePalma, N., Orkin, J., Crowdsourcing human-robot interaction: new methods and system evaluation in a public environment (2013) J Hum Rob Interact, 2 (1), pp. 82-111. , et al; de Vries, H., Shuster, K., Batra, D., et al. Talk the walk: navigating New York city through grounded dialogue. Preprint, 2018; Striegnitz, K., Denis, A., Gargett, A., et al. Report on the second second challenge on generating instructions virtual environments (GIVE-2.5). In: Proceedings of European Workshop on Natural Language Generation, Nancy, France; 2011. p. 270–279; Das, A., Datta, S., Gkioxari, G., et al. Embodied question answering. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA; 2018. p. 1–10","Inamura, T.; National Institute of InformaticsJapan; email: inamura@nii.ac.jp",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Article in Press","All Open Access, Bronze",Scopus,2-s2.0-85105985158
"Lin G., Shan B., Ma Y., Tian X., Yu J.","57222714573;55793394800;36486719600;16314037100;36026903400;","Adaptive neural network command filtered backstepping impedance control for uncertain robotic manipulators with disturbance observer",2021,"Transactions of the Institute of Measurement and Control",,,,"","",,,"10.1177/01423312211009376","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105910185&doi=10.1177%2f01423312211009376&partnerID=40&md5=1ab5fb05f67f1e2ea66f68b879800031","School of Automation, Qingdao University, China; Shandong Key Laboratory of Industrial Control Technology, Qingdao University, China; School of Control Science and Engineering, Shandong University, China","Lin, G., School of Automation, Qingdao University, China, Shandong Key Laboratory of Industrial Control Technology, Qingdao University, China; Shan, B., School of Automation, Qingdao University, China, Shandong Key Laboratory of Industrial Control Technology, Qingdao University, China; Ma, Y., School of Automation, Qingdao University, China, Shandong Key Laboratory of Industrial Control Technology, Qingdao University, China; Tian, X., School of Control Science and Engineering, Shandong University, China; Yu, J., School of Automation, Qingdao University, China, Shandong Key Laboratory of Industrial Control Technology, Qingdao University, China","In this paper, an adaptive neural network command filtered backstepping impedance control method is developed for uncertain robotic manipulators with disturbance observer. First, an adaptive neural network algorithm is used to estimate the uncertain dynamics in the robot system. Second, impedance control is introduced to adjust the force and position relationship in physical human–robot interaction (pHRI). Third, a disturbance observer is employed to estimate the unknown external disturbance in the environment and compensate the control system to improve the safety of pHRI. Then, the command filtered technique can overcome problems of the ‘computational complexity’ and ‘singularity’ of traditional backstepping design. Finally, the simulation results are provided to illustrate the effectiveness of the proposed control method in pHRI. © The Author(s) 2021.","Adaptive neural network control; command filtered control; disturbance observer; impedance control; physical human–robot interaction","Adaptive control systems; Adaptive filtering; Backstepping; Flexible manipulators; Human robot interaction; Industrial manipulators; Man machine systems; Robotics; Adaptive neural network algorithm; Adaptive neural networks; Backstepping design; Disturbance observer; External disturbances; Impedance control methods; Robotic manipulators; Uncertain dynamics; Neural networks",,,,,"2017YFB1303503; 21-1-2-6-nsh; TSQN20161026; National Natural Science Foundation of China, NSFC: 61973179","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by the National Key Research and the Development Plan under Grant 2017YFB1303503, in part by the National Natural Science Foundation of China under Grant 61973179, in part by the Taishan Scholar Special Project Found under Grant TSQN20161026, in part by the Qingdao Key Research and Development Special Project under Grant 21-1-2-6-nsh.",,,,,,,,,,"Chen, M., Robust tracking control for self-balancing mobile robots using disturbance observer (2017) IEEE/CAA Journal of Automatica Sinica, 4 (3), pp. 458-465; Chen, M., Shi, P., Lim, C.C., Adaptive neural fault-tolerant control of a 3-DOF model helicopter system (2016) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46 (2), pp. 260-270. , (, a; Chen, M., Shao, S., Jiang, B., Adaptive neural control of uncertain nonlinear systems using disturbance observer (2017) IEEE Transactions on Cybernetics, 4 (10). , (,): 3310–3123; Chen, W., Ballance, D.J., Gawthrop, P.J., A nonlinear disturbance observer for robotic manipulators (2000) IEEE Transactions on Industrial Electronics, 47 (4), pp. 932-938; Chen, W.H., Yang, J., Lei, G., Disturbance observer-based control and related methods: An overview (2016) IEEE Transactions on Industrial Electronics, 63 (2), pp. 1083-1095. , (, b; Dong, W., Farrell, J.A., Polycarpou, M., Command filtered adaptive backstepping (2012) IEEE Transactions on Control Systems Technology, 20 (3), pp. 566-580; Farrell, J.A., Polycarpou, M., Sharma, M., Command filtered backstepping (2009) IEEE Transactions on Automatic Control, 54 (6), pp. 1391-1395; Ge, S.S., Wang, C., Adaptive neural control of uncertain MIMO nonlinear systems (2004) IEEE Transactions on Neural Networks, 15 (3), pp. 674-692; Ge, S.S., Hang, C.C., Woon, L.C., Adaptive neural network control of robot manipulators in task space (1997) IEEE Transactions on Industrial Electronics, 44 (6), pp. 746-752; Ge, S.S., Lee, T.H., Harris, C.J., (1998) Adaptive Neural Network Control of Robotic Manipulators, , London, World Science; He, W., Dong, Y., Adaptive fuzzy neural network control for a constrained robot using impedance learning (2018) IEEE Transactions on Neural Networks and Learning Systems, 29 (4), pp. 1174-1186; He, W., Chen, Y., Yin, Z., Adaptive neural network control of an uncertain robot with full-state constraints (2016) IEEE Transactions on Cybernetics, 46 (3), pp. 620-629. , (, a; He, W., Dong, Y., Sun, C., Adaptive neural impedance control of a robotic manipulator with input saturation (2016) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46 (3), pp. 334-344. , (, b; Hogan, N., Impedance control: An approach to manipulation. 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"Wang X., Sun J., Gao Z., Zhao L., Liu J., He N.","55982882200;57223094231;8264349600;36916248900;57218452315;57223088917;","Cartesian Admittance Control Based on Maxwell Model for Human Robot Interaction",2021,"Advances in Intelligent Systems and Computing","1305 AISC",,,"451","458",,,"10.1007/978-981-33-4575-1_43","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104793955&doi=10.1007%2f978-981-33-4575-1_43&partnerID=40&md5=28e698631e13db613e87ac7d63f98444","Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China; XCMG Construction Machinery, Xuzhou, China","Wang, X., Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China; Sun, J., Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China; Gao, Z., Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China; Zhao, L., Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China; Liu, J., Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, China; He, N., XCMG Construction Machinery, Xuzhou, China","Classical impedance control methods use Voigt model to describe relationship between external force and displacement. It is a kind of viscoelastic model shows elastic mechanical properties. This feature always generates a repulsive force because spring plays a major role and make user uncomfortable. By contrast, Maxwell model shows plastic mechanical properties and it shows the plastic properties. This characteristic will improve the comfortableness of human robot interaction. Therefore, we present a Maxwell model-based Cartesian admittance control that uses input of position loop to the manipulator. We compared two methods by experiment and result shows that our method can provide a good comfortableness in the case of having same effect of traditional Cartesian admittance control. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.","Admittance control; Human robot interaction; Industrial robot","Man machine systems; Process engineering; Viscoelasticity; Admittance control; Elastic mechanical properties; Impedance control methods; Maxwell models; Plastic property; Position loops; Repulsive forces; Viscoelastic modeling; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 2020-MS-032, 61903357; China Postdoctoral Science Foundation: 2020M672600; Department of Education of Liaoning Province: LNJC201912","This work was supported by the National key R&D plan tasks 2018YFF0300304-04, National Natural Science Foundation of China under contact 61903357, Scientific research project of Liaoning Provincial Department of Education LNJC201912, Liaoning Provincial Natural Science Foundation of China2020-MS-032 and China Postdoctoral Science Foundation2020M672600.","Acknowledgements This work was supported by the National key R&D plan tasks 2018YFF0300304-04, National Natural Science Foundation of China under contact 61903357, Scientific research project of Liaoning Provincial Department of Education LNJC201912, Liaoning Provincial Natural Science Foundation of China 2020-MS-032 and China Postdoctoral Science Foundation 2020M672600.",,,,,,,,,"Dhillon, B.S., (2012) Robot Reliability and Safety, , Springer Science & Business Media, Berlin; Goodrich, M.A., Schultz, A.C., (2008) Human-Robot Interaction: A Survey, , Now Publishers Inc; Qiao, H., Dalay, B.S., Parkin, R.M., Precise robotic chamferless peg-hole insertion operation without force feedback and remote centre compliance (RCC) (1994) J. 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Control, 107 (1), pp. 1-7; Ott, C., Mukherjee, R., Nakamura, Y., Unified impedance and admittance control (2010) 2010 IEEE International Conference on Robotics and Automation, pp. 554-561. , , pp. , IEEE, Anchorage; Scherzinger, S., Roennau, A., Dillmann, R., Forward dynamics compliance control (FDCC): A new approach to Cartesian compliance for robotic manipulators (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4568-4575. , , pp. , IEEE, Vancouver; Lee, D., Lee, W., Park, J., Task space control of articulated robot near kinematic singularity: Forward dynamics approach (2020) IEEE Robot. Autom. Lett., 5 (2), pp. 752-759; Maciej, B., Hassan, O., Bernard, B., Model predictive impedance control (2020) 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 4702-4708. , , pp. , IEEE, Paris; Spriggs, T.W., Huppler, J.D., Bird, R.B., An experimental appraisal of viscoelastic models (1966) Trans. Soc. Rheol., 10 (1), pp. 191-213; Ehlers, W., Markert, B., On the viscoelastic behaviour of fluid saturated porous materials (2000) Granular Matter, 2 (3), pp. 153-161; Fu, L., Zhao, J., Design and implementation of plastic deformation behavior by Cartesian impedance control based on Maxwell model (2018) Complexity, 2018, pp. 1-9; Fu, L., Zhao, J., Maxwell model-based compliance control for human robot friendly interaction (2020) IEEE Trans. Cogn. Dev. Syst.; Liu, G., Han, B., Li, Q., DOREP: An educational experiment platform for robot control based on MATLAB and the real-time controller (2019) International Conference on Intelligent Robotics and Applications, pp. 555-565. , , pp. , Springer, Shenyang","Gao, Z.; Faculty of Information and Control Engineering, China; email: gzj@sjzu.edu.cn","Li Y.Zhu Q.Qiao F.Fan Z.Chen Y.",,"Springer Science and Business Media Deutschland GmbH","2nd International Symposium on Simulation and Process Modelling, ISSPM 2020","29 August 2020 through 30 August 2020",,257579,21945357,9789813345744,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85104793955
"Beraldo G., Tonin L., Cesta A., Menegatti E.","57202942006;36810606700;6701779736;7007056708;","Shared approaches to mentally drive telepresence robots",2021,"CEUR Workshop Proceedings","2806",,,"22","27",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101290718&partnerID=40&md5=6d9b64c0d880b48d2c281ef218196d5d","Department of Information Engineering, University of Padova, Italy; Institute for Cognitive Science and Technology, National Research Council, ISTC-CNR, Italy","Beraldo, G., Department of Information Engineering, University of Padova, Italy, Institute for Cognitive Science and Technology, National Research Council, ISTC-CNR, Italy; Tonin, L., Department of Information Engineering, University of Padova, Italy; Cesta, A., Institute for Cognitive Science and Technology, National Research Council, ISTC-CNR, Italy; Menegatti, E., Department of Information Engineering, University of Padova, Italy","Recently there has been a growing interest in designing human-in-the-loop applications based on shared approaches that fuse the user’s commands with the perception of the context. In this scenario, we focus on user-supervised telepresence robots, designed to improve the quality of life of people suffering from severe physical disabilities or elderly who cannot move anymore. In this regard, we introduce brain-machine interfaces that enable users to directly control the robot through their brain activity. Since the nature of this interface, characterized by low bit rate and noise, herein, we present different methodologies to augment the human-robot interaction and to facilitate the research and the development of these technologies. © 2020 Copyright for this paper by its authors.","1 neurorobotics; Behavior-based systems; Brain-machine interface; Telerobotics and teleoperation","Brain; Brain computer interface; Robotics; Visual communication; Brain activity; Brain machine interface; Human-in-the-loop; Low Bit Rate; Physical disability; Quality of life; Telepresence robots; Human robot interaction",,,,,,,,,,,,,,,,"Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., Vaughan, T. M., Brain–computer interfaces for communication andcontrol (2002) Clinical neurophysiology, 113 (6), pp. 767-791; Lee, K., Liu, D., Perroud, L., Chavarriaga, R., Millán, J. d. R., A brain-controlled exoskeleton with cascaded event-related desynchro-nization classifiers (2017) Robotics and Autonomous Systems, 90, pp. 15-23; He, Y., Eguren, D., Azorìn, J. M., Grossman, R. G., Luu, T. P., Contreras-Vidal, J. L., Brain–machine interfaces for controlling lower-limb powered robotic systems (2018) Journal of neural engineering, 15 (2), p. 021004; Tonin, L., Leeb, R., Tavella, M., Perdikis, S., Millán, The role of shared-control in BCI-based telepresence 2010 IEEE International Conference on Systems, Man and Cybernetics, pp. 1462-1466. , J. D. R IEEE; Iturrate, I., Antelis, J. 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Y., ROS: an open-source robot operating system (2009) IEEE International Conference on Robotics and Automation workshop on open source software, 3 (3). , 2., Kobe, Japan; Tonin, L., Beraldo, G., Tortora, S., Tagliapietra, L., Del, J., Millán, R., Menegatti, E., ROS-Neuro: A common middleware for BMI and robotics. The acquisition and recorder packages (2019) 2019 IEEE International Conference on Systems, Man, and Cybernetics (SMC2019), , October; Beraldo, G., Tortora, S., Menegatti, E., Tonin, L., ROS-Neuro: implementation of a closed-loop BMI based on motor imagery 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020), , October 2020",,"Finzi A.Castellini A.Buoncompagni L.Anzalone S.",,"CEUR-WS","7th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2020","26 November 2020",,166814,16130073,,,,"English","CEUR Workshop Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85101290718
[No author name available],[No author id available],"AIRO 2020 - Proceedings of the 7th Italian Workshop on Artificial Intelligence and Robotics, co-located with the 19th International Conference of the Italian Association for Artificial Intelligence, AIxIA 2020",2021,"CEUR Workshop Proceedings","2806",,,"","",58,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101249080&partnerID=40&md5=fb8750bedb29bbd6660b078e80e5d089",,"","The proceedings contain 10 papers. The topics discussed include: supervised hand-guidance during human robot collaborative task execution: a case study; runtime verification of the ARIAC competition: can a robot be agile and safe at the same time?; knowledge-driven conversation for social robots: exploring crowdsourcing mechanisms for improving the system capabilities; robot choreographies: artificial evolution between novelty and similarity; shared approaches to mentally drive telepresence robots; development of a wizard-of-Oz based framework for QTRobot to facilitate the learning of handwriting in children with dysgraphia; persuasive and polite sentences to drive human-robot interaction in smart homes for elderly care; and optimal control of point-to-point navigation in turbulent flows using reinforcement learning.",,,,,,,,,,,,,,,,,,,,"Finzi A.Castellini A.Buoncompagni L.Anzalone S.",,"CEUR-WS","7th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2020","26 November 2020",,166814,16130073,,,,"English","CEUR Workshop Proc.",Conference Review,"Final","",Scopus,2-s2.0-85101249080
"Craig M.J.A., Edwards C.","57208160215;55467888500;","Feeling for Our Robot Overlords: Perceptions of Emotionally Expressive Social Robots in Initial Interactions",2021,"Communication Studies","72","2",,"251","265",,1,"10.1080/10510974.2021.1880457","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101231618&doi=10.1080%2f10510974.2021.1880457&partnerID=40&md5=44b23a3e31725456bea4220e77632d2f","Kent State University, Kent, OH, United States; Communication and Social Robotics Labs, Western Michigan University, United States","Craig, M.J.A., Kent State University, Kent, OH, United States; Edwards, C., Communication and Social Robotics Labs, Western Michigan University, United States","Human-to-human scripting accounts for a user’s tendency in human-robot interactions (HRI) to utilize scripts just as they would if talking to another person. Can a robot’s emotional verbal expression, however, impact perceptions and further elicit human-to-human scripting? The present study examines the effects of a robot’s emotional expression of joy and sorrow on participant perceptions of anthropomorphism, animacy, likability, perceived intelligence, credibility, social presence, and uncertainty in an initial interaction. Results indicated that robot expressions of joy were rated significantly more likable, intelligent, credible, and socially present than expressions of sorrow. Implications for future research are discussed in light of our findings related to participant impressions of emotional robots and human-to-human scripting. © 2021 Central States Communication Association.","credibility; emotional expression; Human-Machine Communication (HMC); human-to-human interaction script; impression formation; social Robotics",,,,,,,,,,,,,,,,,"Abelson, R.P., Script processing in attitude formation and decision making (1976) Cognition and social behavior, pp. 33-45. , Carroll J.S., Payne J.W., (eds), Lawrence Erlbaum,. 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(4). , https://doi.org/10.7771/1481-4374.1278; Beattie, A., Edwards, A.P., Edwards, C., A bot and a smile: Interpersonal impressions of chatbots and humans using emoji in computer-mediated communication (2020) Communication Studies, 71 (3), pp. 409-427. , https://doi.org/10.1080/10510974.2020.1725082; Beck, A., Cañamero, L., Bard, K.A., Towards an affect space for robots to display emotional body language (2010) Proceedings of the 19th International Symposium in Robot and Human Interactive Communication, pp. 464-469. , https://doi.org/10.1109/roman.2010.5598649, IEEE, &; Berger, C.R., Calabrese, R.J., Some explorations in initial interaction and beyond: Toward a developmental theory of interpersonal communication (1975) Human Communication Research, 1 (2), pp. 99-112. , https://doi.org/10.1111/hcre.1975.1.issue-2; Bickmore, T., Cassell, J., Relational agents; A model and implementation of building user trust (2001) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 396-403. , https://doi.org/10.1145/365024.365304, ACM, &; Bickmore, T.W., Picard, R.W., Establishing and maintaining long-term human computer relationships (2005) ACM Transactions on Computer-Human Interaction, 12 (2), pp. 293-327. , https://doi.org/10.1145/1067860.1067867; Billings, D.R., Schaefer, K.E., Chen, J.Y., Hancock, P.A., Human-robot interaction: Developing trust in robots (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction (pp, pp. 109-110. , https://doi.org/10.1145/2157689.2157709, ACM, &; Biocca, F., Harms, C., Burgoon, J.K., Toward a more robust theory and measure of social presence: Review and suggested criteria (2003) Presence, 12 (5), pp. 456-480. , https://doi.org/10.1162/105474603322761270; Boumans, R., van Meulen, F., Hindriks, K., Neerincx, M., Rikkert, M.O., A feasibility study of a social robot collecting patient reported outcome measurements from older adults (2019) International Journal of Social Robotics, 12 (1). , https://doi.org/10.1007/s12369-019-00561-8, 259–226; Breazeal, C., Toward sociable robots (2003) Robotics and Autonomous Systems, 42 (3-4), pp. 167-175. , https://doi.org/10.1016/S0921-8890(02)00373-1; Burgoon, J.K., A communication model of personal space violations: Explication and an initial test (1978) Human Communication Research, 4 (2), pp. 129-142. , https://doi.org/10.1111/j.1468-2958.1978.tb00603.x; Burgoon, J.K., Interpersonal expectations, expectancy violations, and emotional communication (1993) Journal of Language and Social Psychology, 12 (1-2), pp. 30-48. , https://doi.org/10.1177/0261927x93121003; Carpentier, F., Effects of priming social goals on personal interest in television news (2009) Journal of Broadcasting & Electronic Media, 53 (2), pp. 300-316. , https://doi.org/10.1080/08838150902908114; Castro-González, Á., Admoni, H., Scassellati, B., Effects of form and motion on judgments of social robots׳ animacy, likability, trustworthiness and unpleasantness (2016) International Journal of Human-computer Studies, 90, pp. 27-38. , https://doi.org/10.1016/j.ijhcs.2016.02.004; Choi, J.G., Kim, M., The usage and evaluation of anthropomorphic form in robot design (2009) Undisciplined! 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Eds., ed; Edwards, A., Edwards, C., Westerman, D., Spence, P.R., Initial expectations, interactions, and beyond with social robots (2019) Computers in Human Behavior, 90, pp. 308-314. , https://doi.org/http://dx.doi.org/10.1016/j.chb.2018.08.042; Edwards, A., Omilion-Hodges, L., Edwards, C., How do patients in a medical interview perceive a robot versus human physician? (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 109-110. , https://doi.org/10.1145/3029798.3038308, ACM, &; Edwards, C., Beattie, A.J., Edwards, A., Spence, P.R., Differences in perceptions of communication quality between a Twitterbot and human agent for information seeking and learning (2016) Computers in Human Behavior, 65, pp. 666-671. , https://doi.org/http://dx.doi.org/10.1016/j.chb.2016.07.003; Edwards, C., Edwards, A., Albrehi, F., Spence, P., Interpersonal impressions of a social robot versus human in the context of performance evaluations (2020) Communication Education, pp. 1-18. , https://doi.org/10.1080/03634523.2020.1802495; Edwards, C., Edwards, A., Spence, P.R., Westerman, D., Initial interaction expectations with robots: Testing the human-to-human interaction script (2016) Communication Studies, 67 (2), pp. 227-238. , https://doi.org/10.1080/10510974.2015.1121899; Edwards, C., Edwards, A., Stoll, B., Lin, X., Massey, N., Evaluations of an artificial intelligence instructor’s voice: Social Identity Theory in human-robot interactions (2019) Computers in Human Behavior, 90, pp. 357-362. , https://doi.org/10.1016/j.chb.2018.08.027; Ficocelli, M., Terao, J., Nejat, G., Promoting interactions between humans and robots using robotic emotional behavior (2016) IEEE Transactions on Cybernetics, 46 (12), pp. 2911-2923. , https://doi.org/10.1109/TCYB.2015.2492999; Fogg, B.J., Tseng, H., The elements of computer credibility (1999) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 80-87. , https://doi.org/10.1145/302979.303001, ACM, &,. 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Summit, United States; email: mcraig27@kent.edu",,,"Routledge",,,,,10510974,,,,"English","Commun. Stud.",Article,"Final","",Scopus,2-s2.0-85101231618
"Abrams A.M.H., der Pütten A.M.R.-V.","57201548735;56976422300;","I–C–E Framework: Concepts for Group Dynamics Research in Human-Robot Interaction: Revisiting Theory from Social Psychology on Ingroup Identification (I), Cohesion (C) and Entitativity (E)",2020,"International Journal of Social Robotics","12","6",,"1213","1229",,1,"10.1007/s12369-020-00642-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082944167&doi=10.1007%2fs12369-020-00642-z&partnerID=40&md5=aa3fa7aee3e33d1820e3994d2ec22786","Chair Individual and Technology, RWTH Aachen University, Aachen, Germany","Abrams, A.M.H., Chair Individual and Technology, RWTH Aachen University, Aachen, Germany; der Pütten, A.M.R.-V., Chair Individual and Technology, RWTH Aachen University, Aachen, Germany","The research community of human-robot interaction relies on theories and phenomena from the social sciences in order to study and validate robotic developments in interaction. These studies mainly concerned one (human) on one (robot) interactions in the past. The present paper shifts the attention to groups and group dynamics and reviews relevant concepts from the social sciences: ingroup identification (I), cohesion (C) and entitativity (E). Ubiquitous robots will be part of larger social settings in the near future. A conceptual framework, the I–C–E framework, is proposed as a theoretical foundation for group (dynamics) research in HRI. Additionally, we present methods and possible measures for these relevant concepts and outline topics for future research. © 2020, The Author(s).","Cohesion; Entitativity; Group dynamics; HRI; Human-robot interaction; ICE framework; Ingroup identification","Agricultural robots; Behavioral research; Dynamics; Group theory; Human robot interaction; Man machine systems; Cohesion; Conceptual frameworks; Entitativity; Group dynamics; Research communities; Social psychology; Theoretical foundations; Ubiquitous robots; Social robots",,,,,,,,,,,,,,,,"Abrams, D., Hogg, M.A., Comments on the motivational status of self-esteem in social identity and intergroup discrimination (1988) Eur J Soc Psychol, 18 (4), pp. 317-334; Abrams, D., Hogg, M.A., Hinkle, S., Otten, S., The social identity perspective on small groups (2005) Theories of Small Groups. SAGE, pp. 99-138. , Poole MS, Hollingshead AB, London; Admoni, H., Hayes, B., Feil-Seifer, D., Ullman, D., Scassellati, B., ) Are you looking at me? 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"Escobedo P., Ntagios M., Dahiya R.","57188743232;57205626715;24331529200;","Electronic Skin with Energy Autonomous Proximity Sensing for Human-Robot Interaction",2020,"Proceedings of IEEE Sensors","2020-October",,"9278896","","",,,"10.1109/SENSORS47125.2020.9278896","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098698341&doi=10.1109%2fSENSORS47125.2020.9278896&partnerID=40&md5=6ec9baa1fb74ddaf8521c0062713c9bd","Bendable Electronics and Sensing Technologies (BEST), University of Glasgow, Glasgow, G12 8QQ, United Kingdom","Escobedo, P., Bendable Electronics and Sensing Technologies (BEST), University of Glasgow, Glasgow, G12 8QQ, United Kingdom; Ntagios, M., Bendable Electronics and Sensing Technologies (BEST), University of Glasgow, Glasgow, G12 8QQ, United Kingdom; Dahiya, R., Bendable Electronics and Sensing Technologies (BEST), University of Glasgow, Glasgow, G12 8QQ, United Kingdom","Electronic skin or eSkin is critical to enhance the interactive capabilities of robots through physical contact. The current approaches to develop this capability involve using multiple sensors distributed on robot's body. Energy requirement of such eSkin is currently met through batteries, which is not ideal as it may reduce the operational time and requires frequent charging. In this work, we present an eSkin consisting of distributed energy autonomous proximity sensors. The eSkin contains an array of miniaturized solar cells to generate energy needed for infrared LEDs based proximity sensors. As a proof of concept, the eSkin has been integrated in an industrial robot arm UR5 and proximity sensing has been shown to enable safe Human-Robot Interaction (HRI). © 2020 IEEE.","Electronic Skin; Energy Autonomy; Human-Robot Interaction; Proximity sensing","Industrial robots; Man machine systems; Proximity sensors; Distributed energies; Electronic skin; Energy requirements; Multiple sensors; Physical contacts; Proof of concept; Proximity sensing; Safe human-robot interaction; Human robot interaction",,,,,"H2020-Intereg-IVA5055; Engineering and Physical Sciences Research Council, EPSRC: EP/M002527/1, EP/R029644/1","ACKNOWLEDGMENT This work is supported in part by Engineering and Physical Sciences Research Council (EPSRC) through engineering fellowship for growth (EP/R029644/1 and EP/M002527/1) and North West Centre for Advanced Manufacturing (NW CAM) project supported by the European Union’s INTERREG VA Programme (H2020-Intereg-IVA5055), managed by the Special EU Programmes Body (SEUPB). The views and opinions in this document do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB).",,,,,,,,,,"Dahiya, R., E-skin: From humanoids to humans [point of view] (2019) Proc. Ieee, 107 (2), pp. 247-252. , Feb; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Sci. Robot, 3 (21). , Aug; Leite, I., Martinho, C., Paiva, A., Social robots for long-term interaction: A survey (2013) Int. J. Soc. Robot, 5 (2), pp. 291-308. , Apr; Dahiya, R.S., Valle, M., (2013) Robotic Tactile Sensing: Technologies and System, , Dordrecht: Springer; Argall, B.D., Billard, A.G., A survey of tactile human-robot interactions (2010) Rob. Auton. Syst, 58 (10), pp. 1159-1176. , Oct; Yamaguchi, T., Kashiwagi, T., Arie, T., Akita, S., Takei, K., Humandlike electronic skind integrated soft robotic hand (2019) Adv. Intell. Syst, 1 (2), p. 1900018. , Jun; Zhang, C., Liu, S., Huang, X., Guo, W., Li, Y., Wu, H., A stretchable dual-mode sensor array for multifunctional robotic electronic skin (2019) Nano Energy, 62, pp. 164-170. , no. March; Dahiya, R., Large-Area soft e-skin: The challenges beyond sensor designs (2019) Proc. Ieee, 107 (10), pp. 2016-2033. , Oct; Baek, S., Flexible piezocapacitive sensors based on wrinkled microstructures: Toward low-cost fabrication of pressure sensors over large areas (2017) Rsc Adv., 7 (63), pp. 39420-39426; Dharmasena, R.D.I.G., Energy scavenging and powering e-skin functional devices (2019) Proc. Ieee, 107 (10), pp. 2118-2136; García Núñez, C., Manjakkal, L., Dahiya, R., Energy autonomous electronic skin (2019) Npj Flex. Electron., 3 (1), p. 1. , Dec; Manjakkal, L., Núñez, C.G., Dang, W., Dahiya, R., Flexible self-charging supercapacitor based on graphene-Ag-3d graphene foam electrodes (2018) Nano Energy, 51, pp. 604-612. , no. March, Sep; Jiang, C., Choi, H.W., Cheng, X., Ma, H., Hasko, D., Nathan, A., Printed subthreshold organic transistors operating at high gain and ultralow power (2019) Science (80-.)., 363 (6428), pp. 719-723. , Feb; Jiang, C., Cheng, X., Nathan, A., Flexible ultralow-power sensor interfaces for e-skin (2019) Proc. Ieee, 107 (10), pp. 2084-2105. , Oct; Dahiya, R., Akinwande, D., Chang, J.S., Flexible electronic skin: From humanoids to humans [scanning the issue] (2019) Proc Ieee, 107 (10), pp. 2011-2015. , Oct; Yu, K., Rich, S., Lee, S., Fukuda, K., Yokota, T., Someya, T., Organic photovoltaics: Toward self-powered wearable electronics (2019) Proc. Ieee, 107 (10), pp. 2137-2154. , Oct; Min, G., Manjakkal, L., Mulvihill, D.M., Dahiya, R.S., Triboelectric nanogenerator with enhanced performance via an optimized low permittivity substrate (2019) Ieee Sens. J., PP (C), pp. 1-1; Escobedo, P., De Vargas-Sansalvador, I.M.P., Carvajal, M., Capitán-Vallvey, L.F., Palma, A.J., Martínez-Olmos, A., Flexible passive tag based on light energy harvesting for gas threshold determination in sealed environments (2016) Sensors Actuators B Chem., 236, pp. 226-232. , Nov; Sadasivuni, K.K., Kafy, A., Zhai, L., Ko, H.U., Mun, S., Kim, J., Transparent and flexible cellulose nanocrystal/reduced graphene oxide film for proximity sensing (2015) Small, 11 (8), pp. 994-1002; Sadasivuni, K.K., Kafy, A., Zhai, L., Ko, H.-U., Mun, S., Kim, J., Transparent and flexible cellulose nanocrystal/reduced graphene oxide film for proximity sensing (2015) Small, 11 (8), pp. 994-1002. , Feb; Lumelsky, V.J., Shur, M.S., Wagner, S., Sensitive skin (2001) Ieee Sens. J., 1 (1), pp. 41-51. , Jun; Escobedo, P., Ntagios, M., Shakthivel, D., Navaraj, W., Dahiya, R., Energy generating electronic skin with intrinsic tactile sensing without touch sensors (2020) Ieee Trans. Robot; Gupta, S., Vilouras, A., Dahiya, R., Polydimethylsiloxane as polymeric protective coating for fabrication of ultra-thin chips (2019) Microelectron. Eng., 301, p. 111157. , Oct; Dahiya, R., Gottardi, G., Laidani, N., Pdms residues-free micro/macrostructures on flexible substrates (2015) Microelectron. Eng., 136, pp. 57-62. , Mar; Ntagios, M., Nassar, H., Pullanchiyodan, A., Navaraj, W.T., Dahiya, R., Robotic hands with intrinsic tactile sensing via 3d printed soft pressure sensors (2019) Adv. Intell. Syst, p. 1900080; Navaraj, W., Dahiya, R., Fingerprintdenhanced capacitived piezoelectric flexible sensing skin to discriminate static and dynamic tactile stimuli (2019) Adv. Intell. Syst, p. 1900051. , 1900051, Sep; Lasota, P.A., Fong, T., Shah, J.A., A survey of methods for safe human-robot interaction (2017) Found. Trends Robot, 5 (3), pp. 261-349","Dahiya, R.; Bendable Electronics and Sensing Technologies (BEST), United Kingdom; email: Ravinder.Dahiya@glasgow.ac.uk",,"IEEE;IEEE Sensors Council","Institute of Electrical and Electronics Engineers Inc.","2020 IEEE Sensors, SENSORS 2020","25 October 2020 through 28 October 2020",,165760,19300395,9781728168012,,,"English","Proc. IEEE Sens.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85098698341
"Zou W., Duan P., Chen Y., Yu N., Shi L.","57191196344;57219796718;57211521668;23096713200;35230888900;","Variable stiffness control with strict frequency domain constraints for physical human-robot interaction",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340895","7146","7151",,,"10.1109/IROS45743.2020.9340895","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102413893&doi=10.1109%2fIROS45743.2020.9340895&partnerID=40&md5=f26ffe6469f6cb668ea1dc37e3347042","Hong Kong University of Science and Technology, Department of Electronic and Computer Engineering, Hong Kong; Xeno Dynamics Co., Ltd, Shenzhen, China; Nankai University, Institute of Robotics and Automatic Information Systems, Tianjin Key Laboratory of Intelligent Robotics, Tianjin, China","Zou, W., Hong Kong University of Science and Technology, Department of Electronic and Computer Engineering, Hong Kong; Duan, P., Xeno Dynamics Co., Ltd, Shenzhen, China; Chen, Y., Hong Kong University of Science and Technology, Department of Electronic and Computer Engineering, Hong Kong; Yu, N., Nankai University, Institute of Robotics and Automatic Information Systems, Tianjin Key Laboratory of Intelligent Robotics, Tianjin, China; Shi, L., Hong Kong University of Science and Technology, Department of Electronic and Computer Engineering, Hong Kong","Variable impedance control is advantageous for physical human-robot interaction to improve safety, adaptability and many other aspects. This paper presents a gain-scheduled variable stiffness control approach under strict frequency-domain constraints. Firstly, to reduce conservativeness, we characterize and constrain the impedance rendering, actuator saturation, disturbance/noise rejection and passivity requirements into their specific frequency bands. This relaxation makes sense because of the restricted frequency properties of the interactive robots. Secondly, a gain-scheduled method is taken to regulate the controller gains with respect to the desired stiffness. Thirdly, the scheduling function is parameterized via a nonsmooth optimization method. Finally, the proposed approach is validated by simulations, experiments and comparisons with a gain-fixed passivity-based PID method. © 2020 IEEE.",,"Agricultural robots; Disturbance rejection; Frequency domain analysis; Intelligent robots; Man machine systems; Stiffness; Actuator saturations; Frequency properties; Nonsmooth optimization; Physical human-robot interactions; Scheduling functions; Specific frequencies; Variable impedance control; Variable stiffness; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: U1913208, 61873135, U1713223","This work has been supported by the National Natural Science Foundation of China (61873135, U1713223, U1913208) and the Hong Kong ITF Fund GHP/001/18SZ.",,,,,,,,,,"Hogan, N., Impedance control: An approach to manipulation (1984) American Control Conference, pp. 304-313; Roveda, L., Vicentini, F., Pedrocchi, N., Tosatti, L.M., Impedance control based force-tracking algorithm for interaction robotics tasks: An analytically force overshoots-free approach (2015) International Con-ference on Informatics in Control, Automation and Robotics, pp. 386-391; Ferraguti, F., Preda, N., Manurung, A., Bonfe, M., Lambercy, O., Gassert, R., Muradore, R., Secchi, C., An energy tankbased interactive control architecture for autonomous and teleoperated robotic surgery (2015) IEEE Transactions on Robotics, 31 (5), pp. 1073-1088; Ryu, D., Song, J.B., Kang, S., Kim, M., Frequency domain stability observer and active damping control for stable haptic interaction (2008) IET Control Theory & Applications, 2 (4), pp. 261-268; Li, X., Liu, Y.H., Yu, H., Iterative learning impedance control for rehabilitation robots driven by series elastic actuators (2018) Automatica, 90, pp. 1-7; Zhang, T., Xia, J., Interconnection and damping assignment passivity-based impedance control of a compliant assistive robot for physical human-robot interactions (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 538-545; Dimeas, F., Aspragathos, N., Online stability in human-robot cooperation with admittance control (2016) IEEE Transactions on Haptics, 9 (2), pp. 267-278; Landi, C.T., Ferraguti, F., Sabattini, L., Secchi, C., Bonfe, M., Fantuzzi, C., Variable admittance control preventing undesired oscillating behaviors in physical human-robot interaction (2017) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3611-3616; Lee, K., Buss, M., Force tracking impedance control with variable target stiffness (2008) IFAC Proceedings Volumes, 41 (2), pp. 6751-6756; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) IEEE Transactions on Robotics, 31 (4), pp. 850-863; Tsumugiwa, T., Yokogawa, R., Hara, K., Variable impedance control based on estimation of human arm stiffness for human-robot cooperative calligraphic task (2002) IEEE International Conference on Robotics and Automation, pp. 644-650; Duchaine, V., Gosselin, C.M., General model of human-robot cooperation using a novel velocity based variable impedance control (2007) Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, pp. 446-451; Lecours, A., Mayer-St-Onge, B., Gosselin, C., Variable admittance control of a four-degree-of-freedom intelligent assist device (2012) IEEE International Conference on Robotics and Automation, pp. 3903-3908; Buchli, J., Stulp, F., Theodorou, E., Schaal, S., Learning variable impedance control (2011) The International Journal of Robotics Research, 30 (7), pp. 820-833; Dimeas, F., Aspragathos, N., Reinforcement learning of variable admittance control for human-robot co-manipulation (2015) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1011-1016; Zhang, L., Li, Z., Yang, C., Adaptive neural network based variable stiffness control of uncertain robotic systems using disturbance observer (2017) IEEE Transaction on Industrial Electronics, 64 (3), pp. 2236-2245; Ferraguti, F., Secchi, C., Fantuzzi, C., A tank-based approach to impedance control with variable stiffness (2013) IEEE International Conference on Robotics and Automation, pp. 4948-4953; Kastritsi, T., Dimeas, F., Doulgeri, Z., Progressive automation with DMP synchronization and variable stiffness control (2018) IEEE Robotics and Automation Letters, 3 (4), pp. 3789-3796; Vanderborght, B., Albu-Schffer, A., Bicchi, A., Burdet, E., Caldwell, D.G., Carloni, R., Catalano, M., Ganesh, G., Variable impedance actuators: A review (2013) Robotics and Autonomous Systems, 61 (12), pp. 1601-1614; Haninger, K., Tomizuka, M., Robust passivity and passivity relaxation for impedance control of fiexible-joint robots with inner-loop torque control (2018) IEEE/ASME Transactions on Mechatronics, 23 (6), pp. 2671-2680; Yu, N., Zou, W., Sun, Y., Passivity guaranteed stiffness control with multiple frequency band specifications for a cable-driven series elastic actuator (2019) Mechanical Systems and Signal Processing, 117, pp. 709-722; Yu, N., Zou, W., Stiffness control of a series elastic actuator with restricted frequency domain specifications (2019) Control Theory & Applications, 36 (5), pp. 711-719; Colgate, J.E., (1988) The Control of Dynamically Interacting Systems, , PhD Dissertation, Massachusetts Institute of Technology; Anderson, B.D.O., The small-gain theorem, the passivity theorem and their equivalence (1972) Journal of the Franklin Institute, 293 (2), pp. 105-115; Apkarian, P., Gahinet, P., Buhr, C., Multi-model, multi-objective tuning of fixed-structure controllers (2014) European Control Conference, pp. 856-861; Gahinet, P., Apkarian, P., Automated tuning of gain-scheduled control systems (2013) IEEE Conference on Decision and Control, pp. 2740-2745; Yu, N., Zou, W., Tan, W., Yang, Z., Augmented virtual stiffness rendering of a cable-driven SEA for human-robot interaction (2017) IEEE/CAA Journal of Automatica Sinica, 4 (4), pp. 714-723; Yu, N., Zou, W., Impedance control of a cable-driven SEA with mixed H2=H1 synthesis (2017) Assembly Automation, 37 (3), pp. 296-303","Yu, N.; Nankai University, China; email: nyu@nankai.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102413893
"Capelle E., Benson W.N., Anderson Z., Weinberg J.B., Gorlewicz J.L.","57222351348;57222350032;57207823359;7201642867;55337060600;","Design and implementation of a haptic measurement glove to create realistic human-telerobot interactions",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340976","9781","9788",,1,"10.1109/IROS45743.2020.9340976","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102410864&doi=10.1109%2fIROS45743.2020.9340976&partnerID=40&md5=85d4bf33b9929541d85dd09531772fd4","Saint Louis University, Department of Mechanical and Aerospace Engineering, Saint Louis, MO  63103, United States; Southern Illinois University Edwardsville, Department of Computer Science, Edwardsville, IL  62025, United States","Capelle, E., Saint Louis University, Department of Mechanical and Aerospace Engineering, Saint Louis, MO  63103, United States; Benson, W.N., Saint Louis University, Department of Mechanical and Aerospace Engineering, Saint Louis, MO  63103, United States; Anderson, Z., Southern Illinois University Edwardsville, Department of Computer Science, Edwardsville, IL  62025, United States; Weinberg, J.B., Southern Illinois University Edwardsville, Department of Computer Science, Edwardsville, IL  62025, United States; Gorlewicz, J.L., Saint Louis University, Department of Mechanical and Aerospace Engineering, Saint Louis, MO  63103, United States","Although research indicates that telepresence robots offer a more socially telepresent alternative to conventional forms of remote communication, the lack of touch-based interactions presents challenges for both remote and local users. In order to address these challenges, we have designed and implemented a robotic manipulator emulating a human arm. However, contact interactions like handshakes with a robotic manipulator may feel awkward and unnatural to local users. In this work, we present the design of a wearable haptic measurement glove (HMG) and use it to collect force and inertial data on handshakes in human-human and humanrobot interactions in the interest of developing intelligent shared control algorithms for natural, human-like contact interactions in human-robot interactions. © 2020 IEEE.",,"Agricultural robots; Flexible manipulators; Industrial manipulators; Intelligent robots; Machine design; Robotics; Visual communication; Wearable technology; Contact interaction; Design and implementations; Human like; Human telerobot interaction; Remote communication; Robotic manipulators; Telepresence robots; Touch based interactions; Human robot interaction",,,,,,,,,,,,,,,,"Lee, M.K., Takayama, L., ""now, i Have a Body"": Uses and social norms for mobile remote presence in the workplace (2011) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Ser. CHI '11, pp. 33-42. , New York, NY, USA: ACM; Tanaka, K., Nakanishi, H., Ishiguro, H., Robot conferencing: Physically embodied motions enhance social telepresence (2014) CHI '14 Extended Abstracts on Human Factors in Computing Systems, Ser. CHI EA '14, pp. 1591-1596. , New York, NY, USA: ACM; Tanaka, K., Nakanishi, H., Ishiguro, H., Physical embodiment can produce robot operators pseudo presence (2015) Frontiers in ICT, 2; Sakamoto, D., Kanda, T., Ono, T., Ishiguro, H., Hagita, N., Android as a telecommunication medium with a human-like presence (2007) 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 193-200. , March; Dreyfuss, E., (2015) My Life As A Robot, , https://www.wired.com/2015/09/my-life-as-a-robot-doublerobotics-telecommuting-longread/, Sept; Nakanishi, H., Tanaka, K., Wada, Y., Remote handshaking: Touch enhances video-mediated social telepresence (2014) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Ser. CHI '14, pp. 2143-2152. , New York, NY, USA: ACM; Newhart, V.A., Virtual inclusion via telepresence robots in the classroom (2014) CHI 14 Extended Abstracts on Human Factors in Computing Systems, Ser. CHI EA 14, p. 951956. , New York, NY, USA: Association for Computing Machinery; Tanaka, F., Cicourel, A., Movellan, J.R., Socialization between toddlers and robots at an early childhood education center (2007) Proceedings of the National Academy of Sciences, 104 (46), pp. 17954-17958; Stahl, C., Anastasiou, D., Latour, T., Social telepresence robots: The role of gesture for collaboration over a distance (2018) Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference, Ser. PETRA 18, p. 409414. , https://doi.org/10.1145/3197768.3203180, New York, NY, USA: Association for Computing Machinery; Slack, J.T., DeProw, K., Anderson, Z., Albacete Di Bartolomeo, R.M., Weinberg, J.B., Gorlewicz, J.L., Design of a lightweight, ergonomic manipulator for enabling expressive gesturing in telepresence robots (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, Ser. IROS. IEEE, pp. 5491-5496; Tsui, K.M., Desai, M., Yanco, H.A., Uhlik, C., Exploring use cases for telepresence robots (2011) Proceedings of the 6th International Conference on Human-robot Interaction, Ser. HRI '11, pp. 11-18. , New York, NY, USA: ACM; Hertenstein, M.J., Keltner, D., App, B., Bulleit, B.A., Jaskolka, A.R., Touch communicates distinct emotions (2006) Emotion, 6 (3), pp. 528-533; Hertenstein, M.J., Holmes, R., McCullough, M., Keltner, D., The communication of emotion via touch (2009) Emotion, 9 (4), pp. 566-573; App, B., McIntosh, D.N., Reed, C.L., Hertenstein, M.J., Nonverbal channel use in communication of emotion: How may depend on why (2011) Emotion, 11 (3), pp. 603-617; Samani, H.A., Parsani, R., Rodriguez, L.T., Saadatian, E., Dissanayake, K.H., Cheok, A.D., Kissenger: Design of a kiss transmission device (2012) Proceedings of the Designing Interactive Systems Conference, Ser. DIS 12, p. 4857. , New York, NY, USA: Association for Computing Machinery; Wang, R., Quek, F., Tatar, D., Teh, K.S., Cheok, A., Keep in touch: Channel, expectation and experience (2012) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Ser. CHI '12, pp. 139-148. , New York, NY, USA: ACM; Chaplin, W.F., Phillips, J.B., Brown, J.D., Clanton, N.R., Stein, J.L., Handshaking, gender, personality, and first impressions (2000) Journal of Personality and Social Psychology, 79 (1); Melnyk, A., Henaff, P., Khomenko, V., Borysenko, V., Sensor network architecture to measure characteristics of a handshake between humans (2014) 2014 IEEE 34th International Scientific Conference on Electronics and Nanotechnology (ELNANO). IEEE, pp. 264-268; Wang, Z., Hoelldampf, J., Buss, M., Design and performance of a haptic data acquisition glove (2007) Proceedings of the 10th Annual International Workshop on Presence, pp. 349-357; Wiley, S., Blackwell, T., Casner, D., Holson, B., (2012) Self-balancing Robot Having A Shaft-mounted Head, , U. S. Patent. Nov. 6; Chang, W.-C., Tseng, M.-Y., Cheng, N.-J., (2018) Virtual Reality System and Method for Controlling Operation Modes of Virtual Reality System, , Apr. 24; Huang, Y.-C., Shih, W.-H., Tseng, H.-Y., Chen, C.-T., Chang, C.-W., Tsai, S.-C., Lin, Y.-C., (2018) Wireless Control Device, Position Calibrator and Accessory, , Dec. 25; (2017) Prime One Gloves-Manus VR, , https://manus-vr.com/prime-one-gloves/; Mpu-9250 Product Specification Revision 1. 0, , InvenSense, San Jose, CA, USA; Force sensing resistor integration guide and evaluation parts catalog Interlink Electronics, , Camarillo, CA, USA; Anderson, Z., Capelle, E., Benson, W., Gorlewicz, J., Weinberg, J., Video Comparison of Hhi and Hri Handshakes, , https://sites.google.com/slu.edu/gorlewiczlab/research/enhancing-teleoperative-systems",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102410864
"Xu Y.T., Chen X., Liu X., Meger D., Dudek G.","57222350571;57221569702;57206746907;23009425800;7006413576;","PresSense: Passive respiration sensing via ambient WiFi signals in noisy environments",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341474","4032","4039",,,"10.1109/IROS45743.2020.9341474","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102399011&doi=10.1109%2fIROS45743.2020.9341474&partnerID=40&md5=7da09f72b3d5887892a2131b692d50c6","Samsung AI Center, Montreal, Quebec, Canada","Xu, Y.T., Samsung AI Center, Montreal, Quebec, Canada; Chen, X., Samsung AI Center, Montreal, Quebec, Canada; Liu, X., Samsung AI Center, Montreal, Quebec, Canada; Meger, D., Samsung AI Center, Montreal, Quebec, Canada; Dudek, G., Samsung AI Center, Montreal, Quebec, Canada","Passive sensing with ambient WiFi signals is a promising technique that will enable new types of human-robot interactions while preserving users' privacy. Here, we present PresSense, a system for human respiration sensing in noisy environments. Unlike existing WiFi-based respiration sensors, we employ a human presence detector, improving the robustness in scenarios where no human is present in an Area Of Interest (AOI). We also integrate our novel feature, Peak Distance Histogram (PDH), with other classic WiFi features to achieve better accuracy when someone is present in the AOI. We tested our system using commodity WiFi devices in an office room. Our PresSense outperforms the state of the arts in both respiration rate estimation and presence detection. © 2020 IEEE.",,"Agricultural robots; Intelligent robots; Wireless local area networks (WLAN); Area of interest; Distance histogram; Human respiration; Noisy environment; Passive sensing; Presence detections; Respiration rate; State of the art; Human robot interaction",,,,,,,,,,,,,,,,"Li, Z., Gogia, P.C., Kaess, M., Dense surface reconstruction from monocular vision and lidar (2019) ICRA, pp. 6905-6911; Cen, S.H., Newman, P., Radar-only ego-motion estimation in difficult settings via graph matching (2019) ICRA, pp. 298-304; Robert Hogan, F., Bauzá, M., Canal, O., Donlon, E., Rodriguez, A., Tactile regrasp: Grasp adjustments via simulated tactile transformations (2018) IROS, pp. 2963-2970; Koyama, K., Murakami, K., Senoo, T., Shimojo, M., Ishikawae, M., High-speed, small-deformation catching of soft objects based on active vision and proximity sensing (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 578-585; Zuo, X., Geneva, P., Lee, W., Liu, Y., Huang, G., Lic-fusion: Lidar-inertial-camera odometry (2019) IROS, pp. 5848-5854; Chen, X., Ma, C., Allegue, M., Liu, X., Taming the inconsistency of wi-fi fingerprints for device-free passive indoor localization (2017) INFOCOM, pp. 1-9; Chen, X., Li, H., Zhou, C., Liu, S., Wu, D., Dudek, G., Fido: Ubiquitous fine-grained wifi-based localization for unlabelled users via domain adaptation (2020) WWW, pp. 1-10; Biswas, J., Veloso, M.M., Wifi localization and navigation for autonomous indoor mobile robots (2010) ICRA, pp. 4379-4384; Kotaru, M., Raj Joshi, K., Bharadia, D., Katti, S., Spotfi: Decimeter level localization using wifi (2015) SIGCOMM, pp. 269-282; Qian, K., Wu, C., Zhang, Y., Zhang, G., Yang, Z., Liu, Y., Widar2.0: Passive human tracking with a single wi-fi link (2018) MobiSys, pp. 350-361; Zheng, Y., Zhang, Y., Qian, K., Zhang, G., Liu, Y., Wu, C., Yang, Z., Zero-effort cross-domain gesture recognition with wi-fi (2019) MobiSys, pp. 313-325; Liu, X., Cao, J., Tang, S., Wen, J., Wi-sleep: Contactless sleep monitoring via wifi signals (2014) RTSS, pp. 346-355. , IEEE Computer Society; Liu, J., Wang, Y., Chen, Y., Yang, J., Chen, X., Cheng, J., Tracking vital signs during sleep leveraging off-The-shelf wifi (2015) MobiHoc, pp. 267-276; Liu, X., Cao, J., Tang, S., Wen, J., Guo, P., Contactless respiration monitoring via off-The-shelf wifi devices (2016) IEEE Trans. Mob. Comput, 15 (10), pp. 2466-2479; Wu, C., Yang, Z., Zhou, Z., Liu, X., Liu, Y., Cao, J., Non-invasive detection of moving and stationary human with wifi (2015) IEEE Journal on Selected Areas in Communications, 33 (11), pp. 2329-2342; Wang, X., Yang, C., Mao, S., Tensorbeat: Tensor decomposition for monitoring multiperson breathing beats with commodity wifi (2017) ACM TIST, 9 (1), pp. 81-827; Wang, X., Yang, C., Mao, S., Phasebeat: Exploiting CSI phase data for vital sign monitoring with commodity wifi devices (2017) ICDCS, pp. 1230-1239. , IEEE Computer Society; Zhang, D., Hu, Y., Chen, Y., Zeng, B., Breathtrack: Tracking indoor human breath status via commodity wifi (2019) IEEE Internet of Things Journal, 6 (2), pp. 3899-3911; Wang, H., Zhang, D., Ma, J., Wang, Y., Wang, Y., Wu, D., Gu, T., Xie, B., Human respiration detection with commodity wifi devices: Do user location and body orientation matter? (2016) UbiComp, pp. 25-36; Wang, P., Guo, B., Xin, T., Wang, Z., Yu, Z., Tinysense: Multi-user respiration detection using wi-fi CSI signals (2017) Healthcom, pp. 1-6; Zeng, Y., Wu, D., Gao, R., Gu, T., Zhang, D., Fullbreathe: Full human respiration detection exploiting complementarity of CSI phase and amplitude of wifi signals (2018) IMWUT, 2 (3), pp. 1481-14819; Zeng, Y., Wu, D., Xiong, J., Yi, E., Gao, R., Zhang, D., Farsense: Pushing the range limit of wifi-based respiration sensing with CSI ratio of two antennas (2019) IMWUT, 3 (3), pp. 1211-12126; Korany, B., Karanam, C.R., Cai, H., Mostofi, Y., Xmodal-id: Using wifi for through-wall person identification from candidate video footage (2019) MobiCom, pp. 361-3615; IEEE. IEEE 802.11n standard; Li, X., Zhang, D., Lv, Q., Xiong, J., Li, S., Zhang, Y., Mei, H., Indotrack: Device-free indoor human tracking with commodity wi-fi (2017) IMWUT, 1 (3), pp. 721-7222; He, Z., Guo, L., Lu, Z., Wen, X., Zheng, W., Zhou, S., Contact-free in-home health monitoring system with commodity wi-fi (2019) ICC Workshops, pp. 1-6; George, E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M., (2015) Time Series Analysis: Forecasting and Control, , John Wiley & Sons; Di Domenico, S., Pecoraro, G., Cianca, E., De Sanctis, M., Trained-once device-free crowd counting and occupancy estimation using wifi: A doppler spectrum based approach (2016) WiMob, pp. 1-8. , IEEE Computer Society; Chen, J., Jönsson, P., Tamura, M., Gu, Z., Matsushita, B., Eklundh, L., A simple method for reconstructing a high-quality ndvi time-series data set based on the savitzky-golay filter (2004) Remote Sensing of Environment, 91 (3-4), pp. 332-344; Halperin, D., Hu, W., Sheth, A., Wetherall, D., Tool release: Gathering 802.11n traces with channel state information (2011) SIGCOMM Comput. Commun. Rev., 41 (1), pp. 53-53; Chen, X., Li, H., Zhou, C., Liu, S., Dudek, G., Mofi: Environment-independent device-free human motion detection via wifi (2019) RTSS@Work, pp. 1-2; Xie, Y., Li, Z., Li, M., Precise power delay profiling with commodity wifi (2015) MobiCom, pp. 53-64; Xi, W., Zhao, J., Li, X., Zhao, K., Tang, S., Liu, X., Jiang, Z., Electronic frog eye: Counting crowd using wifi (2014) INFOCOM, pp. 361-369; Qian, K., Wu, C., Yang, Z., Liu, Y., He, F., Xing, T., Enabling contactless detection of moving humans with dynamic speeds using CSI (2018) ACM Trans. Embedded Comput. Syst., 17 (2), pp. 521-5218",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102399011
"Küster D., Putze F., Alves-Oliveira P., Paetzel M., Schultz T.","15064259400;22036416700;56515305600;57191708013;7201770266;","Modeling Socio-Emotional and Cognitive Processes from Multimodal Data in the Wild",2020,"ICMI 2020 - Proceedings of the 2020 International Conference on Multimodal Interaction",,,,"883","885",,1,"10.1145/3382507.3420053","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096639766&doi=10.1145%2f3382507.3420053&partnerID=40&md5=33305f35c5dacf528a7da87466e2b70d","University of Bremen, Bremen, Germany; University of Washington, Washington, WA, United States; Uppsala University, Uppsala, Sweden","Küster, D., University of Bremen, Bremen, Germany; Putze, F., University of Bremen, Bremen, Germany; Alves-Oliveira, P., University of Washington, Washington, WA, United States; Paetzel, M., Uppsala University, Uppsala, Sweden; Schultz, T., University of Bremen, Bremen, Germany","Detecting, modeling, and making sense of multimodal data from human users in the wild still poses numerous challenges. Starting from aspects of data quality and reliability of our measurement instruments, the multidisciplinary endeavor of developing intelligent adaptive systems in human-computer or human-robot interaction (HCI, HRI) requires a broad range of expertise and more integrative efforts to make such systems reliable, engaging, and user-friendly. At the same time, the spectrum of applications for machine learning and modeling of multimodal data in the wild keeps expanding. From the classroom to the robot-assisted operation theatre, our workshop aims to support a vibrant exchange about current trends and methods in the field of modeling multimodal data in the wild. © 2020 ACM.","attention; emotion recognition; engagement; field studies; human-robot-interaction (hri); multimodal modeling","Adaptive systems; Human computer interaction; Interactive computer systems; Social robots; Cognitive process; Data quality; Human users; Intelligent-adaptive systems; Measurement instruments; Multi-modal data; User friendly; Data reduction",,,,,"19F2105F","This workshop was partially supported by the project SmartHelm1, which is funded by the German Federal Ministry of Transport and Digital Infrastructure (""BMVI"") as part of the research initiative „mFUND“ (19F2105F).",,,,,,,,,,"Alves-Oliveira, P., Küster, D., Kappas, A., Paiva, A., Psychological science in hri: Striving for a more integrated field of research (2016) 2016 AAAI Fall Symposium Series; Anguiozar, N.U., St-Onge, D., Measuring cognitive load: Heart-rate variability (HRV) and pupillometry assessment (2020) Proceedings of the 2020 International Conference on Multimodal Interaction (ICMI '20), , October 25-29, 2020, Virtual event, Netherlands. Association for Computing Machinery, New York, NY, USA; Belpaeme, T., Baxter, P.E., Read, R., Wood, R., Cuayáhuitl, H., Kiefer, B., Racioppa, S., Humbert, R., (2013) Multimodal Child-Robot Interaction: Building Social Bonds, 1 (2), pp. 33-53. , https://doi.org/10.5898/JHRI.1.2.Belpaeme, 2013; Cacioppo, J.T., Tassinary, L.G., (1990) Principles of Psychophysiology: Physical, Social, and Inferential Elements, , Cambridge University Press; Chatterjee, S., Scheck, K., Küster, D., Putze, F., Moturu, H., Schering, J., Gómez, J.M., Schultz, T., Smarthelm: Towards multimodal detection of attention in an outdoor augmented reality biking scenario (2020) Proceedings of the 2020 International Conference on Multimodal Interaction (ICMI '20), , October 25-29, 2020, Virtual event, Netherlands. Association for Computing Machinery, New York, NY, USA; Foster, M.E., Alami, R., Gestranius, O., Lemon, O., Niemelä, M., Odobez, J., Pandey, A.K., The mummer project: Engaging human-robot interaction in real-world public spaces (2016) Social Robotics, pp. 753-763. , https://doi.org/10.1007/978-3-319-47437-3-74, Arvin Agah, John-John Cabibihan, Ayanna M. Howard, Miguel A. Salichs, and Hongsheng He (Eds.) 9979 Springer International PublishingSeries Title: Lecture Notes in Computer Science. 1 https://www.bmvi.de/SharedDocs/DE/Artikel/DG/mfund-projekte/smarthelm.html; Gumuslu, E., Barkana, D.E., Kose, H., Emotion recognition using EEG and physiological data for robot-assisted rehabilitation systems (2020) Proceedings of the 2020 International Conference on Multimodal Interaction (ICMI '20), , October 25-29, 2020, Virtual event, Netherlands. Association for Computing Machinery, New York, NY, USA; Jones, A., Küster, D., Basedow, C.A., Alves-Oliveira, P., Serholt, S., Hastie, H., Corrigan, L.J., Castellano, G., Empathic robotic tutors for personalised learning: A multidisciplinary approach (2015) Social Robotics, 9388, pp. 285-295. , https://doi.org/10.1007/978-3-319-25554-5-29SeriesTitle:LectureNotesinComputerScience, Adriana Tapus, Elisabeth André, Jean-Claude Martin, François Ferland, and Mehdi Ammi (Eds.) 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"Fuentes-Moraleda L., Díaz-Pérez P., Orea-Giner A., Muñoz- Mazón A., Villacé-Molinero T.","57210112910;57219433965;57212307539;56550017800;56563004100;","Interaction between hotel service robots and humans: A hotel-specific Service Robot Acceptance Model (sRAM)",2020,"Tourism Management Perspectives","36",,"100751","","",,13,"10.1016/j.tmp.2020.100751","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092740275&doi=10.1016%2fj.tmp.2020.100751&partnerID=40&md5=85670912934df9ce7bc85bfd7c687a66","Faculty of Social Sciences and Law, Business Economics Department, Centro de Estudios Turísticos, CETUR, Rey Juan Carlos University, c/Camino del Molino s/n, Fuenlabrada, Madrid  28943, Spain; Faculty of Social Sciences and Law, Business Economics Department, Rey Juan Carlos University, Paseo de los Artilleros s/n, 28032, Vicálvaro, Madrid, Spain; Faculty of Social Sciences and Law, Business Economics Department, Rey Juan Carlos University, Paseo de los Artilleros s/n, Vicálvaro, Madrid  28032, Spain","Fuentes-Moraleda, L., Faculty of Social Sciences and Law, Business Economics Department, Centro de Estudios Turísticos, CETUR, Rey Juan Carlos University, c/Camino del Molino s/n, Fuenlabrada, Madrid  28943, Spain; Díaz-Pérez, P., Faculty of Social Sciences and Law, Business Economics Department, Rey Juan Carlos University, Paseo de los Artilleros s/n, 28032, Vicálvaro, Madrid, Spain; Orea-Giner, A., Faculty of Social Sciences and Law, Business Economics Department, Rey Juan Carlos University, Paseo de los Artilleros s/n, 28032, Vicálvaro, Madrid, Spain, Faculty of Social Sciences and Law, Business Economics Department, Rey Juan Carlos University, Paseo de los Artilleros s/n, Vicálvaro, Madrid  28032, Spain; Muñoz- Mazón, A., Faculty of Social Sciences and Law, Business Economics Department, Rey Juan Carlos University, Paseo de los Artilleros s/n, Vicálvaro, Madrid  28032, Spain; Villacé-Molinero, T., Faculty of Social Sciences and Law, Business Economics Department, Rey Juan Carlos University, Paseo de los Artilleros s/n, 28032, Vicálvaro, Madrid, Spain","The growing implementation of robotics in hospitality and tourism requires broader research into customers' experience with service robots. This study explores human-robot interaction (HRI) in the context of tourists interacting with hotel service robots. The data, 7994 online TripAdvisor reviews of 74 hotels, were subjected to a content analysis based on the Service Robot Acceptance Model (sRAM) and its dimensions (functional, social-emotional and relational). A sentiment analysis was also carried out. The results identify the principal dimensions and variables involved in HRI and the feelings robots inspire in different types of travellers. Guests most often comment on the functional dimension. Robots' functions determine this experience and influence the interaction between robots and hotel guests. © 2020 Elsevier Ltd","Content analysis; Hotels; HRI (human-robot interaction); Service robots; sRAM (Service Robot Acceptance Model)",,,,,,,,,,,,,,,,,"Aoki, H., Fujimoto, Y., Suzuki, S., Sato-Shimokawara, E., Yamaguchi, T., Difference in physiological responses by different cultural greetings using a robot. 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"Kaburlasos V.G., Lytridis C., Bazinas C., Chatzistamatis S., Sotiropoulou K., Najoua A., Youssfi M., Bouattane O.","6701308189;8901635200;57205365418;57201375640;57224998985;57204701141;36171388900;6506839198;","Head Pose Estimation Using Lattice Computing Techniques",2020,"2020 28th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2020",,,"9238315","","",,2,"10.23919/SoftCOM50211.2020.9238315","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096584855&doi=10.23919%2fSoftCOM50211.2020.9238315&partnerID=40&md5=7f8e78429dc62895731fbed0f99af144","International Hellenic University, Human-Machines Interaction Laboratory (HUMAIN-Lab), Kavala, Greece; Hassan II University of Casablanca, Laboratory SSDIA, ENSET Mohammedia, Mohammedia, Morocco","Kaburlasos, V.G., International Hellenic University, Human-Machines Interaction Laboratory (HUMAIN-Lab), Kavala, Greece; Lytridis, C., International Hellenic University, Human-Machines Interaction Laboratory (HUMAIN-Lab), Kavala, Greece; Bazinas, C., International Hellenic University, Human-Machines Interaction Laboratory (HUMAIN-Lab), Kavala, Greece; Chatzistamatis, S., International Hellenic University, Human-Machines Interaction Laboratory (HUMAIN-Lab), Kavala, Greece; Sotiropoulou, K., International Hellenic University, Human-Machines Interaction Laboratory (HUMAIN-Lab), Kavala, Greece; Najoua, A., Hassan II University of Casablanca, Laboratory SSDIA, ENSET Mohammedia, Mohammedia, Morocco; Youssfi, M., Hassan II University of Casablanca, Laboratory SSDIA, ENSET Mohammedia, Mohammedia, Morocco; Bouattane, O., Hassan II University of Casablanca, Laboratory SSDIA, ENSET Mohammedia, Mohammedia, Morocco","Visual stimuli are essential in many applications in human robot interaction. However, such tasks are usually computationally intensive. Also, data received from the various sensors on a robot require different data representation and processing techniques, which increases the complexity and makes the fusion of sensory data for decision making more difficult. An alternative approach is the use of the Lattice Computing (LC) paradigm for hybrid mathematical modelling based on mathematical lattice theory that unifies rigorously numerical data and non-numerical data. This paper presents an application of this approach, and more specifically a novel method for head pose estimation using LC techniques, as an initial step towards using LC as a unified methodology in social robot interaction applications. © 2020 University of Split, FESB.","Lattice Computing; pose estimation; social robots; tree structures","Computer networks; Data handling; Decision making; Estimation; Human robot interaction; Computing techniques; Data representations; Head Pose Estimation; Mathematical lattice theory; Processing technique; Robot interactions; Unified methodology; Visual stimulus; Lattice theory",,,,,,,,,,,,,,,,"Breazeal, C., Dautenhahn, K., Kanda, T., Social robotics (2016) Springer Handbook of Robotics, pp. 1935-1972; Lytridis, C., Bazinas, C., Papakostas, G.A., Kaburlasos, V., On measuring engagement level during child-robot interaction in education (2020) Advances in Intelligent Systems and Computing, 1023, pp. 3-13. , M. Merdan, W. Lepuschitz, G. 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"Xue T., Wang W., Ma J., Liu W., Pan Z., Han M.","57205218623;35286155000;56210799000;57020033400;57197869982;57214111483;","Progress and Prospects of Multimodal Fusion Methods in Physical Human-Robot Interaction: A Review",2020,"IEEE Sensors Journal","20","18","9094690","10355","10370",,3,"10.1109/JSEN.2020.2995271","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090213507&doi=10.1109%2fJSEN.2020.2995271&partnerID=40&md5=9e2e748d8039af560899e8c731f5cdd0","Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China","Xue, T., Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Wang, W., Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Ma, J., Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Liu, W., Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Pan, Z., Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Han, M., Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China","Recent advances in physical Human-Robot Interaction (pHRI) have shown the potential and feasibility of robot systems for active and safe collaboration with humans. This induces high demand for real-time perception about the dynamic unity formed by robot, human, environment and operating objects. Single sensory modality is inadequate to give a robust estimate of the state of the unity. This paper provides a comprehensive review of state-of-the-art multi-modal fusion methods exploring their classifications, feasibilities and challenges based on the fact that pHRI is spatio-temporal sharing, multi-modal and multi-task. Finally, several directions of research are recommended based on the discussion of multi-modal fusion methods. © 2020 IEEE.","multi-modal; multi-modal fusion; multi-task; Physical human-robot interaction; spatio-temporal sharing","Man machine systems; Multi-modal fusion; Physical human-robot interactions; Physical humanrobot interaction (phri); Real time perception; Robust estimate; Sensory modality; Spatio temporal; State of the art; Social robots",,,,,"YG2017QN61; National Natural Science Foundation of China, NSFC: 51675329, 51675342, 51775332, GZ2016KF001; Ministry of Science and Technology of the People's Republic of China, MOST: 2018IM020100; National Key Scientific Instrument and Equipment Development Projects of China: 2016YFC0104104, 2016YFF0101602","Manuscript received March 25, 2020; revised May 9, 2020; accepted May 11, 2020. Date of publication May 18, 2020; date of current version August 14, 2020. This work was supported in part by the Special Program for Innovation Method of the Ministry of Science and Technology, China under Grant 2018IM020100, in part by the National Key Scientific Instruments and Equipment Development Program of China under Grant 2016YFF0101602 and Grant 2016YFC0104104, in part by the National Natural Science Foundation of China under Grant 51675329, Grant 51675342, and Grant 51775332, in part by the State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System GZ2016KF001, and in part by the Cross Fund for Medical and Engineering of Shanghai Jiao Tong University under Grant YG2017QN61. The associate editor coordinating the review of this article and approving it for publication was Prof. Dongsoo Har. (Corresponding author: Weiming Wang.) 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"Okada Y., Taniguchi R., Tatsumi A., Okubo M., Kimoto M., Iio T., Shimohara K., Shiomi M.","57219852700;57219846427;57209305732;57219850977;57050144300;24724391700;57203054726;8397042200;","Effects of Touch Behaviors and Whispering Voices in Robot-Robot Interaction for Information Providing Tasks",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223431","7","13",,2,"10.1109/RO-MAN47096.2020.9223431","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095750136&doi=10.1109%2fRO-MAN47096.2020.9223431&partnerID=40&md5=25f83308f3b9562cf3179a184a63c302","Atr, Kyoto, Japan","Okada, Y., Atr, Kyoto, Japan; Taniguchi, R., Atr, Kyoto, Japan; Tatsumi, A., Atr, Kyoto, Japan; Okubo, M., Atr, Kyoto, Japan; Kimoto, M., Atr, Kyoto, Japan; Iio, T., Atr, Kyoto, Japan; Shimohara, K., Atr, Kyoto, Japan; Shiomi, M., Atr, Kyoto, Japan","Using multiple robots for information providing tasks is one effective approach to attract people by showing conversational interaction between robots. In this study, we investigate the effectiveness of showing such intimate interaction between robots as touching because it has positive effects on human-robot interactions, even though it has received less attention than robot-robot interaction. We prepared touch behaviors and whispering voices between robots and conducted experiments where robots provided information to participants. Our experiment results showed the effectiveness of both touch behaviors and whispering voices between robots in information providing tasks. On the other hands, we also found that whispering voices decreased their likeability, although touch behavior did not have such negative effects. © 2020 IEEE.",,"Agricultural robots; Conversational interaction; Effective approaches; Information providing; Likeability; Multiple robot; Robot interactions; Social robots",,,,,"Japan Society for the Promotion of Science, KAKEN: JP 19J01290, JP18H03311; Japan Science and Technology Agency, JST; Core Research for Evolutional Science and Technology, CREST: JPMJCR18A1; Precursory Research for Embryonic Science and Technology, PRESTO: JPMJPR1851","1This research work was supported in part by JST CREST Grant Number JPMJCR18A1, Japan JSPS KAKENHI Grant Numbers JP18H03311, and JP 19J01290, and JST, PRESTO Grant Number JPMJPR1851, Japan.",,,,,,,,,,"Alves-Oliveira, P., Ribeiro, T., Petisca, S., Di Tullio, E., Melo, F.S., Paiva, A., An empathic robotic tutor for school classrooms: Considering expectation and satisfaction of children as end-users (2015) International Conference on Social Robotics, pp. 21-30; Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., Hagita, N., A communication robot in a shopping mall (2010) Robotics, IEEE Transactions on, 26 (5), pp. 897-913; Satake, S., Hayashi, K., Nakatani, K., Kanda, T., Field trial of an information-providing robot in a shopping mall (2015) The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015), pp. 1832-1839; Heikkilä, P., Lammi, H., Niemelä, M., Belhassein, K., Sarthou, G., Tammela, A., Clodic, A., Alami, R., Should a robot guide like a human? 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Cham: Springer International Publishing 2017; Sumioka, H., Nakae, A., Kanai, R., Ishiguro, H., Huggable communication medium decreases cortisol levels (2013) Scientific Reports, 3, p. 3034; Avelino, J., Correia, F., Catarino, J., Ribeiro, P., Moreno, P., Bernardino, A., Paiva, A., The power of a hand-shake in human-robot interactions (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1864-1869; Block, A.E., Kuchenbecker, K.J., Softness, warmth, and responsiveness improve robot hugs (2019) International Journal of Social Robotics, 11 (1), pp. 49-64; Nakagawa, K., Matsumura, R., Shiomi, M., Effect of robot's play-biting in non-verbal communication (2020) Journal of Robotics and Mechatronics, 32 (1), pp. 86-96; Samani, H.A., Parsani, R., Rodriguez, L.T., Saadatian, E., Dissanayake, K.H., Cheok, A.D., Kissenger: Design of a kiss transmission device (2012) Proceedings of the Designing Interactive Systems Conference, pp. 48-57; Bartneck, C., Kuli, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81; Sugiura, T., Developmental change in the relation between two affiliation motives and interpersonal alienation (2000) Japanese Journal of Educational Psychology, 48 (3), pp. 352-360",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095750136
"Luria M., Pusateri J., Oden Choi J., Aronson R., Yildirim N., Steenson M.W.","56895782800;57215317661;57191966220;57201505550;57218706672;36710641900;","Medieval robots: The role of historical automata in the design of future robots",2020,"DIS 2020 Companion - Companion Publication of the 2020 ACM Designing Interactive Systems Conference",,,,"191","195",,,"10.1145/3393914.3395890","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090123322&doi=10.1145%2f3393914.3395890&partnerID=40&md5=5d26dba55a9551d76d911683baa43da4","Human-Computer Interaction, Carnegie Mellon University, United States; Robotics Institute, Carnegie Mellon University, United States; School of Design, Carnegie Mellon University, United States","Luria, M., Human-Computer Interaction, Carnegie Mellon University, United States; Pusateri, J., Human-Computer Interaction, Carnegie Mellon University, United States; Oden Choi, J., Human-Computer Interaction, Carnegie Mellon University, United States; Aronson, R., Robotics Institute, Carnegie Mellon University, United States; Yildirim, N., Human-Computer Interaction, Carnegie Mellon University, United States; Steenson, M.W., School of Design, Carnegie Mellon University, United States","In this work, we ask about the relevance of medieval automata in modern culture, and about what we can learn from imagined robots in the distant past about the future design of robots. Using an iterative and critical making process, we plan to reconstruct ""The Alabaster Chamber"", or ""Chambre de Beautes"", a room-sized robotic structure that was described in Le Roman de Troiein the beginning of the 12th century. By prototyping and tangibly introducing this robotic space among multi-disciplinary scholars in the fields of human-computer interaction, human-robot interaction and digital humanities, we set out to provoke discussion about alternative interactions and experiences with technology that we may or may not want to consider. © 2020 Owner/Author.","Automata; Critical making; Historical robots; Prototyping","Automata theory; Human computer interaction; Machine design; Robotics; Digital humanities; Future designs; Making process; Robotic structures; Human robot interaction",,,,,"ING1510103-BIO; National Medical Research Council, NMRC: 16may021, CBRG/0025/2012, CIRGmay014","This work was supported by the following funding sources: Singapore National Medical Research Council (NMRC) Open Fund – Young Investigator Research Grant ( 16may021 ); NMRC Clinician Scientist – Individual Research Grant ( CIRGmay014 ); NMRC Cooperative Basic Research Grant ( CBRG/0025/2012 ); SMART Innovation Centre’s Innovation Grant ( ING1510103-BIO ).",,,,,,,,,,"Buxton, B., (2010) Sketching User Experiences: Getting the Design Right and the Right Design, , Morgan kaufmann; Capek, K., (2004) RUR (Rossum's Universal Robots), , Penguin; De Sainte-Maure, B., (1904) Le Roman de Troie, 2. , Firmin Didot et Cie; Dunne, A., Raby, F., (2013) Speculative Everything: Design, Fiction, and Social Dreaming, , MIT press; Hoffman, G., Ju, W., Designing robots with movement in mind (2014) Journal of Human-Robot Interaction, 3 (1), pp. 91-122. , 2014; Hoffman, G., Zuckerman, O., Hirschberger, G., Luria, M., Shani-Sherman, T., Design and evaluation of a peripheral robotic conversation companion (2015) 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 3-10. , IEEE; Luria, M., Sheriff, O., Boo, M., Forlizzi, J., Zoran, A., Destruction, catharsis and emotional release in human robot interaction (2020) ACM Transactions on Human-Robot Interaction (THRI), , 2020; Luria, M., Zimmerman, J., Forlizzi, J., (2019) Championing Research Through Design in HRI, , 2019; Ratto, M., Critical making: Conceptual and material studies in technology and social life (2011) The Information Society, 27 (4), pp. 252-260. , 2011; Sayers, J., Prototyping the past (2015) Visible Language, 49 (3). , 2015; Suguitan, M., Hoffman, G., Blossom: A handcrafted open-source robot (2019) ACM Transactions on Human-Robot Interaction (THRI), 8 (1), pp. 1-27. , 2019; Truitt, E.R., Trei poete, sages dotors, qui mout sorent di nigromance"": Knowledge and automata in twelfth-century French literature (2004) Configurations, 12 (2), pp. 167-193. , 2004; Rachel Truitt, E., (2015) Medieval Robots: Mechanism, Magic, Nature, and Art, , University of Pennsylvania Press; Zimmerman, J., Forlizzi, J., Evenson, S., Research through design as a method for interaction design research in hci (2007) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 493-502",,,"ACM SIGCHI","Association for Computing Machinery, Inc","2020 ACM Conference on Designing Interactive Systems, DIS 2020","6 July 2020 through 10 July 2020",,161617,,9781450379878,,,"English","DIS Companion - Companion Publ. ACM Des. Interact. Syst. Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85090123322
"Song Y.","57205427691;","Building a ‘Deeper’ Trust: Mapping the Facial Anthropomorphic Trustworthiness in Social Robot Design through Multidisciplinary Approaches",2020,"Design Journal","23","4",,"639","649",,3,"10.1080/14606925.2020.1766871","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085191386&doi=10.1080%2f14606925.2020.1766871&partnerID=40&md5=b615211eab1261c0ef74c2cade14e40c","The Hong Kong Polytechnic University, Kowloon, Hong Kong","Song, Y., The Hong Kong Polytechnic University, Kowloon, Hong Kong","Overview: As a robotic application in artificial intelligence (AI), a social robot is designed for social communication and interactions with humans. Regarding human nature to anthropomorphize objectives, people’s trustworthiness perception towards a social robot is prominent in Human-Robot Interaction (HRI). However, how to achieve a trustworthy looking for social robots is still a challenging problem, this project tries to contribute to this research gap by exploring the meaning of facial anthropomorphic trustworthiness, the effect of specific facial features and their combinations on anthropomorphic trustworthiness, and the effect of dynamic facial features on anthropomorphic trustworthiness under different daily contexts through multidisciplinary approaches. Theoretical contributions and practical implications are discussed in this project. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.","anthropomorphism; facial trustworthiness; human-robot interactions; multidisciplinary approaches; social robot design",,,,,,"University Grants Committee, UGC; Hong Kong Polytechnic University, PolyU: G-RL6B","I would like to thank my supervisors, Yan Tina Luximon (Chief supervisor) and Benny Ding Leung (Co-supervisor), from the School of Design at the Hong Kong Polytechnic University. I would like to thank the support of the UGC Funding Scheme from the Hong Kong Polytechnic University (G-RL6B).","I would like to thank my supervisors, Yan Tina Luximon (Chief supervisor) and Benny Ding Leung (Co-supervisor), from the School of Design at the Hong Kong Polytechnic University. I would like to thank the support of the UGC Funding Scheme from the Hong Kong Polytechnic University (G-RL6B).",,,,,,,,,"Balas, B., Pacella, J., Trustworthiness Perception Is Disrupted in Artificial Faces (2017) Computers in Human Behavior, 77, pp. 240-248; Baumgartner, T., Heinrichs, M., Vonlanthen, A., Fischbacher, U., Fehr, E., Oxytocin Shapes the Neural Circuitry of Trust and Trust Adaptation in Humans (2008) Neuron, 58 (4), pp. 639-650; Belk, R.W., Possessions and the Extended Self (1988) Journal of Consumer Research, 15 (2), p. 139; Berger, J., (1972) Way of Seeing, , London: Penguin Group; Bilgin, Y., Qualitative Method versus Quantitative Method in Marketing Research: An Application Example at Oba Restaurant (2017) Qualitative versus Quantitative Research, , edited by Sonyel Oflazoğl, 1–28. London, UK: IntechOpen; Brenton, H., Gillies, M., Ballin, D., Chatting, D., The Uncanny Valley: Does It Exist and Is It Related to Presence (2005) 19th British HCI Group Annual Conference, , Edingburgh, UK; Colborn, D., Frequency and Urgency (1994) The Promotion of Continence in Adult Nursing, pp. 54-64. , Springer,. edited by Pat Webb. San Diego, California, US; Colquitt, J.A., Scott, B.A., LePine, J.A., Trust, Trustworthiness, and Trust Propensity: A Meta-Analytic Test of Their Unique Relationships with Risk Taking and Job Performance (2007) The Journal of Applied Psychology, 92 (4), pp. 909-927; Decety, J., Sommerville, J.A., Shared Representations between Self and Other: A Social Cognitive Neuroscience View (2003) Trends in Cognitive Sciences, 7 (12), pp. 527-533; Etcoff, N.L., Beauty and the Beholder (1994) Nature, 368 (6468), pp. 186-187; Etcoff, N.L., Stock, S., Haley, L.E., Vickery, S.A., House, D.M., Cosmetics as a Feature of the Extended Human Phenotype: Modulation of the Perception of Biologically Important Facial Signals (2011) PLoS One, 6 (10); Frowd, C.D., Bruce, V., Smith, A.J., Hancock, P.J.B., Improving the Quality of Facial Composites Using a Holistic Cognitive Interview (2008) Journal of Experimental Psychology. Applied, 14 (3), pp. 276-287; Hakim, C., (1987) Research Design: Strategies and Choices in the Design of Social Research, , London, UK: Routledge; Maeng, A., Aggarwal, P., Facing Dominance: Anthropomorphism and the Effect of Product Face Ratio on Consumer Preference (2018) Journal of Consumer Research, 44 (5), pp. 1104-1122; Mathur, M.B., Reichling, D.B., Navigating a Social World with Robot Partners: A Quantitative Cartography of the Uncanny Valley (2016) Cognition, 146, pp. 22-32; McGinn, C., Why Do Robots Need a Head? The Role of Social Interfaces on Service Robots (2020) International Journal of Social Robotics, 12 (1), pp. 215-281; Okubo, M., Ishikawa, K., Kobayashi, A., No Trust on the Left Side: Hemifacial Asymmetries for Trustworthiness and Emotional Expressions (2013) Brain and Cognition, 82 (2), pp. 181-186; Palinko, O., Rea, F., Sandini, G., Sciutti, A., (2015) Eye Gaze Tracking for a Humanoid Robot, pp. 318-324. , IEEE-RAS International Conference on Humanoid Robots, Seoul, Korea; Prakash, A., Rogers, W.A., Why Some Humanoid Faces Are Perceived More Positively than Others: Effects of Human-Likeness and Task (2015) International Journal of Social Robotics, 7 (2), pp. 309-331; Saunderson, S., Nejat, G., How Robots Influence Humans: A Survey of Nonverbal Communication in Social Human–Robot Interaction (2019) International Journal of Social Robotics, 11 (4), pp. 534-575; Stroessner, S.J., Benitez, J., The Social Perception of Humanoid and Non-Humanoid Robots: Effects of Gendered and Machinelike Features (2019) International Journal of Social Robotics, 11 (2), pp. 305-315; Timoshenko, A., Hauser, J.R., Identifying Customer Needs from User-Generated Content (2019) Marketing Science, 38 (1), pp. 1-20; Todorov, A., Said, C.P., Engell, A.D., Oosterhof, N.N., Understanding Evaluation of Faces on Social Dimensions (2008) Trends in Cognitive Sciences, 12 (12), pp. 455-460; Walters, M.L., (2008) The Design Space for Robot Appearance and Behaviour for Social Robot Companions, , Hatfield, Hertfordshire, UK: University of Hertfordshire; Westlund, J.K., Lee, J.J., Plummer, L., Faridi, F., Gray, J., Berlin, M., Quintus-Bosz, H., Tega: A Social Robot (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), p. 561. , IEEE, Christchurch, New Zealand; Yu, M., Saleem, M., Gonzalez, C., Developing Trust: First Impressions and Experience (2014) Journal of Economic Psychology, 43, pp. 16-29","Song, Y.; School of Design, Hong Kong; email: yao.song@connect.polyu.hk",,,"Taylor and Francis Ltd.",,,,,14606925,,,,"English","Des. J.",Article,"Final","",Scopus,2-s2.0-85085191386
"Hunte K., Chen S., Yi J., Su H.","57203656812;57191615566;7402736580;55059793600;","Assist-as-needed control of a wearable lightweight knee robotic device",2020,"IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM","2020-July",,"9159021","1477","1482",,1,"10.1109/AIM43001.2020.9159021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090380562&doi=10.1109%2fAIM43001.2020.9159021&partnerID=40&md5=2c82c5e5a728a4ea1c09baefbb1a678c","Rutgers University, Department of Electrical and Computer Engineering, Piscataway, NJ  08854, United States; Rutgers University, Department of Mechanical and Aerospace Engineering, Piscataway, NJ  08854, United States; City University of New York, Department of Mechanical Engineering, New York, NY  10023, United States","Hunte, K., Rutgers University, Department of Electrical and Computer Engineering, Piscataway, NJ  08854, United States; Chen, S., Rutgers University, Department of Mechanical and Aerospace Engineering, Piscataway, NJ  08854, United States; Yi, J., Rutgers University, Department of Mechanical and Aerospace Engineering, Piscataway, NJ  08854, United States; Su, H., City University of New York, Department of Mechanical Engineering, New York, NY  10023, United States","Knee joint actuation plays a critical role to keep human walking locomotion and balance under abnormal conditions, such as foot slip or work-related musculoskeletal disorders etc. Wearable assistive robotic knee devices provide additional support and actuation for human walkers. We present assist-as-needed control strategy for a lightweight, highly-backdrivable soft knee assistive device. The control design takes advantages of the custom-built high-performance knee assistive device and the muscle synergy-based human walking actuation model. A model predictive control (MPC) design is used for real-time tuning physical human-robot interactions. Human-in-the-loop simulation results are presented to demonstrate the performance of the robotic control systems under normal walking condition. © 2020 IEEE.",,"Intelligent mechatronics; Joints (anatomy); Machine design; Man machine systems; Model predictive control; Predictive control systems; Robotics; Walking aids; Wearable technology; Abnormal conditions; Assistive devices; Assistive robotics; Control strategies; Human-in-the-loop simulations; Physical human-robot interactions; Robotic control system; Work-related musculoskeletal disorders; Social robots",,,,,"CMMI-1762556; National Science Foundation, NSF: 1762556","The work was supported in part by the US National Science Foundation under award CMMI-1762556.",,,,,,,,,,"Trkov, M., Chen, K., Yi, J., Liu, T., Inertial sensor-based slip detection in human walking (2020) IEEE Trans. Automat. Sci. Eng., 17 (1), pp. 348-360; Trkov, M., Chen, K., Yi, J., Bipedal model and extended hybrid zero dynamics of human walking with foot slips (2019) ASME J. Computat. Nonlinear Dyn., 14 (10). , article 101002; Reid, C.R., Bush, P.M., Karwowski, W., Durrani, S.K., Occupational postural activity and lower extremity discomfort: A review (2010) Int. J. Ind. Ergonom., 40, pp. 247-256; Zhang, J., Fiers, P., Witte, K.A., Jackson, R.W., Poggensee, K.L., Atkeson, C.G., Collins, S.H., Human-in-the-loop optimization of exoskeleton assistance during walking (2017) Science, 356, pp. 1280-1284; Trkov, M., Wu, S., Chen, K., Yi, J., Liu, T., Zhao, Q., Design and characterization of a robotic knee assistive device (rokad) for slip-induced fall prevention during walking (2017) Proc. IFAC World Congress, pp. 10218-10223. , Toulouse, France; Yan, T., Cempini, M., Oddo, C.M., Vitiello, N., Review of assistive strategies in powered lower-limb orthoses and exoskeletons (2015) Robot. Auton. Syst., 64, pp. 120-136; Young, A.J., Ferris, D.P., State of the art and future directions for lower limb robotic exoskeletons (2017) IEEE Trans. Neural Syst. Rehab. Eng., 25 (2), pp. 171-182; Ma, Y., Wu, X., Yi, J., Wang, C., Chen, C., A review on humanexoskeleton coordination towards lower limb robotic exoskeleton systems (2019) Int. J. Robot. Automat., 34 (4), pp. 431-451; Sulzer, J.S., Roiz, R.A., Peshkin, M.A., Patton, J.L., A highly backdrivable, lightweight knee actuator for investigating gait in stroke (2009) IEEE Trans. Robotics, 25 (3), pp. 539-548; Shepherd, M.K., Rouse, E.J., Design and validation of a torquecontrollable knee exoskeleton for sit-to-stand assistance (2017) IEEE/ASME Trans. Mechatronics, 22 (4), pp. 1695-1704; Lerner, Z.F., Damiano, D.L., Bulea, T.C., A lower-extremity exoskeleton improves knee extension in children with crouch gait from cerebral palsy (2017) Sci. Transl. Med., 9 (404), p. eaam9145; Sridar, S., Nguyen, P.H., Zhu, M., Lam, Q.P., Polygerinos, P., Development of a soft-inflatable exosuit for knee rehabilitation (2017) Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst., pp. 3722-3727. , Vancouver, BC; Ding, Y., Kim, M., Kuindersma, S., Walsh, C.J., Human-in-theloop optimization of hip assistance with a soft exosuit during walking (2018) Sci. Robot., 3 (15), p. eaar5438; Yu, S., Huang, T.-H., Wang, D., Lynn, B., Sayd, D., Silivanov, V., Park, Y.S., Su, H., Design and control of a high-torque and highly backdrivable hybrid soft exoskeleton for knee injury prevention during squatting (2019) IEEE Robot. Automat. Lett., 4 (4), pp. 4579-4586; Yu, S., Huang, T.-H., Yang, X., Jiao, C., Yang, J., Hu, H., Zhang, S., Su, H., Quasi-direct drive actuation for a lightweight hip exoskeleton with high backdrivability and high bandwidth (2020) IEEE/ASME Trans. Mechatronics, , in press; McIntosh, A.S., Beatty, K.T., Dwan, L.N., Vickers, D.R., Gait dynamics on an inclined walkway (2006) J. Biomech., 39 (13), pp. 2491-2502; Wensing, P.M., Wang, A., Seok, S., Otten, D., Lang, J., Kim, S., (2017) Proprioceptive Actuator Design in the mit Cheetah: Impact Mitigation and High-bandwidth Physical Interaction for Dynamic Legged Robots, 33 (3), pp. 509-522; Chvatal, S.A., Ting, L.H., Common muscle synergies for balance and walking (2013) Front. Comput. Neurosci., 7. , article 48; Sawers, A., Pai, Y.-P., Bhatt, T., Ting, L.H., Neuromuscular responses differ between slip-induced falls and recoveries in older adults (2017) J. Neurophysiol., 117, pp. 509-522; Chen, S., Yi, J., Liu, T., Strength capacity estimation of human upper limb in human-robot interactions with muscle synergy models (2018) Proc. IEEE/ASME Int. Conf. Adv. Intelli. Mechatronics, pp. 1051-1056. , Auckland, New Zealand; Chen, S., Yi, J., Liu, T., Muscle synergy-based control of human-manipulator interactions (2020) Proc. IEEE/ASME Int. Conf. Adv. Intelli. Mechatronics, , Boston, MA; http://opensim.stanford.edu/",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020","6 July 2020 through 9 July 2020",,162314,,9781728167947,,,"English","IEEE ASME Int Conf Adv Intellig Mechatron AIM",Conference Paper,"Final","",Scopus,2-s2.0-85090380562
"Liu X., Liu X., Guo D., Liu H., Sun F., Min H.","57219434366;57221423025;34868037800;35272617500;57204699218;35792579800;","Self-Supervised Learning for Alignment of Objects and Sound",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9197566","1588","1594",,,"10.1109/ICRA40945.2020.9197566","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092693091&doi=10.1109%2fICRA40945.2020.9197566&partnerID=40&md5=3124954e45e66b6e88a9ffe735e36db6","Tsinghua University, Beijing National Research Center for Information Science and Technology, Institute for Artificial Intelligence, Beijing, 100084, China","Liu, X., Tsinghua University, Beijing National Research Center for Information Science and Technology, Institute for Artificial Intelligence, Beijing, 100084, China; Liu, X., Tsinghua University, Beijing National Research Center for Information Science and Technology, Institute for Artificial Intelligence, Beijing, 100084, China; Guo, D., Tsinghua University, Beijing National Research Center for Information Science and Technology, Institute for Artificial Intelligence, Beijing, 100084, China; Liu, H., Tsinghua University, Beijing National Research Center for Information Science and Technology, Institute for Artificial Intelligence, Beijing, 100084, China; Sun, F., Tsinghua University, Beijing National Research Center for Information Science and Technology, Institute for Artificial Intelligence, Beijing, 100084, China; Min, H., Tsinghua University, Beijing National Research Center for Information Science and Technology, Institute for Artificial Intelligence, Beijing, 100084, China","The sound source separation problem has many useful applications in the field of robotics, such as human-robot interaction, scene understanding, etc. However, it remains a very challenging problem. In this paper, we utilize both visual and audio information of videos to perform the sound source separation task. A self-supervised learning framework is proposed to implement the object detection and sound separation modules simultaneously. Such an approach is designed to better find the alignment between the detected objects and separated sound components. Our experiments, conducted on both the synthetic and real datasets, validate this approach and demonstrate the effectiveness of the proposed model in the task of object and sound alignment. © 2020 IEEE.",,"Acoustic generators; Agricultural robots; Alignment; Audio acoustics; Human robot interaction; Object detection; Robotics; Separation; Supervised learning; Audio information; Real data sets; Scene understanding; Sound separation; Sound source separation; Source separation",,,,,"National Natural Science Foundation of China, NSFC: 91848206, U1613212","This work was supported in part by the National Natural Science Foundation of China under Grants U1613212 and 91848206.",,,,,,,,,,"Liu, H., Sun, F., Zhang, X., Robotic material perception using active multimodal fusion (2018) Ieee Transactions on Industrial Electronics, 66 (12), pp. 9878-9886; Okuno, H.G., Nakadai, K., Robot audition: Its rise and perspectives (2015) 2015 Ieee International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5610-5614; Morito, T., Sugiyama, O., Kojima, R., Nakadai, K., Partially shared deep neural network in sound source separation and identification using a uav-embedded microphone array (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1299-1304; Frchette, M., Ltourneau, D., Valin, J.-M., Michaud, F., Integration of sound source localization and separation to improve dialogue management on a robot (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), pp. 2358-2363; Nguyen And AuthorJongSuk Choi, Q., Selection of the closest sound source for robot auditory attention in multi-source scenarios (2016) Journal of Intelligent Robotic Systems, pp. 239-251; Kojima, R., Sugiyama, O., Suzuki, R., Nakadai, K., Taylor, C.E., Semi-automatic bird song analysis by spatial-cue-based integration of sound source detection, localization, separation, and identification (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1287-1292; Liu, H., Zhang, Z., Zhu, Y., Zhu, S.-C., Self-supervised incremental learning for sound source localization in complex indoor environment (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 2599-2605; He, W., Motlicek, P., Odobez, J.-M., Deep neural networks for multiple speaker detection and localization (2018) 2018 Ieee International Conference on Robotics and Automation (ICRA), pp. 74-79; Grondin, F., Michaud, F., Noise mask for tdoa sound source localization of speech on mobile robots in noisy environments (2016) 2016 Ieee International Conference on Robotics and Automation (ICRA), pp. 4530-4535; Cherry, E.C., Some experiments on the recognition of speech, with one and with two ears (1953) The Journal of the Acoustical Society of America, p. 975979; McDermott, J.H., The cocktail party problem (2009) Current Biology, p. R1024R1027; Wang, D., Chen, J., Supervised speech separation based on deep learning: An overview (2018) IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26 (10), pp. 1702-1726. , Oct; Virtanen, T., Sound source separation using sparse coding with temporal continuity objective (2003) Proc Icmc, 2003; Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria (2007) Ieee Transactions on Audio, Speech, and Language Processing, 15 (3), pp. 1066-1074. , March; Hershey, J.R., Chen, Z., Roux, J.L., Watanabe, S., Deep clustering: Discriminative embeddings for segmentation and separation (2016) 2016 Ieee International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 31-35; Yu, D., Kolbk, M., Tan, Z.-H., Jensen, J., Permutation invariant training of deep models for speaker-independent multi-talker speech separation (2017) 2017 Ieee International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 241-245; Liu, H., Guo, D., Zhang, X., Zhu, W., Sun, F., Toward image-to-tactile cross-modal perception for visually-impaired people Ieee Transactions on Automation Science and Engineering; Ravulapalli, S., Sarkar, S., Association of sound to motion in video using perceptual organization (2006) 18th International Conference on Pattern Recognition (ICPR'06), pp. 1216-1219; Sigg, C., Fischer, B., Ommer, B., Roth, V., Buhmann, J., Nonnegative cca for audiovisual source separation (2007) 2007 Ieee Workshop on Machine Learning for Signal Processing, pp. 253-258; Gao, R., Feris, R., Grauman, K., Learning to separate object sounds by watching unlabeled video (2018) The European Conference on Computer Vision (ECCV), , September; Ephrat, A., Mosseri, I., Lang, O., Dekel, T., Wilson, K., Hassidim, A., Freeman, W.T., Rubinstein, M., Looking to listen at the cocktail party: A speaker-independent audio-visual model for speech separation (2018) Acm Transactions on Graphics (Proc. SIGGRAPH), 37; Pu, J., Panagakis, Y., Petridis, S., Pantic, M., Audio-visual object localization and separation using low-rank and sparsity (2017) 2017 Ieee International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2901-2905; Afouras, T., Chung, J.S., Zisserman, A., (2018) The Conversation: Deep Audio-visual Speech Enhancement; Zhao, H., Gan, C., Rouditchenko, A., Vondrick, C., McDermott, J., Torralba, A., The sound of pixels (2018) The European Conference on Computer Vision (ECCV), , September; Rouditchenko, A., Zhao, H., Gan, C., McDermott, J., Torralba, A., Self-supervised audio-visual co-segmentation (2019) Icassp 2019-2019 Ieee International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2357-2361; Owens, A., Efros, A.A., Audio-visual scene analysis with selfsupervised multisensory features (2018) The European Conference on Computer Vision (ECCV), , September; Gao, R., Grauman, K., Co-separating sounds of visual objects (2019) Iccv; Wang, F., Guo, D., Liu, H., Zhou, J., Sun, F., Sound-indicated visual object detection for robotic exploration (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 8070-8076; Zhou, J., Wang, F., Guo, D., Liu, H., Sun, F., Video-guided sound source separation (2019) Icira 2019: Intelligent Robotics and Applications, pp. 415-426; Ren, S., He, K., Girshick, R., Sun, J., Faster r-cnn: Towards realtime object detection with region proposal networks (2015) Advances in Neural Information Processing Systems, pp. 91-99; Ronneberger, O., Fischer, P., Brox, T., U-net: Convolutional networks for biomedical image segmentation (2015) Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015, pp. 234-241. , Cham: Springer International Publishing; Gemmeke, J.F., Ellis, D.P., Freedman, D., Jansen, A., Lawrence, W., Moore, R.C., Plakal, M., Ritter, M., Audio set: An ontology and human-labeled dataset for audio events (2017) Acoustics, Speech and Signal Processing (ICASSP), 2017 Ieee International Conference On. Ieee, pp. 776-780; Vincent, E., Gribonval, R., Févotte, C., Performance measurement in blind audio source separation (2006) Ieee Transactions on Audio, Speech, and Language Processing, 14 (4), pp. 1462-1469",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","",Scopus,2-s2.0-85092693091
"Connolly J.","57215561944;","Preventing robot abuse through emotional robot responses",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"558","560",,1,"10.1145/3371382.3377433","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083227914&doi=10.1145%2f3371382.3377433&partnerID=40&md5=b73277bd964d5a42e15b484fe6d4bd78","Yale University, New Haven, CT, United States","Connolly, J., Yale University, New Haven, CT, United States","My research concerns group influence and prosocial behavior in a Human-Robot Interaction (HRI) context. My collaborators and I created and ran an experiment (N = 30) to measure if the emotional responses of a group of robots could induce participants to take prosocial action against robot abuse. Participants completed a collaborative block-building task with a confederate, during which the confederate abused one robot after it made mistakes. We measured participants' responses to these events. The results of the study indicate that humans are more likely to prosocially intervene when the bystander robots react in sadness as opposed to when they ignore the abuse. They motivate further research on social influence and group dynamics within HRI. © 2020 ACM.","Human-robot interaction; Prosociality; Robot abuse","Agricultural robots; Behavioral research; Man machine systems; Block buildings; Emotional response; Emotional robots; Group dynamics; Human robot Interaction (HRI); Social influence; Human robot interaction",,,,,,,,,,,,,,,,"Alterovitz, R., Koenig, S., Likhachev, M., Robot planning in the real world: Research challenges and opportunities (2016) Artificial Intelligence Magazine, 37, pp. 76-84. , July 2016; Brsci, D., Kidokoro, H., Suehiro, Y., Kanda, T., Escaping from children's abuse of social robots (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI '15), pp. 59-66. , https://doi.org/10.1145/2696454.2696468, ACM, New York, NY, USA; Chidambaram, V., Chiang, Y., Mutlu, B., Designing persuasive robots: How robots might persuade people using vocal and nonverbal cues (2012) 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 293-300. , https://doi.org/10.1145/2157689.2157798; Fraune, M., Kawakami, S., Sabanovic, S., De Silva, R., Okada, M., Three's company, or a crowd: The effects of robot number and behavior on HRI in Japan and the USA (2015) Proceedings of Robotics: Science and Systems. Rome, Italy, , https://doi.org/10.15607/RSS.2015.XI.033; Fraune, M.R., Sherrin, S., Sabanovi, S., Smith, E.R., Rabble of robots effects: Number and type of robots modulates attitudes, emotions, and stereotypes (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction (HRI '15), pp. 109-116. , https://doi.org/10.1145/2696454.2696483, ACM, New York, NY, USA; Glas, D.F., Satake, S., Ferreri, F., Kanda, T., Hagita, N., Ishiguro, H., The network robot system: Enabling social humanrobot interaction in public spaces (2013) J. Hum.-Robot Interact, 1 (2), pp. 5-32. , https://doi.org/10.5898/JHRI.1.2.Glas, Jan. 2013; Keijsers, M., Bartneck, C., Mindless robots get bullied (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (HRI '18), pp. 205-214. , https://doi.org/10.1145/3171221.3171266, ACM, New York, NY, USA; Lucas, H., Poston, J., Yocum, N., Carlson, Z., Feil-Seifer, D., Too big to be mistreated Examining the role of robot size on perceptions of mistreatment (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 1071-1076. , https://doi.org/10.1109/ROMAN.2016.7745241; Nomura, T., Uratani, T., Kanda, T., Matsumoto, K., Kidokoro, H., Suehiro, Y., Yamada, S., Why do children abuse robots (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts (HRI'15 Extended Abstracts), pp. 63-64. , https://doi.org/10.1145/2701973.2701977, ACM, New York, NY, USA; Paiva, A., Santos, F., Santos, F., (2018) Engineering Pro-Sociality with Autonomous Agents, , https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16799; Salmivalli, C., Participant role approach to school bullying: Implications for interventions (1999) Journal of Adolescence, 22 (4), pp. 453-459. , 1999; Salmivalli, C., Participant roles in bullying: How can peer bystanders be utilized in interventions (2014) Theory into Practice, 53 (10), pp. 286-292. , https://doi.org/10.1080/00405841.2014.947222, 2014; Salmivalli, C., Voeten, R., Poskiparta, E., Bystanders matter: Associations between reinforcing, defending, and the frequency of bullying behavior in classrooms (2011) Journal of Clinical Child and Adolescent Psychology : The Official Journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division, 53 (40), pp. 668-676. , https://doi.org/10.1080/15374416.2011.597090, 09 2011; Salomons, N., Van Der Linden, M., Strohkorb Sebo, S., Scassellati, B., Humans conform to robots: Disambiguating trust, truth, and conformity (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (HRI '18), pp. 187-195. , https://doi.org/10.1145/3171221.3171282, ACM, New York, NY, USA; Salvini, P., Ciaravella, G., Yu, W., Ferri, G., Manzi, A., Mazzolai, B., Laschi, C., Dario, P., How safe are service robots in urban environments Bullying a robot (2010) 19th International Symposium in Robot and Human Interactive Communication, pp. 1-7. , https://doi.org/10.1109/ROMAN.2010.5654677; Song, S., Yamada, S., Bioluminescence-inspired human-robot interaction: Designing expressive lights that affect human's willingness to interact with a robot (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (HRI '18), pp. 224-232. , https://doi.org/10.1145/3171221.3171249, ACM, New York, NY, USA; Tan, X.Z., Vázquez, M., Carter, E.J., Morales, C.G., Steinfeld, A., Inducing bystander interventions during robot abuse with social mechanisms (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (HRI '18), pp. 169-177. , https://doi.org/10.1145/3171221.3171247, ACM, New York, NY, USA; Wittenburg, P., Brugman, H., Russel, A., Klassmann, A., Sloetjes, H., ELAN: A professional framework for multimodality research (2006) 5th Int'l Conference on Language Resources and Evaluation (LREC), pp. 1556-1559","Connolly, J.; Yale UniversityUnited States; email: joe.connolly@yale.edu",,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083227914
"Williams T., Szafir D., Chakraborti T., Soh Khim O., Rosen E., Booth S., Groechel T.","7404172627;50362161600;41261182500;57216331095;57195424425;57194724923;57215285175;","Virtual, augmented, and mixed reality for human-robot interaction (VAM-HRI)",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"663","664",,2,"10.1145/3371382.3374850","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083172037&doi=10.1145%2f3371382.3374850&partnerID=40&md5=7108f9d9081fca7ea0e227354acf6f02","Colorado School of Mines, Golden, CO, United States; University of Colorado Boulder, Boulder, CO, United States; IBM Research AI, Cambridge, MA, United States; National University of Singapore, Singapore, Singapore; Brown University, Providence, RI, United States; Massachusetts Institute of Technology, Cambridge, MA, United States; University of Southern California, Los Angeles, CA, United States","Williams, T., Colorado School of Mines, Golden, CO, United States; Szafir, D., University of Colorado Boulder, Boulder, CO, United States; Chakraborti, T., IBM Research AI, Cambridge, MA, United States; Soh Khim, O., National University of Singapore, Singapore, Singapore; Rosen, E., Brown University, Providence, RI, United States; Booth, S., Massachusetts Institute of Technology, Cambridge, MA, United States; Groechel, T., University of Southern California, Los Angeles, CA, United States","The 3rd International Workshop on Virtual, Augmented, and Mixed Reality for Human-Robot Interactions (VAM-HRI) will bring together HRI, Robotics, and Mixed Reality researchers to address challenges in mixed reality interactions between humans and robots. Topics relevant to the workshop include development of robots that can interact with humans in mixed reality, use of virtual reality for developing interactive robots, the design of augmented reality interfaces that mediate communication between humans and robots, comparisons of the capabilities and perceptions of robots and virtual agents, and best design practices. VAM-HRI 2020 will follow on the success of VAM-HRI 2018 and 2019, and advance the cause of this nascent research community. © 2020 ACM.","Augmented reality; Human-robot interaction; Mixed reality; Virtual reality","Agricultural robots; Augmented reality; Machine design; Man machine systems; Mixed reality; Virtual addresses; Design practice; Interactive robot; International workshops; Reality interface; Research communities; Virtual agent; Human robot interaction",,,,,,,,,,,,,,,,"Teja Chadalavada, R., Andreasson, H., Krug, R., Lilienthal, A.J., That's on my mind! robot to human intention communication through on-board projection on shared floor space (2015) ECMR; Chakraborti, T., Sreedharan, S., Kulkarni, A., Kambhampati, S., (2017) Alternative Modes of Interaction in Proximal Human-in-the-Loop Operation of Robots, , CoRR, abs/1703.08930; Chakraborti, T., Sreedharan, S., Kulkarni, A., Kambhampati, S., Projection-aware task planning and execution for human-in-the-loop operation of robots (2018) IROS; Christensen, H.I., Batzinger, T., Bekris, K., Bohringer, K., Bordogna, J., Bradski, G., Brock, O., Eastman, R., A roadmap for us robotics: From internet to robotics (2009) CCC and CRA; Diaz, C., Walker, M., Szafir, D.A., Szafir, D., Designing for depth perceptions in augmented reality (2017) Proc. ISMAR; Gadre, S.Y., Rosen, E., Chien, G., Phillips, E., Tellex, S., Konidaris, G., End-user robot programming using mixed reality (2019) Proc. ICRA; Hedayati, H., Walker, M., Szafir, D., Improving collocated robot teleoperation with augmented reality (2018) Proc. HRI; Huang, B., Bayazit, D., Ullman, D., Gopalan, N., Tellex, S., Flight, camera, action! using natural language and mixed reality to control a drone (2019) ICRA; Leutert, F., Herrmann, C., Schilling, K., A spatial augmented reality system for intuitive display of robotic data (2013) Proc. HRI; Rosen, E., Whitney, D., Phillips, E., Chien, G., Tompkin, J., Konidaris, G., Tellex, S., Communicating and controlling robot arm motion intent through mixed-reality head-mounted displays (2019) IJRR; Sato, S., Sakane, S., A human-robot interface using an interactive hand pointer that projects a mark in the real work space (2000) Proc. ICRA; Shen, J., Jin, J., Gans, N., A multi-view camera-projector system for object detection and robot-human feedback (2013) Proc. ICRA; Szafir, D., Mutlu, B., Fong, T., Communication of intent in assistive free flyers (2014) Proceedings of HRI. ACM; Szafir, D., Mutlu, B., Fong, T., Communicating directionality in flying robots (2015) Proceedings of HRI. ACM; Turk, M., Fragoso, V., Computer vision for mobile augmented reality (2015) Mobile Cloud Visual Media Computing; Vogel, C., (2016) Projection-based Interaction and Safeguarding, , https://goo.gl/61zfnE, FourByThree; Walker, M., Hedayati, H., Lee, J., Szafir, D., Communicating robot motion intent with augmented reality (2018) Proc. HRI; Watanabe, A., Ikeda, T., Morales, Y., Shinozawa, K., Miyashita, T., Hagita, N., Communicating robotic navigational intentions (2015) IROS, pp. 5763-5769. , IEEE; Whitney, D., Rosen, E., Ullman, D., Phillips, E., Tellex, S., Ros reality: A virtual reality framework using consumer-grade hardware for ros-enabled robots (2018) IROS. IEEE; Williams, T., Briggs, G., Oosterveld, B., Scheutz, M., Going beyond literal command-based instructions: Extending robotic natural language interaction capabilities (2015) Proceedings of AAAI; Williams, T., Szafir, D., Chakraborti, T., Ben Amor, H., The 1st international workshop on virtual, augmented, and mixed reality for humanrobot interaction (2018) AI Magazine; Williams, T., Szafir, D., Chakraborti, T., Ben Amor, H., Virtual, augmented, and mixed reality for human-robot interaction (2018) Comp. HRI; Zhang, Y., Sreedharan, S., Kulkarni, A., Chakraborti, T., Hankui Zhuo, H., Kambhampati, S., Plan explicability and predictability for robot task planning (2017) ICRA. IEEE; Zhu, D., Veloso, M., Virtually adapted reality and algorithm visualization for autonomous robots (2016) RoboCup Symposium",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083172037
"Wijnen L., Lemaignan S., Bremner P.","57193755961;18234152100;57206776256;","Towards using virtual reality for replicating HRI studies",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"514","516",,3,"10.1145/3371382.3378374","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083168765&doi=10.1145%2f3371382.3378374&partnerID=40&md5=808e4b58eb8c04fc3cd6d7811e53a04b","Bristol Robotics Laboratory, Bristol, United Kingdom","Wijnen, L., Bristol Robotics Laboratory, Bristol, United Kingdom; Lemaignan, S., Bristol Robotics Laboratory, Bristol, United Kingdom; Bremner, P., Bristol Robotics Laboratory, Bristol, United Kingdom","As a first step into utilizing Virtual Reality (VR) for Human-Robot interaction(HRI) studies we attempted to replicate a study from Kahn et al., which was interested in peoples' secret keeping behaviour with regards to robotic tour guides compared to human tour guides. Some changes had to be made to the original study but the essence of the experiment was maintained. Results suggest that there are many differences in how the participants in this study viewed the various robot behaviours when compared to how participants viewed the guides in the original study. As such no conclusive statements can be made about the overall suitability of VR as a platform for conducting HRI studies. © 2020 ACM.","Human-robot interaction (hri); Replication; Trust; Virtual reality (vr)","Agricultural robots; Man machine systems; Virtual reality; Human robot Interaction (HRI); Tour guide; Human robot interaction",,,,,"Engineering and Physical Sciences Research Council, EPSRC; University of Bristol","This project was funded by the EPSRC and part of the FARSCOPE CDT at the Bristol Robotics Laboratory, University of the West of England and University of Bristol",,,,,,,,,,"Duguleana, M., Grigorie, B.F., Mogan, G., Evaluating human-robot interaction during a manipulation experiment conducted in immersive virtual reality (2011) International Conference on Virtual and Mixed Reality. Springer, pp. 164-173; Flook, R., Shrinah, A., Wijnen, L., Eder, K., Melhuish, C., Lemaignan, S., On the impact of different types of errors on trust in human-robot interaction: Are laboratory-based HRI experiments trustworthy (2019) Interaction Studies, 20 (3), pp. 455-486. , 2019; Kahn, P.H., Jr., Kanda, T., Ishiguro, H., Gill, B.T., Shen, S., Gary, H.E., Ruckert, J.H., Will people keep the secret of a humanoid robot: Psychological intimacy in hri (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 173-180; Sanchez-Vives, M.V., Slater, M., From presence to consciousness through virtual reality (2005) Nature Reviews Neuroscience, 6 (4), p. 332. , 2005; Schuemie, M.J., Van Der Straaten, P., Krijn, M., Van Der Mast, C.A., Research on presence in virtual reality: A survey (2001) CyberPsychology & Behavior, 4 (2), pp. 183-201. , 2001; Weistroffer, V., Paljic, A., Fuchs, P., Hugues, O., Chodacki, J., Ligot, P., Morais, A., Assessing the acceptability of human-robot co-presence on assembly lines: A comparison between actual situations and their virtual reality counterparts (2014) The 23rd IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 377-384; Wingen, T., Berkessel, J., Englich, B., (2019) No Replication, No Trust How Low Replicability Influences Trust in Psychology, , 2019",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083168765
"De Carolis B., Gena C., Lieto A., Rossi S., Sciutti A.","57213789426;6507316953;36782747200;55619320887;36957816700;","Workshop on adapted interaction with social robots (cAESAR)",2020,"International Conference on Intelligent User Interfaces, Proceedings IUI",,,,"3","4",,1,"10.1145/3379336.3379360","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082172723&doi=10.1145%2f3379336.3379360&partnerID=40&md5=1776e9db2de06e107d1485280513ad91","Department of Computer Science, University of Bari, Bari, Italy; Department of Computer Science, University of Torino Torino, Italy; Electrical Engineering and Information Technologies Department, University of Napoli Federico II, Italy; Italian Institute of Technology, Genova, Italy","De Carolis, B., Department of Computer Science, University of Bari, Bari, Italy; Gena, C., Department of Computer Science, University of Torino Torino, Italy; Lieto, A., Department of Computer Science, University of Torino Torino, Italy; Rossi, S., Electrical Engineering and Information Technologies Department, University of Napoli Federico II, Italy; Sciutti, A., Italian Institute of Technology, Genova, Italy","Human Robot Interaction (HRI) is a field of study dedicated to understanding, designing, and evaluating robotic systems for use by, or with, humans. In HRI there is a consensus about the design and implementation of robotic systems that should be able to adapt their behaviour on the basis of user actions and behaviour. The robot should adapt to emotions, personalities, and it should also have memory of past interactions with the user in order to become believable. This is of particular importance in the field of social robotics and social HRI. The aim of this Workshop is to bring together researchers and practitioners who are working on various aspects of social robotics and adaptive interaction. © 2020 International Conference on Intelligent User Interfaces, Proceedings IUI. All rights reserved.","Adaptivity; HRI; Human Behavior Understanding; Social Robots","Behavioral research; Robotics; User interfaces; Adaptive interaction; Adaptivity; Design and implementations; Human behavior understanding; Human robot Interaction (HRI); Robotic systems; Social robotics; User action; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 804388; European Research Council, ERC","This workshop will be organized in the framework of project wHISPER (investigating Human Shared PErception with Robots) funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 804388).",,,,,,,,,,"Alonso, R., Concas, E., Reforgiato Recupero, D., Holistic, A., Flexible and Scalable Social Robot Architecture Employing Voice Assistant Technologies; Ahmad, M.I., Mubin, O., Orlando, J., (2017) A Systematic Review of Adaptivity in Human-Robot Interaction; Brelstaff, G., Brelstaff, A., Affective HRI: A Potential Impedance Mismatch; Costantini, S., De Gasperis, G., Di Mascio, T., Migliarini, P., Salutari, A., Proposal of A Empathic Multi-agent Robot Design Based on Theory of Mind; De Carolis, B.N., Lops, P., Cataldo, M., Semeraro, G., Towards A Social Robot As Interface for Tourism Recommendations; Forbrig, P., Alexandru Bundea, Q., Using the Social Humanoid Robot Pepper for Training Tasks; Gena, C., Mattutino, C., Brighenti, S., Nazzario, M., Social Assistive Robotics for Autistic Children; Gena, C., Cena, F., Mattutino, C., Botta, M., Cloudbased User Modeling for Social Robots: A First Attempt; Goodrich, M.A., Schultz, A.C., Human-robot interaction: A survey (2007) Found. Trends Hum.-Comput. Interact., 1 (3), pp. 203-275. , https://doi.org/10.1561/1100000005, (January 2007); Manca, M., Paterno, F., Santoro, C., Zedda, E., Adaptation in Humanoid Robots Serious Games with for Mild Cognitive Impairment Older Adults; Manzi, F., Di Dio, C., Itakura, S., Kanda, T., Ishiguro, H., Davide Massaro and Antonella Marchetti, Moral Evaluation of Human and Robot Interactions in Japanese Preschoolers; Sciutti, A., Mara, M., Tagliasco, V., Sandini, G., Humanizing human-robot interaction: On the importance of mutual understanding (2018) IEEE Technology and Society Magazine, 37 (1), pp. 22-29. , March; Wecker, A., Lavee, M., Social Robots and Religion: The Case of Judaism",,,"ACM Special Interest Group on Artificial Intelligence (SIGAI);Specialist Interest Group in Computer-Human Interaction of the ACM (SIGCHI)","Association for Computing Machinery","25th International Conference on Intelligent User Interfaces, IUI 2020","17 March 2020 through 20 March 2020",,158231,,9781450375139,,,"English","Int Conf Intell User Interfaces Proc IUI",Conference Paper,"Final","",Scopus,2-s2.0-85082172723
"Ghosh A., Soto D.A.P., Veres S.M., Rossiter A.","56729623500;57196546622;35579593700;7103035939;","Human robot interaction for future remote manipulations in industry 4.0",2020,"IFAC-PapersOnLine","53",,,"10223","10228",,2,"10.1016/j.ifacol.2020.12.2752","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107780953&doi=10.1016%2fj.ifacol.2020.12.2752&partnerID=40&md5=269a1536446a404bf8e3799e0d33142a","The University of Sheffield, Sheffield, S1 3JD, United Kingdom","Ghosh, A., The University of Sheffield, Sheffield, S1 3JD, United Kingdom; Soto, D.A.P., The University of Sheffield, Sheffield, S1 3JD, United Kingdom; Veres, S.M., The University of Sheffield, Sheffield, S1 3JD, United Kingdom; Rossiter, A., The University of Sheffield, Sheffield, S1 3JD, United Kingdom","In the nuclear industry it is still common to rely on tele-operated robots. Tele-operation however can be strenuous and demanding on operating personnel and productivity can be low without advanced HRI interfaces. Today, the world is moving towards Industry 4.0. With that vision, this paper introduces the concept of Remotely Instructed Robots (RIRs), which are reliable yet rely on human intelligence. RIRs can accept high and low level instructions from the operator and execute tasks based on operators' descriptions and at a variety of complexity levels. The paper outlines an agent model of RIRs and furthermore, presents how it could be implemented inside nuclear gloveboxes to achieve novel human robot interaction. Copyright © 2020 The Authors.","Digital twin; Industry4.0; Remotely Instructed robots; Safe Human-robot Interaction","Industrial robots; Industry 4.0; Man machine systems; Nuclear industry; Agent model; Complexity levels; Gloveboxes; Human intelligence; Operating personnel; Remote manipulation; Teleoperated robots; Social robots",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/R026084/1","This work was supported by EPSRC Grant No. EP/R026084/1, Robotics and Artificial Intelligence for Nuclear (RAIN), UK.",,,,,,,,,,"Aitken, J.M., Veres, S.M., Shaukat, A., Gao, Y., Cucco, E., Dennis, L.A., Fisher, M., Mort, P.E., Autonomous nuclear waste management (2018) IEEE Intelligent Systems, 33 (6), pp. 47-55; Allspaw, J., Roche, J., Lemiesz, N., Yannuzzi, M., Yanco, H.A., Remotely teleoperating a humanoid robot to perform fine motor tasks with virtual reality- (2018) Proceedings of the 1st International Workshop on Virtual, Augmented, and Mixed Reality for HRI (VAMHRI).; Baccarini, M., Maravita, A., (2013) Beyond the Boundaries of the Hand: Plasticity of Body-space Interactions following Tool-use.; Cancedda, L., Cannavò, A., Garofalo, G., Lamberti, F., Montuschi, P., Paravati, G., Mixed realitybased user interaction feedback for a hand-controlled interface targeted to robot teleoperation (2017) International Conference on Augmented Reality, Virtual Reality and Computer Graphics, pp. 447-463. , Springer; Davis, J., Edgar, T., Graybill, R., Korambath, P., Schott, B., Swink, D., Wang, J., Wetzel, J., Smart manufacturing (2015) Annual Review of Chemical and Biomolecular Engineering, 6, pp. 141-160; Ghosh, A., Veres, S.M., Paredes-Soto, D., Clarke, J.E., Rossiter, J.A., Intuitive programming with remotely instructed robots inside future gloveboxes (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 209-211; Heyer, C., Human-robot interaction and future industrial robotics applications (2010) Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on, pp. 4749-4754. , IEEE; Hokayem, P.F., Spong, M.W., Bilateral teleoperation: An historical survey (2006) Automatica, 42 (12), pp. 2035-2057; Hu, H., Li, J., Xie, Z., Wang, B., Liu, H., Hirzinger, G., A robot arm/hand teleoperation system with telepresence and shared control (2005) Proceedings, 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics., pp. 1312-1317. , IEEE; Jang, I., Carrasco, J., Weightman, A., Lennox, B., Intuitive bare-hand teleoperation of a robotic manipulator using virtual reality and leap motion (2019) Annual Conference towards Autonomous Robotic Systems, pp. 283-294. , Springer; Kahol, K., Leyba, M.J., Deka, M., Deka, V., Mayes, S., Smith, M., Ferrara, J.J., Panchanathan, S., Effect of fatigue on psychomotor and cognitive skills (2008) The American Journal of Surgery, 195 (2), pp. 195-204; King, R., Hamilton, D., Augmented virtualised reality-applications and benefits in remote handling for fusion (2009) Fusion Engineering and Design, 84 (2-6), pp. 1055-1057; Lii, N.Y., Chen, Z., Pleintinger, B., Borst, C.H., Hirzinger, G., Schiele, A., Toward understanding the effects of visual-and force-feedback on robotic hand grasping performance for space teleoperation (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3745-3752. , IEEE; Lincoln, N.K., Veres, S.M., Natural language programming of complex robotic bdi agents (2013) Journal of Intelligent & Robotic Systems, 71 (2), pp. 211-230; Nur, N.M., Dawal, S.Z.M., Dahari, M., Sanusi, J., Muscle activity, time to fatigue, and maximum task duration at different levels of production standard time (2015) Journal of Physical Therapy Science, 27 (7), pp. 2323-2326; Poupyrev, I., Ichikawa, T., Manipulating objects in virtual worlds: Categorization and empirical evaluation of interaction techniques (1999) Journal of Visual Languages & Computing, 10 (1), pp. 19-35; Rademaker, R.L., Wu, D.A., Bloem, I.M., Sack, A.T., Intensive tool-practice and skillfulness facilitate the extension of body representations in humans (2014) Neuropsychologia, 56, pp. 196-203; Rakita, D., Mutlu, B., Gleicher, M., (2018) An Autonomous Dynamic Camera Method for e Ective Remote Teleoperation.; Rao, A.S., Agentspeak (l): Bdi agents speak out in a logical computable language (1996) European Workshop on Modelling Autonomous Agents in A Multi-Agent World, pp. 42-55. , Springer; Rollow, T., Type a accident investigation of the march 16, 2000 plutonium-238 multiple intake event at the plutonium facility, los alamos national laboratory, new mexico, united states department of energy, office of oversight (2000) Safety and Health.; Seed, A., Byrne, R., Animal tool-use (2010) Current Biology, 20 (23), pp. R1032-R1039; Talha, M., Ghalamzan, E.A.M., Takahashi, C., Kuo, J., Ingamells, W., Stolkin, R., Towards robotic decommissioning of legacy nuclear plant: Results of human-factors experiments with tele-robotic manipulation, and a discussion of challenges and approaches for decommissioning (2016) 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 166-173; Talha, M., Ghalamzan, E., Takahashi, C., Kuo, J., Ingamells, W., Stolkin, R., Towards robotic decommissioning of legacy nuclear plant: Results of human-factors experiments with tele-robotic manipulation, and a discussion of challenges and approaches for decommissioning (2016) 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp. 166-173. , IEEE; Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui, F., Digital twin-driven product design, manufacturing and service with big data (2018) The International Journal of Advanced Manufacturing Technology, 94 (9-12), pp. 3563-3576; Thoben, K.D., Wiesner, S., Wuest, T., Industrie 4.0"" and smart manufacturing - A review of research issues and application examples (2017) International Journal of Automation Technology, 11 (1), pp. 4-16; Wang, L., Törngren, M., Onori, M., Current status and advancement of cyber-physical systems in manufacturing (2015) Journal of Manufacturing Systems, 37, pp. 517-527; Whitney, D., Rosen, E., Phillips, E., Konidaris, G., Tellex, S., Comparing robot grasping teleoperation across desktop and virtual reality with ros reality (2017) Proceedings of the International Symposium on Robotics Research.; Wooldridge, M., (2009) An Introduction to Multiagent Systems, , John Wiley & Sons",,,,"Elsevier B.V.","21st IFAC World Congress 2020","12 July 2020 through 17 July 2020",,145388,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85107780953
"Cha E., Fitter N.T., Kim Y., Fong T., Matarić M.","55638511800;55921515500;56066777600;7102098734;7005611984;","Generating Expressive Light Signals for Appearance-Constrained Robots",2020,"Springer Proceedings in Advanced Robotics","11",,,"595","607",,,"10.1007/978-3-030-33950-0_51","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107029883&doi=10.1007%2f978-3-030-33950-0_51&partnerID=40&md5=7a65ef0c834898374ad2529d488178f9","University of Southern California, Los Angeles, CA  90089, United States; NASA Ames Research Center, Mountain View, CA  94035, United States; Oregon State University, Corvallis, OR  97331, United States","Cha, E., University of Southern California, Los Angeles, CA  90089, United States; Fitter, N.T., University of Southern California, Los Angeles, CA  90089, United States, Oregon State University, Corvallis, OR  97331, United States; Kim, Y., NASA Ames Research Center, Mountain View, CA  94035, United States; Fong, T., NASA Ames Research Center, Mountain View, CA  94035, United States; Matarić, M., University of Southern California, Los Angeles, CA  90089, United States","Our goal is to enable robots to communicate information about their internal state using expressive light signals. Since nonverbal cues are typically given context by a robot’s other actions, we combined light signals, varying their color, pattern, and frequency, with robot base motion and investigated the effects of these nonverbal signals on human observers’ attribution of certain task-related properties (e.g., urgency and safety) to the robot. The results show that variations in light signal parameters affect human attribution of common notification properties. Using these findings, we present a first step towards systematically generating new light signals that can convey different state-related properties relevant for human-robot interaction, validated in a video-based study. © 2020, Springer Nature Switzerland AG.",,,,,,,,,,,,,,,,,,"Arrabito, G.R., Mondor, T.A., Kent, K.J., Judging the urgency of non-verbal auditory alarms: A case study (2004) Ergonomics, 47 (8), pp. 821-840; Baraka, K., Paiva, A., Veloso, M., Expressive lights for revealing mobile service robot state (2015) Robot 2015: Second Iberian Robotics Conference, pp. 107-119. , , pp. , Springer, Cham; Baraka, K., Rosenthal, S., Veloso, M., Enhancing human understanding of a mobile robot’s state and actions using expressive lights (2016) IEEE International Symposium on Robot and Human Interactive Communication, pp. 652-657. , , pp. , IEEE; Cha, E., Kim, Y., Fong, T., Matarić, M.J., A survey of nonverbal signaling methods for non-humanoid robots (2018) Found. Trends Robot., 6 (4), pp. 211-323; Cha, E., Matarić, M., Using nonverbal signals to request help during human-robot collaboration (2016) IEEE/RSJ International Conference on Intelligent Robots and Systems, Pp. 5070–5076. IEEE; Cohn, T.E.: Method and apparatus for enhancing visual perception of display lights, warning lights and the like, and of stimuli used in testing for ocular disease, 20 January 1998. US Patent 5,710,560 (1998); Dautenhahn, K., Walters, M., Woods, S., Koay, K.L., Nehaniv, C.L., Sisbot, A., Alami, R., Siméon, T., How may I serve you? A robot companion approaching a seated person in a helping context (2006) ACM/IEEE International Conference on Human-Robot Interaction, pp. 172-179. , , pp. , ACM; Dragan, A.D., Lee, K.C.T., Srinivasa, S.S., Legibility and predictability of robot motion (2013) ACM/IEEE International Conference on Human-Robot Interaction, Pp. 301–308. IEEE; Guillaume, A., Drake, C., Rivenez, M., Pellieux, L., Chastres, V., Perception of urgency and alarm design (2002) International Conference on Auditory Display. Else-Vier; Harrison, C., Horstman, J., Hsieh, G., Hudson, S., Unlocking the expressivity of point lights (2012) ACM Conference on Human Factors in Computing Systems, pp. 1683-1692. , , pp. , ACM; Lewis, C.F., McBeath, M.K., Bias to experience approaching motion in a three-dimensional virtual environment (2004) Perception, 33 (3), pp. 259-276; McCrickard, D.S., Chewar, C.M., Somervell, J.P., Ndiwalana, A., A model for notification systems evaluation-assessing user goals for multitasking activity (2003) ACM Trans. Comput. Hum. Interact., 10 (4), pp. 312-338; Pousman, Z., Stasko, J., A taxonomy of ambient information systems: Four patterns of design (2006) Working Conference on Advanced Visual Interfaces, pp. 67-74. , , pp. , ACM; Song, S., Yamada, S., Effect of expressive lights on human perception and interpretation of functional robot (2018) Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, p. LBW629. , , p. , ACM; Szafir, D., Mutlu, B., Fong, T., Communicating directionality in flying robots (2015) ACM/IEEE International Conference on Human-Robot Interaction, pp. 19-26. , , pp. , ACM; Szafir, D.J., (2015) Human Interaction with Assistive Free-Flying Robots, , PhD thesis; Wool, L.E., Komban, S.J., Kremkow, J., Jansen, M., Li, X., Alonso, J.M., Zaidi, Q., Salience of unique hues and implications for color theory (2015) J. Vis., 15 (2), p. 10; Zhou, A., Hadfield-Menell, D., Nagabandi, A., Dragan, A.D., Expressive robot motion timing (2017) ACM/IEEE International Conference on Human-Robot Interaction, pp. 22-31. , , pp., ACM","Fitter, N.T.; Oregon State UniversityUnited States; email: naomi.fitter@oregonstate.edu",,,"Springer Science and Business Media B.V.",,,,,25111256,,,,"English","Springer. Proc. Adv. Robot.",Book Chapter,"Final","",Scopus,2-s2.0-85107029883
"Latupeirissa A.B., Panariello C., Bresin R.","57216733848;57216726720;55879760500;","Exploring emotion perception in sonic HRI",2020,"Proceedings of the Sound and Music Computing Conferences","2020-June",,,"434","441",,2,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101259342&partnerID=40&md5=b80d1635779cbb4688cb9249347f3f5e","Sound and Music Computing, KTH Royal Institute of Technology","Latupeirissa, A.B., Sound and Music Computing, KTH Royal Institute of Technology; Panariello, C., Sound and Music Computing, KTH Royal Institute of Technology; Bresin, R., Sound and Music Computing, KTH Royal Institute of Technology","Despite the fact that sounds produced by robots can affect the interaction with humans, sound design is often an overlooked aspect in Human-Robot Interaction (HRI). This paper explores how different sets of sounds designed for expressive robot gestures of a humanoid Pepper robot can influence the perception of emotional intentions. In the pilot study presented in this paper, it has been asked to rate different stimuli in terms of perceived affective states. The stimuli were audio, audio-video and video only and contained either Pepper's original servomotors noises, sawtooth, or more complex designed sounds. The preliminary results show a preference for the use of more complex sounds, thus confirming the necessity of further exploration in sonic HRI. Copyright © 2020 Adrian B. Latupeirissa et al.",,"Audio acoustics; Behavioral research; Machine design; Affective state; Audio videos; Complex sounds; Human robot Interaction (HRI); Pilot studies; Robot gestures; Sound designs; Human robot interaction",,,,,"86892; Vetenskapsrådet, VR","This project was funded by Grant 2017-03979 from the Swedish Research Council and by NordForsk's Nordic University Hub ""Nordic Sound and Music Computing Network - NordicSMC"", project number 86892.","This project was funded by Grant 2017-03979 from the Swedish Research Council and by NordForsk’s Nordic University Hub “Nordic Sound and Music Computing Network - NordicSMC”, project number 86892.",,,,,,,,,"Frid, E., Bresin, R., Alexanderson, S., Perception of Mechanical Sounds Inherent to Expressive Gestures of a NAO Robot-Implications for Movement Sonification of Humanoids (2018) Proceedings of the Sound and Music Computing Conference; Häring, M., Bee, N., André, E., Creation and evaluation of emotion expression with body movement, sound and eye color for humanoid robots (2011) 2011 ROMAN, pp. 204-209. , July; Löffler, D., Schmidt, N., Tscharn, R., Multimodal expression of artificial emotion in social robots using color, motion and sound (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 334-343. , ACM; Song, S., Yamada, S., Expressing emotions through color, sound, and vibration with an appearanceconstrained social robot (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human- Robot Interaction, ser. HRI '17, pp. 2-11. , http://doi.acm.org/10.1145/2909824.3020239, New York, NY, USA: ACM, [Online]. Available; Komatsu, T., Toward making humans empathize with artificial agents by means of subtle expressions (2005) International Conference on Affective Computing and Intelligent Interaction, pp. 458-465. , Springer; Yilmazyildiz, S., Read, R., Belpeame, T., Verhelst, W., Review of semantic-free utterances in social human-robot interaction (2016) International Journal of Human-Computer Interaction, 32 (1), pp. 63-85; McGinn, C., Torre, I., Can you tell the robot by the voice? an exploratory study on the role of voice in the perception of robots (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 211-221. , IEEE; Erden, M., Emotional postures for the humanoid-robot Nao (2013) International Journal of Social Robotics, 5 (4), pp. 441-456; Dael, N., Mortillaro, M., Scherer, K. R., Emotion expression in body action and posture (2012) Emotion, 12 (5), p. 1085; Panariello, C., Sköld, S., Frid, E., Bresin, R., (2019) From vocal sketching to sound models by means of a soundbased musical transcription system, pp. 1-7; Kappas, A., Küster, D., Dente, P., Basedow, C., Simply the B.E.S.T.! Creation and Validation of the Bremen Emotional Sounds Toolkit (2015) International Convention of Psychological Science; Bresin, R., Friberg, A., Emotion rendering in music: Range and characteristic values of seven musical variables (2011) Cortex, 47 (9), pp. 1068-1081; Russell, J. A., A circumplex model of affect (1980) Journal of personality and social psychology, 39 (6), p. 1161; Kynčl, L., Jiříček, O., Psychoacoustic product sound quality evaluation (2001) 17th International Congress on acoustics; Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y. C., de Visser, E. J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527. , https://doi.org/10.1177/0018720811417254, pMID: 22046724. [Online]. Available; Schaefer, K. E., Chen, J. Y. C., Szalma, J. L., Hancock, P. A., A meta-analysis of factors influencing the development of trust in automation: Implications for understanding autonomy in future systems (2016) Human Factors, 58 (3), pp. 377-400. , https://doi.org/10.1177/0018720816634228, pMID: 27005902. [Online]. Available",,"Spagnol S.Valle A.","ARDUINO","CERN","17th Sound and Music Computing Conference, SMC 2020","24 June 2020 through 26 June 2020",,167060,25183672,9788894541502,,,"English","Proc. Sound Music Comput. Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85101259342
"Chen M., Hsu D., Lee W.S.","57190131729;34569904000;55663394400;","Guided Exploration of Human Intentions for Human-Robot Interaction",2020,"Springer Proceedings in Advanced Robotics","14",,,"921","938",,1,"10.1007/978-3-030-44051-0_53","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097163270&doi=10.1007%2f978-3-030-44051-0_53&partnerID=40&md5=84dfea668395dd88d22ba1b71f8c4e8c","National University of Singapore, Singapore, 117417, Singapore","Chen, M., National University of Singapore, Singapore, 117417, Singapore; Hsu, D., National University of Singapore, Singapore, 117417, Singapore; Lee, W.S., National University of Singapore, Singapore, 117417, Singapore","Robot understanding of human intentions is essential for fluid human-robot interaction. Intentions, however, cannot be directly observed and must be inferred from behaviors. We learn a model of adaptive human behavior conditioned on the intention as a latent variable. We then embed the human behavior model into a principled probabilistic decision model, which enables the robot to (i) explore actively in order to infer human intentions and (ii) choose actions that maximize its performance. Furthermore, the robot learns from the demonstrated actions of human experts to further improve exploration. Preliminary experiments in simulation indicate that our approach, when applied to autonomous driving, improves the efficiency and safety of driving in common interactive driving scenarios. © 2020, Springer Nature Switzerland AG.",,,,,,,"National Research Foundation Singapore, NRF; Singapore-MIT Alliance for Research and Technology Centre, SMART: MOE2016-T2-2-068","Acknowledgments. This work is supported in part by the Singapore NRF through the SMART Future Urban Mobility IRG and MOE AcRF grant MOE2016-T2-2-068.",,,,,,,,,,"Abbeel, P., Ng, A.Y., Apprenticeship learning via inverse reinforcement learning (2004) Proceedings of the International Conference on Machine Learning, p. 1. , , p. , ACM; Astington, J.W., The Child’s Discovery of the Mind (1993) Vol, 31. , Harvard University Press, Cambridge; Bai, H., Cai, S., Ye, N., Hsu, D., Lee, W.S., Intention-aware online POMDP planning for autonomous driving in a crowd (2015) Proceedings of the IEEE International Conference on Robotics & Automation, Pp. 454–460. IEEE; Bandyopadhyay, T., Won, K.S., Frazzoli, E., Hsu, D., Lee, W.S., Rus, D., Intention-aware motion planning (2013) Proceedings International Workshop on the Algorithmic Foundations of Robotics, pp. 475-491. , Frazzoli, E., Lozano-Perez, T., Roy, N., Rus, D. (eds.), pp. , Springer, Heidelberg; Bratman, M.: Intention, Plans and Practical Reason. 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Mach. Learn. Res., 12, pp. 2825-2830; Rasmussen, C.E., Gaussian processes in machine learning (2004) Advanced Lectures on Machine Learning, pp. 63-71. , Bousquet, O., von Luxburg, U., Ratsch, G. (eds.), pp. , Springer, Heidelberg; Rubinstein, A., (1998) Modeling Bounded Rationality, , MIT Press, Cambridge; Sadigh, D., Sastry, S.S., Seshia, S.A., Dragan, A., Information gathering actions over human internal state (2016) Proceedings of the IEEE International Conference on Intelligent Robots and Systems, pp. 66-73. , , pp. , IEEE; Sadigh, D., Sastry, S., Seshia, S.A., Dragan, A.D., Planning for autonomous cars that leverage effects on human actions (2016) Proceedings of the Robotics: Science & Systems; Sunberg, Z.N., Ho, C.J., Kochenderfer, M.J., The value of inferring the internal state of traffic participants for autonomous freeway driving (2017) American Control Conference (ACC), pp. 3004-3010. , , pp. , IEEE; Vert, J.P., Tsuda, K., Schölkopf, B., A primer on kernel methods (2004) Kernel Methods Comput. Biol., 47, pp. 35-70","Chen, M.; National University of SingaporeSingapore; email: chenmin@comp.nus.edu.sg",,,"Springer Science and Business Media B.V.",,,,,25111256,,,,"English","Springer. Proc. Adv. Robot.",Book Chapter,"Final","All Open Access, Green",Scopus,2-s2.0-85097163270
[No author name available],[No author id available],"JOWO 2020 - Proceedings of the Joint Ontology Workshops, co-located with the Bolzano Summer of Knowledge, BOSK 2020",2020,"CEUR Workshop Proceedings","2708",,,"","",264,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095607871&partnerID=40&md5=360ed2d6c654e81a1758f59edecc2fd6",,"","The proceedings contain 24 papers. The topics discussed include: rdf triples extraction from company web pages: comparison of state-of-the-art deep models; on the evaluation of retrofitting for supervised short-text classification; semantic enriched deep learning for document classification; bridging the gap between an ontology and deep neural models by pattern mining; what are we debating about when we debate about processes and events?; critical discussion of ZHOU'S 'what the progressive aspect tells us about processes'; on the ontological foundations of cellular development; functional parthood: a dispositional perspective; using linked data to help robots understand product-related actions; and what say you: an ontological representation of imperative meaning for human-robot interaction.",,,,,,,,,,,,,,,,,,,,"Hammar K.Kutz O.Dimou A.Hahmann T.Hoehndorf R.Masolo C.Vita R.",,"CEUR-WS","2020 Joint Ontology Workshops, JOWO 2020","31 August 2020 through 7 October 2020",,164284,16130073,,,,"English","CEUR Workshop Proc.",Conference Review,"Final","",Scopus,2-s2.0-85095607871
"Buono P., Castellano G., Decarolis B., MacChiarulo N.","13006302400;7005355310;57216848767;57204515186;","Social assistive robots in elderly care: Exploring the role of empathy",2020,"CEUR Workshop Proceedings","2702",,,"12","19",,3,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094831887&partnerID=40&md5=9c71a17ac85c7fe16e8b3521eec02d0e","Dipartimento di Informatica, Università Degli Studi di Bari Aldo Moro, Bari, Italy","Buono, P., Dipartimento di Informatica, Università Degli Studi di Bari Aldo Moro, Bari, Italy; Castellano, G., Dipartimento di Informatica, Università Degli Studi di Bari Aldo Moro, Bari, Italy; Decarolis, B., Dipartimento di Informatica, Università Degli Studi di Bari Aldo Moro, Bari, Italy; MacChiarulo, N., Dipartimento di Informatica, Università Degli Studi di Bari Aldo Moro, Bari, Italy","The COVID-19 emergency has shown that elderly people living in Assisted Living Houses (ALHs) have been highly exposed to the virus. Besides health problems, during the social distancing restrictions, the elderly were also strongly affected by loneliness due to a lack of contact with their loved ones. Innovative solutions for ALH based on Social Assistive Robotics can reduce the risk of infection and, at the same time, improve the quality of life of elderly people. In this work, after a brief overview on the Pepper4Elderly project, we focus on the role of empathy and affective behaviors in human-robot interaction when the robot is used as a caring agent to assist and entertain the elderly guests of ALHs. © 2020 CEUR-WS. All rights reserved.","Assistive Living Houses; Emphatic Behaviour Model; Pepper; Social Assistive Robots","Behavioral research; Ecosystems; Internet of things; Social robots; Viruses; Affective behaviors; Assistive robotics; Assistive robots; Elderly care; Elderly people; Exposed to; Innovative solutions; Quality of life; Assisted living",,,,,,,,,,,,,,,,"Li, A. X., Florendo, M., Miller, L. E., Ishiguro, H., Saygin, A. P., Robot form and motion influences social attention (2015) Proc. Int. Conf. on Human-Robot Interaction, HRI '15, pp. 43-50. , https://doi.org/10.1145/2696454.2696478, Association for Computing Machinery, New York, NY, USA; Anzalone, S. M., Boucenna, S., Ivaldi, S., Chetouani, M., Evaluating the engagement with social robots (2015) International Journal of Social Robotics, 7, pp. 465-478; Traeger, M. L., Strohkorb Sebo, S., Jung, M., Scassellati, B., Christakis, N. A., Vulnerable robots positively shape human conversational dynamics in a human- robot team (2020) Proc. of the National Academy of Sciences, 117, pp. 6370-6375. , https://www.pnas.org/content/117/12/6370, arXiv:https://www.pnas.org/content/117/12/6370.full.pdf; Decety, J., Bartal, I., Uzefovsky, F., Knafo-Noam, A., Empathy as a driver of prosocial behaviour: Highly conserved neurobehavioural mechanisms across species (2016) Philosophical Transactions of the Royal Society B: Biological Sciences, 371, p. 20150077; de Vignemont, F., Singer, T., The empathic brain: how, when and why? (2006) Trends in Cognitive Sciences, 10, pp. 435-441; Anderson, C., Keltner, D., The role of empathy in the formation and maintenance of social bonds (2002) Behavioral and Brain Sciences, 25, pp. 21-22; Leite, I., Pereira, A., Mascarenhas, S., Martinho, C., Prada, R., Paiva, A., The influence of empathy in human-robot relations (2012) International Journal of Human-Computer Studies, 71; Paiva, A., Leite, I., Boukricha, H., Wachsmuth, I., Empathy in virtual agents and robots: A survey (2017) ACM Trans. Interact. Intell. Syst, 7. , https://doi.org/10.1145/2912150; Charrier, L., Galdeano, A., Cordier, A., Lefort, M., (2018) Empathy display influence on human-robot interactions: a pilot study; Carolis, B. D., Ferilli, S., Palestra, G., Simulating empathic behavior in a social assistive robot (2017) Multimedia Tools Appl, 76, pp. 5073-5094. , https://doi.org/10.1007/s11042-016-3797-0; Bui, H., Chong, N. Y., An integrated approach to human-robot-smart environment interaction interface for ambient assisted living (2018) 2018 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), pp. 32-37; Napoli, C. D., Rossi, S., A layered architecture for socially assistive robotics as a service (2019) 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 352-357; Casiddu, N., Cesta, A., Cortellessa, G., Orlandini, A., Porfirione, C., Divano, A., Micheli, E., Zallio, M., (2015) Robot Interface Design: The Giraff Telepresence Robot for Social Interaction, 11, pp. 499-509; Manca, M., Paternò, F., Santoro, C., Zedda, E., Braschi, C., Franco, R., Sale, A., The impact of serious games with humanoid robots on mild cognitive impairment older adults (2020) International Journal of Human-Computer Studies; Rao, A. S., George, M. P., Bdi agents: From theory to practice (1995) Proceedings of the First International Conference on Multi-Agent Systems (ICMAS-95), pp. 312-319; Nicholson, A. E., Brady, J. M., Dynamic belief networks for discrete monitoring (1994) IEEE Transactions on Systems, Man, and Cybernetics, 24, pp. 1593-1610; Freksa, C., Fuzzy logic: An interface between logic and human reasoning (1994) IEEE Expert, 9, pp. 20-21; Carolis, B. D., Novielli, N., Recognizing signals of social attitude in interacting with ambient conversational systems (2014) Multimodal User Interfaces, 8, pp. 43-60; Broekens, J., Heerink, M., Rosendal, H., Assistive social robots in elderly care: A review (2009) Gerontechnology, 8, pp. 94-103; Bahadori, S., Cesta, A., Grisetti, G., Iocchi, L., Leone, R., Nardi, D., Oddi, A., Rasconi, R., Robocare: Pervasive intelligence for the domestic care of the elderly (2003) AI∗IA Magazine Special Issue; Chen, N., Song, J., Li, B., Providing aging adults social robots' companionship in home-based elder care (2019) Journal of Healthcare Engineering, 2019, pp. 1-7; Picard, R., (2004) Toward machines with emotional intelligence, pp. 29-30; Baron-Cohen, S., (2011) The Science of Evil: On Empathy and the Origins of Cruelty, , Basic Books, NY; Paiva, A., Leite, I., Boukricha, H., Wachsmuth, I., Empathy in virtual agents and robots: A survey (2017) ACM Trans. Interact. Intell. Syst, 7. , https://doi.org/10.1145/2912150; Guo, G., Guo, R., Li, X., Facial expression recognition influenced by human aging (2013) IEEE Transactions on Affective Computing, 4, pp. 291-298; Wang, S., Wu, S., Gao, Z., Ji, Q., Facial expression recognition through modeling age-related spatial patterns (2015) Multimedia Tools and Applications, 75, pp. 3937-3954; Yu, D., Deng, L., Deep learning and its applications to signal and information processing [exploratory dsp] (2011) IEEE Signal Processing Magazine, 28, pp. 145-154; Ebner, N., Riediger, M., Lindenberger, U., Faces - a database of facial expressions in young, middle-aged, and older women and men: Development and validation (2010) Behavior research methods, 42, pp. 351-362",,"Desolda G.Deufemia V.Gena C.Matera M.Paterno F.Treccani B.",,"CEUR-WS","1st International Workshop on Empowering People in Dealing with Internet of Things Ecosystems, EMPATHY 2020","29 September 2020",,164030,16130073,,,,"English","CEUR Workshop Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85094831887
"Roesler O.","57208167935;","A Cross-Situational Learning Based Framework for Grounding of Synonyms in Human-Robot Interactions",2020,"Advances in Intelligent Systems and Computing","1093 AISC",,,"225","236",,,"10.1007/978-3-030-36150-1_19","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079100022&doi=10.1007%2f978-3-030-36150-1_19&partnerID=40&md5=d533e6fe2f9dfb1cc5115ded730007c0","Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, 1050, Belgium","Roesler, O., Artificial Intelligence Lab, Vrije Universiteit Brussel, Brussels, 1050, Belgium","Natural human-robot interaction requires robots to link words to objects and actions through grounding. Although grounding has been investigated in previous studies, not many considered grounding of synonyms and the majority of employed models only worked offline. In this paper, we try to fill this gap by introducing an online learning framework for grounding synonymous object and action names using cross-situational learning. Words are grounded through geometric characteristics of objects and kinematic features of the robot joints during action execution. An interaction experiment between a human tutor and HSR robot is used to evaluate the proposed framework. The results show that the employed framework is able to successfully ground all used words. © 2020, Springer Nature Switzerland AG.","Cross-situational learning; Human-robot interaction; Language grounding","Man machine systems; Robotics; Action execution; Cross-situational learning; Geometric characteristics; Language grounding; Offline; Online learning; Robot joint; Human robot interaction",,,,,,,,,,,,,,,,"Aly, A., Taniguchi, A., Taniguchi, T., A generative framework for multimodal learning of spatial concepts and object categories: An unsupervised part-of-speech tagging and 3D visual perception based approach (2017) IEEE International Conference on Development and Learning and the International Conference on Epigenetic Robotics (Icdl-Epirob), , Lisbon, Portugal, September; Blythe, R.A., Smith, K., Smith, A.D.M., Learning times for large lexicons through cross-situational learning (2010) Cogn. Sci., 34, pp. 620-642; Clark, E.V., The principle of contrast: A constraint on language acquisition (1987) Mechanisms of Language Acquisition, pp. 1-33. , Lawrence Erlbaum Associates; Craye, C., Filliat, D., Goudou, J.F., Environment exploration for object-based visual saliency learning (2016) IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, May; Dawson, C.R., Wright, J., Rebguns, A., Escárcega, M.V., Fried, D., Cohen, P.R., A generative probabilistic framework for learning spatial language IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL), , Osaka, Japan, August 2013; Ester, M., Kriegel, H.P., Sander, J., Xu, X., A density-based algorithm for discovering clusters in large spatial databases with noise (1996) Proceedings of the 2Nd International Conference on Knowledge Discovery and Data Mining (KDD), pp. 226-231. , Portland, Oregon, USA, August; Filin, S., Pfeifer, N., Segmentation of airborne laser scanning data using a slope adaptive neighborhood (2006) ISPRS J. Photogram. Remote Sens. (P&RS), 60, pp. 71-80; Fischler, M.A., Bolles, R.C., Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography (1981) Commun. ACM (CACM), 24 (6), pp. 381-395; Fisher, C., Hall, D.G., Rakowitz, S., Gleitman, L., When it is better to receive than to give: Syntactic and conceptual constraints on vocabulary growth (1994) Lingua, 92, pp. 333-375; Fontanari, J.F., Tikhanoff, V., Cangelosi, A., Ilin, R., Perlovsky, L.I., Cross-situational learning of object-word mapping using neural modeling fields (2009) Neural Netw, 22 (5-6), pp. 579-585; Fontanari, J.F., Tikhanoff, V., Cangelosi, A., Perlovsky, L.I., A cross-situational algorithm for learning a lexicon using neural modeling fields (2009) International Joint Conference on Neural Networks (IJCNN), Atlanta, GA, USA, June; Harnad, S., The symbol grounding problem (1990) Physica D, 42, pp. 335-346; Hubert, L., Arabie, P., Comparing partitions (1985) J. Classif., 2 (1), pp. 193-218; (2017) International Federation of Robotics: World Robotics 2017-Service Robots; Kemp, C.C., Edsinger, A., Torres-Jara, E., Challenges for robot manipulation in human environments (2007) IEEE Robot. Autom. Mag., 14 (1), pp. 20-29; Koster, K., Spann, M., MIR: An approach to robust clustering-application to range image segmentation (2000) IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI), 22 (5), pp. 430-444; Nguyen, A., Le, B., 3D point cloud segmentation: A survey (2013) 6Th IEEE Conference on Robotics, Automation and Mechatronics (RAM), , IEEE, Manila, November; Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Duchesnay, E., Scikit-learn: Machine learning in python (2011) J. Mach. Learn. Res., 12, pp. 2825-2830; Pinker, S., (1989) Learnability and Cognition, , MIT Press, Cambridge; Roesler, O., Aly, A., Taniguchi, T., Hayashi, Y., A probabilistic framework for comparing syntactic and semantic grounding of synonyms through cross-situational learning ICRA-18 Workshop on Representing a Complex World: Perception, Inference, and Learning for Joint Semantic, Geometric, and Physical Understanding, , Brisbane, Australia, May 2018; Roesler, O., Aly, A., Taniguchi, T., Hayashi, Y., Evaluation of word representations in grounding natural language instructions through computational human-robot interaction (2019) Proceedings of the 14Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), , Daegu, South Korea, March; Rusu, R.B., Bradski, G., Thibaux, R., Hsu, J., Fast 3D recognition and pose using the viewpoint feature histogram (2010) Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan, pp. 2155-2162. , October; Sappa, A.D., Devy, M., Fast range image segmentation by an edge detection strategy (2002) Proceedings of the Third International Conference on 3-D Digital Imaging and Modeling (3DIM), , Quebec City, Quebec, Canada, August; Schnabel, R., Wahl, R., Klein, R., Efficient ransac for point-cloud shape detection (2007) Comput. Graphics Forum, 26 (2), pp. 214-226; Schubert, E., Sander, J., Ester, M., Kriegel, H.P., Xu, X., DBSCAN revisited, revisited: Why and how you should (still) use DBSCAN (2017) ACM Trans. Database Syst. (TODS), 42 (3), p. 19; She, L., Yang, S., Cheng, Y., Jia, Y., Chai, J.Y., Xi, N., Back to the blocks world: Learning new actions through situated human-robot dialogue (2014) Proceedings of the SIGDIAL 2014 Conference, pp. 89-97. , Philadelphia, U.S.A., June; Siskind, J.M., A computational study of cross-situational techniques for learning word-to-meaning mappings (1996) Cognition, 61, pp. 39-91; Smith, A.D.M., Smith, K., (2012) Cross-Situational Learning, pp. 864-866. , https://doi.org/10.1007/978-1-4419-1428-61712, Springer, Boston; Smith, K., Smith, A.D.M., Blythe, R.A., Cross-situational learning: An experimental study of word-learning mechanisms (2011) Cogn. Sci., 35 (3), pp. 480-498; Steels, L., Loetzsch, M., The grounded naming game (2012) Experiments in Cultural Language Evolution, pp. 41-59. , Steels, L. (ed.) , John Benjamins, Amsterdam; Strom, J., Richardson, A., Olson, E., Graph-based segmentation for colored 3D laser point clouds (2010) International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan; Taniguchi, A., Taniguchi, T., Cangelosi, A., Cross-situational learning with Bayesian generative models for multimodal category and word learning in robots (2017) Front. Neurorobot., 11, p. 66; Tellex, S., Kollar, T., Dickerson, S., Walter, M.R., Banerjee, A.G., Teller, S., Roy, N., Approaching the symbol grounding problem with probabilistic graphical models (2011) AI Mag, 32 (4), pp. 64-76; (2017) HSR Manual, , 2017.4.17 edn., April","Roesler, O.; Artificial Intelligence Lab, Belgium; email: oliver@roesler.co.uk","Silva M.F.Luis Lima J.Reis L.P.Sanfeliu A.Tardioli D.",,"Springer","4th Iberian Robotics Conference, ROBOT 2019","20 November 2019 through 22 November 2019",,236649,21945357,9783030361495,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85079100022
"Nicolis D.","57195351043;","A General Framework for Shared Control in Robot Teleoperation with Force and Visual Feedback",2020,"SpringerBriefs in Applied Sciences and Technology",,,,"119","131",,,"10.1007/978-3-030-32094-2_9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073222643&doi=10.1007%2f978-3-030-32094-2_9&partnerID=40&md5=01bfeff7fc9ce07bca5a3e385030cc21","Politecnico di Milano, Piazza Leonardo da Vinci, 32, Milano, 20133, Italy","Nicolis, D., Politecnico di Milano, Piazza Leonardo da Vinci, 32, Milano, 20133, Italy","In the last decade, the topic of human robot interaction has received increasing interest from research and industry, as robots must now interface with human users to accomplish complex tasks. In this scenario, robotics engineers are required to take the human component into account in the robot design and control. This is especially true in telerobotics, where interaction with the user plays an important role in the controlled system stability. By means of a thorough analysis and practical experiments, this contribution aims at giving a concrete idea of the aspects that need to be considered in the design of a complete control framework for teleoperated systems, that are able to seamlessly integrate with a human operator through shared control. © 2020, The Author(s).",,"Feedback; Industrial research; Machine design; System stability; Visual communication; Visual servoing; Complete control; Controlled system; Human components; Human operator; Robot teleoperation; Shared control; Tele-operated systems; Visual feedback; Human robot interaction",,,,,,,,,,,,,,,,"Abbott, J.J., Virtual fixtures for bilateral telemanipulation (2005) Mechanical Engineering, , The Johns Hopkins University; Abi-Farraj, F., Pedemonte, N., Giordano, P.R., A visual-based shared control architecture for remote telemanipulation (2016) IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS’16; Ahola, J.M., Koskinen, J., Seppälä, T., Heikkilä, T., Development of impedance control for human/robot interactive handling of heavy parts and loads (2015) Proceedings of the International Conference on ASME 2015 International Design Engineering Technical Conference and Computers and Information in Engineering Conference (IDETC/CIE), p. 8. , IEEE, Boston, MA; Anderson, R.J., Spong, M.W., Bilateral control of teleoperators with time delay (1989) IEEE Trans Autom Control, 34 (5), pp. 494-501; Andrea, Z., Andrea, C., Luigi, P., Paolo, R., (2018) Prediction of Human Activity Patterns for Human-Robot Collaborative Assembly Tasks, , IEEE Trans Ind Inform; Bartolini, G., Ferrara, A., Levant, A., Usai, E., On second order sliding mode controllers (1999) Variable Structure Systems, Sliding Mode and Nonlinear Control. Springer, Berlin, pp. 329-350. , pp; Chatelain, P., Krupa, A., Navab, N., Optimization of ultrasound image quality via visual servoing (2015) 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 5997-6002. , pp, IEEE; Hirche, S., Ferre, M., Barrio, J., Melchiorri, C., Buss, M., Bilateral control architectures for telerobotics (2007) Advances in Telerobotics. Springer, Berlin, pp. 163-176. , pp; Hogan, N., Impedance control: An approach to manipulation: Part ii-implementation (1985) J Dyn Syst, Meas, Control, 107 (1), pp. 8-16; Lawrence, D.A., Stability and transparency in bilateral teleoperation (1993) IEEE Trans Robot Autom, 9 (5), pp. 624-637; Negrello, F., Settimi, A., Caporale, D., Lentini, G., Poggiani, M., Kanoulas, D., Muratore, L., Ciarleglio, L., (2018) Humanoids at Work, , IEEE Robot Autom Mag; Nicolis, D., Palumbo, M., Zanchettin, A.M., Rocco, P., Occlusion-free visual servoing for the shared autonomy teleoperation of dual-arm robots (2018) IEEE Robot Autom Lett, 3 (2), pp. 796-803; Nicolis, D., Zanchettin, A.M., Rocco, P., A hierarchical optimization approach to robot teleoperation and virtual fixtures rendering (2017) Ifac-Papersonline, 50 (1), pp. 5672-5679. , 2017. 20th IFAC World Congress; Nicolis, D., Zanchettin, A.M., Rocco, P., Human intention estimation based on neural networks for enhanced collaboration with robots (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1326-1333. , pp, IEEE; Peer, A., Hirche, S., Weber, C., Krause, I., Buss, M., Miossec, S., Evrard, P., Kheddar, A., Intercontinental multimodal tele-cooperation using a humanoid robot (2018) 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 405-411. , pp, IEEE; Ryu, J.-H., Artigas, J., Preusche, C., A passive bilateral control scheme for a teleoperator with time-varying communication delay (2010) Mechatronics, 20 (7), pp. 812-823; Schmaus, P., Leidner, D., Krüger, T., Schiele, A., Pleintinger, B., Bayer, R., Lii, N.Y., Preliminary insights from the meteron supvis justin space-robotics experiment (2018) IEEE Robot Autom Lett, 3 (4), pp. 3836-3843; Shibuya Hoppman. Shibuya Hoppman Webpage, pp. 2012-2016; (2017), http://www.smartsurg-project.eu/; Utkin, V.I., (2013) Sliding Modes in Control and Optimization, , Springer Science & Business Media, Berlin; Vrublevskis, J., Berthoud, L., Guest, M., Smith, C., Bennett, A., Gaubert, F., Schroeven-Deceuninck, H., van Winnendael, M., (2018) Description of European Space Agency (ESA) Concept Development for a Mars Sample Receiving Facility (MSRF), p. 2071. , Second international mars sample return; Zeestraten, M.J.A., Havoutis, I., Calinon, S., Programming by demonstration for shared control with an application in teleoperation (2018) IEEE Robot Autom Lett, 3, pp. 1848-1855","Nicolis, D.; Politecnico di Milano, Piazza Leonardo da Vinci, 32, Italy; email: davide.nicolis@polimi.it",,,"Springer Verlag",,,,,2191530X,,,,"English","SpringerBriefs Appl. Sci. Technol.",Book Chapter,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85073222643
"Gordić Z., Jovanović K.","57192680971;36137004200;","Influence of Unmodelled External Forces on the Quality of Collision Detection",2020,"Advances in Intelligent Systems and Computing","980",,,"319","328",,,"10.1007/978-3-030-19648-6_37","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065994541&doi=10.1007%2f978-3-030-19648-6_37&partnerID=40&md5=0e5240f110258fd1225c34d73ce19e81","School of Electrical Engineering, University of Belgrade, Belgrade, 11000, Serbia","Gordić, Z., School of Electrical Engineering, University of Belgrade, Belgrade, 11000, Serbia; Jovanović, K., School of Electrical Engineering, University of Belgrade, Belgrade, 11000, Serbia","Physical Human-Robot Interaction requires collision detection to enable a safe sharing of workspace between humans and robots, mostly using model based algorithms. Majority of robot tasks involve physical interaction with the environment, and consequently the forces occurring during the interaction. This paper presents an experimental testing of influence of unmodelled but intentional forces on the quality of collision detection algorithms. Results from testing of different manipulation and assembly tasks are shown and discussed in terms of their significance to collision detection and similarity with real collisions. Presented results and conclusions from this paper may serve as guidelines for future collision detection related work by offering a better understanding and insight on the relevance of intentional external forces. © Springer Nature Switzerland AG 2020.","Collision detection; Industrial robot; Physical Human-Robot Interaction","Industrial robots; Man machine systems; Robotics; Assembly tasks; Collision detection; Collision detection algorithm; Experimental testing; External force; Model-based algorithms; Physical human-robot interactions; Physical interactions; Human robot interaction",,,,,,"The work was partially supported by the Serbian Ministry of education, science, and technological development, grant No. TR35003 and H2020 project “A Pan European Network of Robotics DIHs for Agile Production (DIH²)”.",,,,,,,,,,"Geravand, M., Flacco, F., Luca, A., Human-robot physical interaction and collaboration using an industrial robot with a closed control architecture (2013) In: Proceedings of IEEE International Conference on Robotics and Automation, 2013, pp. 4000-4007. , pp; de Luca, A., Albu-Schäffer, A., Haddadin, S., Hirzinger, G., Collision detection and safe reaction with the DLR-III lightweight manipulator arm (2006) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2006), Beijing, China, pp. 1623-1630. , pp; Yamada, Y., Hirasawa, Y., Huand, S., Umetani, Y., Fail-safe human/robot contact in the safety space (1996) IEEE International Workshop on Robot and Human Communication, pp. 59-64. , pp; de Luca, A., Flacco, F., Integrated control for pHRI: Collision avoidance, detection, reaction and collaboration (2012) Proceedings of 4Th IEEERAS & EMBS Conference on Biomedical Robotics and Biomechatronics, 2012, pp. 288-295; Yamada, Y., Hirasawa, Y., Huang, S., Umetani, Y., Suita, K., Human-robot contact in the safeguarding space (1997) IEEE/ASME Trans. Mech., 2 (4), pp. 230-236; Chen, W., Sun, Y., Huang, Y., (2010) A Collision Detection System for an Assistive Robotic. Communications in Computer and Information Science, pp. 117-123. , pp; Ferraguti, F., Secchi, C., Fantuzzi, C., A tank-based approach to impedance control with variable stiffness (2013) IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, pp. 4948-4953. , pp; Schindlbeck, C., Haddadin, S., Unified passivity-based Cartesian force/impedance control for rigid and flexible joint robots via task-energy tanks (2015) IEEE International Conference on Robotics and Automation (ICRA), Seattle, pp. 440-447. , pp; Bahloul, A., Tliba, S., Chitour, Y., Dynamic parameters identification of an industrial robot with and without payload (2018) 18Th IFAC Symposium on System Identification, SYSID 2018, IFAC, July 2018, Stockholm, pp. 443-448; Swevers, J., Verdonck, W., Schutter, J.D., Dynamic model identification for industrial robots (2007) IEEE Control Syst. Mag., 27 (5), pp. 58-71; Brunot, M., Janot, A., Carillo, F., Garnier, H., A pragmatic and systematic statistical analysis for identification of industrial robots (2017) Proceedings of the IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2017, Munich, Germany, pp. 559-564. , pp; Gautier, M., Numerical calculation of the base inertial parameters of robots (1990) In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 1990, pp. 1020-1025. , pp; Gautier, M., Khalil, W., On the identification of the inertial parameters of robots (1988) In: Proceedings of the IEEE International on Decision and Control (CDC), 1988, pp. 2264-2269. , pp; Gautier, M., Khalil, W., Direct calculation of minimum set of inertial parameters of serial robots (1990) IEEE Trans. Robot. Autom., 6, pp. 368-373; Janot, A., Vandanjon, P.-O., Gautier, M., A generic instrumental variable approach for industrial robot identification (2014) IEEE Trans. Control Syst. Technol., 22 (1), pp. 132-145; Jin, J., Gans, N., Parameter identification for industrial robots with a fast and robust trajectory design approach (2015) Robot. Comput.-Integr. Manuf., 31, pp. 21-29; Khosla, P.K., Kanade, T., Parameter identification of robot dynamics (1985) In: Proceedings of the IEEE International on Decision and Control (CDC), 1985, pp. 1754-1760. , pp; Walter, E., Pronzato, L., (1997) Identification of Parametric Models from Experimental Data. Communications and Control Engineering, , 1st edn. Springer, London; Deniša, M., Gams, A., Ude, A., Petrič, T., Learning compliant movement primitives through demonstration and statistical generalization (2016) IEEE/ASME Trans. Mech., 21 (5), pp. 2581-2594","Gordić, Z.; School of Electrical Engineering, Serbia; email: zavisa@etf.rs","Gorges D.Berns K.","Deutsche Forschungsgemeinschaft;Deutsches Forschungszentrum für Künstliche Intelligenz;Fraunhofer Zentrum Kaiserslautern;Technische Universität Kaiserslautern","Springer Verlag","28th International Conference on Robotics in Alpe-Adria-Danube Region, RAAD 2019","19 June 2019 through 21 June 2019",,225939,21945357,9783030196479,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85065994541
"Ge R., Zheng Z., Tian X., Liao L.","36135213200;57297660600;37011501600;57203993651;","Human-robot interaction: When investors adjust the usage of robo-advisors in peer-to-peer lending",2021,"Information Systems Research","32","3",,"774","785",,1,"10.1287/ISRE.2021.1009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117251988&doi=10.1287%2fISRE.2021.1009&partnerID=40&md5=96aa74994f1ea24d9e8a5c0a9408fd43","Department of Electronic Commerce, Shanghai Business School, Shanghai, 200235, China; Jindal School of Management, University of Texas at Dallas, Richardson, TX  75080, United States; PBC School of Finance, Tsinghua University, Beijing, 100083, China","Ge, R., Department of Electronic Commerce, Shanghai Business School, Shanghai, 200235, China; Zheng, Z., Jindal School of Management, University of Texas at Dallas, Richardson, TX  75080, United States; Tian, X., PBC School of Finance, Tsinghua University, Beijing, 100083, China; Liao, L., PBC School of Finance, Tsinghua University, Beijing, 100083, China","We study the human-robot interaction of financial-advising services in peer-topeer lending (P2P). Many crowdfunding platforms have started using robo-advisors to help lenders augment their intelligence in P2P loan investments. Collaborating with one of the leading P2P companies, we examine how investors use robo-advisors and how the human adjustment of robo-advisor usage affects investment performance. Our analyses show that, somewhat surprisingly, investors who need more help from robo-advisors- that is, those encountered more defaults in their manual investing-are less likely to adopt such services. Investors tend to adjust their usage of the service in reaction to recent robo-advisor performance. However, interestingly, these human-in-the-loop interferences often lead to inferior performance. Copyright: © 2021 INFORMS.","Augmented intelligence; Human-in-the-loop; Peer-to-peer lending; Robo-advisor","Crowdsourcing; Finance; Intelligent robots; Man machine systems; Augmented intelligence; Human-in-the-loop; Humans-robot interactions; Investment performance; Peer-to-peer lending; Performance; Robo-advisor; Human robot interaction",,,,,"19YJC630041; National Natural Science Foundation of China, NSFC: 71532004, 71790591, 71825002, 71850013","Funding: R. Ge received financial support from the Ministry of Education in China Research Program for Humanities and Social Sciences [Grant No. 19YJC630041]. E. Zheng received financial support from the National Natural Science Foundation of China (NSFC) [Grants 71850013 and 71532004]. T. Xuan received financial support from the NSFC [Grants 71825002 and 71790591].",,,,,,,,,,"Arnold, V, Collier, P, Leech, S, Sutton, S, The effect of experience and complexity on order and recency bias in decision making by professional accountants (2000) Accounting Finance, 40 (2), pp. 109-134; Attenberg, J, Ipeirotis, P, Provost, F, Beat the machine: Challenging humans to find a predictive model's ""unknown unknowns (2015) J. Data Inform. 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Psych, 54 (3), pp. 180-187; D'Hondt, C, De Winne, R, Ghysels, E, Raymond, S, (2019) Artificial intelligence alter egos: Who benefits from robo-investing?, , https://dx.doi.org/10.2139/ssrn.3415981, Preprint, submitted November 6; Dietvorst, BJ, Simmons, JP, Massey, C, Algorithm aversion: People erroneously avoid algorithms after seeing them err (2015) J. Experiment. Psych. Gen, 144 (1), pp. 114-126; Dietvorst, BJ, Simmons, JP, Massey, C, Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them (2016) Management Sci, 64 (3), pp. 1155-1170; Duarte, J, Siegel, S, Young, L, Trust and credit: The role of appearance in peer-to-peer lending (2012) Rev. Financial Stud, 25 (8), pp. 2455-2483; Featherman, M, Pavlou, P, Predicting e-services adoption: A perceived risk facets perspective (2003) Internat. J. Human Comput. 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"Bi J., He M., Luo M., Hu F.","57318199300;57202929033;57318132700;55926734100;","Interactive Intention Prediction Model for Humanoid Robot Based on Visual Features",2021,"ACM International Conference Proceeding Series",,,"3483852","36","41",,,"10.1145/3483845.3483852","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118309051&doi=10.1145%2f3483845.3483852&partnerID=40&md5=d923a5b0121dfdfbe53efbf119782e92","College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China","Bi, J., College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China; He, M., College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China; Luo, M., College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China; Hu, F., College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China","It is necessary for humanoid robots to have the capability of human-like social environment perception, which could enable robots to interact with people intelligently. In order to improve the interaction capability of humanoid robots in complex and changeable social environments, an interactive intention prediction model(IIPM) is proposed, which can quantitatively predict the intensity of interactive intention by the visual features of face orientation, social distance and facial expression in the actual social environment. Based on this model, humanoid robots can make autonomous decisions and select interactive person to carry out interactive tasks reasonably. Finally, the prediction accuracy of the IIPM is proved by single-person and multi-person experiments, which provides an effective and accurate solution for natural human-robot interaction (HRI). © 2021 ACM.","Human-robot interaction; Interactive intention prediction; Robot vision; Weight distribution","Agricultural robots; Anthropomorphic robots; Computer vision; Forecasting; Man machine systems; Environment perceptions; Human like; Humanoid robot; Humans-robot interactions; Intention predictions; Interactive intention prediction; Prediction modelling; Social environment; Visual feature; Weight distributions; Human robot interaction",,,,,"cstc2019jscx-msxmX0006; Chongqing University of Technology, CQUT: 20201010","The author wishes to thank Dr. Wang and Dr. Jin from the Robotics Department of Chongqing University of Technology for their help in this paper.","This work is supported by Technology Innovation and Application Project of Chongqing Municipal (cstc2019jscx-msxmX0006) and Graduate Student Innovation Program of Chongqing University of Technology (Grant No. clgycx 20201010). The author wishes to thank Dr. Wang and Dr. Jin from the Robotics Department of Chongqing University of Technology for their help in this paper.",,,,,,,,,"Mavridis, N., A review of verbal and non-verbal human-robot interactive communication (2015) Robotics and Autonomous Systems, 63, pp. 22-35. , https://doi.org/10.1016/j.robot.2014.09.031; Mubin, O., An image based non-verbal behaviour analysis of hri (2017) International Conference on Social Robotics, , https://doi.org/10.1007/978-3-319-70022-9_3, Springer, Cham; Tasaki, T., Dynamic communication of humanoid robot with multiple people based on interaction distance (2006) Information and Media Technologies, 1 (1), pp. 285-295. , https://doi.org/10.11185/imt.1.285; Bednarik, R., Vrzakova, H., Hradis, M.L., What do you want to do next: A novel approach for intent prediction in gaze-based interaction (2012) Proceedings of the Symposium on Eye Tracking Research and Applications (ETRA '12)., pp. 83-90. , https://doi.org/10.1145/2168556.2168569, Association for Computing Machinery, New York, NY, USA; Wang, W., Human computer interaction: Intention recognition based on EEG and eyet racking (2021) Acta Aeronautica et Astronautica Sinica (In Chinese), 42 (2), pp. 292-302. , https://10.7527/S10006893.2020.24290; Ren, S., Research on interactive intent recognition based on facial expression and line of sight direction (2019) Advanced Data Mining and Applications. 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Springer, Cham; Baltrusaitis, T., Zadeh, A., Lim, Y.C., Morency, L., Openface 2. 0: Facial behavior analysis toolkit (2018) 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 59-66. , https://10.1109/FG.2018.00019; Saaty, T.L., Decision making with the analytic hierarchy process (2008) International Journal of Services Sciences, 1 (1), pp. 83-98. , https://doi.org/10.1504/IJSSci.2008.01759; Zadeh, A., Lim, Y.C., Baltrušaitis, T., Morency, L., Convolutional experts constrained local model for 3d facial landmark detection (2017) 2017 IEEE International Conference on Computer VisionWorkshops (ICCVW), pp. 2519-2528. , https://; Hall, E.T., Proxemics [and comments and replies] (1968) Current Anthropology, 9 (2-3), pp. 83-108. , https://doi:10.1086/200975; Fiore, S.M., Toward understanding social cues and signals in human-robot interaction: Effects of robot gaze and proxemic behavior (2013) Frontiers in Psychology, 4 (2), p. 859. , https://; Truong, X.T., Ngo, T.D., Social interactive intention prediction and categorization (2019) ICRA 2019Workshop on MoRobAE-Mobile Robot Assistants for the Elderly, , Montreal Canada, May 20-24; Nurmi, J.-E., Optimistic, approach-oriented, andavoidance strategies in social situations: Three studies on loneliness and peer relationships (1996) Eur. J. Pers., 10, pp. 201-219. , https://doi.org/10.1002/(SICI)1099-0984(199609)10:3>201::AIDPER257<3.0.CO;2-#; Ekman, P., Rosenberg, E.L., (1997) What the Face Reveals: Basic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS)., , Oxford University Press, USA; Tian, Y.-I., Kanade, T., Cohn, J.F., Recognizing action units for facial expression analysis (2001) IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 (2), pp. 97-115. , https://10.1109/34.908962, Feb","He, M.; College of Mechanical Engineering, China; email: hemiaoj991@cqut.edu.cn",,,"Association for Computing Machinery","2nd International Conference on Control, Robotics and Intelligent System, CCRIS 2021","20 August 2021 through 22 August 2021",,172904,,9781450390453,,,"English","ACM Int. Conf. Proc. Ser.",Conference Paper,"Final","",Scopus,2-s2.0-85118309051
"Chen X., Hong B., Lin Z., Hou J., Gao Z., Lv S.","57247643300;56688092700;57222289155;57222289528;57208238321;57208248517;","Lumped Mass Model for Flexible Cable: A Review",2021,"Journal of Physics: Conference Series","1995","1","012029","","",,,"10.1088/1742-6596/1995/1/012029","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112750349&doi=10.1088%2f1742-6596%2f1995%2f1%2f012029&partnerID=40&md5=e54d24b1146d4255e829210c7c131528","School of Mechanical Engineering, Tianjin University, Tianjin, China; Internal Combustion Engine Research Institute, Tianjin University, Tianjin, China; Tianjin Tianbo Science and Technology Co., Ltd., Tianjin, China","Chen, X., School of Mechanical Engineering, Tianjin University, Tianjin, China; Hong, B., Internal Combustion Engine Research Institute, Tianjin University, Tianjin, China; Lin, Z., School of Mechanical Engineering, Tianjin University, Tianjin, China; Hou, J., School of Mechanical Engineering, Tianjin University, Tianjin, China, Tianjin Tianbo Science and Technology Co., Ltd., Tianjin, China; Gao, Z., School of Mechanical Engineering, Tianjin University, Tianjin, China, Tianjin Tianbo Science and Technology Co., Ltd., Tianjin, China; Lv, S., School of Mechanical Engineering, Tianjin University, Tianjin, China, Tianjin Tianbo Science and Technology Co., Ltd., Tianjin, China","Model of a flexible cable plays a great role in the applications of underwater towing system, mooring line system, etc. A complete mathematical model is a basis to accurately describe the dynamic characteristics of flexible cable. This article presents a state of art comprehensive review of lumped mass model. First, the mathematical principle of the lumped mass model, including geometric simplification, force analysis, and solution, are systematically summarized.Second, the dynamic response of cable under different boundary conditions is introduced, and boundary conditions are categoried. Third, the numerical solution of the lumped mass model are assessed. Finally, based on the application of guide dog robot, the authors discussed the research future and trends. The authors aim to ealize human-robot interaction through a flexible cable. © Published under licence by IOP Publishing Ltd.",,"Boundary conditions; Human robot interaction; Mooring; Different boundary condition; Dynamic characteristics; Flexible cable; Force analysis; Geometric simplification; Lumped mass model; Mathematical principle; Numerical solution; Mooring cables",,,,,,,,,,,,,,,,"Mankala, K.K., Agrawal, S.K., Dynamic Modeling and Simulation of Satellite Tethered Systems (2005) Journal of Vibration & Acoustics, 127, pp. 144-156; Koh, C.G., Rong, Y., Dynamic analysis of large displacement cable motion with experimental verification (2004) Journal of Sound & Vibration, 272, pp. 187-206; Johansen, V., Modelling of inextensible cable dynamics with experiments (2006) International Journal of Non-Linear Mechanics, 41, pp. 543-555; Chai, Y.T., Varyani, K.S., Barltrop, N, Three-dimensional Lump-Mass formulation of a catenary riser with bending, torsion and irregular seabed interaction effect (2002) Ocean Engineering, 29, pp. 1503-1525; Zhang, D., Dynamic analysis of submarine cable during the process of laying back to the seabed (2019) Ships and Offshore Structures, pp. 1-9; Hall, M., Goupee, A., Validation of a lumped-mass mooring line model with DeepCwind semisubmersible model test data (2015) Ocean Engineering, 104, pp. 590-603; Peng, L., Liu, Y., Study of release process of the aeronautic towed decoy (2015) Aircraft Design, pp. 1-5. , 035; Peng, W., (2011) Reaearch on the Tether Dynamic in the Releasing Process of Towed Decoy Missile, , (National University of Defense Technology); Pol Ac Hek, W.H., Calculation of transient motion of submerged cables (1960) Mathematics of Computation, 14, pp. 27-46; Wang, Z., (2019) Analysis of Single-Point Mooring Submerged Buoy System Excited by Flow in Shallow Sea 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA); Jingrui, Z., Mo, F., Menglan, D, (2013) Research on Dynamic Response of Mooring Lines in Deep Water Ship Engineering, pp. 103-107; Hu, X.Z., Liu, S.J., Numerical simulation of deepwater deployment for offshore structures with deploying cables (2015) Journal of Central South University, 22, pp. 922-930; Hong, S.M., Ha, K.N., Kim, J.Y., Dynamics Modeling and Motion Simulation of USV/UUV with Linked Underwater Cable (2020) Journal of Marine Science and Engineering, 8, p. 318; Lingzhi, (2016) Dynamics of a taut mooring line accounting for the embedded anchor chains Ocean Engineering; Fei, W., Guoliang, H., Semi-Dynamic Modeling and Simulation Study of Underwater Faired Cable (2012) Journal of Shanghai Jiaotong University (Science), pp. 1658-1664; Fei, W., Guoliang, H., Shengchun, W, (2011) Dynamic Research on the Coupling Response of Cable-Towing Ship System Journal of Shanghai Jiaotong University (Science), pp. 570-575; Dynamics and control of a towed underwater vehicle system, part I: model development (2003) Ocean Engineering, 30, pp. 453-470. , A B.B. et al; Gao, P., A Dynamic Model for Continuous Lowering Analysis of Deep-Sea Equipment (2020) Based on the Lumped-Mass Method Applied Sciences, 10, p. 3177; Duan, Q.J., Modeling of variable length Cable Driven Parallel Robot (2010) IEEE/ASME International Conference on Mechatronic & Embedded Systems & Applications; Duan, Q.J., (2010) Deployment/Retrieval Modeling of Cable-Driven Parallel Robot Mathematical Problems in Engineering, 2010, pp. 242-256; Fei, W., (2007) SIMULATION AND CONTROL RESEARCH OF MARINE TOWED SEISMIC SYSTEM, , (Shanghai Jiao Tong University); Zhongxian, Z., Yong, Y., HeLong, S., (2015) Dynamic Simulation of Mooring System Based on Lumped Mass Method Navigation of China, 38, pp. 41-45; nan, C., (2008) Study on Application of Lump-MassMethod to Calculating Snap Mooring Tension in the Deep Sea, , (Tianjin University); Wang, F., Dynamic Effect Research of Cable-Lead-In Rod on Towed System (2019) Journal of Shanghai Jiaotong University (Science), 24, pp. 745-753; Quisenberry, J., Arena, A., (2006) Discrete Cable Modeling and Dynamic Analysis Aiaa Aerospace Sciences Meeting & Exhibit","Hong, B.; Internal Combustion Engine Research Institute, China; email: hongbin@tju.edu.cn",,,"IOP Publishing Ltd","2021 3rd International Conference on Computer Modeling, Simulation and Algorithm, CMSA 2021","4 July 2021 through 5 July 2021",,171117,17426588,,,,"English","J. Phys. Conf. Ser.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85112750349
"Jarosz M., Nawrocki P., Sniezynski B., Indurkhya B.","57212304491;24587357500;23393638000;6603120668;","Multi-platform intelligent system for multimodal human-computer interaction",2021,"Computing and Informatics","40","1",,"83","103",,1,"10.31577/cai_2021_1_83","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112301251&doi=10.31577%2fcai_2021_1_83&partnerID=40&md5=cf6d3b91b9b8019e3d45aaf0d2c32278","Institute of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, al. A. Mickiewicza 30, Krakow, 30-059, Poland; Institute of Philosophy, Jagiellonian University, Golebia 24, Krakow, 31-007, Poland","Jarosz, M., Institute of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, al. A. Mickiewicza 30, Krakow, 30-059, Poland; Nawrocki, P., Institute of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, al. A. Mickiewicza 30, Krakow, 30-059, Poland; Sniezynski, B., Institute of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, al. A. Mickiewicza 30, Krakow, 30-059, Poland; Indurkhya, B., Institute of Philosophy, Jagiellonian University, Golebia 24, Krakow, 31-007, Poland","We present a flexible human-robot interaction architecture that incorporates emotions and moods to provide a natural experience for humans. To determine the emotional state of the user, information representing eye gaze and facial expression is combined with other contextual information such as whether the user is asking questions or has been quiet for some time. Subsequently, an appropriate robot behaviour is selected from a multi-path scenario. This architecture can be easily adapted to interactions with non-embodied robots such as avatars on a mobile device or a PC. We present the outcome of evaluating an implementation of our proposed architecture as a whole, and also of its modules for detecting emotions and questions. Results are promising and provide a basis for further development. © 2021 Slovak Academy of Sciences. All rights reserved.","Human-computer interaction; Humanoid robot; Intelligent system architecture; Multi-platform; Multimodal system","Human computer interaction; Intelligent systems; Contextual information; Emotional state; Eye-gaze; Facial Expressions; Multi-platform; Multimodal human computer interaction; Multipaths; Proposed architectures; Human robot interaction",,,,,"Narodowe Centrum Badań i Rozwoju, NCBR; National Center for Research and Development, NCRD","The researhc presentedin this paper was supported by funds from the PolishMin-istry of Science and Higher Education allocated to the AGHUniversity of Science and Technolog.yMateuszJarosz’s workwas supported in part by the National Center for Researhc and Developmetn(NCBR) under Grant No. POLTUR2//2018.5 We wish to thank Anna Kolota for her assistance with testing.",,,,,,,,,,"Fine, S., Singer, Y., Tishby, N., The Hierarchical Hidden Markov Model: Analysis and Applications (1998) Machine Learning, 32 (1), pp. 41-62; Ardila, L. R., Coronado, E., Hendra, H., Phan, J., Zainalkefli, Z., Venture, G., Adaptive Fuzzy and Predictive Controllers for Expressive Robot Arm Movement During Human and Environment Interaction (2019) International Journal of Mechanical Engineering and Robotics Research, 8 (2), pp. 207-219; Rincon, L., Coronado, E., Law, C., Venture, G., Adaptive Cognitive Robot Using Dynamic Perception with Fast Deep-Learning and Adaptive On-Line Predictive Control (2019) Advances in Mechanism and Machine Science (IFToMM WC 2019), 73, pp. 2429-2438. , Uhl, T. (Ed): Springer, Cham, Mechanisms and Machine Science; Hagane, S., Ardila, L. K. R., Katsumata, T., Bonnet, V., Fraisse, P., Venture, G., Adaptive Generalized Predictive Controller and Cartesian Force Control for Robot Arm Using Dynamics and Geometric Identification (2018) Journal of Robotics and Mechatronics, 30 (6), pp. 927-942; Abiyev, R. H., Akkaya, N., Aytac, E., Control of Soccer Robots Using Behaviour Trees (2013) 2013 9th Asian Control Conference (ASCC), pp. 1-6. , IEEE, Istanbul, Turkey; Marzinotto, A., Colledanchise, M., Smith, C., Ögren, P., Towards a Unified Behavior Trees Framework for Robot Control (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 5420-5427. , Hong Kong, China; Perzanowski, D., Schultz, A. C., Adams, W., Marsh, E., Bugajska, M., Building a Multimodal Human-Robot Interface (2001) IEEE Intelligent Systems, 16 (1), pp. 16-21; Wood, E., Baltrusaitis, T., Zhang, X., Sugano, Y., Robinson, P., Bulling, A., Rendering of Eyes for Eye-Shape Registration and Gaze Estimation (2015) Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), pp. 3756-3764. , Santiago, Chile; Baltrusaitis, T., Robinson, P., Morency, L.-P., Constrained Local Neural Fields for Robust Facial Landmark Detection in the Wild (2013) Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 354-361. , Sydney, Australia; Jarosz, M., Nawrocki, P., Placzkiewicz, L., Sniezynski, B., Zielinski, M., Indurkhya, B., Detecting Gaze Direction Using Robot-Mounted and Mobile-Device Cameras (2019) Computer Science, 20 (4); Arriaga, O., Valdenegro-Toro, M., Plöger, P., (2017) Real-Time Convolutional Neural Networks for Emotion and Gender Classification, , arXiv: 1710.07557; Ando, A., Asakawa, R., Masumura, R., Kamiyama, H., Kobashikawa, S., Aono, Y., Automatic Question Detection from Acoustic and Phonetic Features Using Feature-Wise Pre-Training (2018) Proceedings of INTERSPEECH, pp. 1731-1735; Chollet, F., Xception: Deep Learning with Depthwise Separable Convolutions (2017) Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800-1807; Zhang, H., McLoughlin, I., Song, Y., Robust Sound Event Recognition Using Convolutional Neural Networks (2015) 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 559-563; Livingstone, S.R., Russo, F. A., The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A Dynamic, Multimodal Set of Facial and Vocal Expressions in North American English (2018) PLoS ONE, 13 (5), p. e0196391. , Art; Alonso-Martin, F., Malfaz, M., Sequeira, J., Gorostiza, J. F., Salichs, M. A., A Multimodal Emotion Detection System During Human-Robot Interaction (2013) Sensors, 13 (11), pp. 15549-15581; Breazeal, C., Emotion and Sociable Humanoid Robots (2003) International Journal of Human-Computer Studies, 59 (1-2), pp. 119-155; Beer, J. M., Fisk, A.D., Rogers, W. A., Toward a Framework for Levels of Robot Autonomy in Human-Robot Interaction (2014) Journal of Human-Robot Interaction, 3 (2), pp. 74-99; Lee, K. W., Kim, H. R., Yoon, W. C., Yoon, Y.S., Kwon, D. S., Designing a Human-Robot Interaction Framework for Home Service Robot (2005) IEEE International Workshop on Robot and Human Interactive Communication (ROMAN 2005), pp. 286-293; Coronado, E., Mastrogiovanni, F., Venture, G., Development of Intelligent Behaviors for Social Robots via User-Friendly and Modular Programming Tools (2018) 2018 IEEEWorkshop on Advanced Robotics and Its Social Impacts (ARSO), pp. 62-68. , Genova, Italy; Zguda, P., Ko lota, A., Jarosz, M., Sondej, F., Izui, T., Dziok, M., Belowska, A., Indurkhya, B., On the Role of Trust in Child-Robot Interaction (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1-6. , New Delhi, India; Zguda, P., Ko lota, A., Jarosz, M., Sondej, F., Izui, T., Dziok, M., Indurkhya, B., Why Don't You Have a Wife?! (2019) Free Format Dialogue in CRI. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Late Breaking Results Poster, , IEEE; Edward, M., (2020) NLP Augmentation, , https://github.com/makcedward/nlpaug; Röning, J., Holappa, J., Kellokumpu, V., Tikanmäki, A., Pietikäinen, M., Minotaurus: A System for Affective Human-Robot Interaction in Smart Environments (2014) Cognitive Computation, 6 (4), pp. 940-953; Liu, Z., Wu, M., Cao, W., Chen, L., Xu, J., Zhang, R., Mao, J., A Facial Expression Emotion Recognition Based Human-Robot Interaction System (2017) IEEE/CAA Journal of Automatica Sinica, 4 (4), pp. 668-676; Liu, Z. T., Pan, F. F., Wu, M., Cao, W. H., Chen, L. F., Xu, J. P., Zhang, R., Zhou, M. T., A Multimodal Emotional Communication Based Humans-Robots Interaction System (2016) 2016 35th Chinese Control Conference (CCC), pp. 6363-6368. , IEEE",,,,"Slovak Academy of Sciences, Institute of Theatre and Film Research of the Center for Research in Art",,,,,13359150,,,,"English","Comput. Inf.",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85112301251
"Hebbar V., Langbort C.","57224541893;23569981700;","A model for tacit communication in collaborative human-uav search-and-rescue",2021,"Entropy","23","8","1027","","",,,"10.3390/e23081027","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112455705&doi=10.3390%2fe23081027&partnerID=40&md5=c1e26aa93cbb83f183e6ee0b56c61e82","Coordinated Science Laboratory, University of Illinois Urbana-Champaign, Champaign, IL  61820, United States","Hebbar, V., Coordinated Science Laboratory, University of Illinois Urbana-Champaign, Champaign, IL  61820, United States; Langbort, C., Coordinated Science Laboratory, University of Illinois Urbana-Champaign, Champaign, IL  61820, United States","Tacit communication can be exploited in human robot interaction (HRI) scenarios to achieve desirable outcomes. This paper models a particular search and rescue (SAR) scenario as a modified asymmetric rendezvous game, where limited signaling capabilities are present between the two players—rescuer and rescuee. We model our situation as a co-operative Stackelberg signaling game, where the rescuer acts as a leader in signaling its intent to the rescuee. We present an efficient game-theoretic approach to obtain the optimal signaling policy to be employed by the rescuer. We then robustify this approach to uncertainties in the rescue topology and deviations in rescuee behavior. The paper thus introduces a game-theoretic framework to model an HRI scenario with implicit communication capacity. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Game theory; Human robot interaction; Signaling",,,,,,,,,,,,,,,,,"Grover, P., Sahai, A., Implicit and explicit communication in decentralized control Proceedings of the 48th Annual Allerton Conference on Communication, Control, and Computing, pp. 278-285. , Allerton, Monticello, IL, USA, 29 September–1 October 2010; [CrossRef]; Sobel, J., Signaling Games (2009) Encyclopedia of Complexity and Systems Science, , Meyers, R., Ed.; Springer: New York, NY, USA, [CrossRef]; Baillieul, J., Özcimder, K., The control theory of motion-based communication: Problems in teaching robots to dance Proceedings of the American Control Conference (ACC), pp. 4319-4326. , Montreal, QC, Canada, 27–29 June 2012; [CrossRef]; Santos, M., Egerstedt, M., From Motions to Emotions: Can the Fundamental Emotions be Expressed in a Robot Swarm? (2020) Int. J. Soc. Robot, 13, pp. 751-764. , [CrossRef]; Dragan, A.D., Lee, K.C.T., Srinivasa, S.S., Legibility and Predictability of Robot Motion Proceedings of the 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 301-308. , Tokyo, Japan, 4–6 March 2013; [CrossRef]; Dragan, A.D., Bauman, S., Forlizzi, J., Srinivasa, S.S., Effects of Robot Motion on Human-Robot Collaboration Proceedings of the 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 51-58. , Portland, OR, USA, 2–5 March 2015; [CrossRef]; Kamenica, E., Gentzkow, M., Bayesian Persuasion (2011) Am. Econ. Rev, 101, pp. 2590-2615. , [CrossRef]; Alpern, S., Gal, S., (2003) The Theory of Search Games and Rendezvous, , Springer: Boston, MA, USA; Szafir, D., Mutlu, B., Fong, T., Communication of Intent in Assistive Free Flyers Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 358-365. , Bielefeld, Germany, 3–6 March 2014; [CrossRef]; Szafir, D., Mutlu, B., Fong, T., Communicating Directionality in Flying Robots Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 19-26. , Portland, OR, USA, 2–5 March 2015; [CrossRef]; Jan, O., Horowitz, A.J., Peng, Z.R., Using global positioning system data to understand variations in path choice (2000) Transp. Res. Rec, 1725, pp. 37-44. , [CrossRef]; Zhu, S., Levinson, D., Do people use the shortest path? An empirical test of Wardrop’s first principle (2015) PLoS ONE, 10, p. e0134322. , [CrossRef]; Bongiorno, C., Zhou, Y., Kryven, M., Theurel, D., Rizzo, A., Santi, P., Tenenbaum, J., Ratti, C., Vector-based Pedestrian Navigation in Cities, , arXiv 2021, arXiv:2103.07104; Dantzig, G.B., (1963) Linear Programming and Extensions, , Princeton University Press: Princeton, NJ, USA; Hebbar, V., (2020) A Stackelberg Signaling Game for Co-Operative Rendezvous in Uncertain Environments in a Search-and-Rescue Context, , Master’s Thesis, University of Illinois Urbana-Champaign, Champaign, IL, USA; Sigal, C.E., Pritsker, A.A.B., Solberg, J.J., The Stochastic Shortest Route Problem (1980) Oper. Res, 28, pp. 1122-1129. , [CrossRef]; Frank, H., Shortest Paths in Probabilistic Graphs (1969) Oper. Res, 17, pp. 583-599. , [CrossRef]; Berstimas, D., Sim, M., Robust discrete optimization and network flows (2003) Math. Program, 98, pp. 49-71. , [CrossRef]; Ben-Tal, A., El Ghaoui, L., Nemirovski, A., (2009) Robust Optimization, , Princeton University Press: Princeton, NJ, USA; Valiant, L.G., The Complexity of Enumeration and Reliability Problems (1979) SIAM J. Comput, 8, pp. 410-421. , [CrossRef]; Malleson, N., Vanky, A., Hashemian, B., Santi, P., Verma, S.K., Courtney, T.K., Ratti, C., The characteristics of asymmetric pedestrian behavior: A preliminary study using passive smartphone location data (2018) Trans. GIS, 22, pp. 616-634. , [CrossRef]; Byers, T.H., Waterman, M.S., Determining all optimal and near-optimal solutions when solving shortest path problems by dynamic programming (1984) Oper. Res, 32, pp. 1381-1384. , [CrossRef]; Naor, D., Brutlag, D., On near-optimal alignments of biological sequences (1994) J. Comput. Biol, 1, pp. 349-366. , [CrossRef] [PubMed]; Eppstein, D., Finding the k Shortest Paths (1999) Soc. Ind. Appl. Math, 28, pp. 652-673; Yen, J.Y., Finding the k Shortest Loopless Paths in a Network (1971) Manag. Sci, 17, pp. 712-716. , [CrossRef]","Hebbar, V.; Coordinated Science Laboratory, United States; email: vhebbar2@illinois.edu",,,"MDPI AG",,,,,10994300,,,,"English","Entropy",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85112455705
"Weiss A., Wortmeier A.-K., Kubicek B.","57226290772;57226272537;30967566800;","Cobots in Industry 4.0: A Roadmap for Future Practice Studies on Human-Robot Collaboration",2021,"IEEE Transactions on Human-Machine Systems","51","4","9490032","335","345",,1,"10.1109/THMS.2021.3092684","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111070979&doi=10.1109%2fTHMS.2021.3092684&partnerID=40&md5=ee2205bdbadf30d33b22d58f36c2c126","Human-Computer Interaction Group, Faculty of Informatics, Tu Wien, Vienna, 1040, Austria; University of Stuttgart, Stuttgart, 70174, Germany; University of Graz, Graz, 8010, Austria","Weiss, A., Human-Computer Interaction Group, Faculty of Informatics, Tu Wien, Vienna, 1040, Austria; Wortmeier, A.-K., University of Stuttgart, Stuttgart, 70174, Germany; Kubicek, B., University of Graz, Graz, 8010, Austria","With the vision of Industry 4.0 and cobots, working conditions in industrial settings are starting to change. We review related literature from the fields of human-robot interaction, work and organizational psychology, and sociology of work, as well as an exemplary project case study, and identify research gaps regarding the implications of cobots for work environments. We argue that we are in a transition phase from automation to actual collaboration with robots in manufacturing, and that this will open up a new problem space for investigations, in which a practice lens will be crucial. Based on this, we propose a research agenda for social practice and workplace studies to explore the sociotechnical environment of Industry 4.0 involving cobots at the individual, team, and organizational levels. © 2013 IEEE.","human-robot interaction (HRI); Human-robot teams; Industry 4.0; sociotechnical systems; workplace","Aerospace industry; Industrial robots; Industry 4.0; Sociology; Industrial settings; Organizational levels; Organizational psychology; Robot interactions; Social practices; Sociotechnical environments; Work environments; Workplace studies; Social robots",,,,,"Deutsche Forschungsgemeinschaft, DFG: EXC 2120/1-390831618; Austrian Science Fund, FWF: V587-G29","Manuscript received October 1, 2020; revised March 22, 2021; accepted May 30, 2021. Date of publication; date of current version. The work of Ann-Kathrin Wortmeier was supported by German Research Foundation under Grant EXC 2120/1-390831618. The work of Astrid Weiss was supported by Austrian Science Foundation (FWF) under Grant V587-G29 (SharedSpace). This article was recommended by Associate Editor B. Hu. (Corresponding author: Astrid Weiss.) Astrid Weiss is with the Human–Computer Interaction Group, Faculty of Informatics, TU Wien, 1040 Vienna, Austria (e-mail: astrid.weiss@tuwien.ac.at).",,,,,,,,,,"Kolbeinsson, A., Lagerstedt, E., Lindblom, J., Foundation for a classification of collaboration levels for human-robot cooperation in manufacturing (2019) Prod. Manuf. Res., 7 (1), pp. 448-471; Weiss, A., Buchner, R., Tscheligi, M., Fischer, H., Exploring humanrobot cooperation possibilities for semiconductormanufacturing (2011) Proc. Int. Conf. Collaboration Technol. Syst., pp. 173-177; Palanque, P., Campos, P.F., Nocera, J.A., Clemmensen, T., Roto, V., User experience in an automated world (2019) Proc. IFIP Conf. Human-Comput. 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Syst.",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85111070979
"Meng Y., Zhao Z., Chen W., Xiao X., Meng M.Q.-H.","57268442600;57214761947;57282059800;55920059700;57221185664;","Workspace analysis of a dual-arm mobile robot system for coordinated operation",2021,"2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021",,,,"1058","1063",,,"10.1109/RCAR52367.2021.9517634","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115375266&doi=10.1109%2fRCAR52367.2021.9517634&partnerID=40&md5=8d42cbaadb46d0d0d10c031e7bf81122","Southern University of Science and Technology, Department of Electronic and Electrical Engineering, Shenzhen, 518055, China; Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong; Shenzhen Research Institute, Chinese University of Hong Kong, Shenzhen, Hong Kong","Meng, Y., Southern University of Science and Technology, Department of Electronic and Electrical Engineering, Shenzhen, 518055, China; Zhao, Z., Southern University of Science and Technology, Department of Electronic and Electrical Engineering, Shenzhen, 518055, China; Chen, W., Southern University of Science and Technology, Department of Electronic and Electrical Engineering, Shenzhen, 518055, China; Xiao, X., Southern University of Science and Technology, Department of Electronic and Electrical Engineering, Shenzhen, 518055, China; Meng, M.Q.-H., Southern University of Science and Technology, Department of Electronic and Electrical Engineering, Shenzhen, 518055, China, Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong, Shenzhen Research Institute, Chinese University of Hong Kong, Shenzhen, Hong Kong","With the development of robotics, more and more complicated tasks are expected to be completed by robots, while the operation ability of single-arm robots is limited. To meet this requirement, a mobile dual-arm robot is designed, and the workspace is analyzed. The designed mobile dual-arm robot has a simple and easy-to-operate software system framework and a humanoid looking for effective Human-Robot-Interaction. After solving the forward kinematics, we provide the workspace analysis through the Monte Carlo method. With the extracted boundary of the workspace, the characteristics of the coordinated workspace of the dual-arm robot are provided. To verify the feasibility and effectiveness of our system, we use the designed dual-arm mobile robot to demonstrate manipulation tasks with equipped sensors. © 2021 IEEE.",,"Mobile robots; Monte Carlo methods; Robotic arms; Robotics; Coordinated operation; Dual arm; Dual-arm robot; Forward kinematics; Humans-robot interactions; Mobile robot systems; Simple++; Software-systems; System framework; Workspace analysis; Human robot interaction",,,,,,"This work is partially supported by Shenzhen Key Laboratory of Robot Perception and Intelligence (ZDSYSS2020081017180001), Shenzhen Science and Technology Innovation projects (JCYJ20170413161616163, JCYJ20170413161503220). This work is also supported by Hong Kong RGC CRF grant (C4063-18G), and Hong Kong RGC GRF grant (14200618). 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IEEE, pp. 164-169; Xiao, X., Gao, H., Li, C., Qiu, L., Kumar, K.S., Cai, C.J., Bhola, B.S., Ren, H., Portable body-attached positioning mechanism toward robotic needle intervention (2020) IEEE/ASME Transactions on Mechatronics, 25 (2), pp. 1105-1116; Xiao, X., Li, C., Gu, X., Yan, Y., Wu, Y., Wu, Q., Chiong, E., Ren, H., A tubular dual-roller bending mechanism towards robotic transurethral prostate biopsy (2020) IEEE/ASME Transactions on Mechatronics, pp. 1-1; Bergsma, M., Mooij, E., Application of taylor series integration to reentry problems (2016) AIAA Atmospheric Flight Mechanics Conference, p. 0024; Tian, H.-B., Shang, W.-F., Ma, H.-W., Coordinate workspace analysis for the manipulators of a dual-arm mobile robot (2015) Modular Machine Tool & Automatic Manufacturing Technique, p. 06; Richard, B., Robotics innovations at the 2009 assembly technology expo (2010) Industrial Robot, 37 (5), pp. 427-430; Chen, W., Gu, S., Zhu, L., Zhang, H., Zhu, H., Guan, Y., Representation of truss-style structures for autonomous climbing of biped pole-climbing robots (2018) Robotics and Autonomous Systems, 101, pp. 126-137. , https://www.sciencedirect.com/science/article/pii/S0921889017303093; Zhou, H., Yang, G., Lv, H., Huang, X., Yang, H., Pang, Z., Iotenabled dual-arm motion capture and mapping for telerobotics in home care (2019) IEEE Journal of Biomedical and Health Informatics, 24 (6), pp. 1541-1549; Zhang, X., Ma, J., Ma, W.K., Yang, L., Yin, A.G., Analysis of singular configuration and workspace of eod robots arm (2015) Journal of Tianjin Polytechnic University","Meng, M.Q.-H.; Southern University of Science and Technology, China; email: max.meng@ieee.org",,"IEEE Robotics and Automation Society (RA);Shanghai Jiao Tong University;Shenzhen Institute of Advanced Technology (SIAT)","Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021","15 July 2021 through 19 July 2021",,171512,,9781665436786,,,"English","IEEE Int. Conf. Real-Time Comput. Robot., RCAR",Conference Paper,"Final","",Scopus,2-s2.0-85115375266
[No author name available],[No author id available],"2021 IEEE International Conference on Advanced Robotics and Its Social Impacts, ARSO 2021",2021,"Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO","2021-July",,,"","",253,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116508933&partnerID=40&md5=1463d700027048553fa3b65f22f4c799",,"","The proceedings contain 22 papers. The topics discussed include: can’t hide your disappointment: using human pose and facial cues for intent prediction in a target game; intelligent agent deception and the influence on human trust and interaction; an efficient multi-vehicle routing strategy for goods delivery services; a GAN-based approach to communicative gesture generation for social robots; the role of cybersecurity and HPC in the explainability of autonomous robots behavior; using sessions, episodes and parameterized testing in ROSPIT for constructing re-usable test specifications; agility metrics in the ARIAC competition; influence of personality traits on helping behavior in human-robot interaction; preferences of seniors for robots delivering a message with congruent approaching behavior; and aiding emergency evacuations using obstacle-aware path clearing.",,,,,,,,,,,,,,,,,,,,,,"IEEE Computer Society","2021 IEEE International Conference on Advanced Robotics and Its Social Impacts, ARSO 2021","8 July 2021 through 10 July 2021",,172042,21627568,9781665449533,,,"English","Proc. IEEE Workshop Adv. Rob. Soc. Impacts, ARSO",Conference Review,"Final","",Scopus,2-s2.0-85116508933
"Megalingam R.K., Kota A.H., Vijaya Krishna Tejaswi P.","35119069500;57226379059;57237614100;","Optimal Approach to Speech Recognition with ROS",2021,"Proceedings of the 6th International Conference on Communication and Electronics Systems, ICCES 2021",,,"9489085","111","116",,,"10.1109/ICCES51350.2021.9489085","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113796062&doi=10.1109%2fICCES51350.2021.9489085&partnerID=40&md5=6aa69fd1dc8a234aa58650c8c1090865","Amrita Vishwa Vidyapeetham, Department of Electronics and Communication Engineering, Amritapuri, India","Megalingam, R.K., Amrita Vishwa Vidyapeetham, Department of Electronics and Communication Engineering, Amritapuri, India; Kota, A.H., Amrita Vishwa Vidyapeetham, Department of Electronics and Communication Engineering, Amritapuri, India; Vijaya Krishna Tejaswi, P., Amrita Vishwa Vidyapeetham, Department of Electronics and Communication Engineering, Amritapuri, India","Among several ways of controlling a robotic system, voice control is one of the most sophisticated ways of issuing commands. Speech processing plays a key role in voice control. The main objective of this research work is to deliver a cost-efficient speech processing module and provide an audio response to the user. With the increase in usage of autonomous systems in recent times, users are looking towards cost efficient ways of Human-Robot Interaction. As ROS is an open source platform and the fact that many autonomous systems are being built over this platform has driven this research work. This research work was carried out to design one such module that can be integrated with the existing software stack of Robot Operating System easily. © 2021 IEEE.","Human-Robot Interaction; Optimal; RaspberyyPi; RobotOperatingSystem; SpeechProcessing","Machine design; Open source software; Open systems; Speech processing; Speech recognition; Autonomous systems; Cost-efficient; Open source platforms; Optimal approaches; Robot operating system; Robotic systems; Software stacks; Voice control; Social robots",,,,,,"We would like to express our sincere gratitude to Almighty for giving us the opportunity to carry out the research in a successful manner during these tough times.",,,,,,,,,,"Priya, J.K., Sree, S.S., Navya, T., Gupta, D., Implementation of phonetic level speech recognition in kannada using htk (2018) 2018 International Conference on Communication and Signal Processing ICCSP, pp. 0082-0085. , Chennai, India; Poorna, S.S., Jeevitha, C.Y., Nair, S.J., Santhosh, S., Nair, G.J., Emotion recognition using multi-parameter speech feature classification (2015) 2015 International Conference on Computers, Communications, and Systems (ICCCS, pp. 217-222. , Kanyakumari, India; Jayakumar, A., Raghunath, M., Sakthipriya, M.S., Akhila, S., Sadanandan, A., Nedungadi, P., Enhancing speech recognition in developing language learning systems for low cost Androids (2016) 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT, pp. 80-84. , New Delhi, India; Bajpai, S., Radha, D., Smart phone as a controlling device for smart home using speech recognition (2019) 2019 International Conference on Communication and Signal Processing (ICCSP, pp. 0701-0705. , Chennai, India; Megalingam, R.K., Manaswini, M., Yannam, J., Naick, V.S., Chowdary, G.N., Human robot interaction on navigation platform using robot operating system (2020) 2020 Fourth International Conference on Inventive Systems and Control (ICISC, pp. 898-905. , Coimbatore, India; Megalingam, R.K., Nair, R.N., Prakhya, S.M., Mohan, M., Low power, intelligent, wireless, home security system for the elderly people (2011) 2011 3rd International Conference on Electronics Computer Technology, Kanyakumari, India, pp. 320-324; Megalingam, R.K., Nagalla, D., Pasumarthi, R.K., Gontu, V., Allada, P.K., ROS based, simulation and control of a wheeled robot using gamer's steering wheel (2018) 2018 4th International Conference on Computing Communication and Automation (ICCCA, pp. 1-5. , Greater Noida, India; Megalingam, R.K., Rajendraprasad, A., Manoharan, S.K., Comparison of planned path and travelled path using ros navigation stack (2020) 2020 International Conference for Emerging Technology (INCET), Belgaum, India, pp. 1-6; Wang, J., Shi, Z., A speech interaction system based on cloud service under ros (2019) 2019 Chinese Control Conference (CCC, pp. 4721-4725. , Guangzhou, China; Wang, C., Design of speech recognition system (2013) 2013 IEEE Third International Conference on Information Science and Technology (ICIST, pp. 1042-1044. , Yangzhou, China; Kiran, K.S., Mandal, A., Kumar, K.R.P., Mitra, P., Veni, S., A Comparative study of dictionary learning algorithms on speech recognition task (2018) 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 588-594",,,,"Institute of Electrical and Electronics Engineers Inc.","6th IEEE International Conference on Communication and Electronics Systems, ICCES 2021","8 July 2021 through 10 July 2021",,170997,,9781665435871,,,"English","Proc. Int. Conf. Commun. Electron. Syst., ICCES",Conference Paper,"Final","",Scopus,2-s2.0-85113796062
"Ma X., Wang H., Ma Q.","57226447854;57226455332;57204794814;","Generator with triangulation for pedestrians trajectory prediction",2021,"Tehnicki Vjesnik","28","4",,"1285","1290",,,"10.17559/TV-20210311111839","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111600054&doi=10.17559%2fTV-20210311111839&partnerID=40&md5=338ee96b7f622bea053db072fb784b1c","Hebei University of Economics and Business, School of Management Science and Engineering, Hebei University of Economics and Business, Shijiazhuang, 050061, China; Hebei University of Economics and Business, Modern Educational Technology Center, Hebei University of Economics and Business, Shijiazhuang, 050061, China; Beijing Jiaotong University, Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China","Ma, X., Hebei University of Economics and Business, School of Management Science and Engineering, Hebei University of Economics and Business, Shijiazhuang, 050061, China; Wang, H., Hebei University of Economics and Business, Modern Educational Technology Center, Hebei University of Economics and Business, Shijiazhuang, 050061, China; Ma, Q., Beijing Jiaotong University, Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China","Pedestrian trajectory prediction is a basic task in computer vision field. The prosperity of artificial intelligence makes the automatic drive, human-robot interaction and surveillance video attract a great deal of attention. Generally, researchers always place emphasis on pedestrian trajectory. The focuses of pedestrian trajectory prediction task are motion pattern modelling and spatio-temporal interaction modelling in the current study. In our paper, we present a GAN-based framework to model pedestrian motion pattern. A Delaunay triangulation algorithm is applied to map the pedestrian interaction. From the perspective of space, both the position interaction and motion interaction of pedestrians can be considered. For example, the influence of the movement direction and motion potential energy of pedestrians on the surrounding pedestrians can be modelled. © 2021, Strojarski Facultet. All rights reserved.","Delaunay triangulation; Generative adversarial nets; Pedestrian trajectory prediction","Artificial intelligence; Forecasting; Potential energy; Security systems; Social robots; Time and motion study; Trajectories; Triangulation; Delau-nay triangulations; GaN based; Motion pattern; Pedestrian motion; Pedestrian trajectories; Spatiotemporal interactions; Surveillance video; Trajectory prediction; Motion analysis",,,,,"GH171046","This work is supported by the Fundamental Research Project of Humanities and Social Sciences in Colleges and Universities of Hebei Province (NO. GH171046).",,,,,,,,,,"Zolghadr, J., Cai, Y., Locating a two-wheeled robot using extended Kalman filter (2015) Tehnički vjesnik, 22 (6), p. 14811488. , https://doi.org/10.17559/TV-20140531190647; Qiu, Y., Liu, C., Modelling and stimulation of target tracking and localization in wireless sensor network (2014) Tehnički vjesnik, 21 (2), pp. 233-238; Alexandre, A., Kratarth, G., Vignesh, R., Alexandre, R., Li, F. F., Silvio, S., Social LSTM: Human Trajectory Prediction in Crowded Spaces (2016) IEEE Conference on Computer Vision and Pattern Recognition, , https://doi.org/10.1109/CVPR.2016.110; Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, Da, Ozair, S., Courville, A., Bengio, Y., Generative Adversarial Nets (2014) the International Conference on Neural Information Processing Systems, 2, pp. 2673-2680; Mirza, M., Osindero, Si, (2014) Conditional Generative Adversarial Nets, , https://arxiv.org/abs/1411.1784; Przemyslaw, A. L., Terrence, S., Julie, A. S., A Survey of Methods for Safe Human-Robot Interaction (2017) Now Foundations and Trends, , https://doi.org/10.1561/2300000052; Kucner, T. P., Magnusson, M., Schaffernicht, E., Bennetts, V. H., Lilienthal, A. J., Enabling Flow Awareness for Mobile Robots in Partially Observable Environments (2017) IEEE Robotics and Automation Letters, 2 (2), pp. 1093-1100. , https://doi.org/10.1109/LRA.2017.2660060; Ryoo, M. S., Fuchs, T. J., Xia, L., Aggarwal, J. K., Matthies, L., Robot-Centric Activity Prediction from First-Person Videos: What Will They Do to Me? (2015) Proceedings of HRI, pp. 295-302. , https://doi.org/10.1145/2696454.2696462; Rehder, E., Wirth, F., Lauer, M., Stiller, C., Pedestrian Prediction by Planning Using Deep Neural Networks (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 5903-5908. , https://doi.org/10.1109/ICRA.2018.8460203; Alexandre, A., Vignesh, R., Li, F. F., Sociallyaware Large-scale Crowd Forecasting (2014) IEEE Conference on Computer Vision and Pattern Recognition (CVPR), , https://doi.org/10.1109/CVPR.2014.283; Agrim, G., Justin, J., Li, F. F., Silvio, S., Alexandre, A., Social GAN: Socially acceptable trajectories with Generative adversarial networks (2018) IEEE Conference on Computer Vision and Pattern Recognition (CVPR), , https://doi.org/10.1109/CVPR.2018.00240; Sirin, H., Meiqing, W., He, W., Siew, K. L., Situation-Aware Pedestrian Trajectory Prediction with Spatio-Temporal Attention Model (2019) 24th Computer Vision Winter Workshop (CVWW); Daksh, V., Srinivasaraghavan, G., Human Trajectory Prediction using Spatially Aware Deep Attention Models (2017) 31st Conference on Neural Information Processing Systems (NIPS); Ehsan, P., Christoph, H. L., Back to square one: Probabilistic Trajectory Forecasting without Bells and Whistles (2018) 32nd Conference on Neural Information Processing Systems (NIPS); Nikhil, N., Morris, B. T., Convolutional Neural Network for Trajectory Prediction (2018) European Conference on Computer Vision-ECCV 2018 Workshops, , https://doi.org/10.1007/978-3-030-11015-4_16; Huynh, M., Gita, A., (2018) Scene-LSTM: A Model for Human Trajectory Prediction; Amir, S., Vineet, K., Ali, S., Noriaki, H., Hamid, R., Silvio, S., SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints (2019) IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), , https://doi.org/10.1109/CVPR.2019.00144; Stefano, P., Andreas, E., Konrad, S., Lcu, van G., You'll never walk alone: Modeling social behavior for multitarger tracking (2009) IEEE 12th International Conference on Computer Vision (ICCV), , https://doi.org/10.1109/ICCV.2009.5459260; Alon, L., Yiorgos, C., Dani, L., Crowds by example (2007) Computer Graphics forum, 26 (3), pp. 655-664. , https://doi.org/10.1111/j.1467-8659.2007.01089.x; Pu, Z., Wanli, O., Pengfei, Z., Jianru, X., SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction (2019) IEEE Conference on Computer Vision and Pattern Recognition (CVPR), , https://doi.org/10.1109/CVPR.2019.01236","Wang, H.; Hebei University of Economics and Business, China; email: wanght@heuet.edu.cn",,,"Strojarski Facultet",,,,,13303651,,,,"English","Teh. Vjesn.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85111600054
"Guo Y., Shi C., Yang X.J.","57201850598;55791297900;23975212000;","Reverse Psychology in Trust-Aware Human-Robot Interaction",2021,"IEEE Robotics and Automation Letters","6","3","9382074","4851","4858",,1,"10.1109/LRA.2021.3067626","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103276225&doi=10.1109%2fLRA.2021.3067626&partnerID=40&md5=bdf627e6421394a00d6e58ca10df2177","Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, United States","Guo, Y., Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, United States; Shi, C., Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, United States; Yang, X.J., Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, United States","To facilitate effective human-robot interaction (HRI), trust-aware HRI has been proposed, wherein the robotic agent explicitly considers the human's trust during its planning and decision making. The success of trust-aware HRI depends on the specification of a trust dynamics model and a trust-behavior model. In this study, we proposed one novel trust-behavior model, namely the reverse psychology model, and compared it against the commonly used disuse model. We examined how the two models affect the robot's optimal policy and the human-robot team performance. Results indicate that the robot will deliberately 'manipulate' the human's trust under the reverse psychology model. To correct this 'manipulative' behavior, we proposed a trust-seeking reward function that facilitates trust establishment without significantly sacrificing the team performance. © 2016 IEEE.","human-robot interaction; Trust; trust dynamics","Agricultural robots; Decision making; Man machine systems; Robot programming; Human robot Interaction (HRI); Human-robot-team; Optimal policies; Psychology Model; Reward function; Team performance; Trust dynamics; Trust establishment; Social robots",,,,,"Army Research Laboratory, ARL: W911NF-20-2-0087","Manuscript received October 15, 2020; accepted February 21, 2021. Date of publication March 19, 2021; date of current version April 14, 2021. This letter was recommended for publication by Associate Editor A. LaViers and Editor G. Venture upon evaluation of the reviewers’ comments. This work was supported by ARL Cooperative Agreement Number W911NF-20-2-0087. (Corresponding author: X. Jessie Yang.) The authors are with the Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48105, U.S. (e-mail: yaohuig@umich.edu; shicong@umich.edu; xijyang@umich.edu). Digital Object Identifier 10.1109/LRA.2021.3067626",,,,,,,,,,"Sheridan, T.B., Human-robot interaction: Status and challenges (2016) Hum. Factors, 58 (4), pp. 525-532; Azevedo-Sa, H., Jayaraman, S.K., Yang, X.J., Robert, L.P., Tilbury, D.M., Context-adaptive management of drivers' trust in automated vehicles (2020) IEEE Robot. Automat. Lett., 5 (4), pp. 6908-6915. , Oct; Yang, X.J., Unhelkar, V.V., Li, K., Shah, J.A., Evaluating effects of user experience and system transparency on trust in automation (2017) Proc. 12thACM/IEEE Int. Conf.Hum.-Robot Interaction (HRI '17), pp. 408-416. , ACM; Du, N., Huang, K.Y., Yang, X.J., Not all information is equal: Effects of disclosing different types of likelihood information on trust, compliance and reliance, and task performance in human-automation teaming (2020) Hum. Factors, 62 (6), pp. 987-1001; Chen, M., Nikolaidis, S., Soh, H., Hsu, D., Srinivasa, S., Trust-aware decision making for human-robot collaboration: Model learning and planning (2020) ACMTrans.Hum.-Robot Interaction (THRI), 9 (2), pp. 1-23; Guo, Y., Yang, X.J., Modeling and predicting trust dynamics in humanrobot teaming: A Bayesian inference approach (2020) Int. J. Social Robot., , Jul; Lee, J.D., See, K.A., Trust in automation: Designing for appropriate reliance (2004) Hum. Factors, 46 (1), pp. 50-80; Xu, A., Dudek, G., Optimo: Online probabilistic trust inference model for asymmetric human-robot collaborations (2015) Proc. 10th ACM/IEEE Int. Conf. Hum.-Robot Interaction, pp. 221-228; Lu, Y., (2020) Detecting and Overcoming Trust Miscalibration in Real Time Using An Eye-tracking Based Technique, , Ph.D. dissertation, University of Michigan; Hu, W.-L., Akash, K., Jain, N., Reid, T., Real-time sensing of trust in human-machine interactions (2016) IFAC-PapersOnLine, 49 (32), pp. 48-53; Azevedo-Sa, H., Jayaraman, S.K., Esterwood, C.T., Yang, X.J., Robert, L.P., Tilbury, D.M., Real-time estimation of drivers' trust in automated driving systems (2020) Int. J. Social Robot.; MacDonald, G., Nail, P.R., Harper, J.R., Do people use reverse psychology? an exploration of strategic self-anticonformity (2011) Social Influence, 6 (1), pp. 1-14; Lee, J.D., Moray, N., Trust control strategies and allocation of function in human-machine systems (1992) Ergonom., 35 (10), pp. 1243-1270; Soh, H., Xie, Y., Chen, M., Hsu, D., Multi-task trust transfer for humanrobot interaction (2020) Int. J. Robot. Res., 39 (2-3), pp. 233-249; Yang, X.J., Guo, Y., Schemanske, C., From trust to trust dynamics: Combining empirical and computational approaches to model and predict trust dynamics in human-autonomy interaction Human-Automation Interaction: G. Transportation. Duffy, , Vincent, J. Landry, Steven, J. D. Lee, and N. A. Stanton, Eds. Springer to be published; Losey, D.P., Sadigh, D., Robots that take advantage of human trust (2019) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 7001-7008; Wang, N., Pynadath, D.V., Hill, S.G., The impact of pomdp-generated explanations on trust and performance in human-robot teams (2016) Proc. Int. Conf. Auton. Agents Multiagent Syst., pp. 997-1005; Mayer, R.C., An integrative model of organizational trust (1995) Acad. Manage. Rev., 20 (3), pp. 709-734. , Jul; Klimoski, R.J., Karol, B.L., The impact of trust on creative problem solving groups (1976) J. Appl. Psychol., 61 (5), pp. 630-633. , Oct; Faulkner, P., Simpson, T., (2017) The Philosophy of Trust, , London, U.K.: Oxford University Press; Lewis, M., Sycara, K., Walker, P., (2018) The Role of Trust in Human-Robot Interaction, pp. 135-159. , Cham: Springer International Publishing","Yang, X.J.; Department of Industrial and Operations Engineering, United States; email: xijyang@umich.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85103276225
"Vranken T., Sliwa B., Wietfeld C., Schreckenberg M.","57208494900;57193692610;16176324400;7003981225;","Adapting a cellular automata model to describe heterogeneous traffic with human-driven, automated, and communicating automated vehicles",2021,"Physica A: Statistical Mechanics and its Applications","570",,"125792","","",,6,"10.1016/j.physa.2021.125792","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100387170&doi=10.1016%2fj.physa.2021.125792&partnerID=40&md5=77ab4e72dfe91b1e9d88880d4fc0f6c0","Physics of Transport and Traffic, University Duisburg–Essen, Germany; Communication Networks Institute, TU Dortmund University, Germany","Vranken, T., Physics of Transport and Traffic, University Duisburg–Essen, Germany; Sliwa, B., Communication Networks Institute, TU Dortmund University, Germany; Wietfeld, C., Communication Networks Institute, TU Dortmund University, Germany; Schreckenberg, M., Physics of Transport and Traffic, University Duisburg–Essen, Germany","This paper introduces a cellular automaton design for single lane highway sections with a reduced time step length of 0.1 s. It is able to model automated and human vehicle agents in heterogeneous as well as homogeneous traffic. Furthermore, it allows agents to adopt different behaviour patterns depending on whether they follow automated or human vehicle agents. A distinction between communicating and non-communicating automated vehicles is also possible. Simulations showed that autonomous vehicles are expected to increase road capacity, reduce the frequency and lifetime of traffic jams, and improve traffic synchronisation. Homogeneous communicating automated vehicles traffic can increase the capacity up to about 480% compared to conventional traffic. Furthermore, potential problems with the human–robot interaction that could result in accidents were identified and removed. © 2021","Automated vehicle model; Cellular automata model; Heterogeneous traffic model; Traffic","Automation; Cellular automata; Human robot interaction; Traffic congestion; Automated vehicles; Behaviour patterns; Cellular automata modeling; Heterogeneous traffic; Potential problems; Robot interactions; Single-lane highway; Time step length; Road vehicles",,,,,"Deutsche Forschungsgemeinschaft, DFG: SFB 876","Part of the work on this paper has been supported by Deutsche Forschungsgemeinschaft (DFG), Germany within the Collaborative Research Center SFB 876 “Providing Information by Resource-Constrained Analysis”, project B4.",,,,,,,,,,"Pettigrew, S., Why public health should embrace the autonomous car (2017) Aust. N. Z. J. Public Health, 41 (1), pp. 5-7; Do, W., Rouhani, O.M., Miranda-Moreno, L., Simulation-based connected and automated vehicle models on highway sections: A literature review (2019) J. Adv. Transp., 2019; Schreurs, M.A., Steuwer, S.D., Autonomous driving—Political, legal, social, and sustainability dimensions (2016) Autonomous Driving, pp. 149-171. , Maurer M. Gerdes J.C. Lenz B. Winner H. Springer Open Berlin; Goodin, C., Carrillo, J.T., McInnis, D.P., Cummins, C.L., Durst, P.J., Gates, B.Q., Newell, B.S., Unmanned ground vehicle simulation with the virtual autonomous navigation environment (2017) 6th International Conference on Military Technologies, pp. 160-165. , Křivánek V. Technologies I.C.o.M. IEEE Piscataway, NJ; Fagnant, D.J., Kockelman, K., Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations (2015) Transp. Res. A, 77, pp. 167-181; Abboud, K., Omar, H.A., Zhuang, W., Interworking of DSRC and cellular network technologies for V2X communications: A survey (2016) IEEE Trans. Veh. Technol., 65 (12), pp. 9457-9470; Marzoug, R., Téllez, B.C., Téllez, M.C., Pérez, G.A.M., Bassam, A., Oubram, O., Optimization of traffic intersection using connected vehicles (2019) Internat. J. Modern Phys. C, 30 (6); Ameen, H., Mahamad, A., Saon, S., Nor, D., Ghazi, K., A review on vehicle to vehicle communication system applications (2020) Indones. J. Electr. Eng. Comput. Sci., 18, pp. 188-198; Marzoug, R., Lakouari, N., Oubram, O., Ez-Zahraouy, H., Cisneros-Villalobos, L., Velásquez-Aguilar, J.G., Impact of information feedback strategy on the car accidents in two-route scenario (2018) Internat. J. Modern Phys. C, 29 (9); Marchant, G.E., Lindor, R.A., The coming collision between autonomous vehicles and the liability system (2012) Santa Clara Law Rev., 52 (4), pp. 1321-1340. , https://digitalcommons.law.scu.edu/lawreview/vol52/iss4/6, URL; Lohmann, M.F., Liability issues concerning self-driving vehicles (2016) Eur. J. Risk Regul., 7 (2), pp. 335-340; Ma, J., Zhou, F., Huang, Z., Melson, C.L., James, R., Zhang, X., Hardware-in-the-loop testing of connected and automated vehicle applications: A use case for queue-aware signalized intersection approach and departure (2018) Transp. Res. Rec.: J. Transp. Res. Board, 2672 (22), pp. 36-46; Treiber, J., Hennecke, F., Helbing, Z., Congested traffic states in empirical observations and microscopic simulations (2000) Phys. Rev. E, 62 (2), pp. 1805-1824; Kesting, A., Treiber, M., Schönhof, M., Helbing, D., Adaptive cruise control design for active congestion avoidance (2008) Transp. Res. C, 16 (6), pp. 668-683; Shladover, S.E., Su, D., Lu, X.-Y., Impacts of cooperative adaptive cruise control on freeway traffic flow (2012) Transp. Res. Rec.: J. Transp. Res. Board, 2324 (1), pp. 63-70; Kerner, B.S., Failure of classical traffic flow theories: Stochastic highway capacity and automatic driving (2016) Physica A, 450, pp. 700-747; Ngoduy, D., Instability of cooperative adaptive cruise control traffic flow: A macroscopic approach (2013) Commun. Nonlinear Sci. Numer. Simul., 18 (10), pp. 2838-2851; Delis, A.I., Nikolos, I.K., Papageorgiou, M., Macroscopic traffic flow modeling with adaptive cruise control: Development and numerical solution (2015) Comput. Math. Appl., 70 (8), pp. 1921-1947; VanderWerf, J., Shladover, S., Kourjanskaia, N., Miller, M., Krishnan, H., Modeling effects of driver control assistance systems on traffic (2001) Transp. Res. Rec.: J. Transp. Res. 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Rec.; Ye, L., Yamamoto, T., Modeling connected and autonomous vehicles in heterogeneous traffic flow (2018) Physica A, 490, pp. 269-277; Yao, B.F.S., Shet, R.A., Managing connected automated vehicles in mixed traffic considering communication reliability: a platooning strategy (2020) Transp. Res. Procedia, 47, pp. 43-50; Ghiasi, A., Hussain, O., Qian, Z.S., Li, X., A mixed traffic capacity analysis and lane management model for connected automated vehicles: a markov chain method (2017) Transp. Res. B, 106, pp. 266-292; van Arem, B., van Driel, C.J.G., Visser, R., The impact of cooperative adaptive cruise control on traffic-flow characteristics (2006) IEEE Trans. Intell. Transp. Syst., 7 (4), pp. 429-436; Dickie, D.A., Boyle, L.N., Drivers’ understanding of adaptive cruise control limitations (2009) Proc. Hum. Factors Ergon. Soc. Annu. Meet., 53 (23), pp. 1806-1810; Funkhouser, K., Drews, F., Putting the brakes on autonomous vehicle control (2016) Proc. Hum. Factors Ergon. Soc. Annu. 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E, 53 (2), pp. R1297-R1300; Bundestag, D., Straßenverkehrs-Ordnung: StVO (2013); Nyholm, S., Smids, J., Automated cars meet human drivers: responsible human-robot coordination and the ethics of mixed traffic (2018) Ethics Inf. Technol., 9 (4), p. 332; Gerdes, J.C., Thornton, S.M., Implementable ethics for autonomous vehicles (2015) Autonomes Fahren, Vol. 14, pp. 87-102. , Maurer M. Gerdes J.C. Lenz B. Winner H. Springer Vieweg Berlin and Heidelberg; Kerner, B.S., Introduction to Modern Traffic Flow Theory and Control: The Long Road to Three-Phase Traffic Theory (2009), Springer-Verlag Berlin Heidelberg Berlin, Heidelberg; Sliwa, B., Liebig, T., Vranken, T., Schreckenberg, M., Wietfeld, C., System-of-systems modeling, analysis and optimization of hybrid vehicular traffic (2019), 2019 Annual IEEE International Systems Conference, SysCon, Orlando, Florida, USA; Gaudet, B., Review of Cooperative Truck Platooning Systems (2014), National Research Council of Canada. Automotive and Surface Transportation","Vranken, T.; Physics of Transport and Traffic, Germany; email: tim.vranken@uni-due.de",,,"Elsevier B.V.",,,,,03784371,,PHYAD,,"English","Phys A Stat Mech Appl",Article,"Final","",Scopus,2-s2.0-85100387170
"Keet C.M.","8964217800;","Natural language generation requirements for social robots in sub-Saharan Africa",2021,"2021 IST-Africa Conference, IST-Africa 2021",,,,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118951389&partnerID=40&md5=2d7725b9cfb894bab394c9e3ea48256d","University of Cape Town, 18 University Avenue, Cape Town, 7701, South Africa","Keet, C.M., University of Cape Town, 18 University Avenue, Cape Town, 7701, South Africa","Robots are deployed in Africa mainly in manufacturing, yet they may assist in society as future oriented technologies as well. They may ameliorate, e.g., service delivery issues and skills shortages. In this discussion paper, several uses and use cases relevant to Sub-Saharan Africa are described and requirements identified. We zoom in on human-robot interaction in Niger-Congo B ('bantu') languages. Use cases for healthcare and education elucidate specific requirements for the natural language generation component of robots in society. In contrast to typical generation systems, it demands i) combining data-to-text and knowledge-to-text in one system, ii) generating different types of sentences so as to switch between written and spoken language, and iii) processing non-trivial numbers. © 2021 IST-Africa Institute and Authors.","Natural Language Generation; Niger-Congo B (Bantu) Languages; Robots; Technology-enabled Healthcare and Learning","Human robot interaction; Natural language processing systems; Generation requirements; Humans-robot interactions; Natural language generation; Niger; Niger-congo B (bantu) language; Service delivery; Skill shortage; Social robots; Sub-saharan africa; Technology-enabled healthcare and learning; Health care",,,,,"National Research Foundation, NRF: 120852","Acknowledgements This work was financially supported by the National Research",,,,,,,,,,"Barnard, E., Davel, M.H., Van Heerden, C., The nchlt speech corpus of the South African languages (2014) Spoken Language Technologies for Under-Resourced Languages, pp. 194-200. , May; Byamugisha, J., (2019) Ontology Verbalization in Agglutinating Bantu Languages: A Study of Runyankore and Its Generalizability, , Department of Computer Science, University of Cape Town, South Africa; Byamugisha, J., Keet, C.M., DeRenzi, B., Evaluation of a runyankore grammar engine for healthcare messages (2017) Proceedings of INLG'17. ACL, pp. 105-113. , Santiago de Compostela, Spain. 4-7 Sept; Calitz, A.P., Poisat, P., Cullen, M., The future African workplace: The use of collaborative robots in manufacturing (2017) SA Journal of Human Resource Management, 15, p. a901; Cawsey, J.A., Jones, B.R., Pearson, J., The evaluation of a personalized health information system for patients with cancer (2000) User Modeling and User-Adapted Interaction, 10 (1), pp. 47-72; (2020) South Africa's Ratio of Industrial Robots to Employees Amongst the Global Top 40. Online, , https://www.consultancy.co.za/news/654/south-africas-ratio-of-industrial-robots-to-employeesamongst-the-global-top-40, Consultancy co. za; 4 June 2018. Last accessed: 2 Dec; Davids, N., Touch down for sa's banking siri (2019) UCT News, , https://www.news.uct.ac.za/news/audio/-article/2019-09-12-touch-down-for-sas-banking-siri, 12 Sept; Last accessed: 4 Dec 2020; Dube, M., Suleman, H., Proceedings of icadl'19 Language Identification for South African Bantu Languages Using Rank Order Statistics, pp. 283-289. , Springer LNCS 11853; Edwards, B.I., Cheok, A.D., Why not robot teachers: Artificial intelligence for addressing teacher shortage (2018) Applied Artificial Intelligence, 32 (4), pp. 345-360; Weizenbaum, J., Eliza-a computer program for the study of natural language communication between man and machine (1966) Communications of the ACM, 9 (1), pp. 36-42; Fischinger, D., Einramhof, P., Papoutsakis, K., Hobbit, a care robot supporting independent living at home: First prototype and lessons learned (2016) Robotics and Autonomous Systems, 75 (A), pp. 60-78; Gardent, C., Perez-Beltrachini, L., Using fb-ltag derivation trees to generate transformation-based grammar exercises (2012) Proceedings of TAG+11, pp. 117-125; Gatt, A., Reiter, E., Simplenlg: A realization engine for practical applications (2009) Proceedings of ENLG'09, pp. 90-93. , March 30-31, Athens, Greece. ACL; Gilbert, N., Keet, C.M., Automating question generation and marking of language learning exercises for isizulu (2018) Proceedings of CNL'18, 304, pp. 31-40. , FAIA. IOS Press; Green, J., Mine rescue robots requirements outcomes from an industry workshop (2013) Proceedings of RobMech'13, pp. 111-116. , Durban, South Africa; Hussey, N., The language barrier: The overlooked challenge to equitable health care (2012) SAHR, pp. 189-195. , 13; Keet, C.M., Khumalo, L., Toward verbalizing logical theories in isizulu Proceedings of CNL'14, 8625, pp. 78-89. , Springer LNAI; Keet, C.M., Khumalo, L., Toward a knowledge-to-text controlled natural language of isizulu (2017) Language Resources and Evaluation, 51, pp. 131-157; Keet, C.M., Xakaza, M., Khumalo, L., Verbalising owl ontologies in isizulu with python The Semantic Web: ESWC 2017 Satellite Events, pp. 59-64. , Springer LNCS, 10577; Mahlaza, Z., (2018) Grammars for Generating IsiXhosa and IsiZulu Weather Bulletin Verbs, , Department of Computer Science, University of Cape Town, South Africa; Lange, H., Ljunglöf, P., Putting control into language learning Proceedings of CNL'18. IOS Press FAIA, 304, pp. 61-70; Marais, L., Awezamed: A multilingual, multimodal speech-to-speech translation application for maternal health care (2020) Proceedings of FUSION'20, pp. 1-8. , Rustenburg, SA; Masinga, P., Campbell, H., Trimble, J.A., A framework for human collaborative robots, operations in South African automotive industry (2015) Proceedings of IEEM'15, pp. 1494-1497. , Singapore; Nxumalo, M., No 'killer robot' plans for sandf (2018) IOL Daily News, , https://www.iol.co.za/dailynews/news/no-killer-robot-plans-for-sandf-16827299, 3 Sept; last accessed: 4 Dec 2020; Nthite, T., Tsoeu, M., End-to-end text-to-speech synthesis for under resourced South African languages (2020) Proceedings of SAUPEC/RobMech/PRAS'20, p. 6. , Cape Town, South Africa abstract; de Sripada, O.R., Sripada, S., Adapting simplenlg for Brazilian Portuguese realisation (2014) Proceedings of INLG'14 ACL, pp. 93-94. , Philadelphia, USA, 19-21 June; Oluoch, T., De Keizer, N., Langat, P., A structured approach to recording AIDS-defining illnesses in Kenya: A snomed ct based solution (2015) Journal of Biomedical Informatics, 56, pp. 387-394; Sharkey, A.J.C., (2016) Should We Welcome Robot Teachers? Ethics in Information Technology, 18, pp. 283-297; Vaudry, P.-L., Lapalme, G., Adapting simplenlg for bilingual english-French realisation (2013) Proceedings of ENLG'13, pp. 183-187. , August 8-9, 2013, Sofia, Bulgaria. ACL","Keet, C.M.; University of Cape Town, 18 University Avenue, South Africa; email: mkeet@cs.uct.ac.za","Cunningham M.Cunningham P.",,"Institute of Electrical and Electronics Engineers Inc.","2021 IST-Africa Conference, IST-Africa 2021","10 May 2021 through 14 May 2021",,173211,,9781905824670,,,"English","IST-Africa Conf., IST-Africa",Conference Paper,"Final","",Scopus,2-s2.0-85118951389
"Suguitan M., Hoffman G.","57201559535;35726707500;","You Are (Not) the Robot: Variable Perspective Motion Control of a Social Telepresence Robot",2021,"Conference on Human Factors in Computing Systems - Proceedings",,,,"","",,,"10.1145/3411763.3451571","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105807225&doi=10.1145%2f3411763.3451571&partnerID=40&md5=df9e73618357c2dd60cc86356ee8a7e5","Human-Robot Collaboration and Companionship Lab, Cornell University, United States; Cornell University, United States","Suguitan, M., Human-Robot Collaboration and Companionship Lab, Cornell University, United States; Hoffman, G., Cornell University, United States","COVID-19 has dramatically limited opportunities for in-person human-robot interaction research and shifted focus towards remote technologies such as telepresence robots. Telepresence robots enable rich communication and agency through their physical presence and controllability, but their screen-oriented designs and button-centric controls abstract users away from their own physicality. In this demonstration, we present a telepresence system for remotely controlling a social robot using a smartphone's motion sensors. Users can select between a first-person perspective from the robot's internal camera or a third-person perspective showing the robot's whole body. Users can also record their movements for later playback. This system has applications as an embodied remote communication platform and for crowdsourcing demonstrations of user-crafted robot movements. © 2021 Owner/Author.","crowdsourcing; human-robot interaction; motion control; telepresence","Human engineering; Motion sensors; Social robots; Visual communication; First-person perspectives; Remote communication; Remotely controlling; Robot movements; Tele-presence systems; Telepresence robots; Whole body; Machine design",,,,,,,,,,,,,,,,"Adalgeirsson, S., Breazeal, C., MeBot: A robotic platform for socially embodied telepresence (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 15-22. , https://doi.org/10.1109/HRI.2010.5453272; Ainasoja, A.E., Pertuz, S., Kämäräinen, J., Smartphone teleoperation for self-balancing telepresence robots (2019) VISIGRAPP (4: VISAPP), pp. 561-568. , https://doi.org/10.5220/0007406405610568; Denisova, A., Cairns, P., First person vs. third person perspective in digital games: Do player preferences afect immersion (2015) Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (Seoul, Republic of Korea) (CHI '15), pp. 145-148. , https://doi.org/10.1145/2702123.2702256, Association for Computing Machinery, New York, NY, USA; Galvan Debarba, H., Bovet, S., Salomon, R., Blanke, O., Her Belin, B., Boulic, R., Characterizing first and third person viewpointsand their alternation for embodied interaction in virtual reality (2017) PLOS. ONE, 12 (12), pp. 1-19. , https://doi.org/10.1371/journal.pone.0190109, 12 2017; Goldberg, K., (2001) The Robot in the Garden: Telerobotics and Telepistemology in the Age of the Internet, , MIT Press; Gomez, R., Szapiro, D., Merino, L., Brock, H., Nakamura, K., Sabanovic, S., Emoji to robomoji: Exploring afective telepresence through haru (2020) International Conference on Social Robotics, pp. 652-663. , https://doi.org/10.1007/978-3-030-62056-1_54, Springer; Gorisse, G., Christmann, O., Armand Amato, E., Richir, S., First-and third-person perspectives in immersive virtual environments: Presence and performance analysis of embodied users (2017) Frontiers in Robotics and AI 4, p. 33. , https://doi.org/10.3389/frobt.2017.00033, 2017; Jeong, K., Sung, J., Lee, H., Kim, A., Kim, H., Park, C., Jeong, Y., Kim, J., Fribo: A social networking robot for increasing social connectedness through sharing daily home activities from living noise data (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (Chicago, IL, USA) (HRI '18), pp. 114-122. , https://doi.org/10.1145/3171221.3171254, Association for Computing Machinery, New York, NY, USA; Jonggil, A., Jounghyun Kim, G., SPRinT: A mixed approach to a hand-held robot interface for telepresence (2018) International Journal of Social Robotics, 10 (4), pp. 537-552. , https://doi.org/10.1007/s12369-017-0463-2, 2018; Kilteni, K., Groten, R., Slater, M., The sense of embodiment in virtual reality (2012) Presence: Teleoperators and Virtual Environments, 21 (4), pp. 373-387. , https://doi.org/10.1162/PRES_a_00124, 2012; Lau, F., Gopinath, D., Argall, B.D., (2020) A JavaScript Framework for Crowdsourced Human-Robot Interaction Experiments: RemoteHRI, , Association for the Advancement of Artifcial Intelligence; Mandlekar, A., Zhu, Y., Garg, A., Booher, J., Spero, M., Tung, A., Gao, J., Fei-Fei, L., RoboTurk: A crowdsourcing platform for robotic skill learning through imitation (2018) Proceedings of the 2nd Conference on Robot Learning (Proceedings of Machine Learning Research 87). PMLR, pp. 879-893. , http://proceedings.mlr.press/v87/mandlekar18a.html; Murphy, R.R., Babu Manjunath Gandudi, V., Adams, J., (2020) Applications of Robots for COVID-19 Response, , [cs. RO]; Sakashita, M., Minagawa, T., Koike, A., Suzuki, I., Kawahara, K., Ochiai, Y., You as a puppet: Evaluation of telepresence user interface for puppetry (2017) Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology (Québec City, QC, Canada) (UIST '17), pp. 217-228. , https://doi.org/10.1145/3126594.3126608, Association for Computing Machinery, New York, NY, USA; Sirkin, D., Ju, W., Consistency in physical and on-screen action improves perceptions of telepresence robots (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction (Boston, Massachusetts, USA) (HRI '12), pp. 57-64. , https://doi.org/10.1145/2157689.2157699, Association for Computing Machinery, New York, NY, USA; Dan Stiehl, W., Ki Lee, J., Breazeal, C., Nalin, M., Morandi, A., Sanna, A., The huggable: A platform for research in robotic companions for pediatric care (2009) Proceedings of the 8th International Conference on Interaction Design and Children (Como, Italy) (IDC '09), pp. 317-320. , https://doi.org/10.1145/1551788.1551872, Association for Computing Machinery, New York, NY, USA; Suguitan, M., Gomez, R., Hofman, G., MoveAE: Modifying afective robot movements using classifying variational autoencoders (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 481-489. , https://doi.org/10.1145/3319502.3374807; Suguitan, M., Hofman, G., Blossom: A handcrafted open-source robot (2019) ACM Transactions on Human-Robot Interaction, 8 (1), p. 27. , https://doi.org/10.1145/3310356, March 2019",,,"ACM Special Interest Group on Computer-Human Interaction (SIGCHI)","Association for Computing Machinery","2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI EA 2021","8 May 2021 through 13 May 2021",,168787,,9781450380959,,,"English","Conf Hum Fact Comput Syst Proc",Conference Paper,"Final","",Scopus,2-s2.0-85105807225
"Esterwood C., Essenmacher K., Yang H.","57216331965;57223996509;57224005501;","A meta-analysis of human personality and robot acceptance in human-robot interaction",2021,"Conference on Human Factors in Computing Systems - Proceedings",,,,"","",,4,"10.1145/3411764.3445542","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106713994&doi=10.1145%2f3411764.3445542&partnerID=40&md5=59b4fa1826b5a75089aa1b8a0d6adfab","School of Information, University of Michigan, Ann Arbor, MI, United States","Esterwood, C., School of Information, University of Michigan, Ann Arbor, MI, United States; Essenmacher, K., School of Information, University of Michigan, Ann Arbor, MI, United States; Yang, H., School of Information, University of Michigan, Ann Arbor, MI, United States","Human personality has been identifed as a predictor of robot acceptance in the human-robot interaction (HRI) literature. Despite this, the HRI literature has provided mixed support for this assertion. To better understand the relationship between human personality and robot acceptance, this paper conducts a meta-analysis of 26 studies. Results found a positive relationship between human personality and robot acceptance. However, this relationship varied greatly by the specifc personality trait along with the study sample's age, gender diversity, task, and global region. This meta-analysis also identifed gaps in the literature. Namely, additional studies are needed that investigate both the big fve personality traits and other personality traits, examine a more diverse age range, and utilize samples from previously unexamined regions of the globe. © 2021 ACM.","Adaptability; Anxiety; Enjoyment; Hri; Human-robot interaction; Individual diferences; Meta-analysis; Personality; Robot acceptance; Sociability; Social presence; Trust","Human engineering; Man machine systems; Gender diversity; Global regions; Human robot Interaction (HRI); Meta analysis; Mixed supports; Personality traits; Robot acceptances; Study samples; Social robots",,,,,,,,,,,,,,,,"(1937) Personality: A Psychological Interpretation., , Gordon Willard Allport., 1937; Aly, A., Tapus, A., A model for synthesizing a combined verbal and nonverbal behavior based on personality traits in human-robot interaction (2013) 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI)., pp. 325-332. , IEEE; Andrist, S., Mutlu, B., Tapus, A., Look like me: Matching robot personality via gaze to increase motivation (2015) Proceedings of the 33rd Annual Acm Conference on Human Factors in Computing Systems., pp. 3603-3612. , ACM; Ashton, M.C., Lee, K., Vries De, R.E., The hexaco honesty-humility, agreeableness, and emotionality factors: A review of research and theory (2014) Pers. 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"Maithani H., Ramon J.A.C., Lequievre L., Mezouar Y., Alric M.","57209266536;35769026000;34167996000;6602736108;57212599321;","Exoscarne: Assistive strategies for an industrial meat cutting system based on physical human-robot interaction",2021,"Applied Sciences (Switzerland)","11","9","3907","","",,1,"10.3390/app11093907","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106658381&doi=10.3390%2fapp11093907&partnerID=40&md5=19609ee91d6152daef17dc8cd3c5b375","Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, F-63000, France; Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, 15782, Spain; ADIV, ZAC des Gravanches, 10 Rue Jacqueline Auriol, Clermont-Ferrand, 63100, France","Maithani, H., Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, F-63000, France; Ramon, J.A.C., Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, 15782, Spain; Lequievre, L., Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, F-63000, France; Mezouar, Y., Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, F-63000, France; Alric, M., ADIV, ZAC des Gravanches, 10 Rue Jacqueline Auriol, Clermont-Ferrand, 63100, France","Musculoskeletal disorders of the wrist are common in the meat industry. A proof of concept of a physical human-robot interaction (pHRI)-based assistive strategy for an industrial meat cutting system is demonstrated which can be transferred to an exoskeleton later. We discuss how a robot can assist a human in pHRI, specifically in the context of an industrial project i.e for the meat cutting industry. We developed an impedance control-based system that enables a KUKA LWR robot to provide assistive forces to a professional butcher while simultaneously allowing motion of the knife (tool) in all degrees of freedom. We developed two assistive strategies—a force amplification strategy and an intent prediction strategy—and integrated them into an impedance controller. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Assistive robotics; Collaborative robots; Physical human-robot interaction",,,,,,"Horizon 2020 Framework Programme, H2020: 869855","Funding: This research received funding from the French government research program Investisse-ments d‘Avenir through the UMTs ACTIA Mécarnéo-AgRobErgo and the project Exoscarne (Call P3A-ICF2A-2I2A, FranceAgriMer) and from the European Union’s Horizon 2020 research and innovation programme under grant agreement nº 869855 (SoftManBot project).",,,,,,,,,,"(2020) Statistiques Accidents du Travail et Maladies Professionnelles, , Carsat Bretagne: Rennes, France; (2019) L’Assurance Maladie-Risques Professionels, Éléments Statistiques et Financiers; Caisse nationale de l’Assurance Maladie des travailleurs salariés, , Rapport Annuel Paris, France, 2020; (2019) État de Santé des Salariés de la Filière Viande du Régime Agricole en Bretagne, , Institut de Veille Sanitaire: Saint-Maurice, France; De Medeiros Esper, I., From, P.J., Mason, A., Robotisation and intelligent systems in abattoirs (2021) Trends Food Sci. 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J, 34, pp. 376-385; Bischoff, R., Kurth, J., Schreiber, G., Koeppe, R., Albu-Schäffer, A., Beyer, A., Eiberger, O., Grunwald, G., The KUKA-DLR Lightweight Robot arm-a new reference platform for robotics research and manufacturing (2010) Proceedings of the ISR 2010 (41st International Symposium on Robotics) and ROBOTIK 2010 (6th German Conference on Robotics), pp. 1-8. , VDE, Munich, Germany, 7-9 June; Albu-Schaffer, A., Hirzinger, G., Cartesian impedance control techniques for torque controlled light-weight robots (2002) Proceedings of the 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292), 1, pp. 657-663. , Washington, DC, USA, 11-15 May; Schreiber, G., Stemmer, A., Bischoff, R., The fast research interface for the kuka lightweight robot (2010) Proceedings of the IEEE Workshop on Innovative Robot Control Architectures for Demanding (Research) Applications How to Modify and Enhance Commercial Controllers (ICRA 2010), pp. 15-21. , Anchorage, AK, USA, 3-7 May; (2010) KUKA LWR Document Manual, , KUKA Roboter GmbH: Augsburg, Germany; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., ROS: An open-source Robot Operating System (2009) ICRA Workshop on Open Source Software; IEEE ICRA 2009, 3, p. 5. , Kobe, Japan; http://docs.ros.org/indigo/api/orocos_kdl/html/""target=""_blank"">http://docs.ros.org/indigo/api/orocos_kdl/html/</a, Available online: <a href=""> (accessed on 21 April 2021); Corke, P, (2017) Robotics, Vision and Control: Fundamental Algorithms in MATLAB® Second Edition, Completely Revised, 118. , Springer: Berlin/Heidelberg, Germany; https://in.mathworks.com/help/robotics/""target=""_blank, Available online: <a href="""">https://in.mathworks.com/help/robotics/</a> (accessed on 21 April 2021)","Maithani, H.; Université Clermont Auvergne, France; email: harshmaithani09@gmail.com",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85106658381
"Boateng A., Zhang Y.","57222464331;55922457000;","Virtual shadow rendering for maintaining situation awareness in proximal human-robot teaming",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447221","494","498",,,"10.1145/3434074.3447221","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102762115&doi=10.1145%2f3434074.3447221&partnerID=40&md5=f31b6468a383d9385cbe21b4ed929c1b","School of Computing, Informatics, and Decision Systems Engineering, Tempe, AZ, United States","Boateng, A., School of Computing, Informatics, and Decision Systems Engineering, Tempe, AZ, United States; Zhang, Y., School of Computing, Informatics, and Decision Systems Engineering, Tempe, AZ, United States","One focus of augmented reality (AR) in robotics has been on enriching the interface for human-robot interaction. While such an interface is often made intuitive to interact with, it invariably imposes novel objects into the environment. In situations where the human already has a focus, such as in a human-robot teaming task, these objects can potentially overload our senses and lead to degraded teaming performance. In this paper, we propose using AR objects to solely augment natural objects to avoid disrupting our natural senses while adding critical information about the current situation. In particular, our case study focuses on addressing the limited field of view of humans by incorporating persistent virtual shadows of robots for maintaining situation awareness in proximal human-robot teaming tasks. © 2021 ACM.","Augmented reality; Human-robot interaction; Virtual shadow","Agricultural robots; Augmented reality; Man machine systems; Current situation; Field of views; Human robots; Natural objects; Situation awareness; Social robots",,,,,"National Aeronautics and Space Administration, NASA: NNX17AD06G; Air Force Office of Scientific Research, AFOSR: FA9550-18-1-0067; National Science Foundation, NSF: IIS-1844524","We thank the reviewers for their helpful comments. This research is supported in part by the NSF grant IIS-1844524, the NASA grant NNX17AD06G, and the AFOSR grant FA9550-18-1-0067.",,,,,,,,,,"(2015) Unity-Manual: Shadows, , 2015; Trends in educational augmented reality studies: A systematic review (2018) Malaysia Online Journal of Educational Technology, 6 (2), pp. 60-74. , 2018; Andersen, R.S., Madsen, O., Moeslund, T.B., Amor, H.B., Projecting robot intentions into human environments (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 294-301; Baranovski, I., Stankovski, S., Ostojic, G., Horvat, S., Sran, T., Augmented reality support for self-service automated systems Journal of Graphic Engineering and Design (JGED), 11 (1), p. 6; Bork, F., Eck, U., Navab, N., Birds vs. fish: Visualizing out-of-view objects in augmented reality using 3d minimaps (2019) 2019 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), pp. 285-286. , https://doi.org/10.1109/ISMAR-Adjunct.2019.00-28; Chen, H., Liu, X., Yin, D., Tang, J., A survey on dialogue systems (2017) ACM SIGKDD Explorations Newsletter, 19 (2), pp. 25-35. , https://doi.org/10.1145/3166054.3166058, Nov 2017; Cooke, N.J., Gorman, J.C., Myers, C.W., Duran, J.L., Interactive team cognition (2013) Cognitive Science, 37 (2), pp. 255-285. , https://doi.org/10.1111/cogs.12009, 2013 arXiv https://onlinelibrary. wiley. com/doi/pdf/10. 1111/cogs. 12009; Henderson, S.J., Feiner, S., (2009) Evaluating the Benefits of Augmented Reality for Task Localization in Maintenance of An Armored Personnel Carrier Turret., pp. 135-144; Hong, A., Zeydan, B., Charreyron, S., Ergeneman, O., Pane, S., Toy, M., Petruska, A., Nelson, B., Real-time holographic tracking and control of microrobots (2017) IEEE Robotics and Automation Letters, 2, p. 1. , https://doi.org/10.1109/LRA.2016.2579739, 01 2017; Kamphuis, C., Barsom, E., Schijven, M., Christoph, N., Augmented reality in medical education? (2014) Perspectives on Medical Education, 3 (4), pp. 300-311. , https://doi.org/10.1007/s40037-013-0107-7, September 2014; Livingston, M., Julier, S., Brown, D., Ii, J., Gabbard, J., Hix, D., (2008) An Augmented Reality System for Military Operations in Urban Terrain, , 12 2008; Mekni, M., Lemieux, A., (2014) Augmented Reality: Applications, Challenges and Future Trends; Michalos, G., Karagiannis, P., Makris, S., Tokçalar, Ö., Chryssolouris, G., Augmented reality (ar) applications for supporting human-robot interactive cooperation (2016) Procedia CIRP, 41, pp. 370-375. , https://doi.org/10.1016/j.procir.2015.12.005, 2016 Research and Innovation in Manufacturing: Key Enabling Technologies for the Factories of the Future-Proceedings of the 48th CIRP Conference on Manufacturing Systems; Moore, D., Ju, W., Sound as implicit influence on human-robot interactions (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (HRI '18), pp. 311-312. , https://doi.org/10.1145/3173386.3176918, Association for Computing Machinery, New York, NY, USA; Oliff, H., Liu, Y., Kumar, M., Williams, M., Ryan, M., Reinforcement learning for facilitating human-robot-interaction in manufacturing (2020) Journal of Manufacturing Systems, 56, pp. 326-340. , https://doi.org/10.1016/j.jmsy.2020.06.018, url= http://www. sciencedirect. com/science/article/pii/S0278612520301084 [n. d.]; Benites Paradeda, R., Hashemian, M., Afonso Rodrigues, R., Paiva, A., How facial expressions and small talk may influence trust in a robot (2016) Social Robotics, , Springer International Publishing; Pilet, J., Lepetit, V., Fua, P., Fast non-rigid surface detection, registration and realistic augmentation (2008) Internationa Journal in Computer Vission, pp. 109-122; Stoeva, D., Gelautz, M., Body language in affective human-robot interaction (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI '20), pp. 606-608. , https://doi.org/10.1145/3371382.3377432, Association for Computing Machinery; Urban, E.C., (1999) AThe Information Warrior; Vazquez-Alvarez, Y., Oakley, I., Brewster, S.A., (2012) Auditory Display Design for Exploration in Mobile Audio-augmented Reality., pp. 987-999; Wang, Y., Samaras, D., Estimation of multiple directional light sources for synthesis of augmented reality images (2003) Graphical Models, 65 (4), pp. 185-205. , 2003",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102762115
"Hannibal G.","56512012200;","Focusing on the vulnerabilities of robots through expert interviews for trust in human-robot interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447178","288","293",,,"10.1145/3434074.3447178","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102759582&doi=10.1145%2f3434074.3447178&partnerID=40&md5=b351c81ed2e7ba96a3bcaf78acc9cbb1","Institute of Visual Computing and Human-Centered Technology, TU Wien, Austria","Hannibal, G., Institute of Visual Computing and Human-Centered Technology, TU Wien, Austria","The practical value of trust in human-robot Interaction (HRI) has been to strength human long-term collaboration and interaction with robots. While much work focuses on determining the various factors influencing trust in HRI, vulnerability as a precondition of trust has not yet been explored from a robot-centered perspective. Based on eight semi-structured interviews with experts, I set out to identify robot vulnerabilities for then to present a systematic overview that resulted in a total of 13 categories grouped into four different themes. In the discussion, I specifically focus on how the experts interpreted the notion of vulnerability as it would relate to robots and how malicious human behavior can be problematic when aiming to ensure mutual trust in HRI. © 2021 ACM.","Concept; Ethics; Expert interviews; Trust; Vulnerability","Agricultural robots; Behavioral research; Man machine systems; Human behaviors; Human robot Interaction (HRI); Mutual trust; Semi structured interviews; Social robots",,,,,"E056-05","Many thanks to Dr. Mateusz Tokarski for transcribing the interviews, Dr. Astrid Weiss for the helpful comments on previous versions, and the reviewers for their feedback. This work was supported by the Trust Robot Doctoral College (E056-05) at TU Wien.",,,,,,,,,,"Baier, A., Trust and antitrust (1986) Ethics, 96 (2), pp. 231-260. , 1986; Booth, S., Tompkin, J., Pfister, H., Waldo, J., Gajos, K., Nagpal, R., Piggybacking robots: Human-robot overtrust in university dormitory security (2017) Proceedings of the 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI '17), pp. 426-434. , ACM Press, Vienna, Austria; Braun, V., Clarke, V., Hayfield, N., Terry, G., Thematic analysis (2019) Handbook of Research Methods in Health Social Sciences, 48, pp. 843-860. , P. Liamputtong (Ed.) 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Reframing the question about robot rights (2018) Information, 9 (4), p. 73. , 3 2018; Tolmeijer, S., Weiss, A., Hanheide, M., Lindner, F., Powers, T.M., Dixon, C., Tielman, M.L., Taxonomy of trust-relevant failures and mitigation strategies (2020) 15th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 3-12. , ACM, Cambridge, United Kingdom; Traeger, M.L., Strohkorb Sebo, S., Jung, M., Scassellati, B., Christakis, N.A., Vulnerable robots positively shape human conversational dynamics in a human-robot team (2020) Proceedings of the National Academy of Sciences (PNAS), 117 (12), pp. 6370-6375. , 3 2020; Ullman, D., Malle, B.F., What does it mean to trust a robot? steps toward a multidimensional measure of trust (2018) Proceedings of the 13th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 263-264. , ACM, Chicago (IL), USA; Van Den Brule, R., Dotsch, R., Bijlstra, G., Wigboldus, J.D.H., Haselager, P., Do robot performance and behavioral style affect human trust? 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The development of a human-robot interaction trust scale (2012) International Journal of Social Robotics, 4 (3), pp. 235-248. , 2012","Hannibal, G.; Institute of Visual Computing and Human-Centered Technology, Austria; email: glenda.hannibal@tuwien.ac.at",,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102759582
"Ramaraj P.","57222275570;","Robots that help humans build better mental models of robots",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3446365","595","597",,1,"10.1145/3434074.3446365","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102751263&doi=10.1145%2f3434074.3446365&partnerID=40&md5=de0747a1f45856e085bcede60686c254","University of Michigan, Ann Arbor, MI, United States","Ramaraj, P., University of Michigan, Ann Arbor, MI, United States","Interactive Task Learning (ITL) is an approach to teaching robots new tasks through language and demonstration. It relies on the fact that people have experience teaching each other. However, this can be challenging if the human instructor does not have an accurate mental model of a robot. This mental model consists of the robot's knowledge, capabilities, shortcomings, goals, and intentions. The research question that we investigate is ""How can the robot help the human build a better mental model of the robot?""We study human-robot interaction failures to understand the role of mental models in resolving them. We also discuss a human-centred interaction model design that is informed by human subject studies and plan-based theories of dialogue, specifically Collaborative Discourse Theory. © 2021 Owner/Author.","Cognitive systems; Interaction failures; Interactive task learning; Mental models","Agricultural robots; Cognitive systems; Man machine systems; Collaborative discourse theory; Human subjects; Interaction model; Mental model; Plan-based theory; Research questions; Task learning; Teaching robots; Human robot interaction",,,,,,,,,,,,,,,,"Andrist, S., Bohus, D., Kamar, E., Horvitz, E., What went wrong and why? diagnosing situated interaction failures in the wild (2017) International Conference on Social Robotics, pp. 293-303. , https://www.microsoft.com/en-us/research/publication/what-went-wrongand-why-diagnosing-situated-interaction-failures-in-the-wild/, Springer; Braun, V., Clarke, V., Using thematic analysis in psychology (2006) Qualitative Research in Psychology, 3 (2), pp. 77-101. , https://biotap.utk.edu/wpcontent/uploads/2019/10/Using-thematic-analysis-in-psychology-1.pdf.pdf, 2006; Breazeal, C., Hofman, G., Lockerd, A., Teaching and working with robots as a collaboration (2004) AAMAS, 4, pp. 1030-1037; Breazeal, C., Kidd, C.D., Lockerd Thomaz, A., Hofman, G., Berlin, M., Efects of nonverbal communication on efciency and robustness in human-robot teamwork (2005) IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 708-713. , http://alumni.media.mit.edu/~guy/publications/BreazealIROS05.pdf; Brooks, D.J., (2017) A Human-centric Approach to Autonomous Robot Failures, , https://search.proquest.com/docview/1927724896, Ph. D. Dissertation. University of Massachusetts Lowell; Chai, J.Y., Fang, R., Liu, C., She, L., Collaborative language grounding toward situated human-robot dialogue (2016) AI Magazine, 37 (4). , http://www.cse.msu.edu/~jchai/Papers/AIMagazine2016.pdf, 2016; Chao, C., Cakmak, M., Thomaz, A.L., Transparent active learning for robots (2010) Human-Robot Interaction (HRI), 2010 5th ACM/IEEE International Conference On. IEEE, pp. 317-324. , https://www.cc.gatech.edu/~athomaz/papers/Chao-hri10.pdf; Gluck, K.A., Laird, J.E., (2019) Interactive Task Learning., 26. , https://ieeexplore.ieee.org/document/8012335, MIT Press; Grosz, B., Sidner, C.L., Attention, intentions, and the structure of discourse (1986) Computational Linguistics, , https://dash.harvard.edu/handle/1/2579648, 1986; Honig, S., Oron-Gilad, T., Understanding and resolving failures in human-robot interaction: Literature review and model development (2018) Frontiers in Psychology, 9, p. 861. , https://www.semanticscholar.org/paper/Understanding-and-Resolving-Failures-in-Human-Robot-Honig-OronGilad/d48d57abe467393121fe58c6cd60f51f4c8b82a1, 2018; Lazewatsky, D.A., Smart, W.D., Context-sensitive in-the-world interfaces for mobile manipulation robots (2012) RO-MAN, 2012 IEEE, pp. 989-994. , http://people.oregonstate.edu/~smartw/library/papers/2012/roman2012.pdf; Marge, M., Rudnicky, A.I., Towards overcoming miscommunication in situated dialogue by asking questions (2011) AAAI Fall Symposium: Building Representations of Common Ground with Intelligent Agents, , http://www.cs.cmu.edu/~mrmarge/MargeAAAIfss11.pdf; Mohan, S., Klenk, M., Shreve, M., Evans, K., Ang, A., Maxwell, J., (2020) Characterizing An Analogical Concept Memory for Newellian Cognitive Architectures, , 2020; Mohan, S., Mininger, A.H., Kirk, J.R., Laird, J.E., Acquiring grounded representations of words with situated interactive instruction (2012) Advances in Cognitive Systems, 2, pp. 113-130. , Citeseer; Mohseni-Kabir, A., Rich, C., Chernova, S., Sidner, C.L., Miller, D., Interactive hierarchical task learning from a single demonstration (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction (Portland, Oregon, USA) (HRI '15), pp. 205-212. , https://doi.org/10.1145/2696454.2696474, Association for Computing Machinery, New York, NY, USA; Norman, D.A., Some observations on mental models (2014) Mental Models, pp. 15-22. , Psychology Press; Perlmutter, L., Kernfeld, E., Cakmak, M., Situated language understanding with human-like and visualization-based transparency (2016) Robotics: Science and Systems, , https://homes.cs.washington.edu/~lrperlmu/perlmutter16rss.pdf; Ramaraj, P., Klenk, M., Mohan, S., Understanding intentions in human teaching to design interactive task learning robots (2020) RSS 2020 Workshop: AI & Its Alternatives in Assistive & Collaborative Robotics: Decoding Intent; Ramaraj, P., Laird, J.E., Establishing common ground for learning robots (2018) RSS 2018: Workshop on Models and Representations for Natural Human-Robot Communication, , http://www2.ece.rochester.edu/projects/rail/mrhrc2018/papers/ramaraj05.pdf; Ramaraj, P., Sahay, S., Kumar, S.H., Lasecki, W.S., Laird, J.E., Towards using transparency mechanisms to build better mental models (2019) Advances in Cognitive Systems: 7th Goal Reasoning Workshop, 7, pp. 1-6; Rosenthal, S., Veloso, M., Dey, A.K., Acquiring accurate human responses to robots' questions (2012) International Journal of Social Robotics, 4 (2), pp. 117-129. , http://www.cs.cmu.edu/~mmv/papers/12ijsrRosenthalVelosoDey.pdf, 2012; Tellex, S., Knepper, R.A., Li, A., Rus, D., Roy, N., Asking for help using inverse semantics (2014) Robotics: Science and Systems Foundation, , http://cs.brown.edu/courses/csci2951-k/papers/tellex14.pdf; Thomaz, A.L., Breazeal, C., Teachable robots: Understanding human teaching behavior to build more efective robot learners (2008) Artifcial Intelligence, 172 (6-7), pp. 716-737. , https://www.sciencedirect.com/science/article/pii/S000437020700135X, 2008; Wu, E., Gopalan, N., MacGlashan, J., Tellex, S., Wong, L.L.S., (2016) Social Feedback for Robotic Collaboration, , https://h2r.cs.brown.edu/wp-content/uploads/2016/07/wu16thesis.pdf, 2016","Ramaraj, P.; University of MichiganUnited States; email: preetir@umich.edu",,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102751263
"Bhatia S., Björling E.A., Budhiraja T.","57205199791;55301519700;57222470524;","Exploring web-based VR for participatory robot design",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447139","109","112",,1,"10.1145/3434074.3447139","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102746834&doi=10.1145%2f3434074.3447139&partnerID=40&md5=6465ccd577003d1bffa81b42b95bbf85","University of Washington, Seattle, WA, United States; Indraprastha Institute of Information Technology, New Delhi, India","Bhatia, S., University of Washington, Seattle, WA, United States; Björling, E.A., Indraprastha Institute of Information Technology, New Delhi, India; Budhiraja, T., University of Washington, Seattle, WA, United States","With the pandemic preventing access to universities and consequently limiting in-person user studies, it is imperative to explore other mediums for conducting user studies for human-robot interaction. Virtual reality (VR) presents a novel and promising research platform that can potentially offer a creative and accessible environment for HRI studies. Despite access to VR being limited given its hardware requirements (e.g. need for headsets), web-based VR offers universal access to VR utilities through web browsers. In this paper, we present a participatory design pilot study, aimed at exploring the use of co-design of a robot using web-based VR. Results seem to show that web-based VR environments are engaging and accessible research platforms to gather environment and interaction data in HRI. © 2021 ACM.","Co-design; Robot design; Virtual reality; Web-based VR","Agricultural robots; Machine design; Man machine systems; Virtual reality; Web browsers; Websites; Co-designs; Participatory design; Pilot studies; Research platforms; Robot designs; Universal access; User study; Web based; Human robot interaction",,,,,,,,,,,,,,,,"Bell, B.T., Understanding adolescents (2016) Perspectives on HCI Research with Teenagers, pp. 11-27. , Springer; Björling, E.A., Rose, E., Participatory research principles in humancentered design: Engaging teens in the co-design of a social robot (2019) Multimodal Technologies and Interaction, 3 (1), p. 8. , 2019; CoSpaces Edu for AR CR Creation in Education, , https://cospaces.io/edu/about.html, Delightex; Fanini, B., Cinque, L., Encoding immersive sessions for online, interactive vr analytics (2020) Virtual Reality, 24 (3), pp. 423-438. , 2020; Fundamentals of WebXR, , https://developer.mozilla.org/en-US/docs/Web/API/WebXR_Device_API/Fundamentals, The Blender Foundation; Frydenberg, M., Andone, D., Does creating shared projects in virtual reality capture students' interest in technology? (2019) An International Project in STEM Education. In 2019 IEEE Integrated STEM Education Conference (ISEC), pp. 311-315; Hudák, M., Korecko, Š., Sobota, B., Advanced user interaction forweb-based collaborative virtual reality (2020) 2020 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), pp. 000343-000348; Kalegina, A., Schroeder, G., Allchin, A., Berlin, K., Cakmak, M., Characterizing the design space of rendered robot faces (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 96-104; Kappas, A., Küster, D., Dente, P., Basedow, C., Simply the best! creation and validation of the bremen emotional sounds toolkit (2015) International Convention of Psychological Science; Kim, A.S., Björling, E.A., Bhatia, S., Li, D., Designing a collaborative virtual reality game for teen-robot interactions (2019) Proceedings of the 18th ACM International Conference on Interaction Design and Children, pp. 470-475; Liu, O., Rakita, D., Mutlu, B., Gleicher, M., Understanding human-robot interaction in virtual reality (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 751-757; Marjanovic, U., Tegeltija, S., Medic, N., Lazarevic, M., Tasic, N., Lalic, B., Content Development for Virtual Reality Training. Department of Industrial Engineering and Management Novi Sad, p. 253. , Serbia; Mozilla Developer Network, , https://www.blender.org/; Schuler, D., Namioka, A., (1993) Participatory Design: Principles and Practices, , CRC Press; Sherman, W.R., (2019) VR Developer Gems, , CRC Press; Terada, K., Yamauchi, A., Ito, A., Artificial emotion expression for a robot by dynamic color change (2012) 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 314-321; Weistroffer, V., Paljic, A., Callebert, L., Fuchs, P., A methodology to assess the acceptability of human-robot collaboration using virtual reality (2013) Proceedings of the 19th ACM Symposium on Virtual Reality Software and Technology, pp. 39-48; Wijnen, L., Bremner, P., Lemaignan, S., Giuliani, M., Performing human-robot interaction user studies in virtual reality 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), p. 794; Wijnen, L., Lemaignan, S., Bremner, P., Towards using virtual reality for replicating hri studies (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 514-516; Woodard, M., Barany, A., Facilitating creative processes through a culturally responsive computing summer camp (2020) SITE Interactive Conference. Association for the Advancement of Computing in Education (AACE), pp. 385-387",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102746834
"Savery R.","57190783994;","Machine learning driven musical improvisation for mechanomorphic human-robot interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3446351","559","561",,2,"10.1145/3434074.3446351","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102742995&doi=10.1145%2f3434074.3446351&partnerID=40&md5=792b96c5869a99b706e7b2873b0c3f17","Georgia Tech Center for Music Technology, Atlanta, GA, United States","Savery, R., Georgia Tech Center for Music Technology, Atlanta, GA, United States","As industrial robots and social robots become prevalent in commercial and home settings it is crucial to improve forms of communication with human collaborators and companions. In this work, I describe the use of musical improvisation to generate emotional musical prosody for improved human-robot interaction. This aims to develop a canny approach, where robots perform in a mechanomorphic manner improving collaboration opportunities with humans. I have currently collected a new 12-hour dataset and developed a Conditional Variational Autoencoder to generate new phrases. Generations have then been used to compare the impact of prosody on anthropomorphism, animacy, likeability, perceived intelligence, and trust. Future work will incorporate prosody into groups of robots and humans, using personality to drive emotional decisions and emotion contagion. © 2021 Owner/Author.","Audio; Emotion; Music; Prosody; Robotics; Sound","Agricultural robots; Industrial robots; Machine learning; Man machine systems; Animacy; Auto encoders; Likeability; Social robots",,,,,,,,,,,,,,,,"Bartneck, C., Kulic, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81. , 2009; Bates, J., The role of emotion in believable agents (1994) Commun. ACM, 37 (7), pp. 122-125. , https://doi.org/10.1145/176789.176803, July 1994; Ellis Benson, B., (2003) The Improvisation of Musical Dialogue: A Phenomenology of Music, , Cambridge University Press; Bethel, C.L., Murphy, R.R., Review of human studies methods in hri and recommendations (2010) International Journal of Social Robotics, 2 (4), pp. 347-359. , 2010; Breazeal, C., Aryananda, L., Recognition of affective communicative intent in robot-directed speech (2002) Autonomous Robots, 12 (1), pp. 83-104. , 2002; Briot, J., Hadjeres, G., Pachet, F., (2017) Deep Learning Techniques for Music Generation-a Survey, , 2017; Crumpton, J., Bethel, C.L., A survey of using vocal prosody to convey emotion in robot speech (2016) International Journal of Social Robotics, 8 (2), pp. 271-285. , 2016; Dautenhahn, K., Walters, M., Woods, S., Koay, K.L., Nehaniv, C.L., Sisbot, A., Alami, R., Siméon, T., How may i serve you? A robot companion approaching a seated person in a helping context (2006) Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction (Salt Lake City, Utah, USA) (HRI '06), pp. 172-179. , https://doi.org/10.1145/1121241.1121272, Association for Computing Machinery, New York, NY, USA; Ferreira, L., Whitehead, J., Learning to generate music with sentiment (2019) ISMIR, pp. 384-390; Hausen, M., Torppa, R., Salmela, V.R., Vainio, M., Särkämö, T., Music and speech prosody: A common rhythm (2013) Frontiers in Psychology, 4, p. 566. , 2013; Marie Higgins, K., (2011) The Music of Our Lives, , 2011; Jones, R., Communication in the real world: An introduction to communication studies (2013) The Saylor Foundation; Mateas, M., (1999) Artificial Intelligence Today, pp. 297-328. , http://dl.acm.org/citation.cfm?id=1805750.1805762, Springer-Verlag, Berlin, Heidelberg, Chapter An Oz-centric Review of Interactive Drama and Believable Agents; Mavridis, N., A review of verbal and non-verbal human-robot interactive communication (2015) Robotics and Autonomous Systems, 63, pp. 22-35. , 2015; Moore, R.K., Appropriate voices for artefacts: Some key insights (2017) 1st International Workshop on Vocal Interactivity In-and-between Humans, Animals and Robots; Moore, R.K., Is spoken language all-or-nothing? (2017) Implications for Future Speech-based Human-machine Interaction. In Dialogues with Social Robots, pp. 281-291. , Springer; Norberto Pires, J., Azar, A.S., Advances in robotics for additive/hybrid manufacturing: Robot control, speech interface and path planning (2018) Industrial Robot: An International Journal, , 2018; Sacharin, V., Schlegel, K., Scherer, K.R., (2012) Geneva Emotion Wheel Rating Study (Report), , Geneva, Switzerland: University of Geneva. Swiss Center for Affective Sciences (2012); Saunderson, S., Nejat, G., How robots influence humans: A survey of nonverbal communication in social human-robot interaction (2019) International Journal of Social Robotics, 11 (4), pp. 575-608. , 2019; Savery, R., Rose, R., Weinberg, G., Establishing human-robot trust through music-driven robotic emotion prosody and gesture (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1-7; Savery, R., Rose, R., Weinberg, G., Finding shimi's voice: Fostering human-robot communication with music and a NVIDIA jetson tx2 (2019) Proceedings of the 17th Linux Audio Conference, p. 5; Savery, R., Weinberg, G., A survey of robotics and emotion: Classifications and models of emotional interaction (2020) Proceedings of the 29th International Conference on Robot and Human Interactive Communication; Savery, R., Zahray, L., Weinberg, G., Emotional musical prosody for the enhancement of trust in robotic arm communication (2020) Trust, Acceptance and Social Cues in Human-Robot Interaction, RO-MAN 2020; Savery, R., Zahray, L., Weinberg, G., Prosodycvae: A conditional convolutionalvariational autoencoder for real-timeemotional music prosody generation (2020) 2020 Joint Conference on AI Music Creativity. CSMC+MUME; Schaefer, K.E., Measuring trust in human robot interactions: Development of the (2016) Trust Perception Scale-HRI, pp. 191-218. , https://doi.org/10.1007/978-1-4899-7668-0_10, Springer US, Boston, MA; Sloboda, J., Music: Where cognition and emotion meet (1999) Conference Proceedings: Opening the Umbrella; An Encompassing View of Music Education; Australian Society for Music Education, XII National Conference, p. 175. , University of Sydney, NSW, Australia 09-13 July Australian Society for Music Education; Sturm, B.L., Felipe Santos, J., Ben-Tal, O., Korshunova, I., (2016) Music Transcription Modelling and Composition Using Deep Learning, , 2016; Forde Thompson, W., Glenn Schellenberg, E., Husain, G., Decoding speech prosody: Do music lessons help? (2004) Emotion, 4 (1), p. 46. , 2004; Wennerstrom, A., (2001) The Music of Everyday Speech: Prosody and Discourse Analysis, , Oxford University Press; Wu, J., Hu, C., Wang, Y., Hu, X., Zhu, J., A hierarchical recurrent neural network for symbolic melody generation (2019) IEEE Transactions on Cybernetics, 50 (6), pp. 2749-2757. , 2019; Zhao, K., Li, S., Cai, J., Wang, H., Wang, J., An emotional symbolic music generation system based on lstm networks (2019) 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 2039-2043","Savery, R.; Georgia Tech Center for Music TechnologyUnited States; email: rsavery3@gatech.edu",,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102742995
"Cheng G., Xu L.","57210413948;35270512100;","Compliance Control of a Lower Limb Rehabilitation Robot in Mirror Therapy",2021,"2021 7th International Conference on Mechatronics and Robotics Engineering, ICMRE 2021",,,"9384851","77","82",,,"10.1109/ICMRE51691.2021.9384851","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104846651&doi=10.1109%2fICMRE51691.2021.9384851&partnerID=40&md5=232ba7de21e140b1bbaa05f2febb6b8f","Anhui University of Science and Technology, Huainan, China; Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China","Cheng, G., Anhui University of Science and Technology, Huainan, China; Xu, L., Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China","Robot-assisted rehabilitation at lower limbs have been developed extensively. Mirror therapy is applied for hemiparesis rehabilitation in this paper. The robotic system is designed which includes a sitting/lying lower limb rehabilitation robot and an attitude and heading reference system. The trajectory of the healthy limb is defined as the reference training trajectory of the robot which drives the impaired limb, then mirror image movement between patient's bilateral limbs is established. Human-robot interaction compliance control is the major concern regarding training safety. The hierarchy control frame is proposed which consists of a low-level trajectory tracking loop and a high-level admittance control loop. Experiment results validate the feasibility of the proposed scheme in providing compliance rehabilitation training. © 2021 IEEE.","compliance control; lower limb rehabilitation robot; mirror therapy","Agricultural robots; Control systems; Educational robots; Human robot interaction; Mirrors; Neuromuscular rehabilitation; Robotics; Trajectories; Admittance control; Attitude and heading reference systems; Control frames; Mirror therapies; Rehabilitation training; Robot-assisted rehabilitation; Robotic systems; Trajectory tracking; Compliance control",,,,,,,,,,,,,,,,"P. Langhorne, P., F. Coupar, F., A. Pollock, A., Motor recovery after stroke: A systematic review (2009) Lancet Neurology, 8 (8), pp. 741-754; Lum, P.S., Burgar, C.G., Shor, P.C., Majmundar, M., van der Loos, M., Robot-assisted movement training compared with conventionaltherapy techniques for the rehabilitation of upper-limb motor function after stroke (2002) Arch. Phys. Med. Rehabil., 83 (7), pp. 952-959. , Jul; Huo, W., Mohammed, S., Moreno, J.C., Lower limb wearable robots for assistance and rehabilitation: A state of the art (2016) IEEE Systems Journal, 10 (3), pp. 1068-1081; Meng, W., Liu, Q., Zhou, Z.D., Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation (2015) Mechatronics, 31, pp. 132-145; Trinnachoke, E., Viboon, S., A lower limb rehabilitation robot in sitting position with a review of training activities (2018) Journal of Healthcare Engineering, 4 (1), pp. 1-18; Altschuler, E.L., Wisdom, S.B., Stone, L., Rehabilitation of hemiparesis after stroke with a mirror (1999) Lancet, 353 (9169), pp. 2035-2036; Michielsen, M.E., Selles, R.W., van der Geest, J.N., Motor recovery and cortical reorganization after mirror therapy in chronic stroke patients: A phase II randomized controlled trial (2011) Neurorehabilitation and Neural Repair, 25 (3), pp. 223-233; Kim, J., Kim, J., Robot-assisted mirroring exercise as a physical therapy for hemiparesis rehabilitation (2017) Proceedings of Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society, pp. 4243-4246. , South Korea, July 11-15; Long, Y., Du, Z.J., Wang, W.D., Dong, W., Development of a wearable exoskeleton rehabilitation system based on hybrid control mode (2016) International Journal of Advanced Robotic Systems, 13, pp. 1-10; Bradley, D., Acosta-Marquez, C., Hawley, M., Brownsell, S., Enderby, P., Mawson, S., Nexos-The design, development and evaluation of a rehabilitation system for the lower limbs (2009) Mechatronics, 19 (3), pp. 245-257; Hogan, N., Impedance control: An approach to manipulation: Parts I-III (1985) Journal of Dynamic Systems Measurement and Control-Transactions of the Asme, 107 (1), pp. 1-24; Saglia, J.A., Tsagarakis, N.G., Dai, J.S., Control strategies for patient-assisted training using the ankle rehabilitation robot (ARBOT) (2013) IEEE/ASME Transactions on Mechatronics, 18 (6), pp. 1799-1808; Wu, J., Gao, J., Song, R., The design and control of a 3DOF lower limb rehabilitation robot (2016) Mechatronics, 33, pp. 13-22; Duschau, W.A., Caprez, A., Riener, R., Patient-cooperative control increases active participation of individuals with SCI during robot-aided gait training (2010) Journal of Neuro Engineering and Rehabilitation, 7, pp. 43-1-43-13; Xu, D., Zhang, M.M., Xu, H., Interactive compliance control of a wrist rehabilitation device (WRED) with enhanced training safety (2019) Journal of Healthcare Engineering, 33, pp. 1-10",,,,"Institute of Electrical and Electronics Engineers Inc.","7th International Conference on Mechatronics and Robotics Engineering, ICMRE 2021","3 February 2021 through 5 February 2021",,168272,,9780738132051,,,"English","Int. Conf. Mechatronics Robot. Eng., ICMRE",Conference Paper,"Final","",Scopus,2-s2.0-85104846651
"Alenljung B., Lindblom J.","24402835300;7006805787;","Analysing action and intention recognition in human-robot interaction with anemone",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12763 LNCS",,,"181","200",,,"10.1007/978-3-030-78465-2_14","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120694190&doi=10.1007%2f978-3-030-78465-2_14&partnerID=40&md5=843b3a88b135d4a135d1944894794848","University of Skövde, Skövde, Sweden","Alenljung, B., University of Skövde, Skövde, Sweden; Lindblom, J., University of Skövde, Skövde, Sweden","The ANEMONE is a methodological approach for user experience (UX) evaluation of action and intention recognition in human-robot interaction that has activity theory as its theoretical lens in combination with the seven stages of action model and UX evaluation methodology. ANEMONE has been applied in a case where a prototype has been evaluated. The prototype was a workstation in assembly in manufacturing consisting of a collaborative robot, a pallet, a tablet, and a workbench, where one operator is working in the same physical space as one robot. The purpose of this paper is to provide guidance on how to use ANEMONE, with a particular focus on the data analysis part, through describing a real example together with lessons learned and recommendations. © Springer Nature Switzerland AG 2021.","Action Recognition; Activity Theory; Evaluation; Human-Robot Collaboration; Human-Robot Interaction; Intention Recognition; Seven Stages of Action Model; User Experience (UX); User-centered","Activity coefficients; Collaborative robots; Human computer interaction; Action modeling; Action recognition; Activity Theory; Evaluation; Human-robot collaboration; Humans-robot interactions; Intention recognition; Seven stage of action model; User experience (UX); User-centred; Users' experiences; Man machine systems",,,,,"Horizon 2020 Framework Programme, H2020: 637107; European Commission, EC; Stiftelsen för Kunskaps- och Kompetensutveckling, KKS: 20140220","Acknowledgements. We wish to thank the participants that have contributed to our UX evaluation. We also want to thank our colleagues Wei Wang, Magnus Holm, Richard Sennington, and Bernard Schmidt who have provided assistance during data collection and configured the robot platform used in the present study. We also thank Christine Lindblom for designing Fig. 4. This work was financially supported by the Knowledge Foundation, Stockholm, under SIDUS grant agreement no. 20140220 (AIR, action and intention recognition in human interaction with autonomous systems) as well as the European Commission under the Horizon 2020 Programme under grant agreement no. 637107 (SYMBIO-TIC).",,,,,,,,,,"Lindblom, J., Alenljung, B., The ANEMONE: Theoretical foundations for UX evaluation of action and intention recognition in human-robot interaction (2020) Sensors, 20, p. 4284. , https://doi.org/10.3390/s20154284; Nouwen, R., van Rooij, R., Sauerland, U., Schmitz, H.-C., Introduction (2011) VIC 2009. LNCS, Vol. 6517, pp. 1-12. , https://doi.org/10.1007/978-3-642-18446-8_1, Nouwen, R., van Rooij, R., Sauerland, U., Schmitz, H.-C. (eds.), pp. , Springer, Heidelberg; Kulichenko, A.D., Polovko, S.A., Yu Smirnova, E., Shubin, P.K., Ulanov, V.N., Ontology for group of rescuing robots (2020) IOP Conference Series: Earth and Environmental Science, 539. , https://doi.org/10.1088/1755-1315/539/1/012121; Lytridis C., et al.: Social robots as cyber-physical actors in entertainment and education. In: International Conference on Software, Telecommunications and Computer Networks (Soft-COM), Split, Croatia, pp. 1–6. IEEE (2019). https://doi.org/10.23919/SOFTCOM.2019.890 3630; Mankodiya, K., Gandhi, R., Narasimhan, P.: Challenges and Opportunities for Embedded Computing in Retail Environments. In: Martins, F., Lopes, L., Paulino, H. (eds.) S-CUBE 2012. LNICSSITE, vol. 102, pp. 121–136. 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Work, 11, pp. 243-267. , https://doi.org/10.1023/A:1015298005381; Kaptelinin, V., The Interaction Design Foundation (2013) The Encyclopedia of Human-Computer Interaction, , https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-com puter-interaction-2nd-ed/user-experience-and-experience-design, Activity theory. In: Soegaard, M., Dam, R.F., (eds.) , 2nd edn, Aarhus, Denmark; Law, E.L.C., Sun, X., Evaluating user experience of adaptive digital educational games with activity theory (2012) Int. J. Hum Comput Stud., 70, pp. 478-497. , https://doi.org/10.1016/j.ijhcs. 2012.01.007","Alenljung, B.; University of SkövdeSweden; email: beatrice.alenljung@his.se","Kurosu M.",,"Springer Science and Business Media Deutschland GmbH","Human Computer Interaction thematic area of the 23rd International Conference on Human-Computer Interaction, HCII 2021","24 July 2021 through 29 July 2021",,262079,03029743,9783030784645,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85120694190
"Zhang H., Li Y., Guo Y., Chen X., Ren Q.","57283678300;57284340500;57284340600;57268888600;55332437900;","Control of Pneumatic Artificial Muscles with SNN-based Cerebellar-Like Model",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"824","828",,,"10.1007/978-3-030-90525-5_79","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119843611&doi=10.1007%2f978-3-030-90525-5_79&partnerID=40&md5=6bc116f8469150aa2058925b087fa780","College of Control Science and Engineering, Zhejiang University, Hangzhou, 310013, China","Zhang, H., College of Control Science and Engineering, Zhejiang University, Hangzhou, 310013, China; Li, Y., College of Control Science and Engineering, Zhejiang University, Hangzhou, 310013, China; Guo, Y., College of Control Science and Engineering, Zhejiang University, Hangzhou, 310013, China; Chen, X., College of Control Science and Engineering, Zhejiang University, Hangzhou, 310013, China; Ren, Q., College of Control Science and Engineering, Zhejiang University, Hangzhou, 310013, China","Soft robotics technologies have gained growing interest in recent years, which allows various applications from manufacturing to human-robot interaction. Pneumatic artificial muscle (PAM), a typical soft actuator, has been widely applied to soft robots. The compliance and resilience of soft actuators allow soft robots to behave compliant when interacting with unstructured environments, while the utilization of soft actuators also introduces nonlinearity and uncertainty. Inspired by Cerebellum’s vital functions in control of human’s physical movement, a neural network model of Cerebellum based on spiking neuron networks (SNNs) is designed. This model is used as a feed-forward controller in controlling a 1-DOF robot arm driven by PAMs. The simulation results show that this Cerebellar-based system achieves good performance and increases the system’s response speed. © 2021, Springer Nature Switzerland AG.","Cerebellum-like controller; Mckibben; Nonlinear systems; Spiking neural network; STDP","Brain; Controllers; Human robot interaction; Muscle; Neurons; Pneumatic actuators; Pneumatic control; Pneumatic drives; Cerebellum-like controller; Mckibben; Network-based; Neural-networks; Pneumatic artificial muscle; Soft actuators; Soft robot; Spiking neural network; Spiking neuron networks; STDP; Neural networks",,,,,,,,,,,,,,,,"Abadía, I., Naveros, F., Garrido, J.A., Ros, E., Luque, N.R., On robot compliance: a cerebellar control approach (2021) IEEE Trans. Cybern., 51 (5), pp. 2476-2489. , https://doi.org/10.1109/TCYB.2019.2945498; Caporale, N., Dan, Y., Spike timing-dependent plasticity: A hebbian learning rule (2008) Ann. Rev. Neurosci., 31, pp. 25-46; Carrillo, R.R., Ros, E., Boucheny, C., Coenen, O.J.M.: A real-time spiking cerebellum model for learning robot control. Biosystems 94(1), 18–27 (2008). https://doi.org/10.1016/j.biosystems.2008.05.008. https://www.sciencedirect.com/science/article/pii/S0303264708001226. seventh International Workshop on Information Processing in Cells and Tissues; Hazan, H., et al.: BindsNet: a machine learning-oriented spiking neural networks library in python. Front. Neuroinform. 12, 89 (2018). https://doi.org/10.3389/fninf. 2018.00089. https://www.frontiersin.org/article/10.3389/fninf.2018.00089; Miall, R., The cerebellum and visually controlled movements (1999) IEE Workshop on Self-Learning Robots III Brainstyle Robotics: The Cerebellum beyond Function Approximation (Ref. No. 1999/049), Pp. 2/1–2/5, , https://doi.org/10.1049/ic:19990257; Yang, J., Song, T., A prediction scheme in spiking neural network (SNN) hardware for ultra-low power consumption (2020) 2020 International Soc Design Conference (ISOCC), pp. 310-311. , https://doi.org/10.1109/ISOCC50952.2020.9333106, pp","Zhang, H.; College of Control Science and Engineering, China; email: lifelongyuanzhb@gmail.com","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85119843611
"Shardyko I., Samorodova M., Titov V.","56125560000;57196085142;56125730200;","Series elastic actuator control based on active damping injection with positive torque feedback",2021,"Proceedings - 2021 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2021",,,,"613","618",,,"10.1109/ICIEAM51226.2021.9446352","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116251012&doi=10.1109%2fICIEAM51226.2021.9446352&partnerID=40&md5=58beb96bcf8e4c6fdf097f82f15f4a62","Department of Robotics, Russian State Scientific Center for Robotics and Technical Cybernetics, Saint-Petersburg, Russian Federation","Shardyko, I., Department of Robotics, Russian State Scientific Center for Robotics and Technical Cybernetics, Saint-Petersburg, Russian Federation; Samorodova, M., Department of Robotics, Russian State Scientific Center for Robotics and Technical Cybernetics, Saint-Petersburg, Russian Federation; Titov, V., Department of Robotics, Russian State Scientific Center for Robotics and Technical Cybernetics, Saint-Petersburg, Russian Federation","The paper considers the control of serial elastic actuators (SEA) that have become a leading trend in the field of safe robotics and human-robot interaction. Many researchers in this field give particular attention to closed-loop impedance control. In particular, controlling the damping of substantially compliant SEA was considered by several authors in the last few years. One such approach is based on active damping injection that introduces a viscous friction term into the behavior of SEA spring via pure control action. The present paper proposes an extension of this approach that gives way to the explicit and simple tuning of the controller along with the well-defined behavior of the closed-loop system. For the proposed controller stability analysis and simulation studies are conducted. © 2021 IEEE.","Control system; Damping injection; MATLAB/Simulink; Positive torque feedback; Regulation; Series elastic actuator","Actuators; Closed loop systems; Controllers; Damping; Feedback; MATLAB; Active damping; Actuator control; Damping injection; Elastic actuators; MATLAB/ SIMULINK; Positive torque feedback; Positive torques; Regulation; Series elastic actuators; Torque feedback; Human robot interaction",,,,,"Ministry of Education and Science of the Russian Federation, Minobrnauka: 075-01195-21-02","This research is supported by the Ministry of Science and Higher Education of the Russian Federation. State assignment №075-01195-21-02.",,,,,,,,,,"Haninger, K., Lu, J., Tomizuka, M., Motion control of series-elastic actuators (2016) 2016 American Control Conf; Paine, N., Holley, J., Actuator control for the NASA-JSC Valkyrie humanoid robot: A decoupled dynamics approach for torque control of series elastic robots (2015) Journal of Field Robotics, 32 (3); Bodie, K., Bellicose, C.D., Hutter, M., Anypulator: Design and control of a safe robotic arm (2016) 2016 IEEE/RSJ Int. Conf. On Intelligent Robots and Systems; Gill, A.P., Late motor processing in low-impedance robots: Impedance control of series-elastic actuators (2004) Proc. of the 2004 American Control Conf., 4; Spong, M.W., Modeling and control of elastic joint robots (1987) ASME J. Dyn. Syst., Meas. 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On Robotics and Automation; Lin, H., McInroy, J.E., Hamann, J.C., Analysis and design of motion control systems with positive force/torque feedback using robust control methods (2005) IEEE Transactions on Control Systems Technology, 13 (5), pp. 752-759; Kosuge, K., Takeuchi, H., Furuta, K., Motion control of a robot arm using joint torque sensors (1988) Proc. of the 27th IEEE Conf. of Decision and Control; Aghili, F., Buehler, M., Hollerbach, J.M., (1998) Disturbance Attenuation and Load Decoupling with H Positive Joint Torque Feedback; Jun, M., Enhancing joint torque control of series elastic actuators with physical damping (2017) 2017 IEEE Int. Conf. On Robotics and Automation; Shardyko, I., Samorodova, M., Titov, V., Development of control system for a sea-joint based on active damping injection 2020 Int. Conf. On Industrial Engineering, Applications and Manufacturing, p. 2020; Samorodova, M., Shardyko, I., Titov, V., Control system of manipulator with elastic joints based on regulator with active damping (2020) Extreme Robotics, 1 (1), pp. 146-154; Kottenstette, N., Antsaklis, P.J., Relationships between positive real, passive dissipative, & positive systems (2010) Proc. of the 2010 American Control Conf, pp. 409-416; Dalyaev, I., On gain selection of active damping injection controller for series elastic actuators (2020) 2020 Int. Conf. Nonlinearity, Information and Robotics, pp. 1-6",,,,"Institute of Electrical and Electronics Engineers Inc.","2021 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2021","17 May 2021 through 21 May 2021",,171855,,9781728145877,,,"English","Proc. - Int. Conf. Ind. Eng., Appl. Manuf., ICIEAM",Conference Paper,"Final","",Scopus,2-s2.0-85116251012
"Kaszuba S., Leotta F., Nardi D.","57224850698;37118890600;7006582655;","A Preliminary Study on Virtual Reality Tools in Human-Robot Interaction",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12980 LNCS",,,"81","90",,,"10.1007/978-3-030-87595-4_7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115860973&doi=10.1007%2f978-3-030-87595-4_7&partnerID=40&md5=1b190fdd30e382c291be516cfa9882a1","Department of Computer, Control, and Management Engineering (DIAG), Sapienza University of Rome, via Ariosto 25, Rome, Italy","Kaszuba, S., Department of Computer, Control, and Management Engineering (DIAG), Sapienza University of Rome, via Ariosto 25, Rome, Italy; Leotta, F., Department of Computer, Control, and Management Engineering (DIAG), Sapienza University of Rome, via Ariosto 25, Rome, Italy; Nardi, D., Department of Computer, Control, and Management Engineering (DIAG), Sapienza University of Rome, via Ariosto 25, Rome, Italy","Choosing the best interaction modalities and protocols in Human-Robot Interaction (HRI) is far from being straightforward, as it strictly depends on the application domain, the tasks to be executed, the types of robots and sensors involved. In the last years, a growing number of HRI researchers exploited Virtual Reality (VR) as a mean to evaluate proposed solutions, focusing in particular on safety and correctness of collaborative tasks. This allows to prove the effectiveness and robustness of a certain approach in a simulated environment, thus permitting to converge more easily to the best solution, also avoiding to experiment potentially harmful actions in a real scenario. In this paper, we aim at reviewing existing VR based approaches targeting or embodying HRI. © 2021, Springer Nature Switzerland AG.","Communication modalities; Human-robot interaction; Speech acts; Virtual Reality","Man machine systems; Speech communication; Virtual reality; Applications domains; Collaborative tasks; Communication modalities; Humans-robot interactions; Simulated environment; Speech acts; Virtual reality tools; Human robot interaction",,,,,"101016906","Acknowledgements. The work of S. Kaszuba, F. Leotta and D. Nardi has been partly supported by the H2020 EU project CANOPIES - A Collaborative Paradigm for Human Workers and Multi-Robot Teams in Precision Agriculture Systems, Grant Agreement 101016906.",,,,,,,,,,"Arntz, A., Eimler, S.C., Experiencing AI in VR: A qualitative study on designing a human-machine collaboration scenario (2020) HCI International 2020-Late Breaking Posters, pp. 299-307. , Stephanidis, C., Antona, M., Ntoa, S. 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IEEE; Shi, D., Collins, E.G., Goldiez, B., Donate, A., Liu, X., Dunlap, D., Human-aware robot motion planning with velocity constraints (2008) International Symposium on Collaborative Technologies and Systems, pp. 490-497. , pp., 2008; Shu, B., Sziebig, G., Pieters, R., (2019) Architecture for Safe Human-Robot Collaboration: Multi-Modal Communication in Virtual Reality for Efficient Task Execution, pp. 2297-2302. , pp; Wijnen, L., Bremner, P., Lemaignan, S., Giuliani, M., Performing human-robot interaction user studies in virtual reality* (2020) 29Th IEEE International Conference on Robot and Human Interactive Communication, pp. 794-794. , RO-MAN), pp; Yan, H., Ang, M.A., Poo, A., A survey on perception methods for human-robot interaction in social robots (2014) Int. J. Soc. Rob., 6, pp. 85-119; Zacharaki, A., Kostavelis, I., Gasteratos, A., Dokas, I., Safety bounds in human robot interaction: A survey (2020) Saf. Sci., 127","Leotta, F.; Department of Computer, via Ariosto 25, Italy; email: leotta@diag.uniroma1.it","De Paolis L.T.Arpaia P.Bourdot P.",,"Springer Science and Business Media Deutschland GmbH","8th International Conference on Augmented Reality, Virtual Reality and Computer Graphics, AVR 2021","7 September 2021 through 10 September 2021",,265419,03029743,9783030875947,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85115860973
"Wang H., Li X., Zhang X.","57211739989;23028270800;35180693900;","Multimodal Human-robot Interaction on Service Robot",2021,"IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)",,,"9391068","2290","2295",,,"10.1109/IAEAC50856.2021.9391068","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104640348&doi=10.1109%2fIAEAC50856.2021.9391068&partnerID=40&md5=a6f6f2c1a8f03a1e7d8dc19bbe15951c","Beijing University of Technology, Faculty of Information Technology, Beijing, 100124, China; Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China","Wang, H., Beijing University of Technology, Faculty of Information Technology, Beijing, 100124, China, Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China; Li, X., Beijing University of Technology, Faculty of Information Technology, Beijing, 100124, China, Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China; Zhang, X., Beijing University of Technology, Faculty of Information Technology, Beijing, 100124, China, Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China","In order to improve the efficiency of multimodal fusion in human-robot interaction (HRI), an improved technique is proposed to synthesize visual and audio data. The robotic auditory system uses a microphone array to obtain auditory information and uses the MUSIC algorithm to determine the azimuth of the sound source, and uses end-to-end gating CNN recognizes speech results; For the visual system, a two-layer neural network system is used to detect and recognize dynamic gestures. An improved D-S evidence theory algorithm based on the rule intention voter is designed to fuse the output results of the two modules for determining intention of the current interactive object. Experimental results validate the efficiency and accuracy of multimodal fusion system. © 2021 IEEE.","audio; human-robot interaction; multimodal fusion; service robot; visual","Efficiency; Man machine systems; Multilayer neural networks; Network layers; Auditory systems; Human robot Interaction (HRI); Improved techniques; Interactive objects; Microphone arrays; Multi-modal fusion; MUSIC algorithms; Neural network systems; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 61703012; Natural Science Foundation of Beijing Municipality: 4182010","The research work is financially supported by National Natural Science Foundation of China (No. 61703012), Beijing Natural Science Foundation (No. 4182010).",,,,,,,,,,"Liu, H., Wang, L., Gesture recognition for human-robot collaboration: A review[J] (2017) International Journal of Industrial Ergonomics, p. 690; Narain, B.R., Priscilla, D.M.S., (2017) Gesture Command Recognition System for Human Machine Interaction[J]., , Social Science Electronic Publishing; Ahuja, M.K., Singh, A., Static vision based hand gesture recognition using principal component analysis[C] (2016) Ieee International Conference on Moocs.; Xu, D., Wu, X., Chen, Y.L., Online dynamic gesture recognition for human robot interaction[J] (2015) Journal of Intelligent & Robotic Systems; Gao, Q., Liu, J., Ju, Z., (2017) Static Hand Gesture Recognition with Parallel CNNs for Space Human-Robot Interaction[J].; Mi, J., Tang, S., Deng, Z., Object affordance based multimodal fusion for natural human-robot interaction[J] (2019) Cognitive Systems Research, 54, pp. 128-137; Liu, H., Fang, T., Zhou, T., Deep learning-based multimodal control interface for human-robot collaboration[J] (2018) Procedia Cirp on SciVerse ScienceDirect; Schmidt, A., Deleforge, A., Kellermann, W., Ego-noise reduction using a motor data-guided multichannel dictionary[C] (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).; Trick, S., Koert, D., Peters, J., Multimodal uncertainty reduction for intention recognition in human-robot interaction[C] (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).; Schiffer, S., Ferrein, A., Lakemeyer, G., Caesar: An intelligent domestic service robot[J] (2012) Intelligent Service Robotics, 5 (4), pp. 259-273; Rifinski, D., Erel, H., Feiner, A., Human-human-robot interaction: Robotic object's responsive gestures improve interpersonal evaluation in human interaction[J] (2020) Human-Computer Interaction, pp. 1-27; Meng, J., Feng, Z., Xu, T., A method of fusing gesture and speech for human-robot interaction[C] (2020) Proceedings of 2020 the 6th International Conference on Computing and Data Engineering., pp. 265-269; Liu, H., Fang, T., Zhou, T., Towards robust human-robot collaborative manufacturing: Multimodal fusion[J] (2018) Ieee Access, 6, pp. 74762-74771","Wang, H.; Beijing University of Technology, China; email: 19801355105@163.com",,"Chengdu Union Institute of Science and Technology;Chongqing Geeks Education Technology Co., Ltd;Chongqing Global Union Academy of Science and Technology;Global Union Academy of Science and Technology;IEEE Beijing Section",,"5th IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2021","12 March 2021 through 14 March 2021",,168296,26896621,,,,"English","Adv. Inf. Tech. Electron. and Autom. Control Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85104640348
"Wilcock G., Jokinen K.","36603777300;23397498500;","Towards Increasing Naturalness and Flexibility in Human-Robot Dialogue Systems",2021,"Lecture Notes in Electrical Engineering","714",,,"109","114",,,"10.1007/978-981-15-9323-9_9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102648529&doi=10.1007%2f978-981-15-9323-9_9&partnerID=40&md5=5dbbb7269130788615a04e0b3f4eed54","CDM Interact, Helsinki, Finland; Artificial Intelligence Research Center, AIST Tokyo Waterfront, Tokyo, Japan","Wilcock, G., CDM Interact, Helsinki, Finland; Jokinen, K., Artificial Intelligence Research Center, AIST Tokyo Waterfront, Tokyo, Japan","The chapter discusses some approaches to increasing the naturalness and flexibility of human-robot interaction, with examples from the WikiTalk dialogue system. WikiTalk enables robots to talk fluently about thousands of topics using Wikipedia-based talking. However, there are three challenging areas that need to be addressed to make the system more natural: speech interaction, face recognition, interaction history. We address these challenges and describe more context-aware approaches taking the individual partner into account when generating responses. Finally, we discuss the need for a Wikipedia-based listening capability to enable robots to follow the changing topics in human conversation. This would allow robots to join in the conversation using Wikipedia-based talking to make new topically relevant dialogue contributions. © 2020, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.",,"Face recognition; Speech processing; Speech recognition; Context-aware approaches; Dialogue systems; Human-robot dialogue; Interaction history; Speech interaction; Wikipedia; Social robots",,,,,"New Energy and Industrial Technology Development Organization, NEDO","Acknowledgements The first author thanks Prof. Tatsuya Kawahara of Kyoto University for the opportunity to participate in the ERICA robot project. The second author acknowledges the support of the New Energy and Industrial Technology Development Organisation (NEDO) in Japan.",,,,,,,,,,"Amos, B., Ludwiczuk, B., Satyanarayanan, M., OpenFace: a general-purpose face recognition library with mobile applications. Technical report, CMU-CS-16-118 (2016) CMU; Fu, Z., A paper list for style transfer in text (2018) [Github], , https://github.com/fuzhenxin/Style-Transfer-in-Text; Jokinen, K., Bridging gaps between planning and open-domain spoken dialogues (2015) Language Production, Cognition, and the Lexicon, pp. 347-360. , Gala N, Rapp R, Bel-Enguix G, Springer, Berlin, pp; Jokinen, K., Fukuda, K., Iino, N., Nishimura, S., Nishimura, T., Oota, Y., Watanabe, K., Yoshida, Y., Ethical and privacy issues in interactive service applications for elder people concerning dialogues and social robots (2019) 7Th Serviceology Conference, , Tokyo; Jokinen, K., Nishimura, S., Watanabe, K., Nishimura, T., Human-robot dialogues for explaining activities (2018) 9Th International Workshop on Spoken Dialogue Systems, , IWSDS 2018). Singapore; Jokinen, K., Wilcock, G., Multimodal signals and holistic interaction structuring. In: Proceedings of 24th International Conference on Computational Linguistics (COLING 2012). Mumbai (2012) Pp 527–538; Jokinen, K., Wilcock, G., Multimodal open-domain conversations with the Nao robot (2014) Natural Interaction with Robots, Knowbots and Smartphones: Putting Spoken Dialogue Systems into Practice, pp. 213-224. , Mariani J, Rosset S, Garnier-Rizet M, Devillers L, Springer, Berlin, pp; Kawahara, T., Spoken dialogue system for a human-like conversational robot ERICA (2018) 9Th International Workshop on Spoken Dialogue Systems (IWSDS 2018), , Singapore; Kawahara, T., Uesato, M., Yoshino, K., Takanashi, K., Toward adaptive generation of backchannels for attentive listening agents (2015) Proceedings of the 6Th International Workshop on Spoken Dialogue Systems (IWSDS 2015, , Busan, South Korea; Kazemi, V., Sullivan, J., One millisecond face alignment with an ensemble of regression trees (2014) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867-1874. , , pp; Kim, S., Banchs, R.E., Li H (2015) Wikification of concept mentions within spoken dialogues using domain constraints from wikipedia Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp. 2225-2229. , http://www.aclweb.org/anthology/D15-1199, Lisbon, Portugal, pp; Lala, D., Milhorat, P., Inoue, K., Ishida, M., Takanashi, K., Kawahara, T., Attentive listening system with backchanneling, response generation and flexible turn-taking (2017) Proceedings of the SIGDIAL 2017 Conference, pp. 127-136. , Saarbrücken, Germany, pp; Mihalcea, R., Csomai, A., Wikify! Linking documents to encyclopedic knowledge (2007) Proceedings of the 16Th ACM Conference on Information and Knowledge Management (CIKM 2007), pp. 233-242. , , pp. , Lisbon; Milde, B., Wacker, J., Radomski, S., Muhlhäuser, M., Biemann, C., Ambient search: A document retrieval system for speech streams. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations (2016) Osaka; Milne, D., Witten, I., Learning to link with Wikipedia. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM 2008). Napa Valley (2008) Pp 509– 518; Schroff, F., Kalenichenko, D., Philbin, J., Facenet: A unified embedding for face recognition and clustering (2015) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815-823. , , pp; Wilcock G, Jokinen K (215) Multilingual WikiTalk: Wikipedia-based talking robots that switch languages. In: Proceedings of the 16th Annual SIGdial Meeting on Discourse and Dialogue. Prague","Jokinen, K.; Artificial Intelligence Research Center, Japan; email: kristiina.jokinen@aist.go.jp",,,"Springer Science and Business Media Deutschland GmbH",,,,,18761100,,,,"English","Lect. Notes Electr. Eng.",Book Chapter,"Final","",Scopus,2-s2.0-85102648529
"Ong P., Cortés J.","57201802455;24802051900;","Opportunistic robot control for interactive multiobjective optimization under human performance limitations",2021,"Automatica","123",,"109263","","",,,"10.1016/j.automatica.2020.109263","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094839404&doi=10.1016%2fj.automatica.2020.109263&partnerID=40&md5=3e48543d988024c119541e7fe56b063a","Department of Mechanical and Aerospace Engineering, University of California, San Diego, United States","Ong, P., Department of Mechanical and Aerospace Engineering, University of California, San Diego, United States; Cortés, J., Department of Mechanical and Aerospace Engineering, University of California, San Diego, United States","This paper proposes opportunistic state-triggered strategies for solving convex multiobjective optimization problems that involve human–robot interaction. The robot is aware of the multiple objective functions defining the problem, but requires human input to find the most desirable Pareto solution. In order to avoid overloading the human with queries, we view her as a limited resource to the robot, and design event-triggered controllers that opportunistically prescribe the information exchanges among them. We consider various models of human performance, starting with an ideal one where queries are responded instantaneously, and later considering constraints on the response time and the interaction frequency. For each model, we formally establish the asymptotic convergence to the desired optimizer and rule out the existence of Zeno behavior. © 2020 Elsevier Ltd","Event-triggered control; Human–robot interaction; Multiobjective optimization; Multiple criteria decision making","Machine design; Multiobjective optimization; Asymptotic convergence; Information exchanges; Interaction frequencies; Interactive multiobjective optimization; Models of human performance; Multi-objective optimization problem; Multiple objective functions; Robot interactions; Human robot interaction",,,,,"National Science Foundation, NSF: 1917177, ECCS-1917177","This work was supported by National Science Foundation Award ECCS-1917177 . The material in this paper was presented at the 56th IEEE Conference on Decision and Control, December 12–15, 2017, Melbourne, Australia ( Ong & Cortés, 2017 ). This paper was recommended for publication in revised form by Associate Editor Zhihua Qu under the direction of Editor Daniel Liberzon.",,,,,,,,,,"Crandall, J.W., Goodrich, M.A., Olsen, D.R., Jr., Nielsen, C.W., Validating human–robot interaction schemes in multitasking environments (2005) IEEE Transactions on Systems, Man & Cybernetics. Part A: Systems & Humans, 35 (4), pp. 438-449; Debreu, G., Representation of a preference ordering by a numerical function (1954) Decision Processes, 3, pp. 159-165; Dolk, V.S., Borgers, D.P., Heemels, W., (2014), P. M. H. Dynamic event-triggered control: Tradeoffs between transmission intervals and performance. 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In IEEE international symposium on robot and human interactive communication (pp. 799–804); Heemels, W.P.M.H., Johansson, K.H., Tabuada, P., (2012), An introduction to event-triggered and self-triggered control. In IEEE conf. on decision and control (pp. 3270–3285); Hetel, L., Daafouz, J., Iung, C., Stabilization of arbitrary switched linear systems with unknown time-varying delays (2006) IEEE Transactions on Automatic Control, 51 (10), pp. 1668-1674; Khalil, H.K., Nonlinear systems (2002), 3rd ed. Prentice Hall; Kia, S.S., Cortés, J., Martinez, S., Distributed convex optimization via continuous-time coordination algorithms with discrete-time communication (2015) Automatica, 55, pp. 254-264; Li, L., Wang, X., Lemmon, M., (2012), D. Stabilizing bit-rate of disturbed event triggered control systems. In Proceedings of the 4th IFAC conference on analysis and design of hybrid systems (pp. 70–75); Luque, M., Yang, J.B., Wong, B.Y.H., Project method for multiobjective optimization based on gradient projection and reference points (2009) IEEE Transactions on Systems, Man & Cybernetics. Part A: Systems & Humans, 39 (4), pp. 864-879; Miettinen, K., Ruiz, F., Wierzbicki, A.P., Introduction to multiobjective optimization: Interactive approaches (2008) Multiobjective optimization: Interactive and evolutionary approaches, pp. 27-57. , Branke Jürgen Deb Kalyanmoy Miettinen Kaisa Słowiński Roman Springer Berlin, Heidelberg; Miskowicz, M., Event-based control and signal processing (2015), CRC Press; Ong, P., (2017), Cortés, J. Event-triggered interactive gradient descent for real-time multi-objective optimization. In IEEE conf. on decision and control (pp. 5445–5450); Ong, P., (2018), Cortés, J. Event-triggered control design with performance barrier. In IEEE conf. on decision and control (pp. 951–956); Peitz, S., Dellnitz, M., A survey of recent trends in multiobjective optimal control – surrogate models, feedback control and objective reduction (2018) Mathematical and Computational Applications, 23; Peitz, S., Schäfer, K., Ober-Blöbaum, S., Eckstein, J., Köhler, U., Dellnitz, M., A multiobjective MPC approach for autonomously driven electric vehicles (2017) IFAC-PapersOnLine, 50 (1), pp. 8674-8679; Peters, J.R., Srivastava, V., Taylor, G.S., Surana, A., Eckstein, M.P., Bullo, F., Human supervisory control of robotic teams: Integrating cognitive modeling with engineering design (2015) IEEE Control Systems, 35 (6), pp. 57-80; Richert, D., Cortés, J., Distributed linear programming with event-triggered communication (2016) SIAM Journal on Control and Optimization, 54 (3), pp. 1769-1797; Sakawa, M., Interactive multiobjective decision making by the sequential proxy optimization technique: SPOT (1982) European Journal of Operational Research, 9 (4), pp. 386-396; Shaikh, M.T., Goodrich, M., (2017), A. Design and evaluation of adverb palette: A GUI for selecting tradeoffs in multi-objective optimization problems. In ACM/IEEE international conference on human-robot interaction (pp. 389–397); Steinfeld, A., Fong, T., Kaber, D.B., Lewis, M., Scholtz, J., Schultz, A., (2006), C. Common metrics for human–robot interaction. In HRI (pp. 33–40); Tabuada, P., Event-triggered real-time scheduling of stabilizing control tasks (2007) IEEE Transactions on Automatic Control, 52 (9), pp. 1680-1685; Wang, X., Lemmon, M.D., Event-triggering in distributed networked control systems (2011) IEEE Transactions on Automatic Control, 56 (3), pp. 586-601; Weimer, J., Araújo, J., Johansson, K.H., Distributed event-triggered estimation in networked systems (2012) IFAC Proceedings Volumes, 45 (9), pp. 178-185; Wu, W., Reimann, S., Görges, D., Liu, S., Suboptimal event-triggered control for time-delayed linear systems (2015) IEEE Transactions on Automatic Control, 60 (5), pp. 1386-1391; Xin, B., Chen, L., Chen, J., Ishibuchi, H., Hirota, K., Liu, B., Interactive multiobjective optimization: A review of the state-of-the-art (2018) IEEE Access, 6, pp. 41256-41279; Yang, J.B., Gradient projection and local region search for multiobjective optimisation (1999) European Journal of Operational Research, 112 (2), pp. 432-459","Ong, P.; Department of Mechanical and Aerospace Engineering, United States; email: piong@ucsd.edu",,,"Elsevier Ltd",,,,,00051098,,ATCAA,,"English","Automatica",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85094839404
"Maniscalco U., Messina A., Storniolo P.","22734994000;57193569361;6506554923;","The human–robot interaction in robot-aided medical care",2021,"Smart Innovation, Systems and Technologies","189",,,"233","242",,3,"10.1007/978-981-15-5784-2_19","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086304170&doi=10.1007%2f978-981-15-5784-2_19&partnerID=40&md5=75095e9ce0385a0e509965ebb1c09fb5","Istituto di Calcolo e Reti ad Alte Prestazioni - C.N.R. Human Robot Interaction Group, Via Ugo La Malfa, 153, Palermo, Italy","Maniscalco, U., Istituto di Calcolo e Reti ad Alte Prestazioni - C.N.R. Human Robot Interaction Group, Via Ugo La Malfa, 153, Palermo, Italy; Messina, A., Istituto di Calcolo e Reti ad Alte Prestazioni - C.N.R. Human Robot Interaction Group, Via Ugo La Malfa, 153, Palermo, Italy; Storniolo, P., Istituto di Calcolo e Reti ad Alte Prestazioni - C.N.R. Human Robot Interaction Group, Via Ugo La Malfa, 153, Palermo, Italy","This article deals with the problem of the interaction and engagement between humans and humanoid Robots in circumstances where it is essential to be sure that engagement has occurred and it persists for the right time. In particular, in Robot-aided medical care (but also in other critical situations), it is essential to be sure that the flow of information between the human and the humanoid Robot effectively occurs between the actual patient and the Robot. Many sensory data of the Robot will be involved in a data fusion algorithm to provide robustness and stability in the Human–Robot interaction. The methodology described in this article has been realized on real Robots (Nao and Pepper) and implemented through Python scripts in ROS (Robot Operating System). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020.","Human–Robot interaction; Robot; Robotics","Anthropomorphic robots; Data fusion; Intelligent systems; Medical robotics; Data fusion algorithm; Humanoid robot; Real robot; Robot interactions; Robot operating system; Sensory data; Human robot interaction",,,,,"Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR: ARS01_00900","Acknowledgements This research was partially supported by the project AMICO—Assistenza Medicale In COntextual Awareness, with funding from the National Programs of the Italian Ministry of Education, Universities and Research (code: ARS01_00900).",,,,,,,,,,"Sidner, C.L., Lee, C., Kidd, C.D., Lesh, N., Rich, C., Explorations in engagement for humans and Robots (2005) Artif. Intell., 166 (1-2), pp. 140-164. , ISSN 0004-3702; Ehrlich, S., Wykowska, A., Ramirez-Amaro, K., Cheng, G., When to engage in interaction and how? EEG-based enhancement of robot’s ability to sense social signals in HRI (2014) 14Th IEEE-RAS International Conference on Humanoid Robots (Humanoids). IEEE, pp. 1104-1109. , pp., 2014; Bartl, C., Dorner, D., Psi: A theory of the integration of cognition, emotion and motivation (1998) Proceedings of the 2Nd European Conference on Cognitive Modelling. DTIC Document, pp. 66-73. , pp; Patterson, M.L., (1983) Nonverbal Behavior. a Functional Perspective, , Springer, New York; Cassell, J., Nudge nudge wink wink: Elements of face-to-face conversation for embodied conversational agents (2000) Embodied Conversational Agents, pp. 1-18. , Cassell, J., Sullivan, J., Prevost, S., Churchill, E. (eds.), pp., MIT Press, Cambridge, MA; Rauterberg, M., Dtwyler, M., Sperisen, M., From competition to collaboration through a shared social space (1995) Proceedings of the East-West International Conference on Human-Computer Interaction (EWHCI95), 2, pp. 94-101. , Blumental, B., Gornostaev, J., Unger, C. (eds.), vol., pp; Hall, E.T., (1968) Proxemics. Curr. Anthropol., 9, pp. 83-108; Argyle, M., Cook, M., Gaze and Mutual Gaze (1976) Cambridge University Press, New York; Miyauchi, D., Sakurai, A., Makamura, A., Kuno, Y., Active eye contact for human–robot communication (2004) Proceedings of CHI 2004–Late Breaking Results, Vol. CD Disc 2, pp. 1099-1104. , pp., ACM Press, New York; Minutolo, A., Esposito, M., de Pietro, G., A conversational chatbot based on kowledge-graphs for factoid medical questions (2017) Somet, pp. 139-152. , pp., September; Caggianese, G., de Pietro, G., Esposito, M., Gallo, L., Minutolo, A., Neroni, P., Discovering Leonardo with artificial intelligence and holograms: A user study (2020) Pattern Recognit. Lett.","Maniscalco, U.; Istituto di Calcolo e Reti ad Alte Prestazioni - C.N.R. Human Robot Interaction Group, Via Ugo La Malfa, 153, Italy; email: umberto.maniscalco@icar.cnr.it","Zimmermann A.Howlett R.J.Jain L.C.",,"Springer","International KES Conference on Human Centred Intelligent Systems, KES-HCIS 2020","17 June 2020 through 19 June 2020",,240529,21903018,9789811557835,,,"English","Smart Innov. Syst. Technol.",Conference Paper,"Final","",Scopus,2-s2.0-85086304170
"Chebotareva E., Magid E., Carballo A., Hsia K.-H.","56095311100;7006190065;27567451000;7005777255;","Basic User Interaction Features for Human-Following Cargo Robot TIAGo Base",2020,"Proceedings - International Conference on Developments in eSystems Engineering, DeSE","2020-December",,"9450794","206","211",,,"10.1109/DeSE51703.2020.9450794","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112535145&doi=10.1109%2fDeSE51703.2020.9450794&partnerID=40&md5=28d5ea943bf1c7269d066793ca71298c","Institute of Information Technology and Intelligent Systems, Kazan Federal University, Laboratory of Intelligent Robotic Systems, Intelligent Robotics Department, Kazan, Russian Federation; Nagoya University, Institute of Innovation for Future Society, Nagoya, Japan; National Yunlin University of Science and Technology, Department of Electrical Engineering, Douliu, Taiwan","Chebotareva, E., Institute of Information Technology and Intelligent Systems, Kazan Federal University, Laboratory of Intelligent Robotic Systems, Intelligent Robotics Department, Kazan, Russian Federation; Magid, E., Institute of Information Technology and Intelligent Systems, Kazan Federal University, Laboratory of Intelligent Robotic Systems, Intelligent Robotics Department, Kazan, Russian Federation; Carballo, A., Nagoya University, Institute of Innovation for Future Society, Nagoya, Japan; Hsia, K.-H., National Yunlin University of Science and Technology, Department of Electrical Engineering, Douliu, Taiwan","The ability to follow a human is necessary for many types of service and social robots. A development of human-following robots requires taking into account the features of a perception of such robots by users. This paper presents results of human-robot interaction experiments with TIAGo Base cargo robot when it follows a user. Eight people took part in the experiments. The participants interacted with the robot using voice commands. TIAGo Base tracked a person using a single laser rangefinder. The purpose of the experiments was to identify the features of a user interaction with TIAGo Base robot through voice commands when the robot carries a load and follows the user. In addition, we identified a preferable distance between TIAGo Base robot and a user. © 2020 IEEE.","human-following robot; human-robot interaction; human-robot proxemics; voice commands","Range finders; Human following; Single lasers; User interaction; Voice command; Social robots",,,,,"Russian Foundation for Basic Research, РФФИ: 19-58-70002","The reported study was funded by the Russian Foundation for Basic Research (RFBR), according to the research project No. 19-58-70002. Special thanks to PAL Robotics for their kind professional support with TIAGo Base robot software and hardware related issues.",,,,,,,,,,"The world's first auto-follow robotic suitcase (2020) Cowarobot, , https://cowarobotrover.com/, [Accessed: 15-Jul-2020]; (2020) Gita, , https://mygita.com/, [Accessed: 15-Jul-2020]; Orita, Y., Fukao, T., Robust human tracking of a crawler robot (2019) Journal of Robotics and Mechatronics, 31 (2), pp. 194-202; Sato, Y., Arai, M., Suzuki, R., Kobayashi, Y., Kuno, Y., Yamazaki, K., Yamazaki, A., A maneuverable robotic wheelchair able to move adaptively with a caregiver by considering the situation (2013) 2013 IEEE RO-MAN, pp. 282-287; Chebotareva, E., Safin, R., Shafikov, A., Masaev, D., Shaposhnikov, A., Shayakhmetov, I., Gerasimov, Y., Magid, E., Emotional social robot emotico (2019) 12th International Conference on the Developments in ESystems Engineering; Koide, K., Miura, J., Menegatti, E., Monocular person tracking and identification with on-line deep feature selection for person following robots (2020) Robotics and Autonomous Systems, 124, p. 103348; Chen, B.X., Sahdev, R., Tsotsos, J.K., Integrating stereo vision with a cnn tracker for a person-following robot (2017) International Conference on Computer Vision Systems, pp. 300-313; Guerrero-Higueras, Á.M., Álvarez-Aparicio, C., Olivera, M.C.C., Rodrguez-Lera, F.J., Fernández-Llamas, C., Rico, F.M., Matellán, V., Tracking people in a mobile robot from 2d lidar scans using full convolutional neural networks for security in cluttered environments (2018) Frontiers in Neurorobotics, 12; Chebotareva, E., Hsia, K.-H., Yakovlev, K., Magid, E., Laser rangefinder and monocular camera data fusion for human-following algorithm by pmb-2 mobile robot in simulated gazebo environment (2020) Smart Innovation, Systems and Technologies, 187; Islam, M.J., Hong, J., Sattar, J., Person-following by autonomous robots: A categorical overview (2019) The International Journal of Robotics Research, 38 (14), pp. 1581-1618; Honig, S.S., Oron-Gilad, T., Zaichyk, H., Sarne-Fleischmann, V., Olatunji, S., Edan, Y., Toward socially aware person-following robots (2018) IEEE Transactions on Cognitive and Developmental Systems, 10 (4), pp. 936-954; Saunderson, S., Nejat, G., How robots influence humans: A survey of nonverbal communication in social human-robot interaction (2019) International Journal of Social Robotics, 11, pp. 575-608; Mumm, J., Mutlu, B., Human-robot proxemics: Physical and psychological distancing in human-robot interaction (2011) 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 331-338; Hall, E.T., (1966) The Hidden Dimension, , N. Y. : Doubleday; Walters, M., Dautenhahn, K., Boekhorst, R., Koay, K., Kaouri, C., Woods, S., Nehaniv, C., Werry, I., The influence of subjects' personality traits on personal spatial zones in a human-robot interaction experiment (2005) IEEE International Workshop on Robot and Human Interactive Communication, pp. 347-352; Takayama, L., Pantofaru, C., Influences on proxemic behaviors in human-robot interaction (2009) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5495-5502; Duncan, B.A., Murphy, R.R., A preliminary model for comfortable approach distance based on environmental conditions and personal factors (2012) 2012 International Conference on Collaboration Technologies and Systems (CTS), pp. 622-627; Lin, W., Yueh, H.-P., The relationship between robot appearance and interaction with child users: How distance matters (2016) Cross-Cultural Design, pp. 229-236. , P.-L. P. Rau, Ed; Okita, S.Y., Ng-Thow-Hing, V., Sarvadevabhatla, R.K., Captain may i. proxemics study examining factors that influence distance between humanoid robots, children, and adults, during human-robot interaction (2012) 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 203-204; Rajamohan, V., Scully-Allison, C., Dascalu, S., Feil-Seifer, D., Factors influencing the human preferred interaction distance (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1-7; Rossi, S., Staffa, M., Bove, L., Capasso, R., Ercolano, G., User's personality and activity influence on hri comfortable distances (2017) Social Robotics, pp. 167-177; Obaid, M., Sandoval, E.B., Zotowski, J., Moltchanova, E., Basedow, C.A., Bartneck, C., Stop that is close enough. how body postures influence human-robot proximity (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN), pp. 354-361; Brandl, C., Mertens, A., Schlick, C., Human-robot interaction in assisted personal services: Factors influencing distances that humans will accept between themselves and an approaching service robot (2016) Human Factors and Ergonomics in Manufacturing & Service Industries, 26, pp. 713-727; Hiroi, Y., Nakayama, T., Kuroda, H., Miyake, S., Ito, A., Effect of robot height on comfortableness of spoken dialog (2012) 2012 5th International Conference on Human System Interactions, pp. 29-34; Sun, Y., Sun, L., Liu, J., Human comfort following behavior for service robots (2016) 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 649-654; Sun, Y., Sun, L., Liu, J., Performance evaluation of human comfortable following model for service robots (2018) 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 144-147; Hamacher, A., Bianchi-Berthouze, N., Pipe, A.G., Eder, K., Believing in bert: Using expressive communication to enhance trust and counteract operational error in physical human-robot interaction (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 493-500; Bereznikov, D., Zakiev, A., Network failure detection and autonomous return for pmb-2 mobile robot (2020) International Conference on Artificial Life and Robotics (ICAROB), pp. 444-447; (2016) PMB-2 Technical Specifications, , http://palrobotics.com/wp-content/uploads/2016/07/PMB-2-Datasheet.pdf, [Accessed: 15-Jul-2020]; Pages, J., Marchionni, L., Ferro, F., Tiago: The modular robot that adapts to different research needs (2016) International Workshop on Robot Modularity, IROS; (2020) Open Source Speech Recognition Toolkit, , https://cmusphinx.github.io/, CMUSphinx, [Accessed: 15-Jul-2020]; Lavrenov, R., Zakiev, A., Magid, E., Automatic mapping and filtering tool: From a sensor-based occupancy grid to a 3d gazebo octomap (2017) 2017 International Conference on Mechanical, System and Control Engineering (ICMSC). IEEE, pp. 190-195; Babenko, B., Yang, M.-H., Belongie, S., Visual tracking with online multiple instance learning (2009) 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 983-990; Lavrenov, R.O., Magid, E.A., Matsuno, F., Svinin, M.M., Suthakorn, J., Development and implementation of spline-based path planning algorithm in ros/gazebo environment (2019) Trudy SPIIran, 18 (1), pp. 57-84; Tsoy, T., Sabirova, L., Lavrenov, R., Magid, E., Master program students experiences in robot operating system course (2018) 2018 11th International Conference on Developments in ESystems Engineering (DeSE). IEEE, pp. 186-191; Bohannon, R., Comfortable and maximum walking speed of adults aged 20-79 years: Reference values and determinants (1997) Age and Ageing, 26, pp. 15-19",,,,"Institute of Electrical and Electronics Engineers Inc.","13th International Conference on Developments in eSystems Engineering, DeSE 2020","13 December 2020 through 17 December 2020",,170860,21611343,9781665422383,,,"English","Proc. - Int. Conf. Dev. eSystems Eng., DeSE",Conference Paper,"Final","",Scopus,2-s2.0-85112535145
"Hannum C., Li R., Wang W.","57222726387;57201552259;57201554862;","Trust or Not?: A Computational Robot-Trusting-Human Model for Human-Robot Collaborative Tasks",2020,"Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",,,"9378119","5689","5691",,,"10.1109/BigData50022.2020.9378119","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103823107&doi=10.1109%2fBigData50022.2020.9378119&partnerID=40&md5=07879882ffd8dbc790f3e2b203b0ef5d","Montclair State University, Department of Computer Science, Montclair, NJ, United States","Hannum, C., Montclair State University, Department of Computer Science, Montclair, NJ, United States; Li, R., Montclair State University, Department of Computer Science, Montclair, NJ, United States; Wang, W., Montclair State University, Department of Computer Science, Montclair, NJ, United States","The trust of a robot in its human partner is a significant issue in human-robot interaction, which is seldom explored in the field of robotics. This study addresses a critical issue of robots' trust in humans during the human-robot collaboration process based on the data of human motions, past interactions of the human-robot pair, and the human's current performance in the co-carry task. The trust level is evaluated dynamically throughout the collaborative task that allows the trust level to change if the human performs false positive actions, which can help the robot avoid making unpredictable movements and causing injury to the human. Experimental results showed that the robot effectively assisted the human in collaborative tasks through the proposed computational trust model. © 2020 IEEE.","computational model; human factors; human-robot interaction; robotics; trust","Big data; Collaborative tasks; Computational trust model; Critical issues; Current performance; False positive; Human motions; Human robots; Human-robot collaboration; Social robots",,,,,,,,,,,,,,,,"Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human-robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266; Wang, W., Li, R., Chen, Y., Diekel, Z.M., Jia, Y., Facilitating human-robot collaborative tasks by teaching-learning-collaboration from human demonstrations (2018) IEEE Transactions on Automation Science and Engineering, 16 (2), pp. 640-653; Wang, W., Chen, Y., Jia, Y., Which way is the best?: Evaluation and optimization of dual-arm robot path planning for human-robot collaborative tasks in smart manufacturing contexts (2019) Dynamic Systems and Control Conference, , 59148; Ososky, S., Schuster, D., Phillips, E., Jentsch, F.G., Building appropriate trust in human-robot teams (2013) 2013 AAAI Spring Symposium Series, pp. 60-65; Billings, D.R., Schaefer, K.E., Chen, J.Y., Hancock, P.A., Humanrobot interaction: Developing trust in robots (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 109-110; Rossi, A., Dautenhahn, K., Koay, K.L., Saunders, J., Investigating human perceptions of trust in robots for safe hri in home environments (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 375-376; Stormont, D.P., Analyzing human trust of autonomous systems in hazardous environments (2008) Proc. Of the Human Implications of Human-Robot Interaction Workshop at AAAI, pp. 27-32; Freedy, A., DeVisser, E., Weltman, G., Coeyman, N., Measurement of trust in human-robot collaboration (2007) 2007 International Symposium on Collaborative Technologies and Systems, pp. 106-114; Lucas, B.D., Kanade, T., (1981) An Iterative Image Registration Technique with An Application to Stereo Vision, pp. 121-130; Bier, S., Li, R., Wang, W., A full-dimensional robot teleoperation platform (2020) 2020 IEEE International Conference on Mechanical and Aerospace Engineering, pp. 186-191; Wang, W., Liu, N., Li, R., Chen, Y., Jia, Y., HuCoM: A model for human comfort estimation in personalized human-robot collaboration (2018) ASME 2018 Dynamic Systems and Control Conference, pp. 1-6. , American Society of Mechanical Engineers",,"Wu X.Jermaine C.Xiong L.Hu X.T.Kotevska O.Lu S.Xu W.Aluru S.Zhai C.Al-Masri E.Chen Z.Saltz J.","Ankura TM;IBM;IEEE;IEEE Computer Society","Institute of Electrical and Electronics Engineers Inc.","8th IEEE International Conference on Big Data, Big Data 2020","10 December 2020 through 13 December 2020",,168025,,9781728162515,,,"English","Proc. - IEEE Int. Conf. Big Data, Big Data",Conference Paper,"Final","",Scopus,2-s2.0-85103823107
"Yu W., Perrusquía A.","57218834579;57192589965;","Simplified Stable Admittance Control Using End-Effector Orientations",2020,"International Journal of Social Robotics","12","5",,"1061","1073",,9,"10.1007/s12369-019-00579-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095386744&doi=10.1007%2fs12369-019-00579-y&partnerID=40&md5=5e87540af5e0d029e1219bad90be228c","Departamento de Control Automático, CINVESTAV-IPN (National Polytechnic Institute), Av.IPN 2508, Mexico City, 07360, Mexico","Yu, W., Departamento de Control Automático, CINVESTAV-IPN (National Polytechnic Institute), Av.IPN 2508, Mexico City, 07360, Mexico; Perrusquía, A., Departamento de Control Automático, CINVESTAV-IPN (National Polytechnic Institute), Av.IPN 2508, Mexico City, 07360, Mexico","Admittance control is used mainly for human–robot interaction. It transforms forces and torques to the desired position and orientation of the end effector. When the admittance control is in the task space, it needs the Jacobian matrix, while in the joint space, it requires the inverse kinematics. This paper modifies the admittance control using only the orientation components of the end-effector to avoid the calculation of the inverse kinematics and the Jacobian matrix. We use geometric properties, adaptive control and sliding mode control to approximate them. The stability of those controllers is proven. Experiments are presented in real time with a 2-DOF pan and tilt robot and a 4-DOF exoskeleton. The results of the experiment show the effectiveness of the proposed controllers. © 2019, Springer Nature B.V.","Admittance control; Sliding mode control; Stability","Agricultural robots; Convergence of numerical methods; End effectors; Exoskeleton (Robotics); Inverse kinematics; Inverse problems; Jacobian matrices; Sliding mode control; Social robots; Adaptive Control; Admittance control; Desired position; Forces and torques; Geometric properties; Joint space; Robot interactions; Task space; Controllers",,,,,,,,,,,,,,,,"Dimeas, F., Aspragathos, N., Online stability in human–robot cooperations with admittance control (2016) IEEE Trans Haptics, 9 (2), pp. 267-278; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human–robot physical interaction (2015) IEEE Trans Robot, 31 (4), pp. 850-863; Bonitz, R.G., Hsia, T.C., Internal force-based impedance control for cooperating manipulators (1996) IEEE Trans Robot Autom, 12 (1), pp. 78-89; Abdossalami, A., Sirouspour, S., Adaptive control of haptic interaction with impedance and admittance type virtual environments (2008) Symposium on Haptic Interfaces for Virtual Environments and Teleoperator Systems, pp. 145-152; Kazerooni, H., Herm, M.G., The dynamics and control of a haptic interface device (1994) IEEE Trans Robot Autom, 10 (4), pp. 453-464; Garrido, J., (2015) Aprendizaje Por demostración En El Espacio Articular Para El Seguimiento De Trayectorias Aplicado En Un Exoesqueleto De 4 Grados De Libertad, , Centro de Investigación y Estudios Avanzados del Instituto Politécnico Nacional, México; Dohring, M., Newman, W., The passivity of natural admittance control implementations (2003) IEEE International Conference on Robotics and Automation, pp. 371-376; Yu, W., Rosen, J., Li, X., PID admittance control for an upper limb exoskeleton (2011) American Control Conference, pp. 1124-1129; Hogan, N., Impedance control: an approach to manipulation (1985) J Dyn Syst Measurement Control, 107, pp. 1-24; Yu, W., Carmona, R., Li, X., Neural PID admittance control of a robot (2013) American Control Conference, pp. 4963-4968; Kang, S.H., Jin, M., Chang, P.H., A solution to the accuracy/robustness dilemma in impedance control (2009) IEEE ASME Trans Mechatron, 14, pp. 182-194; Chih, M., Huang, A.C., (2004) Adpative impedance control of robot manipulators based on function approximation technique, robotica, pp. 395-403. , Cambridge University Press, Cambridge; Lu, W.S., Meng, Q.H., Impedance control with adaptation for robotic manipulators (1991) IEEE Trans Robot Autom, 7 (3), pp. 408-415; Kelly, R., Carelli, R., Amestegui, M., Ortega, R., On adaptive impedance control of robots manipulators (1989) IEEE Robot Autom, 1, pp. 572-577; Tee, K.P., Yan, R., Li, H., Adaptive admittance control of a robot manipulator under task space constraint (2010) IEEE International Conference on Robotics and Automation, pp. 5181-5186; Singh, S.K., Popa, D.O., An analysis of some fundamental problems in adaptive control of force and impedance behavior: theory and experiments (1995) IEEE Trans Robot Autom, 11 (6), pp. 912-921; Ferreti, G., Magnani, G.A., Rocco, P., Impedance control for elastic joints industrial manipulators (2004) IEEE Trans Robot Autom, 20 (3), pp. 488-498; Irawan, A., Moktadir, M., Tan, Y.Y., (2015) PD-FLC with Admittance Control for Hexapod robot’s Leg Positioning on Seabed, , In, IEEE American control conference; Kiguchi, K., Tanaka, T., Fukuda, T., Nuero-fuzzy control of a robotic exoskeleton with EMG signals (2004) IEEE Trans Fuzzy Syst, 12 (4), pp. 481-490; Mohammadi, H., Richter, H., Robust tracking/impedance control: Application to prosthetics (2015) American Control Conference, pp. 2673-2678; Yu, W., Rosen, J., A novel linear PID controller for an upper limb exoskeleton (2010) 49Th IEEE Conference on Decision and Control, pp. 3548-3553; Tufail, M., de Silva, C.W., Impedance control schemes for bilateral teleoperation (2014) International Conference on Computer Science and Education, pp. 44-49; Perrusquía, A., Yu, W., Soria, A., Lozano, R., Stable admittance control without inverse kinematics (2017) 20Th IFAC World Congress (IFAC2017), , Toulose; Ramírez, D., Arturo, O., Parra Vega, V., Díaz Montiel, M.G., Pozas Cardenas, M.J., Hernández Gómez, R.A., Cartesian sliding PD control of robots manipulators for tracking in finite time: Theory and experiments (2008) DAAAM International Scientific Book, Chapter, 23, pp. 257-272; Kelly, R., Santibáñez, V., (2003) Control de Movimiento de Robots Manipuladores, , Pearson Prentice Hall, Upper Saddle River; Spong, M.W., Hutchinson, S., Vidyasagar, M., (2004) Robot Dynamics and Control, , Wiley, Eglinton; Perrusquía, A., Yu, W., Task space human–robot interaction using angular velocity Jacobian (2019) 2019 International Symposium on Medical Robotics, , ISMR; Roy, S., Edan, Y., Investigating joint-action in short-cycle repetitive handover tasks: the role of giver versus receiver and its implications for human–robot collaborative system design (2018) Int J Social Robot; Someshwar, R., Kerner, Y., Optimization of waiting time in HR coordination (2013) 2013 IEEE International Conference on Systems, Man, and Cybernetics (SMC13), pp. 1918-1923","Yu, W.; Departamento de Control Automático, Av.IPN 2508, Mexico; email: yuw@ctrl.cinvestav.mx",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85095386744
"Akgun S.A., Ghafurian M., Crowley M., Dautenhahn K.","57209982972;57190254083;16318327500;7003305185;","Using Emotions to Complement Multi-Modal Human-Robot Interaction in Urban Search and Rescue Scenarios",2020,"ICMI 2020 - Proceedings of the 2020 International Conference on Multimodal Interaction",,,,"575","584",,1,"10.1145/3382507.3418871","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096649137&doi=10.1145%2f3382507.3418871&partnerID=40&md5=a95811358f628c14b47197db8cc66506","University of Waterloo, Systems Design Engineering, Waterloo, ON, Canada; University of Waterloo, Electrical and Computer Engineering, Waterloo, ON, Canada; University of Waterloo, Electrical and Computer Engineering and Systems Design Engineering, Waterloo, Canada","Akgun, S.A., University of Waterloo, Systems Design Engineering, Waterloo, ON, Canada; Ghafurian, M., University of Waterloo, Electrical and Computer Engineering, Waterloo, ON, Canada; Crowley, M., University of Waterloo, Electrical and Computer Engineering, Waterloo, ON, Canada; Dautenhahn, K., University of Waterloo, Electrical and Computer Engineering and Systems Design Engineering, Waterloo, Canada","An experiment is presented to investigate whether there is consensus in mapping emotions to messages/situations in urban search and rescue scenarios, where efficiency and effectiveness of interactions are key to success. We studied mappings between 10 specific messages, presented in two different communication styles, reflecting common situations that might happen during search and rescue missions, and the emotions exhibited by robots in those situations. The data was obtained through a Mechanical Turk study with 78 participants. Our findings support the feasibility of using emotions as an additional communication channel to improve multi-modal human-robot interaction for urban search and rescue robots, and suggests that these mappings are robust, i.e. are not affected by the robot's communication style. © 2020 ACM.","affective communication; emotions; human-robot interaction; multi-modal communication; search and rescue; urban search and rescue","Interactive computer systems; Man machine systems; Mapping; Communication styles; Mechanical turks; Multi-modal; Search and rescue; Urban search and rescue; Social robots",,,,,,,,,,,,,,,,"https://www.fema.gov/about-agency, [n.d.]. About the Agency; Technical rescue program development manual (2013) CreateSpace Independent Publishing Platform, , https://books.google.ca/booksid=yVxLEcFoGoIC, Federal Emergency Management Agency and U.S.F. 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IEEE, pp. 948-953; Ekman, P., An argument for basic emotions (1992) Cognition & Emotion, 6 (3-4), pp. 169-200. , 1992; Finzi, A., Orlandini, A., Human-robot interaction through mixed-initiative planning for rescue and search rovers (2005) Congress of the Italian Association for Artificial Intelligence. Springer, pp. 483-494; Funke, F., Reips, U., Why semantic differentials in webbased research should be made from visual analogue scales and not from 5-point scales (2012) Field Methods, 24 (3), pp. 310-327. , 2012; Ghafurian, M., Budnarain, N., Hoey, J., Role of emotions in perception of humanness of virtual agents (2019) Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems. 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Springer, pp. 152-165; Gwaltney-Brant, S., Murphy, L., Wismer, T., Albretsen, J., General toxicologic hazards and risks for search-and-rescue dogs responding to urban disasters (2003) Journal of the American Veterinary Medical Association, 222, pp. 292-295. , https://doi.org/10.2460/javma.2003.222.292, 03 2003; Hada, Y., Takizawa, O., 2-4 Development of communication technology for search and rescue robots (2011) Journal of the National Institute of Information and Communications Technology, 58, pp. 131-151. , 03 2011; Heise, D.R., Expressive order: Confirming sentiments in social actions (2007) Springer Science & Business Media; Hellström, T., Bensch, S., Understandable robots (2018) Paladyn, Journal of Behavioral Robotics, 9, pp. 110-123. , https://doi.org/10.1515/pjbr-2018-0009, 07 2018; Hoffmann, L., Derksen, M., Kopp, S., What a pity, pepper! how warmth in robots' language impacts reactions to errors during a collaborative task (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (Cambridge, United Kingdom) (HRI '20), pp. 245-247. , https://doi.org/10.1145/3371382.3378242, Association for Computing Machinery, New York, NY, USA; Hogan, D.E., Burstein, J.L., Disaster medicine (2007) Lippincott Williams & Wilkins, , https://books.google.ca/booksid=hgLccSb8DIUC; Hortensius, R., Hekele, F., Cross, E.S., The perception of emotion in artificial agents (2018) IEEE Transactions on Cognitive and Developmental Systems, 10 (4), pp. 852-864. , 2018; Jones, B., Tang, A., Neustaedter, C., Remote communication in wilderness search and rescue: Implications for the design of emergency distributed-collaboration tools for network-sparse environments (2020) Proc. ACM Hum.-Comput. Interact. 4, GROUP, p. 26. , https://doi.org/10.1145/3375190, Jan. 2020; Kayukawa, Y., Takahashi, Y., Tsujimoto, T., Terada, K., Inoue, H., Influence of emotional expression of real humanoid robot to human decision-making (2017) 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-6; Kleiner, A., Dornhege, C., Dali, S., Mapping disaster areas jointly: RFID-Coordinated SLAM by Hurnans and Robots (2007) 2007 IEEE International Workshop on Safety, Security and Rescue Robotics. IEEE, pp. 1-6; Kolling, A., Walker, P., Chakraborty, N., Sycara, K., Lewis, M., Human interaction with robot swarms: A survey (2016) IEEE Transactions on Human-Machine Systems, 46 (1), pp. 9-26. , https://doi.org/10.1109/THMS.2015.2480801, 2016; Kruijff, G.M., Janíek, M., Keshavdas, S., Larochelle, B., Zender, H., Smets, N.J.J.M., Mioch, T., Colas, F., Experience in system design for human-robot teaming in urban search and rescue (2014) Field and Service Robotics. 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"Žlajpah L., Petrič T.","6603769815;36447813900;","Unified Virtual Guides Framework for Path Tracking Tasks",2020,"Robotica","38","10",,"1807","1823",,5,"10.1017/S0263574719000973","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069928312&doi=10.1017%2fS0263574719000973&partnerID=40&md5=6236a54713056f2f86c9a78ecf6547a9","Department of Automation Biocybernetics and Robotics, JoŽef Stefan Institute, Ljubljana, Slovenia","Žlajpah, L., Department of Automation Biocybernetics and Robotics, JoŽef Stefan Institute, Ljubljana, Slovenia; Petrič, T., Department of Automation Biocybernetics and Robotics, JoŽef Stefan Institute, Ljubljana, Slovenia","In this paper, we propose a novel unified framework for virtual guides. The human-robot interaction is based on a virtual robot, which is controlled by the admittance control. The unified framework combines virtual guides, control of the dynamic behavior, and path tracking. Different virtual guides and active constraints can be realized by using dead-zones in the position part of the admittance controller. The proposed algorithm can act in a changing task space and allows selection of the tasks-space and redundant degrees-of-freedom during the task execution. The admittance control algorithm can be implemented either on a velocity or on acceleration level. The proposed framework has been validated by an experiment on a KUKA LWR robot performing the Buzz-Wire task. © 2020 Cambridge University Press.","Admittance control; Human-robot cooperation; Path following; Redundant robots; Virtual guides","Agricultural robots; Degrees of freedom (mechanics); Admittance control; Human-robot cooperation; Path following; Redundant robot; Virtual guides; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 820767",,,,,,,,,,,"De Santis, A., Siciliano, B., De Luca, A., Bicchi, A., An atlas of physical human-robot interaction (2008) Mech. Mach. Theory, 43 (3), pp. 253-270; Haddadin, S., Croft, E., Physical Human-Robot Interaction (2016), pp. 1835-1874. , Springer Handbook of Robotics, (B. Siciliano B and O. Khatib, eds.) (Springer International Publishing, Cham; Ikemoto, S., Amor, H.B., Minato, T., Jung, B., Ishiguro, H., Physical human-robot interaction: Mutual learning and adaptation (2012) Ieee Robot. Auto. Mag., 19 (4), pp. 24-35; Nemec, B., Likar, N., Gams, A., Ude, A., Human robot cooperation with compliance adaptation along the motion trajectory (2017) Auto. Robot., 42 (5), pp. 1023-1035; Mörtl, A., Lawitzky, M., Kucukyilmaz, A., Sezgin, M., Basdogan, C., Hirche, S., The role of roles: Physical cooperation between humans and robots (2012) Int. J. Robot. Res., 31 (13), pp. 1656-1674; Petrič, T., Goljat, R., Babič, J., Cooperative Human-Robot Control Based on Fitts' Law (2016) 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), Cancun, Mexico (IEEE, pp. 345-350; Peternel, L., Petrič, T., Oztop, E., Babič, J., Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach (2014) Auto. Robot., 36 (1-2), pp. 123-136; Bicchi, A., Peshkin, M.A., Colgate, J.E., Safety for Physical Human-Robot Interaction (2008) Springer Handbook of Robotics, pp. 1335-1348. , (B. Siciliano B and O. Khatib, eds.) (Springer, Berlin, Heidelberg; Petrič, T., Cevzar, M., Babič, J., Utilizing Speed-Accuracy Trade-Off Models for Human-Robot Coadaptation during Cooperative Groove Fitting Task (2017) 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), Birmingham, Uk, pp. 107-112; Rosenberg, L., Virtual Fixtures: Perceptual Tools for Telerobotic Manipulation (1993) Proceedings of Ieee Virtual Reality Annual International Symposium, Seattle, WA, Usa, pp. 76-82; Bowyer, S.A., Davies, B.L., y Rodriguez Baena, F., Active constraints/virtual fixtures: A survey (2014) Ieee Trans. Robot., 30, pp. 138-157; Bettini, A., Marayong, P., Lang, S., Okamura, A., Hager, G.D., Visual assisted control for manipulation using virtual fixtures (2004) Ieee Trans. Robot., 20 (6), pp. 953-966; Abbott, J.J., Marayong, P., Okamura, A.M., Haptic Virtual Fixtures for Robot-Assisted Manipulation (2007) Robotics Research. Springer Tracts in Advanced Robotics, 28, pp. 49-64. , (S. Thrun, R. Brooks and H. Durrant-Whyte, eds.), Springer, Berlin, Heidelberg; Hager, G.D., Human-Machine Cooperative Manipulation with Vision-Based Motion Constraints (2010) Lecture Notes in Control and Information Sciences Visual Servoing Via Advanced Numerical Methods, 401, pp. 55-70. , (G. Chesi and K. Hashimoto, eds.), Springer, London; Restrepo, S.S., (2018) Intuitive Iterative and Assisted Virtual Guides Programming for Human-Robot Comanipulation, , PhD thesis; Raiola, G., Lamy, X., Stulp, F., Co-manipulation with Multiple Probabilistic Virtual Guides (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015, pp. 7-13. , Hamburg, Germany; Lee, D., Ott, C., Incremental kinesthetic teaching of motion primitives using the motion refinement tube (2011) Auto. Robot., 31 (2-3), pp. 115-131; Pezzementi, Z., Okamura, A.M., Hager, G.D., Dynamic Guidance with Pseudoadmittance Virtual Fixtures (2007) Proceedings 2007 Ieee International Conference on Robotics and Automation, Roma, Italy (IEEE, pp. 1761-1767; Kragic, D., Marayong, P., Li, M., Okamura, A.M., Hager, G.D., Human-Machine collaborative systems for microsurgical applications (2005) Int. J. Robot. Res., 24 (9), pp. 731-741; Restrepo, S.S., Raiola, G., Chevalier, P., Lamy, X., Sidobre, D., Iterative Virtual Guides Programming for Human-Robot Comanipulation (2017) 2017 Ieee International Conference on Advanced Intelligent Mechatronics (AIM), Munich, Germany (IEEE, pp. 219-226; Raiola, G., Restrepo, S.S., Chevalier, P., Rodriguez-Ayerbe, P., Lamy, X., Tliba, S., Stulp, F., Co-manipulation with a library of virtual guiding fixtures (2017) Autonomous Robots, 42 (5), pp. 1037-1051; Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., (2009) Robotics-Modelling, Planning and Control, , SpringerVerlag, London; Ott, C., Mukherjee, R., Nakamura, Y., Unified Impedance and Admittance Control (2010) 2010 Ieee International Conference on Robotics and Automation, Anchorage, AK, Usa, pp. 554-561; Duchaine, V., Mayer St-Onge, B., Gao, D., Gosselin, C., Stable and intuitive control of an intelligent assist device (2012) Ieee Trans. Haptics, 5 (2), pp. 148-159; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive Human-Robot Physical Interaction (2015) Ieee Trans. Robot., 31, pp. 850-863; Ranatunga, I., Lewis, F., Popa, D.O., Tousif, S.M., Adaptive admittance control for Human-Robot interaction using model reference design and adaptive inverse filtering (2017) Ieee Trans. Control Systems Technology, 25 (1), pp. 1-10; Žlajpah, L., Petrič, T., Virtual Guides for Redundant Robots Using Admittance Control for Path Tracking Tasks (2019) Advances in Service and Industrial Robotics: Proceedings of the 27th International Conference on Robotics in Alpe-Adria Danube Region (RAAD 2018), pp. 13-23. , (N. A. Aspragathos, P. N. Koustoumpardis and V. C. Moulianitis, eds.) (Springer International Publishing, Cham; Albu-Schaffer, A., Ott, C., Hirzinger, G., A unified passivity-based control framework for position, torque and impedance control of flexible joint robots (2007) Int. J. Robot. Res., 26, pp. 23-39; Nakanishi, J., Cory, R., Mistry, M., Schaal, S., Peters, J., Operational space control: A theoretical and empirical comparison (2008) Int. J. Robot. Res., 27, pp. 737-757; Duchaine, V., Gosselin, C.M., General Model of Human-Robot Cooperation Using a Novel Velocity Based Variable Impedance Control (2007) Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (WHC'07), pp. 445-451. , Tsukaba, Japan; Lecours, A., Mayer-St-Onge, B., Gosselin, C., Variable Admittance Control of a Four-Degree-of-Freedom Intelligent Assist Device (2012) 2012 Ieee International Conference on Robotics and Automation, 2, pp. 3903-3908. , Saint Paul, MN, USA; Žlajpah, L., Kinematic Control of Redundant Robots in Changing Task Space (2016) Advances in Robot Design and Intelligent Control. Raad 2016. Advances in Intelligent Systems and Computing, 540, pp. 3-11. , (A. Rodić and T. Borangiu, eds.), Springer, Cham); Ude, A., Nemec, B., Petri, T., Morimoto, J., Orientation in Cartesian Space Dynamic Movement Primitives (2014) 2014 Ieee International Conference on Robotics and Automation (ICRA) Hong Kong, China, pp. 2997-3004; Žlajpah, L., On Time Optimal Path Control of Manipulators with Bounded Joint Velocities and Torques (1996) Ieee International Conference on Robotics and Automation, Minneapolis, Minnesota, pp. 1572-1577; Mihelj, M., Podobnik, J., Virtual Fixtures (2012) Haptics for Virtual Reality and Teleoperation, pp. 179-200. , (M. Mihelj and J. Podobnik, eds.) (Springer, Netherlands","Žlajpah, L.; Department of Automation Biocybernetics and Robotics, Slovenia; email: tadej.petric@ijs.si",,,"Cambridge University Press",,,,,02635747,,ROBOD,,"English","Robotica",Article,"Final","",Scopus,2-s2.0-85069928312
"Tian H., Duan X., Cui T., Wang J., Shi Q., Dong J.","57214923370;55646016600;57190121476;57221537440;57216691464;57221535949;","A Dual-six-dimensional Force Sensors Calibration Method for pHRI based on Ridge Regression",2020,"2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020",,,"9303303","515","520",,1,"10.1109/RCAR49640.2020.9303303","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099343195&doi=10.1109%2fRCAR49640.2020.9303303&partnerID=40&md5=c9a5b5c9fd247f7449bf81cd429c037f","Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, China","Tian, H., Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, China; Duan, X., Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, China; Cui, T., Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, China; Wang, J., Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, China; Shi, Q., Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, China; Dong, J., Beijing Institute of Technology, Beijing Advanced Innovation Center for Intelligent Robots and Systems, China","During robot teaching and physical human-robot interaction (pHRI), external forces and torques are required to be measured. However, the gravity acted on collaborative tools is a known-structure but unknown parameters input for pHRI systems. It is an essential process that gravity parameters need to be estimated and compensated using six-dimension force sensor calibration in a pHRI linear system based on Newton-Euler (NE) equation. In the previous work, the collaborative robot (cbot) system interacting with human and environment has 2 orthogonal installation six-dimension force sensors, where sensors' biasing and static wrenches exist. To solve this problem for both six-dimension force sensors in the same time, a regression algorithm in 3 steps is proposed using ridge regression and least square regression. © 2020 IEEE.",,"Agricultural robots; Calibration; Linear systems; Regression analysis; Robotics; Calibration method; Collaborative robots; Collaborative tools; Least square regression; Physical humanrobot interaction (phri); Regression algorithms; Six-dimension force sensors; Six-dimensional force; Social robots",,,,,"4192053, L182025, NO.L182025; National Natural Science Foundation of China, NSFC: 61673064; National Key Research and Development Program of China, NKRDPC: 2019YFC0118000","* Research supported by National key R&D program of China (No. 2019YFC0118000) & National Natural Science Foundation of China (Grant No. 61673064) & Beijing Nature Science Fund (NO.L182025&4192053).","ACKNOWLEDGMENT Research supported by National key R&D program of China (No. 2019YFC0118000) & National Natural Science Foundation of China (Grant No. 61673064) & Beijing Nature Science Fund (NO. L182025&4192053).",,,,,,,,,"Seraji, H., Adaptive admittance control: An approach to explicit force control in compliant motion [C] (1994) International Conference on Robotics and Automation, pp. 2705-2712; Ranatunga, I., Lewis, F.L., Popa, D.O., Adaptive Admittance Control for Human-Robot Interaction Using Model Reference Design and Adaptive Inverse Filtering [J] (2017) Ieee Transactions on Control Systems and Technology, 25 (1), pp. 278-285; Ma, D., Hollerbach, J.M., Identifying mass parameters for gravity compensation and automatic torque sensor calibration [C] (1996) International Conference on Robotics and Automation, pp. 661-666; Tauro, R.A., Kaiser, B., Worn, H., Static compensation for wrist mounted force torque sensor [C] (2009) International Conference on Intelligent Engineering Systems, pp. 105-109; Gong, D., Jia, Y., Cheng, Y., On-line and simultaneous calibration of wrist-mounted Force/Torque sensor and tool Forces/Torques for manipulation [C] (2013) Robotics and Biomimetics, pp. 619-624; Zhan, Y., Duan, X., Cui, T., Craniotomy robot system based on human-machine parallel collaboration [C] (2016) International Conference on Mechatronics and Automation, pp. 1119-1124; Han, D., Duan, X., Li, M., Interaction Control for Manipulator with compliant end-effector based on hybrid position-force control [C] (2017) International Conference on Mechatronics and Automation, pp. 863-868; Ivaldi, S., Fumagalli, M., Randazzo, M., Computing robot internal/external wrenches by means of inertial tactile and F/T sensors: Theory and implementation on the iCub [C] (2011) Ieee-ras International Conference on Humanoid Robots, pp. 521-528; Maric, P., Potkonjak, V., Geometrical Parameter Estimation for Industrial Manipulators Using Two-step Estimation Schemes [J] (1999) Journal of Intelligent and Robotic Systems, 24 (1), pp. 89-97; Liu, G., Iagnemma, K., Dubowsky, S., A base force/torque sensor approach to robot manipulator inertial parameter estimation [C] (1998) International Conference on Robotics and Automation, pp. 3316-3321; Zhao, J., Yang, F., Liu, W., An Approximation Model Based on Kernel Ridge Regression for Robot Kinematics Simulation [C] (2019) Computer Supported Cooperative Work in Design, pp. 313-318; Keemink, A.Q., Der Kooij, H.V., Stienen, A.H., Admittance control for physical human-robot interaction [J] (2018) The International Journal of Robotics Research, 37 (11), pp. 1421-1444; Ott, C., Nakamura, Y., Base force/torque sensing for position based Cartesian impedance control [C] (2009) Intelligent Robots and Systems, pp. 3244-3250; Van Den Berg, J., Miller, S., Duckworth, D., Superhuman performance of surgical tasks by robots using iterative learning from human-guided demonstrations [C] 2010 (2010) Ieee International Conference on Robotics and Automation. Ieee, pp. 2074-2081; Inoue, T., De Magistris, G., Munawar, A., Deep reinforcement learning for high precision assembly tasks [C] (2017) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Ieee, (2017), pp. 819-825","Duan, X.; Beijing Institute of Technology, China; email: duanstar@bit.edu.cn",,"Harbin Institute of Technology;IEEE Robotics and Automation Society (RA);Shanghai Jiao Tong University","Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020","28 September 2020 through 29 September 2020",,166261,,9781728172927,,,"English","IEEE Int. Conf. Real-Time Comput. Robot., RCAR",Conference Paper,"Final","",Scopus,2-s2.0-85099343195
"Sparrow R.","56254128900;","Do Robots Have Race?: Race, Social Construction, and HRI",2020,"IEEE Robotics and Automation Magazine","27","3","8886688","144","150",,4,"10.1109/MRA.2019.2927372","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079816057&doi=10.1109%2fMRA.2019.2927372&partnerID=40&md5=14a2f4126a9b5097528d09d1f1d9fdee","Philosophical and Australian Research, Monash University, Clayton, VIC, Australia","Sparrow, R., Philosophical and Australian Research, Monash University, Clayton, VIC, Australia","In a paper presented at the 2018 ACM/IEEE International Conference on Human Robot Interaction (HRI 2018), Bartneck and colleagues (myself included) argued that people attribute race to humanoid robots [1]. The experiments we conducted suggested that people are willing to identify robots as being of different races when asked and that, at least when confronted with the shooter bias test [2], people also behave in ways that suggest they attribute race to robots. This article joins a small number of other articles in providing evidence that people attribute racial and/or ethnic identities to robots [3], [4]. While the methods we used in our research have come in for some criticism [5], the article has at least highlighted this question as a topic for future research. © 1994-2011 IEEE.",,"Agricultural robots; Anthropomorphic robots; Bias test; Humanoid robot; Social constructions; Social robots",,,,,"CE140100012","The research for this article was supported under the Australian Research Council’s Centers of Excellence funding scheme (Project CE140100012). The views expressed here are those of the author and are not necessarily those of the Australian Research Council. I thank Max Cappuccio for the invitation to present at the Second Joint United Arab Emirates Symposium on Social Robotics, in 2016, which first prompted me to work on this topic. Some of the research for this article was conducted while I was a visiting fellow in the Department of Philosophy at Carnegie Mellon University (CMU). I also thank David Danks for the opportunity to visit CMU. My thinking on the race politics of robots was influenced by conversations and correspondence with Christoph Bartneck and Frie-derike Eyssel, in the course of our collaboration on an earlier manuscript, and with Max Cappuccio, Louis Chude-Sokei, Ayanna Howard, Ole Koksvik, Sascha Morrell, Illah Nourbakhsh, and Aaron Steinfeld. I am grateful to Ole Koksvik and Mark Howard for assistance with bibliographic research and with preparing the manuscript for publication.",,,,,,,,,,"Bartneck, C., Robots and racism (2018) Proc. 2018 ACM/IEEE Int. Conf. Human-Robot Interaction (HRI 2018), pp. 196-204; Correll, J., Park, B., Judd, C.M., Wittenbrink, B., The police officer's dilemma: Using ethnicity to disambiguate potentially threatening individuals (2002) J. Pers. Soc. Psychol, 83 (6), pp. 1314-1329; Eyssel, F., Loughnan, S., It don't matter if you're Black or White"" Effects of robot appearance and user prejudice on evaluations of a newly developed robot companion (2013) Proc. Int. Conf. Social Robotics, pp. 422-431; Strait, M., Ramos, A.S., Contreras, V., Garcia, N., Robots racialized in the likeness of marginalized social identities are subject to greater dehumanization than those racialized as White (2018) Proc. 27th IEEE Int. Symp. Robot and Human Interactive Communication (ROMAN), pp. 452-457; Ogunyale, T., Bryant, D.A., Howard, A., Does removing stereotype priming remove bias A pilot human-robot interaction study (2018) Proc. 5th Workshop Fairness, Accountability, and Transparency in Machine Learning, pp. 3-7; Reeves, B., Nass, C., (1998) The Media Equation: How People Treat Computers, Television, and New Media like Real People and Places, , Cambridge, UK: Cambridge Univ. Press; Sparrow, R., Robots, rape, and representation (2017) Int. J. Social Robotics, 9 (4), pp. 465-477; Bernotat, J., Eyssel, F., Sachse, J., Shape it-The influence of robot body shape on gender perception in robots (2017) Proc. Int. Conf. Social Robotics, pp. 75-84; Eyssel, F., Hegel, F., S)he's got the look: Gender stereotyping of robots (2012) J. Appl. Soc. Psychol, 42 (9), pp. 2213-2230; Otterbacher, J., Talias, M., S/he's too warm/agentic!: The influence of gender on uncanny reactions to robots Proc. 2017 ACM/IEEE Int. Conf. Human-Robot Interaction, pp. 214-223; Siegel, M., Breazeal, C., Norton, M.I., Persuasive robotics: The influence of robot gender on human behavior 2009 IEEE/RSJ Int. Conf. Intelligent Robots and Systems, pp. 2563-2568; Howard, A., Borenstein, J., Hacking the human bias in robotics (2018) ACM Trans. Human-Robot Interaction (THRI), 7 (1); Howard, A., Borenstein, J., The ugly truth about ourselves and our robot creations: The problem of bias and social inequity (2018) Sci. Eng. Ethics, 24 (5), pp. 1521-1536; Templeton, A.R., Biological races in humans (2013) Stud. History Philos. Sci. Part C: Stud. History Philos. Biol. Biomed. Sci, 44 (3), pp. 262-271; Denton, N.A., Deane, G.D., Researching race and ethnicity: Methodological issues (2010) the SAGE Handbook of Race and Ethnic Studies, , P. H. Collins and J. Solomos, Eds. Newbury Park, CA: Sage; Root, M., How we divide the world (2018) Proc. 1998 Biennial Meetings Philosophy Sci. Assoc. Part II: Symposia Papers, 67, pp. S268-S639; Appiah, K.A., Race, culture, identity: Misunderstood connections (1996) Color Conscious: The Political Morality of Race, pp. 30-105. , K. A. Appiah and A. Gutmann, Eds. Princeton, NJ: Princeton Univ. Press; Zack, N., (2003) Philosophy of Science and Race, pp. 87-88. , Evanston, IL: Routledge; Smedley, A., Smedley, B.D., Race as biology is fiction, racism as a social problem is real: Anthropological and historical perspectives on the social construction of race (2005) Amer. Psychologist, 60 (1), pp. 16-26; Boden, M., Principles of robotics: Regulating robots in the real world (2017) Connection Sci, 29 (2), pp. 124-129; Sparrow, R., Sparrow, L., In the hands of machines the future of aged care (2006) Minds Mach, 16 (2), pp. 141-161; Phillips, E., Zhao, X., Ullman, D., Malle, B.F., What is humanlike: Decomposing robots' human-like appearance using the anthropomorphic roBOT (ABOT) database Proc. 2018 ACM/IEEE Int. Conf. Human-Robot Interaction, 18, pp. 105-113; Johnson, B., Energy slaves: Carbon technologies, climate change, and the stratified history of the fossil economy (2016) Amer. Quart, 68 (4), pp. 955-979; (1931) Mechanical Men That Excel Any Human Being, p. 48. , San Antonio Light; Roediger, D.R., (2006) Working Toward Whiteness: How America's Immigrants Became White: The Strange Journey from Ellis Island to the Suburbs, , New York: Basic Books; Morrell, S., Zombies, robots, race, and modern labour (2015) Affirmations: Modern, 2 (2), pp. 101-134; Rieder, J., (2012) Colonialism and the Emergence of Science Fiction, , Middletown, CT: Wesleyan Univ. Press; Chude-Sokei, L., (2016) The Sound of Culture: Diaspora and Black Technopoetics, , Middletown, CT: Wesleyan Univ. Press; Hampton, G.J., (2015) Imagining Slaves and Robots in Literature, Film, and Popular Culture: Reinventing Yesterday's Slave with Tomorrow's Robot, , London: Lexington Books; Kakoudaki, D., (2014) Anatomy of A Robot: Literature, Cinema, and the Cultural Work of Artificial People, , New Brunswick, NJ: Rutgers Univ. Press; Bernardi, D., (1999) Star Trek and History: Race-ing Toward A White Future, , New Brunswick, NJ: Rutgers Univ. Press; Bryson, J.J., Robots should be slaves (2010) Close Engagements with Artificial Companions: Key Social, Psychological, pp. 63-74. , Ethical and Design Issues, Y. Wilks, Ed. Amsterdam, The Netherlands: John Benjamins Publishing; Peterson, S., Designing people to serve (2011) Robot Ethics: The Ethical and Social Implications of Robotics, pp. 283-298. , P. Lin, G. A. Bekey, and K. Abney, Eds. Cambridge, MA: MIT Press; Eyssel, F., Kuchenbrandt, D., Social categorization of social robots: Anthropomorphism as a function of robot group membership (2012) Brit. J. Soc. Psychol, 51 (4), pp. 724-731; Payne, B.K., Krosnick, J.A., Pasek, J., Lelkes, Y., Akhtar, O., Tompson, T., Implicit and explicit prejudice in the 2008 American presidential election (2010) J. Exp. Soc. Psychol, 46 (2), pp. 367-374; Stanley, D.A., Sokol-Hessner, P., Banaji, M.R., Phelps, E.A., Kahneman, D., Implicit race attitudes predict trustworthiness judgments and economic trust decisions (2011) Proc. National Academy of Sciences of the United States of America, 108 (19), pp. 7710-7715; Richeson, J.A., Sommers, S.R., Toward a social psychology of race and race relations for the twenty-first century (2016) Annu. Rev. Psychol, 67 (1), pp. 439-463","Sparrow, R.; Philosophical and Australian Research, Australia; email: robert.sparrow@monash.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10709932,,IRAME,,"English","IEEE Rob Autom Mag",Article,"Final","",Scopus,2-s2.0-85079816057
"Kuhnlenz K., Westermann M., Kuhnlenz B.","8611373500;57219848640;55909854300;","Impact of Human Gaze Behavior and Robot Appearance on Motion Uncertainty during Cooperative Hand Movement Tasks",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223447","1301","1306",,,"10.1109/RO-MAN47096.2020.9223447","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095787139&doi=10.1109%2fRO-MAN47096.2020.9223447&partnerID=40&md5=e033336b8d58b359507f7558cc4baa08","Coburg University of Applied Sciences and Arts, Department of Electrical Engineering and Computer Science, Coburg, D-96450, Germany","Kuhnlenz, K., Coburg University of Applied Sciences and Arts, Department of Electrical Engineering and Computer Science, Coburg, D-96450, Germany; Westermann, M., Coburg University of Applied Sciences and Arts, Department of Electrical Engineering and Computer Science, Coburg, D-96450, Germany; Kuhnlenz, B., Coburg University of Applied Sciences and Arts, Department of Electrical Engineering and Computer Science, Coburg, D-96450, Germany","Results from a study on human gaze behavior during a synchronous human-robot arm movement task with incongruent motions and robot appearance variation are presented. Significant interactions of human gaze behavior and robot appearance (presence or absence of a robot head) towards deviations of human hand movements from task direction are found. Depending on the level of humanoid embodiment, different gaze strategies lead to different deviations from task motion, which may be explained with different allocation of attention in pursuit and static point fixation behaviors. Besides a better understanding of gaze dependent impact of robot appearance on interaction, a practical implication is the potential for deriving respective situational uncertainty models of human motion in order to improve safety and efficiency of close human-robot interaction. © 2020 IEEE.",,"Agricultural robots; Behavioral research; Allocation of attentions; Gaze behavior; Hand movement; Human motions; Motion uncertainty; Robot appearance; Safety and efficiencies; Uncertainty models; Social robots",,,,,,,,,,,,,,,,"Atsma, J., Koning, A., Van Lier, R., Multiple object tracking: Anticipatory attention doesn't 'bounce (2012) Journal of Vision, 12 (13), pp. 1-11; Fukushima, K., Fukushima, J., Warabi, T., Barnes, G.R., Cognitive processes involved in smooth pursuit eye movements: Behavioral evidence (2013) Neural Substrate and Clinical Correlation, Frontiers in Systems Neuroscience, 7; Khan, A.Z., Lefevre, P., Heinen, S.J., Blohm, G., The default allocation of attention is broadly ahead of smooth pursuit (2010) Journal of Vision, 10 (7), pp. 1-17; Tatler, B.W., Land, M.F., Everyday visual attention (2016) Handbook of Attention, pp. 391-422. , MIT Press, A. Kingstone, J. M. Fawcett, and E. F. Risko (eds.); Kupferberg, A., Huber, M., Helfer, B., Lenz, C., Knoll, A., Glasauer, S., Moving just like you: Motor interference depends on similar motility of agent and observer (2012) PLoS One, 7 (6); Kühnlenz, K., Kühnlenz, B., Motor interference of incongruent motions increases workload in close hri (2020) Advanced Robotics, Taylor and Francis, 34 (6), pp. 400-406; Kühnlenz, B., Erhart, M., Kainert, M., Wang, Z.-Q., Wilm, J., Kühnlenz, K., Impact of trajectory profiles on user stress in close human-robot interaction (2018) At-Automatisierungstechnik, de Gruyter Oldenbourg, 66 (6), pp. 483-491; Bley, H., Reinhart, G., Seliger, G., Bernardi, M., Korne, T., Appropriate human involvement in assembly and disassembly (2004) CIRP Annals-Manufacturing Technology, 53, pp. 487-509; Bortot, D., Born, M., Bengler, Directly or on detours? How should industrial robots approximate humans? (2013) Proc. of ACM/IEEE HRI; Brecher, C., Müller, S., Kur, S., Lohse, W., Towards anthropomorphic movements for industrial robots (2013) Proceedings of the 4th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management: Human Body Modeling and Ergonomics; Chaminade, T., Franklin, D., Oztop, E., Cheng, G., Motor interference between humans and humanoid robots: Effect of biological and artificial motion (2005) Proc. of the IEEE International Conference on Development and Learning; Cook, J., Saygin, A.P., Swain, R., Blakemore, S.-J., Reduced sensitivity to minimum-jerk biological motion in autism spectrum conditions (2009) Neuropsychologia, 47 (14), pp. 3275-3278; Faber, M., Bützler, J., Schlick, C., Human-robot cooperation in future production systems: Analysis of requirements for designing an ergonomic work system (2015) Procedia Manufacturing, 3, pp. 510-517; Fehd, H.M., Seiffert, A.E., Eye movements during multiple object tracking: Where do participants look? (2008) Cognition, 108 (1), pp. 1-13; Fehd, H.M., Seiffert, A.E., Looking at the center of the targets helps multiple object tracking (2010) Journal of Vision, 10 (4), pp. 1-13; Flash, T., Hogan, N., The coordination of arm movements: An experimentally confirmed mathematical model (1985) Journal of Neuroscience, 5 (7), pp. 1688-1703; Frielink-Loing, A.F., Koning, A., Van Lier, R., Distinguishing influences of overt and covert attention in anticipatory attentional target tracking (2017) Journal of Vision, 17 (4); Huber, M., Rickert, M., Knoll, A., Brandt, T., Glasauer, S., Human-robot interaction in handing-over tasks (2008) Proc. of the IEEE Int. Symp. on Robot and Human Interactive Communication (ROMAN); Kilner, J., Paulignan, Y., Blakemore, S., An interference effect of observed biological movement on action (2003) Current Biology, 13 (6), pp. 522-525; Kilner, J., Hamilton, A., Blakemore, S., Interference effect of observed human movement on action is due to velocity profile of biological motion (2007) Social Neuroscience, 2 (3), pp. 158-166; Kirsch, A., Kruse, T., Sisbot, A., Alami, R., Lawitzky, M., Brscíc, D., Hirche, S., Glasauer, S., Plan-based control of joint human-robot activities (2010) KI-Künstliche Intelligenz, 24 (3), pp. 223-231; Kuz, S., Petruck, H., Heisterueber, M.A., Patel, H., Schumann, B., Schlick, C., Mirror neurons and human-robot interaction in assembly cells (2015) Proc. AHFE; Lasota, P., Fong, T., Shah, J., A survey of methods for safe human-robot interaction (2017) Foundations and Trends in Robotics, 5, pp. 261-349; Mayer, M., Odenthal, B., Ewert, D., Kempf, T., Behnen, D., Büscher, C., Kuz, S., Brecher, C., Self-optimising assembly systems based on cognitive technologies (2012) Integrative Production Technology for High-Wage Countries, , Editor: C Brecher; Oztop, E., Franklin, D., Chaminade, T., Cheng, G., Humanhumanoid interaction: Is a humanoid robot perceived as a human? (2005) Int. J. of Humanoid Robots, 2, pp. 537-559; Petruck, H., Nelles, J., Faber, M., Giese, H., Geibel, M., Mostert, S., Mertens, A., Nitsch, V., Human-robot cooperation in manual assembly-interaction concepts for the future workplace (2019) Proc. of the AHFE 2019 Int. Conf. on Human Factors in Robots and Unmanned Systems; Rinkenauer, G., Böckenkamp, A., Weigert, F., Man-robot collaboration in the context of industry 4. 0: Approach-avoidance tendencies as an indicator for the affective quality of interaction (2017) Advances in Ergonomic Design of Systems, Products and Processes, , Schlick C. et al. (eds) . Springer, Berlin, Heidelberg; Shen, Y., Reinhart, G., Safe assembly motion-a novel approach for applying human-robot co-operation in hybrid assembly systems (2013) Proc. of the IEEE Int. Conf. on Mechatronics and Automation (ICMA)",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095787139
"Diaz M., Quintana J.J., Santiago R., Ferrer M.A., Gonzalez A.","36760594500;24341642700;57219244040;55636321172;57219237404;","Towards a Man - Machine interaction in programming robotic subject",2020,"Proceedings - 2020 14th Technologies Applied to Electronics Teaching Conference, TAEE 2020",,,"9163670","","",,,"10.1109/TAEE46915.2020.9163670","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091863494&doi=10.1109%2fTAEE46915.2020.9163670&partnerID=40&md5=b515357f30895dca4801e23152ec47a7","ESCOEX Faculty, Universidad Del Atlantico Medio, Las Palmas, Spain; Universidad de Las Palmas de Gran Canaria, Department of Electronic and Automatic Engineering, Las Palmas, Spain; Institute for Technological Development and Innovation in Communication IDeTIC, Universidad de Las Palmas de Gran Canaria, Las Palmas, Spain; Universidad Del Atlantico Medio, Department of Educational Research, Las Palmas, Spain","Diaz, M., ESCOEX Faculty, Universidad Del Atlantico Medio, Las Palmas, Spain; Quintana, J.J., Universidad de Las Palmas de Gran Canaria, Department of Electronic and Automatic Engineering, Las Palmas, Spain; Santiago, R., Universidad de Las Palmas de Gran Canaria, Department of Electronic and Automatic Engineering, Las Palmas, Spain; Ferrer, M.A., Institute for Technological Development and Innovation in Communication IDeTIC, Universidad de Las Palmas de Gran Canaria, Las Palmas, Spain; Gonzalez, A., Universidad Del Atlantico Medio, Department of Educational Research, Las Palmas, Spain","This article begins by generally addressing the fundamentals of educational robotics, to later conclude that human-robot interaction practices constitute an activity that meets the purposes of this new discipline, and that significantly improves motivation and involvement of students in academic work. Specifically, teachers are provided with a proposal for laboratory practice that develops robot control by hand movement. This lab activity, aimed at developing students' programming skills, includes the installation of Leap Motion, which recognizes the hand gestures, and the steps to follow to achieve its communication with the ABB industrial robot, model IRB 120, which will execute the commands within specific coordinates that the student will program. through the connection between Leap Motion-Matlab RobotStudio-Robot ABB IRB 120. © 2020 IEEE.","ABB Robot; Hand Gestures; Human-Robot Interaction; Leap Motion; Robot Programming","E-learning; Human robot interaction; MATLAB; Robot programming; Robotics; Students; Academic work; Educational robotics; Hand gesture; Hand movement; Laboratory practices; Programming skills; Robot controls; Educational robots",,,,,,,,,,,,,,,,"Ruiz-Velasco, E., (2007) Educatronics Innovation in the Learning of Science and Technology, , Madrid, Spain: Ediciones Daz de Santos S. A; Papert, S., (1980) Mindstorms: Children, Computers, and Powerful Ideas, , Basic Books, Inc; Aliane, N., (2006) Una Experiencia de Aprendizaje Basado en Proyecto en Una Asignatura de Robótica; Lombana, N.B., Uso de la robótica educativa como estrategia didáctica en el aula (2015) Praxis & Saber, 6 (11), pp. 215-234; Rebollo, G.O., Albaladejo, I.M.R., Cuadra, F.G., Sánchez, A.C., Implantación de la nueva asignatura robótica en enseñanza secundaria y bachillerato (2015) Investigación en la Escuela, (87), pp. 65-79; Sabanovíc, S., Emotion in robot cultures: Cultural models of affect in social robot design (2010) Proceedings of the Conference on Design & Emotion (D&E2010); Reeves, B., Nass, C., (1996) The Media Equation Cambridge University Press, , New York; Krathwohl, D.R., Anderson, L.W., (2009) A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives, , Longman; Goodrich, M.A., Schultz, A.C., Human-robot interaction: A survey (2008) Foundations and Trends® in Human-Computer Interaction, 1 (3), pp. 203-275; Murphy, R.R., Nomura, T., Billard, A., Burke, J.L., Humanrobot interaction (2010) IEEE Robotics Automation Magazine, 17 (2), pp. 85-89. , June; Quintana, J.J., Diaz, M., Ferrer, M.A., Stirring up the learning to program robotic arms through the generation of student handwriting (2018) 2018 XIII Technologies Applied to Electronics Teaching Conference (TAEE). IEEE, pp. 1-6; (2019) Technical Specifications Leap Motion, , blog.showleap.com/category/leapmotion, last visit: November 29th; (2019) Leap Motion Official Website, , www.leapmotion.com, last visit: November 29th; (2019) Robotstudio Technical Manual, , www.infoplc.net/files/descargas/abbrobotica/infoplcnet3hac032104-005revdes.pdf, RobotStudio last visit: November 29th; (2019) Choose A C++ Compiler, , https://www.mathworks.com/help/matlab/matlabexternal/choose-c-or-c-compilers.html, last visit: November 29th; Perry, J., (2019) Matleap: Matlab Interface to the Leap Motion Controller, , github.com/jeffsp/matleap, last visit: November 29th; Del Nozal, A.R., Aguilar, F.G.E., Gutierrez, D., Robotic platform for active multidisciplinary learning (2018) Proceedings of the XIII Congress of Technology, Learning and Teaching of Electronics, pp. 348-354. , Tenerife, June 20-22; Santiago, R., (2019) Reconocimiento de Gestos de la Mano para El Movimiento de un Robot, , Bachelor's thesis, Escuela de ingenieros Industriales y Civiles, Universidad de Las Palmas de Gran Canaria, nov",,"Alves G.R.Fidalgo A.V.Felgueiras M.C.Costa R.J.",,"Institute of Electrical and Electronics Engineers Inc.","14th Technologies Applied to Electronics Teaching Conference, TAEE 2020","8 July 2020 through 10 July 2020",,162362,,9781728167329,,,"English","Proc. - Technol. Appl. Electron. Teach. Conf., TAEE",Conference Paper,"Final","",Scopus,2-s2.0-85091863494
"Chiou Y.-M., Barnes T., Mouza C., Shen C.-C.","57208131647;55892654700;57203042637;7402860169;","Social robot teaches cybersecurity",2020,"Extended Abstracts - Proceedings of the 2020 ACM Interaction Design and Children Conference, IDC 2020",,,,"199","204",,,"10.1145/3397617.3397824","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092044171&doi=10.1145%2f3397617.3397824&partnerID=40&md5=b788ee1d77b838c1746fec64e76dc94c","University of Delaware, United States","Chiou, Y.-M., University of Delaware, United States; Barnes, T., University of Delaware, United States; Mouza, C., University of Delaware, United States; Shen, C.-C., University of Delaware, United States","Social robots have recently been gaining attention in the education field. Given their capabilities, researchers can use social robots in various ways that support human-robot interactions. In this paper, we present an interactive cybersecurity education program to teach children about foundation cybersecurity concepts using a social robot. To create child-robot interactions in cybersecurity education, we devised three processes. First, in collaboration with practicing teachers we developed an interactive story to support student engagement and learning of cybersecurity concepts. Second, we prototyped animations for the story on the social robot. Third, we use a mixed-methods approach to pilot test our cybersecurity education program. Our research highlights the potential of social robot use in education, both for child-robot interaction and K-12 cybersecurity education. © 2020 Owner/Author.","collaborative learning; cybersecurity education; interest development; K-12 education; social robots; STEM","Educational robots; Security of data; Software testing; Child-robot interactions; Cyber security; Cyber-security educations; Education field; Interactive stories; Mixed method; Pilot tests; Student engagement; Social robots",,,,,"National Science Foundation, NSF: 1821794",,,,,,,,,,,"Bruckman, A., Biggers, M., Ericson, B., McKlin, T., Dimond, J., DiSalvo, B., Hewner, M., Yardi, S., Georgia computes: Improving the entire computing education pipeline Proceedings of the 40th Acm Technical Symposium on Computer Science Education (SIGCSE '09), 2009. , 2009, Chattanooga, TN; Prepare and Inspire: K-12 Education in Science, Technology, Engineering, and Mathematics (STEM) for America's Future, , http://www.whitehouse.gov/sites/default/files/microsites/ostp/pcast-stemedreport.pdf, PCAST, 2010, Washington, D.C. Retrieved from, 2010. Accessed:03/26/2020; Kelleher, C., Pausch, R., Kiesler, S., Storytelling alice motivates middle school girls to learn computer programming Proceedings of the Sigchi Conference on Human Factors in Computing Systems, pp. 1455-1464. , 2007, April, ACM; De Vecchi, N., Kenny, A., Dickson-Swift, V., Kidd, S., How digital storytelling is used in mental health: A scoping review International Journal of Mental Health Nursing, 25 (3), pp. 183-193. , 2016; Sawyer, C.B., Willis, J.M., Introducing digital storytelling to influence the behavior of children and adolescents Journal of Creativity in Mental Health, 6 (4), pp. 274-283. , 2011; Fridin, M., Storytelling by a kindergarten social assistive robot: A tool for constructive learning in preschool education Computers & Education, 70, pp. 53-64. , 2014; Hoffman, G., Kubat, R., Breazeal, C., A hybrid control system for puppeteering a live robotic stage actor Robot and Human Interactive Communication, 2008. RO-MAN 2008. The 17th Ieee International Symposium on, pp. 354-359. , 2008, August, IEEE; Kelleher, C., Pausch, R., Kiesler, S., Storytelling alice motivates middle school girls to learn computer programming Proceedings of the Sigchi Conference on Human Factors in Computing Systems, pp. 1455-1464. , 2007, April, ACM; Leyzberg, D., Spaulding, S., Toneva, M., Scassellati, B., The physical presence of a robot tutor increases cognitive learning gains Proceedings of the Cognitive Science Society, 34 (34), pp. 1882-1887. , 2012, January; Michaelis, J.E., Mutlu, B., Supporting interest in science learning with a social robot Proceedings of the 18th Acm International Conference on Interaction Design and Children, Idc 2019, pp. 71-82. , 2019; Zenbo AsusTek Computer Inc, , https://zenbo.asus.com, Accessed: 03/26/2020; Ivankova, N.V., Creswell, J.W., Stick, S.L., Using mixed-methods sequential explanatory design: From theory to practice Field Methods, 18 (1), pp. 3-20. , 2006; Creswell, J.W., Revisiting mixed methods and advancing scientific practices The Oxford Handbook of Multimethod and Mixed Methods Research Inquiry, , 2015, In S. Hesse-Biber, & R. Burke Johnson. New York, NY: Oxford University Press; The Common Sense Census: Media Use by Tweens and Teens, , https://www.commonsensemedia.org/sites/default/files/uploads/research/census-researchreport.pdf, Accessed:03/26/2020; Why K-12 Students Need to Be Taught to Guard Their Data Online, , https://edtechmagazine.com/k12/article/2019/12/why-k-12-students-need-be-taught-guard-their-data-online, Accessed:03/26/2020",,,"Elsevier;Google;iREAD","Association for Computing Machinery","2020 ACM Interaction Design and Children Conference, IDC 2020","17 June 2020 through 24 June 2020",,161885,,9781450380201,,,"English","Ext. Abstr. - Proc. ACM Interact. Des. Child. Conf., IDC",Conference Paper,"Final","",Scopus,2-s2.0-85092044171
"Guo B., Han J., Li X., Yan L.","57186085600;16232750100;56103283100;57217389678;","Dynamics Modeling and Simulation of Gait Rehabilitation Robot Based on Lie Groups and Lie Algebras Theory [步态康复机器人动力学李群李代数建模及仿真]",2020,"Xitong Fangzhen Xuebao / Journal of System Simulation","32","6",,"1126","1135",,,"10.16182/j.issn1004731x.joss.18-0769","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087177694&doi=10.16182%2fj.issn1004731x.joss.18-0769&partnerID=40&md5=871dcf2f1b32c474ffd0eae1aec20b64","School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, China; Henan Provincial Key Laboratory of Robotics and Intelligent System, Luoyang, 471003, China; Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Luoyang, 471003, China; Wuhan COBOT Technology Co., Ltd., Wuhan, 430074, China","Guo, B., School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, China, Henan Provincial Key Laboratory of Robotics and Intelligent System, Luoyang, 471003, China; Han, J., School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, China, Henan Provincial Key Laboratory of Robotics and Intelligent System, Luoyang, 471003, China, Collaborative Innovation Center of Machinery Equipment Advanced Manufacturing of Henan Province, Luoyang, 471003, China; Li, X., School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, China, Henan Provincial Key Laboratory of Robotics and Intelligent System, Luoyang, 471003, China; Yan, L., Wuhan COBOT Technology Co., Ltd., Wuhan, 430074, China","A pneumatic-driven gait rehabilitation robot based on rigid mechanism and flexible driver is designed. To explore the dynamic characteristics of the human-robot harmony system constructed by patients and the exoskeleton robot, the dynamic models of human, robot and human-robot system in different gait phases are established based on Lie group and Lie algebra theory. The driving and energy characteristics of three rehabilitation training modes of passive, active assistance and resistance are analyzed. The robot joint driven moment and the human active moment are simulated to verify the Lie group and Lie algebraic modeling method and the dynamic model. It provides a reference to the construction of the robot driving system and the design of the human-robot interaction control strategy. © 2020, The Editorial Board of Journal of System Simulation. All right reserved.","Dynamics; Gait rehabilitation robot; Human-robot harmony; Lie group and Lie algebra theory; Simulation",,,,,,,,,,,,,,,,,"Sun, Y, Paulus, D, Eyssen, M, A Systematic Review and Meta-analysis of Acute Stroke Unit Care: What's beyond the Statistical Significance (2013) BMC Medical Research Methodology, 13 (1), pp. 132-142. , [J]. (S1471-2288); Ji, Wen, Wang, Jianhui, Fang, Xiaoke, Method and Realization of Intelligent System of Rehabilitation Training for Stroke (2014) Journal of System and Simulation, 26 (4), pp. 836-842. , 纪雯, 王建辉, 方晓柯, 等. 脑卒中康复训练智能方法及实现[J]. 系统仿真学报, 2014, 26(4): 836-842. (S1004-731X); Hou, Zengguang, Zhao, Xingang, Cheng, Long, Recent Advances in Rehabilitation Robots and Intelligent Assistance Systems (2016) Acta Automatica Sinica, 42 (12), pp. 1765-1779. , 侯增广, 赵新刚, 程龙, 等. 康复机器人与智能辅助系统的研究进展[J]. 自动化学报, 2016, 42(12): 1765-1779. [J]. (S0254-4156); Ding, H, Yang, X J, Zheng, N N, Tri-Co Robot: a Chinese Robotic Research Initiative for Enhanced Robot Interaction Capabilities (2018) National Science Review, 5 (6), pp. 799-801. , [J]. (S2095-5138); Riener, R, Lünenburger, L, Maier, I C, Locomotor Training in Subjects with Sensorimotor Deficits: an Overview of the Robotic Gait Orthosis Lokomat (2010) Journal of Healthcare Engineering, 1 (2), pp. 197-216. , [J]. (S2040-2295); Van Kammen, K, Boonstra, A M, van der Woude, L H, The Combined Effects of Guidance Force, Bodyweight Support and Gait Speed on Muscle Activity during Able-bodied Walking in the Lokomat (2016) Clinical Biomechanics, 36 (1), pp. 65-73. , [J]. (S0268-0033); Hassan, M, Kadone, H, Suzuki, K, Wearable Gait Measurement System with an Instrumented Cane for Exoskeleton Control (2014) Sensors, 14 (1), pp. 1705-1722. , [J]. (S1530-437X); Zhu, Yihui, (2008) Rehabilitation assessment, pp. 20-22. , 诸毅晖. 康复评定学[M]. 上海: 上海科学技术出版社, 2008: 20-22. [M]. Shanghai: Shanghai Science and Technology Press; Meng, W, Liu, Q, Zhou, Z D, Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation (2015) Mechatronics, 31 (10), pp. 132-145. , [J]. (S0957-4158); Dai, Jiansheng, (2014) Geometrical foundations and screw algebra for mechanisms and robotics, pp. 91-94. , 戴建生. 机构学与机器人学的几何基础与旋量代数[M]. 北京: 高等教育出版社, 2014: 91-94. [M]. Beijing: Higher Education Press; Yu, Jingjun, Liu, Xinjun, Ding, Xilun, (2016) Mathematic foundation of mechanisms and robotics, pp. 111-117. , 于靖军, 刘辛军, 丁希伦. 机器人机构学的数学基础[M]. 北京: 机械工业出版社, 2016: 111-117.. [M]. Beijing: China Machine Press; Winter, David A., (2009) Biomechanics and Motor Control of Human Movement, pp. 75-77. , [M]. Hoboken, USA: John Wiley & Sons, Inc; Corke, P., (2017) Robotics, Vision and Control: Fundamental Algorithms in MATLAB, pp. 263-271. , [M]. Second edition. Springer: Springer Publishing Company, Inc",,,,"Acta Simulata Systematica Sinica",,,,,1004731X,,XFXUF,,"Chinese","Xitong Fangzhen Xuebao",Article,"Final","",Scopus,2-s2.0-85087177694
[No author name available],[No author id available],"HRI 2020 - Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"","",681,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083225577&partnerID=40&md5=3ac39d64493c61bfb258fe3ced60830d",,"","The proceedings contain 228 papers. The topics discussed include: arts + health: new approaches to arts and robots in health care; robonomics: the study of robot-human peer-to-peer financial transactions and agreements; dog sit! domestic dogs (canis familiaris) follow a robot’s sit commands; the confucian matador: three defenses against the mechanical bull; adapting to a robot: adapting gardening and the garden to fit a robot lawn mower; four years in review: statistical practices of Likert scales in human-robot interaction studies; would you turn off a robot because it confronts you with your own mortality?; voice puppetry: towards conversational HRI WoZ experiments with synthesized voices; an application of HRI in low-cost digital manufacturing; and demonstration of a social robot for control of remote autonomous systems.",,,,,,,,,,,,,,,,,,,,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Review,"Final","",Scopus,2-s2.0-85083225577
"Hashemian M., Mascarenhas S., Couto M., Paiva A., Santos P.A., Prada R.","56513639000;35105447700;56506742400;7102683774;7102995678;6603926477;","The application of social power in persuasive social robots",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"564","566",,,"10.1145/3371382.3377447","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083169594&doi=10.1145%2f3371382.3377447&partnerID=40&md5=609a63e15a33ab64f8cfb080f843599d","INESC-ID, Universidade de Lisboa, Lisbon, Portugal","Hashemian, M., INESC-ID, Universidade de Lisboa, Lisbon, Portugal; Mascarenhas, S., INESC-ID, Universidade de Lisboa, Lisbon, Portugal; Couto, M., INESC-ID, Universidade de Lisboa, Lisbon, Portugal; Paiva, A., INESC-ID, Universidade de Lisboa, Lisbon, Portugal; Santos, P.A., INESC-ID, Universidade de Lisboa, Lisbon, Portugal; Prada, R., INESC-ID, Universidade de Lisboa, Lisbon, Portugal","The technology of the future will bring an increasing number of robots into our daily life. This has motivated a number of researchers to explore diverse factors to promote social interaction with robots. This PhD project aims at investigating social power dynamics in Human-Robot Interaction. Social power is defined as one's ability to influence others to do something which they would not do otherwise. Different theories classify alternative ways to achieve social power, such as providing rewards, using coercion, or acting as an expert. After conceptualizing social power to allow implementation in social agents, we studied how those power strategies affect persuasion when using robots. Specifically, we attempted to design persuasive robots by creating persuasive strategies inspired from social power. © 2020 ACM.","HRI; Human-robot interaction; Persuasion; Social power","Agricultural robots; Machine design; Man machine systems; Daily lives; Persuasive robots; PhD project; Power dynamics; Power strategies; Social agents; Social interactions; Social robots",,,,,"Fundação para a Ciência e a Tecnologia, FCT: UID/CEC/50021/2019; Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção, INCT-EN","This work was funded by AMIGOS project (PTDC/EEISII/7174/2014), and partially supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with reference UID/CEC/50021/2019.",,,,,,,,,,"French, J.R., Raven, B., Cartwright, D., The bases of social power (1959) Classics of Organization Theory, 7, pp. 311-320; Foucault, M., The history of sexuality: An introduction (1990) Vintage; Russell, B., Power: A new social analysis (2004) Routledge; Reeves, B., Nass, C.I., (1996) The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places, , Cambridge university press; Ju, W., Power in human robot interactions (2016) What Social Robots Can and Should Do: Proceedings of Robophilosophy 2016/TRANSOR 2016, 290, p. 13; Hou, Y.-Y.T., Jung, M., Robots in power (2018) Proceedings of Longitudinal Human-Robot Teaming Workshop at HRI18. ACM, pp. 325-338; Chidambaram, V., Chiang, Y.-H., Mutlu, B., Designing persuasive robots: How robots might persuade people using vocal and nonverbal cues (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 293-300; Ham, J., Bokhorst, R., Cuijpers, R., Van Der Pol, D., Cabibihan, J.-J., Making robots persuasive: The influence of combining persuasive strategies (gazing and gestures) by a storytelling robot on its persuasive power (2011) International Conference on Social Robotics. Springer, pp. 71-83; Lee, S.A., Liang, Y., The role of reciprocity in verbally persuasive robots (2016) Cyberpsychology, Behavior, and Social Networking, 19 (8), pp. 524-527; Ghazali, A.S., Ham, J., Barakova, E., Markopoulos, P., Assessing the effect of persuasive robots interactive social cues on users' psychological reactance, liking, trusting beliefs and compliance (2019) Advanced Robotics, 33 (7-8), pp. 325-337; Billings, D.R., Schaefer, K.E., Chen, J.Y., Hancock, P.A., Human-robot interaction: Developing trust in robots (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 109-110; Simsarian, K.T., (2000) Toward Human-robot Collaboration, , Ph.D. dissertation, Numerisk analys och datalogi; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Langer, A., Feingold-Polak, R., Mueller, O., Kellmeyer, P., Levy-Tzedek, S., Trust in socially assistive robots: Considerations for use in rehabilitation (2019) Neuroscience & Biobehavioral Reviews; Hashemian, M., Prada, R., Santos, P.A., Mascarenhas, S., Enhancing social believability of virtual agents using social power dynamics (2018) Proceedings of the 18th International Conference on Intelligent Virtual Agents, pp. 147-152; Hashemian, M., Paradeda, R., Guerra, C., Paiva, A., Do you trust me investigating the formation of trust in social robots (2019) EPIA Conference on Artificial Intelligence. Springer, pp. 357-369; Briñol, P., Petty, R.E., Durso, G.R., Rucker, D.D., Power and persuasion: Processes by which perceived power can influence evaluative judgments (2017) Review of General Psychology, 21 (3), p. 223; Mourali, M., Nagpal, A., The powerful select, the powerless reject: Power's influence in decision strategies (2013) Journal of Business Research, 66 (7), pp. 874-880; Hashemian, M., Paiva, A., Mascarenhas, S., Santos, P.A., Prada, R., The power to persuade: A study of social power in human-robot interaction (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 1-8; Hashemian, M., Couto, M., Mascarenhas, S., Paiva, A., Santos, P.A., Prada, R., Persuasive social robots using reward/coercion strategies Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI '20 Companion). IEEE, 2020",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083169594
[No author name available],[No author id available],"HRI 2020 - Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"","",642,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081996091&partnerID=40&md5=ff995bb8833b1bf3474e28d612bb8050",,"","The proceedings contain 66 papers. The topics discussed include: taxonomy of trust-relevant failures and mitigation strategies; why should we gender? the effect of robot gendering and occupational stereotypes on human trust and perceived competency; examining profiles for robotic risk assessment: does a robot's approach to risk affect user trust?; effects of anthropomorphism and accountability on trust in human robot interaction; when humans aren't optimal: robots that collaborate with risk-aware humans; behavioral responses to robot conversational failures; working with a social robot in school: a long-term real-world unsupervised deployment; the persistence of first impressions: the effect of repeated interactions on the perception of a social robot; and in storage, yet on display: an empirical investigation of robots' value as social signals.",,,,,,,,,,,,,,,,,,,,,"ACM SIGAI;ACM SIGCHI;IEEE Robotics and Automation Society( IEEE RAS)","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158224,21672148,9781450367462,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Review,"Final","",Scopus,2-s2.0-85081996091
"Akbari A., Diab M., Rosell J.","56574458000;57196709757;38562674300;","Contingent task and motion planning under uncertainty for human-robot interactions",2020,"Applied Sciences (Switzerland)","10","5","1665","","",,5,"10.3390/app10051665","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081908186&doi=10.3390%2fapp10051665&partnerID=40&md5=bdb4f0a375bb000c5730e0cf30a5cbd7","Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya, Barcelona, 08034, Spain","Akbari, A., Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya, Barcelona, 08034, Spain; Diab, M., Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya, Barcelona, 08034, Spain; Rosell, J., Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya, Barcelona, 08034, Spain","Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human-robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment. © 2020 by the authors.","Manipulation planning; Perception; Robot-human interactions; Task and motion planning",,,,,,"DPI2016-80077-R","This work was partially supported by the Spanish Government through the project DPI2016-80077-R. Mohammed Diab is supported by the Spanish Government through the grant 2017.",,,,,,,,,,"Hoffmann, J., Brafman, R., Contingent planning via heuristic forward search with implicit belief states (2005) In Proceedings of the International Conference on Automated Planning and Scheduling, , Monterey, CA, USA, 5-10 June; Stilman, M., Schamburek, J.U., Kuffner, J., Asfour, T., Manipulation planning among movable obstacles (2007) Proceedings of the 2007 IEEE International Conference on Robotics and Automation, pp. 3327-3332. , Roma, Italy, 10-14 April; Stilman, M., Kuffner, J., Planning among movable obstacles with artificial constraints (2008) Int. J. Robot. Res, 27, pp. 1295-1307. , [CrossRef]; Rodríguez, C., Suárez, R., Combining motion planning and task assignment for a dual-arm system (2016) In Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4238-4243. , Daejeon, Korea, 9-14 October; Zarandi, M.F., Mosadegh, H., Fattahi, M., Two-machine robotic cell scheduling problem with sequence-dependent setup times (2013) Comput. Oper. Res, 40, pp. 1420-1434. , [CrossRef]; Foumani, M., Smith-Miles, K., Gunawan, I., Moeini, A., A framework for stochastic scheduling of two-machine robotic rework cells with in-process inspection system (2017) Comput. Ind. Eng, 112, pp. 492-502. , [CrossRef]; Ghallab, M., Nau, D., Traverso, P., (2004) Automated Planning: Theory & Practice;, , Elsevier: San Francisco, CA, USA; Lagriffoul, F., Andres, B., Combining task and motion planning: A culprit detection problem (2016) Int. J. Robot. Res, 35, pp. 890-927. , [CrossRef]; Dantam, N., Kingston, Z.K., Chaudhuri, S., Kavraki, L.E., Incremental Task and Motion Planning: A Constraint-Based Approach (2016) In Proceedings of the Robotics: Science and Systems, , Cambridge, MA, USA, 18-22 June; He, K., Lahijanian, M., Kavraki, L.E., Vardi, M.Y., Towards manipulation planning with temporal logic specifications (2015) Proceedings of the International Conference on Robotics and Automation, pp. 346-352. , Seattle, WA, USA, 26-30 May; Akbari, A., Muhayyudin, Rosell, J., Task and Motion Planning Using Physics-based Reasoning (2015) In Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation, , Luxembourg, 8-11 September; Srivastava, S., Fang, E., Riano, L., Chitnis, R., Russell, S., Abbeel, P., Combined task and motion planning through an extensible planner-independent interface layer (2014) In Proceedings of the IEEE International Conference on Robotics and Automation Robotics and Automation, pp. 639-646. , Hong Kong, China, 31 May-5 June; Diab, M., Akbari, A., Muhayyuddin, Rosell, J., PMK-A Knowledge Processing Framework for Autonomous Robotics Perception and Manipulation (2019) Sensors, 19, p. 1166. , [CrossRef] [PubMed]; Cambon, S., Alami, R., Gravot, F., A hybrid approach to intricate motion, manipulation and task planning (2009) Int. J. Robot. Res, 28, pp. 104-126. , [CrossRef]; Garrett, C.R., Lozano-Pérez, T., Kaelbling, L.P., FFRob: An efficient heuristic for task and motion planning (2015) Algorithmic Foundations of Robotics XI;, pp. 179-195. , Springer: Cham Switzerland; Akbari, A., Muhayyuddin, Rosell, J., Reasoning-based Evaluation of Manipulation Actions for Efficient Task Planning (2015) In Proceedings of the ROBOT2015: Second Iberian Robotics Conference, , Lisbon, Portugal,19-21 November; Akbari, A., Muhayyudin, Rosell, J., Task Planning Using Physics-based Heuristics on Manipulation Actions (2016) In Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation, , Berlin, Germany, 6-9 September; Akbari, A., Muhayyuddin, Rosell, J., Knowledge-oriented task and motion planning for multiple mobile robots (2019) J. Exp. Theor. Artif. Intell, 31, pp. 137-162. , [CrossRef]; Akbari, A., Lagriffoul, F., Rosell, J., Combined heuristic task and motion planning for bi-manual robots (2018) Auton. Robot, pp. 1-16. , [CrossRef]; Bryce, D., Kambhampati, S., Smith, D.E., Planning graph heuristics for belief space search (2006) J. Artif. Intell. Res, 26, pp. 35-99. , [CrossRef]; Petrick, R.P., Bacchus, F., Extending the Knowledge-Based Approach to Planning with Incomplete Information and Sensing (2004) Proceedings of the International Conference on Automated Planning and Scheduling, Whistler, pp. 2-11. , BC, USA, 3-7 June; Bonet, B., Geffner, H., Planning under partial observability by classical replanning: Theory and experiments (2011) In Proceedings of the IJCAI Proceedings-International Joint Conference on Artificial Intelligence, 22, p. 1936. , Barcelona, Catalonia, Spain, 16-22 July 22; Shani, G., Brafman, R.I., Replanning in domains with partial information and sensing actions (2011) IJCAI, 2011, pp. 2021-2026; Maliah, S., Brafman, R.I., Karpas, E., Shani, G., Partially Observable Online Contingent Planning Using Landmark Heuristics (2014) In Proceedings of the International Conference on Automated Planning and Scheduling, , Portsmouth, NH, USA, 21-26 June; Gaschler, A., Petrick, R., Kröger, T., Knoll, A., Khatib, O., Robot task planning with contingencies for run-time sensing (2013) In Proceedings of the International Conference on Robotics and Automation Workshop on Combining Task and Motion Planning, , Karlsruhe, Germany, 6-10 May; Nouman, A., Yalciner, I.F., Erdem, E., Patoglu, V., Experimental evaluation of hybrid conditional planning for service robotics (2016) Proceedings of the International Symposium on Experimental Robotics, pp. 692-702. , Tokyo, Japan,3-6 October; Hoffmann, J., Nebel, B., The FF planning system: Fast plan generation through heuristic search (2001) J. Artif. Intell. Res, pp. 253-302. , [CrossRef]; Pednault, E.P.D., ADL: Exploring the Middle Ground Between STRIPS and the Situation Calculus (1989) Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning, pp. 324-332. , Toronto, ON, Canada, 15-18 May 1989; Morgan Kaufmann Publishers Inc.: San Francisco, CA, USA; Rosell, J., Pérez, A., Aliakbar, A., Muhayyuddin, Palomo, L., García, N., The Kautham Project: A teaching and research tool for robot motion planning (2014) In Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation, , Barcelona, Spain, 16-19 September; Sucan, I., Moll, M., Kavraki, L.E., others., The open motion planning library (2012) Robot. Autom. Mag, 19, pp. 72-82. , [CrossRef]; Kuffner, J.J., LaValle, S.M., RRT-connect: An efficient approach to single-query path planning (2000) In Proceedings of the International Conference on Robotics and Automation, 2, pp. 995-1001. , San Francisco, CA, USA, 24-28 April 2; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., ROS: An open-source Robot Operating System (2009) In Proceedings of the International Conference on Robotics and Automation Workshop on Open Source Software, 3, p. 5. , Kobe, Japan, 12-17 May","Akbari, A.; Institute of Industrial and Control Engineering, Spain; email: ali.akbari101@gmail.com",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85081908186
"Demircan E., Yung S., Choi M., Baschshi J., Nguyen B., Rodriguez J.","55324598000;57213421030;57213420744;57213423068;57213422834;57195477448;","Operational space analysis of human muscular effort in robot assisted reaching tasks",2020,"Robotics and Autonomous Systems","125",,"103429","","",,1,"10.1016/j.robot.2020.103429","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077794606&doi=10.1016%2fj.robot.2020.103429&partnerID=40&md5=5321ac09ffa28cdf3c754f0cee7bd8b3","Department of Mechanical and Aerospace Engineering, Biomedical Engineering, California State University, Long Beach, United States; Department of Mechanical and Aerospace Engineering, California State University, Long Beach, United States; Department of Computer Science, California State University, Long Beach, United States","Demircan, E., Department of Mechanical and Aerospace Engineering, Biomedical Engineering, California State University, Long Beach, United States; Yung, S., Department of Mechanical and Aerospace Engineering, California State University, Long Beach, United States; Choi, M., Department of Computer Science, California State University, Long Beach, United States; Baschshi, J., Department of Mechanical and Aerospace Engineering, California State University, Long Beach, United States; Nguyen, B., Department of Mechanical and Aerospace Engineering, California State University, Long Beach, United States; Rodriguez, J., Department of Mechanical and Aerospace Engineering, California State University, Long Beach, United States","Human motor performance is a key area of investigation in both biomechanics and robotics. In robotics, understanding human muscular control is important to synthesize prosthetic motions and ensure safe human–robot interaction. Building controllable biomechanical models can help in quantifying the characteristics of a subject's motion and in designing effective treatments, like motion training. This paper presents the task-based motion analysis of muscular effort using an upper-body musculoskeletal model, validated through motion capture experiments and dynamic simulations. To study the contribution of robotic assistance in improving human motor skills, the muscular effort of the task of reaching with and without robotic assistance was investigated for 10 subjects. Reduction of 21.4% in the arm muscular effort was observed for the tasks with robotic assistance. © 2020 Elsevier B.V.","Dynamics; Human performance augmentation; Rehabilitation robotics","Biomechanics; Dynamics; Robotics; Bio-mechanical models; Human motor skills; Human performance; Muscular effort; Musculoskeletal model; Operational space; Rehabilitation robotics; Robot interactions; Human robot interaction",,,,,"California State University, CSU; California State University Long Beach, CSULB","This work was supported by CSULB Faculty Seed Grant.This work was supported by California State University, Long Beach Faculty Seed Grant. The authors would like to thank the Human Performance and Robotics Lab members for participating in the data collection, Vijay Anandani for assisting with the motion capture experiments, and Dipti Chaudhari for proofreading the manuscript. The authors are also grateful to Kensei Morita for providing illustrations.","This work was supported by California State University, Long Beach Faculty Seed Grant. The authors would like to thank the Human Performance and Robotics Lab members for participating in the data collection, Vijay Anandani for assisting with the motion capture experiments, and Dipti Chaudhari for proofreading the manuscript. The authors are also grateful to Kensei Morita for providing illustrations. Emel Demircan is an Assistant Professor at the Departments of Mechanical and Aerospace Engineering and Bio medical Engineering at California State University, Long Beach. Dr. Demircan obtained her Ph.D in Mechanical Engineering from Stanford University in 2012. She was a postdoctoral scholar at Stanford from 2012 to 2014 and a visiting assistant professor at the University of Tokyo from 2014 to 2015. She was also a part-time scientist at Lucile Salter Packard Children’s Hospital Gait Analysis Lab at Stanford University. Dr. Demircan’s research focuses on the application of dynamics and control theory for the simulation and analysis of bio mechanical and robotic systems. Her research interests include cyber-physical systems, rehabilitation robotics, sports bio mechanics, natural motion generation in humanoid robotics, and human motion synthesis. Dr. Demircan is an OpenSim Fellow; and the founder & co-chair of the IEEE RAS Technical Committee on “Human Movement Understanding”. She is actively collaborating with clinical, athletic, and industrial partners; and she is involved in professional and educational activities within the IEEE Robotics Society.",,,,,,,,,"De Sapio, V., Warren, J., Khatib, O., Delp, S., Simulating the task-level control of human motion: a methodology and framework for implementation (2005) Vis. Comput., 21 (5), pp. 289-302; Mainprice, J., Berenson, D., Human-robot collaborative manipulation planning using early prediction of human motion (2013) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 299-306; Gielniak, M.J., Liu, C.K., Thomaz, A.L., Generating human-like motion for robots (2013) Int. J. Robot. Res., 32 (11), pp. 1275-1301; Do, M., Azad, P., Asfour, T., Dillmann, R., Imitation of human motion on a humanoid robot using non-linear optimization (2008) IEEE-RAS International Conference on Humanoid Robots, pp. 545-552; Asfour, T., Regenstein, K., Azad, P., Schroder, J., Bierbaum, A., Vahrenkamp, N., Dillmann, R., Armar-iii: An integrated humanoid platform for sensory-motor control (2006) IEEE-RAS International Conference on Humanoid Robots, pp. 169-175; Khatib, O., Yokoi, K., Brock, O., Chang, K., Casal, A., Robots in human environments: Basic autonomous capabilities (1999) Int. J. Robot. Res., 18 (7), pp. 684-696; Mombaur, K., Laumond, J.P., Truong, A., An inverse optimal control approach to human motion modeling (2011) Springer Tracts Adv. Robot. Robot. Res., 70, pp. 451-468; Mombaur, K., Truong, A., Laumond, J.P., From human to humanoid locomotion—an inverse optimal control approach (2009) Springer Tracts Adv. Robot. Robot. Res., 28 (3), pp. 369-383; Mainprice, J., Hayne, R., Berenson, D., Goal set inverse optimal control and iterative re-planning for predicting human reaching motions in shared workspaces (2016) IEEE Trans. Robot., 32 (4), pp. 897-908; Kulic, D., Croft, E.A., Safe planning for human–robot interaction (2005) J. Robot. Syst., 22 (7), pp. 383-396; De Sapio, V., An approach for goal-oriented neuromuscular control of digital humans in physics-based simulations (2014) Int. J. Hum. 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Graph., 35 (4), pp. 1-11; Murai, A., Yamane, K., Nakamura, Y., Modeling and identification of human neuromusculoskeletal network based on biomechanical property of muscle (2008) International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 3706, , no. 9, Journal Article; Khatib, O., Demircan, E., De Sapio, V., Sentis, L., Delp, S., Robotics-based synthesis of human motion (2009) J. Physiol. - Paris, 103 (3-5), pp. 211-219; Crowninshield, R.D., Brand, R.A., A physiologically based criterion of muscle force prediction in locomotion (1981) J. Biomech., 14 (11), pp. 793-801; Coscia, M., Cheung, V.C., Tropea, P., Koenig, A., Monaco, V., Bennis, C., Micera, S., Bonato, P., The effect of arm weight support on upper limb muscle synergies during reaching movements (2014) J. NeuroEng. Rehabil., 11 (22); Coscia, M., Cheung, V., Tropea, P., Koenig, A., Monaco, V., Bennis, C., Micera, S., Bonato, P., The effect of arm weight support on upper limb muscle synergies during reaching movements (2014) J. NeuroEng. Rehabil., 1 (22); d'Avella1, A., Lacquaniti, F., Control of reaching movements by muscle synergy combinations (2013) Front. Comput. Neurosci., 7 (42); Keisuke, T., Bumsuk, L., Yasufumi, S., Kazuhiro, T., Naoki, W., Kenji, S., Hideomi, W., Muscle activation patterns in acceleration-based phases during reach-to-grasp movement (2016) J. Phys. Ther. Sci., 28 (11), pp. 3105-3111; Khatib, O., A unified approach for motion and force control of robot manipulators: the operational space formulation (1987) IEEE J. Robot. Autom., RA-3 (1), pp. 43-53; Holzbaur, K.R., Murray, W.M., Delp, S.L., A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control (2005) Ann. Biomed. Eng., 33, pp. 829-840; Delp, S.L., Anderson, F.C., Arnold, A.S., Loan, P., Habib, A., John, C.T., Opensim: open-source software to create and analyze dynamic simulations of movement (2007) IEEE Trans. Biomed. Eng., 54 (11), pp. 1940-1950; Hill, A.V., The heat of shortening and dynamics constants of muscles (1938) Proc. R. Soc. Lond. B, 126 (843), pp. 359-411; Zajac, F.E., Muscle and tendon: Properties, models, scaling, and application to biomechanics and motor control (1989) Crit. Rev. Biomed. Eng., 17 (4), pp. 359-411; Delp, S.L., Loan, P.J., Hoy, M.G., Zajac, F.E., Topp, E.L., Rosen, J.M., An interactive graphics- based model of the lower extremity to study orthopaedic surgical procedures (1990) IEEE Trans. Biomed. Eng., 37, pp. 757-767; Kendall, F., McCreary, E., Provance, P., Muscles Testing and Function, Vol. 1 (1993), Williams & Wilkins Philadelphi; Khatib, O., Sentis, L., Park, J., Warren, J., Whole-body dynamic behavior and control of human-like robots (2004) Int. J. Hum. Robot., 1, pp. 29-43; Version 1.0.6 (2014), Kinova Robotics Quebec, Canada; Version Sports 1.7.1 (2015), ANT Neuro Enschede, The Netherlands; Natural point,inc. (2016), http://www.optitrack.com/products/prime-13/, web:; Buchanan, T., Lloyd, D., Manal, K., Besier, T., Estimation of muscle forces and joint moments using a forward-inverse dynamics model (2005) Med. Sci. Sports Exerc., 37, pp. 1911-1916; Khatib, O., Inertial properties in robotics manipulation: An object-level framework (1995) Int. J. Robot. Res., 14 (1), pp. 19-36; Demircan, E., Murai, A., Khatib, O., Nakamura, Y., Muscular effort for the characterization of human postural behaviors (2014) Proc. of Experimental Robotics, , Springer Netherlands","Demircan, E.; Department of Mechanical and Aerospace Engineering, United States; email: emel.demircan@csulb.edu",,,"Elsevier B.V.",,,,,09218890,,RASOE,,"English","Rob Autom Syst",Article,"Final","",Scopus,2-s2.0-85077794606
"Hartl-Nesic C., Bischof B., Glück T., Kugi A.","57212931662;57192593802;36666210000;57217374033;","Path following control with path progress concepts: An assembly scenario [Pfadfolgeregelung mit Konzepten für den Pfadfortschritt: Ein Assemblierungsszenario]",2020,"At-Automatisierungstechnik","68","1",,"44","57",,,"10.1515/auto-2019-0114","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078173347&doi=10.1515%2fauto-2019-0114&partnerID=40&md5=30ea5700ee95d07c9988f2f0927785f3","ACIN, Institut für Automatisierungs- und Regelungstechnik, TU Wien, Wien, 1040, Austria; Center for Vision, Automation and Control, AIT Austrian Institute of Technology, Wien, 1210, Austria","Hartl-Nesic, C., ACIN, Institut für Automatisierungs- und Regelungstechnik, TU Wien, Wien, 1040, Austria; Bischof, B., Center for Vision, Automation and Control, AIT Austrian Institute of Technology, Wien, 1210, Austria; Glück, T., Center for Vision, Automation and Control, AIT Austrian Institute of Technology, Wien, 1210, Austria; Kugi, A., ACIN, Institut für Automatisierungs- und Regelungstechnik, TU Wien, Wien, 1040, Austria, Center for Vision, Automation and Control, AIT Austrian Institute of Technology, Wien, 1210, Austria","Collaborative robots are becoming increasingly popular in industry, and collaboration between humans and robots constitutes an important component of modern production systems. Path following control restricts the robot motion to a geometric path in the Cartesian space and thus provides a systematic approach to the design of safe physical human-robot-interaction concepts. In this work, four concepts are presented which link the movement along the path to system states or exogenous inputs: autonomous mode, collaborative mode, cooperative mode and gesture control. Furthermore, the controller parameters are linked to the path progress such that the impedance behavior of the robot varies along the path. The presented concepts are demonstrated in an interactive assembly scenario with the light-weight robot Kuka LBR iiwa 14 R820. © 2020 Walter de Gruyter GmbH, Berlin/Boston.","assembling; cobot; collaborative robot; human-robot-interaction; path",,,,,,,,,,,,,,,,,"Bischof, B., Glück, T., Kugi, A., Combined path following and compliance control for fully actuated rigid body systems in 3-D space (2016) IEEE Transactions on Control Systems Technology, 25 (5), pp. 1750-1760; Bischof, B., A path/surface following control approach to generate virtual fixtures (2018) IEEE Transactions on Robotics, 34 (6), pp. 1577-1592; Bischof, B., Path following control for elastic joint robots (2017) IFAC-PapersOnLine, 50 (1), pp. 4806-4811; Bishop, R.L., There is more than one way to frame a curve (1975) The American Mathematical Monthly, 82 (3), pp. 246-251; Consolini, L., Path following for the PVTOL aircraft (2010) Automatica, 46 (8), pp. 1284-1296; De-Luca, A., Book, W.J., Robots with flexible elements (2016) Springer Handbook of Robotics, pp. 287-319. , Berlin Heidelberg: Springer; De-Luca, A., Flacco, F., Integrated control for pHRI: Collision avoidance, detection, reaction and collaboration (2012) Proceedings of the 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 288-295. , Rom, Italien; De-Luca, A., Collision detection and safe reaction with the dlr-III lightweight manipulator arm (2006) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1623-1630. , Peking, China; Do-Carmo, M.P., (2016) Differential Geometry of Curves and Surfaces: Revised and Updated Second Edition, , Mineola, New York: Courier Dover Publications; Faulwasser, T., Implementation of nonlinear model predictive path-following control for an industrial robot (2016) IEEE Transactions on Control Systems Technology, 25 (4), pp. 1505-1511; Flixeder, S., Force-based cooperative manipulation of highly deformable materials (2017) Modellierung und Regelung Komplexer Dynamischer Systeme, , Hrsg. von A. Kugi and K. Schlacher. Bd. 36. Aachen, Deutschland: Shaker; Gill, R.J., Spline path following for redundant mechanical systems (2015) IEEE Transactions on Robotics, 31 (6), pp. 1378-1392; Haddadin, S., Collision detection and reaction: A contribution to safe physical human-robot interaction (2008) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3356-3363. , Nizza, Frankreich; Haddadin, S., Konzepte für den Roboterassistenten der Zukunft (2010) At - Automatisierungstechnik, 58 (12), pp. 695-708; Ignatyev, A.O., Stability of a linear oscillator with variable parameters (1997) Electronic Journal of Differential Equations 1997, 17, pp. 1-6; Ijspeert, A.J., Nakanishi, J., Schaal, S., Movement imitation with nonlinear dynamical systems in humanoid robots (2002) Proceedings of the IEEE International Conference on Robotics and Automation, 2, pp. 1398-1403. , Washington, DC, USA; Ijspeert, A.J., Dynamical movement primitives: Learning attractor models for motor behaviors (2013) Neural Computation, 25 (2), pp. 328-373; Karlsson, M., Two-degree-of-freedom control for trajectory tracking and perturbation recovery during execution of dynamical movement primitives (2017) IFAC-PapersOnLine, 50 (1), pp. 1923-1930; Khoramshahi, M., Billard, A., A dynamical system approach to task-adaptation in physical human-robot interaction (2019) Autonomous Robots, 43 (4), pp. 927-946; Kim, M.-J., Kim, M.-S., Shin, S.Y., A general construction scheme for unit quaternion curves with simple high order derivatives (1995) Proceedings of SIGGRAPH, pp. 369-376. , Los Angeles, CA, USA, S; Krasnosel'Skii, M.A., Pokrovskii, A.V., (1989) Systems with Hysteresis, , Berlin Heidelberg: Springer; Nielsen, C., Fulford, C., Maggiore, M., Path following using transverse feedback linearization: Application to a maglev positioning system (2009) Proceedings of the American Control Conference, pp. 3045-3050. , St. Louis, MO, USA; Ott, C., (2008) Cartesian Impedance Control of Redundant and Flexible-Joint Robots, , Berlin Heidelberg: Springer; Parusel, S., Haddadin, S., Albu-Schäffer, A., Modular state-based behavior control for safe human-robot interaction: A lightweight control architecture for a lightweight robot (2011) Proceedings of the IEEE International Conference on Robotics and Automation, pp. 4298-4305. , Shanghai, China; Piegl, L., Tiller, W., (1997) The NURBS Book, , 2. Aufl. Berlin Heidelberg: Springer; Rugh, W.J., (1996) Linear System Theory, , 2. Aufl. Upper Saddle River, New Jersy: Prentice Hall; Siciliano, B., (2010) Robotics: Modelling, Planning and Control, , London, UK: Springer; Spong, M.W., Modeling and control of elastic joint robots (1987) Journal of Dynamic Systems, Measurement, and Control, 109 (4), pp. 310-318; Le-Tien, L., Entkopplungsregelung und Reibungskompensation für einen Roboter mit elastischen verkoppelten Gelenken (2010) At - Automatisierungstechnik, 58 (9), pp. 499-511; Urbanek, H., Albu-Schäffer, A., Van-Der-Smagt, P., Learning from demonstration: Repetitive movements for autonomous service robotics (2004) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 4, pp. 3495-3500. , Sendai, Japan; Xiang, X., Synchronized path following control of multiple homogenous underactuated AUVs (2012) Journal of Systems Science and Complexity, 25 (1), pp. 71-89","Hartl-Nesic, C.; ACIN, Austria; email: hartl@acin.tuwien.ac.at",,,"De Gruyter Oldenbourg",,,,,01782312,,,,"German","At-Automatisierungstechnik",Article,"Final","",Scopus,2-s2.0-85078173347
"Forgó Z., Portela M.A.V., Hypki A., Kuhlenkötter B.","6504636728;57222052773;6506910986;7801348331;","Dual arm robot control by hands gestures using ROS",2020,"52nd International Symposium on Robotics, ISR 2020",,,,"115","120",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101187186&partnerID=40&md5=501e58d0f42ead8bfbf504b710b68642","Mechanical Engineering Department, Sapientia University, Tirgu Mures, Romania; Chair of Production Systems, Ruhr-Universität Bochum, Bochum, Germany","Forgó, Z., Mechanical Engineering Department, Sapientia University, Tirgu Mures, Romania; Portela, M.A.V., Chair of Production Systems, Ruhr-Universität Bochum, Bochum, Germany; Hypki, A., Chair of Production Systems, Ruhr-Universität Bochum, Bochum, Germany; Kuhlenkötter, B., Chair of Production Systems, Ruhr-Universität Bochum, Bochum, Germany","The human-robot interaction can be realized during robot programming or robot program execution. In both cases a methodology is needed through which the actions of the user will influence the operation of the robot. This paper will present a further development of previous work concerning human-robot communication. It is based on the LeapMotion hand-tracking sensor implemented on Robot Operating System (ROS), which is used to control a dual arm collaborative robot, the ABB YuMi, by two hands of an operator: the overall system and the architecture of the ROS nodes are presented. Using this approach, the proof of concept is done: the robot is controlled by hand gestures in the ABB RobotStudio virtual environment and in reality as well. © 2020 VDE Verlag GMBH.",,"Agricultural robots; Robot programming; Robotics; Collaborative robots; Dual-arm robot; Hand gesture; Hand tracking; Human-robot communication; Proof of concept; Robot operating systems (ROS); Robot programs; Social robots",,,,,,,,,,,,,,,,"Craig, J. J., Introduction to Robotics (2005) Mechanics and Control, , (3rd. ed). Pearson Education, Inc., Upper Saddle River, NJ. ISBN 0-13-123629-6; Miller, M. R., Miller, R., (2017) Robots and Robotics: Principles, Systems, and Industrial Applications, , McGraw Hill Education. ISBN 9781259859786; Freund, E., Rokossa, D., Roßmann, J., Processoriented Approach to an efficient. Offline Programming of Industrial Robots (1998) Proceedings of the 24th; Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200), pp. 208-213. , https://doi.org/10.1109/IECON.1998.723992, Aachen, Germany; Orendt, E. M., Riedl, M., Henrich, D., Robust One-Shot Robot Programming by Demonstration Using Entity-Based Resources (2017) Proceedings of the 26th International Conference on Robotics in Alpe- Adria-Danube Region; Vuletic, T., Duffy, A., Hay, L., McTeague, C., Campbell, G., Grealy, M., Systematic literature review of hand gestures used in human computer interaction interfaces (2019) International Journal of Human- Computer Studies, 129 (2019), pp. 74-94. , https://doi.org/10.1016/j.ijhcs.2019.03.011; (2019) IFR World Robotics Presentation - Press-conference, , https://ifr.org/downloads/press2018/IFRWorldRoboticsPresentation-18Sept2019.pdf, International Federation of Robotics. Retrieved September 27, 2019 from; Bischoff, R., The KUKA-DLR Lightweight Robot arm-a new reference platform for robotics research and manufacturing (2010) Proceedings of the 41st international symposium on Robotics (ISR) and 6th German conference on robotics (ROBOTIK), pp. 1-8. , VDI Verlag, München; Bedakaa, A. K., Lina, C.-Y., CAD-based robot path planning and simulation using OPEN CASCADE (2018) Proceedings of the International Conference on Robotics and Smart Manufacturing (RoSMa2018), 133, pp. 779-785. , https://doi.org/10.1016/j.procs.2018.07.119ProcediaComputerScience, Elsevier, 1-8; Bravo, F. A., González, A. M., González, E., A review of intuitive robot programming environments for educational purposes (2017) 2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC), pp. 1-6. , https://doi.org/10.1109/CCAC.2017.8276396, Cartagena; Rieder, M., Konzept zur effizienten Integration und Erweiterung von Roboter-Picksystemen (2019) Proceedings of the 28, , Deutscher Materialfluss-Kongress; Magrinia, E., Ferragutib, F., Rongab, A. J., Pinic, F., De Luca, A., Lealic, F., Human-robot coexistence and interaction in open industrial cells (2019) Robotics and Computer-Integrated Manufacturing, 61 (2019). , https://doi.org/10.1016/j.rcim.2019.101846; (2019) Franka Emika Users Guide, , https://www.franka.de, Retrieved September 27, 2019 from; (2019) Product description for robot FANUC CR- 35iA, , https://www.fanuc.eu, Retrieved September 27,2019 from; Mavridis, N., A review of verbal and non-verbal human-robot interactive communication (2015) Robotics and Autonomous Systems, 63 (1), pp. 22-35. , https://doi.org/10.1016/j.robot.2014.09.031, (2015); Mendes, N., Ferrer, J., Vitorino, J., Safeea, M., Neto, P., Human Behavior and Hand Gesture Classification for Smart Human-robot Interaction (2017) Procedia Manufacturing, 11 (2017), pp. 91-98. , https://doi.org/10.1016/j.promfg.2017.07.156; Forgó, Z., Hypki, A., Kuhlenkötter, B., Gesture based robot programming using ROS platform (2018) 50th International Symposium on Robotics (ISR), pp. 1-8. , VDI Verlag, München, Germany; (2019) ABB ROS packages, , http://wiki.ros.org/abb, Retrieved September 27, 2019 from; Cherubini, A., Collaborative manufacturing with physical human-robot interaction (2016) Robotics and Computer-Integrated Manufacturing, 40 (2016), pp. 1-13. , https://doi.org/10.1016/j.rcim.2015.12.007; (2019) ABB experimental ROS packages, , http://wiki.ros.org/abb-experimental, Retrieved September 27, 2019 from; (2019) ROS package for ABB YuMi robot, , https://github.com/ADVRHumanoids/abb-yumi-rospkg, Retrieved September 27, 2019 from; (2019) Vendor specific ROS packages, , fromhttp://wiki.ros.org/Industrial#Vendor-Specific-Packages, 2019. Retrieved September 27",,,,"VDE Verlag GmbH","52nd International Symposium on Robotics, ISR 2020","9 December 2020 through 10 December 2020",,166460,,9783800754298,,,"English","Int. Symp. Robot., ISR",Conference Paper,"Final","",Scopus,2-s2.0-85101187186
"Atamna A., Clavel C.","56711884200;22033255800;","HRI-RNN: A user-robot dynamics-oriented RNN for engagement decrease detection",2020,"Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH","2020-October",,,"4198","4202",,,"10.21437/Interspeech.2020-1261","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098149582&doi=10.21437%2fInterspeech.2020-1261&partnerID=40&md5=58addca09edd3dd3bb99338404a5e479","LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France","Atamna, A., LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France; Clavel, C., LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France","Natural and fluid human-robot interaction (HRI) systems rely on the robot's ability to accurately assess the user's engagement in the interaction. Current HRI systems for engagement analysis, and more broadly emotion recognition, only consider user data while discarding robot data which, in many cases, affects the user state. We present a novel recurrent neural architecture for online detection of user engagement decrease in a spontaneous HRI setting that exploits the robot data. Our architecture models the user as a distinct party in the conversation and uses the robot data as contextual information to help assess engagement. We evaluate our approach on a real-world highly imbalanced data set, where we observe up to 2.13% increase in F1 score compared to a standard gated recurrent unit (GRU). Copyright © 2020 ISCA",,"Recurrent neural networks; Speech communication; Architecture models; Contextual information; Emotion recognition; Engagement analysis; Human robot Interaction (HRI); Imbalanced Data-sets; Neural architectures; On-line detection; Social robots",,,,,"ITN 7659552; Agence Nationale de la Recherche, ANR: ANR-17-MAOI","This work is supported by the Data Science & Artificial Intelligence for Digitalized Industry & Services chair of Télécom Paris, the European project H2020 ANIMATAS (ITN 7659552), and the French National Research Agency’s grant ANR-17-MAOI.",,,,,,,,,,"Sidner, C. L., Lee, C., Kidd, C. D., Lesh, N., Rich, C., Explorations in Engagement for Humans and Robots (2005) Artificial Intelligence, pp. 140-164; Dermouche, S., Pelachaud, C., Engagement modeling in dyadic interaction (2019) International Conference on Multimodal Interaction, (ICMI), pp. 440-445; Barrière, V., Clavel, C., Essid, S., Attitude Classification in Adjacency Pairs of a Human-Agent Interaction with Hidden Conditional Random Fields (2018) IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4949-4953; Langlet, C., Clavel, C., Improving Social Relationships in Face-to-Face Human-Agent Interactions: When the Agent Wants to Know User's Likes and Dislikes (2015) Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 1064-1073; Ben-Youssef, A., Varni, G., Essid, S., Clavel, C., On-the-Fly Detection of User Engagement Decrease in Spontaneous Human-Robot Interaction Using Recurrent and Deep Neural Networks (2019) International Journal of Social Robotics; Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y., Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation (2014) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724-1734; Liu, T., Kappas, A., Predicting engagement breakdown in HRI using thin-slices of facial expressions (2018) Workshops of the AAAI Conference on Artificial Intelligence, pp. 37-43; Inoue, K., Lala, D., Takanashi, K., Kawahara, T., Engagement Recognition in Spoken Dialogue via Neural Network by Aggregating Different Annotators' Models (2018) Annual Conference of the International Speech Communication Association (Interspeech), pp. 616-620; Min, W., Park, K., Wiggins, J., Mott, B., Wiebe, E., Boyer, K. E., Lester, J., Predicting Dialogue Breakdown in Conversational Pedagogical Agents with Multimodal LSTMs (2019) Artificial Intelligence in Education, pp. 195-200; Ben Youssef, A., Clavel, C., Essid, S., Early Detection of User Engagement Breakdown in Spontaneous Human-Humanoid Interaction (2019) IEEE Transactions on Affective Computing; Hochreiter, S., Schmidhuber, J., Long Short-Term Memory (1997) Neural Comput, 9 (8), pp. 1735-1780; Yun, W., Lee, D., Park, C., Kim, J., Kim, J., Automatic Recognition of Children Engagement from Facial Video using Convolutional Neural Networks (2018) IEEE Transactions on Affective Computing; Majumder, N., Poria, S., Hazarika, D., Mihalcea, R., Gelbukh, A. F., Cambria, E., DialogueRNN: An Attentive RNN for Emotion Detection in Conversations (2019) Proceedings of the AAAI Conference on Artificial Intelligence, pp. 6818-6825; Hazarika, D., Poria, S., Mihalcea, R., Cambria, E., Zimmermann, R., ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection (2018) Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2594-2604; Hazarika, D., Poria, S., Zadeh, A., Cambria, E., Morency, L.-P., Zimmermann, R., Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos (2018) Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), pp. 2122-2132; Ghosal, D., Majumder, N., Poria, S., Chhaya, N., Gelbukh, A., DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation (2019) Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP); Zhang, D., Wu, L., Sun, C., Li, S., Zhu, Q., Zhou, G., Modeling both Context- and Speaker-Sensitive Dependence for Emotion Detection in Multi-speaker Conversations (2019) Proceedings of the International Joint Conference on Artificial Intelligence, pp. 5415-5421; Kipf, T. N., Welling, M., Semi-Supervised Classification with Graph Convolutional Networks (2017) ICLR; Rollet, N., Clavel, C., Talk to you later”: Doing Social Robotics with Conversation Analysis. Towards the Development of an Automatic System for the Prediction of Disengagement (2020) Interaction Studies, pp. 269-293; Ben-Youssef, A., Clavel, C., Essid, S., Bilac, M., Chamoux, M., Lim, A., UE-HRI: A New Dataset for the Study of User Engagement in Spontaneous Human-robot Interactions (2017) Proceedings of the ACM International Conference on Multimodal Interaction, pp. 464-472; Wittenburg, P., Brugman, H., Russel, A., Klassmann, A., Sloetjes, H., ELAN: a Professional Framework for Multimodality Research (2006) LREC, pp. 1556-1559; Baltrusaitis, T., Zadeh, A., Lim, Y. C., Morency, L.-P., OpenFace 2.0: Facial Behavior Analysis Toolkit (2018) IEEE International Conference on Automatic Face & Gesture Recognition, pp. 59-66; Eyben, F., Wöllmer, M., Schuller, B., openSMILE: The Munich Versatile and Fast Open-Source Audio Feature Extractor (2010) Proceedings of the international conference on Multimedia, pp. 1459-1462; Joder, C., Essid, S., Richard, G., Temporal Integration for Audio Classification with Application to Musical Instrument Classification (2009) IEEE Transactions on Audio, Speech and Language Processing, 17 (1), pp. 174-186; Kingma, D. P., Ba, J., ADAM: A Method for Stochastic Optimization (2015) ICLR",,,"Alibaba Group;Amazon Alexa;Apple;et al.;Intel;Magic Data","International Speech Communication Association","21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020","25 October 2020 through 29 October 2020",,165507,2308457X,,,,"English","Proc. Annu. Conf. Int. Speech. Commun. Assoc., INTERSPEECH",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85098149582
"Megalingam R.K., Manaswini M., Yannam J., Naick V.S., Chowdary G.N.","35119069500;57216287217;57219228453;57210336095;57210337979;","Human Robot Interaction on Navigation platform using Robot Operating System",2020,"Proceedings of the 4th International Conference on Inventive Systems and Control, ICISC 2020",,,"9171065","898","905",,2,"10.1109/ICISC47916.2020.9171065","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091772063&doi=10.1109%2fICISC47916.2020.9171065&partnerID=40&md5=484da35d93bbe6c2c97579396175d8f2","Amrita Vishwa Vidyapeetham, Dept. of Electronics and Communication Engineering, Amritapuri, India","Megalingam, R.K., Amrita Vishwa Vidyapeetham, Dept. of Electronics and Communication Engineering, Amritapuri, India; Manaswini, M., Amrita Vishwa Vidyapeetham, Dept. of Electronics and Communication Engineering, Amritapuri, India; Yannam, J., Amrita Vishwa Vidyapeetham, Dept. of Electronics and Communication Engineering, Amritapuri, India; Naick, V.S., Amrita Vishwa Vidyapeetham, Dept. of Electronics and Communication Engineering, Amritapuri, India; Chowdary, G.N., Amrita Vishwa Vidyapeetham, Dept. of Electronics and Communication Engineering, Amritapuri, India","In the last few decades, automation has been one of the leading and emerging technologies to develop sophisticated and robust robotic solutions for various applications like military, agriculture, transport, shopping, house, hospitals, etc. One of these emerging fields is service robotics which is implemented in the shop, home, and hospital environments. As these domains deal with the interaction between Humans and Robots, the concept called Human-Robot Interaction (HRI) has come into existence. In service robotics, voice is the most important mode of communication for HRI. This research proposes the implementation and integration of such HRI on an autonomous navigation system using the Robot Operating System (ROS). This paper also discusses the system architecture that is developed for this research. © 2020 IEEE.","autonomous; integration; mapping; navigation; speech","Agricultural robots; Hospitals; Man machine systems; Military applications; Navigation systems; Robot learning; Robotics; Autonomous navigation systems; Emerging technologies; Hospital environment; Human robot Interaction (HRI); Robot operating system; Robot operating systems (ROS); Robotic solutions; System architectures; Social robots",,,,,,"It is gladdening to thank Humanitarian Technology Labs and the Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Kollam who provided us with a highly encouraging work environment and lab facilities, which was the backbone for completion of this project.",,,,,,,,,,"Voisan, E., Paulis, B., Precup, R., Dragan, F., Ros-based robot navigation and human interaction in indoor environment (2015) 2015 Ieee 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics, Timisoara, pp. 31-36; https://www.google.com; https://www.who.int/news-room/fact-sheets/detail/ageing-and-health; Tharaniya Soundhari, M., Brilly Sangeetha, S., Intelligent interface based speech recognition for home automation using android application (2015) 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pp. 1-11. , Coimbatore; Li, R., Liu, J., Zhang, L., Hang, Y., Lidar/mems imu integrated navigation (slam) method for a small uav in indoor environments (2014) 2014 Dgon Inertial Sensors and Systems (ISS), pp. 1-15. , Karlsruhe; Opromolla, R., Fasano, G., Rufino, G., Grassi, M., Savvaris, A., Lidar-inertial integration for uav localization and mapping in complex environments (2016) 2016 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 649-656. , Arlington, va; Megalingam, R.K., Vishnu, S., Sekhar, S., Sasikumar, V., Sreekumar, S., Nair, T.R., Design and implementation of an android application for smart shopping (2019) 2019 International Conference on Communication and Signal Processing (ICCSP), pp. 0470-0474. , Chennai, India; Kannan Megalingam, R., Ravi Teja, C., Sreekanth, S., Raj, A., Ros based autonomous indoor navigation simulation using slam algorithm (2018) International Journal of Pure and Applied Mathematics, 118 (7), pp. 199-205; Megalingam, R.K., Nair, R.N., Prakhya, S.M., Automated voice based home navigation system for the elderly and the physically challenged (2011) 2011 2nd International Conference on Wireless Communication, Vehicular Technology, Information Theory and Aerospace & Electronic Systems Technology (Wireless VITAE), pp. 1-5. , Chennai; Mishra, S., Kankal, R., Lunawat, S., Ram, P., Gupta, V., Interactive automation interface using android app and speech recognition over wireless lan and internet (2016) International Conference & Workshop on Electronics & Telecommunication Engineering (ICWET 2016), pp. 93-99. , Mumbai; Gatesichapakorn, S., Takamatsu, J., Ruchanurucks, M., Ros based autonomous mobile robot navigation using 2d lidar and rgb-d camera (2019) 2019 First International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP), pp. 151-154. , Bangkok, Thailand; Cheng, Y., Wang, G.Y., Mobile robot navigation based on lidar (2018) 2018 Chinese Control and Decision Conference (CCDC), pp. 1243-1246. , Shenyang; Zhang, Y., Lu, Z., Wang, C., Liu, C., Wang, Y., Voice control dual arm robot based on ros system (2018) 2018 Ieee International Conference on Intelligence and Safety for Robotics (ISR), pp. 232-237. , Shenyang; Li, Z., Xiong, Y., Zhou, L., Ros-based indoor autonomous exploration and navigation wheelchair (2017) 2017 10th International Symposium on Computational Intelligence and Design (ISCID), pp. 132-135. , Hangzhou",,,,"Institute of Electrical and Electronics Engineers Inc.","4th International Conference on Inventive Systems and Control, ICISC 2020","8 January 2020 through 10 January 2020",,162544,,9781728128139,,,"English","Proc. Int. Conf. Inven. Syst. Control, ICISC",Conference Paper,"Final","",Scopus,2-s2.0-85091772063
"Tian X., Lubold N., Friedman L., Walker E.","57218283983;56453541900;57205026659;14049520000;","Understanding Rapport over Multiple Sessions with a Social, Teachable Robot",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12164 LNAI",,,"318","323",,1,"10.1007/978-3-030-52240-7_58","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088561780&doi=10.1007%2f978-3-030-52240-7_58&partnerID=40&md5=f800e420249cf9203f65c5f92e1e7194","University of Pittsburgh, Pittsburgh, PA, United States; Arizona State University, Tempe, AZ, United States; University of Pennsylvania, Philadelphia, PA, United States","Tian, X., University of Pittsburgh, Pittsburgh, PA, United States; Lubold, N., Arizona State University, Tempe, AZ, United States; Friedman, L., University of Pennsylvania, Philadelphia, PA, United States; Walker, E., University of Pittsburgh, Pittsburgh, PA, United States","Social robots have been shown to be effective educational tools. Rapport, or interpersonal closeness, can lead to better human-robot interactions and positive learning outcomes. Prior research has investigated the effects of social robots on student rapport and learning in a single session, but little is known about how individuals build rapport with a robot over multiple sessions. We reported on a case study in which 7 middle school students explained mathematics concepts to an intelligent teachable robot named Emma for five sessions. We modeled learners’ rapport-building linguistic strategies to understand whether the ways middle school students build rapport with the robot over time follow the same trends as human conversation, and how individual differences might mediate the rapport between human and robot. © 2020, Springer Nature Switzerland AG.","Case study; Human-robot interaction; Long-term hri; Multiple sessions interaction; Rapport","Artificial intelligence; Economic and social effects; Intelligent robots; Social robots; Students; Educational tools; Individual Differences; Interpersonal closeness; Learning outcome; Middle school students; Multiple sessions; Educational robots",,,,,"National Science Foundation, NSF: IIS-1637809","This work is supported by the National Robotics Initiative and the National Science Foundation, grant #CISE-IIS-1637809. We would like to thank Mesut Erhan Unal for his guidance in the ICA modeling process.",,,,,,,,,,"Ädel, A., Rapport building in student group work (2011) J. Pragmat, 43 (12), pp. 2932-2947; Beckmann, C.F., Smith, S.M., Probabilistic independent component analysis for functional magnetic resonance imaging (2004) IEEE Trans. Med. Imaging, 23 (2), pp. 137-152; Bracewell, D.B., Tomlinson, M.T., Wang, H., (2012) Identification of social acts in dialogue, , Technical report, Language Computer Corporation Richardson United States; Delorme, A., Makeig, S., Eeglab: an open source toolbox for analysis of single-trial eeg dynamics including independent component analysis (2004) J. Neurosci. Methods, 134 (1), pp. 9-21; Gordon, G., Breazeal, C., Engel, S., Can children catch curiosity from a social robot? (2015) 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 91-98. , IEEE; Gratch, J., Virtual rapport (2006) IVA 2006. LNCS (LNAI), 4133, pp. 14-27. , https://doi.org/10.1007/118218302, Gratch, J., Young, M., Aylett, R., Ballin, D., Olivier, P. (eds) Springer, Heidelberg; Gremler, D.D., Gwinner, K.P., Rapport-building behaviors used by retail employees (2008) J. Retail, 84 (3), pp. 308-324; Lakin, J.L., Chartrand, T.L., Using nonconscious behavioral mimicry to create affiliation and rapport (2003) Psychol. Sci, 14 (4), pp. 334-339; Lubold, N., Walker, E., Pon-Barry, H., Ogan, A., Automated pitch convergence improves learning in a social, teachable robot for middle school mathematics (2018) AIED 2018. LNCS (LNAI), 10947, pp. 282-296. , https://doi.org/10.1007/978-3-319-93843-121, Penstein Rosé, C., et al. (eds) Springer, Cham; Lubold, N., Walker, E., Pon-Barry, H., Ogan, A., Comfort with robots influences rapport with a social, entraining teachable robot (2019) AIED 2019. LNCS (LNAI), 11625, pp. 231-243. , https://doi.org/10.1007/978-3-030-23204-720, Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Springer, Cham; McDaniel, B., D’Mello, S., King, B., Chipman, P., Tapp, K., Graesser, A., Facial features for affective state detection in learning environments (2007) Proceedings of the Annual Meeting of the Cognitive Science Society, 29; Michaelis, J.E., Mutlu, B., Supporting interest in science learning with a social robot (2019) Proceedings of the 18th ACM International Conference on Interaction Design and Children, pp. 71-82. , ACM; Ogan, A., Finkelstein, S., Mayfield, E., D’Adamo, C., Matsuda, N., Cassell, J., Oh dear stacy!” social interaction, elaboration, and learning with teachable agents (2012) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 39-48; Ogan, A., Finkelstein, S., Walker, E., Carlson, R., Cassell, J., Rudeness and rapport: insults and learning gains in peer tutoring (2012) ITS 2012. LNCS, 7315, pp. 11-21. , https://doi.org/10.1007/978-3-642-30950-22, Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds) Springer, Heidelberg; Park, H.W., Rosenberg-Kima, R., Rosenberg, M., Gordon, G., Breazeal, C., Growing growth mindset with a social robot peer (2017) 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 137-145. , IEEE; Pennebaker, J.W., Boyd, R.L., Jordan, K., Blackburn, K., (2015) The development and psychometric properties of liwc2015, , Technical report; Seo, S.H., Griffin, K., Young, J.E., Bunt, A., Prentice, S., Loureiro-Rodríguez, V., Investigating people’s rapport building and hindering behaviors when working with a collaborative robot (2018) Int. J. Social Robot, 10 (1), pp. 147-161; Spencer-Oatey, H., (2005) (Im) politeness, face and perceptions of rapport: unpackaging their bases and interrelationships; Tickle-Degnen, L., Rosenthal, R., The nature of rapport and its nonverbal correlates (1990) Psychol. Inq, 1 (4), pp. 285-293; Zhao, R., Papangelis, A., Cassell, J., Towards a dyadic computational model of rapport management for human-virtual agent interaction (2014) IVA 2014. LNCS (LNAI), 8637, pp. 514-527. , https://doi.org/10.1007/978-3-319-09767-162, Bickmore, T., Marsella, S., Sidner, C. (eds) Springer, Cham","Tian, X.; University of PittsburghUnited States; email: xiaoyi-tian@pitt.edu","Bittencourt I.I.Cukurova M.Luckin R.Muldner K.Millan E.",,"Springer","21st International Conference on Artificial Intelligence in Education, AIED 2020","6 July 2020 through 10 July 2020",,241949,03029743,9783030522391,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85088561780
"Hempel T., Al-Hamadi A.","57217701662;57199426558;","SLAM-based multistate tracking system for mobile human-robot interaction",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12131 LNCS",,,"368","376",,2,"10.1007/978-3-030-50347-5_32","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087533586&doi=10.1007%2f978-3-030-50347-5_32&partnerID=40&md5=0467294eb815418bf2281a74b756d3b9","Neuro-Information Technology (NIT), Otto-von-Guericke University Magdeburg, Universitaetsplatz 2, Magdeburg, 39106, Germany","Hempel, T., Neuro-Information Technology (NIT), Otto-von-Guericke University Magdeburg, Universitaetsplatz 2, Magdeburg, 39106, Germany; Al-Hamadi, A., Neuro-Information Technology (NIT), Otto-von-Guericke University Magdeburg, Universitaetsplatz 2, Magdeburg, 39106, Germany","The transfer from the utilization of simple robots for specifically predefined tasks to the integration of generalized autonomous systems poses a number of challenges for the collaboration between humans and robots. These include the independent orientation of robots in unknown environments and the intuitive interaction with human cooperation partners. We present a robust human-robot interaction (HRI) system that proactively searches for interaction partners and follows them in unknown real environments. For this purpose, an algorithm for simultaneous localization and mapping of the environment is integrated along with a dynamic system for determination of the partner’s willingness and the tracking of the partner’s localization. Interruptions of interactions are recovered by a separate recovery mode that is able to identify prior collaboration partners. © Springer Nature Switzerland AG 2020.","Human-robot interaction; Interaction willingness detection; Slam","Image analysis; Man machine systems; SLAM robotics; Tracking (position); Autonomous systems; Collaboration partner; Human robot Interaction (HRI); Intuitive interaction; Real environments; Recovery modes; Simultaneous localization and mapping; Tracking system; Social robots",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 03ZZ0448G-L","This work is funded by the Federal Ministry of Education and Research (BMBF) (RoboAssist no. 03ZZ0448G-L) within 3Dsensation alliance.",,,,,,,,,,"Awais, M., Henrich, D., Human-robot collaboration by intention recognition using probabilistic state machines (2010) 19th International Workshop on Robotics in Alpe-Adria-Danube Region (RAAD 2010), pp. 75-80. , https://doi.org/10.1109/RAAD.2010.5524605, June; Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M., Visual object tracking using adaptive correlation filters (2010) 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2544-2550; Bradski, G., The OpenCV library (2000) Dr. Dobb’s J. Softw. Tools; Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O., Monoslam: real-time single camera slam (2007) IEEE Trans Pattern Anal. Mach. Intell, 29, pp. 1052-1067; Engel, J., Koltun, V., Cremers, D., (2016) Direct sparse odometry, , http://arxiv.org/abs/1607.02565, CoRR abs/1607.02565; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2015) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778; Kazemi, V., Sullivan, J., One millisecond face alignment with an ensemble of regression trees (2014) 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867-1874; Kerl, C., Sturm, J., Cremers, D., Dense visual slam for RGB-D cameras (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2100-2106. , https://doi.org/10.1109/IROS.2013.6696650, November; Li, S., Zhang, L., Diao, X., Deep-learning-based human intention prediction using RGB images and optical flow (2019) J. Intell. Robot. Syst, 97 (1), pp. 95-107. , https://doi.org/10.1007/s10846-019-01049-3; Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B., FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges (2003) Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI), IJCAI, Acapulco, Mexico; Mur-Artal, R., Montiel, J.M.M., Tardós, J.D., (2015) ORB-SLAM: a versatile and accurate monocular slam system, , CoRR abs/1502.00956; Mur-Artal, R., Tardos, J.D., ORB-SLAM2: an open-source slam system for monocular, stereo, and RGB-D cameras (2017) IEEE Trans. Rob, 33 (5), pp. 1255-1262. , https://doi.org/10.1109/tro.2017.2705103; Newcombe, R.A., Lovegrove, S., Davison, A.J., DTAM: dense tracking and mapping in real-time (2011) ICCV, pp. 2320-2327. , Metaxas, D.N., Quan, L., Sanfeliu, A., Gool, L.J.V. (eds) IEEE; Pire, T., Fischer, T., Castro, G., De Cristóforis, P., Civera, J., Berlles, J., S-PTAM: stereo parallel tracking and mapping (2017) Robot. Auton. Syst, 93, pp. 27-42. , https://doi.org/10.1016/j.robot.2017.03.019; Redmon, J., Farhadi, A., (2018) Yolov3: an incremental improvement, , http://arxiv.org/abs/1804.02767.cite, arxiv:1804.02767Comment. Technical report; Svenstrup, M., Tranberg, S., Andersen, H.J., Bak, T., Pose estimation and adaptive robot behaviour for human-robot interaction (2009) 2009 IEEE International Conference on Robotics and Automation, pp. 3571-3576. , https://doi.org/10.1109/ROBOT.2009.5152690, May; Tistarelli, M., Grosso, E., Human face analysis: from identity to emotion and intention recognition (2010) ICEB 2010. LNCS, 6005, pp. 76-88. , https://doi.org/10.1007/978-3-642-12595-911, Kumar, A., Zhang, D. (eds) Springer, Heidelberg; Wolf, L., Hassner, T., Taigman, Y., Effective unconstrained face recognition by combining multiple descriptors and learned background statistics (2011) IEEE Trans. Pattern Anal. Mach. Intell, 33 (10), pp. 1978-1990. , https://doi.org/10.1109/TPAMI.2010.230","Hempel, T.; Neuro-Information Technology (NIT), Universitaetsplatz 2, Germany; email: thorsten.hempel@ovgu.de","Campilho A.Karray F.Wang Z.",,"Springer","17th International Conference on Image Analysis and Recognition, ICIAR 2020","24 June 2020 through 26 June 2020",,241149,03029743,9783030503468,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85087533586
"Joshi S.","57201549303;","Towards community-robot interactions",2020,"ECSCW 2019 - Proceedings of the 17th European Conference on Computer Supported Cooperative Work",,,,"","",,,"10.18420/ecscw2019_dc5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084997873&doi=10.18420%2fecscw2019_dc5&partnerID=40&md5=31d4a54bacbee5e331efb84b981b4d1d","Indiana University Bloomington, United States","Joshi, S., Indiana University Bloomington, United States","Technical advances in multi-user robotics are supporting their increased real-world robot deployments in community and organizational settings. However, interactions with robots in these settings are viewed mostly as an aggregate of individual interactions with robots, separate from their social context. Through my Ph.D. research, I raise awareness about the need for a community perspective to Human-Robot Interactions (HRI) and use real-world studies to demonstrate the conceptual and methodological relevance of community-centric approaches to robot use. The goal of my research is to provide a value framework of 'Community-Robot Interactions' for systematic study of the use and development of robots for communities and probe the role of the robots as a community resource having theoretical and design implications for HRI. © Copyright 2019 held by Swapna Joshi.",,"Interactive computer systems; Machine design; Community resources; Design implications; Human robot Interaction (HRI); Organizational setting; Robot interactions; Social context; Systematic study; Technical advances; Social robots",,,,,,,,,,,,,,,,"Joshi, S., Šabanović, S., A communal perspective on shared robots as social catalysts (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 732-738; Joshi, S., Šabanović, S., Robots for inter-generational interactions: Implications for nonfamilial community settings (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 478-486; Kidd, C.D., Taggart, W., Turkle, S., A sociable robot to encourage social interaction among the elderly (2006) Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on, pp. 3972-3976; Nourbakhsh, I., Acedo, C., Sargent, R., Strebel, C., Tomokiyo, L., Belalcazar, C., (2010) GigaPan Conversations: Diversity and Inclusion in the Community; Šabanović, S., Robots in society, society in robots (2010) International Journal of Social Robotics, 2 (4), pp. 439-450; Steinfeld, A., Fong, T., Kaber, D., Lewis, M., Scholtz, J., Schultz, A., Goodrich, M., Common metrics for human-robot interaction (2006) Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction, pp. 33-40; Yanco, H.A., Drury, J., Classifying human-robot interaction: An updated taxonomy (2004) Systems, Man and Cybernetics, 2004 IEEE International Conference on, 3, pp. 2841-2846","Joshi, S.; Indiana University BloomingtonUnited States; email: swapna@iu.edu",,,"European Society for Socially Embedded Technologies (EUSSET)","17th European Conference on Computer Supported Cooperative Work, ECSCW 2019","8 June 2019 through 12 June 2019",,159630,,,,,"English","ECSCW - Proc. Eur. Conf. Computer Supported Cooper. Work",Conference Paper,"Final","",Scopus,2-s2.0-85084997873
"Arif Khan M., Pushp S., Hazarika S.M.","57211310291;57199504420;24343762400;","ccaROS: A ROS Node for Cognitive Collaborative Architecture for an Intelligent Wheelchair",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","11886 LNCS",,,"212","221",,,"10.1007/978-3-030-44689-5_19","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083977617&doi=10.1007%2f978-3-030-44689-5_19&partnerID=40&md5=b0b72f9bf6915f8a22e8d9ff0bff7e02","Human Centered Computing Lab, Central University of Jharkhand, Ranchi, India; Biomimetic and Cognitive Robotics Lab, Tezpur University, Tezpur, Assam, India; Indian Institute of Technology, Guwahati, India","Arif Khan, M., Biomimetic and Cognitive Robotics Lab, Tezpur University, Tezpur, Assam, India; Pushp, S., Human Centered Computing Lab, Central University of Jharkhand, Ranchi, India; Hazarika, S.M., Indian Institute of Technology, Guwahati, India","For effective Human-Robot Interaction (HRI), an intelligent wheelchair (IW) need to be cognitively enhanced. Robot Operating System (ROS) has been steadily gaining popularity among robotics researchers as an open source framework for robot control. This paper presents ccaROS - a new ROS node for a Cognitive Collaborative Architecture to achieve better HRI for an IW. The design of the ROS node is presented. It provides mechanisms for obstacle avoidance, detection and adaption of user’s navigational strategy; seamless switching of driving control from machine to human and vice-versa. This would not only assist to achieve safe navigation but also allow retention of residual skills of the user. The effectiveness of ccaROS has been evaluated through simulation studies within a ROS-USARSim environment. © 2020, Springer Nature Switzerland AG.","Cognitive Collaborative Architecture; Intelligent wheelchair; ROS node","Human computer interaction; Intelligent robots; Open systems; Wheelchairs; Collaborative architectures; Human robot Interaction (HRI); Intelligent wheelchair; Open source frameworks; Robot operating systems (ROS); Safe navigations; Seamless switching; Simulation studies; Human robot interaction",,,,,,,,,,,,,,,,"Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y., An integrated theory of the mind (2004) Psychol. Rev., 111 (4), p. 1036; Balakirsky, S., Kootbally, Z., USARSim/ROS: A combined framework for robotic control and simulation (2012) ASME/ISCIE 2012 International Symposium on Flexible Automation, pp. 101-108. , pp; Carlson, T., Demiris, Y., Collaborative control for a robotic wheelchair: Evaluation of performance, attention, and workload (2012) IEEE Trans. Syst. Man Cybern. Part B Cybern., 42 (3), pp. 876-888; Cooper, R.A., Boninger, M.L., Engineering better wheelchairs to enhance community participation (2006) IEEE Trans. Neural Syst. Rehabil. Eng., 14 (4), pp. 438-455; Crum, R.M., Anthony, J.C., Bassett, S.S., Folstein, M.F., Population-based norms for the mini-mental state examination by age and educational level (1993) JAMA, 269 (18), pp. 2386-2391; Langley, P., Choi, D., A unified cognitive architecture for physical agents Proceedings of the National Conference on Artificial Intelligence, 21, p. 1469. , AAAI Press; MIT Press, Menlo Park; Cambridge; London (1999, 2006); Pushp, S., Bhardwaj, B., Hazarika, S.M., Cognitive decision making for navigation assistance based on intent recognition (2017) MIKE 2017. LNCS (LNAI), 10682, pp. 81-89. , https://doi.org/10.1007/978-3-319-71928-3_9, Ghosh, A., Pal, R., Prasath, R. (eds.), pp., Springer, Cham; Pushp, S., Saikia, A., Khan, A., Hazarika, S.M., A cognitively enhanced collaborative control architecture for an intelligent wheelchair: Formalization, implementation and evaluation (2018) Cogn. Syst. Res., 49, pp. 114-127; Quigley, M., ROS: An open-source robot operating system (2009) ICRA Workshop on Open Source Software, 3, p. 5; Saikia, A., CBDI-based collaborative control for a robotic wheelchair (2016) Proced. Comput. Sci., 84, pp. 127-131; Schultz, A., Using computational cognitive models to build better human-robot interaction (2006) NAE US FOE Symposium; Urdiales, C., Wheelchair collaborative control for disabled users navigating indoors (2011) Artif. Intell. Med., 52 (3), pp. 177-191","Pushp, S.; Human Centered Computing Lab, India; email: sumantpushp@gmail.com","Tiwary U.S.Chaudhury S.",,"Springer","11th International Conference on Intelligent Human Computer Interaction, IHCI 2019","12 December 2019 through 14 December 2019",,239139,03029743,9783030446888,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85083977617
"Yang L., Li Z., Lei Q., Xu J., Deng Y., Zhong Y.","57208427789;57200183907;56423257200;57218110539;57214761534;57215563808;","Teaching a robot to draw: Hand gesture demonstration based on human-robot interaction",2020,"Proceedings of SPIE - The International Society for Optical Engineering","11433",,"114330M","","",,1,"10.1117/12.2559707","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081179526&doi=10.1117%2f12.2559707&partnerID=40&md5=925f860b902ee2da680ab4164ad31d89","College of Mechanical Engineering, Changchun University of Technology, Changchun, China; Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Guangzhou, China","Yang, L., College of Mechanical Engineering, Changchun University of Technology, Changchun, China; Li, Z., College of Mechanical Engineering, Changchun University of Technology, Changchun, China; Lei, Q., Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Guangzhou, China; Xu, J., Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Guangzhou, China; Deng, Y., Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Guangzhou, China; Zhong, Y., Guangzhou Institute of Advanced Technology, Chinese Academy of Sciences, Guangzhou, China","With the development of collaborative robots, robot programming by demonstration (PBD) plays an important role in human-robot interaction, it aims to transfer new skills from observations of tasks demonstrated by humans to robots. In this paper, we proposed a new approach to teach a robot to draw pictures based on human fingertip recognition and hand motion tracking. Combining the robot operating system (ROS), OpenCV and Moveit (motion planning libraries), we capture the finger movement trajectory by using Kinect2 depth camera. Then the trajectory waypoint is sent to the ur5 robotic arm through topic communication to complete the trajectory tracking task. The experiment indicates that the proposed approach allows inexperienced users to efficiently teach a robot to track the demonstrated trajectory. © 2020 SPIE.","Fingertip recognition; Human-robot interaction; Programming by demonstration; Robot operating system","Computer vision; Demonstrations; Man machine systems; Motion capture; Motion planning; Motion tracking; Robot programming; Trajectories; Collaborative robots; Fingertip recognition; Hand motion tracking; Programming by demon-stration; Robot operating system; Robot operating systems (ROS); Robot programming by demonstration; Trajectory tracking; Human robot interaction",,,,,"2017215102008; 2015B010919002; 201907010012; 2016GJ004; Guangdong Innovative and Entrepreneurial Research Team Program: 2014ZT05G132; Shenzhen Peacock Plan: 201607010041, 201704030091","This research is supported by the Major Projects of Guangzhou City of China (Grant No. 201907010012)，the Guangdong Innovative and Entrepreneurial Research Team Program (Grant No.2014ZT05G132), Shenzhen Peacock Plan (Grant No.KQTD2015033117354154), the Major Projects of Guangzhou City of China (Grant No.201704030091, 201607010041), the Major Projects of Guangdong Province of China (Grant No.2015B010919002), the Major Projects of Dongguan City of China (Grant No.2017215102008), the Nansha District International Science and Technology Cooperation Project of Guangzhou City of China (Grant No.2016GJ004).",,,,,,,,,,"Han, J., Shao, L., Xu, D., Shotton, J., Enhanced computer vision with microsoft kinect sensor: A review (2013) IEEE TRANSACTIONS on CYBERNETICS, 43 (5). , Oct; Billard, A., Calinon, S., Dillmann, R., Schaal, S., Robot programming by demonstration (2008) Springer Handbook of Robotics, pp. 1371-1394. , Springer; Haage, M., Teaching assembly by demonstration using advanced human robot interaction and a knowledge integration framework (2017) Procedia Manufacturing, 11, pp. 164-173; Moe, S., Schjølberg, I., Real-time hand guiding of industrial manipulator in 5 dof using microsoft kinect and accelerometer (2013) IEEE RO-MAN.; Hyun Lim, G., Pedrosa, E., Amaral, F., Lau, N., Rich and robust human-robot interaction on gesture recognition for assembly tasks (2017) IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), , April 26-28, Coimbra, Portugal; Kurien, M., Kim, M., Kopsida, M., Brilakis, I., Real-time simulation of construction workers using combined human body and hand tracking for robotic construction worker system (2018) Automation in Construction, 86, pp. 125-137. , February; Orlando Russo, L., Airò Farulla, G., Pianu, D., PARLOMA-A novel human-robot interaction system for deaf-blind remote communication (2015) International Journal of Advanced Robotic Systems, , January 1; Landa-Hurtado, Mamani-Macaya, Valenzuela-Coloma, Kinect-based trajectory teaching for industrial robots (2014) 14th Pan-American Congress of Applied Mechanics-PACAM XIV, , March 24-28; Ekvall, S., Kragic, D., Robot learning from demonstration: A task-level planning approach (2008) International Journal of Advanced Robotic Systems, 5 (3), pp. 223-234; Profanter, S., Perzylo, A., Somani, N., Rickert, M., Knoll, A., Analysis and semantic modeling of modality preferences in industrial human-robot interaction (2015) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , Sept 28-Oct 2, Hamburg, Germany; Marino, D.L., Manic, M., Fast trajectory simplification algorithm for natural user interfaces in robot programming by demonstration (2016) IEEE 25th International Symposium on Industrial Electronics (ISIE).; Lambrecht, J., Walzel, H., Kruger, J., Robust finger gesture recognition on handheld devices for spatial programming of industrial robots (2013) The 22nd IEEE International Symposium on Robot and Human Interactive Communication.; Heimann, O., Hügle, J., Krüger, J., (2017) Gesture Based Robot Programming Using Process Knowledge-An Example for Welding Applications, , 12-15 Sept; Zhang, X., Zhou, H., Cheng, H., Huang, Y., Teaching-playback of robot manipulator based on human gesture recognition and motion tracking (2015) Proceedings of the 2015 IEEE Conference on Robotics and Biomimetics.; Liu, H., Wang, L., Gesture recognition for human-robot collaboration: A review (2017) International Journal of Industrial Ergonomics.; Ganapathyraju, S., Hand gesture recognition using convexity hull defects to control an industrial robot (2013) International Conference on Instrumentation Control and Automation.; Tsarouchi, P., Makris, S., Chryssolouris, G., Human-robot interaction review and challenges on task planning and programming (2016) International Journal of Computer Integrated Manufacturing.; Feng, Z., Xu, S., Zhang, X., Real-time fingertip tracking and detection using kinect depth sensor for a new writing-in-the air system (2012) Proceedings of the 4th International Conference on Internet Multimedia Computing and Service.; Kumar Singh, A., Chakraborty, P., Nandi, G.C., Sketch drawing by NAO humanoid robot (2016) TENCON 2015-2015 IEEE Region 10 Conference.; Ju, Z., Ji, X., Li, J., Liu, H., An integrative framework of human hand gesture segmentation for human-robot interaction (2017) IEEE Systems Journal, 11 (3). , Sept; Morshidi, M., Tjahjadi, T., Gravity optimised particle filter for hand tracking (2014) Pattern Recognition.","Yang, L.; Changchun Univ. of Technology (China)China","Osten W.Nikolaev D.Zhou J.","American Science and Engineering Institute","SPIE","12th International Conference on Machine Vision, ICMV 2019","16 November 2019 through 18 November 2019",,157925,0277786X,9781510636439,PSISD,,"English","Proc SPIE Int Soc Opt Eng",Conference Paper,"Final","",Scopus,2-s2.0-85081179526
"McGinn C.","36651232500;","Why Do Robots Need a Head? The Role of Social Interfaces on Service Robots",2020,"International Journal of Social Robotics","12","1",,"281","295",,10,"10.1007/s12369-019-00564-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067296208&doi=10.1007%2fs12369-019-00564-5&partnerID=40&md5=753a788c3050b56b957c168a517a8ba0","Trinity College Dublin, Dublin, Ireland","McGinn, C., Trinity College Dublin, Dublin, Ireland","Developing effective ways for robots to communicate with humans presents many significant design challenges and requires detailed consideration of a wide range of factors. To facilitate communication between people and machines, it is common for robots to possess head-like features capable of providing social feedback through facial expressions, attention, gaze, etc. This paper explores the multifaceted roles that robotic head-like interfaces play in human–robot interaction. The research makes two main contributions. First, the paper outlines the motivations for using social interfaces on service robots and reviews key design insights from past studies in the field. Second, a taxonomy for classifying robot heads is proposed. This taxonomy has broad appeal for designers, as it gives structure to a large, disorganised design space. © 2019, Springer Nature B.V.","Human–robot interaction; Robot design; Robot head; Social interface; Taxonomy","Agricultural robots; Machine design; Mobile robots; Taxonomies; Design challenges; Design insights; Facial Expressions; Robot designs; Robot head; Robot interactions; Social feedbacks; Social interfaces; Social robots",,,,,,,,,,,,,,,,"Robots and robotic devices–vocabulary (2012) ISO, 8373, p. 2012; Robots and robotic devices–safety requirements for personal care robots (2014) ISO, 13482, p. 2014; (2016), http://changingminds.org/explanations/emotions/basic%20emotions.htm, Accessed 30 Sept 2010; Adamides, G., Christou, G., Katsanos, C., Xenos, M., Hadzilacos, T., Usability guidelines for the design of robot teleoperation: a taxonomy (2015) IEEE Trans Hum Mach Syst, 45 (2), pp. 256-262; Álvarez, M., Galán, R., Matía, F., Rodríguez-Losada, D., Jiménez, A., An emotional model for a guide robot (2010) IEEE Trans Syst Man Cybern Part A Syst Hum, 40 (5), pp. 982-992; Aly, A., Griffiths, S., Stramandinoli, F., Metrics and benchmarks in human-robot interaction: recent advances in cognitive robotics (2017) Cognit Syst Res, 43, pp. 313-323; Azenkot, S., Feng, C., Cakmak, M., Enabling building service robots to guide blind people: A participatory design approach (2016) The Eleventh ACM/IEEE International Conference on Human Robot Interaction. 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"Sejima Y., Sato Y., Watanabe T.","56036801300;55728748100;55670249000;","Development of a Pupil Response System with Empathy Expression in Face-to-Face Body Contact",2020,"Advances in Intelligent Systems and Computing","952",,,"95","102",,1,"10.1007/978-3-030-20441-9_11","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067292321&doi=10.1007%2f978-3-030-20441-9_11&partnerID=40&md5=fca96565406ecfbf207a0f55a1651a8b","Faculty of Computer Science and Systems Engineering, Okayama Prefectural University, 111 Kuboki, Soja, Okayama, Japan","Sejima, Y., Faculty of Computer Science and Systems Engineering, Okayama Prefectural University, 111 Kuboki, Soja, Okayama, Japan; Sato, Y., Faculty of Computer Science and Systems Engineering, Okayama Prefectural University, 111 Kuboki, Soja, Okayama, Japan; Watanabe, T., Faculty of Computer Science and Systems Engineering, Okayama Prefectural University, 111 Kuboki, Soja, Okayama, Japan","Pupil response is closely related to human affects and emotions. Focusing on the pupil response in human- robot interaction, we developed a pupil response interface using hemisphere displays for enhancing affective expression. This interface can generate pupil response like human by speech input and enhance affective expression. In this study, for the basic research of forming an intimate communication between human and pet-robot, we analyzed the pupil response during his or her body contact stroking forearm or head by using a pupil measurement device. Based on the analysis, we developed an advanced pupil response system for enhancing intimacy. This system generates the empathy expression when the talker touches any surface of hemisphere displays. The effectiveness of the system was confirmed experimentally. © 2020, Springer Nature Switzerland AG.","Emotional expression; Empathy; Human-robot interaction; Nonverbal communication; Pet robot","Man machine systems; Body contacts; Emotional expressions; Empathy; Face to face; Measurement device; Non-verbal communications; Pet Robots; Pupil response; Human robot interaction",,,,,"Japan Society for the Promotion of Science, JSPS: JP16K01560","Acknowledgments. This work was supported by JSPS KAKENHI Grant Numbers JP16K01560.",,,,,,,,,,"Faria, M., Costigliola, A., Alves-Oliveira, P., Paiva, A., Follow me: Communicating Intentions with a spherical robot (2016) Proceedings of 25Th IEEE International Symposium on Robot and Human Interactive Communication, pp. 664-669. , ROMAN 2016), pp; Nambo, H., Kimura, H., Hara, M., Abe, K., Tajima, T., Improvement of the owner distinction method for healing-type pet robots (2009) Trans. Inst. Electr. Eng. Jpn. C Publ. Electron. Inf. Syst. Soc., 129 (9), pp. 1662-1668. , in Japanese; Funakoshi, K., Kimura, N., Nakano, M., Iwahashi, N., Learning names without vocabulary and wording limitation by service robots (2010) IEICE Trans. Inf. Syst. (Jpn. Ed.), 93 (5), pp. 610-620. , in Japanese; Tachi, Y., Tsujisawa, N., Misawa, T., Abe, K., Kimura, H., Nambo, H., Emotion recognition system using a pet robot (2006) IEICE Trans. Inf. Syst. (Jpn. Ed.), 89 (3), pp. 447-456. , in Japanese; Watanabe, T., Human-entrained embodied interaction and communication technology (2011) Emotional Engineering, pp. 161-177. , pp., Springer; Shibata, T., Wada, K., Robot Therapy”: Mental health care by using animal type robot (2012) J. Inst. Electron. Inf. Commun. Eng., 95 (5), pp. 442-445. , in Japanese; Hess, E.H., Attitude and pupil size (1965) Sci. Am., 212 (4), pp. 46-54; Sejima, Y., Sato, Y., Watanabe, T., Development of an expressible pupil response interface using hemispherical displays (2015) Proceedings of 24Th IEEE International Symposium on Robot and Human Interactive Communication, pp. 258-290. , ROMAN 2015), pp; Daibo, I., (1998) Communication in Behavior, pp. 47-49. , pp., SAIENSU-SHA Co., Ltd., Tokyo, in Japanese; Horiuchi, S., (2000) Touch Care: Tatting as a Nursing Technique, pp. 87-92. , pp., Life Support, Inc, in Japanese; Yamaguchi, H., (2000) Healing Power of Hand, pp. 74-77. , pp., Soshisha Publishing Co., Ltd., Tokyo, in Japanese; Argyle, M., Dean, J., Eye contact, distance and affiliation (1965) Sociometry, 41 (3), pp. 289-304","Sejima, Y.; Faculty of Computer Science and Systems Engineering, 111 Kuboki, Japan; email: sejima@ss.oka-pu.ac.jp","Fukuda S.",,"Springer Verlag","AHFE International Conference on Affective and Pleasurable Design, 2019","24 July 2019 through 28 July 2019",,226989,21945357,9783030204402,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85067292321
"Shingarey D., Kaul L., Asfour T.","57207195598;56715810700;15724268700;","Torque-Based Velocity Control for Safe Human-Humanoid Interaction",2020,"Advances in Intelligent Systems and Computing","980",,,"61","68",,,"10.1007/978-3-030-19648-6_8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065984472&doi=10.1007%2f978-3-030-19648-6_8&partnerID=40&md5=dc4e1c3e7ff97c9a660fbc2925ed34cc","Karlsruhe Institute of Technology (KIT), Institute for Anthropomatics and Robotics (IAR), High Performance Humanoid Technologies (H2T), Adenauerring 2, Karlsruhe, 76131, Germany","Shingarey, D., Karlsruhe Institute of Technology (KIT), Institute for Anthropomatics and Robotics (IAR), High Performance Humanoid Technologies (H2T), Adenauerring 2, Karlsruhe, 76131, Germany; Kaul, L., Karlsruhe Institute of Technology (KIT), Institute for Anthropomatics and Robotics (IAR), High Performance Humanoid Technologies (H2T), Adenauerring 2, Karlsruhe, 76131, Germany; Asfour, T., Karlsruhe Institute of Technology (KIT), Institute for Anthropomatics and Robotics (IAR), High Performance Humanoid Technologies (H2T), Adenauerring 2, Karlsruhe, 76131, Germany","Torque-controlled robots are essential for safe human-robot interaction (HRI). In this paper we address the question of how joint level torque control can be implemented and seamlessly integrated into tasks that require precise velocity control. We present a control scheme that takes a desired velocity and torque as input to generate control output driving the joints at this velocity, and simultaneously realizing compliant behavior for safe HRI. We propose a novel method to integrate torque and velocity control into a single controller. The controller consists of an inner torque control loop embedded in an outer velocity control loop, and is hence called Torque-Based Velocity Control (TBVC). Experiments demonstrating the performance of the proposed control scheme are carried out on the humanoid ARMAR-6. © Springer Nature Switzerland AG 2020.","Humanoid robotics; Torque control","Anthropomorphic robots; Controllers; Robotics; Torque; Torque control; Velocity; Velocity control; Compliant behavior; Control schemes; Humanoid interaction; Humanoid robotics; Safe human-robot interaction; Single controllers; Torque control loop; Torque-based; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020: 643950",,,,,,,,,,,"Siciliano, B., Khatib, O., Springer Handbook of Robotics (2008) Springer, Heidelberg; Spong, M.W., Hutchinson, S., Vidyasagar, M., (2006) Robot Modeling and Control, 3. , vol., Wiley, New York; Albu-Schäffer, A., Ott, C., Hirzinger, G., A unified passivity-based control framework for position, torque and impedance control of flexible joint robots (2007) Int. J. Rob. Res., 26 (1), pp. 23-39; Asfour, T., Schill, J., Peters, H., Klas, C., Bucker, J., Sander, C., Schulz, S., Bartenbach, V., ARMAR-4: A 63 DOF torque controlled humanoid robot (2013) IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp. 390-396. , pp; Englsberger, J., Werner, A., Ott, C., Henze, B., Roa, M.A., Garofalo, G., Burger, R., Schmid, K., (2014) Overview of the Torque-Controlled Humanoid Robot TORO. In: 2014 14Th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp. 916-923. , pp., IEEE; Hyon, S.H., Hale, J.G., Cheng, G., Full-body compliant human-humanoid interaction: Balancing in the presence of unknown external forces (2007) IEEE Trans. Rob., 23 (5), pp. 884-898; Albu-Schäffer, A., Haddadin, S., Ott, C., Stemmer, A., Wimböck, T., Hirzinger, G., The DLR lightweight robot: Design and control concepts for robots in human environments (2007) Ind. Rob. Int. J., 34 (5), pp. 376-385; Sciavicco, L., Siciliano, B., (2012) Modelling and Control of Robot Manipulators, , Springer, London; Hutter, M., Remy, C.D., Hoepflinger, M.A., Siegwart, R., High compliant series elastic actuation for the robotic leg ScarlETH (2012) Field Robotics, pp. 507-514. , pp., World Scientific; Mosadeghzad, M., Medrano-Cerda, G.A., Saglia, J.A., Tsagarakis, N.G., Caldwell, D.G., Comparison of various active impedance control approaches, modeling, implementation, passivity, stability and trade-offs (2012) 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 342-348. , pp., IEEE; Rader, S., Kaul, L., Weiner, P., Asfour, T., Highly integrated sensor-actuator-controller units for modular robot design (2017) IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pp. 1160-1166. , pp; Asfour, T., Kaul, L., Wächter, M., Ottenhaus, S., Weiner, P., Rader, S., Grimm, R., Haubert, H., ARMAR-6: A collaborative humanoid robot for industrial environments (2018) IEEE/RAS International Conference on Humanoid Robots (Humanoids), pp. 447-454. , pp","Asfour, T.; Karlsruhe Institute of Technology (KIT), Adenauerring 2, Germany; email: asfour@kit.edu","Gorges D.Berns K.","Deutsche Forschungsgemeinschaft;Deutsches Forschungszentrum für Künstliche Intelligenz;Fraunhofer Zentrum Kaiserslautern;Technische Universität Kaiserslautern","Springer Verlag","28th International Conference on Robotics in Alpe-Adria-Danube Region, RAAD 2019","19 June 2019 through 21 June 2019",,225939,21945357,9783030196479,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85065984472
"Schoeller F., Miller M., Salomon R., Friston K.J.","16043722800;57225871612;35173762600;36080215500;","Trust as Extended Control: Human-Machine Interactions as Active Inference",2021,"Frontiers in Systems Neuroscience","15",,"669810","","",,,"10.3389/fnsys.2021.669810","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118135335&doi=10.3389%2ffnsys.2021.669810&partnerID=40&md5=ab4893cfad72678bbb37ff140bdeca5e","Massachusetts Institute of Technology, Cambridge, MA, United States; Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel; Center for Human Nature, Artificial Intelligence and Neuroscience, Hokkaido University, Sapporo, Japan; Department of Informatics, University of Sussex, Brighton, United Kingdom; Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom","Schoeller, F., Massachusetts Institute of Technology, Cambridge, MA, United States, Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel; Miller, M., Center for Human Nature, Artificial Intelligence and Neuroscience, Hokkaido University, Sapporo, Japan, Department of Informatics, University of Sussex, Brighton, United Kingdom; Salomon, R., Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel; Friston, K.J., Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom","In order to interact seamlessly with robots, users must infer the causes of a robot’s behavior–and be confident about that inference (and its predictions). Hence, trust is a necessary condition for human-robot collaboration (HRC). However, and despite its crucial role, it is still largely unknown how trust emerges, develops, and supports human relationship to technological systems. In the following paper we review the literature on trust, human-robot interaction, HRC, and human interaction at large. Early models of trust suggest that it is a trade-off between benevolence and competence; while studies of human to human interaction emphasize the role of shared behavior and mutual knowledge in the gradual building of trust. We go on to introduce a model of trust as an agent’ best explanation for reliable sensory exchange with an extended motor plant or partner. This model is based on the cognitive neuroscience of active inference and suggests that, in the context of HRC, trust can be casted in terms of virtual control over an artificial agent. Interactive feedback is a necessary condition to the extension of the trustor’s perception-action cycle. This model has important implications for understanding human-robot interaction and collaboration–as it allows the traditional determinants of human trust, such as the benevolence and competence attributed to the trustee, to be defined in terms of hierarchical active inference, while vulnerability can be described in terms of information exchange and empowerment. Furthermore, this model emphasizes the role of user feedback during HRC and suggests that boredom and surprise may be used in personalized interactions as markers for under and over-reliance on the system. The description of trust as a sense of virtual control offers a crucial step toward grounding human factors in cognitive neuroscience and improving the design of human-centered technology. Furthermore, we examine the role of shared behavior in the genesis of trust, especially in the context of dyadic collaboration, suggesting important consequences for the acceptability and design of human-robot collaborative systems. © Copyright © 2021 Schoeller, Miller, Salomon and Friston.","active inference; cobotics; control; extended mind hypothesis; human computer interaction; human-robot interaction; trust","Article; boredom; cognitive neuroscience; competence; empowerment; feedback system; human; human computer interaction; man machine interaction; sensorimotor function; social psychology; trust; visual information",,,,,"DLV-692739","MM carried out this work with the support of Horizon 2020 European Union ERC Advanced Grant XSPECT – DLV-692739.",,,,,,,,,,"Abramson, L.Y., Metalsky, G.I., Alloy, L.B., Hopelessness depression: a theory-based subtype of depression (1989) Psychol. 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"Irfan B., Hellou M., Belpaeme T.","57194725723;57216352354;13103663500;","Coffee With a Hint of Data: Towards Using Data-Driven Approaches in Personalised Long-Term Interactions",2021,"Frontiers in Robotics and AI","8",,"676814","","",,,"10.3389/frobt.2021.676814","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116906661&doi=10.3389%2ffrobt.2021.676814&partnerID=40&md5=ad9267333be43d76bc6f84f6b5e0aaa2","Centre for Robotics and Neural Systems, University of Plymouth, Plymouth, United Kingdom; Polytech Sorbonne, Paris, France; IDLab-imec, Ghent University, Ghent, Belgium","Irfan, B., Centre for Robotics and Neural Systems, University of Plymouth, Plymouth, United Kingdom; Hellou, M., Polytech Sorbonne, Paris, France; Belpaeme, T., Centre for Robotics and Neural Systems, University of Plymouth, Plymouth, United Kingdom, IDLab-imec, Ghent University, Ghent, Belgium","While earlier research in human-robot interaction pre-dominantly uses rule-based architectures for natural language interaction, these approaches are not flexible enough for long-term interactions in the real world due to the large variation in user utterances. In contrast, data-driven approaches map the user input to the agent output directly, hence, provide more flexibility with these variations without requiring any set of rules. However, data-driven approaches are generally applied to single dialogue exchanges with a user and do not build up a memory over long-term conversation with different users, whereas long-term interactions require remembering users and their preferences incrementally and continuously and recalling previous interactions with users to adapt and personalise the interactions, known as the lifelong learning problem. In addition, it is desirable to learn user preferences from a few samples of interactions (i.e., few-shot learning). These are known to be challenging problems in machine learning, while they are trivial for rule-based approaches, creating a trade-off between flexibility and robustness. Correspondingly, in this work, we present the text-based Barista Datasets generated to evaluate the potential of data-driven approaches in generic and personalised long-term human-robot interactions with simulated real-world problems, such as recognition errors, incorrect recalls and changes to the user preferences. Based on these datasets, we explore the performance and the underlying inaccuracies of the state-of-the-art data-driven dialogue models that are strong baselines in other domains of personalisation in single interactions, namely Supervised Embeddings, Sequence-to-Sequence, End-to-End Memory Network, Key-Value Memory Network, and Generative Profile Memory Network. The experiments show that while data-driven approaches are suitable for generic task-oriented dialogue and real-time interactions, no model performs sufficiently well to be deployed in personalised long-term interactions in the real world, because of their inability to learn and use new identities, and their poor performance in recalling user-related data. © Copyright © 2021 Irfan, Hellou and Belpaeme.","conversational artificial intelligence; data-driven architectures; dataset; few-shot learning; lifelong learning; long-term human-robot interaction; personalisation; task-oriented dialogue",,,,,,"674868","The authors would like to thank Ghent University IDLab (Belgium) for access to the servers for the experiments, and Cit? Internationale Universitaire de Paris (France) for hosting the user study with the barista robot.","This work has been supported by the EU H2020 Marie Skłodowska-Curie Actions Innovative Training Networks project APRIL (grant 674868) and SoftBank Robotics Europe",,,,,,,,,"Adiwardana, D., Luong, M.-T., So, D.R., Hall, J., Fiedel, N., Thoppilan, R., (2020) Towards a Human-like Open-Domain Chatbot, , https://arxiv.org/abs/2001.09977; Bai, B., Weston, J., Collobert, R., Grangier, D., Supervised Semantic Indexing (2009) Advances in Information Retrieval. 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Linguistics, 46, pp. 53-93; Zhu, L., Yang, Y., Compound Memory Networks for Few-Shot Video Classification (2018) Proceedings of the European Conference on Computer Vision, , Munich, Germany, ECCV, In; Zhu, L., Yang, Y., Label Independent Memory for Semi-supervised Few-Shot Video Classification (2020) IEEE Trans. Pattern Anal. Mach. Intell, 1, p. 1","Irfan, B.; Centre for Robotics and Neural Systems, United Kingdom; email: bahar.irfan@plymouth.ac.uk",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85116906661
"Cremer S., Saadatzi M.N., Wijayasinghe I.B., Das S.K., Saadatzi M.H., Popa D.O.","56383673500;36642836100;36679469200;56383202800;36188694200;7102861346;","SkinSim: A Design and Simulation Tool for Robot Skin with Closed-Loop pHRI Controllers",2021,"IEEE Transactions on Automation Science and Engineering","18","3","9123915","1302","1314",,,"10.1109/TASE.2020.3001269","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112686472&doi=10.1109%2fTASE.2020.3001269&partnerID=40&md5=2e3452a367d6079aaea7ca122e22d018","Department of Electrical Engineering, The University of Texas at Arlington, Arlington, TX, United States; Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, United States","Cremer, S., Department of Electrical Engineering, The University of Texas at Arlington, Arlington, TX, United States; Saadatzi, M.N., Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, United States; Wijayasinghe, I.B., Department of Electrical Engineering, The University of Texas at Arlington, Arlington, TX, United States; Das, S.K., Department of Electrical Engineering, The University of Texas at Arlington, Arlington, TX, United States; Saadatzi, M.H., Department of Electrical Engineering, The University of Texas at Arlington, Arlington, TX, United States; Popa, D.O., Department of Electrical Engineering, The University of Texas at Arlington, Arlington, TX, United States","The importance of tactile sensing for physical human-robot interaction (pHRI) and dexterous manipulation is well known. SkinSim is a novel simulation environment for tactile robot skins in which one can study design tradeoffs involved in deploying whole-body, dense sensor arrays. In this article, scalable modeling approaches are presented for simulating pressure-sensitive robot skin patches, with simultaneous consideration of sensing element geometry and mechanical structure, signal quality, data processing, and closed-loop force controller performance. The open-source simulation architecture of SkinSim is compatible with Gazebo and robot operating system (ROS) programming environments and supports both robot skin dynamic models, as well as tactile sensing element models. An experimentally validated force dispersion model was introduced for the simulation of sensors embedded in a mechanical damping layer. Simulation examples of robot skin with different tactile resolutions are presented using parameter values extracted from an experimental testbed. Thus, simulation results were experimentally validated and the skin sensor density impact on a simple pHRI controller performance was evaluated. Performance measures include center of pressure (COP) estimation error and control signal settling time, overshoot, and steady-state errors. Results suggest that while COP errors decrease in denser sensor skins, controller performance also deteriorates. Therefore, optimal robot skin designs will have to consider application-dependent tradeoffs. Similar results were confirmed in simulation with a larger skin patch containing approximately 4000 tactels and deployed on the end-effector of a collaborative mobile manipulator. Note to Practitioners - This article was motivated by the expensive and time-consuming process of designing tactile skin for robots. When designing pressure-sensitive whole-body sensor arrays for physical human-robot interaction, there are tradeoffs related to sensor resolution, accuracy, and response time, among others. Instead of building prototypes and evaluating them experimentally, the SkinSim simulation environment allows automatic testing of skins with simultaneous consideration of sensing element geometry and mechanical structure, signal quality, data processing, and closed-loop force controller performance. The user can specify several configurations to test and thereby explore the best tradeoffs before actually prototyping any hardware. © 2004-2012 IEEE.","Force feedback; human-robot interaction; simulation; tactile sensors","Automatic testing; Commerce; Controllers; Data handling; End effectors; Errors; Force control; Machine design; Man machine systems; Manipulators; Open source software; Open systems; Robot programming; Controller performance; Dexterous manipulation; Physical human-robot interactions; Physical humanrobot interaction (phri); Programming environment; Robot operating systems (ROS); Simulation architecture; Simulation environment; Human robot interaction",,,,,"1849213; National Science Foundation, NSF: IIP#1643989, IIS#1208623, MRI#1828355","Manuscript received September 4, 2019; revised February 26, 2020; accepted April 23, 2020. Date of publication June 24, 2020; date of current version July 2, 2021. This article was recommended for publication by Associate Editor Y. Pan and Editor K. Saitou upon evaluation of the reviewers’ comments. This work was supported in part by the U.S. National Science Foundation under Grant MRI#1828355, Grant IIP#1643989, and Grant IIS#1208623 and in part by the Kentucky EPSCOR through the RII Track-1 Project under Grant #1849213. (Corresponding author: Sven Cremer.) Sven Cremer is with the Department of Electrical Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA (e-mail: sven.cremer@mavs.uta.edu).",,,,,,,,,,"Argall, B.D., Billard, A.G., A survey of tactile human-robot interactions (2010) Robot. Auton. Syst., 58 (10), pp. 1159-1176; O'Neill, J., Lu, J., Dockter, R., Kowalewski, T., Practical, stretchable smart skin sensors for contact-Aware robots in safe and collaborative interactions (2015) Proc. Ieee Int. Conf. Robot. Autom. (ICRA), pp. 624-629. , May; Kragic, D., Crinier, S., Brunn, D., Christensen, H.I., Vision and tactile sensing for real world tasks (2003) Proc. Ieee Int. Conf. Robot. Autom., pp. 1545-1550. , Sep; Lee, M., Nicholls, H., Tactile sensing for mechatronics-A state of the art surveymechatronics (1999) Mechatronics, 9 (1), pp. 1-31; Hsiao, K., Chitta, S., Ciocarlie, M., Jones, E.G., Contact-reactive grasping of objects with partial shape information (2010) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 1228-1235. , Oct; Cheng, G., Dean-Leon, E., Bergner, F., Rogelio Guadarrama Olvera, J., Leboutet, Q., Mittendorfer, P., A comprehensive realization of robot skin: Sensors, sensing, control, and applications (2019) Proc. Ieee, 107 (10), pp. 2034-2051. , Oct; Dahiya, R., E-Skin: From humanoids to humans (2019) Proc. Ieee, 107 (2), pp. 247-252. , Feb; Saadatzi, M.N., Baptist, J.R., Yang, Z., Popa, D.O., Modeling and fabrication of scalable tactile sensor arrays for flexible robot skins (2019) Ieee Sensors J., 19 (17), pp. 7632-7643. , Sep; Dahiya, R., Metta, G., Valle, M., Sandini, G., Tactile sensing-From humans to humanoids (2010) Ieee Trans. Robot., 26 (1), pp. 1-20. , Feb; Cabibihan, J.-J., Pattofatto, S., Jomâa, M., Benallal, A., Carrozza, M.C., Towards humanlike social touch for sociable robotics and prosthetics: Comparisons on the compliance, conformance and hysteresis of synthetic and human fingertip skins (2009) T. J. Social Robot., 1 (1), pp. 29-40. , Jan; Ge, S.S., Lee, T.H., Zhu, G., Energy-based robust controller design for multi-link flexible robots (1996) Mechatronics, 6 (7), pp. 779-798. , Oct; Luo, Z.-H., Direct strain feedback control of flexible robot arms: New theoretical and experimental results (1993) Ieee Trans. Autom. Control, 38 (11), pp. 1610-1622. , Nov; Jackson, J., Microsoft robotics studio: A technical introduction (2007) Ieee Robot. Autom. Mag., 14 (4), pp. 82-87. , Dec; Echeverria, G., Lassabe, N., Degroote, A., Lemaignan, S., Modular open robots simulation engine: MORSE (2011) Proc. Ieee Int. Conf. Robot. Autom., pp. 46-51. , May; Rohmer, E., Singh, S.P.N., Freese, M., V-REP: A versatile and scalable robot simulation framework (2013) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 1321-1326. , Nov; Koenig, N., Howard, A., Design and use paradigms for Gazebo, an open-source multi-robot simulator (2004) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), pp. 2149-2154. , Sep./Oct; Joergensen, J.A., Ellekilde, L.P., Petersen, H.G., Robworksiman open simulator for sensor based grasping (2010) Proc. (41st Int. Symp. Robot.) Robotik 2010 (6th German Conf. Robot.), pp. 1-8. , Jun; Diankov, R., (2010) Automated Construction of Robotic Manipulation Programs, , Ph.D. dissertation, Robot. Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA; Mirtich, B., Canny, J., Impulse-based simulation of rigid bodies (1995) Proc. Symp. Interact. 3D Graph. (SI3D), pp. 181-189; Coumans, E., Bullet physics library (2013) Open Source: Bulletphys.org, 15 (49), p. 5; Ashley-Rollman, M.P., Pillai, P., Goodstein, M.L., Simulating multi-million-robot ensembles (2011) Proc. Ieee Int. Conf. Robot. Autom., pp. 1006-1013. , May; Baker, M., Destruction and dynamic artist tools for film and game production (2011) Acm Siggraph, , presented at the; Habib, A., Ranatunga, I., Shook, K., Popa, D.O., SkinSim: A simulation environment for multimodal robot skin (2014) Proc. Ieee Int. Conf. Autom. Sci. Eng. (CASE), pp. 1226-1231. , Aug; Quigley, M., ROS: An open-source robot operating system (2009) Proc. Icra Workshop Open-Source Softw., 3, p. 5; Hollis, B., Patterson, S., Trinkle, J., Compressed sensing for tactile skins (2016) Proc. Ieee Int. Conf. Robot. Autom. (ICRA), pp. 150-157. , May; Shook, K.R., (2014) Experimental Testbed for Robotic Skin Characterization and Interaction Control, , M.S. Thesis, Dept. Elect. Eng., Univ. Texas Arlington, Arlington, TX, USA; Anghinolfi, D., Cannata, G., Mastrogiovanni, F., Nattero, C., Paolucci, M., On the problem of the automated design of large-scale robot skin (2013) Ieee Trans. Autom. Sci. Eng., 10 (4), pp. 1087-1100. , Oct; Wei, X., Joneja, A., Tang, K., An improved algorithm for the automated design of large-scaled robot skin (2015) Ieee Trans. Autom. Sci. Eng., 12 (1), pp. 372-377. , Jan; Wei, X., Zhao, B., Joneja, A., Xi, J., On the polygon containment problem on an isometric grid (2017) Ieee Trans. Autom. Sci. Eng., 14 (2), pp. 1075-1083. , Apr; Cannata, G., Maggiali, M., Metta, G., Sandini, G., An embedded artificial skin for humanoid robots (2008) Proc. Ieee Int. Conf. Multisensor Fusion Integr. Intell. Syst., pp. 434-438. , Aug; Cheng, M.-Y., Tsao, C.-M., Yang, Y.-J., An anthropomorphic robotic skin using highly twistable tactile sensing array (2010) Proc. 5th Ieee Conf. Ind. Electron. Appl., pp. 650-655. , Jun; Strohmayr, M., Worn, H., Hirzinger, G., The DLR artificial skin step I: Uniting sensitivity and collision tolerance (2013) Proc. Ieee Int. Conf. Robot. Automat., pp. 1012-1018. , May; Bordoni, F., D'Amico, A., Noise in sensors (1990) Sens. Actuators A, Phys., 21 (1-3), pp. 17-24. , Feb; Yu, X., Thaysen, J., Hansen, O., Boisen, A., Optimization of sensitivity and noise in piezoresistive cantilevers (2002) J. Appl. Phys., 92 (10), pp. 6296-6301. , Nov; Martins, R., Filipe Ferreira, J., Dias, J., Touch attention Bayesian models for robotic active haptic exploration of heterogeneous surfaces (2014) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 1208-1215. , Sep; Tenzer, Y., Jentoft, L.P., Howe, R.D., The feel of MEMS barometers: Inexpensive and easily customized tactile array sensors (2014) Ieee Robot. Autom. Mag., 21 (3), pp. 89-95. , Sep; Smith, R., (2006) Open Dynamics Engine v0.5 User Guide, , https//:www.ode.org, May, [Online]; Volpe, R., Khosla, P., A theoretical and experimental investigation of explicit force control strategies for manipulators (1993) Ieee Trans. Autom. Control, 38 (11), pp. 1634-1650. , Nov; Shook, K.R., Habib, A., Lee, W.H., Popa, D.O., Experimental testbed for robot skin characterization and interaction control (2014) Proc. Spie, 9116. , Jun., Art. no. 911606; Åström, K.J., Hägglund, T., (1988) Automatic Tuning of Pid Controllers, , Research Triangle, NC, USA: Instrument Society of America; Saadatzi, M.N., Abubakar, S., Das, S.K., Saadatzi, M.H., Popa, D.O., Adaptive controller for physical interaction with an omni-directional mobile nurse assistant robot (2020) Proc. Asme Idetc Mech. Robot. Conf., pp. 5-10. , Aug","Cremer, S.; Department of Electrical Engineering, United States; email: sven.cremer@mavs.uta.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15455955,,,,"English","IEEE Trans. Autom. Sci. Eng.",Article,"Final","",Scopus,2-s2.0-85112686472
"Pointeau G., Mirliaz S., Mealier A.-L., Dominey P.F.","55804281800;57195393665;57050343800;7004714357;","Learning to Use Narrative Function Words for the Organization and Communication of Experience",2021,"Frontiers in Psychology","12",,"591703","","",,1,"10.3389/fpsyg.2021.591703","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102904120&doi=10.3389%2ffpsyg.2021.591703&partnerID=40&md5=a920903d5ef58c835c31de66e2f68c9a","INSERM, UMR 1093-CAPS, Université Bourgogne Franche-Comté, UFR des Sciences du Sport, Dijon, France; Robot Cognition Laboratory, Marey Institute, Dijon, France; École Normale Supérieure de Rennes, Bruz, France","Pointeau, G., INSERM, UMR 1093-CAPS, Université Bourgogne Franche-Comté, UFR des Sciences du Sport, Dijon, France, Robot Cognition Laboratory, Marey Institute, Dijon, France; Mirliaz, S., INSERM, UMR 1093-CAPS, Université Bourgogne Franche-Comté, UFR des Sciences du Sport, Dijon, France, Robot Cognition Laboratory, Marey Institute, Dijon, France, École Normale Supérieure de Rennes, Bruz, France; Mealier, A.-L., INSERM, UMR 1093-CAPS, Université Bourgogne Franche-Comté, UFR des Sciences du Sport, Dijon, France, Robot Cognition Laboratory, Marey Institute, Dijon, France; Dominey, P.F., INSERM, UMR 1093-CAPS, Université Bourgogne Franche-Comté, UFR des Sciences du Sport, Dijon, France, Robot Cognition Laboratory, Marey Institute, Dijon, France","How do people learn to talk about the causal and temporal relations between events, and the motivation behind why people do what they do? The narrative practice hypothesis of Hutto and Gallagher holds that children are exposed to narratives that provide training for understanding and expressing reasons for why people behave as they do. In this context, we have recently developed a model of narrative processing where a structured model of the developing situation (the situation model) is built up from experienced events, and enriched by sentences in a narrative that describe event meanings. The main interest is to develop a proof of concept for how narrative can be used to structure, organize and describe experience. Narrative sentences describe events, and they also define temporal and causal relations between events. These relations are specified by a class of narrative function words, including “because, before, after, first, finally.” The current research develops a proof of concept that by observing how people describe social events, a developmental robotic system can begin to acquire early knowledge of how to explain the reasons for events. We collect data from naïve subjects who use narrative function words to describe simple scenes of human-robot interaction, and then employ algorithms for extracting the statistical structure of how narrative function words link events in the situation model. By using these statistical regularities, the robot can thus learn from human experience about how to properly employ in question-answering dialogues with the human, and in generating canonical narratives for new experiences. The behavior of the system is demonstrated over several behavioral interactions, and associated narrative interaction sessions, while a more formal extended evaluation and user study will be the subject of future research. Clearly this is far removed from the power of the full blown narrative practice capability, but it provides a first step in the development of an experimental infrastructure for the study of socially situated narrative practice in human-robot interaction. © Copyright © 2021 Pointeau, Mirliaz, Mealier and Dominey.","discourse marker; narrative; narrative practice; reservoir computing; situation model",,,,,,"FP7 Information and Communication Technologies, ICT: 612139; European Commission, EC","We thank Th?ophile Peyraud for help in collecting data on use of narrative function words. Funding. This research was supported by the European Union, FP7 ICT Contract 612139 What You Say Is What You Did, WYSIWYD, and the French R?gion Bourgogne Franche Comt? Project ANER RobotSelf.","This research was supported by the European Union, FP7 ICT Contract 612139 What You Say Is What You Did, WYSIWYD, and the French Région Bourgogne Franche Comté Project ANER RobotSelf.",,,,,,,,,"Bloom, L., Lahey, M., Hood, L., Lifter, K., Fiess, K., Complex sentences: acquisition of syntactic connectives and the semantic relations they encode (1980) J. Child Lang, 7, pp. 235-261. , 7410493; Bruner, J., The narrative construction of reality (1991) Crit. Inquiry, 18, pp. 1-21; Bruner, J.S., (1990) Acts of Meaning, , Cambridge, Harvard University Press; Bruner, J.S., (2009) Actual Minds, Possible Worlds, , Cambridge, Harvard University Press; Crangle, C., Suppes, P., (1994) Language and Learning for Robots, 41. , Stanford, CA, CSLI; Dominey, P., Boucher, J., Learning to talk about events from narrated video in a construction grammar framework (2005) Artif. Intel, 167, pp. 31-61; Dominey, P., Mallet, A., Yoshida, E., Progress in programming the hrp-2 humanoid using spoken language (2007) Paper Presented at the IEEE International Conference on Robotics and Automation, , a, Piscataway, NJ; Dominey, P., Mallet, A., Yoshida, E., Real-time cooperative behavior acquisition by a humanoid apprentice (2007) Paper Presented at the International Conference on Humanoid Robotics, , b, Pittsburg, CA; Dominey, P., Mallet, A., Yoshida, E., Real-Time spoken-language programming for cooperative interaction with a humanoid apprentice (2009) Intl. J. Humanoids Rob, 6, pp. 147-171; Dominey, P.F., A conceptuocentric shift in the characterization of language: comment on Jackendoff (2003) Behav. Brain Sci, 26, p. 674; Dominey, P.F., Dodane, C., Indeterminacy in language acquisition: the role of child directed speech and joint attention (2004) J. 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Dominey, P.F.; Robot Cognition Laboratory, France; email: peter.dominey@inserm.fr",,,"Frontiers Media S.A.",,,,,16641078,,,,"English","Front. Psychol.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85102904120
"Ducastel V., Langlois K., Rossini M., Grosu V., Vanderborght B., Lefeber D., Verstraten T., Geeroms J.","57208834298;57195477049;57200377594;36727530600;57191490554;55161380600;56743227400;55922115000;","Smarcos: Off-the-shelf smart compliant actuators for human–robot applications",2021,"Actuators","10","11","289","","",,,"10.3390/act10110289","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119068101&doi=10.3390%2fact10110289&partnerID=40&md5=654778449bec23f0d0ffc963f4243e30","Robotics & Multibody Mechanics Research Group, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium; IMEC Brussels, Kapeldreef 75, Leuven, 3001, Belgium; Flanders Make, Oude Diestersebaan 133, Lommel, 3920, Belgium","Ducastel, V., Robotics & Multibody Mechanics Research Group, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium, IMEC Brussels, Kapeldreef 75, Leuven, 3001, Belgium; Langlois, K., Robotics & Multibody Mechanics Research Group, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium, IMEC Brussels, Kapeldreef 75, Leuven, 3001, Belgium; Rossini, M., Robotics & Multibody Mechanics Research Group, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium, Flanders Make, Oude Diestersebaan 133, Lommel, 3920, Belgium; Grosu, V., Robotics & Multibody Mechanics Research Group, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium; Vanderborght, B., Robotics & Multibody Mechanics Research Group, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium, IMEC Brussels, Kapeldreef 75, Leuven, 3001, Belgium; Lefeber, D., Robotics & Multibody Mechanics Research Group, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium, Flanders Make, Oude Diestersebaan 133, Lommel, 3920, Belgium; Verstraten, T., Robotics & Multibody Mechanics Research Group, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium, Flanders Make, Oude Diestersebaan 133, Lommel, 3920, Belgium; Geeroms, J., Robotics & Multibody Mechanics Research Group, Vrije Universiteit Brussel, Pleinlaan 2, Brussels, 1050, Belgium, Flanders Make, Oude Diestersebaan 133, Lommel, 3920, Belgium","With the growing popularity of Human–Robot Interactions, a series of robotic assistive devices have been created over the last decades. However, due to the lack of easily integrable resources, the development of these custom made devices turns out to be long and expensive. Therefore, the SMARCOS, a novel off-the-shelf Smart Variable Stiffness Actuator for human-centered robotic applications is proposed in this paper. This modular actuator combines compliant elements and sensors as well as low-level controller and high-bandwidth communication. The characterisation of the actuator is presented in this manuscript, followed by two use-cases wherein the benefits of such technology can be truly exploited. The actuator provides a lightweight design that can serve as the building blocks to facilitate the development of robotic applications. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Healthcare; Human–Robot Interactions (HRI); Smart Variable Stiffness Actuator (SVSA)",,,,,,"Fonds Wetenschappelijk Onderzoek, FWO","Funding: The work of Marco Rossini and Joost Geeroms was supported by the Research Foundation-Flanders (FWO) under grant no.S000118N SBO Exo4Work project.","The work of Marco Rossini and Joost Geeroms was supported by the Research FoundationFlanders (FWO) under grant no.S000118N SBO Exo4Work project.Tom Verstraten is a postdoctoral fellow of the Research Foundation-Flanders (FWO).",,,,,,,,,"Goodrich, M., Human–Robot Interaction: A Survey (2007) Found. Trends Hum.-Comput. Interact, 1, pp. 203-275. , [CrossRef]; Veale, A., Towards compliant and wearable robotic orthoses: A review of current and emerging actuator technologies (2016) Med. Eng. Phys, 38, pp. 317-325. , [CrossRef] [PubMed]; Vanderborght, B., Variable impedance actuators: A review (2013) Robot. Auton. 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Theory, 116, pp. 248-261. , [CrossRef]; Petit, F., Bidirectional antagonistic variable stiffness actuation: Analysis, design & implementation Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 4189-4198. , Anchorage, AK, USA, 3–7 May 2010; Tsagarakis, N., A new variable stiffness actuator (CompAct-VSA): Design and modelling Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 378-383. , San Francisco, CA, USA, 25–30 September 2011; Friedl, W., FAS A flexible antagonistic spring element for a high performance over actuated hand Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1366-1372. , San Francisco, CA, USA, 25–30 September 2011; Wolf, S., The DLR FSJ: Energy based design of a variable stiffness joint Proceedings of the 2011 IEEE International Conference on Robotics and Automation, pp. 5082-5089. , Shanghai, China, 9–13 May 2011; Vanderborght, B., MACCEPA 2.0: Adjustable compliant actuator with stiffening characteristic for energy efficient hopping Proceedings of the 2009 IEEE International Conference on Robotics and Automation, pp. 544-549. , Kobe, Japan, 12–17 May 2009; Brackx, B., Design of a modular add-on compliant actuator to convert an orthosis into an assistive exoskeleton Proceedings of the 5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 485-490. , Sao Paulo, Brazil, 12–15 August 2014; Grosu, V., Design of Smart Modular Variable Stiffness Actuators for Robotic-Assistive Devices (2017) IEEE/ASME Trans. Mechatron, 22, pp. 1777-1785. , [CrossRef]; Catalano, M., VSA-CubeBot. A modular variable stiffness platform for multi degrees of freedom systems Proceedings of the 2011 IEEE International Conference on Robotics and Automation, pp. 5090-5095. , Shanghai, China, 9–13 May 2011; Catalano, M., VSA-CubeBot. VSA-HD: From the enumeration analysis to the prototypical implementation Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3676-3681. , Taipei, Taiwan, 18–22 October 2010; Marchal-Crespo, L., Review of control strategies for robotic movement training after neurologic injury (2009) J. Neuroeng. Rehabil, 6, pp. 1-15. , [CrossRef] [PubMed]; Riener, R., Patient-cooperative strategies for robot-aided treadmill training: First experimental results (2005) IEEE Trans. Neural Syst. Rehabil. 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Theory, 130, pp. 71-85. , [CrossRef]; Rossi, L.F., Design of a modular distributed control system for robotic exoskeletons Proceedings of the ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC), pp. 1-4. , Manaus, Brazil, 9–11 January 2012; Slavnić, S., Mobile robotic gait rehabilitation system CORBYS-overview and first results on orthosis actuation Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2087-2094. , Chicago, IL, USA, 14–18 September 2014; Vo-gia, L., Development of a 7DOF soft manipulator arm for the compliant humanoid robot COMAN Proceedings of the 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), pp. 1106-1111. , Bali, Indonesia, 5–10 December 2014; Van Ham, R., MACCEPA, the mechanically adjustable compliance and controllable equilibrium position actuator: Design and implementation in a biped robot (2007) Robot. Auton. Syst, 10, pp. 761-768. , [CrossRef]; Grosu, V., Multi-Axis Force Sensor for Human–Robot Interaction Sensing in a Rehabilitation Robotic Device (2017) Sensors, 17, p. 1294. , [CrossRef]; Winter, D., (2009) Biomechanics and Motor Control of Human Movement, , 4th ed.; John Wiley & Sons: Hoboken, NJ, USA; Huysamen, K., Kinematic and kinetic functional requirements for industrial exoskeletons for lifting tasks and overhead lifting (2020) Ergonomics, 64, pp. 818-830. , [CrossRef] [PubMed]; Grosu, V., Instrumenting Complex Exoskeletons for Improved Human–Robot Interaction (2015) IEEE Instrum. Meas. Mag, 18, pp. 5-10. , [CrossRef]; Grioli, G., Variable stiffness actuators: The user’s point of view (2015) Sensors, 34, pp. 727-743. , [CrossRef]; Langlois, K., EtherCAT Tutorial: An Introduction for Real-Time Hardware Communication on Windows [Tutorial] (2018) IEEE Robot. Autom. Mag, 25, pp. 22-122. , [CrossRef]; Rosati, G., Planar Robotic Systems for Upper-Limb Post-Stroke Rehabilitation (2008) ASME Int. Mech. Eng. Congr. Expo, 48630, pp. 115-124; Flores, E., Improving patient motivation in game development for motor deficit rehabilitation Proceedings of the International Conference on Advances in Computer Entertainment Technology, pp. 381-384. , Yokohama, Japan, 3–5 December 2008","Ducastel, V.; Robotics & Multibody Mechanics Research Group, Pleinlaan 2, Belgium; email: Vincent.Ducastel@vub.be",,,"MDPI",,,,,20760825,,,,"English","Actuators",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85119068101
"Piccinelli N., Muradore R.","57205375915;14827262300;","A bilateral teleoperation with interaction force constraint in unknown environment using non linear model predictive control",2021,"European Journal of Control","62",,,"185","191",,,"10.1016/j.ejcon.2021.06.030","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111609549&doi=10.1016%2fj.ejcon.2021.06.030&partnerID=40&md5=0f65a8fd601eba706fd181e2dff35c6e","Department of Computer Science, University of Verona, Strada Le Grazie 15, Verona, 37134, Italy","Piccinelli, N., Department of Computer Science, University of Verona, Strada Le Grazie 15, Verona, 37134, Italy; Muradore, R., Department of Computer Science, University of Verona, Strada Le Grazie 15, Verona, 37134, Italy","In critical scenarios, the interaction forces between a robot and the environment could lead to damages and dangerous situations. Complex tasks like grasping fragile objects or physical human-robot interaction in collaborative robotics require the capability of controlling forces. In bilateral teleoperation, force feedback is used to provide telepresence to the operator. In such situation, the force is commonly measured by a force/torque sensor at the end effector of the remote robot. Even if force feedback allows the operator to feel the interaction with the environment this does not prevent unsafe motion. In this paper, we propose a model predictive control (MPC) based bilateral teleoperation able to guarantee safe interaction with the environment by constraining the forces. The method does not assume any prior knowledge of the environment. © 2021 European Control Association","Bilateral teleoperation; Model predictive control; Optimal control; Robotics","Biofeedback; End effectors; Feedback; Human robot interaction; Predictive control systems; Remote control; Visual communication; Bilateral teleoperation; Capability of controlling; Dangerous situations; Force/torque sensor; Interaction forces; Nonlinear model predictive control; Physical human-robot interactions; Unknown environments; Model predictive control",,,,,"Horizon 2020 Framework Programme, H2020: 2018-2022, 779813; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR; Horizon 2020","This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 779813 (SARAS project, www.saras-project.eu ). This work has been partly supported by the project MIUR “Dipartimenti di Eccellenza” 2018-2022.","This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 779813 (SARAS project, www.saras-project.eu). This work has been partly supported by the project MIUR ?Dipartimenti di Eccellenza? 2018-2022.",,,,,,,,,"Bemporad, A., Predictive control of teleoperated constrained systems with unbounded communication delays (1998) IEEE Conference on Decision and Control, pp. 2133-2138; Brogliato, B., Lozano, R., Maschke, B., Egeland, O., Dissipative systems analysis and control (2007), Springer; Ferraguti, F., Preda, N., De Rossi, G., Bonfe, M., Muradore, R., Fiorini, P., Secchi, C., A two-layer approach for shared control in semi-autonomous robotic surgery (2015) 2015 European Control Conference, ECC 2015; Franken, M., Stramigioli, S., Misra, S., Secchi, C., MacChelli, A., Bilateral telemanipulation with time delays: A two-layer approach combining passivity and transparency (2011) IEEE Transactions on Robotics, 27 (4), pp. 741-756; Garofalo, G., Mansfeld, N., Jankowski, J., Ott, C., Sliding mode momentum observers for estimation of external torques and joint acceleration (2019) International Conference on Robotics and Automation, pp. 6117-6123; Lawrence, D.A., Stability and Transparency in Bilateral Teleoperation (1993) IEEE Transactions on Robotics and Automation, 9 (5), pp. 624-637; Lee, D., Huang, K., Passive-set-position-modulation framework for interactive robotic systems (2010) IEEE Transactions on Robotics, 26 (2), pp. 354-369; Loschi, F., Piccinelli, N., Diego, D., Riccardo, M., Fiorini, P., Secchi, C., An optimized two-layer approach for efficient and robustly stable bilateral teleoperation (2021) International Conference on Robotics and Automation; Minelli, M., Ferraguti, F., Piccinelli, N., Muradore, R., Secchi, C., An energy-shared two-layer approach for multi-master-multi-slave bilateral teleoperation systems (2019) International Conference on Robotics and Automation, pp. 423-429; Muradore, R., Fiorini, P., A review of bilateral teleoperation algorithms (2016) Acta Polytechnica Hungarica, 13 (1), pp. 191-208; Niemeyer, G., Slotine, J.J.E., Stable Adaptive Teleoperation (1991) IEEE Journal of Oceanic Engineering, 16 (1), pp. 152-162; Park, J., Khatib, O., A haptic teleoperation approach based on contact force control (2006) The International Journal of Robotics Research, 25 (5-6), pp. 575-591; Piccinelli, N., Muradore, R., A passivity-based bilateral teleoperation architecture using distributed nonlinear model predictive control (2020) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 11466-11472; Ryu, J.H., Artigas, J., Preusche, C., A passive bilateral control scheme for a teleoperator with time-varying communication delay (2010) Mechatronics, 20 (7), pp. 812-823; Santos, L., Cortesão, R., Computed-torque control for robotic-assisted tele-echography based on perceived stiffness estimation (2018) IEEE Transactions on Automation Science and Engineering, 15 (3), pp. 1337-1354; Sartori, E., Tadiello, C., Secchi, C., Muradore, R., Tele-echography using a two-layer teleoperation algorithm with energy scaling (2019) International Conference on Robotics and Automation, pp. 1569-1575; Sheng, J., Spong, M.W., Model predictive control for bilateral teleoperation systems with time delays (2004) Canadian Conference on Electrical and Computer Engineering, 4, pp. 1877-1880; Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., Robotics: Modelling, Planning and Control (2008), Springer; Sirouspour, S., Shahdi, A., Model predictive control for transparent teleoperation under communication time delay (2006) IEEE Transactions on Robotics; Slama, T., Trevisani, A., Aubry, D., Oboe, R., Kratz, F., Experimental analysis of an internet-based bilateral teleoperation system with motion and force scaling using a model predictive controller (2008) IEEE Transactions on Industrial Electronics, 55 (9), pp. 3290-3299; Wahrburg, A., Morara, E., Cesari, G., Matthias, B., Ding, H., Cartesian contact force estimation for robotic manipulators using kalman filters and the generalized momentum (2015) IEEE International Conference on Automation Science and Engineering","Piccinelli, N.; Department of Computer Science, Strada Le Grazie 15, Italy; email: nicola.piccinelli@univr.it",,,"Elsevier Ltd",,,,,09473580,,EJCOF,,"English","Eur J Control",Article,"Final","",Scopus,2-s2.0-85111609549
"Muszynski M., Roman-Rangel E., Tian L., Kostoulas T., Chaspari T., Amelidis P.","56785701100;36100690400;56151869000;57203183748;55351228300;57211521740;","Workshop on Multimodal Affect and Aesthetic Experience",2021,"ICMI 2021 - Proceedings of the 2021 International Conference on Multimodal Interaction",,,,"868","869",,,"10.1145/3462244.3480981","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118975581&doi=10.1145%2f3462244.3480981&partnerID=40&md5=c44db85d0587942f422532d2b50f0ea4","University of Geneva, Geneva, Switzerland; Instituto Tecnologico Autonomo de Mexico, Mexico City, Mexico; Monash University, Melbourne, Australia; University of the Aegean, Samos, Greece; Texas Am University, College Station, TX, United States; Bournemouth University, Bournemouth, United Kingdom","Muszynski, M., University of Geneva, Geneva, Switzerland; Roman-Rangel, E., Instituto Tecnologico Autonomo de Mexico, Mexico City, Mexico; Tian, L., Monash University, Melbourne, Australia; Kostoulas, T., University of the Aegean, Samos, Greece; Chaspari, T., Texas Am University, College Station, TX, United States; Amelidis, P., Bournemouth University, Bournemouth, United Kingdom","The term ""aesthetic experience""corresponds to inner states of individuals exposed to art. Investigating form, content, and aesthetic values of artistic objects, indoor and outdoor spaces, urban areas, and modern interactive technology is essential to improve social behaviour, quality of life, and health of humans in the long term. Quantifying and interpreting the aesthetic experience of art receivers in different contexts can contribute towards (a) creating art and (b) better understanding humans' affective reactions to aesthetic stimuli. Focusing on different types of artistic content, such as movies, music, urban art, ancient artwork, and modern interactive technology, the goal of the Second International Workshop on Multimodal Affect and Aesthetic Experience is to enhance the interdisciplinary collaboration among researchers from the following domains: affective computing, aesthetics, human-robot interaction, and digital archaeology and art. © 2021 Owner/Author.","Aesthetic experience; Affective computing; Digital archaeology; Digital art; Emotions; Human-robot interaction; Machine Learning; Multimodal modeling; Signal processing","E-learning; History; Human robot interaction; Machine learning; Man machine systems; Signal processing; Affective Computing; Digital archeology; Digital art; Emotion; Esthetic experience; Humans-robot interactions; Machine-learning; Multi-modal; Multimodal models; Signal-processing; Arts computing",,,,,,,,,,,,,,,,"Bazin, A., (1967) What Is Cinema?, 1; Chaspari, T., Tsiartas, A., Stein, L.I., Cermak, S.A., Narayanan, S.S., Sparse representation of electro dermal activity with knowledge-driven dictionaries (2014) Ieee Transactions on Biomedical Engineering, 62 (3), pp. 960-971. , 2014; Cupchik, G.C., Vartanian, O., Crawley, A., Mikulis, D.J., Viewing artworks: Contributions of cognitive control and perceptual facilitation to aesthetic experience (2009) Brain and Cognition, 70 (1), pp. 84-91. , 2009; Fletcher, C., Hulusic, V., Amelidis, P., (2019) Virtual Reality Ear Training System: A Study on Spatialised Audio in Interval Recognition, p. 4; Kim, J., Yadav, M., Ahn, C.R., Chaspari, T., (2019) Saliency Detection Analysis of Pedestrians' Physiological Responses to Assess Adverse Built Environment Features, pp. 90-97; Kim, J., Yadav, M., Chaspari, T., Ahn, C.R., Saliency detection analysis of collective physiological responses of pedestrians to evaluate neighborhood built environments (2020) Advanced Engineering Informatics, 43, p. 101035. , 2020; Kostoulas, T., Chanel, G., Muszynski, M., Lombardo, P., Pun, T., (2015) Dynamic Time Warping of Multimodal Signals for Detecting Highlights in Movies, pp. 35-40; Kostoulas, T., Chanel, G., Muszynski, M., Lombardo, P., Pun, T., (2015) Identifying Aesthetic Highlights in Movies from Clustering of Physiological and Behavioral Signals, p. 6; Kostoulas, T., Chanel, G., Muszynski, M., Lombardo, P., Pun, T., Films, affective computing and aesthetic experience: Identifying emotional and aesthetic highlights from multimodal signals in a social setting (2017) Frontiers in Ict, 4, p. 11. , 2017; Kostoulas, T., Muszynski, M., Chaspari, T., Amelidis, P., (2020) Multimodal Affect and Aesthetic Experience, pp. 888-889; Lombardo, P., Émotion et souvenir chez aldo rossi (2011) Faces-Journal D Architecture, 69, pp. 34-41. , 2011; Lombardo, P., (2014) Literature, Emotions, and the Possible: Hazlitt and Stendhal, pp. 117-134; Markovic, S., Components of aesthetic experience: Aesthetic fascination, aesthetic appraisal, and aesthetic emotion (2012) I-Perception, 3 (1), pp. 1-17. , 2012; Muszynski, M., (2018) Recognizing Film Aesthetics, Spectators' Affect and Aesthetic Emotions from Multimodal Signals, , Ph.D. Dissertation. University of Geneva; Muszynski, M., Kostoulas, T., Chanel, G., Lombardo, P., Pun, T., (2015) Spectators' Synchronization Detection Based on Manifold Representation of Physiological Signals: Application to Movie Highlights Detection, pp. 235-238; Muszynski, M., Kostoulas, T., Lombardo, P., Pun, T., Chanel, G., (2016) Synchronization among Groups of Spectators for Highlight Detection in Movies, pp. 292-296; Muszynski, M., Kostoulas, T., Lombardo, P., Pun, T., Chanel, G., (2018) Aesthetic Highlight Detection in Movies Based on Synchronization of Spectators' Reactions; Muszynski, M., Tian, L., Lai, C., Moore, J., Kostoulas, T., Lombardo, P., Pun, T., Chanel, G., (2019) Recognizing Induced Emotions of Movie Audiences from Multimodal Information; Roman-Rangel, E., Can, G., Marchand-Maillet, S., Hu, R., Gayol, C.P., Krempel, G., Spotak, J., Gatica-Perez, D., (2016) Transferring Neural Representations for Low-dimensional Indexing of Maya Hieroglyphic Art, pp. 842-855; Santana, P.S., Roman-Rangel, E., (2021) Quantifying Visual Similarity for Artistic Styles, pp. 187-197; Tian, L., Muszynski, M., Lai, C., Moore, J.D., Kostoulas, T., Lombardo, P., Pun, T., Chanel, G., (2017) Recognizing Induced Emotions of Movie Audiences: Are Induced and Perceived Emotions the Same?, pp. 28-35; Tian, L., Oviatt, S., A taxonomy of social errors in human- robot interaction (2021) Acm Transactions on Human-Robot Interaction (THRI), 10 (2), pp. 1-32. , 2021; Yadav, M., Chaspari, T., Kim, J., Ahn, C.R., (2018) Capturing and Quantifying Emotional Distress in the Built Environment, p. 8",,,"ACM SIGCHI","Association for Computing Machinery, Inc","23rd ACM International Conference on Multimodal Interaction, ICMI 2021","18 October 2021 through 22 October 2021",,172814,,9781450384810,,,"English","ICMI - Proc. Int. Conf. Multimodal Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85118975581
"Jørgensen J., Christiansen M.B.","57199227765;57312498500;","The Sounds of Softness. Designing Sound for Human-Soft Robot Interaction",2021,"Frontiers in Robotics and AI","8",,"674121","","",,,"10.3389/frobt.2021.674121","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117916142&doi=10.3389%2ffrobt.2021.674121&partnerID=40&md5=ae70693c4642aced8ed66c6e69215a38","Center for Soft Robotics, SDU Biorobotics, University of Southern Denmark, Odense, Denmark","Jørgensen, J., Center for Soft Robotics, SDU Biorobotics, University of Southern Denmark, Odense, Denmark; Christiansen, M.B., Center for Soft Robotics, SDU Biorobotics, University of Southern Denmark, Odense, Denmark","In this article, we report on research and creative practice that explores the aesthetic interplay between movement and sound for soft robotics. Our inquiry seeks to interrogate what sound designs might be aesthetically engaging and appropriate for soft robotic movement in a social human-robot interaction setting. We present the design of a soft sound-producing robot, SONŌ, made of pliable and expandable silicone and three sound designs made for this robot. The article comprises an articulation of the underlying design process and results from two empirical interaction experiments (N = 66, N = 60) conducted to evaluate the sound designs. The sound designs did not have statistically significant effects on people’s perception of the social attributes of two different soft robots. Qualitative results, however, indicate that people’s interpretations of the sound designs depend on robot type. © Copyright © 2021 Jørgensen and Christiansen.","human-robot interaction; practice-based artistic research; social robotics; soft robotics; sound design",,,,,,,"We acknowledge help received from Cao Danh Do with 3D prints and fabrication and from Carsten Albertsen with designing and building the electronics. We would like to thank all participants in the study.",,,,,,,,,,"Abidi, H., Cianchetti, M., On Intrinsic Safety of Soft Robots (2017) Front. Robot. 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"Wang J., Liu Y., Yue T., Wang C., Mao J., Wang Y., You F.","57203558641;57219442494;57226787478;57227169900;57228079300;57218648909;56192500500;","Robot transparency and anthropomorphic attribute effects on human–robot interactions",2021,"Sensors","21","17","5722","","",,,"10.3390/s21175722","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113345256&doi=10.3390%2fs21175722&partnerID=40&md5=2c32eade53714a44b31fbe2ad84f8b13","Car Interaction Design Lab, College of Arts and Media, Tongji University, Shanghai, 201804, China; Shenzhen Research Institute, Sun Yat‐Sen University, Shenzhen, 518057, China; College of Design and Innovation, Tongji University, Shanghai, 200092, China","Wang, J., Car Interaction Design Lab, College of Arts and Media, Tongji University, Shanghai, 201804, China, Shenzhen Research Institute, Sun Yat‐Sen University, Shenzhen, 518057, China; Liu, Y., Car Interaction Design Lab, College of Arts and Media, Tongji University, Shanghai, 201804, China, College of Design and Innovation, Tongji University, Shanghai, 200092, China; Yue, T., Car Interaction Design Lab, College of Arts and Media, Tongji University, Shanghai, 201804, China; Wang, C., Car Interaction Design Lab, College of Arts and Media, Tongji University, Shanghai, 201804, China; Mao, J., Car Interaction Design Lab, College of Arts and Media, Tongji University, Shanghai, 201804, China; Wang, Y., Car Interaction Design Lab, College of Arts and Media, Tongji University, Shanghai, 201804, China; You, F., Car Interaction Design Lab, College of Arts and Media, Tongji University, Shanghai, 201804, China, Shenzhen Research Institute, Sun Yat‐Sen University, Shenzhen, 518057, China","Anthropomorphic robots need to maintain effective and emotive communication with humans as automotive agents to establish and maintain effective human–robot performances and pos-itive human experiences. Previous research has shown that the characteristics of robot communication positively affect human–robot interaction outcomes such as usability, trust, workload, and per-formance. In this study, we investigated the characteristics of transparency and anthropomorphism in robotic dual‐channel communication, encompassing the voice channel (low or high, increasing the amount of information provided by textual information) and the visual channel (low or high, increasing the amount of information provided by expressive information). The results showed the benefits and limitations of increasing the transparency and anthropomorphism, demonstrating the significance of the careful implementation of transparency methods. The limitations and future di-rections are discussed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Anthropomorphism; Human–robot interactions; On‐board robot; Robot transparency","Anthropomorphic robots; Transparency; Amount of information; Channel communication; Robot communication; Robot interactions; Robot performance; Textual information; Visual channels; Voice channels; Social robots; human; interpersonal communication; robotics; trust; Communication; Humans; Robotics; Trust",,,,,"2020‐1509; 1717; National Office for Philosophy and Social Sciences, NPOPSS: 19FYSB040, 202002JG26; International Science and Technology Cooperation Programme, ISTCP: GHZ20190823164803756","This research was supported by the National Social Science Fund of China (No. 19FYSB040), CES?Kingfar Excellent Young Scholar Joint Research Funding (No. 202002JG26), China Scholarship Council Foundation (2020?1509), Shanghai Automotive Industry Science and Technology Development Foundation (No. 1717), and Shenzhen Collaborative Innovation Project: International Science and Technology Cooperation (No. GHZ20190823164803756).","Funding: This research was supported by the National Social Science Fund of China (No. 19FYSB040), CES‐Kingfar Excellent Young Scholar Joint Research Funding (No. 202002JG26), China Scholarship Council Foundation (2020‐1509), Shanghai Automotive Industry Science and Technol‐ ogy Development Foundation (No. 1717), and Shenzhen Collaborative Innovation Project: Interna‐ tional Science and Technology Cooperation (No. GHZ20190823164803756).",,,,,,,,,"Mavridis, N., A review of verbal and non‐verbal human–robot interactive communication (2015) Robot. 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Human‐Computer Interact, 2010, pp. 1-17; Jeon, M., Walker, B.N., Yim, J, Effects of specific emotions on subjective judgment, driving performance, and perceived work-load (2014) Transp. Res. Part F: Traffic Psychol. Behav, 24, pp. 197-209. , B; Dula, C.S., Geller, E., Geller, Risky, aggressive, or emotional driving: Addressing the need for consistent communication in research (2003) J. Saf. Res, 34, pp. 559-566; Fischer, K., How People Talk with Robots: Designing Dialogue to Reduce User Uncertainty (2011) AI Mag, 32, pp. 31-38; Ososky, S., Schuster, D., Phillips, E., Jentsch, F.G., Building appropriate trust in human‐robot teams (2013) 2013 AAAI Spring Symposium Series, pp. 60-65. , AAAI: Menlo Park, CA, USA; Lewis, J.R., Psychometric evaluation of an after‐scenario questionnaire for computer usability studies: The ASQ (1991) SIGCHI Bull, 23, pp. 78-81; Pauzie, A., A method to assess the driver mental workload: The driving activity load index (DALI) (2009) Intell. Transp. Syst. IET, 2, pp. 315-322; Bradley, M.M., Lang, P.J., Measuring emotion: The self‐assessment manikin and the semantic differential (1994) J. Behav. Ther. Exp. Psychiatry, 25, pp. 49-59; Shneiderman, B., Designing the user interface strategies for effective human‐computer interaction (1987) SIGBIO Newsl, 9, p. 6","Liu, Y.; Car Interaction Design Lab, China; email: liuyujia@tongji.edu.cn
You, F.; Car Interaction Design Lab, China; email: youfang@tongji.edu.cn",,,"MDPI AG",,,,,14248220,,,"34502613","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85113345256
"Paplu S.H., Arif M.N.I., Berns K.","57204053864;57223303742;7006119626;","Utilizing Semantic and Contextual Information during Human-Robot Interaction",2021,"IEEE International Conference on Development and Learning, ICDL 2021",,,,"","",,,"10.1109/ICDL49984.2021.9515611","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114553612&doi=10.1109%2fICDL49984.2021.9515611&partnerID=40&md5=39057b64a7021ac60f25858042e50f1a","Technische Universität Kaiserslautern, Department of Computer Science, Kaiserslautern, Germany","Paplu, S.H., Technische Universität Kaiserslautern, Department of Computer Science, Kaiserslautern, Germany; Arif, M.N.I., Technische Universität Kaiserslautern, Department of Computer Science, Kaiserslautern, Germany; Berns, K., Technische Universität Kaiserslautern, Department of Computer Science, Kaiserslautern, Germany","Humans are aware of the context and the environment while communicating with an interaction partner. Although equipping a robot with such cognitive skills is still a far cry, integrating contextual information into a robot is a step towards making Human-Robot Interaction (HRI) more meaningful, realistic or 'human-like'. This paper focuses on establishing HRI based on semantic and contextual information in the conversation. The humanoid robot called ROBIN recognizes the user utterances using a speech recognizer called Julius. The approach proposed extracts named entities, contextual information and the sentiment of each user utterance, with robot's responses triggered by a chatbot while gestures and facial expressions invoked by the sentiment labels of the user speech. The human-centered evaluation results based on the experimental scenarios show that the approach ensures more fluid and natural interaction. © 2021 IEEE.","Binary Relationships; Context-based Interaction; Human-Robot Interaction; Named Entity Recognition; Speech Recognition","Anthropomorphic robots; Man machine systems; Semantics; Cognitive skill; Contextual information; Facial Expressions; Human robot Interaction (HRI); Human-centered evaluation; Named entities; Natural interactions; Speech recognizer; Human robot interaction",,,,,,,,,,,,,,,,"Nardi, B.A., (1996) Context and Consciousness: Activity Theory and Humancomputer Interaction, , mit Press; Quintas, J., Menezes, P., Dias, J., Context-based perception and understanding of human intentions (2013) 2013 IEEE RO-MAN. IEEE, pp. 346-347; Potter, J., Cognition as context (whose cognition?) (1998) Research on Language and Social Interaction, 31 (1), pp. 29-44; Menezes, P., Quintas, J., Dias, J., The role of context information in human-robot interaction (2014) 23rd IEEE International Symposium on Robot and Human Interactive Communication Workshop on Interactive Robots for Aging And/or Impaired People.; Al-Darraji, S., (2016) Perception of Nonverbal Cues for Human-Robot Interaction, , Germany: Verlag Dr. Hut; Zafar, Z., Paplu, S.H., Berns, K., Automatic assessment of human personality traits: A step towards intelligent human-robot interaction (2018) 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), pp. 1-9; Maas, J.F., Spexard, T., Fritsch, J., Wrede, B., Sagerer, G., Biron, what's the topic? a multi-modal topic tracker for improved human-robot interaction (2006) ROMAN 2006-The 15th IEEE International Symposium on Robot and Human Interactive Communication. 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IEEE, pp. 1-6; http://www.acapela-group.com/, Voice synthesis- Text to Speech; Reichardt, M., F¨ohst, T., Berns, K., (2012) Introducing Finroc: A Convenient Real-time Framework for Robotics Based on A Systematic Design Approach, , Robotics Research Lab, Department of Computer Science, University of Kaiserslautern, Kaiserslautern, Germany, Technical Report; Al-Darraji, S., Berns, K., Rodíc, A., Action unit based facial expression recognition using deep learning (2016) International Conference on Robotics in Alpe-Adria Danube Region, pp. 413-420. , Springer",,,"et al.;IEEE;IEEE Computational Intelligence Society;Microsoft;NSFC;Peking University","Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Development and Learning, ICDL 2021","23 August 2021 through 26 August 2021",,171395,,9781728162423,,,"English","IEEE Int. Conf. Dev. Learn., ICDL",Conference Paper,"Final","",Scopus,2-s2.0-85114553612
"Yazdani A., Novin R.S., Merryweather A., Hermans T.","57208065928;56513986100;35603782000;36730868000;","Is the Leader Robot an Adequate Sensor for Posture Estimation and Ergonomic Assessment of A Human Teleoperator?",2021,"IEEE International Conference on Automation Science and Engineering","2021-August",,,"1946","1952",,,"10.1109/CASE49439.2021.9551471","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110789836&doi=10.1109%2fCASE49439.2021.9551471&partnerID=40&md5=5bad9a59bf0afb61e15c45cfcd3f29dd","University of Utah, Department of Mechanical Eng. and Robotics Center, Salt Lake City, UT, United States; School of Computing and Robotics Center, University of Utah, Salt Lake City, UT, United States; Nvidia Corporation, Seattle, WA, United States","Yazdani, A., University of Utah, Department of Mechanical Eng. and Robotics Center, Salt Lake City, UT, United States; Novin, R.S., University of Utah, Department of Mechanical Eng. and Robotics Center, Salt Lake City, UT, United States; Merryweather, A., University of Utah, Department of Mechanical Eng. and Robotics Center, Salt Lake City, UT, United States; Hermans, T., School of Computing and Robotics Center, University of Utah, Salt Lake City, UT, United States, Nvidia Corporation, Seattle, WA, United States","Ergonomic assessment of human posture plays a vital role in understanding work-related safety and health. Current posture estimation approaches face occlusion challenges in teleoperation and physical human-robot interaction. We investigate if the leader robot is an adequate sensor for posture estimation in teleoperation and we introduce a new probabilistic approach that relies solely on the trajectory of the leader robot for generating observations. We model the human using a redundant, partially-observable dynamical system and we infer the posture using a standard particle filter. We compare our approach with postures from a commercial motion capture system and also two least-squares optimization approaches for human inverse kinematics. The results reveal that the proposed approach successfully estimates human postures and ergonomic risk scores comparable to those estimates from gold-standard motion capture. © 2021 IEEE.",,"Dynamical systems; Ergonomics; Human robot interaction; Remote control; 'current; Ergonomic assessment; Estimation approaches; Face occlusion; Human postures; Human teleoperators; Leader robots; Posture estimation; Safety and healths; Work-related; Inverse kinematics",,,,,"Defense Advanced Research Projects Agency, DARPA: N66001-19-2-4035","This work is supported by DARPA under grant N66001-19-2-4035.",,,,,,,,,,"Vos, T., Barber, R.M., Bell, B., Bertozzi-Villa, A., Biryukov, S., Bolliger, I., Charlson, F., Dicker, D., Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: A systematic analysis for the global burden of disease study 2013 (2015) The Lancet, 386 (9995), pp. 743-800; Dempsey, P.G., Kocher, L.M., Nasarwanji, M.F., Pollard, J.P., Whitson, A.E., Emerging ergonomics issues and opportunities in mining (2018) International Journal of Environmental Research and Public Health, 15 (11), p. 2449; Peternel, L., Fang, C., Laghi, M., Bicchi, A., Tsagarakis, N., Ajoudani, A., Human arm posture optimisation in bilateral teleoperation through interface reconfiguration (2020) IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob 2020); Yu, S., Lee, J., Park, B., Kim, K., Cho, I., Ergonomic analysis of a telemanipulation technique for a pyroprocess demonstration facility (2014) Nuclear Engineering and Technology, 46 (4), pp. 489-500; Yazdani, A., Sabbagh Novin, R., Posture estimation and optimization in ergonomically intelligent teleoperation systems (2021) Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, pp. 604-606; Zanchettin, A.M., Ceriani, N.M., Rocco, P., Ding, H., Matthias, B., Safety in human-robot collaborative manufacturing environments: Metrics and control (2016) IEEE Transactions on Automation Science and Engineering, 13 (2), pp. 882-893; Fridovich-Keil, D., Bajcsy, A., Fisac, J.F., Herbert, S.L., Wang, S., Dragan, A.D., Tomlin, C.J., Confidence-aware motion prediction for real-time collision avoidance1 (2020) The International Journal of Robotics Research, 39 (2-3), pp. 250-265; Mangalam, K., Adeli, E., Lee, K.-H., Gaidon, A., Niebles, J.C., Disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision (2020) The IEEE Winter Conference on Applications of Computer Vision, pp. 2784-2793; Alvarez, M., Torricelli, D., Del Ama, A.J., Pinto, D., Gonzalez-Vargas, J., Moreno, J.C., Gil-Agudo, A., Pons, J.L., Simultaneous estimation of human and exoskeleton motion: A simplified protocol (2017) IEEE Int. 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Conf. Autom. Sci. Eng.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85110789836
"Lee W.-Y., Sakashita M., Ricci E., Claure H., Guimbretiere F., Jung M.","57204952765;57190945486;57221146458;57213577721;6508204254;15073123400;","Interactive vignettes: Enabling large-scale interactive HRI research",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"1289","1296",,,"10.1109/RO-MAN50785.2021.9515376","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115084338&doi=10.1109%2fRO-MAN50785.2021.9515376&partnerID=40&md5=79b4c2a5dccdc7f2f808d2636bbecfd3","Cornell University, United States; Cornell University, Information Science Department, United States","Lee, W.-Y., Cornell University, United States; Sakashita, M., Cornell University, United States; Ricci, E., Cornell University, United States; Claure, H., Cornell University, United States; Guimbretiere, F., Cornell University, Information Science Department, United States; Jung, M., Cornell University, Information Science Department, United States","We propose the use of interactive vignettes as an alternative to traditional text- and video-based vignettes for conducting large-scale Human-Robot Interaction (HRI) studies. Interactive vignettes maintain the advantages of traditional vignettes while offering additional affordances for participant interaction and data collection through interactive elements. We discuss the core affordances of interactive vignettes, including explorability, responsiveness, and non-linearity, and look into how these affordances can enable HRI research with more complex scenarios. To demonstrate the strength of the approach, we present a case study of our own research project with N=87 participants and show the data we collect through interactive vignettes. We suggest that the use of interactive vignettes can benefit HRI researchers in learning how participants interact with, respond to, and perceive a robot's behavior in pre-defined scenarios. © 2021 IEEE.",,"Agricultural robots; Data acquisition; Affordances; Data collection; Human robot Interaction (HRI); Interactive elements; Social robots",,,,,"National Science Foundation, NSF: 1925100; Office of Naval Research, ONR: N00014-19-1-2299","This project was in part sponsored by the Office of Naval Research (N00014-19-1-2299) and by the National Science Foundation (1925100). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Office of Naval Research nor the National Science Foundation.",,,,,,,,,,"Alxander, C.S., Becker, H.J., The use of vignettes in survey research (1978) Public Opinion Quarterly, 42 (1), pp. 93-104. , https://doi.org/10.1086/268432, 01; Malle, B.F., Scheutz, M., Arnold, T., Voiklis, J., Cusimano, C., Sacrifice one for the good of many? People apply different moral norms to human and robot agents (2015) 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 117-124; Fussell, S.R., Kiesler, S., Setlock, L.D., Yew, V., How people anthropomorphize robots (2008) 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI). 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"Zhang T., Du Q., Yang G., Chen C.-Y., Wang C., Fang Z.","57268859400;57268716500;7405752170;55597280800;57202194907;35322011900;","A Review of Compliant Control for Collaborative Robots",2021,"Proceedings of the 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021",,,,"1103","1108",,,"10.1109/ICIEA51954.2021.9516193","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115443067&doi=10.1109%2fICIEA51954.2021.9516193&partnerID=40&md5=be2094c7f0bd1e32185c230161331d5e","Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China; Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China; University of Chinese Academy of Sciences, Beijing, China","Zhang, T., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China, University of Chinese Academy of Sciences, Beijing, China; Du, Q., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China, University of Chinese Academy of Sciences, Beijing, China; Yang, G., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China; Chen, C.-Y., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China; Wang, C., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China; Fang, Z., Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China, Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China","Collaborative robots that can directly interact with humans have become a new trend in the robotics field. To achieve safe and friendly human-robot interaction, the compliant control of collaborative robots is extremely important, especially the compliant control of each joint. Also, the number of publications has increased yearly. In this article, we summarize the compliant control methods that have been proposed and applied in the robot system and classify compliant control methods into three categories: force-based which needs to know the joint torque/force and limited by the performance of force controller; model-based which can achieve high control accuracy but limited by the model accurate; external force-based which can increase the transparency but needs to know the external force. Then we propose possible future research directions. © 2021 IEEE.","collaborative robot; compliant control; human-robot interaction; impedance control; joint control","Agricultural robots; Human robot interaction; Man machine systems; Collaborative robots; Compliant control; Control methods; External force; Force-based; Humans-robot interactions; Impedance control; Joint control; Robots system; Three categories; Collaborative robots",,,,,"National Natural Science Foundation of China, NSFC: 2018B10058, 92048201; National Key Research and Development Program of China, NKRDPC: 2018YFB1308900","This work was supported in part by National Key R&D Program of China (Grant No.2018YFB1308900), in part by the National Natural Science Foundation of China (Grant No.92048201) and in part by the Major Special Projects of the Plan “Science and Technology Innovation 2025” in Ningbo (Grant No.2018B10058)",,,,,,,,,,"Calanca, A., Muradore, R., Fiorini, P., A review of algorithms for compliant control of stiff and fixed-compliance robots (2016) IEEE/ASME Transactions on Mechatronics, 21 (2), pp. 613-624; Wang, W., Li, R., Chen, Y., Diekel, Z.M., Jia, Y., Facilitating human-robot collaborative tasks by teaching-learning-collaboration from human demonstrations (2019) IEEE Transactions on Automation Science and Engineering, 16 (2), pp. 640-653; Robla-Gomez, S., Becerra, V.M., Llata, J.R., Gonzalez-Sarabia, E., Torre-Ferrero, C., Perez-Oria, J., Working together: A review on safe human-robot collaboration in industrial environments (2017) IEEE Access, 5, pp. 26754-26773; Schumacher, M., Wojtusch, J., Beckerle, P., Von Stryk, O., An introductory review of active compliant control (2019) Robotics and Autonomous Systems, 119, pp. 185-200; Hogan, N., Impedance control: An approach to manipulation 1984 American Control Conference, Conference Proceedings, pp. 304-313; Mason Matthew, T., Compliance and force control for computer controlled manipulators (1981) Systems, Man and Cybernetics, IEEE Transactions on, 11 (6), pp. 418-432; Chiaverini, S., Sciavicco, L., Force/position control of manipulators in task space with dominance in force (1988) Ifac Proceedings Volumes; Albu-Schaffer, A., Ott, C., Hirzinger, G., A passivity based cartesian impedance controller for flexible joint robots-part II: Full state feedback, impedance design and experiments IEEE International Conference on Robotics and Automation, 2004. 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Raiola, G., Cardenas, C.A., Tadele, T.S., De Vries, T., Stramigioli, S., Development of a safety-and energy-aware impedance controller for collaborative robots (2018) IEEE Robotics and Automation Letters, 3 (2), pp. 1237-1244; Haddadin, S., De Luca, A., Albu-Schaffer, A., Robot collisions: A survey on detection, isolation, and identification (2017) IEEE Transactions on Robotics, 33 (6), pp. 1292-1312; Lee, S.-D., Song, J.-B., Sensorless collision detection based on friction model for a robot manipulator (2016) International Journal OfPrecision Engineering and Manufacturing, 17 (1), pp. 11-17; Jorda, M., Herrero, E.G., Khatib, O., Contact-driven posture behavior for safe and interactive robot operation 2019 International Conference on Robotics and Automation (ICRA), Conference Proceedings, pp. 9243-9249",,,,"Institute of Electrical and Electronics Engineers Inc.","16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021","1 August 2021 through 4 August 2021",,171511,,9781665422482,,,"English","Proc. IEEE Conf. Ind. Electron. Appl. ICIEA",Conference Paper,"Final","",Scopus,2-s2.0-85115443067
"Kim T., Shi K., Kong K.","57259693300;57259652000;24332189800;","A compact transmitted-force-sensing series elastic actuator with optimized planar torsional spring for exoskeletons",2021,"IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM","2021-July",,,"572","577",,,"10.1109/AIM46487.2021.9517640","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114961827&doi=10.1109%2fAIM46487.2021.9517640&partnerID=40&md5=1fc5f95616445ec6f826da742f98648d","KAIST, Dept. of Mechanical Engineering, Daejeon, South Korea","Kim, T., KAIST, Dept. of Mechanical Engineering, Daejeon, South Korea; Shi, K., KAIST, Dept. of Mechanical Engineering, Daejeon, South Korea; Kong, K., KAIST, Dept. of Mechanical Engineering, Daejeon, South Korea","Series elastic actuator (SEA), which enables torque-based control, is widely used in human-robot interaction applications such as exoskeletons. However, it is not yet widely used due to the trade-off between size and control performance. In this study, a compact transmitted-force-sensing SEA (TFSEA) module is introduced, applying a cycloid reducer and a planar torsional spring(PTS). A novel spring design with the optimal circumferential profile, which can maximize the length and the radius of curvature, is proposed and quantitatively compared to the previously studied profiles using finite element analysis. The proposed spring design showed a higher deformation when the same torque was applied. Low hysteresis and symmetry of the deformation are verified by experiment. Also, high-frequency torque control performance is evaluated by a reference-tracking experiment using a feedback controller. © 2021 IEEE.","Cycloid reducer; Exoskeletal robotics; Planar torsional spring; Series elastic actuator; Torque control","Actuators; Biofeedback; Deformation; Economic and social effects; Exoskeleton (Robotics); Human robot interaction; Intelligent mechatronics; Control performance; Feedback controller; High frequency HF; Radius of curvature; Reference-tracking; Series elastic actuators; Torque based controls; Torsional springs; Springs (components)",,,,,"HJ20C0007; Ministry of Trade, Industry and Energy, MOTIE; Korea Health Industry Development Institute, KHIDI","This work was supported by the Technology Innovation Program (20007096, Super Human Robot Suit Project) funded By the Ministry of Trade, Industry and Energy (MOTIE, Korea), and a grant of the Korea Health Technology RD Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health Welfare, Republic of Korea (grant number: HJ20C0007)",,,,,,,,,,"Chen, T., Casas, R., Lum, P.S., An elbow exoskeleton for upper limb rehabilitation with series elastic actuator and cable-driven differential (2019) IEEE Transactions on Robotics, 35 (6), pp. 1464-1474; Zhang, S., Zhu, Q., Wu, J., Xiong, R., Gu, Y., Design and compliance control of rehabilitation exoskeleton for elbow joint anchylosis (2020) Proc. Of the 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 1896-1901; Dos Santos, W.M., Caurin, G.A., Siqueira, A.A., Design and control of an active knee orthosis driven by a rotary series elastic actuator (2017) Control Engineering Practice, 58, pp. 307-318; Kong, K., Bae, J., Tomizuka, M., Control of rotary series elastic actuator for ideal force-mode actuation in human-robot interaction applications (2009) IEEE/ASME Transactions on Mechatronics, 14 (1), pp. 105-118; Sergi, F., Accoto, D., Carpino, G., Tagliamonte, N.L., Guglielmelli, E., Design and characterization of a compact rotary series elastic actuator for knee assistance during overground walking (2012) Proc. Of the 2012 4th IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 1931-1936; Herodotou, P., Wang, S., Design, modelling, and experimental evaluation of a compact elastic actuator for a gait assisting exoskeleton (2019) Proc. Of the 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), pp. 331-336; Lee, C., Oh, S., Configuration and performance analysis of a compact planetary geared elastic actuator (2016) Proc. Of the IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, pp. 6391-6396; Carpino, G., Accoto, D., Sergi, F., Luigi Tagliamonte, N., Guglielmelli, E., A novel compact torsional spring for series elastic actuators for assistive wearable robots (2012) Journal of Mechanical Design, 134 (12); Wang, Y., Chen, Y., Chen, K., Wu, Y., Huang, Y., A flat torsional spring with corrugated flexible units for series elastic actuators (2017) Proc. Of the 2017 2nd International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 138-143; Jessop, D.M., Pain, M.T., Maximum velocities in flexion and extension actions for sport (2016) Journal of Human Kinetics, 50 (1), pp. 37-44; Widlund, T., Yang, S., Hsu, Y.-Y., Lu, N., Stretchability and compliance of freestanding serpentine-shaped ribbons (2014) International Journal of Solids and Structures, 51 (23-24), pp. 4026-4037",,,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021","12 July 2021 through 16 July 2021",,171450,,9781665441391,,,"English","IEEE ASME Int Conf Adv Intellig Mechatron AIM",Conference Paper,"Final","",Scopus,2-s2.0-85114961827
"Xie B., Park C.H.","57215674653;57226671958;","Empathetic robot with transformer-based dialogue agent",2021,"2021 18th International Conference on Ubiquitous Robots, UR 2021",,,"9494669","290","295",,,"10.1109/UR52253.2021.9494669","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112425777&doi=10.1109%2fUR52253.2021.9494669&partnerID=40&md5=c54e4be5c2855e6e801c71365ad8ebcd","The George Washington University, School of Engineering and Applied Science, Department of Biomedical Engineering, Washington, DC  20052, United States","Xie, B., The George Washington University, School of Engineering and Applied Science, Department of Biomedical Engineering, Washington, DC  20052, United States; Park, C.H., The George Washington University, School of Engineering and Applied Science, Department of Biomedical Engineering, Washington, DC  20052, United States","Natural human-robot interaction (HRI) attracts considerable interest in letting robots understand users' emotional states. This paper demonstrates a method to introduce the affection model to a robotic system's conversational agent to provide natural and empathetic HRI. We use a large-scale pre-trained language model and fine-tune it on a dialogue dataset with empathetic characteristics. Based on recent progress in deep learning and natural language processing, we extend the current methods and enable our robotic agent to perform advanced sentiment analysis using our trained affection model. This dialogue agent will allow the robot to provide natural responses along with emotion classification with the estimations of arousal and valence levels. We evaluate our model using different metrics in comparison with recent studies and showing its emotion detection capability. © 2021 IEEE.","Affection model; Empathetic dialogue agent; Human-robot interaction (HRI); Transformer","Agricultural robots; Deep learning; Large dataset; Robotics; Sentiment analysis; Conversational agents; Emotion classification; Emotion detection; Emotional state; Human robot Interaction (HRI); NAtural language processing; Natural response; Recent progress; Human robot interaction",,,,,"National Science Foundation, NSF: 1846658","This research is supported by the National Science Foundation (NSF) under the grant #1846658.",,,,,,,,,,"Castellano, G., Leite, I., Pereira, A., Martinho, C., Paiva, A., McOwan, P.W., Affect recognition for interactive companions: Challenges and design in real world scenarios (2010) Journal on Multimodal User Interfaces, 3 (1), pp. 89-98; Castellano, G., Paiva, A., Kappas, A., Aylett, R., Hastie, H., Barendregt, W., Nabais, F., Bull, S., Towards empathic virtual and robotic tutors (2013) International Conference on Artificial Intelligence in Education. 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[Online]; Kim, J.C., Azzi, P., Jeon, M., Howard, A.M., Park, C.H., Audiobased emotion estimation for interactive robotic therapy for children with autism spectrum disorder (2017) 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). IEEE, pp. 39-44; Mishne, G., Experiments with mood classification in blog posts (2005) Proceedings of ACM SIGIR 2005 Workshop on Stylistic Analysis of Text for Information Access, 19, pp. 321-327. , Citeseer; Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., Fidler, S., Aligning books and movies: Towards story-like visual explanations by watching movies and reading books (2015) Proceedings of the IEEE International Conference on Computer Vision, pp. 19-27; Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., Weston, J., (2018) Personalizing Dialogue Agents: I Have A Dog, Do You Have Pets Too?",,,,"Institute of Electrical and Electronics Engineers Inc.","18th International Conference on Ubiquitous Robots, UR 2021","12 July 2021 through 14 July 2021",,170755,,9781665438995,,,"English","Int. Conf. Ubiquitous Robot., UR",Conference Paper,"Final","",Scopus,2-s2.0-85112425777
"Baumgartl H., Sauter D., Schenk C., Atik C., Buettner R.","57204587967;57204584246;57150585500;57274958900;24831738000;","Vision-based hand gesture recognition for human-computer interaction using MobileNetV2",2021,"Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021",,,,"1667","1674",,,"10.1109/COMPSAC51774.2021.00249","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115864701&doi=10.1109%2fCOMPSAC51774.2021.00249&partnerID=40&md5=6db0885d7b29c2631d05ec203b6ba159","Aalen University, Aalen, Germany","Baumgartl, H., Aalen University, Aalen, Germany; Sauter, D., Aalen University, Aalen, Germany; Schenk, C., Aalen University, Aalen, Germany; Atik, C., Aalen University, Aalen, Germany; Buettner, R., Aalen University, Aalen, Germany","In recent years, the demand for gesture recognition has increased enormously due to many applications such as computer games, human-robot interaction, assistance systems, sports, sign language interpreters, and e-commerce. The recognition of hand gestures is one of the most important gesture recognition methods. With simple hand gestures, devices in the smart home area (TV, radio, vacuum cleaner robots, etc.) should be easier to operate. Our method is based on a convolutional neural network, or more precisely on MobileNetV2. With this lean and fast network, we have been able to achieve an accuracy of 99.96 percent in recognition of hand gestures, so that in the future, we will be able to offer an application in the field of HumanComputer Interaction to interact more easily with the ever-increasing number of technologies in everyday life. © 2021 IEEE.","Convolutional neural network; Hand gesture recognition; Human-computer interaction; Image classification; Mobilenet","Application programs; Automation; Computer games; Convolutional neural networks; Human computer interaction; Palmprint recognition; Social robots; Assistance system; Hand gesture; Hand-gesture recognition; Sign language; Smart homes; Vision based; Gesture recognition",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 13FH176PX8, 13FH4E05IA, 13FH566KX9; Ministerium für Wirtschaft, Arbeit und Wohnungsbau Baden-Württemberg, WM BW: 36-3400.7/107","ACKNOWLEDGMENT This research is funded by the German Federal Ministry of Education and Research (13FH176PX8, 13FH4E05IA, 13FH566KX9) and the Baden-Württemberg Ministry of Economics, Affairs, Labour and Housing (36-3400.7/107).","This research is funded by the German Federal Ministry of Education and Research (13FH176PX8, 13FH4E05IA, 13FH566KX9) and the Baden-W?rttemberg Ministry of Economics, Affairs, Labour and Housing (36-3400.7/107).",,,,,,,,,"Just, A., Marcel, S., A comparative study of two state-of-the-art sequence processing techniques for hand gesture recognition (2009) Computer Vision and Image Understanding, 113 (4), pp. 532-543; Sun, Y., Intelligent human computer interaction based on non redundant EMG signal (2020) Alexandria Engineering Journal, 59 (3), pp. 1149-1157; Kheratkar, N., Bhavani, S., Jarali, A., Pathak, A., Kumbhar, S., Gesture controlled home automation using CNN (2020) ICICCS 2020 Proc., pp. 620-626; Shao, J., Ortmeyer, C., Finch, D., Smart home appliance control (2008) IEEE IAS 2008 Proc., pp. 1-6; Bien, Z.Z., Park, K.-H., Jung, J.-W., Do, J.-H., Intention reading is essential in human-friendly interfaces for the elderly and the handicapped (2005) IEEE Transactions on Industrial Electronics, 52 (6), pp. 1500-1505; Levin-Sagi, M., Pasher, E., Carlsson, V., Klug, T., Ziegert, T., Zinnen, A., A comprehensive human factors analysis of wearable computers supporting a hospital ward round (2007) IFAWC 2007 Proc., pp. 1-12; Shimada, A., Yamashita, T., Taniguchi, R., Hand gesture based TV control system - Towards both user & machine-friendly gesture applications (2013) FCV 2013 Proc., pp. 121-126; Bhuiyan, M., Picking, R., Gesture-controlled user interfaces, what have we done and what's next? 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Comput., Softw., Appl. Conf., COMPSAC",Conference Paper,"Final","",Scopus,2-s2.0-85115864701
"Clark A., Ahmad I.","57218220057;7201878459;","Interfacing with Robots without the use of Touch or Speech",2021,"ACM International Conference Proceeding Series",,,,"347","353",,,"10.1145/3453892.3461330","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109273502&doi=10.1145%2f3453892.3461330&partnerID=40&md5=823e7d34b0078fcf04c3d4acb1785610","University of Texas, Arlington, United States","Clark, A., University of Texas, Arlington, United States; Ahmad, I., University of Texas, Arlington, United States","As the field of robotics develops, so do the methods of interfacing with and controlling those robots. Many modern robots can be communicated with using commands that require neither speech nor touch. The two main motivations behind this trend are the assistance of people with disabilities and the desire for more natural Human-Robot Interaction (HRI). In allowing nonverbal communication with robots, accessibility of the systems increases to allow more people to interact with and benefit from robotics systems. Additionally, nonverbal communication provides more natural communication, which can lead to many benefits in HRI. This paper provides an overview of existing technologies in touchless and nonverbal human-robot interfaces; it presents the most prevalent interface methods, their uses, and limitations. Discussions are provided on future directions for research. © 2021 ACM.","EEG; emotion detection; gaze detection; HRI","Robotics; Human robot Interaction (HRI); Natural communication; Non-verbal communications; Non-verbal human; People with disabilities; Robotics systems; Touchless; Human robot interaction",,,,,,,,,,,,,,,,"Specification of Gaze Accuracy and Gaze Precision, Tobii X2-30 Eye Tracker, , https://www.tobiipro.com/siteassets/tobii-pro/technical-specifications/tobii-pro-x2-30-technical-specification.pdf/?v=1.0; Al-Rahayfeh, A., Faezipour, M., Application of head flexion detection for enhancing eye gaze direction classification (2014) 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, pp. 966-969. , https://doi.org/10.1109/EMBC.2014.6943753, 2014; Ballard, P., Stockman, G.C., Computer operation via face orientation (1992) [1992] Proceedings. 11th IAPR International Conference on Pattern Recognition, pp. 407-410. , The Hague, Netherlands; Bell, C.J., Shenoy, P., Chalodhorn, R., Rao, N.R.P., Control of a humanoid robot by a noninvasive brain-computer interface in humans (2008) Journal of Neural Engineering, 5 (2), pp. 214-220. , https://doi.org/10.1088/1741-2560/5/2/012, 2008; Chen, L., Wu, M., Zhou, M., She, J., Dong, F., Hirota, K., Information-driven multirobot behavior adaptation to emotional intention in human-robot interaction (2018) IEEE Transactions on Cognitive and Developmental Systems, 10 (3), pp. 647-658. , https://doi.org/10.1109/TCDS.2017.2728003, 2018; Chowdhury, A., Raza, H., Dutta, A., Prasad, G., EEGEMG based hybrid brain computer interface for triggering hand exoskeleton for neuro-rehabilitation (2017) ACM International Conference Proceeding Series Part F1320, pp. 1-6. , https://doi.org/10.1145/3132446.3134909, 2017; Cohn, J.F., Tronick, E.Z., Mother-infant face-to-face interaction: The sequence of dyadic states at 3, 6, and 9 months (1987) Developmental Psychology, 23 (1), pp. 68-77. , https://doi.org/10.1037/0012-1649.23.1.68, 1987; Coronado, E., Villalobos, J., Bruno, B., Mastrogiovanni, F., Gesture-based robot control: Design challenges and evaluation with humans (2017) Proceedings-IEEE International Conference on Robotics and Automation, 1, pp. 2761-2767. , https://doi.org/10.1109/ICRA.2017.7989321, 2017; Lynn Craig, T., Nelson, C.A., Li, S., Zhang, X., Human gaze commands classification: A shape based approach to interfacing with robots (2016) 2016 12th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA). IEEE, pp. 1-6. , https://doi.org/10.1109/MESA.2016.7587154, Auckland; Ekman, P., Friesen, W.V., Constants across cultures in the face and emotion (1971) Journal of Personality and Social Psychology, 17, pp. 124-129. , https://web-b-ebscohost-com.ezproxy.uta.edu/ehost/detail/detail?vid=8{&}sid=b31892f2-afd0-4f1b-88c8-b95be726296b{%}40pdc-v-sessmgr04{&}bdata=JnNpdGU9ZWhvc3QtbGl2ZQ{%}3D{%}3D{#}AN=1971-07999-001{&}db=pdh, 1971; Fedorova, A.A., Shishkin, S.L., Nuzhdin, Y.O., Velichkovsky, B.M., Gaze based robot control: The communicative approach (2015) 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER). 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Palinko, O., Sciutti, A., Wakita, Y., Matsumoto, Y., Sandini, G., If looks could kill: Humanoid robots play a gaze-based social game with humans (2016) IEEE-RAS International Conference on Humanoid Robots, pp. 905-910. , https://doi.org/10.1109/HUMANOIDS.2016.7803380, 2016; Pavlidis, I., Levine, J., Thermal image analysis for polygraph testing (2002) IEEE Engineering in Medicine and Biology Magazine, 21 (6). , 2002; Pigou, L., Van Den Oord, A., Dieleman, S., Van Herreweghe, M., Dambre, J., Beyond temporal pooling: Recurrence and temporal convolutions for gesture recognition in video (2018) International Journal of Computer Vision, 126 (2-4), pp. 430-439. , https://doi.org/10.1007/s11263-016-0957-7, 2018; Revathy, A., Shanmugapriya, P., Mohan, V., Performance comparison of speaker and emotion recognition (2015) 2015 3rd International Conference on Signal Processing, Communication and Networking, ICSCN 2015, pp. 1-6. , https://doi.org/10.1109/ICSCN.2015.7219844, 2015; Gwendoline Riseborough, M., Physiographic gestures as decoding facilitators: Three experiments exploring a neglected facet of communication (1981) Journal of Nonverbal Behavior, 5 (3), pp. 172-183. , https://doi.org/10.1007/BF00986134, 1981; Soekadar, S.R., Witkowski, M., Gómez, C., Opisso, E., Medina, J., Cortese, M., Cempini, M., Vitiello, N., Hybrid EEG/EOG-based brain/neural hand exoskeleton restores fully independent daily living activities after quadriplegia (2016) Science Robotics, 1 (1), p. eaag3296. , https://doi.org/10.1126/scirobotics.aag3296, dec 2016; Sorelli, M., Kopietz, C., Zaunseder, S., Bocchi, L., Pulse decomposition analysis in camera-based photoplethysmography (2019) Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 3179-3182. , https://doi.org/10.1109/EMBC.2019.8857856, 2019; Stawicki, P., Gembler, F., Volosyak, I., Driving a semiautonomous mobile robotic car controlled by an ssvep-based bci (2016) Computational Intelligence and Neuroscience 2016, , https://doi.org/10.1155/2016/4909685, 2016; Xu, T., Zhang, H., Yu, C., See you see me: The role of eye contact in multimodal human-robot interaction (2016) ACM Transactions on Interactive Intelligent Systems, 6 (1), pp. 1-22. , https://doi.org/10.1145/2882970, 2016; Soo Yoo, B., Hwan Kim, J., Fuzzy integral-based gaze control of a robotic head for human robot interaction (2015) IEEE Transactions on Cybernetics, 45 (9), pp. 1769-1783. , https://doi.org/10.1109/TCYB.2014.2360205, 2015; Yordanov, Y., Tsenov, G., Mladenov, V., Humanoid robot control with EEG brainwaves (2017) Proceedings of the 2017 IEEE 9th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2017, 1, pp. 238-242. , https://doi.org/10.1109/IDAACS.2017.8095083, 2017; Zhang, G., Paulin Hansen, J., Accessible control of telepresence robots based on eye tracking (2019) Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications (ETRA), pp. 1-3. , https://doi.org/10.1145/3314111.3322869, Denver; Zhang, N., Jiang, J., Tang, J., Zhou, Z., Hu, D., A novel steady-state visually evoked potential-based brain-computer-interface paradigm to steer a humanoid robot (2014) Proceedings-2014 4th International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2014, pp. 774-778. , https://doi.org/10.1109/IMCCC.2014.164, 2014",,,"The College of Engineering, University of Texas at Arlington (UTA);The Department of Computer Science and Engineering at UTA;The Human Centered Computing Laboratory (Heracleia) at UTA;The iPerform Industry-University NSF Center at UTA;The National Center for Scientific Research (NCSR)-Demokritos;The National Science Foundation (NSF)","Association for Computing Machinery","14th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2021","29 June 2021 through 1 July 2021",,169934,,9781450387927,,,"English","ACM Int. Conf. Proc. Ser.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85109273502
"Chen K.","57219527386;","Emotion Regulation with Markov Decision Process for Human-robot Interaction",2021,"Proceedings - 2021 IEEE International Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2021",,,"9475923","80","85",,,"10.1109/CogSIMA51574.2021.9475923","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112867106&doi=10.1109%2fCogSIMA51574.2021.9475923&partnerID=40&md5=7057025f26a2f0aedc22a49ac1dd25bc","University of Texas at Austin, Department of Mechanical Engineering, Austin, TX  78712, United States","Chen, K., University of Texas at Austin, Department of Mechanical Engineering, Austin, TX  78712, United States","Emotion Regulation is an essential aspect since it helps people avoid harmful emotions. Shaping emotions and satisfying people are some of the key reasons to value a social robot in human-robot interaction. Thus, in this paper, a multi-weighted Markov Decision Process Emotion Regulation (MDPER) robot is created to maximize the transfer from a negative emotional arousal to positive one while minimizing the robot service spendings in cost and steps. By using an emotion regulation method external stimuli as a robot action set, the MDPER robot generates a series of its actions to help people regulate their emotions. Personality and emotion/intention degrees are weighted by the analytic hierarchy process under a specialized Markov Decision Process framework. The simulation and the experiment are implemented to prove that the MDPER system successfully achieves the goal. © 2021 IEEE.","Emotion Regulation; Human-Robot Interaction; Markov Decision Process","Behavioral research; Man machine systems; Markov processes; Emotion regulations; External stimulus; Markov Decision Processes; Robot actions; Robot service; Weighted Markov decision process; Social robots",,,,,,,,,,,,,,,,"Gross, J.J., Emotion regulation: Current status and future prospects (2015) Psychological Inquiry, 26 (1), pp. 1-26; Gross, J.J., (2013) Handbook of Emotion Regulation, , Guilford publications; Gross, J.J., John, O.P., Individual differences in two emotion regulation processes: Implications for affect, relationships, and wellbeing (2003) Journal of Personality and Social Psychology, 85 (2), p. 348; Evers, C., Stok, F.M., De Ridder, D.T., Feeding your feelings: Emotion regulation strategies and emotional eating (2010) Personality and Social Psychology Bulletin, 36 (6), pp. 792-804; Vinola, C., Vimaladevi, K., A survey on human emotion recognition approaches, databases and applications (2015) Elcvia Electronic Letters on Computer Vision and Image Analysis, 14 (2), pp. 24-44; Iengo, S., Origlia, A., Staffa, M., Finzi, A., Attentional and emotional regulation in human-robot interaction (2012) 2012 Ieee RO-MAN: The 21st Ieee International Symposium on Robot and Human Interactive Communication, pp. 1135-1140; Jung, M.F., Affective grounding in human-robot interaction (2017) 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 263-273; Desideri, L., Ottaviani, C., Malavasi, M., Di Marzio, R., Bonifacci, P., Emotional processes in human-robot interaction during brief cognitive testing (2019) Computers in Human Behavior, 90, pp. 331-342; Hao, M., Cao, W., Liu, Z., Wu, M., Yuan, Y., Emotion regulation based on multi-objective weighted reinforcement learning for human robot interaction (2019) 2019 12th Asian Control Conference (ASCC), pp. 1402-1406; Young, J.E., Sung, J., Voida, A., Sharlin, E., Igarashi, T., Christensen, H.I., Grinter, R.E., Evaluating human-robot interaction (2011) International Journal of Social Robotics, 3 (1), pp. 53-67; Nass, C., Moon, Y., Machines and mindlessness: Social responses to computers (2000) Journal of Social Issues, 56 (1), pp. 81-103; Forlizzi, J., DiSalvo, C., Service robots in the domestic environment: A study of the roomba vacuum in the home (2006) Proceedings of the 1st Acm SIGCHI/SIGART Conference on Human-robot Interaction, pp. 258-265; Knox, W.B., Stone, P., Reinforcement learning from simultaneous human and MDP reward (2012) Aamas, pp. 475-482; Coan, J.A., Allen, J.J., (2007) Handbook of Emotion Elicitation and Assessment, , Oxford university press; Campbell-Sills, L., Barlow, D.H., Brown, T.A., Hofmann, S.G., Effects of suppression and acceptance on emotional responses of individuals with anxiety and mood disorders (2006) Behaviour Research and Therapy, 44 (9), pp. 1251-1263; Blanchette, I., Richards, A., The influence of affect on higher level cognition: A review of research on interpretation, judgement, decision making and reasoning (2010) Cognition & Emotion, 24 (4), pp. 561-595; Saaty, T.L., (1990) Multi Criteria Decision Making: The Analytic Hierarchy Process: Planning, Priority Setting Resource Allocation; Mikhailov, L., Tsvetinov, P., Evaluation of services using a fuzzy analytic hierarchy process (2004) Applied Soft Computing, 5 (1), pp. 23-33; Saaty, T.L., What is the analytic hierarchy process? (1988) Mathematical Models for Decision Support, pp. 109-121. , Springer; Zahedi, F., The analytic hierarchy processa survey of the method and its applications (1986) Interfaces, 16 (4), pp. 96-108; Limonero, J.T., Fernández-Castro, J., Soler-Oritja, J., Moleiro, M.A.L., Emotional intelligence and recovering from induced negative emotional state (2015) Frontiers in Psychology, 6, p. 816; Kurdi, B., Lozano, S., Banaji, M.R., Introducing the open affective standardized image set (oasis) (2017) Behavior Research Methods, 49 (2), pp. 457-470; White, E.L., Rickard, N.S., Emotion response and regulation to happy and sad music stimuli: Partial synchronization of subjective and physiological responses (2016) Musicae Scientiae, 20 (1), pp. 11-25; Eerola, T., Vuoskoski, J.K., A comparison of the discrete and dimensional models of emotion in music (2011) Psychology of Music, 39 (1), pp. 18-49; Kotov, R., Gamez, W., Schmidt, F., Watson, D., Linking big personality traits to anxiety, depressive, and substance use disorders: A meta analysis (2010) Psychological Bulletin, 136 (5), p. 768","Chen, K.; University of Texas at Austin, United States; email: kunyechen@utexas.edu","McGeorge N.Ruvinsky A.Teichmann M.Motus L.Freiman M.",,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2021","14 May 2021 through 22 May 2021",,170822,,9781728176987,,,"English","Proc. - IEEE Int. Conf. Cogn. Comput. Asp. Situat. Manag., CogSIMA",Conference Paper,"Final","",Scopus,2-s2.0-85112867106
"Cheng P.-J., Ting H.-Y., Huang H.-P.","57137415500;57196039825;7405609488;","Importing the Human Factor into Safe Human-Robot Interaction Function Using the Bond Graph Method",2021,"Robotica","39","5",,"772","786",,,"10.1017/S0263574720000715","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095816834&doi=10.1017%2fS0263574720000715&partnerID=40&md5=5120e627ab5fdea0be53058f7ca8d1a1","Department of Product Development, TECHMAN ROBOT Inc., Taoyuan, 33383, Taiwan; Department of Mechanical Engineering, National Taiwan University, Taipei, 10617, Taiwan","Cheng, P.-J., Department of Product Development, TECHMAN ROBOT Inc., Taoyuan, 33383, Taiwan; Ting, H.-Y., Department of Mechanical Engineering, National Taiwan University, Taipei, 10617, Taiwan; Huang, H.-P., Department of Mechanical Engineering, National Taiwan University, Taipei, 10617, Taiwan","The variable stiffness actuator (VSA) is helpful to realize the post-collision safety strategies for safe human-robot interaction.1 The stiffness of the robot will be reduced to protect the user from injury when the collision between the robot and human occurs. However, The VSA has a mechanism limit constraint that can cause harm to users even if the stiffness is minimized. Accordingly, in this article, a concept combining danger index and robust fault detection and isolation is presented and applied to active-passive variable stiffness elastic actuator (APVSEA). APVSEA can actively change joint stiffness with the change of danger index. Experimental results show that this concept can effectively confirm the fault mode and provide additional protection measures to ensure the safety of users when the joint stiffness has been adjusted to the minimum. Copyright © The Author(s), 2020. Published by Cambridge University Press.","Bond graph method; Fault detection; Human-robot interaction; KEYWORDS: Safe; Variable stiffness actuator","Actuators; Agricultural robots; Fault detection; Stiffness; Bond graph method; Elastic actuators; Joint stiffness; Post-collision; Protection measures; Robust fault detection and isolation; Safe human-robot interaction; Variable stiffness; Human robot interaction",,,,,,,,,,,,,,,,"Tadele, T.S., De Vries, T., Stramigioli, S., The safety of domestic robotics: A survey of various safetyrelated publications (2014) IEEE Robot. Autom. Mag., 21 (3), pp. 134-142; Bicchi, A., Peshkin, M.A., Colgate, J.E., Safety for Physical Human-Robot Interaction (2008) Springer Handbook of Robotics, pp. 1335-1348. , (B. Siciliano and O. 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Eng., 27 (3), pp. 313-326; Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.N., A review of process fault detection and diagnosis part III: Process history based methods (2003) Comp. Chem. Eng., 27 (3), pp. 327-346; Medjaher, K., A Bond Graph Model-based Fault Detection and Isolation (2011) Maintenance Modelling and Applications, pp. 497-506. , (J. Andrews, CH. Bérenguer and L. Jackson, eds.), Ch. 6 (Det Norske Veritas, Hovik); Samantaray, A.K., Belkacem, B.O., Bond Graph Model-based Quantitative FDI (2008) Model-based Process Supervision: A Bond Graph Approach, Ch. 5 (Springer, London), pp. 177-228; Bouallegue, W., Bouabdallah, S.B., Tagina, M., A New Adaptive Fuzzy FDI Method for Bond Graph Uncertain Parameters Systems (2011) Proceedings of the IEEE International Conference on Electronics, Circuits and Systems (ICECS), pp. 643-648. , Beirut, Lebanon (December); Bouallegue, W., Bouabdallah, S.B., Tagina, M., Causal Approaches and Fuzzy Logic in FDI of Bond Graph Uncertain Parameters Systems (2011) Proceedings of the International Conference on Communications, Computing and Control Applications (CCCA), Hammamet, Tunisia (March), pp. 1-6; Emami, K., Nener, B., Sreeram, V., Trinh, H., Fernando, T., A Fault Detection Technique for Dynamical Systems (2013) Proceedings of the IEEE Conference on Industrial and Information Systems (ICIIS), Sri Lanka (August), pp. 201-206; Pourbabaee, B., Meskin, N., Khorasani, K., Sensor Fault Detection and Isolation using Multiple Robust Filters for Linear Systems with Time-Varying Parameter Uncertainty and Error Variance Constraints (2014) Proceedings of the IEEE Conference on Control Applications (CCA), Juan les Antibes, France (October), pp. 382-389; Djeziri, M.A., Merzouki, R., Bunamama, B.O., Dauphin-Tanguy, G., Robust Fault Diagnosis by Using Bond Graph Approach (2007) IEEE Trans. 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Siciliano, ed.), Ch. 3 (Springer, Berlin); Beyl, T., Nicolai, P., Raczkowsky, J., Worn, H., Comparetti, M.D., Momi, E.D., Multi Kinect People Detection for Intuitive and Safe Human Robot Cooperation in the Operating Room (2013) Proceedings of the International Conference on Advanced Robotics (ICAR), pp. 1-6. , Montevideo, Uruguay (November); Luca, A.D., Flacco, F., Integrated Control for pHRI: Collision Avoidance, Detection, Reaction and Collaboration (2012) Proceedings of the IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 288-295. , Rome, Italy (June); Morato, C., Kaipa, K.N., Zhao, B., Gupta, S.K., Toward safe human robot collaboration by using multiple kinects based real-time human tracking (2014) J. Com. Info. Sci. Engin., 14 (1), pp. 0110061-0110069; Randelli, G., Bonanni, T., Iocchi, L., Nardi, D., Knowledge acquisition through human-robot multimodal interaction (2013) Intel. Serv. Robot., 6 (1), pp. 19-31; Yun, S.S., A gaze control of socially interactive robots in multiple-person interaction (2017) Robotica, 35 (11), pp. 2122-2138; Flacco, F., Kroger, T., Luca, A.D., Khatib, O., A Depth Space Approach to Human-Robot Collision Avoidance (2012) Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 338-345. , Saint Paul, Minnesota (May); Rybski, P., Anderson-Sprecher, P., Huber, D., Niessl, C., Simmons, R., Sensor Fusion for Human Safety in Industrial Workcells (2012) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3612-3619. , Vilamoura, Portugal (October); Cheng, P.J., Huang, H.P., Model matching contorl for an active-passive variable stiffness actuator (2015) Int. J. Robot. Auto., 6 (3), pp. 48-64; Cheng, P.J., Ting, H.Y., Huang, H.P., Safe human robot inter action using model matching control (2016) J. Chinese Soc. Mech. Engin. (JCSME), 37 (6), pp. 587-596; Wang, R.J., Huang, H.P., Mechanically stiffness-adjustable actuator using a leaf spring for safe physical human-robot interaction (2012) Mechanika, 18 (1), pp. 77-83; (2013) IFA, BG/BGIA Risk Assessment Recommendations According to Machinery Directive: Design of Workplaces with Collaborative Robots","Huang, H.-P.; Department of Mechanical Engineering, Taiwan; email: hanpang@ntu.edu.tw",,,"Cambridge University Press",,,,,02635747,,ROBOD,,"English","Robotica",Article,"Final","",Scopus,2-s2.0-85095816834
"Agrusti F., Gasparetti F., Gena C., Sansonetti G., Tkalcic M.","36006338200;6506843445;6507316953;22735492400;24438438300;","SOcial and Cultural IntegrAtion with PersonaLIZEd Interfaces (SOCIALIZE)",2021,"International Conference on Intelligent User Interfaces, Proceedings IUI",,,"3450709","9","11",,,"10.1145/3397482.3450709","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104473996&doi=10.1145%2f3397482.3450709&partnerID=40&md5=5d8061d97d43c83a86ac2d5ce90454f4","Roma Tre University, Rome, Italy; University of Torino, Turin, Italy; University of Primorska, Koper, Slovenia","Agrusti, F., Roma Tre University, Rome, Italy; Gasparetti, F., Roma Tre University, Rome, Italy; Gena, C., University of Torino, Turin, Italy; Sansonetti, G., Roma Tre University, Rome, Italy; Tkalcic, M., University of Primorska, Koper, Slovenia","This is the first edition of the SOcial and Cultural IntegrAtion with PersonaLIZEd Interfaces (SOCIALIZE) workshop. The main goal is to bring together all those interested in the development of interactive techniques that may contribute to foster the social and cultural inclusion of a broad range of users. More specifically, we intend to attract research that takes into account the interaction peculiarities typical of different realities, with a focus on disadvantaged and at-risk categories (e.g., refugees and migrants) and vulnerable groups (e.g., children, elderly, autistic and disabled people). Among others, we are also interested in human-robot interaction techniques aimed at the development of social robots, that is, autonomous robots that interact with people by engaging in social-affective behaviors, abilities, and rules related to their collaborative role. © 2021 Owner/Author.","Human-robot interaction; Interactive techniques; Recommender systems; Social and cultural inclusion","Social robots; User interfaces; Affective behaviors; Disabled people; Interactive techniques; Personalized interface; Risk categories; Vulnerable groups; Economic and social effects",,,,,,,,,,,,,,,,"Alencar, A., Refugee integration and social media: A local and experiential perspective (2018) Information, Communication & Society, 21 (11), pp. 1588-1603. , 2018; Berkovsky, S., Taib, R., Hijikata, Y., Braslavsku, P., Knijnenburg, B., A cross-cultural analysis of trust in recommender systems (2018) Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (UMAP '18). ACM, pp. 285-289. , New York, NY, USA; Caidi, N., Allard, D., Quirke, L., Information practices of immigrants (2010) Annual Rev. Info. Sci & Technol., 44 (1), pp. 491-531. , Jan. 2010; Concone, F., Giaconia, R., Lo Re, G., Morana, M., A smart assistant for visual recognition of painted scenes (2021) SOCIALIZE 2021; D'Aniello, G., Gaeta, M., Cultural situation awareness in e-learning systems (2021) SOCIALIZE 2021; De Carolis, B.N., D'Errico, F., Palestra, G., Macchiarulo, N., Socially inclusive robots: Learning culture-related gestures by playing with pepper (2021) SOCIALIZE 2021; Ferrato, A., Limongelli, C., Mezzini, M., Sansonetti, G., Exploiting micro facial expressions for more inclusive user interfaces (2021) SOCIALIZE 2021; Fogli, A., Sansonetti, G., Exploiting semantics for context-aware itinerary recommendation (2019) Personal and Ubiquitous Computing, 23 (2), pp. 215-231. , 2019; Gasparetti, F., Sansonetti, G., Micarelli, A., Tourism recommender systems as a vehicle for social and cultural inclusion (2021) SOCIALIZE 2021; Gena, C., Sugar, salt & pepper-humanoid robotics for autism (2021) SOCIALIZE 2021; Mauro, N., Ardissono, L., Cocomazzi, S., Cena, F., Information extraction for inclusive recommender systems (2021) SOCIALIZE 2021; Sansonetti, G., Point of interest recommendation based on social and linked open data (2019) Personal and Ubiquitous Computing, 23 (2), pp. 199-214. , 2019; Sansonetti, G., Gasparetti, F., Micarelli, A., Cena, F., Gena, C., Enhancing cultural recommendations through social and linked open data (2019) User Modeling and User-Adapted Interaction, 29 (1), pp. 121-159. , 2019; Wecker, A., Kuflik, T., Mulholland, P., Diaz-Agudo, B., Anthony Pedersen, T., Introducing empathy into recommender systems as a tool for promoting social cohesion (2021) SOCIALIZE 2021",,,"AAAS;Google;Microsoft;Multimodal Technologies and Interaction;Science Robotics;Texas A and M University","Association for Computing Machinery","26th International Conference on Intelligent User Interfaces: Where HCI Meets AI, IUI 2021","14 April 2021 through 17 April 2021",,168380,,9781450380188,,,"English","Int Conf Intell User Interfaces Proc IUI",Conference Paper,"Final","",Scopus,2-s2.0-85104473996
"Bolignari M., Rizzello G., Zaccarian L., Fontana M.","57207828233;55970038200;56223118400;16042084500;","Smith-Predictor-Based Torque Control of a Rolling Diaphragm Hydrostatic Transmission",2021,"IEEE Robotics and Automation Letters","6","2","9363528","2970","2977",,,"10.1109/LRA.2021.3062310","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101858489&doi=10.1109%2fLRA.2021.3062310&partnerID=40&md5=a45422087e812b871780c0764d809c65","Department of Industrial Engineering, University of Trento, Trento, 38123, Italy; Department of Systems Engineering, Department of Material Science and Engineering, Saarland University, Saarbrücken, DE, DE 66123, Germany; LAAS-CNRS, Université de Toulouse, Centre National de la Recherche Scientifique, Toulouse, 31400, France; TeCIP Institute, Scuola Superiore Sant'Anna, Pisa, 56100, Italy","Bolignari, M., Department of Industrial Engineering, University of Trento, Trento, 38123, Italy; Rizzello, G., Department of Systems Engineering, Department of Material Science and Engineering, Saarland University, Saarbrücken, DE, DE 66123, Germany; Zaccarian, L., LAAS-CNRS, Université de Toulouse, Centre National de la Recherche Scientifique, Toulouse, 31400, France; Fontana, M., TeCIP Institute, Scuola Superiore Sant'Anna, Pisa, 56100, Italy","Rolling Diaphragm Hydrostatic Transmissions (RDHT) are high-performance low-cost solutions to delocalize heavy actuators away from the joints of robotic systems. Exploiting a low-cost pressure-based sensing technique, we propose here a Smith-predictor-based joint torque control of an RDHT-based actuation system. We also use a load-cell sensor for ground truth validation. The developed feedback controller is conveniently tuned based on an arbitrary pre-specified closed-loop natural frequency and damping ratio. This preserves the open-loop bandwidth while removing the intrinsic oscillations of the lightly damped highly transparent open-loop plant. Experimental tests validate the proposed control strategy, both in a stand-alone torque setpoint configuration and in a series of Human-Robot-Interaction tests confirming the significant advantages of the closed-loop control architecture. © 2016 IEEE.","actuator remotization; HRI; Hydrostatic transmission; rolling diaphragm; smith predictor; torque control","Agricultural robots; Biofeedback; Costs; Human robot interaction; Hydraulics; Hydrostatic pressure; Torque control; Transmissions; Closed-loop control; Control strategies; Feedback controller; Hydrostatic transmission; Intrinsic oscillations; Joint torque control; Low-cost solution; Sensing techniques; Closed loop control systems",,,,,"Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR; Università degli Studi di Trento, UNITN","Manuscript received October 15, 2020; accepted February 6, 2021. Date of publication February 25, 2021; date of current version March 18, 2021. This letter was recommended for publication by Associate Editor D. Zanotto and Editor C. Gosselin upon evaluation of the reviewers’ comments. This work was supported by MIUR under the Program Department of Excellence of the DII, University of Trento, and Scuola Superiore Sant’Anna. (Corresponding author: Marco Fontana.) Marco Bolignari is with the Department of Industrial Engineering, University of Trento, Trento 38123, Italy (e-mail: marco.bolignari@unitn.it).",,,,,,,,,,"Hirzinger, G., Albu-Schaffer, A., Hahnle, M., Schaefer, I., Sporer, N., On a new generation of torque controlled light-weight robots (2001) Proc. Ieee Int. Conf. Robot. Automat., pp. 3356-3363; Freudenstein, F., On the conceptual design of a novel class of robot configurations (1989) J. Mechanisms, Transmissions, Automat. Des., 111 (1), pp. 47-53; Massie, T.H., The phantom haptic interface: A device for probing virtual objects (1994) Proc. Asme Symp. Haptic Interfaces Virtual Env. Teleop. Syst., 55 (1), pp. 295-300. , Chicago, IL; Fontana, M., Fabio, S., Marcheschi, S., Bergamasco, M., Haptic hand exoskeleton for precision grasp simulation (2013) J. Mechanisms Robot., 5 (4), pp. 0410141-0410149; Chen, D., Yun, Y., Deshpande, A.D., Experimental characterization of bowden cable friction (2014) Proc. Ieee Int. Conf. Robot. Automat., pp. 5927-5933; Whitney, J.P., Glisson, M.F., Brockmeyer, E.L., Hodgins, J.K., A low-friction passive fluid transmission and fluid-tendon soft actuator (2014) Proc. Ieee Int. Conf. Intell. Robots Syst., pp. 2801-2808; Whitney, J.P., Chen, T., Mars, J., Hodgins, J.K., A hybrid hydrostatic transmission and human-safe haptic telepresence robot (2016) Proc. Ieee Int. Conf. Robot. Autom., pp. 690-695; Véronneau, C., Bigué, J.-P.L., Lussier-Desbiens, A., Plante, J.-S., A high-bandwidth back-drivable hydrostatic power distribution system for exoskeletons based on magnetorheological clutches (2018) Ieee Robot. Automat. Lett., 3 (3), pp. 2592-2599. , Jul; Schwarm, E., Gravesmill, K.M., Whitney, J.P., A floating-piston hydrostatic linear actuator and remote-direct-drive 2-Dof gripper (2019) Proc. Ieee Int. Conf. Robot. Autom., pp. 7562-7568; Burkhard, N., A rolling-diaphragm hydrostatic transmission for remote mr-guided needle insertion (2017) Proc. Ieee Int. Conf. Robot. Autom., pp. 1148-1153; Chiaverini, S., Siciliano, B., Villani, L., A survey of robot interaction control schemes with experimental comparison (1999) IEEE/ASME Trans. On Mechatronics, 4 (3), pp. 273-285. , Sep; Albu-Schäffer, A., Ott, C., Hirzinger, G., A unified passivity-based control framework for position, torque and impedance control of flexible joint robots (2007) Int. J. Robot. Res., 26 (1), pp. 23-39; Calanca, A., Muradore, R., Fiorini, P., A review of algorithms for compliant control of stiff and fixed-compliance robots (2016) IEEE/ASME Trans. Mechatronics, 21 (2), pp. 613-624. , Apr; Paine, N., Oh, S., Sentis, L., Design and control considerations for high-performance series elastic actuators (2014) IEEE/ASME Trans. On Mecha-tronics, 19 (3), pp. 1080-1091. , Jun; Véronneau, C., Denis, J., Lebel, L.-P., Denninger, M., Plante, J.-S., Girard, A., A lightweight force-controllable wearable arm based on magnetorheological-hydrostatic actuators (2019) Proc. Int. Conf. Robot. Automat., pp. 4018-4024; Bolignari, M., Fontana, M., Design and experimental characterization of a high performance hydrostatic transmission for robot actuation (2020) Mec-canica, 55, pp. 1-11; Palmor, Z., Time-delay compensation smith predictor and its modifications (1996) The Control Handbook, pp. 224-237. , W. Levine, Ed Boca Raton, FL, USA: CRC press; Kim, B., Deshpande, A.D., An upper-body rehabilitation exoskeleton harmony with an anatomical shoulder mechanism: Design, modeling, control, and performance evaluation (2017) Int. J. Robot. Res., 36 (4), pp. 414-435; Sugar, T.G., Design and control of rupert: A device for robotic upper extremity repetitive therapy (2007) Ieee Trans. Neural Syst. Rehabil. Eng., 15 (3), pp. 336-346. , Sep; Shin, D., Sardellitti, I., Park, Y.-L., Khatib, O., Cutkosky, M., Design and control of a bio-inspired human-friendly robot (2010) Int. J. Robot. Res., 29 (5), pp. 571-584; An, C.H., Hollerbach, J.M., Dynamic stability issues in force control of manipulators (1987) Proc. Ieee Dyn. Stability Issues Force Control Manipulators, pp. 821-827; Véronneau, C., Multifunctional remotely actuated 3-dof supernumerary robotic arm based on magnetorheological clutches and hydrostatic transmission lines (2020) Ieee Robot. Automat. Lett., 5 (2), pp. 2546-2553. , Apr","Fontana, M.; TeCIP Institute, Italy; email: marco.fontana@santannapisa.it",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85101858489
"Guo J., Chen P., Jiang Y., Yokoi H., Togo S.","57223042110;57205209850;21742756100;7201964047;53878687100;","Real-time object detection with deep learning for robot vision on mixed reality device",2021,"LifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies",,,"9391811","82","83",,2,"10.1109/LifeTech52111.2021.9391811","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104665503&doi=10.1109%2fLifeTech52111.2021.9391811&partnerID=40&md5=5c8254e20e2b5272132eba20bec7c2b5","Department of Mechanical and Intelligent Systems Engineering, The University of Electro-Communications, Tokyo, Japan; Center for Neuroscience and Biomedical Engineering, The University of Electro-Communications, Tokyo, Japan","Guo, J., Department of Mechanical and Intelligent Systems Engineering, The University of Electro-Communications, Tokyo, Japan; Chen, P., Department of Mechanical and Intelligent Systems Engineering, The University of Electro-Communications, Tokyo, Japan; Jiang, Y., Center for Neuroscience and Biomedical Engineering, The University of Electro-Communications, Tokyo, Japan; Yokoi, H., Department of Mechanical and Intelligent Systems Engineering, The University of Electro-Communications, Tokyo, Japan; Togo, S., Department of Mechanical and Intelligent Systems Engineering, The University of Electro-Communications, Tokyo, Japan","Mixed reality device sensing capabilities are valuable for robots, for example, the inertial measurement unit (IMU) sensor and time-of-flight (TOF) depth sensor can support the robot in navigating its environment. This paper demonstrates a deep learning (YOLO model) background, real-time object detection system implemented on mixed reality device. The goal of the system is to create a real-time communication system between HoloLens and Ubuntu systems to enable real-time object detection using the YOLO model. The experimental results show that the proposed method has a fast speed to achieve real-time object detection using HoloLens. This enables Microsoft HoloLens as a device for robot vision. To enhance human-robot interaction, we will apply it to a wearable robot arm system to automatically grasp objects in the future. © 2021 IEEE.","Deep learning; Microsoft HoloLens; Object detection; Robot vision; User interface","Deep learning; Mixed reality; Object detection; Object recognition; Real time systems; Social robots; Depth sensors; Inertial measurement unit; MicroSoft; Object detection systems; Real time; Real-time communication system; Time of flight; Wearable robots; Computer vision",,,,,,,,,,,,,,,,"Bahri, H., Krcmarík, D., Kocí, J., Accurate object detection system on hololens using yolo algorithm (2019) 2019 International Conference on Control, Artificial Intelligence, Robotics Optimization (ICCAIRO), pp. 219-224; Redmon, J., Divvala, S., Girshick, R., Farhadi, A., (2016) You Only Look Once: Unified, Real-Time Object Detection; Smith, E., HoloLensCameraStream for Unity, , https://github.com/VulcanTechnologies/HoloLensCameraStream, Accessed: Nov.19, 2020; Ros-Sharp, , https://github.com/siemens/ros-sharp, Accessed: November 18, 2020; Bjelonic, M., YOLO ROS:Real-Time Object Detection for ROS, , https://github.com/leggedrobotics/darknet_ros, Accessed: Nov.20, 2020; Tossy, Yolov4-for-Darknet_ros, , https://github.com/Tossy0423/yolov4-for-darknet_ros, Accessed: Nov.20, 2020; Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M., (2020) YOLOv4: Optimal Speed and Accuracy of Object Detection",,,"IEEE;IEEE Life Sciences","Institute of Electrical and Electronics Engineers Inc.","3rd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2021","9 March 2021 through 11 March 2021",,168367,,9781665418751,,,"English","LifeTech - IEEE Glob. Conf. Life Sci. Technol.",Conference Paper,"Final","",Scopus,2-s2.0-85104665503
"Luria M., Hoggenmüller M., Lee W.-Y., Hespanhol L., Jung M., Forlizzi J.","56895782800;56422380000;57204952765;55295736000;15073123400;6602845027;","Research through design approaches in human-robot interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444868","685","687",,1,"10.1145/3434074.3444868","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102777898&doi=10.1145%2f3434074.3444868&partnerID=40&md5=c8c19fc6275147e8320253e04dea465f","Human-Computer Interaction Intitute, Carnegie Mellon University, United States; Sydney School of Architecture, Design and Planning, The University of Sydney, Australia; Mechanical and Aerospace Engineering, Cornell University, United States; Sydney School of Architecture, Design and Planning, The University of Sydney, Australia; Information Science, Cornell University, United States","Luria, M., Human-Computer Interaction Intitute, Carnegie Mellon University, United States; Hoggenmüller, M., Sydney School of Architecture, Design and Planning, The University of Sydney, Australia; Lee, W.-Y., Mechanical and Aerospace Engineering, Cornell University, United States; Hespanhol, L., Sydney School of Architecture, Design and Planning, The University of Sydney, Australia; Jung, M., Information Science, Cornell University, United States; Forlizzi, J., Human-Computer Interaction Intitute, Carnegie Mellon University, United States","This workshop sets out to bring researchers across the Human-Robot Interaction (HRI), Human-Computer Interaction, and Design fields that use, or are interested in using Research through Design (RtD) in their work. RtD is a research approach that uses design practices, such as ideation and prototyping, to generate knowledge. RtD focuses on understanding what is the right thing to design, and has the potential to bring new perspectives that break through fixation within a field. In our workshop, we will attempt to classify current HRI practices of RtD, identify under-explored topics and methods, and discuss challenges on conducting this type of work in the HRI field. The workshop will result in defining next steps for better integration of RtD approaches in the HRI community and guidelines to include more researchers in RtD practice. © 2021 Owner/Author.","Design research; Human-robot interaction; Research through design","Agricultural robots; Human computer interaction; Machine design; Design approaches; Design practice; Hri communities; Human robot Interaction (HRI); Research approach; Human robot interaction",,,,,"National Science Foundation, NSF: SES-1734456","This workshop is supported by the National Science Foundation, grant SES-1734456.",,,,,,,,,,"Alves-Oliveira, P., Arriaga, P., Paiva, A., Hoffman, G., Yolo, a robot for creativity: A co-design study with children (2017) Proceedings of the 2017 Conference on Interaction Design and Children, pp. 423-429; Auger, J., Living with robots: A speculative design approach (2014) Journal of Human-Robot Interaction, 3 (1), pp. 20-42. , 2014; Bødker, S., When second wave hci meets third wave challenges (2006) Proceedings of the 4th Nordic Conference on Human-Computer Interaction: Changing Roles (Oslo, Norway) (NordiCHI '06), pp. 1-8. , https://doi.org/10.1145/1182475.1182476, Association for Computing Machinery, New York, NY, USA; Buchanan, R., Wicked problems in design thinking (1995) The Idea of Design, pp. 3-20. , Victor Margolin and Richard Buchanan (Eds.) MIT Press; Buxton, B., (2010) Sketching User Experiences: Getting the Design Right and the Right Design, , Morgan Kaufmann; Dunne, A., Raby, F., Technological dreams series no. 1 (2007) Robots, , http://dunneandraby.co.uk/content/projects/10/0; Gaver, W., What should we expect from research through design? (2012) Proc. Of the SIGCHI Conference on Human Factors in Computing Systems, pp. 937-946; Gaver, W.W., Bowers, J., Boucher, A., Gellerson, H., Pennington, S., Schmidt, A., Steed, A., Walker, B., The drift table: Designing for ludic engagement (2004) CHI'04 Extended Abstracts on Human Factors in Computing Systems. ACM, pp. 885-900; Hoggenmueller, M., Hespanhol, L., Tomitsch, M., Stop and smell the chalk flowers: A robotic probe for investigating urban interaction with physicalised displays (2020) Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1-14; Koskinen, I., Zimmerman, J., Binder, T., Redstrom, J., Wensveen, S., (2011) Design Research through Practice: From the Lab, Field, and Showroom, , Elsevier; Lee, W., Jung, M., Ludic-hri: Designing playful experiences with robots (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 582-584; Luce Lupetti, M., Shybo-design of a research artefact for human-robot interaction studies (2017) Journal of Science and Technology of the Arts, 9 (1), pp. 57-69. , 2017; Luce Lupetti, M., Zaga, C., Cila, N., (2020) First International Workshop on Designerly HRI Knowledge; Luria, M., Reig, S., Zhi Tan, X., Steinfeld, A., Forlizzi, J., Zimmerman, J., Re-embodiment and co-embodiment: Exploration of social presence for robots and conversational agents (2019) Proceedings of the 2019 on Designing Interactive Systems Conference. ACM, pp. 633-644; Luria, M., Sheriff, O., Boo, M., Forlizzi, J., Zoran, A., Destruction, catharsis, and emotional release in human-robot interaction (2020) ACM Transactions on Human-Robot Interaction (THRI), 9 (4), pp. 1-19. , 2020; Luria, M., Zheng, R., Huffman, B., Shuangni, H., Zimmerman, J., Forlizzi, J., Social boundaries for personal agents in the interpersonal space of the home (2020) Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM; Luria, M., Zimmerman, J., Forlizzi, J., Championing research through design in hri (2019) 2019 CHI Conference on Human Factors in Computing Systems, , 2019; Odom, W., Selby, M., Sellen, A., Kirk, D., Banks, R., Regan, T., Photobox: On the design of a slow technology (2012) Proceedings of the Designing Interactive Systems Conference, pp. 665-668; Odom, W., Zimmerman, J., Forlizzi, J., Teenagers and their virtual possessions: Design opportunities and issues (2011) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, pp. 1491-1500; Suguitan, M., Hoffman, G., Blossom: A handcrafted open-source robot (2019) ACM Transactions on Human-Robot Interaction (THRI), 8 (1), pp. 1-27. , 2019; Yang, Q., Banovic, N., Zimmerman, J., Mapping machine learning advances from hci research to reveal starting places for design innovation (2018) Proc. Of the CHI Conference on Human Factors in Computing Systems, pp. 1-11; Zimmerman, J., Forlizzi, J., Research through design in hci (2014) Ways of Knowing in HCI, pp. 167-189. , Springer; Zimmerman, J., Forlizzi, J., Evenson, S., Research through design as a method for interaction design research in hci (2007) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, pp. 493-502",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102777898
"Tsoi N., Connolly J., Adéníran E., Hansen A., Pineda K.T., Adamson T., Thompson S., Ramnauth R., Vázquez M., Scassellati B.","57214458789;57215561944;57189747461;57222465435;57222471568;57215831563;57216992263;57222463910;37017787700;57203072853;","Challenges deploying robots during a pandemic: An effort to fight social isolation among children",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444665","234","242",,,"10.1145/3434073.3444665","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102768068&doi=10.1145%2f3434073.3444665&partnerID=40&md5=806a2959dbd45a22cdda50d1085dad88","Yale University, United States","Tsoi, N., Yale University, United States; Connolly, J., Yale University, United States; Adéníran, E.; Hansen, A.; Pineda, K.T.; Adamson, T.; Thompson, S.; Ramnauth, R.; Vázquez, M.; Scassellati, B.","The practice of social distancing during the COVID-19 pandemic re-sulted in billions of people quarantined in their homes. In response, we designed and deployed VectorConnect, a robot teleoperation system intended to help combat the efects of social distancing in children during the pandemic. VectorConnect uses the of-the-shelf Vector robot to allowits users to engage in physical play while being geographically separated.We distributed the system to hundreds of users in a matter of weeks. This paper details the development and deployment of the system, our accomplishments, and the obstacles encountered throughout this process. Also, it provides recommen-dations to best facilitate similar deployments in the future.We hope that this case study about Human-Robot Interaction practice serves as inspiration to innovate in times of global crises. © 2021 IEEE Computer Society. All rights reserved.","Human-Robot Interaction; Robot Teleoperation; Social Isolation","Agricultural robots; Man machine systems; Physical play; Robot teleoperation; Social isolation; Social robots",,,,,"National Science Foundation, NSF: 1924802",,,,,,,,,,,"Altig, D., Baker, S., Barrero, J.M., Bloom, N., Bunn, P., Chen, S., Davis, S.J., Mihaylov, E., Economic uncertainty before and during the COVID-19 pandemic (2020) Journal OfPublic Economics, p. 104274; Bailey, R., Armour, K., Kirk, D., Jess, M., Pickup, I., Sandford, R., The educational benefts claimed for physical education and school sport:an academic review (2009) Research Papers in Education, 24 (1), pp. 1-27. , https://doi.org/10.1080/02671520701809817, BERA Physical Education, and Sport Pedagogy Special Interest Group 2009; Bainbridge, W.A., Hart, J.W., Kim, E.S., Scassellati, B., The benefts of interactions with physically present robots over video-displayed agents (2011) International Journal of Social Robotics, 3 (1), pp. 41-52. , https://doi.org/10.1007/s12369-010-0082-7, Jan; Barbour, A.C., The impact of playground design on the play behaviors ofchildren with difering levels of physical competence (1999) Early Childhood ResearchQuarterly, 14 (1), pp. 75-98. , https://doi.org/10.1016/S0885-2006(99)80007-6; Beer, J.M., Takayama, L., Mobile remote presence systemsfor older adults: Acceptance, Benefts, and Concerns (2011) 2011 6th ACM/IEEEInternational Conference on Human-Robot Interaction (HRI)., pp. 19-26. , https://doi.org/10.1145/1957656.1957665; Bellini, S., (2008) Building Social Relationships: A Systematic Approach to Teaching Social Interaction Skills to Children and Adolescents with Autism Spectrum Disordersand Other Social Difculties, , AApC publishing; Bjorklund, D.F., Brown, R.D., Physical play and cognitivedevelopment: Integrating activity, cognition, and education (1998) Child Development, 69 (3), pp. 604-606; Cabral, L., Xu, L., (2020) Seller Reputation and Price Gouging: Evidence from the COVID-19 Pandemic, , Technical Report. Mimeo; Cacioppo, J.T., Cacioppo, S., The growing problem of lone-liness (2018) The Lancet, 391, p. 426. , https://doi.org/10.1016/S0140-6736(18)30142-9, 10119, Feb 2018, Publisher: Elsevier; Cesta, A., Cortellessa, G., Orlandini, A., Tiberio, L., Long-term evaluation of a telepresence robot for the elderly: Methodologyand ecological case study (2016) International Journal of Social Robotics, 8 (3), pp. 421-441; Combs, M.L., Slaby, D.A., Social-skills training withchildren (1977) Advances in Clinical Child Psychology., pp. 161-201. , Springer; Durodié, B., Handling uncertainty and ambiguity in the COVID-19 pan-demic (2020) Psychological Trauma: Theory, Research, Practice, and Policy, 12, p. S61; Eyssel, F., Reich, N., Loneliness makes the heart growfonder (of robots)-On the efects of loneliness on psychological anthropomorphism. In2013 (2013) 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 121-122; Fernaeus, Y., Håkansson, M., Jacobsson, M., Ljungblad, S., How do you play with a robotic toy animal? A long-term study of Pleo (2010) Proceedings of the 9th International Conference on Interaction Design and Children., pp. 39-48; (2020) Flutter-beautiful Native Apps in Record Time, , https://futter.dev/, Flutter, Retrieved September28, 2020 from; (2020) Center for Robot-AssistedSearchand Rescue, , http://crasar.org/, Center for Robot-AssistedSearch and Rescue, Retrieved October 5, 2020 from; Golberstein, E., Wen, H., Miller, B.F., Coronavirus disease 2019 (COVID-19) and mental health for children and adolescents (2020) JAMA Pedi-atrics, 174 (9), pp. 819-820. , https://doi.org/10.1001/jamapediatrics.2020.1456, 09 2020; Holt-Lunstad, J., The potential public health relevance of socialisolation and loneliness: Prevalence, epidemiology, and risk factors (2017) Public Policy & Aging Report, 27 (4), pp. 127-130; Kristofersson, A., Coradeschi, S., Loutf, A., A review ofmobile robotic telepresence (2013) Advances in Human-Computer Interaction; Lee, K.M., Jung, Y., Kim, J., Kim, S.R., Arephysically embodied social agents better than disembodied social agents?: The efects of physical embodiment, tactile interaction, and people's loneliness inhuman-robot interaction (2006) International Journal of Human-computer Studies, 64 (10), pp. 962-973; Lynch, S.A., Simpson, C.G., Social skills: Laying the foundation for success (2010) Dimensions of Early Childhood, 38 (2), pp. 3-12; Martelaro, N., Nneji, V.C., Ju, W., Hinds, P., Tell memore designing HRI to encourage more trust, disclosure, and companionship (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI).IEEE, pp. 181-188; Matarić, M.J., Socially assistive robotics: Human augmentation versusautomation (2017) Science Robotics, 2 (4), p. eaam5410; Mendelson, H., Moon, K., Modeling success and engagement for theapp economy (2018) Proceedings of the 2018 World Wide Web Conference., pp. 569-578; Odekerken-Schröder, G., Mele, C., Russo-Spena, T., Mahr, D., Ruggiero, A., Mitigating loneliness with companion robots in theCOVID-19 pandemic and beyond: An integrative framework and research agenda (2020) Journal of Service Management; Pellegrini, A.D., Smith, P.K., Physical activity play: The nature and function of a neglected aspect of play (1998) Child Development, 69 (3), pp. 577-598; Rae, I., Mutlu, B., Takayama, L., Bodies in motion: Mobil-ity, presence, and task awareness in telepresence (2014) Proceedings of the SIGCHIConference on Human Factors in Computing Systems., pp. 2153-2162; Read, J.C., MacFarlane, S., Casey, C., Endurability, engagement and expectations: Measuring children's fun (2002) Interaction Design and Children, 2, pp. 1-23. , Shaker Publishing Eindhoven; Scassellati, B., Vázquez, M., The potential of socially assistiverobots during infectious disease outbreaks (2020) Science Robotics, 5, p. 44; Seelye, A.M., Wild, K.V., Larimer, N., Maxwell, S., Kearns, P., Kaye, J.A., Reactions to a remote-controlledvideo-communication robot in seniors' homes: A pilot study of feasibilityand acceptance (2012) Telemedicine and E-Health, 18 (10), pp. 755-759. , https://doi.org/10.1089/tmj.2012.0026, PMID: 23082794; Soares, N., Kay, J.C., Craven, G., Mobile robotic telepres-ence solutions for the education of hospitalized children (2017) Perspectives in Healthinformation Management, 14. , Fall; Stoll, B., Reig, S., He, L., Kaplan, I., Jung, M.F., Fussell, S.R., Wait, can you move the robot? Examining telepresence robot use incollaborative teams (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction., pp. 14-22; Strain, P.S., Odom, S.L., Peer social initiations: Efective intervention for social skills development of exceptional children (1986) Exceptional Children, 52 (6), pp. 543-551; Sung, J., Christensen, H.I., Grinter, R.E., Robots in thewild: Understanding long-term use (2009) Proceedings of the 4th ACM/IEEE Interna-tional Conference on Human Robot Interaction., pp. 45-52; Tan, X.Z., Vázquez, M., Carter, E.J., Morales, C.G., Steinfeld, A., Inducing bystander interventions during robot abuse withsocial mechanisms (2018) Proceedings of the 2018 ACM/IEEE International Conferenceon Human-Robot Interaction., pp. 169-177; Tanaka, F., Takahashi, T., Matsuzoe, S., Tazawa, N., Morita, M., Child-operated telepresence robot: A feld trial con-necting classrooms between Australia and Japan (2013) 2013 IEEE/RSJ InternationalConference on Intelligent Robots and Systems. IEEE, pp. 5896-5901; Tanaka, F., Takahashi, T., Matsuzoe, S., Tazawa, N., Morita, M., Telepresence robot helps children in communicating withteachers who speak a diferent language (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-robot Interaction., pp. 399-406; Tsui, K.M., Desai, M., Yanco, H.A., Uhlik, C., Exploring use cases for telepresence robots (2011) 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 11-18; Yang, G.-Z., Nelson, B.J., Murphy, R.R., Choset, H., Christensen, H., Collins, S.H., Dario, P., Jacob-Stein, N., (2020) Combating COVID-19-The Role of Robotics in Managing Publichealth and Infectious Diseases; Zhong, B.-L., Chen, S.-L., Tu, X., Conwell, Y., Lonelinessand cognitive function in older adults: Findings from the Chinese longitudinal healthy longevity survey (2017) The Journals of Gerontology: Series B, 72 (1), pp. 120-128",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS)","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167644,21672148,9781450382892,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102768068
"Johal W., Bruno B., Olsen J.K., Chetouani M., Lemaignan S., Sandygulova A.","55912460600;36922823500;55417042300;8964513700;18234152100;55193519700;","Robots for learning - Learner-centred design",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444873","715","716",,,"10.1145/3434074.3444873","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102736527&doi=10.1145%2f3434074.3444873&partnerID=40&md5=c4c2d7e8b9024b0d87352032fb51d75b","University of New South Wales Sydney, Sydney, NSW, Australia; Swiss Federal Institute of Technology in Lausanne (EPFL), Lausanne, Vaud, Switzerland; University of San Diego, San Diego, CA, United States; Sorbonne University, Paris, France; Bristol Robotics Lab, Bristol, United Kingdom; Nazarbayev University, Nur-Sultan, Kazakhstan","Johal, W., University of New South Wales Sydney, Sydney, NSW, Australia; Bruno, B., Swiss Federal Institute of Technology in Lausanne (EPFL), Lausanne, Vaud, Switzerland; Olsen, J.K., University of San Diego, San Diego, CA, United States; Chetouani, M., Sorbonne University, Paris, France; Lemaignan, S., Bristol Robotics Lab, Bristol, United Kingdom; Sandygulova, A., Nazarbayev University, Nur-Sultan, Kazakhstan","The Robots for Learning workshop series aims at advancing the research topics related to the use of social robots in educational contexts. This year's half-day workshop follows on previous events in Human-Robot Interaction conferences focusing on efforts to dis-cuss potential benchmarks in design, methodology and evaluation of new robotics systems that help learners. In this 6th edition of the workshop, we will be investigating in particular methods from technologies for education and online learning. Since the past few months, online and remote learning has been put in place in several countries to cope with the health and safety measures due to theCovid-19 pandemic. In this workshop, we aim to discuss strategies to design robotics system able to provide embodied assistance to the remote learners and to demonstrate long-term learning effects. © 2021 Owner/Author.","Human-robot interaction; Robot in education; Robots for learning","Agricultural robots; E-learning; Learning systems; Machine design; Man machine systems; Robotics; Social robots; Educational context; Half-day workshop; Health and safety; Learner-centred design; Learning workshops; Long-term learning; Research topics; Robotics systems; Educational robots",,,,,"Australian Research Council, ARC: DE210100858; Horizon 2020: 761758, 765955; Staatssekretariat für Bildung, Forschung und Innovation, SBFI: SFG1038; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; Nazarbayev University, NU: 091019CRP2107; National Centre of Competence in Research Robotics","Researchers organising this workshop would like to thank the following agencies for their funding support: the Australian Research Council (DE210100858), the Swiss National Science Foundation through the National Centre of Competence in Research Robotics, The European Union Horizon 2020 through the ANIMATAS project (No 765955) and HumanE AI NET project (No 761758), the Nazarbayev University Collaborative Research Program (No 091019CRP2107) and the Swiss State Secretariat for Education, Research and Innovation (SFG1038).",,,,,,,,,,"(2020) Robots for Learning Website, , https://robot4learning.github.io/; Johal, W., Research trends in social robots for learning (2020) Current Robotics Reports, pp. 1-9; Papamitsiou, Z., Economides, A.A., Learning analytics and educational data mining in practice: A systematic literature reviewof empirical evidence (2014) Journal of Educational Technology & Society, 17 (4), pp. 49-64. , 2014; Wright, D.B., Research methods for education with technology: Four concerns, examples, and recommendations (2019) Frontiers in Education, 4, p. 147. , https://doi.org/10.3389/feduc.2019.00147, 2019",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102736527
"Antonioni E., Bisconti P., Massa N., Nardi D., Suriani V.","57221475860;57205669543;57221471216;7006582655;57197733555;","Questioning Items’ Link in Users’ Perception of a Training Robot for Elders",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"509","518",,,"10.1007/978-3-030-90525-5_44","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119835939&doi=10.1007%2f978-3-030-90525-5_44&partnerID=40&md5=4a8cc33cd0bde92dbb08786fbe6ba84e","Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy; Sant’Anna School of Advanced Studies, DIRPOLIS Institute, Pisa, Italy; Department of Psychology, Sapienza University of Rome, Rome, Italy","Antonioni, E., Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy; Bisconti, P., Sant’Anna School of Advanced Studies, DIRPOLIS Institute, Pisa, Italy; Massa, N., Department of Psychology, Sapienza University of Rome, Rome, Italy; Nardi, D., Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy; Suriani, V., Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy","Socially Assistive robots are becoming more common in modern society. These robots can accomplish a variety of tasks for people that are exposed to isolation and difficulties. Among those, elderly people are the largest part, and with them, robotics can play new roles. Elderly people are the ones who usually suffer a major technological gap, and it is worth evaluating their perception when dealing with robots. To this end, the present work addresses the interaction of elderly people during a training session with a humanoid robot. The analysis has been carried out by means of a questionnaire, using four key factors: Motivation, Usability, Likability, and Sociability. The results can contribute to the design and the development of social interaction between robots and humans in training contexts to enhance the effectiveness of human-robot interaction. © 2021, Springer Nature Switzerland AG.","Ageing society; Robot trainer; Social robots","Economic and social effects; Human robot interaction; Machine design; Man machine systems; Aging societies; Elderly people; Exposed to; Humanoid robot; Large parts; Robot trainer; Social robots; Socially assistive robots; Training sessions; User perceptions; Anthropomorphic robots",,,,,,"We want to thank the Fondazione Mondo Digitale for its essential support in recruiting the participants to this study and helping us in administering the questionnaires. A special mention to Cecilia Stajano for her help and availability.",,,,,,,,,,"Antonioni, E., Suriani, V., Massa, N., Nardi, D., Autonomous and remote controlled humanoid robot for fitness training (2020) Companion Publication of the 2020 International Conference on Multimodal Interaction, pp. 235-239. , https://doi.org/10.1145/3395035.3425301, pp., ICMI 2020 Companion. Association for Computing Machinery, New York, NY, USA; Brawley, L., Vallerand, R., (1985) Effects of Informational and Controlling Fitness Leaders on participants’ Interest and Intention to Pursue Engagement in a Fitness Program, , Unpublished manuscript, Waterloo, Canada; Diaz, R., Miller, E.K., Kraus, E., Fredericson, M., Impact of adaptive sports participation on quality of life (2019) Sports Med. Arthrosc. Rev., 27 (2), pp. 73-82; Esteban, P.G., How to build a supervised autonomous system for robot-enhanced therapy for children with autism spectrum disorder (2017) Paladyn J. Behav. Robot., 8 (1), pp. 18-38; Griffiths, S., (2021) Exercise with Social Robots: Companion Or Coach? Arxiv Preprint Arxiv, 2103, p. 12940; Hayslett, H.T., (2014) Statistics, , Elsevier, New York; Kim, S.S., Kim, J., Badu-Baiden, F., Giroux, M., Choi, Y.: Preference for robot service or human service in hotels? Impacts of the COVID-19 pandemic. Int. J. Hosp. Manag. 93, 102795 (2021). https://doi.org/10.1016/j.ijhm.2020.102795, https://linkinghub.elsevier.com/retrieve/pii/S0278431920303479; Lee, I.M., Paffenbarger, R.S., Do physical activity and physical fitness avert premature mortality? (1996) Exerc. Sport Sci. Rev., 24 (1); Leveille, S.G., Guralnik, J.M., Ferrucci, L., Langlois, J.A., Aging successfully until death in old age: Opportunities for increasing active life expectancy (1999) Am. J. Epidemiol., 149 (7), pp. 654-664; Lotfi, A., Langensiepen, C., Yahaya, S.W., Socially assistive robotics: Robot exercise trainer for older adults (2018) Technologies, 6 (1), p. 32; Mageau, G.A., Vallerand, R.J., The coach-athlete relationship: A motivational model (2003) J. Sports Sci., 21 (11), pp. 883-904; Nomura, T., Suzuki, T., Kanda, T., Kato, K., Measurement of negative attitudes toward robots (2006) Interact. Stud., 7 (3), pp. 437-454; Otte, F.W., Davids, K., Millar, S.K., Klatt, S., When and how to provide feedback and instructions to athletes?-How sport psychology and pedagogy insights can improve coaching interventions to enhance self-regulation in training (2020) Front. Psychol., 11 (1444); Pelletier, L.G., Rocchi, M.A., Vallerand, R.J., Deci, E.L., Ryan, R.M., Validation of the revised sport motivation scale (SMS-ii) (2013) Psychol. Sport Exerc., 14 (3), pp. 329-341; Shao, M., Alves, S.F.D.R., Ismail, O., Zhang, X., Nejat, G., Benhabib, B., You are doing great! only one rep left: An affect-aware social robot for exercising (2019) 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Pp. 3811–3817. IEEE; Sharkey, A., Sharkey, N., Granny and the robots: Ethical issues in robot care for the elderly (2012) Ethics Inf. Technol., 14 (1), pp. 27-40. , https://doi.org/10.1007/s10676-010-9234-6; Smuha, N.A., The EU approach to ethics guidelines for trustworthy artificial intelligence (2019) Comput. Law Rev. Int., 20 (4), pp. 97-106; Steptoe, A., Owen, N., Kunz-Ebrecht, S.R., Brydon, L., Loneliness and neuroen-docrine, cardiovascular, and inflammatory stress responses in middle-aged men and women (2004) Psychoneuroendocrinology, 29 (5), pp. 593-611; Steptoe, A., Shankar, A., Demakakos, P., Wardle, J., Social isolation, loneliness, and all-cause mortality in older men and women (2013) Proc. Natl. Acad. Sci., 110 (15), pp. 5797-5801","Suriani, V.; Department of Computer, Italy; email: suriani@diag.uniroma1.it","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119835939
"Zhao Z., Li X., Liu M., Li X., Gao H., Hao L.","57208514891;56811045500;57215590838;57189643229;57225106846;18835526000;","A novel human-robot interface based on soft skin sensor designed for the upper-limb exoskeleton",2021,"Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science",,,,"","",,,"10.1177/09544062211035801","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116376837&doi=10.1177%2f09544062211035801&partnerID=40&md5=74d18f716e9bf3e6f12e7bd5a94e5119","School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China; State Key Laboratory of Syntherical Automation for Process Industries, Northeastern University, Shenyang, China","Zhao, Z., School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China; Li, X., State Key Laboratory of Syntherical Automation for Process Industries, Northeastern University, Shenyang, China; Liu, M., School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China; Li, X., School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China; Gao, H., School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China; Hao, L., School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China","The upper-limb exoskeleton is capable of enhancing human arm strength beyond normal levels, whereas deriving the operator’s desired action straightforward turns out to be one of the significant difficulties facing human-robot interaction research. In the study, the human-robot interface was presented to regulate the exoskeleton tracking human elbow motion trajectory that employed the contact force signals between the exoskeleton and its operator as the primary means of information transportation. The signals were recorded by adopting the novel soft skin sensors attached to the bracket on the exoskeleton linkage, which could reflect the human arm motion intention through the virtual admittance model and adaptive control. Subsequently, a 1-DOF upper-limb exoskeleton was designed to illustrate the performance of the proposed sensor and the interaction control method in the human-robot cooperation experiment. © IMechE 2021.","contact force; human-robot interface; soft skin sensor; Upper-limb exoskeleton; virtual admittance control","Exoskeleton (Robotics); Man machine systems; Virtual reality; Admittance control; Contact forces; Human arm; Human-Robot Interface; Skin sensors; Soft skin sensor; Upper limbs; Upper-limb exoskeleton; Virtual admittance control; Virtual admittances; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 61503070, 61873054, 62073063","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors acknowledge the financial support from National Natural Science Foundation of China (Grant No. 62073063, 61873054 and 61503070).",,,,,,,,,,"He, W., Wang, T., He, X., Dynamical modeling and boundary vibration control of a rigid-flexible wing system (2020) IEEE/ASME Trans Mechatron, 25, pp. 2711-2719; He, W., Mu, X., Zhang, L., Modeling and trajectory tracking control for flapping-wing micro aerial vehicles (2021) IEEE/CAA J Autom Sinica, 8, pp. 148-156; Yu, X., He, W., Li, H., Adaptive fuzzy full-state and output-feedback control for uncertain robots with output constraint. In:, New York: IEEE; Xinfeng, G., Xingchen, L., Yao, S., Model-free tracking control of continuum manipulators with global stability and assigned accuracy. 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A safe human -robot interactive control structure with human arm movement detection for an uer-limb wearable robot used during lifting tasks., 2020; 17: 172988142093757","Hao, L.; School of Mechanical Engineering and Automation, China; email: haolina@me.neu.edu.cn",,,"SAGE Publications Ltd",,,,,09544062,,PMCSE,,"English","Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci.",Article,"Article in Press","",Scopus,2-s2.0-85116376837
"Gan J., Wang N., Zuo L.","57284548200;55446268900;57283666100;","Surface electromyography (semg)-based intention recognition and control design for human-robot interaction in uncertain environment",2021,"Sensors and Materials","33","9",,"3153","3168",,,"10.18494/SAM.2021.3230","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116323513&doi=10.18494%2fSAM.2021.3230&partnerID=40&md5=de8aade6443a4a7b5aacb8abc4ff1f96","Key Laboratory of Autonomous Systems and Networked Control, College of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China; Bristol Robotics Laboratory, University of West of England, Frenchay, Coldharbour Ln, BristolBS34 8QZ, United Kingdom; School of Electronic and Control Engineering, Chang'an University, Xi'an, 710064, China","Gan, J., Key Laboratory of Autonomous Systems and Networked Control, College of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China; Wang, N., Bristol Robotics Laboratory, University of West of England, Frenchay, Coldharbour Ln, BristolBS34 8QZ, United Kingdom; Zuo, L., School of Electronic and Control Engineering, Chang'an University, Xi'an, 710064, China","An important direction of human-robot interaction (HRI) is making robots respond to complex and dexterous tasks intelligently. To achieve this, biological signals based on surface electromyography (sEMG) have widely been used to identify human intentions rapidly and effectively. We propose an algorithm that can recognize human intentions conveyed by different hand gestures through analyzing sEMG data. This will facilitate the selection of the most appropriate interaction mode and level during HRI for the robot. We also propose an admittance control framework combining a tan-type barrier Lyapunov function (BLF) and a radial basis function neural network (RBFNN) to ensure the interaction and tracking performance and to guarantee the stability of the system in uncertain environments. Experiments performed on a Baxter robot verify the effectiveness of the proposed framework. © 2021 M Y U Scientific Publishing Division. All rights reserved.","Barrier lyapunov function; Human-robot interaction; Radial basis function neural network; Surface electromyography","Human robot interaction; Machine design; Man machine systems; Radial basis function networks; System stability; Barrier lyapunov function; Biological signals; Control design; Human intentions; Humans-robot interactions; Intention recognition; Lyapunov's functions; Radial basis function neural networks (RBF); Surface electromyography; Uncertain environments; Lyapunov functions",,,,,"National Natural Science Foundation of China, NSFC: 61803039","This work was supported in part by the National Nature Science Foundation of China (NSFC) under Grant 61803039.",,,,,,,,,,"Wan, W., Lu, F., Wu, Z., Harada, K., (2017) Neurocomputing, 259, p. 85. , https://doi.org/10.1016/j.neucom.2017.01.077; Wan, W., Harada, K., Kanehiro, F., (2019) IEEE Trans. Ind. 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"Inkulu A.K., Bahubalendruni M.V.A.R., Dara A., SankaranarayanaSamy K.","57220037591;56453400700;57214799940;16040406500;","Challenges and opportunities in human robot collaboration context of Industry 4.0 - a state of the art review",2021,"Industrial Robot",,,,"","",,2,"10.1108/IR-04-2021-0077","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111820692&doi=10.1108%2fIR-04-2021-0077&partnerID=40&md5=438fe909bd9dc99e9cd19e28f7a7528c","Industrial Robotics and Manufacturing Automation Laboratory, Department of Mechanical Engineering, National Institute of Technology Puducherry, Karaikal, India","Inkulu, A.K., Industrial Robotics and Manufacturing Automation Laboratory, Department of Mechanical Engineering, National Institute of Technology Puducherry, Karaikal, India; Bahubalendruni, M.V.A.R., Industrial Robotics and Manufacturing Automation Laboratory, Department of Mechanical Engineering, National Institute of Technology Puducherry, Karaikal, India; Dara, A., Industrial Robotics and Manufacturing Automation Laboratory, Department of Mechanical Engineering, National Institute of Technology Puducherry, Karaikal, India; SankaranarayanaSamy, K., Industrial Robotics and Manufacturing Automation Laboratory, Department of Mechanical Engineering, National Institute of Technology Puducherry, Karaikal, India","Purpose: In the present era of Industry 4.0, the manufacturing automation is moving toward mass production and mass customization through human–robot collaboration. The purpose of this paper is to describe various human–robot collaborative (HRC) techniques and their applicability for various manufacturing methods along with key challenges. Design/methodology/approach: Numerous recent relevant research literature has been analyzed, and various human–robot interaction methods have been identified, and detailed discussions are made on one- and two-way human–robot collaboration. Findings: The challenges in implementing human–robot collaboration for various manufacturing process and the challenges in one- and two-way collaboration between human and robot are found and discussed. Originality/value: The authors have attempted to classify the HRC techniques and demonstrated the challenges in different modes. © 2021, Emerald Publishing Limited.","Automation; HRI; Industry 4.0; Mass customization; Mass production; Robots","Agricultural robots; Industrial robots; Industry 4.0; Machine design; Design/methodology/approach; Human-robot collaboration; Manufacturing Automation; Manufacturing methods; Manufacturing process; Mass customization; Robot interactions; State-of-the art reviews; Social robots",,,,,,,,,,,,,,,,"Aliev, K., Antonelli, D., Bruno, G., Task-based programming and sequence planning for human-robot collaborative assembly (2019) IFAC-PapersOnLine, 52 (13), pp. 1638-1643; Angleraud, A., Codd-Downey, R., Netzev, M., Houbre, Q., Pieters, R., Virtual teaching for assembly tasks planning (2020) IEEE International Conference on Human-Machine; Antonelli, D., Astanin, S., Qualification of a collaborative human-robot welding cell (2016) Procedia CIRP, 41, pp. 352-357; Asadi, E., Li, B., Chen, I.M., Pictobot: a cooperative painting robot for interior finishing of industrial developments (2018) IEEE Robotics & Automation Magazine, 25 (2), pp. 82-94; Asokan, A., Pothen, A.J., Vijayaraj, R.K., ARMatron – a wearable gesture recognition glove: for control of robotic devices in disaster management and human rehabilitation (2016) International Conference on Robotics and Automation for Humanitarian Applications, pp. 1-5; Bahubalendruni, M.R., Biswal, B.B., A review on assembly sequence generation and its automation (2016) Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 230 (5), pp. 824-838; Bahubalendruni, M.R., Biswal, B.B., Kumar, M., Nayak, R., Influence of assembly predicate consideration on optimal assembly sequence generation (2015) Assembly Automation, 35 (4), pp. 309-316; Battini, D., Faccio, M., Persona, A., Sgarbossa, F., New methodological framework to improve productivity and ergonomics in assembly system design (2011) International Journal of Industrial Ergonomics, 41 (1), pp. 30-42; Bogue, R., Robots in recycling and disassembly (2019) Industrial Robot: The International Journal of Robotics Research and Application, 46 (4), pp. 461-466; Bojanova, I., The digital revolution: what's on the horizon? 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Robot",Review,"Article in Press","",Scopus,2-s2.0-85111820692
"Stock-Homburg R.","56364282100;","Survey of Emotions in Human–Robot Interactions: Perspectives from Robotic Psychology on 20 Years of Research",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00778-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107511010&doi=10.1007%2fs12369-021-00778-6&partnerID=40&md5=a832415e614bf48c0259fade200a457f","Professor for Marketing and HR, Technical University Darmstadt, Hochschulstr. 1, Darmstadt, 64289, Germany","Stock-Homburg, R., Professor for Marketing and HR, Technical University Darmstadt, Hochschulstr. 1, Darmstadt, 64289, Germany","Knowledge production within the interdisciplinary field of human–robot interaction (HRI) with social robots has accelerated, despite the continued fragmentation of the research domain. Together, these features make it hard to remain at the forefront of research or assess the collective evidence pertaining to specific areas, such as the role of emotions in HRI. This systematic review of state-of-the-art research into humans’ recognition and responses to artificial emotions of social robots during HRI encompasses the years 2000–2020. In accordance with a stimulus–organism–response framework, the review advances robotic psychology by revealing current knowledge about (1) the generation of artificial robotic emotions (stimulus), (2) human recognition of robotic artificial emotions (organism), and (3) human responses to robotic emotions (response), as well as (4) other contingencies that affect emotions as moderators. © 2021, The Author(s).","Artificial emotions; Emotions; Human–robot interaction (HRI); Review; Social robots; Survey","Agricultural robots; Robotics; Artificial emotions; Human recognition; Interdisciplinary fields; Knowledge production; Research domains; Robot interactions; Robotic psychology; Systematic Review; Social robots",,,,,"Deutsche Forschungsgemeinschaft, DFG","This research was funded by the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft), Zentrum für verantwortungsbewusste Digitalisierung (ZEVEDI), and the leap in time foundation.",,,,,,,,,,"Abd Latif, M.H., Yusof, H.M., Sidek, S., Rusli, N., Thermal imaging based affective state recognition (2015) 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS). 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"Arora A.S., Fleming M., Arora A., Taras V., Xu J.","35736838900;57221963295;55437932800;35077310500;55506799900;","Finding “H” in HRI: Examining human personality traits, robotic anthropomorphism, and robot likeability in human-robot interaction",2021,"International Journal of Intelligent Information Technologies","17","1",,"19","38",,,"10.4018/IJIIT.2021010102","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100821953&doi=10.4018%2fIJIIT.2021010102&partnerID=40&md5=bb1f99a68cfe0255d02951c2e531081d","University of the District of Columbia, United States; University of North Carolina, Greensboro, United States","Arora, A.S., University of the District of Columbia, United States; Fleming, M., University of the District of Columbia, United States; Arora, A., University of the District of Columbia, United States; Taras, V., University of North Carolina, Greensboro, United States; Xu, J., University of the District of Columbia, United States","The study examines the relationship between the big five personality traits (extroversion, agreeableness, conscientiousness, neuroticism, and openness) and robot likeability and successful HRI implementation in varying human-robot interaction (HRI) situations. Further, this research investigates the influence of human-like attributes in robots (a.k.a. robotic anthropomorphism) on the likeability of robots. The research found that robotic anthropomorphism positively influences the relationship between human personality variables (e.g., extraversion and agreeableness) and robot likeability in human interaction with social robots. Further, anthropomorphism positively influences extraversion and robot likeability during industrial robotic interactions with humans. Extraversion, agreeableness, and neuroticism were found to play a significant role. This research bridges the gap by providing an in-depth understanding of the big five human personality traits, robotic anthropomorphism, and robot likeability in social-collaborative robotics. Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.","Agreeableness; Big Five; Conscientiousness; Extroversion; HRI; HRI Implementation; Neuroticism; Openness; Robot Likeability; Robotic Anthropomorphism; Social-Collaborative Robotics; Uncanny Effect","Economic and social effects; Industrial robots; Man machine systems; Robotics; Human interactions; Human like; Human robot Interaction (HRI); In-depth understanding; Industrial robotics; Likeability; Personality traits; Personality variables; Social robots",,,,,"National Science Foundation, NSF; U.S. Department of Defense, DOD; U.S. Department of Energy, USDOE; National Aeronautics and Space Administration, NASA; U.S. Department of Agriculture, USDA","Jiajun Xu (PhD) is an Associate Professor in the Department of Mechanical Engineering, and Director of the Center for Advanced Manufacturing at the University of the District of Columbia (UDC). He received his Ph.D. in Mechanical Engineering from the University of Maryland-College Park. His current research interests include: in-situ characterization and multiscale modeling of energy transport inside nanostructured materials, thermal management and energy conversion using nano-enhanced phase change materials, in-situ monitoring and process optimization of direct metal laser sintering-based (DMLS) additive manufacturing, and environment-friendly wastewater treatment using nanoparticle-infused mesoporous materials. His research work is sponsored by the National Aeronautics and Space Administration, National Science Foundation, Department of Defense, Department of Energy, and the U.S. Department of Agriculture.","Anshu Saxena Arora, Ph.D., PMP is Associate Professor of Marketing and Director of Logistics and International Trade (LIT) Analytics Center in the School of Business and Public Administration at the University of the District of Columbia. Previously, she worked as the Associate Professor of Marketing at the Jay S. Sidhu School of Business and Leadership, Wilkes University, Wilkes-Barre, PA. Before joining Wilkes University, she was Associate Professor of Marketing and Chairperson of Department of Business (Management, Marketing and Supporting Areas) in the College of Business Administration, Savannah State University, Savannah, Georgia, USA. Dr. Arora was awarded Ph.D. in the area of Consumer Navigation Behavior in Hypermedia CMEs from the Indian Institute of Technology, Delhi, India. She is a Certified Project Management Professional (PMP) from Project Management Institute, USA and holds a Foundation Certificate in IT Service Management from Information Systems Examination Board, UK. In July 2019, Dr. Arora received the prestigious National Science Foundation (NSF) grant for establishing the Logistics & International Trade (LIT) Analytics Center of Excellence and developing STEM-Business focused LIT program at the University of the District of Columbia. She is the Senior Editor of Marketing for the International Journal of Emerging Markets (IJoEM) published by Emerald Publications. She is also the Series Editor for International Marketing and Management Research published by Palgrave Macmillan Publications, UK. Dr. Arora has been a Visiting Professor at ISC Paris Business School in France, University of California, Davis, and Thunderbird School of Global Management at Arizona State University. She has more than a decade of industrial and academic experience. She has worked in companies like Hyundai Motors India Limited, Lufthansa German Airlines, and Siemens India. 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Technologies",Article,"Final","",Scopus,2-s2.0-85100821953
"Duque Domingo J., Gómez-García-Bermejo J., Zalama E.","57190025605;57217391062;56018068000;","Optimization of a Robotics Gaze Control System",2021,"Advances in Intelligent Systems and Computing","1285",,,"213","226",,,"10.1007/978-3-030-62579-5_15","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097379203&doi=10.1007%2f978-3-030-62579-5_15&partnerID=40&md5=967a9e977560dee183d8e61ea0706504","ITAP-DISA, University of Valladolid, Pl. Santa Cruz 8, Valladolid, 47002, Spain; CARTIF Foundation, Boecillo, Valladolid, 47151, Spain","Duque Domingo, J., ITAP-DISA, University of Valladolid, Pl. Santa Cruz 8, Valladolid, 47002, Spain; Gómez-García-Bermejo, J., ITAP-DISA, University of Valladolid, Pl. Santa Cruz 8, Valladolid, 47002, Spain, CARTIF Foundation, Boecillo, Valladolid, 47151, Spain; Zalama, E., ITAP-DISA, University of Valladolid, Pl. Santa Cruz 8, Valladolid, 47002, Spain, CARTIF Foundation, Boecillo, Valladolid, 47151, Spain","Gaze control is an important issue in the interaction between a robot and humans. In particular, the decision of who to pay attention to in a multi-party conversation is a way of improving a robot’s naturalness in human-robot interaction. A system based on a competitive neural network can decide who to look at with a smooth transition in the focus of attention when significant changes in stimuli are produced. One important aspect in this process is the configuration of the different parameters of such a neural network. The weights of the different stimuli have to be computed in order to achieve behavior similar to humans. This article explains how these weights can be obtained by solving an optimization problem. The experiments carried out and some results are also presented. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.","Competitive network; Computer vision; Gaze control; Gaze engagement; HRI; Humanoid robot; ROS","Man machine systems; Neural networks; Robotics; Competitive neural network; Focus of Attention; Gaze control; Multi-party conversations; Optimization problems; Smooth transitions; Human robot interaction",,,,,,,,,,,,,,,,"Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: A review (2017) J. 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Santa Cruz 8, Spain; email: jaime.duque@uva.es","Bergasa L.M.Ocana M.Barea R.Lopez-Guillen E.Revenga P.",,"Springer Science and Business Media Deutschland GmbH","21st International Workshop of Physical Agents, WAF 2020","19 November 2020 through 20 November 2020",,251329,21945357,9783030625788,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85097379203
"Lee A., Jo W., Kannan S.S., Min B.-C.","55662789800;56152510500;57214751493;39161762500;","Investigating the Effect of Deictic Movements of a Multi-Robot",2021,"International Journal of Human-Computer Interaction","37","3",,"197","210",,,"10.1080/10447318.2020.1812908","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090305173&doi=10.1080%2f10447318.2020.1812908&partnerID=40&md5=ffbda5c33f8651691773f7eb99767871","SMART Lab, Department of Computer and Information Technology, Purdue University, West Lafayette, IN, United States; Purdue Polytechnic Institute, Purdue University, West Lafayette, IN, United States","Lee, A., SMART Lab, Department of Computer and Information Technology, Purdue University, West Lafayette, IN, United States, Purdue Polytechnic Institute, Purdue University, West Lafayette, IN, United States; Jo, W., SMART Lab, Department of Computer and Information Technology, Purdue University, West Lafayette, IN, United States; Kannan, S.S., SMART Lab, Department of Computer and Information Technology, Purdue University, West Lafayette, IN, United States; Min, B.-C., SMART Lab, Department of Computer and Information Technology, Purdue University, West Lafayette, IN, United States","While research on human-robot interaction is ongoing as robots become more readily available and easier to use, the study of interactions between a human and a team of multiple robots represents a relatively new field of research. In particular, how multi-robots could be used for everyday users and how the characteristics of multi-robots would affect human perception and cognition has not been explored. In this paper, we specifically focus on physical affordances generated by the movements of multi-robots, and investigate the effects of deictic movements of multi-robots on information retrieval by conducting a delayed free recall task. We conclude with further discussion of how the movements of the multi-robot reshape the way of people perceiving information and what should be considered to design a multi-robot-based display. © 2020 Taylor & Francis Group, LLC.",,"Industrial robots; Machine design; Multipurpose robots; Affordances; Human perception; Multiple robot; Multirobots; Social robots",,,,,"National Science Foundation, NSF: IIS-1846221; Directorate for Computer and Information Science and Engineering, CISE: 1846221","This material is based upon work supported by the National Science Foundation under Grant No. IIS-1846221.",,,,,,,,,,"Ahmad, M.I., Mubin, O., Orlando, J., Adaptive social robot for sustaining social engagement during long-term children–robot interaction (2017) International Journal of Human–Computer Interaction, 33 (12), pp. 943-962. , https://doi.org/10.1080/10447318.2017.1300750; Alonso-Mora, J., Breitenmoser, A., Rufli, M., Siegwart, R., Beardsley, P., Image and animation display with multiple mobile robots (2012) The International Journal of Robotics Research, 31 (6), pp. 753-773. , https://doi.org/10.1177/0278364912442095; Becker, A., Habibi, G., Werfel, J., Rubenstein, M., McLurkin, J., Massive uniform manipulation: Controlling large populations of simple robots with a common input signal (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 520-527. , IEEE, &; Bernardos, A.M., Gómez, D., Casar, J.R., A comparison of head pose and deictic pointing interaction methods for smart environments (2016) International Journal of Human-computer Interaction, 32 (4), pp. 325-351. , https://doi.org/10.1080/10447318.2016.1142054; Biernacki, P., Waldorf, D., Snowball sampling: Problems and techniques of chain referral sampling (1981) Sociological Methods & Research, 10 (2), pp. 141-163. , https://doi.org/10.1177/004912418101000205; Bradski, G., Kaehler, A., (2008) Learning OpenCV: Computer vision with the OpenCV library, , O’Reilly Media, Inc; Braun, V., Clarke, V., Using thematic analysis in psychology (2006) Qualitative Research in Psychology, 3 (2), pp. 77-101. , https://doi.org/10.1191/1478088706qp063oa; Cacace, J., Finzi, A., Lippiello, V., Implicit robot selection for human multi-robot interaction in search and rescue missions (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 803-808. , IEEE, &; Carenini, G., Conati, C., Hoque, E., Steichen, B., Toker, D., Enns, J., Highlighting interventions and user differences: Informing adaptive information visualization support (2014) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1835-1844. , ACM, &; Chong, S.C., Evans, K.K., Distributed versus focused attention (count vs estimate) (2011) Wiley Interdisciplinary Reviews. 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Interact.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85090305173
"Edwards C., Edwards A., Albrehi F., Spence P.","55467888500;17433903500;57218534688;15847046400;","Interpersonal impressions of a social robot versus human in the context of performance evaluations",2021,"Communication Education","70","2",,"165","182",,5,"10.1080/03634523.2020.1802495","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089448146&doi=10.1080%2f03634523.2020.1802495&partnerID=40&md5=2218969b604b881ddfd7c625faffc170","Communication and Social Robotics Labs, School of Communication, Western Michigan University, Kalamazoo, MI, United States; Department of Communication, Wayne State University, Detroit, MI, United States; Communication and Social Robotics Labs, Nicholson School of Communication and Media, University of Central Florida, Orlando, FL, United States","Edwards, C., Communication and Social Robotics Labs, School of Communication, Western Michigan University, Kalamazoo, MI, United States; Edwards, A., Communication and Social Robotics Labs, School of Communication, Western Michigan University, Kalamazoo, MI, United States; Albrehi, F., Department of Communication, Wayne State University, Detroit, MI, United States; Spence, P., Communication and Social Robotics Labs, Nicholson School of Communication and Media, University of Central Florida, Orlando, FL, United States","Extending previous research on the Computers Are Social Actors paradigm and the human-to-human interaction script, this study examines the interpersonal impressions of a social robot evaluator versus a human evaluator in a performance evaluation context. A between-subjects experiment was conducted to measure participants’ impressions of the credibility, task and social attractiveness, and social presence of a social robot or a human evaluator of their public speaking performances. Additionally, participants rated the competence of the feedback they received. Both evaluators (human and social robot) garnered mostly “favorable” interpersonal impressions. Moreover, both agents’ feedback was rated favorably. In all cases, the human evaluator was rated significantly higher than the social robot evaluator. Implications for the use of social robots in educational evaluator roles and for future human–robot interaction research are discussed. © 2020 National Communication Association.","attraction; communication; credibility; human–machine communication; human–robot interaction; social presence",,,,,,,,,,,,,,,,,"Beattie, A., Edwards, A.P., Edwards, C., A bot and a smile: Interpersonal impressions of chatbots and humans using emoji in computer-mediated communication (2020) Communication Studies, pp. 1-19. , https://doi.org/10.1080/10510974.2020.1725082; Biocca, F., Harms, C., Burgoon, J.K., Toward a more robust theory and measure of social presence: Review and suggested criteria (2003) Presence: Teleoperators and Virtual Environments, 12 (5), pp. 456-480. , https://doi.org/10.1162/105474603322761270; Bodie, G.D., A racing heart, rattling knees, and ruminative thoughts: Defining, explaining, and treating public speaking anxiety (2010) Communication Education, 59 (1), pp. 70-105. , https://doi.org/ps://doi.org/10.1080/03634520903443849, –, htt; 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Educ.",Article,"Final","",Scopus,2-s2.0-85089448146
"Choi Y., Oh M., Choi M., Kim S.","55685955800;57211928364;56594110900;57209619163;","Exploring the influence of culture on tourist experiences with robots in service delivery environment",2021,"Current Issues in Tourism","24","5",,"717","733",,12,"10.1080/13683500.2020.1735318","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081200701&doi=10.1080%2f13683500.2020.1735318&partnerID=40&md5=d0b786ec0e3a72cbd23ef3517bec0922","School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Kowloon, Hong Kong; School of Events, Tourism & Hospitality Management, Leeds Beckett University, Leeds, United Kingdom","Choi, Y., School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Kowloon, Hong Kong; Oh, M., School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Kowloon, Hong Kong; Choi, M., School of Events, Tourism & Hospitality Management, Leeds Beckett University, Leeds, United Kingdom; Kim, S., School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Kowloon, Hong Kong","Robots and artificial intelligence represent a newly emerging trend in tourism and hospitality. However, studies examining how cultural perceptions influence tourists’ experiences interacting with service robots are lacking. In response to the industrial trend, the experiential components of robot-staffed hotels are assessed in this study. A qualitative approach is adopted to compare the semantic networks of Japanese and non-Japanese tourists’ online reviews, using 1,498 reviews from nine robot-staffed hotels in Japan. The results indicate that hotel guests’ interaction with robots is one of the main experiential components in robot-staffed hotels. The semantic network analysis results demonstrate noticeable differences, with Japanese reviews demonstrating more emotional responses to human-robot interaction and non-Japanese reviews valuing the functional and technical aspects of robot-provided services more. © 2020 Informa UK Limited, trading as Taylor & Francis Group.","artificial intelligence; human-robot interaction; online reviews; R-tourism; Robot; semantic network analysis","artificial intelligence; culture; network analysis; perception; qualitative analysis; questionnaire survey; robotics; tourism; Japan",,,,,,,,,,,,,,,,"Alexis, P., R-Tourism: Introducing the potential impact of robotics and service automation in tourism (2017) Ovidius University Annals, Series Economic Sciences, 17 (1), pp. 211-216; Ballestar, M.T., Díaz-Chao, Á., Sainz, J., Torrent-Sellens, J., Knowledge, robots and productivity in SMEs: Explaining the second digital wave (2020) Journal of Business Research, 108, pp. 119-131. , https://doi.org/10.1016/j.jbusres.2019.11.017; Bartneck, C., Nomura, T., Kanda, T., Suzuki, T., Kennsuke, K., (2005) Proceedings of the 11th international conference on Human-Computer Interaction International, , July,A cross-cultural study on attitudes towards robots., Las Vegas, NV; Bartneck, C., Suzuki, T., Kanda, T., Nomura, T., The influence of people’s culture and prior experiences with Aibo on their attitude towards robots (2006) Ai & Society, 21 (1-2), pp. 217-230. , https://doi.org/10.1007/s00146-006-0052-7; Bird, J., (2018), https://www.forbes.com/sites/jonbird1/2018/09/04/the-robot-restaurant-revolution-jetsons-like-dream-or-automation-nightmare/#4e4482a66cef, September,The robot restaurant revolution: ‘Jetsons-like’ dream or automation nightmare? 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(Eds; Ziemke, T., Thill, S., Robots are not embodied! Conceptions of embodiment and their implications for social human–robot interaction (2014) Frontiers in Artificial Intelligence and Applications, 273, pp. 49-53. , https://doi.org/10.3233/978-1-61499-480-0-49","Choi, Y.; School of Hotel and Tourism Management, Hong Kong; email: young.choi@polyu.edu.hk",,,"Routledge",,,,,13683500,,,,"English","Curr. Issues Tour.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85081200701
"Funk M., Dieber B., Pichler H., Coeckelbergh M.","57079610200;35223140600;7005305909;22233758600;","Gamification of Trust in HRI?",2020,"Frontiers in Artificial Intelligence and Applications","335",,,"632","642",,,"10.3233/FAIA200972","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098865290&doi=10.3233%2fFAIA200972&partnerID=40&md5=62f78dc78b5118c546261a0e07679e39","Philosophy of Media and Technology, University of Vienna, Austria; Faculty of Computer Science, University of Vienna, Austria; Institute for Robotics and Mechatronics, Joanneum Research, Austria","Funk, M., Philosophy of Media and Technology, University of Vienna, Austria, Faculty of Computer Science, University of Vienna, Austria; Dieber, B., Institute for Robotics and Mechatronics, Joanneum Research, Austria; Pichler, H., Institute for Robotics and Mechatronics, Joanneum Research, Austria; Coeckelbergh, M., Philosophy of Media and Technology, University of Vienna, Austria","In this transdisciplinary paper we discuss the question whether trust in human-robot-interaction (HRI) can be gained by gamification. Therefore, the concept of credibility will be introduced. A specific focus is on the question concerning the implementation of ethical rules in robotic safety systems. With a focus on Wittgenstein as a philosopher of technology, we argue that in many fields of application cultural issues play a crucial role that cannot be controlled in a top-down approach. Instead, we follow a process-oriented bottom-up understanding of trust, which pays attention to different social situations of normative practices. In order to combine our transdisciplinary philosophical and engineering points of view, a model for 'gamifying trust' including ethical reflection as well as a short sketch of a possible technical implementation are presented. © 2020 The authors and IOS Press. All rights reserved.","Annoying Valley; credibility; robot ethics; transdisciplinarity; Trust","Gamification; Philosophical aspects; Robotics; Bottom up; Cultural issue; Human robot Interaction (HRI); Process-oriented; Technical implementation; Top down approaches; Social robots",,,,,"780073; Österreichische Forschungsförderungsgesellschaft, FFG; Baqai Medical University, BMU","This work has been supported by the European Union´s Horizon 2020 research and innovation program under the CSA project “INBOTS—Inclusive Robotics for a better Society” (grant agreement No 780073), by the Austrian Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMU) under the program “CredRoS—Credible and Safe Robot Systems”, and by the Austrian Research Promotion Agency (FFG) under the project “dAIalog.at—Förderung von Fairness und Vertrauen in KI durch Formate der partizipativen Technikgestaltung”. We also want to express our thankfulness to the three anonymous reviewers of an earlier version of this paper, who provided critical remarks and helped to improve the quality.",,,,,,,,,,"Dignum, V., Dignum, F., Vazquez-Salceda, J., Clodic, A., Gentile, M., Mascarenhas, S., Augello, A., Design for values for social robot architectures (2018) Envisioning Robots in Society-Power, Politics, and Public Space, pp. 43-52. , Coeckelbergh M, Loh J, Funk M, Seibt J, Nørskov M, editors. Amsterdam a.o.: IOS Press; Ess, C., Trust, social identity, and computation (2014) The Complexity of Trust, Computing, and Society, pp. 199-226. , Harper R, editor. Cambridge: Cambridge University Press; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De, V.E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Lewis, M., Sycara, K., Walker, P., The role of trust in human-robot interaction (2018) Foundations of Trusted Autonomy, pp. 135-159. , Abbass HA, Scholz J, Reid DJ, editors. Cham: Springer; Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K., (2015) Towards Safe and Trustworthy Social Robots: Ethical Challenges and Practical Issues, pp. 584-593. , Tapus A, André E, Martin J-C, Ferland F, Ammi M, editors. Social Robotics. Cham: Springer International Publishing; Schaefer, K.E., Chen, J.Y.C., Szalmaand, J.L., Hancock, P.A., A Meta-Analysis of factors influencing the development of trust in automation: Implications for understanding autonomy in future systems (2016) Human Factors, 58 (3), pp. 377-400; Tolmeijer, S., Weiss, A., Hanheide, M., Lindner, F., Powers, T.M., Dixon, C., Tielman, M.L., Taxonomy of trust-relevant failures and mitigation strategies (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, , https://dl.acm.org/doi/pdf/10.1145/3319502.3374793, accessed 2020 Apr 23; Seibt, J., Introduction (2014) Sociable Robots and the Future of Social Relations, pp. vii-viii. , Seibt J, Hakli R, Nørskov M, editors. Amsterdam a.o.: IOS Press; Nørskov, M., Editors preface (2016) Social Robots. Boundaries, Potential, Challenges, pp. xv-xxii. , Nørskov M, editor. Farnham, Burlington: Ashgate; Seibt, J., Classifying forms and modes of co-working in the ontology of asymmetric social interactions (OASIS) (2018) Envisioning Robots in Society-Power, Politics, and Public Space, pp. 133-146. , Coeckelbergh M, Loh J, Funk M, Seibt J, Nørskov M, editors. Amsterdam a.o.: IOS Press; Seibt, J., Borggreen, G., Fischer, K., Hasse, C., Liu, H.Y., Nørskov, M., Working with and alongside robots: Forms and modes of co-working (2018) Envisioning Robots in Society-Power, Politics, and Public, Space, pp. 128-132. , Coeckelbergh M, Loh J, Funk M, Seibt J, Nørskov M, editors., Amsterdam a.o.: IOS Press; Coeckelbergh, M., (2012) Growing Moral Relations. Critique of Moral Status Ascription, , New York: Palgrave Macmillan; Ihde, D., (2009) Postphenomenology and Technoscience, , The Peking University Lectures. Albany NY: State University of New York Press; Ropohl, G., (2016) Ethik und Technikbewertung., , Frankfurt a.M.: Suhrkamp; Janich, P., (2006) Was Ist Information? 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Available from:, accessed 2020 Apr 23; Barlow, M., Trusted autonomous game play Foundations of Trusted Autonomy, pp. 125-132. , https://doi.org/10.1007/978-3-319-64816-3_7, Abbass HA, Scholz J, Reid DJ, editors. [Internet]. Cham: Springer International Publishing; 2018 [cited 2020 Oct 23]. (Studies in Systems, Decision, and Control)","Funk, M.; Department of Philosophy, Universitätsstraße 7 (NIG), Austria; email: funkmichael@posteo.de","Norskov M.Seibt J.Quick O.S.",,"IOS Press BV","4th Conference on Robophilosophy 2020: Culturally Sustainable Social Robotics","18 August 2020 through 21 August 2020",,165874,09226389,9781643681542,,,"English","Front. Artif. Intell. Appl.",Conference Paper,"Final","",Scopus,2-s2.0-85098865290
"Kopets E.E., Karimov A.I., Kolev G.Y., Scalera L., Butusov D.N.","57200196143;56702297800;57209465601;57191853169;54388644800;","Interactive robot for playing russian checkers",2020,"Robotics","9","4","107","1","15",,,"10.3390/robotics9040107","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097807886&doi=10.3390%2frobotics9040107&partnerID=40&md5=6ccab284c690cb447b8b6acde3ea3090","Youth Research Institute, ETU LETI, St. Petersburg, 197376, Russian Federation; Polytechnic Department of Engineering and Architecture, University of Udine, Udine, 33100, Italy","Kopets, E.E., Youth Research Institute, ETU LETI, St. Petersburg, 197376, Russian Federation; Karimov, A.I., Youth Research Institute, ETU LETI, St. Petersburg, 197376, Russian Federation; Kolev, G.Y., Youth Research Institute, ETU LETI, St. Petersburg, 197376, Russian Federation; Scalera, L., Polytechnic Department of Engineering and Architecture, University of Udine, Udine, 33100, Italy; Butusov, D.N., Youth Research Institute, ETU LETI, St. Petersburg, 197376, Russian Federation","Human–robot interaction in board games is a rapidly developing field of robotics. This paper presents a robot capable of playing Russian checkers designed for entertaining, training, and research purposes. Its control program is based on a novel unsupervised self-learning algorithm inspired by AlphaZero and represents the first successful attempt of using this approach in the checkers game. The main engineering challenge in mechanics is to develop a board state acquisition system non-sensitive to lighting conditions, which is achieved by rejecting computer vision and utilizing magnetic sensors instead. An original robot face is designed to endow the robot an ability to express its attributed emotional state. Testing the robot at open-air multiday exhibitions shows the robustness of the design to difficult exploitation conditions and the high interest of visitors to the robot. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","AlphaZero; Checkers; Draughts; Human–robot interaction; Robotics",,,,,,,,,,,,,,,,,"Sussman, The, Hall, M., Performingdigital bipolarthe Intelligentsensors Machine:TLE4945LDeceptionlocated underand Enchantmentblack cells inonthetheLifesecondof the AutomatonPCB determineChess Player.the TDR/Thestate of theDramacell Rev.due 1999,to neodymium43, pp. 81-96. , [CrossRef]magnet tokens mounted into each checker. The; (2002) The does Turk: not The matter Life and since Times the of the color Famous and the Eighteenth-Century type of checker Chess-Playing (man or king) Machine; is nonambiguously determined by gameplay, , magnet Standage, polarity T. Walker Books: London, UK, ISBN 0-8027-1391-2; Kovács, G., Petunin, A., Ivanko, J., Yusupova, N., From the first chess-automaton to the mars pathfinder (2016) Acta References Polytech. Hung, 13, pp. 61-81; Gao, M.Y., PerformingSastre-Rienietz,the IntelligentM.; Zell,Machine:A. 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Public Health, 16, p. 5021. , [CrossRef] [PubMed]; Lemaignan, S., Jacq, A., Hood, D., Garcia, F., Paiva, A., Dillenbourg, P., Learning by teaching a robot: The case of handwriting (2016) IEEE Robot. Autom. Mag, 23, pp. 56-66. , [CrossRef]","Karimov, A.I.; Youth Research Institute, Russian Federation; email: aikarimov@etu.ru",,,"MDPI AG",,,,,22186581,,,,"English","Robotics",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85097807886
"Zhou X., He J., Bhushan, Qi W., Hu Y., Dai J., Xu Y.","57190885661;23974252200;57219413325;57194568340;57188985422;57217565414;57219422676;","Hybrid Iμmuscle Signals Powered Teleoperation Control of Serial Manipulator Incorporating Passivity Adaptation",2020,"ICARM 2020 - 2020 5th IEEE International Conference on Advanced Robotics and Mechatronics",,,"9195357","228","233",,1,"10.1109/ICARM49381.2020.9195357","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092649306&doi=10.1109%2fICARM49381.2020.9195357&partnerID=40&md5=209402d3f22c2d4bc5304ed77d60668f","Politecnico di Milano, Department of Electronics, Information and Bioengineering (DEIB), Milan, MI, 20133, Italy; State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, 410012, China; Sapienza University of Rome, Control Engineering at DIAG, Rome, RM, 00185, Italy; Technical University of Munich, Department of Informatics, Munich, 85748, Germany","Zhou, X., Politecnico di Milano, Department of Electronics, Information and Bioengineering (DEIB), Milan, MI, 20133, Italy; He, J., State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, 410012, China; Bhushan, Sapienza University of Rome, Control Engineering at DIAG, Rome, RM, 00185, Italy; Qi, W., Politecnico di Milano, Department of Electronics, Information and Bioengineering (DEIB), Milan, MI, 20133, Italy; Hu, Y., Technical University of Munich, Department of Informatics, Munich, 85748, Germany; Dai, J., State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, 410012, China; Xu, Y., State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha, 410012, China","With the widespread application of robots, Human-Robot Interaction (HRI) is an essential aspect in our daily lives. Especially when there is a great requirement of natural and simple control interface for intuitive controls. However, it is inevitable that the robot will interfere with the external environment while controlling the robot. In this paper, a hybrid Iμmuscle teleoperation control method for a serial manipulator is proposed to control the robot adaptation to the external environment in a safer way. The serial manipulator can be controlled by the proposed interface in real-time, applying forearm-motion estimates based on a hybrid of IMU and EMG signals. Moreover, in the process of HRI, safety can be enhanced by passivity control. To show its effectiveness, the interface is demonstrated in real-time experiments, including obstacle experiment. © 2020 IEEE.",,"Agricultural robots; Industrial manipulators; Motion estimation; Redundant manipulators; Remote control; Robotics; Control interfaces; External environments; Human robot Interaction (HRI); Intuitive controls; Motion estimates; Passivity control; Real-time experiment; Serial manipulators; Human robot interaction",,,,,,,,,,,,,,,,"Kaptelinin, V., Kiselev, A., Loutfi, A., Hellström, T., Robots in contexts: Human-robot interaction as physically and socially embedded (2017) Proceedings of the European Conference on Cognitive Ergonomics 2017, pp. 203-204; Li, Z., Chen, F., Bicchi, A., Sun, Y., Fukuda, T., Guest editorial neuro-robotics systems: Sensing, cognition, learning, and control (2019) IEEE Transactions on Cognitive and Developmental Systems, 11 (2), pp. 145-147; Crandall, J.W., Goodrich, M.A., Characterizing efficiency of human robot interaction: A case study of shared-control teleoperation (2002) IEEE/RSJ International Conference on Intelligent Robots and Systems, 2, pp. 1290-1295; Su, H., Shuai, L., Jagadesh, M., Bascetta, L., Ferrigno, G., De Momi, E., Manipulability optimization control of a serial redundant robot for robot-assisted minimally invasive surgery (2019) ICRA 2019-IEEE International Conference on Robotics and Automation, pp. 1-6; Yao, S., Zhuang, Y., Li, Z., Song, R., Adaptive admittance control for an ankle exoskeleton using an emg-driven musculoskeletal model (2018) Frontiers in Neurorobotics, 12, pp. 16-28; Carey, M.W., Kurz, E.M., Matte, J.D., Perrault, T.D., Padir, T., Novel eod robot design with dexterous gripper and intuitive teleoperation (2012) World Automation Congress 2012, pp. 1-6; Su, H., Yang, C., Mdeihly, H., Rizzo, A., Ferrigno, G., De Momi, E., Neural network enhanced robot tool identification and calibration for bilateral teleoperation (2019) IEEE Access, 7, pp. 122041-122051; Wang, L., Wang, X.V., Safety in human-robot collaborative assembly (2018) Cloud-Based Cyber-Physical Systems in Manufacturing, pp. 211-241. , Springer; Su, H., Qi, W., Yang, C., Aliverti, A., Ferrigno, G., De Momi, E., Deep neural network approach in human-like redundancy optimization for anthropomorphic manipulators (2019) IEEE Access, 7, pp. 124207-124216; Xu, Y., Yang, C., Liu, X., Li, Z., A teleoperated shared control scheme for mobile robot based semg (2018) 2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 288-293; Li, Z., Yuan, Y., Luo, L., Su, W., Zhao, K., Xu, C., Huang, J., Pi, M., Hybrid brain/muscle signals powered wearable walking exoskeleton enhancing motor ability in climbing stairs activity (2019) IEEE Transactions on Medical Robotics and Bionics, 1 (4), pp. 218-227; Li, Z., Li, J., Zhao, S., Yuan, Y., Kang, Y., Chen, C.P., Adaptive neural control of a kinematically redundant exoskeleton robot using brain-machine interfaces (2018) IEEE Transactions on Neural Networks and Learning Systems; Gao, H., Luo, L., Pi, M., Li, Z., Li, Q., Zhao, K., Huang, J., EEG-based volitional control of prosthetic legs for walking in different terrains (2019) IEEE Transactions on Automation Science and Engineering; Meeker, C., Ciocarlie, M., (2018) Emg-controlled Non-anthropomorphic Hand Teleoperation Using A Continuous Teleoperation Subspace; Zhang, H., Zhao, Z., Yu, Y., Gui, K., Sheng, X., Zhu, X., A feasibility study on an intuitive teleoperation system combining imu with semg sensors (2018) International Conference on Intelligent Robotics and Applications, pp. 465-474; Su, H., Qi, W., Hu, Y., Sandoval, J., Zhang, L., Schmirander, Y., Chen, G., Ferrigno, G., Towards model-free tool dynamic identification and calibration using multi-layer neural network (2019) Sensors, 19 (17), p. 3636; Franken, M., Stramigioli, S., Misra, S., Secchi, C., Macchelli, A., Bilateral telemanipulation with time delays: A two-layer approach combining passivity and transparency (2011) IEEE Transactions on Robotics, 27 (4), pp. 741-756; Desoer, C.A., Vidyasagar, M., Feedback systems: Input-output properties (1975) Siam, 55; Schaft, A.V.D., (1999) L2-gain and Passivity in Nonlinear Control, , Springer-Verlag; Hogan, N., Impedance control-an approach to manipulation. i-theory. II-implementation. III-applications (1985) ASME Transactions Journal of Dynamic Systems and Measurement Control B, 107, pp. 1-24. , Mar; Ferraguti, F., Secchi, C., Fantuzzi, C., A tank-based approach to impedance control with variable stiffness (2013) 2013 IEEE International Conference on Robotics and Automation, pp. 4948-4953; Ferraguti, F., Preda, N., Manurung, A., Bonfe, M., Lambercy, O., Gassert, R., Muradore, R., Secchi, C., An energy tankbased interactive control architecture for autonomous and teleoperated robotic surgery (2015) IEEE Transactions on Robotics, 31 (5), pp. 1073-1088; Schindlbeck, C., Haddadin, S., Unified passivity-based cartesian force/impedance control for rigid and flexible joint robots via taskenergy tanks (2015) 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 440-447; Hogan, N., Controlling impedance at the man/machine interface (1989) Proceedings, 1989 International Conference on Robotics and Automation, pp. 1626-1631; Mokaram, S., Aitken, J.M., Martinez-Hernandez, U., Eimontaite, I., Cameron, D., Rolph, J., Gwilt, I., Law, J., A rosintegrated api for the kuka lbr iiwa collaborative robot (2017) IFACPapersOnLine, 50 (1), pp. 15859-15864; Panariello, D., Caporaso, T., Grazioso, S., Di Gironimo, G., Lanzotti, A., Knopp, S., Pelliccia, L., Klimant, P., Using the kuka lbr iiwa robot as haptic device for virtual reality training of hip replacement surgery (2019) 2019 Third IEEE International Conference on Robotic Computing (IRC), pp. 449-450; Schreiber, G., Stemmer, A., Bischoff, R., The fast research interface for the kuka lightweight robot (2010) IEEE Workshop on Innovative Robot Control Architectures for Demanding (Research) Applications How to Modify and Enhance Commercial Controllers (ICRA 2010), pp. 15-21; Sathiyanarayanan, M., Rajan, S., Myo armband for physiotherapy healthcare: A case study using gesture recognition application (2016) 2016 8th International Conference on Communication Systems and Networks (COMSNETS), pp. 1-6; Su, H., Ovur, S.E., Zhou, X., Qi, W., Ferrigno, G., De Momi, E., Depth vision guided hand gesture recognition using electromyographic signals (2020) Advanced Robotics",,,"Anhui Robotics Society;Artificial Intelligence and Robotics Association of Guangdong Province;Deltason;IEEE/CAA Journal of Automatica Sinica","Institute of Electrical and Electronics Engineers Inc.","5th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2020","18 December 2020 through 21 December 2020",,163161,,9781728164793,,,"English","ICARM - IEEE Int. Conf. Adv. Robot. Mechatronics",Conference Paper,"Final","",Scopus,2-s2.0-85092649306
"Chakraborty S., Timoney J.","57221833067;24578248900;","Robot Human Synchronization for Musical Ensemble: Progress and Challenges",2020,"2020 5th International Conference on Robotics and Automation Engineering, ICRAE 2020",,,"9310916","93","99",,1,"10.1109/ICRAE50850.2020.9310916","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100341977&doi=10.1109%2fICRAE50850.2020.9310916&partnerID=40&md5=4194a7e5f598729399a717021cbdee13","Maynooth University, Dept. of Computer Science, Ireland","Chakraborty, S., Maynooth University, Dept. of Computer Science, Ireland; Timoney, J., Maynooth University, Dept. of Computer Science, Ireland","Human beings are principally efficient at synchronizing and predicting within a swiftly altering realtime environment. For instance, various activities that involve harmonized movements, like catching a ball and dancing, needs firm decision making, based on partial information. However, the synchronization of tasks offers serious difficulty for the current architecture of robots. This is even more difficult in Human-robot interactions. A potential solution is to consider all entities as coupled oscillators. Then, Kuramoto's synchronous oscillator concept should then be applicable in an adaptive way. However, in real-life, uncertain sensory environments creates huge challenges. This paper reviews the progress of human robot synchronization for a musical performance. The results are presented in a quantitative manner. The paper provides a discussion of the research findings, along with an indication of the major future implications. © 2020 IEEE.","complex system; Music Ensemble; robot control; synchronization","Agricultural robots; Decision making; Robotics; Synchronization; Coupled oscillators; Human being; Human robots; Musical performance; Partial information; Real-time environment; Human robot interaction",,,,,,,,,,,,,,,,"Solis, J., Petersen, K., Ninomiya, T., Takeuchi, M., Takanishi, A., Development of anthropomorphic musical performance robots: From understanding the nature of music performance to its application to entertainment robotics (2009) 2009 Ieee/rsj International Conference on Intelligent Robots and Systems, pp. 2309-2314. , October, ieee; Weinberg, G., Driscoll, S., The interactive robotic percussionist: New developments in form, mechanics, perception and interaction design (2007) Proceedings of the Acm/ieee International Conference on Human-robot Interaction, pp. 97-104. , March, acm; Weinberg, G., Driscoll, S., Toward robotic musicianship (2006) Computer Music Journal, 30 (4), pp. 28-45; Mizumoto, T., Takeda, R., Yoshii, K., Komatani, K., Ogata, T., Okuno, H.G., A robot listens to music and counts its beats aloud by separating music from counting voice (2008) 2008 Ieee/rsj International Conference on Intelligent Robots and Systems, pp. 1538-1543. , September, ieee; Simsekli, U., Sönmez, O., Kurt, B.K., Cemgil, A.T., Combined perception and control for timing in robotic music performances (2012) Eurasip Journal on Audio, Speech, and Music Processing, 2012 (1), p. 8; Oliveira, J.L., Ince, G., Nakamura, K., Nakadai, K., Online audio beat tracking for a dancing robot in the presence of ego-motion noise in a real environment (2012) 2012 Ieee International Conference on Robotics and Automation, pp. 403-408. , May, ieee; Oliveira, J.L., Nakamura, K., Langlois, T., Gouyon, F., Nakadai, K., Lim, A., Reis, L.P., Okuno, H.G., Making a robot dance to diverse musical genre in noisy environments (2014) 2014 Ieee/rsj International Conference on Intelligent Robots and Systems, pp. 1896-1901. , September, ieee; Kitchenham, B., Charters, S., (2007) Guidelines for Performing Systematic Literature Reviews in Software Engineering; Petticrew, M., Roberts, H., (2008) Systematic Reviews in the Social Sciences: A Practical Guide, , John Wiley & Sons; Acebrón, J.A., Bonilla, L.L., Vicente, C.J.P., Ritort, F., Spigler, R., The kuramoto model: A simple paradigm for synchronization phenomena (2005) Reviews of Modern Physics, 77 (1), p. 137; Desmet, F., Leman, M., Lesaffre, M., De Bruyn, L., Statistical analysis of human body movement and group interactions in response to music (2009) Advances in Data Analysis, Data Handling and Business Intelligence, pp. 399-408. , Springer, Berlin, Heidelberg; Dikker, S., Wan, L., Davidesco, I., Kaggen, L., Oostrik, M., McClintock, J., Rowland, J., Poeppel, D., Brain-to-brain synchrony tracks real-world dynamic group interactions in the classroom (2017) Current Biology, 27 (9), pp. 1375-1380; Gil Martínez, A.D., (2019) Modelling Human-robot Interactions As a Network of Kuramoto 'S Oscillators, , Master's thesis, Universitat Politècnica de Catalunya; Goebl, W., Palmer, C., Synchronization of timing and motion among performing musicians. Music perception (2009) An Interdisciplinary Journal, 26 (5), pp. 427-438; Hennig, H., Synchronization in human musical rhythms and mutually interacting complex systems (2014) Proceedings of the National Academy of Sciences, 111 (36), pp. 12974-12979; Hoffman, G., Weinberg, G., Gesture-based human-robot jazz improvisation (2010) 2010 Ieee International Conference on Robotics and Automation, pp. 582-587. , May, IEEE; Huang, K.Y., Challenges in human-computer interaction design for mobile devices (2009) Proceedings of the World Congress on Engineering and Computer Science, 1, pp. 236-241. , October; Holzinger, A., Human-computer interaction and knowledge discovery (hci-kdd): What is the benefit of bringing those two fields to work together? (2013) International Conference on Availability, Reliability, and Security, , Springer, Berlin, Heidelberg; Hu, Y., Brain-to-brain synchronization across two persons predicts mutual prosociality (2017) Social Cognitive and Affective Neuroscience, 12 (12), pp. 1835-1844; Kapur, A., Singer, E., Benning, M.S., Tzanetakis, G., Integrating hyper instruments, musical robots & machine musicianship for north indian classical music (2007) Proceedings of the 7th International Conference on New Interfaces for Musical Expression, pp. 238-241. , June, ACM; Keller, P.E., Musical ensemble synchronisation (2007) Proceedings of the Inaugural International Conference on Music Communication Science, pp. 80-83; Murata, K., Nakadai, K., Takeda, R., Okuno, H.G., Torii, T., Hasegawa, Y., Tsujino, H., A beat-tracking robot for human-robot interaction and its evaluation (2008) Humanoids 2008-8th IEEE-RAS International Conference on Humanoid Robots, pp. 79-84. , December, IEEE; Otsuka, T., Mizumoto, T., Nakadai, K., Takahashi, T., Komatani, K., Ogata, T., Okuno, H.G., Music-ensemble robot that is capable of playing the theremin while listening to the accompanied music (2010) International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 102-112. , June, Springer, Berlin, Heidelberg; Patel, A.D., Iversen, J.R., Bregman, M.R., Schulz, I., Schulz, C., Investigating the human-specificity of synchronization to music (2008) Proceedings of the 10th International Conference on Music and Cognition. Sapporo, Japan, pp. 100-104. , August; Rodriguez, E., George, N., Lachaux, J.P., Martinerie, J., Renault, B., Varela, F.J., Perception's shadow: Long-distance synchronization of human brain activity (1999) Nature, 397 (6718), p. 430; Shiu, Y., Cho, N., Chang, P.C., Kuo, C.C.J., Robust on-line beat tracking with kalman filtering and probabilistic data association (kf-pda) (2008) Ieee Transactions on Consumer Electronics, 54 (3), pp. 1369-1377; Suzuki, K., Camurri, A., Ferrentino, P., Hashimoto, S., Intelligent agent system for human-robot interaction through artificial emotion (1998) SMC'98 Conference Proceedings. 1998 Ieee International Conference on Systems, Man, and Cybernetics (Cat. No. 98CH36218), 2, pp. 1055-1060. , October, IEEE; Volpe, G., D'Ausilio, A., Badino, L., Camurri, A., Fadiga, L., Measuring social interaction in music ensembles (2016) Philosophical Transactions of the Royal Society B: Biological Sciences, 371 (1693), p. 20150377; Bradley, K.W., Stone, P., Breazeal, C., Training a robot via human feedback: A case study (2013) International Conference on Social Robotics, , Springer, Cham; Heggli, O.A., A kuramoto model of self-other integration across interpersonal synchronization strategies (2019) Plos Computational Biology, 15 (10), p. e1007422; Ardila, L.R., Adaptive fuzzy and predictive controllers for expressive robot arm movement during human and environment interaction (2019) International Journal of Mechanical Engineering and Robotics Research, 8 (2); Goto, K., A method for driving humanoid robot based on human gesture (2020) International Journal of Mechanical Engineering and Robotics Research, 9 (3); Goto, K., A method for driving humanoid robot based on human gesture (2020) International Journal of Mechanical Engineering and Robotics Research, 9 (3); Iqbal, T., Moosaei, M., Riek, L.D., Tempo adaptation and anticipation methods for human-robot teams (2016) RSS, Planning HRI: Shared Autonomy Collab. Robot. Workshop",,,,"Institute of Electrical and Electronics Engineers Inc.","5th International Conference on Robotics and Automation Engineering, ICRAE 2020","20 November 2020 through 22 November 2020",,166395,,9781728189819,,,"English","Int. Conf. Robot. Autom. Eng., ICRAE",Conference Paper,"Final","",Scopus,2-s2.0-85100341977
"Yelwande S.S., Dandavate R.M.","57221596507;57220890142;","Study of Emotion Recognition Models for Socially Aware Robots and Subsequent Path Mapping",2020,"Proceedings of the 4th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2020",,,"9297630","1230","1233",,1,"10.1109/ICECA49313.2020.9297630","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099568249&doi=10.1109%2fICECA49313.2020.9297630&partnerID=40&md5=fb559d1113df2e104d8128a972d627fa","Sinhgad College of Engineering, Electronics and Telecommunication Department, Pune, India","Yelwande, S.S., Sinhgad College of Engineering, Electronics and Telecommunication Department, Pune, India; Dandavate, R.M., Sinhgad College of Engineering, Electronics and Telecommunication Department, Pune, India","Globally various robots working in harmony to perform a variety of tasks it is extremely important to map a path for hassle-free performance without restrictions and smooth movement even in the most complex situations. A common example of such complex situations is a robot moving in a crowded place full of human beings. The bot must find its way amidst these circumstances without disturbing the overall environmental setup. One such unique method for this to happen is human-robot interaction based on emotions. In this paper, various methods are discussed and present a substantial comparison between different methods of socially aware robotics based on facial expressions and emotions. The methods incorporated are GAIT based emotion learning, Affective Cognitive Learning, and Decision-Making model, and Pleasure-Arousal-Dominance Model. © 2020 IEEE.","Affective Cognitive Learning and Decision-Making model (ACLDM); Emotion Recognition; GAIT based learning; Pleasure-Arousal-Dominance (PAD)","Decision making; Cognitive learning; Decision making models; Emotion recognition; Facial Expressions; Human being; Robots working; Smooth movement; Social robots",,,,,,,,,,,,,,,,"Bera, A., Randhavane, T., Manocha, D., Modelling multichannel emotions using facial expression and t rajectory cues for improving sociallyaware Robot Navigation 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW; Zhang, H., Liu, S., Design of autonomous navigation system based on affective cognit ive learning and decision-making (2009) International Conference on Robotics and Biomimetics, December 19-23; Narayanan, V., Murali Manoghar, B., Ssahank Dorbala, V., Manocha, D., Bera, A., (2020) ProxEmo: Gait-based Emotion Learning and Multi-view Proxemic Fusion for SociallyAware Robot Navigation; Kahn, G., Villaflor, A., Ding, B., Abbeel, P., Levine, S., Self-supervised deep reinforcement learning with generalized computation graphs for robot navigation (2018) IEEE International Conference on Robotics and Automation (ICRA); Mehrabian, A., (1996) PleasureArousal-dominance. Current Psychology, 14, pp. 261-292; Kruse, T., Kumar Pandey, A., Alami, R., Kirsch, A., HumanAware robot navigation: A survey (2013) Robot Ics and Autonomous Systems, 61 (12), pp. 1726-1743; Thias Luber, M., Spinello, L., Silva, J., Arras, K.O., SociallyAware robot navigation: A learning Approach (2012) IROS; Ekman, P., Facial expression and emotion (1993) American 1 Psychologist; Ahn, H., Picard, R.W., AffectiveCognit ive learningAnd decision making: The Role of Emotions The 18 Th European Meet Ing on Cybernet Ics and Systems Research (EMCSR 2006), Vienna, Austria; Pandey, A.K., Alami, R., Framework towards a socially aware mobile robot motion in humanCentered dynamic environment (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct; Mit Tal, T., Bhattacharya, U., Chandra, R., Bera, A., Manocha, D., M2er: Multiplicative multimodal Emotion recognition using facial, textual, and speechCues (2020) AAAI; Ranganathan, G., An economical robot ic armplaying chess using visual servoing (2020) Journal of Innovat Ive Image Processing (JIIP), 2 (3), pp. 141-146; Pandian, A.P., Artificial intelligence application in smart warehousing environment for automated logist ics (2019) Journal of Art Ificial Intelligence, 1 (2), pp. 63-72; Chiu, M., Shu, J., Hui, P., Emotion recognition through gait on mobile devices (2018) 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), March, pp. 800-805",,,,"Institute of Electrical and Electronics Engineers Inc.","4th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2020","5 November 2020 through 7 November 2020",,166221,,9781728163871,,,"English","Proc. Int. Conf. Electron., Commun. Aerosp. Technol., ICECA",Conference Paper,"Final","",Scopus,2-s2.0-85099568249
"Velasco-Guillen R.J., Grosu V., Carmona-Ortiz V.A., Vanderborght B., Lefeber D., Font-Llagunes J.M., Beckerle P.","57219802929;36727530600;57219806154;57191490554;55161380600;26534251200;36964076200;","A Stiffness-Fault-Tolerant Control Strategy for an Elastically Actuated Powered Knee Orthosis",2020,"Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics","2020-November",,"9224403","660","665",,,"10.1109/BioRob49111.2020.9224403","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095588015&doi=10.1109%2fBioRob49111.2020.9224403&partnerID=40&md5=1374ddbfdc08385c57bf08fcf1b9921c","Technische Universität Dortmund, Elastic Lightweight Robotics Group, Dortmund, Germany; Vrije Universiteit Brussel and Flanders Make, Robotics and MultiBody Mechanics Research Group, Brussels, Belgium; Universitat Politècnica de Catalunya, Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Barcelona, Spain; Institute for Mechatronic Systems, Technische Universität Darmstadt, Darmstadt, Germany","Velasco-Guillen, R.J., Technische Universität Dortmund, Elastic Lightweight Robotics Group, Dortmund, Germany; Grosu, V., Vrije Universiteit Brussel and Flanders Make, Robotics and MultiBody Mechanics Research Group, Brussels, Belgium; Carmona-Ortiz, V.A., Universitat Politècnica de Catalunya, Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Barcelona, Spain; Vanderborght, B., Vrije Universiteit Brussel and Flanders Make, Robotics and MultiBody Mechanics Research Group, Brussels, Belgium; Lefeber, D., Vrije Universiteit Brussel and Flanders Make, Robotics and MultiBody Mechanics Research Group, Brussels, Belgium; Font-Llagunes, J.M., Universitat Politècnica de Catalunya, Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Barcelona, Spain; Beckerle, P., Technische Universität Dortmund, Elastic Lightweight Robotics Group, Dortmund, Germany, Institute for Mechatronic Systems, Technische Universität Darmstadt, Darmstadt, Germany","Elastic actuators can provide safe human-robot interaction and energy efficient mobility. For this purpose they are ideal for wearable robotic applications. However, such actuators are subject to stiffness faults. We present a stiffness-fault-tolerant control strategy for complex elastic actuators, capable of adapting to changes in output stiffness, and demonstrate it on a smart variable stiffness actuator based on the MACCEPA concept. We develop the dynamics of the actuator and a model-based impedance control scheme. Biomechanical data extracted from the flexion/extension of a real knee joint are used as trajectory reference for the evaluation of the control concept in simulation. Results show that the controlled actuator is capable of tracking a reference trajectory under fault conditions and interaction disturbance while maintaining physical human-robot characteristics. © 2020 IEEE.",,"Actuators; Agricultural robots; Energy efficiency; Fault tolerance; Joints (anatomy); Physiological models; Robotics; Stiffness; Biomechanical data; Elastic actuators; Fault tolerant control; Impedance control; Reference trajectories; Robotic applications; Safe human-robot interaction; Variable stiffness; Human robot interaction",,,,,"Deutsche Forschungsgemeinschaft, DFG: BE 5729/1","*This work was supported by a Deutsche Forschungsgemeinschaft (DFG) Research Grant (no. BE 5729/1).",,,,,,,,,,"Verstraten, T., Beckerle, P., Furnémont, R., Mathijssen, G., Vanderborght, B., Lefeber, D., Series and parallel elastic actuation: Impact of natural dynamics on power and energy consumption (2016) Mechanism and Machine Theory, 102, pp. 232-246. , aug; Filippini, R., Sen, S., Bicchi, A., Toward soft robots you can depend on (2008) IEEE Robotics Automation Magazine, 15 (3), pp. 31-41; Vanderborght, B., Albu-Schaeffer, A., Bicchi, A., Burdet, E., Caldwell, D., Carloni, R., Catalano, M., Wolf, S., Variable impedance actuators: A review (2013) Robotics and Autonomous Systems, 61 (12), pp. 1601-1614. , Dec; Beckerle, P., Verstraten, T., Mathijssen, G., Furnemont, R., Vanderborght, B., Lefeber, D., Series and parallel elastic actuation: Influence of operating positions on design and control (2017) IEEE/ASME Transactions on Mechatronics, 22 (1), pp. 521-529. , Feb; Beckerle, P., Practical relevance of faults, diagnosis methods, and tolerance measures in elastically actuated robots (2016) Control Engineering Practice, 50, pp. 95-100. , May; Mrak, B., Lenaerts, B., Driesen, W., Desmet, W., Optimal magnetic spring for compliant actuation-validated torque density benchmark (2019) Actuators, 8 (1), p. 18. , Multidisciplinary Digital Publishing Institute; Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M., (2010) Diagnosis and Fault-Tolerant Control, , Springer; Ott, C., (2008) Cartesian Impedance Control of Redundant and Flexible-joint Robots, , Springer; Rocon, E., Ruiz, A., Raya, R., Schiele, A., Pons, J.L., Belda-Lois, J., Poveda, R., Moreno, J., Human-robot physical interaction (2008) Wearable Robots: Biomechatronic Exoskeletons, pp. 127-163; Hogan, N., Impedance control: An approach to manipulation (1984) 1984 American Control Conference. IEEE, pp. 304-313; Menard, T., Grioli, G., Bicchi, A., A stiffness estimator for agonistic-antagonistic variable-stiffness-actuator devices (2014) IEEE Transactions on Robotics, 30 (5), pp. 1269-1278. , Oct; Flacco, F., De Luca, A., Sardellitti, I., Tsagarakis, N.G., Online estimation of variable stiffness in flexible robot joints (2012) The International Journal of Robotics Research, 31 (13), pp. 1556-1577. , Nov; Stuhlenmiller, F., Velasco-Guillen, R.J., Rinderknecht, S., Beckerle, P., (2019) Fault-tolerant Physical Human-robot Interaction Via Stiffness Adaptation of Elastic Actuators, , paper presented at Human-Friendly Robotics Workshop, Reggio Emilia, Italy; Stuhlenmiller, F., Perner, G., Rinderknecht, S., Beckerle, P., A stiffness-fault-tolerant control strategy for reliable physical humanrobot interaction (2019) Human Friendly Robotics, pp. 3-14. , Springer International Publishing; Ham, R.V., Vanderborght, B., Damme, M.V., Verrelst, B., Lefeber, D., Maccepa, the mechanically adjustable compliance and controllable equilibrium position actuator: Design and implementation in a biped robot (2007) Robotics and Autonomous Systems, 55 (10), pp. 761-768; Bacek, T., Moltedo, M., Rodriguez-Guerrero, C., Geeroms, J., Vanderborght, B., Lefeber, D., Design and evaluation of a torquecontrollable knee joint actuator with adjustable series compliance and parallel elasticity (2018) Mechanism and Machine Theory, 130, pp. 71-85; Andersson, S., Söderberg, A., Björklund, S., Friction models for sliding dry, boundary and mixed lubricated contacts (2007) Tribology International, 40 (4), pp. 580-587; Crowder, R., (2006) Electric Drives and Electromechanical Systems: Applications and Control, , Elsevier; Collins, J.A., Busby, H.R., Staab, G.H., (2009) Mechanical Design of Machine Elements and Machines: A Failure Prevention Perspective, , John Wiley & Sons; Rodriguez-Cianca, D., Weckx, M., Jimenez-Fabian, R., Torricelli, D., Gonzalez-Vargas, J., Sanchez-Villamañan, M., Sartori, M., Lefeber, D., A variable stiffness actuator module with favorable mass distribution for a bio-inspired biped robot (2019) Frontiers in Neurorobotics, 13, p. 20; Lee, E.T., Wang, J.W., (2003) Appendix A: Newton-Raphson Method, pp. 428-432. , John Wiley & Sons, Ltd; Liu, Y., Anxi, Y., Zhu, J., Liang, D., Unscented kalman filtering in the additive noise case (2010) Science China Technological Sciences, 53, pp. 929-941. , 04; Winter, D.A., (2009) Biomechanics and Motor Control of Human Movement, , 4th ed. John Wiley & Sons, Ltd",,,,"IEEE Computer Society","8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020","29 November 2020 through 1 December 2020",,164150,21551774,9781728159072,,,"English","Proc. IEEE RAS EMBS Int. Conf. Biomed. Rob. Biomechatronics, BioRob",Conference Paper,"Final","",Scopus,2-s2.0-85095588015
"Jang J., Park J.H.","57221048821;36064300200;","Parameter identification of an unknown object in human-robot collaborative manipulation",2020,"International Conference on Control, Automation and Systems","2020-October",,"9268257","1086","1091",,,"10.23919/ICCAS50221.2020.9268257","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098087839&doi=10.23919%2fICCAS50221.2020.9268257&partnerID=40&md5=c881d08fec844863e0121fd90ef4832f",,"Jang, J.; Park, J.H.","In human-robot collaborative manipulation of an object, if the robot knows the intention of the human, the efficiency of the work would greatly increase. For the robot to know of the human intention, it should have the information of the force applied by the human, which can be more accurately if it can estimate the inertial and dimensional parameters online. However, the force applied by the human will disturb the parameter identification process. This paper presents a strategy to identify the inertial and dimensional parameters of an unknown object online for physical human-robot interactions. Extended Kalman filter is used for identification under the assumption that the force applied by the human is an unknown external disturbance. This approach was evaluated in simulations of physical human-robot object manipulation task. © 2020 Institute of Control, Robotics, and Systems - ICROS.","Extended Kalman Filter; Human-Robot Collaboration; Parameter Identification; Physical Human-Robot Interaction; Recognizing the Intention of the Human","Kalman filters; Parameter estimation; Dimensional parameters; External disturbances; Human intentions; Human robots; Object manipulation; Physical human-robot interactions; Unknown objects; Human robot interaction",,,,,"Ministry of Science, ICT and Future Planning, MSIP; National Research Council of Science and Technology, NST","research project, ""Development of core machinery technologies for autonomous operation and manufacturing"", which has been supported by a grant from National Research Council of Science & Technology under the R&D Program of Ministry of Science, ICT and Future Planning.",,,,,,,,,,"Atkeson, C.G., An, C.H., Hollerbach, J.M., Estimation of inertial parameters of manipulator loads and links (1986) The International Journal of Robotics Research, 5 (3); Ikeura, R., Inooka, H., Variable impedance control of a robot for cooperation with a human (1995) IEEE International Conference on Robotics and Automation, pp. 3097-3102; Tsumugiwa, T., Yokogawa, R., Hara, K., Variable impedance control based on estimation of human arm stiffness for human-robot cooperative calligraphic task (2002) IEEE International Conference on Robotics and Automation, pp. 644-650; Duchaine, V., Gosselin, C.M., General model of human-robot cooperation using a novel velocity based variable impedance con-trol (2007) IEEE EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, pp. 446-451; Gribovskaya, E., Kheddar, A., Billard, A., Motion learning and adaptive impedance for robot control during physical interaction with humans (2011) IEEE International Conference on Robotics and Automation, pp. 4326-4332; Dimeas, F., Asoragatgis, N., Fuzzy learning variable admittance control for human-robot cooperation (2014) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4770-4776; Du, Z., Wang, W., Yan, Z., Dong, W., Wang, W., Variable admittance control based fuzzy reinforcement learning for minimally invasive surgery manipulator (2017) Sensors, 17 (4), pp. 1-15; Sharkawy, A.N., Koustournpardis, P.N., Aspragathos, N., Variable admittance control for human-robot collaboration based on online neural network training (2018) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1334-1339; Khalil, W., Dombre, E., (2002) Modeling, Identification and Control of Robots, , Tatlor and Francis Books; Radkhah, K., Kulic, D., Croft, E., Dynamic parameter identification for the crs a460 robot (2007) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3842-3847; Wu, J., Wang, J., You, Z., An overview of dynamic parameter identification of robots (2010) Robots and Computer-Integrated Manufacturing, 26 (5), pp. 414-419; Ljung, L., (1999) System Identification: Theory for the User, , Prentice-Hall; Choi, J.S., Yoon, J.H., Park, J.H., Kim, P.J., A numerical algorithm to identify independent grouped parameters of robot manipulator for control (2011) Advanced Intelligent Mechatronics, IEEE/ASME International Conference on, pp. 373-378; Traversaro, S., Brossette, S., Escande, A., Nori, F., Identification of fully physical consistent inertial parameters using optimization on manifolds (2016) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5446-5451; Wensing, P.M., Kim, S., Slotine, J.J.E., Linear matrix inequalities for physically consistent inertial parameter identification: A statistical perspective on the mass distribution (2018) IEEE Robotics and Automation Letters, 3 (1), pp. 60-67; Cehajic, D., Dohmann, P.B., Hirche, S., Estimating unknown object dynamics in humanrobot manipulation tasks (2017) IEEE International Conference on Robotics and Automation, pp. 1730-1737; Wenzel, T.A., Burnham, K.J., Blundell, M.V., Williams, R.A., Dual extended kalman filter for vehicle state and parameter estimation (2006) Vehicle System Dynamics, 44 (2), pp. 153-171; Best, M.C., Newton, A.P., Tuplin, S., The identifying extended kalman filter: Parametric system identification of a vehicle handling model (2007) Proceedings of the Institution of Mechanical Engineers Part K Journal of Multi-body Dynamics, 221 (1), pp. 87-98; Hong, S., Lee, C., Borrelli, F., Hendrick, J.K., A novel approach for vehicle inertial parameter identification using a dual kalman filter (2015) Intelligent Transportation Systems, IEEE Transactions on, 16 (1), pp. 151-160; Yu, Z., Wang, J., Simultaneous estimation of vehicle's center of gravity and inertial parameters based on ackermann's steering geometry (2017) ASME Transactions on Journal of Dynamic Systems, Measurement, and Control, 139 (3), p. 031006; Keesman, K.J., (2011) System Identification: An Introduction, , Springer; Adewusi, S., Thomas, M., Hung, V.V., Natural frequencies of the hand-arm system using finite element method (2012) American Conference on Human Vibration, pp. 17-18; Xu, Y., Hollerbach, J.M., Identification of human joint mechanical properties from single trial data (1998) IEEE Transactions on Biomedical Engineering, 45 (8), pp. 1051-1060","Park, J.H.email: jongpark@hanyang.ac.kr
Park, J.H.email: jongpark@hanyang.ac.kr",,,"IEEE Computer Society","20th International Conference on Control, Automation and Systems, ICCAS 2020","13 October 2020 through 16 October 2020",,165535,15987833,9788993215205,,,"English","Int. Conf. Control, Autom. Syst.",Conference Paper,"Final","",Scopus,2-s2.0-85098087839
"Gochoo M., Vogan A.A., Khalid S., Alnajjar F.","56038574900;57212061191;57212061480;14526943700;","AI and Robotics-Based Cognitive Training for Elderly: A Systematic Review",2020,"2020 IEEE / ITU International Conference on Artificial Intelligence for Good, AI4G 2020",,,"9311076","129","134",,,"10.1109/AI4G50087.2020.9311076","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100345302&doi=10.1109%2fAI4G50087.2020.9311076&partnerID=40&md5=d571fa9fa6f3e5d08b75be728f36038a","College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates; University College, United Arab Emirates University, Al Ain, United Arab Emirates","Gochoo, M., College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates; Vogan, A.A., University College, United Arab Emirates University, Al Ain, United Arab Emirates; Khalid, S., College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates; Alnajjar, F., College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates","AI has evolved and is now being used widely in various sectors. Applications involving AI have been growing exponentially, especially in sectors of health and education. Advanced elderly cognitive training and performance improvement are soon going to be a necessity to meet the requirements of growing elderly population. Technology is advancing rapidly and AI in robotics can now be involved in cognitive training and elderly care. This review, first examines the existing research available for robots used in elderly care; be it form of cognitive training or affective therapy. Second, examines the effects of human robot interaction as a strategy for elderly to combat cognitive decline and improve cognitive function. And finally, puts light on the future of AI in cognitive training and how AI can be utilized to solve many challenges currently faced. © 2020 IEEE.","Artificial intelligence; cognitive decline; elderly cognitive training; robotics","Human robot interaction; Robotics; Cognitive decline; Cognitive functions; Cognitive training; Elderly care; Elderly populations; Systematic Review; Educational robots",,,,,,,,,,,,,,,,"Goralski, M.A., Tan, T.K., Artificial intelligence and sustainable development (2020) The International Journal of Management Education, 18 (1), p. 100330; Alnajjar, F.S., Renawi, A.M., Cappuccio, M., Mubain, O., A low-cost autonomous attention assessment system for robot intervention with autistic children (2019) 2019 Ieee Global Engineering Education Conference (EDUCON), , Apr; Al Braiki, B., Artificial intelligence in education and assessment methods (2020) Bulletin of Electrical Engineering and Informatics, 9 (5), pp. 1998-2007. , October; Marr, B., (2018) What Is Artificial Intelligence and How Will It Change Our World?, , https://www.bernardmarr.com/default.asp?contentID=963, Last accessed: February 2020; Barr, N., Pennycook, G., Stolz, J.A., Fugelsang, J.A., The brain in your pocket: Evidence that smartphones are used to supplant thinking (2015) Computers in Human Behavior, 48, pp. 473-480; Abdi, J., Al-Hindawi, A., Ng, T., Vizcaychipi, M.P., Scoping review on the use of socially assistive robot technology in elderly care (2018) Bmj Open, 8 (2), p. e018815. , Feb; (2015) United Nations: World Population Prospects the 2015 Revisions, , http://esa.un.org/unpd/wpp, Available online at, Last accessed: January 2020; McColl, D., Louie, W.-Y.G., Nejat, G., Brian 2.1: A socially assistive robot for the elderly and cognitively impaired (2013) Ieee Robotics & Automation Magazine, 20 (1), pp. 74-83. , Mar; Bozoki, A., Radovanovic, M., Winn, B., Heeter, C., Anthony, J.C., Effects of a computer-based cognitive exercise program on age-related cognitive decline (2013) Archives of Gerontology and Geriatrics, 57 (1), pp. 1-7. , Jul; Dementia Statistics, Alzeihmer's Disease International, , https://www.alz.co.uk/research/statistics, Available online at; Ten Brinke, L.F., Best, J.R., Crockett, R.A., Liu-Ambrose, T., The effects of an 8-week computerized cognitive training program in older adults: A study protocol for a randomized controlled trial (2018) Bmc Geriatrics, 18 (1). , Jan; Willis, S.L., Tennstedt, S.L., Marsiske, M., Ball, K., Elias, J., Koepke, K.M., Morris, J.N., Wright, E., Long-term effects of cognitive training on everyday functional outcomes in older adults (2006) Jama, 296 (23), p. 2805. , and for the ACTIVE Study Group, Dec; Kelly, M.E., Loughrey, D., Lawlor, B.A., Robertson, I.H., Walsh, C., Brennan, S., The impact of cognitive training and mental stimulation on cognitive and everyday functioning of healthy older adults: A systematic review and meta-Analysis (2014) Ageing Research Reviews, 15, pp. 28-43. , May; Rebok, G.W., Ball, K., Guey, L.T., Jones, R.N., Kim, H.-Y., King, J.W., Marsiske, M., Willis, S.L., Ten-year effects of the advanced cognitive training for independent and vital elderly cognitive training trial on cognition and everyday functioning in older adults (2014) Journal of the American Geriatrics Society, 62 (1), pp. 16-24. , Jan; Kueider, A.M., Parisi, J.M., Gross, A.L., Rebok, G.W., Computerized cognitive training with older adults: A systematic review (2012) PLoS One, 7 (7), p. 40588. , Jul; Boot, W.R., Kramer, A.F., (2014) The Brain-Games Conundrum: Does Cognitive Training Really Sharpen the Mind?; Corbett, A., Owen, A., Hampshire, A., Grahn, J., Stenton, R., Dajani, S., Burns, A., Ballard, C., The effect of an online cognitive training package in healthy older adults: An online randomized controlled trial (2015) Journal of the American Medical Directors Association, 16 (11), pp. 990-997. , Nov; Zhang, H., Wang, Z., Wang, J., Lyu, X., Wang, X., Liu, Y., Zeng, X., Yu, X., Computerized multi-domain cognitive training reduces brain atrophy in patients with amnestic mild cognitive impairment (2019) Translational Psychiatry, 9 (1). , Jan; Alnajjar, F., Khalid, S., Vogan, A.A., Shimoda, S., Nouchi, R., Kawashima, R., Emerging cognitive intervention technologies to meet the needs of an aging population: A systematic review (2019) Frontiers in Aging Neuroscience, 11. , Oct; Vogan, A.A., Alnajjar, F., Gochoo, M., Khalid, S., Robots, ai, and cognitive training in an era of mass age-related cognitive decline: A systematic review (2020) Ieee Access, 8, pp. 18284-18304; Feingold Polak, R., Elishay, A., Shachar, Y., Stein, M., Edan, Y., Levy Tzedek, S., Differences between young and old users when interacting with a humanoid robot (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction-HRI '18; Kim, G.H., Jeon, S., Im, K., Kwon, H., Lee, B.H., Kim, G.Y., Jeong, H., Na, D.L., Structural brain changes after traditional and robot-Assisted multi-domain cognitive training in community-dwelling healthy elderly (2015) Plos One, 10 (4), p. 0123251. , Apr; Tanaka, M., Ishii, A., Yamano, E., Ogikubo, H., Okazaki, M., Kamimura, K., Konishi, Y., Watanabe, Y., Effect of a human-type communication robot on cognitive function in elderly women living alone (2012) Medical Science Monitor, 18 (9), pp. CR550-CR557; Pino, O., Palestra, G., Trevino, R., De Carolis, B., The humanoid robot nao as trainer in a memory program for elderly people with mild cognitive impairment (2019) International Journal of Social Robotics, , Feb; Jøranson, N., Pedersen, I., Rokstad, A.M.M., Ihlebaek, C., Change in quality of life in older people with dementia participating in paro-Activity: A cluster-randomized controlled trial (2016) Journal of Advanced Nursing, 72 (12), pp. 3020-3033. , Aug; Soler, V.M., Agüera-Ortiz, L., Rodríguez, J.O., Rebolledo, C.M., Muñoz, P., Pérez, A.R., Social robots in advanced dementia (2015) Front. Aging Neurosci., 7, p. 133; Otake-Matsuura, M., Tokunaga, S., Watanabe, K., Abe, M.S., Sekiguchi, T., Sugimoto, H., Kishimoto, T., Kudo, T., (2019) Photo-Integrated Conversation Moderated by Robots for Cognitive Health in Older Adults: A Randomized Controlled Trial, , Aug; Liang, A., Piroth, I., Robinson, H., MacDonald, B., Fisher, M., Nater, U.M., Skoluda, N., Broadbent, E., A pilot randomized trial of a companion robot for people with dementia living in the community (2017) Journal of the American Medical Directors Association, 18 (10), pp. 871-878. , Oct; Takayanagi, K., Kirita, T., Shibata, T., Comparison of verbal and emotional responses of elderly people with mild/moderate dementia and those with severe dementia in responses to seal robot, paro (2014) Frontiers in Aging Neuroscience, 6. , Sep; Klimova, B., Computer-based cognitive training in aging (2016) Frontiers in Aging Neuroscience, 8. , Dec; Broekens, J., Heerink, M., Rosendal, H., Assistive social robots in elderly care: A review (2009) Gerontechnology, 8 (2). , Apr; Calo, C.J., Hunt-Bull, N., Lewis, L., Metzler, T., Ethical implications of using the paro robot with a focus on dementia patient care,"" (2011) 25th Assoc. Advancement Artif. Intell. (AAAI) Conf. Artif. Intell., Hum.-Robot Interact. Eldercare, , San Francisco, CA, USA, Aug; Giullian, N., Ricks, D., Atherton, A., Colton, M., Goodrich, M., Brinton, B., Detailed requirements for robots in autism therapy (2010) 2010 Ieee International Conference on Systems, Man and Cybernetics, , Oct; Atherton, J.A., Goodrich, M.A., Supporting clinicians in robot assisted therapy for autism spectrum disorder: Creating and editing robot animations with full-body motion tracking"" (2011) Robot. Sci. 2011 Syst. Workshop Hum.-Robot Interact., Perspect. Contrib. Robot. Hum.Sci, , Los Angeles, CA, USA, Jun","Alnajjar, F.; College of Information Technology, United Arab Emirates; email: fady.alnajjar@uaeu.ac.ae",,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE / ITU International Conference on Artificial Intelligence for Good, AI4G 2020","21 September 2020 through 25 September 2020",,166493,,9781728170312,,,"English","IEEE / ITU Int. Conf. Artif. Intell. Good, AI4G",Conference Paper,"Final","",Scopus,2-s2.0-85100345302
"Skarga-Bandurova I., Siriak R., Biloborodova T., Cuzzolin F., Bawa V.S., Mohamed M.I., Dinesh Jackson Samuel R.","56946900400;57210371049;57195589622;57203968719;57215592780;57219372359;57211886149;","Surgical hand gesture prediction for the operating room",2020,"Studies in Health Technology and Informatics","273",,,"97","103",,,"10.3233/SHTI200621","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092430853&doi=10.3233%2fSHTI200621&partnerID=40&md5=eb2a0daacc1f152c1bbb4cbb679cf084","Oxford Brookes University, United Kingdom; Volodymyr Dahl East Ukrainian National University, Ukraine","Skarga-Bandurova, I., Oxford Brookes University, United Kingdom; Siriak, R., Volodymyr Dahl East Ukrainian National University, Ukraine; Biloborodova, T., Volodymyr Dahl East Ukrainian National University, Ukraine; Cuzzolin, F., Oxford Brookes University, United Kingdom; Bawa, V.S., Oxford Brookes University, United Kingdom; Mohamed, M.I., Oxford Brookes University, United Kingdom; Dinesh Jackson Samuel, R., Oxford Brookes University, United Kingdom","Technological advancements in smart assistive technology enable navigating and manipulating various types of computer-aided devices in the operating room through a contactless gesture interface. Understanding surgeon actions is crucial to natural human-robot interaction in operating room since it means a sort of prediction a human behavior so that the robot can foresee the surgeon's intention, early choose appropriate action and reduce waiting time. In this paper, we present a new deep network based on Convolution Long Short-Term Memory (ConvLSTM) for gesture prediction configured to provide natural interaction between the surgeon and assistive robot and improve operating-room efficiency. The experimental results prove the capability of reliably recognizing unfinished gestures on videos. We quantitatively demonstrate the latter ability and the fact that GestureConvLSTM improves the baseline system performance on LSA64 dataset. © 2020 The authors and IOS Press.","ConvLSTM; GestureConvLSTM; Hand gesture; Operating room; Prediction; Surgeon","Behavioral research; Forecasting; Nanotechnology; Operating rooms; Robotic surgery; Wearable technology; Assistive robots; Assistive technology; Baseline systems; Computer-aided device; Gesture interfaces; Human behaviors; Natural interactions; Technological advancement; Human robot interaction; computer; computer interface; gesture; human; operating room; self help device; Computers; Gestures; Humans; Operating Rooms; Self-Help Devices; User-Computer Interface",,,,,"Horizon 2020 Framework Programme, H2020: 779813; Horizon 2020","This paper is supported by European Union Horizon 2020 research and innovation program under grant agreement No 779813, project SARAS (Smart Autonomous Robotic Assistant Surgeon).",,,,,,,,,,"Bezemer, J., (2010) Gesture beyond Conversation Handbook of Multimodal Analysis, , ed. C. Jewitt; López-Casado, C., Bauzano, E., Rivas-Blanco, I., Pérez-Del-Pulgar, C.J., Muñoz, V.F., A gesture recognition algorithm for hand-Assisted laparoscopic surgery (2019) Sensors (Basel)., 19 (23), p. 5182. , Nov 26;, PMID: 31779237; PMCID: PMC6929113; Lee, A.R., Cho, Y., Jin, S., Kim, N., Enhancement of surgical hand gesture recognition using a capsule network for a contactless interface in the operating room (2020) Computer Methods and Programs in Biomedicine., 190, p. 105385. , Jul; Van Amsterdam, B., Clarkson, M.J., Stoyanov, D., Multi-task reccurent neural network for surgical gesture recognition and progress prediction 2020 Ieee International Conference on Robotics and Automation (ICRA), , https://arxiv.org/pdf/2003.04772.pdf; Tang, H., Wang, W., Xu, D., Yan, Y., Sebe, N., Gesturegan for hand gesture-to-gesture translation in the wild 2018 Acm Multimedia Conference (MM '18, , https://doi.org/10.1145/3240508.3240704, October 22-26, 2018 Seoul, Republic of Korea. ACM, New York, NY, USA; Xingjian, S.H.I., Convolutional lstm network: A machine learning approach for precipitation nowcasting (2015) Advances in Neural Information Processing Systems.; Keras, , https://keras.io/examples/conv-lstm; Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R., (2012) Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors, , 3 Jul; Siriak, R., Skarga-Bandurova, I., Boltov, Y., Deep convolutional network with long short-term memory layers for dynamic gesture recognition (2019) 10th Ieee International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) 2019, 1, pp. 158-162; LeCun, Y., Bengio, Y., Convolutional networks for images, speech, and time series (1995) The Handbook of Brain Theory and Neural Networks, 3361 (10), p. 1995; Google Collaboration Using Gpu Graphics, , https://colab.research.google.com/notebooks/intro.ipynb]; Ronchetti, F., Quiroga, F., Estrebou, C., Lanzarini, L., Rosete, A., Lsa64: An argentinian sign language dataset (2016) Xx Ii Congreso Argentino de Ciencias de la Computación (CACIC); Aggarwal, C.C., Hinneburg, A., Keim, D.A., On the surprising behavior of distance metrics in high dimensional space (2001) International Conference on Database Theory, pp. 420-434. , Springer, Berlin, Heidelberg","Skarga-Bandurova, I.; School of Engineering, United Kingdom; email: iskargabandurova@brookes.ac.uk","Blobel B.Lhotska L.Pharow P.Sousa F.",,"IOS Press BV","17th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2020","14 September 2020 through 16 September 2020",,162917,09269630,9781643681122,,"33087597","English","Stud. Health Technol. Informatics",Conference Paper,"Final","",Scopus,2-s2.0-85092430853
"Buisan G., Sarthou G., Bit-Monnot A., Clodic A., Alami R.","57204733130;57212393872;56035400000;14053492500;7003803131;","Efficient, Situated and Ontology based Referring Expression Generation for Human-Robot collaboration",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223485","349","356",,1,"10.1109/RO-MAN47096.2020.9223485","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095781043&doi=10.1109%2fRO-MAN47096.2020.9223485&partnerID=40&md5=fba2be49013f95833aa013c23f9d4579","Université de Toulouse, Cnrs, LAAS-CNRS, Toulouse, France","Buisan, G., Université de Toulouse, Cnrs, LAAS-CNRS, Toulouse, France; Sarthou, G., Université de Toulouse, Cnrs, LAAS-CNRS, Toulouse, France; Bit-Monnot, A., Université de Toulouse, Cnrs, LAAS-CNRS, Toulouse, France; Clodic, A., Université de Toulouse, Cnrs, LAAS-CNRS, Toulouse, France; Alami, R., Université de Toulouse, Cnrs, LAAS-CNRS, Toulouse, France","In Human-Robot Interaction (HRI), ensuring nonambiguous communication between the robot and the human is a key point for carrying out fluently a collaborative task. With this work, we propose a method which allows the robot to generate the optimal set of assertions that are necessary in order to produce an unambiguous reference. In this paper, we present a novel approach to the Referring Expression Generation (REG) problem and its integration into a robotic system. Our method is a domain-independent approach based on an ontology as a knowledge base. We show how this generation can be performed on an ontology which is not dedicated to this task. We then validate our method through simulated situations, compare it with state of the art approach and on a real robotic system. © 2020 IEEE.",,"Agricultural robots; Knowledge based systems; Ontology; Robotics; Collaborative tasks; Domain-independent approach; Human robot Interaction (HRI); Human-robot collaboration; Knowledge base; Referring expressions; Robotic systems; State-of-the-art approach; Social robots",,,,,"Allen Institute for Artificial Intelligence, AI2; Agence Nationale de la Recherche, ANR: ANR-16-CE33-0017","ACKNOWLEDGEMENT Authors want to thank Amandine Mayima for her work on the integrated scenario. This work has been funded by the French Agence Nationale de la Recherche JointAction4HRI project ANR-16-CE33-0017 and the Artificial and Natural Intelligence Toulouse Institute (ANITI).",,,,,,,,,,"Beetz, M., Bálint-Benczédi, F., Blodow, N., Nyga, D., Wiedemeyer, T., Márton, Z.-C., Robosherlock: Unstructured information processing for robot perception (2015) IEEE ICRA; Dale, R., Cooking up referring expressions (1989) 27th Annual Meeting of the Association for Computational Linguistics; Dale, R., (1992) Generating Referring Expressions: Constructing Descriptions in A Domain of Objects and Processes, , The MIT Press; Dale, R., Reiter, E., Computational interpretations of the gricean maxims in the generation of referring expressions (1995) Cognitive Science, 19 (2); FitzGerald, N., Artzi, Y., Zettlemoyer, L., Learning distributions over logical forms for referring expression generation (2013) Proc. of the Conf. on Empirical Methods in Natural Language Processing; Fokoue, A., Kershenbaum, A., Ma, L., Schonberg, E., Srinivas, K., The summary abox: Cutting ontologies down to size (2006) The Semantic Web-ISWC. Springer Berlin Heidelberg, p. 4273; Krahmer, E., Erk S, V., Verleg, A., Graph-based generation of referring expressions (2003) Computational Linguistics, 29 (1); Krahmer, E., Van Deemter, K., Computational generation of referring expressions: A survey (2012) Computational Linguistics, 38 (1); Lemaignan, S., Ros, R., Sisbot, E.A., Alami, R., Beetz, M., Grounding the interaction: Anchoring situated discourse in everyday human-robot interaction (2012) Int. Journal of Social Robotics, 4 (2); Li, S., (2017) Automatically Evaluating and Generating Clear Robot Explanations, , Master's thesis, Carnegie Mellon Uni., Pittsburgh, PA; Li, S., Scalise, R., Admoni, H., Rosenthal, S., Srinivasa, S.S., Spatial references and perspective in natural language instructions for collaborative manipulation (2016) IEEE RO-MAN; Miller, R.B., Response time in man-computer conversational transactions (1968) Proceedings of the December 9-11, Fall Joint Computer Conference, Part i, , Association for Computing Machinery; Reiter, E., Dale, R., (2000) Building Natural Language Generation Systems, , Cambridge University Press; Ros, R., Lemaignan, S., Sisbot, E.A., Alami, R., Steinwender, J., Hamann, K., Warneken, F., Which one? Grounding the referent based on efficient human-robot interaction (2010) IEEE RO-MAN; Sarthou, G., Clodic, A., Alami, R., Ontologenius : A long-term semantic memory for robotic agents (2019) IEEE RO-MAN; Viethen, J., Mitchell, M., Krahmer, E., Graphs and spatial relations in the generation of referring expressions (2013) Proceedings of the 14th European Workshop on Natural Language Generation; Williams, T., Scheutz, M., A framework for resolving open-world referential expressions in distributed heterogeneous knowledge bases (2016) Thirtieth AAAI Conference on Artificial Intelligence; Williams, T., Scheutz, M., Referring expression generation under uncertainty: Algorithm and evaluation framework (2017) Proc. of the 10th International Conference on Natural Language Generation; Williams, T., Yazdani, F., Suresh, P., Scheutz, M., Beetz, M., Dempster-shafer theoretic resolution of referential ambiguity (2019) Autonomous Robots, 43 (2); Yamakata, Y., Kawahara, T., Okuno, H.G., Minoh, M., Belief network based disambiguation of object reference in spoken dialogue system (2004) Transactions of the Japanese Society for Artificial Intelligence, 19",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85095781043
"Monaikul N., Abbasi B., Rysbek Z., Di Eugenio B., Zefran M.","56405627600;57190124754;57202321417;6603126702;7003754282;","Role Switching in Task-Oriented Multimodal Human-Robot Collaboration",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223461","1150","1156",,,"10.1109/RO-MAN47096.2020.9223461","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095777567&doi=10.1109%2fRO-MAN47096.2020.9223461&partnerID=40&md5=536f548f38633a27a02a021c4f83901e","University of Illinois at Chicago, Natural Language Processing Lab, Chicago, IL  60607, United States; California State University Channel Islands, Computer Science Department, Camarillo, CA  93012, United States","Monaikul, N., University of Illinois at Chicago, Natural Language Processing Lab, Chicago, IL  60607, United States; Abbasi, B., California State University Channel Islands, Computer Science Department, Camarillo, CA  93012, United States; Rysbek, Z., University of Illinois at Chicago, Natural Language Processing Lab, Chicago, IL  60607, United States; Di Eugenio, B., University of Illinois at Chicago, Natural Language Processing Lab, Chicago, IL  60607, United States; Zefran, M., University of Illinois at Chicago, Natural Language Processing Lab, Chicago, IL  60607, United States","In a collaborative task and the interaction that accompanies it, the participants often take on distinct roles, and dynamically switch the roles as the task requires. A domestic assistive robot thus needs to have similar capabilities. Using our previously proposed Multimodal Interaction Manager (MIM) framework, this paper investigates how role switching for a robot can be implemented. It identifies a set of primitive subtasks that encode common interaction patterns observed in our data corpus and that can be used to easily construct complex task models. It also describes an implementation on the NAO robot that, together with our original work, demonstrates that the robot can take on different roles. We provide a detailed analysis of the performance of the system and discuss the challenges that arise when switching roles in human-robot interactions. © 2020 IEEE.",,"Agricultural robots; Assistive robots; Collaborative tasks; Complex task; Human-robot collaboration; Interaction pattern; Multi-Modal Interactions; Role switching; Task-oriented; Social robots",,,,,"National Science Foundation, NSF: 1705058, 1762924, CMMI-1762924, IIS-1705058","∗First three authors contributed equally to this work. 1N. Monaikul and B. Di Eugenio are with the Natural Language Processing Lab, Computer Science Department, University of Illinois at Chicago, Chicago, IL 60607, USA. 2B. Abbasi is with the Computer Science Department, California State University Channel Islands, Camarillo, CA 93012, USA. 3Z. Rysbek and Miloˇs Zˇefran are with the Robotics Lab, Electrical and Computer Engineering Department, University of Illinois at Chicago, Chicago, IL 60607, USA. This work has been supported by the National Science Foundation grants IIS-1705058 and CMMI-1762924.",,,,,,,,,,"Abbasi, B., Monaikul, N., Rysbek, Z., Di Eugenio, B., Zefran, M., A multimodal human-robot interaction manager for assistive robots (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 6756-6762; Chen, L., Javaid, M., Di Eugenio, B., Zefran, M., The roles and recognition of haptic-ostensive actions in collaborative multimodal human-human dialogues (2015) Computer Speech & Language, 34 (1), pp. 201-231; Huang, C.-M., Mutlu, B., Learning-based modeling of multimodal behaviors for humanlike robots (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction, Ser. HRI '14. ACM; Lee, J.K., Breazeal, C., Human social response toward humanoid robot's head and facial features (2010) CHI'10 Extended Abstracts on Human Factors in Computing Systems. ACM, pp. 4237-4242; Salem, M., Eyssel, F., Rohlfing, K., Kopp, S., Joublin, F., To err is human (-like): Effects of robot gesture on perceived anthropomorphism and likability (2013) International Journal of Social Robotics, 5 (3), pp. 313-323; Hemminghaus, J., Kopp, S., Towards adaptive social behavior generation for assistive robots using reinforcement learning (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, Ser. HRI '17. ACM, pp. 332-340; Erol, K., Hendler, J., Nau, D.S., HTN planning: Complexity and expressivity (1994) AAAI, 94, pp. 1123-1128; Russell, S.J., Norvig, P., (2016) Artificial Intelligence: A Modern Approach, , Malaysia; Pearson Education Limited; Nooraei, B., Rich, C., Sidner, C.L., A real-time architecture for embodied conversational agents: Beyond turn-taking (2014) ACHI, 14, pp. 381-388; Mohseni-Kabir, A., Rich, C., Chernova, S., Sidner, C.L., Miller, D., Interactive hierarchical task learning from a single demonstration (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 205-212; Clair, A.S., Saldanha, C., Boteanu, A., Chernova, S., Interactive hierarchical task learning via crowdsourcing for robot adaptability (2016) Refereed Workshop Planning for Human-Robot Interaction: Shared Autonomy and Collaborative Robotics at Robotics: Science and Systems, Ann Arbor, Michigan. RSS; Peng, B., Li, X., Li, L., Gao, J., Celikyilmaz, A., Lee, S., Wong, K.-F., Composite task-completion dialogue policy learning via hierarchical deep reinforcement learning (2017) Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP); Gopalan, N., Tellex, S., Modeling and solving human-robot collaborative tasks using pomdps (2015) RSSWorkshop on Model Learning for Human-Robot Communication; Papadimitriou, C.H., Tsitsiklis, J.N., The complexity of markov decision processes (1987) Mathematics of Operations Research, 12 (3), pp. 441-450; Boutilier, C., Planning, learning and coordination in multiagent decision processes (1996) Proceedings of the 6th Conference on Theoretical Aspects of Rationality and Knowledge, pp. 195-210. , Morgan Kaufmann Publishers Inc; Gasíc, M., Kim, D., Tsiakoulis, P., Young, S., Distributed dialogue policies for multi-domain statistical dialogue management (2015) 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 5371-5375; Freedy, A., Sert, O., Freedy, E., McDonough, J., Weltman, G., Tambe, M., Gupta, T., Cabrera, P., Multiagent adjustable autonomy framework (maaf) for multirobot, multi-human teams (2008) 2008 International Symposium on Collaborative Technologies and Systems, pp. 498-505. , May; Martinson, E., Arkin, R., Learning to role-switch in multirobot systems (2003) 2003 IEEE International Conference on Robotics and Automation (Cat. No. 03CH37422), 2, pp. 2727-2734. , Sep. 2; Hsu, H.C.-H., Liu, A., A flexible architecture for navigation control of a mobile robot (2007) IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 37 (3), pp. 310-318. , May; McMillen, C., Veloso, M., Distributed, play-based role assignment for robot teams in dynamic environments (2006) Distributed Autonomous Robotic Systems 7, pp. 145-154. , M. Gini and R. Voyles, Eds. Tokyo: Springer Japan; Bussy, A., Gergondet, P., Kheddar, A., Keith, F., Crosnier, A., Proactive behavior of a humanoid robot in a haptic transportation task with a human partner (2012) 2012 IEEE ROMAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication, pp. 962-967. , Sep; Evrard, P., Kheddar, A., Homotopy-based controller for physical human-robot interaction (2009) RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 1-6. , Sep; Thobbi, A., Gu, Y., Sheng, W., Using human motion estimation for human-robot cooperative manipulation (2011) 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2873-2878. , Sep; Chu-Carroll, J., Brown, M.K., Tracking initiative in collaborative dialogue interactions (1997) Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics, pp. 262-270; Balkanski, C., Hurault-Plantet, M., Cooperative requests and replies in a collaborative dialogue model (2000) International Journal of Human-Computer Studies, 53, pp. 915-968; Pennington, J., Socher, R., Manning, C., Glove: Global vectors for word representation (2014) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532-1543; Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C., Mobilenetv2: Inverted residuals and linear bottlenecks (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510-4520; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778; Devlin, J., Chang, M.-W., Lee, K., Toutanova, K., (2018) Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding; Hirst, G., McRoy, S., Heeman, P., Edmonds, P., Horton, D., Repairing conversational misunderstandings and nonunderstandings (1994) Speech Communication, 15 (3-4), pp. 213-229",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095777567
"Ye S., Feigh K., Howard A.","57193499397;15831610200;26643170000;","Learning in Motion: Dynamic Interactions for Increased Trust in Human-Robot Interaction Games",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223437","1186","1189",,,"10.1109/RO-MAN47096.2020.9223437","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095735091&doi=10.1109%2fRO-MAN47096.2020.9223437&partnerID=40&md5=74bfb142371d9fab0e75afbaa05d3b0c","Georgia Institute of Technology, Atlanta, GA, United States","Ye, S., Georgia Institute of Technology, Atlanta, GA, United States; Feigh, K., Georgia Institute of Technology, Atlanta, GA, United States; Howard, A., Georgia Institute of Technology, Atlanta, GA, United States","Embodiment of actions and tasks has typically been analyzed from the robot's perspective where the robot's embodiment helps develop and maintain trust. However, we ask a similar question looking at the interaction from the human perspective. Embodied cognition has been shown in the cognitive science literature to produce increased social empathy and cooperation. To understand how human embodiment can help develop and increase trust in human-robot interactions, we created conducted a study where participants were tasked with memorizing greek letters associated with dance motions with the help of a humanoid robot. Participants either performed the dance motion or utilized a touch screen during the interaction. The results showed that participants' trust in the robot increased at a higher rate during human embodiment of motions as opposed to utilizing a touch screen device. © 2020 IEEE.",,"Agricultural robots; Anthropomorphic robots; Man machine systems; Touch screens; Cognitive science; Dynamic interaction; Embodied cognition; Greek letters; Human perspectives; Humanoid robot; Social robots",,,,,,,,,,,,,,,,"Barsalou, L.W., Niedenthal, P.M., Barbey, A.K., Ruppert, J.A., Social embodiment (2003) The Psychology of Learning and Motivation: Advances in Research and Theory, 43, pp. 43-92. , Elsevier Science, Barsalou, Lawrence W. : Emory University, Dept of Psychology, Atlanta, GA, US, 30322 barsalou@emory. edu; Bartneck, C., Kulic, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2008) International Journal of Social Robotics, 1, pp. 71-81; Breazeal, C., Gray, J., Berlin, M., An embodied cognition approach to mindreading skills for socially intelligent robots (2009) The International Journal of Robotics Research, 28 (5), pp. 656-680; Dijksterhuis, A., Bargh, J.A., The perception-behavior expressway: Automatic effects of social perception on social behavior (2001) Volume 33 of Advances in Experimental Social Psychology, pp. 1-40. , Academic Press; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, C.J.Y., Visser De, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Lee, J.D., See, K.A., Trust in automation: Designing for appropriate reliance (2004) Human Factors, 46 (1), pp. 50-80; Madhavan, P., Wiegmann, D.A., Similarities and differences between human-human and human-automation trust: An integrative review (2007) Theoretical Issues in Ergonomics Science, 8 (4), pp. 277-301; Meltzoff, A.N., Elements of a developmental theory of imitation (2002) The Imitative Mind: Development, Evolution, and Brain Bases., Cambridge Studies in Cognitive Perceptual Development., pp. 19-41. , Cambridge University Press, New York, NY, US; Rambusch, J., Ziemke, T., The role of embodiment in situated learning (2005) Proceedings of the 27th Annual Conference of the Cognitive Science Society : CogSci05, pp. 1803-1808. , University of Skövde, School of Humanities and Informatics; Schaefer, K.E., (2016) Measuring Trust in Human Robot Interactions: Development of the Trust Perception Scale-HRI, pp. 191-218. , Springer US, Boston, MA; Gregory Trafton, J., Hiatt, L.M., Harrison, A.M., Tamborello, F.P., Khemlani, S.S., Schultz, A.C., ACT-r/e: An embodied cognitive architecture for human-robot interaction (2013) J. Hum.-Robot Interact., 2 (1), pp. 30-55. , feb; Wainer, J., Feil-Seifer, D.J., Shell, D.A., Mataric, M.J., The role of physical embodiment in human-robot interaction (2006) ROMAN 2006-The 15th IEEE International Symposium on Robot and Human Interactive Communication, pp. 117-122; Wilson, R.A., Foglia, L., Embodied cognition (2017) Edward N Zalta, Editor, the Stanford Encyclopedia of Philosophy, , Metaphysics Research Lab, Stanford University, spring 2017 edition",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095735091
"Mistry K., Rizvi B., Rook C., Iqbal S., Zhang L., Joy C.P.","56421741200;57201979799;57219548946;56937895600;56290594900;57204468914;","A Multi-Population FA for Automatic Facial Emotion Recognition",2020,"Proceedings of the International Joint Conference on Neural Networks",,,"9207516","","",,,"10.1109/IJCNN48605.2020.9207516","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093823373&doi=10.1109%2fIJCNN48605.2020.9207516&partnerID=40&md5=e6e48864c9e1ac9b988a2abdc1e69894","Northumbria University, Dept. of Computer Information Sciences, Newcastle, United Kingdom; Newcastle University, Transitional and Clinical Institute, Newcastle, United Kingdom; Teesside University, Dept. of Computer Science Information System, Middlesbrough, United Kingdom","Mistry, K., Northumbria University, Dept. of Computer Information Sciences, Newcastle, United Kingdom; Rizvi, B., Northumbria University, Dept. of Computer Information Sciences, Newcastle, United Kingdom; Rook, C., Northumbria University, Dept. of Computer Information Sciences, Newcastle, United Kingdom; Iqbal, S., Newcastle University, Transitional and Clinical Institute, Newcastle, United Kingdom; Zhang, L., Northumbria University, Dept. of Computer Information Sciences, Newcastle, United Kingdom; Joy, C.P., Teesside University, Dept. of Computer Science Information System, Middlesbrough, United Kingdom","Automatic facial emotion recognition system is popular in various domains such as health care, surveillance and human-robot interaction. In this paper we present a novel multi-population FA for automatic facial emotion recognition. The overall system is equipped with horizontal vertical neighborhood local binary patterns (hvnLBP) for feature extraction, a novel multi-population FA for feature selection and diverse classifiers for emotion recognition. First, we extract features using hvnLBP, which are robust to illumination changes, scaling and rotation variations. Then, a novel FA variant is proposed to further select most important and emotion specific features. These selected features are used as input to the classifier to further classify seven basic emotions. The proposed system is evaluated with multiple facial expression datasets and also compared with other state-of-the-art models. © 2020 IEEE.","facial expression recognition; feature optimization; local binary pattern","Feature extraction; Neural networks; Pattern recognition systems; Social robots; Speech recognition; Basic emotions; Emotion recognition; Facial emotions; Facial Expressions; Illumination changes; Local binary patterns; Multi population; State of the art; Face recognition",,,,,,,,,,,,,,,,"Zhang, L., Fielding, B., Kinghorn, P., Mistry, K., A vision enriched intelligent agent with image description generation (2016) Proceedings of International Conference of Autonomous Agents and Multiagent Systems., , Singapore; G'Mussel, A.S., Hewig, J., The value of a smile: Facial expression affects ultimatum-game responses (2013) Judgment and Decision Making, 8 (3), pp. 381-385; Vural, E., Cetin, M., Ercil, A., Littlewort, G., Bartlett, M., Movellan, J., Automated drowsiness detection for improved driver safety (2008) Proceedings of the International Conference on Automotive Technologies, 2008; Zhang, L., Jiang, M., Farid, D., Hossain, A.M., Intelligent facial emotion recognition and semantic-based topic detection for a humanoid robot (2013) Expert Systems with Applications, 40, pp. 5160-5168. , 2013; Zhang, L., Mistry, K., Jiang, M., Neoh, S.C., Hossain, M.A., Adaptive facial point detection and emotion recognition for a humanoid robot (2015) Computer Vision and Image Understanding, 140, pp. 93-114. , 2015; Xue, B., Zhang, M., Browne, W.N., Particle swarm optimization for feature selection in classification: A multi-objective approach (2013) Ieee Trans. 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"Papageorgiou D., Papageorgiou D., Kastritsi T., Doulgeri Z., Doulgeri Z., Rovithakis G.A., Rovithakis G.A.","57203254778;57203254778;57203901418;35548695300;35548695300;7004020947;7004020947;","A Passive pHRI Controller for Assisting the User in Partially Known Tasks",2020,"IEEE Transactions on Robotics","36","3","8995640","802","815",,3,"10.1109/TRO.2020.2969018","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087049492&doi=10.1109%2fTRO.2020.2969018&partnerID=40&md5=04c7be3b122006e481e2ada60d7763fe","Center for Research and Technology Hellas, Thessaloniki, 57001, Greece; Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece","Papageorgiou, D., Center for Research and Technology Hellas, Thessaloniki, 57001, Greece, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece; Papageorgiou, D., Center for Research and Technology Hellas, Thessaloniki, 57001, Greece, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece; Kastritsi, T., Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece; Doulgeri, Z., Center for Research and Technology Hellas, Thessaloniki, 57001, Greece, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece; Doulgeri, Z., Center for Research and Technology Hellas, Thessaloniki, 57001, Greece, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece; Rovithakis, G.A., Center for Research and Technology Hellas, Thessaloniki, 57001, Greece, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece; Rovithakis, G.A., Center for Research and Technology Hellas, Thessaloniki, 57001, Greece, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece","In this article, a passive physical human-robot interaction (pHRI) controller is proposed to enhance pHRI performance in terms of precision, cognitive load, and user effort, in cases partial knowledge of the task is available. Partial knowledge refers to a subspace of SE(3) determined by the desired task, generally mixing both position and orientation variables, and is mathematically approximated by parametric expressions. The proposed scheme, which utilizes the notion of virtual constraints and the prescribed performance control methodology, is proved to be passive with respect to the interaction force, while guaranteeing constraint satisfaction in all cases. The control scheme is experimentally validated and compared with a dissipative control scheme utilizing a KUKA LWR4+ robot in a master-slave task; experiments also include an application to a robotic assembly case. © 2004-2012 IEEE.","Active constraints; passivity; physical human-robot interaction (pHRI); robot control","Agricultural robots; Controllers; Robotic assembly; Constraint Satisfaction; Dissipative control; Interaction forces; Parametric expressions; Physical humanrobot interaction (phri); Position and orientations; Prescribed performance; Virtual constraints; Human robot interaction",,,,,"MIS-5000432; Horizon 2020 Framework Programme, H2020: 644938, 820767; European Commission, EC; European Social Fund, ESF","Manuscript received January 21, 2019; accepted December 26, 2019. Date of publication February 12, 2020; date of current version June 4, 2020. This article was recommended for publication by Associate Editor F. Ficuciello and Editor A. Billard upon evaluation of the reviewers’ comments. This work was supported by the EU Horizon 2020 Research and Innovation Program through the SARA-Fun Project under Grant 644938 and the CoLLaboratE Project under Grant 820767. The work of T. Kastritsi was supported by Greece and the European Union (European Social Fund-ESF) through the Operational Programme Human Resources Development, Education and Lifelong Learning in the context of the project “Strengthening Human Resources Research Potential via Doctorate Research” (MIS-5000432), implemented by the State Scholarships Foundation (IKY). (Corresponding author: Zoe Doulgeri.) D. Papageorgiou, Z. Doulgeri, and G. A. Rovithakis are with the Center for Research and Technology Hellas, 57001 Thessaloniki, Greece, and also with the Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece (e-mail: dimpapag@iti.gr; doulgeri@iti.gr; robi@iti.gr).","This work was supported by the EU Horizon 2020 Research and Innovation Program through the SARAFun Project under Grant 644938 and the CoLLaboratE Project under Grant 820767. The work of T. Kastritsi was supported by Greece and the European Union (European Social Fund-ESF) through the Operational Programme Human Resources Development, Education and Lifelong Learning in the context of the project ""Strengthening Human Resources Research Potential via Doctorate Research"" (MIS-5000432), implemented by the State Scholarships Foundation (IKY). 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Autom, 4, pp. 3993-3998. , Apr; Khalil, H., (2002) Nonlinear Systems, , 3rd ed. Englewood Cliffs NJ USA: Prentice-Hall; Fitts, P.M., The information capacity of the human motor system in controlling the amplitude of movement (1954) J. Exp. Psychol., 47 (6), pp. 381-391; Azor, R., Bar-Itzhack, I., Deutschmann, J., Harman, R., Angular-rate estimation using delayed quaternion measurements (2001) J. Guid., Control, Dyn., 24 (3), pp. 436-443; Chou, J.C.K., Quaternion kinematic and dynamic differential equations (1992) IEEE Trans. Robot. Autom., 8 (1), pp. 53-64. , Feb; Bertsekas, D.P., (1982) Constrained Optimization and Lagrange Multiplier Methods, pp. 1-94. , http://www.sciencedirect.com/science/article/pii/B9780120934805500052, New York, NY USA: Academic, ch. 1, [online]","Doulgeri, Z.; Center for Research and Technology HellasGreece; email: doulgeri@iti.gr",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15523098,,,,"English","IEEE Trans. Rob.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85087049492
"Hoang V.T.","57212478072;","HGM-4: A new multi-cameras dataset for hand gesture recognition",2020,"Data in Brief","30",,"105676","","",,4,"10.1016/j.dib.2020.105676","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084535880&doi=10.1016%2fj.dib.2020.105676&partnerID=40&md5=3c9ca87f8147ab39e614121d5741dbae","Ho Chi Minh City Open University, Viet Nam","Hoang, V.T., Ho Chi Minh City Open University, Viet Nam","Gesture recognition technology is rapidly growing in the recent years due to the demands of many application such as computer game and sport, human robot interaction, assistant systems, sign language interpretation and e-commerce. One of the most important of gesture recognition is hand-gesture recognition. For example, it can be used to control all devices (television, radio, air-condition, and doors) by just hand gestures for smart home application. The HGM-4 dataset is built for hand gesture recognition (the full dataset is available from: https://data.mendeley.com/datasets/jzy8zngkbg/4) which contains total 4,160 color images (1280 × 700 pixels) of 26 hand gestures captured by four cameras at different position. The training and testing set are defined to create a benchmark framework for comparing the experimental results. © 2020 The Author(s)","Biometric recognition; Hand gesture recognition; Image classification; Multiple cameras; One hand gesture; Sign language",,,,,,,,,,,,,,,,,"Chansri, C., Srinonchat, J., Hand Gesture Recognition for Thai Sign Language in Complex Background Using Fusion of Depth and Color Video (2016) Procedia Computer Science, 86, pp. 257-260; Dinh, D.-L., Kim, J.T., Kim, T.-S., Hand Gesture Recognition and Interface via a Depth Imaging Sensor for Smart Home Appliances (2014) Energy Procedia, 62, pp. 576-582; Dominio, F., Donadeo, M., Zanuttigh, P., Combining multiple depth-based descriptors for hand gesture recognition (2014) Pattern Recognition Letters, 50, pp. 101-111; Guan, H., Chang, J.S., Chen, L., Feris, R.S., Turk, M., Multi-view Appearance-based 3D Hand Pose Estimation (2006) 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’06), New York, NY, USA, , IEEE 154–154; Just, A., Marcel, S., A comparative study of two state-of-the-art sequence processing techniques for hand gesture recognition (2009) Computer Vision, and Image Understanding, 113, pp. 532-543; Maqueda, A.I., del-Blanco, C.R., Jaureguizar, F., García, N., Human–computer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns (2015) Computer Vision and Image Understanding, 141, pp. 126-137; Ren, Z., Yuan, J., Zhang, Z., Robust Hand Gesture Recognition Based on Finger-Earth Mover's Distance with a Commodity Depth Camera (2011) Proceedings of the 19th ACM International Conference on Multimedia, Association for Computing Machinery, New York, NY, USA, pp. 1093-1096; Pisharady, P.K., Saerbeck, M., Recent methods and databases in vision-based hand gesture recognition: A review (2015) Computer Vision, and Image Understanding, 141, pp. 152-165; Poon, G., Kwan, K.C., Pang, W.-M., Real-time Multi-view Bimanual Gesture Recognition (2018) 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP), Shenzhen, pp. 19-23. , IEEE; Poon, G., Kwan, K.C., Pang, W.-M., Occlusion-robust bimanual gesture recognition by fusing multi-views (2019) Multimed Tools Appl, 78, pp. 23469-23488; Zhang, Y., Cao, C., Cheng, J., Lu, H., EgoGesture: A New Dataset and Benchmark for Egocentric Hand Gesture Recognition (2018) IEEE Trans. Multimedia., 20, pp. 1038-1050; Shukla, D., The IMHG dataset: A Multi-View Hand Gesture RGB-D Dataset for Human-Robot Interaction, (n.d.) 4; Tavakoli, M., Benussi, C., Alhais Lopes, P., Osorio, L.B., de Almeida, A.T., Robust hand gesture recognition with a double channel surface EMG wearable armband and SVM classifier (2018) Biomedical Signal Processing and Control, 46, pp. 121-130; Otsu, N., A threshold selection method from gray-level histograms, IEEE Transactions on Systems (1979) Man and Cybernetics, 9, pp. 62-66",,,,"Elsevier Inc.",,,,,23523409,,,,"English","Data Brief",Data Paper,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85084535880
"Trautman P., Patel K.","36803082100;57218262734;","Real time crowd navigation from first principles of probability theory",2020,"Proceedings International Conference on Automated Planning and Scheduling, ICAPS","30",,,"459","467",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088530636&partnerID=40&md5=bd7b079bededb2a4e0cc56b8966a4a17","Honda Research Institute, San Jose, CA, United States","Trautman, P., Honda Research Institute, San Jose, CA, United States; Patel, K., Honda Research Institute, San Jose, CA, United States","Constructing realistic and real time human-robot interaction models is a core challenge in crowd navigation. In this paper we derive a robot-agent interaction density from first principles of probability theory; we call our approach “first order interacting Gaussian processes” (foIGP). Furthermore, we compute locally optimal solutions'with respect to multifaceted agent “intent” and “flexibility”'in near real time on a laptop CPU. We test on challenging scenarios from the ETH crowd dataset and show that the safety and efficiency statistics of foIGP is competitive with human safety and efficiency statistics. Further, we compute the safety and efficiency statistics of dynamic window avoidance, a physics based model variant of foIGP, a Monte Carlo inference based approach, and the best performing deep reinforcement learning algorithm; foIGP outperforms all of them. Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.",,"Deep learning; Efficiency; Inference engines; Learning algorithms; Probability; Reinforcement learning; Scheduling; Statistical tests; Agent interaction; First principles; Gaussian Processes; Monte Carlo inference; Optimal solutions; Physics-based modeling; Probability theory; Safety and efficiencies; Human robot interaction",,,,,,,,,,,,,,,,"Adams, E., To survive the streets, robocars must learn to think like humans (2017) Wired; Alahi, A., Goel, K., Social-LSTM: Human trajectory prediction in crowded spaces (2016) CVPR; Aoude, G., Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion (2011) Autonomous Robots; Burgard, W., Cremers, A., The interactive museum tour-guide robot (1998) AAAI; Canny, J., Reif, J., New lower bound techniques for robot motion planning problems (1987) Symposium on Foundations of Computer Science; Chen, Y., Everett, M., Liu, M., How, J., Socially aware motion planning with deep reinforcement learning (2017) IROS; Chen, C., Liu, Y., Kreiss, S., Alahi, A., Crowd-robot interaction: Crowd-aware robot navigation with attention-based deep reinforcement learning (2019) ICRA; Curtis, S., Best, A., Manocha, D., Menge: A modular framework for simulating crowd movement (2016) Collective Dynamics; Doucet, A., Johansen, A., A tutorial on particle filtering and smoothing: Fifteen years later (2008) Oxford Handbook of Nonlinear Filtering, , Oxford Handbooks; Du Toit, N., Burdick, J., Robot motion planning in dynamic, uncertain environments (2012) IEEE Transactions on Robotics; Fan, T., Cheng, X., Pan, J., Long, P., Liu, W., Yang, R., Manocha, D., Getting robots unfrozen and unlost in dense pedestrian crowds (2019), RAL; Fox, D., Burgard, W., Thrun, S., The dynamic window approach to collision avoidance (1997) IEEE RAM; Fulgenzi, C., Dynamic obstacle avoidance in uncertain environment combining pvos and occupancy grid (2007) ICRA; Fulgenzi, C., Tay, C., Spalanzani, A., Laugier, C., Probabilistic navigation in dynamic environment using rapidly-exploring random trees and Gaussian processes (2008) IROS; Gloor, C., (2016) Pedsim: pedestrian crowd simulation; Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., Alahi, A., Social GAN: Socially acceptable trajectories with generative adversarial networks (2018) CVPR; Hall, E., (1966) The Hidden Dimension, , Doubleday; Helbing, D., Molnar, P., Social force model for pedestrian dynamics (1995) Physical Review E; Ivanovich, B., Pavone, M., The trajectron: Probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs (2019) ICCV; Ivanovich, B., Schmerling, E., Leung, K., Pavone, M., Generative modeling of multimodal multi-human behavior (2018) IROS; Joseph, J., Doshi-Velez, F., Roy, N., A bayesian nonparametric approach to modeling mobility patterns (2011) Autonomous Robots; Kretzschmar, H., Spies, M., Sprunk, C., Burgard, W., Socially compliant mobile robot navigation via inverse reinforcement learning (2016) IJRR; Kruse, T., Alami, R., Pandey, A., Kirsch, A., Human-aware robot navigation: a survey (2013) RAS; Kuderer, M., Kretzschmar, H., Sprunk, C., Burgard, W., Feature-based prediction of trajectories for socially compliant navigation (2012) RSS; Lerner, A., Chrysanthou, Y., Lischinski, D., Crowds by example (2007) Computer graphics forum; Mead, R., Atrash, A., Mataric, M., Proxemic feature recognition for interactive robots: automating social science metrics (2011) ICSR; Pellegrini, S., Ess, A., Schindler, K., Van Gool, L., You'll never walk alone: modeling social behavior for multi-target tracking (2009) International Conference on Computer Vision; Pettre, J., Babel, M., Hayet, J., Salaris, P., Salvini, P., (2018) From freezing to jostling robots: Current challenges and new paradigms for safe robot navigation in dense crowds; Pfeiffer, M., Paolo, G., Sommer, H., Nieto, J., Siegwart, R., Cadena, C., A data-driven model for interaction-aware pedestrian motion prediction in object cluttered environments (2018) ICRA; Rasmussen, C. E., Williams, C., (2006) Gaussian Processes for Machine Learning, , MIT Press; Rios-Martinez, J., Understanding human interaction for probabilistic autonomous navigation using risk-RRT (2011) IROS; Sadigh, D., Landolfi, N., Sastry, S., Seshia, S., Dragan, A., Planning for cars that coordinate with people: Leveraging effects on human actions for planning and active information gathering over human internal state (2018) Autonomous Robots; Snape, J., van den Berg, J., Guy, S., Manocha, D., The hybrid reciprocal velocity obstacle (2011) IEEE Transactions on Robotics; Sturlaugson, L., Sheppard, J., Inference complexity in continuous time bayesian networks (2014) UAI; Svenstrup, M., Bak, T., Andersen, J., Trajectory planning for robots in dynamic human environments (2010) IROS; Tai, L., Zhang, J., Lin, X., Burgard, W., Socially compliant navigation through raw depth inputs with generative adversarial imitation learning (2018) ICRA; Thompson, S., A probabilistic model of human motion and navigation intent for mobile robot path planning (2009) IROS; Thrun, S., Beetz, M., Probabilistic algorithms and the interactive museum tour-guide robot minerva (2000), IJRR; Trautman, P., Krause, A., Unfreezing the robot: Navigation in dense interacting crowds (2010) IROS; Trautman, P., Ma, J., Krause, A., Murray, R. M., Robot navigation in dense crowds: the case for cooperation (2013) ICRA; Trautman, P., (2013) Robot Navigation in Dense Crowds: Statistical Models and Experimental Studies of Human Robot Cooperation, , Ph.D. Dissertation, California Institute of Technology; Trautman, P., (2017) Sparse Interacting Gaussian Processes: Efficiency and Optimality Theorems of Autonomous Crowd Navigation, , CDC; Unhelkar, V., Human-robot co-navigation using anticipatory indicators of human walking motion (2015) ICRA; van den Berg, J., Lin, M., Manocha, D., Reciprocal velocity obstacles for real-time multi-agent navigation (2008) ICRA; Vemula, A., Muelling, K., Oh, J., Social attention: Modeling attention in human crowds (2018) ICRA; Ziebart, B. D., Maas, A., Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior (2008) UbiComp",,"Beck J.C.Buffet O.Hoffmann J.Karpas E.Sohrabi S.",,"AAAI press","30th International Conference on Automated Planning and Scheduling, ICAPS 2020","26 October 2020 through 30 October 2020",,161572,23340835,,,,"English","Proc. Int. Conf. Automated Plan. Sched., ICAPS",Conference Paper,"Final","",Scopus,2-s2.0-85088530636
"Shih B., Lathrop E., Adibnazari I., Martin R., Park Y.-L., Tolley M.T.","57196032468;57218167198;57204818997;57218165086;23009880400;24333439100;","Classification of components of affective touch using rapidly-manufacturable soft sensor skins",2020,"2020 3rd IEEE International Conference on Soft Robotics, RoboSoft 2020",,,"9116023","182","187",,,"10.1109/RoboSoft48309.2020.9116023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088146462&doi=10.1109%2fRoboSoft48309.2020.9116023&partnerID=40&md5=55645e006a4a4a824d3106d79e9c55d8","UC San Diego, Department of Mechanical and Aerospace Engineering, United States; Seoul National University, Department of Mechanical and Aerospace Engineering, South Korea","Shih, B., UC San Diego, Department of Mechanical and Aerospace Engineering, United States; Lathrop, E., UC San Diego, Department of Mechanical and Aerospace Engineering, United States; Adibnazari, I., UC San Diego, Department of Mechanical and Aerospace Engineering, United States; Martin, R., UC San Diego, Department of Mechanical and Aerospace Engineering, United States; Park, Y.-L., Seoul National University, Department of Mechanical and Aerospace Engineering, South Korea; Tolley, M.T., UC San Diego, Department of Mechanical and Aerospace Engineering, United States","Affective touches play an important role in everyday communication because a significant amount of human interactions are through physical contacts. To facilitate robust, safe human-robot interactions (HRIs), we require robots with soft skins that can interpret affective touches. In this paper, we describe the fabrication of rapidly manufacturable, soft sensor skins using liquid metal embedded silicone elastomer as a resistive element and trained a recurrent neural network (RNN) to distinguish between a variety of pokes and rubs from human users. On a 2 × 2 sensor array, we obtained an average classification accuracy of 97% for ten different types of pokes and rubs, demonstrating that the combination of a soft sensor and machine learning can classify the interactions. Our approach is a step towards intelligent soft robots that can understand social interactions through touches. © 2020 IEEE.",,"Agricultural robots; Intelligent robots; Recurrent neural networks; Robotics; Silicones; Classification accuracy; Human interactions; Physical contacts; Recurrent neural network (RNN); Resistive elements; Safe human-robot interaction; Silicone elastomers; Social interactions; Human robot interaction",,,,,"National Science Foundation, NSF; Office of Naval Research, ONR: N00014-17-1-2062, N00014-18-1-2277","The authors thank the Office of Naval Research for support (grant numbers N00014-17-1-2062 and N00014-18-1-2277). E.L. is supported by an NSF Graduate Research Fellowship.",,,,,,,,,,"Rus, D., Tolley, M.T., Design, fabrication and control of soft robots (2015) Nature, 521 (7553), p. 467; Whitesides, G.M., Soft robotics (2018) Angewandte Chemie International Edition, 57 (16), pp. 4258-4273; Tolley, M.T., Shepherd, R.F., Mosadegh, B., Galloway, K.C., Wehner, M., Karpelson, M., Wood, R.J., Whitesides, G.M., A resilient, untethered soft robot (2014) Soft Robotics, 1 (3), pp. 213-223; Deimel, R., Brock, O., Soft hands for reliable grasping strategies (2015) Soft Robotics, pp. 211-221. , Springer; Katzschmann, R.K., DelPreto, J., MacCurdy, R., Rus, D., Exploration of underwater life with an acoustically controlled soft robotic fish (2018) Science Robotics, 3 (16), p. eaar3449; Hawkes, E.W., Blumenschein, L.H., Greer, J.D., Okamura, A.M., A soft robot that navigates its environment through growth (2017) Science Robotics, 2 (8), p. eaan3028; Hughes, J., Maiolino, P., Iida, F., An anthropomorphic soft skeleton hand exploiting conditional models for piano playing (2018) Science Robotics, 3 (25), p. eaau3098; Shih, B., Shah, D.S., Li, J., Thuruthel, T.G., Park, Y.-L., Iida, F., Bao, Z., Tolley, M.T., Electronic Skins for Intelligent Soft Robots, , submitted; Park, Y.-L., Chen, B.-R., Wood, R.J., Design and fabrication of soft artificial skin using embedded microchannels and liquid conductors (2012) IEEE Sensors Journal, 12 (8), pp. 2711-2718; Shih, B., Drotman, D., Christianson, C., Huo, Z., White, R., Chris-Tensen, H.I., Tolley, M.T., Custom soft robotic gripper sensor skins for haptic object visualization (2017) 2017 IEEE/RSJ IROS, pp. 494-501; Shih, B., Christianson, C., Gillespie, K., Lee, S., Mayeda, J., Huo, Z., Tolley, M.T., Design considerations for 3d printed, soft, multimaterial resistive sensors for soft robotics (2019) Frontiers in Robotics and AI, 6, p. 30; Hellebrekers, T., Kroemer, O., Majidi, C., Soft magnetic skin for continuous deformation sensing (2019) Advanced Intelligent Systems, 1 (4), p. 1900025; Zhao, H., O'Brien, K., Li, S., Shepherd, R.F., Optoelectronically innervated soft prosthetic hand via stretchable optical waveguides (2016) Science Robotics, 1 (1), p. eaai7529; Boutry, C.M., Negre, M., Jorda, M., Vardoulis, O., Chortos, A., Khatib, O., Bao, Z., A hierarchically patterned, bioinspired e-skin able to detect the direction of applied pressure for robotics (2018) Science Robotics, 3 (24), p. eaau6914; Sonar, H.A., Yuen, M.C.-S., Kramer-Bottiglio, R., Paik, J., An any-resolution distributed pressure localization scheme using a capacitive soft sensor skin (2018) IEEE RoboSoft, No. CONF; Thuruthel, T.G., Shih, B., Laschi, C., Tolley, M.T., Soft robot perception using embedded soft sensors and recurrent neural networks (2019) Science Robotics, 4 (26), p. eaav1488; Soter, G., Conn, A., Hauser, H., Rossiter, J., Bodily aware soft robots: Integration of proprioceptive and exteroceptive sensors (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 2448-2453; Piacenza, P., Sherman, S., Ciocarlie, M., Data-driven super-resolution on a tactile dome (2018) IEEE Robotics and Automation Letters, 3 (3), pp. 1434-1441; Van Meerbeek, I., De Sa, C., Shepherd, R., Soft optoelectronic sensory foams with proprioception (2018) Science Robotics, 3 (24), p. eaau2489; Kim, D., Kwon, J., Han, S., Park, Y.-L., Jo, S., Deep full-body motion network (dfm-net) for a soft wearable motion sensing suit (2018) IEEE/ASME Transactions on Mechatronics; Wang, H., Totaro, M., Beccai, L., Toward perceptive soft robots: Progress and challenges (2018) Advanced Science, 5 (9), p. 1800541; Pfeifer, R., Bongard, J., (2006) How the Body Shapes the Way We Think: A New View of Intelligence; Goodrich, M.A., Schultz, A.C., Human-robot interaction: A survey (2008) Foundations and Trends in Human-Computer Interaction, 1 (3), pp. 203-275; Field, T., Touch for socioemotional and physical well-being: A review (2010) Developmental Review, 30 (4), pp. 367-383; Von Mohr, M., Kirsch, L.P., Fotopoulou, A., The soothing function of touch: Affective touch reduces feelings of social exclusion (2017) Scientific Reports, 7 (1), p. 13516; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Computation, 9 (8), pp. 1735-1780; Yohanan, S., MacLean, K.E., The role of affective touch in human-robot interaction: Human intent and expectations in touching the haptic creature (2012) International Journal of Social Robotics, 4 (2), pp. 163-180; Alonso-Martin, F., Gamboa-Montero, J., Castillo, J., Castro-González, A., Salichs, M., Detecting and classifying human touches in a social robot through acoustic sensing and machine learning (2017) Sensors, 17 (5), p. 1138; Pan, S.J., Yang, Q., A survey on transfer learning (2009) IEEE Transactions on Knowledge and Data Engineering, 22 (10), pp. 1345-1359",,,,"Institute of Electrical and Electronics Engineers Inc.","3rd IEEE International Conference on Soft Robotics, RoboSoft 2020","15 May 2020 through 15 July 2020",,161217,,9781728165707,,,"English","IEEE Int. Conf. Soft Robot., RoboSoft",Conference Paper,"Final","",Scopus,2-s2.0-85088146462
"Aeraiz-Bekkis D., Ganesh G., Yoshida E., Yamanobe N.","57217309959;15622746500;35276803100;6507370973;","Robot Movement Uncertainty Determines Human Discomfort in Co-worker Scenarios",2020,"2020 6th International Conference on Control, Automation and Robotics, ICCAR 2020",,,"9108085","59","66",,1,"10.1109/ICCAR49639.2020.9108085","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087054942&doi=10.1109%2fICCAR49639.2020.9108085&partnerID=40&md5=f002418c1239733568a8e0b045ea677d","University of Montpellier, Montpellier, France; CNRS-UM LIRMM UMR5506, Montpellier, France; CNRS-AIST JRL UMI3218/RL, Tsukuba, Japan; ISRI AIRC AIST, Tsukuba, Tokyo, Japan","Aeraiz-Bekkis, D., University of Montpellier, Montpellier, France; Ganesh, G., CNRS-UM LIRMM UMR5506, Montpellier, France; Yoshida, E., CNRS-AIST JRL UMI3218/RL, Tsukuba, Japan; Yamanobe, N., ISRI AIRC AIST, Tsukuba, Tokyo, Japan","long term success of a human-robot interaction will depend on how comfortable and safe a human feels with it. But which feature of a robot's movement determines human comfort? To address this question, here we considered four different models of human discomfort. We then designed an empirical human-robot co-worker task that enables us to both, quantify the discomfort experienced by the human co-worker by analyzing behavioral changes, and examine which model of discomfort explains the changes best. Using this task, we show that the perceived uncertainty in a robot's movement is a key determinant of human discomfort, and we discuss how movement uncertainty can give a unified explanation for the modulation of human comfort with robots, and trust in them, as reported in several previous studies. © 2020 IEEE.","comfort; human perception; human robot interaction; movement uncertainty; robot predictability","Agricultural robots; Robotics; Behavioral changes; Human comforts; Human robots; Key determinants; Robot movements; Social robots",,,,,"JPMJER1701; Japan Society for the Promotion of Science, JSPS: JP17H01804","This work was supported by JSPS KAKENHI Grant Number JP17H01804. GG was partially supported by JST ERATO Grant Number JPMJER1701.",,,,,,,,,,"Variable impedance actuators: A review (2013) Robotics and Autonomous Systems, 61 (12), pp. 1601-1614. , http://www.sciencedirect.com/science/article/pii/S0921889013001188, Dec; Kazanzides, P., Safety design for medical robots (2009) Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp. 7208-7211; Ganesh, G., Albu-Schaffer, A., Haruno, M., Kawato, M., Burdet, E., Biomimetic motor behavior for simultaneous adaptation of force, impedance and trajectory in interaction tasks (2010) Proceedings of IEEE International Conference on Robotics and Automation. IEEE, pp. 2705-2711; Haddadin, S., Albu-Schaffer, A., Luca, A.D., Hirzinger, G., Collision detection and reaction: A contribution to safe physical human-robot interaction (2008) Proceedings OfIEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 3356-3363; Li, Y., Ganesh, G., Jarrasse, N., Haddadin, S., Albu-Schaffer, A., Burdet, E., Force, impedance, and trajectory learning for contact tooling and haptic identification (2018) IEEE Transactions on Robotics, 34 (5), pp. 1170-1244. , https://ieeexplore.ieee.org/abstract/document/8362715, Oct; Sisbot, E., Marin-Urias, L., Alami, R., Simeon, T., A human aware mobile robot motion planner (2007) Robotics, IEEE Transactions on, 23 (5), pp. 874-883. , Nov; Rajamohan, V., Scully-Allison, C., Dascalu, S., Feil-Seifer, D., Factors influencing the human preferred interaction distance (2019) Proceedings OfIEEE International Conference on Robot and Human Interactive Communication, pp. 1-7; Sanders, T., Oleson, K.E., Billings, D.R., Chen, J.Y., Hancock, P.A., A model of human-robot trust: Theoretical model development (2011) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 55 (1), pp. 1432-1436. , Sep; Li, D., Rau, P.-L.P., Li, Y., A cross-cultural study: Effect of robot appearance and task (2010) International Journal OfSocial Robotics, 2 (2), pp. 175-186. , Jun; Parasuraman, R., Sheridan, T., Wickens, C., Situation awareness, mental workload, and trust in automation: Viable, empirically supported cognitive engineering constructs (2008) Journal OfCognitive Engineering and Decision Making, 2, pp. 140-160. , Jul; Lewis, M., Sycara, K., Walker, P., The role of trust in human-robot interaction (2018) Foundations OfTrusted Autonomy, Ser. Studies in Systems, 17, pp. 135-159. , Decision and Control, A. H., S. J., and R. D., Eds. Springer; Rousseau, D.M., Sitkin, S.B., Burt, R.S., Camerer, C., Not so different after all: A cross-discipline view of trust (1998) Academy of Management Review, 23 (3), pp. 393-404. , Jul; Muir, B., Moray, N., Trust in automation. Part II. Experimental studies of trust and human intervention in a process control simulation (1996) Ergonomics, 39, pp. 429-460. , Apr; Koppenborg, M., Nickel, P., Naber, B., Lungfiel, A., Huelke, M., Effects of movement speed and predictability in human-robot collaboration (2017) Human Factors and Ergonomics in Manufacturing & Service Industries, 27 (4), pp. 197-209. , May; Koay, K., Dautenhahn, K., Woods, S., Walters, M., Empirical results from using a comfort level device in human-robot interaction studies (2006) Proceedings OfACM/IEEE International Conference on Human-Robot Interaction, pp. 194-201. , Jan; Johansson, R., Westling, G., Backstrom, A., Flanagan, J., Eye-hand coordination in object manipulation (2001) The Journal Ofneuroscience : The Official Journal of the Society for Neuroscience, 21 (7), p. 691732. , Oct; Takagi, A., Ganesh, G., Yoshioka, T., Kawato, M., Burdet, E., Physically interacting individuals estimate the partner's goal to enhance their movements (2017) Nature Human Behaviour, 1 (3), p. 23973374. , https://doi.org/10.1038/s41562-017-0054, Mar; Ganesh, G., Takagi, A., Osu, R., Yoshioka, T., Kawato, M., Burdet, E., Two is better than one: Physical interactions improve motor performance in humans (2014) Scientific Reports, 4, p. 3824. , Jan; Dragan, A.D., Bauman, S., Forlizzi, J., Srinivasa, S.S., Effects of robot motion on human-robot collaboration (2015) Proceedings of ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 51-58; Dragan, A.D., Lee, K.C., Srinivasa, S.S., Legibility and predictability of robot motion (2013) Proceedings OfACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 301-308; Axelrod, R., (1997) The Complexity OfCooperation: Agent-Based Models of Competition and Collaboration, , Princeton: Princeton University Press; Lewandowsky, S., Mundy, M., Tan, G., The dynamics of trust: Comparing humans to automation (2010) Journal Ofexperimental Psychology. 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Cham: Springer International Publishing; Walters, M., Koay, K., Woods, S., Syrdal, D.S., Dautenhahn, K., Robot to human approaches: Preliminary results on comfortable distances and preferences (2007) AAAI Spring Symposium-Technical Report, , Jan; Walters, M.L., Dautenhahn, K., Koay, K.L., Kaouri, C., Boekhorst, R., Nehaniv, C., Werry, I., Lee, D., Close encounters: Spatial distances between people and a robot of mechanistic appearance (2005) 5th IEEE-RAS International Conference on Humanoid Robots, p. 450455. , Dec; Kanda, T., Ishiguro, H., (2017) Human-Robot Interaction in Social Robotics, , CRC Press Mar; Wobbrock, J., Findlater, L., Gergle, D., Higgins, J., (2011) The Aligned Rank Transform for Nonparametric Factorial Analyses Using only Anova Procedures, pp. 143-146. , May; Wojciechowska, A., Frey, J., Sass, S., Shafir, R., Cauchard, J.R., Collocated human-drone interaction: Methodology and approach strategy (2019) Proceedings of ACM/IEEE International Conference on Human-Robot Interaction, pp. 172-181; Bussy, A., (2013) Approche Cognitive pour la Representation de l'Interaction Proximale Haptique Entre un Homme et un humanoi'de, , Ph.D. dissertation, LIRMM-Laboratoire d'Informatique de Robotique et de Microelectronique de Montpellier; Li, Y., Carboni, G., Gonzalez, F., Campolo, D., Burde, E., Measuring emotion: The self-assessment manikin and the semantic differential (2019) Nature Machine Intelligence, 1 (1), pp. 36-43. , Jun",,,"Beijing Jiaotong University;Xidian University","Institute of Electrical and Electronics Engineers Inc.","6th International Conference on Control, Automation and Robotics, ICCAR 2020","20 April 2020 through 23 April 2020",,160765,,9781728161396,,,"English","Int. Conf. Control, Autom. Robot., ICCAR",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85087054942
"Antonello M., Chiesurin S., Ghidoni S.","55804432000;57213813064;23392337500;","Enhancing semantic segmentation with detection priors and iterated graph cuts for robotics",2020,"Engineering Applications of Artificial Intelligence","90",,"103467","","",,2,"10.1016/j.engappai.2019.103467","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077984169&doi=10.1016%2fj.engappai.2019.103467&partnerID=40&md5=759ab2f8ec241e0a6171c0e7fe002885","Intelligent Autonomous Systems Laboratory (IAS-Lab), Department of Information Engineering (DEI), University of Padova, Via Ognissanti 72, Padova, 35129, Italy; School of Mathematical and Computer Sciences, Heriot Watt University, Edinburgh Campus, Boundary Rd N, EH14 4AS, Edinburgh, Scotland, United Kingdom","Antonello, M., Intelligent Autonomous Systems Laboratory (IAS-Lab), Department of Information Engineering (DEI), University of Padova, Via Ognissanti 72, Padova, 35129, Italy; Chiesurin, S., School of Mathematical and Computer Sciences, Heriot Watt University, Edinburgh Campus, Boundary Rd N, EH14 4AS, Edinburgh, Scotland, United Kingdom; Ghidoni, S., Intelligent Autonomous Systems Laboratory (IAS-Lab), Department of Information Engineering (DEI), University of Padova, Via Ognissanti 72, Padova, 35129, Italy","To foster human–robot interaction, autonomous robots need to understand the environment in which they operate. In this context, one of the main challenges is semantic segmentation, together with the recognition of important objects, which can aid robots during exploration, as well as when planning new actions and interacting with the environment. In this study, we extend a multi-view semantic segmentation system based on 3D Entangled Forests (3DEF) by integrating and refining two object detectors, Mask R-CNN and You Only Look Once (YOLO), with Bayesian fusion and iterated graph cuts. The new system takes the best of its components, successfully exploiting both 2D and 3D data. Our experiments show that our approach is competitive with the state-of-the-art and leads to accurate semantic segmentations. © 2020 Elsevier Ltd","Mapping; Object detection; Segmentation and categorization; Semantic scene understanding","Graphic methods; Human robot interaction; Mapping; Object detection; Object recognition; Robot programming; Semantics; Bayesian fusion; Important object; Multi-views; Object detectors; Robot interactions; Scene understanding; Semantic segmentation; State of the art; Image segmentation",,,,,"Horizon 2020 Framework Programme, H2020: 723853; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR; Università degli Studi di Padova, UNIPD","This work was supported by IAS-Lab in the Department of Information Engineering of the University of Padua, Italy, the European Union's Horizon 2020 research and innovation programme under grant agreement No. 723853 and MIUR (Italian Minister for Education) under the initiative Departments of Excellence (Law 232/2016).","This work was supported by IAS-Lab in the Department of Information Engineering of the University of Padua, Italy , the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 723853 and MIUR (Italian Minister for Education) under the initiative Departments of Excellence (Law 232/2016).",,,,,,,,,"Abdulla, W., Mask r-cnn for object detection and instance segmentation on keras and tensorflow (2017), https://github.com/matterport/Mask_RCNN; Anand, A., Koppula, H.S., Joachims, T., Saxena, A., Contextually guided semantic labeling and search for three-dimensional point clouds (2013) Int. 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Vis., 57 (2), pp. 137-154; Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Wang, X., Deep high-resolution representation learning for visual recognition (2019), arXiv preprint; Wolf, D., Prankl, J., Vincze, M., Fast semantic segmentation of 3d point clouds using a dense crf with learned parameters (2015) 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 4867-4873. , IEEE; Wolf, D., Prankl, J., Vincze, M., Enhancing semantic segmentation for robotics: the power of 3-d entangled forests (2016) IEEE Robot. Autom. Lett., 1 (1), pp. 49-56","Antonello, M.; Intelligent Autonomous Systems Laboratory (IAS-Lab), Via Ognissanti 72, Italy; email: morris.antonello@dei.unipd.it",,,"Elsevier Ltd",,,,,09521976,,EAAIE,,"English","Eng Appl Artif Intell",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85077984169
"Georgiou T., Baillie L., Ross M.K., Broz F.","57210666931;17433535300;57216350248;25925714600;","Applying the participatory design workshop method to explore how socially assistive robots could assist stroke survivors",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"203","205",,1,"10.1145/3371382.3378232","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083189663&doi=10.1145%2f3371382.3378232&partnerID=40&md5=71fa9ebd783e557c16219e282cfbd7f0","School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, United Kingdom","Georgiou, T., School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, United Kingdom; Baillie, L., School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, United Kingdom; Ross, M.K., School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, United Kingdom; Broz, F., School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, United Kingdom","The field of human robot interaction (HRI) is still relatively new and often borrows methods and principles from the more established field of HCI. HRI researchers are adopting HCI methods, however, these methodologies may need slight modifications and adaptations in order to better investigate the unique challenges of working in HRI. In this paper, we present our findings which utilised one such method: Participatory Design (PD) workshop. We held the workshop in our assistive living lab with ten stroke survivors. This workshop aimed to explore the design of new socially assistive robotic technologies that could be used to support stroke survivors in the home environment. Some of our findings were unanticipated, which suggested that some adaptations to the existing framework for PD workshops need to be revised in order to be useful to HRI research. © 2020 ACM.","Hci; HRI; Participatory design; Socially assistive robots; Stroke","Agricultural robots; Machine design; Man machine systems; Assistive living; Assistive robotics; HCI methods; Home environment; Human robot Interaction (HRI); Participatory design; Socially assistive robots; Stroke survivors; Human robot interaction",,,,,,,,,,,,,,,,"Ayoade, M., Baillie, L., A novel knee rehabilitation system for the home (2014) Proc. of the SIGCHI Conference on Human Factors in Computing Systems (CHI'14), pp. 2521-2530. , https://doi.org/10.1145/2556288.2557353; Baillie, L., (2002) The Home Workshop A Method for Investigating the Home, , Napier University; Bødker, S., Buur, J., The design collaboratorium: A place for usability design (2002) ACM Transactions on Computer-Human Interaction, 9 (2), pp. 152-169. , https://doi.org/10.1145/513665.513670; Braun, V., Clarke, V., Using thematic analysis in psychology (2006) Qualitative Research in Psychology, 3, pp. 77-101. , https://doi.org/10.1191/1478088706qp063oa; Glaser, B.G., Strauss, A.L., (1967) The Discovery of Grounded Theory-Strategies for Qualitative Research, , Transaction Publishers, New Jersey; Lee, H.R., Sabanovi, S., Chang, W.L., Nagata, S., Piatt, J., Bennett, C., Hakken, D., Steps toward participatory design of social robots: Mutual learning with older adults with depression (2017) ACM/IEEE International Conference on Human-Robot Interaction, pp. 244-253. , https://doi.org/10.1145/2909824.3020237; Prange, G.B., Jannink, M.J.A., Groothuis-Oudshoorn, C.G.M., Hermens, H.J., Ijzerman, M.J., Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke (2006) The Journal of Rehabilitation Research and Development, 43 (2), p. 171. , https://doi.org/10.1682/JRRD.2005.04.0076; Uzor, S., Baillie, L., Skelton, D., Senior designers : Empowering seniors to design enjoyable falls rehabilitation tools (2012) Proc. of the SIGCHI Conference on Human Factors in Computing Systems (CHI'12), pp. 1179-1188. , https://doi.org/10.1145/2207676.2208568; Wada, K., Shibata, T., Saito, T., Tanie, K., Effects of robot assisted activity to elderly people who stay at a health service facility for the aged (2003) Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat No, 03CH37453), pp. 2847-2852. , https://doi.org/10.1109/IROS.2003.1249302; Wade, E., Parnandi, A.R., Matari, M.J., Using socially assistive robotics to augment motor task performance in individuals post-stroke (2011) IEEE International Conference on Intelligent Robots and Systems, pp. 2403-2408; Westlund, J.M.K., Martinez, M., Archie, M., Das, M., Breazeal, C., Effects of framing a robot as a social agent or as a machine on children's social behavior (2016) 25th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN, 2016, pp. 688-693. , https://doi.org/10.1109/ROMAN.2016.7745193",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083189663
"Bianco F., Ognibene D.","57212374624;14834121900;","From psychological intention recognition theories to adaptive theory of mind for robots: Computational models",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"136","138",,1,"10.1145/3371382.3378364","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083180556&doi=10.1145%2f3371382.3378364&partnerID=40&md5=9e49a1de576e19e3e8055dd1e5a83376","School of Computer Science and Electronic Engineering, University of Essex, United Kingdom; Department of Psychology, University of Essex, United Kingdom","Bianco, F., School of Computer Science and Electronic Engineering, University of Essex, United Kingdom, Department of Psychology, University of Essex, United Kingdom; Ognibene, D., School of Computer Science and Electronic Engineering, University of Essex, United Kingdom","Progress in robots' application to everyday scenarios has increased the interest in human-robot interaction (HRI) research. However, robots' limited social skills are associated with decreased humans' positive attitude during HRI. Here, we put forward the idea of developing adaptive Theory of Mind (ToM) model-based systems for social robotics, able to deal with new situations and interact with different users in new tasks. Therefore, we grouped current research from developmental psychology debating the computational processes underlying ToM for HRI strategy development. Defining a model describing adaptive ToM processes may in fact aid the development of adaptive robotic architectures for more flexible and successful HRI. Finally, we hope with this report to both further promote the cross-talk between the fields of developmental psychology and robotics and inspire future investigations in this direction. © 2020 ACM.","Action recognition; Belief recognition; Intention recognition; Prediction; Theory of mind; User state","Agricultural robots; Computation theory; Man machine systems; Robotics; Computational model; Computational process; Developmental psychology; Human robot Interaction (HRI); Model-based systems; Psychological intention; Robotic architectures; Strategy development; Social robots",,,,,,,,,,,,,,,,"Abubshait, A., Wiese, E., You look human, but act like a machine: Agent appearance and behavior modulate different aspects of human-robot interaction (2017) Front Psychol, 8, p. 1393; Sinnema, L., Alimardani, M., (2019) The Attitude of Elderly and Young Adults Towards A Humanoid Robot As A Facilitator for Social Interaction; Vignolo, A., Sciutti, A., Rea, F., Michael, J., (2019) Spatiotemporal Coordination Supports A Sense of Commitment in Human-Robot Interaction; Sheridan, T.B., Human-robot interaction: Status and challenges (2016) Human Factors, 58 (4), pp. 525-532; Roselli, C., Ciardo, F., Wykowska, A., Robots Improve Judgments on Self-generated Actions: An Intentional Binding Study; Yang, J.B.G.Z., Dupont, P.E., Fisher, P., Floridi, L., Full, R., The grand challenges of science robotics (2018) Science Robotics, 3, p. eaar7650; Bianco, F., Ognibene, D., Functional Advantages of An Adaptive Theory of Mind for Robotics: A Review of Current Architectures; Bianco, F., Ognibene, D., Transferring Adaptive Theory of Mind to Social Robots: Insights from Developmental Psychology; Lemaignan, S., Dillenbourg, P., Mutual modelling in robotics: Inspirations for the next steps (2015) ACM/IEEE International Conference on Human-Robot Interaction, pp. 303-310. , 03/02 2015; Csibra, G.G.G., Obsessed with goals': Functions and mechanisms of teleological interpretation of actions in humans (2007) Acta Psychol, 124 (1), pp. 60-78; Southgate, V., Johnson, M.H., Csibra, G., Infants attribute goals even to biomechanically impossible actions (2008) Cognition, 107 (3), pp. 1059-1069. , Jun; Cangelosi, A., Schlesinger, M., From babies to robots: The contribution of developmental robotics to developmental psychology (2018) Child Development Perspectives, 12 (3), pp. 183-188; Demiris, Y., Dearden, A., From motor babbling to hierarchical learning by imitation: A robot developmental pathway (2005) Proceedings of the 5th International Workshop on Epigenetic Robotics, pp. 31-37; Woodward, A.L., Infants selectively encode the goal object of an actor's reach (1998) Cognition, 69 (1), pp. 1-34. , Nov; Monroy, C., Gerson, S., Hunnius, S., Infants' motor proficiency and statistical learning for actions (2017) Front Psychol, 8, p. 2174; Southgate, V., Johnson, M.H., Osborne, T., Csibra, G., Predictive motor activation during action observation in human infants (2009) Biol Lett, 5 (6), pp. 769-772. , Dec 23; Skerry, A.E., Carey, S.E., Spelke, E.S., First-person action experience reveals sensitivity to action efficiency in prereaching infants (2013) Proceedings of the National Academy of Sciences, 110 (46), pp. 18728-18733; Gergely, G., Csibra, G., Teleological reasoning in infancy: The nai;ve theory of rational action (2003) Trends Cogn Sci, 7 (7), pp. 287-292. , Jul; Hiatt, L., Harrison, A., Trafton, J., (2011) Accommodating Human Variability in Human-Robot Teams Through Theory of Mind; Winfield, A.F.T., Experiments in artificial theory of mind: From safety to story-telling (2018) Frontiers in Robotics and AI, 5. , 75 2018-June-26; Keysers, C., Gazzola, V., Integrating simulation and theory of mind: From self to social cognition (2007) Trends in Cognitive Sciences, 11 (5), pp. 194-196; Baker, C.L., Jara-Ettinger, J., Saxe, R., Tenenbaum, J.B., Rational quantitative attribution of beliefs, desires and percepts in human mentalizing (2017) Nature Human Behaviour, 1 (4), p. 0064; Hamlin, J., Ullman, T., Tenenbaum, J., Goodman, N., Baker, C., The mentalistic basis of core social cognition: Experiments in preverbal infants and a computational model (2013) Dev Sci, 16 (2), pp. 209-226. , Mar",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85083180556
"Kühnlenz K., Kühnlenz B.","8611373500;55909854300;","Motor interference of incongruent motions increases workload in close HRI",2020,"Advanced Robotics","34","6",,"400","406",,2,"10.1080/01691864.2020.1717614","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079174910&doi=10.1080%2f01691864.2020.1717614&partnerID=40&md5=9d2ff086dc7a23814604f217c38e2654","Robotics Research Lab, Department of Electrical Engineering and Computer Science, Coburg University of Applied Sciences and Arts, Coburg, Germany; Academic Center for Sciences and Humanities, Coburg University of Applied Sciences and Arts, Coburg, Germany","Kühnlenz, K., Robotics Research Lab, Department of Electrical Engineering and Computer Science, Coburg University of Applied Sciences and Arts, Coburg, Germany; Kühnlenz, B., Academic Center for Sciences and Humanities, Coburg University of Applied Sciences and Arts, Coburg, Germany","This paper investigates the effect of motor interference in human arm motions performed in close vicinity to robot arm motions and resulting workload. Human and robot perform congruent (parallel) and incongruent (orthogonal) motions in front of each other and hand positions are recorded through an optical tracking system. The experimental results show a significant increase of human hand position variability as well as a decrease of perceived performance and an increase of perceived effort between congruent and incongruent motion conditions. Moreover, it is found that an increase of motor interference results in higher workload. The results further suggest that even though humanoid form might be considered more intuitive in close human–robot interaction, humanoid design of an interacting robot might not always be the best choice in terms of cooperative positioning accuracy and workload. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","Human–robot interaction; interaction studies; robots for manufacturing","Machine design; Motion tracking; Human arm motion; Interaction studies; Motion conditions; Motor interference; Optical tracking systems; Perceived performance; Positioning accuracy; Robot interactions; Human robot interaction",,,,,"Bundesministerium für Bildung und Frauen, BMBF: 13FH001AN8","This work was supported by Bundesministerium f?r Bildung und Forschung [13FH001AN8].",,,,,,,,,,"Kilner, J., Paulignan, Y., Blakemore, S., An interference effect of observed biological movement on action (2003) Curr Biol, 13 (6), pp. 522-525; Buccino, G., Binkofski, F., Fink, G.R., Action observation activates premotor and parietal areas in a somatotopic manner: an fMRI study (2001) Eur J Neurosci, 13, pp. 400-404. , et al; Buccino, G., Binkofski, F., Riggio, L., The mirror neuron system and action recognition (2004) Brain Lang, 89 (2), pp. 370-376; Fadiga, L., Fogassi, L., Pavesi, G., Motor facilitation during action observation: a magnetic stimulation study (1995) J Neurophysiol, 73 (6), pp. 2608-2611. , et al; Brass, M., Bekkering, H., Prinz, W., Movement observation affects movement execution in a simple response task (2001) Acta Psychol, 106 (1-2), pp. 3-22; Kilner, J., Hamilton, A., Blakemore, S., Interference effect of observed human movement on action is due to velocity profile of biological motion (2007) Soc Neurosci, 2 (3), pp. 158-166; Kupferberg, A., Glasauer, S., Huber, M., Biological movement increases acceptance of humanoid robots as human partners in motor interaction (2011) AI Soc, 26 (4), pp. 339-345. , et al; Flash, T., Hogan, N., The coordination of arm movements: an experimentally confirmed mathematical model (1985) J Neurosci, 5 (7), pp. 1688-1703; Cook, J., Saygin, A.P., Swain, R., Reduced sensitivity to minimum-jerk biological motion in autism spectrum conditions (2009) Neuropsychologia, 47 (14), pp. 3275-3278. , et al; Huber, M., Rickert, M., Knoll, A., Human-robot interaction handing-over tasks. Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN); Munich, Germany; 2008, et al; Oztop, E., Franklin, D., Chaminade, T., Human-humanoid interaction: is a humanoid robot perceived as a human? (2005) Int J Humanoid Robots, 2, pp. 537-559. , et al; Chaminade, T., Franklin, D., Oztop, E., Motor interference between humans and humanoid robots: effect of biological and artificial motion. Proceedings of the 4th International Conference on Development and Learning; Osaka, Japan; 2005, et al; Kuz, S., Schlick, C., Anthropomorphic motion control for safe and efficient human-robot cooperation assembly system. Proceedings of the Triennial Congress of the International Ergonomics Association (IEA); Melbourne, Australia; 2015; Petruck, H., Kuz, S., Mertens, A., Increasing safety human-robot collaboration by using anthropomorphic speed profiles of robot movements. In: Schlick C, Trzcieliski S, editors. Advances ergonomics of manufacturing: managing the enterprise of the future. Springer, Berlin; 2016. (Advances intelligent systems and computing; 490), et al; Duffy, B.R., Anthropomorphism and the social robot (2003) Rob Auton Syst, 42 (3-4), pp. 177-190; Hart, S., Staveland, L., Development of NASA-TLX (task load index): results of empirical and theoretical research. In: Hancock P, Meshkati N, editors. Human mental workload. North-Holland Elsevier Science, Amsterdam; 1988; Fron, C., Korn, O., A short history of the perception of robots and automata from antiquity to modern times. In: Korn O, editor. Social robots: technological, societal and ethical aspects of human-robot interaction. Springer, Berlin; 2019; Draper, J., Blair, L., Workload, flow and telepresence during teleoperation. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA); Minneapolis, USA; 1996; Kaber, D., Onal, E., Endsley, M., Design of automation for telerobots and the effect on performance, operator situation awareness and subjective workload (2000) Hum Factors Ergon Manuf, 10, pp. 409-430; Scholtz, J., Antonishek, B., Young, J., Evaluation of operator interventions autonomous off-road driving. Proceedings of the NIST Performance Metrics for Intelligent Systems Workshop; Gaithersburg, USA; 2003","Kühnlenz, K.; Robotics Research Lab, Germany; email: kolja.kuehnlenz@hs-coburg.de",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Final","",Scopus,2-s2.0-85079174910
"Beane M.I.","56747852500;","In storage, yet on display: An empirical investigation of robots' value as social signals",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"83","91",,2,"10.1145/3319502.3374775","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082004008&doi=10.1145%2f3319502.3374775&partnerID=40&md5=a997bfa1a04c4214410b32cb5c4988cf","Technology Management, University of California, Santa Barbara, Santa Barbara, CA, United States","Beane, M.I., Technology Management, University of California, Santa Barbara, Santa Barbara, CA, United States","This paper suggests that humans value robotic systems as signals-costly, visible commitments that can secure access to preferred resources. This contrasts with HRI research, design, engineering and deployment, which have focused on robots' instrumental value-namely how designing and interacting with them may produce more or less productivity. Drawing on a multiyear ethnography of a ""failed"" robotic telepresence deployment in a teaching hospital, this paper shows that robots' signaling value can significantly outweigh-and even contravene-any practical utility that they may provide through use. This analysis further suggests that-unlike nontechnological organizational signals-robots' signaling value is highly contingent on the observability of their use. Considering robots' signaling value in complex social systems such as organizations promises improved robotic systems development and deployment techniques and improved prediction regarding human-robot interaction. © 2020 Association for Computing Machinery.","Ethnography; HRI theory; Robotic telepresence; Signaling theory","Hospitals; Machine design; Man machine systems; Robotics; Social robots; Visual communication; Complex social systems; Empirical investigation; Ethnography; HRI theory; Improved robotic systems; Robotic systems; Signaling theory; Telepresence; Signaling",,,,,,,,,,,,,,,,"Mutlu, B., Forlizzi, J., Robots in organizations: The role of workflow, social, and environmental factors in human-robot interaction (2008) Proceedings of the 3rd ACM/IEEE International Conference on Human Robot Interaction, pp. 287-294; Lee, M.K., Takayama, L., Now, i have a body: Uses and social norms for mobile remote presence in the workplace (2011) Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, pp. 33-42; Stubbs, K., Hinds, P., Wettergreen, D., Challenges to grounding in human-robot interaction (2006) Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-robot Interaction, New York, NY, USA, pp. 357-358; Sauppé, A., Mutlu, B., The social impact of a robot co-worker in industrial settings (2015) Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 3613-3622; Yanco, A.H.A., Norton, Ober, W., Shane, D., Skinner, A., Vice, J., Analysis of human-robot interaction at the darpa robotics challenge trials (2015) J. Field Robot., 32 (3), pp. 420-444; Jung, M., Hinds, P., Robots in the wild: A time for more robust theories of human-robot interaction (2018) ACM Trans Hum-Robot Interact, 7 (1), pp. 21-25. , May; Summers, L., Opinion | Picking on Robots Won't Deal with Job Destruction, , Washington Post, 05-Mar-2017; Delaney, K.J., The robot that takes your job should pay taxes, says bill gates Quartz, , http://qz.com/911968/bill-gates-the-robot-that-takes-your-job-should-pay-taxes, 17-Feb-2017. [Online]. 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Visual Information and Knowledge Management, pp. 503-523; Wilcox, R., Shah, J., Optimization of multi-agent workflow for human-robot collaboration in assembly manufacturing (2012) Infotech@ Aerospace, 2012, p. 2535; Casper, J., Murphy, R.R., Human-robot interactions during the robot-assisted urban search and rescue response at the world trade center (2003) IEEE Trans. Syst. Man Cybern. Part B Cybern., 33 (3), pp. 367-385; Spence, M., Job market signaling (1973) Q. J. Econ., 87 (3), pp. 355-374; Barley, S.R., Kunda, G., Bringing work back in (2001) Organ. Sci., 12 (1), pp. 76-95; Bechky, B.A., Making organizational theory work: Institutions, occupations, and negotiated orders (2011) Organ. Sci., 22 (5), pp. 1157-1167. , Sep; Admoni, H., Weng, T., Hayes, B., Scassellati, B., Robot nonverbal behavior improves task performance in difficult collaborations (2016) The Eleventh ACM/IEEE International Conference on Human Robot Interaction, Piscataway, NJ, USA, pp. 51-58; Rule, A., Forlizzi, J., Designing interfaces for multi-user, multi-robot systems (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, New York, NY, USA, pp. 97-104; Hayes, B., Shah, J.A., Improving robot controller transparency through autonomous policy explanation (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, New York, NY, USA, pp. 303-312; Wagoner, A., Humanoid robots rescuing humans and extinguishing fires for cooperative fire security system using harms (2015) 2015 6th International Conference on Automation, Robotics and Applications (ICARA), pp. 411-415; Guneysu Ozgur, A., Iterative design of an upper limb rehabilitation game with tangible robots (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 241-250; Stricker, R., Interactive mobile robots guiding visitors in a university building (2012) 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication, pp. 695-700; Sung, J.Y., Guo, L., Grinter, R., Christensen, H., My roomba is rambo': Intimate home appliances (2007) UbiComp 2007 Ubiquitous Comput., pp. 145-162; Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D., User acceptance of information technology: Toward a unified view (2003) MIS Q., pp. 425-478; Anderson, P., Tushman, M.L., Technological discontinuities and dominant designs: A cyclical model of technological change (1990) Adm. 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E-Health, 21 (8), pp. 637-643; Smedegaard, C.V., Reframing the role of novelty within social hri: From noise to information (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 411-420; Rogers, E.M., (2003) Diffusion of Innovations, 5th Edition, 5th Edition, , New York: Free Press; Murphy, R.R., Human-robot interaction in rescue robotics (2004) IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., 34 (2), pp. 138-153; Kuss, A., Hollmann, R., Dietz, T., Hägele, M., Manufacturing knowledge for industrial robot systems: Review and synthesis of model architecture (2016) 2016 IEEE International Conference on Automation Science and Engineering (CASE), pp. 348-354; Orlikowski, W.J., Scott, S.V., Sociomateriality: Challenging the separation of technology, work and organization (2008) Acad. Manag. Ann., 2 (1), pp. 433-474; Leonardi, P.M., Theoretical foundations for the study of sociomateriality (2013) Inf. Organ., 23 (2), pp. 59-76. , Apr; Darling, K., (2015) Who's Johnny' Anthropomorphic Framing in Human-Robot Interaction, Integration, and Policy, , Social Science Research Network, Rochester, NY, SSRN Scholarly Paper ID 2588669, Mar; Amiot, C.E., Bastian, B., Toward a psychology of human-animal relations (2015) Psychol. Bull., 141 (1), p. 6; Sevillano, V., Fiske, S.T., Animals as social objects (2016) Eur. Psychol.; Goldstein, D.S., Kopin, I.J., Evolution of concepts of stress (2007) Stress, 10 (2), pp. 109-120; Cannon, W.B., (1963) The Wisdom of the Body Rev. and Enl. Ed Edition, , New York: W. W. Norton & Company; Livy, T., McDonald, A.H., (1976) Rome and the Mediterrean: The History of Rome from Its Foundation, Books, pp. 31-45. , 1st edition. Harmondsworth: Penguin Classics","Beane, M.I.; Technology Management, United States; email: mattbeane@ucsb.edu",,"ACM SIGAI;ACM SIGCHI;IEEE Robotics and Automation Society( IEEE RAS)","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158224,21672148,9781450367462,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85082004008
"Gull M.A., Bai S., Bak T.","57194162801;23092693700;7005968177;","A review on design of upper limb exoskeletons",2020,"Robotics","9","1","16","","",,61,"10.3390/robotics9010016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082558547&doi=10.3390%2frobotics9010016&partnerID=40&md5=7c0628500523940fd408ffc0b0d595ae","Department of Mechanical and Manufacturing Engineering, Aalborg University, Aalborg, 9220, Denmark; Department of Electronics Systems, Aalborg University, Aalborg, 9220, Denmark","Gull, M.A., Department of Mechanical and Manufacturing Engineering, Aalborg University, Aalborg, 9220, Denmark; Bai, S., Department of Mechanical and Manufacturing Engineering, Aalborg University, Aalborg, 9220, Denmark; Bak, T., Department of Electronics Systems, Aalborg University, Aalborg, 9220, Denmark","Exoskeleton robotics has ushered in a new era of modern neuromuscular rehabilitation engineering and assistive technology research. The technology promises to improve the upper-limb functionalities required for performing activities of daily living. The exoskeleton technology is evolving quickly but still needs interdisciplinary research to solve technical challenges, e.g., kinematic compatibility and development of effective human-robot interaction. In this paper, the recent development in upper-limb exoskeletons is reviewed. The key challenges involved in the development of assistive exoskeletons are highlighted by comparing available solutions. This paper provides a general classification, comparisons, and overview of the mechatronic designs of upper-limb exoskeletons. In addition, a brief overview of the control modalities for upper-limb exoskeletons is also presented in this paper. A discussion on the future directions of research is included. © 2020 by the authors.","Assistive technology; Exoskeleton review; Human-robot interaction; Neuromuscular rehabilitation; Upper-limb exoskeleton",,,,,,"Aalborg Universitet, AAU","This work is supported in part by Innovative Fund Denmark Grand Solutions project Exo-aider and AAU EXOTIC project.",,,,,,,,,,"Bai, S., Virk, G.S., Sugar, T., (2018) Wearable Exoskeleton Systems: Design, Control and Applications;, , Institution of Engineering and Technology: London, UK; Kim, S., Nussbaum, M.A., Esfahani, M.I.M., Alemi, M.M., Jia, B., Rashedi, E., Assessing the influence of a passive, upper extremity exoskeletal vest for tasks requiring arm elevation: Part II-""Unexpected"" effects on shoulder motion, balance, and spine loading (2018) Appl. Ergon, 70, pp. 323-330. , [CrossRef]; Carmeli, E., Peleg, S., Bartur, G., Elbo, E., Vatine, J.J., HandTutorTM enhanced hand rehabilitation after stroke-A pilot study (2011) Physiother. Res. 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"Li Y., Tee K.P., Yan R., Ge S.S.","35174564100;55663612200;55953742700;25924925100;","Reinforcement learning for human-robot shared control",2020,"Assembly Automation","40","1",,"105","117",,4,"10.1108/AA-10-2018-0153","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074020841&doi=10.1108%2fAA-10-2018-0153&partnerID=40&md5=a8c59346d6b1a4df5eec71a6e8ba44ef","University of Sussex, Falmer, United Kingdom; Institute for Infocomm Research, Singapore, Singapore; Sichuan University, Chengdu, China; National Universit of Singapore, Singapore, Singapore","Li, Y., University of Sussex, Falmer, United Kingdom; Tee, K.P., Institute for Infocomm Research, Singapore, Singapore; Yan, R., Sichuan University, Chengdu, China; Ge, S.S., National Universit of Singapore, Singapore, Singapore","Purpose: This paper aims to propose a general framework of shared control for human–robot interaction. Design/methodology/approach: Human dynamics are considered in analysis of the coupled human–robot system. Motion intentions of both human and robot are taken into account in the control objective of the robot. Reinforcement learning is developed to achieve the control objective subject to unknown dynamics of human and robot. The closed-loop system performance is discussed through a rigorous proof. Findings: Simulations are conducted to demonstrate the learning capability of the proposed method and its feasibility in handling various situations. Originality/value: Compared to existing works, the proposed framework combines motion intentions of both human and robot in a human–robot shared control system, without the requirement of the knowledge of human’s and robot’s dynamics. © 2019, Emerald Publishing Limited.","Control; Cooperative robots; Robotics","Closed loop systems; Control engineering; Dynamics; Machine design; Man machine systems; Reinforcement learning; Robotics; Control objectives; Cooperative robots; Design/methodology/approach; Human-robot shared control; Learning capabilities; Motion intention; Robot interactions; Shared-control systems; Social robots",,,,,,,,,,,,,,,,"Bertsekas, D.P., Homer, M.L., Logan, D.A., Patek, S.D., Sandell, N.R., Missile defense and interceptor allocation by neuro-dynamic programming (2000) IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 30 (1), pp. 42-51; Buerger, S.P., Hogan, N., Complementary stability and loop shaping for improved human-robot interaction (2007) IEEE Transactions on Robotics, 23 (2), pp. 232-244; 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University of SussexUnited Kingdom; email: hit.li.yn@gmail.com",,,"Emerald Group Holdings Ltd.",,,,,01445154,,ASAUD,,"English","Assem. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85074020841
"Wang B., Li Y., Lang H., Wang Y.","57209905481;57194270657;24331841200;8693672000;","Hand gesture recognition and motion estimation using the Kinect sensor",2020,"Mechatronic Systems and Control","48","1",,"17","24",,2,"10.2316/J.2020.201-0014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080877814&doi=10.2316%2fJ.2020.201-0014&partnerID=40&md5=776916516c92eb9250f69768d839f866","Department of Automotive, Mechanical and Manufacturing Engineering, Ontario Tech University, Oshawa, ON, Canada; Department of Mechatronics Engineering, Kennesaw State University, Marietta, GA  30060, United States","Wang, B., Department of Automotive, Mechanical and Manufacturing Engineering, Ontario Tech University, Oshawa, ON, Canada; Li, Y., Department of Automotive, Mechanical and Manufacturing Engineering, Ontario Tech University, Oshawa, ON, Canada; Lang, H., Department of Automotive, Mechanical and Manufacturing Engineering, Ontario Tech University, Oshawa, ON, Canada; Wang, Y., Department of Mechatronics Engineering, Kennesaw State University, Marietta, GA  30060, United States","This paper proposes and implements an efficient method to recognize hand gestures and estimate the motion of each fingertip using a combination of RGB image and depth image data acquired from Microsoft's Kinect sensor. The real-time performance of hand identification, tracking and motion estimation is achieved without any assistance of additional electronic devices. To guarantee the robust performance, differences between subsequent frames are analysed and specific algorithms are utilized in the proposed system. In addition, the calibration using both focal length and angle range of the camera is applied to coordinate with different devices to obtain accurate speed estimation. The experiment results show the robust and real-time performance. The proposed approach can be widely applied to robotic applications, such as visual servoing and human-robot interactions. © 2020 Acta Press. All rights reserved.","Hand gesture; Human-computer interaction; Microsoft Kinect; Speed Estimation","Human computer interaction; Human robot interaction; Motion sensors; Motion tracking; Palmprint recognition; Visual servoing; Hand gesture; Hand identifications; Hand-gesture recognition; Microsoft kinect; Real time performance; Robotic applications; Robust performance; Speed estimation; Motion estimation",,,,,,,,,,,,,,,,"Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A., Face recognition: A literature survey (2003) ACM Computing Surveys, 35 (4), pp. 399-458; Dailey, D.J., Cathey, F.W., Pumrin, S., An algorithm to estimate mean traffic speed using uncalibrated cameras (2000) IEEE Transactions on Intelligent Transportation Systems, 1, pp. 98-107; Ren, Z., Yuan, J., Meng, J., Zhang, Z., Robust partbased hand gesture recognition using Kinect sensor (2013) IEEE Transactions on Multimedia, 15 (5), pp. 1110-1120; Dewaele, G., Devernay, F., Horaud, R., Hand motion from 3D point trajectories and a smooth surface model (2004) Proc. Eur. Conf. Computer Vision, pp. 495-507. , Prague, Czech Republic; Wachs, J.P., Kolsch, M., Stern, H., Edan, Y., Vision-based hand-gesture applications (2011) Communications of the ACM, 54, pp. 60-71; Alostaz, A., Optimized automated tracking of a moving object with a robotic eye system (2016) Control and Intelligent Systems, 44 (1), pp. 18-26; Kim, L.H., Design and implementation of artificial intelligent motorized wheelchair system using speech recognition and joystick (2005) Control and Intelligent Systems, 33 (2), pp. 102-109; Raheja, J.L., Shyam, R., Kumar, U., Prasad, P.B., Real-time robotic hand control using hand gestures (2010) Second International Conf. on Machine Learning and Computing, , February 9-11; Chaudhary, A., (2017) Robust Hand Gesture Recognition for Robotic Hand Control, , (New York: Springer); Bora, D.J., Gupta, A.K., Khan, F.A., Comparing the performance of L∗A∗B∗ and HSV color spaces with respect to color image segmentation (2015) International Journal of Emerging Technology and Advanced Engineering, 5 (2), pp. 192-203; Schmid, C., Mohr, R., Bauckhage, C., Evaluation of interest point detectors (2000) International Journal of Computer Vision, 37 (2), pp. 151-172; Lowe, D.G., Distinctive image features from scale-invariant key points (2004) International Journal of Computer Vision, 60 (2), pp. 91-110; Szeliski, R., (2011) Computer Vision: Algorithms and Applications, , (New York, NJ: Springer); Ghosh, D.K., Ari, S., On an algorithm for Vision-based hand gesture recognition (2016) Signal, Image and Video Processing, 10 (4), pp. 655-662; Sun, C., Zhang, T., Xu, C., Latent support vector machine modeling for sign language recognition with Kinect (2015) ACM Transactions on Intelligent Systems and Technology, 6 (2), pp. 1-20; Pisharady, P.K., Saerbeck, M., Recent methods and databases in vision-based hand gesture recognition: A review (2015) Computer Vision and Image Understanding, 141 (141), pp. 152-165; Yao, Y., Fu, Y., Contour Model-based hand-gesture recognition using the Kinect sensor (2014) IEEE Transactions on Circuits and Systems for Video Technology, 24 (11); Suzuki, S., Abe, K., Topological structural analysis of digital binary images by border following (1985) Computer Vision Graphics Image Processing, 30 (1), pp. 33-46; Russell, B., Norvig, K., (2003) Artificial Intelligence: A Modern Approach, , (Englewood Cliffs, NJ: Prentice-Hall); Ying, W., Lang, H., De Silva, C.W., A hybrid visual servo controller for robust grasping by wheeled mobile robots (2010) IEEE/ASME Transaction on Mechatronics, 15 (5), pp. 757-769; Lang, H., Khan, M., Tan, K.K., De Silva, C.W., Application of visual servo control in autonomous mobile rescue robots (2016) International Journal of Computers, Communications & Control, 11 (5), pp. 685-696","Lang, H.; Department of Automotive, Canada; email: Haoxiang.Lang@uoit.ca",,,"Acta Press",,,,,25611771,,,,"English","Mechatronic Syst. Control",Article,"Final","",Scopus,2-s2.0-85080877814
"Wang P., Wang J., Paranamana P., Shafto P.","57209450763;57222273215;57208568316;6508237891;","A mathematical theory of cooperative communication",2020,"Advances in Neural Information Processing Systems","2020-December",,,"","",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108411071&partnerID=40&md5=2147c99fc0d75836e04a0ea8fda2fbf4","Rutgers University, Newark, United States","Wang, P., Rutgers University, Newark, United States; Wang, J., Rutgers University, Newark, United States; Paranamana, P., Rutgers University, Newark, United States; Shafto, P., Rutgers University, Newark, United States","Cooperative communication plays a central role in theories of human cognition, language, development, culture, and human-robot interaction. Prior models of cooperative communication are algorithmic in nature and do not shed light on why cooperation may yield effective belief transmission and what limitations may arise due to differences between beliefs of agents. Through a connection to the theory of optimal transport, we establishing a mathematical framework for cooperative communication. We derive prior models as special cases, statistical interpretations of belief transfer plans, and proofs of robustness and instability. Computational simulations support and elaborate our theoretical results, and demonstrate fit to human behavior. The results show that cooperative communication provably enables effective, robust belief transmission which is required to explain feats of human learning and improve human-machine interaction. © 2020 Neural information processing systems foundation. All rights reserved.",,"Behavioral research; Computation theory; Man machine systems; Social robots; Computational simulation; Human behaviors; Human cognition; Human machine interaction; Mathematical frameworks; Mathematical theory; Optimal transport; Statistical interpretation; Cooperative communication",,,,,"National Science Foundation, NSF: MRI 1828528, SMA-1640816; U.S. Department of Defense, DOD: 72531RTREP; Army Research Office, ARO: HR00112020039; Defense Advanced Research Projects Agency, DARPA: FA8750-17-2-0146; Air Force Research Laboratory, AFRL","This material is based on research sponsored by the Air Force Research Laboratory and DARPA under agreement number FA8750-17-2-0146 and the Army Research Office and DARPA under agreement HR00112020039. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.","This work was also supported by DoD grant 72531RTREP, NSF SMA-1640816, NSF MRI 1828528 to PS.","This project was supported by DARPA grant HR00112020039 the content of the information does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.",,,,,,,,"Bonawitz, Elizabeth, Shafto, Patrick, Gweon, Hyowon, Goodman, Noah D, Spelke, Elizabeth, Schulz, Laura, The double-edged sword of pedagogy: Instruction limits spontaneous exploration and discovery (2011) Cognition, 120 (3), pp. 322-330; Boyd, Robert, Richerson, Peter J, Henrich, Joseph, The cultural niche: Why social learning is essential for human adaptation (2011) Proceedings_of_the_National_Academy_of_Sciences, 108, pp. 10918-10925. , (Supplement 2); Brenier, Yann, Polar factorization and monotone rearrangement of vector-valued functions (1991) Communications_on_pure_and_applied_mathematics, 44 (4), pp. 375-417; Bridgers, Sophie, Jara-Ettinger, Julian, Gweon, Hyowon, Young children consider the expected utility of others’ learning to decide what to teach (2019) Nature_human_behaviour, pp. 1-9; Chater, Nick, Oaksford, Mike, (2008) The_probabilistic_mind:_Prospects_for_Bayesian_cognitive_science, , OUP Oxford; Csibra, Gergely, Gergely, György, Natural pedagogy (2009) Trends_in_cognitive_sciences, 13 (4), pp. 148-153; Csiszar, Imre, A geometric interpretation of darroch and ratcliff’s generalized iterative scaling (1989) The Annals_of_Statistics, pp. 1409-1413; Cuturi, Marco, Sinkhorn distances: Lightspeed computation of optimal transport (2013) Advances_in neural_information_processing_systems, pp. 2292-2300; Dantzig, George B, Programming of interdependent activities: Ii mathematical model (1949) Econometrica, Journal_of_the_Econometric_Society, pp. 200-211; Darroch, John N, Ratcliff, Douglas, Generalized iterative scaling for log-linear models (1972) The_annals of_mathematical_statistics, pp. 1470-1480; Eaves, Baxter S, Shafto, Patrick, (2016) Toward a general, scaleable framework for bayesian teaching with applications to topic models, , arXiv_preprint_arXiv:1605.07999; Eaves, Baxter S, Feldman, Naomi H, Griffiths, Thomas L, Shafto, Patrick, Infant-directed speech is consistent with teaching (2016) Psychological_review, 123 (6), p. 758; Fisac, Jaime F, Gates, Monica A, Hamrick, Jessica B, Liu, Chang, Hadfield-Menell, Dylan, Palaniappan, Malayandi, Malik, Dhruv, Dragan, Anca D, (2017) Pragmatic-pedagogic value alignment, , arXiv_preprint_arXiv:1707.06354; Franke, Michael, Jäger, Gerhard, Probabilistic pragmatics, or why bayes’ rule is probably important for pragmatics (2016) Zeitschrift_für_sprachwissenschaft, 35 (1), pp. 3-44; Franklin, Joel, Lorenz, Jens, On the scaling of multidimensional matrices (1989) Linear_Algebra_and_its applications, 114, pp. 717-735; Genevay, Aude, Peyré, Gabriel, Cuturi, Marco, (2017) Learning generative models with sinkhorn divergences, , arXiv_preprint_arXiv:1706.00292; 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Ho, Mark K, Littman, Michael L, Cushman, Fiery, Austerweil, Joseph L, Effectively learning from pedagogical demonstrations (2018) Proceedings_of_the_Annual_Conference_of_the_Cognitive_Science Society; Idel, Martin, (2016) A review of matrix scaling and sinkhorn’s normal form for matrices and positive maps, , arXiv_preprint_arXiv:1609.06349; Jara-Ettinger, Julian, Theory of mind as inverse reinforcement learning (2019) Current_Opinion_in_Behavioral Sciences, 29, pp. 105-110; Jara-Ettinger, Julian, Gweon, Hyowon, Schulz, Laura E, Tenenbaum, Joshua B, The naive utility calculus: Computational principles underlying commonsense psychology (2016) Trends_in_cognitive sciences, 20 (8), pp. 589-604; Jern, Alan, Lucas, Christopher G, Kemp, Charles, People learn other people’s preferences through inverse decision-making (2017) Cognition, 168, pp. 46-64; Kantorovich, Leonid V, On the translocation of masses (2006) Journal_of_Mathematical_Sciences, 133 (4), pp. 1381-1382; Knight, Philip A, The sinkhorn–knopp algorithm: convergence and applications (2008) SIAM_Journal_on Matrix_Analysis_and_Applications, 30 (1), pp. 261-275; 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Cognitive Science Society; Shafto, Patrick, Goodman, Noah D, Frank, Michael C, Learning from others: The consequences of psychological reasoning for human learning (2012) Perspectives_on_Psychological_Science, 7 (4), pp. 341-351; Shafto, Patrick, Goodman, Noah D, Griffiths, Thomas L, A rational account of pedagogical reasoning: Teaching by, and learning from, examples (2014) Cognitive_Psychology, 71, pp. 55-89; Sinkhorn, Richard, Knopp, Paul, Concerning nonnegative matrices and doubly stochastic matrices (1967) Pacific_Journal_of_Mathematics, 21 (2), pp. 343-348; Sperber, Dan, Wilson, Deirdre, (1986) Relevance:_Communication_and_cognition, 142. , Harvard University Press Cambridge, MA; Sutton, Richard S, Barto, Andrew G, (1998) Introduction_to_reinforcement_learning, 2. , MIT press Cambridge; Tenenbaum, Joshua B, Kemp, Charles, Griffiths, Thomas L, Goodman, Noah D, How to grow a mind: Statistics, structure, and abstraction (2011) science, 331 (6022), pp. 1279-1285; Tomasello, M., (1999) The_cultural_origins_of_human_cognition, , Harvard University Press, Cambridge, MA; Villani, Cédric, (2008) Optimal_transport:_old_and_new, 338. , Springer Science & Business Media; Wang, Pei, Paranamana, Pushpi, Shafto, Patrick, Generalizing the theory of cooperative inference (2019), AIStats; Yang, Scott Cheng-Hsin, Yu, Yue, Givchi, Arash, Wang, Pei, Vong, Wai Keen, Shafto, Patrick, Optimal cooperative inference (2018) AISTATS, volume 84 of Proceedings_of_Machine_Learning Research, pp. 376-385. , PMLR; Zhu, Xiaojin, Machine teaching for bayesian learners in the exponential family (2013) Advances_in Neural_Information_Processing_Systems, pp. 1905-1913; Zhu, Xiaojin, Machine teaching: An inverse problem to machine learning and an approach toward optimal education (2015) Twenty-Ninth_AAAI_Conference_on_Artificial_Intelligence",,,"Apple;et al.;Microsoft;PDT Partners;Sony;Tenstorrent","Neural information processing systems foundation","34th Conference on Neural Information Processing Systems, NeurIPS 2020","6 December 2020 through 12 December 2020",,169463,10495258,,,,"English","Adv. neural inf. proces. syst.",Conference Paper,"Final","",Scopus,2-s2.0-85108411071
"Sheikholeslami S., Lee G., Hart J.W., Srinivasa S., Croft E.A.","57117573300;57208850335;25031145200;6602084313;7003817722;","A Study of Reaching Motions for Collaborative Human-Robot Interaction",2020,"Springer Proceedings in Advanced Robotics","11",,,"584","594",,,"10.1007/978-3-030-33950-0_50","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107057338&doi=10.1007%2f978-3-030-33950-0_50&partnerID=40&md5=cc2c540946041eb6bc5b06563f933066","Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada; Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, United States; Faculty of Computer Science, University of Texas at Austin, Austin, TX, United States; Faculty of Engineering, Monash University, Clayton, Australia","Sheikholeslami, S., Department of Mechanical Engineering, University of British Columbia, Vancouver, BC, Canada; Lee, G., Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, United States; Hart, J.W., Faculty of Computer Science, University of Texas at Austin, Austin, TX, United States; Srinivasa, S., Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, United States; Croft, E.A., Faculty of Engineering, Monash University, Clayton, Australia","In human-human interactions, individuals naturally achieve fluency by anticipating the partner’s actions. This predictive ability is largely lacking in collaborative robots, leading to inefficient human-robot interactions. Fluent meshing in human-robot collaboration requires the robot to make its intentions clear to its human collaborator. We propose a unified generative model of human reaching motions that allows the robot to (a) infer human intent, and then (b) plan its motion to be legible, or intent-expressive. We conducted a study on human reaching motion and constructed an elliptical motion model that is shown to yield a good fit to empirical data. In future studies, we plan to confirm the effectiveness of this model in predicting human intent and conveying robot intent for achieving fluency in human-robot handovers. © 2020, Springer Nature Switzerland AG.",,,,,,,,,,,,,,,,,,"Tomasello, M., Carpenter, M., Call, J., Behne, T., Moll, H., (2005) Behav. Brain Sci., 28 (5), pp. 675-691; Dragan, A.D., Bauman, S., Forlizzi, J., Srinivasa, S.S., (2015) In: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction (ACM), Pp, pp. 51-58; Cakmak, M., Srinivasa, S.S., Lee, M.K., Kiesler, S., Forlizzi, J., (2011) In: Proceedings of the 6Th International Conference on Human-Robot Interaction. ACM, New York, NY, USA,, HRI 2011, Pp, pp. 489-496; Beetz, M., Stulp, F., Esden-Tempski, P., Fedrizzi, A., Klank, U., Kresse, I., Maldonado, A., Ruiz, F., (2009) Autonom. Robots, 28 (1), p. 21; Dragan, A.D., Lee, K.C.T., Srinivasa, S.S., (2013) In: 2013 8Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Pp, pp. 301-308; Hoffman, G., Breazeal, C., (2007) IEEE Trans. Robot., 23 (5), p. 952; Lasseter, J.: In: ACM Siggraph Computer Graphics, vol. 21, pp. 35–44. ACM (1987); Flash, T., Hogan, N., (1985) J. Neurosci., 5 (7), p. 1688; Argall, B.D., Chernova, S., Veloso, M., Browning, B., (2009) Robot. Auton. Syst., 57 (5), p. 469; Abbeel, P., Ng, A.Y., In: Proceedings of the Twenty-First International Conference ON Machine Learning, p. 1 (2004) ACM; Dragan, A.D., Gordon, G.J., Srinivasa, S.S., In: Robotics Research, pp. 309–326 (2017) Springer; Sciutti, A., Sandini, G., (2017) IEEE Trans. Neural Syst. Rehabil. Eng., 25 (12), p. 2295; Flash, T., Hogans, N., (1985) J. Neurosci., 5, p. 1688; Harris, C., Wolpert, D.M., Signal-dependent noise determines motor planning (1998) Nature, 394 (6695), p. 780; Wu, G., van der Helm, F.C., (2005) J. Biomech., 38 (5), p. 981; Todorov, E., Jordan, M., (2002) Nat. Neurosci., 5, p. 1226; Morasso, P., (1981) Exp. Brain Res., 42 (2), p. 223; Collewijn, H., Erkelens, C.J., Steinman, R.M., (1988) J. Physiol., 404 (1), p. 157; Nurunnabi, A., Belton, D., West, G., (2012) ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Science, 1-3, pp. 269-274. , vol., pp; Hartley, R.I., Zisserman, A., (2004) Multiple View Geometry in Computer Vision, , 2nd edn. Cambridge University Press, Cambridge, ). ISBN: 0521540518; Ayoub, A.B., (1993) Math. Mag., 66 (5), p. 322","Croft, E.A.; Faculty of Engineering, Australia; email: elizabeth.croft@monash.edu",,,"Springer Science and Business Media B.V.",,,,,25111256,,,,"English","Springer. Proc. Adv. Robot.",Book Chapter,"Final","",Scopus,2-s2.0-85107057338
"Guggemos J., Seufert S., Sonderegger S.","57200695415;6603394178;57209238903;","Pepper: A humanoid robot with personality?",2020,"17th International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2020",,,,"345","348",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099529742&partnerID=40&md5=b5a96d8315d23f79c09a396a73fc3381","University of St.Gallen, St. Jakob-Str. 21, St. Gallen, 9000, Switzerland","Guggemos, J., University of St.Gallen, St. Jakob-Str. 21, St. Gallen, 9000, Switzerland; Seufert, S., University of St.Gallen, St. Jakob-Str. 21, St. Gallen, 9000, Switzerland; Sonderegger, S., University of St.Gallen, St. Jakob-Str. 21, St. Gallen, 9000, Switzerland","Perceived personality plays an important role in human-robot interaction. This paper uses the Big Five Inventory-2 (BFI-2-S) to assess the perceived personality of a type Pepper social robot. Pepper provided a sample of its capabilities during an introduction to academic writing university course. N = 462 students evaluated Pepper's personality by answering the BFI-2-S questions. A confirmatory factor analysis with the implied five-factor structure did not converge. A subsequent exploratory factor analysis yielded inconclusive results. Although we were not able to provide evidence for a Big Five personality structure of the social robot, perceived personality may be an important characteristic of a social robot: The latent correlation of a factor labeled 'positive characteristics' with the intention to use the robot equals .487. © 2020 17th International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2020. All rights reserved.","Big Five; Pepper; Personality; Social Robot",,,,,,,,,,,,,,,,,"Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Science Robotics, 3 (21), pp. 1-9. , https://doi.org/10.1126/scirobotics.aat5954; Cooney, M., Leister, W., Using the Engagement Profile to Design an Engaging Robotic Teaching Assistant for Students (2019) Robotics, 8 (1), pp. 1-26. , https://doi.org/10.3390/robotics8010021; Craenen, B. G.W., Deshmukh, A., Foster, M. E., Vinciarelli, A., Shaping Gestures to Shape Personality: Big-Five Traits, Godspeed Scores and the Similarity-Attraction Effect (2018) Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems, pp. 2221-2223. , M. Dastani, M. Sukthankar, E. André, & S. Koenig (Eds), Stockholm: International Foundation for Autonomous Agents and Multiagent Systems; Gardecki, A., Podpora, M., (2017) Experience from the operation of the Pepper humanoid robots, , https://doi.org/10.1109/PAEE.2017.8008994, Piscataway, NJ: IEEE; Goldberg, L. R., An alternative ""description of personality"": The Big-Five factor structure (1990) Journal of Personality and Social Psychology, 59 (6), pp. 1216-1229. , https://doi.org/10.1037/0022-3514.59.6.1216; Goretzko, D., Pham, T. T. H., Bühner, M., Exploratory factor analysis: Current use, methodological developments and recommendations for good practice (2019) Current Psychology, , https://doi.org/10.1007/s12144-019-00300-2, Advance online publication; Guggemos, J., Burkhard, M., Seufert, S., Sonderegger, S., Social Robots as Teaching Assistance System in Higher Education: Conceptual Framework for the Development of Use Cases (2020) Proceedings of the 12th International Conference on Computer Supported Education (CSEDU), pp. 125-132. , https://doi.org/10.5220/0009794801250132; Guggemos, J., Seufert, S., Sonderegger, S., Humanoid robots in higher education: Evaluating the acceptance of Pepper in the context of an academic writing course using the UTAUT (2020) British Journal of Educational Technology, 51 (5), pp. 1864-1883. , https://doi.org/10.1111/bjet.13006; Lee, K. M., Peng, W., Jin, S.-A., Yan, C., Can Robots Manifest Personality?: An Empirical Test of Personality Recognition, Social Responses, and Social Presence in Human-Robot Interaction (2006) Journal of Communication, 56 (4), pp. 754-772. , https://doi.org/10.1111/j.1460-2466.2006.00318.x; McNeish, D., Thanks coefficient alpha, we'll take it from here (2018) Psychological Methods, 23 (3), pp. 412-433. , https://doi.org/10.1037/met0000144; Mou, Y., Shi, C., Shen, T., Xu, K., A Systematic Review of the Personality of Robot: Mapping Its Conceptualization, Operationalization, Contextualization and Effects (2020) International Journal of Human-Computer Interaction, 36 (6), pp. 591-605. , https://doi.org/10.1080/10447318.2019.1663008; Mubin, O., Stevens, C. J., Shahid, S., Mahmud, A. A., Dong, J.-J., A Review of the applicability of robots in education (2013) Technology for Education and Learning, 1 (1), pp. 1-7. , https://doi.org/10.2316/Journal.209.2013.1.209-0015; Soto, C. J., John, O. P., Short and extra-short forms of the Big Five Inventory-2: The BFI-2-S and BFI-2-XS (2017) Journal of Research in Personality, 68, pp. 69-81. , https://doi.org/10.1016/j.jrp.2017.02.004; Walters, M. L., Syrdal, D. S., Dautenhahn, K., te Boekhorst, R., Koay, K. L., Avoiding the uncanny valley: robot appearance, personality and consistency of behavior in an attention-seeking home scenario for a robot companion (2008) Autonomous Robots, 24 (2), pp. 159-178. , https://doi.org/10.1007/s10514-007-9058-3; Watkins, M. W., Exploratory Factor Analysis: A Guide to Best Practice (2018) Journal of Black Psychology, 44 (3), pp. 219-246. , https://doi.org/10.1177/0095798418771807",,,,"IADIS Press","17th International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2020","18 November 2020 through 20 November 2020",,166336,,9789898704221,,,"English","Int. Conf. Cogn. Explor. Learn. Digit. Age, CELDA",Conference Paper,"Final","",Scopus,2-s2.0-85099529742
"Amiri S., Shirazi M.S., Zhang S.","57204472065;55778609400;53980860100;","Learning and reasoning for robot sequential decision making under uncertainty",2020,"AAAI 2020 - 34th AAAI Conference on Artificial Intelligence",,,,"2726","2733",,5,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094086042&partnerID=40&md5=6df6fbe7c78dd33973b68993d7b1fc4d","SUNY Binghamton, Binghamton, NY  13902, United States; University of Indianapolis, Indianapolis, IN  44227, United States","Amiri, S., SUNY Binghamton, Binghamton, NY  13902, United States; Shirazi, M.S., University of Indianapolis, Indianapolis, IN  44227, United States; Zhang, S., SUNY Binghamton, Binghamton, NY  13902, United States","Robots frequently face complex tasks that require more than one action, where sequential decision-making (SDM) capabilities become necessary. The key contribution of this work is a robot SDM framework, called LCORPP, that supports the simultaneous capabilities of supervised learning for passive state estimation, automated reasoning with declarative human knowledge, and planning under uncertainty toward achieving long-term goals. In particular, we use a hybrid reasoning paradigm to refine the state estimator, and provide informative priors for the probabilistic planner. In experiments, a mobile robot is tasked with estimating human intentions using their motion trajectories, declarative contextual knowledge, and human-robot interaction (dialog-based and motion-based). Results suggest that, in efficiency and accuracy, our framework performs better than its no-learning and no-reasoning counterparts in office environment. Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.",,"Artificial intelligence; Decision making; Robot programming; Space division multiple access; State estimation; Automated reasoning; Contextual knowledge; Informative Priors; Motion trajectories; Office environments; Planning under uncertainty; Sequential decision making; Sequential decision making under uncertainties; Human robot interaction",,,,,"National Science Foundation, NSF: 1925044, IIS-1925044; Ford Motor Company","This work has taken place in the Autonomous Intelligent Robotics (AIR) Group at SUNY Binghamton. AIR research is supported in part by grants from the National Science Foundation (IIS-1925044), Ford Motor Company, and SUNY Research Foundation.",,,,,,,,,,"Aditya, S., Saha, R., Yang, Y., Baral, C., Spatial knowledge distillation to aid visual reasoning (2019) WACV, pp. 227-235; Amiri, S., Wei, S., Zhang, S., Sinapov, J., Thomason, J., Stone, P., Multi-modal predicate identification using dynamically learned robot controllers (2018) IJCAI, pp. 4638-4645; Argall, B. D., Chernova, S., Veloso, M., Browning, B., A survey of robot learning from demonstration (2009) Robotics and autonomous systems, 57 (5), pp. 469-483; Bach, S. H., Hinge-loss markov random fields and probabilistic soft logic (2017) The JMLR, 18 (1), pp. 3846-3912; Baral, C., Gelfond, M., Rushton, N., Probabilistic reasoning with answer sets (2009) TPLP, 9 (1), pp. 57-144; Chen, X., Li, L.-J., Fei-Fei, L., Gupta, A., Iterative visual reasoning beyond convolutions (2018) CVPR; Chen, H., Tan, H., (2019) Enabling robots to understand incomplete natural language instructions using commonsense reasoning, , arXiv preprint arXiv:1904.12907; Chitnis, R., Kaelbling, L. P., Lozano-Pérez, T., (2018) Integrating human-provided information into belief state representation using dynamic factorization, , arXiv preprint arXiv:1803.00119; Chollet, F., (2015) Keras, , https://keras.io; Ferreira, L. A., Bianchi, R. A., Santos, P. E., de Mantaras, R. L., Answer set programming for non-stationary markov decision processes (2017) Applied Intelligence, 47 (4), pp. 993-1007; Gelfond, M., Kahl, Y., (2014) Knowledge representation, reasoning, and the design of intelligent agents: The answer-set programming approach, , Cambridge University Press; Graves, A., Mohamed, A.-r., Hinton, G., Speech recognition with deep recurrent neural networks (2013) IEEE-ICASSP; Griffith, S., Subramanian, K., Policy shaping: Integrating human feedback with reinforcement learning (2013) NIPS; Hawes, N., Burbridge, C., Jovan, F., The strands project: Long-term autonomy in everyday environments (2017) IEEE Robotics & Automation Magazine, 24 (3), pp. 146-156; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural computation, 9 (8), pp. 1735-1780; Hopfield, J. J., Neural networks and physical systems with emergent collective computational abilities (1982) Proceedings of the national academy of sciences, 79 (8), pp. 2554-2558; Illanes, L., Yan, X., Icarte, R. T., McIlraith, S. A., Symbolic planning and model-free reinforcement learning: Training taskable agents (2019) 4th Multidisciplinary Conference on RLDM; Kaelbling, L. P., Littman, M. L., Cassandra, A. R., Planning and acting in partially observable stochastic domains (1998) Artificial intelligence, 101 (1-2), pp. 99-134; Kato, Y., Kanda, T., Ishiguro, H., May i help you?: Design of human-like polite approaching behavior (2015) HRI; Khandelwal, P., Zhang, S., Sinapov, J., Leonetti, M., Thomason, J., Yang, F., BWIBots: A platform for bridging the gap between AI and human–robot interaction research (2017), The IJRR; Kingma, D. P., Ba, J., (2014) Adam: A method for stochastic optimization, , arXiv:1412.6980; Kurniawati, H., Hsu, D., Lee, W. S., Sarsop: Efficient point-based pomdp planning by approximating optimally reachable belief spaces (2008) Robotics: Science and Systems; Leonetti, M., Iocchi, L., Stone, P., A synthesis of automated planning and reinforcement learning for efficient, robust decision-making (2016) Artificial Intelligence, 241, pp. 103-130; Lifschitz, V., Answer sets and the language of answer set programming (2016) AI Magazine, 37 (3), pp. 7-12; Lu, D., Zhang, S., Stone, P., Chen, X., Leveraging commonsense reasoning and multimodal perception for robot spoken dialog systems (2017) IROS; Lu, K., Zhang, S., Stone, P., Chen, X., (2018) Robot represention and reasoning with knowledge from reinforcement learning, , arXiv:1809.11074; Lyu, D., Sdrl: interpretable and data-efficient deep reinforcement learning leveraging symbolic planning (2019) AAAI; Puterman, M. L., (2014) Markov decision processes: discrete stochastic dynamic programming, , John Wiley & Sons; Richardson, M., Domingos, P., Markov logic networks (2006) Machine learning, 62 (1-2), pp. 107-136; Sridharan, M., Rainge, S., Integrating reinforcement learning and declarative programming to learn causal laws in dynamic domains (2014) International Conference on Social Robotics; Sridharan, M., Gelfond, M., Zhang, S., Wyatt, J., REBA: A refinement-based architecture for knowledge representation and reasoning in robotics (2019) JAIR; Taylor, M. E., Borealis, A., Improving reinforcement learning with human input (2018) IJCAI, pp. 5724-5728; Thomaz, A. L., Breazeal, C., Reinforcement learning with human teachers: Evidence of feedback and guidance with implications for learning performance (2006) AAAI; Veloso, M. M., The increasingly fascinating opportunity for human-robot-ai interaction: The cobot mobile service robots (2018) ACM THRI, 7 (1), p. 5; Vinyals, O., Toshev, A., Bengio, S., Erhan, D., Show and tell: A neural image caption generator (2015) CVPR; Yang, F., Lyu, D., Liu, B., Gustafson, S., (2018) Peorl: Integrating symbolic planning and hierarchical reinforcement learning for robust decision-making, , arXiv preprint arXiv:1804.07779; Zellers, R., Bisk, Y., Farhadi, A., Choi, Y., From recognition to cognition: Visual commonsense reasoning (2019) CVPR; Zellers, R., Holtzman, A., (2019) Hellaswag: Can a machine really finish your sentence?, , arXiv preprint arXiv:1905.07830; Zhang, S., Stone, P., Corpp: Commonsense reasoning and probabilistic planning, as applied to dialog with a mobile robot (2015) AAAI, pp. 1394-1400; Zhang, S., Sridharan, M., Wyatt, J. L., Mixed logical inference and probabilistic planning for robots in unreliable worlds (2015) IEEE Transactions on Robotics, 31 (3), pp. 699-713",,,"Association for the Advancement of Artificial Intelligence","AAAI press","34th AAAI Conference on Artificial Intelligence, AAAI 2020","7 February 2020 through 12 February 2020",,166426,,9781577358350,,,"English","AAAI - AAAI Conf. Artif. Intell.",Conference Paper,"Final","",Scopus,2-s2.0-85094086042
"Sandygulova A., CohenMiller A., Oralbayeva N., Johal W., Asselborn T., Dillenbourg P.","55193519700;57193846239;57219172690;55912460600;57201704646;8912010400;","Cognitive Learning with a Robot: The Case of Script Acquisition",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12226 LNCS",,,"154","162",,,"10.1007/978-3-030-57506-9_12","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091562612&doi=10.1007%2f978-3-030-57506-9_12&partnerID=40&md5=cd8235efcea59f431e138cf380322809","Nazarbayev University, Nur-Sultan, Kazakhstan; University of New South Wales, Sydney, Australia; CHILI Lab, EPFL, Lausanne, Switzerland","Sandygulova, A., Nazarbayev University, Nur-Sultan, Kazakhstan; CohenMiller, A., Nazarbayev University, Nur-Sultan, Kazakhstan; Oralbayeva, N., Nazarbayev University, Nur-Sultan, Kazakhstan; Johal, W., University of New South Wales, Sydney, Australia, CHILI Lab, EPFL, Lausanne, Switzerland; Asselborn, T., CHILI Lab, EPFL, Lausanne, Switzerland; Dillenbourg, P., CHILI Lab, EPFL, Lausanne, Switzerland","This research is situated in a specialized context offering a rarely occurring opportunity for research questions and technical objectives about language acquisition. The Kazakh language transition from Cyrillic to Latin alphabet in Kazakhstan raises challenges to teach the whole population to write and read in the new script. We propose an unique interdisciplinary approach by integrating innovative solutions from robotics, computer vision fields and pedagogical strategies from education, linguistics and cognitive sciences that will assist various demographic groups in this challenging endeavor. This paper presents the proposed system—a human-robot interaction application designed to assist primary school children in learning a new script and its associated handwriting. The system was deployed in a series of experiments where children increase their knowledge of the new script when practicing handwriting with a robot. © 2020, Springer Nature Switzerland AG.","Child learning; Human-robot interaction; Language learning; Social robot — Cognitive learning theory — Interdisciplinarity","Application programs; Computer simulation languages; Computer vision; Educational robots; Embedded systems; Social robots; Cognitive learning; Cognitive science; Demographic groups; Innovative solutions; Language acquisition; Pedagogical strategies; Primary school children; Research questions; Formal methods",,,,,"Singapore Eye Research Institute, SERI: SFG 1038; Schweizerischer Nationalfonds zur F&#x00F6;rderung der Wissenschaftlichen Forschung, SNF; National Centre of Competence in Research Robotics; Nazarbayev University, NU: 091019CRP2107","Acknowledgments. This work was supported by the Nazarbayev University Collaborative Research Program grant (award number is 091019CRP2107). This research was also funded by the Swiss State Secretariat for Education, Research and Innovation (SERI) as parts of the CIS Region Seeding Grants (Project SFG 1038), and the NCCR Robotics (SNSF).",,,,,,,,,,"Altynsarina, E., (2018) Kazakhstan adopts new version of Latin-based Kazakh alphabet, , https://astanatimes.com/2018/02/kazakhstan-adopts-new-version-of-latin-based-kazakh-alphabet/; Kadirova, R.B., (2018) The Sociolinguistic Attitudes of Kazakhs Towards the Latin Alphabet and Orthography Reform in Kazakh, , https://cdr.lib.unc.edu/concern/dissertations/br86b405f; Crisp, S., Soviet language planning 1917–53 (1990) Language Planning in the Soviet Union, , https://doi.org/10.1007/978-1-349-20301-72, Kirkwood, M. (ed) Palgrave Macmillan, London; Hatcher, L., Script change in Azerbaijan: acts of identity (2008) Int. J. Soc. Lang, 2008 (192), pp. 105-116. , https://doi.org/10.1515/IJSL.2008.038; Mubin, O., Shahid, C.J., Al Mahmud, A., Dong, J.J., A review of the applicability of robots in education (2013) Technol. Educ. Learn, 1 (209), p. 13. , https://doi.org/10.2316/journal.209.2013.1.209-0015; Balkibekov, K., Meiirbekov, S., Tazhigaliyeva, N., Sandygulova, A., Should robots win or lose? robot’s losing playing strategy positively affects child learning (2016) Robot and Human Interactive Communication (RO-MAN), pp. 706-711. , IEEE, August; Sysoev, I., Hershman, A., Fine, S., Traweek, C., Roy, D., Speechblocks: a constructionist early literacy app (2017) Proceedings of the 2017 Conference on Interaction Design and Children, pp. 248-257. , ACM; Dewi, R., Priandani, N., Brata, K., Fanani, L., Usability evaluation of mobile-based application for Javanese script learning media (2018) J. Inf. Technol. Comput. 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(eds) Springer, Cham; Hood, D., Lemaignan, S., Dillenbourg, P., The cowriter project: teaching a robot how to write (2015) 2015 Human-Robot Interaction Conference, , https://doi.org/10.1145/2701973.2702091, Portland, USA, 3–5 March; Jacq, A., Lemaignan, S., Garcia, F., Dillenbourg, P., Paiva, A., Building successful long child-robot interactions in a learning context (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 239-246. , Christchurch, New zealand, 07–10 March; Lemaignan, S., Learning by teaching a robot: the case of handwriting (2016) Robot. Autom. Mag, 23 (2), pp. 56-66. , https://doi.org/10.1109/Mra.2016.2546700; Yadollahi, E., Johal, W., Paiva, A., Dillenbourg, P., When deictic gestures in a robot can harm child-robot collaboration (2018) Proceedings of the 17th ACM Conference on Interaction Design and Children, pp. 195-206. , June; Jamet, F., Masson, O., Jacquet, B., Stilgenbauer, J.L., Baratgin, J., Learning by teaching with humanoid robot: a new powerful experimental tool to improve chil-dren’s learning ability (2018) J. Robot; Huong, L.P.H., The more knowledgeable peer, target language use, and group participation (2007) Can. Modern Lang. Rev./ La revue canadienne des langues vivantes, 64 (2), pp. 333-354. , https://www.muse.jhu.edu/article/233947; Vygotsky, L.S., (1978) Mind in Society: The Development of Higher Psychological Processes, , Harvard University Press, Cambridge; Jobeen, A., Kazemian, B., Shahbaz, M., The role of error analysis in teaching and learning of second and foreign language (2015) Educ. Linguis. Res, 1 (2), pp. 52-62. , https://ssrn.com/abstract=2659714https://doi.org/10.5296/elr.v1i1.8189, 13 September; CohenMiller, A., Pate, E., A model for developing interdisciplinary research theoretical frameworks (2019) Qual. Rep, 24 (6), pp. 1211-1226. , https://nsuworks.nova.edu/tqr/vol24/iss6/2; Klein, J.T., (1990) Interdisciplinarity: History, Theory, and Practice, , Wayne State University Press, Detroit; Kim, A., Cowriting kazakh: transitioning to a new Latin script using social robots (2019) Proceedings of the 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE; Sandygulova, A., CoWriting kazakh: learning a new script with a robot (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI 2020), p. 8. , https://doi.org/10.1145/3319502.3374813, Cambridge, United Kingdom, ACM, New York, USA; Al-Azami, S., Kenner, C., Ruby, M., Gregory, E., Transliteration as a bridge to learning for bilingual children (2010) Int. J. Bilingual Educ. Bilingualism, 13 (6), pp. 683-700; Plowman, L., Stephen, C., McPake, J., (2010) Growing up with Technology: Young Children Learning in a Digital World, , Routledge, London; Yelland, N.J., A pedagogy of multiliteracies: young children and multimodal learning with tablets (2018) Br. J. Educ. Technol, 49 (5), pp. 847-858; Kim, B., Reeves, T.C., Reframing research on learning with technology: in search of the meaning of cognitive tools (2007) Instr. Sci, 35 (3), pp. 207-256; Schunk, D., (2020) Learning Theories: An Educational Perspective, , 8th edn. Pearson, London; Li, J.X., James, K.H., Handwriting generates variable visual output to facilitate symbol learning (2016) J. Exp. Psychol. Gen, 145 (3), pp. 298-313. , https://doi.org/10.1037/xge0000134; James, K.H., Engelhardt, L., The effects of handwriting experience on functional brain development in pre-literate children (2012) Trends Neurosci. Educ, 1 (1), pp. 32-42. , https://doi.org/10.1016/j.tine.2012.08.001; James, K.H., The importance of handwriting experience on the development of the literate brain (2017) Curr. Dir. Psychol. Sci, 26 (6), pp. 502-508. , https://doi.org/10.1177/0963721417709821; Mayer, C., Wallner, S., Budde-Spengler, N., Braunert, S., Arndt, P.A., Kiefer, M., Literacy training of kindergarten children with pencil, keyboard or tablet stylus: the influence of the writing tool on reading and writing performance at the letter and word level (2020) Front. Psychol, 10, p. 3054. , https://doi.org/10.3389/fpsyg.2019.03054; (2020), https://pld-literacy.org/, Primary School Literacy Resources PLD (Promoting Literacy Development) (2019). Accessed 29 Jan; Richmond, J.E., (2010) School aged children: visual perception and reversal recognition of letters and numbers separately and in context; Shuell, T.J., Cognitive conceptions of learning (1986) Rev. Educ. Res, 56 (4), pp. 411-436","Sandygulova, A.; Nazarbayev UniversityKazakhstan; email: anara.sandygulova@nu.edu.kz","Camara J.Steffen M.",,"Springer Science and Business Media Deutschland GmbH","3rd International Workshop on Formal Co-Simulation of Cyber-Physical Systems, CoSim-CPS 2019, International Workshop on Automated and verifiable Software System Development, ASYDE 2019, International Workshop on Cognition: Interdisciplinary Foundations, Models and Applications, CIFMA 2019 and 17th International Workshop on Foundations of  Coordination Languages and Self-Adaptiveness of Software Applications, FOCLASA 2019, co-located with the 17th International Conference on Software Engineering and Formal Methods, SEFM 2019","16 September 2019 through 20 September 2019",,245179,03029743,9783030575052,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85091562612
"Andrews J., Lad M., Chandrasekaran B.","57216313842;57216313136;56740438200;","A Robotic System for Elderly Care and Monitoring using Human Robot Interaction",2020,"2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020",,,"9031215","576","579",,,"10.1109/CCWC47524.2020.9031215","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083084843&doi=10.1109%2fCCWC47524.2020.9031215&partnerID=40&md5=2b69755562735a8906df9f7b4b30824c","Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States","Andrews, J., Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States; Lad, M., Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States; Chandrasekaran, B., Florida Polytechnic University, Department of Electrical and Computer Engineering, Lakeland, FL, United States","Human-Robot Interaction is an emerging field in the area of robotics and growth in this field finds application in several areas of the society. In this paper an initial progress of the design of a robotic system for elderly care is proposed. The paper describes the basic architecture of this proposed system and presents an initial implementation of this robot. This is a work in progress, and the robotic system will be updated and developed as the research progresses when more sensors and other components are interfaced providing more functionalities to the robot. Also, another goal of the project is contribution in undergraduate electrical and computer engineering education where students understand and learn to work on both hardware and software aspects in the design of an assistive robotic system. © 2020 IEEE.","Human-Robot Interaction; Machine Learning; Motion Control; Robotic System Architecture; Sensors; Social Robotics","Computer hardware; Engineering research; Man machine systems; Robotics; Students; Assistive robotics; Elderly care; Electrical and computer engineering; Hardware and software; Robotic systems; Work in progress; Human robot interaction",,,,,,,,,,,,,,,,"Sharkey, A., Sharkey, N., Children, the elderly, and interacti ve robots (2011) IEEE Robotics & Automation Magazine, 18 (1), pp. 32-38. , Apr; (2008), http://www.impactlab.net/2008/10/27lrosie-the-robot-maid-made-bytoyota, Say Goodbye To Housekeep ing, New Robot Maid. 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IEEE; Fong, T., Nourbakhsh, I., Dautenhahn, K., (2002) A Survey of Socially Interactive Robots : Concepts, Design and Applications: Technical Report CMU-RI-TR-02-29; Duffy, B.R., Rooney, C., O'Hare, G.M., O'Donoghue, R., What is a social robot (1999) 10th Irish Conference on Artificial Intelligence & Cognitive Science, , September University College Cork, Ireland, 1-3 September 1999; Broekens, J., Heerink, M., Rosendal, H., Assistive social robots in elderly care : A review (2009) Gerontechnology, 8 (2), pp. 94-103; Graf, B., Hans, M., Schraft, R.D., Care-o-bot II-development of a next generation robotic home assistant (2004) Autonomous Robots, 16 (2), pp. 193-205; Bahadori, S., Cesta, A., Grisetti, G., Iocchi, L., Leone, R., Nardi, D., Oddi, A., Rasconi, R., Robocare: An integrated robotic system for the domestic care of the elderly (2003) Proceedings of Workshop on Ambient Intelligence AI 1A-03, Pisa, , Italy; https:llwww.xilinx.comlproductslboards-and-kitslzcul04.html, [Accessed: 4-November-2019]; http://www.tul.com.twlProductsPYNQ-Z2.html, November-2019; http://www.pynq.io, [Accessed: 4-November-2019]; https:llwww.tensorflow.org/, [Accessed: 4-November-2019]; Gael, L., https:llwww.thingiverse.comlthing:90033, [Accessed: 4-November-2019]; http://pinktentacle.coml2008/11/, Motoman SDAIO [Accessed: 4November-2019]; https:llwccftech.comlsony-emotional-robots/, SONY, Emotional Robot [Accessed: 4-November-2019]; Westlund, J.K., Lee, J.J., Plummer, L., Faridi, F., Gray, J., Berlin, M., Quintus-Bo Sz, H., Breazeal, C., Tega: A social robot (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), p. 561; Sabelli, A.M., Kanda, T., Hagita, N., A conversational robot in an elderly care center : An ethnographic study (2011) 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 37-44. , March IEEE",,"Chakrabarti S.Paul R.","IEEE Region 1;IEEE Region 6;IEEE USA;Institute of Engineering and Management (IEM);University of Engineering and Management (UEM);UNLV","Institute of Electrical and Electronics Engineers Inc.","10th Annual Computing and Communication Workshop and Conference, CCWC 2020","6 January 2020 through 8 January 2020",,158422,,9781728137834,,,"English","Annu. Comput. Commun. Workshop Conf., CCWC",Conference Paper,"Final","",Scopus,2-s2.0-85083084843
"Nakagawa K., Matsumura R., Shiomi M.","23985467600;25958195100;8397042200;","Effect of robot’s play-biting in non-verbal communication",2020,"Journal of Robotics and Mechatronics","32","1",,"86","96",,4,"10.20965/jrm.2020.p0086","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079844344&doi=10.20965%2fjrm.2020.p0086&partnerID=40&md5=6ec8266fbff8f018f8f2153421fd0b4c","Karakuri Products Inc., 2-1-1 Nihonbashi-hongokucho, Chuo-ku, Tokyo, 103-0021, Japan; Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai, Keihanna Science City, Kyoto, 619-0288, Japan","Nakagawa, K., Karakuri Products Inc., 2-1-1 Nihonbashi-hongokucho, Chuo-ku, Tokyo, 103-0021, Japan, Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai, Keihanna Science City, Kyoto, 619-0288, Japan; Matsumura, R., Karakuri Products Inc., 2-1-1 Nihonbashi-hongokucho, Chuo-ku, Tokyo, 103-0021, Japan, Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai, Keihanna Science City, Kyoto, 619-0288, Japan; Shiomi, M., Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai, Keihanna Science City, Kyoto, 619-0288, Japan","This paper focuses on “play-biting” as a touch communication method used by robots. We investigated an appropriate play-biting behavior and its effect on interaction. The touching action has positive effects in human-robot interactions. However, as biting is a defenseless act, it may cause a negative effect as well. Therefore, we first examine biting manner and the appearance of the robot using a virtual play-biting system in Experiment 1. Next, based on the result of experiment, the play-biting system is implemented in a stuffed animal robot. We verified the impressions created by the robot and its effect on mitigating stress in Experiment 2. Consequently, the play-biting communication gave positive and lively impression, and effect of reducing the physiological index of stress, in comparison to only touching the robot. © 2020, Fuji Technology Press. All rights reserved.","Human-robot interaction; Play-biting; Stress; Therapy; Touch","Man machine systems; Stresses; Animal robot; Communication method; Non-verbal communications; Physiological indices; Play-biting; Therapy; Touch; Human robot interaction",,,,,"JPMJCR18A1","This work was partially supported by JST CREST Grant Number JPMJCR18A1, Japan.",,,,,,,,,,"Fisher, J.D., Rytting, M., Heslin, R., Hands Touching Hands: Affective and Evaluative Effects of an Interpersonal Touch (1976) So-Ciometry, 39, pp. 416-421; Gueguen, N., Jacob, C., Boulbry, G., The effect of touch on compliance with a restaurant’s employee suggestion (2007) Int. 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Mechatron., 26 (4), pp. 418-425; Hauser, S., McIntyre, S., Israr, A., Olausson, H., Gerling, G., Uncovering Human-to-Human Physical Interactions that Underlie Emotional and Affective Touch Communication (2019) Proc. of IEEE World Haptics Conf, pp. 407-412; Kasuga, T., Hashimoto, M., Human-Robot Handshaking using Neural Oscillators (2005) Proc. of the 2005 IEEE Int. Conf. on Robotics and Automation, pp. 3802-3807; Wang, Z., Giannopoulos, E., Slater, M., Peer, A., Handshake: Realistic Human-Robot Interaction in Haptic Enhanced Virtual Re-ality (2011) Presence, 20 (4), pp. 371-392; Jindai, M., Watanabe, T., Shibata, S., Yamamoto, T., Develop-ment of a Handshake Robot System Based on a Handshake Approaching Motion Model (2008) J. Robot. Mechatron., 20 (4), pp. 650-659; Bailenson, J.N., Yee, N., Brave, S., Merget, D., Koslow, D., Vir-tual Interpersonal Touch: Expressing and Recognizing Emotions Through Haptic Devices (2007) Human-Computer Interaction, 22 (3), pp. 325-353; Yamashita, Y., Ishihara, H., Ikeda, T., Asada, M., Investigation of Causal Relationship Between Touch Sensations of Robots and Personality Impressions by Path Analysis (2019) Int. J. of Social Robotics, 11 (1), pp. 141-150; Fukuda, H., Shiomi, M., Nakagawa, K., Ueda, K., “‘Midas touch’ in human-robot interaction: Evidence from event-related potentials during the ultimatum game (2012) Proc. of the 7Th Annual ACM/IEEE Int. Conf. on Human-Robot Interaction (HRI’12), pp. 131-132; Shiomi, M., Nakata, A., Kanbara, M., Hagita, N., A hug from a robot encourages prosocial behavior (2017) 2017 26Th IEEE Int. Symp. on Robot and Human Interactive Communication (RO-MAN), pp. 418-423; Sumioka, H., Nakae, A., Kanai, R., Ishiguro, H., Huggable communication medium decreases cortisol levels (2013) Scientific Reports, 3; Cramer, H., Amin, A.K., Evers, V., Kemper, N., Touched by robots: Effects of physical contact and robot proactiveness (2009) Workshop on the Reign of Catz and Dogz in CHI’09; Willemse, C.J.A.M., Toet, A., van Erp, J.B.F., Affective and Behavioral Responses to Robot-Initiated Social Touch: Toward Understanding the Opportunities and Limitations of Physical Contact in Human-Robot Interaction (2017) Frontiers in ICT, 4, p. 12; Kanoh, M., Babyloid (2014) J. Robot. 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Conf. on Human-Robot Interaction (HRI ’07), pp. 145-152; Hoffmann, L., Krämer, N.C., Investigating the Effects of Physical and Virtual Embodiment in Task-oriented and Conversational Contexts (2013) Int. J. Hum.-Comput. Stud., 71 (7-8), pp. 763-774; Mathur, M.B., Reichling, D.B., Navigating a social world with robot partners: A quantitative cartography of the Uncanny Valley (2016) Cognition, 146, pp. 22-32; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots (2009) Int. J. of Social Robotics, 1 (1), pp. 71-81; Tokuda, K., The Validity of Temporary Mood Scale (2011) Ritsumeikan J. of Human Sciences, (22), pp. 1-6; Malik, M., Bigger, J.T., Camm, A.J., Kleiger, R.E., Malliani, A., Moss, A.J., Schwartz, P.J., Heart rate variability: Standards of measurement, physiological interpretation, and clinical use (1996) European Heart J, 17 (3), pp. 354-381; Zheng, X., Shiomi, M., Minato, T., Ishiguro, H., What kinds of robot’s touch will match expressed emotions? (2020) IEEE Robotics and Automation Letters, 5 (1), pp. 127-134",,,,"Fuji Technology Press",,,,,09153942,,,,"English","J. Rob. Mechatronics",Article,"Final","",Scopus,2-s2.0-85079844344
"Lisini Baldi T., Farina F., Garulli A., Giannitrapani A., Prattichizzo D.","56960321600;7006298343;7003697493;6507678339;7004048807;","Upper Body Pose Estimation Using Wearable Inertial Sensors and Multiplicative Kalman Filter",2020,"IEEE Sensors Journal","20","1","8830389","492","500",,16,"10.1109/JSEN.2019.2940612","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077819388&doi=10.1109%2fJSEN.2019.2940612&partnerID=40&md5=f01b6246b57485b7f3c0967500460365","Dipartimento di Ingegneria Dell'informazione e Scienze Matematiche, Universita' di Siena, Siena, 53100, Italy; Department of Electrical, Electronic and Information Engineering 'Guglielmo Marconi, Alma Mater Studiorum Università di Bologna, Bologna, Bologna, 40126, Italy; Department of Information Engineering and Mathematics, University of Siena, Siena, 53100, Italy","Lisini Baldi, T., Dipartimento di Ingegneria Dell'informazione e Scienze Matematiche, Universita' di Siena, Siena, 53100, Italy; Farina, F., Department of Electrical, Electronic and Information Engineering 'Guglielmo Marconi, Alma Mater Studiorum Università di Bologna, Bologna, Bologna, 40126, Italy; Garulli, A., Dipartimento di Ingegneria Dell'informazione e Scienze Matematiche, Universita' di Siena, Siena, 53100, Italy; Giannitrapani, A., Dipartimento di Ingegneria Dell'informazione e Scienze Matematiche, Universita' di Siena, Siena, 53100, Italy; Prattichizzo, D., Department of Information Engineering and Mathematics, University of Siena, Siena, 53100, Italy","Estimating the limbs pose in a wearable way may benefit multiple areas such as rehabilitation, teleoperation, human-robot interaction, gaming, and many more. Several solutions are commercially available, but they are usually expensive or not wearable/portable. We present a wearable pose estimation system (WePosE), based on inertial measurements units (IMUs), for motion analysis and body tracking. Differently from camera-based approaches, the proposed system does not suffer from occlusion problems and lighting conditions, it is cost effective and it can be used in indoor and outdoor environments. Moreover, since only accelerometers and gyroscopes are used to estimate the orientation, the system can be used also in the presence of iron and magnetic disturbances. An experimental validation using a high precision optical tracker has been performed. Results confirmed the effectiveness of the proposed approach. © 2001-2012 IEEE.","body tracking; inertial sensors; Kalman filter; Wearable sensors","Cost effectiveness; Human robot interaction; Inertial navigation systems; Kalman filters; Body tracking; Experimental validations; Inertial measurements units; Inertial sensor; Lighting conditions; Magnetic disturbance; Occlusion problems; Outdoor environment; Wearable sensors",,,,,"Horizon 2020 Framework Programme, H2020: 688857","Manuscript received May 10, 2019; revised August 30, 2019; accepted September 7, 2019. Date of publication September 10, 2019; date of current version December 20, 2019. This work was supported in the European Union’s Horizon 2020 Research and Innovation Programme of the project “SOFTPRO - Synergy-based Open-source Foundations and Technologies for Prosthetics and RehabilitatiOn” under Grant 688857. The associate editor coordinating the review of this article and approving it for publication was Dr. Ying Zhang. (Tommaso Lisini Baldi and Francesco Farina contributed equally to this work.) (Corresponding authors: Tommaso Lisini Baldi; Francesco Farina.) T. Lisini Baldi, A. Garulli, and A. Giannitrapani are with the Dipar-timento di Ingegneria dell’informazione e scienze matematiche, Uni-versita’ di Siena, 53100 Siena, Italy (e-mail: lisini@diism.unisi.it; garulli@diism.unisi.it; giannitrapani@diism.unisi.it).",,,,,,,,,,"Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., Havinga, P., Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey (2010) Proc. 23th Int. Conf. Archit.comput. Syst., pp. 1-10. , Feb; Mohammadi, M., Lisini Baldi, T., Scheggi, S., Prattichizzo, D., Fingertip force estimation via inertial and magnetic sensors in deformable object manipulation (2016) Proc. 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(RO-MAN), pp. 936-941. , Aug. /Sep; Mukhopadhyay, S.C., Wearable sensors for human activity monitoring: A review (2015) IEEE Sensors J., 15 (3), pp. 1321-1330. , Mar; Michel, D., Qammaz, A., Argyros, A.A., Markerless 3D human pose estimation and tracking based on RGBD cameras: An experimental evaluation (2017) Proc. 10th Int. Conf. Pervasive Technol. Rel. Assistive Environ., pp. 115-122; Legnani, G., Zappa, B., Casolo, F., Adamini, R., Magnani, P.L., A model of an electro-goniometer and its calibration for biomechanical applications (2000) Med. Eng. Phys., 22 (10), pp. 711-722; Nguyen, K.D., Chen, I.-M., Luo, Z., Yeo, S.H., Duh, H.B.-L., A wearable sensing system for tracking and monitoring of functional arm movement (2011) IEEE/ASME Trans. Mechatronics, 16 (2), pp. 213-220. , Apr; Poitras, I., Validity and reliability of wearable sensors for joint angle estimation: A systematic review (2019) Sensors, 19 (7), p. 1555; Kieliba, P.J., Comparison of three hand pose reconstruction algorithms using inertial and magnetic measurement units (2018) Proc. IEEE-RAS 18th Int. Conf. Humanoid Robots (Humanoids), pp. 1-9. , Nov; Morris, J.R.W., Accelerometry-A technique for the measurement of human body movements (1973) J. Biomech., 6 (6), pp. 729-732; Foxlin, E., Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter (1996) Proc. IEEE Virtual Reality Annu. Int. Symp., pp. 185-194. , Mar. /Apr; El-Gohary, M., McNames, J., Human joint angle estimation with inertial sensors and validation with a robot arm (2015) IEEE Trans. Biomed. Eng., 62 (7), pp. 1759-1767. , Jul; Roetenberg, D., Luinge, H., Slycke, P., Xsens MVN: Full 6DOF human motion tracking using miniature inertial sensors (2009) Xsens Motion Technol. 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Robot., pp. 1-7. , Jun. /Jul; El-Gohary, M., Holmstrom, L., Huisinga, J., King, E., McNames, J., Horak, F., Upper limb joint angle tracking with inertial sensors (2011) Proc Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., pp. 5629-5632. , Aug. /Sep; Miezal, M., Taetz, B., Bleser, G., On inertial body tracking in the presence of model calibration errors (2016) Sensors, 16 (7), p. 1132; (1987) Man-System Integration, Standards NASA 3000, , NASA, Washington, DC, USA; Lisini Baldi, T., Farina, F., Garulli, A., Giannitrapani, A., Prattichizzo, D., (2018) WePosE Code Package, A Library for Wearable Pose Estimation Using Inertial Sensors, , http://wepose.diism.unisi.it","Farina, F.; Department of Electrical, Italy; email: franc.farina@unibo.it",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,1530437X,,,,"English","IEEE Sensors J.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85077819388
"Zheng T., Wang Y., Yang G., Shen W., Fang Z., Yang K.","55324533100;56063652100;7405752170;57208214458;35322011900;57188988931;","Self-calibration method for two DOF cable-driven joint module",2020,"Advances in Intelligent Systems and Computing","1031 AISC",,,"983","991",,,"10.1007/978-981-13-9406-5_118","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077541383&doi=10.1007%2f978-981-13-9406-5_118&partnerID=40&md5=8822657b1d4d48a7dcc257da9fcb95e2","Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China; University of Chinese Academy of Sciences, Beijing, China","Zheng, T., Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China; Wang, Y., Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China, University of Chinese Academy of Sciences, Beijing, China; Yang, G., Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China; Shen, W., Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China, University of Chinese Academy of Sciences, Beijing, China; Fang, Z., Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China; Yang, K., Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo, China","A modular cable-driven robot consists of a number of consecutively connected cable-driven joint modules. It has the advantages of lightweight structure and compliant behavior so that it can perform intrinsically safe human–robot interactions. However, a modular cable-driven robot normally has a low positioning accuracy due to the manufacturing and assembly errors in each of the cable-driven joint modules. In this work, a self-calibration method is proposed for the 2-DOF cable-driven universal joint module by utilizing its actuation redundancy. Different from the conventional self-calibration method where the absolute cable lengths need to be measured, the proposed error model is formulated based on the relative lengths changes of the diving cables. The effectiveness of the self-calibration method is verified through computer simulations. © Springer Nature Singapore Pte Ltd. 2020.","Cable-driven robot; Identification; Kinematics; Self-calibration","Calibration; Human robot interaction; Identification (control systems); Intelligent computing; Kinematics; Actuation redundancy; Cable driven robots; Compliant behavior; Intrinsically safe; Positioning accuracy; Robot interactions; Self calibration; Self-calibration method; Cables",,,,,"61409230101; QJD1602033; National Natural Science Foundation of China, NSFC: 51475448, 51705510","Acknowledgements This research is supported by National Natural Science Foundation of China (Project code: 51705510 and 51475448) and Pre-research Project of Equipment Development Department of PRC Central Military Commission (61409230101), and Qianjiang Talent Project (QJD1602033).",,,,,,,,,,"Lafourcade, P., Llibre, M., Design of a parallel wire-driven manipulator for wind tunnels Proceedings Workshop on Fundamental Issues and Future Research Directions for Parallel Mechanisms and Manipulators, , Quebec City, Canada; Kawamura, S., Choe, W., Tanaka, S., Development of an ultrahigh speed robot FALCON using wire drive system (1995) Proceedings IEEE International Conference on Robotics and Automation; Suzuki, Y., Kuwahara, H., Ohnishi, K., Development and verification of tendon-driven rotary actuator for haptics with flexible actuators and a PE line (2010) Proceedings IEEE International Workshop on Advanced Motion Control, Nagaoka, Japan; Chen, I.M., Yang, G.L., Tan, C.T., Yeo, S.H., Local POE model for robot kinematic calibration (2001) Mech. 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Eng., 10 (2), pp. 380-391; Mustafa, S.K., Yang, G., Yeo, S.H., Lin, W., Kinematic calibration of a 7-DOF self-calibrated modular cable-driven robotic arm (2008) IEEE International Conference on Robotics and Automation ICRA, p. 1288; Jing, W., Tao, P.Y., Yang, G., Shimada, K., Calibration of industry robots with consideration of loading effects using product-of-exponential and gaussian process (2016) IEEE International Conference on Robotics and Automation, , Stockholm, Sweden; Lau, D., Initial length and pose calibration for cable-driven parallel robots with relative length feedback (2017) Initial Length and Pose Calibration for Cable-Driven Parallel Robots with Relative Length Feedback, pp. 140-151; Wang, H., Gao, T., Kinugawa, J., Kosuge, K., Finding measurement configurations for accurate robot calibration: Validation with a cable-driven robot (2017) IEEE Trans. Rob., 33 (5), pp. 1156-1169; Tao, P.Y., Mustafa, K.S., Robot Work Cell Calibration and Error Compensation (2015) Springerlink","Yang, G.; Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment TechnologyChina; email: glyang@nimte.ac.cn","Jain V.Patnaik S.Popentiu Vladicescu F.Sethi I.K.",,"Springer","4th International Conference on Intelligent Computing, Communication and Devices, ICCD 2018","7 December 2018 through 9 December 2018",,235529,21945357,9789811394058,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85077541383
"Huang J.-Y., Lee W.-P., Dong B.-W.","55945102500;55567492800;57211254940;","Learning Emotion Recognition and Response Generation for a Service Robot",2020,"Mechanisms and Machine Science","78",,,"286","297",,2,"10.1007/978-3-030-30036-4_26","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073148353&doi=10.1007%2f978-3-030-30036-4_26&partnerID=40&md5=1b42df3f28276923bda51d784d9f03f7","National Sun Yat-sen University, Kaohsiung City, 80424, Taiwan","Huang, J.-Y., National Sun Yat-sen University, Kaohsiung City, 80424, Taiwan; Lee, W.-P., National Sun Yat-sen University, Kaohsiung City, 80424, Taiwan; Dong, B.-W., National Sun Yat-sen University, Kaohsiung City, 80424, Taiwan","Building dialoguing services for robots to provide natural human-robot interactions and to enhance user experiences is now advocated. With this type of services, a robot can work as a consultant and provide domain-specific knowledge to end users. In this study, we adopt a service-oriented framework to develop emotion-aware dialogues for a service robot. Our work includes several unique features: it trains classifiers to recognize users’ emotions in conversation, learns a deep neural model to generate answers in response to users’ questions, and uses the emotional information to determine the answer sentences produced by the dialoguing model. A series of experiments are conducted for performance evaluation. The results are compared with other machine learning methods, and they show the promise and potential of the presented approach. © 2020, Springer Nature Switzerland AG.","Dialogue modeling; Emotion recognition; Machine learning; Service robot","Classification (of information); Learning systems; Machine learning; Mobile robots; Speech recognition; User experience; Dialogue modeling; Domain-specific knowledge; Emotion recognition; Emotional information; Machine learning methods; Response generation; Service oriented frameworks; Service robots; Human robot interaction",,,,,,,,,,,,,,,,"Waibel, M., Beetz, M., Civera, J., D’Andrea, R., RoboEarth-a world wide web for robots (2011) IEEE Robot. Autom. Mag., 18 (2), pp. 69-82; Mohanarajah, G., Hunziker, D., D’Andrea, R., Waibel, M., Rapyuta: A cloud robotics platform (2015) IEEE Trans. Autom. Sci. Eng., 12 (2), pp. 481-493; Bajaj, N.M., Spiers, A.J., Dollar, A.M., State of the art in artificial wrists: A review of prosthetic and robotic wrist design (2019) IEEE Trans. Rob., 35 (1), pp. 261-277; Gao, J., Galley, M., Li, L., Neural approaches to conversational AI (2018) Proceedings of the 41St ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 1371-1374. , pp; Shang, L., Lu, Z., Li, H., Neural responding machine for short-text conversation (2015) Proceedings of the 53Rd Annual Meeting of the Association for Computational Linguistics, 1, pp. 1577-1586. , vol., pp; Zhou, H., Huang, M., Zhang, T., Zhu, X., Emotional chatting machine: Emotional conversation generation with internal and external memory (2018) Proceedings of AAAI, pp. 730-738. , pp; Sun, X., Peng, X., Ding, S., Emotional human-machine conversation generation based on long short-term memory (2018) Cogn. Comput., 10 (3), pp. 389-397; Appel, O., Chiclana, F., Carter, J., Fujita, H., A hybrid approach to the sentiment analysis problem at the sentence level (2016) Knowl.-Based Syst., 108, pp. 110-124; Pennington, J., Socher, R., Manning, C.D., GloVe: Global vectors for word representation (2014) Proceedings of International Conference on Empirical Methods in Natural Language Processing, pp. 1532-1543. , pp; Kingma, D.P., Ba, J., Adam: A method for stochastic optimization (2015) Proceedings of International Conference for Learning Representations; Bajaj, N.M., Spiers, A.J., Dollar, A.M., State of the art in artificial wrists: A review of prosthetic and robotic wrist design (2019) IEEE Trans. Rob., 35 (1), pp. 261-277; Phan, D.A., Shindo, H., Matsumoto, Y., Multiple emotions detection in conversation transcripts (2016) Proceedings of the 30Th Pacific Asia Conference on Language, Information and Computation, pp. 85-94. , pp; Feng, M., Xiang, B., Glass, M.R., Wang, L., Applying deep learning to answer selection: A study and an open task (2015) Arxiv:1508.01585V2 [Cs.Cl]; Huang, J.-Y., Lin, T.-A., Lee, W.-P., Using deep learning and an external knowledge base to develop human-robot dialogues (2018) Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, pp. 3709-3714. , pp","Lee, W.-P.; National Sun Yat-sen UniversityTaiwan; email: wplee@mail.nsysu.edu.tw",,,"Springer Science and Business Media B.V.",,,,,22110984,,,,"English","Mech. Mach. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85073148353
"Tirupachuri Y., Nava G., Latella C., Ferigo D., Rapetti L., Tagliapietra L., Nori F., Pucci D.","57193026479;57192430048;57189343334;57194087376;57199999801;56938576600;57197776930;55201907900;","Towards partner-aware humanoid robot control under physical interactions",2020,"Advances in Intelligent Systems and Computing","1038",,,"1073","1092",,2,"10.1007/978-3-030-29513-4_78","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072827302&doi=10.1007%2f978-3-030-29513-4_78&partnerID=40&md5=386ca7f45a38501eefa04ffdf32048b3","RBCS, Istituto Italiano di Tecnologia, Genova, Italy; Dynamic Interaction Control, Istituto Italiano di Tecnologia, Genova, Italy; DIBRIS, University of Genova, Genova, Italy; School of Computer Science, University of Manchester, Manchester, United Kingdom; DeepMind, London, United Kingdom","Tirupachuri, Y., RBCS, Istituto Italiano di Tecnologia, Genova, Italy, Dynamic Interaction Control, Istituto Italiano di Tecnologia, Genova, Italy, DIBRIS, University of Genova, Genova, Italy; Nava, G., Dynamic Interaction Control, Istituto Italiano di Tecnologia, Genova, Italy, DIBRIS, University of Genova, Genova, Italy; Latella, C., Dynamic Interaction Control, Istituto Italiano di Tecnologia, Genova, Italy; Ferigo, D., Dynamic Interaction Control, Istituto Italiano di Tecnologia, Genova, Italy, School of Computer Science, University of Manchester, Manchester, United Kingdom; Rapetti, L., Dynamic Interaction Control, Istituto Italiano di Tecnologia, Genova, Italy, School of Computer Science, University of Manchester, Manchester, United Kingdom; Tagliapietra, L., Dynamic Interaction Control, Istituto Italiano di Tecnologia, Genova, Italy; Nori, F., DeepMind, London, United Kingdom; Pucci, D., Dynamic Interaction Control, Istituto Italiano di Tecnologia, Genova, Italy","The topic of physical human-robot interaction received a lot of attention from the robotics community because of many promising application domains. However, studying physical interaction between a robot and an external agent, like a human or another robot, without considering the dynamics of both the systems may lead to many shortcomings in fully exploiting the interaction. In this paper, we present a coupled-dynamics formalism followed by a sound approach in exploiting helpful interaction with a humanoid robot. In particular, we propose the first attempt to define and exploit the human help for the robot to accomplish a specific task. As a result, we present a task-based partner-aware robot control techniques. The theoretical results are validated by conducting experiments with two iCub humanoid robots involved in physical interaction. © Springer Nature Switzerland AG 2020.","Humanoids; Physical human-robot interaction; Physical robot-robot interaction","Anthropomorphic robots; Intelligent systems; Man machine systems; Coupled dynamics; External agents; Humanoid robot controls; Humanoids; Physical human-robot interactions; Physical interactions; Robot controls; Robot interactions; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020: 642961, 731540; Parents Against Childhood Epilepsy, PACE","This work is supported by PACE project, Marie Sk?lodowska-Curie grant agreement No. 642961 and An.Dy project which has received funding from the European Union?s Horizon 2020 Research and Innovation Programme under grant agreement No. 731540. The authors would like to thank Yue Hu, Stefano Dafarra, Giulio Romualdi and, Aiko Dinale for their support in conducting the experiments.",,,,,,,,,,"Agravante, D.J., Cherubini, A., Bussy, A., Gergondet, P., Kheddar, A., Collaborative human-humanoid carrying using vision and haptic sensing (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 607-612. , pp., IEEE; Samuel, A., Berniker, M., Herr, H., Powered ankle-foot prosthesis to assist level-ground and stair-descent gaits (2008) Neural Netw, 21 (4), pp. 654-666; Bussy, A., Gergondet, P., Kheddar, A., Keith, F., Crosnier, A., Proactive behavior of a humanoid robot in a haptic transportation task with a human partner (2012) RO-MAN, 2012 IEEE, pp. 962-967. , IEEE; Bussy, A., Kheddar, A., Crosnier, A., Keith, F., Human-humanoid haptic joint object transportation case study (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3633-3638. , pp., IEEE; Caux, S., Mateo, E., Zapata, R., Balance of biped robots: Special double-inverted pendulum (1998) 1998 IEEE International Conference on Systems, Man, and Cybernetics 1998, 4, pp. 3691-3696. , IEEE; Cho, E., Chen, R., Merhi, L.-K., Xiao, Z., Pousett, B., Menon, C., Force myography to control robotic upper extremity prostheses: A feasibility study (2016) Front. 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Interact., 1 (3), pp. 203-275; Herzog, A., Righetti, L., Grimminger, F., Pastor, P., Schaal, S., Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics (2014) 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 981-988. , pp., IEEE; Hirai, K., Hirose, M., Haikawa, Y., Takenaka, T., The development of Honda humanoid robot (1998) Proceedings of the 1998 IEEE International Conference on Robotics and Automation 1998, 2, pp. 1321-1326. , IEEE; Hofmann, A., Popovic, M., Herr, H., Exploiting angular momentum to enhance bipedal center-of-mass control (2009) IEEE International Conference on Robotics and Automation 2009, ICRA 2009, pp. 4423-4429. , IEEE; Hyon, S.-H., Hale, J.G., Cheng, G., Full-body compliant human-humanoid interaction: Balancing in the presence of unknown external forces (2007) IEEE Trans. Robot., 23 (5), pp. 884-898; Ikemoto, S., Amor, H.B., Minato, T., Jung, B., Ishiguro, H., Physical human-robot interaction: Mutual learning and adaptation (2012) IEEE Robot. Autom. Mag., 19 (4), pp. 24-35; Khalil, W., Dombre, E., (2004) Modeling, Identification and Control of Robots, , Butterworth-Heinemann, Oxford; Koolen, T., Bertrand, S., Thomas, G., de Boer, T., Tingfan, W., Smith, J., Englsberger, J., Pratt, J., Design of a momentum-based control framework and application to the humanoid robot atlas (2016) Int. J. Humanoid Rob., 13 (1); Kyrkjebø, E., Inertial human motion estimation for physical human-robot interaction using an interaction velocity update to reduce drift (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 163-164. , pp., ACM; Latella, C., Lorenzini, M., Lazzaroni, M., Romano, F., Traversaro, S., Akhras, M.A., Pucci, D., Nori, F., Towards real-time whole-body human dynamics estimation through probabilistic sensor fusion algorithms (2018) Auton. Robots., 43, pp. 1591-1603; Losey, D.P., McDonald, C.G., Battaglia, E., O’Malley, M.K., A review of intent detection, arbitration, and communication aspects of shared control for physical human-robot interaction (2018) Appl. Mech. Rev., 70 (1); Marsden, J.E., Ratiu, T.S., (2010) Introduction to Mechanics and Symmetry: A Basic Exposition of Classical Mechanical Systems, , Springer, Heidelberg; Mattar, E.A., Al-Junaid, H.J., Al-Seddiqi, H.H., Biomimetic based EEG learning for robotics complex grasping and dexterous manipulation (2018) Biomimetic Prosthetics, , InTech; McMullen, D.P., Hotson, G., Katyal, K.D., Wester, B.A., Fifer, M.S., McGee, T.G., Harris, A., Ravitz, A.D., Demonstration of a semi-autonomous hybrid brain-machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic (2014) IEEE Trans. Neural Syst. Rehabil. Eng., 22 (4), pp. 784-796; Metta, G., Natale, L., Nori, F., Sandini, G., Vernon, D., Fadiga, L., von Hofsten, C., Santos-Victor, J., The iCub humanoid robot: An open-systems platform for research in cognitive development (2010) Neural Netw, 23 (8-9), pp. 1125-1134; Natale, L., Bartolozzi, C., Pucci, D., Wykowska, A., Metta, G., ICub: The not-yet-finished story of building a robot child (2017) Sci. Robot., 2 (13); Nava, G., Romano, F., Nori, F., Pucci, D., Stability analysis and design of momentum-based controllers for humanoid robots (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 680-687. , pp., IEEE; Nori, F., Traversaro, S., Eljaik, J., Romano, F., Del Prete, A., Pucci, D., ICub whole-body control through force regulation on rigid non-coplanar contacts (2015) Front. Robot. AI, 2 (6), p. 18; Ott, C., Roa, M.A., Hirzinger, G., Posture and balance control for biped robots based on contact force optimization (2011) 2011 11Th IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp. 26-33. , pp., IEEE; Pattacini, U., Nori, F., Natale, L., Metta, G., Sandini, G., An experimental evaluation of a novel minimum-jerk Cartesian controller for humanoid robots (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1668-1674. , pp., October; Peternel, L., Babič, J., Learning of compliant human-robot interaction using full-body haptic interface (2013) Adv. Robot., 27 (13), pp. 1003-1012; Peternel, L., Tsagarakis, N., Caldwell, D., Ajoudani, A., Robot adaptation to human physical fatigue in human-robot co-manipulation (2018) Auton. Robots., 42, pp. 1011-1021; Pucci, D., Romano, F., Traversaro, S., Nori, F., Highly dynamic balancing via force control (2016) 2016 IEEE-RAS 16Th International Conference on Humanoid Robots (Humanoids), p. 141. , pp., November; Radmand, A., Scheme, E., Englehart, K., A characterization of the effect of limb position on EMG features to guide the development of effective prosthetic control schemes (2014) 2014 36Th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 662-667. , IEEE; Rasouli, M., Chellamuthu, K., Cabibihan, J.-J., Kukreja, S.L., Towards enhanced control of upper prosthetic limbs: A force-myographic approach (2016) 2016 6Th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), pp. 232-236. , IEEE; Rautaray, S.S., Agrawal, A., Vision based hand gesture recognition for human computer interaction: A survey (2015) Artif. Intell. Rev., 43 (1), pp. 1-54; Reily, B., Han, F., Parker, L.E., Zhang, H., Skeleton-based bio-inspired human activity prediction for real-time human-robot interaction (2018) Auton. Robots., 42 (6), pp. 1281-1298; Romano, F., Nava, G., Azad, M., Čamernik, J., Dafarra, S., Dermy, O., Latella, C., Nori, F., TheCoDyCoproject achievements and beyond: Toward human aware whole-body controllers for physical human robot interaction (2018) IEEE Robot. Autom. Lett., 3 (1), pp. 516-523; Sarac, M., Koyas, E., Erdogan, A., Cetin, M., Patoglu, V., Brain computer interface based robotic rehabilitation with online modification of task speed (2013) 2013 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 1-7. , pp., IEEE; Song, R., Tong, K., Hu, X., Li, L., Assistive control system using continuous myoelectric signal in robot-aided arm training for patients after stroke (2008) IEEE Trans. Neural Syst. Rehabil. Eng., 16 (4), pp. 371-379; Stephens, B.J., Atkeson, C.G., Dynamic balance force control for compliant humanoid robots (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1248-1255. , IEEE; Wensing, P.M., Orin, D.E., Generation of dynamic humanoid behaviors through task-space control with conic optimization (2013) 2013 IEEE International Conference on Robotics and Automation (ICRA), pp. 3103-3109. , IEEE; Yap, H.K., Mao, A., Goh, J.C.H., Yeow, C.-H., Design of a wearable FMG sensing system for user intent detection during hand rehabilitation with a soft robotic glove (2016) 2016 6Th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), pp. 781-786. , IEEE; Zhou, Y., Fang, Y., Zeng, J., Li, K., Liu, H., A multi-channel EMG-driven FES solution for stroke rehabilitation (2018) International Conference on Intelligent Robotics and Applications, pp. 235-243. , pp., Springer","Tirupachuri, Y.; Dynamic Interaction Control, Italy; email: Yeshasvi.Tirupachuri@iit.it","Bi Y.Bhatia R.Kapoor S.",,"Springer Verlag","Intelligent Systems Conference, IntelliSys 2019","5 September 2019 through 6 September 2019",,231009,21945357,9783030295127,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85072827302
"Song J., Zhu A., Tu Y., Zou J.","57203911021;8876295500;57210522898;57360137700;","Multijoint passive elastic spine exoskeleton for stoop lifting assistance",2021,"International Journal of Advanced Robotic Systems","18","6",,"","",,,"10.1177/17298814211062033","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120476781&doi=10.1177%2f17298814211062033&partnerID=40&md5=d4ed9828e7d9e71753c6178a2c08de0a","Institute of Robotics Intelligent Systems, Xi’an Jiaotong University, Xi’an, China; Shaanxi Key Laboratory of Intelligent Robots, Xi’an, China; Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an, China","Song, J., Institute of Robotics Intelligent Systems, Xi’an Jiaotong University, Xi’an, China, Shaanxi Key Laboratory of Intelligent Robots, Xi’an, China; Zhu, A., Institute of Robotics Intelligent Systems, Xi’an Jiaotong University, Xi’an, China; Tu, Y., Institute of Robotics Intelligent Systems, Xi’an Jiaotong University, Xi’an, China, Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an, China; Zou, J., Institute of Robotics Intelligent Systems, Xi’an Jiaotong University, Xi’an, China","In the task of carrying heavy objects, it is easy to cause back injuries and other musculoskeletal diseases. Although wearable robots are designed to reduce this danger, most existing exoskeletons use high-stiffness mechanisms, which are beneficial to load-bearing conduction, but this restricts the natural movement of the human body, thereby causing ergonomic risks. This article proposes a back exoskeleton composed of multiple elastic spherical hinges inspired by the biological spine. This spine exoskeleton can assist in the process of bending the body and ensure flexibility. We deduced the kinematics model of this mechanism and established an analytical biomechanical model of human–robot interaction. The mechanism of joint assistance of the spine exoskeleton was discussed, and experiments were conducted to verify the flexibility of the spine exoskeleton and the effectiveness of the assistance during bending. © The Author(s) 2021.","Back exoskeleton; bionic spine; human-robot interaction; multijoint mechanism; passive elastic mechanism","Bionics; Exoskeleton (Robotics); Back exoskeleton; Back injuries; Bionic spine; Elastic mechanism; Humans-robot interactions; Multi-joint; Multijoint mechanism; Musculoskeletal disease; Passive elastic mechanism; Stoop lifting; Human robot interaction",,,,,"2021A02002; 2020GXLH-Y-007, 2020GY-207, 2021GY-286, 2021GY-333; 2020XXX001; National Natural Science Foundation of China, NSFC: 52175061, U1813212","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the National Natural Science Foundation of China (No. U1813212, 52175061), Shaanxi Provincial Key R&D Program (2020GXLH-Y-007, 2021GY-333, 2021GY-286, 2020GY-207), Shanxi Provincial Key Research Project (2020XXX001), Xinjiang Funded by Autonomous Region Major Science and Technology Special Project (2021A02002).",,,,,,,,,,"Waters, T.R., Putz-Anderson, V., Garg, A., Revised NIOSH equation for the design and evaluation of manual lifting tasks (1993) Ergonomics, 36 (7), pp. 749-776; van Dieën, J.H., Kingma, I., Effects of antagonistic co-contraction on differences between electromyography based and optimization based estimates of spinal forces (2005) Ergonomics, 48 (4), pp. 411-426; Kazerooni, H., Human power amplifier for lifting load including apparatus for preventing slack in lifting cable: U.S. Patent 6,386,513, , 2002 May 14; Abdoli-Eramaki, M., Stevenson, J.M., Reid, S.A., Mathematical and empirical proof of principle for an on-body personal lift augmentation device (PLAD) (2007) J Biomech, 40 (8), pp. 1694-1700; Abdoli-E, M., Agnew, M.J., Stevenson, J.M., An on-body personal lift augmentation device (PLAD) reduces EMG amplitude of erector spinae during lifting tasks (2006) Clin Biomech, 21 (5), pp. 456-465; Imamura, Y., Tanaka, T., Suzuki, Y., Motion-based-design of elastic material for passive assistive device using musculoskeletal model (2011) J Robot Mechatron, 23 (6), pp. 978-990; Wehner, M., Rempel, D., Kazerooni, H., Lower extremity exoskeleton reduces back forces in lifting ASME 2009 dynamic systems and control conference, 2, pp. 49-56. , California, USA, October 2009, In:, ume; Ulrey, B.L., Fathallah, F.A., Effect of a personal weight transfer device on muscle activities and joint flexions in the stooped posture (2013) J Electromyogr Kinesiol, 23 (1), pp. 195-205; Bosch, T., van Eck, J., Knitel, K., The effects of a passive exoskeleton on muscle activity, discomfort and endurance time in forward bending work (2016) Appl Ergon, 54, pp. 212-217; Taal, S.R., Sankai, Y., Exoskeletal spine and shoulder girdle for full body exoskeletons with human versatility IEEE international conference on robotics & automation, pp. 2217-2222. , IEEE, Shanghai, China, 9–13 May 2011, In; Looze, M.P.D., Bosch, T., Krause, F., Exoskeletons for industrial application and their potential effects on physical work load (2016) Ergonomics, 59, pp. 671-681; Näf, M.B., De Rijcke, L., Guerrero, C.R., Towards low back support with a passive biomimetic exo-spine 2017 International conference on rehabilitation robotics (ICORR), pp. 1165-1170. , London, UK, 17–20 July 2017, In; Yang, X., Huang, T.-H., Hu, H., Spine-inspired continuum soft exoskeleton for stoop lifting assistance (2019) IEEE Robot Autom Lett, 44, pp. 4547-4554; Abdoli, M., Agnew, M.J., Stevenson, J.M., An on-body personal lift augmentation device (PLAD) reduces EMG amplitude of erector spinae during lifting tasks (2006) Clin Biomech, 21, pp. 456-465; Abdoli, E.M., Stevenson, J.M., The effect of on-body lift assistive device on the lumbar 3D dynamic moments and EMG during asymmetric freestyle lifting (2008) Clin Biomech, 23, pp. 372-380; Frost, D.M., Abdoli-E, M., Stevenson, J.M., PLAD (personal lift assistive device) stiffness affects the lumbar flexion/extension moment and the posterior chain EMG during symmetrical lifting tasks (2009) J Electromyogr Kinesiol, 19, pp. e403-e412; Imamura, Y., Tanaka, T., Suzuki, Y., Motion-based-design of elastic material for passive assistive device using musculoskeletal model (2011) J Robot Mechatron, 23 (6), pp. 978-990; Näf, M.B., Koopman, A.S., Saskia, B., Passive back support exoskeleton improves range of motion using flexible beams (2018) Front Robot AI, 5, p. 72; Yang, X., Huang, T.H., Hu, H., Spine-inspired continuum soft exoskeleton for stoop lifting assistance (2019) IEEE Robot Autom Lett, 4 (4), pp. 4547-4554; Park, J., Jin, X., Agrawal, S.K., Second spine: upper body assistive device for human load carriage (2015) ASME J Mech Robot, 7 (1), p. 011012. , (1 February 2015); Park, J., Zanotto, D., Vashista, V., Second spine: a device to relieve stresses on the upper body during loaded walking 5th IEEE RAS/EMBS international conference on biomedical robotics and biomechatronics, pp. 689-694. , Sao Paulo, Brazil, 12–15 August 2014, In; Smith, T.J., Fernie, G.R., Functional biomechanics of the Spine (1991) Spine, 16 (10), pp. 1197-1203","Zhu, A.; Institute of Robotics Intelligent Systems, China; email: abzhu@mail.xjtu.edu.cn",,,"SAGE Publications Inc.",,,,,17298806,,,,"English","Int. J. Adv. Rob. Syst.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85120476781
"Veling L., McGinn C.","57194232749;36651232500;","Qualitative Research in HRI: A Review and Taxonomy",2021,"International Journal of Social Robotics","13","7",,"1689","1709",,,"10.1007/s12369-020-00723-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101254831&doi=10.1007%2fs12369-020-00723-z&partnerID=40&md5=0c614b4f78e87e232bcaa24d0301741e","Maynooth University, Maynooth, Ireland; Trinity College Dublin, Dublin, Ireland","Veling, L., Maynooth University, Maynooth, Ireland; McGinn, C., Trinity College Dublin, Dublin, Ireland","The field of human–robot interaction (HRI) is young and highly inter-disciplinary, and the approaches, standards and methods proper to it are still in the process of negotiation. This paper reviews the use of qualitative methods and approaches in the HRI literature in order to contribute to the development of a foundation of approaches and methodologies for these new research areas. In total, 73 papers that use qualitative methods were systematically reviewed. The review reveals that there is widespread use of qualitative methods in HRI, but very different approaches to reporting on it, and high variance in the rigour with which the approaches are applied. We also identify the key qualitative methods used. A major contribution of this paper is a taxonomy categorizing qualitative research in HRI in two dimensions: by ’study type’ and based on the specific qualitative method used. © 2021, Springer Nature B.V.","Ethnography; Grounded theory; Human–robot interaction; Interviews; Participatory design; Qualitative methods; Social robotics; Social science; Survey; Taxonomy","Agricultural robots; Social robots; Qualitative method; Qualitative research; Robot interactions; Two-dimension; Taxonomies",,,,,,,,,,,,,,,,"Steinfeld, A., Fong, T., Kaber, D., Lewis, M., Scholtz, J., Schultz, A., Goodrich, M., Common metrics for human-robot interaction (2006) Proceedings of the 1St ACM SIGCHI/SIGART Conference on human–robot Interaction, pp. 33-40; (2002) A Taxonomy for human–robot Interaction, pp. 111-119. , In, Proceedings of the AAAI fall symposium on human–robot interaction, sn; Broekens, J., Heerink, M., Rosendal, H., Assistive social robots in elderly care: a review (2009) Gerontechnology, 8 (2), pp. 94-103; Dautenhahn, K., Methodology & themes of human-robot interaction: a growing research field (2007) Int J Adv Robot Syst, 4 (1), p. 15; Bemelmans, R., Gelderblom, G.J., Jonker, P., De Witte, L., Socially assistive robots in elderly care: a systematic review into effects and effectiveness (2012) J Am Med Dir Assoc, 13 (2), pp. 114-120; Kachouie, R., Sedighadeli, S., Khosla, R., Chu, M.-T., Socially assistive robots in elderly care: a mixed-method systematic literature review (2014) Int J Human–Comput Interact, 30 (5), pp. 369-393; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int J Soc Robot, 1 (1), pp. 71-81; Bethel, C.L., Murphy, R.R., Review of human studies methods in hri and recommendations (2010) Int J Soc Robot, 2 (4), pp. 347-359; Shah, S.K., Corley, K.G., Building better theory by bridging the quantitative–qualitative divide (2006) J Manag Stud, 43 (8), pp. 1821-1835; Lindblom, J., Andreasson, R., Current challenges for ux evaluation of human–robot interaction (2016) Advances in Ergonomics of Manufacturing: Managing the Enterprise of the Future. 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J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85101254831
"Selvaggio M., Cognetti M., Nikolaidis S., Ivaldi S., Siciliano B.","57188639260;54792819500;55635433000;26643860200;7006263661;","Autonomy in physical human-robot interaction: A brief survey",2021,"IEEE Robotics and Automation Letters","6","4","9501975","7989","7996",,,"10.1109/LRA.2021.3100603","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112635301&doi=10.1109%2fLRA.2021.3100603&partnerID=40&md5=ccc50c75cc5f15c2efb93ae757e1ceb0","Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, 80138, Italy; Department of Electronic Engineering, National University of Ireland Maynooth, Maynooth, W23 F2K8, Ireland; Department of Computer Science, University of Southern California, Los Angeles, CA  90007, United States; Inria, Loria, Université de Lorraine, CNRS, Nancy, 54000, France","Selvaggio, M., Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, 80138, Italy; Cognetti, M., Department of Electronic Engineering, National University of Ireland Maynooth, Maynooth, W23 F2K8, Ireland; Nikolaidis, S., Department of Computer Science, University of Southern California, Los Angeles, CA  90007, United States; Ivaldi, S., Inria, Loria, Université de Lorraine, CNRS, Nancy, 54000, France; Siciliano, B., Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, 80138, Italy","Sharing the control of a robotic system with an autonomous controller allows a human to reduce his/her cognitive and physical workload during the execution of a task. In recent years, the development of inference and learning techniques has widened the spectrum of applications of shared control (SC) approaches, leading to robotic systems that are capable of seamless adaptation of their autonomy level. In this perspective, shared autonomy (SA) can be defined as the design paradigm that enables this adapting behavior of the robotic system. This letter collects the latest results achieved by the research community in the field of SC and SA with special emphasis on physical human-robot interaction (pHRI). Architectures and methods developed for SC and SA are discussed throughout the letter, highlighting the key aspects of each methodology. A discussion about open issues concludes this letter. © 2016 IEEE.","Human-centered robotics; Human-robot collaboration; Physical human-robot interaction","Agricultural robots; Control theory; Learning systems; Man machine systems; Robotics; Autonomy levels; Design paradigm; Learning techniques; Physical human-robot interactions; Physical humanrobot interaction (phri); Physical workloads; Research communities; Shared autonomy; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 101017008; Horizon 2020","Manuscript received February 24, 2021; accepted July 19, 2021. Date of publication July 29, 2021; date of current version August 20, 2021. This letter was recommended for publication by Associate Editor K. Hashtrudi-Zaad and Editor J.-H. Ryu upon evaluation of the reviewers’ comments. This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement 101017008. (Corresponding author: Mario Selvaggio.) Mario Selvaggio and Bruno Siciliano are with the Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples 80138, Italy (e-mail: mar.selvaggio@gmail.com; siciliano@ieee.org).","This work was supported in part by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement 101017008.",,,,,,,,,"Ferrell, W.R., Sheridan, T.B., Supervisory control of remote manipulation (1967) IEEE Spectr., 4 (10), pp. 81-88. , Oct; Sheridan, T., Human and Computer Control of Undersea Teleoperators (1978) Massachusetts Inst of Tech Cambridge Man-Machine Systems Lab, , Dec; Thrun, S., Toward robotic cars (2010) Commun. 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"Birch B., Griffiths C.A., Morgan A.","57223398635;57219637654;57224184276;","Environmental effects on reliability and accuracy of MFCC based voice recognition for industrial human-robot-interaction",2021,"Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture","235","12",,"1939","1948",,,"10.1177/09544054211014492","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105786317&doi=10.1177%2f09544054211014492&partnerID=40&md5=7779fddaf3206f0d2a533723bf494501","College of Engineering, Swansea University, Swansea, United Kingdom","Birch, B., College of Engineering, Swansea University, Swansea, United Kingdom; Griffiths, C.A., College of Engineering, Swansea University, Swansea, United Kingdom; Morgan, A., College of Engineering, Swansea University, Swansea, United Kingdom","Collaborative robots are becoming increasingly important for advanced manufacturing processes. The purpose of this paper is to determine the capability of a novel Human-Robot-interface to be used for machine hole drilling. Using a developed voice activation system, environmental factors on speech recognition accuracy are considered. The research investigates the accuracy of a Mel Frequency Cepstral Coefficients-based feature extraction algorithm which uses Dynamic Time Warping to compare an utterance to a limited, user-dependent dictionary. The developed Speech Recognition method allows for Human-Robot-Interaction using a novel integration method between the voice recognition and robot. The system can be utilised in many manufacturing environments where robot motions can be coupled to voice inputs rather than using time consuming physical interfaces. However, there are limitations to uptake in industries where the volume of background machine noise is high. © IMechE 2021.","Cobots; dynamic time warping; human-robot-interaction; industrial robots; MEL frequency cepstral coefficients; speech recognition; voice control","Drilling machines (machine tools); Industrial robots; Man machine systems; Manufacture; Social robots; Advanced manufacturing; Collaborative robots; Dynamic time warping; Environmental factors; Feature extraction algorithms; Human-Robot Interface; Manufacturing environments; Mel frequency cepstral co-efficient; Speech recognition",,,,,,,,,,,,,,,,"Chen, C.H., (1976) Pattern recognition and artificial intelligence: proceedings of the joint workshop on pattern recognition and artificial intelligence, Held at Hyannis, Massachusetts, June 1–3, 1976, , New York, NY, Academic Press, Academic Press Rapid Manuscript Reproduction; Juang, B.H., Rabiner, L.R., Automatic speech recognition–a brief his- tory of the technology development (2005) Georgia Institute of Technology Atlanta Rutgers University and the University of California Santa Barbara, 1, p. 67. , p; 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College of Engineering, United Kingdom; email: c.a.griffiths@swansea.ac.uk",,,"SAGE Publications Ltd",,,,,09544054,,PIBME,,"English","Proc Inst Mech Eng Part B J Eng Manuf",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85105786317
"Bhat M., Mahto G., Kesaria S., Femandes V., Arya K.","57339046200;57339000800;57338888000;57338979400;24483089200;","Real-time gesture control UAV with a low resource framework",2021,"2021 International Symposium of Asian Control Association on Intelligent Robotics and Industrial Automation, IRIA 2021",,,,"19","24",,,"10.1109/IRIA53009.2021.9588681","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119100808&doi=10.1109%2fIRIA53009.2021.9588681&partnerID=40&md5=f0311fa5b688203c337f4ba778111cf7","BMS College of Engineering, Dept. of Electrical and Electronics, Bangalore, 560019, India; UPU Government Polytechnic, Dept. of Computer Science and Engg., Durg, 491001, India; Indian Institute of Technology, Dept. of Systems and Control, Mumbai, 400076, India; Indian Institute of Technology, Dept. of Computer Science and Engg., Mumbai, 400076, India","Bhat, M., BMS College of Engineering, Dept. of Electrical and Electronics, Bangalore, 560019, India; Mahto, G., UPU Government Polytechnic, Dept. of Computer Science and Engg., Durg, 491001, India; Kesaria, S., Indian Institute of Technology, Dept. of Systems and Control, Mumbai, 400076, India; Femandes, V., Indian Institute of Technology, Dept. of Computer Science and Engg., Mumbai, 400076, India; Arya, K., Indian Institute of Technology, Dept. of Computer Science and Engg., Mumbai, 400076, India","This study showcases a low-resource framework that enables people with no technical know-how to interact with drones, it also explores the capabilities of 2D- computer vision and deep learning techniques for gesture based interface systems on a low-cost micro drone with an onboard RGB camera. This Human-Robot Interaction system processes the real-time human pose to allow a user to command the drone, i.e., by providing direction to move and execute actions. A linear PD controller and image processing techniques are implemented to track humans whilst maintaining a safe distance from the user by perceiving depth information through pose estimation. We incorporated the gesture recognition results into a drone using the Robot Operating System (ROS) and evaluated system performance indoor and outdoor. This low computation framework can be applied further to control robotic arms or mobile robots. © 2021 IEEE.","AI and ML in robotics; AUV control; DJI Tello drone; gesture recognition; Human Robot Interaction; Robot Operating System; Robotic control","Aircraft detection; Deep learning; Drones; Gesture recognition; Human computer interaction; Man machine systems; Real time systems; Robotics; Technology transfer; AI and ML in robotic; AUV control; DJI tello drone; Gesture control; Gestures recognition; Humans-robot interactions; Real- time; Robot operating system; Robotic controls; Technical know hows; Human robot interaction",,,,,,,,,,,,,,,,"Tomic, T., Toward a fully autonomous UAV: Research platform for indoor and outdoor urban search and rescue (2012) IEEE Robot. Autom. 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Symp. Asian Control Assoc. Intell. Robot. Ind. Autom., IRIA",Conference Paper,"Final","",Scopus,2-s2.0-85119100808
"Dönmez Özkan Y., Mirnig A.G., Meschtscherjakov A., Demir C., Tscheligi M.","57280922100;56436732500;23467162500;57282916500;56948770700;","Mode awareness interfaces in automated vehicles, robotics, and aviation: A literature review",2021,"Proceedings - 13th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2021",,,,"147","158",,,"10.1145/3409118.3475125","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116223533&doi=10.1145%2f3409118.3475125&partnerID=40&md5=6d2bcf46f1fee753818321238c5eff45","Center for Human-Computer Interaction, University of Salzburg, Salzburg, Austria; University of Salzburg, Austrian Institute of Technology, Austria","Dönmez Özkan, Y., Center for Human-Computer Interaction, University of Salzburg, Salzburg, Austria; Mirnig, A.G., Center for Human-Computer Interaction, University of Salzburg, Salzburg, Austria; Meschtscherjakov, A., Center for Human-Computer Interaction, University of Salzburg, Salzburg, Austria; Demir, C., Center for Human-Computer Interaction, University of Salzburg, Salzburg, Austria; Tscheligi, M., University of Salzburg, Austrian Institute of Technology, Austria","With increasing automation capabilities and a push towards full automation in vehicles, mode awareness, i.e., the driver's awareness of the vehicle's current automation mode, becomes an important factor. While issues surrounding mode awareness are known, research concerning and design towards mode awareness appears to not yet be a focal point in the automated driving domain. In this paper, we provide a state-of-the art on mode awareness from the related domains of automated driving, aviation, and Human-Robot Interaction. We present a summary of existing mode awareness interface solutions as well as existing techniques and recognized gaps concerning mode awareness. We found that existing interfaces are often simple, sometimes outdated, yet are difficult to meaningfully expand without overloading the user. We also found predictive approaches as a promising strategy to lessen the need for mode awareness via separate indicators. © 2021 Association for Computing Machinery.","Automated driving; Aviation; Human-robot interaction; Mode awareness; Mode confusion; Systematic literature review","Automation; Man machine systems; Vehicles; 'current; Automated driving; Automated vehicles; Focal points; Humans-robot interactions; Literature reviews; Mode awareness; Mode confusion; State of the art; Systematic literature review; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020: 875597; Horizon 2020; Innovation and Networks Executive Agency, INEA","This research was conducted within the project HADRIAN, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875597. The Innovation and Networks Executive Agency (INEA) is not responsible for any use that may be made of the information this document contains.","This research was conducted within the project HADRIAN, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 875597. 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"Lombardi M., Liuzza D., di Bernardo M.","57222618203;35746581800;7006817977;","Dynamic Input Deep Learning Control of Artificial Avatars in a Multi-Agent Joint Motor Task",2021,"Frontiers in Robotics and AI","8",,"665301","","",,,"10.3389/frobt.2021.665301","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113328826&doi=10.3389%2ffrobt.2021.665301&partnerID=40&md5=3915b8b470a3343bccc5b09f60aeb702","Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom; Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy; ENEA Fusion and Nuclear Safety Department, Frascati, Italy; Scuola Superiore Meridionale, University of Naples Federico II, Naples, Italy","Lombardi, M., Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom, Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy; Liuzza, D., ENEA Fusion and Nuclear Safety Department, Frascati, Italy; di Bernardo, M., Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom, Scuola Superiore Meridionale, University of Naples Federico II, Naples, Italy","In many real-word scenarios, humans and robots are required to coordinate their movements in joint tasks to fulfil a common goal. While several examples regarding dyadic human robot interaction exist in the current literature, multi-agent scenarios in which one or more artificial agents need to interact with many humans are still seldom investigated. In this paper we address the problem of synthesizing an autonomous artificial agent to perform a paradigmatic oscillatory joint task in human ensembles while exhibiting some desired human kinematic features. We propose an architecture based on deep reinforcement learning which is flexible enough to make the artificial agent interact with human groups of different sizes. As a paradigmatic coordination task we consider a multi-agent version of the mirror game, an oscillatory motor task largely used in the literature to study human motor coordination. © Copyright © 2021 Lombardi, Liuzza and di Bernardo.","human-robot interaction; mirror game; movement coordination; nonlinear control; reinforcement learning; virtual player",,,,,,"Engineering and Physical Sciences Research Council, EPSRC; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR: ARS01 00861","This research was developed with the economic support of MIUR (Italian Ministry of University and Research) performing the activities of the project ARS01 00861 “Integrated collaborative systems for smart factory - ICOSAF”, and EPSRC PhD Scholarship to ML.",,,,,,,,,,"Alderisio, F., Fiore, G., Salesse, R.N., Bardy, B.G., di Bernardo, M., Interaction Patterns and Individual Dynamics Shape the Way We Move in Synchrony (2017) Sci. Rep, 7, pp. 6846-6910. , a; Alderisio, F., Lombardi, M., Fiore, G., di Bernardo, M., A Novel Computer-Based Set-Up to Study Movement Coordination in Human Ensembles (2017) Front. Psychol, 8, p. 967. , b; Freeman, C.T., Rogers, E., Hughes, A.-M., Burridge, J.H., Meadmore, K.L., Iterative Learning Control in Health Care: Electrical Stimulation and Robotic-Assisted Upper-Limb Stroke Rehabilitation (2012) IEEE Control. Syst, 32, pp. 18-43; Haken, H., Kelso, J.A.S., Bunz, H., A Theoretical Model of Phase Transitions in Human Hand Movements (1985) Biol. Cybern, 51, pp. 347-356; Hentout, A., Aouache, M., Maoudj, A., Akli, I., Human-Robot Interaction in Industrial Collaborative Robotics: a Literature Review of the Decade 2008-2017 (2019) Adv. Robotics, 33, pp. 764-799; Kralemann, B., Cimponeriu, L., Rosenblum, M., Pikovsky, A., Mrowka, R., Phase Dynamics of Coupled Oscillators Reconstructed from Data (2008) Phys. Rev. E Stat. Nonlin Soft Matter Phys, 77, p. 066205; Lamb, M., Lorenz, T., Harrison, S.J., Kallen, R., Minai, A., Richardson, M.J., Papac: A Pick and Place Agent Based on Human Behavioral Dynamics (2017) Proceedings of the 5th International Conference on Human Agent Interaction, pp. 131-141; Liu, Y., Nejat, G., Robotic Urban Search and rescue: A Survey From the Control Perspective (2013) J. Intell. Robot Syst, 72, pp. 147-165; Lombardi, M., Liuzza, D., di Bernardo, M., Using Learning to Control Artificial Avatars in Human Motor Coordination Tasks (2021) IEEE Transactions on Robotics, , 19 May 2021, early access; Lombardi, M., Liuzza, D., di Bernardo, M., Deep Learning Control of Artificial Avatars in Group Coordination Tasks (2019) 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 714-719; Lombardi, M., Liuzza, D., di Bernardo, M., Generation and Classification of Individual Behaviours for Virtual Players Control in Motor Coordination Tasks (2018) 2018 European Control Conference (ECC), pp. 2374-2379; Miro, X.A., Kuhn, R., Brayda, L., (2008) Interactive Personalized Robot for home Use, , US Patent US7349758B2; Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Human-Level Control Through Deep Reinforcement Learning (2015) Nature, 518, pp. 529-533; Noy, L., Dekel, E., Alon, U., The Mirror Game as a Paradigm for Studying the Dynamics of Two People Improvising Motion Together (2011) Proc. 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Physio, 3, p. 405; Russell, S., Norvig, P., (2003) Artificial Intelligence: A Modern Approach, , 3rd Edn, Hoboken, New Jersey, Prentice-Hall; Slowinski, P., Rooke, E., Di Bernardo, M., Tanaseva-Atanasova, K., Kinematic Characteristics of Motion in the Mirror Game (2014) 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 748-753; Sutton, R.S., Barto, A.G., (2018) Reinforcement Learning: An Introduction, , MIT press; Wiltermuth, S.S., Heath, C., Synchrony and Cooperation (2009) Psychol. Sci, 20, pp. 1-5; Zhai, C., Alderisio, F., Slowinski, P., Tsaneva-Atanasova, K., di Bernardo, M., Design and Validation of a Virtual Player for Studying Interpersonal Coordination in the Mirror Game (2018) IEEE Trans. Cybern, 48, pp. 1018-1029; Zhai, C., Alderisio, F., Słowiński, P., Tsaneva-Atanasova, K., di Bernardo, M., Design of a Virtual Player for Joint Improvisation With Humans in the Mirror Game (2016) Plos one, 11, p. e0154361","di Bernardo, M.; Department of Engineering Mathematics, United Kingdom; email: mario.dibernardo@unina.it",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85113328826
"Tang W., Lin Y., Zhang C., Liang Y., Wang J., Wang W., Ji C., Zhou M., Yang H., Zou J.","57190687537;57201636158;57212207277;57217104608;57190862983;57192617116;57230723900;57229942400;7406558218;57188818112;","Self-contained soft electrofluidic actuators",2021,"Science Advances","7","34","eabf8080","","",,,"10.1126/sciadv.abf8080","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113379334&doi=10.1126%2fsciadv.abf8080&partnerID=40&md5=9e2143aa1a494b8c2b99641bf0aea83e","State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China; Institute of Marine Science and Technology, Shandong University, Qingdao, 266237, China; School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, 310027, China; Ningbo Research Institute, Zhejiang University, Ningbo, 315100, China","Tang, W., State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China; Lin, Y., State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China; Zhang, C., State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China; Liang, Y., State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China; Wang, J., State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China; Wang, W., State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China; Ji, C., Institute of Marine Science and Technology, Shandong University, Qingdao, 266237, China; Zhou, M., School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, 310027, China; Yang, H., State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China; Zou, J., State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China, Ningbo Research Institute, Zhejiang University, Ningbo, 315100, China","Soft robotics revolutionized human-robot interactions, yet there exist persistent challenges for developing high-performance soft actuators that are powerful, rapid, controllable, safe, and portable. Here, we introduce a class of self-contained soft electrofluidic actuators (SEFAs), which can directly convert electrical energy into the mechanical energy of the actuators through electrically responsive fluids that drive the outside elastomer deformation. The use of special dielectric liquid enhances fluid flow capabilities, improving the actuation performance of the SEFAs. SEFAs are easily manufactured by using widely available materials and common fabrication techniques, and display excellent comprehensive performances in portability, controllability, rapid response, versatility, safety, and actuation. An artificial muscle stretching a joint and a soft bionic ray swimming in a tank demonstrate their effective performance. Hence, SEFAs offer a platform for developing soft actuators with potential applications in wearable assistant devices and soft robots. Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).",,"Dielectric liquids; Flow of fluids; Human robot interaction; Artificial muscle; Comprehensive performance; Effective performance; Electrical energy; Fabrication technique; Mechanical energies; Soft actuators; Wearable assistant; Mechanical actuators",,,,,"National Natural Science Foundation of China, NNSFC: 51875507, 51890885; Fundamental Research Funds for the Central Universities: 2020QNA4003","This work is supported by the National Natural Science Foundation of China (award nos. 51875507 and 51890885) and the Fundamental Research Funds for the Central Universities (no. 2020QNA4003).",,,,,,,,,,"Cianchetti, M., Laschi, C., Menciassi, A., Paolo, D., Biomedical applications of soft robotics (2018) Nat. Rev. Mater, 3, pp. 143-153; Rus, D., Tolley, M. T., Design, fabrication and control of soft robots (2015) Nature, 521, pp. 467-475; Kim, Y., Parada, G. 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Z., A review on miniaturized ultrasonic wireless power transfer to implantable medical devices (2019) IEEE Access, 7, pp. 2092-2106; Yang, J.-W., Do, H.-L., Soft-switching dual-flyback DC-DC converter with improved efficiency and reduced output ripple current (2017) IEEE Trans. Indust. Electron, 64, pp. 3587-3594; Hsu, W.-C., Chen, J.-F., Hsieh, Y.-P., Wu, Y.-M., Design and steady-state analysis of parallel resonant DC-DC converter for high-voltage power generator (2017) IEEE Trans. Power Electron, 32, pp. 957-966; Zhang, Z., Pang, H., Georgiadis, A., Cecati, C., Wireless power transfer-An overview (2018) IEEE Trans. Indust. Electron, 66, pp. 1044-1058; De Clercq, H., Puers, R., Contactless energy transfer at the bedside featuring an online power optimization strategy (2014) Sens. Actuat. A Phys, 217, pp. 160-167; Yuk, H., Zhang, T., Lin, S., Parada, G. A., Zhao, X., Tough bonding of hydrogels to diverse non-porous surfaces (2016) Nat. Mater, 15, pp. 190-196; Yang, D., Verma, M. S., So, J.-H., Mosadegh, B., Keplinger, C., Lee, B., Khashai, F., Whitesides, G. M., Buckling pneumatic linear actuators inspired by muscle (2016) Adv. Mater. Technol, 1, p. 1600055; Madden, J. D. W., Vandesteeg, N. A., Anquetil, P. A., Madden, P. G. A., Takshi, A., Pytel, R. Z., Lafontaine, S. R., Hunter, I. W., Artificial muscle technology: Physical principles and naval prospects (2004) IEEE J. Oceanic Eng, 29, pp. 706-728","Zhang, C.; State Key Laboratory of Fluid Power and Mechatronic Systems, China; email: chao.zhang@zju.edu.cn
Zou, J.; State Key Laboratory of Fluid Power and Mechatronic Systems, China; email: junzou@zju.edu.cn",,,"American Association for the Advancement of Science",,,,,23752548,,,"34417171","English","Sci. Adv.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85113379334
"Gao Y., Huang H., Xu W., Li B., Xi F.","57226694839;55637358900;14038527700;55628528575;57225885521;","Environment Information-based Impedance Control",2021,"2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2021",,,,"204","208",,,"10.1109/CYBER53097.2021.9588296","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119333926&doi=10.1109%2fCYBER53097.2021.9588296&partnerID=40&md5=e7dc69ba56c7fd2bd96c9886aa04a6c6","School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China; Harbin Institute of Technology, Shenzhen, China","Gao, Y., School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China; Huang, H., Harbin Institute of Technology, Shenzhen, China; Xu, W., School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China; Li, B., Harbin Institute of Technology, Shenzhen, China; Xi, F., School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China","The regulation of contact force between manipulators and objects is important in robotics applications such as grinding, assembly, and human-robot interaction, etc. Impedance control is one of the classical methods of force control, but it suffers from lack of prior knowledge of the environment. Many researches combine impedance control with adaptive law to compensate the unknow environment parameter, which work well on continuous surfaces. But the methods always cause large overshoot if the end-effector is not close enough to the object before contact established. We proposed a novel approach which integrates depth camera into impedance control framework. The approach employs depth camera to extract environment information, which then is fed to the impedance controller. The method enables robots to see the shape of objects and perform force tracking on complex surfaces and even incontinuous surfaces. © 2021 IEEE.",,"Cameras; Environmental regulations; Manipulators; Assembly robots; Classical methods; Contact forces; Depth camera; Environment information; Grinding robots; Humans-robot interactions; Impedance control; Prior-knowledge; Robotics applications; Human robot interaction",,,,,"Special Project for Research and Development in Key areas of Guangdong Province: 2019B090915001; Shenzhen Research and Development Program: JCYJ20200109112818703","* This work was supported by the Key-Area Research and Development Program of Guangdong Province (Grant No. 2019B090915001), and the Shenzhen Research and Development Program of China (Grant No. JCYJ20200109112818703). Corresponding author: Hailin Huang Yinghao Gao, Hailin Huang, and Wenfu Xu are with the School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, 518055, China (e-mail: 19S053035@stu.hit.edu.cn,",,,,,,,,,,"Raibert, M.H., Craig, J.J., Hybrid position-force control of manipulators english (1981) J Dyn Syst-T Asme, 103 (2), pp. 126-133; Hogan, N., Impedance control-An approach to manipulation english (1985) J Dyn Syst-T Asme, 107 (1), pp. 1-7; Seraji, H., Colbaugh, R., Force tracking in impedance control English (1997) Int J Robot Res, 16 (1), pp. 97-117. , Feb; Song, P., Yu, Y., Zhang, X., A tutorial survey and comparison of impedance control on robotic manipulation (2019) Robotica, 37 (5), pp. 801-836. , May; Colbaugh, R., Seraji, H., Glass, K., Direct adaptive impedance control of manipulators the 30th (1991) IEEE Conference on Decision and Control, , Brighton, UK; Duan, J., Gan, Y., Chen, M., Dai, X., Adaptive variable impedance control for dynamic contact force tracking in uncertain environment (2018) Robot Auton Syst, 102, pp. 54-65; Bonilla, I., Mendoza, M., Gonzalez-Galvan, E.J., Chavez-Olivares, C., Path-Tracking maneuvers with industrial robot manipulators using uncalibrated vision and Impedance Control (2012) IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews, 42 (6); Koch, H., Koenig, A., Weigl-Seitz, A., Kleinmann, K., Suchy, J., Multisensor contour following with vision, force, and acceleration sensors for an industrial Robot (2013) IEEE Transactions on Instrumentation and Measurement, 62 (2), pp. 268-280. , Feb; Zhou, Y., Global vision-based impedance control for robotic wall polishing (2019) 2019 Ieee/Rsj International Conference on Intelligent Robots and Systems, (IEEE International Conference on Intelligent Robots and Systems, pp. 6022-6027; Zhang, G., Wang, J., Cao, F., Li, Y., Chen, X., 3D curvature grinding path planning based on point cloud data (2016) 2016 12th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), , Auckland, New Zealand, AUG 29-312016: IEEE",,,"IEEE Robotics and Automation Society;Shenyang Institute of Automation CAS;Shenzhen Academy of Robotics","Institute of Electrical and Electronics Engineers Inc.","11th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2021","27 July 2021 through 31 July 2021",,173761,,9781665425278,,,"English","IEEE Annu. Int. Conf. CYBER Technol. Autom., Control, Intell. Syst., CYBER",Conference Paper,"Final","",Scopus,2-s2.0-85119333926
"Farkas Z.V., Nádas G., Kolossa J., Korondi P.","57213312212;57226323570;57226302558;55809786600;","Robot compatible environment and conditions",2021,"Periodica Polytechnica Civil Engineering","65","3",,"784","791",,,"10.3311/PPci.17495","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111088541&doi=10.3311%2fPPci.17495&partnerID=40&md5=b7eee6ce694511b4cec0a8019729d562","Department of Machine and Product Design, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, H-1111, Hungary; Department of Architecture and Design, Faculty of Art and Creative Industries, Budapest Metropolitan University, Nagy Lajos király útja 1-9, Budapest, H-1148, Hungary; Department of Residential Building Design, Faculty of Architecture, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, H-1111, Hungary; Department of Mechatronics, Optics and Engineering Informatics, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Bertalan Lajos utca 4-6, Budapest, H-1111, Hungary; TRAX Technology Solutions, Rákóczi út 42, Budapest, H-1072, Hungary","Farkas, Z.V., Department of Machine and Product Design, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, H-1111, Hungary, TRAX Technology Solutions, Rákóczi út 42, Budapest, H-1072, Hungary; Nádas, G., Department of Architecture and Design, Faculty of Art and Creative Industries, Budapest Metropolitan University, Nagy Lajos király útja 1-9, Budapest, H-1148, Hungary; Kolossa, J., Department of Residential Building Design, Faculty of Architecture, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, H-1111, Hungary; Korondi, P., Department of Mechatronics, Optics and Engineering Informatics, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Bertalan Lajos utca 4-6, Budapest, H-1111, Hungary","Service robot technology is progressing at a fast pace. Accurate robot-friendly indoor localization and harmonization of built environment in alignment with digital, physical, and social environment becomes emphasized. This paper proposes the novel approach of Robot Compatible Environment (RCE) within the architectural space. Evolution of service robotics in connection with civil engineering and architecture is discussed, whereas optimum performance is to be achieved based on robots’ capabilities and spatial affordances. For ubiquitous and safe human-robot interaction, robots are to be integrated into the living environment. The aim of the research is to highlight solutions for various interconnected challenges within the built environment. Our goal is to reach findings on comparison of robotic and accessibility standards, synthesis of navigation, access to information and social acceptance. Checklists, recommendations, and design process are introduced within the RCE framework, proposing a holistic approach. © 2021, Budapest University of Technology and Economics. All rights reserved.","Architecture; Home environment; Human-robot interaction; RCE; Robot compatible environment; Service robots","Indoor positioning systems; Robotics; Architectural space; Holistic approach; Indoor localization; Living environment; Optimum performance; Safe human-robot interaction; Social acceptance; Social environment; Human robot interaction; research; robotics; technology",,,,,"Hungarian Scientific Research Fund, OTKA: K12051; Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NKFIA; Innovációs és Technológiai Minisztérium","Research Fund (OTKA K12051). The research reported in this paper and carried out at BME has been supported by the NRDI Fund (TKP2020 IES, Grant No. BME-IE-MIFM) based on the charter of bolster issued by the NRDI Office under the auspices of the Ministry for Innovation and Technology.","The authors wish to thank the support of the Hungarian Research Fund (OTKA K12051). The research reported in this paper and carried out at BME has been supported by the NRDI Fund (TKP2020 IES, Grant No. BME-IE-MIFM) based on the charter of bolster issued by the NRDI Office under the auspices of the Ministry for Innovation and Technology.",,,,,,,,,"Franck, K. A., Lepori, R. B., (2000) Architecture inside out, , Wiley Academy, West Sussex, UK; Kavraki, L. E., Svestka, P., Latombe, J.-C., Overmars, M. H., Probabilistic roadmaps for path planning in high-dimensional con-figuration spaces (1996) IEEE Transactions on Robotics and Automation, (4), pp. 566-580. , https://doi.org/10.1109/70.508439, (12); Choset, H., Lynch, K. M., Hutchinson, S., Kantor, G. A., Burgard, W., Kavraki, L. E., Thrun, S., (2005) Principles of Robot Motion, , https://doi.org/10.1017/S0263574706212803, MIT Press, Cambridge, MA, US; Katz, D., Kenney, J., Brock, O., ""How Can Robots Succeed in Unstructured Environments? (2008) Workshop on Robot Manipulation: Intelligence in Human Environments at Robotics, Science and Systems, , http://www.robotics.tu-berlin.de/fileadmin/fg170/Publikationen_pdf/Katz-08b.pdf, presented at Zurich, Switzerland, June, 28, [online]; Smarr, C.-A., Fausset, C. B., Rogers, W. A., (2011) Understanding the Potential for Robot Assistance for Older Adults in the Home Environment, , http://hdl.handle.net/1853/39670, Georgia Institute of Technology, Atlanta, GA, USA, Rep. HFA-TR-1102, [online]; Niitsuma, M., Beppu, W., Ichikawa, T., Kovács, Sz., Korondi, P., Hashimoto, H., Implementation of robot behaviors based on ethological approach for monitoring support system in Intelligent Space (2011) IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 536-541. , https://doi.org/10.1109/AIM.2011.6027060, Budapest, Hungary; Martinecz, A., Rácz, R., Niitsuma, M., Korondi, P., Control of Mogi Robi (2011) International Engineering Symposium at Bánki (IESB 2011), p. 15. , presented at Budapest, Hungary, Nov, –16; Shibata, T., Kawaguchi, Y., Wada, K., Investigation on peo-ple living with Paro at home (2010) RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 470-475. , https://doi.org/10.1109/ROMAN.2009.5326201, Toyama, Japan; Humanics Ergonomics ""Accessibility standards"", , http://www.humanics-es.com/recc-disabled.htm#-standards, [online] [Accessed: 15 February 2021]; Ziegler, J., Working Area of Wheelchairs Details about Some Dimensions that are specified in ISO 7176-5 (2003) International Workshop on Space Requirements for Wheeled Mobility, p. 9. , presented at Center for Inclusive Design and Environmental Access, Buffalo, NY, USA, Oct, –11; Farkas, Z. V., Korondi, P., Fodor, L., Safety aspects and guide-lines for robot compatible environment (2012) IECON 2012-38th Conference of IEEE Industrial Electronics Society, pp. 5547-5552. , https://doi.org/10.1109/IECON.2012.6388949, Montreal, QC, Canada; Rasmussen, J., (1987) Information Processing and Human-Machine Interaction. An Approach to Cognitive Engineering, , North-Holland, New York, NY, US; Gibson, J. J., The theory of affordances (1977) Perceiving, Acting and Knowing, pp. 67-82. , Shaw, R., Bransford, J. (eds) Wiley, New York, NY, USA; Kovács, Á. T., Micsik, A., Method for Evaluating a Building Information Model (2019) Periodica Polytechnica Civil Engineering, 63 (2), pp. 541-549. , https://doi.org/10.3311/PPci.12563; Diankov, R., Kanade, T., Kuffner, J., Integrating Grasp Planning and Visual Feedback for Reliable Manipulation (2009) 9th IEEE-RAS International Conference on Humanoid Robots, pp. 646-652. , https://doi.org/10.1109/ICHR.2009.5379526, Paris, France","Nádas, G.; Department of Architecture and Design, Nagy Lajos király útja 1-9, Hungary; email: gnadas@metropolitan.hu",,,"Budapest University of Technology and Economics",,,,,05536626,,,,"English","Period. Polytech. Civ. Eng.",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85111088541
"Liu Z.-T., Rehman A., Wu M., Cao W.-H., Hao M.","56101506800;57215525124;57218664297;7402082944;57196186360;","Speech emotion recognition based on formant characteristics feature extraction and phoneme type convergence",2021,"Information Sciences","563",,,"309","325",,4,"10.1016/j.ins.2021.02.016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102301429&doi=10.1016%2fj.ins.2021.02.016&partnerID=40&md5=47c72714b8b2df34924e401fb36944b0","School of Automation, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, 430074, China","Liu, Z.-T., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China, Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, 430074, China; Rehman, A., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China, Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, 430074, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China, Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, 430074, China; Cao, W.-H., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China, Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, 430074, China; Hao, M., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China, Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, 430074, China","Speech Emotion Recognition (SER) has numerous applications including human-robot interaction, online gaming, and health care assistance. While deep learning-based approaches achieve considerable precision, they often come with high computational and time costs. Indeed, feature learning strategies must search for important features in a large amount of speech data. In order to reduce these time and computational costs, we propose pre-processing step in which speech segments with similar formant characteristics are clustered together and labeled as the same phoneme. The phoneme occurrence rates in emotional utterances are then used as the input features for classifiers. Using six databases (EmoDB, RAVDESS, IEMOCAP, ShEMO, DEMoS and MSP-Improv) for evaluation, the level of accuracy is comparable to that of current state-of-the-art methods and the required training time was significantly reduced from hours to minutes. © 2021 Elsevier Inc.","Clustering; Cross-corpus; Emotion recognition; Formants extraction; Phonemes; Speech","Classification (of information); Deep learning; Human robot interaction; Speech; Speech recognition; Clusterings; Computational costs; Cross-corpus; Emotion recognition; Features extraction; Formant extraction; Phoneme; Speech; Speech emotion recognition; Time cost; Extraction",,,,,"Natural Science Foundation of Hubei Province: 2015CFA010, 2018CFB447; Higher Education Discipline Innovation Project: B17040; China University of Geosciences, Wuhan, CUG: 1910491T01; Wuhan Science and Technology Project: 2020010601012175, 2017010201010133; National Natural Science Foundation of China, NSFC: 61403422, 61976197, 61273102","This work was supported in part by the National Natural Science Foundation of China under Grant 61976197, 61403422 and 61273102, in part by the Hubei Provincial Natural Science Foundation of China under Grant 2018CFB447 and 2015CFA010, in part by the Wuhan Science and Technology Project under Grant 2017010201010133 and 2020010601012175, in part by the 111 Project under Grant B17040, and in part by the Fundamental Research Funds for National University, China University of Geosciences, Wuhan, under Grant 1910491T01.",,,,,,,,,,"Finegan, E., Language: Its structure and use (2014), Cengage Learning; Slavova, V., Towards emotion recognition in texts–a sound-symbolic experiment (2019) Int. 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"Hoggenmüller M., Lee W.-Y., Hespanhol L., Jung M., Tomitsch M.","56422380000;57204952765;55295736000;15073123400;16240454600;","Eliciting New Perspectives in RtD Studies through Annotated Portfolios: A Case Study of Robotic Artefacts",2021,"DIS 2021 - Proceedings of the 2021 ACM Designing Interactive Systems Conference: Nowhere and Everywhere",,,,"1875","1886",,,"10.1145/3461778.3462134","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110170820&doi=10.1145%2f3461778.3462134&partnerID=40&md5=50d4866798c5ba5e38ff3d0393de7c96","Design Lab, Sydney School of Architecture, Design and Planning, University of Sydney, Australia; Robots in Groups Lab, Cornell University, United States; Cafa Beijing Visual Art Innovation Institute, China","Hoggenmüller, M., Design Lab, Sydney School of Architecture, Design and Planning, University of Sydney, Australia; Lee, W.-Y., Robots in Groups Lab, Cornell University, United States; Hespanhol, L., Design Lab, Sydney School of Architecture, Design and Planning, University of Sydney, Australia; Jung, M., Robots in Groups Lab, Cornell University, United States; Tomitsch, M., Design Lab, Sydney School of Architecture, Design and Planning, University of Sydney, Australia, Cafa Beijing Visual Art Innovation Institute, China","In this paper, we investigate how to elicit new perspectives in research-through-design (RtD) studies through annotated portfolios. Situating the usage in human-robot interaction (HRI), we used two robotic artefacts as a case study: we first created our own annotated portfolio and subsequently ran online workshops during which we asked HRI experts to annotate our robotic artefacts. We report on the different aspects revealed about the value, use, and further improvements of the robotic artefacts through using the annotated portfolio technique ourselves versus using it with experts. We suggest that annotated portfolios - when performed by external experts - allow design researchers to obtain a form of creative and generative peer critique. Our paper offers methodological considerations for conducting expert annotation sessions. Further, we discuss the use of annotated portfolios to unveil designerly HRI knowledge in RtD studies. © 2021 ACM.","annotated portfolios; human-robot interaction; intermediate-level knowledge; research through design; urban robots","Robotics; Design researchers; Expert annotations; External experts; Human robot Interaction (HRI); Human robot interaction",,,,,,,,,,,,,,,,"Alves-Oliveira, P., Arriaga, P., Paiva, A., Hoffman, G., Children as robot designers (2021) Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (Boulder, CO, USA) (HRI '21), pp. 399-408. , https://doi.org/10.1145/3434073.3444650, Association for Computing Machinery, New York, NY, USA; Anderson-Bashan, L., Megidish, B., Erel, H., Wald, I., Hoffman, G., Zuckerman, O., Grishko, A., The greeting machine: An abstract robotic object for opening encounters (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)., pp. 595-602. , https://doi.org/10.1109/ROMAN.2018.8525516; Bardzell, J., Bardzell, S., Dalsgaard, P., Gross, S., Halskov, K., Documenting the research through design process (2016) Proceedings of the 2016 ACM Conference on Designing Interactive Systems (Brisbane, QLD, Australia) (DIS '16), pp. 96-107. , https://doi.org/10.1145/2901790.2901859, Association for Computing Machinery, New York, NY, USA; Bardzell, J., Bolter, J., Löwgren, J., Interaction criticism: Three readings of an interaction design, and what they get us (2010) Interactions, 17 (2), pp. 32-37. , https://doi.org/10.1145/1699775.1699783, March; Bowers, J., The logic of annotated portfolios: Communicating the value of 'research through design (2012) Proceedings of the Designing Interactive Systems Conference (Newcastle upon Tyne, United Kingdom) (DIS '12), pp. 68-77. , https://doi.org/10.1145/2317956.2317968, Association for Computing Machinery, New York, NY, USA; Braun, V., Clarke, V., Using thematic analysis in psychology (2006) Qualitative Research in Psychology, 3 (2), pp. 77-101; Cheon, E., Makoto Su, N., Futuristic autobiographies: Weaving participant narratives to elicit values around robots (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (Chicago, IL, USA) (HRI '18), pp. 388-397. , https://doi.org/10.1145/3171221.3171244, Association for Computing Machinery, New York, NY, USA; Cross, N., Designerly ways of knowing (1982) Design Studies, 3 (4), pp. 221-227. , https://doi.org/10.1016/0142-694X(82)90040-0, Special Issue Design Education; Leora Culén, A., Børsting, J., Gaver, W., Strategies for annotating portfolios: Mapping designs for new domains (2020) Proceedings of the 2020 ACM Designing Interactive Systems Conference (Eindhoven, Netherlands) (DIS '20), pp. 1633-1645. , https://doi.org/10.1145/3357236.3395490, Association for Computing Machinery, New York, NY, USA; Dalsgaard, P., Dindler, C., Between theory and practice: Bridging concepts in HCI research (2014) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Toronto, Ontario, Canada) (CHI '14), pp. 1635-1644. , https://doi.org/10.1145/2556288.2557342, Association for Computing Machinery, New York, NY, USA; Desjardins, A., Wakkary, R., Odom, W., Lin, H., Lorenz Schilling, M., Exploring DIY tutorials as a way to disseminate research through design (2017) Interactions, 24 (4), pp. 78-82. , https://doi.org/10.1145/3098319, June; Döring, T., Sylvester, A., Schmidt, A., Ephemeral user interfaces: Valuing the aesthetics of interface components that do not last (2013) Interactions, 20 (4), pp. 32-37. , https://doi.org/10.1145/2486227.2486235, 7 2013; Dunne, A., Raby, F., (2007) Technological Dreams Series: No. 1, Robots, , http://dunneandraby.co.uk/content/projects/10/0, last accessed: June 2020; Van, D.K., Landay, D.J., Hong, J.I., (2002) The Design of Sites: Patterns, Principles, and Processes for Crafting A Customer-Centered Web Experience, , Addison-Wesley Longman Publishing Co., Inc., USA; Fallman, D., Stolterman, E., Establishing criteria of rigour and relevance in interaction design research (2010) Digital Creativity, 21 (4), pp. 265-272. , https://doi.org/10.1080/14626268.2010.548869; Gamboa, M., Obaid, M., Ljungblad, S., Ritual drones: Designing and studying critical flying companions (2021) Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (Boulder, CO, USA) (HRI '21 Companion), pp. 562-564. , https://doi.org/10.1145/3434074.3446363, Association for Computing Machinery, New York, NY, USA; Gaver, B., Bowers, J., Annotated portfolios (2012) Interactions, 19 (4), pp. 40-49. , https://doi.org/10.1145/2212877.2212889, 7 2012; Gaver, W., Designing for homo ludens (2002) I3 Magazine, 12, pp. 2-6; Gaver, W., What should we expect from research through design? 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ACM Des. Interact. Syst. Conf.: Nowhere Everywhere",Conference Paper,"Final","",Scopus,2-s2.0-85110170820
"Villamar Gómez L., Miura J.","57222276718;7007070630;","Ontology-based knowledge management with verbal interaction for command interpretation and execution by home service robots",2021,"Robotics and Autonomous Systems","140",,"103763","","",,5,"10.1016/j.robot.2021.103763","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102117068&doi=10.1016%2fj.robot.2021.103763&partnerID=40&md5=79d9b02179cba7e043b6fc8b8073c26d","Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Aichi, 441-8580, Japan","Villamar Gómez, L., Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Aichi, 441-8580, Japan; Miura, J., Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Aichi, 441-8580, Japan","This paper describes a system for service robots that combines ontological knowledge reasoning and human–robot interaction to interpret natural language commands and successfully perform household chores, such as finding and delivering objects. Knowledge and context reasoning is essential for providing more efficient service robots, given their diverse and continuously changing environments. Moreover, since they are in contact with humans, robots require such skills as interaction and language. Therefore, we developed a system with specific modules to manage robots’ knowledge and reasoning, command analysis, decision-making, and talking interaction. The system relies on inference methods and verbal interaction to understand commands and clarify uncertain information. We tested our system inside a simulated environment where the robot receives commands with missing or unclear information. The system's performance was compared with the average performance of human subjects who completed the same commands in the simulation. © 2021 Elsevier B.V.","Human–robot interaction; Knowledge management; Ontology-based; Service robots","Agricultural robots; Decision making; Knowledge management; Mobile robots; Ontology; Changing environment; Home service robot; Knowledge reasoning; Ontology-based knowledge managements; Robot interactions; Simulated environment; System's performance; Uncertain informations; Social robots",,,,,"Japan Society for the Promotion of Science, KAKEN: 17H01799","This work was supported in part by the Japan Society for the Promotion of Sciences (JSPS) KAKENHI under Grant 17H01799.",,,,,,,,,,"Nielsen, S., Bonnerup, E., Hansen, A., Nilsson, J., Nellemann, L., Hansen, K., Hammershøi, D., Subjective experience of interacting with a social robot at a danish airport (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), IEEE RO-MAN proceedings, pp. 1163-1170. , IEEE United States; Bruno, B., Recchiuto, C.T., Papadopoulos, I., Saffiotti, A., Koulouglioti, C., Menicatti, R., Mastrogiovanni, F., Sgorbissa, A., Knowledge representation for culturally competent personal robots: Requirements, design principles, implementation, and assessment (2019) Int. 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Intell., 33","Villamar Gómez, L.; Department of Computer Science and Engineering, Toyohashi, Japan; email: liliana@aisl.cs.tut.ac.jp",,,"Elsevier B.V.",,,,,09218890,,RASOE,,"English","Rob Autom Syst",Article,"Final","",Scopus,2-s2.0-85102117068
"Ding Z., Song C., Xu J., Dou Y.","57226590844;35085626400;55714104900;57216907884;","Human-Robot Interaction System Design for Manipulator Control Using Reinforcement Learning",2021,"Proceedings - 2021 36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021",,,"9486647","660","665",,,"10.1109/YAC53711.2021.9486647","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112049639&doi=10.1109%2fYAC53711.2021.9486647&partnerID=40&md5=a446dc9bc4e9687a0625cccde469a88c","Beijing Institute of Technology, School of Automation, Beijing, China","Ding, Z., Beijing Institute of Technology, School of Automation, Beijing, China; Song, C., Beijing Institute of Technology, School of Automation, Beijing, China; Xu, J., Beijing Institute of Technology, School of Automation, Beijing, China; Dou, Y., Beijing Institute of Technology, School of Automation, Beijing, China","In this article, a novel human-robot interaction (HRI) system is presented and applied in the robotic arm coordinated operation control task. The presented HRI system includes two parts, the impedance model controller and the robotic arm controller, which allows the operator to manipulate the robotic arm to accomplish the given task with minimal human effort. First, the model-based reinforcement learning (RL) method is applied in the impedance model for operator adaptation. The impedance model controller can transform human input into the specific signal for the manipulator. Second, a novel adaptive manipulator controller is designed. In contrast to existing controllers, a velocity-free filter is implemented in our controller, which is developed to replace the manipulator actuator's speed signal. The effectiveness of the presented HRI system is verified by the simulation based on real manipulator parameters. © 2021 IEEE.","Adaptive impedance control; Human-robot interaction; Reinforcement learning","Controllers; Industrial manipulators; Learning systems; Machine design; Man machine systems; Manipulators; Reinforcement learning; Robotic arms; Robotics; Arm controllers; Coordinated operation; Existing controllers; Human robot Interaction (HRI); Impedance modeling; Manipulator control; Manipulator parameters; Model-based reinforcement learning; Social robots",,,,,,,,,,,,,,,,"Cai, S., Ma, Z., Skibniewski, M.J., Bao, S., Construc-Tion automation and robotics for high-rise buildings over the past decades: A comprehensive review (2019) Advanced Engineering Informatics, 42, p. 100989; Han, D., Huang, R., Liu, X., Yang, Y., Combined spacecraft stabilization control after multiple impacts during the capture of a tumbling target by a space robot (2020) Acta Astronáutica, 176, pp. 24-32; Goodrich, M.A., Schultz, A.C., (2008) Human-Robot Inter-Action: A Survey, , Now Publishers Inc; Liu, Z., Liu, J., He, W., Dynamic modeling and vibration control of a flexible aerial refueling hose (2016) Aerospace Science and Technology, 55, pp. 92-102; Yang, Y., Vamvoudakis, K.G., Modares, H., Yin, Y., Wunsch, D., Hamiltonian-driven hybrid adaptive dynamic programming (2020) IEEE Transactions on Systems, Man and Cybernetics: Systems; Liu, Z., Zhao, Z., Ahn, C.K., Boundary constrained control of flexible string systems subject to disturbances (2020) IEEE Transactions on Circuits and Systems II: Express Briefs, 67 (1), pp. 112-116; Yang, Y., Gao, W., Modares, H., Xu, C., Robust actor-critic learning for continuous-Time nonlinear systems with unmodeled dynamics (2021) IEEE Transactions on Fuzzy Systems; Lee, H., Kim, J., A survey on robot teaching: Categorization and brief review (2013) Applied Mechanics and Materials, 330, pp. 648-656; Li, Z., Zhao, T., Chen, F., Hu, Y., Su, C.Y., Fukuda, T., Reinforcement learning of manipulation and grasping using dynamical movement primitives for a humanoidlike mobile manipulator (2018) IEEE/ ASME Trans. 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Annu. Conf. Chin. Assoc. Autom., YAC",Conference Paper,"Final","",Scopus,2-s2.0-85112049639
"Serj M.F., Asgari M., Lavi B., Valls D.P., Garcia M.A.","57203514304;55747224400;57192083628;57203516997;57212612196;","A time-distributed convolutional long short-term memory for hand gesture recognition",2021,"2021 29th Iranian Conference on Electrical Engineering, ICEE 2021",,,,"478","482",,,"10.1109/ICEE52715.2021.9544445","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117435990&doi=10.1109%2fICEE52715.2021.9544445&partnerID=40&md5=48b0d8b6e7294da3393d2dc4351c426f","Rovira i Virgili University, Department of Computer Engineering and Mathematics, Tarragona, Spain; K.N. Toosi University of Technology, Faculty of Electrical Engineering, Tehran, Iran; University of Campinas, Institute of Computing, Sao Paulo, Brazil; Autonomous University of Madrid, Department of Electronics and Communication Technology, Spain","Serj, M.F., Rovira i Virgili University, Department of Computer Engineering and Mathematics, Tarragona, Spain; Asgari, M., K.N. Toosi University of Technology, Faculty of Electrical Engineering, Tehran, Iran; Lavi, B., University of Campinas, Institute of Computing, Sao Paulo, Brazil; Valls, D.P., Rovira i Virgili University, Department of Computer Engineering and Mathematics, Tarragona, Spain; Garcia, M.A., Autonomous University of Madrid, Department of Electronics and Communication Technology, Spain","The applications of human-robot interaction have recently raised many research interests, and hand gesture recognition to recognize human gestures in video-based problems is one of them. In the recent decade, deep learning techniques have proven their promising performance in the fields of pattern recognition and computer vision. This study presents an improved version of the Convolutional Neural Network in combination with Long Short-Term Memory for hand gesture recognition. The proposed structure is fully considered in a time-distributed framework to effectively train the network of the frame-level classification. Hence, employing a time-distributed framework, a TD-CNN-LSTM method is developed. Finally, the efficacy of our proposed architecture is evaluated on the recent publicly available GRIT corpus dataset, and we also show that our method outperforms the recent state of the art CNN-LSTM method. © 2021 IEEE.","Convolutional neural networks; Deep learning; Hand gesture recognition; Long short-term memory","Brain; Convolution; Convolutional neural networks; Gesture recognition; Human robot interaction; Palmprint recognition; Convolutional neural network; Deep learning; Distributed framework; Hand-gesture recognition; Human gestures; Humans-robot interactions; Learning techniques; Performance; Proposed architectures; Research interests; Long short-term memory",,,,,,,,,,,,,,,,"Nadeem, A., Jalal, A., Kim, K., Human actions tracking and recognition based on body parts detection via artificial neural network (2020) 2020 3rd International Conference on Advancements in Computational Sciences (ICACS), pp. 1-6; Rigoll, G., Kosmala, A., Eickeler, S., High performance real-time gesture recognition using hidden markov models (1997) International Gesture Workshop, pp. 69-80. , Berlin, Heidelberg; Darrell, T., Pentland, A., Space-time gestures (1993) Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 335-340. , New York, USA; Liu, G., Schulte, O., Deep reinforcement learning in ice hockey for context-aware player evaluation (2018) Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pp. 3442-3448; Jirak, D., Barros, P., Wermter, S., Dynamic gesture recognition using echo state networks (2015) Proceedings, 475. , Belgium: Presses universitaires de Louvain; Rautaray, S.S., Agrawal, A., Vision based hand gesture recognition for human computer interaction: A survey (2015) Artificial Intelligence Review, 43 (1), pp. 1-54; Nagi, J., Ducatelle, F., Di Caro, G.A., Ciresan, D., Meier, U., Giusti, A., Nagi, F., Gambardella, L.M., Max-pooling convolutional neural networks for vision-based hand gesture recognition (2011) 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 342-347. , Kuala Lumpur, Malaysia; Bilal, S., Akmeliawati, R., Salami, M.J.E., Shafie, A.A., Visionbased hand posture detection and recognition for sign language - A study (2011) 2011 4th International Conference on Mechatronics (ICOM), pp. 1-6. , Kuala Lumpur, Malaysia; Jmaa, A.B., Mahdi, W., Jemaa, Y.B., Hamadou, A.B., Hand localization and fingers features extraction: Application to digit recognition in sign language (2009) International Conference on Intelligent Data Engineering and Automated Learning, pp. 151-159. , Berlin, Heidelberg; Hinton, G.E., Osindero, S., Teh, Y.W., A fast learning algorithm for deep belief nets (2006) Neural Computation, 18 (7), pp. 1527-1554; Ciresan, D., Meier, U., Masci, J., Schmidhuber, J., Multi-column deep neural network for traffic sign classification (2012) Neural Networks, 32, pp. 333-338; Khalil-Hani, M., Sung, L.S., A convolutional neural network approach for face verification (2014) 2014 International Conference on High Performance Computing Simulation (HPCS), pp. 707-714. , Bologna, Italy; Khaloozadeh, A., Robust extended kalman filtering for nonlinear systems with unknown input: A ubb model approach (2020) IET Radar Sonar Navig., 14 (11), pp. 1837-1844; Karnowski, T.P., Arel, I., Rose, D., Deep spatiotemporal feature learning with application to image classification (2010) 2010 Ninth International Conference on Machine Learning and Applications, pp. 883-888. , Washington, DC, USA; Barros, P., Parisi, G.I., Jirak, D., Wermter, S., Real-time gesture recognition using a humanoid robot with a deep neural architecture (2014) 2014 IEEE-RAS International Conference on Humanoid Robots, pp. 646-651. , Madrid, Spain; Wu, D., Pigou, L., Kindermans, P., Le, N.D., Shao, L., Dambre, J., Odobez, J., Deep dynamic neural networks for multimodal gesture segmentation and recognition (2016) IEEE Trans. Pattern Anal. Mach. Intell., 38 (8), pp. 1583-1597; Molchanov, P., Yang, X., Gupta, S., Kim, K., Tyree, S., Kautz, J., Online detection and classification of dynamic hand gestures with recurrent 3d convolutional neural networks (2016) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4207-4215. , Las Vegas, NV, USA; Tsironi, E., Barros, P., Weber, C., Wermter, S., An analysis of convolutional long short-term memory recurrent neural networks for gesture recognition (2017) Neurocomputing, 268, pp. 76-86; Tsironi, E., Barros, P., Wermter, S., Gesture recognition with a convolutional long short-term memory recurrent neural network (2016) ESANN 2016 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2. , Bruges, Belgium; Chollet, F., (2017) Keras, , https://github.com/fchollet/keras",,,,"Institute of Electrical and Electronics Engineers Inc.","29th Iranian Conference on Electrical Engineering, ICEE 2021","18 May 2021 through 20 May 2021",,172292,,9781665433655,,,"English","Iranian Conf. Electr. Eng., ICEE",Conference Paper,"Final","",Scopus,2-s2.0-85117435990
"Zhao J., Wei Z., Yang X., Zhang G., Wang Z.","57033063400;57204134894;57200070940;57218001667;57217204735;","Mechanoplastic tribotronic two-dimensional multibit nonvolatile optoelectronic memory",2021,"Nano Energy","82",,"105692","","",,2,"10.1016/j.nanoen.2020.105692","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098104029&doi=10.1016%2fj.nanoen.2020.105692&partnerID=40&md5=c1e67cef876feca12720a985bf5592d4","Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, 100081, China; School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China; School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, 100190, China; Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, 100190, China; Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China; School of Material Science and Engineering, Georgia Institute of Technology, Atlanta, GA  30332–0245, United States","Zhao, J., Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, 100081, China, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China; Wei, Z., School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, 100190, China, Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, 100190, China; Yang, X., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China; Zhang, G., School of Physical Sciences, University of Chinese Academy of Sciences, Beijing, 100190, China, Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, 100190, China; Wang, Z., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, School of Material Science and Engineering, Georgia Institute of Technology, Atlanta, GA  30332–0245, United States","The multi-bit nonvolatile memory based on two-dimensional material is promising for the future semiconductor industry due to the request of device's miniaturization and structure flexibility. Here, we report a novel MoS2 nonvolatile memory combined with nanographene as floating gate realize multilevel storage state triggerd by both mechanical and optical stimulation without applying extra gate voltages. The device demonstrates excellent characteristics with high programming/erasing current ratio &gt; 107, performance stability over 200 cycles and retention time excess 105 s. The extremely high photoresponsivity of MoS2 optical memory ~ 12000 A/W is firstly reported. In addition, the stable flexibility enables integrated devices feasibility for wearable memories in image capturing and recording. The tribotronic-optoelectronic dual-gate memory opens up a new route to development of future human-robot interaction system integrating both sensing and storage. © 2020 Elsevier Ltd","Charge trapping; Mechanical stimulation; Nonvolatile storage; Photoelectronic memory; Twodimensional materials","Image recording; Layered semiconductors; Molybdenum compounds; Semiconductor device manufacture; Social robots; Sulfur compounds; Integrated device; Multilevel storage; Non-volatile memory; Optical stimulation; Performance stability; Photoresponsivity; Semiconductor industry; Two-dimensional materials; Nonvolatile storage",,,,,"2019YFB1309701; National Natural Science Foundation of China, NSFC: 61804009","This research was financially supported by the National Key R&D program of China (Grant No. 2019YFB1309701 ), National Natural Science Foundation of China (NSFC) (Grant No. 61804009 ), and Beijing Institute of Technology Research Fund Program for Young Scholars .",,,,,,,,,,"Castro Neto, A.H., Guinea, F., Peres, N.M.R., Novoselov, K.S., Geim, A.K., The electronic properties of graphene (2009) Rev. 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Mater., 12, pp. 207-211; Jang, S., Hwang, E., Lee, J.H., Park, H.S., Cho, J.H., Graphene-graphene oxide floating gate transistor memory (2015) Small, 11, pp. 311-318; Tseng, R.J., Huang, J.X., Ouyang, J., Kaner, R.B., Yang, Y., Polyaniline nanofiber/gold nanoparticle nonvolatile memory (2005) Nano Lett., 5, pp. 1077-1080; Kim, S.H., Yi, S.G., Park, M.U., Lee, C.J., Kim, M., Yoo, K., Multilevel MoS2 optical memory with photoresponsive top floating gates (2019) ACS Appl. Mater. Interfaces, 11, pp. 25306-25312; Lopez-Sanchez, O., Lembke, D., Kayci, M., Radenovic, A., Kis, A., Ultrasensitive photodetectors based on monolayer MoS2 (2013) Nat. Nanotechnol., 8, pp. 497-501; Yin, Z.Y., Li, H., Li, H., Jiang, L., Shi, Y.M., Sun, Y.H., Lu, G., Zhang, H., Single-Layer MoS2 Phototransistors (2012) ACS Nano, 6, pp. 74-80; Wang, H.N., Zhang, C.J., Chan, W.M., Tiwari, S., Rana, F., Ultrafast response of monolayer molybdenum disulfide photodetectors (2015) Nat. 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Wang, Z.; Beijing Institute of Nanoenergy and Nanosystems, China; email: zhong.wang@mse.gatech.edu",,,"Elsevier Ltd",,,,,22112855,,,,"English","Nano Energy",Article,"Final","",Scopus,2-s2.0-85098104029
"Yoon S., Alimardani M., Hiraki K.","57222470662;55836794100;7102170992;","The effect of robot-guided meditation on intra-brain EEG phase synchronization",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447184","318","322",,1,"10.1145/3434074.3447184","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102770263&doi=10.1145%2f3434074.3447184&partnerID=40&md5=02446a06d02176f8014f230c95de1009","Department of Cognitive Science and Artificial, Intelligence Tilburg University, Tilburg, Netherlands; Graduate School of Arts and Sciences, University of Tokyo, Tokyo, Japan","Yoon, S., Department of Cognitive Science and Artificial, Intelligence Tilburg University, Tilburg, Netherlands; Alimardani, M., Department of Cognitive Science and Artificial, Intelligence Tilburg University, Tilburg, Netherlands; Hiraki, K., Graduate School of Arts and Sciences, University of Tokyo, Tokyo, Japan","In this study, we examined the effect of mindfulness meditation facilitated by human-robot interaction (HRI) on brain activity. EEG signals were collected from two groups of participants; Meditation group who practiced mindfulness meditation with a social robot and Control group who only listened to a lecture by the robot. We compared brain functional connectivity between the two groups by computing EEG phase synchrony during HRI session. The results revealed a significantly lower global phase synchrony in beta frequency band of the Meditation group, which has been previously reported as an indication of reduced cognitive processing and achieving the mindful state in experienced meditators. Our findings demonstrate the potential of Socially-Assistive Robots (SAR) for integration in mental healthcare and optimization of the intervention effects. Additionally, our study puts forward new measures for objective monitoring of HRI effect on the user's neurophysiological responses. © 2021 ACM.","Brain connectivity; Brain-computer interface (BCI); EEG phase synchronization; Human-robot interaction (HRI); Mental health; Mindfulness meditation; Socially-assistive robots (SAR)","Agricultural robots; Brain; Man machine systems; Neurophysiology; Brain activity; Cognitive processing; Control groups; Functional connectivity; Human robot Interaction (HRI); Phase synchronization; Phase synchrony; Socially assistive robots; Human robot interaction",,,,,"Japan Society for the Promotion of Science, KAKEN: 15F15046; Core Research for Evolutional Science and Technology, CREST: JPMJCR18A4; Cabinet Office, Government of Japan; Council for Science, Technology and Innovation, CSTI","This research was supported by ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan), Grant-in-Aid for JSPS Research Fellow 15F15046, and JST CREST (grant# JPMJCR18A4).",,,,,,,,,,"Miller, E., Polson, D., Apps, avatars, and robots: The future of mental healthcare (2019) Issues Ment. 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AI, 7 (125); Alimardani, M., Keshmiri, S., Sumioka, H., Hiraki, K., Classification of EEG signals for a hypnotrack bci system (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 240-245; Mattout, J., Brain-computer interfaces: A neuroscience paradigm of social interaction?. A matter of perspective (2012) Frontiers in Human Neuroscience, 6 (114); Alimardani, M., Hiraki, K., Development of a real-time brain-computer interface for interactive robot therapy: An exploration of EEG and emg features during hypnosis (2017) International Journal of Computer and Information Engineering, 11 (2), pp. 187-195; Lomas, T., Ivtzan, I., Fu, C.H.Y., A systematic review of the neurophysiology of mindfulness on EEG oscillations (2015) Neurosci. Biobehav. Rev., 57, pp. 401-410; Yu, X., Activation of the anterior prefrontal cortex and serotonergic system is associated with improvements in mood and EEG changes induced by zen meditation practice in novices (2011) Int. 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Cogn., 30, pp. 13-23; Lehmann, D., Faber, P.L., Tei, S., Pascual-Marqui, R.D., Milz, P., Kochi, K., Reduced functional connectivity between cortical sources in five meditation traditions detected with lagged coherence using EEG tomography (2012) Neuroimage, 60 (2), pp. 1574-1586; Payne, J.R., Experienced meditators exhibit no differences to demographically matched controls in theta phase synchronization, p200, or p300 during an auditory oddball task (2020) Mindfulness (N. Y)., 11 (3), pp. 643-659; Hebert, R., Lehmann, D., Tan, G., Travis, F., Arenander, A., Enhanced EEG alpha time-domain phase synchrony during transcendental meditation: Implications for cortical integration theory (2005) Signal Processing, 85 (11), pp. 2213-2232; Rodriguez-Larios, J., Wong, K.F., Lim, J., Alaerts, K., Mindfulness training is associated with changes in alpha-theta cross-frequency dynamics during meditation (2020) Mindfulness (N. Y).; Van Lutterveld, R., Van Dellen, E., Pal, P., Yang, H., Stam, C.J., Brewer, J., Meditation is associated with increased brain network integration (2017) Neuroimage, 158, pp. 18-25. , May; Williams, J.M.G., Teasdale, J.D.S.Z., Segal, Z.V., Kabat-Zinn, J., (2007) The Mindful Way through Depression: Freeing Yourself from Chronic Unhappiness.; Cacioppo, J.T., Tassinary, L.G., Berntson, G.G., (2007) Handbook of Psychophysiology, 2; He, P., Li, P., Sun, H., Feature extraction of acoustic signals based on complex morlet wavelet (2011) Procedia Eng., 15, pp. 464-468; Lachaux, J.P., Rodriguez, E., Martinerie, J., Varela, F.J., Measuring phase synchrony in brain signals (1999) Hum. Brain Mapp., 8 (4), pp. 194-208; Corain, L., Salmaso, L., A critical review and a comparative study on conditional permutation tests for two-way anova (2007) Commun. Stat. Simul. Comput., 36 (4), pp. 791-805; Hauswald, A., Übelacker, T., Leske, S., Weisz, N., What it means to be zen: Marked modulations of local and interareal synchronization during open monitoring meditation (2015) Neuroimage, 108, pp. 265-273; Jo, H.G., Naranjo, J.R., Hinterberger, T., Winter, U., Schmidt, S., Phase synchrony in slow cortical potentials is decreased in both expert and trained novice meditators (2019) Neurosci. Lett., 701, pp. 142-145; Ray, W.J., Cole, H.W., EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes (1985) Science, 228 (4700), pp. 750-752; Lang, E.W., Tomé, A.M., Keck, I.R., Górriz-Sáez, J.M., Puntonet, C.G., Brain connectivity analysis: A short survey (2012) Comput. Intell. Neurosci., 2012 (3); Gao, Y., Selective attention enhances beta-band cortical oscillation to speech under 'cocktail-party' listening conditions (2017) Front. Hum. Neurosci., 11, p. 34; Tjon, D.M., Tinga, A.M., Alimardani, M., Louwerse, M.M., Brain activity reflects sense of presence in 360 video for virtual reality (2019) The 28th International Conference on Information Systems Development (ISD2019); Alimardani, M., Nishio, S., Ishiguro, H., Humanlike robot hands controlled by brain activity arouse illusion of ownership in operators (2013) Scientific Reports, 3, p. 2396; Tan, D., Nijholt, A., Brain-computer interfaces and human-computer interaction (2010) Brain-Computer Interfaces, pp. 3-19. , Springer, London; Alimardani, M., Qurashi, S., Mind perception of a sociable humanoid robot: A comparison between elderly and young adults (2019) Iberian Robotics Conference, pp. 96-108. , Springer; Kont, M., Alimardani, M., Engagement and mind perception within human-robot interaction: A comparison between elderly and young adults (2020) International Conference on Social Robotics, pp. 344-356. , Springer, Cham",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102770263
"Kamino W., Joshi S., Šabanovic S.","57220182215;57201549303;14036199300;","'Food' for human robot interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447144","131","135",,,"10.1145/3434074.3447144","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102769591&doi=10.1145%2f3434074.3447144&partnerID=40&md5=4834d412ab76e61b2b08c7aeaa2f66e0","Indiana University Bloomington, Bloomington, IN, United States","Kamino, W., Indiana University Bloomington, Bloomington, IN, United States; Joshi, S., Indiana University Bloomington, Bloomington, IN, United States; Šabanovic, S., Indiana University Bloomington, Bloomington, IN, United States","'Food', when mentioned in Human-Robot Interaction (HRI) research, is most often in the context of functional applications of automation, delivery, and assistance. Food has, however, not been explored as a medium for social expression or building relationships with social robots. Using web-based examples of robot food and our pilot collection of LOVOT and AIBO robot user's Tweets about their practices of feeding their robots, we show how food has the potential to sustain interactions, increase enjoyment, sociability and companionship in HRI, enhance life-likeness, autonomy, and agency for robots, and open up opportunities for community building among robot users. We present design implications of food for HRI, and urge HRI researchers to envision food as a facet of Human-Robot relationships and < - Human-Food-Robot - > interaction as a celebratory, provocative, and promising domain for HRI and social robot design. © 2021 ACM.","Food; Human-robot interaction; Social robot","Agricultural robots; Machine design; Man machine systems; AIBO robot; Community buildings; Design implications; Functional applications; Human robot Interaction (HRI); Human robots; Web based; Social robots",,,,,,,,,,,,,,,,"Beardsworth, A., Keil, T., (2002) Sociology on the Menu: An Invitation to the Study of Food and Society, , Routledge; Bell, D., Valentine, G., (1997) Consuming Geographies: We Are Where We Eat, , Psychology Press; Bhattacharjee, T., Gordon, E.K., Scalise, R., Cabrera, M.E., Caspi, A., Cakmak, M., Srinivasa, S.S., Is more autonomy always better? exploring preferences of users with mobility impairments in robot-assisted feeding (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 181-190; Bugmann, G., Copleston, S.N., What can a personal robot do for you? (2011) Conference towards Autonomous Robotic Systems, pp. 360-371. , Springer; Cahlander, R.L., Carroll, D.W., Hanson, R.A., Hollingsworth, A., Reinertsen, J.O., (1990) Food Preparation Robot; (2012) Leisure Food Series (Cycad Leaf/Sugar Cane/Chunk of Ice), , https://www.pleoworld.com/pleo_rb/eng/product.php?&c1id=2&id=5, Innvo Labs Corporation; (2012) Mint Leaf, , https://www.pleoworld.com/pleo_rb/eng/product.php?&c1id=2&id=26, Innvo Labs Corporation; (2012), www.pleoworld.com, Innvo Labs Corporation; (2020), https://us.aibo.com/, Sony Corporation; Cruz-Maya, A., Tapus, A., Teaching nutrition and healthy eating by using multimedia with a kompai robot: Effects of stress and user's personality (2016) 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 644-649; Dubé, L., LeBel, J.L., Lu, J., Affect asymmetry and comfort food consumption (2005) Physiology & Behavior, 86 (4), pp. 559-567. , 2005; Evers, C., Marijn Stok, F., De Ridder, D.T.D., Feeding your feelings: Emotion regulation strategies and emotional eating (2010) Personality and Social Psychology Bulletin, 36 (6), pp. 792-804. , 2010; Friedman, B., Kahn, P.H., Jr., Hagman, J., Hardware companions? what online aibo discussion forums reveal about the human-robotic relationship (2003) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 273-280; Jeff Goldblatt, J., (1990) Special Events: The Art and Science of Celebration, , Van Nostrand Reinhold; Grimes, A., Harper, R., Celebratory technology: Newdirections for food research in hci (2008) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 467-476; Esther Hamburg, M., Finkenauer, C., Schuengel, C., Food for love: The role of food offering in empathic emotion regulation (2014) Frontiers in Psychology, 5, p. 32. , 2014; Iqbal, J., Hameed Khan, Z., Khalid, A., Prospects of robotics in food industry (2017) Food Science and Technology, 37 (2), pp. 159-165. , 2017; Kyung Lee, M., Forlizzi, J., Rybski, P.E., Crabbe, F., Chung, W., Finkle, J., Glaser, E., Kiesler, S., The snackbot: Documenting the design of a robot for long-term human-robot interaction (2009) Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction, pp. 7-14; Leite, I., Martinho, C., Paiva, A., Social robots for long-term interaction: A survey (2013) International Journal of Social Robotics, 5 (2), pp. 291-308. , 2013; Lupton, D., Food, memory and meaning: The symbolic and social nature of food events (1994) The Sociological Review, 42 (4), pp. 664-685. , 1994; Lupton, D., (1996) Food, the Body and the Self, , Sage; Nussey, S., (2020) SoftBank Brings Food Service Robot to Labour-strapped Japan, , https://www.reuters.com/article/softbank-group-robotics-idUSKBN26J0GC; Palinko, O., Fischer, K., Ruiz Ramirez, E., Damsgaard Nissen, L., Langedijk, R.M., A drink-serving mobile social robot selects who to interact with using gaze (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 384-385; Park, D., Hoshi, Y., Kemp, C.C., A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder (2018) IEEE Robotics and Automation Letters, 3 (3), pp. 1544-1551. , 2018; Randall, N., Joshi, S., Liu, X., Health-e-eater: Dinnertime companion robot and magic plate for improving eating habits in children from low-income families (2018) Companion of the 2018 ACM/IEEE International Conference on Human-robot Interaction, pp. 361-362; Randall, N., Joshi, S., Liu, X., Health-e-eater: Dinnertime companion robot and magic plate for improving eating habits in children from low-income families (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (Chicago, IL, USA) (HRI '18), pp. 361-362. , https://doi.org/10.1145/3173386.3177828, Association for Computing Machinery, New York, NY, USA; (2019) IEEE Spectrum., , https://robots.ieee.org/robots/starship/; Troisi, J.D., Gabriel, S., Chicken soup really is good for the soul ""comfort food"" fulfills the need to belong (2011) Psychological Science, 22 (6), pp. 747-753; Yamazaki, A., Masuda, R., Autonomous foods handling by chopsticks for meal assistant robot (2012) ROBOTIK 2012; 7th German Conference on Robotics. VDE, pp. 1-6",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102769591
"Babel F., Kraus J., Hock P., Asenbauer H., Baumann M.","57204529534;57158564500;57015145500;57222463837;7202494638;","Investigating the validity of online robot evaluations: Comparison of findings from an one-sample online and laboratory study",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447141","116","120",,,"10.1145/3434074.3447141","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102766713&doi=10.1145%2f3434074.3447141&partnerID=40&md5=6b4f987de0b1ad8a75f5d31223ee8fa0","Dept. Human Factors, Ulm University, Ulm, Germany","Babel, F., Dept. Human Factors, Ulm University, Ulm, Germany; Kraus, J., Dept. Human Factors, Ulm University, Ulm, Germany; Hock, P., Dept. Human Factors, Ulm University, Ulm, Germany; Asenbauer, H., Dept. Human Factors, Ulm University, Ulm, Germany; Baumann, M., Dept. Human Factors, Ulm University, Ulm, Germany","As the number of online studies in the field of human-robot interaction (HRI) increases, the comparability of the results of online studies with those of laboratory experiments needs further investigation. In this one-sample study, 29 participants experienced three different commercially-available service robots first in an online session and then in a lab experiment and evaluated the robots regarding their trust, fear and intention to use the robot. Furthermore, several robot characteristics were evaluated (e.g. humanness, uncanniness). Overall, study results indicate high comparability of findings from online and lab experiments for trust, fear and robot characteristics like humanness and uncanniness. Same relative differences between the robots were found for both presentation methods except for intention to use and robot reliability. This preliminary study provides insights into online study validity and makes recommendations for future research. © 2021 ACM.","Intention to use; Method comparison; Research method validity; Robot evaluation; Service robots; Trust","Agricultural robots; Laboratories; Man machine systems; Human robot Interaction (HRI); Intention to use; Laboratory experiments; Laboratory studies; Online robots; Online studies; Service robots; Social robots",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV7967","The study is part of the research project ‘RobotKoop’ funded by the German Ministry of Education and Research (grant no. 16SV7967)",,,,,,,,,,"Bartneck, C., Kulic, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81. , 2009; Dimitrova, D.S., Kaishev, V.K., Tan, S., Computing the kolmogorov-smirnov distribution when the underlying cdf is purely discrete, mixed, or continuous (2020) J. Stat. Softw., 95 (1), pp. 1-42. , https://doi.org/10.18637/jss.v095.i10, 2020; Feil-Seifer, D., Haring, K.S., Rossi, S., RWagner, A., Williams, T., Where to next? The impact of COVID-19 on human-robot interaction research (2020) ACM Trans. Human-Robot Interact., 10 (1), pp. 1-7. , https://doi.org/10.1145/3405450, 2020; Gong, L., How social is social responses to computers? The function of the degree of anthropomorphism in computer representations (2008) Comput. Human Behav., 24 (4), pp. 1494-1509. , https://doi.org/10.1016/j.chb.2007.05.007, 2008; Chang Ho, C., MacDorman, K.F., Measuring the uncanny valley effect: Refinements to indices for perceived humanness, attractiveness, and eeriness (2017) Int. J. Soc. Robot., 9 (1), pp. 129-139. , https://doi.org/10.1007/s12369-016-0380-9, jan 2017; Hock, P., Babel, F., Rukzio, E., Muehl, K., Baumann, M., Online experiments as a supplement of automated driving simulator studies: A methodological insight (2019) Adjun. Proc.-11th Int. ACM Conf. Automot. User Interfaces Interact. Veh. Appl. AutomotiveUI 2019, pp. 282-286. , https://doi.org/10.1145/3349263.3351334, Association for Computing Machinery, Inc; Hock, P., Kraus, J., Babel, F., Walch, M., Rukzio, E., Baumann, M., How to design valid simulator studies for investigating user experience in automated driving-review and hands-on considerations (2018) Proc.-10th Int. ACM Conf. Automot. User Interfaces Interact. Veh. Appl. AutomotiveUI 2018, pp. 105-117. , https://doi.org/10.1145/3239060.3239066; Honig, S., Oron-Gilad, T., Comparing laboratory user studies and video-enhanced web surveys for eliciting user gestures in human-robot interactions (2020) ACM/IEEE Int. Conf. Human-Robot Interact, pp. 248-250. , https://doi.org/10.1145/3371382.3378325; Jian, J., Bisantz, A.M., Drury, C.G., Foundations for an empirically determined scale of trust in automated systems (2000) International Journal of Cognitive Ergonomics, 4 (1), pp. 53-71. , 2000; David Kidd, C., (2000) Sociable Robots: The Role of Presence and Task in Human-Robot Interaction, , https://dspace.mit.edu/handle/1721.1/61863, MSc Thesis. Master Thesis; Maria Kraus, J., (2020) Psychological Processes in the Formation and Calibration of Trust in Automation, , https://doi.org/10.18725/OPARU-32583, Dissertation. Dissertation Ulm University; Maria Kraus, J., Babel, F., Baumann, M., Initial reactions to an autonomous service robot in public space: An interview study (2019) AOW Symp. Eng. Psychol, p. 3; Li, J., The benefit of being physically present: A survey of experimental works comparing copresent robots, telepresent robots and virtual agents (2015) Int. J. Hum. Comput. Stud., 77, pp. 23-37. , https://doi.org/10.1016/j.ijhcs.2015.01.001, 2015; Madsen, M., Gregor, S., Measuring human-computer trust (2000) 11th Australasian Conference on Information Systems, 53, pp. 6-8. , Citeseer; Mišeikis, J., Caroni, P., Duchamp, P., Gasser, A., Marko, R., Mišeikiene, N., Zwilling, F., Früh, H., (2020) Lio-A Personal Robot Assistant for Human-robot Interaction and Care Applications, 5 (4), pp. 5339-5346. , 2020; Pages, J., Marchionni, L., Ferro, F., Tiago: The modular robot that adapts to different research needs (2016) Int. Work. Robot Modul, pp. 3-6. , https://clawar.org/wp-content/uploads/2016/10/P2.pdf; Steele, M., Chaseling, J., Powers of discrete goodness-of-fit test statistics for a uniform null against a selection of alternative distributions (2006) Commun. Stat. Simul. Comput., 35 (4), pp. 1067-1075. , https://doi.org/10.1080/03610910600880666, 2006; Venkatesh, V., Davis, F.D., A theoretical extension of the technology acceptance model: Four longitudinal field studies (2000) Management Science, 46 (2), pp. 186-204. , 2000; Wallot, S., Leonardi, G., Deriving inferential statistics from recurrence plots: A recurrence-based test of differences between sample distributions and its comparison to the two-sample kolmogorov-smirnov test (2018) Chaos, 28, p. 085712. , https://doi.org/10.1063/1.5024915, 2018; Woods, S.N., Walters, M.L., Lee Koay, K., Dautenhahn, K., Methodological issues in hri: A comparison of live and video-based methods in robot to human approach direction trials (2006) Proc.-IEEE Int. Work. Robot Hum. Interact. Commun, pp. 51-58. , https://doi.org/10.1109/ROMAN.2006.314394",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102766713
"Rheman J., Fraune M.R.","57222468094;56085661200;","Not in my house!: Children playing an online game with robots show low trust and closeness with ingroup robots",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447204","417","420",,,"10.1145/3434074.3447204","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102759020&doi=10.1145%2f3434074.3447204&partnerID=40&md5=5c3b3c4fa85d29d5124483929592ba1c","Psychology Department, New Mexico State University, Las Cruces, NM, United States","Rheman, J., Psychology Department, New Mexico State University, Las Cruces, NM, United States; Fraune, M.R., Psychology Department, New Mexico State University, Las Cruces, NM, United States","This paper presents preliminary research on whether children will accept a robot as part of their ingroup, and on how a robot's group membership affects trust, closeness, and social support. Trust is important in human-robot interactions because it affects if people will follow robots' advice. In this study, we randomly assigned 11- and 12-year-old participants to a condition such that participants were either on a team with the robot (ingroup) or were opponents of the robot (outgroup) for an online game. Thus far, we have eight participants in the ingroup condition. Our preliminary results showed that children had a low level of trust, closeness, and social support with the robot. Participants had a much more negative response than we anticipated. We speculate that there will be a more positive response with an in-person setting rather than a remote one. © 2021 ACM.","Child-robot interaction; Closeness; Group membership; Social support; Trust","Agricultural robots; Man machine systems; Social networking (online); Group memberships; On-line games; Social support; Social robots",,,,,"National Science Foundation, NSF: 1849591",,,,,,,,,,,"Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y.C., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Hum. Factors, 53 (5), pp. 517-527; Sembroski, C.E., Fraune, M.R., Sabanovic, S., He said, she said, it said: Effects of robot group membership and human authority on people's willingness to follow their instructions (2017) RO-MAN 2017-26th IEEE International Symposium on Robot and Human Interactive Communication, 2017, pp. 56-61. , Janua; Wildschut, T., Pinter, B., Vevea, J.L., Insko, C.A., Schopler, J., Beyond the group mind: A quantitative review of the interindividual-intergroup discontinuity effect (2003) Psychol. Bull., 129 (5), pp. 698-722; Balliet, D., Wu, J., De Dreu, C.K.W., Ingroup favoritism in cooperation: A meta-analysis (2014) Psychological Bulletin, 140 (6), pp. 1556-1581; Reeves, B., Nass, C., (1996) How People Treat Comptuers, Television and New Media like Real People and Places.; Kahn, P.H., 'robovie, you'll have to go into the closet now': Children's social and moral relationships with a humanoid robot (2012) Dev. Psychol., 48 (2), pp. 303-314; Mayer, R.C., Davis, J.H., Schoorman, F.D., An integrative model of organizational trust (1995) Acad. Manag. Rev., 20 (3), pp. 709-734; Kristin, S., (2009) The Perception and Measurement of Human-robot Trust, , University of Central Florida; Van Straten, C.L., Kühne, R., Peter, J., De Jong, C., Barco, A., Closeness, trust, and perceived social support in child-robot relationship formation: Development and validation of three self-report scales (2020) Interact. Stud., 21 (1), pp. 57-84; Di Dio, C., Shall i trust you?. From child-robot interaction to trusting relationships Frontiers in Psychology, 11, p. 2020; Sherif, M., Harvey, O.J., Hood, W.R., (2010) The Robbers Cave Experiment: Intergroup Conflict and Cooperation. [Orig. Pub. As Intergroup Conflict and Group Relations], p. 228. , http://site.ebrary.com/lib/anuau/reader.action?docID=10477834; Fehr, B., Ernst, B., Helen, R., Egalitarianism in young children (2008) Nature, 454 (7208), pp. 1079-1083; Yu, J., Zhu, L., Leslie, A.M., Children's sharing behavior in mini-dictator games: The role of in-group favoritism and theory of mind (2016) Child Development, 87 (6), pp. 1747-1757; Fraune, M.R., Our robots, our team: Robot anthropomorphism moderates group effects in human-robot teams Frontiers in Psychology, 11, p. 2020; Pearson, Y., Borenstein, J., Creating 'companions' for children: The ethics of designing esthetic features for robots (2014) AI Soc., 29 (1), pp. 23-31; Bickmore, R., Timothy, P., Establishing and maintaining long term human computer relationships (2005) AMC Trans. Comput. Interact., 12 (2), pp. 293-327; Ruffman, T., Olson, D.R., Ash, T., Keenan, T., The abcs of deception: Do young children understand deception in the same way as adults? (1993) Developmental Psychology, 29 (1), pp. 74-87; Gongola, J., Scurich, N., Quas, J.A., Detecting deception in children: A meta-analysis (2017) Law and Human Behavior, 41 (1), pp. 44-54; Talwar, K., Victoria, M., Susan, L., White lie-telling in children for politeness purposes (2007) Int. J. Behav. Dev., (1978), pp. 1-7; Marris, E., Millions of students are returning to us universities in a vast unplanned pandemic experiment (2020) Nature, 584 (7822), pp. 510-512",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102759020
"Szafr D., Szafr D.A.","57222471065;57222471064;","Connecting human-robot interaction and data visualization",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444683","281","292",,1,"10.1145/3434073.3444683","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102756048&doi=10.1145%2f3434073.3444683&partnerID=40&md5=a08f24fe0d63252e41066b86430b101c","ATLAS Institute and Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States","Szafr, D., ATLAS Institute and Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States; Szafr, D.A., ATLAS Institute and Department of Computer Science, University of Colorado Boulder, Boulder, CO, United States","Human-robot interaction (HRI) research frequently explores how to design interfaces that enable humans to efectively teleoperate and supervise robots. One of the principle goals of such systems is to support data collection, analysis, and human decision making, which requires representing robot data in ways that support fast and accurate analyses by humans. However, the interfaces for these systems do not always use best-practice principles for efectively visualizing data. We present a new framework to scafold reasoning about robot interface design that emphasizes the need to consider data visualization for supporting analysis and decision-making pro-cesses, detail several data visualization best practices relevant to HRI, identify a set of core data tasks that commonly occur in HRI, and highlight several promising opportunities for further synergis-tic activities at the intersection of these two research areas. © 2021 IEEE Computer Society. All rights reserved.","Data Visualization; Human-Robot Interaction (HRI); Information Visualization; InfoVis; Robot Interface Design; VIS","Agricultural robots; Data visualization; Decision making; Machine design; Man machine systems; Visualization; Accurate analysis; Best practices; Data collection; Design interface; Human decision making; Human robot Interaction (HRI); Robot interface; Supporting analysis; Social robots",,,,,"National Science Foundation, NSF: 1764092","This work was supported by the NSF under Award #1764092.",,,,,,,,,,"Adamides, G., Katsanos, C., Constantinou, I., Christou, G., Xenos, M., Hadzilacos, T., Edan, Y., Design and devel-opment of a semi-autonomous agricultural vineyard sprayer: Human-robot interaction aspects (2017) Journal of Field Robotics, 34 (8), pp. 1407-1426; Adams, J.A., Critical considerations for human-robot interface develop-ment (2002) Proceedings of 2002 AAAI Fall Symposium., pp. 1-8; Adams, J.A., Unmanned vehicle situation awareness: A path forward (2007) Human Systems Integration Symposium., pp. 31-89; Albers, D., Correll, M., Gleicher, M., Task-driven evaluation of aggregation in time series visualization (2014) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems., pp. 551-560; Amar, R., Eagan, J., Stasko, J., Low-level components of analytic activity in information visualization (2005) IEEE Symposium on Information Visualization, 2005. 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Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85102756048
"Hao L., Pagani R., Beschi M., Legnani G.","57222724511;57211168733;49962916100;35606616800;","Dynamic and friction parameters of an industrial robot: Identification, comparison and repetitiveness analysis",2021,"Robotics","10","1","49","","",,1,"10.3390/robotics10010049","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103838732&doi=10.3390%2frobotics10010049&partnerID=40&md5=e1b65fc6ec465e161f42c0c2280baa52","Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze 38, Brescia, 25123, Italy; Istituto di Sistemi e Tecnologie Industriali per il Manifatturiero Avanzato, National Research Council (CNR-STIIMA), Via Corti 12, Milan, 20133, Italy","Hao, L., Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze 38, Brescia, 25123, Italy; Pagani, R., Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze 38, Brescia, 25123, Italy; Beschi, M., Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze 38, Brescia, 25123, Italy, Istituto di Sistemi e Tecnologie Industriali per il Manifatturiero Avanzato, National Research Council (CNR-STIIMA), Via Corti 12, Milan, 20133, Italy; Legnani, G., Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze 38, Brescia, 25123, Italy, Istituto di Sistemi e Tecnologie Industriali per il Manifatturiero Avanzato, National Research Council (CNR-STIIMA), Via Corti 12, Milan, 20133, Italy","This paper describes the results of dynamic tests performed to study the robustness of a dynamics model of an industrial manipulator. The tests show that the joint friction changes during the robot operation. The variation can be identified in a double exponential law and thus the variation can be predicted. The variation is due to the heat generated by the friction. A model is used to estimate the temperature and related friction variation. Experimental data collected on two robots EFORT ER3A-C60 are presented and discussed. Repetitive tests performed on different days showed that the inertial and friction parameters can be robustly estimated and that the value of the measured joint friction can be used to estimate the unexpected conditions of the joints. Future applications may include sensorless identification of collisions, predictive maintenance programs, or human–robot interaction. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Friction; Identification; Modelling; Robotics; Temperature",,,,,,,"The authors thank Evolut Spa, Brescia (IT), Robox Spa, Novara (IT) and Efort Intelligent Equipment Co. 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Legnani, G.; Department of Mechanical and Industrial Engineering, Via Branze 38, Italy; email: giovanni.legnani@unibs.it
Legnani, G.; Istituto di Sistemi e Tecnologie Industriali per il Manifatturiero Avanzato, Via Corti 12, Italy; email: giovanni.legnani@unibs.it",,,"MDPI AG",,,,,22186581,,,,"English","Robotics",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85103838732
"Shimoga G., Choi D.-S., Kim S.-Y.","57189094612;55516294700;23397120200;","Bio-inspired soft robotics: Tunable photo-actuation behavior of azo chromophore containing liquid crystalline elastomers",2021,"Applied Sciences (Switzerland)","11","3","1233","1","18",,2,"10.3390/app11031233","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100600492&doi=10.3390%2fapp11031233&partnerID=40&md5=4a4eb58c1592270fd73fd857a463f2c0","Advanced Technology Research Center, Future Convergence Engineering, Korea University of Technology and Education, Cheonan-si Chungcheongnam-do, 330–708, South Korea; School of Compute Science, College of Engineering and Information Technology, Semyung University, Jecheon Chungcheongbuk-do, 27136, South Korea","Shimoga, G., Advanced Technology Research Center, Future Convergence Engineering, Korea University of Technology and Education, Cheonan-si Chungcheongnam-do, 330–708, South Korea; Choi, D.-S., School of Compute Science, College of Engineering and Information Technology, Semyung University, Jecheon Chungcheongbuk-do, 27136, South Korea; Kim, S.-Y., Advanced Technology Research Center, Future Convergence Engineering, Korea University of Technology and Education, Cheonan-si Chungcheongnam-do, 330–708, South Korea","Bio-inspiration relentlessly sparks the novel ideas to develop innovative soft robotic structures from smart materials. The conceptual soft robotic designs inspired by biomimetic routes have resulted in pioneering research contributions based on the understanding of the material selection and actuation properties. In an attempt to overcome the hazardous injuries, soft robotic systems are used subsequently to ensure safe human–robot interaction. In contrast to dielectric elastomer actuators, prolific efforts were made by understanding the photo-actuating properties of liquid crystalline elastomers (LCEs) containing azo-derivatives to construct mechanical structures and tiny portable robots for specific technological applications. The structure and material properties of these stimuli-responsive polymers can skillfully be controlled by light. In this short technical note, we highlight the potential high-tech importance and the photo-actuation behavior of some remarkable LCEs with azobenzene chromophores. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Actuators; Azobenzene; Bio-inspired; Opto-mechanical; Photo-actuation; Soft robotics",,,,,,"National Research Foundation of Korea, NRF; Kementerian Pendidikan Malaysia, KPM: NRF-2018R1A6A1A03025526, NRF-2020R1I1A3065371","This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education (NRF-2020R1I1A3065371). The work was also supported by Priority Research Centers Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1A6A1A03025526).",,,,,,,,,,"Mayer, S., Zentel, R., Liquid crystalline polymers and elastomers (2002) Curr. Opin. Solid State Mater. Sci, 6, pp. 545-551. , [CrossRef]; Ula, S.W., Traugutt, N.A., Volpe, R.H., Patel, R.R., Yu, K., Yakachi, C.M., Liquid crystal elastomers: An introduction and review of emerging technologies (2018) Liq. Cryst. Rev, 6, pp. 78-107. , [CrossRef]; Zentel, R., Liquid Crystal Elastomers (1989) Adv. 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Kim, S.-Y.; Advanced Technology Research Center, South Korea; email: sykim@koreatech.ac.kr",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85100600492
"Meyer L., Araiza-Illan G., Rachman L., Gaudrain E., Başkent D.","57344599500;57226211024;56196550100;18436403700;6602445586;","Perception of a Humanoid Robot as an Interface for Auditory Testing",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13054 LNAI",,,"198","202",,,"10.1007/978-3-030-89177-0_21","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119327093&doi=10.1007%2f978-3-030-89177-0_21&partnerID=40&md5=cb103fbd666b04b6480d19e6641b70a6","Department of Otorhinolaryngology/Head and Neck Surgery, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands; University Medical Centre Groningen, W.J. Kolff Institute for Biomedical Engineering and Materials Science, University of Groningen, Groningen, Netherlands; Lyon Neuroscience Research Centre, CNRS UMR 5292, INSERM UMRS 1028, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France","Meyer, L., Department of Otorhinolaryngology/Head and Neck Surgery, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands, University Medical Centre Groningen, W.J. Kolff Institute for Biomedical Engineering and Materials Science, University of Groningen, Groningen, Netherlands; Araiza-Illan, G., Department of Otorhinolaryngology/Head and Neck Surgery, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands, University Medical Centre Groningen, W.J. Kolff Institute for Biomedical Engineering and Materials Science, University of Groningen, Groningen, Netherlands; Rachman, L., Department of Otorhinolaryngology/Head and Neck Surgery, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands, University Medical Centre Groningen, W.J. Kolff Institute for Biomedical Engineering and Materials Science, University of Groningen, Groningen, Netherlands; Gaudrain, E., Department of Otorhinolaryngology/Head and Neck Surgery, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands, Lyon Neuroscience Research Centre, CNRS UMR 5292, INSERM UMRS 1028, Université Claude Bernard Lyon 1, Université de Lyon, Lyon, France; Başkent, D., Department of Otorhinolaryngology/Head and Neck Surgery, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands, University Medical Centre Groningen, W.J. Kolff Institute for Biomedical Engineering and Materials Science, University of Groningen, Groningen, Netherlands","Perception tasks that require long and frequent testing can result in a loss of attention and focus, contributing to erroneous and inconsistent data. To maintain engagement and improve reliability of data, a more interactive interface could be used. This study aims to investigate if a humanoid NAO robot could provide such an interface to improve or maintain engagement during testing. More specifically, human-robot interaction (HRI) will be explored when performing various auditory perception tasks, played in the form of games, on the NAO robot. Evaluation of the HRI will be performed using questionnaires derived from the system usability, Godspeed, and similarity-attraction scales, as well as through video analysis. Future research will extend the evaluation of the HRI to children and aim to further improve the engagement and social acceptability of the NAO robot as a game interface. © 2021, Springer Nature Switzerland AG.","Auditory perception; HRI; Robot perception; Task engagement","Anthropomorphic robots; Human robot interaction; Petroleum reservoir evaluation; Surveys; Auditory perception; Erroneous datum; Humanoid robot; Humans-robot interactions; Inconsistent data; Interactive interfaces; Robot perception; System usability; Task engagement; Video analysis; Audition",,,,,,,,,,,,,,,,"Gaudrain, E., Başkent, D., Discrimination of voice pitch and vocal-tract length in cochlear implant users (2018) Ear Hear, 39 (2), pp. 226-237; Looije, R., van der Zalm, A., Neerincx, M.A., Beun, R.-J., Help, I need some body the effect of embodiment on playful learning (2012) 2012 IEEE RO-MAN: The 21St IEEE International Symposium on Robot and Human Interactive Communication, Paris, France, Pp. 718–724. IEEE, , http://ieeexplore.ieee.org/document/6343836/; Nagels, L., Gaudrain, E., Vickers, D., Hendriks, P., Başkent, D., Development of voice perception is dissociated across gender cues in school-age children (2020) Sci. Rep., 10 (1); Ivaldi, S., Lefort, S., Peters, J., Chetouani, M., Provasi, J., Zibetti, E., Towards engagement models that consider individual factors in HRI: On the relation of extroversion and negative attitude towards robots to gaze and speech during a human–robot assembly task: Experiments with the iCub humanoid (2017) Int. J. Soc. Robot., 9 (1), pp. 63-86; Bernier, E.P., Scassellati, B., The similarity-attraction effect in human-robot interaction (2010) 2010 IEEE 9Th International Conference on Development and Learning, Ann Arbor, MI, USA, Pp. 286–290. IEEE, , http://ieeexplore.ieee.org/document/5578828/; Brooke, J., (1996) Sus-A Quick and Dirty Usability Scale, , In: Jordan, P.W., Thomas, B., Weermeester, B.A., McClellend, A.L. (eds.) Usability Evaluation in Industry. Taylor and Francis, London; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot., 1 (1), pp. 71-81; Lee, K.M., Peng, W., Jin, S.-A., Yan, C., Can robots manifest personality?: An empirical test of personality recognition, social responses, and social presence in human-robot interaction (2006) J. Commun., 56 (4), pp. 754-772; Eddins, D.A., Liu, C., Psychometric properties of the coordinate response measure corpus with various types of background interference (2012) J. Acoust. Soc. Am., 131 (2), pp. EL177-EL183; Giuliani, M., Mirnig, N., Stollnberger, G., Stadler, S., Buchner, R., Tscheligi, M.: Systematic analysis of video data from different human–robot interaction studies: a categorization of social signals during error situations. Front Psychol. 6,931 (2015). http://journal.frontiersin. org/Article/10.3389/fpsyg.2015.00931/abstract; Desideri, L., Ottaviani, C., Malavasi, M., Di Marzio, R., Bonifacci, P., Emotional processes in human-robot interaction during brief cognitive testing (2019) Comput. Hum. Behav., 90, pp. 331-342","Meyer, L.; Department of Otorhinolaryngology/Head and Neck Surgery, Netherlands; email: L.Meyer@rug.nl","Fox C.Gao J.Ghalamzan Esfahani A.Saaj M.Hanheide M.Parsons S.",,"Springer Science and Business Media Deutschland GmbH","22th Annual Conference Towards Autonomous Robotic Systems, TAROS 2021","8 September 2021 through 10 September 2021",,268049,03029743,9783030891763,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119327093
"Bao S., Huang B., Yuan J., Wang B., Xia L., Wang M., Liu Y.","36896475500;57198829129;55709574700;57199072058;57283888400;57284111000;57283888500;","Service robots in wuhan cabin hospitals",2021,"Sensors and Materials","33","9",,"3187","3200",,,"10.18494/SAM.2021.3235","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116365297&doi=10.18494%2fSAM.2021.3235&partnerID=40&md5=49518908d25b10be5632fa289cb0d4ab","Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai, 200444, China; CloudMinds Technologies Inc., 85 Hong'an Road, Fangshan District, Beijing, 102488, China","Bao, S., Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai, 200444, China; Huang, B., CloudMinds Technologies Inc., 85 Hong'an Road, Fangshan District, Beijing, 102488, China; Yuan, J., Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai, 200444, China; Wang, B., CloudMinds Technologies Inc., 85 Hong'an Road, Fangshan District, Beijing, 102488, China; Xia, L., Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, 99 Shangda Road, Baoshan District, Shanghai, 200444, China; Wang, M., CloudMinds Technologies Inc., 85 Hong'an Road, Fangshan District, Beijing, 102488, China; Liu, Y., CloudMinds Technologies Inc., 85 Hong'an Road, Fangshan District, Beijing, 102488, China","medical service robot, human-robot interaction, COVID-19, sensor fusion The cabin hospitals used for COVID-19 victims have an infectious environment and thus require highly autonomous systems to provide various services. This article presents a multirobot architecture designed for cabin hospitals with special focus on the sensors used by service robots. The robots' functions include autonomous disinfection, delivery, cleaning, temperature measurement, physical interaction, and conversation assistance. The robots collaborate with each other by using multiple sensors in large and complicated scenes and evolve together. The robots are controlled by cloud controllers considering a human-centered multi-robot collaboration mechanism. This article presents the details of these robots, including the hardware and software architecture of the robots with sensors, the communication modules, and the cloudbased central controllers. It also presents several applications of the robots and discusses open problems. © 2021 M Y U Scientific Publishing Division. All rights reserved.",,"Disinfection; Hospitals; Human robot interaction; Industrial robots; Irradiation; Temperature measurement; Collaboration mechanisms; Humans-robot interactions; Medical services; Multi-robot collaboration; Multiple sensors; Multirobots; Physical interactions; Robot functions; Sensor fusion; Service robots; Mobile robots",,,,,,,,,,,,,,,,"Organization, W. H., Report of the WHO China Joint Mission on Coronavirus Disease 2019 (COVID-19), , http://asean-china-center.org/resources/file/P020200318828172605354.pdf, (2020). (accessed Feb. 2020); Roser, M., Ritchie, H., Ortiz-Ospina, E., Hasell, J., Coronavirus Disease (COVID-19) - Statistics and Research (2020) Our World in Data, , https://ourworldindata.org/coronavirus; Hellewell, J., Abbott, S., Gimma, A., Bosse, N. I., Jarvis, C. I., Russell, T. W., Munday, J. D., Eggo, R. M., (2020) Lancet Global Health, 8, p. 488496. , https://doi.org/10.1016/S2214-109X(20)30074-7; Louie, W.-Y. 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Wang, B.; CloudMinds Technologies Inc., 85 Hong'an Road, Fangshan District, China; email: charly@cloudminds.com",,,"M Y U Scientific Publishing Division",,,,,09144935,,,,"English","Sens. Mater.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85116365297
"Frijns H.A., Schmidbauer C.","57192806605;57209321116;","Design Guidelines for Collaborative Industrial Robot User Interfaces",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12934 LNCS",,,"407","427",,,"10.1007/978-3-030-85613-7_28","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115275286&doi=10.1007%2f978-3-030-85613-7_28&partnerID=40&md5=ffae8943a3626445acb5b7d37d513e7e","Institute of Management Science, TU Wien, Vienna, Austria","Frijns, H.A., Institute of Management Science, TU Wien, Vienna, Austria; Schmidbauer, C., Institute of Management Science, TU Wien, Vienna, Austria","Collaborative industrial robot (cobot) systems are deployed to automate tasks or as a tool for Human-Robot Interaction (HRI) scenarios, especially for manufacturing applications. A large number of manufacturers of this technology have entered the cobot market in recent years. Manufacturers intend to offer easy control possibilities to make cobots suitable for different user groups, but there are few evaluation tools for assessing user interface (UI) design specifically for cobots. Therefore, we propose a set of design guidelines for cobots based on existing literature on heuristics and cobot UI design. The guidelines were further developed on the basis of modified heuristic evaluations by researchers with robotics expertise, as well as interviews with cobot UI/User Experience (UX) design experts. The resulting design guidelines are intended for identification of usability problems during heuristic evaluation of the UI design of cobot systems. © 2021, IFIP International Federation for Information Processing.","Collaborative robots; Design guidelines; Heuristic evaluation; Human-robot collaboration; Human-robot interaction; User interface design","End effectors; Human computer interaction; Industrial robots; Machine design; Manufacture; Social robots; Design-expert; Evaluation tool; Heuristic evaluation; Human robot Interaction (HRI); Manufacturing applications; UI designs; Usability problems; User groups; User interfaces",,,,,,"Acknowledgements. Supported by Doctoral College TrustRobots, TU Wien. The first author of this publication was responsible for the research project and writing of the publication, and was supported by the second author. We thank Hans Küffner-McCauley, Tanja Zigaert, Michael Hornáˇcek, all paper reviewers, evaluation study participants, and interviewees.",,,,,,,,,,"Adamides, G., Christou, G., Katsanos, C., Xenos, M., Hadzilacos, T., Usability guidelines for the design of robot teleoperation: A taxonomy (2015) IEEE Trans. Human-Mach. Syst., 45 (2), pp. 256-262. , https://doi.org/10.1109/THMS.2014.2371048; Amershi, S., Guidelines for human-AI interaction (2019) Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems-Chi ’19, pp. 1-13. , https://doi.org/10.1145/3290605.3300233, pp., ACM Press; Bangor, A., Kortum, P.T., Miller, J.T.: An empirical evaluation of the system usability scale. Int. J. Hum.-Comput. 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IEEE, , https://doi.org/10.1109/ROMAN.2010.5598735","Frijns, H.A.; Institute of Management Science, Austria; email: helena.frijns@tuwien.ac.at","Ardito C.Lanzilotti R.Malizia A.Malizia A.Petrie H.Piccinno A.Desolda G.Inkpen K.",,"Springer Science and Business Media Deutschland GmbH","18th IFIP TC 13 International Conference on Human-Computer Interaction, INTERACT 2021","30 August 2021 through 3 September 2021",,264499,03029743,9783030856120,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85115275286
"Nouri Rahmat Abadi B., Hirata Y.","56040083700;7402006534;","Control of planar passive wire-driven support systems using servo brakes",2021,"Mechanics Based Design of Structures and Machines",,,,"","",,,"10.1080/15397734.2021.1945931","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109745656&doi=10.1080%2f15397734.2021.1945931&partnerID=40&md5=34cffde303930fe17196e155f59662db","Department of Mechanical Engineering, Shiraz University, Shiraz, Iran; Department of Robotics, Tohoku University, Sendai, Japan","Nouri Rahmat Abadi, B., Department of Mechanical Engineering, Shiraz University, Shiraz, Iran; Hirata, Y., Department of Robotics, Tohoku University, Sendai, Japan","In this research, control of a passive wire-driven system for the physical support of human beings is discussed. The considered support system does not have any active motor and the required force for steering the system is provided by a human operator. In the cases that the handling point deviates from the desired path, the servo brake units apply force and eliminate the position error. Thus, the robot is completely safe in human–robot interaction and can be used for sports training and rehabilitation. In this article, the controllable workspace of the system for tracking some curves is obtained, a controller based on sliding mode is proposed, and a redundancy resolution method is suggested for two applications of the system. The efficiency of the control strategy is investigated by simulating the movement of the robot for each application. © 2021 Taylor & Francis Group, LLC.","control; Passive motion and force support system; servo brake; wire-driven robots; workspace","Brakes; Educational robots; Control strategies; Controllable workspace; Human operator; Position errors; Redundancy resolution methods; Robot interactions; Sports trainings; Support systems; Human robot interaction",,,,,,,,,,,,,,,,"Bouguila, L., Ishii, M., Sato, M., A large workspace haptic device for human-scale virtual environments (2000) August, pp. 86-91. , Springer-Verlag, Berlin, Heidelberg:, and; Dellon, B., Matsuoka, Y., Path guidance control for a safer large scale dissipative haptic display (2008) IEEE, pp. 2073-2078. , Pasadena, CA:, and,. 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October 18–22; Hirata, Y., Suzuki, K., Kosuge, K., Improvement in the performance of passive motion support system with wires based on analysis of brake control (2011) 2011 IEEE International Conference on Robotics and Automation, pp. 4272-4277. , IEEE, Shanghai, China:, and,. May 9–13; Hirata, Y., Tozaki, Y., Kosuge, K., Development of wire-type motion support system controlled by servo brakes (2011) 2011 IEEE International Conference on Robotics and Biomimetics, pp. 2150-2155. , IEEE, Karon Beach, Thailand:, and,. December 7–11; Hirata, Y., Tozaki, Y., Kosuge, K., Wire-type human support system controlled by servo brakes (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3356-3361. , IEEE, Vilamoura-Algarve, Portugal:, and,. October 7–12; Hirata, Y., Shirai, R., Kosuge, K., Position and orientation control of passive wire-driven motion support system using servo brakes (2017) IEEE, 3, pp. 3702-3707. , Singapore:, and,. 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May 12–17","Nouri Rahmat Abadi, B.; Department of Mechanical Engineering, Iran",,,"Bellwether Publishing, Ltd.",,,,,15397734,,,,"English","Mech. Based Des. Struct. Mach.",Article,"Article in Press","",Scopus,2-s2.0-85109745656
"Acarturk C., Indurkya B., Nawrocki P., Sniezynski B., Jarosz M., Usal K.A.","25652940800;57224519586;24587357500;23393638000;57212304491;57224528832;","Gaze Aversion In Conversational Settings: An Investigation Based On Mock Job Interview",2021,"Journal of Eye Movement Research","14","1",,"1","13",,,"10.16910/jemr.14.1.1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107763107&doi=10.16910%2fjemr.14.1.1&partnerID=40&md5=ce1e2bee2c7e90372dc3284070542ca6","Department of Cognitive Science, Middle East Technical University, Turkey; Department of Cognitive Science, Jagiellonian University, Poland; Institute of Computer Science, AGH University of Science and Technology, Poland","Acarturk, C., Department of Cognitive Science, Middle East Technical University, Turkey; Indurkya, B., Department of Cognitive Science, Jagiellonian University, Poland; Nawrocki, P., Institute of Computer Science, AGH University of Science and Technology, Poland; Sniezynski, B., Institute of Computer Science, AGH University of Science and Technology, Poland; Jarosz, M., Institute of Computer Science, AGH University of Science and Technology, Poland; Usal, K.A., Department of Cognitive Science, Middle East Technical University, Turkey","We report the results of an empirical study on gaze aversion during dyadic human-to-human conversation in an interview setting. To address various methodological challenges in assessing gaze-to-face contact, we followed an approach where the experiment was conducted twice, each time with a different set of interviewees. In one of them the interviewer’s gaze was tracked with an eye tracker, and in the other the interviewee’s gaze was tracked. The gaze sequences obtained in both experiments were analyzed and modeled as Discrete-TimeMarkov Chains. The results show that the interviewer made more frequent and longer gaze contacts compared to the interviewee. Also, the interviewer made mostly diagonal gaze aversions, whereas the interviewee made sideways aversions (left or right). We discuss the relevance of this research for Human-Robot Interaction, and discuss some future research problems. © 2021. Journal of Eye Movement Research All Rights Reserved.","face contact; gaze aversion; Gaze in conversation; Human Human Interaction",,,,,,"POLTUR2/5/2018; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK: 117E021; Narodowe Centrum Badań i Rozwoju, NCBR","This research was supported in part by a grant by TUBITAK (The Scientific and Technological Research Council of Turkey) Grant No 117E021 and NCBR (The National Center for Research and Development of Poland) Grant No POLTUR2/5/2018. The authors thank Efecan Yılmaz, Gizem Özen, Elif Esmer, Gamze Eşdur, Bengisu Çağıltay and Faruk Büyüktekin for their technical support.",,,,,,,,,,"Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: A review (2017) Journal of Human Robot Interaction, 6 (1), pp. 25-63. , https://doi.org/10.5898/JHRI.6.1.Admoni; Andrist, S., Mutlu, B., Gleicher, M., Conversational gaze aversion for virtual agents (2013) Intelligent Virtual Agents, pp. 249-262. , https://doi.org/10.1007/978-3-642-40415-3, Aylett, R., Krenn, B., Pelachaud, C., and Shimodaira,H. (Eds), –).Springer, Berlin Heidelberg; Andrist, S., Tan, X. Z., Gleicher, M., Mutlu, B., Conversational gaze aversion for humanlike robots (2014) Proceedings of the 9th ACM/IEEE International Conference on Human-Robot Interaction, pp. 25-32. , http://dx.doi.org/10.1145/2559636.2559666; Archer, D., Akert, R. 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"Fusté-Forné F.","57186638300;","Robot chefs in gastronomy tourism: What's on the menu?",2021,"Tourism Management Perspectives","37",,"100774","","",,9,"10.1016/j.tmp.2020.100774","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097002171&doi=10.1016%2fj.tmp.2020.100774&partnerID=40&md5=009edaf472fe25415030a64e91879582","Department of Business, Faculty of Tourism, University of Girona, Spain","Fusté-Forné, F., Department of Business, Faculty of Tourism, University of Girona, Spain","The use of robots in hospitality and tourism is rapidly evolving. Restaurants progressively include robots as part of their staff, not only as waiters but also as chefs. The robotization of tourism and gastronomic experiences is a vital challenge that both service providers and customers need to cope with. Within this context, the paper investigates the perceptions of tourists towards the use of robots in restaurants. Drawing from a qualitative research design and built on a grounded theory approach, the results reveal the opportunities derived from the implementation of robots in hospitality and tourism. On the other hand, tourists also perceive the growing presence of robots in food services as a form of dehumanization of the gastronomic experience. Theoretical and practical implications are discussed with regard to a new experiencescape that is increasingly dominated by human-robot interactions. © 2020 Elsevier Ltd","Culinary heritage; Dining experience; Food tourism; Restaurant experience; Service robot; Tourist experience",,,,,,,,,,,,,,,,,"Ab Karim, S., Chi, C.G.Q., Culinary tourism as a destination attraction: An empirical examination of destinations' food image (2010) Journal of Hospitality Marketing and Management, 19 (6), pp. 531-555; Andersson, T.D., The tourist in the experience economy (2007) Scandinavian Journal of Hospitality and Tourism, 7 (1), pp. 46-58; Barnett, W., Foos, A., Gruber, T., Keeling, D., Keeling, K., Nasr, L., Consumer perceptions of interactive service robots: A value-dominant logic perspective (2014) Paper presented at The 23rd IEEE International Symposium on Robot and Human Interactive Communication, Edinburgh, pp. 1134-1139; Belanche, D., Casaló, L.V., Flavián, C., Frontline robots in tourism and hospitality: Service enhancement or cost reduction? 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"Chen L., Wu M., Pedrycz W., Hirota K.","55774663200;35231682600;57210775415;7402829702;","Weight-Adapted Convolution Neural Network for Facial Expression Recognition",2021,"Studies in Computational Intelligence","926",,,"57","75",,,"10.1007/978-3-030-61577-2_5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096214776&doi=10.1007%2f978-3-030-61577-2_5&partnerID=40&md5=5f2ca7267d065983f30c584327505854","School of Automation, China University of Geosciences, Wuhan, China; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Tokyo Institute of Technology, Yokohama, Japan","Chen, L., School of Automation, China University of Geosciences, Wuhan, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, China; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Hirota, K., Tokyo Institute of Technology, Yokohama, Japan","The weight-adapted convolution neural network (WACNN) is proposed to extract discriminative expression representations for recognizing facial expression. It aims to make good use of the convolution neural network’s potential performance in avoiding local optimal and speeding up convergence by hybrid genetic algorithm (HGA) with optimal initial population, in such a way that it realizes deep and global emotion understanding in human-robot interaction. Moreover, the idea of novelty search is introduced to solve the deception problem in the HGA, which can expend the search space to help genetic algorithm jump out of local optimum and optimize large-scale parameters. In the proposal, the facial expression image preprocessing is conducted first, then the low-level expression features are extracted by using principal component analysis. 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"Giorgi I., Watson C., Pratt C., Masala G.L.","57207956597;57217472049;57217113972;8581904600;","Designing robot verbal and nonverbal interactions in socially assistive domain for quality ageing in place",2021,"Smart Innovation, Systems and Technologies","189",,,"255","265",,1,"10.1007/978-981-15-5784-2_21","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086305106&doi=10.1007%2f978-981-15-5784-2_21&partnerID=40&md5=c72a9418ae4dd6c245416ccaf1335063","The University of Manchester, Manchester, United Kingdom; Manchester Metropolitan University, Manchester, United Kingdom","Giorgi, I., The University of Manchester, Manchester, United Kingdom; Watson, C., Manchester Metropolitan University, Manchester, United Kingdom; Pratt, C., Manchester Metropolitan University, Manchester, United Kingdom; Masala, G.L., Manchester Metropolitan University, Manchester, United Kingdom","Endowing robots with the role of social assistance in silver care could be a powerful tool to combat chronic loneliness in ageing adults. These robots can be tasked with functional and affective care to support quotidian living and grant companionship that helps lessen the burden of cognitive decline and impairment emerging from social isolation. To accomplish such imperative tasks, artificial agents must be adept at communicating naturally with the human elder. In this work, we aim to enable human–robot interaction by designing human-like verbal and nonverbal behaviours of an autonomous robot companion. We employed the robot on a trial run using customisable algorithms to address a range of needs, while thriving social and emotional attachment with the potential senior user, with the final intent being that such endeavours can help achieve quality ageing in place. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020.","Aged care; Eldercare; Human–robot interaction; Natural communication; Robot companion; Socially assistive; Verbal and nonverbal interaction","Intelligent systems; Artificial agents; Cognitive decline; Emotional attachments; Human like; Non-verbal behaviours; Robot interactions; Senior user; Social isolation; Social robots",,,,,"Interreg: 2S05-014","Acknowledgements Authors of this paper acknowledge the funding provided by the Interreg 2 Seas Mers Zeeën AGE’In project (2S05-014) to support the work described in this publication.",,,,,,,,,,"Bartels, S., Naslund, J., The underside of the silver tsunami-older adults and mental health care (2013) N. 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(eds.), Paris, France; Romeo, M., Hernandez Garcia, D., Jones, R., Cangelosi, A., Deploying a deep learning agent for HRI with potential “end-users” at multiple sheltered housing sites (2019) 7Th International Conference on Human-Agent Interaction, Kyoto, Japan; Luperto, M., Romeo, M., Lunardini, F., Basilico, N., Abbate, C., Jones, R., Cangelosi, A., Borghese, N.A., Evaluating the Acceptability of Assistive Robots for Early Detection of Mild Cognitive Impairment (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, Macao, China; Johnson, D.O., Cuijpers, R.H., Juola, J.F., Torta, E., Simonov, M., Frisiello, A., Beck, C., Socially assistive robots: A comprehensive approach to extending independent living (2014) Int. J. Soc. Robot., 6, pp. 195-211. , https://doi.org/10.1007/s12369-0130217-8; Tanaka, M., Ishii, A., Yamano, E., Ogikubo, H., Okazaki, M., Kamimura, K., Watanabe, Y., Effect of a human-type communication robot on cognitive function in elderly women living alone (2012) Med. Sci. Monit. Int. Med. J. Exp. Clin. Res., 18 (9), pp. CR550-CR557. , https://doi.org/10.12659/msm.883350; Softbank Robotics Webpage, , https://www.softbankrobotics.com/emea/en/nao; https://www.softbankrobotics.com/emea/en/industries/healthcare","Masala, G.L.; Manchester Metropolitan UniversityUnited Kingdom; email: g.masala@mmu.ac.uk","Zimmermann A.Howlett R.J.Jain L.C.",,"Springer","International KES Conference on Human Centred Intelligent Systems, KES-HCIS 2020","17 June 2020 through 19 June 2020",,240529,21903018,9789811557835,,,"English","Smart Innov. Syst. Technol.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85086305106
"Remmers P.","57221325856;","The Artificial Nature of Social Robots: A Phenomenological Interpretation of Two Conflicting Tendencies in Human-Robot Interaction",2020,"Frontiers in Artificial Intelligence and Applications","335",,,"78","85",,,"10.3233/FAIA200902","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098889175&doi=10.3233%2fFAIA200902&partnerID=40&md5=db1e1c844196e7903332b0da7a18f94f","Institute of Philosophy, History of Literature, Science, and Technology, Technische Universität Berlin, Germany","Remmers, P., Institute of Philosophy, History of Literature, Science, and Technology, Technische Universität Berlin, Germany","Effects of anthropomorphism or zoomorphism in social robotics motivate two opposing tendencies in the philosophy and ethics of robots: A ârational' tendency that discourages excessive anthropomorphism because it is based on an illusion and a âvisionary' tendency that promotes the relational reality of human-robot interaction. I argue for two claims: First, the opposition between these tendencies cannot be resolved and leads to a kind of technological antinomy. Second, we can deal with this antinomy by way of an analogy between our treatment of robots as social interactors and the perception of objects in pictures according to a phenomenological theory of image perception. Following this analogy, human-or animal-likeness in social robots is interpreted neither as a psychological illusion, nor as a relational reality. Instead, robots belong to a special ontological category shaped by perception and interaction, similar to objects in images. © 2020 The authors and IOS Press. All rights reserved.","anthropomorphism; Human-robot interaction; illusion; image perception; phenomenology","Man machine systems; Robotics; Image perception; Interactors; Phenomenological interpretation; Phenomenological theory; Social robotics; Social robots",,,,,,,,,,,,,,,,"Mayer, M.P., Kuz, S., Schlick, C.M., Using anthropomorphism to improve the human-machine interaction in industrial environments (part II) (2013) In: International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, pp. 93-100. , http://link.springer.com/chapter/10.1007/978-3-642-39182-8_11, Internet]. Springer, [cited 2017 Feb 21]; Goetz, J., Kiesler, S., Powers, A., Matching robot appearance and behavior to tasks to improve human-robot cooperation In: The 12th Ieee International Workshop on Robot and Human Interactive Communication, 2003 Proceedings Roman 2003, pp. 55-60. , http://ieeexplore.ieee.org/abstract/document/1251796, Internet]. IEEE; 2003 [cited 2017 Feb 21]; Bryson, J.J., Robots should be slaves (2010) Close Engagements with Artificial Companions: Key Social, Psychological, Ethical and Design Issues. Amsterdam, pp. 63-74. , In: Wilks Y, editor. Philadelphia: John Benjamins; Johnson, D.G., Verdicchio, M., Why robots should not be treated like animals (2018) Ethics Inf Technol., 20 (4), pp. 291-301. , Sep 24; Gunkel, D.J., A vindication of the rights of machines (2014) Philos Technol., 27 (1), pp. 113-132. , Mar; Coeckelbergh, M., The moral standing of machines: Towards a relational and non-cartesian moral hermeneutics (2014) Philos Technol, 27 (1), pp. 61-77. , Mar; Remmers, P., The ethical significance of human likeness in robotics and ai (2019) Ethics Prog, 10 (2), pp. 52-67. , Oct 10; Dennett, D.C., (1987) The Intentional Stance, , Cambridge (Massachusetts): MIT Press; Remmers, P., Mensch-roboter-interaktion (2018) Philosophische und Ethische Perspektiven, , Berlin: Logos Verlag; Gombrich, E.H., (1984) Art and Illusion: A Study in the Psychology of Pictorial Representation, , 7th ed. London: Phaidon; Husserl, E., Phantasie, Bildbewusstsein, Erinnerung (1980) Zur Phänomenologie der Anschaulichen Vergegenwärtigungen. Texte Aus Dem Nachlass (1898-1925), , Marbach E, editor. Den Haag: Martinus Nijhoff; Wiesing, L., (2010) Artificial Presence: Philosophical Studies in Image Theory, , Stanford, CA: Stanford University Press; Beiboer, J., Sandoval, E.B., Validating the accuracy of imaged-based research into the uncanny valley: An experimental proposal (2019) In: Proceedings of the 14th ACM/IEEE International Conference on Human-robot Interaction, pp. 608-609. , Daegu, Republic of Korea: IEEE Press","Remmers, P.; Technische Universität Berlin, Sekr. H 72, Straße des 17. Juni 135, Germany; email: peter.remmers@tu-Berlin.de","Norskov M.Seibt J.Quick O.S.",,"IOS Press BV","4th Conference on Robophilosophy 2020: Culturally Sustainable Social Robotics","18 August 2020 through 21 August 2020",,165874,09226389,9781643681542,,,"English","Front. Artif. Intell. Appl.",Conference Paper,"Final","",Scopus,2-s2.0-85098889175
"Clodic A., Dignum F., Fernández Castro V., Hakli R.","14053492500;7003423512;57195054016;6507886338;","Social Models for Social Robotics",2020,"Frontiers in Artificial Intelligence and Applications","335",,,"515","519",,,"10.3233/FAIA200950","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098863607&doi=10.3233%2fFAIA200950&partnerID=40&md5=533d94d44b16f82bf4a1f4ccdd25df44","LAAS-CNRS, Université de Toulouse, France; Department of Computing Science, Umeå University, Sweden; Institut Jean Nicod, Cnrs Umr 8129, DEC, ENS, Psl University, France; Department of Practical Philosophy, University of Helsinki, Finland","Clodic, A., LAAS-CNRS, Université de Toulouse, France; Dignum, F., Department of Computing Science, Umeå University, Sweden; Fernández Castro, V., LAAS-CNRS, Université de Toulouse, France, Institut Jean Nicod, Cnrs Umr 8129, DEC, ENS, Psl University, France; Hakli, R., Department of Practical Philosophy, University of Helsinki, Finland","Social robotics is one of the most important emerging technologies, with potentially profound socio-cultural impact. However, the current interdisciplinary research areas of 'social robotics' and 'Human-Robot Interaction' (HRI) are not yet equipped with the necessary conceptual tools in order to design interactions between humans and robots. New approaches for effective yet context-adequate social interactions are needed, that observe overarching ethical principles and take larger socio-cultural perspectives into account. With this workshop, we aim to clarify questions arising with this new technology. How far can robots go-now and in the future-to fulfill the requirements of full-blown social agents? How and where do ethical requirements dovetail with the elements (conditions, principles, and procedures) for social agency? This workshop will be part of the 'Toward a Framework for Joint Action' series (fja.sciencesconf.org). © 2020 The authors and IOS Press. All rights reserved.","HRI; social model; Social robotics; sociality","End effectors; Machine design; Philosophical aspects; Robotics; Emerging technologies; Ethical principles; Interdisciplinary research; New approaches; Social interactions; Social models; Social robotics; Socio-cultural; Social robots",,,,,"ANR-16-CE33-0017, ANR-19-PI3A-0004; Allen Institute for Artificial Intelligence, AI2; Academy of Finland","This work has been supported by the Artificial and Natural Intelligence Toulouse Institute - Institut 3iA ANITI ANR-19-PI3A-0004 and JointAction4HRI project ANR-16-CE33-0017, and by the Academy of Finland.",,,,,,,,,,"Breazeal, C., Toward sociable robots (2003) Robotics and Autonomous Systems, 42 (3-4), pp. 167-175. , Mar; Seibt, J., Varieties of the ""as If"" five ways to simulate an action (2014) Frontiers in Artificial Intelligence and Applications, 273, pp. 97-104. , https://doi.org/10.3233/978-1-61499-480-0-97; Seibt, J., Flensborg, D.M., Vestergaard, C., Integrative social robotics, value-driven design, and transdisciplinarity (2020) Interaction Studies, 21 (1), pp. 111-144; Fiebich, A., Nguyen, N., Schwarzkopf, S., (2015) Cooperation with Robots? A Two-Dimensional Approach, pp. 25-43. , https://doi.org/10.1007/978-3-319-15515-92, Collective Agency and Cooperation in Natural and Artificial Systems. Springer International Publishing, ; Misselhorn, C., (2015) Collective Agency and Cooperation in Natural and Artificial Systems: Explanation, Implementation and Simulation, , Cham: Springer; Goffman, E., (1963) Behavior in Public Places; Notes on the Social Organization of Gatherings, , New York: Free Press of Glencoe; Pacherie, E., The phenomenology of action: A conceptual framework (2008) Cognition, 107 (1), pp. 179-217. , Apr; Vesper, C., Butterfill, S., Knoblich, G., Sebanz, N., A minimal architecture forjoint action (2010) Neural Networks, 23 (8-9), pp. 998-1003. , Oct; Vesper, C., Abramova, E., Ciardo, F., Crossey, B., Effenberg, A., Joint action: Mental representations, shared information and general mechanisms for coordinating with others (2017) Frontiers in Psychology, , https://doi.org/10.3389/fpsyg.2016.02039, Jan;07","Clodic, A.7 Avenue du Colonel Roche, France; email: aurelie.clodic@laas.fr","Norskov M.Seibt J.Quick O.S.",,"IOS Press BV","4th Conference on Robophilosophy 2020: Culturally Sustainable Social Robotics","18 August 2020 through 21 August 2020",,165874,09226389,9781643681542,,,"English","Front. Artif. Intell. Appl.",Conference Paper,"Final","",Scopus,2-s2.0-85098863607
"Yu C., Tapus A.","57212384467;23010519200;","SRG3: Speech-driven Robot Gesture Generation with GAN",2020,"16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020",,,"9305330","759","766",,,"10.1109/ICARCV50220.2020.9305330","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100082832&doi=10.1109%2fICARCV50220.2020.9305330&partnerID=40&md5=956fde26a433c404690df97bbf3b958f","Autonomous System and Robotics Lab/U2IS, Ensta Paris, Institut Polytechnique de Paris, Department of Computer Science and System Engineering, Palaiseau, France","Yu, C., Autonomous System and Robotics Lab/U2IS, Ensta Paris, Institut Polytechnique de Paris, Department of Computer Science and System Engineering, Palaiseau, France; Tapus, A., Autonomous System and Robotics Lab/U2IS, Ensta Paris, Institut Polytechnique de Paris, Department of Computer Science and System Engineering, Palaiseau, France","The human gestures occur spontaneously and usually they are aligned with speech, which leads to a natural and expressive interaction. Speech-driven gesture generation is important in order to enable a social robot to exhibit social cues and conduct a successful human-robot interaction. In this paper, the generation process involves mapping acoustic speech representation to the corresponding gestures for a humanoid robot. The paper proposes a new GAN (Generative Adversarial Network) architecture for speech to gesture generation. Instead of the fixed mapping from one speech to one gesture pattern, our end-to-end GAN structure can generate multiple mapped gestures patterns from one speech (with multiple noises) just like humans do. The generated gestures can be applied to social robots with arms. The evaluation result shows the effectiveness of our generative model for speech-driven robot gesture generation. © 2020 IEEE.",,"Agricultural robots; Anthropomorphic robots; Computer vision; Mapping; Robotics; Speech; Acoustic speech; Adversarial networks; Evaluation results; Expressive interactions; Generation process; Generative model; Human gestures; Humanoid robot; Social robots",,,,,"China Scholarship Council, CSC","This work was supported by China Scholarship Council (CSC).",,,,,,,,,,"Salem, M., Kopp, S., Wachsmuth, I., Rohlfing, K., Joublin, F., Generation and evaluation of communicative robot gesture (2012) International Journal of Social Robotics, 4 (2), pp. 201-217; Yu, C., Tapus, A., Interactive robot learning for multimodal emotion recognition (2019) International Conference on Social Robotics, pp. 633-642. , Springer; Tapus, A., Bandera, A., Vazquez-Martin, R., Calderita, L.V., Perceiving the person and their interactions with the others for social robotics-a review (2019) Pattern Recognition Letters, 118, pp. 3-13; Yu, C., Tapus, A., Multimodal emotion recognition with thermal and rgb-d cameras for human-robot interaction (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 532-534; Rossi, S., Ferland, F., Tapus, A., User profiling and behavioral adaptation for hri: A survey (2017) Pattern Recognition Letters, 99, pp. 3-12; Aly, A., Tapus, A., Multimodal adapted robot behavior synthesis within a narrative human-robot interaction (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 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IEEE, pp. 2857-2864; Kingma, D.P., Welling, M., (2013) Auto-encoding Variational Bayes; Karras, T., Aila, T., Laine, S., Herva, A., Lehtinen, J., Audio-driven facial animation by joint end-to-end learning of pose and emotion (2017) ACM Transactions on Graphics (TOG), 36 (4), pp. 1-12; Lee, H.-Y., Yang, X., Liu, M.-Y., Wang, T.-C., Lu, Y.-D., Yang, M.-H., Kautz, J., Dancing to music (2019) Advances in Neural Information Processing Systems, pp. 3581-3591; Tang, T., Jia, J., Mao, H., Dance with melody: An lstm-autoencoder approach to music-oriented dance synthesis (2018) Proceedings of the 26th ACM International Conference on Multimedia, pp. 1598-1606; Aly, A., Tapus, A., Towards an intelligent system for generating an adapted verbal and nonverbal combined behavior in human-robot interaction (2016) Autonomous Robots, 40 (2), pp. 193-209; Cassell, J., Vilhjálmsson, H.H., Bickmore, T., Beat: The behavior expression animation toolkit (2004) Life-Like Characters, pp. 163-185. , Springer; Yoon, Y., Ko, W.-R., Jang, M., Lee, J., Kim, J., Lee, G., Robots learn social skills: End-to-end learning of co-speech gesture generation for humanoid robots (2019) 2019 International Conference on Robotics and Automation (ICRA). IEEE, pp. 4303-4309; Sutskever, I., Vinyals, O., Le, Q.V., Sequence to sequence learning with neural networks (2014) Advances in Neural Information Processing Systems, pp. 3104-3112; Hasegawa, D., Kaneko, N., Shirakawa, S., Sakuta, H., Sumi, K., Evaluation of speech-to-gesture generation using bi-directional lstm network (2018) Proceedings of the 18th International Conference on Intelligent Virtual Agents, pp. 79-86; Kucherenko, T., Hasegawa, D., Henter, G.E., Kaneko, N., Kjellström, H., Analyzing input and output representations for speech-driven gesture generation (2019) Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents, pp. 97-104; LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., Gradient-based learning applied to document recognition (1998) Proceedings of the IEEE, 86 (11), pp. 2278-2324; LeCun, Y., Bengio, Y., Convolutional networks for images, speech, and time series (1995) The Handbook of Brain Theory and Neural Networks, 3361 (10), p. 1995; Dai, W., Dai, C., Qu, S., Li, J., Das, S., Very deep convolutional neural networks for raw waveforms (2017) 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 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IEEE, pp. 3487-3491; Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A., Image-to-image translation with conditional adversarial networks (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125-1134; Franz, S., Nolte-Holube, R., Wallhoff, F., (2013) Nafome: Nao Follows Me-tracking, Reproduction and Simulation of Human Motion, , Jade University of Applied Sciences, Germany; Yavşan, E., Uçar, A., Gesture imitation and recognition using kinect sensor and extreme learning machines (2016) Measurement, 94, pp. 852-861; Pepper-joints-aldebaran 2. 0. 6. 8 Documentation, , http://doc.aldebaran.com/2-0/family/juliettetechnical/jointsjuliette.html, Date last accessed 20th-May-2020; Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y., Realtime multi-person 2d pose estimation using part affinity fields (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7291-7299; Martinez, J., Hossain, R., Romero, J., Little, J.J., A simple yet effective baseline for 3d human pose estimation (2017) ICCV; Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C., Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments (2014) IEEE Transactions on Pattern Analysis and Machine Intelligence, 36 (7), pp. 1325-1339. , jul; Fabrice, B., Ffmpeg, , https://ffmpeg.org; Kingma, D.P., Ba, J., (2014) Adam: A Method for Stochastic Optimization",,,"IEEE","Institute of Electrical and Electronics Engineers Inc.","16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020","13 December 2020 through 15 December 2020",,166416,,9781728177090,,,"English","IEEE Int. Conf. Control, Autom., Robot. Vis., ICARCV",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85100082832
"Pinzon-Arenas J., Jiménez-Moreno R.","57201298021;57188664966;","Tool delivery robot using convolutional neural network",2020,"International Journal of Electrical and Computer Engineering","10","6",,"6300","6308",,,"10.11591/IJECE.V10I6.PP6300-6308","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092293699&doi=10.11591%2fIJECE.V10I6.PP6300-6308&partnerID=40&md5=0c9bdaade8e7f56421f155bcc7ba7a9f","Mechatronics Engineering Program, Faculty of Engineering, Militar Nueva Granada University, Carrera 11 #101-80, Bogotá D.C., Colombia","Pinzon-Arenas, J., Mechatronics Engineering Program, Faculty of Engineering, Militar Nueva Granada University, Carrera 11 #101-80, Bogotá D.C., Colombia; Jiménez-Moreno, R., Mechatronics Engineering Program, Faculty of Engineering, Militar Nueva Granada University, Carrera 11 #101-80, Bogotá D.C., Colombia","In the following article, it is presented a human-robot interaction system where algorithms were developed to control the movement of a manipulator in order to allow it to search and deliver, in the hand of the user, a desired tool with a certain orientation. A convolutional neural network (CNN) was used to detect and recognize the user's hand, geometric analysis for the adjustment of the delivery status of the tool from any position of the robot and any orientation of the gripper, and a trajectory planning algorithm for the movement of the manipulator. It was possible to use the activations of a CNN developed in previous works for the detection of the position and orientation of the hand in the workspace and thus track it in real time, both in a simulated environment and in a real environment. © 2020 Institute of Advanced Engineering and Science. All rights reserved.","Convolutional neural network; Delivery status adjustment; Hand tracking; Human-robot interaction; Path planning",,,,,,,,,,,,,,,,,"Henschel, Anna, Hortensius, Ruud, Cross, Emily S., Social Cognition in the Age of Human-Robot Interaction (2020) Trends in Neurosciences, 43 (6), pp. 373-384; Zacharaki, Angeliki, Kostavelis, Ioannis, Antonios Gasteratos, Ioannis Dokas, Safety bounds in human robot interaction: A survey (2020) Safety Science, 127; Liu, Yueyue, Li, Zhijun, Liu, Huaping, Kan, Zhen, Skill transfer learning for autonomous robots and human-robot cooperation: A survey (2020) Robotics and Autonomous Systems, 128; Ehsan Hashemi-Petroodi, S., Thevenin, Simon, Kovalev, Sergey, Dolgui, Alexandre, Operations management issues in design and control of hybrid human-robot collaborative manufacturing systems: a survey (2020) Annual Reviews in Control; Hamed, Amin, Masouleh, Mehdi Tale, Kalhor, Ahmad, Design and characterization of a bio-inspired 3-DOF Tactile/Force sensor and implementation on a 3-DOF decoupled parallel mechanism for human-robot interaction purposes (2020) Mechatronics, 66; Taylor, J., Bordeaux, L., Cashman, T., Corish, B., Keskin, C., Sharp, T., Efficient and precise interactive hand tracking through joint, continuous optimization of pose and correspondences (2016) ACM Transactions on Graphics (TOG), 35 (4), pp. 1-12; Fang, Jing, Yuan, Yuan, Human-in-the-loop optimization of wearable robots to reduce the human metabolic energy cost in physical movements (2020) Robotics and Autonomous Systems, 127; Wang, Yong, Wei, Xian, Shen, Hao, Ding, Lu, Wan, Jiuqing, Robust fusion for RGB-D tracking using CNN features (2020) Applied Soft Computing, 92; Yuan, Di, Kang, Wei, He, Zhenyu, Robust visual tracking with correlation filters and metric learning (2020) Knowledge-Based Systems, 195; Pinzón-Arenas, Javier Orlando, Pachón-Suescún, César Giovany, Jiménez-Moreno, Robinson, Assistant robot through deep learning (2020) International Journal of Electrical and Computer Engineering (IJECE), 10 (1), pp. 1053-1062; Qin, Jiaohua, Pan, Wenyan, Xiang, Xuyu, Tan, Yun, Hou, Guimin, A biological image classification method based on improved CNN (2020) Ecological Informatics, 58; Bora, Mayur Bhargab, Daimary, Dinthisrang, Amitab, Khwairakpam, Kandar, Debdatta, Handwritten Character Recognition from Images using CNN-ECOC (2020) Procedia Computer Science, 167, pp. 2403-2409; Vasan, Danish, Alazab, Mamoun, Wassan, Sobia, Safaei, Babak, Zheng, Qin, Image-Based malware classification using ensemble of CNN architectures (IMCEC) (2020) Computers & Security, 92; Useche, Paula, Jiménez-Moreno, Robinson, Baquero, Javier Martinez, Algorithm of detection, classification and gripping of occluded objects by CNN techniques and Haar classifiers (2020) International Journal of Electrical and Computer Engineering (IJECE), 10 (2), pp. 1308-1316; Pachón-Suescún, César Giovany, Enciso-Aragon, Carlos J., Jiménez-Moreno, Robinson, Robotic navigation algorithm with machine vision (2020) International Journal of Electrical and Computer Engineering (IJECE), 10 (1), pp. 1329-1342; Morgenstern, Rachelle, Su, Edwin P., The gripper table mounted retraction system: A tireless surgical assistant (2018) Seminars in Arthroplasty, 29 (3), pp. 134-136; Arenas, J. O. P., Murillo, P. C. U., Moreno, R. J., Convolutional neural network architecture for hand gesture recognition (2017) IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), pp. 1-4; Dewi, Tresna, Nurmaini, Siti, Risma, Pola, Oktarina, Yurni, Roriz, Muhammad, Inverse kinematic analysis of 4 DOF pick and place arm robot manipulator using fuzzy logic controller (2020) International Journal of Electrical and Computer Engineering (IJECE), 10 (2), pp. 1376-1386; Paula Catalina Useche, M., Jiménez-Moreno, Robinson, Detection and gripping of an occluded tool using DAG-CNN (2020) ARPN Journal of Engineering and Applied Sciences, 15 (9), pp. 1069-1078; Kouzehgar, Maryam, Tamilselvam, Yokhesh Krishnasamy, Heredia, Manuel Vega, Elara, Mohan Rajesh, Self-reconfigurable façade-cleaning robot equipped with deep-learning-based crack detection based on convolutional neural networks (2019) Automation in Construction, 108; Wang, Zeli, Li, Heng, Zhang, Xiaoling, Construction waste recycling robot for nails and screws: Computer vision technology and neural network approach (2019) Automation in Construction, 97, pp. 220-228; Simonyan, K., Zisserman, A., (2014) Very deep convolutional networks for large-scale image recognition, , arXiv preprint arXiv:1409.1556; Enciso-Aragón, C. J., Pachón-Suescún, C. G., Jimenez-Moreno, R., Quality control system by means of CNN and fuzzy systems (2018) International Journal of Applied Engineering Research, 13 (16), pp. 12846-12853; Zhang, Fengyi, Liu, Zhiyong, Xiong, Fangzhou, Su, Jianhua, Qiao, Hong, WAGNN: A Weighted Aggregation Graph Neural Network for robot skill learning (2020) Robotics and Autonomous Systems, 130; Furrer, F., Burri, M., Achtelik, M., Siegwart, R., RotorS-A modular Gazebo MAV simulator framework (2016) Robot Operating System (ROS), pp. 595-625; Wang, Yong, Wei, Xian, Tang, Xuan, Shen, Hao, Ding, Lu, CNN tracking based on data augmentation (2020) Knowledge-Based Systems, 194; Hu, Tianhao, Tang, Tang, Lin, Ronglai, Chen, Ming, Han, Shufa, Wu, Jie, A simple data augmentation algorithm and a self-adaptive convolutional architecture for few-shot fault diagnosis under different working conditions Measurement, 156, p. 2020","Jiménez-Moreno, R.; Mechatronics Engineering Program, Carrera 11 #101-80, Colombia; email: robinson.jimenez@unimilitar.edu.co",,,"Institute of Advanced Engineering and Science",,,,,20888708,,,,"English","Int. J. Electr. Comput. Eng.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85092293699
"Yang C., Wang X., Zhang X., Zhou L.","8601073100;57222002206;57213542264;57221999273;","Safe Trajectory Tracking of Manipulator based on Sliding Mode Control",2020,"2020 7th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2020",,,"9336908","353","358",,,"10.1109/ICCSS52145.2020.9336908","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100910836&doi=10.1109%2fICCSS52145.2020.9336908&partnerID=40&md5=8d8d20fcf488e85af9c57d1c35ab0bce","School of Information and Control Engineering, China University of Mining and Technology, XuZhou, China","Yang, C., School of Information and Control Engineering, China University of Mining and Technology, XuZhou, China; Wang, X., School of Information and Control Engineering, China University of Mining and Technology, XuZhou, China; Zhang, X., School of Information and Control Engineering, China University of Mining and Technology, XuZhou, China; Zhou, L., School of Information and Control Engineering, China University of Mining and Technology, XuZhou, China","In view of the collision problem between the manipulator and operators in the industrial scene of human-robot interaction, a safe trajectory tracking method of manipulator based on sliding mode control is proposed. The method uses a sliding mode controller to achieve high-precision and fast tracking of joint trajectory. On this basis, a collision response strategy is added, that is, the estimated external torque and desired joint velocity are used as inputs to modify the interpolation time of fifth-order spline trajectory. Simulation results show that the method we proposed works well on AUBO-iS. When the manipulator touches a stationary obstacle, it slows down and then remains stationary. If the obstacle is approaching, the manipulator moves backward along the desired trajectory. After removing the obstacle, the manipulator moves forward along the desired traj ectory. © 2020 IEEE.","collision detection; human-robot interaction; sliding mode control; trajectory scaling","Human robot interaction; Industrial manipulators; Industrial robots; Interpolation; Sliding mode control; Trajectories; Collision problem; Collision response; Desired trajectories; Joint trajectories; Sliding mode controller; Stationary obstacles; Trajectory tracking; Trajectory tracking method; Manipulators",,,,,"National Natural Science Foundation of China, NSFC: 61873272","This work is supported by the National Natural Science Foundation of China (Grant. 61873272).",,,,,,,,,,"Villani, V., Pini, F., Leali, F.F., Survey on human-robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266; Min, J.K., Ahn, K.H., Park, H.C., A novel reactive-type joint torque sensor with high torsional stiffness for robot applications"" (2019) Mechatronics, 63, p. 102265; Seok, D.Y., Kim, Y.B., Lee, S.Y., Kim, J., Choi, H.R., Ultra-thin joint torque sensor with enhanced sensitivity for robotic application"" (2020) Ieee Robotics and Automation Letters, 5 (4), pp. 5873-5880; Shi, Y.G., Zhang, W., Yang, T., Yang, T., Liu, L., Cui, Y.J., Flexible joints of picking manipulator based on current feedback (2020) Ieee Access, 8, pp. 85329-85338; Cho, C.N., Kim, J.H., Kim, Y.L., Song, J.B., Kyung, J.H., Collision detection algorithm to distinguish between intended contact and unexpected collision (2012) Advanced. Robotics, 26 (16), pp. 1825-1840; Magrini, E., Flacco, F., Luca, A.D., Estimation of contact forces using a virtual force sensor (2014) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2126-2133. , USA:Chicago; Haddadin, S., Albu-Schaffer, A., Luca, A.D., Collision detection and reaction: A contribution to safe physical human-robot interaction (2008) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3356-3363. , IEEE, France: Nice; Luca, A.D., Albu-Schaffer, A., Haddadin, S., Hirzinger, G., Collision detection and safe reaction with the dlr-iii lightweight manipulator arm (2007) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1623-1630. , IEEE, China: Beijing; Tarnopolskaya, T., Fulton, N., Maurer, H., Synthesis of optimal bang-bang control for cooperative collision avoidance for aircraft (ships) with unequal linear speeds (2012) Journal of Optimization Theory Applications, 155 (1), pp. 115-144; Jia, Y., Misra, A.K., Robust trajectory tracking control of a dual-Arm space robot actuated by control moment gyroscopes (2017) Acta Astronautica, 137, pp. 287-301; Zhu, Y., Qiao, J., Zhang, Y., Guo, L., Adaptive sliding mode disturbance observer-based composite control with prescribed performance of space manipulators for target capturing (2019) Ieee Transactions on Industrial Electronics, 66 (3), pp. 1973-1983; Gui, H., Vukovich, G., Adaptive fault-tolerant spacecraft attitude control using a novel integral terminal sliding mode: Adaptive fault-tolerant spacecraft attitude control (2017) International Journal of Robust and Nonlinear Control, 27 (16), pp. 3174-3196; Xu, B., Sun, F., Pan, Y., Chen, B., Disturbance observer based composite learning fuzzy control of nonlinear systems with unknown dead zone (2017) Ieee Transactions on Systems, Man, and Cybernetics: Systems, 47 (8), pp. 1-9; Walker, M.W., Wee, L.B., Adaptive control of space-based robot manipulators (1991) Ieee Transactions on Robotics and Automation, 7 (6), pp. 828-835; Jia, S., Shan, J., Finite-time trajectory tracking control of space manipulator under actuator saturation (2020) Ieee Transactions on Industrial Electronics, 67 (3), pp. 2086-2096; Wang, L., Chai, T., Zhai, L., Neural-network-based terminal sliding-mode control of robotic manipulators including actuator dynamics (2009) Ieee Transactions on Industrial Electronics, 56 (9), pp. 3296-3304; Jaemin, B., Jin, M., Han, S., A new adaptive sliding-mode control scheme for application to robot manipulators (2016) Ieee Transactions on Industrial Electronics, 63 (6), pp. 3628-3637; Liu, J.K., (2015) Matlab Simulation of Sliding Mode Variable Structure Control: Sliding Mode Control Design and Matlab Simulation the Design Method of Advanced Control System, , Beijing: Tsinghua University Press","Yang, C.; School of Information and Control Engineering, China; email: chunyuyang@cumt.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.","7th International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2020","13 November 2020 through 15 November 2020",,166936,,9781728162461,,,"English","Int. Conf. Inf., Cybern., Comput. Soc. Syst., ICCSS",Conference Paper,"Final","",Scopus,2-s2.0-85100910836
"Schaper M.-M., Malinverni L., Valero C.","56747538800;55321736900;57203384502;","Robot Presidents: Who should rule the world?: Teaching Critical Thinking in AI through Reflections upon Food Traditions",2020,"PervasiveHealth: Pervasive Computing Technologies for Healthcare",,,,"","",,3,"10.1145/3419249.3420085","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117542702&doi=10.1145%2f3419249.3420085&partnerID=40&md5=e8b848aea7c1115ad51d0b5f19e60cf0","Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Design and Visual Arts Department, University of Barcelona, Barcelona, Spain","Schaper, M.-M., Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Malinverni, L., Design and Visual Arts Department, University of Barcelona, Barcelona, Spain; Valero, C., Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain","The goal of this study is to explore new educational strategies to stimulate children's critical reflections upon ethical and social issues on Human-Robot Interaction. We use food practices as a conversational prop with children to reflect upon the complexity of social norms and cultural traditions that surround a family meal. We bridge then these concepts with the critical reflection upon the potentials and limitations of AI to address issues that arise from cultural diversity in society. Therefore, we conducted an exploratory study with 23 primary school students in a cultural diversity context. The results showed that our strategy sparked key discussions on wicked problems about AI, gender inequality and how traditions shape norms and rules in our social life. We point towards possible future directions to engage children in reflective practice through playful embodied design methods and human-food interactions. © 2020 Owner/Author.","AI Literacy; education; Educational Robotics; Ethics; food traditions; K-12; reflective methods","Educational robots; Human computer interaction; Critical reflections; Critical thinking; Cultural diversity; Cultural traditions; Educational strategy; Exploratory studies; Human food interactions; Reflective practices; Social robots",,,,,"Ministerio de Economía y Competitividad, MINECO: MDM-2015-0502","Supported by the Maria de Maeztu Program of Spanish Ministry of Economy and Competitiveness (Grant MDM-2015-0502). We also thank all children and the teacher for their motivation and contributions during the workshop sessions.",,,,,,,,,,"Bertran, F.A., Duval, J., Segura, E.M., Chasing play potentials in food culture (2020) Proceedings of the 2020 ACM Designing Interactive Systems Conference, ACM, pp. 979-991. , http://doi.org/10.1145/3357236.3395575; Bertran, F.A., Jhaveri, S., Lutz, R., Isbister, K., Wilde, D., Making sense of human-food interaction (2019) Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, ACM, pp. 1-13; Anwar, S., Bascou, N.A., A systematic review of studies on educational robotics (2019) Journal of Pre-College Engineering Education Research (J-PEER)9, 2; Bertran, F.A., Isbister, K., Technology for situated and emergent play: A bridging concept and design agenda (2020) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1-14. , http://doi.org/10.1145/3313831.3376859, April; Charisi, V., Malinverni, L., Rubegni, E., Schaper, M., Creating opportunities for children's critical reflections on ai, robotics and other intelligent technologies (2020) International Conference of Interaction Design and Children, ACM, , New York, NY, USA; Chisik, Y., Bertran, F.A., Schaper, M., Segura, E.M., Vidal, L.T., Wilde, D., Chasing play potentials in food culture: Embracing children's perspectives (2020) International Conference of Interaction Design and Children 2020, American Medical Association; Chisik, Y., Pons, P., Jaen, J., Gastronomy meets ludology (2018) Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts-CHI PLAY '18 Extended Abstracts, ACM Press, pp. 155-168. , http://doi.org/10.1145/3270316.3272056; Denning, P.J., The profession of IT moods, wicked problems, and learning (2013) Communications of the ACM, 56 (3), pp. 30-32. , http://doi.org/10.1145/2428556.2428566; Druga, S., Vu, S.T., Likhith, E., Qiu, T., Inclusive AI literacy for kids around the world (2019) ACM International Conference Proceeding Series, pp. 104-111. , http://doi.org/10.1145/3311890.3311904; Karoff, H.S., Reconceptualising children's play: Exploring the connections between spaces, practices and emotional moods (2015) Children's Spatialities: Embodiment, Emotion and Agency, pp. 112-127. , http://doi.org/10.1057/9781137464989-7, Abigail Hackett, Lisa Procter and Julie Seymour (eds.). Palgrave Macmillan UK, London; Linhorst, M., (2017) Could A Robot Be President Politico Magazine, , https://www.politico.com/magazine/story/2017/07/08/robotpresident-215342, Retrieved May 30 2020; Malinverni, L., Valero, C., What is a robot An artistic approach to understand children's imaginary about robots Proceeding of the Conference on Interaction Design and Children-IDC'20; Parasecoli, F., Scholliers, P., Cultural history of food (2015) Bloomsbury Academic, pp. 1-6; Schaper, M., Iversen, O.S., Malinverni, L., Pares, N., FUBImethod: Strategies to engage children in the co-design of Full-Body interactive experiences (2019) International Journal of Human-Computer Studies, 132, pp. 52-69. , http://doi.org/10.1016/j.ijhcs.2019.07.008; Touretzky, D., Gardner-McCune, C., Martin, F., Seehorn, D., Envisioning ai for k-12: What should every child know about ai (2019) Proceedings of the AAAI Conference on Artificial Intelligence, 33, pp. 9795-9799. , http://doi.org/10.1609/aaai.v33i01.33019795; (2019) Workshop Report: AI and Child Rights Policy, , United Nations Children's Fund (UNICEF); Verma, S., Weapons of math destruction: How big data increases inequality and threatens democracy (2019) Vikalpa: The Journal for Decision Makers, , http://doi.org/10.1177/0256090919853933; Vincent-Lancrin, S., Van Der Vlies, R., (2020) Trustworthy Artificial Intelligence (AI) in Education: Promises and Challenges, 218; Visser, M., (1991) The Rituals of Dinner, , Pennsylvania State University",,,,"ICST","11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, NordiCHI 2020","25 October 2020 through 29 October 2020",,164330,21531633,9781450375795,,,"English","PervasiveHealth: Pervasive Computing Technol Healthc.",Conference Paper,"Final","",Scopus,2-s2.0-85117542702
"Daher K., Casas J., Khaled O.A., Mugellini E.","57211264069;57205030813;8987146800;8987147000;","Empathic Chatbot Response for Medical Assistance",2020,"Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents, IVA 2020",,,,"","",,,"10.1145/3383652.3423864","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096965302&doi=10.1145%2f3383652.3423864&partnerID=40&md5=fe8b298b7d7e4ba7330d713c3261bfca","University of Applied Sciences and Arts, Western Switzerland, Fribourg, Switzerland","Daher, K., University of Applied Sciences and Arts, Western Switzerland, Fribourg, Switzerland; Casas, J., University of Applied Sciences and Arts, Western Switzerland, Fribourg, Switzerland; Khaled, O.A., University of Applied Sciences and Arts, Western Switzerland, Fribourg, Switzerland; Mugellini, E., University of Applied Sciences and Arts, Western Switzerland, Fribourg, Switzerland","Is it helpful for a medical physical health chatbot to show empathy? How can a chatbot show empathy only based on short-term text conversations? We have investigated these questions by building two different medical assistant chatbots with the goal of providing a diagnosis for physical health problem to the user based on a short conversation. One chatbot was advice-only and asked only the necessary questions for the diagnosis without responding to the user's emotions. Another chatbot, capable of showing empathy, responded in a more supportive manner by analyzing the user's emotions and generating appropriate responses with a high empathic accuracy. Using the RoPE scale questionnaire for empathy perception in a human-robot interaction, our empathic chatbot was rated significantly better in showing empathy and was preferred by a majority of the preliminary study participants (N=12). © 2020 Owner/Author.","conversational agent; emotion detection; empathy; healthcare computing; pattern matching","Diagnosis; Human robot interaction; Chatbot; Chatbots; Physical health; Short term; Intelligent virtual agents",,,,,,,,,,,,,,,,"Charrier, L., Rieger, A., Galdeano, A., Cordier, A., Lefort, M., Hassas, S., The rope scale: A measure of how empathic a robot is perceived (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)., pp. 656-657. , https://doi.org/10.1109/HRI.2019.8673082, ISSN: 2167-2121; Ekman, P., Friesen, W.V., Ellsworth, P., (2013) Emotion in the Human Face: Guidelines for Research and An Integration of Fndings., 11. , Elsevier; Falala-Séchet, C., Antoine, L., Thiriez, I., Bungener, C., Owlie: A chatbot that provides emotional support for coping with psychological difculties (2019) Proceedings of the 19th Acm International Conference on Intelligent Virtual Agents., pp. 236-237; Fast, E., Chen, B., Bernstein, M.S., Empath: Understanding topic signals in large-scale text (2016) Proceedings of the 2016 Chi Conference on Human Factors in Computing Systems (CHI '16), pp. 4647-4657. , https://doi.org/10.1145/2858036.2858535, Association for Computing Machinery, San Jose, California, USA; Feng, J., Lazar, J., Preece, J., Empathy and online interpersonal trust: A fragile relationship (2004) Behaviour & Information Technology, 23 (2), pp. 97-106. , https://doi.org/10.1080/01449290310001659240, March 2004; Fitzpatrick, K.K., Darcy, A., Vierhile, M., Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety usinga fully automated conversational agent (woebot): A randomized controlled trial (2017) Jmir Mental Health, 4 (2), p. e19. , https://doi.org/10.2196/mental.7785, 2017; Hall, J.A., Schwartz, R., Empathy present and future (2019) The Journal of Social Psychology, 159 (3), pp. 225-243. , https://doi.org/10.1080/00224545.2018.1477442, May 2019; Liu, B., Shyam Sundar, S., Should machines express sympathy and empathy? Experiments with a health advice chatbot (2018) Cyberpsychology Behavior and Social Networking, 21 (10), pp. 625-636. , https://doi.org/10.1089/cyber.2018.0110, Oct. 2018; Seehausen, M., Kazzer, P., Bajbouj, M., Heekeren, H.R., Jacobs, A.M., Klann-Delius, G., Menninghaus, W., Prehn, K., Efects of empathic social responses on the emotions of the recipient (2016) Brain and Cognition, 103, pp. 50-61. , https://doi.org/10.1016/j.bandc.2015.11.004, March 2016; Spring, T., Casas, J., Daher, K., Mugellini, E., Omar, K.A., (2018) Empathic Response Generation in Chatbots, p. 10. , http://ceur-ws.org/Vol-2458/paper1.pdf, 2018; Weizenbaum, J., Elizaða computer program for the study of natural language communication between man and machine (1966) Commun. Acm, 9 (1), pp. 36-45. , https://doi.org/10.1145/365153.365168, Jan. 1966; Xu, A., Liu, Z., Guo, Y., Sinha, V., Akkiraju, R., A new chatbot for customer service on social media (2017) Chi, , https://doi.org/10.1145/3025453.3025496",,,"ACM Special Interest Group on Artificial Intelligence (SIGAI)","Association for Computing Machinery, Inc","20th ACM International Conference on Intelligent Virtual Agents, IVA 2020","20 October 2020 through 22 October 2020",,164271,,9781450375863,,,"English","Proc. ACM Int. Conf. Intell. Virtual Agents, IVA",Conference Paper,"Final","",Scopus,2-s2.0-85096965302
"Zhang R., Lv H., Zhou H., Zhang Y., Liu C., Yang G.","57220861990;57204468637;57211515368;57220876565;57220876417;54961736000;","A Gait Recognition System for Interaction with a Homecare Mobile Robot",2020,"IECON Proceedings (Industrial Electronics Conference)","2020-October",,"9254412","3391","3396",,,"10.1109/IECON43393.2020.9254412","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097770235&doi=10.1109%2fIECON43393.2020.9254412&partnerID=40&md5=c35cd44bee22cb23d1430c448b385737","School of Mechanical Engineering Zhejiang University, State Key Laboratory of Fluid Power and Mechatronic Systems, Hangzhou, China","Zhang, R., School of Mechanical Engineering Zhejiang University, State Key Laboratory of Fluid Power and Mechatronic Systems, Hangzhou, China; Lv, H., School of Mechanical Engineering Zhejiang University, State Key Laboratory of Fluid Power and Mechatronic Systems, Hangzhou, China; Zhou, H., School of Mechanical Engineering Zhejiang University, State Key Laboratory of Fluid Power and Mechatronic Systems, Hangzhou, China; Zhang, Y., School of Mechanical Engineering Zhejiang University, State Key Laboratory of Fluid Power and Mechatronic Systems, Hangzhou, China; Liu, C., School of Mechanical Engineering Zhejiang University, State Key Laboratory of Fluid Power and Mechatronic Systems, Hangzhou, China; Yang, G., School of Mechanical Engineering Zhejiang University, State Key Laboratory of Fluid Power and Mechatronic Systems, Hangzhou, China","With the development of intelligent sensing and human-robot interaction technology, homecare robots play an increasingly important role in the field of homecare services. At the same time, the interaction between the operator and the homecare robot is particularly valued. This paper proposed a homecare robot interaction system based on a wearable inertial motion capture device. In this system, the wearable motion capture device is used to capture the operator's motion signals. After processing the motion signals and recognizing the corresponding motion poses, the system controls the homecare robot to imitate the intention of the operator. In this paper, we focused on controlling the movement of the homecare robot through the operator's lower limb motion data and designed a novel gait recognition algorithm. The proposed system was evaluated experimentally, proving that the system has strong performance and practicality. © 2020 IEEE.","gait recognition; homecare; human-robot interaction; intelligent sensing; wearable motion capture device","Gait analysis; Industrial electronics; Motion capture; Pattern recognition; Wearable technology; Gait recognition; Home-care services; Homecare robots; Inertial motion captures; Intelligent sensing; Lower-limb motion; Motion signals; System control; Human robot interaction",,,,,"K18-508116-008-03; 2020XGZX017; National Natural Science Foundation of China, NSFC: 51890884, 51975513; Natural Science Foundation of Zhejiang Province: LR20E050003; State Key Laboratory of Fluid Power and Mechatronic Systems, SKLoFP: 51821093, SKLoFP_ZZ_2002; Thousand Young Talents Program of China","This work was supported in part by the National Natural Science Foundation of China under Grant 51975513, the Major Research Plan of National Natural Science Foundation of China under Grant 51890884, the Natural Science Foundation of Zhejiang Province, China under Grant LR20E050003, the Zhejiang University Special Scientific Research Fund for COVID-19 Prevention and Control under Grant 2020XGZX017, the Director Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems (Grant No. SKLoFP_ZZ_2002), the Science Fund for Creative Research Groups of the National Natural Science Foundation of China under Grant 51821093, Robotics Institute of Zhejiang University under Grant K18-508116-008-03, and China's Thousand Talents Plan Young Professionals Program.",,,,,,,,,,"ByungSoo, K., Ho-Jin, C., Hong, C., Kim, J., Oh Chul, K., Yoo, C.D., Neural network-based autonomous navigation for a homecare mobile robot (2017) The 2017 IEEE International Conference on Big Data and Smart Computing (BigComp, , 13-16 Feb. 2017; Park, C., Kang, S., Kim, J., Oh, J., A study on service robot system for elder care (2012) The 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI, , 26-28 Nov. 2012; Pineau, J., Montemerlo, M., Pollack, M., Roy, N., Thrun, S., Towards robotic assistants in nursing homes: Challenges and results (2003) Robotics and Autonomous Systems, 42 (3), pp. 271-281. , 03/31/2003; Ha, Y., Byun, Y., A ubiquitous homecare service system using a wearable user interface device (2012) 2012 IEEE/ACIS 11th International Conference on Computer and Information Science, pp. 649-650; Fan, J., A robotic coach architecture for elder care (rocare) based on multi-user engagement models (2017) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (8), pp. 1153-1163; Joo-Ho, L., Morioka, K., Ando, N., Hashimoto, H., Cooperation of distributed intelligent sensors in intelligent environment (2004) IEEE/ASME Transactions on Mechatronics, 9 (3), pp. 535-543; Jeong-Yean, Y., Dong-Soo, K., The effect of multiple robot interaction on human-robot interaction (2012) 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI, pp. 30-33; Yang, G., A health-iot platform based on the integration of intelligent packaging, unobtrusive bio-sensor, and intelligent medicine box (2014) IEEE Transactions on Industrial Informatics, 10 (4), pp. 2180-2191; Prayudi, I., Kim, D., Design and implementation of imu-based human arm motion capture system (2012) 2012 IEEE International Conference on Mechatronics and Automation, pp. 670-675; Seo, E., Park, C., Kim, D., Song, J., Quaternion-based orientation estimation with static error reduction (2011) 2011 IEEE International Conference on Mechatronics and Automation, pp. 1624-1629; Roetenberg, D., Luinge, H.J., Baten, C.T.M., Veltink, P.H., Compensation of magnetic disturbances improves inertial and magnetic sensing of human body segment orientation (2005) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 13 (3), pp. 395-405; Qureshi, U., Golnaraghi, F., An algorithm for the in-field calibration of a mems imu (2017) IEEE Sensors Journal, 17 (22), pp. 7479-7486; Kang, M., Kang, H., Lee, C., Moon, K., The gesture recognition technology based on imu sensor for personal active spinning (2018) 2018 20th International Conference on Advanced Communication Technology (ICACT, pp. 546-552; Lu, X., Huang, F., Chen, Z., Gu, J., Gait-Event-based human intention recognition approach for lower limb (2018) 2018 IEEE International Conference on Information and Automation (ICIA, pp. 734-739; Yang, G., A novel gesture recognition system for intelligent interaction with a nursing-care assistant robot (2018) Applied Sciences, 8, p. 2349. , 11/22; Ltd, N., (2017) Perception-Neuron, , http://www.noitom.com/; Ito, K., Gaussian filter for nonlinear filtering problems (2000) Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No. 00CH37187, 2 (2), pp. 1218-1223",,,"IEEE Industrial Electronics Society (IES);SPECS - Smart Grid + Power Electronics Consortium Singapore;The Institute of Electrical and Electronics Engineers (IEEE)","IEEE Computer Society","46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020","19 October 2020 through 21 October 2020",,165032,,9781728154145,IEPRE,,"English","IECON Proc",Conference Paper,"Final","",Scopus,2-s2.0-85097770235
"Yamane K.","35410577600;","Kinematic Redundancy Resolution for Humanoid Robots by Human Motion Database",2020,"IEEE Robotics and Automation Letters","5","4","9206142","6948","6955",,,"10.1109/LRA.2020.3026972","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091932463&doi=10.1109%2fLRA.2020.3026972&partnerID=40&md5=7d0ad5daa38bab9642137a4bcb300c62","Honda Research Institute Usa, San Jose, CA  95134, United States","Yamane, K., Honda Research Institute Usa, San Jose, CA  95134, United States","In this letter, we present a method for resolving kinematic redundancy using a human motion database, with application to teleoperation of bimanual humanoid robots using low-cost devices. Handheld devices for virtual reality applications can realize low-cost interfaces for operating such robots but available information does not uniquely determine the arm configuration. The resulting arm motions may be unnatural and inconsistent due to the kinematic redundancy. The idea explored in this paper is to construct a human motion database in advance using an interface that can directly measure the whole arm configuration such as motion capture. During teleoperation, the database is used to infer the appropriate arm configuration, grasp forces, and object trajectory based on the endeffector trajectories measured by low-cost devices. The database employs Bayesian Interaction Primitives that have been used for modeling human-robot interactions. © 2016 IEEE.","datasets for human motion; Human and humanoid motion analysis and synthsis; learning from demonstration; telerobotics and teleoperation","Agricultural robots; Anthropomorphic robots; Costs; Database systems; Kinematics; Redundancy; Redundant manipulators; Remote control; Arm motions; End-effector trajectory; Hand held device; Human motion database; Humanoid robot; Kinematic redundancy; Low-cost devices; Object trajectories; Human robot interaction",,,,,,,,,,,,,,,,"Mohr, F.W., Computer-enhanced robotic cardiac surgery: Experience in 148 patients (2001) J. Thoracic Cardiovascular Surgery, 121 (5), pp. 842-853; Sheridan, T.B., Space teleoperation through time delay: Review and prognosis (1993) Ieee Trans. Robot. Autom., 9 (5), pp. 592-606. , Oct; Murphy, R.R., Human-robot interaction in rescue robotics (2004) Ieee Trans. Syst., Man, Cybern., Part C (Appl. 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"Marvel J.A., Bagchi S., Zimmerman M., Antonishek B.","35074587400;57208164628;57208165783;8620417100;","Towards effective interface designs for collaborative HRI in manufacturing",2020,"ACM Transactions on Human-Robot Interaction","9","4","25","","",,8,"10.1145/3385009","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089016586&doi=10.1145%2f3385009&partnerID=40&md5=576751ea93fc4b0260992e912f909233","U.S. National Institute of Standards and Technology, 100 Bureau Dr., Stop 8230, Gaithersburg, MD  20899, United States","Marvel, J.A., U.S. National Institute of Standards and Technology, 100 Bureau Dr., Stop 8230, Gaithersburg, MD  20899, United States; Bagchi, S., U.S. National Institute of Standards and Technology, 100 Bureau Dr., Stop 8230, Gaithersburg, MD  20899, United States; Zimmerman, M., U.S. National Institute of Standards and Technology, 100 Bureau Dr., Stop 8230, Gaithersburg, MD  20899, United States; Antonishek, B., U.S. National Institute of Standards and Technology, 100 Bureau Dr., Stop 8230, Gaithersburg, MD  20899, United States","We present a comprehensive framework and test methodology for the evaluation of human-machine interfaces (HMI) and human-robot interactions (HRI) in collaborative manufacturing applications. An overview of the challenges that face current- and next-generation collaborative robot systems is presented, specifically focused on the interactions between man and machine, and a series of objectively quantitative and subjectively qualitative metrics are given to guide the development and assessment of interfaces and interactions. A generalized set of guidelines for the design of HMI is also proposed to address these challenges and thereby enable effective and intuitive diagnostics and error corrections when process failures occur. These guidelines are aimed at aiding researchers in developing effective interface and interaction technologies, maximizing operator situation awareness in human-robot collaborative manufacturing teams, promoting effective process and system diagnostics reporting, and enabling faster responses to equipment or application errors. © 2020 Public Domain.","benchmarking; data sets; peformance measures; repeatability studies; Test methods and metrics; use cases","Agricultural robots; Error correction; Man machine systems; Manufacture; Collaborative manufacturing; Collaborative robots; Human Machine Interface; Human robot Interaction (HRI); Interaction technology; Interface designs; Situation awareness; System diagnostics; Social robots",,,,,,,,,,,,,,,,"Armstrong, A.C., The fragility of group flow: The experiences of two small groups in a middle schoolmathematics classroom (2008) J. Math. 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"Song S., She Y., Wang J., Su H.-J.","57200117296;56545216500;7701333092;8379272100;","Toward Tradeoff between Impact Force Reduction and Maximum Safe Speed: Dynamic Parameter Optimization of Variable Stiffness Robots",2020,"Journal of Mechanisms and Robotics","12","5","054503","","",,4,"10.1115/1.4046839","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087949007&doi=10.1115%2f1.4046839&partnerID=40&md5=2eee324caeec4dc6af4cf150ca312578","Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA  02139, United States; Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH  43210, United States","Song, S., Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States; She, Y., Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA  02139, United States; Wang, J., Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States; Su, H.-J., Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH  43210, United States","Variable stiffness robots may provide an effective way of trading-off between safety and speed during physical human-robot interaction. In such a compromise, the impact force reduction capability and maximum safe speed are two key performance measures. To quantitatively study how dynamic parameters such as mass, inertia, and stiffness affect these two performance measures, performance indices for impact force reduction capability and maximum speed of variable stiffness robots are proposed based on the impact ellipsoid in this paper. The proposed performance indices consider different impact directions and kinematic configurations in the large. Combining the two performance indices, the global performance of variable stiffness robots is defined. A two-step optimization method is designed to achieve this global performance. A two-link variable stiffness link robot example is provided to show the efficacy of the proposed method. © 2020 by ASME.","dynamic parameter optimization; dynamics; impact force reduction; maximum safe speed; physical human-robot interaction; robot design; soft robots; Variable stiffness robot","Agricultural robots; Optimal systems; Stiffness; Dynamic parameters; Global performance; Kinematic configuration; Performance indices; Performance measure; Physical human-robot interactions; Two-step optimizations; Variable stiffness; Human robot interaction",,,,,"National Science Foundation, NSF: 1637656","The work was partially supported by NSF Award #1637656.",,,,,,,,,,"ISO10218: Robots and Robotic Devices-Safety Requirements for Industrial Robots Part 1: Robots (2011), [1] International Organization for Standardization; Bicchi, A., Tonietti, G., Fast and ""Soft-Arm"" Tactics (2004), [2]; Robot Arm Design] IEEE Rob. Autom. 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Anat, 120 (1), pp. 33-54. , [37]; (2016) ISO/TS 15066:2016 Robots and Robotic Devices-Collaborative Robots, , [38] International Organization for Standardization; She, Y., Meng, D., Shi, H., Su, H.J., Dynamic Modeling of a 2D Compliant Link for Safety Evaluation in Human-Robot Interactions (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, , [39] Hamburg, Germany, Sept. 28-Oct. 2; Morrison, T., Li, C, Pei, X., Su, H.J., A Novel Rotating Beam Link for Variable Stiffness Robotic Arms (2019) 2019 International Conference on Robotics and Automation (ICRA), , [40] Montreal, Canada, May 20-24",,,,"American Society of Mechanical Engineers (ASME)",,,,,19424302,,,,"English","J. Mech. Robot.",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85087949007
"Wittmann J., Jankowski J., Wahrmann D., Rixen D.J.","57219851425;57220950510;56423769300;7004338263;","Hierarchical Motion Planning Framework for Manipulators in Human-Centered Dynamic Environments",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223549","525","532",,,"10.1109/RO-MAN47096.2020.9223549","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095758882&doi=10.1109%2fRO-MAN47096.2020.9223549&partnerID=40&md5=56c24aa5126310f8da855923d88dbb01","Technical University of Munich, Department of Applied Mechanics, Germany; Franka Emika GmbH, Munich, Germany","Wittmann, J., Technical University of Munich, Department of Applied Mechanics, Germany; Jankowski, J., Technical University of Munich, Department of Applied Mechanics, Germany; Wahrmann, D., Franka Emika GmbH, Munich, Germany; Rixen, D.J., Technical University of Munich, Department of Applied Mechanics, Germany","Collaborating robots face rising challenges with respect to autonomy and safety as they are deployed in flexible automation applications. The ability to perform the required tasks in the presence of humans and obstacles is key for the integration of these machines in industry. In this work we introduce a framework for motion planning of manipulators that builds upon the most promising existing approaches by combining them in an advantageous way. It includes a new Obstacle-related Sampling Rejection Probabilistic Roadmap planner (ORSR-PRM) that represents the free workspace in an efficient way. Using this representation, dynamic obstacles can be avoided in real-time using an attractor-based online trajectory generation. The resulting motions satisfy kinematic and dynamic joint limits, ensuring a safe human-robot interaction. We validate the functionality and performance of the presented framework in simulations and experiments. © 2020 IEEE.",,"Agricultural robots; Collision avoidance; Manipulators; Motion planning; Robot programming; Dynamic environments; Dynamic obstacles; Flexible automation; Joint limits; On-line trajectory generations; Probabilistic roadmap planners; Real time; Safe human-robot interaction; Social robots",,,,,"AZ-1318-17","This work is supported by the Bayerische Forschungs-stiftung (BFS, project number AZ-1318-17).",,,,,,,,,,"(2019) World Robotics Industrial and Service Robots, , International Federation of Robotics (IFR). 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Rep; Hauser, K., Lazy collision checking in asymptotically-optimal motion planning (2015) 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 2951-2957; Karaman, S., Frazzoli, E., (2011) Sampling-based Algorithms for Optimal Motion Planning; Cefalo, M., Oriolo, G., A general framework for task-constrained motion planning with moving obstacles (2019) Robotica, 37 (3), pp. 575-598; Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots (1985) Proceedings. 1985 IEEE International Conference on Robotics and Automation, 2, pp. 500-505; Haddadin, S., Urbanek, H., Parusel, S., Burschka, D., Roßmann, J., Albu-Schäffer, A., Hirzinger, G., Real-time reactive motion generation based on variable attractor dynamics and shaped velocities (2010) IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010-Conference Proceedings; Robotic Operating System., , https://www.ros.org, Stanford Artificial Intelligence Laboratory et al; Amato, N.M., Bayazit, O.B., Dale, L.K., Jones, C., Vallejo, D., (1998) Obprm: An Obstacle-based Prm for 3d Workspaces; Sun, Z., Hsu, D., Jiang, T., Kurniawati, H., Reif, J.H., Narrow passage sampling for probabilistic roadmap planning (2005) IEEE Transactions on Robotics, 21 (6), pp. 1105-1115; Boor, V., Overmars, M.H., Der Van Stappen, A.F., The Gaussian sampling strategy for probabilistic roadmap planners (1999) Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No. 99CH36288C), 2, pp. 1018-1023; (2020) Introducing the Franka Emika Robot-soft-robot Performance, Smart and Industry-ready. Enabling Automation for Anyone, Anywhere, , https://www.franka.de/, FRANKA EMIKA GmbH; Pan, J., Chitta, S., Manocha, D., Fcl: A general purpose library for collision and proximity queries (2012) 2012 IEEE International Conference on Robotics and Automation, pp. 3859-3866",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095758882
"Vysocký A., Grushko S., Oščádal P., Kot T., Babjak J., Jánoš R., Sukop M., Bobovský Z.","57211522769;57218209694;57216509401;55836715700;56209228400;55016528600;36615762200;57222544092;","Analysis of precision and stability of hand tracking with leap motion sensor",2020,"Sensors (Switzerland)","20","15","4088","1","15",,3,"10.3390/s20154088","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088316527&doi=10.3390%2fs20154088&partnerID=40&md5=c87fdcd3f8fb4970f18a93fb0736cc63","Department of Robotics, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, Ostrava, 70800, Czech Republic; Department of Robotics, Faculty of Mechanical Engineering, Technical University of Kosice, Kosice, 04200, Slovakia","Vysocký, A., Department of Robotics, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, Ostrava, 70800, Czech Republic; Grushko, S., Department of Robotics, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, Ostrava, 70800, Czech Republic; Oščádal, P., Department of Robotics, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, Ostrava, 70800, Czech Republic; Kot, T., Department of Robotics, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, Ostrava, 70800, Czech Republic; Babjak, J., Department of Robotics, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, Ostrava, 70800, Czech Republic; Jánoš, R., Department of Robotics, Faculty of Mechanical Engineering, Technical University of Kosice, Kosice, 04200, Slovakia; Sukop, M., Department of Robotics, Faculty of Mechanical Engineering, Technical University of Kosice, Kosice, 04200, Slovakia; Bobovský, Z., Department of Robotics, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, Ostrava, 70800, Czech Republic","In this analysis, we present results from measurements performed to determine the stability of a hand tracking system and the accuracy of the detected palm and finger’s position. Measurements were performed for the evaluation of the sensor for an application in an industrial robot-assisted assembly scenario. Human–robot interaction is a relevant topic in collaborative robotics. Intuitive and straightforward control tools for robot navigation and program flow control are essential for effective utilisation in production scenarios without unnecessary slowdowns caused by the operator. For the hand tracking and gesture-based control, it is necessary to know the sensor’s accuracy. For gesture recognition with a moving target, the sensor must provide stable tracking results. This paper evaluates the sensor’s real-world performance by measuring the localisation deviations of the hand being tracked as it moves in the workspace. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Collaborative robot; Gesture; Hand tracking; Leap motion; Robot","Gesture recognition; Human robot interaction; Motion sensors; Motion tracking; Control tools; Hand tracking; Localisation; Moving targets; Program flow; Real-world performance; Robot interactions; Robot navigation; Palmprint recognition; forecasting; genetic procedures; gesture; hand; human; motion; robotics; Biosensing Techniques; Forecasting; Gestures; Hand; Humans; Motion; Robotics",,,,,"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV, VEGA: 1/0389/18","This work was supported with the Research Platform focused on Industry 4.0 and Robotics in Ostrava Agglomeration project, project number CZ.02.1.01/0.0/0.0/17_049/0008425 within the Operational Programme Research, Development and Education. This article has also been supported by specific research project SP2020/141 and financed by the state budget of the Czech Republic. This project was elaborated with the support of VEGA 1/0389/18 Research on kinematically redundant mechanisms.",,,,,,,,,,"Sheridan, T.B., Human-Robot Interaction: Status and Challenges (2016) Hum. Factors, 58, pp. 525-532; Chen, B., Hua, C., Dai, B., He, Y., Han, J., Online control programming algorithm for human-robot interaction system with a novel real-time human gesture recognition method (2019) Int. J. Adv. Robot. Syst, 16; Maurtua, I., Fernández, I., Tellaeche, A., Kildal, J., Susperregi, L., Ibarguren, A., Sierra, B., Natural multimodal communication for human-robot collaboration (2017) Int. J. Adv. Robot. Syst; Aliprantis, J., Konstantakis, M., Nikopoulou, R., Mylonas, P., Caridakis, G., Natural Interaction in Augmented Reality Context (2019) Proceeding of the Visual Pattern Extraction and Recognition for Cultural Heritage Understanding, pp. 50-61. , Pisa, Italy, 30 January 2019; Bachmann, D., Weichert, F., Rinkenauer, G., Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller (2018) Sensors, 18; Leap Motion Coordinate Systems, , https://developer-archive.leapmotion.com/documentation/objc/devguide/Leap_Coordinate_Mapping.html, (accessed on 16 June 2020); Vysocký, A., Pastor, R., Novák, P., Interaction with Collaborative Robot Using 2D and TOF Camera Proceeding of the Modelling and Simulation for Autonomous Systems (MESAS 2018), pp. 477-489. , Prague, Czech Republic, 17–19 October 2018, 2019; Bachmann, D., Weichert, F., Rinkenauer, G., Evaluation of the Leap Motion Controller as a New Contact-Free Pointing Device (2015) Sensors, 15, pp. 214-233; Guna, J., Jakus, G., Pogačnik, M., Tomažič, S., Sodnik, J., An Analysis of the Precision and Reliability of the Leap Motion Sensor and Its Suitability for Static and Dynamic Tracking (2014) Sensors, 14, pp. 3702-3720; Smeragliuolo, A.H., Hill, N.J., Disla, L., Putrino, D., Validation of the Leap Motion Controller Using Markered Motion Capture Technology (2016) J. Biomech, 49, pp. 1742-1750; Vosinakis, S., Koutsabasis, P., Evaluation of Visual Feedback Techniques for Virtual Grasping with Bare Hands using Leap Motion and Oculus Rift (2017) Virtual Real; Oropesa, I., de Jong, T., Sánchez-González, P., Dankelman, J., Gómez, E., Feasibility of tracking laparoscopic instruments in a box trainer using a Leap Motion Controller (2016) Measurement, 80, pp. 115-124; Pititeeraphab, Y., Choitkunnan, P., Thongpance, N., Kullathum, K., Pintavirooj, C., Robot-arm control system using LEAP motion controller Proceeding of the 2016 International Conference on Biomedical Engineering (BME-HUST), pp. 109-112. , Hanoi, Vietnam, 5–6 October 2016, 2016; Gunawardane, H., Medagedara, N., Madhusanka, B., Control of Robot Arm Based on Hand Gesture using Leap Motion Sensor Technology (2017) Int. J. Robot. Mechatronics; Staretu, I., Moldovan, C., Leap Motion Device Used to Control a Real Anthropomorphic Gripper (2016) Int. J. Adv. Robot. Syst, 13, p. 113; Jin, H., Chen, Q., Chen, Z., Hu, Y., Zhang, J., Multi-LeapMotion sensor based demonstration for robotic refine tabletop object manipulation task (2016) Caai Trans. Intell. Technol, 1, pp. 104-113","Vysocký, A.; Department of Robotics, Czech Republic; email: ales.vysocky@vsb.cz",,,"MDPI AG",,,,,14248220,,,"32707927","English","Sensors",Letter,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85088316527
"Dong J., Xu J., Zhou Q., Hu S.","57217493071;56260858000;57215325244;57217485746;","Physical human–robot interaction force control method based on adaptive variable impedance",2020,"Journal of the Franklin Institute","357","12",,"7864","7878",,4,"10.1016/j.jfranklin.2020.06.007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087315934&doi=10.1016%2fj.jfranklin.2020.06.007&partnerID=40&md5=66c9a158e1c27e25b13102f8afe53591","College of Information Engineering, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou, Zhejiang  310023, China","Dong, J., College of Information Engineering, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou, Zhejiang  310023, China; Xu, J., College of Information Engineering, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou, Zhejiang  310023, China; Zhou, Q., College of Information Engineering, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou, Zhejiang  310023, China; Hu, S., College of Information Engineering, Zhejiang University of Technology, 288 Liuhe Road, Hangzhou, Zhejiang  310023, China","This study presents a method for extracting the actual interaction force from a force sensor signal by measuring the gravity of the end-effector. The variable impedance control problem of commercial industrial robots is studied, to investigate the uncertain contact impedance characteristics between the human palm and a robot end-effector in the physical human-robot interaction process. A speed-based variable impedance adaptive interaction control method is proposed. The damping parameters of the admittance controller are adaptively adjusted according to the interactive force tracking error. The desired speed of the robot is also given, which is tracked in the Cartesian space speed mode of a Stäubli TX-90 robot to achieve interactive force control. An experimental comparison is made between the control strategies of the constant impedance and adaptive variable impedance. The experimental results show that the proposed method is effective. © 2020",,"End effectors; Force control; Industrial robots; Adaptive interaction; Control strategies; Experimental comparison; Force sensor signals; Interaction forces; Physical human-robot interactions; Robot interactions; Variable impedance control; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 61374103, U1709213","This work was supported in part by the National Natural Science Foundation of China under Grants U1709213 and 61374103 .",,,,,,,,,,"Duchaine, V., Gosselin, C.M., General model of human-robot cooperation using a novel velocity based variable impedance control (2007) Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, Tsukuba, Japan, pp. 446-451; Agravante, D.J., Cherubini, A., Bussy, A., Gergondet, P., Kheddar, A., Collaborative human-humanoid carrying using visionand haptic sensing (2014) IEEE International Conference on Robotics and Automation, Hong Kong, China, pp. 607-612; Gams, A., Nemec, B., Ijspeert, A.J., Ude, A., Coupling movement primitives: interaction with the environment and bimanual tasks (2014) IEEE Trans. Robot., 30 (4), pp. 816-830; Donner, P., Buss, M., Cooperative swinging of complex pendulumlike objects: experimental evaluation (2016) IEEE Trans. Robot., 32 (3), pp. 744-753; Palunko, I., Donner, P., Buss, M., Hirche, S., Cooperative suspended object manipulation using reinforcement learning and energy-based control (2014) IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, United States, pp. 885-891; Haddadin, S., Luca, A.A.D., Collision detection and reaction: a contribution to safe physical human-robot interaction (2008) IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France, pp. 3356-3363; Corrales, J.A., Candelas, F.A., Torres, F., Safe human-robot interaction based on dynamic sphere-swept line bounding volumes (2011) Robot. Comput. Integr. Manuf., 27 (1), pp. 177-185; Tian, Z., Gao, X., Su, S., Qiu, J., Du, X., Guizani, M., Evaluating reputation management schemes of internet of vehicles based on evolutionary game theory (2019) IEEE Trans. Veh. Technol., 68 (6), pp. 5971-5980; Tian, Z., Su, S., Shi, W., Du, X., Guizani, M., Yu, X., A data-driven model for future internet route decision modeling (2019) Future Gener. Comput. Syst., 95, pp. 212-220; Tian, Z., Shi, W., Wang, Y., Zhu, C., Du, X., Su, S., Real-time lateral movement detection based on evidence reasoning network for edge computing environment (2019) IEEE Trans. Ind. Inform., 15 (7), pp. 4285-4294; Li, H., Stable and compliant motion of physical human-robot interaction coupled with a moving environment using variable admittance and adaptive control (2018) IEEE Robot. Autom. Lett., 3, pp. 2493-2500; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Trans. Robot., 31 (5), pp. 089-1100; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human–robot physical interaction (2015) IEEE Trans. Robot., 31, pp. 850-863; Hogan, N., Impedance control - an approach to manipulation.I - theory. II - implementation. III- applications (1985) Asme Trans. J. Dyn. Syst. Meas. Control, 107, pp. 304-313; Craig, J.J., Raibert, M.H., A systematic method of hybrid position/force control of a manipulator (1979) IEEE International Computer Software and Applications Conference, Chicago, United States, pp. 446-451; Zhen, Y., (2015), An overview on compliance control of robot based on the impedance control, nanjing; Bowyer, S., Rodriguez, Y.B.F., Dissipative control for physical human–robot interaction (2017) IEEE Trans. Robot., 31 (6), pp. 1281-1293; Aydin, Y., Tokatli, O., Patoglu, V., Stable physical human–robot interaction using fractional order admittance control (2018) IEEE Trans. Hapt., 1, pp. 464-475; Kosuge, K., Yoshida, H., Fukuda, T., Dynamic control for robot–human collaboration (1993) Proc. of IEEE International Workshop on Robot and Human Communication, Nagoya, Japan, pp. 398-401; Corteville, B., Aertbelin, E., Bruyninckx, H., Schutter, J.D., Brussel, H.V., Human-inspired robot assistant for fast point-to-point movements (2007) Proceedings of the IEEE International Conference on Robotics and Automation, Roma, Italy, pp. 3639-3644; Li, K., Chen, R., Nuchkrua, T., Boonto, S., Dual loop compliant control based on human prediction for physical human–robot interaction (2019) Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Hiroshima, Japan, pp. 459-464; Li, K., Zhao, H., Nuchkrua, T., Yuan, Y., Ding, H., Sparse bayesian learning-based adaptive impedance control in physical human–robot interaction (2018) IEEE International Conference on Robotics and Biomimetics (ROBIO), Kuala Lumpur, Malaysia, pp. 2379-2385; Li, Z., Liu, J., Huang, Z., Peng, Y., Pu, H., Ding, L., Adaptive impedance control of human-robot cooperation using reinforcement learning (2017) IEEE Trans. Ind. Electron., 64 (10), pp. 8013-8022; Chen, X., Wang, N., Cheng, H., Yang, C., Neural learning enhanced variable admittance control for human–robot collaboration (2020) IEEE Access, 8, pp. 25727-25737; Duan, J., Gan, Y., Chen, M., Adaptive variable impedance control for dynamic contact force tracking in uncertain environment (2018) Robot. Auton. Syst., 102, pp. 54-65; Seraji, H., Colbaugh, R., Force tracking in impedance control (2002) IEEE International Conference on Robotics and Automation, Washington, United States, pp. 499-506; Jung, S., Hsia, T.C., Bonitz, R.G., Force tracking impedance control of robot manipulators under unknown environment (2004) IEEE Trans. Control Syst. Technol., 12 (3), pp. 474-483; Lecours, A., Mayerstonge, B., Gosselin, C., Variable admittance control of a four-degree-of-freedom intelligent assist device (2012) IEEE International Conference on Robotics and Automation, Saint Paul minnesota, United States, pp. 3903-3908; John, J.C., Introduction to Robotics: Mechanics and Control (2003), Pearson Education International; Zhao, M., Application and Software Development of Process Control System for Assembly Robot (2016), Southeast University","Xu, J.; College of Information Engineering, 288 Liuhe Road, China; email: xujm@zjut.edu.cn",,,"Elsevier Ltd",,,,,00160032,,JFINA,,"English","J Franklin Inst",Article,"Final","",Scopus,2-s2.0-85087315934
"Ramos A., Hashtrudi-Zaad K.","57216857142;6701420299;","Estimation of Energy Absorption Capability of Arm Using Force Myography for Stable Human-Machine Interaction",2020,"Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS","2020-July",,"9175410","4758","4761",,,"10.1109/EMBC44109.2020.9175410","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091045097&doi=10.1109%2fEMBC44109.2020.9175410&partnerID=40&md5=fd6eaa034a167a557675591c08cb1c1c","Queen's University, Department of Electrical and Computer Engineering, Kingston, ON  K7L3N6, Canada","Ramos, A., Queen's University, Department of Electrical and Computer Engineering, Kingston, ON  K7L3N6, Canada; Hashtrudi-Zaad, K., Queen's University, Department of Electrical and Computer Engineering, Kingston, ON  K7L3N6, Canada","Human-robot interactions help in various industries and enhance the user experience in different ways. However, constant safety monitoring is needed in environments where human users are at risk, such as rehabilitation therapy, space exploration, or mining. One way to improve safety and performance in robotic tasks is to include biological information of the user in the control system. This can help regulate the energy that is delivered to the user. In this work, we estimate the energy absorbing capabilities of the human arm, using the metric Excess of Passivity (EOP). EOP data from healthy subjects were obtained based on Forcemyography of the subjects' arm, to expand the sources of biological information and improve estimations.Clinical relevance - This protocol can help determine the ability of rehabilitation patients to withstand robotic stimulation with high amplitudes of therapeutic forces, as needed in assistive therapy. © 2020 IEEE.","Arm impedance; control systems; excess of passivity; human-robot interaction; rehabilitation robotics; robot control; stability; upper-limb; variability.","Energy absorption; Man machine systems; Patient rehabilitation; Patient treatment; Robotics; Safety engineering; Space research; User experience; Biological information; Energy absorbing capability; Energy absorption capability; Healthy subjects; Human machine interaction; Rehabilitation therapy; Safety monitoring; Space explorations; Human robot interaction",,,,,,,,,,,,,,,,"Chang, W.H., Kim, Y.-H., Robot-assisted therapy in stroke rehabilitation (2013) Journal of Stroke, 15 (9), pp. 174-181; Mukherjee, D., Patil, C.G., Epidemiology and the global burden of stroke (2011) World Neurosurgery, 76 (6), pp. 85-90. , Dec; (2017), www.heartandstroke.ca/-/media/pdf-files/canada/stroke-report/, Heart&Stroke Stroke report; Loureiro, R.C.V., Harwin, W.S., Nagai, K., Johnson, M., Advances in upper limb stroke rehabilitation: A technology push (2011) Medical & Biological Engineering & Computing, 49 (10), p. 1103; Tyryshkin, K., Coderre, A.M., Glasgow, J.I., Herter, T.M., Bagg, S.D., Dukelow, S.P., Scott, S.H., A robotic object hitting task to quantify sensorimotor impairments in participants with stroke (2014) Journal of NeuroEngineering and Rehabilitation, 11 (47); Kim, H., Miller, L.M., Fedulow, I., Simkins, M., Abrams, G.M., Byl, N., Rosen, J., Kinematic data analysis for post-stroke patients following bilateral versus unilateral rehabilitation with an upper limb wearable robotic system (2013) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21 (2), pp. 153-164; Emerson, I., Potgieter, J., Xu, W.L., Control implementation for an integrated robotic and virtual mirror therapy system for stroke rehabilitation (2016) IEEE International Workshop on Advanced Motion Control (AMC), pp. 479-485; Hossain, M.S., Hardy, S., Alamri, A., Alelaiwi, A., Hardy, V., Wilhelm, C., Ar-based serious game framework for post-stroke rehabilitation (2016) Multimedia Systems, 22 (6), pp. 659-674; Franken, M., Stramigioli, S., Misra, S., Secchi, C., Macchelli, A., Bilateral telemanipulation with time delays: A two-layer approach combining passivity and transparency (2011) IEEE Transactions on Robotics, 27 (4), pp. 741-756; Winters, J., Stark, L., Seif-Naraghi, A.H., An analysis of the sources of musculoskeletal system impedance (1988) Journal of Biomechanics, 21 (12), pp. 1011-1025; Haddadi, A., Razi, K., Hashtrudi-Zaad, K., Operator dynamics consideration for less conservative coupled stability condition in bilateral teleoperation (2015) IEEE/ASME Transactions on Mechatronics, 20 (5), pp. 2463-2475; Atashzar, S.F., Shahbazi, M., Tavakoli, M., Patel, R.V., A passivitybased approach for stable patient-robot interaction in haptics-enabled rehabilitation systems: Modulated time-domain passivity control (2017) IEEE Transactions on Control Systems Technology, 25 (3), pp. 991-1006; Atashzar, S.F., Shahbazi, M., Tavakoli, M., Patel, R.V., A graspbased passivity signature for haptics-enabled human-robot interaction: Application to design of a new safety mechanism for robotic rehabilitation (2017) The International Journal of Robotics Research, 36 (5-7), pp. 778-799; Dyck, M., Jazayeri, A., Tavakoli, M., Is the human operator in a teleoperation system passive (2013) World Haptics Conference, pp. 683-688; Ramos, A., Hashtrudi-Zaad, K., Estimation of upper-limb energy absorption capabilities for stable human-robot interactions (2020) IEEE Haptics Symposium, pp. 115-120",,,,"Institute of Electrical and Electronics Engineers Inc.","42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020","20 July 2020 through 24 July 2020",,162693,1557170X,9781728119908,,,"English","Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS",Conference Paper,"Final","",Scopus,2-s2.0-85091045097
"Yu M., Liu Y.-J., Zhang Y., Zhao G., Yu C., Shi Y.","56693403400;35933567100;57202223712;36026171100;36555230900;7404964393;","Interactions with reconfigurable modular robots enhance spatial reasoning performance",2020,"IEEE Transactions on Cognitive and Developmental Systems","12","2","8703129","300","310",,1,"10.1109/TCDS.2019.2914162","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069704281&doi=10.1109%2fTCDS.2019.2914162&partnerID=40&md5=3f0977eff643acc8c694f594af81e6e3","CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China; Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China; Department of Computer Science and Technology, MOE-Key Laboratory of Pervasive Computing, Tsinghua University, Beijing, 100084, China","Yu, M., CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China, Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China; Liu, Y.-J., Department of Computer Science and Technology, MOE-Key Laboratory of Pervasive Computing, Tsinghua University, Beijing, 100084, China; Zhang, Y., CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China, Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China; Zhao, G., CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China, Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China; Yu, C., Department of Computer Science and Technology, MOE-Key Laboratory of Pervasive Computing, Tsinghua University, Beijing, 100084, China; Shi, Y., Department of Computer Science and Technology, MOE-Key Laboratory of Pervasive Computing, Tsinghua University, Beijing, 100084, China","Reconfigurable modular robots (RMRobots) can change their shape and functionality (e.g., locomotion styles) to fit different environments, and have been widely investigated in applications, such as exploration and inspection. In this paper, we present a new application of RMRobots for improving human spatial ability which plays a significant role in developing an individual's performance and achievement in science, technology, engineering, and mathematics (STEM). Two user studies are conducted, and the results show that: 1) the task performance of interacting with RMRobots has a significant positive relationship with mental rotation, a widely used measure of spatial ability; and 2) interacting with RMRobots can effectively improve the performance on a task related to spatial reasoning skills according to behavioral data and electroencephalograph (EEG) indices. Our presented study broadens RMRobot research in the area of human-robot interaction. © 2016 IEEE.","Electroencephalograph (eeg); Human-robot interaction; Mental rotation; Reconfigurable modular robots (rmrobots); Spatial ability","Electroencephalography; Modular robots; Behavioral data; Locomotion styles; Mental rotation; New applications; Science , technology , engineering , and mathematics; Spatial abilities; Spatial reasoning; Task performance; Human robot interaction",,,,,"2016YFB1001200; National Natural Science Foundation of China, NSFC: 31771226, 61725204, U1736220","Manuscript received November 6, 2018; revised April 24, 2019; accepted April 27, 2019. Date of publication April 30, 2019; date of current version June 10, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant U1736220, Grant 31771226, and Grant 61725204; and in part by the National Key Research and Development Plan under Grant 2016YFB1001200. (Corresponding authors: Yong-Jin Liu; Guozhen Zhao.) M. Yu is with the CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China, and also with the Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China (e-mail: yumj14@mails.tsinghua.edu.cn).",,,,,,,,,,"Young, C.J., Levine, S.C., Mix, K.S., The connection between spatial and mathematical ability across development (2018) Front. Psychol, 9, p. 755. , Jun; Harris, J., Hirsh-Pasek, K., Newcombe, N.S., Understanding spatial transformations: Similarities and differences between mental rotation and mental folding (2013) Cogn. Process, 14 (2), pp. 105-115; Shepard, R.N., Metzler, J., Mental rotation of three-dimensional objects (1971) Science, 171 (3972), pp. 701-703; Wai, J., Lubinski, D., Benbow, C.P., Spatial ability for STEM domains: Aligning over 50 years of cumulative psychological knowledge solidifies its importance (2009) J. Educ. 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"Swofford M., Peruzzi J., Tsoi N., Thompson S., Martín-Martín R., Savarese S., Vázquez M.","57216989114;57216991169;57214458789;57216992263;36996641700;6603319910;37017787700;","Improving Social Awareness Through DANTE: Deep Affinity Network for Clustering Conversational Interactants",2020,"Proceedings of the ACM on Human-Computer Interaction","4","CSCW1","20","","",,4,"10.1145/3392824","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085662246&doi=10.1145%2f3392824&partnerID=40&md5=66a9018a82569c3002dc189f619f3538","Stanford University, Stanford, United States; Yale University, New Haven, United States","Swofford, M., Stanford University, Stanford, United States; Peruzzi, J., Stanford University, Stanford, United States; Tsoi, N., Yale University, New Haven, United States; Thompson, S., Yale University, New Haven, United States; Martín-Martín, R., Stanford University, Stanford, United States; Savarese, S., Stanford University, Stanford, United States; Vázquez, M., Yale University, New Haven, United States","We propose a data-driven approach to detect conversational groups by identifying spatial arrangements typical of these focused social encounters. Our approach uses a novel Deep Affinity Network (DANTE) to predict the likelihood that two individuals in a scene are part of the same conversational group, considering their social context. The predicted pair-wise affinities are then used in a graph clustering framework to identify both small (e.g., dyads) and large groups. The results from our evaluation on multiple, established benchmarks suggest that combining powerful deep learning methods with classical clustering techniques can improve the detection of conversational groups in comparison to prior approaches. Finally, we demonstrate the practicality of our approach in a human-robot interaction scenario. Our efforts show that our work advances group detection not only in theory, but also in practice. © 2020 ACM.","f-formations; group conversations; proxemic interactions; spatial analysis","Deep learning; Group theory; Learning systems; Clustering techniques; Data-driven approach; Graph clustering; Group detection; Learning methods; Social awareness; Social context; Spatial arrangements; Human robot interaction",,,,,"National Science Foundation, NSF: 1924802, IIS-1924802; Toyota Physical and Chemical Research Institute, TPCRI","The Toyota Research Institute provided funds to assist with this research. This work is also partially funded by the National Science Foundation under Grant No. (IIS-1924802). Any opinions, fndings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily re?ect the views of any Toyota entity nor of the National Science Foundation.","The Toyota Research Institute provided funds to assist with this research. This work is also partially funded by the National Science Foundation under Grant No. (IIS-1924802). 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"Galin R., Meshcheryakov R., Kamesheva S., Samoshina A.","57209776926;23035794100;57217104550;57217106709;","Cobots and the benefits of their implementation in intelligent manufacturing",2020,"IOP Conference Series: Materials Science and Engineering","862","3","032075","","",,4,"10.1088/1757-899X/862/3/032075","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086256862&doi=10.1088%2f1757-899X%2f862%2f3%2f032075&partnerID=40&md5=f0dd2a309e94d281d4829deb31ed97d4","V A Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, 65 Profsoyuznaya Street, Moscow, 117997, Russian Federation","Galin, R., V A Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, 65 Profsoyuznaya Street, Moscow, 117997, Russian Federation; Meshcheryakov, R., V A Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, 65 Profsoyuznaya Street, Moscow, 117997, Russian Federation; Kamesheva, S., V A Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, 65 Profsoyuznaya Street, Moscow, 117997, Russian Federation; Samoshina, A., V A Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, 65 Profsoyuznaya Street, Moscow, 117997, Russian Federation","The perspective of robotics in intelligent manufacturing becomes with Industry 4.0. Increasing the profitability of production is the main goal, which also depends on the organization of safety and efficiently human-robot interaction in a shared work place. Perspective robotic solutions integrated into intelligent manufacturing makes able to contribute an increase of automation in production with safety compliance. The portability and compactness of robots allow to optimize the production process and improve efficiency. An important stage of robotics was the ability to take robots outside of barriers and place them next to humans. This robotic solutions market is growing up every year. This indicates a high demand for the introduction of robots in intelligent manufacturing. Already today we can see how the main players in automated manufacturing have taken the first steps towards Industry 4.0. © 2020 IOP Publishing Ltd. All rights reserved.",,,,,,,"Russian Foundation for Basic Research, RFBR: 19-08-00331","The reported study was partially funded by RFBR according to the research project № 19-08-00331.",,,,,,,,,,"Capek, K., (2010) R. U. R.: Rossum's Universal Robots, p. 88; Schwab, K., (2016) The Fourth Industrial Revolution; Mihelj, M., (2019) Robotics Collaborative Robots, 12, pp. 173-187; Galin, R., Meshcheryakov, R., Review on Human-Robot Interaction during Collaboration in a Shared Workspace (2019) Interactive Collaborative Robotics. ICR 2019. Lecture Notes in Computer Science, pp. 63-74; Villani, V., Survey on human-robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266; https://ifr.org/worldrobotics, IFR. Executive Summary World Robotics 2019 Industrial Robots. Homepage last accessed 02/15/2020; Robots and Robotic Devices - Safety Requirements for Industrial Robots - Part 1, 2: Robot Systems and Integration; Endsley, M., Kaber, D., Level of automation effects on performance, situation awareness and workload in a dynamic control task (1999) Ergonomics, 42 (3), pp. 462-492; Durso, F., En route operational errors and situation awareness (1998) International Journal of Aviaton Psychology, 8 (2), pp. 177-194; Mazney, Image-guided navigation: The surgeon's perspective on performance consequences and human factors issues (2009) International Journal of Medical Robotics and Computer Assisted Surgery, 5 (3), pp. 297-308; Charalambous, G., Human-automation collaboration in manufacturing: Identifying key implementation factors (2013) ICMR2013, pp. 301-306; Haddadin, S., New insights concerning intrinsic joint elasticity for safety (2010) IEEE/RSJ International Conference on Proceedings of Intelligent Robots and Systems (IROS), pp. 2181-2187; Tsai, C.S., Hu, J.S., Tomizuka, M., Ensuring safety in human-robot coexistence environment (2014) IEEE/RSJ International Conference on Proceedings of Intelligent Robots and Systems (IROS), pp. 4191-4196; Galin, R., Meshcheryakov, R., Human-Robot Interaction Efficiency and Human-Robot Collaboration (2020) Industry 4.0 Issues & New Intelligent Control Paradigms. Studies in Systems, Decision and Control, 272, pp. 55-63; Galin, R., Meshcheryakov, R., (2019) IOP Conf. Ser.: Mater. Sci. Eng., 537; Liu, C., Tomizuka, M., Designing the Robot Behavior for Safe Human-Robot Interactions (2017) Trends in Control and Decision-Making for Human-Robot Collaboration Systems, 11, pp. 241-270; Information, M., (2017) BMW Group Harnesses Potential of Innovative Automation and Flexible Assistance Systems in Production, pp. 1-6; Anandan, T., Robots and humans: Sefaty collaboration (2017) Control Engineering, 6, pp. 46-49",,"Kovalev I.V.Voroshilova A.A.",,"Institute of Physics Publishing","2nd International Conference on Modernization, Innovations, Progress: Advanced Technologies in Material Science, Mechanical and Automation Engineering, MIP: Engineering 2020","16 April 2020 through 18 April 2020",,160471,17578981,,,,"English","IOP Conf. Ser. Mater. Sci. Eng.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85086256862
"Campbell J., Yamane K.","57033311200;35410577600;","Learning Whole-Body Human-Robot Haptic Interaction in Social Contexts",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9196933","10177","10183",,1,"10.1109/ICRA40945.2020.9196933","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092702891&doi=10.1109%2fICRA40945.2020.9196933&partnerID=40&md5=6779a532f7fdeca70708321d3bb28aa9","School of Computing Informatics and Decision Systems Engineering, Arizona State University, United States; Honda Research Institute, United States","Campbell, J., School of Computing Informatics and Decision Systems Engineering, Arizona State University, United States; Yamane, K., Honda Research Institute, United States","This paper presents a learning-from-demonstration (LfD) framework for teaching human-robot social interactions that involve whole-body haptic interaction, i.e. direct human-robot contact over the full robot body. The performance of existing LfD frameworks suffers in such interactions due to the high dimensionality and spatiotemporal sparsity of the demonstration data. We show that by leveraging this sparsity, we can reduce the data dimensionality without incurring a significant accuracy penalty, and introduce three strategies for doing so. By combining these techniques with an LfD framework for learning multimodal human-robot interactions, we can model the spatiotemporal relationship between the tactile and kinesthetic information during whole-body haptic interactions. Using a teleoperated bimanual robot equipped with 61 force sensors, we experimentally demonstrate that a model trained with 121 sample hugs from 4 participants generalizes well to unseen inputs and human partners. © 2020 IEEE.",,"Agricultural robots; Robotics; Bimanual robots; Data dimensionality; Haptic interactions; High dimensionality; Learning from demonstration; Social context; Social interactions; Spatio-temporal relationships; Social robots",,,,,,,,,,,,,,,,"Argall, B.D., Billard, A.G., A survey of tactile human. Robot interactions (2010) Robotics and Autonomous Systems, 58 (10), pp. 1159-1176; Campbell, J., Ben Amor, H., Bayesian interaction primitives: A slam approach to human-robot interaction (2017) Conference on Robot Learning, pp. 379-387; Campbell, J., Stepputtis, S., Ben Amor, H., Probabilistic multimodal modeling for human-robot interaction tasks (2019) Robotics: Science and Systems; Campbell, J., Hitzmann, A., Stepputtis, S., Ikemoto, S., Hosoda, K., Ben Amor, H., Learning interactive behaviors for musculoskeletal robots using Bayesian interaction primitives (2019) IEEE/RSJ Interna-tional Conference on Intelligent Robots and Systems; Kaplish, A., Yamane, K., Motion regargeting and control for teleoperated physical human-robot interaction (2019) IEEE-RAS International Conference on Humanoid Robots, , (in press); Wada, K., Shibata, T., Saito, T., Tanie, K., Effects of robotassisted activity for elderly people and nurses at a day service center (2004) Proceedings of the IEEE, 92 (11), pp. 1780-1788; Rus, D., Tolley, M.T., Design, fabrication and control of soft robots (2015) Nature, 521 (7553), p. 467; Shiomi, M., Nakata, A., Kanbara, M., Hagita, N., A robot that encourages self-disclosure by hug (2017) International Conference on Social Robotics, pp. 324-333. , Springer; Block, A.E., Kuchenbecker, K.J., Softness, warmth, and responsiveness improve robot hugs (2019) International Journal of Social Robotics, 11 (1), pp. 49-64; Miyashita, T., Tajika, T., Ishiguro, H., Kogure, K., Hagita, N., Haptic communication between humans and robots (2007) Robotics Research, pp. 525-536. , Springer; Noda, T., Miyashita, T., Ishiguro, H., Hagita, N., Super-fiexible skin sensors embedded on the whole body, self-organizing based on haptic interactions (2012) Human-Robot Interaction in Social Robotics, p. 183; Kaboli, M., Long, A., Cheng, G., Humanoids learn touch modalities identification via multi-modal robotic skin and robust tactile descriptors (2015) Advanced Robotics, 29 (21), pp. 1411-1425; Mittendorfer, P., Yoshida, E., Cheng, G., Realizing whole-body tactile interactions with a self-organizing, multi-modal artificial skin on a humanoid robot (2015) Advanced Robotics, 29 (1), pp. 51-67; Guadarrama-Olvera, J.R., Bergner, F., Dean, E., Cheng, G., Enhancing biped locomotion on unknown terrain using tactile feedback (2018) 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids). IEEE, pp. 1-9; Bagewadi, K., Campbell, J., Amor, H.B., (2019) Multimodal Dataset of Human-robot Hugging Interaction; Kim, J., Alspach, A., Yamane, K., 3d printed soft skin for safe human-robot interaction (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 2419-2425; Calinon, S., Evrard, P., Gribovskaya, E., Billard, A., Kheddar, A., Learning collaborative manipulation tasks by demonstration using a haptic interface (2009) 2009 International Conference on Advanced Robotics. IEEE, pp. 1-6; Peternel, L., Babific, J., Learning of compliant human. Robot interaction using full-body haptic interface (2013) Advanced Robotics, 27 (13), pp. 1003-1012; Evensen, G., The ensemble kalman filter: Theoretical formulation and practical implementation (2003) Ocean Dynamics, 53 (4), pp. 343-367; Chen, S., Cowan, C.F., Grant, P.M., Orthogonal least squares learning algorithm for radial basis function networks (1991) IEEE Transac-tions on Neural Networks, 2 (2), pp. 302-309; Peng, H., Long, F., Ding, C., Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and minredundancy (2005) IEEE Transactions on Pattern Analysis & Machine Intelligence, (8), pp. 1226-1238; Vergara, J.R., Estávez, P.A., A review of feature selection methods based on mutual information (2014) Neural Computing and Applications, 24 (1), pp. 175-186; Kraskov, A., Stögbauer, H., Grassberger, P., Estimating mutual information (2004) Physical Review e, 69 (6), p. 066138; Ross, B.C., Mutual information between discrete and continuous data sets (2014) PloS One, 9 (2), p. e87357; Kwak, N., Choi, C.-H., Input feature selection by mutual information based on parzen window (2002) IEEE Transactions on Pattern Analysis & Machine Intelligence, (12), pp. 1667-1671; Introducing the Franka Emika Robot, , https://www.franka.de/, Franka Emika GmbH; ShokacPot/ShokacCube Product Outline, , http://www.touchence.jp/en/cube/index.html, Touchence Inc; Miniature and Subminiature Orientation Sensors, , https://inertiallabs.com/3os.html; Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., Sheikh, Y., (2018) OpenPose: Realtime multi-person 2d pose estimation using part afnity fields",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85092702891
"Miao Q., Peng Y., Liu L., McDaid A., Zhang M.","55916271000;56918917700;56168963500;34973342900;57203987000;","Subject-specific compliance control of an upper-limb bilateral robotic system",2020,"Robotics and Autonomous Systems","126",,"103478","","",,6,"10.1016/j.robot.2020.103478","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079681002&doi=10.1016%2fj.robot.2020.103478&partnerID=40&md5=7ba0ab6c395b70dfaaabee8af11f9f0b","Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China; Department of Physical Education and Sports Science, Zhejiang University, Hangzhou, 310058, China; School of Big Data & Software Engineering, Chongqing University, Chongqing, 400030, China; Department of Mechanical Engineering, the University of Auckland, Auckland, 1010, New Zealand","Miao, Q., Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China; Peng, Y., Department of Physical Education and Sports Science, Zhejiang University, Hangzhou, 310058, China; Liu, L., School of Big Data & Software Engineering, Chongqing University, Chongqing, 400030, China; McDaid, A., Department of Mechanical Engineering, the University of Auckland, Auckland, 1010, New Zealand; Zhang, M., Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China","This paper proposes a new compliance control strategy on a robot-assisted bilateral upper limb rehabilitation system. The robotic compliance regulation was achieved by modifying predefined training trajectories in real time, based on measured human–robot interaction force and human users’ position within subject-specific workspace. Experimental data were collected from healthy subjects, and indicate that human users can follow predefined training trajectories well under real-time compliance variation, with the maximum normalized root mean square error (NRMSE) value no greater than 1.44%. Preliminary findings are encouraging, which demonstrates the availability of the proposed subject-specific compliance adaptation strategy, and the potential with enhanced training safety and efficacy. Future work will consider conducting a direct comparison between a bilateral upper limb rehabilitation device (BULReD)-assisted compliance training with or without subject-specific adaptation on a large sample of participants with upper limb disabilities. © 2020 Elsevier B.V.","Bilateral; Compliance control; Subject-specific workspace; Upper-limb robotics","Human robot interaction; Mean square error; Neuromuscular rehabilitation; Robotics; Adaptation strategies; Bilateral; Compliance regulations; Robot interactions; Root mean square errors; Subject-specific; Upper limbs; Upper-limb rehabilitation; Compliance control",,,,,"National Natural Science Foundation of China, NSFC: 20190004, 61903181","This work is partly supported by the National Natural Science Foundation of China (Grant No. 61903181 ).","This work is partly supported by the National Natural Science Foundation of China (Grant No. 61903181). Work on human beings that is submitted to Robotics and Autonomous Systems should comply with the principles laid down in the Declaration of Helsinki; Recommendations guiding physicians in biomedical research involving human subjects. Adopted by the 18th World Medical Assembly, Helsinki, Finland, June 1964, amended by the 29th World Medical Assembly, Tokyo, Japan, October 1975, the 35th World Medical Assembly, Venice, Italy, October 1983, and the 41st World Medical Assembly, Hong Kong, September 1989. You should include information as to whether the work has been approved by the appropriate ethical committees related to the institution(s) in which it was performed and that subjects gave informed consent to the work. The study was approved by the Southern University of Science and Technology, Human Participants Ethics Committee (20190004).",,,,,,,,,"Van Delden, A., Peper, C.L.E., Kwakkel, G., Beek, P.J., A systematic review of bilateral upper limb training devices for poststroke rehabilitation, Stroke research and treatment, Vol. 2012 (2012); Brackenridge, J., Bradnam, L.V., Lennon, S., Costi, J.J., Hobbs, D.A., A review of rehabilitation devices to promote upper limb function following stroke (2016) Neurosci. Biomed. Eng., 4 (1), pp. 25-42; Lo, H.S., Xie, S.Q., Exoskeleton robots for upper-limb rehabilitation: State of the art and future prospects (2012) Med. Eng. Phys., 34 (3), pp. 261-268; Proietti, T., Crocher, V., Roby-Brami, A., Jarrassé, N., Upper-limb robotic exoskeletons for neurorehabilitation: a review on control strategies (2016) IEEE Rev. Biomed. Eng., 9, pp. 4-14; Klamroth-Marganska, V., Three-dimensional, task-specific robot therapy of the arm after stroke: a multicentre, parallel-group randomised trial (2014) Lancet Neurol., 13 (2), pp. 159-166; Trujillo, P., Quantitative EEG for predicting upper limb motor recovery in chronic stroke robot-assisted rehabilitation (2017) IEEE Trans. Neural Syst. Rehabil. Eng., 25 (7), pp. 1058-1067; Mazzoleni, S., Tran, V.-D., Dario, P., Posteraro, F., Wrist robot-assisted rehabilitation treatment in subacute and chronic stroke patients: From distal-to-proximal motor recovery (2018) IEEE Trans. Neural Syst. Rehabil. Eng., 26 (9), pp. 1889-1896; Simkins, M., Kim, H., Abrams, G., Byl, N., Rosen, J., Robotic unilateral and bilateral upper-limb movement training for stroke survivors afflicted by chronic hemiparesis (2013), IEEE International Conference on Rehabilitation Robotics 6650506; Ang, K.K., A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke (2015) Clin. EEG Neurosci., 46 (4), pp. 310-320; Hesse, S., Schulte-Tigges, G., Konrad, M., Bardeleben, A., Werner, C., Robot-assisted arm trainer for the passive and active practice of bilateral forearm and wrist movements in hemiparetic subjects1 (2003) Arch. Phys. Med. 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Therapy, 86 (11), pp. 1466-1478; Zhang, M., Meng, W., Davies, T.C., Zhang, Y., Xie, S.Q., A robot-driven computational model for estimating passive ankle torque with subject-specific adaptation (2016) IEEE Trans. Biomed. Eng., 63 (4), pp. 814-821; Miao, Q., Zhang, M., Wang, Y., Xie, S.Q., Design and Interaction Control of a New Bilateral Upper Limb Rehabilitation Device (BULReD); Miao, Q., Zhang, M., McDaid, A., Peng, Y., Xie, S.Q., A robot-assisted bilateral upper limb training strategy with subject-specific workspace: A pilot study (2020) Robot. Auton. Syst., 124. , 02/01/2020; Miao, Q., McDaid, A., Zhang, M., Kebria, P., Li, H., A three-stage trajectory generation method for robot-assisted bilateral upper limb training with subject-specific adaptation (2018) Robot. Auton. Syst., 105, pp. 38-46; Winter, D.A., Biomechanics and motor control of human movement (1990) Physiotherapy, 74 (2), p. 94; Cao, J., Xie, S.Q., Das, R., MIMO sliding mode controller for gait exoskeleton driven by pneumatic muscles (2018) IEEE Trans. Control Syst. Technol., 26 (1), pp. 274-281; Yu, H., Huang, S., Chen, G., Thakor, N.J.M., Control design of a novel compliant actuator for rehabilitation robots, 23(8) (2013), pp. 1072-1083; Chen, G., Qi, P., Guo, Z., Yu, H., Mechanical design and evaluation of a compact portable knee–ankle–foot robot for gait rehabilitation (2016) Mech. Mach. Theory, 103, pp. 51-64. , 09/01/2016; Emken, J.L., Benitez, R., Sideris, A., Bobrow, J.E., Reinkensmeyer, D.J., Motor adaptation as a greedy optimization of error and effort (2007) J. Neurophysiol., 97 (6), pp. 3997-4006","Zhang, M.; Department of Biomedical Engineering, China; email: zhangmm@sustech.edu.cn",,,"Elsevier B.V.",,,,,09218890,,RASOE,,"English","Rob Autom Syst",Article,"Final","",Scopus,2-s2.0-85079681002
"Phillips A., Vinoo A., Fitter N.T.","57214242258;57214241854;55921515500;","May i Draw Your Attention? Initial Lessons from the Large-Scale Generative Mark Maker",2020,"IEEE Robotics and Automation Letters","5","2","8950238","690","697",,1,"10.1109/LRA.2020.2964167","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078535772&doi=10.1109%2fLRA.2020.2964167&partnerID=40&md5=f94f48309bb9c3918cfb2ca213511394","Collaborative Robotics and Intelligent Systems Institute, Oregon State University, Corvallis, OR  97331, United States","Phillips, A., Collaborative Robotics and Intelligent Systems Institute, Oregon State University, Corvallis, OR  97331, United States; Vinoo, A., Collaborative Robotics and Intelligent Systems Institute, Oregon State University, Corvallis, OR  97331, United States; Fitter, N.T., Collaborative Robotics and Intelligent Systems Institute, Oregon State University, Corvallis, OR  97331, United States","Everyday robots are emerging in contexts from household chores to entertainment, and an accompanying need arises to understand perceptions of these systems. We propose a sleek 2D hanging pen plotter as a useful tool for both furthering art and studying human responses to ambient everyday robots. Aspects of this system and the pieces it produced were grounded in ideas from conceptual and generative art. We installed our robotic system in three different spaces in the wild, recorded video footage of the installations, consulted with expert artists, and interviewed a subset of exhibit visitors. Key findings included visitor assumptions of robot responsiveness and differences in user behaviors between performing arts and engineering environments. The exploratory products of this work can benefit artists who employ generative concepts in their work, as well as roboticists who seek further understanding of human-robot interaction in the wild. © 2016 IEEE.","cognitive human-robot interaction; Entertainment robotics; human-centered robotics","Behavioral research; Man machine systems; Robotics; Cognitive human-robot interactions; Engineering environment; Entertainment robotics; Generative art; Human centered robotics; Human response; Performing arts; Robotic systems; Human robot interaction",,,,,,,,,,,,,,,,"LeWitt, S., Paragraphs on conceptual art (1967) Artforum, 5 (10), pp. 79-83; Nake, F., Paragraphs on computer art, past and present (2010) Proc. CAT London Conf., pp. 55-63; Penny, S., Art and robotics: Sixty years of situated machines (2013) AI Soc., 28 (2), pp. 147-156; Moon, H., Drawing machine (2012) The Leroy Neimen Center, , https://docs.google.com/document/d/1g-FMeDZ88smfVqHV8uzMAkh1ZlKNEXVdjzkow91zGo/edit, Chicago, IL; (2019) Robot Muralist, , https://www.sprayprinter.com/, Accessed:Aug.27,2019; McCormack, J., Gifford, T., Hutchings, P., Autonomy, authenticity, authorship and intention in computer generated art (2019) Proc. Int. Conf. Comput. Intell. Music Sound, Art Design (Part EvoStar), pp. 35-50; Nake, F., Computer art: A personal recollection (2005) Proc. ACM Conf. Creativity Cognition, pp. 54-62; Nees, G., Regency graphics and the esthetics laboratory: Picture generation by point-distinction and pseudodistance-minimizing (1990) Leonardo, 23 (4), pp. 355-361; Mohr, M., Kurtz, T., (1994) Manfred Mohr. Waser, , https://www.galeriecharlot.com/media/public/artist/manfred-mohrgaleriecharlot-1gc4gt.pdf; Reas, C., Behavioral sculpture (2001) Master of Science Thesis, Media Laboratory, Massachusetts Institute of Technology, , Cambridge, MA, USA; Levillain, F., Zibetti, E., Lefort, S., Interacting with nonanthropomorphic robotic artworks and interpreting their behaviour (2017) Int. J. Social Robot., 9 (1), pp. 141-161; Srikaew, A., Cambron, M., Northrup, S., Peters, I.I.R., Wilkes, M., Kawamura, K., Humanoid drawing robot (1998) Proc. IASTED Int.Conf. Robot. Manuf., Citeseer; Tresset, P., Leymarie, F.F., Portrait drawing by Paul the robot (2013) Comput. Graph., 37 (5), pp. 348-363; (2019) Scribit Robot Home Page, , https://scribit.design/, Accessed:Aug.27,2019; Dream, D., (2019) Deep Dream Generator, , https://deepdreamgenerator.com/, Accessed:Aug.27,2019; Colton, S., Creativity versus the perception of creativity in computational systems (2008) Proc. AAAI Spring Symp.: Creative Intell. Syst., 8, pp. 14-20; Hoenig, F., Defining computational aesthetics (2005) Computational Aesthetics in Graphics, Visualization and Imaging, pp. 13-18. , L. Neumann, M. Sbert, B. Gooch, and W. Purgathofer, Eds., Geneve, Switzerland: Eurographics Assoc; Arman, P.V., (2019) Cloudpainter, , http://www.cloudpainter.com/, Accessed:Aug.28,2019; Lehni, J., (2016) Viktor, Rita, and Hector Projects, , http://juerglehni.com/works, Accessed:Aug.27,2019; Phillips, A., Vinoo, A., Fitter, N.T., (2019) LSGMM Project Opensource Repository, , https://github.com/shareresearchteam/Drawing-Machine-LSGMM; Sassolino, A., (2019) Afasia 1 Sculpture, , https://www.arcangelosassolino.it/, Accessed:Sep.1,2019; Chen, C., Information visualization (2010) Wiley Interdisciplinary Rev.: Comput. Statist., 2 (4), pp. 387-403","Fitter, N.T.; Collaborative Robotics and Intelligent Systems Institute, United States; email: naomi.fitter@oregonstate.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85078535772
"Petrick R.P.A., Foster M.E.","36447706600;23091020100;","Knowledge engineering and planning for social human-robot interaction: A case study",2020,"Knowledge Engineering Tools and Techniques for AI Planning",,,,"261","277",,2,"10.1007/978-3-030-38561-3_14","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097144353&doi=10.1007%2f978-3-030-38561-3_14&partnerID=40&md5=b349e967e986b87ffe48000956289015","Department of Computer Science, Heriot-Watt University, Edinburgh, United Kingdom; School of Computing Science, University of Glasgow, Glasgow, United Kingdom","Petrick, R.P.A., Department of Computer Science, Heriot-Watt University, Edinburgh, United Kingdom; Foster, M.E., School of Computing Science, University of Glasgow, Glasgow, United Kingdom","The core task of automated planning is goal-directed action selection; this task is not unique to the planning community, but is also relevant to numerous other research areas within AI. One such area is interactive systems, where a fundamental component called the interaction manager selects actions in the context of conversing with humans using natural language. Although this has obvious parallels to automated planning, using a planner to address the interaction management task relies on appropriate engineering of the underlying planning domain and planning problem to capture the necessary dynamics of the world, the agents involved, their actions, and their knowledge. In this chapter, we describe work on using domain-independent automated planning for action section in social human-robot interaction, focusing on work from the JAMES (Joint Action for Multimodal Embodied Social Systems) robot bartender project. © Springer Nature Switzerland AG 2020.",,,,,,,,,,,,,,,,,,"(2020) Alexa Skills Kit Official Site, , https://developer.amazon.com/en-GB/alexa/alexa-skills-kit, accessed: 2020-02-09; Appelt, D., (1985) Planning English Sentences, , Cambridge University Press; Asher, N., Lascarides, A., (2003), Logics of Conversation. Cambridge University Press; Benotti, L., Accommodation through tacit sensing (2008) Proceedings of LONDIAL 2008, pp. 75-82. , London, United Kingdom; Brenner, M., Kruijff-Korbayova, I., A continual multiagent planning approach to situated dialogue (2008) Proceedings of LONDIAL 2008, pp. 67-74; Bui, T.H., (2006) Multimodal dialogue management - state of the art. Tech. Rep. 06-01, , University of Twente (UT), Enschede, The Netherlands; Cohen, P., Levesque, H., Rational interaction as the basis for communication (1990) Intentions in Communication, pp. 221-255. , MIT Press, Cambridge, MA; Fikes, R.E., Nilsson, N.J., STRIPS: A new approach to the application of theorem proving to problem solving (1971) Artificial Intelligence, 2, pp. 189-208; Foster, M.E., Petrick, R.P.A., Separating representation, reasoning, and implementation for interaction management. Lessons from automated planning (2017) Dialogues with Social Robots: Enablements, pp. 93-107. , https://doi.org/10.1007/978-981-10-2585-3_7, Analyses, and Evaluation, Springer Singapore, Singapore; Foster, M.E., Gaschler, A., Giuliani, M., Isard, A., Pateraki, M., Petrick, R.P.A., Two people walk into a bar: Dynamic multi-party social interaction with a robot agent (2012) Proceedings of ICMI 2012, pp. 3-10. , https://doi.org/10.1145/2388676.2388680; Foster, M.E., Gaschler, A., Giuliani, M., Automatically classifying user engagement for dynamic multi-party human-robot interaction (2017) International Journal of Social Robotics, 9 (5), pp. 659-674. , https://doi.org/10.1007/s12369-017-0414-y; Fox, M., Long, D., Magazzeni, D., Explainable planning (2017) Proceedings of the IJCAI Workshop on Explainable AI; Giuliani, M., Petrick, R.P.A., Foster, M.E., Gaschler, A., Isard, A., Pateraki, M., Sigalas, M., Comparing task-based and socially intelligent behaviour in a robot bartender (2013) Proceedings of ICMI 2013, , https://doi.org/10.1145/2522848.2522869; (2020) Dialogflow, , https://dialogflow.com/, accessed: 2020-02-09; Hovy, E., (1988) Generating natural language under pragmatic constraints, , Lawrence Erlbaum Associates, Hillsdale, NJ, USA; (2019) ICAPS Competitions, , http://www.icaps-conference.org/index.php/Main/, Competitions, accessed: 2019-08-01; Isard, A., Matheson, C., Rhetorical structure for natural language generation in dialogue (2012) Proceedings of SemDial-2012 (SeineDial), pp. 161-162; Janarthanam, S., Hastie, H., Deshmukh, A., Aylett, R., Foster, M.E., A reusable interaction management module: Use case for empathic robotic tutoring (2015) Proceedings of goDIAL 2015, , Gothenburg, Sweden; Johnston, M., Bangalore, S., Vasireddy, G., Stent, A., Ehlen, P., Walker, M., Whittaker, S., Maloor, P., MATCH: An architecture for multimodal dialogue systems (2002) Proceedings of ACL 2002, pp. 376-383. , Philadelphia, Pennsylvania, USA; Jokinen, K., McTear, M., Spoken dialogue systems (2009) Synthesis Lectures on Human Language Technologies, 2 (1), pp. 1-151; Koller, A., Stone, M., Sentence generation as planning (2007) Proceedings of ACL 2007, pp. 336-343. , Prague, Czech Republic; Larsson, S., Traum, D.R., Information state and dialogue management in the TRINDI dialogue move engine toolkit (2000) Natural Language Engineering, 6 (3-4), pp. 323-340. , https://doi.org/10.1017/S1351324900002539; Lison, P., A hybrid approach to dialogue management based on probabilistic rules (2015) Computer Speech & Language, , https://doi.org/10.1016/j.csl.2015.01.001; Loth, S., Huth, K., de Ruiter, J.P., Automatic detection of service initiation signals used in bars (2013) Frontiers in Psychology, 4 (557). , https://doi.org/10.3389/fpsyg.2013.00557; Mann, W.C., Thompson, S.A., Rhetorical structure theory: Toward a functional theory of text organization (1988) Text, 8 (3), pp. 243-281; McDermott, D., Ghallab, M., Howe, A., Knoblock, C., Ram, A., Veloso, M., Weld, D., Wilkins, D., (1998) PDDL - The Planning Domain Definition Language, , (Version 1.2). Technical Report CVC TR-98-003/DCS TR-1165, Yale Center for Computational Vision and Control; McTear, M., Callejas, Z., Griol, D., (2016) The Conversational Interface, , https://doi.org/10.1007/978-3-319-32967-3, Springer International Publishing; Olaso, J.M., Milhorat, P., Himmelsbach, J., Boudy, J., Chollet, G., Schlögl, S., Torres, M.I.T., A multi-lingual evaluation of the vAssist spoken dialog system: Comparing Disco and RavenClaw (2016) Proceedings of IWSDS 2016, , Saariselkä, Finland; Papaioannou, I., Dondrup, C., Lemon, O., Human-robot interaction requires more than slot filling - multi-threaded dialogue for collaborative tasks and social conversation (2018) Proceedings of the FAIM/ISCA Workshop on Artificial Intelligence for Multimodal Human Robot Interaction, pp. 61-64; Pateraki, M., Sigalas, M., Chliveros, G., Trahanias, P., Visual human-robot communication in social settings (2013) Proceedings of ICRA Workshop on Semantics, Identification and Control of Robot-Human-Environment Interaction; Peltason, J., Wrede, B., The curious robot as a case-study for comparing dialog systems (2011) AI Magazine, 32 (4), pp. 85-99. , https://doi.org/10.1609/aimag.v32i4.2382; Perrault, C.R., Allen, J.F., A plan-based analysis of indirect speech acts (1980) American Journal of Computational Linguistics, 6 (3-4), pp. 167-182; Petrick, R.P.A., Bacchus, F., A knowledge-based approach to planning with incomplete information and sensing (2002) Proceedings of AIPS 2002, pp. 212-221; Petrick, R.P.A., Bacchus, F., Extending the knowledge-based approach to planning with incomplete information and sensing (2004) Proceedings of ICAPS 2004, pp. 2-11; Petrick, R.P.A., Foster, M.E., Planning for social interaction in a robot bartender domain (2013) Proceedings of ICAPS 2013, , Rome, Italy; Rintanen, J., Complexity of planning with partial observability (2004) Proceedings of ICAPS 2004, pp. 345-354; Wang, Z., Lemon, O., (2013) A simple and generic belief tracking mechanism for the dialog state tracking challenge: On the believability of observed information, , Proceedings of SIGDIAL 2013; White, M., Efficient realization of coordinate structures in Combinatory Categorial Grammar (2006) Research on Language and Computation, 4 (1), pp. 39-75; Young, R.M., Moore, J.D., (1994) DPOCL: A principled approach to discourse planning, pp. 13-20. , Proceedings of INLG 2004, Kennebunkport, Maine, USA","Petrick, R.P.A.; Department of Computer Science, United Kingdom; email: R.Petrick@hw.ac.uk",,,"Springer International Publishing",,,,,,9783030385613; 9783030385606,,,"English","Knowl. Eng. Tools and Techniques for AI Plan.",Book Chapter,"Final","",Scopus,2-s2.0-85097144353
"Lee W.-Y., Jung M.","57204952765;15073123400;","Ludic-HRI: Designing playful experiences with robots",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"582","584",,5,"10.1145/3371382.3377429","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083203305&doi=10.1145%2f3371382.3377429&partnerID=40&md5=2d5188ef15e25b99354fb84f2639a9cf","Cornell University, Ithaca, NY, United States","Lee, W.-Y., Cornell University, Ithaca, NY, United States; Jung, M., Cornell University, Ithaca, NY, United States","People are inherently playful, and playfulness matters not only when engaging in actual play but also in all other activities. Based on this, I propose using a ludic design approach as a means to broaden the design space for Human-Robot Interaction (HRI). In this paper, I discuss the application of ludic design in HRI and explore how ludic activities can act as a mechanism for achieving new understandings from people during their interactions with robots. Two projects, BubbleBot and Sketching Robot, are presented as cases of designing ludic activities with robots. In my work, I have investigated how people perceived robots by applying exploratory research through design methods in creating the ludic experiences. I am continuously learning from my design and exploring potential areas for designing robots while identifying new values and goals for having robots in our lives. © 2020 ACM.","Interaction design; Ludic design; Research through design","Agricultural robots; Machine design; Man machine systems; Design method; Design spaces; Exploratory research; Human robot Interaction (HRI); Ludic design; Ludic experiences; Human robot interaction",,,,,,,,,,,,,,,,"Petersson Brooks, E., Ludic engagement designs: Creating spaces for playful learning (2013) International Conference on Universal Access in Human-Computer Interaction. Springer, pp. 241-249; Fasola, J., Mataric, M.J., Using socially assistive human-robot interaction to motivate physical exercise for older adults (2012) Proc. IEEE, 100 (8), pp. 2512-2526. , 2012; Gao, Y., Petersson Brooks, E., Designing ludic engagement in an interactive virtual dressing room system-a comparative study (2013) International Conference of Design, User Experience, and Usability. Springer, pp. 504-512; Gaver, W., Designing for homo ludens (2002) I3 Magazine, 12, pp. 2-6. , 2002 June; Gaver, W.W., Bowers, J., Boucher, A., Gellerson, H., Pennington, S., Schmidt, A., Steed, A., BrendanWalker, The drift table: Designing for ludic engagement (2004) CHI'04 Extended Abstracts on Human Factors in Computing Systems. ACM, pp. 885-900; Derek, A.H., How designers play: The ludic modalities of the creative process (2016) Design Issues, 32 (4), pp. 16-28. , 2016; Johan, H., (1950) Homo Ludens: A Study of the Play Element in Culture; Kahn, P.H., Jr., Kanda, T., Ishiguro, H., Gill, B.T., Shen, S., Ruckert, J.H., Gary, H.E., Human creativity can be facilitated through interacting with a social robot (2016) The Eleventh ACM/IEEE International Conference on Human Robot Interaction. IEEE Press, pp. 173-180; Lee, W., Tsung-Yu Hou, Y., Zaga, C., Jung, M., Design for serendipitous interaction: Bubblebot-bringing people together with bubbles (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 759-760; Liang, R., Chung, W., Kao, H., Lin, T., Intouch: Crossing social interaction with perception (2013) International Conference of Design, User Experience, and Usability. Springer, pp. 306-315; Luria, M., Zimmerman, J., Forlizzi, J., (2019) Championing Research Through Design in HRI, , arXiv preprint arXiv:1908.07572 2019; Won Park, H., Grover, I., Spaulding, S., Gomez, L., Breazeal, C., A model-free affective reinforcement learning approach to personalization of an autonomous social robot companion for early literacy education (2019) Proceedings of the AAAI Conference on Artificial Intelligence, 33, pp. 687-694; Riek, L.D., Wizard of oz studies in hri: A systematic review and new reporting guidelines (2012) Journal of Human-Robot Interaction, 1 (1), pp. 119-136. , 2012; Sengers, P., Boehner, K., David, S., Kaye, J., Reflective design (2005) Proceedings of the 4th Decennial Conference on Critical Computing: Between Sense and Sensibility. ACM, pp. 49-58; Shu, T., Ryoo, M.S., Zhu, S., (2016) Learning Social Affordance for Human-robot Interaction, , arXiv preprint arXiv:1604.036922016; Vallverdú, J., Trovato, G., Emotional affordances for human-robot interaction (2016) Adaptive Behavior, 24 (5), pp. 320-334. , 2016; Yan, H., Ang, M.H., Neow Poo, A., A survey on perception methods for human-robot interaction in social robots (2014) International Journal of Social Robotics, 6 (1), pp. 85-119. , 2014; Zimmerman, J., Forlizzi, J., Evenson, S., Research through design as a method for interaction design research in HCI (2007) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, pp. 493-502",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083203305
"Bloem A.C., Barakova E., Hootsmans I.M., Opheij L.M., Toebosch R.H.A., Kerzel M., Barros P.","57216354066;35614959800;57216335668;57216355692;57216331178;41861835400;55734354000;","Improving emotional expression recognition of robots using regions of interest from human data",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"142","144",,1,"10.1145/3371382.3378359","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083200505&doi=10.1145%2f3371382.3378359&partnerID=40&md5=39d7102b3c1d6813a260673b8e54e68b","Industrial Design Eindhoven, University of Technology, Eindhoven, Netherlands; Knowledge Technology, Departmentof Informatics, University of Hamburg, Hamburg, Germany; Cognitive Architecture for Collaborative Technologies (CONTACT), Unit Istituto Italiano di Tecnologia, Genova, Italy","Bloem, A.C., Industrial Design Eindhoven, University of Technology, Eindhoven, Netherlands; Barakova, E., Industrial Design Eindhoven, University of Technology, Eindhoven, Netherlands; Hootsmans, I.M., Industrial Design Eindhoven, University of Technology, Eindhoven, Netherlands; Opheij, L.M., Industrial Design Eindhoven, University of Technology, Eindhoven, Netherlands; Toebosch, R.H.A., Industrial Design Eindhoven, University of Technology, Eindhoven, Netherlands; Kerzel, M., Knowledge Technology, Departmentof Informatics, University of Hamburg, Hamburg, Germany; Barros, P., Cognitive Architecture for Collaborative Technologies (CONTACT), Unit Istituto Italiano di Tecnologia, Genova, Italy","This paper is the first step of an attempt to equip social robots with emotion recognition capabilities comparable to those of humans. Most of the recent deep learning solutions for facial expression recognition under-perform when deployed in Human-Robot-Interaction scenarios, although they are capable of breaking records on the most varied benchmarks on facial expression recognition. The main reason for that we believe is that they are using techniques that are developed for recognition of static pictures, while in real-life scenarios, we infer emotions from intervals of expression. Utilising on the feature of CNN to form regions of interests that are similar to human gaze patterns, we use recordings from human-gaze patterns to train such a network to infer facial emotions from 3 seconds video footage of humans expressing 6 basic emotions. © 2020 ACM.","Attention maps; Emotion recognition; HRI; Neural networks","Agricultural robots; Deep learning; Man machine systems; Social robots; Basic emotions; Emotion recognition; Emotional expressions; Facial emotions; Facial expression recognition; Human data; Regions of interest; Video footage; Face recognition",,,,,,,,,,,,,,,,"Abdi, H., RV coefficient and congruence coefficient (2007) Encyclopedia of Measurement and Statistics, 849, p. 853. , 2007; Arbib, M.A., Fellous, J., Emotions: From brain to robot (2004) Trends in Cognitive Sciences, 8 (12), pp. 554-561. , 2004; Arjovsky, M., Chintala, S., Bottou, L., Wasserstein generative adversarial networks (2017) International Conference on Machine Learning, pp. 214-223; Barakova, E.I., Gorbunov, R., Rauterberg, M., Automatic interpretation of affective facial expressions in the context of interpersonal interaction (2015) IEEE Transactions on Human-machine Systems, 45 (4), pp. 409-418. , 2015; Barros, P., Parisi, G., Stefan, W., A personalized affective memory model for improving emotion recognition (2019) International Conference on Machine Learning, pp. 485-494; Barros, P., Wermter, S., Developing crossmodal expression recognition based on a deep neural model (2016) Adaptive Behavior, 24 (5), pp. 373-396. , 2016; Breazeal, C., Edsinger, A., Fitzpatrick, P., Scassellati, B., Active vision for sociable robots (2001) IEEE Transactions on Systems, Man, and Cyberneticspart A: Systems and Humans, 31 (5), pp. 443-453. , 2001; Breazeal, C., Scassellati, B., (1999) A Context-dependent Attention System for A Social Robot. Rn, 255, p. 3. , 1999; Chen, C., Garrod, O.G.B., Zhan, J., Beskow, J., Schyns, P.G., Jack, R.E., Reverse engineering psychologically valid facial expressions of emotion into social robots (2018) 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018). IEEE, pp. 448-452; Chen, C., Hensel, L.B., Duan, Y., Ince, R.A.A., Garrod, O.G.B., Beskow, J., Jack, R.E., Schyns, P.G., Equipping social robots with culturally-sensitive facial expressions of emotion using data-driven methods (2019) 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019). IEEE, pp. 1-8; Chen, L., Zhou, M., Su, W., Wu, M., She, J., Hirota, K., Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction (2018) Information Sciences, 428, pp. 49-61. , 2018; Dhall, A., Goecke, R., Lucey, S., Gedeon, T., Collecting large, richly annotated facial-expression databases from movies (2012) IEEE Multimedia, 19 (3), pp. 34-41. , 2012; Fernandez, M.P.D., Guerrero, P.F.A., Ingen, T., Cunha, A., (2019) FERAtt: Facial Expression Recognition with Attention Net, , arXiv preprint arXiv:1902.03284 2019; Gudi, A., Emrah Tasli, H., Den Uyl, T.M., Maroulis, A., Deep learning based facs action unit occurrence and intensity estimation (2015) 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 6, pp. 1-5. , IEEE; Hazarika, D., Gorantla, S., Poria, S., Zimmermann, R., Self-attentive feature-level fusion for multimodal emotion detection (2018) 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, pp. 196-201; Hessels, R.S., Kemner, C., Van Den Boomen, C., Hooge, I.T.C., The area-of-interest problem in eyetracking research: A noise-robust solution for face and sparse stimuli (2016) Behavior Research Methods, 48 (4), pp. 1694-1712. , 2016; Huang, K., Chung-Hsien, W., Hong, Q., Su, M., Chen, Y., Speech emotion recognition using deep neural network considering verbal and nonverbal speech sounds (2019) ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 5866-5870; Kret, M.E., Roelofs, K., Stekelenburg, J.J., De Gelder, B., Emotional signals from faces, bodies and scenes influence observers' face expressions, fixations and pupil-size (2013) Frontiers in Human Neuroscience, 7. , 2013; Metta, G., Sandini, G., Vernon, D., Natale, L., Nori, F., The iCub humanoid robot: An open platform for research in embodied cognition (2008) Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems. ACM, pp. 50-56; Mollahosseini, A., Hasani, B., Mahoor, M.H., (2017) Affectnet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild, , arXiv preprint arXiv:1708.03985 2017; Ruiz-Garcia, A., Webb, N., Palade, V., Eastwood, M., Elshaw, M., Deep learning for real time facial expression recognition in social robots (2018) International Conference on Neural Information Processing. Springer, pp. 392-402; Sariyanidi, E., Gunes, H., Cavallaro, A., Automatic analysis of facial affect: A survey of registration, representation, and recognition (2014) IEEE Transactions on Pattern Analysis and Machine Intelligence, 37 (6), pp. 1113-1133. , 2014; Sciutti, A., Rea, F., Sandini, G., When you are young,(robot's) looks matter (2014) Developmental Changes in the Desired Properties of A Robot Friend. in the 23rd IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 567-573; Soleymani, M., Pantic, M., Pun, T., Multimodal emotion recognition in response to videos (2012) IEEE Transactions on Affective Computing, 3 (2), pp. 211-223. , 2012; Wang, F., Chen, H., Kong, L., Sheng, W., Real-time facial expression recognition on robot for healthcare (2018) 2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR). IEEE, pp. 402-406; Bagher Zadeh, A., Pu Liang, P., Poria, S., Cambria, E., Philippe Morency, L., Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph (2018) The 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1, pp. 2236-2246; Zafeiriou, S., Kollias, D., Nicolaou, M.A., Papaioannou, A., Zhao, G., Kotsia, I., Aff-wild: Valence and arousal 'in-thewild'challenge (2017) Computer Vision and Pattern Recognition Workshops (CVPRW). 2017 IEEE Conference On. IEEE, pp. 1980-1987; Zeiler, M.D., Fergus, R., Visualizing and understanding convolutional networks (2014) European Conference on Computer Vision. Springer, pp. 818-833",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083200505
"Honig S., Oron-Gilad T.","57201589559;16042913400;","Comparing laboratory user studies and video-enhancedweb surveys for eliciting user gestures in human-robot interactions",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"248","250",,3,"10.1145/3371382.3378325","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083166516&doi=10.1145%2f3371382.3378325&partnerID=40&md5=26512668011217823a7ee1e7fb2d47ae","Ben-Gurion University of the Negev, Be'er Sheva, Israel","Honig, S., Ben-Gurion University of the Negev, Be'er Sheva, Israel; Oron-Gilad, T., Ben-Gurion University of the Negev, Be'er Sheva, Israel","Laboratory studies are time consuming and costly. We aimed to examine whether the gestures users naturally select to communicate commands to a mobile robot during a laboratory user study can be comparable to those selected during an online video-enhanced survey. 64 participants were divided into two experimental groups according to the interaction methodology. In both conditions, participants instructed the robot to perform eight different tasks using only upper body gestures. For 7 of the 8 tasks we did not find evidence that that the physical gestures by which the participants chose to communicate with the robot depended on the interaction methodology. Our investigation, while still preliminary, may suggest that video enhanced surveys can be used for human robot interaction design and evaluation, especially in the preliminary stages of defining users' existing mental models and expectations. © 2020 ACM.","Human-robot communication; Laboratory studies; Online surveys; Scenario-based evaluation; User gestures; Video surveys","Agricultural robots; Machine design; Man machine systems; Surveys; Experimental groups; Laboratory studies; Mental model; Online video; Upper bodies; User study; Human robot interaction",,,,,"Leona M. and Harry B. Helmsley Charitable Trust; Ben-Gurion University of the Negev, BGU","The first author is supported by The Helmsley Charitable Trust through the Agricultural, Biological, Cognitive Robotics Initiative and the Marcus Endowment Fund, and by Ben-Gurion University through the High-tech, Bio-tech and Chemo-tech Scholarship.",,,,,,,,,,"Fuchs, M., Funke, F., Video Web Survey. Results of an experimental comparison with a text-based Web survey. in Challenges of a changing world (2007) Proceedings of the Fifth International Conference of the Association for Survey Computing. Association for Survey Computing Berkeley, pp. 63-80; David Kidd, C., (2003) Sociable Robots: The Role of Presence and Task in Humanrobot Interaction, , Ph.D. Dissertation. Massachusetts Institute of Technology; Peshkova, E., Hitz, M., Ahlström, D., Exploring user-defined gestures and voice commands to control an unmanned aerial vehicle (2017) Intelligent Technologies for Interactive Entertainment, pp. 47-62. , Ronald Poppe, John-Jules Meyer, Remco Veltkamp, and Mehdi Dastani (Eds.). Springer International Publishing, Cham; Powers, A., Kiesler, S., The advisor robot: Tracing people's mental model from a robot's physical attributes (2006) Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-robot Interaction (HRI '06), pp. 218-225. , https://doi.org/10.1145/1121241.1121280, ACM, New York, NY, USA; Ratchford, M., Barnhart, M., Development and validation of the technology adoption propensity (TAP) index (2012) Journal of Business Research, 65 (8), pp. 1209-1215. , https://doi.org/10.1016/j.jbusres.2011.07.001, 2012; Riek, L.D., Rabinowitch, T., Bremner, P., Pipe, A.G., Fraser, M., Robinson, P., Cooperative gestures: Effective signaling for humanoid robots (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 61-68. , https://doi.org/10.1109/HRI.2010.5453266; Rossi, A., Dautenhahn, K., Lee Koay, K., Walters, M.L., How the timing and magnitude of robot errors influence peoples' trust of robots in an emergency scenario (2017) Social Robotics, Abderrahmane Kheddar, Eiichi Yoshida, Shuzhi Sam Ge, Kenji Suzuki, John-john Cabibihan, pp. 42-52. , friederike eyssel, and hongsheng he (eds.). springer international publishing, cham; Saez-Pons, J., Lehmann, H., Syrdal, D.S., Dautenhahn, K., Development of the sociability of non-anthropomorphic robot home companions (2014) 4th International Conference on Development and Learning and on Epigenetic Robotics, pp. 111-116. , https://doi.org/10.1109/DEVLRN.2014.6982964; Sverre Syrdal, D., Dautenhahn, K., Lee Koay, K., Walters, M.L., The negative attitudes towards robots scale and reactions to robot behaviour in a live human-robot interaction study (2009) Adaptive and Emergent Behaviour and Complex Systems, , 2009; Sverre Syrdal, D., Otero, N., Dautenhahn, K., Video prototyping in human-robot interaction: Results from a qualitative study (2008) Proceedings of the 15th European Conference on Cognitive Ergonomics: The Ergonomics of Cool Interaction (ECCE '08), 29 (8). , https://doi.org/10.1145/1473018.1473055, ACM, New York, NY, USA, Article; Weiss, A., Wurhofer, D., Bernhaupt, R., Altmaninger, M., Tscheligi, M., A methodological adaptation for heuristic evaluation of HRI (2010) 19th International Symposium in Robot and Human Interactive Communication, pp. 1-6. , https://doi.org/10.1109/ROMAN.2010.5598735; Woods, S.N., Walters, M.L., Koay, K.L., Dautenhahn, K., Methodological issues in hri: A comparison of live and video-based methods in robot to human approach direction trials (2006) ROMAN 2006-The 15th IEEE International Symposium on Robot and Human Interactive Communication, pp. 51-58. , https://doi.org/10.1109/ROMAN.2006.314394; Xu, Q., Ng, J., Tan, O., Huang, Z., Tay, B., Park, T., Methodological issues in scenario-based evaluation of human-robot interaction (2015) International Journal of Social Robotics, 7 (2), pp. 279-291. , 2015",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083166516
"Velner E., Boersma P.P.G., De Graaf M.M.A.","57215835492;57207613111;55919980400;","Intonation in robot speech: Does it work the same as with people?",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"569","578",,2,"10.1145/3319502.3374801","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082002607&doi=10.1145%2f3319502.3374801&partnerID=40&md5=ef5a3f3023d8e472a45601be195718b8","University of Amsterdam, Netherlands; Utrecht University, Netherlands","Velner, E., University of Amsterdam, Netherlands; Boersma, P.P.G., University of Amsterdam, Netherlands; De Graaf, M.M.A., Utrecht University, Netherlands","Human-robot interaction (HRI) research aims to design natural interactions between humans and robots. Intonation, a social signaling function in human speech investigated thoroughly in linguistics, has not yet been studied in HRI. This study investigates the effect of robot speech intonation in four conditions (no intonation, focus intonation, end-of-utterance intonation, or combined intonation) on conversational naturalness, social engagement, and people's humanlike perception of the robot collecting objective and subjective data of participant conversations (n = 120). Our results showed that humanlike intonation partially improved subjective naturalness but not observed fluency, and that intonation partially improved social engagement but did not affect humanlike perceptions of the robot. Given that our results mainly differed from our hypotheses based on human speech intonation, we discuss the implications and provide suggestions for future research to further investigate conversational naturalness in robot speech intonation. © 2020 Association for Computing Machinery.","Conversation Analysis; Human-Robot Interaction; Linguistics; Speech Intonation; Turn Taking","Human robot interaction; Linguistics; Machine design; Man machine systems; Speech; Conversation analysis; Human robot Interaction (HRI); Human speech; Humanlike perception; Natural interactions; Social engagement; Social signaling; Turn-taking; Social robots",,,,,,,,,,,,,,,,"Aarestrup, M., Jensen, L.C., Fischer, K., The sound makes the greeting: Interpersonal functions of intonation in human-robot interaction (2015) 2015 AAAI Spring Symposium Series; (2011) Language Manual: HQ and HD Dutch, , Acapela Group, Mons, Belgium, March; Andersen, P.A., Coussoule, A.R., The perceptual world of the communication apprehensive: The effect of communication apprehension and interpersonal gaze on interpersonal perception Communication Quarterly, 28 (1), pp. 44-54. , 1980; Banse, R., Scherer, K.R., Acoustic profiles in vocal emotion expression Journal of Personality and Social Psychology, 70 (3), p. 614. , 1996; Bartneck, C., Croft, E., Kulic, D., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots International Journal of Social Robotics, 1 (1), pp. 71-81. , 2009; Baumann, S., Grice, M., The intonation of accessibility Journal of Pragmatics, 38 (10), pp. 1636-1657. , 2006; Baxter, P., Kennedy, J., Senft, E., Lemaignan, S., Belpaeme, T., From characterising three years of hri to methodology and reporting recommendations (2016) Eleventh ACM/IEEE International Conference on Human Robot Interaction, pp. 391-398; Becker-Asano, C., Kanda, T., Ishi, C., Ishiguro, H., How about laughter perceived naturalness of two laughing humanoid robots 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, pp. 1-6. , 2009; Boersma, P., Weenink, D., (2019) Praat: Doing Phonetics by Computer [Computer Program]; Botinis, A., Granström, B., Möbius, B., Developments and paradigms in intonation research Speech Communication, 33 (4), pp. 263-296. , 2001; Breazeal, C., Emotive qualities in robot speech (2001) Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, 3, pp. 1388-1394; Breazeal, C., Social interactions in hri: The robot view IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 34 (2), pp. 181-186. , 2004; Castellano, G., Pereira, A., Leite, I., Paiva, A., McOwan, P.W., Detecting user engagement with a robot companion using task and social interactionbased features (2009) Proceedings of the 2009 International Conference on Multimodal Interfaces, pp. 119-126. , ACM; Chen, A.J., (2005) On the Universal and Language-specific Perception of Paralinguistic Intonational Meaning, , PhD thesis, University of Utrecht; Ciechanowski, L., Przegalinska, A., Magnuski, M., Gloor, P., In the shades of the uncanny valley: An experimental study of human-chatbot interaction Future Generation Computer Systems, 92, pp. 539-548. , 2019; Clayman, S.E., (2012) Turn-constructional Units and the Transition-relevance Place, pp. 151-166; Crumpton, J., Bethel, C.L., A survey of using vocal prosody to convey emotion in robot speech International Journal of Social Robotics, 8 (2), pp. 271-285. , 2016; De Graaf, M.M., Allouch, S.B., The relation between people's attitude and anxiety towards robots in human-robot interaction (2013) 2013 IEEE RO-MAN, pp. 632-637; De Graaf, M.M.A., Allouch, S.B., The influence of prior expectations of a robot's lifelikeness on users' intentions to treat a zoomorphic robot as a companion International Journal of Social Robotics, 9 (1), pp. 17-32. , 2017; Eyssel, F., Kuchenbrandt, D., Bobinger, S., Effects of anticipated humanrobot interaction and predictability of robot behavior on perceptions of anthropomorphism (2011) Proceedings of the 6th International Conference on Human-Robot Interaction, pp. 61-68. , ACM; Field, A.P., (2015) Discovering Statistics Using SPSS, , SAGE, London, England; Fiske, S.T., Cuddy, A.J., Glick, P., Xu, J., A model of (often mixed) stereotype content: Competence and warmth respectively follow from perceived status and competition (2002) Journal of Personality and Social Psychology, 82 (6), pp. 878-902; Florent, G., Acapela TTS for Windows, Mac and Linux-User's Guide, 40 Ed, , Acapela Group, Mons, Belgium, June 2015; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots Robotics and Autonomous Systems, 42 (3-4), pp. 143-166. , 2003; Gårding, E. Intonation in swedish (1998) Intonation Systems: A Survey of Twenty Languages, pp. 96-111. , D. Hirst and A. Di Cristo, Eds. Cambridge University Press; Garcia, A.C., Baker Jacobs, J., The eyes of the beholder: Understanding the turn-taking system in quasi-synchronous computer-mediated communication Research on Language and Social Interaction, 32 (4), pp. 337-367. , 1999; Ghosh, S., Pherwani, J., (2015) Enabling Naturalness and Humanness in Mobile Voice Assistants; Grice, M., Baumann, S., (2007) An Introduction to Intonation-functions and Models, pp. 25-52; Heerink, M., Krose, B., Evers, V., Wielinga, B., Measuring acceptance of an assistive social robot: A suggested toolkit (2009) RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 528-533. , September; Hill, J., Ford, W.R., Farreras, I.G., Real conversations with artificial intelligence: A comparison between human-human online conversations and human-chatbot conversations (2015) Computers in Human Behavior, 49, pp. 245-250; Hirst, D., Di Cristo, A.E., (1998) Intonation Systems: A Survey of Twenty Languages, , Cambridge University Press; Ho, C.C., MacDorman, K.F., Measuring the uncanny valley effect (2017) International Journal of Social Robotics, 9 (1), pp. 129-139; Hung, V., Elvir, M., Gonzalez, A., DeMara, R., Towards a method for evaluating naturalness in conversational dialog systems (2009) 2009 IEEE International Conference on Systems, Man and Cybernetics, pp. 1236-1241. , October; Igic, A., Watson, C., Teutenberg, J., Broadbent, E., Tamagawa, R., Mac-Donald, B., Towards a flexible platform for voice accent and expression selection on a healthcare robot (2009) Proceedings of the Australasian Language Technology Association Workshop, pp. 109-113. , 2009; Kim, Y., Kwak, S.S., Kim, M.S., Am i acceptable to you effect of a robot's verbal language forms on people's social distance from robots (2013) Computers in Human Behavior, 29 (3), pp. 1091-1101; Krahmer, E., Swerts, M., On the alleged existence of contrastive accents Speech Communication, 34 (4), pp. 391-405. , 2001; Leite, I., Castellano, G., Pereira, A., Martinho, C., Paiva, A., Long-term interactions with empathic robots: Evaluating perceived support in children (2012) International Conference on Social Robotics, pp. 298-307. , Springer; Mavridis, N., A review of verbal and non-verbal human-robot interactive communication (2015) Robotics and Autonomous Systems, 63, pp. 22-35; Mazeland, H.J., Inleiding in de Conversatieanalyse, , Coutinho, 2003; Mihalicek, V., Wilson, C.E., (2011) Language Files: Materials for An Introduction to Language and Linguistics, , Columbus OH: The Ohio State University Press; Mirenda, P.L., Donnellan, A.M., Yoder, D.E., Gaze behavior: A new look at an old problem Journal of Autism and Developmental Disorders, 13 (4), pp. 397-409. , 1983; Mirnig, N., Weiss, A., Skantze, G., Al Moubayed, S., Gustafson, J., Beskow, J., Granström, B., Tscheligi, M., Face-to-face with a robot: What do we actually talk about (2013) International Journal of Humanoid Robotics, 10 (1), p. 1350011; Mori, M., MacDorman, K.F., Kageki, N., The uncanny valley [from the field] (2012) IEEE Robotics & Automation Magazine, 19 (2), pp. 98-100; Nishio, S., Ogawa, K., Kanakogi, Y., Itakura, S., Ishiguro, H., Do robot appearance and speech affect people's attitude evaluation through the ultimatum game (2018) Geminoid Studies: Science and Technologies for Humanlike Teleoperated Androids, pp. 263-277; Nomura, T., Kanda, T., Suzuki, T., Kato, K., Prediction of human behavior in human-robot interaction using psychological scales for anxiety and negative attitudes toward robots (2008) IEEE Transactions on Robotics, 24 (2), pp. 442-451; Rich, C., Ponsler, B., Holroyd, A., Sidner, C.L., Recognizing engagement in human-robot interaction (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 375-382. , IEEE; Rodriguez, I., Martínez-Otzeta, J.M., Lazkano, E., Ruiz, T., Adaptive emotional chatting behavior to increase the sociability of robots (2017) International Conference on Social Robotics, pp. 666-675. , Springer; Ronanki, S., Henter, G.E., Wu, Z., King, S., A template-based approach for speech synthesis intonation generation using lstms (2016) INTERSPEECH, pp. 2463-2467; Sacks, H., Schegloff, E.A., Jefferson, G., (1978) A Simplest Systematics for the Organization of Turn Taking for Conversation, pp. 7-55. , Academic Press; Salem, M., Eyssel, F., Rohlfing, K., Kopp, S., Joublin, F., To err is human (-like): Effects of robot gesture on perceived anthropomorphism and likability (2013) International Journal of Social Robotics, 5 (3), pp. 313-323; Schegloff, E.A., Discourse as an interactional achievement: Some uses of 'uh huh' and other things that come between sentences (1982) Analyzing Discourse: Text and Talk, 71, p. 93; Schegloff, E.A., Turn organization: One intersection of grammar and interaction (1996) Studies in Interactional Sociolinguistics, 13, pp. 52-133; Sirithunge, H.C., Muthugala, M.V.J., Jayasekara, A.B.P., Chandima, D.P., A wizard of oz study of human interest towards robot initiated human-robot interaction (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 515-521; Steinfeld, A., Fong, T., Kaber, D., Lewis, M., Scholtz, J., Schultz, A., Goodrich, M., Common metrics for human-robot interaction (2006) Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-robot Interaction, pp. 33-40. , March, ACM; Stivers, T., Enfield, N.J., Brown, P., Englert, C., Hayashi, M., Heinemann, T., Hoymann, G., Yoon, K., Universals and cultural variation in turn-taking in conversation Proceedings of the National Academy of Sciences, 106 (26), pp. 10587-10592. , 2009; Hart, T.J., Intonation in dutch (1998) Intonation Systems: A Survey of Twenty Languages, pp. 96-111. , D. Hirst and A. Di Cristo, Eds. Cambridge University Press; Tay, B.T., Low, S.C., Ko, K.H., Park, T., Types of humor that robots can play Computers in Human Behavior, 60, pp. 19-28. , 2016; Van Straten, C.L., (2016) Looks Good Sounds Nice: Intonation and Bodily Appearance in Robot-mediated Communicative Treatment for Children with Autism; Wild, B., Erb, M., Bartels, M., Are emotions contagious evoked emotions while viewing emotionally expressive faces: Quality, quantity, time course and gender differences (2001) Psychiatry Research, 102 (2), pp. 109-124",,,"ACM SIGAI;ACM SIGCHI;IEEE Robotics and Automation Society( IEEE RAS)","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158224,21672148,9781450367462,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85082002607
"Zhang K., Yu Z., Zhang D., Wang Z., Guo B.","57207105166;16178409600;57192814397;56086598300;55265626800;","RaCon: A gesture recognition approach via Doppler radar for intelligent human-robot interaction",2020,"2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020",,,"9156109","","",,1,"10.1109/PerComWorkshops48775.2020.9156109","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092003912&doi=10.1109%2fPerComWorkshops48775.2020.9156109&partnerID=40&md5=0e7470d998edfea6c36344a3aba1462f","School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China","Zhang, K., School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China; Yu, Z., School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China; Zhang, D., School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China; Wang, Z., School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China; Guo, B., School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, China","As an important entrance for human-robot interaction, the hand gesture recognition based on wireless sensor has received great attention in recent years. By recognizing fine-grained arm movements, remotely deployed collaborative robot could work more accurately to satisfy human demands. Existing approaches mostly use wearable sensors or wireless devices to recognize human movement, which is with strict position requirements. In this paper, we propose a robust gesture recognition method based on double Doppler radars. Specifically, we use two Doppler radars to collect two sources of doppler signal of a gesture. Then 6 types of gestures with different angles between people and the radar were classified by employing an improved dynamic time warping (DTW) algorithm. Furthermore, we demonstrate the practicability of the proposed method by developing a cooperative robot control system and the average recognition accuracy is 96%. © 2020 IEEE.","Doppler radar; Gesture recognition; Human-robot interaction; Signal synthesis analysis; Wireless sensing","Doppler radar; Intelligent robots; Man machine systems; Social robots; Ubiquitous computing; Wearable sensors; Collaborative robots; Cooperative robot control; Doppler signals; Dynamic time warping algorithms; Hand-gesture recognition; Human movements; Recognition accuracy; Wireless devices; Gesture recognition",,,,,,,,,,,,,,,,"Jacob, M., Li, Y.-T., Akingba, G., Wachs, J.P., Gestonurse: A robotic surgical nurse for handling surgical instruments in the operating room (2012) Journal of Robotic Surgery, 6 (1), pp. 53-63; Park, T., Lee, J., Hwang, I., Yoo, C., Nachman, L., Song, J., E-gesture: A collaborative architecture for energy-efficient gesture recognition with hand-worn sensor and mobile devices (2011) Proceedings of the 9th Acm Conference on Embedded Networked Sensor Systems, pp. 260-273; Fang, B., Lane, N.D., Zhang, M., Boran, A., Kawsar, F., Bodyscan: Enabling radio-based sensing on wearable devices for contactless ac-tivity and vital sign monitoring (2016) Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services, pp. 97-110; Gupta, S., Morris, D., Patel, S., Tan, D., Soundwave: Using the doppler effect to sense gestures (2012) Proceedings of the Sigchi Conference on Human Factors in Computing Systems, pp. 1911-1914; Ketabdar, H., Moghadam, P., Naderi, B., Roshandel, M., Magnetic signatures in air for mobile devices (2012) Proceedings of the 14th International Conference on Human-computer Interaction with Mobile Devices and Services Companion, pp. 185-188; Agrawal, S., Constandache, I., Gaonkar, S., Roy Choudhury, R., Caves, K., DeRuyter, F., Using mobile phones to write in air (2011) Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, pp. 15-28; Abdelnasser, H., Youssef, M., Harras, K.A., Wigest: A ubiquitous wifi-based gesture recognition system (2015) 2015 Ieee Conference on Computer Communications INFOCOM, pp. 1472-1480; Li, H., Yang, W., Wang, J., Xu, Y., Huang, L., Wifinger: Talk to your smart devices with finger-grained gesture (2016) Proceedings of the 2016 Acm International Joint Conference on Pervasive and Ubiquitous Computing, pp. 250-261; Al-Qaness, M., Li, F., Wiger: Wifi-based gesture recognition system (2016) Isprs International Journal of Geo-Information, 5 (6), p. 92; Peng, Z., Li, C., Muñoz-Ferreras, J.-M., Gómez-Garćia, R., An fmcw radar sensor for human gesture recognition in the presence of multiple targets (2017) 2017 First Ieee MTT-S International Microwave Bio Conference IMBIOC, pp. 1-3; Chetty, K., Chen, Q., Ritchie, M., Woodbridge, K., A low-cost through-the-wall fmcw radar for stand-off operation and activity de-tection (2017) Radar Sensor Technology Xxi 10188 1018808, International Society for Optics and Photonics; Khan, M.A.A.H., Kukkapalli, R., Waradpande, P., Kulandaivel, S., Banerjee, N., Roy, N., Robucci, R., Ram: Radar-based activity monitor (2016) Ieee Infocom 2016-The 35th Annual Ieee International Conference on Computer Communications, pp. 1-9; Li, Z., Robucci, R., Banerjee, N., Patel, C., Tongue-n-cheek: Non-contact tongue gesture recognition (2015) Proceedings of the 14th International Conference on Information Processing in Sensor Networks, pp. 95-105; Dekker, B., Jacobs, S., Kossen, A., Kruithof, M., Huizing, A., Geurts, M., Gesture recognition with a low power fmcw radar and a deep convolutional neural network (2017) 2017 European Radar Conference EURAD, pp. 163-166; Kim, Y., Toomajian, B., Hand gesture recognition using micro-doppler signatures with convolutional neural network (2016) Ieee Access, 4, pp. 7125-7130; Lou, X., Yu, Z., Wang, Z., Zhang, K., Guo, B., Gesture-radar: Enabling natural human-computer interactions with radar-based adaptive and robust arm gesture recognition (2018) 2018 Ieee International Conference on Systems, Man, and Cybernetics (SMC, pp. 4291-4297",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020","23 March 2020 through 27 March 2020",,162492,,9781728147161,,,"English","IEEE Int. Conf. Pervasive Comput. Commun. Workshops, PerCom Workshops",Conference Paper,"Final","",Scopus,2-s2.0-85092003912
"Meng J., Feng Z., Xu T.","56198200400;7403443516;56683649700;","A method of fusing gesture and speech for human-robot interaction",2020,"PervasiveHealth: Pervasive Computing Technologies for Healthcare",,,,"265","269",,2,"10.1145/3379247.3379261","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082172399&doi=10.1145%2f3379247.3379261&partnerID=40&md5=d64c7e21ac9bd9d5d2260c19eff14801","Department of Information Science and Engineering, University of Jinan, Jinan, 250022, China; Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, 250022, China","Meng, J., Department of Information Science and Engineering, University of Jinan, Jinan, 250022, China, Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, 250022, China; Feng, Z., Department of Information Science and Engineering, University of Jinan, Jinan, 250022, China, Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, 250022, China; Xu, T., Department of Information Science and Engineering, University of Jinan, Jinan, 250022, China, Shandong Provincial Key Laboratory of Network Based Intelligent Computing, Jinan, 250022, China","Aiming at the problem of using the unimodal model in human-robot interaction, in order to improve the operator's ability to control and interact with the robot and thus realize the natural and friendly interaction between human and robot, we propose a novel multimodal fusion architecture based on gesture and speech. Firstly, the convolutional neural network and Baidu API are used to recognize gesture and speech respectively. Secondly, the result of gesture prediction probability and the result of speech forward matching are normalized. Finally, the proposed multimodal fusion algorithm is used to fuse the two results, and the output results are filled into the intention slot. The operator's intention is determined by judging the fill integrity of intents the intention slot. The experimental results show that the proposed multimodal fusion architecture is superior to the unimodal model in recognition accuracy and interaction efficiency. © 2020 ACM International Conference Proceeding Series. All rights reserved.","Human-robot interaction; Intention understanding; Multimodal fusion","Convolutional neural networks; Man machine systems; Network architecture; Friendly interaction; Intention understanding; Interaction efficiency; Multi-modal fusion; Prediction probabilities; Recognition accuracy; Unimodal; Human robot interaction",,,,,,,,,,,,,,,,"Liu, H., Wang, L., Gesture recognition for human-robot collaboration: A review (2018) International Journal of Industrial Ergonomics, 68, pp. 355-367. , https://doi.org/10.1016/j.ergon.2017.02.004, (Nov. 2018); Narain, B.R., Priscilla, D., Gesture command recognition system for human machine interaction Australian Journal of Basic and Applied Sciences., 10 (5), pp. 41-45. , (Feb. 2016); Ahuja, M.K., Singh, A., Static vision based hand gesture recognition using principal component analysis (2015) 2015 IEEE 3rd International Conference on MOOCs, Innovation and Technology in Education (MITE). IEEE, pp. 402-406. , Amritsar, India; Xu, D., Wu, X., Chen, Y.L., Xu, Y., Online dynamic gesture recognition for human robot interaction Journal of Intelligent & Robotic Systems, 77, pp. 583-596. , (Apr. 2015); Gao, Q., Liu, J., Ju, Z., Li, Y., Zhang, T., Zhang, L., Static hand gesture recognition with parallel CNNs for space human-robot interaction (2017) International Conference on Intelligent Robotics and Applications, pp. 462-473. , Springer, Cham; Mi, J., Tang, S., Deng, Z., Goerner, M., Zhang, J., Object affordance based multimodal fusion for natural Human-Robot interaction Cognitive Systems Research, 54, pp. 128-137. , (May. 2019); Liu, H., Fang, T., Zhou, T.Y., Wang, Y.Q., Wang, L.H., Deep learning-based multimodal control interface for human-robot collaboration Procedia CIRP., 72, pp. 3-8. , (May. 2018); Schmidt, A., Deleforge, A., Kellermann, W., Ego-noise reduction using a motor data-guided multichannel dictionary (2016) IEEE/RSJ International Conference on Intelligent Robots & Systems, , IEEE, Daejeon, South Korea; Ye, S., Realization of speech intelligence control of artificial intelligence Technology Information., 17 (7), pp. 25-27; Trick, S., Koert, D., Peters, J., Rothkopf, C., (2019) Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction, , arXiv preprint arXiv; Rodriguez, S., Pérez, K., Quintero, C., López, J., Rojas, E., Calderón, J., Identification of multimodal human-robot interaction using combined kernels (2016) Innovations in Bio-Inspired Computing and Applications, pp. 263-273. , Springer, Cham; Liu, H., Fang, T., Zhou, T., Wang, L., Towards robust human-robot collaborative manufacturing: Multimodal fusion IEEE Access, 6, pp. 74762-74771; Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, C.A., (2016) SSD: Single Shot MultiBox Detector. in European Conference on Computer Vision, pp. 21-37. , Springer, Cham",,,,"ICST","6th International Conference on Computing and Data Engineering, ICCDE 2020 and 2020 International Conference on Artificial Intelligence in Electronics Engineering, AIEE 2020","4 January 2020 through 6 January 2020",,158230,21531633,9781450376730,,,"English","PervasiveHealth: Pervasive Computing Technol Healthc.",Conference Paper,"Final","",Scopus,2-s2.0-85082172399
"Chen L., Li M., Lai X., Hirota K., Pedrycz W.","55774663200;57204930290;7101868375;7402829702;57210775415;","CNN-based broad learning with efficient incremental reconstruction model for facial emotion recognition",2020,"IFAC-PapersOnLine","53",,,"10236","10241",,,"10.1016/j.ifacol.2020.12.2754","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107736841&doi=10.1016%2fj.ifacol.2020.12.2754&partnerID=40&md5=36f12ebbef7bea30644d954c10b40781","School of Automation, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Tokyo Institute of Technology, Yokohama, 226-8502, Japan; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB  T6R 2G7, Canada","Chen, L., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Li, M., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Lai, X., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Hirota, K., Tokyo Institute of Technology, Yokohama, 226-8502, Japan; Pedrycz, W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB  T6R 2G7, Canada","Convolutional neural network-based broad learning with efficient incremental reconstruction model (CNNBL) is proposed to recognize emotions in human-robot interaction. It aims to extract deep and abstract features from facial emotional images, and reduce the influence of the complex structure and slow network updates on facial emotion recognition in deep learning. Feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotion recognition. When the network needs to expand, the network is dynamically updated by incremental learning algorithm. We verified the experimental performance through k-fold cross validation. According to the recognition results, the accuracy on JAFFE database of our proposal is greater than that of the state of the art, such as the Local Binary Patterns with Softmax and Deep Attentive Multi-path convolutional neural network. Copyright © 2020 The Authors.","Broad Learning; Convolution Neural Networks; Emotion Recognition; Human-Robot Interaction","Convolution; Convolutional neural networks; Deep learning; Face recognition; Human robot interaction; Learning systems; Regression analysis; Speech recognition; Complex structure; Emotion recognition; Facial emotions; Incremental learning; K fold cross validations; Local binary patterns; Ridge-regression algorithm; State of the art; Learning algorithms",,,,,"National Natural Science Foundation of China, NSFC: 61603356, 61733016, 61973286; China University of Geosciences, Wuhan, CUG: 201839; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project: B17040","This work was supported by the National Natural Science Foundation of China under Grants 61973286, 61603356, and 61733016, the 111 project under Grant B17040, and the Fundamental Research Funds for the Central Universities, China University of Geosciences (No. 201839).",,,,,,,,,,"Coelho, J.P., Pinho, T.M., Boaventura-Cunha, J., A new brain emotional learning simulink toolbox for control systems design (2017) IFAC-PapersOnLine, 50 (1), pp. 16009-16014; Luo, Z.J., Chen, J.H., Takiguchi, T., Ariki, Y., Emotional voice conversion using dual supervised adversarial networks with continuous wavelet transform f0 features (2019) IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27 (10), pp. 1535-1548; Tsalamlal, M.Y., Amorim, M.A., Martin, J.C., Ammi, M., Combining facial expression and touch for perceiving emotional valence (2018) IEEE Transactions on Affective Computing, 9 (4), pp. 437-449; Chen, L.F., Wu, M., Zhou, M.T., Liu, Z.T., She, J.H., Hirota, K., Dynamic emotion understanding in human-robot interaction based on two-layer fuzzy SVR-TS model (2017) IEEE Transactions on Systems, Man, and Cybernetics: Systems; Chen, L.F., Su, W.J., Feng, Y., Wu, M., She, J.H., Hirota, K., Two-layer fuzzy multiple random forest for speech emotion recognition in human-robot interaction (2020) Information Sciences, 509, pp. 150-163; Yin, Z., Wang, Y., Liu, L., Physiological feature based emotion recognition via an ensemble deep autoencoder with parsimonious structure (2017) IFACPapersOnLine, 50 (1), pp. 6940-6945; Zhang, T., Zheng, W., Cui, Z., Zong, Y., Li, Y., Spatial-temporal recurrent neural network for emotion recognition (2019) IEEE Transactions on Cybernetics, 49 (3), pp. 839-847; Kim, L.W., DeepX: Deep Learning Accelerator for Restricted Boltzmann Machine Artificial Neural Networks (2018) IEEE Transactions on Neural Networks and Learning Systems, 29 (5), pp. 1441-1453; Wang, C.Y., Wang, J.C., Santoso, A., Chiang, C.C., Wu, C.H., Sound event recognition using auditory-receptive-field binary pattern and hierarchical-diving deep belief network (2018) IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26 (8), pp. 1336-1351; Lecun, Y., Bottou, L., Bengio, Y., Haffner, P., Gradient-based learning applied to document recognition (1998) Proceedings of the IEEE, 86 (11), pp. 2278-2324; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Computation, 9 (8), pp. 1735-1780; Chen, L.F., Zhou, M.T., Wu, M., She, J.H., Liu, Z.T., Dong, F.Y., Hirota, K., Three-layer weighted fuzzy SVR for emotional intention understanding in humanrobot interaction (2018) IEEE Transactions on Fuzzy Systems, 26 (5), pp. 2524-2538; Chen, C.L.P., Liu, Z., An effective and efficient incremental learning system without the need for deep architecture (2017) IEEE Transactions on Neural Networks and Learning Systems, 29 (1), pp. 10-24; Li, Y., Zeng, J.B., Shan, S.G., Chen, X., Occlusion Aware Facial Expression Recognition Using CNN with Attention Mechanism (2019) IEEE Transactions on Image Processing, 28 (5), pp. 2439-2450; Chen, L.F., Feng, Y., Maram, M.A., Wang, Y.W., Wu, M., Hirota, K., Pedrycz, W., Multi-SVM based Demp-ster-Shafer Theory for Gesture Intention Understanding Using Sparse Coding Feature (2019) Applied Soft Computing; Pao, Y.-H., Takefuji, Y., Functional-link net computing: Theory system architecture and functionalities (1992) Computer, 25 (5), pp. 76-79; Igelnik, B., Pao, Y.-H., Stochastic choice of basis functions in adaptive function approximation and the functional-link net (1995) IEEE Transactions on Neural Networks, 6 (6), pp. 1320-1329; Ali, H., Hariharan, M., Yaacob, S., Adom, A.H., Facial expressions recognition based on cognition and mapped binary patterns (2018) IEEE Access, 6, pp. 18795-18803; Lopes, A.T., Aguiar, E.D., Souza, S.F.D., Santos, T.O., Facial expression recognition with convolutional neural networks: Coping with few data and the training sample order (2017) Pattern Recognition, 61, pp. 610-628; Xie, S.Y., Hu, H.F., Wu, Y.B., Deep multi-path convolutional neural network joint with salient region attention for facial expression recognition (2019) Pattern Recognition, 92, pp. 177-191","Lai, X.; School of Automation, China; email: laixz@cug.edu.cn",,,"Elsevier B.V.","21st IFAC World Congress 2020","12 July 2020 through 17 July 2020",,145388,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85107736841
"Lestingi L., Askarpour M., Bersani M.M., Rossi M.","57219181705;57191286316;35730853100;7403708768;","Formal Verification of Human-Robot Interaction in Healthcare Scenarios",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12310 LNCS",,,"303","324",,4,"10.1007/978-3-030-58768-0_17","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091577651&doi=10.1007%2f978-3-030-58768-0_17&partnerID=40&md5=7d38e35ffa4c0ccb5b814b58b096023b","Politecnico di Milano, Milan, Italy; McMaster University, Hamilton, Canada","Lestingi, L., Politecnico di Milano, Milan, Italy; Askarpour, M., McMaster University, Hamilton, Canada; Bersani, M.M., Politecnico di Milano, Milan, Italy; Rossi, M., Politecnico di Milano, Milan, Italy","We present a model-driven approach for the creation of formally verified scenarios involving human-robot interaction in healthcare settings. The work offers an innovative take on the application of formal methods to human modeling, as it incorporates physiology-related aspects. The model, based on the formalism of Hybrid Automata, includes a stochastic component to capture the variability of human behavior, which makes it suitable for Statistical Model Checking. The toolchain is meant to be accessible to a wide range of professional figures. Therefore, we have laid out a user-friendly representation format for the scenario, from which the full formal model is automatically generated and verified through the Uppaal tool. The outcome is an estimation of the probability of success of the mission, based on which the user can refine the model if the result is not satisfactory. © 2020, Springer Nature Switzerland AG.","Healthcare robotics; Human-robot interaction; Model-driven approach; Service robots; Statistical Model Checking","Behavioral research; Formal verification; Health care; Man machine systems; Model checking; Stochastic models; Stochastic systems; Automatically generated; Human behaviors; Hybrid automatons; Model driven approach; Probability of success; Statistical model checking; Stochastic component; User friendly; Human robot interaction",,,,,,"Aknowledgements. The Italian Ministry of Education, University and Research is acknowledged for the support provided through the Project “Department of Excellence LIS4.0 - Lightweight and Smart Structures for Industry 4.0”.",,,,,,,,,,"(2020), https://github.com/LesLivia/hritoolchain, HRI Toolchain; Adam, C., Johal, W., Pellier, D., Fiorino, H., Pesty, S., Social human-robot interaction: a new cognitive and affective interaction-oriented architecture (2016) ICSR 2016. LNCS (LNAI), 9979, pp. 253-263. , https://doi.org/10.1007/978-3-319-47437-325, Agah, A., Cabibihan, J.-J., Howard, A.M., Salichs, M.A., He, H. (eds) Springer, Cham; Agha, G., Palmskog, K., A survey of statistical model checking (2018) ACM Trans. Model. Comput. Simul. (TOMACS), 28 (1), pp. 1-39; Alur, R., The algorithmic analysis of hybrid systems (1995) Theoret. Comput. Sci, 138 (1), pp. 3-34; Alur, R., Dill, D.L., A theory of timed automata (1994) Theoret. Comput. Sci, 126 (2), pp. 183-235; Arai, R., Schlingloff, H., Model-based performance prediction by statistical model checking an industrial case study of autonomous transport robots (2017) Concurrency, Specification and Programming; Askarpour, M., Mandrioli, D., Rossi, M., Vicentini, F., Formal model of human erroneous behavior for safety analysis in collaborative robotics (2019) Robot. Comput.-Integr. Manuf, 57, pp. 465-476; Askarpour, M., Menghi, C., Belli, G., Bersani, M., Pelliccione, P., Mind the gap: robotic mission planning meets software engineering (2020) Proceedings of the 8th International Conference on Formal Methods in Software Engineering; Bersani, M.M., Soldo, M., Menghi, C., Pelliccione, P., Rossi, M., PuRSUE-from specification of robotic environments to synthesis of controllers (2020) Formal Aspects Comput, pp. 1-41. , https://doi.org/10.1007/s00165-020-00509-0; Chettibi, T., Haddad, M., Lehtihet, H., Khalil, W., Suboptimal trajectory generation for industrial robots using trapezoidal velocity profiles (2006) IROS, pp. 729-735. , IEEE; David, A., Larsen, K.G., Legay, A., Mikučionis, M., Poulsen, D.B., Uppaal SMC tutorial (2015) Int. J. Softw. Tools Technol. Transfer, 17 (4), pp. 397-415. , https://doi.org/10.1007/s10009-014-0361-y; Zhao, X., Towards integrating formal verification of autonomous robots with battery prognostics and health management (2019) SEFM 2019. LNCS, 11724, pp. 105-124. , https://doi.org/10.1007/978-3-030-30446-16, Ölveczky, P.C., Salaün, G. (eds) Springer, Cham; Foughali, M., Ingrand, F., Seceleanu, C., Statistical model checking of complex robotic systems (2019) SPIN 2019. LNCS, 11636, pp. 114-134. , https://doi.org/10.1007/978-3-030-30923-77, Biondi, F., Given-Wilson, T., Legay, A. (eds) Springer, Cham; de Graaf, M.M., Ben Allouch, S., van Dijk, J.A., Why would I use this in my home? A model of domestic social robot acceptance (2019) Hum.-Comput. Interact, 34 (2), pp. 115-173; Hadley, M., A deterministic model of the free will phenomenon (2018) J. Conscious. Explor. Res, 9 (1), pp. 01-19; Halder, R., Proença, J., Macedo, N., Santos, A., Formal verification of ROS-based robotic applications using timed-automata (2017) International FME Workshop on Formal Methods in Software Engineering (FormaliSE), pp. 44-50. , IEEE; Herd, B., Miles, S., McBurney, P., Luck, M., Quantitative analysis of multiagent systems through statistical model checking (2015) EMAS 2015. LNCS (LNAI), 9318, pp. 109-130. , https://doi.org/10.1007/978-3-319-26184-37, Baldoni, M., Baresi, L., Dastani, M. (eds) Springer, Cham; (2010) Safety of machinery-General principles for design-Risk assessment and risk reduction, , ISO 12100: ISO; (2014) Robots and robotic devices-Safety requirements for personal care robots, , ISO 13482: ISO; (2006) Safety of machinery-Safety-related parts of control systems-Part 1: General principles for design, , ISO 13849–1: ISO; Jaber, M.Y., Givi, Z., Neumann, W.P., Incorporating human fatigue and recovery into the learning-forgetting process (2013) Appl. Math. Model, 37 (12–13), pp. 7287-7299; Jacobs, T., Virk, G.S., ISO 13482-the new safety standard for personal care robots (2014) International Symposium on Robotics, pp. 1-6. , VDE; Konz, S., Work/rest: Part ii-the scientific basis (knowledge base) for the guide 1 (2000) Ergonomics Guidelines and Problem Solving, 1 (401), p. 38; Larsen, K.G., Pettersson, P., Yi, W., Uppaal in a nutshell (1997) Int. J. Softw. Tools Technol. Transfer, 1 (1), pp. 134-152. , https://doi.org/10.1007/s100090050010; Luckcuck, M., Farrell, M., Dennis, L.A., Dixon, C., Fisher, M., Formal specification and verification of autonomous robotic systems: a survey (2019) ACM Comput. Surv. (CSUR), 52 (5), pp. 1-41; Payne, P., Lopetegui, M., Yu, S., A review of clinical workflow studies and methods (2019) Cognitive Informatics. HI, pp. 47-61. , https://doi.org/10.1007/978-3-030-16916-94, Zheng, K., Westbrook, J., Kannampallil, T.G., Patel, V.L. (eds) Springer, Cham; Porfirio, D., Sauppé, A., Albarghouthi, A., Mutlu, B., Authoring and verifying human-robot interactions (2018) Proceedings of ACM Symposium on User Interface Software and Technology, pp. 75-86; Rasouli, A., Tsotsos, J.K., Autonomous vehicles that interact with pedestrians: a survey of theory and practice (2019) IEEE Trans. Intell. Transp. Syst, 21, pp. 900-918; Sinkaram, C., Rajakumar, K., Asirvadam, V., Modeling battery management system using the lithium-ion battery (2012) International Conference on Control System, Computing and Engineering, pp. 50-55. , IEEE; Vicentini, F., Askarpour, M., Rossi, M.G., Mandrioli, D., Safety assessment of collaborative robotics through automated formal verification (2019) IEEE Trans. Rob, , https://doi.org/10.1109/TRO.2019.2937471; Webster, M., Formal verification of an autonomous personal robotic assistant (2014) AAAI Spring Symposium Series","Lestingi, L.; Politecnico di MilanoItaly; email: livia.lestingi@polimi.it","de Boer F.Cerone A.",,"Springer Science and Business Media Deutschland GmbH","18th International Conference on Software Engineering and Formal Methods, SEFM 2020","14 September 2020 through 18 September 2020",,245269,03029743,9783030587673,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85091577651
"Yılmaz E., Fal M., Acartürk C.","57218312261;57190405035;25652940800;","The role of gaze as a deictic cue in human robot interaction",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12197 LNAI",,,"466","478",,,"10.1007/978-3-030-50439-7_32","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088744941&doi=10.1007%2f978-3-030-50439-7_32&partnerID=40&md5=ab36db527830cc5c9093e3f5990e382d","Institute of Informatics, Department of Cognitive Science, Middle East Technical University, Ankara, Turkey; TAI, Turkish Aerospace Industries, Ankara, Turkey","Yılmaz, E., Institute of Informatics, Department of Cognitive Science, Middle East Technical University, Ankara, Turkey; Fal, M., Institute of Informatics, Department of Cognitive Science, Middle East Technical University, Ankara, Turkey, TAI, Turkish Aerospace Industries, Ankara, Turkey; Acartürk, C., Institute of Informatics, Department of Cognitive Science, Middle East Technical University, Ankara, Turkey","Gaze has a major role in social interaction. As a deictic reference, gaze aims at attracting visual attention of a communication partner to a referred entity in the environment. Gaze direction in natural faces is a well-investigated domain of research at behavioral and neurophysiological levels. However, our knowledge about deictic role of gaze in Human Robot Interaction is limited. The present study focuses on a comparative analysis of the deictic role of gaze direction in alternative face morphologies. We report an experimental study that investigated deictic gaze in a virtual reality environment. Human participants identified object locations by utilizing deictic gaze cues provided by avatar faces, as well as natural human faces. Our findings reveal a facilitating role in the accuracy of objection detection in favor of gaze embedded in natural faces compared to gaze embedded in synthetic avatar face morphologies. © Springer Nature Switzerland AG 2020.","Deictic gaze; Human-avatar interaction; Human-robot interaction; Joint attention; Synthetic faces","Behavioral research; Human computer interaction; Visual communication; Communication partners; Comparative analysis; Gaze direction; Human faces; Object location; Social interactions; Virtual-reality environment; Visual Attention; Human robot interaction",,,,,,,,,,,,,,,,"Perrett, D., Smith, P., Potter, D., Visual cells in the temporal cortex sensitive to face view and gaze direction (1985) Proceedings of the Royal Society of London, Series B, Biological Sciences, 223 (1232), pp. 293-317. , Royal Society; Stiefelhagen, R., Zhu, J., Head orientation and gaze direction in meetings (2002) CHI 2002 Extended Abstracts on Human Factors in Computing Systems (CHI EA 2002), pp. 858-859. , Association for Computing Machinery, New York City; Katzenmaier, M., Stiefelhagen, B., Schultz, T., Identifying the addressee in human-human-robot interactions based on head pose and speech (2004) ICMI 2004-Sixth International Conference on Multimodal Interfaces, pp. 144-151. , Association for Computing Machinery, New York City; Tan, Z., Thomsen, N.B., Duan, X., iSocioBot: a multimodal interactive social robot (2018) Int. J. Soc. Robot, 10 (1), pp. 5-19; Galantucci, B., An experimental study of the emergence of human communication systems (2005) Cogn. Sci, 29 (5), pp. 737-767; Sebanz, N., Bekkering, H., Knoblich, G., Joint action: bodies and minds moving together (2006) Trends Cogn. Sci, 10 (2), pp. 70-76; Haddadin, S., Croft, E., Erratum to: Physical human–robot interaction (2016) Springer Handbook of Robotics, p. E1. , https://doi.org/10.1007/978-3-319-32552-1_81, Siciliano, B., Khatib, O. (eds) Springer, Cham; Fang, R., Doering, M., Chai, J.Y., Embodied collaborative referring expression generation in situated human-robot interaction (2015) Proceedings of the 10th Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 271-278. , Association for Computing Machinery, New York City; Lemaignan, S., Warnier, M., Sisbot, E.A., Artificial cognition for social human–robot interaction: an implementation (2017) Artif. Intell, 247 (1), pp. 45-69; Piwek, P., Salience in the generation of multimodal referring acts (2009) Proceedings of the 2009 International Conference on Multimodal Interfaces, pp. 207-210; Admoni, H., Scassellati, B., Social eye gaze in human-robot Interaction: a review (2017) J. Hum.-Robot Interact, 6 (1), pp. 25-63; Ruhland, K., Peters, C.E., Andrist, S., A review of eye gaze in virtual agents, social robotics and HCI: behaviour generation, user interaction and perception (2015) Comput. Graph. Forum, 34 (6), pp. 299-326; Yücel, Z., Salah, A.A., Meriçli, Ç., Joint attention by gaze interpolation and saliency (2013) IEEE Trans. Cybern, 43 (3), pp. 829-842; Breazeal, C., Dautenhahn, K., Kanda, T., Social robots (2016) Springer Handbook of Robotics, pp. 1935-1961. , https://doi.org/10.1007/978-3-319-32552-1, Siciliano, B., Khatib, O. (eds), 2nd edn. Springer Handbooks, Heidelberg; Devault, D., Kariaeva, N., Kothari, A., An information-state approach to collaborative reference (2005) Proceedings of the ACL Interactive Poster and Demonstration Sessions, pp. 1-4; Mutlu, B., Shiwa, T., Kanda, T., Footing in human-robot conversations: how robots might shape participant roles using gaze cues (2009) Hum. Factors, 2 (1), pp. 61-68; Shannon, C.E., A mathematical theory of communication (1948) Bell Syst. Tech. J, 1 (1), pp. 379-423; Holmqvist, K., Andersson, R., Paradigms-visual search (2011) Eye Tracking: A Comprehensive Guide to Methods and Measures, pp. 409-413. , Oxford University Press, Oxford","Acartürk, C.; Institute of Informatics, Turkey; email: acarturk@metu.edu.tr","Schmorrow D.D.Fidopiastis C.M.",,"Springer","14th International Conference on Augmented Cognition, AC 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242429,03029743,9783030504380,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85088744941
"Persiani M., Hellström T.","57202026166;55926741800;","Intent Recognition from Speech and Plan Recognition",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12092 LNAI",,,"212","223",,1,"10.1007/978-3-030-49778-1_17","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088564119&doi=10.1007%2f978-3-030-49778-1_17&partnerID=40&md5=9d389fde6451139a15df779533d9342c","Umeå University, Umeå, Sweden","Persiani, M., Umeå University, Umeå, Sweden; Hellström, T., Umeå University, Umeå, Sweden","In multi-agent systems, the ability to infer intentions allows artificial agents to act proactively and with partial information. In this paper we propose an algorithm to infer a speakers intentions with natural language analysis combined with plan recognition. We define a Natural Language Understanding component to classify semantic roles from sentences into partially instantiated actions, that are interpreted as the intention of the speaker. These actions are grounded to arbitrary, hand-defined task domains. Intent recognition with partial actions is statistically evaluated with several planning domains. We then define a Human-Robot Interaction setting where both utterance classification and plan recognition are tested using a Pepper robot. We further address the issue of missing parameters in declared intentions and robot commands by leveraging the Principle of Rational Action, which is embedded in the plan recognition phase. © 2020, Springer Nature Switzerland AG.","Algorithms; Intent recognition; Natural Language Understanding; Plan recognition; Semantic Role Labeling","Multi agent systems; Semantics; Social robots; Artificial agents; Intent recognition; Natural language analysis; Natural language understanding; Partial information; Plan recognition; Planning domains; Utterance classification; Speech recognition",,,,,"Horizon 2020 Framework Programme, H2020: 721619",,,,,,,,,,,"Baker, C.F., Fillmore, C.J., Lowe, J.B., The berkeley framenet project (1998) Proceedings of the 17th International Conference on Computational Linguistics, 1, pp. 86-90. , Volume Association for Computational Linguistics; Bastianelli, E., Castellucci, G., Croce, D., Iocchi, L., Basili, R., Nardi, D., Huric: a human robot interaction corpus (2014) LREC, pp. 4519-4526; Bensch, S., Jevtić, A., Hellström, T., On interaction quality in human-robot interaction (2017) International Conference on Agents and Artificial Intelligence (ICAART), pp. 182-189; Breazeal, C., Aryananda, L., Recognition of affective communicative intent in robot-directed speech (2002) Auton. Robots, 12 (1), pp. 83-104. , https://doi.org/10.1023/A:1013215010749; Chakraborti, T., Kambhampati, S., Scheutz, M., Zhang, Y., (2017) Ai challenges in human-robot cognitive teaming, , arXiv preprint arXiv:1707.04775; Chen, H., Tan, H., Kuntz, A., Bansal, M., Alterovitz, R., Enabling robots to understand incomplete natural language instructions using commonsense reasoning (2019) CoRR; Demiris, Y., Prediction of intent in robotics and multi-agent systems (2007) Cogn. Process, 8 (3), pp. 151-158. , https://doi.org/10.1007/s10339-007-0168-9; He, L., Lee, K., Lewis, M., Zettlemoyer, L., Deep semantic role labeling: what works and what’s next (2017) Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 1, pp. 473-483. , Long Papers); Kelley, R., Browne, K., Wigand, L., Nicolescu, M., Hamilton, B., Nicolescu, M., Deep networks for predicting human intent with respect to objects (2012) 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 171-172. , March; McDermott, D., (1998) PDDL-the planning domain definition language; Ramírez, M., Geffner, H., Probabilistic plan recognition using off-the-shelf classical planners (2010) Twenty-Fourth AAAI Conference on Artificial Intelligence; Rani, P., Liu, C., Sarkar, N., Vanman, E., An empirical study of machine learning techniques for affect recognition in human-robot interaction (2006) Pattern Anal. Appl, 9 (1), pp. 58-69; Redmon, J., Divvala, S., Girshick, R., Farhadi, A., You only look once: unified, real-time object detection (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788; Schaefer, K.E., Chen, J.Y., Wright, J., Aksaray, D., Roy, N., Challenges with incorporating context into human-robot teaming (2017) 2017 AAAI Spring Symposium Series; Sukthankar, G., Geib, C., Bui, H.H., Pynadath, D., Goldman, R.P., (2014) Plan, Activity, and Intent Recognition: Theory and Practice, , Newnes, London; Teixeira, A., A critical analysis of speech-based interaction in healthcare robots: making a case for the increased use of speech in medical and assistive robots (2014) Speech and Automata in Health Care, pp. 1-29; Tellex, S., Understanding natural language commands for robotic navigation and mobile manipulation (2011) Twenty-Fifth AAAI Conference on Artificial Intelligence; Thomas, B.J., Jenkins, O.C., Roboframenet: verb-centric semantics for actions in robot middleware (2012) 2012 IEEE International Conference on Robotics and Automation, pp. 4750-4755. , IEEE; Tomasello, M., Carpenter, M., Call, J., Behne, T., Moll, H., Understanding and sharing intentions: the origins of cultural cognition (2005) Behav. Brain Sci, 28 (5), pp. 675-691; Wei, J.W., Zou, K., (2019) Eda: Easy data augmentation techniques for boosting performance on text classification tasks, , arXiv preprint arXiv:1901.11196","Persiani, M.; Umeå UniversitySweden; email: michelep@cs.umu.se","Demazeau Y.Holvoet T.Corchado J.M.Costantini S.",,"Springer","18th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2020","7 October 2020 through 9 October 2020",,241869,03029743,9783030497774,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85088564119
"Chan C.-C., Tsai C.-C.","57214161666;7404967092;","Collision-free speed alteration strategy for human safety in human-robot coexistence environments",2020,"IEEE Access","8",,"9072145","80120","80133",,3,"10.1109/ACCESS.2020.2988654","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084934346&doi=10.1109%2fACCESS.2020.2988654&partnerID=40&md5=0b60dedc5f0062e47a9b3752c55d55ff","Department of Electrical Engineering, National Chung Hsing University, Taichung, 40227, Taiwan","Chan, C.-C., Department of Electrical Engineering, National Chung Hsing University, Taichung, 40227, Taiwan; Tsai, C.-C., Department of Electrical Engineering, National Chung Hsing University, Taichung, 40227, Taiwan","This paper presents a novel real-time collision-free speed alteration strategy using a danger index and an elite real-coded genetic algorithm (ERGA) for environments in which humans and robots coexist or cooperate, in order to guarantee the safety of an operator who works with a collaborative robot. A danger index based on ellipsoid modeling of the operator and robot describes the degree of safety during human-robot interactions. The ERGA and a penalty function are used to solve the constrained nonlinear optimization problem to change the handling speed of the robot. Comparative simulation results show the superiority of the proposed method by comparing to two existing methods. The applicability of the proposed method is verified using two experiments involving a 6-DoF industrial manipulator with an EtherCAT network protocol, an RGB-D sensor and a real-time operation system. © 2013 IEEE.","Collaborative robot; danger index; elite real-coded genetic algorithm (ERGA); ellipsoid modeling; human-robot coexistence; human-robot cooperation; RGB-D sensor; speed alteration","Constrained optimization; Genetic algorithms; Industrial manipulators; Network protocols; Nonlinear programming; Collaborative robots; Collision-free; Comparative simulation; Constrained non-linear optimizations; Penalty function; Real coded genetic algorithm; Real time operation system; Rgb-d sensors; Human robot interaction",,,,,"Ministry of Science and Technology, Taiwan, MOST: MOST 108-2218-E-005-002","This work was supported by the Ministry of Science and Technology, Taiwan, under Grant MOST 108-2218-E-005-002.",,,,,,,,,,"Dean-Leon, E., Ramirez-Amaro, K., Bergner, F., Dianov, I., Cheng, G., Integration of robotic technologies for rapidly deployable robots (2018) IEEE Trans. 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Electron., 50 (2), pp. 385-393. , Apr; Balan, L., Bone, G., Real-time 3D collision avoidance method for safe human and robot coexistence (2006) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 276-282. , Beijing, China, Oct; Kuhn, S., Gecks, T., Henrich, D., Velocity control for safe robot guidance based on fused vision and force/torque data (2006) Proc. IEEE Int. Conf. Multisensor Fusion Integr. Intell. Syst., pp. 485-492. , Berlin, Germany, Sep; Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots (1986) Int. J. Robot. Res., 5 (1), pp. 90-98. , Sep; Parigi Polverini, M., Zanchettin, A.M., Rocco, P., Real-time collision avoidance in human-robot interaction based on kinetostatic safety field (2014) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 4136-4141. , Chicago, IL, USA, Sep; Kulic, D., Croft, E., Safe planning for human-robot interaction (2004) Proc. IEEE Int. Conf. On Robot. 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Electron., 58 (10), pp. 4813-4821. , Oct; Orfanus, D., Indergaard, R., Prytz, G., Wien, T., Ethercat-based platform for distributed control in high-performance industrial applications (2013) Proc. IEEE 18th Conf. Emerg. Technol. Factory Autom. (ETFA), pp. 1-8. , Cagliari, Italy, Sep; Kant, K., Zucker, S., Planning collision-free trajectories in timevarying environments: A two-level hierarchy (1998) Proc. IEEE Int. Conf. Robot. Autom., pp. 1644-1649. , Philadelphia, PA, USA, Apr; Hwang, K.S., Tsai, M.D., On-line collision-avoidance trajectory planning of two planar robots based on geometric modeling (1999) J. Inf. Sci. Eng., 15, pp. 131-152. , Jan; Ratiu, M., Adriana Prichici, M., Industrial robot trajectory optimization-A review (2017) MATEC Web Conf., 126. , Oct; Sidobre, D., Desormeaux, K., Smooth cubic polynomial trajectories for human-robot interactions (2019) J. Intell. 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Eng., 17 (1), pp. 463-474. , Jan; Rojas, R.A., Garcia, M.A.R., Wehrle, E., Vidoni, R., A variational approach to minimum-jerk trajectories for psychological safety in collaborative assembly stations (2019) IEEE Robot. Autom. Lett., 4 (2), pp. 823-829. , Apr; Kim, E., Kirschner, R., Yamada, Y., Okamoto, S., Estimating probability of human hand intrusion for speed and separation monitoring using interference theory (2020) Robot. Comput.-Integr. Manuf., 61. , Feb; Lasota, P.A., Rossano, G.F., Shah, J.A., Toward safe closeproximity human-robot interaction with standard industrial robots (2014) Proc. IEEE Int. Conf. Autom. Sci. Eng. (CASE), pp. 339-344. , Taipei, Taiwan, Aug; Kanazawa, A., Kinugawa, J., Kosuge, K., Adaptive motion planning for a collaborative robot based on prediction uncertainty to enhance human safety and work efficiency (2019) IEEE Trans. Robot., 35 (4), pp. 817-832. , Aug; Kruse, T., Pandey, A.K., Alami, R., Kirsch, A., Human-aware robot navigation: A survey (2013) Rob. Auton. Syst., 61 (12), pp. 1726-1743. , Dec; Bhagya, S.M., Samarakoon, P., Viraj, M.A., Muthugala, J., Buddhika, A.G., Jayasekara, P., Elara, M.R., An exploratory study on proxemics preferences of humans in accordance with attributes of service robots (2019) Proc. 28th IEEE Int. Conf. Robot Human Interact. Commun. (RO-MAN), pp. 1-7. , New Delhi, India, Oct; Tong, J., Zhou, J., Liu, L., Pan, Z., Yan, H., Scanning 3D full human bodies using kinects (2012) IEEE Trans. Vis. Comput. Graphics, 18 (4), pp. 643-650. , Apr; Moon, S., Park, Y., Ko, D.W., Suh, I.H., Multiple kinect sensor fusion for human skeleton tracking using Kalman filtering (2016) Int. J. Adv. Robot. Syst., 13 (2), p. 65. , Mar; Wu, C.-J., Quigley, A., Harris-Birtill, D., Out of sight: A toolkit for tracking occluded human joint positions (2017) Pers. Ubiquitous Comput., 21 (1), pp. 125-135. , Feb; Kumar, S., Arora, S., Sahin, F., Speed and separation monitoring using on-robot Time-of-Flight laser-ranging sensor arrays (2019) Proc. IEEE 15th Int. Conf. Autom. Sci. Eng. (CASE), pp. 1684-1691. , Vancouver, BC, Canada, Aug; Darwish, W., Li, W., Tang, S., Wu, B., Chen, W., A robust calibration method for consumer grade RGB-D sensors for precise indoor reconstruction (2019) IEEE Access, 7, pp. 8824-8833; Zhou, X., Li, J., Chen, S., Cai, H., Liu, H., Multiple perspective object tracking via context-aware correlation filter (2018) IEEE Access, 6, pp. 43262-43273. , Aug","Tsai, C.-C.; Department of Electrical Engineering, Taiwan; email: cctsai@nchu.edu.tw",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85084934346
"Irimia E., Mitrofan M., Mititelu V.B.","34976661200;55801333200;14042533100;","Evaluating the Wordnet and CoRoLa-based word embedding vectors for Romanian as resources in the task of microworlds lexicon expansion",2020,"Proceedings of the 10th Global WordNet Conference",,,,"176","184",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082487100&partnerID=40&md5=b3cfeb759b7e4b9d5648ce973ef9f0e8","RACAI, Bucharest, Romania","Irimia, E., RACAI, Bucharest, Romania; Mitrofan, M., RACAI, Bucharest, Romania; Mititelu, V.B., RACAI, Bucharest, Romania","Within a larger frame of facilitating human-robot interaction, we present here the creation of a core vocabulary to be learned by a robot. It is extracted from two tokenised and lemmatized scenarios pertaining to two imagined microworlds in which the robot is supposed to play an assistive role. We also evaluate two resources for their utility for expanding this vocabulary so as to better cope with the robot's communication needs. The language under study is Romanian and the resources used are the Romanian wordnet and word embedding vectors extracted from the large representative corpus of contemporary Romanian, CoRoLa. The evaluation is made for two situations: one in which the words are not semantically disambiguated before expanding the lexicon, and another one in which they are disambiguated with senses from the Romanian wordnet. The appropriateness of each resource is discussed. © Copyright by Oficyna Wydawnicza Politechniki Wrocławskiej, Wrocław 2019.",,"Embeddings; Ontology; Assistive; Lexicon expansions; Microworlds; Romanians; Wordnet; Human robot interaction",,,,,,,,,,,,,,,,"Agirre, E., Alfonseca, E., Hall, K., Kravalova, J., Paşca, M., Soroa, A., A study on similarity and relatedness using distributional and wordnet-based approaches (2009) Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 19-27. , 2009; Bakarov, A., (2018) A Survey of Word Embeddings Evaluation Methods, , arXiv preprint 2018; Mititelu, V.B., Increasing the effectiveness of the Romanian Wordnet in NLP applications (2013) CSJM, 21 (3), pp. 320-331; Mititelu, V.B., Tufiş, D., Irimia, E., The reference corpus of the contemporary Romanian language (CoROLA) (2018) Proceedings of LREC 2018, pp. 1178-1185. , Japan; Mititelu, V.B., Mitrofan, M., Leaving no stone unturned when identifying and classifying verbal multiword expressions in the Romanian wordNet (2019) Proceedings of the 10th Global WordNet Conference, , Wroclaw, Poland, this; Cruse, A.D., (1986) Lexical Semantics, , Cambridge, CUP; Fellbaum, C., (1998) WordNet: An Electronic Lexical Database, , ed. Cambridge, MA: MIT Press; Grice, P., Logic and conversation (1975) Syntax and Semantics. 3: Speech Acts, p. 4158. , Cole, Morgan, J. New York: Academic Press; Ion, R., Teprolin: An extensible, online text preprocessing platform for Romanian (2018) Proceedings of the 13th International Conference Linguistic Resources and Tools for Processing the Romanian Language, , Iaşi, 22-23 November 2018; Kleiber, G., Tamba, I., L'hyponymie revisité: Inclusion et hiérarchie (1990) Langages, No. 98: L'Hyponymie et l'Hyperonymie, , Larousse; Leeuwenberg, A., Vela, M., Dehdari, J., van Genabith, J., A minimally supervised approach for synonym extraction with word embeddings (2016) The Prague Bulletin of Mathematical Linguistics, pp. 111-142. , 2016; Luong, H.P., Gauch, S., Speretta, M., Enriching concept descriptions in an amphibian ontology with vocabulary extracted from wordnet (2009) 22nd IEEE International Symposium on Computer-Based Medical Systems, pp. 1-6. , 2009; Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J., Distributed representations of words and phrases and their compositionality (2013) Advances in Neural Information Processing Systems; Miller, G.A., WordNet: A lexical database for English (1995) Communications of the ACM, 38 (11), pp. 39-41; Niles, I., Pease, A., Towards a standard upper ontology (2001) Proceedings of the 2nd International Conference on Formal Ontology in Information Systems, pp. 2-9; Ono, M., Miwa, M., Sasaki, Y., Word embedding-based antonym detection using thesauri and distributional information (2015) Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 984-989; Pandey, A.K., Gelin, R., A mass-produced sociable humanoid robot: Pepper: The first machine of its kind (2018) IEEE Robotics Automation Magazine, pp. 40-48; Paiş, V., Tufiş, D., Computing distributed representations of words using the CoRoLa corpus (2018) Proceedings of the Romanian Academy Series A - Mathematics Physics Technical Sciences Information Science, 19, pp. 185-191; Pennington, J., Socher, R., Manning, C., Glove: Global vectors for word representation (2014) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532-1543. , 2014; Rizov, B., Dimitrova, T., Mititelu, V.B., Hydra for web: A multilingual wordNet viewer (2015) Proceedings of the 11th International Conference “Linguistic Resources and Tools for Processing the Romanian Language, pp. 19-30. , Iaşi, Romania; Snow, R., Prakash, S., Jurafsky, D., Ng, A.Y., Learning to merge word senses (2007) Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 1005-1014; Speretta, M., Gauch, S., Using text mining to enrich the vocabulary of domain ontologies (2008) Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology-Volume 01, pp. 549-552. , 2008; Stan, A., Yamagishi, J., King, S., Aylett, M., The Romanian Speech Synthesis (RSS) corpus: Building a high quality HMM-based speech synthesis system using a high sampling rate (2011) Speech Communication, 53, pp. 442-450; Tufiş, D., Cristea, D., Stamou, S., BalkaNet: Aims, methods, results and perspectives. A general overview (2004) Journal on Information Science and Technology, Special Issue on BalkaNet, Romanian Academy, 7 (1-2), pp. 7-41; Tufiş, D., Ştefanescu, D., Experiments with a differential semantics annotation for WordNet 3.0 (2012) Decision Support Systems, 53 (4), pp. 695-703; Tufiş, D., Mititelu, V.B., Stefanescu, D., Ion, R., The Romanian wordnet in a nutshell (2013) Language Resources and Evaluation, 47, pp. 1305-1314; Tufiş, D., Mititelu, V.B., The lexical ontology for Romaniany (2015) Language Production, Cognition, and the Lexicon, pp. 491-504. , Nuria Gala, Reinhard Rapp and Gemma Bel-Enguix eds; Tufiş, D., Mititelu, V.B., Irimia, E., Mitrofan, M., Ion, R., Cioroiu, G., Making pepper understand and respond in Romanian (2019) Proceedings of CSCS22, , press; Vossen, P., (2002) EuroWordNet General Document Version 3, , Report, University of Amsterdam",,"Fellbaum C.Vossen P.Rudnicka E.Maziarz M.Piasecki M.",,"Oficyna Wydawnicza Politechniki Wroclawskiej","10th Global WordNet Conference, GWC 2019","23 July 2019 through 27 July 2019",,158320,,9788374931083,,,"English","Proc. Glob. WordNet Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85082487100
"Silva V., Soares F., Esteves J.S., Pereira A.P., Matos D.","57191270842;7006778773;24775662800;55984040600;55348411400;","Adequacy of Game Scenarios for an Object with Playware Technology to Promote Emotion Recognition in Children with Autism Spectrum Disorder",2020,"Advances in Intelligent Systems and Computing","1026",,,"293","298",,,"10.1007/978-3-030-27928-8_44","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071457173&doi=10.1007%2f978-3-030-27928-8_44&partnerID=40&md5=739e41d44ace72f09341f7608dee1afc","Algoritmi Research Centre, University of Minho, Guimarães, Portugal; Department of Industrial Electronics, University of Minho, Guimarães, Portugal; Research Centre on Education, Institute of Education, University of Minho, Braga, Portugal; ID+ – IPCA-ESD, Campus do IPCA, Barcelos, Portugal","Silva, V., Algoritmi Research Centre, University of Minho, Guimarães, Portugal; Soares, F., Algoritmi Research Centre, University of Minho, Guimarães, Portugal, Department of Industrial Electronics, University of Minho, Guimarães, Portugal; Esteves, J.S., Algoritmi Research Centre, University of Minho, Guimarães, Portugal, Department of Industrial Electronics, University of Minho, Guimarães, Portugal; Pereira, A.P., Research Centre on Education, Institute of Education, University of Minho, Braga, Portugal; Matos, D., ID+ – IPCA-ESD, Campus do IPCA, Barcelos, Portugal","Every human interaction is emphasized by the participants’ emotional states and manifestations. Being unable to understand the emotional state of the others is a significant deficit for anyone. This is the situation children with Autism Spectrum Disorder (ASD) usually find themselves in. Thus, researchers are exploring different technologies, such as Objects based on Playware Technology and social robots, in order to improve the emotion recognition skills of individuals with ASD. Following this trend, an improved Object of Playware Technology to be used in a storytelling activity as an add-on to the human-robot interaction with children with ASD, is proposed in the present work. Additionally, an evaluation of the images developed for the game scenario was conducted through an online questionnaire. Overall, the storytelling scenarios were well interpreted by the participants, matching a mean accuracy of 90%. © Springer Nature Switzerland AG 2020.","Human-robot interaction; Playware; Tangible interfaces","Diseases; Man machine systems; Speech recognition; Systems engineering; Children with autisms; Emotion recognition; Emotional state; Human interactions; Objects-based; Online questionnaire; Playware; Tangible interfaces; Human robot interaction",,,,,"FundaÃ§Ã£o para a CiÃªncia e a Tecnologia, FCT; Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção, INCT-EN: SFRH/BD/SFRH/BD/133314/2017, UID/CEC/00319/2019","The authors would like to express their acknowledgments to COMPETE: POCI-01-0145-FEDER-007043 and FCT ? Funda??o para a Ci?ncia e Tecnologia within the Project Scope: UID/CEC/00319/2019. Vinicius Silva also thanks FCT for the PhD scholarship SFRH/BD/SFRH/BD/133314/2017. The authors thank the teachers and students of the Elementary School of Gualtar (EB1/JI Gualtar) in Braga for the participation.","Acknowledgments. The authors would like to express their acknowledgments to COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019. Vinicius Silva also thanks FCT for the PhD scholarship SFRH/BD/SFRH/BD/133314/2017. The authors thank the teachers and students of the Elementary School of Gualtar (EB1/JI Gualtar) in Braga for the participation.",,,,,,,,,"Lund, H.H., Dam Pedersen, M., Beck, R., Modular robotic tiles: Experiments for children with autism (2009) Artif. Life Robot., 13 (2), pp. 394-400; Barajas, A.O., Al Osman, H., Shirmohammadi, S., A serious game for children with autism spectrum disorder as a tool for play therapy (2017) 2017 IEEE 5Th International Conference on Serious Games and Applications for Health, Segah 2017; Tapus, A., Children with autism social engagement in interaction with Nao, an imitative robot (2012) Interact. Stud., 13, pp. 315-347; Kim, E., Paul, R., Shic, F., Scassellati, B., Bridging the research gap: Making HRI useful to individuals with autism (2012) J. Hum.-Robot Interact., 1, pp. 26-54; Costa, S., (2014) Affective Robotics for Socio-Emotional Skills Development in Children with Autism Spectrum Disorders, , University of Minho; Silva, V., Soares, F., Esteves, J.S., Pereira, A.P., PlayCube: Designing a tangible playware module for human-robot interaction (2019) Advances in Intelligent Systems and Computing; Silva, V., Soares, F., Esteves, J.S., Pereira, A.P., Building a hybrid approach for a game scenario using a tangible interface in human robot interaction (2018) Göbel, S. Et Al, , (eds), Springer, Cham","Silva, V.; Algoritmi Research Centre, Portugal; email: a65312@alunos.uminho.pt","Ahram T.Karwowski W.Pickl S.Taiar R.",,"Springer Verlag","2nd International Conference on Human Systems Engineering and Design: Future Trends and Applications, IHSED 2019","16 September 2019 through 18 September 2019",,230069,21945357,9783030279271,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85071457173
"Santos N.B., Bavaresco R.S., Tavares J.E.R., Ramos G.D.O., Barbosa J.L.V.","57225018313;57213193262;57220937413;57201406798;8375489800;","A systematic mapping study of robotics in human care",2021,"Robotics and Autonomous Systems","144",,"103833","","",,3,"10.1016/j.robot.2021.103833","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108970520&doi=10.1016%2fj.robot.2021.103833&partnerID=40&md5=64d815af046315dc535024cef48b7f8e","Applied Computing Graduate Program (PPGCA), University of Vale do Rio dos Sinos (UNISINOS), Av. Unisinos 950, São Leopoldo, Rio Grande do Sul, Brazil","Santos, N.B., Applied Computing Graduate Program (PPGCA), University of Vale do Rio dos Sinos (UNISINOS), Av. Unisinos 950, São Leopoldo, Rio Grande do Sul, Brazil; Bavaresco, R.S., Applied Computing Graduate Program (PPGCA), University of Vale do Rio dos Sinos (UNISINOS), Av. Unisinos 950, São Leopoldo, Rio Grande do Sul, Brazil; Tavares, J.E.R., Applied Computing Graduate Program (PPGCA), University of Vale do Rio dos Sinos (UNISINOS), Av. Unisinos 950, São Leopoldo, Rio Grande do Sul, Brazil; Ramos, G.D.O., Applied Computing Graduate Program (PPGCA), University of Vale do Rio dos Sinos (UNISINOS), Av. Unisinos 950, São Leopoldo, Rio Grande do Sul, Brazil; Barbosa, J.L.V., Applied Computing Graduate Program (PPGCA), University of Vale do Rio dos Sinos (UNISINOS), Av. Unisinos 950, São Leopoldo, Rio Grande do Sul, Brazil","The World Health Organization (WHO) reported that more than 1 billion people live with some form of disability. Moreover, the number of elderly is increasing in recent years. According to the United Nations (UN), in 2050, there will be 2.1 billion people above 60 years of age worldwide. Many of these people live alone in their homes or clinics and rely on some kind of help to fulfill their specific needs. In this context, emerging opportunities for the application of robotics to support ubiquitous healthcare may reflect in reducing medical costs and increasing the convenience of patients and people in general. This paper presents a systematic mapping study to identify the application of service robots in the assistance of human care, focusing on the employment of computational technologies and unexplored research gaps in the literature. The study conducted searches in eight scientific repositories in the area of service robots through a systematic filtering process to remove bias. Afterward, the filtering process allowed to reduce from an initial sample of 9372 to 69 studies. As a result, these studies were reviewed entirely, analyzed, and categorized to answer six research questions. In addition, the study proposed four taxonomies illustrating the state-of-the-art of robotics in human care. The results highlight therapy and entertainment as the most common categories of the usage of robotics in human care. The most widely-used technologies to integrate with smart environments are smartphone sensors, smart device integration, wearables, and cloud services. The most frequently used mean of human–robot interaction is verbal communication, which is useful to help the elderly, children, and people with a mental health disorder. The most commonly cited diseases were cognition impairment, autism spectrum disorder, and motor impairment. Finally, we observed a trend in the growth of the use of service robots to improve the intelligence of the environment supporting human care. The scientific contribution of this article are four taxonomies that classify and group caregiver robots according to the application, integration with a smart environment, human–robot interaction, and target audience. This study also allowed the learning of 11 lessons on methodological and technological aspects based on the profound research performed. © 2021 Elsevier B.V.","Assistive robot; Caregiver robot; Cognitive impairment; Human caregiver; Service robot; Systematic mapping study","Agricultural robots; Mapping; Medical robotics; mHealth; Mobile robots; Robotics; Taxonomies; Wearable technology; Autism spectrum disorders; Computational technology; Scientific contributions; Systematic mapping studies; Technological aspects; Ubiquitous health care; Verbal communications; World Health Organization; Social robots",,,,,"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, FAPERGS","The authors would like to thank the Foundation for Research Support of the State of Rio Grande do Sul (FAPERGS), Brazil , the Coordination for Improvement of Higher Education Personnel - Brazil (CAPES) - Financing Code 001, the National Council for Scientific and Technological Development (CNPq), Brazil and the University of Vale do Rio dos Sinos (Unisinos), Brazil for the support of the development of this work. The authors especially acknowledge the support of the Applied Computing Graduate Program (PPGCA), Brazil and the Mobile Computing Laboratory (Mobilab) of Unisinos, Brazil .",,,,,,,,,,"The MIT Press Reader, The czech play that gave us the word ‘robot’ (2019), http://thereader.mitpress.mit.edu/origin-word-robot-rur/, (Accessed 8 April 2021); Frontiers in Robotics and IA, Toward growing robots: A historical evolution from cellular to plant-inspired robotics (2018), https://www.frontiersin.org/articles/10.3389/frobt.2018.00016/full, URL: (Accessed 7 February 2021); The Robot Report, 10 robotics companies to watch in 2019 (2019), https://www.therobotreport.com/robotics-companies-to-watch-in-2019/, (Accessed 5 March 2021); Mitsubishi Eletric, Industrial robots-MELFA (2020), https://www.mitsubishielectric.com/fa/products/rbt/robot/index.html, (Accessed 3 April 2021); ABB Robotics, ABB robotics (2020), https://new.abb.com/products/robotics, URL: (Accessed 11 April 2021); Onrom Robotics, Robotics (2020), https://automation.omron.com/en/us/products/category/robotics, (Accessed 3 April 2021); iRobot, Roomba robot vacuums (2020), https://www.irobot.com/roomba, (Accessed 19 March 2021); Maytronics, Residential robotic pool cleaners (2020), https://maytronicsus.com/products/residential-pool-cleaning-robots/, (Accessed 11 April 2021); UB Robot, UBTech (2020), https://www.ubtrobot.com/?ls=pt, (Accessed 29 March 2021); Garcia, E., Jimenez, M.A., De Santos, P.G., Armada, M., The evolution of robotics research (2007) IEEE Robot. 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Rehabil., 4, p. 5; Graf, B., Hans, M., Schraft, R.D., Care-o-bot II - development of a next generation robotic home assistant (2004) Auton. Robots, 16 (2), pp. 193-205; Bien, Z., Chung, M.J., Chang, P.H., Kwon, D.S., Kim, D.J., Han, J.S., Kim, J.H., Lim, S.C., Integration of a rehabilitation robotic system (KARES II) with human-friendly man-machine interaction units (2004) Auton. Robots, 16 (2), pp. 165-191; Dario, P., Guglielmelli, E., Laschi, C., Teti, G., MOVAID: a personal robot in everyday life of disabled and elderly people (2021) Technol. Disabil., 10 (2), pp. 77-93; Wagner, A., Barbosa, J.L.V., Barbosa, D.N.F., A model for profile management applied to ubiquitous learning environments (2014) Expert Syst. Appl., 41 (4, Part 2), pp. 2023-2034; Barbosa, J., ao Tavares, J., Cardoso, I., Alves, B., Martini, B., TrailCare: An indoor and outdoor context-aware system to assist wheelchair users (2018) Int. J. Hum.-Comput. Stud., 116, pp. 1-14; Tavares, J., Barbosa, J., Cardoso, I., Costa, C., Yamin, A., Real, R., Hefestos: an intelligent system applied to ubiquitous accessibility (2016) Univers. Access Inf. Soc., 15 (4), pp. 589-607","Santos, N.B.; Applied Computing Graduate Program (PPGCA), Av. Unisinos 950, São Leopoldo, Brazil; email: nicas16@edu.unisinos.br",,,"Elsevier B.V.",,,,,09218890,,RASOE,,"English","Rob Autom Syst",Article,"Final","",Scopus,2-s2.0-85108970520
"Mogster J., Stoelen M., Kyrkjebo E.","57262066200;36175043400;15925412000;","Estimating robot body torque for two-handed cooperative physical human-robot interaction",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"279","284",,,"10.1109/RO-MAN50785.2021.9515470","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115099840&doi=10.1109%2fRO-MAN50785.2021.9515470&partnerID=40&md5=6f937e53a7528d0d7951fe70175c6b03","Western Norway University of Applied Sciences, Department of Computer Science, Electrical Engineering and Mathematical Sciences, Faculty of Engineering and Science, Forde, Norway; Centre for Robotics and Neural Systems (CRNS), University of Plymouth, Plymouth, United Kingdom","Mogster, J., Western Norway University of Applied Sciences, Department of Computer Science, Electrical Engineering and Mathematical Sciences, Faculty of Engineering and Science, Forde, Norway; Stoelen, M., Western Norway University of Applied Sciences, Department of Computer Science, Electrical Engineering and Mathematical Sciences, Faculty of Engineering and Science, Forde, Norway, Centre for Robotics and Neural Systems (CRNS), University of Plymouth, Plymouth, United Kingdom; Kyrkjebo, E., Western Norway University of Applied Sciences, Department of Computer Science, Electrical Engineering and Mathematical Sciences, Faculty of Engineering and Science, Forde, Norway","Cooperative physical Human Robot Interaction (pHRI) aims to combine the best of human problem-solving skills with the strength, speed and accuracy of a robot. When humans and robots physically interact, there will be interaction Forces and Torques (F/Ts) that must be within safe limits to avoid threats to human life and unacceptable damage to equipment. When measured, these F/Ts can be limited by safety rated emergency stops, and one can design a compliant robot behavior to reduce interaction F/Ts, and avoid unnecessary emergency stops. Several recent collaborative robots offer measurements of interaction F/Ts by utilizing torque sensors in joints or observers for joint torque, and the classical end-effector F/T sensor can provide measurements of interaction at the working end of a robot. The end-effector wrench can be calculated from joint torques if and only if there is no interaction on the robot body. Typically, safety limits are evaluated around a single point of contact - on the end-effector or elsewhere on the robot body. This approach fails when a human uses both hands to interact with a robot, e.g. when hand guiding or otherwise cooperating with the robot placing one hand on the robot end-effector and the other hand on the robot elbow. Having two points of contact that are evaluated as one will limit the allowed F/Ts of the sum of the contacts rather than individually. In this paper, we introduce the body torque as the interaction on the body that is not the result of interactions on the end-effector. We then use this body torque, which is a more accurate representation of the forces applied to the robot body, to limit the body interaction F/Ts to ensure safe human-robot interaction. Furthermore, the body torque can be used to design null-space compliance for a redundant robot. Distinguishing body torque is a step towards safe cooperative pHRI, where body torque, unknown end-effector loads, and end-effector interaction F/T are all important measurements for safety, control and compliance. © 2021 IEEE.",,"Agricultural robots; Compliance control; End effectors; Machine design; Man machine systems; Torque; Collaborative robots; Human problem solving; Interaction forces; Measurements of; Physical human-robot interactions; Physical humanrobot interaction (phri); Robot end effector; Safe human-robot interaction; Social robots",,,,,"Norges Forskningsråd: 28077","*This work was funded by the Research Council of Norway through grant number 28077. 1Department of Computer science, Electrical engineering and Mathematical sciences, Faculty of Engineering and Science, Western Norway University of Applied Sciences, Førde, Norway jmog@hvl.no 2Centre for Robotics and Neural Systems (CRNS), University of Plymouth, Plymouth, UK Fig. 1. Example of hand guiding a robot using both hands. Joint torque measurements alone will not allow distinguishing interaction on end-effector from interaction on the body, nor will this scenario allow calculating the end-effector wrench from joint torque.",,,,,,,,,,"Haddadin, S., Croft, E., (2016) Physical Human-Robot Interaction, pp. 1835-1874. , ch. 69. Springer International Publishing; Robots and robotic devices-collaborative robots (2016) ISO, , https://www.iso.org/standard/62996.html, ISO/TS 15066, Tech. Rep. 15066; Robots and robotic devices-vocabulary (2012) ISO, , https://www.iso.org/standard/55890.html, ISO 8373, Tech. Rep. 8373; (2017) KUKA Sunrise. OS 1. 14 Operating and Programming Instructions for System Integrators, KUKA, , Aug; Haddadin, S., (2014) Towards Safe Robots, , Springer Berlin Heidelberg; Sadeghian, H., Ficuciello, F., Villani, L., Keshmiri, M., Global impedance control of dual-arm manipulation for safe interaction (2012) IFAC Proceedings Volumes, 45 (22), pp. 767-772; Sadeghian, H., Villani, L., Keshmiri, M., Siciliano, B., Task-space control of robot manipulators with null-space compliance (2014) IEEE Transactions on Robotics, 30 (2), pp. 493-506. , apr; Vigoriti, F., Ruggiero, F., Lippiello, V., Villani, L., Control of redundant robot arms with null-space compliance and singularity-free orientation representation (2018) Robotics and Autonomous Systems, 100, pp. 186-193. , feb; Likar, N., Zlajpah, L., External joint torque-based estimation of contact information (2014) International Journal of Advanced Robotic Systems, 11 (7), p. 107. , jan; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) IEEE Transactions on Robotics, 31 (4), pp. 850-863. , aug; Ficuciello, F., Villani, L., Siciliano, B., Impedance control of redundant manipulators for safe humanrobot collaboration (2016) Acta Polytechnica Hungarica, 13 (1). , jan; Spong, M.W., Hutchinson, S., Vidyasagar, M., (2005) Robot Modeling and Control, , John Wiley & Sons Inc; Corke, P., (2017) Robotics, Vision and Control, , Springer International Publishing; Hogan, N., Impedance control: An approach to manipulation (1984) 1984 American Control Conference. IEEE, , https://ieeexplore.ieee.org/document/4788393/citations#citations, jul",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115099840
"Lima M.R., Wairagkar M., Natarajan N., Vaitheswaran S., Vaidyanathan R.","57219193243;56938674800;57222631333;26667288600;35570517600;","Robotic Telemedicine for Mental Health: A Multimodal Approach to Improve Human-Robot Engagement",2021,"Frontiers in Robotics and AI","8",,"618866","","",,3,"10.3389/frobt.2021.618866","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103546097&doi=10.3389%2ffrobt.2021.618866&partnerID=40&md5=2a49dceb971bb6fc55e946d3e581ac0f","Department of Mechanical Engineering, Imperial College London, UK Dementia Research Institute—Care Research and Technology Centre, London, United Kingdom; Schizophrenia Research Foundation (SCARF), Chennai, India","Lima, M.R., Department of Mechanical Engineering, Imperial College London, UK Dementia Research Institute—Care Research and Technology Centre, London, United Kingdom; Wairagkar, M., Department of Mechanical Engineering, Imperial College London, UK Dementia Research Institute—Care Research and Technology Centre, London, United Kingdom; Natarajan, N., Schizophrenia Research Foundation (SCARF), Chennai, India; Vaitheswaran, S., Schizophrenia Research Foundation (SCARF), Chennai, India; Vaidyanathan, R., Department of Mechanical Engineering, Imperial College London, UK Dementia Research Institute—Care Research and Technology Centre, London, United Kingdom","COVID-19 has severely impacted mental health in vulnerable demographics, in particular older adults, who face unprecedented isolation. Consequences, while globally severe, are acutely pronounced in low- and middle-income countries (LMICs) confronting pronounced gaps in resources and clinician accessibility. Social robots are well-recognized for their potential to support mental health, yet user compliance (i.e., trust) demands seamless affective human-robot interactions; natural ‘human-like’ conversations are required in simple, inexpensive, deployable platforms. We present the design, development, and pilot testing of a multimodal robotic framework fusing verbal (contextual speech) and nonverbal (facial expressions) social cues, aimed to improve engagement in human-robot interaction and ultimately facilitate mental health telemedicine during and beyond the COVID-19 pandemic. We report the design optimization of a hybrid face robot, which combines digital facial expressions based on mathematical affect space mapping with static 3D facial features. We further introduce a contextual virtual assistant with integrated cloud-based AI coupled to the robot’s facial representation of emotions, such that the robot adapts its emotional response to users’ speech in real-time. Experiments with healthy participants demonstrate emotion recognition exceeding 90% for happy, tired, sad, angry, surprised and stern/disgusted robotic emotions. When separated, stern and disgusted are occasionally transposed (70%+ accuracy overall) but are easily distinguishable from other emotions. A qualitative user experience analysis indicates overall enthusiastic and engaging reception to human-robot multimodal interaction with the new framework. The robot has been modified to enable clinical telemedicine for cognitive engagement with older adults and people with dementia (PwD) in LMICs. The mechanically simple and low-cost social robot has been deployed in pilot tests to support older individuals and PwD at the Schizophrenia Research Foundation (SCARF) in Chennai, India. A procedure for deployment addressing challenges in cultural acceptance, end-user acclimatization and resource allocation is further introduced. Results indicate strong promise to stimulate human-robot psychosocial interaction through the hybrid-face robotic system. Future work is targeting deployment for telemedicine to mitigate the mental health impact of COVID-19 on older adults and PwD in both LMICs and higher income regions. © Copyright © 2021 Lima, Wairagkar, Natarajan, Vaitheswaran and Vaidyanathan.","COVID-19; dementia; human-robot interaction; intelligent virtual assistant; low- and middle-income countries; multimodal interaction; social robots; telemedicine",,,,,,"UKDRI-7003; CMC Microsystems, CMC; Global Challenges Research Fund, GCRF; Imperial College London; Christian Medical College, Vellore, CMC","This work was supported by Research England Grand Challenge Research Fund (GCRF) through Imperial College London, the Dementia Research Institute Care Research & Technology Center (DRI-CRT) (UKDRI-7003), the Imperial College London’s President’s PhD Scholarships, and the United Kingdom MedTech SuperConnector (MTSC).","We thank all participants involved in the evaluation of human-robot interactions, both at Imperial College London and SCARF India. 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ACM, 9, pp. 36-45; Wolters, M.K., Kelly, F., Kilgour, J., Kilgour, J., (2016) Designing a spoken dialogue interface to an intelligent cognitive assistant for people with dementia, , Newbury Park, CA, SAGE Publications Ltd","Vaidyanathan, R.; Department of Mechanical Engineering, United Kingdom; email: r.vaidyanathan@imperial.ac.uk",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85103546097
"Hamanaka F., Yoshida S., Yamazoe H.","57345688400;57345225900;8517824300;","Impression Evaluation of Interaction among Robots based on Collision Avoidance Behaviors",2021,"HAI 2021 - Proceedings of the 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction",,,,"272","276",,,"10.1145/3472307.3484659","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119328273&doi=10.1145%2f3472307.3484659&partnerID=40&md5=a73a8a732f2b53c8f3cbcfdf960ac825","School of Engineering, University of Hyogo, Japan; Graduate School of Engineering, University of Hyogo, Japan","Hamanaka, F., School of Engineering, University of Hyogo, Japan; Yoshida, S., Graduate School of Engineering, University of Hyogo, Japan; Yamazoe, H., School of Engineering, University of Hyogo, Japan","For achieving effective human-robot interaction, robot behaviors must be designed that humans can easily predict and understand. Humanoid robots are equipped with such mechanisms as facial expressions or gestures for communicating their internal states. Although humans can easily grasp such facial expressions or gestures, unfortunately, non-humanoid robots lack such mechanisms. In this research, we employ robot behaviors and their interactions with the other robots for expressing intentions and emotions considering situations where multiple non-humanoid robots collaboratively perform tasks. Here, we focus on such interaction behaviors among robots as collision avoidance and evaluate the effects of the differences of such behaviors on human impressions of the robots. Our experimental results of the subjective evaluations showed that the human impressions of the robots were affected by not only their speed but also by their interactions, including the collision avoidance behaviors of the robots. © 2021 Owner/Author.","collision avoidance behaviors; human impressions; interaction among robots; non-humanoid robots","Behavioral research; Collision avoidance; Human robot interaction; Avoidance behaviour; Collision avoidance behavior; Collisions avoidance; Facial Expressions; Facial gestures; Human impressions; Humanoid robot; Interaction among robot; Non-humanoid robot; Robot behavior; Anthropomorphic robots",,,,,"Japan Society for the Promotion of Science, KAKEN: JP18K11383, JP21K11968","This research was supported in part by JSPS KAKENHI Grant Numbers JP18K11383 and JP21K11968.",,,,,,,,,,"Agnihotri, A., Chan, A., Hedaoo, S., Knight, H., Distinguishing Robot Personality from Motion (2020) Companion of the 2020 ACM/ IEEE International Conference on Human-Robot Interaction, pp. 87-89. , https://doi.org/10.1145/3371382.3378389; Bradley, M.M., Lang, P.J., 1994. 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The research domain and scale construction of adjective-pairs in a semantic differential method in Japan (1985) The Japanese Journal of Educational Psychology, 33 (3), pp. 253-260. , https://doi.org/10.5926/jjep1953.33.3_253(inJapanese; Kim, L.H., Follmer, S., 2017. Ubiswarm: Ubiquitous robotic interfaces and investigation of abstract motion as a display (2017) Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, 1 (3), pp. 1-20. , https://doi.org/10.1145/3130931; Li, J., Cuadra, A., Mok, B., Reeves, B., Kaye, J., Ju, W., 2019. Communicating dominance in a nonanthropomorphic robot using locomotion (2019) ACM Transactions on Human-Robot Interaction (THRI, 8 (1), pp. 1-14. , https://doi.org/10.1145/3310357; Papenmeier, F., Uhrig, M., Kirsch, A., 2019. Human understanding of robot motion: The role of velocity and orientation (2019) International Journal of Social Robotics, 11 (1), pp. 75-88. , https://doi.org/10.1007/s12369-018-0493-4; Saerbeck, M., Bartneck, C., Perception of affect elicited by robot motion (2010) 2010 5th ACM/ IEEE International Conference on Human-Robot Interaction (HRI, pp. 53-60. , https://doi.org/10.1109/HRI.2010.5453269; Schulz, T., Holthaus, P., Amirabdollahian, F., Lee Koay, K., Torresen, J., Herstad, J., Differences of Human Perceptions of a Robot Moving using Linear or Slow in, Slow out Velocity Profiles When Performing a Cleaning Task (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN, pp. 1-8. , https://doi.org/10.1109/ro-man46459.2019.8956355; Song, S., Yamada, S., Designing expressive lights and in-situ motions for robots to express emotions (2018) Proceedings of the 6th International Conference on Human-Agent Interaction, pp. 222-228. , https://doi.org/10.1145/3284432.3284458; Tamura, Y., Dai Le, P., Hitomi, K., Chandrasiri, N.P., Bando, T., Yamashita, A., Asama, H., Development of pedestrian behavior model taking account of intention (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 382-387. , https://doi.org/10.1109/IROS.2012.6385599; Yoshioka, G., Sakamoto, T., Takeuchi, Y., Inferring affective states from observation of a robot's simple movements (2015) 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN, pp. 185-190. , https://doi.org/10.1109/ROMAN.2015.7333582",,,"CyberAgent","Association for Computing Machinery, Inc","9th International User Modeling, Adaptation and Personalization Human-Agent Interaction, HAI 2021","9 November 2021 through 11 November 2021",,173654,,9781450386203,,,"English","HAI - Proc. Int. User Model., Adapt. Pers. Human-Agent Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85119328273
"Liberman-Pincu E., David A., Sarne-Fleischmann V., Edan Y., Oron-Gilad T.","57221689984;57354238300;55078262300;7004434501;16042913400;","Comply with me: Using design manipulations to affect human–robot interaction in a covid-19 officer robot use case",2021,"Multimodal Technologies and Interaction","5","11","71","","",,,"10.3390/mti5110071","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120007952&doi=10.3390%2fmti5110071&partnerID=40&md5=9cc2022294dec01915b013beeea2b31a","Department of Industrial Engineering and Management, Agricultural, Biological, Cognitive Robotics Initiative, Ben-Gurion University of the Negev, Be’er Sheva, 8410501, Israel","Liberman-Pincu, E., Department of Industrial Engineering and Management, Agricultural, Biological, Cognitive Robotics Initiative, Ben-Gurion University of the Negev, Be’er Sheva, 8410501, Israel; David, A., Department of Industrial Engineering and Management, Agricultural, Biological, Cognitive Robotics Initiative, Ben-Gurion University of the Negev, Be’er Sheva, 8410501, Israel; Sarne-Fleischmann, V., Department of Industrial Engineering and Management, Agricultural, Biological, Cognitive Robotics Initiative, Ben-Gurion University of the Negev, Be’er Sheva, 8410501, Israel; Edan, Y., Department of Industrial Engineering and Management, Agricultural, Biological, Cognitive Robotics Initiative, Ben-Gurion University of the Negev, Be’er Sheva, 8410501, Israel; Oron-Gilad, T., Department of Industrial Engineering and Management, Agricultural, Biological, Cognitive Robotics Initiative, Ben-Gurion University of the Negev, Be’er Sheva, 8410501, Israel","This study examines the effect of a COVID-19 Officer Robot (COR) on passersby compliance and the effects of its minor design manipulations on human–robot interaction. A robotic application was developed to ensure participants entering a public building comply with COVID restrictions of a green pass and wearing a face mask. The participants’ attitudes toward the robot and their perception of its authoritativeness were explored with video and questionnaires data. Thematic analysis was used to define unique behaviors related to human–COR interaction. Direct and extended interactions with minor design manipulation of the COR were evaluated in a public scenario setting. The results demonstrate that even minor design manipulations may influence users’ attitudes toward officer robots. The outcomes of this research can support manufacturers in rapidly adjusting their robots to new domains and tasks and guide future designs of authoritative socially assistive robots (SARs). © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Compliance; COVID-19; Design manipulation; Human–robot interaction; Robot authority; Service robot; Social assistive robot; Thematic analysis; Visual qualities",,,,,,"3-15625; Leona M. and Harry B. Helmsley Charitable Trust; Ben-Gurion University of the Negev, BGU","Funding: This research was supported by Ministry of Innovation, Science and Technology, Israel (grant 3-15625), and by Ben-Gurion University of the Negev through the Helmsley Charitable Trust, the Agricultural, Biological and Cognitive Robotics Initiative, the W. Gunther Plaut Chair in Manufacturing Engineering and by the George Shrut Chair in Human performance Management.",,,,,,,,,,"Marques, L., Murphy, R., Althoefer, K., Tadokoro, S., Laschi, C., Robotics Responds to the COVID-19 Outbreak [From the Guest Editors] (2021) IEEE Robot. Autom. Mag, 28, pp. 16-17. , [CrossRef]; (2020) Executive Summary World Robotics 2020 Service Robots, , https://ifr.org/img/worldrobotics/Executive_Summary_WR_2020_Service:Robots.pdf, IFR. (accessed on 5 May 2021); Tavakoli, M., Carriere, J., Torabi, A., Robotics, smart wearable technologies, and autonomous intelligent systems for healthcare during the COVID-19 pandemic: An analysis of the state of the art and future vision (2020) Adv. Intell. 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Stud, 52, pp. 1215-1224. , [CrossRef] [PubMed]; Lund, S., Madgavkar, A., Manyika, J., Smit, S., Ellingrud, K., Meaney, M., Robinson, O., The Future of Work after COVID-19, , https://www.mckinsey.com/featured-insights/future-of-work/the-future-of-work-after-covid-19, McKinsey Global Institute: 2021. 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Geriatr, 54, pp. 121-126. , [CrossRef] [PubMed]; Björklund, L., (2018) Knock on Wood: Does Material Choice Change the Social Perception of Robots?, , Master’s Thesis, KTH, Stockholm, Sweden; Liberman-Pincu, E., Permet, Y., Oron-Gilad, T., (2021) Judging a SAR by Its Cover; The Effect of Body Structure, Outline, and Color on Users’ Perception, , https://www.researchgate.net/publication/354947812_Judging_a_socially_assistive_robot_SAR_by_its_cover_The_effect_of_body_structure_outline_and_color_on_users\T1\textquoteright_perception, (accessed on 16 November 2021); Liberman-Pincu, E., Van Grondelle, E.D., Oron-Gilad, T., Designing robots with relationships in mind: Suggesting two models of human-socially assistive robot (SAR) relationship Proceedings of the HRI ’21 Companion: Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, pp. 555-558. , Boulder, CO, USA, 8–11 March 2021; Liberman-Pincu, E., Oron-Gilad, T., Impacting the perception of socially assistive robots-evaluating the effect of visual qualities among children Proceedings of the 2021 30th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 612-618. , Vancouver, BC, Canada, 8–12 August 2021; Trovato, G., Do, M., Terlemez, Ö., Mandery, C., Ishii, H., Bianchi-Berthouze, N., Asfour, T., Takanishi, A., Is hugging a robot weird? 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Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, pp. 168-176. , Boulder, CO, USA, 8–11 March 2021; https://corona.health.gov.il/en/directives/green-pass-info/, Available online: (accessed on 16 November 2021); Fan, T., Chen, Z., Zhao, X., Liang, J., Shen, C., Manocha, D., Pan, J., Zhang, W., (2020) Autonomous social distancing in urban environments using a quadruped robot, , arXiv arXiv:2008.08889; Sathyamoorthy, A.J., Patel, U., Savle, Y.A., Paul, M., Manocha, D., (2020) COVID-robot: Monitoring social distancing constraints in crowded scenarios, , arXiv arXiv:2008.06585; Tsai, Y.L., (2021) One Service Robot’s Exploration of Entertainment Methods during COVID-19, , Master’s Thesis, Oregon State University, Corvallis, OR, USA; Bana, P.R., (2021) Robot vs. Stick: The Impact of Anthropomorphism on the Use of Hand Sanitizer, , Master’s Thesis, Oregon State University, Corvallis, OR, USA; McCaffrey, C., Taylor, A., Roy, S., Banisetty, S.B., Mead, R., Williams, T., Can robots be used to encourage social distancing? Proceedings of the HRI ’21 Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, pp. 475-478. , Boulder, CO, USA, 8–11 March 2021; Natarajan, M., Gombolay, M., Effects of anthropomorphism and accountability on trust in human robot interaction Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 33-42. , Cambridge, UK, 23–26 March 2020; Haring, K.S., Satterfield, K.M., Tossell, C.C., de Visser, E.J., Lyons, J.R., Mancuso, V.F., Finomore, V.S., Funke, G.J., Robot authority in human-robot teaming: Effects of human-likeness and physical embodiment on compliance (2021) Front. Psychol, 12, p. 625713. , [CrossRef] [PubMed]; D’Egidio, G., Patel, R., Rashidi, B., Mansour, M., Sabri, E., Milgram, P., A study of the efficacy of flashing lights to increase the salience of alcohol-gel dispensers for improving hand hygiene compliance (2014) Am. J. Infect. 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Bull, 14, pp. 459-467. , [CrossRef]; https://www.onerobotix.co.il/, Available online: (accessed on 16 November 2021); David, A., (2021) Robotic Inspector for COVID-19, , Final Project Report; Dept. of Industrial Engineering and Management, Ben-Gurion University of the Negev: Beer Sheva, Israel; Woods, S., Walters, M., Koay, K.L., Dautenhahn, K., Comparing human robot interaction scenarios using live and video based methods: Towards a novel methodological approach Proceedings of the 9th IEEE International Workshop on Advanced Motion Control, pp. 750-755. , Istanbul, Turkey, 27–29 March 2006; Woods, S.N., Walters, M.L., Koay, K.L., Dautenhahn, K., Methodological issues in HRI: A comparison of live and video-based methods in robot to human approach direction trials Proceedings of the ROMAN 2006—The 15th IEEE International Symposium on Robot and Human Interactive Communication, pp. 51-58. , Hatfield, UK, 6–8 September 2006; Gou, M.S., Webb, T.L., Prescott, T., The effect of direct and extended contact on attitudes towards social robots (2021) Heliyon, 7, p. e06418; Fraune, M.R., Šabanović, S., Kanda, T., Human group presence, group characteristics, and group norms affect human-robot interaction in naturalistic settings (2019) Front. 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Applications and Case Studies, Proceedings of the 11th International Conference, VAMR 2019, Held as Part of the 21st HCI International Conference, HCII 2019, pp. 63-78. , Orlando, FL, USA, 26–31 July Springer: Cham, Switzerland, 2019","Liberman-Pincu, E.; Department of Industrial Engineering and Management, Israel; email: elapin@post.bgu.ac.il",,,"MDPI",,,,,24144088,,,,"English","Multimodal Tech. Inter.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85120007952
"Holthaus P., Wachsmuth S.","36699613400;6506127757;","It was a Pleasure Meeting You: Towards a Holistic Model of Human–Robot Encounters",2021,"International Journal of Social Robotics","13","7",,"1729","1745",,2,"10.1007/s12369-021-00759-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101906514&doi=10.1007%2fs12369-021-00759-9&partnerID=40&md5=4fb120f3a00561a3546e2101c519cceb","University of Hertfordshire, College Lane, Hatfield, AL10 9AB, United Kingdom; Bielefeld University, Inspiration 1, Bielefeld, 33619, Germany","Holthaus, P., University of Hertfordshire, College Lane, Hatfield, AL10 9AB, United Kingdom; Wachsmuth, S., Bielefeld University, Inspiration 1, Bielefeld, 33619, Germany","Social signals are commonly used to facilitate the usability of humanoid robots. While providing the robot with an extended expressibility, these signals are often applied only in structured interactions where parts of the familiarization or farewell processes are disregarded in the evaluation. In order to establish the consideration of a more comprehensive view, this article presents a holistic model of human encounters with a social robot. We propose and discuss particular robot signals, which aim to express the robot’s social awareness, for each of the model’s phases. We present an interaction study with humans that are inexperienced in interacting with robots to investigate the effects of these signals. Results verify that the implementation of proposed signals is beneficial for the participants’ user experience. The study further reveals a strong interdependency of a robot’s social signals and the importance of addressing entire encounters in human–robot interaction studies. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.","Holistic human–robot encounters; Nonverbal signals; User study","Agricultural robots; Anthropomorphic robots; User experience; Expressibility; Holistic model; Humanoid robot; Interaction studies; Robot interactions; Social awareness; Social signals; Social robots",,,,,"Deutsche Forschungsgemeinschaft, DFG","This work has been funded in part by the German Research Foundation (DFG) within the Collaborative Research Center 673, Alignment in Communication.",,,,,,,,,,"Andrist, S., Tan, X.Z., Gleicher, M., Mutlu, B., Conversational gaze aversion for humanlike robots (2014) In: 2014 9Th ACM/IEEE International Conference on Human–robot Interaction (HRI). IEEE, pp. 25-32. , https://doi.org/10.1145/2559636.2559666; Barnes, J., Fakhrhosseini, M., Jeon, M., Park, C.H., Howard, A., The influence of robot design on acceptance of social robots (2017) 2017 14Th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). 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J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85101906514
"Koniakowsky I., Loew A., Forster Y., Naujoks F., Keinath A.","57225151023;57216964965;57191722779;56307873900;35102520600;","Context and Culture affect the Psychometrics of Questionnaires evaluating Speech-based Assistants",2021,"Adjunct Proceedings - 13th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2021",,,,"115","118",,,"10.1145/3473682.3480276","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116119361&doi=10.1145%2f3473682.3480276&partnerID=40&md5=d1105e0a60369ed950f9972034c137d0","Ulm University, Germany; Technical University Munich, Germany; BMW Group, Germany","Koniakowsky, I., Ulm University, Germany; Loew, A., Technical University Munich, Germany; Forster, Y., BMW Group, Germany; Naujoks, F., BMW Group, Germany; Keinath, A., BMW Group, Germany","Intelligent Personal Assistants (IPAs) have grown into technologically mature systems. However, instruments used for evaluating the usability and user experience of IPAs were developed two decades ago. This bears the danger for research and development to apply inadequate measurements to a novel technology. In this study, more recent scales from human-robot-interaction were used for evaluating speech-based assistants in vehicles in a Chinese sample. However, it cannot be assumed, that adapting a questionnaire from another context and culture leads to objective, reliable and valid measurements. Therefore, data was examined regarding internal consistency and factor structure. Cronbach's alpha was considerably high. However, a factor analysis did not support the assumed four-factor structure but rather a two-factorial solution. Findings suggest that adapting a questionnaire from a different context and culture, can affect its psychometrics. Consequently, the underlying postulated constructs should be treated with caution. © 2021 Owner/Author.","Intelligent personal assistant; questionnaire; speech-based assistant; validation","Human robot interaction; Cronbach's alphas; Factor structure; Humans-robot interactions; Internal consistency; Internal factors; Questionnaire; Research and development; Speech-based assistant; Users' experiences; Validation; Surveys",,,,,,,,,,,,,,,,"Visual-manual NHTSA driver distraction guidelines for in-vehicle electronic devices (2012) NHTSA, , Washington, DC: National Highway Traffic Safety Administration (NHTSA), Department of Transportation (DOT; Peissner, M., Doebler, V., Metze, F., Can voice interaction help reducing the level of distraction and prevent accidents? 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Eng, (634), pp. 287-303. , https://doi.org/10.1017/S1351324900002497, September 2000; Laugwitz, B., Held, T., Schrepp, M., Construction and Evaluation of a User Experience Questionnaire (2008) HCI and Usability for Education and Work, Andreas Holzinger, Ed. 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Ed, , Erlbaum Hillsdale, NJ; Tabachnick, B.G., Fidell, L.S., 2019 Using Multivariate Statistics (Seventh Edition). Always Learning, , Pearson, New York; Anthoine, E., Moret, L., Regnault, A., Sebille, V., Hardouin, J., Sample size used to validate a scale: A review of publications on newly-developed patient reported outcomes measures (2014) Health and Quality of Life Outcomes, 12, p. 176. , https://doi.org/10.1186/s12955-014-0176-2; Forster, Y., Hergeth, S., Naujoks, F., Krems, J.F., Keinath, A., Self-report measures for the assessment of human-machine interfaces in automated driving (2019) Cogn Tech Work, 22 (4), pp. 703-720. , https://doi.org/10.1007/s10111-019-00599-8",,,"ACM SIGCOMM;L3 Pilot;seeingmachines;WiVW","Association for Computing Machinery, Inc","13th ACM International Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2021","9 September 2021 through 14 September 2021",,171874,,9781450386418,,,"English","Adjun. Proc. - Int. ACM Conf. Automot. User Interfaces Interact. Veh. Appl., AutomotiveUI",Conference Paper,"Final","",Scopus,2-s2.0-85116119361
"Zimmer M., Al-Yacoub A., Ferreira P., Hubbard E.-M., Lohse N.","57210560802;57190946378;7202018234;36514743200;6603252771;","Mental workload of local vs remote operator in human-machine interaction case study",2021,"Advances in Transdisciplinary Engineering","15",,,"33","38",,,"10.3233/ATDE210008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116394552&doi=10.3233%2fATDE210008&partnerID=40&md5=ead8633d3417c9c1b681185d8d40cfd8","Loughborough University, Intelligent Automation Centre, Epinal Way, Loughborough, LE11 3TU, United Kingdom","Zimmer, M., Loughborough University, Intelligent Automation Centre, Epinal Way, Loughborough, LE11 3TU, United Kingdom; Al-Yacoub, A., Loughborough University, Intelligent Automation Centre, Epinal Way, Loughborough, LE11 3TU, United Kingdom; Ferreira, P., Loughborough University, Intelligent Automation Centre, Epinal Way, Loughborough, LE11 3TU, United Kingdom; Hubbard, E.-M., Loughborough University, Intelligent Automation Centre, Epinal Way, Loughborough, LE11 3TU, United Kingdom; Lohse, N., Loughborough University, Intelligent Automation Centre, Epinal Way, Loughborough, LE11 3TU, United Kingdom","Since late 2019, a novel Coronavirus disease 2019 (COVID-19) has spread globally. As a result, businesses were forced to send their workforce into remote working, wherever possible. While research in this area has seen an increase in studying and developing technologies that allow and support such remote working style, not every sector is currently prepared for such a transition. Especially the manufacturing sector has faced challenges in this regard. In this paper, the mental workload of two groups of participants is studied during a human-robot interaction task. Participants were asked to bring a robotised cell used in a dispensing task to full production by tuning system parameters. After the experiment, a self-assessment of the participants' perceived mental workload using the NASA Task Load Index (NASA-TLX) was used. The results show that remote participants tend to have lower perceived workload compared to the local participants. © 2021 The authors and IOS Press.","Ergonomics; Human-Robot Interaction; Mental Workload; NASA-TLX; Remote Working","Ergonomics; Human robot interaction; Man machine systems; Case-studies; Coronaviruses; Human machine interaction; Humans-robot interactions; Load index; Mental workload; NASA task load index; Remote operator; Remote working; Working styles; NASA",,,,,"EP/L014998/1, EP/R032718/1; Engineering and Physical Sciences Research Council, EPSRC","The authors gratefully acknowledge the financial support of the Engineering and Physical Sciences Research Council (EPSRC) Centre for Doctoral Training in Embedded Intelligence (EP/L014998/1) and “Digital Toolkit for optimisation of operators and technology in manufacturing partnerships” (DigiTOP) (EP/R032718/1).",,,,,,,,,,"Wang, B., Liu, Y., Qian, J., Parker, S.K., (2021) Achieving Effective Remote Working during the COVID-19 Pandemic : A Work Design Perspective, 70 (1), pp. 16-59; Young, M.S., Brookhuis, K.A., Wickens, C.D., Hancock, P.A., State of science: Mental workload in ergonomics (2015) Ergonomics, 58 (1), pp. 1-17; Kantowitz, B.H., Attention and mental workload (2000) Proc. Hum. Factors Ergon. Soc. Annu. Meet, 44 (21), pp. 159-456. , https://doi.org/10.1177/154193120004402121; Hart, S.G., Staveland, L.E., Development of nasa-tlx (task load index): Results of empirical and theoretical research (1988) Human Mental Workload, 52, pp. 139-183. , P. A. Hancock and N. B. T.-A. in P. Meshkati, Eds. North-Holland; ILO encyclopaedia of occupational health and safety Mental Workload, , https://www.iloencyclopaedia.org/k2-feed1/item/628-mental-workload, accessed Apr. 09, 2021; Huey, B.M., Wickens, C.D., (1993) Workload Transition-Implications for Individual and Team Performance, , Washington, DC: National Academy Press; Cain, B., A review of the mental workload literature (2007) Def. Res. Dev. Toronto, (1998), pp. 41-434; Lysaght, R.J., (1989) Operator Workload: Comprehensive Review and Evaluation of Operator Workload Methodologies; Stanton, N., Hedge, A., Brookhuis, K., Salas, E., Hendrick, H., (2005) Handbook of Human Factors and Ergonomics Methods, , CRC Press; Rubio, S., Diaz, E., Martin, J., Puente, J.M., Evaluation of subjective mental workload: A comparison of swat, nasa-tlx, and workload profile methods (2004) Appl. Psychol, 53 (1), pp. 61-86; Gurevich, P., Lanir, J., Cohen, B., (2015) Design and Implementation of TeleAdvisor: A Projection-Based Augmented Reality System for Remote Collaboration, 24 (6); Flor, N.V., Side-by-side collaboration : A case study (1998) Int. J. Human-Computer Stud, 49, p. 201222; Tang, J.C., Findings from observational studies of collaborative work (1991) Int. J. Man-Machine Stud, 34, pp. 143-160; Kraut, R.E., Fussell, S.R., Siegel, J., Visual information as a conversational resource in collaborative physical tasks (2003) Human-Computer Interact, 18, pp. 13-49; Gergle, D., Kraut, R.E., Fussell, S.R., Using visual information for grounding and awareness in collaborative tasks (2013) Human-Computer Interact, 28 (1), pp. 1-39. , Jan; Fussell, S.R., Setlock, L.D., Yang, J., Mauer, E., Kramer, A.D.I., Gestures over video streams to support remote collaboration on physical tasks (2004) Human-Computer Interact, 19, pp. 273-309; Thoben, K., Wiesner, S., Wuest, T., Industrie 4.0' and smart manufacturing-a review of research issues and application examples (2017) Int. J. Autom. Technol, pp. 4-16","Zimmer, M.; Loughborough University, Epinal Way, United Kingdom; email: m.zimmer2@lboro.ac.uk","Shafik M.Case K.",,"IOS Press BV","18th International Conference on Manufacturing Research, ICMR 2021","7 September 2021 through 10 September 2021",,171961,,9781614994398,,,"English","Adv. Transdiscipl. Eng.",Conference Paper,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85116394552
"Zhang X., Hu H., Tang D., Zhang C., Fu J., Zhao P.","57207821400;57219031688;57211916579;57191861902;57202545348;56581020200;","Magnetic flexible tactile sensor via direct ink writing",2021,"Sensors and Actuators, A: Physical","327",,"112753","","",,1,"10.1016/j.sna.2021.112753","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104584757&doi=10.1016%2fj.sna.2021.112753&partnerID=40&md5=9c6478979b2466d1c51d770f11499ad0","The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China; The Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China; Zhejiang University, University of Illinois at Urbana, Champaign Institute, Haining, 314400, China","Zhang, X., The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China, The Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China; Hu, H., Zhejiang University, University of Illinois at Urbana, Champaign Institute, Haining, 314400, China; Tang, D., The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China, The Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China; Zhang, C., The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China, The Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China; Fu, J., The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China, The Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China; Zhao, P., The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China, The Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China","The development of flexible tactile sensors is an emerging field due to their wide applications ranging from human-robot interactions to wearable electronics. This paper proposes a flexible tactile sensor based on the magnetic field for detecting both forces and positions, which exhibited a linear relationship. The direct ink writing technology was used to fabricate magnetic elastomers for the quick test of variable patterns. The introduced folding magnetization allows for a higher magnetic flux density in the designated position which made it possible to get rid of magnets. A pattern was designed and printed. The linear relationship was found to be linear (R2 &gt; 0.98) between the magnetic field signal and the magnitude and position of the load force. Moreover, the untethered sensor was proven to work well in the sealed box, which is expected to serve as the electrical skin of robots or the tactile sensor in extreme environments. © 2021","Flexible; Linear; Magnetic; Tactile sensor; Untethered","Flexible electronics; Human robot interaction; Wearable sensors; Flexible; Flexible tactile sensors; Humans-robot interactions; Linear; Linear relationships; Magnetic elastomers; Magnetic-field; Tactile sensors; Untethered; Writing technology; Magnetic fields",,,,,"National Natural Science Foundation of China, NSFC: 51635006, 51821093, 51875519; Natural Science Foundation of Zhejiang Province, ZJNSF: LZ18E050002","The authors acknowledge the financial support of the National Natural Science Foundation of China (Grant nos. 51875519 , 51821093 , and 51635006 ) and the Zhejiang Provincial Natural Science Foundation of China (Grant no. LZ18E050002 ).",,,,,,,,,,"Ray, T.R., Choi, J., Bandodkar, A.J., Krishnan, S., Gutruf, P., Tian, L., Ghaffari, R., Rogers, J.A., Bio-integrated wearable systems: a comprehensive review (2019) Chem. Rev., 119, pp. 5461-5533; Melzer, M., Mönch, J.I., Makarov, D., Zabila, Y., Bermúdez, G.S.C., Karnaushenko, D., Baunack, S., Schmidt, O.G., Wearable magnetic field sensors for flexible electronics (2015) Adv. Mater., 27, pp. 1274-1280; Doshi, S.M., Thostenson, E.T., Thin and flexible carbon nanotube-based pressure sensors with ultrawide sensing range (2018) ACS Sens., 3, pp. 1276-1282; Zhang, X., Turkani, V.S., Hajian, S., Bose, A.K., Maddipatla, D., Hanson, A.J., Narakathu, B.B., Atashbar, M.Z., Novel printed carbon nanotubes based flexible resistive humidity sensor (2019) IEEE Sens. J., pp. 12592-12601; Dhong, C., Kayser, L.V., Arroyo, R., Shin, A., Finn, M., Kleinschmidt, A.T., Lipomi, D.J., Role of fingerprint-inspired relief structures in elastomeric slabs for detecting frictional differences arising from surface monolayers (2018) Soft Matter, 14, pp. 7483-7491; Mu, C., Song, Y., Huang, W., Ran, A., Sun, R., Xie, W., Zhang, H., Flexible normal-tangential force sensor with opposite resistance responding for highly sensitive artificial skin (2018) Adv. Funct. Mater., 28, pp. 1-9; Yang, Y.J., Cheng, M.Y., Chang, W.Y., Tsao, L.C., Yang, S.A., Shih, W.P., Chang, F.Y., Fan, K.C., An integrated flexible temperature and tactile sensing array using PI-copper films (2008) Sens. Actuators A Phys., 143, pp. 143-153; Kim, K., Lee, K.R., Kim, W.H., Park, K.B., Kim, T.H., Kim, J.S., Pak, J.J., Polymer-based flexible tactile sensor up to 32 × 32 arrays integrated with interconnection terminals (2009) Sens. Actuators A Phys., 156, pp. 284-291; Cheng, M.Y., Tsao, C.M., Lai, Y.Z., Yang, Y.J., The development of a highly twistable tactile sensing array with stretchable helical electrodes (2011) Sens. Actuators A Phys., 166, pp. 226-233; Emamian, S., Narakathu, B.B., Chlaihawi, A.A., Bazuin, B.J., Atashbar, M.Z., Screen printing of flexible piezoelectric based device on polyethylene terephthalate (PET) and paper for touch and force sensing applications (2017) Sens. Actuators A Phys., 263, pp. 639-647; Seminara, L., Pinna, L., Valle, M., Basirico, L., Loi, A., Cosseddu, P., Bonfiglio, A., Metta, G., Piezoelectric polymer transducer arrays for flexible tactile sensors (2013) IEEE Sens. J., 13, pp. 4022-4029; Gagino, M., Katsikis, G., Olcum, S., Virot, L., Cochet, M., Thuaire, A., Manalis, S.R., Agache, V., Suspended nanochannel resonator arrays with piezoresistive sensors for high-throughput weighing of nanoparticles in solution (2020) ACS Sens., 5, pp. 1230-1238; Chen, X., Shao, J., Tian, H., Li, X., Wang, C., Luo, Y., Li, S., Scalable imprinting of flexible multiplexed sensor arrays with distributed piezoelectricity-enhanced micropillars for dynamic tactile sensing (2020) Adv. Mater. Technol., 5, pp. 1-9; Zhang, Q., Wang, Y.L., Xia, Y., Zhang, P.F., Kirk, T.V., Chen, X.D., Textile-only capacitive sensors for facile fabric integration without compromise of wearability (2019) Adv. Mater. 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Sci., 2, pp. 1-21; Zhang, Q., Wang, Y.L., Xia, Y., Kirk, T.V., Chen, X.D., Textile-only capacitive sensors with a lockstitch structure for facile integration in any areas of a fabric (2020) ACS Sens., 5, pp. 1535-1540; Takahashi, T., Takei, K., Gillies, A.G., Fearing, R.S., Javey, A., Carbon nanotube active-matrix backplanes for conformal electronics and sensors (2011) Nano Lett., 11, pp. 5408-5413; Prakash Gupta, M., Chen, L., Estrada, D., Behnam, A., Pop, E., Kumar, S., Impact of thermal boundary conductances on power dissipation and electrical breakdown of carbon nanotube network transistors (2012) J. Appl. Phys., 112; Kawasetsu, T., Horii, T., Ishihara, H., Asada, M., Mexican-hat-like response in a flexible tactile sensor using a magnetorheological elastomer (2018) Sensors (Switzerland), 18, pp. 1-16; Wang, H., De Boer, G., Kow, J., Alazmani, A., Ghajari, M., Hewson, R., Culmer, P., Design methodology for magnetic field-based soft tri-axis tactile sensors (2016) Sensors (Switzerland), 16; Li, Y., Qi, Z., Yang, J., Zhou, M., Zhang, X., Ling, W., Zhang, Y., Huang, X., Origami NdFeB flexible magnetic membranes with enhanced magnetism and programmable sequences of polarities (2019) Adv. Funct. Mater., 29, pp. 1-10; Lai, C.W., Yu, S.S., 3D printable strain sensors from deep eutectic solvents and cellulose nanocrystals (2020) ACS Appl. Mater. Interfaces, 12, pp. 34235-34244; Kim, Y., Yuk, H., Zhao, R., Chester, S.A., Zhao, X., Printing ferromagnetic domains for untethered fast-transforming soft materials (2018) Nature, 558, pp. 274-279; Jiang, Y., Liu, Z., Matsuhisa, N., Qi, D., Leow, W.R., Yang, H., Yu, J., Chen, X., Auxetic mechanical metamaterials to enhance sensitivity of stretchable strain sensors (2018) Adv. Mater., 30, pp. 1-8; Maddipatla, D., Narakathu, B.B., Ochoa, M., Rahimi, R., Zhou, J., Yoon, C.K., Jiang, H., Atashbar, M.Z., Rapid prototyping of a novel and flexible paper based oxygen sensing patch via additive inkjet printing process (2019) RSC Adv., 9, pp. 22695-22704","Zhao, P.; The State Key Laboratory of Fluid Power and Mechatronic Systems, China; email: pengzhao@zju.edu.cn",,,"Elsevier B.V.",,,,,09244247,,SAAPE,,"English","Sens Actuators A Phys",Article,"Final","",Scopus,2-s2.0-85104584757
"Mucchiani C., Cacchione P., Johnson M., Mead R., Yim M.","57200142286;6602728456;57203731750;23009115600;7005922938;","Deployment of a socially assistive robot for assessment of COVID-19 symptoms and exposure at an elder care setting",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"1189","1195",,,"10.1109/RO-MAN50785.2021.9515551","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115111476&doi=10.1109%2fRO-MAN50785.2021.9515551&partnerID=40&md5=5900cd2c025cc6d6fec96073d984560e","University of Pennsylvania, United States; Semio INC","Mucchiani, C., University of Pennsylvania, United States; Cacchione, P., University of Pennsylvania, United States; Johnson, M., University of Pennsylvania, United States; Mead, R., Semio INC; Yim, M., University of Pennsylvania, United States","This work investigates the deployment of an affordable socially assistive robot (SAR) at an older adult day care setting for the screening of COVID-19 symptoms and exposure. Despite the focus on older adults, other stakeholders (clinicians and caregivers) were included in the study due to the need for daily COVID-19 screening. The investigation considered which aspects of human-robot-interaction (HRI) are relevant when designing social agents for patient screening. The implementation was based upon the current screening procedure adopted by the deployment facility, and translated into robot dialogues and gesturing motion. Post-interaction surveys with participants informed their preferences for the type of interaction and system usability. Observer surveys evaluated users' reaction, verbal and physical engagement. Results indicated general acceptance of the social agent and possible improvements to the current version of the robot to encourage a broader adoption by the stakeholders. © 2021 IEEE.",,"Agricultural robots; Surveys; Current screening; Elder care; Human robot Interaction (HRI); Older adults; Social agents; Socially assistive robots; System usability; Social robots",,,,,"National Science Foundation, NSF: TT 1827673","This work was funded by the NSF grant PFI:TT 1827673 Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. 1 University of Pennsylvania 2 Semio INC",,,,,,,,,,"Sefcik, J.S., Johnson, M.J., Yim, M., Lau, T., Vivio, N., Mucchiani, C., Cacchione, P.Z., Stakeholders' perceptions sought to inform the development of a low-cost mobile robot for older adults: A qualitative descriptive study (2018) Clinical Nursing Research, 27 (1), pp. 61-80; (2020) Centers for Disease and Control Prevention (CDC), , https://www.cdc.gov/coronavirus/2019-ncov/need-extraprecautions/older-adults.html, Accessed on 08/21; (2020) Centers for Disease and Control Prevention (CDC), , https://www.cdc.gov/coronavirus/2019-ncov/faq.html, Accessed on 08/21; Smarr, C.A., Domestic robots for older adults: Attitudes, preferences, and potential (2014) Int J Soc Robot, 6, pp. 229-247; Pineau, J., Montemerlo, M., Pollack, M., Towards robotic assistants in nursing homes: Challenges and results (2003) Special Issue on Socially Interactive Robots, Robotics and Autonomous Systems, 42 (3-4), pp. 271-281; Heerink, M., Kröse, B., Evers, V., Wielinga, B., Assessing acceptance of assistive social agent technology by older adults: The almere model (2010) International Journal of Social Robotics, 2 (4), pp. 361-375. , Dec; Johnson, M.J., Ma, J., Sefcik, J.S., Cacchione, P.Z., Mucchiani, C., Lau, T., Yim, M., Task and design requirements for an affordable mobile service robot for elder care in an all-inclusive care for elders assisted-living setting (2017) International Journal of Social Robotics, pp. 1-20; Seifer, D., Mataric, M., Defining socially assistive robotics (2005) Proceedings of the IEEE 9th International Conference on Rehabilitation Robotics, pp. 465-468. , June 28-July 1 Chicago, IL, USA; Kidd, C.D., (2008) Designing for Long-term Human-robot Interaction and Application to Weight Loss; Fasola, J., Mataric, M.J., Robot exercise instructor: A socially assistive robot system to monitor and encourage physical exercise for the elderly (2010) 19th International Symposium in Robot and Human Interactive Communication, pp. 416-421. , September IEEE; Wada, K., Shibata, T., Saito, T., Tanie, K., Effects of robotassisted activity for elderly people and nurses at a day service center (2004) Proceedings of the IEEE, 92 (11), pp. 1780-1788; Kim, E., Newland, E., Paul, R., Scassellati, B., Robotic tools for prosodic training for children with asd: A case study (2008) International Meeting for Autism Research (IMFAR); Breazeal, C.L., (2004) Designing Sociable Robots, , MIT press; Ekman, P., Facial expressions Handbook of Cognition and Emotion, , Eds. 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Sussex; Graneheim, U.H., Lundman, B., Qualitative content analysis in nursing research: Concepts, procedures and measures to achieve trustworthiness (2004) Nurse Education Today, 24 (2), pp. 105-112; Hsieh, H.F., Shannon, S.E., Three approaches to qualitative content analysis (2005) Qualitative Health Research, 15 (9), pp. 1277-1288; Mucchiani, C., Sharma, S., Johnson, M., Sefcik, J., Vivio, N., Huang, J., Cacchione, P., Lau, T., Evaluating older adults' interaction with a mobile assistive robot 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 840-847. , IEEE, September; Costa, J.T., Yim, M., Designing for uniform mobility using holonomicity 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 2448-2453; Mucchiani, C., Torres, W.O., Edgar, D., Johnson, M.J., Cacchione, P.Z., Yim, M., Development and deployment of a mobile manipulator for assisting and entertaining elders living in supportive apartment living facilities (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 121-128. , August IEEE; Collins, F., Yim, M., Design of a spherical robot arm with the spiral zipper prismatic joint 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 2137-2143; Krosnick, J.A., Alwin, D.F., An evaluation of a cognitive theory of response-order effects in survey measurement (1987) Public Opinion Quarterly, 51 (2), pp. 201-219; Cacchione, P.Z., Caio, M., Lima, K., Mead, R.R., Yim, M., Johnson, M., Older adults, clinicians and caregivers preferences for design features of an affordable mobile service robot in the care of older adults Special Issue: Interaction in Robot Assistive Elderly Care. 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Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85115111476
"Rysbek Z., Oh K.H., Abbasi B., Zefran M., Di Eugenio B.","57202321417;57262205400;57190124754;7003754282;6603126702;","Physical action primitives for collaborative decision making in human-human manipulation",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"1319","1325",,,"10.1109/RO-MAN50785.2021.9515363","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115084365&doi=10.1109%2fRO-MAN50785.2021.9515363&partnerID=40&md5=b3087a3efcf5c12a1d4284239f077f20","University of Illinois at Chicago, Robotics Lab, Department of Electrical and Computer Engineering, Chicago, IL  60607, United States; California State University Channel Islands, Department of Computer Science, Camarillo, CA  93012, United States; University of Illinois at Chicago, Natural Language Processing Lab, Department of Computer Science, Chicago, IL  60607, United States","Rysbek, Z., University of Illinois at Chicago, Robotics Lab, Department of Electrical and Computer Engineering, Chicago, IL  60607, United States; Oh, K.H., University of Illinois at Chicago, Robotics Lab, Department of Electrical and Computer Engineering, Chicago, IL  60607, United States; Abbasi, B., California State University Channel Islands, Department of Computer Science, Camarillo, CA  93012, United States; Zefran, M., University of Illinois at Chicago, Robotics Lab, Department of Electrical and Computer Engineering, Chicago, IL  60607, United States; Di Eugenio, B., University of Illinois at Chicago, Natural Language Processing Lab, Department of Computer Science, Chicago, IL  60607, United States","Human-human collaboration is characterized by a back-and-forth, where an action of one agent elicits the response of the other. This interaction is inherently multimodal and includes both high-level modalities such as language and low-level ones such as force exchanges. In this work, we investigate human collaborative manipulation: we show distinct patterns that can be identified in low-level physical data and that can be interpreted as primitives used by humans to negotiate about various aspects of the motion and to execute the motion. These primitives provide a high-level interpretation of the interaction and can be used to connect low-level behavior to language. We describe the human study used to collect the data, the data analysis process, and discuss how the identified primitives could be used by a robot's interaction manager to mediate physical Human-Robot Interaction (pHRI). © 2021 IEEE.",,"Agricultural robots; Decision making; High level languages; Analysis process; Collaborative decision making; Human manipulation; Interaction managers; Multi-modal; Physical action; Physical data; Physical humanrobot interaction (phri); Social robots",,,,,"National Science Foundation, NSF: CMMI-1762924, IIS-1705058","This work has been supported by the National Science Foundation grants IIS-1705058 and CMMI-1762924.",,,,,,,,,,"Abbasi, B., Monaikul, N., Rysbek, Z., Eugenio, B.D., Zefran, M., A multimodal human-robot interaction manager for assistive robots (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6756-6762. , Nov; Noohi, E., Zefran, M., Patton, J.L., A model for human-human collaborative object manipulation and its application to human-robot interaction (2016) IEEE Transactions on Robotics, 32 (4), pp. 880-896. , Aug; Calinon, S., Evrard, P., Gribovskaya, E., Billard, A., Kheddar, A., Learning collaborative manipulation tasks by demonstration using a haptic interface (2009) International Conference on Advanced Robotics, 2009. 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RO-MAN 2008. The 17th IEEE International Symposium On. IEEE, pp. 107-112. , http://ieeexplore.ieee.org/xpls/absall.jsp?arnumber=4600651; Strabala, K.W., Lee, M.K., Dragan, A.D., Forlizzi, J.L., Srinivasa, S., Cakmak, M., Micelli, V., Towards seamless human-robot handovers (2013) Journal of Human-Robot Interaction, 2 (1), pp. 112-132. , http://hri-journal.org/index.php/HRI/article/view/114; Flash, T., Hogan, N., The coordination of arm movements: An experimentally confirmed mathematical model (1985) Journal of Neuroscience, 5 (7), pp. 1688-1703; Noohi, E., Zefran, M., Patton, J.L., A model for human-human collaborative object manipulation and its application to human-robot interaction (2016) IEEE Transactions on Robotics, 32 (4), pp. 880-896; Al-Saadi, Z., Sirintuna, D., Kucukyilmaz, A., Basdogan, C., A novel haptic feature set for the classification of interactive motor behaviors in collaborative object transfer (2020) IEEE Transactions on Haptics, pp. 1-1; Mojtahedi, K., Whitsell, B., Artemiadis, P., Santello, M., Communication and inference of intended movement direction during human-human physical interaction (2017) Frontiers in Neurorobotics, 11. , Apr; Evrard, P., Kheddar, A., Homotopy-based controller for physical human-robot interaction (2009) RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 1-6. , Sep; Van Der Wel, R., Knoblich, G., Sebanz, N., Let the force be with us: Dyads exploit haptic coupling for coordination (2011) Journal of Experimental Psychology. 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IEEE, pp. 1098-1103; Woods, D.L., Wyma, J.M., Yund, E.W., Herron, T.J., Reed, B., Factors influencing the latency of simple reaction time (2015) Frontiers in Human Neuroscience, 9. , Mar; Kaufman, L., Rousseeuw, P.J., (2009) Finding Groups in Data: An Introduction to Cluster Analysis, 344. , John Wiley & Sons; Sakoe, H., Chiba, S., Dynamic programming algorithm optimization for spoken word recognition (1978) IEEE Transactions on Acoustics, Speech, and Signal Processing, 26 (1), pp. 43-49; Howard, C., Di Eugenio, B., Jordan, P., Katz, S., Exploring initiative as a signal of knowledge co-construction during collaborative problem solving (2017) Cognitive Science, 41 (6), pp. 1422-1449. , Aug; Hogan, N., Impedance control: An approach to manipulation: Part i-theory (1985) Journal of Dynamic Systems, Measurement, and Control, 107 (1), pp. 1-7. , https://doi.org/10.1115/1.3140702, 03",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115084365
"Petrak B., Stapels J.G., Weitz K., Eyssel F., Andre E.","57212246381;57217912785;57191854355;9846264800;7006887726;","To move or not to move? Social acceptability of robot proxemics behavior depending on user emotion",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"975","982",,,"10.1109/RO-MAN50785.2021.9515502","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115068607&doi=10.1109%2fRO-MAN50785.2021.9515502&partnerID=40&md5=ab881cb8eda4069e49c3fd3396147048","Augsburg University, Human-Centered Artificial Intelligence, Germany; Bielefeld University, Center for Cognitive Interaction Technology, Applied Social Psychology and Gender Research, !!!Department of Psychology, Germany","Petrak, B., Augsburg University, Human-Centered Artificial Intelligence, Germany; Stapels, J.G., Bielefeld University, Center for Cognitive Interaction Technology, Applied Social Psychology and Gender Research, !!!Department of Psychology, Germany; Weitz, K., Augsburg University, Human-Centered Artificial Intelligence, Germany; Eyssel, F., Bielefeld University, Center for Cognitive Interaction Technology, Applied Social Psychology and Gender Research, !!!Department of Psychology, Germany; Andre, E., Augsburg University, Human-Centered Artificial Intelligence, Germany","Various works show that proxemics occupies an important role in human-robot interaction and that appropriate proxemic interaction depends on many characteristics of humans and robots. However, there is none that shows the relationship between an emotional state expressed by a user and a proxemic reaction of the robot to it, in a social interaction between these interactants. In the current experiment (N = 82), we investigate this using an online study in which we examine which proxemic response (i.e., approaching, not moving, moving away) to a person's expressed emotional state (i.e., anger, fear, disgust, surprise, sadness, joy) is perceived as appropriate. The quantitative and qualitative data collected suggests that the robot's approach was considered appropriate for the expressed fear, sadness, and joy, whereas moving away was perceived as inappropriate in most scenarios. Further exploratory findings underline the importance of appropriate nonverbal behavior on the perception of the robot. © 2021 IEEE.",,"Agricultural robots; Behavioral research; Emotional state; Nonverbal behavior; Online studies; Qualitative data; Social acceptability; Social interactions; User emotions; Social robots",,,,,"Augsburg University; Bundesministerium für Bildung und Forschung, BMBF: 16SV7960, FKZ 16SV7959; Universität Bielefeld","∗This research has been partially funded by the German Federal Ministry of Education and Research (BMBF) in the project “VIVA” (FKZ 16SV7959 and 16SV7960) 1Björn Petrak, Katharina Weitz, and Elisabeth André are with Human-Centered Artificial Intelligence, Augsburg University, Germany bjoern.petrak@uni-a.de katharina.weitz@uni-a.de elisabeth.andre@uni-a.de 2Julia G. Stapels, and Friederike Eyssel are with Center for Cognitive Interaction Technology, Applied Social Psychology and Gender Research, Department of Psychology, Bielefeld University, Germany julia.stapels@uni-bielefeld.de friederike.eyssel@uni-bielefeld.de",,,,,,,,,,"Pradhan, A., Findlater, L., Lazar, A., Phantom friend ""or ""just a box with information"" personification and ontological categorization of smart speaker-based voice assistants by older adults (2019) Proceedings of the ACM on Human-Computer Interaction, 3, pp. 1-21; Petrak, B., Weitz, K., Aslan, I., Andre, E., Let me show you your new home: Studying the effect of proxemic-awareness of robots on users' first impressions (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2019; Hall, E., (1982) The Hidden Dimension, , New York: Anchor Books; Syrdal, D.S., Koay, K.L., Walters, M.L., Dautenhahn, K., A personalized robot companion?-the role of individual differences on spatial preferences in hri scenarios (2007) Proceedings-IEEE International Workshop on Robot and Human Interactive Communication, pp. 1143-1148; Walters, M.L., Oskoei, M.A., Syrdal, D.S., Dautenhahn, K., A long-term human-robot proxemic study (2011) Proceedings-IEEE International Workshop on Robot and Human Interactive Communication; Walters, M.L., Syrdal, D.S., Koay, K.L., Dautenhahn, K., Te Boekhorst, R., Human approach distances to a mechanical-looking robot with different robot voice styles (2008) Proceedings of the 17th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN, pp. 707-712; Obaid, M., Sandoval, E.B., Zlotowski, J., Moltchanova, E., Basedow, C.A., Bartneck, C., Stop! that is close enough. how body postures influence human-robot proximity (2016) 25th IEEE International Symposium on Robot and Human Interactive Communication, ROMAN 2016, pp. 354-361; Bhagya, S., Samarakoon, P., Viraj, M., Muthugala, J., Buddhika, A., Jayasekara, P., Elara, M.R., An exploratory study on proxemics preferences of humans in accordance with attributes of service robots (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). 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Springer, Cham, oct; Eyssel, F., Kuchenbrandt, D., Social categorization of social robots: Anthropomorphism as a function of robot group membership (2012) British Journal of Social Psychology, 51 (4), pp. 724-731. , dec; Touré-Tillery, M., McGill, A.L., Who or what to believe: Trust and the differential persuasiveness of human and anthropomorphized messengers (2015) Journal of Marketing, 79 (4), pp. 94-110; Lamm, H., Stephan, E., Zur messung von einsamkeit: Entwicklung einer deutschen fassung des fragebogens von russell und peplau (1986) Psychologie und Praxis, (3), pp. 132-134; Reich-Stiebert, N., Eyssel, F., Learning with educational companion robots? Toward attitudes on education robots, predictors of attitudes, and application potentials for education robots (2015) International Journal of Social Robotics, 7 (5), pp. 875-888",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85115068607
"Adamik M., Dudzinska K., Herskind A.J., Rehm M.","57261763800;57262205100;57262060900;10039896700;","The difference between trust measurement and behavior: Investigating the effect of personalizing a robot's appearance on trust in HRI",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"880","885",,,"10.1109/RO-MAN50785.2021.9515487","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115052157&doi=10.1109%2fRO-MAN50785.2021.9515487&partnerID=40&md5=3d5ad164081643accd2a038d0bc2d4fe","Aalborg University, HRI-lab, Technical Faculty of IT and Design, Aalborg, 9000, Denmark","Adamik, M., Aalborg University, HRI-lab, Technical Faculty of IT and Design, Aalborg, 9000, Denmark; Dudzinska, K., Aalborg University, HRI-lab, Technical Faculty of IT and Design, Aalborg, 9000, Denmark; Herskind, A.J., Aalborg University, HRI-lab, Technical Faculty of IT and Design, Aalborg, 9000, Denmark; Rehm, M., Aalborg University, HRI-lab, Technical Faculty of IT and Design, Aalborg, 9000, Denmark","With the increased use of social robots in critical applications, like elder care and rehabilitation, it becomes necessary to investigate the user's trust in robots to prevent over- and under-utilization of the robotic systems. While several studies have shown how trust increases through personalised behaviour, there is a lack of research concerned with the influence of personalised physical appearance. This study explores the effect of personalised physical appearance on trust in human-robot-interaction (HRI). In an online game, 60 participants interacted with a robot, where half of the participants were asked to personalise the robot prior to the game. Trust was measured through a trust-related questionnaire as well as by evaluating user behaviour during the game. Results indicate that personalised physical appearance does not directly correlate to higher trust perceptions, however, there was significant evidence that players exhibit more trusting behaviours in a game against a personalised robot. © 2021 IEEE.",,"Agricultural robots; Behavioral research; Critical applications; Elder care; Human robot Interaction (HRI); On-line games; Robotic systems; Trust measurement; User behaviour; Social robots",,,,,,,,,,,,,,,,"Savioke Announces First Las Vegas Installation and New Relay Features, , https://www.savioke.com/blog/2018/1/8/gpwkugbcn9xev881b1wpko1p63ekai, Accessed: 2020-09-17; Ajzen, I., The theory of planned behavior (1991) Organizational Behavior and Human Decision Processes, 50 (2), pp. 179-211. , Theories of Cognitive Self-Regulation; Baker, A.L., Phillips, E.K., Ullman, D., Keebler, J.R., Toward an understanding of trust repair in human-robot interaction (2018) ACM Transactions on Interactive Intelligent Systems, 8 (4), pp. 1-30. , November; Barber, B., (1983) The Logic and Limits of Trust, , Rutgers University Press, New Brunswick, NJ; Berg, J., Dickhaut, J., McCabe, K., Trust, reciprocity, and social history (1995) Games and Economic Behavior, 10 (1), pp. 122-142. , July; Bohnet, I., (2010) Trust in Experiments, pp. 253-257. , Palgrave Macmillan UK, London; Chen, M., Nikolaidis, S., Soh, H., Hsu, D., Srinivasa, S.S., (2018) Planning with Trust for Human-robot Collaboration, , CoRR, abs/1801. 04099; Churamani, N., Anton, P., Brügger, M., Fließwasser, E., Hummel, T., Mayer, J., Mustafa, W., Wermter, S., The impact of personalisation on human-robot interaction in learning scenarios (2017) Proceedings of the 5th International Conference on Human Agent Interaction. ACM, , October; Talking Robots to Be Piloted in Preschools and First Years of Primary Education, , https://www.tampere.fi/en/city-of-tampere/info/current-issues/2018/03/19032018_1.html, City of Tampere. Accessed: 2020-03-31; David, M.D.B.P., Gunsch, G., The influence of task load and automation trust on deception detection (2004) Group Decision and Negotiation, 13 (2), pp. 173-189; Kate Devitt, S., Trustworthiness of autonomous systems (2018) Foundations of Trusted Autonomy, pp. 161-184. , Springer International Publishing; Evans, A.M., Revelle, W., Survey and behavioral measurements of interpersonal trust (2008) Journal of Research in Personality, 42 (6), pp. 1585-1593; Gambetta, D., (2000) Can We Trust Trust? Trust: Making and Breaking Cooperative Relations, Electronic Edition, pp. 213-237. , Department of Sociology, University of Oxford; Gomez, R., Haru, M., The unassuming big-eyed robot helping researchers study social robotics (2020) IEEE Spectrum; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, C.J.Y., Visser De, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Hegel, F., Muhl, C., Wrede, B., Hielscher-Fastabend, M., Sagerer, G., Understanding social robots (2009) 2009 Second International Conferences on Advances in Computer-Human Interactions, pp. 169-174; Jessup, S.A., Schneider, T.R., Alarcon, G.M., Ryan, T.J., Capiola, A., The measurement of the propensity to trust automation (2019) Virtual, Augmented and Mixed Reality. Applications and Case Studies, pp. 476-489. , Springer International Publishing; Jian, J., Bisantz, A.M., Drury, C.G., Foundations for an empirically determined scale of trust in automated systems (2000) International Journal of Cognitive Ergonomics, 4 (1), pp. 53-71; Kachouie, R., Sedighadeli, S., Khosla, R., Chu, M., Socially assistive robots in elderly care: A mixed-method systematic literature review (2014) International Journal of Human-Computer Interaction, 30 (5), pp. 369-393; Cai Kok, B., Soh, H., Trust in robots: Challenges and opportunities (2020) Current Robotics Reports, 1 (4), pp. 297-309. , September; Langer, A., Feingold-Polak, R., Mueller, O., Kellmeyer, P., Levy-Tzedek, S., Trust in socially assistive robots: Considerations for use in rehabilitation (2019) Neuroscience & Biobehavioral Reviews, 104, pp. 231-239. , September; Mayer, R.C., Davis, J.H., David Schoorman, F., An integrative model of organizational trust (1995) The Academy of Management Review, 20 (3), p. 709. , July; McCabe, K.A., Rigdon, M.L., Smith, V.L., Positive reciprocity and intentions in trust games (2003) Journal of Economic Behavior & Organization, 52 (2), pp. 267-275. , October; Merritt, S.M., Ilgen, D.R., Not all trust is created equal: Dispositional and history-based trust in human-automation interactions (2008) Human Factors, 50 (2), pp. 194-210. , PMID: 18516832; Muir, B.M., Moray, N., Trust in automation. part II. experimental studies of trust and human intervention in a process control simulation (1996) Ergonomics, 39 (3), pp. 429-460. , PMID: 8849495; Pillutla, M., Malhotra, D., Murnighan, J., Attribution of trust and the calculus of reciprocity (2003) Journal of Experimental Social Psychology, 39 (9), pp. 448-455; Benítez Sandoval, E., Brandstetter, J., Obaid, M., Bartneck, C., Reciprocity in human-robot interaction: A quantitative approach through the prisoner's dilemma and the ultimatum game (2015) International Journal of Social Robotics, 8 (2), pp. 303-317. , December; Schaefer, K.E., (2013) The Perception and Measurement of Human-robot Trust.; Schaefer, K.E., Measuring trust in human robot interactions: Development of the ""trust perception scale"" (2016) Robust Intelligence and Trust in Autonomous Systems, pp. 191-218. , Springer US",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115052157
"Deng F., Zhou Y., Song S., Jiang Z., Chen L., Su J., Sun Z., Zhang J.","57226857111;57203575344;57207760100;57208391128;57217044554;57045140900;25637891100;57202953338;","Say what you are looking at: An attention-based interactive system for autistic children",2021,"Applied Sciences (Switzerland)","11","16","7426","","",,,"10.3390/app11167426","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113176896&doi=10.3390%2fapp11167426&partnerID=40&md5=ea178fa765eb3a144a86efc2addd24d3","School of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China; School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, China; Department of Mathematical Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China; Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518129, China; DoGoodly International Education Center (Shenzhen) Co., Ltd., Smart Children Education Center, Shenzhen, 518000, China; School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China; School of Science and Engineering, The Chinese University of Hong (Shenzhen), Shenzhen, 518172, China; Institute of Robotics and Intelligent Manufacturing, The Chinese University of Hong Kong (Shenzhen), Shenzhen, 518172, China","Deng, F., School of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China; Zhou, Y., School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, China; Song, S., Department of Mathematical Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China; Jiang, Z., Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518129, China; Chen, L., DoGoodly International Education Center (Shenzhen) Co., Ltd., Smart Children Education Center, Shenzhen, 518000, China; Su, J., School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University, Suzhou, 215123, China; Sun, Z., School of Science and Engineering, The Chinese University of Hong (Shenzhen), Shenzhen, 518172, China; Zhang, J., Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518129, China, Institute of Robotics and Intelligent Manufacturing, The Chinese University of Hong Kong (Shenzhen), Shenzhen, 518172, China","Gaze-following is an effective way for intention understanding in human–robot interaction, which aims to follow the gaze of humans to estimate what object is being observed. Most of the existing methods require people and objects to appear in the same image. Due to the limitation in the view of the camera, these methods are not applicable in practice. To address this problem, we propose a method of gaze following that utilizes a geometric map for better estimation. With the help of the map, this method is competitive for cross-frame estimation. On the basis of this method, we propose a novel gaze-based image caption system, which has been studied for the first time. Our experiments demonstrate that the system follows the gaze and describes objects accurately. We believe that this system is competent for autistic children’s rehabilitation training, pension service robots, and other applications. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Human–robot interaction; Image caption; Simultaneous localization and mapping; Visual attention",,,,,,"SKLSAOP1702; 2019-INT020, AC01202101014; Science, Technology and Innovation Commission of Shenzhen Municipality: JCYJ20170410172100520, RCYX20200714114736115","Funding: This research was funded by the Shenzhen Science and Technology Innovation Commission, grant numbers JCYJ20170410172100520 and RCYX20200714114736115. This research was also funded by the Open Program of Neusoft Corporation, item number SKLSAOP1702, as well as the Shenzhen Institute of Artificial Intelligence and Robotics for Society, grant number 2019-INT020 and AC01202101014. The APC was funded by the Shenzhen Science and Technology Innovation Commission.",,,,,,,,,,"Anamaria, P.C., Ramona, S., Sebastian, P., Pintea, S., Saldien, J., Rusu, A., David, D., Vanderborght, D., Can the social robot probo help children with autism to identify situation-based emotions? A series of single case experiments (2013) Int. J. Hum. Robot, 10, p. 1350025. , [CrossRef]; Peca, A., Tapus, A., Aly, A., Pop, C., Jisa, L., Pintea, S., Rusu, A., David, D., (2012) Exploratory Study: Children’s with Autism Awareness of Being Imitated by Nao Robot, , arXiv arXiv:2003.03528; Santner, K., Fritz, G., Paletta, L., Mayer, H., Visual recovery of saliency maps from human attention in 3D environments Proceedings of the IEEE International Conference on Robotics & Automation, , Karlsruhe, Germany, 6–10 May 2013. [CrossRef]; Wang, H., Pi, J., Qin, T., Shen, S., Shi, B.E., SLAM-based localization of 3D gaze using a mobile eye tracker Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications, pp. 1-5. , Warsaw, Poland, 14–17 June 2018; [CrossRef]; Fathi, A., Hodgins, J.K., Rehg, J.M., Social interactions: A first-person perspective Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 1226-1233. , Providence, RI, USA, 16–21 June 2012; [CrossRef]; Marin-Jimenez, M.J., Zisserman, A., Eichner, M., Ferrari, V., Detecting People Looking at Each Other in Videos (2014) Int. J. Comput. Vis, 106, pp. 282-296. , [CrossRef]; Park, H.S., Jain, E., Sheikh, Y., Predicting Primary Gaze Behavior Using Social Saliency Fields Proceedings of the IEEE International Conference on Computer Vision, , Sydney, Australia, 1–8 December 2013. [CrossRef]; Recasens, Adriá Recasens Continente, (2016) Where are they looking? Diss. Massachusetts Institute of Technology, , http://gazefollow.csail.mit.edu/, (accessed on 2 June 2021); Lian, D., Yu, Z., Gao, S., Believe It or Not, We Know What You Are Looking at! 2018, , https://github.com/sviplab/GazeFollowing/, (accessed on 2 June 2021); Parks, D., Borji, A., Itti, L., Augmented saliency model using automatic 3D head pose detection and learned gaze following in natural scenes (2015) Vis. Res, 116, pp. 113-126. , [CrossRef] [PubMed]; Chong, E., Ruiz, N., Wang, Y., Zhang, Y., Rozga, A., Rehg, J.M., Connecting Gaze, Scene, and Attention: Generalized Attention Estimation via Joint Modeling of Gaze and Scene Saliency Proceedings of the European Conference on Computer Vision (ECCV), , Munich, Germany, 8–14 September 2018. [CrossRef]; Chong, E., Wang, Y., Ruiz, N., Regh, J.M., Detecting Attended Visual Targets in Video Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Online Conference, , 14–19 June 2020. [CrossRef]; Recasens, A., Vondrick, C., Khosla, A., Torralba, A., (2016) Following Gaze Across Views, , arXiv arXiv:1612.03094; Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R.S., Bengio, Y., Show, attend and tell: Neural image caption generation with visual attention Proceedings of the 32nd International Conference on Machine Learning, pp. 2048-2057. , Lille, France, 6–11 July 2015; Lu, J., Xiong, C., Parikh, D., Socher, R., Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), , Honolulu, HI, USA, 21–26 July 2017. [CrossRef]; Medsker, L.R., Jain, L.C., Recurrent neural networks (2001) Des. Appl, 5, pp. 64-67; Cornia, M., Baraldi, L., Serra, G., Cucchiara, R., Paying More Attention to Saliency: Image Captioning with Saliency and Context Attention (2018) Acm Trans. Multimed. Comput. Commun. Appl, 14, pp. 1-21. , [CrossRef]; Cornia, M., Baraldi, L., Serra, G., Cucchiara, R., Visual saliency for image captioning in new multimedia services Proceedings of the IEEE International Conference on Multimedia & Expo Workshops, , Hong Kong, China, 10–14 July 2017. [CrossRef]; Liu, Y., Wu, Q., Tang, L., Shi, H., Gaze-assisted Multi-stream Deep Neural Network for Action Recognition (2017) IEEE Access, 5, pp. 19432-19441. , [CrossRef]; Sugano, Y., Bulling, A., (2016) Seeing with Humans: Gaze-Assisted Neural Image Captioning, , arXiv arXiv:1608.05203; Johnson, J., Andrej, K., Li, F.-F., Densecap: Fully convolutional localization networks for dense captioning Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), , Las Vegas, NV, USA, 27–30 June 2016. [CrossRef]; Ren, S., He, K., Girshick, R., Sun, J., Faster R-CNN: Towards Real-time Object Detection with Region Proposal Networks (2015) Adv. Neural inf. Process. Syst, 28, pp. 91-99. , [CrossRef] [PubMed]; Yang, L., Tang, K., Yang, J., Li, L.-J., Dense Captioning with Joint Inference and Visual Context Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), , Minneapolis, MN, USA, 17–22 June 2017. [CrossRef]; Wang, E.K., Zhang, X., Wang, F., Wu, T.Y., Chen, C.M., Multilayer Dense Attention Model for Image Caption (2019) IEEE Access, 7, pp. 66358-66368. , [CrossRef]; Zhang, B., Zhou, L., Song, S., Chen, L., Jiang, Z., Zhang, J., Image Captioning in Chinese and Its Application for Children with Autism Spectrum Disorder Proceedings of the ICMLC 12th International Conference on Machine Learning and Computing, , Shenzhen, China, 15–17 February 2020. [CrossRef]; Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Shamma, D.A., Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations (2017) Int. J. Comput. Vis, 123, pp. 32-73. , [CrossRef]; Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Bernstein, M., ImageNet Large Scale Visual Recognition Challenge (2015) Int. J. Comput. Vis, 115, pp. 211-252. , [CrossRef]; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Comput, 9, pp. 1735-1780. , [CrossRef] [PubMed]; Jia, D., Wei, D., Socher, R., Li, L.-J., Li, K., Fei-Fei, L., ImageNet: A large-scale hierarchical image database Proceedings of the IEEE Computer Vision & Pattern Recognition, pp. 248-255. , Miami, FL, USA, 20–25 June 2009; [CrossRef]; Vinyals, O., Toshev, A., Bengio, S., Erhan, D., Show and Tell: A Neural Image Caption Generator Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), , Boston, MA, USA, 7–12 June 2015. [CrossRef]; Lu, J., Yang, J., Batra, D., Parikh, D., Neural Baby Talk Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, , Salt Lake City, UT, USA, 18–23 June 2018. [CrossRef]; Redmon, J., Farhadi, A., (2018) YOLOv3: An Incremental Improvement, , arXiv arXiv:1804.02767; Baltrusaitis, T., Zadeh, A., Lim, Y.C., Morency, L.-P., OpenFace 2.0: Facial Behavior Analysis Toolkit Proceedings of the IEEE 13th International Conference on Automatic Face & Gesture Recognition, pp. 59-66. , IEEE Computer Society, Xi’an, China, 15–19 May 2018; [CrossRef]; Mur-Artal, R., Tardós, J.D., ORB-SLAM2: An Open-source SLAM System for Monocular, Stereo, and RGB-D Cameras (2017) IEEE Trans. Robot, 33, pp. 1255-1262. , [CrossRef]; Montabone, S., Soto, A., Human detection using a mobile platform and novel features derived from a visual saliency mechanism (2010) Image Vis. Comput, 28, pp. 391-402. , [CrossRef]; Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y., (2019) BERTScore: Evaluating Text Generation with BERT, , https://github.com/Tiiiger/bert_score/, (accessed on 2 June 2021)","Su, J.; School of AI and Advanced Computing, China; email: Jionglong.Su@xjtlu.edu.cn
Sun, Z.; School of Science and Engineering, China; email: sunzhenglong@cuhk.edu.cn
Zhang, J.; Institute of Artificial Intelligence and Robotics for SocietyChina; email: zhangjiaming@cuhk.edu.cn",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85113176896
"Zhang Y., Wang J., Chen W., Zhang J., Chen Z.","57268784900;56075130700;35776127700;57345728500;57206982817;","Design and Optimization of Human-Machine Interaction Wearable Device for Lower Limb Rehabilitation Exoskeleton",2021,"Proceedings of the 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021",,,,"2101","2106",,,"10.1109/ICIEA51954.2021.9516326","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115443362&doi=10.1109%2fICIEA51954.2021.9516326&partnerID=40&md5=69d04450c71aaebfaf18bb3e77a57a3d","School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100083, China; Hangzhou Innovation Institue, Beihang University, Hangzhou, 310051, China; School of Mechanical Engineering and Automation, Beihang University, Beijing, 100083, China; First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang, 310000, China","Zhang, Y., School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100083, China, Hangzhou Innovation Institue, Beihang University, Hangzhou, 310051, China; Wang, J., School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100083, China, Hangzhou Innovation Institue, Beihang University, Hangzhou, 310051, China; Chen, W., School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100083, China, Hangzhou Innovation Institue, Beihang University, Hangzhou, 310051, China; Zhang, J., Hangzhou Innovation Institue, Beihang University, Hangzhou, 310051, China, School of Mechanical Engineering and Automation, Beihang University, Beijing, 100083, China; Chen, Z., First Affiliated Hospital, College of Medicine, Zhejiang University, Zhejiang, 310000, China","In this article, we designed a wearable device for the lower limb rehabilitation exoskeleton robots. The device is mainly composed of three parts: a slide rail mechanism, a detecting human-machine interaction force structure and a binding part. By adding passive degree of freedom to automatically compensate for the joint axis deviation between the exoskeleton and the human body, thereby reducing the radial parasitic force on the human leg. In order to verify the effect of automatic compensation of passive degrees of freedom, we conduct statics modeling for human-robot interaction coupling. At the same time, we also designed a device for detecting interaction force, which can obtain human movement intentions in rehabilitation training, and provide a hardware foundation for the research of human-robot cooperative control of rehabilitation exoskeleton. The detection device has the advantages of low cost, compact structure, and good detection performance. © 2021 IEEE.","human-robot coupling; interaction force detection; rehabilitation exoskeleton; wearable device","Degrees of freedom (mechanics); Exoskeleton (Robotics); Human robot interaction; Machine design; Man machine systems; Neuromuscular rehabilitation; Force detection; Human machine interaction; Human robots; Human-robot coupling; Interaction force detection; Interaction forces; Limb rehabilitation; Lower limb; Rehabilitation exoskeleton; Wearable devices; Wearable technology",,,,,,,,,,,,,,,,"Kawamoto, H., Kamibayashi, K., Nakata, Y., Pilot study of locomotion improvement using hybrid assistive limb in chronic stroke patients (2013) Bmc Neurology, 13 (1), pp. 141-148; Kozlowski, A.J., Bryce, T.N., Dijkers, M.P., Time and effort required by persons with spinal cord injury to learn to use a powered exoskeleton for assisted walking (2015) Topics in Spinal Cord Injury Rehabilitation, 21 (2), p. 110; Campbell, M., Exoskeleton helps the paralysed walk again (2010) New Entist, 208 (2782), p. 21; Hal, S.Y., Hybrid assistive limb based on cybernics (2010) The 13th International Symposium, Isrr 2007, , Hiroshima, Japan, Nov; Kawabata, T., Satoh, H., Sankai, Y., Working posture control of robot suit hal for reducing structural stress (2009) International Conference on Robotics & Biomimetics; Cruciger, O., Schildhauer, T.A., Meindl, R.C., Impact of locomotion training with a neurologic controlled hybrid assistive limb (hal) exoskeleton on neuropathic pain and health related quality of life (hrqol) in chronic sci: Acasestudy (2016) Disability and Rehabilitation: Assistive Technology, 11 (6), pp. 529-534; Yamamoto, K., Ishii, M., Stand alone wearable power assisting suit -sensing and control systems (2004) IEEE International Workshop on Robot & Human Interactive Communication, pp. 661-666; McGeer, T., Passive dynamic walking (1990) The International Journal of Robotics Research, 9 (2), pp. 62-82; Tao, H., Yu, Y., Zhang, Q., Sun, H., Ge, Y., A control system based on mcu for wearable power assist legs (2011) IEEE International Conference on Robotics and Biomimetics, pp. 2193-2198. , Karon Beach, Thailand; http://activelink.co.jp/doc/668.html; Veneman, J.F., Kruidhof, R., Hekman, E.E.G., Design and evaluation of the lopes exoskeleton robot for interactive gait rehabilitation (2007) IEEE Transactions on Neural Systems AndRehabilitation Engineering, 15 (3), pp. 379-386; Vallery, H., Veneman, J., Asseldonk, E.V., Compliant actuation of rehabilitationrobots (2008) Robotics & Automation Magazine, 15 (3), pp. 60-69; Meuleman, J., Van Asseldonk, E., Van Oort, G., Lopes 11 -design and evaluation of an admittance controlled gait training robot with shadow-leg approach (2016) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24 (3), pp. 352-363; Veneman, J.F., Ekkelenkamp, R., Kruidhof, R., Design of a series elastic-And bowden cable-based actuation system for use as torque-Actuator in exoskeleton-Type training (2006) International Journal of Robotics Research, 25 (3), pp. 261-281; Kong, K., Bae, J., Tomizuka, M., Control of rotary series elastic actuator for ideal force-mode actuation in human-robot interaction applications (2009) IEEE/ASME Transactions on Mechatronics, 14 (1), pp. 105-118; Iwaki, H., Pinskerova, V., Freeman, M.A.R., Tibiofemoral movement 1: The shapes and relative movements of the femur and tibia in the unloaded cadaver knee (2000) Journal of Bone & . Joint Surgery, British Volume, 82 (8), pp. 1189-1195","Wang, J.; School of Automation Science and Electrical Engineering, China; email: jhwangbuaa@126.com",,,"Institute of Electrical and Electronics Engineers Inc.","16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021","1 August 2021 through 4 August 2021",,171511,,9781665422482,,,"English","Proc. IEEE Conf. Ind. Electron. Appl. ICIEA",Conference Paper,"Final","",Scopus,2-s2.0-85115443362
"Ostovar A., Bensch S., Hellström T.","55420091600;16315104000;55926741800;","Natural language guided object retrieval in images",2021,"Acta Informatica","58","4",,"243","261",,,"10.1007/s00236-021-00400-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110811104&doi=10.1007%2fs00236-021-00400-2&partnerID=40&md5=81aadd90738ce7182f8ddc9074b9ad6a","Department of Computing Science, Umeå University, Umeå, 901 87, Sweden","Ostovar, A., Department of Computing Science, Umeå University, Umeå, 901 87, Sweden; Bensch, S., Department of Computing Science, Umeå University, Umeå, 901 87, Sweden; Hellström, T., Department of Computing Science, Umeå University, Umeå, 901 87, Sweden","The ability to understand the surrounding environment and being able to communicate with interacting humans are important functionalities for many automated systems where visual input (e.g., images, video) and natural language input (speech or text) have to be related to each other. Possible applications are automatic image caption generation, interactive surveillance systems, or human robot interaction. In this paper, we propose algorithms for automatic responses to natural language queries about an image. Our approach uses a predefined neural net for detection of bounding boxes and objects in images, spatial relations between bounding boxes are modeled with a neural net, the queries are analyzed with a syntactic parser, and algorithms to map natural language to properties in the images are introduced. The algorithms make use of semantic similarity and antonyms. We evaluate the performance of our approach with test users assessing the quality of our system’s generated answers. © 2021, The Author(s).",,"Automation; Natural language processing systems; Neural networks; Semantics; Social robots; Syntactics; Automated systems; Automatic response; Natural language queries; Semantic similarity; Spatial relations; Surrounding environment; Surveillance systems; Syntactic parsers; Visual languages",,,,,,"The authors gratefully acknowledge the permission to use the images from the Visual Genome dataset [20] under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). The following changes were made to the original images: Fig.?2 , three bounding boxes and indices added; Figs.?3 , 4 and 6 , blue and red bounding boxes and labels ?person? and ?monitor? added; Fig.?5 , four bounding boxes added; Fig.?6 , bounding boxes, arrows and green lines added. 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Syst., 1 (2), p. 5; Ren, S., He, K., Girshick, R.B., Sun, J., Faster R-CNN: towards real-time object detection with region proposal networks (2017) IEEE Trans. Pattern Anal. Mach. Intell., 39 (6), pp. 1137-1149; Rohrbach, A., Rohrbach, M., Hu, R., Darrell, T., Schiele, B., Grounding of textual phrases in images by reconstruction (2016) European Conference on Computer Vision, pp. 817-834. , Springer; Shih, K.J., Singh, S., Hoiem, D., Where to look: Focus regions for visual question answering (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4613-4621; Vinyals, O., Toshev, A., Bengio, S., Erhan, D., Show and tell: A neural image caption generator (2015) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3156-3164; Yu, L., Lin, Z., Shen, X., Yang, J., Lu, X., Bansal, M., Berg, T.L., Mattnet: Modular attention network for referring expression comprehension (2018) 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18–22, 2018, Pp. 1307–1315. IEEE Computer Society; Zhan, Y., Yu, J., Yu, T., Tao, D., On exploring undetermined relationships for visual relationship detection (2019) IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5123-5132. , https://doi.org/10.1109/CVPR.2019.00527, 2019; Zhang, L., Zhang, S., Shen, P., Zhu, G., Afaq Ali Shah, S., Bennamoun, M., Relationship detection based on object semantic inference and attention mechanisms (2019) Proceedings of the 2019 on International Conference on Multimedia Retrieval, ICMR’19, pp. 68-72. , https://doi.org/10.1145/3323873.3325025, Association for Computing Machinery, New York, NY, USA; Zhao, J., Wang, T., Yatskar, M., Ordonez, V., Chang, K., Men also like shopping: Reducing gender bias amplification using corpus-level constraints (2017) Palmer, M., Hwa, R., Riedel, S. (Eds.) Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Copenhagen, Denmark, September 9–11, 2017, Pp. 2979–2989. Association for Computational Linguistics; Zhu, Y., Jiang, S., (2018) Deep Structured Learning for Visual Relationship Detection, , AAAI","Bensch, S.; Department of Computing Science, Sweden; email: suna@cs.umu.se",,,"Springer Science and Business Media Deutschland GmbH",,,,,00015903,,AINFA,,"English","Acta Inform.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85110811104
"Yousaf S.N., Ghonasgi K., Esmatloo P., Deshpande A.D.","57212314873;57190752341;57195415034;7202168327;","An actuated indenter for characterization of soft tissue towards human-centered design",2021,"IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM","2021-July",,,"1243","1248",,,"10.1109/AIM46487.2021.9517510","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114960480&doi=10.1109%2fAIM46487.2021.9517510&partnerID=40&md5=9e90cf78a9384ccda6f03b321864db58","The University of Texas at Austin, Department of Mechanical Engineering, Austin, TX  78712, United States","Yousaf, S.N., The University of Texas at Austin, Department of Mechanical Engineering, Austin, TX  78712, United States; Ghonasgi, K., The University of Texas at Austin, Department of Mechanical Engineering, Austin, TX  78712, United States; Esmatloo, P., The University of Texas at Austin, Department of Mechanical Engineering, Austin, TX  78712, United States; Deshpande, A.D., The University of Texas at Austin, Department of Mechanical Engineering, Austin, TX  78712, United States","The viscoelastic properties of human soft tissue influence the nature of interaction at attachment points in wearable devices. Characterizing these properties is especially critical for understanding physical human-robot interaction (pHRI) for exoskeleton design, prosthetics, and similar fields. This paper presents the design and control of a novel actuated indenter for the measurement of human soft tissue properties. The accuracy of position (0.025 mm) and force (111 mN) measurements allows for repeatable and controlled tissue deformation and monitoring at 36 different angular locations (10° increments). Controlled indentation displacement with force feedback is used in three types of applications on a human subject's forearm. The soft tissue stiffness profile, viscoelastic relaxation behavior, and frequency domain response are measured. The ability to perform multiple characterizations with one indenter device broadens the scope of a human-centered approach to design, modeling, and perception as they relate to pHRI interfaces. © 2021 IEEE.",,"Biofeedback; Exoskeleton (Robotics); Frequency domain analysis; Human robot interaction; Intelligent mechatronics; Machine design; Viscoelasticity; Angular locations; Design and control; Frequency domain response; Human-centered designs; Physical humanrobot interaction (phri); Tissue deformations; Viscoelastic properties; Viscoelastic relaxation; Tissue",,,,,"National Science Foundation, NSF: 1941260, 2019704, DGE-1610403","This work was funded, in part, by the National Science Foundation (NSF) grant numbers 2019704 and 1941260, as well as the NSF Graduate Research Fellowship Program (GRFP) grant number DGE-1610403.",,,,,,,,,,"Varghese, R.J., Mukherjee, G., King, R., Keller, S., Deshpande, A.D., Designing variable stiffness profiles to optimize the physical human robot interface of hand exoskeletons (2018) 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), pp. 1101-1108; Sengeh, D.M., Herr, H., A variable-impedance prosthetic socket for a transtibial amputee designed from magnetic resonance imaging data (2013) JPO: Journal of Prosthetics and Orthotics, 25 (3), pp. 129-137; Groves, R.B., Coulman, S.A., Birchall, J.C., Evans, S.L., An anisotropic, hyperelastic model for skin: Experimental measurements, finite element modelling and identification of parameters for human and murine skin (2013) Journal of the Mechanical Behavior of Biomedical Materials, 18, pp. 167-180; Liu, Y., Kerdok, A.E., Howe, R.D., A nonlinear finite element model of soft tissue indentation (2004) International Symposium on Medical Simulation., pp. 67-76. , Springer; Bolanowski, S.J., Jr., Gescheider, G.A., Verrillo, R.T., Checkosky, C.M., Four channels mediate the mechanical aspects of touch (1988) The Journal of the Acoustical Society of America, 84 (5), pp. 1680-1694; Richardson, K.A., Imhoff, T.T., Grigg, P., Collins, J.J., Using electrical noise to enhance the ability of humans to detect subthreshold mechanical cutaneous stimuli (1998) Chaos: An Interdisciplinary Journal of Nonlinear Science, 8 (3), pp. 599-603; Ferrington, D., Nail, B., Rowe, M., Human tactile detection thresholds: Modification by inputs from specific tactile receptor classes (1977) The Journal of Physiology, 272 (2), pp. 415-433; Zheng, Y.-P., Mak, A.F., An ultrasound indentation system for biomechanical properties assessment of soft tissues in-vivo (1996) IEEE Transactions on Biomedical Engineering, 43 (9), pp. 912-918; Oflaz, H., Baran, O., A new medical device to measure a stiffness of soft materials (2014) Acta of Bioengineering and Biomechanics, 16 (1); Petron, A., Duval, J.-F., Herr, H., Multi-indenter device for in vivo biomechanical tissue measurement (2016) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (5), pp. 426-435; Oh, D., Lee, S., Choi, Y., Simultaneous stiffness measurement device for a human forearm (2020) IEEE Access, 8, pp. 15313-15321; Xiong, S., Goonetilleke, R.S., Witana, C.P., Rodrigo, W., An indentation apparatus for evaluating discomfort and pain thresholds in conjunction with mechanical properties of foot tissue in vivo (2010) Journal of Rehabilitation Research & Development, 47 (7); Oh, D., Lee, S., Choi, Y., Development of 6-axis stiffness measurement device for prosthetic socket design (2019) The Journal of Korea Robotics Society, 14 (1), pp. 58-64; Samur, E., Sedef, M., Basdogan, C., Avtan, L., Duzgun, O., A robotic indenter for minimally invasive measurement and characterization of soft tissue response (2007) Medical Image Analysis, 11 (4), pp. 361-373; Pailler-Mattei, C., Bec, S., Zahouani, H., Vivo measurements of the elastic mechanical properties of human skin by indentation tests (2008) Medical Engineering & Physics, 30 (5), pp. 599-606; Jones, D.H., Kilgour, R.D., Comtois, A.S., Test-retest reliability of pressure pain threshold measurements of the upper limb and torso in young healthy women (2007) The Journal of Pain, 8 (8), pp. 650-656; Ahn, B., Kim, J., Measurement and characterization of soft tissue behavior with surface deformation and force response under large deformations (2010) Medical Image Analysis, 14 (2), pp. 138-148","Deshpande, A.D.; The University of Texas at Austin, United States; email: ashish@austin.utexas.edu",,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021","12 July 2021 through 16 July 2021",,171450,,9781665441391,,,"English","IEEE ASME Int Conf Adv Intellig Mechatron AIM",Conference Paper,"Final","",Scopus,2-s2.0-85114960480
"Buck A.R., Anderson D.T., Keller J.M., Luke R.H., Scott G.","41261132900;55483345800;7403486724;7102188774;7402930105;","A Fuzzy Spatial Relationship Graph for Point Clouds Using Bounding Boxes",2021,"IEEE International Conference on Fuzzy Systems","2021-July",,,"","",,1,"10.1109/FUZZ45933.2021.9494462","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109219071&doi=10.1109%2fFUZZ45933.2021.9494462&partnerID=40&md5=3ee29e708d4f2c554c925170ba8abb44","University of Missouri, Electrical Engineering and Computer Science (EECS) Department, Columbia, MO, United States; Us Army Devcom C5ISR Center, Fort Belvoir, VA, United States","Buck, A.R., University of Missouri, Electrical Engineering and Computer Science (EECS) Department, Columbia, MO, United States; Anderson, D.T., University of Missouri, Electrical Engineering and Computer Science (EECS) Department, Columbia, MO, United States; Keller, J.M., University of Missouri, Electrical Engineering and Computer Science (EECS) Department, Columbia, MO, United States; Luke, R.H., Us Army Devcom C5ISR Center, Fort Belvoir, VA, United States; Scott, G., University of Missouri, Electrical Engineering and Computer Science (EECS) Department, Columbia, MO, United States","Three dimensional point cloud data sets are easy to acquire and manipulate, but are often too large to process directly for embedded real-time applications. The spatial information in a point cloud can be represented in a variety of reduced forms, such as voxel grids, Gaussian mixture models, or spatial semantic structures. In this article, we show how a segmented point cloud can be represented as a spatial relationship graph using bounding boxes and triangular fuzzy numbers. This model is a lightweight encoding of the relative distance and direction between objects, and can be used to describe and query for particular spatial configurations using linguistic terms in a multicriteria framework. We show how this approach can be applied on a hand-segmented subset of the NPM3D data set with several illustrative examples. The work herein has useful applications in many applied domains, such as human-robot interaction with unmanned aerial systems. © 2021 IEEE.",,"Antennas; Fuzzy sets; Semantics; Embedded real-time applications; Gaussian Mixture Model; Spatial configuration; Spatial informations; Spatial relationships; Three-dimensional point clouds; Triangular fuzzy numbers; Unmanned aerial systems; Social robots",,,,,,,,,,,,,,,,"Dhawale, A., Yang, X., Michael, N., Reactive collision avoidance using real-time local Gaussian mixture model maps (2018) Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3545-3550. , October; Wu, W., Qi, Z., Fuxin, L., Pointconv: Deep convolutional networks on 3d point clouds (2019) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), , June; Thomas, H., Qi, C.R., Deschaud, J.-E., Marcotegui, B., Goulette, F., Guibas, L.J., KPConv: Flexible and deformable convolution for point clouds (2019) Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), , October; Roynard, X., Deschaud, J.-E., Goulette, F., Paris-Lille-3d: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification (2018) The International Journal of Robotics Research, 37 (6), pp. 545-557; Chronis, G., Skubic, M., Sketch-based navigation for mobile robots (2003) The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03, 1, pp. 284-289; Skubic, M., Matsakis, P., Forrester, B., Chronis, G., Extracting navigation states from a hand-drawn map (2001) Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164), 1, pp. 259-264; Haldekar, M., Ganesan, A., Oates, T., (2017) Identifying Spatial Relations in Images Using Convolutional Neural Networks; Matsakis, P., Keller, J.M., Wendling, L., Marjamaa, J., Sjahputera, O., Linguistic description of relative positions in images (2001) IEEE Trans. Syst. Man Cybern. Part B, 31 (4), pp. 573-588. , https://doi.org/10.1109/3477.938261; Matsakis, P., Keller, J.M., Sjahputera, O., Marjamaa, J., The use of force histograms for affine-invariant relative position description (2004) IEEE Trans. Pattern Anal. Mach. Intell., 26 (1), pp. 1-18; Reed, J., Naeem, M., Matsakis, P., A first algorithm to calculate force histograms in the case of 3d vector objects (2014) Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods-ICPRAM,, INSTICC, pp. 104-112. , SciTePress; Schonberger, J.L., Frahm, J.-M., Structure-frommotion revisited (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), , June; Godard, C., Mac Aodha, O., Firman, M., Brostow, G.J., Digging into self-supervised monocular depth estimation (2019) Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), , October; Klir, G.J., Yuan, B., (1995) Fuzzy Sets and Fuzzy Logic: Theory and Applications, , Upper Saddle River, NJ: Prentice Hall",,,"IEEE Computational Intelligence Society","Institute of Electrical and Electronics Engineers Inc.","2021 IEEE CIS International Conference on Fuzzy Systems, FUZZ 2021","11 July 2021 through 14 July 2021",,171254,10987584,9781665444071,PIFSF,,"English","IEEE Int Conf Fuzzy Syst",Conference Paper,"Final","",Scopus,2-s2.0-85109219071
"Tornbjerg K., Kanstrup A.M., Skov M.B., Rehm M.","57024237200;6602398981;7004242735;10039896700;","Investigating human-robot cooperation in a hospital environment: Scrutinising visions and actual realisation of mobile robots in service work",2021,"DIS 2021 - Proceedings of the 2021 ACM Designing Interactive Systems Conference: Nowhere and Everywhere",,,,"381","391",,,"10.1145/3461778.3462101","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110216201&doi=10.1145%2f3461778.3462101&partnerID=40&md5=ce52842e8f0a89527134c56e5c8d1174","Department of Planning, Aalborg University, Denmark; Department of Computer Science, Aalborg University, Denmark; Department of Architecture, Design and Media Technology, Aalborg University, Denmark","Tornbjerg, K., Department of Planning, Aalborg University, Denmark; Kanstrup, A.M., Department of Planning, Aalborg University, Denmark; Skov, M.B., Department of Computer Science, Aalborg University, Denmark; Rehm, M., Department of Architecture, Design and Media Technology, Aalborg University, Denmark","This study analysed work activity in a hospital basement where humans and robots interacted and cooperated on logistics tasks. The robots were deployed to automate parts of courier processes and improve the work environment for the hospital's kitchen staff. Human-robot cooperation was studied through ethnographic fieldwork relating to mobile service robots and hospital kitchen staff. The results highlighted problems arising through the assumption that the gplug and play' service robots could effectively automate work tasks. The analysis revealed the complexity of human-robot interaction in dynamic work settings such as hospitals and identified contradictions between the envisioning and realisation of robots at work, as well as the visible and invisible procedures underpinning human-robot cooperation. Consequently, we emphasise the importance of considering robots as agents of change and draw attention to the new work practices that arise when robots assume the roles of workers in dynamic work settings. © 2021 Owner/Author.","hospital; Human-robot cooperation; invisible work; service staff","Hospitals; Mobile robots; Hospital environment; Human-robot cooperation; Mobile service robots; Service robots; Work activities; Work environments; Work practices; Work task; Social robots",,,,,,,,,,,,,,,,"Regioner, D., (2015) Pres På Sundhedsvaesenet: Derfor Stiger Sygehusudgifterne-Sådan Holder VI Vaeksten Nede, , https://www.regioner.dk/media/2209/2015-pres-paa-sundhedsvaesenet.pdf, Retrieved February 10, 2021 from; Layers of silence, arenas of voice: The ecology of visible and invisible work (1999) Comput. Support. Coop. Work (CSCW), 9. , Leigh Star, Strauss, Kluwer Academic Publishers, Netherlands; Suchman, L.A., (1987) Plans and Situated Actions: The Problem of Humanmachine Communication, , Cambridge University Press, USA; Ljungblad, S., Serholt, S., Barendregt, W., Lindgren, P., Obaid, M., (2016) Are We Really Addressing the Human in Human-Robot; Šabanović, S., Robots in society, society in robots (2010) Int J of Soc Robot, 2 (4), pp. 439-450; Fernaeus, Y., Jacobsson, M., Ljungblad, S., Erik Holmqvist, L., Are we living in a robot cargo cult? (2009) Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI' 09), pp. 279-280; MacKenzie, D., Wajcman, J., Introductory essay: The social shaping of technology (1999) The Social Shaping of Technology (2nd. Ed.), pp. 3-27. , Donald MacKenzie and Judy Wajcman (Eds.). Open University Press, Buckingham, UK; Ljungblad, S., Serholt, S., Milosevic, T., Toft Norgaard, R., Ni Bhroin, N., Lindgren, P., Ess, C., Obaid, M., Critical robotics-Exploring a new paradigm (2018) Proceedings of the 10th Nordic Conference on Human-Computer Interaction (NordiCHI'18), , Oslo, Norway; Ljungblad, S., Serholt, S., Barendregt, W., Lindgren, P., Obaid, M., Are we really addressing the human in human-robot interaction? Adopting the phenomenologically-situated paradigm (2016) What Social Robots Can and Should Do: Proceedings of Robophilosophy 2016/TRANSOR 2016, pp. 99-103. , J. Seibt, M. Nørskov & S. Schack (Eds.), IOS Press, Amsterdam, Netherlands; Mettler, T., Raptis, D., What constitutes the field of health information systems? Fostering a systematic framework and research agenda (2011) Health Informatics Journal., 18, pp. 147-156; Haidegger, T., Barreto, M., Gonçalves, P., Habib, M.K., Ragavan, S.K.V., Li, H., Vaccarella, A., Prestes, E., Applied ontologies and standards for service robots (2013) Robot. Auton. Syst., 61 (11), pp. 1215-1223; (2012) Robots and Robotic Devices-Vocabulary, , https://www.iso.org/obp/ui/fr/#iso:std:iso:8373:ed-2:v1:en, International Organization for Standardization, ISO Standard No. 8373: 2012; Deery, J., Courier robot keeps hospital staff 'on the job (1997) The Journal for Healthcare Quality (JHQ): January-February, 19 (1), pp. 22-23; Kirschling, T.E., Rough, S.S., Ludwig, B.C., Determining the feasibility of robotic courier medication delivery in a hospital setting (2009) Am J Health Syst Pharm., 66 (19), pp. 1754-1762; Rossetti, M.D., Kumar, A., Felder, R.A., Mobile robot simulation of clinical laboratory deliveries (1998) Winter Simul. Conf. Proc., 2 (1987), pp. 1415-1422; Evans, J., Krishnamurthy, B., Barrows, B., Skewis, T., Lumelsky, V., Handling real-world motion planning: A hospital transport robot (1992) IEEE Control Systems Magazine, 12 (1), pp. 15-19; Mutlu, B., Forlizzi, J., Robots in organizations: The role of workflow, social, and environmental factors in human-robot interaction (2008) Proc. 3rd ACM/IEEE International Conference on Human-Robot Interaction. Living with Robot., pp. 287-294; Ljungblad, S., Kotrbova, J., Jacobsson, M., Crame, H., Niechwiadowicz, K., Hospital robot at work: Something alien or an intelligent colleague? (2012) Proc. ACM Conf. Comput. Support. Coop. Work. CSCW, pp. 177-186. , February; Mettler, T., Sprenger, M., Winter, R., Service robots in hospitals: New perspectives on niche evolution and technology affordances (2017) European Journal of Information Systems, 26 (5), pp. 451-468; Cresswell, K., Cunningham-Burley, S., Sheikh, A., Health care robotics: Qualitative exploration of key challenges and future directions (2018) Journal of Medical Internet Research, 20 (7), p. e10410; Schmidt, K., Bannon, L., Constructing CSCW: The first quarter century (2013) Comput. Support. Coop. Work (CSCW), 22, pp. 345-372; Luff, P., Hindmarsh, J., Heath, C., (2000) Workplace Studies: Recovering Work Pratice and Informing System Design, , Cambridge University Press; Health, C., Luff, P., (2000) Technology in Action, , Cambridge University Press, 2000; Rodden, T.A., Mariani, J.A., Blair, G., Supporting cooperative applications (1992) Computer Supported Cooperative Work (CSCW): An International Journal, 1 (1-2), pp. 41-68. , 1992; Suchman, L., (1987) Plans and Situated Actions: The Problem of Human-machine Communication"", , Cambridge University Press; Crabtree, A., Rodden, T.A., Benford, S.D., Moving with the times: IT research and the boundaries of CSCW (2005) Comput. Support. Coop.Work (CSCW): The Journal of Collaborative Computing, 14 (3), pp. 217-251; Corbin, J., Strauss, A., Managing chronic illness at home: Three lines of work (1985) Qualitative Sociology, 8, pp. 224-247; Schmith, K., The concept of 'work' in CSCW (2011) Comput. Support. Coop. Work (CSCW), 20, pp. 341-401; Biernacki, P., Waldorf, D., Snowball sampling: Problems and techniques of chain referral sampling (1981) Sociological Methods and Research., 10, pp. 141-163; Spradley, J.P., (1979) The Ethnographic Interview., , Holt, Rinehart and Winston, New York, USA; Everett, M., Barrett, M., Guided tour: A method for deepening the relational quality in narrative research (2012) Qualitative Research Journal., 12, pp. 32-46; Blomberg, J., Burrell, M., Guest, G., An ethnographic approach to design (2002) The Human-computer Interaction Handbook: Fundamentals, Evolving Technologies and Emerging Applications., pp. 964-986. , L. Erlbaum Associates, Inc; Feil-Seifer, D.J., Matarić, M.J., Ethical principles for socially assistive robotics (2011) IEEE Robot. Autom. Mag., 18 (1), pp. 24-31; Goodrich, M., Schultz, A., Human-robot interaction: A survey (2007) Foundations and Trends in Human-Computer Interaction., 1, pp. 203-275; Sperschneider, W., Bagger, K., Ethnographic fieldwork under industrial constraints: Towards Design-in-Context (2000) International Journal of Humancomputer Interaction-IJHCI; Braun, V., Clarke, V., Using thematic analysis in psychology (2006) Qualitative Research in Psychology, 3 (2), pp. 77-101; Beyer, H., Holtzblatt, K., (1999) Contextual Design. Interactions., 6, pp. 32-42; Friis Jørgensen, J., Introduktion til arbejdsgangsanalyse og design (2019) Sølvkjaer, Bergstedt. Sundhedsteknologi i Praksis, pp. 207-230. , FADL's Forlag, Copenhagen; Nardi, B., O'Day, V., (1999) Information Ecologies, Using Technology with Heart, , The MIT Press, USA",,,"ACM Special Interest Group on Computer-Human Interaction (SIGCHI)","Association for Computing Machinery, Inc","2021 ACM Designing Interactive Systems Conference: Nowhere and Everywhere, DIS 2021","28 June 2021 through 2 July 2021",,169894,,9781450384766,,,"English","DIS - Proc. ACM Des. Interact. Syst. Conf.: Nowhere Everywhere",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85110216201
"Alves-Oliveira P., Lupetti M.L., Luria M., Löffler D., Gamboa M., Albaugh L., Kamino W., K. Ostrowski A., Puljiz D., Reynolds-Cuéllar P., Scheunemann M., Suguitan M., Lockton D.","56515305600;56685107500;56895782800;56096474900;57222464648;57190166429;57220182215;57217659921;55809478000;57197787644;56549821200;57201559535;34875194700;","Collection of Metaphors for Human-Robot Interaction",2021,"DIS 2021 - Proceedings of the 2021 ACM Designing Interactive Systems Conference: Nowhere and Everywhere",,,,"1366","1379",,,"10.1145/3461778.3462060","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110173056&doi=10.1145%2f3461778.3462060&partnerID=40&md5=03240c08b21f31984263a9ece05fd2b0","University of Washington, United States; Delft University of Technology, Netherlands; Carnegie Mellon University, United States; University of Siegen, Germany; Chalmers University of Technology, Sweden; Indiana University, United States; Mit Media Lab Mit, United States; Karlsruhe Institute of Technology, Germany; University of Hertfordshire, United Kingdom; Cornell University, United States; Eindhoven University of Technology, Netherlands","Alves-Oliveira, P., University of Washington, United States; Lupetti, M.L., Delft University of Technology, Netherlands; Luria, M., Carnegie Mellon University, United States; Löffler, D., University of Siegen, Germany; Gamboa, M., Chalmers University of Technology, Sweden; Albaugh, L., Carnegie Mellon University, United States; Kamino, W., Indiana University, United States; K. Ostrowski, A., Mit Media Lab Mit, United States; Puljiz, D., Karlsruhe Institute of Technology, Germany; Reynolds-Cuéllar, P., Mit Media Lab Mit, United States; Scheunemann, M., University of Hertfordshire, United Kingdom; Suguitan, M., Cornell University, United States; Lockton, D., Eindhoven University of Technology, Netherlands","The word ""robot""frequently conjures unrealistic expectations of utilitarian perfection: tireless, efficient and flawless agents. However, real-world robots are far from perfect - they fail and make mistakes. Thus, roboticists should consider altering their current assumptions and cultivating new perspectives that account for a more complete range of robot roles, behaviors, and interactions. To encourage this, we explore the use of metaphors for generating novel ideas and reframing existing problems, eliciting new perspectives of human-robot interaction. Our work makes two contributions. We (1) surface current assumptions that accompany the term ""robots,""and (2) present a collection of alternative perspectives of interaction with robots through metaphors. By identifying assumptions, we provide a comprehensible list of aspects to reconsider regarding robots' physicality, roles, and behaviors. Through metaphors, we propose new ways of examining how we can use, relate to, and co-exist with the robots that will share our future. © 2021 ACM.","design research; fixation; human-robot interaction; metaphors; robot assumptions","Man machine systems; Existing problems; Real-world; Reframing; Surface current; Human robot interaction",,,,,,,,,,,,,,,,"Ahmad, M.I., Mubin, O., Orlando, J., Adaptive social robot for sustaining social engagement during long-term children-robot interaction (2017) International Journal of Human-Computer Interaction, 33 (12), pp. 943-962; Alves-Oliveira, P., Arriaga, P., Paiva, A., Hoffman, G., Guide to build yolo, a creativity-stimulating robot for children (2019) HardwareX, 6, p. e00074; Arthur, K., (1964) The Act of Creation, 751. , London: Hutchinson; Auger, J., Living with robots: A speculative design approach (2014) Journal of Human-Robot Interaction, 3 (1), pp. 20-42; Baraka, K., Alves-Oliveira, P., Ribeiro, T., An extended framework for characterizing social robots (2020) Human-Robot Interaction, pp. 21-64. , Springer, Cham; Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., Šabanovic, S., (2020) Humanrobot Interaction: An Introduction, , Cambridge University Press; Boer, L., Bewley, H., Reconfiguring the appearance and expression of social robots by acknowledging their otherness (2018) Conference on Designing Interactive Systems, pp. 667-677. , June; Bradshaw, J.M., Hoffman, R.R., Woods, D.D., Johnson, M., The seven deadly myths of"" autonomous systems (2013) Ieee Intelligent Systems, 28 (3), pp. 54-61; Casakin, H., Metaphors in the design studio: Implications for education (2004) Ds 33: Proceedings of E&pde 2004, the 7th International Conference on Engineering and Product Design Education, pp. 265-273. , Delft, the Netherlands, 02.-03.09. 2004; Casakin, H.P., Assessing the use of metaphors in the design process (2006) Environment and Planning B: Planning and Design, 33 (2), pp. 253-268; Casakin, H.P., Metaphors in design problem solving: Implications for creativity (2007) International Journal of Design, 1 (2); Cheon, E., Su, N.M., Futuristic autobiographies: Weaving participant narratives to elicit values around robots (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 388-397. , February; Cila, N., Hekkert, P., Visch, V., Source selection in product metaphor generation: The effects of salience and relatedness (2014) International Journal of Design, 8 (1); Coyne, R., (1995) Designing Information Technology in the Postmodern Age: From Method to Metaphor, , Mit Press; Coulton, P., Lindley, J.G., More-than human centred design: Considering other things (2019) The Design Journal, 22 (4), pp. 463-481; Crilly, N., Fixation and creativity in concept development: The attitudes and practices of expert designers (2015) Design Studies, 38, pp. 54-91; Dorst, K., The core of 'design thinking' and its application (2011) Design Studies, 32 (6), pp. 521-532; Dunne, A., (2008) Hertzian Tales: Electronic Products, Aesthetic Experience, and Critical Design, , MIT press; Fernaeus, Y., Ljungblad, S., Jacobsson, M., Taylor, A., Where third wave hci meets hri: Report from a workshop on user-centred design of robots (2009) 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 293-294. , March), IEEE; Dragan, A.D., Lee, K.C., Srinivasa, S.S., Legibility and predictability of robot motion (2013) 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 301-308. , March), IEEE; Dunne, A., Raby, F., (2007) Technological Dreams Series: No. 1. 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"Yan F., Iliyasu A.M., Hirota K.","51061778800;36494641800;7402829702;","Emotion space modelling for social robots",2021,"Engineering Applications of Artificial Intelligence","100",,"104178","","",,5,"10.1016/j.engappai.2021.104178","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099412219&doi=10.1016%2fj.engappai.2021.104178&partnerID=40&md5=950d880bf1d76bbe3ec8f53b0b87866e","School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China; College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia; School of Computing, Tokyo Institute of Technology, Yokohama, 226-8502, Japan; School of Automation, Beijing Institute of Technology, Beijing, 100081, China","Yan, F., School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China; Iliyasu, A.M., College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia, School of Computing, Tokyo Institute of Technology, Yokohama, 226-8502, Japan; Hirota, K., School of Computing, Tokyo Institute of Technology, Yokohama, 226-8502, Japan, School of Automation, Beijing Institute of Technology, Beijing, 100081, China","Improving the interaction between artificial systems and their users is an important issue in artificial intelligence. In current trends in social robotics, this is accomplished by making such systems not just intelligent but also emotionally sensitive. Therefore, artificial emotional intelligence (AEI) is focused on simulating and extending natural emotion (especially human emotion) to provide robots with the capability to recognise and express emotions in human–robot interaction (HRI). In this treatise, we present an overview of the advances made in AEI by highlighting the progress recorded in the areas of emotion classification, emotional robots, and emotion space modelling in HRI. This review is aimed at providing readers with a succinct, yet adequate compendium of the progresses made in the emerging AEI sub-discipline. It is hoped this effort will stimulate further interest aimed at the pursuit of more advanced algorithms and models for available technologies and possible applications to other domains. © 2021 Elsevier Ltd","Artificial emotional intelligence; Communication atmosphere; Emotion space; Human–robot interaction; Quantum computing; Social robots","Artificial intelligence; Emotional intelligence; Artificial systems; Emotion classification; Emotional robots; Express emotions; Natural emotions; Robot interactions; Social robotics; Sub-disciplines; Social robots",,,,,"2020/01/12173","This study is sponsored by the Prince Sattam Bin Abdulaziz University, Saudi Arabia via the Deanship for Scientific Research funding for the Advanced Computational Intelligence and Intelligent Systems Engineering (ACIISE) Research Group Project Number 2020/01/12173 .",,,,,,,,,,"Aerts, D., Sozzo, S., Veloz, T., Quantum structure in cognition and the foundations of human reasoning (2015) Internat. J. Theoret. 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Process., 15, pp. 1-35","Iliyasu, A.M.; College of Engineering, Saudi Arabia; email: a.iliyasu@psau.edu.sa",,,"Elsevier Ltd",,,,,09521976,,EAAIE,,"English","Eng Appl Artif Intell",Short Survey,"Final","",Scopus,2-s2.0-85099412219
"Anshar M., Williams M.-A.","23092764900;7410005152;","Simplified Pain Matrix Method for Artificial Pain Activation Embedded into Robot Framework",2021,"International Journal of Social Robotics","13","2",,"187","195",,,"10.1007/s12369-020-00632-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079865878&doi=10.1007%2fs12369-020-00632-1&partnerID=40&md5=62a32dcf374b5bce77c10ecca9a42091","Electrical Department, Faculty of Engineering, Universitas Hasanuddin, Makassar, Indonesia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia","Anshar, M., Electrical Department, Faculty of Engineering, Universitas Hasanuddin, Makassar, Indonesia; Williams, M.-A., Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia","Physical interaction in Human Robot Interaction is a predominant factor to determine the existence of collaboration between humans and robots. However, a prolonged physical interaction may lead humans to experience what is called as pain which possibly arises due to uncoordinated movements between humans and robots, internal humans’ physical constraints or other feasible causes. This paper introduces a novel method,simplified Pain Matrix (PM), which a PM to generate and activate the kinds of synthetic pain embedded into a robot framework with simplifications on some features of the Pain Matrix. Two scenarios of experiments are constructed and dedicated to demonstrate the activation procedure of the proposed pain mechanism, and the results show a reliable outcome by accurately invoking a particular synthetic pain based on stimulus obtained from the robot joint sensor, which is then followed by reactive prevention actions generated by the Robot Mind. © 2020, Springer Nature B.V.","Data sequence; Elbow data; Faulty joint; HRI; Inflammatory pain; Joint position; Proprioceptive pain; Sensory malfunction pain; Synthetic pain","Agricultural robots; Chemical activation; Health; Matrix algebra; Data sequences; Elbow data; Inflammatory pain; Proprioceptive pain; Sensory malfunction pain; Synthetic pain; Social robots",,,,,"CSR-2AIR; University of Technology Sydney, UTS","This work expresses the appreciation to all members of the Centre of Social, Cognitive, Social Robotics and Advanced Artificial Intelligent Research (CSR-2AIR), Department of Electrical Engineering, University of Hasanuddin UNHAS. This project is part of a joint research between UTS and UNHAS through Indonesia-Australia Social and Cognitive Robotics (IASCR) research collaboration project.",,,,,,,,,,"Hsu, M.C., Schubiner, H., Stracks, J.S., Clauw, D.J., Sustained pain reduction through affective self-awareness in fibromyalgia: a randomized controlled trial (2010) J Gen Intern Med, 25 (10), pp. 1064-1070; Steen, E., Haugli, L., From pain to self-awareness–a qualitative analysis of the significance of group participation for persons with chronic musculoskeletal pain (2001) Patient Educ Couns, 42 (1), pp. 35-46; Anshar, M., Williams, M.-A., Evolving synthetic pain into an adaptive self-awareness framework for robots (2016) Biol Inspired Cogn Archit, 16, pp. 8-18; Anshar, M., Williams, M.-A., Evolving robot empathy towards humans with motor disabilities through artificial pain generation (2018) Am Inst Math Sci Neurosci, 5 (1), pp. 56-73; Anshar, M., Williams, M.-A., Evolving artificial pain from fault detection through pattern data analysis (2017) IEEE International Conference on Real-Time Computing and Robotics (RCAR), pp. 694-699; Finger, S., (1994) Pain, book section 11, pp. 148-164. , Oxford University Press, New York; Anshar, M., (2017) Evolving Robot Empathy through the Generation of Artificial Pain in an Adaptive Self-Awareness Framework for human–robot Collaborative Tasks., , Thesis; Moayedi, M., Davis, K.D., Theories of pain: from specificity to gate control (2013) J Neurophys, 109 (1), pp. 5-12; Reichling, D.B., Green, P.G., Levine, J.D., The fundamental unit of pain is the cell (2013) PAIN, 154, pp. S2-S9. , (,),., ®","Anshar, M.; Electrical Department, Indonesia; email: muh.anshar@gmail.com",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85079865878
"Liu C., Li X., Li Q., Xue Y., Liu H., Gao Y.","56449910700;56928444700;57216469222;57220162554;57220158513;57220163000;","Robot recognizing humans intention and interacting with humans based on a multi-task model combining ST-GCN-LSTM model and YOLO model",2021,"Neurocomputing","430",,,"174","184",,3,"10.1016/j.neucom.2020.10.016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097068975&doi=10.1016%2fj.neucom.2020.10.016&partnerID=40&md5=de36db287534a50a998d989125397c74","Faculty of Information and Technology, Beijing University of Technology, Beijing, China","Liu, C., Faculty of Information and Technology, Beijing University of Technology, Beijing, China; Li, X., Faculty of Information and Technology, Beijing University of Technology, Beijing, China; Li, Q., Faculty of Information and Technology, Beijing University of Technology, Beijing, China; Xue, Y., Faculty of Information and Technology, Beijing University of Technology, Beijing, China; Liu, H., Faculty of Information and Technology, Beijing University of Technology, Beijing, China; Gao, Y., Faculty of Information and Technology, Beijing University of Technology, Beijing, China","It is hoped that the robot could interact with the human when the robots help us in our daily lives. And understanding humans’ specific intention is the first crucial task for human-robot interaction. In this paper, we firstly develop a multi-task model for recognizing humans’ intention, which is composed of two sub-tasks: human action recognition and hand-held object identification. For the front subtask, an effective ST-GCN-LSTM model is proposed by fusing the Spatial Temporal Graph Convolutional Networks and Long Short Term Memory Networks. And for the second subtask, the YOLO v3 model is adopted for the hand-held object identification. Then, we build a framework for robot interacting with the human. Finally, these proposed models and the interacting framework are verified on several datasets and the testing results show the effectiveness of the proposed models and the framework. © 2020 Elsevier B.V.","Human Intention Recognition; Human-Robot Interaction; ST-GCN-LSTM","Convolutional neural networks; Human robot interaction; Palmprint recognition; Convolutional networks; Daily lives; Human-action recognition; Multi-task model; Object identification; Short term memory; Spatial temporals; Subtasks; Long short-term memory; article; human; long short term memory network; robotics",,,,,"2018YFB1702704, 2018YFC1602704, 61673053, 61873006; National Natural Science Foundation of China, NSFC: 61703230","The work was supported by the National Natural Science Foundation of China under Grant 61703230, the National Key Research and Project under Grant 2018YFC1602704 and Grant 2018YFB1702704, and in part by the National Natural Science Foundation of China under Grant 61873006, and Grant 61673053.",,,,,,,,,,"Shu, T., Gao, X., Ryoo, M.S., Zhu, S., Learning social affordance grammar from videos: transferring human interactions to human-robot interactions (2017) IEEE International Conference on Robotics and Automation, pp. 1669-1676; Shu, T., Ryoo, M.S., Zhu, S., Learning social affordance for human-robot interaction (2016), International Joint Conference on Artificial Intelligence; Pinto, L., Gupta, A., Supersizing self-supervision: learning to grasp from 50k tries and 700 robot hours (2016) IEEE International Conference on Robotics and Automation, pp. 3406-3413; Welschehold, T., Dornhege, C., Burgard, W., Learning manipulation actions from human demonstrations (2016) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3772-3777; Liu, C., Fang, B., Sun, F., Li, X., Huang, W., Learning to grasp familiar objects based on experience and objects shape affordance (2019) IEEE Trans. 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Ben Amor, J. Peters, G. Maeda, Learning multiple collaborative tasks with a mixture of interaction primitives; Ewerton, M., Neumann, G., Lioutikov, R., Amor, H.B., Peters, J., Maeda, G., (2015), Modeling spatio-temporal variability in human-robot interaction with probabilistic movement primitives; Paraschos, A., Daniel, C., Peters, J., Neumann, G., (2013), pp. 2616-2624. , Probabilistic movement primitives; Vochten, M., Laet, T.D., Schutter, J.D., Generalizing demonstration motions and adaptive motion generation using an invariant rigid body trajectory representation (2016) IEEE International Conference on Robotics and Automation; Vakanski, A., Mantegh, I., Irish, A., Janabi-Sharifi, F., Trajectory learning for robot programming by demonstration using hidden markov model and dynamic time warping (2012) IEEE Trans. Syst. Man Cybern. B Cybern., 42 (4), pp. 1039-1052","Li, X.; Faculty of Information and Technology, China; email: lixiaolibjut@bjut.edu.cn",,,"Elsevier B.V.",,,,,09252312,,NRCGE,,"English","Neurocomputing",Article,"Final","",Scopus,2-s2.0-85097068975
"Honour A., Banisetty S.B., Feil-Seifer D.","57222472566;57195150511;14033850500;","Perceived social intelligence as evaluation of socially navigation",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447226","519","523",,1,"10.1145/3434074.3447226","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102761864&doi=10.1145%2f3434074.3447226&partnerID=40&md5=a04e1c4437e7dd997be18b0e96f3bf5d","University of Nevada Reno, Reno, NV, United States","Honour, A., University of Nevada Reno, Reno, NV, United States; Banisetty, S.B., University of Nevada Reno, Reno, NV, United States; Feil-Seifer, D., University of Nevada Reno, Reno, NV, United States","As Human-Robot Interaction becomes more sophisticated, measuring the performance of a social robot is crucial to gauging the effectiveness of its behavior. However, social behavior does not necessarily have strict performance metrics that other autonomous behavior can have. Indeed, when considering robot navigation, a socially-appropriate action may be one that is sub-optimal, resulting in longer paths, longer times to get to a goal. Instead, we can rely on subjective assessments of the robot's social performance by a participant in a robot interaction or by a bystander. In this paper, we use the newly-validated Perceived Social Intelligence (PSI) scale to examine the perception of non-humanoid robots in non-verbal social scenarios. We show that there are significant differences between the perceived social intelligence of robots exhibiting SAN behavior compared to one using a traditional navigation planner in scenarios such as waiting in a queue and group behavior. © 2021 ACM.","Metrics; Social intelligence; Socially-aware navigation","Agricultural robots; Anthropomorphic robots; Intelligent robots; Man machine systems; Navigation; Autonomous behaviors; Performance metrics; Robot interactions; Robot navigation; Social intelligence; Social performance; Social scenarios; Subjective assessments; Social robots",,,,,"National Science Foundation, NSF: IIS-1719027, IIS-1757929","This work was supported by the National Science Foundation (IIS-1757929, IIS-1719027).",,,,,,,,,,"Althoff, D., Kuffner, J.J., Wollherr, D., Buss, M., Safety assessment of robot trajectories for navigation in uncertain and dynamic environments (2012) Autonomous Robots, 32 (3), pp. 285-302. , 2012; Authors, A., Anonymized for blind review (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1-9; Authors, A., (2019) Anonymized for Blind Review, , Anonymized [cs. RO]; Barchard, K.A., Lapping-Carr, L., Shane Westfall, R., Balajee Banisetty, S., Feil-Seifer, D., (2018) Perceived Social Intelligence (PSI) Scales Test Manual; Barchard, K.A., Lapping-Carr, L., Shane Westfall, R., Fink-Armold, A., Balajee Banisetty, S., Feil-Seifer, D., Measuring the perceived social intelligence of robots (2020) ACM Transactions on Human-Robot Interaction (THRI), 9 (4), pp. 1-29. , 2020; Bartneck, C., Croft, E., Kulic, D., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81. , https://doi.org/10.1007/s12369-008-0001-3, 2009; Charalampous, K., Kostavelis, I., Gasteratos, A., Recent trends in social aware robot navigation: A survey (2017) Robotics and Autonomous Systems, 93, pp. 85-104. , 2017; Feil-Seifer, D., Distance-based computational models for facilitating robot interaction with children (2012) Journal of Human-Robot Interaction, 1 (1). , 2012; Ferrer, G., Sanfeliu, A., Multi-objective cost-to-go functions on robot navigation in dynamic environments (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3824-3829; Ford, M.E., Tisak, M.S., A further search for social intelligence (1983) Journal of Educational Psychology, 75 (2), p. 196. , 1983; Twitchell Hall, E., (1966) The Hidden Dimension, , 1966; Heider, F., Simmel, M., An experimental study of apparent behavior (1944) The American Journal of Psychology, 57 (2), pp. 243-259. , 1944; Kendon, A., Spacing and orientation in co-present interaction (2010) Development of Multimodal Interfaces: Active Listening and Synchrony, pp. 1-15. , Springer; Kim, B., Pineau, J., Socially adaptive path planning in human environments using inverse reinforcement learning (2016) International Journal of Social Robotics, 8 (1), pp. 51-66. , 2016; Kruse, T., Kumar Pandey, A., Alami, R., Kirsch, A., Human-aware robot navigation: A survey (2013) Robotics and Autonomous Systems, 61 (12), pp. 1726-1743. , 2013; Lu, D.V., Smart, W.D., Towards more efficient navigation for robots and humans (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 1707-1713; Mutlu, B., Forlizzi, J., Robots in organizations: The role of workflow, social, and environmental factors in human-robot interaction (2008) 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 287-294; Nomura, T., Kato, K., Kanda, T., Suzuki, T., Experimental investigation into influence of negative attitudes toward robots on human-robot interaction (2006) AI & Society, 2 (2), pp. 138-150. , https://doi.org/10.1007/s00146-005-0012-7, Feb 2006; Obaid, M., Sandoval, E.B., Zlotowski, J., Moltchanova, E., Basedow, C.A., Bartneck, C., Stop! that is close enough. how body postures influence human-robot proximity (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 354-361; Okal, B., Arras, K.O., Learning socially normative robot navigation behaviors with Bayesian inverse reinforcement learning (2016) 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 2889-2895; (2018) R: A Language and Environment for Statistical Computing, , https://www.R-project.org, R Core Team. R Foundation for Statistical Computing, Vienna, Austria; Islas Ramírez, O.A., Khambhaita, H., Chatila, R., Chetouani, M., Alami, R., Robots learning how and where to approach people (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 347-353; Sebastian, M., Balajee Banisetty, S., Feil-Seifer, D., Sociallyaware navigation planner using models of human-human interaction 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 405-410",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102761864
"Winkle K., Melsión G.I., McMillan D., Leite I.","57201504964;57222471378;57192374849;25031658100;","Boosting robot credibility and challenging gender norms in responding to abusive behaviour: A case for feminist robots",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3446910","29","37",,,"10.1145/3434074.3446910","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102739249&doi=10.1145%2f3434074.3446910&partnerID=40&md5=6a7bbe7065f328f44b33c1462658f927","KTH Royal Institute of Technology, Stockholm, Sweden; DSV, Stockholm University, Stockholm, Sweden","Winkle, K., KTH Royal Institute of Technology, Stockholm, Sweden; Melsión, G.I., KTH Royal Institute of Technology, Stockholm, Sweden; McMillan, D., DSV, Stockholm University, Stockholm, Sweden; Leite, I., KTH Royal Institute of Technology, Stockholm, Sweden","Inspired by the recent UNESCO report I'd Blush if I Could, we tackle some of the issues regarding gendered AI through exploring the impact of feminist social robot behaviour on human-robot interaction. Specifically we consider (i) use of a social robot to encourage girls to consider studying robotics (and expression of feminist sentiment in this context), (ii) if/how robots should respond to abusive, and antifeminist sentiment and (iii) how ('female') robots can be designed to challenge current gender-based norms of expected behaviour. We demonstrate that whilst there are complex interactions between robot, user and observer gender, we were able to increase girls' perceptions of robot credibility and reduce gender bias in boys. We suggest our work provides positive evidence for going against current digital assistant/traditional human gender-based norms, and the future role robots might have in reducing our gender biases. © 2021 ACM.","Feminism; Gender; Robot abuse; Social human robot interaction","Agricultural robots; Behavioral research; Economic and social effects; Man machine systems; Digital assistants; Gender bias; Positive evidence; Social robots",,,,,,"We thank our video actors and aids Marcus Klasson and Sarah Gillet; Truls Nyberg for the translation of our experimental measures and Madeline Balaam for discussions on feminist design. Finally, we are immensely grateful to Sofia Partsinevelou for facilitating this study at IES and giving us crucial feedback throughout. This work was partially supported by the Digital Futures research center.",,,,,,,,,,"Bardzell, S., Feminist hci: Taking stock and outlining an agenda for design (2010) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '10), pp. 1301-1310. , https://doi.org/10.1145/1753326.1753521, Association for Computing Machinery, New York, NY, USA; Bryant, D., Borenstein, J., Howard, A., Why should we gender? The effect of robot gendering and occupational stereotypes on human trust and perceived competency (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI '20), pp. 13-21. , https://doi.org/10.1145/3319502.3374778, Association for Computing Machinery, New York, NY, USA; Crowelly, C.R., Villanoy, M., Scheutzz, M., Schermerhornz, P., Gendered voice and robot entities: Perceptions and reactions of male and female subjects (2009) 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3735-3741. , https://doi.org/10.1109/IROS.2009.5354204ISSN:2153-0866; D'Ignazio, C., Klein, L.F., (2020) Data Feminism, , MIT Press. Google-Books-ID: x5nSDwAAQBAJ; Diáz-Garciá, C., González-Moreno, A., Jose Sáez-Martínez, F., Gender diversity within r&d teams: Its impact on radicalness of innovation (2013) Innovation, 15 (2), pp. 149-160. , https://doi.org/10.5172/impp.2013.15.2.149, 2013; Edwards, C., Myers, S.A., Perceived instructor credibility as a function of instructor aggressive communication (2007) Communication Research Reports, 24 (1), pp. 47-53. , https://doi.org/10.1080/08824090601128141, Publisher:Routledge_eprint https://doi.org/10.1080/08824090601128141 Feb; Eyssel, F., Hegel, F., (s)he's got the look: Gender stereotyping of robots1 (2012) Journal of Applied Social Psychology, 42 (9), pp. 2213-2230. , https://doi.org/10.1111/j.1559-1816.2012.00937.x_eprint, 2012 https://onlinelibrary. wiley. com/doi/pdf/10. 1111/j. 1559-1816. 2012. 00937. x; Fridin, M., Belokopytov, M., Acceptance of socially assistive humanoid robot by preschool and elementary school teachers (2014) Computers in Human Behavior, 33, pp. 23-31. , https://doi.org/10.1016/j.chb.2013.12.016, April 2014; Gass, R.H., Seiter, J.S., (2015) Persuasion: Social Influence and Compliance Gaining, , Routledge. Google-Books-ID: qvMvCgAAQBAJ; Shazwani Ghazali, A., Ham, J., Barakova, E., Markopoulos, P., Assessing the effect of persuasive robots interactive social cues on users' psychological reactance, liking, trusting beliefs and compliance: Advanced robotics (2019) Advanced Robotics, 33, pp. 325-337. , https://www.tandfonline.com/doi/full/10.1080/01691864.2019.1589570, 2019: Vol 33, No 7-8; Infante, D.A., (1987) Arguing Constructively, , Waveland Press. Google-Books-ID: zXoYAAAAQBAJ; Infante, D.A., Rancer, A.S., A conceptualization and measure of argumentativeness (1982) Journal of Personality Assessment, 46 (1), pp. 72-80. , https://doi.org/10.1207/s15327752jpa4601_13, Feb 1982 Publisher: Routledge _eprint https://doi. org/10. 1207/s15327752jpa4601_13; Jackson, R.B., Williams, T., Language-capable robots may inadvertently weaken human moral norms (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 401-410. , https://doi.org/10.1109/HRI.2019.8673123ISSN:2167-2148; Blake Jackson, R., Williams, T., Smith, N., Exploring the role of gender in perceptions of robotic noncompliance (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI '20), pp. 559-567. , https://doi.org/10.1145/3319502.3374831, Association for Computing Machinery, New York, NY, USA; Kuchenbrandt, D., Häring, M., Eichberg, J., Eyssel, F., André, E., Keep an eye on the task! how gender typicality of tasks influence human-robot interactions (2014) International Journal of Social Robotics, 6 (3), pp. 417-427. , https://doi.org/10.1007/s12369-014-0244-0, Aug 2014; Mellén, J., Angervall, P., Gender and choice: Differentiating options in swedish upper secondary stem programmes (2020) Journal of Education Policy, pp. 1-19; Mills, S., (2003) Gender and Politeness, , Cambridge University Press. Google-Books-ID: ngnddB7tXw8C; (2019) It's Just Everywhere ""A Study on Sexism in Schools and How We Tackle It. Technical Report, , https://unesdoc.unesco.org/ark:/48223/pf0000367416.page=1, National Education Union and UKFEMINISTA; Nomura, T., Robots and gender (2016) Gender and the Genome, 1 (1), pp. 18-25. , https://doi.org/10.1089/gg.2016.29002, Dec 2016. nom Publisher: Mary Ann Liebert, Inc., publishers; Odenbring, Y., Johansson, T., Tough-girl femininity, sisterhood and respectability: Minority girls' perceptions of sexual harassment in an urban secondary school (2019) NORA-Nordic Journal of Feminist and Gender Research, 27 (4), pp. 258-270. , https://doi.org/10.1080/08038740.2019.1653967, Oct 2019 Publisher: Routledge _eprint https://doi. org/10. 1080/08038740. 2019. 1653967; Stoet, G., Geary, D.C., The gender-equality paradox in science, technology, engineering, and mathematics education (2018) Psychological Science, 29 (4), pp. 581-593. , 2018; Sullivan, A., Umaschi Bers, M., Investigating the use of robotics to increase girls' interest in engineering during early elementary school (2019) International Journal of Technology and Design Education, 29 (5), pp. 1033-1051. , 2019; West, M., Kraut, R., Ei Chew, H., (2019) I'D Blush if i Could: Closing Gender Divides in Digital Skills through Education, , https://unesdoc.unesco.org/ark:/48223/pf0000367416.page=1, Technical Report; Winkle, K., Lemaignan, S., Caleb-Solly, P., Leonards, U., Turton, A., Bremner, P., Effective persuasion strategies for socially assistive robots (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 277-285. , https://doi.org/10.1109/HRI.2019.8673313; Ye, H., Jeong, H., Zhong, W., Bhatt, S., Izzetoglu, K., Ayaz, H., Suri, R., The effect of anthropomorphization and gender of a robot on human-robot interactions (2020) Advances in Neuroergonomics and Cognitive Engineering (Advances in Intelligent Systems and Computing), pp. 357-362. , https://doi.org/10.1007/978-3-030-20473-0_34, Hasan Ayaz (Ed.) Springer International Publishing, Cham; Öhrn, E., Challenging sexism? gender and ethnicity in the secondary school (2009) Scandinavian Journal of Educational Research, 53 (6), pp. 579-590. , https://doi.org/10.1080/00313830903302091, Publisher:Routledge_eprint https://doi.org/10.1080/00313830903302091 Dec",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102739249
"Rosen E., Groechel T., Walker M.E., Chang C.T., Forde J.Z.","57195424425;57215285175;57201144448;57222472876;57219478857;","Virtual, augmented, and mixed reality for human-robot interaction (VAM-HRI)",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444879","721","723",,,"10.1145/3434074.3444879","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102736758&doi=10.1145%2f3434074.3444879&partnerID=40&md5=015acdc0d591abcb0ab07ba135e5b016","Brown University, Providence, RI, United States; University of Southern California, Los Angeles, CA, United States; University of Colorado Boulder, Boulder, CO, United States","Rosen, E., Brown University, Providence, RI, United States; Groechel, T., University of Southern California, Los Angeles, CA, United States; Walker, M.E., University of Colorado Boulder, Boulder, CO, United States; Chang, C.T., University of Colorado Boulder, Boulder, CO, United States; Forde, J.Z., Brown University, Providence, RI, United States","The 4th International Workshop on Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) will bring together HRI, robotics, and mixed reality researchers to address challenges in mixed reality interactions between humans and robots. Topics relevant to the workshop include development of robots that can interact with humans in mixed reality, use of virtual reality for developing interactive robots, the design of augmented reality interfaces that mediate communication between humans and robots, the investigations of mixed reality interfaces for robot learning, comparisons of the capabilities and perceptions of robots and virtual agents, and best design practices. Special topics of interest this year include VAM-HRI research during the COVID-19 pandemic as well as the ethical implications of VAM-HRI research. VAM-HRI 2021 will follow on the success of VAM-HRI 2018-20 and advance the cause of this nascent research community. © 2021 Owner/Author.","Augmented reality; Human-robot interaction; Mixed reality; Virtual reality","Agricultural robots; Augmented reality; Educational robots; Machine design; Man machine systems; Robot learning; Social robots; Virtual addresses; Design practice; Ethical implications; Interactive robot; International workshops; Mixed reality interfaces; Reality interface; Research communities; Virtual agent; Mixed reality",,,,,,,,,,,,,,,,"Brooks, C., Szafir, D., (2020) Visualization of Intended Assistance for Acceptance of Shared Control; Teja Chadalavada, R., Andreasson, H., Krug, R., Lilienthal, A.J., That's on my mind! robot to human intention communication through on-board projection on shared floor space (2015) ECMR; Chakraborti, T., Sreedharan, S., Kulkarni, A., Kambhampati, S., (2017) Alternative Modes of Interaction in Proximal Human-in-the-Loop Operation of Robots, p. 08930. , CoRR, abs/1703; Chakraborti, T., Sreedharan, S., Kulkarni, A., Kambhampati, S., Projection-aware task planning and execution for human-in-the-loop operation of robots (2018) IROS; Christensen, H.I., Batzinger, T., Bekris, K., Bohringer, K., Bordogna, J., Bradski, G., Brock, O., Eastman, R., A roadmap for us robotics: From internet to robotics (2009) CCC and CRA; Diaz, C., Walker, M., Albers Szafir, D., Szafir, D., Designing for depth perceptions in augmented reality (2017) Proc. ISMAR; Groechel, T., Shi, Z., Pakkar, R., Mataric, M.J., Using socially expressive mixed reality arms for enhancing low-expressivity robots (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1-8; Hedayati, H., Walker, M., Szafir, D., Improving collocated robot teleoperation with augmented reality (2018) Proc. HRI; Hetrick, R., Amerson, N., Kim, B., Rosen, E., Visser De, E.J., Phillips, E., Comparing virtual reality interfaces for the teleoperation of robots (2020) 2020 Systems and Information Engineering Design Symposium (SIEDS), pp. 1-7; Huang, B., Bayazit, D., Ullman, D., Gopalan, N., Tellex, S., Flight, camera, action! using natural language and mixed reality to control a drone (2019) ICRA; Leutert, F., Herrmann, C., Schilling, K., A spatial augmented reality system for intuitive display of robotic data (2013) Proc. HRI; Mahajan, K., Groechel, T.R., Pakkar, R., Lee, H., Cordero, J., Mataric, M.J., Adapting usability metrics for a socially assistive, kinesthetic, mixed reality robot tutoring environment (2020) International Conference on Social Robotics; Rosen, E., Kumar, N., Gopalan, N., Ullman, D., Konidaris, G., Tellex, S., Building plannable representations with mixed reality (2020) 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1-9; Rosen, E., Whitney, D., Fishman, M., Ullman, D., Tellex, S., Mixed reality as a bidirectional communication interface for human-robot interaction (2020) 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1-9; Sato, S., Sakane, S., A human-robot interface using an interactive hand pointer that projects a mark in the real work space (2000) Proc. ICRA; Shen, J., Jin, J., Gans, N., A multi-view camera-projector system for object detection and robot-human feedback (2013) Proc. ICRA; Szafir, D., Mutlu, B., Fong, T., Communication of intent in assistive free flyers (2014) Proceedings of HRI. ACM; Szafir, D., Mutlu, B., Fong, T., Communicating directionality in flying robots (2015) Proceedings of HRI. ACM; Turk, M., Fragoso, V., Computer vision for mobile augmented reality (2015) Mobile Cloud Visual Media Computing.; Walker, M., Hedayati, H., Lee, J., Szafir, D., Communicating robot motion intent with augmented reality (2018) Proc. HRI; Watanabe, A., Ikeda, T., Morales, Y., Shinozawa, K., Miyashita, T., Hagita, N., Communicating robotic navigational intentions (2015) IROS, pp. 5763-5769; Williams, T., Briggs, G., Oosterveld, B., Scheutz, M., Going beyond literal command-based instructions: Extending robotic natural language interaction capabilities (2015) Proceedings of AAAI; Williams, T., Szafir, D., Chakraborti, T., Ben Amor, H., The 1st international workshop on virtual, augmented, and mixed reality for humanrobot interaction (2018) AI Magazine; Williams, T., Szafir, D., Chakraborti, T., Ben Amor, H., Virtual, augmented, and mixed reality for human-robot interaction (2018) Comp. HRI; Zhang, Y., Sreedharan, S., Kulkarni, A., Chakraborti, T., Hankui Zhuo, H., Kambhampati, S., Plan explicability and predictability for robot task planning (2017) ICRA. IEEE",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102736758
"Zamfirescu-Pereira J.D., Sirkin D., Goedicke D., Lc R., Friedman N., Mandel I., Martelaro N., Ju W.","57209393657;36172695500;57202043646;57215536959;57215417038;57204948937;56144702700;7005898423;","Fake it to make it: Exploratory prototyping in HRI",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3446909","19","28",,1,"10.1145/3434074.3446909","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102728912&doi=10.1145%2f3434074.3446909&partnerID=40&md5=4ce37c0af8eed2a28627726f6827273b","University of California, Berkeley, Berkeley, CA, United States; Stanford University, Stanford, CA, United States; Cornell Tech, New York, NY, United States; City University of Hong Kong, Hong Kong, Hong Kong; Carnegie Mellon University, Pittsburgh, PA, United States","Zamfirescu-Pereira, J.D., University of California, Berkeley, Berkeley, CA, United States; Sirkin, D., Stanford University, Stanford, CA, United States; Goedicke, D., Cornell Tech, New York, NY, United States; Lc, R., City University of Hong Kong, Hong Kong, Hong Kong; Friedman, N., Cornell Tech, New York, NY, United States; Mandel, I., Cornell Tech, New York, NY, United States; Martelaro, N., Carnegie Mellon University, Pittsburgh, PA, United States; Ju, W., Cornell Tech, New York, NY, United States","Exploratory prototyping techniques are critical to devising new robot forms, actions, and behaviors, and to eliciting human responses to designed interactive features, early in the design process. In this opinion piece, we establish the contribution of exploratory prototyping to the field of human-robot interaction, arguing research engaged in design exploration-rather than controlled experimentation-should be focused on flexibility rather than specificity, possibility rather than replicability, and design insights as incubated subjectively through the designer rather than dispassionately proven by statistical analysis. We draw on literature in HCI for examples of published design explorations in academic venues, and to suggest how analogous contributions can be valued and evaluated by the HRI community. Lastly, we present and examine case studies of three design methods we have used in our own design work: physical prototyping with human-in-the-loop control, video prototyping, and virtual simulations. © 2021 Owner/Author.","Design; Evaluation; Experimentation; Exploratory prototyping; Hci; Hri; Prototyping; Video prototyping; Virtual simulation; Wizard of oz","Agricultural robots; Machine design; Man machine systems; Controlled experimentation; Design Exploration; Design insights; Hri communities; Human-in-the-loop control; Interactive features; Physical prototyping; Virtual simulations; Human robot interaction",,,,,,,,,,,,,,,,"Balit, E., Vaufreydaz, D., Reignier, P., Integrating animation artists into the animation design of social robots: An open-source robot animation software (2016) The Eleventh ACM/IEEE International Conference on Human Robot Interaction, pp. 417-418. , IEEE Press; Balit, E., Vaufreydaz, D., Reignier, P., Pear: Prototyping expressive animated robots-a framework for social robot prototyping (2018) 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018); Bartneck, C., Hu, J., Rapid prototyping for interactive robots (2004) The 8th Conference on Intelligent Autonomous Systems (IAS-8), pp. 136-145. , IOPress; Bojanovich, C., Grand central terminal (2012) Wikipedia, , https://commons.wikimedia.org/wiki/File:Grand_Central_Terminal_Lobby.jpg; (2005) The 'Double Diamond' Design Process Model, , British Design Council; Burns, C., Dishman, E., Verplank, W., Lassiter, B., Actors, hairdos & videotape-informance design (1994) Conference Companion on Human Factors in Computing Systems. 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IEEE, pp. 303-308; Wobbrock, J.O., Kientz, J.A., Research contributions in human-computer interaction (2016) Interactions, 23 (3), pp. 38-44. , 2016; Woods, S., Walters, M., Lee Koay, K., Dautenhahn, K., Comparing human robot interaction scenarios using live and video based methods: Towards a novel methodological approach (2006) 9th IEEE International Workshop on Advanced Motion Control, 2006. IEEE, pp. 750-755; NWoods, S., Walters, M.L., Lee Koay, K., Dautenhahn, K., Methodological issues in hri: A comparison of live and video-based methods in robot to human approach direction trials (2006) Robot and Human Interactive Communication. ROMAN. IEEE, pp. 51-58; Yang, S., Ka-Jun Mok, B., Sirkin, D., Ive Page, H., Maheshwari, R., Fischer, K., Ju, W., Experiences developing socially acceptable interactions for a robotic trash barrel (2015) Robot and Human Interactive Communication (RO-MAN), 2015 24th IEEE International Symposium On. IEEE, pp. 277-284; Yee, N., Bailenson, J.N., Urbanek, M., Chang, F., Merget, A., The unbearable likeness of being digital: The persistence of nonverbal social norms in online virtual environments (2007) CyberPsychology& Behavior, 10 (1), pp. 115-121. , 2007; Zimmerman, J., Forlizzi, J., Evenson, S., Research through design as a method for interaction design research in hci (2007) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, pp. 493-502",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85102728912
"Jutinico A.L., Flórez-Cediel O., Siqueira A.A.G.","54929830800;57205201618;7003788864;","Complementary stability of Markovian systems: Series elastic actuators and human-robot interaction",2021,"IFAC-PapersOnLine","54","4",,"112","117",,,"10.1016/j.ifacol.2021.10.019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120656080&doi=10.1016%2fj.ifacol.2021.10.019&partnerID=40&md5=14d1d30150dccf3345ca9ac39ba5eeef","Universidad Distrital Francisco José de Caldas, Bogotá, Colombia; University of São Paulo, São Carlos, Brazil","Jutinico, A.L., Universidad Distrital Francisco José de Caldas, Bogotá, Colombia; Flórez-Cediel, O., Universidad Distrital Francisco José de Caldas, Bogotá, Colombia; Siqueira, A.A.G., University of São Paulo, São Carlos, Brazil","This paper presents an extension of the complementary stability analysis for discrete-time Markovian systems applied in interaction control. The control strategy considers an internal loop force control for Series Elastic Actuators based on the Robust Regulator for Discrete-time Markov Jump Linear systems and extern loop impedance control to regulate the interaction between the human and robot. Two examples show the complementary stability analysis of the human-robot interaction system, where is regarded as different values of the virtual impedances, different levels for the robustness of the force control, uncertainties, and abrupt changes for human parameters. Simulation results show the capacity of control strategy to deal with active interaction models, where the Mean Square Stability guarantees safe for the user. Additionally, from results it is understood the transmissibility of impedance achieved is a consequence of the robust performance given by the force controller. Copyright © 2021 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)","Complementary stability; Markovian jump linear systems; Robotic rehabilitation; Robust control","Actuators; Control system stability; Discrete time control systems; Force control; Human robot interaction; Linear systems; Man machine systems; Robust control; Uncertainty analysis; Virtual reality; Complementary stability; Control strategies; Discrete time; Humans-robot interactions; Interaction controls; Markovian; Markovian jump linear systems; Robotic rehabilitation; Series elastic actuators; Stability analyze; Markov processes",,,,,,,,,,,,,,,,"Ajoudani, A., Zanchettin, A., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human-robot collaboration (2018) Autonomous Robots, 42, pp. 957-975; Buerger, S.P., Hogan, N., Complementary stability and loop shaping for improved human-robot interaction (2007) IEEE Transactions on Robotics, 23 (2), pp. 232-244; Cerri, J.P., Terra, M.H., Recursive robust regulator for discrete-time Markovian jump linear systems (2017) IEEE Transactions on Automatic Control; Costa, O.L., Fragoso, M.D., Marques, R., Discrete-time Markov jump linear systems (2005) Probability and Its Applications, , Springer, London; Escalante, F.M., Jutinico, A.L., Jaimes, J.C., Terra, M.H., Siqueira, A.A.G., Markovian robust filtering and control applied to rehabilitation robotics (2021) IEEE/ASME Transactions on Mechatronics, 26 (1), pp. 491-502; Hogan, N., Impedance control: An approach to manipulation (1985) Journal of Dynamic Systems, Measurement and Control, 107 (1), pp. 1-24. , Parts 1-3; Jutinico, A.L., Escalante, F.M., Jaimes, J.C., Terra, M.H., Siqueira, A.A.G., Markovian robust compliance control based on electromyographic signals (2018) 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), pp. 1218-1223; Jutinico, A.L., Jaimes, J.C., Escalante, F.M., Perez-Ibarra, J.C., Terra, M.H., Siqueira, A.A.G., Impedance control for robotic rehabilitation: A robust Markovian approach (2017) Frontiers in Neurorobotics, 11, p. 43; Lee, H., Rouse, E.J., Krebs, H.I., Summary of human ankle mechanical impedance during walking (2016) IEEE Journal of Translational Engineering in Health and Medicine, 4, pp. 1-7; Pratt, G.A., Williamson, M.M., Series elastic actuators (1995) Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots, pp. 399-406",,,,"Elsevier B.V.","4th IFAC Conference on Embedded Systems, Computational Intelligence and Telematics in Control CESCIT 2021","5 July 2021 through 7 July 2021",,146688,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85120656080
"Ai L., Zhou T., Wu L., Qian W., Xiao X., Guo Z.","57274604900;57274605000;57316126700;57215699888;57020463800;37047279500;","Design of a 6 DOF Cable-Driven Upper Limb Exoskeleton",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13013 LNAI",,,"728","736",,,"10.1007/978-3-030-89095-7_69","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118206155&doi=10.1007%2f978-3-030-89095-7_69&partnerID=40&md5=923888624825d04dcefa320dc5a1b40d","School of Power and Mechanical Engineering, Wuhan University, Wuhan, China","Ai, L., School of Power and Mechanical Engineering, Wuhan University, Wuhan, China; Zhou, T., School of Power and Mechanical Engineering, Wuhan University, Wuhan, China; Wu, L., School of Power and Mechanical Engineering, Wuhan University, Wuhan, China; Qian, W., School of Power and Mechanical Engineering, Wuhan University, Wuhan, China; Xiao, X., School of Power and Mechanical Engineering, Wuhan University, Wuhan, China; Guo, Z., School of Power and Mechanical Engineering, Wuhan University, Wuhan, China","To provide post-stroke patients with rehabilitation training, this paper presents a six degrees of freedom (DOF) cable-driven upper limb exoskeleton, where 3 active DOF, 1 active DOF, and 2 passive DOF are given to the shoulder, elbow, and wrist, respectively. Carbon fiber materials are mainly used to consist of the frame of the prototype, achieving the lightness and compactness design. Cable-driven series elastic actuators are used to minimize the inertia and complexity of the exoskeleton’s joints and provide compliant actuation, resulting in better human-robot interaction. A double parallelogram mechanism is designed to transfer rotation from a remote center to the glenohumeral joint. To evaluate the performance of the prototype, PID controllers are designed to conduct position tracking and torque tracking experiments in the elbow joint. The experimental results show that the prototype has a good tracking performance and is capable to assist patients in rehabilitation training. © 2021, Springer Nature Switzerland AG.","Cable-driven SEA; Carbon fiber materials; Double parallelogram mechanism; Upper limb exoskeleton","Cables; Carbon fibers; Degrees of freedom (mechanics); Exoskeleton (Robotics); Human robot interaction; Joints (anatomy); Manipulators; Patient rehabilitation; Three term control systems; 6 degree of freedom; Cable-driven; Cable-driven SEA; Carbon-fiber materials; Double parallelogram mechanism; Parallelogram mechanisms; Post stroke patients; Rehabilitation training; Upper limb exoskeleton; Upper limbs; Mechanisms",,,,,"WHU-2020-SYJS-10","Acknowledgements. This work was supported by the Experimental Technology Project of Wuhan University in 2020 under the research project number WHU-2020-SYJS-10.",,,,,,,,,,"Kim, J., Global stroke statistics 2019 (2020) Int. J. Stroke, 15 (8), pp. 819-838; Brewer, B.R., McDowell, S.K., Worthen-Chaudhari, L.C., Poststroke upper extremity rehabilitation: A review of robotic systems and clinical results (2007) Top Stroke Rehabil, 14 (6), pp. 22-44; Asl, H.J., Yamashita, M., Narikiyo, T., Kawanishi, M., Field-based assist-as-needed control schemes for rehabilitation robots (2020) IEEE/ASME Trans. Mechatron., 25 (4), pp. 2100-2111; Trigili, E., Design and experimental characterization of a shoulder-elbow exoskeleton with compliant joints for post-stroke rehabilitation (2019) IEEE/ASME Trans. Mechatron., 24 (4), pp. 1485-1496; Keller, U., van Hedel, H.J., Klamroth-Marganska, V., Riener, R., ChARMin: The first actuated exoskeleton robot for pediatric arm rehabilitation (2016) IEEE/ASME Trans. Mechatron., 21 (5), pp. 2201-2213; Xiao, F., Gao, Y., Wang, Y., Zhu, Y., Zhao, J., Design and evaluation of a 7-DOF cable-driven upper limb exoskeleton (2018) J. Mech. Sci. 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Mechatron., 22 (1), pp. 161-172; Zhang, Q., Xu, B., Guo, Z., Xiao, X., Design and modeling of a compact rotary series elastic actuator for an elbow rehabilitation robot (2017) ICIRA 2017. LNCS (LNAI), Vol. 10464, pp. 44-56. , https://doi.org/10.1007/978-3-319-65298-6_5, Huang, YongAn, Wu, H., Liu, H., Yin, Z. (eds.), pp. , Springer, Cham; Chen, B., Zhao, X., Ma, H., Qin, L., Liao, W., Design and characterization of a magnetorheological series elastic actuator for a lower extremity exoskeleton (2017) Smart Mater. Struct., 26 (10); Sun, J., Guo, Z., Zhang, Y., Xiao, X., Tan, J., A novel design of serial variable stiffness actuator based on an archimedean spiral relocation mechanism (2018) IEEE/ASME Trans. Mechatron., 23 (5), pp. 2121-2131; Cummings, J.P., Ruiken, D., Wilkinson, E.L., Lanighan, M.W., Grupen, R.A., Sup, F.C., A compact, modular series elastic actuator (2016) J. Mech. Robot., 8 (4); Pan, Y., Guo, Z., Gu, D., Bioinspired design and control of robots with intrinsic compliance (2020) Front Neurorobotics, 14; Sun, J., Zhang, Y., Zhang, C., Guo, Z., Xiao, X., Mechanical design of a compact serial variable stiffness actuator (SVSA) based on lever mechanism (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017, Pp. 33–38. IEEE; Bai, S., Christensen, S., Islam, M.R.U., An upper-body exoskeleton with a novel shoulder mechanism for assistive applications (2017) 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2017, Pp. 1041–1046. IEEE; Christensen, S., Bai, S., Kinematic analysis and design of a novel shoulder exoskeleton using a double parallelogram linkage (2018) J. Mech. Robot., 10 (4)","Guo, Z.; School of Power and Mechanical Engineering, China; email: guozhao@whu.edu.cn","Liu X.Nie Z.Yu J.Xie F.Song R.",,"Springer Science and Business Media Deutschland GmbH","14th International Conference on Intelligent Robotics and Applications, ICIRA 2021","22 October 2021 through 25 October 2021",,267169,03029743,9783030890940,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85118206155
"Sharifi M., Azimi V., Mushahwar V.K., Tavakoli M.","55224489900;49360919000;6603751651;8668966400;","Impedance Learning-Based Adaptive Control for Human-Robot Interaction",2021,"IEEE Transactions on Control Systems Technology",,,,"","",,1,"10.1109/TCST.2021.3107483","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114368286&doi=10.1109%2fTCST.2021.3107483&partnerID=40&md5=e91eb8393c712f9553905d8eae67e9fc","Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, also with the Division of Physical Medicine and Rehabilitation, Department of Medicine, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology (SMART) Network Group, University of Alberta, Edmonton, AB T6G 2E1, Canada (e-mail: m.sharifi@ualberta.ca); Department of Mechanical Engineering, Auburn University, Auburn, AL 36849 USA.; Division of Physical Medicine and Rehabilitation, Department of Medicine, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology (SMART) Network Group, University of Alberta, Edmonton, AB T6G 2E1, Canada.; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology (SMART) Network Group, University of Alberta, Edmonton, AB T6G 2E1, Canada.","Sharifi, M., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, also with the Division of Physical Medicine and Rehabilitation, Department of Medicine, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology (SMART) Network Group, University of Alberta, Edmonton, AB T6G 2E1, Canada (e-mail: m.sharifi@ualberta.ca); Azimi, V., Department of Mechanical Engineering, Auburn University, Auburn, AL 36849 USA.; Mushahwar, V.K., Division of Physical Medicine and Rehabilitation, Department of Medicine, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology (SMART) Network Group, University of Alberta, Edmonton, AB T6G 2E1, Canada.; Tavakoli, M., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology (SMART) Network Group, University of Alberta, Edmonton, AB T6G 2E1, Canada.","In this article, a new learning-based time-varying impedance controller is proposed and tested to facilitate an autonomous physical human-robot interaction (pHRI). Novel adaptation laws are formulated for online adjustment of robot impedance based on human behavior. Two other sets of update rules are defined for intelligent coping with the robot's structured and unstructured uncertainties. These rules ensure stability via Lyapunov's theorem and provide uniform ultimate boundedness (UUB) of the closed-loop system's response, without a need for HRI force/torque measurement. Accordingly, the convergence of response signals, including errors in tracking, online impedance learning, robot parameter adaptation, and controller gain variation, is proven to operate in a bounded region (compact set) in the presence of robot and human uncertainties and bounded disturbances. The performance of the developed intelligent impedance-varying control strategy is investigated through comprehensive experimental studies in a repetitive following task with a moving target. IEEE","Autonomous impedance variation; Dynamics; Force; Impedance; nonlinear adaptive control; physical human-robot interaction (pHRI); Robot sensing systems; robot stability; Robots; Stability analysis; Task analysis; uniform ultimate boundedness (UUB).","Behavioral research; Closed loop systems; Controllers; Man machine systems; Bounded disturbances; Control strategies; Controller gain variations; Impedance controllers; On-line adjustment; Physical humanrobot interaction (phri); Uniform ultimate boundedness; Unstructured uncertainty; Human robot interaction",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10636536,,IETTE,,"English","IEEE Trans Control Syst Technol",Article,"Article in Press","",Scopus,2-s2.0-85114368286
"Rodriguez-Guerra D., Sorrosal G., Cabanes I., Calleja C.","57226775119;55837381200;6505573517;36650360000;","Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments",2021,"IEEE Access","9",,"9493209","108557","108578",,,"10.1109/ACCESS.2021.3099287","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112639785&doi=10.1109%2fACCESS.2021.3099287&partnerID=40&md5=c3c0d6f6402b80ef9fdef5149fc472d7","Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), Arrasate, 20500, Spain; Faculty of Engineering in Bilbao, University of the Basque Country (UPV/EHU), Bilbao, 48013, Spain","Rodriguez-Guerra, D., Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), Arrasate, 20500, Spain; Sorrosal, G., Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), Arrasate, 20500, Spain; Cabanes, I., Faculty of Engineering in Bilbao, University of the Basque Country (UPV/EHU), Bilbao, 48013, Spain; Calleja, C., Ikerlan Technology Research Centre, Basque Research and Technology Alliance (BRTA), Arrasate, 20500, Spain","The demand for collaborative robots is growing in industrial environments due to their versatility and low prices. Thus, more collaborative solutions are emerging for industrial scenarios. However, implementing scenarios where robots work autonomously while synchronizing their operations in a safe industrial environment with shopfloor workers is not easy. To fill the gap existing in the safe implementation of industrial collaborative scenarios, this manuscript presents a review based on five identified challenges that gathers the primary vital aspects to bear in mind while developing applications for them. Thus, a four-level classification is proposed, which collects the identified challenges and the previous developments in the field of human-robot interaction. The five identified challenges pretends to be the missing enabling key for implementing industrial collaborative scenarios in modern industrial plants. Lastly, a discussion and conclusion are exposed to analyze the degree of development in the field and its potential growth. © 2013 IEEE.","Cobots; HRI challenges; human-robot interaction; industrial collaborative scenarios; industrial safety; review","Industrial robots; Man machine systems; Collaborative robots; Exposed to; Industrial environments; Industrial scenarios; Potential growth; Shop floor; Human robot interaction",,,,,,,,,,,,,,,,"Hentout, A., Mustapha, A., Maoudj, A., Akli, I., Key challenges and open issues of industrial collaborative robotics (2018) Proc. Workshop Hum.-Robot Interact., from Service Ind. (RO-MAN), pp. 1-4. , Aug; Karlsson, K., Jonsson, M., Overview of SAAB in commercial aeronautics. Clean Sky 2 and ITD AIRFRAME research and SAAB aerostructures automation projects and strategy (2019) Proc. 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"Yang Y., Liu Y., Lv X., Ai J., Li Y.","57225153778;57221170196;57213861615;16300678100;57225148706;","Anthropomorphism and customers’ willingness to use artificial intelligence service agents",2021,"Journal of Hospitality Marketing and Management",,,,"","",,17,"10.1080/19368623.2021.1926037","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109276812&doi=10.1080%2f19368623.2021.1926037&partnerID=40&md5=f707ee6ee364eb4e90fb3647302f4205","School of Economic Mathematics, Southwestern University of Finance and Economics, Chengdu, China; School of Sports Science and Physical Education, Southwestern University of Finance and Economics, Chengdu, China; School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, China","Yang, Y., School of Economic Mathematics, Southwestern University of Finance and Economics, Chengdu, China; Liu, Y., School of Sports Science and Physical Education, Southwestern University of Finance and Economics, Chengdu, China; Lv, X., School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, China; Ai, J., School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, China; Li, Y., School of Business Administration, Faculty of Business Administration, Southwestern University of Finance and Economics, Chengdu, China","Previous studies provide inconsistent evidence regarding the effect of anthropomorphism on customers’ willingness to use AI service agents. This paper explains the reason by introducing service context. Two situational experiments are used to demonstrate that under the context of high perceived control, customers expect AI service agents with more anthropomorphic designs to perform better and prefer highly human-like AI service agents. However, under the context of low perceived control, customers perceive stronger threat in facing AI service agents with more anthropomorphic designs and prefer less human-like AI service agents. Moreover, we find that this effect is significant only in social scenarios. These findings provide new insights into previous inconsistent evidence regarding anthropomorphic design’s influence on customers’ willingness to use AI service agents. Our findings also have important implications for AI service agents design in different service contexts and advance the literature on human–robot interaction and marketing. © 2021 Taylor & Francis Group, LLC.","Anthropomorphism; artificial intelligence (AI) service agents; perceived threat; performance expectation; willingness to use",,,,,,,,,,,,,,,,,"Aggarwal, P., Mcgill, A.L., Is that car smiling at me? Schema congruity as a basis for evaluating anthropomorphized products (2007) Journal of Consumer Research, 34 (4), pp. 468-479. , https://doi.org/10.1086/518544; Ambroise, L., Valette-Florence, P., The brand personality metaphor and inter-product stability of a specific barometer (2010) Recherche et Applications En Marketing (English Edition), 25 (2), pp. 3-28. , https://doi.org/10.1177/205157071002500201; Axelrod, L., Hone, K.S., E-motional advantage: Performance and satisfaction gains with affective computing. 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"Gemeinboeck P., Saunders R.","10043705000;7201706570;","Moving beyond the mirror: relational and performative meaning making in human–robot communication",2021,"AI and Society",,,,"","",,1,"10.1007/s00146-021-01212-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108156277&doi=10.1007%2fs00146-021-01212-1&partnerID=40&md5=3c61dbfbf223405d77f889915611972d","Department of Media Theory, University of Applied Arts Vienna, Vienna, Austria; Centre for Transformative Media Technologies, School of Arts, Social Sciences and Humanities, Swinburne University of Technology, Melbourne, Australia; Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands","Gemeinboeck, P., Department of Media Theory, University of Applied Arts Vienna, Vienna, Austria, Centre for Transformative Media Technologies, School of Arts, Social Sciences and Humanities, Swinburne University of Technology, Melbourne, Australia; Saunders, R., Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands","Current research in human–robot interaction often focuses on rendering communication between humans and robots more ‘natural’ by designing machines that appear and behave humanlike. Communication, in this human-centric approach, is often understood as a process of successfully transmitting information in the form of predefined messages and gestures. This article introduces an alternative arts-led, movement-centric approach, which embraces the differences of machinelike robotic artefacts and, instead, investigates how meaning is dynamically enacted in the encounter of humans and machines. Our design approach revolves around a novel embodied mapping methodology, which serves to bridge between human–machine asymmetries and socioculturally situate abstract robotic artefacts. Building on concepts from performativity, material agency, enactive sense-making and kinaesthetic empathy, our Machine Movement Lab project opens up a performative-relational model of human–machine communication, where meaning is generated through relational dynamics in the interaction itself. © 2021, The Author(s).","Agency; Embodiment; Human–robot interaction; Meaning-making; Movement; Performance; Performativity","Bridges; Robotics; Design approaches; Machine communications; Mapping methodology; Meaning makings; Performativity; Relational Model; Robot communication; Robot interactions; Social robots",,,,,"Seventh Framework Programme, FP7: 621403; Fourth Framework Programme, FP4; Australian Government; Australian Research Council, ARC: DP160104706; Austrian Science Fund, FWF: AR545","This research was funded in part by the Australian Government through the Australian Research Council (DP160104706); an EU Framework Programme (FP7) European Research Area Chairs Scheme project (621403); and the Austrian Government through the Austrian Science Fund (FWF): AR545.",,,,,,,,,,"Alač, M., Social robots: things or agents? 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Series on Advances in Consciousness Research 14, , John Benjamins, Amsterdam; Sheets-Johnstone, M., From movement to dance (2012) PhenomenolCognSci, 11, pp. 39-57; Smuts, B., Embodied communication in nonhuman animals (2008) Human development in the 21st century, pp. 136-146. , Fogel A, King B, Shanker S, (eds), Cambridge University Press, Cambridge; Stevens, C., McKechnie, S., Thinking in action: thought made visible in contemporary dance (2005) Cogn Process, 6, pp. 243-252; Suchman, L., (2007) Human-machine reconfigurations: plans and situated actions, , Cambridge University Press, Cambridge; Suschke, S., (2003) Müller machtTheater: ZehnInszenierungen und einEpilog, , Theater der Zeit, Berlin; Turkle, S., (2005) The second self: computers and the human spirit, , Twentieth Anniversary, The MIT Press, Cambridge; Turkle, S., Authenticity in the age of digital companions (2007) Interact Stud, 8 (3), pp. 501-517; Turkle, S., (2011) Alone together: why we expect more from technology and less from each other, , Basic Books, New York; Uexküll, J.V., A stroll through the worlds of animals and men: a picture book of invisible worlds (1957) trans) Instinctive behavior: the development of a modern concept, pp. 5-80. , Schiller CH, (ed), International Universities Press, New York; Varela, F.J., Thompson, E., Rosch, E., (1991) The embodied mind: cognitive science and human experience, , MIT Press, Cambridge; Vlachos, E., Jochum, E., Demers, L.P., The effects of exposure to different social robots on attitudes toward preferences (2016) Interact Stud, 17 (3), pp. 390-404; Ziemke, T., Introduction to the special issue on situated and embodied cognition (2002) Cognit Syst Res, 3, pp. 271-274; Ziemke, T., Sharkey, N., A stroll through the worlds of robots and animals (2001) Semiotica, 134, pp. 1-4","Saunders, R.; Leiden Institute of Advanced Computer Science, Netherlands; email: r.saunders@liacs.leidenuniv.nl",,,"Springer Science and Business Media Deutschland GmbH",,,,,09515666,,,,"English","AI Soc.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85108156277
"Saxena S., Tripathi S., Sudarshan T.S.B.","57215559197;14008732700;36893577200;","Deep Facial Emotion Recognition System Under Facial Mask Occlusion",2021,"Communications in Computer and Information Science","1377 CCIS",,,"381","393",,,"10.1007/978-981-16-1092-9_32","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107363502&doi=10.1007%2f978-981-16-1092-9_32&partnerID=40&md5=c402d10c5ae10827a8f2721897cc1673","Department of Electronics and Communication Engineering, PES University, Bangalore, India; Department of Computer Science and Engineering, PES University, Bangalore, India","Saxena, S., Department of Electronics and Communication Engineering, PES University, Bangalore, India; Tripathi, S., Department of Electronics and Communication Engineering, PES University, Bangalore, India; Sudarshan, T.S.B., Department of Computer Science and Engineering, PES University, Bangalore, India","Over the past few decades, automated machine based Facial Emotion Recognition (FER) has made a significant progress, guided by its relevance for applications in various areas of neuroscience, health, defense, entertainment and Human Robot Interaction (HRI). Most of the work on FER is focused on controlled conditions for non-occluded faces. In the present Covid-19 scenario, when more individuals cover their face partially to prevent spread of corona virus, developing a system/technique that can recognize facial emotion with facial mask constraint is desirable. In this research work, a FER system built using Convolution Neural Networks (CNN) technique is proposed. In the proposed work, eyes and forehead segment is used to train model. This system is implemented using Raspberry Pi 3 B + and Robotic Process automation (RPA) for HRI. The proposed system achieves 79.87% average accuracy with 5-fold cross validation and also tested in real time scenario. © 2021, Springer Nature Singapore Pte Ltd.","CNN; Facial emotion recognition; Facial mask occlusion; Raspberry Pi III; Robotic process automation platform","Computer vision; Neural networks; Social robots; Speech recognition; Automated machines; Controlled conditions; Convolution neural network; Cross validation; Facial emotions; Human robot Interaction (HRI); Mask constraints; Process automation; Face recognition",,,,,,"The authors would like to thank all the volunteers for the experimentation and also would like to thank the host organization for providing CMU Multi PIE database. We thank all other researchers for making other relevant databases available for such research experiments.",,,,,,,,,,"Mehrabian, A., Communication without words (1968) Psychology Today, 2, pp. 53-56; Dautenhahn, K., Methodology & themes of human-robot interaction: A growing research field (2007) Int. J. Adv. Robotic Syst., 4 (1), p. 15; Happy, S.L., Dasgupta, A., Patnaik, P., Routray, A., Automated alertness and emotion detection for empathic feedback during e-learning (2013) IEEE 5Th Int. Conference on Technology for Education (T4E), Kharagpur, India, pp. 47-50. , , pp; Coco, M.D., Leo, M., Distante, C., Palestra, G., Automatic emotion recognition in robot-children interaction for ASD treatment (2015) IEEE International Conference on Computer Vision Workshop, Santiago, pp. 537-545. , , pp; Good Fellow, I.J., Challenges in representation learning: A report on three machine learning contests (2013) Workshop Challenges in Representation Learning (ICM12013), Pp. 1– 8; Rosalind, W.P., (2000) Affective Computing, , MIT press, Cambridge; Suja, P., Tripathi, S., Real-time emotion recognition from facial images using Raspberry Pi II (2016) 3Rd International Conference on Signal Processing and Integrated Networks, (SPIN), IEEE, Noida, India, pp. 666-670. , , pp; Gamborino, E., Yueh, H., Lin, W., Yeh, S., Fu, L., Mood estimation as a social profile predictor in an autonomous, multi-session, emotional support robot for children (2019) 28Th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), New Delhi, India, Pp. 1–6; Gross, R., (2007) Guide to the CMU Multi-Pie Database. Carnegie Mellon University, , Technical report, The Robotics Institute; Bourel, F., Chibelushi, C.C., Low, A.A., Recognition of facial expressions in the presence of occlusion (2001) 12Th British Machine Vision Conference, pp. 213-222. , , pp; Bourel, F., Robust facial expression recognition using a state-based model of spatially-localised facial dynamics (2002) Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 106-111. , , pp; Towner, H., Slater, M., Reconstruction and recognition of occluded facial expressions using PCA (2007) ACII 2007. LNCS, Vol. 4738, Pp. 36–, p. 47. , https://doi.org/10.1007/978-3-540-74889-2_4, Paiva, A.C.R., Prada, R., Picard, R.W. (eds.) , Springer, Heidelberg; Kotsia, I., An analysis of facial expression recognition under partial facial image occlusion (2008) Image Vis. Comput., 26 (7), pp. 1052-1067; Xia, M., Robust facial expression recognition based on RPCA and AdaBoost (2009) 10Th Workshop on Image Analysis for Multimedia Interactive Services, pp. 113-116. , , pp; Cotter, S.F., Recognition of occluded facial expressions using a fusion of localized sparse representation Classifiers (2011) IEEE Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop, pp. 437-442. , , pp; Liu, S., Facial expression recognition under partial occlusion based on weber local descriptor histogram and decision fusion (2014) 33Rd Chinese Control Conference, pp. 4664-4668. , , pp; Cheng, Y., A deep structure for facial expression recognition under partial occlusion (2014) Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 211-214. , , pp; Tősér, Z., Jeni, L.A., Lőrincz, A., Cohn, J.F., Deep learning for facial action unit detection under large head poses (2016) ECCV 2016. LNCS, Vol. 9915, pp. 359-371. , https://doi.org/10.1007/978-3-319-49409-8_29, Hua, G., Jégou, H. (eds.), pp. , Springer, Cham; Viola, P., Jones, M.J., Robust real-time face detection (2004) Int. J. Comput. Vision, 57, pp. 137-154; https://www.uipath.com/; https://www.raspberrypi.org/products/raspberry-pi-3-model-b-plus/","Saxena, S.; Department of Electronics and Communication Engineering, India; email: suchitrasaxena10@gmail.com","Singh S.K.Roy P.Raman B.Nagabhushan P.",,"Springer Science and Business Media Deutschland GmbH","5th International Conference on Computer Vision and Image Processing, CVIP 2020","4 December 2020 through 6 December 2020",,256959,18650929,9789811610912,,,"English","Commun. Comput. Info. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85107363502
"Valori M., Scibilia A., Fassi I., Saenz J., Behrens R., Herbster S., Bidard C., Lucet E., Magisson A., Schaake L., Bessler J., Prange-Lasonder G.B., Kühnrich M., Lassen A.B., Nielsen K.","39362641500;57207199424;6506998602;57190879394;55540144500;57223361358;9236211000;24781182200;57223356896;24829528200;57204240176;14045673000;57223374318;57223139063;57204240797;","Validating safety in human–robot collaboration: Standards and new perspectives",2021,"Robotics","10","2","65","","",,3,"10.3390/robotics10020065","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105719099&doi=10.3390%2frobotics10020065&partnerID=40&md5=0f15324d6e791f550a1030057e80020d","Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Milan, 20133, Italy; Fraunhofer IFF, Magdeburg, 39106, Germany; Université Paris-Saclay, CEA List, Palaiseau, F-91120, France; CEA, CEA Tech Grand Est, Metz, F-57075, France; Roessingh Research and Development, Enschede, 7522 AH, Netherlands; Department of Biomedical Signals and Systems, University of Twente, Enschede, 7522 NB, Netherlands; Department of Biomechanical Engineering, University of Twente, Enschede, 7522 NB, Netherlands; Department of Robot Technology, Danish Technological Institute, Odense, 5230, Denmark","Valori, M., Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Milan, 20133, Italy; Scibilia, A., Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Milan, 20133, Italy; Fassi, I., Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Milan, 20133, Italy; Saenz, J., Fraunhofer IFF, Magdeburg, 39106, Germany; Behrens, R., Fraunhofer IFF, Magdeburg, 39106, Germany; Herbster, S., Fraunhofer IFF, Magdeburg, 39106, Germany; Bidard, C., Université Paris-Saclay, CEA List, Palaiseau, F-91120, France; Lucet, E., Université Paris-Saclay, CEA List, Palaiseau, F-91120, France; Magisson, A., CEA, CEA Tech Grand Est, Metz, F-57075, France; Schaake, L., Roessingh Research and Development, Enschede, 7522 AH, Netherlands; Bessler, J., Roessingh Research and Development, Enschede, 7522 AH, Netherlands, Department of Biomedical Signals and Systems, University of Twente, Enschede, 7522 NB, Netherlands; Prange-Lasonder, G.B., Roessingh Research and Development, Enschede, 7522 AH, Netherlands, Department of Biomechanical Engineering, University of Twente, Enschede, 7522 NB, Netherlands; Kühnrich, M., Department of Robot Technology, Danish Technological Institute, Odense, 5230, Denmark; Lassen, A.B., Department of Robot Technology, Danish Technological Institute, Odense, 5230, Denmark; Nielsen, K., Department of Robot Technology, Danish Technological Institute, Odense, 5230, Denmark","Human–robot collaboration is currently one of the frontiers of industrial robot implementation. In parallel, the use of robots and robotic devices is increasing in several fields, substituting humans in “4D”—dull, dirty, dangerous, and delicate—tasks, and such a trend is boosted by the recent need for social distancing. New challenges in safety assessment and verification arise, due to both the closer and closer human–robot interaction, common for the different application domains, and the broadening of user audience, which is now very diverse. The present paper discusses a cross-domain approach towards the definition of step-by-step validation procedures for collaborative robotic applications. To outline the context, the standardization framework is analyzed, especially from the perspective of safety testing and assessment. Afterwards, some testing procedures based on safety skills, developed within the framework of the European project COVR, are discussed and exemplary presented. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Collaborative robots; Robot standards; Robotic safety",,,,,,"Horizon 2020 Framework Programme, H2020: 779966","Funding: This research was funded by the European Union’s Horizon 2020 research and innovation programme, under grant agreement No 779966.",,,,,,,,,,"Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266; Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human–robot collaboration (2018) Auton. Robot, 42, pp. 957-975; World Robotic Report 2020, , Https://ifr.org/ifr-press-releases/news/record-2.7-million-robots-work-infactories-around-the-globe, IFR.. (accessed on 30 January 2021); Peters, B.S., Armijo, P.R., Krause, C., Choudhury, S.A., Oleynikov, D., Review of emerging surgical robotic technology (2018) Surg. Endosc, 32, pp. 1636-1655; Riek, L.D., Healthcare robotics (2017) Commun. ACM, 60, pp. 68-78; Duckett, T., Pearson, S., Blackmore, S., Grieve, B., Wilson, P., Gill, H., Hunter, A.J., Georgilas, I., (2018) Agricultural Robotics: The Future of Robotic Agriculture, , UK-RAS Network: London, UK; Kimmig, R., Verheijen, R.H.M., Rudnicki, M., Robot assisted surgery during the COVID-19 pandemic, especially for gyne-cological cancer: A statement of the Society of European Robotic Gynaecological Surgery (SERGS) (2020) J. Gynecol. Oncol, 31, p. e59; Tavakoli, M., Carriere, J., Torabi, A., Robotics, smart wearable technologies, and autonomous intelligent systems for healthcare during the COVID-19 pandemic: An analysis of the state of the art and future vision (2020) Adv. Intell. Syst, p. 2; Zeng, Z., Chen, P.-J., Lew, A.A., From high-touch to high-tech: COVID-19 drives robotics adoption (2020) Tour. Geogr, 22, pp. 724-734; EUnited Robotics, , Https://www.eu-nited.net/eunited+aisbl/robotics/news/european-charter-for-robots-and-humans-released-by-engineering-association-eunited.html, Good Work Charter of the European Robotics Industry. (accessed on 30 January 2021); Vasic, M., Billard, A., Safety issues in human-robot interactions Proceedings of the 2013 IEEE International Conference on Robotics and Automation, pp. 197-204. , Karlsruhe, Germany, 6–10 May 2013; Guiochet, J., Do Hoang, Q.A., Kaâniche, M., Powell, D., Applying Existing Standards to a Medical Rehabilitation Robot: Limits and Challenges (2012) Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , Vilamoura, Portugal, 7–12 October; Gopinath, V., Johansen, K., Derelöv, M., Gustafsson, Å., Axelsson, S., Safe collaborative assembly on a continuously moving line with large industrial robots (2021) Robot. Comput. Manuf, 67, p. 102048; Saenz, J., Behrens, R., Schulenburg, E., Petersen, H., Gibaru, O., Neto, P., Elkmann, N., Methods for considering safety in design of robotics applications featuring human-robot collaboration (2020) Int. J. Adv. Manuf. Technol, 107, pp. 2313-2331; Gualtieri, L., Rauch, E., Vidoni, R., Emerging research fields in safety and ergonomics in industrial collaborative robotics: A systematic literature review (2021) Robot. Comput. Manuf, 67, p. 101998; Kumar, S., Savur, C., Sahin, F., Survey of human–robot collaboration in industrial settings: Awareness, intelligence, and compliance (2021) IEEE Trans. Syst. Man Cybern. Syst, 51, pp. 280-297; Marvel, J.A., Performance metrics of speed and separation monitoring in shared workspaces (2013) IEEE Trans. Autom. Sci. Eng, 10, pp. 405-414; (2006) Directive 2006/42/EC on Machinery, , European Parliament. European Parliament: Brussels, Belgium; (2017) Regulation (EU) 2017/745 on Medical Devices, , European Parliament. European Parliament: Brussels, Belgium; Bessler, J., Prange-Lasonder, G.B., Schaake, L., Saenz, J.F., Bidard, C., Fassi, I., Valori, M., Buurke, J.H., Safety assessment of rehabilitation robots: A review identifying safety skills and current knowledge gaps (2021) Front. Robot. AI, p. 8; (2001) Directive 2001/95/EC on General Product Safety, , European Parliament. European Parliament: Brussels, Belgium; (2013) Low Voltage Directive 2014/13/EU, , European Parliament. European Parliament: Brussels, Belgium; (2014) Directive 2014/30/EU on the Harmonization of the Laws of the Member States Relating to Electromagnetic Compatibility, , European Parliament. European Parliament: Brussels, Belgium; (2014) Radio Equipment Directive 2014/53/EU, , European Parliament. European Parliament: Brussels, Belgium; Toolkit, , Https://www.safearoundrobots.com/home, COVR. (accessed on 30 January 2021); (2010) Safety of Machinery—General Principles for Design—Risk Assessment and Risk Reduction, , ISO 12100:2010. International Or-ganization for Standardization: Geneva, Switzerland; (2019) Medical Devices—Application of Risk Management to Medical Devices, , ISO 14971:2019. International Organization for Standardization: Geneva, Switzerland; (2011) Robots and Robotic Devices—Safety Requirements for Industrial Robots—Part 1: Robots, , ISO 10218-1:2011. International Organization for Standardization: Geneva, Switzerland; (2011) Robots and Robotic Devices—Safety Requirements for Industrial Robots—Part 2: Robot Systems and Inte-gration, , ISO 10218-2:2011. International Organization for Standardization: Geneva, Switzerland; (2016) Robots and Robotic Devices—Collaborative Robots, , ISO/TS 15066:2016. International Organization for Standardization: Geneva, Switzerland; (2010) Safety of Machinery—Positioning of Safeguards with Respect to the Approach Speeds of Parts of the Human Body, , ISO 13855:2010. International Organization for Standardization: Geneva, Switzerland; (2018) Agricultural Machinery and Tractors—Safety of Highly Automated Machinery, , ISO 18497:2018. International Organization for Standardization: Geneva, Switzerland; (2019) Safety of Machinery—Two-Hand Control Devices—Principles for Design and Selection, , ISO 13851:2019. International Organi-zation for Standardization: Geneva, Switzerland; (2014) Robots and Robotic Devices—Safety Requirements for Personal Care Robots, , ISO 13482:2014. International Organization for Standardization: Geneva, Switzerland; (2020) Industrial Trucks—Safety Requirements and Verification—Part 4: Driverless Industrial Trucks and Their Systems, , ISO 3691-4:2020. International Organization for Standardization: Geneva, Switzerland; (2020) Medical Electrical Equipment—Part 2-78: Particular Requirements for Basic Safety and Essential Performance of Medical Robots for Rehabilitation, Assessment, Compensation or Alleviation, , IEC 80601-2-78:2020. International Electrotechnical Commission: Geneva, Switzerland; (2020) Application of ISO 13482—Part 1: Safety-Related Test Methods, , ISO/TR 23482-1:2020. International Organization for Stand-ardization: Geneva, Switzerland; (2018) Robotics—Safety Design for Industrial Robot Systems—Part 1: End-Effectors, , ISO/TR 20218-1:2018. International Organization for Standardization: Geneva, Switzerland; (2017) Robotics—Safety Requirements for Industrial Robots—Part 2: Manual Load/Unload Stations, , ISO/TR 20218-2:2017. International Organization for Standardization: Geneva, Switzerland; (2020) Robotics—Safety Requirements for Robot Systems in an Industrial Environment—Part 2: Robot Systems, Robot Applications and Robot Cells Integration, , ISO/DIS 10218-2:2020. International Organization for Standardization: Geneva, Switzerland; (2016) Robotics—Performance Criteria and Related Test Methods for Service Robots—Part 1: Locomotion for Wheeled Robots, , ISO 18646-1:2016. International Organization for Standardization: Geneva, Switzerland; (2019) Robotics—Performance Criteria and Related Test Methods for Service Robots—Part 2: Navigation, , ISO 18646-2:2019. International Organization for Standardization: Geneva, Switzerland; Saenz, J., Lassen, A.B., Bidard, C., Burke, J.H., Nielsen, K., Schaake, L., Vicentini, F., COVR—Towards simplified evaluation and validation of collaborative robotics applications across a wide range of domains using robot safety skills (2018) Proceedings of the 9th International Conference on Safety of Industrial Automated Systems (SIAS), , Nancy, France, 10–12 October; (2017) Design of Systems with ""Power and Force Limiting"" Function; Fachbereich Holz und Metall, German Social Accident Insurance (DGUV), , Collaborative robot systems. Heidelberg, Germany; (1997) EN 1525-Safety of Industrial Trucks—Driverless Trucks and Their Systems, , CEN. European Committee for Standardization: Brussels, Belgium; Herbster, S., Behrens, R., Elkmann, N., A New Conversion Method to Evaluate the Hazard Potential of Collaborative Robots in Free Collisions (2021) Experimental Robotics, pp. 222-232. , Springer International Publishing: Berlin/Heidelberg, Germany; Diederick, C., Niehorster, L., Lappe, M., The accuracy and precision of position and orientation tracking in the HTC vive virtual reality system for scientific research (2017) I-Perception, 8, p. 2041669517708205","Valori, M.; Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Italy; email: marcello.valori@stiima.cnr.it",,,"MDPI AG",,,,,22186581,,,,"English","Robotics",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85105719099
"Contreras-Calderón M.G., Laribi M.A., Castillo-Castañeda E.","57217291187;36500011400;55989796100;","New Variable Stiffness Joint (VSJ): Study and Simulation",2021,"Mechanisms and Machine Science","103",,,"284","293",,,"10.1007/978-3-030-75271-2_30","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105656627&doi=10.1007%2f978-3-030-75271-2_30&partnerID=40&md5=6122bb9bad7adf6e1d3e9e797b34420e","Instituto Politécnico Nacional, CICATA-Unidad Querétaro, Querétaro, Mexico; Department of GMSC, Pprime Institute, CNRS – University of Poitiers, ENSMA – UPR 3346, Poitiers, France","Contreras-Calderón, M.G., Instituto Politécnico Nacional, CICATA-Unidad Querétaro, Querétaro, Mexico; Laribi, M.A., Department of GMSC, Pprime Institute, CNRS – University of Poitiers, ENSMA – UPR 3346, Poitiers, France; Castillo-Castañeda, E., Instituto Politécnico Nacional, CICATA-Unidad Querétaro, Querétaro, Mexico","Variable stiffness joints are devices that allow changing the joint stiffness of a mechanism. The development of this joint has increased, and various applications have emerged, most to improve human-robot interaction. This paper presents a proposal design of a new variable stiffness joint (VSJ). The working principle consists of antagonist extension springs with a rack and pinion mechanism. The springs are attached to a set of racks that allow varying the spring length, the main factor in varying the stiffness. The racks engage with a set of gears; one has a motor attached, which is responsible for varying the joint stiffness. Its mathematical model describes the joint behavior to maintain a constant force at the end of a link. Its simulation is performed with a constant force placed at the end of the link to evaluate the joint’s performance in a rotational situation. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Antagonist springs; Compliant joint; Human-robot interaction; Variable stiffness joint","Human robot interaction; Springs (components); Constant force; Extension springs; Joint behavior; Joint stiffness; Rack and pinions; Variable stiffness; Stiffness",,,,,,,,,,,,,,,,"Howell, L., Magleby, S., Olsen, B., (2013), Compliant Mechanisms. Wiley, New York; Medina, J., Jardón, A., Balaguer, C., Control desacoplado de un actuador de rigidez variable para robots asistenciales (2016) Revista Panamericana De Automática E Informática Industrial, 13, pp. 80-91; Pratt, G., Williamson, M., Series elastic actuators (1995) IEEE/RSJ International Conference on Intelligent Robots and Systems, p. 399. , , p. , IEEE, Pittsburgh; Tsagarakis, N.G., Laffranchi, M., Cannella, F., Caldwell, D.G., Antagonistic and series elastic actuators: A comparative analysis on the energy consumption (2009) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5678-5684. , St. Louis, USA, pp. , IEEE; López-Martínez, J., Giménez-Fernández, A., García-Vallejo, D., Jardón-Huete A., Balaguer-Bernaldo C.: Diseño y simulación de un actuador de rigidez variable. Asociación Española de Ingeniería Mecánica. XIX Congreso Nacional de Ingeniería Mecánica (2012). ISSN 0212-5072; Yuwang, L., Xiagang, L., Zhongqiu, Y., Jinguo, L., Design and analysis of spring parallel variable stiffness actuator based on antagonistic principle (2019) Mech. Mach. Theory, 140, pp. 44-58; English, C., Russell, D., Mechanics and stiffness limitations of a variable stiffness actuator for use in prosthetic limbs (1999) Mech. Mach. Theory, 32 (1), pp. 7-25; Braun, D.J., Howard, M., Vijayakumar, S., Exploiting variable stiffness in explosive movement tasks (2012) Robotics: Science and Systems VII, 7, pp. 25-32. , , vol., pp; Jafari, A., Tsagarakis, G., Vanderborght, B., Caldwell, G., A novel actuator with adjustable stiffness (AwAS) (2010) IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, pp. 4201-4206. , , pp; Schiavi, R., Bicchi, A., Tonietti, G., Design and control of a variable stiffness actuator for safe and fast physical human/robot interaction (2005) Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 526-531. , Barcelona, Spain, pp; Contreras-Calderon, M.G., Castillo-Castañeda, E., Design of a variable stiffness joint for a five-bar-mechanism (2020) Advances in Service and Industrial Robotics. Results of RAAD, Switzerland AG, Pp. 54–63; Robinson, M., (2015) A Compliant Mechanism-Based Variable-Stiffness Joint, , Master thesis, Utha; Kim, B., Song, J., Hybrid dual actuator unit: A design of a variable stiffness actuator based on an adjustable moment arm mechanism (2010) IEEE International Conference on Robotics and Automation Anchorage Convention District, Anchorage, Alaska, pp. 1655-1660. , , pp; Lee springs. https://www.leespring.mx/es/conoce-mas-resortes-de-extension. Acceded 08 Feb 2021","Contreras-Calderón, M.G.; Instituto Politécnico Nacional, Mexico; email: ma.gpe.contreras.c@gmail.com",,,"Springer Science and Business Media B.V.",,,,,22110984,,,,"English","Mech. Mach. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85105656627
"Jahn K., Nissen A.","57193132674;57213596633;","Towards dual processing of social robots: Differences in the automatic and reflective system",2021,"International Conference on Information Systems, ICIS 2020 - Making Digital Inclusive: Blending the Local and the Global",,,,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103465958&partnerID=40&md5=8abd3eff43a8b98eb614ca494b252ff9","University of Siegen, Kohlbettstraße 15, Siegen, 57072, Germany; University of Duisburg-Essen, Universitätsstraße 9, Essen, 45141, Germany","Jahn, K., University of Siegen, Kohlbettstraße 15, Siegen, 57072, Germany; Nissen, A., University of Duisburg-Essen, Universitätsstraße 9, Essen, 45141, Germany","Social robots increasingly diffuse into our lives in work, health, and many other areas. However, theoretical approaches that explain how social robots should be designed to maximize experiential and performance-related outcomes in human-robot interaction (HRI) are still rare. To close this research gap, we aim to develop a dual process model of HRI with the help of two experiments. Results of the first experiment show that individuals categorize humans and robots differently in the automatic and reflective system, leading to different forms of robotic biases in these systems. Specifically, whereas humans show a bias against all types of robots in the reflective system, they only show biases against robots with low anthropomorphism in the automatic system. With the second experiment, we aim to complement these results from a neurophysiological perspective to gain more insights into cognitive processes during classification and evaluation of robots. © ICIS 2020. All rights reserved.","Dual process theory; Robotic bias; Uncanny valley; Who said what task","Blending; Information systems; Information use; Automatic systems; Classification and evaluations; Cognitive process; Dual process; Human robot Interaction (HRI); Reflective systems; Theoretical approach; Social robots",,,,,"005-1706-0006; Deutsche Forschungsgemeinschaft, DFG: 416228727 – SFB 1410","This paper was funded by the Ministry for Culture and Science of the State North Rhine-Westphalia (Project ID 005-1706-0006) and the German Research Foundation (DFG) (Project ID 416228727 – SFB 1410).",,,,,,,,,,"Bockelman Morrow, P., Fiore, S. M., Supporting human-robot teams in social dynamicism: An overview of the metaphoric inference framework (2012) Proceedings of the Human Factors and Ergonomics Society, 56 (1), pp. 1718-1722; Chaminade, T., Zecca, M., Blakemore, S. J., Takanishi, A., Frith, C. D., Micera, S., Dario, P., Umiltà, M. A., Brain response to a humanoid robot in areas implicated in the perception of human emotional gestures (2010) PLoS ONE, 5, p. 7; Epley, N., Waytz, A., Cacioppo, J. T., On Seeing Human: A Three-Factor Theory of Anthropomorphism (2007) Psychological Review, 114 (4), pp. 864-886; Eyssel, F., Kuchenbrandt, D., Social categorization of social robots: Anthropomorphism as a function of robot group membership: Social categorization and social robots (2012) British Journal of Social Psychology, 51 (4), pp. 724-731. , 2012; Fogassi, L., Ferrari, P. F., Gesierich, B., Rozzi, S., Chersi, F., Rizzolotti, G., Neuroscience: Parietal lobe: From action organization to intention understanding (2005) Science, 308 (5722), pp. 662-667; Gong, L., The boundary of racial prejudice: Comparing preferences for computer-synthesized White, Black, and robot characters (2008) Computers in Human Behavior. Including the Special Issue: Internet Empowerment, 24 (5), pp. 2074-2093. , 2008; Hehman, E., Ingbretsen, Z. A., Freeman, J. B., The neural basis of stereotypic impact on multiple social categorization (2014) NeuroImage, 101, pp. 704-711. , 2014; Kätsyri, J., Förger, K., Mäkäräinen, M., Takala, T., A review of empirical evidence on different uncanny valley hypotheses: support for perceptual mismatch as one road to the valley of eeriness (2015) Frontiers in Psychology, (6), p. 390; Krach, S., Hegel, F., Wrede, B., Sagerer, G., Binkofski, F., Kircher, T., Can machines think? Interaction and perspective taking with robots investigated via fMRI (2008) PLoS ONE, 3, p. 7; Lobato, E. J., Wiltshire, T. J., Fiore, S. M., A dual-process approach to understanding human-robot interaction (2013) Proceedings of the Human Factors and Ergonomics Society, pp. 1263-1267; MacDorman, K. F., Chattopadhyay, D., Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not (2016) Cognition, 146, pp. 190-205. , 2016; MacDorman, K. F., Chattopadhyay, D., Categorization-based stranger avoidance does not explain the uncanny valley effect (2017) Cognition, 161, pp. 132-135. , 2017; MacDorman, K. F., Green, R. D., Ho, C.-C., Koch, C. T., Too real for comfort? Uncanny responses to computer generated faces (2009) Computers in Human Behavior, 25 (3), pp. 695-710. , 2009; McKnight, H., Choudhury, V., Kacmar, C., The Impact of Initial Consumer Trust on Intentions to Transact with a Web Site: A Trust Building Model (2002) The Journal of Strategic Information Systems, 11 (3-4), pp. 297-323; Martini, M. C., Gonzalez, C. A., Wiese, E., Seeing Minds in Others - Can Agents with Robotic Appearance Have Human-Like Preferences? (2016) PLOS ONE, 11 (1), p. e0146310; Mathur, M. B., Reichling, D. B., An uncanny game of trust: social trustworthiness of robots inferred from subtle anthropomorphic facial cues (2009) Proceedings of the 4th ACM/IEEE international conference on Human robot interaction - HRI'09, the 4th ACM/IEEE international conference, pp. 313-314. , La Jolla, California; Mathur, M. B., Reichling, D. B., Lunardini, F., Geminiani, A., Antonietti, A., Ruijten, P. A., Levitan, C. A., Aczel, B., Uncanny but not confusing: Multisite study of perceptual category confusion in the Uncanny Valley Computers in Human Behavior, 103, pp. 21-30. , (2020). (September 2019); Miura, N., Sugiura, M., Takahashi, M., Miyamoto, A., Kawashima, R., The effect of emotional valence and body structure on emotional empathy to humanoid robot: an fMRI study (2009) NeuroImage, 47, pp. 39-41. , Supplement 1 2009; Molenberghs, P., Louis, W. R., Insights From fMRI Studies Into Ingroup Bias (2018) Frontiers in Psychology, 9. , Publisher: Frontiers, 1868; Mori, The uncanny valley (1970) Energy, 7 (4), pp. 33-35; Morrison, S., Decety, J., Molenberghs, P., The neuroscience of group membership (2012) Neuropsychologia, 50 (8), pp. 2114-2120. , 2012; Mukamel, R., Ekstrom, A. D., Kaplan, J., Iacoboni, M., Fried, I., Single-Neuron Responses in Humans during Execution and Observation of Actions (2010) Current Biology, 20 (8), pp. 750-756; Nomura, T., Robots and Gender (2017) Gender and the Genome, 1 (1), pp. 18-26; Pascalis, O., Haan, M. de, Nelson, C. A., Is face processing species-specific during the first year of life? (2002) Science, 296 (5571), pp. 1321-1323; Pfeuffer, N., Benlian, A., Gimpel, H., Hinz, O., Anthropomorphic Information Systems (2019) Business and Information Systems Engineering, 61 (4), pp. 523-533; Pietraszewski, D., Cosmides, L., Tooby, J., The Content of Our Cooperation, Not the Color of Our Skin: An Alliance Detection System Regulates Categorization by Coalition and Race, but Not Sex (2014) PLoS ONE, 9 (2), p. e88534. , 2014; Riedl, R., Mohr, P., Kenning, P., Davis, F., Heekeren, H., Trusting humans and avatars: A brain imaging study based on evolution theory (2014) Journal of Management Information Systems, 30 (4), pp. 83-114; Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K., Would You Trust a (Faulty) Robot? Effects of Error, Task Type and Personality on Human-Robot Cooperation and Trust (2015) 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 1-8. , March; Schindler, S., Zell, E., Botsch, M., Kissler, J., Differential effects of face-realism and emotion on event-related brain potentials and their implications for the uncanny valley theory (2017) Scientific Reports, 7 (1), pp. 1-13. , 2017; Sesko, A. K., Biernat, M., Prototypes of race and gender: The invisibility of Black women (2010) Journal of Experimental Social Psychology, 46 (2), pp. 356-360; Seyama, J., Nagayama, R. S., The Uncanny Valley: Effect of Realism on the Impression of Artificial Human Faces (2007) PRESENCE: Teleoperators and Virtual Environments, (16), pp. 337-351; Seymour, M., Riemer, K., Kay, J., Interactive Realistic Digital Avatars - Revisiting the Uncanny Valley (2017) Proceedings of the 50th Hawaii International Conference on System Sciences, pp. 547-556. , (2017); Siegel, M., Breazeal, C., Norton, M. I., Persuasive Robotics: The influence of robot gender on human behavior (2009) 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2563-2568. , IEEE, 2009; Sloman, S. A., The empirical case for two systems of reasoning (1996) Psychological Bulletin, 119 (1), pp. 3-22; Stock, R., Merkle, M., Eidens, D., Hannig, M., Heineck, P., Nguyen, M. A., Völker, J., When robots enter our workplace: Understanding employee trust in assistive robots 40th International Conference on Information Systems, ICIS 2019, pp. 1-9. , (2020); Strait, M. K., Floerke, V. A., Ju, W., Maddox, K., Remedios, J. D., Jung, M. F., Urry, H. L., Understanding the Uncanny: Both Atypical Features and Category Ambiguity Provoke Aversion toward Humanlike Robots (2017) Frontiers in Psychology, (8), p. 1366; Strait, M., Canning, C., Scheutz, M., Investigating the effects of robot communication strategies in advice-giving situations based on robot appearance, interaction modality and distance (2014) ACM/IEEE International Conference on Human-Robot Interaction; Wang, Y., Quadflieg, S., In our own image? Emotional and neural processing differences when observing human-human vs human-robot interactions (2014) Social Cognitive and Affective Neuroscience, 10 (11), pp. 1515-1524",,,,"Association for Information Systems","2020 International Conference on Information Systems - Making Digital Inclusive: Blending the Local and the Global, ICIS 2020","13 December 2020 through 16 December 2020",,167844,,9781733632553,,,"English","Int. Conf. Inf. Syst., ICIS - Mak. Digit. Incl.: Blending Local Glob.",Conference Paper,"Final","",Scopus,2-s2.0-85103465958
"Bisconti P.","57205669543;","Will sexual robots modify human relationships? A psychological approach to reframe the symbolic argument",2021,"Advanced Robotics","35","9",,"561","571",,,"10.1080/01691864.2021.1886167","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100942700&doi=10.1080%2f01691864.2021.1886167&partnerID=40&md5=3e614bd38c330b99912ed4dd49c02e2b","DIRPOLIS Institute, Sant’Anna School of Advanced Studies, Pisa, Italy","Bisconti, P., DIRPOLIS Institute, Sant’Anna School of Advanced Studies, Pisa, Italy","The purpose of this paper is to understand if and how interactions with Sexual Robots will modify users’ relational abilities in human-human relations. We first underline that, in today’s scholar discussion on the ‘symbolic argument’, there is no theoretical framework explaining the process of symbolic shift between human-robot interactions (HRI) and human-human interactions (HHI). To clarify the symbolic shift mechanism, we propose the concept of objectual mediation. Moreover, under the lens of Winnicott’s object-relation theory, we argue that HRI can structurally modify users relationality, degrading intersubjective abilities: Sexual Robots may blurry the limit between the ‘internal object’ and the external one. We conclude that, under certain conditions, interactions with Sexual Robots may bring the user back to an infantile object-relation setting, worsening the user ability to manage relational frustration. This will negatively impact on human-human interactions and social values in general. © 2021 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","companion robots; human-robot interactions; philosophy of technology; psychology; Sexual robots","Agricultural robots; Human relations; Human relationships; Human robot Interaction (HRI); Human-human interactions; Object-relations; Shift mechanism; Social values; Theoretical framework; Human robot interaction",,,,,,,,,,,,,,,,"Sullins, J.P., Robots, love, and sex: the ethics of building a love machine (2012) IEEE Trans Affect Comput, 3 (4), pp. 398-409; Danaher, J., Robotic rape and robotic child sexual abuse: should they be criminalised? (2017) Criminal law and Philosophy, 11 (1), pp. 71-95; Bendel, O., Sex robots from the perspective of machine ethics (2017) Love and sex with robots. LSR 2016. Lecture notes in computer science, vol 10237, , Cheok A., Devlin K., Levy D., (eds), Cham: Springer,. In:, editor; Levy, D., (2009) Love+ sex with robots: the evolution of human-robot relationships, , New York.: Duckworth Overlook; Strikwerda, L., Present and future instances of virtual rape in light of three categories of legal philosophical theories on rape (2015) Philos Technol, 28 (4), pp. 491-510; Maras, M.H., Shapiro, L.R., Child sex dolls and robots: more than just an Uncanny Valley (2017) J Internet Law, 21, pp. 3-17; Cox-George, C., Bewley, S., I, Sex robot: the health implications of the sex robot industry (2018) BMJ Sex Reproductive Health, 44, pp. 161-164; Gutiu, S.M., The roboticization of consent (2016) Robot Law, , Edward Elgar Publishing,. In; Devlin, K., In defence of sex machines: why trying to ban sex robots is wrong (2015) The Conversation; Levy, D., Loebner, H., Robot prostitutes as alternatives to human sex workers (2007) IEEE international conference on robotics and automation, , Rome:. In; Sparrow, R., Robots, rape, and representation (2017) Int J Soc Robot, 9 (4), pp. 465-477; Danaher, J., McArthur, N., (2017) Robot sex: social and ethical implications, , Cambridge, MA, and London.: MIT Press; Coeckelbergh, M., Robot rights? towards a social-relational justification of moral consideration (2010) Ethics Inf Technol, 12 (3), pp. 209-221; Drummond, A., Sauer, J.D., Garea, S.S., The infamous relationship between violent video game use and aggression: uncharted moderators and small effects make it a far cry from certain (2018) Video game influences on aggression, cognition, and attention, , https://link.springer.com/chapter/10.1007/978-3-319-95495-0_3, Ferguson C., (ed), Cham: Springer,. In:, editor; Ferguson, C.J., Barr, H., Figueroa, G., Digital poison? three studies examining the influence of violent video games on youth (2015) Comput Human Behav, 50, pp. 399-410; Peeters, A., Haselager, P., Designing virtuous sex robots (2019) Int J Soc Robot, pp. 1-12; Richardson, K., Sex robot matters: slavery, the prostituted, and the rights of machines (2016) IEEE Technol Soc Mag, 35 (2), pp. 46-53; Richardson, K., (2015) An anthropology of robots and AI: annihilation anxiety and machines, , New York: Routledge; Richardson, K., Technological animism: the uncanny personhood of humanoid machines (2016) Social Analysis, 60 (1), pp. 110-128; Danaher, J., The symbolic-consequences argument in the sex robot debate (2017) Robot sex: social and ethical implications, pp. 125-159. , Danaher J., McArthur N., (eds), Cambridge MA: MIT Press,. In:, editors.,. p; Van de Poel, I., An ethical framework for evaluating experimental technology (2016) Sci Eng Ethics, 22 (3), pp. 667-686; Summers, F., (2014) Object relations theories and psychopathology: a comprehensive text, , New York / London: Routledge; Piaget, J., (2013) The construction of reality in the child, , Routledge; Fromberg, D.P., Bergen, D., (1998) Play from birth to twelve and beyond: contexts, perspectives, and meanings, , New York: Psychology Press, editors; Benjamin, J., (2013) The bonds of love: psychoanalysis, feminism, and the problem of domination, , New York: Pantheon; Bisconti, P., Piermattei, S., Sexual Robots: The social-relational approach and the concept of subjective reference (2020) Human-computer interaction. Multimodal and natural interaction. HCII 2020. Lecture notes in computer science, 12182. , Kurosu M., (ed), Cham: Springer,. In:, editor., vol; Turkle, S., Taggart, W., Kidd, C.D., Relational artifacts with children and elders: The complexities of cybercompanionship (2006) Conn Sci, 18 (4), pp. 347-361; Breazeal, C.L., (2004) Designing sociable robots, , MIT press; Winnicott, D.W., The use of an object (1969) Int J Psycho-Analysis, 50, pp. 711-716; Sandler, J., On internal object relations (1990) J Am Psychoanal Assoc, 38 (4), pp. 859-880; Ogden, T.H., The concept of internal object relations (1983) Int J Psycho-Analysis, 64, pp. 227-241; Winnicott, D.W., Transitional objects and transitional phenomena in  (1991) Playing and reality, pp. 86-104. , New York: Psychology Press, Routledge;,. p; Knafo, D., Guys and dolls: relational life in the technological era (2015) Psychoanal Dialogues, 25 (4), pp. 481-502; Scheutz, M., Arnold, T., Are we ready for sex robots? (2016) 2016 11th ACM/IEEE International Conference on human-robot interaction (HRI), pp. 351-358. , March, IEEE, In:,. p; Watson, L., Flanigan, J., (2020) Debating sex work, , Oxford: Oxford University Press; Gunkel, D.J., (2018) Robot rights, , Cambridge: MIT Press","Bisconti, P.; DIRPOLIS Institute, Italy; email: piercosma.biscontilucidi@santannapisa.it",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85100942700
"Sharifi M., Zakerimanesh A., Mehr J.K., Torabi A., Mushahwar V.K., Tavakoli M.","55224489900;57193490432;57221662636;57203129433;6603751651;8668966400;","Impedance Variation and Learning Strategies in Human-Robot Interaction",2021,"IEEE Transactions on Cybernetics",,,,"","",,3,"10.1109/TCYB.2020.3043798","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099722915&doi=10.1109%2fTCYB.2020.3043798&partnerID=40&md5=f1fa0e522a31ca7831fab1349e60cfc0","Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, also with the Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada (e-mail: m.sharifi@ualberta.ca); Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada.; Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, also with the Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada.; Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada.","Sharifi, M., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, also with the Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada (e-mail: m.sharifi@ualberta.ca); Zakerimanesh, A., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada.; Mehr, J.K., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, also with the Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada.; Torabi, A., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada.; Mushahwar, V.K., Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Alberta, Edmonton, AB T6G 2E1, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada.; Tavakoli, M., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada, and also with the Sensory Motor Adaptive Rehabilitation Technology Network, University of Alberta, Edmonton, AB T6G 2E1, Canada.","In this survey, various concepts and methodologies developed over the past two decades for varying and learning the impedance or admittance of robotic systems that physically interact with humans are explored. For this purpose, the assumptions and mathematical formulations for the online adjustment of impedance models and controllers for physical human-robot interaction (HRI) are categorized and compared. In this systematic review, studies on: 1) variation and 2) learning of appropriate impedance elements are taken into account. These strategies are classified and described in terms of their objectives, points of view (approaches), and signal requirements (including position, HRI force, and electromyography activity). Different methods involving linear/nonlinear analyses (e.g., optimal control design and nonlinear Lyapunov-based stability guarantee) and the Gaussian approximation algorithms (e.g., Gaussian mixture model-based and dynamic movement primitives-based strategies) are reviewed. Current challenges and research trends in physical HRI are finally discussed. IEEE","Human-robot interaction (HRI); impedance and admittance models; impedance control; impedance learning; impedance variation; robot learning; robot stability",,,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21682267,,,,"English","IEEE Trans. Cybern.",Article,"Article in Press","",Scopus,2-s2.0-85099722915
"Martínez-Colón A., Viciana-Abad R., Perez-Lorenzo J.M., Evers C., Naylor P.A.","56982933400;8203882800;12344712400;24476173800;7005918686;","Evaluation of a Multi-speaker System for Socially Assistive HRI in Real Scenarios",2021,"Advances in Intelligent Systems and Computing","1285",,,"151","166",,1,"10.1007/978-3-030-62579-5_11","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097382580&doi=10.1007%2f978-3-030-62579-5_11&partnerID=40&md5=90e06c6d6ed7266cf2d0271061e40edb","Universidad de Jaén, Jaén, Spain; University of Southampton, Southampton, United Kingdom; Imperial College London, London, United Kingdom","Martínez-Colón, A., Universidad de Jaén, Jaén, Spain; Viciana-Abad, R., Universidad de Jaén, Jaén, Spain; Perez-Lorenzo, J.M., Universidad de Jaén, Jaén, Spain; Evers, C., University of Southampton, Southampton, United Kingdom; Naylor, P.A., Imperial College London, London, United Kingdom","In the field of social human-robot interaction, and in particular for social assistive robotics, the capacity of recognizing the speaker’s discourse in very diverse conditions and where more than one interlocutor may be present, plays an essential role. The use of a mics. array that can be mounted in a robot supported by a voice enhancement module has been evaluated, with the goal of improving the performance of current automatic speech recognition (ASR) systems in multi-speaker conditions. An evaluation has been made of the improvement in terms of intelligibility scores that can be achieved in the operation of two off-the-shelf ASR solutions in situations that contemplate the typical scenarios where a robot with these characteristics can be found. The results have identified the conditions in which a low computational cost demand algorithm can be beneficial to improve intelligibility scores in real environments. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.","Array; ASR; Beamforming; Intelligibility; Masking","Speech recognition; Assistive; Assistive robotics; Automatic speech recognition system; Computational costs; Intelligibility scores; Real environments; Social human-robot interactions; Speaker systems; Social robots",,,,,"Ministerio de Ciencia, Innovación y Universidades, MCIU","Acknowledgements. This work has been funded by the National Research Project TEST-RTI2018-099522-A-C44’: “Test-beds for the Evaluation of Social Awareness in Assistance Robotics” and thanks to the collaboration with CSP group at Imperial College London, funded by the Spanish Ministry of Science, Innovation and University through the lectures mobility programme (Jose Castillejo’s 2018 grant). Most of the information about the typical life in a retirement home and Felipe’s robot name have been gathered from the experiences during the work developed in Vitalia Teatinos and supported by the Regional Project AT17-5509-UMA ‘ROSI’.",,,,,,,,,,"Kriegel, J., Grabner, V., Tuttle-Weidinger, L., Ehrenmüller, I., Socially assistive robots (SAR) in in-patient care for the elderly (2019) Stud. Health Technol. Inform, 260, pp. 178-185. , https://doi.org/10.3233/978-1-61499-971-3-178; Beckert, E., Event-based experiments in an assistive environment using wireless sensor networks and voice recognition (2009) 2Nd International Conference on Pervasive Technologies Related to Assistive Environments (PETRA09), pp. 1-8. , https://doi.org/10.1145/1579114.1579131, ACM, Corfu; Martínez, J., Towards a robust robotic assistant for comprehensive geriatric assessment procedures: Updating the CLARC system (2018) 27Th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN), pp. 820-825. , https://doi.org/10.1109/ROMAN.2018.8525818, IEEE Press, Nanjing; Wang, D., Chen, J., Supervised speech separation based on deep learning: An overview (2018) IEEE/ACM Trans. Audio Speech Lang. Process., 26, pp. 1702-1726. , https://doi.org/10.3233/978-1-61499-971-3-178; Okuno, H.G., Nakadai, K., Kim, H., Robot audition: Missing feature theory approach and active audition (2009) Springer Tracts in Advanced Robotics (14Th Conference Robotics Research), 70, pp. 227-244. , https://doi.org/10.1007/978-3-642-19457-314; Valin, J., Robust recognition of simultaneous speech by a mobile robot (2007) IEEE Trans. Robot., 23, pp. 742-752. , https://doi.org/10.1109/TRO.2007.900612; Chang, X., MIMO-speech: End-to-end multi-channel multi-speaker speech recognition (2020) IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 237-244. , https://doi.org/10.1109/ASRU46091.2019.9003986; Jankowski, C., Mruthyunjaya, V., Lin, R., (2020) Improved Robust ASR for Social Robots in Public Spaces; Biocca, F., The cyborg’s dilemma: Embodiment in virtual environments (1997) Second International Conference on Cognitive Technology Humanizing the Information Age, pp. 12-26. , https://doi.org/10.1109/CT.1997.617676, Japan; Kennedy, J., Child speech recognition in human-robot interaction: Evaluations and recommendations (2017) 12Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), p. 82?90. , https://doi.org/10.1145/2909824.3020229, IEEE/ACM, Vienna; Garnerin, M., Rossato, S., Laurent, B., Gender representation in french broadcast corpora and its impact on ASR performance (2019) 1St International Workshop on AI for Smart TV Content Production, Access and Delivery (AI4TV 2019), pp. 3-9. , https://doi.org/10.1145/3347449.3357480, ACM, New York; Nikunen, J., Diment, A., Virtanen, T., Separation of moving sound sources using multichannel NMF and acoustic trackings (2018) IEEE/ACM Trans. Audio Speech Lang. Process., 26, pp. 281-295. , https://doi.org/10.1109/TASLP.2017.2774925; Reche, P.J., Binaural lateral localization of multiple sources in real environments using a kurtosis-driven split-EM algorithm (2018) Eng. Appl. Artif. Intell., 69, pp. 137-146. , https://doi.org/10.1016/j.engappai.2017.12.013; Souden, M., Benesty, J., Affes, S., A study of the LCMV and MVDR noise reduction filters (2010) IEEE Trans. Signal Process., 58, pp. 4925-4935. , https://doi.org/10.1109/TSP.2010.2051803; Yu, Z.L., Er, M.J., An extended generalized sidelobe canceller in time and frequency domain (2004) 2004 IEEE International Symposium on Circuits and Systems, pp. 629-633. , https://doi.org/10.1109/ISCAS.2004.1328825, IEEE, Vancouver; Griffiths, L., Jim, C., An alternative approach to linearly constrained adaptive beamforming (1982) IEEE Trans. Antennas Propag., 30, pp. 27-34; Morgan, J.P., (2017) Time-Frequency Masking Performance for Improved Intelligibility with Microphone Arrays, , Master Thesis in the College of Engineering at the University of Kentucky; Taal, C.H., A short-time objective intelligibility measure for time-frequency weighted noisy speech (2010) IEEE Transactions on Antennas and Propagation, pp. 4214-4217. , https://doi.org/10.1109/ICASSP.2010.5495701, IEEE, Dallas; Miller, G.A., The masking of speech (1947) Psychol. Bull., 44, pp. 105-129; Martinez-Colon, A., Attentional mechanism based on a microphone array for embedded devices and a single camera (2018) Workshop of Physical Agents, pp. 165-178. , Springer. Madrid; Ni, F., Zhou, Y., Liu, H., A robust GSC beamforming method for speech enhancement using linear microphone array (2019) IEEE 21St International Workshop on Multimedia Signal Processing (MMSP), pp. 1-5. , IEEE. Kuala Lumpur; Li, C., Benesty, J., Chen, J., Beamforming based on null-steering with small spacing linear microphone arrays (2018) J. Acoust. Soc. Am., 143, pp. 2651-2664","Viciana-Abad, R.; Universidad de JaénSpain; email: rviciana@ujaen.es","Bergasa L.M.Ocana M.Barea R.Lopez-Guillen E.Revenga P.",,"Springer Science and Business Media Deutschland GmbH","21st International Workshop of Physical Agents, WAF 2020","19 November 2020 through 20 November 2020",,251329,21945357,9783030625788,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85097382580
"Zhao J., Gong S., Xie B., Duan Y., Zhang Z.","57129187500;57207468351;36462894100;57216927327;56725003500;","Human arm motion prediction in human-robot interaction based on a modified minimum jerk model",2021,"Advanced Robotics","35","3-4",,"205","218",,,"10.1080/01691864.2020.1840432","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095734096&doi=10.1080%2f01691864.2020.1840432&partnerID=40&md5=13ff995712b835cd5e226c11ab3cd3a2","College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China; Department of Electrical and Computer Engineering, University of Kentucky, United States","Zhao, J., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China; Gong, S., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China; Xie, B., Department of Electrical and Computer Engineering, University of Kentucky, United States; Duan, Y., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China; Zhang, Z., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China","Accurate prediction of human motion is essential to ensure the efficiency and safety of human-robot interaction (HRI), especially when humans and robots interact closely in a shared environment. A novel method is developed in this work to address three fundamental problems in human arm motion prediction, i.e. given the early-stage fingertip trajectory of a human arm reaching motion, how to predict the motion duration, the motion destination, and the remaining fingertip trajectory. First, a modified minimum jerk model (MMJM), containing three input parameters—the motion duration, the motion destination, and the early-stage fingertip trajectory, is developed to express and predict the remaining fingertip trajectory. Next, these unknown parameters are determined by determining the optimal starting time of motion prediction and employing Gaussian process regression models (GPRs). Finally, the proposed human arm motion prediction method is validated by simulations and HRI experiments. © 2020 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","Gaussian process regression; Human-robot interaction; minimum jerk model; motion prediction","Agricultural robots; Forecasting; Man machine systems; Motion estimation; Regression analysis; Trajectories; Accurate prediction; Fingertip trajectory; Gaussian process regression model; Human arm motion; Human robot Interaction (HRI); Input parameter; Minimum jerk model; Motion prediction; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 51975008; Beijing Municipal Natural Science Foundation: 3192002","This work was supported by the National Natural Science Foundation of China (No. 51975008) and the Beijing Municipal Natural Science Foundation (No. 3192002).",,,,,,,,,,"Hentout, A., Aouache, M., Maoudj, A., Human–robot interaction in industrial collaborative robotics: a literature review of the decade 2008–2017 (2019) Adv Robot, 33 (15-16), pp. 764-799; Unhelkar, V.V., Lasota, P.A., Tyroller, Q., Human-Aware robotic Assistant for Collaborative Assembly: Integrating human motion prediction With planning in time (2018) IEEE Robot Autom Lett, 3 (3), pp. 2394-2401; Gui, L.Y., Zhang, K., Wang, Y.X., Teaching Robots to Predict Human Motion. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2018, October 1, 2018 - October 5, 2018. 2018. Madrid, Spain: Institute of Electrical and Electronics Engineers Inc; Reily, B., Han, F., Parker, L.E., Skeleton-based bio-inspired human activity prediction for real-time human–robot interaction (2018) Auton Robots, 42 (6), pp. 1281-1298; Li, Y., Wang, Z., Yang, X., Efficient convolutional hierarchical autoencoder for human motion prediction (2019) Vis Comput, 35 (6-8), pp. 1143-1156; Long, Y., Du, Z.-J., Wang, W.-D., Human motion intent learning based motion assistance control for a wearable exoskeleton (2018) Robot Comput Integr Manuf, 49, pp. 317-327; Torricelli, D., Cortés, C., Lete, N., A Subject-Specific kinematic model to predict human motion in Exoskeleton-Assisted Gait (2018) Front Neurorobot, 12, p. 18; Hua, Y., Fan, J., Liu, G., A novel Weight-Bearing lower Limb Exoskeleton based on motion intention prediction and Locomotion state Identification (2019) IEEE Access, 7, pp. 37620-37638; Bi, L., Feleke, A.G., Guan, C., A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration (2019) Biomed Signal Process Control, 51, pp. 113-127; Mainprice, J., Berenson, D., Human-robot collaborative manipulation planning using early prediction of human motion. 2013 26th IEEE/RSJ International Conference on Intelligent Robots and Systems: New Horizon, IROS 2013, November 3, 2013 - November 8, 2013. 2013. Tokyo, Japan: Institute of Electrical and Electronics Engineers Inc; Perez-D'Arpino, C., Shah, J.A., Fast target prediction of human reaching motion for cooperative human-robot manipulation tasks using time series classification. 2015 IEEE International Conference on Robotics and Automation, ICRA 2015, May 26, 2015 - May 30, 2015. 2015. Seattle, WA, United states: Institute of Electrical and Electronics Engineers Inc; Luo, R., Berenson, D., A framework for unsupervised online human reaching motion recognition and early prediction. IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015, September 28, 2015 - October 2, 2015. 2015. Messeplatz 1, Hamburg, Germany: Institute of Electrical and Electronics Engineers Inc; Luo, R., Hayne, R., Berenson, D., Unsupervised early prediction of human reaching for human–robot collaboration in shared workspaces (2018) Auton Robots, 42 (3), pp. 631-648; Park, J.S., Park, C., Manocha, D., I-Planner: intention-aware motion planning using learning-based human motion prediction (2019) Int J Rob Res, 38 (1), pp. 23-39; Butepage, J., Kjellstrom, H., Kragic, D., Anticipating Many Futures: Online Human Motion Prediction and Generation for Human-Robot Interaction. 2018 IEEE International Conference on Robotics and Automation, ICRA 2018, May 21, 2018 - May 25, 2018. 2018. Brisbane, QLD, Australia: Institute of Electrical and Electronics Engineers Inc; Liu, H., Wang, L., Human motion prediction for human-robot collaboration (2017) J Manuf Syst, 44, pp. 287-294; Zhou, T., Wachs, J.P., Early prediction for physical human robot collaboration in the operating room (2018) Auton Robots, 42 (5), pp. 977-995; Wang, Y., Sheng, Y., Wang, J., Optimal collision-free robot trajectory generation based on time series prediction of human motion (2018) IEEE Robotics and Automation Letters, 3 (1), pp. 226-233; Wang, W., Qin, L., Yuan, X., Bionic control of exoskeleton robot based on motion intention for rehabilitation training (2019) Adv Robot, 33 (12), pp. 590-601; Pereira, A., Althoff, M., Overapproximative human Arm occupancy prediction for collision avoidance (2018) IEEE Trans Autom Sci Eng, 15 (2), pp. 818-831; Mainprice, J., Hayne, R., Berenson, D., Predicting human reaching motion collaborative tasks using Inverse Optimal Control and iterative re-planning. 2015 IEEE International Conference on Robotics and Automation, ICRA 2015, May 26, 2015 - May 30, 2015. 2015. Seattle, WA, United states: Institute of Electrical and Electronics Engineers Inc; Mainprice, J., Hayne, R., Berenson, D., Goal Set inverse optimal control and iterative replanning for predicting human reaching motions in shared workspaces (2016) IEEE Trans Robot, 32 (4), pp. 897-908; Lasota, P.A., Shah, J.A., A multiple-predictor approach to human motion prediction. 2017 IEEE International Conference on Robotics and Automation, ICRA 2017, May 29, 2017 - June 3, 2017. 2017. Singapore, Singapore: Institute of Electrical and Electronics Engineers Inc; Amor, H.B., Neumann, G., Kamthe, S., Interaction primitives for human-robot cooperation tasks. 2014 IEEE International Conference on Robotics and Automation, ICRA 2014, May 31, 2014 - June 7, 2014. 2014. Hong Kong, China: Institute of Electrical and Electronics Engineers Inc; Maeda, Y., Hara, T., Arai, T., Human-robot cooperative manipulation with motion estimation. 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 29, 2001 - November 3, 2001. 2001. Maui, HI, United states: Institute of Electrical and Electronics Engineers Inc; Flash, T., Hogan, N., The coordination of arm movements - an experimentally confirmed mathematical-model (1985) J Neurosci, 5 (7), pp. 1688-1703; Seeger, M., Gaussian processes for machine learning (2004) Int J Neural Syst, 14 (2), pp. 69-106; Kong, D., Chen, Y., Li, N., Gaussian process regression for tool wear prediction (2018) Mech Syst Signal Process, 104, pp. 556-574; Dubey, R.V., Euler, J.A., Babcock, S.M., Real-time implementation of an optimization scheme for seven-degree-of-freedom redundant manipulators (1991) IEEE Trans Robot Autom, 7 (5), pp. 579-588","Xie, B.; Department of Electrical and Computer Engineering, United States; email: Biyun.Xie@uky.edu",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Final","",Scopus,2-s2.0-85095734096
"Zhichao P., Wenhua H., Hongji T., Minlei X., Ruwei L.","26029652000;23480871000;16178490900;57284393900;57284394000;","Attention-based sequence modeling for categorical emotion recognition with modulation spectral feature",2020,"Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020",,,,"349","353",,,"10.1109/ICISCE50968.2020.00080","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116381954&doi=10.1109%2fICISCE50968.2020.00080&partnerID=40&md5=4ca7c36eeb03e27677889fa12f6ca7c0","Hunan University of Humanities, Science and Technology, Information School, Loudi, China","Zhichao, P., Hunan University of Humanities, Science and Technology, Information School, Loudi, China; Wenhua, H., Hunan University of Humanities, Science and Technology, Information School, Loudi, China; Hongji, T., Hunan University of Humanities, Science and Technology, Information School, Loudi, China; Minlei, X., Hunan University of Humanities, Science and Technology, Information School, Loudi, China; Ruwei, L., Hunan University of Humanities, Science and Technology, Information School, Loudi, China","Emotion information from speech can effectively help robots or virtual agents to understand speaker's intentions in natural human-robot interaction. Previous studies have indicated that temporal-modulation cues are important for emotional speech perception. Utterance-level modulation spectral features (MSF) obtained from temporal modulation cues are commonly used for emotion recognition. However, MSF is a kind of statistical feature and cannot reflect the temporal dynamics of speech signals very well. In this paper, we propose a categorical emotion recognition method by modeling time sequence of segment-level MSF. Considering that the voice regions of an utterance are more emotional, we use a temporal attention model to capture the salient regions of emotion representation in each utterance. Our experiments with the IEMOCAP database demonstrate that the proposed method can be effectively used to recognize the emotion state of speech from segment-level modulation spectral feature. © 2020 IEEE.","attention-based sequence modeling; Categorical emotion; modulation spectral feature; speech emotion recognition","Behavioral research; Human robot interaction; Speech; Speech recognition; Virtual reality; Attention-based sequence modeling; Categorical emotion; Emotion recognition; Humans-robot interactions; Modulation spectral feature; Sequence models; Spectral feature; Speech emotion recognition; Temporal modulations; Virtual agent; Modulation",,,,,"18A414; S202010553050","This study was partly supported by the Research Foundation of Education Bureau of Hunan Province, China (Grant No. 18A414), and was partly supported by the Undergraduate Innovation and Entrepreneurship Training Program (No. S202010553050)",,,,,,,,,,"Wu, S., Falk, T.H., Chan, W.Y., Automatic speech emotion recognition using modulation spectral features (2011) Speech Commun., 53 (5), pp. 768-785; Zhu, Z., Miyauchi, R., Araki, Y., Unoki, M., Modulation spectral features for predicting vocal emotion recognition by simulated cochlear implants (2016) Interspeech 2016, pp. 262-266; Busso, C., IEMOCAP: Interactive emotional dyadic motion capture database (2008) Lang. Resour. Eval., 42 (4), pp. 335-359; Moore, B.C.J., Glasberg, B.R., Suggested formulae for calculating auditory-filter bandwidths and excitation patterns (1983) J. Acoust. Soc. Am., 74 (3), pp. 750-753; Patterson, R.D., Unoki, M., Irino, T., Extending the domain of center frequencies for the compressive gammachirp auditory filter (2003) J. Acoust. Soc. Am., 114 (3), pp. 1529-1542; Peng, Z., Zhu, Z., Unoki, M., Dang, J., Akagi, M., Dimensional emotion recognition from speech using modulation spectral features and recurrent neural networks (2019) 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 524-528; Wang, Z.-Q., Tashev, I., Learning utterance-level representations for speech emotion and age/gender recognition using deep neural networks (2017) Proc. 42nd IEEE Int. Conf. Acoust., Speech and Signal Process., pp. 5150-5154; Lee, J., Tashev, I., High-level feature representation using recurrent neural network for speech emotion recognition (2015) Proc. INTERSPEECH 2015 16th Annu. Conf. Int. Speech Commun. Assoc., pp. 1537-1540; Neumann, M., Vu, N.T., Attentive convolutional neural network based speech emotion recognition: A study on the impact of input features, signal length, and acted speech (2017) Proc. INTERSPEECH 2017 18th Annu. Conf. Int. Speech Commun. Assoc., 2017 (3), pp. 1263-1267. , Augus; Li, Y., Zhao, T., Kawahara, T., Improved end-to-end speech emotion recognition using self attention mechanism and multitask learning (2019) Proc. Interspeech 2019, pp. 2803-2807; Li, R., Wu, Z., Jia, J., Zhao, S., Meng, H., Dilated residual network with multi-head self-attention for speech emotion recognition (2019) ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6675-6679; Schuller, B., Steidl, S., Batliner, A., The interspeech 2009 emotion challenge (2009) Proc. INTERSPEECH 2009 10th Annu. Conf. Int. Speech Commun. Assoc.; Schuller, B., The interspeech 2013 computational paralinguistics challenge: Social signals, conflict, emotion, autism (2013) Proc. INTERSPEECH 2013 14th Annu. Conf. Int. Speech Commun. Assoc.; Eyben, F., Wöllmer, M., Schuller, B., Opensmile: The munich versatile and fast open-source audio feature extractor (2010) Proc. 18th ACM Int. Conf. Multimedia, pp. 1459-1462",,"Li S.Dai Y.Ma J.Cheng Y.","et al.;Hunan University;Hunan University of Humanities, Science and Technology;Swinburne University of Technology;Wayne State University;Xiamen University","Institute of Electrical and Electronics Engineers Inc.","7th International Conference on Information Science and Control Engineering, ICISCE 2020","18 December 2020 through 20 December 2020",,171872,,9781728164069,,,"English","Proc. - Int. Conf. Inf. Sci. Control Eng., ICISCE",Conference Paper,"Final","",Scopus,2-s2.0-85116381954
"Ky Q.-M., Huynh D.-N., Le M.-H.","57221559719;57221564881;36809760500;","Receptionist and Security Robot Using Face Recognition with Standardized Data Collecting Method",2020,"Proceedings of 2020 5th International Conference on Green Technology and Sustainable Development, GTSD 2020",,,"9303087","597","603",,1,"10.1109/GTSD50082.2020.9303087","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099463918&doi=10.1109%2fGTSD50082.2020.9303087&partnerID=40&md5=224b2332ceb109da770df5ec7db2f927","HCMC University of Technology and Education, Faculty of Electrical and Electronics Engineering, Ho Chi Minh City, Viet Nam","Ky, Q.-M., HCMC University of Technology and Education, Faculty of Electrical and Electronics Engineering, Ho Chi Minh City, Viet Nam; Huynh, D.-N., HCMC University of Technology and Education, Faculty of Electrical and Electronics Engineering, Ho Chi Minh City, Viet Nam; Le, M.-H., HCMC University of Technology and Education, Faculty of Electrical and Electronics Engineering, Ho Chi Minh City, Viet Nam","Face recognition has become the front runner for deep learning applications in the real world and this paper focuses on its implementation in a human-robot interaction and security system. For this specific project, it is inherent that restraints are created to allow the system to produce greater performance within the requirements of a receptionist and security robot. A k-nearest neighbors classifier is applied to further enhance the accuracy of face recognition. By sequencing images from videos, we create large datasets to train our own classifier in various conditions to increase its accuracy and lower false-positive rates in poor lighting environments. With the goal of creating a service robot, we have standardized our method of data collection for new inputs that will assist the recognition process in variable conditions of operation. The resulting product is a system that can accurately predict known and unknown faces with Asian features. © 2020 IEEE.","deep learning; face recognition; human robot interaction; images sequencing; k-nearest neighbor classifier; lighting; receptionist and security; standardized dataset","Classification (of information); Data acquisition; Deep learning; Environmental technology; Large dataset; Nearest neighbor search; Planning; Social robots; Sustainable development; Data collecting; Data collection; False positive rates; K-nearest neighbors classifiers; Lighting environment; Recognition process; Service robots; Variable conditions; Face recognition",,,,,,,,,,,,,,,,"Cokun, M., Ugar, A., Yildirim, O., Demir, Y., Face recognition based on convolutional neural network (2017) 2017 International Conference on Modern Electrical and Energy Systems (MEES), pp. 376-379; Deng, J., Guo, J., Xue, N., Zafeiriou, S., (2018) Arcface: Additive Angular Margin Loss for Deep Face Recognition, , [c s.C V ]; Schroff, F., Kalenichenko, D., Philbin, J., Facenet: A unified embedding for face recognition and clustering (2015) 2015 Ieee Conference on Computer Vision and Pattern Recognition (CVPR), , http://dx.doi.org/10.1109/CVPR.2015.7298682, Jun; Shepley, A.J., (2019) Deep Learning for Face Recognition: A Critical Analysis; Bartneck, C., Nomura, T., Kanda, T., Suzuki, T., Kennsuke, K., (2005) Cultural Differences in Attitudes towards Robots, , Jan; Sabanovic, S., Inventing Japan's 'robotics culture' : The repeated assembly of science, technology, and culture in social robotics (2014) Social Studies of Science, 44 (3), pp. 342-367. , PMID: 25051586; Pandey, A.K., Gelin, R., A mass-produced sociable humanoid robot: Pepper: The first machine of its kind (2018) Ieee Robotics & Automation Magazine, p. 11. , Jul; Holthaus, P., Wachsmuth, S., (2014) The Receptionist Robot, , Mar; Bazzano, F., Lamberti, F., Human-robot interfaces for interactive receptionist systems and wayfinding applications (2018) Robotics, 7, p. 56. , Sep; Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E., (2007) Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments, , University of Massachusetts, Amherst, Tech. Rep. 0749, Oct; Xiong, Z., Wang, Z., Du, C., Zhu, R., Xiao, J., Lu, T., An asian face dataset and how race influences face recognition (2018) Advances in Multimedia Information Processing-PCM 2018, pp. 372-383. , R. Hong, W. Cheng, T. Yamasaki, M. Wang, and C. Ngo, Eds., Cham: Springer International Publishing, ISBN: 9783-030-00767-6; Li, D., Zhang, X., Song, L., Zhao, Y., Multiple-step model training for face recognition (2018) International Conference on Applications and Techniques in Cyber Security and Intelligence, pp. 146-153. , J. Abawajy, K. R. Choo, and R. Islam, Eds., Cham: Springer International Publishing, ISBN: 978-3-319-67071-3; Dalal, N., Triggs, B., Histograms of oriented gradients for human detection (2005) 2005 Ieee Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 1 (1), pp. 886-893; Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., Pantic, M., 300 Faces in-the-wild challenge: The first facial landmark localization challenge (2013) 2013 Ieee International Conference on Computer Vision Workshops, pp. 397-403; Sagonas, C., Antonakos, E., Tzimiropoulos, G., Zafeiriou, S., Pantic, M., 300 Faces in-the-wild challenge: Database and results (2016) Image and Vision Comput Ing, 47. , Jan; Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., Pantic, M., A semi-automatic methodology for facial landmark annotation (2013) 2013 Ieee Conference on Com Puter Vision and Pattern Recognition Workshops, pp. 896-903; Kazemi, V., Sullivan, J., One millisecond face alignment with an ensemble of regression trees (2014) 2014 Ieee Conference on Computer Vision and Pattern Recognition, pp. 1867-1874. , Jun; Viola, P., Jones, M., Rapid object detection using a boosted cascade of simple features (2001) Proceedings of the 2001 Ieee Computer Society Conference on Computer Vision and Pattern Recognition. Cvpr 2001, 1, p. 1. , Dec; Zhang, K., Zhang, Z., Li, Z., Qiao, Y., Joint face detection and alignment using multitask cascaded convolutional networks (2016) Ieee Signal Processing Letters, 23 (10), pp. 1499-1503. , http://dx.doi.org/10.1109/LSP.2016.2603342, Oct, ISSN: 15582361; Koch, G., Zemel, R., Salakhutdinov, R., Siamese neural networks for one-shot image recognition (2015) Icml Deep Learning Workshop, 2. , Lille; Cunningham, P., Delany, S., K-nearest neighbour classifiers (2007) Mult Classif Syst, , Apr; MacIel, F., Cleger-Tamayo, S., Khurshid, A., Martins, P., (2019) Data Collection and Image Processing Tool for Face Recognition, pp. 386-392. , in. Sep, ISBN: 978-3-030-30711-0",,,"IEEE;IEEE Systems, Man, and Cybernetics Society (SMC);Springer","Institute of Electrical and Electronics Engineers Inc.","5th International Conference on Green Technology and Sustainable Development, GTSD 2020","27 November 2020 through 28 November 2020",,166256,,9781728199825,,,"English","Proc. Int. Conf. Green Technol. Sustain. Dev., GTSD",Conference Paper,"Final","",Scopus,2-s2.0-85099463918
"Abe N., Rye D., Loke L.","57220706221;7005365020;22938119300;","Designing an Experiment for Generating Human Experiences of Robot Cooperativeness and Uncooperativeness",2020,"HAI 2020 - Proceedings of the 8th International Conference on Human-Agent Interaction",,,,"248","250",,,"10.1145/3406499.3418750","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096538155&doi=10.1145%2f3406499.3418750&partnerID=40&md5=d6ddcafa9d63a7674019a9774c9592b4","Sydney Institute for Robotics and Intelligent Systems, University of SydneyNSW  2006, Australia; Australian Centre for Field Robotics, University of SydneyNSW  2006, Australia; School of Architecture, Design and Planning, University of SydneyNSW  2006, Australia","Abe, N., Sydney Institute for Robotics and Intelligent Systems, University of SydneyNSW  2006, Australia; Rye, D., Australian Centre for Field Robotics, University of SydneyNSW  2006, Australia; Loke, L., School of Architecture, Design and Planning, University of SydneyNSW  2006, Australia","While existing approaches to human-robot collaboration typically focus on how to build robots that can work safely and fluently with humans on collaborative tasks, our research focuses on how people experience interaction with a robot and interpret its behaviour as collaborative or non-collaborative. By applying a microsociological theory to analyse the process of interaction as it unfolds, our project aims to identify the boundary between cooperativeness and uncooperativeness of the robot in the shared task of a human and a robot carrying an object to a destination. The paper presents the experimental protocol designed based on the microsociological theory that is less known in current human-robot interaction studies. The aim of the experiment is to enable participants to experience expected and unexpected interactions with a robot and to account for how the interaction affects participant interpretation of robot cooperativeness and uncooperativeness. © 2020 Owner/Author.","experiment design; human-robot collaboration; interaction; microsociology; robot path","Behavioral research; Collaborative tasks; Designing an experiment; Experimental protocols; Human-robot collaboration; Research focus; Social robots",,,,,,"This project is funded by the Sydney Institute for Robotics and Intelligent Systems, University of Sydney.",,,,,,,,,,"Abe, N., Rye, D., Loke, L., A microsociological approach to understanding the robot collaborative motion in human-robot interaction (2019) Proceedings of the 31st Australian Conference on Human-Computer-Interaction, pp. 407-411. , https://doi.org/10.1145/3369457.3369503, Fremantle, WA, Australia; Agravante, D.J., Cherubini, A., Sherikov, A., Wieber, P.-B., Kheddar, A., Human-humanoid collaborative carrying (2019) IEEE Trans. Robot., 35 (4), pp. 833-846. , https://doi.org/10.1109/TRO.2019.2914350, (August) 2019; Casalino, A., Cividini, F., Maria Zanchettin, A., Piroddi, L., Rocco, P., Human-robot collaborative assembly: A use-case application (2018) IFAC-PapersOnLine, 51 (11), pp. 194-199. , https://doi.org/10.1016/j.ifacol.2018.08.257, 2018; Chartier, G., Berthoz, A., Brian, É., Jaisson, M., Violence and uncertainty: Interactional sketches for a cognitive analysis of violent actions (2017) Social Science Information, 56 (2), pp. 198-219. , https://doi.org/10.1177/0539018417694772, (June) 2017; Donner, P., Christange, F., Lu, J., Buss, M., Cooperative dynamic manipulation of unknown flexible objects: Joint energy injection based on simple pendulum fundamental dynamics (2017) Int J of Soc Robotics, 9 (4), pp. 575-599. , https://doi.org/10.1007/s12369-017-0415-x, (September) 2017; Dragan, A.D., Bauman, S., Forlizzi, J., Srinivasa, S.S., Effects of robot motion on human-robot collaboration (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction - HRI '15, pp. 51-58. , https://doi.org/10.1145/2696454.2696473, ACM Press, Portland, Oregon, USA; Dragan, A.D., Lee, T.K.C., Srinivasa, S.S., Legibility and predictability of robot motion (2013) 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 301-308. , https://doi.org/10.1109/HRI.2013.6483603, IEEE, Tokyo, Japan; Flacco, F., Kroger, T., De Luca, A., Khatib, O., A depth space approach to human-robot collision avoidance (2012) 2012 IEEE International Conference on Robotics and Automation, pp. 338-345. , https://doi.org/10.1109/ICRA.2012.6225245, IEEE, Saint Paul, MN; Ragaglia, M., Bascetta, L., Rocco, P., Maria Zanchettin, A., Integration of perception, control and injury knowledge for safe humanrobot interaction (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 1196-1202. , https://doi.org/10.1109/ICRA.2014.6907005, IEEE, Hong Kong, China",,,"Cyber Agent;frontiers in Robotics and AI","Association for Computing Machinery, Inc","8th International Conference on Human-Agent Interaction, HAI 2020","10 November 2020 through 13 November 2020",,164494,,9781450380546,,,"English","HAI - Proc. Int. Conf. Hum.-Agent Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85096538155
"De Souza I.E., Azevedo H.","57219923353;57201827214;","A simulated environment for long-term interactions with episodic memory",2020,"2020 Latin American Robotics Symposium, 2020 Brazilian Symposium on Robotics and 2020 Workshop on Robotics in Education, LARS-SBR-WRE 2020",,,"9306974","","",,,"10.1109/LARS/SBR/WRE51543.2020.9306974","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100294204&doi=10.1109%2fLARS%2fSBR%2fWRE51543.2020.9306974&partnerID=40&md5=6fb2f3d31d8c4999afe875d33ced11fb","Center for Information Technology Renato Archer (CTI) Campinas-SP, Brazil","De Souza, I.E., Center for Information Technology Renato Archer (CTI) Campinas-SP, Brazil; Azevedo, H., Center for Information Technology Renato Archer (CTI) Campinas-SP, Brazil","The expansion of robots use in the domestic environment demanded the development of robotic systems with the capacity to maintain long-term interaction with human beings in the execution of everyday tasks. This need produced an increase in research in Human-Robot Interaction (HRI) in particular in the area of social robotics. However, in a long-term interaction, the use of social robots requires the ability to learn users' preferences and adapt their behavior according to changes in the environment, presenting a natural and friendly interaction with human beings, generating engagement and enabling future interactions. In this article, we present a cognitive system with episodic memory for a robotic agent in a domestic environment. The cognitive system selects and stores the most important interactions with users, learning with each person's preference. The system was tested and validated using the snack and drink suggestions scenario. © 2020 IEEE.","cognitive system; episodic memory; LIDA; long-term human-robot interaction; Social robotics","Agricultural robots; Cognitive systems; Educational robots; Robotics; Domestic environments; Episodic memory; Friendly interaction; Human robot Interaction (HRI); Long-term interaction; Robotic systems; Simulated environment; Social robotics; Social robots",,,,,"Ministério da Ciência, Tecnologia e Inovação, MCTI; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq: PCI-301629/2020-0","The authors are grateful to MCTI (Ministry of Science, Technology and Innovations) and CNPq (Brazilian Council for Scientific and Technological Development) for the grant number PCI-301629/2020-0.",,,,,,,,,,"Goodrich, M.A., Schultz, A.C., Human-robot interaction: A survey (2008) Foundations and Trends® in Human-Computer Interaction, 1 (3), pp. 203-275; Hegel, F., Muhl, C., Wrede, B., Hielscher-Fastabend, M., Sagerer, G., Understanding social robots (2009) 2009 Second International Conferences on Advances in Computer-Human Interactions. IEEE, pp. 169-174; Fasola, J., Mataric, M.J., Using socially assistive human-robot interaction to motivate physical exercise for older adults (2012) Proceedings of the IEEE, 100 (8), pp. 2512-2526; Short, E., Swift-Spong, K., Greczek, J., Ramachandran, A., Litoiu, A., Grigore, E.C., Feil-Seifer, D., Scassellati, B., How to train your dragonbot: Socially assistive robots for teaching children about nutrition through play (2014) The 23rd IEEE International Symposium on Robot and Human Interactive Communication, pp. 924-929; Mondada, F., Bonani, M., Riedo, F., Briod, M., Pereyre, L., Retornaz, P., Magnenat, S., Bringing robotics to formal education: The thymio opensource hardware robot (2017) IEEE Robotics Automation Magazine, 24 (1), pp. 77-85; Ahmad, M., Mubin, O., Orlando, J., A systematic review of adaptivity in human-robot interaction (2017) Multimodal Technologies and Interaction, 1 (3), p. 14; Tapus, A., Aly, A., User adaptable robot behavior (2011) 2011 International Conference on Collaboration Technologies and Systems (CTS). IEEE, pp. 165-167; Wang, S., Christensen, H.I., Tritonbot: First lessons learned from deployment of a long-term autonomy tour guide robot (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 158-165; Hawes, N., Burbridge, C., Jovan, F., Kunze, L., Lacerda, B., Mudrova, L., Young, J., Hanheide, M., The strands project: Long-term autonomy in everyday environments (2017) IEEE Robotics Automation Magazine, 24 (3), pp. 146-156; Ahmad, M.I., Mubin, O., Orlando, J., Adaptive social robot for sustaining social engagement during long-term children-robot interaction (2017) International Journal of Human-Computer Interaction, 33 (12), pp. 943-962; Nocentini, O., Fiorini, L., Acerbi, G., Sorrentino, A., Mancioppi, G., Cavallo, F., A survey of behavioral models for social robots (2019) Robotics, 8 (3), p. 54; Leite, I., Martinho, C., Paiva, A., Social robots for long-term interaction: A survey (2013) International Journal of Social Robotics, 5 (2), pp. 291-308; Aly, A., Griffiths, S., Stramandinoli, F., Metrics and benchmarks in human-robot interaction: Recent advances in cognitive robotics (2017) Cognitive Systems Research, 43, pp. 313-323. , http://www.sciencedirect.com/science/article/pii/S1389041716300912; Belo, J.P.R., Romero, R.A.F., Azevedo, H., Rhs simulator for robotic cognitive systems (2017) 2017 Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR), pp. 1-6; Edirisinghe, M.M.S.N., Jayasekara, A.G.B.P., Use of autobiographical memory for enhancing adaptive hri in multi-user domestic environment (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 1118-1123; Leite, I., Castellano, G., Pereira, A., Martinho, C., Paiva, A., Empathic robots for long-term interaction (2014) International Journal of Social Robotics, 6 (3), pp. 329-341; Azevedo, H., Belo, J.P.R., Romero, R.A., Using ontology as a strategy for modeling the interface between the cognitive and robotic systems (2020) Journal of Intelligent & Robotic Systems, 99 (3), pp. 431-449; Franklin, S., Madl, T., D'Mello, S., Snaider, J., Lida: A systems-level architecture for cognition, emotion, and learning (2014) IEEE Transactions on Autonomous Mental Development, 6 (1), pp. 19-41. , March; Johal, W., Pellier, D., Adam, C., Fiorino, H., Pesty, S., A cognitive and affective architecture for social human-robot interaction (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts, pp. 71-72; Chella, A., Lanza, F., Seidita, V., A cognitive architecture for humanrobot teaming interaction (2018) Proceedings of the 6th International Workshop on Artificial Intelligence and Cognition. Palermo (July 2-4 2018); Snaider, J., McCall, R., Franklin, S., The lida framework as a general tool for agi (2011) International Conference on Artificial General Intelligence, pp. 133-142. , Springer; Madl, T., Franklin, S., Chen, K., Trappl, R., Spatial working memory in the lida cognitive architecture (2013) Proceedings of the 12th International Conference on Cognitive Modelling, pp. 384-390; Azevedo, H., Souza, I.E., A simulated environment for long-term interactions (2019) 2019 Latin American Robotics Symposium (LARS), 2019 Brazilian Symposium on Robotics (SBR) and 2019 Workshop on Robotics in Education (WRE), pp. 311-316",,"Goncalves L.M.G.Drews Junior P.L.J.da Silva B.M.F.Fernandes Curvelo C.da.C.Fabro J.A.dos Santos D.H.de Melo J.C.P.","Brazilian Computer Society (SBC);IEEE Northeast Brazil RAS-CS Joint Chapter","Institute of Electrical and Electronics Engineers Inc.","17th Latin American Robotics Symposium, 12th Brazilian Symposium on Robotics and 11th Workshop on Robotics in Education, LARS-SBR-WRE 2020","9 November 2020 through 12 November 2020",,166442,,9780738111537,,,"English","Lat. American Robot. Symp., Braz. Symp. Robot. Workshop Robot. Educ., LARS-SBR-WRE",Conference Paper,"Final","",Scopus,2-s2.0-85100294204
"Almada Campos A.C., Adorno B.V.","57221815873;35408793200;","Development of Human-Robot Communication Technologies for Future Interaction Experiments",2020,"2020 Latin American Robotics Symposium, 2020 Brazilian Symposium on Robotics and 2020 Workshop on Robotics in Education, LARS-SBR-WRE 2020",,,"9306965","","",,,"10.1109/LARS/SBR/WRE51543.2020.9306965","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100294194&doi=10.1109%2fLARS%2fSBR%2fWRE51543.2020.9306965&partnerID=40&md5=199cb744facdcbd215d5983e9667a347","Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais (UFMG) Av. Antônio Carlos 6627, Belo Horizonte, MG, 31270-901, Brazil","Almada Campos, A.C., Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais (UFMG) Av. Antônio Carlos 6627, Belo Horizonte, MG, 31270-901, Brazil; Adorno, B.V., Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais (UFMG) Av. Antônio Carlos 6627, Belo Horizonte, MG, 31270-901, Brazil","This paper describes our development of human-robot communication technologies, which will be used in future experiments to investigate the effects of different types of communication in human-robot interactions. Using the human kinematic chain and facial points, obtained with methods from the literature, we propose geometric analyses for recognition and interpretation of human pointing gestures and gaze direction. We developed a virtual agent with voice, facial expressions, and gaze direction to interact with humans. The pointing gestures system presented mean success rate of 77.8% (s.d. 16.4%) in experiments and the implementation of the facial points detection algorithm resulted in mean errors of 0.52 cm (s.d. 0.29 cm) in the horizontal iris centers distance and 0.67 cm (s.d. 0.31 cm) in the vertical distance between eyes and mouth, both important attributes to estimate human gaze direction. Those results are satisfactory for the intended application. © 2020 IEEE.",,"Agricultural robots; Educational robots; Kinematics; Robotics; Detection algorithm; Facial Expressions; Gaze direction; Geometric analysis; Human kinematics; Human-robot communication; Pointing gestures; Virtual agent; Social robots",,,,,"Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq: 133046/2019-2","This work was supported by the Brazilian agency CNPq (grant number 133046/2019-2).",,,,,,,,,,"Ljungblad, S., Kotrbova, J., Jacobsson, M., Cramer, H., Niechwiadowicz, K., Hospital robot at work: Something alien or an intelligent colleague (2012) Proc. ACM 2012 Conf. Comput. Support. Coop. Work-CSCW '12, pp. 177-186. , New York, New York, USA: ACM Press; Unhelkar, V.V., Dorr, S., Bubeck, A., Lasota, P.A., Perez, J., Siu, H.C., Boerkoel, J.C., Shah, J.A., Mobile robots for moving-floor assembly lines: Design, evaluation, and deployment (2018) IEEE Robot. Autom. Mag, 25 (2), pp. 72-81. , jun; Crossman, M.K., Kazdin, A.E., Kitt, E.R., The influence of a socially assistive robot on mood, anxiety, and arousal in children (2018) Prof. Psychol. Res. Pract, 49 (1), pp. 48-56. , feb; Abdi, J., Al-Hindawi, A., Ng, T., Vizcaychipi, M.P., Scoping review on the use of socially assistive robot technology in elderly care (2018) BMJ Open, 8 (2), p. e018815. , feb; Meghdari, A., Shariati, A., Alemi, M., Nobaveh, A.A., Khamooshi, M., Mozaffari, B., Design performance characteristics of a social robot companion arash for pediatric hospitals (2018) Int. J. Humanoid Robot, 15 (5), p. 1850019. , oct; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Rob. Auton. Syst, 42, pp. 143-166. , mar; Breazeal, C., Kidd, C., Thomaz, A., Hoffman, G., Berlin, M., Effects of nonverbal communication on efficiency and robustness in humanrobot teamwork (2005) 2005 IEEE/RSJ Int. Conf. Intell. Robot. Syst. IEEE; Bauer, A., Wollherr, D., Buss, M., Human-robot collaboration: A survey (2008) Int. J. Humanoid Robot, 5 (1), pp. 47-66. , mar; Gildert, N., Millard, A.G., Pomfret, A., Timmis, J., The need for combining implicit and explicit communication in cooperative robotic systems (2018) Front. Robot. AI, 5, pp. 1-6. , JUN, jun; Venture, G., Kulíc, D., Robot expressive motions: A survey of generation and evaluation methods (2019) ACM Trans. Human-Robot Interact, 8 (4). , nov; Goodrich, M.A., Schultz, A.C., Human-robot interaction: A survey (2007) Found. TrendsR Human-Computer Interact, 1 (3), pp. 203-275; Lenz, A., Skachek, S., Hamann, K., Steinwender, J., Pipe, A.G., Melhuish, C., The bert2 infrastructure: An integrated system for the study of human-robot interaction (2010) 2010 10th IEEE-RAS Int. Conf. Humanoid Robot. IEEE, pp. 346-351. , dec; Zhang, H., Fricker, D., Yu, C., A multimodal real-time platform for studying human-avatar interactions (2010) Int. Conf. Intell. Virtual Agents, pp. 49-56; Lazzeri, N., Mazzei, D., De Rossi, D., Development and testing of a multimodal acquisition platform for human-robot interaction affective studies (2014) J. Human-Robot Interact, 3 (2); Adorno, B.V., (2017) Robot Kinematic Modeling and Control Based on Dual Quaternion Algebra-Part I: Fundamentals; Viola, P., Jones, M., Rapid object detection using a boosted cascade of simple features (2001) Proc. 2001 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.-CVPR 2001. IEEE Comput. Soc; Timm, F., Barth, E., Accurate eye centre localisation by means of gradients (2011) Proc. Int. Conf. Comput. Vis. Theory Appl. SciTePress-Science and and Technology Publications, pp. 125-130; Kalegina, A., Schroeder, G., Allchin, A., Berlin, K., Cakmak, M., Characterizing the design space of rendered robot faces (2018) Proc. 2018 ACM/IEEE Int. Conf. Human-Robot Interact.-HRI '18, pp. 96-104. , February. New York, New York, USA: ACM Press",,"Goncalves L.M.G.Drews Junior P.L.J.da Silva B.M.F.Fernandes Curvelo C.da.C.Fabro J.A.dos Santos D.H.de Melo J.C.P.","Brazilian Computer Society (SBC);IEEE Northeast Brazil RAS-CS Joint Chapter","Institute of Electrical and Electronics Engineers Inc.","17th Latin American Robotics Symposium, 12th Brazilian Symposium on Robotics and 11th Workshop on Robotics in Education, LARS-SBR-WRE 2020","9 November 2020 through 12 November 2020",,166442,,9780738111537,,,"English","Lat. American Robot. Symp., Braz. Symp. Robot. Workshop Robot. Educ., LARS-SBR-WRE",Conference Paper,"Final","",Scopus,2-s2.0-85100294194
"Shiomi M., Okumura S., Kimoto M., Iio T., Shimohara K.","8397042200;57197865704;57050144300;24724391700;57203054726;","Two is better than one: Social rewards from two agents enhance offline improvements in motor skills more than single agent",2020,"PLoS ONE","15","11 November","e0240622","","",,5,"10.1371/journal.pone.0240622","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095677874&doi=10.1371%2fjournal.pone.0240622&partnerID=40&md5=a402077f58c8427c2c12a125af06c62f","Department of Agent Interaction Design Laboratory, Advanced Telecommunications Research Institute International, Kyoto, Japan; Department of Information Systems Design, Doshisha University, Kyoto, Japan; Department of Information and Computer Science, Keio University, Kanagawa, Japan; Social Cognitive Engineering Laboratory, University of Tsukuba, Ibaraki, Japan; JST, PRESTO, Japan","Shiomi, M., Department of Agent Interaction Design Laboratory, Advanced Telecommunications Research Institute International, Kyoto, Japan; Okumura, S., Department of Agent Interaction Design Laboratory, Advanced Telecommunications Research Institute International, Kyoto, Japan, Department of Information Systems Design, Doshisha University, Kyoto, Japan; Kimoto, M., Department of Agent Interaction Design Laboratory, Advanced Telecommunications Research Institute International, Kyoto, Japan, Department of Information and Computer Science, Keio University, Kanagawa, Japan; Iio, T., Department of Agent Interaction Design Laboratory, Advanced Telecommunications Research Institute International, Kyoto, Japan, Social Cognitive Engineering Laboratory, University of Tsukuba, Ibaraki, Japan, JST, PRESTO, Japan; Shimohara, K., Department of Information Systems Design, Doshisha University, Kyoto, Japan","Social rewards as praise from others enhance offline improvements in human motor skills. Does praise from artificial beings, e.g., computer-graphics-based agents (displayed agents) and robots (collocated agents), also enhance offline improvements in motor skills as effectively as praise from humans? This paper answers this question via two subsequent days’ experiment. We investigated the effect of the number of agents and their sense of presence toward offline improvement in motor skills because they are essential factors to change social effects and people’s behaviors in human-agent and human-robot interaction. Our 96 participants performed a finger-tapping task. Our results showed that those who received praise from two agents showed significantly better offline motor skill improvement than people who were praised by just one agent and those who received no praise. However, we identified no significant effects related to the sense of presence. © 2020 Shiomi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",,"adult; article; controlled study; female; finger tapping test; human; human experiment; major clinical study; male; motor performance; praise; reward; robotics; algorithm; learning; motor performance; physiology; psychomotor performance; social behavior; young adult; Algorithms; Female; Humans; Learning; Male; Motor Skills; Psychomotor Performance; Reward; Social Behavior; Young Adult",,,,,,,,,,,,,,,,"Bandura, A., Self-efficacy: toward a unifying theory of behavioral change (1977) Psychological review, 84 (2), p. 191. , https://doi.org/10.1037//0033-295x.84.2.191, PMID: 847061; Ames, C., Motivation: What teachers need to know (1990) Teachers college record, 91 (3), pp. 409-421; Kohls, G., Peltzer, J., Herpertz-Dahlmann, B., Konrad, K., Differential effects of social and non-social reward on response inhibition in children and adolescents (2009) Developmental science, 12 (4), pp. 614-625. , https://doi.org/10.1111/j.1467-7687.2009.00816.x, PMID: 19635087; Chalk, K., Bizo, L. A., Specific praise improves on-task behaviour and numeracy enjoyment: A study of year four pupils engaged in the numeracy hour (2004) Educational Psychology in Practice, 20 (4), pp. 335-351; Aliyev, R., Karakus, M., Ulus, H., Teachers’ Verbal Cues That Cause Students to Feel Various Emotions (2013) Anthropologist, 16 (1–2), pp. 263-272; Henderlong, J., Lepper, M. R., The effects of praise on children’s intrinsic motivation: a review and synthesis (2002) Psychological bulletin, 128 (5), p. 774. , https://doi.org/10.1037/0033-2909.128.5.774, PMID: 12206194; Kulkarni, S., Mitra, S., Management of Remote Mediation for Children’s Education over the Internet (2010) Global Learn, pp. 2044-2049; Catano, V. M., Relation of improved performance through verbal praise to source of praise (1975) Perceptual and Motor Skills, 41 (1), pp. 71-74; Sugawara, S. K., Tanaka, S., Okazaki, S., Watanabe, K., Sadato, N., Social rewards enhance offline improvements in motor skill (2012) PLoS ONE, 7 (11), p. e48174. , https://doi.org/10.1371/journal.pone.0048174, PMID: 23144855; Buss, A. H., (2014) Social Behavior and Personality (Psychology Revivals), , Routledge; Nass, C., Steuer, J., Tauber, E. R., Computers are social actors (1994) Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 72-78; Johnson, D., Gardner, J., Wiles, J., Experience as a moderator of the media equation: the impact of flattery and praise (2004) International Journal of Human-Computer Studies, 61 (3), pp. 237-258; Mumm, J., Mutlu, B., Designing motivational agents: The role of praise, social comparison, and embodiment in computer feedback (2011) Computers in Human Behavior, 27 (5), pp. 1643-1650; Leyzberg, D., Spaulding, S., Scassellati, B., Personalizing robot tutors to individuals’ learning differences (2014) Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction, pp. 423-430; Warren, Z. E., Zheng, Z., Swanson, A. R., Bekele, E., Zhang, L., Crittendon, J. A., Can robotic interaction improve joint attention skills? (2015) Journal of autism and developmental disorders, 45 (11), pp. 3726-3734. , https://doi.org/10.1007/s10803-013-1918-4, PMID: 24014194; Reeves, B., Nass, C., (1996) How people treat computers, television, and new media like real people and places, , CSLI Publications and Cambridge; Kahn, P. H., Kanda, T., Ishiguro, H., Freier, N. G., Severson, R. L., Gill, B. T., Robovie, you’ll have to go into the closet now”: Children’s social and moral relationships with a humanoid robot (2012) Developmental psychology, 48 (2), p. 303. , https://doi.org/10.1037/a0027033, PMID: 22369338; Hayashi, K., Shiomi, M., Kanda, T., Hagita, N., Are Robots Appropriate for Troublesome and Communicative Tasks in a City Environment? (2012) IEEE Transactions on Autonomous Mental Development, 4 (2), pp. 150-160; Robertson, E. M., Pascual-Leone, A., Miall, R. C., Current concepts in procedural consolidation (2004) Nature reviews. 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Brainstorming Emerging E-collaboration Concepts and Applications, 1, p. 250; Sakamoto, D., Hayashi, K., Kanda, T., Shiomi, M., Koizumi, S., Ishiguro, H., Humanoid Robots as a Broadcasting Communication Medium in Open Public Spaces (2009) International Journal of Social Robotics, 1 (2), pp. 157-169; Shiomi, M., Hagita, N., Do Synchronized Multiple Robots Exert Peer Pressure? (2016) Proceedings of the Fourth International Conference on Human Agent Interaction, pp. 27-33. , Biopolis, Singapore; Iio, T., Yoshikawa, Y., Ishiguro, H., Pre-scheduled Turn-Taking between Robots to Make Conversation Coherent (2016) Proceedings of the Fourth International Conference on Human Agent Interaction, pp. 19-25. , Biopolis, Singapore; Powers, A., Kiesler, S., Fussell, S., Torrey, C., Comparing a computer agent with a humanoid robot (2007) Human-Robot Interaction (HRI), 2007 2nd ACM/IEEE International Conference on, pp. 145-152; Bainbridge, W. A., Hart, J. W., Kim, E. S., Scassellati, B., (2011) The benefits of interactions with physically present robots over video-displayed agents, 3 (1), pp. 41-52. , J. I. J. o. S. R; Li, J., The benefit of being physically present: A survey of experimental works comparing copresent robots, telepresent robots and virtual agents (2015) International Journal of Human-Computer Studies, 77, pp. 23-37; Okumura, S., Kimoto, M., Shiomi, M., Iio, T., Shimohara, K., Hagita, N., Do Social Rewards from Robots Enhance Offline Improvements in Motor Skills? (2017) International Conference on Social Robotics, pp. 32-41; Carifio, J., Perla, R. J., Ten common misunderstandings, misconceptions, persistent myths and urban legends about Likert scales and Likert response formats and their antidotes (2007) Journal of Social Sciences, 3 (3), pp. 106-116; Senft, E., Lemaignan, S., Baxter, P. E., Bartlett, M., Belpaeme, T., Teaching robots social autonomy from in situ human guidance (2019) Science Robotics, 4 (35), p. eaat1186; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Science robotics, 3 (21), p. eaat5954; Willemse, C. J., van Erp, J. B., Social Touch in Human–Robot Interaction: Robot-Initiated Touches can Induce Positive Responses without Extensive Prior Bonding (2019) International Journal of Social Robotics, 11 (2), pp. 285-304; Zajonc, R. B., Social facilitation (1965) Science, 149 (3681), pp. 269-274. , https://doi.org/10.1126/science.149.3681.269, PMID: 14300526; Karau, S. J., Williams, K. D., Social loafing: A meta-analytic review and theoretical integration (1993) Journal of personality and social psychology, 65 (4), p. 681; Asch, S. E., Opinions and Social Pressure (1955) Scientific American, 193, pp. 17-26; Shiomi, M., Hagita, N., Do the number of robots and the participant’s gender influence conformity effect from multiple robots? (2019) Advanced Robotics, 33 (15–16), pp. 756-763; Pan, X., Gillies, M., Barker, C., Clark, D. M., Slater, M., Socially anxious and confident men interact with a forward virtual woman: an experimental study (2012) PloS one, 7 (4); Llobera, J., Spanlang, B., Ruffini, G., Slater, M., Proxemics with multiple dynamic characters in an immersive virtual environment (2010) ACM Trans. Appl. Percept, 8 (1), p. 3. , Article; Kim, K., Boelling, L., Haesler, S., Bailenson, J., Bruder, G., Welch, G. F., Does a digital assistant need a body? The influence of visual embodiment and social behavior on the perception of intelligent virtual agents in AR (2018) 2018 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 105-114","Shiomi, M.; Department of Agent Interaction Design Laboratory, Japan; email: m-shiomi@atr.jp",,,"Public Library of Science",,,,,19326203,,POLNC,"33147230","English","PLoS ONE",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85095677874
"Edge C., Sakib Enan S., Fulton M., Hong J., Mo J., Barthelemy K., Bashaw H., Kallevig B., Knutson C., Orpen K., Sattar J.","57210802284;57219441628;57207943000;57210802713;57215561535;57219766515;57215872406;57219763481;57219765086;57219759437;15726321000;","Design and experiments with LoCO AUV: A low cost open-source autonomous underwater vehicle",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341007","1761","1768",,,"10.1109/IROS45743.2020.9341007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102401806&doi=10.1109%2fIROS45743.2020.9341007&partnerID=40&md5=d92c643f3be8fcb67b733701eef1ac2a","University of Minnesota Computer Science; University of Minnesota Aerospace Engineering; Clarkson University Computer Science; University of Minnesota Mechanical Engineering; University of Minnesota - Duluth Computer Science; Computer Science at University of Minnesota","Edge, C., University of Minnesota Computer Science; Sakib Enan, S., University of Minnesota Computer Science; Fulton, M., University of Minnesota Computer Science; Hong, J., University of Minnesota Computer Science; Mo, J., University of Minnesota Computer Science; Barthelemy, K., University of Minnesota Aerospace Engineering; Bashaw, H., Clarkson University Computer Science; Kallevig, B., University of Minnesota Mechanical Engineering; Knutson, C., University of Minnesota - Duluth Computer Science; Orpen, K., University of Minnesota Aerospace Engineering; Sattar, J., Computer Science at University of Minnesota","In this paper we present the LoCO AUV, a Low-Cost, Open Autonomous Underwater Vehicle. LoCO is a general-purpose, single-person-deployable, vision-guided AUV, rated to a depth of 100 meters. We discuss the open and expandable design of this underwater robot, as well as the design of a simulator in Gazebo. Additionally, we explore the platform's preliminary local motion control and state estimation abilities, which enable it to perform maneuvers autonomously. In order to demonstrate its usefulness for a variety of tasks, we implement a variety of our previously presented human-robot interaction capabilities on LoCO, including gestural control, diver following, and robot communication via motion. Finally, we discuss the practical concerns of deployment and our experiences in using this robot in pools, lakes, and the ocean. All design details, instructions on assembly, and code will be released under a permissive, open-source license. © 2020 IEEE.",,"Agricultural robots; Autonomous vehicles; Costs; Intelligent robots; Machine design; Open systems; Social robots; User experience; Visual servoing; Gestural control; Low costs; Open source license; Open sources; Robot communication; Underwater robots; Autonomous underwater vehicles",,,,,"National Science Foundation, NSF: 1757916",,,,,,,,,,,"Foley, B.P., Dellaporta, K., Sakellariou, D., Bingham, B.S., Camilli, R., Eustice, R.M., Evagelistis, D., Theodoulou, T., The 2005 chios ancient shipwreck survey: New methods for underwater archaeology (2009) Hesperia: The Journal of the American School of Classical Studies at Athens, 78 (2), pp. 269-305; Williams, S.B., Pizarro, O., Jakuba, M., Barrett, N., Auv benthic habitat mapping in south eastern Tasmania (2010) Field and Service Robotics, pp. 275-284. , A. Howard, K. Iagnemma, and A. Kelly, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg; Sariel, S., Balch, T., Stack, J., Distributed multi-auv coordination in naval mine countermeasure missions (2006) Georgia Institute of Technology, Tech. Rep.; Petillot, Y.R., Reed, S.R., Bell, J.M., Real time AUV pipeline detection and tracking using side scan sonar and multi-beam echosounder (2002) Oceans '02 MTS/IEEE, 1, pp. 217-222. , Oct; Gafurov, S.A., Klochkov, E.V., Autonomous unmanned underwater vehicles development tendencies (2015) Procedia Engineering, 106, pp. 141-148; Widditsch, H.R., Spurv\-The first decade (1973) Defense Technical Information Center, , Fort Belvoir, VA, Tech. Rep., Oct; Nodland, W., Ewart, T., Bendiner, W., Miller, J., Aagaard, E., Spurv II-an unmanned, free-swimming submersible developed for oceanographic research (1981) Oceans 81, pp. 92-98. , Sep; Allen, B., Stokey, R., Austin, T., Forrester, N., Goldsborough, R., Purcell, M., Alt, C.V., REMUS: A small, low cost AUV; System description, field trials and performance results (1997) Oceans '97. MTS/IEEE Conference Proceedings, pp. 994-1000. , Oct; Stokey, R.P., Roup, A., Alt, C.V., Allen, B., Forrester, N., Austin, T., Goldsborough, R., Kukulya, A., Development of the REMUS 600 autonomous underwater vehicle (2005) Proceedings of Oceans 2005 MTS/IEEE, 2, pp. 1301-1304. , Sep; Eriksen, C.C., Osse, T.J., Light, R.D., Wen, T., Lehman, T.W., Sabin, P.L., Ballard, J.W., Chiodi, A.M., Seaglider: A long-range autonomous underwater vehicle for oceanographic research (2001) Ieee Journal of Oceanic Engineering, 26 (4), pp. 424-436. , Oct; Osse, T.J., Eriksen, C.C., The deepglider: A full ocean depth glider for oceanographic research (2007) Oceans 2007, pp. 1-12. , Sep; Dudek, G., Giguere, P., Prahacs, C., Saunderson, S., Sattar, J., Torres-Méndez, L.A., Jenkin, M., Georgiades, C., AQUA: An amphibious autonomous robot (2007) Computer, 40, pp. 46-53. , Feb; Hackbarth, A., Kreuzer, E., Solowjow, E., HippoCampus: A micro underwater vehicle for swarm applications (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2258-2263. , Sep; Amory, A., Maehle, E., SEMBIO-A small energy-efficient swarm AUV (2016) Oceans 2016 MTS/IEEE Monterey, pp. 1-7. , Sep; Gonçalves, C.S., Ferreira, B.M., Matos, A.C., Design and development of shad-A small hovering auv with differential actuation (2016) Oceans 2016 MTS/IEEE Monterey, pp. 1-4. , Sep; Okamoto, A., Tamura, K., Sasano, M., Sawada, K., Seta, T., Inaba, S., Ura, T., Itoh, Y., Development of hoveringtype AUV HOBALINfor exploring seafloor hydrothermal deposits (2016) Oceans 2016 MTS/IEEE Monterey, pp. 1-4. , Sep; Carreras, M., Hernández, J.D., Vidal, E., Palomeras, N., Ribas, D., Ridao, P., Sparus II auv-A hovering vehicle for seabed inspection (2018) Ieee Journal of Oceanic Engineering, 43 (2), pp. 344-355. , Apr; Underwood, A., Murphy, C., Design of a micro-AUV for autonomy development and multi-vehicle systems (2017) Oceans 2017-Aberdeen, pp. 1-6. , Jun; Wu, J., Peng, S., Xu, T., Hu, R., Wang, S., Pan, M., Weng, X., Test bed auv for docking algorithm research (2018) Oceans 2018 MTS/IEEE Charleston, pp. 1-6. , Oct; Meier, L., Honegger, D., Pollefeys, M., PX4: A node-based multithreaded open source robotics framework for deeply embedded platforms (2015) 2015 Ieee International Conference on Robotics and Automation (ICRA), pp. 6235-6240. , May; Https://ardupilot.org/, (Accessed on 03/01/2020); Https://github.com/ Neufieldrobotics/spinnaker Sdk Camera Driver, , (FLIR) Spinnaker based camera driver; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., ROS: An open-source robot operating system (2009) Icra Workshop on Open Source Software, 3, p. 5. , Kobe, Japan; Julier, S.J., Uhlmann, J.K., New extension of the kalman filter to nonlinear systems (1997) Signal Processing, Sensor Fusion, and Target Recognition VI, 3068, pp. 182-193. , International Society for Optics and Photonics; Https://github.com/cra-ros-pkg/robot Localization, , robot localization; Kato, H., Billinghurst, M., Marker tracking and hmd calibration for a video-based augmented reality conferencing system (1999) Proceedings of the 2nd International Workshop on Augmented Reality (IWAR 99), , San Francisco, October; Koenig, N., Howard, A., Design and use paradigms for Gazebo, an open-source multi-robot simulator (2004) 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3, pp. 2149-2154. , Sep; Weissmann, S., Pinkall, U., Underwater rigid body dynamics (2012) Acm Transactions on Graphics (TOG), 31. , July; Islam, M.J., Ho, M., Sattar, J., Dynamic reconfiguration of mission parameters in underwater human-robot collaboration (2018) 2018 Ieee International Conference on Robotics and Automation (ICRA), pp. 6212-6219. , May; Fulton, M., Edge, C., Sattar, J., Robot communication via motion: Closing the underwater human-robot interaction loop (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 4660-4666. , May; Islam, M.J., Fulton, M., Sattar, J., Toward a generic diverfollowing algorithm: Balancing robustness and efficiency in deep visual detection (2019) Ieee Robotics and Automation Letters, 4 (1), pp. 113-120. , Jan",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102401806
"Lanza D., Menicatti R., Sgorbissa A.","57219625601;57197737505;6602687901;","Abductive recognition of context-dependent utterances in human-robot interaction",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341155","10975","10981",,,"10.1109/IROS45743.2020.9341155","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102399158&doi=10.1109%2fIROS45743.2020.9341155&partnerID=40&md5=accee494a008dd404cb36d289548b1ac","University of Genova, Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS), Genova, 16145, Italy","Lanza, D., University of Genova, Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS), Genova, 16145, Italy; Menicatti, R., University of Genova, Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS), Genova, 16145, Italy; Sgorbissa, A., University of Genova, Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS), Genova, 16145, Italy","Context-dependent meaning recognition in natural language utterances is one of the key problems of computational pragmatics. Abductive reasoning seems apt for modeling and understanding these phenomena. In fact, it presents observations through hypotheses, allowing us to understand subtexts and implied meanings without exact deductions. For this reason in this paper, we are going to explore abductive reasoning and context modeling in human-robot interaction. Rather than a radical inferential approach, we assumed a conventional approach towards context-depending meanings, i.e, they are conventionally encoded rather than inferred from the utterances. In order to address the problem, a case study is presented, analyzing whether such a system could manage correctly these linguistic phenomena. The results obtained confirm the validity of a conventional approach in context modeling and, on this basis, further models are proposed to work around the limitations of the case study. © 2020 IEEE.",,"Agricultural robots; Intelligent robots; Man machine systems; Abductive reasoning; Context dependent; Context modeling; Conventional approach; In contexts; Linguistic phenomena; Natural languages; Social robots",,,,,,,,,,,,,,,,"Krämer, N.C., Von Der Pütten, A., Eimler, S., Human-agent and human-robot interaction theory: Similarities to and differences from human-human interaction (2012) Human-Computer Interaction: The Agency Perspective, Ser. Studies in Computational Intelligence, M. Zacarias and J. V. de Oliveira, Eds. Berlin, Heidelberg: Springer, pp. 215-240. , https://doi.org/10.1007/978-3-642-25691-29; Briggs, G., Williams, T., Scheutz, M., Enabling robots to understand indirect speech acts in task-based interactions (2017) Journal of Human-Robot Interaction, 6 (1), pp. 64-94. , https://doi.org/10.5898/JHRI.6.1.Briggs, May; Bach, K., (2007) Literal Meaning. Philosophy and Phenomenological Research, 75 (2), pp. 487-492. , https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1933-1592.2007.00088.x; DeRose, K., Assertion, knowledge, and context (2002) Philosophical Re-view, 111 (2), pp. 167-203. , publisher: Duke University Press; Recchiuto, C.T., Papadopoulos, C., Hill, T., Castro, N., Bruno, B., Papadopoulos, I., Sgorbissa, A., Designing an experimental and a reference robot to test and evaluate the impact of cultural competence in socially assistive robotics (2019) 2019 28th Ieee International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1-8. , iSSN: 1944-9437 Oct; Rehurek, R., Sojka, P., Software framework for topic modelling with large corpora (2010) Proceedings of the Lrec 2010 Workshop on New Challenges for Nlp Frameworks, pp. 45-50; Paul, G., AI approaches to abduction abductive reasoning and learning (2000) Ser. Handbook of Defeasible Reasoning and Uncertainty Management Systems, pp. 35-98. , https://doi.org/10.1007/978-94-017-1733-52, D. M. Gabbay and R. Kruse, Eds. Dordrecht: Springer Netherlands; Menzies, T., Applications of abduction: Knowledge-level modelling (1996) International Journal of Human-Computer Studies, 45 (3), pp. 305-335. , http://www.sciencedirect.com/science/article/pii/S1071581996900543, Sept; Austin, J.L., (1975) How to Do Things with Words: The William James Lectures Delivered at Harvard University in 1955, , Oxford, New York: Oxford University Press, Sept; Bunt, H., Black, W., The abc of computational pragmatics (2000) Abduction, Belief and Context in Dialogue: Studies in Computational Pragmatics, 1. , http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.22.4842, ser. Natural Language Processing, H. Bunt and W. Black, Eds. Amsterdam: John Benjamins Publishing Company; Kaplan, D., Demonstratives: An essay on the semantics, logic, metaphysics and epistemology of demonstratives and other indexicals (1989) Themes from Kaplan, J. Almog, J. Perry, and H. Wettstein, Eds. Oxford University Press, pp. 481-563; Bar-Hillel, Y., Logical syntax and semantics (1954) Language, 30 (2), pp. 230-237. , https://www.jstor.org/stable/410265, publisher: Linguistic Society of America; Domaneschi, F., Penco, C., Presupposizioni (2017) APhEx Portale Italiano di Filosoa Analitica, (15). , aphex.it/index.php?Temi=557D03012202740321040302777327; Grice, H.P., Logic and conversation (1975) Syntax and Semantics, 3, pp. 22-40. , (1989). .Studies in the Way of Words. Harvard University Press academic press ed., P. Cole and J. Morgan, Eds. reprinted as ch.2 of Grice, H.P; Wilson, D., Sperber, D., On choosing the context for utterance interpretation (1981) Foregrounding Background, J. Allwood and E. Hjelmquist, Eds. Doxa; Peirce, C.S., On the natural classification of arguments (1867) Proceed-ings of the American Academy of Arts and Sciences, 7, pp. 261-287; Poole, D., Mackworth, A., (2017) Artificial Intelligence, , https://artint.info/2e/, Foundations of Computational Agents, 2nd ed. Cambridge University Press; Kuhn, M., Johnson, K., (2013) Applied Predictive Modeling, , https://www.springer.com/gp/book/9781461468486, New York: Springer-Verlag; Kohavi, R., Provost, F., Glossary of terms (1998) Machine Learning, 30 (2), pp. 271-274. , https://doi.org/10.1023/A:1017181826899, Feb; Fadaee, M., Bisazza, A., Monz, C., Data augmentation for low-resource neural machine translation (2017) Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Vancouver, pp. 567-573. , https://www.aclweb.org/anthology/P17-2090, Canada: Association for Computational Linguistics, July; Xie, Z., Wang, S.I., Li, J., Lévy, D., Nie, A., Jurafsky, D., Ng, A.Y., (2019) Data Noising As Smoothing in Neural Network Language Models, , https://collaborate.princeton.edu/en/publications/data-noising-as-smoothing-in-neural-network-language-models, Jan; Yu, S., Yang, J., Liu, D., Li, R., Zhang, Y., Zhao, S., Hierarchical data augmentation and the application in text classification (2019) Ieee Access, 7, pp. 185476-185485. , conference Name: IEEE Access",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102399158
"Sun L., Wu Z., Ma H., Tomizuka M.","56007948700;57218304895;57205548314;35567325500;","Expressing diverse human driving behavior with probabilistic rewards and online inference",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341371","2020","2026",,,"10.1109/IROS45743.2020.9341371","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098423049&doi=10.1109%2fIROS45743.2020.9341371&partnerID=40&md5=557fb105050ab8cb7fbe5e3ff626c35c","University of California, Department of Mechanical Engineering, Berkeley, 94720, United States","Sun, L., University of California, Department of Mechanical Engineering, Berkeley, 94720, United States; Wu, Z., University of California, Department of Mechanical Engineering, Berkeley, 94720, United States; Ma, H., University of California, Department of Mechanical Engineering, Berkeley, 94720, United States; Tomizuka, M., University of California, Department of Mechanical Engineering, Berkeley, 94720, United States","In human-robot interaction (HRI) systems, such as autonomous vehicles, understanding and representing human behavior are important. Human behavior is naturally rich and diverse. Cost/reward learning, as an efficient way to learn and represent human behavior, has been successfully applied in many domains. Most of traditional inverse reinforcement learning (IRL) algorithms, however, cannot adequately capture the diversity of human behavior since they assume that all behavior in a given dataset is generated by a single cost function. In this paper, we propose a probabilistic IRL framework that directly learns a distribution of cost functions in continuous domain. Evaluations on both synthetic data and real human driving data are conducted. Both the quantitative and subjective results show that our proposed framework can better express diverse human driving behaviors, as well as extracting different driving styles that match what human participants interpret in our user study. © 2020 IEEE.",,"Agricultural robots; Cost functions; Intelligent robots; Probability distributions; Reinforcement learning; Social robots; Continuous domain; Driving styles; Human behaviors; Human driving behavior; Human robot Interaction (HRI); Inverse reinforcement learning; Online inferences; Synthetic data; Behavioral research",,,,,,,,,,,,,,,,"Sun, L., Zhan, W., Tomizuka, M., Dragan, A.D., Courteous autonomous cars (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 663-670; Schmerling, E., Leung, K., Vollprecht, W., Pavone, M., Multimodal probabilistic model-based planning for human-robot interaction (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1-9; Sun, L., Peng, C., Zhan, W., Tomizuka, M., A fast integrated planning and control framework for autonomous driving via imitation learning (2018) ASME 2018 Dynamic Systems and Control Conference, , American Society of Mechanical Engineers Digital Collection; Bansal, M., Krizhevsky, A., Ogale, A., (2018) Chauffeurnet: Learning to Drive by Imitating the Best and Synthesizing the Worst; Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., Wierstra, D., (2015) Continuous Control with Deep Reinforcement Learning; Modares, H., Ranatunga, I., Lewis, F.L., Popa, D.O., Optimized assistive human-robot interaction using reinforcement learning (2015) IEEE Transactions on Cybernetics, 46 (3), pp. 655-667; Shalev-Shwartz, S., Shammah, S., Shashua, A., (2016) Safe, Multiagent, Reinforcement Learning for Autonomous Driving; Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., Quillen, D., Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection (2018) The International Journal of Robotics Research, 37 (4-5), pp. 421-436; Hirschfeld, L.A., Gelman, S.A., (1994) Mapping the Mind: Domain Specificity in Cognition and Culture, , Cambridge University Press; Ng, A.Y., Russell, S.J., Algorithms for inverse reinforcement learning (2000) Icml, pp. 663-670; Abbeel, P., Ng, A.Y., Apprenticeship learning via inverse reinforcement learning (2004) Proceedings of the Twenty-first International Conference on Machine Learning. ACM, p. 1; Ramachandran, D., Amir, E., Bayesian inverse reinforcement learning (2007) IJCAI, 7, pp. 2586-2591; Ziebart, B.D., Maas, A.L., Bagnell, J.A., Dey, A.K., Maximum entropy inverse reinforcement learning (2008) AAAI, 8, pp. 1433-1438. , Chicago, IL, USA; Levine, S., Koltun, V., Continuous inverse optimal control with locally optimal examples (2012) Proceedings of the 29th International Coference on International Conference on Machine Learning, pp. 475-482. , Omnipress; Wu, Z., Sun, L., Zhan, W., Yang, C., Tomizuka, M., Efficient sampling-based maximum entropy inverse reinforcement learning with application to autonomous driving (2020) IEEE Robotics and Automation Letters; Naumann, M., Sun, L., Zhan, W., Tomizuka, M., (2020) Analyzing the Suitability of Cost Functions for Explaining and Imitating Human Driving Behavior, 1 (1); Babes, M., Marivate, V., Subramanian, K., Littman, M.L., Apprenticeship learning about multiple intentions (2011) Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 897-904; Choi, J., Kim, K.-E., Nonparametric Bayesian inverse reinforcement learning for multiple reward functions (2012) Advances in Neural Information Processing Systems, pp. 305-313; Nikolaidis, S., Ramakrishnan, R., Gu, K., Shah, J., Efficient model learning from joint-action demonstrations for human-robot collaborative tasks (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-robot Interaction. ACM, pp. 189-196; Zhan, W., Sun, L., Wang, D., Shi, H., Clausse, A., Naumann, M., Kummerle, J., De La Fortelle, A., (2019) Interaction Dataset: An International, Adversarial and Cooperative Motion Dataset in Interactive Driving Scenarios with Semantic Maps",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85098423049
"Yu Y., Chen C., Sheng X., Zhu X.","57202953119;57207921162;7102570458;7406185799;","A Motor Unit-specific Images Based Scheme for Continuous Estimation of Wrist Torques - A Pilot Study",2020,"Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics","2020-October",,"9283000","3182","3187",,1,"10.1109/SMC42975.2020.9283000","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098863450&doi=10.1109%2fSMC42975.2020.9283000&partnerID=40&md5=5f223403322827e5813ab60c5a5f0840","Shanghai Jiao Tong University, State Key Laboratory of Mechanical System and Vibration, Shanghai, China","Yu, Y., Shanghai Jiao Tong University, State Key Laboratory of Mechanical System and Vibration, Shanghai, China; Chen, C., Shanghai Jiao Tong University, State Key Laboratory of Mechanical System and Vibration, Shanghai, China; Sheng, X., Shanghai Jiao Tong University, State Key Laboratory of Mechanical System and Vibration, Shanghai, China; Zhu, X., Shanghai Jiao Tong University, State Key Laboratory of Mechanical System and Vibration, Shanghai, China","Neural interface using motor units (MUs) decomposed from surface electromyography (sEMG) has provided a novel approach for the intuitive human-robot interaction. However, existing feature extraction methods from decomposed MUs are simplex, ignoring the inherent spatial information and the subtle interactions between different MUs. In this study, we proposed a MU-specific images based scheme for extracting features from decomposed MUs and further estimating wrist torques continuously. Specifically, MU-specific images were reconstructed from decomposed MUs using sEMG and fed into a convolutional neural network for feature extraction and estimating wrist torques. The results demonstrated that the proposed scheme significantly outperformed three conventional regression methods using decomposed spike count features, with R2 equal to 0.86 ± 0.05 in pronation/supination and 0.90 ± 0.05 in flexion/extension. This study provides a novel scheme for estimation of continuous movement using decomposed MUs and potentially paves the way of neural interface. © 2020 IEEE.","EMG decomposition; feature extraction; motor unit-specific images; neural interface","Biomedical signal processing; Convolutional neural networks; Extraction; Feature extraction; Human robot interaction; Regression analysis; Extracting features; Feature extraction methods; Neural interfaces; Pilot studies; Regression method; Spatial informations; Subtle interaction; Surface electromyography; Image processing",,,,,"2018YFB1307202; National Natural Science Foundation of China, NSFC: 51620105002, 91948302; Science and Technology Commission of Shanghai Municipality, STCSM: 18JC1410400","This work is supported in part by the China National Key R&D Program (Grant No.2018YFB1307202), the National Natural Science Foundation of China (Grant Nos. 91948302, 51620105002), and the Science and Technology Commission of Shanghai Municipality (Grant No.18JC1410400). *Xinjun Sheng is the corresponding author.",,,,,,,,,,"Farina, D., Jiang, N., Rehbaum, H., Holobar, A., Graimann, B., Dietl, H., Aszmann, O.C., The extraction of neural information from the surface EMG for the control of upper-limb prostheses: Emerging avenues and challenges (2014) Ieee Transactions on Neural Systems and Rehabilitation Engineering, 22 (4), pp. 797-809; Hakonen, M., Piitulainen, H., Visala, A., Current state of digital signal processing in myoelectric interfaces and related applications (2015) Biomedical Signal Processing and Control, 18, pp. 334-359; Yu, Y., Chen, C., Sheng, X., Zhu, X., Multi-DoF continuous estimation for wrist torques using stacked autoencoder (2020) Biomedical Signal Processing and Control, 57, p. 101733; Pan, L., Crouch, D.L., Huang, H., Myoelectric control based on a generic musculoskeletal model: Toward a multi-user neural-machine interface (2018) Ieee Transactions on Neural Systems and Rehabilitation Engineering, 26 (7), pp. 1435-1442; Kumar, D.K., Jelfs, B., Sui, X., Arjunan, S.P., Prosthetic hand control: A multidisciplinary review to identify strengths, shortcomings, and the future (2019) Biomedical Signal Processing and Control, 53, p. 101588; Englehart, K., Hudgins, B., A robust, real-time control scheme for multifunction myoelectric control (2003) Ieee Transactions on Biomedical Engineering, 50 (7), pp. 848-854; Ameri, A., Kamavuako, E.N., Scheme, E.J., Englehart, K.B., Parker, P.A., Support vector regression for improved real-time, simultaneous myoelectric control (2014) Ieee Transactions on Neural Systems and Rehabilitation Engineering, 22 (6), pp. 1198-1209; Jiang, N., Rehbaum, H., Vujaklija, I., Graimann, B., Farina, D., Intuitive, online, simultaneous, and proportional myoelectric control over two degrees-of-freedom in upper limb amputees (2013) Ieee Transactions on Neural Systems and Rehabilitation Engineering, 22 (3), pp. 501-510; Lin, C., Wang, B., Jiang, N., Farina, D., Robust extraction of basis functions for simultaneous and proportional myoelectric control via sparse non-negative matrix factorization (2018) Journal of Neural Engineering, 15 (2), p. 026017; Jiang, N., Englehart, K.B., Parker, P.A., Extracting simultaneous and proportional neural control information for multiple-DOF prostheses from the surface electromyographic signal (2008) Ieee Transactions on Biomedical Engineering, 56 (4), pp. 1070-1080; Ameri, A., Akhaee, M.A., Scheme, E., Englehart, K., Regression convolutional neural network for improved simultaneous EMG control (2019) Journal of Neural Engineering, 16 (3), p. 036015; Geng, W., Du, Y., Jin, W., Wei, W., Hu, Y., Li, J., Gesture recognition by instantaneous surface EMG images (2016) Scientific Reports, 6, p. 36571; Kapelner, T., Vujaklija, I., Jiang, N., Negro, F., Aszmann, O.C., Principe, J., Farina, D., Predicting wrist kinematics from motor unit discharge timings for the control of active prostheses (2019) Journal of Neuroengineering and Rehabilitation, 16 (1), p. 47; Farina, D., Vujaklija, I., Sartori, M., Kapelner, T., Negro, F., Jiang, N., Bergmeister, K., Aszmann, O.C., Man/machine interface based on the discharge timings of spinal motor neurons after targeted muscle reinnervation (2017) Nature Biomedical Engineering, 1 (2), pp. 1-12; Chen, C., Chai, G., Guo, W., Sheng, X., Farina, D., Zhu, X., Prediction of finger kinematics from discharge timings of motor units: Implications for intuitive control of myoelectric prostheses (2018) Journal of Neural Engineering; Twardowski, M.D., Roy, S.H., Li, Z., Contessa, P., De Luca, G., Kline, J.C., Motor unit drive: A neural interface for real-time upper limb prosthetic control (2018) Journal of Neural Engineering, 16 (1), p. 016012; Holobar, A., Zazula, D., Gradient convolution kernel compensation applied to surface electromyograms (2007) International Conference on Independent Component Analysis and Signal Separation., pp. 617-624. , Springer; Holobar, A., Zazula, D., Multichannel blind source separation using convolution kernel compensation (2007) Ieee Transactions on Signal Processing, 55 (9), pp. 4487-4496; Holobar, A., Zazula, D., Correlation-based decomposition of surface electromyograms at low contraction forces (2004) Medical and Biological Engineering and Computing, 42 (4), pp. 487-495; Ning, Y., Zhu, X., Zhu, S., Zhang, Y., Surface EMG decomposition based on K-means clustering and convolution kernel compensation (2014) Ieee Journal of Biomedical and Health Informatics, 19 (2), pp. 471-477; Mohebian, M.R., Marateb, H.R., Karimimehr, S., Mañanas, M.A., Kranjec, J., Holobar, A., Non-invasive decoding of the motoneurons: A guided source separation method based on convolution kernel compensation with clustered initial points (2019) Frontiers in Computational Neuroscience, 13, p. 14; Negro, F., Muceli, S., Castronovo, A.M., Holobar, A., Farina, D., Multichannel intramuscular and surface EMG decomposition by convolutive blind source separation (2016) Journal of Neural Engineering, 13 (2), p. 026027; Holobar, A., Minetto, M.A., Farina, D., Accurate identification of motor unit discharge patterns from high-density surface EMG and validation with a novel signal-based performance metric (2014) Journal of Neural Engineering, 11 (1), p. 016008; Farina, D., Holobar, A., Merletti, R., Enoka, R.M., Decoding the neural drive to muscles from the surface electromyogram (2010) Clinical Neurophysiology, 121 (10), pp. 1616-1623; Chen, C., Yu, Y., Ma, S., Sheng, X., Lin, C., Farina, D., Zhu, X., Hand gesture recognition based on motor unit spike trains decoded from highdensity electromyography (2020) Biomedical Signal Processing and Control, 55, p. 101637; Farina, D., Merletti, R., Enoka, R.M., The extraction of neural strategies from the surface EMG: An update (2014) Journal of Applied Physiology, 117 (11), pp. 1215-1230","Sheng, X.; Shanghai Jiao Tong University, China; email: xjsheng@sjtu.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020","11 October 2020 through 14 October 2020",,165855,1062922X,9781728185262,PICYE,,"English","Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.",Conference Paper,"Final","",Scopus,2-s2.0-85098863450
"Aspragathos N., Moulianitis V., Koustoumpardis P.","7003803146;6506258012;6507626671;","Special Issue on Human-Robot Interaction (HRI)",2020,"Robotica","38","10",,"1715","1716",,1,"10.1017/S0263574720000946","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094175066&doi=10.1017%2fS0263574720000946&partnerID=40&md5=c485875cb87df880de7ea7d72aae4e86","Robotics Group, Mechanical and Aeronautics Engineering Department, University of Patras, Greece; Department of Product and Systems Design Engineering, University of the Aegean, Greece","Aspragathos, N., Robotics Group, Mechanical and Aeronautics Engineering Department, University of Patras, Greece; Moulianitis, V., Department of Product and Systems Design Engineering, University of the Aegean, Greece; Koustoumpardis, P., Robotics Group, Mechanical and Aeronautics Engineering Department, University of Patras, Greece","Human-robot interaction (HRI) is one of the most rapidly growing research fields in robotics and promising for the future of robotics technology. Despite the fact that numerous significant research results in HRI have been presented during the last years, there are still challenges in several critical topics of HRI, which could be summarized as: (i) collision and safety, (ii) virtual guides, (iii) cooperative manipulation, (iv) teleoperation and haptic interfaces, and (v) learning by observation or demonstration. In physical HRI research, the complementarity of the human and the robot capabilities is carefully considered for the advancement of their cooperation in a safe manner. New advanced control systems should be developed so the robot will acquire the ability to adapt easily to the human intentions and to the given task. The possible applications requiring co-manipulation are cooperative transportation of bulky and heavy objects, manufacturing processes such as assembly and surgery. ©",,,,,,,,,,,,,,,,,,"Gordić, Z., Jovanović, K., Collision detection on industrial robots in repetitive tasks using modified dynamic time warping (2020) Robotica, 38 (10), pp. 1717-1736; Sharkawy, A., Koustoumpardis, P., Aspragathos, N., Neural network design for manipulator collision detection based only on the joint position sensors (2020) Robotica, 38 (10), pp. 1737-1755; Huber, G., Wollherr, D., An online trajectory generator on SE (3) for human-robot collaboration (2020) Robotica, 38 (10), pp. 1756-1777; Restrepo, S.S., Raiola, G., Guerry, J., D'Elia, E., Lamy, X., Sidobre, D., Toward an intuitive and iterative 6D virtual guide programming framework for assisted human-robot comanipulation (2020) Robotica, 38 (10), pp. 1778-1806; Žlajpah, L., Petrič, T., Unified virtual guides framework for path tracking tasks (2020) Robotica, 38 (10), pp. 1807-1823; Papageorgiou, D., Dimeas, F., Kastritsi, T., Doulgeri, Z., Kinesthetic guidance utilizing DMP synchronization and assistive virtual fixtures for progressive automation (2020) Robotica, 38 (10), pp. 1824-1841; Alevizos, K., Bechlioulis, C., Kyriakopoulos, K., Physical human-robot cooperation based on robust motion intention estimation (2020) Robotica, 38 (10), pp. 1842-1866; Koskinopoulou, M., Maniadakis, M., Trahanias, P., Speed adaptation in learning from demonstration through latent space formulation (2020) Robotica, 38 (10), pp. 1867-1879; Torabi, A., Khadem, M., Zareinia, K., Sutherland, G.R., Tavakoli, M., Using a redundant user interface in teleoperated surgical systems for task performance enhancement (2020) Robotica, 38 (10), pp. 1880-1894","Koustoumpardis, P.; Robotics Group, Greece; email: koust@upatras.gr",,,"Cambridge University Press",,,,,02635747,,ROBOD,,"English","Robotica",Editorial,"Final","All Open Access, Bronze",Scopus,2-s2.0-85094175066
"Luria M., Sheriff O., Boo M., Forlizzi J., Zoran A.","56895782800;57219436931;57219432618;6602845027;26657025200;","Destruction, catharsis, and emotional release in human-robot interaction",2020,"ACM Transactions on Human-Robot Interaction","9","4","22","","",,7,"10.1145/3385007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090123724&doi=10.1145%2f3385007&partnerID=40&md5=cc3a21969080c092bcb5e39dc955946d","Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA  15206, United States; Hebrew University, Edmond J. Safra Campus, Jerusalem, 91904, Israel","Luria, M., Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA  15206, United States; Sheriff, O., Hebrew University, Edmond J. Safra Campus, Jerusalem, 91904, Israel; Boo, M., Hebrew University, Edmond J. Safra Campus, Jerusalem, 91904, Israel; Forlizzi, J., Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA  15206, United States; Zoran, A., Hebrew University, Edmond J. Safra Campus, Jerusalem, 91904, Israel","The intersection between social, technical, and economic factors biases new product development to focus on utilitarian value. However, objects that serve alternative goals, behaviors and emotions have accompanied humankind for millennia. This article speculates about robotic objects for one non-utilitarian behavior and its implications: destruction. Robots and objects for destruction have a shared history of embodiment and heavily rely on their embodiment for interaction. Yet the topic of destruction is not very common in the field of human-robot interaction (HRI). Thus, we (1) present a survey of ethnographic investigations that show modes of HRI related to destruction, and (2) develop speculative concepts of interaction that demonstrate these ideas in HRI. By exemplifying a broad range of speculative uses of destruction in HRI and grounding it in literature, we hope this theoretical and conceptual article will bring a fresh perspective on alternative interactions with robots. © 2020 Owner/Author.","catharsis; creation; destruction; embodied interaction; HRI; human-object interaction; human-robot interaction; Interaction design; material culture; social robots; speculative design","Agricultural robots; Man machine systems; Product development; Economic factors; Human robot Interaction (HRI); New product development; Human robot interaction",,,,,,,,,,,,,,,,"Alvares, C.A., (1980) Homo Faber, , M. 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"Gokul H., Kanna S.V., Akshay Kumar H., Ravikumar V.","57211684222;57221788255;57193071724;57220164120;","Design of Imitative Control Modalities for a 3 Degree of Freedom Robotic Arm",2020,"4th International Conference on Computer, Communication and Signal Processing, ICCCSP 2020",,,"9315273","","",,,"10.1109/ICCCSP49186.2020.9315273","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100202360&doi=10.1109%2fICCCSP49186.2020.9315273&partnerID=40&md5=fe529d596432f08c68e34d2b5e4dda2a","Sri Sivasubramaniya Nadar College of Engineering, Department of Electrical and Electronics, Chennai, India","Gokul, H., Sri Sivasubramaniya Nadar College of Engineering, Department of Electrical and Electronics, Chennai, India; Kanna, S.V., Sri Sivasubramaniya Nadar College of Engineering, Department of Electrical and Electronics, Chennai, India; Akshay Kumar, H., Sri Sivasubramaniya Nadar College of Engineering, Department of Electrical and Electronics, Chennai, India; Ravikumar, V., Sri Sivasubramaniya Nadar College of Engineering, Department of Electrical and Electronics, Chennai, India","This research article focuses on enabling real-time Human-Robot Interaction (HRI) with two input control modalities designed to control a robotic arm. The robotic arm comprises of 3 Degrees of Freedom (DOF) and is controlled using the following 2 strategies: Inertial Sensor-glove and Image-based visual servoing. The Mathematical model of the given 3 DOF robotic arm was derived and the two proposed methods were explicitly designed for the same. The inertial sensor-glove, worn by the user, is embedded with inertial sensors that provide data to track the motion of the user's arm. The Image-based visual servoing modality is a mono-vision based approach which tracks the motion of a target object held by the user using a camera and operates similar to a mouse-pointer but in 3 dimensions. Fitts's Targeting tasks were performed to analyse the performance of these Human-Robot Interactive input modalities. © 2020 IEEE.","Fitts's law; Human computer interaction; image processing; robotic arm; sensors","Degrees of freedom (mechanics); Human robot interaction; Inertial navigation systems; Mammals; Robotic arms; Robotics; Signal processing; Wearable sensors; 3 degrees of freedom; Human robot Interaction (HRI); Image based visual servoing; Inertial sensor; Interactive input; Mono visions; Mouse pointers; Target object; Visual servoing",,,,,,,,,,,,,,,,"Commonly Used Statistics, Osha Data and Statistics, , https://www.osha.gov/oshstats/commonstats.html, Construction's Fatal Four; Kaur, H., Rani, J., A review: Study of various techniques of hand gesture recognition (2016) 2016 Ieee 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), pp. 1-5. , Delhi; Kumra, S., Saxena, R., Mehta, S., Design and development of 6-dof robotic arm controlled by man machine interface (2012) 2012 Ieee International Conference on Computational Intelligence and Computing Research, pp. 1-5. , Coimbatore; Szab, R., Gontean, A., Robotic arm control with stereo vision made in labwindows/cvi (2015) 2015 38th International Conference on Telecommunications and Signal Processing (TSP), pp. 1-5. , Prague; Dahari, M., Tan, J., Forward and inverse kinematics model for robotic welding process using kr-16ks kuka robot (2011) 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization, pp. 1-6. , Kuala Lumpur; Jiang, H., Wachs, J.P., Pendergast, M., Duerstock, B.S., 3d joystick for robotic arm control by individuals with high level spinal cord injuries (2013) 2013 Ieee 13th International Conference on Rehabilitation Robotics (ICORR), pp. 1-5. , Seattle, wa; Forsslund, J., Yip, M., Sallns, E.-L., Wooden-haptics: A starting kit for crafting force-reflecting spatial hap-tic devices (2015) Proceedings of the Ninth International Conference on Tangible, Embedded, and Embodied Interaction (TEI 15), p. 133140. , DOI:https://doi.org/10.1145/2677199.2680595, Association for Computing Machinery, New York, ny, usa; Wittmann, F., Lambercy, O., Gassert, R., Magnetometer-based drift correction during rest in imu arm motion tracking (2019) Sensors (Basel), 19 (6), p. 1312. , Published 2019 Mar 15; Hutchinson, S., Hager, G.D., Corke, P.I., A tutorial on visual servo control (1996) Ieee Transactions on Robotics and Automation, 12 (5), p. 651670; Ganesan, P., Rajini, V., Sathish, B.S., Shaik, K.B., Hsv color space based segmentation of region of interest in satellite images (2014) 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. 101-105. , Kanyakumari; Fitts's Law,The Glossary of Human Computer Interaction, , https://www.interaction-design.org/literature/book/the-glossary-of-human-computer-interaction/fitts-s-law, [online]; MacKenzie, I.B.W., Extending fitts' law to two-dimensional tasks (1992) Proceedings of the Sigchi Conference on Human Factors in Computing Systems, Series Chi '92., pp. 219-226; Yip, M.C., Tavakoli, M., Howe, R.D., Performance analysis of a haptic telemanipulation task under time delay (2011) J Adv Robot, 25 (5), p. 651673; Zareinia, K., Maddahi, Y., Ng, C., Sepehri, N., Sutherland, G.R., Performance evaluation of haptic hand-controllers in a robot-Assisted surgical system (2015) Int. J. Med. Robot. Comput.-Assisted Surgery, 11 (4), p. 486501; Guo, S., A novel robot-Assisted endovascular catheteriza-tion system with haptic force feedback (2019) Ieee Transactions on Robotics, 35 (3), pp. 685-696. , June",,,"Navis;SSN","Institute of Electrical and Electronics Engineers Inc.","4th International Conference on Computer, Communication and Signal Processing, ICCCSP 2020","28 September 2020 through 29 September 2020",,166542,,9781728165097,,,"English","Int. Conf. Comput., Commun. Signal Process., ICCCSP",Conference Paper,"Final","",Scopus,2-s2.0-85100202360
"Du P., Huang Z., Liu T., Ji T., Xu K., Gao Q., Sibai H., Driggs-Campbell K., Mitra S.","57215358806;57215057068;57219502659;57219797049;57210161704;57219497401;57194177206;55005983200;24463145900;","Online Monitoring for Safe Pedestrian-Vehicle Interactions",2020,"2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020",,,"9294366","","",,2,"10.1109/ITSC45102.2020.9294366","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099565384&doi=10.1109%2fITSC45102.2020.9294366&partnerID=40&md5=c9c652ff2794404e73cfb013c7dfb08f","Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, United States; Department of Computer Science, University of Illinois at Urbana-Champaign, United States","Du, P., Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, United States; Huang, Z., Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, United States; Liu, T., Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, United States; Ji, T., Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, United States; Xu, K., Department of Computer Science, University of Illinois at Urbana-Champaign, United States; Gao, Q., Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, United States; Sibai, H., Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, United States; Driggs-Campbell, K., Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, United States; Mitra, S., Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, United States","As autonomous systems begin to operate amongst humans, methods for safe interaction must be investigated. We consider an example of a small autonomous vehicle in a pedestrian zone that must safely maneuver around people in a free-form fashion. We investigate two key questions: How can we effectively integrate pedestrian intent estimation into our autonomous stack? Can we develop an online monitoring framework to give rigorous assurances on the safety of such human-robot interactions? We present a pedestrian intent estimation framework that can accurately predict future pedestrian trajectories given multiple possible goal locations. We integrate this into a reachability-based online monitoring and decision making scheme that formally assesses the safety of these interactions with nearly real-time performance (approximately 0. 1s). These techniques are both tested in simulation and integrated on a test vehicle with a complete in-house autonomous stack, demonstrating safe interaction in real-world experiments. © 2020 IEEE.",,"Automobile testing; Behavioral research; Decision making; Electronic assessment; Intelligent systems; Intelligent vehicle highway systems; Partial discharges; Pedestrian safety; Vehicles; Autonomous systems; Online monitoring; Pedestrian trajectories; Reachability; Real time performance; Real world experiment; Small autonomous vehicles; Vehicle interactions; Human robot interaction",,,,,"FA9550-17-1-0236; National Science Foundation, NSF","*This research is supported by research grants from the National Science Foundation (FMITF:1918531) and the United States Air Force Office of Scientific Research (FA9550-17-1-0236). We are grateful to the Illinois Center for Autonomy, Coordinated Science Lab, and AutonomouStuff for supporting our experimental infrastructure. † Z. Huang, T. Liu, T. Ji, K. Xu, and Q. 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"Pollmann K., Ziegler D.","57128090300;56236807100;","Personal quizmaster: A pattern approach to personalized interaction experiences with the MiRo robot",2020,"PervasiveHealth: Pervasive Computing Technologies for Healthcare",,,"3410414","485","489",,2,"10.1145/3404983.3410414","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091771263&doi=10.1145%2f3404983.3410414&partnerID=40&md5=9ec79575623cc2dfc31dbfba2af27c7b","Institute for Industrial Engineering, Fraunhofer IAO, Stuttgart, Germany","Pollmann, K., Institute for Industrial Engineering, Fraunhofer IAO, Stuttgart, Germany; Ziegler, D., Institute for Industrial Engineering, Fraunhofer IAO, Stuttgart, Germany","In Human-Robot Interaction, personalization has been proposed as a strategy to increase acceptance for social robots. The present paper describes how behavioral design patterns can be used to tailor the interaction experience to the individual user's characteristics and needs. To demonstrate this approach, we designed a quiz game application for the MiRo robot. The robot acts as the quizmaster and shows different behaviors (coach-like/empathic vs. challenging/provocative) depending on the type of user who is playing the game (community-focused vs. competition-focused player). We describe the process of creating the two quizmaster personalities and related behavioral patterns as well as the technical background for integrating them with the interaction model for the quiz game. The result is a Wizard-of-Oz demonstration of the personalizable quiz game that is accompanied by an interactive video prototype remote for user studies and demo purposes. © 2020 Owner/Author.","behavioral patterns; multimodal behavioral expressions; personalized human-robot interaction; social robot","User experience; Behavioral patterns; Design Patterns; Interaction experiences; Interaction model; Interactive video; Personalizations; Technical background; Wizard of Oz; Social robots",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV7941","The NIKA project is funded by the German Federal Ministry of Education and Research (BMBF 16SV7941).",,,,,,,,,,"Alexander, C., (1977) A Pattern Language: Towns, Buildings, Construction, , Oxford University Press; Ballesteros, S., Kraft, E., Santana, S., Tziraki, C., Maintaining older brain functionality: A targeted review (2015) Neuroscience & Biobehavioral Reviews, 55, pp. 453-477. , 2015; Borchers, J.O., A pattern approach to interaction design (2000) Proceedings of the 3rd International Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques, pp. 369-378. , ACM, New York; Churamani, N., Anton, P., Brügger, M., Fließwasser, E., Hummel, T., Mayer, J., Mustafa, W., Wermter, S., The impact of personalisation on human-robot interaction in learning scenarios (2017) Proceedings of the 5th International Conference on Human Agent Interaction (Bielefeld, Germany) (HAI '17), pp. 171-180. , https://doi.org/10.1145/3125739.3125756, ACM, New York, NY, USA; Collins, E.C., Prescott, T.J., Mitchinson, B., Saying it with light: A pilot study of affective communication using the miro robot (2015) Biomimetic and Biohybrid Systems, pp. 243-255. , https://doi.org/10.1007/978-3-319-22979-9_25, Springer International Publishing; (2019) MIRO-E: Python, , http://labs.consequentialrobotics.com/miro-e/docs/index.php?page=Technical_Libraries_Python, Retrieved June 8, 2020 from Consequential Robotics; Constantine, L.L., Lockwood, D.L.A., Software for use: A practical guide to the models and methods of usage-centered design (1999) Pearson Education; Dautenhahn, K., Robots we like to live with?!-a developmental perspective on a personalized, life-long robot companion (2004) RO-MAN 2004. 13th IEEE InternationalWorkshop on Robot and Human Interactive Communication (IEEE Catalog No. 04TH8759), pp. 17-22. , https://doi.org/10.1109/ROMAN.2004.1374720; (2018) MyUI Runtime Environment, , https://github.com/FraunhoferIAO/myui-runtime, Fraunhofer IAO Retrieved June 8, 2020 from; Kahn, P.H., Freier, N.G., Kanda, T., Ishiguro, H., Ruckert, J.H., Severson, R.L., Kane, S.K., Design patterns for sociality in human-robot interaction (2008) Proceedings of the 3rd ACM/IEEE International Conference on Human Robot Interaction. ACM, pp. 97-104; Kobsa, A., Generic user modeling systems (2001) User Modeling and User-Adapted Interaction, 11 (1-2), pp. 49-63. , https://doi.org/10.1023/a:1011187500863, March 2001; Lamers, M.H., Verbeek, F.J., (2011) Human-Robot Personal Relationships-Third International Conference, HRPR 2010, 59. , https://doi.org/10.1007/978-3-642-19385-9, Leiden, The Netherlands, June 23-24, 2010, Revised Selected Papers Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer; Nishimuta, I., Itoyama, K., Yoshii, K., Okuno, H.G., Multi-party interactions by quizmaster robot in speech-based jeopardy! Like games (2017) 2017 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1787-1792; Park, D.C., Lodi-Smith, J., Drew, L., Haber, S., Hebrank, A., Bischof, G.N., Aamodt, W., The impact of sustained engagement on cognitive function in older adults: The synapse project (2014) Psychological Science, 25 (1), pp. 103-112. , 2014; Peissner, M., Häbe, D., Janssen, D., Sellner, T., Myui: Generating accessible user interfaces from multimodal design patterns (2012) Proceedings of the 4th ACM SIGCHI Symposium on Engineering Interactive Computing Systems-EICS '12, , https://doi.org/10.1145/2305484.2305500, ACM Press; Pollmann, K., Behavioral design patterns for social, assistive robots-insights from the nika research project (2019) Mensch und Computer 2019-Workshopband, , 2019; Shah, T.M., Weinborn, M., Verdile, G., Sohrabi, H.R., Martins, R.N., Enhancing cognitive functioning in healthly older adults: A systematic review of the clinical significance of commercially available computerized cognitive training in preventing cognitive decline (2017) Neuropsychology Review, 27 (1), pp. 62-80. , 2017; Sverre Syrdal, D., Lee Koay, K., Walters, M.L., Dautenhahn, K., A personalized robot companion-the role of individual differences on spatial preferences in hri scenarios (2007) RO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication, pp. 1143-1148. , https://doi.org/10.1109/ROMAN.2007.4415252; Thompson, C., Mohamed, S., Louie, W.G., Chen He, J., Li, J., Nejat, G., The robot tangy facilitating trivia games: A team-based user-study with long-term care residents (2017) 2017 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), pp. 173-178; Tidwell, J., (2010) Designing Interfaces: Patterns for Effective Interaction Design, , 2nd edition O'Reilly Media, Inc., Canada; Ziegler, D., Peissner, M., Modelling of polymorphic user interfaces at the appropriate level of abstraction (2018) Advances in Intelligent Systems and Computing, pp. 45-56. , https://doi.org/10.1007/978-3-319-94229-2_5, Springer International Publishing",,"Alt F.Schneegass S.Hornecker E.",,"ICST","2020 Conference on Mensch und Computer, MuC 2020","6 September 2020 through 9 September 2020",,162854,21531633,9781450375405,,,"English","PervasiveHealth: Pervasive Computing Technol Healthc.",Conference Paper,"Final","",Scopus,2-s2.0-85091771263
"Adamides G.","25958862000;","Heuristic Evaluation of the User Interface for a Semi-Autonomous Agricultural Robot Sprayer",2020,"Agris On-line Papers in Economics and Informatics","12","3",,"3","12",,,"10.7160/aol.2020.120301","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102276576&doi=10.7160%2faol.2020.120301&partnerID=40&md5=4a0918018763e2776b6b0033984c779c","Rural Development Department, Agricultural Research Institute, Nicosia, Cyprus","Adamides, G., Rural Development Department, Agricultural Research Institute, Nicosia, Cyprus","This study presents the heuristic evaluation, as a usability inspection method for Human-Robot Interaction (HRI) systems. First, the methodology to engineer a semi-autonomous agricultural robot sprayer is presented, and then findings from heuristic usability evaluation studies that were carried out on a human-robot interface for a semi-autonomous agricultural vineyard robot sprayer. The following research-based heuristics for the design of robot teleoperation were used: Platform architecture and scalability, Error prevention and recovery, Visual design, Information presentation, Robot state awareness, Interaction effectiveness and efficiency, Robot environment/surroundings awareness, and Cognitive factors. In each evaluation study, usability problems were identified, and specific suggestions were documented for HRI usability improvement. In each design iteration, a smaller number of usability issues were identified.. Results of the final heuristic evaluation showed that the system is at a good level of usability and is expected to provide satisfactory services to its typical users. © 2020, Agris On-line Papers in Economics and Informatics. All Rights Reserved","Agricultural robot; heuristic evaluation; human-robot interaction; usability study","agricultural technology; error analysis; heuristics; information technology; robotics; spray; vineyard; visual analysis",,,,,,,,,,,,,,,,"Adamides, G., (2016) User Interfaces for Human-Robot Interaction: Application on a Semi- Autonomous Agricultural Robot Sprayer, , [1] PhD Doctoral Dissertation, Open University of Cyprus, Nicosia. ISBN 978-9963-695-49-2; Adamides, G., Christou, G., Katsanos, C., Xenos, M., Hadzilacos, T., Usability Guidelines for the Design of Robot Teleoperation: A Taxonomy (2015) IEEE Transactions on Human-Machine Systems, 45 (2), pp. 256-262. , [2] ISSN 2168-2291; Adamides, G., Katsanos, C., Christou, G., Xenos, M., Papadavid, G., Hadzilacos, T., User interface considerations for telerobotics: The case of an agricultural robot sprayer (2014) Second international conference on remote sensing and geoinformation of the environment (RSCy2014), 9229, p. 92291W. , [3] International Society for Optics and Photonics; Alan, D., Janet, F., Gregory, A., Russell, B., (2004) Human-computer interaction, , [4] England: Pearson Education Limited. ISBN 978-0-13-046109-4; Albert, W., Tullis, T., (2008) Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics (Interactive Technologies), , [5] Morgan Kaufmann. E-ISBN 978-0-12-415781-1; Arad, B., Balendonck, J., Barth, R., Ben‐Shahar, O., Edan, Y., Hellström, T., Hemming, J., van Tuijl, B., Development of a sweet pepper harvesting robot (2020) Journal of Field Robotics, 34 (6), pp. 1027-1039. , [6] E-ISSN 1556-4967; Åstrand, B., Baerveldt, A.-J., An Agricultural Mobile Robot with Vision-Based Perception for Mechanical Weed Control (2002) Autonomous Robots, 13, pp. 21-35. , [7] E-ISSN 1573-7527, ISSN 0929-5593; Berenstein, R., Edan, Y., Evaluation of marking techniques for a human-robot selective vineyard sprayer (2012) the International Conference of Agricultural Engineering, , [8] Paper presented at Valencia, Spain; Berenstein, R., Edan, Y., Human-robot collaborative site-specific sprayer (2017) Journal of Field Robotics, 34 (8), pp. 1519-1530. , [9] E-ISSN 1556-4967; Berenstein, R., Shahar, O. B., Shapiro, A., Edan, Y., Grape clusters and foliage detection algorithms for autonomous selective vineyard sprayer (2010) Intelligent Service Robotics, 3 (4), pp. 233-243. , [10] E-ISSN 1861-2784, ISSN 1861-2776; Brown, D., Giles, D., Oliver, M., Klassen, P., Targeted spray technology to reduce pesticide in runoff from dormant orchards (2008) Crop Protection, 27, pp. 545-552. , [11] ISSN 0261-2194; Dumas, J. S., Redish, J. C., A practical guide to usability testing (1999) Intellect Books, , [12] ISBN 1-84150-020-8; Edan, Y., Han, S., Kondo, N., Automation in Agriculture (2009) Springer Handbook of Automation, pp. 1095-1128. , [13] Berlin, Heidelberg: Springer Berlin Heidelberg. ISBN 10: 3540788301, ISBN 13: 9783540788300; Endalew, A. M., Debaer, C., Rutten, N., Vercammen, J., Delele, M. A., Ramon, H., Nicolaï, B. M., Verboven, P., Modelling the effect of tree foliage on sprayer airflow in orchards (2011) Boundary-Layer Meteorology, 138 (1), pp. 139-162. , [14] E-ISSN 1573-1472, ISSN 0006-8314; How to Feed the World in 2050 (2009) Population and Development Review, 35 (4), pp. 837-839. , [15] FAO E-ISSN ISSN:1728-4457; (2010) Climate-smart Agriculture: Policies, Practices and Financing for Food Security, Adaptation and Mitigation, , [16] FAO Food and Agriculture Organization of the United Nations, Rome, Italy; Fong, T., Thorpe, C., Baur, C., Collaboration, dialogue, human-robot interaction (2001) Robotics Research, Springer Tracts in Advanced Robotics, 6. , [17] Jarvis R. A., Zelinsky A. (eds) Springer, Berlin, Heidelberg; Hertz, T., Zahniser, S., Is there a farm labor shortage? (2013) American Journal of Agricultural Economics, 95 (2), pp. 476-481. , [18] E-ISSN 1467-8276, ISSN 0002-9092; Huffman, W. E., Evenson, R. E., Structural and productivity change in US agriculture, 1950–1982 (2001) Agricultural economics, 24 (2), pp. 127-147. , [19] ISSN 0169-5150; Kargar, B., Amir, H., Shirzadifar, A. M., Automatic weed detection system and smart herbicide sprayer robot for corn fields (2013) the First RSI/ISM International Conference on Robotics and Mechatronics, , [20] Paper presented at; McNulty, P. B., Grace, P. M., Agricultural Mechanization and Automation (2009) Encyclopedia of Life Support Systems (EOLSS), I. , [21] J. K. Schueller (Ed), Eolss Publishers Company Limited. E-ISBN 978-1-84826-096-2, ISBN 978-1-84826-546-2; Nielsen, J., How to conduct a heuristic evaluation (1994) Nielsen Norman Group, 1, pp. 1-8. , [22] (a); Nielsen, J., (1994) Usability engineering, , [23] (b) Morgan Kaufmann. ISBN 978-0125184069; Nielsen, J., Usability inspection methods (1994) Conference companion on Human factors in computing systems, pp. 413-414. , [24] (c); Nielsen, J., Severity ratings for usability problems (1995) Papers and Essays, , http://www.useit.com/papers/heuristic/severityrating.html, [25] [Online]. Available: [Accessed: 23 June, 2020]; Nielsen, J., Landauer, T. K., A mathematical model of the finding of usability problems (1993) the Proceedings of the INTERACT‘93 and CHI‘93 conference on Human factors in computing systems, pp. 206-213. , [26] Paper presented at ISBN 978-0-89791-575-5; Nielsen, J., Molich, R., Heuristic evaluation of user interfaces (1990) the Proceedings of the SIGCHI conference on Human factors in computing systems: Empowering people, , [27] Paper presented at ISBN 978-1-4503-0228-9; Oberti, R., Marchi, M., Tirelli, P., Calcante, A., Iriti, M., Tona, E., Hočevar, M., Ulbrich, H., Selective spraying of grapevines for disease control using a modular agricultural robot (2016) Biosystems Engineering, 146, pp. 203-215. , [28] ISSN 1537-5110; Poole, A., Linden, B., Eye Tracking in Human-Computer Interaction and Usability Research: Current Status and Future (2005) Encyclopedia of human computer interaction: IGI Global, , [29] C. Ghaoui (Ed), E-ISBN13: 9781591407980, ISBN13: 9781591405627, ISBN10: 1591405629; Sistler, F. E., Robotics and intelligent machines in agriculture (1987) IEEE Journal of Robotics and Automation, 3 (1), pp. 3-6. , [30] ISSN 0882-4967; Yanco, H. A., Drury, J., Classifying human-robot interaction: an updated taxonomy (2004) 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583), 3, pp. 2841-2846. , [31] ISBN 0-7803-8566-7, ISSN 1062-922X","Adamides, G.; Rural Development Department, Cyprus; email: gadamides@ari.gov.cy",,,"Faculty of Economics and Management",,,,,18041930,,,,"English","Agris On-line Pap. Econ. Inf.",Article,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85102276576
"Razin Y.S., Feigh K.M.","57195955923;15831610200;","Hitting the Road: Exploring Human-Robot Trust for Self-Driving Vehicles",2020,"Proceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020",,,"9209525","","",,1,"10.1109/ICHMS49158.2020.9209525","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093929491&doi=10.1109%2fICHMS49158.2020.9209525&partnerID=40&md5=4064718752f24b841d60c4608a7131fe","Georgia Institute of Technology, School of Aerospace Engineering, Atlanta, GA, United States","Razin, Y.S., Georgia Institute of Technology, School of Aerospace Engineering, Atlanta, GA, United States; Feigh, K.M., Georgia Institute of Technology, School of Aerospace Engineering, Atlanta, GA, United States","With self-driving cars making their way on to our roads, we ask not what it would take for them to gain acceptance among consumers, but what impact they may have on other drivers. How they will be perceived and whether they will be trusted will likely have a major effect on traffic flow and vehicular safety. This work first undertakes an exploratory factor analysis to validate a trust scale for human-robot interaction and shows how previously validated metrics and general trust theory support a more complete model of trust that has increased applicability in the driving domain. We experimentally test this expanded model in the context of human-Automation interaction during simulated driving, revealing how using these dimensions uncovers significant biases within human-robot trust that may have particularly deleterious effects when it comes to sharing our future roads with automated vehicles. © 2020 IEEE.","autonomous vehicles; human-robot interaction trust; trust scale validation","Autonomous vehicles; Factor analysis; Man machine systems; Automated vehicles; Deleterious effects; Exploratory factor analysis; Human-automation interactions; Self drivings; Simulated driving; Trust theories; Vehicular safety; Social robots",,,,,"National Science Foundation, NSF","This work was done with support from Grant #1544814 as part of the NSF Cyber-Physical Systems Program.",,,,,,,,,,"Gefen, D., Karahanna, E., Straub, D.W., Trust and TAM in online shopping: An intergrated model (2003) MIS Quaterly, 27 (1), pp. 51-90; Barber, B., (1983) The Logic and Limits of Trust. Rutgers, , University Press, New Brunswick, NJ; Mayer, R.C., Davis, J.H., Schoorman, F.D., An integrative model of organizational trust (1995) Academy of Management Review, 20 (3), pp. 709-734; Jian, J.-Y., Bisantz, A.M., Drury, C.G., Foundations for an empirically determined scale of trust in automated systems (2000) Intl J. Of Cognitive Ergonomics, 4 (1), pp. 53-71; Ekman, F., Johansson, M., Sochor, J., Creating appropriate trust for autonomous vehicle systems: A framework for HMI design Transportation Research Board, Tech. Rep, 2016; Mirnig, A.G., Sutter, C., A framework for analyzing and calibrating trust in automated vehicles AutomotiveUI ?16 Adjunct, 2016; Gold, C., Körber, M., Hohenberger, C., Lechner, D., Bengler, K., Trust in automation-before and after the experience of take-over scenarios in a highly automated vehicle (2015) Procedia Manufacturing, 3, pp. 3025-3032; Sanders, T., Oleson, K.E., Billings, D.R., Chen, J.Y., Hancock, P.A., A model of human-robot trust: Theoretical model development (2011) Proc. Of the HFES Annual Mtg, 55 (1), pp. 1432-1436. , SAGE Publications Sage CA: Los Angeles, CA; Murphy, R., Schreckenghost, D., Survey of metrics for human-robot interaction (2013) Proc. Of the 8th ACM/IEEE Intl. Conf. Of Human-Robot Interaction. IEEE Press, pp. 197-198; Deutsch, M., Trust and Suspicion (1958) J. Of Conflict Resolution, 2, pp. 265-279; Lee, J.D., See, K.A., Trust in automation: Designing for appropriate reliance (2004) Human Factors, 46 (1), pp. 50-80; Chien, S.-Y., Semnani-Azad, Z., Lewis, M., Sycara, K., Towards the development of an inter-cultural scale to measure trust in automation (2014) Intl. Conf. On Cross-Cultural Design, pp. 35-46; Körber, M., Theoretical considerations and development of a questionnaire to measure trust in automation Congress of the Intl. Ergonomics Association, 2018, pp. 13-30. , Springer; Castelfranchi, C., Falcone, R., Theory, T., (2010) A Socio-Cognitive and Computational Model, 18. , West Sussex: John Wiley, Sons; Rotter, J., A new scale for the measurement of interpersonal trust (1967) Journal of Personality, 35 (4), pp. 651-655; Lewicki, R.O.Y.J., McAllister, D.J., Bies, R.J., Trust and distrust: New relationships and realities (1998) Academy of Management Review, 23 (3), pp. 438-458; McKnight, D.H., Chervany, N.L., What trust means in e-commerce customer relationships: An interdisciplinary conceptual typology (2001) International Journal of Electronic Commerce, 6 (2), pp. 35-59; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-Analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Masalonis, A.J., Parasuraman, R., Trust as a construct for evaluation of automated aids: Past and future theory and research (1999) Proc. Of the HFES Annual Mtg, 43 (3), pp. 184-187; Sheridan, T.B., Parasuraman, R., Human-Automation interaction (2005) Revs. Of Human Factors and Ergonomics, 1 (1), pp. 89-129; Schaefer, K.E., Chen, J.Y.C., Szalma, J.L., Hancock, P.A., A metaanalysis of factors influencing the development of trust in automation: Implications for understanding autonomy in future systems (2016) Human Factors, 58 (3), pp. 377-400; Goillau, P., Kelly, C., Boardman, M., Jeannot, E., Guidelines for trust in future ATM systems : Measures. European Organisation for the Safety of Air Navigation. Brussels (2003) Belgium; Krueger, F., Meyer-Lindenberg, A., Toward a model of interpersonal trust drawn from neuroscience, psychology, and economics (2019) Trends in Neurosciences, 42 (2), pp. 92-101; Shah, S., Dey, D., Lovett, C., Kapoor, A., Airsim: High-fidelity visual and physical simulation for autonomous vehicles (2017) Field and Service Robotics, , https://arxiv.org/abs/1705.05065; (2019) A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, , https://www.R-project.org/, R Core Team R; Kuznetsova, A., Brockhoff, P.B., Christensen, R.H.B., (2017) LmerTest Package: Tests in Linear Mixed Effects Models, , https://www.jstatsoft.org/article/view/v082i13/0; Revelle, W., (2018) Psych: Procedures for Psychological, Psychometric, and Personality Research, Northwestern University, Evanston, Illinois, , https://CRAN.Rproject.org/package=psych, r package version 1.8.12; Multal, A.S., (2019) DescTools: Tools for Descriptive Statistics, , https://cran.rproject.org/package=DescTools, r package version 0.99.31; Falissard, B., (2012) Psy: Various Procedures Used in Psychometry, , https://CRAN.Rproject.org/package=psy, r package version 1.1; Rosseel, Y., (2012) Lavaan: An R Package for Structural Equation Modeling, , http://www.jstatsoft.org/v48/i02/; Wickham, H., (2016) ggplot2: Elegant Graphics for Data Analysis, , https://ggplot2.tidyverse.org; Gutzwiller, R., Chiou, E., Craig, S., Lewis, C., Lematta, G., Hsiung, C.-P., Positive bias in the ?trust in automated systems survey an examination of the jian et al. (2000) scale (2019) Proc. Of the HFES Annual Meeting, 63 (11), pp. 217-221; Clifton, J.D., Managing validity versus reliability trade-offs in scalebuilding decisions Psychological Methods, 2019; Spain, R.D., Bustamante, E.A., Bliss, J.P., Towards an empirically developed scale for system trust: Take two (2008) Proc. Of the HFES Annual Mtg, 52 (19), pp. 1335-1339. , SAGE Publications Sage CA: Los Angeles, CA; Lewis, M., Sycara, K., Walker, P., The role of trust in human-robot interaction Foundations of Trusted Autonomy, 2018, pp. 135-159. , Springer, Cham; Muir, B.M., Trust in automation: Part i (1994) Ergonomics, 37 (11), pp. 1905-1922; Gottman, J.M., The Science of Trust: Emotional Attunement for Couples. New York, London (2011), WW Norton Company; Axelrod, R., Dion, D., The further evolution of cooperation (1988) Science, 242 (4884), pp. 1385-1390",,"Fortino G.Wang F.-Y.Nurnberger A.Kaber D.Falcone R.Mendonca D.Yu Z.Guerrieri A.",,"Institute of Electrical and Electronics Engineers Inc.","1st IEEE International Conference on Human-Machine Systems, ICHMS 2020","7 September 2020 through 9 September 2020",,163564,,9781728158716,,,"English","Proc. IEEE Int. Conf. Hum.-Mach. Syst., ICHMS",Conference Paper,"Final","",Scopus,2-s2.0-85093929491
"Ansari R.J., Giordano G., Sjöberg J., Karayiannidis Y.","55752525100;57191346403;57218613709;8693741600;","Human grasp position estimation for human–robot cooperative object manipulation",2020,"Robotics and Autonomous Systems","131",,"103600","","",,1,"10.1016/j.robot.2020.103600","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088290447&doi=10.1016%2fj.robot.2020.103600&partnerID=40&md5=0d3fb9ac8e211177afb5e94c4a6b708a","Department of Electrical Engineering, Chalmers University of Technology41296 Göteborg, Sweden; Zenuity AB41756 Göteborg, Sweden","Ansari, R.J., Department of Electrical Engineering, Chalmers University of Technology41296 Göteborg, Sweden; Giordano, G., Zenuity AB41756 Göteborg, Sweden; Sjöberg, J., Department of Electrical Engineering, Chalmers University of Technology41296 Göteborg, Sweden; Karayiannidis, Y., Department of Electrical Engineering, Chalmers University of Technology41296 Göteborg, Sweden","This paper addresses the problem of human grasp position estimation in a physical human–robot object handling scenario. The problem is formulated as a linear regression by considering the human grasp position and their exerted torque as unknown parameters. We propose a modified least-squares algorithm to estimate the parameters by evaluating the quality of the estimates based on the assumption that the parameters should remain constant for a period of time. The solution is model-agnostic in terms of the human force/torque model – requiring only force/torque measurements on the robot side and proprioception – and is model-based in terms of the object model. The proposed grasp position estimation method is compared statistically with a conventional contact point estimation method using the collected experimental data. Moreover, the performance of the developed method is evaluated through various scenarios of physical human–robot interaction. © 2020 Elsevier B.V.","Collaborative robots; Estimation; Object manipulation; Physical human–robot interaction","Agricultural robots; Contact points; Model-based OPC; Modified least squares; Object handling; Object manipulation; Object model; Position estimation; Robot interactions; Human robot interaction",,,,,"Vetenskapsrådet, VR","This work has been supported by the Swedish Research Council (VR) .",,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human–robot collaboration (2018) Auton. Robots, pp. 1-19; Ćehajić, D., (2017), pp. 1730-1737. , P.B. g. Dohmann, S. Hirche, Estimating unknown object dynamics in human-robot manipulation tasks, in: 2017 IEEE International Conference on Robotics and Automation, ICRA; Karayiannidis, Y., Smith, C., Viña, F.E., Kragic, D., Online contact point estimation for uncalibrated tool use (2014) Robotics and Automation, ICRA, 2014 IEEE International Conference on, pp. 2488-2494. , IEEE; Karayiannidis, Y., Smith, C., Viña, F.E., Kragic, D., Online kinematics estimation for active human-robot manipulation of jointly held objects (2013), pp. 4872-4878. , 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems; Manuelli, L., Tedrake, R., Localizing external contact using proprioceptive sensors: The contact particle filter (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pp. 5062-5069. , IEEE; Karayiannidis, Y., Smith, C., Kragic, D., Mapping human intentions to robot motions via physical interaction through a jointly-held object (2014) The 23rd IEEE International Symposium on Robot and Human Interactive Communication, pp. 391-397. , IEEE; Dumora, J., Geffard, F., Bidard, C., Brouillet, T., Fraisse, P., Experimental study on haptic communication of a human in a shared human-robot collaborative task (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5137-5144. , IEEE; Madan, C.E., Kucukyilmaz, A., Sezgin, T.M., Basdogan, C., Recognition of haptic interaction patterns in dyadic joint object manipulation (2014) IEEE Trans. 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Math. Lett., 74, pp. 33-45","Ansari, R.J.; Department of Electrical Engineering, Sweden; email: raminj@chalmers.se",,,"Elsevier B.V.",,,,,09218890,,RASOE,,"English","Rob Autom Syst",Article,"Final","",Scopus,2-s2.0-85088290447
"Müller F., Jäkel J., Thomas U.","57188974322;33967731400;7004226574;","Stability analysis for a passive/active human model in physical human–robot interaction with multiple users",2020,"International Journal of Control","93","9",,"2104","2119",,2,"10.1080/00207179.2018.1541508","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057307857&doi=10.1080%2f00207179.2018.1541508&partnerID=40&md5=04d07a1b5460b3b51a2fdd1641a7c393","Chair of Robotics and Human-Machine-Interaction Lab, Chemnitz University of Technology, Chemnitz, Germany; Department of Electrical Engineering and Information Technology, Leipzig University of Applied Science, Leipzig, Germany","Müller, F., Chair of Robotics and Human-Machine-Interaction Lab, Chemnitz University of Technology, Chemnitz, Germany; Jäkel, J., Department of Electrical Engineering and Information Technology, Leipzig University of Applied Science, Leipzig, Germany; Thomas, U., Chair of Robotics and Human-Machine-Interaction Lab, Chemnitz University of Technology, Chemnitz, Germany","Human–robot–human interaction (HRH), understood as a physical human–robot interaction (pHRI) with two humans, can be applied when lifting heavy, bulky and large-sized objects with a robot. In combination with a virtual environment, this system can become nonlinear. In this article, we prove sufficient stability conditions for a stationary point of such a particular type of nonlinear multiple time-delay systems. In addition, a new human model consisting of a passive and an active part will be introduced and validated on experimental data. The derived stability conditions are applied to a single-user pHRI system including this human model. The results indicate that these conditions are very conservative. Then four approaches for the analysis of a multi-user pHRI are introduced and compared with each other. Finally, a potential HRH application with a nonlinear environment in the form of a potential force field is presented. © 2018 Informa UK Limited, trading as Taylor & Francis Group.","human–robot–human; impedance control; Lyapunov–Krasovskii functional; nonlinear time-delay systems; Physical human–robot interaction","Delay control systems; System stability; Time delay; Virtual reality; Impedance control; Krasovskii functional; Multiple time-delay system; Nonlinear environments; Nonlinear time delay systems; Potential force fields; Robot interactions; Stability condition; Human robot interaction",,,,,,,,,,,,,,,,"Agarwal, P., Deshpande, A.D., (2015) Impedance and force-field control of the index finger module of a hand exoskeleton for rehabilitation, , August,. 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IEEE/RSJ international conference on Intelligent Robots and Systems (IROS), Sendai, Japan (4, 3883–3888","Müller, F.; Robotics and Human-Machine-Interaction Lab, Reichenhainer Str. 70, Germany; email: florian.mueller@etit.tu-chemnitz.de",,,"Taylor and Francis Ltd.",,,,,00207179,,IJCOA,,"English","Int J Control",Article,"Final","",Scopus,2-s2.0-85057307857
"Choi Y., Choi M., Oh M., Kim S.","55685955800;56594110900;57211928364;57209619163;","Service robots in hotels: understanding the service quality perceptions of human-robot interaction",2020,"Journal of Hospitality Marketing and Management","29","6",,"613","635",,44,"10.1080/19368623.2020.1703871","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077157890&doi=10.1080%2f19368623.2020.1703871&partnerID=40&md5=0cdefb4039372e3fc880bdd2d4037f01","School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Kowloon, Hong Kong; School of Events, Tourism Hospitality Management, Leeds Beckett University, Leeds, United Kingdom","Choi, Y., School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Kowloon, Hong Kong; Choi, M., School of Events, Tourism Hospitality Management, Leeds Beckett University, Leeds, United Kingdom; Oh, M., School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Kowloon, Hong Kong; Kim, S., School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Kowloon, Hong Kong","Hotel industry started to adopt service robots, which are considered a future workforce. However, no attempt was conducted to examine the dimensionality of service quality of service robots. This paper aims to understand the influence of human–robot interaction from the viewpoint of hoteliers and guests. Two studies are conducted in this respect. Study 1 organizes focus-group interviews with hotel managers from various departments to elicit themes related to guest–robot interaction and robot-delivered services. Based on the findings in Study 1, Study 2 conducts an experiment to examine and compare hotel guests’ perceptions about the quality of services provided by human staff and service robots, as well as their joint services. Human staff services are perceived higher than the services of service robots in terms of interaction quality and physical service environment. However, no significant difference in outcome quality is noted. © 2019 Taylor & Francis Group, LLC.","artificial intelligence; human–robot interaction; service quality; Service robots",,,,,,"Hong Kong Polytechnic University, PolyU","This work was supported by the internal grant?[YBX4]?of the The Hong?Kong Polytechnic University.",,,,,,,,,,"Ariffin, A.A.M., Maghzi, A., A preliminary study on customer expectations of hotel hospitality: Influences of personal and hotel factors (2012) International Journal of Hospitality Management, 31 (1), pp. 191-198; Assaker, G., Hallak, R., Moderating effects of tourists’ novelty-seeking tendencies on destination image, visitor satisfaction, and short-and long-term revisit intentions (2013) Journal of Travel Research, 52, pp. 600-613; Bailenson, J.N., Yee, N., Digital chameleons: Automatic assimilation of nonverbal gestures in immersive virtual environments (2005) Psychological Science, 16, pp. 814-819; Bartneck, C., Forlizzi, J., A design-centred framework for social human-robot interaction (2004) RO-MAN 2004. 13th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No. 04TH8759), pp. 591-594. , September, IEEE, Okayama, Japan, &; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81; Bickmore, T.W., Pfeifer, L.M., Jack, B.W., Taking the time to care: Empowering low health literacy hospital patients with virtual nurse agents (2009) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1265-1274. , Boston: ACM, MA, &; Brady, M.K., Cronin, J.J., Some new thoughts on conceptualizing perceived service quality: A hierarchical approach (2001) Journal of Marketing, 65, pp. 34-49; Breen, R.L., A practical guide to focus-group research (2006) Journal of Geography in Higher Education, 30 (3), pp. 463-475; Chan, A.P.H., Tung, V.W.S., Examining the effects of robotic service on brand experience: The moderating role of hotel segment (2019) Journal of Travel & Tourism Marketing, 36 (4), pp. 458-468; Choi, T.Y., Chu, R., Determinants of hotel guests’ satisfaction and repeat patronage in the Hong Kong hotel industry (2001) International Journal of Hospitality Management, 20, pp. 277-297; Cronin, J.J., Taylor, S.A., Measuring service quality: A reexamination and extension (1992) Journal of Marketing, 56, pp. 55-68; Epley, N., Waytz, A., Cacioppo, J.T., On seeing human: A three-factor theory of anthropomorphism (2007) Psychological Review, 114 (4), pp. 864-886; Gefen, D., Karahanna, E., Straub, D.W., Trust and TAM in online shopping: An integrated model (2003) MIS Quarterly, 7, pp. 51-90; Robots: The next generation of hoteliers, , https://www.hotel-icon.com/icon-edit/robots-the-next-generation-of-hoteliers, Retrived from; Hutt, C., (1970), Specific and diverse exploration. 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Hoboken, NJ; (2016), https://www.tnooz.com/article/robots-hotels-work-with-humans-not-replace-them/, March; Tsarouchi, P., Makris, S., Chryssolouris, G., Human–Robot interaction review and challenges on task planning and programming (2016) International Journal of Computer Integrated Manufacturing, 29 (8), pp. 916-931; Tung, V.W.S., Au, N., Exploring customer experiences with robotics in hospitality (2018) International Journal of Contemporary Hospitality Management, 30, pp. 2680-2697; Tung, V.W.S., Law, R., The potential for tourism and hospitality experience research in human-robot interactions (2017) International Journal of Contemporary Hospitality Management, 2, pp. 2498-2513; Tussyadiah, I.P., Park, S., Consumer evaluation of hotel service robots (2018) Information and communication technologies in tourism 2018, pp. 308-320. , Stangl B., Pesonen J., (eds), Cham, Switzerland: Springer, &,. (Eds; van Doorn, J., Mende, M., Noble, S.M., Hulland, J., Ostrom, A.L., Grewal, D., Petersen, J.A., Domo Arigato Mr. Roboto emergence of automated social presence in organizational frontlines and customers’ service experiences (2017) Journal of Service Research, 20 (1), pp. 43-58; Wirtz, J., Patterson, P., Kunz, W., Gruber, T., Lu, V.N., Paluch, S., Martins, A., Brave new World: Service robots in the frontline (2018) Journal of Service Management, 29 (5), pp. 907-931; Xu, D., Wu, X., Chen, Y.L., Xu, Y., Online dynamic gesture recognition for human robot interaction (2015) Journal of Intelligent & Robotic Systems, 77 (3-4), pp. 583-596; Yu, C.E., Ngan, H.F.B., The power of head tilts: Gender and cultural differences of perceived human vs human-like robot smile in service (2019) Tourism Review, 74 (3), pp. 428-442; Ziemke, T., Thill, S., Robots are not embodied! Conceptions of embodiment and their implications for social human–Robot interaction (2014) Frontiers in Artificial Intelligence and Applications, 273, pp. 49-53","Choi, Y.; School of Hotel and Tourism Management, 17 Science Museum Road 818, TST East, Hong Kong; email: young.choi@polyu.edu.hk",,,"Routledge",,,,,19368623,,,,"English","J. Hosp. Mark. Manage.",Article,"Final","",Scopus,2-s2.0-85077157890
"Wengefeld T., Hochemer D., Lewandowski B., Kohler M., Beer M., Gross H.-M.","56731144600;57219856177;57201701924;57219849705;57219848498;7202668837;","A Laser Projection System for Robot Intention Communication and Human Robot Interaction",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223517","259","265",,2,"10.1109/RO-MAN47096.2020.9223517","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095787314&doi=10.1109%2fRO-MAN47096.2020.9223517&partnerID=40&md5=f6571c3c09492916cb9744b38f2424b9","Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Ilmenau, 98694, Germany","Wengefeld, T., Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Ilmenau, 98694, Germany; Hochemer, D., Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Ilmenau, 98694, Germany; Lewandowski, B., Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Ilmenau, 98694, Germany; Kohler, M., Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Ilmenau, 98694, Germany; Beer, M., Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Ilmenau, 98694, Germany; Gross, H.-M., Neuroinformatics and Cognitive Robotics Lab, Ilmenau University of Technology, Ilmenau, 98694, Germany","In order to deploy service robots in environments where they encounter and/or cooperate with persons, one important key factor is human acceptance. Hence, information on which upcoming actions of the robot are based has to be made transparent and understandable to the human. However, considering the restricted power resources of mobile robot platforms, systems for visualization not only have to be expressive but also energy efficient. In this paper, we applied the well-known technique of laser scanning on a mobile robot to create a novel system for intention visualization and human-robot-interaction. We conducted user tests to compare our system to a low-power consuming LED video projector solution in order to evaluate the suitability for mobile platforms and to get human impressions of both systems. We can show that the presented system is preferred by most users in a dynamic test setup on a mobile platform. © 2020 IEEE.",,"Agricultural robots; Drilling platforms; Energy efficiency; Man machine systems; Mobile robots; Visualization; Energy efficient; Human impressions; Laser projection system; Laser scanning; Mobile platform; Mobile robot platforms; Power resources; Service robots; Social robots",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 03ZZ0437D, 03ZZ0460, 16SV7829K","This work has received funding from the German Federal Ministry of Education and Research (BMBF) to the project 3D-PersA2 (grant agreement no. 03ZZ0460), the project ROTATOR (grant agreement no. 03ZZ0437D) as part of the research alliance 3DSensation and the project FRAME (grant agreement no. 16SV7829K).",,,,,,,,,,"Lewandowski, B., Socially compliant human-robot interaction for autonomous scanning tasks in supermarket environments (2020) Proc. of RO-MAN; Gross, H.-M., TOOMAS: Interactie shopping guide robots in everyday use. final implementation and experiences from long-term field trials (2009) Proc. of IROS, pp. 2005-2012; Trinh, T.Q., Autonomous mobile gait training robot for orthopedic rehabilitation in a clinical environment (2020) Proc. of RO-MAN; Gross, H.-M., Mobile robot companion for walking training of stroke patients in clinical post-stroke rehabilitation (2017) Proc. of ICRA, pp. 1028-1035; Gross, H.M., Living with a mobile companion robot in your own apartment-final implementation and results of a 20-weeks field study with 20 seniors (2019) Proc. of ICRA, pp. 2253-2259; Gross, H.-M., Robot companion for domestic health assistance: Implementation, test and case study under everyday conditions in private apartments (2015) Proc. of IROS, pp. 5992-5999; Foukarakis, M., Applying a multimodal user interface development framework on a domestic service robot (2017) Proc. of the 10th Int. Conf. on PErvasive Technologies Related to Assistive Environments, pp. 378-384; Stricker, R., R2D2 reloaded: Dynamic video projection on a mobile service robot (2015) Proc. of ECMR, pp. 1-6; Chadalavada, R.T., That's on my mind! robot to human intention communication through on-board projection on shared oor space (2015) Proc. of ECMR, pp. 1-6; Panek, P., User acceptance of a mobile led projector on a socially assistive robot (2012) Ambient Assisted Living, pp. 77-91. , Springer; Sasai, T., Development of a guide robot interacting with the user using information projection. basic system (2011) IEEE Int. Conf. on Mechatronics and Automation, pp. 1297-1302; Matsumaru, T., Mobile robot with preliminary-announcement and display function of forthcoming motion using projection equipment (2006) Proc. of RO-MAN, pp. 443-450; Yamashita, S., Evaluation of robots that signals a pedestrian using face orientation based on moving trajectory analysis (2019) Proc. of RO-MAN, pp. 1-8; Neggers, M., Investigating the understandability and ef-ciency of directional cues in robot navigation (2019) Proc. of RO-MAN; Van, G.D.T., Designing an expressive head for a help requesting socially assistive robot (2020) Human-Friendly Robotics (HFR) 2019, pp. 88-102. , Springer; Hietanen, A., Proof of concept of a projection-based safety system for human-robot collaborative engine assembly (2019) Proc. of RO-MAN; Bolano, G., Transparent robot behavior using augmented reality in close human-robot interaction (2019) Proc. of RO-MAN, pp. 1-7; Sharpe, L.T., A luminous ef-ciency function, v (-) for daylight adaptation (2005) Journal of Vision, 5 (11), p. 3; Hall, E.T., A system for the notation of proxemic behavior (1963) American Anthropologist, 65 (5), pp. 1003-1026; Wengefeld, T., A multi modal people tracker for real time human robot interaction (2019) Proc. of RO-MAN, p. 8; Wengefeld, T., Real-time person orientation estimation using colored pointclouds (2019) Proc. of ECMR. IEEE, p. 7; Lewandowski, B., Deep orientation: Fast and robust upper body orientation estimation for mobile robotic applications (2019) Proc. of IROS, pp. 441-448; Philippsen, R., An interpolated dynamic navigation function (2005) Proc. of ICRA, pp. 3782-3789; Dijkstra, E.W., A note on two problems in connexion with graphs (1959) Numerische Mathematik, 1 (1), pp. 269-271; Mueller, S., Local real-time motion planning using evolutionary optimization (2017) Towards Autonomous Robotic Systems (TAROS) Ser. LNCS, pp. 211-221. , 10454. Springer; Fox, D., The dynamic window approach to collision avoidance (1997) IEEE Robotics Automation Magazine, 4 (1), pp. 23-33. , March; Liebner, J., Now i need help! passing doors and using elevators as an assistance requiring robot (2019) Int. Conf. on Social Robotics (ICSR), pp. 527-537. , Springer",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095787314
"Speranza S., Recchiuto C.T., Bruno B., Sgorbissa A.","57195588438;36452602300;36922823500;6602687901;","A Model for the Representation of the Extraversion-Introversion Personality Traits in the Communication Style of a Social Robot",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223537","75","81",,1,"10.1109/RO-MAN47096.2020.9223537","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095756032&doi=10.1109%2fRO-MAN47096.2020.9223537&partnerID=40&md5=19544c8d07763be875b14ee9fb2a3b15","University of Genoa, Dibris, Genoa, 16145, Italy; Chili Lab and BioRob Lab, Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland","Speranza, S., University of Genoa, Dibris, Genoa, 16145, Italy; Recchiuto, C.T., University of Genoa, Dibris, Genoa, 16145, Italy; Bruno, B., Chili Lab and BioRob Lab, Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland; Sgorbissa, A., University of Genoa, Dibris, Genoa, 16145, Italy","Personality is one of the most important factors in human interactions, which retains its importance in human- robot interactions with social robots. This work focusses on the varied linguistic strategies which characterize the personality traits of extraversion and introversion, analysing the main features that differentiate both personalities and eventually proposing a verbal communication model for the extraverted and introverted personality of a conversational social robot. The model classifies and converts phrases, with the result of building different communication styles. A pilot study, involving human subjects and the humanoid robot Pepper, programmed to mimic both extraverted and introverted personality types, has been conducted, with the twofold aim of assessing if differences between the two personalities of the robot can be perceived, and analyzing the effects of different personality traits on verbal interaction with human subjects. Preliminary results seem to confirm the law of attraction in human-human interaction for the extraverted personality. © 2020 IEEE.",,"Agricultural robots; Anthropomorphic robots; Communication styles; Human interactions; Human-human interactions; Humanoid robot; Personality traits; Personality types; Verbal communications; Verbal interaction; Social robots",,,,,"737858","This work has been supported by the European Commission Horizon2020 Research and Innovation Programme under Grant Agreement No. 737858.",,,,,,,,,,"Rahbar, F., Anzalone, S., Varni, G., Zibetti, E., Ivaldi, S., Chetouani, M., Predicting extraversion from non-verbal features during a face-to-face human-robot interaction (2015) Int. Conf. on Social Robotics, pp. 543-553. , Springer; Andrist, S., Spannan, E., Mutlu, B., Rhetorical robots: Making robots more effective speakers using linguistic cues of expertise (2013) Proceedings of the 8th ACM/IEEE Int. Conf. on Human-robot Interaction, pp. 341-348. , IEEE Press; Meerbeek, B., Hoonhout, J., Bingley, P., Terken, J.M.B., The influence of robot personality on perceived and preferred level of user control (2008) Interaction Studies, 9 (2), pp. 204-229; Park, E., Jin, D., Pobil Del, A.P., The law of attraction in human-robot interaction (2012) Int. Journal of Advanced Robotic Systems, 9 (2), p. 35; Goldberg, L.R., An alternative description of personality : The big-five factor structure (1990) Journal of Personality and Social Psychology, 59 (6), p. 1216; Eysenck, H.J., (1950) Dimensions of Personality, 5. , Transaction Publishers; Thorne, A., The press of personality: A study of conversations between introverts and extraverts (1987) Journal of Personality and Social Psychology, 53 (4), p. 718; Neuman, G.A., Wagner, S.H., Christiansen, N.D., The relationship between work-team personality composition and the job performance of teams (1999) Group & Organization Management, 24 (1), pp. 28-45; Hendrick, C., Brown, S.R., (1971) Introversion, Extraversion, and Interpersonal Attraction; Roselyn Lee, J., Nass, C.I., Trust in computers: The computers-are-social-actors (casa) paradigm and trustworthiness perception in human-computer communication (2010) Trust and Technology in A Ubiquitous Modern Environment: Theoretical and Methodological Perspectives, pp. 1-15. , IGI Global; Celiktutan, O., Gunes, H., Computational analysis of humanrobot interactions through first-person vision: Personality and interaction experience (2015) 2015 24th IEEE Int. Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 815-820. , IEEE; Lohse, M., Hanheide, M., Wrede, B., Walters, M.L., Koay, K.L., Syrdal, D.S., Severinson-Eklundh, K., Evaluating extrovert and introvert behaviour of a domestic robot: A video study (2008) Proceedings of the 17th IEEE Int. Symposium on Robot and Human Interactive Communication RO-MAN '08, pp. 488-493. , Münich, Germany; Ludewig, Y., Döring, N., Exner, N., Design and evaluation of the personality trait extraversion of a shopping robot (2012) 2012 IEEE RO-MAN: The 21st IEEE Int. Symposium on Robot and Human Interactive Communication, pp. 372-379. , IEEE; Kim, H., Kwak, S.S., Kim, M., Personality design of sociable robots by control of gesture design factors (2008) RO-MAN 2008-The 17th IEEE Int. Symposium on Robot and Human Interactive Communication, pp. 494-499. , IEEE; Heerink, M., Kröse, B., Evers, V., Wielinga, B., (2008) The Influence of Social Presence on Acceptance of A Companion Robot by Older People; De Ruyter, B., Saini, P., Markopoulos, P., Van Breemen, A., Assessing the effects of building social intelligence in a robotic interface for the home (2005) Interacting with Computers, 17 (5), pp. 522-541; Bruno, B., Tommaso Recchiuto, C., Papadopoulos, I., Saffiotti, A., Koulouglioti, C., Menicatti, R., Mastrogiovanni, F., Sgorbissa, A., Knowledge representation for culturally competent personal robots: Requirements, design principles, implementation, and assessment (2019) Int. Journal of Social Robotics, pp. 1-24; Sgorbissa, A., Papadopoulos, I., Bruno, B., Koulouglioti, C., Recchiuto, C., Encoding guidelines for a culturally competent robot for elderly care (2018) 2018 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 1988-1995. , IEEE; Abdul Khaliq, A., Köckemann, U., Pecora, F., Saffiotti, A., Bruno, B., Tommaso Recchiuto, C., Sgorbissa, A., Young Chong, N., Culturally aware planning and execution of robot actions (2018) 2018 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp. 326-332. , IEEE; Recchiuto, C., Gava, L., Grassi, L., Grillo, A., Lagomarsino, M., Lanza, D., Liu, Z., Sgorbissa, A., Cloud services for culture aware conversation: Socially assistive robots and virtual assistants (2020) 2020 IEEE 17th Int. Conf. on Ubiquitos Robots. IEEE, , In-press; Tommaso Recchiuto, C., Antonio, S., A feasibility study of culture-aware cloud services for conversational robots (2020) Robotics and Automation Letters. IEEE, , In press; Gill, A.J., Oberlander, J., Taking care of the linguistic features of extraversion (2002) Proceedings of the Annual Meeting of the Cognitive Science Society, 24; Lynn, R., Martin, T., National differences for thirtyseven nations in extraversion, neuroticism, psychoticism and economic, demographic and other correlates (1995) Personality and Individual Differences, 19 (3), pp. 403-406; Brooke, J., Sus-a quick and dirty usability scale (1996) Usability Evaluation in Industry, 189 (194), pp. 4-7; Polkosky, M.D., (2005) Toward A Social-cognitive Psychology of Speech Technology: Affective Responses to Speech-based E-service; Lewis, J.R., Hardzinski, M.L., Investigating the psychometric properties of the speech user interface service quality questionnaire (2015) Int. Journal of Speech Technology, 18 (3), pp. 479-487; McKnight, P.E., Najab, J., Mann-whitney u test (2010) The Corsini Encyclopedia of Psychology, p. 1; Eve-Cahoon, H., Understanding the introvert preference (2003) Journal of Nursing Education, 42 (5), pp. 191-193","Recchiuto, C.T.; University of Genoa, Italy; email: carmine.recchiuto@dibris.unige.it",,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095756032
"Christodoulou P., May Reid A.A., Pnevmatikos D., Del Rio C.R., Fachantidis N.","57207456867;57219850965;16319919000;55614100100;6603461712;","Students participate and evaluate the design and development of a social robot",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223490","739","744",,2,"10.1109/RO-MAN47096.2020.9223490","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095752250&doi=10.1109%2fRO-MAN47096.2020.9223490&partnerID=40&md5=8f45b4f28a83ede769dbba1872bfa8e7","University of Western Macedonia, Primary Education Department, Florina, 53100, Greece; University of Cádiz, Engineering Faculty, Cádiz, 11003, Spain; University of Macedonia, Educational Social Policy Department, Thessaloniki, 54636, Greece","Christodoulou, P., University of Western Macedonia, Primary Education Department, Florina, 53100, Greece; May Reid, A.A., University of Cádiz, Engineering Faculty, Cádiz, 11003, Spain; Pnevmatikos, D., University of Western Macedonia, Primary Education Department, Florina, 53100, Greece; Del Rio, C.R., University of Cádiz, Engineering Faculty, Cádiz, 11003, Spain; Fachantidis, N., University of Macedonia, Educational Social Policy Department, Thessaloniki, 54636, Greece","Scholars have highlighted the importance of the humanoid appearance and the integration of various social cues for the design of Socially Assistive Robots for Education (SAR). However, designing a SAR for education omitting the stakeholders that will exploit it might prove a risky task. The aim of the current study, on the one hand, was to present the design of a SAR for Science Technology Engineering and Mathematics (STEM) education developed through stakeholders' involvement in various steps of the approach. On the other hand, the study aimed to present the evaluation of the prototype robot through a STEM-oriented robot-assisted collaborative online teaching-learning sequence. Preliminary results indicate that participants endorsed the appearance and non-verbal behavior of the robot above chance level, while gender and age-related differences were revealed regarding the most appealing feature of the robot. Implications for Human-Robot Interaction are discussed. © 2020 IEEE.",,"Agricultural robots; Economic and social effects; Educational robots; Engineering education; Machine design; STEM (science, technology, engineering and mathematics); Age-related differences; Design and Development; Engineering and mathematics; Nonverbal behavior; Online teaching; Prototype robot; Science technologies; Socially assistive robots; Social robots",,,,,"Horizon 2020: 709515","*Research supported by Horizon 2020 [No 709515]. P. Christodoulou & D. Pnevmatikos are with the Primary Education Department, University of Western Macedonia, Florina, 53100, Greece (email: pchristodoulou & dpnevmat @uowm.gr). A. A. May Reid & C. Rioja del Rio are with the Engineering Faculty, University of Cádiz, Cádiz, 11003, Spain (e-mail: alecia.reid & carlos.rioja @gm.uca.es).",,,,,,,,,,"Mubin, O., Stevens, C.J., Shahid, S., Al Mahmud, A., Dong, J.J., A review of the applicability of robots in education (2013) Journal of Technology in Education and Learning, 1, pp. 1-7; Reich-Stiebert, N., Eyssel, F., Hohnemann, C., Involve the user! changing attitudes toward robots by user participation in a robot prototyping process (2019) Comput Human Behav, 91, pp. 290-296. , Feb; Feil-Seifer, D., Mataric, M.J., Defining socially assistive robotics (2005) 9th International Conference on Rehabilitation Robotics, pp. 465-468. , Chicago; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Sci. Robot, 3, pp. 1-9. , Aug; Ham, J., Cuijpers, R.H., Cabibihan, J.J., Combining robotic persuasive strategies: The persuasive power of a storytelling robot that uses gazing and gestures (2015) Int J Soc Robot, 7, pp. 479-487; Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: A review (2017) J. Hum. Robot Interact., 6, pp. 25-63. , May; Meltzoff, A.N., Brooks, R., Shon, A.P., Rao, R.P., Social robots are psychological agents for infants: A test of gaze following (2010) Neural Netw, 23, pp. 966-972. , Oct; Kleinke, C.L., Gaze and eye contact: A research review (1986) Psychol. Bull., 100, pp. 78-100. , https://doi.org/10.1037/0033-2909.100.1.78; Kompatsiari, K., Ciardo, F., Tikhanoff, V., Metta, G., Wykowska, A., It's in the eyes: The engaging role of eye contact in hri (2019) Int J Soc Robot, , https://doi.org/10.1007/s12369-019-00565-4; Mutlu, B., Forlizzi, J., Hodgins, J., A storytelling robot: Modeling and evaluation of human-like gaze behavior (2006) 6th IEEE-RAS International Conference on Humanoid Robots, pp. 518-523. , Italy; Rosenthal-Von Der Pütten, A.M., Krämer, N.C., Herrmann, J., The effects of humanlike and robot-specifc afective nonverbal behavior on perception, emotion, and behavior (2018) Int J Soc Robot, 10 (5), pp. 569-582. , Jan; Ghazali, A.S., Ham, J., Barakova, E.I., Markopoulos, P., Effects of robot facial characteristics and gender in persuasive human-robot interaction (2018) Front. Robot. AI., 5. , Jun; Salem, M., Eyssel, F., Rohlfing, K., Kopp, S., Joublin, F., To err is human (-like): Effects of robot gesture on perceived anthropomorphism and likability (2013) Int J Soc Robot, 5 (3), pp. 313-323. , May; Narahara, H., Maeno, T., Factors of gestures of robots for smooth communication with humans (2007) RoboComm '07: Proceedings of the 1st International Conference on Robot Communication and Coordination, Greece, pp. 1-4; Mumm, J., Mutlu, B., Human-robot proxemics: Physical and psychological distancing in human-robot interaction (2011) Proceedings of the 6th International Conference on Human-robot Interaction, USA, pp. 331-338; Könings, K.D., Seidel, T., Van Merriënboer, J.J., Participatory design of learning environments: Integrating perspectives of students, teachers, and designers (2014) Instr Sci, 42, pp. 1-9. , Dec; Bovill, C., An investigation of co-created curricula within higher education in the UK, Ireland and the USA (2014) Innovations in Education and Teaching International, 51, pp. 15-25. , Feb; Hurst, A., Tobias, J., Empowering individuals with do-it-yourself assistive technology (2011) Proc. 13th Intern. ACM SIGACCESS Conf. Computers and Accessibility, Scotland, pp. 11-18. , K. F. McCoy, Y. Yesilada, Eds; Blancas, M., Vouloutsi, V., Fernando, S., Sánchez-Fibla, M., Zucca, R., Prescott, T.J., Mura, A., Verschure, P.F., Analyzing children's expectations from robotic companions in educational settings (2017) IEEE-RAS 17th International Conference on Humanoid Robotics, UK, pp. 749-755; Obaid, M., Baykal, G.E., Yantaç, A.E., Barendregt, W., Developing a prototyping method for involving children in the design of classroom robots (2018) Int J Soc Robot, 10, pp. 279-291. , Apr; Reich-Stiebert, N., Eyssel, F., Hohnemann, C., Exploring university students' preferences for educational robot design by means of a user-centered design approach (2019) Int J Soc Robot, , May; Pnevmatikos, D., Christodoulou, P., Fachantidis, N., Stakeholders' involvement in participatory design approaches of learning environments: A systematic review (2020) EDULEARN20 Proceedings, Virtual, , to be assigned; Clabaugh, C., Ragusa, G., Sha, F., Matari, M., Designing a socially assistive robot for personalized number concepts learning in preschool children (2015) 2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pp. 314-319. , USA; Short, E., Swift-Spong, K., Greczek, J., Ramachandran, A., Litoiu, A., Grigore, E.C., Levonisova, S., How to train your dragonbot: Socially assistive robots for teaching children about nutrition through play The 23rd IEEE International Symposium on Robot and Human Interactive Communication, 204, pp. 924-929. , Scotland; Davison, D.P., Wijnen, F.M., Van, M.D.J., Reidsma, D., Evers, V., Designing a social robot to support children's inquiry learning: A contextual analysis of children working together at school (2019) Int J Soc Robot, pp. 1-25. , May; Assaad, M., Makio, J., Makela, T., Kankaanranta, M., Fachantidis, N., Dagdilelis, V., Piashkun, S.V., Attracting european youths to stem education and careers: A pedagogical approach to a hybrid learning environment (2017) Int. J. Educ. Pedag. Sci., 4, p. 1730; Mäkelä, T., Pnevmatikos, D., Immonen, H., Fachantidis, N., Kankaanranta, M., Christodoulou, P., Considering various stakeholders' views in the design of a hybrid stem learning environment : Perceptions from Finland and Greece (2017) Proc. 9th International Conference on Education and New Learning Technologies, Barcelona, pp. 5517-5526. , L. G. Chova, A. L. Martínez, & I. C. Torres, Eds; Assaad, M., Pnevmatikos, D., Christodoulou, P., Makeyeu, H., Fenyvesi, K., Mäki-Kuutti, M., User wishes on the integration of social media and ict in a hybrid learning environment: Comparing focus group results from emerging and developed european economies (2018) International Conference on ICT Management for Global Competitiveness and Economic Growth in Emerging Economies Wroc?Aw, pp. 110-114; Pnevmatikos, D., Christodoulou, P., Fachantidis, N., Designing A Socially Assistive Robot for Education Through A Participatory Design Approach: Guiding Principles for the Developers, , under review; Fachantidis, N., Dimitriou, A.G., Pliasa, S., Dagdilelis, V., Pnevmatikos, D., Perlantidis, P., Papadimitriou, A., Android os mobile technologies meets robotics for expandable, exchangeable, reconfigurable, educational, stem-enhancing, socializing robot (2017) Interactive Mobile Communication, Technologies and Learning, 725, pp. 487-497. , Nov; Psillos, D., Kariotoglou, P., Theoretical issues related to designing and developing teaching-learning sequences (2016) Iterative Design of Teaching-learning Sequences, pp. 11-34. , D. Psillos, P. Kariotoglou, Eds., , The Netherlands: Springer; Vosniadou, S., Ioannides, C., Dimitrakopoulou, A., Papademetriou, E., Designing learning environments to promote conceptual change in science (2001) Learn. Instr., 11, pp. 381-419. , Aug; Appel, M., Izydorczyk, D., Weber, S., Mara, M., Lischetzke, T., The uncanny of mind in a machine: Humanoid robots as tools, agents, and experiencers (2020) Comput. Hum. Behav., 102, pp. 274-286. , Jan; Gray, K., Wegner, D.M., Feeling robots and human zombies: Mind perception and the uncanny valley (2012) Cognition, 125, pp. 125-130. , Oct; Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: A review (2017) J. Hum. Robot Interact., 6, pp. 25-63. , May; Frances, S.J., Sex differences in nonverbal behavior (1979) Sex Roles, 5, pp. 519-535. , https://doi.org/10.1007/BF00287326, Aug; Tung, F.W., Influence of gender and age on the attitudes of children towards humanoid robots (2011) International Conference on Human-Computer Interaction, pp. 637-646. , Berlin; Reeves, B., Nass, C., (1996) The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places, , Cambridge: University Press; Tajfel, H., Turner, J.C., An integrative theory of inter-group conflict (1979) The Social Psychology of Intergroup Relations, Monterey, pp. 33-47. , W. G. Austin, S. Worchel, Ed; Eyssel, F., Kuchenbrandt, D., Social categorization of social robots: Anthropomorphism as a function of robot group membership (2012) Br. J. Soc. Psychol., 51, pp. 724-731. , Nov; Williams, J.M., Currie, C., Self-esteem and physical development in early adolescence: Pubertal timing and body image (2000) J. Early Adolesc., 20, pp. 129-149",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095752250
"Scheunemann M.M., Cuijpers R.H., Salge C.","56549821200;6602683032;31767711200;","Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223478","1340","1347",,2,"10.1109/RO-MAN47096.2020.9223478","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095742220&doi=10.1109%2fRO-MAN47096.2020.9223478&partnerID=40&md5=b2675f85f54c58338810c0c5bec140e0","University of Hertfordshire, School of Computer Science, Hatfield, AL10 0UT, United Kingdom; Eindhoven University of Technology, Eindhoven, 5600 MB, Netherlands","Scheunemann, M.M., University of Hertfordshire, School of Computer Science, Hatfield, AL10 0UT, United Kingdom; Cuijpers, R.H., Eindhoven University of Technology, Eindhoven, 5600 MB, Netherlands; Salge, C., University of Hertfordshire, School of Computer Science, Hatfield, AL10 0UT, United Kingdom","A solid methodology to understand human perception and preferences in human-robot interaction (HRI) is crucial in designing real-world HRI. Social cognition posits that the dimensions Warmth and Competence are central and universal dimensions characterizing other humans [1]. The Robotic Social Attribute Scale (RoSAS) proposes items for those dimensions suitable for HRI and validated them in a visual observation study. In this paper we complement the validation by showing the usability of these dimensions in a behavior based, physical HRI study with a fully autonomous robot. We compare the findings with the popular Godspeed dimensions Animacy, Anthropomorphism, Likeability, Perceived Intelligence and Perceived Safety. We found that Warmth and Competence, among all RoSAS and Godspeed dimensions, are the most important predictors for human preferences between different robot behaviors. This predictive power holds even when there is no clear consensus preference or significant factor difference between conditions. © 2020 IEEE.",,"Agricultural robots; Behavioral research; Man machine systems; Human perception; Human robot Interaction (HRI); Physical human-robot interactions; Predictive power; Robot behavior; Social attributes; Social cognition; Visual observations; Social robots",,,,,,,,,,,,,,,,"Fiske, S.T., Cuddy, A.J.C., Glick, P., Universal dimensions of social cognition: Warmth and competence (2007) Trends in Cognitive Sciences, 11, pp. 77-83; Steinfeld, A., Fong, T., Kaber, D., Lewis, M., Scholtz, J., Schultz, A., Goodrich, M., Common metrics for human-robot interaction (2006) Proceeding of the 1st ACM SIGCHI/SIGART Conference on Humanrobot interaction-HRI'06, pp. 33-40. , ACM. ACM Press; Bartneck, C., Kulíc, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81; Weiss, A., Bartneck, C., Meta analysis of the usage of the godspeed questionnaire series (2015) 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 381-388; Ho, C.-C., MacDorman, K.F., Revisiting the uncanny valley theory: Developing and validating an alternative to the godspeed indices (2010) Computers in Human Behavior, 26 (6), pp. 1508-1518; Carpinella, C.M., Wyman, A.B., Perez, M.A., Stroessner, S.J., The robotic social attributes scale (rosas): Development and validation (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, Ser. HRI '17, pp. 254-262. , New York, NY, USA: ACM; Judd, C.M., James-Hawkins, L., Yzerbyt, V., Kashima, Y., Fundamental dimensions of social judgment: Understanding the relations between judgments of competence and warmth (2005) Journal of Personality and Social Psychology, 89 (6), pp. 899-913; Cuddy, A.J.C., Fiske, S.T., Glick, P., The bias map: Behaviors from intergroup affect and stereotypes (2007) Journal of Personality and Social Psychology, 92, pp. 631-648. , 4; Stroessner, S.J., On the social perception of robots: Measurement, moderation, and implications (2020) Living with Robots, pp. 21-47. , R. Pak, E. J. de Visser, and E. Rovira, Eds. Academic Press; Goldman, A., De Vignemont, F., Is social cognition embodied? (2009) Trends in Cognitive Sciences, 13 (4), pp. 154-159; Dautenhahn, K., I could be you: The phenomenological dimension of social understanding (1997) Cybernetics and Systems, 28 (5), pp. 417-453; Beer, J.M., Fisk, A.D., Rogers, W.A., Toward a framework for levels of robot autonomy in human-robot interaction (2014) Journal of Human-Robot Interaction, 3 (2), pp. 74-99; Scheunmann, M.M., (2020) Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction, , https://mms.ai/ro-man2020, supplementary material for this paper; Scheunemann, M.M., Salge, C., Dautenhahn, K., Intrinsically motivated autonomy in human-robot interaction: Human perception of predictive information in robots (2019) Towards Autonomous Robotic Systems, pp. 325-337. , Cham: Springer International Publishing; (2020) Sphero Support & Help, , https://support.sphero.com/category/kxwbdyqeyq-bb-8, Sphero, Inc; Scheunemann, M.M., Dautenhahn, K., Salem, M., Robins, B., Utilizing bluetooth low energy to recognize proximity, touch and humans (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 362-367; Oudeyer, P.-Y., Kaplan, F., What is intrinsic motivation? A typology of computational approaches (2009) Frontiers in Neurorobotics, 1, p. 6; Martius, G., Der, R., Ay, N., Information driven self-organization of complex robotic behaviors (2013) PLOS ONE, 8 (5), pp. 1-14; Der, R., Martius, G., (2012) The Playful Machine: Theoretical Foundation and Practical Realization of Self-Organizing Robots, Ser. Cognitive Systems Monographs, 15. , Springer-Verlag Berlin Heidelberg; Scheunemann, M.M., Salge, C., Polani, D., Dautenhahn, K., (2020) Human Perception of Intrinsically Motivated Autonomy in Human-Robot Interaction, , https://arxiv.org/pdf/2002.05936; Nehmzow, U., (2006) Scientific Methods in Mobile Robotics, 24 (1-2). , Springer-Verlag GmbH",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85095742220
"Fallatah A., Chun B., Balali S., Knight H.","57208162008;57209366609;57203386896;26321722700;","Would you please buy me a Coffee?"": How microculturesimpact people's helpful actions toward robots",2020,"DIS 2020 - Proceedings of the 2020 ACM Designing Interactive Systems Conference",,,,"939","950",,3,"10.1145/3357236.3395446","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090495477&doi=10.1145%2f3357236.3395446&partnerID=40&md5=2d1e75a60288bf663c6845b82dbee0e7","Oregon State University, School of Electrical Engineering and Computer Science, United States; Oregon State University, School of Language, Culture, and Society, United States","Fallatah, A., Oregon State University, School of Electrical Engineering and Computer Science, United States; Chun, B., Oregon State University, School of Language, Culture, and Society, United States; Balali, S., Oregon State University, School of Electrical Engineering and Computer Science, United States; Knight, H., Oregon State University, School of Electrical Engineering and Computer Science, United States","Robots sometimes face hardware and algorithmic challenges that exceed their capabilities, e.g., an armless robot pressing an elevator button. Previous work suggests that rather than augmenting the robot capabilities, sometimes robots can simply ask for help. A central contribution of this paper is the discovery of how people's helping behaviors vary within local microcultures, i.e., shared patterns of behaviors and norms linked to local atmospheric conditions and situations. Our methods combine techniques from both social robotics research and ethnography to investigate how people's helping behaviors toward robots vary across six cafes on a single college campus. We deploy a simple robot to request help ordering items, analyzing the 268 interaction instances to find significant variations in both help and care behaviors toward the robot. Microcultural and situational factors influence this help, motivating the inclusion of cultural criteria into the behavioral predictions of human-robot interaction systems. © 2020 ACM.","Ethnography; Human-robot interaction","Social robots; Atmospheric conditions; College campus; Situational factors; Social robotics; Behavioral research",,,,,,,,,,,,,,,,"Agnihotri, A., Knight, H., Persuasive chairbots: A (mostly) robot-recruited experiment (2019) Proceedings of the 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI '19), , IEEE, Daegu, Korea; Backhaus, N., Rosen, P.H., Scheidig, A., Gross, H., Wischniewski, S., Somebody help me, please?!"" interaction design framework for needy mobile service robots (2018) Proceedings of IEEE Workshop on Advanced Robotics and Its Social Impacts (ARSO '18), pp. 54-61. , IEEE, Genoa, Italy; Bajones, M., Weiss, A., Vincze, M., (2016) Help, Anyone? 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An in-The-wild study template for robots approaching unsuspecting participants (2018) Workshop on the Social Robots in the Wild at the International Conference on Human-Robot Interaction (HRI '18), , ACM/IEEE, Chicago, United State; Sauppé, A., Mutlu, B., The social impact of a robot co-worker in industrial settings (2015) Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 3613-3622. , ACM, Seoul, Korea; Song, H., Joong Kim, M., Jeong, S., Suk, H., Kwon, D., Design of idle motions for service robot via video ethnography (2009) Proceedings of the 18th IEEE International Symposium on Robot and Human Interactive Communication(RO-MAN '09), pp. 195-199. , IEEE, Toyama, Japan; Sorenson, J., Toward a pragmatic and social engineering ethics (2019) Paladyn, Journal of Behavioral Robotics, 10 (1), pp. 207-218. , 2019; Sung, J., Grinter, R.E., Christensen, H.I., Guo, L., Housewives or technophiles?: Understanding domestic robot owners (2008) Proceedings of the 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI '08), pp. 129-136. , IEEE, Amsterdam, The Netherlands; Wallace, J., Ethics and inscription in social robot design. A visual ethnography (2019) Paladyn, Journal of Behavioral Robotics, 10 (1), pp. 66-76. , 2019; Wasson, C., Odell Butler, M., Copeland-Carson, J., (2016) Applying Anthropology in the Global Village, , Routledge, London, United Kingdom, England; Weiss, A., Igelsböck, J., Tscheligi, M., Bauer, A., Kühnlenz, K., Wollherr, D., Buss, M., Robots asking for directions: The willingness of passers-by to support robots (2010) Proceedings of the 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI '10), pp. 23-30. , http://dx.doi.org/10.1109/HRI.2010.5453273, IEEE, Osaka, Japan; Yang, S., Ka-Jun Mok, B., Sirkin, D., Page Ive, H., Maheshwari, R., Fischer, K., Ju, W., Experiences developing socially acceptable interactions for a robotic trash barrel (2015) Proceedings of the 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN '15), pp. 277-284. , IEEE, Kobe, Japan",,,"ACM SIGCHI","Association for Computing Machinery, Inc","2020 ACM Conference on Designing Interactive Systems, DIS 2020","6 July 2020 through 10 July 2020",,161555,,9781450369749,,,"English","DIS Companion - Companion Publ. ACM Des. Interact. Syst. Conf.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85090495477
"Zheng H., Yokoi H., Jiang Y., Duan F.","57219131194;7201964047;21742756100;24537140100;","An Online sEMG Motion Classification Framework for Tele-operating the Robotic Hand",2020,"Chinese Control Conference, CCC","2020-July",,"9188795","6491","6496",,,"10.23919/CCC50068.2020.9188795","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091397815&doi=10.23919%2fCCC50068.2020.9188795&partnerID=40&md5=ed1301f94efc0b9eb446eb7016f3b8f8","Nankai University, College of Artificial Intelligence, Tianjin, 300350, China; University of Electro-Communication, Brain Science Inspired Life Support Research Center, Tokyo, 182-8585, Japan","Zheng, H., Nankai University, College of Artificial Intelligence, Tianjin, 300350, China; Yokoi, H., University of Electro-Communication, Brain Science Inspired Life Support Research Center, Tokyo, 182-8585, Japan; Jiang, Y., University of Electro-Communication, Brain Science Inspired Life Support Research Center, Tokyo, 182-8585, Japan; Duan, F., Nankai University, College of Artificial Intelligence, Tianjin, 300350, China","Seamless communication between human intended motions and robot actions is essential for Human-Robot Interaction (HRI). When tele-operating a robotic hand, it is a natural and effective way via surface electromyography (sEMG) signals. This paper proposes an online sEMG motion classification framework for tele-operating the robotic hand. The whole framework consists of offline training and online recognition phases. In the offline training phase, three features were selected from four candidates and Artificial Neural Network (ANN) won the election among three classifiers. Inthe online recognition phase, two-thresholds data segmentation and majority voting techniques were designed, and three subjects participated in online experiment to verify the feasibility of this framework. The online experimental results show that the average total accuracy is 73.56% and the average vote accuracy is 91.67%. The outcomes of this study have shown the promising potential of sEMG-based HRI. © 2020 Technical Committee on Control Theory, Chinese Association of Automation.","Human-Robot Interaction (HRI); online classification; surface electromyography signal (sEMG)","Electronic voting; Motion analysis; Robotic arms; Robotics; Data segmentation; Human robot Interaction (HRI); Motion classification; On-line experiments; On-line recognition; Seamless communication; Surface electromyography; Voting techniques; Social robots",,,,,"2017YFE0129700; National Natural Science Foundation of China, NSFC: 61673224, 61850410524; National Science Fund for Distinguished Young Scholars: 18JCJQJC46100","*This work was partially supported by the National Key R&D Program of China (No. 2017YFE0129700), the National Natural Science Foundation of China (No. 61673224), the Research Fellowship for International Young Scientists (No. 61850410526 and 61850410524), the Tianjin Natural Science Foundation for Distinguished Young Scholars (No. 18JCJQJC46100)",,,,,,,,,,"Chen, X., Zhao, B., Wang, Y., Control of a 7-dof robotic arm system with an ssvep-based bci[j] (2018) International Journal of Neural Systems, 28 (8), p. 1850018; Onose, G., Grozea, C., Anghelescu, A., On the feasibility of using motor imagery eeg-based brain-computer interface in chronic tetraplegics for assistive robotic arm control: A clinical test and long-term post-trial follow-up[j] (2012) Spinal Cord, 50 (8), pp. 599-608; Frisoli, A., Loconsole, C., Leonardis, D., A new gaze-bci-driven control of an upper limb exoskeleton for rehabilitation in real-world tasks[j] (2012) IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42 (6), pp. 1169-1179; Novak, D., Riener, R., Enhancing patient freedom in rehabilitation robotics using gaze-based intention detection[c] (2013) 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR). 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IEEE, pp. 1-7; Hamaya, M., Matsubara, T., Noda, T., Learning assistive strategies for exoskeleton robots from user-robot physical interaction[j] (2017) Pattern Recognition Letters, 99, pp. 67-76; Siu, H.C., Arenas, A.M., Sun, T., Implementation of a surface electromyography-based upper extremity exoskeleton controller using learning from demonstration[j] (2018) Sensors, 18 (2), p. 467; Atzori, M., Cognolato, M., Muller, H., Deep learning with convolutional neural networks applied to electromyography data: A resource for the classification of movements for prosthetic hands (2016) Frontiers in Neurorobotics, 10; Xu, L., Chen, X., Cao, S., Feasibility study of advanced neural networks applied to semg-based force estimation[j] (2018) Sensors, 18 (10), p. 3226; Wei, W., Wong, Y., Du, Y., A multi-stream convolutional neural network for semg-based gesture recognition in muscle-computer interface[j] (2019) Pattern Recognition Letters, 119, pp. 131-138; Zia Ur Rehman, M., Waris, A., Gilani, S.O., Multiday emg-based classification of hand motions with deep learning techniques[j] (2018) Sensors, 18 (8), p. 2497; Côté-Allard, U., Fall, C.L., Drouin, A., Deep learning for electromyographic hand gesture signal classification using transfer learning[j] (2019) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27 (4), pp. 760-771; Smith, L.H., Determining the optimal window length for pattern recognition-based myoelectric control: Balancing the competing effects of classification error and controller delay (2011) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19 (2), pp. 186-192; Van Erp, M., Vuurpijl, L., Schomaker, L., An overview and comparison of voting methods for pattern recognition (2002) Frontiers in Handwriting Recognition, 2002. Proceedings. Eighth International Workshop On. IEEE, pp. 195-200; Kim, M.K., Lee, J.M., Ko, H., A preliminary analysis of analysis window size and voting size with a time delay for a robust real-time semg pattern recognition[c] (2014) 2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). IEEE, pp. 121-126",,"Fu J.Sun J.","Systems Engineering Society of China (SESC);Technical Committee on Control Theory (TCCT) of Chinese Association of Automation (CAA)","IEEE Computer Society","39th Chinese Control Conference, CCC 2020","27 July 2020 through 29 July 2020",,162927,19341768,9789881563903,,,"English","Chinese Control Conf., CCC",Conference Paper,"Final","",Scopus,2-s2.0-85091397815
"Viet Tuyen N.T., Elibol A., Chong N.Y.","57197734080;24733903500;7007012034;","Learning from Humans to Generate Communicative Gestures for Social Robots",2020,"2020 17th International Conference on Ubiquitous Robots, UR 2020",,,"9144985","284","289",,2,"10.1109/UR49135.2020.9144985","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094321837&doi=10.1109%2fUR49135.2020.9144985&partnerID=40&md5=6ada600a2c541adc4186659dd6d2724e","Japan Advanced Institute of Science and Technology, School of Information Science, Ishikawa, Japan","Viet Tuyen, N.T., Japan Advanced Institute of Science and Technology, School of Information Science, Ishikawa, Japan; Elibol, A., Japan Advanced Institute of Science and Technology, School of Information Science, Ishikawa, Japan; Chong, N.Y., Japan Advanced Institute of Science and Technology, School of Information Science, Ishikawa, Japan","Non-verbal behaviors play an essential role in human-human interaction, allowing people to convey their intention and attitudes, and affecting social outcomes. Of particular importance in the context of human-robot interaction is that the communicative gestures are expected to endow social robots with the capability of emphasizing its speech, describing something, or showing its intention. In this paper, we propose an approach to learn the relation between human behaviors and natural language based on a Conditional Generative Adversarial Network (CGAN). We demonstrated the validity of our model through a public dataset. The experimental results indicated that the generated human-like gestures correctly convey the meaning of input sentences. The generated gestures were transformed into the target robot's motion, being the robot's personalized communicative gestures, which showed significant improvements over the baselines and could be widely accepted and understood by the general public. © 2020 IEEE.",,"Agricultural robots; Behavioral research; Adversarial networks; Communicative gestures; General publics; Human behaviors; Human-human interactions; Natural languages; Nonverbal behavior; Social outcomes; Social robots",,,,,"Air Force Office of Scientific Research, AFOSR: AFOSRAOARD/FA2386-19-1-4015; Horizon 2020 Framework Programme, H2020; Horizon 2020: 737858; Ministry of Internal Affairs and Communications, MIC","This work was supported by the EU-Japan coordinated RandD project on Culture Aware Robots and Environmental Sensor Systems for Elderly Support commissioned by the Ministry of Internal Affairs and Communications of Japan and EC Horizon 2020 Research and Innovation Programme under grant agreement No. 737858.","This work was supported by the EU-Japan coordinated R&D project on “Culture Aware Robots and Environmental Sensor Systems for Elderly Support” commissioned by the Ministry of Internal Affairs and Communications of Japan and EC Horizon 2020 Research and Innovation Programme under grant agreement No. 737858. The authors are also grateful for financial supports from the Air Force Office of Scientific Research (AFOSRAOARD/FA2386-19-1-4015).",,,,,,,,,"Knapp, M.L., Hall, J.A., Horgan, T.G., (2013) Nonverbal Communication in Human Interaction., , Cengage Learning; Breazeal, C.L., (2002) Designing Sociable Robots., , MIT press; Salem, M., Eyssel, F., Rohlfing, K., Kopp, S., Joublin, F., To err is human(-like): Effects of robot gesture on perceived anthropomorphism and likability (2013) International Journal of Social Robotics, 5 (3), pp. 313-323; Bruno, B., Chong, N.Y., Kamide, H., Kanoria, S., Lee, J., Lim, Y., Pandey, A.K., Sgorbissa, A., Paving the way for culturally competent robots: A position paper (2017) Ieee International Symposium on Robot and Human Interactive Communication., pp. 553-560; Cassell, J., Vilhjálmsson, H.H., Bickmore, T., Beat: The behavior expression animation toolkit (2004) Life-Like Characters., pp. 163-185. , Springer; Marsella, S., Xu, Y., Lhommet, M., Feng, A., Scherer, S., Shapiro, A., Virtual character performance from speech (2013) Acm SIGGRAPH/Eurographics Symposium on Computer Animation., pp. 25-35; Shimazu, A., Hieida, C., Nagai, T., Nakamura, T., Takeda, Y., Hara, T., Nakagawa, O., Maeda, T., Generation of gestures during presentation for humanoid robots (2018) Ieee International Symposium on Robot and Human Interactive Communication., pp. 961-968; Plappert, M., Mandery, C., Asfour, T., Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks (2018) Robotics and Autonomous Systems, 109, pp. 13-26; Mirza, M., Osindero, S., (2014) Conditional Generative Adversarial Nets; Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., Generative adversarial nets (2014) Advances in Neural Information Processing Systems, pp. 2672-2680; Wang, K., Gou, C., Duan, Y., Lin, Y., Zheng, X., Wang, F.-Y., Generative adversarial networks: Introduction and outlook (2017) IEEE/CAA Journal of Automatica Sinica, 4 (4), pp. 588-598; Yan, Y., Xu, J., Ni, B., Zhang, W., Yang, X., Skeleton-Aided articulated motion generation (2017) Acm International Conference on Multimedia., pp. 199-207; Ke, Q., Bennamoun, M., An, S., Sohel, F., Boussaid, F., A new representation of skeleton sequences for 3d action recognition (2017) Ieee Conference on Computer Vision and Pattern Recognition, pp. 3288-3297; Kiros, R., Zhu, Y., Salakhutdinov, R.R., Zemel, R., Urtasun, R., Torralba, A., Fidler, S., Skip-Thought vectors (2015) Advances in Neural Information Processing Systems, pp. 3294-3302; Chung, J., Gulcehre, C., Cho, K., Bengio, Y., (2014) Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling; Radford, A., Metz, L., Chintala, S., (2015) Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks; Gauthier, J., Conditional generative adversarial nets for convolutional face generation (2014) Class Project for Stanford CS231N: Convolutional Neural Networks for Visual Recognition, Winter Semester, 2014 (5), p. 2; Vondrick, C., Pirsiavash, H., Torralba, A., Generating videos with scene dynamics (2016) Advances in Neural Information Processing Systems, pp. 613-621; Donahue, C., McAuley, J., Puckette, M., (2018) Adversarial Audio Synthesis; Nair, V., Hinton, G.E., Rectified linear units improve restricted boltzmann machines (2010) International Conference on Machine Learning, pp. 807-814; Tuyen, N.T.V., Jeong, S., Chong, N.Y., Emotional bodily expressions for culturally competent robots through long term humanrobot interaction (2018) IEEE/RSJ International Conference on Intelligent Robots and Systems., pp. 2008-2013; Mandery, C., Terlemez, O., Do, M., Vahrenkamp, N., Asfour, T., The kit whole-body human motion database (2015) International Conference on Advanced Robotics., pp. 329-336; Plappert, M., Mandery, C., Asfour, T., The kit motion-language dataset (2016) Big Data, 4 (4), pp. 236-252; Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., Fidler, S., Aligning books and movies: Towards story-like visual explanations by watching movies and reading books (2015) Ieee International Conference on Computer Vision, pp. 19-27; Hussein, M.E., Torki, M., Gowayyed, M.A., El-Saban, M., Human action recognition using a temporal hierarchy of covariance descriptors on 3d joint locations (2013) International Joint Conference on Artificial Intelligence, 13, pp. 2466-2472; Tuyen, N.T.V., Jeong, S., Chong, N.Y., Learning human behavior for emotional body expression in socially assistive robotics (2017) International Conference on Ubiquitous Robots and Ambient Intelligence., pp. 45-50; Kingma, D.P., Ba, J., (2014) Adam: A Method for Stochastic Optimization",,,,"Institute of Electrical and Electronics Engineers Inc.","17th International Conference on Ubiquitous Robots, UR 2020","22 June 2020 through 26 June 2020",,161946,,9781728157153,,,"English",,Conference Paper,"Final","",Scopus,2-s2.0-85094321837
"Wei D., Zhou H., Yang H.Y.","57219626342;55574208996;7406558218;","Human-robot negotiation of intentions based on virtual fixtures for shared task execution",2020,"2020 17th International Conference on Ubiquitous Robots, UR 2020",,,"9144859","297","302",,1,"10.1109/UR49135.2020.9144859","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094320788&doi=10.1109%2fUR49135.2020.9144859&partnerID=40&md5=924500fd060f78900dd206eb62d809eb","Zhejiang University, State Key Laboratory of Fluid Power and Mechatronic Systems, School of Management, Hangzhou, 310063, China","Wei, D., Zhejiang University, State Key Laboratory of Fluid Power and Mechatronic Systems, School of Management, Hangzhou, 310063, China; Zhou, H., Zhejiang University, State Key Laboratory of Fluid Power and Mechatronic Systems, School of Management, Hangzhou, 310063, China; Yang, H.Y., Zhejiang University, State Key Laboratory of Fluid Power and Mechatronic Systems, School of Management, Hangzhou, 310063, China","Robots are increasingly working side-by-side with human to fuse their complementary capabilities in cooperating with them for tasks in a wide range of applications, such as exoskeleton and industry or health-care. In order to promote natural interaction between humans and robots, the ability of humans to negotiate intentions through haptic channels has inspired a number of studies aimed at improving human-robot interaction performance. In this work, we propose a novel human-robot negotiation policy and introduce adaptive virtual fixture technology into traditional mechanisms to integrate bilateral intentions. In the policy, virtual fixtures are used to generate and adjust virtual paths while negotiation with human partners, speeding up people's perception of robot task, making negotiation more efficient. Moreover, the path will adapt online to the estimated human intention, providing better solutions for both dyads while ensuring performance. The proposed strategy is verified in collaborative obstacle avoidance experiments. © 2020 IEEE.",,"Agricultural robots; Exoskeleton (Robotics); Fixtures (tooling); Industrial robots; Human intentions; Human robots; Natural interactions; Robot tasks; Side by sides; Task executions; Virtual fixture; Virtual paths; Human robot interaction",,,,,"2018YFB2001203, 51821093; National Natural Science Foundation of China, NSFC: 51890885","We set three layers of traditional negotiation standard to be combined with the virtual fixture, modified the reference point position and the size of local virtual ’tube’. With the verification of obstacle avoidance task experiments, the policy outperforms the traditional negotiation approach and realize efficient human robot interaction. Acknowledgements: The research leading to these results has received partial funding from National Key R&D Program of China no.2018YFB2001203, ""Electro-hydraulic system of basic research"" innovation group III no.51821093, and Major Projects of the National Natural Science Foundation of China no.51890885. Reference: [1] Giordano, P. Robuffo, et al. ""Robotic assembly of complex planar parts: An experimental evaluation."" 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2008. [2] De Santis, Agostino, et al. ""An atlas of physical human–robot interaction."" Mechanism and Machine Theory 43.3 (2008): 253-270. [3] Krebs, Hermano et al. ""Rehabilitation robotics: Performance- based progressive robot-assisted therapy."" Autonomous robots 15.1 (2003): 7-20. [4] Dollar, Aaron M., and Hugh Herr. ""Lower extremity exoskeletons and active orthoses: Challenges and state-of-the-art."" IEEE Transactions on robotics 24.1 (2008): 144-158. [5] Jarrasse, Nathanael, Vittorio Sanguineti, and Etienne Burdet. ""Slaves no longer: review on role assignment for human–robot joint motor action."" Adaptive Behavior 22.1 (2014): 70-82. [6] Mörtl, Alexander, et al. ""The role of roles: Physical cooperation between humans and robots."" The International Journal of Robotics Research 31.13 (2012): 1656-1674. [7] C. A. C. Parker and E. A. Croft, “Experimental investigation of humanrobot cooperative carrying,” IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011. [8] R. Groten, D. Feth, R. L. Klatzky, and A. Peer, “The role of haptic feedback for the integration of intentions in shared task execution,” IEEE Transactions on Haptics, Vol 6, No 1, 2013. [9] A. Takagi, F. Usai, G. Ganesh, V. Sanguineti, and E. Burdet, “Haptic communication between humans is tuned by the hard or soft mechanics of interaction,” PLOS Computational Biology, vol. 14, pp. 1–17, 03 2018. [10] L. Roche and L. Saint-Bauzel, “Implementation of haptic communication in comanipulative tasks: A statistical state machine model,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Automation [11] G. J. Maeda, G. Neumann, M. Ewerto, R. Lioutikov, O. Kroemer, and J. Peters, “Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks,” Autonomous Robots,pp. 593–612, 2017 [12] Kucukyilmaz, Ayse, Tevfik Metin Sezgin, and Cagatay Basdogan. ""Intention recognition for dynamic role exchange in haptic collaboration."" IEEE transactions on haptics 6.1 (2012): 58-68. [13] L. B. Rosenberg, “The use of virtual fixtures as perceptual overlays to enhance operator performance in remote environments,” p. 52, 09 1992 [14] Hogan, Neville. ""Impedance control: An approach to manipulation: Part I—Theory."" (1985): 1-7. [15] Wang, Hong ling, Joseph Kearney, and Kendall Atkinson. ""Robust and efficient computation of the closest point on a spline curve."" Proceedings of the 5th International Conference on Curves and Surfaces. 2002.","The research leading to these results has received partial funding from National Key RandD Program of China no.2018YFB2001203, Electro-hydraulic system of basic research innovation group III no.51821093, and Major Projects of the National Natural Science Foundation of China no.51890885.",,,,,,,,,"Giordano, P.R., Robotic assembly of complex planar parts: An experimental evaluation (2008) 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.; De Santis, A., An atlas of physical human-robot interaction (2008) Mechanism and Machine Theory, 43 (3), pp. 253-270; Krebs, H., Rehabilitation robotics: Performancebased progressive robot-Assisted therapy (2003) Autonomous Robots, 15 (1), pp. 7-20; Dollar, A.M., Herr, H., Lower extremity exoskeletons and active orthoses: Challenges and state-of-Theart (2008) Ieee Transactions on Robotics, 24 (1), pp. 144-158; Jarrasse, N., Sanguineti, V., Burdet, E., Slaves no longer: Review on role assignment for human-robot joint motor action (2014) Adaptive Behavior, 22 (1), pp. 70-82; Mörtl, A., The role of roles: Physical cooperation between humans and robots (2012) The International Journal of Robotics Research, 31 (13), pp. 1656-1674; Parker, C.A.C., Croft, E.A., Experimental investigation of humanrobot cooperative carrying (2011) IEEE/RSJ International Conference on Intelligent Robots and Systems; Groten, R., Feth, D., Klatzky, R.L., Peer, A., The role of haptic feedback for the integration of intentions in shared task execution (2013) Ieee Transactions on Haptics, 6 (1); Takagi, A., Usai, F., Ganesh, G., Sanguineti, V., Burdet, E., Haptic communication between humans is tuned by the hard or soft mechanics of interaction (2018) Plos Computational Biology, 14 (3), pp. 1-17; Roche, L., Saint-Bauzel, L., Implementation of haptic communication in comanipulative tasks: A statistical state machine model 2016 IEEE/RSJ International Conference on Intelligent Robots and Automation; Maeda, G.J., Neumann, G., Ewerto, M., Lioutikov, R., Kroemer, O., Peters, J., Probabilistic movement primitives for coordination of multiple human-robot collaborative tasks (2017) Autonomous Robots, pp. 593-612; Kucukyilmaz, A., Sezgin, T.M., Basdogan, C., Intention recognition for dynamic role exchange in haptic collaboration (2012) Ieee Transactions on Haptics, 6 (1), pp. 58-68; Rosenberg, L.B., (1992) The Use of Virtual Fixtures As Perceptual Overlays to Enhance Operator Performance in Remote Environments, 9, p. 52; Hogan, N., (1985) Impedance Control: An Approach to Manipulation: Part I-Theory., pp. 1-7; Wang, H.L., Kearney, J., Atkinson, K., Robust and efficient computation of the closest point on a spline curve (2002) Proceedings of the 5th International Conference on Curves and Surfaces.",,,,"Institute of Electrical and Electronics Engineers Inc.","17th International Conference on Ubiquitous Robots, UR 2020","22 June 2020 through 26 June 2020",,161946,,9781728157153,,,"English",,Conference Paper,"Final","",Scopus,2-s2.0-85094320788
"Martin A.E.H., Dean-Leon E., Cheng G.","57215572222;9736968400;10243873000;","TACTO-Selector: Enhanced Hierarchical Fusion of PBVS with Reactive Skin Control for Physical Human-Robot Interaction",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9196979","9121","9127",,,"10.1109/ICRA40945.2020.9196979","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092691569&doi=10.1109%2fICRA40945.2020.9196979&partnerID=40&md5=67d436067909baf332ea3168301ad180","Technical University of Munich, Institute for Cognitive Systems, Germany","Martin, A.E.H., Technical University of Munich, Institute for Cognitive Systems, Germany; Dean-Leon, E., Technical University of Munich, Institute for Cognitive Systems, Germany; Cheng, G., Technical University of Munich, Institute for Cognitive Systems, Germany","In a physical Human-Robot Interaction for industrial scenarios is paramount to guarantee the safety of the user while keeping the robot's performance. Hierarchical task approaches are not sufficient since they tend to sacrifice the low priority tasks in order to guarantee the consistency of the main task. To handle this problem, we enhance the standard hierarchical fusion by introducing a novel interactive task-reconfiguring approach (TACTO-Selector) that uses the information of the tactile interaction to adapt the dimension of the tasks, therefore guaranteeing the execution of the safety task while performing the other task as good as possible. In this work, we hierarchically combine a 6 DOF Position-Based Visual Servoing (PBVS) task with a reactive skin control. This approach was evaluated on a 6 DOF industrial robot showing an improvement of 36.37% on average in tracking error reduction compared with a standard approach. © 2020 IEEE.",,"Accident prevention; Agricultural robots; Man machine systems; Robotics; Visual servoing; Hierarchical fusions; Industrial scenarios; Low priorities; Main tasks; Physical human-robot interactions; Position based visual servoing; Tactile interaction; Tracking errors; Human robot interaction",,,,,,,,,,,,,,,,"Haddadin, S., Albu-Schaffer, A., De Luca, A., Hirzinger, G., Collision detection and reaction: A contribution to safe physical human-robot interaction (2008) 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3356-3363. , Sep; Haddadin, S., Haddadin, S., Khoury, A., Rokahr, T., Parusel, S., Burgkart, R., Bicchi, A., Albu-Schffer, A., On making robots understand safety: Embedding injury knowledge into control (2012) The International Journal of Robotics Research, 31 (13), pp. 1578-1602; Povse, B., Koritnik, D., Bajd, T., Munih, M., Correlation between impact-energy density and pain intensity during robot-man collision (2010) 2010 3rd IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 179-183. , Sep; Lasota, P.A., Fong, T., Shah, J.A., A survey of methods for safe human-robot interaction (2017) Foundations and TrendsR in Robotics, 5 (4), pp. 261-349; Heinzmann, J., Zelinsky, A., Quantitative safety guarantees for physical human-robot interaction (2003) The International Journal of Robotics Research, 22 (7-8), pp. 479-504; Kock, S., Bredahl, J., Eriksson, P., Myhr, M., Behnisch, K., (2006) Taming the Robot, pp. 11-14. , 01; Rybski, P., Anderson-Sprecher, P., Huber, D., Niessl, C., Simmons, R., Sensor fusion for human safety in industrial workcells (2012) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , October; Flacco, F., Krger, T., De Luca, A., Khatib, O., A depth space approach to human-robot collision avoidance (2012) 2012 IEEE Interna-tional Conference on Robotics and Automation, pp. 338-345. , May; Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots Proceedings. 1985 IEEE International Conference on Robotics and Automation, 2, pp. 500-505. , March 1985; Vogel, C., Walter, C., Elkmann, N., A projection-based sensor system for safe physical human-robot collaboration (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5359-5364. , Nov; Lasota, P.A., Rossano, G.F., Shah, J.A., Toward safe closeproximity human-robot interaction with standard industrial robots (2014) 2014 IEEE International Conference on Automation Science and Engineering (CASE), pp. 339-344. , Aug; Zanchettin, A.M., Ceriani, N.M., Rocco, P., Ding, H., Matthias, B., Safety in human-robot collaborative manufacturing environments: Metrics and control (2016) IEEE Transactions on Automation Science and Engineering, 13 (2), pp. 882-893. , April; Mansfeld, N., Djellab, B., Veuthey, J.R., Beck, F., Ott, C., Haddadin, S., Improving the performance of biomechanically safe velocity control for redundant robots through reflected mass minimization (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5390-5397. , Sep; Buizza Avanzini, G., Ceriani, N.M., Zanchettin, A.M., Rocco, P., Bascetta, L., Safety control of industrial robots based on a distributed distance sensor (2014) IEEE Transactions on Control Systems Technology, 22 (6), pp. 2127-2140. , Nov; Lacevic, B., Rocco, P., Kinetostatic danger field-a novel safety assessment for human-robot interaction (2010) 2010 IEEE/RSJ Interna-tional Conference on Intelligent Robots and Systems, pp. 2169-2174. , Oct; Lacevic, B., Rocco, P., Safety-oriented control of robotic manipulators a kinematic approach (2011) IFAC Proceedings Volumes, 44 (1), pp. 11508-11513. , 18th IFAC World Congress; Wosch, T., Feiten, W., Reactive motion control for human-robot tactile interaction (2002) Proceedings 2002 IEEE International Confer-ence on Robotics and Automation (Cat. 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IEEE, pp. 2849-2854; Dean-Leon, E., Bergner, F., Ramirez-Amaro, K., Cheng, G., From multi-modal tactile signals to a compliant control (2016) 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 892-898. , Nov; Dietrich, A., Ott, C., Albu-Schffer, A., An overview of null space projections for redundant, torque-controlled robots (2015) The International Journal of Robotics Research, 34 (11), pp. 1385-1400; Dean-Leon, E., Pierce, B., Bergner, F., Mittendorfer, P., Ramirez-Amaro, K., Burger, W., Cheng, G., Tomm: Tactile omnidirectional mobile manipulator (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 2441-2447. , May; Mittendorfer, P., Yoshida, E., Cheng, G., Realizing whole-body tactile interactions with a self-organizing, multi-modal artificial skin on a humanoid robot (2015) Advanced Robotics, 29 (1), pp. 51-67; Romero Ramirez, F., Muoz-Salinas, R., Medina-Carnicer, R., Speeded up detection of squared fiducial markers (2018) Image and Vision Computing, 76, p. 06",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","",Scopus,2-s2.0-85092691569
"Yang Z., Ge S., Wan F., Liu Y., Song C.","57218160010;57217115510;57195419628;57196080160;41662155000;","Scalable Tactile Sensing for an Omni-adaptive Soft Robot Finger",2020,"2020 3rd IEEE International Conference on Soft Robotics, RoboSoft 2020",,,"9116026","572","577",,1,"10.1109/RoboSoft48309.2020.9116026","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088150295&doi=10.1109%2fRoboSoft48309.2020.9116026&partnerID=40&md5=c20972504717fd0435568fc8f380fb9f","Southern University of Science and Technology, Department of Mechanical and Energy Engineering, Shenzhen, Guangdong, 518055, China; AncoraSpring Inc. and Currently A Visiting Scholar, SUSTech Institute of Robotics, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China; Southern University of Science and Technology, Department of Mechanical and Energy Engineering, Shenzhen, Guangdong, 518055, China","Yang, Z., Southern University of Science and Technology, Department of Mechanical and Energy Engineering, Shenzhen, Guangdong, 518055, China; Ge, S., Southern University of Science and Technology, Department of Mechanical and Energy Engineering, Shenzhen, Guangdong, 518055, China; Wan, F., AncoraSpring Inc. and Currently A Visiting Scholar, SUSTech Institute of Robotics, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China; Liu, Y., Southern University of Science and Technology, Department of Mechanical and Energy Engineering, Shenzhen, Guangdong, 518055, China; Song, C., Southern University of Science and Technology, Department of Mechanical and Energy Engineering, Shenzhen, Guangdong, 518055, China","Robotic fingers made of soft material and compliant structures usually lead to superior adaptation when interacting with the unstructured physical environment. In this paper, we present an embedded sensing solution using optical fibers for an omni-adaptive soft robotic finger with exceptional adaptation in all directions. In particular, we managed to insert a pair of optical fibers inside the finger's structural cavity without interfering with its adaptive performance. The resultant integration is scalable as a versatile, low-cost, and moisture-proof solution for physically safe human-robot interaction. In addition, we experimented with our finger design for an object sorting task and identified sectional diameters of 94% objects within the ± 6 mm error and measured 80% of the structural strains within ± 0.1 mm/mm error. The proposed sensor design opens many doors in future applications of soft robotics for scalable and adaptive physical interactions in the unstructured environment. © 2020 IEEE.","adaptive grasping; optical fiber; soft robot; tactile sensing","Agricultural robots; Optical fibers; Robotic arms; Robotics; Compliant structures; Embedded sensing; Future applications; Physical environments; Physical interactions; Safe human-robot interaction; Structural strain; Unstructured environments; Human robot interaction",,,,,"Southern University of Science and Technology, SUSTech","*This work was supported by Southern University of Science and Technology and AncoraSpring Inc.",,,,,,,,,,"Laschi, C., Mazzolai, B., Cianchetti, M., Soft robotics: Technologies and systems pushing the boundaries of robot abilities (2016) Science Robotics, 1 (1). , https://robotics.sciencemag.org/content/1/1/eaah3690, [online]; Wang, H., Totaro, M., Beccai, L., Toward perceptive soft robots: Progress and challenges (2018) Advanced Science, 5 (9), p. 1800541; Thuruthel, T.G., Shih, B., Laschi, C., Tolley, M.T., Soft robot perception using embedded soft sensors and recurrent neural networks (2019) Science Robotics, 4 (26), p. eaav1488; Wang, Z., Hirai, S., Soft gripper dynamics using a line-segment model with an optimization-based parameter identification method (2017) IEEE Robotics and Automation Letters, 2 (2), pp. 624-631; Tan, N., Gu, X., Ren, H., Simultaneous robot-world, sensor-tip, and kinematics calibration of an underactuated robotic hand with soft fingers (2017) IEEE Access, 6, pp. 22705-22715; Rus, D., Tolley, M.T., Design, fabrication and control of soft robots (2015) Nature, 521 (7553), p. 467; Boonvisut, P., Çavuşoglu, M.C., Estimation of soft tissue mechanical parameters from robotic manipulation data (2012) IEEE/ASME Transactions on Mechatronics, 18 (5), pp. 1602-1611; Xu, P.A., Mishra, A., Bai, H., Aubin, C., Zullo, L., Shepherd, R., Optical lace for synthetic afferent neural networks (2019) Science Robotics, 4 (34), p. eaaw6304; Da Silva, J.G., De Carvalho, A.A., Da Silva, D.D., A strain gauge tactile sensor for finger-mounted applications (2002) IEEE Transactions on Instrumentation and Measurement, 51 (1), pp. 18-22; Chin, L., Yuen, M.C., Lipton, J., Trueba, L.H., Kramer-Bottiglio, R., Rus, D., A simple electric soft robotic gripper with high-deformation haptic feedback (2019) International Conference on Robotics and Automation; Zhao, H., O'Brien, K., Li, S., Shepherd, R.F., Optoelectronically innervated soft prosthetic hand via stretchable optical waveguides (2016) Science Robotics, 1 (1), p. eaai7529; Park, M., Bok, B.-G., Ahn, J.-H., Kim, M.-S., Recent advances in tactile sensing technology (2018) Micromachines, 9 (7), p. 321; Sundaram, S., Kellnhofer, P., Li, Y., Zhu, J.-Y., Torralba, A., Matusik, W., Learning the signatures of the human grasp using a scalable tactile glove (2019) Nature, 569 (7758), p. 698; Van Meerbeek, I., De Sa, C., Shepherd, R., Soft optoelectronic sensory foams with proprioception (2018) Science Robotics, 3 (24), p. eaau2489; Stassi, S., Cauda, V., Canavese, G., Pirri, C.F., Flexible tactile sensing based on piezoresistive composites: A review (2014) Sensors, 14 (3), pp. 5296-5332; Lipton, J.I., MacCurdy, R., Manchester, Z., Chin, L., Cellucci, D., Rus, D., Handedness in shearing auxetics creates rigid and compliant structures (2018) Science, 360 (6389), pp. 632-635; Sartiano, D., Sales, S., Low cost plastic optical fiber pressure sensor embedded in mattress for vital signal monitoring (2017) Sensors, 17 (12), p. 2900; Shintake, J., Cacucciolo, V., Floreano, D., Shea, H., Soft robotic grippers (2018) Advanced Materials, 30 (29), p. 1707035; Truby, R.L., Katzschmann, R.K., Lewis, J.A., Rus, D., Soft robotic fingers with embedded ionogel sensors and discrete actuation modes for somatosensitive manipulation (2019) 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft), pp. 322-329. , ieee; Chin, L., Lipton, J., Yuen, M.C., Kramer-Bottiglio, R., Rus, D., Automated recycling separation enabled by soft robotic material classification (2019) 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft), pp. 102-107. , ieee; Calli, B., Singh, A., Walsman, A., Srinivasa, S., Abbeel, P., Dollar, A.M., The ycb object and model set: Towards common benchmarks for manipulation research (2015) 2015 International Conference on Advanced Robotics (ICAR), pp. 510-517. , ieee",,,,"Institute of Electrical and Electronics Engineers Inc.","3rd IEEE International Conference on Soft Robotics, RoboSoft 2020","15 May 2020 through 15 July 2020",,161217,,9781728165707,,,"English","IEEE Int. Conf. Soft Robot., RoboSoft",Conference Paper,"Final","",Scopus,2-s2.0-85088150295
"Kim W., Kim N., Lyons J.B., Nam C.S.","56517258500;57194709022;15623605700;8421659200;","Factors affecting trust in high-vulnerability human-robot interaction contexts: A structural equation modelling approach",2020,"Applied Ergonomics","85",,"103056","","",,14,"10.1016/j.apergo.2020.103056","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078050068&doi=10.1016%2fj.apergo.2020.103056&partnerID=40&md5=aac34a90f9ba06478f84f1ca11a045bd","Department of Industrial & Management Engineering, Sungkyul University, South Korea; Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, United States; Air Force Research Laboratory, United States","Kim, W., Department of Industrial & Management Engineering, Sungkyul University, South Korea; Kim, N., Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, United States; Lyons, J.B., Air Force Research Laboratory, United States; Nam, C.S., Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, United States","The current research proposed and tested a structural equation model (SEM) that describes hypothesized relationships among factors affecting trust in human-robot interaction (HRI) such as trustworthiness, human-likeness, intelligence, perfect automation schema (PAS), and affect. A video stimulus depicting an autonomous guard robot interacting with humans was employed as a stimulus via Amazon's Mechanical Turk to recruit 233 participants. Human-related and robot-related metrics were found to affect trustworthiness that subsequently affected trust. In particular, ability (as a trustworthiness facet) was a dominant factor affecting trust in HRI. Integrity was found to mediate the relationships between robot- and human-related metrics and trustworthiness. This study also showed a correlation between intelligence and trustworthiness, as well as between PAS and trustworthiness. The findings of the present study have significant implications for both theory and practice on factors and levels that affect trust in HRI. © 2020 Elsevier Ltd","Amazon's mechanical turk; Autonomous guard robot; Human-robot interaction; Structural equation modelling; Trustworthiness","Intelligent robots; Man machine systems; Amazon's mechanical turks; Dominant factor; Human likeness; Human robot Interaction (HRI); Structural equation modeling; Structural equation modelling; Theory and practice; Trustworthiness; Human robot interaction; adult; article; female; human; human experiment; intelligence; major clinical study; male; robotics; structural equation modeling; theoretical study; trust; videorecording; artificial intelligence; beneficence; computer interface; latent class analysis; man machine interaction; middle aged; young adult; Adult; Artificial Intelligence; Beneficence; Female; Humans; Latent Class Analysis; Male; Man-Machine Systems; Middle Aged; Robotics; Trust; User-Computer Interface; Young Adult",,,,,"Air Force Research Laboratory, AFRL","This research was partly supported by the U.S. Air Force Research Laboratory Summer Faculty Fellowship Program. 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Fitts Department of Industrial & Systems Engineering, United States; email: csnam@ncsu.edu",,,"Elsevier Ltd",,,,,00036870,,AERGB,"32174344","English","Appl. Ergon.",Article,"Final","",Scopus,2-s2.0-85078050068
"Xiang G., Su J.","56157775800;7401682511;","Interactive Natural Motion Planning for Robot Systems Based on Representation Space",2020,"International Journal of Social Robotics","12","2",,"345","354",,,"10.1007/s12369-019-00552-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065175809&doi=10.1007%2fs12369-019-00552-9&partnerID=40&md5=d8f10447a1c4529f2589ed3c19f91956","Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China","Xiang, G., Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China; Su, J., Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China","Endowing robot with interactive natural motion planning is of great importance, since user has to get more and more involved for better realization with versatile tasks under increasingly complicated environments. In this paper, studies on interactive natural motion planning are summarized and a general theoretic framework for which is presented in triple-folds. Firstly, the motion planning model is formulated based on representation space, and essential guidelines on planning algorithm selection are also proposed. Then user intention inference, which consists of grid-based intention model and Bayesian filtering based inferring algorithm, is investigated to mitigate the impact of network induced imperfections during human–robot interaction. Finally, for further rejecting various uncertainties, H∞ control theory based disturbance observer is proposed. All three algorithmic design procedures are stated in detail and the efficiency of which are presented via existing results, followed by discussions on the future directions. © 2019, Springer Nature B.V.","Bayesian filtering; Disturbance observer; Intention inference; Interactive natural motion planning; Representation space; Robot","Agricultural robots; Inference engines; Motion planning; Social robots; Algorithmic design procedures; Bayesian filtering; Disturbance observer; Intention modeling; Natural motions; Planning algorithms; Representation space; Robot interactions; Robot programming",,,,,"Northwest Fisheries Science Center, NWFSC: 61521063, 61533012, 91748120; National Natural Science Foundation of China, NSFC","This work was supported by National Natural Science Foundation of China (NFSC) under Grants 61533012, 91748120 and 61521063.",,,,,,,,,,"Tan, M., Wang, S., Research progress on robotics (2013) Acta Automat Sinica, 39 (7), pp. 1119-1128; Toit, N.E.D., Burdick, J.W., Robot motion planning in dynamic, uncertain environments (2012) IEEE Trans Robot, 28 (1), pp. 101-115; Ikemoto, S., Amor, H.B., Minato, T., Jung, B., Physical human–robot interaction: mutual learning and adaptation (2016) IEEE Robot Autom Mag, 19 (4), pp. 24-35; Torres, L.G., Baykal, C., Alterovitz, R., Interactive-rate motion planning for concentric tube robots (2014) IEEE International Conference on Robotics Automation (ICRA), pp. 1915-1821; Muhlig, M., Gienger, M., Steil, J.J., Human-robot interaction for learning and adaptation of object movements (2010) IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 4901-4907; Calinon, S., Sardellitti, I., Caldwell, D.G., Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies (2010) Intelligent Robot Syst, 6219 (1), pp. 249-254; Schulman, J., Duan, Y., Ho, J., Lee, A., Awwal, I., Bradlow, H., Pan, J., Abbeel, P., Motion planning with sequential convex optimization and convex collision checking (2014) Int J Robot Res, 33 (9), pp. 1251-1270; Karaman, S., Frazzoli, E., Sampling-based algorithms for optimal motion planning (2011) Int J Robot Res, 30 (7), pp. 846-894; Lozano-Perez, T., A simple motion-planning algorithm for general robot manipulators (1987) IEEE J Robot Autom, 3 (3), pp. 224-238; Ibeas, A., De la Sen, M., Robustly stable adaptive control of a tandem of master-slave robotic manipulators with force reflection by using a multiestimation scheme (2006) IEEE Trans Syst Man Cybern B Cybern, 36 (5), pp. 1162-1179; Ani, H.M., Vijay, K., Luiz, C., Decentralized controllers for shape generation with robotic swarms (2008) Robotica, 26 (5), pp. 691-701; Olfati-Saber, R., Flocking for multi-agent dynamic systems: algorithms and theory (2006) IEEE Trans Automat Contr, 51 (3), pp. 401-420; Hauser, K., (2014) The minimum constraint removal problem with three robotics applications, , Sage Publications Inc, Thousand Oaks; Su, J., Xie, W., Motion planning and coordination for robot systems based on representation space (2011) IEEE Trans Syst Man Cybern B Cybern, 41 (1), pp. 248-259; Xie, W., Su, J., Lin, Z., New coordination scheme for multi-robot systems based on state space models (2008) J Syst Eng Electron, 19 (4), pp. 722-734; Zhang, L., Gao, H., Kaynak, O., Network-induced constraints in networked control systems: a survey (2012) IEEE Trans Ind Inf, 9 (1), pp. 403-416; Bemporad, A., Heemels, M., Johansson, M., (2010) Networked control systems, , Springer, London; Gupta, R.A., Chow, M.Y., Networked control system: overview and research trends (2010) IEEE Trans Ind Electron, 57 (7), pp. 2527-2535; Walsh, G.C., Ye, H., Bushnell, L., Stability analysis of networked control systems (2002) IEEE Trans Contr Syst Technol, 10 (3), pp. 438-446; Quevedo, D.E., Nesic, D., Input-to-state stability of packetized predictive control over unreliable networks affected by packet-dropouts (2011) IEEE Trans Automat Contr, 56 (2), pp. 370-375; Sinopoli, B., Schenato, L., Franceschetti, M., Poolla, K., Kalman filtering with intermittent observations (2004) IEEE Trans Automat Contr, 1 (9), pp. 1453-1464; Rodriguezseda, E.J., Lee, D., Spong, M.W., Experimental comparison study of control architectures for bilateral teleoperators (2009) IEEE Trans Robot, 25 (6), pp. 1304-1318; Janabi-Sharifi, F., Hassanzadeh, I., Experimental analysis of mobile-robot teleoperation via shared impedance control (2011) IEEE Trans Syst Man Cybern B Cybern, 41 (2), pp. 591-606; Niemeyer, G., Telemanipulation with time delays (2004) Int J Robot Res, 23 (9), pp. 873-890; Mitra, P., Niemeyer, G., Model-mediated telemanipulation (2008) Int J Robot Res, 27 (2), pp. 253-262; Su, J., Representation and inference of user intention for internet robot (2014) IEEE Trans Syst Man Cybern Syst, 44 (8), pp. 995-1002; Andreft, N., Horaud, R., Espiau, B., Robot hand-eye calibration using structure-from-motion (2001) Int J Robot Res, 20 (3), pp. 228-248; Su, J., Camera calibration based on receptive fields (2007) Pattern Recognit, 40 (10), pp. 2837-2845; Gao, Z., On the centrality of disturbance rejection in automatic control (2014) ISA Trans, 53 (4), pp. 850-857; Li, S., Yang, J., Chen, W.H., Chen, X., (2014) Disturbance observer-based control: methods and applications, , CRC Press Inc, Boca Raton; Guo, L., Cao, S., Anti-disturbance control theory for systems with multiple disturbances: a survey (2014) ISA Trans, 53 (4), pp. 846-849; Silva, G.J., Datta, A., Bhattacharyya, S.P., New results on the synthesis of PID controllers (2002) IEEE Trans Automat Contr, 47 (2), pp. 241-252; Zhou, K., Doyle, J.C., Glover, K., (1996) Robust and optimal control, , Prentice Hall, New Jersey; Aström, K.J., (2012) Introduction to stochastic control theory, , Courier Corporation, Chelmsford; Aström, K.J., (2013) Adaptive control, , Courier Corporation, Chelmsford; Utkin, V., Guldner, J., Shi, J., (2009) Sliding mode control in electro-mechanical systems, , CRC Press Inc, Boca Raton; Lewis, F.W., Jagannathan, S., Yesildirak, A., (1998) Neural network control of robot manipulators and non-linear systems, , CRC Press Inc, Boca Raton; Lee, C.C., Fuzzy logic in control systems: fuzzy logic controller. II (1990) IEEE Trans Syst Man Cybern B Cybern, 20 (2), pp. 419-435; Han, J., From pid to active disturbance rejection control (2009) IEEE Trans Ind Electron, 56 (3), pp. 900-906; Ohnishi, K., Shibata, M., Murakami, T., Motion control for advanced mechatronics (2002) IEEE/ASME Trans Mechatron, 1 (1), pp. 56-67; Su, J., Robotic uncalibrated visual serving based on ADRC (2015) Control Decis, 30 (1), pp. 1-8. , (in Chinese; Elfes, A., Matthies, L., Sensor integration for robot navigation: Combining sonar and stereo range data in a grid-based representataion (2007) IEEE Conference on Decision and Control, pp. 1802-1807; Su, J., Wang, L., Yun, J.N., A design of disturbance observer in standard H∞ control framework (2015) Int J Robust Nonlinear Control, 25 (16), pp. 2894-2910; Yun, J.N., Su, J., Yong, I.K., Yong, C.K., Robust disturbance observer for two-inertia system (2013) IEEE Trans Ind Electron, 60 (7), pp. 2700-2710; Yun, J.N., Su, J.B., Design of a disturbance observer for a two-link manipulator with flexible joints (2014) IEEE Tran Control Syst Technol, 22 (2), pp. 809-815; Wang, L., Su, J., Robust disturbance rejection control for attitude tracking of an aircraft (2015) IEEE Trans Control Syst Technol, 23 (6), pp. 2361-2368; Ma, Z., Su, J., Robust uncalibrated visual servoing control based on disturbance observer (2015) ISA Trans, 59, pp. 193-204; Shim, H., Jo, N.H., An almost necessary and sufficient condition for robust stability of closed-loop systems with disturbance observer (2009) Automatica, 45 (1), pp. 296-299; Jo, N.H., Joo, Y., Shim, H., Son, Y.I., A note on disturbance observer with unknown relative degree of the plant (2014) Automatica, 50 (6), pp. 1730-1734; Yang, J., Li, S., Yu, X., Sliding-mode control for systems with mismatched uncertainties via a disturbance observer (2012) IEEE Trans Ind Electron, 60 (1), pp. 160-169; Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van den Driessche, G., Schrittwieser, J., Lanctot, M., Mastering the game of go with deep neural networks and tree search (2016) Nature, 529 (7587), pp. 484-489; Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B., Human-level concept learning through probabilistic program induction (2015) Science, 350 (6266), pp. 1332-1338; Lewis, F.L., Vrabie, D., Vamvoudakis, K.G., Reinforcement learning and feedback control: using natural decision methods to design optimal adaptive controllers (2012) IEEE Control Syst Mag, 32 (6), pp. 76-105","Su, J.; Department of Automation, China; email: jbsu@sjtu.edu.cn",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85065175809
"Shree V., Chao W.-L., Campbell M.","57195424642;56735573500;7403371580;","Interactive natural language-based person search",2020,"IEEE Robotics and Automation Letters","5","2","8972387","1851","1858",,,"10.1109/LRA.2020.2969921","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079780847&doi=10.1109%2fLRA.2020.2969921&partnerID=40&md5=8b9ebf22ab997402727d6cf33b4fc0e6","Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY  14850, United States; Department of Computer Science and Engineering, Ohio State University, Columbus, OH  43210, United States","Shree, V., Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY  14850, United States; Chao, W.-L., Department of Computer Science and Engineering, Ohio State University, Columbus, OH  43210, United States; Campbell, M., Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY  14850, United States","In this work, we consider the problem of searching people in an unconstrained environment, with natural language descriptions. Specifically, we study how to systematically design an algorithm to effectively acquire descriptions from humans. An algorithm is proposed by adapting models, used for visual and language understanding, to search a person of interest (POI) in a principled way, achieving promising results without the need to re-design another complicated model. We then investigate an iterative question-answering (QA) strategy that enable robots to request additional information about the POI's appearance from the user. To this end, we introduce a greedy algorithm to rank questions in terms of their significance, and equip the algorithm with the capability to dynamically adjust the length of human-robot interaction according to model's uncertainty. Our approach is validated not only on benchmark datasets but on a mobile robot, moving in a dynamic and crowded environment. © 2016 IEEE.","AI-based methods; cognitive human-robot interaction; human detection and tracking","Iterative methods; Man machine systems; Natural language processing systems; Visual languages; Benchmark datasets; Cognitive human-robot interactions; Greedy algorithms; Human detection and tracking; Language understanding; Natural languages; Question Answering; Unconstrained environments; Human robot interaction",,,,,"Office of Naval Research, ONR: N00014-17-1-2175","Manuscript received September 10, 2019; accepted January 6, 2020. Date of publication January 28, 2020; date of current version February 11, 2020. This letter was recommended for publication by Associate Editor Eren Erdal Aksoy and Editor T. Asfour upon evaluation of the reviewers’ comments. This work was supported by Office of Naval Research DOD under Grant N00014-17-1-2175. (Corresponding author: Vikram Shree.) V. Shree and M. Campbell are with the Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14850 USA (e-mail: vs476@cornell.edu; mc288@cornell.edu).",,,,,,,,,,"Antol, S., Vqa: Visual question answering (2015) Proc. Int. Conf. Comput. Vision, pp. 2425-2433; Wang, Z., Zero-shot person re-identification via cross-view consistency (2015) IEEE Trans. Multimedia, 18 (2), pp. 260-272. , Feb; Chang, S., Han, W., Tang, J., Qi, G.-J., Aggarwal, C.C., Huang, T.S., Heterogeneous network embedding via deep architectures (2015) Proc. 21th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, pp. 119-128; Roth, J., Liu, X., On the exploration of joint attribute learning for person re-identification (2014) Proc. Asian Conf. Comput. Vision, pp. 673-688; Layne, R., Hospedales, T.M., Gong, S., Attributes-based reidentification (2014) Person Re-Identification., pp. 93-117. , Berlin, Germany: Springer; Su, C., Zhang, S., Xing, J., Gao, W., Tian, Q., Deep attributes driven multi-camera person re-identification (2016) Proc. Eur. Conf. Comput. Vision, pp. 475-491; Yin, Z., Adversarial attribute-image person re-identification (2018) Proc. 27th Int. Joint Conf. Artif. Intell., pp. 1100-1106; Li, S., Xiao, T., Li, H., Zhou, B., Yue, D., Wang, X., Person search with natural language description (2017) Proc. Conf. Comput. Vision Pattern Recognit., pp. 5187-5196; Liao, S., Hu, Y., Zhu, X., Li, S.Z., Person re-identification by local maximal occurrence representation and metric learning (2015) Proc. Int. Conf. Comput. Vision, pp. 2197-2206; Liao, S., Li, S.Z., Efficient PSD constrained asymmetric metric learning for person re-identification (2015) Proc. Int. Conf. Comput. Vision, pp. 3685-3693; Jiang, Y., Natarajan, V., Chen, X., Rohrbach, M., Batra, D., Parikh, D., (2018) Pythia v0. 1: The Winning Entry to the Vqa Challenge 2018; Chen, T., Xu, C., Luo, J., Improving text-based person search by spatial matching and adaptive threshold (2018) Proc. IEEEWinter Conf. Appl. Comput. Vision, pp. 1879-1887; Li, S., Xiao, T., Li, H., Yang, W., Wang, X., Identity-aware textual-visual matching with latent co-attention (2017) Proc. Int. Conf. Comput. Vision, pp. 1890-1899; Lu, P., Li, H., Zhang, W., Wang, J., Wang, X., Co-attending free-form regions and detections with multi-modalmultiplicative feature embedding for visual question answering (2018) Proc. Conf. Artif. Intell., pp. 7218-7225; Singh, A., Pythia-a platform for vision & language research (2018) Proc. NeurIPS SysML Workshop; Pennington, J., Socher, R., Manning, C., Glove: Global vectors for word representation (2014) Proc. Conf. Empirical Methods Natural Lang. Process., pp. 1532-1543; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2016) Proc. Conf. Comput. Vision Pattern Recognit., pp. 770-778; Girshick, R., Fast r-cnn (2015) Proc. Int. Conf. Comput. Vision, pp. 1440-1448; Krishna, R., Visual genome: Connecting language and vision using crowdsourced dense image annotations (2017) Int. J. Comput. Vision, 123 (1), pp. 32-73; Vinyals, O., Toshev, A., Bengio, S., Erhan, D., Show and tell: A neural image caption generator (2015) Proc. Conf. Comput. Vision Pattern Recognit., pp. 3156-3164; Nemhauser, G.L., Wolsey, L.A., Maximizing submodular set functions: Formulations and analysis of algorithms (1981) North-Holland Mathematics Studies, 59, pp. 279-301. , Amsterdam, The Netherlands: Elsevier; Shree, V., Chao, W.-L., Campbell, M., An empirical study of person reidentification with attributes (2019) Proc. 28th IEEE Int. Conf. Robot Human Interactive Commun., pp. 1-8; He, K., Gkioxari, G., Dollár, P., Girshick, R., Mask r-cnn (2017) Proc. Int. Conf. Comput. Vision, pp. 2980-2988","Shree, V.; Sibley School of Mechanical and Aerospace Engineering, United States; email: vs476@cornell.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85079780847
"Hansen A.K., Nilsson J., Jochum E.A., Herath D.","57204949260;57204951769;57074708900;8366358100;","On the importance of posture and the interaction environment exploring agency, animacy and presence in the lab vs wild using mixed-methods",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"227","229",,1,"10.1145/3371382.3378288","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083268311&doi=10.1145%2f3371382.3378288&partnerID=40&md5=02b880ee71ef188888cf6061893ce74b","Aalborg University, Aalborg, Denmark; University of Canberra, Canberra, ACT, Australia","Hansen, A.K., Aalborg University, Aalborg, Denmark; Nilsson, J., Aalborg University, Aalborg, Denmark; Jochum, E.A., Aalborg University, Aalborg, Denmark; Herath, D., University of Canberra, Canberra, ACT, Australia","This work explores three concepts relevant to the study of human-robot interaction: posture, setting and evaluation methods. The first concept is the importance of a robot's posture on its perceived interaction affordances. Early findings suggest that the same robot presented in different postural arrangements may significantly impact the way the interaction is perceived. Second, there is growing evidence to suggest the importance of situating interaction studies in-the-wild. We observed that the environment an interaction is situated in strongly affects the outcome, an indication that experiments constrained to the laboratory may not reveal useful social aspects relevant to understanding HRI fully. Finally, in order to conduct in-the-wild studies, we argue that current practice of using single-strand methods may not be sufficient; we instead explore a mixed-methods approach to study the complex social and environmental interplay between the robot, the participant and the bystanders. © 2020 ACM.","HRI; In the wild; Mixed methods; Robotic art","Agricultural robots; Man machine systems; Social aspects; Affordances; Animacy; Current practices; Evaluation methods; Interaction environment; Interaction studies; Mixed method; Social and environmental; Social robots",,,,,,,,,,,,,,,,"Jung, M., Hinds, P., Robots in the wild: A time for more robust theories of human-robot interaction ACM Transactions on Human-Robot Interaction (THRI), 7 (1), p. 2. , 2018; Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J.W., Wellman, M., Machine behaviour Nature, 568 (7753), pp. 477-486. , 2019/04/01 2019; Silvera-Tawil, D., Velonaki, M., Rye, D., Human-robot interaction with humanoid Diamandini using an open experimentation method (2015) City; Herath, D., Jochum, E., Vlachos, E., An experimental study of embodied interaction and human perception of social presence for interactive robots in public settings (2017) IEEE Transactions on Cognitive and Developmental Systems; Aly, A., Griffiths, S., Stramandinoli, F., Metrics and benchmarks in human-robot interaction: Recent advances in cognitive robotics Cognitive Systems Research, 43, pp. 313-323. , 2017/06/01/ 2017; Bartneck, C., Kuli, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots International Journal of Social Robotics, 1 (1), pp. 71-81. , January 01 2009; Sim, D.Y.Y., Loo, C.K., Extensive assessment and evaluation methodologies on assistive social robots for modelling human-robot interaction-A review Information Sciences, 301, pp. 305-344. , 2015/04/20/ 2015; Vlachos, E., Jochum, E., Demers, L.-P., The effects of exposure to different social robots on attitudes toward preferences Interaction Studies, 17 (3), pp. 390-404. , 2017; Andrés, A., Pardo, D.E., Díaz, M., Angulo, C., New instrumentation for human robot interaction assessment based on observational methods Journal of Ambient Intelligence and Smart Environments, 7 (4), pp. 397-413. , 2015; De Graaf, M.M.A., Ben Allouch, S., Exploring influencing variables for the acceptance of social robots Robotics and Autonomous Systems, 61 (12), pp. 1476-1486. , 2013/12/01/ 2013; Kroos, C., Herath, D.C., (2011) Stelarc Evoking Agency: Attention Model and Behaviour Control in A Robotic Art Installation Leonardo; Braun, V., Clarke, V., Using thematic analysis in psychology Qualitative Research in Psychology, 3 (2), pp. 77-101. , 2006/01/01 2006",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083268311
"Parekh S., Lim J.C.","57216352229;57216339387;","Receptivity & interaction of social robots in hospitals",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"389","391",,2,"10.1145/3371382.3378243","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083253172&doi=10.1145%2f3371382.3378243&partnerID=40&md5=f82815cb5de209fc683279b474db8bb1","NCS Pte Ltd, Singapore, Singapore","Parekh, S., NCS Pte Ltd, Singapore, Singapore; Lim, J.C., NCS Pte Ltd, Singapore, Singapore","For social robots to be deployed in an interaction-centred environment such as a hospital, entities need to understand how to design human-robot interactions for the technology to be adopted successfully. For instance, if the robot was to be placed at a hospital concierge, it would need to engage visitors similar to a human concierge, either through verbal or non-verbal gestures (social cues). In this study, we investigate two hypotheses. Firstly, we hypothesized that various attention-drawing social cues significantly correlate to the receptivity of the robot. Secondly, we hypothesized that humans preferred medium of information transfer is through verbal interaction. We set up a humanoid concierge robot, Cruzr, in a hospital for 5 days as a trial. Our findings indicate an increase in receptivity when Cruzr performed a social cue as compared to neutral mode and that the preferred mode of communication was touch over voice. © 2020 ACM.","Concierge social robots; Hospital; Human-robot interaction; Mode of interaction; Receptivity","Agricultural robots; Hospitals; Machine design; Man machine systems; Attention drawing; Information transfers; Social cues; Verbal interaction; Social robots",,,,,,,,,,,,,,,,"Bruce, A., Nourbakhsh, I., Simmons, R., The role of expressiveness and attention in human-robot interaction (2002) Robotics and Automation 2002 Proceedings. ICRA'02. IEEE International Conference, 4, pp. 4138-4142; Breazeal, C., (2002) Designing Sociable Robots, , Cambridge, Massachusetts: MIT Press; Breazeal, C., Toward sociable robots Robotics and Autonomous Systems, 42 (3-4), pp. 167-175. , 2003; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Robotics and Autonomous Systems, 42 (3-4), pp. 143-166; Goetz, J., Kiesler, S., Powers, A., Matching robot appearance and behaviour to tasks to improve human-robot cooperation (2003) The 12th IEEE International Workshop on Robot and Human Interactive Communication, 29, pp. 55-60. , Oct. 31-Nov. 2, Milbrae, CA; Hegel, F., Krach, S., Kircher, T., Wrede, B., Sagerer, G., Understanding social robots: A user study on anthropomorphism (2008) Robot and Human Interactive Communication. 2008. RO-MAN 2008 the 17th IEEE International Symposium on, pp. 574-579. , IEEE; Ibrahim, H., Zheng-Hua, T., Nicolai, T., Xiaodong, D., (2016) User Acceptance of Social Robots; Nitto, H., Taniyama, D., Inagak, H., Social acceptance and impact of robots and artificial intelligence (2017) Nomura Research Institute, Ltd. (NRI); Gehle, R., Pitsch, K., Dankert, T., Wrede, S., How to open an interaction between robot and museum visitor Strategies to establish a focused encounter in hri (2017) ACM/IEEE International Conference on Human-Robot Interaction. NY, USA: ACM, pp. 187-195; Finke, M., Koay, K.L., Dautenhahn, K., Nehaniv, C.L., Walters, M.L., Saunders, J., Hey, i'm over here-how can a robot attract people's attention (2005) IEEE International Workshop on Robot and Human Interactive Communication (ROMAN). Aug, pp. 7-12; Torta, E., Van Heumen, J., Cuijpers, R.H., Juola, J.F., How can a robot attract the attention of its human partner a comparative study over different modalities for attracting attention (2012) Social Robotics. Berlin, Heidelberg: Springer, pp. 288-297; O'Brien, H.L., Toms, E.G., The development and evaluation of a survey to measure user engagement (2009) Journal of the American Society for Information Science and Technology, 61 (1), pp. 50-69",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083253172
"Law T.","57212389268;","Measuring relational trust in human-robot interactions",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"579","581",,3,"10.1145/3371382.3377435","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083161577&doi=10.1145%2f3371382.3377435&partnerID=40&md5=4a5bc90aaabc93a13647203a16db44e3","Tufts University, Medford, MA, United States","Law, T., Tufts University, Medford, MA, United States","Trust is an integral part of almost any human-robot interaction (HRI). The most technologically advanced robot can sit unused if a human interactant does not trust it. Conversely, a robot that is overly trusted may be assumed to be more advanced than it truly is, resulting in over-reliance on an imperfect system [8]. One of the most widely used definitions for trust in HRI is that trust is ""the attitude that an agent will help achieve an individual's goals in a situation characterized by uncertainty and vulnerability."" [6]. As robots initially emerged into society in factory roles, their goals were clear and their performance was concretely measurable with metrics such as time to completion, number of errors in a given behavior, and behavior consistency over time. The humans who worked with them, therefore, could base their trust in the robots on how effectively they achieved these clearly defined goals. © 2020 ACM.","Human-robot interaction; Human-robot trust; Relational trust; Trust","Agricultural robots; Man machine systems; Behavior consistency; Human robot Interaction (HRI); Imperfect systems; Integral part; Over reliance; Human robot interaction",,,,,"National Science Foundation, NSF: IIS-1316809; Air Force Office of Scientific Research, AFOSR: FA9550-18-1-0465","This project was funded in part by the National Science Foundation grant IIS-1316809 and the AFOSR grant FA9550-18-1-0465.",,,,,,,,,,"Caruso, D.R., Salovey, P., (2004) The Emotionally Intelligent Manager: How to Develop and Use the Four Key Emotional Skills of Leadership, , John Wiley & Sons; Clarke, N., Emotional intelligence and its relationship to transformational leadership and key project manager competences (2010) Project Management Journal, 41 (2), pp. 5-20. , 2010; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y.C., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527. , 2011; Law, T., Chita-Tegmark, M., Scheutz, M., Forthcoming. The Interplay between Emotional Intelligence, Trust, and Gender in Human-robot Interaction. International Journal of Social Robotics (Forthcoming); Law, T., Scheutz, M., Forthcoming. Trust: Recent Concepts and Evaluations in Human-robot Interaction. in Trust in Human-Robot Interaction: Research and Applications, , Chang Nam and Joe Lyons (Eds.). Elsevier, San Diego, CA, Chapter 2; Lee, J.D., See, K.A., Trust in automation: Designing for appropriate reliance (2004) Human Factors, 46 (1), pp. 50-80. , 2004; Mayer, R.C., Davis, J.H., The effect of the performance appraisal system on trust for management: A field quasi-experiment (1999) Journal of Applied Psychology, 84 (1), p. 123. , 1999; Parasuraman, R., Riley, V., Humans and automation: Use, misuse, disuse, abuse (1997) Human Factors, 39 (2), pp. 230-253. , 1997; Salovey, P., Mayer, J.D., Emotional intelligence (1990) Imagination, Cognition and Personality, 9 (3), pp. 185-211. , 1990; Ullman, D., Malle, B.F., What does it mean to trust a robot: Steps toward a multidimensional measure of trust (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 263-264","Law, T.; Tufts UniversityUnited States; email: theresa.law@tufts.edu",,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083161577
"Wallkotter S., Stower R., Kappas A., Castellano G.","57197799646;57217980101;7101642010;23007528900;","A robot by any other frame: Framing and behaviour influence mind perception in virtual but not real-world environments",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"609","618",,6,"10.1145/3319502.3374800","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082012040&doi=10.1145%2f3319502.3374800&partnerID=40&md5=9b061380bfcc022efbceda60ab78ea29","Uppsala University, Uppsala, Sweden; Jacobs University, Bremen, Germany","Wallkotter, S., Uppsala University, Uppsala, Sweden; Stower, R., Jacobs University, Bremen, Germany; Kappas, A., Jacobs University, Bremen, Germany; Castellano, G., Uppsala University, Uppsala, Sweden","Mind perception in robots has been an understudied construct in human-robot interaction (HRI) compared to similar concepts such as anthropomorphism and the intentional stance. In a series of three experiments, we identify two factors that could potentially influence mind perception and moral concern in robots: How the robot is introduced (framing), and how the robot acts (social behaviour). In the first two online experiments, we show that both framing and behaviour independently influence participants' mind perception. However, when we combined both variables in the following real-world experiment, these effects failed to replicate. We hence identify a third factor post-hoc: The online versus real-world nature of the interactions. After analysing potential confounds, we tentatively suggest that mind perception is harder to influence in real-world experiments, as manipulations are harder to isolate compared to virtual experiments, which only provide a slice of the interaction. © 2020 Association for Computing Machinery.","Ethics; Framing; Human-robot interaction; Mind perception; Moral concern; Robot agency; Social behaviour; Social robotics; User study","Ethical aspects; Human robot interaction; Man machine systems; Robotics; Virtual reality; Framing; Moral concerns; Social behaviour; Social robotics; User study; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 765955","This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 765955.",,,,,,,,,,"Abubshait, A., Wiese, E., You look human, but act like a machine: Agent appearance and behavior modulate different aspects of human-robot interaction (2017) Frontiers in Psychology, 8, pp. 1-12. , http://doi.org/10.3389/fpsyg.2017.01393, August, 2017; Imtiaz Ahmad, M., Mubin, O., Shahid, S., Orlando, J., Robot's adaptive emotional feedback sustains children's social engagement and promotes their vocabulary learning: A long-term child-robot interaction study (2019) Adaptive Behavior, 27 (4), pp. 243-266. , http://doi.org/10.1177/1059712319844182, 2019; Andrist, S., Mutlu, B., Tapus, A., Look like me: Matching robot personality via gaze to increase motivation (2015) Conference on Human Factors in Computing Systems-Proceedings, 2015, pp. 3603-3612. , http://doi.org/10.1145/2702123.2702592, April. Association for Computing Machinery; Anjomshoae, S., Najjar, A., Calvaresi, D., Främling, K., Explainable agents and robots: Results from a systematic literature review (2019) Proceedings of the 18th International Conference on Autonomous Agents and Multi-Agent Systems. 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Broadbent, E., Kumar, V., Li, X., Sollers, J., Stafford, R.Q., MacDonald, B.A., Wegner, D.M., Robots with display screens: A robot with a more humanlike face display is perceived to have more mind and a better personality (2013) PLoS ONE, S8 (8), p. e72589. , http://doi.org/10.1371/journal.pone.0072589, (aug 2013; Caruana, N., Spirou, D., Brock, J., Human agency beliefs influence behaviour during virtual social interactions (2017) PeerJ; Cohen, J., Quantitative methods in psychology (1992) Nature, 141 (3570), p. 613. , http://doi.org/10.1038/141613a0, 1992; Corrigan, L.J., Peters, C., Castellano, G., Identifying task engagement: Towards personalised interactions with educational robots (2013) Proceedings-2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013, pp. 655-658. , http://doi.org/10.1109/ACII.2013.114, IEEE; Deng, E., Mutlu, B., Mataric, M.J., Embodiment in socially interactive robots (2019) Foundations and Trends in Robotics, 7 (4), pp. 251-356. , http://doi.org/10.1561/2300000056, 2019; Epley, N., Waytz, A., Cacioppo, J.T., On seeing human: A three-factor theory of anthropomorphism (2007) Psychological Review, 114 (4), pp. 864-886. , http://doi.org/10.1037/0033-295X.114.4.864, 2007; Eyssel, F.A., Kuchenbrandt, D., Bobinger, S., Ruiter, L., Hegel, F., If you sound like me, you must be more human (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction-HRI '12, 125. , http://doi.org/10.1145/2157689.2157717, ACM Press, New York, New York, USA; Faul, F., Erdfelder, E., Lang, A.G., Buchner, A., G power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences (2007) Behavior Research Methods, 39 (2), pp. 175-191. , http://doi.org/10.3758/BF03193146, 2007; Fink, J., Anthropomorphism and human likeness in the design of robots and human-robot interaction (2012) International Conference on Social Robotics., pp. 199-208; Fischer, K., Lohan, K., Saunders, J., Nehaniv, C., Wrede, B., Rohlfing, K., The impact of the contingency of robot feedback on hri (2013) Proceedings of the 2013 International Conference on Collaboration Technologies and Systems, CTS 2013, pp. 210-217. , http://doi.org/10.1109/CTS.2013.6567231, IEEE; Ghazali, A.S., Ham, J., Markopoulos, P., Barakova, E., Investigating the effect of social cues on social agency judgement (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 586-587. , http://doi.org/10.1109/HRI.2019.8673266, IEEE; Maartje, M.A., De Graaf, People's explanations of robot behavior subtly reveal mental state inferences (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI, pp. 239-248; Gray, H.M., Gray, K., Wegner, D.M., Dimensions of mind perception (2007) Science, 315 (5812), p. 619. , http://doi.org/10.1126/science.1134475, feb 2007; Groom, V., Srinivasan, V., Bethel, C.L., Murphy, R., Dole, L., Nass, C., Responses to robot social roles and social role framing (2011) 2011 International Conference on Collaboration Technologies and Systems (CTS), pp. 194-203. , http://doi.org/10.1109/CTS.2011.5928687, IEEE; Hoffmann, L., Krämer, N.C., How should an artificial entity be embodied comparing the effects of a physically present robot and its virtual representation (2011) HRI 2011 Workshop on Social Robotic Telepresence, pp. 14-20. , 2011; Irfan, B., Ramachandran, A., Spaulding, S., Glas, D.F., Leite, I., Koay, K.L., Personalization in long-term human-robot interaction (2019) ACM/IEEE International Conference on Human-Robot Interaction, 2019, pp. 685-686. , http://doi.org/10.1109/HRI.2019.8673076, March IEEE; Ishii, T., Watanabe, K., How people attribute minds to non-living entities (2019) 2019 11th International Conference on Knowledge and Smart Technology, KST 2019, pp. 213-217. , http://doi.org/10.1109/KST.2019.8687324, Institute of Electrical and Electronics Engineers Inc; Jung, M.F., Lee, J.J., Depalma, N., Adalgeirsson, S.O., Hinds, P.J., Breazeal, C., Engaging robots: Easing complex humanrobot teamwork using backchanneling (2013) Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW, pp. 1555-1566. , http://doi.org/10.1145/2441776.2441954; Koda, T., Nishimura, Y., Nishijima, T., How robot's animacy affects human tolerance for their malfunctions (2016) ACM/IEEE International Conference on Human-Robot Interaction, 2016, pp. 455-456. , http://doi.org/10.1109/HRI.2016.7451803, April IEEE; Kose-Bagci, H., Dautenhahn, K., Nehaniv, C.L., Emergent dynamics of turn-taking interaction in drumming games with a humanoid robot (2008) RO-MAN 2008-The 17th IEEE International Symposium on Robot and Human Interactive Communication, pp. 346-353. , http://doi.org/10.1109/ROMAN.2008.4600690, IEEE; Kwon, M., Jung, M.F., Knepper, R.A., Human expectations of social robots (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2016, pp. 463-464. , http://doi.org/10.1109/HRI.2016.7451807, April. IEEE; Laham, S.M., Expanding the moral circle: Inclusion and exclusion mindsets and the circle of moral regard (2009) Journal of Experimental Social Psychology, 45 (1), pp. 250-253. , http://doi.org/10.1016/j.jesp.2008.08.012, 2009; Ligthart, M., Truong, K.P., Selecting the right robot: Influence of user attitude, robot sociability and embodiment on user preferences (2015) Proceedings-IEEE International Workshop on Robot and Human Interactive Communication 2015-Novem, pp. 682-687. , http://doi.org/10.1109/ROMAN.2015.7333598, 2015; Malle, B.F., Scheutz, M., Forlizzi, J., Voiklis, J., Which robot am i thinking about the impact of action and appearance on people's evaluations of a moral robot (2016) ACM/IEEE International Conference on Human-Robot Interaction, 2016, pp. 125-132. , http://doi.org/10.1109/HRI.2016.7451743, April. IEEE Computer Society; Marchesi, S., Ghiglino, D., Ciardo, F., Perez-Osorio, J., Baykara, E., Wykowska, A., Do we adopt the intentional stance toward humanoid robots (2019) Frontiers in Psychology, 10, p. 450. , http://doi.org/10.3389/fpsyg.2019.00450, 2019; Mirnig, N., Stollnberger, G., Miksch, M., Stadler, S., Giuliani, M., Tscheligi, M., To err is robot: How humans assess and act toward an erroneous social robot (2017) Frontiers in Robotics and AI, 4, pp. 1-15. , http://doi.org/10.3389/frobt.2017.00021, (may 2017; Mutlu, B., Forlizzi, J., Hodgins, J., A storytelling robot: Modeling and evaluation of human-like gaze behavior (2006) Proceedings of the 2006 6th IEEE-RAS International Conference on Humanoid Robots, HUMANOIDS, pp. 518-523. , http://doi.org/10.1109/ICHR.2006.321322; Nomura, T., Kanda, T., Yamada, S., Measurement of moral concern for robots (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 540-541. , http://doi.org/10.1109/HRI.2019.8673095, IEEE; Nomura, T., Otsubo, K., Kanda, T., Preliminary investigation of moral expansiveness for robots (2019) Proceedings of IEEE Workshop on Advanced Robotics and Its Social Impacts, ARSO 2018-September, pp. 91-96. , http://doi.org/10.1109/ARSO.2018.8625717, 2019; Park, E., Kong, H., Lim, H.T., Lee, J., You, S., Angel, P., Pobil, D., The effect of robot's behavior vs appearance on communication with humans (2011) HRI 2011-Proceedings of the 6th ACM/IEEE International Conference on Human-Robot Interaction, pp. 219-220. , http://doi.org/10.1145/1957656.1957740, IEEE; Phillips, E., Zhao, X., Ullman, D., Malle, B.F., What is human-like: Decomposing robots' human-like appearance using the anthropomorphic robot (abot) database (2018) ACM/IEEE International Conference on Human-Robot Interaction, pp. 105-113. , http://doi.org/10.1145/3171221.3171268, ACM Press, New York, New York, USA; Rea, D.J., Young, J.E., It's all in your head: Using priming to shape an operator's perceptions and behavior during teleoperation (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction-HRI, 18, pp. 32-40. , http://doi.org/10.1145/3171221.3171259; Rea, D.J., Young, J.E., Methods and effects of priming a teloperator's perception of robot capabilities (2019) ACM/IEEE International Conference on Human-Robot Interaction, 2019, pp. 739-741. , http://doi.org/10.1109/HRI.2019.8673186, March IEEE; Salem, M., Eyssel, F.A., Rohlfing, K., Kopp, S., Joublin, F., To err is human(-like): Effects of robot gesture on perceived anthropomorphism and likability (2013) International Journal of Social Robotics, 5 (3), pp. 313-323. , http://doi.org/10.1007/s12369-013-0196-9, aug 2013; Schellen, E., Wykowska, A., Intentional mindset toward robotsopen questions and methodological challenges (2019) Frontiers in Robotics AI, 6. , http://doi.org/10.3389/frobt.2018.00139, JAN (jan 2019); Schneider, S., Kummert, F., Does the user's evaluation of a socially assistive robot change based on presence and companionship type (2017) ACM/IEEE International Conference on Human-Robot Interaction, pp. 277-278. , http://doi.org/10.1145/3029798.3038418; Seo, S.H., Geiskkovitch, D., Nakane, M., King, C., Young, J.E., Poor thing! would you feel sorry for a simulated robot: A comparison of empathy toward a physical and a simulated robot (2015) ACM/IEEE International Conference on Human-Robot Interaction 2015, pp. 125-132. , http://doi.org/10.1145/2696454.2696471, March; Short, E., Hart, J., Vu, M., Scassellati, B., No fair!! an interaction with a cheating robot (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 219-226. , http://doi.org/10.1109/HRI.2010.5453193, IEEE; Stenzel, A., Chinellato, E., Bou, T.M.A., Pobil Del, Á.P., Lappe, M., Liepelt, R., When humanoid robots become human-like interaction partners: Corepresentation of robotic actions (2012) Journal of Experimental Psychology: Human Perception and Performance, 38 (5), pp. 1073-1077. , http://doi.org/10.1037/a0029493, oct 2012; Thellman, S., Ziemke, T., Social attitudes toward robots are easily manipulated (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction-HRI '17. ACM Press, New York, New York, USA, pp. 299-300. , http://doi.org/10.1145/3029798.3038336; Tsiakas, K., Abujelala, M., Lioulemes, A., Makedon, F., An intelligent interactive learning and adaptation framework for robot-based vocational training (2017) 2016 IEEE Symposium Series on Computational Intelligence, , http://doi.org/10.1109/SSCI.2016.7850066, SSCI 2016 October (2017); Vanman, E.J., Kappas, A., Danger, will robinson! the challenges of social robots for intergroup relations (2019) Social and Personality Psychology Compass, 13 (8), pp. 1-13. , http://doi.org/10.1111/spc3.12489, aug 2019; Westlund, K.J.M., Martinez, M., Archie, M., Das, M., Breazeal, C., A study to measure the effect of framing a robot as a social agent or as a machine on children's social behavior (2016) ACM/IEEE International Conference on Human-Robot Interaction, 2016, pp. 459-460. , http://doi.org/10.1109/HRI.2016.7451805, April. IEEE Computer Society; Wiese, E., Metta, G., Wykowska, A., Robots as intentional agents: Using neuroscientific methods to make robots appear more social (2017) Frontiers in Psychology, 8. , http://doi.org/10.3389/fpsyg.2017.01663, OCT (oct 2017); Wiese, E., Wykowska, A., Zwickel, J., Mueller, H.J., I see what you mean: How attentional selection is shaped by ascribing intentions to others (2012) PloS One; Wigdor, N., De Greeff, J., Looije, R., Neerincx, M.A., Howto improve human-robot interaction with conversational fillers (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 219-224. , http://doi.org/10.1109/ROMAN.2016.7745134, IEEE; Wills, P., Baxter, P., Kennedy, J., Senft, E., Belpaeme, T., Socially contingent humanoid robot head behaviour results in increased charity donations (2016) ACM/IEEE International Conference on Human-Robot Interaction, 2016, pp. 533-534. , http://doi.org/10.1109/HRI.2016.7451842, April IEEE; Wykowska, A., Wiese, E., Prosser, A., Mueller, H.J., Beliefs about the minds of others influence howwe process sensory information (2014) PloS One; Zotowski, J., Proudfoot, D., Yogeeswaran, K., Bartneck, C., Anthropomorphism: Opportunities and challenges in humanârobot interaction (2015) International Journal of Social Robotics, 7 (3), pp. 347-360. , http://doi.org/10.1007/s12369-014-0267-6, jun 2015",,,"ACM SIGAI;ACM SIGCHI;IEEE Robotics and Automation Society( IEEE RAS)","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158224,21672148,9781450367462,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85082012040
"Siegfried R., Aminian B., Odobez J.-M.","57195685304;57217013696;57203103085;","ManiGaze: A dataset for evaluating remote gaze estimator in object manipulation situations",2020,"Eye Tracking Research and Applications Symposium (ETRA)",,,"35","","",,,"10.1145/3379156.3391369","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085733447&doi=10.1145%2f3379156.3391369&partnerID=40&md5=2dfdcf53acfbce914e088651a1b91dfa","Idiap Reseach Institute, Martigny, Switzerland; EPFL, Lausanne, Switzerland","Siegfried, R., Idiap Reseach Institute, Martigny, Switzerland, EPFL, Lausanne, Switzerland; Aminian, B., Idiap Reseach Institute, Martigny, Switzerland, EPFL, Lausanne, Switzerland; Odobez, J.-M., Idiap Reseach Institute, Martigny, Switzerland, EPFL, Lausanne, Switzerland","Gaze estimation allows robots to better understand users and thus to more precisely meet their needs. In this paper, we are interested in gaze sensing for analyzing collaborative tasks and manipulation behaviors in human-robot interactions (HRI), which differs from screen gazing and other communicative HRI settings. Our goal is to study the accuracy that remote vision gaze estimators can provide, as they are a promising alternative to current accurate but intrusive wearable sensors. In this view, our contributions are: 1) we collected and make public a labeled dataset involving manipulation tasks and gazing behaviors in an HRI context; 2) we evaluate the performance of a state-of-the-art gaze estimation system on this dataset. Our results show a low default accuracy, which is improved by calibration, but that more research is needed if one wishes to distinguish gazing at one object amongst a dozen on a table. © 2020 ACM.","Dataset; Gaze estimation; Human-robot interaction; Remote recording","Behavioral research; Human robot interaction; Collaborative tasks; Gaze estimation; Human robot Interaction (HRI); Labeled dataset; Manipulation task; Object manipulation; Remote vision; State of the art; Eye tracking",,,,,"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF: 30214; Horizon 2020: 688147","This research has been supported by the European Union Horizon 2020 research and innovation program (grant no. 688147, MuMMER project) and by the SNSF (grant no. 30214, project ROSALIS).",,,,,,,,,,"Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: A review (2017) Journal of Human-Robot Interaction, 6 (1), pp. 25-53. , 2017; Andrist, S., Mutlu, B., Gleicher, M., Conversational gaze aversion for virtual agents (2013) IVA, 8108, pp. 249-262; Aronson, R.M., Santini, T., Kübler, T.C., Kasneci, E., Srinivasa, S., Admoni, H., Eye-hand behavior in human-robot shared manipulation (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 4-13; Bader, T., Vogelgesang, M., Klaus, E., Multimodal integration of natural gaze behavior for intention recognition during object manipulation (2009) Proceedings of the 2009 International Conference on Multimodal Interfaces (ICMIMLMI '09), pp. 199-206; Chennamma, H.R., Yuan, X., A survey on eye-gaze tracking techniques (2013) CoRR, , http://arxiv.org/abs/1312.6410, abs/1312. 6410 2013; Cook, M., Gaze and mutual gaze in social encounters: How long-and when- we look others ""in the eye"" is one of the main signals in nonverbal communication (1977) American Scientist, 65 (3), pp. 328-333. , http://www.jstor.org/stable/27847843, 1977; Fischer, T., Jin Chang, H., Demiris, Y., Rt-gene: Real-time eye gaze estimation in natural environments (2018) Proceedings of the European Conference on Computer Vision (ECCV), pp. 334-352; Hayhoe, M., Ballard, D., Eye movements in natural behavior (2005) Trends in Cognitive Sciences, 9 (4), pp. 188-194. , 2005; Huang, C., Mutlu, B., Anticipatory robot control for efficient human-robot collaboration (2016) The Eleventh ACM/IEEE International Conference on Human Robot Interaction (HRI '16), pp. 83-90. , IEEE Press; Ishii, R., Otsuka, K., Kumano, S., Yamato, J., Prediction of who will be the next speaker and when using gaze behavior in multiparty meetings (2016) ACM Trans. Interact. Intell. Syst., 6 (1), p. 31. , 2016; Johansson, R.S., Westling, G., Bäckström, A., Randall Flanagan, J., Eye-hand coordination in object manipulation (2001) Journal of Neuroscience, 21 (17), pp. 6917-6932. , 2001; Kellnhofer, P., Recasens, A., Stent, S., Matusik, W., Torralba, A., Gaze360: Physically unconstrained gaze estimation in the wild (2019) Proceedings of the IEEE International Conference on Computer Vision, pp. 6912-6921; Moon, A., Zheng, M., Troniak, D.M., Blumer, B.A., Gleeson, B., MacLean, K., Pan, J.X.M.K., Croft, E.A., Meet me where i'm gazing: How shared attention gaze affects human-robot handover timing (2014) HRI 2014-Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction. IEEE, Institute of Electrical and Electronics Engineers, pp. 334-341; Alberto Funes Mora, K., Odobez, J., Geometric generative gaze estimation (g3e) for remote rgb-d cameras (2014) CVPR, pp. 1773-1780; Alberto Funes Mora, K., Odobez, J., Gaze estimation in the 3d space using rgb-d sensors-towards head-pose and user invariance (2016) International Journal of Computer Vision, 118 (2), pp. 194-216. , 2016; Morimoto, C.H., Mimica, M.M.R., Eye gaze tracking techniques for interactive applications (2005) Comput. Vis. Image Underst., 98 (1), pp. 4-24. , 2005; Newman, B.A., Aronson, R.M., Srinivasa, S.S., Kitani, K., Admoni, H., (2018) HARMONIC: A Multimodal Dataset of Assistive Human-Robot Collaboration, , 2018; Nguyen, L.S., Frauendorfer, D., Mast, M.S., Gatica-Perez, D., Hire me: Computational inference of hirability in employment interviews based on nonverbal behavior (2014) IEEE Transactions on Multimedia, 16 (4), pp. 1018-1031. , 2014; Siegfried, R., Yu, Y., Odobez, J., Towards the use of social interaction conventions as prior for gaze model adaptation (2017) ICMI. ACM, pp. 154-162; Smith, B.A., Yin, Q., Feiner, S.K., Nayar, S.K., Gaze locking: Passive eye contact detection for human-object interaction (2013) Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology. ACM, pp. 271-280; Sugano, Y., Matsushita, Y., Sato, Y., Learning-by-synthesis for appearance-based 3d gaze estimation (2014) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1821-1828; Eye movements and vision (1967) Plenum, , 1967 Yarbus; Yu, Y., Funes, K., Odobez, J.-M., Headfusion: 360 degree head pose tracking combining 3d morphable model and 3d reconstruction (2018) IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 40 (1), pp. 2653-2667. , (Nov. 2018); Zhang, X., Sugano, Y., Bulling, A., Evaluation of appearance-based methods and implications for gaze-based applications (2019) Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, p. 416; Zhang, X., Sugano, Y., Fritz, M., Bulling, A., Mpiigaze: Real-world dataset and deep appearance-based gaze estimation (2017) IEEE Transactions on Pattern Analysis and Machine Intelligence, 41 (1), pp. 162-175. , 2017",,"Spencer S.N.","Blickshift;et al.;IMOTIONS;Neuroiconica;Pupil Labs;tobii pro","Association for Computing Machinery","2020 ACM Symposium on Eye Tracking Research and Applications, ETRA 2020","2 June 2020 through 5 June 2020",,160051,,9781450371346,,,"English","Eye Track. Res. Appl. Symp. (ETRA)",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85085733447
"Zhao Z., Papalexakis E.E., Ma X.","57208521157;36987222700;56242668800;","Learning physical common sense as knowledge graph completion via BERT data augmentation and constrained tucker factorization",2020,"EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference",,,,"3293","3298",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117826724&partnerID=40&md5=98f7f19055ae90c226441359c62c98ee","Department of Computer Science and Engineering, HKUST, Hong Kong; Department of Computer Science and Engineering, University of California, Riverside, United States","Zhao, Z., Department of Computer Science and Engineering, HKUST, Hong Kong; Papalexakis, E.E., Department of Computer Science and Engineering, University of California, Riverside, United States; Ma, X., Department of Computer Science and Engineering, HKUST, Hong Kong","Physical common sense plays an essential role in the cognition abilities of robots for human-robot interaction. Machine learning methods have shown promising results on physical commonsense learning in natural language processing but still suffer from model generalization. In this paper, we formulate physical commonsense learning as a knowledge graph completion problem to better use the latent relationships among training samples. Compared with completing general knowledge graphs, completing a physical commonsense knowledge graph has three unique characteristics: training data are scarce, not all facts can be mined from existing texts, and the number of relationships is small. To deal with these problems, we first use a pre-training language model BERT to augment training data, and then employ constrained tucker factorization to model complex relationships by constraining types and adding negative relationships. We compare our method with existing state-of-the-art knowledge graph embedding methods and show its superior performance. © 2020 Association for Computational Linguistics",,"Computational linguistics; Factorization; Human robot interaction; Learning algorithms; Learning systems; Natural language processing systems; Common sense; Completion problem; Data augmentation; General knowledge; Humans-robot interactions; Knowledge graphs; Machine learning methods; Model generalization; Training data; Training sample; Knowledge graph",,,,,"National Science Foundation, NSF: OAC-1808591; Bourns College of Engineering, University of California, Riverside, BCOE","The authors thank Dr. Mingfei Sun for helpful discussions, as well as all anonymous reviewers for insightful comments. E. Papalexakis was supported by a UCR-China collaboration grant by the Bourns College of Engineering at UCR, and by the National Science Foundation CDSE Grant no. OAC-1808591.",,,,,,,,,,"Auer, Sören, Bizer, Christian, Kobilarov, Georgi, Lehmann, Jens, Cyganiak, Richard, Ives, Zachary, DBpedia: A nucleus for a web of open data (2007) Proceedings of the 6th International The Semantic Web and 2nd Asian Conference on Asian Semantic Web Conference, p. 722735. , page Berlin, Heidelberg. Springer-Verlag; Balazevic, Ivana, Allen, Carl, Hospedales, Timothy, TuckER: Tensor factorization for knowledge graph completion (2019) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLPIJCNLP), pp. 5185-5194. , Hong Kong, China. Association for Computational Linguistics; Bisk, Yonatan, Zellers, Rowan, LeBras, Ronan, Gao, Jianfeng, Choi, Yejin, PIQA: reasoning about physical commonsense in natural language (2020) The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, pp. 7432-7439. , New York, NY, USA, February 7-12, 2020, pages AAAI Press; Bollacker, Kurt, Evans, Colin, Paritosh, Praveen, Sturge, Tim, Taylor, Jamie, Freebase: A collaboratively created graph database for structuring human knowledge (2008) Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, SIGMOD 08, p. 12471250. , page New York, NY, USA. Association for Computing Machinery; Bordes, Antoine, Usunier, Nicolas, Garcia-Durán, Alberto, Weston, Jason, Yakhnenko, Oksana, Translating embeddings for modeling multi-relational data (2013) Proceedings of the 26th International Conference on Neural Information Processing Systems, 2, p. 27872795. , NIPS13, page Red Hook, NY, USA. Curran Associates Inc; Bosselut, Antoine, Rashkin, Hannah, Sap, Maarten, Malaviya, Chaitanya, Celikyilmaz, Asli, Choi, Yejin, COMET: Commonsense transformers for automatic knowledge graph construction (2019) Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4762-4779. , Florence, Italy. Association for Computational Linguistics; Cambria, Erik, White, Bebo, Jumping NLP curves: A review of natural language processing research (2014) IEEE Computational intelligence magazine, 9 (2), pp. 48-57; Chang, Kai-Wei, Yih, Wen-tau, Yang, Bishan, Meek, Christopher, Typed tensor decomposition of knowledge bases for relation extraction (2014) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1568-1579. , Doha, Qatar. Association for Computational Linguistics; Clancy, Ryan, Ilyas, Ihab F., Lin, Jimmy, Scalable knowledge graph construction from text collections (2019) Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER), pp. 39-46. , Hong Kong, China. Association for Computational Linguistics; Devlin, Jacob, Chang, Ming-Wei, Lee, Kenton, Toutanova, Kristina, BERT: Pre-training of deep bidirectional transformers for language understanding (2019) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1, pp. 4171-4186. , (Long and Short Papers), pages Minneapolis, Minnesota. Association for Computational Linguistics; Forbes, Maxwell, Holtzman, Ari, Choi, Yejin, Do neural language representations learn physical commonsense? (2019) Proceedings of the 41st Annual Conference of the Cognitive Science Society; Han, Xu, Cao, Shulin, Lv, Xin, Lin, Yankai, Liu, Zhiyuan, Sun, Maosong, Li, Juanzi, OpenKE: An open toolkit for knowledge embedding (2018) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 139-144. , Brussels, Belgium. Association for Computational Linguistics; Ji, Guoliang, He, Shizhu, Xu, Liheng, Liu, Kang, Zhao, Jun, Knowledge graph embedding via dynamic mapping matrix (2015) Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, 1, pp. 687-696. , Long Papers), pages Beijing, China. Association for Computational Linguistics; Annervaz, K M, Chowdhury, Somnath Basu Roy, Dukkipati, Ambedkar, Learning beyond datasets: Knowledge graph augmented neural networks for natural language processing (2018) Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1, pp. 313-322. , pages New Orleans, Louisiana. Association for Computational Linguistics; Kazemi, Seyed Mehran, Poole, David, Simple embedding for link prediction in knowledge graphs (2018) Advances in Neural Information Processing Systems, pp. 4284-4295. , Curran Associates, Inc; Nickel, Maximilian, Murphy, Kevin, Tresp, Volker, Gabrilovich, Evgeniy, A review of relational machine learning for knowledge graphs (2016) Proceedings of the IEEE, 104 (1), pp. 11-33; Nickel, Maximilian, Tresp, Volker, Kriegel, Hans-Peter, A three-way model for collective learning on multi-relational data (2011) Proceedings of the 28th International Conference on International Conference on Machine Learning, ICML'11, p. 809816. , page Madison, WI, USA. Omnipress; Ostermann, Simon, Roth, Michael, Modi, Ashutosh, Thater, Stefan, Pinkal, Manfred, SemEval-2018 task 11: Machine comprehension using commonsense knowledge (2018) Proceedings of The 12th International Workshop on Semantic Evaluation, pp. 747-757. , New Orleans, Louisiana. Association for Computational Linguistics; Peters, Matthew E., Neumann, Mark, Logan, Robert, Schwartz, Roy, Joshi, Vidur, Singh, Sameer, Smith, Noah A., Knowledge enhanced contextual word representations (2019) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLPIJCNLP), pp. 43-54. , Hong Kong, China. Association for Computational Linguistics; Rajpurkar, Pranav, Zhang, Jian, Lopyrev, Konstantin, Liang, Percy, SQuAD: 100,000+ questions for machine comprehension of text (2016) Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 2383-2392. , Austin, Texas. Association for Computational Linguistics; She, Lanbo, Chai, Joyce, Interactive learning of grounded verb semantics towards human-robot communication (2017) Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 1, pp. 1634-1644. , Long Papers), pages Vancouver, Canada. Association for Computational Linguistics; Stickland, Asa Cooper, Murray, Iain, BERT and PALs: Projected attention layers for efficient adaptation in multi-task learning (2019) Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pp. 5986-5995. , Long Beach, California, USA. PMLR; Trouillon, Tho, Welbl, Johannes, Riedel, Sebastian, Gaussier, Eric, Bouchard, Guillaume, Complex embeddings for simple link prediction (2016) Proceedings of The 33rd International Conference on Machine Learning, volume 48 of Proceedings of Machine Learning Research, pp. 2071-2080. , New York, New York, USA. PMLR; Wang, Quan, Mao, Zhendong, Wang, Bin, Guo, Li, Knowledge graph embedding: A survey of approaches and applications (2017) IEEE Transactions on Knowledge and Data Engineering, 29 (12), pp. 2724-2743; Yang, Bishan, Yih, Scott Wen-tau, He, Xiaodong, Gao, Jianfeng, Deng, Li, Embedding entities and relations for learning and inference in knowledge bases (2015) Proceedings of the International Conference on Learning Representations (ICLR) 2015; Zhou, Hao, Young, Tom, Huang, Minlie, Zhao, Haizhou, Xu, Jingfang, Zhu, Xiaoyan, Commonsense knowledge aware conversation generation with graph attention (2018) Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pp. 4623-4629. , International Joint Conferences on Artificial Intelligence Organization",,,"Amazon Science;Apple;Baidu;Bloomberg Engineering;et al.;Google Research","Association for Computational Linguistics (ACL)","2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020","16 November 2020 through 20 November 2020",,172724,,9781952148606,,,"English","EMNLP - Conf. Empir. Methods Nat. Lang. Process., Proc. Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85117826724
"Chumkamon S., Kawamoto K., Inthiam J., Yokkampon U., Hayashi E.","25031430200;57224981509;56811526500;57221336666;7101665914;","Robot motion and grasping for blindfold handover",2020,"Proceedings of International Conference on Artificial Life and Robotics","2020",,,"255","258",,,"10.5954/ICAROB.2020.OS24-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108818649&doi=10.5954%2fICAROB.2020.OS24-4&partnerID=40&md5=ae7587a3111d645480707859a2289db5","Hayashi Laboratory, Kyushu Institute of Technology, Japan","Chumkamon, S., Hayashi Laboratory, Kyushu Institute of Technology, Japan; Kawamoto, K., Hayashi Laboratory, Kyushu Institute of Technology, Japan; Inthiam, J., Hayashi Laboratory, Kyushu Institute of Technology, Japan; Yokkampon, U., Hayashi Laboratory, Kyushu Institute of Technology, Japan; Hayashi, E., Hayashi Laboratory, Kyushu Institute of Technology, Japan","Autonomous robots in human-robot interaction (HRI) recently are becoming part of human life as the number of service or personal robots increasingly used in our home. In order to fulfill the gap of HRI merit, we would like to propose a system of the autonomous robot motion and grasping creation for assisting the disabled person such as the blind people for handover tasks to help blind people in pick and place tasks. In this paper, we develop the robot motion to receive the object by handing over from the blindfold human to represent the blindness. To determine the target of the object and human hand, we implement the 6DOF pose detection using a point cloud and hand detection using the Single Shot Detection model in Deep learning for planning motion using 9DOF arm robot with hand. We finally experiment and evaluate the tasks from blindfold-robot handover tasks. © The 2020 International Conference on Artificial Life and Robotics (ICAROB2020).","Factory Automation; Human-Robot Interaction; Motion Planning",,,,,,,,,,,,,,,,,"Beeson, Patrick, Ames, Barrett, TRAC-IK: An open-source library for improved solving of generic inverse kinematics (2015) 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 928-935. , IEEE; Sucan, I.A., Moll, M., Kavraki, L.E., The open motion planning library (2012) IEEE Robotics & Automation Magazine, 19 (4), pp. 72-82; Zhu, Z., Hu, H., Robot learning from demonstration in robotic assembly: A survey (2018) Robotics, 7 (2), p. 17; Liu, Wei, Anguelov, Dragomir, Erhan, Dumitru, Szegedy, Christian, Reed, Scott, Fu, Cheng-Yang, Berg, Alexander C., SSD: Single shot multibox detector (2016) European conference on computer vision, pp. 21-37. , Springer, Cham",,,,"ALife Robotics Corporation Ltd","25th International Conference on Artificial Life and Robotics, ICAROB 2020","13 January 2020 through 16 January 2020",,257439,24359157,,,,"English","Proc. International. Conference. Artificial. Life. Robot.",Conference Paper,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85108818649
"Shi L., Copot C., Vanlanduit S.","57211018738;35301588200;7004271926;","Visual Intention Classification by Deep Learning for Gaze-based Human-Robot Interaction",2020,"IFAC-PapersOnLine","53","5",,"750","755",,,"10.1016/j.ifacol.2021.04.168","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107846337&doi=10.1016%2fj.ifacol.2021.04.168&partnerID=40&md5=46cd8486b12b4282cf39b89f042d74b4","Op3Mech, University of Antwerp, Groenenborgerlaan 171, Antwerp, 2020, Belgium; Op3Mech, University of Antwerp, Groenenborgerlaan 171, Antwerp, 2020, Belgium; Op3Mech, University of Antwerp, Groenenborgerlaan 171, Antwerp, 2020, Belgium","Shi, L., Op3Mech, University of Antwerp, Groenenborgerlaan 171, Antwerp, 2020, Belgium; Copot, C., Op3Mech, University of Antwerp, Groenenborgerlaan 171, Antwerp, 2020, Belgium; Vanlanduit, S., Op3Mech, University of Antwerp, Groenenborgerlaan 171, Antwerp, 2020, Belgium","In this work, we propose a deep learning model to classify a human's visual intention in gaze-based Human-Robot Interaction(HRI). We consider a scenario in which a human wears a pair of eye tracking glasses and can select an object by gaze and a robotic manipulator picks up the object. A neural network is trained as a binary classifier to classify if a human is looking at an object. The network architecture is based on Fully Convolutional Net(FCN), Convolutional Block Attention Modules(CBAM) and Residual Blocks. We evaluate our model with two experiments. In one experiment we test the performance in the scenario where only a single object exists and the other one multiple objects exist. The results show that our proposed network is accurate and it can generalize well. The F1 score on the single object is 0.971 and 0.962 on multiple objects. © 2020 Elsevier B.V.. All rights reserved.","deep learning; gaze intention; HRI","Convolution; Convolutional neural networks; Deep learning; Eye tracking; Industrial manipulators; Man machine systems; Manipulators; Network architecture; Object tracking; Binary classifiers; F1 scores; Human robot Interaction (HRI); Learning models; Multiple objects; Robotic manipulators; Single object; Human robot interaction",,,,,,,,,,,,,,,,"Anantrasirichai, N., Gilchrist, I.D., Bull, D.R., Fixation identification for low-sample-rate mobile eye trackers (2016) 2016 Ieee International Conference on Image Processing (ICIP), pp. 3126-3130; Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G., (2015) The Ucr Time Series Classification Archive, , www.cs.ucr.edu/eamonn/timeseriesdata, 2015). URL; Cui, Z., Chen, W., Chen, Y., (2016) Multi-scale Con-volutional Neural Networks for Time Series Classification, , arXiv preprint arXiv:1603.06995; Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.A., Deep neural network ensembles for time series classification (2019) 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1-6; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2016) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 770-778; Hessels, R.S., Niehorster, D.C., Kemner, C., Hooge, I.T., Noise-robust fixation detection in eye movement data: Identification by two-means clustering (i2mc) (2017) Behavior Research Methods, 49 (5), pp. 1802-1823; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Computation, 9 (8), pp. 1735-1780; Hoppe, S., Bulling, A., (2016) End-to-end Eye Movement Detection Using Convolutional Neural Networks, , arXiv preprint arXiv:1609.02452; Hu, J., Shen, L., Sun, G., Squeeze-and-excitation networks (2018) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 7132-7141; Hu, Y., Li, J., Huang, Y., Gao, X., Channel-wise and spatial feature modulation network for single image super-resolution (2019) Ieee Transactions on Circuits and Systems for Video Technology; Ioffe, S., Szegedy, C., (2015) Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, , arXiv preprint arXiv:1502.03167; Karim, F., Majumdar, S., Darabi, H., Chen, S., Lstm fully convolutional networks for time series classification (2017) Ieee Access, 6, pp. 1662-1669; Karim, F., Majumdar, S., Darabi, H., Harford, S., Multivariate lstm-fcns for time series classification (2019) Neural Networks, 116, pp. 237-245; Kassner, M., Patera, W., Bulling, A., Pupil: An open source platform for pervasive eye tracking and mobile gaze-based interaction (2014) Adjunct Proceedings of the 2014 Acm International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp '14 Adjunct, pp. 1151-1160. , http://doi.acm.org/10.1145/2638728.2641695, ACM, New York, NY, USA. URL; Le Guennec, A., Malinowski, S., Tavenard, R., (2016) Data Augmentation for Time Series Classification Using Convolutional Neural Networks; Long, J., Shelhamer, E., Darrell, T., Fully convolutional networks for semantic segmentation (2015) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 3431-3440; Nair, V., Hinton, G.E., Rectified linear units improve restricted boltzmann machines (2010) Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807-814; Redmon, J., Farhadi, A., (2016) Yolo9000: Better, Faster, Stronger, , arXiv preprint arXiv:1612.08242; Roy, A.G., Navab, N., Wachinger, C., Recalibrating fully convolutional networks with spatial and channel squeeze and excitation blocks (2019) Ieee Transactions on Medical Imaging, 38 (2), pp. 540-549; Salvucci, D.D., Goldberg, J.H., Identifying fixations and saccades in eye-tracking protocols (2000) Proceedings of the 2000 Symposium on Eye Tracking Research & Applications, pp. 71-78; Serra, J., Pascual, S., Karatzoglou, A., Towards a universal neural network encoder for time series (2018) Ccia, pp. 120-129; Shi, L., Copot, C., Vanlanduit, S., (2019) What Are You Looking At? Detecting Human Intention in Gaze Based Human-robot Interaction, , arXiv preprint arXiv:1909.07953; Wang, Z., Yan, W., Oates, T., Time series classification from scratch with deep neural networks: A strong baseline (2017) 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1578-1585; Woo, S., Park, J., Lee, J.Y., So Kweon, I., Cbam: Convolutional block attention module (2018) Proceedings of the European Conference on Computer Vision (ECCV), pp. 3-19; Zemblys, R., Niehorster, D.C., Holmqvist, K., Gazenet: End-to-end eye-movement event detection with deep neural networks (2019) Behavior Research Methods, 51 (2), pp. 840-864; Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W., Ni, J., Chawla, N.V., A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data (2019) Proceedings of the Aaai Conference on Artificial Intelligence, 33, pp. 1409-1416; Zhao, B., Lu, H., Chen, S., Liu, J., Wu, D., Convolutional neural networks for time series classification (2017) Journal of Systems Engineering and Electronics, 28 (1), pp. 162-169; Zheng, Y., Liu, Q., Chen, E., Ge, Y., Zhao, J.L., Time series classification using multi-channels deep convolutional neural networks (2014) International Conference on Web-Age Information Management, pp. 298-310. , Springer",,,,"Elsevier B.V.","3rd IFAC Workshop on Cyber-Physical and Human Systems, CPHS 2020","3 December 2020 through 5 December 2020",,145888,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85107846337
"Huang J., Wu W., Zhang Z., Chen Y.","56326298100;57211553119;57201427541;57203833448;","A Human Decision-Making Behavior Model for Human-Robot Interaction in Multi-Robot Systems",2020,"IEEE Access","8",,,"197853","197862",,2,"10.1109/ACCESS.2020.3035348","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102885228&doi=10.1109%2fACCESS.2020.3035348&partnerID=40&md5=9d040a2176f35fa6240fbce49794d085","College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China","Huang, J., College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China; Wu, W., College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China; Zhang, Z., College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China; Chen, Y., College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China","In this paper, a novel human decision-making behavior model is proposed for human-robot interaction (HRI) for the control of multi-robot systems (MRS). The proposed human drift diffusion model (HDDM) combines the traditional drift diffusion model (DDM) and the null-space-based behavioral control (NSBC) method by introducing a data-processing station and a human cognitive system. In the HDDM, the evolution of human-decision information is computed. By using a threshold of such information to trigger human interaction, accurate human decision-making timing can be obtained. In addition, a cooperative controller is designed for robots to follow human instructions. Simulations under various scenarios showthat by using the proposedHDDMand the controller, robots can complete human instructions more accurately comparing to traditional methods. An experiment using a group of quadrotors subject to external wind disturbances also demonstrate the effectiveness of the proposedHDDMin real-world uncertain environments. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.","Human decisionmaking behavior model; Human drift diffusion model; Human intervention; Multi-robot system","Behavioral research; Cognitive systems; Controllers; Data handling; Decision making; Diffusion; Industrial robots; Man machine systems; Multipurpose robots; Robot learning; Drift-diffusion model; Human cognitive systems; Human decision making; Human robot Interaction (HRI); Multirobot systems; Null-space-based behavioral control; Processing stations; Uncertain environments; Social robots",,,,,"National Natural Science Foundation of China, NSFC: 61603094","This work was supported in part by the National Natural Science Foundation of China under Grant 61603094.",,,,,,,,,,"Huang, J., Zhou, N., Cao, M., Adaptive fuzzy behavioral control of second-order autonomous agents with prioritized missions: Theory and experiments (2019) Ieee Trans. Ind. Electron, 66 (12), pp. 9612-9622. , Dec; Ren, W., Beard, R.W., (2008) Distributed Consensus in Multi-Vehicle Cooper-Ative Control, , London, U.K.: Springer; Hu, W., Liu, L., Feng, G., Output consensus of heterogeneous linear multi-agent systems by distributed event-triggered/self-triggered strategy (2017) Ieee Trans. Cybern, 47 (8), pp. 1914-1924. , , Aug; Cruz, J., Leader-follower strategies for multilevel systems (1978) Ieee Trans. Autom. Control, 23 (2), pp. 244-255. , Apr; He, S., Wang, M., Dai, S.-L., Luo, F., Leader-follower formation control of USVs with prescribed performance and collision avoidance (2019) Ieee Trans. Ind. Informat, 15 (1), pp. 572-581. , Jan; Ren, W., Beard, R.W., Decentralized scheme for spacecraft formation-ying via the virtual structure approach (2004) J. Guid., Control, Dyn., 27 (1), pp. 73-82. , Jan; Balch, T., Arkin, R.C., Behavior-based formation control for multirobot teams (1998) Ieee Trans. Robot. Autom., 14 (6), pp. 926-939. , Dec; Chen, J., Gan, M., Huang, J., Dou, L., Fang, H., Formation control of multiple euler-Lagrange systems via null-space-based behavioral control (2016) Sci. China Inf. Sci., 59 (1), pp. 1-11. , Jan; Antonelli, G., Chiaverini, S., Kinematic control of platoons of autonomous vehicles (2006) Ieee Trans. Robot., 22 (6), pp. 1285-1292. , Dec; Chen, Y., Zhang, Z., Huang, J., Dynamic task priority planning for null-space behavioral control of multi-agent systems (2020) Ieee Access, 8, pp. 149643-149651; Antonelli, G., Arrichiello, F., Chakraborti, S., Chiaverini, S., Experiences of formation control of multi-robot systems with the null-spacebased behavioral control (2007) Proc. Ieee Int. Conf. Robot. Autom., pp. 1068-1073. , Apr; Goodrich, M.A., Schultz, A.C., Human-robot interaction: A survey (2007) Found. Trends Hum.-Comput. Interact, 1 (3), pp. 203-275; Morioka, K., Lee, J.-H., Hashimoto, H., Human-following mobile robot in a distributed intelligent sensor network (2004) Ieee Trans. Ind. Elec-tron., 51 (1), pp. 229-237. , Feb; Franchi, A., Giordano, P.R., Secchi, C., Son, H.I., Bulthoff, H.H., A passivity-based decentralized approach for the bilateral teleoperation of a group of UAVs with switching topology (2011) Proc. Ieee Int. Conf. Robot. Autom., pp. 898-905. , May; Sadrfaridpour, B., Wang, Y., Collaborative assembly in hybrid manufacturing cells: An integrated framework for human-robot interaction (2018) Ieee Trans. Autom. Sci. Eng., 15 (3), pp. 1178-1192. , Jul; Fang, H., Shang, C., Chen, J., An optimization-based shared control framework with applications in multi-robot systems (2018) Sci. China Inf. Sci., 61 (1), pp. 201-203. , Jan; Scheggi, S., Morbidi, F., Prattichizzo, D., Human-robot formation control via visual and vibrotactile haptic feedback (2014) Ieee Trans. 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Control, 44, pp. 342-354; Pentland, A., Liu, A., Modeling and prediction of human Behavior (1999) Neural Comput, 11 (1), pp. 229-242. , Jan; Gao, J., Lee, J.D., Extending the decision-eld theory to model operators' reliance on automation in supervisory control situations (2006) Ieee Trans. Syst., Man, Cybern. A, Syst. Humans, 36 (5), pp. 943-959. , Sep; Ratcliff, R., McKoon, G., The diffusion decision model: Theory and data for two-choice decision tasks (2008) Neural Comput, 20 (4), pp. 873-922. , Apr; Ratcliff, R., Gomez, P., McKoon, G., A diffusion model account of the lexical decision task (2004) Psychol. Rev., 111 (1), pp. 159-170; Stewart, A., Cao, M., Nedic, A., Tomlin, D., Leonard, N., Towards human-robot teams: Model-based analysis of human decision making in two-alternative choice tasks with social feedback (2012) Proc. Ieee, 100 (3), pp. 751-775. , Mar; Antonelli, G., Arrichiello, F., Chiaverini, S., The null-space-based behavioral control for mobile robots (2005) Proc. Int. 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Robot., 25 (5), pp. 985-994. , Oct","Chen, Y.; College of Electrical Engineering and Automation, China; email: ytchen_fzu@163.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85102885228
"Kothig A., Muñoz J., Mahdi H., Aroyo A.M., Dautenhahn K.","57210748983;56645651500;57220178635;57192384790;7003305185;","HRI Physio Lib: A Software Framework to Support the Integration of Physiological Adaptation in HRI",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"36","47",,2,"10.1007/978-3-030-62056-1_4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097202082&doi=10.1007%2f978-3-030-62056-1_4&partnerID=40&md5=a9c655dd0b6fc62a6a66fafc4f12885c","Social and Intelligent Robotics Research Lab (SIRRL), University of Waterloo, Waterloo, Canada","Kothig, A., Social and Intelligent Robotics Research Lab (SIRRL), University of Waterloo, Waterloo, Canada; Muñoz, J., Social and Intelligent Robotics Research Lab (SIRRL), University of Waterloo, Waterloo, Canada; Mahdi, H., Social and Intelligent Robotics Research Lab (SIRRL), University of Waterloo, Waterloo, Canada; Aroyo, A.M., Social and Intelligent Robotics Research Lab (SIRRL), University of Waterloo, Waterloo, Canada; Dautenhahn, K., Social and Intelligent Robotics Research Lab (SIRRL), University of Waterloo, Waterloo, Canada","The rise of available physiological sensors in recent years can be attributed to the widespread popularity of commercial sensing technologies equipped with health monitoring technology. To make more engaging human-robot interaction (HRI), social robots should have some ability to infer their social partner’s affective state. Measurements of the autonomic nervous system via non-invasive physiological sensors provide a convenient window into a person’s affective state, namely their emotions, behavior, stress, and engagement. HRI research has included physiological sensors in-the-loop, however implementations are often specific to studies, and do not lend well for reusability. To address this gap, we propose a modular, flexible and extensible framework designed to work with popular robot platforms. Our framework will be compatible with both lab and consumer grade sensors, and includes essential tools and processing algorithms for affective state estimation geared towards real-time HRI applications. © 2020, Springer Nature Switzerland AG.","Affective computing; Human-robot interaction; Physiological sensors; Software framework","Computer programming; Physiology; Reusability; Robotics; Autonomic nervous system; Extensible framework; Human robot Interaction (HRI); Physiological adaptations; Physiological sensors; Processing algorithms; Sensing technology; Software frameworks; Social robots",,,,,,,,,,,,,,,,"(2020) Eventide, , http://www.okazolab.com/; (2020) Unpack Human Behaviour, , https://imotions.com/; Barco, A., Albo-Canals, J., Garriga, C., Engagement based on a customization of an ipod-lego robot for a long-term interaction for an educational purpose (2014) Proceeding of ACM/IEEE HRI 2014, pp. 124-125. , IEEE; Batista, D., da Silva, H.P., Fred, A., Moreira, C., Reis, M., Ferreira, H.A., Benchmarking of the bitalino biomedical toolkit against an established gold standard (2019) Healthc. 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Continuous Engagement Assessment from a robot’s Point of View., , arXiv preprint arXiv; Dillen, N., Ilievski, M., Law, E., Nacke, L.E., Czarnecki, K., Schneider, O., Keep calm and ride along: Passenger comfort and anxiety as physiological responses to autonomous driving styles (2020) Proceeding of CHI Conference on Human Factors in Computing Systems, pp. 1-13; D’Mello, S., A time for emoting: When affect-sensitivity is and isn’t effective at promoting deep learning (2010) International Conference on Intelligent Tutoring Systems, pp. 245-254; Essa, I.A., Pentland, A.P., Coding, analysis, interpretation, and recognition of facial expressions (1997) IEEE Trans. Pattern Anal. Mach. Intell., 19 (7), pp. 757-763; Fairclough, S., Gilleade, K., Construction of the biocybernetic loop: A case study (2012) Proceeding of ACM ICMI, pp. 571-578; Fairclough, S.H., Fundamentals of physiological computing (2009) Interact. Comput., 21 (1-2), pp. 133-145; Fan, J., A robotic coach architecture for elder care (Rocare) based on multi-user engagement models (2016) IEEE Trans. Neural Syst. Rehabil. Eng., 25 (8), pp. 1153-1163; Fan, Y., Lu, X., Li, D., Liu, Y., Video-based emotion recognition using CNN-RNN and c3d hybrid networks (2016) Proceeding of ACM ICMI, pp. 445-450. , 2016; Fiorini, L., Mancioppi, G., Semeraro, F., Fujita, H., Cavallo, F., Unsupervised emotional state classification through physiological parameters for social robotics applications (2020) Knowl.-Based Syst., 190; Foster, M.E., Gaschler, A., Giuliani, M., Automatically classifying user engagement for dynamic multi-party human-robot interaction (2017) Int. J. Soc. Robot., 9 (5), pp. 659-674; Gonzalez Billandon, J., Can a robot catch you lying? A machine learning system to detect lies during interactions (2019) Front. Robot. AI, 6 (64); Gunes, H., Piccardi, M., Bi-modal emotion recognition from expressive face and body gestures (2007) J. 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AI, 4, p. 36; Shan, C., Gong, S., McOwan, P.W., Beyond facial expressions: Learning human emotion from body gestures (2007) BMVC, pp. 1-10; Sjak-Shie, E.E., (2019) Physiodata Toolbox, , https://PhysioDataToolbox.leidenuniv.nl, (version 0.5) [computer software]; Szafir, D., Mutlu, B., Pay attention! designing adaptive agents that monitor and improve user engagement (2012) Proceeding of IGCHI Conference on Human Factors in Computing Systems, pp. 11-20; Tsiakas, K., Abujelala, M., Makedon, F., Task engagement as personalization feedback for socially-assistive robots and cognitive training (2018) Technologies, 6 (2), p. 49; Vogel, E.A., (2020) About One-In-Five Americans Use a Smart Watch Or Fitness Tracker, , https://www.pewresearch.org/fact-tank/2020/01/09/about-one-in-five-americans-use-a-smart-watch-or-fitness-tracker/","Kothig, A.; Social and Intelligent Robotics Research Lab (SIRRL), Canada; email: austin.kothig@uwaterloo.ca","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097202082
"Petisca S., Paiva A., Esteves F.","57188997854;7102683774;7004375639;","Perceptions of People’s Dishonesty Towards Robots",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"132","143",,,"10.1007/978-3-030-62056-1_12","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097184981&doi=10.1007%2f978-3-030-62056-1_12&partnerID=40&md5=bbf33bd98f7dce756f5a39763b470261","Instituto Universitário de Lisboa (ISCTE-IUL) and INESC-ID, Lisbon, Portugal; Instituto Superior Técnico (IST) and INESC-ID, Lisbon, Portugal; Mid Sweden University and ISCTE-IUL, Sundsvall, Sweden","Petisca, S., Instituto Universitário de Lisboa (ISCTE-IUL) and INESC-ID, Lisbon, Portugal; Paiva, A., Instituto Superior Técnico (IST) and INESC-ID, Lisbon, Portugal; Esteves, F., Mid Sweden University and ISCTE-IUL, Sundsvall, Sweden","Dishonest behavior is an issue in human-human interactions and the same might happen in human-robot interactions. To ascertain people’s perceptions of dishonesty, we asked participants to evaluate five different scenarios where someone was being dishonest towards a human or a robot, but we varied the level of autonomy the robot presented. We asked them how guilty they would feel by being dishonest towards a robot, and why do they think people would be dishonest with robots. We see that, regardless of being a human or the autonomy the robot presented, people always evaluated as being wrong to be dishonest. They reported feeling low guilt with a robot. And they expressed that people will be dishonest mostly because of lack of capabilities in the robot to prevent dishonesty, absence of presence, and a human tendency for dishonesty. These results bring implications for the developments of autonomous robots in the future. © 2020, Springer Nature Switzerland AG.","Dishonesty; Human-robot interaction; Unethical behavior","Robotics; Human-human interactions; Level of autonomies; Human robot interaction",,,,,"Fundação para a Ciência e a Tecnologia, FCT: Ref.SFRH/BD/118013/2016, UIDB/50021/2020; Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção, INCT-EN","This work was supported by national funds through Funda??o para a Ci?ncia e a Tecnologia (FCT) with reference UIDB/50021/2020 and Sofia Petisca acknowledges an FCT Grant (Ref.SFRH/BD/118013/2016).","This work was supported by national funds through Funda¸cão para a Ciência e a Tecnologia (FCT) with reference UIDB/50021/2020 and Sofia Petisca acknowledges an FCT Grant (Ref.SFRH/BD/118013/2016).",,,,,,,,,"Fischbacher, U., Föllmi-Heusi, F., Lies in disguise–an experimental study on cheating (2013) J. Eur. Econ. 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Mark. Res., 45 (6), pp. 633-644. , https://doi.org/10.1509/jmkr.45.6.633; Petisca, S., Esteves, F., Paiva, A., Cheating with robots: How at ease do they make us feel? (2019) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2102-2107. , https://doi.org/10.1109/IROS40897.2019.8967790; Sandoval, E.B., Brandstetter, J., Bartneck, C., Can a robot bribe a human?: The measurement of the negative side of reciprocity in human robot interaction (2016) The Eleventh ACM/IEEE International Conference on Human Robot Interaction, pp. 117-124. , https://doi.org/10.1109/HRI.2016.7451742, IEEE Press; Shalvi, S., Eldar, O., Bereby-Meyer, Y., Honesty requires time (And lack of justifications) (2012) Psychol. Sci., 23 (10), pp. 1264-1270. , https://doi.org/10.1177/0956797612443835; Ullman, D., Leite, I., Phillips, J., Kim-Cohen, J., Scassellati, B., Smart human, smarter robot: How cheating affects perceptions of social agency (2014) Proceedings of the Annual Meeting of the Cognitive Science Society, 36; Zhong, C.B., Bohns, V.K., Gino, F., Good lamps are the best police: Darkness increases dishonesty and self-interested behavior (2010) Psychol. Sci., 21 (3), pp. 311-314. , https://doi.org/10.1177/0956797609360754","Petisca, S.; Instituto Universitário de Lisboa (ISCTE-IUL) and INESC-IDPortugal; email: Sofia_Petisca@iscte-iul.pt","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097184981
"Williams T., Zhu Q., Grollman D.","7404172627;56300893800;15845413500;","An experimental ethics approach to robot ethics education",2020,"AAAI 2020 - 34th AAAI Conference on Artificial Intelligence",,,,"13485","13492",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097179536&partnerID=40&md5=799819fb3338df835260fa8665cb3a78","Colorado School of Mines, Golden, CO  80402, United States; Plus One Robotics, Boulder, CO  80301, United States","Williams, T., Colorado School of Mines, Golden, CO  80402, United States; Zhu, Q., Colorado School of Mines, Golden, CO  80402, United States; Grollman, D., Plus One Robotics, Boulder, CO  80301, United States","We propose an experimental ethics-based curricular module for an undergraduate course on Robot Ethics. The proposed module aims to teach students how human subjects research methods can be used to investigate potential ethical concerns arising in human-robot interaction, by engaging those students in real experimental ethics research. In this paper we describe the proposed curricular module, describe our implementation of that module within a Robot Ethics course offered at a medium-sized engineering university, and statistically evaluate the effectiveness of the proposed curricular module in achieving desired learning objectives. While our results do not provide clear evidence of a quantifiable benefit to undergraduate achievement of the described learning objectives, we note that the module did provide additional learning opportunities for graduate students in the course, as they helped to supervise, analyze, and write up the results of this undergraduate-performed research experiment. © 2020, Association for the Advancement of Artificial Intelligence.",,"Artificial intelligence; Human robot interaction; Philosophical aspects; Students; Engineering universities; Ethical concerns; Graduate students; Human subjects; Learning objectives; Learning opportunity; Robot ethics; Undergraduate Courses; Educational robots",,,,,"National Science Foundation, NSF: IIS-1849348; Colorado School of Mines, CSM","This work was supported in part by a grant from the National Science Foundation (IIS-1849348) and in part by Colorado School of Mines 2018 Tech Fee CS-01. The authors would like to extend thanks to the graduate students of Colorado School of Mines’ 2019 Robot Ethics class for their assistance in supervising the experiments described in this paper.",,,,,,,,,,"Allen, J. E., Guinn, C. 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L., Increasing student participation in undergraduate research benefits students, faculty, and department (2008) Teaching of Psychology, 35 (3), pp. 194-197; Winkle, K., Lemaignan, S., Caleb-Solly, P., Leonards, U., Turton, A., Bremner, P., Effective persuasion strategies for socially assistive robots (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 277-285. , IEEE",,,"Association for the Advancement of Artificial Intelligence","AAAI press","34th AAAI Conference on Artificial Intelligence, AAAI 2020","7 February 2020 through 12 February 2020",,166426,,9781577358350,,,"English","AAAI - AAAI Conf. Artif. Intell.",Conference Paper,"Final","",Scopus,2-s2.0-85097179536
"Ghafurian M., Lakatos G., Tao Z., Dautenhahn K.","57190254083;8432728600;57220188286;7003305185;","Design and Evaluation of Affective Expressions of a Zoomorphic Robot",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"1","12",,4,"10.1007/978-3-030-62056-1_1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097170643&doi=10.1007%2f978-3-030-62056-1_1&partnerID=40&md5=539ea939e0c31a2fbf1278726f2feb07","Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada; Department of Computer Science, University of Hertfordshire, Hatfield, United Kingdom; David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada","Ghafurian, M., Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada; Lakatos, G., Department of Computer Science, University of Hertfordshire, Hatfield, United Kingdom; Tao, Z., David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada; Dautenhahn, K., Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada","Social robots that are capable of showing affective expressions can improve human-robot interaction and users’ experiences in many ways. This capability is important in many application areas. In this paper, we describe the design and evaluation of 11 affective expressions for Miro, an animal-like robot. Affective expressions were inspired by the animal and human behavior literature. The designs were evaluated through a video study on Mechanical Turk with 88 participants. Five of the expressions—happy, sad, excited, surprised, and tired—were correctly recognized by more than half of the participants. While fewer participants were able to recognize the other, more complex affective expressions, we observed a significant correlation between the recognition of the robot’s displayed affective states and participants’ understanding of human emotions. This suggested that the reduced accuracy in the recognition of the other affective expressions can be due to the general challenges involved in recognizing complex emotions. © 2020, Springer Nature Switzerland AG.","Affective expressions; Design of social robots; Miro; Zoomorphic robots","Animals; Behavioral research; Machine design; Robotics; Affective state; Application area; Complex emotions; Design and evaluations; Human behaviors; Human emotion; Mechanical turks; Social robots",,,,,"University of Waterloo, UW","Acknowledgment. This research was undertaken, in part, thanks to funding from the Canada 150 Research Chairs Program and the Network for Aging Research at the University of Waterloo. We thank Jesse Hoey, Chrystopher L. Nehaniv, and the anonymous reviewers for their comments on this work.",,,,,,,,,,"Bartneck, C., Duenser, A., Moltchanova, E., Zawieska, K., Comparing the similarity of responses received from studies in amazon mechanical turk to studies conducted online and with direct recruitment (2015) Plos One, 10 (4), pp. 1-23; Breazeal, C., Emotion and sociable humanoid robots (2003) Int. J. Hum. Comput. Stud., 59 (1-2), pp. 119-155; Buckland, E., Volk, H., Burn, C., Abeyesinghe, S., Owner perceptions of companion dog expressions of positive emotional states and the contexts in which they occur (2014) Anim. Welf., 23 (3), pp. 287-296; Burn, C.C., Bestial boredom: A biological perspective on animal boredom and suggestions for its scientific investigation (2017) Anim. 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Behav., 59, pp. 411-419; Ghafurian, M., Budnarain, N., Hoey, J., Role of emotions in perception of humanness of virtual agents (2019) Proceedings of the 18Th International Conference on Autonomous Agents and Multiagent Systems, pp. 1979-1981; Hampshire, V., Robertson, S., Using the facial grimace scale to evaluate rabbit wellness in post-procedural monitoring (2015) Lab Anim, 44 (7), p. 259; Hasegawa, M., Ohtani, N., Ohta, M., Dogs’ body language relevant to learning achievement (2014) Anim, 4 (1), pp. 45-58; Keating, S.C., Thomas, A.A., Flecknell, P.A., Leach, M.C., Evaluation of emla cream for preventing pain during tattooing of rabbits: Changes in physiological, behavioural and facial expression responses (2012) Plos One, 7 (9); Klausz, B., Kis, A., Persa, E., Miklósi, Á., Gácsi, M., A quick assessment tool for human-directed aggression in pet dogs (2014) Aggressive Behav, 40 (2), pp. 178-188; Konok, V., Dóka, A., Miklósi, Á., The behavior of the domestic dog (Canis famil-iaris) during separation from and reunion with the owner: A questionnaire and an experimental study (2011) Appl. 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LNCS (LNAI), 9793, pp. 179-191. , https://doi.org/10.1007/978-3-319-42417-017, Lepora, N.F.F., Mura, A., Mangan, M., Verschure, P.F.M.J.F.M.J., Desmulliez, M., Prescott, T.J.J. (eds.) , Springer, Cham; Morris, P.H., Doe, C., Godsell, E., Secondary emotions in non-primate species? Behavioural reports and subjective claims by animal owners (2008) Cogn. Emot., 22 (1), pp. 3-20; Nijdam, N.A., (2009) Mapping Emotion to Color. Book Mapping Emotion to Color, pp. 2-9; O’Brien, H.L., Toms, E.G., What is user engagement? A conceptual framework for defining user engagement with technology (2008) J. Am. Soc. Inf. Sci. Technol., 59 (6), pp. 938-955; Quaranta, A., Siniscalchi, M., Vallortigara, G., Asymmetric tail-wagging responses by dogs to different emotive stimuli (2007) Curr. Biol., 17 (6), pp. R199-R201; Reisenzein, R., Bördgen, S., Holtbernd, T., Matz, D., Evidence for strong dissociation between emotion and facial displays: The case of surprise (2006) J. Pers. Soc. Psychol., 91 (2), p. 295; Ritschel, H., Aslan, I., Mertes, S., Seiderer, A., André, E., Personalized synthesis of intentional and emotional non-verbal sounds for social robots (2019) 8Th International Conference on Affective Computing and Intelligent Interaction, pp. 1-7; Saldien, J., Goris, K., Vanderborght, B., Vanderfaeillie, J., Lefeber, D., Expressing emotions with the social robot probo (2010) Int. J. Soc. Robot., 2 (4), pp. 377-389; Schirmer, A., Seow, C.S., Penney, T.B., Humans process dog and human facial affect in similar ways (2013) Plos One, 8 (9); Tami, G., Gallagher, A., Description of the behaviour of domestic dog (Canis famil-iaris) by experienced and inexperienced people (2009) Appl. Anim. Behav. Sci., 120 (3-4), pp. 159-169; Terwogt, M.M., Hoeksma, J.B., Colors and emotions: Preferences and combinations (1995) J. Gen. Psychol., 122 (1), pp. 5-17; Topál, J., Miklósi, Á., Csányi, V., Dóka, A., Attachment behavior in dogs (Canis familiaris): A new application of ainsworth’s (1969) strange situation test (1998) J. Comp. Psychol., 112 (3), p. 219; Vas, J., Topál, J., Gácsi, M., Miklósi, A., Csányi, V., A friend or an enemy? Dogs’ reaction to an unfamiliar person showing behavioural cues of threat and friendliness at different times (2005) Appl. Anim. Behav. Sci., 94 (1-2), pp. 99-115; Velásquez, J.D., An emotion-based approach to robotics (1999) Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems, 1, pp. 235-240; Widen, S.C., Russell, J.A., The “disgust face” conveys anger to children (2010) Emotion, 10 (4), p. 455; Widen, S.C., Russell, J.A., Brooks, A., Anger and disgust: Discrete or overlapping categories (2004) 2004 APS Annual Convention, Boston College, Chicago, IL","Ghafurian, M.; Department of Electrical and Computer Engineering, Canada; email: moojan@uwaterloo.ca","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097170643
"Williams T., Grollman D., Han M., Jackson R.B., Lockshin J., Wen R., Nahman Z., Zhu Q.","7404172627;15845413500;57220177795;57205545565;57220182621;57210420554;57220189686;56300893800;","“Excuse Me, Robot”: Impact of Polite Robot Wakewords on Human-Robot Politeness",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"404","415",,,"10.1007/978-3-030-62056-1_34","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097168656&doi=10.1007%2f978-3-030-62056-1_34&partnerID=40&md5=605a8fa817f8d49c3a18ff900a2ea2ea","Colorado School of Mines, Golden, CO  80401, United States; Plus One Robotics, Boulder, CO, United States","Williams, T., Colorado School of Mines, Golden, CO  80401, United States; Grollman, D., Plus One Robotics, Boulder, CO, United States; Han, M., Colorado School of Mines, Golden, CO  80401, United States; Jackson, R.B., Colorado School of Mines, Golden, CO  80401, United States; Lockshin, J., Colorado School of Mines, Golden, CO  80401, United States; Wen, R., Colorado School of Mines, Golden, CO  80401, United States; Nahman, Z., Colorado School of Mines, Golden, CO  80401, United States; Zhu, Q., Colorado School of Mines, Golden, CO  80401, United States","While the ultimate goal of natural-language based Human-Robot Interaction (HRI) may be free-form, mixed-initiative dialogue, social robots deployed in the near future will likely primarily engage in wakeword-driven interaction, in which users’ commands are prefaced by a wakeword such as “Hey, Robot”. This style of interaction helps to allay user privacy concerns, as the robot’s full speech recognition module need not be employed until the target wakeword is used. Unfortunately, there are a number of concerns in the popular media surrounding this style of interaction, with consumers fearing that it is training users (in particular, children) to be rude towards technology, and by extension, rude towards other humans. In this paper, we present a study that demonstrates how an alternate style of wakeword, i.e., “Excuse me, Robot” may allay this concern, by priming users to phrase commands as Indirect Speech Acts. © 2020, Springer Nature Switzerland AG.","Indirect speech acts; Persuasive robotics; Wakewords","Robotics; Speech recognition; Freeforms; Human robot Interaction (HRI); Human robots; Mixed initiative; Natural languages; Speech acts; Speech recognition modules; User privacy; Social robots",,,,,"National Science Foundation, NSF","Acknowledgments. This work was funded in part by Grants IIS-1849348 and IIS-1909847 from the National Science Foundation. We would also like to thank the students of the Spring 2019 Robot Ethics class at the Colorado School of Mines for their help conducting this experiment.",,,,,,,,,,"Allen, J.E., Guinn, C.I., Horvtz, E., Mixed-initiative interaction (1999) IEEE Intell. Syst. Appl., 14 (5), pp. 14-23; Baig, E., (2018) Kids were Being Rude to Alexa, So Amazon Updated It, , https://www.usatoday.com/story/tech/columnist/baig/2018/04/25/amazon-echo-dot-kids-alexa-thanks-them-saying-please/547911002/; (2018) BBC: Amazon Alexa to Reward Kids Who Say: ‘please, , https://www.bbc.com/news/technology-43897516; Bonfert, M., Spliethöver, M., Arzaroli, R., Lange, M., Hanci, M., Porzel, R., If you ask nicely: A digital assistant rebuking impolite voice commands (2018) Proceedings of the 2018 on International Conference on Multimodal Interaction, pp. 95-102. , ACM; Briggs, G., Scheutz, M., How robots can affect human behavior: Investigating the effects of robotic displays of protest and distress (2014) Int. J. Soc. Rob., 6, pp. 343-355; Brown, P., Levinson, S., (1987) Politeness: Some Universals in Language Usage, , Cambridge University Press, Cambridge; Bürkner, P.C., BRMS: An r package for bayesian multilevel models using stan (2017) J. Stat. Softw., 80 (1), pp. 1-28; Carpinella, C.M., Wyman, A.B., Perez, M.A., Stroessner, S.J., The robotic social attributes scale (ROSAS): Development and validation (2017) Proceeding of HRI; Cassell, J., Torres, O.E., Prevost, S., (1999) Turn Taking versus Discourse Structure. Machine Conversations, pp. 143-15533. , Springer, Berlin; Danescu-Niculescu-mizil, C., Sudhof, M., Jurafsky, D., Leskovec, J., Potts, C., A computational approach to politeness with application to social factors (2013) Proc. ACL; Elgin, M., (2018) The Case against Teaching Kids to Be Polite to Alexa, , https://www.fastcompany.com/40588020/the-case-against-teaching-kids-to-be-polite-to-alexa, Accessed 26 Jun 2019; Faucet, D., (2019) Voice Faucet, , https://www.deltafaucet.com/Voice; Ghosh, J., Li, Y., Mitra, R., On the use of cauchy prior distributions for bayesian logistic regression (2015) Bayesian Anal, 13, pp. 359-383; Gino, F., Understanding ordinary unethical behavior: Why people who value morality act immorally (2015) Curr. Opin. Behav. Sci., 3, pp. 107-111; Gordon, K., (2018) Alexa and the Age of Casual Rudeness, , https://www.theatlantic.com/family/archive/2018/04/alexa-manners-smart-speakers-command/558653/, Accessed 26 Jun 2019; Horvitz, E., Principles of mixed-initiative user interfaces (1999) Proceeding of CHI; Jackson, R.B., Wililams, T., On perceived social and moral agency in natural language capable robots (2019) HRI WS on the Dark Side of HRI; Jackson, R.B., Williams, T., Language-capable robots may inadvertently weaken human moral norms (2019) Proceeding of HRI, pp. 401-410. , IEEE; Jarosz, A.F., Wiley, J., What are the odds? A practical guide to computing and reporting bayes factors (2014) J. Prob. Solving, 7 (2); JASP Team, et al.: Jasp. Version 0.8. 0.0. software (2016); Kennedy, J., Baxter, P., Belpaeme, T., Children comply with a robot’s indirect requests (2014) Proceeding of HRI, pp. 198-199. , ACM; Lai, K.L., Confucian moral cultivation: Some parallels with musical training (2003) The Moral Circle and the Self: Chinese and Western Approaches. Open Court; Lockshin, J., Williams, T., We need to start thinking ahead”: The impact of social context on linguistic norm adherence (2020) Proceedings of the 42Nd Annual Meeting of the Cognitive Science Society (COGSCI); Mayer, R.E., Sobko, K., Mautone, P.D., Social cues in multimedia learning: Role of speaker’s voice (2003) J. Educ. Psychol., 95 (2), p. 419; Puett, M., Gross-Loh, C., (2016) The Path: What Chinese Philosophers Can Teach Us about the Good Life, , Simon and Schuster, United States; Roubroeks, M., Ham, J., Midden, C., When artificial social agents try to persuade people: The role of social agency on the occurrence of psychological reactance (2011) Int. J. Soc. Robot., 3 (2), pp. 155-165; Rouder, J.N., Speckman, P.L., Sun, D., Morey, R.D., Iverson, G., Bayesian t tests for accepting and rejecting the null hypothesis (2009) Psy. Bul. Rev., 16, pp. 225-237; Searle, J.R., Indirect speech acts (1975) Syntax Semant, 3, pp. 59-82; Siegel, M., Breazeal, C., Norton, M.I., Persuasive robotics: The influence of robot gender on human behavior (2009) Proceeding of IROS. IEEE; Traum, D., Rickel, J., Embodied agents for multi-party dialogue in immersive virtual worlds (2002) Proceeding of AAMAS, pp. 766-773. , ACM; Truong, A., (2016) Parents are Worried the Amazon Echo is Conditioning Their Kids to Be Rude, , https://qz.com/701521/parents-are-worried-the-amazon-echo-is-conditioning-their-kids-to-be-rude/; Verbeek, P.P., (2011) Moralizing Technology: Understanding and Designing the Morality of Things, , University of Chicago Press, United States; Williams, T., Thames, D., Novakoff, J., Scheutz, M., ”Thank you for sharing that interesting fact!”: Effects of capability and context on indirect speech act use in task-based human-robot dialogue (2018) Proceeding of HRI; Williams, T., Zhu, Q., Grollman, D.H., An experimental ethics approach to robot ethics education (2020) Proceedings of the 10Th Symposium on Educational Advances in Artificial Intelligence (EAAI), pp. 13428-13435; Winkle, K., Lemaignan, S., Caleb-Solly, P., Leonards, U., Turton, A., Bremner, P., Effective persuasion strategies for socially assistive robots (2019) Proceeding of HRI","Williams, T.; Colorado School of MinesUnited States; email: twilliams@mines.edu","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097168656
"Ogas E., Avila L., Larregay G., Moran D.","57209026120;55262988600;57201909195;57209028659;","Object grasping with a robot arm using aconvolutional network",2020,"International Journal of Mechatronics and Automation","7","3",,"113","121",,,"10.1504/IJMA.2020.109057","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095133766&doi=10.1504%2fIJMA.2020.109057&partnerID=40&md5=8c7a0aa2fe7d7b9d1ed4b7455dec8f57","Laboratorio de Mecatrónica, FICA-UNSL, San Luis, 5730, Argentina; Laboratorio de Investigación y Desarrollo en Inteligencia Computacional, FCFMyN-UNSL, San Luis, D5700HHW, Argentina","Ogas, E., Laboratorio de Mecatrónica, FICA-UNSL, San Luis, 5730, Argentina; Avila, L., Laboratorio de Investigación y Desarrollo en Inteligencia Computacional, FCFMyN-UNSL, San Luis, D5700HHW, Argentina; Larregay, G., Laboratorio de Mecatrónica, FICA-UNSL, San Luis, 5730, Argentina; Moran, D., Laboratorio de Mecatrónica, FICA-UNSL, San Luis, 5730, Argentina","The latest advances in human-robot interaction make possible the spread of collaborative robotic systems in the industrial environment. In this context, most of the industrial robots will serve as human assistants in assembly lines, involved in complex manufacturing tasks. This implies the need to improve the ability of robots to manipulate objects in unstructured scenarios that are often prohibitively expensive to model. In this work, we use a convolutional network for recognising different objects on the work plane. Later, a grasping algorithm is used to estimate the best robot gripper posture so that the robot can pick up the desired object from the cluster. We use the Hough transform and the friction cones theory to identify a set of candidate grasping points on the piece. In general, we found that our implementation performs well and the robot was able to pick up a variety of objects. © 2020 Inderscience Enterprises Ltd.","Deep learning; Friction cones; Hough transform; Industrial robot; Object grasping",,,,,,,,,,,,,,,,,"Alex, O.-H., (2016) Robot Vision Vs. Computer Vision: What's the Difference?, , https://blog.robotiq.com/robot-vision-vscomputer-vision-whats-the-difference, (accessed 27 August 2018); Bradski, G., Kaehler, A., (2016) Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library, , O'Reilly Media, Inc, Sebastopol, Russia; Craig, J., (2005) Introduction to Robotics: Mechanics and Control, , Pearson Education Inc., Publishing as Prentice Hall, Juárez, Mexico; Heikkila, J., Silven, O., A four-step camera calibration procedure with implicit image correction (1997) Proceedings of Ieee Computer Society Conference on Computer Vision and Pattern Recognition, Ieee Comput. Soc., pp. 1106-1112; Hossain, D., Capi, G., Jindai, M., Kaneko, S., Pick-place of dynamic objects by robot manipulator based on deep learning and easy user interface teaching systems (2017) Industrial Robot: An International Journal, 44 (1), pp. 11-20; Jiang, Y., Moseson, S., Saxena, A., Efficient grasping from RGBD images: Learning using a new rectangle representation' (2011) 2011 Ieee International Conference on Robotics and Automation, pp. 3304-3311; Johns, E., Leutenegger, S., Davison, A.J., Deep learning a grasp function for grasping under gripper pose uncertainty (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4461-4468; Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks (2012) Advances in Neural Information Processing Systems, pp. 1097-1105; Krug, R., Dimitrov, D., Charusta, K., Iliev, B., On the efficient computation of independent contact regions for force closure grasps (2010) IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, Iros 2010 - Conference Proceedings, pp. 586-591; Lenz, I., Lee, H., Saxena, A., Deep learning for detecting robotic grasps (2015) International Journal of Robotics Research, 34 (4-5), pp. 705-724. , SAGE Publications, Sage UK, London, England; Matas, J., Galambos, C., Kittler, J., Robust detection of lines using the progressive probabilistic Hough transform (2000) Computer Vision and Image Understanding, 78 (1), pp. 119-137. , Academic Press; McIlhagga, W., The canny edge detector revisited (2011) International Journal of Computer Vision, 91 (3), pp. 251-261. , Springer USA; Miller, A.T., Knoop, S., Christensen, H.I., Allen, P.K., Automatic grasp planning using shape primitives (2003) Proceedings - Ieee International Conference on Robotics and Automation, 2, pp. 1824-1829; Mukhopadhyay, P., Chaudhuri, B.B., A survey of Hough transform (2015) Pattern Recognition, 48 (3), pp. 993-1010. , Pergamon; Pelossof, R., Miller, A., Allen, P., Jebara, A., An SVM learning approach to robotic grasping (2004) Robotics and Automation', Proceedings, 2004 Ieee International Conference on ICRA'04, pp. 3512-3518; Ranzato, M., Huang, F.J., Boureau, Y.-L., LeCun, Y., Unsupervised learning of invariant feature hierarchies with applications to object recognition (2007) 2007 Ieee Conference on Computer Vision and Pattern Recognition, pp. 1-8; Rawat, W., Wang, Z., Deep convolutional neural networks for image classification: A comprehensive review (2017) Neural Computation, 29 (9), pp. 2352-2449. , http://journals-info@mit.edu, MIT Press, One Rogers St., Cambridge, MA 02142-1209, USA; Redmon, J., Angelova, A., Real-time grasp detection using convolutional neural networks (2015) 2015 Ieee International Conference on Robotics and Automation (ICRA), pp. 1316-1322; Saxena, A., Driemeyer, J., Ng, A.Y., Robotic grasping of novel objects using vision (2008) The International Journal of Robotics Research, 27 (2), pp. 157-173. , Sage Publications, Sage UK, London, England; Sezgin, M., Sankur, B., Survey over image thresholding techniques and quantitative performance evaluation (2004) Journal of Electronic Imaging, International Society for Optics and Photonics, 13 (1), p. 146; Shapiro, L., Stockman, G., (2001) Computer Vision, , Prentice Hall, Upper Saddle River, NJ, USA; Smith, G., Lee, E., Goldberg, K., Böhringer, K., Craig, J., Computing parallel-jaw grips (1999) Ieee International Conference on Robotics and Automation, , http://ford.ieor.berkeley.edu/grip/, (accessed 27 August 2018); Sural, S., Gang, Q., Pramanik, S., Segmentation and histogram generation using the HSV color space for image retrieval (2002) Proceedings International Conference on Image Processing, 2, pp. II589-II592; Turksoy, K., Hajizadeh, I., Samadi, S., Feng, J., Sevil, M., Park, M., Quinn, L., Real-time insulin bolusing for unannounced meals with artificial pancreas (2017) Control Engineering Practice, 59, pp. 159-164; Wan, W., Using intelligent robots to assemble automobile parts (2017) Advances in Automobile Engineering, Omics International, 6 (1), pp. 1-2; Zhang, Z., A flexible new technique for camera calibration (2000) Ieee Transactions on Pattern Analysis and Machine Intelligence, 22 (11), pp. 1330-1334","Avila, L.; Laboratorio de Investigación y Desarrollo en Inteligencia Computacional, Argentina",,,"Inderscience Publishers",,,,,20451059,,,,"English","Int. J. Mechatronics Autom.",Article,"Final","",Scopus,2-s2.0-85095133766
"Fadda R., Congiu S., Doneddu G., Striano T.","24824317300;28267464200;36241366800;7004181055;","Inspiring robots: Developmental trajectories of gaze following in humans",2020,"Rivista Internazionale di Filosofia e Psicologia","11","2",,"211","222",,,"10.4453/rifp.2020.0013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093904241&doi=10.4453%2frifp.2020.0013&partnerID=40&md5=75ff838fcd6340e8899e65f2ae614788","Dipartimento di Pedagogia, Psicologia, Filosofia, Università degli Studi di Cagliari, via Is Mirrionis, 1, Cagliari (I), 09123, Italy; Centro per l’Autismo e disturbi correlati, Nuovo Centro Fisioterapico Sardo, via Is Mirrionis, 1, Cagliari (I), 09123, Italy; Department of Psychology, City University of New York, Hunter College, 695 Park Avenue, New York, 10021, United States","Fadda, R., Dipartimento di Pedagogia, Psicologia, Filosofia, Università degli Studi di Cagliari, via Is Mirrionis, 1, Cagliari (I), 09123, Italy; Congiu, S., Dipartimento di Pedagogia, Psicologia, Filosofia, Università degli Studi di Cagliari, via Is Mirrionis, 1, Cagliari (I), 09123, Italy; Doneddu, G., Centro per l’Autismo e disturbi correlati, Nuovo Centro Fisioterapico Sardo, via Is Mirrionis, 1, Cagliari (I), 09123, Italy; Striano, T., Department of Psychology, City University of New York, Hunter College, 695 Park Avenue, New York, 10021, United States","The ability to respond to gaze cueing is essential for successful social interactions and social learning. An active area of research in human robot interactions (HRI) focuses on the computational encoding of biologically realistic gaze cueing responses in robots. Studies of human development are a primary source of guidance for this field of research. The investigation of how perceived gazes constrain the developmental trajectories of visual attention in humans from childhood to adulthood might reveal important factors to implement realistic gaze following in social robots. This study investigated spontaneous gaze following in 2 and 4-year-old children and adults. Participants saw faces of an adult gazing toward an object. We found that accuracy of gaze following improved significantly with age. The results are discussed considering the development of the executive control of visual attention in humans and its possible implication in implementing gaze following in social robotics. © Roberta Fadda, Sara Congiu, Giuseppe Doneddu, Tricia Striano 2020.","Developmental Trajectories; Eye Tracking; Gaze Following; Social Robotics; Visual Attention",,,,,,,,,,,,,,,,,"ADAMONI, H., SCASELLATI, B., Social eye gaze in Human-Robot interaction: A review (2017) Journal of Human-Robot Interaction, VI (1), pp. 25-63; ANDRIST, S., MUTLU, B., GLEICHER, M., Conversational gaze aversion for virtual agents (2013) Intelligent virtual agents, pp. 249-262. , E.R. AYLETT, B. KRENN, C. PELACHAUD, H. SHIMODAIRA (eds), Springer, Cham; ARGALL, B.D., CHERNOVA, S., VELOSO, M., BROWNING, B., A survey of robot learning from demonstration (2009) Robotics & Autonomous Systems, LVII (5), pp. 469-483; ASADA, M., HOSODA, K., KUNIYOSHI, Y., ISHIGURO, H., INUI, T., YOSHIKAWA, Y., OGINO, M., YOSHIDA, C., Cognitive developmental robotics: A survey (2009) IEEE Transactions on Autonomous Mental Development, I (1), pp. 12-34; BIRMINGHAM, E., BISCHOF, W.F., KINGSTONE, A., Saliency does not account for fixations to eyes within social scenes (2009) Vision Research, XLIX (24), pp. 2992-3000; BORJI, A., ITTI, L., State-of-the-Art in Visual Attention Modelling (2012) IEEE Transactions on Pattern Analysis & Machine Intelligence, XXXV (1), pp. 185-207; BUTTERWORTH, G., JARRETT, N., What minds have common in space: Spatial mechanisms serving joint visual attention in infancy (1991) British Journal of Developmental Psychology, IX (1), pp. 55-72; CHAO, F., WANG, Z., SHANG, C., MENG, Q., JIANG, M., ZHOU, C., SHEN, Q., A developmental approach to robotic pointing via human-robot interaction (2014) Information Sciences, CCLXXXIII (1), pp. 288-303; COOK, J., BARBALAT, G., BLAKEMORE, S.J., Top-down modulation on the perception of other people in schizophrenia and autism (2012) Frontiers in Human Neuroscience, VI, p. 175. , Art. Nr; CSIBRA, G., GERGELY, G., Social learning and social cognition: The case of pedapeda gogy (2006) Processes of change in brain and cognitive development. Attention and performance, pp. 249-274. , Y. MUNAKATA, M.H. JOHNSON (eds), Oxford University Press, Oxford, gogy; D’ENTREMONT, B., HAINS, S.M.J., MUIR, D.W., A demonstration of gaze following in 3-to 6-month-olds (1997) Infant Behavior & Development, XX (4), pp. 569-572; DRIVER, J., DAVIS, G., RICCIARDELLI, P., KIDD, P., MAXWELL, E., BARON-COHEN, S., Gaze perception triggers reflexive visuospatial orienting (1999) Visual Cognition, V (5), pp. 509-540; EMERY, N.J., The eyes have it: The neuroethology, function and evolution of social gaze (2000) Neuroscience & Biobehavioral Reviews, XXIV (6), pp. 581-604; FALK-YTTER, T., THORUP, E., BÖLTE, S., Brief report: Lack of processing bias for the objects other people attend in the 3year-olds with autism (2015) Journal of Autism & Developmental Disorders, XLV (6), pp. 1897-1904; FARRONI, T., MASSACCESI, S., PIVIDORI, D., JOHNSON, M.H., Gaze following in newborns (2004) Infancy, V (1), pp. 39-60; FECTEAU, J., MUNOZ, D.P., Salience, relevance, and firing: A priority map for target selection (2006) Trends in Cognitive Sciences, X (8), pp. 382-390; GROSSBERG, S., MELTZOFF, A., MOVELLAN, J., NEWCOMBE, N., Social cognition: From babies to robots (2010) Neural Networks, XXIII (8-9). , (eds) Special; HIETANEN, J.K., LEPPÄNEN, J.M., PELTOLA, M.J., LINNAAHO, K., RUUHIALA, H.J., Seeing direct and averted gaze activates the approach-avoidance motivational brain systems (2008) Neuropsychologia, XLVI (9), pp. 2423-2430; HOEHL, S., REID, V., MOONEY, J., STRIANO, T., What are you looking at? Infants’ neural processing of an adult’s objectdirected eye gaze (2008) Developmental Science, XI (1), pp. 10-16; ITAKURA, S., Gaze following and joint visual attention in non-human animals (2004) Japanese Psychological Research, XLVI (3), pp. 216-226; ITTI, L., KOCH, C., Computational modeling of visual attention (2001) Nature Reviews Neuroscience, II (3), pp. 194-203; LUNGARELLA, M., METTA, G., PFEIFER, R., SANDINI, G., Developmental robotics: A survey (2003) Connect Science, XV (4), pp. 151-190; MELTZOFF, A., BROOKS, R., SHON, A.P., RAO, R.P., Social” robots are psychological agents for infants: A test of gaze following (2010) Neural Networks, XXIII (8-9), pp. 966-972; MITAKE, H., HASEGAWA, S., KOIKE, Y., SATO, M., Reactive virtual human with bottom-up and top-down visual attention for gaze generation in real time interaction (2007) IEEE-Virtual Reality Conference, pp. 211-214. , 10-14 March 2007, Charlotte (NC); MUNDY, P., Neurodevelopment of joint attention (2016) Autism and joint attention. Development, neuroscience and clinical fundamentals, pp. 210-262. , P. MUNDY, Guilford Press, New York; PARISE, E., CLEVELAND, A., COSTABILE, A., STRIANO, T., Influence of vocal cues on learning about objects in joint attention contexts (2007) Infant Behavior & Development, XXX (2), pp. 380-384; RISKO, E.F., LAIDLAW, K.E., FREETH, M., FOULSHAM, T., KINGSTONE, A., Social attention with real versus reel stimuli: Toward an empirical approach to concerns about ecological validity (2012) Frontiers in Human Neuroscience, VI, p. 143. , Art. Nr. doi; RUEDA, M.R., FAN, J., MCCANDLISS, B.D., HALPARIN, J.D., GRUBER, D.B., LERCARI, L.P., POSNER, M.I., Development of attentional networks in childhood (2004) Neuropsychologia, XLII (8), pp. 1029-1040; RUHLAND, K., PETERS, C.E., ANDRIST, S., BADLER, J.B., BADLER, N.I., GLEICHER, M., MUTLU, B., MCDONNEL, R., A review of eye gaze in virtual agents, social robotics and HCI: Behaviour generation, user interaction and perception (2015) Computer Graphics Forum, XXXIV (6), pp. 299-326; SAGE, K., BALDWIN, D., Social gating and pedagogy: Mechanisms for learning and implications for robotics (2010) Neural Networks, XXIII (8-9), pp. 1091-1098; SAKITA, K., OGAWARA, K., MURAKAMI, S., KAWAMURA, K., IKEUCHI, K., Flexible cooperation between human and robot by interpreting human intention from gaze information (2004) IEEE/RSJ International Conference on Intelligent Robots and Systems, I. , doi; SERENCES, J.T., YANTIS, S., Selective visual attention and perceptual coherence (2006) Trends in Cognitive Sciences, X (1), pp. 38-45; STOESZ, B.M., JAKOBSON, L.S., Developmental changes in attention to faces and bodies in static and dynamic scenes (2014) Frontiers in Psychology, V, p. 193. , Art. Nr; STRIANO, T., STAHL, D., Sensitivity to triadic attention in early infancy (2005) Developmental Science, VIII (4), pp. 333-343; STRIANO, T., STAHL, D., CLEVELAND, A., HOEHL, S., Sensitivity to triadic attention between 6 weeks and 3 months of age (2007) Infant Behavior & Development, XXX (3), pp. 529-534","Fadda, R.; Dipartimento di Pedagogia, via Is Mirrionis, 1, Italy; email: robfadda@unica.it",,,"Mimesis International",,,,,20394667,,,,"English","Rivista Internazionale di Filosofia e Psicologia",Article,"Final","",Scopus,2-s2.0-85093904241
"Zinina A.A., Zaidelman L.Y., Kotov A.A., Arinkin N.A.","57034067600;57203319992;57033665400;57195642237;","The perception of robot's emotional gestures and speech by children solving a spatial puzzle [ВОСПРИЯТИЕ ЭМОЦИОНАЛЬНЫХ ЖЕСТОВ И РЕЧИ РОБОТА ДЕТЬМИ, РЕШАЮЩИМИ ПРОСТРАНСТВЕННУЮ ГОЛОВОЛОМКУ]",2020,"Komp'juternaja Lingvistika i Intellektual'nye Tehnologii","2020-June","19",,"811","826",,1,"10.28995/2075-7182-2020-19-811-826","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093872511&doi=10.28995%2f2075-7182-2020-19-811-826&partnerID=40&md5=1408dfd7c42f3fc8c3362262fc91dc2e","Kurhcatov Institute, Moscow, Russian Federation; Russian State University for the Humanities, Moscow, Russian Federation","Zinina, A.A., Kurhcatov Institute, Moscow, Russian Federation, Russian State University for the Humanities, Moscow, Russian Federation; Zaidelman, L.Y., Kurhcatov Institute, Moscow, Russian Federation, Russian State University for the Humanities, Moscow, Russian Federation; Kotov, A.A., Kurhcatov Institute, Moscow, Russian Federation, Russian State University for the Humanities, Moscow, Russian Federation; Arinkin, N.A., Kurhcatov Institute, Moscow, Russian Federation, Russian State University for the Humanities, Moscow, Russian Federation","The emotional behavior of a companion robot is important for human-robot interaction in the situation of training tasks. We examined the influence of emotional gestures and emotional speech of the robot on its perception by primary school students (N=52, male, female, mean age 9.8) in the situation of joint solution of the spatial Tangram puzzle. It was shown that emotional gestures make a significant contribution to the attractiveness of the robot for the child. It was also found that test subjects prefer the robot with emotional gestures and speech over the robot with neutral gesture and speech behavior. The study also analyzed the communicative behavior of children, identified typical communicative signs that are typical for interaction start with the robot, for monitoring the game and for difficult situations. We described typical mistakes that children make when assembling a puzzle together with the robot. © 2020 ABBYY PRODUCTION LLC. All rights reserved.","Affective robot tutors; Multimodal communication; Robot-to-human interaction",,,,,,"Russian Science Foundation, RSF: 19-78-00113","1 The research is supported by the Russian Science Foundation (project No 19-78-00113).",,,,,,,,,,"Alemi, M., Meghdari, A., Ghazisaedy, M., Employing humanoid robots for teaching English language in Iranian junior high-schools (2014) International Journal of Humanoid Robotics, 11, p. 1450022. , (03); Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Science robotics, 3 (21), p. eaat5954; Belpaeme, T., Vogt, P., Van den Berghe, R., Bergmann, K., Göksun, T., De Haas, M., Papadopoulos, F., In Guidelines for designing social robots as second language tutors (2018) International Journal of Social Robotics, 10 (3), pp. 325-341; Brown, L. N., Howard, A. 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F., An iterative design methodology for user-friendly natural language office information applications (1984) ACM Transactions on Office Information Systems, 2 (1), pp. 26-41. , March 1984; Kennedy, J., Baxter, P., Belpaeme, T., The robot who tried too hard: Social behaviour of a robot tutor can negatively affect child learning (2015) 2015 10th ACM/ IEEE International Conference on Human-Robot Interaction (HRI), pp. 67-74; Kennedy, J., Baxter, P., Senft, E., Belpaeme, T., Higher nonverbal immediacy leads to greater learning gains in child-robot tutoring interactions (2015) International conference on social robotics, pp. 327-336. , Springer, Cham; Kirschner, D., Velik, R., Yahyanejad, S., Brandstötter, M., Hofbaur, M., YuMi, come and play with Me! 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W., Rosenberg-Kima, R., Rosenberg, M., Gordon, G., Breazeal, C., Growing growth mindset with a social robot peer (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 137-145; Pereira, A., Leite, I., Mascarenhas, S., Martinho, C., Paiva, A., Using empathy to improve human-robot relationships (2010) International Conference on Human-Robot Personal Relationship, pp. 130-138. , Springer, Berlin, Heidelberg; Schodde, T., Hoffmann, L., Kopp, S., How to manage affective state in child-robot tutoring interactions? (2017) 2017 International Conference on Companion Technology, pp. 1-6; Shiomi, M., Kanda, T., Howley, I., Hayashi, K., Hagita, N., Can a social robot stimulate science curiosity in classrooms? (2015) International Journal of Social Robotics, 7 (5), pp. 641-652; Shore, T., Androulakaki, T., Skantze, G., KTH Tangrams: A Dataset for Research on Alignment and Conceptual Pacts in Task-Oriented Dialogue (2018) Eleventh International Conference on Language Resources and Evaluation (LREC 2018), pp. 768-775; van den Berghe, R., Verhagen, J., Oudgenoeg-Paz, O., van der Ven, S., Leseman, P., Social robots for language learning: A review (2019) Review of Educational Research, 89 (2), pp. 259-295; Wainer, J., Feil-Seifer, D. J., Shell, D. A., Mataric, M. J., The role of physical embodiment in human-robot interaction (2006) ROMAN 2006-The 15th IEEE International Symposium on Robot and Human Interactive Communication, pp. 117-122; Zinina, A., Zaydelman, L., Arinkin, N., Kotov, A., Non-verbal behavior of the robot companion: a contribution to the likeability (2019) Procedia Computer Science. Postproceedings of the 10th Annual International Conference on Biologically Inspired Cognitive Architectures, , BICA. Elsevier",,,,"ABBYY PRODUCTION LLC","2020 Annual International Conference on Computational Linguistics and Intellectual Technologies, Dialogue 2020","17 June 2020 through 20 June 2020",,163840,22217932,,,,"English","Komp'ut. Lingvist. Intellekt. Tehnol.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85093872511
"Bibi S., Anjum N., Amjad T., McRobbie G., Ramzan N.","55747021900;18041896100;48861062100;56169082200;16069861100;","Human Interaction Anticipation by Combining Deep Features and Transformed Optical Flow Components",2020,"IEEE Access","8",,"9151125","137646","137657",,1,"10.1109/ACCESS.2020.3012557","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089590377&doi=10.1109%2fACCESS.2020.3012557&partnerID=40&md5=c0b0d2c0e4a645df6e79c88e2f7ed9ed","Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan; Department of Computer Science, Capital University of Science and Technology, Islamabad, Pakistan; School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley, United Kingdom","Bibi, S., Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan; Anjum, N., Department of Computer Science, Capital University of Science and Technology, Islamabad, Pakistan; Amjad, T., Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan; McRobbie, G., School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley, United Kingdom; Ramzan, N., Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan","The anticipation of ongoing human interactions is not only highly dynamic and challenging problem but extremely crucial in applications such as remote monitoring, video surveillance, human-robot interaction, anti-terrorists and anti-crime securities. In this work, we address the problem of anticipating the interactions between people monitored by single as well as multiple camera views. To this end, we propose a novel approach that integrates Deep Features with novel hand-crafted features, namely Transformed Optical Flow Components (TOFCs). In order to validate the performance of the proposed approach, we have tested the proposed approach in real outdoor environments, captured using single as well as multiple cameras, having shadow and illumination variations as well as cluttered backgrounds. The results of the proposed approach are also compared with the state-of-the-art approaches. The experimental results show that the proposed approach is promising to anticipate real human interactions. © 2013 IEEE.","deep learning; Human interaction anticipation; transformed optical flow; video surveillance","Cameras; Optical flows; Security systems; Terrorism; Cluttered backgrounds; Human interactions; Illumination variation; Multiple cameras; Outdoor environment; Remote monitoring; State-of-the-art approach; Video surveillance; Human robot interaction",,,,,,,,,,,,,,,,"Rodriguez, B., Fernando, C., Li, H., Action anticipation by predicting future dynamic images (2018) Proc. Eur. Conf. Comput. Vis. (ECCV), pp. 89-105; Niebles, J.C., Fei-Fei, L., A hierarchical model of shape and appearance for human action classification (2007) Proc. IEEE Conf. Comput. Vis. Pattern Recognit, pp. 1-8. , Jun; Ben-Arie, J., Wang, Z., Pandit, P., Rajaram, S., Human activity recognition using multidimensional indexing (2002) IEEE Trans. Pattern Anal. Mach. Intell, 24 (8), pp. 1091-1104. , Aug; Kong, Y., Fu, Y., Close human interaction recognition using patchaware models (2016) IEEE Trans. Image Process, 25 (1), pp. 167-178. , Jan; Chen, C.-W., Aghajan, H., Multiview social behavior analysis in work environments (2011) Proc. 5th ACM/IEEE Int. Conf. Distrib. Smart Cameras, pp. 1-6. , Aug; Ryoo, M.S., Aggarwal, J.K., Recognition of composite human activities through context-free grammar based representation (2006) Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), 2, pp. 1709-1718. , Jun; Choi, W., Shahid, K., Savarese, S., Learning context for collective activity recognition (2011) Proc. CVPR, pp. 3273-3280. , Jun; Ryoo, M.S., Human activity prediction: Early recognition of ongoing activities from streaming videos (2011) Proc. Int. Conf. Comput. Vis, pp. 1036-1043. , Nov; Ke, Q., Bennamoun, M., An, S., Boussaid, F., Sohel, F., Human interaction prediction using deep temporal features (2016) Proc. Eur. Conf. Comput. Vis. Cham, Switzerland: Springer, pp. 403-414; Ke, Q., Bennamoun, M., An, S., Sohel, F., Boussaid, F., Leveraging structural context models and ranking score fusion for human interaction prediction (2018) IEEE Trans. Multimedia, 20 (7), pp. 1712-1723. , Jul; Lan, T., Chen, T.-C., Savarese, S., A hierarchical representation for future action prediction (2014) Proc. Eur. Conf. Comput. Vis. Cham, Switzerland: Springer, pp. 689-704; O'Mahony, N., Campbell, S., Carvalho, A., Harapanahalli, S., Hernandez, G.V., Krpalkova, L., Riordan, D., Walsh, J., Deep learning vs. traditional computer vision (2019) Proc. Sci. Inf. Conf. Cham, Switzerland: Springer, pp. 128-144; Azorin-Lopez, J., Saval-Calvo, M., Fuster-Guillo, A., Garcia-Rodriguez, J., A novel prediction method for early recognition of global human behaviour in image sequences (2016) Neural Process. Lett, 43 (2), pp. 363-387. , Apr; Barnachon, M., Bouakaz, S., Boufama, B., Guillou, E., Ongoing human action recognition with motion capture (2014) Pattern Recognit, 47 (1), pp. 238-247. , Jan; Sun, Q., Liu, H., Liu, M., Zhang, T., Human activity prediction by mapping grouplets to recurrent self-organizing map (2016) Neurocomputing, 177, pp. 427-440. , Feb; Wang, H., Yang, W., Yuan, C., Ling, H., Hu, W., Human activity prediction using temporally-weighted generalized time warping (2017) Neuro-computing, 225, pp. 139-147. , Feb; Choi, S., Kim, E., Oh, S., Human behavior prediction for smart homes using deep learning (2013) Proc. IEEE RO-MAN, pp. 173-179. , Aug; Dutta, V., Zielinska, T., Predicting human actions taking into account object affordances (2019) J. Intell. Robot. Syst, 93 (3-4), pp. 745-761. , Mar; Ke, Q., Liu, J., Bennamoun, M., Rahmani, H., An, S., Sohel, F., Boussaid, F., Global regularizer and temporal-aware cross-entropy for skeleton-based early action recognition (2018) Proc. Asian Conf. Comput. Vis. Cham, pp. 729-745. , Switzerland: Springer; Liu, J., Shahroudy, A., Wang, G., Duan, L.-Y., Kot, A.C., Skeletonbased online action prediction using scale selection network (2020) IEEE Trans. Pattern Anal. Mach. Intell, 42 (6), pp. 1453-1467. , Jun; Ye, Q., Zhong, H., Qu, C., Zhang, Y., Human interaction recognition based on whole-individual detection (2020) Sensors, 20 (8), p. 2346. , Apr; Wu, S., Chen, Y.-C., Li, X., Wu, A.-C., You, J.-J., Zheng, W.-S., An enhanced deep feature representation for person re-identification (2016) Proc. IEEE Winter Conf. Appl. Comput. Vis. (WACV), pp. 1-8. , Mar; Vondrick, C., Pirsiavash, H., Torralba, A., Anticipating visual representations from unlabeled video (2016) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 98-106. , Jun; De Freitas, N., (2010) Deep Learning of Invariant Spatio-temporal Features from Video, , Univ. British Columbia,Vancouver, BC, Canada, Tech. Rep; Majtner, T., Yildirim-Yayilgan, S., Hardeberg, J.Y., Combining deep learning and hand-crafted features for skin lesion classification (2016) Proc. 6th Int. Conf. Image Process. Theory, Tools Appl. (IPTA), pp. 1-6. , Dec; Acar, E., Hopfgartner, F., Albayrak, S., Understanding affective content of music videos through learned representations (2014) Proc. Int. Conf. Multimedia Modeling. Cham, Switzerland: Springer, pp. 303-314; Dollár, P., Appel, R., Belongie, S., Perona, P., Fast feature pyramids for object detection (2014) IEEE Trans. Pattern Anal. Mach. Intell, 36 (8), pp. 1532-1545. , Aug; Horn, B.K., Schunck, B.G., Determining optical-ow (1981) Proc. SPIE, 281, pp. 319-331. , Nov; Caetano, C., De Melo, V.H.C., Dos Santos, J.A., Schwartz, W.R., Activity recognition based on a magnitude-orientation stream network (2017) Proc. 30th SIBGRAPI Conf. Graph., Patterns Images (SIBGRAPI), pp. 47-54. , Oct; Krizhevsky, A., Sutskever, I., Hinton, G.E., ImageNet classification with deep convolutional neural networks (2012) Proc. Adv. Neural Inf. Pro-cess. Syst, pp. 1097-1105; Colque, R.V.H.M., Caetano, C., De Andrade, M.T.L., Schwartz, A.R., Histograms of optical-ow orientation and magnitude and entropy to detect anomalous events in videos (2017) IEEE Trans. Circuits Syst. Video Technol, 27 (3), pp. 673-682. , Mar; Mosteller, F., Tukey, J.W., (1977) Data Analysis and Regression: A Second Course in Statistics, , Reading, MA, USA: Addison-Wesley; Ryoo, M., Aggarwal, J., (2015) Ut-Interaction Dataset, ICPR Contest on Semantic Description of Human Activities (SDHA) (2010), , http://cvrc.ece.utexas.edu/SDHA2010/Human-Interaction.html; Nadal, C., Legault, R., Suen, C.Y., Complementary algorithms for the recognition of totally unconstrained handwritten numerals (1990) Proc. 10th Int. Conf. Pattern Recognit, 1, pp. 443-449; Mi, A., Wang, L., Qi, J., A multiple classifier fusion algorithm using weighted decision templates (2016) Sci. Program, 2016, pp. 1-10. , Sep","Anjum, N.; Department of Computer Science, Pakistan; email: nadeem.anjum@cust.edu.pk",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85089590377
"Zhang J., Liu S., Zhang Y., Zhu R.","57209619459;57094270100;57218215917;7202446527;","A Method to Extract Motion Velocities of Limb and Trunk in Human Locomotion",2020,"IEEE Access","8",,"9130674","120553","120561",,2,"10.1109/ACCESS.2020.3006336","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088308070&doi=10.1109%2fACCESS.2020.3006336&partnerID=40&md5=24f0c2e31c79c4ac09c361e58b42b0a0","Department of Precision Instrument, State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, China","Zhang, J., Department of Precision Instrument, State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, China; Liu, S., Department of Precision Instrument, State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, China; Zhang, Y., Department of Precision Instrument, State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, China; Zhu, R., Department of Precision Instrument, State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, China","Human motion monitoring is important in applications of movies, animation, sport training, physical rehabilitation, and human-robot interaction. There is a demand for a simple and easy-to-use method to recognize motions of limbs and trunk. In this paper, we developed a wearable velocity tracking device using two orthogonally-placed micro flow sensors to implement three-dimensional motion velocity measurement. In addition, we proposed a functional link artificial neural network (FLANN) model to extract trunk velocity and relative limb velocity from an absolute limb motion detected by the wearable tracking device according to their different dynamic features. Experiments were conducted to validate the effectiveness of the velocity tracking methodology. Results showed the proposed method with wearable device enables real-time measurements of motion velocities of limb and trunk, which were free of accumulated error, robust for dynamic walking and running, and simple to use. © 2013 IEEE.","feature extraction; Human motion; kinematic velocity track; micro flow sensor; motions of limb and trunk; wearable device","Educational robots; Human robot interaction; Sports; Velocity; Wearable technology; Accumulated errors; Functional link artificial neural networks; Human locomotions; Micro flow sensors; Physical rehabilitation; Real time measurements; Three-dimensional motion; Velocity tracking; Motion tracking",,,,,"National Natural Science Foundation of China, NSFC: 51735007; Tsinghua University, THU: 2019GQG1009","This work was supported in part by the National Natural Science Foundation of China under Grant 51735007, and in part by the Institute for Guo Qiang, Tsinghua University, under Grant 2019GQG1009.",,,,,,,,,,"Ramos, J., Kim, S., Dynamic locomotion synchronization of bipedal robot and human operator via bilateral feedback teleoperation (2019) Sci. 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Technol., 22 (8). , Aug; Lotand, E., Rezaee, A.A., A competitive functional link artificial neural network as a universal approximator (2018) Soft Comput., 22 (14), pp. 4613-4625. , Jul; Dash, S.K., Bisoi, R., Dash, P.K., A hybrid functional link dynamic neural network and evolutionary unscented Kalman filter for short-term electricity price forecasting (2016) Neural Comput. Appl., 27 (7), pp. 2123-2140. , Oct; Ljung, L., Prediction error estimation methods (2002) Circuits, Syst., Signal Process., 21 (1), pp. 11-21. , Jan; Wilamowski, B.M., Yu, H., Improved computation for Levenberg-Marquardt training (2010) IEEE Trans. Neural Netw., 21 (6), pp. 930-937. , Jun","Zhu, R.; Department of Precision Instrument, China; email: zr_gloria@mail.tsinghua.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85088308070
"Joshi S., Sabanovic S.","57201549303;14036199300;","A tactical urbanist approach to facilitate exploratory HRI research in public spaces",2020,"ECSCW 2019 - Proceedings of the 17th European Conference on Computer Supported Cooperative Work",,,,"","",,,"10.18420/ecscw2019_p05","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085057124&doi=10.18420%2fecscw2019_p05&partnerID=40&md5=62068d5e98534d876a5bbcc4221489f5","Indiana University Bloomington, United States","Joshi, S., Indiana University Bloomington, United States; Sabanovic, S., Indiana University Bloomington, United States","Human-Robot Interaction (HRI) research in public spaces often encounters delays and restrictions due to the need for sophisticated technology, regulatory approvals, and public or community support. To remedy these concerns, we suggest HRI can apply the core philosophy of Tactical Urbanism, a concept from urban planning, to catalyze HRI in public spaces, gather community feedback and information on the feasibility of future implementations of robots in the public, and create social impact and forge connections with the community while spreading awareness about robots as a public resource. As a case study, we share tactics used, and strategies followed in conducting a pop-up style study of'A robotic mailbox to raise awareness about homelessness.' We discuss benefits of the approach that could enable the social studies of HRI not only to match but to precede, the fast-paced technological advancement and deployment of robots in public spaces. © Copyright 2019 held by Swapna Joshi and Selma Sabanovic.",,"Interactive computer systems; Regional planning; Robot programming; Social robots; Community support; Human robot Interaction (HRI); Public resources; Public space; Regulatory approvals; Social impact; Social study; Technological advancement; Economic and social effects",,,,,,,,,,,,,,,,"Bascelli, C.M., Where the sidewalk ends and robots deliver: Setting a framework for regulating personal delivery devices (2018) Rutgers Computer & Tech. LJ, 44, p. 93; Jensen, B., Tomatis, N., Mayor, L., Drygajlo, A., Siegwart, R., Robots meet humans: Interaction in public spaces (2005) IEEE Transactions on Industrial Electronics, 52 (6), pp. 1530-1546; Leite, I., Martinho, C., Paiva, A., Social robots for long-term interaction: A survey (2013) International Journal of Social Robotics, 5 (2), pp. 291-308; Lydon, M., Garcia, A., A tactical urbanism how-to (2015) Tactical Urbanism, pp. 171-208. , Springer",,,,"European Society for Socially Embedded Technologies (EUSSET)","17th European Conference on Computer Supported Cooperative Work, ECSCW 2019","8 June 2019 through 12 June 2019",,159630,,,,,"English","ECSCW - Proc. Eur. Conf. Computer Supported Cooper. Work",Conference Paper,"Final","",Scopus,2-s2.0-85085057124
"González E., Páez J., Luis-Ferreira F., Sarraipa J., Gonçalves R.","7402466812;57210786174;55927426500;24342291200;6602141538;","Human-Robot Scaffolding, an Architecture to Support the Learning Process",2020,"Advances in Intelligent Systems and Computing","1092 AISC",,,"528","541",,1,"10.1007/978-3-030-35990-4_43","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082115067&doi=10.1007%2f978-3-030-35990-4_43&partnerID=40&md5=8373ee33d17abb5f6617616f922424c3","Facultad de Ingeniería, Pontificia Universidad Javeriana, Bogotá, Colombia; Facultad de Ciencias y Educación, Universidad Distrital Francisco José de Caldas, Bogotá, Colombia; CTS, UNINOVA, Dep.º de Eng.ª Electrotécnica Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal","González, E., Facultad de Ingeniería, Pontificia Universidad Javeriana, Bogotá, Colombia; Páez, J., Facultad de Ciencias y Educación, Universidad Distrital Francisco José de Caldas, Bogotá, Colombia; Luis-Ferreira, F., CTS, UNINOVA, Dep.º de Eng.ª Electrotécnica Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal; Sarraipa, J., CTS, UNINOVA, Dep.º de Eng.ª Electrotécnica Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal; Gonçalves, R., CTS, UNINOVA, Dep.º de Eng.ª Electrotécnica Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal","Recognizing and diagnosing learner’s cognitive and emotional state to intervene assertively is an important aspect to improve learning processes. This mission that can be supported by social robots in educational contexts. A cognitive architecture to manage the robot’s social behavior with handling capacity is presented. The human-robot scaffolding architecture is composed of three systems: multimodal fusion, believes, and scaffolding. Those recognize verbal and nonverbal data from user and from the mechanical assembly task, acknowledges the user’s cognitive and emotional state according to the learning task and configure the actions of the robot based on the Flow Theory. It establishes relations between challenges and skills during the learning process, presenting also the theoretical analysis and explorative actions with children to build each subsystem of architecture. The present research contributes to the field of human-robot interaction by suggesting an architecture that seeks the robot’s proactive behavior according to learner’s needs. © 2020, Springer Nature Switzerland AG.","Constructionism; Human-robot interaction; Scaffolding; Social robots","Architecture; Human robot interaction; Learning systems; Man machine systems; Robotics; Scaffolds; Cognitive architectures; Constructionism; Educational context; Handling capacity; Mechanical assembly; Multi-modal fusion; Proactive behavior; Scaffolding; Social robots",,,,,"European Commission, EC","Acknowledgements. The work has also been promoted under the project CARELINK, AAL-CALL-2016-049 funded by AAL JP, and co-funded by the European Commission and National Funding Authorities FCT from Portugal and the national institutions from Ireland, Belgium and Switzerland.",,,,,,,,,,"Alibali, M.W., Spencer, R.C., Knox, L., Kita, S., Spontaneous gestures influence strategy choices in problem solving (2011) Psychol. Sci., 22 (9), pp. 1138-1144; Knoll, A., Hildenbrandt, B., Zhang, J., Instructing cooperating assembly robots through situated dialogues in natural language (1997) 1997 Proceedings of the IEEE International Conference on Robotics and Automation, 1, pp. 888-894. , vol., pp., IEEE; Knoll, A.C., Distributed contract networks of sensor agents with adaptive reconfiguration: Modelling, simulation, implementation and experiments (2001) J. Frankl. 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Robot., 5 (3), pp. 313-323","Luis-Ferreira, F.; CTS, Portugal; email: flf@uninova.pt","Silva M.F.Luis Lima J.Reis L.P.Sanfeliu A.Tardioli D.",,"Springer","4th Iberian Robotics Conference, ROBOT 2019","20 November 2019 through 22 November 2019",,236649,21945357,9783030359898,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85082115067
"Pereira R., Garrote L., Barros T., Carona C., Bento L.C., Nunes U.J.","57212084884;56841438900;57212082760;26533871200;14070145000;57200710287;","Expressive Robotic Head for Human-Robot Interaction Studies",2020,"IFMBE Proceedings","76",,,"1627","1637",,,"10.1007/978-3-030-31635-8_200","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075875323&doi=10.1007%2f978-3-030-31635-8_200&partnerID=40&md5=4e7c439a31b647ec937b92d55957ea70","Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal; Department of Electrical and Computer Engineering, University of Coimbra, Coimbra, Portugal; Cerebral Palsy Association of Coimbra, Center for Research in Neuropsychology and Cognitive-Behavioural Intervention, University of Coimbra, Coimbra, Portugal","Pereira, R., Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal; Garrote, L., Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal, Department of Electrical and Computer Engineering, University of Coimbra, Coimbra, Portugal; Barros, T., Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal; Carona, C., Cerebral Palsy Association of Coimbra, Center for Research in Neuropsychology and Cognitive-Behavioural Intervention, University of Coimbra, Coimbra, Portugal; Bento, L.C., Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal; Nunes, U.J., Institute of Systems and Robotics, University of Coimbra, Coimbra, Portugal, Department of Electrical and Computer Engineering, University of Coimbra, Coimbra, Portugal","In this paper we present an improvement of the ISR-RobotHead prototype interaction capabilities having in view future Human-Robot Interactions studies. To accomplish that, a new hardware and software architectures and new facial expressions were developed. This new prototype (ISR-RobotHead v2.0) uses LCDs to display six human facial emotional expressions, avoiding the use of interlinked mechanical systems. The prototype also incorporates cameras, microphones, speakers, and LEDs on the robot’s cheeks. To validate the new cartoon facial expressions, an exploratory study was performed with children (4–6 years old) and adults (23–24 years old), who were asked to identify the expression displayed by the ISR-RobotHead v2.0; for children, the evaluation of accuracy in the detection of emotional expressions was performed through a corresponding number of cards picturing child’s emotional facial expressions. Preliminary results successfully validate the proposed robot’s non-verbal communication. © 2020, Springer Nature Switzerland AG.","Facial expressions; Human-robot interaction; LCD-based robotic head; Speech recognition","Biochemical engineering; Liquid crystal displays; Man machine systems; Medical computing; Robotics; Speech recognition; Emotional expressions; Exploratory studies; Facial Expressions; Hardware and software architectures; Mechanical systems; Non-verbal communications; Robotic head; Human robot interaction",,,,,"UID/PSI/00730/2019; UID/EEA/00048/2013; Fundação para a Ciência e a Tecnologia, FCT: AMS-HMI12; European Regional Development Fund, ERDF; Programa Operacional Temático Factores de Competitividade, POFC","Acknowledgements. The authors would like to thank Professor Rosa Roldão for facilitating the conduction of experimental tests with children at “Centro de A¸cão Social do Concelho de Ílhavo”. This work was partially supported by FCT Project AMS-HMI12 (https://sites.google.com/site/amshmi12/), ISR-UC and University of Coimbra, and by UID/EEA/00048/2013 with FEDER funding through programs QREN and COMPETE. It was also supported by Center for Research in Neuropsychology and Cognitive-Behavior Intervention (UID/PSI/00730/2019) and Cerebral Palsy Association of Coimbra.","The authors would like to thank Professor Rosa Rold?o for facilitating the conduction of experimental tests with children at ?Centro de A??o Social do Concelho de ?lhavo?. This work was partially supported by FCT Project AMS-HMI12 (https://sites.google.com/site/amshmi12/), ISR-UC and University of Coimbra, and by UID/EEA/00048/2013 with FEDER funding through programs QREN and COMPETE. It was also supported by Center for Research in Neuropsychol-ogy and Cognitive-Behavior Intervention (UID/PSI/00730/2019) and Cerebral Palsy Association of Coimbra.",,,,,,,,,"Ahn, H.S., Kim, P.J., Choi, J.H., Mansoor, S.B., Kang, W.S., Yoon, S.M., Na, J.H., Ko, H.S., Emotional head robot with behavior decision model and face recognition (2007) 2007 International Conference on Control, Automation and Systems, pp. 2719-2724. , pp., October; Anzalone, S.M., Boucenna, S., Ivaldi, S., Chetouani, M., Evaluating the engagement with social robots (2015) Int. J. Soc. Robot., 7, pp. 465-478; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Sci. Robot., 3 (21), p. eaat5954; Breazeal, C., Emotion and sociable humanoid robots (2003) Int. J. Hum.-Comput. Stud., 59, pp. 119-155; http://www.bluefrogrobotics.com/en/buddy/; Costa, A., Steffgen, G., Lera, F., Nazarikhorram, A., Ziafati, P., Socially assistive robots for teaching emotional abilities to children with Autism Spectrum Disorder (2017) 3Rd Workshop on Child-Robot Interaction at HRI; Han, J., Jo, M., Park, S., Kim, S., The educational use of home robots for children (2005) ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, pp. 378-383. , pp., August; Kȩdzierski, J., Muszyński, R., Zoll, C., Oleksy, A., Frontkiewicz, M., EMYS– emotive head of a social robot (2013) Int. J. Soc. Robot, 5, pp. 237-249; Këpuska, V., Bohouta, G., Comparing speech recognition systems (Microsoft API, Google API And CMU Sphinx) (2017) Int. J. Eng. Res. Appl., 7, pp. 20-24; Libin, A.V., Libin, E.V., Person-robot interactions from the robopsychologists’ point of view: The robotic psychology and robotherapy approach (2004) Proc. IEEE, 92, pp. 1789-1803; Lobue, V., Thrasher, C., The child affective facial expression (CAFE) set: Validity and reliability from untrained adults (2015) Front. Psychol., 5, p. 1532; Loureiro, R., Lopes, A., Carona, C., Almeida, D., Faria, F., Garrote, L., Premebida, C., Nunes, U.J., ISR-RobotHead: Robotic head with LCD-based emotional expressiveness (2017) 2017 IEEE 5Th Portuguese Meeting on Bioengineering (ENBENG); Lütkebohle, I., Hegel, F., Schulz, S., Hackel, M., Wrede, B., Wachsmuth, S., Sagerer, G., (2010) The Bielefeld Anthropomorphic Robot Head Flobi. In: 2010 IEEE International Conference on Robotics and Automation, pp. 3384-3391. , pp., May; McColl, D., Hong, A., Hatakeyama, N., Nejat, G., Benhabib, B., A survey of autonomous human affect detection methods for social robots engaged in naturalHRI (2016) J.Intell.Robot.Syst., 82, pp. 101-133; Mori, M., Macdorman, K.F., Kageki, N., The uncanny valley [From the Field] (2012) IEEE Robot. Autom. Mag., 19, pp. 98-100; Orejana, J.R., Macdonald, B.A., Ahn, H.S., Peri, K., Broadbent, E., Healthcare Robots in Homes of Rural Older Adults (2015) Social Robotics. ICSR 2015. Lecture Notes in Computer Science, 9388, pp. 512-521. , Tapus, A., André, E., Martin, JC., Fer-land, F., Ammi, M., pp., Springer, Cham; Pereira, R., (2018) Isr-Robothead V2.0: New Hardware and Software Modules. Master Degree Dissertation, , Coimbra, University; Schulz, T., Tørresen, J., Herstad, J., Animation techniques in human-robot interaction user studies: A systematic literature review (2018) Clinical Orthopaedics and Related Research; Sosnowski, S., Bittermann, A., Kuhnlenz, K., Buss, M., Design and Evaluation of Emotion-Display EDDIE (2006) 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3113-3118. , pp., October; Tan, X.Z., Vázquez, M., Carter, E.J., Morales, C.G., Steinfeld, A., Inducing bystander interventions during robot abuse with social mechanisms (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 169-177. , pp","Pereira, R.; Institute of Systems and Robotics, Portugal; email: ricardo.pereira@isr.uc.pt","Henriques J.de Carvalho P.Neves N.",,"Springer","15th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2019","26 September 2019 through 28 September 2019",,232529,16800737,9783030316341,,,"English","IFMBE Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85075875323
"Arias E., Encalada P., Tigre F., Granizo C., Gordon C., Garcia M.V.","57209978602;57204520420;57209969066;57209653305;56419359600;56597269000;","A ConvNet-Based Approach Applied to the Gesticulation Control of a Social Robot",2020,"Advances in Intelligent Systems and Computing","1066",,,"186","195",,2,"10.1007/978-3-030-32022-5_18","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075674016&doi=10.1007%2f978-3-030-32022-5_18&partnerID=40&md5=48f84fae60e5645005ab782192722ca5","Universidad Tecnica de Ambato, UTA, Ambato, 180103, Ecuador; University of Basque Country, UPV/EHU, Bilbao, 48013, Spain","Arias, E., Universidad Tecnica de Ambato, UTA, Ambato, 180103, Ecuador; Encalada, P., Universidad Tecnica de Ambato, UTA, Ambato, 180103, Ecuador; Tigre, F., Universidad Tecnica de Ambato, UTA, Ambato, 180103, Ecuador; Granizo, C., Universidad Tecnica de Ambato, UTA, Ambato, 180103, Ecuador; Gordon, C., Universidad Tecnica de Ambato, UTA, Ambato, 180103, Ecuador; Garcia, M.V., Universidad Tecnica de Ambato, UTA, Ambato, 180103, Ecuador, University of Basque Country, UPV/EHU, Bilbao, 48013, Spain","This document presents the enforcement of a facial gesture recognition system through applying a Convolutional Neural Network (CNN) algorithm for gesticulation of an interactive social robot with humanoid appearance, which was designed in order to accomplish the thematic proposed. Furthermore, it is incorporated into it a hearing communication system for Human-Robot interaction throughout the use of visemes, by coordinating the robots mouth movement with the processed audio of the text converted to the robot’s voice (text to speech). The precision achieved by the CNN incorporated in the social-interactive robot is 61%, while the synchronization system between the robot’s mouth and the robot’s audio-voice differs from 0.1 s. In this way, it is pretended to endow mechanisms social robots for a naturally interaction with people, thus facilitating the appliance of them in the fields of childrens teaching-learning, medical therapies and as entertainment means. © 2020, Springer Nature Switzerland AG.","Deep Learning; Human-Robot interaction; Neural networks; Social robots; Visemes","Audition; Convolutional neural networks; Deep learning; Deep neural networks; Human robot interaction; Man machine systems; Medical robotics; Neural networks; Speech communication; Childrens; Facial gestures; Interactive robot; Medical therapy; Synchronization systems; Teaching-learning; Text to speech; Visemes; Social robots",,,,,"1919-CU-P-2017","Acknowledgment. This work was financed in part by Universidad Tecnica de Ambato (UTA) and their Research and Development Department (DIDE) under project 1919-CU-P-2017.",,,,,,,,,,"Barnes, J., Fakhrhosseini, M., Jeon, M., Park, C.-H., Howard, A., The influence of robot design on acceptance of social robots (2017) 2017 14Th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 51-55. , pp., IEEE, Jeju; Mead, R., Mataric, M.J., Autonomous human-robot proxemics: A robot-centered approach (2016) The Eleventh ACM/IEEE International Conference on Human Robot Interaction, pp. 573-573. , pp., IEEE Press; Benavides, J., (2016) Diseo Y Construccin De Un Robot Interactivo Para El Tratamiento De Personas Con El Trastorno Del Espectro Autista (TEA), , Universidad de las Fuerzas Armadas (ESPE; Sojib, N., Islam, S., Rupok, M.H., Hasan, S., Amin, M.R., Iqbal, M.Z., Design and development of the social humanoid robot named Ribo (2017) 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), pp. 314-317. , pp., IEEE, Dhaka; Lapusan, C., Rad, C.-R., Besoiu, S., Plesa, A., Design of a humanoid robot head for studying human-robot interaction (2015) 2015 7Th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Pp. WR-15–WR-18. IEEE, Bucharest; Chen, L., Zhou, M., Su, W., Wu, M., She, J., Hirota, K., Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction (2018) Inf. Sci., 428, pp. 49-61; Faria, D.R., Vieira, M., Faria, F.C.C., Premebida, C., Affective facial expressions recognition for human-robot interaction (2017) 2017 26Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 805-810. , pp., IEEE, Lisbon; Chen, J., Chen, Z., Chi, Z., Fu, H., Facial expression recognition based on facial components detection and hog features (2014) International Workshops on Electrical and Computer Engineering Subfields, Istanbul, Turkey, Pp. 884–888; Soni, L.N., Datar, A., Datar, S., Implementation of Viola-Jones Algorithm based approach for human face detection (2017) Int. J. Curr. Eng. Technol., 7, pp. 1819-1823; (2016) Robocity16 Open Conference on Future Trends in Robotics, , Fernndez, R., Montes, H. (eds.), Consejo Superior de Investigaciones Cientificas, Madrid; Cheng, H., Ji, G., Design and implementation of a low cost 3D printed humanoid robotic platform (2016) 2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 86-91. , pp., IEEE, Chengdu; Lecun, Y., Bengio, Y., Hinton, G., Deep learning (2015) Nature, 521, p. 436; Kumar, P., Happy, S.L., Routray, A., A real-time robust facial expression recognition system using HOG features (2016) 2016 International Conference on Computing, Analytics and Security Trends (CAST), pp. 289-293. , pp., IEEE, Pune; Meghdari, A., Shouraki, S.B., Siamy, A., Shariati, A., The real-time facial imitation by a social humanoid robot (2016) 2016 4Th International Conference on Robotics and Mechatronics (ICROM), pp. 524-529. , pp., IEEE, Tehran; Fernandez, M.C.D., Gob, K.J.E., Leonidas, A.R.M., Ravara, R.J.J., Bandala, A.A., Dadios, E.P., Simultaneous face detection and recognition using Viola-Jones Algorithm and Artificial Neural Networks for identity verification (2014) 2014 IEEE Region 10 Symposium, pp. 672-676. , pp., IEEE, Kuala Lumpur; Wang, Y.-Q., An analysis of the Viola-Jones face detection algorithm (2014) Image Process. on Line, 4, pp. 128-148; Sang, D.V., van Dat, N., Thuan, D.P., Facial expression recognition using deep convolutional neural networks (2017) 2017 9Th International Conference on Knowledge and Systems Engineering (KSE), pp. 130-135. , pp., IEEE, Hue; Ashwin, T.S., Jose, J., Raghu, G., Reddy, G.R.M., An E-learning system with multifacial emotion recognition using supervised machine learning (2015) 2015 IEEE Seventh International Conference on Technology for Education (T4E), pp. 23-26. , pp., IEEE, Warangal; Vu, T.H., Nguyen, L., Guo, T., Monga, V., Deep network for simultaneous decomposition and classification in UWB-SAR imagery (2018) 2018 IEEE Radar Conference (Radarconf 2018), pp. 0553-0558. , pp., IEEE, Oklahoma City; Khan, S., Rahmani, H., Shah, S.A.A., Bennamoun, M., A Guide to Convolutional Neural Networks for Computer Vision (2018) Synthesis Lectures on Computer Vision, 8, pp. 1-207. , vol., pp","Encalada, P.; Universidad Tecnica de Ambato, Ecuador; email: pg.encalada@uta.edu.ec","Botto-Tobar M.Leon-Acurio J.Diaz Cadena A.Montiel Diaz P.",,"Springer Science and Business Media Deutschland GmbH","1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019","29 May 2019 through 31 May 2019",,233339,21945357,9783030320218,,,"English",,Conference Paper,"Final","",Scopus,2-s2.0-85075674016
"Gao Q., Ogenyi U.E., Liu J., Ju Z., Liu H.","57214809236;57203484980;7410106559;25655056800;54958434200;","A Two-Stream CNN Framework for American Sign Language Recognition Based on Multimodal Data Fusion",2020,"Advances in Intelligent Systems and Computing","1043",,,"107","118",,3,"10.1007/978-3-030-29933-0_9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072869151&doi=10.1007%2f978-3-030-29933-0_9&partnerID=40&md5=eb6a1ac5f2c1480087a63f6cadb8256b","State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; University of Chinese Academy of Sciences, Beijing, 100049, China; School of Computing, University of Portsmouth, Portsmouth, PO1 3HE, United Kingdom","Gao, Q., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China, University of Chinese Academy of Sciences, Beijing, 100049, China; Ogenyi, U.E., School of Computing, University of Portsmouth, Portsmouth, PO1 3HE, United Kingdom; Liu, J., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; Ju, Z., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China, School of Computing, University of Portsmouth, Portsmouth, PO1 3HE, United Kingdom; Liu, H., School of Computing, University of Portsmouth, Portsmouth, PO1 3HE, United Kingdom","At present, vision-based hand gesture recognition is very important in human-robot interaction (HRI). This non-contact method enables natural and friendly interaction between people and robots. Aiming at this technology, a two-stream CNN framework (2S-CNN) is proposed to recognize the American sign language (ASL) hand gestures based on multimodal (RGB and depth) data fusion. Firstly, the hand gesture data is enhanced to remove the influence of background and noise. Secondly, hand gesture RGB and depth features are extracted for hand gesture recognition using CNNs on two streams, respectively. Finally, a fusion layer is designed for fusing the recognition results of the two streams. This method utilizes multimodal data to increase the recognition accuracy of the ASL hand gestures. The experiments prove that the recognition accuracy of 2S-CNN can reach 92.08 $$\%$$ on ASL fingerspelling database and is higher than that of baseline methods. © 2020, Springer Nature Switzerland AG.","CNN; Hand gesture recognition; Multimodal data fusion","Artificial intelligence; Computer hardware description languages; Data fusion; Human robot interaction; Palmprint recognition; American sign language; Friendly interaction; Hand-gesture recognition; Human robot Interaction (HRI); Multi-modal data; Multimodal data fusion; Noncontact methods; Recognition accuracy; Gesture recognition",,,,,"2018YFB1304600; 050102; National Natural Science Foundation of China, NSFC: 51575338, 51575407, 51575412, 51775541; Chinese Academy of Sciences, CAS: Y4A3210301; Seventh Framework Programme, FP7: 611391; State Key Lab of Digital Manufacturing Equipment and Technology: DMETKF2017003","Research supported in part by the Key Research Program of the Chinese Academy of Sciences under Grant Y4A3210301, in part by the Research Fund of China Manned Space Engineering under Grant 050102, in part by the Natural Science Foundation of China under Grant 51775541, 51575412, 51575338 and 51575407, in part by the EU Seventh Framework Programme (FP7)-ICT under Grant 611391, in part by the Research Project of State Key Lab of Digital Manufacturing Equipment & Technology of China under Grant DMETKF2017003, in part by National Key R&D Program Projects 2018YFB1304600.",,,,,,,,,,"Goodrich, M.A., Schultz, A.C., Human crobot interaction: A survey (2008) Found. Trends in Hum. Comput. Interact., 1 (3), pp. 203-275; Liu, J., Luo, Y., Ju, Z., An interactive astronaut-robot system with gesture control (2016) Comput. Intell. Neurosci., , 7845102 (2016); Rautaray, S.S., Agrawal, A., Vision based hand gesture recognition for human computer interaction: A survey (2015) Artif. Intell. Rev., 43 (1), pp. 1-54; Lecun, Y., Bengio, Y., Hinton, G., Deep learning (2015) Nature, 521 (7553), p. 436; Wang, T., Li, Y., Hu, J., Khan, A., Liu, L., Li, C., Ran, M., A survey on vision-based hand gesture recognition (2018) International Conference on Smart Multimedia, pp. 219-231. , pp., August; Oyedotun, O.K., Khashman, A., Deep learning in vision-based static hand gesture recognition (2017) Neural Comput. Appl., 28 (12), pp. 3941-3951; Nagi, J., Ducatelle, F., Di Caro, A.G., Cirean, D., Meier, U., Giusti, A., Gambardella, L.M., Max-pooling convolutional neural networks for vision-based hand gesture recognition (2011) Conference on 2011 IEEE International in Signal and Image Processing Applications (ICSIPA), pp. 342-347. , pp; Kimm, Y., Toomajian, B., Hand gesture recognition using micro-Doppler signatures with convolutional neural network (2016) IEEE Access, 4, pp. 7125-7130; Yamashita, T., Watasue, T., Hand posture recognition based on bottom-up structured deep convolutional neural network with curriculum learning (2014) Image Processing (ICIP), 2014 IEEE International Conference On, pp. 853-857. , pp; Gao, Q., Liu, J., Ju, Z., Li, Y., Zhang, T., Zhang, L., Static hand gesture recognition with parallel CNNs for space human-robot interaction (2017) International Conference on Intelligent Robotics and Applications, pp. 462-473. , pp; Flores, C.J.L., Cutipa, A.G., Enciso, R.L., Application of convolutional neural networks for static hand gestures recognition under different invariant features (2017) IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), pp. 1-4. , pp., 2017; Zhang, Z., Tian, Z., Zhou, M., HandSense: Smart multimodal hand gesture recognition based on deep neural networks (2018) Journal of Ambient Intelligence and Humanized Computing, pp. 1-16; Hao, S., Wang, W., Ye, Y., Nie, T., Bruzzone, L., Two-stream deep architecture for hyperspectral image classification (2018) IEEE Trans. Geosci. Remote Sens., 56 (4), pp. 2349-2361; Zhang, Z., Microsoft kinect sensor and its effect (2012) IEEE Multimedia, 19 (2), pp. 4-10; Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A., Inception-v4, inception-resnet and the impact of residual connections on learning (2017) AAAI, 4, p. 12; Simonyan, K., Zisserman, A., (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition; He, K., Zhang, X., Ren, S., Sun, J., Identity mappings in deep residual networks (2016) European Conference on Computer Vision, pp. 630-645. , pp; Pugeault, N., Bowden, R., Spelling it out: Real-time ASL fingerspelling recognition (2011) IEEE International Conference On, Computer Vision Workshops (ICCV Workshops), pp. 1114-1119. , pp., 2011; Kuznetsova, A., Leal-Taix, L., Rosenhahn, B., Real-time sign language recognition using a consumer depth camera (2013) Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 83-90. , pp; Keskin, C., Kraç, F., Kara, Y.E., Akarun, L., Real time hand pose estimation using depth sensors (2013) Consumer Depth Cameras for Computer Vision, pp. 119-137; Dong, C., Leu, M.C., Yin, Z., American sign language alphabet recognition using microsoft Kinect (2015) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 44-52. , pp","Liu, J.; State Key Laboratory of Robotics, China; email: liujinguo@sia.cn","Ju Z.Zhou D.Gegov A.Yang L.Yang C.",,"Springer Verlag","19th Annual UK Workshop on Computational Intelligence, UKCI 2019","4 September 2019 through 6 September 2019",,231289,21945357,9783030299323,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85072869151
"Arias E., Encalada P., Tigre F., Granizo C., Gordon C., Garcia M.V.","57209978602;57204520420;57209969066;57209653305;56419359600;56597269000;","Convolutional neural network applied to the gesticulation control of an interactive social robot with humanoid aspect",2020,"Advances in Intelligent Systems and Computing","1038",,,"1039","1053",,1,"10.1007/978-3-030-29513-4_76","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072829111&doi=10.1007%2f978-3-030-29513-4_76&partnerID=40&md5=380eafe72e670e62adad3237328a0c4f","Universidad Tecnica de Ambato, UTA, Ambato, 180103, Ecuador; University of Basque Country, UPV/EHU, Bilbao, 48013, Spain","Arias, E., Universidad Tecnica de Ambato, UTA, Ambato, 180103, Ecuador; Encalada, P., Universidad Tecnica de Ambato, UTA, Ambato, 180103, Ecuador; Tigre, F., Universidad Tecnica de Ambato, UTA, Ambato, 180103, Ecuador; Granizo, C., Universidad Tecnica de Ambato, UTA, Ambato, 180103, Ecuador; Gordon, C., Universidad Tecnica de Ambato, UTA, Ambato, 180103, Ecuador; Garcia, M.V., Universidad Tecnica de Ambato, UTA, Ambato, 180103, Ecuador, University of Basque Country, UPV/EHU, Bilbao, 48013, Spain","This document presents the enforcement of a facial gesture recognition system through applying a convolutional neural network algorithm for gesticulation of an interactive social robot with humanoid appearance, which was designed in order to accomplish the thematic proposed. Furthermore, it is incorporated into it a hearing communication system for Human-Robot interaction throughout the use of visemes, by coordinating the robot’s mouth movement with the processed audio of the text converted to the robot’s voice (text to speech). The precision achieved by the convolutional neural network incorporated in the social-interactive robot is 61%, while the synchronization system between the robot’s mouth and the robot’s audio-voice differs from 0.1 s. In this manner, it is pretended to endow mechanisms social robots for a more naturally interaction with people, thus facilitating the appliance of them in the fields of children’s teaching-learning, medical therapies and as entertainment means. © Springer Nature Switzerland AG 2020.","Deep learning; Human-Robot Interaction; Neural networks; Social robots; Visemes","Audition; Convolution; Deep learning; Deep neural networks; Intelligent systems; Man machine systems; Neural networks; Speech communication; Convolutional neural network; Facial gestures; Interactive robot; Medical therapy; Social robots; Synchronization systems; Teaching-learning; Visemes; Human robot interaction",,,,,,"The authors thank the Technical University of Ambato and the ?Direcci?n de Investigaci?n y Desarrollo? (DIDE) for their support in carrying out this research, in the execution of the project ?Plataforma M?vil Omnidireccional KUKA dotada de Inteligencia Artificial utilizando estrategias de Machine Learnig para Navegaci?n Segura en Espacios no Controlados?, project code: PFISEI27.",,,,,,,,,,"Barnes, J., Fakhrhosseini, M., Jeon, M., Park, C.-H., Howard, A., The influence of robot design on acceptance of social robots (2017) 2017 14Th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 51-55. , pp., IEEE, Jeju; Mead, R., Mataric, M.J., Autonomous human-robot proxemics: A robot-centered approach (2016) The Eleventh ACM/IEEE International Conference on Human Robot Interaction, p. 573. , p., IEEE Press; Rubio Benavides, J.A., (2016) Disenño Y construcción De Un Robot Interactivo Para El Tratamiento De Personas Con El Trastorno Del Espectro Autista (TEA), , Universidad de las Fuerzas Armadas (ESPE; Sojib, N., Islam, S., Rupok, M.H., Hasan, S., Amin, M.R., Iqbal, M.Z., Design and development of the social humanoid robot named Ribo (2017) 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), pp. 314-317. , pp., IEEE, Dhaka; Lapusan, C., Rad, C.-R., Besoiu, S., Plesa, A., Design of a humanoid robot head for studying human-robot interaction (2015) 2015 7Th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Pp. WR-15-WR-18. IEEE, Bucharest; Chen, L., Zhou, M., Su, W., Wu, M., She, J., Hirota, K., Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction (2018) Inf. Sci., 428, pp. 49-61; Faria, D.R., Vieira, M., Faria, F.C.C., Premebida, C., Affective facial expressions recognition for human-robot interaction (2017) 2017 26Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 805-810. , pp., IEEE, Lisbon; Chen, J., Chen, Z., Chi, Z., Fu, H., Facial expression recognition based on facial components detection and hog features (2014) International Workshops on Electrical and Computer Engineering Subfields, pp. 884-888. , pp., Istanbul; Soni, L.N., Datar, A., Datar, S., Implementation of Viola-Jones algorithm based approach for human face detection (2017) Int. J. Curr. Eng. Technol., 7, pp. 1819-1823; (2016) Robocity16 Open Conference on Future Trends in Robotics, , Fernández, R., Montes, H. (eds.), Consejo Superior de Investigaciones Cientificas, Madrid; Cheng, H., Ji, G., Design and implementation of a low cost 3D printed humanoid robotic platform (2016) 2016 IEEE International Conference on Cyber Technology in Automation. Control, and Intelligent Systems (CYBER), pp. 86-91. , pp., IEEE, Chengdu; Le, T.-L., Dong, V.-T., Toward a Vietnamese facial expression recognition system for human-robot interaction (2011) The 2011 International Conference on Advanced Technologies for Communications (ATC 2011), pp. 252-255. , pp., IEEE, Da Nang; Nakaoka, S., Kanehiro, F., Miura, K., Morisawa, M., Fujiwara, K., Kaneko, K., Kajita, S., Hirukawa, H., Creating facial motions of cybernetic human HRP-4C (2009) 2009 9Th IEEE-RAS International Conference on Humanoid Robots, pp. 561-567. , pp., IEEE, Paris; Wang, K., Li, R., Zhao, L., Real-time facial expressions recognition system for service robot based-on ASM and SVMs (2010) 2010 8Th World Congress on Intelligent Control and Automation, pp. 6637-6641. , pp., IEEE, Jinan; Deng, J., Pang, G., Zhang, Z., Pang, Z., Yang, H., Yang, G., CGAN based Facial Expression Recognition for Human-Robot Interaction (2019) IEEE Access, 7, pp. 9848-9859. , https://doi.org/10.1109/ACCESS.2019.2891668; Lecun, Y., Bengio, Y., Hinton, G., Deep learning (2015) Nature, 521, p. 436; Kumar, P., Happy, S.L., Routray, A., A real-time robust facial expression recognition system using HOG features (2016) 2016 International Conference on Computing. Analytics and Security Trends (CAST), pp. 289-293. , pp., IEEE, Pune; Meghdari, A., Shouraki, S.B., Siamy, A., Shariati, A., The real-time facial imitation by a social humanoid robot (2016) 2016 4Th International Conference on Robotics and Mechatronics (ICROM), pp. 524-529. , pp., IEEE, Tehran; Fernandez, M.C.D., Gob, K.J.E., Leonidas, A.R.M., Ravara, R.J.J., Bandala, A.A., Dadios, E.P., Simultaneous face detection and recognition using Viola-Jones Algorithm and Artificial Neural Networks for identity verification (2014) 2014 IEEE Region 10 Symposium, pp. 672-676. , pp., IEEE, Kuala Lumpur; Wang, Y.-Q., An analysis of the Viola-Jones face detection algorithm (2014) Image Process. on Line, 4, pp. 128-148; Sang, D.V., van Dat, N., Thuan, D.P., Facial expression recognition using deep convolutional neural networks (2017) 2017 9Th International Conference on Knowledge and Systems Engineering (KSE), pp. 130-135. , pp., IEEE, Hue; Ashwin, T.S., Jose, J., Raghu, G., Reddy, G.R.M., An E-learning system with multifacial emotion recognition using supervised machine learning (2015) 2015 IEEE Seventh International Conference on Technology for Education (T4E), pp. 23-26. , pp., IEEE, Warangal; Vu, T.H., Nguyen, L., Guo, T., Monga, V., Deep network for simultaneous decomposition and classification in UWB-SAR imagery (2018) 2018 IEEE Radar Conference (Radarconf18), pp. 0553-0558. , pp., IEEE, Oklahoma City; Khan, S., Rahmani, H., Shah, S.A., Bennamoun, M., A guide to convolutional neural networks for computer vision (2018) Synth. Lect. Comput. Vis., 8, pp. 1-207; Kingma, D.P., Ba, J., (2014) Adam: A Method for Stochastic Optimization. Arxiv, 6980 (cs), p. 1412; Deng, X., Liu, Q., Deng, Y., Mahadevan, S., An improved method to construct basic probability assignment based on the confusion matrix for classification problem (2016) Inf. Sci., 340-341, pp. 250-261; Liu, L., Li, B., Chen, I.-M., Goh, T.J., Sung, M., Interactive robots as social partner for communication care (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 2231-2236. , pp., IEEE, Hong Kong; Encalada, P., Alvarado, B., Matia, F., (2016) Facial Expressions and Voice Control of an Interacive robot” Robocity, , Chap. 27","Garcia, M.V.; Universidad Tecnica de Ambato, Ecuador; email: mv.garcia@uta.edu.ec","Bi Y.Bhatia R.Kapoor S.",,"Springer Verlag","Intelligent Systems Conference, IntelliSys 2019","5 September 2019 through 6 September 2019",,231009,21945357,9783030295127,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85072829111
"Oh S., Oh Y.H., Ju D.Y.","57209477562;56963267700;55804211500;","Understanding the Preference of the Elderly for Companion Robot Design",2020,"Advances in Intelligent Systems and Computing","962",,,"92","103",,2,"10.1007/978-3-030-20467-9_9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067900824&doi=10.1007%2f978-3-030-20467-9_9&partnerID=40&md5=f6c56c9b0efe37041f3a979c3c016da3","Technology and Design Research Center, Yonsei Institute of Convergence Technology, Yonsei University, Incheon, South Korea; School of Integrated Technology, Yonsei Institute of Convergence Technology, Yonsei University, Incheon, South Korea","Oh, S., Technology and Design Research Center, Yonsei Institute of Convergence Technology, Yonsei University, Incheon, South Korea; Oh, Y.H., School of Integrated Technology, Yonsei Institute of Convergence Technology, Yonsei University, Incheon, South Korea; Ju, D.Y., Technology and Design Research Center, Yonsei Institute of Convergence Technology, Yonsei University, Incheon, South Korea","Companion robots have been utilized as technical solutions to alleviate the problems of the elderly. Various types of companion robots have been suggested by human&#x2013;robot interaction researchers. However, the appearance of some companion robots lacks analysis from the formative perspective. Therefore, we developed five robot design concepts based on literature reviews and the formative analysis of commercialized robots. In total, 19 participants of different age groups were interviewed to rate their preference for the developed design concepts. Cross-tabulation analysis and qualitative findings showed that the preference for the design concepts differed with the age group. The elderly preferred rounded and anthropomorphic robot designs. When evaluating the appearance of a robot, they preferred an intimate design enabling talking to the robot. However, the younger adults preferred neat and tidy designs with less detailed design elements. They considered the actual usage of the robot, emphasizing on maintenance and sanitation. &#x00A9; 2020, Springer Nature Switzerland AG.","Appearance; Companion robot; Design concept; Elderly","Anthropomorphic robots; Human engineering; Human robot interaction; Appearance; Companion robot; Design concept; Elderly; Formative analysis; Literature reviews; Robot interactions; Technical solutions; Machine design",,,,,"National Research Foundation, NRF; Ministry of Science, ICT and Future Planning, MSIP: IITP-2018-2017-0-01015, NRF-2017M3C8A8091770; National Research Foundation of Korea, NRF; Institute for Information and Communications Technology Promotion, IITP","Acknowledgments. This research was supported by Research Program to Solve Social Issues of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2017M3C8A8091770). This research was supported by the MIST (Ministry of Science and ICT), Korea, under the “ICT Consilience Creative Program” (IITP-2018-2017-0-01015) supervised by the IITP (Institute for Information & Communications Technology Promotion). We would like to thank Jimin Ryu for designing the companion robot images.","This research was supported by Research Program to Solve Social Issues of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2017M3C8A8091770). This research was supported by the MIST (Ministry of Science and ICT), Korea, under the “ICT Consilience Creative Program” (IITP-2018-2017-0-01015) supervised by the IITP (Institute for Information & Communications Technology Promotion). We would like to thank Jimin Ryu for designing the companion robot images.",,,,,,,,,"Czaja, S.J., Sharit, J., Age differences in attitudes toward computers (1998) J Geron. Ser. B., pp. 329-340. , 53B; Schraft, R.D., Schaeffer, C., May, T., Care-O-bot: The concept of a system for assisting elderly or disabled persons in home environments (1998) IECON ’98. Proceedings of the 24Th Annual Conference of the IEEE Industrial Electronics Society, pp. 2476-2481. , pp., IEEE; Brunel, F.F., Bloch, P.H., Arnold, T.J., Individual differences in the centrality of visual product aesthetics: Concept and measurement (2003) J. Consum. Res., 29, pp. 551-565; Del Pobil, A.P., Sundar, S.S., Interaction science perspective on HRI: Designing robot morphology (2010) Proceedings of the 5Th ACM/IEEE International Conference on Human-Robot Interaction, p. 5; Broadbent, E., Stafford, R., Macdonald, B., Acceptance of healthcare robots for the older population: Review and future directions (2009) Int. J. Soc. Robot., 1, p. 319; Syrdal, D.S., Dautenhahn, K., Woods, S.N., Walters, M.L., Koay, K.L., Looking good? Appearance preferences and robot personality inferences at zero acquaintance (2007) AAAI Spring Symposium: Multidisciplinary Collaboration for Socially Assistive Robotics; Li, D., Rau, P.L.P., Li, Y., A cross-cultural study: Effect of robot appearance and task (2010) Int. J. Soc. Robot., 2, pp. 175-186; Wu, Y.-H., Fassert, C., Rigaud, A.-S., Designing robots for the elderly: Appearance issue and beyond (2012) Arch. Gerontol. Geriatr., 54, pp. 121-126; Walters, M.L., Koay, K.L., Syrdal, D.S., Dautenhahn, K., Te Boekhorst, R., Preferences and perceptions of robot appearance and embodiment in human-robot interaction Trials (2009) Procs New Front. Human-Robot Interact; Heerink, M., Kröse, B., Evers, V., Wielinga, B., Assessing acceptance of assistive social agent technology by older adults: The Almere model (2010) Int. J. Soc. Robot., 2, pp. 361-375; Syrdal, D.S., Dautenhahn, K., Woods, S., Walters, M.L., Koay, K.L.: ‘Doing the right thing wrong’-personality and tolerance to uncomfortable robot approaches (2006) Robot and Human Interactive Communication, 2006. ROMAN 2006. the 15Th IEEE International Symposium, pp. 183-188; Preuß, D., Legal, F., Living with the animals: Animal or robotic companions for the elderly in smart homes? (2017) J. Med. Ethics., 43, pp. LP-410; Banks, M.R., Willoughby, L.M., Banks, W.A., Animal-assisted therapy and loneliness in nursing homes: Use of robotic versus living dogs (2008) J. Am. Med. Dir. Assoc., 9, pp. 173-177; Friedmann, E., Galik, E., Thomas, S.A., Hall, P.S., Chung, S.Y., McCune, S., Evaluation of a Pet-assisted living intervention for improving functional status in assisted living residents with mild to moderate cognitive impairment: A pilot study (2014) Am. J. Alzheimer’s Dis. Other Dementias®., 30, pp. 276-289; Shibata, T., Therapeutic seal robot as biofeedback medical device: Qualitative and quantitative evaluations of robot therapy in dementia care (2012) Proc. IEEE, 100, pp. 2527-2538; Shibata, T., Wada, K., Tanie, K., Statistical analysis and comparison of questionnaire results of subjective evaluations of seal robot in Japan and UK (2003) 2003 IEEE International Conference on Robotics and Automation, pp. 3152-3157. , pp., IEEE; Yu, R., Hui, E., Lee, J., Poon, D., Ng, A., Sit, K., Ip, K., Shibata, T., Use of a therapeutic, socially assistive pet robot (PARO) in improving mood and stimulating social interaction and communication for people with dementia: Study protocol for a randomized controlled trial (2015) JMIR Res. Protoc., 4, p. e45; Libin, A.V., Libin, E.V., Person-robot Interactions from the robopsychologists’ point of view: The robotic psychology and robotherapy approach (2004) Proc. IEEE, 92, pp. 1789-1803; Minato, T., Shimada, M., Ishiguro, H., Itakura, S., Development of an android robot for studying human-robot interaction (2004) Bt-Innovations in Applied Artificial Intelligence. Presented at The; Prakash, A., Rogers, W.A., Why some humanoid faces are perceived more positively than others: Effects of human-likeness and task (2015) Int. J. Soc. Robot., 7, pp. 309-331; Breazeal, C., Social robots: From research to commercialization (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, p. 1. , p., ACM; Oh, Y.H., Kim, J., Ju, D.Y., Analysis of design elements to enhance acceptance of companion robot in older adults (2018) RO-MAN 2018-The 27Th IEEE International Symposium on Robot and Human Interactive Communication, Workshop on Social Cues in Robot Interaction, Trust and Acceptance. IEEE; Lazar, A., Thompson, H.J., Piper, A.M., Demiris, G., Rethinking the design of robotic pets for older adults (2016) Proceedings of the 2016 ACM Conference on Designing Interactive Systems, pp. 1034-1046. , pp., ACM; Lee, S., Choi, J., Enhancing user experience with conversational agent for movie recommendation: Effects of self-disclosure and reciprocity (2017) Int. J. Hum Comput Stud., 103, pp. 95-105","Ju, D.Y.; Technology and Design Research Center, South Korea; email: dyju@yonsei.ac.kr","Chen J.",,"Springer Verlag","AHFE International Conference on Human Factors in Robots and Unmanned Systems, 2019","24 July 2019 through 28 July 2019",,227169,21945357,9783030204662,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85067900824
"Tello J.A., Zapata M., Paillacho D.","57209328960;56818703100;56084736200;","Kinematic optimization of the robot head movements for the evaluation of human-robot interaction in social robotics",2020,"Advances in Intelligent Systems and Computing","975",,,"108","118",,2,"10.1007/978-3-030-20216-3_11","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067392738&doi=10.1007%2f978-3-030-20216-3_11&partnerID=40&md5=ecbbb79bf2a8fe35c496f36e730a614f","Research Center of Mechatronics and Interactive Systems, Universidad Tecnológica Indoamérica, Machala y Sabanilla, Quito, 172103, Ecuador; ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador","Tello, J.A., Research Center of Mechatronics and Interactive Systems, Universidad Tecnológica Indoamérica, Machala y Sabanilla, Quito, 172103, Ecuador; Zapata, M., Research Center of Mechatronics and Interactive Systems, Universidad Tecnológica Indoamérica, Machala y Sabanilla, Quito, 172103, Ecuador; Paillacho, D., ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador","This paper presents the simplification of the head movements from the analysis of the biomechanical parameters of the head and neck at the mechanical and structural level through CAD modeling and construction with additive printing in ABS/PLA to implement non-verbal communication strategies and establish behavior patterns in the social interaction. This is using in the denominated MASHI (Multipurpose Assistant robot for Social Human-robot Interaction) experimental robotic telepresence platform, implemented by a display with a fish-eye camera along with the mechanical mechanism, which permits 4 degrees of freedom (DoF). In the development of mathematical-mechanical modeling for the kinematics codification that governs the robot and the autonomy of movement, we have the Pitch, Roll, and Yaw movements, and the combination of all of them to establish an active communication through telepresence. For the computational implementation, it will be show the rotational matrix to describe the movement. © Springer Nature Switzerland AG 2020.","3D additive printing; Biomechanics; Computing engineering; Human robotic interaction; Lightweight mechanical design","3D modeling; 3D printers; Additives; Biomechanics; Computer aided design; Degrees of freedom (mechanics); Human computer interaction; Kinematics; Man machine systems; Robotics; Visual communication; Biomechanical parameters; Computational implementations; Experimental robotics; Human-Robotic interaction; Kinematic optimization; Mechanical design; Non-verbal communications; Social human-robot interactions; Human robot interaction",,,,,,,,,,,,,,,,"Nordin, A.I., Hudson, M., Denisova, A., Beeston, J., (2016) Perceptions of Telepresence Robot Form, 4. , vol; Victornunez, D., Rodriguez, V., México, E.M., Modelo Vrml Interactivo De Un Robot Humanoide Bioloid; Danev, L., Hamann, M., Fricke, N., Hollarek, T., Paillacho, D., Development of animated facial expressions to express emotions in a robot: RobotIcon, 2017 IEEE 2nd Ecuador Tech (2017) Chapters Meet. ETCM, 2017 (Janua), pp. 1-6. , vol., pp; Nourbakhsh, I.R., Robots and Education in the Classroom and in the Museum: On the Study of Robots, and Robots for Study; Sirimanna, S.W.D.T.S., Jayasekara, A.G.B.P., Edirisinghe, E.A.N.S., Lakmal, R.A.P., Lakshitha, T.G.G., (2015) Design and Simulation of a Human-Like Robot Neck Mechanism, 2015 Electr. Eng. Conf. [Eecon], 1893. , December; Pinzón Ríos, I.D., Cabeza hacia adelante: Una mirada desde la biomecánica y sus implicaciones sobre el movimiento corporal humano TT-forward head: A biomechanical perspective and its implications for the human body movement (2015) Rev. Univ. Ind. Santander, Salud, 47 (1), pp. 75-83; Ölçücüoğlu, O., Human-like robot head design a thesis submitted to the Graduate School of Natural and Applied Sciences of (2007) Middle East Technical University; Díaz-Boladeras, M., Paillacho, D., Angulo, C., Torres, O., González-Diéguez, J., Albo-Canals, J., Evaluating group-robot interaction in crowded public spaces: A week-long exploratory study in the wild with a humanoid robot guiding visitors through a science museum (2015) Int. J. Humanoid Robot., 12 (4); Dine, A., Vosniakos, G.-C., On the development of a robot-operated 3D-printer (2018) Procedia Manuf., 17 (Jan.), pp. 6-13; Eisenberg, M., 3D printing for children: What to build next? (2013) Int. J. Child-Computer Interact., 1, pp. 7-13; Avilés Arriaga, H.H., Herrera Rivas, H., Nuño Maganda, M.A., Un Esquema 3D para la Descripción Visual de Gestos Dinámicos, no (2014) December; Hernández, X.R., (2017) Rediscovering the Experimental Robotic Platform MASHI THESIS, No. January, P., p. 49; Diaz, M., Paillacho, D., Angulo, C., An exploratory study of group-robot social interactions in a cultural center (2015) IEEE/RSJ Int. Conf. Intell. Robot. Syst., pp. 44-48. , IROS 2015","Tello, J.A.; Research Center of Mechatronics and Interactive Systems, Machala y Sabanilla, Ecuador; email: jorgealvarez@uti.edu.ec","Alexander T.Di Nicolantonio M.Rossi E.",,"Springer Verlag","AHFE International Conference on Additive Manufacturing, Modeling Systems and 3D Prototyping, 2019","24 July 2019 through 28 July 2019",,226919,21945357,9783030202156,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85067392738
"Wang Z., Bu T., Li Y., Wei D., Tao B., Yin Z., Zhang C., Wu H.","57354931700;57195735354;57208774257;57354460900;7006868490;7401498779;56136402100;57222816837;","Multidimensional Force Sensors Based on Triboelectric Nanogenerators for Electronic Skin",2021,"ACS Applied Materials and Interfaces","13","47",,"56320","56328",,,"10.1021/acsami.1c17506","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119987298&doi=10.1021%2facsami.1c17506&partnerID=40&md5=91cff13e60f9f2be9c499178e79765d5","Flexible Electronics Research Center, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China; Cas Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China; School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China","Wang, Z., Flexible Electronics Research Center, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China, Cas Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China; Bu, T., Cas Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; Li, Y., Flexible Electronics Research Center, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China; Wei, D., Flexible Electronics Research Center, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China; Tao, B., Flexible Electronics Research Center, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China; Yin, Z., Flexible Electronics Research Center, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China; Zhang, C., Cas Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; Wu, H., Flexible Electronics Research Center, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Hubei, Wuhan, 430074, China","The ability to detect multidimensional forces is highly desired for electronic skin (E-skin) sensors. Here, based on single-electrode-mode triboelectric nanogenerators (S-TENGs), fully elastic E-skin that can simultaneously sense normal pressure and shear force has been proposed. With the hemispherical curve-structure design and further structural optimization, the pressure sensor exhibits a high linearity and sensitivity of 144.8 mV/kPa in the low-pressure region. By partitioning the lower tribolayer into two symmetric parts, a multidimensional force sensor has been fabricated in which the output voltage sum and ratio of the two S-TENGs can be used for normal pressure and shear force sensing, respectively. When the multidimensional force sensors are mounted at a two-fingered robotic manipulator, the change of the grabbing state can be recognized, indicating that the sensor may have great application potential in tactile sensing for robotic manipulation, human-robot interactions, environmental awareness, and object recognition. © 2021 American Chemical Society.","electronic skin; multidimensional force sensor; robotics; tactile sensing; triboelectric nanogenerators","Curve fitting; Human robot interaction; Manipulators; Object recognition; Robotics; Structural optimization; Triboelectricity; Electronic skin; Multidimensional force sensor; Nanogenerators; Normal pressure; Pressure force; Shear force; Single electrodes; Skin sensors; Tactile sensing; Triboelectric nanogenerator; Nanogenerators",,,,,"DMETKF2020014; 2019AEA171; National Natural Science Foundation of China, NSFC: 51820105008, 92048302, U2013213","We acknowledge the support from the National Natural Science Foundation of China (No. U2013213, 51820105008, and 92048302) the Technology Innovation Project of Hubei Province of China under Grant 2019AEA171, and the Open Research Foundation of State Key Laboratory of Digital Manufacturing Equipment & Technology (DMETKF2020014). We also acknowledge the support from Flexible Electronics Research Center of HUST for providing experiment facility.","We acknowledge the support from the National Natural Science Foundation of China (No. U2013213, 51820105008, and 92048302), the Technology Innovation Project of Hubei Province of China under Grant 2019AEA171, and the Open Research Foundation of State Key Laboratory of Digital Manufacturing Equipment & Technology (DMETKF2020014). We also acknowledge the support from Flexible Electronics Research Center of HUST for providing experiment facility.",,,,,,,,,"Chortos, A., Liu, J., Bao, Z.N., Pursuing Prosthetic Electronic Skin (2016) Nat. Mater., 15, pp. 937-950; Hammock, M.L., Chortos, A., Tee, B.C., Tok, J.B., Bao, Z.N., 25th anniversary article: The Evolution of Electronic skin (e-skin): A Brief History, Design Considerations, and Recent Progress (2013) Adv. 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Commun., 9, p. 2458; Chen, H.T., Bai, L., Li, T., Zhao, C., Zhang, J.S., Zhang, N., Song, G.F., Xu, Y., Wearable and Robust Triboelectric Nanogenerator Based on Crumpled Gold Films (2018) Nano Energy, 46, pp. 73-80; Dong, K., Wu, Z.Y., Deng, J.N., Wang, A.C., Zou, H.Y., Chen, C.Y., Hu, D.M., Wang, Z.L., A Stretchable Yarn Embedded Triboelectric Nanogenerator as Electronic Skin for Biomechanical Energy Harvesting and Multifunctional Pressure Sensing (2018) Adv. Mater., 30, p. e1804944; Boutry, C.M., Negre, M., Jorda, M., Vardoulis, O., Chortos, A., Khatib, O., Bao, Z.N., A Hierarchically Patterned, Bioinspired E-Skin Able to Detect the Direction of Applied Pressure for Robotics (2018) Sci. Robot., 3, p. eaau6419; Ha, M.J., Lim, S.D., Cho, S.W., Lee, Y.O., Na, S.Y., Baig, C.G., Ko, H.H., Skin-Inspired Hierarchical Polymer Architectures with Gradient Stiffness for Spacer-Free, Ultrathin, and Highly Sensitive Triboelectric Sensors (2018) Acs Nano, 12, pp. 3964-3974; Yao, G., Xu, L., Cheng, X.W., Li, Y.Y., Huang, X., Guo, W., Liu, S.Y., Wu, H., Bioinspired Triboelectric Nanogenerators as Self-Powered Electronic Skin for Robotic Tactile Sensing (2020) Adv. Funct. Mater., 30, p. 1907312; Tan, C.X., Dong, Z.G., Li, Y.H., Zhao, H.G., Huang, X.Y., Zhou, Z.C., Jiang, J.W., Sun, B., A High Performance Wearable Strain Sensor with Advanced Thermal Management for Motion Monitoring (2020) Nat. Commun., 11, p. 3530; Yamada, T., Hayamizu, Y., Yamamoto, Y., Yomogida, Y., Izadi-Najafabadi, A., Futaba, D.N., Hata, K., A Stretchable Carbon Nanotube Strain Sensor for Human-Motion Detection (2011) Nat. 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Zhang, C.; Cas Center for Excellence in Nanoscience, China; email: czhang@binn.cas.cn",,,"American Chemical Society",,,,,19448244,,,,"English","ACS Appl. Mater. Interfaces",Article,"Final","",Scopus,2-s2.0-85119987298
"Dai B., Zhang L., Zhao C., Bachman H., Becker R., Mai J., Jiao Z., Li W., Zheng L., Wan X., Huang T.J., Zhuang S., Zhang D.","57226564502;56939513100;21740400900;57191866159;57221280911;57326205200;57204427978;57219535976;57326205300;57325636300;7404961232;57226704503;8365460000;","Biomimetic apposition compound eye fabricated using microfluidic-assisted 3D printing",2021,"Nature Communications","12","1","6458","","",,,"10.1038/s41467-021-26606-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118660467&doi=10.1038%2fs41467-021-26606-z&partnerID=40&md5=f368e15f46990d5261272b5124c56ed2","Engineering Research Center of Optical Instrument and System, the Ministry of Education, Shanghai Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, 200093, China; Department of Physics, University of Dayton, Dayton, OH  45469, United States; Department of Electro-Optics and Photonics, University of Dayton, Dayton, OH  45469, United States; Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC  27709, United States; Department of Biomedical Engineering, Duke University, Durham, NC  27709, United States; Alfred E. Mann Institute for Biomedical Engineering, University of Southern California, Los Angeles, CA  90089, United States","Dai, B., Engineering Research Center of Optical Instrument and System, the Ministry of Education, Shanghai Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, 200093, China; Zhang, L., Engineering Research Center of Optical Instrument and System, the Ministry of Education, Shanghai Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, 200093, China; Zhao, C., Department of Physics, University of Dayton, Dayton, OH  45469, United States, Department of Electro-Optics and Photonics, University of Dayton, Dayton, OH  45469, United States; Bachman, H., Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC  27709, United States; Becker, R., Department of Biomedical Engineering, Duke University, Durham, NC  27709, United States; Mai, J., Alfred E. Mann Institute for Biomedical Engineering, University of Southern California, Los Angeles, CA  90089, United States; Jiao, Z., Engineering Research Center of Optical Instrument and System, the Ministry of Education, Shanghai Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, 200093, China; Li, W., Engineering Research Center of Optical Instrument and System, the Ministry of Education, Shanghai Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, 200093, China; Zheng, L., Engineering Research Center of Optical Instrument and System, the Ministry of Education, Shanghai Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, 200093, China; Wan, X., Engineering Research Center of Optical Instrument and System, the Ministry of Education, Shanghai Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, 200093, China; Huang, T.J., Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC  27709, United States; Zhuang, S., Engineering Research Center of Optical Instrument and System, the Ministry of Education, Shanghai Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, 200093, China; Zhang, D., Engineering Research Center of Optical Instrument and System, the Ministry of Education, Shanghai Key Laboratory of Modern Optical System, University of Shanghai for Science and Technology, Shanghai, 200093, China","After half a billion years of evolution, arthropods have developed sophisticated compound eyes with extraordinary visual capabilities that have inspired the development of artificial compound eyes. However, the limited 2D nature of most traditional fabrication techniques makes it challenging to directly replicate these natural systems. Here, we present a biomimetic apposition compound eye fabricated using a microfluidic-assisted 3D-printing technique. Each microlens is connected to the bottom planar surface of the eye via intracorporal, zero-crosstalk refractive-index-matched waveguides to mimic the rhabdoms of a natural eye. Full-colour wide-angle panoramic views and position tracking of a point source are realized by placing the fabricated eye directly on top of a commercial imaging sensor. As a biomimetic analogue to naturally occurring compound eyes, the eye’s full-colour 3D to 2D mapping capability has the potential to enable a wide variety of applications from improving endoscopic imaging to enhancing machine vision for facilitating human–robot interactions. © 2021, The Author(s).",,"arthropod; evolution; eye; mapping method; robotics; three-dimensional modeling; visual analysis; Arthropoda",,,,,"National Outstanding Youth Science Fund Project of National Natural Science Foundation of China, IUSS: 61775140; National Natural Science Foundation of China, NSFC; Science and Technology Commission of Shanghai Municipality, STCSM: 18142200800; National Key Research and Development Program of China, NKRDPC: 2016YFD0500603; Shanghai Rising-Star Program: 20QA1407000","This research was supported by grants from the National Key Research and Development Program of China (Grant no. 2016YFD0500603), the National Natural Science Foundation of China (Grant no. 61775140), the Shanghai Science and Technology Commission (Grant no. 18142200800) and Shanghai Rising-Star Program (Grant no. 20QA1407000).",,,,,,,,,,"Parker, A., (2003) In the Blink of an Eye Ch, 8. , Perseus Pub; Vannier, J., Exceptional preservation of eye structure in arthropod visual predators from the Middle Jurassic (2016) Nat. 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Huang, T.J.; Department of Mechanical Engineering and Materials Science, United States; email: tony.huang@duke.edu",,,"Nature Research",,,,,20411723,,,,"English","Nat. Commun.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85118660467
"Carneiro D., Silva F., Georgieva P.","57202607993;7201708129;6701326860;","Robot anticipation learning system for ball catching",2021,"Robotics","10","4","113","","",,,"10.3390/robotics10040113","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118233491&doi=10.3390%2frobotics10040113&partnerID=40&md5=1e8ad04d36b5c6549819dcc187505656","Department of Electronics, Telecommunications and Informatics, Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, Aveiro, 3810-193, Portugal","Carneiro, D., Department of Electronics, Telecommunications and Informatics, Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, Aveiro, 3810-193, Portugal; Silva, F., Department of Electronics, Telecommunications and Informatics, Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, Aveiro, 3810-193, Portugal; Georgieva, P., Department of Electronics, Telecommunications and Informatics, Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, Aveiro, 3810-193, Portugal","Catching flying objects is a challenging task in human–robot interaction. Traditional techniques predict the intersection position and time using the information obtained during the free-flying ball motion. A common pain point in these systems is the short ball flight time and uncertainties in the ball’s trajectory estimation. In this paper, we present the Robot Anticipation Learning System (RALS) that accounts for the information obtained from observation of the thrower’s hand motion before the ball is released. RALS takes extra time for the robot to start moving in the direction of the target before the opponent finishes throwing. To the best of our knowledge, this is the first robot control system for ball-catching with anticipation skills. Our results show that the information fused from both throwing and flying motions improves the ball-catching rate by up to 20% compared to the baseline approach, with the predictions relying only on the information acquired during the flight phase. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Anticipation learning; Ball catching; Human–robot interaction; Neural network; Trajectory prediction",,,,,,"Fundação para a Ciência e a Tecnologia, FCT: UIDB/00127/2020","Acknowledgments: This work was supported by the Foundation for Science and Technology (FCT) in the context of the project UIDB/00127/2020.",,,,,,,,,,"Nemec, B., Ude, A., Reinforcement learning of ball-in-a-cup playing robot Proceedings of the 2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011, , Karon Beach, Thailand, 7–11 December 2011. [CrossRef]; Kober, J., Peters, J., Policy search for motor primitives in robotics (2011) Mach. Learn, 84, pp. 171-203. , [CrossRef]; Bujarbaruah, M., Zheng, T., Shetty, A., Sehr, M., Borrelli, F., Learning to Play Cup-and-Ball with Noisy Camera Observations Proceedings of the 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), , Hong Kong, China, 20–21 August 2020. 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Perform, 20, p. 591. , [CrossRef] [PubMed]; Cesqui, B., Russo, M., Lacquaniti, F., d’Avella, A., Grasping in one-handed catching in relation to performance (2016) PLoS ONE, 11, p. e0158606. , [CrossRef] [PubMed]; Carneiro, D., Silva, F., Georgieva, P., The role of early anticipations for human-robot ball catching Proceedings of the 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 10-16. , Torres Vedras, Portugal, 25–27 April 2018; Hove, B., Slotine, J.J.E., Experiments in robotic catching (1991) Proceedings of the 1991 American Control Conference, pp. 380-386. , Boston, MA, USA, 26–28 June; Hong, W., Slotine, J.J.E., Experiments in hand-eye coordination using active vision (1997) Experimental Robotics IV, pp. 130-139. , Springer: Berlin/Heidelberg, Germany; Nishiwaki, K., Ionno, A., Nagashima, K., Inaba, M., Inoue, H., The humanoid saika that catches a thrown ball (1997) Proceedings of the 6th IEEE International Workshop on Robot and Human Communication, pp. 94-99. , RO-MAN’97 SENDAI, Sendai, Japan, 29 September–1 October; Frese, U., Bauml, B., Haidacher, S., Schreiber, G., Schäfer, I., Hahnle, M., Hirzinger, G., Off-the-shelf vision for a robotic ball catcher (2001) Proceedings of the 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No. 01CH37180), 3, pp. 1623-1629. , Maui, HI, USA, 29 October–3 November; Riley, M., Atkeson, C.G., Robot catching: Towards engaging human-humanoid interaction (2002) Auton. Robot, 12, pp. 119-128. , [CrossRef]; Park, G.R., Kim, K., Kim, C., Jeong, M.H., You, B.J., Ra, S., Human-like catching motion of humanoid using evolutionary algorithm (ea)-based imitation learning Proceedings of the RO-MAN 2009—The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 809-815. , Toyama, Japan, 27 September–2 October 2009; Kim, S., Gribovskaya, E., Billard, A., Learning motion dynamics to catch a moving object Proceedings of the 2010 10th IEEE-RAS International Conference on Humanoid Robots, pp. 106-111. , Nashville, TN, USA, 6–8 December 2010; Bäuml, B., Wimböck, T., Hirzinger, G., Kinematically optimal catching a flying ball with a hand-arm-system Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2592-2599. , Taipei, Taiwan, 18–22 October 2010; Dong, K., Pereida, K., Shkurti, F., Schoellig, A.P., Catch the Ball: Accurate high-speed motions for mobile manipulators via inverse dynamics learning Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , Las Vegas, NV, USA, 24 October 2020–24 January 2021. [CrossRef]; Yu, H., Guo, D., Yin, H., Chen, A., Xu, K., Wang, Y., Xiong, R., (2021) Neural Motion Prediction for In-flight Uneven Object Catching, , arXiv arXiv:2103.08368v1; Allen, P.K., Timcenko, A., Yoshimi, B., Michelman, P., Automated Tracking and Grasping of a Moving Object with a Robotic Hand-Eye System (1993) IEEE Trans. Robot. Autom, 9, pp. 152-165. , [CrossRef]; Deguchi, K., Sakurai, H., Ushida, S., A goal oriented just-in-time visual servoing for ball catching robot arm (2008) Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, , IROS, Nice, France, 22–26 September [CrossRef]; Cigliano, P., Lippiello, V., Ruggiero, F., Siciliano, B., Robotic Ball Catching with an Eye-in-Hand Single-Camera System (2015) IEEE Trans. Control Syst. Technol, 23, pp. 1657-1671. , [CrossRef]; Sato, M., Takahashi, A., Namiki, A., High-speed catching by multi-vision robot hand Proceedings of the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , Las Vegas, NV, USA, 24 October 2020–24 January 2021. [CrossRef]; Ardakani, M.M.G., Olofsson, B., Robertsson, A., Johansson, R., Real-time trajectory generation using model predictive control (2015) Proceedings of the 2015 IEEE International Conference on Automation Science and Engineering (CASE), , Gothenburg, Sweden, 24–28 August [CrossRef]; Stone, J.A., Maynard, I., North, J.S., Panchuk, D., Davids, K., Emergent perception–action couplings regulate postural adjustments during performance of externally-timed dynamic interceptive actions (2015) Psychol. Res, 79, pp. 829-843. , [CrossRef] [PubMed]; Sigurdsson, G.A., Russakovsky, O., Gupta, A., (2017) What Actions are Needed for Understanding Human Actions in Videos?, , arXiv arXiv:1708.02696; Yoshikawa, T., (1990) Foundations of Robotics: Analysis and Control, , MIT Press: Cambridge, MA, USA","Georgieva, P.; Department of Electronics, Portugal; email: petia@ua.pt",,,"MDPI",,,,,22186581,,,,"English","Robotics",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85118233491
"Tsfasman M., Saravanan A., Viner D., Goslinga D., De Wolf S., Raman C., Jonker C.M., Oertel C.","57378020800;57377399200;57377399300;57376934700;57377708700;57211681203;7006702150;50561763400;","Towards a Real-time Measure of the Perception of Anthropomorphism in Human-robot Interaction",2021,"MuCAI 2021 - Proceedings of the 2nd ACM Multimedia Workshop on Multimodal Conversational AI, co-located with ACM MM 2021",,,,"13","18",,1,"10.1145/3475959.3485394","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119377600&doi=10.1145%2f3475959.3485394&partnerID=40&md5=f7b4de2e2a07d2423c89cd419ff82ebc","Delft University of Technology, Delft, Netherlands","Tsfasman, M., Delft University of Technology, Delft, Netherlands; Saravanan, A., Delft University of Technology, Delft, Netherlands; Viner, D., Delft University of Technology, Delft, Netherlands; Goslinga, D., Delft University of Technology, Delft, Netherlands; De Wolf, S., Delft University of Technology, Delft, Netherlands; Raman, C., Delft University of Technology, Delft, Netherlands; Jonker, C.M., Delft University of Technology, Delft, Netherlands; Oertel, C., Delft University of Technology, Delft, Netherlands","How human-like do conversational robots need to look to enable long-term human-robot conversation? One essential aspect of long-term interaction is a human's ability to adapt to the varying degrees of a conversational partner's engagement and emotions. Prosodically, this can be achieved through (dis)entrainment. While speech-synthesis has been a limiting factor for many years, restrictions in this regard are increasingly mitigated. These advancements now emphasise the importance of studying the effect of robot embodiment on human entrainment. In this study, we conducted a between-subjects online human-robot interaction experiment in an educational use-case scenario where a tutor was either embodied through a human or a robot face. 43 English-speaking participants took part in the study for whom we analysed the degree of acoustic-prosodic entrainment to the human or robot face, respectively. We found that the degree of subjective and objective perception of anthropomorphism positively correlates with acoustic-prosodic entrainment. © 2021 Owner/Author.","acoustic-prosodic entrainment; human-robot interaction; multi-modal; prosody",,,,,,,,,,,,,,,,,"Akata, Zeynep, Balliet, Dan, de Rijke, Maarten, Dignum, Frank, Dignum, Virginia, Eiben, Guszti, Fokkens, Antske, Welling, Max, A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artifcial Intelligence (2020) Computer, 53 (8), pp. 18-28. , https://doi.org/10.1109/MC.2020.2996587, [1] (2020); Moubayed, Samer Al, Beskow, Jonas, Skantze, Gabriel, Granström, Björn, Furhat: A Back-Projected Human-Like Robot Head for Multiparty Human-Machine Interaction (2012) Cognitive Behavioural Systems, pp. 114-130. , [2] Anna Esposito, Antoni-etta M. Esposito, Alessandro Vinciarelli, Rüdiger Hofmann, and Vincent C. Müller (Eds). 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Springer Berlin Heidelberg, Berlin, Heidelberg; Breazeal, Cynthia, Regulation and Entrainment in Human-Robot Interaction (2002) The International Journal of Robotics Research, 21 (10-11), pp. 883-902. , https://doi.org/10.1177/0278364902021010096, [6] (2002), arXiv:https://doi.org; De Looze, Céline, Oertel, Catharine, Rauzy, Stéphane, Campbell, Nick, Measuring dynamics of mimicry by means of prosodic cues in conversational speech (2011) ICPhS 2011, , [7]; de Visser, E.J., Monfort, S.S., McKendrick, R., Smith, M.A.B., McKnight, P.E., Krueger, F., Parasuraman, R., Almost human: Anthropomorphism increases trust resilience in cognitive agents (2016) Journal of Experimental Psychology: Applied, 22 (3), pp. 331-349. , https://doi.org/10.1037/xap0000092, [8] (2016), cited By 111; Fink, Julia, Anthropomorphism and Human Likeness in the Design of Robots and Human-Robot Interaction (2012) Social Robotics, pp. 199-208. , [9] Shuzhi Sam Ge, Oussama Khatib, John-John Cabibihan, Reid Simmons, and Mary-Anne Williams (Eds). Springer Berlin Heidelberg, Berlin, Heidelberg; Gálvez, Ramiro H., Gauder, Lara, Luque, Jordi, Gravano, Agustín, A unifying framework for modeling acoustic/prosodic entrainment: defnition and evaluation on two large corpora (2020) Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 215-224. , https://aclanthology.org/2020.sigdial-1.27, [10] Association for Computational Linguistics, 1st virtual meeting; Gog, Tamara, Paas, Fred, Marcus, Nadine, Ayres, Paul, Sweller, John, The Mirror Neuron System and Observational Learning: Implications for the Efectiveness of Dynamic Visualizations (2008) Educational Psychology Review, 21, pp. 21-30. , https://doi.org/10.1007/s10648-008-9094-3, [11] (03 2008); Jadoul, Yannick, Thompson, Bill, de Boer, Bart, Introducing Parselmouth: A Python interface to Praat (2018) Journal of Phonetics, 71, pp. 1-15. , https://doi.org/10.1016/j.wocn.2018.07.001, [12] (2018); Kim, Ran Hee, Moon, Yeop, Choi, Jung Ju, Kwak, Sonya S., The Efect of Robot Appearance Types on Motivating Donation (2014) 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 210-211. , [13]; 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K., Dickens, L., Jeong, Sooyeon, Harris, P., DeSteno, D., Breazeal, C., (2015) A Comparison of Children Learning New Words from Robots, , [36] Tablets, People; Yuan, Jiahong, Liberman, Mark, Cieri, Christopher, (2007) Towardsan integrated understanding of speech overlaps in conversation, , [37] (01 2007)","Tsfasman, M.; Delft University of TechnologyNetherlands; email: M.Tsfasman@tudelft.nl
Saravanan, A.; Delft University of TechnologyNetherlands; email: A.Saravanan@student.tudelft.nl",,"ACM SIGMM","Association for Computing Machinery, Inc","2nd ACM Multimedia Workshop on Multimodal Conversational AI, MuCAI 2021, co-located with ACM MM 2021","24 October 2021",,174214,,9781450386791,,,"English","MuCAI - Proc. ACM Multimed. Workshop Multimodal Conversational AI, co-located ACM MM",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85119377600
"Scheutz C., Law T., Scheutz M.","57325224500;57212389268;6603548841;","Envirobots: How human–robot interaction can facilitate sustainable behavior",2021,"Sustainability (Switzerland)","13","21","12283","","",,,"10.3390/su132112283","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118545630&doi=10.3390%2fsu132112283&partnerID=40&md5=425b738a2eb1e430b478e597e6579564","Human–Robot Interaction Laboratory, Tufts University, Medford, MA  021555, United States","Scheutz, C., Human–Robot Interaction Laboratory, Tufts University, Medford, MA  021555, United States; Law, T., Human–Robot Interaction Laboratory, Tufts University, Medford, MA  021555, United States; Scheutz, M., Human–Robot Interaction Laboratory, Tufts University, Medford, MA  021555, United States","Environmental psychology aims to study human behavior with regard to the environment and how psychological techniques can be used to motivate behavior change. We argue that these concepts can be applied to interactive robots designed for other tasks, which then enables them to encourage sustainability behaviors in humans. We first present a literature review on the current state of social robots that are used to encourage sustainable behaviors. We next present eight hypothetical scenarios which are informed by the progress that has already been made in social robots in sustainability, as well as notable gaps where further environmental psychological concepts could be utilized. These scenarios encompass possible robots that range from limited sensing and no manipulation capabilities, to more sophisticated sensing and no manipulation, to sophisticated sensing and manipulation capabilities. We present these scenarios in which human–robot interaction could potentially result in pro-environmental behavioral changes in humans as recommendations for robot designers interested in helping design social robots for sustainability. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Ecological psychology; Human–robot interaction; Sustainability robots","design method; human behavior; psychology; robotics; sustainability",,,,,,,,,,,,,,,,"Nations, U., The Sustainable Development Goals Report 2020, , https://unstats.un.org/sdgs/report/2020/, (accessed on 11 June 2021); Turesinin, M., Kabir, A.M.H., Mollah, T., Sarwar, S., Hosain, M.S., Aquatic Iguana: A Floating Waste Collecting Robot with IoT Based Water Monitoring System Proceedings of the 2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI), pp. 21-25. , Yogyakarta, Indonesia, 1–2 October 2020; Davidson, E., The Design of an Autonomous Recycling Robot (2008) Grace Allen Scholars Theses, , https://digitalcommons.usf.edu/cgi/viewcontent.cgi?article=1001&context=honors_gast, (accessed on 11 June 2021); Rahmawati, E., Sucahyo, I., Asnawi, A., Faris, M., Taqwim, M., Mahendra, D., A water surface cleaning robot (2019) J. Phys. Conf. Ser, 1417, p. 012006; Wang, Z., Li, H., Zhang, X., Construction waste recycling robot for nails and screws: Computer vision technology and neural network approach (2019) Autom. Constr, 97, pp. 220-228; Ivosevic, B., Han, Y.G., Cho, Y., Kwon, O., The use of conservation drones in ecology and wildlife research (2015) J. Ecol. Environ, 38, pp. 113-118; Breazeal, C., Toward sociable robots (2003) Robot. Auton. Syst, 42, pp. 167-175; Duffy, B.R., Anthropomorphism and the social robot (2003) Robot. Auton. 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"Kontogiorgos D., Tran M., Gustafson J., Soleymani M.","57126340100;57225828825;24480882100;57188866370;","A Systematic Cross-Corpus Analysis of Human Reactions to Robot Conversational Failures",2021,"ICMI 2021 - Proceedings of the 2021 International Conference on Multimodal Interaction",,,,"112","120",,,"10.1145/3462244.3479887","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119019530&doi=10.1145%2f3462244.3479887&partnerID=40&md5=33a6e6344d1d9bc7012a770395d30b70","Kth Royal Institute of Technology, Stockholm, Sweden; University of Southern California, Los Angeles, United States","Kontogiorgos, D., Kth Royal Institute of Technology, Stockholm, Sweden; Tran, M., University of Southern California, Los Angeles, United States; Gustafson, J., Kth Royal Institute of Technology, Stockholm, Sweden; Soleymani, M., University of Southern California, Los Angeles, United States","In this paper, we analyze multimodal behavioral responses to robot failures across different tasks. Two multimodal datasets are examined in which humans interact with guided-task robots in task-oriented dialogues. In both datasets, the robots simulated failures of conversational breakdown and miscommunication typically observed in human-robot interactions. We closely examine human reactions to these failures looking at facial and acoustic features. Our analyses identify the significant behavioral features for automatic detection of such failures in interaction. We also examine human responses to different types of robot failures and if failures occurred early or late in the interaction cause variation in the responses. Our findings indicate that several nonverbal behaviors are consistently present in responses to robots' failures, e.g., gaze and speech prosody, whereas, linguistic features appear to be task-dependent. We discuss how these findings may generalize to other tasks, and how autonomous robots may identify opportunities to detect and recover from failures in interactions with humans. © 2021 ACM.","behavioral responses; miscommunication; social signal processing","Human robot interaction; Linguistics; Man machine systems; Signal processing; Behavioral response; Cross corpus analysis; Facial feature; Human reaction; Humans-robot interactions; Miscommunication; Multi-modal; Multi-modal dataset; Social signal processing; Task-oriented; Behavioral research",,,,,"Army Research Office, ARO: W911NF-20-2-0053; Foundation for Advancement in Cancer Therapy, FACT: GMT14-0082; Stiftelsen för Strategisk Forskning, SSF","This research was sponsored by the Swedish Foundation for Strategic Research (FACT: GMT14-0082) and by the U.S. Army Research Office accomplished under Cooperative Agreement Number W911NF-20-2-0053. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein. We thank Ron Artstein and Gale Lucas for sharing the Negotiation corpus.",,,,,,,,,,"Andrist, S., Bohus, D., Kamar, E., Horvitz, E., What went wrong and why? 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Int. Conf. Multimodal Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85119019530
"Wang J., Shen L.","57300372100;9243089200;","Semi-adaptable human hand motion prediction based on neural networks and Kalman filter",2021,"Journal of Physics: Conference Series","2029","1","012091","","",,,"10.1088/1742-6596/2029/1/012091","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117363007&doi=10.1088%2f1742-6596%2f2029%2f1%2f012091&partnerID=40&md5=8a11dd6084084b96c3028d08958eb0fc","School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China","Wang, J., School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China; Shen, L., School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China","This paper focuses on predicting trajectories of the human hand in order to improve the safety for human-robot interactions. In this work, the position and orientation are represented by two curves in the operation space such that the same algorithm can be used for both position and orientation prediction. The motion prediction is achieved in two steps. Firstly, the neural network (NN) model is applied for offline training to model the human hand motion. Secondly, the Kalman filter is added to adjust the weight coefficients of the NN model’s output layer online when a set of new data is measured, such that the NN model is adaptive to new data. An experiment study has been conducted to validate the effectiveness of the proposed algorithm. The result shows that the proposed algorithm achieves a higher prediction accuracy and requires a smaller amount of data to achieve optimal performance compared with the advanced method. © 2021 Institute of Physics Publishing. All rights reserved.",,"Forecasting; Human robot interaction; Kalman filters; Human hand motions; Human hands; Humans-robot interactions; Motion prediction; Neural network model; Neural-networks; Orientation prediction; Position and orientations; Position predictions; Prediction-based; Motion estimation",,,,,,,,,,,,,,,,"Luo, R. C., Mai, L., Human Intention Inference and On-Line Human Hand Motion Prediction for Human-Robot Collaboration (2019) IEEE/RSJ International Conference on Intelligent Robots and Systems. (IROS), pp. 5958-5964. , Macau; Ding, H., Reißig, G., Wijaya, K., Human arm motion modeling and long-term prediction for safe and efficient Human-Robot-Interaction (2011) IEEE International Conference on Robotics and Automation, pp. 5875-5880. , Shanghai; Wang, J. M., Fleet, D. J., Hertzmann, A., Gaussian Process Dynamical Models for Human Motion (2008) IEEE Transactions on Pattern Analysis and Machine Intelligence, 30 (2), pp. 283-298; Mainprice, J., Berenson, D., Human-robot collaborative manipulation planning using early prediction of human motion (2013) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 299-306. , Tokyo; Mainprice, J., Hayne, R., Berenson, D., Predicting human reaching motion in collaborative tasks using Inverse Optimal Control and iterative re-planning (2015) IEEE International Conference on Robotics and Automation (ICRA), pp. 885-892. , Seattle; utepage, J. B¨, Black, M. J., Kragic, D., Kjellstr¨om, H., Deep Representation Learning for Human Motion Prediction and Classification (2017) the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1591-1599. , Honolulu, HI; Ghosh, P., Song, J., Aksan, E., Learning Human Motion Models for Long-Term Predictions (2017) the International Conference on 3D Vision (3DV), pp. 458-466. , Qingdao; Cheng, Y., Zhao, W., Liu, C., Tomizuka, M., Human motion prediction using semi-adaptable neural networks (2019) IEEE Amer. Control Conf, pp. 4884-4890; Zha, X. F., Optimal pose trajectory planning for robot manipulators (2002) Mechanism and Machine Theory, 37 (10), pp. 1063-1086","Wang, J.; School of Mechatronic Engineering and Automation, China; email: juxing@i.shu.edu.cn",,,"IOP Publishing Ltd","2021 2nd International Conference on Mechatronics Technology and Intelligent Manufacturing, ICMTIM 2021","13 August 2021 through 15 August 2021",,172431,17426588,,,,"English","J. Phys. Conf. Ser.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85117363007
"Mizanoor Rahman S.M., Ikeura R.","37039570500;6701347027;","Cognitive Feedforward Learning Control for Object Manipulation with a Power Assist Robotic System",2021,"Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021",,,,"","",,,"10.1109/ICHMS53169.2021.9582629","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118958230&doi=10.1109%2fICHMS53169.2021.9582629&partnerID=40&md5=2e3c3f83c15e893e18b991f99a97e96e","University of West Florida, Dept. of Intelligent Systems and Robotics, Pensacola, FL, United States; Mie University, Dept. of Mechanical Engineering, Mie, Tsu, Japan","Mizanoor Rahman, S.M., University of West Florida, Dept. of Intelligent Systems and Robotics, Pensacola, FL, United States; Ikeura, R., Mie University, Dept. of Mechanical Engineering, Mie, Tsu, Japan","In the first step, a 1-DOF power assist robotic system (PARS) was developed for object manipulation with it, and the dynamics for human-robot co-manipulation of objects was derived reflecting human cognition (weight perception). Then, an admittance control scheme with position feedback and velocity controller was derived from the weight-perception-based dynamics. In a user study, human subjects lifted objects with the system. An evaluation scheme was developed to evaluate human-robot interaction (HRI) and co-manipulation performance. A reinforcement learning method was implemented to learn the admittance control parameters resulting in satisfactory HRI and manipulation performance. The results showed that inclusion of weight perception in the dynamics and the learning control were effective to produce satisfactory HRI and performance. In the second step, a novel variable admittance feedforward adaptive control algorithm was proposed, which helped further improve the HRI and manipulation performance. Then, effectiveness of the adaptive feedforward learning control method was validated using a multi-DOF PARS for manipulating heavy objects. © 2021 IEEE.","cognition; feedforward control; learning control; object manipulation; power assist robot","Dynamics; Feedforward control; Intelligent robots; Learning algorithms; Man machine systems; Power control; Reinforcement learning; Robotics; Admittance control; Cognition; Humans-robot interactions; Learning control; Object manipulation; Performance; Power assist; Power assist robots; Robotic systems; Weight perception; Human robot interaction",,,,,,,,,,,,,,,,"Kazerooni, H., Extender: A case study for human-robot interaction via transfer of power and information signals (1993) Proc. Of Ieee Int. Workshop on Robot and Human Communication, pp. 10-20; Niinuma, A., Miyoshi, T., Terashima, K., Miyashita, Y., Evaluation of effectiveness of a power-assisted wire suspension system compared to conventional machine Proc. Of 2009 Ieee Int. Conference on Mechatronics and Automation, 2009, pp. 369-374; Doi, T., Yamada, H., Ikemoto, T., Naratani, H., Simulation of a pneumatic hand crane power-assist system (2008) Journal of Robotics and Mechatronics, 20 (6), pp. 896-902; Hara, S., A smooth switching from power-assist control to automatic transfer control and its application to a transfer machine (2007) Ieee Trans. On Industrial Electronics, 54 (1), pp. 638-650; Yagi, E., Harada, D., Kobayashi, M., Upper-limb power-assist control for agriculture load lifting (2009) International Journal of Automation Technology, 3 (6), pp. 716-722; Dimeas, F., Koustoumpardis, P., Aspragathos, N., Admittance neuro-control of a lifting device to reduce human effort (2013) Advanced Robotics, 27 (13), pp. 1013-1022; Hara, H., Sankai, Y., Hal equipped with passive mechanism (2012) Proc. Of Ieee Int. Symp. Of System Integration, pp. 1-6; Gosselin, C., Laliberte, T., Mayer-St-Onge, B., Foucault, S., Lecours, A., Duchaine, V., Paradis, N., Menassa, R., A friendly beast of burden: A human-assistive robot for handling large payloads (2013) Ieee Robotics & Automation Magazine, 20 (4), pp. 139-147; Lecours, A., Mayer-St-Onge, B., Gosselin, C., Variable admittance control of a four-degree-of-freedom intelligent assist device (2012) Proc. Of Ieee Int. Conf. Of Robotics and Automation, pp. 3903-3908; Lamy, X., Colledani, F., Geffard, F., Measson, Y., Morel, G., Human force amplification with industrial robot: Study of dynamic limitations 2010 Ieee Int. Conf. On Intelligent Robots and Systems, pp. 2487-2494; (2021), http://psuf.panasonic.co.jp/alc/en/index.html, accessed July 31; (2021), http://www.rb3d.com/en/cobots-range/, accessed July 31; Colgate, J., Peshkin, M., Klostermeyer, S., Intelligent assist devices in industrial applications: A review (2003) Proc. Of 2003 IEEE/RSJ Int. Conf. Of Intelligent Robots and Systems, 3, pp. 2516-2521; Olivier, D., Sylvain, A., Fares, K., Philippe, G., Cobomanip: A new generation of intelligent assist device (2014) Proc. Of 41st International Symposium on Robotics, pp. 1-8; Gordon, A., Forssberg, H., Johansson, R., Westling, G., Visual size cues in the programming of manipulative forces during precision grip (1991) Experimental Brain Research, 83 (3), pp. 477-482; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., Unimanual and bimanual weight discrimination in lifting objects with a power assist system (2009) Proc. Of ICROS-SICE Int. Joint Conf, pp. 4787-4792; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., A psychophysical model of the power assist system for lifting objects (2009) Proc. Of 2009 Ieee Int. Conf. Of Systems, Man, and Cybernetics, pp. 4125-4130; Kaniarasu, P., Steinfeld, A., Effects of blame on trust in human robot interaction (2014) Proc. Of the 23rd Ieee Int. Symposium on Robot and Human Interactive Communication, pp. 850-855; Marc, O., Martin, S., Humans integrate visual and haptic information in a statistically optimal fashion (2002) Nature, 415, pp. 429-433; Seki, H., Iso, M., Hori, Y., How to design force sensorless power assist robot considering environmental characteristics-position control based or force control based (2002) Proc. Of Annual Conf. Of Ieee Industrial Electronics Society, 3, pp. 2255-2260; Zatsiorsky, V., Gao, F., Latash, M., Motor control goes beyond physics: Differential effects of gravity and inertia on finger forces during manipulation of hand-held objects (2005) Experimental Brain Research, 162 (3), pp. 300-308; Rahman, S.M.M., Ikeura, R., Cognition-based variable admittance control for active compliance in flexible manipulation of heavy objects with a power assist robotic system (2018) Robotics and Biomimetics, 5 (7), pp. 1-25. , November; Rahman, S.M.M., Ikeura, R., Cognition-based control and optimization algorithms for optimizing human-robot interactions in power assisted object manipulation (2016) Journal of Information Science and Engineering, 32 (5), pp. 1325-1344; Rahman, S.M.M., Ikeura, R., Calibrating intuitive and natural human-robot interaction and performance for power-assisted heavy object manipulation using cognition-based intelligent admittance control schemes (2018) International Journal of Advanced Robotic Systems, 15 (4), pp. 1-18. , July-August; Rahman, S.M.M., Machine learning-based cognitive position and force controls for power-assisted human-robot collaborative manipulation (2021) Machines, 9 (2), p. 28; Rahman, S.M.M., Human features-based variable admittance control for improving hri and performance in power-assisted heavy object manipulation (2019) Proc. Of 2019 12th International Conference on Human System Interaction (HSI), pp. 87-92; Rahman, S.M.M., Admittance-based bio-inspired cognitive pid control to optimize human-robot interaction in power-assisted object manipulation (2020) Proc. Of 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 679-684; Wheeless, S., Rahman, S.M.M., Sensor fusion-based supervised learning approach to developing collaborative manipulation system with variable autonomy (2021) Proc. Of International Conference on Intelligent Human Systems Integration, pp. 24-30. , February 22-24, Palermo, Italy; Rahman, S.M.M., Psychophysics-based cognitive reinforcement learning to optimize human-robot interaction in power-assisted object manipulation (2021) Proc. Of International Conference on Intelligent Human Systems Integration, pp. 56-62. , February 22-24, Palermo, Italy; Rahman, S.M.M., Image processing-based supervised learning to predict robot intention for multimodal interactions between a virtual human and a social robot (2021) Proc. Of International Conference on Intelligent Human Systems Integration, pp. 533-539. , February 22-24, Palermo, Italy; Rahman, S.M.M., Trustworthy power assistance in object manipulation with a power assist robotic system (2019) Proc. Of Ieee SoutheastCon, pp. 1-6. , April 11-14, 2019, USA; Rahman, S.M.M., Cognitive cyber-physical system (c-cps) for human-robot collaborative manufacturing (2019) Proc. Of 14th Ieee International Conference on System of Systems Engineering (IEEE SoSE, pp. 125-130. , May 19-22, 2019, Anchorage, Alaska, USA; Rahman, S.M.M., Performance metrics for human-robot collaboration: An automotive manufacturing case (2021) Proc. Of 2021 Ieee International Workshop on Metrology for Automotive (MetroAutomotive 2021), pp. 260-265. , July 1-2, Bologna, Italy; Rahman, S.M.M., Wang, Y., Walker, I.D., Mears, L., Pak, R., Remy, S., Trust-based compliant robot-human handovers of payloads in collaborative assembly in flexible manufacturing (2016) Proc. Of the 12th Ieee Int. Conf. Of Automation Science and Engineering (IEEE Case, pp. 355-360. , Texas, USA, August 21-24, 2016; Rahman, S.M.M., Sadr, B., Wang, Y., Trust-based optimal subtask allocation and model predictive control for human-robot collaborative assembly in manufacturing (2015) Proc. Of Asme Dynamic Systems and Controls Conference, p. 4. , Columbus, Ohio, October 28-30, Paper No. DSCC2015-9850",,"Nurnberger A.Fortino G.Guerrieri A.Kaber D.Mendonca D.Schilling M.Yu Z.",,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021","8 September 2021 through 10 September 2021",,173215,,9781665401708,,,"English","Proc. IEEE Int. Conf. Human-Mach. Syst., ICHMS",Conference Paper,"Final","",Scopus,2-s2.0-85118958230
"Khalifa A., Al-Hamadi A.","57242588900;6603014610;","A Survey on Loss Functions for Deep Face Recognition Network",2021,"Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021",,,,"","",,,"10.1109/ICHMS53169.2021.9582652","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118956703&doi=10.1109%2fICHMS53169.2021.9582652&partnerID=40&md5=3dbcf0a5886834a4945462443440563e","Otto-von-Guericke University, Neuro-Information Technology, Magdeburg, Germany","Khalifa, A., Otto-von-Guericke University, Neuro-Information Technology, Magdeburg, Germany; Al-Hamadi, A., Otto-von-Guericke University, Neuro-Information Technology, Magdeburg, Germany","With the increased collaboration between humans and robots in daily life, face recognition becomes one of the essential aspects of human-robot interaction (HRI). The robot requires a highly accurate face recognition system to operate in different environments and conditions. The role of achieving high accuracy face recognition is the enhancement of learning discriminative feature representation, which is almost entirely around minimizing the intra-class distance and maximizing the inter-class distance. The loss function is used on deep Convolutional Neural Networks (CNNs) to enhance this discriminative power of the deeply learned features. Softmax loss is one of the most used loss functions in many CNNs. However, softmax loss did not have the sufficient discriminative power needed for face recognition. Recently, many researchers work on developing novel loss functions to improve discriminatory power mainly, the intra-class distance of deep features. This survey paper's main objective is to compare the multiple loss functions used for deep face recognition networks showing the weakness for each loss function. © 2021 IEEE.","CNN; Deep Learning; Face Recognition; HRI; Loss Functions","Convolutional neural networks; Deep neural networks; Human robot interaction; Surveys; Class-distance; Convolutional neural network; Daily lives; Deep learning; Discriminative power; Face recognition systems; Highly accurate; Humans-robot interactions; Intra class; Loss functions; Face recognition",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 03ZZ0448L, 03zz04x02b","This work is funded by the Egyptian and the Federal Ministry of Education and Research of Germany (BMBF) (RoboAssist no. 03ZZ0448L, and Robo-Lab no. 03zz04x02b ) within the Zwanzig20 Alliance 3Dsensation.",,,,,,,,,,"Strazdas, D., Hintz, J., Felßberg, A.-M., Al-Hamadi, A., Robots and wizards: An investigation into natural human-robot interaction (2020) Ieee Access, 8 (635), p. 207. , 207 642; Saxen, F., Handrich, S., Werner, P., Othman, E., Al-Hamadi, A., Detecting arbitrarily rotated faces for face analysis (2019) 2019 Ieee International Conference on Image Processing (ICIP). Ieee, pp. 3945-3949; Wang, M., Deng, W., Deep face recognition: A survey (2021) Neurocomputing, 429, pp. 215-244; Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Rabinovich, A., Going deeper with convolutions (2015) 2015 Ieee Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-9; Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks (2012) Advances in Neural Information Processing Systems, 25. , F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds, Curran Associates, Inc; Zhong, Y., Deng, W., Hu, J., Zhao, D., Li, X., Wen, D., Sface: Sigmoid-constrained hypersphere loss for robust face recognition (2021) Ieee Transactions on Image Processing; Saxen, F., Werner, P., Handrich, S., Othman, E., Dinges, L., Al-Hamadi, A., Face attribute detection with mobilenetv2 and nasnet-mobile (2019) 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 176-180; Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., Liu, W., Cosface: Large margin cosine loss for deep face recognition (2018) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 5265-5274; Deng, J., Guo, J., Xue, N., Zafeiriou, S., Arcface: Additive angular margin loss for deep face recognition (2019) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4690-4699; Liu, H., Zhu, X., Lei, Z., Li, S.Z., Adaptiveface: Adaptive margin and sampling for face recognition (2019) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11947-11956; Deng, J., Guo, J., Liu, T., Gong, M., Zafeiriou, S., Sub-center arcface: Boosting face recognition by large-scale noisy web faces (2020) European Conference on Computer Vision, pp. 741-757. , Springer; Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L., Sphereface: Deep hypersphere embedding for face recognition (2017) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 212-220; Hu, W., Huang, Y., Zhang, F., Li, R., Noise-tolerant paradigm for training face recognition cnns (2019) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11887-11896; Zhong, Y., Deng, W., Wang, M., Hu, J., Peng, J., Tao, X., Huang, Y., Unequal-training for deep face recognition with long-tailed noisy data (2019) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7812-7821; Yi, D., Lei, Z., Liao, S., Li, S.Z., (2014) Learning Face Representation from Scratch; Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J., Ms-celeb-1m: A dataset and benchmark for large-scale face recognition (2016) European Conference on Computer Vision, pp. 87-102. , Springer; Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A., Vggface2: A dataset for recognising faces across pose and age (2018) 2018 13th Ieee International Conference on Automatic Face & Gesture Recognition (FG 2018). Ieee, pp. 67-74; Cao, J., Li, Y., Zhang, Z., Celeb-500k: A large training dataset for face recognition (2018) 2018 25th Ieee International Conference on Image Processing (ICIP). Ieee, pp. 2406-2410; Kemelmacher-Shlizerman, I., Seitz, S.M., Miller, D., Brossard, E., The megaface benchmark: 1 million faces for recognition at scale (2016) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 4873-4882; Ng, H.-W., Winkler, S., A data-driven approach to cleaning large face datasets (2014) 2014 Ieee International Conference on Image Processing (ICIP). Ieee, pp. 343-347; Lanitis, A., Taylor, C.J., Cootes, T.F., Toward automatic simulation of aging effects on face images (2002) Ieee Transactions on Pattern Analysis and Machine Intelligence, 24 (4), pp. 442-455; Whitelam, C., Taborsky, E., Blanton, A., Maze, B., Adams, J., Miller, T., Kalka, N., Allen, K., Iarpa janus benchmark-b face dataset (2017) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition Workshops, pp. 90-98; Klare, B.F., Klein, B., Taborsky, E., Blanton, A., Cheney, J., Allen, K., Grother, P., Jain, A.K., Pushing the frontiers of unconstrained face detection and recognition: Iarpa janus benchmark a (2015) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 1931-1939; Maze, B., Adams, J., Duncan, J.A., Kalka, N., Miller, T., Otto, C., Jain, A.K., Cheney, J., Iarpa janus benchmark-c: Face dataset and protocol (2018) 2018 International Conference on Biometrics (ICB), pp. 158-165; Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E., Labeled faces in the wild: A database forstudying face recognition in unconstrained environments (2008) Workshop on Faces in'Real-life'Images: Detection, Alignment, and Recognition; Sengupta, S., Chen, J.-C., Castillo, C., Patel, V.M., Chellappa, R., Jacobs, D.W., Frontal to profile face verification in the wild (2016) 2016 Ieee Winter Conference on Applications of Computer Vision (WACV). Ieee, pp. 1-9; Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S., Agedb: The first manually collected, in-the-wild age database (2017) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition Workshops, pp. 51-59; Zheng, T., Deng, W., Hu, J., (2017) Cross-age Lfw: A Database for Studying Cross-age Face Recognition in Unconstrained Environments; Zheng, T., Deng, W., (2018) Cross-pose Lfw: A Database for Studying Crosspose Face Recognition in Unconstrained Environments, 5. , Beijing University of Posts and Telecommunications, Tech. Rep; Zeng, D., Shi, H., Du, H., Wang, J., Lei, Z., Mei, T., (2020) Npcface: A Negative-positive Cooperation Supervision for Training Large-scale Face Recognition",,"Nurnberger A.Fortino G.Guerrieri A.Kaber D.Mendonca D.Schilling M.Yu Z.",,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021","8 September 2021 through 10 September 2021",,173215,,9781665401708,,,"English","Proc. IEEE Int. Conf. Human-Mach. Syst., ICHMS",Conference Paper,"Final","",Scopus,2-s2.0-85118956703
"Ho J., Wang C.-M.","57189332540;8687380500;","Human-Centered AI using Ethical Causality and Learning Representation for Multi-Agent Deep Reinforcement Learning",2021,"Proceedings of the 2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021",,,,"","",,,"10.1109/ICHMS53169.2021.9582667","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118919258&doi=10.1109%2fICHMS53169.2021.9582667&partnerID=40&md5=645bce754f6391d69de06806823bc0f9","Taiwan International Graduate Program, Social Networks and Human-Centered Computing Program, Taiwan; Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan","Ho, J., Taiwan International Graduate Program, Social Networks and Human-Centered Computing Program, Taiwan; Wang, C.-M., Institute of Information Science, Academia Sinica, Taipei, 115, Taiwan","Human-Centered Computing and AI are two fields devoted to several cross-intersecting interests in the modern AI design. They consider human factors and the machine learning algorithms to enhance compatibility and reliability for human-robot interaction and cooperation. In this work, we propose a novel design concept for the challenging issues that have raised ethical dilemmas; an augmented ethical causality with successor representation for policy gradient models Human-Centered AI with environments. The proposed system leverages Human-Centered AI for using explainable knowledge to construct the ethical causality, and shows it significantly outperformed the statistical approach and baselines alone by further considering meta parametric Human-Centered ethical priorities, when compared to other approaches in the simulated game theory Deep Reinforcement Learning environments. The experimental results aim to efficiently and effectively access the cause, effect and impact of causal inference and multi-agent heterogeneity in the DRL environments for natural, general and significant causal learning representations. © 2021 IEEE.","Ethical Causality; Human-CenteredAI; Multi-Agent Deep Reinforcement Learning; Successor Representation","Computation theory; Computer aided instruction; Game theory; Human robot interaction; Learning algorithms; Multi agent systems; Philosophical aspects; Reinforcement learning; Ethical causality; Human-centered computing; Human-centeredai; Human-robot-cooperation; Humans-robot interactions; Machine learning algorithms; Multi agent; Multi-agent deep reinforcement learning; Novel design; Successor representation; Deep learning",,,,,,,,,,,,,,,,"Scurto, H., Designing deep reinforcement learning for human parameter exploration (2021) Acm Transactions on Computer-Human Interaction (TOCHI), 28 (1), pp. 1-35; Li, G., Human-centered reinforcement learning: A survey (2019) Ieee Transactions on Human-Machine Systems, 49 (4); Krening, S., Feigh, K.M., Interaction algorithm effect on human experience with reinforcement learning (2018) Acm Transactions on Human-Robot Interaction (THRI), 7 (2), pp. 1-22; He, W., Li, Z., Philip Chen, C.L., A survey of human-centered intelligent robots: Issues and challenges (2017) IEEE/CAA Journal of Automatica Sinica, 4 (4), pp. 602-609; Kessler, F., Taylor, A., Thomaz, A., Interactive reinforcement learning from imperfect teachers (2021) Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction; Frazier, S., Riedl, M., Improving deep reinforcement learning in minecraft with action advice (2019) Proceedings of the Aaai Conference on Artificial Intelligence and Interactive Digital Entertainment, 15 (1); Ho, J., Wang, C., Explainable and adaptable augmentation in knowledge attention network for multi-agent deep reinforcement learning systems (2020) 2020 Ieee Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE). Ieee; Cheema, N., Predicting mid-air interaction movements and fatigue using deep reinforcement learning (2020) Proceedings of the 2020 Chi Conference on Human Factors in Computing Systems; Ghadirzadeh, A., Human-centered collaborative robots with deep reinforcement learning (2020) Ieee Robotics and Automation Letters; Oyeleke, R.O., Chang, C.K., Margrett, J., Situationcentered goal reinforcement of activities of daily living in smart home environments (2020) Expert Systems, 37 (1), p. e12487; Alamdari, N., Lobarinas, E., Kehtarnavaz, N., Personalization of hearing aid compression by human-in-the-loop deep reinforcement learning (2020) Ieee Access, 8, pp. 203503-203515; MacGlashan, J., Interactive learning from policy-dependent human feedback (2017) International Conference on Machine Learning. Pmlr; Gu, S., Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates (2017) 2017 Ieee International Conference on Robotics and Automation. Ieee; Ritschel, H., Andre, E., Real-time robot personality adaptation based on reinforcement learning and social signals (2017) Proceedings of the Companion of the 2017 Acm/ieee International Conference on Human-robot Interaction; Moreira, I., Deep reinforcement learning with interactive feedback in a human-robot environment (2020) Applied Sciences, 10 (16), p. 5574; Puiutta, E., Veith, E.M.S.P., Explainable reinforcement learning: A survey (2020) International Cross-Domain Conference for Machine Learning and Knowledge Extraction, , Springer, Cham; Knox, W.B., Stone, P., Reinforcement learning from human reward: Discounting in episodic tasks (2012) 2012 Ieee RO-MAN: The 21st Ieee International Symposium on Robot and Human Interactive Communication. Ieee; Ho, J., Wang, C., User-centric and real-time activity recognition using smart glasses (2016) Green, Pervasive, and Cloud Computing, pp. 196-210. , Springer, Cham; (2021) Fast Reinforcement Learning through the Composition of Behaviours, , https://deepmind.com; MacHado, M.C., Bellemare, M.G., Bowling, M., Count-based exploration with the successor representation (2020) Proceedings of the Aaai Conference on Artificial Intelligence; Barreto, A., Transfer in deep reinforcement learning using successor features and generalised policy improvement (2018) International Conference on Machine Learning. Pmlr; Razin, Y.S., Feigh, K.M., Hitting the road: Exploring human-robot trust for self-driving vehicles (2020) 2020 Ieee International Conference on Human-Machine Systems (ICHMS). Ieee; Cicirelli, F., Thermal comfort management leveraging deep reinforcement learning and human-in-the-loop (2020) 2020 Ieee International Conference on Human-Machine Systems (ICHMS). Ieee; Das, D., Chernova, S., Leveraging rationales to improve human task performance (2020) Proceedings of the 25th International Conference on Intelligent User Interfaces; Lu, S., (2021) Decentralized Policy Gradient Descent Ascent for Safe Multi-Agent Reinforcement Learning; Hoffman, M., (2020) Acme: A Research Framework for Distributed Reinforcement Learning; Cabi, S., (2019) Scaling Data-driven Robotics with Reward Sketching and Batch Reinforcement Learning; Lee, X.Y., Spatiotemporally constrained action space attacks on deep reinforcement learning agents (2020) Proceedings of the Aaai Conference on Artificial Intelligence, 34 (4); Scholkopf, B., Toward causal representation learning (2021) Proceedings of the Ieee; Naumov, P., Yew, R., (2021) Ethical Dilemmas in Strategic Games; (2021) Learning from Human Preferences, , https://deepmind.com; Scholkopf, B., (2019) Causality for Machine Learning",,"Nurnberger A.Fortino G.Guerrieri A.Kaber D.Mendonca D.Schilling M.Yu Z.",,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Human-Machine Systems, ICHMS 2021","8 September 2021 through 10 September 2021",,173215,,9781665401708,,,"English","Proc. IEEE Int. Conf. Human-Mach. Syst., ICHMS",Conference Paper,"Final","",Scopus,2-s2.0-85118919258
"Miao Q., Sun C., Zhang M., Chu K.","55916271000;57222347794;57203987000;57222899147;","Performance-based Adaptive Control Strategy for Robot-assisted Rehabilitation [基于任务表现的机器人辅助康复自适应控制策略]",2021,"Jiqiren/Robot","43","5",,"539","546 and 556",,,"10.13973/j.cnki.robot.200555","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117734936&doi=10.13973%2fj.cnki.robot.200555&partnerID=40&md5=e1aad0a56c86ef433c88a8107ff15aa4","Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China","Miao, Q., Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China; Sun, C., Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China; Zhang, M., Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China; Chu, K., Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China","A performance-based adaptive control strategy is proposed for robot-assisted upper-limb coordinated rehabilitation training, with aims to provide subject-specific robotic assistance for patients with limb movement disorders and promote their active engagement in effective rehabilitation training. Firstly, an end-effector for upper-limb bilateral rehabilitation and a coordinated training task are presented. Then, kinematic parameters based on clinical scales and coordinated training indicators are introduced, and a fuzzy neural network model is constructed to relate the multiple task measures to the robot admittance parameter. Finally, the coordinated training experiments are conducted with human subjects to validate the proposed method and compare it with the human-robot interactive strategy mentioned in the previous study. Experimental results indicate that the proposed strategy achieves a good ability of tracking desired performance indicators and keeping human-robot interaction smooth, and it can adaptively provide tailored robotic assistance, which enhances subjects' active engagement in training. © 2021, Science Press. All right reserved.","Adaptive control; Fuzzy neural network; Human-robot interaction; Performance; Upper-limb rehabilitation","Fuzzy inference; Fuzzy neural networks; Human robot interaction; Man machine systems; Neuromuscular rehabilitation; Robotics; Adaptive Control; Adaptive control strategy; Fuzzy-neural-networks; Humans-robot interactions; Performance; Performance based; Rehabilitation training; Robot-assisted rehabilitation; Upper limbs; Upper-limb rehabilitation; Adaptive control systems",,,,,,,,,,,,,,,,"Zhang, J J, Cheah, C C., Passivity and stability of human-robot interaction control for upper-limb rehabilitation robots (2015) IEEE Transactions on Robotics, 31 (2), pp. 233-245. , [J]; Lin, D J, Finklestein, S P, Cramer, S C., New directions in treatments targeting stroke recovery (2018) Stroke, 49 (12), pp. 3107-3114. , [J]; Hou, Z G, Zhao, X G, Cheng, L, Recent advances in rehabilitation robots and intelligent assistance systems (2016) Acta Automatica Sinica, 42 (12), pp. 1765-1779. , 侯增广, 赵新刚, 程龙, 等. 康复机器人与智能辅助系统的研究进展[J]. 自动化学报, 2016, 42(12): 1765-1779. [J]; Gassert, R, Dietz, V., Rehabilitation robots for the treatment of sensorimotor deficits: A neurophysiological perspective (2018) Journal of Neuroengineering and Rehabilitation, 15 (1). , [J]; Mehrholz, J, Hädrich, A, Platz, T, Electromechanical and robot-assisted arm training for improving generic activities of daily living, arm function, and arm muscle strength after stroke (2012) Cochrane Database of Systematic Reviews, (6). , [J]; Pehlivan, A U, Losey, D P, O'Malley, M K., Minimal assist-asneeded controller for upper limb robotic rehabilitation (2016) IEEE Transactions on Robotics, 32 (1), pp. 113-124. , [J]; Zhang, M M, Xie, S Q, Li, X L, Adaptive patient-cooperative control of a compliant ankle rehabilitation robot (CARR) with enhanced training safety (2018) IEEE Transactions on Industrial Electronics, 65 (2), pp. 1398-1407. , [J]; Wu, Q C, Wang, X S, Chen, B, Development of a minimalintervention-based admittance control strategy for upper extremity rehabilitation exoskeleton (2018) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48 (6), pp. 1005-1016. , [J]; Luo, L C, Peng, L, Wang, C, A greedy assist-as-needed controller for upper limb rehabilitation (2019) IEEE Transactions on Neural Networks and Learning Systems, 30 (11), pp. 3433-3443. , [J]; Miao, Q, Peng, Y X, Liu, L, Subject-specific compliance control of an upper-limb bilateral robotic system (2020) Robotics and Autonomous Systems, 126. , [J]; Krebs, H I, Palazzolo, J J, Dipietro, L, Rehabilitation robotics: Performance-based progressive robot-assisted therapy (2003) Autonomous Robots, 15 (1), pp. 7-20. , [J]; Papaleo, E, Zollo, L, Spedaliere, L, Patient-tailored adaptive robotic system for upper-limb rehabilitation (2013) IEEE International Conference on Robotics and Automation, pp. 3860-3865. , [C] Piscataway, USA: IEEE; Miao, Q, Zhang, M M, McDaid, A, A robot-assisted bilateral upper limb training strategy with subject-specific workspace: A pilot study (2020) Robotics and Autonomous Systems, 124. , [J]; Miao, Q, Li, Z J, Chu, K Y, Performance-based iterative learning control for task-oriented rehabilitation: A pilot study in robot-assisted bilateral training (2021) IEEE Transactions on Cognitive and Developmental Systems, , [J]; Fehlberg, M A, Gleeson, B T, Provancher, W R., Active Handrest: A large workspace tool for precision manipulation (2012) International Journal of Robotics Research, 31 (3), pp. 289-301. , [J]; Bosecker, C, Dipietro, L, Volpe, B, Kinematic robot-based evaluation scales and clinical counterparts to measure upper limb motor performance in patients with chronic stroke (2010) Neurorehabilitation and Neural Repair, 24 (1), pp. 62-69. , [J]; Flash, T, Hogan, N., The coordination of arm movements: An experimentally confirmed mathematical model (1985) Journal of Neuroscience, 5 (7), pp. 1688-1703. , [J]","Zhang, M.; Department of Biomedical Engineering, China; email: zhangmm@sustech.edu.cn",,,"Chinese Academy of Sciences",,,,,10020446,,JIQIE,,"Chinese","Jiqiren",Article,"Final","",Scopus,2-s2.0-85117734936
"Malinowska J.K.","57189242771;","What Does It Mean to Empathise with a Robot?",2021,"Minds and Machines","31","3",,"361","376",,1,"10.1007/s11023-021-09558-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103210495&doi=10.1007%2fs11023-021-09558-7&partnerID=40&md5=399cfc5d10e16a1b04196e0ba6b26a9c","Faculty of Philosophy, Adam Mickiewicz University, Poznan, Poland","Malinowska, J.K., Faculty of Philosophy, Adam Mickiewicz University, Poznan, Poland","Given that empathy allows people to form and maintain satisfying social relationships with other subjects, it is no surprise that this is one of the most studied phenomena in the area of human–robot interaction (HRI). But the fact that the term ‘empathy’ has strong social connotations raises a question: can it be applied to robots? Can we actually use social terms and explanations in relation to these inanimate machines? In this article, I analyse the range of uses of the term empathy in the field of HRI studies and social robotics, and consider the substantial, functional and relational positions on this issue. I focus on the relational (cooperational) perspective presented by Luisa Damiano and Paul Dumouchel, who interpret emotions (together with empathy) as being the result of affective coordination. I also reflect on the criteria that should be used to determine when, in such relations, we are dealing with actual empathy. © 2021, The Author(s).","Empathy; Human–robot interactions; Neuroscience; Social robots","Artificial intelligence; Philosophical aspects; Robot interactions; Social relationships; Social robotics; Social robots",,,,,"Harish-Chandra Research Institute, HRI; Narodowe Centrum Nauki, NCN: DEC-2015/19/D/HS1/00965; Ministerstwo Edukacji i Nauki, MNiSW","This research was funded by the Ministry of Science and Higher Education in Poland, National Science Centre, No. DEC-2015/19/D/HS1/00965. The article is based on the conference presentation: ?The growing need for reliable conceptual analysis in HRI studies: the example of the term ?empathy?? presented at Robophilosophy conference in 2020) which was printed in a form of conference proceedings (Malinowska, 2020). However, it is significantly developed and expanded version of the text. I am grateful to the reviewers for their insightful comments.","This research was funded by the Ministry of Science and Higher Education in Poland, National Science Centre, No. DEC-2015/19/D/HS1/00965. The article is based on the conference presentation: “The growing need for reliable conceptual analysis in HRI studies: the example of the term ‘empathy’” presented at Robophilosophy conference in 2020) which was printed in a form of conference proceedings (Malinowska, ). However, it is significantly developed and expanded version of the text. I am grateful to the reviewers for their insightful comments.",,,,,,,,,"Adamo, S.A., Do insects feel pain? 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The Biological and Socio-Cognitive Factors Shaping people’s Empathy with Social Robots, , Unpublished manuscript; Metzinger, T., (2009) The Ego Tunnel: The science of the mind and the myth of the self, , Basic Books; Misselhorn, C., Empathy with inanimate objects and the uncanny valley (2009) Minds and Machines, 19 (3), p. 345; Nagata, S., Sekiguchi, M., Asakawa, K., Mobile robot control by a structured hierarchical neural network (1990) IEEE Control Systems Magazine, 10 (3), pp. 69-76; Nehaniv, C.L., Dautenhahn, K.E., (2007) Imitation and social learning in robots, humans and animals: Behavioural, social and communicative dimensions, , Cambridge University Press; Nickelsen, N.C.M., ‘Active Citizenship’ and feeding assistive robotics (2019) Designing robots, designing humans, p. 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Would you feel sorry for a simulated robot? A comparison of empathy toward a physical and a simulated robot. 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (pp. 125–132) (2015) IEEE; Singer, T., Lamm, C., The social neuroscience of empathy (2009) Annals of the New York Academy of Sciences, 1156 (1), pp. 81-96; Singer, T., Klimecki, O.M., Empathy and compassion (2014) Current Biology, 24 (18), pp. R875-R878; Sparks, A., McDonald, S., Lino, B., O'Donnell, M., Green, M.J., Social cognition, empathy and functional outcome in schizophrenia (2010) Schizophrenia Research, 122 (1-3), pp. 172-178; Stephan, W.G., Finlay, K., The role of empathy in improving intergroup relations (1999) Journal of Social Issues, 55 (4), pp. 729-743; Stueber, K., ; Tapus, A., Mataric, M.J., (2007) Emulating Empathy in Socially Assistive Robotics, , AAAI spring symposium, Multidisciplinary collaboration for socially assistive robotics; Wermter, S., Weber, C., Elshaw, M., Panchev, C., Erwin, H., Pulvermüller, F., Towards multimodal neural robot learning (2004) Robotics and Autonomous Systems, 47 (2-3), pp. 171-175; Williams, M.A., Robot social intelligence (2012) International Conference on Social Robotics, pp. 45-55. , Springer","Malinowska, J.K.; Faculty of Philosophy, Poland; email: malinowska@amu.edu.pl",,,"Springer Science and Business Media B.V.",,,,,09246495,,MMACE,,"English","Minds Mach",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85103210495
"Sutjipto S., Woolfrey J., Carmichael M.G., Paul G.","57202214637;57190128692;36801255200;7202627449;","Cartesian Inertia Optimization via Redundancy Resolution for Physical Human-Robot Interaction",2021,"IEEE International Conference on Automation Science and Engineering","2021-August",,,"570","575",,,"10.1109/CASE49439.2021.9551663","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117006375&doi=10.1109%2fCASE49439.2021.9551663&partnerID=40&md5=dc62f70037ca4d94050fd89bb8bb0fde","University of Technology Sydney (UTS), Centre for Autonomous Systems (UTS:CAS), Sydney, Australia","Sutjipto, S., University of Technology Sydney (UTS), Centre for Autonomous Systems (UTS:CAS), Sydney, Australia; Woolfrey, J., University of Technology Sydney (UTS), Centre for Autonomous Systems (UTS:CAS), Sydney, Australia; Carmichael, M.G., University of Technology Sydney (UTS), Centre for Autonomous Systems (UTS:CAS), Sydney, Australia; Paul, G., University of Technology Sydney (UTS), Centre for Autonomous Systems (UTS:CAS), Sydney, Australia","The objective of introducing robotic manipulators into human-centric domains is to improve the efficacy of tasks in a safe and practical manner. The shift toward collaborative manipulator platforms has facilitated physical human-robot interaction (pHRI) in such environments. Often, these platforms are kinematically redundant and possess more degrees of freedom (DOF) than needed to complete a desired task. When no additional task is defined, it is possible for the manipulator to converge upon joint configurations that are unfavourable for the collaborative task. Consequently, there is potential for the posture of the manipulator to affect the interaction experienced. This paper investigates an inertia-based optimization control method for redundant manipulators interacting with an active agent. The inertia-based reconfiguration is evaluated through simulations and quantified with real-life experiments conducted with a robot-robot dyad. It was found that resolving redundancy to reconfigure the Cartesian inertia reduced the energy expenditure of the active agent during the interaction. © 2021 IEEE.",,"Degrees of freedom (mechanics); Human robot interaction; Man machine systems; Manipulators; Active agents; Cartesians; Collaborative tasks; Control methods; Human-centric; Joint configuration; Optimisations; Optimization control; Physical humanrobot interaction (phri); Redundancy resolution; Redundancy",,,,,,"Sheila Sutjipto is supported by an Australian Government Research Training Program Scholarship. The authors would like to thank Richardo Khonasty and Dinh Tung Le for helping setup the experiments, and to Yujun Lai for giving detailed feedback on the manuscript.",,,,,,,,,,"Hogan, N., Impedance control: An approach to manipulation: Part i-theory (1985) Journal of Dynamic Systems, Measurement, and Control, 107 (1), pp. 1-7; Ikeura, R., Inooka, H., Variable impedance control of a robot for cooperation with a human (1995) Proceedings-IEEE International Conference on Robotics and Automation, 3, pp. 3097-3102; Tsumugiwa, T., Yokogawa, R., Hara, K., Variable impedance control with virtual stiffness for human-robot cooperative peg-in-hole task (2002) IEEE International Conference on Intelligent Robots and Systems, 2, pp. 1075-1081; Erden, M.S., Billard, A., Robotic assistance by impedance compensation for hand movements while manual welding (2016) IEEE Transactions on Cybernetics, 46 (11), pp. 2459-2472. , 11; Ajoudani, A., Tsagarakis, N.G., Bicchi, A., Choosing poses for force and stiffness control (2017) IEEE Transactions on Robotics, 33 (6), pp. 1483-1490. , 12; Woolfrey, J., Lu, W., Liu, D., A control method for joint torque minimization of redundant manipulators handling large external forces (2019) Journal of Intelligent and Robotic Systems: Theory and Applications, 96 (1), pp. 3-16. , 10; Lai, Y., Sutjipto, S., Clout, M.D., Carmichael, M.G., Paul, G., GAVRe 2 : Towards data-driven upper-limb rehabilitation with adaptive-feedback gamification (2019) 2018 IEEE International Conference on Robotics and Biomimetics, ROBIO 2018, pp. 164-169. , Institute of Electrical and Electronics Engineers Inc., 3; Sutjipto, S., Lai, Y., Carmichael, M.G., Paul, G., Fitts' law in the presence of interface inertia (2020) Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2020, pp. 4749-4752. , July. Institute of Electrical and Electronics Engineers Inc., 7; Ott, C., (2008) Cartesian Impedance Control of Redundant and Flexible-Joint Robots, , Springer Berlin Heidelberg; Asada, H., Dynamic analysis and design of robot manipulators using inertia ellipsoids (1984) Proceedings-IEEE International Conference on Robotics and Automation, pp. 94-102. , Institute of Electrical and Electronics Engineers Inc; Walker, I.D., Impact configurations and measures for kinematically redundant and multiple armed robot systems (1994) IEEE Transactions on Robotics and Automation, 10 (5), pp. 670-683; Sharma, S., Suomalainen, M., Kyrki, V., Compliant manipulation of free-floating objects (2018) Proceedings-IEEE International Conference on Robotics and Automation, pp. 865-872. , Institute of Electrical and Electronics Engineers Inc., 9; Vigni, F., Knoop, E., Prattichizzo, D., Malvezzi, M., The role of closed-loop hand control in handshaking interactions (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 878-885. , 4; Carmichael, M.G., Khonasty, R., Aldini, S., Liu, D., Human preferences in using damping to manage singularities during physical human-robot collaboration (2020) Proceedings-IEEE International Conference on Robotics and Automation, pp. 10184-10190. , Institute of Electrical and Electronics Engineers Inc., 5; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) IEEE Transactions on Robotics, 31 (4), pp. 850-863. , 8; Gil, J.J., Rubio, Á., Savall, J., Decreasing the apparent inertia of an impedance haptic device by using force feedforward (2009) IEEE Transactions on Control Systems Technology, 17 (4), pp. 833-838; Khatib, O., A unified approach for motion and force control of robot manipulators: The operational space formulation (1987) IEEE Journal on Robotics and Automation, 3 (1), pp. 43-53; Yoshikawa, T., Manipulability of robotic mechanisms (1985) The International Journal of Robotics Research, 4 (2), pp. 3-9. , 6","Sutjipto, S.; University of Technology Sydney (UTS), Australia; email: sheila.sutjipto@uts.edu.au",,,"IEEE Computer Society","17th IEEE International Conference on Automation Science and Engineering, CASE 2021","23 August 2021 through 27 August 2021",,172370,21618070,9781665418737,,,"English","IEEE Int. Conf. Autom. Sci. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85117006375
"Murimi R.","55191236400;","On the Design of Social Robots Using Sheaf Theory and Smart Contracts",2021,"Frontiers in Robotics and AI","8",,"559380","","",,,"10.3389/frobt.2021.559380","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114367237&doi=10.3389%2ffrobt.2021.559380&partnerID=40&md5=2879ab92eab67db9ba984c8ce58d7acc","University of Dallas, Gupta College of Business, Irving, TX, United States","Murimi, R., University of Dallas, Gupta College of Business, Irving, TX, United States","The incorporation of robots in the social fabric of our society has taken giant leaps, enabled by advances in artificial intelligence and big data. As these robots become increasingly adept at parsing through enormous datasets and making decisions where humans fall short, a significant challenge lies in the analysis of robot behavior. Capturing interactions between robots, humans and IoT devices in traditional structures such as graphs poses challenges in the storage and analysis of large data sets in dense graphs generated by frequent activities. This paper proposes a framework that uses the blockchain for the storage of robotic interactions, and the use of sheaf theory for analysis of these interactions. Applications of our framework for social robots and swarm robots incorporating imperfect information and irrationality on the blockchain sheaf are proposed. This work shows the application of such a framework for various blockchain applications on the spectrum of human-robot interaction, and identifies key challenges that arise as a result of using the blockchain for robotic applications. © Copyright © 2021 Murimi.","blockchain; imperfect information; irrationality; robotics; sheaf theory; smart contracts",,,,,,,,,,,,,,,,,"Abdallah, A., Maarof, M.A., Zainal, A., Fraud Detection System: A Survey (2016) J. Netw. 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"Kumar K.S., Zhang L., Kalairaj M.S., Banerjee H., Xiao X., Jiayi C.C., Huang H., Lim C.M., Ouyang J., Ren H.","57214440720;57213177233;57211583803;57199500702;55920059700;57226759192;57216913876;13410511900;57226364489;14030501000;","Stretchable and Sensitive Silver Nanowire-Hydrogel Strain Sensors for Proprioceptive Actuation",2021,"ACS Applied Materials and Interfaces","13","31",,"37816","37829",,1,"10.1021/acsami.1c08305","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112506846&doi=10.1021%2facsami.1c08305&partnerID=40&md5=d858f7507778be432d56948cba0fd863","Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore; Department of Materials Science and Engineering, National University of Singapore, Blk EA, #03-09, 9 Engineering Drive 1, Singapore, 117575, Singapore; Singapore Institute of Manufacturing Technology, A∗STAR Singapore, Fusionopolis Two, 4 Fusionopolis Way, Singapore, 138635, Singapore; Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055, China; Department of Otolaryngology-Head and Neck Surgery, Singapore General Hospital, Singapore, 169856, Singapore; Duke-NUS Graduate Medical School, 169547, Singapore; Department of Electronic Engineering, Shun Hing Institute of Advanced Engineering, Chinese University of Hong Kong (CUHK), Hong Kong, Hong Kong; Institute of Materials, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, 1015, Switzerland","Kumar, K.S., Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore; Zhang, L., Department of Materials Science and Engineering, National University of Singapore, Blk EA, #03-09, 9 Engineering Drive 1, Singapore, 117575, Singapore; Kalairaj, M.S., Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore; Banerjee, H., Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore, Institute of Materials, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, 1015, Switzerland; Xiao, X., Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore, Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055, China; Jiayi, C.C., Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore, Singapore Institute of Manufacturing Technology, A∗STAR Singapore, Fusionopolis Two, 4 Fusionopolis Way, Singapore, 138635, Singapore; Huang, H., Singapore Institute of Manufacturing Technology, A∗STAR Singapore, Fusionopolis Two, 4 Fusionopolis Way, Singapore, 138635, Singapore; Lim, C.M., Department of Otolaryngology-Head and Neck Surgery, Singapore General Hospital, Singapore, 169856, Singapore, Duke-NUS Graduate Medical School, 169547, Singapore; Ouyang, J., Department of Materials Science and Engineering, National University of Singapore, Blk EA, #03-09, 9 Engineering Drive 1, Singapore, 117575, Singapore; Ren, H., Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore, Department of Electronic Engineering, Shun Hing Institute of Advanced Engineering, Chinese University of Hong Kong (CUHK), Hong Kong, Hong Kong","Safer human-robot interactions mandate the adoption of proprioceptive actuation. Strain sensors can detect the deformation of tools and devices in unstructured and capricious environments. However, such sensor integration in surgical/clinical settings is challenging due to confined spaces, structural complexity, and performance losses of tools and devices. Herein, we report a highly stretchable skin-like strain sensor based on a silver nanowire (AgNW) layer and hydrogel substrate. Our facile fabrication method utilizes thermal annealing to modulate the gauge factor (GF) by forming multidimensional wrinkles and a layered conductive network. The developed AgNW-hydrogel (AGel) sensors sustain and exhibit a strain-sensitive profile (max. GF = ∼70) with high stretchability (200%). Due to its conformability, the sensor demonstrates efficacy in integration and motion monitoring with minimal mechanical constraints. We provide contextual cognizance of tooltip during a transoral procedure by incorporating AGel sensors and showing the fabrication methodology's versatility by developing a hybrid self-sensing actuator with real-time performance feedback. © 2021 American Chemical Society.","biorobotic interface; medical sensors; proprioceptive actuation; silver nanowire; strain sensors","Biofeedback; Electric resistance measurement; Human robot interaction; Hydrogels; Network layers; Silver; Conductive networks; Fabrication methodology; Facile fabrication; Mechanical constraints; Real time performance; Self-sensing actuators; Sensor integration; Structural complexity; Silver nanowires",,,,,"R397000353114; Ministry of Science and Technology, Taiwan, MOST; Chinese University of Hong Kong, CUHK; Shun Hing Institute of Advanced Engineering, SHIAE; National Key Research and Development Program of China, NKRDPC: 2018YFB1307700, 2018YFB1307703","This work was supported by the National Key R&D Program of China under Grant no. 2018YFB1307700 (with subprogram no. 2018YFB1307703) from the Ministry of Science and Technology (MOST) of China, the Shun Hing Institute of Advanced Engineering (SHIAE project #BME-p1-21) at the Chinese University of Hong Kong (CUHK), and the Singapore Academic Research Fund under Grant no. 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Today, 19, p. 100610; Kang, T.-H., Chang, H., Choi, D., Kim, S., Moon, J., Lim, J.A., Lee, K.-Y., Yi, H., Hydrogel-Templated Transfer-Printing of Conductive Nanonetworks for Wearable Sensors on Topographic Flexible Substrates (2019) Nano Lett., 19, pp. 3684-3691; Ponraj, G., Kirthika, S.K., Lim, C.M., Ren, H., Soft Tactile Sensors with Inkjet-Printing Conductivity and Hydrogel Biocompatibility for Retractors in Cadaveric Surgical Trials (2018) IEEE Sens. J., 18, pp. 9840-9847; Kumar, K.S., Nguyen, T.D., Kalairaj, M.S., Hema, V.M., Cai, C.J., Huang, H., Lim, C.M., Ren, H., Deployable Telescopic Tubular Mechanisms with a Steerable Tongue Depressor towards Self-Administered Oral Swab (2021) Front. Robot. AI, 8, p. 612959; Shi, Y., Ma, C., Peng, L., Yu, G., Conductive ""smart"" Hybrid Hydrogels with PNIPAM and Nanostructured Conductive Polymers (2015) Adv. Funct. Mater., 25, pp. 1219-1225; Sun, X., Qin, Z., Ye, L., Zhang, H., Yu, Q., Wu, X., Li, J., Yao, F., Carbon Nanotubes Reinforced Hydrogel as Flexible Strain Sensor with High Stretchability and Mechanically Toughness (2020) Chem. Eng. J., 382, p. 122832; Liu, Q., Chen, J., Li, Y., Shi, G., High-Performance Strain Sensors with Fish-Scale-Like Graphene-Sensing Layers for Full-Range Detection of Human Motionss (2016) ACS Nano, 10, pp. 7901-7906; Sun, J.-Y., Zhao, X., Illeperuma, W.R.K., Chaudhuri, O., Oh, K.H., Mooney, D.J., Vlassak, J.J., Suo, Z., Highly Stretchable and Tough Hydrogels (2012) Nature, 489, pp. 133-136; Ahn, Y., Lee, H., Lee, D., Lee, Y., Highly Conductive and Flexible Silver Nanowire-Based Microelectrodes on Biocompatible Hydrogel (2014) ACS Appl. Mater. Interfaces, 6, pp. 18401-18407; Fang, F., Huang, G.-W., Xiao, H.-M., Li, Y.-Q., Hu, N., Fu, S.-Y., Largely Enhanced Electrical Conductivity of Layer-Structured Silver Nanowire/Polyimide Composite Films by Polyaniline (2018) Compos. Sci. Technol., 156, pp. 144-150; Wang, D.-Y., Tao, L.-Q., Liu, Y., Zhang, T.-Y., Pang, Y., Wang, Q., Jiang, S., Ren, T.-L., High Performance Flexible Strain Sensor Based on Self-Locked Overlapping Graphene Sheets (2016) Nanoscale, 8, pp. 20090-20095; Wei, Y., Chen, S., Yuan, X., Wang, P., Liu, L., Multiscale Wrinkled Microstructures for Piezoresistive Fibers (2016) Adv. Funct. Mater., 26, pp. 5078-5085; Ding, Y., Cui, Y., Liu, X., Liu, G., Shan, F., Welded Silver Nanowire Networks as High-Performance Transparent Conductive Electrodes: Welding Techniques and Device Applications (2020) Appl. Mater. Today, 20, p. 100634; Kholid, F.N., Huang, H., Zhang, Y., Fan, H.J., Multiple Electrical Breakdowns and Electrical Annealing Using High Current Approximating Breakdown Current of Silver Nanowire Network (2016) Nanotechnology, 27, p. 025703; Kalairaj, M.S., Banerjee, H., Lim, C.M., Chen, P.-Y., Ren, H., Hydrogel-Matrix Encapsulated Nitinol Actuation with Self-Cooling Mechanism (2019) RSC Adv., 9, pp. 34244-34255; Zhu, Y., Qin, Q., Xu, F., Fan, F., Ding, Y., Zhang, T., Wiley, B.J., Wang, Z.L., Size Effects on Elasticity, Yielding, and Fracture of Silver Nanowires: In situ Experiments (2012) Phys. Rev. B, 85, p. 045443; Ho, X., Cheng, C.K., Tey, J.N., Wei, J., Biaxially Stretchable Transparent Conductors that Use Nanowire Networks (2014) J. Mater. Res., 29, p. 2965; Wang, Y., Lee, S., Yokota, T., Wang, H., Jiang, Z., Wang, J., Koizumi, M., Someya, T., A Durable Nanomesh On-Skin Strain Gauge for Natural Skin Motion Monitoring with Minimum Mechanical Constraints (2020) Sci. Adv., 6, p. eabb7043; Gu, X., Li, C., Xiao, X., Lim, C.M., Ren, H., A Compliant Transoral Surgical Robotic System Based on a Parallel Flexible Mechanism (2019) Ann. Biomed. Eng., 47, pp. 1329-1344; Faris, C., Koury, E., Philpott, J., Sharma, S., Tolley, N., Narula, A., Estimation of Tracheostomy Tube Cuff Pressure by Pilot Balloon Palpation (2007) J. Laryngol. Otol., 121, pp. 869-871; Ganner, C., The Accurate Measurement of Endotracheal Tube Cuff Pressures (2001) Br. J. Nurs., 10, pp. 1127-1134; Yuk, H., Zhang, T., Parada, G.A., Liu, X., Zhao, X., Skin-Inspired Hydrogel-Elastomer Hybrids with Robust Interfaces and Functional Microstructures (2016) Nat. Commun., 7, p. 12028","Ren, H.; Department of Biomedical Engineering, 4 Engineering Drive 3, Singapore; email: hlren@ieee.org
Ouyang, J.; Department of Materials Science and Engineering, Blk EA, #03-09, 9 Engineering Drive 1, Singapore; email: mseoj@nus.edu.sg",,,"American Chemical Society",,,,,19448244,,,"34323464","English","ACS Appl. Mater. Interfaces",Article,"Final","",Scopus,2-s2.0-85112506846
"Alam S., Johnston B., Vitale J., Williams M.-A.","57262165500;56648269000;56412528400;7410005152;","Would you trust a robot with your mental health? The interaction of emotion and logic in persuasive backfiring",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"384","391",,,"10.1109/RO-MAN50785.2021.9515385","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115087575&doi=10.1109%2fRO-MAN50785.2021.9515385&partnerID=40&md5=44068d97753efda61529b8a88769bcbb","University of Technology Sydney, Australia; University of New South Wales, Australia","Alam, S., University of Technology Sydney, Australia; Johnston, B., University of Technology Sydney, Australia; Vitale, J., University of Technology Sydney, Australia; Williams, M.-A., University of New South Wales, Australia","Building trust in robots through social interactions has a major impact on user experience and adoption of robot technologies. The role of trust in such interactions is associated with the persuasive influence a robot has on humans. A persuasive attempt may decrease trusting attitudes towards robots if it leads to persuasive backfiring, which refers to the creation of an attitude change in a direction opposite to the one intended by the intervention. In order to explore persuasive backfiring in the context of Human-Robot Interaction, this research study tests the interaction between emotion and logic as elements present both in the attitudes to be influenced, and in the persuasive appeal delivered by a robot. Results indicate a significant backfiring effect when emotions are used to influence attitudes that are based on logic. This observation has practical design implications for persuasive robots, especially in high-stakes fields such as Psychotherapy and Urban Search and Rescue. © 2021 IEEE.",,"Agricultural robots; Computer circuits; Machine design; User experience; Attitude change; Design implications; Mental health; Persuasive robots; Research studies; Robot technology; Social interactions; Urban search and rescue; Social robots",,,,,,,,,,,,,,,,"Stibe, A., Cugelman, B., Persuasive backfiring: When behavior change interventions trigger unintended negative outcomes (2016) International Conference on Persuasive Technology, pp. 65-77. , Springer; Haddock, G., Maio, G.R., Arnold, K., Huskinson, T., Should persuasion be affective or cognitive? The moderating effects of need for affect and need for cognition (2008) Personality and Social Psychology Bulletin, 34 (6), pp. 769-778; Moray, N., Inagaki, T., Laboratory studies of trust between humans and machines in automated systems (1999) Transactions of the Institute of Measurement and Control, 21 (4-5), pp. 203-211; Lewis, M., Sycara, K., Walker, P., The role of trust in human-robot interaction (2018) Foundations of Trusted Autonomy, pp. 135-159. , Springer, Cham; Petty, R.E., Cacioppo, J.T., (1996) Attitudes and Persuasion: Classic and Contemporary Approaches, , Westview Press; Ashman, M., (2018) Introduction to Professional Communications; Orji, R., Mandryk, R.L., Vassileva, J., Gender, age, and responsiveness to cialdini's persuasion strategies (2015) International Conference on Persuasive Technology, pp. 147-159. , Springer; Ham, J., Spahn, A., Shall i show you some other shirts too? The psychology and ethics of persuasive robots (2015) A Construction Manual for Robots' Ethical Systems, pp. 63-81. , Springer; Rosenthal-Von Der Pütten, A.M., Krämer, N.C., Hoffmann, L., Sobieraj, S., Eimler, S.C., An experimental study on emotional reactions towards a robot (2013) International Journal of Social Robotics, 5 (1), pp. 17-34; Shao, M., Snyder, M., Nejat, G., Benhabib, B., User affect elicitation with a socially emotional robot (2020) Robotics, 9 (2), p. 44; Xu, J., Broekens, J., Hindriks, K., Neerincx, M.A., Effects of bodily mood expression of a robotic teacher on students (2014) 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 2614-2620; Clarkson, J.J., Tormala, Z.L., Rucker, D.D., Cognitive and affective matching effects in persuasion: An amplification perspective (2011) Personality and Social Psychology Bulletin, 37 (11), pp. 1415-1427; Crites, S.L., Jr., Fabrigar, L.R., Petty, R.E., Measuring the affective and cognitive properties of attitudes: Conceptual and methodological issues (1994) Personality and Social Psychology Bulletin, 20 (6), pp. 619-634; Fabrigar, L.R., Petty, R.E., The role of the affective and cognitive bases of attitudes in susceptibility to affectively and cognitively based persuasion (1999) Personality and Social Psychology Bulletin, 25 (3), pp. 363-381; Brehm, J.W., (1966) A Theory of Psychological Reactance.; Roubroeks, M.A.J., Ham, J.R.C., Midden, C.J.H., The dominant robot: Threatening robots cause psychological reactance, especially when they have incongruent goals (2010) Persuasive Technology, pp. 174-184. , T. Ploug, P. Hasle, and H. Oinas-Kukkonen, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg; Ghazali, A.S., Ham, J., Barakova, E., Markopoulos, P., Pardon the rude robot: Social cues diminish reactance to high controlling language (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 411-417; Ghazali, A., Ham, J., Barakova, E., Markopoulos, P., Assessing the effect of persuasive robots interactive social cues on users' psychological reactance, liking, trusting beliefs and compliance (2019) Advanced Robotics, 33 (7-8), pp. 325-337; Roubroeks, M., Ham, J., Midden, C., When artificial social agents try to persuade people: The role of social agency on the occurrence of psychological reactance (2011) International Journal of Social Robotics, 3 (4), pp. 155-165; Sweldens, S., Corneille, O., Yzerbyt, V., The role of awareness in attitude formation through evaluative conditioning (2014) Personality and Social Psychology Review, 18 (2), pp. 187-209; Evans, J.S.B., In two minds: Dual-process accounts of reasoning (2003) Trends in Cognitive Sciences, 7 (10), pp. 454-459; Kahneman, D., A perspective on judgment and choice: Mapping bounded rationality (2003) American Psychologist, 58 (9), p. 697; Kahneman, D., Frederick, S., Representativeness revisited: Attribute substitution in intuitive judgment (2002) Heuristics and Biases: The Psychology of Intuitive Judgment, 49, p. 81; Lieberman, M.D., Gaunt, R., Gilbert, D.T., Trope, Y., (2002) Reflexion and Reflection: A Social Cognitive Neuroscience Approach to Attributional Inference.; Sloman, S.A., The empirical case for two systems of reasoning (1996) Psychological Bulletin, 119 (1), p. 3; Smith, E.R., DeCoster, J., Dual-process models in social and cognitive psychology: Conceptual integration and links to underlying memory systems (2000) Personality and Social Psychology Review, 4 (2), pp. 108-131; Kim, T., Duhachek, A., Artificial intelligence and persuasion: A construal-level account (2020) Psychological Science, 31 (4), pp. 363-380; Barry, A.M., Perception theory (2005) Handbook of Visual Communication: Theory, Methods, and Media, pp. 45-62; Shah, A.P., Why are certain accents judged the way they are? decoding qualitative patterns of accent bias (2019) Advances in Language and Literary Studies, 10 (3), pp. 128-139; Van Giesen, R.I., Fischer, A.R., Van Dijk, H., Van Trijp, H.C., Affect and cognition in attitude formation toward familiar and unfamiliar attitude objects (2015) PloS One, 10 (10), p. 0141790; Villata, S., Cabrio, E., Jraidi, I., Benlamine, S., Chaouachi, M., Frasson, C., Gandon, F., Emotions and personality traits in argumentation: An empirical evaluation 1 (2017) Argument & Computation, 8 (1), pp. 61-87; Elliott, V., Thinking about the coding process in qualitative data analysis (2018) The Qualitative Report, 23 (11), pp. 2850-2861; Siegel, M., Breazeal, C., Norton, M., (2009) Persuasive Robotics: The Influence of Robot Gender on Human Behavior, pp. 2563-2568; Rocklage, M.D., Rucker, D.D., Nordgren, L.F., Persuasion, emotion, and language: The intent to persuade transforms language via emotionality (2018) Psychological Science, 29 (5), pp. 749-760; Giles, H., Raki, T., Language attitudes: Social determinants and consequences of language variation (2014) The Oxford Handbook of Language and Social Psychology, pp. 11-26; Cialdini, R., (1993) B. 1993 Influence: The Psychology of Persuasion, , New York: Quill/William Morrow; Jack, A.I., Friedman, J.P., Boyatzis, R.E., Taylor, S.N., Why do you believe in god? Relationships between religious belief, analytic thinking, mentalizing and moral concern (2016) PloS One; Jack, A.I., Dawson, A.J., Begany, K.L., Leckie, R.L., Barry, K.P., Ciccia, A.H., Snyder, A.Z., Fmri reveals reciprocal inhibition between social and physical cognitive domains (2013) NeuroImage, pp. 385-401; Billings, D.R., Schaefer, K.E., Chen, J.Y., Hancock, P.A., Human-robot interaction: Developing trust in robots (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 109-110; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K., Would you trust a (faulty) robot? effects of error, task type and personality on human-robot cooperation and trust (2015) 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 1-8; Van Kleef, G.A., Van Den Berg, H., Heerdink, M.W., The persuasive power of emotions: Effects of emotional expressions on attitude formation and change (2015) Journal of Applied Psychology, 100 (4), p. 1124",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115087575
"Zhang Z., Zheng J., Thalmann N.M.","57261577100;57194827755;55218502200;","Engagement intention estimation in multiparty human-robot interaction",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"117","122",,,"10.1109/RO-MAN50785.2021.9515373","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115079686&doi=10.1109%2fRO-MAN50785.2021.9515373&partnerID=40&md5=91b137988b6e187540ef561fd6b5f6bd","Nanyang Technological University, School of Computer Science and Engineering, Singapore; Nanyang Technological University, Institute of Media Innovation, Singapore","Zhang, Z., Nanyang Technological University, School of Computer Science and Engineering, Singapore, Nanyang Technological University, Institute of Media Innovation, Singapore; Zheng, J., Nanyang Technological University, School of Computer Science and Engineering, Singapore; Thalmann, N.M., Nanyang Technological University, Institute of Media Innovation, Singapore","As the applications of intelligent agents (IAs) are gradually increasing in daily life, they are expected to have reasonable social intelligence to interact with people by appropriately interpreting human behavior and intention. This paper presents a method to estimate whether people have willingness to join in a conversation, which helps to endow IAs with the capability of detecting potential participants. The method is built on the CNN-LSTM network, which takes image features and social signals as input, making use of general information conveyed in images, semantic social cues proven by social psychology studies, and temporal information in the sequence of inputs. The network is designed to have a multi-branch structure with the flexibility of accommodating different types of inputs. We also discuss the signal transition in multiparty human-robot interaction scenarios. The method is evaluated on three datasets with social signals and/or images as inputs. The results show that the proposed method can infer human engagement intention well. © 2021 IEEE.",,"Agricultural robots; Behavioral research; Intelligent agents; Long short-term memory; Man machine systems; Semantics; General information; Human behaviors; Intention estimation; Multi-branch structures; Signal transition; Social intelligence; Social psychology; Temporal information; Social robots",,,,,"National Research Foundation Singapore, NRF","This research is supported by the National Research Foundation, Singapore under its International Research Centres in Singapore Funding Initiative. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.",,,,,,,,,,"Murphy, J., Gretzel, U., Pesonen, J., Marketing robot services in hospitality and tourism: The role of anthropomorphism (2019) Journal of Travel & Tourism Marketing, 36 (7), pp. 784-795. , Feb; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Science Robotics, 3 (21); Tahir, Y., Dauwels, J., Thalmann, D., Thalmann, N.M., A user study of a humanoid robot as a social mediator for two-person conversations (2018) International Journal of Social Robotics, pp. 1-14; Wan, S., Gu, Z., Ni, Q., Cognitive computing and wireless communications on the edge for healthcare service robots (2020) Computer Communications, 149, pp. 99-106. , Jan; Breazeal, C.L., (2002) Designing Sociable Robots, , MIT press; Dautenhahn, K., Socially intelligent robots: Dimensions of human-robot interaction (2007) Philosophical Transactions of the Royal Society B: Biological Sciences, 362 (1480), pp. 679-704. , Feb; Oertel, C., Castellano, G., Chetouani, M., Nasir, J., Obaid, M., Pelachaud, C., Peters, C., Engagement in human-agent interaction: An overview (2020) Frontiers in Robotics and AI, 7, p. 92; Michalowski, M.P., Sabanovic, S., Simmons, R., A spatial model of engagement for a social robot (2006) 9th IEEE International Workshop on Advanced Motion Control. IEEE, pp. 762-767; Bohus, D., Horvitz, E., Models for multiparty engagement in openworld dialog (2009) Proc. Of the SIGDIAL 2009 Conference. ACL, pp. 225-234; Vaufreydaz, D., Johal, W., Combe, C., Starting engagement detection towards a companion robot using multimodal features (2016) Robotics and Autonomous Systems, 75, pp. 4-16; Foster, M.E., Gaschler, A., Giuliani, M., Automatically classifying user engagement for dynamic multi-party human-robot interaction (2017) International Journal of Social Robotics, 9 (5), pp. 659-674; Ozaki, Y., Ishihara, T., Matsumura, N., Nunobiki, T., Yamada, T., Decision-making prediction for human-robot engagement between pedestrian and robot receptionist (2018) 27th IEEE Intl Symp on Robot and Human Interactive Communication. IEEE, pp. 208-215; Ito, K., Kong, Q., Horiguchi, S., Sumiyoshi, T., Nagamatsu, K., Anticipating the start of user interaction for service robot in the wild (2020) IEEE International Conference on Robotics and Automation (ICRA), pp. 9687-9693; Yumak, Z., Ren, J., Thalmann, N.M., Yuan, J., Modelling multiparty interactions among virtual characters, robots, and humans (2014) Presence: Teleoperators and Virtual Environments, 23 (2), pp. 172-190; Burgoon, J.K., Magnenat-Thalmann, N., Pantic, M., Vinciarelli, A., (2017) Social Signal Processing, , Cambridge University Press; Gatica-Perez, D., Automatic nonverbal analysis of social interaction in small groups: A review (2009) Image and Vision Computing, 27 (12), pp. 1775-1787; Knapp, M.L., Hall, J.A., Horgan, T.G., Nonverbal communication in human interaction (2013) Cengage Learning; Mead, R., Atrash, A., Mataríc, M.J., Proxemic feature recognition for interactive robots: Automating metrics from the social sciences (2011) International Conference on Social Robotics, pp. 52-61; Hall, E.T., Birdwhistell, R.L., Bock, B., Proxemics (1968) Current Anthropology, 9 (2-3), pp. 83-108; Kendon, A., Ferber, A., A description of some human greetings (1973) Comparative Ecology and Behaviour of Primates, 591 (668); Langton, S.R., Watt, R.J., Bruce, V., Do the eyes have it? cues to the direction of social attention (2000) Trends in Cognitive Sciences, 4 (2), pp. 50-59; Monkaresi, H., Bosch, N., Calvo, R.A., D'Mello, S.K., Automated detection of engagement using video-based estimation of facial expressions and heart rate (2017) IEEE Transactions on Affective Computing, 8 (1), pp. 15-28; LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D., Backpropagation applied to handwritten zip code recognition (1989) Neural Computation, 1 (4), pp. 541-551; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Computation, 9 (8), pp. 1735-1780; Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., Sheikh, Y., Openpose: Realtime multi-person 2D pose estimation using part affinity fields (2019) IEEE Transactions on Pattern Analysis and Machine Intelligence, 43 (1), pp. 172-186; Baltrusaitis, T., Zadeh, A., Lim, Y.C., Morency, L.-P., Openface 2. 0: Facial behavior analysis toolkit (2018) 13th IEEE Intl Conf on Automatic Face & Gesture Recognition. IEEE, pp. 59-66; Parkhi, O.M., Vedaldi, A., Zisserman, A., Deep face recognition (2015) Proc. Of the British Machine Vision Conference, pp. 411-4112; Kato, Y., Kanda, T., Ishiguro, H., May i help you?-design of human-like polite approaching behavior (2015) 10th International Conference on Human-Robot Interaction, pp. 35-42; Ryoo, M.S., Matthies, L., First-person activity recognition: What are they doing to me? (2013) IEEE Conference on Computer Vision and Pattern Recognition (CVPR), , Portland, OR, June; Ben-Youssef, A., Clavel, C., Essid, S., Bilac, M., Chamoux, M., Lim, A., Ue-hri: A new dataset for the study of user engagement in spontaneous human-robot interactions (2017) Proc. Of the 19th ACM Intl Conf on Multimodal Interaction. ACM, pp. 464-472",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115079686
"Nguyen A., Xie B.","57262028100;36462894100;","Human arm motion prediction in reaching movements",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"1117","1123",,,"10.1109/RO-MAN50785.2021.9515461","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115055716&doi=10.1109%2fRO-MAN50785.2021.9515461&partnerID=40&md5=4671dc538de2f5881c97b581f3eec0d0","University of Texas at Austin, Department of Electrical and Computer Engineering, Austin, TX  78712, United States; University of Kentucky, Department of Electrical and Computer Engineering, Lexington, KY  40506, United States","Nguyen, A., University of Texas at Austin, Department of Electrical and Computer Engineering, Austin, TX  78712, United States; Xie, B., University of Kentucky, Department of Electrical and Computer Engineering, Lexington, KY  40506, United States","There is an increasing interest in accurately predicting natural human arm motions for areas like human-robot interaction, wearable robots, and ergonomic simulations. This paper studies the problem of predicting natural fingertip and joint trajectories in human arm reaching movements. Compared to the widely-used minimum jerk model, the 5-parameter logistic model can represent natural fingertip trajectories more accurately. Based on 3520 human arm motions recorded by a motion capture system, regression learning is used to predict the five parameters representing the fingertip trajectory for a given target point. Then, the elbow swivel angle is predicted using regression learning to resolve the kinematic redundancy of the human arm at discrete fingertip positions. Finally, discrete joint angles are solved based on the predicted elbow swivel angles and then fitted to a continuous 5-parameter logistic function to obtain the joint trajectory. This method is verified using 48 test motions, and the results show that this method can generate accurate human arm motions. © 2021 IEEE.",,"Agricultural robots; Forecasting; Social robots; Trajectories; Ergonomic simulation; Fingertip trajectory; Joint trajectories; Kinematic redundancy; Logistic functions; Minimum jerk model; Motion capture system; Reaching movements; Motion estimation",,,,,"University of Kentucky, UK","Support of this research was provided by the Woodrow W. Everett, Jr. SCEEE Development Fund in cooperation with the Southeastern Association of Electrical Engineering Department Heads and the University of Kentucky Electrical and Computer Engineering Undergraduate Research Fellowship program","*Support of this research was provided by the Woodrow W. Everett, Jr. SCEEE Development Fund in cooperation with the Southeastern Association of Electrical Engineering Department Heads and the University of Kentucky Electrical and Computer Engineering Undergraduate Research Fellowship program.",,,,,,,,,"Nef, T., Guidali, M., Riener, R., Armin III-arm therapy exoskeleton with an ergonomic shoulder actuation (2009) Applied Bionics and Biomechanics, 6 (2), pp. 127-142; Klamroth-Marganska, D., Verena, B.M., Javier, C.K., Curt, P., Armin, D.P., Volker, E.P., Thierry, F.M., Robert, P., Three-dimensional, task-specific robot therapy of the arm after stroke: A multicentre, parallel-group randomised trial (2014) Lancet Neurology, 13 (2), pp. 159-166; Mao, Y., Agrawal, S.K., Design of a cable-driven arm exoskeleton (carex) for neural rehabilitation (2012) IEEE Transactions on Robotics, 28 (4), pp. 922-931; Perry, J., Rosen, J., Burns, S., Upper-limb powered exoskeleton design (2007) IEEE/ASME Transactions on Mechatronics, 12 (4), pp. 408-417; Li, S., Wang, H., Zhang, S., Wang, S., Han, K., Human motion trajectory prediction in human-robot collaborative tasks (2019) IOP Conference Series: Materials Science and Engineering, 646, p. 12067; Mainprice, J., Berenson, D., Human-robot collaborative manipulation planning using early prediction of human motion (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 299-306; Zanchettin, A.M., Bascetta, L., Rocco, P., Achieving humanlike motion: Resolving redundancy for anthropomorphic industrial manipulators (2013) IEEE Robotics Automation Magazine, 20 (4), pp. 131-138; Gielniak, M.J., Liu, C.K., Thomaz, A.L., Generating human-like motion for robots (2013) The International Journal of Robotics Research, 32 (11), pp. 1275-1301; Alexopoulos, K., Mavrikios, D., Chryssolouris, G., Ergotoolkit: An ergonomic analysis tool in a virtual manufacturing environment (2013) International Journal of Computer Integrated Manufacturing, 26 (5), pp. 440-452; Mehrabi, N., Sharif Razavian, R., Ghannadi, B., McPhee, J., Predictive simulation of reaching moving targets using nonlinear model predictive control (2016) Frontiers in Computational Neuroscience, 10, pp. 143-143; Pavlov, A.N., Runnova, A.E., Maksimenko, V.A., Pavlova, O.N., Grishina, D.S., Hramov, A.E., Recognizing arm motions by fluctuation analysis of EEG signals (2019) Technical Physics Letters, 45 (2), pp. 129-131; Bundy, D.T., Szrama, N., Pahwa, M., Leuthardt, E.C., Unilateral, 3D arm movement kinematics are encoded in ipsilateral human cortex (2018) The Journal of Neuroscience, 38 (47), pp. 10042-10056; Sharifi, M., Pourtakdoust, S.H., Parnianpour, M., Optimal control of human-like musculoskeletal arm: Prediction of trajectory and muscle forces: Optimal control of human-like musculoskeletalarm (2017) Optimal Control Applications Methods, 38 (2), pp. 167-183; Uno, Y., Kawato, M., Suzuki, R., Formation and control of optimal trajectory in human multijoint arm movement. minimumtorque-change model (1989) Biological Cybernetics, 61 (2), pp. 89-101; Kang, T., He, J., Tillery, S.I.H., Determining natural arm configuration along a reaching trajectory (2005) Experimental Brain Research, 167 (3), pp. 352-361; Nakano, E., Imamizu, H., Osu, R., Uno, Y., Gomi, H., Yoshioka, T., Kawato, M., Quantitative examinations of internal representations for arm trajectory planning: Minimum commanded torque change model (1999) Journal of Neurophysiology, 81 (5), pp. 2140-2155; Flash, T., Hogan, N., The coordination of arm movements: An experimentally confirmed mathematical model (1985) The Journal of Neuroscience, 5 (7), pp. 1688-1703; Kashima, T., Iwaseya, M., Trajectory control of biomimetic robots for demonstrating human arm movements (2011) Artificial Life and Robotics, 16 (3), pp. 338-342; Kim, H., Miller, L.M., Byl, N., Abrams, G.M., Rosen, J., Redundancy resolution of the human arm and an upper limb exoskeleton (2012) IEEE Transactions on Biomedical Engineering, 59 (6), pp. 1770-1779; Kashima, T., Kashima, T., Hori, K., Hori, K., Control of biomimetic robots based on analysis of human arm trajectories in 3D movements (2016) Artificial Life and Robotics, 21 (1), pp. 24-30; Yang, J., Marler, R.T., Beck, S., Abdel-Malek, K., Kim, J., Realtime optimal reach-posture prediction in a new interactive virtual environment (2006) Journal of Computer Science and Technology, 21 (2), pp. 189-198; Asfour, T., Dillmann, R., Human-like motion of a humanoid robot arm based on a closed-form solution of the inverse kinematics problem (2003) Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No. 03CH37453), 2, pp. 1407-1412; Tolani, D., Goswami, A., Badler, N.I., Real-time inverse kinematics techniques for anthropomorphic limbs (2000) Graphical Models, 62 (5), pp. 353-388; Kim, H., Li, Z., Milutinovic, D., Rosen, J., Resolving the redundancy of a seven dof wearable robotic system based on kinematic and dynamic constraint (2012) 2012 IEEE International Conference on Robotics and Automation, pp. 305-310; Dinse, G.E., An em algorithm for fitting a four-parameter logistic model to binary dose-response data (2011) Journal of Agricultural, Biological, and Environmental Statistics, 16 (2), pp. 221-232; Maeda, Y., Hara, T., Arai, T., Human-robot cooperative manipulation with motion estimation (2001) Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No. 01CH37180), 4, pp. 2240-2245",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115055716
"Arduengo M., Sentis L.","57218328029;6507774039;","The Robot Economy: Here It Comes",2021,"International Journal of Social Robotics","13","5",,"937","947",,,"10.1007/s12369-020-00686-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088838323&doi=10.1007%2fs12369-020-00686-1&partnerID=40&md5=af5a5bf54fd7245f2ab44eda8dcccbb6","Human Centered Robotics Lab (University of Texas at Austin), Austin, United States; Department of Aerospace Engineering (University of Texas at Austin), Human Centered Robotics Lab (University of Texas at Austin), Austin, United States","Arduengo, M., Human Centered Robotics Lab (University of Texas at Austin), Austin, United States; Sentis, L., Department of Aerospace Engineering (University of Texas at Austin), Human Centered Robotics Lab (University of Texas at Austin), Austin, United States","Automation is not a new phenomenon, and questions about its effects have long followed its advances. More than a half-century ago, US President Lyndon B. Johnson established a national commission to examine the impact of technology on the economy, declaring that automation “can be the ally of our prosperity if we will just look ahead”. In this paper, our premise is that we are at a technological inflection point in which robots are developing the capacity to greatly increase their cognitive and physical capabilities, and thus raising questions on labor dynamics. With increasing levels of autonomy and human-robot interaction, intelligent robots could soon accomplish new human-like capabilities such as engaging into social activities. Therefore, an increase in automation and autonomy brings the question of robots directly participating in some economic activities as autonomous agents. In this paper, a technological framework describing a robot economy is outlined and the challenges it might represent in the current socio-economic scenario are pondered. © 2020, Springer Nature B.V.","Blockchain; Cloud robotics; Intelligent robots; IoRT; Robot economy","Agricultural robots; Automation; Autonomous agents; Economics; Intelligent robots; Economic activities; Human like; Inflection points; Labor dynamics; Physical capabilities; Social activities; Socio-economics; Technological framework; Social robots",,,,,,"The authors would like to thank Carme Torras from Institut de Rob?tica i Inform?tica Industrial (CSIC-UPC) in Barcelona, Spain.",,,,,,,,,,"Kuipers, B.C.S., 395T: Intelligent Robotics -UT at Austin., , http://www.cs.utexas.edu/~kuipers/cs395T-S06.html, Accessed on dec 2019; Crews, J., (2016) Robonomics, , Createspace Independent Publishing, Scotts Valley; (2018) Towards Blockchain-Based Robonomics: Autonomous Agents Behavior Validation, Arxiv E-Preprints. 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Basic Books; Freeman, R.B., (2015) Who Owns the Robots Rules the World, IZA World of Labor; Vermeulen, B., Kesselhut, J., Puka, A., Saviotti, P., The impact of automation on employment: just the usual structural change (2018) Sustainability, 10, p. 1661; Acemoglu, D., Restrepo, P., Robots and jobs: Evidence from US labor markets (2017) National Bureau of Economic Research (NBER) Working Paper Series, , Working Paper 18629; Sachs, J.D., Kotlikoff, L.J., Smart machines and long-term misery (2012) National Bureau of Economic Research (NBER) Working Paper Series, , Working Paper 18629; Autor, D., Skills, education, and the rise of earnings inequality among the other 99 percent (2014) Science, 344, p. 843; Dellot, B., Wallace-Stephens, F., The age of automation-AI, robotics and the future of low-skilled work (2017) Technical Report, Royal Society for Arts, Manufactures and Commerce (Rsa)-Action and Research Centre; (2017) Trade and Development Report-Beyon Austeriy: Towards a Global New Deal, , Technical report, United Nations, New York and Geneva; Turner, A., (2018) Capitalism in the age of robots: work, income and wealth in the 21st-century, in Lecture at School of Advanced International Studies, , John Hopkins University, Washington DC; Ivanov, S., Webster, C., (2017) The robot as a consumer: a research agenda, in marketing: experience and perspectives conference, , University of Economics, Varna; Scheutz, M., The inherent dangers of unidirectional emotional bonds between humans and social robots (2014) Robot ethics: the ethical and social implications of robotics, , Lin P, Abney K, Bekey G, (eds), MIT Press, Cambridge; Cardenas, I., Kim, J.H., (2018) Robot–human agreements and finantial transactions enabled by a blockchain and smart contracts, in HRI’18: Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, , USA (ACM), Chicago; Song, J., The truth about smart contracts. 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Aws.amazon.com/robomaker/ (2019) Accessed on Dec; Ray, P.P., Internet of Robotic Things: Concept, Technologies, and Challenges (2016) IEEE Access, 4, p. 9489; Simoens, P., Dragone, M., Saffiotti, A., The internet of robotic things: a review of concept, added value and applications (2018) Int J Adv Robot Syst; Vermesan, O., Broring, A., Tragos, E., Serrano, M., Bacciu, D., Chessa, S., Gallicchio, C., Bahr, R., Internet of robotic things: converging sensing, actuacing, hypoconnectivity, artificial intelligence and IoT Platforms (2017) Cognitive hyperconnected digital transformation: internet of things intelligence evolution, , Vermesan O, Bacquet J, (eds), River Publishers, Denmark; Swan, M., (2015) Blockchain: blueprint for a new economy, , O’Reilly Media Inc., Champaign; Castelló, E., The blockchain: A new framework for robotic swarm systems, ArXiv e-prints (2017) Arxiv, 1608, p. 00695; Djukic, B., (2019) Do Robots Dream of Crypto? Medium.Com/Anyledger/Do-Robots-Dream-Of-Blockchain-8Bbe7b24f654, , Accessed on, dec; Hanada, Y., Hsiao, L., Levis, P., Smart contracts for machine-to-machine communication: Posibilities and limitations, ArXiv e-prints (2018) (2018) Arxiv, 1806, p. 00555; Korinek, A., Stiglitz, J., Artificial intelligence and its implications for income distribution and unemployment (2019) Economics of artificial intelligence: an agenda, , Agrawal A, Gans J, Goldfarb A, (eds), The University Chicago Press, Chicago; Manyika, J., Amd, M.C., Miremadi, M., Bighi, J., George, K., Willmott, P., Dewhurst, M., (2017) A Future that Works: Automation. Employment and Productivity, , Technical report; Gillham, J., Rimmington, L., Dance, H., Verweij, G., Rao, A., Roberts, K.B., Paich, M., (2018) The Macroeconomic Impact of AI, , Technical report, PricewaterhouseCoopers LLP; (2014) Strategic Research Agenda for Robotics in Europa, p. 2020. , Technical report, SPARC; Artificial intelligence, automation (2016) And the Economy, , Technical report, US Gov; Prassler, E., Kosuge, K., Domestic robots (2008) Handbook of robotics, , Siciliano B, KhatibSpringer O, (eds), 1, Springer, Berlin; (2019) Robotou Blockchain-Enabled Service Robot Ecosystem. Whitepaper Version 1.2. Www.Robotou.Ai. Accessed On, , dec; Delvau, M., Report with recommendations to the commission on civil law rules on robotics. Technical report, European parliament (2017) A8-0005/2017; Alexandre, F., The legal status of artificially intelligent robots-personhood, taxation and control (2017) Technical Report, Dissertation Project, Master of Laws, Tilburg University, the Netherlands; Study of European civil law rules in robotics (2016) Technical Report, Directorate-General for Internal Policies, European Parliament, Brussels; Yang, G.-Z., Bellingham, J., Dupont, P.E., Fischer, P., Floridi, L., Full, R., Jacobstein, N., Wood, R., The grand challenges of science robotics (2018) Sci Robot, 3, p. 14; Torras, C., (2018) The vestigial heart (with materials to debate/teach Ethics in social robotics and AI), , The MIT Press, Cambridge; European Agency for Safety and Health at Work (2015) the Future of Work: robotics—Discussion Paper, , Technical report; Rotman, D., Who will own the robots (2015) MIT Technology Review (June)","Arduengo, M.; Human Centered Robotics Lab (University of Texas at Austin)United States; email: miguel.arduengo@upc.edu",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85088838323
"Tuli T.B., Terefe T.O., Rashid M.M.U.","57202939176;57218107503;57218098148;","Telepresence Mobile Robots Design and Control for Social Interaction",2021,"International Journal of Social Robotics","13","5",,"877","886",,,"10.1007/s12369-020-00676-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087927500&doi=10.1007%2fs12369-020-00676-3&partnerID=40&md5=cbe24af4a1522bb4c5e1e2f47dc68c73","Department of Electromechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia; Department of Mechanical Engineering, Mizan Tepi University, Mizan Tepi, Ethiopia; Department of Electrical and Electronic Engineering, National Institute of Textile Engineering and Research (NITER), Dhaka, 1350, Bangladesh","Tuli, T.B., Department of Electromechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia; Terefe, T.O., Department of Mechanical Engineering, Mizan Tepi University, Mizan Tepi, Ethiopia; Rashid, M.M.U., Department of Electrical and Electronic Engineering, National Institute of Textile Engineering and Research (NITER), Dhaka, 1350, Bangladesh","Human–robot interaction has extended its application horizon to simplify how human beings interact with each other through a remotely controlled telepresence robot. The fast growth of communication technologies such as 4G and 5G has elevated the potential to establish stable audio–video-data transmission. However, human–robot physical interactions are still challenging regarding maneuverability, controllability, stability, drive layout, and autonomy. Hence, this paper presents a systematic design and control approach based on the customer’s needs and expectations of telepresence mobile robots for social interactions. A system model and controller design are developed using the Lagrangian method and linear quadratic regulator, respectively, for different scenarios such as flat surface, inclined surface, and yaw (steering). The robot system is capable of traveling uphill (30∘) and has a variable height (600–1200 mm). The robot is advantageous in developing countries to fill the skill gaps as well as for sharing knowledge and expertise using a virtual and mobile physical presence. © 2020, Springer Nature B.V.","Lagrangian model; LQR; QFD; Telepresence mobile robot","5G mobile communication systems; Agricultural robots; Controllers; Developing countries; Digital storage; Lagrange multipliers; Mobile robots; Social robots; Visual communication; Communication technologies; Controller designs; Design and control; Linear quadratic regulator; Physical interactions; Social interactions; Telepresence robots; Video data transmission; Machine design",,,,,,,,,,,,,,,,"Asada, H., Slotine, J.J., Slotine, J.J.E., (1986) Robot Analysis and Control, , Wiley. Google-Books-ID: KUG1VGkL3loC; Bhattacharya, A., Sarkar, B., Mukherjee, S.K., Integrating AHP with QFD for robot selection under requirement perspective (2005) Int J Prod Res, 43 (17), pp. 3671-3685; Dautenhahn, K., Socially intelligent robots: dimensions of human–robot interaction (2007) Philos Trans R Soc B Biol Sci, 362 (1480), pp. 679-704; de Graaf, M.M.A., An ethical evaluation of human–robot relationships (2016) Int J Soc Robot, 8 (4), pp. 589-598; Esfandiari, R.S., Lu, B., (2018) Modeling and analysis of dynamic systems, , CRC Press, Boca Raton; Giuliani, M., Lenz, C., Müller, T., Rickert, M., Knoll, A., Design principles for safety in human–robot interaction (2010) Int J Soc Robot, 2 (3), pp. 253-274; Harmo, P., Halme, A., Pitkänen, H., Virekoski, P., Halinen, M., Suomela, J., Moving eye—interactive telepresence over internet with a ball shaped mobile robot (2001) IFAC Proc Vol, 34 (9), pp. 481-486; Hiyama, A., Kosugi, A., Fukuda, K., Kobayashi, M., Hirose, M., Facilitating remote communication between senior communities with telepresence robots (2017) Human aspects of IT for the aged population. Applications, services and contexts. Lecture notes in computer science, pp. 501-515. , Zhou J, Salvendy G, (eds), Springer, Cham; Robot hazards: From safety to security (2019) Arxiv:1806.06681 [Cs]; Kristoffersson, A., Coradeschi, S., Loutfi, A., A review of mobile robotic telepresence (2013) Adv Hum-Comput Interact, 2013, pp. 1-17; Lazzeri, N., Mazzei, D., Cominelli, L., Cisternino, A., De Rossi, D.E., Designing the mind of a social robot (2018) Appl Sci, 8 (2), p. 302; Lee, H., Kim, Y.H., Lee, K.K., Yoon, D.K., You, B.J., Designing the appearance of a telepresence robot, M4K: a case study (2016) Cultural robotics. Lecture notes in computer science, pp. 33-43. , Koh JT, Dunstan BJ, Silvera-Tawil D, Velonaki M, (eds), Springer, Cham; Liao, H., Chang, Y., Wu, D., Gou, X., Improved approach to quality function deployment based on Pythagorean fuzzy sets and application to assembly robot design evaluation (2019) Front Eng Manag, , https://doi.org/10.1007/s42524-019-0038-z; Melendez-Fernandez, F., Galindo, C., Gonzalez-Jimenez, J., A web-based solution for robotic telepresence (2017) Int J Adv Robot Syst, 14 (6). , (,),., (,)., https://doi.org/10.1177/1729881417743738; Moniz, A.B., Krings, B.J., Robots working with humans or humans working with robots? Searching for social dimensions in new human–robot interaction in industry (2016) Societies, 6 (3), p. 23; Nise, N.S., (2004) Control systems engineering, , 4, Wiley, Hoboken; Ogata, K., (2014) System Dynamics. Pearson Education Limited, , OCLC; Osawa, M., Imai, M., A robot for test bed aimed at improving telepresence system and evasion from discomfort stimuli by online learning (2019) Int J Soc Robot, , https://doi.org/10.1007/s12369-019-00551-w; Pasawang, T., Chatchanayuenyong, T., Sa-Ngiamvibool, W., (2015) Qfd-Based Conceptual Design of an Autonomous Underwater Robot; Rae, I., Takayama, L., Mutlu, B., The influence of height in robot-mediated communication (2013) 8Th ACM/IEEE International Conference on human–robot Interaction (HRI), pp. 1-8. , https://doi.org/10.1109/HRI.2013.6483495, 2167-2148; Rodogno, R., Ethics and social robotics (2016) Ethics Inf Technol, 18 (4), pp. 241-242; (2016) Springer Handbook of Robotics, 2Nd Edn. Springer Handbooks., , Siciliano B, Khatib O; Tan, Q., Denojean-Mairet, M., Wang, H., Zhang, X., Pivot, F.C., Treu, R., Toward a telepresence robot empowered smart lab (2019) Smart Learn Environ, 6 (1), p. 5; Tan, Z., Hu, Y., Xu, K., Virtual reality based immersive telepresence system for remote conversation and collaboration (2017) Next generation computer animation techniques. Lecture notes in computer science, pp. 234-247. , Chang J, Zhang JJ, Magnenat Thalmann N, Hu SM, Tong R, Wang W, (eds), Springer, Berlin; Trevelyan, J.P., Kang, S.C., Hamel, W.R., Robotics in hazardous applications (2008) Springer handbook of robotics, pp. 1101-1126. , Siciliano B, Khatib O, (eds), Springer, Berlin; Tsai, T.C., Hsu, Y.L., Ma, A.I., King, T., Wu, C.H., (2007) Developing a telepresence robot for interpersonal communication with the elderly in a home environment, , https://doi.org/10.1089/tmj.2006.0068; Tsui, K.M., Yanco, H.A., Design challenges and guidelines for social interaction using mobile telepresence robots (2013) Rev Hum Factors Ergon, 9 (1), pp. 227-301; Tuli, T.B., Mathematical modeling and dynamic simulation of gantry robot using bond graph (2018) Information and communication technology for development for Africa. Lecture notes of the institute for computer sciences, social informatics and telecommunications engineering, pp. 228-237. , Mekuria F, Nigussie EE, Dargie W, Edward M, Tegegne T, (eds), Springer, Cham; Yigit, T., Kose, U., Sengoz, N., Robotics rights and ethics rules (2018) J Multidiscip Dev, 3 (1), pp. 30-37","Tuli, T.B.; Department of Electromechanical Engineering, Ethiopia; email: tadele.belay@aastu.edu.et",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85087927500
"Lin D., Yang C., Dai S.","57218105380;55671767000;36877968100;","Teleoperation Control of Flexible Joint Continuum Robot",2021,"2021 7th International Conference on Control Science and Systems Engineering, ICCSSE 2021",,,,"57","62",,,"10.1109/ICCSSE52761.2021.9545144","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116525638&doi=10.1109%2fICCSSE52761.2021.9545144&partnerID=40&md5=8b6a50cc42471f42e6d32670070f14f7","South China University of Technology, School of Automation Science and Engineering, Guangzhou, China; Foshan Newthinking Intelligent Technology Company Ltd., Foshan, 528231, China","Lin, D., South China University of Technology, School of Automation Science and Engineering, Guangzhou, China, Foshan Newthinking Intelligent Technology Company Ltd., Foshan, 528231, China; Yang, C., South China University of Technology, School of Automation Science and Engineering, Guangzhou, China, Foshan Newthinking Intelligent Technology Company Ltd., Foshan, 528231, China; Dai, S., South China University of Technology, School of Automation Science and Engineering, Guangzhou, China, Foshan Newthinking Intelligent Technology Company Ltd., Foshan, 528231, China","With the development of soft materials and flexible machinery, continuous flexible robots are widely used in the field of robots. Compared with the traditional discrete rigid robot, the continuum robot has flexible joint and multi-section structure. In this paper, we proposed a human-robot interaction control strategy with error feedback, which is based on the teleoperation for flexible joint continuum robot. We apply master-slave operation modes on our control system, which are used to control the end effector of the continuum robot. At the same time, a novel tip-following control method based on forward kinematic model is used to control the end-effector following the desired trajectory that assigned by the input devices touch X. The control method is considering joint angle limit and navigating the end effector of continuum robot in real time. In this paper, a 24 degrees of freedom(DOF) continuum robot with compliant joint is used in the simulation, and the effectiveness of the proposed teleoperation control strategy is testified. © 2021 IEEE.","Continuum robot; human-robot interaction strategy; teleoperation; tip-following control","Degrees of freedom (mechanics); End effectors; Flexible manipulators; Kinematics; Machine design; Machinery; Man machine systems; Remote control; Robotics; Continuum robot; Control methods; Control strategies; Flexible joints; Following control; Human-robot interaction strategy; Humans-robot interactions; Interaction strategy; Softer materials; Tip-following control; Human robot interaction",,,,,"2018IT100322; 2019B1515120076, 2020B1515120054; National Natural Science Foundation of China, NSFC: 61861136009, U20A20200","This work was supported in part by National Nature Science Foundation of China (NSFC) under Grants U20A20200 and 61861136009, in part by Guangdong Basic and Applied Basic Research Foundation under Grants 2019B1515120076 and 2020B1515120054, in part by the Foshan Science and Technology Innovation Team Special Project under Grant 2018IT100322. ∗Corresponding author is C. Yang. Email: cyang@ieee.org.",,,,,,,,,,"Walker, I.D., Continuous backbone continuum robot manipulators (2013) International Scholarly Research Notices; Simaan, N., Snake-like units using flexible backbones and actuation redundancy for enhanced miniaturization (2005) Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 3012-3017. , April; Kim, Y.J., Cheng, S., Kim, S., A stiffness-adjustable hyperredundant manipulator using a variable neutral-line mechanism for minimally invasive surgery (2013) IEEE Transactions on Robotics, 30 (2), pp. 382-395; Camarillo, D.B., Carlson, C.R., Salisbury, J.K., Configuration tracking for continuum manipulators with coupled tendon drive (2009) IEEE Transactions on Robotics, 25 (4), pp. 798-808; Webster, R.J., Romano, J.M., Cowan, N.J., Mechanics of precurved-tube continuum robots (2009) IEEE Trans. Rob., 25 (1), pp. 67-78; Ayadi, N., Turki, M., Ghribi, R., Derbel, N., Identification and development of a real time motion control for a mobile robot's dc gearmotor (2017) International Journal of Computer Applications in Technology, 55 (1); Dong, X., Raffles, M., Cobos-Guzman, S., A novel continuum robot using twin-pivot compliant joints: Design, modeling, and validation (2016) Journal of Mechanisms and Robotics, 8 (2), p. 021010; Dong, X., Raffles, M., Cobos-Guzman, S., Axinte, D., Kell, J., A novel continuum robot using twin-pivot compliant joints: Design, modeling, and validation (2016) Journal of Mechanisms and Robotics, 8 (2); Casper, J., Murphy Robin, R., Human-robot interactions during the robot-assisted urban search and rescue response at the world trade center (2003) IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 33 (3), pp. 367-385; Csencsits, M., Jones, B.A., McMahan, W., Iyengar, V., Walker, I.D., User interfaces for continuum robot arms (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3123-3130; Burgner-Kahrs, J., Rucker, D.C., Choset, H., Continuum robots for medical applications: A survey (2015) IEEE Transactions on Robotics, 31 (6), pp. 1261-1280; Mahmoud, R.A., Ueno, A., Tatsumi, S., An interactive tele-operated anthropomorphic robot hand: Osaka city university hand (2012) International Journal of Mechanical Engineering and Robotics Research, 1 (1), pp. 120-135. , April; Mochiyama, H., Shimemura, E., Kobayashi, H., Shape control of manipulators with hyper degrees of freedom (1999) The International Journal of Robotics Research, 18 (6), pp. 584-600; Braganza, D., Dawson, D.M., Walker, I.D., Nath, N., A neural network controller for continuum robots (2007) IEEE Transactions on Robotics, 23 (6), pp. 1270-1277; Trivedi, D., Lotfi, A., Rahn, C.D., Geometrically exact models for soft robotic manipulators (2008) IEEE Transactions on Robotics, 24 (4), pp. 773-780; Rucker, D.C., Jones, B.A., Webster Iii, R.J., A geometrically exact model for externally loaded concentric-tube continuum robots (2010) IEEE Transactions on Robotics, 26 (5), pp. 769-780; Chirikjian, G.S., Burdick, J.W., (1991) Kinematics of Hyper-redundant Robot Locomotion with Applications to Grasping, , IEEE Computer Society Press; Kang, R., Branson, D.T., Guglielmino, E., Caldwell, D.G., Dynamic modeling and control of an octopus inspired multiple continuum arm robot (2012) Computers and Mathematics with Applications, 64 (5), pp. 1004-1016; Palmer, D., Cobos-Guzman, S., Axinte, D., Real-time method for tip following navigation of continuum snake arm robots (2014) Robotics and Autonomous Systems, 62 (10), pp. 1478-1485; Jones, B.A., Walker, I.D., Kinematics for multisection continuum robots (2006) IEEE Transactions on Robotics, 22 (1), pp. 43-55; Yang, C., Wang, X., Li, Z., Li, Y., Su, C.Y., Teleoperation control based on combination of wave variable and neural networks (2016) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47 (8), pp. 2125-2136","Yang, C.; South China University of Technology, China; email: cyang@ieee.org",,"China Agricultural University;IEEE","Institute of Electrical and Electronics Engineers Inc.","7th International Conference on Control Science and Systems Engineering, ICCSSE 2021","30 July 2021 through 1 August 2021",,171942,,9781665444057,,,"English","Int. Conf. Control Sci. Syst. Eng., ICCSSE",Conference Paper,"Final","",Scopus,2-s2.0-85116525638
"D'Antonio E., Galofaro E., Patane F., Casadio M., Masia L.","57203838033;57197792870;8900790700;9735851100;8900790600;","A dual arm haptic exoskeleton for dynamical coupled manipulation",2021,"IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM","2021-July",,,"1237","1242",,,"10.1109/AIM46487.2021.9517707","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114963434&doi=10.1109%2fAIM46487.2021.9517707&partnerID=40&md5=ee7697072ba111be6e1874a3c9c5da89","Institute of Computer Engineering (ZITI), Heidelberg University, Assistive Robotics and Interactive Exosuits (ARIES) Lab, Heidelberg, Germany; University Niccolò Cusano, Mechanical Measurements and Microelectronics (M3Lab) Lab, Engineering Department, Rome, Italy; Department of Informatics, Bioengineering, Robotics, and System Engineering (DIBRIS), University of Genoa, Genoa, Italy","D'Antonio, E., Institute of Computer Engineering (ZITI), Heidelberg University, Assistive Robotics and Interactive Exosuits (ARIES) Lab, Heidelberg, Germany; Galofaro, E., Institute of Computer Engineering (ZITI), Heidelberg University, Assistive Robotics and Interactive Exosuits (ARIES) Lab, Heidelberg, Germany, Department of Informatics, Bioengineering, Robotics, and System Engineering (DIBRIS), University of Genoa, Genoa, Italy; Patane, F., University Niccolò Cusano, Mechanical Measurements and Microelectronics (M3Lab) Lab, Engineering Department, Rome, Italy; Casadio, M., Department of Informatics, Bioengineering, Robotics, and System Engineering (DIBRIS), University of Genoa, Genoa, Italy; Masia, L., Institute of Computer Engineering (ZITI), Heidelberg University, Assistive Robotics and Interactive Exosuits (ARIES) Lab, Heidelberg, Germany","Most activities of daily living require bimanual motor coordination during interactions with the external environment. Recent advancements in haptics and virtual reality provide researchers with new instruments to study human-robot interaction and implement experimental paradigms to involve multiple degrees of freedom in the simulation of complex interactive environments. We developed a pick and place task on a bimanual 6 DoFs exoskeleton to explore, for the first time, dynamical coupled motor strategies in a 3D workspace. The robot rendered the force feedback resulting from the interaction with a variety of virtual objects. We tested healthy subjects during several experimental conditions corresponding to multiple simulated dynamics. Results highlighted the system ability in reproducing virtual materials of different stiffness. The dependency of subjects' performance on the virtual objects' properties and targets' direction has also been detected, suggesting that providing haptic rendering on a wearable bimanual exoskeleton could represent a starting point for the development of a fully customized and measurable haptic environment. © 2021 IEEE.",,"Biofeedback; Degrees of freedom (mechanics); Human robot interaction; Intelligent mechatronics; Activities of Daily Living; Experimental conditions; External environments; Haptic environment; Interactive Environments; Motor co-ordination; Multiple degrees of freedom; Virtual materials; Exoskeleton (Robotics)",,,,,,,,,,,,,,,,"Shumway-Cook, A., Woollacott, M.H., (2007) Motor Control: Translating Research into Clinical Practice, , Lippincott Williams & Wilkins; Johansson, R.S., Theorin, A., Westling, G., Andersson, M., Ohki, Y., Nyberg, L., How a lateralized brain supports symmetrical bimanual tasks (2006) PLoS Biol, 4 (6); Nozaki, D., Scott, S.H., Multi-compartment model can explain partial transfer of learning within the same limb between unimanual and bimanual reaching (2009) Exp. Brain Res., 194 (3), pp. 451-463; Casadio, M., Sanguineti, V., Squeri, V., Masia, L., Morasso, P., Inter-limb interference during bimanual adaptation to dynamic environments (2010) Exp. Brain Res., 202 (3), pp. 693-707; Tieri, G., Morone, G., Paolucci, S., Iosa, M., Virtual reality in cognitive and motor rehabilitation: Facts, fiction and fallacies (2018) Expert Rev. Med. Devices, 15 (2), pp. 107-117; Schultheis, M.T., Rizzo, A.A., The application of virtual reality technology in rehabilitation (2001) Rehabil. Psychol., 46 (3), p. 296; D'Antonio, E., Tieri, G., Patané, F., Morone, G., Iosa, M., Stable or able? Effect of virtual reality stimulation on static balance of poststroke patients and healthy subjects (2020) Hum. Mov. Sci., 70; Sacks, R., Perlman, A., Barak, R., Construction safety training using immersive virtual reality (2013) Constr. Manag. Econ., 31 (9), pp. 1005-1017; Saposnik, G., Efficacy and safety of non-immersive virtual reality exercising in stroke rehabilitation (EVREST): A randomised, multicentre, single-blind, controlled trial (2016) Lancet Neurol., 15 (10), pp. 1019-1027; Faria, A.L., Andrade, A., Soares, L., Badia, S.B.I., Benefits of virtual reality based cognitive rehabilitation through simulated activities of daily living: A randomized controlled trial with stroke patients (2016) J. Neuroeng. Rehabil., 13 (1), p. 96; Laver, K.E., Lange, B., George, S., Deutsch, J.E., Saposnik, G., Crotty, M., Virtual reality for stroke rehabilitation (2017) Cochrane Database Syst. Rev., (11); Bohil, C.J., Alicea, B., Biocca, F.A., Virtual reality in neuroscience research and therapy (2011) Nat. Rev. Neurosci., 12 (12), pp. 752-762; Contu, S., Hughes, C.M.L., Masia, L., The role of visual and haptic feedback during dynamically coupled bimanual manipulation (2016) IEEE Trans. Haptics, 9 (4), pp. 536-547; O'Malley, M.K., Gupta, A., Haptic interfaces (2008) HCI beyond GUI Des. Haptic, Speech, Olfactory, Other Nontradit. Interfaces, pp. 25-64; Laycock, S.D., Day, A.M., Recent developments and applications of haptic devices (2003) Comput. Graph. Forum, 22 (2), pp. 117-132. , Jun; Massie, T.H., Salisbury, J.K., The phantom haptic interface: A device for probing virtual objects (1994) Proceedings of the ASME Winter Annual Meeting, Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, 55 (1), pp. 295-300; Colgate, J.E., Brown, J.M., Factors affecting the z-width of a haptic display (1994) Proceedings of the 1994 IEEE International Conference on Robotics and Automation, pp. 3205-3210; Adams, R.J., Hannaford, B., Stable haptic interaction with virtual environments (1999) IEEE Trans. Robot. Autom., 15 (3), pp. 465-474; Zilles, C.B., Salisbury, J.K., A constraint-based god-object method for haptic display (1995) Proceedings 1995 Ieee/rsj International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots, 3, pp. 146-151; Ruffaldi, E., Barsotti, M., Leonardis, D., Bassani, G., Frisoli, A., Bergamasco, M., Evaluating virtual embodiment with the alex exoskeleton (2014) International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, pp. 133-140; Pirondini, E., Evaluation of the effects of the Arm Light Exoskeleton on movement execution and muscle activities: A pilot study on healthy subjects (2016) J. Neuroeng. Rehabil., pp. 1-21; Frisoli, A., Exoskeletons for upper limb rehabilitation (2018) Rehabilitation Robotics, Elsevier, pp. 75-87; Unity Technologies (2018) SteamVR; Sankaranarayanan, G., Hannaford, B., Virtual coupling schemes for position coherency in networked haptic environments (2006) The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006, pp. 853-858. , BioRob 2006; Park, J., The relationship between controlled joint torque and endeffector force in underactuated robotic systems (2011) Robotica, 29 (4), p. 581; Huang, K., Chitrakar, D., Rydén, F., Chizeck, H.J., Evaluation of haptic guidance virtual fixtures and 3D visualization methods in telemanipulation-a user study (2019) Intell. Serv. Robot., 12 (4), pp. 289-301; Hulin, T., Sagardia, M., Artigas, J., Schaetzle, S., Kremer, P., Preusche, C., Human-scale bimanual haptic interface (2008) Proceedings; Gupta, A., O'Malley, M.K., Design of a haptic arm exoskeleton for training and rehabilitation (2006) IEEE/ASME Trans. Mechatronics, 11 (3), pp. 280-289; Frisoli, A., Salsedo, F., Bergamasco, M., Rossi, B., Carboncini, M.C., A force-feedback exoskeleton for upper-limb rehabilitation in virtual reality (2009) Appl. Bionics Biomech., 6 (2), pp. 115-126","D'antonio, E.; Institute of Computer Engineering (ZITI), Germany; email: erika.dantonio@ziti.uni-heidelberg.de",,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021","12 July 2021 through 16 July 2021",,171450,,9781665441391,,,"English","IEEE ASME Int Conf Adv Intellig Mechatron AIM",Conference Paper,"Final","",Scopus,2-s2.0-85114963434
"Qi W., Su H., Zhang J., Song R., Ferrigno G., de Momi E., Aliverti A.","57194568340;57203702628;57283102500;26431599600;7005615860;57203951877;7006361013;","Active learning strategy of finger flexion tracking using sEMG for robot hand control",2021,"2021 6th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2021",,,,"753","758",,,"10.1109/ICARM52023.2021.9536163","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116244541&doi=10.1109%2fICARM52023.2021.9536163&partnerID=40&md5=f738691e64766799baa2713ddcfbff54","The Institute of Advanced Technology, University of Science and Technology of China, Hefei, 5089, China; The Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, 20133, Italy; The Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, 510000, China; The Shenzhen Research Institute of Sun Yat-sen University, Shenzhen, 510810, China","Qi, W., The Institute of Advanced Technology, University of Science and Technology of China, Hefei, 5089, China, The Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, 20133, Italy; Su, H., The Institute of Advanced Technology, University of Science and Technology of China, Hefei, 5089, China, The Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, 20133, Italy; Zhang, J., The Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, 20133, Italy; Song, R., The Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, 510000, China, The Shenzhen Research Institute of Sun Yat-sen University, Shenzhen, 510810, China; Ferrigno, G., The Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, 20133, Italy; de Momi, E., The Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, 20133, Italy; Aliverti, A., The Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, 20133, Italy","Capturing biosignals from wearable devices is widely applied in the human-robot interaction (HRI) area. For example, surface electromyography (sEMG) signals are always adopted to track finger flexion for hand robot control. However, it is difficult to extract features from the weak sEMG signals with several noises. The existing regression model cannot be dealing with changes in real robot control scenarios. This paper proposed an sEMG based finger flexion tracking framework for robot hand control using the active learning strategy. It consists of an offline regression model and an online model updating module—the former aims to build the regression model based on the processed sEMG and finger angles. The latter is to update the model when it gets a trigger. The comparison results prove the performance of the active learning strategy in the online scenario. By comparing the overall updating times and errors, the decision tree method saves more computational time. At the same time, Gaussian regression obtains a higher accuracy. © 2021 IEEE.",,"Decision trees; Human robot interaction; Learning systems; Palmprint recognition; Active learning strategies; Biosignals; Finger flexion; Humans-robot interactions; Regression modelling; Robot hand control; Robots control; Surface electromyography; Surface electromyography signals; Wearable devices; Regression analysis",,,,,,"*This work was supported by the Opening Project of Shanghai Robot R&D and Transformation Functional Platform.",,,,,,,,,,"Delph, M.A., Fischer, S.A., Gauthier, P.W., Luna, C.H.M., Clancy, E.A., Fischer, G.S., A soft robotic exomusculature glove with integrated semg sensing for hand rehabilitation (2013) 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR), pp. 1-7; Zhou, Y., Fang, Y., Gui, K., Li, K., Zhang, D., Liu, H., Semg bias-driven functional electrical stimulation system for upper-limb stroke rehabilitation (2018) IEEE Sensors Journal, 18 (16), pp. 6812-6821; Qi, W., Aliverti, A., A multimodal wearable system for continuous and real-time breathing pattern monitoring during daily activity (2019) IEEE Journal of Biomedical and Health Informatics, 24 (8), pp. 2199-2207; Meattini, R., Benatti, S., Scarcia, U., de Gregorio, D., Benini, L., Melchiorri, C., An semg-based human–robot interface for robotic hands using machine learning and synergies (2018) IEEE Transactions on Components, Packaging and Manufacturing Technology, 8 (7), pp. 1149-1158; Luo, R., Sun, S., Zhang, X., Tang, Z., Wang, W., A low-cost end-to-end semg-based gait sub-phase recognition system (2019) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28 (1), pp. 267-276; Qian, K., Zhang, Z., Yamamoto, Y., Schuller, B.W., Artificial intelligence internet of things for the elderly: From assisted living to health-care monitoring (2021) IEEE Signal Processing Magazine, 38 (4), pp. 78-88; Brunelli, D., Farella, E., Giovanelli, D., Milosevic, B., Minakov, I., Design considerations for wireless acquisition of multichannel semg signals in prosthetic hand control (2016) IEEE Sensors Journal, 16 (23), pp. 8338-8347; Naik, G.R., Baker, K.G., Nguyen, H.T., Dependence independence measure for posterior and anterior emg sensors used in simple and complex finger flexion movements: Evaluation using sdica (2014) IEEE Journal of Biomedical and Health Informatics, 19 (5), pp. 1689-1696; Arteaga, M.V., Castiblanco, J.C., Mondragon, I.F., Colorado, J.D., Alvarado-Rojas, C., Emg-driven hand model based on the classification of individual finger movements (2020) Biomedical Signal Processing and Control, 58, pp. 101-834; Qi, W., Ovur, S.E., Li, Z., Marzullo, A., Song, R., Multi-sensor guided hand gestures recognition for teleoperated robot using recurrent neural network (2021) IEEE Robotics and Automation Letters, 6 (3), pp. 6039-6045; Barsotti, M., Dupan, S., Vujaklija, I., Došen, S., Frisoli, A., Farina, D., Online finger control using high-density emg and minimal training data for robotic applications (2018) IEEE Robotics and Automation Letters, 4 (2), pp. 217-223; Botelho, D.P., Curran, K., Lowery, M.M., Anatomically accurate model of emg during index finger flexion and abduction derived from diffusion tensor imaging (2019) PLoS Computational Biology, 15 (8); Hidayat, A.A., Arief, Z., Yuniarti, H., Lovett scalling with myo armband for monitoring finger muscles therapy of post-stroke people (2016) 2016 International Electronics Symposium (IES), pp. 66-70; Borghetti, M., Sardini, E., Serpelloni, M., Sensorized glove for measuring hand finger flexion for rehabilitation purposes (2013) IEEE Transactions on Instrumentation and Measurement, 62 (12), pp. 3308-3314; Polygerinos, P., Wang, Z., Galloway, K.C., Wood, R.J., Walsh, C.J., Soft robotic glove for combined assistance and at-home rehabilitation (2015) Robotics and Autonomous Systems, 73, pp. 135-143; Naik, G.R., Nguyen, H.T., Nonnegative matrix factorization for the identification of emg finger movements: Evaluation using matrix analysis (2014) IEEE Journal of Biomedical and Health Informatics, 19 (2), pp. 478-485; Ngeo, J.G., Tamei, T., Shibata, T., Continuous and simultaneous estimation of finger kinematics using inputs from an emg-to-muscle activation model (2014) Journal of Neuroengineering and Rehabilitation, 11 (1), pp. 1-14; Connolly, J., Condell, J., O’Flynn, B., Sanchez, J.T., Gardiner, P., Imu sensor-based electronic goniometric glove for clinical finger movement analysis (2017) IEEE Sensors Journal, 18 (3), pp. 1273-1281; Pizzolato, S., Tagliapietra, L., Cognolato, M., Reggiani, M., Müller, H., Atzori, M., Comparison of six electromyography acquisition setups on hand movement classification tasks (2017) PloS ONE, 12 (10); Liu, M.-J., Xiong, C.-H., Xiong, L., Huang, X.-L., Biomechanical characteristics of hand coordination in grasping activities of daily living (2016) PloS ONE, 11 (1); Oskoei, M.A., Hu, H., Support vector machine-based classification scheme for myoelectric control applied to upper limb (2008) IEEE Transactions on Biomedical Engineering, 55 (8), pp. 1956-1965; Scheme, E.J., Englehart, K.B., Hudgins, B.S., Selective classification for improved robustness of myoelectric control under nonideal conditions (2011) IEEE Transactions on Biomedical Engineering, 58 (6), pp. 1698-1705; Lagarias, J.C., Reeds, J.A., Wright, M.H., Wright, P.E., Convergence properties of the nelder–mead simplex method in low dimensions (1998) SIAM Journal on Optimization, 9 (1), pp. 112-147; Fan, R.-E., Chen, P.-H., Lin, C.-J., Joachims, T., Working set selection using second order information for training support vector machines (2005) Journal of Machine Learning Research, 6 (12); Loh, W.-Y., Regression tress with unbiased variable selection and interaction detection (2002) Statistica Sinica, pp. 361-386; Huang, P.-S., Avron, H., Sainath, T.N., Sindhwani, V., Ramabhadran, B., Kernel methods match deep neural networks on timit (2014) 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 205-209",,,"Gaitech Robotics;Publishing House of Electronics Industry;Quanser;Sensors;Springer;Unitree Robotics","Institute of Electrical and Electronics Engineers Inc.","6th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2021","3 July 2021 through 5 July 2021",,171776,,9780738133645,,,"English","IEEE Int. Conf. Adv. Robot. Mechatronics, ICARM",Conference Paper,"Final","",Scopus,2-s2.0-85116244541
"Mauzé B., Laurent G.J., Dahmouche R., Clévy C.","57216521395;35345138000;24780378500;6506507449;","Micrometer Positioning Accuracy With a Planar Parallel Continuum Robot",2021,"Frontiers in Robotics and AI","8",,"706070","","",,,"10.3389/frobt.2021.706070","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110526953&doi=10.3389%2ffrobt.2021.706070&partnerID=40&md5=8d055b9819f61025bd32dc64d6733ec4","FEMTO-ST Institute, University Bourgogne Franche-Comté, Besançon, France","Mauzé, B., FEMTO-ST Institute, University Bourgogne Franche-Comté, Besançon, France; Laurent, G.J., FEMTO-ST Institute, University Bourgogne Franche-Comté, Besançon, France; Dahmouche, R., FEMTO-ST Institute, University Bourgogne Franche-Comté, Besançon, France; Clévy, C., FEMTO-ST Institute, University Bourgogne Franche-Comté, Besançon, France","Parallel Continuum Robots (PCR) have several advantages over classical articulated robots, notably a large workspace, miniaturization capabilities and safe human-robot interactions. However, their low accuracy is still a serious drawback. Indeed, several conditions have to be met for PCR to reach a high accuracy, namely: a repeatable mechanical structure, a correct kinematic model, and a proper estimation of the model’s parameters. In this article, we propose a methodology that allows reaching a micrometer accuracy with a PCR. This approach emphasizes the importance of using a repeatable continuum mechanism, identifying the most influential parameters of an accurate kinematic model of the robot and precisely measuring them. The experimental results show that the proposed approach allows to reach an accuracy of 3.3 µm in position and 0.5 mrad in orientation over a 10 mm long circular path. These results push the current limits of PCR accuracy and make them good potential candidates for high accuracy automatic positioning tasks. © Copyright © 2021 Mauzé, Laurent, Dahmouche and Clévy.","accuracy; continuum robots; cosserat-rod; kirchhoff-rod; micro-positioning; parallel robots; robot calibration; soft robots",,,,,,"ANR-17-EURE-0002; ANR-10-EQPX-44-01; Agence Nationale de la Recherche, ANR; Conseil régional de Bourgogne-Franche-Comté","This work was supported by µSpider project (ANR-19-CE33-0002-02), by Région Bourgogne Franche-Comté and the EIPHI Graduate School (ANR-17-EURE-0002). This work has been partly supported by the French ROBOTEX network and its FEMTO-ST technological facility (ANR-10-EQPX-44-01).",,,,,,,,,,"Altuzarra, O., Caballero, D., Campa, F.J., Pinto, C., Position Analysis in Planar Parallel Continuum Mechanisms (2019) Mechanism Machine Theor, 132, pp. 13-29; Altuzarra, O., Merlet, J.-P., Certified Kinematics Solution of 2-dof Planar Parallel Continuum Mechanisms (2019) Adv. Mechanism Machine Sci, 73, pp. 197-208; Andre, A.N., Sandoz, P., Mauze, B., Jacquot, M., Laurent, G.J., Sensing One Nanometer over Ten Centimeters: A Microencoded Target for Visual In-Plane Position Measurement (2020) Ieee/asme Trans. Mechatron, 25, pp. 1193-1201; Black, C.B., Till, J., Rucker, D.C., Parallel Continuum Robots: Modeling, Analysis, and Actuation-Based Force Sensing (2018) IEEE Trans. Robot, 34, pp. 29-47; Bryson, C.E., Rucker, D.C., Toward Parallel Continuum Manipulators (2014) IEEE International Conference on Robotics and Automation, pp. 778-785. , Hong Kong, China, IEEE; Campa, F.J., Diez, M., Diaz-Caneja, D., Altuzarra, O., A 2 Dof Continuum Parallel Robot for Pick & Place Collaborative Tasks (2019) Advances in Mechanism and Machine Science, 73, pp. 1979-1988. , Uhl T., (ed), Cham, Springer International Publishing,, Editor; Dehghani, M., Moosavian, S.A.A., Characteristics Identification of Continuum Robots for Exact Modeling (2013) IEEE RSI/ISM International Conference on Robotics and Mechatronics, pp. 26-31. , Tehran, IEEE; (1998) Manipulating Industrial Robots – Performance Criteria and Related Test Methods.Standard ISO 9283:1998, , Geneva, Switzerland, International Organization for Standardization; Lilge, S., Nuelle, K., Boettcher, G., Spindeldreier, S., Burgner-Kahrs, J., “Tendon Actuated Continuous Structures in Planar Parallel Robots: A Kinematic Analysis (2021) ASME. 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Lett, 1, pp. 828-835; Rao, P., Peyron, Q., Lilge, S., Burgner-Kahrs, J., How to Model Tendon-Driven Continuum Robots and Benchmark Modelling Performance (2021) Front. Robot. AI, 7, p. 630245; Rone, W.S., Ben-Tzvi, P., Mechanics Modeling of Multisegment Rod-Driven Continuum Robots (2014) J. Mech. Robotics, 6, p. 041006; Takano, R., Mochiyama, H., Takesue, N., Real-time Shape Estimation of Kirchhoff Elastic Rod Based on Force/torque Sensor (2017) IEEE International Conference on Robotics and Automation, pp. 2508-2515. , Singapore, IEEE; Till, J., Rucker, D.C., Elastic Stability of Cosserat Rods and Parallel Continuum Robots (2017) IEEE Trans. Robot, 33, pp. 718-733; Trivedi, D., Lotfi, A., Rahn, C.D., Geometrically Exact Models for Soft Robotic Manipulators (2008) IEEE Trans. Robot, 24, pp. 773-780; Wu, G., Shi, G., Experimental Statics Calibration of a Multi-Constraint Parallel Continuum Robot (2019) Mechanism Machine Theor, 136, pp. 72-85; Wu, G., Shi, G., Shi, Y., Modeling and Analysis of a Parallel Continuum Robot Using Artificial Neural Network (2017) IEEE International Conference on Mechatronics, pp. 153-158. , Churchill, VIC, IEEE; Yang, Z., Zhu, X., Xu, K., Continuum Delta Robot: A Novel Translational Parallel Robot with Continuum Joints (2018) IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 748-755. , Auckland, IEEE","Dahmouche, R.; FEMTO-ST Institute, France; email: redwan.dahmouche@femto-st.fr",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85110526953
"Bärmann L., Peller-Konrad F., Constantin S., Asfour T., Waibel A.","57224322064;57224322866;57201881982;15724268700;35575129700;","Deep Episodic Memory for Verbalization of Robot Experience",2021,"IEEE Robotics and Automation Letters","6","3","9444649","5808","5815",,,"10.1109/LRA.2021.3085166","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107341053&doi=10.1109%2fLRA.2021.3085166&partnerID=40&md5=5819970574301e18706cd21b952cd72d","Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany","Bärmann, L., Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany; Peller-Konrad, F., Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany; Constantin, S., Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany; Asfour, T., Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany; Waibel, A., Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany","The ability to verbalize robot experience in natural language is key for a symbiotic human-robot interaction. While first works approached this problem using template-based verbalization on symbolic episode data only, we explore a novel way in which deep learning methods are used for the creation of an episodic memory from experiences as well as the verbalization of such experience in natural language. To this end, we first collected a complex dataset consisting of more than a thousand multimodal robot episode recordings both from simulation as well as real robot executions, together with representative natural language questions and answers about the robot's past experience. Second, we propose and evaluate an episodic memory verbalization model consisting of a speech encoder and decoder based on the Transformer architecture, combined with an LSTM-based episodic memory auto-encoder, and evaluate the model on simulated and real data from robot execution examples. Our experimental results provide a proof-of-concept for episodic-memory-based verbalization of robot experience. © 2016 IEEE.","AI-enabled robotics; Learning from experience; natural dialog for HRI; sensorimotor learning","Agricultural robots; Computer simulation languages; Deep learning; Human robot interaction; Learning systems; Signal encoding; Auto encoders; Episodic memory; Learning methods; Multi-modal; Natural language questions; Natural languages; Proof of concept; Template-based; Long short-term memory",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 01IS18040 A","Manuscript received December 21, 2020; accepted May 14, 2021. Date of publication May 31, 2021; date of current version June 15, 2021. This letter was recommended for publication by Editor M. Vincze upon evaluation of the reviewers’ comments. This work was supported by the German Federal Ministry of Education and Research (BMBF) under the project OML (01IS18040 A). (Corresponding author: Leonard Bärmann.) The authors are with the Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology, Karlsruhe, Germany (e-mail: lbaermann@gmx.de; fabian.peller-konrad@kit.edu; stefan.constantin@kit.edu; asfour@kit.edu; waibel@kit.edu).",,,,,,,,,,"Rosenthal, S., Selvaraj, S.P., Veloso, M., Verbalization: Narration of autonomous robot experience (2016) Proc. Int. Joint Conf. Artif. Intel, pp. 862-868; Tulving, E., Episodic and semantic memory (1972) Organization of Memory, pp. 381-403. , E. Tulving & W. Donaldson, Academic Press; Rothfuss, J., Ferreira, F., Aksoy, E.E., Zhou, Y., Asfour, T., Deep episodic memory: Encoding, recalling, and predicting episodic experiences for robot action execution (2018) IEEE Robot. Automat. Let., 3 (4), pp. 4007-4014. , Oct; Asfour, T., ARMAR-III: An integrated humanoid platform for sensory-motor control (2006) Proc. IEEE-RAS Int. Conf. Humanoid Robots, pp. 169-175; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Comput., 9 (8), pp. 1735-1780; Sutskever, I., Vinyals, O., Le, Q.V., Sequence to sequence learning with neural networks (2014) Proc. Int. Conf. Neural Inf. Process. Syst, pp. 3104-3112; Vaswani, A., Attention is all you need (2017) Proc. Int. Conf. Neural Inf. Process. Syst, pp. 6000-6010; Raffel, C., Exploring the limits of transfer learning with a unified text-to-text transformer (2020) J. Mach. Learn. Res., 21 (140), pp. 1-67; Perera, V., Selveraj, S.P., Rosenthal, S., Veloso, M., Dynamic generation and refinement of robot verbalization (2016) Proc. IEEE Int. Symp. Rob. Hum. Interact. Comm, pp. 212-218; Zhu, Q., Perera, V., Wachter, M., Asfour, T., Veloso, M.M., Autonomous narration of humanoid robot kitchen task experience (2017) Proc. IEEE-RAS Int. Conf. Humanoid Robots, pp. 390-397; Winkler, J., Tenorth, M., Bozcuoglu, A.K., Beetz, M., CRAMm memories for robots performing everyday manipulation activities (2014) Proc. Adv. Cogn. Syst., 3, pp. 47-66; Beetz, M., Bessler, D., Haidu, A., Pomarlan, M., Bozcuoglu, A.K., Bartels, G., Knowrob 2.0 -A 2nd generation knowledge processing framework for cognition-enabled robotic agents (2018) Proc. IEEE Int. Conf. Robot. Automat, pp. 512-519; Kenfack, F.K., Siddiky, F.A., Balint-Benczedi, F., Beetz, M., RobotVQA - A scene-graph- And deep-learning-based visual question answering system for robot manipulation (2020) Proc. IEEE/RSJ Int. Conf. Intel. Rob. Syst, pp. 9667-9674; Park, G.-M., Yoo, Y.-H., Kim, D.-H., Kim, J.-H., Deep ART neural model for biologically inspired episodic memory and its application to task performance of robots (2018) IEEE Trans. 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Pattern Recognit; Lei, J., Yu, L., Berg, T., Bansal, M., TVQA+: Spatio-temporal grounding for video question answering (2020) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8211-8225. , Association for Computational Linguistics, Jul; Yu, Z., ActivityNet-QA: A dataset for understanding complex web videos via question answering (2019) AAAI, 33 (1), pp. 9127-9134; Zhao, Z., Xiao, S., Song, Z., Lu, C., Xiao, J., Zhuang, Y., Open-ended video question answering via multi-modal conditional adversarial networks (2020) IEEE Trans. Image Process, 29, pp. 3859-3870; Kumar, A., Askme anything: Dynamicmemory networks for natural language processing (2016) Proc. Int. Conf. Mach. Learn., 48, pp. 1378-1387; Wu, C.-S., Socher, R., Xiong, C., Global-to-local memory pointer networks for task-oriented dialogue (2019) Proc. Int. Conf. Learn. Representations, pp. 1-11; Bahdanau, D., Cho, K., Bengio, Y., Neural machine translation by jointly learning to align and translate (2015) Proc. Int. Conf. Learn. Representations, pp. 1-11; Kollar, T., (2017) Generalized Grounding Graphs: A Probabilistic Framework for Understanding Grounded Commands; Kollar, T., Tellex, S., Roy, D., Roy, N., Grounding verbs of motion in natural language commands to robots (2014) Proc. Exp. Robot., 79, pp. 31-47. , Springer Berlin Heidelberg; Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., Sheikh, Y., OpenPose: Realtime multi-person 2 D pose estimation using part affinity fields (2021) IEEE Trans. On Pattern Analysis and Machine Intelligence, 43 (1), pp. 172-186. , Jan; Vahrenkamp, N., Wächter, M., Kröhnert, M., Welke, K., Asfour, T., The robot software framework ArmarX (2015) It - Inf. Technol., 57 (2), pp. 99-111; Bordes, A., Boureau, Y.-L., Weston, J., Learning end-to-end goal-oriented dialog (2017) Proc. Int. Conf. Learn. Representations, pp. 1-10","Bärmann, L.; Institute for Anthropomatics and Robotics, Germany; email: lbaermann@gmx.de",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85107341053
"Aroyo A.M., Pasquali D., Kothig A., Rea F., Sandini G., Sciutti A.","57192384790;57216357588;57210748983;54386065400;35280694500;36957816700;","Expectations Vs. Reality: Unreliability and Transparency in a Treasure Hunt Game with Icub",2021,"IEEE Robotics and Automation Letters","6","3","9440726","5681","5688",,1,"10.1109/LRA.2021.3083465","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107173590&doi=10.1109%2fLRA.2021.3083465&partnerID=40&md5=5434d41398a3cecc77d889f35b6324fc","Social and Intelligent Robotics Research Laboratory (SIRRL), Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON  N2L 3G1, Canada; DIBRIS, Università di Genova, Genova, GE 16152, Italy; Department of Robotics, Brain and Cognitive Sciences, Italian Institute of Technology (IIT), Genova, 16163, Italy","Aroyo, A.M., Social and Intelligent Robotics Research Laboratory (SIRRL), Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON  N2L 3G1, Canada; Pasquali, D., DIBRIS, Università di Genova, Genova, GE 16152, Italy; Kothig, A., Social and Intelligent Robotics Research Laboratory (SIRRL), Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON  N2L 3G1, Canada; Rea, F., Department of Robotics, Brain and Cognitive Sciences, Italian Institute of Technology (IIT), Genova, 16163, Italy; Sandini, G., Department of Robotics, Brain and Cognitive Sciences, Italian Institute of Technology (IIT), Genova, 16163, Italy; Sciutti, A., Department of Robotics, Brain and Cognitive Sciences, Italian Institute of Technology (IIT), Genova, 16163, Italy","Trust is essential in human-robot interactions, and in times where machines are yet to be fully reliable, it is important to study how robotic hardware faults can affect the human counterpart. This experiment builds on a previous research that studied trust changes in a game-like scenario with the humanoid robot iCub. Several robot hardware failures (validated in another online study) were introduced in order to measure changes in trust due to the unreliability of the iCub. A total of 68 participants took part in this study. For half of them, the robot adopted a transparent approach, explaining each failure after it happened. Participants' behaviour was also compared to the 61 participants that played the same game with a fully reliable robot in the previous study. Against all expectations, introducing manifest hardware failures does not seem to significantly affect trust, while transparency mainly deteriorates the quality of interaction with the robot. © 2016 IEEE.","acceptability and trust; human-robot collaboration; Social HRI; transparency; unreliability","Agricultural robots; Anthropomorphic robots; Transparency; Hardware failures; Humanoid robot; Online studies; Quality of interaction; Robot hardware; Robotic hardware; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 804388; European Research Council, ERC","Manuscript received February 24, 2021; accepted May 5, 2021. Date of publication May 25, 2021; date of current version June 7, 2021. Researchers of the SIRRL Laboratory at University of Waterloo are supported in part, thanks to funding from the Canada 150 Research Chairs Program. This work has been supported by a Starting Grant from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme G.A. No 804388, wHiSPER. (Corresponding author: Alexander Mois Aroyo) Alexander M. Aroyo and Austin Kothig are with the Social and Intelligent Robotics Research Laboratory (SIRRL), Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada (e-mail: alexander.aroyo@uwaterloo.ca; austin.kothig@uwaterloo.ca).",,,,,,,,,,"Lee, J.D., See, K.A., Trust in automation: Designing for appropriate reliance (2004) Hum. Factors, 46 (1), pp. 50-80; Hancock, P.A., Billings, D.R., Schaefer, K.E., Can you trust your robot? (2011) Ergonom. Des., 19 (3), pp. 24-29; Sandini, G., Mohan, V., Sciutti, A., Morasso, P., Social cognition for human-robot symbiosis-challenges and building blocks (2018) Front. 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Elsevier, pp. 197-208. , Academic Press","Aroyo, A.M.; Department of Electrical and Computer Engineering, Canada; email: alexander.aroyo@uwaterloo.ca",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85107173590
"Filippini C., Spadolini E., Cardone D., Bianchi D., Preziuso M., Sciarretta C., del Cimmuto V., Lisciani D., Merla A.","57205533040;57217130929;54788843200;36542224000;55274452200;57217132874;57217129270;57217137122;24169413000;","Facilitating the Child–Robot Interaction by Endowing the Robot with the Capability of Understanding the Child Engagement: The Case of Mio Amico Robot",2021,"International Journal of Social Robotics","13","4",,"677","689",,10,"10.1007/s12369-020-00661-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086407639&doi=10.1007%2fs12369-020-00661-w&partnerID=40&md5=5e3cbb58b3ea872703a66930d2495822","Department of Neurosciences, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, Chieti, Italy; Next2U s.r.l., Pescara, Italy; Ud’Anet s.r.l., Torrevecchia Teatina, Italy; Lisciani Giochi s.r.l., Teramo, Italy","Filippini, C., Department of Neurosciences, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, Chieti, Italy; Spadolini, E., Next2U s.r.l., Pescara, Italy; Cardone, D., Department of Neurosciences, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, Chieti, Italy; Bianchi, D., Ud’Anet s.r.l., Torrevecchia Teatina, Italy; Preziuso, M., Ud’Anet s.r.l., Torrevecchia Teatina, Italy; Sciarretta, C., Ud’Anet s.r.l., Torrevecchia Teatina, Italy; del Cimmuto, V., Ud’Anet s.r.l., Torrevecchia Teatina, Italy; Lisciani, D., Lisciani Giochi s.r.l., Teramo, Italy; Merla, A., Department of Neurosciences, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, Chieti, Italy, Next2U s.r.l., Pescara, Italy","Social Robots (SRs) are substantially becoming part of modern society, given their frequent use in many areas of application including education, communication, assistance, and entertainment. The main challenge in human–robot interaction is in achieving human-like and affective interaction between the two groups. This study is aimed at endowing SRs with the capability of assessing the emotional state of the interlocutor, by analyzing his/her psychophysiological signals. The methodology is focused on remote evaluations of the subject’s peripheral neuro-vegetative activity by means of thermal infrared imaging. The approach was developed and tested for a particularly challenging use case: the interaction between children and a commercial educational robot, Mio Amico Robot, produced by LiscianiGiochi©. The emotional state classified from the thermal signal analysis was compared to the emotional state recognized by a facial action coding system. The proposed approach was reliable and accurate and favored a personalized and improved interaction of children with SRs. © 2020, The Author(s).","Computational psychophysiology; Emotions; Human-machine interaction; Infrared imaging; Social robot","Agricultural robots; Social robots; Thermography (imaging); Affective interaction; Emotional state; Facial Action Coding System; Human like; Psychophysiological signals; Remote evaluation; Robot interactions; Thermal infrared imaging; Educational robots",,,,,"Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR: 1/06/2016, F/050326/01-02/X32","This study was funded by MIUR PON Project Sensing Robot—n. F/050326/01-02/X32 FONDO PER LA CRESCITA SOSTENIBILE (F.C.S.) Horizon 2020 - PON I&C 2014/2020 (D.M. 1/06/2016). 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J. Soc. Rob.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85086407639
"Kansizoglou I., Misirlis E., Gasteratos A.","57212487280;57225136886;6701673537;","Learning Long-Term Behavior through Continuous Emotion Estimation",2021,"ACM International Conference Proceeding Series",,,,"502","506",,,"10.1145/3453892.3461626","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109269310&doi=10.1145%2f3453892.3461626&partnerID=40&md5=8b8575505560d7661fa6c25503eb441e","Democritus University of Thrace, Greece","Kansizoglou, I., Democritus University of Thrace, Greece; Misirlis, E., Democritus University of Thrace, Greece; Gasteratos, A., Democritus University of Thrace, Greece","Emotions convey concise information regarding an individual's internal state, while in the long-term they can be used to form an opinion about his/her overall personality. The latter can be proved particularly vital in many human-robot interaction tasks, like in the case of an assisted living robotic agent, where the human's mood may in turn require the adaptation of the robot's behavior. As a result, the paper at hand proposes a novel approach enabling an artificial agent to conceive and gradually learn the personality of a human, by tracking his emotional variations throughout their interaction time. To achieve that, the facial landmarks of the subject are extracted and fed into a Deep Neural Network architecture that estimates the two coefficients of human emotions, viz., arousal and valence, as introduced by the broadly known Russell's model. Finally, by creating a dashboard for user-friendly display of our results, we present both momentarily and in the long-term the monitored fluctuations of a person's emotional state. © 2021 ACM.","emotion recognition; Neural networks","Deep learning; Deep neural networks; Network architecture; Artificial agents; Emotion estimation; Emotional state; Facial landmark; Interaction time; Internal state; Long-term behavior; Robotic agents; Human robot interaction",,,,,"T2EDK-01862","This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH - CREATE - INNOVATE grant number [T2EDK-01862].",,,,,,,,,,"Odilon Clement Allognon, S., De S Britto, A., Koerich, A.L., Continuous emotion recognition via deep convolutional autoencoder and support vector regressor (2020) 2020 International Joint Conference on Neural Networks, pp. 1-8; Breaban, A., Van De Kuilen, G., Noussair, C.N., Prudence, emotional state, personality, and cognitive ability (2016) Frontiers in Psychology, 7, p. 1688. , 2016; Charalampous, K., Kostavelis, I., Gasteratos, A., Recent trends in social aware robot navigation: A survey (2017) Robotics and Autonomous Systems, 93, pp. 85-104. , 2017; Ekman, P., Friesen, W.V., O'Sullivan, M., Chan, A., Diacoyanni-Tarlatzis, I., Heider, K., Krause, R., Ricci-Bitti, P.E., Universals and cultural differences in the judgments of facial expressions of emotion (1987) Journal of Personality and Social Psychology, 53, p. 712. , 1987; Ioffe, S., Szegedy, C., Batch normalization: Accelerating deep network training by reducing internal covariate shift (2015) International Conference on Machine Learning, pp. 448-456; Jacobs, E., Broekens, J., Jonker, C., Emergent dynamics of joy, distress, hope and fear in reinforcement learning agents (2014) Adaptive Learning Agents Workshop at AAMAS2014; Kansizoglou, I., Bampis, L., Gasteratos, A., An active learning paradigm for online audio-visual emotion recognition (2019) IEEE Transactions on Affective Computing, , 2019; Kansizoglou, I., Bampis, L., Gasteratos, A., (2020) Deep Feature Space: A Geometrical Perspective, , 2020; Kazemi, V., Sullivan, J., One millisecond face alignment with an ensemble of regression trees (2014) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867-1874; Kingma, D.P., Ba, J., Adam: A method for stochastic optimization (2015) ICLR; Lee, H., Kang, B., Continuous emotion estimation of facial expressions on jaffe and ck+ datasets for human-robot interaction (2020) Intelligent Service Robotics, 13, pp. 15-27. , 2020; Mollahosseini, A., Hasani, B., Mahoor, M.H., Affectnet: A database for facial expression, valence, and arousal computing in the wild (2017) IEEE Transactions on Affective Computing, 10, pp. 18-31. , 2017; Nocentini, O., Fiorini, L., Acerbi, G., Sorrentino, A., Mancioppi, G., Cavallo, F., A survey of behavioral models for social robots (2019) Robotics, 8, p. 54. , 2019; Ringeval, F., Sonderegger, A., Sauer, J., Lalanne, D., Introducing the recola multimodal corpus of remote collaborative and affective interactions (2013) 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1-8; Rossi, S., Ferland, F., Tapus, A., User profiling and behavioral adaptation for hri: A survey (2017) Pattern Recognition Letters, 99, pp. 3-12. , 2017; Russell, J.A., Feldman Barrett, L., Core affect, prototypical emotional episodes, and other things called emotion: Dissecting the elephant (1999) Journal of Personality and Social Psychology, 76, p. 805. , 1999; Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., Dropout: A simple way to prevent neural networks from overfitting (2014) The Journal of Machine Learning Research, 15, pp. 1929-1958. , 2014; Tsintotas, K.A., Bampis, L., Gasteratos, A., Probabilistic appearance-based place recognition through bag of tracked words (2019) IEEE Robotics and Automation Letters, 4, pp. 1737-1744. , 2019; Tzirakis, P., Trigeorgis, G., Nicolaou, M.A., Schuller, B.W., Zafeiriou, S., End-to-end multimodal emotion recognition using deep neural networks (2017) IEEE Journal of Selected Topics in Signal Processing, 11, pp. 1301-1309. , 2017; Viola, P., Jones, M., Rapid object detection using a boosted cascade of simple features (2001) Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001. I-I",,,"The College of Engineering, University of Texas at Arlington (UTA);The Department of Computer Science and Engineering at UTA;The Human Centered Computing Laboratory (Heracleia) at UTA;The iPerform Industry-University NSF Center at UTA;The National Center for Scientific Research (NCSR)-Demokritos;The National Science Foundation (NSF)","Association for Computing Machinery","14th ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2021","29 June 2021 through 1 July 2021",,169934,,9781450387927,,,"English","ACM Int. Conf. Proc. Ser.",Conference Paper,"Final","",Scopus,2-s2.0-85109269310
"Cila N., Zaga C., Lupetti M.L.","55078412200;56429717300;56685107500;","Learning from robotic artefacts: A quest for strong concepts in Human-Robot Interaction",2021,"DIS 2021 - Proceedings of the 2021 ACM Designing Interactive Systems Conference: Nowhere and Everywhere",,,,"1356","1365",,,"10.1145/3461778.3462095","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110114214&doi=10.1145%2f3461778.3462095&partnerID=40&md5=9a1181c58904de666c21c253e89d2d00","Department of Human-Centered Deign, Delft University of Technology, Netherlands; Human-Centred Design Group, University of Twente, Netherlands","Cila, N., Department of Human-Centered Deign, Delft University of Technology, Netherlands; Zaga, C., Human-Centred Design Group, University of Twente, Netherlands; Lupetti, M.L., Department of Human-Centered Deign, Delft University of Technology, Netherlands","This paper is a methodological replication of Barendregt et al. [11], who urged Child-Computer Interaction field to embrace Intermediate Level Knowledge as a meaningful and valid way of generating knowledge. We extend this epistemological gap to the Human-Robot Interaction (HRI). Currently, artefact-centered papers - papers that present the development of an artefact - seem to be one of the primary ways that the HRI field generates knowledge. In this paper, we made an analysis of all papers presented at the HRI Conference from 2006 to 2020. Our results indicate that the 41,2 % of the papers were artefact-centered; and the impact of them, measured in the number of citations, was significantly lower than other kinds of papers. We used 23 artefact-centered papers to formulate two strong concepts and investigate how the foundational design epistemology about intermediate-level knowledge and RtD can contribute to other design-related disciplines to produce useful and valuable knowledge. © 2021 Owner/Author.","Artefact-centered paper; Epistemology; Human-Robot Interaction; Intermediate-level knowledge; Strong concepts","Educational robots; Man machine systems; Robotics; Child-computer interactions; Human robot Interaction (HRI); Intermediate level; Human robot interaction",,,,,,,,,,,,,,,,"Orn Adalgeirsson, S., Breazeal, C., Mebot: A robotic platform for socially embodied telepresence (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI). 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ACM Des. Interact. Syst. Conf.: Nowhere Everywhere",Conference Paper,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85110114214
"Yakob M.Y.B., Baharuddin M.Z.B., Khairudin A.R.M., Karim M.H.B.A.","57226750969;35329255600;57204805520;57226741989;","Telecontrol of Prosthetic Robot Hand Using Myo Armband",2021,"2021 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2021 - Proceedings",,,"9495919","288","293",,,"10.1109/I2CACIS52118.2021.9495919","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112570063&doi=10.1109%2fI2CACIS52118.2021.9495919&partnerID=40&md5=c4edcb86d3c6fbcfbacaaac6ddcb282c","Universiti Tenaga Nasional, Master of Electrical, College of Graduate and Studies, Selangor, Malaysia; Universiti Tenaga Nasional, College of Engineering, Department of Electrical and Electronics Engineering, Selangor, Malaysia; Universiti Tenaga Nasional, College of Engineering, Department of Foundation and Diploma Studies Engineering, Selangor, Malaysia","Yakob, M.Y.B., Universiti Tenaga Nasional, Master of Electrical, College of Graduate and Studies, Selangor, Malaysia; Baharuddin, M.Z.B., Universiti Tenaga Nasional, College of Engineering, Department of Electrical and Electronics Engineering, Selangor, Malaysia; Khairudin, A.R.M., Universiti Tenaga Nasional, College of Engineering, Department of Foundation and Diploma Studies Engineering, Selangor, Malaysia; Karim, M.H.B.A., Universiti Tenaga Nasional, College of Engineering, Department of Foundation and Diploma Studies Engineering, Selangor, Malaysia","Bionic prosthetic hands are widely utilised to support people who have upper limb amputations and incapability by making it movable and controllable. However, the price of bionic prosthetic arms in the current market is quite expensive, hence limiting people from owning it. Nevertheless, Myo Armband appears to provide fine hand movement and sensitive to input, making it a suitable alternative to gesture-based wireless control. Hence, in this project, it will be used to read and control signals for the 3d printed robotic prosthetic arm. The control of prosthetic robot arm is achieved by sensing the gestures of amputees' upper limb. Bluetooth is used for the wireless communication between Myo armband and the computer, while communication between computer and the prosthetic robot arm used serial communication. It is observed that fist gestures from Myo armband controlling the prosthetic robot hand have been successfully achieved. The proposed configuration can be used in human-robot interaction and telecontrol studies. © 2021 IEEE.","3D Robot Hand; Electromyography; Myo Armband; Prosthetic Robot Hand","3D printers; Automation; Bionics; End effectors; Human robot interaction; Intelligent systems; Process control; Robotic arms; Control signal; Hand movement; Prosthetic arm; Prosthetic hands; Serial communications; Upper limb amputation; Wireless communications; Wireless control; Artificial limbs",,,,,"Collaborative Research in Engineering, Science and Technology Centre, CREST: 1-6, BOLD2020, RJO 10517844/083","This work was supported by the Collaborative Research in Engineering, Science & Technology (CREST) and BOLD2020 grant under (RJO 10517844/083), Development of 3D Printed Stem/Steam Automation Project for Schools and Foundation via Rapid Prototyping REFERENCES [1] S. J. Gutiérrez, E. Cardiel and P. R. 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[Online]. [Accessed 19 December 2018]; Lee, Y.S., Kim, D., Kim, H.J., Arm motion estimation algorithm using myo armband (2017) 2017 First IEEE International Conference on Robotic Computing, 1 (3), pp. 376-381; Wiedmer, X., (2017) Servomotor's List, , http://new.aranacorp.com/en/servomotors-list/, Arana Corp, 23 August. [Online]. [Accessed 12 January 2019]",,,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2021","26 June 2021",,170757,,9781665403436,,,"English","IEEE Int. Conf. Autom. Control Intell. Syst., I2CACIS - Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85112570063
"Elbeleidy S., Rosen D., Liu D., Shick A., Williams T.","57208691436;57226012063;57211683310;37011551100;7404172627;","Analyzing Teleoperation Interface Usage of Robots in Therapy for Children with Autism",2021,"Proceedings of Interaction Design and Children, IDC 2021",,,,"112","118",,1,"10.1145/3459990.3460715","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110164075&doi=10.1145%2f3459990.3460715&partnerID=40&md5=981d222b0fe4e734e83c63cf1dcfb6dc","MIRRORLab Colorado School of Mines, United States; Atlas Institute University of Colorado Boulder, United States","Elbeleidy, S., MIRRORLab Colorado School of Mines, United States; Rosen, D., MIRRORLab Colorado School of Mines, United States; Liu, D., Atlas Institute University of Colorado Boulder, United States; Shick, A., Atlas Institute University of Colorado Boulder, United States; Williams, T., MIRRORLab Colorado School of Mines, United States","Therapist-operated robots can play a uniquely impactful role in helping children with autism practice and acquire social skills. While extensive research within Human-Robot Interaction has focused on teleoperation interfaces for robots in general, little work has been done on teleoperation interface design for robots in the context of therapy for children with autism. Moreover, while clinical research has shown the positive impact robots can have on children with autism, much of that research has been performed in a controlled environment, with little understanding of the way these robots are used in practice. We analyze archival data of therapists teleoperating robots as part of their regular therapy sessions, to (1) determine common themes and difficulties in therapists' use of teleoperation interfaces, and (2) provide design recommendations to improve therapists' overall experience. We believe that following these recommendations will help maximize the effectiveness of therapy for children with autism when using Socially Assistive Robotics and the scale at which robots can be deployed in this domain. © 2021 ACM.","Autism; Case Studies; Social Skills Training; Socially Assistive Robotics","Clinical research; Diseases; Economic and social effects; Machine design; Remote control; Archival data; Assistive robotics; Children with autisms; Controlled environment; Design recommendations; Social skills; Teleoperating robots; Teleoperation interface; Social robots",,,,,,,,,,,,,,,,"Allspaw, J., Roche, J., Remotely teleoperating a humanoid robot to perform fne motor tasks with virtual reality (2018) Proceedings of the 1st International Workshop on Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI); Begum, M., Serna, R.W., Kontak, D., Allspaw, J., Kuczynski, J., Yanco, H.A., Suarez, J., Measuring the efcacy of robots in autism therapy: How informative are standard hri metrics' (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction., pp. 335-342; Begum, M., Serna, R.W., Yanco, H.A., Are robots ready to deliver autism interventions? 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A survey on the roles and benefts of social robots in the therapy of children with autism (2013) International Journal of Social Robotics, 5 (4), pp. 593-618. , 2013; Chaminade, T., Da Fonseca, D., Rosset, D., Lutcher, E., Cheng, G., Deruelle, C., Fmri study of young adults with autism interacting with a humanoid robot (2012) 2012 Ieee RO-MAN: The 21st Ieee International Symposium on Robot and Human Interactive Communication, pp. 380-385; Chung, E.Y.-H., Robotic intervention program for enhancement of social engagement among children with autism spectrum disorder (2019) Journal of Developmental and Physical Disabilities, 31 (4), pp. 419-434. , 2019; Chung, E.Y.-H., Robot-mediated social skill intervention programme for children with autism spectrum disorder: An aba time-series study (2020) International Journal of Social Robotics, pp. 1-13. , 2020); Clabaugh, C., Becerra, D., Deng, E., Ragusa, G., Mataric, M., Month-long, in-home case study of a socially assistive robot for children with autism spectrum disorder (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction., pp. 87-88; Clabaugh, C.E., Mahajan, K., Jain, S., Pakkar, R., Becerra, D., Shi, Z., Deng, E., Mataric, M., Long-term personalization of an in-home socially assistive robot for children with autism spectrum disorders (2019) Frontiers in Robotics and Ai, 6, p. 110. , 2019; Costa, A.P., Charpiot, L., Rodríguez Lera, F., Ziafati, P., Nazarikhorram, A., Torre Der, L.Van, Stefgen, G., A comparison between a person and a robot in the attention, imitation, and repetitive and stereotypical behaviors of children with autism spectrum disorder (2018) Proceedings Workshop on Social Human-robot Interaction of Human-care Service Robots at HRI2018; Damm, O., Malchus, K., Jaecks, P., Krach, S., Paulus, F., Naber, M., Jansen, A., Stenneken, P., Diferent gaze behavior in human-robot interaction in asperger's syndrome: An eye-tracking study (2013) 2013 Ieee RO-MAN, pp. 368-369; Dautenhahn, K., Robots as social actors: Aurora and the case of autism (1999) Proc. CT99, the Third International Cognitive Technology Conference, August, San Francisco, 359, p. 374. , Citeseer; Dawe, J., Sutherland, C., Barco, A., Broadbent, E., Can social robots help children in healthcare contexts? A scoping review (2019) Bmj Paediatrics Open, 3, p. 1. , 2019; Diehl, J.J., Schmitt, L.M., Villano, M., Crowell, C.R., The clinical use of robots for individuals with autism spectrum disorders: A critical review (2012) Research in Autism Spectrum Disorders, 6 (1), pp. 249-262. , 2012; Feil-Seifer, D., Mataric, M.J., Towards the integration of socially assistive robots into the lives of children with asd (2009) International Conference on Human-Robot Interaction Workshop on Societal Impact: How Socially Accepted Robots Can Be Integrated in Our Society, 21; Giannopulu, I., Multimodal cognitive nonverbal and verbal interactions: The neurorehabilitation of autistic children via mobile toy robots (2013) Iaria International Journal of Advances in Life Sciences, 5. , 2013; Grice, P.M., Kemp, C.C., Assistive mobile manipulation: Designing for operators with motor impairments (2016) Rss 2016 Workshop on Socially and Physically Assistive Robotics for Humanity; (2021) Fine Art Miracles, , https://fneartmiracles.com, Fine Art Miracles Inc; Ismail, L.I., Verhoeven, T., Dambre, J., Wyfels, F., Leveraging robotics research for children with autism: A review (2019) International Journal of Social Robotics, 11 (3), pp. 389-410. , 2019; Kabacinska, K., Prescott, T.J., Robillard, J.M., Socially assistive robots as mental health interventions for children: A scoping review (2020) International Journal of Social Robotics, pp. 1-17. , 2020); Kenny, L., Hattersley, C., Molins, B., Buckley, C., Povey, C., Pellicano, E., Which terms should be used to describe autism? Perspectives from the uk autism community (2016) Autism, 20 (4), pp. 442-462. , 2016; Kim, E.S., Berkovits, L.D., Bernier, E.P., Leyzberg, D., Shic, F., Paul, R., Scassellati, B., Social robots as embedded reinforcers of social behavior in children with autism (2013) Journal of Autism and Developmental Disorders, 43 (5), pp. 1038-1049. , 2013; Kot, T., Novák, P., Utilization of the oculus rift hmd in mobile robot teleoperation (2014) Applied Mechanics and Materials, 555, pp. 199-208. , Trans Tech Publ; Le Goc, M., Kim, L.H., Parsaei, A., Fekete, J.-D., Dragicevic, P., Follmer, S., Zooids: Building blocks for swarm user interfaces (2016) Proceedings of the 29th Annual Symposium on User Interface Software and Technology., pp. 97-109; Lichtenstern, M., Angermann, M., Frassl, M., Berthold, G., Julian, B.J, Rus, D., Pose and paste-an intuitive interface for remote navigation of a multi-robot system (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1632-1639; Lipton, J.I., Fay, A.J., Rus, D., Baxter's homunculus: Virtual reality spaces for teleoperation in manufacturing (2017) Ieee Robotics and Automation Letters, 3 (1), pp. 179-186. , 2017; Mataric, M.J., Scassellati, B., Socially assistive robotics (2016) Springer Handbook of Robotics, pp. 1973-1994. , Springer; Michaud, F., Salter, T., Duquette, A., Mercier, H., Lauria, M., Larouche, H., Larose, F., Assistive technologies and child-robot interaction (2007) Aaai Spring Symposium on Multidisciplinary Collaboration for Socially Assistive Robotics; (2021) PEERbots, , https://peerbots.org, PEERbots; Pennisi, P., Tonacci, A., Tartarisco, G., Billeci, L., Ruta, L., Gangemi, S., Pioggia, G., Autism and social robotics: A systematic review (2016) Autism Research, 9 (2), pp. 165-183. , 2016; Pioggia, G., Sica, M.L., Ferro, M., Igliozzi, R., Muratori, F., Ahluwalia, A., De Rossi, D., Human-robot interaction in autism: Face, an android-based social therapy (2007) RO-MAN 2007-the 16th Ieee International Symposium on Robot and Human Interactive Communication, pp. 605-612; Rabbitt, S.M., Kazdin, A.E., Scassellati, B., Integrating socially assistive robotics into mental healthcare interventions: Applications and recommendations for expanded use (2015) Clinical Psychology Review, 35, pp. 35-46. , 2015; Robins, B., Dautenhahn, K., Te Boekhorst, R., Billard, A., Efects of repeated exposure to a humanoid robot on children with autism (2004) Designing a More Inclusive World, pp. 225-236. , Springer; (2021) Movia Robotics, , https://moviarobotics.com, Movia Robotics; Scassellati, B., Admoni, H., Mataric, M., Robots for use in autism research (2012) Annual Review of Biomedical Engineering, 14. , 2012; Scassellati, B., Boccanfuso, L., Huang, C., Mademtzi, M., Qin, M., Salomons, N., Ventola, P., Shic, F., Improving social skills in children with asd using a long-term, in-home social robot (2018) Science Robotics, 3, p. 21. , 2018; Shamsuddin, S., Yussof, H., Mohamed, S., Akhtar Hanapiah, F., Idzhar Ismail, L., Stereotyped behavior of autistic children with lower iq level in hri with a humanoid robot (2013) 2013 Ieee Workshop on Advanced Robotics and Its Social Impacts, pp. 175-180; Shick, A., Romibo robot project: An open-source efort to develop a low-cost sensory adaptable robot for special needs therapy and education (2013) Acm Siggraph 2013 Studio Talks (Anaheim, California) (SIGGRAPH '13), 1p. , https://doi.org/10.1145/2503673.2503689, Association for Computing Machinery, New York, NY, USA; So, W.-C., Wong, M.K.-Y., Lam, W.-Y., Cheng, C.-H., Ku, S.-Y., Lam, K.-Y., Huang, Y., Wong, W.-L., Who is a better teacher for children with autism? Comparison of learning outcomes between robot-based and human-based interventions in gestural production and recognition (2019) Research in Developmental Disabilities, 86, pp. 62-75. , 2019; Steinfeld, A., Interface lessons for fully and semi-Autonomous mobile robots (2004) Ieee International Conference on Robotics and Automation, 2004. Proceedings. ICRA'04. 2004, 3, pp. 2752-2757; Tsui, K.M., Dalphond, J.M., Brooks, D.J., Medvedev, M.S., McCann, E., Allspaw, J., Kontak, D., Yanco, H.A., Accessible human-robot interaction for telepresence robots: A case study. Paladyn (2015) Journal of Behavioral Robotics, 1. , open-issue, (2015); Wainer, J., Ferrari, E., Dautenhahn, K., Robins, B., The efectiveness of using a robotics class to foster collaboration among groups of children with autism in an exploratory study (2010) Personal and Ubiquitous Computing, 14 (5), pp. 445-455. , 2010; Wainer, J., Robins, B., Amirabdollahian, F., Dautenhahn, K., Using the humanoid robot kaspar to autonomously play triadic games and facilitate collaborative play among children with autism (2014) Ieee Transactions on Autonomous Mental Development, 6 (3), pp. 183-199. , 2014",,,"BRIDGES;EUGAIN;Hellenic Republic - National and Kapodistrian University of Athens;LUMS","Association for Computing Machinery, Inc","2021 ACM Interaction Design and Children, IDC 2021","24 June 2021 through 30 June 2021",,169893,,9781450384520,,,"English","Proc. Interact. Des. Child., IDC",Conference Paper,"Final","",Scopus,2-s2.0-85110164075
"White N.T., Cagiltay B., Michaelis J.E., Mutlu B.","57226025784;57217587286;56734228100;15060220700;","Designing Emotionally Expressive Social Commentary to Facilitate Child-Robot Interaction",2021,"Proceedings of Interaction Design and Children, IDC 2021",,,,"314","325",,,"10.1145/3459990.3460714","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110077058&doi=10.1145%2f3459990.3460714&partnerID=40&md5=08e4c0e791f4ffd4eede8c6dc0fbb605","Computer Sciences University of Wisconsin, Madison, United States; Department of Computer Sciences, University of Wisconsin, Madison, United States; University of Illinois at Chicago, United States; Department of Computer Sciences, University of Wisconsin-Madison, United States","White, N.T., Computer Sciences University of Wisconsin, Madison, United States; Cagiltay, B., Department of Computer Sciences, University of Wisconsin, Madison, United States; Michaelis, J.E., University of Illinois at Chicago, United States; Mutlu, B., Department of Computer Sciences, University of Wisconsin-Madison, United States","Emotion expression in human-robot interaction has been widely explored, however little is known about how such expressions should be coupled with feelings and opinions expressed by a social robot. We explored how 12 children experienced emotionally expressive social commentaries from a reading companion robot across five interaction styles that differed in their non-verbal emotional expressiveness and opinionated conversational styles (neutral, divergent, or convergent opinions). We found that, while the robot's opinions and non-verbal emotion expressions affected children's experiences with the robot, the speech content of the commentaries was the more prominent factor in their experience. Additionally, children differed in their perceptions of social commentary: while some expressed a sense of connection-making with the robot's self-disclosure commentaries, others felt distracted by them or felt like the robot was off-topic. We recommend designers pay particular attention to the robot's speech content and consider children's individual differences in designing emotional and opinionated speech. © 2021 ACM.","Emotion Expressions; Reading Companion Robots; Robot Opinions; Social Commentary","Felt; Felts; Child-robot interactions; Companion robot; Emotion expression; Individual Differences; Interaction styles; Self-disclosure; Speech content; Social robots",,,,,"National Science Foundation, NSF: 1906854","We would like to thank our colleagues Olivia Zhao and Emmanuel Senft for their valuable feedback throughout the design and execution of this study. We would also like to thank Gigi Mitchell for her help in providing the study resources to the participants. This work was supported by the National Science Foundation under award 1906854, “STEM-Mates: Designing Companion Robots with Socially Situated Interest Scaffolds for Informal, In-home STEM Learning.”",,,,,,,,,,"Ahmad, M.I., Mubin, O., Orlando, J., Adaptive social robot for sustaining social engagement during long-term children-robot interaction (2017) International Journal of Human-Computer Interaction, 33 (12), pp. 943-962. , 2017; Ahmad, M.I., Mubin, O., Shahid, S., Orlando, J., Robot's adaptive emotional feedback sustains children's social engagement and promotes their vocabulary learning: A long-term child-robot interaction study (2019) Adaptive Behavior, 27 (4), pp. 243-266. , 2019; Ali, S., Moroso, T., Breazeal, C., Can children learn creativity from a social robot? 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Ieee, pp. 449-455; Yadollahi, E., Johal, W., Paiva, A., Dillenbourg, P., When deictic gestures in a robot can harm child-robot collaboration (2018) Proceedings of the 17th Acm Conference on Interaction Design and Children., pp. 195-206; Zhao, F., White, N., Cagiltay, B., Niedenthal, P., Michaelis, J., Mutlu, B., Designing emotional expressions for a reading companion robot Afective Science. Society for Afective Science, , in press",,,"BRIDGES;EUGAIN;Hellenic Republic - National and Kapodistrian University of Athens;LUMS","Association for Computing Machinery, Inc","2021 ACM Interaction Design and Children, IDC 2021","24 June 2021 through 30 June 2021",,169893,,9781450384520,,,"English","Proc. Interact. Des. Child., IDC",Conference Paper,"Final","",Scopus,2-s2.0-85110077058
"Yuan F., Klavon E., Liu Z., Lopez R.P., Zhao X.","57222106767;57224198801;57222116271;16307485800;55705069200;","A Systematic Review of Robotic Rehabilitation for Cognitive Training",2021,"Frontiers in Robotics and AI","8",,"605715","","",,4,"10.3389/frobt.2021.605715","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107229517&doi=10.3389%2ffrobt.2021.605715&partnerID=40&md5=d06f48d8f29a468daf0bd1140fb4e95d","Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States; School of Nursing, MGH Institute of Health Professions, Boston, MA, United States","Yuan, F., Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States; Klavon, E., Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States; Liu, Z., Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States; Lopez, R.P., School of Nursing, MGH Institute of Health Professions, Boston, MA, United States; Zhao, X., Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States","A large and increasing number of people around the world experience cognitive disability. Rehabilitation robotics has provided promising training and assistance approaches to mitigate cognitive deficits. In this article, we carried out a systematic review on recent developments in robot-assisted cognitive training. We included 99 articles in this work and described their applications, enabling technologies, experiments, and products. We also conducted a meta analysis on the articles that evaluated robot-assisted cognitive training protocol with primary end users (i.e., people with cognitive disability). We identified major limitations in current robotics rehabilitation for cognitive training, including the small sample size, non-standard measurement of training and uncontrollable factors. There are still multifaceted challenges in this field, including ethical issues, user-centered (or stakeholder-centered) design, the reliability, trust, and cost-effectiveness, personalization of the robot-assisted cognitive training system. Future research shall also take into consideration human-robot collaboration and social cognition to facilitate a natural human-robot interaction. © Copyright © 2021 Yuan, Klavon, Liu, Lopez and Zhao.","cognitive disability; human-robot interaction; multimodal interaction; rehabilitation robotics; robot-assisted cognitive training; socially assistive robotics",,,,,,,,,,,,,,,,,"Ab Aziz, A., Ahmad, F., Yusof, N., Ahmad, F.K., Yusof, S.A.M., Designing a robot-assisted therapy for individuals with anxiety traits and states (2015) 2015 International Symposium on Agents, Multi-Agent Systems and Robotics (ISAMSR), pp. 98-103. , Putrajaya, IEEE; Abdollahi, H., Mollahosseini, A., Lane, J.T., Mahoor, M.H., A pilot study on using an intelligent life-like robot as a companion for elderly individuals with dementia and depression (2017) 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pp. 541-546. , Birmingham, IEEE; Adam, C., Johal, W., Pellier, D., Fiorino, H., Pesty, S., Social human-robot interaction: a new cognitive and affective interaction-oriented architecture (2016) International Conference on Social Robotics, pp. 253-263. , Springer; Agrigoroaie, R., Ferland, F., Tapus, A., The enrichme project: Lessons learnt from a first interaction with the elderly (2016) International Conference on Social Robotics, pp. 735-745. , Kansas City, MO, Springer; Agrigoroaie, R.M., Tapus, A., Developing a healthcare robot with personalized behaviors and social skills for the elderly (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 589-590. , Christchurch, IEEE; Ahn, H.S., Lee, M.H., Broadbent, E., MacDonald, B.A., Is entertainment services of a healthcare service robot for older people useful to young people? 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"Tziafas G., Kasaei H.","57223769158;6602182120;","Few-Shot Visual Grounding for Natural Human-Robot Interaction",2021,"2021 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2021",,,"9429801","50","55",,1,"10.1109/ICARSC52212.2021.9429801","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107144568&doi=10.1109%2fICARSC52212.2021.9429801&partnerID=40&md5=db487d53ef790f79264044272c691c69","University of Groningen, Department of Artificial Intelligence, Netherlands","Tziafas, G., University of Groningen, Department of Artificial Intelligence, Netherlands; Kasaei, H., University of Groningen, Department of Artificial Intelligence, Netherlands","Natural Human-Robot Interaction (HRI) is one of the key components for service robots to be able to work in human-centric environments. In such dynamic environments, the robot needs to understand the intention of the user to accomplish a task successfully. Towards addressing this point, we propose a software architecture that segments a target object from a crowded scene, indicated verbally by a human user. At the core of our system, we employ a multi-modal deep neural network for visual grounding. Unlike most grounding methods that tackle the challenge using pre-Trained object detectors via a two-stepped process, we develop a single stage zero-shot model that is able to provide predictions in unseen data. We evaluate the performance of the proposed model on real RGB-D data collected from public scene datasets. Experimental results showed that the proposed model performs well in terms of accuracy and speed, while showcasing robustness to variation in the natural language input. © 2021 IEEE.",,"Agricultural robots; Deep neural networks; Man machine systems; Object detection; Dynamic environments; Grounding methods; Human robot Interaction (HRI); Human-centric; Natural languages; Object detectors; Service robots; Target object; Human robot interaction",,,,,,,,,,,,,,,,"International Conference on Robotics and Automation, Icra 2019, Montreal, QC, Canada, May 20-24, 2019, 2019; Chen, K., Kovvuri, R., Nevatia, R., Query-guided regression network with context policy for phrase grounding (2017) CoRR, abs/1708.01676; Escalante, H.J., Hernandez, C.A., Gonzalez, J.A., Lopez-Lopez, A., Montes, M., Morales, E.F., Enrique Sucar, L., Grubinger, M., (2010) The Segmented and Annotated Iapr TC-12 Benchmark. Comput. Vis. 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Chapter Contrast Limited Adaptive Histogram Equalization, pp. 474-485. , Academic Press Professional, Inc., San Diego, CA, USA",,"Santos V.Lau N.Neto P.Lopes A.C.","IEEE Portugal Section;IEEE Portugal Section RA Chapter;Sociedade Portuguesa de Robotica (SPR)","Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2021","28 April 2021 through 29 April 2021",,169060,,9781665431989,,,"English","IEEE Int. Conf. Autonomous Robot Syst. Competitions, ICARSC",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85107144568
"Lutz C., Tamò-Larrieux A.","56201845000;57208304288;","Do Privacy Concerns About Social Robots Affect Use Intentions? Evidence From an Experimental Vignette Study",2021,"Frontiers in Robotics and AI","8",,"627958","","",,,"10.3389/frobt.2021.627958","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105543912&doi=10.3389%2ffrobt.2021.627958&partnerID=40&md5=64b1cd48da14e4ebdf19d457b5eb738f","Nordic Centre for Internet and Society, BI Norwegian Business School, Oslo, Norway; Institute for Work and Employment Research, University of St. Gallen, St. Gallen, Switzerland","Lutz, C., Nordic Centre for Internet and Society, BI Norwegian Business School, Oslo, Norway; Tamò-Larrieux, A., Institute for Work and Employment Research, University of St. Gallen, St. Gallen, Switzerland","While the privacy implications of social robots have been increasingly discussed and privacy-sensitive robotics is becoming a research field within human–robot interaction, little empirical research has investigated privacy concerns about robots and the effect they have on behavioral intentions. To address this gap, we present the results of an experimental vignette study that includes antecedents from the privacy, robotics, technology adoption, and trust literature. Using linear regression analysis, with the privacy-invasiveness of a fictional but realistic robot as the key manipulation, we show that privacy concerns affect use intention significantly and negatively. Compared with earlier work done through a survey, where we found a robot privacy paradox, the experimental vignette approach allows for a more realistic and tangible assessment of respondents' concerns and behavioral intentions, showing how potential robot users take into account privacy as consideration for future behavior. We contextualize our findings within broader debates on privacy and data protection with smart technologies. © Copyright © 2021 Lutz and Tamò-Larrieux.","privacy; privacy paradox; social influence; social robots; survey; trust",,,,,,"Universität St. Gallen, HSG: 1031564; Norges Forskningsråd: 275347; Universität Zürich, UZH","CL received funding from the Research Council of Norway under Grant Agreement 275347 Future Ways of Working in the Digital Economy. AT-L received funding from the Digital Society Initiative fellowship (University of Zurich, Switzerland) and the International Postdoctoral Fellowship Grant (University of St. Gallen, Switzerland; Project Number 1031564).",,,,,,,,,,"Acharya, A., (2015) Are we ready for driver-less vehicles? Security vs. privacy – A social perspective, , https://arxiv.org/abs/1412.5207, Available online at; Acquisti, A., Grossklags, J., Privacy and rationality in individual decision making (2005) IEEE Secur. 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Soc, 16, pp. 479-500","Lutz, C.; Nordic Centre for Internet and Society, Norway; email: christoph.lutz@bi.no",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85105543912
"Pipitone A., Chella A.","35748950600;35562825400;","What robots want? Hearing the inner voice of a robot",2021,"iScience","24","4","102371","","",,2,"10.1016/j.isci.2021.102371","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105269325&doi=10.1016%2fj.isci.2021.102371&partnerID=40&md5=7bd7b997d9bbda461550f8c8402dd980","Department of Engineering, University of Palermo, Viale delle Scienze, Palermo, Italy; ICAR CNR, Via Ugo La Malfa, Palermo, Italy","Pipitone, A., Department of Engineering, University of Palermo, Viale delle Scienze, Palermo, Italy; Chella, A., Department of Engineering, University of Palermo, Viale delle Scienze, Palermo, Italy, ICAR CNR, Via Ugo La Malfa, Palermo, Italy","The inner speech is thoroughly studied in humans, and it represents an interdisciplinary research issue involving psychology, neuroscience, and pedagogy. A few papers only, mostly theoretical, analyze the role of inner speech in robots. The present study investigates the potential of the robot's inner speech while cooperating with human partners. A cognitive architecture is designed and integrated with standard robot routines into a complex framework. Two threads of interaction are discussed by setting the robot operations with and without inner speech. Thanks to the robotic self-dialog, the partner can easily trace the robot's processes. Moreover, the robot can better solve conflicts leading to successful goal achievements. The results show that functional and transparency requirements, according to the international standards ISO/TS:2016 and COMEST/Unesco for collaborative robots, are better met when inner speech accompanies human-robot interaction. The inner speech could be applied in many robotics contexts, such as learning, regulation, and attention. © 2021 The Author(s)","Cognitive Neuroscience; Human-Centered Computing; Psychology; Robotics",,,,,,"Air Force Office of Scientific Research, AFOSR: FA9550-19-1-7025","This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-19-1-7025 .",,,,,,,,,,"Alderson-Day, B., Fernyhough, C., Inner speech: development, cognitive functions, phenomenology, and neurobiology (2015) Psychol. Bull., 141, pp. 931-965; Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y., An integrated theory of the mind (2004) Psychol. Rev., 111, pp. 1036-1060; Anderson, J.R., Matessa, M., Lebiere, C., Act-r: a theory of higher level cognition and its relation to visual attention (1997) Human Comput. Interact., 12 (4), pp. 439-462; Beazley, M.B., Glass, C.R., Chambless, D.L., Arnkoff, D.B., Cognitive self-statements in social phobia: a comparison across three types of social situations (2001) Cogn. Ther. Res., 25, pp. 781-799; Chella, A., Pipitone, A., Morin, A., Racy, F., Developing self-awareness in robots via inner speech (2020) Front. Robot. AI, 7, p. 16; Report of Comest on Robotics Ethics (2017), World Commission on the Ethics of Scientific Knowledge and Technology SHS/YES/COMEST-10/17/2 REV; Emerson, M., Miyake, A., The role of inner speech in task switching: a dual-task investigation (2003) J. Mem. Lang., 48, pp. 148-168; Gade, M., Paelecke, M., Talking matters - evaluative and motivational inner speech use predicts performance in conflict tasks (2019) Sci. Rep., 9 (1), p. 9531; Howard, D., Riek, L., A Code of Ethics for the Human-Robot Interaction Profession, (We Robot 2014) (2015), pp. 1-10; Robots and Robotic Devices (2016); Morin, A., Characteristics of an effective internal dialogue in the acquisition of self-information (1995) Imagin. Cogn. Personal., 15, pp. 45-58; Morin, A., Self-awareness deficits following loss of inner speech: Dr. jill bolte taylor's case study (2009) Conscious. Cogn., 18, pp. 524-529; Morin, A., Self-awareness part 1: definitions, measures, effects, function, and antecedents (2011) Soc. Personal. Psychol. Compass, 5, pp. 807-823; Morin, A., Inner speech (2012) Encyclopedia of Human Behavior, pp. 436-443. , W. Hirstein San Diego Elsevier; Quigley, M., Conley, K., Gerkey, B.P., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., Ros: An Open-Source Robot Operating System (2009), ICRA Workshop on Open Source Software; Tappan, M., Mediated moralities: sociocultural approaches to moral development (2005) Handbook of Moral Development, p. 24. , Psychology Press; Vygotsky, L., Thought and Language (1962), MIT; Webster, M., Western, D., Araiza-Illan, D., Dixon, C., Eder, K., Fisher, M., Pipe, A.G., An Assurance-Based Approach to Verification and Validation of Human-Robot Teams (2016), abs/1608.07403, CoRR","Pipitone, A.; Department of Engineering, Viale delle Scienze, Italy; email: arianna.pipitone@unipa.it",,,"Elsevier Inc.",,,,,25890042,,,,"English","iScience",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85105269325
"Hasegawa N., Takahashi Y.","7203075504;57197248202;","Control of soap bubble ejection robot using facial expressions",2021,"International Journal of Manufacturing, Materials, and Mechanical Engineering","11","2",,"1","16",,,"10.4018/IJMMME.2021040101","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103126646&doi=10.4018%2fIJMMME.2021040101&partnerID=40&md5=4bef38a1b67fea67417952d93a1b6ccd","Kindai University Technical College, Japan; Kanagawa Institute of Technology, Japan","Hasegawa, N., Kindai University Technical College, Japan; Takahashi, Y., Kanagawa Institute of Technology, Japan","This research has developed a soap bubble ejection robot as an amusement system that reads emotions from human facial expressions and controls the ejection of soap bubbles to improve human-robot interaction. A subject's response to soap bubble ejection is read by a built-in face recognition sensor which sends data to a control system which in turn controls the next ejection. Soap bubbles are often used to research children's emotions/emotional responses. First, evaluation experiments of the control system were performed using face photographs that show human emotions. The experimental results revealed that soap bubbles were ejected in the case of indifference, and the ejection stopped in the case of joy. Through the experimental results, it was confirmed that the control system worked properly when face photographs were used and also verified the effectiveness of the facial recognition sensor. Secondly, evaluation experiments were conducted with an actual human, and it was confirmed from the results that the control system operates as designed. © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.","Amusement robots; Facial expression recognition; Human emotion; Human-robot interaction; Microcomputer control; Robot control; Robot mechanisms; Soap bubble generation",,,,,,,,,,,,,,,,,"Darrow, C. W., Gullickson, G. R., The peripheral mechanism of the galvanic skin response (1970) Psychophysiology, 6 (5), pp. 597-600. , PMID:5485543; Dogan, D., Bogosyan, S., Acarman, T., Evaluation of driver stress level with survey, galvanic skin response sensor data, and force-sensing data (2019) Advances in Mechanical Engineering, 11 (12), pp. 1-19; Fletcher, M. D., Jones, S. L., White, P. R., Dolder, C. N., Leighton, T. G., Lineton, B., Effects of very high-frequency sound and ultrasound on humans. Part I: Adverse symptom after exposure to audible very-high frequency sound (2018) The Journal of the Acoustical Society of America, 144 (4), pp. 2511-2520. , PMID:30404512; Fujimori, B., Yokota, T., Central and peripheral mechanisms of galvanic skin reflex (1962) Acta Neurovegetativa, 24 (1-4), pp. 241-249; Hasegawa, N., Takahashi, Y., Preliminary research into control system using quantified human emotions (2017) Proceedings of International Conference on Control, Automation and Systems, pp. 1519-1524; Hasegawa, N., Takahashi, Y., How recognition of human facial expression can be incoporated in robot control (2019) Proceedings of International Conference on Research and Education in Mechatronics; Hasegawa, N., Takahashi, Y., Incorporation of Human Facial Expression into Robot Control (2019) Handbook of Research on Advanced Mechatronic Systems and Intelligent Robotics, , IGI Global; Jeroh, O. E., Powers, L. S., Roveda, J. M., Determination of Stress in Humans Using Data Fusion of Off-the-Shelf Wearable Sensors Data for Electrocardiogram and Galvanic Skin Response (2018) Journal of Biomedical Science, 7 (3), pp. 1-5; Kato, N., Incipient Scientific Thinking in five-year-old children's activities: through sandbox play, soap bubble play, playing in the water color and mud ball play (2013) Cross-Cultural Business and Cultural Studies, 18 (1), pp. 17-28; Kikhia, B., Stavropoulos, T. G., Andreadis, S., Karvonen, N., Kompatsiaris, I., Sävenstedt, S., Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia (1989) Sensors (Basel), 16, pp. 1-17; Kitamura, K., Takamura, N., Iwai, Y., Sato, K., Detection of unconscious facial reactions to uncomfortable illumination (2014) IEEJ Transactions on Electronics. 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M., A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee (2012) Sensors (Basel), 12 (5), pp. 6075-6101. , PMID:22778631; Yabe, T., Development process of musical expression under varying environmental configurations: Practical study of bubble play in four-year-old preschool class (2012) Journal of the Study of School Music Education Practice, 16, pp. 49-56; Yamada, S., A study on environmental setting to expression activity (1): the case of two soap bubble arts (2017) Shukutoku University Junior College Bulletin, 57, pp. 73-87; Yokota, T., Takahashi, T., Kondo, M., Fujimori, B., Studies on the diphasic wave form of the galvanic skin reflex (1959) Electroencephalography and Clinical Neurophysiology, 11 (4), pp. 687-696. , PMID:13846558",,,,"IGI Global",,,,,21561680,,,,"English","Int. J. Manuf. Mater. Mech. Eng.",Article,"Final","",Scopus,2-s2.0-85103126646
"Nuzzi C., Pasinetti S., Pagani R., Coffetti G., Sansoni G.","57203633842;55763186300;57211168733;57219625215;7003728854;","HANDS: an RGB-D dataset of static hand-gestures for human-robot interaction",2021,"Data in Brief","35",,"106791","","",,1,"10.1016/j.dib.2021.106791","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100596728&doi=10.1016%2fj.dib.2021.106791&partnerID=40&md5=f85d430ae8da0836040b03a775e84465","Department of Mechanical and Industrial Engineering (DIMI), University of BresciaBrescia, Italy","Nuzzi, C., Department of Mechanical and Industrial Engineering (DIMI), University of BresciaBrescia, Italy; Pasinetti, S., Department of Mechanical and Industrial Engineering (DIMI), University of BresciaBrescia, Italy; Pagani, R., Department of Mechanical and Industrial Engineering (DIMI), University of BresciaBrescia, Italy; Coffetti, G., Department of Mechanical and Industrial Engineering (DIMI), University of BresciaBrescia, Italy; Sansoni, G., Department of Mechanical and Industrial Engineering (DIMI), University of BresciaBrescia, Italy","The HANDS dataset has been created for human-robot interaction research, and it is composed of spatially and temporally aligned RGB and Depth frames. It contains 12 static single-hand gestures performed with both the right-hand and the left-hand, and 3 static two-hands gestures for a total of 29 unique classes. Five actors (two females and three males) have been acquired performing the gestures, each of them adopting a different background and light conditions. For each actor, 150 RGB frames and their corresponding 150 Depth frames per gesture have been collected, for a total of 2400 RGB frames and 2400 Depth frames per actor. Data has been collected using a Kinect v2 camera intrinsically calibrated to spatially align RGB data to Depth data. The temporal alignment has been performed offline using MATLAB, aligning frames with a maximum temporal distance of 66 ms. This dataset has been used in [1] and it is freely available at http://dx.doi.org/10.17632/ndrczc35bt.1. © 2021 The Authors","Classification; Hand-Gesture Recognition; Human-Robot Interaction; Object Detector",,,,,,,,,,,,,,,,,"Nuzzi, C., Pasinetti, S., Pagani, R., Ghidini, S., Beschi, M., Coffetti, G., Sansoni, G., MEGURU: a gesture-based robot program builder for Meta-Collaborative workstations (2021) Rob. Comput. Integr. Manuf., 68; Barczak, A.L.C., Reyes, N.H., Abastillas, M., Piccio, A., Susnjak, T., A new 2D static hand gesture colour image dataset for ASL gestures (2011) Res. Lett. Inform. Math. Sci., 15, pp. 12-20; Hoang, V.T., HGM-4: A new multi-cameras dataset for hand gesture recognition (2020) Data in Brief, 30; Hsiao, Y.-S., Sanchez-Riera, J., Lim, T., Hua, K.-L.C.W.-H., LaRED: A Large RGB-D extensible hand gesture dataset (2014) Proceedings of the 5th ACM Multimedia Systems Conference, Singapore; Pisharady, P.K., Vadakkepat, P., Loh, A.P., Attention based detection and recognition of hand postures against complex backgrounds (2013) Int. J. Comput. Vision, 101 (3), pp. 403-419; Ren, Z., Yuan, J.Z.Z., Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth camera (2011) Proceedings of the 19th ACM International Conference on Multimedia, Scottsdale, Arizona, USA; Suau, X., Alcoverro, M., López-Méndez, A., Ruiz-Hidalgo, J., Casas, J.R., Real-time fingertip localization conditioned on hand gesture classification (2014) Image Vision Comput., 32 (8), pp. 522-532; Guyon, I., Athitsos, V., Jangyodsuk, P., Escalante, H.J., The ChaLearn gesture dataset (CGD 2011) (2014) Mach. Vis. Appl., 25 (8), pp. 1929-1951; Nuzzi, C., Pasinetti, S., Pagani, R., Franco, D., Sansoni, G., Hand gesture recognition for collaborative workstations: a smart command system prototype (2019) Image Analysis and Processing – ICIAP 2019, Trento; Sell, J., O'Connor, P., The Xbox one system on a chip and kinect sensor (2014) IEEE Micro, 34 (2), pp. 44-53; Quigley, M., Gerkey, B.P., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Ng, A., ROS: an open-source Robot Operating System (2009) ICRA Workshop on Open Source Software; Xiang, L., Echtler, F., Kerl, C., Wiedemeyer, T., Lars, H., Gordon, R., Facioni, F., Alistair, libfreenect2: Release 0.2 (2016); Wiedemeyer, T., (2015), https://github.com/code-iai/iai_kinect2, “IAI Kinect2,” [Online]. Available: [Accessed January 2018]; D'Elia, Y., (2013), https://www.thregr.org/∼wavexx/software/facedetect/, “facedetect,” [Online]. Available: [Accessed November 2020]","Nuzzi, C.; Department of Mechanical and Industrial Engineering (DIMI), Italy; email: c.nuzzi@unibs.it",,,"Elsevier Inc.",,,,,23523409,,,,"English","Data Brief",Data Paper,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85100596728
"Lu L., Zhang P., Zhang T.C.","56449741500;57191751453;57220035158;","Leveraging “human-likeness” of robotic service at restaurants",2021,"International Journal of Hospitality Management","94",,"102823","","",,13,"10.1016/j.ijhm.2020.102823","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098556750&doi=10.1016%2fj.ijhm.2020.102823&partnerID=40&md5=435345f7df3bc8e5d3215229cf244f2f","School of Sport, Tourism, and Hospitality Management, Temple University, 1810 N. 13th Street, Speakman Hall 329, Philadelphia, PA  19122, United States; Department of Retailing and Tourism Management, College of Agriculture, Food and Environment, University of Kentucky, Erikson Hall, Lexington, KY  40506-0050, United States; Rosen College of Hospitality Management, University of Central Florida, 9907 Universal Blvd, Orlando, FL  32821, United States","Lu, L., School of Sport, Tourism, and Hospitality Management, Temple University, 1810 N. 13th Street, Speakman Hall 329, Philadelphia, PA  19122, United States; Zhang, P., Department of Retailing and Tourism Management, College of Agriculture, Food and Environment, University of Kentucky, Erikson Hall, Lexington, KY  40506-0050, United States; Zhang, T.C., Rosen College of Hospitality Management, University of Central Florida, 9907 Universal Blvd, Orlando, FL  32821, United States","Despite the rise of human-robot interaction research, the mixed findings of human-likeness in consumer evaluation exist. Focusing on the restaurant sector, this research investigates how service robots’ varying levels of human-likeness of attributes (i.e., visual, vocal and verbal) influence consumption outcomes (e.g., service encounter evaluation, revisit intentions and positive word of mouth intentions) and the underlying mechanisms through cognition (i.e., perceived credibility) and positive emotion per Appraisal Theory. Drawing on a consumer experiment involving a total of 587 participants, results suggest that humanlike voice emerges as a dominant attribute affecting all three consumption outcomes. Humanlike language style positively affects service encounter evaluation but barely affects the other two outcomes. The significant effect of humanlike voice on three consumption outcomes is only explained by positive emotion whereas the effect of humanlike language style on service encounter evaluation is explained by both cognition (i.e., perceived credibility) and emotion. © 2020 Elsevier Ltd","Human-likeness; Language; Physical appearance; Restaurant; Service robot; Voice",,,,,,,,,,,,,,,,,"Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot., 1 (1), pp. 71-81; Bartsch, S., What sounds beautiful is good?” How employee vocal attractiveness affects customer's evaluation of the voice-to-voice service encounter (2008) Aktuelle Forschungsfragen Im Dienstleistungsmarketing (Current Research Questions in Service Marketing), Gabler, pp. 45-68; Belk, R., Understanding the robot: comments on Goudey and Bonnin (2016) (2016) Rech. Appl. 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Robot., 7 (3), pp. 347-360","Lu, L.; School of Sport, 1810 N. 13th Street, Speakman Hall 329, United States; email: lu.lu0001@temple.edu",,,"Elsevier Ltd",,,,,02784319,,IJHMD,,"English","Int. J. Hosp. Manage.",Article,"Final","",Scopus,2-s2.0-85098556750
"Delgosha M.S., Hajiheydari N.","25421093400;55490674700;","How human users engage with consumer robots? A dual model of psychological ownership and trust to explain post-adoption behaviours",2021,"Computers in Human Behavior","117",,"106660","","",,5,"10.1016/j.chb.2020.106660","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098183132&doi=10.1016%2fj.chb.2020.106660&partnerID=40&md5=06e917f628cd2f48ce94b8c45c8c9cec","Bristol Business School, University of the West of England, Coldharbour Ln, Frenchay, Campus, Stoke Gifford, Bristol, BS16 1QY, United Kingdom; Sheffield University Management School, Conduit Road, Sheffield, S10 1FL, United Kingdom; University of Tehran, Iran","Delgosha, M.S., Bristol Business School, University of the West of England, Coldharbour Ln, Frenchay, Campus, Stoke Gifford, Bristol, BS16 1QY, United Kingdom; Hajiheydari, N., Sheffield University Management School, Conduit Road, Sheffield, S10 1FL, United Kingdom, University of Tehran, Iran","Consumer robots are technically evolving and have a growing presence in our daily lives with enhanced interactive capabilities. While there is insightful literature on robot adoption, so far, research has done less to examine the post-adoption interaction of human-consumer robots. Drawing on trust in technology model and psychological ownership theory, this study proposes a conceptual dual model to explain robot users’ post-adoption behaviours, while considers the moderating roles of anthropomorphism and social presence. We empirically corroborated our model by asking from 403 current robot owners to illustrate theoretical paths to their post-adoption behaviours including cognitive absorption and intention to explore. This study contributes to the extant literature of human-robot interaction by proposing a theoretically grounded and empirically tested framework that contextualizes psychological ownership theory, uncertainty reduction theory, and trust in technology model. We also highlight the implications for practitioners to leverage trust and psychological ownership mechanisms together for encouraging users to actively engage with robots. © 2020 Elsevier Ltd","Anthropomorphism; Cognitive absorption; Consumer robots; Intention to explore; Psychological ownership; Social presence; Trust","Behavioral research; Human computer interaction; Cognitive absorptions; Human users; Intention to explore; Post adoptions; Psychological ownership; Social presence; Trust in technologies; Uncertainty reduction theory; Social robots; adoption; article; consumer; drawing; human; human experiment; physician; psychological model; robotics; theoretical study; trust; uncertainty",,,,,,,,,,,,,,,,"Agarwal, R., Karahanna, E., Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage (2000) MIS Quarterly, pp. 665-694; Airenti, G., The cognitive bases of anthropomorphism: From relatedness to empathy (2015) International Journal of Social Robotics, 7 (1), pp. 117-127; Anjomshoae, S., Najjar, A., Calvaresi, D., Främling, K., May). 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Hum. Behav.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85098183132
"Salek Shahrezaie R., Banisetty S.B., Mohammadi M., Feil-Seifer D.","57211603950;57195150511;57222360508;14033850500;","Towards deep reasoning on social rules for socially aware navigation",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447225","515","518",,2,"10.1145/3434074.3447225","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102779459&doi=10.1145%2f3434074.3447225&partnerID=40&md5=b7c801b82dae499c0ad57d7fb5345447","University of Nevada Reno, Reno, NV, United States; WHIP Mobility Group, Selangor, Malaysia","Salek Shahrezaie, R., University of Nevada Reno, Reno, NV, United States; Banisetty, S.B., University of Nevada Reno, Reno, NV, United States; Mohammadi, M., WHIP Mobility Group, Selangor, Malaysia; Feil-Seifer, D., University of Nevada Reno, Reno, NV, United States","This work presents ideation and preliminary results of using contextual information and information of the objects present in the scene to query applicable social navigation rules for the sensed context. Prior work in socially-Aware Navigation (SAN) shows its importance in human-robot interaction as it improves the interaction quality, safety and comfort of the interacting partner. In this work, we are interested in automatic detection of social rules in SAN and we present three major components of our method, namely: a Convolutional Neural Network-based context classifier that can autonomously perceive contextual information using camera input; a YOLO-based object detection to localize objects with a scene; and a knowledge base of social rules relationships with the concepts to query them using both contextual and detected objects in the scene. Our preliminary results suggest that our approach can observe an on-going interaction, given an image input, and use that information to query the social navigation rules required in that particular context. © 2021 ACM.","Human-robot interaction; Knowledge graph; Social rules","Agricultural robots; Air navigation; Automata theory; Classification (of information); Convolutional neural networks; Knowledge based systems; Man machine systems; Navigation; Object detection; Automatic Detection; Contextual information; Image inputs; Interaction quality; Knowledge base; Social navigation; Social rules; Social robots",,,,,"National Science Foundation, NSF: -1719027","The authors would also like to acknowledge the financial support of this work by the National Science Foundation (NSF, #IIS-1719027).",,,,,,,,,,"Balajee Banisetty, S., (2020) Multi-Context Socially-Aware Navigation Using Non-Linear Optimization, , Ph. D. Dissertation. University of Nevada, Reno; Balajee Banisetty, S., Feil-Seifer, D., (2018) Towards A Unified Planner for Socially-aware Navigation, , 2018; Balajee Banisetty, S., Forer, S., Yliniemi, L., Nicolescu, M., Feil-Seifer, D., (2019) Socially-aware Navigation: A Non-linear Multi-objective Optimization Approach, , 2019; Balajee Banisetty, S., Sebastian, M., Feil-Seifer, D., (2016) Socially-Aware Navigation: Action Discrimination to Select Appropriate Behavior; Bera, A., Randhavane, T., Manocha, D., The emotionally intelligent robot: Improving socially-aware human prediction in crowded environments (2019) Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops; Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Zhang, J., (2016) End to End Learning for Self-driving Cars, , 2016; Crespo, J., Carlos Castillo, J., Martinez Mozos, O., Barber, R., Semantic information for robot navigation: A survey (2020) Applied Sciences, 10 (2), p. 497. , https://doi.org/10.3390/app10020497, Jan 2020; Forer, S., Balajee Banisetty, S., Yliniemi, L., Nicolescu, M., Feil-Seifer, D., Socially-aware navigation using non-linear multiobjective optimization (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1-9; Gruber, T.R., Toward principles for the design of ontologies used for knowledge sharing? (1995) International Journal of Human-Computer Studies, 43 (5), pp. 907-928. , https://doi.org/10.1006/ijhc.1995.1081, 1995; Kim, B., Pineau, J., Socially adaptive path planning in human environments using inverse reinforcement learning (2016) International Journal of Social Robotics, 8 (1), pp. 51-66. , 2016; Kretzschmar, H., Spies, M., Sprunk, C., Burgard, W., Socially compliant mobile robot navigation via inverse reinforcement learning (2016) The International Journal of Robotics Research, 35 (11), pp. 1289-1307. , 2016; Kruse, T., Kumar Pandey, A., Alami, R., Kirsch, A., Human-aware robot navigation: A survey (2013) Robotics and Autonomous Systems, 61 (12), pp. 1726-1743. , 2013; Lemaignan, S., Warnier, M., Akin Sisbot, E., Clodic, A., Alami, R., Artificial cognition for social human-robot interaction: An implementation (2017) Artificial Intelligence, 247, pp. 45-69. , https://doi.org/10.1016/j.artint.2016.07.002, 2017 Special Issue on AI and Robotics; Lu, D.V., Hershberger, D., Smart, W.D., Layered costmaps for context-sensitive navigation (2014) Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference On. IEEE, pp. 709-715; Marder-Eppstein, E., Berger, E., Foote, T., Gerkey, B., Konolige, K., The office marathon: Robust navigation in an indoor office environment (2010) 2010 IEEE International Conference on Robotics and Automation. IEEE, pp. 300-307; Okal, B., Arras, K.O., Learning socially normative robot navigation behaviors with Bayesian inverse reinforcement learning (2016) 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 2889-2895; Quattoni, A., Torralba, A., Recognizing indoor scenes (2009) IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp. 413-420. , https://doi.org/10.1109/CVPR.2009.5206537; Ramon-Vigo, R., Perez-Higueras, N., Caballero, F., Merino, L., Transferring human navigation behaviors into a robot local planner (2014) Robot and Human Interactive Communication, 2014 RO-MAN: The 23rd IEEE International Symposium On. IEEE, pp. 774-779; Rios-Martinez, J., Spalanzani, A., Laugier, C., Understanding human interaction for probabilistic autonomous navigation using risk-rrt approach (2011) 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 2014-2019; Saxena, A., Jain, A., Sener, O., Jami, A., Misra, D.K., Koppula, H.S., (2015) RoboBrain: Large-Scale Knowledge Engine for Robots; Sebastian, M., Balajee Banisetty, S., Feil-Seifer, D., Sociallyaware navigation planner using models of human-human interaction (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 405-410; Tsetsos, V., Anagnostopoulos, C., Kikiras, P., Hadjiefthymiades, S., Semantically enriched navigation for indoor environments (2006) IJWGS, 2, pp. 453-478. , https://doi.org/10.1504/IJWGS.2006.011714, 01 2006; Zhao, L., Li, S., Object detection algorithm based on improved yolov3 (2020) Electronics, 9, p. 537. , https://doi.org/10.3390/electronics9030537, 03 2020",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102779459
"Elbeleidy S., Shick A., Williams T.","57208691436;37011551100;7404172627;","Teleoperation interface usage in robot-assisted childhood ASD therapy",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447151","162","166",,1,"10.1145/3434074.3447151","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102761891&doi=10.1145%2f3434074.3447151&partnerID=40&md5=0bf85b8ef827489f86a4be692f302d3f","MIRRORLab, Colorado School of Mines, Golden, CO, United States; ATLAS Institute, University of Colorado Boulder, Boulder, CO, United States","Elbeleidy, S., MIRRORLab, Colorado School of Mines, Golden, CO, United States; Shick, A., ATLAS Institute, University of Colorado Boulder, Boulder, CO, United States; Williams, T., MIRRORLab, Colorado School of Mines, Golden, CO, United States","Therapist-operated robots can play a uniquely impactful role in helping children with Autism Spectrum Disorder (ASD) practice and acquire social skills. While extensive research within Human Robot Interaction has focused on teleoperation interfaces for robots in general, little work has been done on teleoperation interface design for robots in the context of ASD therapy. Moreover, while clinical research has shown the positive impact robots can have on children with Autism, much of that research has been performed in a controlled environment, with little understanding of the way these robots are used ""in the wild"". We analyze archival data of therapists teleoperating robots as part of their regular therapy sessions, to (1) determine common themes and difficulties in therapists' use of teleoperation interfaces, and (2) provide design recommendations to improve therapists' overall experience. We believe that following these recommendations will help maximize the effectiveness of ASD therapy with Socially Assistive Robots and the scale at which it can be deployed. © 2021 ACM.","Autism; Case studies; In-the-wild studies; Social skills training; Socially assistive robotics","Agricultural robots; Clinical research; Diseases; Economic and social effects; Machine design; Man machine systems; Remote control; Archival data; Children with autisms; Controlled environment; Design recommendations; Social skills; Socially assistive robots; Teleoperating robots; Teleoperation interface; Social robots",,,,,,,,,,,,,,,,"Allspaw, J., Roche, J., Remotely teleoperating a humanoid robot to perform fine motor tasks with virtual reality (2018) Proceedings of the 1st InternationalWorkshop on Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI); Boccanfuso, L., Scarborough, S., Abramson, R.K., Hall, A.V., Wright, H.H., O'Kane, J.M., A low-cost socially assistive robot and robotassisted intervention for children with autism spectrum disorder: Field trials and lessons learned (2017) Autonomous Robots, 41 (3), pp. 637-655. , 2017; Bosch, J., Ridao, P., Garcia, R., Gracias, N., Towards omnidirectional immersion for rov teleoperation (2016) Proceedings of Jornadas de Automática, , Madrid, Spain 2016; Cabibihan, J., Javed, H., Ang, M., Mariam Aljunied, S., Why robots? A survey on the roles and benefits of social robots in the therapy of children with autism (2013) International Journal of Social Robotics, 5 (4), pp. 593-618. , 2013; Chaminade, T., Da Fonseca, D., Rosset, D., Lutcher, E., Cheng, G., Deruelle, C., Fmri study of young adults with autism interacting with a humanoid robot (2012) 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 380-385; Chung, E.Y.-H., Robotic intervention program for enhancement of social engagement among children with autism spectrum disorder (2019) Journal of Developmental and Physical Disabilities, 31 (4), pp. 419-434. , 2019; Yin-Han Chung, E., Robot-mediated social skill intervention programme for children with autism spectrum disorder: An aba time-series study (2020) International Journal of Social Robotics, pp. 1-13; Elise Clabaugh, C., Mahajan, K., Jain, S., Pakkar, R., Becerra, D., Shi, Z., Deng, E., Mataric, M., Long-term personalization of an in-home socially assistive robot for children with autism spectrum disorders (2019) Frontiers in Robotics and AI, 6, p. 110. , 2019; Damm, O., Malchus, K., Jaecks, P., Krach, S., Paulus, F., Naber, M., Jansen, A., Stenneken, P., Different gaze behavior in human-robot interaction in asperger's syndrome: An eye-tracking study (2013) 2013 IEEE RO-MAN. IEEE, pp. 368-369; Dautenhahn, K., Robots as social actors: Aurora and the case of autism (1999) Proc. CT99, the Third International Cognitive Technology Conference, 359, p. 374. , August, San Francisco. Citeseer; Dawe, J., Sutherland, C., Barco, A., Broadbent, E., Can social robots help children in healthcare contexts? 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IEEE, pp. 1632-1639; Lipton, J.I., Fay, A.J., Rus, D., Baxter's homunculus: Virtual reality spaces for teleoperation in manufacturing (2017) IEEE Robotics and Automation Letters, 3 (1), pp. 179-186. , 2017; Mataric, M.J., Scassellati, B., Socially assistive robotics (2016) Springer Handbook of Robotics, pp. 1973-1994. , Springer; Michaud, F., Salter, T., Duquette, A., Mercier, H., Lauria, M., Larouche, H., Larose, F., Assistive technologies and childrobot interaction (2007) AAAI Spring Symposium on Multidisciplinary Collaboration for Socially Assistive Robotics; (2021) PEERbots, , https://peerbots.org; Pennisi, P., Tonacci, A., Tartarisco, G., Billeci, L., Ruta, L., Gangemi, S., Pioggia, G., Autism and social robotics: A systematic review (2016) Autism Research, 9 (2), pp. 165-183. , 2016; Pioggia, G., Sica, M.L., Ferro, M., Igliozzi, R., Muratori, F., Ahluwalia, A., De Rossi, D., Human-robot interaction in autism: Face, an android-based social therapy (2007) RO-MAN 2007-the 16th IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 605-612; Rabbitt, S.M., Kazdin, A.E., Scassellati, B., Integrating socially assistive robotics into mental healthcare interventions: Applications and recommendations for expanded use (2015) Clinical Psychology Review, 35, pp. 35-46. , 2015; Robins, B., Dautenhahn, K., Te Boekhorst, R., Billard, A., Effects of repeated exposure to a humanoid robot on children with autism (2004) Designing A More Inclusive World, pp. 225-236. , Springer; Scassellati, B., Admoni, H., Mataric, M., Robots for use in autism research (2012) Annual Review of Biomedical Engineering, 14. , 2012; Scassellati, B., Boccanfuso, L., Huang, C., Mademtzi, M., Qin, M., Salomons, N., Ventola, P., Shic, F., Improving social skills in children with asd using a long-term, in-home social robot (2018) Science Robotics, 3 (21). , 2018; Shamsuddin, S., Yussof, H., Mohamed, S., Akhtar Hanapiah, F., Ismail Idzhar, L., Stereotyped behavior of autistic children with lower iq level in hri with a humanoid robot (2013) 2013 IEEE Workshop on Advanced Robotics and Its Social Impacts. IEEE, pp. 175-180; Steinfeld, A., Interface lessons for fully and semi-autonomous mobile robots (2004) IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA'04, 3, pp. 2752-2757. , 2004. IEEE; Tsui, K.M., Dalphond, J.M., Brooks, D.J., Medvedev, M.S., McCann, E., Allspaw, J., Kontak, D., Yanco, H.A., Accessible human-robot interaction for telepresence robots: A case study (2015) Paladyn, Journal of Behavioral Robotics, 1. , open-issue 2015; Wainer, J., Ferrari, E., Dautenhahn, K., Robins, B., The effectiveness of using a robotics class to foster collaboration among groups of children with autism in an exploratory study (2010) Personal and Ubiquitous Computing, 14 (5), pp. 445-455. , 2010; Wainer, J., Robins, B., Amirabdollahian, F., Dautenhahn, K., Using the humanoid robot kaspar to autonomously play triadic games and facilitate collaborative play among children with autism (2014) IEEE Transactions on Autonomous Mental Development, 6 (3), pp. 183-199. , 2014",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102761891
"Choi B.J., Park J., Park C.H.","57222469899;57192667984;36080995200;","Formal verification for human-robot interaction in medical environments",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447155","181","185",,,"10.1145/3434074.3447155","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102761207&doi=10.1145%2f3434074.3447155&partnerID=40&md5=e8b98c59ac8302d4c7b7f8f09188f8b8","Potomac School, McLean, VA, United States; George Washington University, Washington, DC, United States","Choi, B.J., Potomac School, McLean, VA, United States; Park, J., George Washington University, Washington, DC, United States; Park, C.H., George Washington University, Washington, DC, United States","We present a formal verification method that provides a model-based approach to human-robot interaction (HRI) in medical settings by utilizing linear temporal logic (LTL). We define high-level HRI procedures with an LTL-based framework to create algorithmically sound robots that can function independently in dynamic HRI environments. Our approach's theoretical infallibility confers particular advantages for medical robots, where safety and informative communications are crucial. In order to establish the viability of our proposed method, and with the ongoing COVID-19 pandemic in mind, we developed an LTL knowledge base for a medical robot tasked with HRI-intensive roles of patient reception and triage. We designed robotic simulations based on our LTL architecture to test our approach, employing randomized inputs to generate unpredictable HRI environments. We then conducted formal verification via an automata-theoretic approach by evaluating our simulated robot against generalized Büchi automata. We hope our LTL-based approach can enable future achievements in HRI. © 2021 ACM.","Formal verification; Human-robot interaction; Linear temporal logic; Robotic medical receptionist","Agricultural robots; Automata theory; Formal verification; Knowledge based systems; Man machine systems; Medical robotics; Temporal logic; Automata-theoretic approach; Formal verification methods; Human robot Interaction (HRI); Informative communication; Linear temporal logic; Medical settings; Model based approach; Robotic simulation; Human robot interaction",,,,,"National Science Foundation, NSF: 1846658","This research is partially funded by the National Science Foundation (NSF) under the grant #1846658.",,,,,,,,,,"Alimohamadi, Y., Sepandi, M., Taghdir, M., Hosamirudsari, H., Determine the most common clinical symptoms in COVID-19 patients: A systematic review and meta-analysis (2020) Journal of Preventive Medicine and Hygiene, 61 (3), p. 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IEEE, pp. 2020-2025; Finucane, C., Jing, G., Kress-Gazit, H., Ltlmop: Experimenting with language, temporal logic and robot control (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 1988-1993; Karaman, S., Frazzoli, E., Complex mission optimization for multiple-uavs using linear temporal logic (2008) 2008 American Control Conference. IEEE, pp. 2003-2009; Hameed Khan, Z., Siddique, A., Won Lee, C., Robotics utilization for healthcare digitization in global COVID-19 management (2020) International Journal of Environmental Research and Public Health, 17 (11), p. 3819. , 2020; Lestingi, L., Askarpour, M., Bersani, M.M., Rossi, M., Formal verification of human-robot interaction in healthcare scenarios (2020) International Conference on Software Engineering and Formal Methods, pp. 303-324. , Springer; Palmieri, J.J., Stern, T.A., Lies in the doctor-patient relationship (2009) Primary Care Companion to the Journal of Clinical Psychiatry, 11 (4), p. 163. , 2009; Pnueli, A., The temporal logic of programs (1977) 18th Annual Symposium on Foundations of Computer Science (Sfcs 1977), pp. 46-57; Porfirio, D., Sauppé, A., Albarghouthi, A., Mutlu, B., Authoring and verifying human-robot interactions (2018) Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology, pp. 75-86; Porfirio, D., Sauppé, A., Albarghouthi, A., Mutlu, B., Computational tools for human-robot interaction design (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 733-735; Rozier, K.Y., Linear temporal logic symbolic model checking (2011) Computer Science Review, 5 (2), pp. 163-203. , 2011; Vogel, L., (2019) Why Do Patients Often Lie to Their Doctors?; Webster, M., Salem, M., Dixon, C., Fisher, M., Dautenhahn, K., (2014) Formal Verification of An Autonomous Personal Robotic Assistant, , 2014; Xu, N., Li, J., Niu, Y., Shen, L., An ltl-based motion and action dynamic planning method for autonomous robot (2016) IFAC-PapersOnLine, 49 (5), pp. 91-96. , 2016",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102761207
"Li C., Park J., Kim H., Chrysostomou D.","57192578646;57222472003;57222473138;24477398900;","How can i help you? An intelligent virtual assistant for industrial robots",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447163","220","224",,1,"10.1145/3434074.3447163","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102759831&doi=10.1145%2f3434074.3447163&partnerID=40&md5=c1d90e33beb362b0b0d6cb4fea8c4fba","Aalborg University, Dept. of Materials and Production, Aalborg East, Denmark","Li, C., Aalborg University, Dept. of Materials and Production, Aalborg East, Denmark; Park, J., Aalborg University, Dept. of Materials and Production, Aalborg East, Denmark; Kim, H., Aalborg University, Dept. of Materials and Production, Aalborg East, Denmark; Chrysostomou, D., Aalborg University, Dept. of Materials and Production, Aalborg East, Denmark","In the light of recent trends toward introducing Artificial Intelligence (AI) to enhance Human-Robot Interaction (HRI), intelligent virtual assistants (VA) driven by Natural Language Processing (NLP) receives ample attention in the manufacturing domain. However, most VAs either tightly bind with a specific robotic system or lack efficient human-robot communication. In this work, we implement a layer of interaction between the robotic system and the human operator. This interaction is achieved using a novel VA, called Max, as an intelligent and robust interface. We expand the research work in three directions. Firstly, we introduce a RESTful style Client-Server architecture for Max. Secondly, inspired by studies of human-human conversations, we embed conversation strategies into human-robot dialog policy generation to create a more natural and humanized conversation environment. Finally, we evaluate Max over multiple real-world scenarios from the exploration of an unknown environment to package delivery, with the means of an industrial robot. © 2021 ACM.","Client-server architecture; Human-robot interaction; Natural language processing; User experience; Virtual assistant","Agricultural robots; Client server computer systems; Industrial robots; Intelligent robots; Man machine systems; Natural language processing systems; Robotics; Client-server architectures; Human robot Interaction (HRI); Human-robot communication; Manufacturing domains; NAtural language processing; Real-world scenario; Unknown environments; Virtual assistants; Social robots",,,,,"P0008749; Horizon 2020 Framework Programme, H2020: 857061; Ministry of Trade, Industry and Energy, MOTIE; Korea Institute for Advancement of Technology, KIAT","The authors would like to acknowledge support by the H2020-WIDESPREAD project no. 857061 ""Networking for Research and Development of Human Interactive and Sensitive Robotics Taking Advantage of Additive Manufacturing – R2P2"". This research was also supported by MOTIE (Ministry of Trade, Industry, and Energy) in Korea, under the Fostering Global Talents for Innovative Growth Program related to Robotics(P0008749) supervised by the Korea Institute for Advancement of Technology (KIAT). Authors would also like to thank the research assistants Martin Bieber and Galadrielle Humblot-Renaux for their significant help with setting up the robot demos.",,,,,,,,,,"(2020) Developer Documentation, , https://developer.amazon.com/documentation, Retrieved Oct 2, 2020 from; (2020) SiriKit, , https://developer.apple.com/documentation/sirikit/, Retrieved Oct 5, 2020 from; Banchs, R.E., Li, H., Iris: A chat-oriented dialogue system based on the vector space model (2012) Proceedings of the ACL 2012 System Demonstrations. Association for Computational Linguistics, pp. 37-42. , https://www.aclweb.org/anthology/P12-3007, Jeju Island, Korea; Devlin, J., Chang, M., Lee, K., Toutanova, K., (2018) Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding, , 2018; El Hafi, L., Isobe, S., Tabuchi, Y., Katsumata, Y., Nakamura, H., Fukui, T., Matsuo, T., Taniguchi, T., System for augmented human-robot interaction through mixed reality and robot training by non-experts in customer service environments (2020) Advanced Robotics, 34 (3-4), pp. 157-172. , https://doi.org/10.1080/01691864.2019.1694068, 2020; Hentout, A., Aouache, M., Maoudj, A., Akli, I., Human-robot interaction in industrial collaborative robotics: A literature review of the decade 2008-2017 (2019) Advanced Robotics, 33 (15-16), pp. 764-799. , https://doi.org/10.1080/01691864.2019.1636714, 2019; Huang, J., Lee, W., Chen, C., Dong, B., Developing emotion-aware human-robot dialogues for domain-specific and goal-oriented tasks (2020) Robotics, 9 (2), p. 31. , https://doi.org/10.3390/robotics9020031, 2020; Huber, A., Weiss, A., Developing human-robot interaction for an industry 4. 0 robot: How industry workers helped to improve remote-hri to physical-hri (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (Vienna, Austria) (HRI '17), pp. 137-138. , https://doi.org/10.1145/3029798.3038346, Association for Computing Machinery, New York, NY, USA; Ishiguro, H., Kawahara, T., Nakamura, Y., Autonomous dialogue technologies in symbiotic human-robot interaction (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (Cambridge, United Kingdom) (HRI '20), pp. 650-651. , https://doi.org/10.1145/3371382.3374855, Association for Computing Machinery, New York, NY, USA; Li, Z., Kiseleva, J., De Rijke, M., Dialogue generation: From imitation learning to inverse reinforcement learning (2019) Proceedings of the AAAI Conference on Artificial Intelligence, 33, pp. 6722-6729. , https://doi.org/10.1609/aaai.v33i01.33016722; Lindblom, J., Alenljung, B., Billing, E., Evaluating the user experience of human-robot interaction (2020) Number 1 in Springer Series on Bio-and Neurosystems, pp. 231-256. , https://doi.org/10.1007/978-3-030-42307-0_9, Springer; Lu, K., Zhang, S., Stone, P., Chen, X., Learning and reasoning for robot dialog and navigation tasks (2020) Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 107-117. , https://www.aclweb.org/anthology/2020.sigdial-1.14, Association for Computational Linguistics, 1st virtual meeting; Matuszek, C., Grounded language learning: Where robotics and nlp meet (2018) Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, pp. 5687-5691. , https://doi.org/10.24963/ijcai.2018/810; Ötting, S.K., Masjutin, L., Steil, J.J., Maier, G.W., Let's work together: A meta-analysis on robot design features that enable successful human-robot interaction at work (2020) Human Factors, , https://doi.org/10.1177/0018720820966433, 2020; Prati, E., Peruzzini, M., Pellicciari, M., Raffaeli, R., How to include user experience in the design of human-robot interaction (2021) Robotics and Computer-Integrated Manufacturing, 68, p. 102072. , https://doi.org/10.1016/j.rcim.2020.102072, 2021; Ritter, A., Cherry, C., Dolan, W.B., Data-driven response generation in social media (2011) Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 583-593. , https://www.aclweb.org/anthology/D11-1054, Association for Computational Linguistics, Edinburgh, Scotland, UK; Rueben, M., Elprama, S.A., Chrysostomou, D., Jacobs, A., Introduction to (re)using questionnaires in human-robot interaction research (2020) Number 1 in Springer Series on Bio-and Neurosystems, pp. 125-144. , https://doi.org/10.1007/978-3-030-42307-0_5, Springer; Schmidt, M., Niehues, J., Waibel, A., (2017) Towards An Open-Domain Social Dialog System, pp. 271-278. , https://doi.org/10.1007/978-981-10-2585-3_21, Springer Singapore, Singapore; Tamosiunaite, M., Javad Aein, M., Matthias Braun, J., Kulvicius, T., Markievicz, I., Kapociute-Dzikiene, J., Valteryte, R., Wörgötter, F., Cut & recombine reuse of robot action components based on simple language instructions (2019) The International Journal of Robotics Research, 38 (10-11), pp. 1179-1207. , https://doi.org/10.1177/0278364919865594, 1 Sept 2019; Tao, C., Wu, W., Xu, C., Hu, W., Zhao, D., Yan, R., One time of interaction may not be enough: Go deep with an interaction-over-interaction network for response selection in dialogues (2019) Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1-11. , https://doi.org/10.18653/v1/P19-1001, Association for Computational Linguistics, Florence, Italy; Thomason, J., Padmakumar, A., Sinapov, J., Walker, N., Jiang, Y., Yedidsion, H., Hart, J., Mooney, R., Jointly improving parsing and perception for natural language commands through human-robot dialog (2020) Journal of Artificial Intelligence Research, 67, pp. 327-374. , https://doi.org/10.1613/jair.1.11485, 2020; Thomason, J., Padmakumar, A., Sinapov, J., Walker, N., Jiang, Y., Yedidsion, H., Hart, J., Mooney, R.J., Improving grounded natural language understanding through human-robot dialog (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 6934-6941. , https://doi.org/10.1109/ICRA.2019.8794287; Tian, Z., Yan, R., Mou, L., Song, Y., Feng, Y., Zhao, D., How to make context more useful? An empirical study on context-aware neural conversational models (2017) Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 231-236. , https://doi.org/10.18653/v1/P17-2036, Association for Computational Linguistics, Vancouver, Canada; Vinyals, O., Le, Q., (2015) A Neural Conversational Model, , 2015; Yu, Z., Xu, Z., Black, A.W., Rudnicky, A., Strategy and policy learning for non-task-oriented conversational systems (2016) Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 404-412. , https://doi.org/10.18653/v1/W16-3649, Association for Computational Linguistics, Los Angeles",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102759831
"Yang Y., Williams A.B.","57221330208;7405838909;","Improving human-robot collaboration efficiency and robustness through non-verbal emotional communication",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447191","354","356",,,"10.1145/3434074.3447191","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102753447&doi=10.1145%2f3434074.3447191&partnerID=40&md5=337b2112a6a55951609878b6345936a8","University of Kansas, Lawrence, KS, United States","Yang, Y., University of Kansas, Lawrence, KS, United States; Williams, A.B., University of Kansas, Lawrence, KS, United States","Emotional expression plays a very important role in human-robot interaction. It can greatly improve the quality of interaction between humans and robots. In the case of non-verbal communication, emotional expression can greatly shorten the social distance between humans and robots. However, in practical HRI applications, the efficiency and robustness are also an important part we need to consider. In this paper, we demonstrate the impact on the efficiency and robustness of human-robot teamwork in the case of implicit non-verbal communication. We designed an interactive experiment using a collaborative Sawyer robot. In the experiment, we let the robot play a tic-tac-toe game with a person, and judged whether there was a positive or negative effect by comparing the effects of various emotional factors. Our experiments demonstrated that the emotional expression in the case of non-verbal communication has a positive impact on the efficiency and robustness in a collaborative human-robot task. © 2021 ACM.","Collaboration; Emotional expression; Non-verbal communication; Teamwork","Agricultural robots; Efficiency; Man machine systems; Emotional communications; Emotional expressions; Emotional factors; Human-robot collaboration; Non-verbal communications; Quality of interaction; Social distance; Tic-Tac-Toe game; Social robots",,,,,,,,,,,,,,,,"Breazeal, C., Kidd, C.D., Thomaz, A.L., Hoffman, G., Berlin, M., Effects of nonverbal communication on efficiency and robustness in human-robot teamwork (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 708-713. , https://doi.org/10.1109/IROS.2005.1545011; Hoffman, G., Breazeal, C., Collaboration in human-robot teams (2004) AIAA 1st Intelligent Systems Technical Conference., p. 6434; Hoffman, G., Breazeal, C., Effects of anticipatory action on human-robot teamwork efficiency, fluency, and perception of team (2007) Proceedings of the ACM/IEEE International Conference on Human-robot Interaction (Arlington, Virginia, USA) (HRI '07), pp. 1-8. , https://doi.org/10.1145/1228716.1228718, ACM, New York, NY, USA; Hoffman, G., Cakmak, M., Chao, C., Timing in human-robot interaction (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-robot Interaction (Bielefeld, Germany) (HRI '14), pp. 509-510. , https://doi.org/10.1145/2559636.2560029, ACM, New York, NY, USA; Jung, M.F., Šabanovic, S., Eyssel, F., Fraune, M., Robots in groups and teams (2017) Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (Portland, Oregon, USA) (CSCW '17 Companion), pp. 401-407. , https://doi.org/10.1145/3022198.3022659, ACM, New York, NY, USA; Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., Berg, A.C., Ssd: Single shot multibox detector (2016) European Conference on Computer Vision, pp. 21-37. , Springer; Lockerd, A., Breazeal, C., Tutelage and socially guided robot learning (2004) 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(IEEE Cat. No. 04CH37566), 4, pp. 3475-3480; Mehrabian, A., (2017) Nonverbal Communication, , Routledge; Nakata, T., Mori, T., (1998) Expression of Emotion and Intention by Robot Body Movement; Novikova, J., Watts, L., Inamura, T., Emotionally expressive robot behavior improves human-robot collaboration (2015) 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 7-12. , https://doi.org/10.1109/ROMAN.2015.7333645; Reyes, M., Meza, I., Pineda, L.A., The positive effect of negative feedback in hri using a facial expression robot (2016) Cultural Robotics, pp. 44-54. , Jeffrey T. K. V. Koh, Belinda J. Dunstan, David Silvera-Tawil, and Mari Velonaki (Eds.) Springer International Publishing, Cham; Russell, E., Williams, A.B., Effects of smile emotional model on humanoid robot user interaction (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts (Portland, Oregon, USA) (HRI'15 Extended Abstracts), pp. 113-114. , https://doi.org/10.1145/2701973.2702049, Association for Computing Machinery, New York, NY, USA; Streater, J., Elias, J., Bockelman Morrow, P., Fiore, S., Towards an interdisciplinary understanding of perspective for human-robot teamwork (2011) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 55 (1), pp. 1481-1485. , https://doi.org/10.1177/1071181311551308, 2011 arXiv https://doi. org/10. 1177/1071181311551308",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102753447
"Friedman N., Love K., Bremers A., Parry A.J., Lc R., Amgalan B., Liu J., Ju W.","57215417038;57222463840;57222471955;57222463816;57215536959;57222466975;57202049558;7005898423;","Designing functional clothing for human-robot interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444870","703","705",,,"10.1145/3434074.3444870","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102742936&doi=10.1145%2f3434074.3444870&partnerID=40&md5=af276c9546dd0907f4e0dce732a5a655","Cornell Tech, New York City, NY, United States; New York University, New York, NY, United States; Bay Area Artist Collective, Mixed Baggage, Los Angeles, CA, United States; City University of Hong Kong, Hong Kong, Hong Kong; Parsons School of Design, New York, NY, United States; Cornell University, Ithaca, NY, United States","Friedman, N., Cornell Tech, New York City, NY, United States; Love, K., New York University, New York, NY, United States; Bremers, A., Cornell Tech, New York City, NY, United States; Parry, A.J., Bay Area Artist Collective, Mixed Baggage, Los Angeles, CA, United States; Lc, R., City University of Hong Kong, Hong Kong, Hong Kong; Amgalan, B., Parsons School of Design, New York, NY, United States; Liu, J., Cornell University, Ithaca, NY, United States; Ju, W., Cornell Tech, New York City, NY, United States","We believe that we can design robot clothes to help robots become better robots-help them to be useful in a wider array of contexts, or to better adapt or function in the contexts they are already in. We propose that robot clothing should avoid mere mimicry of human apparel, and instead be motivated by what robots need. While we have seen robots dressed in clothes in the last few decades, we believe that robot clothes can be designed with thoughtful intention and should be studied as its own field. In this workshop, we explore this new area within human robot interaction by bringing together HRI researchers, designers, fashion and costume designers, and artists. We will focus on potential functions of robot clothes, discuss potential trends, and design clothes for robots together in an interactive prototyping session. Through this workshop, we hope to build a community of people who will push forward the field of robot clothing design. © 2021 Owner/Author.","Design; Fashion; Human-robot interaction","Agricultural robots; Clothes; Machine design; Man machine systems; Can design; Clothing design; Interactive prototyping; On potentials; Push forwards; Human robot interaction",,,,,,,,,,,,,,,,"(2014) Big Hero, p. 6; (2020) Robots: Your Guide to the World of Robotics: Sophia, , https://robots.ieee.org/robots/sophia/, Accessed: 2020-09-10",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102742936
[No author name available],[No author id available],"HRI 2021 - Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"","",755,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102741405&partnerID=40&md5=df0652de36636aca7d61d69bcc324feb",,"","The proceedings contain 181 papers. The topics discussed include: sex robots in care: setting the stage for a discussion on the potential use of sexual robot technologies for persons with disabilities; robots as moral advisors: the effects of deontological, virtue, and Confucian role ethics on encouraging honest behavior; boosting robot credibility and challenging gender norms in responding to abusive behavior: a case for feminist robots; who wants to grant robots rights?; infants respond to robot’s need for assistance in pursuing action-based goals; assisted human-robot-interaction for industrial assembly: application of spatial augmented reality (SAR) for collaborative assembly tasks; looks can permit deceiving: how reward or punishment decisions are influenced by robot embodiment; perceptual effects of ambient sound on an artificial agent’s rate of speech; the haunted desk: exploring non-volitional behavior change with everyday robotics; and a user-centered agile approach to the development of a real-world social robot application for reception areas.",,,,,,,,,,,,,,,,,,,,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Review,"Final","",Scopus,2-s2.0-85102741405
"Azhar M.Q., Jiang W., Schattner-Elmaleh N.","15055219300;57222462907;57222463696;","Collaboration education suite for children with ASD",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447209","439","441",,,"10.1145/3434074.3447209","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102727535&doi=10.1145%2f3434074.3447209&partnerID=40&md5=f45ae6d7b72145fbb5856d0fe24473ba","Computer Information Systems, BMCC, The City University of New York, United States","Azhar, M.Q., Computer Information Systems, BMCC, The City University of New York, United States; Jiang, W., Computer Information Systems, BMCC, The City University of New York, United States; Schattner-Elmaleh, N., Computer Information Systems, BMCC, The City University of New York, United States","Our project explores whether multiple robots can be deployed as a therapeutic tool to help children with Autism Spectrum Disorder (ASD) practice social interaction and collaboration. We implemented a human-robot interaction (HRI) solution using two different assistive social robots; the NAO humanoid robot and the Cozmo robot. We have several modules available through an iOS app that we developed for children with ASD to engage with these robots in scenarios focused on social interaction and collaboration. We are also in the process of refining an intelligent web interface so that we, as well as therapists and caregivers, can access relevant data collected by our HRI software. The web interface is designed to help caregivers and therapists, along with our team, monitor their child's or patient's progress, and understand which methods work best. The overall goal is to conduct an HRI user study with children with ASD to test how our software solution performs. © 2021 ACM.","Autism spectrum disorder; Cozmo robot; Human-robot collaboration; Human-robot interaction; Interface; Nao humanoid robot; Social interaction","Agricultural robots; Anthropomorphic robots; Educational robots; End effectors; Man machine systems; Software testing; Children with autisms; Human robot Interaction (HRI); Humanoid robot; Intelligent web; Multiple robot; Social interactions; Software solution; Therapeutic tools; Social robots",,,,,,,,,,,,,,,,"Scassellati, B., Boccanfuso, L., Huang, C., Mademtzi, M., Qin, M., Salomons, N., Ventola, P., Shic, F., (2018) Improving Social Skills in Children with ASD Using A Long-term, In-home Social Robot; Waltz, E., (2018) Therapy Robot Teaches Social Skills to Children with Autism, , 08; Shamsuddin, S., Initial response of autistic children in human-robot interaction therapy with humanoid robot nao (2012) 2012 IEEE 8th International Colloquium on Signal Processing and Its Applications, pp. 188-193. , Melaka; Wood, L.J., Zaraki, A., Robins, B., Developing kaspar: A humanoid robot for children with autism (2019) Int J of Soc Robotics; Joint-attention development in children with asd by using social robotics as an educational tool Conference Proceedings EDUNOVATIC 2018: 3rd Virtual International Conference, (2018), pp. 21-25; Burt, K.C., Using robots and digitech for students with disabilities (2018) The Journal of Digital Learning and Teaching Victoria, 5 (1), pp. 27-29; Esteban, P., Baxter, P., Belpaeme, T., How to build a supervised autonomous system for robot-enhanced therapy for children with autism spectrum disorder (2017) Paladyn, Journal of Behavioral Robotics, 8 (1), pp. 18-38. , Retrieved 24 Feb. 2020; Feil-Seifer, D., Mataric, M.J., B3ia: A control architecture for autonomous robot-assisted behavior intervention for children with autism spectrum disorders (2008) RO-MAN 2008-The 17th IEEE International Symposium on Robot and Human Interactive Communication, pp. 328-333. , Munich; Yussof, H., Haziq Salleh, M., Azfar Miskam, M., Shamsuddin, S., Rahman Omar, A., (2015) ASKNAO Apps Targeting at Social Skills Development for Children with Autism; Billing, Z., Erik, T., Robot-enhanced therapy for children with autism (2018) SweCog 2018: 14th National Conference of the Swedish Cognitive Science Society, Linköping, , October 11 and 12; Stanton, C.M., Kahn, P.H., Jr., Severson, R.L., Ruckert, J.H., Gill, B.T., Robotic Animals Might Aid in the Social Development of Children with Autism; Kumazaki, H., Yoshikawa, Y., Yoshimura, Y., The impact of robotic intervention on joint attention in children with autism spectrum disorders (2018) Molecular Autism, 9 (46); Kline, F.M., Deshler, D.D., Schumaker, J.B., Implementing learning strategy instruction in class settings: A research perspective (1992) Promoting Academic Competence and Literacy in School, pp. 361-406. , M. Pressley, K. Harris, & J. T. Guthrie (Eds.) San Diego, CA: Academic Press",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102727535
"Wang T., Okada S., Guo A., Makikawa M., Shiozawa N.","57221770010;14625556000;57211276989;55903676500;16176616200;","Effect of assist robot on muscle synergy during sit-to-stand movement",2021,"ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics",,,"9419525","367","368",,,"10.1109/ISR50024.2021.9419525","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106535802&doi=10.1109%2fISR50024.2021.9419525&partnerID=40&md5=e17c8ba1de76ebe8d2eb31d054884c4f","Ritsumeikan University, Faculty of Science and Engineering, Department of Robotics, 1-1-1 Nojihigashi, Kusatsu, Shiga, Japan; Graduate School of Medicine, Osaka University, Department of Health Science, 1-7 Yamada-oka, Suita, Osaka, Japan; Rit-sumeikan University, Faculty of Sport and Health Science, 1-1-1 Noji-higashi, Kusatsu, Shiga, Japan","Wang, T., Ritsumeikan University, Faculty of Science and Engineering, Department of Robotics, 1-1-1 Nojihigashi, Kusatsu, Shiga, Japan; Okada, S., Ritsumeikan University, Faculty of Science and Engineering, Department of Robotics, 1-1-1 Nojihigashi, Kusatsu, Shiga, Japan; Guo, A., Graduate School of Medicine, Osaka University, Department of Health Science, 1-7 Yamada-oka, Suita, Osaka, Japan; Makikawa, M., Ritsumeikan University, Faculty of Science and Engineering, Department of Robotics, 1-1-1 Nojihigashi, Kusatsu, Shiga, Japan; Shiozawa, N., Rit-sumeikan University, Faculty of Sport and Health Science, 1-1-1 Noji-higashi, Kusatsu, Shiga, Japan","Sit-to-Stand (STS) is an important movement in daily life and could be difficult for older people. Assist robot is expected to provide STS support. STS movement does not only require kinetic coordination, but also muscle coordination. Thus, understanding the muscle coordination during STS with robot assistance is a key issue of human-robot-interaction. The purpose of this study is to reveal the effect of assist robot on muscle coordination during STS through muscle synergy theory. Six subjects did self-performed and robot-assist STSs in total ten times. Surface electromyogram (EMG) of eight muscles was measured to extract muscle synergy using non-negative matrix factorization (NMF). As a result, two muscle synergies appeared in self-performed STS. There were three muscle synergies when STS was assisted by the robot. The spatial patterns of muscle synergies for lower limb muscles differed with and without robot assistance. Using the robot delayed the activation of muscle synergy at the temporal pattern. © 2021 IEEE.",,"Agricultural robots; Factorization; Human robot interaction; Matrix algebra; Robotics; Muscle coordination; Muscle synergies; Nonnegative matrix factorization; Robot assistance; Sit-to-stand movements; Spatial patterns; Surface electromyogram; Temporal pattern; Muscle",,,,,,,,,,,,,,,,"Wang, T., Jeong, H., Ohno, Y., Evaluation of self-reliance support robot through relative phase (2017) IEEE Access, 5, pp. 17816-17823; Kogami, H., Effect of physical therapy on muscle synergy structure during standing-up motion of hemiplegic patients (2018) IEEE Robot. Autom. Lett., 3 (3), pp. 2229-2236; Hirai, H., Pham, H., Ariga, Y., Uno, K., Miyazaki, F., Motor control based on the muscle synergy hypothesis (2016) Cognitive Neuroscience Robotics A, pp. 25-50. , M. Kasaki, H. Ishiguro, M. Asada, M. Osaka, and T. Fujikado, Eds. Tokyo: Springer; Hanawa, H., Muscle synergies underlying sit-to-stand tasks in elderly people and their relationship with kinetic characteristics (2017) J. Electromyogr. Kinesiol., 37, pp. 15-20. , https://doi.org/10.1016/j.jelekin.2017.08.004",,,"IEEE Robotics and Automation Society;Nagoya University","Institute of Electrical and Electronics Engineers Inc.","2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021","4 March 2021 through 6 March 2021",,168860,,9781665438629,,,"English","ISR - IEEE Int. Conf. Intell. Saf. Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85106535802
"Kirschner R.J., Mansfeld N., Abdolshah S., Haddadin S.","57223928721;55556621500;37071846300;23008499000;","Experimental analysis of impact forces in constrained collisions according to ISO/TS 15066",2021,"ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics",,,"9419494","1","5",,,"10.1109/ISR50024.2021.9419494","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106528951&doi=10.1109%2fISR50024.2021.9419494&partnerID=40&md5=c5844b1c9c3a558ec1d162a0e03ccf82","Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, Technical University of Munich, Munich, 80797, Germany","Kirschner, R.J., Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, Technical University of Munich, Munich, 80797, Germany; Mansfeld, N.; Abdolshah, S.; Haddadin, S.","For the user of a collaborative robot, it is important to select robot parameters and trajectories such that the task is fulfilled while ensuring human safety at the same time. In human robot interaction (HRI), constrained collisions can be particularly hazardous to the human and recently, collision test devices were developed that assess safety in such scenarios. In this paper, we propose the concept of a constrained collision force map (CCFM), which relates the robot impact velocity and the collision reaction method its parameterization to the peak collision force in a constrained collision scenario. The CCFM is a tool that will help practitioners to implement both safe and efficient HRI applications and to understand the robot's collision behavior. In this work, we derive the CCFM for three robots (UR10e, UR5e, and Franka Emika Panda) for varying contact thresholds, contact stiffnesses, and robot poses. Finally, we compare our results with the force estimation suggested by ISO/TS 15066:2016. © 2021 IEEE.",,"Agricultural robots; End effectors; Robotics; Collaborative robots; Collision behavior; Collision reactions; Collision scenarios; Contact threshold; Experimental analysis; Human robot Interaction (HRI); Impact velocities; Human robot interaction",,,,,"5140951; 5140953; Horizon 2020 Framework Programme, H2020: 732737, 871899","ACKNOWLEDGMENT The authors would like to thank Joao Jantalia for helping with conducting the experiments. We gratefully acknowledge the funding of the Lighthouse Initiative Geriatronics by StMWi Bayern (Project X, grant no. 5140951) and LongLeif GaPa gGmbH (Project Y, grant no. 5140953), the European Union’s Horizon 2020 research and innovation program as part of the project ILIAD under grant no. 732737 and the support by the European Union’s Horizon 2020 research and innovation program as part of the project I.AM. under grant no. 871899. Please note that S. Haddadin has a potential conflict of interest as shareholder of Franka Emika GmbH.",,,,,,,,,,"ISO/TS 15066:2017, Robots and Robotic Devices-Collaborative Robots, , Beuth-Verlag, Berlin; Bhanoday, V., Matthias, B., Ahmad, A., A design metric for safety assessment of industrial robot design suitable for power-and forcelimited collaborative operation International Journal of Intelligent Robotics and Applications, 2 (2), pp. 226-234. , 2018; Kirschner, D., Schlotzhauer, A., Brandstotter, M., Hofbaur, M., Validation of relevant parameters of sensitive manipulators for human-robot collaboration (2018) Advances in Service and Industrial Robotics, RAAD 2017, Mechanisms and Machine Science, 49. , Springer, Cham; Haddadin, S., Haddadin, S., Khoury, A., Rokahr, T., Parusel, S., Burgkart, R., Albu-Schaffer, A., On making robots understand safety: Embedding injury knowledge into control (2012) The International Journal of Robotics Research, 31, pp. 1578-1602. , 13th ed; Schlotzhauer, A., Kaiser, L., Wachter, J., Brandstotter, M., Hofbaur, M., On the trustability of the safety measures of collaborative robots: 2D Collision-force-map of a sensitive manipulator for safe HRC (2019) 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), pp. 1676-1683. , Vancouver, BC, Canada; Mansfeld, N., Hamad, M., Becker, M., Marin, A.G., Haddadin, S., Safety map: A unified representation for biomechanics impact data and robot instantaneous dynamic properties (2018) IEEE Robotics and Automation Letters, 3 (3), pp. 1880-1887. , July; Haddadin, S., De Luca, A., Albu-Schaffer, A., Robot collisions: A survey on detection, isolation, and identification (2017) IEEE Transactions on Robotics, 33 (6), pp. 1292-1312. , Dec; (1998) Manipulating Industrial Robots-Performance Criteria and Related Test Methods, , ISO 9283, Beuth Verlag, Berlin; Cohen, B., Sucan, I.A., Chitta, S., A generic infrastructure for benchmarking motion planners (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 589-595. , Vilamoura; Steinfeld, A., Common metrics for human-robot interaction (2006) 2006 1st ACM SIGCHI/SIGART Conference on Human-robot Interaction (HRI '06), pp. 33-40. , New York, NY, USA; Kimble, K., Van Wyk, K., Falco, J., Messina, E., Sun, Y., Shibata, M., Uemura, W., Yokokohji, Y., Benchmarking protocols for evaluating small parts robotic assembly systems (2020) IEEE Robotics and Automation Letters, 5 (2), pp. 883-889; Behrens, R., (2019) Biomechanische Grenzwerte F¨ur Die Sichere Mensch-Roboter-Kollaboration, , Springer Vieweg, in Springer Fachmedien Wiesbaden GmbH; Haddadin, S., Albu-Schffer, A., Hirzinger, G., Dummy crashtests for the evaluation of rigid human-robot impacts (2007) International Workshop on Technical Challenges for Dependable Robots in Human Environments","Kirschner, R.J.; Institute for Robotics and System Intelligence, Germany; email: robin-jeanne.kirschner@tum.de",,"IEEE Robotics and Automation Society;Nagoya University","Institute of Electrical and Electronics Engineers Inc.","2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021","4 March 2021 through 6 March 2021",,168860,,9781665438629,,,"English","ISR - IEEE Int. Conf. Intell. Saf. Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85106528951
"Kirschner R.J., Burr L., Porzenheim M., Mayer H., Abdolshah S., Haddadin S.","57223928721;57223932406;57223928669;13406394400;37071846300;23008499000;","Involuntary motion in human-robot interaction: Effect of interactive user training on the occurrence of human startle-surprise motion",2021,"ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics",,,"9419526","28","32",,,"10.1109/ISR50024.2021.9419526","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106486695&doi=10.1109%2fISR50024.2021.9419526&partnerID=40&md5=f5eeff59e98d3bdfb44cd7a4994923e9","Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, University of Munich, Munich, 80797, Germany; Munich Center of Technology and Sociology, Technical University Munich, Munich, 80797, Germany","Kirschner, R.J., Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, University of Munich, Munich, 80797, Germany; Burr, L., Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, University of Munich, Munich, 80797, Germany; Porzenheim, M., Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, University of Munich, Munich, 80797, Germany; Mayer, H., Munich Center of Technology and Sociology, Technical University Munich, Munich, 80797, Germany; Abdolshah, S., Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, University of Munich, Munich, 80797, Germany; Haddadin, S., Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, University of Munich, Munich, 80797, Germany","In human-robot interaction (HRI) high relative velocity between robot and human at collision may lead to severe injury. Human rapid involuntary motion (IM) may therefore be a safety hazard resulting from startle or surprise (StSu). The arousal that may cause such motion can be reduced by habituation effects, indicating that user application training leads to improved safety in HRI. We suggest using an introductory hands-on user training and investigate whether it can influence the occurrence of IM. An exploratory experiment was performed with 21 participants. During the experiment a robot frontally approached the human six times. The experiment was repeated before and after a hands-on training session. We observe whether the participants show cues of StSu at the approach and compare the quantity of these cues occurring before and after hands-on robot introductory training. Our findings show promising results for the hands-on introductory training to positively effect the psychological and physical safety in HRI. © 2021 IEEE.",,"Agricultural robots; Educational robots; Man machine systems; Robotics; Hands-on-trainings; Human robot Interaction (HRI); Relative velocity; Safety hazards; User training; Human robot interaction",,,,,"5140951; 5140953; Horizon 2020 Framework Programme, H2020: 732737, KoBo34, V5ARA202; Bundesministerium für Bildung und Forschung, BMBF: 16SV7985","ACKNOWLEDGMENT The authors would like to thank Jan Schreiber for supporting the experiments and Nico Mansfeld for proof reading. We gratefully acknowledge the funding of the Lighthouse Initiative Geriatronics by StMWi Bayern (Project X, grant no. 5140951) and LongLeif GaPa gGmbH (Project Y, grant no. 5140953), the European Union’s Horizon 2020 research and innovation programme as part of the project ILIAD under grant no. 732737 and the funding of project KoBo34 (Verbundprojektnummer V5ARA202) by the BMBF (grant no. 16SV7985). The support by the Bavarian Institute for Digital Transformation is gratefully acknowledged. Please note that S. Haddadin has a potential conflict of interest as shareholder of Franka Emika GmbH.",,,,,,,,,,"Haddadin, S., Haddadin, S., Khoury, A., On making robots understand safety: Embedding injury knowledge into control (2012) The International Journal of Robotics Research., 31 (13), pp. 1578-1602; (2017) Robots and Robotic Devices-Collaborative Robots, , ISO/TS 15066, Beuth-Verlag, Berlin; Dhillon, B.S., (2012) Robot Reliability and Safety, , Springer Science and Business Media; Martin, W.L., Murray, P.S., Bates, P.R., Fear-potentiated startle: A review from an aviation perspective (2015) The International Journal of Avaiation Psychology, 25 (2), pp. 97-107; Hattori, T., Yamada, Y., Mori, S., Okamoto, S., Hara, S., Psychological experiments on avoidance action characteristics for estimating avoidability of harm to eyes from robots (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5400-5405. , Vilamoura; Kirschner, R.J., Burr, L., Mayer, H., Mansfeld, N., Abdolshah, S., Haddadin, S., Towards preventing involuntary human motion in hri: Parameter identification and control concept (2020) , IEEE Transactions on Cognitive and Developmental Systems, , to be published; Grillon, C., Fear-potentiated startle in humans: Effects of anticipatory anxiety on the acoustic blink reflex (1991) Psychophysiology, 28 (5), pp. 588-595; Kulic, D., Croft, E., Anxiety detection during human-robot interaction (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 616-621. , Edmonton, Alta; Haddadin, S., Johannsmeier, L., Becker, M., Schappler, M., Lilge, T., Haddadin, S., Roboterfabrik: A pilot to link and unify German robotics education to match industrial and societal demands (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, p. 375; Share, P., Pender, J., Preparing for a robot future? Social professions, social robotics and the challenges ahead (2018) Irish Journal of Applied Social Studies, 18 (1), p. 4; De Lange, F.P., Heilbron, M., Kok, P., How do expectations shape perception? (2018) Trends in Cognitive Sciences, 22 (9), pp. 764-779; Haddadin, S., Albu-Schffer, A., Hirzinger, G., Requirements for safe robots: Measurements, analysis and new insights (2009) The International Journal of Robotics Research, 28 (11-12), pp. 1507-1527; Burke, M.J., Sarpy, S.A., Smith-Crowe, K., Chan-Serafin, S., Salvador, R.O., Islam, G., Relative effectiveness of worker safety and health training methods (2006) American Journal of Public Health, 96 (2), pp. 315-324. , February 1; Parsons, H.M., Human factors in industrial robot safety (1986) Journal of Occupational Accidents, 8 (1-2), pp. 25-47; Weitschat, R., Ehrensperger, J., Maier, M., Aschemann, H., Safe and efficient human-robot collaboration Part I: Estimation of human arm motions (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1993-1999. , Brisbane, QLD; Grillon, C., Baas, J., A review of the modulation of the startle reflex by affective states and its application in psychiatry (2003) Clinical Neurophysiology, 114 (9), pp. 1557-1579; Davis, M., The mammalian startle response (1984) Neural Mechanism of Startle Behavior, pp. 287-351. , R.C. Eaton (ed.), New York: Plenum Press; Coulson, M., Attributing emotion to static body postures: Recognition accuracy, confusions, and viewpoint dependence (2004) Journal of Nonverbal Behavior, 28 (2), pp. 117-139; Ekman, P., Friesen, W.V., Simons, R.C., Is the startle reaction an emotion (1985) Journal of Personality and Social Psychology, 49 (5), pp. 1416-1426; Allwood, J., Cerrato, L., Jokinen, K., Navarretta, C., Paggio, P., The MUMIN coding scheme for the annotation of feedback, turn management and sequencing phenomena (2007) Language Resources and Evaluation, 41 (3-4), pp. 273-287; Wittenburg, P., Brugman, H., Russel, A., Klassmann, A., Sloetjes, H., ELAN: A professional framework for multimodality research? (2006) Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC 2006), pp. 1556-1559",,,"IEEE Robotics and Automation Society;Nagoya University","Institute of Electrical and Electronics Engineers Inc.","2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021","4 March 2021 through 6 March 2021",,168860,,9781665438629,,,"English","ISR - IEEE Int. Conf. Intell. Saf. Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85106486695
"Liu J., Yamada Y., Akiyama Y.","57218350116;35410760400;55516094900;","Calculating the supplied energy for physical human-robot interaction",2021,"ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics",,,"9419517","157","160",,,"10.1109/ISR50024.2021.9419517","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106482164&doi=10.1109%2fISR50024.2021.9419517&partnerID=40&md5=eeb8bdf6dc27fefe2d6cf7b0c21724ac","Nagoya University, Faculty of Department of Mechanical Systems Engineering, Nagoya, 464-8603, Japan","Liu, J., Nagoya University, Faculty of Department of Mechanical Systems Engineering, Nagoya, 464-8603, Japan; Yamada, Y., Nagoya University, Faculty of Department of Mechanical Systems Engineering, Nagoya, 464-8603, Japan; Akiyama, Y., Nagoya University, Faculty of Department of Mechanical Systems Engineering, Nagoya, 464-8603, Japan","As the safety of robotics is highly demanded, injury criteria are widely investigated and employed in control strategies because of their guiding roles, in which the energy (density) tolerances are of priority owing to unification of other physical variables for injury criteria. However, the calculation method for the supplied energy of the physical Human-Robot Interaction (pHRI) is not addressed by considering the viscoelasticity of the human soft tissue. This paper presents four collision modes for classifying the pHRI and proposes a generic method for calculating the supplied energy of each collision mode. Consequently, specific calculation instances are given by treating the human soft tissue as a three-element Maxwell model with maximum/allowable compressive displacement. The feasibility and precision of the proposed calculation method are verified by comparing the computational and simulation results. This calculation method is intended to provide a way of directly determining or indirectly converting the allowable velocity or mass of an approaching robot. © 2021 IEEE.","Calculation method; Collision mode; Human soft tissue; Physical Human-Robot Interaction; Supplied energy; Viscoelasticity","Agricultural robots; Man machine systems; Robotics; Tissue; Tissue engineering; Generic method; In-control; Injury criteria; Maxwell models; Physical human-robot interactions; Physical humanrobot interaction (phri); Physical variables; Soft tissue; Human robot interaction",,,,,,,,,,,,,,,,"Haddadin, S., (2013) Towards Safe Robots: Approaching Asimov's 1st Law, 90. , Springer; Versace, J., (1971) A Review of the Severity Index, , tech. rep., SAE Technical Paper; Bicchi, A., Tonietti, G., Fast and"" soft-arm"" tactics [robot arm design] (2004) IEEE Robotics & Automation Magazine, 11 (2), pp. 22-33; Oberer, S., Schraft, R.D., Robot-dummy crash tests for robot safety assessment (2007) Proceedings 2007 IEEE International Conference on Robotics and Automation, pp. 2934-2939; Yamada, Y., Umetani, Y., Hirasawa, Y., Proposal of a psychophysiological experiment system applying the reaction of human pupillary dilation to frightening robot motions (1999) IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 99CH37028), 2, pp. 1052-1057; Alami, R., Albu-Schäffer, A., Bicchi, A., Bischoff, R., Chatila, R., De Luca, A., De Santis, A., Hirzinger, G., Safe and dependable physical human-robot interaction in anthropic domains: State of the art and challenges (2006) 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1-16; Povse, B., Koritnik, D., Kamnik, R., Bajd, T., Munih, M., Industrial robot and human operator collision (2010) 2010 IEEE International Conference on Systems, Man and Cybernetics, pp. 2663-2668; Haddadin, S., Haddadin, S., Khoury, A., Rokahr, T., Parusel, S., Burgkart, R., Bicchi, A., Albu-Schäffer, A., On making robots understand safety: Embedding injury knowledge into control (2012) The International Journal of Robotics Research, 31 (13), pp. 1578-1602; Sugiura, R., Fujikawa, T., Nishikata, R., Nishimoto, T., Soft tissue bruise injury by blunt impact in human-robot interaction-difference of tolerance between chest and extremities (2019) 2019 19th International Conference on Control, Automation and Systems (ICCAS), pp. 792-797; (2016) Ts 15066: 2016: Robots and Robotic Devices-collaborative Robots, , I. ISO Geneva, Switzerland: International Organization for Standardization; ISO-ISO 12100:2010-Safety of machinery-General principles for design-Risk assessment and risk reduction.""; Tadele, T.S., De Vries, T.J., Stramigioli, S., Combining energy and power based safety metrics in controller design for domestic robots (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 1209-1214; Raiola, G., Cardenas, C.A., Tadele, T.S., De Vries, T., Stramigioli, S., Development of a safety-and energy-aware impedance controller for collaborative robots (2018) IEEE Robotics and Automation Letters, 3 (2), pp. 1237-1244; Fujikawa, T., Sugiura, R., Nishikata, R., Nishimoto, T., Critical contact pressure and transferred energy for soft tissue injury by blunt impact in human-robot interaction (2017) 2017 17th International Conference on Control, Automation and Systems (ICCAS), pp. 867-872; Haddadin, S., Albu-Schaffer, A., De Luca, A., Hirzinger, G., Collision detection and reaction: A contribution to safe physical human-robot interaction (2008) 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3356-3363; Iki, Y., Yamada, Y., Akiyama, Y., Okamoto, S., Niwa, K., Liu, J., Evaluation of contact between a human hand and a robot end tip for designing a dummy (2019) The Proceedings of JSME Annual Conference on Robotics and Mechatronics (Robomec), 2019, pp. 201-208; Fung, Y., (2013) Biomechanics: Mechanical Properties of Living Tissues, , Springer Science & Business Media; Winter, D.A., (2009) Biomechanics and Motor Control of Human Movement, , John Wiley & Sons","Liu, J.; Nagoya University, Japan; email: liu.jian@k.mbox.nagoya-u.ac.jp",,"IEEE Robotics and Automation Society;Nagoya University","Institute of Electrical and Electronics Engineers Inc.","2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021","4 March 2021 through 6 March 2021",,168860,,9781665438629,,,"English","ISR - IEEE Int. Conf. Intell. Saf. Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85106482164
"Escalante F.M., Jutinico A.L., Jaimes J.C., Terra M.H., Siqueira A.A.G.","57195420315;54929830800;57195319020;7003287549;7003788864;","Markovian Robust Filtering and Control Applied to Rehabilitation Robotics",2021,"IEEE/ASME Transactions on Mechatronics","26","1","9241443","491","502",,6,"10.1109/TMECH.2020.3034245","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101284081&doi=10.1109%2fTMECH.2020.3034245&partnerID=40&md5=0e4d5345084d16eda90e1ae83cec4d64","Department of Electrical Engineering, University of São Paulo, São Carlos, 13566590, Brazil; Universidad Antonio Nariño, Bogotá, 110231, Colombia; Department of Mechanical Engineering, University of São Paulo, São Carlos, 13566590, Brazil","Escalante, F.M., Department of Electrical Engineering, University of São Paulo, São Carlos, 13566590, Brazil; Jutinico, A.L., Universidad Antonio Nariño, Bogotá, 110231, Colombia; Jaimes, J.C., Department of Mechanical Engineering, University of São Paulo, São Carlos, 13566590, Brazil; Terra, M.H., Department of Electrical Engineering, University of São Paulo, São Carlos, 13566590, Brazil; Siqueira, A.A.G., Department of Mechanical Engineering, University of São Paulo, São Carlos, 13566590, Brazil","In this article, we deal with rehabilitation robot robust control in order to obtain best performance in the human-robot interaction. This class of system is subject to abrupt changes due to exogenous inputs and parameter variations caused, mainly, by the human dynamic behavior. In this context, Markovian jump linear systems offer suitable tools to model and control of this class of interactions. We propose force and impedance controllers based on robust recursive Markovian versions of the standard Kalman filter and the linear quadratic regulator. Different levels of the individual's actuation during the interaction with the robot define the Markov states. The measurement of electromyographic signals is used as jump parameter among Markov states. Also, a serious game is used to generate visual feedback, to promote active user participation, and to guide his movement routine. We compare our proposal with two other Markovian-based approaches and show the effectiveness our method through experiments with an ankle rehabilitation platform. © 1996-2012 IEEE.","Markovian jump linear systems (MJLS); robotic rehabilitation; robust control; robust estimation","Behavioral research; Biofeedback; Kalman filters; Linear systems; Markov processes; Robotics; Robust control; Serious games; Visual communication; Visual servoing; Electromyographic signal; Impedance controllers; Linear quadratic regulator; Markovian jump linear systems; Modeling and control; Rehabilitation robot; Rehabilitation robotics; Standard Kalman filters; Human robot interaction",,,,,"2014/50851-0, 465755/2014-3; 2018213; Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS): 728, 753; Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP: 2011/04074-3, 2013/14756-0, 2019/05937-7; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq: 132221/2013-6, 142080/2016-0","Manuscript received August 18, 2019; revised March 1, 2020, June 14, 2020, and September 20, 2020; accepted October 1, 2020. Date of publication October 27, 2020; date of current version February 16, 2021. Recommended by Technical Editor T. Shimono and Senior Editor M. K. O’Malley. This work was supported in part by the Pro-Rectory of Research of University of São Paulo, Coordination for the Improvement of Higher Education Personnel (CAPES)-Finance Code 001, Ministry of Science, Technology and Innovation of Colombia Minciencias (before Colciencias) under Grant 728 and Grant 753, in part by the Univer-sidad Antonio Nariño under Project 2018213 and PFAN-UAN, in part by the National Council for Scientific and Technological Development (CNPq) under Grant 132221/2013-6 and Grant 142080/2016-0, in part by São Paulo Research Foundation (FAPESP) under Grant 2011/04074-3, Grant 2013/14756-0, and Grant 2019/05937-7, and in part by the Project INCT-INSac (National Institute of Science and Technology for Cooperative Autonomous Systems)— under Grant CNPq 465755/2014-3 and Grant FAPESP 2014/50851-0. (Corresponding author: Andres L. Jutinico.) Felix M. Escalante and Marco H. Terra are with the Department of Electrical Engineering, University of São Paulo, São Carlos 13566590, Brazil (e-mail: maurinho707@usp.br; terra@sc.usp.br).",,,,,,,,,,"Losey, D.P., McDonald, C.G., Battaglia, E., O'Malley, M.K., A review of intent detection, arbitration, and communication aspects of shared control for physical human-robot interaction (2018) Appl. Mechanics Rev., 70 (1), pp. 1-19. , Feb; Huo, W., Mohammed, S., Moreno, J.C., Amirat, Y., Lower limb wearable robots for assistance and rehabilitation: A state of the art (2016) Ieee Syst. J., 10 (3), pp. 1068-1081. , Sep; Jamwal, P.K., Hussain, S., Xie, S.Q., Review on design and control aspects of ankle rehabilitation robots (2015) Disability. Rehabil. Assistive. Technol., 10 (2), pp. 93-101. , Dec; Dos Santos, W.M., Caurin, G.A., Siqueira, A.A., Design and control of an active knee orthosis driven by a rotary series elastic actuator (2017) Control Eng. Pract., 58, pp. 307-318. , Jan; Pratt, G.A., Williamson, M.M., Series elastic actuators (1995) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. Hum. Robot Interact. Cooperative Robots, Aug., 1, pp. 399-406; Hogan, N., Impedance control: An approach to manipulation (1985) J. Dyn. Syst., Meas. Control, 107 (1), pp. 1-24; Kong, K., Bae, J., Tomizuka, M., Control of rotary series elastic actuator for ideal force-mode actuation in human-robot interaction applications (2009) IEEE/ASME Trans. Mechatronics, 14 (1), pp. 105-118. , Feb; Wang, M., Sun, L., Yin, W., Dong, S., Liu, J., Continuous robust control for series elastic actuator with unknown payload parameters and external disturbances (2017) IEEE/CAA J. Automatica Sinica, 4 (4), pp. 620-627. , Oct; Calanca, A., Fiorini, P., Impedance control of series elastic actuators based on well-defined force dynamics (2017) Robot. Auton. Syst., 96, pp. 81-92. , Oct; Lee, H., Rouse, E.J., Krebs, H.I., Summaryofhuman ankle mechanical impedance during walking (2016) Ieee J. Translational. Eng. Health Med., 4. , Aug; Geertsen, S.S., Kirk, H., Lorentzen, J., Jorsal, M., Johansson, C.B., Nielsen, J.B., Impaired gait function in adults with cerebral palsy is associated with reduced rapid force generation and increased passive stiffness (2015) Clin. Neurophysiol., 126 (12), pp. 2320-2329. , Dec; Buerger, S.P., Hogan, N., Complementary stability and loop shaping for improved human-robot interaction (2007) Ieee Trans. Robot., 23 (2), pp. 232-244. , Apr; Li, X., Pan, Y., Chen, G., Yu, H., Adaptive human-robot interaction control for robots driven by series elastic actuators (2017) Ieee Trans. Robot., 33 (1), pp. 169-182. , Feb; Li, X., Pan, Y., Chen, G., Yu, H., Multi-modal control scheme for rehabilitation robotic exoskeletons (2017) Int. J. Robot. Res., 36 (5-7), pp. 759-777. , Feb; Calanca, A., Capisani, L., Fiorini, P., Robust force control of series elastic actuators (2014) Actuators, 3 (3), pp. 182-204. , Sep; Nogueira, S.L., Siqueira, A.A., Inoue, R.S., Terra, M.H., Markov jump linear systems-based position estimation for lower limb exoskeletons (2014) Sensors, 14 (1), pp. 1835-1849. , Jan; Global Kalman filter approaches to estimate absolute angles of lower limb segments (2017) Biomed. Eng. Online, 16 (1), pp. 1-20. , S.L. Nogueiraet al., May; Jutinico, A.L., Jaimes, J.C., Escalante, F.M., Perez-Ibarra, J.C., Terra, M.H., Siqueira, A.A.G., Impedance control for robotic rehabilitation: A robust Markovian approach (2017) Front. Neurorobotics, 11. , Aug; Jutinico, A.L., Jaimes, J.C., Escalante, F.M., Perez-Ibarra, J.C., Terra, M.H., Siqueira, A.A.G., Recursive robust regulator for discrete-time Markovian jump linear systems: Control of series elastic actuators (2017) Proc. 20th Int. Federation Accountants World Congr, 50, pp. 1340-1345. , Jul; Jutinico, A.L., Escalante, F.M., Jaimes, J.C., Terra, M.H., Siqueira, A.A.G., Markovian robust compliance control based on electromyo-graphic signals (2018) Proc. 7th Ieee Int. Conf. Biomed. Robot. Biomecha-tronics, Enschede, the Netherlands, Aug., pp. 1218-1223; Escalante, F.M., Jutinico, A.L., Jaimes, J.C., Siqueira, A.A.G., Terra, M.H., Robust Kalman filter and robust regulator for discrete-time Marko-vian jump linear systems: Control of series elastic actuators (2018) Proc. Ieee Conf. Control Technol. Appl., Copenhagen, Denmark, Aug., pp. 976-981; Costa, O.L., Fragoso, M.D., Marques, R., (2005) Discrete-Time Markov Jump Linear Systems (Probability and Its Applications), , Berlin, Germany: Springer; Do Valle Costa, O.L., Fragoso, M.D., Todorov, M.G., (2012) Continuous-Time Markov Jump Linear Systems, , Berlin, Germany: Springer; Cerri, J.P., Terra, M.H., Recursive robust regulator for discrete-time Markovian jump linear systems (2017) Ieee Trans. Autom. Control, 62 (11), pp. 6004-6011. , Nov; Ishihara, J.Y., Terra, M.H., Cerri, J.P., Optimal robust filtering for systems subject to uncertainties (2015) Automatica, 52, pp. 111-117. , Nov; Gonçalves, A., Siqueira, A.A., Dos Santos, W.M., Consoni, L.J., Do Amaral, L.M., Franzolin Ob, S.De, Development and evaluationof arobotic platform for rehabilitation of ankle movements (2013) Proc. 22nd Int. Congr. Mech. Eng., Ribeirão Preto, Brazil, pp. 8291-8298. , Nov; Milner-Brown, H.S., Stein, R.B., Yemm, R., Changes in firing rate of human motor units during linearly changing voluntary contractions (1973) J. Physiol., 230 (2), pp. 371-390. , Apr; Lloyd, D.G., Besier, T.F., An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo (2003) J. Biomech., 36 (6), pp. 765-776. , Jun; Cerri, J.P., (2013) Control and Filtering for Uncertain Discrete-time Markovian Jump Linear Systems, , Ph.D. dissertation, São Carlos School Eng., Univ. São Paulo, São Paulo, Brazil","Jutinico, A.L.; Universidad Antonio NariñoColombia; email: ajutinico@uan.edu.co",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10834435,,IATEF,,"English","IEEE ASME Trans Mechatron",Article,"Final","",Scopus,2-s2.0-85101284081
"Sheron P.S.F., Sridhar K.P., Baskar S., Shakeel P.M.","57218371092;55608797500;56396373800;57211443039;","Projection-dependent input processing for 3D object recognition in human robot interaction systems",2021,"Image and Vision Computing","106",,"104089","","",,3,"10.1016/j.imavis.2020.104089","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098654356&doi=10.1016%2fj.imavis.2020.104089&partnerID=40&md5=3902f0935d63908decb9ab766128101f","Department of Electronics and Communication, Karpagam Academy of Higher Education, Coimbatore, India; Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia","Sheron, P.S.F., Department of Electronics and Communication, Karpagam Academy of Higher Education, Coimbatore, India; Sridhar, K.P., Department of Electronics and Communication, Karpagam Academy of Higher Education, Coimbatore, India; Baskar, S., Department of Electronics and Communication, Karpagam Academy of Higher Education, Coimbatore, India; Shakeel, P.M., Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia","Human-Robot Interaction (HRI) provides assisted services in different real-time applications. The robotic systems identify objects through digital visualization wherein a three-dimensional (3D) image is converged to a plane-based projection. The projection is analyzed using the co-ordinates and identification points for recognizing the object. In such a converging process, the misidentification of projections in different planes results in recognition errors. This article proposes projection-dependent input processing (PDIP) method to reduce the misidentifications in object recognition. In this method, the input is the visualizing image projected in all the possible dimensions to identify the conjoining indices. The conjoined indices without intersection are segregated using labeled analysis. The non-correlating indices are identified in the possible dimension projections to prevent errors. The deviations in planes and indices matching are prevented by correlating the input with similar stored inputs with labels. The proposed method is verified using the metrics recognition ratio (96.4%), time (630.36 ms), complexity (5.93), and error (0.605). © 2020 Elsevier B.V.","3D images; Dimension modeling; HRI; Principle component analysis; Space projection","Errors; Man machine systems; Object recognition; Three dimensional computer graphics; 3d object recognition; Digital visualization; Human robot Interaction (HRI); Identification point; Real-time application; Recognition error; Recognition ratio; Three-dimensional (3D) image; Social robots",,,,,"Department of Science and Technology, Ministry of Science and Technology, India, डीएसटी; Science and Engineering Research Board, SERB; Department of Science and Technology, Government of West Bengal, DST","The authors would like to thank SERB, Science and Engineering Research Board, and Department of Science and Technology(DST), New Delhi, India for the funding to carry out the research work at our institution.","The authors would like to thank SERB, Science and Engineering Research Board , and Department of Science and Technology(DST) , New Delhi, India for the funding to carry out the research work at our institution.",,,,,,,,,"Boutteau, R., Rossi, R., Qin, L., Merriaux, P., Savatier, X., A vision-based system for robot localization in large industrial environments (2019) J. 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Remote Sens., 161, pp. 13-26; Rahman, M.M., Tan, Y., Xue, J., Shao, L., Lu, K., 3D object detection: learning 3D bounding boxes from scaled-down 2D bounding boxes in RGB-D images (2019) Inf. Sci., 476, pp. 147-158; Ren, Y., Chen, C., Li, S., Kuo, C.-C.J., Context-assisted 3D (C3D) object detection from RGB-D images (2018) J. Vis. Commun. Image Represent., 55, pp. 131-141; Wang, D., He, Y., Liu, Y., Li, D., Wu, S., Qin, Y., Xu, Z., 3D object detection algorithm for panoramic images with multi-scale convolutional neural network (2019) IEEE Access., 7, pp. 171461-171470; Guo, B., Dai, H., Li, Z., Huang, W., Efficient planar surface-based 3D mapping method for mobile robots using stereo vision (2019) IEEE Access., 7, pp. 73593-73601; Berz, E.L., Tesch, D.A., Hessel, F.P., Machine-learning-based system for multi-sensor 3D localization of stationary objects (2018) IET Cyber-Phys. Syst. Theory Applic., 3 (2), pp. 81-88; Crivellaro, A., Rad, M., Verdie, Y., Yi, K.M., Fua, P., Lepetit, V., Robust 3D object tracking from monocular images using stable parts (2018) IEEE Trans. Pattern Anal. Mach. Intell., 40 (6), pp. 1465-1479; Zhang, J., Sun, J., Instance-based object recognition in 3D point clouds using discriminative shape primitives (2017) Mach. Vis. Appl., 29 (2), pp. 285-297; Yang, Y., Qiu, F., Li, H., Zhang, L., Wang, M.-L., Fu, M.-Y., Large-scale 3D semantic mapping using stereo vision (2018) Int. J. Autom. Comput., 15 (2), pp. 194-206; https://shape.cs.princeton.edu/benchmark/","Sheron, P.S.F.; Department of Electronics and Communication, India; email: febinsheronpps@gmail.com",,,"Elsevier Ltd",,,,,02628856,,IVCOD,,"English","Image Vision Comput",Article,"Final","",Scopus,2-s2.0-85098654356
"Sun T., Gong L., Li X., Xie S., Chen Z., Hu Q., Filliat D.","57219691816;36637488200;57222189326;57222182583;57216690652;57222187386;55976331500;","RobotDrlSim: A Real Time Robot Simulation Platform for Reinforcement Learning and Human Interactive Demonstration Learning",2021,"Journal of Physics: Conference Series","1746","1","012035","","",,1,"10.1088/1742-6596/1746/1/012035","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101841563&doi=10.1088%2f1742-6596%2f1746%2f1%2f012035&partnerID=40&md5=f1774170832a927eaf3eb3ac738f9a87","School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Computer Science and System Engineering Department and Inria Flowers Team, Ensta, Institut Polytechnique Paris, Palaiseau, France","Sun, T., School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Gong, L., School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Li, X., School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Xie, S., School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Chen, Z., School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Hu, Q., School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Filliat, D., Computer Science and System Engineering Department and Inria Flowers Team, Ensta, Institut Polytechnique Paris, Palaiseau, France","Deep reinforcement learning (DRL) techniques give robotics research an AI boost in many applications. In order to simultaneously accommodate the complex robotic behaviour simulation and DRL algorithm verification, a new simulation platform, namely the RobotDrlSim, is proposed. First, we design a standardized API interfacing mechanism for coordinating diverse environments on RobotDrlSim platform, where PyBullet simulator is equipped with an API to form a physical engine for robotics simulation. Second, benchmark DRL models are included in the baseline library for evaluation. Third, real-Time human-robot interactions can be captured and imported to drive the RobotDrlSim tasks, which provide big data-stream for reinforcement learning. Experimentations show that cutting-edge DRL algorithms developed in python can be seamlessly deployed to the robots, and human interactions can be availed in training the robots. RobotDrlSim is valid for efficiently developing DRL algorithms for artificial intelligence models of robots, and it is especially suitable for the robot educational purposes. © Published under licence by IOP Publishing Ltd.",,"Data streams; Deep learning; Digital storage; Human robot interaction; Reinforcement learning; Robotics; Simulation platform; Algorithm verification; Behaviour simulations; Cutting edges; Demonstration learning; Human interactions; Physical engines; Real-time robots; Robotics research; Educational robots",,,,,,,,,,,,,,,,"Levine, S., Krizhevsky, A., (2012) Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection; Vinyals, O., Grandmaster level in StarCraft II using multi-Agent reinforcement learning (2019) Nature, 575, pp. 350-354; Silver, D., Mastering the game of Go with deep neural networks and tree search (2016) Nature, 529, pp. 484-489; Mnih, V., (2013) Playing Atari with Deep Reinforcement Learning CoRR abs/1312.5, , http://arxiv.org/abs/1312.5602, [Online]. Available; Coumans, E., (2013) Bullet physics library Open source bulletphysics. org, 15, p. 5; Coumans, E., Bai, Y., PyBullet, a Python module for physics simulation for games, robotics and machine learning; Langevin, G., (2014), InMoov; Plasencia, A., Shichkina, Y., Suárez, I., Ruiz, Z., Open source robotic simulators platforms for teaching deep reinforcement learning algorithms (2019) Procedia Comput. Sci, 150, pp. 162-170; James, S., Johns, E., (2016) 3D Simulated Robot Manipulation Using Deep Reinforcement Learning; Ferigo, D., Traversaro, S., Metta, G., Pucci, D., Gym-Ignition: Reproducible Robotic Simulations for Reinforcement Learning Proc (2020) 2020 IEEE/SICE Int. Symp. Syst. Integr. SII, 2020, pp. 885-890; Kirtas, M., Tsampazis, K., Passalis, N., Tefas, A., (2020) Deepbots: A Webots-Based Deep Reinforcement Learning Framework for Robotics Artificial Intelligence Applications and Innovations, pp. 64-75; Matas, J., (2018) Learning end-To-end robotic manipulation of deformable objects, , https://www.imperial.ac.uk/media/imperial-college/faculty-of-engineering/computing/public/1718-ug-projects/Jan-Matas-Learning-end-To-end-robotic-manipulation-of-deformable-objects.pdf, [Online]. Available; Finn, C., Abbeel, P., Eecs, P., Edu, B., (2016) Guided Cost Learning : Deep Inverse Optimal Control via Policy Optimization, p. 48; Quillen, D., Jang, E., Nachum, O., Finn, C., Ibarz, J., Levine, S., (2018) Deep Reinforcement Learning for Vision-Based Robotic Grasping : A Simulated Comparative Evaluation of Off-Policy Methods, pp. 6284-6291; Hill, A., (2018) GitHub repository (GitHub) Stable Baselines","Gong, L.; School of Mechanical Engineering, China; email: gongliang_m@sjtu.edu.cn",,,"IOP Publishing Ltd","2020 3rd International Conference on Modeling, Simulation and Optimization Technologies and Applications, MSOTA 2020","22 November 2020 through 23 November 2020",,167331,17426588,,,,"English","J. Phys. Conf. Ser.",Conference Paper,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85101841563
"Yoon H.-S., Park S.-W., Yoo J.-H.","16551453900;57221540482;7402295457;","Real-time hair segmentation using mobile-unet",2021,"Electronics (Switzerland)","10","2","99","1","12",,1,"10.3390/electronics10020099","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099416370&doi=10.3390%2felectronics10020099&partnerID=40&md5=43da1521b44f0c7a7640029dfe9f4378","HRI Section, Artificial Intelligence Research Laboratory, Electronics and Telecommunications Research Institute (ETRI), 218 Gajung-ro, Yueseong-gu, Daejeon, 34129, South Korea; ICT Department, ETRI School, University of Science & Technology, Daejeon, 34129, South Korea","Yoon, H.-S., HRI Section, Artificial Intelligence Research Laboratory, Electronics and Telecommunications Research Institute (ETRI), 218 Gajung-ro, Yueseong-gu, Daejeon, 34129, South Korea, ICT Department, ETRI School, University of Science & Technology, Daejeon, 34129, South Korea; Park, S.-W., ICT Department, ETRI School, University of Science & Technology, Daejeon, 34129, South Korea; Yoo, J.-H., HRI Section, Artificial Intelligence Research Laboratory, Electronics and Telecommunications Research Institute (ETRI), 218 Gajung-ro, Yueseong-gu, Daejeon, 34129, South Korea, ICT Department, ETRI School, University of Science & Technology, Daejeon, 34129, South Korea","We described a real-time hair segmentation method based on a fully convolutional network with the basic structure of an encoder–decoder. In one of the traditional computer vision techniques for hair segmentation, the mean shift and watershed methodologies suffer from inaccuracy and slow execution due to multi-step, complex image processing. It is also difficult to execute the process in real-time unless an optimization technique is applied to the partition. To solve this problem, we exploited Mobile-Unet using the U-Net segmentation model, which incorporates the optimization techniques of MobileNetV2. In experiments, hair segmentation accuracy was evaluated by different genders and races, and the average accuracy was 89.9%. By comparing the accuracy and execution speed of our model with those of other models in related studies, we confirmed that the proposed model achieved the same or better performance. As such, the results of hair segmentation can obtain hair information (style, color, length), which has a significant impact on human-robot interaction with people. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Computer vision; Deep learning; FCN; Hair segmentation; HRI; Mobile-Unet",,,,,,"Ministry of Trade, Industry and Energy, MOTIE: 0077553; Korea Evaluation Institute of Industrial Technology, KEIT; Institute for Information and Communications Technology Promotion, IITP; Ministry of Science and ICT, South Korea, MSIT: 2019-0-00330","Funding: This research was supported by an Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean government (MSIT) (2019-0-00330, Development of AI Technology for Early Screening of Infant/Child Autism Spectrum Disorders based on Cognition of the Psychological Behavior and Response), and a Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by the Korean government (MOTIE) (0077553, Development of Social Robot Intelligence for Social Human–Robot Interaction of Service Robot).",,,,,,,,,,"Onyeulo, E.B., Gandhi, V., What Makes a Social Robot Good at Interacting with Humans? (2020) Information, 11, p. 43. , [CrossRef]; Breazeal, C., Scassellati, B., Robots that imitate humans (2002) Trends Cogn. Sci, 6, pp. 481-487. , [CrossRef]; Chai, M., Shao, T., Wu, H., Weng, Y., Zhou, K., Autohair: Fully automatic hair modeling from a single image (2016) ACM Trans. Graph, 35. , [CrossRef]; Yacoob, Y., Davis, L., Detection, analysis and matching of hair Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV), pp. 741-748. , Beijing, China, 17–21 October 2005; Wang, D., Chai, X., Zhang, H., Chang, H., Zeng, W., Shan, S., A novel coarse-to-fine hair segmentation method (2011) J. Softw, 24, pp. 233-238; Wang, D., Shan, S., Zhang, H., Zeng, W., Chen, X., Data driven hair segmentation with isomorphic manifold inference (2014) Image Vis. Comput, 32, pp. 739-750. , [CrossRef]; Guo, W., Aarabi, P., Hair segmentation using heuristically-trained neural networks (2016) IEEE Trans. Neural Netw. Learn. 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Vis, 128, pp. 479-499. , [CrossRef]; Seyed-Iman, M., Mehrdad, F., Ang, L., Nir, L., Akihiro, M., Hassan, G., (2019) Improved Knowledge Distillation via Teacher Assistant, , arXiv arXiv:1902.03393; Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L., MobileNetV2: Inverted Residuals and Linear Bottlenecks Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510-4520. , Salt Lake City, UT, USA, 18–23 June 2018; Jang, J., Jeon, S.H., Kim, J., Yoon, H., Robust Deep Age Estimation Method Using Artificially Generated Image Set (2017) ETRI J, 39, pp. 643-651. , [CrossRef]; Wei, L., Dragomir, A., Dumitru, E., Christian, S., Scott, R., Fu, C.-Y., Berg, A.C., (2016) SSD: Single Shot MultiBox Detector, , arXiv arXiv:1512.02325v5; Ronneberger, O., Fischer, P., Brox, T., U-net: Convolutional networks for biomedical image segmentation Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234-241. , Munich, Germany, 5–9 October 2015; Levinshtein, A., Chang, C., Phung, E., Kezele, I., Guo, W., Aarabi, P., (2018) Real-time deep hair matting on mobile devices, , arXiv arXiv:1712.07168v2; Kae, A., Sohn, K., Lee, H., Learned-Miller, E., Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2019-2026. , Portland, OR, USA, 23–28 June 2013; Ileni, T., Borza, D., Darabant, A.S., A Deep Learning Approach to Hair Segmentation and Color Extraction from Facial Images Proceedings of the ACIVS 2018: Advanced Concepts for Intelligent Vision Systems, pp. 438-449. , Poitiers, France, 24–27 September 2018; Yan, Y., Berthelier, A., Duffner, S., Naturel, X., Garcia, C., Chateau, T., Human Hair Segmentation in the Wild Using Deep Shape Prior (2019) Proceedings of the Third Workshop on Computer Vision for AR/VR, pp. 1-4. , Long Beach, CA, USA, 17 June; Tkachenka, A., Karpiak, G., Vakunov, A., Kartynnik, Y., Ablavatski, A., Bazarevsky, V., Pisarchyk, S., Real-time Hair Segmentation and Recoloring on Mobile GPUs (2019) Proceedings of the CVPR Workshop on Computer Vision for Augmented and Virtual Reality, pp. 1-3. , Long Beach, CA, USA, 17 June","Yoon, H.-S.; HRI Section, 218 Gajung-ro, Yueseong-gu, South Korea; email: yoonhs@etri.re.kr
Yoon, H.-S.; ICT Department, South Korea; email: yoonhs@etri.re.kr",,,"MDPI AG",,,,,20799292,,,,"English","Electronics (Switzerland)",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85099416370
"Strathearn C., Ma M.","57204127919;57197837250;","The multimodal turing test for realistic humanoid robots with embodied artificial intelligence",2021,"CEUR Workshop Proceedings","3007",,,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120619585&partnerID=40&md5=0f36d5f43f10bfe2ba5b30f97937e03b","School of Computing and Digital Technologies, Staffordshire University, United Kingdom; Provost, Falmouth University, United Kingdom","Strathearn, C., School of Computing and Digital Technologies, Staffordshire University, United Kingdom; Ma, M., Provost, Falmouth University, United Kingdom","Alan Turing developed the Turing Test as a method to determine whether artificial intelligence (AI) can deceive human interrogators into believing it is sentient by competently answering questions at a confidence rate of 30%+. However, the Turing Test is concerned with natural language processing (NLP) and neglects the significance of appearance, communication and movement. The theoretical proposition at the core of this paper: ‘can machines emulate human beings?’ is concerned with both functionality and materiality. Many scholars consider the creation of a realistic humanoid robot (RHR) that is perceptually indistinguishable from a human as the apex of humanity’s technological capabilities. Nevertheless, no comprehensive development framework exists for engineers to achieve higher modes of human emulation, and no current evaluation method is nuanced enough to detect the causal effects of the Uncanny Valley (UV) effect. The Multimodal Turing Test (MTT) provides such a methodology and offers a foundation for creating higher levels of human likeness in RHRs for enhancing human-robot interaction (HRI). Copyright © 2021 for this paper by its authors.","Artificial intelligence; Embodied artificial intelligence; HRI; Humanoid robots; Turing test","Artificial intelligence; Human robot interaction; Intelligent robots; Natural language processing systems; Testing; 'current; Development frameworks; Embodied artificial intelligence; Higher mode; Human being; Humanoid robot; Humans-robot interactions; Multi-modal; Technological capability; Turing tests; Anthropomorphic robots",,,,,,,,,,,,,,,,"(2014) The Fake Kid Who Passed the Turing Test, , AAmoth. D Ret:time.com/2847900/eugene-goostman-turing-test Acc 5.2.20; (2011) AI tricks people into thinking it is human, , newscientist.com/article/dn20865-software-tricks-people-into-thinking-itishuman/#ixzz6Ez0JgQi1, Aron. J Retrieved: Acc: 25.02.20; Face to Face With The Future of AI (2020) Horizon Magazine, , https://www.staffs.ac.uk/alumni/horizon-alumni-magazinepp.8-11, Barnfield. N Riley Raven; (1994) Simulacra and simulation, , Baudrillard. J Trans: Ann Arbor: University of Michigan Press, ISBN-10: 0472065211; Bringsjord, S., Caporale, C., Noel, R., The Total Turing Test (2000) J-LLI, 9 (4), pp. 397-418. , www.jstor.org/stable/40180234; Burleigh. T, Schoenherr. J, Lacroix. G (2013). Does the uncanny valley exist? Computers in Human Behaviour, 29, pp. 759-771; (2019) What is New in Robotics?, , Cole. E Retrieved: blog.robotiq.com/whats-new-in-robotics-06.12.2019. A:25.02.20; (2014) Why Eugene Goostman Did Not Pass the Turing Test, , https://www.huffingtonpost.co.uk/jackcopeland/turingtesteugenegoostman, Copeland. J Retrieved: Acc:25.02.20; (2019) A tangible Turing Test, , https://www.neowin.net/news/a-tangible-turing-testthe-loebner-prizeiscoming-to-swansea-this-weekend/, Fawaz. A Retrieved: Acc: 21.02.20; Teaching to the Turing Test with Cleverbot (2014) Transformations: The Journal of Inclusive Scholarship and Pedagogy, 24 (1-2), pp. 56-66. , Gehl. R Retrieved February 25, 2020; (2016) Robots Could Hack Turing Test by Keeping Silent, , www.scientificamerican.com/article/robots-could-hack-turingtest-by-keeping-silent/, Ghose. T Retrieved: Acc: 25.02.20; Harnad, S., The Turing Test Is Not A Trick: Turing Indistinguishability Is A Scientific Criterion (1992) SIGART Bulletin, 3 (4), pp. 9-10. , (October 1992); Harnad, S., Minds Machine’s and Turing (2000) Journal of Logic, Language and Information, 9, p. 425. , https://doi.org/10.1023/A:1008315308862; (2014) What is the Turing Test?, , www.theguardian.com/technology/2014/jun/09/what-is-the-alan-turingtest, Hern. A Retrieved: Acc: 25.02.20; (2019) Advanced Robotics Forced Scientist To Invent A New Turing Test, , https://futurism.com/the-byte/scientists-invented-new-turing-test, Houser. K Retrieved: Acc: 18.04.20; Ishiguro, H., Android science: Toward a new cross-interdisciplinary framework. J-Comp-Sci, Corpus ID: 6105971Jacquet, B., Baratgin, J., & Jamet, F. (2019). Cooperation in Online Conversations (2005), 10, p. 727. , https://doi.org/10.3389/fpsyg.2019.00727, Journal of Psychology; Karniel, A, Avraham, G, Peles, Ba, Levy-Tzedek, (2010) Turing-Like Handshake Test for Motor Intelligence, , JoVE; Kasaki, M., Ishiguro, H., Asada, M., Osaka, M., Fujikado, T., (2016) Cognitive neuroscience Robotics: Synthetic Approaches to human understanding; (2019) Just because they are robots?, , www.ameinfo.com/industry/technology/robots-treat-humanoids-racialgender-bias, Khatib. H Retrieved: Acc: 15.09.19; (2019) There is no AGI, , https://arxiv.org/abs/1906.05833, Landgrebe. J Smith. B Retrieved: Acc: 25.02.20; (2014) The computer actually got an F on the Turing Test, , wired.com/2014/06/turing-test-not-so-fast/, Mann. A Ret: Acc:9.2.20; Marzano, G, Novembre, A., Machine’s that Dream: A New Challenge in Behavioral-Basic Robotics (2016) Procedia Computer Science, 104, pp. 146-151; (2019) Will androids ever be able to convince people they are human?, , www.researchgate.net/publication/341756234_Mr_Robot_Will_androids_ever_be_able_to_convince_people_they_are_human_Guardian_Online.Acc07.07.20, Mathieson. S Retrieved; The Uncanny Valley (1970) Energy, (7), pp. 33-35. , Mori. M; Oppy, G. R., Dowe, D. L., The Turing Test (2003) Stanford Encyclopedia of Philosophy, 1, pp. 1-26. , (online); (2019) You should fear Super Stupidity, , towardsdatascience.com/you-should-fear-super-stupidity-not-super-intelligence-19f93a46fa4d.Acc.17.02.20, Pereira. D not ASI Retrieved; Rapaport, W., How to Pass a Turing Test (2000) Journal of Logic, Language, and Information, 9 (4), pp. 467-490. , www.jstor.org/stable/40180238, Retrieved: Acc: 24.04 2020; (2016) Does the Fifth Amendment ‘expose a serious flaw’ in Turing Test?, , www.wired.co.uk/article/major-flaw-turing-testsilence, Reynolds. E Retrieved:. Acc:25.0 2.20; (2014) A Machine Did not ‘Pass’ the Turing Test, , https://www.vice.com/en_uk/article/gq8ddw/eugene-goostman-alanturing-test-kevin-warwick, Robbins. M Retrieved: Acc: 25.02.20; (2014) Scientists dispute if ‘Eugene Goostman’ passed Turing Test, , www.theguardian.com/technology/2014/jun/09/scientistsdisagree-over-whether-Turing-test-has-beenpassed.Acc.19.04.20, Sample. I & Hern. A Retrieved; The Truly Total Turing Test (1998) Minds Mach, 8 (2), pp. 263-272. , Schweizer. P (May 1998); Searle, J, Minds, brains, and programs (1980) Behavioural and Brain Sciences, 3 (3), pp. 417-457; (2012) Alan Turing: The experiment that shaped AI, , bbc.co.uk/news/technology-18475646, Sharkey. N Retrieved: Acc:22.02.20; Stock-Homburg. R, Peters. J, Schneider. K, Prasad. V, Nukovic. L (2020) Evaluation of the HTT for anthropomorphic Robots Int Conf HRI; (2019) Can machine’s think? A new Turing Test may have the answer, , www.weforum.org/agenda/2019/08/our-turing-test-for-androids-willjudge-how-lifelike-humanoid-robots-can-be/, The World Economic Forum Retrieved: Acc: 14.03.20; Turing, A., Computing Machinery and Intelligence (1950) Mind, (236), pp. 433-460. , doi.org; (2019) The hobbyists competing to make AI human, , www.bbc.co.uk/news/technology-49578503, Wakefield. J Retrieved: Acc: 25.02.20; Warwick, K., Shah, H., Passing the Turing Test Does Not Mean the End of Humanity (2015) Cognitive Computation, 8, pp. 409-419; Whitby, B, (1996) Reflections on AI: the legal, moral and ethical dimensions, , Intellect, Oxford, UK. ISBN 9781871516685",,"Stein A.Tomforde S.Botev J.Lewis P.",,"CEUR-WS","Joint LIFELIKE Workshops on 8th Edition in the Evolution of the Workshop Series of Autonomously Learning and Optimizing Systems, SAOS 2020 and 9th Edition in the Evolution of the Workshop Series of Autonomously Learning and Optimizing Systems, SAOS 2021","19 July 2021",,173932,16130073,,,,"English","CEUR Workshop Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85120619585
"Zhong B., Guo K., Yu H., Zhang M.","57211180683;57213609079;7405853761;57203987000;","Toward Gait Symmetry Enhancement via a Cable-Driven Exoskeleton Powered by Series Elastic Actuators",2021,"IEEE Robotics and Automation Letters",,,,"","",,,"10.1109/LRA.2021.3130639","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120574437&doi=10.1109%2fLRA.2021.3130639&partnerID=40&md5=f8c5e4024a595c84e02c797880016550","Department of Biomedical Engineering, Southern University of Science and Technology, Southern University of Science and Technology, Shenzhen, China, 518055 (e-mail: 11955001@mail.sustech.edu.cn); Department of biomedical engineering, Southern University of Science and Technology, Shenzhen, China, 518000 (e-mail: 11930454@mail.sustech.edu.cn); Biomedical Engineering, National University of Singapore, Singapore, Singapore, 117576 (e-mail: bieyhy@nus.edu.sg); Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China, 518055 (e-mail: zhangmm@sustech.edu.cn)","Zhong, B., Department of Biomedical Engineering, Southern University of Science and Technology, Southern University of Science and Technology, Shenzhen, China, 518055 (e-mail: 11955001@mail.sustech.edu.cn); Guo, K., Department of biomedical engineering, Southern University of Science and Technology, Shenzhen, China, 518000 (e-mail: 11930454@mail.sustech.edu.cn); Yu, H., Biomedical Engineering, National University of Singapore, Singapore, Singapore, 117576 (e-mail: bieyhy@nus.edu.sg); Zhang, M., Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China, 518055 (e-mail: zhangmm@sustech.edu.cn)","AbstractGait rehabilitation is essential for chronic stroke patients to regain independent walking ability and quality of life, of which symmetry is regarded as a gold standard of gait quality. In this letter, a cable-driven lower limb exoskeleton powered by series elastic actuators (SEAs) has been developed, and a generalized gait-phase-based assistive strategy has been presented aiming to enhance the gait symmetry of chronic stroke patients. This strategy features a parameterized assistive force generation method that allows individuals customization to achieve more synchronized assistance with their walking gait patterns. A compliant human-robot interaction is guaranteed by SEA, and the exoskeleton exhibits low passive impedance and good transparency. The proposed assistive strategy was evaluated with five able-bodied subjects walked with the assistance from exoskeleton while adding artificial impairments to mimic a pathological gait with deficits (i.e., reduced knee flexion and foot-drop). Experimental results proved its efficacy in enhancing temporal walking gait symmetry to levels comparable to participants normal gait. IEEE","Actuators; cable-driven; Exoskeletons; Force; Gait symmetry enhancement; Knee; Legged locomotion; lower limb exoskeleton; Springs; Stroke (medical condition); stroke rehabilitation; walking assistance","Cables; Exoskeleton (Robotics); Human robot interaction; Patient rehabilitation; Physiological models; Walking aids; Cable-driven; Exoskeleton; Force; Gait symmetries; Gait symmetry enhancement; Knee; Legged locomotion; Low limb exoskeleton; Lower limb; Medical conditions; Stroke (medical condition); Stroke rehabilitation; Walking assistance; Actuators",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Article in Press","",Scopus,2-s2.0-85120574437
"Stange S., Kopp S.","57215826942;35722438500;","Explaining Before or After Acting? How the Timing of Self-Explanations Affects User Perception of Robot Behavior",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"142","153",,,"10.1007/978-3-030-90525-5_13","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119872612&doi=10.1007%2f978-3-030-90525-5_13&partnerID=40&md5=75b73f88a776649d8005b1f6a4794eb7","Faculty of Technology, Bielefeld University, Bielefeld, Germany","Stange, S., Faculty of Technology, Bielefeld University, Bielefeld, Germany; Kopp, S., Faculty of Technology, Bielefeld University, Bielefeld, Germany","Explanations are a useful tool to improve human-robot interaction and the topic of what a good explanation should entail has received much attention. While a robot’s behavior can be justified upon request after its execution, the intention to act can also be signaled by a robot prior to the execution. In this paper we report results from a pre-registered study on the effects of a social robot proactively giving a self-explanation before vs. after the execution of an undesirable behavior. Contrary to our expectations we found that explaining a behavior before its execution did not yield positive effects on the users’ perception of the robot or the behavior. Instead, the robot’s behavior was perceived as less desirable when explained before the execution rather than afterwards. Exploratory analyses further revealed that even though participants felt less uncertain about what was going to happen next, they also felt less in control, had lower trust and lower contact intentions with a robot that explained before it acted. © 2021, Springer Nature Switzerland AG.","Explainable robots; Explanation timing; Human-robot-interaction","Felt; Human robot interaction; Man machine systems; Explainable robot; Explanation timing; Exploratory analysis; Humans-robot interactions; In-control; Robot behavior; Self explanations; Social robots; User perceptions; Timing circuits",,,,,"Bundesministerium für Bildung und Forschung, BMBF: FKZ 16SV7959","This research was supported by the German Federal Ministry of Education and Research (BMBF) in the project ‘VIVA’ (FKZ 16SV7959).",,,,,,,,,,"Anjomshoae, S., Najjar, A., Calvaresi, D., Främling, K., Explainable agents and robots: Results from a systematic literature review (2019) 18Th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), Montreal, Canada, May 13–17, 2019, Pp. 1078–1088. International Foundation for Autonomous Agents and Multiagent Systems; Baraka, K., Paiva, A., Veloso, M.: Expressive lights for revealing mobile service robot state. In: Robot 2015: Second Iberian Robotics Conference. AISC, vol. 417, pp. 107–119. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27146-0 9; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot., 1 (1), pp. 71-81; Besold, T.R., Uckelman, S.L., The what, the why, and the how of artificial explanations in automated decision-making (2018) Corr; Cha, E., Kim, Y., Fong, T., Mataric, M.J., A survey of nonverbal signaling methods for non-humanoid robots (2018) Found. 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Methods, 39 (2), pp. 175-191; Hilton, D.J., Conversational processes and causal explanation (1990) Psychol. Bull., 107 (1), p. 65; Lyons, J.B., et al.: Shaping trust through transparent design: theoretical and experimental guidelines. In: Savage-Knepshield, P., Chen, J. (eds.) Advances in Human Factors in Robots and Unmanned Systems, vol. 499, pp. 127–136. Springer, Basel, Switzerland (2017). https://doi.org/10.1007/978-3-319-41959-6 11; Pipitone, A., Chella, A., What robots want? Hearing the inner voice of a robot (2021) Iscience, 24 (4); Priester, J.R., Petty, R.E., The gradual threshold model of ambivalence: relating the positive and negative bases of attitudes to subjective ambivalence (1996) J. Personal. Soc. Psychol., 71 (3), p. 431; Putnam, V., Conati, C., Exploring the need for explainable artificial intelligence (XAI) in intelligent tutoring systems (ITS) (2019) CEUR Workshop Proceedings, pp. 23-27. , pp; Reich-Stiebert, N., Eyssel, F., Learning with educational companion robots? Toward attitudes on education robots, predictors of attitudes, and application potentials for education robots (2015) Int. J. Soc. Robot., 7 (5), pp. 875-888; Reysen, S., Construction of a new scale: The reysen likability scale (2005) Soc. Behav. Personal. Int. J., 33 (2), pp. 201-208; Rosenfeld, A., Richardson, A., Explainability in human-agent systems (2019) Auton. Agents Multi-Agent Syst., 33 (6), pp. 673-705; Stange, S., Kopp, S., Effects of a social robot’s self-explanations on how humans understand and evaluate its behavior (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 619-627. , pp; Stange, S., Kopp, S., Effects of referring to robot vs. User needs in self-explanations of undesirable robot behavior (2021) Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, pp. 271-275. , pp; Touré-Tillery, M., McGill, A.L., Who or what to believe: Trust and the differential persuasiveness of human and anthropomorphized messengers (2015) J. Market., 79 (4), pp. 94-110; Walton, D., A new dialectical theory of explanation (2004) Philos. Explor., 7 (1), pp. 71-89","Stange, S.; Faculty of Technology, Germany; email: sstange@techfak.uni-bielefeld.de","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119872612
"Woods D., Yuan F., Jao Y.-L., Zhao X.","57352094000;57222106767;56650809800;55705069200;","Social Robots for Older Adults with Dementia: A Narrative Review on Challenges & Future Directions",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"411","420",,,"10.1007/978-3-030-90525-5_35","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119870516&doi=10.1007%2f978-3-030-90525-5_35&partnerID=40&md5=e0a430433771ae692dbc5102406fdc55","University of Tennessee, Knoxville, TN  37996, United States; Pennsylvania State University, University Park, PA  16802, United States","Woods, D., University of Tennessee, Knoxville, TN  37996, United States; Yuan, F., University of Tennessee, Knoxville, TN  37996, United States; Jao, Y.-L., Pennsylvania State University, University Park, PA  16802, United States; Zhao, X., University of Tennessee, Knoxville, TN  37996, United States","Worldwide, approximately 50 million people live with Alzheimer’s disease or other dementias and there are nearly 10 million new cases every year. Social robots have been a promising approach to supplement human caregivers in dementia care. In this narrative review, we reviewed 62 articles to gain insight into the attitudes and perceptions among people with dementia and other stakeholders worldwide towards using social robots to assist dementia care. Then, we discussed some critical factors and challenges found in these studies influencing people’s perceptions as well as future directions in this field. The primary influencing challenges include cultural factors, users’ limited experiences with technologies, methodological challenges underlying qualitative studies as well as technological malfunctions in current robot system. We further suggested several aspects to be taken into more consideration in future research, including collaborations with other stakeholders, design of individual or group use for dementia care, an adaptive level of autonomy in a social robot and long-term human-robot interaction. © 2021, Springer Nature Switzerland AG.","Dementia care; Human-robot interaction; Social robots","Machine design; Man machine systems; Neurodegenerative diseases; Alzheimer; Attitudes and perceptions; Critical challenges; Critical factors; Dementia cares; Gain insight; Humans-robot interactions; New case; Older adults; Social robots; Human robot interaction",,,,,,,,,,,,,,,,"Appel, L., Peisachovich, E., Sinclair, D., Jokel, R., da Silva, C., SafeHome: A serious game to promote safe environments for persons living with dementia (2020) Cureus, 12 (2), p. e6949; Arthanat, S., Begum, M., Gu, T., Laroche, D.P., Xu, D., Zhang, N., Caregiver perspectives on a smart home-based socially assistive robot for individuals with Alzheimer’s disease and related dementia (2020) Disability and Rehabilitation: Assistive Technology, pp. 789-798. , pp; Aymerich-Franch, L., Why it is time to stop ostracizing social robots (2020) Nat. Mach. Intell., 2 (7), p. 364; Bartl, A., Bosch, S., Brandt, M., Dittrich, M., Lugrin, B., The influence of a social robot’s persona on how it is perceived and accepted by elderly users (2016) International Conference on Social Robotics, pp. 681-691. , pp., Social Robotics, Kansas; Boumans, R.J., (2020) Feasibility and Effectiveness of Social Robots in Acquiring Patient Reported Outcomes from Older Adults, p. 231. , p; Cavallo, F., Robotic services acceptance in smart environments with older adults: User satisfaction and acceptability study (2018) J. Med. Int. Res., 20 (9); Cesta, A., Cortellessa, G., Orlandini, A., Tiberio, L., Long-term evaluation of a telepresence robot for the elderly: Methodology and ecological case study (2016) Int. J. Soc. Robot., 8 (3), pp. 421-441; Chen, L.-Y., Sumioka, H., Ke, L.-J., Shiomi, M., Chen, L.-K., Effects of teleoperated humanoid robot application in older adults with neurocognitive disorders in Taiwan: A report of three cases (2020) Aging Med. Healthc., 11 (2), pp. 67-71; Coco, K., Kangasniemi, M., Rantanen, T., Care personnel’s attitudes and fears toward care robots in elderly care: A comparison of data from the care personnel in Finland and Japan (2018) J. Nurs. Scholarship, 50 (6), pp. 634-644; Coşar, S., ENRICHME: Perception and interaction of an assistive robot for the elderly at home (2020) Int. J. Soc. Robot., 12 (3), pp. 779-805; Damholdt, M., Attitudinal change in elderly citizens toward social robots: The role of personality traits and beliefs about robot functionality (2015) Front. Psychol., , https://doi.org/10.3389/fpsyg.2015.01701; Di Nuovo, A., The multi-modal interface of Robot-Era multi-robot services tailored for the elderly (2017) Intel. Serv. Robot., 11 (1), pp. 109-126; Do, H.M., Sheng, W., Harrington, E.E., Bishop, A.J., Clinical screening interview using a social robot for geriatric care (2021) IEEE Trans. Automat. Sci. 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A Qualitative Research on the Acceptance of NAO Robots in Elderly Care; Hanson, S.T., Anderson, H.J., Bak, T., Practical evaluation of robots for elderly in Denmark — an overview (2010) ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, Osaka; Harmo, P., Taipalus, T., Knuuttila, J., Vallet, J., Halme, A., Needs and Solutions-Home Automation and Service Robots for the Elderly and Disabled (2005) IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton; Heerink, M.B., Assessing acceptance of assistive social agent technology by older adults: The almere model (2010) Int. J. Soc. Robot., 2, pp. 361-375; Heernik, M., Krose, B.J., Evers, V., Wielinga, B.J., Studying the acceptance of a robotic agent by elderly users (2006) Int. J. Model. Ident. Control, 33, p. 43; Johansson-Pajala, R.-M., Care robot orientation: What, who and how? Potential users’ perceptions (2020) Int. J. Soc. 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IEEE; Whelan, S., Murphy, K., Barrett, E., Krusche, C., Santorelli, A., Casey, D., Factors affecting the acceptability of social robots by older adults including people with dementia or cognitive impairment: A literature review (2018) Int. J. Soc. Robot., 10 (5), pp. 643-668; Wilson, G., Robot-Enabled Support of Daily Activities in Smart Home Environments (2018) Cognit. Syst. Res.; Wu, Y.-H., Fassert, C., Rigaud, A.-S., Designing robots for the elderly: Appearance issue and beyond (2012) Archiv. Gerontol. Geriatr., 54 (1), pp. 121-126; Yuan, F., (2021) Assessing the Acceptability of a Humanoid Robot for Alzheimer’s Disease and Related Dementia Care Using an Online Survey, , https://arxiv.org/abs/2104.12903; Yuan, F., Klavon, E., Liu, Z., Lopez, R.P., Zhao, X., A systematic review of robotic rehabilitation for cognitive training (2021) Front. Robot. AI, 8 (105); Zhang, Y., Chignell, M.H., Flattening the curve of cognitive decline: A survey of cognitive interventions for people living with dementia (a review) (2020) International Symposium on Human Factors and Ergonomics in Health Care, pp. 110-114. , pp., Sage Journals, London","Yuan, F.; University of TennesseeUnited States; email: fyuan6@vols.utk.edu","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119870516
"Lin C., Wu W., Lin G., Cai S., Xie L.","57217101292;57216627291;57217102959;57190013652;8218534100;","Design and Control of a Seven Degrees-of-Freedom Semi-exoskeleton Upper Limb Robot",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"596","605",,,"10.1007/978-3-030-90525-5_52","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119865251&doi=10.1007%2f978-3-030-90525-5_52&partnerID=40&md5=cea734c98284bc3ff3aac1c95f014de1","Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangdong, Guangzhou, China; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore","Lin, C., Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangdong, Guangzhou, China; Wu, W., Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangdong, Guangzhou, China; Lin, G., Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangdong, Guangzhou, China; Cai, S., Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; Xie, L., Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangdong, Guangzhou, China","Previous studies have shown that patient’s voluntary participation is one of the key factors in improving rehabilitation effects. End-effector and exoskeleton type robots have been developed to support rehabilitation training at different impedance levels. However, these robots either fail to take the movement of the shoulder girdle into account or suffer from complex and massive shoulder mechanisms. In this paper, we merge the advantages of the end-effector and exoskeleton type robots and propose a simple and effective semi-exoskeleton upper limb robot with seven degrees of freedom to support the impedance training of the human shoulder complex and elbow joint. Besides, an admittance control scheme is developed to generate desired movements during training. Experiments on five subjects are conducted to assess the feasibility and performance of the proposed robot. Results show that the proposed robot has satisfactory performance in terms of shoulder kinematic compatibility and human-robot interaction. This study could pave way for a practical rehabilitation robot for patients with stroke in real-life. © 2021, Springer Nature Switzerland AG.","Admittance control; Rehabilitation; Semi-exoskeleton robot; Upper limb","Degrees of freedom (mechanics); Exoskeleton (Robotics); Human robot interaction; Machine design; Neuromuscular rehabilitation; Admittance control; Design and control; Exoskeleton robots; Key factors; Performance; Rehabilitation effects; Rehabilitation training; Semi-exoskeleton robot; Upper limbs; Voluntary participation; End effectors",,,,,"201903010028, 202002030324; 6141A02033124; AC01202005011; 2020B2020; National Natural Science Foundation of China, NSFC: 52075177; Guangdong Medical Research Foundation: 2018KZDXM002, 2019A050505001","Acknowledgements. This work was supported in part by the National Natural Science Foundation of China (Grant No. 52075177), Joint Fund of the Ministry of Education for Equipment Pre-Research (Grant No. 6141A02033124), Research Foundation of Guangdong Province (Grant No. 2019A050505001 and 2018KZDXM002), Guangzhou Research Foundation (Grant No. 202002030324 and 201903010028), Zhongshan Research Foundation (Grant No.2020B2020), and Shenzhen Institute of Artificial Intelligence and Robotics for Society (Grant No. AC01202005011).",,,,,,,,,,"Burton, J.K., Predicting discharge to institutional long-term care after stroke: A systematic review and metaanalysis (2018) J. Am. Geriatr. Soc., 66, pp. 161-169; Veerbeek, J.M., What is the evidence for physical therapy poststroke? A systematic review and meta-analysis (2014) Plos One, 9 (2); Liu, L., Wang, D., Lawrence Wong, K.S., Wang, Y., Stroke and stroke care in China huge burden, significant workload, and a national priority (2011) Stroke, 42, pp. 3651-3654; Li, G., Cai, S., Xie, L., Cooperative control of a dual-arm rehabilitation robot for upper limb physiotherapy and training (2019) IEEE/ASME International Conference Advanced Intelligent Mechatronics, AIM. 2019-July, Pp. 802–807; Cai, S., SVM-based classification of sEMG signals for upper-limb self-rehabilitation training (2019) Front. Neurorobot., 13 (1-10); Proietti, T., Crocher, V., Roby-Brami, A., Jarrasse, N., Upper-limb robotic exoskeletons for neurorehabilitation: A review on control strategies (2016) IEEE Rev. Biomed. Eng., 9, pp. 4-14; Hogan, N., Impedance control: An approach to manipulation: Part I-theory (1985) J. Dyn. Syst. Meas. Control. Trans. ASME., 107, pp. 1-7; Keemink, A.Q.L., van der Kooij, H., Stienen, A.H.A., Admittance control for physical human– robot interaction (2018) Int. J. Rob. Res., 37, pp. 1421-1444; Culmer, P.R., A control strategy for upper limb robotic rehabilitation with a dual robot system (2010) IEEE/ASME Trans. Mech., 15, pp. 575-585; Miller, L.M., Rosen, J., Comparison of multi-sensor admittance control in joint space and task space for a seven degree of freedom upper limb exoskeleton (2010) 2010 3Rd IEEE RAS EMBS International Conference Biomedical Robot. Biomechatronics, Biorob 2010, Pp. 70– 75; Huang, J., Huo, W., Xu, W., Mohammed, S., Amirat, Y., Control of upper-limb power-assist exoskeleton using a human-robot interface based on motion intention recognition (2015) IEEE Trans. Autom. Sci. Eng., 12, pp. 1257-1270; Loureiro, R., Amirabdollahian, F., Topping, M., Driessen, B., Harwin, W., Upper limb robot mediated stroke therapy-GENTLE/s approach (2003) Auton. Robots., 15, pp. 35-51; Zhang, L., Guo, S., Sun, Q., Development and assist-as-needed control of an end-effector upper limb rehabilitation robot (2020) Appl. Sci., 10 (19), p. 6684; Perry, J.C., Rosen, J., Design of a 7 degree-of-freedom upper-limb powered exoskeleton (2006) Proceedings of First IEEE/RAS-EMBS International Conference Biomedical Robotics Biomechatronics, 2006, Biorob 2006, Pp. 805–810; Gunasekara, M., Gopura, R., Jayawardena, S., 6-REXOS: Upper limb exoskeleton robot with improved pHRI (2015) Int. J. Adv. Robot. Syst., 12 (4), p. 47; Dromerick, A.W., Edwards, D.F., Kumar, A., Hemiplegic shoulder pain syndrome: Frequency and characteristics during inpatient stroke rehabilitation (2008) Arch. Phys. Med. Rehabil., 89, pp. 1589-1593; Axes, S.E., Decoupling, T., Short papers of joint rotations and translations (2009) IEEE Trans. Robot., 25, pp. 628-633; Taal, S.R., Sankai, Y., Exoskeletal spine and shoulder girdle for full body exoskeletons with human versatility (2011) Proceedings-Ieee International Conference Robotics Automation, pp. 2217-2222. , pp; Park, H.S., Ren, Y., Zhang, L.Q., IntelliArm: An exoskeleton for diagnosis and treatment of patients with neurological impairments (2008) Proceedings 2Nd Bienn. IEEE/RAS-EMBS International Conference on Biomedical Roboticsbiomechatronics, Biorob 2008, Pp. 109– 114; Ball, S.J., Brown, I.E., Scott, S.H., MEDARM: A rehabilitation robot with 5DOF at the shoulder complex (2007) IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM; Balasubramanian, S., Melendez-Calderon, A., Roby-Brami, A., Burdet, E., On the analysis of movement smoothness (2015) J. Neuroeng. Rehabil., 12 (1), pp. 1-11","Xie, L.; Shien-Ming Wu School of Intelligent Engineering, Guangdong, China; email: melhxie@scut.edu.cn","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119865251
"Johari K., Tong C.T.Z., Subbaraju V., Kim J.-J., Tan U.-X.","57208880924;57352244800;55199535800;57352397500;22235331000;","Gaze Assisted Visual Grounding",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"191","202",,,"10.1007/978-3-030-90525-5_17","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119849930&doi=10.1007%2f978-3-030-90525-5_17&partnerID=40&md5=6804b98994b3e5f9e2e9ef540e870628","Singapore University of Technology and Design, Singapore, Singapore; Institute of High Performance Computing, A*STAR, Singapore, Singapore; Institute for Infocomm Research, A*STAR, Singapore, Singapore","Johari, K., Singapore University of Technology and Design, Singapore, Singapore; Tong, C.T.Z., Singapore University of Technology and Design, Singapore, Singapore; Subbaraju, V., Institute of High Performance Computing, A*STAR, Singapore, Singapore; Kim, J.-J., Institute for Infocomm Research, A*STAR, Singapore, Singapore; Tan, U.-X., Singapore University of Technology and Design, Singapore, Singapore","There has been an increasing demand for visual grounding in various human-robot interaction applications. However, the accuracy is often limited by the size of the dataset that can be collected, which is often a challenge. Hence, this paper proposes using the natural implicit input modality of human gaze to assist and improve the visual grounding accuracy of human instructions to robotic agents. To demonstrate the capability, mechanical gear objects are used. To achieve that, we utilized a transformer-based text classifier and a small corpus to develop a baseline phrase grounding model. We evaluate this phrase grounding system with and without gaze input to demonstrate the improvement. Gaze information (obtained from Microsoft Hololens2) improves the performance accuracy from 26% to 65%, leading to more efficient human-robot collaboration and applicable to hands-free scenarios. This approach is data-efficient as it requires only a small training dataset to ground the natural language referring expressions. © 2021, Springer Nature Switzerland AG.","Gaze tracking; Human-robot interaction; Visual grounding","Classification (of information); Human robot interaction; Man machine systems; Text processing; Gaze-tracking; Grounding systems; Humans-robot interactions; Input modalities; Mechanical; MicroSoft; Performance; Robotic agents; Text classifiers; Visual grounding; Eye tracking",,,,,"Agency for Science, Technology and Research, A*STAR: A18A2b0046","This research is supported by the Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme (Project # A18A2b0046).",,,,,,,,,,"Bhardwaj, R., Majumder, N., Poria, S., Hovy, E., (2021) More Identifiable Yet Equally Performant Transformers for Text Classification. 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Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1 26","Johari, K.; Singapore University of Technology and DesignSingapore; email: kritika_johari@mymail.sutd.edu.sg","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119849930
"Cardenas-Hernandez F.P., Romano G., Drachsler H.","57315264500;57210712330;26326216500;","Multimodal immersive learning with artificial intelligence for robot and running application cases",2021,"CEUR Workshop Proceedings","2979",,,"15","21",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118157453&partnerID=40&md5=348f86bcb1462b399bdf7c5803b2400c","Information Center for Education, DIPF, Leibniz Institute for Research and Information in Education, Frankfurt am Main, 60323, Germany","Cardenas-Hernandez, F.P., Information Center for Education, DIPF, Leibniz Institute for Research and Information in Education, Frankfurt am Main, 60323, Germany; Romano, G., Information Center for Education, DIPF, Leibniz Institute for Research and Information in Education, Frankfurt am Main, 60323, Germany; Drachsler, H., Information Center for Education, DIPF, Leibniz Institute for Research and Information in Education, Frankfurt am Main, 60323, Germany","In research different MMLA applications were presented that provide a solution for a particular psychomotor learning task, e.g. CPR or table tennis. A common limitation in all applications is that they are domain specific. In this sense, we present the MILKI-PSY project, whose main goal is to provide a one-for-all system across different domains. Inherently given that different psychomotor learning tasks across domains have certain aspects in common, it would make a one-for-all system possible. Additionally, we present ideas for the MMLA data collection through different sensors and its respective storage, annotation, preparation, and exploitation. The proposed ideas are with respect to two learning tasks: running in the field of sports and collaborative montage in the field of human-robot interaction. Further, we suggest that the system must give to the user the freedom to decide what sensor data to use, and which feedback to receive. Ultimately, we opt for a scalable solution that can be provided to a larger audience. © 2021 CEUR-WS. All rights reserved.","Learning; Multimodal interaction; Sensors","Digital storage; Human robot interaction; Intelligent robots; Learning systems; Different domains; Domain specific; Immersive learning; Learning; Learning tasks; Multi-modal; Multimodal Interaction; Psychomotors; Running applications; Table-tennis; Sports",,,,,,,,,,,,,,,,"Di Mitri, D., Schneider, J., Klemke, R., Specht, M., Drachsler, H., Multimodal Pipeline: A generic approach for handling multimodal data for supporting learning (2019) First workshop on AI-based Multimodal Analytics for Understanding Human Learning in Real-world Educational Contexts, , China; Di Mitri, D., Schneider, J., Trebing, K., Sopka, S., Specht, M., Drachsler, H., Real-Time Multimodal Feedback with the CPR Tutor (2020) AIED 2020. LNCS (LNAI), 12163, pp. 141-152. , Artificial Intelligence in Education, Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds) Springer, Switzerland; Mat Sanusi, K. A., Di Mitri, D., Limbu, B., Klemke, R., Table Tennis Tutor: Forehand Strokes Classification Based on Multimodal Data and Neural Networks (2021) Sensors, 21 (9), p. 3121. , Switzerland; Schneider, J., Börner, D., Rosmalen, P., Specht, M., Presentation Trainer, your Public Speaking Multimodal Coach (2015) Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, 17, pp. 539-546. , USA; Cherubini, A., Navarro-Alarcon, D., Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges, and Opportunities (2021) Frontiers in Neurorobotics, 14, p. 113; https://www.hopkinsmedicine.org/health/conditionsand-diseases/vital-signs-body-temperature-pulse-rate-respiration-rate-blood-pressure, Johns Hopkins Medicine Homepage, last accessed 2021/07/12; Strohbach, M., Daubert, J., Ravkin, H., Lischka, M., Big data storage (2016) New Horizons for a Data-Driven Economy, pp. 119-141. , Cavanillas J.M., Curry E., Wahlster, W. (eds), 1st edn., Springer, Switzerland; Di Mitri, D., Schneider, J., Specht, M., Drachsler, H., From signals to knowledge: A conceptual model for multimodal learning analytics (2018) Journal of Computer Assisted Learning, 34 (4), pp. 338-349; Di Mitri, D., Schneider, J., Klemke, R., Specht, M., Drachsler, H., Read Between the Lines: An Annotation Tool for Multimodal Data for Learning (2019) Proceedings of the 9th International Conference on Learning Analytics & Knowledge - LAK19, pp. 51-60. , Association for Computing Machinery, USA; Schneider, J., Di Mitri, D., Limbu, B., Drachsler, H., Multimodal Learning Hub: A Tool for Capturing Customizable Multimodal Learning Experiences (2018) Lifelong Technology-Enhanced Learning, pp. 45-58. , Pammer-Schindler, Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M., (eds), Springer, Switzerland","Cardenas-Hernandez, F.P.; Information Center for Education, Germany; email: cardenas@dipf.de","Klemke R.Klemke R.Sanusi K.A.M.Sanusi K.A.M.Majonica D.Richert A.Varney V.Keller T.Schneider J.Di Mitri D.Ciordas-Hertel G.-P.Cardenas-Hernandez F.P.Romano G.Kravcik M.Paassen B.Klamma R.Slupczynski M.Klatt S.Geisen M.Baumgartner T.Riedl N.",,"CEUR-WS","1st International Workshop on Multimodal Immersive Learning Systems, MILeS 2021","20 September 2021 through 24 September 2021",,172550,16130073,,,,"English","CEUR Workshop Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85118157453
"Sadangharn P.","57199327836;","A Multidimensional Analysis of Robotic Deployment in Thai Hotels",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00833-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117352862&doi=10.1007%2fs12369-021-00833-2&partnerID=40&md5=8e2f28f6fca831592fa83298065c1377","Faculty of Management and Tourism, Burapha University, Chonburi, Thailand","Sadangharn, P., Faculty of Management and Tourism, Burapha University, Chonburi, Thailand","Several hotel businesses are interested in robotic deployment to improve customer service, enhance hotel productivity, and gain a competitive advantage. In this study, I deployed a robot in a real working environment to explore stakeholders’ perceptions and investigate factors involved in robotic deployment in Thai hotels. A qualitative research method, interpretative phenomenological analysis, was utilized. As there was no robotic deployment in Thai hotels, Peanut, a navigation service robot, was temporarily assigned to work in the hotels to allow key informants—hotel executives, human resource managers, reception managers, hotel staff, and hotel guests—to understand the phenomenon and experience robotic deployment. The findings revealed that stakeholders in Thai hotels accepted the robot; however, this acceptance was contingent on various robot, human, and organizational dimensions. Greater user acceptance can promote more widespread robotic deployment. This study has important implications for human–robot interactions in the hotel industry, especially in Thailand where this technology has not yet been applied. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Acceptance of robot; Human–robot interaction; Robotic deployment; Service robot","Competition; Hotels; Human resource management; Human robot interaction; Managers; Robotics; Acceptance of robot; Competitive advantage; Customer-service; Humans-robot interactions; Multi-dimensional analysis; Qualitative research methods; Robotic deployment; Service robots; Stakeholder perception; Working environment; Mobile robots",,,,,"Thailand Research Fund, TRF; Burapha University, BUU: RSA6280029","This research was funded by the Thailand Research Fund and Burapha University, grant number RSA6280029. 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Weber, R.P., (1990) Basic content analysis, , Sage, Beverly Hills, CA; Hsieh, H.F., Shannon, S.E., Three approaches to qualitative content analysis (2005) Qual Health Res, 15 (9), pp. 1277-1288; Gibbs, G.R., Thematic coding and categorizing (2007) Qualitative research kit: analyzing qualitative data, pp. 38-55. , Gibbs GR, (ed), SAGE Publications Ltd, London; Tesch, R., (1990) Qualitative Research: Analysis Types and Software Tools, , Falmer, Bristol; Morgan, D.L., Qualitative content analysis: a guide to paths not taken (1993) Qual Health Res, 3, pp. 112-121; Morse, J.M., Field, P.A., (1995) Qualitative research methods for health professionals, , 2, Sage, Thousand Oaks, CA; Coffey, A., Atkinson, P., (1996) Making sense of qualitative data: Complementary research strategies, , Sage, Thousand Oaks, CA; Payne, G., Williams, M., Generalization in qualitative research (2005) Sociology, 39, pp. 295-314","Sadangharn, P.; Faculty of Management and Tourism, Thailand; email: pornrat@buu.ac.th",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Article in Press","All Open Access, Bronze, Green",Scopus,2-s2.0-85117352862
"Roda-Sanchez L., Garrido-Hidalgo C., García A.S., Olivares T., Fernández-Caballero A.","57202112308;57193328014;16174733600;6602909884;6602230534;","Comparison of RGB-D and IMU-based gesture recognition for human-robot interaction in remanufacturing",2021,"International Journal of Advanced Manufacturing Technology",,,,"","",,,"10.1007/s00170-021-08125-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117054754&doi=10.1007%2fs00170-021-08125-9&partnerID=40&md5=040856967e70e4a15f003b547cb1a8b9","Instituto de Investigación en Informática de Albacete, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain","Roda-Sanchez, L., Instituto de Investigación en Informática de Albacete, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Garrido-Hidalgo, C., Instituto de Investigación en Informática de Albacete, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; García, A.S., Instituto de Investigación en Informática de Albacete, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Olivares, T., Instituto de Investigación en Informática de Albacete, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Fernández-Caballero, A., Instituto de Investigación en Informática de Albacete, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, 02071, Spain","With product life-cycles getting shorter and limited availability of natural resources, the paradigm shift towards the circular economy is being impulsed. In this domain, the successful adoption of remanufacturing is key. However, its associated process efficiency is to date limited given high flexibility requirements for product disassembly. With the emergence of Industry 4.0, natural human-robot interaction is expected to provide numerous benefits in terms of (re)manufacturing efficiency and cost. In this regard, vision-based and wearable-based approaches are the most extended when it comes to establishing a gesture-based interaction interface. In this work, an experimental comparison of two different movement-estimation systems—(i) position data collected from Microsoft Kinect RGB-D cameras and (ii) acceleration data collected from inertial measurement units (IMUs)—is addressed. The results point to our IMU-based proposal, OperaBLE, having recognition accuracy rates up to 8.5 times higher than these of Microsoft Kinect, which proved to be dependent on the movement’s execution plane, subject’s posture, and focal distance. © 2021, The Author(s).","Computer vision; Human-robot interaction; Internet of Things; Remanufacturing; Wearables","Computer vision; Efficiency; Gesture recognition; Human robot interaction; Life cycle; Man machine systems; Motion estimation; Wearable technology; Circular economy; Gestures recognition; Humans-robot interactions; Inertial measurements units; Microsoft's kinect; Paradigm shifts; Process efficiency; Product life cycles; Remanufacturing; Unit-based; Internet of things",,,,,"European Regional Development Fund, ERDF: RTI2018-098156-B-C52; Electronic Components and Systems for European Leadership, ECSEL: 876852","Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was partially supported by the Spanish Ministerio de Ciencia e Innovación (FEDER, EU funds) under RTI2018-098156-B-C52 project. This work was also partially supported by VALU3S, a European co-funded innovation project that has been granted by the ECSEL Joint Undertaking (JU) [grant number 876852]. The funding of the project comes from the Horizon 2020 research programme and participating countries. National funding is provided by Germany, including the Free States of Saxony and Thuringia, Austria, Belgium, Finland, France, Italy, the Netherlands, Slovakia, Spain, Sweden, and Turkey. This work was also partially supported by Grupos I+D funding under 2021-GRIN-31042. The publication is part of the project PCI2020-112001, funded by MCIN/AEI/10.13039/501100011033 and by the European Union “NextGenerationEU”/PRTR. Luis Roda-Sanchez is pursuing his Industrial PhD under DIN2018-010177 fellowship from Spanish Ministerio de Ciencia e Innovación. Celia Garrido-Hidalgo holds 2019-PREDUCLM-10703 fellowship from FEDER, UE at Universidad de Castilla-La Mancha.",,,,,,,,,,"Ramírez, F.J., Castellani, M., Xu, W., Autonomous remanufacturing (2020) The International Journal of Advanced Manufacturing Technology, , (,),.,., https://doi.org/10.1007/s00170-020-05559-5; Zlamparet, G.I., Ijomah, W., Miao, Y., Awasthi, A.K., Zeng, X., Li, J., Remanufacturing strategies: a solution for WEEE problem (2017) J Clean Prod, 149, pp. 126-136; Geissdoerfer, M., Savaget, P., Bocken, N.M., Hultink, E.J., The circular economy–a new sustainability paradigm? 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IEEE, pp. 168-172","Roda-Sanchez, L.; Instituto de Investigación en Informática de Albacete, Spain; email: Luis.Roda@alu.uclm.es",,,"Springer Science and Business Media Deutschland GmbH",,,,,02683768,,IJATE,,"English","Int J Adv Manuf Technol",Article,"Article in Press","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85117054754
"Yang J., Chew E., Liu P.","57220006927;23989981600;56428216700;","Service Humanoid Robotics: Review and Design of a Novel Bionic-Companionship Framework",2021,"Lecture Notes in Mechanical Engineering",,,,"185","194",,,"10.1007/978-981-16-4803-8_20","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113779012&doi=10.1007%2f978-981-16-4803-8_20&partnerID=40&md5=aa8fa971eab858aea6930d64516d352c","EUREKA Robotics Lab, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, C5 2YB, United Kingdom; The Department of Computer Science, University of York, York, YO10 5GH, United Kingdom","Yang, J., EUREKA Robotics Lab, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, C5 2YB, United Kingdom; Chew, E., EUREKA Robotics Lab, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, C5 2YB, United Kingdom; Liu, P., The Department of Computer Science, University of York, York, YO10 5GH, United Kingdom","At present, industrial robotics focused more on motion control and vision; whereas Humanoid Service Robotics (HSRs) are increasingly being investigated among researchers’ and practitioners’ field of speech interactions. The problematic and quality of human-robot interaction (HRI) has become one of the hot potatoes concerned in academia. This paper proposes a novel interactive framework suitable for HSRs. The proposed framework is grounded on the novel integration of Trevarthen [24 Companionship Theory and neural image generation algorithm in computer vision. By integrating the image-to-natural interactivities generation, and communicate with the environment to better interact with the stakeholder, thereby changing from interaction to a bionic-companionship. In addition, the article also reviews the research of neural image generation algorithms and summarizes the application cases of the algorithm structure in the field of robotics from a critical perspective. We believe that the new interactive bionic-companionship framework can enable HSRs to further develop towards robot companions. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.","Human-robot interaction; Humanoid robotics; Social robotics",,,,,,,,,,,,,,,,,"CANBOT Homepage, https://www.canbotrobots.com/html/yy-detail.html. Accessed 6 April 2020; Cascianelli, S., Costante, G., Ciarfuglia, T.A., Valigi, P., Fravolini, M.L., Full-GRU natural language video description for service robotics applications (2018) IEEE Robot. Autom. Lett., 3 (2), pp. 841-848; Chen, L., He, Y., Fan, L., Let the robot tell: Describe car image with natural language via LSTM (2017) Pattern Recogn. Lett., 98, pp. 75-82; Cho, K., (2014) Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation, , EMNLP; Dautenhahn, K., Woods, S., Kaouri, C., Walters, M.L., Koay, K.L., Werry, I., What is a robot companion-friend, assistant or butler? 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Abdul Majeed A.Liu P.Platts J.Myung H.Kim J.Kim J.",,"Springer Science and Business Media Deutschland GmbH","8th International Conference on Robot Intelligence Technology and Applications, RiTA 2020","11 December 2020 through 13 December 2020",,263529,21954356,9789811648021,,,"English","Lect. Notes Mech. Eng.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85113779012
"Wang A., Li W., yu J.","55366674200;57221684733;57211813056;","PD Controller of a Lower Limb Exoskeleton Robot Based on Sliding Mode RBF Neural Network",2021,"Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST","387",,,"427","436",,,"10.1007/978-3-030-82562-1_40","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113385279&doi=10.1007%2f978-3-030-82562-1_40&partnerID=40&md5=8c8e79e90a273c938de8052c800714b6","School of Electric and Information Engineering, Zhongyuan University of Technology, Zhengzhou, 450007, China; Zhongyuan University of Technology, Zhengzhou, 450007, China; Zhongyuan-Petersburg Aviation College, Zhongyuan University of Technology, Zhengzhou, 450007, China","Wang, A., School of Electric and Information Engineering, Zhongyuan University of Technology, Zhengzhou, 450007, China; Li, W., Zhongyuan University of Technology, Zhengzhou, 450007, China; yu, J., Zhongyuan-Petersburg Aviation College, Zhongyuan University of Technology, Zhengzhou, 450007, China","The lower limb exoskeleton robot (LLER) is a human-robot interaction device that combines human functions and mechanical characteristics. Due to the complexity and strong coupling of human gait, it is difficult for LLER to be worn comfortably and safely for training. In such a scenario, the paper proposes a kind of proportional-derivative(PD) controller of LLER based on sliding mode RBF neural network(SMRBF-nn). In order to verify the effectiveness of the proposed control scheme, pertinent experiments were carried out. The gait data of the subject was collected through the motion capture system. A simulating model was established, different control methods, like conventional SMRBF-nn controller and PD controller based on SMRBF-nn, have been tested on the LLER. The experimental results show that the control strategy proposed in this paper can not only make LLER track the human body’s gait trajectory, but also output appropriate torque when there is a disturbance. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.","Human gait; LLER; PD; SMRBF-nn","Exoskeleton (Robotics); Human robot interaction; Multimedia systems; Radial basis function networks; Control methods; Control schemes; Control strategies; Exoskeleton robots; Mechanical characteristics; Motion capture system; Proportional-derivative controllers; RBF Neural Network; Controllers",,,,,"182102410056, 202102210097, 202102210135; 2017GGJS117; National Natural Science Foundation of China, NNSFC: 62073297, U1813201","The authors would like to thank the Henan Province Science and Technology R&D projects (182102410056, 202102210097, 202102210135), Young Backbone Teachers in Henan Province (2017GGJS117), and National Natural Science Foundation (62073297, U1813201) for their support of this work.",,,,,,,,,,"Rifa, H., Mohammed, S., Djouani, K., Toward lower limbs functional rehabilitation through a knee-joint exoskeleton (2016) IEEE Trans. Control Syst. Technol., 25 (2), pp. 712-719; Ma, Y., Wu, X., Yi, J., A review on human-exoskeleton coordination towards lower limb robotic exoskeleton systems (2019) Int. J. Rob. Autom., 34 (431-451); Guerra-Filho, G., Optical motion capture: Theory and implementation (2005) RITA, 12 (2), pp. 61-90; Chevallereau, C., Aoustin, Y., Optimal reference trajectories for walking and running of a biped robot (2001) Robotica, 19 (5), pp. 557-569; Feng, S., Xinjilefu, X., Atkeson, C.G., Robust dynamic walking using online foot step optimization (2016) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5373-5378. , , pp. , IEEE, Daejeon; Li, G., Kawamura, K., Barrance, P., Prediction of muscle recruitment and its effect on joint reaction forces during knee exercises (1998) Ann. Biomed. Eng. J. Biomed. Eng. Soc., 26 (4), pp. 725-733; Zhou, Y., Li, Z., Xiao, S., Auto-conditioned recurrent networks for extended complex human motion synthesis (2018) 6Th International Conference on Learning Representations, , Vancouver; Luu, T.P., Low, K.H., Qu, X., An individual-specific gait pattern prediction model based on generalized regression neural networks (2014) Gait Posture, 39 (1), pp. 443-448; Rossomando, F.G., Soria, C., Carelli, R., Autonomous mobile robots navigation using RBF neural compensator (2011) Control Eng. Pract., 19 (3), pp. 215-222; Aole, S., Elamvazuthi, I., Waghmare, L., Improved active disturbance rejection control for trajectory tracking control of lower limb robotic rehabilitation exoskeleton (2020) Sensor, 20 (13), p. 3681","Wang, A.; School of Electric and Information Engineering, China; email: a.wang@zut.edu.cn","Fu W.Xu Y.Wang S.Zhang Y.",,"Springer Science and Business Media Deutschland GmbH","3rd EAI International Conference on Multimedia Technology and Enhanced Learning, ICMTEL 2021","8 April 2021 through 9 April 2021",,262979,18678211,9783030825614,,,"English","Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85113385279
"Geissler F., Qutub S., Roychowdhury S., Asgari A., Peng Y., Dhamasia A., Graefe R., Pattabiraman K., Paulitsch M.","57205540604;57226773065;57226776336;57226765484;57226763993;57226762923;57219417971;8887951000;22980623100;","Towards a safety case for hardware fault tolerance in convolutional neural networks using activation range supervision",2021,"CEUR Workshop Proceedings","2916",,,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112610462&partnerID=40&md5=75eb2d2581761dcbf0a533870baf6de1","Intel, Germany; University of British Columbia, Canada","Geissler, F., Intel, Germany; Qutub, S., Intel, Germany; Roychowdhury, S., Intel, Germany; Asgari, A., University of British Columbia, Canada; Peng, Y., Intel, Germany; Dhamasia, A., Intel, Germany; Graefe, R., Intel, Germany; Pattabiraman, K., University of British Columbia, Canada; Paulitsch, M., Intel, Germany","Convolutional neural networks (CNNs) have become an established part of numerous safety-critical computer vision applications, including human robot interactions and automated driving. Real-world implementations will need to guarantee their robustness against hardware soft errors corrupting the underlying platform memory. Based on the previously observed efficacy of activation clipping techniques, we build a prototypical safety case for classifier CNNs by demonstrating that range supervision represents a highly reliable fault detector and mitigator with respect to relevant bit flips, adopting an eight-exponent floating point data representation. We further explore novel, non-uniform range restriction methods that effectively suppress the probability of silent data corruptions and uncorrectable errors. As a safety-relevant end-to-end use case, we showcase the benefit of our approach in a vehicle classification scenario, using ResNet-50 and the traffic camera data set MIOVision. The quantitative evidence provided in this work can be leveraged to inspire further and possibly more complex CNN safety arguments. © 2021 CEUR-WS. All rights reserved.",,"Chemical activation; Classification (of information); Computer hardware; Convolution; Digital arithmetic; Fault tolerance; Human computer interaction; Human robot interaction; Radiation hardening; Safety factor; Clipping techniques; Computer vision applications; Floating-point data; Hardware fault tolerance; Real-world implementation; Silent data corruptions; Uncorrectable errors; Vehicle classification; Convolutional neural networks",,,,,"Intel Corporation; Bundesministerium für Verkehr und Digitale Infrastruktur, BMVI: 01MM19008; Natural Sciences and Engineering Research Council of Canada, NSERC","Our research was partially funded by the Federal Ministry of Transport and Digital Infrastructure of Germany in the project Providentia++ (01MM19008). Further, this research was partially supported by a grant from the Natural Sciences and Engineering Research Council of Canada (NSERC), and a research gift from Intel to UBC.",,,,,,,,,,"(2018) Tech. Rep, , https://www.iso.org/standard/68383.html, International Organization for Standardization, ISO 26262, [Online]. Available; Road vehicles - Safety of the intended functionality (2019) Tech. Rep, , https://www.iso.org/standard/70939.html, International Organization for Standardization, [Online]. Available; Athavale, J., Baldovin, A., Graefe, R., Paulitsch, M., Rosales, R., AI and Reliability Trends in Safety-Critical Autonomous Systems on Ground and Air (2020) Proceedings - 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, DSN-W 2020, pp. 74-77; Dixit, H. D., Pendharkar, S., Beadon, M., Mason, C., Chakravarthy, T., Muthiah, B., Sankar, S., Silent Data Corruptions at Scale (2021) Association for Computing Machinery, 1 (1). , [Online]. Available: arxiv.org/abs/2102. 11245; Neale, A., Sachdev, M., Neutron Radiation Induced Soft Error Rates for an Adjacent-ECC Protected SRAM in 28 nm CMOS (2016) IEEE Transactions on Nuclear Science, 63 (3), pp. 1912-1917; Li, G., Hari, S. K. S., Sullivan, M., Tsai, T., Pattabiraman, K., Emer, J., Keckler, S. W., Understanding error propagation in Deep Learning Neural Network (DNN) accelerators and applications (2017) Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017; Hoang, L.-H., Hanif, M. A., Shafique, M., (2019) FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation, , https://arxiv.org/abs/1912.00941, [Online]. Available; Chen, Z., Li, G., Pattabiraman, K., (2020) Ranger: Boosting Error Resilience of Deep Neural Networks through Range Restriction, , https://arxiv.org/abs/2003.13874, [Online]. Available; Cluzeau, J. M., Henriquel, X., Rebender, G., Soudain, G., van Dijk, L., Gronskiy, A., Haber, D., Polak, R., Concepts of Design Assurance for Neural Networks (CoDANN) (2020) Public Report Extract Version 1.0, pp. 1-104. , https://www.easa.europa.eu/document-library/general-publications/concepts-design-assurance-neural-networks-codann, [Online]. Available; Koopman, P., Osyk, B., Safety argument considerations for public road testing of autonomous vehicles (2019) SAE Technical Papers, , 2019-April April; Chen, Z., Li, G., Pattabiraman, K., Debardeleben, N., BinFI: An efficient fault injector for safety-critical machine learning systems (2019) International Conference for High Performance Computing, Networking, Storage and Analysis, SC; Hong, S., Frigo, P., Kaya, Y., Giuffrida, C., Dumitras, T., Terminal brain damage: Exposing the graceless degradation in deep neural networks under hardware fault attacks (2019) Proceedings of the 28th USENIX Security Symposium; Lotfi, A., Hukerikar, S., Balasubramanian, K., Racunas, P., Saxena, N., Bramley, R., Huang, Y., Resiliency of automotive object detection networks on GPU architectures (2019) Proceedings - International Test Conference, 2019, pp. 1-9. , Novem; Hanif, M. A., Shafique, M., SalvagedNn: Salvaging deep neural network accelerators with permanent faults through saliency-driven fault-aware mapping (2020) Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, , https://royalsocietypublishing.org/doi/10.1098/rsta.2019.0164, [Online]. Available; Mahmoud, A., Sastry Hari, S. K., Fletcher, C. W., Adve, S. V., Sakr, C., Shanbhag, N., Molchanov, P., Keckler, S. W., (2020) Hardnn: Feature map vulnerability evaluation in CNNS; Ponader, J., Kundu, S., Solihin, Y., (2020) MILR: Mathematically Induced Layer Recovery for Plaintext Space Error Correction of CNNs, , http://arxiv.org/abs/2010.14687, [Online]. Available; Zhao, K., Di, S., Li, S., Liang, X., Zhai, Y., Chen, J., Ouyang, K., Chen, Z., FT-CNN: Algorithm-Based Fault Tolerance for Convolutional Neural Networks (2021) IEEE Transactions on Parallel and Distributed Systems, 32 (7), pp. 1677-1689; Schorn, C., Guntoro, A., Ascheid, G., Efficient On-Line Error Detection and Mitigation for Deep Neural Network Accelerators (2018) Safecomp 2018, 11093. , LNCS; Yang, L., Murmann, B., SRAM voltage scaling for energy-efficient convolutional neural networks (2017) Proceedings - International Symposium on Quality Electronic Design, ISQED. IEEE Computer Society, pp. 7-12. , may; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2016) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770-778. , 2016-Decem; Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition (2015) 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings; Krizhevsky, A., Sutskever, I., Hinton, G. E., ImageNet classification with deep convolutional neural networks (2012) Advances in Neural Information Processing Systems, 2, pp. 1097-1105; Deng, Jia, Dong, Wei, Socher, R., Li, Li-Jia, Li, Kai, Fei-Fei, Li, ImageNet: A large-scale hierarchical image database (2009) 2009 IEEE Conference on Computer Vision and Pattern Recognition; Luo, Z., Charron, F. B., Lemaire, C., Konrad, J., Li, S., Mishra, A., Achkar, A., Jodoin, P.-M., MIO-TCD: A new benchmark dataset for vehicle classification and localization (2018) IEEE Transactions on Image Processing; (2019) Tech. Rep, , IEEE, 754-2019 IEEE Standard for Floating-Point Arithmetic; bfloat16 - Hardware Numerics Definition (2018) Tech. Rep, , https://software.intel.com/content/www/us/en/develop/download/bfloat16-hardware-numerics-definition.html, Intel Corporation, [Online]. Available; Theagarajan, R., Pala, F., Bhanu, B., EDeN: Ensemble of Deep Networks for Vehicle Classification (2017) IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Chang, C. K., Lym, S., Kelly, N., Sullivan, M. B., Erez, M., Evaluating and accelerating high-fidelity error injection for HPC (2019) Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018, pp. 577-589; Mahmoud, A., Aggarwal, N., Nobbe, A., Sanchez Vicarte, J. R., Adve, S. V., Fletcher, C. W., Frosio, I., Hari, S. K. S., PyTorchFI: A Runtime Perturbation Tool for DNNs (2020) DSNDSML; (2021) Cuda toolkit documentation, , https://docs.nvidia.com/cuda/floating-point/index.html, Nvidia, [Online]. Available; Krämmer, A., Schöller, C., Gulati, D., Knoll, A., (2019) Providentia - A large scale sensing system for the assistance of autonomous vehicles, , arXiv, [Online]. Available: arxiv:1906.06789","Geissler, F.; IntelGermany; email: florian.geissler@intel.com","Espinoza H.McDermid J.Huang X.Castillo-Effen M.Chen X.C.Hernandez-Orallo J.OhEigeartaigh S.S.Mallah R.Pedroza G.",,"CEUR-WS","2021 Workshop on Artificial Intelligence Safety, AISafety 2021","19 August 2021 through 20 August 2021",,170614,16130073,,,,"English","CEUR Workshop Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85112610462
[No author name available],[No author id available],"11th International Joint Conference on Computational Intelligence, IJCCI 2019",2021,"Studies in Computational Intelligence","922",,,"","",402,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112161731&partnerID=40&md5=0b245838eab2a4cd55be6008e8f68a7d",,"","The proceedings contain 16 papers. The special focus in this conference is on Computational Intelligence. The topics include: Hybrid Strategy Coupling EGO and CMA-ES for Structural Topology Optimization in Statics and Crashworthiness; introduction to Sequential Heteroscedastic Probabilistic Neural Networks; Rule Extraction from Neural Networks and Other Classifiers Applied to XSS Detection; CVaR Q-Learning; deep Convolutional Neural Network Processing of Images for Obstacle Avoidance; an Empirical Study on Insertion and Deletion Mutation in Cartesian Genetic Programming; neural Models to Quantify the Determinants of Truck Fuel Consumption; niching-Based Feature Selection with Multi-tree Genetic Programming for Dynamic Flexible Job Shop Scheduling; preface; near Optimal Solving of the (N 2 –1)-puzzle Using Heuristics Based on Artificial Neural Networks; building Market Timing Strategies Using Trend Representative Testing and Computational Intelligence Metaheuristics; towards a Class-Aware Information Granulation for Graph Embedding and Classification; predicting Cardiovascular Death with Automatically Designed Fuzzy Logic Rule-Based Models; fuzzy Geometric Approach to Collision Estimation Under Gaussian Noise in Human-Robot Interaction.",,,,,,,,,,,,,,,,,,,,"Merelo J.J.Garibaldi J.Linares-Barranco A.Warwick K.Madani K.",,"Springer Science and Business Media Deutschland GmbH","11th International Joint Conference on Computational Intelligence, IJCCI 2019","17 September 2019 through 19 September 2019",,262269,1860949X,9783030705930,,,"English","Stud. Comput. Intell.",Conference Review,"Final","",Scopus,2-s2.0-85112161731
"Rosén J., Lindblom J., Billing E.","57219093157;7006805787;36462270500;","Reporting of Ethical Conduct in Human-Robot Interaction Research",2021,"Lecture Notes in Networks and Systems","268",,,"87","94",,,"10.1007/978-3-030-79997-7_11","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112020768&doi=10.1007%2f978-3-030-79997-7_11&partnerID=40&md5=f6ee34fc5091aec6a323de4af5d9e46c","Interaction Lab, University of Skövde, Högskolevägen 1, Skövde, 541 28, Sweden","Rosén, J., Interaction Lab, University of Skövde, Högskolevägen 1, Skövde, 541 28, Sweden; Lindblom, J., Interaction Lab, University of Skövde, Högskolevägen 1, Skövde, 541 28, Sweden; Billing, E., Interaction Lab, University of Skövde, Högskolevägen 1, Skövde, 541 28, Sweden","The field of Human-Robot Interaction (HRI) is progressively maturing into a distinct discipline with its own research practices and traditions. Aiming to support this development, we analyzed how ethical conduct was reported and discussed in HRI research involving human participants. A literature study of 73 papers from three major HRI publication outlets was performed. The analysis considered how often the following five principles of ethical conduct were reported: ethical board approval, informed consent, data protection and privacy, deception, and debriefing. These five principles were selected as they belong to all major and relevant ethical guidelines for the HRI field. The results show that overall, ethical conduct is rarely reported, with four out of five principles mentioned in less than one third of all papers. The most frequently mentioned aspect was informed consent, which was reported in 49% of the articles. In this work, we aim to stimulate increased acknowledgment and discussion of ethical conduct reporting within the HRI field. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Ethics; Human-Robot Interaction; Methodology",,,,,,,"Special thanks to Oskar MacGregor for his valuable insight on proper ethical conduct. We would also like to thank Erik Lagerstedt and Kajsa Nalin for their support and help on parts of the analysis of the literature study.",,,,,,,,,,"Dautenhahn, K., Socially intelligent robots: Dimensions of human-robot interaction (2007) Philos. Trans. Roy. Soc. B Biol. Sci, 362 (1480), pp. 679-704; Lindblom, J., Andreasson, R., Current challenges for UX evaluation of human-robot interaction (2016) Advances in Ergonomics of Manufacturing: Managing the Enterprise of the Future, pp. 267-277. , https://doi.org/10.1007/978-3-319-41697-7_24, Schlick, C., Trzcieliński, S. (eds.), pp. , Springer, Cham; Baxter, P., Kennedy, J., Senft, E., Lemaignan, S., Belpaeme, T., From characterising three years of HRI to methodology and reporting recommendations (2016) The Eleventh ACM/IEEE International Conference on Human Robot Interaction, pp. 391-398. , , pp. , IEEE Press; Milgram, S., Behavioral study of obedience (1963) Psychol. Sci. Public Interest, 67 (4), pp. 371-378; Haney, C., Banks, C., Zimbardo, P., Study of prisoners and guards in a simulated prison (1973) Naval Res. Rev., 26 (9), pp. 1-17; Weiten, W., Psychology: Themes and Variations (2007) Cengage Learning; American Psychological Association: Ethical Principles of Psychologists and Code of Conduct, , https://www.apa.org/ethics/code, American Psychological Association; European Commission: Ethics in Social Science and Humanities. European Commission. https://ec.europa.eu/info/sites/info/files/6._h2020_ethics-soc-science-humanities_en.pdf; World Medical Association: Declaration of Helsinki World Medical Association, , https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-res earch-involving-human-subjects/; European Union: General Data Protection Regulation. EU. https://gdpr-info.eu; ACM/IEEE International Conference on Human-Robot Interaction. https://dl.acm.org/confer ence/hri; IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). https://www.ieee-ras.org/conferences-workshops/financially-co-sponsored/ro-man; ACM Transactions on Human-Robot Interaction, , https://dl.acm.org/journal/thri; Oates, B.J., (2005) Researching Information Systems and Computing, , Sage; Riek, L., Howard, D., A code of ethics for the human-robot interaction profession (2014) Proceedings of We Robot; Oliveira, R., Friends or foes? Socioemotional support and gaze behaviors in mixed groups of humans and robots (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 279-288. , , pp. , ACM; Rea, D.J., Young, J.E., It’s all in your head: Using priming to shape an operator’s perceptions and behavior during teleoperation (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human Robot Interaction, pp. 32-40. , , pp. , ACM; Schroter, S., Plowman, R., Hutchings, A., Gonzalez, A., Reporting ethics committee approval and patient consent by study design in five general medical journals (2006) J. Med. Ethics, 32 (12), pp. 718-723; Matthias, A., Robot lies in health care: When is deception morally permissible? (2015) Kennedy Inst. Ethics J., 25 (2), pp. 169-162","Rosén, J.; Interaction Lab, Högskolevägen 1, Sweden; email: julia.rosen@his.se","Zallio M.Raymundo Ibanez C.Hernandez J.H.",,"Springer Science and Business Media Deutschland GmbH","AHFE Conference on Human Factors in Robots, Drones and Unmanned Systems, and AHFE Conference on Human Factors in Cybersecurity, 2021","25 July 2021 through 29 July 2021",,261829,23673370,9783030799960,,,"English","Lect. Notes Networks Syst.",Conference Paper,"Final","",Scopus,2-s2.0-85112020768
"Ai Q., Liu Z., Meng W., Liu Q., Xie S.Q.","57221967520;57197763977;57203197619;55577742400;57218448814;","Machine Learning in Robot Assisted Upper Limb Rehabilitation: A Focused Review",2021,"IEEE Transactions on Cognitive and Developmental Systems",,,,"","",,1,"10.1109/TCDS.2021.3098350","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111016559&doi=10.1109%2fTCDS.2021.3098350&partnerID=40&md5=41aa62fb9ce1f392e31b8fef5c58ef91","School of Information Engineering, Wuhan University of Technology, Wuhan, 430070 China.; School of Information Engineering, Wuhan University of Technology, Wuhan, 430070 China. (e-mail: weimeng@whut.edu.cn); School of Electronic and Electrical Engineering, University of Leeds, Leeds, LS2 9JT, UK.","Ai, Q., School of Information Engineering, Wuhan University of Technology, Wuhan, 430070 China.; Liu, Z., School of Information Engineering, Wuhan University of Technology, Wuhan, 430070 China.; Meng, W., School of Information Engineering, Wuhan University of Technology, Wuhan, 430070 China. (e-mail: weimeng@whut.edu.cn); Liu, Q., School of Information Engineering, Wuhan University of Technology, Wuhan, 430070 China.; Xie, S.Q., School of Electronic and Electrical Engineering, University of Leeds, Leeds, LS2 9JT, UK.","Robot-assisted rehabilitation, which can provide repetitive, intensive and high-precision physics training, has a positive influence on motor function recovery of stroke patients. Current robots need to be more intelligent and more reliable in clinical practice. Machine learning algorithms (MLAs) are able to learn from data and predict future unknown conditions, which is of benefit to improve the effectiveness of robot-assisted rehabilitation. In this paper, we conduct a focused review on machine learning-based methods for robot-assisted upper limb rehabilitation. Firstly, the current status of upper rehabilitation robots is presented. Then, we outline and analyze the designs and applications of MLAs for upper limb movement intention recognition, human-robot interaction control and quantitative assessment of motor function. Meanwhile, we discuss the future directions of MLAs-based robotic rehabilitation. This review article provides a summary of MLAs for robotic upper limb rehabilitation and contributes to the design and development of future advanced intelligent medical devices. IEEE","End effectors; Estimation; Exoskeletons; human-robot interaction; intention recognition; Machine learning; quantitative assessment.; Rehabilitation robotics; Robot sensing systems; Robots; Training; upper limb rehabilitation","Educational robots; Intelligent robots; Learning algorithms; Machine design; Machine learning; Robotics; Social robots; Design and Development; Human-robot interaction control; Machine learning algorithm (MLAs); Quantitative assessments; Rehabilitation robot; Robot-assisted rehabilitation; Robotic rehabilitation; Upper-limb rehabilitation; Neuromuscular rehabilitation",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23798920,,,,"English","IEEE Trans. Cogn. Dev. Syst.",Article,"Article in Press","All Open Access, Green",Scopus,2-s2.0-85111016559
"Mees O., Burgard W.","57192430420;7003610380;","Composing Pick-and-Place Tasks by Grounding Language",2021,"Springer Proceedings in Advanced Robotics","19",,,"491","501",,,"10.1007/978-3-030-71151-1_43","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107060233&doi=10.1007%2f978-3-030-71151-1_43&partnerID=40&md5=624f852f5f4993da6617c9eb3e9d1997","University of Freiburg, Freiburg, Germany; Toyota Research Institute, Toyota, United States","Mees, O., University of Freiburg, Freiburg, Germany; Burgard, W., University of Freiburg, Freiburg, Germany, Toyota Research Institute, Toyota, United States","Controlling robots to perform tasks via natural language is one of the most challenging topics in human-robot interaction. In this work, we present a robot system that follows unconstrained language instructions to pick and place arbitrary objects and effectively resolves ambiguities through dialogues. Our approach infers objects and their relationships from input images and language expressions and can place objects in accordance with the spatial relations expressed by the user. Unlike previous approaches, we consider grounding not only for the picking but also for the placement of everyday objects from language. Specifically, by grounding objects and their spatial relations, we allow specification of complex placement instructions, e.g. “place it behind the middle red bowl”. Our results obtained using a real-world PR2 robot demonstrate the effectiveness of our method in understanding pick-and-place language instructions and sequentially composing them to solve tabletop manipulation tasks. Videos are available at http://speechrobot.cs.uni-freiburg.de. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.",,,,,,,"Bundesministerium für Bildung und Forschung, BMBF: 01IS18040B-OML","Acknowledgments. This work has been supported partly by the Freiburg Graduate School of Robotics and the German Federal Ministry of Education and Research under contract number 01IS18040B-OML. We thank Henrich Kolkhorst for his contributions to the speech-to-text pipeline and to Andreas Eitel for valuable discussions.",,,,,,,,,,"Clark, H.H., Brennan, S.E., (1991) Grounding in Communication, , Prespectives on Socially Shared Cognition; Guadarrama, S., Riano, L., Golland, D., Go, D., Jia, Y., Klein, D., Abbeel, P., Darrell, T., (2013) Grounding Spatial Relations for Human-Robot Interaction, p. IROS; Pangercic, D., Pitzer, B., Tenorth, M., Beetz, M., (2012) Semantic Object Maps for Robotic Housework-Representation, Acquisition and Use, , IROS; Hatori, J., Kikuchi, Y., Kobayashi, S., Takahashi, K., Tsuboi, Y., Unno, Y., Ko, W., Tan, J., (2018) Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions, p. ICRA; Paul, R., Arkin, J., Roy, N., M Howard, T., (2016) Efficient Grounding of Abstract Spatial Concepts for Natural Language Interaction with Robot Manipulators, , RSS; Jiang, Y., Lim, M., Zheng, C., Saxena, S., Learning to place new objects in a scene (2012) IJRR, 31 (9), pp. 1021-1043; Mees, O., Emek, A., Vertens, J., Burgard, W., (2020) Learning Object Placements for Relational Instructions by Hallucinating Scene Representations, p. ICRA; Mees, O., Abdo, N., Mazuran, M., Burgard, W., (2017) Metric Learning for Generalizing Spatial Relations to New Objects, p. 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Learn., 28 (1), pp. 41-75; Mees, O., Merklinger, M., Kalweit, G., Burgard, W., Unsupervised robot skill learning from videos (2020) ICRA, Adversarial Skill Networks; Abdo, N., Stachniss, C., Spinello, L., Burgard, W., Organizing objects by predicting user preferences through collaborative filtering (2016) IJRR, 35 (13), pp. 1587-1608; Haustein, J.A., Hang, K., Stork, J.A., Kragic, D., (2019) Object Placement Planning and Optimization for Robot Manipulators, , IROS; Nematollahi, I., Mees, O., Hermann, L., Burgard, W., Unsupervised structured dynamics models from physical interaction (2020) IROS, Hindsight for Foresight; Mees, O., Tatarchenko, M., Brox, T., Burgard, W., (2019) Self-Supervised 3D Shape and Viewpoint Estimation from Single Images for Robotics, , IROS; Varley, J., Dechant, C., Richardson, A., Ruales, J., Allen, P., (2017) Shape Completion Enabled Robotic Grasping, p. IROS; Mousavian, A., Eppner, C., Fox, D., (2019) 6-Dof Graspnet: Variational Grasp Generation for Object Manipulation, , ICCV; Lynch, C., Sermanet, P., (2020) Grounding Language in Play. Arxiv Preprint Arxiv, 2005, p. 07648; Shao, L., Migimatsu, T., Zhang, Q., Yang, K., Bohg, J., (2020) Concept2robot: Learning Manipulation Concepts from Instructions and Human Demonstrations, , RSS","Mees, O.; University of FreiburgGermany; email: meeso@informatik.uni-freiburg.de",,,"Springer Science and Business Media B.V.",,,,,25111256,,,,"English","Springer. Proc. Adv. Robot.",Book Chapter,"Final","All Open Access, Green",Scopus,2-s2.0-85107060233
"Mintrom M., Sumartojo S., Kulić D., Tian L., Carreno-Medrano P., Allen A.","6602830007;55548214000;10244652900;56151869000;56336138700;57219633402;","Robots in public spaces: implications for policy design",2021,"Policy Design and Practice",,,,"","",,2,"10.1080/25741292.2021.1905342","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106246047&doi=10.1080%2f25741292.2021.1905342&partnerID=40&md5=7a38ff690d105b6eb77fc9fe0e0a4d2a","Monash University, Melbourne, Australia","Mintrom, M., Monash University, Melbourne, Australia; Sumartojo, S., Monash University, Melbourne, Australia; Kulić, D., Monash University, Melbourne, Australia; Tian, L., Monash University, Melbourne, Australia; Carreno-Medrano, P., Monash University, Melbourne, Australia; Allen, A., Monash University, Melbourne, Australia","Rapid advances in digital technologies have allowed robots to become more autonomous and efficacious than ever before. Future developments in robotics hold the potential to transform human robot interactions. We can expect to see robots performing a variety of functions in public spaces. Possibilities exist for robots to greatly improve the quality of our lives and to contribute positively to the safety, creative potential, and atmosphere of public spaces. But as this trend develops, the risk emerges of robots transforming public spaces and social interactions in undesirable ways. By reviewing previous public policy approaches to harnessing and regulating disruptive technology, we consider how public policy could simultaneously enhance opportunities created by the presence of robots in public spaces and reduce the risks of undesirable outcomes. We summarize key insights into a policy design checklist to guide policies on robots in public spaces. These insights cover (1) safety, (2) privacy and ethics, (3) productivity, (4) esthetics, (5) co-creation, (6) equitable access, and (7) systemic innovation. © 2021 The Author(s). 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Des. Pract.",Article,"Article in Press","All Open Access, Gold",Scopus,2-s2.0-85106246047
"Shi L., Copot C., Vanlanduit S.","57211018738;35301588200;7004271926;","Gazeemd: Detecting visual intention in gaze-based human-robot interaction",2021,"Robotics","10","2","68","","",,1,"10.3390/robotics10020068","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105740422&doi=10.3390%2frobotics10020068&partnerID=40&md5=8219ff5e8e6576d302ee3a13cee677fb","Department of Electromechanics, Faculty of Applied Engineering, University of Antwerp, Campus Groenenborger, Antwerp, 2020, Belgium; G.Z. 324, Campus Groenenborger, Groenenborgerlaan 171, Antwerp, 2020, Belgium","Shi, L., Department of Electromechanics, Faculty of Applied Engineering, University of Antwerp, Campus Groenenborger, Antwerp, 2020, Belgium, G.Z. 324, Campus Groenenborger, Groenenborgerlaan 171, Antwerp, 2020, Belgium; Copot, C., Department of Electromechanics, Faculty of Applied Engineering, University of Antwerp, Campus Groenenborger, Antwerp, 2020, Belgium; Vanlanduit, S., Department of Electromechanics, Faculty of Applied Engineering, University of Antwerp, Campus Groenenborger, Antwerp, 2020, Belgium","In gaze-based Human-Robot Interaction (HRI), it is important to determine human visual intention for interacting with robots. One typical HRI interaction scenario is that a human selects an object by gaze and a robotic manipulator will pick up the object. In this work, we propose an approach, GazeEMD, that can be used to detect whether a human is looking at an object for HRI application. We use Earth Mover’s Distance (EMD) to measure the similarity between the hypothetical gazes at objects and the actual gazes. Then, the similarity score is used to determine if the human visual intention is on the object. We compare our approach with a fixation-based method and HitScan with a run length in the scenario of selecting daily objects by gaze. Our experimental results indicate that the GazeEMD approach has higher accuracy and is more robust to noises than the other approaches. Hence, the users can lessen cognitive load by using our approach in the real-world HRI scenario. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","EMD; Fixation; Gaze; Human-Robot Interaction",,,,,,,,,,,,,,,,,"Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., Van deWeijer, J., (2011) Eye Tracking: A Comprehensive Guide to Methods and Measures, , OUP: Oxford, UK; Salvucci, D.D., Goldberg, J.H., Identifying fixations and saccades in eye-tracking protocols (2000) Proceedings of the 2000 Symposium on Eye Tracking Research & Applications, pp. 71-78. , Palm Beach Gardens, FL, USA, 6–8 November; Santini, T., Fuhl, W., Kübler, T., Kasneci, E., Bayesian identification of fixations, saccades, and smooth pursuits Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications, pp. 163-170. , Charleston, SC, USA, 14–17 March 2016; Zemblys, R., Niehorster, D.C., Komogortsev, O., Holmqvist, K., Using machine learning to detect events in eye-tracking data (2018) Behav. Res. 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Technol, p. 2; Shojaeizadeh, M., Djamasbi, S., Trapp, A.C., Density of gaze points within a fixation and information processing behavior Proceedings of the International Conference on Universal Access in Human-Computer Interaction, pp. 465-471. , Toronto, ON, Canada, 17–22 July 2016; Wang, H., Shi, B.E., Gaze awareness improves collaboration efficiency in a collaborative assembly task Proceedings of the 11th ACM Symposium on Eye Tracking Research & Applications, pp. 1-5. , Denver, CO, USA, 25–28 June 2019; Moon, A., Troniak, D.M., Gleeson, B., Pan, M.K., Zheng, M., Blumer, B.A., MacLean, K., Croft, E.A., Meet me where i’m gazing: How shared attention gaze affects human-robot handover timing Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction, pp. 334-341. , Bielefeld, Germany, 3–6 March 2014","Copot, C.; Department of Electromechanics, Campus Groenenborger, Belgium; email: cosmin.copot@uantwerpen.be",,,"MDPI AG",,,,,22186581,,,,"English","Robotics",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85105740422
"Paplu S.H., Arif M.N.I., Berns K.","57204053864;57223303742;7006119626;","Adapting behaviour of socially interactive robot based on text sentiment",2021,"Mechanisms and Machine Science","102",,,"221","228",,,"10.1007/978-3-030-75259-0_24","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105534077&doi=10.1007%2f978-3-030-75259-0_24&partnerID=40&md5=2b6cd4089de7e504b409738d52f620d0","Department of Computer Science, RRLAB, Technische Universität Kaiserslautern, Kaiserslautern, 67663, Germany","Paplu, S.H., Department of Computer Science, RRLAB, Technische Universität Kaiserslautern, Kaiserslautern, 67663, Germany; Arif, M.N.I., Department of Computer Science, RRLAB, Technische Universität Kaiserslautern, Kaiserslautern, 67663, Germany; Berns, K., Department of Computer Science, RRLAB, Technische Universität Kaiserslautern, Kaiserslautern, 67663, Germany","The assessment of emotion is a complex process that encompasses information from various channels or sources, e.g., verbal, para-linguistic, non-verbal or even textual cues. Understanding the emotion of an interaction partner and exhibiting proper bodily reactions play a crucial role in establishing an engaging interaction. The evaluation of emotion, in general, is significant as far as emotionally intelligent human-robot interaction is concerned. In this paper, a text-based interaction system has been introduced to establish affective communication. A chatbot system has been implemented on a humanoid robot named ROBIN. The sentiment of the user input texts has been analyzed with MITIE machine learning toolkit. The robot considers emotional states of the users’ text utterances and reacts with appropriate speech, gesture, and facial expressions. This triggers real-time affective behavior from the robot’s perspective. The proposed system has been tested on several interaction scenarios to validate the approach from a human-centered perspective. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.","Chatbot; Human-robot interaction; Text sentiment","Anthropomorphic robots; Affective behaviors; Affective communication; Complex Processes; Emotional state; Facial Expressions; Interaction systems; Interactive robot; Various Channels; Social robots",,,,,,,,,,,,,,,,"Thomaz, A., Hoffman, G., Cakmak, M., Computational human-robot interaction. Found (2016) Trends Robot, 4 (2-3), pp. 105-223; Al-Darraji, S., (2016) Perception of Nonverbal Cues for Human-Robot Interaction, , Verlag Dr. Hut; Milhorat, P., Lala, D., Inoue, K., Zhao, T., Ishida, M., Takanashi, K., Nakamura, S., Kawahara, T., A conversational dialogue manager for the humanoid robot ERICA (2019) Advanced Social Interaction with Agents, pp. 119-131. , pp., Springer; Fridin, M., Storytelling by a kindergarten social assistive robot: A tool for constructive learning in preschool education (2014) Comput. Educ., 70, pp. 53-64; Brill, E., Dumais, S., Banko, M., An analysis of the AskMSR question-answering system (2002) Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), pp. 257-264; Muench, F., Baumel, A., More than a text message: Dismantling digital triggers to curate behavior change in patient-centered health interventions (2017) J. Med. Internet Res., 19 (5); Paplu, S.H., Mishra, C., Berns, K., Pseudo-randomization in automating robot behaviour during human-robot interaction (2020) 2020 Joint IEEE 10Th International Conference on Development and Learning and Epigenetic Robotics (Icdl-Epirob), pp. 1-6. , pp., IEEE; Zafar, Z., Paplu, S.H., Berns, K., Automatic assessment of human personality traits: A step towards intelligent human-robot interaction (2018) 2018 IEEE-RAS 18Th International Conference on Humanoid Robots (Humanoids), pp. 1-9. , pp., IEEE; Weizenbaum, J., ELIZA–a computer program for the study of natural language communication between man and machine (1966) Commun. ACM, 9 (1), pp. 36-45; Serrano, J., Gonzalez, F., Zalewski, J., CleverNAO: The intelligent conversational humanoid robot (2015) 2015 IEEE 8Th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2, pp. 887-892. , vol., pp., IEEE; Russell, E., (2015) Real-Time Topic and Sentiment Analysis in Human-Robot Conversation; Medhat, W., Hassan, A., Korashy, H., Sentiment analysis algorithms and applications: A survey (2014) Ain Shams Eng. J., 5 (4), pp. 1093-1113; King, D.E., Dlib-ml: A machine learning toolkit (2009) J. Mach. Learn. Res., 10, pp. 1755-1758; Bird, S., Klein, E., Loper, E., (2009) Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O’Reilly Media Inc, , Sebastopol; Zhang, Y., Jin, R., Zhou, Z.H., Understanding bag-of-words model: A statistical framework (2010) Int. J. Mach. Learn. Cybern., 1 (1-4), pp. 43-52; Ekman, P., Basic emotions (1999) Handbook of Cognition and Emotion, , vol. 98, no. 45–60, p. 16; Reichardt, M., Föhst, T., Berns, K., (2012) Introducing FINROC: A Convenient Real-Time Framework for Robotics Based on A Systematic Design Approach, , Technical report, Robotics Research Lab, Department of Computer Science, University of Kaiserslautern, Kaiserslautern, Germany","Paplu, S.H.; Department of Computer Science, Germany; email: paplu@cs.uni-kl.de",,,"Springer Science and Business Media B.V.",,,,,22110984,,,,"English","Mech. Mach. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85105534077
"Burema D.","57198492127;","A critical analysis of the representations of older adults in the field of human–robot interaction",2021,"AI and Society",,,,"","",,2,"10.1007/s00146-021-01205-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105386171&doi=10.1007%2fs00146-021-01205-0&partnerID=40&md5=db97c3b2b2a7faf943dfbc4e069fa098","Bremen International Graduate School of Social Sciences (BIGSSS), University of Bremen, Jacobs University, Bremen, Germany","Burema, D., Bremen International Graduate School of Social Sciences (BIGSSS), University of Bremen, Jacobs University, Bremen, Germany","This paper argues that there is a need to critically assess bias in the representations of older adults in the field of Human–Robot Interaction. This need stems from the recognition that technology development is a socially constructed process that has the potential to reinforce problematic understandings of older adults. Based on a qualitative content analysis of 96 academic publications, this paper indicates that older adults are represented as; frail by default, independent by effort; silent and technologically illiterate; burdensome; and problematic for society. Within these documents, few counternarratives are present that do not take such essentialist representations. In these texts, the goal of social robots in elder care is to “enable” older adults to “better” themselves. The older body is seen as “fixable” with social robots, reinforcing an ageist and neoliberal narrative: older adults are reduced to potential care receivers in ways that shift care responsibilities away from the welfare state onto the individual. © 2021, The Author(s).","Ageism; Elder care; Human–robot interaction; Neoliberalism; Older adults; User representations","Economic and social effects; Academic publications; Content analysis; Critical analysis; Elder care; Older adults; Robot interactions; Technology development; Welfare state; Social robots",,,,,"713639; Horizon 2020","Open Access funding enabled and organized by Projekt DEAL. This work was supported by the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 713639.",,,,,,,,,,"Abbey, R., Hyde, S., No country for older people? 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IEEE, Siem Reap, pp. 161-166; Khosla, R., Chu, M.T., Nguyen, K., Enhancing emotional well being of elderly using assistive social robots in Australia (2013) 2013 International Conference on Biometrics and Kansei Engineering, pp. 41-46. , Tokyo; Laslett, P., (1991) A fresh map of life: the emergence of the third age, , Harvard University Press, Harvard; Looije, R., Neerincx, M.A., Cnossen, F., Persuasive robotic assistant for health self-management of older adults: design and evaluation of social behaviors (2010) Int J Hum Comput Stud, 68, pp. 386-397; Lüders, M., Brandtzæg, P.B., ‘My children tell me it’s so simple’: a mixed-methods approach to understand older non-users’ perceptions of social networking sites (2017) New Media Soc, 19, pp. 181-198; Mikkelsen, H.H., Never too late for pleasure: aging, neoliberalism, and the politics of potentiality in Denmark: never too late for pleasure (2017) Am Ethnol, 44, pp. 646-656; Neven, L., Leeson, C.A.P., Beyond determinism: understanding actual use of social robots by older people (2015) Aging and the digital life course, , Prendergast D, Garattini C, (eds), Berghahn Books; Neven, L., Peine, A., (2017) From triple win to triple sin: how a problematic future discourse is shaping the way people age with technology, , Societies; Pavolini, E., Ranci, C., Reforms in long-term care policies in europe: anintroduction (2013) Reforms in long-term care policies in Europe, , Ranci C, Pavolini E, (eds), Springer, New York; Peine, A., Neven, L., From intervention to co-constitution: new directions in theorizing about aging and technology (2019) Gerontologist, 59, pp. 15-21; Pfadenhauer, M., Dukat, C., Robot caregiver or robot-supported caregiving? 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Mind perception and attitudes towards robots predict use of an eldercare robot (2014) Int J Soc Robot, 6, pp. 17-32; Sung, H.C., Chang, S.M., Chin, M.Y., Robot-assisted therapy for improving social interactions and activity participation among institutionalized older adults: a pilot study: robot therapy for older adults (2015) Asia Pac Psychiatry, 7, pp. 1-6; Tapus, A., Mataric, M., Socially assistive robotic music therapist for maintaining attention of older adults with cognitive impairments (2008) Proceedings of AAAI Fall Symposium on AI in Eldercare: New Solutions to Old Problems, , Arlington; Wada, K., Shibata, T., Saito, T., Tanie, K., Psychological, physiological and social effects to elderly people by robot assisted activity at a health service facility for the aged (2003) Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM, 2003, pp. 272-277. , IEEE, Kobe; Wada, K., Shibata, T., Saito, T., Psychological and social effects of one year robot assisted activity on elderly people at a health service facility for the aged (2005) Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 2785-2790. , IEEE, Barcelona; Woolgar, S., Configuring the user: the case of usability trials (1990) Sociol Rev, 38, pp. 58-99; Zlatintsi, A., Rodomagoulakis, I., Pitsikalis, V., Social human–robot interaction for the elderly: Two real-life use cases (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 335-336. , ACM, Vienna","Burema, D.; Bremen International Graduate School of Social Sciences (BIGSSS), Germany; email: burema@tu-berlin.de",,,"Springer Science and Business Media Deutschland GmbH",,,,,09515666,,,,"English","AI Soc.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85105386171
"Zhao F.D., Yang Z.K., Li X.S., Guo D.D., Li H.T.","15060775500;57222759541;55934244700;56263431700;57192095751;","Extract Executable Action Sequences from Natural Language Instructions Based on DQN for Medical Service Robots",2021,"International Journal of Computers, Communications and Control","16","2",,"1","12",,1,"10.15837/ijccc.2021.2.4115","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104002671&doi=10.15837%2fijccc.2021.2.4115&partnerID=40&md5=b0406756b290fa694dfe13deec16fed3","School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China; Xinjiang University of Science and Technology, Korla, 841000, China; The Key Laboratory for Software Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, China","Zhao, F.D., School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China, Xinjiang University of Science and Technology, Korla, 841000, China, The Key Laboratory for Software Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, China; Yang, Z.K., School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China, The Key Laboratory for Software Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, China; Li, X.S., School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China, The Key Laboratory for Software Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, China; Guo, D.D., School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China, The Key Laboratory for Software Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, China; Li, H.T., School of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China, The Key Laboratory for Software Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, China","The emergence and popularization of medical robots bring great convenience to doctors in treating patients. The core of medical robots is the interaction and cooperation between doctors and robots, so it is crucial to design a simple and stable human-robots interaction system for medical robots. Language is the most convenient way for people to communicate with each other, so in this paper, a DQN agent based on long-short term memory (LSTM) and attention mechanism is proposed to enable the robots to extract executable action sequences from doctors' natural language instructions. For this, our agent should be able to complete two related tasks: 1) extracting action names from instructions. 2) extracting action arguments according to the extracted action names. We evaluate our agent on three datasets composed of texts with an average length of 49.95, 209.34, 417.17 words respectively. The results show that our agent can perform better than similar agents. And our agent has a better ability to handle long texts than previous works. © 2021 the authors. Licensee Agora University, Oradea, Romania. All Rights Reserved.","attention mechanism; DQN agent; human-robots interaction; long-short term memory; medical robots",,,,,,"XJEDU2021I029; Hebei Provincial Key Research Projects: 18210336, 202003B043","This work was supported by the Key Research and Development Program of Hebei Province Grant No.18210336 and Qinhuangdao City Science, Technology Research and Development Plan Grant No.202003B043 and Xinjiang Uygur Autonomous Region University Scientific Research Project (Key Natural Science Project), XJEDU2021I029. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the paper.",,,,,,,,,,"BAJENARU, L., Marinescu, I. A., Dobre, C., DRAGHICI, R., Herghelegiu, A. M., Rusu, A., Identifying the Needs of Older People for Personalized Assistive Solutions in Romanian Healthcare System (2020) Studies in Informatics and Control, 29 (3), pp. 363-372. , https://doi.org/10.24846/v29i3y202009, [1] ISSN 1220-1766, 2020; Bastianelli, E., Castellucci, G., Croce, D., Iocchi, L., Basili, R., Nardi, D., HuRIC: a Human Robot Interaction Corpus (2014) Proceedings of the 9th International Conference on Language Resources and Evaluation, pp. 4519-4526. , [2] 2014; Mensio, M., Bastianelli, E., Tiddi, I., Rizzo, G., Mitigating Bias in Deep Nets with Knowl edge Bases: the Case of Natural Language Understanding for Robots (2020) AAAI Spring Symposium: Combining Machine Learning with Know ledge Engineering, 1, pp. 1-9. , [3] 2020; Kaelbling, L. P., Littman, M. L., Moore, A. W., An introduction to reinforcement learning (1995) The Biology and Technology of Intel ligent Autonomous Agents, p. 90127. , [4] Springer, Berlin, Heidelberg, 1995; Shi, H., Li, X., Hwang, K. 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P., Feed-forward networks with attention can solve some long-term memory problems (2016) International Conference on Learning Representations, , [23] 2016; Dozat, T., Incorporating Nesterov Momentum into Adam (2016) International Conference on Learning Representations, 1, pp. 2013-2016. , [24] 2016; Chung, J., Gulcehre, C., Cho, K., Bengio, Y., (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling, , [25] CoRR abs/1412.3555, 2014","Li, X.S.; School of Information Science and Engineering, China",,,"Agora University",,,,,18419836,,,,"English","Int. J. Comput. Commun. Control",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85104002671
"Malinowska J.K.","57189242771;","Can I Feel Your Pain? The Biological and Socio-Cognitive Factors Shaping People’s Empathy with Social Robots",2021,"International Journal of Social Robotics",,,,"","",,3,"10.1007/s12369-021-00787-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103150921&doi=10.1007%2fs12369-021-00787-5&partnerID=40&md5=9b1fce35f24e018fddab0261f6f1b2f0","Faculty of Philosophy, Adam Mickiewicz University, Poznań, Poland; Department of Philosophy, Jagiellonian University, Kraków, Poland","Malinowska, J.K., Faculty of Philosophy, Adam Mickiewicz University, Poznań, Poland, Department of Philosophy, Jagiellonian University, Kraków, Poland","This paper discuss the phenomenon of empathy in social robotics and is divided into three main parts. Initially, I analyse whether it is correct to use this concept to study and describe people’s reactions to robots. I present arguments in favour of the position that people actually do empathise with robots. I also consider what circumstances shape human empathy with these entities. I propose that two basic classes of such factors be distinguished: biological and socio-cognitive. In my opinion, one of the most important among them is a sense of group membership with robots, as it modulates the empathic responses to representatives of our- and other- groups. The sense of group membership with robots may be co-shaped by socio-cognitive factors such as one’s experience, familiarity with the robot and its history, motivation, accepted ontology, stereotypes or language. Finally, I argue in favour of the formulation of a pragmatic and normative framework for manipulations in the level of empathy in human–robot interactions. © 2021, The Author(s).","Anthropomorphism; Empathy; Human–robot interactions; Social robots","Agricultural robots; Group memberships; Robot interactions; Social robotics; Socio-cognitive; Social robots",,,,,"Ministerstwo Edukacji i Nauki, MNiSW: 0068/NPRH4/H2b/83/2016","This research was funded by the Ministry of Science and Higher Education in Poland, National Programme for the Development of Humanities, No. 0068/NPRH4/H2b/83/2016. Fragments of the text in Sect. are based on the articles: “The growing need for reliable conceptual analysis in HRI studies: the example of the term ‘empathy’” presented at Robophilosophy conference in 2020) and “What does it mean to empathise with a robot” []. I am grateful to my colleagues Maciej Musiał and Tomasz Żuradzki for all of their comments and guidance. I would also like to thank the Reviewers for their great help in improving this paper.",,,,,,,,,,"Abe, S., Educational effects using a robot patient simulation system for development of clinical attitude (2018) Eur J Dent Educ, 22 (3), pp. 327-336; Adamo, S.A., Do insects feel pain? A question at the intersection of animal behaviour, philosophy and robotics (2016) Anim Behav, 118, pp. 75-79; Airenti, G., The cognitive bases of anthropomorphism: from relatedness to empathy (2015) Int J Soc Robot, 7 (1), pp. 117-127; Aggarwal, P., McGill, A.L., Is that car smiling at me? 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J. Soc. Rob.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85103150921
"Zafar Z., Ashok A., Berns K.","56462691100;57222516272;7006119626;","Personality traits assessment using p.A.D. Emotional space in human-robot interaction",2021,"VISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","2",,,"111","118",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102974518&partnerID=40&md5=579d9d515e2e418c556711a934135b57","Robotics Research Lab., Department of Computer Science, Technische Universität Kaiserslautern, Germany","Zafar, Z., Robotics Research Lab., Department of Computer Science, Technische Universität Kaiserslautern, Germany; Ashok, A., Robotics Research Lab., Department of Computer Science, Technische Universität Kaiserslautern, Germany; Berns, K., Robotics Research Lab., Department of Computer Science, Technische Universität Kaiserslautern, Germany","Cognitive social robotics is the field of research that is committed to building social robots that facilitate to draw parallels with human beings. Humans assess the behavior and personality of their counterparts to adapt their behavior and show empathy to flourish human-human interaction. Similarly, assessment of human personality is highly critical in realizing natural and intelligent human-robot interaction. Numerous personality traits assessment systems have been reported in the literature; however, most of them target the big five personality traits. From only visual information, this work proposes to use pleasure, arousal, and dominance emotional space for the assessment of personality traits based on the work of Mehrabian. To validate the system, three different scenarios have been developed to assess 12 different personality traits on a social humanoid robot. Experimental results show that the system can assess human personality traits with 84% accuracy in real-time and, hence, it can adapt its behavior according to the perceived personality of the interaction partner. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.","Human-robot interaction; PAD emotional space; Personality trait assessment; Robot adaptivity","Anthropomorphic robots; Computer graphics; Computer vision; Man machine systems; Assessment system; Human being; Human-human interactions; Humanoid robot; Personality traits; Real time; Social robotics; Visual information; Social robots",,,,,,,,,,,,,,,,"Al-Darraji, S., Berns, K., Rodić, A., Action unit based facial expression recognition using deep learning (2017) Advances in Robot Design and Intelligent Control, pp. 413-420. , Rodić, A. and Borangiu, T., editors, pages Cham. 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Stud, 4, p. 57; Mehrabian, A., Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament (1996) Current Psychology, 14 (4), pp. 261-292; Nass, C., Lee, K. M., Does computer-synthesized speech manifest personality? experimental tests of recognition, similarity-attraction, and consistency-attraction (2001) Journal of experimental psychology: applied, 7 (3), p. 171; Rodić, A., Urukalo, D., Vujović, M., Spasojević, S., Tomić, M., Berns, K., Al-Darraji, S., Zafar, Z., Embodiment of human personality with ei-robots by mapping behaviour traits from live-model (2016) International Conference on Robotics in Alpe-Adria Danube Region, pp. 438-448. , Springer; Russell, J. A., A circumplex model of affect (1980) Journal of personality and social psychology, 39 (6), p. 1161; Russell, J. A., Mehrabian, A., Evidence for a three-factor theory of emotions (1977) Journal of research in Personality, 11 (3), pp. 273-294; Salam, H., Celiktutan, O., Hupont, I., Gunes, H., Chetouani, M., Fully automatic analysis of engagement and its relationship to personality in human-robot interactions (2016) IEEE Access, 5, pp. 705-721; Saleh, S., Berns, K., Nonverbal communication with a humanoid robot via head gestures (2015) Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, p. 15. , page ACM; Watson, D., Clark, L. A., Measurement and mismeasurement of mood: Recurrent and emergent issues (1997) Journal of personality assessment, 68 (2), pp. 267-296; Watson, D., Tellegen, A., Toward a consensual structure of mood (1985) Psychological bulletin, 98 (2), p. 219; Wundt, W. M., Judd, C. H., (1897) Outlines of psychology, 1. , Scholarly Press; Zafar, Z., Paplu, S. H., Berns, K., Automatic assessment of human personality traits: A step towards intelligent human-robot interaction (2018) 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids), pp. 1-9. , pages IEEE; Zafar, Z., Venugopal, R., Berns, K., Real-time recognition of human postures for human-robot interaction (2018) 11th International Conference on Advances in Computer-Human Interactions (ACHI), pp. 29-35. , (2018b) pages",,"Paljic A.Peck T.Braz J.Bouatouch K.","Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","SciTePress","16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021","8 February 2021 through 10 February 2021",,167535,,9789897584886,,,"English","VISIGRAPP - Proc. Int. Jt. Conf. Comput. Vis., Imaging Comput. Graph. Theory Appl.",Conference Paper,"Final","",Scopus,2-s2.0-85102974518
"Sørensen A.R., Palinko O., Krüger N.","57222516289;35099082400;7004442773;","Classification of visual interest based on gaze and facial features for human-robot interaction",2021,"VISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","2",,,"198","204",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102972571&partnerID=40&md5=6e7f4f3455e7633ac85f0a764aacb424","The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, Odense M, DK-5230, Denmark","Sørensen, A.R., The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, Odense M, DK-5230, Denmark; Palinko, O., The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, Odense M, DK-5230, Denmark; Krüger, N., The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Campusvej 55, Odense M, DK-5230, Denmark","It is important for a social robot to know if a nearby human is showing interest in interacting with it. We approximate this interest with expressed visual interest. To find it, we train a number of classifiers with previously labeled data. The input features for these are facial features like head orientation, eye gaze and facial action units, which are provided by the OpenFace library. As training data, we use video footage collected during an in-the-wild human-robot interaction scenario, where a social robot was approaching people at a cafeteria to serve them water. The most successful classifier that we trained tested at a 94% accuracy for detecting interest on an unrelated testing dataset. This allows us to create an effective tool for our social robot, which enables it to start talking to people only when it is fairly certain that the addressed persons are interested in talking to it. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.","Classification; Gaze; Human-robot interaction; Visual interest","Classification (of information); Computer graphics; Computer vision; Educational robots; End effectors; Man machine systems; Statistical tests; Effective tool; Eye-gaze; Facial action; Facial feature; Input features; Training data; Video footage; Visual interest; Social robots",,,,,,"This work has been supported by InnovationsFonden Danmark in the context of the Project “Seamless huMan-robot interactiOn fOr THe support of elderly people” (SMOOTH).",,,,,,,,,,"Argyle, M., (1972) Non-verbal communication in human social interaction; Mavridis, N., A review of verbal and non-verbal human–robot interactive communication (2015) Robotics and Autonomous Systems, 63, pp. 22-35; Juel, W. K., Haarslev, F., Ramírez, E. R., Marchetti, E., Fischer, K., Shaikh, D., Krüger, N., SMOOTH Robot: Design for a novel modular welfare robot (2020) Journal of Intelligent & Robotic Systems, 98 (1), pp. 19-37; Palinko, O., Fischer, K., Ruiz Ramirez, E., Damsgaard Nissen, L., Langedijk, R. M., A Drink-Serving Mobile Social Robot Selects who to Interact with Using Gaze (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 384-385. , (March); Baltrušaitis, T., Robinson, P., Morency, L. P., Openface: an open source facial behavior analysis toolkit (2016) 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1-10. , (March) IEEE; Hjortsjö, C. H., Man's face and mimic language (1969), Studentlitteratur; Argyle, M., Cook, M., (1976) Gaze and mutual gaze; Palinko, O., Rea, F., Sandini, G., Sciutti, A., Eye gaze tracking for a humanoid robot (2015) 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 318-324. , (November) IEEE; Munoz-Salinas, R., Aguirre, E., García-Silvente, M., González, A., A fuzzy system for visual detection of interest in human-robot interaction (2005) 2nd International Conference on Machine Intelligence (ACIDCA-ICMI’2005), pp. 574-581; Palinko, O., Rea, F., Sandini, G., Sciutti, A., Robot reading human gaze: Why eye tracking is better than head tracking for human-robot collaboration (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5048-5054. , (October) IEEE; Zhang, Y., (2010) New advances in machine learning, , (Ed). BoD–Books on Demand; Ekman, R., (2012) What the face reveals: Basic and applied studies of spontaneous expression using the Facial Action Coding System (FACS)",,"Paljic A.Peck T.Braz J.Bouatouch K.","Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","SciTePress","16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021","8 February 2021 through 10 February 2021",,167535,,9789897584886,,,"English","VISIGRAPP - Proc. Int. Jt. Conf. Comput. Vis., Imaging Comput. Graph. Theory Appl.",Conference Paper,"Final","",Scopus,2-s2.0-85102972571
"Camblor B., Salotti J.-M., Fage C., Daney D.","57222134687;8423098000;56422336900;36867137800;","Degraded situation awareness in a robotic workspace: accident report analysis",2021,"Theoretical Issues in Ergonomics Science",,,,"","",,,"10.1080/1463922X.2021.1879308","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101596916&doi=10.1080%2f1463922X.2021.1879308&partnerID=40&md5=41df03707362cc13284c648da9ee0edd","Inria, IMS, UMR 5218, Talence, France; Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France","Camblor, B., Inria, IMS, UMR 5218, Talence, France, Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France; Salotti, J.-M., Inria, IMS, UMR 5218, Talence, France, Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France; Fage, C., Inria, IMS, UMR 5218, Talence, France; Daney, D., Inria, IMS, UMR 5218, Talence, France","We adopted an approach based on interactions in a robotic workspace, coupled with Endsley’s model of Situation Awareness (SA), to identify factors that potentially degrade operators’ SA: the eight SA demons. We propose here to study the implications of these demons in 45 industrial accidents involving robotic. These demons were associated with possible interactions a human may have in a robotic workspace. Our results showed that five of the eight SA demons appeared in the accident reports examined. The results showed that the identified SA demons were mainly associated with the first level of SA (perception level). We have also been able to describe five patterns of SA demon occurence in these accidents. SA demons are generally involved in human-robot interactions rather than human-environment interactions. Thanks to this analysis, we found that an operator’s SA is mainly impacted by the Errant Mental Model and Out-of-the-Loop SA demons in human-robot and human-environment interactions. Finally, we propose recommendations regarding workspace design. © 2021 Informa UK Limited, trading as Taylor & Francis Group.","accident analysis; accidents; Industrial robotics; interaction; situation awareness",,,,,,,,,,,,,,,,,"Aghazadeh, F., Hirschfeld, R., Chapleski, R., Industrial Robot Use: survey Results and Hazard Analysis (1993) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 37 (14), pp. 994-998. , and; Arai, T., Kato, R., Fujita, M., Assessment of Operator Stress Induced by Robot Collaboration in Assembly (2010) CIRP Annals, 59 (1), pp. 5-8. , and; Charpentier, P., Sghaier, A., Industrial Robotic: Accident Analysis and Human-Robot Coactivity (2012) Proceedings of the 7th International Conference on the Safety of Industrial Automated Systems, SIAS, pp. 11-12. , and; Chiappe, D.L., Strybel, T.Z., Vu, K.-P.L., Mechanisms for the Acquisition of Situation Awareness in Situated Agents (2012) Theoretical Issues in Ergonomics Science, 13 (6), pp. 625-647. , and; Colgate, J.E., Edward, J., Peshkin, M.A., Wannasuphoprasit, W., Cobots: Robots for Collaboration with Human Operators (1996) Proceedings of the 1996 ASME International Mechanical Engineering Congress and Exposition, , and; De Luca, A., Flacco, F., Integrated Control for pHRI: Collision Avoidance, Detection, Reaction and Collaboration (2012) 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 288-295. , and,. IEEE; Dhillon, B.S., Robot Reliability (1991) Robot Reliability and Safety, pp. 119-149. , Springer,. Springer-Verlag New York; Emika, F., (2018), https://www.franka.de, Panda,. Technical Report. Accessed 2017-10-06.[Online]; Endsley, M.R., Design and Evaluation for Situation Awareness Enhancement (1988) Proceedings of the Human Factors Society Annual Meeting, 32 (2), pp. 97-101; Endsley, M.R., Toward a Theory of Situation Awareness in Dynamic Systems (1995) Human Factors: The Journal of the Human Factors and Ergonomics Society, 37 (1), pp. 32-64; Endsley, M.R., Jones, D.G., SA Demons: The Enemies of Situation Awareness (2016) Designing for Situation Awareness, pp. 50-61. , CRC Press, and,. CRC Press, Taylor and Francis Group, UK; (2011), https://www.iso.org/standard/41571.html, 10218-2: 2011: Robots and Robotic Devices–Safety Requirements for Industrial Robots–Part 2: Robot Systems and Integration,. Standard ISO/IEC TR 10218-2:2011. 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Geneva, CH: International Organization for Standardization; Jiang, B.C., Gainer, C.A., Jr., A Cause-and-Effect Analysis of Robot Accidents (1987) Journal of Occupational Accidents, 9 (1), pp. 27-45; Jones, D.G., Endsley, M.R., Sources of Situation Awareness Errors in Aviation (1996) Aviation, Space, and Environmental Medicine, , and,. 67 (6): 507–512; Karwowski, W., Rahimi, M., Worker Selection of Safe Speed and Idle Condition in Simulated Monitoring of Two Industrial Robots (1991) Ergonomics, 34 (5), pp. 531-546. , and; Koppenborg, M., Nickel, P., Naber, B., Lungfiel, A., Huelke, M., Effects of Movement Speed and Predictability in Human–Robot Collaboration (2017) Human Factors and Ergonomics in Manufacturing & Service Industries, 27 (4), pp. 197-209. , and; Lamy, P., Tissot, C., Analyse de récits d’accidents du travail pour identifier des dérives d’usage et apport des TAL (2016) Congrès Lambda Mu 20 de Maîtrise des Risques et de Sûreté de Fonctionnement, , 11-13 Octobre 2016, Saint Malo, France; Malm, T., Viitaniemi, J., Latokartano, J., Lind, S., Venho-Ahonen, O., Schabel, J., Safety of Interactive Robotics–Learning from Accidents (2010) International Journal of Social Robotics, 2 (3), pp. 221-227. , and; Meziane, R., Li, P., Otis, M.J.D., Ezzaidi, H., Cardou, P., Safer Hybrid Workspace Using Human-Robot Interaction While Sharing Production Activities (2014) 2014 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Proceedings, pp. 37-42. , and,. IEEE; Michalos, G., Makris, S., Tsarouchi, P., Guasch, T., Kontovrakis, D., Chryssolouris, G., Design Considerations for Safe Human-Robot Collaborative Workplaces (2015) Procedia CIrP, 37, pp. 248-253. , and; Ogorodnikova, O., Human Weaknesses and Strengths in Collaboration with Robots (2008) Periodica Polytechnica Mechanical Engineering, 52 (1), pp. 25-33; Or, C.K.L., Duffy, V.G., Cheung, C.C., Perception of Safe Robot Idle Time in Virtual Reality and Real Industrial Environments (2009) International Journal of Industrial Ergonomics, 39 (5), pp. 807-812. , and; Rahimi, M., Karwowski, W., A Research Paradigm in Human-Robot Interaction (1990) International Journal of Industrial Ergonomics, 5 (1), pp. 59-71. , and; Rasmussen, J., Skills, Rules, and Knowledge; Signals, Signs, and Symbols, and Other Distinctions in Human Performance Models (1983) IEEE Transactions on Systems, Man, and Cybernetics, SMC-13 (3), pp. 257-266; Reason, J., (1990) Human Error, , Cambridge University Press,. 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IEEE; Schaub, K., Caragnano, G., Britzke, B., Bruder, R., The European Assembly Worksheet (2013) Theoretical Issues in Ergonomics Science, 14 (6), pp. 616-639. , and; Shappell, S., Detwiler, C., Holcomb, K., Hackworth, C., Boquet, A., Wiegmann, D.A., Human Error and Commercial Aviation Accidents: An Analysis Using the Human Factors Analysis and Classification System (2007) Human Factors, 49 (2), pp. 227-242. , and; Stratmann, T.C., Boll, S., Demon Hunt-the Role of Endsley’s Demons of Situation Awareness in Maritime Accidents (2016) Human-Centered and Error-Resilient Systems Development, pp. 203-212. , Springer, and; Sugimoto, N., Kawaguchi, K., Fault Tree Analysis of Hazards Created by Robots (1983) Proceedings of the 13th International Symposium on Industrial Robots and Robot, 7, pp. 9-13. , and; Suhir, E., Lini, S., Bey, C., Salotti, J.-M., Hourlier, S., Claverie, B., Probabilistic Modelling of the Concept of Anticipation in Aviation (2015) Theoretical Issues in Ergonomics Science, 16 (1), pp. 69-85. , and; Vasic, M., Billard, A., Safety Issues in Human-Robot Interactions (2013) 2013 Ieee International Conference on Robotics and Automation, pp. 197-204. , and,. IEEE; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on Human–Robot Collaboration in Industrial Settings: Safety, Intuitive Interfaces and Applications (2018) Mechatronics, 55, pp. 248-266. , and","Salotti, J.-M.; Inria, France",,,"Taylor and Francis Ltd.",,,,,1463922X,,,,"English","Theor. Issues Ergon. Sci.",Article,"Article in Press","All Open Access, Green",Scopus,2-s2.0-85101596916
"Zhou J., Li Z., Li X., Wang X., Song R.","57219424421;57218308705;57206898593;57201280742;26431599600;","Human-Robot Cooperation Control Based on Trajectory Deformation Algorithm for a Lower Limb Rehabilitation Robot",2021,"IEEE/ASME Transactions on Mechatronics",,,,"","",,1,"10.1109/TMECH.2021.3053562","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100475924&doi=10.1109%2fTMECH.2021.3053562&partnerID=40&md5=9840a4d934940e434873699aeb43f5c1","School of Biomedical Engineering, Sun Yat-Sen University, 26469 Guangzhou, Guangdong, China, 510275 (e-mail: zhouj285@mail2.sysu.edu.cn); University of Science and Technology of China, 12652 Hefei, China, 230026 (e-mail: zjli@ieee.org); , Guangzhou, China, 510006 (e-mail: 525514854@qq.com); Guangdong Work Injury Rehabilitation Center, Guangzhou, China, 510900 (e-mail: xiaoyunwang77@hotmail.com); School of Biomedical Engineering, Sun Yat-Sen University, 26469 Guangzhou, Guangdong, China, 510275 (e-mail: songrong@mail.sysu.edu.cn)","Zhou, J., School of Biomedical Engineering, Sun Yat-Sen University, 26469 Guangzhou, Guangdong, China, 510275 (e-mail: zhouj285@mail2.sysu.edu.cn); Li, Z., University of Science and Technology of China, 12652 Hefei, China, 230026 (e-mail: zjli@ieee.org); Li, X., , Guangzhou, China, 510006 (e-mail: 525514854@qq.com); Wang, X., Guangdong Work Injury Rehabilitation Center, Guangzhou, China, 510900 (e-mail: xiaoyunwang77@hotmail.com); Song, R., School of Biomedical Engineering, Sun Yat-Sen University, 26469 Guangzhou, Guangdong, China, 510275 (e-mail: songrong@mail.sysu.edu.cn)","Although many studies have certified the advantages of human-robot cooperation control with admittance model (AM), robot compliance and movement smoothness need to be further improved. In this paper, a trajectory deformation algorithm (TDA) is developed as a high-level trajectory planner, which can plan subjects desired trajectory based on interaction force during physical human-robot interaction (pHRI). A low-level proportional-derivative (PD) position controller is selected to ensure the lower limb rehabilitation robot (LLRR) can track the desired trajectory. Then, the validity of TDA is verified through simulation and experiment studies. The energy per unit distance (EPUD) and dimensionless squared jerk (DSJ) are chosen as indicators of robot compliance and movement smoothness, respectively. The experimental results demonstrated that both the EPUD and the DSJ values using TDA are smaller than that using the AM, indicating the TDA can improve robot compliance and movement smoothness. Therefore, it may have great potential in fields involving pHRI, such as robot-aided rehabilitation. IEEE","Admittance; Human-robot cooperation control; Impedance; lower limb rehabilitation robot; movement smoothness; Rehabilitation robotics; robot compliance; Robots; Strain; Training; Trajectory; trajectory deformation algorithm","Compliance control; Deformation; Neuromuscular rehabilitation; Trajectories; Desired trajectories; Experiment study; Human-robot cooperation; Interaction forces; Physical humanrobot interaction (phri); Position controller; Proportional derivatives; Robot-aided rehabilitations; Social robots",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10834435,,IATEF,,"English","IEEE ASME Trans Mechatron",Article,"Article in Press","",Scopus,2-s2.0-85100475924
"Kim D., Lee J.","55581286900;16310292700;","Robust control of a system with a pneumatic spring",2021,"Journal of the Franklin Institute","358","1",,"555","574",,1,"10.1016/j.jfranklin.2020.10.037","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097098217&doi=10.1016%2fj.jfranklin.2020.10.037&partnerID=40&md5=199dce56092c574d37c4105eff157a7a","Department of Physical Therapy and Rehabilitation Science, School of Medicine, University of Maryland, BaltimoreMD  21201, United States; Department of Biongineering, School of Engineering, University of Maryland, College ParkMD  20742, United States; Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Wessling, Bavaria  82234, Germany","Kim, D., Department of Physical Therapy and Rehabilitation Science, School of Medicine, University of Maryland, BaltimoreMD  21201, United States, Department of Biongineering, School of Engineering, University of Maryland, College ParkMD  20742, United States; Lee, J., Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Wessling, Bavaria  82234, Germany","Recently, series elasticity has been realized using pneumatics in human-robot interaction systems. Pneumatic circuits provide not only a flexible power transmission, but also the elastic element in a series elastic actuator (SEA). Pneumatic series elastic systems involve more than twice the number of parameters that influence system behaviors in comparison with rigid robotic systems. In this study, a position controller that eliminates the need of identifying a system model by employing the time delay estimation (TDE) technique is proposed for pneumatic SEA systems. The TDE technique is effective in compensating for system dynamics and all uncertainties involved in system behaviors without imposing computation load. TDE error is cancelled out through a learning way, which improves control performance and leads to asymptotic stability. A simulation study demonstrates the robustness of the proposed controllers against uncertainties imposed on the motor system as well as uncertainties on the end-effector. The simulation shows the efficacy of the learning compensation for TDE error. © 2020 The Franklin Institute",,"Asymptotic stability; Bioelectric phenomena; Controllers; Delay control systems; Elasticity; Error compensation; Flexible electronics; Pneumatic actuators; Pneumatics; Robust control; Springs (components); Computation loads; Control performance; Position controller; Series elastic actuators; Simulation studies; System behaviors; System modeling; Time delay estimation; Social robots",,,,,"National Institutes of Health, NIH; Institute for Clinical and Translational Research, University of Wisconsin, Madison, UW ICTR: TL1 TR003100; National Center for Advancing Translational Sciences, NCATS","The authors express our gratitude to Drs. Maolin Jin and Gun-Rae Cho at the Korea Institute of Robot and Convergence for their advice for this study. This publication was in part made possible by the University of Maryland Baltimore Institute for Clinical and Translational Research (ICTR) which is funded in part by Grant Number TL1 TR003100 from the National Center for Advancing Translational Sciences (NCATS) a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the University of Maryland Baltimore ICTR, NCATS or NIH.","The authors express our gratitude to Drs. Maolin Jin and Gun-Rae Cho at the Korea Institute of Robot and Convergence for their advice for this study. This publication was in part made possible by the University of Maryland Baltimore Institute for Clinical and Translational Research (ICTR) which is funded in part by Grant Number TL1 TR003100 from the National Center for Advancing Translational Sciences (NCATS) a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the University of Maryland Baltimore ICTR, NCATS or NIH.",,,,,,,,,"Pratt, G.A., Williamson, M.M., Series elastic actuators (1995) Proceedings of the 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems 95.‘Human Robot Interaction and Cooperative Robots’, 1, pp. 399-406. , IEEE; Zinn, M., Khatib, O., Roth, B., Salisbury, J.K., Playing it safe [human-friendly robots] (2004) Robot. Autom. Mag. 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Control IEEE Trans., 47 (11), pp. 1931-1937","Kim, D.; Department of Physical Therapy and Rehabilitation Science, United States; email: dongwonkim@som.umaryland.edu",,,"Elsevier Ltd",,,,,00160032,,JFINA,,"English","J Franklin Inst",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85097098217
"González-Docasal A., Aceta C., Arzelus H., Álvarez A., Fernández I., Kildal J.","57219985509;57219988202;56303519100;56598789500;42761280800;36695073600;","Towards a Natural Human-Robot Interaction in an Industrial Environment",2021,"Lecture Notes in Electrical Engineering","704",,,"243","255",,,"10.1007/978-981-15-8395-7_18","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096421785&doi=10.1007%2f978-981-15-8395-7_18&partnerID=40&md5=696fbfc0ea92a592899a29761d4de9fd","Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, Donostia – San Sebastián, 20009, Spain; Tekniker, Basque Research and Technology Alliance (BRTA), Parke Teknologikoa Iñaki Goenaga 5, Eibar, Spain","González-Docasal, A., Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, Donostia – San Sebastián, 20009, Spain; Aceta, C., Tekniker, Basque Research and Technology Alliance (BRTA), Parke Teknologikoa Iñaki Goenaga 5, Eibar, Spain; Arzelus, H., Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, Donostia – San Sebastián, 20009, Spain; Álvarez, A., Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, Donostia – San Sebastián, 20009, Spain; Fernández, I., Tekniker, Basque Research and Technology Alliance (BRTA), Parke Teknologikoa Iñaki Goenaga 5, Eibar, Spain; Kildal, J., Tekniker, Basque Research and Technology Alliance (BRTA), Parke Teknologikoa Iñaki Goenaga 5, Eibar, Spain","Nowadays, modern industry has adopted robots as part of their processes. In many scenarios, such machines collaborate with humans to perform specific tasks in their same environment or simply guide them in a natural, safe and efficient way. Our approach improves a previously conducted work on a multi-modal human-robot interaction system with different audio acquisition and speech recognition modules for a more natural communication. The semantic interpreter, with the aid of a knowledge manager, parses the resulting transcription and, using contextual information, selects the order that the operator has uttered and sends it to the robot to be executed. This setup is evaluated in a real manufacture scenario in a laboratory environment with a large set of end users both quantitatively and qualitatively. The gathered results reveal that the system behaves robustly and that the assignment was also considered by the end users as manageable, whilst the system in overall was received with a high level of trust and usability. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.",,"Industrial robots; Man machine systems; Semantics; Speech communication; Speech processing; Speech recognition; Contextual information; Industrial environments; Knowledge managers; Laboratory environment; Multi-modal; Natural communication; Specific tasks; Speech recognition modules; Social robots",,,,,"Eusko Jaurlaritza","Acknowledgements This work was supported by the Department of Economic Development and Competitiveness of the Basque Government via the LangileOK project.",,,,,,,,,,"Amodei, D., Anubhai, R., Battenberg, E., Case, C., Casper, J., Catanzaro, B., Chen, J., Zhu, Z., (2015) Deep Speech 2: End-To-End Speech Recognition in English and Mandarin; Anastasakos, T., McDonough, J., Schwartz, R., Makhoul, J., A compact model for speaker-adaptive training (1996) Proceeding of Fourth International Conference on Spoken Language Processing. 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LREC2012; Park, D.S., Chan, W., Zhang, Y., Chiu, C.C., Zoph, B., Cubuk, E.D., Le, Q.V., (2019) Specaugment: A Simple Data Augmentation Method for Automatic Speech Recognition, p. 2019. , https://doi.org/10.21437/interspeech.2019-2680, Interspeech; Peddinti, V., Chen, G., Manohar, V., Ko, T., Povey, D., Khudanpur, S., JHU ASpIRE system: Robust LVCSR with TDNNS, iVector adaptation and RNN-LMS (2015) 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 539-546. , https://doi.org/10.1109/ASRU.2015.7404842, pp; Peddinti, V., Povey, D., Khudanpur, S., (2015) A Time Delay Neural Network Architecture for Efficient Modeling of Long Temporal Contexts; Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Hannemann, M., Vesely, K., (2011) The Kaldi Speech Recognition Toolkit. In: IEEE 2011 Workshop on Automatic Speech Recognition and Understanding. IEEE Signal Processing Society. IEEE Catalog No. CFP11SRW-USB; Povey, D., Kingsbury, B., Mangu, L., Saon, G., Soltau, H., Zweig, G., FMPE: Discriminatively trained features for speech recognition (2005) In: Proceedings.(Icassp 2005). IEEE International Conference on Acoustics, Speech, and Signal Processing 2005, Vol 1. IEEE, pp. I-961; Pozo, A., Aliprandi, C., Álvarez, A., Mendes, C., Neto, J., Paulo, S., Piccinini, N., Raffaelli, M., (2014) SAVAS: Collecting, Annotating and Sharing Audiovisual Language Resources for Automatic Subtitling","González-Docasal, A.; Vicomtech Foundation, Mikeletegi 57, Spain; email: agonzalezd@vicomtech.org","D'Haro L.F.Callejas Z.Nakamura S.",,"Springer Science and Business Media Deutschland GmbH","11th International Workshop on Spoken Dialogue Systems, IWSDS 2020","21 September 2020 through 23 September 2020",,250759,18761100,9789811583940,,,"English","Lect. Notes Electr. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85096421785
"Dannecker A., Hertig D.","55490617800;57218211054;","Facial Recognition and Pathfinding on the Humanoid Robot Pepper as a Starting Point for Social Interaction",2021,"Studies in Systems, Decision and Control","294",,,"147","160",,1,"10.1007/978-3-030-48332-6_10","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088323374&doi=10.1007%2f978-3-030-48332-6_10&partnerID=40&md5=8b69fad8d4d69c4c39c4225d128f855e","School of Business, Institute for Information Systems, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Peter Merian-Strasse 86, Basel, 4002, Switzerland","Dannecker, A., School of Business, Institute for Information Systems, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Peter Merian-Strasse 86, Basel, 4002, Switzerland; Hertig, D., School of Business, Institute for Information Systems, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Peter Merian-Strasse 86, Basel, 4002, Switzerland","Interaction between humanoid robots and humans is a complex process. Speech, gestures, and recognition of communication partners are important aspects in a well-defined interaction. To seem more natural, a humanoid robot should not be stationary. It should be able to be part of a crowd and wander around a specific area. Therefore, pathfinding is important to give a humanoid robot the ability to connect with people in more than one place. In addition, the recognition of communication partners is the backbone of social interaction. This chapter demonstrates how OpenCV, a well-known computer vision library, supports the robot Pepper in the recognition of communication partners and in addition, how this is the starting point to different types of small talk as the basis for a prototypical interaction process of humanoid robots and humans. Additionally, the navigation functions that allow the robot to move autonomously and enable a better human–robot interaction will be discussed. © 2021, Springer Nature Switzerland AG.","Facial recognition; Humanoid robots; Human–robot interaction; Pathfinding; Robotics",,,,,,,,,,,,,,,,,"Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Robot. Autonom. Syst, 42, pp. 143-166. , https://doi.org/10.1016/S0921-8890(02)00372-X; Hu, J., Edsinger, A., Lim, Y.-J., Donaldson, N., Solano, M., Solochek, A., Marchessault, R., An advanced medical robotic system augmenting healthcare capabilities—robotic nursing assistant (2011) 2011 IEEE International Conference on Robotics and Automation, pp. 6264-6269; Nejat, G., Sun, Y., Nies, M., Assistive robots in health care settings (2009) Home Health Care Manage. 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Fourth IEEE International Conference on Multimodal Interfaces, pp. 123-128; Breazeal, C., Scassellati, B., How to build robots that make friends and influence people (1999) Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289), 2, pp. 858-863; Gorostiza, J.F., Barber, R., Khamis, A.M., Malfaz, M., Pacheco, R., Rivas, R., Corrales, A., Salichs, M.A., Multimodal human-robot interaction framework for a personal robot (2006) ROMAN 2006—The 15th IEEE International Symposium on Robot and Human Interactive Communication, pp. 39-44; Fischer, K., Interpersonal variation in understanding robots as social actors (2011) 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 53-60; Breazeal, C., Toward sociable robots (2003) Robot. Autonom. Syst, 42, pp. 167-175. , https://doi.org/10.1016/S0921-8890(02)00373-1; http://doc.aldebaran.com/2-5/home_pepper.html, Pepper—Documentation—Aldebaran 2.5.11.14a documentation; OpenCV: OpenCV library, , https://opencv.org/; Vossen, P., Baez, S., Bajčetić, L., Kraaijeveld, B., (2018) Leolani: a reference machine with a theory of mind for social communication, , arXiv:1806.01526 [cs]; Turk, M.A., Pentland, A.P., Face recognition using eigenfaces (1991) IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991. 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(eds) Springer, Berlin Heidelberg, Berlin, Heidelberg; Open, CV, Face Recognition with OpenCV—OpenCV 2.4.13.7 documentation, , https://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.html; https://www.merriam-webster.com/dictionary/bright, Definition of bright; https://www.merriam-webster.com/dictionary/contrast, Definition of contrast; Saturation|Definition of Saturation by Merriam-Webster, , https://www.merriam-webster.com/dictionary/saturation; https://www.merriam-webster.com/dictionary/resolution, Definition of resolution; Wang, H., Yu, Y., Yuan, Q., Application of Dijkstra algorithm in robot path-planning (2011) 2011 Second International Conference on Mechanic Automation and Control Engineering, pp. 1067-1069; Niemelä, M., Arvola, A., Aaltonen, I., Monitoring the acceptance of a social service robot in a shopping mall: first results (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 225-226. , ACM, New York, NY, USA; Aaltonen, I., Arvola, A., Heikkilä, P., Lammi, H., Hello pepper, may i tickle you?: children’s and adults’ responses to an entertainment robot at a shopping mall (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 53-54. , ACM, New York, NY, USA; Khambhaita, H., Alami, R., A human-robot cooperative navigation planner (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction—HRI ’17, pp. 161-162. , ACM Press, Vienna, Austria; Kretzschmar, H., Spies, M., Sprunk, C., Burgard, W., Socially compliant mobile robot navigation via inverse reinforcement learning (2016) Int. J. Robot. Res, 35, pp. 1289-1307. , https://doi.org/10.1177/0278364915619772; GitHub—aldebaran/naoqi_navigation_samples: contains samples for NAOqi navigation and exploration features. The samples are packaged into Choregraphe applications, , https://github.com/aldebaran/naoqi_navigation_samples; http://doc.aldebaran.com/2-5/family/pepper_technical/laser_pep.html, Lasers—Aldebaran 2.5.11.14a documentation; http://doc.aldebaran.com/2-5/family/pepper_technical/sonar_pep.html, Sonars—Aldebaran 2.5.11.14a documentation; Documentation—ROS Wiki, , https://wiki.ros.org/","Dannecker, A.; School of Business, Peter Merian-Strasse 86, Switzerland; email: achim.dannecker@fhnw.ch",,,"Springer",,,,,21984182,,,,"English","Stud. Syst. Decis. Control",Book Chapter,"Final","",Scopus,2-s2.0-85088323374
"Khaewratana W., Veinott E.S., Manian Ramkumar S.","57222139024;6506037042;57206388128;","Development of a Generalized Voice-Controlled Human-Robot Interface: One Automatic Speech Recognition System for All Robots",2020,"2020 3rd International Conference on Control and Robots, ICCR 2020",,,"9344123","38","42",,,"10.1109/ICCR51572.2020.9344123","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101614271&doi=10.1109%2fICCR51572.2020.9344123&partnerID=40&md5=df67abaa19ef384f00b9e456260dc867","Michigan Technological University, Department of Cognitive and Learning Sciences, Houghton, MI, United States; College of Engineering Technology, Rochester Institute of Technology, Rochester, NY, United States","Khaewratana, W., Michigan Technological University, Department of Cognitive and Learning Sciences, Houghton, MI, United States; Veinott, E.S., Michigan Technological University, Department of Cognitive and Learning Sciences, Houghton, MI, United States; Manian Ramkumar, S., College of Engineering Technology, Rochester Institute of Technology, Rochester, NY, United States","Automatic speech recognition (ASR) technology has been used by researchers to develop voice-controlled robots. However, the usual pattern is to develop an ASR interface that works specifically with one particular robot. Generalized ASRs, ones that can communicate with multiple robots, are needed. A generalized ASR allows an operator to control several robots that are operated by different programming languages at the same time via a single interface. This paper describes the development and an initial user study of a generalized ASR interface using a commercially available ASR software. The user study provided initial validation of the interface's functionality using a variety of commands. Results indicated a reasonably high input flexibility; the interface successfully recognized 85% of 1200 spoken commands. It also demonstrated output flexibility; the generalized ASR fed the robot a sequence of keystrokes that formed any syntax required to trigger its task programs. Implications for human-robot interaction are discussed. © 2020 IEEE.","automatic speech recognition; generalized; human-robot interface; macro; voice-controlled","Agricultural robots; Robot programming; Speech recognition; Automatic speech recognition; Automatic speech recognition system; Human-Robot Interface; Multiple robot; User study; Voice controlled robots; Voice-controlled; Social robots",,,,,,,,,,,,,,,,"Avutu, S.R., Bhatia, D., Reddy, B.V., Voice control module for low cost local-map navigation based intelligent wheelchair (2017) 2017 IEEE 7th International Advance Computing Conference (IACC) (Pp. 609-613). IEEE, , January; Nishimori, M., Saitoh, T., Konishi, R., Voice controlled intelligent wheelchair (2007) SICE, 2007 Annual Conference, pp. 336-340; Sharifuddin, M.S.I., Nordin, S., Ali, A.M., (2019) 2019 1st International Conference on Artificial Intelligence and Data Sciences (AiDAS) (Pp. 40-43, , , September). Voice control intelligent wheelchair movement using CNNs. In, IEEE; Wang, D., Yu, H., Development of the control system of a voice-operated wheelchair with multi-posture characteristics (2017) 2017 2nd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), pp. 151-155. , June, IEEE; Xue, R., Du, Z., Yan, Z., Ren, B., A design and evaluation of hand-held mechatronic laparoscopic surgical robot (2018) 2018 International Conference on Control and Robots (ICCR), pp. 19-24. , September IEEE; Kondo, Y., Construction of reconfigurable motion database for real-Time human-robot interaction (2013) NAISTAR, pp. 1-101; Liu, P., Glas, D.F., Kanda, T., Ishiguro, H., Data-driven HRI: Learning social behaviors by example from human-human interaction (2016) IEEE Transactions on Robotics, 32 (4), pp. 988-1008; Hsiao, S.W., Wu, C.N., A KE, DSM and ISM based approach for patrol robot development (2018) 2018 International Conference on Control and Robots (ICCR), pp. 30-34. , September IEEE; Iba, S., Paredis, C.J., Khosla, P.K., Interactive multimodal robot programming (2005) The International Journal of Robotics Research, 24 (1), pp. 83-104; Fujita, M., Kitano, H., Development of an autonomous quadruped robot for robot entertainment (1998) Autonomous Robots, 5 (1), pp. 7-18; Ferre, M., Macias-Guarasa, J., Aracil, R., Barrientos, A., Voice Command Generation for Teleoperated Robot Systems (1998) IEEE Xplore, 2, pp. 679-685; Yu, M., Wang, X., Lin, Y., Bai, X., Gaze tracking system for teleoperation. 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(n.d); Yang, D., Iwano, K., Furui, S., Accent analysis for mandarin large vocabulary continuous speech recognition (2008) Asian Workshop on Speech Science and Technology, pp. 87-91; Lee, A., Kawahara, T., Recent development of open-source speech recognition engine julius (2009) Proceedings: APSIPA ASC 2009: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference, pp. 131-137; Fohr, D., Mella, O., Cerisara, C., Illina, I., The automatic news transcription system: ANTS some real time experiments (2004) Proc. ICSLP, pp. 377-380; Cantrell, R., Scheutz, M., Schermerhorn, P., Wu, X., Robust spoken instruction understanding for HRI (2010) 2010 5th ACM/ IEEE International Conference on Human Robot Interaction (HRI) (Pp. 275-282). IEEE, , March; Koriat, A., Greenberg, S.N., The extraction of phrase structure during reading: Evidence from letter detection errors (1994) Psychonomic Bulletin and Review, 1 (3), pp. 345-356; Koriat, A., Greenberg, S.N., The enhancement effect in letter detection: Further evidence for the structural model of reading (1996) Journal of Experimental Psychology: Learning, Memory and Cognition, 22 (5), pp. 1184-1195; Scheutz, M., Schermerhorn, P., Kramer, J., Anderson, D., First steps toward natural human-like HRI (2007) Autonomous Robots, 22 (4), pp. 411-423; Lauria, S., Bugmann, G., Kyriacou, T., Klein, E., Mobile robot programming using natural language (2002) Robotics and Autonomous Systems, 38 (3), pp. 171-181; Pires, J.N., Robot-by-voice: Experiments on commanding an industrial robot using the human voice (2005) Industrial Robot: An International Journal, 32 (6), pp. 505-511; Karat, C.M., Halverson, C., Horn, D., Karat, J., Patterns of entry and correction in large vocabulary continuous speech recognition systems (1999) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 568-575. , May",,,"Beijing Institute of Control Robots and Intelligent Technology;IEEE","Institute of Electrical and Electronics Engineers Inc.","3rd International Conference on Control and Robots, ICCR 2020","26 December 2020 through 29 December 2020",,167205,,9781728175621,,,"English","Int. Conf. Control Robot., ICCR",Conference Paper,"Final","",Scopus,2-s2.0-85101614271
"Gavrilova L., Kotik A., Tsoy T., Martinez-Garcia E.A., Svinin M., Magid E.","57216729760;57216726767;57203976124;56000847100;7003311772;7006190065;","Facilitating a preparatory stage of real-world experiments in a humanoid robot assisted English language teaching using Gazebo simulator",2020,"Proceedings - International Conference on Developments in eSystems Engineering, DeSE","2020-December",,"9450749","222","227",,,"10.1109/DeSE51703.2020.9450749","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112526055&doi=10.1109%2fDeSE51703.2020.9450749&partnerID=40&md5=68c00d1cf73a499eac0c82e05fc3582d","Kazan Federal University, Institute of Information Technology and Intelligent Systems, Intelligent Robotics Department, Kazan, Russian Federation; Autonomous University of Ciudad Juarez, Institute of Engineering and Technology, Department of Industrial Engineering and Manufacturing, Juarez, Mexico; Ritsumeikan University, College of Information Science and Engineering, Information Science and Engineering Department, Kyoto, Japan","Gavrilova, L., Kazan Federal University, Institute of Information Technology and Intelligent Systems, Intelligent Robotics Department, Kazan, Russian Federation; Kotik, A., Kazan Federal University, Institute of Information Technology and Intelligent Systems, Intelligent Robotics Department, Kazan, Russian Federation; Tsoy, T., Kazan Federal University, Institute of Information Technology and Intelligent Systems, Intelligent Robotics Department, Kazan, Russian Federation; Martinez-Garcia, E.A., Autonomous University of Ciudad Juarez, Institute of Engineering and Technology, Department of Industrial Engineering and Manufacturing, Juarez, Mexico; Svinin, M., Ritsumeikan University, College of Information Science and Engineering, Information Science and Engineering Department, Kyoto, Japan; Magid, E., Kazan Federal University, Institute of Information Technology and Intelligent Systems, Intelligent Robotics Department, Kazan, Russian Federation","A simulator is a software application that allows imitating an experimental environment and controlling a process or an instrument. Projects in human-robot interaction (HRI) field face different challenges during real world experiments, which include improper robot behavior, mistakes in an algorithm logic, software and hardware failures, and generic lay participants on a user side. Simulators could help to avoid a number of problems that researchers would definitely face within real world experiments at algorithms' testing stage. This study presents an experimental validation in Gazebo simulator and real-world experiments for English language lessons scenarios with a small size humanoid Robotis DARwin OP2. We investigated advantages of using Gazebo simulator in constructing modular generic HRI blocks and global HRI scenarios in order to optimize a humanoid robot assisted process of studying English. The simulator saved a significant amount of efforts and time at preparatory stages, allowing robot programmers and HRI developers to obtain a quick feedback from users and their requirements for necessary adjustments. © 2020 IEEE.","human-robot interaction; humanoid; modeling; simulation","Anthropomorphic robots; Application programs; Biofeedback; Simulators; English language teaching; English languages; Experimental environment; Experimental validations; Human robot Interaction (HRI); Real world experiment; Software and hardwares; Software applications; Human robot interaction",,,,,"Russian Foundation for Basic Research, РФФИ: 19-58-70002","This research was funded by the Russian Foundation for Basic Research (RFBR), project ID 19-58-70002.",,,,,,,,,,"Milliez, G., Ferreira, E., Fiore, M., Alami, R., Lefèvre, F., Simulating human-robot interactions for dialogue strategy learning (2014) Lecture Notes in Computer Science, pp. 594-606; Dautenhahn, K., Socially intelligent robots: Dimensions of human-robot interaction Philosophical Transactions of the Royal Society B: Biological Sciences, 362 (1480), pp. 679-704; Fong, T., Nourbaksh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Autonomous Systems, 42, pp. 143-166; Lemaignan, S., Hanheide, M., Karg, M., Khambhaita, H., Kunze, L., Lier, F., Lütkebohle, I., Milliez, G., Simulation and hri recent perspectives with the morse simulator Lecture Notes in Computer Science, pp. 13-24; Koenig, N., Howard, A., Design and use paradigms for gazebo, an open-source multi-robot simulator International Conference on Intelligent Robots and Systems (IROS), 3, pp. 2149-2154; Pashkin, A., Lavrenov, R., Zakiev, A., Svinin, M., Pilot communication protocols for group of mobile robots in usar scenarios Proceedings of the International Conference on Developments in ESystems Engineering, DeSE, pp. 37-41. , 9073455; Li, H., Liu, W., Wang, K., Kawashima, K., Magid, E., A cable-pulley transmission mechanism for surgical robot with backdrivable capability (2018) Robotics and Computer-Integrated Manufacturing, 49, pp. 328-334; Lewis, M., Wang, J., Hughes, S., Usarsim: Simulation for the study of human-robot interaction (2007) Journal of Cognitive Engineering and Decision Making, 1 (1), pp. 98-120; Han, J.-H., Kim, J.-H., 3d visibility check in webots for human perspective taking in human-robot interaction Robot Intelligence Technology and Applications, pp. 255-262; Zamora, I., Lopez, N., Vilches, V., Cordero, A., (2016) Extending the Openai Gym for Robotics: A Toolkit for Reinforcement Learning Using Ros and Gazebo; Qian, Z., Xia, W., Xiong, J., Gan, T., Guo, Y., Weng, S., Deng, H., Zhang, J., Manipulation task simulation using ros and gazebo Proceedings of the IEEE International Conference on Robotics and Biomimetics, pp. 2594-2598; Sagitov, A., Gavrilova, L., Tsoy, T., Hongbin, L., Design of simple one-arm surgical robot for minimally invasive surgery Proceedings of the 12th International Conference on Developments in ESystems Engineering, pp. 500-503; Lavrenov, R., Magid, E., Matsuno, F., Svinin, M., Suthakorn, J., Development and implementation of spline-based path planning algorithm in ros/gazebo environment (2019) Trudy SPIIran, 18 (1), pp. 57-84; Farchy, A., Barrett, S., MacAlpine, P., Stone, P., Humanoid robots learning to walk faster: From the real world to simulation and back Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems, (AAMAS'2013), pp. 39-46; Wang, J., Lewis, M., Hughes, S., Koes, M., Carpin, S., Validating usarsim for use in hri research (2005) Proceedings of the Human Factors and Ergonomics Society, pp. 457-461; Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., Hagita, N., A communication robot in a shopping mall (2010) IEEE Transactions on Robotics, 26 (5), pp. 897-913; Koizumi, S., Kanda, T., Shiomi, M., Ishiguro, H., Hagita, N., Preliminary field trial for teleoperated communication robots (2006) Proceedings of the 15th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN06), 6-8, pp. 145-150; Lier, F., Wachsmuth, S., Towards an open simulation environment for the pepper robot Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, RI'18, p. 2. , New York: ACM; Gavrilova, L., Petrov, V., Kotik, A., Sagitov, A., Khalitova, L., Tsoy, T., Pilot study of teaching english language for preschool children with a small-size humanoid robot assistant (2019) Proceedings of the the 12th International Conference on Developments in ESystems Engineering, pp. 253-260; Robotis OP2 Robot Description, , http://www.robotis.us/robotis-op2-us/",,,,"Institute of Electrical and Electronics Engineers Inc.","13th International Conference on Developments in eSystems Engineering, DeSE 2020","13 December 2020 through 17 December 2020",,170860,21611343,9781665422383,,,"English","Proc. - Int. Conf. Dev. eSystems Eng., DeSE",Conference Paper,"Final","",Scopus,2-s2.0-85112526055
"Le D.T., Sutjipto S., Lai Y., Paul G.","57221767704;57202214637;57208211450;7202627449;","Intuitive Virtual Reality based Control of a Real-world Mobile Manipulator",2020,"16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020",,,"9305517","767","772",,1,"10.1109/ICARCV50220.2020.9305517","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100093204&doi=10.1109%2fICARCV50220.2020.9305517&partnerID=40&md5=253552fe0c0f1c4d8942faea754bb808",,"Le, D.T.; Sutjipto, S.; Lai, Y.; Paul, G.","This paper presents an integration of Virtual Reality (VR) interfaces with the control system of a realworld mobile manipulator, ultimately facilitating a natural and intuitive method for human-robot interaction. VR's ability to track movements in 3D space and translate performed motions provide an intuitive platform for users to explore and interact with the virtual environment. Coupled with intuitive controls, such as grabbing and pointing, the VR platform provides a compelling advantage that can be used to solve limitations of traditional remote robot teleoperation methods. This paper summarises the system implemented, which includes a simulation of the robot in Unity3d, as well as analyses critical results of accuracy and performance, from experiments with users of various experience levels. The method used for measuring accuracy with a simulated robot presented a utilitarian validation for contrasting the difference between 2D and VR 3D interfaces. Users' performance and experience under various levels of control latency, which is a crucial factor in remote online robot control, were also measured. © 2020 IEEE.",,"Agricultural robots; Computer vision; Industrial manipulators; Man machine systems; Manipulators; Robotics; Social robots; User experience; 3D interface; Intuitive controls; Measuring accuracy; Mobile manipulator; Online robots; Remote robot; Simulated robot; Vr platforms; Virtual reality",,,,,,,,,,,,,,,,"Shakor, P., Renneberg, J., Nejadi, S., Paul, G., Optimisation of different concrete mix designs for 3d printing by utilizing 6dof industrial robot (2017) Proceedings of the 34th International Symposium on Automation and Robotics in Construction (ISARC)., pp. 268-275. , Tribun EU, s. r. o., Brno, July; Ivanov, S.H., Webster, C., Berezina, K., Adoption of robots and service automation by tourism and hospitality companies (2017) Revista Turismo & Desenvolvimento, 27 (28), pp. 1501-1517; Paul, G., Webb, S., Liu, D., Dissanayake, G., A robotic system for steel bridge maintenance: Field testing (2010) Proceedings Australasian Conference on Robotics and Automation (ACRA), pp. 1-8; Lai, Y., Sutjipto, S., Clout, M.D., Carmichael, M.G., Paul, G., Gavre2: Towards data-driven upper-limb rehabilitation with adaptive-feedback gamification (2018) 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), 12, pp. 164-169; Trevelyan, J., Hamel, W.R., Kang, S.C., Robotics in hazardous applications (2016) Springer Handbook of Robotics, 1, pp. 1521-1548. , Springer International Publishing; Goodrich, M.A., Schultz, A.C., (2008) Human-robot Interaction: A Survey, , Now Publishers Inc; Dejong, B.P., Colgate, J.E., Peshkin, M.A., Improving teleoperation: Reducing mental rotations and translations (2004) Proceedings-IEEE International Conference on Robotics and Automation, 2004 (4), pp. 3708-3714. , Institute of Electrical and Electronics Engineers Inc; Chotiprayanakul, P., Liu, D., Paul, G., (2011) Effect of View Distance and Movement Scale on Haptic-Based Teleoperation of Industrial Robots in Complex Environments, p. 6; Hetrick, R., Amerson, N., Kim, B., Rosen, E., Visser E J, D., Phillips, E., Comparing virtual reality interfaces for the teleoperation of robots (2020) Institute of Electrical and Electronics Engineers (IEEE), 6, pp. 1-7; Lipton, J.I., Fay, A.J., Rus, D., Baxter's homunculus: Virtual reality spaces for teleoperation in manufacturing (2018) IEEE Robotics and Automation Letters, 3 (1), pp. 179-186; Pereira, A., Carter, E.J., Leite, I., Mars, J., Lehman, J.F., Augmented reality dialog interface for multimodal teleoperation (2017) ROMAN 2017-26th IEEE International Symposium on Robot and Human Interactive Communication, pp. 764-771. , 2017-Janua; Rosen, E., Whitney, D., Phillips, E., Chien, G., Tompkin, J., Konidaris, G., Tellex, S., Communicating and controlling robot arm motion intent through mixed-reality head-mounted displays (2019) International Journal of Robotics Research, pp. 1-16; Muhammad, F., Hassan, A., Cleaver, A., Sinapov, J., Creating a shared reality with robots (2019) ACM/IEEE International Conference on Human-Robot Interaction, 3, pp. 614-615. , 2019-March. IEEE Computer Society; Chu, C.-H., Li, P.-C., Augmented reality (ar) based robot path planning for programming by demonstration (2016) Asia Design Engineering Workshop, , December; Araiza-Illan, D., Bernabe San De, A., Hongchao, F., Shin, L.Y., Augmented reality for quick and intuitive robotic packing re-programming (2019) ACM/IEEE International Conference on Human-Robot Interaction, p. 664. , 2019-March. IEEE Computer Society, 3; Ostanin, M., Klimchik, A., Interactive robot programing using mixed reality (2018) IFAC-PapersOnLine, 51 (22), pp. 50-55; Whitney, D., Rosen, E., Phillips, E., Konidaris, G., Tellex, S., Comparing robot grasping teleoperation across desktop and virtual reality with ros reality (2017) ISRR; Gadre, S.Y., Rosen, E., Chien, G., Phillips, E., Tellex, S., Konidaris, G., End-user robot programming using mixed reality (2019) Institute of Electrical and Electronics Engineers (IEEE), 8, pp. 2707-2713; Umetani, Y., Yoshida, K., Resolved motion rate control of space manipulators with generalized jacobian matrix (1989) IEEE Transactions on Robotics and Automation, 5 (3), pp. 303-314; Corke, P., Matlab toolboxes: Robotics and vision for students and teachers (2007) IEEE Robotics and Automation Magazine, 14 (4), pp. 16-17. , 12; Wampler, C.W., Manipulator inverse kinematic solutions based on vector formulations and damped least-squares methods (1986) IEEE Transactions on Systems, Man and Cybernetics, 16 (1), pp. 93-101",,,"IEEE","Institute of Electrical and Electronics Engineers Inc.","16th IEEE International Conference on Control, Automation, Robotics and Vision, ICARCV 2020","13 December 2020 through 15 December 2020",,166416,,9781728177090,,,"English","IEEE Int. Conf. Control, Autom., Robot. Vis., ICARCV",Conference Paper,"Final","",Scopus,2-s2.0-85100093204
"Liu Y., Yang C., Wang M.","57211088566;57204881979;55553729118;","Human-Robot interaction Based on Biosignals",2020,"2020 International Symposium on Autonomous Systems, ISAS 2020",,,"9378844","58","63",,,"10.1109/ISAS49493.2020.9378844","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103792810&doi=10.1109%2fISAS49493.2020.9378844&partnerID=40&md5=95b7a62fc118212c91365efe3a540c8c","South China University of Technology, Guangzhou, China","Liu, Y., South China University of Technology, Guangzhou, China; Yang, C., South China University of Technology, Guangzhou, China; Wang, M., South China University of Technology, Guangzhou, China","This paper introduces a novel manner for human-robot interaction (HRI) based on electroencephalogram (EEG) and surface electromyography (sEMG) signals. the P300-based BCI system aims to provide a target point for the mobile robot and indicates whether or not the robot will perform the grab task. Considering the relative position of the robot and the target cube will lead to the failure of the grasping task, we use different gestures which evoke corresponding sEMG signals to fine-tune the position of the robot. To get the target point closely related to the target character from the BCI system, we use linear discriminant analysis (LDA) to do the P300 detection task. and the average charater recognition for all subjects offline can reach 92%. Similarly, a simple convolutional neural network (CNN) is constructed for gestures classification and obtained 98% accuracy offline. in addition, the validation of the proposed method is verfied by the experimental results on mobile robot and Gaussian mechanical arm. © 2020 IEEE.","CNN; EEG; HRI; LDA; P300; sEMG","Biomedical signal processing; Convolutional neural networks; Discriminant analysis; Electroencephalography; Man machine systems; Mobile robots; Navigation; Detection tasks; Electro-encephalogram (EEG); Human robot Interaction (HRI); Linear discriminant analysis; Mechanical arm; Relative positions; Surface electromyography; Target character; Human robot interaction",,,,,"2019B1515120076; National Natural Science Foundation of China, NSFC: 61811530281, 61861136009, U20A20200; Fundamental Research Funds for the Central Universities","This work was supported in part by National Nature Science Foundation of China (NSFC) under Grant U20A20200, Grant 61811530281, and Grant 61861136009, in part by Guangdong Regional Joint Foundation under Grand 2019B1515120076, and in part by the Fundamental Research for the Central Universities.",,,,,,,,,,"Yang, C., Wu, H., Li, Z., et, al.., Mind control of a robotic arm with visual fusion technology[J] (2017) Ieee Transactions on Industrial Informatics, 14 (9), pp. 3822-3830; Mao, Y., Jin, J., Xu, R., et, al.., The Influence of Visual Attention on the Performance of a Novel Tactile P300 Brain-Computer interface with Cheeks-Stim Paradigm[J] (2020) International Journal of Neural Systems; Wei, M., Lin, F., A novel multi-dimensional features fusion algorithm for the EEG signal recognition of brain s sensorimotor region activated tasks[J] (2020) International Journal of Intelligent Computing and Cybernetics; Damper, R.I., Burnett, J.W., Gray, P.W., Straus, L.P., Symes, R.A., Hand-held text-to-speech device for the non-vocal disabled (1987) J Biomed Eng, 9 (4), pp. 332-340. , Oct; Cecotti, H., Graser, A., Convolutional neural networks for P300 detection with application to brain-computer interfaces[J] (2010) Ieee Transactions on Pattern Analysis and Machine Intelligence, 33 (3), pp. 433-445; Finke, A., Lenhardt, A., Ritter, H., The MindGame: A P300-based braincomputer interface game[J] (2009) Neural Networks, 22 (9), pp. 1329-1333; Achanccaray, D., Flores, C., Fonseca, C., et, al.., A P300-based brain computer interface for smart home interaction through an ANFIS ensemble[C] (2017) 2017 Ieee International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-5; Loureiro, R., Amirabdollahian, F., Topping, M., et, al.., Upper limb robot mediated stroke therapyGENTLE/s approach[J] (2003) Autonomous Robots, 15 (1), pp. 35-51; Oskoei, M.A., Hu, H., Myoelectric control systemsA survey[J] (2007) Biomedical Signal Processing and Control, 2 (4), pp. 275-294; Xiaoling, L., Motor imagery-based EEG signals classification by combining temporal and spatial deep characteristics[J] (2020) International Journal of Intelligent Computing and Cybernetics; Zong, C., Ji, Z., Yu, J., et, al.., An angle-changeable tracked robot with humanrobot interaction in unstructured environments[J] (2020) Assembly Automation; Luo, Z., Yang, G., Study of myoelectric prostheses based on fuzzy control and touch feedback[C] (2005) 2005 International Conference on Neural Networks and Brain, 3, pp. 1815-1819; Sun, Q., Liu, X., Mu, X., et, al.., Estimation for normal contact stiffness of joint surfaces by considering the variation of critical deformation[J] (2020) Assembly Automation; Segalowitz, S.J., Barnes, K.L., The reliability of ERP components in the auditory oddball paradigm[J] (1993) Psychophysiology, 30 (5), pp. 451-459; Townsend, G., LaPallo, B.K., Boulay, C.B., et, al.., A novel P300-based braincomputer interface stimulus presentation paradigm: Moving beyond rows and columns[J] (2010) Clinical Neurophysiology, 121 (7), pp. 1109-1120; Kuang, Y., Cheng, H., Zheng, Y., et, al.., One-shot gesture recognition with attention-based DTW for human-robot collaboration[J] (2019) Assembly Automation; Manyakov, N.V., Chumerin, N., Combaz, A., et, al.., Comparison of classification methods for P300 brain-computer interface on disabled subjects[J] (2011) Computational Intelligence and Neuroscience, 2011; Valakos, D., DAvossa, G., Mylonas, D., P300 response modulation reflects breaches of non-probabilistic expectations[J] (2020) Scientific Reports, 10 (1), pp. 1-11; Lu, J., Plataniotis, K.N., Venetsanopoulos, A.N., Face recognition using LDA-based algorithms[J] (2003) Ieee Transactions on Neural Networks, 14 (1), pp. 195-200; Townsend, J.T., Theoretical analysis of an alphabetic confusion matrix[J] (1971) Perception & Psychophysics, 9 (1), pp. 40-50; Li, X.A., Sun, K., Guo, C., et, al.., Enhanced static modeling of commercial pneumatic artificial muscles[J] (2020) Assembly Automation; Yang, C., Luo, J., Liu, C., et, al.., Haptics electromyography perception and learning enhanced intelligence for teleoperated robot[J] (2018) Ieee Transactions on Automation Science and Engineering, 16 (4), pp. 1512-1521; Bouteraa, Y., Abdallah, I.B., Elmogy, A., Design and control of an exoskeleton robot with EMG-driven electrical stimulation for upper limb rehabilitation[J] (2020) Industrial Robot: The International Journal of Robotics Research and Application; Zeng, C., Yang, C., Cheng, H., et, al.., Simultaneously Encoding Movement and sEMG-based Stiffness for Robotic Skill Learning[J] (2020) Ieee Transactions on Industrial Informatics",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 International Symposium on Autonomous Systems, ISAS 2020","6 December 2020 through 8 December 2020",,168040,,9781665418829,,,"English","Int. Symp. Auton. Syst., ISAS",Conference Paper,"Final","",Scopus,2-s2.0-85103792810
"Dou W.B., Chin W.H., Kubota N.","57221843785;55658768800;7202158579;","Hand gesture communication using deep learning based on relevance theory",2020,"2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020",,,"9322784","","",,1,"10.1109/SCISISIS50064.2020.9322784","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100435767&doi=10.1109%2fSCISISIS50064.2020.9322784&partnerID=40&md5=2cfd9e4a8656ef051319a57924440a9c","Tokyo Metropolitan Univerity, Hino, Japan","Dou, W.B., Tokyo Metropolitan Univerity, Hino, Japan; Chin, W.H., Tokyo Metropolitan Univerity, Hino, Japan; Kubota, N., Tokyo Metropolitan Univerity, Hino, Japan","In recent years, social robots are widely research due to the advancement of electronics hardware and deep learning algorithms breakthrough. Hand gesture recognition playing an important role in human-robot interaction. However, to deploy state-of-art of deep learning methods for hand gesture recognition, researchers have to associate all hand gestures with their respective meanings (label). Re-train the deep learning model is required if a new gesture added for recognition. In addition, the meaning of hand gestures often depending on the environmental conditions. In this paper, we propose a framework that learns and recognizes hand gesture meaning based on surrounding objects. The proposed method consists of two sub frameworks: i) A deep learning model, Mask RCNN to detect and extract features of objects and hand gestures; ii) An incremental learning model, Declarative Memory Recurrent Neural Model to incrementally learn hand gestures meaning based on surrounding objects. The effectiveness of our proposed method is validated through several experiments. © 2020 IEEE.","DM-ARM; Environmental information; Hand gesture recognition; Mask R-CNN","Computation theory; Gesture recognition; Intelligent computing; Intelligent systems; Learning systems; Palmprint recognition; Recurrent neural networks; Social robots; Soft computing; Environmental conditions; Hand gesture; Hand-gesture recognition; Incremental learning; Learning methods; Learning models; Neural modeling; Relevance theories; Learning algorithms",,,,,,,,,,,,,,,,"Yamaguchi, T., Naoyuki, K., Takama, Y., (2009) Introduction to Intelligent Network Systems, 21 (2), p. 234. , https://doi.org/10.3156/jsoft.21.2_234, Print ISSN 1347-7986 https://www. jstage. jst. go. Jp/article/jsoft/21/2/21_234/_article/-char/ja; Nakashima, N., (2012) Pragmatics, , Asakura Publishing Co., Ltd; Imai, K., (2001) An Invitation to Pragmatics, , TAISHUKAN Publishing Co., Ltd; Bengio, Y., Learning deep architectures for ai (2009) Foundations and Trends in Machine Learning, 2 (1), pp. 1-127; Sumi, K., Seki, M., Habe, H., Object Detection-Model of Foreground and Background, pp. 79-98. , IPSJ SIG Technical Report, 2005-CVTM-150; Girshick, R., Fast R-CNN (2015) ICCV; Ren, S., He, K., Girshick, R., Sun, J., Faster R-CNN: Towards realtime object detection with region proposal networks (2015) NIPS; Long, J., Shelhamer, E., Darrell, T., Fully convolutional networks for semantic segmentation (2015) CVPR; Bremner, A., Lewkowicz, D., Spence, C., (2012) Multisensory Development, , Oxford University Press, Oxford, UK",,,,"Institute of Electrical and Electronics Engineers Inc.","Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020","5 December 2020 through 8 December 2020",,166710,,9781728197326,,,"English","Jt. Int. Conf. Soft Comput. Intell. Syst. Int. Symp. Adv. Intell. Syst., SCIS-ISIS",Conference Paper,"Final","",Scopus,2-s2.0-85100435767
"Kaneiwa T., Nakamura T., Kanoh M., Yamada K.","57221846126;55561912200;8641213100;55262561200;","A verification of visualization ability of onomatopoeia thesaurus map for sound-symbolism on japanese vowels",2020,"2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020",,,"9322787","","",,,"10.1109/SCISISIS50064.2020.9322787","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100375419&doi=10.1109%2fSCISISIS50064.2020.9322787&partnerID=40&md5=d0d55d5ac4136a86f744cff4f2d382ea","Nagoya Insititute of Technology, Japan; Chukyo University, Japan; Institute of Advanced Media Arts and Sciences, Japan","Kaneiwa, T., Nagoya Insititute of Technology, Japan; Nakamura, T., Nagoya Insititute of Technology, Japan; Kanoh, M., Chukyo University, Japan; Yamada, K., Institute of Advanced Media Arts and Sciences, Japan","Human Robot Interaction explores smooth communication between human being and machines. The communication in Japanese often uses onomatopoeia. That can easily express features of sound, atmosphere of movement and so on. It has also the characteristic that is called sound symbolism. Sound symbolism is a property that a specific sound makes people imagine a specific impression. Urata et al. focused on sound symbolism and proposed an onomatopoeia thesaurus map. The thesaurus map attempted to visualize semantic similarity between onomatopoeias. However, visualization ability of the map has not been evaluated sufficiently. In this paper, we verified the visualization ability of the map. We formulated a hypothesis based on sound symbolism about Japanese vowels. The experiment evaluated the hypothesis. As the result of the experiment, it was confirmed that the map can express a part of the hypotheses. On the other hand, a part of the result was different from the hypothesis. This paper discussed the experimental result. © 2020 IEEE.","Autoencoder; Onomatopoeia; Sound Symbolism; Thesaurus Map","Intelligent computing; Intelligent systems; Semantics; Soft computing; Thesauri; Visualization; Human being; Japanese vowels; Semantic similarity; Social robots",,,,,,,,,,,,,,,,"Ono, M., (2007) Nihongo Onomatope Jiten [Onomatopoeia-Mimetic 4500 Japanese Onomatopoeia Dictionary], , In Japanese; Urata, D., Ishino, S., Nakamura, T., Kanoh, M., Yamada, K., Visualized onomatopoeia thesaurus maps based on deep autoencoder (2018) Joint 10th International Conference on Soft Computing and Intelligent Systems (SCIS) and 19th International Symposium on Advanced Intelligent Systems (ISIS), pp. 215-219; Hinton, L., Nichols, J., Ohala, J.J., (2006) Sound Symbolism, , Cambridge University Press; Shinohara, K., Onomatope to ninchikagaku-zikken wo motiita onsyotyo no kisokenkyu [onomatopoeia and cognitive science-basic research on sound symbols using experiments] (2015) Nihongogaku, 34 (11), pp. 44-54. , In Japanese; Kaneiwa, T., Nakamura, T., Kanoh, M., Yamada, K., A verification of sound-symbolism visualization using onomatopoeia thesaurus map Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, , to appear In Japanese; HMM-based Text-to-Speech Speech System Open JTalk, , http://openjtalk.sp.nitech.ac.jp/index.php; Japanese Phonology, , https://en.wikipedia.org/wiki/Japanesephonology",,,,"Institute of Electrical and Electronics Engineers Inc.","Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020","5 December 2020 through 8 December 2020",,166710,,9781728197326,,,"English","Jt. Int. Conf. Soft Comput. Intell. Syst. Int. Symp. Adv. Intell. Syst., SCIS-ISIS",Conference Paper,"Final","",Scopus,2-s2.0-85100375419
"Hoorn J.F.","6507060727;","Theory of Robot Communication: II. Befriending a Robot over Time",2020,"International Journal of Humanoid Robotics","17","6","2050027","","",,1,"10.1142/S0219843620500279","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101809973&doi=10.1142%2fS0219843620500279&partnerID=40&md5=c4bc5247cc951062c7366eb4504bf8f5","Department of Computing and School of Design, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong; Department of Communication Science, Vrije Universiteit, Amsterdam, Netherlands","Hoorn, J.F., Department of Computing and School of Design, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, Department of Communication Science, Vrije Universiteit, Amsterdam, Netherlands","In building on theories of Computer-Mediated Communication (CMC), Human-Robot Interaction, and Media Psychology (Mψ; i.e., Theory of Affective Bonding), this paper proposes an explanation of how over time, people experience the mediated or simulated aspects of the interaction with a social robot. In two simultaneously running loops, a more reflective process is balanced with a more affective process. If human interference is detected behind the machine, Robot-Mediated Communication commences, which basically follows CMC assumptions; if human interference remains undetected, Human-Robot Communication (HRC) comes into play, holding the robot for an autonomous social actor. The more emotionally aroused a robot user is, the more likely they develop an affective relationship with what actually is a machine. The main contribution of this paper is an integration of CMC, HRC, and Mψ, outlining a full-blown theory of robot communication connected to friendship formation, accounting for communicative features, modes of processing, as well as psychophysiology. © 2020 The Author(s).","artificial friendship; human-robot interaction; language; neurology of emotion; Robot communication theory","Agricultural robots; Human computer interaction; Turing machines; Affective process; Computer mediated communication; Human interference; Human-robot communication; Media psychologies; Robot communication; Robot mediated communications; Social actors; Social robots",,,,,"AB/rk/2019/100; Hong Kong Polytechnic University, PolyU: 406, AiDLab RP2P3, DI.19.005","This study was performed as part of the employment of the author at The Hong Kong Polytechnic University in the PAL project of the Arti¯cial Intelligence in Design Laboratory (grant number: AiDLab RP2P3). The part at Vrije Universiteit Amsterdam was supported by Communicating with and Relating to Social Robots: Alice Meets Leolani, NWO Open Competition −− Digitalisation (SSH) (grant number: 406.DI.19.005) and VUvereniging (grant number: AB/rk/2019/100). I am thankful to Elly A. Konijn and the anonymous reviewers for commenting on an earlier draft of this paper.",,,,,,,,,,"Hoorn, J. F., Theory of robot communication: I. The medium is the communication partner (2020) Int. J. Humanoid Robotics, 17 (6); Wang, W., Smartphones as social actors? Social dispositional factors in assessing anthropomorphism (2017) Comput. Hum. Behav, 68, pp. 334-344; Short, J., Williams, E., Christie, B., (1976) The Social Psychology of Telecommunications, , (Wiley, London); Sproull, L., Kiesler, S., Reducing social context cues: Electronic mail in organizational communication (1986) Manage. Sci, 32, pp. 1492-1512; Mavridis, N., A review of verbal and non-verbal human-robot interactive communication (2015) Robot. 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Res, 28 (3), pp. 317-348; Donath, J., Signals in social supernets (2007) J. Comput.-Mediat. Commun, 13 (1), pp. 231-251; Daft, R. L., Lengel, R. H., Information richness: A new approach to managerial behavior and organizational design (1984) Research in Organizational Behavior, 6, pp. 191-233. , eds. L. L. Cummings and B. M. Staw, (JAI, Homewood, IL); Hoorn, J. F., Winter, S. D., Here comes the bad news: Doctor robot taking over (2017) Int. J. Soc. Robot, 10 (4), pp. 519-535; Walther, J. B., Selective self-presentation in computer-mediated communication: Hyperpersonal dimensions of technology, language, and cognition (2007) Comput. Hum. Behav, 23 (5), pp. 2538-2557; Buhlmann, H., Gisler, A., (2006) A Course in Credibility Theory and its Applications, , (Springer, New York); Walther, J. 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F., Differential facial articulacy in robots and humans elicit different levels of responsiveness, empathy, and projected feelings (2020) Robotics, 9 (4), p. 92; Wondra, J. D., Ellsworth, P. C., An appraisal theory of empathy and other vicarious emotional experiences (2015) Psychol. Rev, 122 (3), pp. 411-428; Palmer, M. T., Interpersonal communication and virtual reality: Mediating interpersonal relationships (1995) Communication in the Age of Virtual Reality, pp. 277-302. , eds. F. Biocca and M. R. Levy (Erlbaum, Hillsdale, NJ); Collins, N. L., Miller, L. C., Self-disclosure and liking: A meta-analytic review (1994) Psychol. Bull, 116, pp. 457-475; Rubin, Z., Disclosing oneself to a stranger: Reciprocity and its limits (1975) J. Exp. Soc. Psychol, 11 (3), pp. 233-260; Hoorn, J. F., Konijn, E. A., Germans, D. M., Burger, S., Munneke, A., The in-between machine: The unique value proposition of a robot or why we are modelling the wrong things (2015) Proc. 7th Int. Conf. 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"Ryumin D., Kagirov I., Axyonov A., Pavlyuk N., Saveliev A., Kipyatkova I., Zelezny M., Mporas I., Karpov A.","57191960214;25121369400;57203963345;57210803960;55356518900;36018436400;8533765400;14036079800;57219469958;","A multimodal user interface for an assistive robotic shopping cart",2020,"Electronics (Switzerland)","9","12","2093","1","25",,3,"10.3390/electronics9122093","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097430177&doi=10.3390%2felectronics9122093&partnerID=40&md5=6f7b9c7c92efb4c6eae1001154184962","St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg, 199178, Russian Federation; Department of Cybernetics, Faculty of Applied Sciences, University of West Bohemia, Pilsen, 301 00, Czech Republic; School of Engineering and Computer Science, University of Hertfordshire, Herts, Hatfield, AL10 9AB, United Kingdom","Ryumin, D., St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg, 199178, Russian Federation; Kagirov, I., St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg, 199178, Russian Federation; Axyonov, A., St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg, 199178, Russian Federation; Pavlyuk, N., St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg, 199178, Russian Federation; Saveliev, A., St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg, 199178, Russian Federation; Kipyatkova, I., St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg, 199178, Russian Federation; Zelezny, M., Department of Cybernetics, Faculty of Applied Sciences, University of West Bohemia, Pilsen, 301 00, Czech Republic; Mporas, I., School of Engineering and Computer Science, University of Hertfordshire, Herts, Hatfield, AL10 9AB, United Kingdom; Karpov, A., St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg, 199178, Russian Federation","This paper presents the research and development of the prototype of the assistive mobile information robot (AMIR). The main features of the presented prototype are voice and gesture-based interfaces with Russian speech and sign language recognition and synthesis techniques and a high degree of robot autonomy. AMIR prototype’s aim is to be used as a robotic cart for shopping in grocery stores and/or supermarkets. Among the main topics covered in this paper are the presentation of the interface (three modalities), the single-handed gesture recognition system (based on a collected database of Russian sign language elements), as well as the technical description of the robotic platform (architecture, navigation algorithm). The use of multimodal interfaces, namely the speech and gesture modalities, make human-robot interaction natural and intuitive, as well as sign language recognition allows hearing-impaired people to use this robotic cart. AMIR prototype has promising perspectives for real usage in supermarkets, both due to its assistive capabilities and its multimodal user interface. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Assistive robotics; Gesture interface; Multimodal user interface; Service robotics; Sign language processing; Speech recognition; Voice interface",,,,,,"Ministry of Science and Higher Education of the Russian Federation: 0073-2019-0005, 14.616.21.0095, RFMEFI61618X0095","Funding: This research is financially supported by the Ministry of Science and Higher Education of the Russian Federation, the agreement No. 14.616.21.0095 (ID Number: RFMEFI61618X0095), as well as a part of the Russian state research No. 0073-2019-0005.",,,,,,,,,,"Wada, K., Shibata, T., Living with seal robots—Its sociopsychological and physiological influences on the elderly at a care house (2007) IEEE Trans. Robot, 23, pp. 972-980. , [CrossRef]; Wada, K., Shibata, T., Musha, T., Kimura, S., Robot therapy for elders affected by dementia (2008) IEEE Eng. Med. Biol. Mag, 27, pp. 53-60. , [CrossRef]; Wada, K., Shibata, T., Social and physiological influences of robot therapy in a care house (2008) Interact. Stud, 9, pp. 258-276. , [CrossRef]; Bemelmans, R., Gelderblom, G.J., Jonker, P., Witte, L., The potential of socially assistive robotics in care for elderly, a systematic review (2011) Hum. Robot Pers. Relatsh, 59, pp. 83-89; Bemelmans, R., Gelderblom, G.J., Jonker, P., Witte, L., Socially assistive robots in elderly care: A systematic review into effects and effectiveness (2012) J. Am. Med Dir. Assoc, 13, pp. 114-120. , [CrossRef] [PubMed]; Maldonado-Bascon, S., Iglesias-Iglesias, C., Martín-Martín, P., Lafuente-Arroyo, S., Fallen people detection capabilities using assistive robot (2019) Electronics, 8, p. 915. , [CrossRef]; Lotfi, A., Langensiepen, C., Yahaya, S.W., Socially Assistive Robotics: Robot Exercise Trainer for Older Adults (2018) Technologies, 6, p. 32. , [CrossRef]; Piezzo, C., Suzuki, K., Feasibility Study of a Socially Assistive Humanoid Robot for Guiding Elderly Individuals during Walking (2017) Future Internet, 9, p. 30. , [CrossRef]; Sumiya, T., Matsubara, Y., Nakano, M., Sugaya, M., A mobile robot for fall detection for elderly-care (2015) Procedia Comput. 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Karpov, A.; St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), Russian Federation; email: karpov@iias.spb.su",,,"MDPI AG",,,,,20799292,,,,"English","Electronics (Switzerland)",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85097430177
"Lambert A., Norouzi N., Bruder G., Welch G.","57215359617;57204712431;23391698600;35572266400;","A Systematic Review of Ten Years of Research on Human Interaction with Social Robots",2020,"International Journal of Human-Computer Interaction","36","19",,"1804","1817",,3,"10.1080/10447318.2020.1801172","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089786446&doi=10.1080%2f10447318.2020.1801172&partnerID=40&md5=71355593a129a70ba95bbbf3f89b36b3","Department of Computer Science, University of Central Florida, Orlando, FL, United States; Institute for Simulation and Training (IST), University of Central Florida, Orlando, FL, United States; College of Nursing, University of Central Florida, Orlando, FL, United States","Lambert, A., Department of Computer Science, University of Central Florida, Orlando, FL, United States; Norouzi, N., Department of Computer Science, University of Central Florida, Orlando, FL, United States; Bruder, G., Institute for Simulation and Training (IST), University of Central Florida, Orlando, FL, United States; Welch, G., Department of Computer Science, University of Central Florida, Orlando, FL, United States, Institute for Simulation and Training (IST), University of Central Florida, Orlando, FL, United States, College of Nursing, University of Central Florida, Orlando, FL, United States","While research and development related to robotics has been going on for decades, the past decade in particular has seen a marked increase in related efforts, in part due to technological advances, increased technological accessibility and reliability, and increased commercial availability. What have come to be known as social robots are now being used to explore novel forms of human-robot interaction, to understand social norms, and to test expectations and human responses. To capture the contributions of these research efforts, identify the current trends, and future directions, we systematically review 10 years of research in the field of social robotics between 2008 and 2018, which includes 86 publications with 70 user studies. We classify the past work based on the research topics and application areas, and provide information about the publications, their user studies, and the capabilities of the social robots utilized. We also discuss selected papers in detail and outline overall trends. Based on these findings, we identify some areas of potential future research. © 2020 Taylor & Francis Group, LLC.",,"Classification (of information); Robotics; Application area; Commercial availability; Human interactions; Research and development; Research efforts; Social robotics; Systematic Review; Technological advances; Social robots",,,,,"National Science Foundation, NSF: 1800961; Office of Naval Research, ONR: N00014-17-1-2927; Stanford University, SU; University of Florida, UF; University of Central Florida, UCF","This material includes work supported in part by the Collaborative Award Numbers 1800961, 1800947, and 1800922 (Dr. Ephraim P. Glinert, IIS) to the University of Central Florida, University of Florida, and Stanford University respectively; the Office of Naval Research under Award Number N00014-17-1-2927 (Dr. Peter Squire, Code 34); and the AdventHealth Endowed Chair in Healthcare Simulation (Prof. Welch). 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"Prasad P.K., Ertel W.","57221831776;35106910200;","Knowledge Acquisition and Reasoning Systems for Service Robots: A Short Review of the State of the Art",2020,"2020 5th International Conference on Robotics and Automation Engineering, ICRAE 2020",,,"9310835","36","45",,,"10.1109/ICRAE50850.2020.9310835","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100345534&doi=10.1109%2fICRAE50850.2020.9310835&partnerID=40&md5=1358a6bad5ab86cb624a32db8299c0ce","Institute for Artificial Intelligence, Ravensburg-Weingarten University, Weingarten, Germany","Prasad, P.K., Institute for Artificial Intelligence, Ravensburg-Weingarten University, Weingarten, Germany; Ertel, W., Institute for Artificial Intelligence, Ravensburg-Weingarten University, Weingarten, Germany","Understanding the environment is an essential capability for mobile service robots to achieve autonomy. While this has proven to be a very challenging task, researchers have come up with a variety of solutions to enable robots to interact with and understand the environment around them. This capability allows robots to make more intelligent decisions using acquired knowledge. In parallel, equipping robots with situation awareness capability is also of equal importance since the semantic context of various entities can change with the change in the situation. This paper aims to deliver a review of the state of the art artificial intelligence approaches taken by researchers to enable robots to acquire and process knowledge of their environment and situation leading to robotic awareness. The focal points include various techniques used for knowledge acquisition, decision management, reasoning, situation awareness, human-robot interaction, and planning. The goal is to compile the modern techniques used in this field based on which future research directions could be defined. © 2020 IEEE.","Artificial Intelligence; environmental awareness; human-robot interaction; knowledge acquisition; reasoning; service robots; situation awareness","Agricultural robots; Intelligent robots; Knowledge acquisition; Mobile robots; Robot programming; Robotics; Semantics; Decision management; Future research directions; Intelligent decisions; Mobile service robots; Modern techniques; Process knowledge; Reasoning system; Situation awareness; Human robot interaction",,,,,,,,,,,,,,,,"Landauer, T., Dumais, S., A solution to plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge (1997) Psychological Review; Beetz, M., Beßler, D., Haidu, A., Pomarlan, M., Bozcuoglu, A., Bartels, G., Know rob 2.0-a 2nd generation knowledge processing framework for cognition-enabled robotic agents (2018) International Conference on Robotics and Automation, pp. 512-519; 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Acm (2020) Ieee International Conference on Human-Robot Interaction, pp. 43-52; Wei, D., Zhou, H., Yang, H., Human-robot negotiation of intentions based on virtual fixtures for shared task execution (2020) 17th International Conference on Ubiquitous Robots (UR), pp. 297-302; Azevedo, C., Raizer, K., Souza, R., A vision for human-machine mutual understanding (hmmu), trust establishment, and collaboration (2017) Ieee Conference on Cognitive and Computational Aspects of Situation Management, pp. 1-3; Chen, J., Lakhmani, S., Stowers, K., Selkowitz, A., Wright, J., Barnes, M., Situation awareness-based agent transparency and human-Autonomy teaming effectiveness (2018) Theoretical Issues in Ergonomics Science, 19, pp. 259-282; Shah, R., Krasheninnikov, D., Alexander, J., Abbeel, P., Dragan, A.D., Preferences implicit in the state of the world (2019) International Conference on Learning Representations Iclr; Bobu, A., Scobee, D., Fisac, J., Sastry, S., Dragan, A.D., Less is more: Rethinking probabilistic models of human behavior (2020) International Conference on Human-Robot Interaction Hri; Bajcsy, A., Bansal, S., Ratner, E., Dragan, A.D., Tomlin, C., A hamilton-jacobi reachability-based framework for predicting and analyzing human motion (2020) International Conference on Robotics and Automation Icra; Reddy, S., Dragan, A.D., Levine, S., Legg, S., Leike, J., Learning human objectives by evaluating hypothetical behavior (2020) International Conference on Machine Learning Icml; Bozcuoglu, A., Kazhoyan, G., Furuta, Y., Stelter, S., Beetz, M., Okada, K., Inaba, M., The exchange of knowledge using cloud robotics (2018) International Conference on Robotics and Automation, pp. 1-8; Wu, H., Wu, X., Ma, Q., Tian, G., Cloud robot: Semantic map building for intelligent service task (2018) Applied Intelligence, 49, pp. 1-16",,,,"Institute of Electrical and Electronics Engineers Inc.","5th International Conference on Robotics and Automation Engineering, ICRAE 2020","20 November 2020 through 22 November 2020",,166395,,9781728189819,,,"English","Int. Conf. Robot. Autom. Eng., ICRAE",Conference Paper,"Final","",Scopus,2-s2.0-85100345534
"Van Campenhout L., Van Camp M., Vancoppenolle W.","55675156300;57194702096;57220108938;","Exploring Tangible VR as a Tool for Workplace Design",2020,"ISS 2020 - Companion - Proceedings of the 2020 Conference on Interactive Surfaces and Spaces",,,,"33","36",,,"10.1145/3380867.3426202","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096904387&doi=10.1145%2f3380867.3426202&partnerID=40&md5=c2f13c338095b8c3fa29407759a3772c","Product Development, University of Antwerp, Antwerp, Belgium; Ground Eight Achilles Design, Mechelen, Belgium","Van Campenhout, L., Product Development, University of Antwerp, Antwerp, Belgium; Van Camp, M., Product Development, University of Antwerp, Antwerp, Belgium; Vancoppenolle, W., Ground Eight Achilles Design, Mechelen, Belgium","In this paper, we present a demonstrator that combines elements of physical prototyping and Virtual Reality. Our goal is to integrate VR in the design process of workplaces, not as a replacement of physical prototyping, but complementary to it. We call this approach Tangible Virtual Reality. We discuss our own background in embodied interaction, and present the context for our research: an industrial work cell for human-robot interaction, proposed by Audi Brussels, Kuka Belgium and FRS Robotics. We explain and illustrate how we used 3D CAD geometry to create a VR model and a physical prototype, and how we mapped them over one another. The result is a demonstrator that offers a VR experience, enhanced with real tactile information, and channeled by the natural limitations of the physical world. We suggest where the benefits of this physical/virtual design approach lay, and discuss how it could be operationalized in workplace design practice. © 2020 ACM.","embodied interaction; human-robot interaction; virtual reality; workplace design","Computer aided design; Human robot interaction; Industrial research; Industrial robots; Design approaches; Design process; Embodied interaction; Physical prototyping; Physical world; Tactile information; Work cell; Workplace design; Virtual reality",,,,,"Agentschap Innoveren en Ondernemen, VLAIO","We thank all publications support and staff, who wrote and provided helpful comments on previous versions of this document. Lukas Van Campenhout gratefully acknowledges the grant from VLAIO and Flanders Make.",,,,,,,,,,"Amores, J., Benavides, X., Shapira, L., TactileVR: Integrating physical toys into learn and play virtual reality experiences (2016) Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, , https://doi.org/10.1145/2851581.2889438, ACM, New York, NY, USA; Berg, L.P., Vance, J.M., Industry use of virtual reality in product design and manufacturing: A survey (2017) Virtual Real-London, 21 (1), pp. 1-17. , https://doi.org/10.1007/s10055-016-0293-9; Dourish, P., (2001) Where the Action Is - The Foundations of Embodied Interaction, , http://dx.doi.org/10.7551/mitpress/7221.001.0001, MIT Press, Cambridge, MA, USA; Harley, D., Tarun, A.P., Germinario, D., Mazalek, A., Tangible VR: Diegetic tangible objects for virtual reality narratives (2017) Proceedings of the 2017 Conference on Designing Interactive Systems, pp. 1253-1263. , https://doi.org/10.1145/3064663.3064680, ACM, New York, NY, USA; Hornecker, E., Buur, J., Getting a grip on tangible interaction: A framework on physical space and social interaction (2006) Proceedings of the CHI Conference on Design for Tangible Interactions, pp. 437-446. , https://doi.org/10.1145/1124772.1124838, ACM, New York, NY, USA; Pontonnier, C., Dumont, G., Samani, A., Madeleine, P., Badawi, M., Designing and evaluating a workstation in real and virtual environment: Toward virtual reality based ergonomic design sessions (2014) J Multimodal User in, 8 (2), pp. 199-208; Simeone, A.L., Velloso, E., Gellersen, H., Substitutional reality: Using the physical environment to design virtual reality experiences (2015) Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 3307-3316. , https://www.doi.org/10.1145/2702123.2702389, ACM, New York, NY, USA; Van Campenhout, L., Frens, J., Hummels, C., Standaert, A., Peremans, H., The enriching limitations of the physical world (2019) Pers Ubiquitous Comput, 23, pp. 81-98. , https://www.doi.org/10.1007/s00779-018-1176-8; Van Campenhout, L., Frens, J., Overbeeke, K., Standaert, A., Peremans, H., Physical interaction in a dematerialized world (2013) Int J des, 7 (1), pp. 1-18; Verlinden, J.C., De Smit, B., Peeters, A.W.J., Van Gelderen, M.H., Development of a flexible augmented prototyping system (2003) Journal WSCG, 11, pp. 1-3; Witmer, B.G., Singer, M.J., Measuring presence in virtual environments: A presence questionnaire (1998) Presence-Teleop Virt, 7 (3), pp. 225-240. , https://doi.org/10.1162/105474698565686",,,"ACM SIGCHI","Association for Computing Machinery, Inc","2020 Conference on Interactive Surfaces and Spaces, ISS 2020","8 November 2020 through 11 November 2020",,164832,,9781450375269,,,"English","ISS Companion - Proc. Conf. Interact. Surfaces Spaces",Conference Paper,"Final","",Scopus,2-s2.0-85096904387
"Paplu S.H., Mishra C., Berns K.","57204053864;57213798792;7006119626;","Pseudo-Randomization in Automating Robot Behaviour during Human-Robot Interaction",2020,"ICDL-EpiRob 2020 - 10th IEEE International Conference on Development and Learning and Epigenetic Robotics",,,"9278115","","",,2,"10.1109/ICDL-EpiRob48136.2020.9278115","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100018641&doi=10.1109%2fICDL-EpiRob48136.2020.9278115&partnerID=40&md5=9df559bfc04890c00813379424f8af4f","Department of Computer Science, Tu Kaiserslautern, Kaiserslautern, Germany","Paplu, S.H., Department of Computer Science, Tu Kaiserslautern, Kaiserslautern, Germany; Mishra, C., Department of Computer Science, Tu Kaiserslautern, Kaiserslautern, Germany; Berns, K., Department of Computer Science, Tu Kaiserslautern, Kaiserslautern, Germany","Automating robot behavior in a specific situation is an active area of research. There are several approaches available in the literature of robotics to cater for the automatic behavior of a robot. However, when it comes to humanoids or human-robot interaction in general, the area has been less explored. In this paper, a pseudo-randomization approach has been introduced to automatize the gestures and facial expressions of an interactive humanoid robot called ROBIN based on its mental state. A significant number of gestures and facial expressions have been implemented to allow the robot more options to perform a relevant action or reaction based on visual stimuli. There is a display of noticeable differences in the behaviour of the robot for the same stimuli perceived from an interaction partner. This slight autonomous behavioural change in the robot clearly shows a notion of automation in behaviour. The results from experimental scenarios and human-centered evaluation of the system help validate the approach. © ICDL-EpiRob 2020. All rights reserved.","Facial expressions; Gestures; Human-robot interaction; Robot behaviour","Agricultural robots; Anthropomorphic robots; Behavioral research; Man machine systems; Random processes; Robotics; Behavioural changes; Facial Expressions; Human-centered evaluation; Humanoid robot; Mental state; Pseudo-randomization; Robot behavior; Visual stimulus; Human robot interaction",,,,,,,,,,,,,,,,"Olson, J.C., Jacoby, J., Cue utilization in the quality perception process (1972) Acr Special Volumes; Stansfield, S.A., A robotic perceptual system utilizing passive vision and active touch (1988) The International Journal of Robotics Research, 7 (6), pp. 138-161; Partos, T.R., Cropper, S.J., Rawlings, D., You don't see what i see: Individual differences in the perception of meaning from visual stimuli (2016) PloS One, 11 (3), p. 150615; Powers, W.T., Feedback: Beyond behaviorism: Stimulus-response laws are wholly predictable within a control-system model of behavioral organization (1973) Science, 179 (4071), pp. 351-356; Churchland, M.M., Byron, M.Y., Cunningham, J.P., Sugrue, L.P., Cohen, M.R., Corrado, G.S., Newsome, W.T., Scott, B.B., Stimulus onset quenches neural variability: A widespread cortical phenomenon (2010) Nature Neuroscience, 13 (3), p. 369; Duffy, B.R., Anthropomorphism and the social robot (2003) Robotics and Autonomous Systems, 42 (3-4), pp. 177-190; Salem, M., Eyssel, F., Rohlfing, K., Kopp, S., Joublin, F., To err is human (-like): Effects of robot gesture on perceived anthropomorphism and likability (2013) International Journal of Social Robotics, 5 (3), pp. 313-323; Mutlu, B., Shiwa, T., Kanda, T., Ishiguro, H., Hagita, N., Footing in human-robot conversations: How robots might shape participant roles using gaze cues (2009) Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction, pp. 61-68. , New York, NY, USA: ACM; Yoon, Y., Ko, W.-R., Jang, M., Lee, J., Kim, J., Lee, G., Robots learn social skills: End-to-end learning of co-speech gesture generation for humanoid robots (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 4303-4309. , Montreal, QC, Canada, Canada: IEEE; Calinon, S., D'Halluin, F., Sauser, E.L., Caldwell, D.G., Billard, A.G., Learning and reproduction of gestures by imitation (2010) Ieee Robotics & Automation Magazine, 17 (2), pp. 44-54; Calinon, S., Billard, A., Incremental learning of gestures by imitation in a humanoid robot (2007) Proceedings of the ACM/IEEE International Conference on Human-robot Interaction, Ser. Hri '07, pp. 255-262. , http://doi.acm.org/10.1145/1228716.1228751, New York, NY, USA: ACM, [Online]; Ikeuchi, K., Baumert, D., Kudoh, S., Takizawa, M., (2019) Design of Conversational Humanoid Robot Based on Hardware Independent Gesture Generation, , http://arxiv.org/abs/1905.08702, CoRR, vol. abs/1905.08702, [Online]; Neff, M., Kipp, M., Albrecht, I., Seidel, H.-P., Gesture modeling and animation based on a probabilistic re-creation of speaker style (2008) Acm Trans. Graph, 27 (1), pp. 51-524. , http://doi.acm.org/10.1145/1330511.1330516, Mar. [Online]; Fischer, K., Jung, M., Jensen, L.C., DerWieschen, M.V.A., Emotion expression in hri-when and why (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 29-38. , Daegu, Korea (South), Korea (South): IEEE; Gelder, B.D., Van Den Stock, J., The bodily expressive action stimulus test (beast). Construction and validation of a stimulus basis for measuring perception of whole body expression of emotions (2011) Frontiers in Psychology, 2, p. 181; Coulson, M., Attributing emotion to static body postures: Recognition accuracy, confusions, and viewpoint dependence (2004) Journal of Nonverbal Behavior, 28 (2), pp. 117-139; Morris, D., (2015) Bodytalk: A World Guide to Gestures, , London: Jonathan Cape; Ekman, P., Friesen, W., Manual of the facial action coding system (facs) (1978) Trans, , ed. Vol. Consulting Psychologists Press, Palo Alto; Zafar, Z., Venugopal, R., Berns, K., Real-time recognition of human postures for human-robot interaction (2018) Proceedings of the 11th International Conference on Advances in Computer-Human Interactions (ACHI), , Rome, Italy; Reichardt, M., Föhst, T., Berns, K., Introducing finroc: A convenient real-time framework for robotics based on a systematic design approach (2012) Robotics Research Lab, Department of Computer Science, , University of Kaiserslautern, Kaiserslautern, Germany, Technical Report; Shreve, M.A., (2013) Automatic Macro-and Micro-facial Expression Spotting and Applications, , Ph.D. dissertation, University of South Florida; Ekman, P., Basic emotions (1999) Handbook of Cognition and Emotion, 98 (45-60), p. 16; Mianowski, K., Schmitz, N., Berns, K., Mechatronics of the humanoid robot roman (2007) Robot Motion and Control 2007, pp. 341-348. , Springer; Young, J.E., Sung, J., Voida, A., Sharlin, E., Igarashi, T., Christensen, H.I., Grinter, R.E., Evaluating human-robot interaction (2011) International Journal of Social Robotics, 3 (1), pp. 53-67; Hall, E.T., (1910) The Hidden Dimension, 609. , NY, USA: Garden City, NY: Doubleday; Zafar, Z., Berns, K., Recognizing hand gestures for human-robot interaction (2016) Proceedings of the 9th International Conference on Advances in Computer-Human Interactions (ACHI), pp. 333-338; Likert, R., A technique for the measurement of attitudes (1932) Archives of Psychology",,,"Advanced Center of Electrical and Electronic Engineering;Advanced Mining Technology Center (AMTC);Synopsys;The Clover: Ingenieria 2030;Universidad Tecnica Federico Santa Maria","Institute of Electrical and Electronics Engineers Inc.","10th Joint IEEE International Conference on Development and Learning and Epigenetic Robotics, ICDL-EpiRob 2020","26 October 2020 through 30 October 2020",,165847,,9781728173061,,,"English","ICDL-EpiRob - IEEE Int. Conf. Dev. Learn. Epigenetic Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85100018641
"Hmamouche Y., Ochs M., Prévot L., Chaminade T.","57170656400;13608908400;57202367740;8339688200;","Neuroscience to investigate social mechanisms involved in human-robot interactions",2020,"ICMI 2020 Companion - Companion Publication of the 2020 International Conference on Multimodal Interaction",,,,"52","56",,,"10.1145/3395035.3425263","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099210042&doi=10.1145%2f3395035.3425263&partnerID=40&md5=dbac9615797dcb09b7c008cb0d7870a6","Aix Marseille Université, CNRS, LIS, UMR7020, Marseille, France; Aix Marseille Université, CNRS, LIS, UMR7309, Marseille, France; Aix Marseille Université, CNRS, LIS, UMR7289, Marseille, France","Hmamouche, Y., Aix Marseille Université, CNRS, LIS, UMR7020, Marseille, France; Ochs, M., Aix Marseille Université, CNRS, LIS, UMR7020, Marseille, France; Prévot, L., Aix Marseille Université, CNRS, LIS, UMR7309, Marseille, France; Chaminade, T., Aix Marseille Université, CNRS, LIS, UMR7289, Marseille, France","To what extent do human-robot interactions (HRI) rely on social processes similar to human-human interactions (HHI)? To address this question objectively, we use a unique corpus. Brain activity and behaviors were recorded synchronously while participants were discussing with a human (confederate of the experimenter) or a robotic device (controlled by the confederate). Here, we focus on two main regions of interest (ROIs), that form the core of ""the social brain"", the right temporoparietal junction [rTPJ] and right medial prefrontal cortex [rMPFC]. An new analysis approach derived from multivariate time-series forecasting is used. A prediction score describes the ability to predict brain activity for each ROI, and results identify which behavioral features, built from raw recordings of the conversations, are used for this prediction. Results identify some differences between HHI and HRI in the behavioral features predicting activity in these ROIs of the social brain, that could explain significant differences in the level of activity. © 2020 ACM.","Feature selection; Functional MRI; Human-human and human-machine interactions; Multimodal signals processing; Natural conversation; Prediction","Brain; Forecasting; Interactive computer systems; Man machine systems; Neurophysiology; Time series analysis; Analysis approach; Behavioral features; Human robot Interaction (HRI); Human-human interactions; Multivariate time series; Prefrontal cortex; Regions of interest; Robotic devices; Social robots",,,,,,"This research is supported by grants ANR-16-CONV-0002 (ILCB), ANR-11-LABX-0036 (BLRI) and AAP-ID-17-46-170301-11.1 by the Aix-Marseille Université Excellence Initiative (A*MIDEX).",,,,,,,,,,"Al Moubayed, S., Beskow, J., Skantze, G., Granström, B., Furhat: A back-projected human-like robot head for multiparty human-machine interaction (2012) Cognitive Behavioural Systems (Lecture Notes in Computer Science), pp. 114-130. , Anna et al. Esposito (Ed.). Springer Berlin Heidelberg; Baltrusaitis, T., Zadeh, A., Lim, Y.C., Morency, L., Openface 2.0: Facial behavior analysis toolkit (2018) 2018 13th Ieee International Conference on Automatic Face Gesture Recognition (FG 2018), pp. 59-66. , https://doi.org/10.1109/FG.2018.00019; Rauchbauer, B., Nazarian, B., Bourhis, M., Ochs, M., Prévot, L., Chaminade, T., Brain activity during reciprocal social interaction investigated using conversational robots as control condition (2019) Philosophical Transactions of the Royal Society B: Biological Sciences, 374 (1771), p. 20180033. , https://doi.org/10.1098/rstb.2018.0033, April 2019; Bone, D., Lee, C., Narayanan, S., Robust unsupervised arousal rating: A rule-based framework with knowledge-inspired vocal features (2014) Ieee Transactions on A?ective Computing, 5 (2), pp. 201-213. , https://doi.org/10.1109/TAFFC.2014.2326393, June 2014; Chaminade, T., An experimental approach to study the physiology of natural social interactions (2017) Interaction Studies, 18 (2), pp. 254-275. , https://doi.org/10.1075/is.18.2.06gry, Dec. 2017; Chaminade, T., Rosset, D., Da Fonseca, D., Nazarian, B., Lutcher, E., Cheng, G., Deruelle, C., How do we think machines think? An fMRI study of alleged competition with an arti?cial intelligence (2012) Frontiers in Human Neuroscience, p. 6. , https://doi.org/10.3389/fnhum.2012.00103; Chen, H., Liao, Y., Jan, H., Kuo, L., Lee, C., A Gaussian mixture regression approach toward modeling the a?ective dynamics between acoustically-derived vocal arousal score (VC-AS) and internal brain fMRI bold signal response (2016) 2016 Ieee International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5775-5779. , https://doi.org/10.1109/ICASSP.2016.7472784; Dennett, D.C., (1987) The Intentional Stance, , MIT Press, Cambridge, Mass; Fan, L., Li, H., Zhuo, J., Zhang, Y., Wang, J., Chen, L., Yang, Z., Jiang, T., The human brainnetome atlas: A new brain atlas based on connectional architecture (2016) Cerebral Cortex, 26 (8), pp. 3508-3526. , https://doi.org/10.1093/cercor/bhw157, May 2016; Gallagher, H.L., Jack, A.I., Roepstor, A., Frith, C.D., Imaging the Intentional Stance in a Competitive Game (2002) NeuroImage, 16 (3), pp. 814-821. , https://doi.org/10.1006/nimg.2002.1117, July 2002; Gössl, C., Fahrmeir, L., Auer, D.P., Bayesian modeling of the hemodynamic response function in BOLD fMRI (2001) NeuroImage, 14 (1), pp. 140-148; He, H., Bai, Y., Garcia, E.A., Li, S., ADASYN: Adaptive synthetic sampling approach for imbalanced learning (2008) 2008 Ieee International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322-1328; Hmamouche, Y., Ochs, M., Prevot, L., Chaminade, T., Exploring the dependencies between behavioral and neuro-physiological time-series extracted from conversations between humans and arti?cial agents (2020) 9th International Conference on Pattern Recognition Applications and Methods, pp. 353-360. , https://doi.org/10.5220/0008989503530360, SCITEPRESS-Science and Technology Publications, Valletta, Malta; Hmamouche, Y., Prevot, L., Ochs, M., Thierry, C., Brainpredict: A tool for predicting and visualising local brain activity (2020) Proceedings of the 12th Language Resources and Evaluation Conference, pp. 703-709. , European Language Resources Association, Marseille, France; Knops, A., Thirion, B., Hubbard, E.M., Michel, V., Dehaene, S., Recruitment of an area involved in eye movements during mental arithmetic (2009) Science, 324 (5934), pp. 1583-1585. , https://doi.org/10.1126/science.1171599, June 2009; Levitan, R., Hirschberg, J., Measuring acoustic-prosodic entrainment with respect to multiple levels and dimensions (2011) Twelfth Annual Conference of the International Speech Communication Association; Ochs, M., Jain, S., Blache, P., Toward an automatic prediction of the sense of presence in virtual reality environment (2018) Proceedings of the 6th International Conference on Human-Agent Interaction, pp. 161-166; Penny, W.D., Friston, K.J., Ashburner, J.T., Kiebel, S.J., Nichols, T.E., (2011) Statistical Parametric Mapping: The Analysis of Functional Brain Images, , Elsevier; Pernet, C.R., McAleer, P., Latinus, M., Gorgolewski, K.J., Charest, I., Bestelmeyer, P.E.G., Watson, R.H., Belin, P., The human voice areas: Spatial organization and inter-individual variability in temporal and extra-temporal cortices (2015) NeuroImage, 119, pp. 164-174. , https://doi.org/10.1016/j.neuroimage.2015.06.050, Oct. 2015; Rauchbauer, B., Hmamouche, Y., Brigitte, B., Prévot, L., Ochs, M., Chaminade, T., Multimodal corpus of bidirectional conversation of human-human and human-robot interaction during fMRI scanning (2020) Proceedings of the Twelfth International Conference on Language Resources and Evaluation, Lrec 2020, , European Language Resources Association (ELRA); Redcay, E., The superior temporal sulcus performs a common function for social and speech perception: Implications for the emergence of autism (2008) Neuroscience & Biobehavioral Reviews, 32 (1), pp. 123-142. , https://doi.org/10.1016/j.neubiorev.2007.06.004, Jan. 2008; Schilbach, L., Timmermans, B., Reddy, V., Costall, A., Bente, G., Schlicht, T., Vogeley, K., Toward a second-person neuroscience (2013) Behavioral and Brain Sciences, 36 (4), pp. 393-414. , https://doi.org/10.1017/s0140525x12000660, July 2013; De Smedt, T., Daelemans, W., Pattern for python (2012) Journal of Machine Learning Research, 13, pp. 2063-2067. , Jun, 2012; Whiteld-Gabrieli, S., Nieto-Castanon, A., Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks (2012) Brain Connectivity, 2 (3), pp. 125-141. , https://doi.org/10.1089/brain.2012.0073, June 2012; Wykowska, A., Chaminade, T., Cheng, G., Embodied artificial agents for understanding human social cognition (2016) Philosophical Transactions of the Royal Society B: Biological Sciences, 371 (1693), p. 20150375. , https://doi.org/10.1098/rstb.2015.0375, May 2016; Yarkoni, T., Barch, D.M., Gray, J.R., Conturo, T.E., Braver, T.S., Bold correlates of trial-by-trial reaction time variability in gray and white matter: A multi-study fmri analysis (2009) PloS. One, 4 (1), p. e4257. , https://doi.org/10.1371/journal.pone.0004257, Jan. 2009",,,"ACM SIGCHI","Association for Computing Machinery, Inc","2020 International Conference on Multimodal Interaction, ICMI 2020","25 October 2020 through 29 October 2020",,166127,,9781450380027,,,"English","ICMI Companion - Companion Publ. Int. Conf. Multimodal Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85099210042
"Fishman A., Paxton C., Yang W., Fox D., Boots B., Ratliff N.","57219523521;56176594500;57221215081;7402074129;10239214300;14822513400;","Collaborative interaction models for optimized human-robot teamwork",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341369","11221","11228",,3,"10.1109/IROS45743.2020.9341369","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102397806&doi=10.1109%2fIROS45743.2020.9341369&partnerID=40&md5=220b5429c81562a67b3795e5d2b60ebb","NVIDIA; University of Washington","Fishman, A., NVIDIA, University of Washington; Paxton, C., NVIDIA; Yang, W., NVIDIA; Fox, D., NVIDIA, University of Washington; Boots, B., NVIDIA, University of Washington; Ratliff, N., NVIDIA","Effective human-robot collaboration requires informed anticipation. The robot must anticipate the human's actions, but also react quickly and intuitively when its predictions are wrong. The robot must plan its actions to account for the human's own plan, with the knowledge that the human's behavior will change based on what the robot actually does. This cyclical game of predicting a human's future actions and generating a corresponding motion plan is extremely difficult to model using standard techniques. In this work, we describe a novel Model Predictive Control (MPC)-based framework for finding optimal trajectories in a collaborative, multi-agent setting, in which we simultaneously plan for the robot while predicting the actions of its external collaborators. We use human-robot handovers to demonstrate that with a strong model of the collaborator, our framework produces fluid, reactive human-robot interactions in novel, cluttered environments. Our method efficiently generates coordinated trajectories, and achieves a high success rate in handover, even in the presence of significant sensor noise. © 2020 IEEE.",,"Agricultural robots; Behavioral research; Forecasting; Intelligent robots; Model predictive control; Multi agent systems; Predictive control systems; Cluttered environments; Collaborative interaction; Cyclical games; Human robots; Human-robot collaboration; Multi-agent setting; Optimal trajectories; Sensor noise; Social robots",,,,,,,,,,,,,,,,"Ziebart, B.D., Ratliff, N., Gallagher, G., Mertz, C., Peterson, K., Bagnell, J.A.D., Hebert, M., Srinivasa, S., Planning-based prediction for pedestrians (2009) IROS; Mainprice, J., Hayne, R., Berenson, D., Goal set inverse optimal control and iterative re-planning for predicting human reaching motions in shared workspaces (2016) IEEE Transaction on Robotics (TRO); Bai, H., Cai, S., Ye, N., Hsu, D., Lee, A.S., Intention-aware online pomdp planning for autonomous driving in a crowd (2015) 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 454-460; Dragan, A.D., Lee, K.C., Srinivasa, S.S., Legibility and predictability of robot motion (2013) 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 301-308; Koppula, H.S., Jain, A., Saxena, A., Anticipatory planning for humanrobot teams (2016) Experimental Robotics, pp. 453-470. , Springer; Toussaint, M., Lopes, M., Multi-bound tree search for logic-geometric programming in cooperative manipulation domains (2017) International Conference on Robotics and Automation (ICRA), pp. 4044-4051; Peternel, L., Kim, W., Babic, J., Ajoudani, A., Towards ergonomic control of human-robot co-manipulation and handover (2017) 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pp. 55-60; Vogt, D., Stepputtis, S., Jung, B., Amor, H.B., One-shot learning of human-robot handovers with triadic interaction meshes (2018) Autonomous Robots, 42 (5), pp. 1053-1065; Strabala, K., Lee, M.K., Dragan, A.D., Forlizzi, J., Srinivasa, S.S., Cakmak, M., Micelli, V., Toward seamless human-robot handovers (2013) HRI 2013; Huang, C.-M., Cakmak, M., Mutlu, B., Adaptive coordination strategies for human-robot handovers (2015) Robotics: Science and Systems; Maeda, G., Neumann, G., Ewerton, M., Lioutikov, R., Kroemer, O., Peters, J., Probabilistic movement primitives for coordination of multiple humanrobot collaborative tasks (2017) Autonomous Robots, 41, pp. 593-612; Medina, J.R., Duvallet, F., Karnam, M., Billard, A., Ahuman-inspired controller for fluid human-robot handovers (2016) 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 324-331; Murphy, K., A survey of pomdp solution techniques (2000) Environment, 2, p. 10; Mukadam, M., Dong, J., Yan, X., Dellaert, F., Boots, B., Continuous-time Gaussian process motion planning via probabilistic inference (2018) The International Journal of Robotics Research, 37, pp. 1319-1340; Ratliff, N., Toussaint, M., Schaal, S., Understanding the geometry of workspace obstacles in motion optimization (2015) IEEE International Conference on Robotics and Automation (ICRA); Toussaint, M., A tutorial on Newton methods for constrained trajectory optimization and relations to SLAM, Gaussian Process smoothing, optimal control, and probabilistic inference (2017) Geometric and Numerical Foundations of Movements, J.-P. Laumond, Ed, , Springer; Zhou, T., Wachs, J.P., Early prediction for physical human robot collaboration in the operating room (2018) Autonomous Robots, 42 (5), pp. 977-995; Kupcsik, A., Hsu, D., Lee, W.S., Learning dynamic robot-to-human object handover from human feedback (2018) Robotics Research, pp. 161-176. , Springer; Grigore, E.C., Eder, K., Pipe, A.G., Melhuish, C., Leonards, U., Joint action understanding improves robot-to-human object handover (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4622-4629; Stouraitis, T., Chatzinikolaidis, I., Gienger, M., Vijayakumar, S., Dyadic collaborative manipulation through hybrid trajectory optimization."" in Conference on Robot Learning (CoRL) (2018), pp. 869-878; Unhelkar, V.V., Siu, H.C., Shah, J.A., Comparative performance of human and mobile robotic assistants in collaborative fetch-and-deliver tasks (2014) HRI, pp. 82-89; Yamane, K., Revfi, M., Asfour, T., Synthesizing object receiving motions of humanoid robots with human motion database (2013) 2013 IEEE International Conference on Robotics and Automation, pp. 1629-1636; Ratliff, N., Zucker, M., Bagnell, J.A., Srinivasa, S., (2009) CHOMP: Gradient Optimization Techniques for Efficient Motion Planning; Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., Schaal, S., Stomp: Stochastic trajectory optimization for motion planning (2011) 2011 IEEE International Conference on Robotics and Automation, pp. 4569-4574. , May; Chonhyon Park, J.P., Manocha, D., ITOMP: Incremental trajectory optimization for real-time replanning in dynamic environments (2012) International Conference on Automated Planning and Scheduling (ICAPS); Schulman, J., Ho, J., Lee, A., Awwal, I., Bradlow, H., Abbeel, P., (2013) Finding Locally Optimal, Collision-free Trajectories with Sequential Convex Optimization, 6; Toussaint, M., Robot trajectory optimization using approximate inference (2009) ICML, pp. 1049-1056; Toussaint, M., Newton methods for k-order markov constrained motion problems (2014), abs/1407.0414; Garrett, C.R., Paxton, C., Lozano-Pérez, T., Kaelbling, L.P., Fox, D., (2019) Online Replanning in Belief Space for Partially Observable Task and Motion Problems; Silver, D., Veness, J., Monte-Carlo planning in large POMDPs (2010) Advances in Neural Information Processing Systems, pp. 2164-2172; Somani, A., Ye, N., Hsu, D., Lee, W.S., DESPOT: Online POMDP planning with regularization (2013) Advances in Neural Information Processing Systems, pp. 1772-1780; Bandyopadhyay, T., Won, K.S., Frazzoli, E., Hsu, D., Lee, W.S., Rus, D., Intention-aware motion planning (2013) Algorithmic Foundations of Robotics X, pp. 475-491. , Springer; Tarados, E., Vazirani, V.V., (2007) Algorithmic Game Theory, Chapter Basic Solution Concepts and Computational Issues, , Cambridge University Press; Dellaert, F., Kaess, M., Factor graphs for robot perception (2017) Foundations and Trends in Robotics, 6 (1-2), pp. 1-139; Mukadam, M., Yan, X., Boots, B., Gaussian processmotion planning (2016) Proceedings-IEEE International Conference on Robotics and Automation, pp. 9-15. , June. Institute of Electrical and Electronics Engineers Inc., 6 2016; Dong, J., Boots, B., Dellaert, F., Sparse Gaussian Processes for Continuous-time Trajectory Estimation on Matrix Lie Groups, , http://arxiv.org/abs/1705.06020, abs/1705.06020, 2017; Riedmiller, M., Hafner, R., Lampe, T., Neunert, M., Degrave, J., Van De Wiele, T., Mnih, V., Springenberg, J.T., Learning by playing solving sparse reward tasks from scratch Proceedings of the 35th International Conference on Machine Learning, Ser. Proceedings OfMachine Learning Research, 80, pp. 4344-4353. , http://proceedings.mlr.press/v80/riedmiller18a.html, J. Dy and A. Krause, Eds. . Stockholmsmssan, Stockholm Sweden: PMLR, 10-15 Jul 2018; Holland, P.W., Welsch, R.E., Robust regression using iteratively reweighted least-squares (1977) Communications in Statistics-Theory and Methods, 6 (9), pp. 813-827. , https://doi.org/10.1080/03610927708827533; Zhang, Z., Parameter estimation techniques: A tutorial with application to conic fitting (1997) Image Vision Comput., 15, pp. 59-76; Schmidt, T., Newcombe, R.A., Fox, D., DART: Dense articulated real-time tracking (2014) Robotics: Science and Systems; Ziebart, B.D., Maas, A., Bagnell, J.A., Dey, A.K., Human behavior modeling with maximum entropy inverse optimal control (2009) AAAI Spring Symposium on Human Behavior Modeling; Kitani, K.M., Ziebart, B.D., Bagnell, J.A., Hebert, M., Activity forecasting (2012) European Conference on Computer Vision, pp. 201-214. , Springer; Paxton, C., Ratliff, N., Eppner, C., Fox, D., Representing robot task plans as robust logical-dynamical systems (2019) IEEE Conference on Intelligent Robots and Systems (IROS); Rakita, D., Mutlu, B., Gleicher, M., An autonomous dynamic camera method for effective remote teleoperation (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 325-333; Bonatti, R., Ho, C., Wang, W., Choudhury, S., Scherer, S.A., Towards a robust aerial cinematography platform: Localizing and tracking moving targets in unstructured environments (2019) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102397806
"Martinez-Gonzalez A., Villamizar M., Canevet O., Odobez J.-M.","57206663240;15123291100;55575425100;57203103085;","Residual pose: A decoupled approach for depth-based 3D human pose estimation",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340695","10313","10318",,2,"10.1109/IROS45743.2020.9340695","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102396397&doi=10.1109%2fIROS45743.2020.9340695&partnerID=40&md5=5c158d83d16c2f26487c1fb6be37d35f","Idiap Research Institute, Switzerland; Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland","Martinez-Gonzalez, A., Idiap Research Institute, Switzerland, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland; Villamizar, M., Idiap Research Institute, Switzerland; Canevet, O., Idiap Research Institute, Switzerland; Odobez, J.-M., Idiap Research Institute, Switzerland, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland","We propose to leverage recent advances in reliable 2D pose estimation with Convolutional Neural Networks (CNN) to estimate the 3D pose of people from depth images in multi-person Human-Robot Interaction (HRI) scenarios. Our method is based on the observation that using the depth information to obtain 3D lifted points from 2D body landmark detections provides a rough estimate of the true 3D human pose, thus requiring only a refinement step. In that line our contributions are threefold. (i) we propose to perform 3D pose estimation from depth images by decoupling 2D pose estimation and 3D pose refinement; (ii) we propose a deep-learning approach that regresses the residual pose between the lifted 3D pose and the true 3D pose; (iii) we show that despite its simplicity, our approach achieves very competitive results both in accuracy and speed on two public datasets and is therefore appealing for multi-person HRI compared to recent state-of-the-art methods. © 2020 IEEE.",,"Agricultural robots; Convolutional neural networks; Deep learning; Intelligent robots; 3D human pose estimation; 3D pose estimation; Depth information; Human robot Interaction (HRI); Landmark detection; Learning approach; Pose estimation; Pose refinement; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020: 688146",,,,,,,,,,,"Aksan, E., Kaufmann, M., Hilliges, O., Structured prediction helps 3d human motion modelling (2019) The IEEE International Conference on Computer Vision (ICCV), , Oct; Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., Black, M.J., Keep it smpl: Automatic estimation of 3d human pose and shape from a single image (2016) Computer Vision-ECCV 2016, pp. 561-578; Cao, Z., Simon, T., Wei, S., Sheikh, Y., Realtime multi-person 2d pose estimation using part affinity fields (2017) CVPR; Chen, C., Ramanan, D., 3d human pose estimation = 2d pose estimation + matching (2017) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5759-5767. , July; Guo, H., Wang, G., Chen, X., Zhang, C., Towards good practices for deep 3d hand pose estimation (2017) ArXiv Preprint; Habibie, I., Xu, W., Mehta, D., Pons-Moll, G., Theobalt, C., In the wild human pose estimation using explicit 2d features and intermediate 3d representations (2019) IEEE Conf. On Computer Vision and Pattern Recognition (CVPR); Haque, A., Peng, B., Luo, Z., Alahi, A., Yeung, S., Fei-Fei, L., Towards viewpoint invariant 3d human pose estimation (2016) European Conference on Computer Vision (ECCV); Joo, H., Simon, T., Li, X., Liu, H., Tan, L., Gui, L., Banerjee, S., Sheikh, Y., Panoptic studio: A massively multiview system for social interaction capture (2017) IEEE Transactions on Pattern Analysis and Machine Intelligence; Yub Jung, H., Lee, S., Seok Heo, Y., Dong Yun, I., Random tree walk toward instantaneous 3d human pose estimation (2015) CVPR, pp. 2467-2474. , IEEE Computer Society; Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J., End-to-end recovery of human shape and pose (2018) Computer Vision and Pattern Regognition (CVPR); Li, S., Zhang, W., Chan, A.B., Maximum-margin structured learning with deep networks for 3d human pose estimation (2015) IEEE International Conference on Computer Vision (ICCV); Martinez, J., Hossain, R., Romero, J., Little, J.J., A simple yet effective baseline for 3d human pose estimation (2017) ICCV; Martínez-González, A., Villamizar, M., Canévet, O., Odobez, J., Real-time convolutional networks for depth-based human pose estimation (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems; Martínez-González, A., Villamizar, M., Canévet, O., Odobez, J., Efficient convolutional neural networks for depthbased multi-person pose estimation (2019) IEEE Transactions on Circuits and Systems for Video Technology; Moon, G., Chang, J., Mu Lee, K., V2v-posenet: Voxel-to-voxel prediction network for accurate 3d hand and human pose estimation from a single depth map (2018) The IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Newell, A., Yang, K., Deng, J., Stacked hourglass networks for human pose estimation (2016) European Conference on Computer Vision; Pavlakos, G., Zhou, X., Derpanis, K.G., Daniilidis, K., Coarse-to-fine volumetric prediction for singleimage 3D human pose (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Ramakrishna, V., Kanade, T., Sheikh, Y., Reconstructing 3d human pose from 2d image landmarks (2012) European Conference on Computer Vision; Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M., Moore, R., Blake, A., (2012) Efficient Human Pose Estimation from Single Depth Images, , Trans. PAMI, January; Sigal, L., Isard, M., Haussecker, H., Black, M.J., Looselimbed people: Estimating 3D human pose and motion using nonparametric belief propagation (2011) International Journal of Computer Vision, 98 (1), pp. 15-48. , May; Wei Sun, X., Shang, J., Liang, S., Wei, Y., Compositional human pose regression (2017) IEEE International Conference on Computer Vision (ICCV); Taylor, J., Shotton, J., Sharp, T., Fitzgibbon, A.W., The vitruvian manifold: Inferring dense correspondences for oneshot human pose estimation (2012) CVPR; Tome, D., Russell, C., Agapito, L., Lifting from the deep: Convolutional 3d pose estimation from a single image (2017) IEEE Conf. On Computer Vision and Pattern Recognition (CVPR); Wandt, B., Rosenhahn, B., Repnet: Weakly supervised training of an adversarial reprojection network for 3d human pose estimation (2019) CVPR; Wang, K., Zhai, S., Cheng, H., Liang, X., Lin, L., Human pose estimation from depth images via inference embedded multi-task learning (2016) Proceedings of ACM on Multimedia; Ye, M., Wang, X., Yang, R., Ren, L., Pollefeys, M., Accurate 3d pose estimation from a single depth image (2011) 2011 International Conference on Computer Vision, pp. 731-738. , Nov","Martinez-Gonzalez, A.; Idiap Research InstituteSwitzerland; email: angel.martinez@idiap.ch
Villamizar, M.; Idiap Research InstituteSwitzerland; email: michael.villamizar@idiap.ch
Canevet, O.; Idiap Research InstituteSwitzerland; email: olivier.canevet@idiap.ch
Odobez, J.-M.; Idiap Research InstituteSwitzerland; email: odobez@idiap.ch",,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102396397
"Gong S., Zhao J.","57207468351;57129187500;","MMJM: A High-Precision Motion Model for Human Arm Reaching Motion",2020,"2020 3rd International Conference on Mechatronics, Robotics and Automation, ICMRA 2020",,,"9398359","59","63",,,"10.1109/ICMRA51221.2020.9398359","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104632111&doi=10.1109%2fICMRA51221.2020.9398359&partnerID=40&md5=8839e6058bb89098a36d0c70d7b8026d","College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China","Gong, S., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China; Zhao, J., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China","In human-robot interaction, it is essential for robots to accurately model and predict the movements of their human partners, allowing them to adjust their behavior in advance to avoid conflicts with humans and to improve the efficiency and safety of the interaction process. This paper aims to develop a highly accurate motion model to model and predict the motion trajectory of the human hand. Firstly, the causes and characteristics of the errors generated by the existing minimum jerk model (MJM) in modeling the trajectories of real human arm reaching movements are investigated, and a new motion model called MMJM is proposed based on a 2nd order Fourier modification term. Secondly, how to use the proposed MMJM to predict the human arm reaching motion is studied. Finally, the effectiveness of the proposed MMJM is validated by the human arm reaching movement experiments, which shows that the MMJM has better modeling accuracy and prediction accuracy than the existing MJM. © 2020 IEEE.","human motion model; human robot interaction; minimum jerk; motion prediction","Agricultural robots; Forecasting; Robotics; High-precision motion; Highly accurate; Interaction process; Minimum jerk model; Motion modeling; Motion trajectories; Prediction accuracy; Reaching movements; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 51975008; Beijing Municipal Natural Science Foundation: 3192002","ACKNOWLEDGMENT This work was supported by the National Natural Science Foundation of China (No. 51975008) and Beijing Municipal Natural Science Foundation (No. 3192002).",,,,,,,,,,"Kosuge, K., Hirata, Y., Human-Robot Interaction [C] (2014) IEEE International Conference on Robotics & Biomimetics. IEEE; Bötepage, J., Kjellström, H., Kragic, D., Anticipating many futures: Online human motion prediction and generation for human-robot interaction[C] (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 1-9; Ding, H., Reißig, G., Wijaya, K., Human arm motion modeling and long-Term prediction for safe and efficient human-robot-interaction [C] (2011) 2011 IEEE International Conference on Robotics and Automation. IEEE, pp. 5875-5880; Mackenzie, I.S., (1991) Fitts' Law As A Perforance Model in Human-computer Interaction [D], , Unpublished Doctoral Dissertation, University of Toronto; Todorov, E., Jordan, M.I., Smoothness maximization along a predefined path accurately predicts the speed profiles of complex arm movements[J] (1998) Journal of Neurophysiology, 80 (2), pp. 696-714; Flash, T., Hogan, N., The coordination of arm movements: An experimentally confirmed mathematical model[J] (1985) Journal of Neuroscience, 5 (7), pp. 1688-1703; Abdel-Malek, K., Mi, Z., Yang, J., Optimization-based trajectory planning of the human upper body[J] (2006) Robotica, 24 (6), pp. 683-696; Bratt, M., Smith, C., Christensen, H.I., Minimum jerk based prediction of user actions for a ball catching task[C] (2007) 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 2710-2716; Ohmura, R., Kusano, Y., Suzuki, Y., Pointing gesture prediction using minimum-jerk model in human-robot interaction[C] (2014) Proceedings of the Second International Conference on Human-Agent Interaction, pp. 181-184; Maeda, Y., Hara, T., Arai, T., Human-robot cooperative manipulation with motion estimation[C] (2001) Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, 4, pp. 2240-2245. , Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No. 01CH37180). Ieee; Wang, C., Peng, L., Hou, Z.G., Experimental validation of minimum-jerk principle in physical human-robot interaction[c] (2018) International Conference on Neural Information Processing, pp. 499-509. , Springer, Cham; Seki, H., Tadakuma, S., Minimum jerk control of power assisting robot on human arm behavior characteristic[C] (2004) 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583). IEEE, 1, pp. 722-727; Huang, P., Xu, Y., Liang, B., Global minimum-jerk trajectory planning of space manipulator[J] (2006) International Journal of Control, Automation, and Systems, 4 (4), pp. 405-413; Aloulou, A., Boubaker, O., Minimum jerk-based control for a three dimensional bipedal robot[C] (2011) International Conference on Intelligent Robotics and Applications, pp. 251-262. , Springer, Berlin, Heidelberg; Piazzi, A., Visioli, A., Global minimum-jerk trajectory planning of robot manipulators[J] (2000) IEEE Transactions on Industrial Electronics, 47 (1), pp. 140-149",,,"Beijing Institute of Control Robots and Intelligent Technology (BICRI);Shanghai University of Engineering Science (SUES)","Institute of Electrical and Electronics Engineers Inc.","3rd International Conference on Mechatronics, Robotics and Automation, ICMRA 2020","16 October 2020 through 18 October 2020",,168402,,9781728190327,,,"English","Int. Conf. Mechatronics, Robot. Autom., ICMRA",Conference Paper,"Final","",Scopus,2-s2.0-85104632111
"Rose L., Bazzocchi M.C.F., Nejat G.","57218893621;56735955800;12789569200;","End-to-End Deep Reinforcement Learning for Exoskeleton Control",2020,"Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics","2020-October",,"9283306","4294","4301",,2,"10.1109/SMC42975.2020.9283306","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098873802&doi=10.1109%2fSMC42975.2020.9283306&partnerID=40&md5=f6d6a69ff0494ff3fd54b378150e9c43","University of Toronto, Autonomous Systems and Biomechatronics Laboratory, Department of Mechanical and Industrial Engineering, Canada","Rose, L., University of Toronto, Autonomous Systems and Biomechatronics Laboratory, Department of Mechanical and Industrial Engineering, Canada; Bazzocchi, M.C.F., University of Toronto, Autonomous Systems and Biomechatronics Laboratory, Department of Mechanical and Industrial Engineering, Canada; Nejat, G., University of Toronto, Autonomous Systems and Biomechatronics Laboratory, Department of Mechanical and Industrial Engineering, Canada","Patient-specific control and training on lower body exoskeletons can help improve a user's gait during post-stroke rehabilitation by increasing their amount of participation and motor learning. Traditionally, adaptive control techniques have been used to provide personalization and synchronization with exoskeleton users, but they require predefined dynamics models of the user and exoskeleton. However, these models can be difficult to accurately define due to the complexity of the human-robot interaction. Most recently deep reinforcement learning techniques have shown potential to effectively learn control schemes without the need for system dynamics models. In this paper, we present for the first time an end-to-end model-free deep reinforcement learning method for an exoskeleton that can learn to follow a desired gait pattern, while considering a user's existing gait pattern and being robust to their perturbations and interactions. We demonstrate the effectiveness of our proposed method for user personalization of gait training in simulated experiments. © 2020 IEEE.","deep reinforcement learning; exoskeleton control; lower body exoskeletons; patient-specific control","Exoskeleton (Robotics); Gait analysis; Human robot interaction; Learning systems; Patient rehabilitation; Reinforcement learning; End-to-end models; Patient specific; Personalizations; Post-stroke rehabilitation; Reinforcement learning method; Reinforcement learning techniques; Simulated experiments; System dynamics model; Deep learning",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC; Canada Research Chairs","This research is supported by the EMHSeed Fund, the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Canada Research Chairs Program. All authors aerw ith the Autonomous Systems and Biomechatronics Laboratory of the Department of Mechanical and Industrial Engineering at the University of Toronto (lowell.rose@mail.utoronto.ca; michael.bazzocchi@utoronto.ca; nejat@mie.utoronto.ca).",,,,,,,,,,"Rupal, B.S., Rafique, S., Singla, A., Singla, E., Isaksson, M., Virk, G.S., Lower-limb exoskeletons: Research trends and regulatory guidelines in medical and non-medical applications (2017) Int. J. Adv. Robot. Syst, 14 (6), pp. 1-27. , Nov; Hobbs, B., Artemiadis, P., A review of robot-assisted lower-limb stroke therapy: Unexplored paths and future directions in gait rehabilitation (2020) Front. 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Electron, PP (C); Huang, R., Cheng, H., Qiu, J., Zhang, J., Learning physical human-robot interaction with coupled cooperative primitives for a lower exoskeleton (2019) IEEE Trans. Autom. Sci. Eng, 16 (4), pp. 1-9; Pong, V., Gu, S., Dalal, M., Levine, S., Temporal difference models: Model-free deep rl for model-based control (2018) 6th Int. Conf. Learn. Represent. ICLR 2018-Conf. Track Proc., pp. 1-14; Bingjing, G., Jianhai, H., Xiangpan, L., Lin, Y., Human-robot interactive control based on reinforcement learning for gait rehabilitation training robot (2019) Int. J. Adv. Robot. 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Graph, 36 (4); Anand, A.S., Zhao, G., Roth, H., Seyfarth, A., A deep reinforcement learning based approach towards generating human walking behavior with a neuromuscular model (2020) 2019 IEEE-RAS 19th Int. Conf. Humanoid Robot., pp. 537-543; Gil, C.R., Calvo, H., Sossa, H., Learning an efficient gait cycle of a biped robot based on reinforcement learning and artificial neural networks (2019) Appl. Sci, 9 (3). , Feb; García, J., Shafie, D., Teaching a humanoid robot to walk faster through safe reinforcement learning (2020) Eng. Appl. Artif. 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Rev, 36 (5), pp. 823-841. , Sep; Kingma, D.P., Ba, J.L., Adam: A method for stochastic optimization (2015) 3rd International Conference on Learning Representations, ICLR 2015-Conference Track Proceedings; Brockman, G., (2016) OpenAI Gym; Thelen, D., Seth, A., Anderson, F.C., Delp, S.L., OpenSim models gait 2392 and 2354 documentation SimTK, , https://simtkconfluence.stanford.edu:8443/display/OpenSim/Gait+2392+and+2354+Models, [Accessed: 09-May-2020]; Grabke, E.P., Masani, K., Andrysek, J., Lower limb assistive device design optimization using musculoskeletal modeling: A review (2019) J. Med. Device, 13 (4); Khamar, M., Edrisi, M., Zahiri, M., Human-exoskeleton control simulation, kinetic and kinematic modeling and parameters extraction (2019) MethodsX, 6, pp. 1838-1846; Coll Pujals, D., Simulation of the assistance of an exoskeleton on lower limbs joints using opensim (2017) Polytechnic University of Catalonia; Sherman, M.A., Seth, A., Delp, S.L., Simbody: Multibody dynamics for biomedical research (2011) Procedia IUTAM, 2, pp. 241-261; Rose, L., Bazzocchi, M.C.F., De Souza, C., Vaughan-Graham, J., Patterson, K., Nejat, G., A framework for mapping and controlling exoskeleton gait patterns in both simulation and real-world (2020) Proc. of the 2020 Design of Medical Devices Conf.",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020","11 October 2020 through 14 October 2020",,165855,1062922X,9781728185262,PICYE,,"English","Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.",Conference Paper,"Final","",Scopus,2-s2.0-85098873802
"Gleirscher M., Calinescu R.","22034121600;6507842838;","Safety Controller Synthesis for Collaborative Robots",2020,"Proceedings of the IEEE International Conference on Engineering of Complex Computer Systems, ICECCS","2020-October",,"9376211","83","92",,3,"10.1109/ICECCS51672.2020.00017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099897904&doi=10.1109%2fICECCS51672.2020.00017&partnerID=40&md5=b0c21d1e25e689db919eec4bdb3cef54","University of York, Department of Computer Science, York, United Kingdom","Gleirscher, M., University of York, Department of Computer Science, York, United Kingdom; Calinescu, R., University of York, Department of Computer Science, York, United Kingdom","In human-robot collaboration (HRC), software-based automatic safety controllers (ASCs) are used in various forms (e.g. shutdown mechanisms, emergency brakes, interlocks) to improve operational safety. Complex robotic tasks and increasingly close human-robot interaction pose new challenges to ASC developers and certification authorities. Key among these challenges is the need to assure the correctness of ASCs under reasonably weak assumptions. To address this need, we introduce and evaluate a tool-supported ASC synthesis method for HRC in manufacturing. Our synthesis approach is informed by the manufacturing process, risk analysis, and regulations. A synthesised ASC is formally verified against correctness criteria and selected from a design space of feasible controllers according to a set of optimality criteria. Such an ASC can detect the occurrence of hazards, move the process into a safe state, and, in certain circumstances, return the process to an operational state from which it can resume its original task. © 2020 IEEE.","Controller synthesis; human-robot collaboration; probabilistic model checking; software engineering","Controllers; Manufacture; Risk analysis; Risk assessment; Safety engineering; Certification authorities; Collaborative robots; Correctness criterion; Human-robot collaboration; Manufacturing process; Operational safety; Operational state; Optimality criteria; Social robots",,,,,"Lloyd's Register Foundation, LRF","Acknowledgements: This research was funded by the Lloyds Register Foundation under the Assuring Autonomy International Programme grant CSI:Cobot. We are grateful for many insights into manufacturing robot control from our project partners at the University of Sheffield and our industrial collaborator. We also thank David Parker for his advice in the use of PRISM’s policy synthesis facility.",,,,,,,,,,"Nicolaisen, P., Occupational safety and industrial robots (1985) Robot Safety, pp. 33-48. , Bonney and Yong, Eds. IFS; Jones, R.H., (1986) A Study of Safety and Production Problems and Safety Strategies Associated with Industrial Robot Systems, , Ph.D. dissertation, Imperial College; Santis, A.D., Siciliano, B., Luca, A.D., Bicchi, A., An atlas of physical human-robot interaction (2008) Mechanism and Machine Theory, 43 (3), pp. 253-270; Sugimoto, N., Safety engineering on industrial robots and their draft standards for safety requirements (1977) 7th Int. Symposium on Industrial Robots, pp. 461-470; Alami, R., Albu-Schaeffer, A., Bicchi, A., Bischoff, R., Chatila, R., Safe and dependable physical human-robot interaction in anthropic domains: State of the art and challenges (2006) IEEE/RSJ Int. Conf. Intelligent Robots and Systems; Haddadin, S., Albu-Schäffer, A., Hirzinger, G., Requirements for safe robots: Measurements, analysis and new insights (2009) The Int. Journal of Robotics Research, 28 (11-12), pp. 1507-1527; Wang, X.V., Kemény, Z., Váncza, J., Wang, L., Human-robot collaborative assembly in cyber-physical production: Classification framework and implementation (2017) CIRP Annals, 66 (1), pp. 5-8; Kaiser, L., Schlotzhauer, A., Brandstötter, M., Safety-related risks and opportunities of key design-aspects for industrial human-robot collaboration (2018) LNCS, pp. 95-104. , Springer; Matthias, B., Kock, S., Jerregard, H., Kallman, M., Lundberg, I., Safety of collaborative industrial robots: Certification possibilities for a collaborative assembly robot concept (2011) IEEE Int. Symposium on Assembly and Manufacturing (ISAM); Marvel, J.A., Falco, J., Marstio, I., Characterizing task-based humanrobot collaboration safety in manufacturing (2015) IEEE Tran. On Systems, Man, and Cybernetics: Systems, 45 (2), pp. 260-275; (2011) Robots and Robotic Devices - Safety Requirements for Industrial Robots, , https://www.iso.org/standard/51330.html, ISO 10218, Robotic Industries Association (RIA), Standard, [Online]; Gillespie, R.B., Colgate, J.E., Peshkin, M.A., A general framework for cobot control (2001) IEEE Transactions on Robotics and Automation, 17 (4), pp. 391-401; (2016) Robots and Robotic Devices - Collaborative Robots, , https://www.iso.org/standard/62996.html, ISO/TS 15066, Robotic Industries Association (RIA), Standard, [Online]; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human-robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266; Hayes, B., Scassellati, B., Challenges in shared-environment humanrobot collaboration (2013) Collab. Manipulation Workshop at HRI; Helms, E., Schraft, R.D., Hagele, M., Rob@work: Robot assistant in industrial environments (2002) 11th IEEE Int. Workshop on Robot and Human Interactive Communication; Gleirscher, M., Run-time risk mitigation in automated vehicles: A model for studying preparatory steps (2017) 1st IFM Workshop Formal Verification of Autonomous Vehicles (FVAV), 257 (8). , ser. EPTCS; Gleirscher, M., Calinescu, R., Woodcock, J., (2020) Risk Structures: A Design Algebra for Risk-aware Machines, , Department of Computer Science, University of York, Working paper; Gleirscher, M., (2020) YAP against Perils: User's Manual, , http://gleirscher.de/yap/yap-manual.pdf, Technical University of Munich and University of York, [Online]; Askarpour, M., Mandrioli, D., Rossi, M., Vicentini, F., SAFERHRC: Safety analysis through formal verification in human-robot collaboration (2016) LNCS, pp. 283-295. , Springer; Kwiatkowska, M., Norman, G., Parker, D., Stochastic model checking (2007) Formal Methods for the Design of Comp., Comm. And Soft. Sys.: Performance Evaluation (SFM), 4486, pp. 220-270. , ser. LNCS, M. Bernardo and J. Hillston, Eds. Springer; Orlandini, A., Suriano, M., Cesta, A., Finzi, A., Controller synthesis for safety critical planning (2013) IEEE 25th Int. Conference on Tools with Artificial Intelligence, , IEEE, nov; Cesta, A., Orlandini, A., Bernardi, G., Umbrico, A., Towards a planning-based framework for symbiotic human-robot collaboration (2016) IEEE 21st Int. Conference on Emerging Technologies and Factory Automation (ETFA), , sep; Heinzmann, J., Zelinsky, A., Quantitative safety guarantees for physical human-robot interaction (2003) The Int. Journal of Robotics Research, 22 (7-8), pp. 479-504; Long, P., Chevallereau, C., Chablat, D., Girin, A., An industrial security system for human-robot coexistence (2018) Industrial Robot: An Int. Journal, 45 (2), pp. 220-226; Baier, C., Katoen, J.-P., (2008) Principles of Model Checking; Kwiatkowska, M., Norman, G., Parker, D., PRISM 4.0: Verification of probabilistic real-time systems (2011) 23rd CAV, 6806, pp. 585-591. , ser. LNCS, Springer; Leveson, N.G., (1995) Safeware: System Safety and Computers, , Addison-Wesley; Leveson, N.G., (2012) Engineering A Safer World: Systems Thinking Applied to Safety, , ser. Engineering Systems. MIT P; Gleirscher, M., Foster, S., Nemouchi, Y., Evolution of formal modelbased assurance cases for autonomous robots (2019) 17th Int. Conf. SEFM, , ser. LNCS, 11724. Springer; Foster, S., Gleirscher, M., Calinescu, R., Towards deductive verification of control algorithms for autonomous marine vehicles (2020) 25th Int. Conf. ICECCS, Singapore; Calinescu, R., Weyns, D., Gerasimou, S., Iftikhar, M.U., Habli, I., Kelly, T., Engineering trustworthy self-adaptive software with dynamic assurance cases (2018) IEEE Transactions on Software Engineering, 44 (11), pp. 1039-1069. , Nov; Gerasimou, S., Calinescu, R., Tamburrelli, G., Synthesis of probabilistic models for quality-of-service software engineering (2018) Automated Software Engineering, 25 (4), pp. 785-831; Calinescu, R., Autili, M., Cámara, J., Di Marco, A., Gerasimou, S., Inverardi, P., Perucci, A., Kwiatkowska, M., Synthesis and verification of self-aware computing systems (2017) Self-Aware Computing Systems, pp. 337-373. , Springer",,"Li Y.Liew A.",,"Institute of Electrical and Electronics Engineers Inc.","25th International Conference on Engineering of Complex Computer Systems, ICECCS 2020","4 March 2021 through 6 March 2021",,168002,,9781728185583,,,"English","Proc IEEE Int Conf Eng Complex Comput Syst ICECCS",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85099897904
"Mohammed S.N., Hassan A.K.A.","57213186106;55293743800;","Speech emotion recognition using MELBP variants of spectrogram image",2020,"International Journal of Intelligent Engineering and Systems","13","5",,"257","266",,,"10.22266/ijies2020.1031.23","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090386623&doi=10.22266%2fijies2020.1031.23&partnerID=40&md5=7ddbb1aeb0afbd892f84c1b6098bd16d","Computer Sciences Department, University of Technology, Baghdad, Iraq; Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq","Mohammed, S.N., Computer Sciences Department, University of Technology, Baghdad, Iraq, Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq; Hassan, A.K.A., Computer Sciences Department, University of Technology, Baghdad, Iraq","Speech emotion recognition finds many applications in the daily life like conversational agents, human robot interaction, call centres etc. However; the task of emotion recognition from speech signal is not trivial due to the difficulty in determining the effective feature set that can recognize the emotion conveyed within the signal in an accurate manner. Image processing techniques are exploited in this paper to solve speech emotion recognition problem. After converting the signal into 2D spectrogram image representation, four forms of Extended Local Binary Pattern (ELBP) are generated to serve as a source for feature extraction stage. The histograms of multiple blocks from ELBP variants are computed and fed to Deep Belief Network (DBN) for classification purpose. Different tests were performed using Surrey Audio-Visual Expressed Emotion (SAVEE) database and the achieved results showed that when using combined vectors of MELBP, the system gives the best accuracy which is 72.14%. The achieved result outperforms state-of-the-art results on the same database. © 2020, Intelligent Network and Systems Society.","Deep belief network (DBN); Multi-block extended local binary pattern (MELBP); Short term fourier transform (STFT); Spectrogram image; Speech emotion",,,,,,,,,,,,,,,,,"Barros, P., Wermter, S., Developing Crossmodal Expression Recognition Based on A Deep Neural Model (2016) Adaptive Behavior, 24 (5), pp. 373-396; Hajarolasvadi, N., Demirel, H., 3D CNNBased Speech Emotion Recognition Using K-Means Clustering and Spectrograms (2019) Entropy, 21 (479), pp. 1-17; Mustaqeem, M., Kwon, S., A CNN-Assisted Enhanced Audio Signal Processing for Speech Emotion Recognition (2020) Sensors, 20 (183), pp. 1-15; Sondhi, S., Khan, M., Vijay, R., Salhan, A., Chouhan, S., Acoustic Analysis of Speech under Stress (2015) International Journal of Bioinformatics Research and Applications, 11 (5), pp. 417-432; Wang, K., An, N., Li, B., Zhang, Y., Li, L., Speech Emotion Recognition Using Fourier Parameters (2015) IEEE Transactions on Affective Computing, 6 (1), pp. 69-75; Bitouk, D., Verma, R., Nenkova, A., ClassLevel Spectral Features for Emotion Recognition (2010) Speech Communication, 52, pp. 613-625; Mohammed, S., Abdul Hassan, A., A Survey on Emotion Recognition for Human Robot Interaction (2019) Journal of Computing and Information Technology, 27 (4), p. 4768; Wang, K., The Feature Extraction Based on Texture Image Information for Emotion Sensing in Speech (2014) Sensors, 14 (9), p. 1669216714; Papakostas, M., Siantikos, G., Giannakopoulos, T., Spyrou, E., Sgouropoulos, D., Recognizing Emotional States Using Speech Information (2017) Advances in Experimental Medicine and Biology, 989, pp. 155-164; Pikramenos, G., Smyrnis, G., Vernikos, I., Konidaris, T., Spyrou, E., Perantonis, S., Sentiment Analysis from Sound Spectrograms via Soft BoVW and Temporal Structure Modelling (2020) Proc. of the 9th International Conf. on Pattern Recognition Applications and Methods, pp. 361-369. , Valletta, Malta; Mohammed, S., Jabir, A., Abbas, Z., SpinImage Descriptors for Text-Independent Speaker Recognition (2019) Emerging Trends in Intelligent Computing and Informatics. IRICT 2019. Advances in Intelligent Systems and Computing, 1073. , Springer, Cham; Makandar, A., Halalli, B., Image Enhancement Techniques Using Highpass and Lowpass Filters (2015) International Journal of Computer Applications, 109 (14), pp. 12-15; Alías, F., Socoro, J., Sevillano, X., A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sound (2016) Applied Sciences, 6 (143), pp. 1-44; Bondarenko, A., Borisov, A., Research on the Classification Ability of Deep Belief Networks on Small and Medium Datasets (2013) Information Technology and Management Science, 16, pp. 60-65; http://kahlan.eps.surrey.ac.uk/savee/; Hassan, A., Mohammed, S., A Novel Facial Emotion Recognition Scheme Based on Graph Mining (2019) Defence Technology, , http://dx.doi.org/10.1016Zj.dt.2019.12.006; Mohammed, S., Alkinani, F., Hassan, Y., Automatic Computer Aided Diagnostic for COVID-19 Based on Chest X-Ray Image and Particle Swarm Intelligence (2020) International Journal of Intelligent Engineering and Systems, 13 (5), pp. 63-73","Mohammed, S.N.; Computer Sciences Department, Iraq; email: suhailan.mo@sc.uobaghdad.edu.iq",,,"Intelligent Network and Systems Society",,,,,2185310X,,,,"English","Int. J. Intelligent Eng. Syst.",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85090386623
"Li F., Chang D., Liu Y., Cui J., Feng S., Huang N., Chen C.-H., Sourina O.","57196404325;55987550100;36809950300;56479735300;56393590300;57219454003;25921980900;57204345367;","Evaluation of humanoid robot design based on global eye-tracking metrics",2020,"Advances in Transdisciplinary Engineering","12",,,"241","249",,,"10.3233/ATDE200082","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092779875&doi=10.3233%2fATDE200082&partnerID=40&md5=7ecd00d54b24e0fe2bc4e1c4973a0443","Fraunhofer Singapore, Singapore; School of Design, Shanghai Jiao Tong University, Shanghai, China; Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates; Huazhong University of Science and Technology, China; School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore","Li, F., Fraunhofer Singapore, Singapore; Chang, D., School of Design, Shanghai Jiao Tong University, Shanghai, China; Liu, Y., Fraunhofer Singapore, Singapore; Cui, J., Fraunhofer Singapore, Singapore; Feng, S., Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates; Huang, N., Huazhong University of Science and Technology, China; Chen, C.-H., School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore; Sourina, O., Fraunhofer Singapore, Singapore","The first impression of robot appearance normally affects the interaction with physical robots. Hence, it is critically important to evaluate the humanoid robot appearance design. This study towards evaluating humanoid robot design based on global eye-tracking metrics. Two methods are selected to extract global eye-tracking metrics, including bin-analysis-based entropy and approximate entropy. The data are collected from an eye-tracking experiment, where 20 participants evaluate 12 humanoid robot appearance designs with their eye movements recorded. The humanoid robots are evaluated from five aspects, namely smartness, friendliness, pleasure, arousal, and dominance. The results show that the entropy of fixation duration and velocity, approximate entropy of saccades amplitude are positively associated with the subjective feelings induced by robot appearance. These findings can aid in better understanding the first impression of human-robot interaction and enable the eye-tracking-based evaluation of humanoid robot design. By combining the theory of design and bio-signals analysis, the study contributes to the field of Transdisciplinary Engineering. © 2020 The authors and IOS Press.","Evaluation; Eye-tracking; Global metrics; Humanoid robot design","Anthropomorphic robots; Entropy; Eye movements; Human robot interaction; Machine design; Approximate entropy; Biosignals; First impressions; Fixation duration; Humanoid robot; Physical robots; Robot appearance; Eye tracking",,,,,"National Research Foundation Singapore, NRF","The authors appreciate the efforts of Mr. Lin Hei Tang in collecting data and supports of all participants of the experiment. This research is supported by the National Research Foundation, Singapore under its International Research Centres in Singapore Funding Initiative. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.",,,,,,,,,,"Creusen, M.E., Schoormans, J.P., The different roles of product appearance in consumer choice (2005) Journal of product innovation management, 22 (1), pp. 63-81; Chi, C.-F., Dewi, R.S., Samali, P., Hsieh, D.-Y., Preference ranking test for different icon design formats for smart living room and bathroom functions (2019) Applied ergonomics, 81, p. 102891; Mugge, R., Schoormans, J.P., Product design and apparent usability. The influence of novelty in product appearance (2012) Applied ergonomics, 43 (6), pp. 1081-1088; Guo, F., Ding, Y., Liu, W., Liu, C., Zhang, X., Can eye-tracking data be measured to assess product design?: Visual attention mechanism should be considered (2016) International Journal of Industrial Ergonomics, 53, pp. 229-235; Law, E.L.-C., Van Schaik, P., Modelling user experience-An agenda for research and practice (2010) Interacting with computers, 22 (5), pp. 313-322; Chew, L.H., Teo, J., Mountstephens, J., Aesthetic preference recognition of 3D shapes using EEG (2016) Cognitive neurodynamics, 10 (2), pp. 165-173; Kitabata, M., Niigaki, Y., Horita, Y., Consideration of Relationship between Human Preference and Pulse Wave Derived from Brain Activity (2019) IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 102 (9), pp. 1250-1253; Qu, Q.-X., Guo, F., Can eye movements be effectively measured to assess product design?: Gender differences should be considered (2019) International Journal of Industrial Ergonomics, 72, pp. 281-289; Glaholt, M.G., (2014) Eye tracking in the cockpit: a review of the relationships between eye movements and the aviators cognitive state, , Defence Research and Development Toronto (Canada); Kukkonen, S., Exploring eye tracking in design evaluation (2005) Joining Forces, pp. 119-126; Qu, Q.-X., Zhang, L., Chao, W.-Y., Duffy, V., User experience design based on eye-tracking technology: a case study on smartphone APPs, in Advances in applied digital human modeling and simulation (2017) Advances in Applied Digital Human Modeling and Simulation, pp. 303-315. , V.G. Duffy (ed) Springer International Publishing, Cham; Fu, B., Noy, N.F., Storey, M.-A., Eye tracking the user experience-an evaluation of ontology visualization techniques (2017) Semantic Web, 8 (1), pp. 23-41; Vuori, T., Olkkonen, M., Pölönen, M., Siren, A., Häkkinen, J., Can eye movements be quantitatively applied to image quality studies? (2004) Proceedings of the third Nordic conference on Human-computer interaction, pp. 335-338. , R. Raisamo (ed) Association for Computing Machinery, New York; Liu, Y., Li, F., Tang, L.H., Lan, Z., Cui, J., Sourina, O., Chen, C.-H., Detection of Humanoid Robot Design Preferences Using EEG and Eye Tracker (2019) 2019 International Conference on Cyberworlds (CW), pp. 219-224. , IEEE, Piscataway; Olsen, A., The Tobii I-VT fixation filter (2012) Tobii Technology; Mehrabian, A., Framework for a comprehensive description and measurement of emotional states (1995) Genetic, social, and general psychology monographs, 121 (3), pp. 339-361; Hosseini, S.M.F., Hilliger, S., Barnes, J., Jeon, M., Park, C.H., Howard, A.M., Love at first sight: Mere exposure to robot appearance leaves impressions similar to interactions with physical robots (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 615-620. , IEEE, Piscataway; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A metaanalysis of factors affecting trust in human-robot interaction (2011) Human factors, 53 (5), pp. 517-527; Lee, H.R., Sabanovic, S., Culturally variable preferences for robot design and use in South Korea, Turkey, and the United States (2014) Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction, pp. 17-24. , ACM, New York; Farnsworth, B., (2018) Eye Tracking: The Complete Pocket Guide, , https://imotions.com/blog/eyetracking/, accessed June, 1 2020; Harezlak, K., Augustyn, D.R., Kasprowski, P., An Analysis of Entropy-Based Eye Movement Events Detection (2019) Entropy, 21 (2), p. 107; Wang, X., Berg, R., McGeorge, N., LaVergne, D., Benda, N., Bisantz, A., Assessing Interaction Strategies for Health IT: An Entropy Based Approach (2018) Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care, pp. 82-86. , SAGE Publications Sage India: New Delhi, India; Yasui, Y., Tanaka, J., Kakudo, M., Tanaka, M., Relationship between preference and gaze in modified food using eye tracker (2019) Journal of prosthodontic research, 63 (2), pp. 210-215; Li, F., Chen, C.-H., Xu, G., Khoo, L.P., Liu, Y., Proactive mental fatigue detection of traffic control operators using bagged trees and gaze-bin analysis (2019) Advanced Engineering Informatics, 42, p. 100987; Kaufman, L., Richards, W., Spontaneous fixation tendencies for visual forms (1969) Perception & Psychophysics, 5 (2), pp. 85-88; Hooge, I.T.C., Erkelens, C.J., Control of fixation duration in a simple search task (1996) Perception & Psychophysics, 58 (7), pp. 969-976; Blow, M., Dautenhahn, K., Appleby, A., Nehaniv, C.L., Lee, D., The art of designing robot faces: Dimensions for human-robot interaction (2006) HRI '06: Proceedings of the 1st ACM SIGCHI/SIGART conference on Human-robot interaction, pp. 331-332; Ho, C.-H., Lu, Y.-N., Can pupil size be measured to assess design products? (2014) International Journal of Industrial Ergonomics, 44 (3), pp. 436-441","Li, F.; Fraunhofer SingaporeSingapore; email: FLI011@e.ntu.edu.sg","Pokojski J.Gil M.Newnes L.Stjepandic J.Wognum N.","Grupa ZPR;International Society of Transdisciplinary Engineering (ISTE);IOS Press;PROSTEP AG;Warsaw University of Technology","IOS Press BV","27th ISTE International Conference on Transdisciplinary Engineering, TE 2020","1 July 2020 through 10 July 2020",,163625,,9781614994398,,,"English","Adv. Transdiscipl. Eng.",Conference Paper,"Final","All Open Access, Gold",Scopus,2-s2.0-85092779875
"Perez J., Aguilar J., Dapena E.","57222704762;55434810600;6602301492;","MIHR: A Human-robot interaction model",2020,"IEEE Latin America Transactions","18","9","9381793","1521","1529",,,"10.1109/TLA.2020.9381793","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103431079&doi=10.1109%2fTLA.2020.9381793&partnerID=40&md5=e5db4bea40966cf9e36740b4b94fa9b0","LaSDAI, Universidad de Los Andes, Venezuela; CEMISID, Universidad de Los Andes, Venezuela","Perez, J., LaSDAI, Universidad de Los Andes, Venezuela; Aguilar, J., CEMISID, Universidad de Los Andes, Venezuela; Dapena, E., LaSDAI, Universidad de Los Andes, Venezuela","The interactions between people and social robots have generated positive effects on people of different ages in diverse contexts. A model of the interaction process is important to understand the person who interacts, in order to manage the internal dynamics of interaction in the social robots. There are models that describe the interaction between humans and machines, but they don't integrate the three most important elements to be considered during the interactions by the social robots: the modalities of human communication, the capacity of adaptation, and the expression of emotions. In this paper, a review of the interaction models between people and social robots is made, in order to analyze what has been done about these three important elements of the interaction. Then, it is proposed a Human-Robot Interaction Model (MIHR) based on a Human-Human Interaction Model (MIHH) previously developed, which integrates the main elements to be considered during the interactions by the social robots. © 2003-2012 IEEE.","Human-Robot Interaction; ROBOT; Social Robot; Social Robotics","Man machine systems; Different ages; Human communications; Human-human interactions; Interaction model; Interaction process; Internal dynamics; Social robots",,,,,,,,,,,,,,,,"Ribeiro, T., Pereira, A., Di Tullio, E., Paiva, A., The sera ecosystem: Socially expressive robotics architecture for autonomous human-robot interaction (2016) Proc. Symposium on Enabling Computing Research in Socially Intelligent HumanRobot Interaction, pp. 155-163; Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: A review (2017) Journal of HRI, 6 (1), pp. 25-63; Lasota, P., Fong, T., Shah, J., A survey of methods for safe human-robot interaction (2017) Foundations and Trends in Robotics, 5 (4), pp. 261-349; Siregar, R., Syahputra, R., Mustar, M., Human-robot interaction based GUI (2017) Journal of Electrical Technology UMY, 1 (1), pp. 10-19; Briggs, G., Scheutz, M., The pragmatic social robot: Toward socially-sensitive utterance generation in human-robot interactions (2016) Proc. AAAI Fall Symposium Series: Artificial Intelligence for Human-Robot Interaction, pp. 12-15; Ghazali, A., Ham, J., Barakova, E., Markopoulos, P., Effects of robot facial characteristics and gender in persuasive human-robot interaction (2018) Frontiers in Robotics and AI, 5 (73), pp. 1-16; Correia, F., Mascarenhas, S., Prada, R., Melo, F., Paiva, A., Group-based emotions in teams of humans and robots (2018) Proc. International Conference on Human-Robot Interaction, pp. 261-269; Luna, K., Palacios, E., Marin, A., A fuzzy speed controller for a guide robot using an HRI approach (2018) IEEE Latin America Transactions, 16 (8), pp. 2102-2107; Andreasson, R., Alenljung, B., Billing, E., Lowe, R., Affective touch in human-robot interaction: Conveying emotion to the nao robot (2018) Journal of Social Robotics, 10 (4), pp. 473-491; Zafar, Z., Salazar, D., Al-Darraji, S., Urukalo, D., Berns, K., Rodić, A., Human robot interaction using dynamic hand gestures (2017) Proc. International Conference on Robotics, pp. 649-656; Graaf, M., An ethical evaluation of human-robot relationships (2016) Journal of Social Robotics, 8 (4), pp. 589-598; Pérez, J., Aguilar, J., Dapena, E., MIHH: Un Modelo de Interacción Humano-Humano (2018) Revista Venezolana de Computación, 5 (1), pp. 10-19; Scassellati, B., Improving social skills in children with ASD using a long-term, in-home social robot (2018) Science Robotics, 3 (21), pp. 1-9; Pérez, J., Castro, J., LRS1: un robot social de bajo costo para la asignatura Programación 1 (2018) Revista Colombiana de Tecnologías de Avanzada, 32 (2), pp. 68-77; Noguchi, Y., Kamide, H., Tanaka, F., Effects on the self-disclosure of elderly people by using a robot which intermediates remote communication (2018) Proc. 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 612-617; Dumas, B., Lalanne, D., Oviatt, S., Multimodal interfaces: A survey of principles, models and frameworks (2009) Human Machine Interaction, pp. 3-26. , Berlin; Vinciarelli, A., Open challenges in modelling, analysis and synthesis of human behaviour in human-human and human-machine interactions (2015) Cognitive Computation, 7 (4), pp. 397-413; Hussain, J., Model-based adaptive user interface based on context and user experience evaluation (2018) Journal on Multimodal User Interfaces, 12 (1), pp. 1-16; Martins, G., Santos, L., Dias, J., User-adaptive interaction in social robots: A survey focusing on non-physical interaction (2018) International Journal of Social Robotics, pp. 1-21; Hellström, T., Bensch, S., Understandable robots. Paladyn (2018) Journal of Behavioral Robotics, 9 (1), pp. 110-123; Gil, A., Aguilar, J., Puerto, E., Dapena, E., Emergence analysis in a multi-robot system (2018) Proc. XLIV Latin American Computer Conference (CLEI), pp. 662-669; Gil, A., Aguilar, J., Dapena, E., Rivas, R., A control Architecture for Robot Swarms (AMEB) (2019) Journal Cybernetics and Systems, 50 (3), pp. 300-322; Song, S., Yamada, S., Expressing emotions through color, sound, and vibration with an appearance-constrained social robot (2017) Proc. International Conference on Human-Robot Interaction, pp. 2-11; Brinck, I., Balkenius, C., Johansson, B., Making place for social norms in the design of human-robot interaction. what social robots can and should do (2016) Proc. Robophilosophy, pp. 303-312; Pérula, R., Bioinspired decision-making for a socially interactive robot (2018) Cognitive Systems Research, pp. 1-40; Herrera, D., Roberti, F., Toibero, M., Carelli, R., Human-Robot Interaction: Legible behavior rules in passing and crossing events (2016) IEEE Latin America Transactions, 14 (6), pp. 2644-2650; Tsiakas, K., Dagioglou, M., Karkaletsis, V., Makedon, F., Adaptive robot assisted therapy using interactive reinforcement learning (2016) Pro. International Conference on Social Robotics, pp. 11-21; Strohkorb, S., Establishing sustained, supportive human-robot relationships: Building blocks and open challenges (2016) Proc. Spring Symposium on Enabling Computing Research in Socially Intelligent Human-Robot Interaction, pp. 179-182; Araujo, A., Pérez, J., Rodriguez, W., Aplicación de una Red Neuronal Convolucional para el Reconocimiento de Personas a Través de la Voz (2018) Proc. Sexta Conferencia Nacional de Computación, Informática y Sistemas, pp. 77-81; Martins, G., Ferreira, P., Santos, L., Dias, J., A context-aware adaptability model for service robots (2016) Proc. Workshop on Autonomous Mobile Service Robots, pp. 1-7; Nichola, L., Erin, W., Heather, P., Effects of voice-adaptation and social dialogue on perceptions of a robotic learning companion (2016) Proc. 11th International Conf. On Human-Robot Interaction, pp. 255-262; Shen, Q., Dautenhahn, K., Saunders, J., Kose, H., Can real-time, adaptive human-robot motor coordination improve humans overall perception of a robot? 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EChallenges e-2015 Conference, pp. 1-9; Karami, A., Adaptive artificial companions learning from users feedback (2016) Adaptive Behavior, 24 (2), pp. 69-86; Rukavina, S., Affective computing and the impact of gender and age (2016) PloS One, 11 (3), pp. 1-20; Gil, A., Aguilar, J., Rivas, R., Dapena, E., Behavioral module in a control architecture for multi-robots (2016) Revista Ingeniería Al Día, 2 (1), pp. 40-57; Morán, N., Pérez, J., Rodríguez, W., Reconocimiento de Estados Emocionales de Personas Mediante la Voz Utilizando Algoritmos de Aprendizaje de Máquina (2018) Revista Venezolana de Computación, 5 (2), pp. 41-52; Bandhakavi, A., Wiratunga, N., Massie, S., Padmanabhan, D., Lexicon generation for emotion detection from text (2017) IEEE Intelligent Systems, 32 (1), pp. 102-108; Campos, V., Jou, B., Giro, X., From pixels to sentiment: Fine-tuning cnns for visual sentiment prediction (2017) Image and Vision Computing, 65, pp. 15-22; Cordero, J., Aguilar, J., Aguilar, K., Chávez, D., Puerto, E., Recognition of the driving style in vehicle drivers (2020) Sensors, 20 (9); Aguilar, J., Aguilar, K., Cordero, J., Chávez, D., Puerto, E., Different intelligent approaches for modeling the style of car driving (2017) Proc. 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(eds), Cham: Springer; Dapena, E., Pérez, J., Rivas, R., Guijarro, A., Rostro Genérico para Máquinas que Interactúan con Personas (2016) Revista Científica UNET, 28 (2), pp. 121-130; Kitchenham, B., Linkman, S., Law, D., DESMET: A methodology for evaluating software engineering methods and tools (1997) Computing & Control Engineering Journal, 8 (3), pp. 120-126",,,,"IEEE Computer Society",,,,,15480992,,,,"Spanish","IEEE. Lat. Am. Trans.",Article,"Final","",Scopus,2-s2.0-85103431079
"Lin X., Mahmud S., Roman S., Shaker A., Law Z., Lin M., Kim J.-H.","57215601643;57208869422;57220092645;57204178053;57220087316;57220092684;55808026200;","Design of A Novel Transformable Centaur Robot with Multilateral Control Interface for Search and Rescue Missions",2020,"Proceedings - 5th International Conference on Automation, Control and Robotics Engineering, CACRE 2020",,,"9230311","200","205",,,"10.1109/CACRE50138.2020.9230311","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096857476&doi=10.1109%2fCACRE50138.2020.9230311&partnerID=40&md5=030664cb520d9f87241f0f2bdb11d3ce","Kent State University, Computer Science, Kent, OH, United States; High School for Math Science Engineering, New York, United States","Lin, X., Kent State University, Computer Science, Kent, OH, United States; Mahmud, S., Kent State University, Computer Science, Kent, OH, United States; Roman, S., Kent State University, Computer Science, Kent, OH, United States; Shaker, A., Kent State University, Computer Science, Kent, OH, United States; Law, Z., Kent State University, Computer Science, Kent, OH, United States; Lin, M., High School for Math Science Engineering, New York, United States; Kim, J.-H., Kent State University, Computer Science, Kent, OH, United States","Increasing occurrences of natural and man-made disasters have driven the demand for search and rescue (SR) robots. However, state-of-the-art SR robots serve as specialpurpose machines with limited use cases. This is in part due to a lack of human-centered designs, particularly in multilateral control and human-robot interactions. Thus, we propose TeleBot-R2, a novel transformable centaur-robot. This robot extends our work presented at the 2018 World Robot Summit, a hybrid humanoid (immersive telepresence) robot with a multilateral control system. Our new version introduces a dual flipper caterpillar track base with an enhanced mechanically dynamic humanoid upper body. This transformer robot can contract into multiple configurations that change it's support polygon and means of locomotion - allowing it to navigate more efficiently through different terrains. Operational awareness is heightened through a VR immersive control interface that interacts with an AI-assisted multilateral control system. This paper presents our mechanical design, control architecture, immersive interfaces, and AI-assistant. © 2020 IEEE.","Multilateral Control; Virtual Reality","Agricultural robots; Control systems; Machine design; Man machine systems; Robotics; Visual communication; Control architecture; Control interfaces; Different terrains; Human-centered designs; Multilateral control; Multiple configurations; Natural and man-made disasters; Special-purpose machines; Social robots",,,,,,,,,,,,,,,,"Davids, A., Urban search and rescue robots: From tragedy to technology (2002) IEEE Intelligent Systems, 17 (2), pp. 81-83; Faisal, F., Hossain, S.A., Doormor: A functional prototype of a manual search and rescue robot (2019) Intl. Conference on Advances in Science, Engineering and Robotics Technology, pp. 1-6; Kohlbrecher, D.K.P.M.K.D.A.S.S., Rose, C., Von Stryk, O., Robocup rescue 2017 team description paper hector darmstadt (2017) ROBOCUP RESCUE 2017 TDP COLLECTION, pp. 1-5; Zhao, J., Gao, J., Zhao, F., Liu, Y., A search-and-rescue robot system for remotely sensing the underground coal mine environment (2017) Sensors, 17 (10), p. 2426. , Oct; Kamegawa, T., Akiyama, T., Sakai, S., Fujii, K., Une, K., Ou, E., Matsumura, Y., Gofuku, A., Development of a separable search-and-rescue robot composed of a mobile robot and a snake robot (2020) Advanced Robotics, 34 (2), pp. 132-139; Cardenas, I.S., Kim, J., Design of a semi-humanoid telepresence robot for plant disaster response and prevention (2019) IEEE International Conference on Intelligent Robots and Systems, pp. 2748-2753; Garage, W., Ros.org-powering the World's Robots, , https://www.ros.org/; Hess, W., Kohler, D., Rapp, H., Andor, D., Real-time loop closure in 2d lidar slam (2016) 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1271-1278; Sucan, S.C.I., Move It!, , http://moveit.ros.org; Cardenas, I.S., Kim, J., Benitez, M., Vitullo, K.A., Park, M., Chen, C., Ohrn-McDaniels, L., Telesuit: Design and implementation of an immersive user-centric telepresence control suit Int. Symposium on Wearable Computers, UbiComp 2019, London, UK, pp. 261-266; Labbe, M., Michaud, F., Rtab Map, , http://wiki.ros.org/rtabmapros; Redmon, J., Farhadi, A., (2018) Yolov3-an Incremental Improvement, , arXiv; https://colab.research.google.com; Maruyama, Y., Kato, S., Azumi, T., Exploring the performance of ros2 (2016) Proceedings of the 13th International Conference on Embedded Software, Ser. EMSOFT '16, , New York, NY, USA",,"Zhang F.Liu L.","Georgia Institute of Technology;IEEE Robotics and Automation Society (RAS);Sichuan University","Institute of Electrical and Electronics Engineers Inc.","5th International Conference on Automation, Control and Robotics Engineering, CACRE 2020","19 September 2020 through 20 September 2020",,164350,,9781728198880,,,"English","Proc. - Int. Conf. Autom., Control Robot. Eng., CACRE",Conference Paper,"Final","",Scopus,2-s2.0-85096857476
"Martín F., Lera F.J.R., Ginés J., Matellán V.","57199976970;57218835107;57195299911;6507987964;","Evolution of a cognitive architecture for social robots: Integrating behaviors and symbolic knowledge",2020,"Applied Sciences (Switzerland)","10","17","6067","","",,2,"10.3390/app10176067","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090390383&doi=10.3390%2fapp10176067&partnerID=40&md5=d37504c42eadd4f87b9a373101b29a87","Intelligent Robotics Lab, Rey Juan Carlos University, Móstoles, 28943, Spain; Escuela de Ingenierías Industrial e Informática, University of León, León, 24007, Spain; Escuela Internacional de Doctorado, Rey Juan Carlos University, Móstoles, 28933, Spain; Supercomputación Castilla y León, SCAYLE, León, 24007, Spain","Martín, F., Intelligent Robotics Lab, Rey Juan Carlos University, Móstoles, 28943, Spain; Lera, F.J.R., Escuela de Ingenierías Industrial e Informática, University of León, León, 24007, Spain; Ginés, J., Escuela Internacional de Doctorado, Rey Juan Carlos University, Móstoles, 28933, Spain; Matellán, V., Supercomputación Castilla y León, SCAYLE, León, 24007, Spain","This paper presents the evolution of a robotic architecture intended for controlling autonomous social robots. The first instance of this architecture was originally designed according to behavior-based principles. The building blocks of this architecture were behaviors designed as a finite state machine and organized in an ethological inspired way. However, the need of managing explicit symbolic knowledge in human-robot interaction required the integration of planning capabilities into the architecture and a symbolic representation of the environment and the internal state of the robot. A major contribution of this paper is the description of the working memory that integrates these two approaches. This working memory has been implemented as a distributed graph. Another contribution is the use of behavior trees instead of state machine for implementing the behavior-based part of the architecture. This late version of the architecture has been tested in robotic competitions (RoboCup or European Robotics League, among others), whose performance is also discussed in this paper. © 2020 by the authors.","Knowledge representation; Planning; Reactivity; Robotic architectures",,,,,,"732410; RTI2018-100683-B-I00","This work has been partially funded by Ministerio de Ciencia, Innovaci?n y Universidades through grant RTI2018-100683-B-I00 and by MROS project, an ITP from RobMoSys that receives funding from the European Horizon 2020 research and innovation program under grant agreement No 732410.",,,,,,,,,,"Coste-Maniere, E., Simmons, R., Architecture, the backbone of robotic systems (2000) Proceedings 2000 ICRA. Millennium Conference, Proceedings of the IEEE International Conference on Robotics and Automation (Cat. No.00CH37065). 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Rev, 53, pp. 17-94; MacKenzie, D.C., Arkin, R.C., Cameron, J.M., Multiagent mission specification and execution (1997) Robot Colonies, pp. 29-52. , Springer: Berlin/Heidelberg, Germany; Montemerlo, M., Roy, N., Thrun, S., Perspectives on standardization in mobile robot programming: The Carnegie Mellon navigation (CARMEN) toolkit (2003) Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No. 03CH37453, 3, pp. 2436-2441. , 2003; IEEE: Piscataway, NJ, USA, 27-31 October; Kramer, J., Scheutz, M., Development environments for autonomous mobile robots: A survey (2007) Auton. Robot, 22, pp. 101-132; Ahmad, A., Babar, M.A., Software architectures for robotic systems: A systematic mapping study (2016) J. Syst. Softw, 122, pp. 16-39; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., ROS: An open-source Robot Operating System (2009) Proceedings of the ICRA Workshop on Open Source Software, 3, p. 5. , Kobe, Japan, 12-17 May; Agüero, C.E., Cañas, J.M., Martin, F., Perdices, E., Behavior-based iterative component architecture for soccer applications with the nao humano (2010) Proceedings of the 5th Workshop on Humanoids Soccer Robots, 127. , Nashville, TN, USA, 7 December; Cañas Plaza, J., Jesus, R.A., Carlos, A., Martín, F., Jde-neoc: Component oriented software architecture for robotics (2007) J. Phys. Agents, 1, pp. 1-6; Rico, F.M., Lera, F.J.R., Olivera, V.M., Myrabot+: A feasible robotic system for interaction challenges (2014) Proceedings of the 2014 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 273-278. , IEEE: Piscataway, NJ, USA, Espinho, Portugal, 14-15 May 2014; Martín, F., Aguero, C.E., Canas, J.M., A Simple, Efficient, and Scalable Behavior-Based Architecture for Robotic Applications (2016) Robot 2015: Second Iberian Robotics Conference, pp. 611-622. , Springer: Berlin/Heidelberg, Germany, Lisbon, Portugal, 19-21 November 2015; Rodríguez Lera, F., Matellán, V., Hybrid architecture for human-robot interaction: Updating the classical three-layer solutio (2013) Proceedings of the Actas del XV Workshop en Agentes Físicos, , Leon, Spain, 12-13 June; Rodríguez, F.J., Matellán, V., Conde, M.A., Rico, F.M., A motivational architecture to create more human-acceptable assistive robots for robotics competitions (2016) Proceedings of the 2016 International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 144-149. , IEEE: Piscataway, NJ, USA, Braganca, Portugal, 4-6 May 2016; Rodríguez-Lera, F.J., Matellán-Olivera, V., Conde-González, M.á., Martín-Rico, F., HiMoP: A three-component architecture to create more human-acceptable social-assistive robots (2018) Cogn. Process, 19, pp. 233-244; Martín-Rico, F., Ginés, J., Vargas, D., Rodríguez-Lera, F.J., Matellán-Olivera, V., Planning-Centered Architecture for RoboCup SSPL@ Home (2018) Workshop of Physical Agents, pp. 287-302. , Springer: Berlin/Heidelberg, Germany; Koenig, A., Crochon, E., Tram: A Blackboard Architecture for Autonomous Robots (1988) Proceedings of the 1st International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 1, pp. 590-597. , IEA/AIE '88; Association for Computing Machinery: New York, NY, USA, Melbourne, Australia, 6-8 June 1988; Bustos, P., Manso, L., Bandera, A., Bandera, J., García-Varea, I., Martínez-Gómez, J., The CORTEX cognitive robotics architecture: Use cases (2019) Cogn. Syst. Res, 55, pp. 107-123; Paulius, D., Sun, Y., A Survey of Knowledge Representation in Service Robotics (2019) Robot. Auton. Syst, 118, pp. 13-30; Marzinotto, A., Colledanchise, M., Smith, C., Ögren, P., Towards a unified behavior trees framework for robot control (2014) Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 5420-5427. , Hong Kong, China, 31 May-5 June; Colledanchise, M., Ögren, P., How Behavior Trees Modularize Hybrid Control Systems and Generalize Sequential Behavior Compositions, the Subsumption Architecture, and Decision Trees (2017) IEEE Trans. Robot, 33, pp. 372-389; Colledanchise, M., Almeida, D., Ögren, P., Towards Blended Reactive Planning and Acting using Behavior Trees (2019) Proceedings of the 2019 International Conference on Robotics and Automation (ICRA, pp. 8839-8845. , Montreal, QC, USA, 20-24 May; Paredes, R., Martin, F., Matellán, V., Agüero, C.E., (2007) MBA: A Modular Hierarchical Behavior-Based Architecture, , https://www.researchgate.net/publication/233792449_MBA_A_Modular_Hierarchical_Behavior-Based_Architecture, accessed on 1 September 2020; Ghallab, M., Howe, A., Knoblock, C., Mcdermott, D., Ram, A., Veloso, M., Weld, D., Wilkins, D., (1998) PDDL-The Planning Domain Definition Language, , https://www.csee.umbc.edu/courses/671/fall12/hw/hw6/pddl1.2.pdf, accessed on 1 September 2020; Cashmore, M., Fox, M., Long, D., Magazzeni, D., Ridder, B., Carrera, A., Palomeras, N., Carreras, M., ROSPlan: Planning in the Robot Operating Syste (2015) Proceedings of the Twenty-Fifth International Conference on Automated Planning and Scheduling, , Jerusalen, Israel, 7-11 Juny","Martín, F.; Intelligent Robotics Lab, Spain; email: francisco.rico@urjc.es",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85090390383
"Li K., Zhang J., Wang L., Zhang M., Li J., Bao S.","57193804114;57223713086;7409187628;7601556980;57218145193;57218138665;","A review of the key technologies for sEMG-based human-robot interaction systems",2020,"Biomedical Signal Processing and Control","62",,"102074","","",,19,"10.1016/j.bspc.2020.102074","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088051273&doi=10.1016%2fj.bspc.2020.102074&partnerID=40&md5=a8d695a4d93453eaa5daa8b6be345b31","School of Mechanical Engineering, Hebei University of Technology, Tianjin, China; College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, United States; College of Education & Human Development, Texas A&M University, College Station, United States","Li, K., School of Mechanical Engineering, Hebei University of Technology, Tianjin, China, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, United States; Zhang, J., School of Mechanical Engineering, Hebei University of Technology, Tianjin, China; Wang, L., College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, United States; Zhang, M., School of Mechanical Engineering, Hebei University of Technology, Tianjin, China; Li, J., School of Mechanical Engineering, Hebei University of Technology, Tianjin, China; Bao, S., College of Education & Human Development, Texas A&M University, College Station, United States","As physiological signals that are closely related to human motion, surface electromyography (sEMG) signals have been widely used in human-robot interaction systems (HRISs). Some reviews on myoelectric control systems have focused on signal processing, feature extraction and pattern recognition methods. However, to the best of our knowledge, there is no review paper on the comprehensive description of HRISs based on sEMG. Therefore, in this paper, we present a detailed review of the key technologies related to the use of sEMG signals in HRISs. First, the advantages and difficulties of using sEMG signals in HRISs are summarized, and some typical applications are introduced. Second, the research status and existing problems are discussed for four key technologies related to sEMG-based HRISs: signal preprocessing and feature extraction, human motion intention recognition, robustness enhancement for sEMG pattern recognition, and multisource information fusion based on sEMG. Finally, we discuss the bottlenecks hindering the application of sEMG-based HRISs and present prospects for this currently active research area. © 2020 Elsevier Ltd","Human-robot interaction system (HRIS); Multisource information fusion; sEMG pattern recognition; Surface electromyography (sEMG)","Biomedical signal processing; Extraction; Feature extraction; Man machine systems; Pattern recognition systems; Human motion intention; Multi-source information fusion; Myoelectric control; Pattern recognition method; Physiological signals; Signal preprocessing; Surface electromyography; Typical application; Social robots; electromyography; feature extraction; human; motion; review; robotics",,,,,"2019HW0001; CXZZBS2019033; 19JCJQJC61600; National Key Research and Development Program of China, NKRDPC: 2018IRS04, 2018YFB1306500","This work was supported in part by National Key R&D Program of China (2018YFB1306500); Intelligent Robot and System High-precision Innovation Center Open Fund Project (2018IRS04); Tianjin Outstanding Youth Fund Project (19JCJQJC61600); Subsidized Project of Innovative Ability Training for Postgraduates in Hebei Province (CXZZBS2019033); Joint Doctoral Training Foundation of HEBUT (2019HW0001).","This work was supported in part by National Key R&D Program of China ( 2018YFB1306500 ); Intelligent Robot and System High-precision Innovation Center Open Fund Project ( 2018IRS04 ); Tianjin Outstanding Youth Fund Project ( 19JCJQJC61600 ); Subsidized Project of Innovative Ability Training for Postgraduates in Hebei Province ( CXZZBS2019033 ); Joint Doctoral Training Foundation of HEBUT ( 2019HW0001 ).",,,,,,,,,"He, Y.Q., Zhao, Y.W., Han, J.D., Yu, H.B., Wang, T.R., Integration with human beings - new trend of robot technology development (2015) Robot Ind., 5, pp. 74-80; Hu, J., Hou, Z.G., Chen, Y.X., Zhang, F., Wang, W.Q., Lower limb rehabilitation robots and interactive control methods (2014) Acta Autom. Sin., 40 (11), pp. 2377-2390; Ding, Q.C., Xiong, A.B., Zhao, X.G., Han, J.D., A review on researches and applications of s EMG-based motion intent recognition methods (2016) Acta Autom. Sin., 42 (1), pp. 13-25; Bi, L., Feleke, A.G., Guan, C., A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration (2019) Biomed. Signal Process. Control, 51, pp. 113-127; Phinyomark, A., Phukpattaranont, P., Limsakul, C., Feature reduction and selection for EMG signal classification (2012) Expert Syst. 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"García P.L., Crispel S., Saerens E., Verstraten T., Lefeber D.","57209626322;57204236550;57200627658;56743227400;55161380600;","Compact Gearboxes for Modern Robotics: A Review",2020,"Frontiers in Robotics and AI","7",,"103","","",,8,"10.3389/frobt.2020.00103","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090014743&doi=10.3389%2ffrobt.2020.00103&partnerID=40&md5=b52c99a788bd64d9cf37da9e1324e9e6","Robotics and Multibody Mechanics, Vrije Universiteit Brussel, Brussels, Belgium; Robotics and Multibody Mechanics, Flanders Make, Heverlee, Belgium","García, P.L., Robotics and Multibody Mechanics, Vrije Universiteit Brussel, Brussels, Belgium, Robotics and Multibody Mechanics, Flanders Make, Heverlee, Belgium; Crispel, S., Robotics and Multibody Mechanics, Vrije Universiteit Brussel, Brussels, Belgium, Robotics and Multibody Mechanics, Flanders Make, Heverlee, Belgium; Saerens, E., Robotics and Multibody Mechanics, Vrije Universiteit Brussel, Brussels, Belgium, Robotics and Multibody Mechanics, Flanders Make, Heverlee, Belgium; Verstraten, T., Robotics and Multibody Mechanics, Vrije Universiteit Brussel, Brussels, Belgium, Robotics and Multibody Mechanics, Flanders Make, Heverlee, Belgium; Lefeber, D., Robotics and Multibody Mechanics, Vrije Universiteit Brussel, Brussels, Belgium, Robotics and Multibody Mechanics, Flanders Make, Heverlee, Belgium","On the eve of Human-Robot-Interaction (HRI) becoming customary in our lives, the performance of HRI robotic devices remains strongly conditioned by their gearboxes. In most industrial robots, two relatively unconventional transmission technologies—Harmonic Drives© and Cycloid Drives—are usually found, which are not so broadly used in other industries. Understanding the origin of this singularity provides valuable insights in the search for suitable, future robotic transmission technologies. In this paper we propose an assessment framework strongly conditioned by HRI applications, and we use it to review the performance of conventional and emerging robotic gearbox technologies, for which the design criterion is strongly shifted toward aspects like weight and efficiency. The framework proposes to use virtual power as a suitable way to assess the inherent limitations of a gearbox technologies to achieve high efficiencies. This paper complements the existing research dealing with the complex interaction between gearbox technologies and the actuators, with a new gearbox-centered perspective particularly focused on HRI applications. © Copyright © 2020 García, Crispel, Saerens, Verstraten and Lefeber.","cycloid drives; efficiency; gearboxes; harmonic drive; HRI; transmissions; virtual power",,,,,,"Horizon 2020 Framework Programme, H2020: 687662; Fonds Wetenschappelijk Onderzoek, FWO; Horizon 2020; Yokohama National University, YNU","SC, ES (SB Ph.D.) and TV (SB Postdoctoral) are Fellows at the Research Foundation Flanders—Fonds voor Wetenschappelijk Onderzoek (FWO). This work has been partially funded by the European Union’s Horizon 2020 Research and Innovation Programme, under Grant Agreement No. 687662—SPEXOR project.","The authors would like to thank Prof. Yasutaka Fujimoto, of the Yokohama National University, as well as the companies Neugart GmbH, Harmonic Drive SE, Sumitomo Drive Germany GmbH, Genesis Robotics, Innovative Mechatronic Systems B.V., Stam s.r.l. and Wittenstein Galaxy GmbH for the kind support and explanations received, and for giving us permission to use the included images of their devices. Funding. SC, ES (SB Ph.D.) and TV (SB Postdoctoral) are Fellows at the Research Foundation Flanders?Fonds voor Wetenschappelijk Onderzoek (FWO). This work has been partially funded by the European Union's Horizon 2020 Research and Innovation Programme, under Grant Agreement No. 687662?SPEXOR project.",,,,,,,,,"Albu-Schäffer, A., Eiberger, O., Grebenstein, M., Haddadin, S., Ott, C., Wimbock, T., Soft robotics (2008) IEEE Robot. Autom. 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WIN0418002; (2020) Technische Broschüre SP+ und TP+ Getrieben, , Katalog; Wolf, A., (1958) Die Grundgesetze der Umlaufgetriebe, , Braunschweig, Friedr. Vieweg and Sohn; Wolfrom, U., Der Wirkungsgrad von Planetenrädergetrieben (1912) Werkstattstechnik, 6, pp. 615-617; Yu, D., Beachley, N., On the mechanical efficiency of differential gearing (1985) ASME J. Mech. Trans. Autom, 107, pp. 61-67; Zinn, M., Roth, B., Khatib, O., Salisbury, J.K., A new actuation approach for human friendly robot design (2004) Int. J. Robot. Res, 23, pp. 379-398","García, P.L.; Robotics and Multibody Mechanics, Belgium; email: plopezga@vub.be
García, P.L.; Robotics and Multibody Mechanics, Belgium; email: plopezga@vub.be",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Review,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85090014743
"Brock H., Sabanovic S., Nakamura K., Gomez R.","56006712800;14036199300;55489762000;12806763700;","Robust Real-Time Hand Gestural Recognition for Non-Verbal Communication with Tabletop Robot Haru",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223566","891","898",,5,"10.1109/RO-MAN47096.2020.9223566","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095739898&doi=10.1109%2fRO-MAN47096.2020.9223566&partnerID=40&md5=f11918288841294fa0518e12ef71cd3c","Honda Research Institute Japan Co. Ltd., Saitama, 351-0188, Japan; Indiana University at Bloomington (IUB)IN  47408, United States","Brock, H., Honda Research Institute Japan Co. Ltd., Saitama, 351-0188, Japan; Sabanovic, S., Indiana University at Bloomington (IUB)IN  47408, United States; Nakamura, K., Honda Research Institute Japan Co. Ltd., Saitama, 351-0188, Japan; Gomez, R., Honda Research Institute Japan Co. Ltd., Saitama, 351-0188, Japan","In this paper, we present our work in close-distance non-verbal communication with tabletop robot Haru through hand gestural interaction. We implemented a novel hand gestural understanding system by training a machine-learning architecture for real-time hand gesture recognition with the Leap Motion. The proposed system is activated based on the velocity of a user's palm and index finger movement, and subsequently labels the detected movement segments under an early classification scheme. Our system is able to combine multiple gesture labels for recognition of consecutive gestures without clear movement boundaries. System evaluation is conducted on data simulating real human-robot interaction conditions, taking into account relevant performance variables such as movement style, timing and posture. Our results show robustness in hand gesture classification performance under variant conditions. We furthermore examine system behavior under sequential data input, paving the way towards seamless and natural real-time close-distance hand-gestural communication in the future. © 2020 IEEE.",,"Agricultural robots; Gesture recognition; Palmprint recognition; Turing machines; Classification scheme; Gestural communication; Gestural interaction; Hand-gesture recognition; Non-verbal communications; Performance variables; System behaviors; System evaluation; Social robots",,,,,,,,,,,,,,,,"Kendon, A., (2004) Gesture: Visible Action As Utterance, , Cambridge University Press; Triesch, J., Malsburg Der C.Von, A gesture interface for humanrobot-interaction (1998) Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition. IEEE, pp. 546-551; Yang, H.-D., Park, A.-Y., Lee, S.-W., Gesture spotting and recognition for human-robot interaction (2007) IEEE Transactions on Robotics, 23 (2), pp. 256-270; Suarez, J., Murphy, R.R., Hand gesture recognition with depth images: A review (2012) 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 411-417; Liu, H., Wang, L., Gesture recognition for human-robot collaboration: A review (2018) International Journal of Industrial Ergonomics, 68, pp. 355-367; Sauppé, A., Mutlu, B., Robot deictics: How gesture and context shape referential communication (2014) 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 342-349; Pitsch, K., Wrede, S., When a robot orients visitors to an exhibit. referential practices and interactional dynamics in real world hri (2014) The 23rd IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 36-42; Lidoris, G., Rohrmuller, F., Wollherr, D., Buss, M., The autonomous city explorer (ace) project-mobile robot navigation in highly populated urban environments (2009) 2009 IEEE International Conference on Robotics and Automation. IEEE, pp. 1416-1422; Lohan, K.S., Lehmann, H., Dondrup, C., Broz, F., Kose, H., Enriching the human-robot interaction loop with natural, semantic and symbolic gestures (2017) Humanoid Robotics: A Reference, pp. 1-21. , Springer; Kim, H., Kwak, S.S., Kim, M., Personality design of sociable robots by control of gesture design factors (2008) RO-MAN 2008-The 17th IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 494-499; Bremner, P., Leonards, U., Iconic gestures for robot avatars, recognition and integration with speech (2016) Frontiers in Psychology, 7, p. 183; Van Dijk, E.T., Torta, E., Cuijpers, R.H., Effects of eye contact and iconic gestures on message retention in human-robot interaction (2013) International Journal of Social Robotics, 5 (4), pp. 491-501; De Wit, J., Schodde, T., Willemsen, B., Bergmann, K., De Haas, M., Kopp, S., Krahmer, E., Vogt, P., The effect of a robot's gestures and adaptive tutoring on children's acquisition of second language vocabularies (2018) Proceedings of the 2018 Acm/ieee International Conference on Human-robot Interaction, pp. 50-58; Gomez, R., Szapiro, D., Galindo, K., Nakamura, K., Haru: Hardware design of an experimental tabletop robot assistant (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-robot Interaction, pp. 233-240; Briant, M.Z., (2009) Baby Sign Language Basics: Early Communication for Hearing Babies and Toddlers, , Hay House, Inc; Miklósi, Á., Pongrácz, P., Lakatos, G., Topál, J., Csányi, V., A comparative study of the use of visual communicative signals in interactions between dogs (canis familiaris) and humans and cats (felis catus) and humans (2005) Journal of Comparative Psychology, 119 (2), p. 179; Meet Haru, the Unassuming Big-Eyed Robot Helping Researchers Study Social Robotics, , https://spectrum.ieee.org/automaton/robotics/home-robots/hondaresearch-institute-haru-social-robot, accessed: 2020-02-12; Sandry, E., (2015) Robots and Communication, , Springer; Cheng, H., Yang, L., Liu, Z., Survey on 3d hand gesture recognition (2015) IEEE Transactions on Circuits and Systems for Video Technology, 26 (9), pp. 1659-1673; Rautaray, S.S., Agrawal, A., Vision based hand gesture recognition for human computer interaction: A survey (2015) Artificial Intelligence Review, 43 (1), pp. 1-54; Leap Motion, Documentation Development SDK, , https://developer.leapmotion.com/documentation/, accessed: 2019-01-23; Marin, G., Dominio, F., Zanuttigh, P., Hand gesture recognition with jointly calibrated leap motion and depth sensor (2016) Multimedia Tools and Applications, 75 (22), pp. 14991-15015; Bachmann, D., Weichert, F., Rinkenauer, G., Evaluation of the leap motion controller as a new contact-free pointing device (2015) Sensors, 15 (1), pp. 214-233; Köpüklü, O., Gunduz, A., Kose, N., Rigoll, G., Real-time hand gesture detection and classification using convolutional neural networks (2019) 2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019). IEEE, pp. 1-8; Gomez, R., Nakamura, K., Szapiro, D., Merino, L., A holistic approach in designing tabletop robot's expressivity (2020) Proceedings of the International Conference on Robotics and Automation; Simon, T., Joo, H., Matthews, I., Sheikh, Y., Hand keypoint detection in single images using multiview bootstrapping (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1145-1153; Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Lee, J., (2019) Mediapipe: A Framework for Building Perception Pipelines; Filho, I.A.S., Chen, E.N., Da Silva Junior, J.M., Da Silva Barboza, R., Gesture recognition using leap motion: A comparison between machine learning algorithms (2018) ACM SIGGRAPH 2018 Posters, Ser. SIGGRAPH '18, , New York, NY, USA: Association for Computing Machinery; Agrawal, R., Joshi, A., Betke, M., Enabling early gesture recognition by motion augmentation (2018) Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference, pp. 98-101",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095739898
"Tolksdorf N.F., Rohlfing K.J.","57214683267;14024566800;","Parents' Views on Using Social Robots for Language Learning",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223540","634","640",,1,"10.1109/RO-MAN47096.2020.9223540","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095738383&doi=10.1109%2fRO-MAN47096.2020.9223540&partnerID=40&md5=b024166c807aa6b3e4f4b49627fb8c49","Psycholinguistics Paderborn University, Faculty of Arts and Humanities, Paderborn, 33098, Germany","Tolksdorf, N.F., Psycholinguistics Paderborn University, Faculty of Arts and Humanities, Paderborn, 33098, Germany; Rohlfing, K.J., Psycholinguistics Paderborn University, Faculty of Arts and Humanities, Paderborn, 33098, Germany","Research in human-robot interaction envisions applications in a variety of areas. In one of them, robots can be used to improve educational performance. However, most scientific investigations focus on learning outcomes without considering the social implications of a robot available as a learning companion. Therefore, the identification of the underlying challenges and issues faced by different stakeholders involved in the technology implementation processes is still sparse. This paper is concerned with parents as a key stakeholder group that is almost overlooked in the existing literature. We present results of a study on child-robot interaction for language learning, in which parents accompanied their children and evaluated the robot after four sessions of experiencing it within a laboratory setting. The results suggest that parents recognize the robots' potential for language learning within a playful interaction with their children. However, as parents reported, the technical challenges for an adaptive and smooth interaction might impede children's learning gains in the long-term. © 2020 IEEE.",,"Agricultural robots; Educational robots; Child-robot interactions; Educational performance; Learning companions; Playful interactions; Scientific investigation; Stakeholder groups; Technical challenges; Technology implementation; Social robots",,,,,,"This research was supported by the Digital Society research program funded by the Ministry of Culture and Science of the German State of North Rhine-Westphalia.",,,,,,,,,,"Vogt, P., Van, B.D.R., De Haas, M., Hoffman, L., Kanero, J., Mamus, E., Montanier, J.-M., Pandey, A.K., Second language tutoring using social robots: A large-scale study (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 497-505. , Daegu, Korea (South): IEEE, Mar; Kory Westlund, J.M., Jeong, S., Park, H.W., Ronfard, S., Adhikari, A., Harris, P.L., DeSteno, D., Breazeal, C.L., Flat vs. expressive storytelling: Young children's learning and retention of a social robot's narrative (2017) Frontiers in Human Neuroscience, 11 (295), pp. 1-20. , June; Hsiao, H.-S., Chang, C.-S., Lin, C.-Y., Hsu, H.-L., IRobiQ : The influence of bidirectional interaction on kindergarteners' reading motivation, literacy, and behavior (2015) Interactive Learning Environments, 23 (3), pp. 269-292. , May; Michaelis, J.E., Mutlu, B., Reading socially: Transforming the in-home reading experience with a learning-companion robot (2018) Science Robotics, 3 (21), p. eaat5999. , Aug; Ioannou, A., Makridou, E., Exploring the potentials of educational robotics in the development of computational thinking: A summary of current research and practical proposal for future work (2018) Education and Information Technologies, 23 (6), pp. 2531-2544. , Nov; Williams, R., Park, H.W., Breazeal, C., A is for artificial intelligence: The impact of artificial intelligence activities on young children's perceptions of robots (2019) Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems-CHI '19, pp. 1-11. , Glasgow, Scotland Uk: ACM Press; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Science Robotics, 3 (21), p. eaat5954. , Aug; Mishra, P., Koehler, M.J., Technological pedagogical content knowledge: A framework for teacher knowledge (2006) Teachers College Record, 108 (6), pp. 1017-1054. , June; Russell, D.L., Schneiderheinze, A., Understanding innovation in education using activity theory (2005) Journal of Educational Technology & Society, 8 (1), pp. 38-53; Tolksdorf, N.F., Siebert, S., Zorn, I., Horwath, I., Rohlfing, K.J., Ethical considerations of applying robots in kindergarten settings: Towards an approach from a macroperspective (2020) International Journal of Social Robotics, , Feb; Lee, E., Youngjun, L., Kye, Y., Ko, B., Elementary and middle school teachers', students' and parents' perception of robot-aided education in korea (2008) Proc. 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Effect of a robot's verbal language forms on people's social distance from robots (2013) Computers in Human Behavior, 29 (3), pp. 1091-1101. , May; Menne, I.M., Lange, B.P., Unz, D.C., My humorous robot: Effects of a robot telling jokes on perceived intelligence and liking (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction-HRI '18, pp. 193-194. , Chicago, IL, USA: ACM Press; Harris, M., (1992) Language Experience and Early Language Development from Input to Uptake, , Hove, UK: Erlbaum; Schodde, T., Bergmann, K., Kopp, S., Adaptive robot language tutoring based on Bayesian knowledge tracing and predictive decision-making (2017) 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI, pp. 128-136. , Mar; Serholt, S., Barendregt, W., Vasalou, A., Alves-Oliveira, P., Jones, A., Petisca, S., Paiva, A., The case of classroom robots: Teachers' deliberations on the ethical tensions (2017) AI & SOCIETY, 32 (4), pp. 613-631. , Nov",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095738383
"He T., Zhao W., Xu L.","57219133026;57198597735;57194206491;","DOP-Tacotron: A Fast Chinese TTS System with Local-based Attention",2020,"Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020",,,"9164203","4345","4350",,,"10.1109/CCDC49329.2020.9164203","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091580349&doi=10.1109%2fCCDC49329.2020.9164203&partnerID=40&md5=edfa6820a6e7999af77800edd543d520","Zhejiang University, College of Electrical Engineering, Hangzhou, Zhejiang, 310027, China; Zhejiang University, Robotics Institute, Yuyao, Zhejiang, 315400, China","He, T., Zhejiang University, College of Electrical Engineering, Hangzhou, Zhejiang, 310027, China, Zhejiang University, Robotics Institute, Yuyao, Zhejiang, 315400, China; Zhao, W., Zhejiang University, College of Electrical Engineering, Hangzhou, Zhejiang, 310027, China, Zhejiang University, Robotics Institute, Yuyao, Zhejiang, 315400, China; Xu, L., Zhejiang University, College of Electrical Engineering, Hangzhou, Zhejiang, 310027, China, Zhejiang University, Robotics Institute, Yuyao, Zhejiang, 315400, China","As used in human-robot interaction, text-to-speech(TTS) systems can generate human-like speech from written text input to mimic human speakers. End-to-end TTS systems are widely explored in recent years. In this paper, we propose a fast trained end-to-end Chinese TTS system DOP-Tacotron. We propose the DOP module for the encoder and the post-processing network. DOP has almost similar effects with CBHG module while using 35.5% fewer parameters. We use local-based attention mechanism, which always follows the previous attention state. DOP-Tacotron achieves a 3.683 subjective 5-scale mean opinion score of naturalness on Chinese Mandarin, outperforming Tacotron in terms of naturalness. In addition, DOP-Tacotron adds stop-talk-loss to loss for spectrogram, and uses sample-length-batch for mini batch and accurate Chinese pinyin with punctuation as input. Our proposed TTS system can be easily trained since training time of DOP-Tacotron is only 2.5 hours. © 2020 IEEE.","dilated convolution; local-based attention; TTS","Control engineering; Attention mechanisms; Mean opinion scores; Post processing; Sample length; Spectrograms; Text-to-speech system; Training time; Written texts; Social robots",,,,,"K11801","* Corresponding Author This work was supported by Robotics Institute of Zhejiang University under Grant K11801.",,,,,,,,,,"Klatt, D.H., Review of text-to-speech conversion for english (1987) Journal of the Acoustical Society of America, 82 (3), pp. 737-793; Taylor, P., (2009) Text-to-Speech Synthesis; Oord, A.V.D., Kalchbrenner, N., Kavukcuoglu, K., (2016) Pixel Recurrent Neural Networks; Oord, A.V.D., Dieleman, S., Zen, H., Simonyan, K., Kavukcuoglu, K., (2016) Wavenet: A Generative Model for Raw Audio; Arik, S.O., Chrzanowski, M., Coates, A., Diamos, G., Shoeybi, M., (2017) Deep Voice: Real-time Neural Text-to-speech; Arik, S., Diamos, G., Gibiansky, A., Miller, J., Zhou, Y., (2017) Deep Voice 2: Multi-speaker Neural Text-to-speech; Wang, W., Xu, S., Xu, B., (2016) First Step Towards End-to-end Parametric Tts Synthesis: Generating Spectral Parameters with Neural Attention; Sotelo, J., Mehri, S., Kumar, K., Santos, J.F., Kastner, K., Courville, A., Bengio, Y., (2017) Char2wav: End-to-end Speech Synthesis; Mehri, S., Kumar, K., Gulrajani, I., Kumar, R., Jain, S., Sotelo, J., Courville, A., Bengio, Y., (2016) Samplernn: An Unconditional End-to-end Neural Audio Generation Model; Wang, Y., Skerry-Ryan, R.J., Stanton, D., Wu, Y., Saurous, R.A., (2017) Tacotron: A Fully End-to-end Text-to-speech Synthesis Model; Shen, J., Pang, R., Weiss, R.J., Schuster, M., Jaitly, N., Yang, Z., Chen, Z., Skerrv-Ryan, R., Natural tts synthesis by conditioning wavenet on mel spectrogram predictions (2018) 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 4779-4783; Ren, Y., Ruan, Y., Tan, X., Qin, T., Zhao, S., Zhao, Z., Liu, T.-Y., Fastspeech: Fast, robust and controllable text to speech (2019) Advances in Neural Information Processing Systems, pp. 3165-3174; Chin-Teng, L., Rui-Cheng, W., Jyh-Yeong, C., Sheng-Fu, L., A novel prosodic-information synthesizer based on recurrent fuzzy neural network for the Chinese tts system (2004) IEEE Trans Syst Man Cybern B Cybern, 34 (1), pp. 309-324; Ping, W., Peng, K., Gibiansky, A., Arik, S.O., Kannan, A., Narang, S., Raiman, J., Miller, J., (2017) Deep Voice 3: 2000-Speaker Neural Text-to-speech; Chorowski, J., Bahdanau, D., Serdyuk, D., Cho, K., Bengio, Y., Attention-based models for speech recognition (2015) Computer Science, 10 (4), pp. 429-439; Li, N., Liu, S., Liu, Y., Zhao, S., Liu, M., (2019) Neural Speech Synthesis with Transformer Network; Chorowski, J., Bahdanau, D., Serdyuk, D., Cho, K., Bengio, Y., Attention-based models for speech recognition (2015) Computer Science, 10 (4), pp. 429-439; Srivastava, R.K., Greff, K., Schmidhuber, J., (2015) Highway Networks, , arXiv preprint arXiv: 1505.00387; Chung, J., Gulcehre, C., Cho, K., Bengio, Y., (2014) Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling, , arXiv preprint arXiv:1412.3555; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778; Ioffe, S., Szegedy, C., (2015) Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, , arXiv preprint arXiv: 1502.03167; Bahdanau, D., Cho, K., Bengio, Y., (2014) Neural Machine Translation by Jointly Learning to Align and Translate, , arXiv preprint arXiv:1409.0473; Vinyals, O., Kaiser, L., Koo, T., Petrov, S., Sutskever, I., Hinton, G., Grammar as a foreign language (2015) Advances in Neural Information Processing Systems, pp. 2773-2781","Xu, L.; Zhejiang University, China; email: xupower@zju.edu.cn",,"IEEE Control Systems Society (CSS);Northeastern University;State Key Laboratory of Synthetical Automation for Process Industries;Technical Committee on Control Theory, Chinese Association of Automation","Institute of Electrical and Electronics Engineers Inc.","32nd Chinese Control and Decision Conference, CCDC 2020","22 August 2020 through 24 August 2020",,162350,,9781728158549,,,"English","Proc. Chin. Control Decis. Conf., CCDC",Conference Paper,"Final","",Scopus,2-s2.0-85091580349
"Zhang X., Zhao J., Zhang M., Liu X.","55715520000;57218347287;7601556980;57210643565;","Disturbance recognition and collision detection of manipulator based on momentum observer",2020,"Sensors (Switzerland)","20","15","4187","1","15",,,"10.3390/s20154187","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088880398&doi=10.3390%2fs20154187&partnerID=40&md5=b61dd4cb8728d810f5b0f628b91e1dae","School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300132, China","Zhang, X., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300132, China; Zhao, J., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300132, China; Zhang, M., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300132, China; Liu, X., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300132, China","Increasing requirements for the safety of human-robot interaction and the cost-effectiveness of collision detection rapidly promote the development of collision detection technology without torque sensors. To address nonlinear disturbance factors in collision detection that may cause unstable or even incorrect detection, this paper proposed a research strategy that considered the friction as the disturbance term in manipulator motion for the collision detection. The manipulator joint disturbance model was established based on the LuGre dynamic friction model, and the external torque observer was designed based on the generalized momentum. Then, the friction measurement was realized using the external torque observer, and the model parameters were identified through the genetic algorithm. The collision detection can be reduced errors after the friction model by compensating the disturbance and can be applicable to variable working conditions. Finally, the accuracy of the constructed disturbance model and the performance of the proposed collision detection method were validated by the experimental studies. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Collision detection; Human-robot interaction; Momentum observer; Nonlinear disturbance; Parameter identification","Cost effectiveness; Friction; Genetic algorithms; Human robot interaction; Collision detection; Disturbance modeling; Dynamic friction models; Friction measurements; Manipulator joints; Manipulator motion; Nonlinear disturbance; Research strategy; Manipulators; experimental study; friction; genetic algorithm; human; letter; motion; robotics; torque; work environment",,,,,"CXZZBS2020030; 2018YFB1309401; 20180603; Natural Science Foundation of Hebei Province: E2018202338","Funding: This research was supported by the National Key Research and Development Project of China (Grant No. 2018YFB1309401), the Key Natural Science Foundation Project of Hebei Province (Grant No. E2018202338), the Innovation Funding Project for Graduate Students in Hebei Province (CXZZBS2020030), and the Science and Technology Innovation Strategy Funding project in Hebei Province (20180603).",,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human–robot collaboration (2017) Auton. Robots, 42, pp. 957-975. , [CrossRef]; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266. , [CrossRef]; Zacharaki, A., Kostavelis, I., Gasteratos, A., Dokas, I., Safety bounds in human robot interaction: A survey (2020) Saf. Sci, 127, p. 1. , [CrossRef]; (2016) ISO/TS 15066:2016, Robots and Robotic Devices-Collaborative Robots, , International Organization for Standardization. ISO: Geneva, Switzerland; Beetz, M., Bartels, G., Albu-Schäffer, A., Balint-Benczedi, F., Belder, R., Bebler, D., Haddadin, S., Wiedemeyer, T., Robotic agents capable of natural and safe physical interaction with human co-workers Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 6528-6535. , Hamburg, Germany, 28 September–2 October 2015; Vorndamme, J., Schappler, M., Haddadin, S., Collision detection, isolation and identification for humanoids (2017) Proceedings of the IEEE International Conference on Robotics and Automation, pp. 4754-4761. , Singapore, 29 May–3 June; Flacco, F., Kröger, T., DeLuca, A., Khatib, O., A Depth Space Approach to Human-Robot Collision Avoidance Proceedings of the IEEE International Conference on Robotics and Automation, pp. 338-345. , Saint Paul, MN, USA, 14–18 May 2012; Lee, S.D., Kim, B.S., Song, J.B., Human–Robot Collision Model with Effective Mass and Manipulability for Design of a Spatial Manipulator (2013) Adv. Robot, 27, pp. 189-198. , [CrossRef]; Haddadin, S., Albu-Schäffer, A., DeLuca, A., Hirzinger, G., Collision detection and reaction: A contribution to safe physical human-robot interaction Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3356-3363. , Nice, France, 22–26 September 2008; DeLuca, A., Albu-Schäffer, A., Haddadin, S., Hirzinger, G., Collision detection and safe reaction with the DLR-III lightweight manipulator arm Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1623-1630. , Beijing, China, 9–15 October 2006; Haddadin, S., DeLuca, A., Albu-Schäffer, A., Robot Collisions: A Survey on Detection, Isolation, and Identification (2017) IEEE Trans. Robot, 33, pp. 1292-1312. , [CrossRef]; Haddadin, S., Krieger, K., Albu-Schäffer, A., Lilge, T., Exploiting Elastic Energy Storage for “Blind” Cyclic Manipulation: Modeling, Stability Analysis, Control, and Experiments for Dribbling (2018) IEEE Trans. Robot, 34, pp. 91-112. , [CrossRef]; Cho, C.N., Kim, J.H., Kim, Y.L., Song, J.B., Kyung, J.H., Collision Detection Algorithm to Distinguish Between Intended Contact and Unexpected Collision (2012) Adv. Robot, 26, pp. 1825-1840. , [CrossRef]; Magrini, E., Flacco, F., DeLuca, A., Estimation of contact forces using a virtual force sensor Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2126-2133. , Chicago, IL, USA, 14–18 September 2014; Lee, S.D., Song, J.B., Sensorless collision detection based on friction model for a robot manipulator (2016) Int. J. Precis. Eng. Manuf, 17, pp. 11-17. , [CrossRef]; Villagrossi, E., Simoni, L., Beschi, M., Pedrocchi, N., Marini, A., Tosatti, L.M., Visioli, A., A virtual force sensor for interaction tasks with conventional industrial robots (2018) Mechatronics, 50, pp. 78-86. , [CrossRef]; Wahrburg, A., Bös, J., Listmann, K.D., Dai, F., Matthias, B., Ding, H., Motor-current-based estimation of cartesian contact forces and torques for robotic manipulators and its application to force control (2018) IEEE Trans. Autom. Sci. Eng, 15, pp. 879-886. , [CrossRef]; Xiao, J., Tian, Z., Hong, Y., Wang, G., Zhang, Z., Liu, H., Duan, W., A lightweight modular collaborative robot collision detection algorithm (2017) J. Tianjin Univ. Nat. Sci. Eng. Technol. Ed, 50, pp. 1140-1147. , [CrossRef]; Yen, S.H., Tang, P.C., Lin, Y.C., Lin, C.Y., Development of a Virtual Force Sensor for a Low-Cost Collaborative Robot and Applications to Safety Control (2019) Sensors, 19, p. 2603. , [CrossRef] [PubMed]; Jung, B.J., Koo, J.C., Choi, H.R., Choi, H.R., Moon, H., Human-robot collision detection under modeling uncertainty using frequency boundary of manipulator dynamics (2014) J. Mech. Sci. Technol, 28, pp. 4389-4395. , [CrossRef]; Li, Z., Ye, J., Wu, H., A Virtual Sensor for Collision Detection and Distinction with Conventional Industrial Robots (2019) Sensors, 19, p. 2368. , [CrossRef] [PubMed]; Heo, Y.J., Kim, D., Lee, W., Kim, H., Park, J., Chung, W.K., Collision Detection for Industrial Collaborative Robots: A Deep Learning Approach (2019) IEEE Robot. Autom. Lett, 4, pp. 740-746. , [CrossRef]; Jia, J., Zhang, M., Zang, X., Zhang, H., Zhao, J., Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental Design (2019) Sensors, 19, p. 2248. , [CrossRef]; Janot, A., Vandanjon, P.O., Gautier, M., An instrumental variable approach for rigid industrial robots identification (2014) Control Eng. Pract, 25, pp. 85-101. , [CrossRef]; Jiang, Z.H., Ishida, T., Sunawada, M., Neural network aided dynamic parameter identification of robot manipulators Proceedings of the 2006 IEEE International Conference on Systems, Man and Cybernetics, pp. 3298-3303. , Taipei, Taiwan, 8–11 October 2006; Kim, M.J., Beck, F., Ott, C., Albu-Schäffer, A., Model-Free Friction Observers for Flexible Joint Robots with Torque Measurements (2019) IEEE Trans. Robot, 35, pp. 1508-1515. , [CrossRef]; Xu, T., Fan, J., Fang, Q., Wang, S., Zhu, Y., Zhao, J., A Novel Virtual Sensor for Estimating Robot Joint Total Friction Based on Total Momentum (2019) Appl. Sci, 9, p. 3344. , [CrossRef]; Urrea, C., Pascal, J., Design, simulation, comparison and evaluation of parameter identification methods for an industrial robot (2018) Comput. Electr. Eng, 67, pp. 791-806. , [CrossRef]; Bahloul, A., Tliba, S., Chitour, Y., Dynamic Parameters Identification of an Industrial Robot with and without Payload (2018) IFAC-PapersOnLine, 51, pp. 443-448. , [CrossRef]; Wu, H., Li, S., Wu, G., Robot collision detection algorithm based on momentum deviation observer (2015) J. Electr. Mach. Control, 19, pp. 97-104. , [CrossRef]; Zhang, J., Cai, C., Liu, X., Zhang, M., Manipulator collision strategy based on second-order feedforward external force observer (2019) Comput. Integr. Manuf. Syst, 25, pp. 1775-1783. , [CrossRef]; Piatkowski, T., Dahl and LuGre dynamic friction models-the analysis of selected properties (2014) Mech. Mach. Theory, 73, pp. 91-100. , [CrossRef]; Morante, S., Victores, J.G., Martínez, S., Balaguer, C., (2016) Force-Sensorless Friction and Gravity Compensation for Robots, pp. 57-68. , Springer: Cham, Switzerland, ISBN 978-3-319-27148-4; Zhong, C., Xiang, J., Wei, W., Zhang, Y., LuGre dynamic friction compensation based on simplified nonlinear observer (2012) J. Zhejiang Univ. (Eng. Sci.), 46, pp. 764-769. , [CrossRef]; Wu, X., Liu, Y., He, M., Gao, F., Shao, G., Research on Robot Joint Friction Modeling and Compensation (2018) J. Instrum, 39, pp. 44-50. , [CrossRef]; Wen, Y., Chu, M., Sun, H., In Friction parameters identification and compensation of LuGre model base on genetic algorithms Proceedings of the International Symposium on Computers & Informatics, pp. 229-238. , Beijing, China, 17–18 January 2015; Cao, P.F., Gan, Y.H., Dai, X.Z., Model-based sensorless robot collision detection under model uncertainties with a fast dynamics identification (2019) Int. J. Adv. Robot. Syst, 16, p. 15. , [CrossRef]; Delgado, R., You, B.-J., Choi, B.W., Real-time control architecture based on Xenomai using ROS packages for a service robot (2019) J. Syst. Softw, 151, pp. 8-19. , [CrossRef]; Delgado, R., Park, J., Choi, B.W., Open Embedded Real-time Controllers for Industrial Distributed Control Systems (2019) Electronics, 8, p. 223. , [CrossRef]","Zhang, X.; School of Mechanical Engineering, China; email: xjzhang@hebut.edu.cn",,,"MDPI AG",,,,,14248220,,,"32731439","English","Sensors",Letter,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85088880398
"Hunt E.R.","57114430000;","Evolutionary stress factors for adaptable robot 'personalities'",2020,"GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion",,,,"1387","1388",,,"10.1145/3377929.3398078","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089736434&doi=10.1145%2f3377929.3398078&partnerID=40&md5=505460ee0dcdb578ef6d79aba5e754b5","University of Bristol, Bristol, United Kingdom","Hunt, E.R., University of Bristol, Bristol, United Kingdom","Many studies on real multi-robot systems are undertaken in laboratory environments, whereas the outside world is unstable and risky. I argue that the phenomenon of phenotypic plasticity provides a bioinspiration framework for making such systems more resilient in the field. Such plasticity can result in consistent individual behavioural differences among group members, which have been described as 'personalities' in recent years. Personality axes such as shy-bold can be represented by single variables, and may be intuitive in the context of human-robot interaction. However, for plasticity to emerge in artificial evolution, simulated environments need to be more heterogeneous and unpredictable, as in real-world natural history. Behavioral Reaction Norms (BRNs) from developmental biology are a useful concept for the transparent mapping of relevant sensory inputs to robot behavioral change. In this position paper, I identify candidate stress factors in simulated environments for artificial evolution of adaptive BRNs. I also consider some potential benefits and costs of this approach. © 2020 ACM.","Field robotics; Personality; Phenotypic plasticity; Reaction norms","Biology; Multipurpose robots; Artificial evolution; Behavioral changes; Developmental biology; Laboratory environment; Multi-robot systems; Phenotypic plasticity; Potential benefits; Simulated environment; Human robot interaction",,,,,"Royal Academy of Engineering, RAENG","EH is supported by the Royal Academy of Engineering and the Office of the Chief Science Adviser for National Security under the UK Intelligence Community Postdoctoral Fellowship Programme.",,,,,,,,,,"Castello, E., Yamamoto, T., Dalla Libera, F., Liu, W., Winfield, T.A.F., Nakamura, Y., Ishiguro, H., Adaptive foraging for simulated and real robotic swarms: The dynamical response threshold approach (2016) Swarm Intelligence, 10 (1), pp. 1-31. , https://doi.org/10.1007/s11721-015-0117-7, 2016; Dall, X.S.R., Bell, A.M., Bolnick, D.I., Ratnieks, W.F.L., An evolutionary ecology of individual differences (2012) Ecology Letters, 15 (10), pp. 1189-1198. , https://doi.org/10.1111/j.1461-0248.2012.01846.x, 2012; Dingemanse, N.J., Kazem, N.A.J., Réale, D., Wright, J., Behavioural reaction norms: Animal personality meets individual plasticity (2010) Trends in Ecology & Evolution, 25 (2), pp. 81-89. , https://doi.org/10.1016/j.tree.2009.07.013, 2010; Gabriel, W., How stress selects for reversible phenotypic plasticity (2005) Journal of Evolutionary Biology, 18 (4), pp. 873-883. , https://doi.org/10.1111/j.1420-9101.2005.00959.x, 2005; Hunt, E.R., Phenotypic plasticity provides a bioinspiration framework for minimal field swarm robotics (2020) Frontiers in Robotics and AI, 7, p. 23. , https://doi.org/10.3389/frobt.2020.00023, 2020; Hunt, E.R., Mi, B., Fernandez, C., Wong, B.M., Pruitt, J.N., Pinter-Wollman, N., Social interactions shape individual and collective personality in social spiders (2018) Proceedings of the Royal Society B: Biological Sciences, 285 (1886), p. 20181366. , https://doi.org/10.1098/rspb.2018.1366, 2018; Jandt, J.M., Bengston, S., Pinter-Wollman, N., Pruitt, J.N., Raine, N.E., Dornhaus, A., Sih, A., Behavioural syndromes and social insects: Personality at multiple levels (2014) Biological Reviews, 89 (1), pp. 48-67. , https://doi.org/10.1111/brv.12042, 2014; Masuda, N., O'Shea-Wheller, T.A., Doran, C., Franks, N.R., Computational model of collective nest selection by ants with heterogeneous acceptance thresholds (2015) Royal Society Open Science, 2 (6), p. 140533. , https://doi.org/10.1098/rsos.140533, 2015; Norman, V.C., Pamminger, T., Hughes, H.W.O., The effects of disturbance threat on leaf-cutting ant colonies: A laboratory study (2017) Insectes Sociaux, 64 (1), pp. 75-85. , https://doi.org/10.1007/s00040-016-0513-z, 2017; Réale, D., Reader, S.M., Sol, D., McDougall, P.T., Dingemanse, N.J., Integrating animal temperament within ecology and evolution (2007) Biological Reviews, 82 (2), pp. 291-318. , https://doi.org/10.1111/j.1469-185X.2007.00010.x, 2007; Schlichting, C.D., Pigliucci, M., (1998) Phenotypic Evolution: A Reaction Norm Perspective, , Sinauer Associates Incorporated, Sunderland, Massachusetts","Hunt, E.R.; University of BristolUnited Kingdom",,"ACM SIGEVO","Association for Computing Machinery, Inc","2020 Genetic and Evolutionary Computation Conference, GECCO 2020","8 July 2020 through 12 July 2020",,161684,,9781450371278,,,"English","GECCO Companion - Proc. Genet. Evolut. Comput. Conf. Companion",Conference Paper,"Final","",Scopus,2-s2.0-85089736434
"Xu X.C., Liu L.L., Xiong R., Jiang L.","57221066208;57218554104;7102951376;57220699478;","Real-time instance-aware semantic mapping",2020,"Journal of Physics: Conference Series","1507","5","052013","","",,,"10.1088/1742-6596/1507/5/052013","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089509725&doi=10.1088%2f1742-6596%2f1507%2f5%2f052013&partnerID=40&md5=d54e0f0e1bc66d4bd49bc8132fa72a5a","Zhejiang University, Hangzhou, 310027, China","Xu, X.C., Zhejiang University, Hangzhou, 310027, China; Liu, L.L., Zhejiang University, Hangzhou, 310027, China; Xiong, R., Zhejiang University, Hangzhou, 310027, China; Jiang, L., Zhejiang University, Hangzhou, 310027, China","The semantic information helps robots to understand its surroundings like human beings and enables robots to achieve human-robot interaction. In recent years, there have been many interests in semantic mapping. Numerous approaches manage to build a semantic map and achieve good accuracy, but the existing mapping methods which create the metric semantic map ignore the subsequent applications of the semantic map. However, the metric map with the simple semantic class label has no direct benefit to localization. In this paper, we propose an approach to construct an object-centric map with promising applications. Employing the traditional metric and deep learning methods, we can extract objects from the environment and along with semantics. This object representation of the semantic map can be useful in other applications of robots, our local map and global map framework can be useful for navigation. At last, we report on the quality and speed of our object-centric mapping method. © Published under licence by IOP Publishing Ltd.",,"Deep learning; Human robot interaction; Learning systems; Mapping; Springs (components); Learning methods; Mapping method; Metric semantics; Object representations; Semantic class; Semantic information; Semantic map; Semantic mapping; Semantics",,,,,,,,,,,,,,,,"Nüchter, A., Hertzberg, J., (2008) Robotics and Autonomous Systems, Elsevier B. V., 56 (11), pp. 915-926; Fioraio, N., Di Stefano, L., (2013) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1538-1545; Ma, L., Stuckler, J., Kerl, C., (2017) IEEE International Conference on Intelligent Robots and Systems, pp. 598-605; Hermans, A., Floros, G., Leibe, B., (2014) Proceedings - IEEE International Conference on Robotics and Automation, pp. 2631-2638; Sunder Hauf, N., Pham, T.T., Latif, Y., (2017) IEEE International Conference on Intelligent Robots and Systems, pp. 5079-5085; Qi, C.R., Su, H., Mo, K., (2017) Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition; Redmon, J., Farhadi, A., (2017) Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition; Liu, W., Anguelov, D., Erhan, D., (2016) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 21-37; Yang, J., Li, H., Campbell, D., (2016) IEEE Transactions on Pattern Analysis and Machine Intelligence, 38 (11), pp. 2241-2254; Souza, A., Gonsalves, L.M.G., (2016) Robotica, 34 (11), pp. 2592-2609; Geiger, A., Lenz, P., Stiller, C., (2013) International Journal of Robotics Research",,,,"Institute of Physics Publishing","2nd Spring International Conference on Defence Technology, ICDT 2020","20 April 2020 through 24 April 2020",,161644,17426588,,,,"English","J. Phys. Conf. Ser.",Conference Paper,"Final","All Open Access, Gold",Scopus,2-s2.0-85089509725
"Bartneck C., Moltchanova E.","6602256988;6508318897;","Expressing uncertainty in Human-Robot Interaction",2020,"PLoS ONE","15","7 July","e0235361","","",,,"10.1371/journal.pone.0235361","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088155155&doi=10.1371%2fjournal.pone.0235361&partnerID=40&md5=5a1014f72523c99c7464b4e085385f9a","University of Canterbury, Christchurch, New Zealand","Bartneck, C., University of Canterbury, Christchurch, New Zealand; Moltchanova, E., University of Canterbury, Christchurch, New Zealand","Most people struggle to understand probability which is an issue for Human-Robot Interaction (HRI) researchers who need to communicate risks and uncertainties to the participants in their studies, the media and policy makers. Previous work showed that even the use of numerical values to express probabilities does not guarantee an accurate understanding by laypeople. We therefore investigate if words can be used to communicate probability, such as “likely” and “almost certainly not”. We embedded these phrases in the context of the usage of autonomous vehicles. The results show that the association of phrases to percentages is not random and there is a preferred order of phrases. The association is, however, not as consistent as hoped for. Hence, it would be advisable to complement the use of words with numerical expression of uncertainty. This study provides an empirically verified list of probabilities phrases that HRI researchers can use to complement the numerical values. © 2020 Bartneck, Moltchanova. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",,"article; human; probability; robotics; uncertainty; brain computer interface; ethics; risk factor; robotics; Brain-Computer Interfaces; Humans; Probability; Risk Factors; Robotics",,,,,,,,,,,,,,,,"Ulph, F, Townsend, E, Glazebrook, C., How should risk be communicated to children: a cross-sectional study comparing different formats of probability information (2009) BMC medical informatics and decision making, 9 (1), p. 26. , https://doi.org/10.1186/1472-6947-9-26, PMID: 19500337; Fuller, R, Dudley, N, Blacktop, J., Risk communication and older people—understanding of probability and risk information by medical inpatients aged 75 years and older (2001) Age and Ageing, 30 (6), pp. 473-476. , https://doi.org/10.1093/ageing/30.6.473, PMID: 11742775; Zlotowski, J, Yogeeswaran, K, Bartneck, C., Can We Control It? Autonomous Robots are Perceived as Threatening (2017) Internatinoal Journal Of Human-Computer Studies, 100, pp. 48-54. , (April 2017); Delvaux, M., (2017) Report with recommendations to the Commission on Civil Law Rules on Robotics, , http://www.europarl.europa.eu/sides/getDoc.do?pubRef=-//EP//TEXT+REPORT+A8-2017-0005+0+DOC+XML+V0//EN, European Parliament; A8-0005/2017; Ehrenkranz, M., (2018) Gizomodo, , Houston Votes To Ban Businesses From Letting People Screw Humanlike Devices In-Store; Sparrow, R, Howard, M., When human beings are like drunk robots: Driverless vehicles, ethics, and the future of transport (2017) Transportation Research Part C: Emerging Technologies, 80, pp. 206-215. , https://doi.org/10.1016/j.trc.2017.04.014; Spiegelhalter, D., (2019) The art of statistics: how to learn from data, , New York: Basic Books; Wittgenstein, L., (1922) Tractatus logico-philosophicus. International library of psychology, philosophy and scientific method, , New York, London: Harcourt, Brace & company; Kahneman, D., (2011) Thinking, fast and slow, , 1st ed. New York: Farrar, Straus and Giroux; Slovic, P., (2000) The perception of risk. Risk, society, and policy series, , London: Earthscan Pub; Weiss, C., Expressing scientific uncertainty (2003) Law, Probability and Risk, 2 (1), pp. 25-46. , https://doi.org/10.1093/lpr/2.1.25; Fischhoff, B, Bostrom, A, Quadrel, MJ., Risk perception and communication (1993) Annual review of public health, 14 (1), pp. 183-203. , https://doi.org/10.1146/annurev.pu.14.050193.001151, PMID: 8323585; Mosteller, F, Youtz, C., Quantifying Probabilistic Expressions (1990) Statistical Science, 5 (1), pp. 2-12. , https://doi.org/10.1214/ss/1177012251; Joslyn, SL, Nichols, RM., Probability or frequency? Expressing forecast uncertainty in public weather forecasts (2009) Meteorological Applications, 16 (3), pp. 309-314. , https://doi.org/10.1002/met.121; Lindsey, RGG, Hertwig, Samuel, Communicating Statistical DNA Evidence (2003) Jurimetrics, 43, pp. 147-163; Berinsky, AJ, Huber, GA, Lenz, GS., Evaluating Online Labor Markets for Experimental Research: Amazon.com’s Mechanical Turk (2012) Political Analysis, 20 (3), pp. 351-368. , https://doi.org/10.1093/pan/mpr057; Eliot, L., (2019) Essential Stats For Justifying And Comparing Self-Driving Cars To Humans At The Wheel, , https://www.forbes.com/sites/lanceeliot/2019/05/30/essential-stats-forjustifying-and-comparing-self-driving-cars-to-humans-at-the-wheel/#4f19661946ed, Forbes; Kalra, N, Paddock, SM., Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability? (2016) Transportation Research Part A: Policy and Practice, 94, pp. 182-193; Bohn, D., Elon Musk: negative media coverage of autonomous vehicles could be ‘killing people’ (2016) The Verge, , https://www.theverge.com/2016/10/19/13341306/elon-musk-negativemedia-autonomous-vehicles-killing-people; Handmer, J, Proudley, B., Communicating uncertainty via probabilities: The case of weather forecasts (2007) Environmental Hazards, 7 (2), pp. 79-87. , https://doi.org/10.1016/j.envhaz.2007.05.002; Weinstein, ND., What Does It Mean to Understand a Risk? Evaluating Risk Comprehension (1999) JNCI Monographs, 1999 (25), pp. 15-20. , https://doi.org/10.1093/oxfordjournals.jncimonographs.a024192; Friedman, J, Hastie, T, Tibshirani, R., (2001) The elements of statistical learning, , 2nd ed. Springer Series in Statistics. New York: Springer; Agresti, A., (2010) Analysis of ordinal categorical data, 656. , John Wiley & Sons; Allison, P., Convergence problems in logistic regression (2003) Numerical issues in statistical computing for the social scientist, pp. 238-252; Breiman, L., Random forests (2001) Machine learning, 45 (1), pp. 5-32. , https://doi.org/10.1023/A:1010933404324; Strobl, C, Boulesteix, AL, Kneib, T, Augustin, T, Zeileis, A., Conditional variable importance for random forests (2008) BMC bioinformatics, 9 (1), p. 307. , https://doi.org/10.1186/1471-2105-9-307, PMID: 18620558; Paul, J, Verleysen, M, Dupont, P, Identification of statistically significant features from random forests (2013) ECML workshop on Solving Complex Machine Learning Problems with Ensemble Methods, pp. 69-80; Therneau, T, Atkinson, B, Ripley, B., (2017) rpart: Recursive Partitioning and Regression Trees, , https://CRAN.R-project.org/package=rpart; Liaw, A, Wiener, M., Classification and Regression by randomForest (2002) R News, 2 (3), pp. 18-22; Murphy, MA, Evans, JS, Storfer, A., Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics (2010) Ecology, 91, pp. 252-261. , https://doi.org/10.1890/08-0879.1, PMID: 20380214; Bartneck, C, Duenser, A, Moltchanova, E, Zawieska, K., Comparing the similarity of responses received from experiments conducted in Amazon’s Mechanical Turk to experiments conducted with traditional methods (2015) PLOS One, 10 (4), p. e0121595. , https://doi.org/10.1371/journal.pone.0121595, PMID: 25876027; Simons, DJ, Chabris, CF., Common (Mis)Beliefs about Memory: A Replication and Comparison of Telephone and Mechanical Turk Survey Methods (2012) PLOS ONE, 7 (12), p. e51876. , https://doi.org/10.1371/journal.pone.0051876, PMID: 23272183; Stewart, N, Ungemach, C, Harris, AJ, Bartels, DM, Newell, BR, Paolacci, G, The average laboratory samples a population of 7,300 Amazon Mechanical Turk workers (2015) Judgment and Decision Making, 10 (5), pp. 479-491; Huff, C, Tingley, D., Who are these people?” Evaluating the demographic characteristics and political preferences of MTurk survey respondents (2015) Research & Politics, 2 (3), p. 2053168015604648; McCredie, MN, Morey, LC., Who Are the Turkers? A Characterization of MTurk Workers Using the Personality Assessment Inventory (2019) Assessment, 26 (5), pp. 759-766. , https://doi.org/10.1177/1073191118760709, PMID: 29490470; Chmielewski, M, Kucker, SC., An MTurk Crisis? Shifts in Data Quality and the Impact on Study Results (2020) Social Psychological and Personality Science, 11 (4), pp. 464-473. , https://doi.org/10.1177/1948550619875149; Barclay, S, Brown, RV, CW, K, Peterson, CR, Philips, LD, Selvidge, J., (1977) Handbook for Decision Analysis, , Office of Naval Research; Lin, M, Lucas, HC, Shmueli, G., Research commentary—too big to fail: large samples and the p-value problem (2013) Information Systems Research, 24 (4), pp. 906-917. , https://doi.org/10.1287/isre.2013.0480; Nuzzo, R., Statistical errors: P values, the’gold standard’of statistical validity, are not as reliable as many scientists assume (2014) Nature, 506 (7487), pp. 150-153. , https://doi.org/10.1038/506150a, PMID: 24522584; Merkhofer, MW., Quantifying judgmental uncertainty: Methodology, experiences, and insights (1987) IEEE Transactions on Systems, Man, and Cybernetics, 17 (5), pp. 741-752. , https://doi.org/10.1109/TSMC.1987.6499281; Bunn, D, Wright, G., Interaction of judgemental and statistical forecasting methods: issues & analysis (1991) Management science, 37 (5), pp. 501-518. , https://doi.org/10.1287/mnsc.37.5.501","Bartneck, C.; University of CanterburyNew Zealand; email: christoph.bartneck@canterbury.ac.nz",,,"Public Library of Science",,,,,19326203,,POLNC,"32673326","English","PLoS ONE",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85088155155
"Wei Y., Wei Y., Zhao J.","57220668642;55994861800;57129187500;","Designing Human-like Behaviors for Anthropomorphic Arm in Humanoid Robot NAO",2020,"Robotica","38","7",,"1205","1226",,,"10.1017/S026357471900136X","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072936818&doi=10.1017%2fS026357471900136X&partnerID=40&md5=3a6a164cd2a87735d79162c864fb1113","Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China; Vehicle and Transportation Engineering Institute, Henan University of Science and Technology, Luoyang Shi, China","Wei, Y., Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China; Wei, Y., Vehicle and Transportation Engineering Institute, Henan University of Science and Technology, Luoyang Shi, China; Zhao, J., Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, China","Human-like motion of robots can improve human-robot interaction and increase the efficiency. In this paper, a novel human-like motion planning strategy is proposed to help anthropomorphic arms generate human-like movements accurately. The strategy consists of three parts: movement primitives, Bayesian network (BN), and a novel coupling neural network (CPNN). The movement primitives are used to decouple the human arm movements. The classification of arm movements improves the accuracy of human-like movements. The motion-decision algorithm based on BN is able to predict occurrence probabilities of the motions and choose appropriate mode of motion. Then, a novel CPNN is proposed to solve the inverse kinematics problems of anthropomorphic arms. The CPNN integrates different models into a single network and reflects the features of these models by changing the network structure. Through the strategy, the anthropomorphic arms can generate various human-like movements with satisfactory accuracy. Finally, the availability of the proposed strategy is verified by simulations for the general motion of humanoid NAO. © 2019 Cambridge University Press.","Anthropomorphic arm; Bayesian network; Human-like movements; Motion planning; Neural network","Anthropomorphic robots; Bayesian networks; Behavioral research; Inverse kinematics; Inverse problems; Motion planning; Neural networks; Robotic arms; Anthropomorphic arm; Coupling neural networks; Decision algorithms; Human arm movements; Human like; Humanoid robot NAO; Movement primitives; Occurrence probability; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 51475016, 51805149, 51975008; Natural Science Foundation of Beijing Municipality: 3192002","The support of this research by National Natural Science Foundation of China (No. 51975008, No. 51475016 and No. 51805149), Natural Science Foundation of Beijing (Grant No. 3192002) is greatly appreciated.",,,,,,,,,,"Cafolla, D., Ceccarelli, M., An experimental validation of a novel humanoid torso (2017) Roboti. 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Syst., 91, pp. 299-313; Katsiaris, P.T., Artemiadis, P.K., Kyriakopoulos, K.J., Modeling anthropomorphism in dynamic human arm movements (2010) Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3507-3512. , Taipei, Taiwan; Seungsu, K., Chang Hwan, K., Jong Hyeon, P., Human-like arm motion generation for humanoid robots using motion capture database (2006) Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3486-3491. , Beijing, China; Zacharias, F., Schlette, C., Schmidt, F., Borst, C., Rossmann, J., Hirzinger, G., Making planned paths look more human-like in humanoid robot manipulation planning (2011) Proceedings of IEEE International Conference on Robotics and Automation, pp. 1192-1198. , Shanghai, China; Hyunchul, K., Zhi, L., Milutinovic, D., Rosen, J., Resolving the redundancy of a seven dof wearable robotic system based on kinematic and dynamic constraint (2012) Proceedings of IEEE International Conference on Robotics and Automation, pp. 305-310. , Saint Paul, Minnesota; Lenarcic, J., Some issues of the human arm motion obtained from its kinematic model (1997) Proceedings of CASYS, pp. 433-444. , Liege, Belgium; McAtamney, L., Corlett, E.N., Rula: A survey method for the investigation of work-related upper limb disorders (1993) Appl. 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Mech., 18 (2), pp. 624-636; Roitman, A.V., Massaquoi, S.G., Takahashiet, K., Ebner, T.J., Kinematic analysis of manual tracking in monkeys: Characterization of movement intermittencies during a circular tracking task (2004) J. Neurophysio., 91 (2), pp. 901-911; Fishbach, A., Roy, S.A., Bastianenet, C., Miller, L.E., Charles Houk, J., Kinematic properties of on-line error corrections in the monkey (2005) Exp. Brain Res., 164 (4), pp. 442-457; Pasalar, S., Roitman, A.V., Ebner, T.J., Effects of speeds and force fields on submovements during circular manual tracking in humans (2005) Exp. Brain Res., 163 (2), pp. 214-225; Flash, T., Hochner, B., Motor primitives in vertebrates and invertebrates (2005) Curr. Opin. Neurobiol., 15 (6), pp. 660-666; Wei, Y., Zhao, J., Designing robot behavior in human robot interaction based on emotion expression (2016) Ind. Robot, 43 (4), pp. 380-389; Lim, B., Ra, S., Park, F.C., Movement primitives, principal component analysis, and the efficient generation of natural motions (2005) Proceedings of the IEEE International Conference on Robotics and Automation, pp. 4630-4635. , Barcelona, Spain; Chhabra, M., Jacobs, R., Properties of synergies arising from a theory of optimal motor behavior (2006) Neural Comput., 18 (10), pp. 2320-2342; Berret, B., Bonnetblanc, F., Papaxanthis, C., Pozzo, T., Modular control of pointing beyond arm's length (2009) J. Neurosci., 29 (1), pp. 191-205; Okamoto, T., Shiratori, T., Kudoh, S., Nakaoka, S., Ikeuchi, K., Toward a dancing robot with listening capability: Keypose-based integration of lower-, middle-, and upper-body motions for varying music tempos (2014) IEEE Trans. Robot., 30 (3), pp. 771-778; Kulić, D., Ott, C., Lee, D., Ishikawa, J., Nakamura, Y., Incremental learning of full body motion primitives and their sequencing through human motion observation (2012) Int. 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Theory, 14 (3), pp. 462-467; Jun, S.J., Qing, S.S., Study on driving motion capture data based on bvh (2013) Trans. Beijing Inst. Technol., 33 (1), pp. 109-114; Zhao, J., Xie, B., Song, C., Generating human-like movements for robotic arms (2014) Mech. Mach. Theory, 81 (11), pp. 107-128; Chen, C., Zhuang, Y.T., Nie, F.P., Yang, Y., Wu, F., Xiao, J., Learning a 3d human pose distance metric from geometric pose descriptor (2011) IEEE Trans. Visualization Comput. Graphics, 17 (11), pp. 1676-1689; Ou, Y.S., Hu, J.B., Wang, Z.Y., Fu, Y.G., Wu, X.Y., Li, W.Y., A real-time human imitation system using kinect (2015) Int. J. Soc. Robot., 7 (5), pp. 587-600","Wei, Y.; Mechanical Engineering and Applied Electronics Technology, China; email: tsubasafx@foxmail.com",,,"Cambridge University Press",,,,,02635747,,ROBOD,,"English","Robotica",Article,"Final","",Scopus,2-s2.0-85072936818
"Rossi S., Conti D., Garramone F., Santangelo G., Staffa M., Varrasi S., Di Nuovo A.","55619320887;56241181700;57190950101;7006968775;8892560900;57201559032;56962754700;","The role of personality factors and empathy in the acceptance and performance of a social robot for psychometric evaluations",2020,"Robotics","9","2","39","","",,6,"10.3390/ROBOTICS9020039","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086668483&doi=10.3390%2fROBOTICS9020039&partnerID=40&md5=c2ec5388361d460022f6c3d10f29ebcb","Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Napoli, 80125, Italy; College of Business, Technology and Engineering, Sheffield Hallam University, Sheffield, S1 1WB, United Kingdom; Department of Psychology, University of Campania L. Vanvitelli, Caserta, 81100, Italy; Department of Physics E. Pancini, University of Naples Federico II, Napoli, 80126, Italy","Rossi, S., Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Napoli, 80125, Italy; Conti, D., College of Business, Technology and Engineering, Sheffield Hallam University, Sheffield, S1 1WB, United Kingdom; Garramone, F., Department of Psychology, University of Campania L. Vanvitelli, Caserta, 81100, Italy; Santangelo, G., Department of Psychology, University of Campania L. Vanvitelli, Caserta, 81100, Italy; Staffa, M., Department of Physics E. Pancini, University of Naples Federico II, Napoli, 80126, Italy; Varrasi, S., College of Business, Technology and Engineering, Sheffield Hallam University, Sheffield, S1 1WB, United Kingdom; Di Nuovo, A., College of Business, Technology and Engineering, Sheffield Hallam University, Sheffield, S1 1WB, United Kingdom","Research and development in socially assistive robotics have produced several novel applications in the care of senior people. However, some are still unexplored such as their use as psychometric tools allowing for a quick and dependable evaluation of human users' intellectual capacity. To fully exploit the application of a social robot as a psychometric tool, it is necessary to account for the users' factors that might influence the interaction with a robot and the evaluation of user cognitive performance. To this end, we invited senior participants to use a prototype of a robot-led cognitive test and analyzed the influence of personality traits and user's empathy on the cognitive performance and technology acceptance. Results show a positive influence of a personality trait, the ""openness to experience"", on the human-robot interaction, and that other factors, such as anxiety, trust, and intention to use, are influencing technology acceptance and correlate the evaluation by psychometric tests. © 2020 by the authors.","Empathy; Human friendly cognitive robotics; Personality factors; Psychometric evaluation; Social assistive robots; Technology acceptance",,,,,,"703489; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR: 2015KB-L78T","The work of D.C. and A.D.N. was supported by the European Union's H2020 research and innovation program under the Marie Sk?odowska Curie Action-Individual Fellowship grant agreement no. 703489. The work of S.R., F.G., and G.S. was supported by the Italian Ministry of University and Research-MIUR, within the PRIN2015 research project ""UPA4SAR-User-centered Profiling and Adaptation for Socially Assistive Robotics"" (grant no. 2015KB-L78T). The authors gratefully thank all people who participated in this study.","Funding: The work of D.C. and A.D.N. was supported by the European Union’s H2020 research and innovation program under the Marie Skłodowska Curie Action—Individual Fellowship grant agreement no. 703489. The work of S.R., F.G., and G.S. was supported by the Italian Ministry of University and Research—MIUR, within the PRIN2015 research project “UPA4SAR—User-centered Profiling and Adaptation for Socially Assistive Robotics” (grant no. 2015KB-L78T).",,,,,,,,,"Di Nuovo, A., Broz, F., Cavallo, F., Dario, P., New Frontiers of Service Robotics for Active and Healthy Ageing (2016) Int. J. Soc. 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"Tapia J., Knoop E., Mutný M., Otaduy M.A., Bächer M.","57217073853;55922286800;57208437691;6507451572;25627224600;","MakeSense: Automated Sensor Design for Proprioceptive Soft Robots",2020,"Soft Robotics","7","3",,"332","345",,20,"10.1089/soro.2018.0162","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086052742&doi=10.1089%2fsoro.2018.0162&partnerID=40&md5=864a58600e9404b6d324d737e817112e","Disney Research, Stampfenbachstrasse 48, Zurich, Zurich, Switzerland; Department of Computer Science, Universidad Rey Juan Carlos, Madrid, Spain","Tapia, J., Disney Research, Stampfenbachstrasse 48, Zurich, Zurich, Switzerland, Department of Computer Science, Universidad Rey Juan Carlos, Madrid, Spain; Knoop, E., Disney Research, Stampfenbachstrasse 48, Zurich, Zurich, Switzerland; Mutný, M., Disney Research, Stampfenbachstrasse 48, Zurich, Zurich, Switzerland; Otaduy, M.A., Department of Computer Science, Universidad Rey Juan Carlos, Madrid, Spain; Bächer, M., Disney Research, Stampfenbachstrasse 48, Zurich, Zurich, Switzerland","Soft robots have applications in safe human-robot interactions, manipulation of fragile objects, and locomotion in challenging and unstructured environments. In this article, we present a computational method for augmenting soft robots with proprioceptive sensing capabilities. Our method automatically computes a minimal stretch-receptive sensor network to user-provided soft robotic designs, which is optimized to perform well under a set of user-specified deformation-force pairs. The sensorized robots are able to reconstruct their full deformation state, under interaction forces. We cast our sensor design as a subselection problem, selecting a minimal set of sensors from a large set of fabricable ones, which minimizes the error when sensing specified deformation-force pairs. Unique to our approach is the use of an analytical gradient of our reconstruction performance measure with respect to selection variables. We demonstrate our technique on a bending bar and gripper example, illustrating more complex designs with a simulated tentacle. © Copyright 2020, Mary Ann Liebert, Inc., publishers 2020.","computational design; sensor design; soft sensing",,,,,,"Horizon 2020 Framework Programme, H2020: H2020-ICT-645599; European Research Council, ERC: 772738","This work has been supported by the projects SOMA (European Commission, Horizon 2020 Framework Programme, H2020-ICT-645599) and TouchDesign (ERC Consolidator Grant No. 772738).",,,,,,,,,,"Park, Y.-L., Majidi, C., Kramer, R., Hyperelastic pressure sensing with a liquid-embedded elastomer (2010) J Micromech Microeng, 20, p. 125029; O'Brien, B., Gisby, T., Anderson, I.A., Stretch sensors for human body motion (2014) Electroactive Polymer Actuators and Devices (EAPAD) 2014, 9056, p. 905618. , Bellingham, WA: International Society for Optics and Photonics; Mengüç, Y., Park, Y.-L., Pei, H., Wearable soft sensing suit for human gait measurement (2014) Int J Robot Res, 33, pp. 1748-1764; 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"de Kervenoael R., Hasan R., Schwob A., Goh E.","57196059833;57204513876;55911596800;57200792631;","Leveraging human-robot interaction in hospitality services: Incorporating the role of perceived value, empathy, and information sharing into visitors’ intentions to use social robots",2020,"Tourism Management","78",,"104042","","",,69,"10.1016/j.tourman.2019.104042","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075628508&doi=10.1016%2fj.tourman.2019.104042&partnerID=40&md5=f56ef158af2d44659553121590311519","Department of Marketing, Rennes School of Business, Rennes, France; Department of Marketing, La Rochelle Business School, La Rochelle, France; Birmingham University, United Kingdom; Singapore Institute of Management (SIM), Singapore","de Kervenoael, R., Department of Marketing, Rennes School of Business, Rennes, France; Hasan, R., Department of Marketing, Rennes School of Business, Rennes, France; Schwob, A., Department of Marketing, La Rochelle Business School, La Rochelle, France; Goh, E., Birmingham University, United Kingdom, Singapore Institute of Management (SIM), Singapore","Social robots have become pervasive in the tourism and hospitality service environments. The empirical understanding of the drivers of visitors' intentions to use robots in such services has become an urgent necessity for their sustainable deployment. Certainly, using social androids within hospitality services requires organisations' attentive commitment to value creation and fulfilling service quality expectations. In this paper, via structural equation modelling (SEM) and semi-structured interviews with managers, we conceptualise and empirically test visitors' intentions to use social robots in hospitality services. With data collected in Singapore's hospitality settings, we found visitors' intentions to use social robots stem from the effects of technology acceptance variables, service quality dimensions leading to perceived value, and two further dimensions from human robot interaction (HRI): empathy and information sharing. Analysis of these dimensions' importance provides a deeper understanding of novel opportunities managers may take advantage of to position social robot-delivered services in tourism and hospitality strategies. © 2019 Elsevier Ltd","Artificial intelligence; Hospitality services; Human-robot interaction; Intention to use robots; Social robots","artificial intelligence; conceptual framework; ecotourism; hospitality industry; numerical model; robotics; service quality; tourism management; Singapore [Southeast Asia]",,,,,,,,,,,,,,,,"Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: A review (2017) Journal of Human-Robot Interaction, 6 (1), pp. 25-63; Aguirre-Urreta, M.I., Marakas, G.M., Modeling choice between competing technologies: A comparison of mechanisms and information integration (2018) Technology adoption and social issues: Concepts, methodologies, tools, and applications, pp. 177-209. , IGI Global Hershey, PA; Albert, N., Merunka, D., The role of brand love in consumer‐brand relationships (2013) Journal of Consumer Marketing, 30 (3), pp. 258-266; Bagozzi, R.P., The legacy of the technology acceptance model and proposal for a paradigm shift (2007) Journal of the Association for Information Systems, 8 (4), pp. 244-254; Baird, N., Robots, automation and retail: Not so cut and dried (2018), https://www.forbes.com/sites/nikkibaird/2018/06/19/robots-automation-and-retail-not-so-cut-and-dried/, June 19 Forbes Retrieved from; Batson, C.D., These things called empathy: Eight related but distinct phenomena (2009) The social neuroscience of empathy, pp. 3-15. , J. 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"Złotowski J., Khalil A., Abdallah S.","50362395700;55417517900;57192904835;","One robot doesn’t fit all: aligning social robot appearance and job suitability from a Middle Eastern perspective",2020,"AI and Society","35","2",,"485","500",,5,"10.1007/s00146-019-00895-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067299243&doi=10.1007%2fs00146-019-00895-x&partnerID=40&md5=1b1c71786e0f984edd7934e6d0be2ee9","Abu Dhabi University, Abu Dhabi, United Arab Emirates; CITEC, Bielefeld University, Bielefeld, Germany; Queensland University of Technology, Brisbane, Australia","Złotowski, J., Abu Dhabi University, Abu Dhabi, United Arab Emirates, CITEC, Bielefeld University, Bielefeld, Germany, Queensland University of Technology, Brisbane, Australia; Khalil, A., Abu Dhabi University, Abu Dhabi, United Arab Emirates; Abdallah, S., Abu Dhabi University, Abu Dhabi, United Arab Emirates","Social robots are expected to take over a significant number of jobs in the coming decades. The present research provides the first systematic evaluation of occupation suitability of existing social robots based on user perception derived classification of them. The study was conducted in the Middle East since the views of this region are rarely considered in human–robot interaction research, although the region is poised to increasingly adopt the use of robots. Laboratory-based experimental data revealed that a robot’s appearance plays an important role in the perception of its capabilities and preference for it to perform a particular job. Participants showed a preference for machine-like robots to perform dull and dirty occupations and humanoids, but not androids, to perform jobs requiring extensive social interaction with humans. However, other aspects of appearance than morphology determine whether a robot is preferred for a job irrespective of its perceived capability to do it. © 2019, Springer-Verlag London Ltd., part of Springer Nature.","Appearance; Human–robot interaction; Jobs; Middle East; Social robotics","Robotics; Appearance; Jobs; Middle East; Robot interactions; Social robotics; Human robot interaction",,,,,,,,,,,,,,,,"Albirini, A., Teachers’ attitudes toward information and communication technologies: the case of Syrian EFL teachers (2006) Comput Educ, 47 (4), pp. 373-398; Al-Qaradawi, S.Y., (1999) The Lawful and the Prohibited in Islam (Al-Halal Wal Haram Fil Islam), , American Trust Publications; Arras, K.O., Cerqui, D., (2005) Do we want to share our lives and bodies with robots, , A 2000-people survey, technical report Nr. 0605-001 Autonomous Systems Lab Swiss Federal Institute of Technology; Bartneck, C., Who like androids more: Japanese or US Americans? 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"Peng S., Wu S., Yu Y., Xia B., Lovell N.H., Wang C.H.","57202094824;55642602400;57213831150;57208164825;7006684228;18839512000;","Multimodal Capacitive and Piezoresistive Sensor for Simultaneous Measurement of Multiple Forces",2020,"ACS Applied Materials and Interfaces","12","19",,"22179","22190",,22,"10.1021/acsami.0c04448","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084694799&doi=10.1021%2facsami.0c04448&partnerID=40&md5=cd005d3563b638691d812ddea9251bb3","School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW  2052, Australia; School of Engineering, Macquarie University, Sydney, NSW  2109, Australia; Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW  2052, Australia","Peng, S., School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW  2052, Australia; Wu, S., School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW  2052, Australia, School of Engineering, Macquarie University, Sydney, NSW  2109, Australia; Yu, Y., School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW  2052, Australia; Xia, B., Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW  2052, Australia; Lovell, N.H., Graduate School of Biomedical Engineering, University of New South Wales, Sydney, NSW  2052, Australia; Wang, C.H., School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW  2052, Australia","Quantitative information on the magnitudes and directions of multiple contacting forces is crucial for a wide range of applications including human-robot interaction, prosthetics, and bionic hands. Herein we report a highly stretchable sensor integrating capacitive and piezoresistive mechanisms that can simultaneously determine multiple forces. The sensor consists of three layers in a sandwich design. The two facesheets serve as both piezoresistive sensors and electrodes for the capacitive sensor, with the core being a porous structure made by using a simple sugar particle template technique to give them high stretchability. The two facesheets contain segregated conductive networks of silver nanowires (AgNWs) and carbon nanofibers (CNFs). By measuring the changes in the electrical resistance of the facesheets and the capacitance between the facesheets, three separate mechanical stimuli can be determined, including normal pressure, in-plane stretch, and transverse shear force. The newly developed multidirectional sensor offers a significant opportunity for the next generation of wearable sensors for human health monitoring and bionic skin for robots. Copyright © 2020 American Chemical Society.","capacitive sensors; directional sensors; piezoresistive sensors; porous PDMS; sandwiched structures; sugar templating","Bionics; Capacitive sensors; Carbon nanofibers; Human robot interaction; Sandwich structures; Silver nanowires; Conductive networks; Electrical resistances; Mechanical stimulus; Piezo-resistive sensors; Porous structures; Quantitative information; Simultaneous measurement; Template technique; Wearable sensors; baysilon; carbon; dimeticone; nanocomposite; nanofiber; nanowire; silver; chemistry; electric capacitance; electrode; electronic device; human; impedance; porosity; pressure; tensile strength; Young modulus; Carbon; Dimethylpolysiloxanes; Elastic Modulus; Electric Capacitance; Electric Impedance; Electrodes; Humans; Nanocomposites; Nanofibers; Nanowires; Porosity; Pressure; Silver; Tensile Strength; Wearable Electronic Devices",,"carbon, 7440-44-0; dimeticone, 32028-95-8, 68248-27-1, 9004-73-3, 9006-65-9; silver, 7440-22-4; baysilon; Carbon; Dimethylpolysiloxanes; Silver",,,"Australian Research Council, ARC: DE170100284, DE190100311, DP190102790","S.W. and S.P. acknowledge the financial support from the Australian Research Council (ARC) via Discovery Early Career Researcher Award (DE170100284 and DE190100311). C.H.W. acknowledges the financial support from the Australian Research Council (ARC) through the Discovery Project scheme (DP190102790). The authors also thank Dr. Yin Yao for technical assistance of FEI Nova NanoSEM 230 and use of facilities at the Electron Microscope Unit (EMU) at UNSW.",,,,,,,,,,"Luo, N., Huang, Y., Liu, J., Chen, S.-C., Wong, C.P., Zhao, N., Hollow-Structured Graphene-Silicone-Composite-Based Piezoresistive Sensors: Decoupled Property Tuning and Bending Reliability (2017) Adv. Mater., 29 (40), p. 1702675; Choi, D.Y., Kim, M.H., Oh, Y.S., Jung, S.-H., Jung, J.H., Sung, H.J., Lee, H.W., Lee, H.M., Highly Stretchable, Hysteresis-Free Ionic Liquid-Based Strain Sensor for Precise Human Motion Monitoring (2017) ACS Appl. Mater. Interfaces, 9 (2), pp. 1770-1780; Huang, Y., Fan, X., Chen, S.C., Zhao, N., Emerging Technologies of Flexible Pressure Sensors: Materials, Modeling, Devices, and Manufacturing (2019) Adv. Funct. 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Ed., 52 (6), pp. 1654-1659","Peng, S.; School of Mechanical and Manufacturing Engineering, Australia; email: shuhua.peng@unsw.edu.au",,,"American Chemical Society",,,,,19448244,,,"32302480","English","ACS Appl. Mater. Interfaces",Article,"Final","",Scopus,2-s2.0-85084694799
"Ballesteros J., Pastor F., Gómez-De-gabriel J.M., Gandarias J.M., García-Cerezo A.J., Urdiales C.","57117414200;57211139267;7801476081;57200697817;6701640053;6603880612;","Proprioceptive estimation of forces using underactuated fingers for robot-initiated phri",2020,"Sensors (Switzerland)","20","10","2863","","",,,"10.3390/s20102863","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085132575&doi=10.3390%2fs20102863&partnerID=40&md5=b1e824e1160a7cbe1ac202ca2614fc1b","Department of Computer Languages and Science, University of Malaga, Escuela de Ingeniería Informática, Málaga, 29071, Spain; Systems Engineering and Automation Department, University of Malaga, Escuela de Ingenierías Industriales, Málaga, 29071, Spain; Electronics Technology Department, University of Malaga, Escuela de Ingeniería Telecomunicación, Málaga, 29071, Spain","Ballesteros, J., Department of Computer Languages and Science, University of Malaga, Escuela de Ingeniería Informática, Málaga, 29071, Spain; Pastor, F., Systems Engineering and Automation Department, University of Malaga, Escuela de Ingenierías Industriales, Málaga, 29071, Spain; Gómez-De-gabriel, J.M., Systems Engineering and Automation Department, University of Malaga, Escuela de Ingenierías Industriales, Málaga, 29071, Spain; Gandarias, J.M., Systems Engineering and Automation Department, University of Malaga, Escuela de Ingenierías Industriales, Málaga, 29071, Spain; García-Cerezo, A.J., Systems Engineering and Automation Department, University of Malaga, Escuela de Ingenierías Industriales, Málaga, 29071, Spain; Urdiales, C., Electronics Technology Department, University of Malaga, Escuela de Ingeniería Telecomunicación, Málaga, 29071, Spain","In physical Human–Robot Interaction (pHRI), forces exerted by humans need to be estimated to accommodate robot commands to human constraints, preferences, and needs. This paper presents a method for the estimation of the interaction forces between a human and a robot using a gripper with proprioceptive sensing. Specifically, we measure forces exerted by a human limb grabbed by an underactuated gripper in a frontal plane using only the gripper’s own sensors. This is achieved via a regression method, trained with experimental data from the values of the phalanx angles and actuator signals. The proposed method is intended for adaptive shared control in limb manipulation. Although adding force sensors provides better performance, the results obtained are accurate enough for this application. This approach requires no additional hardware: it relies uniquely on the gripper motor feedback—current, position and torque—and joint angles. Also, it is computationally cheap, so processing times are low enough to allow continuous human-adapted pHRI for shared control. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Adaptation; Force estimation; Physical Human; Robot Interaction; Underactuated grippers","Grippers; Regression analysis; Actuator signals; Frontal planes; Interaction forces; Processing time; Regression method; Robot commands; Robot interactions; Under-actuated finger; Human robot interaction; feedback system; finger; human; proprioception; regression analysis; robotics; torque; Feedback; Fingers; Humans; Proprioception; Regression Analysis; Robotics; Torque",,,,,"European Commission, EC: BES-2016-078237","Funding: This work was supported by the Spanish projects RTI2018-093421-B-I00 and RTI2018-096701-B-C21, and the European Commission under grant agreement BES-2016-078237.",,,,,,,,,,"Krishnaswamy, K., Moorthy, S., Oates, T., Survey Data Analysis for Repositioning, Transferring, and Personal Care Robots (2017) In Proceedings of the International Conference on Pervasive Technologies Related to Assistive Environments (PETRA), Rhodes, Greece, 21–23 June, pp. 45-51; Krishnan, R.H., Pugazhenthi, S., Mobility assistive devices and self-transfer robotic systems for elderly, a review (2014) Intell. 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Rev, 70; Wang, X., Yu, H., Li, K., Dong, Q., Huang, X., (2019) Study on Force Interaction System of Upper Limb Rehabilitation Robot. in IOP Conference Series: Materials Science and Engineering, 631. , IOP Publishing: Bristol, UK","Ballesteros, J.; Department of Computer Languages and Science, Spain; email: jballesteros@uma.es",,,"MDPI AG",,,,,14248220,,,"32443547","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85085132575
"Bira N., Menguc Y., Davidson J.R.","57203149419;36461401000;55928739600;","3D-Printed Electroactive Hydraulic Valves for Use in Soft Robotic Applications",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9196993","11200","11206",,,"10.1109/ICRA40945.2020.9196993","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092700306&doi=10.1109%2fICRA40945.2020.9196993&partnerID=40&md5=0201a60e855e87039292eb1df28f117b","Oregon State University, Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Corvallis, OR  97331, United States; Oregon State University, Facebook Reality Labs, Courtesy Faculty, Collaborative Robotics and Intelligent Systems Institute, Redmond, WA  98052, United States","Bira, N., Oregon State University, Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Corvallis, OR  97331, United States; Menguc, Y., Oregon State University, Facebook Reality Labs, Courtesy Faculty, Collaborative Robotics and Intelligent Systems Institute, Redmond, WA  98052, United States; Davidson, J.R., Oregon State University, Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Corvallis, OR  97331, United States","Soft robotics promises developments in the research areas of safety, bio-mimicry, manipulation, human-robot interaction, and alternative locomotion techniques. The research presented here is directed towards developing an improved, low-cost, and open-source method for soft robotic control using electrorheological fluids in compact, 3D-printed electroactive hydraulic valves. We construct high-pressure electrorheological valves and deformable actuators using only commercially available materials and accessible fabrication methods. The printed valves were characterized with industrial-grade electrorheological fluid (RheOil 3.0), but the design is generalizable to other electrorheological fluids. Valve performance was shown to be an improvement over comparable work with demonstrated higher yield pressures at lower voltages (up to 230 kPa), larger flow rates (up to 15 ml/min) and lower response times (1 to 3 seconds, depending on design). The resulting valve and actuator systems enable future novel applications of electrorheological fluid-based control and hydraulics in soft robotics and other disciplines. © 2020 IEEE.",,"Agricultural robots; Electrorheological fluids; High pressure engineering; Human robot interaction; Hydraulic actuators; Hydraulic equipment; Nanofluidics; Rheology; Robotics; Electrorheological valves; Fabrication method; Hydraulic valves; Locomotion technique; Novel applications; Robotic applications; Robotic controls; Valve performance; 3D printers",,,,,"National Science Foundation, NSF","This work was partially funded by a National Science Foundation Graduate Research Fellowship.",,,,,,,,,,"Rich, S.I., Wood, R.J., Majidi, C., Untethered soft robotics (2018) Nature Electronics, 1 (2), p. 102; Lisa Urry A, S.A.W.P.V.M., Cain, M.L., Reece, J.B., (2017) Campbell Biology, , https://books.google.com/books?id=UYfAjwEACAAJ, 11th Ed, ser Biology for Student Pearson Education, Incorporated; Winslow, W.M., (1947) Method and Means for Translating Electrical Impulses into Mechanical Force, , Mar. 25 uS Patent; Whittle, M., Bullough, W., The structure of smart fluids (1992) Nature, 358 (6385), p. 373; Halsey, T.C., Electrorheological fluids (1992) Science, 258 (5083), pp. 761-766; Horváth, B., Szalai, I., Structure of electrorheological fluids: A dielectric study of chain formation (2012) Physical Review E-Statistical, Nonlinear, and Soft Matter Physics, 86 (6), pp. 1-5; Simmonds, A., Electro-rheological valves in a hydraulic circuit (1992) Power International, 38 (430), pp. 9-12; Stangroom, J.E., Electrorheological fluids (1983) Physics in Technology, 14 (6), p. 290; Helal, A.H., (2016) Designing Devices Using Electro-active Yield Stress Fluids, , Ph. D. dissertation, Massachusetts Institute of Technology; Murphy, M.P., Playter, R., (2015) Electrorheological Valve, , Mar. 10 uS Patent; Seed, M., Hobson, G., Tozer, R., Simmonds, A., Voltage-controlled electrorheological brake (1986) Proceedings of the IASTED International Symposium on Measurements, Processes and Controls, pp. 280-284; Hare, N.S., (1992) Electro-rheological Shock Absorber, , Nov. 10 uS Patent; Akella, P., Siegwart, R., Cutkosky, M., Manipulation with soft fingers: Contact force control (1991) Robotics and Automation, 1991. Proceedings., 1991 IEEE International Conference On. IEEE, pp. 652-657; Takesue, N., Zhang, G., Furusho, J., Sakaguchi, M., Precise position control of robot arms using a homogeneous er fluid (1999) IEEE Control Systems, 19 (2), pp. 55-61; Furusho, J., Zhang, G., Sakaguchi, M., Vibration suppression control of robot arms using a homogeneous-type electrorheological fluid (1997) Robotics and Automation, 1997. Proceedings., 1997 IEEE International Conference on, 4, pp. 3441-3448. , IEEE; Nikitczuk, J., Weinberg, B., Mavroidis, C., Control of electrorheological fluid based resistive torque elements for use in active rehabilitation devices (2007) Smart Materials and Structures, 16 (2), p. 418; Davidson, J.R., Krebs, H.I., An electrorheological fluid actuator for rehabilitation robotics (2018) IEEE/ASME Transactions on Mechatronics, 23 (5), pp. 2156-2167; Wen, W., Huang, X., Yang, S., Lu, K., Sheng, P., The giant electrorheological effect in suspensions of nanoparticles (2003) Nature Materials, 2 (11), p. 727; Zatopa, A., Walker, S., Menguc, Y., Fully soft 3d-printed electroactive fluidic valve for soft hydraulic robots (2018) Soft Robotics, P. Soro. 2017. 0019, , http://www.liebertpub.com/doi/10.1089/soro.2017.0019, apr http://www. ncbi. nlm. nih. gov/pubmed/29608429; Sadeghi, A., Beccai, L., Mazzolai, B., Innovative soft robots based on electro-rheological fluids (2012) Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference On. IEEE, pp. 4237-4242; Georgiades, G., Oyadiji, S., Effects of electrode geometry on the performance of electrorheological fluid valves (2003) Journal of Intelligent Material Systems and Structures, 14 (2), pp. 105-111; Tam, W.Y., Yi, G.H., Wen, W., Ma, H., Loy, M.M., Sheng, P., New electrorheological fluid: Theory and experiment (1997) Physical Review Letters, 78 (15), p. 2987; Branyan, C., Fleming, C., Remaley, J., Kothari, A., Tumer, K., Hatton, R.L., Menguc, Y., Soft snake robots: Mechanical design and geometric gait implementation (2017) 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, pp. 282-289. , http://ieeexplore.ieee.org/document/8324431/, dec; Gorissen, B., Reynaerts, D., Konishi, S., Yoshida, K., Kim, J.-W., De Volder, M., Elastic inflatable actuators for soft robotic applications (2017) Advanced Materials, 29 (43), p. 1604977. , https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.201604977; Gurka, M., Johnston, L., Petricevic, R., New electrorheological fluids-characteristics and implementation in industrial and mobile applications (2010) Journal of Intelligent Material Systems and Structures, 21 (15), pp. 1531-1535; (2019) 3d Printed Erf Valve Files, , http://bit.ly/ERValveFiles, BiraN2019 Sep; Ilievski, F., Mazzeo, A.D., Shepherd, R.F., Chen, X., Whitesides, G.M., Soft robotics for chemists (2011) Angewandte Chemie, 123, pp. 1930-1935. , http://onlinelibrary.wiley.com/doi/10.1002/ange.201006464/full; Kollias, A., Dimarogonas, A., Properties of zeolite-and cornstarchbased electrorheological fluids at high shear strain rates (1993) Journal of Intelligent Material Systems and Structures, 4 (4), pp. 519-526",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","",Scopus,2-s2.0-85092700306
"Roth J., Chaudhuri S., Klejch O., Marvin R., Gallagher A., Kaver L., Ramaswamy S., Stopczynski A., Schmid C., Xi Z., Pantofaru C.","57204216954;35090023100;57022010500;57204210251;35966892800;57204218683;57220653555;37036176500;7201732776;57204217862;6506180166;","Ava Active Speaker: An Audio-Visual Dataset for Active Speaker Detection",2020,"ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings","2020-May",,"9053900","4492","4496",,16,"10.1109/ICASSP40776.2020.9053900","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084932569&doi=10.1109%2fICASSP40776.2020.9053900&partnerID=40&md5=0a737791ef07af842fcb953a49239e1c","Google Research","Roth, J., Google Research; Chaudhuri, S., Google Research; Klejch, O., Google Research; Marvin, R., Google Research; Gallagher, A., Google Research; Kaver, L., Google Research; Ramaswamy, S., Google Research; Stopczynski, A., Google Research; Schmid, C., Google Research; Xi, Z., Google Research; Pantofaru, C., Google Research","Active speaker detection is an important component in video analysis algorithms for applications such as speaker diarization, video re-targeting for meetings, speech enhancement, and human-robot interaction. The absence of a large, carefully labeled audio-visual active speaker dataset has limited evaluation in terms of data diversity, environments, and accuracy. In this paper, we present the AVA Active Speaker detection dataset (AVA-ActiveSpeaker) which has been publicly released to facilitate algorithm development and comparison. It contains temporally labeled face tracks in videos, where each face instance is labeled as speaking or not, and whether the speech is audible. The dataset contains about 3.65 million human labeled frames spanning 38.5 hours. We also introduce a state-of-the-art, jointly trained audio-visual model for real-time active speaker detection and compare several variants. The evaluation clearly demonstrates a significant gain due to audio-visual modeling and temporal integration over multiple frames. © 2020 IEEE.","active speaker detection; audio-visual; dataset; multimodal","Audio signal processing; Human robot interaction; Large dataset; Speech communication; Speech enhancement; Algorithm development; Multiple-frame; Real time; Speaker detection; Speaker diarization; State of the art; Temporal integration; Video analysis; Speech recognition",,,,,,,,,,,,,,,,"Hershey, J.R., Movellan, J.R., Audio vision: Using audio-visual synchrony to locate sounds. (1999) Proceedings of NIPS; Papandreou, G., Katsamanis, A., Pitsikalis, V., Maragos, P., Adaptive multimodal fusion by uncertainty compensation with application to audiovisual speech recognition (2009) IEEE Transactions on Audio, Speech and Language Processing, 17 (3), pp. 423-435; Slaney, M., Covell, M., Facesync: A linear operator for measuring synchronization of video facial images and audio tracks (2001) Advances in Neural Information Processing Systems, pp. 814-820; Gebru, I.D., Ba, S., Li, X., Horaud, R., Audio-visual speaker diarization based on spatiotemporal Bayesian fusion (2018) IEEE Trans. Pattern Anal. Mach. Intell., 40 (5), pp. 1086-1099; Gebru, I.D., Ba, S., Li, X., Horaud, R., Audio-visual speaker diarization based on spatiotemporal Bayesian fusion (2017) IEEE Trans. Pattern Anal. Mach. Intell., 39; Rana, A., Ozcinar, C., Smolic, A., Towards generating ambisonics using audio-visual cue for virtual reality (2019) Proceedings of ICASSP; Stefanov, K., Beskow, J., Salvi, G., Visionbased active speaker detection in multiparty interaction (2017) Proc. Int. Workshop Grounding Language Understanding, pp. 47-51; Stefanov, K., Sugimoto, A., Beskow, J., Look who's talking: Visual identification of the active speaker in multi-party human-robot interaction (2016) Proc. Workshop Advancements in Social Signal Processing for Multimodal Interaction. ACM, pp. 22-27; Son Chung, J., Zisserman, A., Out of time: Automated lip sync in the wild (2016) Proc. Workshop Multi-View Lip Reading, Asian Conference on Computer Vision; Shillingford, B., Assael, Y., Hoffman, M.W., Paine, T., Hughes, C., Prabhu, U., Liao, H., De Freitas, N., (2018) Large-scale Visual Speech Recognition, , ar xiv. org/abs/1807. 05162; Shinoda, K., Speaker adaptation techniques for automatic speech recognition (2011) Proc. APSIPA ASC; Hoover, K., Chaudhuri, S., Pantofaru, C., Slaney, M., Sturdy, I., Putting a face to the voice: Fusing audio and visual signals across a video to determine speakers (2018) Proc. ICASSP; Afouras, T., Chung, J.S., Zisserman, A., The conversation: Deep audio-visual speech enhancement (2018) INTERSPEECH; Ephrat, A., Mosseri, I., Lang, O., Dekel, T., Wilson, K., Hassidim, A., Freeman, W.T., Rubinstein, M., Looking to listen at the cocktail party: A speakerindependent audio-visual model for speech separation (2018) ACT Trans. Graph., 37 (112); Cour, T., Jordan, C., Miltsakaki, E., Taskar, B., Movie/script: Alignment and parsing of video and text transcription (2008) Proc. European Conference on Computer Vision; Everingham, M., Sivic, J., Zisserman, A., Taking the bite out of automatic naming of characters in tv video (2009) Image and Vision Computing, 27 (5); Nagrani, A., Chung, J.S., Zisserman, A., Voxceleb: A largescale speaker identification dataset (2018) Proc. Interspeech; Chakravarty, P., Mirzaei, S., Tuytelaars, T., Vanhamme, H., Who's speaking? Audio-supervised classification of active speakers in video (2015) Proc. ACM Int. Conf. Multimodal Interaction, pp. 1-5; Chakravarty, P., Tuytelaars, T., Cross-modal supervision for learning active speaker detection in video (2016) Proc. European Conference on Computer Vision, pp. 1-5; Chakravarty, P., Zegers, J., Tuytelaars, T., Van Hamme, H., Active speaker detection with audio-visual co-training (2016) Proc. ACM Int. Conf. Multimodal Interaction; Chaudhuri, S., Roth, J., Ellis, D., Gallagher, A.C., Kaver, L., Marvin, R., Pantofaru, C., Xi, Z., Ava-speech: A densely labeled dataset of speech activity in movies (2018) Proc. Interspeech; Gu, C., Sun, C., Ross, D.A., Vondrick, C., Pantofaru, C., Li, Y., Vijayanarasimhan, S., Malik, J., Ava: A video dataset of spatiotemporally localized atomic visual actions (2018) Proc. IEEE Conf. Computer Vision and Pattern Recognition; Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G., A convolutional neural network cascade for face detection (2015) Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 5325-5334; Fleiss, J.L., Measuring nominal scale agreement among many raters (1971) Psychological Bulletin, 76, pp. 378-382; Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H., Mobilenets: Efficient convolutional neural networks for mobile vision applications (2017) CoRR, , Abs/1704. 04861",,,"The Institute of Electrical and Electronics Engineers, Signal Processing Society","Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020","4 May 2020 through 8 May 2020",,161907,15206149,9781509066315,IPROD,,"English","ICASSP IEEE Int Conf Acoust Speech Signal Process Proc",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85084932569
"Algabri R., Choi M.-T.","57216776324;16229647800;","Deep-learning-based indoor human following of mobile robot using color feature",2020,"Sensors (Switzerland)","20","9","2699","","",,10,"10.3390/s20092699","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084450964&doi=10.3390%2fs20092699&partnerID=40&md5=8b87e7840b95c8ead8f21ee6bb3ceaab","School of Mechanical Engineering, Sungkyunkwan University, Suwon, 16419, South Korea","Algabri, R., School of Mechanical Engineering, Sungkyunkwan University, Suwon, 16419, South Korea; Choi, M.-T., School of Mechanical Engineering, Sungkyunkwan University, Suwon, 16419, South Korea","Human following is one of the fundamental functions in human–robot interaction for mobile robots. This paper shows a novel framework with state-machine control in which the robot tracks the target person in occlusion and illumination changes, as well as navigates with obstacle avoidance while following the target to the destination. People are detected and tracked using a deep learning algorithm, called Single Shot MultiBox Detector, and the target person is identified by extracting the color feature using the hue-saturation-value histogram. The robot follows the target safely to the destination using a simultaneous localization and mapping algorithm with the LIDAR sensor for obstacle avoidance. We performed intensive experiments on our human following approach in an indoor environment with multiple people and moderate illumination changes. Experimental results indicated that the robot followed the target well to the destination, showing the effectiveness and practicability of our proposed system in the given environment. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Color feature; Deep learning; Human following; Mobile robot; Person identification","Conformal mapping; Human robot interaction; Learning algorithms; Mobile robots; Optical radar; Color features; Hue saturation values; Human following; Illumination changes; Indoor environment; Multiple people; Robot interactions; Simultaneous localization and mapping algorithms; Deep learning; algorithm; color; human; robotics; Algorithms; Color; Deep Learning; Humans; Robotics",,,,,"Ministry of Trade, Industry and Energy, MOTIE","Funding: This work was supported by the Technology Innovation Program (20003762) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea).",,,,,,,,,,"Triebel, R., Arras, K., Alami, R., Beyer, L., Breuers, S., Chatila, R., Chetouani, M., Fiore, M., Spencer: A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports (2016) Field and Service Robotics, pp. 607-622. , Springer: Berlin, Germany, pp; Tomoya, A., Nakayama, S., Hoshina, A., Sugaya, M., A mobile robot for following, watching and detecting falls for elderly care (2017) Procedia Comput. 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Res., 34, pp. 335-356; Schlegel, C., Illmann, J., Jaberg, H., Schuster, M., Wörz, R., (1998) Vision Based Person Tracking with a Mobile Robot, pp. 1-10. , BMVC: London, UK; An, N., Sun, S.Y., Zhao, X.G., Hou, Z.G., Online context-based person re-identification and biometric-based action recognition for service robots (2017) Proceedings of the 2017 29Th Chinese Control and Decision Conference (CCDC), pp. 3369-3374. , Istanbul, Turkey, 12–15 October pp; Bellotto, N., Hu, H., Multisensor-based human detection and tracking for mobile service robots (2008) IEEE Trans. Syst. Man Cybern., 39, pp. 167-181; Choi, M.T., Yeom, J., Shin, Y., Park, I., Robot-Assisted ADHD Screening in Diagnostic Process (2019) J. Intell. Robot. Syst., 95, pp. 351-363; Li, H., Zhao, Q., Li, X., Zhang, X., Object detection based on color and shape features for service robot in semi-structured indoor environment (2019) Int. J. Intell. Robot. 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Biom.; Rusu, R.B., Cousins, S., 3d is here: Point cloud library (pcl) (2011) Proceedings of the 2011 IEEE International Conference on Robotics and Automation, pp. 1-4. , In, Shanghai, China, 9–13 May; Balsa-Comerón, J., Guerrero-Higueras, Á.M., Rodríguez-Lera, F.J., Fernández-Llamas, C., Matellán-Olivera, V., Cybersecurity in Autonomous Systems: Hardening ROS Using Encrypted Communications and Semantic Rules (2017) Iberian Robotics Conference; Springer: Berlin, Germany, pp. 67-78; Redmon, J., Divvala, S., Girshick, R., Farhadi, A., You only look once: Unified, real-time object detection (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 779-788. , 26 June–1 July; Ren, S., He, K., Girshick, R., Sun, J., Faster r-cnn: Towards real-time object detection with region proposal networks. 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Comput., 10, pp. 438-446; Dalal, N., Triggs, B., (2005) Histograms of Oriented Gradients for Human Detection., 1, pp. 886-893. , Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, CA, USA, 20–25 June, Volume, pp; Cheng, X., Jia, Y., Su, J., Wu, Y., (2019) Person-Following for Telepresence Robots Using Web Cameras., pp. 2096-2101. , In Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 3–8 November, pp; Jangbari, P., Patel, D., Review on region of interest coding techniques for medical image compression (2016) Int. J. Comput. Appl., 134, pp. 1-5; Kim, M., Arduengo, M., Walker, N., Jiang, Y., Hart, J.W., Stone, P., Sentis, L., An architecture for person-following using active target search (2018) Arxiv; Zisserman, R.H.A., (2004) Multiple View Geometry in Computer Vision, , Cambridge University Press: Cambridge, UK; Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D., RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments (2012) Int. J. Rob. Res., 31, pp. 647-663; Geng, J., Structured-light 3D surface imaging: A tutorial (2011) Adv. Opt. Photonics, 3, pp. 128-160; Foote, T.T., The transform library (2013) In Proceedings of the 2013 IEEE Conference on Technologies for Practical Robot Applications (Tepra), pp. 1-6. , Woburn, MA, USA, 22–23 April; Grisetti, G., Stachniss, C., Burgard, W., Improved techniques for grid mapping with rao-blackwellized particle filters (2007) IEEE Trans. Robot., 23, p. 34; O’Dwyer, A., (2009) Handbook of PI and PID Controller Tuning Rules, , Imperial College Press: London, UK; Lee, B.J., Choi, J., Baek, C., Zhang, B.T., Robust Human Following by Deep Bayesian Trajectory Prediction for Home Service Robots (2018) Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 7189-7195. , Brisbane, Australia, 21–25 May, ; pp","Choi, M.-T.; School of Mechanical Engineering, South Korea; email: mtchoi@g.skku.edu",,,"MDPI AG",,,,,14248220,,,"32397411","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85084450964
"Guznov S., Lyons J., Pfahler M., Heironimus A., Woolley M., Friedman J., Neimeier A.","37048856900;15623605700;57195985543;57211539921;57190287546;57220963612;57211537114;","Robot Transparency and Team Orientation Effects on Human–Robot Teaming",2020,"International Journal of Human-Computer Interaction","36","7",,"650","660",,4,"10.1080/10447318.2019.1676519","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074368222&doi=10.1080%2f10447318.2019.1676519&partnerID=40&md5=a06eb6176acb8500a7c0582a0e364117","Air Force Research Laboratory, Dayton, United States; Cognitive Performance Research Department, Dayton, United States; Graduate Student Program, Oak Ridge Institute of Science and Education, Belcamp, United States; Department of Psychology, Wright State University, Dayton, United States; Department of Engineering, California State University, Los Angeles, United States; Department of Psychology, Northern Kentucky University, Highland Heights, United States","Guznov, S., Air Force Research Laboratory, Dayton, United States; Lyons, J., Air Force Research Laboratory, Dayton, United States; Pfahler, M., Cognitive Performance Research Department, Dayton, United States; Heironimus, A., Graduate Student Program, Oak Ridge Institute of Science and Education, Belcamp, United States; Woolley, M., Department of Psychology, Wright State University, Dayton, United States; Friedman, J., Department of Engineering, California State University, Los Angeles, United States; Neimeier, A., Department of Psychology, Northern Kentucky University, Highland Heights, United States","Human–robot team members often have to interact in a situation when the team members are not physically collocated requiring effective communication to establish and maintain effective human–robot performance. Previous research suggests that characteristics of robot communication can have positive impact on human–robot interaction outcomes such as trust, situation awareness, workload, and performance. In this study, we investigated the joint effects of robot communication transparency (low to high, increasing the amount of information provided through text messages) and team orientation (i.e., robot’s text messages communicating team membership with We statements) properties of messages sent by a ground robot to a human teammate who was responsible for supervisory control. The results showed both benefits and limitations of increased transparency indicating the importance of careful implementation of transparency methods. Team orientation manipulation showed to be ineffective (and potentially detrimental) also indicating that caution needs to be exercised when implementing elements intended to improve group cohesion and team inclusiveness. Limitations and future directions are discussed. © 2019, © 2019 Taylor & Francis Group, LLC.",,"Transparency; Amount of information; Effective communication; Orientation effect; Robot communication; Robot interactions; Robot performance; Situation awareness; Supervisory control; Human robot interaction",,,,,,,,,,,,,,,,"Adams, B.D., Bruyn, L.E., Houde, S., Angelopoulos, P., (2003) Trust in automated systems literature review (DRDC Toronto No. CR-2003-096), , Toronto, Canada: Defence Research and Development Canada; Bailey, N., Scerbo, M., Automation- induced complacency for monitoring highly reliable systems: The role of task complexity, system experience and operator trust (2007) Theoretical Issues in Ergonomical Science, 8, pp. 321-348; Barelka, A.J., Bobko, P., Wesselmann, E.D., Lyons, J.B., Investigating user suspicion of human and computer teammates: An exploration in two cyber experiments Manuscript submitted for publication, , under review; Bruemmer, D.J., Marble, J.L., Dudenhoeffer, D.D., Mutual initiative in human machine teams (2002) Proceedings of the 7th Conf. on Human Factors and Power Plants, , Scottsdale, AZ; Cannon-Bowers, J.A., Salas, E., Converse, S., Shared mental models in expert team decision making (1993) Current issues in individual and group decision making, pp. 221-246. , Castellan N.J., (ed), Hillsdale, NJ: Erlbaum, &,. 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IEEE International Conference on Robotics and Automation (ICRA), New Orleans, LA; Stowers, K., Kasdaglis, N., Newton, O., Lakhmani, S., Wohleber, R., Chen, J., Intelligent agent transparency: The design and evaluation of an interface to facilitate human and intelligent agent collaboration (2016) Proceedings of the Human Factors and Ergonomics Society 2016 Annual Meeting, pp. 1706-1710. , Washington, DC; Waytz, A., Heafner, J., Epley, N., The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle (2014) Journal of Experimental Social Psychology, 52, pp. 113-117; Wynne, K.T., Lyons, J.B., An integrative model of autonomous agent teammate likeness (2018) Theoretical Issues in Ergonomical Science, 19, pp. 353-374","Guznov, S.; Air Force Research LaboratoryUnited States; email: svyatoslav.guznov.1@us.af.mil",,,"Taylor and Francis Inc.",,,,,10447318,,,,"English","Int. J. Hum.-Comput. Interact.",Article,"Final","",Scopus,2-s2.0-85074368222
"Marullo S., Pozzi M., Prattichizzo D., Malvezzi M.","57215671229;57192436420;7004048807;6601959430;","Cooperative Human-Robot Grasping with Extended Contact Patches",2020,"IEEE Robotics and Automation Letters","5","2","9006844","3121","3128",,,"10.1109/LRA.2020.2975705","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081591041&doi=10.1109%2fLRA.2020.2975705&partnerID=40&md5=89602ea9bc4e77b148dda0711e50d2cf","Department of Information Engineering and Mathematics, University of Siena, Siena, Italy; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, 16121, Italy","Marullo, S., Department of Information Engineering and Mathematics, University of Siena, Siena, Italy, Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, 16121, Italy; Pozzi, M., Department of Information Engineering and Mathematics, University of Siena, Siena, Italy, Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, 16121, Italy; Prattichizzo, D., Department of Information Engineering and Mathematics, University of Siena, Siena, Italy, Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, 16121, Italy; Malvezzi, M., Department of Information Engineering and Mathematics, University of Siena, Siena, Italy, Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, 16121, Italy","Grasping large and heavy objects with a robot assistant is one of the most interesting scenarios in physical Human-Robot Interaction. Many solutions have been proposed in the last 40 years, focusing not only on human safety, but also on comfort and ergonomics. When carrying objects with large planar surfaces, classical contact models developed in robotic grasping cannot be used. This is why we conceived a contact model explicitly considering contact area properties and thus suitable to deal with grasps requiring large contact patches from the robot side. Together with the model, this work proposes a decentralized control strategy to implement cooperative object handling between humans and robots. Experimental results showed that the proposed method performs better than a simpler one, which does not account for contact patch properties. The control strategy is decentralized and needs minimal exteroceptive sensing (force/torque sensor at the robot wrist), so the proposed approach can easily be generalized to human-robot teams composed of more than two agents. © 2020 IEEE.","contact modeling; grasping; Physical human-robot interaction","End effectors; Ergonomics; Man machine systems; Carrying objects; Contact modeling; Control strategies; Force/torque sensor; grasping; Human-robot-team; Physical human-robot interactions; Robotic grasping; Human robot interaction",,,,,,"This work was supported in part by the Progetto Prin 2017 ""TIGHT: Tactile InteGration for Humans and arTificial systems,"" prot. 2017SB48FP, and by the IEEE RAS Technical Com-mittee on Haptics under the ""Innovation in haptics"" research programme.",,,,,,,,,,"Haddadin, S., Croft, E., Physical human-robot interaction (2016) Springer Handbook of Robotics, pp. 1835-1874. , B. Siciliano and O. Khatib, Eds., Berlin, Germany: Springer; Mörtl, A., Lawitzky, M., Kucukyilmaz, A., Sezgin, M., Basdogan, C., Hirche, S., The role of roles: Physical cooperation between humans and robots (2012) Int. J. Robot. Res., 31 (13), pp. 1656-1674; Hayashibara, Y., Takubo, T., Sonoda, Y., Arai, H., Tanie, K., Assist system for carrying a long object with a human-analysis of a human cooperative behavior in the vertical direction (1999) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. Human Environ. Friendly Robotswith High Intell. Emotional Quotients, 2, pp. 695-700; Ikeura, R., Inooka, H., Variable impedance control of a robot for cooperation with a human (1995) Proc. IEEE Int. Conf. Robot. Autom., 3, pp. 3097-3102; Parker, C.A., Croft, E.A., Design & personalization of a cooperative carrying robot controller (2012) Proc. IEEE Int. Conf. Robot. Autom., pp. 3916-3921; Maeda, Y., Hara, T., Arai, T., Human-robot cooperative manipulation with motion estimation (2001) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. Expanding Societal Role Robot. Next Millennium, 4, pp. 2240-2245; Lawitzky, M., Mörtl, A., Hirche, S., Load sharing in human-robot cooperative manipulation (2010) Proc. 19th Int. Symp. Robot Human Interactive Commun., pp. 185-191; Caccavale, F., Uchiyama, M., Cooperative manipulation (2016) Springer Handbook of Robotics, pp. 989-1006. , B. Siciliano and O. Khatib, Eds., Berlin, Germany: Springer; Chiacchio, P., Chiaverini, S., Siciliano, B., Direct and inverse kinematics for coordinated motion tasks of a two-manipulator system (1996) J. Dyn. Syst., Meas., Control, 118 (4), pp. 691-697; Bicchi, A., Hands for dextrous manipulation and robust grasping: A difficult road towards simplicity (2000) IEEE Trans. Robot. Autom., 16 (6), pp. 652-662. , Dec; Prattichizzo, D., Trinkle, J.C., Grasping (2016) Springer Handbook of Robotics, pp. 955-988. , B. Siciliano and O. Khatib, Eds., Berlin, Germany: Springer; Malvezzi, M., Prattichizzo, D., Evaluation of grasp stiffness in underactuated compliant hands (2013) Proc. IEEE Int. Conf. Robot. Autom., pp. 2074-2079; Roa, M.A., Suarez, R., Grasp quality measures: Review and performance (2015) Auton. Robots, 38 (1), pp. 65-88; Erdmann, M., An exploration of nonprehensile two-palm manipulation (1998) Int. J. Robot. Res., 17 (5), pp. 485-503; Gupta, A., Huang, W.H., A carrying task for nonprehensile mobile manipulators (2003) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 3, pp. 2896-2901. , Oct; Barbagli, F., Frisoli, A., Salisbury, K., Bergamasco, M., Simulating human fingers: A soft finger proxy model and algorithm (2004) Proc. 12th Int. Symp. Haptic Interfaces for Virtual Environ. Teleoperator Syst., pp. 9-17; Melchiorri, C., Translational and rotational slip detection and control in robotic manipulation (1999) IFAC Proc. Volumes, 32 (2), pp. 647-652; Mason, M.T., Mechanics and planning of manipulator pushing operations (1986) Int. J. Robot. Res., 5 (3), pp. 53-71; Goyal, S., Ruina, A., Papadopoulos, J., Planar sliding with dry friction part 1. Limit surface and moment function (1991) Wear, 143 (2), pp. 307-330; Dafle, N.C., Holladay, R., Rodriguez, A., Planar in-handmanipulation viamotion cones (2019) Int. J. Robot. Res., 39 (2-3), pp. 163-182; Deimel, R., Eppner, C., Ruiz, J.A., Maertens, M., Brock, O., Exploitation of environmental constraints in human and robotic grasping (2016) Robotics Research, pp. 393-409. , Springer; Dharbaneshwer, S., Subramanian, S.J., Kohlhoff, K., Robotic grasp analysis using deformable solid mechanics (2019) Meccanica, 54, pp. 1767-1784; Johnson, K.L., Johnson, K.L., (1987) Contact Mechanics, , Cambridge, U.K.: Cambridge Univ. Press; Carpinteri, A., (2017) Structural Mechanics: A Unified Approach, , Boca Raton, FL, USA: CRC Press; Balasubramanian, S., Calderon, A.M., Brami, A.R., Burdet, E., On the analysis of movement smoothness (2015) J. Neuroeng. Rehabil., 12 (1)","Marullo, S.; Department of Information Engineering and Mathematics, Italy; email: smarullo@diism.unisi.it",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85081591041
"Szczepanowski R., Cichoń E., Arent K., Sobecki J., Styrkowiec P., Florkowski M., Gakis M.","16302364400;56798323600;6603115384;6602425969;56006845300;57194874330;57201434340;","Education biases perception of social robots [L’éducation biaise la perception des robots sociaux]",2020,"Revue Europeenne de Psychologie Appliquee","70","2","100521","","",,1,"10.1016/j.erap.2020.100521","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078309527&doi=10.1016%2fj.erap.2020.100521&partnerID=40&md5=bce43889463898d510556a8b149b825b","Department of Cybernetics and Robotics, Wroclaw University of Science and Technology, Wroclaw, Poland; WSB University in Toruń, Toruń, Poland; Institute of Psychology, Faculty of Education, Psychology and Sociology, University of Zielona Gora, Zielona Gora, Poland; SWPS University of Social Sciences and Humanities, Wrocław Faculty of Psychology, Wroclaw, Poland; Department of Computer Science and Systems Engineering, Wroclaw University of Science and Technology, Wroclaw, Poland; Institute of Psychology, University of Wroclaw, Wroclaw, Poland; College of Psychological Sciences, Faculty of Education, University of Lower Silesia, Wroclaw, Poland","Szczepanowski, R., College of Psychological Sciences, Faculty of Education, University of Lower Silesia, Wroclaw, Poland; Cichoń, E., WSB University in Toruń, Toruń, Poland, College of Psychological Sciences, Faculty of Education, University of Lower Silesia, Wroclaw, Poland; Arent, K., Department of Cybernetics and Robotics, Wroclaw University of Science and Technology, Wroclaw, Poland; Sobecki, J., Department of Computer Science and Systems Engineering, Wroclaw University of Science and Technology, Wroclaw, Poland; Styrkowiec, P., Institute of Psychology, University of Wroclaw, Wroclaw, Poland; Florkowski, M., Institute of Psychology, Faculty of Education, Psychology and Sociology, University of Zielona Gora, Zielona Gora, Poland; Gakis, M., SWPS University of Social Sciences and Humanities, Wrocław Faculty of Psychology, Wroclaw, Poland","Introduction: Recent research on human–robot interactions (HRI) emphasizes a role of user's attitudes in perceiving robot's with different robot embodiments of varying levels of human likenesses. However, other human factors such as educational background may also help understanding of what conditions contribute to enhance social perception of robot's features. Objectives: This study aimed to determine how people's attitudes towards and familiarization with robots influence social perception of particular features of robots. Method: First, we measured attitudes towards robots among undergraduate students with diverse educational background (engineering vs. psychology). Then, participants were presented with short movies showing the behaviour of three robots with different levels of sociability. Finally, participants evaluated the characteristics of these robots on a scale. Results: People more familiar with social robots and with more positive attitudes towards them evaluate robots with human traits more highly. Conclusion: Human perception of social robots resembles social phenomena related to human perception of other people. © 2020 The Authors","Attitudes; Human–Robot interaction; Social perception; Social robots",,,,,,"Ministerstwo Nauki i Szkolnictwa Wyższego, MNiSW","Research was supported by internal funds of the Faculty of Education at University of Lower Silesia , donation number: 3/WYD/2018 under the decision, no Dec 203549/E[HYPHEN]680/S/2018 issued by the Ministry of Science and Higher Education (Poland) .",,,,,,,,,,"Allport, G.W., The nature of prejudice (1954), Addison-Wesley Reading, MA; Arent, K., Gakis, M., Sobecki, J., Szczepanowski, R., Toward emotion recognition embodied in social robots: implementation of Laban movement analysis into NAO robot (2017) Journal of Automation Mobile Robotics and Intelligent Systems, 11 (4), pp. 3-6; Aronson, E., Worchel, P., Similarity versus liking as determinants of interpersonal attractiveness (1966) Psychonomic Science, 5 (4), pp. 157-158; Bargh, J.A., Chaiken, S., Raymond, P., Hymes, C., The automatic evaluation effect: Unconditional automatic attitude activation with a pronunciation task (1996) Journal of Experimental Social Psychology, 32 (1), pp. 104-128; Bartneck, C., Kanda, T., Ishiguro, H., Hagita, N., My robotic doppelgänger — A critical look at the Uncanny Valley (2009) Proceedings — IEEE International Workshop on Robot and Human Interactive Communication, RO-MAN 2009. The 18th IEEE International Symposium on IEEE, IEEE: Toyama, Japan, pp. 269-276; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81; Bohner, G., Wänke, M., Attitudes and attitude change (2002), Psychology Press Hove, UK; Bornstein, R.F., Exposure and affect: Overview and meta-analysis of research, 1968–1987 (1989) Psychological Bulletin, 106 (2), pp. 265-289; Breazeal, C.L., Designing sociable robots (2002), MIT Press Cambridge, MA; Breazeal, C.L., Social Interactions in HRI: The Robot View (2004) IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 34 (2), pp. 181-186; Breazeal, C., Takanishi, A., Kobayashi, T., Social robots that interact with people (2008) Springer handbook of robotics, , B. Siciliano O. Khatib Springer Berlin, Heidelberg; Breazeal, C., Dautenhahn, K., Kanda, T., Social robotics (2016) Springer handbook of robotics, pp. 1935-1972. , B. Siciliano O. 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Appl.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85078309527
"He J., Van Maris A., Caleb-Solly P.","57216344754;57193757921;16244225300;","Investigating the effectiveness of different interaction modalities for spatial human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"239","241",,,"10.1145/3371382.3378273","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083219014&doi=10.1145%2f3371382.3378273&partnerID=40&md5=2ca101680a19d01f2a5df99d244864d1","Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom","He, J., Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom; Van Maris, A., Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom; Caleb-Solly, P., Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom","With the increasing use of social robots in real environments, one of the areas of research requiring more attention is the study of human-robot interaction (HRI) when a person and robot are moving close to each other. Understanding effective ways to design how a robot should communicate its intention during dynamic movement is based on what people's expectations are and how they interpret different cues from the robot. Building on the existing literature, we tested a range of non-verbal cues such as eye contact, gaze and head nodding as part of the robot's behaviour during close proximate passing. The research aimed to investigate the effects of these cues, as well as their combination with body posture, on the efficiency of passing and the quality of HRI. Our results show that the combination of eye contact and the robot turning sideways is the most effective and appropriate compared to other modalities. © 2020 ACM.","Body posture; Eye contact; Gaze; Human robot interaction modalities; Movement intention; Nod","Agricultural robots; Behavioral research; Man machine systems; Social robots; Body postures; Dynamic movements; Eye contact; Head nodding; Human robot Interaction (HRI); Real environments; Robot turnings; Machine design",,,,,"Horizon 2020 Framework Programme, H2020: 721619","AvM was funded by EC MSCA-ITN grant agreetment No 721619 (SOCRATES project).",,,,,,,,,,"Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: A review (2017) Journal of Human-Robot Interaction, 6 (1), pp. 25-63. , 2017; Boucher, J., Pattacini, U., Lelong, A., Bailly, G., Elisei, F., Fagel, S., Dominey, P.F., Ventre-Dominey, J., I reach faster when i see you look: Gaze effects in human-human and human-robot face-to-face cooperation (2012) Frontiers in Neurorobotics, 6, p. 3. , 2012; Hegel, F., Gieselmann, S., Peters, A., Holthaus, P., Wrede, B., Towards a typology of meaningful signals and cues in social robotics (2011) 2011 RO-MAN. IEEE, pp. 72-78; Knapp, M.L., Hall, J.A., Horgan, T.G., Nonverbal communication in human interaction (2013) Cengage Learning; Liu, C., Ishi, C.T., Ishiguro, H., Hagita, N., Generation of nodding, head tilting and eye gazing for human-robot dialogue interaction (2012) 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 285-292; Moon, A., Daniel, M.T., Gleeson, B., Pan, M., Zheng, M., Blumer, B.A., MacLean, K., Croft, E.A., Meet me where i'm gazing: How shared attention gaze affects human-robot handover timing (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-robot Interaction. ACM, pp. 334-341; Petersen, S., Houston, S., Qin, H., Tague, C., Studley, J., The utilization of robotic pets in dementia care (2017) Journal of Alzheimer's Disease, 55 (2), pp. 569-574. , 2017; Pinillos, R., Marcos, S., Feliz, R., Zalama, E., Gómez-García-Bermejo, J., Long-term assessment of a service robot in a hotel environment (2016) Robotics and Autonomous Systems, 79, pp. 40-57. , 2016; https://www.softbankrobotics.com/emea/en/pepper, SoftBank Robotics. [n.d.]. Pepper the humanoid and programmable robot: SoftBank Robotics; Schneider, B., Pea, R., Real-time mutual gaze perception enhances collaborative learning and collaboration quality (2013) International Journal of Computer-supported Collaborative Learning, 8 (4), pp. 375-397. , 2013; Short, E., Swift-Spong, K., Greczek, J., Ramachandran, A., Litoiu, A., Corina Grigore, E., Feil-Seifer, D., Huang, S., How to train your dragonbot: Socially assistive robots for teaching children about nutrition through play (2014) The 23rd IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 924-929; Warta, S.F., Newton, O.B., Song, J., Best, A., Fiore, S.M., Effects of social cues on social signals in human-robot interaction during a hallway navigation task (2018) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 62, pp. 1128-1132. , SAGE Publications Sage CA: Los Angeles, CA",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083219014
"Marvel J.A., Bagchi S., Zimmerman M., Aksu M., Antonishek B., Wang Y., Mead R., Fong T., Ben Amor H.","35074587400;57208164628;57208165783;57195229251;8620417100;36549975900;23009115600;7102098734;23388323100;","Test methods and metrics for effective HRI in real-world human-robot teams",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"652","653",,,"10.1145/3371382.3374845","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083164764&doi=10.1145%2f3371382.3374845&partnerID=40&md5=865c75c2a91326f57d209fc25e3f4efc","National Institute of Standards and Technology, Gaithersburg, MD, United States; Clemson University, Clemson, SC, United States; Semio AI Inc., Los Angeles, CA, United States; National Aeronautics and Space Administration, Moffet-Field, CA, United States; Arizona State University, Tempe, AZ, United States","Marvel, J.A., National Institute of Standards and Technology, Gaithersburg, MD, United States; Bagchi, S., National Institute of Standards and Technology, Gaithersburg, MD, United States; Zimmerman, M., National Institute of Standards and Technology, Gaithersburg, MD, United States; Aksu, M., National Institute of Standards and Technology, Gaithersburg, MD, United States; Antonishek, B., National Institute of Standards and Technology, Gaithersburg, MD, United States; Wang, Y., Clemson University, Clemson, SC, United States; Mead, R., Semio AI Inc., Los Angeles, CA, United States; Fong, T., National Aeronautics and Space Administration, Moffet-Field, CA, United States; Ben Amor, H., Arizona State University, Tempe, AZ, United States","This second annual, full-day workshop aims to explore the metrology necessary for repeatably and independently assessing the performance of robotic systems in real-world human-robot interaction (HRI) scenarios. This workshop continues the motion toward bridging the gaps between the theory and applications of HRI, enabling reproducible studies in HRI, and accelerating the adoption of cutting edge technologies as the industry state-of-practice. The second annual workshop, ""Test Methods and Metrics for Effective HRI,"" seeks to identify test methods and metrics for the holistic assessment and assurance of HRI performance in practical applications. The focus is on identifying the key performance indicators of seemingly disparate sectors, and to foster the community based on the principles of transparency, repeatability and reproducibility, and establishing trust. The goal is to aid in the advancement of HRI technologies through the development of experimental design, test methods, and metrics for assessing HRI and interface designs. © 2020 ACM.","Metrology; Performance metrics; Repeatability; Replicability; Reproducibility; Test methods","Agricultural robots; Benchmarking; Man machine systems; Testing; Community-based; Cutting edge technology; Human robot Interaction (HRI); Human-robot-team; Interface designs; Key performance indicators; Reproducibilities; State of practice; Human robot interaction",,,,,,,,,,,,,,,,,,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083164764
"Suguitan M., Gomez R., Hoffman G.","57201559535;12806763700;35726707500;","MoveAE: Modifying affective robot movements using classifying variational autoencoders",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"481","489",,3,"10.1145/3319502.3374807","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082021748&doi=10.1145%2f3319502.3374807&partnerID=40&md5=fa3bb1ab8d17b3a0e577d482c114209b","HRC2 Lab, Cornell University, Ithaca, NY, United States; Honda Research Institute Japan, Wako, Saitama, Japan","Suguitan, M., HRC2 Lab, Cornell University, Ithaca, NY, United States; Gomez, R., Honda Research Institute Japan, Wako, Saitama, Japan; Hoffman, G., HRC2 Lab, Cornell University, Ithaca, NY, United States","We propose a method for modifying affective robot movements using neural networks. Social robots use gestures and other movements to express their internal states. However, a robot's interactive capabilities are hindered by the predominant use of a limited set of preprogrammed or hand-animated behaviors, which can be repetitive and predictable, making sustained human-robot interactions difficult to maintain. To address this, we developed a method for modifying existing emotive robot movements by using neural networks. We use hand-crafted movement samples and a classifying variational autoencoder trained on these samples. Our method then allows for adjustment of affective movement features by using simple arithmetic in the network's latent embedding space.We present the implementation and evaluation of this approach and show that editing in the latent space can modify the emotive quality of the movements while preserving recognizability and legibility in many cases. This supports neural networks as viable tools for creating and modifying expressive robot behaviors. © 2020 Association for Computing Machinery.","Affective generation; Deep learning; Neural networks; Social robots","Deep learning; Deep neural networks; Learning systems; Man machine systems; Neural networks; Affective generation; Affective movements; Auto encoders; Internal state; Robot behavior; Robot movements; Social robots",,,,,,,,,,,,,,,,"Alissandrakis, A., Nehaniv, C.L., Dautenhahn, K., Correspondence mapping induced state and action metrics for robotic imitation (2007) IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 37 (2), pp. 299-307. , http://doi.org/10.1109/TSMCB.2006.886947, April, 2007; Argall, B.D., Chernova, S., Veloso, M., Browning, B., A survey of robot learning from demonstration (2009) Robotics and Autonomous Systems, 57 (5), pp. 469-483. , http://doi.org/10.1016/j.robot.2008.10.024, 2009; Chollet, F., (2015) Keras, , http://github.com/fchollet/keras; Desai, R., Anderson, F., Matejka, J., Coros, S., McCann, J., Fitzmaurice, G., Grossman, T., Geppetto: Enabling semantic design of expressive robot behaviors (2019) Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19), p. 14. , http://doi.org/10.1145/3290605.3300599, ACM, New York, NY, USA; Lee, D., Oh, K., Choi, H., The chatbot feels you- acounseling service using emotional response generation (2017) 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 437-440. , http://doi.org/10.1109/BIGCOMP.2017.7881752; Ekman, P., An argument for basic emotions (1992) Cognition and Emotion, 6 (3-4), pp. 169-200. , http://doi.org/10.1080/02699939208411068, 1992; Gockley, R., Bruce, A., Forlizzi, J., Michalowski, M., Mundell, A., Rosenthal, S., Sellner, B., Schultz, A.C., Designing robots for long-term social interaction (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1338-1343. , http://doi.org/10.1109/IROS.2005.1545303; Hashimoto, M., Kondo, H., Tamatsu, Y., Effect of emotional expression to gaze guidance using a face robot (2008) RO-MAN 2008-The 17th IEEE International Symposium on Robot and Human Interactive Communication, pp. 95-100. , http://doi.org/10.1109/ROMAN.2008.4600649; Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., Lerchner, A., Beta-vae: Learning basic visual concepts with a constrained variational framework (2017) ICLR, 2 (5), p. 6. , 2017; Hinton, G.E., Zemel, R.S., Autoencoders, minimum description length and helmholtz free energy (1993) Proceedings of the 6th International Conference on Neural Information Processing Systems (NIPS'93), pp. 3-10. , http://dl.acm.org/citation.cfm.id=2987189.2987190, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA; Hoffman, G., Ju, W., (2014) Designing Robots with Movement in Mind. 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ACM Trans. Hum.-Robot Interact, 8 (1), p. 27. , http://doi.org/10.1145/3310356, (March 2019; Susskind, J.M., Hinton, G.E., Movellan, J.R., Anderson, A.K., Generating facial expressions with deep belief nets (2008) Affective Computing, , InTech; Van Den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Kavukcuoglu, K., (2016) Wavenet: A Generative Model for Raw Audio, , CoRR abs/1609.03499 (2016); Yamane, K., Ariki, Y., Hodgins, J., Animating non-humanoid characters with human motion data (2010) Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA '10), pp. 169-178. , http://dl.acm.org/citation.cfmid=1921427.1921453, Eurographics Association, Goslar Germany, Germany; Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D., (2017) Mixup: Beyond Empirical Risk Minimization; Zhao, S., Song, J., Ermon, S., (2017) Towards Deeper Understanding of Variational Autoencoding Models, , 2017; Zhou, A., Dragan, A.D., Cost functions for robot motion style (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3632-3639. , http://doi.org/10.1109/IROS.2018.8594433",,,"ACM SIGAI;ACM SIGCHI;IEEE Robotics and Automation Society( IEEE RAS)","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158224,21672148,9781450367462,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85082021748
"Strait M., Lier F., Bernotat J., Wachsmuth S., Eyssel F., Goldstone R., Sabanovic S.","57110131000;55389377200;57191710906;6506127757;9846264800;7005787552;14036199300;","A three-site reproduction of the joint simon effect with the NAO robot",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"103","110",,2,"10.1145/3319502.3374783","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081989386&doi=10.1145%2f3319502.3374783&partnerID=40&md5=f37321738eb4672de6cd63652069302d","University of Texas Rio Grande Valley, Edinburg, TX, United States; Corporate Research, Robert Bosch GmbH, Germany; Bielefeld University, Bielefeld, Germany; Indiana University, Bloomington, IN, United States","Strait, M., University of Texas Rio Grande Valley, Edinburg, TX, United States; Lier, F., Corporate Research, Robert Bosch GmbH, Germany, Bielefeld University, Bielefeld, Germany; Bernotat, J., Bielefeld University, Bielefeld, Germany; Wachsmuth, S., Bielefeld University, Bielefeld, Germany; Eyssel, F., Bielefeld University, Bielefeld, Germany; Goldstone, R., Indiana University, Bloomington, IN, United States; Sabanovic, S., Indiana University, Bloomington, IN, United States","The generalizability of empirical research depends on the reproduction of findings across settings and populations. Consequently, generalizations demand resources beyond that which is typically available to any one laboratory. With collective interest in the joint Simon effect (JSE)-a phenomenon that suggests people work more effectively with humanlike (as opposed to mechanomorphic) robots-we pursued a multi-institutional research cooperation between robotics researchers, social scientists, and software engineers. To evaluate the robustness of the JSE in dyadic human-robot interactions, we constructed an experimental infrastructure for exact, lab-independent reproduction of robot behavior. Deployment of our infrastructure across three institutions with distinct research orientations (well-resourced versus resource-constrained) provides initial demonstration of the success of our approach and the degree to which it can alleviate technical barriers to HRI reproducibility. Moreover, with the three deployments situated in culturally distinct contexts (Germany, the U.S. Midwest, and the Mexico-U.S. Border), observation of a JSE at each site provides evidence its generalizability across settings and populations. © 2020 Association for Computing Machinery.","Human-robot interaction; Joint Simon effect; Reproducibility","Cell proliferation; Human robot interaction; Man machine systems; Demand resources; Empirical research; Reproducibilities; Research cooperation; Research orientations; Simon effects; Social scientists; Technical barriers; Social robots",,,,,,,,,,,,,,,,"Jeffrey, J., Arnett, The neglected 95%: Why american psychology needs to become less american (2008) American Psychologist, 63 (7), p. 602. , (2008); Baker, M., (2016) 1, 500 Scientists Lift the Lid on Reproducibility, , http://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970, [Online; accessed 16-September-2019]; Baxter, P., Kennedy, J., Senft, E., Lemaignan, S., Belpaeme, T., From characterising three years of hri to methodology and reporting recommendations (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 391-398. , IEEE; 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Chinellato, E., Antonelli, M., Grzyb, B.J., Pobil Del, A.P., Implicit sensorimotor mapping of the peripersonal space by gazing and reaching (2011) IEEE Transactions on Autonomous Mental Development, 3 (1), pp. 43-53. , (2011); Estimating the reproducibility of psychological science (2015) Science, 349 (6251), p. 4716. , Open Science Collaboration, (2015); Joshua, R., De Leeuw, Jspsych: A javascript library for creating behavioral experiments in a web browser (2015) Behavior Research Methods, 47 (1), pp. 1-12. , (2015); Dolk, T., Hommel, B., Colzato, L.S., Schütz-Bosbach, S., Prinz, W., Liepelt, R., The joint simon effect: A review and theoretical integration (2014) Frontiers in Psychology, 5, p. 974. , (2014); Doyen, S., Klein, O., Pichon, C., Cleeremans, A., Behavioral priming: It's all in the mind, but whose mind (2012) PloS One, 7 (1), p. e29081. , (2012); Duvall, P.M., Matyas, S., Glover, A., (2007) Continuous Integration: Improving Software Quality and Reducing Risk, , Pearson Education; 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Henrich, J., Heine, S.J., Norenzayan, A., The weirdest people in the world (2010) Behavioral and Brain Sciences, 33 (2-3), pp. 61-83. , (2010); Hommel, B., Sr compatibility effects without response uncertainty (1996) The Quarterly Journal of Experimental Psychology Section A, 49 (3), pp. 546-571. , 1996; Irfan, B., Kennedy, J., Lemaignan, S., Papadopoulos, F., Senft, E., Belpaeme, T., Social psychology and human-robot interaction: An uneasy marriage (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 13-20. , ACM; Kepes, S., McDaniel, M.A., How trustworthy is the scientific literature in industrial and organizational psychology (2013) Industrial and Organizational Psychology, 6 (3), pp. 252-268. , (2013); Klein, R.A., Ratliff, K.A., Vianello, M., Reginald B, A., Jr., Bahník, Š., Bernstein, M.J., Bocian, K., Chloe Brumbaugh, C., Investigating variation in replicability (2014) Social Psychology, , (2014); Klein, R.A., Vianello, M., Hasselman, F., Adams, B.G., Reginald, B.A., Jr., Alper, S., Aveyard, M., Bahník, Š., Many labs 2: Investigating variation in replicability across samples and settings (2018) Advances in Methods and Practices in Psychological Science, 1 (4), pp. 443-490. , (2018); Rin Lee, H., Sabanovi, S., Culturally variable preferences for robot design and use in south korea, turkey, and the united states (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-robot Interaction, pp. 17-24. , ACM; Rin Lee, H., Tan, H., Šabanovi, S., That robot is not for me: Addressing stereotypes of aging in assistive robot design (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN), pp. 312-317. , IEEE; Lier, F., Hanheide, M., Natale, L., Schulz, S., Weisz, J., Wachsmuth, S., Wrede, S., Towards automated system and experiment reproduction in robotics (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3298-3305. , IEEE; Lier, F., Wienke, J., Nordmann SvenWachsmuth, A., Wrede, S., The cognitive interaction toolkit-improving reproducibility of robotic systems experiments (2014) International Conference on Simulation, Modeling, and Programming for Autonomous Robots, pp. 400-411. , Springer; 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Stenzel, A., Chinellato, E., Bou Tirado, M.A., Pobil Del, Á.P., Lappe, M., Liepelt, R., When humanoid robots become human-like interaction partners: Corepresentation of robotic actions (2012) Journal of Experimental Psychology: Human Perception and Performance, 38 (5), p. 1073. , (2012); Strait, M., Canning, C., Scheutz, M., Reliability of nirsbased bcis: A placebo-controlled replication and reanalysis of brainput (2014) CHI'14 Extended Abstracts on Human Factors in Computing Systems, pp. 619-630. , ACM; Strait, M., Urry, H.L., Muentener, P., Children's responding to humanlike agents reflects an uncanny valley (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 506-515. , IEEE; Tsai, C., Brass, M., Does the human motor system simulate pinocchio's actions (2007) Coacting with a Human Hand Versus a Wooden Hand in a Dyadic Interaction. Psychological Science, 18 (12), pp. 1058-1062. , (2007); Tsai, C., Kuo, W., Hung, D.L., Tzeng, O.J., Action co-representation is tuned to other humans (2008) Journal of Cognitive Neuroscience, 20 (11), pp. 2015-2024. , (2008)",,,"ACM SIGAI;ACM SIGCHI;IEEE Robotics and Automation Society( IEEE RAS)","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158224,21672148,9781450367462,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85081989386
"Zamani N., Moolchandani P., Fitter N.T., Culbertson H.","57195436408;57201555624;55921515500;16642007100;","Effects of Motion Parameters on Acceptability of Human-Robot Patting Touch",2020,"IEEE Haptics Symposium, HAPTICS","2020-March",,"9086298","664","670",,1,"10.1109/HAPTICS45997.2020.ras.HAP20.36.d8bb0c58","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085013959&doi=10.1109%2fHAPTICS45997.2020.ras.HAP20.36.d8bb0c58&partnerID=40&md5=d0e5db4105abad8f9198c462e648743a","University of Southern California, Department of Computer Science, Los Angeles, CA  90089, United States; Institute of Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA  30332, United States; Institute Oregon State University, Collaborative Robotics and Intelligent Systems (CoRIS), Corvallis, OR  97331, United States; University of Southern California, Departments of Computer Science and Aerospace and Mechanical Engineering, Los Angeles, CA  90089, United States","Zamani, N., University of Southern California, Department of Computer Science, Los Angeles, CA  90089, United States; Moolchandani, P., Institute of Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA  30332, United States; Fitter, N.T., Institute Oregon State University, Collaborative Robotics and Intelligent Systems (CoRIS), Corvallis, OR  97331, United States; Culbertson, H., University of Southern California, Departments of Computer Science and Aerospace and Mechanical Engineering, Los Angeles, CA  90089, United States","Touch between people is essential for forming bonds and communicating emotions. However, it is currently missing in human-robot interactions due to issues with reliability and safety. As robotics transitions to home and service sectors, it is increasingly important to design guidelines and models for human-robot social touch. This paper aims to determine how variations in the motions a robot uses while patting the user's forearm or shoulder affect perception and acceptance of the interaction. We conducted a study with N=10 participants using a Sawyer robot with a hand-like end-effector, varying the force, speed, location, and pause duration of the pat. Participants rated perceived safety, valence, arousal, and dominance of each pat condition. Using these results, we propose guidelines for creating interactions that feel safe and non-dominant using low-speed and low-force trajectories. These results will be useful in helping HRI designers create appropriate human-robot social touch interactions. © 2020 IEEE.",,"Machine design; Service industry; Social robots; Human robots; Low speed; Motion parameters; Reliability and safeties; Service sectors; Touch interaction; End effectors",,,,,,,,,,,,,,,,"Sonneveld, M.H., Schifferstein, H.N., The tactual experience of objects (2008) Product Experience, pp. 41-67; Fitter, N.T., Kuchenbecker, K.J., Equipping the baxter robot with human-inspired hand-clapping skills (2016) Proc. IEEE International Symposium on Robot and Human Interactive Communication, pp. 105-112; Hertenstein, M., Holmes, R., McCullough, M., Keltner, D., The communication of emotion via touch (2009) Emotion (Washington, D.C.), 9 (9), pp. 566-573; Fitter, N.T., Kuchenbecker, K.J., How does it feel to clap hands with a robot? (2019) International Journal of Social Robotics, pp. 1-15; Yohanan, S., MacLean, K.E., The role of affective touch in human-robot interaction: Human intent and expectations in touching the haptic creature (2012) International Journal of Social Robotics, 4 (2), pp. 163-180; Kanda, T., Sato, R., Saiwaki, N., Ishiguro, H., A two-month field trial in an elementary school for long-term human-robot interaction (2007) IEEE Transactions on Robotics, 23 (5), pp. 962-971. , Oct; Lu, D.V., Pileggi, A., Smart, W.D., Actor, director, programmer, bot: The collaboration behind sky sky sky (2017) Proc. CHI Workshop on What Actors Can Teach Robots; Hertenstein, M.J., Keltner, D., App, B., Bulleit, B.A., Jaskolka, A.R., Touch communicates distinct emotions (2006) Emotion, 6 (3), p. 528; Haans, A., IJsselsteijn, W., Mediated social touch: A review of current research and future directions (2006) Virtual Reality, 9 (2-3), pp. 149-159; Bickmore, T.W., Fernando, R., Ring, L., Schulman, D., Empathic touch by relational agents (2010) IEEE Transactions on Affective Computing, 1 (1), pp. 60-71; Routasalo, P., Non-necessary touch in the nursing care of elderly people (1996) J. Of Advanced Nursing, 23 (5), pp. 904-911; Avraham, G., Nisky, I., Fernandes, H.L., Acuna, D.E., Kording, K.P., Loeb, G.E., Karniel, A., Toward perceiving robots as humans: Three handshake models face the turing-like handshake test (2012) IEEE Transactions on Haptics, 5 (3), pp. 196-207; Jeong, S., Santos, K.D., Graca, S., O'Connell, B., Anderson, L., Stenquist, N., Fitzpatrick, K., Weinstock, P., Designing a socially assistive robot for pediatric care (2015) Proc. ACM International Conference on Interaction Design and Children, pp. 387-390; Wada, K., Shibata, T., Saito, T., Tanie, K., Effects of robot-assisted activity for elderly people and nurses at a day service center (2004) Proceedings of the IEEE, 92 (11), pp. 1780-1788; Hu, Y., Zhao, Z., Vimal, A., Hoffman, G., Soft skin texture modulation for social robotics (2018) Proc. IEEE International Conference on Soft Robotics, pp. 182-187; Nakagawa, K., Shiomi, M., Shinozawa, K., Matsumura, R., Ishiguro, H., Hagita, N., Effect of robot's active touch on people's motivation (2011) Proc. ACM/IEEE International Conference on Human-Robot Interaction, pp. 465-472; Chen, T.L., King, C.-H., Thomaz, A.L., Kemp, C.C., Touched by a robot: An investigation of subjective responses to robot-initiated touch (2011) Proc. ACM/IEEE International Conference on Human-Robot Interaction, pp. 457-464; Ammi, M., Demulier, V., Caillou, S., Gaffary, Y., Tsalamlal, Y., Martin, J.-C., Tapus, A., Haptic human-robot affective interaction in a handshaking social protocol (2015) Proc. ACM/IEEE International Conference on Human-Robot Interaction, pp. 263-270; Chan, W.P., Parker, C.A., Loos Der Van, H., Croft, E.A., Grip forces and load forces in handovers: Implications for designing human-robot handover controllers (2012) Proc. ACM/IEEE International Conference on Human-Robot Interaction, pp. 9-16; Iwata, H., Sugano, S., Human-robot-contact-state identification based on tactile recognition (2005) IEEE Transactions on Industrial Electronics, 52 (6), pp. 1468-1477; Yohanan, S., MacLean, K.E., A tool to study affective touch (2009) Proc. ACM CHI Extended Abstracts on Human Factors in Computing Systems, pp. 4153-4158; Argall, B.D., Billard, A.G., A survey of tactile human-robot interactions (2010) Robotics and Autonomous Systems, 58 (10), pp. 1159-1176; Suvilehto, J.T., Glerean, E., Dunbar, R.I., Hari, R., Nummenmaa, L., Topography of social touching depends on emotional bonds between humans (2015) Proceedings of the National Academy of Sciences, 112 (45), pp. 13811-13816; Webb, A., Peck, J., Individual differences in interpersonal touch: On the development, validation, and use of the comfort with interpersonal touch(cit) scale (2015) Journal of Consumer Psychology, 25 (1), pp. 60-77",,,"Facebook;Immersion;Reality Labs","IEEE Computer Society","26th IEEE Haptics Symposium, HAPTICS 2020","28 March 2020 through 31 March 2020",,159714,23247347,9781728102344,,,"English","IEEE Haptics Symp., HAPTICS",Conference Paper,"Final","",Scopus,2-s2.0-85085013959
"Lytridis C., Lekova A., Bazinas C., Manios M., Kaburlasos V.G.","8901635200;7801325136;57205365418;57211818905;6701308189;","WINkNN: Windowed Intervals' Number kNN classifier for efficient time-series applications",2020,"Mathematics","8","3","413","","",,6,"10.3390/math8030413","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082422364&doi=10.3390%2fmath8030413&partnerID=40&md5=7799dab3aac6f584d2b2cf3aa0a7977a","HUMAIN-Lab, International Hellenic University (IHU), Kavala, 65404, Greece; Institute of Robotics, Bulgarian Academy of Sciences, Sofia, 1113, Bulgaria","Lytridis, C., HUMAIN-Lab, International Hellenic University (IHU), Kavala, 65404, Greece; Lekova, A., Institute of Robotics, Bulgarian Academy of Sciences, Sofia, 1113, Bulgaria; Bazinas, C., HUMAIN-Lab, International Hellenic University (IHU), Kavala, 65404, Greece; Manios, M., HUMAIN-Lab, International Hellenic University (IHU), Kavala, 65404, Greece; Kaburlasos, V.G., HUMAIN-Lab, International Hellenic University (IHU), Kavala, 65404, Greece","Our interest is in time series classification regarding cyber-physical systems (CPSs) with emphasis in human-robot interaction. We propose an extension of the k nearest neighbor (kNN) classifier to time-series classification using intervals' numbers (INs). More specifically, we partition a time-series into windows of equal length and from each window data we induce a distribution which is represented by an IN. This preserves the time dimension in the representation. All-order data statistics, represented by an IN, are employed implicitly as features; moreover, parametric non-linearities are introduced in order to tune the geometrical relationship (i.e., the distance) between signals and consequently tune classification performance. In conclusion, we introduce the windowed IN kNN (WINkNN) classifier whose application is demonstrated comparatively in two benchmark datasets regarding, first, electroencephalography (EEG) signals and, second, audio signals. The results by WINkNN are superior in both problems; in addition, no ad-hoc data preprocessing is required. Potential future work is discussed. © 2020 by the authors.","Audio signal; Big data; Cyber-physical system (CPS); Electroencephalography (EEG) signal; Human-robot interaction (HRI); Intervals' number (IN); KNN classification; Time-series",,,,,,"Horizon 2020 Framework Programme, H2020; H2020 Marie Skłodowska-Curie Actions, MSCA: 777720","Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 777720.",,,,,,,,,,"Kaburlasos, V.G., Vrochidou, E., Social Robots for Pedagogical Rehabilitation: Trends and Novel Modeling Principles (2019) Cyber-Physical Systems for Social Applications. Advances in Systems Analysis, Software Engineering, and High Performance Computing (ASASEHPC);, pp. 1-21. , Dimitrova, M., Wagatsuma, H., Eds.; IGI Global: Hershey, PA, USA; Kaburlasos, V.G., Vrochidou, E., Panagiotopoulos, F., Aitsidis, C., Jaki, A., Time Series Classification in Cyber-Physical System Applications by Intervals' Numbers Techniques (2019) Proceedings of the 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1-6. , New Orleans, LA, USA, 23-26 June; Serpanos, D., The Cyber-Physical Systems Revolution (2018) Computer, 51, pp. 70-73. , [CrossRef]; (2019) Cyber-Physical Systems for PEdagogical Rehabilitation in Special Education, , https://cordis.europa.eu/project/rcn/212970_en.html, (accessed on 7 October); Vrochidou, E., Lytridis, C., Bazinas, C., Papakostas, G.A., Kaburlasos, V.G., Fuzzy Lattice Reasoning for Brain Signal Classification (2019) J. Univ. Comput. 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"Soh H., Xie Y., Chen M., Hsu D.","56001754300;57208167074;57190131729;34569904000;","Multi-task trust transfer for human–robot interaction",2020,"International Journal of Robotics Research","39","2-3",,"233","249",,12,"10.1177/0278364919866905","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071515968&doi=10.1177%2f0278364919866905&partnerID=40&md5=8c34395066b37d8542136c148db1620c","Department of Computer Science, School of Computing, National University of Singapore, Singapore","Soh, H., Department of Computer Science, School of Computing, National University of Singapore, Singapore; Xie, Y., Department of Computer Science, School of Computing, National University of Singapore, Singapore; Chen, M., Department of Computer Science, School of Computing, National University of Singapore, Singapore; Hsu, D., Department of Computer Science, School of Computing, National University of Singapore, Singapore","Trust is essential in shaping human interactions with one another and with robots. In this article we investigate how human trust in robot capabilities transfers across multiple tasks. We present a human-subject study of two distinct task domains: a Fetch robot performing household tasks and a virtual reality simulation of an autonomous vehicle performing driving and parking maneuvers. The findings expand our understanding of trust and provide new predictive models of trust evolution and transfer via latent task representations: a rational Bayes model, a data-driven neural network model, and a hybrid model that combines the two. Experiments show that the proposed models outperform prevailing models when predicting trust over unseen tasks and users. These results suggest that (i) task-dependent functional trust models capture human trust in robot capabilities more accurately and (ii) trust transfer across tasks can be inferred to a good degree. The latter enables trust-mediated robot decision-making for fluent human–robot interaction in multi-task settings. © The Author(s) 2019.","human-centered robotics; Human–robot interaction; trust","Bayesian networks; Decision making; Virtual reality; Human centered robotics; Human interactions; Neural network model; Predictive models; Robot interactions; Task representation; trust; Virtual reality simulations; Human robot interaction",,,,,"MOE2016-T2-2-068; National University of Singapore, NUS","Thank you to Indu Prasad for her help with the data analysis. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by a NUS Office of the Deputy President (Research and Technology) Startup Grant and in part by MoE AcRF (Tier 2 grant number MOE2016-T2-2-068).",,,,,,,,,,"Billings, D.R., Schaefer, K.E., Chen, J.Y.C., Hancock, P.A., Human–robot interaction: Developing trust in robots (2012) HRI ’12, pp. 5-8. , In; Bowers, J.S., Davis, C.J., Bayesian just-so stories in psychology and neuroscience (2012) Psychological Bulletin, 138 (3), p. 389; Bowman, S.R., Vilnis, L., Vinyals, O., Dai, A.M., Jozefowicz, R., Bengio, S., Generating sentences from a continuous space (2016) CoNLL 2016, p. 10. , In:, p; Castelfranchi, C., Falcone, R., (2010) Trust Theory: A Socio-Cognitive and Computational Model, , 1st ed, New York, John Wiley & Sons; Chen, M., Nikolaidis, S., Soh, H., Hsu, D., Srinivasa, S., (2017) The role of trust in decision-making for human robot collaboration, , Workshop on Human-Centered Robotics, RSS, In; Chen, M., Nikolaidis, S., Soh, H., Hsu, D., Srinivasa, S., Planning with trust for human-robot collaboration (2018) HRI’18, pp. 307-315. , New York, ACM Press, In; Cho, K., van Merrienboer, B., Gulcehre, C., Learning phrase representations using RNN encoder–decoder for statistical machine translation (2014) EMNLP, pp. 1724-1734. , In; Csató, L., Opper, M., Sparse on-line Gaussian processes (2002) Neural Computation, 14 (3), pp. 641-668; Desai, M., Kaniarasu, P., Medvedev, M., Steinfeld, A., Yanco, H., Impact of robot failures and feedback on real-time trust (2013) HRI ’13, pp. 251-258. , In; Desai, M., (2012) Modeling Trust to Improve Human–Robot Interaction, , University of Massachussetts, Lowell, MA, PhD thesis; Dolgov, D., Thrun, S., Montemerlo, M., Diebel, J., Path planning for autonomous vehicles in unknown semi-structured environments (2010) The International Journal of Robotics Research, 29 (5), pp. 485-501; Gombolay, M., Yang, X.J., Hayes, B., Robotic assistance in the coordination of patient care (2018) The International Journal of Robotics Research, 37 (10), pp. 1300-1316; Griffiths, T.L., Lucas, C.G., Williams, J.J., Kalish, M.L., Modeling human function learning with Gaussian processes (2009) Neural Information Processing Systems, 21, pp. 553-560; Hall, R., Trusting your assistant (1996) Proceedings of the 11th Knowledge-Based Software Engineering Conference, pp. 42-51. , Los Alamitos, CA, IEEE Computer Society Press, In; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human–robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Computation, 9 (8), pp. 1735-1780; Hoffman, R.R., Johnson, M., Bradshaw, J.M., Trust in automation (2013) IEEE Intelligent Systems, 13, pp. 1541-1672; Huang, S.H., Bhatia, K., Abbeel, P., Dragan, A.D., (2018) Establishing (appropriate) trust via critical states, , Workshop on Explainable Robotic Systems, HRI, In; Jian, J.Y., Bisantz, A.M., Drury, C.G., Foundations for an empirically determined scale of trust in automated systems (2000) International Journal of Cognitive Ergonomics, 4 (1), pp. 53-71; Jozefowicz, R., Zaremba, W., Sutskever, I., An empirical exploration of recurrent network architectures (2015) ICML, pp. 2342-2350. , In; Kingma, D.P., Ba, J., (2014) Adam: A method for stochastic optimization; Lee, J., Moray, N., Trust, control strategies and allocation of function in human–machine systems (1992) Ergonomics, 35 (10), pp. 1243-1270; Lee, J.D., Moray, N., Trust, self-confidence, and operators’ adaptation to automation (1994) International Journal of Human–Computer Studies, 40 (1), pp. 153-184; Lee, J.D., See, K.A., Trust in automation: Designing for appropriate reliance (2004) Human Factors, 46 (1), pp. 50-80; Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J., (2013) Distributed representations of words and phrases and their compositionality, 2, pp. 3111-3119. , Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS’13), Lake Tahoe, NV, 5–10 December 2013, In; Muir, B.M., (1989) Operators Trust in and Percentage of Time Spent Using the Automatic Controllers in a Supervisory Process Control Task, , Toronto, ON, University of Toronto Press, :, 0315510145; Muir, B.M., Trust in automation: Part I. 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"Müller P., Sood E., Bulling A.","57188879130;57215279496;6505807414;","Anticipating averted gaze in dyadic interactions",2020,"Eye Tracking Research and Applications Symposium (ETRA)",,,,"","",,3,"10.1145/3379155.3391332","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086181758&doi=10.1145%2f3379155.3391332&partnerID=40&md5=5d9ed077d0804ec2bf095530ac7e5995","Max Planck Institute for Informatics, Saarland Informatics Campus, Germany; Institute for Visualisation and Interactive Systems, University of Stuttgart, Germany","Müller, P., Max Planck Institute for Informatics, Saarland Informatics Campus, Germany; Sood, E., Institute for Visualisation and Interactive Systems, University of Stuttgart, Germany; Bulling, A., Institute for Visualisation and Interactive Systems, University of Stuttgart, Germany","We present the first method to anticipate averted gaze in natural dyadic interactions. The task of anticipating averted gaze, i.e. that a person will not make eye contact in the near future, remains unsolved despite its importance for human social encounters as well as a number of applications, including human-robot interaction or conversational agents. Our multimodal method is based on a long short-term memory (LSTM) network that analyses non-verbal facial cues and speaking behaviour. We empirically evaluate our method for different future time horizons on a novel dataset of 121 YouTube videos of dyadic video conferences (74 hours in total). We investigate person-specific and person-independent performance and demonstrate that our method clearly outperforms baselines in both settings. As such, our work sheds light on the tight interplay between eye contact and other non-verbal signals and underlines the potential of computational modelling and anticipation of averted gaze for interactive applications. © 2020 ACM.","anticipatory human-computer interaction; averted gaze; social interactions","Long short-term memory; Social robots; Computational modelling; Conversational agents; Dyadic interaction; Interactive applications; Multi-modal; Non-verbal signals; Person-independent; Time horizons; Eye tracking",,,,,"Horizon 2020 Framework Programme, H2020; European Research Council, ERC: 801708; Deutsche Forschungsgemeinschaft, DFG: EXC 2075 -390740016; Core Research for Evolutional Science and Technology, CREST: JPMJCR14E1","This work was funded by the European Research Council (ERC; grant agreement 801708). P. M?ller was funded by a JST CREST research grant under Grant No.: JPMJCR14E1, Japan, and E. 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Res. Appl. Symp. (ETRA)",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85086181758
"Liu B., Cai H., Ju Z., Liu H.","57193678111;56763253600;25655056800;54958434200;","Multi-stage adaptive regression for online activity recognition",2020,"Pattern Recognition","98",,"107053","","",,2,"10.1016/j.patcog.2019.107053","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072785186&doi=10.1016%2fj.patcog.2019.107053&partnerID=40&md5=3c9978d7668449741cc10d2bdcf96a88","Intelligent Systems and Biomedical Robotics Group, School of Computing, University of Portsmouth, United Kingdom; Computer Science, Loughborough University, United Kingdom","Liu, B., Intelligent Systems and Biomedical Robotics Group, School of Computing, University of Portsmouth, United Kingdom; Cai, H., Computer Science, Loughborough University, United Kingdom; Ju, Z., Intelligent Systems and Biomedical Robotics Group, School of Computing, University of Portsmouth, United Kingdom; Liu, H., Intelligent Systems and Biomedical Robotics Group, School of Computing, University of Portsmouth, United Kingdom","Online activity recognition which aims to detect and recognize activity instantly from a continuous video stream is a key technology in human-robot interaction. However, the partial activity observation problem, mainly due to the incomplete sequence acquisition, makes it greatly challenging. This paper proposes a novel approach, named Multi-stage Adaptive Regression (MAR), for online activity recognition with the main focus on addressing the partial observation problem. Specifically, the MAR framework delicately assembles overlapped activity observations to improve its robustness against arbitrary activity segments. Then multiple score functions corresponding to each specific performance stage are collaboratively learned via a adaptive label strategy to enhance its power of discriminating similar partial activities. Moreover, the Online Human Interaction (OHI) database is constructed to evaluate the online activity recognition in human interaction scenarios. Extensive experimental evaluations on the Multi-Modal Action Detection (MAD) database and the OHI database show that the MAR method achieves an outstanding performance over the state-of-the-art approaches. © 2019","Adaptive regression; Interaction recognition; Online activity recognition; Partial observation","Database systems; Pattern recognition; Regression analysis; Adaptive regression; Experimental evaluation; Human interactions; Interaction recognition; Online activities; Partial observation; Sequence acquisition; State-of-the-art approach; Human robot interaction",,,,,"Spectrum Pharmaceuticals; Seventh Framework Programme, FP7: 611391","This work was supported in part by the EU Seventh Framework Programme (no. 611391 , Development of Robot-Enhanced therapy for children with AutisM spectrum disorders (DREAM)).","This work was supported in part by the EU Seventh Framework Programme (no. 611391, Development of Robot-Enhanced therapy for children with AutisM spectrum disorders (DREAM)).",,,,,,,,,"Aggarwal, J., Xia, L., Human activity recognition from 3d data: a review (2014) Pattern Recognit. 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Pattern Recognit., pp. 3265-3272; Devanne, M., Berretti, S., Pala, P., Wannous, H., Daoudi, M., Del Bimbo, A., Motion segment decomposition of rgb-d sequences for human behavior understanding (2017) Pattern Recognit., 61, pp. 222-233","Liu, H.; Intelligent Systems and Biomedical Robotics Group, United Kingdom; email: honghai.liu@port.ac.uk",,,"Elsevier Ltd",,,,,00313203,,PTNRA,,"English","Pattern Recogn.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85072785186
"Chen L., Wu M., Zhou M., Liu Z., She J., Hirota K.","55774663200;35231682600;57191199141;56101506800;7102576674;7402829702;","Dynamic Emotion Understanding in Human-Robot Interaction Based on Two-Layer Fuzzy SVR-TS Model",2020,"IEEE Transactions on Systems, Man, and Cybernetics: Systems","50","2","8071157","490","501",,28,"10.1109/TSMC.2017.2756447","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032278026&doi=10.1109%2fTSMC.2017.2756447&partnerID=40&md5=0c7c371937561eff0ad2f9d16fc158c0","School of Automation, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; School of Engineering, Tokyo University of Technology, Tokyo, 192-0982, Japan; Tokyo Institute of Technology, Yokohama, 226-8502, Japan","Chen, L., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Wu, M., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Zhou, M., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Liu, Z., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; She, J., School of Automation, China University of Geosciences, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China, School of Engineering, Tokyo University of Technology, Tokyo, 192-0982, Japan; Hirota, K., Tokyo Institute of Technology, Yokohama, 226-8502, Japan","Two-layer fuzzy support vector regression-Takagi-Sugeno (TLFSVR-TS) model is proposed for emotion understanding in human-robot interaction (HRI), where the real-time dynamic emotion is recognized according to facial expression, and emotional intention understanding is obtained mainly based on human emotions and identification information. It aims to make robots capable of recognizing and understanding human emotions, in such a way that make HRI run smoothly. TLFSVR-TS considers about the priori knowledge inferred from human personal preference to reduce the uncertainty of various people, and multiple support vector regression (SVR) corresponding to different genders/provinces/ages of human to guarantee the local learning ability. Preliminary application experiments are performed in the developing emotional social robot system, where 30 volunteers experience the scenario of 'drinking in the bar.' Results show that the proposal receives higher understanding accuracy than that of TLFSVR, kernel fuzzy c -means clustering is fused with SVR, and SVR. © 2013 IEEE.","Dynamic recognition; emotion understanding; human-robot interaction (HRI); two-layer fuzzy support vector regression-Takagi-Sugeno (TLFSVR-TS) model","Face recognition; Gold; Heuristic algorithms; Interactive computer systems; Man machine systems; Psychology computing; Real time systems; Regression analysis; Robots; Support vector machines; Vectors; Dynamic recognition; Emotion recognition; Emotion understanding; Fuzzy support vector regressions; Human robot Interaction (HRI); Human robot interaction",,,,,"2015CFA010; National Natural Science Foundation of China, NSFC: 61210011, 61403422, 61473313, 61603356; Natural Science Foundation of Hubei Province; China University of Geosciences, Wuhan, CUG: 201548; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project: B17040","Manuscript received July 7, 2017; accepted September 6, 2017. Date of publication October 18, 2017; date of current version January 15, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61603356, Grant 61210011, Grant 61473313, and Grant 61403422, in part by the 111 Project under Grant B17040, in part by the Hubei Provincial Natural Science Foundation of China under Grant 2015CFA010, and in part by the Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan, under Grant 201548. This paper was recommended by Associate Editor P. Chen. (Corresponding author: Min Wu.) L. Chen, M. Wu, M. Zhou, and Z. Liu are with the School of Automation, China University of Geosciences, Wuhan 430074, China, and also with the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China (e-mail: chenluefeng@cug.edu.cn; wumin@cug.edu.cn).","This work was supported in part by the National Natural Science Foundation of China under Grant 61603356, Grant 61210011, Grant 61473313, and Grant 61403422, in part by the 111 Project under Grant B17040, in part by the Hubei Provincial Natural Science Foundation of China under Grant 2015CFA010, and in part by the Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan, under Grant 201548.",,,,,,,,,"Tawari, A., Trivedi, M.M., Face expression recognition by cross modal data association (2013) IEEE Trans. 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Informat., 17 (2), pp. 291-301; Chen, L.-F., Multi-robot behavior adaptation to local and global communication atmosphere in humans-robots interaction (2014) J. Multimodal User Interfaces, 8 (3), pp. 289-303; Tanaka, F., Pepper learns together with children: Development of an educational application (2015) Proc. IEEE RAS 15th Int. Conf. Humanoid Robots, pp. 270-275. , Seoul, South Korea; Bicho, E., The power of prediction: Robots that read intentions (2012) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst, pp. 5458-5459. , Vilamoura, Portugal","Wu, M.; School of Automation, China; email: wumin@cug.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21682216,,,,"English","IEEE Trans. Syst. Man Cybern. Syst.",Article,"Final","",Scopus,2-s2.0-85032278026
"Henschel A., Cross E.S.","57214603084;56497637800;","No evidence for enhanced likeability and social motivation towards robots after synchrony experience",2020,"Interaction Studies","21","1",,"7","23",,1,"10.1075/is.19004.hen","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078822042&doi=10.1075%2fis.19004.hen&partnerID=40&md5=144f792aad19d391e0d3baba475a044d","University of Glasgow, United Kingdom","Henschel, A., University of Glasgow, United Kingdom; Cross, E.S., University of Glasgow, United Kingdom","A wealth of social psychology studies suggests that moving in synchrony with another person can positively influence their likeability and prosocial behavior towards them. Recently, human-robot interaction (HRI) researchers have started to develop real-time, adaptive synchronous movement algorithms for social robots. However, little is known how socially beneficial synchronous movements with a robot actually are. We predicted that moving in synchrony with a robot would improve its likeability and participants' social motivation towards the robot, as measured by the number of questions asked during a free interaction period. Using a between-subjects design, we implemented the synchrony manipulation via a drawing task. Contrary to predictions, we found no evidence that participants who moved in synchrony with the robot rated it as more likeable or asked it more questions. By including validated behavioral and neural measures, future studies can generate a better and more objective estimation of synchrony's effects on rapport with social robots. © John Benjamins Publishing Company","Embodied interaction; Godspeed questionnaires; Human-robot interaction; Interpersonal synchrony; Pepper; Social motivation; Social robots",,,,,,,,,,,,,,,,,"Bartneck, C., Kuli, D., Croft, E., Measurement instruments for the anthropomorphism, animacy, likeability (2009) Perceived Intelligence, and Perceived Safety of Robots, pp. 71-81. , https://doi.org/10.1007/s12369-008-0001-3; Berniere, F., Reznick, S., Rosenthal, R., (1988) Synchrony, Pseudosynchrony and Dissynchrony Measuring the Entrainment Process in Mother Infant Interactions, , https://doi.org/10.1037//0022-3514.54.2.243; Broadbent, E., Interactions with robots: The truths we reveal about ourselves (2017) Annu. Rev. Psychol, 68 (9), pp. 1-926. , https://doi.org/10.1146/annurev-psych-010416-043958; Chevallier, C., Kohls, G., Troiani, V., Brodkin, E.S., Schultz, R.T., The social motivation theory of autism (2012) Trends in Cognitive Sciences, 16 (4), pp. 231-238. , https://doi.org/10.1016/j.tics.2012.02.007; Cross, L., Wilson, A.D., Golonka, S., How moving together brings us together: When coordinated rhythmic movement affects cooperation (2016) Frontiers in Psychology, 7 (DEC), pp. 1-13. , https://doi.org/10.3389/fpsyg.2016.01983; Darling, K., (2015) Who's Jonny; Duffy, B.R., (2000) The Social Robot Paradox, p. 288. , https://doi.org/10.1.1.79.3188, PhD Thesis, November; Duffy, B.R., Joue, G., The paradox of social robotics: A discussion (2005) AAAI Fall 2005 Symposium on Machine Ethics, , Hyatt Regency; Duffy, B.R., Rooney, C.F.B., Hare, G.M.P.O., Donoghue, R.P.S.O., What is a social robot? (1999) Computer, pp. 1-3; Eriksson, J., Matarić, M.J., Winstein, C.J., Hands-off assistive robotics for post-stroke arm rehabilitation (2005) Proceedings of the 2005 IEEE 9th International Conference on Rehabilitation Robotics, 2005, pp. 21-24. , https://doi.org/10.1109/ICORR.2005.1501042; Fasola, J., Matarić, M.J., Using socially assistive human-robot interaction to motivate physical exercise for older adults (2012) Proceedings of the IEEE, 100 (8), pp. 2512-2526. , https://doi.org/10.1109/JPROC.2012.2200539; Feil-Seifer, D., Matarić, M.J., Socially assistive robotics (2011) Robotics & Automation Magazine, IEEE, 18 (1), pp. 24-31. , https://doi.org/10.1109/ICORR.2005.1501143; Hove, M.J., Risen, J.L., It's all in the timing: Interpersonal synchrony increases affiliation (2009) Social Cognition, 27 (6), pp. 949-960. , https://doi.org/10.1521/soco.2009.27.6.949; Irfan, B., Kennedy, J., Senft, E., Belpaeme, T., (2018) Social Psychology and Human-Robot Interaction: An Uneasy Marriage, , https://doi.org/10.1145/3173386.3173389; Kasparov, G., (2017) Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins, , PublicAffairs; Kokal, I., Engel, A., Kirschner, S., Keysers, C., Synchronized drumming enhances activity in the caudate and facilitates prosocial commitment-if the rhythm comes easily (2011) PLoS One, 6 (11). , https://doi.org/10.1371/journal.pone.0027272; Lehmann, H., Saez-Pons, J., Syrdal, D.S., Dautenhahn, K., In good company? Perception of movement synchrony of a non-anthropomorphic robot (2015) PLoSONE, 10 (5), pp. 1-16. , https://doi.org/10.1371/journal.pone.0127747; Lorenz, T., Weiss, A., Hirche, S., Synchrony and reciprocity: Key mechanisms for social companion robots in therapy and care (2016) International Journal of Social Robotics, 8 (1), pp. 125-143. , https://doi.org/10.1007/s12369-015-0325-8; Mogan, R., Fischer, R., Bulbulia, J.A., To be in synchrony or not? A meta-analysis of synchrony's effects on behavior, perception, cognition and affect (2017) JournalofExperimental SocialPsychology, 72, pp. 13-20. , https://doi.org/10.1016/j.jesp.2017.03.009; Mörtl, A., Lorenz, T., Hirche, S., Rhythm patterns interaction - Synchronization behavior for human-robot joint action (2014) PLoS ONE, 9 (4). , https://doi.org/10.1371/journal.pone.0095195; Müller, B.C.N., Brass, M., Kühn, S., Tsai, C.C., Nieuwboer, W., Dijksterhuis, A., van Baaren, R.B., When Pinocchio acts like a human, a wooden hand becomes embodied. 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(2015) IEEE Transactions on Autonomous Mental Development, 7 (1), pp. 52-64. , https://doi.org/10.1109/TAMD.2015.2398451; Syrdal, D.S., Dautenhahn, K., Koay, K.L., Walters, M.L., The negative attitudes towards robots scale and reactions to robot behaviour in a live human-robot interaction study (2009) Adaptive and Emergent Behaviour and Complex Systems; Wheatley, T., Kang, O., Parkinson, C., Looser, C.E., From mind perception to mental connection synchrony as a mechanism for social understanding (2012) Social and Personality Psychology Compass, 6 (8), pp. 589-606. , https://doi.org/10.1111/j.1751-9004.2012.00450.x; Wiese, E., Metta, G., Wykowska, A., Robots as intentional agents: Using neuroscientific methods to make robots appear more social (2017) Frontiers in Psychology, 8 (OCT), pp. 1-19. , https://doi.org/10.3389/fpsyg.2017.01663; Wiese, E., Wykowska, A., Zwickel, J., Müller, H.J., I see what you mean: How attentional selection is shaped by ascribing intentions to others (2012) PLoSONE, 7 (9), pp. 1-7. , https://doi.org/10.1371/journal.pone.0045391","Cross, E.S.; Institute of Neuroscience and Psychology, 62 Hillhead Street, United Kingdom; email: Emily.Cross@glasgow.ac.uk",,,"John Benjamins Publishing Company",,,,,15720373,,,,"English","Interact. Stud.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85078822042
"Madrigal F., Lerasle F., Pibre L., Ferrané I.","53164186700;6602992296;57194605480;14026472900;","Audio-Video detection of the active speaker in meetings",2020,"Proceedings - International Conference on Pattern Recognition",,,"9412681","2536","2543",,1,"10.1109/ICPR48806.2021.9412681","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110516977&doi=10.1109%2fICPR48806.2021.9412681&partnerID=40&md5=27265e9f73cf3b3f0c7d5b50bfbfc2fb","LAAS-CNRS, Toulouse, France; LAAS-CNRS, Université Paul Sabatier, Toulouse, France; IRIT, Université de Toulouse, Toulouse, France; IRIT, Université de Toulouse, Université Paul Sabatier, Toulouse, France","Madrigal, F., LAAS-CNRS, Toulouse, France; Lerasle, F., LAAS-CNRS, Université Paul Sabatier, Toulouse, France; Pibre, L., IRIT, Université de Toulouse, Toulouse, France; Ferrané, I., IRIT, Université de Toulouse, Université Paul Sabatier, Toulouse, France","Meetings are a common activity that provides certain challenges when creating systems that assist them. Such is the case of the Active speaker detection, which can provide useful information for human interaction modeling, or human-robot interaction. Active speaker detection is mostly done using speech, however, certain visual and contextual information can provide additional insights. In this paper we propose an active speaker detection framework that integrates audiovisual features with social information, from the meeting context. Visual cue is processed using a Convolutional Neural Network (CNN) that captures the spatio-temporal relationships. We analyze several CNN architectures with both cues: raw pixels (RGB images) and motion (estimated with optical flow). Contextual reasoning is done with an original methodology, based on the gaze of all participants. We evaluate our proposal with a public benchmark in state-of-art: AMI corpus. We show how the addition of visual and context information improves the performance of the active speaker detection. © 2020 IEEE","Active speaker detection; Audiovisual modeling; Convolutional Networks; Feature fusion","Convolutional neural networks; Human robot interaction; Optical flows; Audio-visual features; Context information; Contextual information; Contextual reasoning; Human interaction model; Social information; Spatio-temporal relationships; Speaker detection; Speech recognition",,,,,"Bpifrance","This work was carried at LAAS-CNRS and supported by the project LinTO, funded by Bpifrance as part of the French project Program d’Investissements d’Avenir 3.",,,,,,,,,,"He, W., Motlicek, P., Odobez, J., Deep neural networks for multiple speaker detection and localization (2018) 2018 IEEE Int. Conf. On Robotics and Automation (ICRA), pp. 74-79. , May; Stefanov, K., Beskow, J., Salvi, G., Self-supervised vision-based detection of the active speaker as support for socially aware language acquisition (2020) IEEE Transactions on Cognitive and Developmental Systems, 12 (2), pp. 250-259; Tao, F., Hansen, J.H.L., Busso, C., An unsupervised visual-only voice activity detection approach using temporal orofacial features (2015) INTERSPEECH; Zhou, Z., Zhao, G., Hong, X., Pietikäinen, M., A review of recent advances in visual speech decoding (2014) Image and Vision Computing, 32 (9), pp. 590-605; Hong, X., Yao, H., Wan, Y., Chen, R., A PCA based visual dct feature extraction method for lip-reading (2006) 2006 Int. Conf. On Intelligent Information Hiding and Multimedia, pp. 321-326. , Dec; Le, N., Odobez, J.-M., Learning multimodal temporal representation for dubbing detection in broadcast media (2016) Proceedings of the 24th ACM International Conference on Multimedia, Ser. MM'16, pp. 202-206. , New York, NY, USA: Association for Computing Machinery; Korshunov, D.C.M.G., Halstead, M., Marcel, S., Tampered speaker inconsistency detection with phonetically aware audio-visual features (2019) Procs. Of the Int. Conf. Of Machine Learning; Bonastre, J., Anguera, X., Sierra, G., Bousquet, P., Speaker modeling using local binary decisions (2011) Interspeech; Patino, J., Delgado, H., Evans, N., The EURECOM submission to the first DIHARD challenge (2018) Conf. Of the International Speech Communication Association, , September 2-6, 2018, Hyderabad, India, Hyderabad, INDIA, 09; Vestman, V., Gowda, D., Sahidullah, M., Alku, P., Kinnunen, T., Speaker recognition from whispered speech: A tutorial survey and an application of time-varying linear prediction (2018) Speech Communication, 99, pp. 62-79; Hrúz, M., Zajíc, Z., Convolutional neural network for speaker change detection in telephone speaker diarization system (2017) 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4945-4949. , March; Sarkar, A., Dasgupta, S., Naskar, S.K., Bandyopadhyay, S., Says who? Deep learning models for joint speech recognition, segmentation and diarization (2018) 2018 IEEE Int. Conf. On Acoustics, Speech and Signal Processing (ICASSP), pp. 5229-5233. , April; Wang, Q., Downey, C., Wan, L., Mansfield, P.A., Moreno, I.L., Speaker diarization with lstm (2018) 2018 IEEE Int. Conf. On Acoustics, Speech and Signal Processing (ICASSP), pp. 5239-5243. , April; Xie, W., Nagrani, A., Chung, J.S., Zisserman, A., Utterance-level aggregation for speaker recognition in the wild (2019) International Conference on Acoustics, Speech, and Signal Processing; Nagrani, W.X.A.Z.A., Chung, J.S., Voxceleb: Large-scale speaker verification in the wild (2019) Computer Speech Language; Tao, F., Busso, C., End-to-end audiovisual speech activity detection with bimodal recurrent neural models (2019) Speech Communication, 113; Petridis, D.C.M.P.S., Shen, J., Visual-only recognition of normal, whispered and silent speech (2018) 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); Afouras, T., Chung, J.S., Senior, A., Vinyals, O., Zisserman, A., Deep audio-visual speech recognition (2018) IEEE Transactions on Pattern Analysis and Machine Intelligence, p. 1; Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M., Learning spatiotemporal features with 3d convolutional networks (2015) IEEE Int. Conf. On Computer Vision (ICCV), , December; Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M., A closer look at spatiotemporal convolutions for action recognition (2018) Computer Vision and Pattern Recognition; Borghi, G., Venturelli, M., Vezzani, R., Cucchiara, R., Poseidon: Face-from-depth for driver pose estimation (2017) Procs. Of the IEEE Conf. Of Computer Vision and Pattern Recognition (CVPR); Liu, G., Yu, Y., Mora, K.A.F., Odobez, J., A differential approach for gaze estimation (2019) IEEE Transactions on Pattern Analysis and Machine Intelligence, p. 1; Ranjan, R., Patel, V.M., Chellappa, R., Hyperface: A deep multitask learning framework for face detection, landmark localization, pose estimation, and gender recognition (2019) IEEE Trans. On Pattern Analysis and Machine Intelligence, 41 (1), pp. 121-135. , Jan; Baltrušaitis, T., Robinson, P., Morency, L.P., 3D constrained local model for rigid and non-rigid facial tracking (2012) Conf. On Computer Vision and Pattern Recognition, pp. 2610-2617. , June; Köstinger, M., Wohlhart, P., Roth, P.M., Bischof, H., Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization (2011) 2011 IEEE Int. Conf. On Computer Vision Workshops (ICCV Workshops), pp. 2144-2151. , Nov; Carletta, J., Unleashing the killer corpus: Experiences in creating the multi-everything ami meeting corpus (2007) Language Resources and Evaluation, 2, pp. 181-190",,,,"Institute of Electrical and Electronics Engineers Inc.","25th International Conference on Pattern Recognition, ICPR 2020","10 January 2021 through 15 January 2021",,169954,10514651,9781728188089,PICRE,,"English","Proc. Int. Conf. Pattern Recognit.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85110516977
"Jhuang Y.-Y., Tsai W.-J.","57226101752;7402907333;","Deeppear: Deep pose estimation and action recognition",2020,"Proceedings - International Conference on Pattern Recognition",,,"9413011","7119","7125",,,"10.1109/ICPR48806.2021.9413011","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110462577&doi=10.1109%2fICPR48806.2021.9413011&partnerID=40&md5=cabbf0a2e3b0c9243322475dd3675bd8","Institutes of Multimedia Engineering, National Chiao Tung University, Hsinchu, Taiwan","Jhuang, Y.-Y., Institutes of Multimedia Engineering, National Chiao Tung University, Hsinchu, Taiwan; Tsai, W.-J., Institutes of Multimedia Engineering, National Chiao Tung University, Hsinchu, Taiwan","Human action recognition has been a popular issue recently because it can be applied in many applications such as intelligent surveillance systems, human-robot interaction, and autonomous vehicle control. Human action recognition using RGB video is a challenging task because the learning of actions is easily affected by the cluttered background. To cope with this problem, the proposed method estimates 3D human poses first which can help remove the cluttered background and focus on the human body. In addition to the human poses, the proposed method also utilizes appearance features nearby the predicted joints to make our action prediction context-aware. Instead of using 3D convolutional neural networks as many action recognition approaches did, the proposed method uses a two-stream architecture that aggregates the results from skeleton-based and appearance-based approaches to do action recognition. Experimental results show that the proposed method achieved state-of-the-art performance on NTU RGB+D which is a large-scale dataset for human action recognition. © 2020 IEEE","3D human pose; Human action recognition","Control system synthesis; Convolutional neural networks; Large dataset; Pattern recognition; Action recognition; Appearance based approach; Autonomous vehicle control; Cluttered backgrounds; Human-action recognition; Intelligent surveillance systems; Large-scale dataset; State-of-the-art performance; Social robots",,,,,"Ministry of Science and Technology, Taiwan, MOST: 108-2221-E-009-069-MY2","This work is sponsored by MOST under the contract number: MOST 108-2221-E-009-069-MY2.",,,,,,,,,,"Simonyan, K., Zisserman, A., Two-stream convolutional networks for action recognition in videos (2014) Advances in Neural Information Processing Systems, pp. 568-576; Wang, P., Li, Z., Hou, Y., Li, W., Action recognition based on joint trajectory maps using convolutional neural networks (2016) Proceedings of the 2016 ACM Conference on Multimedia Conference, pp. 102-106; Liu, M., Liu, H., Chen, C., Enhanced skeleton visualization for view invariant human action recognition (2017) Pattern Recognition, 68, pp. 346-362; Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M., Learning spatiotemporal features with 3D convolutional networks 2015 IEEE International Conference on Computer Vision (ICCV), pp. 4489-4497; Hara, K., Kataoka, H., SatohCan, Y., Spatiotemporal 3D CNNs retrace the history of 2D CNNs and Imagenet? 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6546-6555; Yan, S., Xiong, Y., Lin, D., Spatial temporal graph convolutional networks for skeleton-based action recognition 2018 Association for the Advancement of Artificial Intelligence (AAAI), pp. 7444-7452; Si, C., Jing, Y., Wang, W., Wang, L., Tan, T., Skeleton-based action recognition with spatial reasoning and temporal stack learning 2018 European Conference on Computer Vision (ECCV), pp. 106-121; Tang, Y., Tian, Y., Lu, J., Li, P., Zhou, J., Deep progressive reinforcement learning for skeleton-based action recognition 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5323-5332; Zhu, W., Lan, C., Xing, J., Zeng, W., Li, Y., Shen, L., Xie, X., Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks 2016 Association for the Advancement of Artificial Intelligence (AAAI), pp. 3697-3703; Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N., View adaptive recurrent neural networks for high performance human action recognition from skeleton data 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2136-2145; Li, C., Zhong, Q., Xie, D., Pu, S., Skeleton-based action recognition with convolutional neural networks 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), pp. 597-600; Li, C., Zhong, Q., Xie, D., Pu, S., Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation 2018 International Joint Conference on Artificial Intelligence (IJCAI), pp. 786-792; Newell, A., Yang, K., Deng, J., Stacked hourglass networks for human pose estimation 2016 European Conference on Computer Vision (ECCV), pp. 483-499; Zhou, X., Huang, Q., Sun, X., Xue, X., Wei, Y., Towards 3D human pose estimation in the wild: A Weakly-supervised Approach 2017 IEEE International Conference on Computer Vision (ICCV), pp. 398-407; Zhang, P., Lan, C., Xing, J., Zeng, W., Xue, J., Zheng, N., View adaptive recurrent neural networks for high performance human action recognition from skeleton data 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2117-2126; Luvizon, D., Picard, D., Tabia, H., 2D/3D pose estimation and action recognition using multitask deep learning 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5137-5146; Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E., Squeeze-and-excitation networks 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7132-7141; Shahroudy, A., Liu, J., Ng, T., Wang, G., NTU Rgb+d: A large scale dataset for 3D human activity analysis 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1010-1019; Baradel, F., Wolf, C., Mille, J., Taylor, G.W., Glimpse Clouds: Human activity recognition from unstructured feature points 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 469-478; Liu, M., Yuan, J., Recognizing human actions as the evolution of pose estimation maps 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1159-1168; Ke, Q., Bennamoun, M., An, S., Sohel, F., Boussaid, F., A new representation of skeleton sequences for 3D action recognition 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3288-3297",,,,"Institute of Electrical and Electronics Engineers Inc.","25th International Conference on Pattern Recognition, ICPR 2020","10 January 2021 through 15 January 2021",,169954,10514651,9781728188089,PICRE,,"English","Proc. Int. Conf. Pattern Recognit.",Conference Paper,"Final","",Scopus,2-s2.0-85110462577
"Libert K., Cadieux N., Mosconi E.","57224676805;33067579200;56830307500;","Human-machine interaction and human resource management perspective for collaborative robotics implementation and adoption",2020,"Proceedings of the Annual Hawaii International Conference on System Sciences","2020-January",,,"533","542",,2,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108137874&partnerID=40&md5=e257dc2a56165777ef41690b244d15bc","Business School, University of Sherbrooke, Canada","Libert, K., Business School, University of Sherbrooke, Canada; Cadieux, N., Business School, University of Sherbrooke, Canada; Mosconi, E., Business School, University of Sherbrooke, Canada","The shift towards human-robot collaboration (HRC) has the potential to increase productivity and sustainability, while reducing costs for the manufacturing industries. Indeed, it holds great potential for workplaces, allowing individuals to forsake repetitive or physically demanding jobs to focus on safer and more fulfilling ones. Still, integration of humans and machines in organizations presents great challenges to IS scholars due to the complexity of aligning digitalization and human resources. A knowledge gap does persist about organizational implications when it comes to implement collaborative robotics in the workplace and to support proper HRC. Thus, this paper aims to identify recommended human resources management (HRM) practices from previous research about human-robot interaction (HRI). As our results highlight that few studies attempted to fill the gap, a conceptual framework is proposed. It integrates HRM practices, technology adoption dimensions and main determinants of HRC, in the objective to support collaborative robotics implementation in organizations. © 2020 IEEE Computer Society. All rights reserved.",,"Human resource management; Industrial robots; Man machine systems; Robotics; Conceptual frameworks; Human machine interaction; Human resources management; Human robot Interaction (HRI); Human-robot collaboration; Knowledge gaps; Manufacturing industries; Technology adoption; Social robots",,,,,,,,,,,,,,,,"Oliveira, T., Martins, M.F., Literature review of information technology adoption models at firm level (2011) Electronic Journal of Information Systems Evaluation, 14 (1), pp. 110-121; Sung, T.K., Industry 4.0: A Korea perspective (2018) Technological Forecasting and Social Change, 132, pp. 40-45; Tuptuk, N., Hailes, S., Security of smart manufacturing systems (2018) Journal of Manufacturing Systems, 47, pp. 93-106; Chui, M., Manyika, J., Miremadi, M., Four fundamentals of workplace automation (2015) McKinsey Quarterly, 29 (3), pp. 1-9; Atanasoff, L., Venable, M.A., Technostress: Implications for adults in the workforce (2017) The Career Development Quarterly, 65 (4), pp. 326-338; 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Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85108137874
"Shafiei S.B., Elsayed A.S., Hussein A.A., Iqbal U., Guru K.A.","56577959100;55615772000;56883871500;57220873404;15725274500;","Evaluating the mental workload during robot-assisted surgery utilizing network flexibility of human brain",2020,"IEEE Access","8",,,"204012","204019",,3,"10.1109/ACCESS.2020.3036751","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101830175&doi=10.1109%2fACCESS.2020.3036751&partnerID=40&md5=9da3cb4bcc0c0b9f6dbcd216baa54a1b","Applied Technology Laboratory for Advanced Surgery (ATLAS), Roswell Park Comprehensive Cancer Center, Buffalo, NY  14263, United States; Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY  14263, United States","Shafiei, S.B., Applied Technology Laboratory for Advanced Surgery (ATLAS), Roswell Park Comprehensive Cancer Center, Buffalo, NY  14263, United States, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY  14263, United States; Elsayed, A.S., Applied Technology Laboratory for Advanced Surgery (ATLAS), Roswell Park Comprehensive Cancer Center, Buffalo, NY  14263, United States, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY  14263, United States; Hussein, A.A., Applied Technology Laboratory for Advanced Surgery (ATLAS), Roswell Park Comprehensive Cancer Center, Buffalo, NY  14263, United States, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY  14263, United States; Iqbal, U., Applied Technology Laboratory for Advanced Surgery (ATLAS), Roswell Park Comprehensive Cancer Center, Buffalo, NY  14263, United States, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY  14263, United States; Guru, K.A., Applied Technology Laboratory for Advanced Surgery (ATLAS), Roswell Park Comprehensive Cancer Center, Buffalo, NY  14263, United States, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY  14263, United States","Mental Workload (MWL) is traditionally evaluated by psychophysiological signals using spectral analysis and event-related potentials. Robot-assisted Surgery (RAS) is a complex task that involves human-robot interaction, multitasking, quick and appropriate reactions to various stimuli and unforeseen circumstances, as well as frequent switches between surgical subtasks. There is a lack of standardized methodology for objectively monitoring a surgeon's MWL during RAS. In this study, we propose an innovative framework, using dynamic functional brain network measurements and a deep convolutional neural network, to assess MWL. A model was developed and validated using Electroencephalogram (EEG) data from 22 trainees who performed basic surgical tasks, as well as four surgical fellows and an expert surgeon who carried out cystectomies and prostatectomies. The resulting accuracies of the MWL classification into low, intermediate and high were 93%, 89%, and 91% respectively. The proposed method can be used for continually monitoring mental workload levels in an objective fashion. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.","Brain functional network; Convolutional neural network; Mental workload; Robot-assisted surgery","Convolutional neural networks; Deep neural networks; Electroencephalography; Human robot interaction; Spectrum analysis; Surgical equipment; Transplantation (surgical); Brain networks; Electroencephalogram (EEG) datum; Event related potentials; Mental workload; Network flexibility; Psychophysiological signals; Robot-assisted surgery; Surgical tasks; Robotic surgery",,,,,"Roswell Park Alliance Foundation, Roswell Park Cancer Institute","This work was supported by the Roswell Park Alliance Foundation.",,,,,,,,,,"Argyle, E.M., Marinescu, A., Wilson, M.L., Lawson, G., Sharples, S., Physiological indicators of task demand, fatigue, and cognition in future digital manufacturing environments (2021) Int. J. 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USA, 112 (37), pp. 11678-11683. , Sep","Guru, K.A.; Applied Technology Laboratory for Advanced Surgery (ATLAS), United States; email: khurshid.guru@roswellpark.org",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85101830175
"Prioli J.P.J., Rickli J.L.","57221695780;24537735400;","Collaborative robot based architecture to train flexible automated disassembly systems for critical materials",2020,"Procedia Manufacturing","51",,,"46","53",,,"10.1016/j.promfg.2020.10.008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099860143&doi=10.1016%2fj.promfg.2020.10.008&partnerID=40&md5=83fb32403f978fe283a4bed354c0c3a1","Wayne State University, 4815 Fourth St., Detroit, MI  48202, United States","Prioli, J.P.J., Wayne State University, 4815 Fourth St., Detroit, MI  48202, United States; Rickli, J.L., Wayne State University, 4815 Fourth St., Detroit, MI  48202, United States","The capability of disassembly operations contributes to controlling the volume and flow of materials in reverse feedback cycles. An inability to achieve certain levels of volume or flow in feedback cycles from end-of-use products can risk the efficiency and profitability of recycling, remanufacturing, and reuse. Disassembly systems face a significant challenge to achieving large-scale operations due to uncertainty in end-of-use product quality, quantity and timing. In order to manage these uncertainties efficiently, disassembly systems must be flexible, however, they must be automated to achieve large-scale operations. In this paper, a cyber-physical architecture is conceptualized utilizing human-robot interaction via collaborative robots (Cobot) to train a flexible automated disassembly system. As a first step to achieving this system, this paper proposes and demonstrates a method to extract critical disassembly information from a human-cobot disassembly training work-cell that would be used to distribute tasks to processes in a high volume disassembly line. © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.","Collaborative robots; Critical materials; Disassembly; Remanufacturing; System framework",,,,,,"Oak Ridge National Laboratory, ORNL; Cambridge-MIT Institute, CMI: FA-3.3.11","This research is funded by CMI: Critical Materials Institute (FA-3.3.11), and Oak Ridge National Laboratory.",,,,,,,,,,"Fortier, S.M., Nassar, N.T., Lederer, G.W., Brainard, J., Gambogi, J., McCullough, E.A., (2018) Draft Critical Mineral List-Summary of Methodology and Background Information-US Geological Survey Technical Input Document in Response to Secretarial Order No. 3359, , US Geological Survey; Le Petit, Y., Electric vehicle life cycle analysis and raw material availability (2017) Transport & Environment; Pavel, C.C., Thiel, C., Degreif, S., Blagoeva, D., Buchert, M., Schüler, D., Tzimas, E., Role of substitution in mitigating the supply pressure of rare earths in electric road transport applications (2017) Sustainable Materials and Technologies, 12, pp. 62-72. , Jul 1; Desjardins, J., Here are the raw materials we need to fuel the electric car boom (2016) Business Insider UK; Jahns, T., Getting rare-earth magnets out of EV traction machines: A review of the many approaches being pursued to minimize or eliminate rare-earth magnets from future EV drivetrains (2017) IEEE Electrification Magazine, 5 (1), pp. 6-18. , Mar 7; Chu, S., (2011) Criticality Assessment. 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Sustainable Production, Life Cycle Engineering and Management, , Springer, Cham; Liu, Q., Liu, Z., Xu, W., Tang, Q., Zhou, Z., Pham, D.T., Human-robot collaboration in disassembly for sustainable manufacturing (2019) International Journal of Production Research, 57 (12), pp. 4027-4044. , Jun 18; Djuric, A.M., Urbanic, R.J., Rickli, J.L., A framework for collaborative robot (CoBot) integration in advanced manufacturing systems (2016) SAE International Journal of Materials and Manufacturing, 9 (2), pp. 457-464. , May 1; Basdere, B., Seliger, G., Disassembly factories for electrical and electronic products to recover resources in product and material cycles (2003) Environmental Science & Technology, 37 (23), pp. 5354-5362. , Dec 1; Wang, L., Gao, R., Váncza, J., Krüger, J., Wang, X.V., Makris, S., Chryssolouris, G., Symbiotic human-robot collaborative assembly (2019) CIRP Annals, 68 (2), pp. 701-726. , Jan 1; Wang, L., Collaborative robot monitoring and control for enhanced sustainability (2015) The International Journal of Advanced Manufacturing Technology, 81 (9-12), pp. 1433-1445. , Dec 1; Wang, X.V., Wang, L., From cloud manufacturing to cloud remanufacturing: A cloud-based approach for WEEE recovery (2014) Manufacturing Letters, 2 (4), pp. 91-95. , Oct 1; Jiang, H., Yi, J., Chen, S., Zhu, X., A multi-objective algorithm for task scheduling and resource allocation in cloud-based disassembly (2016) Journal of Manufacturing Systems, 41, pp. 239-255. , Oct 1; Fang, Y., Liu, Q., Li, M., Laili, Y., Pham, D.T., Evolutionary many-objective optimization for mixed-model disassembly line balancing with multi-robotic workstations (2019) European Journal of Operational Research, 276 (1), pp. 160-174. , Jul 1; Fang, Y., Ming, H., Li, M., Liu, Q., Pham, D.T., Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time (2019) International Journal of Production Research, pp. 1-17; Ray, P.P., Internet of robotic things: Concept, technologies, and challenges (2016) IEEE Access, 4, pp. 9489-9500","Prioli, J.P.J.; Wayne State University, 4815 Fourth St., United States; email: fs3719@wayne.edu","Vosniakos G.-C.Pellicciari M.Benardos P.Markopoulos A.",,"Elsevier B.V.","30th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2021","15 June 2021 through 18 June 2021",,166553,23519789,,,,"English","Procedia Manuf.",Conference Paper,"Final","All Open Access, Gold",Scopus,2-s2.0-85099860143
"Ma S., Gupta M.","57204395523;57194439341;","Inertial Safety from Structured Light",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12368 LNCS",,,"744","761",,,"10.1007/978-3-030-58592-1_44","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097441025&doi=10.1007%2f978-3-030-58592-1_44&partnerID=40&md5=450c6a6a9598829370a78f64b4d32afd","University of Wisconsin-Madison, Madison, WI  53706, United States","Ma, S., University of Wisconsin-Madison, Madison, WI  53706, United States; Gupta, M., University of Wisconsin-Madison, Madison, WI  53706, United States","We present inertial safety maps (ISM), a novel scene representation designed for fast detection of obstacles in scenarios involving camera or scene motion, such as robot navigation and human-robot interaction. ISM is a motion-centric representation that encodes both scene geometry and motion; different camera motion results in different ISMs for the same scene. We show that ISM can be estimated with a two-camera stereo setup without explicitly recovering scene depths, by measuring differential changes in disparity over time. We develop an active, single-shot structured light-based approach for robustly measuring ISM in challenging scenarios with textureless objects and complex geometries. The proposed approach is computationally light-weight, and can detect intricate obstacles (e.g., thin wire fences) by processing high-resolution images at high-speeds with limited computational resources. ISM can be readily integrated with depth and range maps as a complementary scene representation, potentially enabling high-speed navigation and robotic manipulation in extreme environments, with minimal device complexity. © 2020, Springer Nature Switzerland AG.",,"Cameras; Computer vision; Human robot interaction; Textures; Complex geometries; Computational resources; Device complexity; Extreme environment; High resolution image; Robotic manipulation; Scene representation; Structured Light; Stereo image processing",,,,,"Defense Advanced Research Projects Agency, DARPA; Wisconsin Alumni Research Foundation, WARF","Acknowledgement. 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Lasers Engineering, 87, pp. 18-31; Vo, M., Narasimhan, S.G., Sheikh, Y., Separating texture and illumination for single-shot structured light reconstruction (2014) IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 433-440. , IEEE, June; Wang, J., Bartels, J., Whittaker, W., Sankaranarayanan, A.C., Narasimhan, S.G., Programmable triangulation light curtains (2018) ECCV 2018. LNCS, 11207, pp. 20-35. , https://doi.org/10.1007/978-3-030-01219-92, Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.), Springer, Cham; Watanabe, Y., Sakaue, F., Sato, J., Time-to-contact from image intensity (2015) IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4176-4183. , Boston, MA, USA, IEEE, June; Yang, Q., Wang, L., Ahuja, N., A constant-space belief propagation algorithm for stereo matching (2010) IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1458-1465. , San Francisco, CA, USA, IEEE, June; Zhang, Z., Microsoft kinect sensor and its effect (2012) IEEE Multimedia, 19 (2), pp. 4-10; Zinn, M., Khatib, O., Roth, B., Salisbury, J., Playing it safe (2004) IEEE Robot. Autom. Mag., 11 (2), pp. 12-21","Gupta, M.; University of Wisconsin-MadisonUnited States; email: mohitg@cs.wisc.edu","Vedaldi A.Bischof H.Brox T.Frahm J.",,"Springer Science and Business Media Deutschland GmbH","16th European Conference on Computer Vision, ECCV 2020","23 August 2020 through 28 August 2020",,249299,03029743,9783030585914,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097441025
"Galanos V., Reisel M.","57191885621;57220572916;","Assessing the Japanese Turn in AI and Robot Ethics: Extracting Meaningful Principles Between Exoticism and Empiricism in the Case of AIBO",2020,"IFIP Advances in Information and Communication Technology","590",,,"141","157",,,"10.1007/978-3-030-62803-1_12","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097402763&doi=10.1007%2f978-3-030-62803-1_12&partnerID=40&md5=e8c4f840195dc957466f3c6d611868f2","Science, Technology and Innovation Studies Subject Group, School of Social and Political Science, University of Edinburgh, Old Surgeons’ Hall, Edinburgh, EH1 1LZ, United Kingdom; Applied Anthropology and Japanese Studies, Rikkyo University, Tokyo, Japan","Galanos, V., Science, Technology and Innovation Studies Subject Group, School of Social and Political Science, University of Edinburgh, Old Surgeons’ Hall, Edinburgh, EH1 1LZ, United Kingdom; Reisel, M., Applied Anthropology and Japanese Studies, Rikkyo University, Tokyo, Japan","The present paper critically examines a recent recurrent pattern of Western scholarship of importing sets of Japanese ethics in artificial intelligence/data/robot ethics contexts without a deeper examination of their meaning and value. The paper’s outline is unfolded as such: (1) We draw on material stemming from an ethnographic participant-observer study that followed a debate between Western and Japanese people confronting the robotic AI pet AIBO. (2) We demarcate how many of the proposed Japanese values are practically relevant to the examination of human-robot interaction and how this feeds into existing questions about privacy and safety, in the context of a global overwhelming AI hype and narrative bias. (3) Finally, we discuss how a long history of Western enthusiasm and occasional misunderstandings of Japanese values comes full circle with the recent trend, and we conclude with a set of open questions that require more dedicated empirical research in order to reach more proper and practical value system in the future design of technology. © 2020, IFIP International Federation for Information Processing.","AI ethics; AIBO; Data ethics; Ethnography; Human-robot interaction; Japanese ethics; Roboethics","Human robot interaction; Philosophical aspects; Privacy by design; Empirical research; Future designs; Observer studies; Recent trends; Recurrent patterns; Robot ethics; Value systems; Artificial intelligence",,,,,,,,,,,,,,,,"Vakkuri, V., Abrahamsson, P., The key concepts of ethics of artificial intelligence (2018) 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), pp. 1-6. , https://doi.org/10.1109/ICE.2018.8436265, pp., IEEE; Allen, C., Smit, I., Wallach, W., Artificial morality: Top-down, bottom-up, and hybrid approaches (2005) Ethics Inf. 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(2019), , https://www.tokyu-hands.co.jp/en/hintmagazine/cleaning-and-laundry/jyokin.html, Tokyu Hands","Galanos, V.; Science, United Kingdom; email: Vassilis.Galanos@ed.ac.uk","Kreps D.Komukai T.Gopal T.V.Ishii K.",,"Springer Science and Business Media Deutschland GmbH","14th IFIP TC 9 International Conference on Human Choice and Computers, HCC14 2020","9 September 2020 through 11 September 2020",,251669,18684238,9783030628024,,,"English","IFIP Advances in Information and Communication Technology",Conference Paper,"Final","",Scopus,2-s2.0-85097402763
"Doyle-Burke D., Haring K.S.","57220185262;55639550500;","Robots Are Moral Actors: Unpacking Current Moral HRI Research Through a Moral Foundations Lens",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"170","181",,,"10.1007/978-3-030-62056-1_15","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097191210&doi=10.1007%2f978-3-030-62056-1_15&partnerID=40&md5=381e3be4cc776999dd91d585b7ce5e9b","University of Denver, Denver, CO  80203, United States","Doyle-Burke, D., University of Denver, Denver, CO  80203, United States; Haring, K.S., University of Denver, Denver, CO  80203, United States","Even with the increasing frequency of robot experiences in our daily lives, relatively little research has been done to explore the moral implications of these human-robot interactions (HRI). Of the existing moral HRI research, much of it assumes a moral dimension without clearly defining what moral framework is being applied. To research such a framework, it is crucial to evaluate current research for a communicable moral framework, that is also in dialogue with key moral frameworks in social science spaces. This work uses a narrative literature review to analyze moral HRI research through the lens of the Moral Foundations Theory. The analysis will situate moral HRI work in the seminal moral theory within social science spaces and show where current gaps in HRI research might exist. Evaluating research through the lens of the moral foundations theory framework will strengthen the existence of a ‘Moral HRI’ field that is related but distinct from general HRI. © 2020, Springer Nature Switzerland AG.","Ethics; Human-Robot Interaction; Morality","Behavioral research; Robotics; Current gap; Daily lives; Human robot Interaction (HRI); Literature reviews; Moral theory; Through the lens; Human robot interaction",,,,,,,,,,,,,,,,"Agrawal, S., Williams, M.A., Robot authority and human obedience (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, , https://doi.org/10.1145/3029798.3038387; Asaro, P., “hands up, don’t shoot!” HRI and the automation of police use of force (2016) J. 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Validating the equivalency of trust in human-robot healthcare scenarios (2018) 2018 27Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), , https://doi.org/10.1109/roman.2018.8525782","Doyle-Burke, D.; University of DenverUnited States; email: Dylan.Doyle-Burke@du.edu","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097191210
"Vrochidou E., Lytridis C., Bazinas C., Papakostas G.A., Kaburlasos V.G.","53867522300;8901635200;57205365418;14060879200;6701308189;","Fuzzy lattice reasoning for brain signal classification",2020,"Journal of Universal Computer Science","26","9",,"1175","1195",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096527977&partnerID=40&md5=a2de38324dfe7262d858190f2aa97756","HUMAIN-Lab, International Hellenic University (IHU), Kavala, Greece","Vrochidou, E., HUMAIN-Lab, International Hellenic University (IHU), Kavala, Greece; Lytridis, C., HUMAIN-Lab, International Hellenic University (IHU), Kavala, Greece; Bazinas, C., HUMAIN-Lab, International Hellenic University (IHU), Kavala, Greece; Papakostas, G.A., HUMAIN-Lab, International Hellenic University (IHU), Kavala, Greece; Kaburlasos, V.G., HUMAIN-Lab, International Hellenic University (IHU), Kavala, Greece","Automated reasoning for rational decision-making is critical in Cyber-Physical System (CPS) applications including human-robot interaction. The aforementioned applications are typically driven by audio-visual signals regarding behavior. Here, in addition, we consider brain signals in a pattern recognition problem. A new Intervals’ Number (IN) induction technique is presented for classification of ElectroEncephaloGraphy (EEG) signals by optimized fuzzy lattice reasoning (FLR). Decision-making is effected by an IN-based, parametric k Nearest Neighbor (ANN) classifier, where the conventional (metric) distance function is replaced by a fuzzy order function (a) for “reasoning by analogy”. Extensive computational experiments demonstrate a superior performance of the proposed techniques comparatively to alternative methods from the literature, especially for a small number of channels. An important advantage is the avoidance of ad hoc feature extraction altogether, due to the employment of INs that can potentially represent all-order data statistics. The potential of the proposed techniques in big data applications is also discussed. © 2020, IICM. All rights reserved.","EEG-based emotion recognition; Electroencephalogram (EEG) human-machine interaction; Fuzzy lattice reasoning (FLR); Intervals’ number (IN)",,,,,,"Horizon 2020 Framework Programme, H2020; H2020 Marie Skłodowska-Curie Actions, MSCA: 777720","This project has received funding from the European Union?s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 777720.",,,,,,,,,,"Akin, M., Comparison of wavelet transform and FFT methods in the analysis of EEG signals (2002) Journal of Medical Systems, 26 (3), pp. 241-247. , https://doi.Org/https://doi.org/10.1023/A:1015075101937, [Akin 2002]; Anderson, K., McOwan, P. 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Univers. Comput. Sci.",Article,"Final","",Scopus,2-s2.0-85096527977
"Wei D., Zhang R., Saadatzi M.N., Olowo O.O., Popa D.O.","57195065540;57195069295;36642836100;57193411592;7102861346;","Organic Piezoresistive Pressure Sensitive Robotic Skin for Physical Human-Robot Interaction",2020,"Proceedings of the ASME Design Engineering Technical Conference","1",,"V001T01A013","","",,,"10.1115/DETC2020-22604","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096345026&doi=10.1115%2fDETC2020-22604&partnerID=40&md5=d682008b15fa4273ab114b09f3c8ac4f","Department of Electrical and Computer Engineering, Next Generation Systems Lab, University of Louisville, Kentucky, United States","Wei, D., Department of Electrical and Computer Engineering, Next Generation Systems Lab, University of Louisville, Kentucky, United States; Zhang, R., Department of Electrical and Computer Engineering, Next Generation Systems Lab, University of Louisville, Kentucky, United States; Saadatzi, M.N., Department of Electrical and Computer Engineering, Next Generation Systems Lab, University of Louisville, Kentucky, United States; Olowo, O.O., Department of Electrical and Computer Engineering, Next Generation Systems Lab, University of Louisville, Kentucky, United States; Popa, D.O., Department of Electrical and Computer Engineering, Next Generation Systems Lab, University of Louisville, Kentucky, United States","Pressure sensitive robotic skins have long been investigated for applications to physical human-robot interaction (pHRI). Numerous challenges related to fabrication, sensitivity, density, and reliability remain to be addressed under various environmental and use conditions. In our previous studies, we designed novel strain gauge sensor structures for robotic skin arrays. We coated these star-shaped designs with an organic polymer piezoresistive material, Poly (3, 4-ethylenedioxythiophene)-ploy(styrenesulfonate) or PEDOT: PSS and integrated sensor arrays into elastomer robotic skins. In this paper, we describe a dry etching photolithographic method to create a stable uniform sensor layer of PEDOT:PSS onto starshaped sensors and a lamination process for creating doublesided robotic skins that can be used with temperature compensation. An integrated circuit and load testing apparatus was designed for testing the resulting robotic skin pressure performance. Experiments were conducted to measure the loading performance of the resulting sensor prototypes and results indicate that over 80% sensor yields are possible with this fabrication process. Copyright © 2020 ASME.","Fabrication; Lamination; Pedot:pss; Phri; Robotic skin sensor","Conducting polymers; Design; Dry etching; Integrated circuit testing; Load testing; Man machine systems; Robotics; Strain gages; Fabrication process; Integrated sensor arrays; Loading performance; Physical human-robot interactions; Physical humanrobot interaction (phri); Piezoresistive materials; Poly-3 ,4-ethylenedioxythiophene; Temperature compensation; Human robot interaction",,,,,"National Science Foundation, NSF: 1828355; Office of Experimental Program to Stimulate Competitive Research, EPSCoR: 1849213","This work was supported by National Science Foundation awards MRI #1828355 and EPSCOR #1849213. We would like to thank Joshua Baptist and the staffs of University of Louisville’s Micro Nano Technology Center (MNTC) for their great suggestions and help in fabricating sensor samples.",,,,,,,,,,"Bartolozzi, C., Natale, L., Nori, F., Metta, G., Robots with a sense of touch (2016) Nature materials, 15 (9), p. 921; Johnsson, M., Balkenius, C., Sense of touch in robots with self-organizing maps (2011) IEEE Transactions on Robotics, 27 (3), pp. 498-507; Ward-Cherrier, B., Cramphorn, L., Lepora, N. F., Tactile manipulation with a TacThumb integrated on the open-hand M2 gripper (2016) IEEE Robotics and Automation Letters, 1 (1), pp. 169-175; Mukai, T., Onishi, M., Odashima, T., Hirano, S., Luo, Z., Development of the tactile sensor system of a human-interactive robot ""RI-MAN"" (2008) IEEE Transactions on robotics, 24 (2), pp. 505-512; Hoshi, T., Shinoda, H., Robot skin based on toucharea-sensitive tactile element Proc. Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006, pp. 3463-3468. , IEEE; Inaba, M., Hoshino, Y., Nagasaka, K., Ninomiya, T., Kagami, S., Inoue, H., A full-body tactile sensor suit using electrically conductive fabric and strings Proc. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS'96, pp. 450-457. , IEEE; Lumelsky, V. J., Shur, M. S., Wagner, S., Sensitive skin (2001) IEEE Sensors Journal, 1 (1), pp. 41-51; Wang, C., Hwang, D., Yu, Z., Takei, K., Park, J., Chen, T., Ma, B., Javey, A., User-interactive electronic skin for instantaneous pressure visualization (2013) Nature materials, 12 (10), p. 899; Argall, B. D., Billard, A. G., A survey of tactile human-robot interactions (2010) Robotics and autonomous systems, 58 (10), pp. 1159-1176; Dahiya, R. S., Metta, G., Valle, M., Sandini, G., Tactile sensing-from humans to humanoids (2009) IEEE transactions on robotics, 26 (1), pp. 1-20; Saadatzi, M. N., Baptist, J. R., Yang, Z., Popa, D. O., Modeling and Fabrication of Scalable Tactile Sensor Arrays for Flexible Robot Skins (2019) IEEE Sensors Journal; Baptist, J., Zhang, R., Wei, D., Saadatzi, M., Popa, D., Fabrication of strain gauge sensor arrays for tactile skins Proc. Proc. SPIE.; Das, S. K., Baptist, J. R., Sahasrabuddhe, R., Lee, W. H., Popa, D. O., Package analysis of 3D-printed piezoresistive strain gauge sensors Proc. Sensors for Next-Generation Robotics III, International Society for Optics and Photonics, p. 985905; Saadatzi, M. N., Baptist, J. R., Wijayasinghe, I. B., Popa, D. O., Characterization of large-area pressure sensitive robot skin Proc. Smart Biomedical and Physiological Sensor Technology XIV, International Society for Optics and Photonics, p. 102160G; Nothnagle, C., Baptist, J. R., Sanford, J., Lee, W. H., Popa, D. O., Wijesundara, M. B., EHD printing of PEDOT: PSS inks for fabricating pressure and strain sensor arrays on flexible substrates Proc. Next-Generation Robotics II; and Machine Intelligence and Bio-inspired Computation: Theory and Applications IX, International Society for Optics and Photonics, p. 949403; Saadatzi, M. N., Yang, Z., Baptist, J. R., Sahasrabuddhe, R. R., Wijayasinghe, I. B., Popa, D. O., Parametric investigation of scalable tactile sensors Proc. Smart Biomedical and Physiological Sensor Technology XIV, International Society for Optics and Photonics, p. 102160A","Wei, D.; Department of Electrical and Computer Engineering, United States; email: danming.wei@louisville.edu",,"Computers and Information in Engineering Division;Design Engineering Division","American Society of Mechanical Engineers (ASME)","ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2020","17 August 2020 through 19 August 2020",,164730,,9780791883907,,,"English","Proc. ASME Des. Eng. Tech. Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85096345026
"Alonso R., Concas E., Recupero D.R.","56188189100;57212245253;57206674454;","A flexible and scalable social robot architecture employing voice assistant technologies",2020,"CEUR Workshop Proceedings","2696",,,"35","39",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094852029&partnerID=40&md5=ad8117264e1541554eeb72e46acd53ea","R2M Solution, Pavia, Italy; University of Cagliari, Cagliari, Italy","Alonso, R., R2M Solution, Pavia, Italy; Concas, E., University of Cagliari, Cagliari, Italy; Recupero, D.R., University of Cagliari, Cagliari, Italy","In this paper a flexible and scalable architecture for Human-Robot Interaction is presented. The architecture allows embedding voice assistant technologies that can trigger different kinds of applications for the interaction between the robot and the user. The robotic platform can host applications not computationally expensive, otherwise they can be run on external computing devices or cloud. Zora, an existing robotic platform based on NAO, has been chosen as the platform where to develop applications on top of it. As voice assistant tool we have employed Google Assistant: its SDK and APIs have been leveraged for some of the applications we have deployed within our architecture. Therefore, the resulting Human-Computer Interaction has two main benefits: on the one hand the user may interact with any of the applications created on top of the robot; on the other hand, the user can also rely on voice assistant tools to receive answers in open-domain. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).","Cloud Robotics; Human-Robot Dialogue; Humanoids Robot; Language Understanding; Voice Assistant Technology","Human computer interaction; Robotics; Social robots; Computing devices; Robot architecture; Robotic platforms; Scalable architectures; End effectors",,,,,,,,,,,,,,,,"Atzeni, Mattia, Recupero, Diego Reforgiato, Deep Learning and Sentiment Analysis for Human-Robot Interaction (2018) The Semantic Web: ESWC 2018 Satellite Events - ESWC 2018 Satellite Events, pp. 14-18. , https://doi.org/10.1007/978-3-319-98192-5_3, Heraklion, Crete, Greece, June 3-7, 2018, Revised Selected Papers; Bardaro, Gianluca, Dessì, Danilo, Motta, Enrico, Osborne, Francesco, Recupero, Diego Reforgiato, Parsing Natural Language Sentences into Robot Actions (2019) Proceedings of the ISWC 2019 Satellite Tracks (Posters & Demonstrations, Industry, and Outrageous Ideas) co-located with 18th International Semantic Web Conference (ISWC 2019), pp. 93-96. , http://ceur-ws.org/Vol-2456/paper24.pdf, Auckland, New Zealand, October 26-30, 2019; Belpaeme, Tony, Kennedy, James, Ramachandran, Aditi, Scassellati, Brian, Tanaka, Fumihide, Social robots for education: A review (2018) Science Robotics, 3, p. 21. , https://doi.org/10.1126/scirobotics.aat5954arXiv:https://robotics.sciencemag.org/content/3/21/eaat5954.full.pdf, (2018); Dridi, Amna, Recupero, Diego Reforgiato, Leveraging semantics for sentiment polarity detection in social media (2017) International Journal of Machine Learning and Cybernetics, , https://doi.org/10.1007/s13042-017-0727-z, (19 Sep 2017); D'Onofrio, Grazia, MARIO Project: Experimentation in the Hospital Setting (2017) Ambient Assisted Living - Italian Forum 2017, eighth Italian on Ambient Assisted Living Forum, ForItAAL 2017, pp. 289-303. , https://doi.org/10.1007/978-3-030-04672-9_20, 14-15 June, 2017, Genoa, Italy; Feil-Seifer, David, Mataric, Maja, Robot-assisted therapy for children with autism spectrum disorders (2008) Robot-assisted therapy for children with autism spectrum disorders, pp. 49-52. , https://doi.org/10.1145/1463689.1463716; Gerina, Federica, Pes, Barbara, Recupero, Diego Reforgiato, Riboni, Daniele, Toward Supporting Food Journaling Using Air Quality Data Mining and a Social Robot (2019) Ambient Intelligence - 15th European Conference, AmI 2019, pp. 318-323. , https://doi.org/10.1007/978-3-030-34255-5_22, Rome, Italy, November 13-15, 2019, Proceedings; Mori, Masahiro, MacDorman, Karl, Kageki, Norri, The Uncanny Valley [From the Field] (2012) IEEE Robotics & Automation Magazine, 19, pp. 98-100. , https://doi.org/10.1109/MRA.2012.2192811, (06 2012); Panayiotou, Marievie, Eteokleous, Nikleia, (2017) ROBOTICS AS MEANS TO INCREASE STUDENTS' STEM ATTITUDES, pp. 216-220. , InScience Press, Chapter Projects and Trends; Recupero, Diego Reforgiato, Dessì, Danilo, Concas, Emanuele, A Flexible and Scalable Architecture for Human-Robot Interaction (2019) Ambient Intelligence - 15th European Conference, AmI 2019, pp. 311-317. , https://doi.org/10.1007/978-3-030-34255-5_21, Rome, Italy, November 13-15, 2019, Proceedings; Recupero, Diego Reforgiato, Spiga, Federico, Knowledge acquisition from parsing natural language expressions for humanoid robot action commands (2019) Information Processing & Management, p. 102094. , https://doi.org/10.1016/j.ipm.2019.102094, (2019); (2019), https://voicebot.ai/2019/02/15/google-assistant-actions-total-4253-in-january-2019-up-2-5x-in-past-year-but-7-5-the-total-numberalexa-skills-in-u-s/, Voicebot. Google Assistant Actions Total 4,253 in January 2019, Up 2.5x in Past Year but 7.5 the Tota Alexa Skills in U.S",,"De Carolis B.Gena C.Lieto A.Rossi S.Sciutti A.",,"CEUR-WS","2020 Workshop on Adapted Interaction with Social Robots, cAESAR 2020","17 March 2020",,163832,16130073,,,,"English","CEUR Workshop Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85094852029
"Sóti G., Mamaev I., Hein B.","57219471624;57192180626;23008891600;","A Modular Deep Learning Architecture for Anomaly Detection in HRI",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12336 LNAI",,,"295","307",,,"10.1007/978-3-030-60337-3_29","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092929430&doi=10.1007%2f978-3-030-60337-3_29&partnerID=40&md5=88b1831a353a3eb6d690782405011f0b","IAR-IPR, Karlsruhe Institute of Technology, Karlsruhe, Germany; Karlsruhe University of Applied Sciences, Karlsruhe, Germany","Sóti, G., IAR-IPR, Karlsruhe Institute of Technology, Karlsruhe, Germany; Mamaev, I., IAR-IPR, Karlsruhe Institute of Technology, Karlsruhe, Germany; Hein, B., IAR-IPR, Karlsruhe Institute of Technology, Karlsruhe, Germany, Karlsruhe University of Applied Sciences, Karlsruhe, Germany","Considering humans as a non-deterministic factor makes anomaly detection in Human-Robot Interaction scenarios rather a challenging problem. Anomalous events like unexpected user interaction or unforeseen environment changes are unknown before they happen. On the other hand, the work process or user intentions could evolve in time. To address this issue, a modular deep learning approach is presented that is able to learn normal behavior patterns in an unsupervised manner. We combined the unsupervised feature extraction learning ability of an autoencoder with a sequence modeling neural network. Both models were firstly evaluated on benchmark video datasets, revealing adequate performance comparable to the state-of-the-art methods. For HRI application, a continuous training approach for real-time anomaly detection was developed and evaluated in an HRI-experiment with a collaborative robot, ToF camera, and proximity sensors. In the user study with 10 subjects irregular interactions and misplaced objects were the most common anomalies, which system was able to detect reliably. © 2020, Springer Nature Switzerland AG.","Anomaly detection; Human-Robot Interaction; Unsupervised learning","Anomaly detection; Benchmarking; Educational robots; Human robot interaction; Learning systems; Robotics; Collaborative robots; Environment change; Learning abilities; Learning approach; Learning architectures; Real-time anomaly detections; Sequence modeling; State-of-the-art methods; Deep learning",,,,,,,,,,,,,,,,"Oldenbourg, R., (1890) Gesundheits-Ingenieur., 13 (39-40); Hasan, M., Learning temporal regularity in video sequences (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Del Giorno, A., Bagnell, J.A., Hebert, M., A discriminative framework for anomaly detection in large videos (2016) ECCV 2016. LNCS, 9909, pp. 334-349. , https://doi.org/10.1007/978-3-319-46454-121, Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.), Springer, Cham; Medel, J.R., (2016) Anomaly Detection Using Predictive Convolutional Long Short-Term Memory Units; Chong, Y.S., Tay, Y.H., Abnormal event detection in videos using spatiotempo-ral autoencoder (2017) ISNN 2017. LNCS, 10262, pp. 189-196. , https://doi.org/10.1007/978-3-319-59081-323, Cong, F., Leung, A., Wei, Q. (eds.) , Springer, Cham; Sabokrou, M., Real-time anomaly detection and localization in crowded scenes (2015) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops; Wang, J.H., Miao, Y.Q., Khamis, A., Karray, F., Liang, J., Adaptation approaches in unsupervised learning: A survey of the state-of-the-art and future directions (2016) ICIAR 2016. LNCS, 9730, pp. 3-11. , https://doi.org/10.1007/978-3-319-41501-71, Campilho, A., Karray, F. (eds.), Springer, Cham; Park, C., Huang, J.Z., Ding, Y., A computable plug-in estimator of minimum volume sets for novelty detection (2009) Oper. Res., 58 (5), pp. 1469-1480; Breaban, M., Luchian, H., Outlier detection with nonlinear projection pursuit (2013) Int. J. Comput. Commun. Control, 8 (1), pp. 30-36; Zhang, K., Hutter, M., Jin, H., A new local distance-based outlier detection approach for scattered real-word data (2009) Adv. Knowl. Discov. Data Min., 5476, pp. 813-822; Cabral, G.G., Oliveira, A.L.I., Cahu, C.B.G., Combining nearest neighbor data description and structural risk minimization for one-class classification (2009) Neural Comput. Appl., 18 (2), pp. 175-183; Miao, Y.Q., Farahat, A.K., Kamel, M.S., Locally adaptive density ratio for detecting novelty in twitter streams (2015) The 6Th International Workshop on Modeling Social Media (Msm)-Behavioral Analytics in Social Media, Big Data and the Web, pp. 799-804; Song, L., Teo, C.H., Smola, A.J., Relative novelty detection (2009) International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 536-543; Sodemann, A.A., Ross, M.P., Borghetti, B.J., A review of anomaly detection in automated surveillance (2012) IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.), 42 (6), pp. 1257-1272; Chen, Z., Li, W., Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network (2017) IEEE Trans. Instrum. Meas., 66 (7), pp. 1693-1702; Khamis, A., The effects of outliers data on neural network performance (2005) Japsc, 5 (8), pp. 1394-1398; Klein, B.D., Rossin, D.F., Data quality in neural network models: Effect of error rate and magnitude of error on predictive accuracy (1999) Omega, 27 (5), pp. 569-582; Bai, S., Kolter, J.Z., Koltun, V., (2018) An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling; Lu, C., Shi, J., Jia, J., Abnormal event detection at 150 fps in matlab (2013) Proceedings of the IEEE International Conference on Computer Vision; Mahadevan, V., Anomaly detection in crowded scenes (2010) 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE; Alagi, H., Navarro, S.E., Mende, M., Hein, B., A versatile and modular capacitive tactile proximity sensor (2016) IEEE Haptics Symposium (HAPTICS), Philadelphia, PA, pp. 290-296. , https://doi.org/10.1109/HAPTICS.2016.7463192, 2016; Wang, T., Snoussi, H., Histograms of optical flow orientation for abnormal events detection (2013) 2013 IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS). IEEE; Finch, T., Incremental calculation of weighted mean and variance (2009) Univ. Cambridge, 4 (11-5), pp. 41-42","Sóti, G.; IAR-IPR, Germany; email: gergely.soti@kit.edu","Ronzhin A.Rigoll G.Meshcheryakov R.",,"Springer Science and Business Media Deutschland GmbH","5th International Conference on Interactive Collaborative Robotics, ICR 2020","7 October 2020 through 9 October 2020",,249569,03029743,9783030603366,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85092929430
[No author name available],[No author id available],"18th International Conference on Software Engineering and Formal Methods, SEFM 2020",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12310 LNCS",,,"","",324,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091556107&partnerID=40&md5=8fa0af31332abc036ecf3a06068f6047",,"","The proceedings contain 17 papers. The special focus in this conference is on Software Engineering and Formal Methods. The topics include: Hoare-Style Logic for Unstructured Programs; synthesis of P-Stable Abstractions; runtime Verification of Contracts with Themulus; sound C Code Decompilation for a Subset of x86-64 Binaries; Statically Checking REST API Consumers; A Layered Implementation of DR-BIP Supporting Run-Time Monitoring and Analysis; formal Verification of Human-Robot Interaction in Healthcare Scenarios; finding and Fixing a Mismatch Between the Go Memory Model and Data-Race Detector: A Story on Applied Formal Methods; Formal Verification of COLREG-Based Navigation of Maritime Autonomous Systems; End-to-End Verification of Initial and Transition Properties of GR(1) Designs in SPARK; Affine Systems of ODEs in Isabelle/HOL for Hybrid-Program Verification; interoperability and Integration Testing Methods for IoT Systems: A Systematic Mapping Study; FRed: Conditional Model Checking via Reducers and Folders; difference Verification with Conditions; A Formal Modeling Approach for Portable Low-Level OS Functionality.",,,,,,,,,,,,,,,,,,,,"de Boer F.Cerone A.",,"Springer Science and Business Media Deutschland GmbH","18th International Conference on Software Engineering and Formal Methods, SEFM 2020","14 September 2020 through 18 September 2020",,245269,03029743,9783030587673,,,"English","Lect. Notes Comput. Sci.",Conference Review,"Final","",Scopus,2-s2.0-85091556107
"Bisconti Lucidi P., Piermattei S.","57205669543;57218311337;","Sexual Robots: The Social-Relational Approach and the Concept of Subjective Reference",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12182 LNCS",,,"549","559",,2,"10.1007/978-3-030-49062-1_37","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088753958&doi=10.1007%2f978-3-030-49062-1_37&partnerID=40&md5=ef1afc8124cbd28b2481264257513824","Sant’Anna School of Advanced Studies, DIRPOLIS Institute, Pisa, Italy; Department of Psychology, Sapienza University of Rome, Rome, Italy","Bisconti Lucidi, P., Sant’Anna School of Advanced Studies, DIRPOLIS Institute, Pisa, Italy; Piermattei, S., Department of Psychology, Sapienza University of Rome, Rome, Italy","In this paper we propose the notion of “subjective reference” as a conceptual tool that explains how and why human-robot sexual interactions could reframe users approach to human-human sexual interactions. First, we introduce the current debate about Sexual Robotics, situated in the wider discussion about Social Robots, stating the urgency of a regulative framework. We underline the importance of a social-relational approach, mostly concerned about Social Robots impact in human social structures. Then, we point out the absence of a precise framework conceptualizing why Social Robots, and Sexual Robots in particular, may modify users’ sociality and relationality. Within a psychological framework, we propose to consider Sexual Robots as “subjective references”, namely objects symbolically referring to human subjects: we claim that, for the user experience, every action performed upon a Sexual Robot is symbolically directed toward a human subject, including degrading and violent practices. This shifting mechanism may transfer the user relational setting from human-robot interactions to human-human interactions. © 2020, Springer Nature Switzerland AG.","Ethics of technology; Human-robot interactions; Psychology of HCI; Social robots","Economic and social effects; Human computer interaction; User experience; Human robots; Human subjects; Human-human interactions; Social structure; Social robots",,,,,,,,,,,,,,,,"Baron-Cohen, S., (2011) Zero Degrees of Empathy: A New Theory of Human Cruelty, , Penguin, London; Bendel, O., Sex robots from the perspective of machine ethics (2017) LSR 2016. LNCS (LNAI), 10237, pp. 17-26. , https://doi.org/10.1007/978-3-319-57738-8_2, Cheok, A.D., Devlin, K., Levy, D. (eds) Springer, Cham; Breazeal, C.L., (2004) Designing Sociable Robots, , MIT Press, Cambridge; Broadbent, E., Interactions with robots: The truths we reveal about ourselves (2017) Annu. Rev. Psychol, 68, pp. 627-652; Coeckelbergh, M., Robot rights? Towards a social-relational justification of moral consideration (2010) Ethics Inf. Technol, 12 (3), pp. 209-221; Cox-George, C., Bewley, S., I, Sex Robot: the health implications of the sex robot industry (2018) BMJ Sex Reprod. Health, 44, pp. 161-164; Danaher, J., Robotic rape and robotic child sexual abuse: should they be criminalized? (2017) Crim. Law Philos, 11 (1), pp. 71-95; Devlin, K., In defence of sex machines: why trying to ban sex robots is wrong (2015) The Conversation; Gunkel, D.J., The rights of machines: caring for robotic care-givers (2015) Machine Medical Ethics. ISCASE, 74, pp. 151-166. , https://doi.org/10.1007/978-3-319-08108-3_10, van Rysewyk, S.P., Pontier, M. (eds) Springer, Cham; Gunkel, D.J., (2018) Robot Rights, , MIT Press, Cambridge (a); Gunkel, D.J., The other question: can and should robots have rights? (2018) Ethics Inf. Technol, 20 (2), pp. 87-99; Levy, D., Loebner, H., Robot prostitutes as alternatives to human sex workers (2007) IEEE International Conference on Robotics and Automation, Rome, 14; Levy, D., (2009) Love+Sex with Robots: The Evolution of Human-Robot Relationships, , Duckworth Overlook, London (a); Levy, D., The ethical treatment of artificially conscious robots (2009) Int. J. Social Robot, 1 (3), pp. 209-216; Levy, D., The ethics of robot prostitutes (2012) Robots Ethics: The Ethical and Social Implications of Robotics, pp. 223-231; Krämer, N.C., Eimler, S., von der Pütten, A., Payr, S., Theory of companions: what can theoretical models contribute to applications and understanding of human-robot interaction? (2011) Appl. Artif. Intell, 25 (6), pp. 474-502; Maras, M.H., Shapiro, L.R., Child sex dolls and robots: more than just an uncanny valley (2017) J. Internet Law, 21, pp. 3-17; Nicolò, A.M., Appunti a proposito di una teoria dei rapporti soggettuali (2003) Interazioni, 2 (20), pp. 55-61; Ogden, T.H., (1989) The Primitive Edge of Experience, , Karnak, London; Richardson, K., Sex robot matters: slavery, the prostituted, and the rights of machines (2016) IEEE Technol. Soc. Mag, 35 (2), pp. 46-53; Richardson, K., Technological animism: the uncanny personhood of humanoid machines (2016) Soc. Anal, 60 (1), pp. 110-128; Richardson, K., The asymmetrical ‘relationship’: parallels between prostitution and the development of sex robots (2016) ACM SIGCAS Comput. Soc, 45 (3), pp. 290-293; Strikwerda, L., Present and future instances of virtual rape in light of three categories of legal philosophical theories on rape (2015) Philos. Technol, 28 (4), pp. 491-510; Sharkey, N., Sharkey, A., The crying shame of robot nannies: an ethical appraisal (2010) Interact. Stud, 11 (2), pp. 161-190; Sharkey, N., The ethical frontiers of robotics (2008) Science, 322 (5909), pp. 1800-1801; Scheutz, M., 13 The inherent dangers of unidirectional emotional bonds between humans and social robots (2011) Robot Ethics: The Ethical and Social Implications of Robotics, p. 205; Sparrow, R., Robots in aged care: a dystopian future? (2016) AI Soc, 31 (4), pp. 445-454; Sparrow, R., Robots, rape, and representation (2017) Int. J. Soc. Robot, 9 (4), pp. 465-477; Su, N.M., Lazar, A., Bardzell, J., Bardzell, S., Of dolls and men: anticipating sexual intimacy with robots (2019) ACM Trans. Comput.-Hum. Interact. (TOCHI), 26 (3), p. 13; Sullins, J.P., Robots, love, and sex: the ethics of building a love machine (2012) IEEE Trans. Affect. Comput, 3 (4), pp. 398-409; Tavani, H., Can social robots qualify for moral consideration? Reframing the question about robot rights (2018) Information, 9 (4), p. 73; Turkle, S., Taggart, W., Kidd, C.D., Dasté, O., Relational artifacts with children and elders: the complexities of cybercompanionship (2006) Connection Sci, 18 (4), pp. 347-361; Winnicott, D.W., Creativity and its origins (1990) Essential Papers on the Psychology of Women, pp. 132-145","Bisconti Lucidi, P.; Sant’Anna School of Advanced Studies, Italy; email: piercosma.biscontilucidi@santannapisa.it","Kurosu M.",,"Springer","Thematic Area on Human Computer Interaction, HCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242229,03029743,9783030490614,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85088753958
"Boos A., Sax M., Reinhardt J.","57218311281;57218311558;57201292569;","Investigating Perceived Task Urgency as Justification for Dominant Robot Behaviour",2020,"Communications in Computer and Information Science","1224 CCIS",,,"117","124",,,"10.1007/978-3-030-50726-8_15","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088747936&doi=10.1007%2f978-3-030-50726-8_15&partnerID=40&md5=3cfb8648d928112977707e85c3b76bd4","Chair of Ergonomics, Technical University of Munich, Boltzmannstrasse 15, Garching, 85747, Germany","Boos, A., Chair of Ergonomics, Technical University of Munich, Boltzmannstrasse 15, Garching, 85747, Germany; Sax, M., Chair of Ergonomics, Technical University of Munich, Boltzmannstrasse 15, Garching, 85747, Germany; Reinhardt, J., Chair of Ergonomics, Technical University of Munich, Boltzmannstrasse 15, Garching, 85747, Germany","For the ubiquitous application of mobile robots, robots will need strategies to efficiently navigate through spaces shared with humans. To accomplish time-critical tasks, it can be necessary for robots to take precedence over humans. Giving a justification for such dominant robot behaviour, based on the urgency of its task, might mitigate negative effects on trust and acceptance and enhance behavioural compliance by waiting for the robot to pass. This study investigates the perceived urgency of different tasks a robot might perform. Therefore, 129 participants rated and compared ten tasks in an online study. The results reveal differences in perceived task urgency, related to time pressure in accomplishing the task. Differences in task urgency also affected the choice of who should be given priority to precede in a spatial conflict (human or robot). Furthermore, the results indicate a bias around human-robot interactions: Priority was more often granted to the human than the robot, when equally urgent tasks were presented for both entities. © 2020, Springer Nature Switzerland AG.","Dominant robot behaviour; Human-robot interaction","Human computer interaction; Online studies; Spatial conflicts; Time pressures; Time-critical; Ubiquitous application; Human robot interaction",,,,,,,,,,,,,,,,"Dillard, J.P., Solomon, D.H., Palmer, M.T., Structuring the concept of relational communication (1999) Commun. Monogr, 66 (1), pp. 49-65; Dragan, A.D., Lee, K.C., Srinivasa, S.S., Legibility and predictability of robot motion (2013) 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 301-308. , IEEE; (2018) International Federation of Robotics: Executive summary world robotics-service robots; Lam, C.P., Chou, C.T., Chiang, K.H., Fu, L.C., Human-centered robot navigation—towards a harmoniously human–robot coexisting environment (2010) IEEE Trans. Robot, 27 (1), pp. 99-112; Li, J., Cuadra, A., Mok, B., Reeves, B., Kaye, J., Ju, W., Communicating dominance in a nonanthropomorphic robot using locomotion (2019) ACM Trans. Hum. Robot. Interact. (THRI), 8 (1), pp. 1-14; Li, J., Ju, W., Nass, C., Observer perception of dominance and mirroring behavior in human-robot relationships (2015) 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 133-140. , IEEE; Milli, S., Hadfield-Menell, D., Dragan, A., Russell, S., Should robots be obedient? (2017) Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17), pp. 4754-4760; Mutlu, B., Forlizzi, J., Robots in organizations: the role of workflow, social, and environmental factors in human-robot interaction (2008) 3rd International Conference on Human-Robot Interaction (HRI), pp. 287-294. , IEEE; Reeves, B., Nass, C., (1996) The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places, , Cambridge University Press, Cambridge; Reinhardt, J., Boos, A., Bloier, M., Bengler, K., Effect of variable motion behavior of a mobile robot on human compliance in human-robot spatial interaction (2020) Fruehjahrskongress der Gesellschaft fuer Arbeitswissenschaft (GfA), p. 66. , GfA-Press; Reinhardt, J., Pereira, A., Beckert, D., Bengler, K., Dominance and movement cues of robot motion: a user study on trust and predictability (2017) IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1493-1498. , IEEE; Rosenfeld, A., Richardson, A., Explainability in human-agent systems (2019) Auton. Agents Multi Agent Syst, 33 (6), pp. 673-705; Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K., Would you trust a (faulty) robot? Effects of error, task type and personality on human-robot cooperation and trust (2015) 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 141-148. , IEEE; Trinh, T.Q., Schroeter, C., Kessler, J., Gross, H.-M., Go ahead, please”: recognition and resolution of conflict situations in narrow passages for polite mobile robot navigation (2015) ICSR 2015. LNCS (LNAI), 9388, pp. 643-653. , https://doi.org/10.1007/978-3-319-25554-564, Springer, Cham; Yetim, F., A framework for organizing justifications for strategic use in adaptive interaction contexts (2008) European Conference on Information Systems (ECIS), pp. 815-825; Zhu, M., Yang, Y., Hsee, C.K., The mere urgency effect (2018) J. Consum. Res, 45 (3), pp. 673-690","Boos, A.; Chair of Ergonomics, Boltzmannstrasse 15, Germany; email: annika.boos@tum.de","Stephanidis C.Antona M.",,"Springer","22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242529,18650929,9783030507251,,,"English","Commun. Comput. Info. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85088747936
"Straßmann C., Grewe A., Kowalczyk C., Arntz A., Eimler S.C.","57191889270;57218311194;57218310370;57209304162;6506752642;","Moral Robots? How Uncertainty and Presence Affect Humans’ Moral Decision Making",2020,"Communications in Computer and Information Science","1224 CCIS",,,"488","495",,,"10.1007/978-3-030-50726-8_64","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088743332&doi=10.1007%2f978-3-030-50726-8_64&partnerID=40&md5=fb7c3808947918db317797a9f2b29155","Institute of Computer Science, University of Applied Sciences Ruhr West, Lützowstraße 5, Bottrop, 46236, Germany","Straßmann, C., Institute of Computer Science, University of Applied Sciences Ruhr West, Lützowstraße 5, Bottrop, 46236, Germany; Grewe, A., Institute of Computer Science, University of Applied Sciences Ruhr West, Lützowstraße 5, Bottrop, 46236, Germany; Kowalczyk, C., Institute of Computer Science, University of Applied Sciences Ruhr West, Lützowstraße 5, Bottrop, 46236, Germany; Arntz, A., Institute of Computer Science, University of Applied Sciences Ruhr West, Lützowstraße 5, Bottrop, 46236, Germany; Eimler, S.C., Institute of Computer Science, University of Applied Sciences Ruhr West, Lützowstraße 5, Bottrop, 46236, Germany","Robots are ubiquitously embedded in today’s work life and are increasingly applied in scenarios where they take moral decisions or at least give recommendations. Accordingly, this study investigates how physical presence and robot’s uncertainty expression affect humans’ moral decision-making and robot perception. In a 2 (uncertainty expression: certain/uncertain)$$\times $$ 2 (physical presence: present/video) between-subjects lab experiment (N = 91) the robot Pepper presented a moral dilemma and expressed its decision. It was then tested if participants align with this decision and how certain they are. Moreover, their judgment on the robot’s decision as well as their perception of the robot were assessed. This study provides important implications for the human-robot interaction. Based on the present results, humans’ decision-making was not affected by behavioral variations of the robot (at least for the dilemma used). However, those variables clearly influence humans’ perception of the robot and therefore the interaction with it. © 2020, Springer Nature Switzerland AG.","Moral decision; Persuasion; Robots; Social robotics; Trolley problem","Decision making; Human computer interaction; Robot perception; Work life; Human robot interaction",,,,,,,,,,,,,,,,"Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot, 1 (1), pp. 71-81; Eagly, A.H., (2013) Sex Differences in Social Behavior: A Social-Role Interpretation, , Psychology Press, London; Eagly, A.H., Carli, L.L., Sex of researchers and sex-typed communications as determinants of sex differences in influenceability: a meta-analysis of social influence studies (1981) Psychol. Bull, 90 (1), pp. 1-20; Eyssel, F., Hegel, F., Horstmann, G., Wagner, C., Anthropomorphic inferences from emotional nonverbal cues: a case study (2010) 19th International Symposium in Robot and Human Interactive Communication, pp. 646-651. , IEEE; Freigang, F., Kopp, S., This is what’s important – using speech and gesture to create focus in multimodal utterance (2016) IVA 2016. LNCS (LNAI), 10011, pp. 96-109. , https://doi.org/10.1007/978-3-319-47665-09, Traum, D., Swartout, W., Khooshabeh, P., Kopp, S., Scherer, S., Leuski, A. (eds) Springer, Cham; Ham, J., Cuijpers, R.H., Cabibihan, J.J., Combining robotic persuasive strategies: the persuasive power of a storytelling robot that uses gazing and gestures (2015) Int. J. Soc. Robot, 7 (4), pp. 479-487; Hoffmann, L., Bock, N., Rosenthal vd Pütten, A.M., The peculiarities of robot embodiment (emcorp-scale) development, validation and initial test of the embodiment and corporeality of artificial agents scale (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 370-378; Kahn, P.H., Robovie, you’ll have to go into the closet now”: children’s social and moral relationships with a humanoid robot (2012) Dev. Psychol, 48 (2), pp. 303-314; Li, J., The benefit of being physically present: a survey of experimental works comparing copresent robots, telepresent robots and virtual agents (2015) Int. J. Hum. Comput. Stud, 77, pp. 23-37; Liptak, A., (2017) Sent to prison by a software program’s secret algorithms, , https://www.nytimes.com/2017/05/01/us/politics/sent-to-prison-by-a-software-programs-secret-algorithms.html, May; Makransky, G., Lilleholt, L., Aaby, A., Development and validation of the multimodal presence scale for virtual reality environments: a confirmatory factor analysis and item response theory approach (2017) Comput. Hum. Behav, 72, pp. 276-285; Nomura, T., Suzuki, T., Kanda, T., Kato, K., Measurement of negative attitudes toward robots (2006) Interact. Stud, 7 (3), pp. 437-454; Stellmach, H., Lindner, F., Perception of an uncertain ethical reasoning robot: a pilot study (2018) Mensch und Computer 2018-Tagungsband; Stellmach, H., Lindner, F., Perception of an uncertain ethical reasoning robot (2019) i-com, 18 (1), pp. 79-91; Tormala, Z.L., The role of certainty (and uncertainty) in attitudes and persuasion (2016) Curr. Opin. Psychol, 10, pp. 6-11; You, D., Maeda, Y., Bebeau, M.J., Gender differences in moral sensitivity: a meta-analysis (2011) Ethics Behav, 21 (4), pp. 263-282","Straßmann, C.; Institute of Computer Science, Lützowstraße 5, Germany; email: carolin.strassmann@hs-ruhrwest.de","Stephanidis C.Antona M.",,"Springer","22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242529,18650929,9783030507251,,,"English","Commun. Comput. Info. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85088743332
"Zou X., Lv C., Zhang J.","57203034052;57203039160;55662802900;","The effect of group membership, system reliability and anthropomorphic appearance on user’s trust in intelligent decision support system",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12187 LNAI",,,"231","242",,,"10.1007/978-3-030-49183-3_18","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088535598&doi=10.1007%2f978-3-030-49183-3_18&partnerID=40&md5=43afa57aec83c56d9873ba7423b5299d","CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China; Department of Industrial Engineering, Tsinghua University, Beijing, 100084, China","Zou, X., CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China, Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China, Department of Industrial Engineering, Tsinghua University, Beijing, 100084, China; Lv, C., CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China, Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China; Zhang, J., CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China, Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China","Past studies have found that the in-group membership of an intelligent agent can improve users’ trust. We explored whether such an effect depends on system reliability levels and anthropomorphic appearance. We manipulated reliability levels (95%, 70%, 45%) and anthropomorphic appearance (human-like vs. computer-like appearance) of an intelligent decision support system in our study. The minimum group paradigm was adopted to manipulate the group membership of the intelligent system (in-group vs out-group). We measured trust by using both subjective rating and compliant behaviors toward the system recommendations. The results showed that the intelligent system with an in-group membership resulted in higher trust as compared to the system with an out-group membership. The magnitude of these effects did not differ across different reliability levels and anthropomorphic appearances. We discussed such findings in light of human-robot interaction theories and potential implications for designing trustworthy decision support system. © Springer Nature Switzerland AG 2020.","Anthropomorphism; Group membership; Human-robot interaction; Intelligent decision support system; Trust","Decision support systems; Ergonomics; Human computer interaction; Intelligent systems; Reliability; Compliant behavior; Group memberships; Human like; Intelligent decision support systems; Reliability level; Subjective rating; System reliability; Social robots",,,,,,,,,,,,,,,,"Lee, J.D., See, K.A., Trust in automation: designing for appropriate reliance (2004) Hum. Factors, 46 (1), pp. 50-80; Park, E., Jenkins, Q., Jiang, X., Measuring trust of human operators in new generation rescue robots (2008) Paper presented at the proceedings of the JFPS international symposium on fluid power; Freedy, A., DeVisser, E., Weltman, G., Coeyman, N., Measurement of trust in human-robot collaboration (2007) 2007 International Symposium on Collaborative Technologies and Systems, pp. 106-114. , IEEE, May; Chavaillaz, A., Wastell, D., Sauer, J., System reliability, performance and trust in adaptable automation (2016) Appl. Ergon, 52, pp. 333-342; Ross, J.M., Szalma, J.L., Hancock, P.A., Barnett, J.S., Taylor, G., The effect of automation reliability on user automation trust and reliance in a search-and-rescue scenario (2008) Paper presented at the proceedings of the human factors and ergonomics society annual meeting; de Vries, P., Midden, C., Bouwhuis, D., The effects of errors on system trust, self-confidence, and the allocation of control in route planning (2003) Int. J. Hum. Comput. Stud, 58 (6), pp. 719-735; Dzindolet, M.T., Peterson, S.A., Pomranky, R.A., Pierce, L.G., Beck, H.P., The role of trust in automation reliance (2003) Int. J. Hum. Comput. Stud, 58 (6), pp. 697-718; Schaefer, K.E., Chen, J.Y., Szalma, J.L., Hancock, P.A., A meta-analysis of factors influencing the development of trust in automation: implications for understanding autonomy in future systems (2016) Hum. Factors, 58 (3), pp. 377-400; Sanchez, J., Fisk, A.D., Rogers, W.A., Reliability and age-related effects on trust and reliance of a decision support aid (2004) Paper presented at the proceedings of the human factors and ergonomics society annual meeting; Farrell, S., Lewandowsky, S., A connectionist model of complacency and adaptive recovery under automation (2000) J. Exp. Psychol. Learn. Mem. Cogn, 26 (2), p. 395; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Hum. Factors, 53 (5), pp. 517-527; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot, 1 (1), pp. 71-81; Madhavan, P., Wiegmann, D.A., Effects of information source, pedigree, and reliability on operator interaction with decision support systems (2007) Hum. Factors, 49 (5), pp. 773-785; Qiu, L., Benbasat, I., Evaluating anthropomorphic product recommendation agents: a social relationship perspective to designing information systems (2008) J. Manag. Inf. Syst, 25 (4), pp. 145-182; Nass, C., Steuer, J., Tauber, E.R., Computer are social actors (1994) Conference on Human Factors in Computing Systems, CHI 1994, p. 204. , Boston, Massachusetts, USA, April 24–28, Conference Companion; Pak, R., Fink, N., Price, M., Bass, B., Sturre, L., Decision support aids with anthropomorphic characteristics influence trust and performance in younger and older adults (2012) Ergonomics, 55 (9), p. 1059; de Visser, E.J., Almost human: anthropomorphism increases trust resilience in cognitive agents (2016) J. Exp. Psychol. Appl, 22 (3), p. 331; Messick, D.M., Mackie, D.M., Intergroup relations (1989) Annu. Rev. Psychol, 40 (1), pp. 45-81; Deligianis, C., Stanton, C.J., McGarty, C., Stevens, C.J., The impact of intergroup bias on trust and approach behaviour towards a humanoid robot (2017) J. Hum.-Robot Interact, 6 (3), pp. 4-20; Kuchenbrandt, D., Eyssel, F., Bobinger, S., Neufeld, M., Minimal group-maximal effect? Evaluation and anthropomorphization of the humanoid robot NAO (2011) ICSR 2011. LNCS (LNAI), 7072, pp. 104-113. , https://doi.org/10.1007/978-3-642-25504-5_11, Mutlu, B., Bartneck, C., Ham, J., Evers, Kanda, T. (eds) Springer, Heidelberg; Häring, M., Kuchenbrandt, D., André, E., Would you like to play with me?: how robots’ group membership and task features influence human-robot interaction (2014) Paper presented at the proceedings of the 2014 ACM/IEEE international conference on human-robot interaction; Kuchenbrandt, D., Eyssel, F., Bobinger, S., Neufeld, M., When a robot’s group membership matters (2013) Int. J. Soc. Robot, 5 (3), pp. 409-417; Nass, C., Moon, Y., Machines and mindlessness: social responses to computers (2000) J. Soc. Issues, 56 (1), pp. 81-103; Pak, R., Mclaughlin, A.C., Bass, B., A multi-level analysis of the effects of age and gender stereotypes on trust in anthropomorphic technology by younger and older adults (2014) Ergonomics, 57 (9), pp. 1277-1289; Tajfel, H., Billig, M.G., Bundy, R.P., Flament, C., Social categorization and intergroup behaviour (1971) Eur. J. Soc. Psychol, 1 (2), pp. 149-178; Van Dongen, K., van Maanen, P.P., Under-reliance on the decision aid: a difference in calibration and attribution between self and aid (2006) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 50 (3), pp. 225-229. , SAGE Publications, Los Angeles, October; Brewer, M.B., In-group bias in the minimal intergroup situation: a cognitive-motivational analysis (1979) Psychol. Bull, 86 (2), p. 307; Brewer, M.B., Kramer, R.M., The psychology of intergroup attitudes and behavior (1985) Annu. Rev. Psychol, 36 (1), pp. 219-243; Kramer, R.M., Brewer, M.B., Effects of group identity on resource use in a simulated commons dilemma (1984) J. Pers. Soc. Psychol, 46 (5), p. 1044; Sun, L., Chen, L., Duan, J., Advice taking in decision-making: strategies, influences and feature research (2017) Adv. Psychol. Sci, 25 (1), pp. 169-179","Zhang, J.; CAS Key Laboratory of Behavioral Science, China; email: zhangjingyu@psych.ac.cn","Harris D.Li W.-C.",,"Springer","17th International Conference on Engineering Psychology and Cognitive Ergonomics, EPCE 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242169,03029743,9783030491826,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85088535598
"Wang Q., Fan X., Luo M., Yin X., Zhu W.","57221578351;7403393922;57218263880;56104006000;57193321215;","Construction of human-robot cooperation assembly simulation system based on augmented reality",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12190 LNCS",,,"629","642",,,"10.1007/978-3-030-49695-1_42","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088524979&doi=10.1007%2f978-3-030-49695-1_42&partnerID=40&md5=c8e51f7dd4f7f60af163d2ac52274f9c","Institute of Intelligent Manufacturing and Information Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Key Lab of Advanced Manufacturing Environment, Shanghai, 200030, China","Wang, Q., Institute of Intelligent Manufacturing and Information Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Fan, X., Institute of Intelligent Manufacturing and Information Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China, Shanghai Key Lab of Advanced Manufacturing Environment, Shanghai, 200030, China; Luo, M., Institute of Intelligent Manufacturing and Information Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Yin, X., Institute of Intelligent Manufacturing and Information Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Zhu, W., Institute of Intelligent Manufacturing and Information Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China","Human-Robot cooperation (HRC) is the developing trend in the field of industrial assembly. Design and evaluation of the HRC assembly workstation considering the human factor is very important. In order to evaluate the transformational construction scenario of a manual assembly workstation to a HRC workstation fast and safely, a HRC assembly simulation system is constructed which is based on Augmented Reality (AR) with human-in-loop interaction. It enables a real operator to interact with virtual robot in a real scene, and the assembly steps of real workers can be restored and mapped to a virtual human model for further ergonomic analysis. Kinect and LeapMotion are used as the sensors for human-robot interaction decision and feedback. An automobile gearbox assembly is taken as an example for different assembly task verification, operators’ data are collected and analyzed by RULA scores and NASA-TLX questionnaires. The result shows that the simulation system can be used for the human factor evaluation of different HRC task configuration schemes. © Springer Nature Switzerland AG 2020.","Augmented Reality; Human factors; Human-Robot Cooperation","Augmented reality; Human computer interaction; Human engineering; Mixed reality; NASA; Surveys; Assembly simulation; Automobile gearbox; Design and evaluations; Ergonomic analysis; Human-robot cooperation; Industrial assemblies; Simulation systems; Virtual human model; Social robots",,,,,"National Natural Science Foundation of China, NSFC: 51475291; Ministry of Industry and Information Technology of the People's Republic of China, MIIT: 19GC04252","The work is partially supported by the NSFC project (51475291) and MIIT project (19GC04252), China. The authors are grateful to the editors and anonymous reviewers for their valuable comments.",,,,,,,,,,"Matsas, E., Vosniakos, G.C., Design of a virtual reality training system for human–robot collaboration in manufacturing tasks (2017) Int. J. Interact. Des. Manuf. (IJIDeM), 11 (2), pp. 139-153. , https://doi.org/10.1007/s12008-015-0259-2; Wang, Q., Cheng, Y., Jiao, W., Johnson, M.T., Zhang, Y., Virtual reality human-robot collaborative welding: a case study of weaving gas tungsten arc welding (2019) J. Manuf. Process, 48, pp. 210-217; Neuhoefer, J.A., Kausch, B., Schlick, C.M., (2009) Embedded augmented reality training system for dynamic human-robot cooperation, , Nato Research and Technology Organization Neuilly-Sur-Seine, France; Ni, D., Yew, A.W.W., Ong, S.K., Nee, A.Y.C., Haptic and visual augmented reality interface for programming welding robots (2017) Adv. Manuf, 5 (3), pp. 191-198. , https://doi.org/10.1007/s40436-017-0184-7; Michalos, G., Karagiannis, P., Makris, S., Tokçalar, Ö., Chryssolouris, G., Augmented reality (AR) applications for supporting human-robot interactive cooperation (2016) Procedia CIRP, 41, pp. 370-375; Crick, C., Jay, G., Osentoski, S., Pitzer, B., Jenkins, O.C., Rosbridge: ROS for Non-ROS users (2017) Robotics Research. STAR, 100, pp. 493-504. , https://doi.org/10.1007/978-3-319-29363-9_28, Christensen, H.I., Khatib, O. (eds) Springer, Cham; Azuma, R.T., A survey of augmented reality. Presence: Teleoper (1997) Virtual Environ, 6 (4), pp. 355-385; Plopski, A., Moser, K.R., Kiyokawa, K., Swan, J.E., Takemura, H., Spatial consistency perception in optical and video see-through head-mounted augmentations (2016) 2016 IEEE Virtual Reality (VR), pp. 265-266. , IEEE; Zhu, W., Fan, X., Zhang, Y., Applications and research trends of digital human models in the manufacturing industry (2019) Virtual Real. Intell. Hardw, 1 (6), pp. 558-579; Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W., OctoMap: an efficient probabilistic 3D mapping framework based on octrees (2013) Auton. Robots, 34 (3), pp. 189-206. , https://doi.org/10.1007/s10514-012-9321-0; Nguyen, V.T., (2014) Enhancing touchless interaction with the leap motion using a haptic glove, , University of Eastern Finland, Joensuu; Hart, S.G., Staveland, L.E., Development of NASA-TLX (Task Load Index): results of empirical and theoretical research (1988) Advances in Psychology, North-Holland, 52, pp. 139-183; Qiu, S., Fan, X., Wu, D., He, Q., Zhou, D., Virtual human modeling for interactive assembly and disassembly operation in virtual reality environment (2013) Int. J. Adv. Manuf. Technol, 69 (9– 12), pp. 2355-2372. , https://doi.org/10.1007/s00170-013-5207-3","Fan, X.; Institute of Intelligent Manufacturing and Information Engineering, China; email: xmfan@sjtu.edu.cn","Chen J.Y.C.Fragomeni G.",,"Springer","12th International Conference on Virtual, Augmented and Mixed Reality, VAMR 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242159,03029743,9783030496944,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85088524979
"Cannavò A., Lamberti F.","57194155419;7006958191;","User interaction feedback in a hand-controlled interface for robot team tele-operation using wearable augmented reality",2020,"Italian Chapter Conference 2017 - Smart Tools and Apps in computer Graphics, STAG 2017",,,,"53","62",,,"10.2312/stag.20171227","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088355620&doi=10.2312%2fstag.20171227&partnerID=40&md5=eb79555ea86acfe3546876deb5fa6f18","Politecnico di Torino, Dipartimento di Automatica e Informatica, Corso Duca degli Abruzzi 24, Torino, 10129, Italy","Cannavò, A., Politecnico di Torino, Dipartimento di Automatica e Informatica, Corso Duca degli Abruzzi 24, Torino, 10129, Italy; Lamberti, F., Politecnico di Torino, Dipartimento di Automatica e Informatica, Corso Duca degli Abruzzi 24, Torino, 10129, Italy","Continuous advancements in the field of robotics and its increasing spread across heterogeneous application scenarios make the development of ever more effective user interfaces for human-robot interaction (HRI) an extremely relevant research topic. In particular, Natural User Interfaces (NUIs), e.g., based on hand and body gestures, proved to be an interesting technology to be exploited for designing intuitive interaction paradigms in the field of HRI. However, the more sophisticated the HRI interfaces become, the more important is to provide users with an accurate feedback about the state of the robot as well as of the interface itself. In this work, an Augmented Reality (AR)-based interface is deployed on a head-mounted display to enable tele-operation of a remote robot team using hand movements and gestures. A user study is performed to assess the advantages of wearable AR compared to desktop-based AR in the execution of specific tasks. © 2017 The Author(s) Eurographics Proceedings © 2017 The Eurographics Association.",,"Augmented reality; Biofeedback; Feedback; Helmet mounted displays; Interface states; User interfaces; Wearable computers; Application scenario; Head mounted displays; Human robot Interaction (HRI); Intuitive interaction; Natural user interfaces; Research topics; Specific tasks; User interaction; Human robot interaction",,,,,,,,,,,,,,,,"AHN, J., AHN, E., KIM, G. J., Robot mediated tele-presence through body motion based control (2015) Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on, pp. 463-468. , IEEE, 2; ARANGO, C. A., MARTINEZ, J. R., PÉREZ, V. Z., Master-slave system using kinect and an industrial robot for teleoperations (2013) Health Care Exchanges (PAHCE), 2013 Pan American, pp. 1-6. , IEEE, 2; BONAIUTO, S., CANNAVÒ, A., PIUMATTI, G., PARAVATI, G., LAMBERTI, F., Tele-operation of robot teams: a comparison of gamepad-, mobile device-and hand tracking-based user interfaces, pp. 2-6. , 1, 1; BASSILY, D., GEORGOULAS, C., GUETTLER, J., LINNER, T., BOCK, T., Intuitive and adaptive robotic arm manipulation using the leap motion controller (2014) ISR/Robotik 2014; 41st International Symposium on Robotics; Proceedings of, pp. 1-7. , VDE, 2; CANCEDDA, L., CANNAVÒ, A., GAROFALO, G., LAMBERTI, F., MONTUSCHI, P., PARAVATI, G., Mixed reality-based user interaction feedback for a hand-controlled interface targeted to robot teleoperation (2017) International Conference on Augmented Reality, Virtual Reality and Computer Graphics, pp. 447-463. , Springer, 1, 2, 3, 4, 6; CHO, S. K., JIN, H. Z., LEE, J. M., YAO, B., Teleoperation of a mobile robot using a force-reflection joystick with sensing mechanism of rotating magnetic field (2010) IEEE/ASME Transactions On Mechatronics, 15 (1), pp. 17-26. , 2; CHENG-JUN, D., PING, D., MING-LU, Z., YAN-FANG, Z., Design of mobile robot teleoperation system based on virtual reality (2009) In Automation and Logistics, 2009. ICAL'09. IEEE International Conference on, pp. 2024-2029. , IEEE, 3; CRAINIC, M.-F., PREITL, S., Ergonomic operating mode for a robot arm using a game-pad with two joysticks (2015) Applied Computational Intelligence and Informatics (SACI), 2015 IEEE 10th Jubilee International Symposium on, pp. 167-170. , IEEE, 2; ERDELJ, M., NATALIZIO, E., Uav-assisted disaster management: Applications and open issues (2016) Computing, Networking and Communications (ICNC), 2016 International Conference on, pp. 1-5. , IEEE, 1; FAISAL, S., CAIRNS, P., CRAFT, B., Infovis experience enhancement through mediated interaction, , 1; FRANK, J. A., KAPILA, V., Towards teleoperation-based interactive learning of robot kinematics using a mobile augmented reality interface on a tablet (2016) Control Conference (ICC), 2016 Indian, pp. 385-392. , IEEE, 2, 3; GALE, N., MIRZA-BABAEI, P., PEDERSEN, I., Heuristic guidelines for playful wearable augmented reality applications (2015) Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play, pp. 529-534. , ACM, 7; GUO, C., SHARLIN, E., Exploring the use of tangible user interfaces for human-robot interaction: a comparative study (2008) In ProceedingsoftheSIGCHIConferenceonHumanFactorsinComputingSystems, pp. 121-130. , ACM, 1; HASHIMOTO, S., ISHIDA, A., INAMI, M., IGARASHI, T., Touchme: An augmented reality based remote robot manipulation (2011) In 21st Int. Conf. on Artificial Reality and Telexistence, Proc. of ICAT2011, 2 (3), p. 7; HORTAL, E., PLANELLES, D., COSTA, A., IÁÑEZ, E., ÚBEDA, A., AZORÍN, J. M., FERNÁNDEZ, E., Svm-based brain-machine interface for controlling a robot arm through four mental tasks (2015) Neurocomputing, 151, pp. 116-121. , 2; JIANG, H., WACHS, J. P., PENDERGAST, M., DUERSTOCK, B. S., 3d joystick for robotic arm control by individuals with high level spinal cord injuries (2013) Rehabilitation Robotics (ICORR), 2013 IEEE International Conference on, pp. 1-5. , IEEE, 1; KIM, M. K., RYU, K., OH, Y., OH, S.-R., KIM, K., Implementation of real-time motion and force capturing system for telemanipulation based on semg signals and imu motion data (2014) Robotics and Automation (ICRA), 2014 IEEE International Conference on, pp. 5658-5664. , IEEE, 2; LOVON-RAMOS, P. W., RIPAS-MAMANI, R., ROSASCUEVAS, Y., TEJADA-BEGAZO, M., MOGROVEJO, R. M., BARRIOSARANIBAR, D., Mixed reality applied to the teleoperation of a 7-dof manipulator in rescue missions (2016) Robotics Symposium and IV Brazilian Robotics Symposium (LARS/SBR), pp. 299-304. , 2016 XIII Latin American IEEE, 3; LEVINE, S. J., SCHAFFERT, S., CHECKA, N., Natural user interface for robot task assignment, 2, p. 3; MATEO, C., BRUNETE, A., GAMBAO, E., HERNANDO, M., Hammer: An android based application for end-user industrial robot programming (2014) In Mechatronic and Embedded Systems and Applications (MESA), 2014 IEEE/ASME 10th International Conference on, pp. 1-6. , IEEE, 1; MALKIN, J., LI, X., HARADA, S., LANDAY, J., BILMES, J., The vocal joystick engine v1. 0 (2011) Computer Speech & Language, 25 (3), pp. 535-555. , 2; NIELSEN, J., MOLICH, R., Heuristic evaluation of user interfaces (1990) Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 249-256. , ACM, 7; NETO, P., PIRES, J. N., MOREIRA, A. 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E. C., POLVI, J., TAKETOMI, T., YAMAMOTO, G., SANDOR, C., KATO, H., Toward standard usability questionnaires for handheld augmented reality (2015) IEEE computer graphics and applications, 35 (5), pp. 66-75. , 7; SUZUKI, T., SEKINE, T., FUJII, T., ASAMA, H., ENDO, I., Cooperative formation among multiple mobile robot teleoperation in inspection task (2000) In Decision and Control, 2000. Proceedings of the 39th IEEE Conference on, 1, pp. 358-363. , IEEE, 1; SAUER, M., ZEIGER, F., SCHILLING, K., Mixed-reality user interface for mobile robot teleoperation in ad-hoc networks (2010) IFAC Proceedings Volumes, 43 (23), pp. 77-82. , 2, 3; UBEDA, A., IANEZ, E., AZORIN, J. M., An integrated electrooculography and desktop input bimodal interface to support robotic arm control (2013) IEEE Transactions on Human-Machine Systems, 43 (3), pp. 338-342. , 2; VENNA, T. V. S. N., PATEL, S., Real-time robot control using leap motion a concept of human-robot interaction ASEE, 2; WEICHERT, F., BACHMANN, D., RUDAK, B., FISSELER, D., Analysis of the accuracy and robustness of the leap motion controller (2013) Sensors, 13 (5), pp. 6380-6393. , 2; YEW, A., ONG, S., NEE, A., Immersive augmented reality environment for the teleoperation of maintenance robots (2017) Procedia CIRP, 61, pp. 305-310. , 3; YANG, Y., YAN, H., DEHGHAN, M., ANG, M. H., Real-time human-robot interaction in complex environment using kinect v2 image recognition (2015) Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), 2015 IEEE 7th International Conference on, pp. 112-117. , IEEE, 2",,"Giachetti A.Pingi P.Stanco F.",,"Eurographics Association","2017 Italian Chapter Conference - Smart Tools and Apps in Computer Graphics, STAG 2017","11 September 2017 through 12 September 2017",,159980,,9783038680482,,,"English","Ital. Chapter Conf. - Smart Tools Apps Comput. Graph., STAG",Conference Paper,"Final","",Scopus,2-s2.0-85088355620
"Goto K., Nishino H., Yatsuda A., Tsutsumi H., Haramaki T.","57198579566;7201979505;57195504682;57195507546;56407141300;","A method for driving humanoid robot based on human gesture",2020,"International Journal of Mechanical Engineering and Robotics Research","9","3",,"447","452",,2,"10.18178/ijmerr.9.3.447-452","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087820724&doi=10.18178%2fijmerr.9.3.447-452&partnerID=40&md5=5ea1c2c5fc00ab06aa3221f9b892cf2d","Graduate School of Engineering, Oita University, Oita, Japan; Faculty of Science and Technology, Oita University, Oita, Japan","Goto, K., Graduate School of Engineering, Oita University, Oita, Japan; Nishino, H., Faculty of Science and Technology, Oita University, Oita, Japan; Yatsuda, A., Graduate School of Engineering, Oita University, Oita, Japan; Tsutsumi, H., Graduate School of Engineering, Oita University, Oita, Japan; Haramaki, T., Faculty of Science and Technology, Oita University, Oita, Japan","When designing human robot communication interface, applying non-verbal interaction modalities such as robot motions is useful. Such modalities should be designed to augment verbal communication contents and set a mood for effective human robot interactions. Designing and implementing a set of useful robot motions, however, is a difficult problem. It requires developers to describe instructions for accurately controlling motors embedded in the robot. In this paper, the authors propose a method for generating a variety of robot motions using human gestures. The proposed method captures human gestures in real time by using motion tracking techniques, converting the acquired data to robot motion instructions, and applying them to a physical robot. We developed a prototype system targeted at supporting multiple motion tracking techniques and physical humanoid robots. We also conducted experiments for controlling the robots by human gestures to verify the effectiveness of the proposed method. It can promote better human robot communication environments with reducing labors in robot motion development. © 2020 by the authors.","Human motion tracking; Human robot interaction; Robot motion design",,,,,,,,,,,,,,,,,"Yatsuda, A., Haramaki, T., Nishino, H., An unsolicited heat stroke alert system for the elderly (2017) Proc. IEEE Int'l Conf. on Consumer Electronics-Taiwan, pp. 347-348; Haramaki, T., Nishino, H., An edge computer based driver monitoring system for assisting safety driving (2018) Proc. 6th Int'l Conf. on Emerging Internet, Data & Web Technologies, pp. 639-650; Yatsuda, A., Haramaki, T., Nishino, H., Development of an adult care system based on interactions with a communication robot (2017) Proc. 12th Int'l Conf. on Broadband and Wireless Computing, Communication and Applications, pp. 197-205; Yatsuda, A., Haramaki, T., Nishino, H., A study on robot motions inducing awareness for elderly care (2018) Proc. IEEE Int 'l Conf. on Consumer Electronics-Taiwan, pp. 391-392; Matsui, D., Minato, T., MacDorman, K., Ishiguro, H., Generating natural motion in an android by mapping human motion (2005) Proc. of IEEE/RSJ Int'l Conf. on Intelligent Robots and Systems, pp. 3301-3308; Imai, M., Why do we have to use a robot? IPSJ Magazine, 59 (8), pp. 692-697. , (in Japanese); Cicirelli, G., Attolico, C., Guaragnella, C., D'Orazio, T., A kinect-based gesture recognition approach for a natural human robot interface International Journal of Advanced Robotic Systems, 12 (3), pp. 1-11; Qureshi, A.H., Nakamura, Y., Yoshikawa, Y., Ishiguro, H., Show, attend and interact: Perceivable human-robot social interaction through neural attention q-network (2017) Proc. IEEE Int'l Conf. on-Robotics and Automation, pp. 1639-1645; Jung, J., Kanda, T., Kim, M.-S., Guidelines for contextual motion design of a humanoid robot International Journal of Social Robotics, 5 (2), pp. 153-169; Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y., Realtime multi-person 2d pose estimation using part affinity fields (2017) Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 1302-1310; Mehta, D., Sridhar, S., Sotnychenko, O., Rhodin, H., Shafiei, M., Seidel, H.-P., Xu, W., Theobalt, C., VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera ACM Trans.-on Graphics, 36 (4), pp. 44.1-44.14; (2018), https://sota.vstone.co.jp/home/, [Accessed December] (in Japanese); (2018), https://www.softbank.jp/robot/pepper/, Pepper. [Online]. [Accessed December] (in Japanese)",,,,"International Journal of Mechanical Engineering and Robotics Research",,,,,22780149,,,,"English","Int. J. Mech. Eng. Robot. Res.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85087820724
"Contreras-Calderón M.G., Castillo-Castañeda E.","57217291187;55989796100;","Design of a Variable Stiffness Joint for a Five-Bar-Mechanism",2020,"Mechanisms and Machine Science","84",,,"54","63",,1,"10.1007/978-3-030-48989-2_7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087031554&doi=10.1007%2f978-3-030-48989-2_7&partnerID=40&md5=5918979541749c0bb747d00373be0faa","Instituto Politécnico Nacional, CICATA-Unidad Querétaro, Querétaro, Mexico","Contreras-Calderón, M.G., Instituto Politécnico Nacional, CICATA-Unidad Querétaro, Querétaro, Mexico; Castillo-Castañeda, E., Instituto Politécnico Nacional, CICATA-Unidad Querétaro, Querétaro, Mexico","Variable stiffness actuators have been developed as an alternative to rigid actuators and have been implemented in human-robot interaction tasks such as industrial or assistance tasks to improve the dynamic performance of robots. This paper presents a new design of a variable stiffness joint to generate resistance to movement in the end-effector of a mechanism. The design is based on the cantilever beam principle, where the beam presents a flexion when applying a force and that keeps its stiffness constant. Movable support is implemented, which slides through the beam and allows to vary its flexion according to the movable support distance that is proportional to the stiffness. Also, tests have been performed with the rotational joint, where the end of the beam has been fixed on the rotational joint axis and the link is rotated to a specific angle to study the contribution of stiffness in the resistance to movement of the end-effector link. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.","Cantilever beam; Five-bar mechanism; Resistance force; Variable stiffness joint","Actuators; End effectors; Stiffness; Dynamic performance; Five-bar mechanism; Rotational joint; Stiffness constants; Variable stiffness; Human robot interaction",,,,,,,,,,,,,,,,"Hogan, N., Impedance control: an approach to manipulation: Part I—theory (1985) ASME. J. Dyn. Syst., Meas. Control, 107 (1), pp. 1-7; Flacco, F., De Luca, A., Stiffness estimation and nonlinear control of robots with variable stiffness actuation (2011) The International Federation of Automatic Control, Milano Italia, pp. 6872-6879; Park, J., Lee, Y., Song, J., Kim, H., Safe joint mechanism based on nonlinear stiffness for safe human-robot collision (2008) IEEE International Conference on Robotics and Automation, Pasadena, CA, pp. 2177-2182; Hyun, D., Yangm, H.S., Park, J., Shim, Y., Variable stiffness mechanism for human-friendly robots (2010) Mech. Mach. Theory, 45 (6), pp. 880-897; Braun, D.J., Howard, M., Vijayakumar, S., Exploiting variable stiffness in explosive movement tasks (2012) Robot. Sci. Syst, 7, pp. 25-32; Medina, J., Jardón, A., Balager, C., Control desacoplado de un actuador de rigidez variable para robots asistenciales (2016) RIAI-Rev. iberoamericana de automática e informática industrial, 13 (1), pp. 80-91; Huete, A.J., Victores, J.G., Martínez, S., Giménez, A., Balaguer, C., Personal autonomy rehabilitation in home environments by a portable assistive robot (2012) IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.), 42 (4), pp. 561-570; Tonietti, G., Schiavi, R., Bicchi, A., (2005) Design and Control of a variable stiffness actuator for safe and fast physical human/robot interaction, pp. 526-531; López, J., Giménez, A., García, D., Jardón, A., Balaguer, C., Diseño y simulación de un actuador de rigidez variable (2012) An. Ing. Mecánica Rev. Asociación Española Ingenieria Mecánica, 1 (18), pp. 154-161; Balderas, R., Briot, S., Chriette, A., Martinet, P., Minimizing input torques of a high-speed five-bar mechanism by using variable stiffness springs (2019) CISM, 584, pp. 61-68; Beléndez, T., Neipp, C., Beléndez, A., Flexión de una Barra Delgada Empotrada en un Extremo: Aproximación para Pequeñas Pendientes (2002) Rev. Brasileira de Ensino de Física, 24 (4), pp. 399-407","Contreras-Calderón, M.G.; Instituto Politécnico Nacional, Mexico; email: ma.gpe.contreras.c@gmail.com",,,"Springer Science and Business Media B.V.",,,,,22110984,,,,"English","Mech. Mach. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85087031554
"Nama T., Deb S., Debnath B., Kumari P.","57205732104;55003599100;57215408546;57215426524;","Designing a humanoid robot integrated Exer-Learning-Interaction(ELI)",2020,"Procedia Computer Science","167",,,"1524","1532",,1,"10.1016/j.procs.2020.03.363","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084475143&doi=10.1016%2fj.procs.2020.03.363&partnerID=40&md5=c148092ee18d8542c4cb3817393d8b08","Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, India","Nama, T., Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, India; Deb, S., Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, India; Debnath, B., Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, India; Kumari, P., Department of Computer Science and Engineering, National Institute of Technology, Agartala, Tripura, India","This is a design of transitive gesture-based human-robot interaction (HRI) for enhancing elementary learning outcome of special need children. It is an experimental Precept Action Sequence (PAS) interaction module, incorporating Leap Motion and Soft bank humanoid robot NAO as an implementation platform. In the primary phase of this work restricted recognition and recall task designed, where natural hand movements of the children captured to train and repeat certain task synchronous with robot movements. Traditional learning contents are transformed with tailored action-reaction mapping for improving the learning experience by the children. It is observed that the playful interaction with a robot on context-dependent evaluation of learning contents make the children engaged and natural learning attention grows. Based on empirical experience, viable suggestions are mentioned for the systematic use of Closed Loop HRI(CLHRI) as an effective medium of addressing children with learning trouble. The work may also be extended as exer-learning, therapeutic exercise feedback etc. © 2020 The Authors. Published by Elsevier B.V.","Action-Reaction Mapping; Closed Loop Interaction; Gesture Teleportation; Human Machine Interaction; Kinetic Movements; Leap Motion Controller; Machine Machine Interaction; NAO Robot; Skeleton Movement","Anthropomorphic robots; Artificial intelligence; Machine design; Human robot Interaction (HRI); Implementation platforms; Interaction modules; Learning experiences; Learning interactions; Playful interactions; Therapeutic exercise; Traditional learning; Human robot interaction",,,,,,,,,,,,,,,,"Guo, C., Sharlin, E., Exploring the Use of Tangible User Interfaces for Human-Robot Interaction: A Comparative Study (2008) CHI 2008 Proceedings, Human-Robot Interaction, , Florence, Italy; Nikolaidis, S., Lasota, P., Ramakrishna, R., Shah, J., Improved human robot team performance through cross-training, an approach inspired by human team training practices (2015) The International Journal of Robotics Research, 34 (14), p. 17111730; Chang, C.-W., Lee, J.-H., Chao, P.-Y., Wang, C.-Y., Chen, G.-D., (2010) Exploring the Possibility of Using Humanoid Robots As Instructional Tools for Teaching A Second Language in Primary School, , Educational Technology Society; 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Antonietti, A., Martina, D., Casellato, C., Dangelo, E., Pedrocchi, A., Control of a Humanoid NAO Robot by an Adaptive Bioinspired Cerebellar Module in 3D Motion Tasks (2019) Hindawi, Computational Intelligence and Neuroscience, 2019, p. 15. , Article ID 4862157; Pirker, J., Pojer, M., Holzinger, A., Gutl, C., (2017) Gesture-based Interactions in Video Games with the Leap Motion Controller, , Conference Paper; Deora, D., Bajaj, N., Indian Sign Language Recognition (2012) International Conference of Emerging Technology Trends in Electronics; Jamet, F., Masson, O., Jacquet, B., Stilgenbauer, J.-L., Baratgin, J., Learning by Teaching with Humanoid Robot: A New Powerful Experimental Tool to Improve Childrens Learning Ability (2018) Hindawi Journal of Robotics, 2018, p. 11. , Article ID 4578762; Lee, J., Chai, J., Reitsma, A.P.S., Hodgins, J.K., Pollard, N.S., Interactive Control of Avatars Animated with Human Motion Data (2013) ACM Transactions on Graphics; Weichert, F., Bachmann, D., Aus Rudak, B., Fisseler, D., Analysis of the Accuracy and Robustness of the Leap Motion Controller (2013) Sensors; Kamiya, N., Konishi, Y., Kamiya, A., Development of diagnosis support system of adiadochocinesis in cerebellar ataxia using Leap Motion Controller (2014) International Journal of Application or Innovation in Engineering Management (IJAIEM), 3 (8); Naidu, C., Ghotkar, A., Hand Gesture Recognition Using Leap Motion Controller International Journal of Science and Research (IJSR); Avola, D., Petracca, A., Cinque, L., Markerless Hand Gesture Interface Based on LEAP Motion Controller; Jamet, F., Masson, O., Jacquet, B., Stilgenbauer, J.-L., Baratgin, J., Learning by Teaching with Humanoid Robot: A New Powerful Experimental Tool to Improve Childrens Learning Ability (2018) Hindawi Journal of Robotics, 2018, p. 11. , https://doi.org/10.1155/2018/4578762, Article ID 4578762, pages; Rodriguez, I., Astigarraga, A., Jauregi, E., Ruiz, T., Lazkano, E., Humanizing NAO robot teleoperation using ROS (2014) 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), , November 18-20, Madrid, Spain; Shamsuddin, S., Yussof, H., Idzhar Ismail, L., Mohamed, S., Akhtar Hanapiah, F., Ismarrubie Zahari, N., Humanoid Robot NAO Interacting with Autistic Children of Moderately Impaired Intelligence to Augment Communication Skills (2012) International Symposium on Robotics and Intelligent Sensors (IRIS 2012); Majgaard, G., Humanoid Robots in the Classroom (2015) IADIS International Journal on, 13 (1), pp. 72-86. , http://www/Internet, (2015): 1645-7641; Chen, Y., Ho, M., Yang, H., A Study of Elementary Students Controlling on Leap Motion (2014) International Journal of Computers; Chan, A., Halevi, T., Memon, N., (2015) Leap Motion Controller for Authentication Via Hand Geometry and Gesture, , Springer International Publishing Switzerland; General, A., Da Silva, B., Esteves, D., Halleran, M., Liut, M., A Comparative Analysis between the Mouse, Trackpad and the Leap Motion (2016) CHI Conference; Qingchao, X., Jiangang, C., The Application of Leap Motion in Astronaut Virtual Training (2017) IOP Conference Series: Materials Science and Engineering; Nifal, M.N.M., Logine, T., Sopitha, S., Kiruthika, P., Space Mouse-Hand movement and gesture recognition using Leap Motion Controller (2017) International Journal of Scientific and Research Publications, 7 (12); Shamsuddin, S.Y., Ismail, H., Mohamed, L., Hanapiah, S., Ismarrubie, F., Zahari, N., Humanoid Robot NAO Interacting with Autistic Children of Moderately Impaired Intelligence to Augment Communication Skills (2012) Procedia Engineering, 41, pp. 1533-1538. , (2012)","Deb, S.; Department of Computer Science and Engineering, India; email: sumandeb.cse@nita.ac.in","Singh V.Asari V.K.Li K.-C.",,"Elsevier B.V.","2019 International Conference on Computational Intelligence and Data Science, ICCIDS 2019","6 September 2019 through 7 September 2019",,159537,18770509,,,,"English","Procedia Comput. Sci.",Conference Paper,"Final","All Open Access, Gold",Scopus,2-s2.0-85084475143
"Sutiphotinun T., Neranon P., Vessakosol P., Romyen A., Hiransoog C., Sookgaew J.","57216639048;55842967500;35491966100;57201210519;57216632882;8680661900;","A human-inspired control strategy: A framework for seamless human-robot handovers",2020,"Journal of Mechanical Engineering Research and Developments","43","3",,"235","245",,2,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084145641&partnerID=40&md5=2358883040cea71ab230d70b50e0aa3b","Department of Mechanical Engineering, Faculty of Engineering; Department of Agricultural Economics, Faculty of Economics Prince of Songkhla University, Hat Yai, Songkhla, 90112, Thailand","Sutiphotinun, T., Department of Mechanical Engineering, Faculty of Engineering; Neranon, P., Department of Mechanical Engineering, Faculty of Engineering; Vessakosol, P., Department of Mechanical Engineering, Faculty of Engineering; Romyen, A., Department of Agricultural Economics, Faculty of Economics Prince of Songkhla University, Hat Yai, Songkhla, 90112, Thailand; Hiransoog, C., Department of Mechanical Engineering, Faculty of Engineering; Sookgaew, J., Department of Mechanical Engineering, Faculty of Engineering","It can be claimed that understanding kinematically and dynamically how two humans physically collaborate while naturally performing object handover tasks is so crucial and can be used as guidelines in seamless human-robot interaction. This paper investigates the human behavioural responses in human-to-human object handover tasks under changing the object's mass, in which the interactive force between the humans and the object displacement are simultaneously measured in real-time. The results contribute the handover sequences distinctively categorized into three phases, i.e. sending, transfer and receiving postures, where the giver agent primarily decides to release the object. The interactive force analysis and the suitable transfer point between the giver and receiver have been carried out. Additionally, to be a better understanding of human dynamic characteristics, MJT and ARX model have been mathematically implemented. These paradigm findings will be useful for developing a robotic behaviour-based approach in seamless human-robot handovers in future. © 2020 Zibeline International Publishing Sdn. Bhd.. All rights reserved.","Auto-regressive exogenous (ARX); Human arm impedance model; Human-human interaction; Human-robot interaction; Minimum jerk trajectory (MJT)",,,,,,,,,,,,,,,,,"Reed, K.B., Patton, J., Peshkin, M., Replicating human-human physical interaction (2007) Proceedings of the 2007 IEEE International Conference on Robotics and Automation, 1-10, pp. 3615-3620; Kupcsik, A., Hsu, D., Lee, W.S., Learning dynamic robot-to-human object handover from human feedback (2018) Robotics Research. Springer Proceedings in Advanced Robotics, 2. , In: Bicchi A., Burgard W. (eds), Springer, Cham; Koay, K., Sisbot, E., Syrdal, D., Walters, M., Dautenhahn, K., Alami, R., Exploratory study of a robot approaching a person in the context of handing over an object (2007) Proceedings of the AAAI Spring Symposium on Multi-disciplinary Collaboration or Socially Assistive Robotics, Palo Alto, California, USA, pp. 18-24; Huber, M., Rickert, M., Knoll, A., Brandt, T., Glasauer, S., Human-robot interaction in handing-over tasks (2008) Robot and Human Interactive Communication. RO-MAN 2008, pp. 107-112; Cakmak, M., Srinivasa, S.S., Min Kyung, L., Forlizzi, J., Kiesler, S., Human preferences for robot-human hand-over configurations (2011) Intelligent Robots and Systems (IROS), pp. 1986-1993; Flash, T., Hogan, N., The coordination of arm movements: An experimentally confirmed mathematical model (1985) The Journal of Neuroscience, 5 (7), pp. 1688-1703; Keppel, G., (1991) Design and Analysis: A Researcher's Handbook, , Prentice-Hall, Inc; Diversi, R., Guidorzi, R., Soverini, U., Identification of ARX and ARARX models in the presence of input and output noises (2010) European Journal of Control, 16 (3), pp. 242-255. , 2010; Design and analysis of experiments (2001) John Wiley and Sons, , New York; Yamamoto, T., Terada, K., Ochiai, A., Murase, K., Development of human support robot as the research platform of a domestic mobile manipulator (2019) Robomech Jurnal, 6 (4), pp. 1-15","Neranon, P.; Department of Mechanical Engineering, email: paramin.n@psu.ac.th",,,"Bangladesh University of Engineering and Technology",,,,,10241752,,JERDF,,"English","J Mech Eng Res Dev",Article,"Final","",Scopus,2-s2.0-85084145641
"Landowska A., Robins B.","8967776800;8585983100;","Robot Eye Perspective in Perceiving Facial Expressions in Interaction with Children with Autism",2020,"Advances in Intelligent Systems and Computing","1150 AISC",,,"1287","1297",,1,"10.1007/978-3-030-44038-1_117","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083955391&doi=10.1007%2f978-3-030-44038-1_117&partnerID=40&md5=b0eebcdd081207c2ed713c54ba881864","Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland; School of Computer Science, University of Hertfordshire, Hatfield, United Kingdom","Landowska, A., Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Gdansk, Poland; Robins, B., School of Computer Science, University of Hertfordshire, Hatfield, United Kingdom","The paper concerns automatic facial expression analysis applied in a study of natural “in the wild” interaction between children with autism and a social robot. The paper reports a study that analyzed the recordings captured via a camera located in the eye of a robot. Children with autism exhibit a diverse level of deficits, including ones in social interaction and emotional expression. The aim of the study was to explore the possibility of applying automatic emotion recognition in analyzing human-robot interaction. The study revealed some challenges, that might be classified as activity-based, child condition-based and setup-based ones. Despite those, the facial expressions in children with autism were on average more positive than in a control group of typically developing children. Children with autism seemed to enjoy the interaction with the robot more. The paper might be interesting for researchers and practitioners who plan to combine social robots and emotion recognition in children with autism. © 2020, Springer Nature Switzerland AG.",,"Diseases; Speech recognition; Activity-based; Automatic emotion recognition; Automatic facial expression analysis; Children with autisms; Emotion recognition; Emotional expressions; Facial Expressions; Social interactions; Social robots",,,,,"2019-1-PL01-KA203-065096; TD1309; University of Hertfordshire, UH","This publication was co-funded by the Erasmus Plus project of European Commission: EMBOA, Affective loop in Socially Assistive Robotics as an intervention tool for children with autism, contract no 2019-1-PL01-KA203-065096. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein. The pre-print of this publication is distributed free of charge. The research was co-funded by Santander Universidades stipend, as well as FETI Funds, Gdansk University of Technology. The study was inspired by ?LUDI-Play for children with disabilities? COST action TD1309, 2014?2018. The videos were analyzed due to the courtesy of Adaptive Systems Research Group, the University of Hertfordshire, led by prof. Kerstin Dautenhahn. Special thanks to Silvia Moros and Dr. Luke Wood for helping us with manual video annotations.",,,,,,,,,,"(2013) Diagnostic and Statistical Manual of Mental Disorders, (DSM-5®), , American Psychiatric Pub; Richardson, K., Coeckelbergh, M., Wakunuma, K., Billing, E., Ziemke, T., Gomez, P., Vanderborght, B., Belpaeme, T., Robot enhanced therapy for children with Autism (DREAM): A social model of autism (2018) IEEE Technol. Soc. Mag., 37 (1), pp. 30-39; Pennisi, P., Tonacci, A., Tartarisco, G., Billeci, L., Ruta, L., Gangemi, S., Pioggia, G., Autism and social robotics: A systematic review (2016) Autism Res, 9, pp. 165-183; Scassellati, B., Admoni, H., Matarić, M., Robots for use in autism research (2012) Ann. Rev. Biomed. Eng., 14 (201), pp. 275-294; Ferari, E., Robins, B., Dautenhahn, K., Robot as a social mediator-a play scenario implementation with children with autism (2009) 8Th International Conference on Interaction Design and Children, Como, Italy; Saldien, J., Goris, K., Yilmazyildiz, S., Verhelst, W., Lefeber, D., On the design of the huggable robot Probo (2008) J. Phys. Agents, 2 (2), pp. 3-11; Kozima, H., Yasuda, Y., Nakagawa, C., Social interaction facilitated by a minimally-designed robot: Findings from longitudinal therapeutic practices for autistic children (2007) Proceedings of 16Th IEEE International Conference on Robot & Human Interactive Communication; Stiehl, W.D., Lee, J.K., Breazeal, C., Nalin, M., Morandi, A., Sanna, A., The huggable: A platform for research in robotic companions for pediatric care (2009) Creative Interactive Play for Disabled Children Workshop at the 8Th International Conference on Interaction Design and Children, Como, Italy; Marti, P., Pollini, A., Rullo, A., Shibata, T., (2005) Engaging with Artificial Pets, , Proceedings of Annual Conference of the European Association of Cognitive Ergonomics, Chania, Greece; Billard, A., Robins, B., Nadel, J., Dautenhahn, K., Building robota, a mini-humanoid robot for the rehabilitation of children with autism (2006) RESNA Assistive Technol. J., 19, pp. 37-49; Tapus, A., Peca, A., Aly, A., Pop, C., Jisa, L., Pintea, S., Rusi, S., David, O., Children with autism social engagement in interaction with Nao, an imitative robot – a series of single case experiments (2012) Interact. Stud., 13 (3), pp. 315-347; Dautenhahn, K., Nehaniv, C.L., Walters, M.L., Robins, B., Kose-Bagci, H., Mirza, N.A., Blow, M., KASPAR-a minimally expressive humanoid robot for human-robot interaction research (2009) Spec. Issue Humanoid Robots Appl. Bionics Biomech., 6 (3), pp. 369-397; (2019), https://robots4autism.com/; Huijnen, C., Lexis, M., de Witte, L., Matching robot KASPAR to autism spectrum disorder (ASD) (2016) Int. J. Soc. Robot, 8, pp. 445-455; Huijnen, C., Lexis, M., Jansens, R., de Witte, L., Mapping robots to therapy and educational objectives for children with autism spectrum disorder (2016) J. Autism Dev. Disord., 46 (6), pp. 2100-2114; Huijnen, C., Lexis, M., Jansens, R., de Witte, L., How to implement robots in interventions for children with autism? A co-creation study involving people with autism, parents and professionals (2017) J. Autism Dev. Disord., 47 (10), pp. 3079-3096; Trevisan, D.A., Hoskyn, M., Birmingham, E., Facial expression production in autism: A meta-analysis (2018) Autism Res, 11 (12), pp. 1586-1601; Czapinski, P., Bryson, S.E., Reduced facial muscle movements in autism: Evidence for dysfunction in the neuromuscular pathway? (2003) Brain Cogn, 51 (2), pp. 177-179; Markodimitraki, M., Kypriotaki, M., Ampartzaki, M., Manolitsis, G., Effects of context and facial expression on imitation tasks in preschool children with autism (2013) Early Child Dev. Care, 183 (9), pp. 1276-1292; Reddy, V., Williams, E., Vaughan, A., Sharing humour and laughter in autism and down’s syndrome (2002) Br. J. Psychol., 93 (2), pp. 219-242; Misailidi, P., Affective expressions during joint attention interactions with an adult: The case of autism (2002) J. Hellenic Psychol. Soc., 9, pp. 9-21; Trevisan, D.A., Bowering, M., Birmingham, A., Alexithymia, but not autism spectrum disorder, may be related to the production of emotional facial expressions (2016) Mol. Autism, 7, p. 46; Rudovic, O., Lee, J., Dai, M., Schuller, B., Picard, R.W., Personalized machine learning for robot perception of affect and engagement in autism therapy (2018) Sci. Robot., 3 (19); Capriola-Hall, N., Trubanova, A., Swain, D., White, S., Group differences in facial emotion expression in Autism: Evidence for the utility of machine classification (2018) Sci. Robot., 3 (19), pp. 828-838","Landowska, A.; Faculty of Electronics, Poland; email: nailie@pg.edu.pl","Barolli L.Amato F.Moscato F.Enokido T.Takizawa M.",,"Springer","Workshops of the 34th International Conference on Advanced Information Networking and Applications, WAINA 2020","15 April 2020 through 17 April 2020",,238959,21945357,9783030440374,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85083955391
"Mahmud S., Lin X., Kim J.-H.","57208869422;57215601643;55808026200;","Interface for Human Machine Interaction for assistant devices: A Review",2020,"2020 10th Annual Computing and Communication Workshop and Conference, CCWC 2020",,,"9031244","768","773",,11,"10.1109/CCWC47524.2020.9031244","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083075131&doi=10.1109%2fCCWC47524.2020.9031244&partnerID=40&md5=8da104565cddb8cf5c15fa8330abdcaf","Computer Science, Kent State University, United States","Mahmud, S., Computer Science, Kent State University, United States; Lin, X., Computer Science, Kent State University, United States; Kim, J.-H., Computer Science, Kent State University, United States","Interface for the Human Machine Interaction has become a prominent area of research due to the rapid growth of automation and robotics in the recent few decades. Although an abundance of frameworks have been emerged to make the interaction between human and machine easy and robust, a substantial portion of these is not flourished in their scope. Through this review, we have tried to reveal different types of interfacing technique between human and machine to exhibit the evolution of the related technologies in developing assistant devices. This review explores the contemporary groundbreaking technologies developed for this purpose and their advantages and limitations. An outline is drawn for forthcoming development in the field of human machine interaction, human computer interaction and human robot interaction by using this review. This review draws a broad perspective on status quo and perspective on the possible future development of interfaces of assistant devices in the field of Human machine interaction. © 2020 IEEE.","Brain Signal; EEG; Eye Gaze; Gesture; Mind Band; Telepresence","Human computer interaction; Human machine interaction; Possible futures; Rapid growth; Status quo; Human robot interaction",,,,,,,,,,,,,,,,"Prashant, P., Joshi, A., Gandhi, V., Brain computer interface: A review (2015) 2015 5th Nirma University International Conference on Engineering (NUiCONE), pp. 1-6. , Nov; Haasbroek, L.J., Advanced human-computer interfaces and intent support: A survey and perspective (1993) Proceedings of IEEE Systems Man and Cybernetics Conference-SMC, 4, pp. 350-355. , Oct 4; Tezuka, T., Goto, A., Kashiwa, K., Yoshikawa, H., Kawano, R., A study on space interface for teleoperation system (1994) Proceedings of 1994 3rd IEEE International Workshop on Robot and Human Communication, pp. 62-67. , July; Pavlovic, V.I., Sharma, R., Huang, T.S., Visual interpretation of hand gestures for human-computer interaction: A review (1997) IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (7), pp. 677-695. , July; Al-Rahayfeh, A., Faezipour, M., Eye tracking and head movement detection: A state-of-art survey (2013) IEEE Journal of Translational Engineering in Health and Medicine, 1, p. 2100212; Pavlovic, A.O., April, N.A., Belle, J.-P.V., Management issues with cloud computing (2013) Proc. in ICCC; Gehlhaar, J., The future of mobile computing (2014) Proc. in the 20th Annual International Conference on Mobile Computing and Networking; Song, W., Zhao, S., Jiang, F., Zhu, K., Cao, L., Shi, Y., Teleoperation robot control system based on mindband sensor (2012) 2012 Fifth International Symposium on Computational Intelligence and Design, 2, pp. 299-302. , Oct; Guan, C., Brain-computer interface for stroke rehabilitation with clinical studies (2013) 2013 International Winter Workshop on Brain-Computer Interface (BCI), pp. 4-5. , Feb; Kim, K., Carlson, T., Lee, S., Design of a robotic wheelchair with a motor imagery based brain-computer interface (2013) 2013 International Winter Workshop on Brain-Computer Interface (BCI), pp. 46-48. , Feb; Akman Aydin, E., Bay, F., Güler, Region based brain computer interface for a home control application (2015) 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1075-1078. , Aug; Lavanya, K.N., Shree, D.R., Nischitha, B.R., Asha, T., Gururaj, C., Gesture controlled robot (2017) 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), pp. 465-469. , Dec; Smith, K.A., Csech, C., Murdoch, D., Shaker, G., Gesture recognition using mm-wave sensor for human-car interface (2018) IEEE Sensors Letters, 2 (2), pp. 1-4. , June; Magoules, F., Zou, Q., A novel contactless human machine interface based on machine learning (2017) 2017 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES), pp. 137-140. , Oct; Raheja, J.L., Shyam, R., Kumar, U., Prasad, P.B., Real-time robotic hand control using hand gestures (2010) 2010 Second International Conference on Machine Learning and Computing, pp. 12-16. , Feb; Panwar, M., Hand gesture based interface for aiding visually impaired (2012) 2012 International Conference on Recent Advances in Computing and Software Systems, pp. 80-85. , April; Quintero, C.P., Fomena, R.T., Shademan, A., Ramirez, O., Jagersand, M., Interactive teleoperation interface for semi-autonomous control of robot arms (2014) 2014 Canadian Conference on Computer and Robot Vision, pp. 357-363. , May; Prasad, S., Kumar, P., Sinha, K.P., A wireless dynamic gesture user interface for hci using hand data glove (2014) 2014 Seventh International Conference on Contemporary Computing (IC3, pp. 62-67. , Aug; Pavlovic, A.O., April, N.A., Belle, J.-P.V., Appearance based recognition of American sign language using gesture segmentation (2013) Proc. in ICCC; Chakraborty, P., Sarawgi, P., Mehrotra, G.A.A., Pradhan, R., Hand gesture recognition: A comparative study (2008) Proc. of the Int. Multi Conf. of Engineers and Computer Scientists; Latif, H.O., Sherkat, N., Lotf, A., Telegaze: Teleoperation through eye gaze (2008) 2008 7th IEEE International Conference on Cybernetic Intelligent Systems, pp. 1-6. , Sep; Lim, Y., Gardi, A., Ezer, N., Kistan, T., Sabatini, R., Eye-tracking sensors for adaptive aerospace human-machine interfaces and interactions (2018) 2018 5th IEEE International Workshop on Metrology for AeroSpace (MetroAeroSpace, pp. 311-316. , June; Ho, H., Low cost and better accuracy eye tracker (2014) 2014 International Symposium on Next-Generation Electronics (ISNE), pp. 1-2. , May; Yu, M., Wang, X., Lin, Y., Bai, X., Gaze tracking system for teleoperation (2014) The 26th Chinese Control and Decision Conference (2014 CCDC, pp. 4617-4622. , May; Ĝego, D., Carreto, C., Figueiredo, L., Teleoperation of a mobile robot based on eye-gaze tracking (2017) 2017 12th Iberian Conference on Information Systems and Technologies (CISTI), , Lisbon; Wanluk, N., Visitsattapongse, S., Juhong, A., Pintavirooj, C., Smart wheelchair based on eye tracking (2016) 2016 9th Biomedical Engineering International Conference (BMEiCON), pp. 1-4. , Dec; Randelli, G., Venanzi, M., Nardi, D., Tangible interfaces for robot teleoperation (2011) 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 231-232. , March; Waranusast, R., Bang-Ngoen, A., Thipakorn, J., Interactive tangible user interface for music learning (2013) 2013 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013, pp. 400-405. , Nov; Chan, L.K.Y., Lau, H.Y.K., A tangible user interface using spatial augmented reality (2010) 2010 IEEE Symposium on 3D User Interfaces (3DUI, pp. 137-138. , March; Henderson, S., Feiner, S., Opportunistic tangible user interfaces for augmented reality (2010) IEEE Transactions on Visualization and Computer Graphics, 16 (1), pp. 4-16. , Jan; Wang, K., You, K., Chen, F., Huang, Z., Mao, Z., Human-machine interface using eye saccade and facial expression physiological signals to improve the maneuverability of wearable robots (2017) 2017 International Symposium on Wearable Robotics and Rehabilitation (WeRob), pp. 1-2. , Nov; Rechy-Ramirez, E.J., Hu, H., Bi-modal human machine interface for controlling an intelligent wheelchair (2013) 2013 Fourth International Conference on Emerging Security Technologies, pp. 66-70. , Sep; Tang, J., Zhou, Z., Yu, Y., A hybrid computer interface for robot arm control (2016) 2016 8th International Conference on Information Technology in Medicine and Education (ITME), pp. 365-369. , Dec","Kim, J.-H.; Computer Science, United States; email: jkim72@kent.edu","Chakrabarti S.Paul R.","IEEE Region 1;IEEE Region 6;IEEE USA;Institute of Engineering and Management (IEM);University of Engineering and Management (UEM);UNLV","Institute of Electrical and Electronics Engineers Inc.","10th Annual Computing and Communication Workshop and Conference, CCWC 2020","6 January 2020 through 8 January 2020",,158422,,9781728137834,,,"English","Annu. Comput. Commun. Workshop Conf., CCWC",Conference Paper,"Final","",Scopus,2-s2.0-85083075131
"De Pietro M., Bertacchini F., Pantano P., Bilotta E.","57215431226;23391850700;7005778416;6602455256;","Modelling on Human Intelligence a Machine Learning System",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","11973 LNCS",,,"410","424",,,"10.1007/978-3-030-39081-5_36","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080855289&doi=10.1007%2f978-3-030-39081-5_36&partnerID=40&md5=2a5806a9f8ff2ab9f6b057c5a31dff5f","University of Calabria, via Pietro Bucci, 17b, Arcavacata di Rende, Italy","De Pietro, M., University of Calabria, via Pietro Bucci, 17b, Arcavacata di Rende, Italy; Bertacchini, F., University of Calabria, via Pietro Bucci, 17b, Arcavacata di Rende, Italy; Pantano, P., University of Calabria, via Pietro Bucci, 17b, Arcavacata di Rende, Italy; Bilotta, E., University of Calabria, via Pietro Bucci, 17b, Arcavacata di Rende, Italy","Recently, a huge set of systems, devoted to emotions recognition has been built, especially due to its application in many work domains, with the aims to understand human behaviour and to embody this knowledge into human-computer interaction or human-robot interaction. The recognition of human expressions is a very complex problem for artificial systems, caused by the extreme elusiveness of the phenomenon that, starting from six basic emotions, creates a series of intermediate variations, difficult to recognize by an artificial system. To overcome these difficulties, and expand artificial knowledge, a Machine Learning (ML) system has been designed with the specific aim to develop a recognition system modelled on human cognitive functions. Cohn-Kanade database images was used as data set. After training the ML, it was tested on a representative sample of unstructured data. The aim is to make computational algorithms more and more efficient in recognizing emotional expressions in the faces of human subjects. © 2020, Springer Nature Switzerland AG.","Artificial intelligence; Emotion recognition; Machine learning","Artificial intelligence; Behavioral research; Computation theory; Computational efficiency; Human computer interaction; Learning systems; Machine learning; Cognitive functions; Cohn-Kanade database; Computational algorithm; Emotion recognition; Emotional expressions; Emotions recognition; Recognition systems; Representative sample; Human robot interaction",,,,,,,,,,,,,,,,"Kulkarni, S.S., Reddy, N.P., Hariharan, S.I., Facial expression (Mood) recognition from facial images using committee neural networks (2009) Biomed. Eng. Online, 8 (1), p. 16; Carroll, J.M., Russell, J.A., Facial expressions in Hollywood’s protrayal of emotion (1997) J. Pers. Soc. Psychol., 72 (1), p. 164; Eibl-Eibesfeldt, I., (1989) Human Ethology, , Aldine de Gruvter, New York; Ekman, P., Friesen, W.V., (1978) Facial Action Coding System: Investigator’s Guide, , Consulting Psychologists Press, Palo Alto; Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I., The extended Cohn-Kanade dataset (CK+): A complete dataset for action unit and emotion-specified expression (2010) Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, pp. 94-101; Sebe, N., Lew, M.S., Sun, Y., Cohen, I., Gevers, T., Huang, T.S., Authentic facial expression analysis (2007) Image Vis. Comput., 25 (12), pp. 1856-1863; Kobayashi, H., Hara, F., Facial interaction between animated 3D face robot and human beings (1997) International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, 4, pp. 3732-3737. , , pp. , IEEE, Orlando; Schachter, S., Singer, J., Cognitive, social, and physiological determinants of emotional state (1962) Psychol. Rev., 69 (5), p. 379; Darwin, C., (1860) On the Origin of Species, , D. Appleton & Company, New York; James, W., (1983) Essays in Psychology, 13. , Harvard University Press, Cambridge; Cannon, W.B., The James-Lange theory of emotions: A critical examination and an alternative theory (1927) Am. J. Psychol., 39 (1-4), pp. 106-124; Walker, D.L., Davis, M., The role of amygdala glutamate receptors in fear learning, fear-potentiated startle, and extinction (2002) Pharmacol. Biochem. Behav., 71 (3), pp. 379-392; Doré, B.P., Tompson, S.H., O’Donnell, M.B., An, L.C., Strecher, V., Falk, E.B., Neural mechanisms of emotion regulation moderate the predictive value of affective and value-related brain responses to persuasive messages (2019) J. Neurosci., 39 (7), pp. 1293-1300; Ledoux, J.E., Hofmann, S.G., The subjective experience of emotion: A fearful view (2018) Curr. Opin. Behav. Sci., 19, pp. 67-72; Lazarus, R.S., Thoughts on the relations between emotion and cognition (1982) Am. Psychol., 37 (9), p. 1019; Frijda, N.H., Appraisal and beyond (1993) Cogn. Emot., 7 (3-4), pp. 225-231; Bourgais, M., Taillandier, P., Vercouter, L., Adam, C., Emotion modeling in social simulation: A survey (2018) J. Artif. Soc. Soc. Simul, 21 (2), pp. 1-5; Wibowo, H., Firdausi, F., Suharso, W., Kusuma, W.A., Harmanto, D., Facial expression recognition of 3D image using facial action coding system (FACS) (2019) Telkomnika, 17 (2), pp. 628-636; Gabriele, L., Bertacchini, F., Tavernise, A., Vaca-Cárdenas, L., Pantano, P., Bilotta, E., Lesson planning by computational thinking skills in Italian pre-service teachers (2019) Inform. Educ., 18 (1), pp. 69-104; Gabriele, L., Tavernise, A., Bertacchini, F., Active learning in a robotics laboratory with university students (2012) Increasing Student Engagement and Retention Using Immersive Interfaces: Virtual Worlds, Gaming, and Simulation, pp. 315-339. , , pp. , Emerald Group Publishing Limited; Abdechiri, M., Faez, K., Amindavar, H., Bilotta, E., The chaotic dynamics of high-dimensional systems (2017) Nonlinear Dyn, 87 (4), pp. 2597-2610; Freeman, W.J., Emotion is from preparatory brain chaos; irrational action is from premature closure (2005) Behav. Brain Sci., 28 (2), pp. 204-205; Bilotta, E., Bossio, E., Pantano, P., Chaos at school: Chua’s circuit for students in junior and senior high school (2010) Int. J. Bifurcat. Chaos, 20 (1), pp. 1-28; Bilotta, E., Pantano, P., Vena, S., Artificial micro-worlds part I: A new approach for studying life-like phenomena (2011) Int. J. Bifurcat. Chaos, 21 (2), pp. 373-398; Bilotta, E., Pantano, P., Artificial micro-worlds part II: Cellular automata growth dynamics (2011) Int. J. Bifurcat. Chaos, 21 (3), pp. 619-645; Bilotta, E., Stranges, F., Pantano, P., A gallery of Chua attractors: Part III (2007) Int. J. Bifurcat. Chaos, 17 (3), pp. 657-734; Lorincz, A., Jeni, L., Szabo, Z., Cohn, J., Kanade, T., Emotional expression classification using time-series kernels (2013) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 889-895; Bertacchini, F., An emotional learning environment for subjects with Autism Spectrum Disorder (2013) International Conference on Interactive Collaborative Learning (ICL), pp. 653-659; Cárdenas, L.A.V., An educational coding laboratory for elementary pre-service teachers: A qualitative approach (2016) Int. J. Eng. Pedagogy (Ijep), 6 (1), pp. 11-17; Bertacchini, F., Bilotta, E., Gabriele, L., Pantano, P., Servidio, R., Using Lego MindStorms in higher education: Cognitive strategies in programming a quadruped robot (2010) Workshop Proceedings of the 18Th International Conference on Computers in Education, pp. 366-371. , , pp; Bertacchini, F., Tavernise, A., Knowledge sharing for cultural heritage 2.0 (2014) Pro-Sumers in a Digital Agora. Int. J. Virtual Communities Soc. Netw., 6 (2), pp. 24-36; Bertacchini, F., Bilotta, E., Pantano, P., Shopping with a robotic companion (2017) Comput. Hum. Behav., 77, pp. 382-395; Mane, S., Shah, G., Facial recognition, expression recognition, and gender identification (2019) Data Management, Analytics and Innovation. AISC, 808, pp. 275-290. , https://doi.org/10.1007/978-981-13-1402-5 21, Balas, V.E., Sharma, N., Chakrabarti, A. (eds.), pp. , Springer, Singapore; Kanade, T., Cohn, J.F., Tian, Y., Comprehensive database for facial expression analysis (2000) Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 46-53; Tian, Y.I., Kanade, T., Cohn, J.F., Recognizing action units for facial expression analysis (2001) IEEE Trans. Pattern Anal. Mach. Intell., 23 (2), pp. 97-115; Wolfram, S., (2017) An Elementary Introduction to the Wolfram Language, , Wolfram Media, Incorporated, Champaign","De Pietro, M.; University of Calabria, via Pietro Bucci, 17b, Italy; email: michela.depietro@unical.it","Sergeyev Y.D.Kvasov D.E.Sergeyev Y.D.Kvasov D.E.",,"Springer","3rd Triennial International Conference and Summer School on Numerical Computations: Theory and Algorithms, NUMTA 2019","15 June 2019 through 21 June 2019",,237489,03029743,9783030390808,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85080855289
"Arora A.S., Arora A.","35736838900;55437932800;","The race between cognitive and artificial intelligence: Examining socio-ethical collaborative robots through anthropomorphism and xenocentrism in human-robot interaction",2020,"International Journal of Intelligent Information Technologies","16","1",,"1","16",,4,"10.4018/IJIIT.2020010101","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079781264&doi=10.4018%2fIJIIT.2020010101&partnerID=40&md5=7d2f2d66f1de1376be56694ffc1aa8ae","University of the District of Columbia, Washington, DC, United States","Arora, A.S., University of the District of Columbia, Washington, DC, United States; Arora, A., University of the District of Columbia, Washington, DC, United States","Research on human-robot interaction (HRI) is growing; however, focus on the congruent socio-behavioral HRI research fields of social cognition, socio-behavioral intentions, and code of ethics is lacking. Humans possess an inherent ability of integrating perception, cognition, and action; while robots may have limitations as they may not recognize an object or a being, navigate a terrain, and/ or comprehend written or verbal language and instructions. This HRI research focuses on issues and challenges for both humans and robots from social, behavioral, technical, and ethical perspectives. The human ability to anthropomorphize robots and adoption of 'intentional mindset' toward robots through xenocentrism have added new dimensions to HRI. Robotic anthropomorphism plays a significant role in how humans can be successful companions of robots. This research explores social cognitive intelligence versus artificial intelligence with a focus on privacy protections and ethical implications of HRI while designing robots that are ethical, cognitively and artificially intelligent, and social human-like agents. Copyright © 2020, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.","Anthropomorphism; Artificial Intelligence; Ethical Designs; Human-Robot Interaction (HRI); Intentional Mindset; Roboethics; Social Cognitive Intelligence; Xenocentrism","Artificial intelligence; Behavioral research; Machine design; Man machine systems; Philosophical aspects; Social robots; Anthropomorphism; Cognitive intelligence; Ethical designs; Human robot Interaction (HRI); Intentional Mindset; Roboethics; Xenocentrism; Intelligent robots",,,,,"National Science Foundation, NSF","Anshu Saxena Arora, Ph.D., PMP is Associate Professor of Marketing in the School of Business and Public Administration at the University of the District of Columbia. Previously, she worked as the Associate Professor of Marketing at the Jay S. Sidhu School of Business and Leadership, Wilkes University, Wilkes-Barre, PA. Before joining Wilkes University, Dr. Arora was Associate Professor of Marketing and Chairperson of Department of Business (Management, Marketing and Supporting Areas) in the College of Business Administration, Savannah State University, Savannah, Georgia, USA. She is a Certified Project Management Professional (PMP) from Project Management Institute (PMI), USA and holds a Foundation Certificate in IT Service Management from Information Systems Examination Board (ISEB), UK. Dr. Arora has been a Visiting Professor at ISC Paris Business School in France, University of California, Davis, and Thunderbird School of Global Management at Arizona State University. She has more than a decade of industrial and academic experience. She has worked in companies like Hyundai Motors India Limited, Lufthansa German Airlines, and Siemens India. In July 2019, Dr. Arora received the prestigious National Science Foundation (NSF) grant for establishing the Logistics & International Trade (LIT) Analytics Center of Excellence and developing STEM-Business focused LIT concentration program at the University of the District of Columbia. She is the Senior Editor of Marketing for the International Journal of Emerging Markets (IJoEM) published by Emerald Publications. She has published more than 40 research papers in national and international journals of repute, and has presented about 50 papers in national and international conferences. Her research interests and expertise include digital / DSMM marketing, marketing analytics, social media strategy mix and social media measurement, stereotypical advertising polysemy, consumer behavior, ambient advertising, innovative experiential learning models in marketing and logistics / supply chain management, relational supply chain strategy relationships, and project management.",,,,,,,,,,"Aggarwal, P., McGill, A.L., Is that car smiling at me? 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J. Intell. Inf. Technologies",Article,"Final","",Scopus,2-s2.0-85079781264
"Kim K., Hong D.","36951388600;7201841542;","Robotic Biceps Exercise Machine: Hardware Using Series Elastic Actuator and Control with Disturbance Observer",2020,"IEEE Access","8",,"8945370","12758","12767",,1,"10.1109/ACCESS.2019.2963101","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078696452&doi=10.1109%2fACCESS.2019.2963101&partnerID=40&md5=f08e14282d2d74822f0a9286a527cbb6","School of Mechanical Engineering, Korea University, Seoul, 02841, South Korea","Kim, K., School of Mechanical Engineering, Korea University, Seoul, 02841, South Korea; Hong, D., School of Mechanical Engineering, Korea University, Seoul, 02841, South Korea","Resistance training is a popular form of exercise owing to its health-related and athletic performance benefits. Robotic exercise machines using human-robot interaction are a promising solution for designing resistance training programs that successfully achieve training gains. We propose a robotic biceps exercise machine that generates a variable resistance force profile and controls the interaction force corresponding to the profile through a range of motion of exercises. A series elastic actuator measures and controls the resistance force. A novel cascade control structure comprising an inner velocity and outer force control is presented. The inner loop disturbance on the dynamics and outer loop disturbance on the kinematics are eliminated by a disturbance observer (DOB) in each loop. The performance of the proposed force control scheme is validated by comparisons with the conventional DOB and proportional-integral (PI) control schemes. Additionally, the resistance force profile and interaction force of a conventional robotic biceps exercise machine are analyzed experimentally. © 2013 IEEE.","Biceps exercise; disturbance observer; human-robot interaction; robotic exercise machine; series elastic actuator; variable resistance","Actuators; Cascade control systems; Exercise equipment; Force control; Man machine systems; Robotics; Two term control systems; Biceps exercise; Disturbance observer; Exercise machines; Series elastic actuators; Variable resistance; Human robot interaction",,,,,"Ministry of Trade, Industry and Energy, MOTIE: 20003739","This work supported by the Ministry of Trade, Industry and Energy (MOTIE), South Korea, through the Industrial Technology Innovation Program under Grant 20003739.",,,,,,,,,,"Braith, R.W., Stewart, K.J., Resistance exercise training-Its role in the prevention of cardiovascular disease (2006) Circulation, 113 (22), pp. 2642-2650. , Jun; Deschenes, M.R., Kraemer, W.J., Performance and physiologic adaptations to resistance training (2002) Amer. J. Phys. Med. Rehabil., 81, pp. S3-S16. , Nov; Kraemer, W.J., Ratamess, N.A., Hormonal responses and adaptations to resistance exercise and training (2005) Sports Med., 35 (4), pp. 339-361; Dunstan, D.W., Daly, R.M., Owen, N., Jolley, D., De Courten, M., Shaw, J., Zimmet, P., High-intensity resistance training improves glycemic control in older patients with type 2 diabetes (2002) Diabetes Care, 25 (10), pp. 1729-1736. , Oct; Fleck, S.J., Kraemer, W.J., Types of strength training (2014) Designing Resistance Training Programs, 4th Ed., pp. 15-44. , Champaign, IL, USA: Human Kinetics; Walker, S., Taipale, R.S., Nyman, K., Kraemer, W.J., Häkkinen, K., Neuromuscular and hormonal responses to constant and variable resistance loadings (2011) Med. Sci. Sports Exerc., 43 (1), pp. 26-33. , Jan; Walker, S., Peltonen, H., Avela, J., Häkkinen, K., Kinetic and electromyographic analysis of single repetition constant and variable resistance leg Press actions (2011) J. Electromyogr. Kinesiol., 21 (2), pp. 262-269. , Apr; Aboodarda, S.J., George, J., Mokhtar, A.H., Thompson, M., Muscle strength and damage following two modes of variable resistance training (2011) J. Sports Sci. Med., 10 (4), pp. 635-642. , Dec; Folland, J., Morris, B., Variable-cam resistance training machines: Do they match the angle-torque relationship in humans? (2008) J. Sports Sci., 26 (2), pp. 163-169. , Jan; Li, P., Horowitz, R., Control of smart exercise machines. I. Problem formulation and nonadaptive control (1997) IEEE/ASME Trans. Mechatronics, 2 (4), pp. 237-247. , Dec; Li, P.Y., Horowitz, R., Control of smart exercise machines. II. Selfoptimizing control (1997) IEEE/ASME Trans. Mechatronics, 2 (4), pp. 248-258. , Dec; Dong, S., Lu, K.-Q., Sun, J., Rudolph, K., Adaptive force regulation of muscle strengthening rehabilitation device with magnetorheological fluids (2006) Ieee Trans. Neural Syst. Rehabil. Eng., 14 (1), pp. 55-63. , Mar; Park, J., Kim, K., Hong, D., Haptic-based resistance training machine and its application to biceps exercises (2011) Int. J. Precis. Eng. Manuf., 12 (1), pp. 21-30. , Feb; Gim, K.G., Hong, D.W., Design of a series elastic resistance mechanism for exercise and rehabilitation (2016) Proc. IEEE-RAS 16th Int. Conf. Humanoid Robots (Humanoids), pp. 1239-1244. , Nov; Lee, C., Oh, S., Dynamic dumbbell-novel muscle training robot with programmable exercise load (2018) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), pp. 7648-7653. , Oct; Pratt, G., Williamson, M., Series elastic actuators (2002) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. Hum. Robot Interact. Cooperat. Robots, pp. 399-406. , Nov; Pratt, J., Krupp, B., Morse, C., Series elastic actuators for high fidelity force control (2002) Ind. Robot., 29 (3), pp. 234-241. , Jun; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) Ieee Trans. Robot., 31 (5), pp. 1089-1100. , Oct; Vallery, H., Veneman, J., Van Asseldonk, E., Ekkelenkamp, R., Buss, M., Kooij Der Van, H., Compliant actuation of rehabilitation robots (2008) Ieee Robot. Autom. Mag., 15 (3), pp. 60-69. , Sep; Li, S., Li, J., Tian, G., Shang, H., Stiffness adjustment for a single-link robot arm driven by series elastic actuator in muscle training (2019) Ieee Access, 7, pp. 65029-65039; Awad, M.I., Gan, D., Hussain, I., Az-Zu'Bi, A., Stefanini, C., Khalaf, K., Zweiri, Y., Seneviratne, L.D., Design of a novel passive binary-controlled variable stiffness joint (BpVSJ) towards passive haptic interface application (2018) Ieee Access, 6, pp. 63045-63057; Kim, K., Lee, Y.S., Implementation of mass-independent impedance control for RFSEA using a linkage arm (2019) Ieee Access, 7, pp. 104823-104832; Ohnishi, K., Shibata, M., Murakami, T., Motion control for advanced mechatronics (1996) IEEE/ASME Trans. Mechatronics, 1 (1), pp. 56-67. , Mar; Sariyildiz, E., Ohnishi, K., On the explicit robust force control via disturbance observer (2015) Ieee Trans. Ind. Electron., 62 (3), pp. 1581-1589. , Mar; Zhang, J., Liu, X., Xia, Y., Zuo, Z., Wang, Y., Disturbance observer-based integral sliding-mode control for systems with mismatched disturbances (2016) Ieee Trans. Ind. Electron., 63 (11), pp. 7040-7048. , Nov; Xu, B., Sun, F., Pan, Y., Chen, B., Disturbance observer based composite learning fuzzy control of nonlinear systems with unknown dead zone (2017) Ieee Trans. Syst., Man, Cybern. Syst., 47 (8), pp. 1854-1862. , Aug; Na, J., Chen, A.S., Herrmann, G., Burke, R., Brace, C., Vehicle engine torque estimation via unknown input observer and adaptive parameter estimation (2018) Ieee Trans. Veh. Technol., 67 (1), pp. 409-422. , Jan; Gao, Z., Liu, X., Chen, M., Unknown input observer based robust fault estimation for systems corrupted by partially-decoupled disturbances (2016) Ieee Trans. Ind. Electron., 63 (4), pp. 2537-2547. , Apr; Sun, L., Zhang, Y., Li, D., Lee, K.Y., Tuning of active disturbance rejection control with application to power plant furnace regulation (2019) Control Eng. Pract., 92. , Nov; Sun, L., Shen, J., Hua, Q., Lee, K.Y., Data-driven oxygen excess ratio control for proton exchange membrane fuel cell (2018) Appl. Energy, 231, pp. 866-875. , Dec; Li, S., Yang, J., Chen, W.-C., Chen, X., Generalized extended state observer based control for systems with mismatched uncertainties (2012) Ieee Trans. Ind. Electron., 59 (12), pp. 4792-4802. , Dec; Kong, K., Bae, J., Tomizuka, M., Control of rotary series elastic actuator for ideal force-mode actuation in human-robot interaction applications (2009) IEEE/ASME Trans. Mechatronics, 14 (1), pp. 105-118. , Feb; Oh, S., Kong, K., High-precision robust force control of a series elastic actuator (2017) IEEE/ASME Trans. Mechatronics, 22 (1), pp. 71-80. , Feb; Li, S., Yang, J., Chen, W.-H., Chen, X., Linear disturbance estimator (2016) Disturbance Observer Based Control: Methods and Applications., pp. 27-41. , Boca Raton, FL, USA: CRC Press; Shim, H., Jo, N.H., An almost necessary and sufficient condition for robust stability of closed-loop systems with disturbance observer (2009) Automatica, 45 (1), pp. 296-299. , Jan","Hong, D.; School of Mechanical Engineering, South Korea; email: dhhong@korea.ac.kr",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85078696452
"Xu T., Fan J., Fang Q., Zhu Y., Zhao J.","57207452238;12781454400;57207454156;9240483200;57203289935;","A new robot collision detection method: A modified nonlinear disturbance observer based-on neural networks",2020,"Journal of Intelligent and Fuzzy Systems","38","1",,"175","186",,5,"10.3233/JIFS-179392","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078340108&doi=10.3233%2fJIFS-179392&partnerID=40&md5=90ec494265eb683b24ca8b5118709803","State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China","Xu, T., State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China; Fan, J., State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China; Fang, Q., State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China; Zhu, Y., State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China; Zhao, J., State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, Heilongjiang, China","Collision detection is the core issue in physical human-robot interactions, and many detection methods based on robot dynamic models have been proposed. However, model uncertainties, especially complicated friction, seriously affect the collision detection performance of these methods. In this paper, a nonlinear disturbance observer (NDO) originally proposed for friction estimation is applied for the first time in robot collision detection. To verify that the collision detection performance of the NDO is better than that of the classical generalized momentum observer (GMO), the detection sensitivity, robustness and external torque estimation accuracy of each method are compared and analyzed. Then, to eliminate the effects of friction uncertainties on the collision detection results, a modified nonlinear disturbance observer (MNDO) based on neural networks is proposed to improve the collision detection performance. To verify the effectiveness of the algorithm, simulations and experiments are conducted with a 6-DOF robot and two single-joint platforms. The results indicate that the proposed algorithm is accurate and effective. © 2020 - IOS Press and the authors. All rights reserved.","friction estimation; GMO; NDO; neural networks; Robot collision detection","Friction; Neural networks; Nonlinear analysis; Simulation platform; Uncertainty analysis; Collision detection; Detection methods; Detection sensitivity; Friction estimation; Model uncertainties; Nonlinear disturbance observer; Physical human-robot interactions; Robot collisions; Human robot interaction",,,,,"2017ZX04005001; National Natural Science Foundation of China, NSFC: 91648201","The authors acknowledge the Major Research Plan of National Natural Science Foundation of China (Grant No. 91648201) and the National Science and Technology Major 04 Special Project (Grant No. 2017ZX04005001).",,,,,,,,,,"(2018) Human Friendly Robotics: 10th International Workshop[ M]., , Springer; Borja, R., De La Pinta, J.R., Álvarez, A., Integration of service robots in the smart home by means of upnp: A surveillance robot case study[J] (2013) Robotics and Autonomous Systems, 61 (2), p. 153; Augugliaro, F., Lupashin, S., Hamer, M., The flight assembled architecture installation: Cooperative construction with flying machines[J] (2014) IEEE Control Systems Magazine, 34 (4), p. 4; Hagn, U., Ortmaier, T., Konietschke, R., Telemanipulator for remote minimally invasive surgery[J] (2008) IEEE Robotics & Automation Magazine, 15 (4), pp. 28-33; Haddadin, S., Croft, E., (2016) Physical Human-robot Interaction[ M], pp. 1835-1841. , Springer handbook of robotics. Springer, Cham; Bicchi, A., Peshkin, M.A., Colgate, J.E., (2008) Safety for Physical Human-robot Interaction[J], pp. 1335-1348. , Springer Handbook of Robotics; Yamada, Y., Hirasawa, Y., Huang, S., Human-robot contact in the safeguarding space[J] (1997) IEEE/ASME Transactions on Mechatronics, 2 (4), pp. 230-236; Lee, S.D., Song, J.B., Sensorless collision detection based on friction model for a robot manipulator[J] (2016) International Journal of Precision Engineering & Manufacturing, 17 (1), pp. 11-17; Doisy, G., (2012) Sensorless Collision Detection and Control by Physical Interaction for Wheeled Mobile Robots[J]; Huang, J., Xie, Z., Jin, M., Adaptive impedancecontrolled manipulator based on collision detection[J] (2009) Chinese Journal of Aeronautics, 22 (1), pp. 105-112; Samraj, J., Jeevitha, K., Aprototype model on cbmir using canny edge detection and neural network for visual search (2017) International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE), 5 (10); Lo, S.Y., Cheng, C.A., Huang, H.P., Virtual impedance control for safe human-robot interaction[J] (2016) Journal of Intelligent & Robotic Systems, 82 (1), pp. 3-19; Cirillo, A., Ficuciello, F., Natale, C., A conformable force/tactile skin for physical human-robot interaction[J] (2016) IEEE Robotics and Automation Letters, 1 (1), pp. 41-48; Hughes, D., Lammie, J., Correll, N., A robotic skin for collision avoidance and affective touch recognition[J] (2018) IEEE Robotics and Automation Letters, 3 (3), pp. 1386-1393; Cho, C.N., Hong, J.T., Kim, H.J., Neural network based adaptive actuator fault detection algorithm for robot manipulators[ j] (2018) Journal of Intelligent & Robotic Systems, pp. 1-11; Zhang, Z., Yue, S., Zhang, G., Fly visual system inspired artificial neural network for collision detection[J] (2015) Neurocomputing, 153, pp. 221-234; Heo, Y.J., Kim, D., Lee, W., Collision detection for industrial collaborative robots:adeep learning approach[J] (2019) IEEE Robotics and Automation Letters; Deepika, D., Thanamani, A.S., Revocation methodologies using cloud security (2017) International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE), 5 (9); Guo, M., Zhang, H., Feng, C., Manipulator residual estimation and its application in collision detection[J] (2018) Industrial Robot: An International Journal, 45 (3), pp. 354-362; Haddadin, S., De Luca, A., Albu-Schäffer, A., Robot collisions: Asurvey on detection, isolation, and identification[J] (2017) IEEE Transactions on Robotics, 33 (6), pp. 1292-1312; Ren, T., Dong, Y., Wu, D., Collision detection and identification for robot manipulators based on extended state observer[J] (2018) Control Engineering Practice, 79, pp. 144-153; Meenakshi, Nimbalkar, P., An improvised recommendation system on top-n, unrated and point of interest recommendations regularized with user trust and item ratings (2017) International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE), 5 (8); Chen, W.H., Ballance, D.J., Gawthrop, P.J., A nonlinear disturbance observer for robotic manipulators[J] (2000) IEEE Transactions on Industrial Electronics, 47 (4), pp. 932-938; Nikoobin, A., Haghighi, R., Lyapunov-based nonlinear disturbance observer for serial n-link robot manipulators[J] (2009) Journal of Intelligent and Robotic Systems, 55 (2-3), pp. 135-153; Yang, Z., Peng, J., Liu, Y., Adaptive neural network force tracking impedance control for uncertain robotic manipulator based on nonlinear velocity observer[J] (2019) Neurocomputing, 331, pp. 263-280; Liu, X., Zhao, F., Ge, S.S., End-effector force estimation for flexible-joint robots with global friction approximation using neural networks[J] (2019) IEEE Transactions on Industrial Informatics, 15 (3), pp. 1730-1741; Paul, A., Ahmad, A., Rathore, M.M., Jabbar, S., Smartbuddy: Defining human behaviors using big data analytics in social internet of things (2016) IEEE Wireless Communications, 23 (5), pp. 68-74; Piltan, F., Yarmahmoudi, M.H., Shamsodini, M., Puma-560 robot manipulator position computed torque control methods using matlab/simulink and their integration into graduate nonlinear control and matlab courses[J] (2012) International Journal of Robotics and Automation, 3 (3), pp. 167-191","Zhu, Y.; State Key Laboratory of Robotics and System, China; email: jzhao@hit.edu.cn",,,"IOS Press",,,,,10641246,,,,"English","J. Intelligent Fuzzy Syst.",Article,"Final","",Scopus,2-s2.0-85078340108
"Hoffman E.M., Polverini M.P., Laurenzi A., Tsagarakis N.G.","57216280061;57208417702;57194457730;56275172000;","A Study on Sparse Hierarchical Inverse Kinematics Algorithms for Humanoid Robots",2020,"IEEE Robotics and Automation Letters","5","1","8908679","235","242",,2,"10.1109/LRA.2019.2954820","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077779869&doi=10.1109%2fLRA.2019.2954820&partnerID=40&md5=79e65b25a819d1ac1a0beda9ccafbfc1","Humanoids and Human Centered Mechatronics Research Line (HHCM), Istituto Italiano di Tecnologia (IIT), Genova, 16163, Italy","Hoffman, E.M., Humanoids and Human Centered Mechatronics Research Line (HHCM), Istituto Italiano di Tecnologia (IIT), Genova, 16163, Italy; Polverini, M.P., Humanoids and Human Centered Mechatronics Research Line (HHCM), Istituto Italiano di Tecnologia (IIT), Genova, 16163, Italy; Laurenzi, A., Humanoids and Human Centered Mechatronics Research Line (HHCM), Istituto Italiano di Tecnologia (IIT), Genova, 16163, Italy; Tsagarakis, N.G., Humanoids and Human Centered Mechatronics Research Line (HHCM), Istituto Italiano di Tecnologia (IIT), Genova, 16163, Italy","In humanoid robotic platforms, classical inverse kinematics algorithms, based on L2-regularization of joint velocities or accelerations, tends to engage the motion of all the available degrees of freedom, resulting in movements of the whole robot structure, which are inherently not sparse. The role of sparsity in motion control has recently gained interest in the robotics community for various reasons, e.g., human-like motions, and human-robot interaction, actuation parsimony, yet an exhaustive mathematical analysis is still missing. In order to address this topic, we here propose and compare possible sparse optimization approaches applied to hierarchical inverse kinematics for humanoid robots. This is achieved through LASSO regression and MILP optimization to resolve the IK problem. A first order formulation of the sparse regression problem is further introduced to reduce chattering on the joint velocity profiles. This article presents the theory behind the proposed approaches and performs a comparison analysis based on simulated and real experiments on different humanoid platforms. © 2016 IEEE.","humanoid robots; kinematics; Optimization and optimal control; redundant robots","Anthropomorphic robots; Degrees of freedom (mechanics); Integer programming; Inverse kinematics; Inverse problems; Kinematics; Regression analysis; Robotics; Comparison analysis; Humanoid robot; Humanoid robotics; Inverse kinematics algorithms; Mathematical analysis; Optimization and optimal control; Redundant robot; Sparse optimizations; Human robot interaction",,,,,"644727","Manuscript received September 5, 2019; accepted November 2, 2019. Date of publication November 21, 2019; date of current version December 6, 2019. This article was recommended for publication by Associate Editor Y. Zhao and Editor D. Song upon evaluation of the reviewers’ comments. This work was supported by the European Unions Horizon 2020 Research and Innovation Program under Grant 644727 (CogIMon) and in part by the INAIL—Teleoperation Project. (Enrico Mingo Hoffman and Matteo Parigi Polverini contributed equally to this work.) (Corresponding author: Enrico Mingo Hoffman.) The authors are with the Humanoids and Human Centered Mechatronics Research Line (HHCM), Istituto Italiano di Tecnologia (IIT), 16163 Genova, Italy (e-mail: enrico.mingo@iit.it; matteo.parigi@iit.it; arturo.laurenzi@iit.it; nikolaos.tsagarakis@iit.it).",,,,,,,,,,"Kanoun, O., Lamiraux, F., Wieber, P.-B., Kinematic control of redundant manipulators: Generalizing the task-priority framework to inequality task (2011) IEEE Trans. Robot., 27 (4), pp. 785-792. , Aug; Gonçalves, V.M., Fraisse, P., Crosnier, A., Adorno, B.V., Parsimonious kinematic control of highly redundant robots (2016) IEEE Robot. Autom. Lett., 1 (1), pp. 65-72. , Jan; Yazdani, M., (2012) Fast Human Movements and Sparse Optimal Control Policies, , Ph.D. dissertation, Elect. Comput. Eng., Univ. California San Diego, San Diego, CA, USA; Tarbouriech, S., Navarro, B., Fraisse, P., Crosnier, A., Cherubini, A., Sallé, D., Dual-arm relative tasks performance using sparse kinematic control (2018) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 6003-6009; Berret, B., Darlot, C., Jean, F., Pozzo, T., Papaxanthis, C., Gauthier, J.P., The inactivation principle: Mathematical solutions minimizing the absolute work and biological implications for the planning of arm movements (2008) PLoS Comput. Biol., 4 (10). , http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.397.6465&rep=rep1&type=pdf; Gallieri, M., Maciejowski, J.M., Lasso MPC: Smart regulation of over-actuated systems (2012) Proc. IEEE Amer. Control Conf., pp. 1217-1222; Aguilera, R.P., Delgado, R., Dolz, D., Agüero, J.C., Quadratic MPC with l0-input constraint (2014) IFAC Proc. Volumes, 47 (3), pp. 10888-10893; Ho, E.S., Komura, T., Lau, R.W., Computing inverse kinematics with linear programming (2005) Proc. ACM Symp. Virtual Reality Softw. Technol., pp. 163-166; Parigi Polverini, M., Mingo Hoffman, E., Laurenzi, A., Tsagarakis, N., Sparse optimization of contact forces for balancing control of multilegged humanoids (2019) IEEE Robot.Autom. Lett., 4 (2), pp. 1117-1124. , Apr; Mansard, N., Khatib, O., Kheddar, A., A unified approach to integrate unilateral constraints in the stack of tasks (2009) IEEE Trans. Robot., 25 (3), pp. 670-685. , Jun; Tibshirani, R., Regression shrinkage and selection via the lasso (1996) J. Roy. Statist. Soc., 58, pp. 267-288; Hoffman, E.M., Rocchi, A., Laurenzi, A., Tsagarakis, N.G., Robot control for dummies: Insights and examples using openSoT (2017) Proc. IEEE-RAS Int. Conf. Humanoid Robots, pp. 736-741; Tsagarakis, N., Morfey, S., Cerda, G., Zhibin, L., Caldwell, D., COMpliant huMANoid COMAN: Optimal joint stiffness tuning for modal frequency control (2013) Proc. IEEE Int. Conf. Robot. Autom., pp. 673-678. , May; Ferreau, H.J., Kirches, C., Potschka, A., Bock, H.G., Diehl, M., QpOASES: A parametric active-set algorithm for quadratic programming (2014) Math. Program. Comput., 6 (4), pp. 327-363; Wolff, J., Buss, M., Invariance control design for constrained nonlinear systems (2005) IFAC Proc. Volumes, 38 (1), pp. 37-42; Muratore, L., Laurenzi, A., Hoffman, E.M., Rocchi, A., Caldwell, D.G., Tsagarakis, N.G., XBotCore: A real-time cross-robot software platform (2017) Proc. IEEE Int. Conf. Robot. Comput., pp. 77-80; Laurenzi, A., Mingo Hoffman, E., Muratore, L., Tsagarakis, N.G., CartesI/O: A ROS based real-time capable cartesian control framework (2019) Proc. IEEE Int. Conf. Robot. Autom., pp. 591-596","Hoffman, E.M.; Humanoids and Human Centered Mechatronics Research Line (HHCM), Italy; email: enrico.mingo@iit.it",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85077779869
"Lee H.-S., Kang B.-Y.","56824620800;26643468300;","Continuous emotion estimation of facial expressions on JAFFE and CK+ datasets for human–robot interaction",2020,"Intelligent Service Robotics","13","1",,"15","27",,2,"10.1007/s11370-019-00301-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076101762&doi=10.1007%2fs11370-019-00301-x&partnerID=40&md5=07b121af7ff92dcf9888078ec04e5f17","School of Mechanical Engineering, Kyungpook National University, Daegu, 41566, South Korea","Lee, H.-S., School of Mechanical Engineering, Kyungpook National University, Daegu, 41566, South Korea; Kang, B.-Y., School of Mechanical Engineering, Kyungpook National University, Daegu, 41566, South Korea","Human–robot interaction was always based on estimation of human emotions from human facial expressions, voice and gestures. Human emotions were always categorized in a discretized manner, while we estimate facial images from common datasets for continuous emotions. Linear regression was used in this study which numerically quantizes human emotions as valence and arousal by displaying the raw images on the two-respective coordinate axis. The face image datasets from the Japanese female facial expression (JAFFE) dataset and the extended Cohn–Kanade (CK+) dataset were used in this experiment. Human emotions for the above-mentioned datasets were interpreted by 85 participants who were used in the experimentation. The best result from a series of experiments shows that the minimum of root mean square error for the JAFFE dataset was 0.1661 for valence and 0.1379 for arousal. The proposed method has been compared with previous methods such as songs, sentences, and it is observed that the proposed method for common datasets testing showed an outstanding emotion estimation performance. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.","Arousal; Emotion estimation; Human–robot interaction; Valence","Mean square error; Arousal; Emotion estimation; Facial Expressions; Human emotion; Human facial expressions; Robot interactions; Root mean square errors; Valence; Human robot interaction",,,,,"National Research Foundation of Korea, NRF: NRF-2019R1A2C1011270","This work was supported by the National Research Foundation of Korea funded by the Korean Government under Grant NRF-2019R1A2C1011270. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.",,,,,,,,,,"Ekman, P., Friesen, W.V., Measuring facial movement (1976) Environm Psychol Nonverbal Behav, 1 (1), pp. 56-75; Ekman, P., An argument for basic emotions (1992) Cogn Emot, 6 (3-4), pp. 169-200; Ekman, P., Strong evidence for universals in facial expressions: a reply to Russell’s mistaken critique (1994) Psychol Bull, 115 (2), pp. 268-287; Picard, R.W., (1995) Affective Computing, , MIT Technical report No. 321; Ma, C., Prendinger, H., Ishizuka, M., Emotion estimation and reasoning based on affective textual interaction (2005) International Conference on Affective Computing and Intelligent Interaction, pp. 622-628. , California, USA; Matsumoto, K., Kita, K., Ren, F., Emotion estimation from sentence using relation between Japanese slangs and emotion expressions (2012) Proceedings of the 26Th Pacific Asia Conference on Language, Information, and Computation, pp. 343-350. , Bali, Indonesia; Cohen, I., Garg, A., Huang, T.S., Emotion recognition from facial expressions using multilevel HMM (2000) Neural information process systems, 2. , Springer, Berlin; Miyakoshi, Y., Kato, S., Facial emotion detection considering partial occlusion of face using Bayesian network (2011) IEEE Computers and Informatics Symposium (ISCI), pp. 96-101. , Kuala Lumpur, Malaysia; Ghimire, D., Lee, J., Geometric feature-based facial expression recognition in image sequences using multi-class adaboost and support vector machines (2013) Sensors, 13 (6), pp. 7714-7734; Pitaloka, D.A., Wulandari, A., Basaruddin, T., Liliana, D.Y., Enhancing CNN with preprocessing stage in automatic emotion recognition (2017) Proc Comput Sci, 116, pp. 523-529; Lin, D.T., Facial expression classification using PCA and hierarchical radial basis function network (2006) J Inf Sci Eng, 22 (5), pp. 1033-1046; Shih, F.Y., Chuang, C.F., Wang, P.S., Performance comparisons of facial expression recognition in JAFFE database (2008) Int J Pattern Recognit Artif Intell, 22 (3), pp. 445-459; Jane, N., Kumar, S., Kumar, A., Shamsolmoali, P., Zareapoor, M., Hybrid deep neural networks for face emotion recognition (2018) Pattern Recognit Lett; Gunawan, A.A.S., Face expression detection on Kinect using active appearance model and fuzzy logic (2015) Proc Comput Sci, 59, pp. 268-274; Liu, P., Han, S., Meng, Z., Tong, Y., Facial expression recognition via a boosted deep belief network (2014) IEEE Conference on Computer Vision and Pattern Recognition, Ohio, USA, pp. 1805-1812; Breuer, R., Kimmel, R., (2017) A deep learning perspective on the origin of facial expressions; Schuller, B., Rigoll, G., Lang, M., Hidden Markov model-based speech emotion recognition (2003) IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. I-401. , Hong Kong, China; Busso, C., Deng, Z., Yildirim, S., Bulut, M., Lee, C.M., Kazemzadeh, A., Lee, S., Narayanan, S., Analysis of emotion recognition using facial expressions, speech and multimodal information (2004) Proceedings of the 6Th International Conference on Multimodal Interfaces, pp. 205-211. , PA, USA; Kim, S.M., Valitutti, A., Calvo, R.A., Evaluation of unsupervised emotion models to textual affect recognition (2010) Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Anal and Generation of Emotion in Text, pp. 62-70. , California, USA; Russell, J.A., A circumplex model of affect (1980) J Pers Soc Psychol, 39 (6), pp. 1161-1178; Russell, J.A., Barrett, L.F., Core affect, prototypical emotional episodes, and other things called emotion: dissecting the elephant (1999) J Pers Soc Psychol, 76 (5), pp. 805-819; Yang, Y.H., Lin, Y.C., Su, Y.F., Chen, H.H., Music emotion classification: A regression approach (2007) 2007 IEEE International Conference on Multimedia and Expo, pp. 208-211. , Beijing, China; Yang, Y.H., Lin, Y.C., Su, Y.F., Chen, H.H., A regression approach to music emotion recognition (2008) IEEE Trans Audio Speech Lang Process, 16 (2), pp. 448-457; Chen, Y.A., Wang, J.C., Yang, Y.H., Chen, H., Linear regression-based adaptation of music emotion recognition models for personalization (2014) 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2149-2153. , Florence, Italy; Sun, K., Yu, J., Huang, Y., Hu, X., An improved valence-arousal emotion space for video affective content representation and recognition (2009) 2009 IEEE International Conference on Multimedia and Expo, pp. 566-569. , New York, USA; Mehrabian, A., Pleasure-arousal-dominance: a general framework for describing and measuring individual differences in temperament (1996) Curr Psychol, 14 (4), pp. 261-292; Grimm, M., Kroschel, K., Emotion estimation in speech using a 3D emotion space concept (2007) Robust speech recognition and understanding, , InTech, Rijeka; Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J., Coding facial expressions with gabor wavelets (1998) Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan, pp. 200-205; Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., The extended cohn-kanade dataset (Ck+): A complete dataset for action unit and emotion-specified expression (2010) 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 94-101. , CA, USA, San Francisco; Bargiela, A., Pedrycz, W., Nakashima, T., Multiple regression with fuzzy data (2007) Fuzzy Sets Syst, 158 (19), pp. 2169-2188; Viola, P., Jones, M.J., Robust real-time face detection (2004) Int J Comput Vis, 57 (2), pp. 137-154; Lienhart, R., Maydt, J., An extended set of haar-like features for rapid object detection (2002) 2002 International Conference on Image Processing, 1, pp. I-900. , New York, USA; Schwegmann, C.P., Kleynhans, W., Salmon, B.P., Synthetic aperture radar ship detection using Haar-like features (2016) IEEE GeoSci Rem Sens Lett, 14 (2), pp. 154-158","Kang, B.-Y.; School of Mechanical Engineering, South Korea; email: kby09@knu.ac.kr",,,"Springer",,,,,18612776,,,,"English","Intelligent Serv. Rob.",Review,"Final","",Scopus,2-s2.0-85076101762
"Sandoval J., Laribi M.A., Zeghloul S.","57197388699;36500011400;6701766953;","Autonomous Robot-Assistant Camera Holder for Minimally Invasive Surgery",2020,"Mechanisms and Machine Science","78",,,"465","472",,3,"10.1007/978-3-030-30036-4_42","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073160238&doi=10.1007%2f978-3-030-30036-4_42&partnerID=40&md5=de9685b1c516e44154996e6fb139a8f4","Department of GMSC, Pprime Institute, CNRS - University of Poitiers - ENSMA - UPR 3346, Poitiers, France","Sandoval, J., Department of GMSC, Pprime Institute, CNRS - University of Poitiers - ENSMA - UPR 3346, Poitiers, France; Laribi, M.A., Department of GMSC, Pprime Institute, CNRS - University of Poitiers - ENSMA - UPR 3346, Poitiers, France; Zeghloul, S., Department of GMSC, Pprime Institute, CNRS - University of Poitiers - ENSMA - UPR 3346, Poitiers, France","In this paper we present an autonomous camera holder robotic system for minimally invasive surgery (MIS). The proposed system is composed of a 7-DoF collaborative robot, i.e. Franka robot, holding the surgical camera and a motion capture system, i.e. Qualisys system, tracking online the surgical tools movements. The robot adapts its movements to continuously monitor the surgical gestures, based on the tools tips coordinates provided by the Qualisys system. The surgical camera is inserted into the patient’s body through a surgical device, i.e. trocar, generating a kinematic constraint commonly known as Remote Center of Motion (RCM) constraint. In order to preserve the patient safety, the RCM constraint is guaranteed by the control approach. Moreover, a compliance control law is implemented to smooth the robot movements as well as to reduce the efforts generated by the human-robot interactions. Robot Operating System (ROS) framework has been used to establish the communication between the robot and Qualisys, using the UDP protocol for data exchange. © 2020, Springer Nature Switzerland AG.","Collaborative robot; Motion capture; Robot-assisted minimally-invasive surgery","Cameras; Compliance control; Electronic data interchange; Human robot interaction; Motion capture; Motion tracking; Surgery; Surgical equipment; Collaborative robots; Kinematic constraints; Minimally invasive surgery; Motion capture system; Remote center of motions; Robot operating systems (ROS); Surgical devices; Surgical gestures; Robotic surgery",,,,,,,,,,,,,,,,"Hoeckelmann, M., Rudas, I.J., Fiorini, P., Kirchner, F., Haidegger, T., Current capabilities and development potential in surgical robotics (2015) Int. J. Adv. Robot. Syste., 12 (16); Bajaj, N.M., Spiers, A.J., Dollar, A.M., State of the art in artificial wrists: A review of prosthetic and robotic wrist design (2019) IEEE Trans. Rob., 35 (1), pp. 261-277; Ateş, G., Majani, R., Dede, M.İ.C., Design of a teleoperation scheme with a wearable master for minimally invasive surgery (2019) New Trends in Medical and Service Robotics: Advances in Theory and Practice, pp. 45-53. , Carbone, G., Ceccarelli, M., Pisla, D. (eds.), pp., Springer, Cham; Abbas, P., Holder-Haynes, J., Taylor, D.J., More than a camera holder: Teaching surgical skills to medical students (2015) J. Surg. Res., 195 (2), pp. 385-389; Taylor, R.H., Menciassi, A., Fichtinger, G., Dario, P., Medical robotics in computer-integrated surgery (2008) Handbook of Robotics, Ch. 66, pp. 1199-1222. , Siciliano, B., Khatib, O. (eds.), 2nd edn., pp., Springer Cambridge; Allaf, M.E., Laparoscopic visual field: Voice versus foot pedal interfaces for control of the aesop robot (1998) Surg. Endosc., 12, pp. 1415-1418; Kraft, B.M., Jäger, C., Kraft, K., Leibl, B.J., Bittner, R., The AESOP robot system in laparoscopic surgery: Increased risk or advantage for surgeon and patient? (2004) Surg. Endosc. Other Int. Tech., 18, pp. 1216-1223; Gilbert, J.M., The endoassist robotic camera holder as an aid to the introduction of laparoscopic colorectal surgery (2009) Ann. Roy. Coll. Surg. Engl., 91 (5), pp. 389-393; Stolzenburg, J., Franz, T., Kallidonis, P., Minh, D., Dietel, A., Hicks, J., Nicolaus, M., Liatsikos, E., Comparison of the FreeHand® robotic camera holder with human assistants during endoscopic extraperitoneal radical prostatectomy (2011) BJU Int, 107, pp. 970-974; Voros, S., Haber, G.P., Menudet, J.F., Long, J.A., Cinquin, P., ViKY robotic scope holder: Initial clinical experience and preliminary results using instrument tracking (2010) IEEE/ASME Trans. Mechatron., 6 (879-886); Fujii, K., Salerno, A., Sriskandarajah, K., Kwok, K., Shetty, K., Yang, G., Gaze contingent cartesian control of a robotic arm for laparoscopic surgery (2013) IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, pp. 3582-3589. , pp., 2013; Kuo, C.-H., Dai, J.S., Dasgupta, P., Kinematic design considerations for minimally invasive surgical robots: An overview (2012) Int. J. Med. Rob. Comput. Assist. Surg., 8 (2), pp. 127-145; Nageotte, F., Zanne, P., Doignon, C., de Mathelin, M., Visual servoing-based endoscopic path following for robot-assisted laparoscopic surgery (2006) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2364-2369. , pp., 2006; Wei, G.-Q., Arbter, K., Hirzinger, G., Real-time visual servoing for laparoscopic surgery (1997) IEEE Eng. Med. Biol. Mag., 16, pp. 40-45; Voros, S., Long, J., Cinquin, P., Automatic localization of laparoscopic instruments for the visual servoing of an endoscopic camera holder (2006) Med. Image Comput. Comput. Assist. Interv., 4190, pp. 535-542; Krupa, A., Gangloff, J., Doignon, C., Mathelin, M., Morel, G., Leroy, J., Soler, L., Marescaux, J., Autonomous 3D positionning of surgical instruments in rototized laparoscopic surgery using visual servoing (2003) IEEE Trans. Robot. Autom., 19 (5), pp. 842-853; Sandoval, J., Poisson, G., Vieyres, P., Improved dynamic formulation for decoupled cartesian admittance control and RCM constraint (2016) 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1124-1129. , pp., IEEE; Sandoval, J., Poisson, G., Vieyres, P., A new kinematic formulation of the RCM constraint for redundant torque-controlled robots (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , IEEE; Ott, C., (2008) Cartesian Impedance Control of Redundant and Flexible-Joint Robots, , Springer-Verlag, Heidelberg","Sandoval, J.; Department of GMSC, France; email: juan.sebastian.sandoval.arevalo@univ-poitiers.fr",,,"Springer Science and Business Media B.V.",,,,,22110984,,,,"English","Mech. Mach. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85073160238
"Gorbenko A., Popov V.","46861096400;8988704500;","Deceptive Actions and Robot Collision Avoidance",2020,"Advances in Intelligent Systems and Computing","948",,,"105","109",,1,"10.1007/978-3-030-25719-4_14","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070195964&doi=10.1007%2f978-3-030-25719-4_14&partnerID=40&md5=0e6875e809851755d2fac7ca563ccce1","Ural Federal University, Lenin st., 51, Ekaterinburg, 620083, Russian Federation","Gorbenko, A., Ural Federal University, Lenin st., 51, Ekaterinburg, 620083, Russian Federation; Popov, V., Ural Federal University, Lenin st., 51, Ekaterinburg, 620083, Russian Federation","The problem of collision avoidance is extensively studied in contemporary robotics. Efficiency and effectiveness of multirobot task cooperation and safety of human-robot interactions depend critically on the quality of the solution of the problem of collision avoidance. Classical path planning and obstacle avoiding approaches can not guarantee a sufficiently high quality of the solution of the problem of multirobot and human collision avoidance. So, to solve the problem with a sufficiently high quality, some special algorithms and approaches are needed. In this paper, we consider the notion of deception in context of the problem. We consider the possibility of generating and using deception to manipulate another robot. In particular, we consider two robots that solve their own tasks. In addition, we consider two robots one of which solves its own task and the second robot simulates the behavior of a pedestrian. We study the possibility of using fraud to improve the robot’s own performance and to improve safety of collision avoidance. © 2020, Springer Nature Switzerland AG.","Collision avoidance; Deception; Human-robot interactions; Robot navigation","Collision avoidance; Industrial robots; Man machine systems; Motion planning; Deception; High quality; In contexts; Multirobots; Obstacle-avoiding; Robot collisions; Robot navigation; Special algorithms; Human robot interaction",,,,,,,,,,,,,,,,"Almasri, M.M., Alajlan, A.M., Elleithy, K.M., Trajectory planning and collision avoidance algorithm for mobile robotics system (2016) IEEE Sensors J, 16, pp. 5021-5028. , https://doi.org/10.1109/JSEN.2016.2553126; Zeng, L., Bone, G.M., Mobile robot collision avoidance in human environments (2013) Int J Adv Robot Syst, 10, pp. 1-14. , https://doi.org/10.5772/54933; Liu, F., Narayanan, A., A human-inspired collision avoidance method for multi-robot and mobile autonomous robots (2013) Principles and Practice of Multi-Agent Systems PRIMA 2013. Lecture Notes in Computer Science, 8291, pp. 181-196. , https://doi.org/10.1007/978-3-642-44927-713, Boella, G., Elkind, E., Savarimuthu, B.T.R., Dignum, F., Purvis, M.K. (eds.), pp. , Springer, Berlin; Burgard, W., Cremers, A.B., Fox, D., Hahnel, D., Lakemeyer, G., Schulz, D., Steiner, W., Thrun, S., Experiences with an interactive museum tour-guide robot (1999) Artif Intell, 114 (1), pp. 3-55; Thrun, S., Bennewitz, M., Burgard, W., Cremers, A.B., Dellaert, F., Fox, D., Hahnel, D., Schulz, D., MINERVA: A second-generation museum tour-guide robot (1999) Proceedings of the 1999 IEEE International Conference on Robotics and Automation, 3, pp. 1999-2005. , , vol., pp; Nourbakhsh, I.R., Kunz, C., Willeke, T., The mobot museum robot installations: A five year experiment (2013) 2003 IEEE/RJS International Conference on Intelligent Robots and Systems, pp. 3636-3641. , , pp; Lacevic, B., Rocco, P., Kinetostatic danger field – a novel safety assessment for human-robot interaction (2010) In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010, pp. 2169-2174. , , pp; Polverini, M.P., Zanchettin, A.M., Rocco, P., A computationally efficient safety assessment for collaborative robotics applications (2017) Robot Comput-Integr Manuf, 46, pp. 25-37; Trautman, P., Ma, J., Murray, R.M., Krause, A., Robot navigation in dense human crowds: Statistical models and experimental studies of human-robot cooperation (2015) Int J Rob Res, 34, pp. 335-356. , https://doi.org/10.1177/0278364914557874; Lo, S., Cheng, C., Huang, H., Virtual impedance control for safe human-robot interaction (2016) J Intell Robot Syst, 82, pp. 3-19. , https://doi.org/10.1007/s10846-015-0250-y; Bond, C.F., Robinson, M., The evolution of deception (1988) J Nonverbal Behav, 12, pp. 295-307; Cheney, D.L., Seyfarth, R.M., (2008) Baboon Metaphysics: The Evolution of a Social Mind, , University Of Chicago Press, Chicago; Hauser, M.D., Costs of deception: Cheaters are punished in rhesus monkeys (Macaca mulatta) (1992) PNAS, 89, pp. 12137-12139; Gerwehr, S., Glenn, R.W., (2000) The Art of Darkness: Deception and Urban Operations. Rand Corporation, , Santa Monica; Wagner, A.R., Arkin, R.C., Acting deceptively: Providing robots with the capacity for deception (2011) Int J Soc Robotics, 3, pp. 5-26. , https://doi.org/10.1007/s12369-010-0073-8; McCleskey, E., (1991) Applying Deception to Special Operations Direct Action Missions, , Defense Intelligence College, Washington; Whaley, B., Towards a general theory of deception (1982) J Strateg Stud, 5, pp. 178-192; Vassallo, C., Olivier, A., Soueres, P., Cretual, A., Stasse, O., Pettre, J., How do walkers avoid a mobile robot crossing their way? (2017) Gait & Posture, 51, pp. 97-103","Popov, V.; Ural Federal University, Lenin st., 51, Russian Federation; email: popovvvv@gmail.com","Samsonovich A.V.",,"Springer Verlag","10th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2019","15 August 2019 through 18 August 2019",,228949,21945357,9783030257187,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85070195964
"Kaindl H.","7003752784;","Human-machine interaction",2020,"Advances in Intelligent Systems and Computing","1018",,,"428","433",,,"10.1007/978-3-030-25629-6_66","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070022092&doi=10.1007%2f978-3-030-25629-6_66&partnerID=40&md5=48409372f23d8f1f022e804f7c8a1cee","Institute of Computer Technology, TU Wien, Vienna, 1040, Austria","Kaindl, H., Institute of Computer Technology, TU Wien, Vienna, 1040, Austria","Usually, courses are given on human-computer interaction, while in recent years there was a major shift towards (mobile) devices and machines, with new human interfaces. Of course, they include embedded computers and software, but their interaction with users poses many new challenges and offers new solutions. This tutorial shows manifold usability problems as observed by the presenter in daily life, beyond those usually known from graphical user interfaces (GUIs) of traditional PCs (including laptop computers). It explains them by human factors usually unknown to embedded engineers and motivates user experience. User-centered and Usage-centered Design are compared with the result that they typically overlap but have a different focus each on Interaction Design. Usability Test and Usability Study are explained and contrasted as well. In addition, this tutorial explains key properties of Multimodal Interfaces and UIs of Mobile Devices. Finally, it culminates in a sketch of specific challenges of Human-Robot Interaction. © 2020, Springer Nature Switzerland AG.","Human-Robot interaction; Interaction design; Mobile devices; Multimodal interfaces; User experience","Graphical user interfaces; Human computer interaction; Interactive computer systems; Laptop computers; Machine design; Mobile computing; User centered design; Embedded computers; Graphical user interface (GUIs); Human machine interaction; Interaction design; Multi-modal interfaces; Usability problems; Usability studies; User experience; Human robot interaction",,,,,,,,,,,,,,,,"Bogdan, C., Ertl, D., Hüttenrauch, H., Göller, M., Green, A., Severinson-Eklundh, K., Falb, J., Kaindl, H., Evaluation of robot body movements supporting communication: Towards HRI on the move (2011) New Frontiers in Human-Robot Interaction, pp. 185-210. , John Benjamins Publishing Company, pp; Bogdan, C., Falb, J., Kaindl, H., Kavaldjian, S., Popp, R., Horacek, H., Arnautovic, E., Szep, A., Generating an abstract user interface from a discourse model inspired by human communication (2008) Proceedings of the 41St Hawaii International Conference on System Sciences (HICSS 2008); Bogdan, C., Kaindl, H., Falb, J., Popp, R., Modeling of interaction design by end users through discourse modeling (2008) Proceedings of the 2008 ACM International Conference on Intelligent User Interfaces (IUI 2008), , Maspalomas, Gran Canaria, Spain. ACM Press; Coutaz, J., Nigay, L., Salber, D., Blandford, A., May, J., Young, R.M., Four easy pieces for assessing the usability of multimodal interaction: The CARE properties (1995) Proceedings of International Conference on Human-Computer Interaction (INTERACT 1995); Falb, J., Kaindl, H., Horacek, H., Bogdan, C., Popp, R., Arnautovic, E., A discourse model for interaction design based on theories of human communication (2006) Extended Abstracts on Human Factors in Computing Systems, CHI 2006, pp. 754-759. , New York, NY, USA, ACM Press; Holtzblatt, K., Beyer, H., (2014) Contextual Design: Evolved, , Morgan & Claypool Publishers; Kaindl, H., Popp, R., Raneburger, D., Ertl, D., Falb, J., Szep, A., Bogdan, C., Robot-supported cooperative work: A shared-shopping scenario (2011) Proceedings of the 44Th Hawaii International Conference on System Sciences (HICSS 2011), pp. 1530-1605; Mukasa, K., Kaindl, H., An integration of requirements and user interface specifications (2008) Proceedings of the 16Th IEEE International Requirements Engineering Conference (RE 2008); Nielsen, J., (1993) Usability Engineering, , Morgan Kaufmann Publishers Inc., San Francisco; Popp, R., Raneburger, D., Kaindl, H., Tool support for automated multi-device GUI generation from discourse-based communication models (2013) Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2013); Preece, J., Sharp, H., Rogers, Y., (2015) Interaction Design: Beyond Human-Computer Interaction, , 4th edn. Wiley, New York; Raneburger, D., Alonso-Ríos, D., Popp, R., Kaindl, H., Falb, J., A user study with GUIs tailored for smartphones (2013) Proceedings of the 14Th IFIP TC 13 International Conference on Human-Computer Interaction-Interact 2013, Part II, LNCS, 8118, pp. 505-512. , Springer; Raneburger, D., Popp, R., Alonso-Ríos, D., Kaindl, H., Falb, J., A user study with GUIs tailored for smartphones and tablet PCs (2013) Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013), pp. 3727-3732; Rathfux, T., Thöner, J., Kaindl, H., Popp, R., Combining design-time generation of web-pages with responsive design for improving low-vision accessibility (2018) Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems","Kaindl, H.; Institute of Computer Technology, Austria; email: hermann.kaindl@tuwien.ac.at","Ahram T.Taiar R.Colson S.Choplin A.",,"Springer Verlag","1st International Conference on Human Interaction and Emerging Technologies, IHIET 2019","22 August 2019 through 24 August 2019",,229139,21945357,9783030256289,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85070022092
"Belmonte L.M., Morales R., García A.S., Segura E., Novais P., Fernández-Caballero A.","56728280100;57160596600;16174733600;56927915300;8248071100;6602230534;","Assisting dependent people at home through autonomous unmanned aerial vehicles",2020,"Advances in Intelligent Systems and Computing","1006",,,"216","223",,2,"10.1007/978-3-030-24097-4_26","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068600015&doi=10.1007%2f978-3-030-24097-4_26&partnerID=40&md5=f64634b0b1eff9bb454e829b7b5d5bdd","Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Escuela Técnica Superior de Ingenieros Industriales, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Escola de Engenharia, Universidade do Minho, Campus de Gualtar, Braga, 4710-057, Portugal; CIBERSAM (Biomedical Research Networking Centre in Mental Health), Madrid, Spain","Belmonte, L.M., Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, 02071, Spain, Escuela Técnica Superior de Ingenieros Industriales, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Morales, R., Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, 02071, Spain, Escuela Técnica Superior de Ingenieros Industriales, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; García, A.S., Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, 02071, Spain, Escuela Técnica Superior de Ingenieros Industriales, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Segura, E., Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, 02071, Spain, Escuela Técnica Superior de Ingenieros Industriales, Universidad de Castilla-La Mancha, Albacete, 02071, Spain; Novais, P., Escola de Engenharia, Universidade do Minho, Campus de Gualtar, Braga, 4710-057, Portugal; Fernández-Caballero, A., Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete, 02071, Spain, Escuela Técnica Superior de Ingenieros Industriales, Universidad de Castilla-La Mancha, Albacete, 02071, Spain, CIBERSAM (Biomedical Research Networking Centre in Mental Health), Madrid, Spain","This work describes a proposal of autonomous unmanned aerial vehicles (AUAVs) for home assistance of dependent people. AUAVs will monitor and recognize human activities during flight to improve their quality of life. However, before bringing such AUAV assistance to real homes, several challenges must be faced to make them viable and practical. Some challenges are technical and some others are related to human factors. In particular, several technical aspects are described for AUAV assistance: (1) flight control, based on our active disturbance rejection control algorithm, (2) flight planning (navigation in obstacle environments), and, (3) processing signals, acquired both from flight-control and monitoring sensors. From the assisted person’s viewpoint, our research focuses on three cues: (1) the user’s perception about AUAV assistance, (2) the influence on human acceptance of AUAV appearance and behavior at home, and (3) the human-robot interaction between assistant AUAV and assisted person. Finally, virtual reality environments are proposed to carry out preliminary tests and user acceptance evaluations. © Springer Nature Switzerland AG 2020.","Autonomous unmanned aerial vehicles; Dependent people; Home assistance","Acceptance tests; Ambient intelligence; Antennas; Application programs; Artificial intelligence; Autonomous vehicles; Disturbance rejection; Unmanned aerial vehicles (UAV); Virtual reality; Active disturbance rejection controls; Appearance and behavior; Autonomous unmanned aerial vehicles; Dependent people; Home assistance; Processing signal; Technical aspects; Virtual-reality environment; Human robot interaction",,,,,"Ministerio de Ciencia, Innovación y Universidades, MCIU; Instituto de Salud Carlos III, ISCIII: FPU014/05283; Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM; European Regional Development Fund, ERDF: DPI2016-80894-R; Agencia Estatal de Investigación, AEI; University of the East, UE","Acknowledgments. This work has been partially supported by Spanish Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación (AEI)/European Regional Development Fund (FEDER, UE) under DPI2016-80894-R grant, and by CIBERSAM of the Instituto de Salud Carlos III. Lidia M. Belmonte holds FPU014/05283 scholarship from Spanish Ministerio de Educación y Formación Pro-fesional.","This work has been partially supported by Spanish Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación (AEI)/European Regional Development Fund (FEDER, UE) under DPI2016-80894-R grant, and by CIBERSAM of the Instituto de Salud Carlos III. Lidia M. Belmonte holds FPU014/05283 scholarship from Spanish Ministerio de Educación y Formación Profesional.",,,,,,,,,"Fernández-Caballero, A., Martínez-Rodrigo, A., Pastor, J.M., Castillo, J.C., Lozano-Monasor, E., López, M.T., Zangróniz, R., Fernández-Sotos, A., Smart environment architecture for emotion recognition and regulation (2016) J. Biomed. Inform., 64, pp. 55-73; Castillo, J.C., Castro-González, Á., Fernández-Caballero, A., Latorre, J.M., Pastor, J.M., Fernández-Sotos, A., Salichs, M.A., Software architecture for smart emotion recognition and regulation of the ageing adult (2016) Cogn. Comput., 8 (2), pp. 357-367; Belmonte, L.M., Morales, R., Fernández-Caballero, A., Somolinos, J.A., A tandem active disturbance rejection control for a laboratory helicopter with variable speed rotors (2016) IEEE Trans. Ind. Electron., 63 (10), pp. 6395-6406; Mahony, R., Kumar, V., Corke, P., Multirotor aerial vehicles: Modeling, estimation, and control of quadrotor (2012) IEEE Robot. Autom. Mag., 19 (3), pp. 20-32; Leishman, R.C., Macdonald, J.C., Beard, R.W., McLain, T.W., Quadrotors and accelerometers: State estimation with an improved dynamic model (2014) IEEE Control Syst. Mag., 34 (1), pp. 28-41; Tanveer, M.H., Ahmed, S.F., Hazry, D., Warsy, F.A., Joyo, M.K., Stabilized controller design for attitude and altitude controlling of quad-rotor under disturbance and noisy conditions (2013) Am. J. Appl. Sci., 10 (8), pp. 819-831; Belmonte, L.M., Morales, R., Fernández-Caballero, A., Somolinos, J.A., Robust decentralized nonlinear control for a twin rotor MIMO system (2016) Sensors, 16 (8), p. 1160; Yu, Y., Lu, G., Sun, C., Liu, H., Robust backstepping decentralized tracking control for a 3-DOF helicopter (2015) Nonlinear Dynam, 82 (1-2), pp. 947-960; Fernández-Caballero, A., Belmonte, L.M., Morales, R., Somolinos, J.A., Generalized proportional integral control for an unmanned quadrotor system (2015) Int. J. Adv. Robot. Syst., 12, p. 85; Bouabdallah, S., Noth, A., Siegwart, R., PID vs LQ control techniques applied to an indoor micro quadrotor (2004) Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robotic Systems, pp. 2451-2456. , Senday, Japan, pp; Bertrand, S., Guenard, N., Hamel, T., Piet-Lahanier, H., Eck, L., A hierarchical controller for miniature VTOL UAVs: Design and stability analysis using singular perturbation theory (2011) Cont. Eng. Pract., 19 (10), pp. 1099-1108; Dydek, Z.T., Annaswamy, A.M., Lavretsky, E., Adaptive control of quadrotor UAVs: A design trade study with flight evaluations (2013) IEEE Trans. Cont. Syst. Tech., 21 (4), pp. 1400-1406; Sun, L., Zuo, Z., Nonlinear adaptive trajectory tracking control for a quad-rotor with parametric uncertainty (2014) Proc. Inst. Mech. Eng. G, 229 (9), pp. 1-13; Castillo, J.C., Castro-González, Á., Alonso-Martín, F., Fernández-Caballero, A., Salichs, M.A., Emotion detection and regulation from personal assistant robot in smart environment (2018) Personal Assistants: Emerging Computational Technologies, pp. 179-195. , Costa, A., Julián, V., Novais, P. (eds.), pp., Springer, New York; Morales, R., Fernández-Caballero, A., Somolinos, J.A., Sira-Ramírez, H., Integration of sensors in control and automation systems (2017) J. Sensors, 2017; Castillo, J.C., Fernández-Caballero, A., Serrano-Cuerda, J., López, M.T., Martínez-Rodrigo, A., Smart environment architecture for robust people detection by infrared and visible video fusion (2017) J. Ambient. Intell. Humaniz. Comput., 8 (2), pp. 223-237; Lozano-Monasor, E., López, M.T., Vigo-Bustos, F., Fernández-Caballero, A., Facial expression recognition in ageing adults: From lab to ambient assisted living (2017) J. Ambient. Intell. Humaniz. Comput., 8 (4), pp. 567-578; Ekman, P., Friesen, W.V., Hager, J.C., (2002) Facial Action Coding System. Manual and Investigator’s Guide. Research Nexus, , Salt Lake City; Lamiraux, F., Sekhavat, S., Laumond, J.P., Motion planning and control for Hilare pulling a trailer (1999) IEEE Trans. Robot. Autom., 15 (4), pp. 640-652; Balch, T., Arkin, R.C., Behavior-based information control for multirobot teams (1998) IEEE Trans. Robot. Autom, 14 (6), pp. 926-939; Shivaram, S., (2015) Structural Health Monitoring of Wind Turbine Blades Using Unmanned Air Vehicles. Master’s Dissertation, , University of Dublin; Whitemore, H., Koweit: How a drone is being used to monitor Health & Safety at the construction site (2015) ENGIE Innovation, , https://innovation.engie.com/en/news/news/smart-buildings/koweit-how-a-drone-is-being-used-to-monitor-health-safety-at-the-construction-site-1/1112; Florence, P.R., Carter, J., Ware, J., Tedrake, R., NanoMap: Fast, uncertainty-aware proximity queries with lazy search over local 3D data (2018) International Conference on Robotics and Automation (ICRA), Brisbane, Australia","Fernández-Caballero, A.; Instituto de Investigación en Informática de Albacete, Spain; email: Antonio.Fdez@uclm.es","Novais P.Carneiro D.Lloret J.Chamoso P.Navarro E.Omatu S.",,"Springer Verlag","10th International Symposium on Ambient Intelligence, ISAmI 2019","26 June 2019 through 28 June 2019",,227779,21945357,9783030240967,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85068600015
"Amanatidis T., Langdon P., Clarkson P.J.","57200382670;55781400200;7103180311;","An Inclusive, Fully Autonomous Vehicle Simulator for the Introduction of Human-Robot Interaction Technologies",2020,"Advances in Intelligent Systems and Computing","964",,,"157","165",,,"10.1007/978-3-030-20503-4_14","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067339081&doi=10.1007%2f978-3-030-20503-4_14&partnerID=40&md5=97e022eb3ea296cedb67166a95fdc506","Engineering Design Centre, Engineering Department, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom","Amanatidis, T., Engineering Design Centre, Engineering Department, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom; Langdon, P., Engineering Design Centre, Engineering Department, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom; Clarkson, P.J., Engineering Design Centre, Engineering Department, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, United Kingdom","As fully autonomous - SAE level 5 - vehicles approach commercialisation, there is need to design and test user interfaces specifically for such use. However, testing in real environments is currently limited or, in many cases, impossible. In this paper, as a solution, we present a dedicated simulator for fully autonomous vehicles. First, we outline the design requirements for this simulator. Inclusive design principles were used to accommodate a large range of diversity in our population. It can thus be used by individuals with visual, auditory and certain physical and cognitive impairments. Second, we describe the capabilities of the simulator in terms of human-robot interaction technologies. We aim to assess both performance and non-performance characteristics of the resulting systems and how they can be integrated in the transportation experience. Third, we collate the knowledge obtained during this project to provide a deeper understanding of human factors in operating fully autonomous vehicles. We hope these results provide a basis for further research and improve the experience of users. &#x00A9; 2020, Springer Nature Switzerland AG.","Automotive simulators; Autonomous vehicles; Human factors and ergonomics; Human-robot interaction; Inclusive design","Autonomous vehicles; Ergonomics; Human computer interaction; Machine design; Man machine systems; Simulators; User interfaces; Cognitive impairment; Commercialisation; Design and tests; Fully-autonomous vehicles; Human factors and ergonomics; Inclusive design; Performance characteristics; Real environments; Human robot interaction",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/N011899/1",,,,,,,,,,,"Fernandez-Duque, D., Johnson, M.L., Cause and effect theories of attention: The role of conceptual metaphors (2002) Review of General Psychology, pp. 153-165. , pp; Dikmen, M., Burns, C.M., Autonomous driving in the real world: Experiences with tesla autopilot and summon (2016) Proceedings of the 8Th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 225-228. , https://doi.org/10.1145/3003715.3005465, pp., ACM, New York; Waymo, , https://waymo.com/; (2019) GM Will Make an Autonomous Car without Steering Wheel Or Pedals By, , https://www.theverge.com/2018/1/12/16880978/gm-autonomous-car-2019-detroit-auto-show-2018; Mitchell, W.J., Borroni-Bird, C.E., Burns, L.D., (2010) Reinventing the Automobile: Personal Urban Mobility for the 21St Century, , MIT Press, Cambridge; Fernandez-Duque, D., Johnson, M.L., Cause and effect theories of attention: The role of conceptual metaphors (2002) Review of General Psychology, pp. 153-165. , pp; Politis, I., Langdon, P., Adebayo, D., Bradley, M., Clarkson, P.J., Skrypchuk, L., Mouzakitis, A., Stanton, N., An evaluation of inclusive dialogue-based interfaces for the takeover of control in autonomous cars (2018) 23Rd International Conference on Intelligent User Interfaces, pp. 601-606. , https://doi.org/10.1145/3172944.3172990, pp., ACM, New York; Wintersberger, P., Riener, A., Frison, A.-K., Automated driving system, male, or female driver: Who’d you prefer? Comparative analysis of passengers’ mental conditions, emotional states & qualitative feedback (2016) Proceedings of the 8Th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 51-58. , https://doi.org/10.1145/3003715.3005410, pp., ACM, New York; Wang, P., Sibi, S., Mok, B., Ju, W., Marionette: Enabling on-road wizard-of-oz autonomous driving studies (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 234-243. , https://doi.org/10.1145/2909824.3020256, pp., ACM, New York; Baltodano, S., Sibi, S., Martelaro, N., Gowda, N., Ju, W., The RRADS platform: A real road autonomous driving simulator (2015) Proceedings of the 7Th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 281-288. , https://doi.org/10.1145/2799250.2799288, pp., ACM, New York; Oliveira, L., Luton, J., Iyer, S., Burns, C., Mouzakitis, A., Jennings, P., Birrell, S., Evaluating how interfaces influence the user interaction with fully autonomous vehicles (2018) Proceedings of the 10Th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 320-331. , https://doi.org/10.1145/3239060.3239065, pp., ACM, New York; Braun, M., Weiser, S., Pfleging, B., Alt, F., A comparison of emotion elicitation methods for affective driving studies (2018) Adjunct Proceedings of the 10Th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 77-81. , https://doi.org/10.1145/3239092.3265945, pp., ACM, New York; Amanatidis, T., Langdon, P., Clarkson, P.J., Needs and expectations for fully autonomous vehicle interfaces (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 51-52. , https://doi.org/10.1145/3173386.3177054, pp., ACM, New York; (2015) Transportation Systems Catapult: Intelligent Mobility: Traveller Needs and U.K. Capability Study; Amanatidis, T., Langdon, P.M., Clarkson, P.J., Inclusivity considerations for fully autonomous vehicle user interfaces (2018) Breaking down Barriers, pp. 207-214. , https://doi.org/10.1007/978-3-319-75028-6_18, pp., Springer, Cham; Clarkson, P.J., Coleman, R., Keates, S., Lebbon, C., (2013) Inclusive Design: Design for the Whole Population, , Springer, London; Langdon, P., Johnson, D., Huppert, F., Clarkson, P.J., A framework for collecting inclusive design data for the UK population (2015) Appl. Ergon. 46(Part B), pp. 318-324. , https://doi.org/10.1016/j.apergo.2013.03.011; Politis, I., Langdon, P., Bradley, M., Skrypchuk, L., Mouzakitis, A., Clarkson, P.J., Designing autonomy in cars: A survey and two focus groups on driving habits of an inclusive user group, and group attitudes towards autonomous cars (2017) Advances in Design for Inclusion, pp. 161-173. , https://doi.org/10.1007/978-3-319-60597-5_15, pp., Springer, Cham","Amanatidis, T.; Engineering Design Centre, Trumpington Street, United Kingdom; email: ta323@cam.ac.uk","Stanton N.",,"Springer Verlag","AHFE International Conference on Human Factors in Transportation, 2019","24 July 2019 through 28 July 2019",,226949,21945357,9783030205027,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85067339081
"Lukić B., Petrič T., Žlajpah L., Jovanović K.","57192677119;36447813900;6603769815;36137004200;","KUKA LWR Robot Cartesian Stiffness Control Based on Kinematic Redundancy",2020,"Advances in Intelligent Systems and Computing","980",,,"310","318",,3,"10.1007/978-3-030-19648-6_36","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065966583&doi=10.1007%2f978-3-030-19648-6_36&partnerID=40&md5=f5d0023f5e69e7bcf7cf5f2f6b8794d0","School of Electrical Engineering, University of Belgrade, Belgrade, Serbia; Jožef Stefan Institute, Ljubljana, Slovenia","Lukić, B., School of Electrical Engineering, University of Belgrade, Belgrade, Serbia; Petrič, T., Jožef Stefan Institute, Ljubljana, Slovenia; Žlajpah, L., Jožef Stefan Institute, Ljubljana, Slovenia; Jovanović, K., School of Electrical Engineering, University of Belgrade, Belgrade, Serbia","This paper is dealing with one important topic for physical human-robot interaction, and that is achieving and/or optimizing of an arbitrary Cartesian stiffness of robot’s end-effector (EE). The focus is given on redundant compliant robots with serial elastic actuators with fixed joint stiffness, but can reconfigure without changing the EE position. The work presented in this paper is an approach where the robot redundancy is exploited to achieve the desired or at least some optimal Cartesian stiffness of robots EE. Robot tasks can be divided into primary and secondary tasks. In our case, the primary task is to track the Cartesian position reference and the secondary task is to optimize EE Cartesian stiffness behavior while keeping the desired EE position. This means that the EE position is a constraint in the robot Cartesian stiffness optimization. The algorithm for the Cartesian stiffness optimization has been initially tested using the simulation, and then evaluated on the 7-DOFs KUKA light weight robot. © Springer Nature Switzerland AG 2020.","Cartesian stiffness control; Null space; Physical human-robot interaction; Robot redundancy","End effectors; Man machine systems; Redundancy; Robotics; Stiffness; Cartesian position; Cartesian stiffness; Elastic actuators; Joint stiffness; Kinematic redundancy; Light weight robots; Null space; Physical human-robot interactions; Human robot interaction",,,,,"Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR: ARRS BI-RS/18-19-053, TR-35003","This paper was partly funded by the Ministry of Education, Science and Technological development of the Republic of Serbia, under contracts TR-35003, and bilateral project ARRS BI-RS/18-19-053.",,,,,,,,,,"Pedrocchi, N., Vicentini, F., Malosio, M., Tosatti, L.M., Safe human-robot cooperation in an industrial environment (2013) Int. J. Adv. Robot. Syst., 10 (1), p. 27; Hogan, N., Impedance Control: An approach to manipulation: Part II—Implementation (1985) J. Dyn. Syst. Measur. Control, 107 (1), pp. 8-16; Haddadin, S., Albu-Schäffer, A., Hirzinger, G., Safe physical human-robot interaction: Measurements, analysis and new insights (2010) Springer Tracts in Advanced Robotics, 66, pp. 395-407. , vol., pp; Pratt, G.A., Williamson, M.M., Series elastic actuators (1995) Proceedings of 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems 95. Human Robot Interaction and Cooperative Robots, 1, pp. 399-406. , vol., pp; Robinson, D.W., Pratt, J.E., Paluska, D.J., Pratt, G.A., Series elastic actuator development for a biomimetic walking robot (1999) Proceedings of 1999 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 561-568. , pp; van Ham, R., Vanderborght, B., van Damme, M., Verrelst, B., Lefeber, D., MACCEPA, the mechanically adjustable compliance and controllable equilibrium position actuator: Design and implementation in a biped robot (2007) Robot. Auton. Syst., 55 (10), pp. 761-768; De, A., Tasch, U., A two-DOF manipulator with adjustable compliance capabilities and comparison with the human finger (1996) J. Robot. Syst., 13 (1), pp. 25-34; Migliore, S.A., Brown, E.A., Deweerth, S.P., Biologically inspired joint stiffness control (2005) In: Proceedings-Ieee International Conference on Robotics and Automation, 2005, pp. 4508-4513. , pp; Vanderborght, B., Albu-Schaeffer, A., Bicchi, A., Burdet, E., Caldwell, D.G., Carloni, R., Catalano, M., Wolf, S., Variable impedance actuators: A review (2013) Robot. Auton. Syst., 61 (12), pp. 1601-1614; Grioli, G., Wolf, S., Garabini, M., Catalano, M., Burdet, E., Caldwell, D., Carloni, R., Bicchi, A., Variable stiffness actuators: The user’s point of view (2015) Int. J. Robot. Res., 34 (6), pp. 727-743; Ham, R., Sugar, T.G., Vanderborght, B., Hollander, K.W., Lefeber, D., Compliant actuator designs (2009) Robot. Autom. Mag., 16 (3), pp. 81-94; de Santis, A., Siciliano, B., de Luca, A., Bicchi, A., An atlas of physical human–robot interaction (2008) Mech. Mach. Theory, 43 (3), pp. 253-270; Edsinger, A., Kemp, C.C., Human-robot interaction for cooperative manipulation: Handing objects to one another (2007) Proceedings-Ieee International Workshop on Robot and Human Interactive Communication, pp. 1167-1172. , pp; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Trans. Robot., 31 (5), pp. 1089-1100; Albu-Schaffer, A., Fischer, M., Schreiber, G., Schoeppe, F., Hirzinger, G., (2004) Soft Robotics: What Cartesian Stiffness Can Obtain with Passively Compliant, Uncoupled Joints?, 4, pp. 3295-3301. , Proceedings of International Conference on Intelligent Robots and Systems, (IROS 2004), (2004); Petit, F., Albu-Schäffer, A., Cartesian impedance control for a variable stiffness robot arm (2011) Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4180-4186. , pp; Wu, H.-C., The Karush–Kuhn–Tucker optimality conditions in an optimization problem with interval-valued objective function (2007) Eur. J. Oper. Res., 176 (1), pp. 46-59; Momoh, J.A., El-Hawary, M.E., Adapa, R., A review of selected optimal power flow literature to 1993. I. Nonlinear and quadratic programming approaches (1999) IEEE Trans. Power Syst., 14 (1), pp. 96-104; Momoh, J.A., El-Hawary, M.E., Adapa, R., A review of selected optimal power flow literature to 1993. II. Newton, linear programming and interior point methods (1999) IEEE Trans. Power Syst., 14 (1), pp. 105-111; Chiaverini, S., Oriolo, G., Maciejewski, A.A., Redundant robots (2016) Springer Handbook of Robotics, 2Nd Edn., pp. 221-242. , pp., Springer, Cham","Lukić, B.; School of Electrical Engineering, Serbia; email: branko@etf.bg.ac.rs","Gorges D.Berns K.","Deutsche Forschungsgemeinschaft;Deutsches Forschungszentrum für Künstliche Intelligenz;Fraunhofer Zentrum Kaiserslautern;Technische Universität Kaiserslautern","Springer Verlag","28th International Conference on Robotics in Alpe-Adria-Danube Region, RAAD 2019","19 June 2019 through 21 June 2019",,225939,21945357,9783030196479,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85065966583
"Zhang Z., Qian K., Schuller B.W., Wollherr D.","57204881490;55869537800;6603767415;23010951900;","An Online Robot Collision Detection and Identification Scheme by Supervised Learning and Bayesian Decision Theory",2021,"IEEE Transactions on Automation Science and Engineering","18","3","9109713","1144","1156",,2,"10.1109/TASE.2020.2997094","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112710359&doi=10.1109%2fTASE.2020.2997094&partnerID=40&md5=c5602bb83b928f12701195fad2b4b32f","Chair of Automatic Control Engineering, Technical University of Munich, Munich, Germany; Educational Physiology Laboratory, Graduate School of Education, The University of Tokyo, Tokyo, Japan; Group on Language, Audio and Music (GLAM), Imperial College London, London, United Kingdom","Zhang, Z., Chair of Automatic Control Engineering, Technical University of Munich, Munich, Germany; Qian, K., Educational Physiology Laboratory, Graduate School of Education, The University of Tokyo, Tokyo, Japan; Schuller, B.W., Group on Language, Audio and Music (GLAM), Imperial College London, London, United Kingdom; Wollherr, D., Chair of Automatic Control Engineering, Technical University of Munich, Munich, Germany","This article is dedicated to developing an online collision detection and identification (CDI) scheme for human-collaborative robots. The scheme is composed of a signal classifier and an online diagnosor, which monitors the sensory signals of the robot system, detects the occurrence of a physical human-robot interaction, and identifies its type within a short period. In the beginning, we conduct an experiment to construct a data set that contains the segmented physical interaction signals with ground truth. Then, we develop the signal classifier on the data set with the paradigm of supervised learning. To adapt the classifier to the online application with requirements on response time, an auxiliary online diagnosor is designed using the Bayesian decision theory. The diagnosor provides not only a collision identification result but also a confidence index which represents the reliability of the result. Compared to the previous works, the proposed scheme ensures rapid and accurate CDI even in the early stage of a physical interaction. As a result, safety mechanisms can be triggered before further injuries are caused, which is quite valuable and important toward a safe human-robot collaboration. In the end, the proposed scheme is validated on a robot manipulator and applied to a demonstration task with collision reaction strategies. The experimental results reveal that the collisions are detected and classified within 20 ms with an overall accuracy of 99.6%, which confirms the applicability of the scheme to collaborative robots in practice. Note to Practitioners - This article is intended to provide a novel online collision event handling scheme for robots in industrial environments. This scheme is designed to quickly and accurately detect an accidental collision and distinguish it from the intentional human-robot interaction. The method takes the raw signals from external torque sensors and provides a collision diagnosis result with a reliability index. The simple structure makes it easy to be implemented as a regular fault monitoring routine for collaborative robots. Different from the conventional methods, the proposed collision identification scheme in this article especially focuses on overcoming the following two challenges in practice: first, to timely and accurately report a collision within its early stage, and second, to ensure a high identification accuracy in a complicated environment, where ubiquitous disturbance and noise are unneglectable. The experimental validation at the end of this article confirms its promising application value in human-robot collaboration. © 2004-2012 IEEE.","Anomaly monitoring; collision detection and identification (CDI); collision event pipeline; fault detection and isolation; human-robot interaction; robot safety; supervised learning","Classification (of information); Decision theory; E-learning; Industrial manipulators; Industrial robots; Man machine systems; Manipulators; Robot applications; Supervised learning; Bayesian decision theory; Experimental validations; Human-robot collaboration; Identification accuracy; Identification scheme; Industrial environments; Physical human-robot interactions; Physical interactions; Social robots",,,,,"Japan Society for the Promotion of Science, KAKEN: P19081; Ministry of Education, Culture, Sports, Science and Technology, Monbusho: 17H00878, 19F19081; China Scholarship Council, CSC: 201506120029; Horizon 2020: 820742","Manuscript received February 25, 2020; revised May 9, 2020; accepted May 18, 2020. Date of publication June 5, 2020; date of current version July 2, 2021. This article was recommended for publication by Associate Editor Y. Pan and Editor D. O. Popa upon evaluation of the reviewers’ comments. This work was supported in part by the Horizon 2020 Program of the project “HR-Recycler” under Grant 820742, in part by the China Scholarship Council under Grant 201506120029, in part by the Zhejiang Lab’s International Talent Fund for Young Professionals (Project HANAMI), China, in part by the Japan Society for the Promotion of Science (JSPS) Postdoctoral Fellowship for Research in Japan under Grant P19081 from JSPS, Japan, and in part by the Grants-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan, under Grant 19F19081 and Grant 17H00878. (Corresponding author: Kun Qian.) Zengjie Zhang and Dirk Wollherr are with the Chair of Automatic Control Engineering, Technical University of Munich, 80333 Munich, Germany (e-mail: zengjie.zhang@tum.de; dw@tum.de).",,,,,,,,,,"Althoff, D., Kuffner, J.J., Wollherr, D., Buss, M., Safety assessment of robot trajectories for navigation in uncertain and dynamic environments (2012) Auto. Robots, 32 (3), pp. 285-302. , Apr; Zanchettin, A.M., Ceriani, N.M., Rocco, P., Ding, H., Matthias, B., Safety in human-robot collaborative manufacturing environments: Metrics and control (2016) IEEE Trans. Autom. Sci. Eng, 13 (2), pp. 882-893. , Apr; Haddadin, S., Albu-Schäffer, A., Hirzinger, G., Safe physical humanrobot interaction: Measurements, analysis and new insights (2010) Robotics Research, pp. 395-407. , Berlin, Germany: Springer; Lasota, P.A., Fong, T., Shah, J.A., A survey of methods for safe human-robot interaction (2017) Found. Trends Robot, 5 (3), pp. 261-349; Haddadin, S., De Luca, A., Albu-Schaffer, A., Robot collisions: A survey on detection, isolation, and identification (2017) IEEE Trans. Robot, 33 (6), pp. 1292-1312. , Dec; Kimmel, M., Hirche, S., Active safety control for dynamic humanrobot interaction (2015) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. IROS, pp. 4685-4691. , Hamburg, Germany, Sep; Wiig, M.S., Pettersen, K.Y., Krogstad, T.R., Collision avoidance for underactuated marine vehicles using the constant avoidance angle algorithm (2020) IEEE Trans. Control Syst. Technol, 28 (3), pp. 951-966. , May; Sun, Y., Zhang, Z., Leibold, M., Hayat, R., Wollherr, D., Buss, M., Protective control for robot manipulator by sliding mode based disturbance reconstruction approach (2017) Proc. IEEE Int. Conf. Adv. Intell. 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"Xu L., Yu F.","57204318560;57196718030;","Factors that influence robot acceptance [机器人接受度的影响因素]",2020,"Kexue Tongbao/Chinese Science Bulletin","65","6",,"496","510",,1,"10.1360/TB-2019-0136","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082528747&doi=10.1360%2fTB-2019-0136&partnerID=40&md5=a8ccee3b6fdb781c06c41951a9ae6801","School of Humanities and Social Sciences, Xi’an Jiaotong University, Xi'an, 710049, China; Department of Social and Behavioural Sciences, City University of Hong Kong, Hong Kong, Hong Kong","Xu, L., School of Humanities and Social Sciences, Xi’an Jiaotong University, Xi'an, 710049, China, Department of Social and Behavioural Sciences, City University of Hong Kong, Hong Kong, Hong Kong; Yu, F., School of Humanities and Social Sciences, Xi’an Jiaotong University, Xi'an, 710049, China","With the development of technology, robots as the main carrier of artificial intelligence will appear more and more in human life and work. However, for ordinary people, robots are still relatively new things. Under such a background, whether people accept robots, under what conditions robots are more acceptable to people, and what kind of robots are more acceptable to people are all important issues that need to be explored. Therefore, this paper focuses on the factors that affect people’s acceptance of robots. Specifically, we systematically review the influencing factors of robot acceptance from three aspects: Robot factors, human factors and human-robot interaction factors. Firstly, there is no doubt that people’s acceptance of robots is affected by the factors pertaining to robot itself. Compared with human factors, robot factors are more controllable and practical for robot designers, producers and sellers, and thus are currently the most concerned issue for the public and researchers. Secondly, characteristics of robots such as their visual features, tactile features, auditory features and behavioral features can significantly affect people’s acceptance of robots. Among these characteristics of robots, the anthropomorphism of robot appearance is a hot research issue. A series of studies indicates that the appropriate degree of anthropomorphism can increase people’s acceptance of robot, but inappropriate degree of anthropomorphism can trigger uncanny valley effect, which in turn significantly decrease robot acceptance. Capabilities of robots, such as their instrumental and social abilities have also been proved to play an important in affecting people’s acceptance of robots. The instrumental capabilities of robots are undoubtedly the basic abilities, which include not only utilitarian factors (e. g., perceived usefulness and perceived ease of use), but also hedonic factors (e. g., perceived enjoyment). The social capabilities of robots are mainly reflected on their abilities to socialize with people, which include communicating with human and expressing their social cues such as emotions and vulnerability. Secondly, human factors (e. g., demographic variables and psychological variables) influencing people’s acceptance of robots should not be neglected. Many studies demonstrate that an individual’s demographic characteristics such as age, gender, education and cultural background play an important role in robot acceptance. In general, young people, males, and those with higher education are more likely to accept robots. Individual’s psychological characteristics such as personality, personal innovativeness and self-efficacy have also been shown to have an impact on people’s acceptance of robots. Thirdly, in addition to robot factors and human factors, human-robot interaction factors affecting robot acceptance have also been paid more and more attention by researchers. Lots of studies indicate that robot-related experience can significantly influence people’s acceptance of robots. Previous experience with robots has a positive effect on users’ attitudes towards robots, trust in robots and their own self-esteem. Meanwhile, people’s perception of robots (e. g., perception of robots’ warmth, competence and social presence) is also important. Researchers have suggested that people’s perception of robots’ warmth and competence is not only relevant to their attitudes towards robots, but also can predict their specific emotional response and behavioral tendency. In sum, we systematically review possible influencing factors of robot acceptance, which include robot factors, human factors and human-robot interaction factors. Based on the theory of mind perception, we suggest that people’s acceptance of robots are both positive and negative. On the one hand, people over-trust robots because of the strong capabilities of robots. On the other hand, people also attack robots because of ignoring robots’ experience. Having considered all possible factors, we believe that anthropomorphism may be the core variable that affects people’s acceptance of robots, and the ultimate problem of robot acceptance is necessarily moral issues concerning robots. © 2020, Science Press. 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"Iglesias A., Garcia J., Garcia-Olaya A., Fuentetaja R., Fernandez F., Romero-Garces A., Marfil R., Bandera A., Ting K.L.H., Voilmy D., Duenas A., Suarez-Mejias C.","8704705100;57196717269;37096880000;15044256600;7401589140;36348607500;6603680386;57201603823;56103475200;57189294668;57196745721;55548102700;","Extending the Evaluation of Social Assistive Robots with Accessibility Indicators: The AUSUS Evaluation Framework",2021,"IEEE Transactions on Human-Machine Systems","51","6",,"601","612",,,"10.1109/THMS.2021.3112976","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118250235&doi=10.1109%2fTHMS.2021.3112976&partnerID=40&md5=1c91e5802084b801c7588fd90323e20d","Computer Science and Engineering Department, Universidad Carlos III de Madrid, Leganés, Spain; Department of Electronic Technology, Universidad de Málaga, Málaga, Spain; University of Technology of Troyes, Troyes, France; Hospital Universitario Virgen Del Rocío, Seville, Spain","Iglesias, A., Computer Science and Engineering Department, Universidad Carlos III de Madrid, Leganés, Spain; Garcia, J., Computer Science and Engineering Department, Universidad Carlos III de Madrid, Leganés, Spain; Garcia-Olaya, A., Computer Science and Engineering Department, Universidad Carlos III de Madrid, Leganés, Spain; Fuentetaja, R., Computer Science and Engineering Department, Universidad Carlos III de Madrid, Leganés, Spain; Fernandez, F., Computer Science and Engineering Department, Universidad Carlos III de Madrid, Leganés, Spain; Romero-Garces, A., Department of Electronic Technology, Universidad de Málaga, Málaga, Spain; Marfil, R., Department of Electronic Technology, Universidad de Málaga, Málaga, Spain; Bandera, A., Department of Electronic Technology, Universidad de Málaga, Málaga, Spain; Ting, K.L.H., University of Technology of Troyes, Troyes, France; Voilmy, D., University of Technology of Troyes, Troyes, France; Duenas, A., Hospital Universitario Virgen Del Rocío, Seville, Spain; Suarez-Mejias, C., Hospital Universitario Virgen Del Rocío, Seville, Spain","The introduction of robots in the real world requires previous evaluation of the envisaged performance. Factors like usability, user experience, social acceptance, or societal impact, among others, have been taken into account in evaluation frameworks defined during the last years. However, one of the most important factors that need to be evaluated in any kind of interaction is whether all the users are able to work with the system with the same opportunities and easiness, and none of the current human-robot interaction (HRI) evaluation frameworks include this factor yet. This article proposes an extension of a popular HRI evaluation framework, including accessibility as a new evaluation factor. The proposed approach, named AUSUS, considers Accessibility, Usability, Social acceptance, User experience, and Societal impact. This article presents a use case of the framework, which is evaluated through a socially assistive robotic platform created to perform comprehensive geriatric assessment: the CLARC system. The details of the evaluation process in a hospital and a retirement home are reported, and the main difficulties and recommendations of using AUSUS are discussed. © 2013 IEEE.","Human-robot interaction; robotics and automation; robots, service robots; systems, man, and cybernetics","Economic and social effects; Human robot interaction; Man machine systems; Mobile robots; Social aspects; Automation robot; Face; Guideline; Humans-robot interactions; Robot service; Robotic and automation - robot - service robot; Service robots; Software; System, man, and cybernetic - human-robot interaction; Users' experiences; Robotics",,,,,"Comunidad de Madrid: 2016-T2/TIC-1712; Ministerio de Ciencia, Innovación y Universidades, MCIU; European Commission, EC: FP7-ICT-601116, PT13/006/001; Agencia Estatal de Investigación, AEI: AT17-5509-UMA, CSO2017-86747-R, EPUC3M17, RTI2018-099522-B-C43, TIN2017-88476-C2-2-R, UMA-CEIATECH-10, UMA18-FEDERJA-074","This work was supported in part by the EU ECHORD++ (FP7-ICT-601116) and PT13/006/001 projects, in part by FEDER/Ministerio de Ciencia, Innovaci?n y Universidades - Agencia Estatal de Investigaci?n RTI2018-099522-B-C43, TIN2017-88476-C2-2-R, and CSO2017-86747-R projects, in part by AT17-5509-UMA, UMA18-FEDERJA-074 and UMA-CEIATECH-10 regional projects, and in part by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M17), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation). The work of Javier Garc?a was supported by the Comunidad de Madrid (Spain) funds under Project 2016-T2/TIC-1712. The work of Karine Lan Hing Ting was supported by Berger Levrault as part of the company?s robotics research delegation to the ActivAgeing Living Lab.",,,,,,,,,,"Zheng, Z., Zhao, H., Swanson, A.R., Weitlauf, A.S., Warren, Z.E., Sarkar, N., Design, development, and evaluation of a noninvasive autonomous robot-mediated joint attention intervention system for young children with ASD (2018) IEEE Trans. Hum.-Mach. Syst., 48 (2), pp. 125-135. , Apr; Erfanian, A., Hu, Y., Zeng, T., Tao framework of multiuser satisfaction for assessing interaction models within collaborative virtual environments (2017) IEEE Trans. Hum.-Mach. 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AAAI Spring Symp., pp. 158-164; Fattal, C., Ioana perspectives on usability and accessibility of an autonomous humanoid robot living with elderly people (2020) Disabil. Rehabil.: Assistive Technol., pp. 1-13","Iglesias, A.; Computer Science and Engineering Department, Spain; email: aiglesia@inf.uc3m.es",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21682291,,,,"English","IEEE Trans. Human Mach. Syst.",Article,"Final","",Scopus,2-s2.0-85118250235
"Breland D.S., Dayal A., Jha A., Yalavarthy P.K., Pandey O.J., Cenkeramaddi L.R.","57222169752;57221788290;57220751513;8717097900;55923600800;15922248600;","Robust Hand Gestures Recognition Using a Deep CNN and Thermal Images",2021,"IEEE Sensors Journal","21","23",,"26602","26614",,,"10.1109/JSEN.2021.3119977","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117802142&doi=10.1109%2fJSEN.2021.3119977&partnerID=40&md5=5a56d174fabdc01a4d53d0be6437b929","Department of Information and Communication Technology, ACPS Research Group, University of Agder, Grimstad, Norway; Department of Engineering Sciences, University of Agder, Grimstad, Norway; Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, India; Department of Electronics and Communication Engineering, SRM University, Andhra Pradesh, Mangalagiri, India","Breland, D.S., Department of Information and Communication Technology, ACPS Research Group, University of Agder, Grimstad, Norway; Dayal, A., Department of Information and Communication Technology, ACPS Research Group, University of Agder, Grimstad, Norway; Jha, A., Department of Engineering Sciences, University of Agder, Grimstad, Norway; Yalavarthy, P.K., Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, India; Pandey, O.J., Department of Electronics and Communication Engineering, SRM University, Andhra Pradesh, Mangalagiri, India; Cenkeramaddi, L.R., Department of Information and Communication Technology, ACPS Research Group, University of Agder, Grimstad, Norway","Medical systems and assistive technologies, human-computer interaction, human-robot interaction, industrial automation, virtual environment control, sign language translation, crisis and disaster management, entertainment and computer games, and so on all use RGB cameras for hand gesture recognition. However, their performance is limited especially in low-light conditions. In this paper, we propose a robust hand gesture recognition system based on high-resolution thermal imaging that is light-independent. A dataset of 14,400 thermal hand gestures is constructed, separated into two color tones. We also propose using a deep CNN to classify high-resolution hand gestures accurately. The proposed models were also tested on Raspberry Pi 4 and Nvidia AGX edge computing devices, and the results were compared to the benchmark models. The model also achieves an accuracy of 98.81% and an inference time of 75.138 ms on Nvidia Jetson AGX. In contrast to hand gesture recognition systems based on RGB cameras, which have limited performance in the dark-light conditions, the proposed system based on reliable high resolution thermal images is well-suited to a wide range of applications. © 2001-2012 IEEE.","deep CNN; hand gesture recognition; High resolution thermal imaging; human-computer interaction; human-robot interaction; machine learning; thermal sensors","Cameras; Computer control systems; Computer games; Computer vision; Disasters; Face recognition; Gesture recognition; Human robot interaction; Image resolution; Infrared imaging; Learning systems; Medical imaging; Palmprint recognition; Security systems; Tumors; Virtual reality; Deep CNN; Gestures recognition; Hand-gesture recognition; High resolution; High resolution thermal imaging; Humans-robot interactions; Thermal images; Thermal sensors; Thermal-imaging; Human computer interaction",,,,,,,,,,,,,,,,"Wachs, J.P., Kölsch, M., Stern, H., Edan, Y., Vision-based handgesture applications (2011) Int. 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"Chen Z., Guo Q., Xiong H., Jiang D., Yan Y.","57217164308;56924157800;57220026418;53663547100;55285589300;","Control and Implementation of 2-DOF Lower Limb Exoskeleton Experiment Platform",2021,"Chinese Journal of Mechanical Engineering (English Edition)","34","1","22","","",,2,"10.1186/s10033-021-00537-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101128989&doi=10.1186%2fs10033-021-00537-8&partnerID=40&md5=6ffa9e8cb3cee65debeaa521c764e630","School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Sichuan, Chengdu  611731, China; Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, Sichuan, 611731, China; Glasgow College, University of Electronic Science and Technology of China, Sichuan, 611731, China; School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Sichuan, 611731, China","Chen, Z., School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Sichuan, Chengdu  611731, China; Guo, Q., School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Sichuan, Chengdu  611731, China, Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, Sichuan, 611731, China; Xiong, H., Glasgow College, University of Electronic Science and Technology of China, Sichuan, 611731, China; Jiang, D., School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Sichuan, 611731, China; Yan, Y., School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Sichuan, Chengdu  611731, China, Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, Sichuan, 611731, China","In this study, a humanoid prototype of 2-DOF (degrees of freedom) lower limb exoskeleton is introduced to evaluate the wearable comfortable effect between person and exoskeleton. To improve the detection accuracy of the human-robot interaction torque, a BPNN (backpropagation neural networks) is proposed to estimate this interaction force and to compensate for the measurement error of the 3D-force/torque sensor. Meanwhile, the backstepping controller is designed to realize the exoskeleton's passive position control, which means that the person passively adapts to the exoskeleton. On the other hand, a variable admittance controller is used to implement the exoskeleton's active follow-up control, which means that the person's motion is motivated by his/her intention and the exoskeleton control tries best to improve the human-robot wearable comfortable performance. To improve the wearable comfortable effect, serval regular gait tasks with different admittance parameters and step frequencies are statistically performed to obtain the optimal admittance control parameters. Finally, the BPNN compensation algorithm and two controllers are verified by the experimental exoskeleton prototype with human-robot cooperative motion. © 2021, The Author(s).","Backstepping controller; BP neural network; Lower limb exoskeleton; Variable admittance strategy","Backpropagation; Controllers; Degrees of freedom (mechanics); Human robot interaction; Position control; Wearable technology; Admittance parameters; Back propagation neural networks; Backstepping controller; Compensation algorithm; Detection accuracy; Experiment platforms; Force/torque sensor; Interaction forces; Exoskeleton (Robotics)",,,,,"Natural Science Foundation of Jilin Province; Sichuan Province Science and Technology Support Program: 2018JY0565, 2020YFG0137; National Natural Science Foundation of China, NSFC: 11872147, 12072068, 51775089","Supported by National Natural Science Foundation of China (Grant Nos. 51775089, 12072068, 11872147), Sichuan Province Science and Technology Support Program of China (Grant Nos. 2020YFG0137, 2018JY0565).",,,,,,,,,,"Khamar, M., Edrisi, M., Designing a backstepping sliding mode controller for an assistant human knee exoskeleton based on nonlinear disturbance observer (2018) Mechatronics, 54, pp. 121-132; Aguirre-Ollinger, G., Colgate, J.E., Peshkin, M.A., Design of an active one-degree-of-freedom lower-limb exoskeleton with inertia compensation (2011) The International Journal of Robotics Research, 30 (4), pp. 486-499; Yang, Y., Ma, L., Huang, D., Development and repetitive learning control of lower limb exoskeleton driven by electrohydraulic actuators (2017) IEEE Transactions on Industrial Electronics, 64 (5), pp. 4169-4178; Li, X., Liu, Y.H., Yu, H., Iterative learning impedance control for rehabilitation robots driven by series elastic actuators (2018) Automatica, 90, pp. 1-7; Shi, D., Zhang, W.X., Zhang, W., A review on lower limb rehabilitation exoskeleton robots (2019) Chinese Journal of Mechanical Engineering, 32 (1), pp. 1-11; Lu, R., Li, Z., Su, C.Y., Development and learning control of a human limb with a rehabilitation exoskeleton (2014) IEEE Transactions on Industrial Electronics, 61 (7), pp. 3776-3785; Guo, Q., Zhang, Y., Jiang, D., A control approach for human-mechatronic-hydraulic-coupled exoskeleton in overload-carrying condition (2016) International Journal of Robotics and Automation, 31 (4), pp. 272-280; Guo, Q., Jiang, D., Method for walking gait identification in a lower extremity exoskeleton based on C4.5 decision tree algorithm (2015) International Journal of Advanced Robotic Systems, 12; Li, Z., Su, C.Y., Li, G., Fuzzy approximation-based adaptive backstepping control of an exoskeleton for human upper limbs (2014) IEEE Transactions on Fuzzy Systems, 23 (3), pp. 555-566; Dong, M.K., Cheng, H., Tran, H.T., Minimizing human-exoskeleton interaction force using compensation for dynamic uncertainty error with adaptive RBF network (2016) Journal of Intelligent & Robotic Systems, 82 (3-4), pp. 413-433; He, W., Li, Z., Dong, Y., Design and adaptive control for an upper limb robotic exoskeleton in presence of input saturation (2019) IEEE Transactions on Neural Networks and Learning Systems, 30 (1), pp. 97-108; Meng, W., Liu, Q., Zhou, Z., Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation (2015) Mechatronics, 31, pp. 132-145; Hogan, N., Impedance control - an approach to manipulation. I - theory. ii - implementation. iii - applications (1985) ASME Transactions Journal of Dynamic Systems & Measurement Control B, 107, pp. 1-24; Keemink, A.Q.L., van der Kooij, H., Stienen, A.H., Admittance control for physical human–robot interaction (2018) The International Journal of Robotics Research, 37 (11), pp. 1421-1444; Sharkawy, A.N., Koustournpardis, P.N., Aspragathos, N., Variable admittance control for human-robot collaboration based on online neural network training (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, pp. 1334-1339; Buchli, J., Stulp, F., Theodorou, E., Learning variable impedance control (2011) The International Journal of Robotics Research, 30 (7), pp. 820-833; Yang, C., Peng, G., Li, Y., Neural networks enhanced adaptive admittance control of optimized robot–environment interaction (2019) IEEE Transactions on Cybernetics, 49 (7), pp. 2568-2579; Ferraguti, F., Landi, C.T., Sabattini, L., A variable admittance control strategy for stable physical human–robot interaction (2019) The International Journal of Robotics Research, 38 (6), pp. 747-765; Landi, C.T., Ferraguti, F., Fantuzzi, C., A passivity-based strategy for coaching in human-robot interaction (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 1-6; Landi, C.T., Ferraguti, F., Fantuzzi, C., Admittance control parameter adaptation for physical human-robot interaction (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017, pp. 2911-2916; Dimeas, F., Aspragathos, N., Online stability in human-robot cooperation with admittance control (2016) IEEE Transactions on Haptics, 9 (2), pp. 267-278; Ryu, D., Song, J.B., Kang, S., Frequency domain stability observer and active damping control for stable haptic interaction (2008) IET Control Theory & Applications, 2 (4), pp. 261-268; Kronander, K., Billard, A., Stability considerations for variable impedance control (2016) IEEE Transactions on Robotics, 32 (5), pp. 1298-1305; Kim, M.J., Lee, W., Choi, J.Y., A passivity-based nonlinear admittance control with application to powered upper-limb control under unknown environmental interactions (2019) IEEE/ASME Transactions on Mechatronics, 24 (4), pp. 1473-1484; Gui, K., Liu, H., Zhang, D., A practical and adaptive method to achieve EMG-based torque estimation for a robotic exoskeleton (2019) IEEE/ASME Transactions on Mechatronics, 24 (2), pp. 483-494; Zhuang, Y., Yao, S., Ma, C., Admittance control based on EMG-driven musculoskeletal model improves the human–robot synchronization (2018) IEEE Transactions on Industrial Informatics, 15 (2), pp. 1211-1218; Yu, X., He, W., Li, Y., Bayesian estimation of human impedance and motion intention for human-robot collaboration (2019) IEEE Transactions on Cybernetics; Dohmann, P.B.G., Hirche, S., Distributed control for cooperative manipulation with event-triggered communication (2020) IEEE Transactions on Robotics, 36 (4), pp. 1038-1052; Ba, K., Ma, G., Yu, B., A nonlinear model-based variable impedance parameters control for position-based impedance control system of hydraulic drive unit (2020) International Journal of Control, Automation and Systems, 18 (7), pp. 1806-1817; Ge, S.S., Lee, T.H., Harris, C.J., (1998) Adaptive neural network control of robotic manipulators, , World Scientific Publishing, Singapore","Guo, Q.; School of Aeronautics and Astronautics, China; email: guoqinguestc@uestc.edu.cn",,,"Springer",,,,,10009345,,CJMEE,,"English","Chin J Mech Eng Engl Ed",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85101128989
"Wu J., Liu Q., Zhang M., Pan Z., Li H., Tan K.C.","57204645027;56651247500;56122449900;57204645835;8615868400;57221544379;","HuRAI: A brain-inspired computational model for human-robot auditory interface",2021,"Neurocomputing","465",,,"103","113",,,"10.1016/j.neucom.2021.08.115","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114916023&doi=10.1016%2fj.neucom.2021.08.115&partnerID=40&md5=52feb38398dc280875dd0123636326a8","Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Department of Computing, The Hong Kong Polytechnic University, Hong Kong","Wu, J., Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Liu, Q., Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Zhang, M., Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Pan, Z., Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Li, H., Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Tan, K.C., Department of Computing, The Hong Kong Polytechnic University, Hong Kong","The deep learning era endows immense opportunities for ubiquitous robotic applications by leveraging big data generated from widespread sensors and ever-growing computing capability. While the growing demands for natural human-robot interaction (HRI) as well as concerns for energy efficiency, real-time performance, and data security motive novel solutions. In this paper, we present a brain-inspired spiking neural network (SNN) based Human-Robot Auditory Interface, namely HuRAI. The HuRAI integrates the voice activity detection, speaker localization and voice command recognition systems into a unified framework that can be implemented on the emerging low-power neuromorphic computing (NC) devices. Our experimental results demonstrate superior modeling capabilities of SNNs, achieving accurate and rapid prediction for each task. Moreover, the energy efficiency analysis reveals a compelling prospect, with up to three orders of magnitude energy savings, over the equivalent artificial neural networks that running on the state-of-the-art Nvidia graphics processing unit (GPU). Therefore, integrating the algorithmic power of large-scale SNN models and the energy efficiency of NC devices offers an attractive solution for real-time, low-power robotic applications. © 2021 Elsevier B.V.","Deep spiking neural networks; Human-computer interface; Neuromorphic computing; Speaker localization; Voice activity detection; Voice command recognition","Brain; Computer graphics; Computer graphics equipment; Deep neural networks; Energy efficiency; Graphics processing unit; Human computer interaction; Human robot interaction; Program processors; Robotics; Ubiquitous computing; Auditory interfaces; Brain-inspired; Deep spiking neural network; Human computer interfaces; Human robots; Neural-networks; Neuromorphic computing; Speaker localization; Voice command recognition; Voice-activity detections; Speech recognition; acoustic analysis; algorithm; Article; artificial neural network; brain analysis; computer model; energy conservation; human; mathematical computing; measurement accuracy; prediction; robotics; speech analysis; spiking neural network; voice recognition",,,,,"A1687b0033; 2019KC0AB02; Inter-American Foundation, IAF; Agency for Science, Technology and Research, A*STAR; National University of Singapore, NUS: I2001E0053; National Natural Science Foundation of China, NNSFC: 62106038; China Postdoctoral Science Foundation: 2020M680148","This research work was supported in part by A*STAR under its RIE2020 Advanced Manufacturing and Engineering Human (AME) Programmatic Grant (Grant No. A1687b0033 , Project Title: Spiking Neural Networks (SNN)), the IAF, A*STAR, SOITEC, NXP and National University of Singapore under FD-fAbrICS: Joint Lab for FD-SOI Always-on Intelligent & Connected Systems ( Award I2001E0053 ). The work of J. Wu was also partially supported by the Zhejiang Lab (No. 2019KC0AB02 ). The work of M. Zhang was aslo partially supported by the National Natural Science Foundation of China under Grant No. 62106038 , China Postdoctoral Science Foundation under Grant No. 2020M680148 , Zhejiang Lab’s International Talent Found for Young Professionals. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the supporting institutions and companies.",,,,,,,,,,"Murray, J.C., Erwin, H.R., Wermter, S., Robotic sound-source localisation architecture using cross-correlation and recurrent neural networks (2009) Neural Networks, 22 (2), pp. 173-189; Antonelo, E.A., Schrauwen, B., Stroobandt, D., Event detection and localization for small mobile robots using reservoir computing (2008) Neural Networks, 21 (6), pp. 862-871; Wang, J., Shim, V.A., Yan, R., Tang, H., Sun, F., Automatic object searching and behavior learning for mobile robots in unstructured environment by deep belief networks (2018) IEEE Trans. Cognitive Dev. 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Inc., Speech command recognition using deep learning; Park, D.S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E.D., Le, Q.V., (1904), Specaugment: A simple data augmentation method for automatic speech recognition, arXiv preprint arXiv08779; Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks (2012), pp. 1097-1105. , Advances in neural information processing systems; Doc, E.S., Speech processing, transmission and quality aspects (stq); distributed speech recognition; advanced front-end feature extraction algorithm; compression algorithms (2002) ETSI ES, 202 (50); Benyassine, A., Shlomot, E., Su, H.-Y., Massaloux, D., Lamblin, C., Petit, J.-P., (1997), pp. 64-73. , Itu-t recommendation g. 729 annex b: a silence compression scheme for use with g. 729 optimized for v. 70 digital simultaneous voice and data applications, IEEE Communications Magazine 35 (9); Rami̇rez, J., Segura, J.C., Beni̇tez, C., De La Torre, A., Rubio, A., Efficient voice activity detection algorithms using long-term speech information (2004) Speech Commun., 42 (3-4), pp. 271-287; Sohn, J., Kim, N.S., Sung, W., A statistical model-based voice activity detection (1999) IEEE Signal Processing Letters, 6 (1), pp. 1-3; Dellaferrera, G., Martinelli, F., Cernak, M., (1910), A bin encoding training of a spiking neural network-based voice activity detection, arXiv preprint arXiv12459; Corporation, N., (2020), https://www.nvidia.com/en-us/deep-learning-ai/products/titan-rtx/, Nvidia Titan RTX architecture; Blouw, P., Choo, X., Hunsberger, E., Eliasmith, C., Benchmarking keyword spotting efficiency on neuromorphic hardware (2019) Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop, pp. 1-8; Liu, Q., Wu, J., Parameter tuning-free missing-feature reconstruction for robust sound recognition (2021) IEEE J. Selected Topics Signal Processing, 15 (1), pp. 78-89; Wu, J., Pan, Z., Zhang, M., Das, R.K., Chua, Y., Li, H., (2019), pp. 3667-3668. , Robust sound recognition: A neuromorphic approach., in: INTERSPEECH; Varga, A., Steeneken, H.J., Assessment for automatic speech recognition: Ii. noisex-92: A database and an experiment to study the effect of additive noise on speech recognition systems (1993) Speech Commun., 12 (3), pp. 247-251","Zhang, M.; Department of Electrical and Computer Engineering, Singapore; email: maluzhang@nus.edu.sg",,,"Elsevier B.V.",,,,,09252312,,NRCGE,,"English","Neurocomputing",Article,"Final","",Scopus,2-s2.0-85114916023
"Ondras J., Celiktutan O., Bremner P., Gunes H.","57201700026;16229290400;57206776256;7005067251;","Audio-driven robot upper-body motion synthesis",2021,"IEEE Transactions on Cybernetics","51","11",,"5445","5454",,1,"10.1109/TCYB.2020.2966730","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119410912&doi=10.1109%2fTCYB.2020.2966730&partnerID=40&md5=7856ce61306fe55a537ff14be5cbdbac","Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, United Kingdom; Department of Software Engineering, Cisco Systems, Lysaker, 1366, Norway; Department of Engineering, Centre for Robotics Research, King's College London, London, WC2R 2LS, United Kingdom; Bristol Robotics Laboratory, University of the West England, Bristol, BS16 1QY, United Kingdom","Ondras, J., Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, United Kingdom, Department of Software Engineering, Cisco Systems, Lysaker, 1366, Norway; Celiktutan, O., Department of Engineering, Centre for Robotics Research, King's College London, London, WC2R 2LS, United Kingdom; Bremner, P., Bristol Robotics Laboratory, University of the West England, Bristol, BS16 1QY, United Kingdom; Gunes, H., Department of Computer Science and Technology, University of Cambridge, Cambridge, CB3 0FD, United Kingdom","Body language is an important aspect of human communication, which an effective human-robot interaction interface should mimic well. Human beings exchange information and convey their thoughts and feelings through gaze, facial expressions, body language, and tone of voice along with spoken words, and infer 65% of the meaning of the communicated messages from these nonverbal cues. Modern robotic platforms are, however, limited in their ability to automatically generate behaviors that align with their speech. In this article, we develop a neural-network-based system that takes audio from a user as an input and generates upper-body gestures, including head, hand, and torso movements of the user on a humanoid robot, namely, Softbank Robotics' Pepper. Our system was evaluated quantitatively as well as qualitatively using Web surveys when driven by natural speech and synthetic speech. We compare the impact of generic and person-specific neural-network models on the quality of synthesized movements. We further investigate the relationships between quantitative and qualitative evaluations and examine how the speaker's personality traits affect the synthesized movements. © 2013 IEEE.","Audio-based motion generation; human-robot interaction; personality perception","Anthropomorphic robots; Man machine systems; Robotics; Speech synthesis; Audio-based; Audio-based motion generation; Body language; Body motions; Humans-robot interactions; Motion generation; Motion synthesis; Personality perception; Synthesised; Upper bodies; Human robot interaction",,,,,,,,,,,,,,,,"McNeill, D., (1992) Hand and Mind: What Gestures Reveal about Thought, , Chicago, IL, USA: Univ. Chicago Press; Munhall, K.G., Jones, J.A., Callan, D.E., Kuratate, T., Vatikiotis-Bateson, E., Visual prosody and speech intelligibility: Head movement improves auditory speech perception (2004) Psychol. Sci., 15 (2), pp. 133-137; Mariooryad, S., Busso, C., Generating human-like behaviors using joint, speech-driven models for conversational agents (2012) IEEE Trans. Audio, Speech, Language Process., 20 (8), pp. 2329-2340. , Oct; Van Welbergen, H., Ding, Y., Sattler, K., Pelachaud, C., Kopp, S., Real-time visual prosody for interactive virtual agents (2015) Proc. Int. Conf. Intell. Virtual Agents, pp. 139-151; Busso, C., Deng, Z., Neumann, U., Narayanan, S., Learning expressive human-like head motion sequences from speech (2008) Data-Driven 3D Facial Animation, pp. 113-131. , London, U.K.: Springer; Yang, Z., Metallinou, A., Erzin, E., Narayanan, S., Analysis of interaction attitudes using data-driven hand gesture phrases (2014) Proc. IEEE Int. Conf. Acoust. Speech Signal Process. 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"Li S., Han K., Li X., Zhang S., Xiong Y., Xie Z.","35484295400;57211891584;57300021600;57205673318;57222561100;57224848111;","Hybrid Trajectory Replanning-Based Dynamic Obstacle Avoidance for Physical Human-Robot Interaction",2021,"Journal of Intelligent and Robotic Systems: Theory and Applications","103","3","41","","",,,"10.1007/s10846-021-01510-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117271171&doi=10.1007%2fs10846-021-01510-2&partnerID=40&md5=79a515d60dc6a4a430bc50e4f6711794","School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Ubtech Robotics Corp Ltd, Shenzhen, 518000, China","Li, S., School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Han, K., School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Li, X., School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Zhang, S., School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China; Xiong, Y., Ubtech Robotics Corp Ltd, Shenzhen, 518000, China; Xie, Z., Ubtech Robotics Corp Ltd, Shenzhen, 518000, China","This paper presents a hierarchical replanning framework that rapidly modulates the ongoing trajectory to help 7-DoF redundant manipulators avoid dynamic obstacles in physical human-robot interaction. In this method, the safety requirements of the end-effector and links are met without adding additional control burden. Firstly, an improved rapidly-exploring random trees planner is utilized to initialize the path in the joint space. To prevent excessive trimming, a heuristic method based on geometric path descriptors is proposed for finding and removing redundant nodes. Secondly, a hybrid scheme combining local path rewiring and redundancy-based node self-motion is used to identify and replan the part of the pre-planned path affected by dynamic obstacles. Lastly, an adaptive online trajectory generator is designed to adjust the trajectory in real time and generate smooth motion primitives for actuators. Simulations and real-world experiments validate the effectiveness and robustness of the proposed framework for whole-arm obstacle avoidance in dynamic scenarios. Compared to other methods, our approach has a 35% higher success rate and a 47.2% reduction in replanning time. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Dynamic obstacle avoidance; Online trajectory generation; Physical human-robot interaction; Redundant manipulator; Trajectory replanning","Dynamics; Heuristic methods; Human robot interaction; Industrial manipulators; Man machine systems; Redundant manipulators; Additional control; Dynamic obstacle avoidance; Dynamic obstacles; On-line trajectory generations; Physical humanrobot interaction (phri); Rapidly-exploring random trees; Re-planning; Redundant manipulator; Safety requirements; Trajectory replanning; Trajectories",,,,,"Special Project for Research and Development in Key areas of Guangdong Province: 2019B010154003","This study was funded by the Key-Area Research and Development Program of Guangdong Province, China (grant number 2019B010154003).",,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human–robot collaboration (2018) Auton. 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"Möller R., Furnari A., Battiato S., Härmä A., Farinella G.M.","57220485973;56348910300;6603989025;6701367190;57191972987;","A survey on human-aware robot navigation",2021,"Robotics and Autonomous Systems","145",,"103837","","",,,"10.1016/j.robot.2021.103837","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113985754&doi=10.1016%2fj.robot.2021.103837&partnerID=40&md5=df1f4569f8763855176592a7eed3ddfe","Department of Mathematics and Informatics, University of Catania, V. Andrea Doria, Catania, 95125, Italy; Philips Research, High Tech Campus 34, Eindhoven, 5656, Netherlands; Cognitive Robotics and Social Sensing Laboratory, ICAR-CNR, Palermo, Italy","Möller, R., Department of Mathematics and Informatics, University of Catania, V. Andrea Doria, Catania, 95125, Italy; Furnari, A., Department of Mathematics and Informatics, University of Catania, V. Andrea Doria, Catania, 95125, Italy; Battiato, S., Department of Mathematics and Informatics, University of Catania, V. Andrea Doria, Catania, 95125, Italy; Härmä, A., Philips Research, High Tech Campus 34, Eindhoven, 5656, Netherlands; Farinella, G.M., Department of Mathematics and Informatics, University of Catania, V. Andrea Doria, Catania, 95125, Italy, Cognitive Robotics and Social Sensing Laboratory, ICAR-CNR, Palermo, Italy","Intelligent systems are increasingly part of our everyday lives and have been integrated seamlessly to the point where it is difficult to imagine a world without them. Physical manifestations of those systems on the other hand, in the form of embodied agents or robots, have so far been used only for specific applications and are often limited to functional roles (e.g. in the industry, entertainment and military fields). Given the current growth and innovation in the research communities concerned with the topics of robot navigation, human–robot-interaction and human activity recognition, it seems like this might soon change. Robots are increasingly easy to obtain and use and the acceptance of them in general is growing. However, the design of a socially compliant robot that can function as a companion needs to take various areas of research into account. This paper is concerned with the navigation aspect of a socially-compliant robot and provides a survey of existing solutions for the relevant areas of research as well as an outlook on possible future directions. © 2021 Elsevier B.V.","Active vision; Activity recognition; Human robot interaction; Robot navigation","Agricultural robots; Entertainment industry; Industrial robots; Intelligent systems; Machine design; Military applications; Navigation; Surveys; Embodied agent; Human activity recognition; Human-aware; Military fields; Possible futures; Research communities; Robot interactions; Robot navigation; Social robots",,,,,"812882; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR: AIM1893589, E64118002540007, F/190050/02/X44 – CUP: B61B19000520008","This research has been supported by Marie Sk?odowska-Curie Innovative Training Networks - European Industrial Doctorates - PhilHumans Project (Contract no. 812882, Web: http://www.philhumans.eu), MIUR AIM - Attrazione e Mobilita Internazionale Linea 1 - AIM1893589 - CUP: E64118002540007 and by MISE - PON I&C 2014-2020 - ENIGMA Project - Prog n. 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Andrea Doria, Italy; email: gfarinella@dmi.unict.it",,,"Elsevier B.V.",,,,,09218890,,RASOE,,"English","Rob Autom Syst",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85113985754
"Shao N., Zhou Q., Shao C., Zhao Y.","57222321048;35797614700;57222317529;57222317626;","Adaptive Control of Robot Series Elastic Drive Joint Based on Optimized Radial Basis Function Neural Network",2021,"International Journal of Social Robotics","13","7",,"1823","1832",,,"10.1007/s12369-021-00762-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102248135&doi=10.1007%2fs12369-021-00762-0&partnerID=40&md5=07e139c50867501e597c5e76aaf4f6ca","School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, China","Shao, N., School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, China; Zhou, Q., School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, China; Shao, C., School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, China; Zhao, Y., School of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, China","For the social robot with serial elastic actuator, the joint dynamics model has the problems of strong coupling and high nonlinear, and the traditional PD control algorithm cannot achieve accurate trajectory tracking effect on the joint position of social robot using series elastic actuator. Therefore, an optimized Radial basis function (RBF) neural network adaptive control algorithm was proposed. The method based on RBF neural network approximates the social robot joint model parameters, an adaptive law was designed to estimate the weights of the neural network and the joint model online. The dynamic plane method is combined to improve the robustness of the control algorithm. The simulation results show that the trajectory tracking error peak of PD control algorithm is 0.2 rad. Compared with PD control algorithm, the trajectory tracking error peak of RBF neural network adaptive control algorithm based on dynamic surface optimization is reduced to ± 0.05 rad, which realizes accurate approximation of the parameters of social robot joint model, and accurate dynamics model approximation provides a theoretical basis for further research on human–robot interaction (HRI) of social robots. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.","Dynamic surface; Human–robot interaction; Radial basis function neural network; Series elastic actuator; Social robot","Actuators; Adaptive control systems; Agricultural robots; Approximation algorithms; Functions; Radial basis function networks; Trajectories; Accurate dynamics model; Elastic actuators; Radial basis function neural networks; RBF Neural Network; Robot interactions; Series elastic actuators; Trajectory tracking; Trajectory tracking errors; Social robots",,,,,"Central South University, CSU","We thank all the participants, as well as the graduate research team led by Professor Zhou Qinyuan from Central South University of Forestry and Technology, for facilitating the discussion and providing valuable feedback. In particular, Dr. Zhou played an important role in the careful guidance in the writing process of this article. Finally, We are very grateful to Chenyang Shao, Yanzhao, Rirong Lu, Xianzhe Hu, members of the Robotics Research Group in the School of Mechatronics Engineering of Central South University of Forestry and Technology for their expertise and efforts in programming robots.",,,,,,,,,,"Wang, Y., Chen, Y., Chen, K., Wu, Y., Huang, Y., A flat torsional spring with corrugated flexible units for series elastic actuators (2017) 2Nd International Conference on Advanced Robotics and Mechatronics (ICARM). IEEE, pp. 138-143; Li, X., Pan, Y., Chen, G., Yu, H., Adaptive human–robot interaction control for robots driven by series elastic actuators (2016) IEEE Trans Rob, 33 (1), pp. 169-182; Calanca, A., Muradore, R., Fiorini, P., Impedance control of series elastic actuators: passivity and acceleration-based control (2017) Mechatronics, 47, pp. 37-48; Zhu, Q., Mao, Y., Xiong, R., Wu, J., Adaptive torque and position control for a legged robot based on a series elastic actuator (2016) Int J Adv Rob Syst, 13 (1), p. 26; Calzado, J., Lindsay, A., Chen, C., Samuels, G., Olszewska, J.I., SAMI: Interactive, multi-sense robot architecture (2018) IEEE 22Nd International Conference on Intelligent Engineering Systems (INES). 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J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85102248135
"Spatola N., Kühnlenz B., Cheng G.","57193889944;55909854300;10243873000;","Perception and Evaluation in Human–Robot Interaction: The Human–Robot Interaction Evaluation Scale (HRIES)—A Multicomponent Approach of Anthropomorphism",2021,"International Journal of Social Robotics","13","7",,"1517","1539",,6,"10.1007/s12369-020-00667-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099425734&doi=10.1007%2fs12369-020-00667-4&partnerID=40&md5=7e7dc65e978eebc84791d218eade0758","Social Cognition in Human-Robot Interaction, Istituto Italiano di Tecnologia, Via Morego, 30, Genova, GE  16163, Italy; Department of Education, University of Potsdam, Am Neuen Palais 10, Potsdam, 14469, Germany; Department of Electrical and Computer Engineering, Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, Munich, 80333, Germany","Spatola, N., Social Cognition in Human-Robot Interaction, Istituto Italiano di Tecnologia, Via Morego, 30, Genova, GE  16163, Italy, Department of Education, University of Potsdam, Am Neuen Palais 10, Potsdam, 14469, Germany; Kühnlenz, B., Department of Electrical and Computer Engineering, Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, Munich, 80333, Germany; Cheng, G., Department of Electrical and Computer Engineering, Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, Munich, 80333, Germany","The evaluation of how (human) individuals perceive robots is a central issue to better understand human–robot interaction (HRI). On this topic, promising proposals have emerged. However, present tools are not able to assess a sufficient part of the composite psychological dimensions involved in the evaluation of HRI. Indeed, the percentage of variance explained is often under the recommended threshold for a construct to be valid. In this article, we consolidate the lessons learned from three different studies and propose a further developed questionnaire based on a multicomponent approach of anthropomorphism by adding traits from psychosocial theory about the perception of others and the attribution and deprivation of human characteristics: the de-humanization theory. Among these characteristics, the attribution of agency is of main interest in the field of social robotics as it has been argued that robots could be considered as intentional agents. Factor analyses reveal a four sub-dimensions scale including Sociability, Agency, Animacy, and the Disturbance. We discuss the implication(s) of these dimensions on future perception of and attitudes towards robots. © 2021, Springer Nature B.V.","Anthropomorphism; Human–robot interaction; Questionnaire; Robot evaluation; Robot perception; Scale","Agricultural robots; Social robots; Animacy; Intentional agents; Multicomponents; Robot interactions; Social robotics; Surveys",,,,,,,,,,,,,,,,"Anderson, M.L., Why is AI so scary? 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"Spatola N., Marchesi S., Wykowska A.","57193889944;57200602584;15924284200;","The Intentional Stance Test-2: How to Measure the Tendency to Adopt Intentional Stance Towards Robots",2021,"Frontiers in Robotics and AI","8",,"666586","","",,,"10.3389/frobt.2021.666586","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117481067&doi=10.3389%2ffrobt.2021.666586&partnerID=40&md5=6cc386ebf07f031291babdca762deef2","Social Cognition in Human-Robot Interaction Laboratory, Italian Institute of Technology, Genova, Italy","Spatola, N., Social Cognition in Human-Robot Interaction Laboratory, Italian Institute of Technology, Genova, Italy; Marchesi, S., Social Cognition in Human-Robot Interaction Laboratory, Italian Institute of Technology, Genova, Italy; Wykowska, A., Social Cognition in Human-Robot Interaction Laboratory, Italian Institute of Technology, Genova, Italy","In human-robot interactions, people tend to attribute to robots mental states such as intentions or desires, in order to make sense of their behaviour. This cognitive strategy is termed “intentional stance”. Adopting the intentional stance influences how one will consider, engage and behave towards robots. However, people differ in their likelihood to adopt intentional stance towards robots. Therefore, it seems crucial to assess these interindividual differences. In two studies we developed and validated the structure of a task aiming at evaluating to what extent people adopt intentional stance towards robot actions, the Intentional Stance task (IST). The Intentional Stance Task consists in a task that probes participants’ stance by requiring them to choose the plausibility of a description (mentalistic vs. mechanistic) of behaviour of a robot depicted in a scenario composed of three photographs. Results showed a reliable psychometric structure of the IST. This paper therefore concludes with the proposal of using the IST as a proxy for assessing the degree of adoption of the intentional stance towards robots. © Copyright © 2021 Spatola, Marchesi and Wykowska.","human-robot interaction (HRI); intentional stance theory; measurement; mentalization; social robotics/HRI",,,,,,"H2020 European Research Council, ERC; es:CEI; fr:CER; pl:ERBN: ERC-2016-StG-715058; European Research Council, ERC",,,,,,,,,,,"Ajzen, I., Fishbein, M., Attitude-behavior Relations: A Theoretical Analysis and Review of Empirical Research (1977) Psychol. Bull, 84 (5), pp. 888-918; Albarracin, D., Shavitt, S., Attitudes and Attitude Change (2018) Annu. Rev. Psychol, 69, pp. 299-327; Bacher, A., Wenzig, K., Vogler, M., Einführung und Zielsetzung). SPSS TwoStep Cluster - A First Evaluation (2004) Univ. 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Robot, 3 (14), p. eaar7650","Spatola, N.; Social Cognition in Human-Robot Interaction Laboratory, Italy; email: nicolasspatola@hotmail.fr",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85117481067
"Nabipour M., Moosavian S.A.A.","57193914460;15926236600;","Human model in the loop design optimization for RoboWalk wearable device",2021,"Journal of Mechanical Science and Technology","35","10",,"4685","4693",,,"10.1007/s12206-021-0935-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115304534&doi=10.1007%2fs12206-021-0935-z&partnerID=40&md5=f872d1723e7b7c93fb392201c367ca43","Centre of Excellence in Robotics and Control, Advanced Robotics & Automated Systems (ARAS) Lab, Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran","Nabipour, M., Centre of Excellence in Robotics and Control, Advanced Robotics & Automated Systems (ARAS) Lab, Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran; Moosavian, S.A.A., Centre of Excellence in Robotics and Control, Advanced Robotics & Automated Systems (ARAS) Lab, Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran","In this paper, a design optimization approach for assistive devices based on the simulation of the robot and human model is proposed. The proposed human model in the loop (HMIL) approach exploits an augmented human-robot model for interaction analysis, and to evaluate and modify the assistive device effectiveness. In this regard, a lower-limb assistive device (RoboWalk) for elderly and a human skeletal system are modeled and augmented to use the human-robot interaction forces in the proposed HMIL strategy. Due to the particular design of RoboWalk, a complete set of constraints are specified to ensure the solution feasibility and compatibility of the device in different poses. This constrained optimization problem is solved by weighted sum particle swarm optimization (WSPSO) and multi-objective PSO (MOPSO) algorithm. As a result of this optimization, several appropriate sets of design parameters are found to minimize the load on human joints, actuator torque and mass. Among the obtained optimal points, the point minimizing an introduced index is selected as the final design. © 2021, The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature.","Assistive device; Exoskeleton; Human model in the loop; Optimal design; Optimization; Particle swarm optimization; Robot","Constrained optimization; Exoskeleton (Robotics); Human robot interaction; Machine design; Man machine systems; Swarm intelligence; Wearable technology; Assistive devices; Design optimization; Exoskeleton; Human model in the loop; Human modelling; Model in the loops; Optimal design; Optimisations; Robot; Robot modeling; Particle swarm optimization (PSO)",,,,,,,,,,,,,,,,"Khadiv, M., Moosavian, S.A.A., Yousefi-Koma, A., Maleki, H., Sadedel, M., Online adaptation for humanoids walking on uncertain surfaces (2017) Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 231, pp. 245-258; Walsh, C.J., Endo, K., Herr, H., A quasi-passive leg exoskeleton for load-carrying augmentation (2007) International Journal of Humanoid Robotics, 4, pp. 487-506; 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Ding, Y., Kim, M., Kuindersma, S., Walsh, C.J., Human-in-the-loop optimization of hip assistance with a soft exosuit during walking (2018) Science Robotics, 3, p. eaar5438; Koller, J.R., Gates, D.H., Ferris, D.P., Remy, C.D., Body-in-the-loop optimization of assistive robotic devices: a validation study (2016) Robotics Science and Systems, 2016, pp. 1-10; Zhang, J., Fiers, P., Witte, K.A., Jackson, R.W., Poggensee, K.L., Atkeson, C.G., Collins, S.H., Human-in-the-loop optimization of exoskeleton assistance during walking (2017) Science, 356, pp. 1280-1284; Gordon, D.F., Matsubara, T., Noda, T., Teramae, T., Morimoto, J., Vijayakumar, S., Bayesian optimisation of exoskeleton design parameters (2018) 7Th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), pp. 653-658; Kennedy, J., Eberhart, R., Particle swarm optimization (1995) Proceedings of ICNN’95-Intemational Conference on Neural Networks, 4, pp. 1942-1948; Cura, T., Multi-robot GSA- and PSO-based optimal path planning in static environments (2009) Nonlinear Analysis: Real World Applications, 10 (4), pp. 2396-2406; 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Jamwal, P.K., Xie, S., Aw, K.C., Kinematic design optimization of a parallel ankle rehabilitation robot using modified genetic algorithm (2009) Robotics and Autonomous Systems, 57, pp. 1018-1027; Li, L., Hoon, K.H., Tow, A., Lim, P.H., Low, K.H., Design and control of robotic exoskeleton with balance stabilizer mechanism (2015) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3817-3823; Kim, Y., Shin, S., Kong, K., Han, D., Optimal level of assistance and weight of hip-joint wearable robot considering influences to the human body (2019) IFAC-PapersOnLine, 52, pp. 13-18; Zhou, L., Li, Y., Bai, S., A human-centered design optimization approach for robotic exoskeletons through biomechanical simulation (2017) Robotics and Autonomous Systems, 91, pp. 337-347; Moosavian, S.A.A., Mohamadi, M., Absalan, F., Augmented modeling of a lower limb assistant robot and human body, The 6th RSI International Conference on Robotics and Mechatronics (2018) IEEE, pp. 337-342; Moosavian, S.A.A., Nabipour, M., RoboWalk: Comprehensive augmented dynamics modeling and performance analysis (2019) International Journal of Robotics, pp. 63-72. , Theory and Applications; Nabipour, M., Moosavian, S.A.A., Dynamics modeling and performance analysis of RoboWalk, The 6th RSI International Conference on Robotics and Mechatronics (2018) IEEE, pp. 445-450; Nasrabadi, A.A.M., Absalan, F., Moosavian, S.A.A., Design, kinematics and dynamics modeling of a lower-limb walking assistant robot, The 4th International Conference on Robotics and Mechatronics (2016) IEEE, pp. 319-324; Moosavian, S.A.A., Nabipour, M., Absalan, F., Akbari, V., (2019) Robowalk: Explicit Augmented Human-Robot Dynamics Modeling for Design Optimization, Arxiv, 1907, p. 04114; Mohamadi, M.R., Akbari, V., Mahdizadeh, O., Nabipour, M., Moosavian, S.A.A., Simulation analysis of human-RoboWalk augmented model (2019) The 7th International Conference on Robotics and Mechatronics, pp. 93-98. , IEEE, Tehran, Iran","Moosavian, S.A.A.; Centre of Excellence in Robotics and Control, Iran; email: moosavian@kntu.ac.ir",,,"Korean Society of Mechanical Engineers",,,,,1738494X,,,,"English","J. Mech. Sci. Technol.",Article,"Final","",Scopus,2-s2.0-85115304534
"Sabater A., Alonso I., Montesano L., Murillo A.C.","57211500603;57196726063;6602849539;10040627500;","Domain and View-Point Agnostic Hand Action Recognition",2021,"IEEE Robotics and Automation Letters","6","4","9507285","7823","7830",,,"10.1109/LRA.2021.3101822","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112615156&doi=10.1109%2fLRA.2021.3101822&partnerID=40&md5=647257a513d0ef38d73f6fac7865487b","DIIS - I3A, Universidad de Zaragoza, Zaragoza, 50018, Spain; Bitbrain Technologies, Zaragoza, 50008, Spain","Sabater, A., DIIS - I3A, Universidad de Zaragoza, Zaragoza, 50018, Spain; Alonso, I., DIIS - I3A, Universidad de Zaragoza, Zaragoza, 50018, Spain; Montesano, L., DIIS - I3A, Universidad de Zaragoza, Zaragoza, 50018, Spain, Bitbrain Technologies, Zaragoza, 50008, Spain; Murillo, A.C., DIIS - I3A, Universidad de Zaragoza, Zaragoza, 50018, Spain","Hand action recognition is a special case of action recognition with applications in human-robot interaction, virtual reality or life-logging systems. Building action classifiers able to work for such heterogeneous action domains is very challenging. There are very subtle changes across different actions from a given application but also large variations across domains (e.g. virtual reality vs life-logging). This work introduces a novel skeleton-based hand motion representation model that tackles this problem. The framework we propose is agnostic to the application domain or camera recording view-point. When working on a single domain (intra-domain action classification) our approach performs better or similar to current state-of-the-art methods on well-known hand action recognition benchmarks. And, more importantly, when performing hand action recognition for action domains and camera perspectives which our approach has not been trained for (cross-domain action classification), our proposed framework achieves comparable performance to intra-domain state-of-the-art methods. These experiments show the robustness and generalization capabilities of our framework. © 2016 IEEE.","deep learning for visual perception; gesture, posture and facial expressions; Human and humanoid motion analysis and synthesis","Agricultural robots; Cameras; Social robots; Virtual reality; Action classifications; Action classifier; Action recognition; Camera perspectives; Cross-domain; Generalization capability; Single domains; State-of-the-art methods; Palmprint recognition",,,,,"Office of Naval Research Global, ONRG: ONRG-NICOPN62909-19-1-2027; Ministerio de Ciencia, Innovación y Universidades, MCIU; Direction Générale de l’Armement, DGA: T45 17R/FSE; European Regional Development Fund, ERDF: PGC2018-098817-A-I00; Agencia Estatal de Investigación, AEI","Manuscript received February 24, 2021; accepted July 17, 2021. Date of publication August 4, 2021; date of current version August 17, 2021. This work was supported in part by MCIU/AEI/FEDER under Grant PGC2018-098817-A-I00, in part by DGA under Grant T45 17R/FSE, and in part by the Office of Naval Research Global under the Grant ONRG-NICOPN62909-19-1-2027. This letter was recommended for publication by Associate Editor D. Pucci and Editor A. Kheddar upon evaluation of the reviewers’ comments. (Corresponding author: Alberto Sabater.) Alberto Sabater, Iñigo Alonso, and Ana C. Murillo are with the DIIS - I3A, Universidad de Zaragoza, Zaragoza, Spain (e-mail: asabater@unizar.es; inigo@unizar.es; acm@unizar.es).",,,,,,,,,,"Krupke, D., Steinicke, F., Lubos, P., Jonetzko, Y., Görner, M., Zhang, J., Comparison of multimodal heading and pointing gestures for co-located mixed reality human-robot interaction (2018) Proc. IEEE/RSJ Int.Conf. Intell. Robots Syst., pp. 1-9; Tanwani, A.K., Calinon, S., A generative model for intention recognition and manipulation assistance in teleoperation (2017) Proc. IEEE/RSJ Int. Conf. Intell. 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Acoust., Speech Signal Process., pp. 3732-3736; Hahn, M., Silva, A., Rehg, J.M., (2019) Action2Vec:Acrossmodal Embedding Approach to Action Learning; Jasani, B., Mazagonwalla, A., (2019) Skeleton Based Zero Shot Action Recognition in Joint Pose-language Semantic Space; Chen, T., Kornblith, S., Norouzi, M., Hinton, G., A simple framework for contrastive learning of visual representations (2020) Proc. Int.Conf.Mach. Learn., PMLR, pp. 1597-1607; Zhu, W., Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks (2016) Proc. AAAI Conf. Artif. Intell., 30 (1)","Sabater, A.; DIIS - I3A, Spain; email: asabater@unizar.es",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85112615156
"Chen Y., Wu H., Wang T., Wang Y., Liang Y.","57201862385;57204426014;57204428349;7601522356;57226442713;","Cross-Modal Representation Learning for Lightweight and Accurate Facial Action Unit Detection",2021,"IEEE Robotics and Automation Letters","6","4","9495228","7619","7626",,,"10.1109/LRA.2021.3098944","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111585737&doi=10.1109%2fLRA.2021.3098944&partnerID=40&md5=9231d1585ed95c840bd465c455f9f9ce","Department of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China","Chen, Y., Department of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China; Wu, H., Department of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China; Wang, T., Department of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China; Wang, Y., Department of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China; Liang, Y., Department of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China","In this letter, we focus on designing an effective method for lightweight and accurate facial action unit (AU) detection, which is essential for emotional communication in most human-robot interaction scenarios. AU detection is a delicate and challenging task because the subtle fleeting appearance changes caused by AUs are very difficult to catch and express. Therefore existing approaches mainly deal with static facial states or frame-level temporal relationships. The dynamic process of facial muscle movement, as the core feature of AU, is yet ignored and rarely exploited by prior studies. Based on such observation, we propose Flow Supervised Module (FSM) to explicitly capture the dynamic facial movement in the form of Flow and use the learned Flow to provide supervision signals for the detection model during the training stage effectively and efficiently. Furthermore, the proposed FSM can be easily incorporated into various backbone networks and boost their performance. Extensive experiments are conducted on two benchmark datasets, DISFA and BP4D, showing state-of-the-art performance with competitive detection speed. © 2016 IEEE.","deep learning for visual perception; emotional robotics; Gesture; posture and facial expressions","Agricultural robots; Benchmarking; Back-bone network; Benchmark datasets; Cross-modal representations; Detection models; Emotional communications; Facial movements; State-of-the-art performance; Temporal relationships; Social robots",,,,,"Ng Teng Fong Charitable Foundation","This work is in part supported by the PKU-NTU Joint Research Institute (JRI) sponsored by a donation from the Ng Teng Fong Charitable Foundation. We thank Diqi Chen for his valuable suggestions on FSNet.",,,,,,,,,,"Friesen, E., Ekman, P., Facial action coding system: A technique for the measurement of facial movement (1978) Palo Alto, 3 (2), p. 5; Ekman, P., An argument for basic emotions (1992) Cogn. 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Neural Inf. Process. Syst. Workshop, pp. 1-4; Simonyan, K., Zisserman, A., (2014) Very Deep Convolutional Networks for Large-scale Image Recognition; Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L., Imagenet: A large-scale hierarchical image database (2009) Proc. Conf. Comput. Vis. Pattern Recognit., pp. 248-255; Zhao, K., Chu, W.-S., De La Torre, F., Cohn, J.F., Zhang, H., (2015) Joint Patch and Multi-label Learning for Facial Action Unit Detection, pp. 2207-2216; Corneanu, C., Madadi, M., Escalera, S., Deep structure inference network for facial action unit recognition (2018) Proceedings of the European Conference on Computer Vision (ECCV), pp. 298-313","Wang, T.; Department of Electronics Engineering and Computer Science, China; email: wangtao@pku.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85111585737
"Yang A., Chen Y., Naeem W., Fei M., Chen L.","56359008700;57226296779;55921826500;56503241800;57191047600;","Humanoid motion planning of robotic arm based on human arm action feature and reinforcement learning",2021,"Mechatronics","78",,"102630","","",,,"10.1016/j.mechatronics.2021.102630","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111052537&doi=10.1016%2fj.mechatronics.2021.102630&partnerID=40&md5=a64243090b5c44831c759ab4633c7f28","School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China; School of Electronics, Electrical Engineering, and Computer Science, Queen's University Belfast, Belfast, Northern Ireland  BT7 1NN, United Kingdom; School of Engineering and Design, Hunan Normal University, Changsha, 410081, China","Yang, A., School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China; Chen, Y., School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China; Naeem, W., School of Electronics, Electrical Engineering, and Computer Science, Queen's University Belfast, Belfast, Northern Ireland  BT7 1NN, United Kingdom; Fei, M., School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China; Chen, L., School of Engineering and Design, Hunan Normal University, Changsha, 410081, China","The use and application of robotic arms in helping the aged and vulnerable persons are increasing gradually. In order to achieve safer and reliable human-robot interaction and its wider adoption, the requirements for the humanoid motion of robotic arms are becoming more stringent. This paper presents a humanoid motion planning method for a robotic arm based on the physics of human arm and reinforcement learning. Firstly, the humanoid motion rules are extracted by analyzing and learning the action data of human arm, which is collected using the VICON optical motion capture system. Then, according to the acquired features and rules, the corresponding reward functions are proposed and the humanoid motion training of the robotic arm is carried out by using the reinforcement learning based on Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) algorithm. Finally, the experiments are carried out to verify whether the robotic arm motions planned by the proposed approach are humanoid, and the observed results show its feasibility and effectiveness in planning the humanoid motion of the robotic arm. © 2021","Human arm action feature; Humanoid motion; Reinforcement learning; Reward function","Educational robots; Human robot interaction; Learning systems; Motion planning; Reinforcement learning; Robotic arms; Robotics; Human arm; Motion planning methods; Optical motion capture; Policy gradient; Reward function; Robot programming",,,,,"JCKY2017413C002; National Science Foundation, NSF; Natural Science Foundation of Shanghai: 18ZR1415100","MINRUI FEI received the B.S. and M.S. degrees in control theory and control engineering from the Shanghai University of Technology, Shanghai, China, and the Ph.D. degree in control theory and control engineering from Shanghai University, Shanghai. He has been a Professor and a Doctoral Supervisor with Shanghai University since 1998, where he is the Director of the Shanghai Key Laboratory of Power Station Automation Technology and Vice-Chairman of the Academic Committee. His research has been funded by the National Science Foundation and several industrial organizations. His current research interests include intelligent control, complex system modeling, networked control systems, and evolutionary computation. He has published over 200 research papers and co-edited over ten conference proceedings in the above areas. He is a Vice-Chairman of Chinese Association for System Simulation, the Standing Director of China Instrument and Control Society, and the Director of Chinese Artificial Intelligence Association.","This work was supported by Natural Science Foundation of Shanghai (18ZR1415100), and Defense Industrial Technology Development Program (JCKY2017413C002).",,,,,,,,,"Toyama, H., Kawamoto, H., Sankai, Y., Development of cybernic robot arm to realize support action cooperated with hemiplegic person's arm (2019) 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1645-1650. , July; Zhou, H., Yang, G., Lv, H., Huang, X., Yang, H., Pang, Z., IoT-enabled dual-arm motion capture and mapping for telerobotics in home care (2020) IEEE Journal of Biomedical and Health Informatics, 24 (6), pp. 1541-1549. , June; Su, H., Qi, W., Yang, C., Aliverti, A., Ferrigno, G., De Momi, E., Deep Neural Network Approach in Human-Like Redundancy Optimization for Anthropomorphic Manipulators (2019) IEEE Access, 7, pp. 124207-124216. , Aug; 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Hu, H., Du, J., Hu, X., The experimental research on joint range of motion (2016) Advances in Physical Ergonomics and Human Factors, 489, pp. 265-274. , Springer Cham; Gates, D.H., Walters, L.S., Cowley, J., Range of motion requirements for upper-limb activities of daily living (2016) Am J Occup Ther, 70 (1), pp. 1-10; Zhang, R., Liu, Z., Zhou, Q., Shen, Y., Li, C., Comfortableness evaluation of range of motion of human upper limb joint (2018) China Safety Science Journal, 28 (8), pp. 75-80; Hazara, M., Kyrki, V., Transferring generalizable motor primitives from simulation to real world (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 2172-2179. , April; Andrychowicz, M., Baker, B., Chociej, M., Learning dexterous in-hand manipulation (2020) The International Journal of Robotics Research, 39 (1), pp. 3-20. , Jan","Yang, A.; School of Mechatronic Engineering and Automation, China; email: aolei@shu.edu.cn",,,"Elsevier Ltd",,,,,09574158,,MECHE,,"English","Mechatronics",Article,"Final","",Scopus,2-s2.0-85111052537
"Oishi S., Koide K., Yokozuka M., Banno A.","54793314500;57191584619;36027020900;55952407700;","4D Attention: Comprehensive Framework for Spatio-Temporal Gaze Mapping",2021,"IEEE Robotics and Automation Letters","6","4","9484834","7240","7247",,,"10.1109/LRA.2021.3097274","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110844998&doi=10.1109%2fLRA.2021.3097274&partnerID=40&md5=8bb13047997dab2b2dbe26f166297bf9","Mobile Robotics Research Team (MR2T), National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, 3058568, Japan","Oishi, S., Mobile Robotics Research Team (MR2T), National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, 3058568, Japan; Koide, K., Mobile Robotics Research Team (MR2T), National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, 3058568, Japan; Yokozuka, M., Mobile Robotics Research Team (MR2T), National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, 3058568, Japan; Banno, A., Mobile Robotics Research Team (MR2T), National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, 3058568, Japan","This study presents a framework for capturing human attention in the spatio-temporal domain using eye-tracking glasses. Attention mapping is a key technology for human perceptual activity analysis or Human-Robot Interaction (HRI) to support human visual cognition; however, measuring human attention in dynamic environments is challenging owing to the difficulty in localizing the subject and dealing with moving objects. To address this, we present a comprehensive framework, 4D Attention, for unified gaze mapping onto static and dynamic objects. Specifically, we estimate the glasses pose by leveraging a loose coupling of direct visual localization and Inertial Measurement Unit (IMU) values. Further, by installing reconstruction components into our framework, dynamic objects not captured in the 3D environment map are instantiated based on the input images. Finally, a scene rendering component synthesizes a first-person view with identification (ID) textures and performs direct 2D-3D gaze association. Quantitative evaluations showed the effectiveness of our framework. Additionally, we demonstrated the applications of 4D Attention through experiments in real situations.1 © 2016 IEEE.","intention recognition; Localization; multi-modal perception for HRI; visual tracking","Agricultural robots; Eye tracking; Glass; Mapping; Textures; 3-D environments; Activity analysis; Dynamic environments; Human robot Interaction (HRI); Inertial measurement unit; Quantitative evaluation; Spatio-temporal domains; Visual localization; Human robot interaction",,,,,"Japan Society for the Promotion of Science, KAKEN: 18K18072; New Energy and Industrial Technology Development Organization, NEDO","Manuscript received March 10, 2021; accepted July 7, 2021. Date of publication July 14, 2021; date of current version July 29, 2021. This work was supported in part by JSPS KAKENHI under Grant No.18K18072, and in part by a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO). (Corresponding author: Shuji Oishi.) The authors are with the Mobile Robotics Research Team (MR2T), National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki 3058568, Japan (e-mail: shuji.oishi@aist.go.jp; k.koide@aist.go.jp; yokotsuka-masashi@aist.go.jp; atsuhiko.banno@aist.go.jp).",,,,,,,,,,"Haji Fathaliyan, A., Wang, X., Santos, V.J., Exploiting threedimensional Gaze tracking for action recognition during bimanual manipulation to enhance human-robot collaboration (2018) Front. Robot. Ai, 5; Dini, A., Murko, C., Yahyanejad, S., Augsdörfer, U., Hofbaur, M., Paletta, L., Measurement and prediction of situation awareness in humanrobot interaction based on a framework of probabilistic attention (2017) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 4354-4361; Maekawa, Y., An analysis of how driver experience affects eye-gaze behavior for robotic wheelchair operation (2019) Proc. IEEE/CVF Int. Conf. Comput. Vis. 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Tools, 5, pp. 9-22; Manhardt, F., Wang, G., Busam, B., Nickel, M., Meier, S., Minciullo, L., Ji, X., Navab, N., (2020) Cps : Improving Class-level 6D Pose and Shape EstimationFrom Monocular Images with Self-supervised Learning; Nakazawa, A., Mitsuzumi, Y., Watanabe, Y., Kurazume, R., Yoshikawa, S., Honda, M., First-person video analysis for evaluating skill level in the Humanitude tender-care technique (2020) J. Intell. Robot. Syst., 98 (1), pp. 103-118. , Apr; Chacón-Quesada, R., Demiris, Y., Augmented reality control of smart wheelchair using Eye-Gaze-Enabled selection of affordances (2018) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. Workshop","Oishi, S.; Mobile Robotics Research Team (MR2T), Japan; email: shuji.oishi@aist.go.jp",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85110844998
"Plooij M., Apte S., Keller U., Baines P., Sterke B., Asboth L., Courtine G., von Zitzewitz J., Vallery H.","55556616400;57202579289;55578353800;57290143200;57207943251;54584914000;6602616445;21935318200;24172820000;","Neglected physical human-robot interaction may explain variable outcomes in gait neurorehabilitation research",2021,"Science Robotics","6","58","eabf1888","","",,,"10.1126/scirobotics.abf1888","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116740088&doi=10.1126%2fscirobotics.abf1888&partnerID=40&md5=dd4ec3f5be90fe3977740dee1dfec265","Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, Delft, 2628 CD, Netherlands; Demcon Advanced Mechatronics, Delfttechpark 23, Delft, Netherlands; Motek, a DIH brand, Hogehilweg 18-C, Amsterdam, 1101 CD, Netherlands; Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; ONWARD, EPFL Innovation Park, Lausanne, Switzerland; Center for Neuroprosthetics (CNP) Valais, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Swiss Children’s Rehab, University Children’s Hospital Zurich, Affoltern am Albis, Switzerland; Department of Rehabilitation Medicine, Erasmus MC, Postbus 2040, Rotterdam, 3000 CA, Netherlands; Center for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Clinical Neuroscience, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland; Defitech Center for Interventional Neurotherapies (NeuroRestore), EPFL/CHUV/UNIL, Lausanne, Switzerland","Plooij, M., Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, Delft, 2628 CD, Netherlands, Demcon Advanced Mechatronics, Delfttechpark 23, Delft, Netherlands, Motek, a DIH brand, Hogehilweg 18-C, Amsterdam, 1101 CD, Netherlands; Apte, S., Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, Delft, 2628 CD, Netherlands, Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Keller, U., ONWARD, EPFL Innovation Park, Lausanne, Switzerland, Center for Neuroprosthetics (CNP) Valais, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, Swiss Children’s Rehab, University Children’s Hospital Zurich, Affoltern am Albis, Switzerland; Baines, P., Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, Delft, 2628 CD, Netherlands; Sterke, B., Motek, a DIH brand, Hogehilweg 18-C, Amsterdam, 1101 CD, Netherlands, Department of Rehabilitation Medicine, Erasmus MC, Postbus 2040, Rotterdam, 3000 CA, Netherlands; Asboth, L., Center for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, Department of Clinical Neuroscience, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland, Defitech Center for Interventional Neurotherapies (NeuroRestore), EPFL/CHUV/UNIL, Lausanne, Switzerland; Courtine, G., ONWARD, EPFL Innovation Park, Lausanne, Switzerland, Center for Neuroprosthetics and Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, Department of Clinical Neuroscience, Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Lausanne, Switzerland, Defitech Center for Interventional Neurotherapies (NeuroRestore), EPFL/CHUV/UNIL, Lausanne, Switzerland; von Zitzewitz, J., ONWARD, EPFL Innovation Park, Lausanne, Switzerland, Center for Neuroprosthetics (CNP) Valais, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Vallery, H., Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, Delft, 2628 CD, Netherlands, Department of Rehabilitation Medicine, Erasmus MC, Postbus 2040, Rotterdam, 3000 CA, Netherlands","During gait neurorehabilitation, many factors influence the quality of gait patterns, particularly the chosen body-weight support (BWS) device. Consequently, robotic BWS devices play a key role in gait rehabilitation of people with neurological disorders. The device transparency, support force vector direction, and attachment to the harness vary widely across existing robotic BWS devices, but the influence of these factors on the production of gait remains unknown. Because this information is key to designing an optimal BWS, we systematically studied these determinants in this work. We report that with a highly transparent device and a conventional harness, healthy participants select a small backward force when asked for optimal BWS conditions. This unexpected finding challenges the view that during human-robot interactions, humans predominantly optimize energy efficiency. Instead, they might seek to increase their feeling of stability and safety. We also demonstrate that the location of the attachment points on the harness strongly affects gait patterns, yet harness attachment is hardly reported in literature. Our results establish principles for the design of BWS devices and personalization of BWS settings for gait neurorehabilitation. Copyright © 2021 The Authors, some rights reserved;",,"Energy efficiency; Human robot interaction; Man machine systems; Neuromuscular rehabilitation; Robotics; Body weight supports; Force vectors; Gait pattern; Gait rehabilitation; Neurological disorders; Neurorehabilitation []; Physical humanrobot interaction (phri); Quality of gait; Support devices; Support forces; Gait analysis",,,,,"nccr – on the move; Eurostars: 10152, PCIG13-GA-2013-618899; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF","Funding: The work in this paper was partially funded by Eurostars project 10152, by a Marie-Curie career integration grant PCIG13-GA-2013-618899, by Wings for Life accelerated translation program, and by the National Center of Competence in Research (NCCR) Robotics of the Swiss National Foundation.",,,,,,,,,,"Pennycott, A., Wyss, D., Vallery, H., Klamroth-Marganska, V., Riener, R., Towards more effective robotic gait training for stroke rehabilitation: A review (2012) J. 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K., Moe-Nilssen, R., Treadmill walking with body weight support: Effect of treadmill, harness and body weight support systems (2008) Gait Posture, 28, pp. 303-308; Tran, V.-T., Sasaki, K., Yamamoto, S.-I., Influence of body weight support systems on the abnormal gait kinematic (2020) Appl. Sci, 10, p. 4685; Dickstein, R., Rehabilitation of gait speed after stroke: A critical review of intervention approaches (2008) Neurorehabil. Neural Repair, 22, pp. 649-660; Takahashi, T., Ishida, K., Hirose, D., Nagano, Y., Okumiya, K., Nishinaga, M., Doi, Y., Yamamoto, H., Vertical ground reaction force shape is associated with gait parameters, timed up and go, and functional reach in elderly females (2004) J. Rehabil. Med, 36, pp. 42-45; Greenwood, D. T., (2006) Advanced Dynamics, , (Cambridge Univ. Press)","Vallery, H.; Department of Biomechanical Engineering, Mekelweg 2, Netherlands; email: h.vallery@tudelft.nl",,,"American Association for the Advancement of Science",,,,,24709476,,,"34550719","English","Sci. Robotics",Article,"Final","",Scopus,2-s2.0-85116740088
"Chen C., Zou J.","57218489025;57188818112;","Adaptive robust control of soft bending actuators: an empirical nonlinear model-based approach [软弯曲执行器的自适应鲁棒控制:一种基于经验非线性模型的方法]",2021,"Journal of Zhejiang University: Science A","22","9",,"681","694",,,"10.1631/jzus.A2100076","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115704438&doi=10.1631%2fjzus.A2100076&partnerID=40&md5=952d8029ed788faefaae9c03d3242eef","State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China","Chen, C., State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China; Zou, J., State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China","Soft robotics, compared with their rigid counterparts, are able to adapt to uncharted environments, are superior in safe human-robot interactions, and have low cost, owing to the native compliance of the soft materials. However, customized complex structures, as well as the nonlinear and viscoelastic soft materials, pose a great challenge to accurate modeling and control of soft robotics, and impose restrictions on further applications. In this study, a unified modeling strategy is proposed to establish a complete dynamic model of the most widely used pneumatic soft bending actuator. First, a novel empirical nonlinear model with parametric and nonlinear uncertainties is identified to describe the nonlinear behaviors of pneumatic soft bending actuators. Second, an inner pressure dynamic model of a pneumatic soft bending actuator is established by introducing a modified valve flow rate model of the unbalanced pneumatic proportional valves. Third, an adaptive robust controller is designed using a backstepping method to handle and update the nonlinear and uncertain system. Finally, the experimental results of comparative trajectory tracking control indicate the validity of the proposed modeling and control method. © 2021, Zhejiang University Press.","Adaptive robust control; Empirical nonlinear model identification; Pneumatic soft bending actuator; TP242.3; Trajectory tracking; Unbalanced pneumatic proportional valve modeling","Backstepping; Dynamic models; Human robot interaction; Pneumatic actuators; Pneumatic equipment; Pneumatics; Robotics; Robust control; Adaptive robust control; Bending actuators; Empirical nonlinear model identification; Nonlinear model identification; Pneumatic soft bending actuator; Proportional valves; Soft bending; Tp242.3; Trajectory-tracking; Unbalanced pneumatic proportional valve modeling; Valve models; Adaptive control systems",,,,,"National Natural Science Foundation of China, NSFC: 51821093, 51875507, U1908228","Project supported by the National Natural Science Foundation of China (Nos. 51875507, 51821093, and U1908228)",,,,,,,,,,"Abbasi, P., Nekoui, M.A., Zareinejad, M., Position and force control of a soft pneumatic actuator (2020) Soft Robotics, 7 (5), pp. 550-563; 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An experimental study (2020) IEEE/CAA Journal of Automatica Sinica, 7 (2), pp. 451-460; Kim, S., Laschi, C., Trimmer, B., Soft robotics: a bioinspired evolution in robotics (2013) Trends in Biotechnology, 31 (5), pp. 287-294; Kwon, J., Yoon, S.J., Park, Y.L., Flat inflatable artificial muscles with large stroke and adjustable force-length relations (2020) IEEE Transactions on Robotics, 36 (3), pp. 743-756; Laschi, C., Mazzolai, B., Cianchetti, M., Soft robotics: technologies and systems pushing the boundaries of robot abilities (2016) Science Robotics, 1 (1), p. eaah3690; Li, M.H., Kang, R.J., Branson, D.T., Model-free control for continuum robots based on an adaptive Kalman filter (2018) IEEE/ASME Transactions on Mechatronics, 23 (1), pp. 286-297; Liu, S., Yao, B., Coordinate control of energy saving programmable valves (2008) IEEE Transactions on Control Systems Technology, 16 (1), pp. 34-45; Luo, C., Wang, K., Li, G.Y., Development of active soft robotic manipulators for stable grasping under slippery conditions (2019) IEEE Access, 7, pp. 97604-97613; Marchese, A.D., Tedrake, R., Rus, D., Dynamics and trajectory optimization for a soft spatial fluidic elastomer manipulator (2016) The International Journal of Robotics Research, 35 (8), pp. 1000-1019; Mohanty, A., Yao, B., Indirect adaptive robust control of hydraulic manipulators with accurate parameter estimates (2011) IEEE Transactions on Control Systems Technology, 19 (3), pp. 567-575; Müller, D., Raisch, A., Hildebrandt, A., Nonlinear model based dynamic control of pneumatic driven quasi continuum manipulators (2020) Proceedings of the IEEE/SICE International Symposium on System Integration, pp. 277-282. , https://doi.org/10.1109/SII46433.2020.9026206; Pang, W., Wang, J.B., Fei, Y.Q., The structure, design, and closed-loop motion control of a differential drive soft robot (2018) Soft Robotics, 5 (1), pp. 71-80; Polygerinos, P., Wang, Z., Overvelde, J.T.B., Modeling of soft fiber-reinforced bending actuators (2015) IEEE Transactions on Robotics, 31 (3), pp. 778-789; Polygerinos, P., Correll, N., Morin, S.A., Soft robotics: review of fluid-driven intrinsically soft devices; manufacturing, sensing, control, and applications in human-robot interaction (2017) Advanced Engineering Materials, 19 (12), p. 1700016; Skorina, E.H., Luo, M., Ozel, S., Feedforward augmented sliding mode motion control of antagonistic soft pneumatic actuators (2015) Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2544-2549. , https://doi.org/10.1109/ICRA.2015.7139540; Tang, Z.Q., Heung, H.L., Tong, K.Y., A probabilistic model-based online learning optimal control algorithm for soft pneumatic actuators (2020) IEEE Robotics and Automation Letters, 5 (2), pp. 1437-1444; Thuruthel, T.G., Ansari, Y., Falotico, E., Control strategies for soft robotic manipulators: a survey (2018) Soft Robotics, 5 (2), pp. 149-163; Wang, T., Zhang, Y.C., Chen, Z., Design and verification of model-based nonlinear controller for fluidic soft actuators (2018) Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 1178-1183. , https://doi.org/10.1109/AIM.2018.8452466; Xiang, Z., (2010) Research on the Key Technologies of Pneumatic High-speed on-off Valve, , PhD Thesis, Zhejiang University, Hangzhou, China: (in Chinese; Yang, Y., Li, Y., Chen, Y., Principles and methods for stiffness modulation in soft robot design and development (2018) Bio-Design and Manufacturing, 1 (1), pp. 14-25; Yao, B., High performance adaptive robust control of nonlinear systems: A general framework and new schemes (1997) Proceedings of the 36Th IEEE Conference on Decision and Control, pp. 2489-2494. , https://doi.org/10.1109/CDC.1997.657530; Yao, B., Bu, F.P., Reedy, J., Adaptive robust motion control of single-rod hydraulic actuators: theory and experiments (2000) IEEE/ASME Transactions on Mechatronics, 5 (1), pp. 79-91; Zhang, C., Zhu, P., Lin, Y., Fluid-driven artificial muscles: bio-design, manufacturing, sensing, control, and applications (2021) Bio-Design and Manufacturing, 4 (1), pp. 123-145; Zhang, J., Sheng, J., O’Neill, C.T., Robotic artificial muscles: current progress and future perspectives (2019) IEEE Transactions on Robotics, 35 (3), pp. 761-781; Zhou, J.S., Chen, X.J., Chang, Y., A soft-robotic approach to anthropomorphic robotic hand dexterity (2019) IEEE Access, 7, pp. 101483-101495","Zou, J.; State Key Laboratory of Fluid Power and Mechatronic Systems, China; email: junzou@zju.edu.cn",,,"Zhejiang University",,,,,1673565X,,,,"English","J. Zhejiang Univ. Sci. A",Article,"Final","",Scopus,2-s2.0-85115704438
"LePage L.","57192906308;","The Importance of Realism, Character, and Genre: How Theatre Can Support the Creation of Likeable Sociable Robots",2021,"International Journal of Social Robotics","13","6",,"1427","1441",,,"10.1007/s12369-020-00637-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097486646&doi=10.1007%2fs12369-020-00637-w&partnerID=40&md5=75a6101ecd8f5d23595a982e13d09d60","Department of Theatre, Film, Television and Interactive Media, University of York, Baird Lane, Heslington, York, YO10 5GB, United Kingdom","LePage, L., Department of Theatre, Film, Television and Interactive Media, University of York, Baird Lane, Heslington, York, YO10 5GB, United Kingdom","Stage plays, theories of theatre, narrative studies, and robotics research can serve to identify, explore, and interrogate theatrical elements that support the effective performance of sociable humanoid robots. Theatre, including its parts of performance, aesthetics, character, and genre, can also reveal features of human–robot interaction key to creating humanoid robots that are likeable rather than uncanny. In particular, this can be achieved by relating Mori's (1970/2012) concept of total appearance to realism. Realism is broader and more subtle in its workings than is generally recognised in its operationalization in studies that focus solely on appearance. For example, it is complicated by genre. A realistic character cast in a detective drama will convey different qualities and expectations than the same character in a dystopian drama or romantic comedy. The implications of realism and genre carry over into real life. As stage performances and robotics studies reveal, likeability depends on creating aesthetically coherent representations of character, where all the parts coalesce to produce a socially identifiable figure demonstrating predictable behaviour. © 2020, The Author(s).","Character; Genre; Realism; Sociable robots; Theatre; Uncanny valley","Agricultural robots; Anthropomorphic robots; Robotics; Social robots; Coherent representations; Effective performance; Humanoid robot; Likeability; Robot interactions; Robotics research; Sociable robots; Stage performance; Theaters",,,,,,,,,,,,,,,,"Birnbaum, G.E., Mizrahi, M., Hoffman, G., Reis, H.T., Finkel, E.J., Sass, O., What robots can teach us about intimacy: the reassuring effects of robot responsiveness to human disclosure (2016) Comput Hum Behav, 63, pp. 416-423; Breazeal, C., Toward sociable robots (2003) Robot Autono Syst, 42, pp. 167-175; Bretan, B., Hoffman, G., Weinberg, G., Emotionally expressive dynamic physical behaviors in robots (2015) Int J Hum Comput Stud, 78, pp. 1-16; Čapek, K., (1999), [1920/21]) R.U.R, Rossum’s universal robots) Majer P and Porter C (trans) Methuen Drama, London; Chemers, M., Phronesis for robots: (re)covering dramaturgy as an interdiscipline (2014) The Routledge companion to dramaturgy, pp. 364-369. , Romanska M, (ed), Routledge, London; Denisart, C., Robots. Dir. by Denisart C in a production by Les Voyages Extraordinaires (2009) Film Recording of Performance, , https://www.youtube.com/watch?v=bmfNbSYBjMA&feature=youtu.be&list=UUMbzJ3oSdOiivKt-08M28dQ; Hanson, D., Olney, A., Pereira, I.A., Zileka, M., Upending the Uncanny Valley (2005) The Twentieth National Conference on Artificial Intelligence and the Seventeenth Innovative Applications of AI Conference, July, pp. 9-13. , Pittsburgh, Pennsylvania, USA; Gray, K., Wegner, D.M., Feeling robots and human zombies: mind perception and the uncanny valley (2012) Cognition, 125, pp. 125-130; Harari, Y.N., (2011) Sapiens: a brief history of humankind, pp. 114-128. , vintage Books, London; Hirata, O., Q&A with Hirata O, Ishiguro H, and Long B (DVD) Recorded in Feb 2013 while in New York touring I, Worker and Sayonara, both written and directed by Hirata O (2013) Performed by Seinendan Theater Company + Osaka University Robot Theater Project; Hoffman, G., (2013), Robots with soul. Ted.com. TED; Hoffman, G., Acting lessons for artificial intelligence (2006) 50Th Anniversary Summit of Artificial Intelligence; Ishiguro, H., Q&A with Hirata O (2013) Ishiguro H, and Long B (DVD) February, p. 2013; Jochum, E.A., Murphey, T., Programming play: puppets, robots, and engineering (2014) The routledge companion to puppetry and material performance, pp. 308-321. , Posner DN, Orenstein C, Bell J, (eds), Routledge, London and New York; Jochum, E., Vlachos, E., Christoffersen, A., Grindsted Nielsen, S., Hameed, I.A., Tan, Z., Using theatre to study interaction with care robots (2016) Int J Soc Robot, 8, p. 467; Kawabe, T., Sasaki, K., Ihaya, K., Yamada, Y., When categorization-based stranger avoidance explains the uncanny valley: a comment on MacDorman and Chattopadhyay (2016) (2017) Cognition, 161, pp. 129-135; Knight, H., (2011) Eight lessons learned about non-verbal interactions through robot theater. Social robotics, pp. 42-51. , Springer, Berlin; Knight, H., Gray, M., Acting lesson with robot: Emotional gestures (2012) Video for the International Conference on human–robot Interaction, March, in Boston, MA, p. 407; Knight, H., Simmons, R., Laban head-motions convey robot state: A call for robot body language (2016) International Conference on Robotics and Automation; Kriz, S., Ferro, T.D., Damera, P., Fictional robots as a data source in HRI research: Exploring the link between science fiction and interactional expectations (2010) 19Th IEEE International Symposium on Robot and Human Interactive Communication, pp. 458-463; LePage, L., ‘Thinking something makes it so’: Performing robots, the workings of mimesis and the importance of character (2016) Twenty-first century drama: what happens now, pp. 279-302. , Adiseshiah S, LePage L, (eds), Palgrave Macmillan, Basingstoke; Lepage, L., (2016) Performance Lecture: Inside Robot Theatre: What Happens When Robots (And Children) Take to the Stage? 20 April, p. 2016. , https://www.robottheatre.co.uk/the-performance-lecture, Bulmershe Theatre, Minghella Studios, University of Reading; Lepage, L., Interacting nao (2018) Video, , https://www.youtube.com/watch?v=xN2eJCL4N64; Lin, T.-D., Theater as a site for technology demonstration and knowledge production: theatrical robots in Japan and Taiwan (2015) East Asian Sci Technol Soc Int J, 9 (2), pp. 187-211; Livermore, M., About a Bot. 22/11 productions (personal film recording of the performance, About a Bot (2018) Written and Produced by Livermore M); Lu, D.V., Smart, W.D., Human–robot interactions as theatre (2011) 20Th IEEE International Symposium on Robot and Human Interactive Communication, July 31—August 3, Atlanta, GA, pp. 473-478. , USA; Mangan, B., The uncanny valley as fringe experience (2015) Interact Stud, 16 (2), pp. 193-199; Mori, M., (2012) [1970]) the Uncanny Valley. Macdorman KF & Kageki N (Trans), pp. 98-100. , https://spectrum.ieee.org/automaton/robotics/humanoids/the-uncanny-valley, In, IEEE robotics & automation magazine (online); Popat, S., Moving, withdrawing and the uncanny (2015) Digital movement: essays in motion technology and performance, Palgrave studies in performance technology, pp. 131-147. , Salazar NS, Popat S, (eds), Palgrave, Basingstoke; Poulton, C., From puppet to robot: technology and the human in Japanese theatre (2014) The Routledge companion to puppetry and material performance, pp. 280-293. , Posner DN, Orenstein C, Bell J, (eds), Routledge, London and New York; Tokyo: Seinendan Theater Company + Osaka University Robot Theater Project (2010) Written and Directed by Hirata O (DVD); Schwind, V., Leicht, K., Jäger, S., Wolf, K., Henze, N., Is there an uncanny valley of virtual animals? A quantitative and qualitative investigation (2018) Int J Hum Comput Stud, 111, pp. 49-61; Soloski, A., Performance review: Seinendan Theater Company + Osaka University Robot theater project. Written and directed by Hirata O. Japan Society, New York City, 7 February 2012 (2012) Theatre J, 65 (3), pp. 400-402; (2015) Pipeline Theatre Company, , A Love Story, (personal film recording of the performance, Spillikin, written and directed by Welch J; States, B.O., (1992) Great reckonings in little rooms: on the phenomenology of theater, , University of California Press, California; Talenti, F., (2013) First Performed at Swain Hall, , Written and dir. by Talenti F, starring a RoboThespian™ (by engineered arts), UNC, then at Brick Theater, New York; Tokyo: Seinendan Theater Company + Osaka University Robot Theater Project (2012) Written and Directed by Hirata O (DVD); (2019) Rimini Protokoll and Thomas Melle, , https://www.rimini-protokoll.de/website/en/videos, (available to view at; Urgen, B.A., Kutas, M., Faygin, A.P., Uncanny valley as a window into predictive processing in the social brain (2018) Neuropsychologia, 114, pp. 181-185; Wang, S., Lilienfeld, S.O., Rochat, P., The Uncanny Valley: existence and explanations (2015) Rev General Psychol, 19 (4), pp. 393-407; Yamada, Y., Kawabe, T., Ihaya, K., Categorization difficulty is associated with negative evaluation in the “uncanny valley” fphenomenon (2013) Jpn Psychol Res, 55 (1), pp. 20-32","LePage, L.; Department of Theatre, Baird Lane, Heslington, United Kingdom; email: louise.lepage@york.ac.uk",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85097486646
"Oliveira R., Arriaga P., Axelsson M., Paiva A.","57201501871;12793958200;57201945537;7102683774;","Humor–Robot Interaction: A Scoping Review of the Literature and Future Directions",2021,"International Journal of Social Robotics","13","6",,"1369","1383",,1,"10.1007/s12369-020-00727-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096385671&doi=10.1007%2fs12369-020-00727-9&partnerID=40&md5=cd3a65a30badb3bc10e3a0907906d9e0","ISCTE-Instituto Universitário de Lisboa (ISCTE-IUL), CIS-IUL and INESC-ID, Lisbon, Portugal; ISCTE-Instituto Universitário de Lisboa (ISCTE-IUL) and CIS-IUL, Lisbon, Portugal; INESC-ID (GAIPS), Lisbon, Portugal; Instituto Superior Técnico-Universidade de Lisboa and INESC-ID, Lisbon, Portugal","Oliveira, R., ISCTE-Instituto Universitário de Lisboa (ISCTE-IUL), CIS-IUL and INESC-ID, Lisbon, Portugal; Arriaga, P., ISCTE-Instituto Universitário de Lisboa (ISCTE-IUL) and CIS-IUL, Lisbon, Portugal; Axelsson, M., INESC-ID (GAIPS), Lisbon, Portugal; Paiva, A., Instituto Superior Técnico-Universidade de Lisboa and INESC-ID, Lisbon, Portugal","Humor is a pervasive feature of everyday social interactions that might be leveraged to improve human–robot interactions (HRI). Our goal is to evaluate how the use of humor can improve HRI and enhance the user’s perception of the robot, as well as to derive implications for future research and development of humorous robots. We conducted a systematic search of 7 digital libraries relevant in the areas of HRI and Psychology for papers that were relevant to our goal. We identified 431 records, published between 2000 and August of 2020, of which 12 matched our eligibility criteria. The included studies reported the results of original empirical research that involved direct or video-mediated interaction of humans and robots. Humor seems to have a positive effect in improving the user’s perception of the robot, as well as the user’s evaluation of the interaction. However, the included studies present a number of limitations in their approaches to robotic humor that need to be surpassed before reaching a final verdict on the value of humor in HRI. © 2020, Springer Nature B.V.","Funny; Human–robot interaction; Humor; Interaction design","Agricultural robots; Digital libraries; Petroleum reservoir evaluation; Eligibility criterion; Empirical research; Implications for futures; Mediated interaction; Research and development; Robot interactions; Social interactions; Systematic searches; Social robots",,,,,"Fundação para a Ciência e a Tecnologia, FCT: FCTUID/CEC/500 21/2013, PD/BD/150570/2020, PTDC/EEISII/7174/2014","This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) (FCTUID/CEC/500 21/2013), through the project AMIGOS (PTDC/EEISII/7174/2014). RO also acknowledges a Ph.D. Grant from FCT with the following reference: PD/BD/150570/2020. The authors are solely responsible for the content of this publication. 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Rob.",Review,"Final","",Scopus,2-s2.0-85096385671
"Niiyama R., Seong Y.A., Kawahara Y., Kuniyoshi Y.","57218118140;26658670700;7202786793;7005371269;","Blower-Powered Soft Inflatable Joints for Physical Human-Robot Interaction",2021,"Frontiers in Robotics and AI","8",,"720683","","",,,"10.3389/frobt.2021.720683","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114429831&doi=10.3389%2ffrobt.2021.720683&partnerID=40&md5=ac8aa3fb011b14591160562affafce9e","Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan; Graduate School of Engineering, The University of Tokyo, Tokyo, Japan","Niiyama, R., Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan; Seong, Y.A., Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan; Kawahara, Y., Graduate School of Engineering, The University of Tokyo, Tokyo, Japan; Kuniyoshi, Y., Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan","Inflatables are safe and lightweight structures even at the human scale. Inflatable robots are expected to be applied to physical human-robot interaction (pHRI). Although active joint mechanisms are essential for developing inflatable robots, the existing mechanisms are complex in structure and it is difficult to integrate actuators, which diminish the advantages of inflatables. This study proposes blower-powered soft inflatable joints that are easy to fabricate and contain enough space for an actuation inside. The joints are driven by tendon wires pulled by linear actuators. We derived a theoretical model for both unilateral and bilateral joints and demonstrated a hugging robot with multiple joints as an application of the proposed joint mechanism. The novelty of the proposed joint mechanism and the inflatable robot is that rigid parts have been thoroughly eliminated and the tendons for actuation have been successfully hidden inside. Moreover, the active control of the internal pressure makes inflatables resistant to punctures. We expect that the contact safety of inflatable robots will facilitate advancement of the pHRI field. © Copyright © 2021 Niiyama, Seong, Kawahara and Kuniyoshi.","inflatable robot; physical human-robot interaction; soft mechanism; soft robotics; tendon wire",,,,,,"Japan Science and Technology Agency, JST: JPMJMS 2013; Exploratory Research for Advanced Technology, ERATO: JPMJER1501","This work was supported by JST ERATO Grant Number JPMJER1501 and JST Moonshot R&D Grant Number JPMJMS 2013. The ERATO supported development of inflatable robots. The Moonshot R&D supported experiments in human-robot interaction.","We would like to thank Babot Inc. (Tokyo, Japan) for the advice and assistance with the manufacturing of the inflatables.",,,,,,,,,"Alspach, A., Kim, J., Yamane, K., Design and Fabrication of a Soft Robotic Hand and Arm System (2018) IEEE International Conference on Soft Robotics (RoboSoft), pp. 369-375. , Livorno, Italy, April 25–27, 2018; Annan, M.M., Mark, R.C., Martin, B., (2016) Inflatable Robots, Robotic Components and Assemblies and Methods Including Same, , US Patent, US20140109560A1, Alexandria, VA, United States, United States Patent and Trademark Office (USPTO; Best, C.M., Wilson, J.P., Killpack, M.D., Control of a Pneumatically Actuated, Fully Inflatable, Fabric-Based, Humanoid Robot (2015) IEEE-RAS International Conference on Humanoid Robots, pp. 1133-1140. , Seoul, South Korea, November 3–5, 2015; Garzotto, F., Gelsomini, M., Kinoe, Y., Puffy: A mobile Inflatable Interactive Companion for Children with Neurodevelopmental Disorder (2017) IFIP Conference on Human-Computer Interaction, pp. 467-492. , Bombay, India, September 25–29, 2017, INTERACT; Gazit, D., Dranger, A.L., (2001) Apparatus and Method for Providing Inflated Undulating Figures, , US Patent, US6186857B1, Alexandria, VA, United States, United States Patent and Trademark Office (USPTO; Hofer, M., D’Andrea, R., Design, Fabrication, Modeling and Control of a Fabric-Based Spherical Robotic Arm (2020) Mechatronics, 68, p. 102369; Kim, H.-J., Kawamura, A., Nishioka, Y., Kawamura, S., Mechanical Design and Control of Inflatable Robotic Arms for High Positioning Accuracy (2018) Adv. Robotics, 32, pp. 89-104. , a; Kim, T., Yoon, S.J., Park, Y.-L., Soft Inflatable Sensing Modules for Safe and Interactive Robots (2018) IEEE Robot. Autom. Lett, 3, pp. 3216-3223. , b; Koren, Y., Weinstein, Y., (1991) Inflatable Structure, , US Patent, US5065640A, Alexandria, VA, United States, United States Patent and Trademark Office (USPTO; Liang, X., Cheong, H., Sun, Y., Guo, J., Chui, C.K., Yeow, C.-H., Design, Characterization, and Implementation of a Two-Dof Fabric-Based Soft Robotic Arm (2018) IEEE Robot. Autom. Lett, 3, pp. 2702-2709; McMahan, W., Jones, B.A., Walker, I.D., Design and Implementation of a Multi-Section Continuum Robot: Air-Octor (2005) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3345-3352. , Edmonton, Canada, August 2–6, 2005, IROS; Ohta, P., Valle, L., King, J., Low, K., Yi, J., Atkeson, C.G., Design of a Lightweight Soft Robotic Arm Using Pneumatic Artificial Muscles and Inflatable Sleeves (2017) Soft Robot, 5, pp. 204-215; Sanan, S., Lynn, P.S., Griffith, S.T., Pneumatic Torsional Actuators for Inflatable Robots (2014) J. Mech. Robotics, 6, p. 031003; Sanan, S., Ornstein, M.H., Atkeson, C.G., Physical Human Interaction for an Inflatable Manipulator (2011) International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 7401-7404. , Boston, MA, United States, August 30–September 4, 2011, EMBC; Seong, Y.A., Niiyama, R., Kawahara, Y., Kuniyoshi, Y., Low-pressure Soft Inflatable Joint Driven by Inner Tendon (2019) EEE International Conference on Soft Robotics, pp. 37-42. , Seoul, South Korea, April 14–18, 2019, RoboSoft; Takahashi, S., (1987) Pneumatic Structure, , US Patent, US4765079, Alexandria, VA, United States, United States Patent and Trademark Office (USPTO; Takeichi, M., Suzumori, K., Endo, G., Nabae, H., Development of a 20-M-Long Giacometti Arm with Balloon Body Based on Kinematic Model with Air Resistance (2017) IEEE International Conference on Intelligent Robots and Systems, pp. 2710-2716. , Vancouver, Canada, September 24–28, 2017, IROS; Voisembert, S., Mechbal, N., Riwan, A., Aoussat, A., Design of a Novel Long-Range Inflatable Robotic Arm: Manufacturing and Numerical Evaluation of the Joints and Actuation (2013) J. Mech. Robotics, 5, p. 045001","Niiyama, R.; Graduate School of Information Science and Technology, Japan; email: niiyama@isi.imi.i.u-tokyo.ac.jp",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85114429831
"Douibi K., Le Bars S., Lemontey A., Nag L., Balp R., Breda G.","57195921260;57088790100;57246197900;57189635497;57204357960;57224086534;","Toward EEG-Based BCI Applications for Industry 4.0: Challenges and Possible Applications",2021,"Frontiers in Human Neuroscience","15",,"705064","","",,2,"10.3389/fnhum.2021.705064","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114266592&doi=10.3389%2ffnhum.2021.705064&partnerID=40&md5=953a9d5d990b005ce1d95af75375497a","Capgemini Engineering, Paris, France; Ecole Strate Design, Sèvres, France","Douibi, K., Capgemini Engineering, Paris, France; Le Bars, S., Capgemini Engineering, Paris, France; Lemontey, A., Capgemini Engineering, Paris, France, Ecole Strate Design, Sèvres, France; Nag, L., Capgemini Engineering, Paris, France; Balp, R., Capgemini Engineering, Paris, France; Breda, G., Capgemini Engineering, Paris, France","In the last few decades, Brain-Computer Interface (BCI) research has focused predominantly on clinical applications, notably to enable severely disabled people to interact with the environment. However, recent studies rely mostly on the use of non-invasive electroencephalographic (EEG) devices, suggesting that BCI might be ready to be used outside laboratories. In particular, Industry 4.0 is a rapidly evolving sector that aims to restructure traditional methods by deploying digital tools and cyber-physical systems. BCI-based solutions are attracting increasing attention in this field to support industrial performance by optimizing the cognitive load of industrial operators, facilitating human-robot interactions, and make operations in critical conditions more secure. Although these advancements seem promising, numerous aspects must be considered before developing any operational solutions. Indeed, the development of novel applications outside optimal laboratory conditions raises many challenges. In the current study, we carried out a detailed literature review to investigate the main challenges and present criteria relevant to the future deployment of BCI applications for Industry 4.0. © Copyright © 2021 Douibi, Le Bars, Lemontey, Nag, Balp and Breda.","BCI applications; BCI challenges; EEG; Industry 4.0; technology transfer","augmented reality; behavior; cognitive load; electroencephalogram; ergonomics; event related potential; evoked response; fatigue; feature classification; feature extraction; functional magnetic resonance imaging; functional near-infrared spectroscopy; human; human robot interaction; industrial operator; industry; industry 4.0; information processing; information transfer rate; machine learning; measurement accuracy; nervous system parameters; neurofeedback; nonmedical occupations; occupational safety; process optimization; reliability; Review; steady state evoked potential; technology; virtual reality",,,,,,,,,,,,,,,,"Abdulkader, S.N., Atia, A., Mostafa, M.S.M., Brain computer interfacing: applications and challenges (2015) Egypt. 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Breda, G.; Capgemini EngineeringFrance; email: gabriele.breda@altran.com",,,"Frontiers Media S.A.",,,,,16625161,,,,"English","Front. Human Neurosci.",Review,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85114266592
"Khiabani H., Ahmadi M.","57212452322;7103163180;","A classical machine learning approach for Emg-based lower limb intention detection for human-robot interaction systems",2021,"ICAS 2021 - 2021 IEEE International Conference on Autonomous Systems, Proceedings",,,,"","",,,"10.1109/ICAS49788.2021.9551190","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117447938&doi=10.1109%2fICAS49788.2021.9551190&partnerID=40&md5=e4b6cc677f1cdf4581c031ef9c7736c3","Carleton University, Department of Mechanical and Aerospace Engineering, Ottawa, ON, Canada","Khiabani, H., Carleton University, Department of Mechanical and Aerospace Engineering, Ottawa, ON, Canada; Ahmadi, M., Carleton University, Department of Mechanical and Aerospace Engineering, Ottawa, ON, Canada","Surface Electromyography (sEMG)-based intention-detection systems of lower limb can intelligently augment human- robot interaction (HRI) systems to detect subject's walking direction prior-to or during walking. Ten Subject-Exclusive (Subj-Ex) and Generalized (Gen) Classical Machine Learning (C-ML)-based models are employed to detect direction intentions and evaluate inter-subject robustness in one knee/foot- gesture and three walking-related scenarios. In each, sEMG signals are collected from eight muscles of nine subjects during at least nine distinct gestures/activities. Linear Discriminant Analysis (LDA) and Random Forest (RF) classifiers, applied to the Time-Domain (TD) feature set (of the four input sets), provided the best accuracy. Subj-Ex approach achieves the highest prediction accuracy, facing occasional competition from the Gen approach. In knee/foot gesture scenario, LDA reaches an accuracy of 91.67%, signifying its applicability to robotic-assisted walking, prosthetics, and orthotics. The overall prediction accuracy among walking- related scenarios, though not as remarkably high as in the knee/foot gesture recognition scenario, can reach up to 75%. © 2021 IEEE.","Assisted Walking; Classical Machine Learning; Direction Detection; Intelligent HRI; Intention Detection; Robotic; Surface EMG","Decision trees; Discriminant analysis; Intelligent robots; Machine learning; Man machine systems; Robotics; Time domain analysis; Walking aids; Assisted walking; Classical machine learning; Direction detections; Humans-robot interactions; Intelligent human- robot interaction; Intention detection; Lower limb; Machine-learning; Robotic; Surface EMG; Human robot interaction",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC: 497303","This research was supported by the Discovery Grant, from Natural Sciences and Engineering Council Canada (NSERC) held by Mojtaba Ahmadi. Support for Hasti Khiabani was provided by the NSERC CREATE Grant 497303.",,,,,,,,,,"Meyer-Heim, A., Van Hedel, H.J.A., Robot-assisted and computer-enhanced therapies for children with cerebral palsy: Current state and clinical implementation (2013) Seminars in Pediatric Neurology, 20, pp. 139-145. , Elsevier; Bi, L., Guan, C., A review on emgbased motor intention prediction of continuous human upper limb motion for human-robot collaboration (2019) Biomedical Signal Processing and Control, 51, pp. 113-127; Hudgins, B., Parker, P., Scott, R.N., A new strategy for multifunction myoelectric control (1993) IEEE Transactions on Biomedical Engineering, 40 (1), pp. 82-94; Englehart, K., Hudgins, B., Parker, P.A., Stevenson, M., Classification of the myoelectric signal using time-frequency based representations (1999) Medical Engineering & Physics, 21 (6-7), pp. 431-438; Englehart, K., Hudgin, B., Parker, P.A., A wavelet-based continuous classification scheme for multifunction myoelectric control (2001) IEEE Transactions on Biomedical Engineering, 48 (3), pp. 302-311; Englehart, K., Hudgins, B., A robust, realtime control scheme for multifunction myoelectric control (2003) IEEE Transactions on Biomedical Engineering, 50 (7), pp. 848-854; Kuzborskij, I., Gijsberts, A., Caputo, B., On the challenge of classifying 52 hand movements from surface electromyography (2012) Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4931-4937; Atzori, M., Gijsberts, A., Kuzborskij, I., Elsig, S., Mittaz Hager, A., Deriaz, O., Castellini, C., Caputo, B., Characterization of a benchmark database for myoelectric movement classification (2014) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23 (1), pp. 73-83; Atzori, M., Gijsberts, A., Castellini, C., Caputo, B., Mittaz Hager, A., Elsig, S., Giatsidis, G., Müller, H., Electromyography data for non-invasive naturally-controlled robotic hand prostheses (2014) Scientific Data, 1 (1), pp. 1-13; Gijsberts, A., Atzori, M., Castellini, C., Müller, H., Caputo, B., Movement error rate for evaluation of machine learning methods for semg-based hand movement classification (2014) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22 (4), pp. 735-744; Janet Rechy-Ramirez, E., Hu, H., Biosignal based control in assistive robots: A survey (2015) Digital Communications and Networks, 1 (2), pp. 85-101; Mohebbi, A., Human-robot interaction in rehabilitation and assistance: A review (2020) Current Robotics Reports, pp. 1-14; Zhang, F., Huang, H., Decoding movement intent of patient with multiple sclerosis for the powered lower extremity exoskeleton (2013) Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4957-4960; Nazmi, N., Azizi Abdul Rahman, M., Yamamoto, S., Anom Ahmad, S., Zamzuri, H., Amri Mazlan, S., A review of classification techniques of emg signals during isotonic and isometric contractions (2016) Sensors, 16 (8), p. 1304; Lyons, K.R., Joshi, S.S., A case study on classification of foot gestures via surface electromyography (2015) Annu. Conf. Rehabil. Eng. Assist. Technol. Soc. Amer., pp. 1-5; Thi Thu Vu, H., Dong, D., Cao, H., Verstraten, T., Lefeber, D., Vanderborght, B., Geeroms, J., A review of gait phase detection algorithms for lower limb prostheses (2020) Sensors, 20 (14), p. 3972; Jochumsen, M., Khan Niazi, I., Detection and classification of single-trial movement-related cortical potentials associated with functional lower limb movements (2020) Journal of Neural Engineering, 17 (3), p. 035009; Hu, B., Rouse, E., Hargrove, L., Fusion of bilateral lower-limb neuromechanical signals improves prediction of locomotor activities (2018) Frontiers in Robotics and AI, 5, p. 78; Surface Electromyography for the Non-invasive Assessment of Muscles, , http://www.seniam.org/; Phinyomark, A., Phukpattaranont, P., Limsakul, C., Feature reduction and selection for emg signal classification (2012) Expert Systems with Applications, 39 (8), pp. 7420-7431",,,"ASI;IEEE;IEEE Signal Processing Society","Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Autonomous Systems, ICAS 2021","11 August 2021 through 13 August 2021",,172357,,9781728172897,,,"English","ICAS - IEEE Int. Conf. Auton. Syst., Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85117447938
"Takahashi N., Inamura T., Mizuchi Y., Choi Y.","57262160700;7102940240;36809967300;16400829000;","Evaluation of the difference of human behavior between VR and real environments in searching and manipulating objects in a domestic environment",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"454","460",,,"10.1109/RO-MAN50785.2021.9515393","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115078203&doi=10.1109%2fRO-MAN50785.2021.9515393&partnerID=40&md5=d33dd004588d919772c4f0db6627ecdc","University of Soka, Faculty of Engineering, 1-236 Tangi-machi, Hachioji-shi, Tokyo, 192-8577, Japan; National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan; Graduate University for Advanced Studies (SOKENDAI), 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan; Tamagawa University, 6-1-1 Tamagawagakuen, Machida, Tokyo, Japan","Takahashi, N., University of Soka, Faculty of Engineering, 1-236 Tangi-machi, Hachioji-shi, Tokyo, 192-8577, Japan, National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan; Inamura, T., National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan, Graduate University for Advanced Studies (SOKENDAI), 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo, Japan; Mizuchi, Y., Tamagawa University, 6-1-1 Tamagawagakuen, Machida, Tokyo, Japan; Choi, Y., University of Soka, Faculty of Engineering, 1-236 Tangi-machi, Hachioji-shi, Tokyo, 192-8577, Japan","It is important to evaluate and analyze human behaviors and reactions to build robots that can interact with people. While immersive virtual reality (VR) is one of the useful tools for the evaluation, it has been concerned that the human behavior may differ between VR and real environments due to the differences in cognitive characteristics. In this study, we investigated the differences in human behavior by focusing on common situations in the home environment; for example, a change in head position and posture is required when searching for an object (e.g., peeking motion) and when high accuracy is required when manipulating an object (e.g., placing an object on a shelf). We found that object manipulation requires accuracy which in turn, requires more time in the VR environment. In addition, the amount of change in head posture during object search and manipulation behavior was larger in the VR environment. Finally, we discuss the possibility of using the findings of this study to design robot competitions to assess the quality of human-robot interaction (HRI). © 2021 IEEE.",,"Agricultural robots; Behavioral research; Machine design; Virtual reality; Cognitive characteristics; Domestic environments; Home environment; Human robot Interaction (HRI); Immersive virtual reality; Object manipulation; Real environments; Robot competitions; Social robots",,,,,"2021-21FP03; Japan Society for the Promotion of Science, KAKEN: 19K20379; New Energy and Industrial Technology Development Organization, NEDO","*This study was supported by the New Energy and Industrial Technology Development Organization (NEDO), JSPS (KAKENHI) under Grant Number 19K20379, and ROIS NII Open Collaborative Research 2021-21FP03.",,,,,,,,,,"Shi, C., Satake, S., Kanda, T., Ishiguro, H., A robot that distributes flyers to pedestrians in a shopping mall (2018) International Journal of Social Robotics, 10 (4), pp. 421-437; Komatsubara, T., Shiomi, M., Kaczmarek, T., Kanda, T., Ishiguro, H., Estimating children's social status through their interaction activities in classrooms with a social robot (2019) International Journal of Social Robotics, 11 (1), pp. 35-48; Shiomi, M., Zanlungo, F., Hayashi, K., Kanda, T., Towards a socially acceptable collision avoidance for a mobile robot navigating among pedestrians using a pedestrian model (2014) International Journal of Social Robotics, 6 (3), pp. 443-455; Mizuchi, Y., Inamura, T., Optimization of criterion for objective evaluation of hri performance that approximates subjective evaluation: A case study in robot competition (2020) Advanced Robotics, 34 (3-4), pp. 142-156; Inamura, T., Mizuchi, Y., Sigverse: A cloud-based vr platform for research on multimodal human-robot interaction (2021) Frontiers in Robotics and AI, 8; Kaufeld, M., Nickel, P., Level of robot autonomy and information AIDS in human-robot interaction affect human mental workload-an investigation in virtual reality (2019) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 278-291. , 11581 LNCS; Stanney, K.M., Mourant, R.R., Kennedy, R.S., Human factors issues in virtual environments: A review of the literature (1998) Presence: Teleoperators and Virtual Environments, 7 (4), pp. 327-351; Kelly, J.W., Cherep, L.A., Klesel, B., Siegel, Z.D., George, S., Comparison of two methods for improving distance perception in virtual reality (2018) ACM Transactions on Applied Perception, 15 (2); Willemsen, P., Colton, M.B., Creem-Regehr, S.H., Thompson, W.B., The effects of head-mounted display mechanical properties and field of view on distance judgments in virtual environments (2009) ACM Transactions on Applied Perception, 6 (2), pp. 57-71; Mizuchi, Y., Inamura, T., Evaluation of human behavior difference with restricted field of view in real and vr environments (2018) RO-MAN 2018-27th IEEE International Symposium on Robot and Human Interactive Communication, pp. 196-201; Pfeil, K., Taranta, E.M., Kulshreshth, A., Wisniewski, P., LaViola, J.J., A comparison of eye-head coordination between virtual and physical realities (2018) Proceedings-SAP 2018: ACM Symposium on Applied Perception; Kollenberg, T., Neumann, A., Schneider, D., Tews, T.K., Hermann, T., Ritter, H., Dierker, A., Koesling, H., Visual search in the (un)real world: How head-mounted displays affect eye movements, head movements and target detection (2010) Eye Tracking Research and Applications Symposium (ETRA), pp. 121-124",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115078203
"Deng W., Sargent B., Bradley N.S., Klein L., Rosales M., Pulido J.C., Mataric M.J., Smith B.A.","57208604155;55838246300;7006451487;57215278503;57208568691;56727734900;7005611984;57200816338;","Using socially assistive robot feedback to reinforce infant leg movement acceleration",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"749","756",,,"10.1109/RO-MAN50785.2021.9515537","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115070998&doi=10.1109%2fRO-MAN50785.2021.9515537&partnerID=40&md5=e6ae83ca86ed0cefad5b42bfd3a187d5","University of Southern California, Division of Biokinesiology and Physical Therapy, Los Angeles, CA  90089, United States; University of Southern California, Department of Computer Science, Los Angeles, CA  90089, United States; University Carlos III of Madrid, Department of Innovation and Product Design, Madrid, Spain; University of Southern California, Developmental Neuroscience and Neurogenetics Program, Saban Research Institute, !!!Division of Research on Children, Youth, and Families, @@@Children's Hospital Los Angeles, ###Department of Pediatrics, $$$Keck School of Medicine, Los Angeles, CA  90089, United States","Deng, W., University of Southern California, Division of Biokinesiology and Physical Therapy, Los Angeles, CA  90089, United States; Sargent, B., University of Southern California, Division of Biokinesiology and Physical Therapy, Los Angeles, CA  90089, United States; Bradley, N.S., University of Southern California, Division of Biokinesiology and Physical Therapy, Los Angeles, CA  90089, United States; Klein, L., University of Southern California, Department of Computer Science, Los Angeles, CA  90089, United States; Rosales, M., University of Southern California, Division of Biokinesiology and Physical Therapy, Los Angeles, CA  90089, United States; Pulido, J.C., University Carlos III of Madrid, Department of Innovation and Product Design, Madrid, Spain; Mataric, M.J., University of Southern California, Department of Computer Science, Los Angeles, CA  90089, United States; Smith, B.A., University of Southern California, Developmental Neuroscience and Neurogenetics Program, Saban Research Institute, !!!Division of Research on Children, Youth, and Families, @@@Children's Hospital Los Angeles, ###Department of Pediatrics, $$$Keck School of Medicine, Los Angeles, CA  90089, United States","Learning movement control is a fundamental process integral to infant development. However, it is still unclear how infants learn to control leg movement. This work explores the potential of using socially assistive robots to provide real-time adaptive reinforcement learning for infants. Ten 6 to 8-month old typically-developing infants participated in a study where a robot provided reinforcement when the infant's right leg acceleration fell within the range of 9 to 20 m/s2. If infants increased the proportion of leg accelerations in this band, they were categorized as ""performers"". Six of the ten participating infants were categorized as performers; the performer subgroup increased the magnitude of acceleration, proportion of target acceleration for right leg, and ratio of right/left leg acceleration peaks within the target acceleration band and their right legs increased movement intensity from the baseline to the contingency session. The results showed infants specifically adjusted their right leg acceleration in response to a robot- provided reward. Further study is needed to understand how to improve human-robot interaction policies for personalized interventions for young infants. © 2021 IEEE.",,"Acceleration; Agricultural robots; Biofeedback; Feedback; Reinforcement learning; Adaptive reinforcement; Infant development; Leg movement; Movement control; Real time; Socially assistive robots; Target acceleration; Social robots",,,,,"National Science Foundation, NSF: CBET-1706964","*Research supported by grant [NSF CBET-1706964] from National Science Foundation.",,,,,,,,,,"Plamondon, R., Alimi, A.M., Speed/accuracy trade-offs in target-directed movements (1997) Behav. Brain Sci., 20 (2), pp. 249-279. , Jun; Thelen, E., Corbetta, D., Spencer, J.P., Development of reaching during the first year: Role of movement speed (1996) J. Exp. Psychol. Hum. Percept. 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Robot., 5 (39). , Feb; Syriopoulou-Delli, C.K., Gkiolnta, E., Review of assistive technology in the training of children with autism spectrum disorders Int. J. Dev. Disabil; Blankenship, M.M., Bodine, C., Socially assistive robots for children with cerebral palsy: A meta-analysis (2021) IEEE Trans. Med. Robot. Bionics, 3 (1), pp. 21-30; Deng, W., Rosales, M., Bradley, N.S., Pulido, J.C., Maja, M.J., Smith, B.A., Toward an understanding of infant behavior changes during contingent learning with a humanoid robot (2019) International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EPIROB), , p. 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Spain; Lester, B.M., Tronick, E.Z., Brazelton, T.B., The neonatal intensive care unit network neurobehavioral scale procedures (2004) Pediatrics, 113 (3), pp. 641-667. , Mar; Smith, B.A., Trujillo-Priego, I.A., Lane, C.J., Finley, J.M., Horak, F.B., Daily quantity of infant leg movement: Wearable sensor algorithm and relationship to walking onset (2015) Sensors, 15 (8), pp. 19006-19020. , Aug; Angulo-Kinzler, R.M., Exploration and selection of intralimb coordination patterns in 3-month-old infants (2001) J. Mot. Behav., 33 (4), pp. 363-376",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115070998
"Vasylkiv Y., Ma Z., Li G., Brock H., Nakamura K., Pourang I., Gomezv R.","57192275802;57218215140;56132381600;56006712800;57262458000;57261770800;57262491900;","Shaping affective robot haru's reactive response",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"989","996",,1,"10.1109/RO-MAN50785.2021.9515525","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115068426&doi=10.1109%2fRO-MAN50785.2021.9515525&partnerID=40&md5=615208bb031456aea9adc9c66e927342","University of Manitoba, Canada; Ocean University of China, China; Honda Research Institute Japan Co., Ltd., Japan","Vasylkiv, Y., University of Manitoba, Canada; Ma, Z., Ocean University of China, China; Li, G., Ocean University of China, China; Brock, H., Honda Research Institute Japan Co., Ltd., Japan; Nakamura, K., Honda Research Institute Japan Co., Ltd., Japan; Pourang, I., University of Manitoba, Canada; Gomezv, R., Honda Research Institute Japan Co., Ltd., Japan","We describe a method of teaching a robot its empathic behavioural response from its interaction with people. We used the input modalities such as relative spatial information, facial expressions, body gestures and speech information as perception input that triggers the robot's empathic response. First, we bootstrap the training through a pre-learning mechanism in which training is conducted by users who know the robotic system. This phase provides simulation-based training using a simple graphical user interface to simulate the input, rewards and correction feedback. In the second phase, we developed an online learning scheme for naive users to personalize their robot further, building on top of the bootstrapped model. Here, we developed a natural user interface that enables natural human-robot interaction via the suite of sensors that allows the users to provide evaluative feedback during the interaction with the robot. We evaluated the system and our results show that bootstrapping is an efficient tool to hasten the robot's learning while online learning provided some form of personalization in the real environment with naive users. © 2021 IEEE.",,"Agricultural robots; Behavioral research; Biofeedback; E-learning; Educational robots; End effectors; Feedback; Graphical user interfaces; Learning systems; Facial Expressions; Learning mechanism; Natural user interfaces; Online learning scheme; Real environments; Simulation-based training; Spatial informations; Speech information; Social robots",,,,,,,,,,,,,,,,"Haru: An Experimental Social Robot from Honda Research, , http://spectrum.ieee.org/automaton/robotics/home-robots/haru-anexperimental-social-robot-from-honda-research, accessed: 2020-02-02; Haraway, D.J., (2003) The Companion Species Manifesto: Dogs, People, and Significant Otherness, 1. , Prickly Paradigm Press Chicago; Gomez, R., Szapiro, D., Galindo, K., Nakamura, K., Haru: Hardware design of an experimental tabletop robot assistant (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-robot Interaction, pp. 233-240; Gomez, R., Szapiro, D., Merino, L., Nakamura, K., A holistic approach in designing tabletop robot's expressivity (2020) 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 1970-1976; Brock, H., Chulani, J.P., Merino, L., Szapiro, D., Gomez, R., Developing a lightweight rock-paper-scissors framework for humanrobot collaborative gaming (2020) IEEE Access, 8, pp. 202958-202968; Nichols, E., Gao, L., Gomez, R., Collaborative storytelling with large-scale neural language models (2020) Motion, Interaction and Games, pp. 1-10; Gomez, R., Szapiro, D., Merino, L., Brock, H., Nakamura, K., Sabanovic, S., Emoji to robomoji: Exploring affective telepresence through haru (2020) International Conference on Social Robotics, pp. 652-663. , Springer; Sutton, R.S., Barto, A.G., (2011) Reinforcement Learning: An Introduction; Li, G., Gomez, R., Nakamura, K., He, B., Human-centered reinforcement learning: A survey (2019) IEEE Transactions on Human-Machine Systems, 49 (4), pp. 337-349; Thomaz, A.L., Breazeal, C., Teachable robots: Understanding human teaching behavior to build more effective robot learners (2008) Artificial Intelligence, 172 (6), pp. 716-737; Knox, W.B., Stone, P., Interactively shaping agents via human reinforcement: The tamer framework (2009) Proceedings of the 5th International Conference on Knowledge Capture, pp. 9-16; Yurii Vasylkiv, G.L.H.B.K.N.I.P., Ma, Z., Gomez, R., Automating behavior selection for affective telepresence robot (2021) International Conference on Robotics and Automation, , Springer; White, C.C., A survey of solution techniques for the partially observed markov decision process (1991) Annals of Operations Research, 32 (1), pp. 215-230; Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H., (2017) Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications; Farag, I., Brock, H., Learning motion disfluencies for automatic sign language segmentation (2019) ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7360-7364; Brock, H., Sabanovic, S., Nakamura, K., Gomez, R., Robust realtime hand gestural recognition for non-verbal communication with tabletop robot haru (2020) 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 891-898; Brock, H., Farag, I., Nakadai, K., Recognition of non-manual content in continuous Japanese sign language (2020) Sensors, 20 (19), p. 5621; Nakamura, K., Gomez, R., Improving separation of overlapped speech for meeting conversations using uncalibrated microphone array (2017) 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 55-62; Ge, F., Yan, Y., Deep neural network based wake-up-word speech recognition with two-stage detection (2017) 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2761-2765; Michaely, A.H., Zhang, X., Simko, G., Parada, C., Aleksic, P., Keyword spotting for google assistant using contextual speech recognition (2017) 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 272-278; Hutto, C., Gilbert, E., Vader: A parsimonious rule-based model for sentiment analysis of social media text (2014) Eighth International Conference on Weblogs and Social Media (ICWSM); Watkins, C.J., Dayan, P., Q-learning (1992) Machine Learning, 8 (3-4), pp. 279-292; Lin, J., Ma, Z., Gomez, R., Nakamura, K., He, B., Li, G., A review on interactive reinforcement learning from human social feedback (2020) IEEE Access, 8, pp. 120757-120765; Li, G., Hung, H., Whiteson, S., Knox, W.B., Learning from human reward benefits from socio-competitive feedback (2014) 4th International Conference on Development and Learning and on Epigenetic Robotics, pp. 93-100; Li, G., Whiteson, S., Knox, W.B., Hung, H., Social interaction for efficient agent learning from human reward (2018) Autonomous Agents and Multi-Agent Systems, 32 (1), pp. 1-25; Even-Dar, E., Mansour, Y., Bartlett, P., Learning rates for qlearning (2003) Journal of Machine Learning Research, 5 (1); Liu, W., Natural user interface-next mainstream product user interface (2010) 2010 IEEE 11th International Conference on Computer-Aided Industrial Design & Conceptual Design, 1, pp. 203-205","Li, G.; Ocean University of ChinaChina",,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115068426
"Noda H., Nishikawa S., Niiyama R., Kuniyoshi Y.","57261770500;36959913600;57218118140;7005371269;","Competitive physical interaction by reinforcement learning agents using intention estimation",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"649","656",,,"10.1109/RO-MAN50785.2021.9515411","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115060714&doi=10.1109%2fRO-MAN50785.2021.9515411&partnerID=40&md5=fd281b5c1dd9cdb0f4f24e21defe0299","University of Tokyo, Graduate School of Information Science and Technology, 7-3-1 Hongo, Bunkyoku, Tokyo, Japan","Noda, H., University of Tokyo, Graduate School of Information Science and Technology, 7-3-1 Hongo, Bunkyoku, Tokyo, Japan; Nishikawa, S., University of Tokyo, Graduate School of Information Science and Technology, 7-3-1 Hongo, Bunkyoku, Tokyo, Japan; Niiyama, R., University of Tokyo, Graduate School of Information Science and Technology, 7-3-1 Hongo, Bunkyoku, Tokyo, Japan; Kuniyoshi, Y., University of Tokyo, Graduate School of Information Science and Technology, 7-3-1 Hongo, Bunkyoku, Tokyo, Japan","The physical human-robot interaction (pHRI) research field is expected to contribute to competitive and cooperative human-robot tasks that involve force interactions. However, compared with human-human interactions, current pHRI approaches lack tactical considerations. Current approaches do not estimate intentions from human behavior and do not select policies that are appropriate for the opponent's changing policy. For this reason, we propose a reinforcement learning model that estimates the opponent's changing policy using time-series observations and expresses the agent's policy in a common latent space, referring to descriptions of tactics in open-skill sports. We verify the performance of the reinforcement learning agent using two novel physical and competitive environments, push-hand game and air-hockey. From this, we confirm that the latent space works properly for policy information because each latent variable that represents the machine agent's own policy and that of the opponent affects the behavior of the agent. Two latent variables can clearly express how the agent estimates the opponent's policy and decides its own policy. © 2021 IEEE.",,"Agricultural robots; Behavioral research; Intelligent agents; Reinforcement learning; Sports; Competitive environment; Human-human interactions; Intention estimation; Physical humanrobot interaction (phri); Physical interactions; Reinforcement learning agent; Reinforcement learning models; Tactical considerations; Social robots",,,,,"Japan Society for the Promotion of Science, KAKEN: JP18K18087; Japan Science and Technology Agency, JST: JPMJMS2013","This work was supported by JST Moonshot R&D Grant Number JPMJMS2013 and JSPS KAKENHI Grant Number JP18K18087.",,,,,,,,,,"Namiki, A., Takahashi, F., Motion generation for a sword-fighting robot based on quick detection of opposite players initial motions (2015) Journal of Robotics and Mechatronics, 27 (5), pp. 543-551; Igeta, K., Namiki, A., A decision-making algorithm for an airhockey robot that decides actions depending on its opponent player's motions (2015) 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, pp. 1840-1845; Shimada, H., Kutsuna, Y., A two-layer tactical system for an airhockey-playing robot (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 6985-6990; Wang, H., Kosuge, K., Control of a robot dancer for enhancing haptic human-robot interaction in waltz (2012) IEEE Transactions on Haptics, 5 (3), pp. 264-273; Granados, D.F.P., Yamamoto, B.A., Dance teaching by a robot: Combining cognitive and physical human-robot interaction for supporting the skill learning process (2017) IEEE Robotics and Automation Letters, 2 (3), pp. 1452-1459; Di Russo, F., Bultrini, A., Benefits of sports participation for executive function in disabled athletes (2010) Journal of Neurotrauma, 27 (12), pp. 2309-2319; Wang, C.-H., Chang, C.-C., Open vs. closed skill sports and the modulation of inhibitory control (2013) PloS One, 8 (2), p. 55773; Shimotsukasa, T., Suyama, S., Sadism and competitive performance in sports: The mediation of gamesmanship (2019) The Japanese Journal of Personality, 27 (3), pp. 263-265; Silver, D., Huang, A., Mastering the game of go with deep neural networks and tree search (2016) Nature, 529 (1), pp. 484-489; Moravcík, M., Schmid, M., Deepstack: Expert-level artificial intelligence in heads-up no-limit poker (2017) Science, 356 (6337), pp. 508-513; Oh, I., Rho, S., Creating pro-level ai for a real-time fighting game using deep reinforcement learning (2021) IEEE Transactions on Games; Vinyals, O., Babuschkin, I., Grandmaster level in starcraft II using multi-agent reinforcement learning (2019) Nature, 575 (7782), pp. 350-354; Berner, C., Brockman, G., (2019) Dota 2 with Large Scale Deep Reinforcement Learning; Littman, M.L., Markov games as a framework for multi-agent reinforcement learning (1994) Machine Learning Proceedings 1994, pp. 157-163. , Elsevier; Lowe, R., Wu, Y., (2017) Multi-agent Actor-critic for Mixed Cooperativecompetitive Environments; Konda, V.R., Tsitsiklis, J.N., Actor-critic algorithms (2000) Advances in Neural Information Processing Systems, pp. 1008-1014. , Citeseer; Iqbal, S., Sha, F., Actor-attention-critic for multi-agent reinforcement learning (2019) International Conference on Machine Learning. PMLR, pp. 2961-2970; Vaswani, A., Shazeer, N., (2017) Attention Is All You Need; Li, S., Wu, Y., Robust multi-agent reinforcement learning via minimax deep deterministic policy gradient (2019) Proceedings of the AAAI Conference on Artificial Intelligence, 33 (1), pp. 4213-4220; Albrecht, S.V., Stone, P., Autonomous agents modelling other agents: A comprehensive survey and open problems (2018) Artificial Intelligence, 258, pp. 66-95; Papoudakis, G., Christianos, F., (2019) Dealing with Non-stationarity in Multi-agent Deep Reinforcement Learning; Papoudakis, G., Albrecht, S.V., (2020) Variational Autoencoders for Opponent Modeling in Multi-agent Systems; Kingma, D.P., Welling, M., (2013) Auto-encoding Variational Bayes; Grover, A., Al-Shedivat, M., Learning policy representations in multiagent systems (2018) International Conference on Machine Learning. PMLR, pp. 1802-1811; Raileanu, R., Denton, E., Modeling others using oneself in multiagent reinforcement learning (2018) International Conference on Machine Learning. PMLR, pp. 4257-4266; Everett, R., Roberts, S., Learning against non-stationary agents with opponent modelling and deep reinforcement learning (2018) 2018 AAAI Spring Symposium Series; Schulman, J., Wolski, F., (2017) Proximal Policy Optimization Algorithms; Bansal, T., Pachocki, J., (2017) Emergent Complexity Via Multi-agent Competition; Baker, B., Kanitscheider, I., (2019) Emergent Tool Use from Multi-agent Autocurricula; Bacon, P.-L., Harb, J., The option-critic architecture (2017) Proceedings of the AAAI Conference on Artificial Intelligence, 31 (1); Brockman, G., Cheung, V., (2016) Openai Gym; Todorov, E., Erez, T., Mujoco: A physics engine for model-based control (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 5026-5033",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115060714
"Stuede M., Westermann K., Schappler M., Spindeldreier S.","57210792780;57218618781;56733031100;57222730669;","Sobi: An interactive social service robot for long-term autonomy in open environments",2021,"2021 10th European Conference on Mobile Robots, ECMR 2021 - Proceedings",,,,"","",,,"10.1109/ECMR50962.2021.9568838","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118967644&doi=10.1109%2fECMR50962.2021.9568838&partnerID=40&md5=9deee21e54387dfe35533fc17eb61a06","Leibniz University Hannover, Institute of Mechatronic Systems, Garbsen, D-30823, Germany","Stuede, M., Leibniz University Hannover, Institute of Mechatronic Systems, Garbsen, D-30823, Germany; Westermann, K., Leibniz University Hannover, Institute of Mechatronic Systems, Garbsen, D-30823, Germany; Schappler, M., Leibniz University Hannover, Institute of Mechatronic Systems, Garbsen, D-30823, Germany; Spindeldreier, S., Leibniz University Hannover, Institute of Mechatronic Systems, Garbsen, D-30823, Germany","Long-term autonomy in service robotics is a current research topic, especially for dynamic, large-scale environments that change over time. We present Sobi, a mobile service robot developed as an interactive guide for open environments, such as public places with indoor and outdoor areas. The robot will serve as a platform for environmental modeling and human-robot interaction. Its main hardware and software components, which we freely license as a documented open source project, are presented. Another key focus is Sobi's monitoring system for long-term autonomy, which restores system components in a targeted manner in order to extend the total system lifetime without unplanned intervention. We demonstrate first results of the long-term autonomous capabilities in a 16-day indoor deployment, in which the robot patrols a total of 66.6 km with an average of 5.5 hours of travel time per weekday, charging autonomously in between. In a user study with 12 participants, we evaluate the appearance and usability of the user interface, which allows users to interactively query information about the environment and directions. © 2021 IEEE.",,"Groundwater; Human robot interaction; Mobile robots; Open source software; Open systems; User interfaces; 'current; Change-over time; Environmental model; Mobile service robots; Open environment; Public places; Research topics; Service robotics; Service robots; Social service; Travel time",,,,,,"This work was funded by the Faculty of Mechanical Engineering of the Leibniz University Hannover. Special thanks go to the product design team of the Hannover University of Applied Sciences and Arts.",,,,,,,,,,"Triebel, R., Spencer: A socially aware service robot for passenger guidance and help in busy airports (2016) Springer Tracts in Advanced Robotics, pp. 607-622; Ivanov, S.H., Webster, C., Berezina, K., Adoption of robots and service automation by tourism and hospitality companies (2017) Revista Turismo & Desenvolvimento, 27 (28), pp. 1501-1517; Burgard, W., Cremers, A.B., Fox, D., Hähnel, D., Lakemeyer, G., Schulz, D., Steiner, W., Thrun, S., The interactive museum tour-guide robot (1998) AAAI Conference on Artificial Intelligence, pp. 11-18; Pinillos, R., Marcos, S., Feliz, R., Zalama, E., Gómez-Garciá-Bermejo, J., Long-term assessment of a service robot in a hotel environment (2016) Robotics and Autonomous Systems, 79, pp. 40-57; Biswas, J., Veloso, M., The 1,000-km challenge: Insights and quantitative and qualitative results (2016) IEEE Intelligent Systems, 31 (3), pp. 86-96; Meeussen, W., Marder-Eppstein, E., Watts, K., Gerkey, B., Long term autonomy in office environments (2011) IEEE International Conference on Robotics and Automation (ICRA), Workshop on Longterm Autonomy; Hawes, N., The strands project: Long-term autonomy in everyday environments (2017) IEEE Robotics & Automation Magazine, 24 (3), pp. 146-156; Duchetto, F.D., Baxter, P., Hanheide, M., Lindsey the tour guide robot-usage patterns in a museum long-term deployment (2019) 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1-8; De Jong, M., Towards a robust interactive and learning social robot (2018) Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, pp. 883-891; Wang, S., Christensen, H.I., Tritonbot: First lessons learned from deployment of a long-term autonomy tour guide robot (2018) RO-MAN, pp. 158-165; Chiang, K.-H., Tseng, S.-H., Wu, Y.-H., Li, G.-H., Lam, C.-P., Fu, L.-C., Multisensor-based outdoor tour guide robot ntu-I (2008) SICE Annual Conference, pp. 1425-1430; Luperto, M., Monroy, J., Ruiz-Sarmiento, J.R., Moreno, F.-A., Basilico, N., Gonzalez-Jimenez, J., Borghese, N.A., Towards long-term deployment of a mobile robot for at-home ambient assisted living of the elderly (2019) European Conference on Mobile Robots (ECMR), pp. 1-6; Antonelli, G., Fault detection/tolerance strategies for AUVs and ROVs (2014) Underwater Robots, pp. 101-116. , Springer; Khalastchi, E., Kalech, M., On fault detection and diagnosis in robotic systems (2018) ACM Computing Surveys (CSUR), 51 (1), p. 9; Colledanchise, M., Almeida, D., Ögren, P., Towards blended reactive planning and acting using behavior trees (2019) ICRA, pp. 8839-8845; Rovida, F., Grossmann, B., Krüger, V., Extended behavior trees for quick definition of flexible robotic tasks (2017) IROS, pp. 6793-6800; Redmon, J., Farhadi, A., (2018) Yolov3: An Incremental Improvement; Yan, Z., Duckett, T., Bellotto, N., Online learning for human classification in 3d lidar-based tracking (2017) IROS, pp. 864-871; Bogoslavskyi, I., Stachniss, C., Fast range image-based segmentation of sparse 3D laser scans for online operation (2016) IROS, pp. 163-169; Linder, T., Breuers, S., Leibe, B., Arras, K.O., On multi-modal people tracking from mobile platforms in very crowded and dynamic environments (2016) ICRA, pp. 5512-5519; Labbé, M., Michaud, F., RTAB-Map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation (2019) Journal of Field Robotics, 36 (2), pp. 416-446; Ehlers, S.F.G., Stuede, M., Nuelle, K., Ortmaier, T., Map management approach for slam in large-scale indoor and outdoor areas (2020) ICRA, pp. 9652-9658; Habich, T.-L., Stuede, M., Labbé, M., Spindeldreier, S., Have i been here before? Learning to close the loop with lidar data in graph-based slam (2021) IEEE International Conference on Advanced Intelligent Mechatronics (AIM), , Accepted for publication; Coulter, R.C., (1992) Implementation of the Pure Pursuit Path Tracking Algorithm, , Carnegie-Mellon UNIV Pittsburgh PA Robotics INST, Tech. Rep; Stuede, M., Wilkening, J., Tappe, S., Ortmaier, T., Voice recognition and processing interface for an interactive guide robot in an university scenario (2019) 2019 19th International Conference on Control, Automation and Systems (ICCAS), pp. 1238-1242; Bartneck, C., Kulic, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81; Weiss, A., Bartneck, C., Meta analysis of the usage of the godspeed questionnaire series (2015) RO-MAN, pp. 381-388; Tan, Z.-H., ISocioBot: A multimodal interactive social robot (2018) International Journal of Social Robotics, 10 (1), pp. 5-19","Stuede, M.; Leibniz University Hannover, Germany; email: marvin.stuede@imes.uni-hannover.de",,,"Institute of Electrical and Electronics Engineers Inc.","10th European Conference on Mobile Robots, ECMR 2021","31 August 2021 through 3 September 2021",,173117,,9781665412131,,,"English","European Conf. Mob. Robot., ECMR - Proc.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85118967644
"Islam M.R., Assad-Uz-zaman M., Brahmi B., Bouteraa Y., Wang I., Rahman M.H.","57193765453;56269799700;57193153269;57202361442;57216898881;57210856870;","Design and development of an upper limb rehabilitative robot with dual functionality",2021,"Micromachines","12","8","870","","",,,"10.3390/mi12080870","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111646707&doi=10.3390%2fmi12080870&partnerID=40&md5=f8e7516f71b57f448c41d1b9248f13d3","Richard J. Resch School of Engineering, University of Wisconsin-Green Bay, Green Bay, WI  54311, United States; Mechanical/Biomedical Engineering Department, University of Wisconsin-Milwaukee, Milwaukee, WI  53211, United States; Electrical and Computer Engineering, Miami University, Oxford, OH  45056, United States; Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia; Control and Energy Management Laboratory (CEM Lab), Ecole Nationale d Ingenieurs de Sfax (ENIS), Institut Superieur de Biotechnologie de Sfax (ISBS), University of Sfax, Sfax, 3038, Tunisia; College of Health Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI  53211, United States","Islam, M.R., Richard J. Resch School of Engineering, University of Wisconsin-Green Bay, Green Bay, WI  54311, United States; Assad-Uz-zaman, M., Mechanical/Biomedical Engineering Department, University of Wisconsin-Milwaukee, Milwaukee, WI  53211, United States; Brahmi, B., Electrical and Computer Engineering, Miami University, Oxford, OH  45056, United States; Bouteraa, Y., Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia, Control and Energy Management Laboratory (CEM Lab), Ecole Nationale d Ingenieurs de Sfax (ENIS), Institut Superieur de Biotechnologie de Sfax (ISBS), University of Sfax, Sfax, 3038, Tunisia; Wang, I., College of Health Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI  53211, United States; Rahman, M.H., Mechanical/Biomedical Engineering Department, University of Wisconsin-Milwaukee, Milwaukee, WI  53211, United States","The design of an upper limb rehabilitation robot for post-stroke patients is considered a benchmark problem regarding improving functionality and ensuring better human–robot interaction (HRI). Existing upper limb robots perform either joint-based exercises (exoskeleton-type functionality) or end-point exercises (end-effector-type functionality). Patients may need both kinds of exercises, depending on the type, level, and degree of impairments. This work focused on designing and developing a seven-degrees-of-freedom (DoFs) upper-limb rehabilitation exoskeleton called ‘u-Rob’ that functions as both exoskeleton and end-effector types device. Furthermore, HRI can be improved by monitoring the interaction forces between the robot and the wearer. Existing upper limb robots lack the ability to monitor interaction forces during passive rehabilitation exercises; measuring upper arm forces is also absent in the existing devices. This research work aimed to develop an innovative sensorized upper arm cuff to measure the wearer’s interaction forces in the upper arm. A PID control technique was implemented for both joint-based and end-point exercises. The experimental results validated both types of functionality of the developed robot. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Dual functionality; End effector; Exoskeleton; Rehabilitation; Sensorized cuff; Upper arm forces; Upper limb","Degrees of freedom (mechanics); Electronic medical equipment; End effectors; Exoskeleton (Robotics); Human robot interaction; Joints (anatomy); Machine design; Point contacts; Three term control systems; Bench-mark problems; Degrees of freedom (DoFs); Design and Development; Post stroke patients; Rehabilitation exercise; Rehabilitative robot; Robot interactions; Upper-limb rehabilitation; Neuromuscular rehabilitation",,,,,,,,,,,,,,,,"Benjamin, E.J., Muntner, P., Alonso, A., Bittencourt, M.S., Callaway, C.W., Carson, A., Chamberlain, A.M., Das, S.R., Heart Disease and Stroke Statistics—2019 Update: A Report from the American Heart Association (2019) Circulation, 139, pp. e56-e528. , [CrossRef] [PubMed]; Teasell, R.W., Kalra, L., What’s New in Stroke Rehabilitation (2004) Stroke, 35, pp. 383-385. , [CrossRef] [PubMed]; Lum, P.S., Burgar, C.G., Shor, P.C., Majmundar, M., Van der Loos, H.M., Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke (2002) Arch. Phys. Med. Rehabil, 83, pp. 952-959. , [CrossRef]; Badesa, F.J., Morales, R., Garcia-Aracil, N., Sabater, J., Casals, A., Zollo, L., Auto-adaptive robot-aided therapy using machine learning techniques (2014) Comput. Methods Programs Biomed, 116, pp. 123-130. , [CrossRef]; Balasubramanian, S., He, J., Adaptive Control of a Wearable Exoskeleton for Upper-Extremity Neurorehabilitation (2012) Appl. Bionics Biomech, 9, pp. 99-115. , [CrossRef]; Brahmi, B., Saad, M., Brahmi, A., Luna, C.O., Rahman, M.H., Compliant control for wearable exoskeleton robot based on human inverse kinematics (2018) Int. J. Adv. Robot. Syst, 15. , [CrossRef]; Campolo, D., Tommasino, P., Gamage, K., Klein, J., Hughes, C.M., Masia, L., H-Man: A planar, H-shape cabled differential robotic manipulandum for experiments on human motor control (2014) J. Neurosci. Methods, 235, pp. 285-297. , [CrossRef]; Chen, Y., Li, G., Zhu, Y., Zhao, J., Cai, H., Design of a 6-DOF upper limb rehabilitation exoskeleton with parallel actuated joints (2014) Bio-Med. Mater. Eng, 24, pp. 2527-2535. , [CrossRef]; Christensen, S., Bai, S., (2017) A Novel Shoulder Mechanism with a Double Parallelogram Linkage for Upper-Body Exoskeletons, pp. 51-56. , Springer International Publishing: Cham, Switzerland; Fraile, J.C., Turiel, J.P., Baeyens, E., Viñas, P., Alonso, R., Cuadrado, A., Franco-Martin, M., García-Bravo, F., E2Rebot: A robotic platform for upper limb rehabilitation in patients with neuromotor disability (2016) Adv. Mech. Eng, 8. , [CrossRef]; Garrido, J., Yu, W., Li, X., Modular design and control of an upper limb exoskeleton (2016) J. Mech. Sci. Technol, 30, pp. 2265-2271. , [CrossRef]; Kiguchi, K., Hayashi, Y., An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot (2012) IEEE Trans. Syst. Man Cybern. Part B, 42, pp. 1064-1071. , [CrossRef]; Kim, B., Deshpande, A.D., Controls for the shoulder mechanism of an upper-body exoskeleton for promoting scapulohumeral rhythm Proceedings of the 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 538-542. , Singapore, 11–14 August 2015; Nef, T., Guidali, M., Riener, R., ARMin III—Arm Therapy Exoskeleton with an Ergonomic Shoulder Actuation (2009) Appl. Bionics Biomech, 6, p. 962956. , [CrossRef]; Nef, T., Mihelj, M., Kiefer, G., Perndl, C., Muller, R., Riener, R., ARMin—Exoskeleton for Arm Therapy in Stroke Patients Proceedings of the 2007 IEEE 10th International Conference on Rehabilitation Robotics, pp. 68-74. , Noordwijk, The Netherlands, 13–15 June 2007; Otten, A., Voort, C., Stienen, A., Aarts, R., van Asseldonk, E., van der Kooij, H., LIMPACT:A Hydraulically Powered Self-Aligning Upper Limb Exoskeleton (2015) IEEE/ASME Trans. 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Eng, 15, pp. 327-335. , [CrossRef]; Lee, S.H., Park, G., Cho, D.Y., Kim, H.Y., Lee, J.-Y., Kim, S., Park, S.-B., Shin, J.-H., Comparisons between end-effector and exoskeleton rehabilitation robots regarding upper extremity function among chronic stroke patients with moderate-to-severe upper limb impairment (2020) Sci. Rep, 10, pp. 1806-1808. , [CrossRef]; Hefter, H., Jost, W.H., Reissig, A., Zakine, B., Bakheit, A.M., Wissel, J., Classification of posture in poststroke upper limb spasticity: A potential decision tool for botulinum toxin A treatment? (2012) Int. J. Rehabil. Res, 35, pp. 227-233. , [CrossRef]; Islam, M.R., Brahmi, B., Ahmed, T., Assad-Uz-Zaman, M., Rahman, M.H., Chapter 9—Exoskeletons in upper limb rehabilitation: A review to find key challenges to improve functionality (2020) Control Theory in Biomedical Engineering, pp. 235-265. , Boubaker, O., Ed.; Academic Press: Cambridge, MA, USA; Sadeque, M., Balachandran, S.K., Chapter 10—Overview of medical device processing (2020) Trends in Development of Medical Devices, pp. 177-188. , Shanmugam, P.S.T., Chokkalingam, L., Bakthavachalam, P., Boubaker, O., Eds.; Academic Press: Cambridge, MA, USA; Jiang, L., Dou, Z.-L., Wang, Q., Wang, Q.-Y., Dai, M., Wang, Z., Wei, X.-M., Chen, Y.-B., Evaluation of clinical outcomes of patients with post-stroke wrist and finger spasticity after ultrasonography-guided BTX-A injection and rehabilitation training (2015) Front. Hum. Neurosci, 9, p. 485. , [CrossRef]; Kwakkel, G., Kollen, B.J., Van Der Grond, J., Prevo, A.J., Probability of Regaining Dexterity in the Flaccid Upper Limb (2003) Stroke, 34, pp. 2181-2186. , [CrossRef]; Bai, S., Christensen, S., Islam, M.R.U., An upper-body exoskeleton with a novel shoulder mechanism for assistive applications Proceedings of the 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pp. 1041-1046. , Munich, Germany, 3–7 July 2017; [CrossRef]; Sutapun, A., Sangveraphunsiri, V., A 4-DOF Upper Limb Exoskeleton for Stroke Rehabilitation: Kinematics Mechanics and Control (2015) Int. J. Mech. Eng. Robot. Res, 4, pp. 269-272. , [CrossRef]; Liu, L., Shi, Y.-Y., Xie, L., A Novel Multi-Dof Exoskeleton Robot For Upper Limb Rehabilitation (2016) J. Mech. Med. Biol, 16, p. 1640023. , [CrossRef]; Madani, T., Daachi, B., Djouani, K., Modular-Controller-Design-Based Fast Terminal Sliding Mode for Articulated Exoskeleton Systems (2016) IEEE Trans. Control. 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Mechatron, 12, pp. 408-417. , [CrossRef]; Stroppa, F., Loconsole, C., Marcheschi, S., Frisoli, A., A Robot-Assisted Neuro-Rehabilitation System for Post-Stroke Patients’ Motor Skill Evaluation with ALEx Exoskeleton (2017) Converging Clinical and Engineering Research on Neurorehabilitation II, pp. 501-505. , Springer International Publishing: Cham, Switzerland; Tang, Z., Zhang, K., Sun, S., Gao, Z., Zhang, L., Yang, Z., An Upper-Limb Power-Assist Exoskeleton Using Proportional Myoelectric Control (2014) Sensors, 14, pp. 6677-6694. , [CrossRef] [PubMed]; Gopura, R., Bandara, D., Kiguchi, K., Mann, G., Developments in hardware systems of active upper-limb exoskeleton robots: A review (2016) Robot. Auton. Syst, 75, pp. 203-220. , [CrossRef]; Kiguchi, K., Rahman, M.H., Sasaki, M., Teramoto, K., Development of a 3DOF mobile exoskeleton robot for human upper-limb motion assist (2008) Robot. Auton. Syst, 56, pp. 678-691. , [CrossRef]; Kim, B., Deshpande, A.D., An upper-body rehabilitation exoskeleton Harmony with an anatomical shoulder mechanism: Design, modeling, control, and performance evaluation (2017) Int. J. Robot. Res, 36, pp. 414-435. , [CrossRef]; Hou, Y., Kiguchi, K., Virtual Tunnel Generation Algorithm for Perception-Assist with an Upper-Limb Exoskeleton Robot Proceedings of the 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS), pp. 204-209. , Shenzhen, China, 25–27 October 2018; [CrossRef]; Islam, M., Spiewak, C., Rahman, M., Fareh, R., A Brief Review on Robotic Exoskeletons for Upper Extremity Rehabilitation to Find the Gap between Research Porotype and Commercial Type (2017) Adv. Robot. Autom, 6, p. 2. , [CrossRef]; Jarrassé, N., Proietti, T., Crocher, V., Robertson, J., Sahbani, A., Morel, G., Roby-Brami, A., Robotic Exoskeletons: A Perspective for the Rehabilitation of Arm Coordination in Stroke Patients (2014) Front. Hum. Neurosci, 8, p. 947. , (In English) [CrossRef]; Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Troy, A., Leonhardt, S., A survey on robotic devices for upper limb rehabilitation (2014) J. Neuroeng. Rehabil, 11, p. 3. , [CrossRef] [PubMed]; Gates, D.H., Walters, L.S., Cowley, J.C., Wilken, J., Resnik, L., Range of Motion Requirements for Upper-Limb Activities of Daily Living (2015) Am. J. Occup. Ther, 70, p. 7001350010. , [CrossRef] [PubMed]; Hamilton, N., Weimar, W., Luttgens, K., (2012) Kinesiology: Scientific Basis of Human Motion, , 12th ed.; McGraw-Hill: New York, NY, USA; Kim, K., Park, D.S., Ko, B.W., Lee, J., Yang, S.N., Kim, J., Song, W.K., Arm motion analysis of stroke patients in activities of daily living tasks: A preliminary study Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1287-1291. , Boston, MA, USA, 30 August–3 September 2011; Namdari, S., Yagnik, G., Ebaugh, D.D., Nagda, S., Ramsey, M.L., Williams, G.R., Mehta, S., Defining functional shoulder range of motion for activities of daily living (2012) J. Shoulder Elb. Surg, 21, pp. 1177-1183. , [CrossRef] [PubMed]; Eckstrand, G.A., Van Cott, H.P., Kinkade, R.G., Human Engineering Guide to Equipment Design (1973) Am. J. Psychol, 86, p. 891. , [CrossRef]; Halder, A.M., Itoi, E., An, K.N., Anatomy and biomechanics of the shoulder (2000) Orthop. Clin. N. Am, 31, pp. 159-176. , [CrossRef]; Winter, D.A., Anthropometry (2009) Biomechanics and Motor Control of Human Movement, p. 370. , John Wiley & Sons: New York, NY, USA; De Caro, J.S., Islam, R., Montenegro, E.M., Brahmi, B., Rahman, M., Inverse Kinematic solution of u-Rob4 an hybrid exoskeleton for stroke rehabilitation Proceedings of the 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 755-764. , Monastir, Tunisia, 22–25 March 2021; [CrossRef]; Islam, R., Zaman, A.U., Swapnil, A.A.Z., Ahmed, T., Rahman, M.H., An ergonomic shoulder for robot-aided rehabilitation with hybrid control (2020) Microsyst. Technol, 27, pp. 159-172. , [CrossRef]; Islam, M.R., Assad-Uz-Zaman, M., Rahman, M.H., Design and control of an ergonomic robotic shoulder for wearable exoskeleton robot for rehabilitation (2020) Int. J. Dyn. Control, 8, pp. 312-325. , [CrossRef]; Islam, R., Rahmani, M., Rahman, M.H., A Novel Exoskeleton with Fractional Sliding Mode Control for Upper Limb Rehabilitation (2020) Robotica, 38, pp. 2099-2120. , [CrossRef]; Denavit, J., Hartenberg, R.S., A Kinematic Notation for Lower-Pair Mechanisms Based on Matrices (1955) J. Appl. Mech, 22, pp. 215-221. , [CrossRef]; Craig, J.J., (2017) Introduction to Robotics: Mechanics and Control, p. 448. , 4th ed.; Pearson: Upper Saddle River, NJ, USA; Lenar, J., Klop, N., Positional kinematics of humanoid arms (2006) Robotica, 24, pp. 105-112. , [CrossRef]; Lakhal, O., Melingui, A., Merzouki, R., Hybrid Approach for Modeling and Solving of Kinematics of a Compact Bionic Handling Assistant Manipulator (2015) IEEE/ASME Trans. Mechatron, 21, pp. 1326-1335. , [CrossRef]; Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., (2009) Robotics (Modelling, Planning and Control), , Springer: London, UK; Arm Exercises for Stroke Patients, , https://www.flintrehab.com/arm-exercises-for-stroke-patients, FlintRehab. (accessed on 12 January 2021); Web, MD., Stroke Rehab to Regain Arm Movement, , https://www.webmd.com/stroke/ss/slideshow-stroke, (accessed on 12 January 2021)","Islam, M.R.; Richard J. Resch School of Engineering, United States; email: islamm@uwgb.edu",,,"MDPI AG",,,,,2072666X,,,,"English","Micromachines",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85111646707
"Mathew V., Ramesh K., Prabha B.","57202280337;57221357009;56769679000;","Performance improvement of Facial Expression Recognition Deep neural network models using Histogram Equalization and Contrast Stretching",2021,"2021 International Conference on System, Computation, Automation and Networking, ICSCAN 2021",,,,"","",,,"10.1109/ICSCAN53069.2021.9526527","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115695590&doi=10.1109%2fICSCAN53069.2021.9526527&partnerID=40&md5=9ca7d8c33d54c193e60b451f0ffafbc5","Hindustan Institute of Technology and Science, CSE Department, India; Loyola-ICAM College of Engineering and Technology, Department of IT, India","Mathew, V., Hindustan Institute of Technology and Science, CSE Department, India; Ramesh, K., Hindustan Institute of Technology and Science, CSE Department, India; Prabha, B., Loyola-ICAM College of Engineering and Technology, Department of IT, India","Human facial expression analysis has been in research for some time now, for its widespread application in healthcare, marketing, security, human-robot interactions, and many more. Deep neural networks are being used for developing facial expression recognition models. The quality and nature of the images used for model training are always deciding factors for the performance of the models developed. This paper describes how the application of two image enhancement methods, Histogram equalization, and Contrast stretching on the training images, improve the performance of neural network models used for facial expression. A basic convolutional neural network model and a transfer learning model are used in this study, for repeated performance analysis using multiple models. The experiments are carried out with three popular Facial Expression Analysis datasets, Real World Affective Faces, FER2013, and FER plus datasets to establish the results. It is found that applying histogram equalization or contrast stretching on the face images to be used in the model training, increases the accuracy of the model by an average of two percent. © 2021 IEEE.","Contrast Stretching; Facial Expression Recognition; Histogram Equalization; Image Pre-processing; Neural Network Model","Convolutional neural networks; Deep neural networks; Face recognition; Graphic methods; Human robot interaction; Image enhancement; Transfer learning; Contrast stretching; Facial expression analysis; Facial expression recognition; Facial Expressions; Histogram equalizations; Human facial expressions; Neural network model; Performance analysis; Equalizers",,,,,,"The authors thank National Institute of Electronics and Information Technology, Calicut, India for providing resources for carrying out the research.",,,,,,,,,,"Revina, I., Emmanuel, W.S., A survey on human face expression recognition techniques (2018) Journal of king saud university; Challenges in representation learning: A report on three machine learning contests (2013) International Conference on Neural Information Processing, , I. J. e. A. Goodfellow; Mathew, V., Toby, T., Chacko, A., Udhayakumar, A., Person re-identification through face detection from videos using deep learning (2019) IEEE International Conference on Advanced Networks and Telecommunications Systems, Goa, India; Simonyan, K., Zisserman, A., Very deep convolutional networks for large-Scale image recognition (2015) International Conference on Learning Representations, San Diego; Pandey, A., Patel, D.S., Face expression recognition: A review (2020) Journal of Emerging Technologies and Innovative Research, 7 (4); Florea, C., Florea, L., Bade, M., Vertan, C., Racoviteanu, A., Annealed label transfer for face expression recognition (2019) British Machine Vision Conference, Cardiff; Automatic live facial expression detection using genetic algorithm with haar wavelet features and SVM (2018) Wireless Personal Communications, 103, pp. 2435-2453; Zhou, N., Liang, R., Shi, W., A lightweight convolutional neural network for real-Time facial expression detection (2020) IEEE Access, 9, pp. 5573-5584; Zhao, D., MacDonald, S., Gaud, T., Uribe-Quevedo, A., Martin, M.V., Kapralos, B., Facial expression detection employing a brain computer interface (2018) International Conference on Information, Intelligence, Systems and Applications, Zakynthos, Greece; Li, J., Soladie, C., Seguier, R., LTP-ML : Micro-Expression detection by recognition of local temporal pattern of facial movements (2018) International Conference on Automatic Face & Gesture Recognition, Xian, China; Murtaza, M., Sharif, M., AbdullahYasmin, M., Ahmad, T., Facial expression detection using six facial expressions hexagon (SFEH) model (2019) IEEE 9th Annual Computing and Communication Workshop and Conference; Verburg, M., Menkovski, V., Micro-expression detection in long videos using optical flow and recurrent neural networks (2019) International Conference on Automatic Face & Gesture Recognition, Lille, France; Li, Y., Zeng, J., Shan, S., Chen, X., Occlusion aware facial expression recognition using cNN with attention mechanism (2019) IEEE Transactions on Image Processing, 28 (5), pp. 2439-2450; Li, J., Lam, E.Y., Facial expression recognition using deep neural networks (2015) IEEE International Conference on Imaging Systems and Techniques, Macau, China; Shin, M., Kim, M., Kwon, D.-S., Baseline cNN structure analysis for facial expression recognition robot and human interactive communication (2016) IEEE International Symposium On, NY, USA; Anila, S., Devarajan, N., Preprocessing technique for face recognition applications (2012) Global journal of computer science and technology, 12 (11), pp. 14-18; Shan, L., Weihong, D., JunPing, D., Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild (2017) Computer Vision and Pattern Recognition, Honolulu, USA; Barsoum, E., Zhang, C., Ferrer, C.C., Zhang, Z., Training deep networks for facial expression recognition with crowd-Sourced label distribution (2016) 18th ACM International Conference on Multimodal Interaction, Tokyo Japan; Ren, S., Cao, X., Wei, Y., Sun, J., Face alignment at 3000 fPS via regressing local binary features (2014) IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA; Patel, S., Goswami, M., Comparative analysis of histogram equalization techniques (2014) International Conference on Contemporary Computing and Informatics, Mysore, India",,,,"Institute of Electrical and Electronics Engineers Inc.","2021 International Conference on System, Computation, Automation and Networking, ICSCAN 2021","30 July 2021 through 31 July 2021",,171607,,9781665439862,,,"English","Int. Conf. Syst., Comput., Autom. Netw., ICSCAN",Conference Paper,"Final","",Scopus,2-s2.0-85115695590
"Chen W., Zhang B., Yao J., Zhao X.","57345074600;57218441703;57221419488;16551339800;","Control of SEA Modular Joint for Rehabilitation Exoskeleton Based on Modified Model Predictive Control",2021,"2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2021",,,,"527","532",,,"10.1109/CYBER53097.2021.9588326","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119350381&doi=10.1109%2fCYBER53097.2021.9588326&partnerID=40&md5=a43c84b6129c5ed786b6e5967ec80e0f","State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China","Chen, W., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China; Zhang, B., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China; Yao, J., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China; Zhao, X., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China","The lower limb rigid rehabilitation exoskeleton is research emphasis in lower limb disability assistance and rehabilitation. Modular joints integration in exoskeleton provides high-efficient human-robot interaction. However, it is difficult to achieve miniaturization of structure design. Meanwhile, the lack of constraint design in control makes it difficult to meet the requirements of interaction. A novel SEA modular joint is proposed and the related control problem under constraints are established in this paper. Benefited from interval switching strategy, the proposed SEA modular joint has variable stiffness characteristics while the control complexity introduced by nonlinearity is reduced. On the control of SEA modular joint, the framework of model predictive control (MPC) is applied. The iterative linearization is presented to resolve the nonlinearity of system and the modified cost function in MPC is presented to achieve the prescribed degree of stability. The trajectory tracking and disturbance experiments are conducted. The results show that the proposed method can realize the tracking of reference trajectory while satisfying control constraints. The controller can reject disturbance effectively and modified cost function enhances MPC performance significantly. © 2021 IEEE.",,"Cost functions; Exoskeleton (Robotics); Human robot interaction; Iterative methods; Joints (anatomy); Predictive control systems; Cost-function; High efficient; Humans-robot interactions; Is researches; Lower limb; Miniaturisation; Model-predictive control; Modified model; Modular joints; Structure design; Model predictive control",,,,,"2019-Z12; China Postdoctoral Science Foundation: 2019M661157","*This work is supported by the Foundation of State Key Laboratory of Robotics under Grant Number (2019-Z12) and China Postdoctoral Science Foundation under Grant Number (2019M661157).",,,,,,,,,,"Shi, D., Zhang, W., Zhang, W., A review on lower limb rehabilitation exoskeleton robots[J] (2019) Chinese Journal of Mechanical Engineering, 32 (1), p. 74; Yan, T., Cempini, M., Oddo, C.M., Review of assistive strategies in powered lower-limb orthoses and exoskeletons[J] (2015) Robotics and Autonomous Systems, 64, pp. 120-136; Zhao, X.G., Tan, X.W., Zhang, B., Development of soft lower extremity exoskeleton and its key technologies: A survey[j] (2020) ROBOT, 42 (3), pp. 365-384; Kwa, H.K., Noorden, J.H., Missel, M., Development of the IHMC mobility assist exoskeleton[C] (2009) IEEE International Conference on Robotics and Automation, pp. 2556-2562; Sankai, Y., HAL: Hybrid assistive limb based on cybernics[M] (2010) Robotics Research, pp. 25-34. , Springer, Berlin, Heidelberg; Jiang, X.Z., (2011) Servo Control of Joint Driven by Two Pneumatic Muscles in Opposing Pair Configuration for Rehabilitation Robot[D], , Wuhan: Huazhong University of Science and Technology; Pratt, G., Williamson, M., Series elastic actuators[C] (1995) IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots, 1, pp. 399-406; Tagliamonte, N., Accoto, D., Passivity constraints for the impedance control of series elastic actuators[J]. Proceedings of the Institution of Mechanical Engineers, Part i (2014) Journal of Systems and Control Engineering, 228 (3), pp. 138-153; Kong, K., Bae, J., Tomizuka, M., A compact rotary series elastic actuator for human assistive systems[J] (2011) IEEE/ ASME Transactions on Mechatronics, 17 (2), pp. 288-297; Calanca, A., Fiorini, P., Human-Adaptive control of series elastic actuators[J] (2014) Robotica, 32 (8), p. 1301; Zhu, Q.G., Xiong, R., Lu, C.J., Novel series elastic actuator design and velocity control[J] (2015) Electric Machines and Control, 19 (7), pp. 83-88; Wang, M., Sun, L., Yin, W., A finite time output feedback control approach for interaction-oriented series elastic Actuators[J] (2016) ROBOT, 38 (5), pp. 513-521; Kim, S., Bae, J., Force-mode control of rotary series elastic actuators in a lower extremity exoskeleton using model-inverse time delay control[J] (2017) IEEE/ ASME Transactions on Mechatronics, 22 (3), pp. 1392-1400; (2016) Springer Handbook of Robotics, pp. 287-295. , [M] Springer; (2012) Nonlinear Model Predictive Control[M], , Birkhäuser; Wang, L., (2009) Model Predictive Control System Design and Implementation Using MATLAB®[ M], , Springer Science & Business Media; Khalil, H.K., (2014) Nonlinear Control[M], , Pearson Higher Ed","Chen, W.; State Key Laboratory of Robotics, China; email: chenwanxin@sia.cn",,"IEEE Robotics and Automation Society;Shenyang Institute of Automation CAS;Shenzhen Academy of Robotics","Institute of Electrical and Electronics Engineers Inc.","11th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2021","27 July 2021 through 31 July 2021",,173761,,9781665425278,,,"English","IEEE Annu. Int. Conf. CYBER Technol. Autom., Control, Intell. Syst., CYBER",Conference Paper,"Final","",Scopus,2-s2.0-85119350381
"Huo Y., Li X., Zhang X., Sun D.","57215558671;57225158100;57259736300;56220414100;","Intention-driven variable impedance control for physical human-robot interaction",2021,"IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM","2021-July",,,"1220","1225",,,"10.1109/AIM46487.2021.9517438","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114961221&doi=10.1109%2fAIM46487.2021.9517438&partnerID=40&md5=f7e4fab7d7b7c69fdc8b4197fd56bb3d","City University of Hong Kong, Department of Biomedical Engineering, Hong Kong; Tsinghua University, Department of Automation, China; Tsinghua Shenzhen International Graduate School, China","Huo, Y., City University of Hong Kong, Department of Biomedical Engineering, Hong Kong; Li, X., Tsinghua University, Department of Automation, China; Zhang, X., Tsinghua Shenzhen International Graduate School, China; Sun, D., City University of Hong Kong, Department of Biomedical Engineering, Hong Kong","The technology of physical human-robot interaction (pHRI) aims to combine the advantages of both humans and robots, and it is now playing a significant role in many industrial and medical applications. However, the open issues of safety and efficiency have not been systematically addressed for pHRI. In this paper, a new intention-driven variable impedance controller is proposed for compliantly-driven robots, where the robot is controlled to track the estimated human motion intention under a variable desired impedance model. The human motion intention is represented with the desired angle of the human limb and approximated with adaptive neural networks (NNs). Then, the impedance parameters are adjusted online according to the offset between the desired angle and the current joint configuration of the robot. Such a formulation allows the robot to predict human behavior for better collaboration and hence improve the safety and the efficiency of pHRI. The stability of the closed-loop system is rigorously proved with Lyapunov methods, and experiments on a lower-limb exoskeleton are conducted to evaluate the performance of the proposed controller. © 2021 IEEE.",,"Behavioral research; Closed loop systems; Compliance control; Efficiency; Exoskeleton (Robotics); Industrial robots; Intelligent mechatronics; Lyapunov methods; Man machine systems; Medical applications; Medical robotics; Adaptive neural networks; Human motion intention; Impedance parameters; Joint configuration; Physical human-robot interactions; Physical humanrobot interaction (phri); Safety and efficiencies; Variable impedance control; Social robots",,,,,"National Natural Science Foundation of China, NNSFC: 61803321","Y. Huo and D. Sun are with the Department of Biomedical Engineering, City University of Hong Kong, Hong Kong. X. Li is with the Department of Automation, Tsinghua University, China. X. Zhang is with the Tsinghua Shenzhen International Graduate School, China. This work was supported by the National Natural Science Foundation of China under Grant no. 61803321. Corresponding author: Xiang Li (xiangli@tsinghua.edu.cn).",,,,,,,,,,"Li, X., Chi, G., Vidas, S., Cheah, C.C., Human-guided robotic co-manipulation: Two illustrative scenarios (2016) IEEE Transactions on Control Systems Technology, 24 (5), pp. 1751-1763; Li, X., Pan, Y., Chen, G., Yu, H., Multi-modal control scheme for rehabilitation robotic exoskeletons (2017) The International Journal of Robotics Research, 36 (5-7), pp. 759-777; Dollar, A.M., Herr, H., Lower extremity exoskeletons and active orthoses: Challenges and Stage-of-the Art (2008) IEEE Trans. Robotics, 24 (1), pp. 144-158; Kazerooni, H., Racine, J.L., Huang, L., Steger, R., On the control of the Berkeley Lower Extremity Exoskeleton (BLEEX) (2005) IEEE Int. Conf. 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Robotics, 31 (3), pp. 672-681; Zhang, J., Cheah, C.C., Passivity and stability of human-robot interaction control for upper-limb rehabilitation robots (2015) IEEE Trans. Robotics, 31 (2), pp. 233-245; Dietrich, A., Ott, C., Hierarchical impedance-based tracking control of kinematically redundant robots (2019) IEEE Transactions on Robotics; Ferraguti, F., Landi, C.T., Sabattini, L., Bonfe, M., Fantuzzi, C., Secchi, C., A variable admittance control strategy for stable physical human-robot interaction (2019) The International Journal of Robotics Research, 38 (6), pp. 747-765; Pratt, G.A., Williamson, M.M., Series elastic actuators (1995) IEEE/RSJ Int. Conf. Intelligent Robots Syst., pp. 399-406; Li, X., Pan, Y., Chen, G., Yu, H., Adaptive human-robot interaction control for robots driven by series elastic actuators (2017) IEEE Transactions on Robotics, 33 (1), pp. 169-182; Arimoto, S., (1996) Control Theory of Non-Linear Mechanical Systems., , Oxford University Press; Cheah, C.C., Li, X., (2015) Task-Space Sensory Feedback Control of Robot Manipulators, , Springer; Li, X., Cheah, C.C., Adaptive neural network control of robot based on a unified objective bound (2013) IEEE Transactions on Control Systems Technology, 22 (3), pp. 1032-1043; Cheah, C.C., Wang, D., Learning impedance control for robotic manipulators (1998) IEEE Trans. Robot. Automat., 14 (3), pp. 452-465; Li, X., Liu, Y.-H., Yu, H., Iterative learning impedance control for rehabilitation robots driven by series elastic actuators (2018) Automatica, 90, pp. 1-7; Li, X., Liu, Y.-H., Yu, H., Adaptive impedance control for compliantly actuated robots with a unified safety measure (2018) The World Congress on Intelligent Control and Automation, pp. 444-449","Li, X.; Tsinghua University, China; email: xiangli@tsinghua.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021","12 July 2021 through 16 July 2021",,171450,,9781665441391,,,"English","IEEE ASME Int Conf Adv Intellig Mechatron AIM",Conference Paper,"Final","",Scopus,2-s2.0-85114961221
"Xie Q., Meng Q., Zeng Q., Dai Y., Wu Z., Chen L., Yu H.","57210424487;55811973700;57222965494;57222961905;57210791553;57229971800;57223021211;","Design of a Soft Wrist Exoskeleton Based on SMA Actuator Module [基于SMA驱动模块的腕部柔性外骨骼设计]",2021,"Jiqiren/Robot","43","4",,"406","413",,,"10.13973/j.cnki.robot.200584","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113373358&doi=10.13973%2fj.cnki.robot.200584&partnerID=40&md5=180681c7cc5b50bb04076536e06353cf","Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai, 200093, China; Shanghai Engineering Research Center of Assistive Devices, Shanghai, 200093, China; Key Laboratory of Neural-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, 200093, China","Xie, Q., Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai, 200093, China, Shanghai Engineering Research Center of Assistive Devices, Shanghai, 200093, China, Key Laboratory of Neural-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, 200093, China; Meng, Q., Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai, 200093, China, Shanghai Engineering Research Center of Assistive Devices, Shanghai, 200093, China, Key Laboratory of Neural-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, 200093, China; Zeng, Q., Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai, 200093, China, Shanghai Engineering Research Center of Assistive Devices, Shanghai, 200093, China, Key Laboratory of Neural-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, 200093, China; Dai, Y., Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai, 200093, China, Shanghai Engineering Research Center of Assistive Devices, Shanghai, 200093, China, Key Laboratory of Neural-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, 200093, China; Wu, Z., Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai, 200093, China, Shanghai Engineering Research Center of Assistive Devices, Shanghai, 200093, China, Key Laboratory of Neural-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, 200093, China; Chen, L., Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai, 200093, China, Shanghai Engineering Research Center of Assistive Devices, Shanghai, 200093, China, Key Laboratory of Neural-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, 200093, China; Yu, H., Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai, 200093, China, Shanghai Engineering Research Center of Assistive Devices, Shanghai, 200093, China, Key Laboratory of Neural-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, 200093, China","A soft wrist exoskeleton based on shape memory alloy (SMA) actuator module is proposed to address the problems of exoskeleton robots in bionics, portability and human-robot interaction. Firstly, the maximum recoverable tensile length and phase transition temperature characteristics of SMA spring are studied, and a hybrid model of SMA spring and manganese steel for SMA actuator module is established. Then, the parameters of the hybrid model are optimized according to the motion range and the required tension of human wrist joint. Finally, a cooling method for SMA springs is proposed by mixing elastic element potential energy, air cooling and high and low current switching, and a control method is designed based on motion angle and phase transition temperature feedback. To verify the practical effect of the soft wrist exoskeleton, wear training and temperature measurement experiments are carried out. The experimental results show that the bionics and flexibility of the exoskeleton conforms to the movement characteristics of the human wrist, and its light weight and portability can improve the assistance effect in daily living activities. © 2021, Science Press. All right reserved.","Bionics; Flexible actuator; Shape memory alloy (SMA); Soft exoskeleton; Wrist exoskeleton","Biofeedback; Bionics; Human robot interaction; Manganese steel; Microactuators; Motion control; Potential energy; Shape-memory alloy; Springs (components); Temperature; Temperature measurement; Control methods; Cooling methods; Daily living activities; Elastic element; Exoskeleton robots; Movement characteristics; Shape memory alloy actuators; SMA actuators; Exoskeleton (Robotics)",,,,,,,,,,,,,,,,"Zhou, M G, Wang, H D, Zeng, X Y, Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017 (2019) The Lancet, 394 (10204), pp. 1145-1158. , [J]; Liu, Z W, Zhao, L, Yu, P, A control method of grasping force for soft exoskeleton hand (2019) Robot, 41 (4), pp. 483-492. , 刘自文, 赵亮, 于鹏, 等. 柔性外骨骼手的抓取力控制方法[J]. 机器人, 2019, 41(4): 483-492. 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[J]; Higuma, T, Kiguchi, K, Arata, J., Low-profile two-degree-of-freedom wrist exoskeleton device using multiple spring blades (2018) IEEE Robotics and Automation Letters, 3 (1), pp. 305-311. , [J]; Park, S J, Park, C H., Suit-type wearable robot powered by shape-memory-alloy-based fabric muscle (2019) Scientific Reports, p. 9. , [J]; Park, S J, Kim, U, Park, C H., A novel fabric muscle based on shape memory alloy springs (2020) Soft Robotics, 7 (3), pp. 321-331. , [J]; Jeong, J, Yasir, I B, Han, J, Design of shape memory alloy-based soft wearable robot for assisting wrist motion (2019) Applied Sciences, 9 (19). , [J]; Zhang, Z Q, Zou, J, Ding, J N, Research status of the soft robot driving (2018) Robot, 40 (5), pp. 648-659. , 张忠强, 邹娇, 丁建宁, 等. 软体机器人驱动研究现状[J]. 机器人, 2018, 40(5): 648-659. [J]; Tian, J W, Wang, T M, Shi, Z Y, Kinematics analysis and experiment of an elephant-trunk-like robot arm (2017) Robot, 39 (5), pp. 585-594. , 杨浩. 仿象鼻机械臂的运动学分析及实验[J]. 机器人, 2017, 39(5): 585-594. [J]; Tian, J W, Wang, T M, Shi, Z Y, Kinematics analysis and experiment of an elephant-trunk-like robot arm (2017) Robot, 39 (5), pp. 585-594. , 田加文, 王田苗, 史震云, 等. 仿象鼻机械臂的运动学分析及实验[J]. 机器人, 2017, 39(5): 585-594. [J]; Howell, L L., (2007), 柔顺机构学[M]. 北京: 高等教育出版社, 2007. Howell L L. Compliance mechanism[M]. Beijing: Higher Education Press; Neumann, D A., (2003), 基于Matlab的人体上肢运动分析及仿真[D]. 天津: 天津科技大学, 2003. Zhang A L. Analysis and simulation of human upper limb movement based on Matlab[D]. Tianjin: Tianjin University of Science and Technology; Zhang, A L., Analysis and simulation of human upper limb movement based on Matlab (2003), 张爱丽. 基于Matlab的人体上肢运动分析及仿真[D]. 天津: 天津科技大学, 2003. [D]. Tianjin: Tianjin University of Science and Technology; Li, R., Characteristic analysis and control research of shape memory alloy flexible actuator (2019), 李瑞. 形状记忆合金柔性驱动器的特性分析与控制研究[D]. 广州: 华南理工大学, 2019. [D]. Guangzhou: South China University of Technology; Li, X G, Zhang, B, Zhao, X G, Adaptive backstepping sliding mode controller for a shape memory alloy actuator (2020) Control Theory & Applications, 37 (1), pp. 137-146. , 李晓光, 张弼, 赵新刚, 等. 形状记忆合金驱动器的自适应滑模反步控制[J]. 控制理论与应用, 2020, 37(1): 137-146. [J]","Meng, Q.; Rehabilitation Engineering and Technology Institute, China; email: qiaoling_meng@126.com
Yu, H.; Rehabilitation Engineering and Technology Institute, China; email: yhl98@hotmail.com",,,"Chinese Academy of Sciences",,,,,10020446,,JIQIE,,"Chinese","Jiqiren",Article,"Final","",Scopus,2-s2.0-85113373358
"Song Y., Luximon A., Luximon Y.","57205427691;6507673422;26656778300;","The effect of facial features on facial anthropomorphic trustworthiness in social robots",2021,"Applied Ergonomics","94",,"103420","","",,4,"10.1016/j.apergo.2021.103420","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103685732&doi=10.1016%2fj.apergo.2021.103420&partnerID=40&md5=bd9280635769f5d5cb8e8978aa7629eb","School of Design, The Hong Kong Polytechnic University, Hung Hom, Hong Kong; EMEDS Ltd., Kowloon, Hong Kong, China","Song, Y., School of Design, The Hong Kong Polytechnic University, Hung Hom, Hong Kong; Luximon, A., EMEDS Ltd., Kowloon, Hong Kong, China; Luximon, Y., School of Design, The Hong Kong Polytechnic University, Hung Hom, Hong Kong","As the nature of human-robot relationships have become increasingly bound to shift from supervisor–machine to friend–companion, people have exhibited an increasing interest in making social judgments toward such anthropomorphic objects, such as trustworthiness. However, the facial features of social robots and their potential effect on anthropomorphic trustworthiness are seldom analyzed and discussed comprehensively. This study examined whether the trustworthiness perception toward a social robot shared similarity with baby schema features on the human face. It also explored the effects of different combinations of baby schema facial features, especially the positions and sizes of the eyes and mouth, on facial anthropomorphic trustworthiness. A 5-way mixed experiment (N = 270) was conducted accordingly. The results indicated that people would experience a high level of facial anthropomorphic trustworthiness toward robots with baby schema features (i.e., large eyes, with medium vertical and horizontal positions of the eyes and mouth). This paper contributes to the literature on facial anthropomorphic trustworthiness in human-robot interaction and provides suggestions for social robot design. © 2021 Elsevier Ltd","Baby schema; Eyes; Facial anthropomorphic trustworthiness; Mouth; Social robot","Anthropomorphic robots; Machine design; Baby schema; Eye; Facial anthropomorphic trustworthiness; Facial feature; Human faces; Human robots; Humans-robot interactions; Mouth; Potential effects; Social robots; Social robots; article; controlled study; face; facies; female; human; human experiment; infant; major clinical study; male; mouth; perception; robotics; facial expression; social interaction; trust; Facial Expression; Humans; Robotics; Social Interaction; Social Perception; Trust",,,,,"15603419; University Grants Committee, UGC; Research Grants Council, University Grants Committee, RGC, UGC; Hong Kong Polytechnic University, PolyU","This research was funded by GRF PolyU. 15603419 from the Research Grants Council of Hong Kong and UGC Funding Scheme (RL6B) from the Hong Kong Polytechnic University.",,,,,,,,,,"Berry, D.S., Attractive faces are not all created equal: joint effects of facial babyishness and attractiveness on social perception (1991) Pers. 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Res., 168, pp. 242-249; Venturoso, L., Gabrieli, G., Truzzi, A., Azhari, A., Setoh, P., Bornstein, M.H., Esposito, G., Effects of baby schema and mere exposure on explicit and implicit face processing (2019) Front. Psychol., 10, p. 2649; Walters, M.L., The Design Space for Robot Appearance and Behaviour for Social Robot Companions (2008), University of Hertfordshire; Westlund, J.K., Lee, J.J., Plummer, L., Faridi, F., Gray, J., Berlin, M., Quintus-Bosz, H., Breazeal, C., Tega: a social robot (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), , IEEE 561–561; Xu, J., Cenfetelli, R.T., Aquino, K., Do different kinds of trust matter? An examination of the three trusting beliefs on satisfaction and purchase behavior in the buyer–seller context (2016) J. Strat. Inf. Syst., 25, pp. 15-31; Yovel, G., Duchaine, B., Specialized face perception mechanisms extract both part and spacing information: evidence from developmental prosopagnosia (2006) J. Cognit. Neurosci., 18, pp. 580-593; Yuan, X., Xu, J., Hussain, S., Wang, H., Gao, N., Zhang, L., Trends and prediction in daily new cases and deaths of COVID-19 in the United States: an internet search-interest based model (2020) Explor. Res. Hypothesis Med., pp. 1-6. , 000; Zebrowitz, L.A., Montepare, J.M., Social psychological face perception: why appearance matters (2008) Soc. Personal. Psychol. Compass, 2, pp. 1497-1517","Luximon, Y.; Jockey Club Innovation Tower (Block V), Hung Hom, Hong Kong; email: yan.luximon@polyu.edu.hk",,,"Elsevier Ltd",,,,,00036870,,AERGB,"33823378","English","Appl. Ergon.",Article,"Final","",Scopus,2-s2.0-85103685732
"Tanneberg D., Ploeger K., Rueckert E., Peters J.","57133071600;57207943024;56743244800;35248912800;","SKID RAW: Skill Discovery from Raw Trajectories",2021,"IEEE Robotics and Automation Letters","6","3","9387162","4696","4703",,2,"10.1109/LRA.2021.3068891","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103301482&doi=10.1109%2fLRA.2021.3068891&partnerID=40&md5=475af40a169a76a2cfbe4dd09a917de9","Intelligent Autonomous Systems, Technische Universität Darmstadt, Darmstadt, Germany; Institute for Robotics and Cognitive Systems, Universität zu Lübeck, Lübeck, Germany","Tanneberg, D., Intelligent Autonomous Systems, Technische Universität Darmstadt, Darmstadt, Germany; Ploeger, K., Intelligent Autonomous Systems, Technische Universität Darmstadt, Darmstadt, Germany; Rueckert, E., Institute for Robotics and Cognitive Systems, Universität zu Lübeck, Lübeck, Germany; Peters, J., Intelligent Autonomous Systems, Technische Universität Darmstadt, Darmstadt, Germany","Integrating robots in complex everyday environments requires a multitude of problems to be solved. One crucial feature among those is to equip robots with a mechanism for teaching them a new task in an easy and natural way. When teaching tasks that involve sequences of different skills, with varying order and number of these skills, it is desirable to only demonstrate full task executions instead of all individual skills. For this purpose, we propose a novel approach that simultaneously learns to segment trajectories into reoccurring patterns and the skills to reconstruct these patterns from unlabelled demonstrations without further supervision. Moreover, the approach learns a skill conditioning that can be used to understand possible sequences of skills, a practical mechanism to be used in, for example, human-robot-interactions for a more intelligent and adaptive robot behaviour. The Bayesian and variational inference based approach is evaluated on synthetic and real human demonstrations with varying complexities and dimensionality, showing the successful learning of segmentations and skill libraries from unlabelled data. © 2016 IEEE.","Deep learning methods; learning categories and concepts; representation learning","Agricultural robots; Bayesian; Human demonstrations; Individual skills; Task executions; Variational inference; Human robot interaction",,,,,"SKILLS4ROBOTS; Nvidia; Horizon 2020 Framework Programme, H2020: 640554, 713010; Deutsche Forschungsgemeinschaft, DFG: 430054590","Manuscript received December 2, 2020; accepted March 8, 2021. Date of publication March 25, 2021; date of current version April 13, 2021. This letter was recommended for publication by Associate Editor M. Burke and Editor D. Kulic upon evaluation of the reviewers’ comments. This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 713010 (GOAL-Robots) and 640554 (SKILLS4ROBOTS), in part by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -No. 430054590 (TRAIN), and in part by NVIDIA. (Corresponding author: Daniel Tanneberg.) Daniel Tanneberg and Kai Ploeger are with the Intelligent Autonomous Systems, Technische Universität Darmstadt, 64289 Darmstadt, Germany (e-mail: daniel@robot-learning.de; kai.ploeger@tu-darmstadt.de).",,,,,,,,,,"Lake, B.M., Ullman, T.D., Tenenbaum, J.B., Gershman, S.J., Building machines that learn and think like people (2017) Behav. Brain Sci., 40, p. e253; Argall, B.D., Chernova, S., Veloso, M., Browning, B., A survey of robot learning from demonstration (2009) Robot. Auton. Syst., 57 (5), pp. 469-483; Lioutikov, R., Neumann, G., Maeda, G., Peters, J., Learning movement primitive libraries through probabilistic segmentation (2017) Int. J. Robot. Res., 36 (8), pp. 879-894; Niekum, S., Osentoski, S., Konidaris, G., Barto, A.G., Learning and generalization of complex tasks from unstructured demonstrations (2012) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 5239-5246; Kadmon Harpaz, N., Ungarish, D., Hatsopoulos, N.G., Flash, T., Movement decomposition in the primary motor cortex (2019) Cereb. Cortex, 29 (4), pp. 1619-1633; Goodrich, M.A., Schultz, A.C., (2008) Human-Robot Interaction: A Survey, , Boston, MA, USA: Now Publishers Inc; Kingma, D.P., Welling, M., Auto-encoding variational bayes (2014) Proc. Int. Conf. Learn. Representations; Eslami, S.A., Attend, infer, repeat: Fast scene understanding with generative models (2016) Proc. Adv. Neural Inf. Process. Syst., pp. 3225-3233; Kipf, T., Compile: Compositional imitation learning and execution (2019) Proc. Int. Conf. Mach. Learn., pp. 3418-3428; Osa, T., Ikemoto, S., Goal-conditioned variational autoencoder trajectory primitives with continuous and discrete latent codes (2020) SN Comput. Sci., 1 (5), pp. 1-10; Noseworthy, M., Paul, R., Roy, S., Park, D., Roy, N., Task-conditioned variational autoencoders for learning movement primitives (2020) Proc. Conf. Robot Learn., pp. 933-944; Steinmetz, F., Nitsch, V., Stulp, F., Intuitive task-level programming by demonstration through semantic skill recognition (2019) IEEE Robot. Automat. Lett., 4 (4), pp. 3742-3749. , Oct; Manschitz, S., Kober, J., Gienger, M., Peters, J., Learning movement primitive attractor goals and sequential skills from kinesthetic demonstrations (2015) Robot. Auton. Syst., 74, pp. 97-107; Shankar, T., Gupta, A., Learning robot skills with temporal variational inference (2020) Proc. Int. Conf. Mach. Learn., pp. 8624-8633; Su, Z., Kroemer, O., Loeb, G.E., Sukhatme, G.S., Schaal, S., Learning manipulation graphs from demonstrations using multimodal sensory signals (2018) Proc. IEEE Int. Conf. Robot. Automat., pp. 2758-2765; Zhang, H., Han, X., Zhang, W., Zhou, W., Complex sequential tasks learning with Bayesian inference and Gaussian mixture model (2018) Proc. IEEE Int. Conf. Robot. Biomimetics, pp. 1927-1934; Ranchod, P., Rosman, B., Konidaris, G., Nonparametric Bayesian reward segmentation for skill discovery using inverse reinforcement learning (2015) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 471-477; Kroemer, O., Daniel, C., Neumann, G., VanHoof, H., Peters, J., Towards learning hierarchical skills for multi-phase manipulation tasks (2015) Proc. IEEE Int. Conf. Robot. Automat., pp. 1503-1510; Burke, M., Hristov, Y., Ramamoorthy, S., Hybrid system identification using switching density networks (2020) Proc. Conf. Robot Learn., pp. 172-181. , PMLR; Burke, M., Penkov, S., Ramamoorthy, S., From explanation to synthesis: Compositional program induction for learning from demonstration (2019) Proc. Robot. : Sci. Syst.; Jaderberg, M., Spatial transformer networks (2015) Proc. Adv. Neural Inf. Process. Syst., pp. 2017-2025; Higgins, I., Beta-vae: Learning basic visual concepts with a constrained variational framework (2017) Proc. Int. Conf. Learn. Representations; Burgess, C.P., (2018) Understanding Disentangling in Beta-vae; Dieng, A.B., Kim, Y., Rush, A.M., Blei, D.M., Avoiding latent variable collapse with generative skip models (2019) Proc. 22nd Int. Conf. Artif. Intell. Statist., pp. 2397-2405; Jang, E., Gu, S., Poole, B., Categorical reparameterizationwith gumbelsoftmax (2017) Proc. Int. Conf. Learn. Representations; Maddison, C.J., Mnih, A., Teh, Y.W., The concrete distribution: A continuous relaxation of discrete random variables (2017) Proc. Int. Conf. Learn. Representations; Dupont, E., Learning disentangled joint continuous and discrete representations (2018) Proc. Adv. Neural Inf. Process. Syst., pp. 710-720; Burda, Y., Grosse, R., Salakhutdinov, R., Importance weighted autoencoders (2016) Proc. Int. Conf. Learn. Representations; Koert, D., Pajarinen, J., Schotschneider, A., Trick, S., Rothkopf, C., Peters, J., Learning intention aware online adaptation of movement primitives (2019) IEEE Robot. Automat. Lett., 4 (4), pp. 3719-3726. , Oct; Kingma, D.P., Ba, J., Adam: A method for stochastic optimization (2015) Proc. Int. Conf. Learn. Representations; Loshchilov, I., Hutter, F., Decoupled weight decay regularization (2019) Proc. Int. Conf. Learn. Representations; Zhao, T., Lee, K., Eskenazi, M., Unsupervised discrete sentence representation learning for interpretable neural dialog generation (2018) Proc. 56th Annu. Meeting Assoc. Comput. Linguistics (Volume 1: Long Papers), pp. 1098-1107; Paraschos, A., Daniel, C., Peters, J., Neumann, G., Using probabilistic movement primitives in robotics (2018) Auton. Robots, 42 (3), pp. 529-551; Adams, R.P., MacKay, D.J., (2007) Bayesian Online Changepoint Detection; Agudelo-España, D., Gomez-Gonzalez, S., Bauer, S., Schölkopf, B., Peters, J., Bayesian online prediction of change points (2020) Proc. Conf. Uncertainty Artif. Intell., pp. 320-329. , PMLR","Tanneberg, D.; Intelligent Autonomous Systems, Germany; email: daniel@robot-learning.de",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85103301482
"Schollmann S., Try P., Gebhard M., Wohle L.","57226632110;57226631649;56785513800;57203154747;","Sensors for assistive robotic drinking with physical contact",2021,"2021 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2021 - Conference Proceedings",,,"9478687","","",,1,"10.1109/MeMeA52024.2021.9478687","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112129080&doi=10.1109%2fMeMeA52024.2021.9478687&partnerID=40&md5=696398a1243bb61241463f1b1ce1456e","Westphalian University of Applied Sciences, Electrical Engineering and Applied Sciences, Gelsenkirchen, Germany","Schollmann, S., Westphalian University of Applied Sciences, Electrical Engineering and Applied Sciences, Gelsenkirchen, Germany; Try, P., Westphalian University of Applied Sciences, Electrical Engineering and Applied Sciences, Gelsenkirchen, Germany; Gebhard, M., Westphalian University of Applied Sciences, Electrical Engineering and Applied Sciences, Gelsenkirchen, Germany; Wohle, L., Westphalian University of Applied Sciences, Electrical Engineering and Applied Sciences, Gelsenkirchen, Germany","This paper proposes two sensor systems for human robot interaction in assistive drinking tasks for motion impaired people. The first sensor system uses a capacitive and resistive sensor to detect proximity and measure contact forces between the user and a regular drinking cup. The cup is held by a robot with a three finger gripper. Control strategies for the drinking process with a regular cup using both the capacitive and resistive measurements were developed and tested. The control strategies were rated by the subjects with a NASA RTLX questionnaire. Statistically significant differences between both strategies are not evident. The second sensor system uses a System on Chip (SoC) with four sensors. It enables motion impaired users to stop the robot without contact to the robot. This is accomplished with a robust detection of a human blowing on the sensor. A sensor fusion algorithm is used for detection. The results suggest the usage of both sensing methods in further robotic systems. However, the detection of a human blow should undergo further tests with motion impaired people. © 2021 IEEE.","Assistive robots; Drinking task; Human-robot interaction; Input modalities; Sensor fusion","Capacitive sensors; NASA; Programmable logic controllers; Robotics; System-on-chip; Assistive robotics; Control strategies; Physical contacts; Resistive measurements; Resistive sensor; Sensor fusion algorithms; Statistically significant difference; System on chips (SoC); Social robots",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV7866K","This research was funded by Federal Ministry of Education and Research of Germany grant number 16SV7866K.",,,,,,,,,,"Bickenbach, J.E., International perspectives on spinal cord injury. Copenhagen: World Health Organization (2013) European Observatory on Health Systems and Policies, Op; Chung, C.-S., Wang, H., Cooper, R.A., Functional assessment and performance evaluation for assistive robotic manipulators: Literature review (2013) The Journal of Spinal Cord Medicine, 36 (4), pp. 273-289; Volosyak, I., Ivlev, O., Graser, A., Rehabilitation robot FRIEND II - The general concept and current implementation (2005) 9th International Conference on Rehabilitation Robotics, 2005, pp. 540-544. , ICORR Chicago, IL, USA, Jun. 2005; Graser, A., A supportive friend at work: Robotic workplace assistance for the disabled (2013) IEEE Robot. Automat. Mag., 20 (4), pp. 148-159; Huete, A.J., Victores, J.G., Martinez, S., Gimenez, A., Balaguer, C., Personal autonomy rehabilitation in home environments by a portable assistive robot (2012) IEEE Trans. Syst., Man, Cybern. C, 42 (4), pp. 561-570; Schroer, S., An autonomous robotic assistant for drinking (2015) 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 6482-6487. , Seattle, WA, USA, May. - May. 2015; Goldau, F.F., Shastha, T.K., Kyrarini, M., Graser, A., Autonomous multi-sensory robotic assistant for a drinking task (2019) IEEE International Conference on Rehabilitation Robotics, 2019, pp. 210-216; Tausch, A., Adolph, L., (2020) Autonome Roboter Für Assistenzfunktionen: Interaktive Grundfertigkeiten - Ergebnisse und Forschungsperspektiven des Förderprogramms ARA1; (2021) KINOVA Gen2 Ultra Lightweight Robot, , https://www.kinovarobotics.com/en/products/robotic-arms/kinova-gen2-ultra-lightweight-robot, (accessed: Jan. 2); Maheu, V., Frappier, J., Archambault, P.S., Routhier, F., Evaluation of the JACO robotic arm: Clinico-economic study for powered wheelchair users with upper-extremity disabilities (2011) IEEE International Conference on Rehabilitation Robotics : [Proceedings], 2011, p. 5975397; (2021) Tacterion Plyon Datasheet, , https://content.tacterion.com/Data_Sheet.pdf, (accessed: Jan. 2); (2021) Wenko Cup, , https://shop.wenko.de/bad/badaccessoires/zahnputzbecher/zahnputzbecher-brasil-petrol, (accessed: Jan. 2); (2021) Bosch BME680 Datasheet, , https://www.boschsensortec.com/media/boschsensortec/downloads/datasheets/bstbme680-ds001.pdf, (accessed: Jan. 2); (2021) Qualisys Miqus M3 Datasheet, , https://cdncontent.qualisys.com/2020/01/PI_Miqus.pdf, (accessed: Jan. 2); Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., ROS: An open-source robot operating system (2009) ICRA Workshop on Open Source Software, (3), p. 5. , Kobe, Japan; (2016) Roboter und Robotikgeräte_- Kollaborierende Roboter (ISO/TS_15066), , DIN ISO/TS 15066:2017-04, Berlin; Woods, D.L., Wyma, J.M., Yund, E.W., Herron, T.J., Reed, B., Factors influencing the latency of simple reaction time (2015) Frontiers in Human Neuroscience, 9, p. 131",,,"IEEE;IEEE Instrumentation and Measurement Society","Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2021","23 June 2021 through 25 June 2021",,171087,,9781665419147,,,"English","IEEE Int. Symp. Med. Meas. Appl., MeMeA - Conf. Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85112129080
"Prihodova K., Jech J.","57220252435;57210259760;","Gender recognition using thermal images from UAV",2021,"International Conference on Information and Digital Technologies 2021, IDT 2021",,,"9497627","83","88",,,"10.1109/IDT52577.2021.9497627","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112421413&doi=10.1109%2fIDT52577.2021.9497627&partnerID=40&md5=608dd98928532b6d13525fa08ec1cd25","University of Pardubice, Faculty of Economics and Administration, Pardubice, Czech Republic","Prihodova, K., University of Pardubice, Faculty of Economics and Administration, Pardubice, Czech Republic; Jech, J., University of Pardubice, Faculty of Economics and Administration, Pardubice, Czech Republic","Gender recognition is one of the issues that computer vision deals with. It is useful for analysing human behaviour, intelligent tracking, or human-robot interaction. The aim of this paper is to recognise the gender of people in outdoor areas, where it is very difficult or impossible to guard all access roads to the place, even in poor lighting conditions or in the dark. In this paper, a model will be designed and tested using a controlled UAV flight, during which images of people were obtained. The sensor is a thermal camera located on the UAV, which is not dependent on ambient lighting, and deep learning methods are used for subsequent image processing and classification. These are convolutional neural networks (AlexNet, GoogLeNet), which will be used to solve binary classification. Optimized networks achieve classification accuracy of 81.6 %% (GoogLeNet) and 82.3% (AlexNet). A freely available database [21] was used to learn CNNs, and a self-created database (images obtained with a thermal camera attached to a UAV) was used to test the networks. © 2021 IEEE.","convolutional neural networks; gender recognition; thermal image; UAV","Behavioral research; Cameras; Convolutional neural networks; Deep learning; Human robot interaction; Image processing; Infrared devices; Learning systems; Lighting; Ambient lighting; Binary classification; Classification accuracy; Gender recognition; Human behaviours; Intelligent tracking; Learning methods; Lighting conditions; Unmanned aerial vehicles (UAV)",,,,,,"This article was supported by grant No. SGS 2021 08, No. SGS 2021 011, and No. SGS 2020 018 of the Student Grant Competition.",,,,,,,,,,"Wayman, J., (2003) A Definition of "" Biometrics, p. 2023. , Collected works"" US National Biometric Test Center, San Jose State University, August; Kong, A.W.K., Zhang, D., Kamel, M.S., An analysis of iriscode (2010) Ieee Transactions on Image Processing, 19 (2), pp. 522-532; Nguyen, D., Pham, T., Lee, Y., Park, K., Deep learning-based enhanced presentation attack detection for iris recognition by combining features from local and global regions based on nir camera sensor (2018) Sensors, 18 (8); Lajevardi, S.M., Arakala, A., Davis, S.A., Horadam, K.J., Retina verification system based on biometric graph matching (2013) Ieee Transactions on Image Processing, 22 (9), pp. 3625-3635; Wang, D., Lu, H., Yang, M.-H., Kernel collaborative face recognition (2015) Pattern Recognition, 48 (10), pp. 3025-3037; Frumkin, D., Wasserstrom, A., Davidson, A., Grafit, A., Authentication of forensic dna samples (2010) Forensic Science International: Genetics, 4 (2), pp. 95-103; Voice biometrics growth likely (2009) Biometric Technology Today, 17 (6), pp. 3-4; Scheidat, T., Kalbitz, M., Vielhauer, C., Biometric authentication based on 2d/3d sensing of forensic handwriting traces (2017) Et Biometrics, 6 (4), pp. 316-324; Hafs, T., Bennacer, L., Boughazi, M., Nait-Ali, A., Empirical mode decomposition for online handwritten signature verification (2016) Iet Biometrics, 5 (3), pp. 190-199; Lee, T., Belhatir, M., Sanei, S., A comprehensive review of past and present vision-based techniques for gait recognition (2014) Multimedia Tools and Applications, 72 (3), pp. 2833-2869; Yang, Z., Ai, H., Demographic classification with local binary patterns (2007) Icb, pp. 464-473; Dantcheva, A., Elia, P., Ross, A., What else does your biometric data reveal? A survey on soft biometrics (2016) Transactions on Information Forensics and Security, 11 (3), pp. 441-467; Mkinen, E., Raisamo, R., Evaluation of gender classification methods with automatically detected and aligned faces (2008) Ieee Trans. Pattern Analysis and Machine Intelligence, 30 (3), pp. 541-547; Benitez-Restrepo, H.D., Bovik, A.C., Rodriguez Pulecio, C.G., Image quality assessment to enhance infrared face recognition (2017) Ieee International Conference on Image Processing (ICIP), Ieee, pp. 805-809; (2020) Infectious Diseases, , https://www.who.int/topics/infectious-diseases/en/, World Health Organization; Mei, X., Zhang, Y., Zhu, H., Ling, Y., Zou, Y., Zhang, Z., Observations about symptomatic and asymptomatic infections of 494 patients with covid-19 in shanghai, china (2020) American Journal of Infection Control, 48 (9), pp. 1045-1050; Civil Aviation Authority Czech Republic, , https://ww.caa.cz/en/flight-operations/unmanned-aircraft/, Unmanned Aircraft; Loth, S.R., Iscan, M.Y., (2000) Sex Determination Encyclopedia of Forensic Sciences, 1. , San Diego, CA, USA: Academic; Carletti, V., Greco, A., Percannella, G., Vento, M., Age from faces in the deeplearning revolution (2020) Ieee Transactions on Pattern Analysis and Machine Intelligence, 42 (9), pp. 2113-2132; Prihodova, K., Jech, J., Low-cost system for gender recognition using convolutional neural network (2019) 34th International-Business-Information-Management-Association (IBIMA) Conference, pp. 6316-6322. , Madrid, Spain; Kopaczka, M., Kolk, R., Merhof, D., A fully annotated thermal face database and its application for thermal facial expression recognition (2018) Ieee International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1-6. , Houston, TX, USA; Rafique, I., Hamid, A., Naseer, S., Asad, M., Awais, M., Yasir, T., Age and gender prediction using deep convolutional neural networks (2019) 2019 International Conference on Innovative Computing (ICIC), pp. 1-6. , Lahore, Pakistan; Khan, K., Attique, M., Syed, I., Gul, A.J.S., (2019) Automatic Gender Classification through Face Segmentation, 11 (6), p. 770; Geetha, A., Sundaram, M., Vijayakumari, B., Gender classification from face images by mixing the classifier outcome of prime distinct descriptors (2019) Soft Computing, 23 (8), pp. 2525-2535; Nguyen, D.T., Park, K.R., Body-based gender recognition using images from visible and thermal cameras (2016) Sensors, 16 (2); Nguyen, D.T., Park, K.R., Enhanced gender recognition system using an improved histogram of oriented gradient (hog) feature from quality assessment of visible light and thermal images of the human body (2016) Sensors, 16 (7), p. 1134; Davis, N., Pittaluga, F., Panetta, K., Facial recognition using human visual system algorithms for robotic and uav platforms (2013) 2013 Ieee Conference on Technologies for Practical Robot Applications (TePRA), pp. 1-5; Prihodova, K., Jech, J., Prevention of the spread of viral disease using artificial intelligence from data obtained by uavs (2021) Shs Web of Conferences. Edp Sciences, p. 01042; Afifi, M., Uk hands: Gender recognition and biometric identification using a large dataset of hand images (2019) Multimedia Tools and Applications, 78 (15), pp. 20835-20854; Prihodova, K., Hub, M., Hand-based biometric system using convolutional neural networks (2020) Acta Informatica Pragensia, 9 (1), pp. 48-57; Karpathy, A., Johnson, J., Fei-Fei, L., (2020) CS23. In: Convolutional Neural Networks for Visual Recognition [Online], , http://cs231n.github.io/convolutional-networks/, [Webpage]; Ren, S., He, K., Girshick, R., Sun, J., Faster r-cnn: Towards realtime object detection with region proposal networks (2017) Ieee Transactions on Pattern Analysis and Machine Intelligence, 39 (6), pp. 1137-1149; Gao, W., Ai, H., Face gender classification on consumer images in a multiethnic environment (2009) International Conference on Biometrics, pp. 169-178. , Springer: Berlin, Heidelberg; Cao, D., Chen, C., Piccirilli, M., Adjeroh, D., Bourlai, T., Ross, A., Can facial metrology predict gender (2011) 2011 International Joint Conference on Biometrics (IJCB), pp. 1-8; Shan, C., Learning local binary patterns for gender classification on real-world face images (2012) Pattern Recognition Letters, 33 (4), pp. 431-437; Chen, C., Ross, A., Evaluation of gender classification methods on thermal and near-infrared face images (2011) 2011 International Joint Conference on Biometrics (IJCB), pp. 1-8; Wang, S., Gao, Z., He, S., He, M., Ji, Q., Gender recognition from visible and thermal infrared facial images (2016) Multimedia Tools and Applications, 75, pp. 8419-8442",,,,"Institute of Electrical and Electronics Engineers Inc.","2021 International Conference on Information and Digital Technologies, IDT 2021","22 June 2021 through 24 June 2021",,170770,,9781665436922,,,"English","Int. Conf. Inf. Digit. Technol., IDT",Conference Paper,"Final","",Scopus,2-s2.0-85112421413
"Sorrentino A., Mancioppi G., Coviello L., Cavallo F., Fiorini L.","57194094756;57205157293;57204653047;14628324200;56902334800;","Feasibility study on the role of personality, emotion, and engagement in socially assistive robotics: A cognitive assessment scenario",2021,"Informatics","8","2","23","","",,,"10.3390/informatics8020023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106529064&doi=10.3390%2finformatics8020023&partnerID=40&md5=e17865162fd38bd7d103cb4d72d29928","The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, 56025, Italy; Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, 56127, Italy; Department of Industrial Engineering, University of Florence, Florence, 50139, Italy","Sorrentino, A., The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, 56025, Italy, Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, 56127, Italy; Mancioppi, G., The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, 56025, Italy, Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, 56127, Italy; Coviello, L., The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, 56025, Italy, Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, 56127, Italy; Cavallo, F., The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, 56025, Italy, Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, 56127, Italy, Department of Industrial Engineering, University of Florence, Florence, 50139, Italy; Fiorini, L., Department of Industrial Engineering, University of Florence, Florence, 50139, Italy","This study aims to investigate the role of several aspects that may influence human- robot interaction in assistive scenarios. Among all, we focused on semi-permanent qualities (i.e., personality and cognitive state) and temporal traits (i.e., emotion and engagement) of the user profile. To this end, we organized an experimental session with 11 elderly users who performed a cognitive assessment with the non-humanoid ASTRO robot. ASTRO robot administered the Mini Mental State Examination test in Wizard of Oz setup. Temporal and long-term qualities of each user profile were assessed by self-report questionnaires and by behavioral features extrapolated by the recorded videos. Results highlighted that the quality of the interaction did not depend on the cognitive state of the participants. On the contrary, the cognitive assessment with the robot significantly reduced the anxiety of the users, by enhancing the trust in the robotic entity. It suggests that the personality and the affect traits of the interacting user have a fundamental influence on the quality of the interaction, also in the socially assistive context. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Cognitive assessment; Quality of interaction; Social assistive robotics",,,,,,"Regione Toscana: CUP 165.24052017.112000015, POR CREO FESR 2014-2020","This research was pursued within the CloudIA project (Regione Toscana, POR CREO FESR 2014-2020, CUP 165.24052017.112000015).",,,,,,,,,,"Cross, E.S., Hortensius, R., Wykowska, A., From social brains to social robots: Applying neurocognitive insights to human-robot interaction (2019) Philos. Trans. R. Soc. B Biol. Sci, 374, p. 20180024; Rossi, S., Ferland, F., Tapus, A., User profiling and behavioral adaptation for HRI: A survey (2017) Pattern Recognit. Lett, 99, pp. 3-12; Clabaugh, C., Matarić, M., Robots for the people, by the people: Personalizing human-machine interaction (2018) Sci. 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Med. Internet Res, 20, p. e264; Esposito, R., Fracasso, F., Limosani, R., Onofrio, G.D., Sancarlo, D., Cesta, A., Dario, P., Cavallo, F., Engagement during Interaction with Assistive Robots (2018) Neuropsychiatry, 8, pp. 739-744; Mukaka, M.M., A guide to appropriate use of Correlation coefficient in medical research (2012) Malawi Med. J, 24, pp. 69-71; Fiorini, L., Mancioppi, G., Becchimanzi, C., Sorrentino, A., Pistolesi, M., Tosi, F., Cavallo, F., Multidimensional evaluation of telepresence robot: Results from a field trial* (2020) Proceedings of the 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1211-1216. , Naples Italy, 31 August-4 September; Conti, D., Commodari, E., Buono, S., Personality factors and acceptability of socially assistive robotics in teachers with and without specialized training for children with disability (2017) Life Span Disabil, 20, pp. 251-272; Bernotat, J., Eyssel, F., A robot at home-How affect, technology commitment, and personality traits influence user experience in an intelligent robotics apartment (2017) Proceedings of the RO-MAN 2017-26th IEEE International Symposium on Robot and Human Interactive Communication, pp. 641-646. , Lisbon, Portugal, 28 August-1 September; Takayama, L., Pantofaru, C., Influences on proxemic behaviors in human-robot interaction (2009) Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 5495-5502. , St. Louis, MO, USA, 10-15 October; Damholdt, M.F., Nørskov, M., Yamazaki, R., Hakli, R., Hansen, C.V., Vestergaard, C., Seibt, J., Attitudinal change in elderly citizens toward social robots: The role of personality traits and beliefs about robot functionality (2015) Front. Psychol, 6, p. 1701; Petersen, S., Houston, S., Qin, H., Tague, C., Studley, J., The Utilization of Robotic Pets in Dementia Care (2016) J. Alzheimer’s Dis, 55, pp. 569-574; Mordoch, E., Osterreicher, A., Guse, L., Roger, K., Thompson, G., Use of social commitment robots in the care of elderly people with dementia: A literature review (2013) Maturitas, 74, pp. 14-20; Wada, K., Shibata, T., Living With Seal Robots-Its Sociopsychological and Physiological Influences on the Elderly at a Care House (2007) IEEE Trans. Robot, 23, pp. 972-980; Fasola, J., Mataric, M.J., Using Socially Assistive Human-Robot Interaction to Motivate Physical Exercise for Older Adults (2012) Proceedings of the IEEE, 100, pp. 2512-2526. , Wuhan, China, 23-25 March; O’ Dwyer, J., Murray, N., Flynn, R., (2018) Affective computing using speech and eye gaze: A review and bimodal system proposal for continuous affect prediction, , arXiv arXiv:1805.06652; Mehta, Y., Majumder, N., Gelbukh, A., Cambria, E., Recent trends in deep learning based personality detection (2020) Artif. Intell. Rev, 53, pp. 2313-2339; Aranha, R.V., Correa, C.G., Nunes, F.L.S., Adapting software with Affective Computing: A systematic review (2019) IEEE Trans. Affect. Comput, p. 1","Fiorini, L.; Department of Industrial Engineering, Italy; email: laura.fiorini@unifi.it",,,"MDPI AG",,,,,22279709,,,,"English","Informatics",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85106529064
"Wang W., Shi W., Hou Z.-G., Chen B., Liang X., Ren S., Wang J., Peng L.","55904592500;57211080015;57218664616;16177239100;57201802748;57201793633;57204683754;55732146000;","Prediction of Human Voluntary Torques Based on Collaborative Neuromusculoskeletal Modeling and Adaptive Learning",2021,"IEEE Transactions on Industrial Electronics","68","6","9089204","5217","5226",,3,"10.1109/TIE.2020.2991999","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100325732&doi=10.1109%2fTIE.2020.2991999&partnerID=40&md5=b7b7d158ec8cacce78c5913abb94bd6d","State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China","Wang, W., State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Shi, W., State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Hou, Z.-G., State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Chen, B., State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Liang, X., State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Ren, S., State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Wang, J., State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Peng, L., State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China","Surface Electromyography (sEMG) based human-robot interaction has been widely studied, where prediction of human voluntary torques is one of the key issues that have not been well addressed. In this article, a torque prediction method based on collaborative neuromusculoskeletal modeling and adaptive learning, is proposed to overcome the limitation of existing methods. First, an sEMG-torque model is designed in comprehensive consideration of the previous research results, the requirement for subject-specific adjustment and the coupling between the muscle or muscle-tendon length and the adjacent joint angles, where the latter two factors have rarely been considered in the literature. Then, by combining the advantages of the stochastic particle swarm optimization and conjugate gradient algorithms, a collaborative optimization method is designed to calibrate simultaneously the undetermined parameters. Moreover, an adaptive learning method based on Gaussian process regression is proposed to learn and predict the estimation errors in real time, by which it is supposed that the torque prediction accuracy can be improved efficiently. Finally, experiments were carried out to validate the performance of the proposed method. © 1982-2012 IEEE.","Adaptive learning; human-robot interaction; neuromusculoskeletal modeling; parameter calibration; surface electromyography (sEMG) processing","Conjugate gradient method; Forecasting; Human robot interaction; Muscle; Particle swarm optimization (PSO); Stochastic systems; Torque; Adaptive learning; Adaptive learning method; Collaborative optimization; Conjugate gradient algorithms; Gaussian process regression; Neuromusculoskeletal models; Stochastic particle swarm optimizations; Surface electromyography; Learning systems",,,,,"National Natural Science Foundation of China, NSFC: 91648208, 91848110, U1913601; Chinese Academy of Sciences, CAS: XDB32000000; National Key Research and Development Program of China, NKRDPC: 2017YFB1302303","Manuscript received September 7, 2019; revised January 17, 2020 and March 16, 2020; accepted April 14, 2020. Date of publication May 7, 2020; date of current version February 17, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 91648208, Grant 91848110, and Grant U1913601, in part by the National Key R&D Program of China under Grant 2017YFB1302303, and in part by the Strategic Priority Research Program of Chinese Academy of Science under Grant XDB32000000. (Corresponding author: Zeng-Guang Hou.) Weiqun Wang, Weiguo Shi, Xu Liang, Shixin Ren, Jiaxing Wang, and Liang Peng are with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China, and also with the University of Chinese Academy of Sciences, Beijing 100049, China (e-mail: weiqun. wang@ia.ac.cn; shiweiguo2017@ia.ac.cn; liangxu2013@ia.ac.cn; renshixin2015@ia.ac.cn; wangjiaxing2016@ia.ac.cn; liang.peng@ ia.ac.cn).",,,,,,,,,,"Hussain, S., State-of-the-art robotic gait rehabilitation orthoses: Design and control aspects (2014) Neuro Rehabil, 35 (4), pp. 701-709; Lotze, M., Braun, C., Birbaumer, N., Anders, S., Cohen, L.G., Motor learning elicited by voluntary drive (2003) Brain, 126, pp. 866-872; Lebedev, M.A., Nicolelis, M.A.L., Brain-machine interfaces: From basic science to neuroprostheses and neurorehabilitation (2017) Physiological Rev, 97 (2), pp. 767-837; Xie, X., Yu, Z.L., Lu, H., Gu, Z., Li, Y., Motor imagery classification based on bilinear sub-manifold learning of symmetric positive-definite matrices (2017) IEEE Trans. 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Eng, 25 (8), pp. 1125-1134. , Aug; Buchanan, T.S., Lloyd, D.G., Manal, K., Besier, T.F., Neuromusculoskeletal modeling: Estimation of muscle forces and joint moments and movements from measurements of neural command (2004) J. Appl. Biomech, 20 (4), pp. 367-395; Na, Y., Kim, J., Dynamic elbow flexion force estimation through a muscle twitch model and sEMG in a fatigue condition (2017) IEEE Trans. Neural Syst. Rehabil. Eng, 25 (9), pp. 1431-1439. , Sep; Na, Y., Choi, C., Lee, H.D., Kim, J., Astudy on estimation of joint force through isometric index finger abduction with the help of sEMG peaks for biomedical applications (2016) IEEE Trans. Cybern, 46 (1), pp. 2-8. , Jan; Potluri, C., Anugolu, M., Schoen, M.P., Subbaram Naidu, D., Urfer, A., Chiu, S., Hybrid fusion of linear, non-linear and spectral models for the dynamic modeling of sEMG and skeletal muscle force: An application to upper extremity amputation (2013) Comput. Biol. Med, 43 (11), pp. 1815-1826; Falisse, A., Rossom, S.V., Jonkers, I., Groote, F.D., EMG-driven optimal estimation of subject-specific Hill model muscle Ctendon parameters of the knee joint actuators (2017) IEEE Trans. Biomed. Eng, 64 (9), pp. 2253-2262. , Sep; Fleischer, C., Hommel, G., Calibration of an EMG-Based Body Model with six Muscles to control a Leg Exoskeleton (2007) Proc. IEEE Int. Conf. Robot. Autom, pp. 2514-2519; Meng, W., Ding, B., Zhou, Z., Liu, Q., Ai, Q., An EMG-based force prediction and control approach for robot-assisted lower limb rehabilitation (2014) Proc. IEEE Int. Conf. Syst., Man Cybern, pp. 2198-2203; Ding, Q., Han, J., Zhao, X., Continuous estimation of human multi-joint angles from sEMG using a state-space model (2017) IEEE Trans. Neural Syst. Rehabil. Eng, 25 (9), pp. 1518-1528. , Sep; Zhang, Q., Kim, K., Sharma, N., Prediction of ankle dorsiflexion moment by combined ultrasound sonography and electromyography (2019) IEEE Trans. Neural Syst. Rehabil. Eng, 28 (1), pp. 318-327. , Jan; Sartori, M., Reggiani, M., Bogert Den Van, A.J., Lloyd, D.G., Estimation of musculotendon kinematics in large musculoskeletal models using multidimensional B-splines (2012) J. Biomech, 45 (3), pp. 595-601; Sartori, M., Reggiani, M., Farina, D., Lloyd, D.G., EMG-driven forward-dynamic estimation of muscle force and joint moment about multiple degrees of freedom in the human lower extremity (2012) PloS One, 7 (12); Corcos, D.M., Gottlieb, G.L., Latash, M.L., Almeida, G.L., Agarwal, G.C., Electromechanical delay: An experimental artifact (1992) J. Electromyography Kinesiol, 2 (2), pp. 59-68; Lloyd, D., Besier, T., An EMG-driven musculoskeletal model for estimation of the human knee joint moments across varied tasks (2003) J. Biomech, 36, pp. 765-776; Zajac, F.E., Muscle and tendon: Properties, models, scaling, and application to biomechanics and motor control (1989) Critical Rev. Biomed. Eng, 17 (4), pp. 359-411; Fleischer, C., (2007) Controlling Exoskeletons with EMG Signals and A Biomechanical Body Model, , Ph.D. Dissertation, der Fakultät IV-Elektrotechnik und Informatik, Technische Universität Berlin, Berlin, Germany; An, K.N., Takahashi, K., Harrigan, T.P., Chao, E.Y., Determination of muscle orientations and moment arms (1984) J. Biomech. Eng, 106 (3), pp. 280-282; Wang, W., GPR and SPSO-CG based gait pattern generation for subject-specific training Sci. China Inf. Sci., Submitted for Publication; Wang, W., Towards patients motion intention recognition: Dynamics modeling and identification of iLeg-An LLRR under motion constraints (2016) IEEE Trans. Syst., Man, Cybern.: Syst, 46 (7), pp. 980-992. , Jul; Tortora, G.J., Derrickson, B.H., (2008) Principles of Anatomy and Physiology, , Hoboken, NJ, USA: Wiley","Wang, W.; State Key Laboratory of Management and Control for Complex Systems, China; email: weiqun.wang@ia.ac.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,02780046,,ITIED,,"English","IEEE Trans Ind Electron",Article,"Final","",Scopus,2-s2.0-85100325732
"Lamola M.J.","56168008300;","An ontic–ontological theory for ethics of designing social robots: a case of Black African women and humanoids",2021,"Ethics and Information Technology","23","2",,"119","126",,,"10.1007/s10676-020-09529-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081409194&doi=10.1007%2fs10676-020-09529-z&partnerID=40&md5=ce417da4ac2939449fb833404e4abcdc","Department of Philosophy, University of Pretoria, Pretoria, South Africa","Lamola, M.J., Department of Philosophy, University of Pretoria, Pretoria, South Africa","Given the affective psychological and cognitive dynamics prevalent during human–robot-interlocution, the vulnerability to cultural-political influences of the design aesthetics of a social humanoid robot has far-reaching ramifications. Building upon this hypothesis, I explicate the relationship between the structures of the constitution social ontology and computational semiotics, and ventures a theoretical framework which I proposes as a thesis that impels a moral responsibility on engineers of social humanoids. In distilling this thesis, the implications of the intersection between the socio-aesthetics of racialised and genderised humanoids and the phenomenology of human–robot-interaction are illuminated by the figuration of the experience of a typical black rural African woman as the user, that is, an interlocutor with an industry-standard socially-situated humanlike robot. The representation of the gravity of the psycho-existential and socio-political ramifications of such woman’s life with humanoids is abstracted and posited as grounds that illustrate the imperative for roboticists to take socio-ethical considerations seriously in their designs of humanoids. © 2020, Springer Nature B.V.","Computational semiotics; Humanoids; Postphenomenology; Robot gender; Robot race; Robotic ethics","Anthropomorphic robots; Computation theory; Machine design; Ontology; Philosophical aspects; Professional aspects; Semiotics; Computational semiotics; Ethical considerations; Humanoids; Moral responsibility; Ontological theories; Political influences; Postphenomenology; Theoretical framework; Social robots",,,,,,,,,,,,,,,,"Addison, A., Yogeeswaran, K., Bartneck, C., Robots Can Be More Than Black And White: Examining Racial Bias Towards Robots (2019) Proceedings of Conference on Artificial Intelligence, Ethics, and Society, , www.aies-conference.com/wp-content/papers/main/AIES-19_paper_99.pdf, Honolulu: Association of Advancement of Artificial Intelligence, Accessed 16 June 2019; Alcoff, M.L., What should white people do? 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Chicago, IEEE; Haraway, D., A manifesto for cyborgs: Science, technology, and socialist feminism in the 1980s (2003) From modernism to post modernism, pp. 464-480. , Cahoone L, (ed), 2, Blackwell, Oxford; Harraway, D., Interview with Donna Haraway with R. Markussen, F. Oesen and N. Lykke (2003) Chasing Techscience: Matrix for materiality, pp. 47-57. , Ihde D, Selinger E, (eds), Indiana University Press, Bloomington; Harris, E.C., Prichard, S.M., Rabins, J.M., (2009) Engineering ethics: Concepts and cases, , 4, Wadsworth, Belmont, CA; Heidegger, M., Being and time (J. Macquarie & E (1962) Robinson, 7, p. 1927. , Ed, Trans.). Oxford, Blackwell. (Original work published; Husserl, E., Introduction to logic and theory of knowledge: Lectures 1906/07 (C. O. Hill (2008) Trans.), , Dordrecht, Springer; Ihde, D., (1990) Technology and the lifeworld: From garden to Earth, , Indiana University Press, Bloomington; Ihde, D., If phenomenology is an Albatross, is post-phenomenology possible? 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Technol.",Article,"Final","",Scopus,2-s2.0-85081409194
"Kompatsiari K., Ciardo F., Tikhanoff V., Metta G., Wykowska A.","55904188300;57164931800;14919826000;6602884280;15924284200;","It’s in the Eyes: The Engaging Role of Eye Contact in HRI",2021,"International Journal of Social Robotics","13","3",,"525","535",,10,"10.1007/s12369-019-00565-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067251508&doi=10.1007%2fs12369-019-00565-4&partnerID=40&md5=f707ebf551d59451bd221f683ede5b74","Istituto Italiano di Tecnologia, Social Cognition in Human-Robot Interaction, Centre for Human Technologies, Via Enrico Melen 83, Genoa, 16152, Italy; Ludwig Maximilian University, Großhaderner Str. 2, Planegg, 82152, Germany; Istituto Italiano di Tecnologia, iCub Facility, Via San Quirico 19, Genoa, 16163, Italy; University of Plymouth, Drake Circus, Plymouth, PL4 8AA, United Kingdom; Luleå University of Technology, Luleå, 97187, Sweden","Kompatsiari, K., Istituto Italiano di Tecnologia, Social Cognition in Human-Robot Interaction, Centre for Human Technologies, Via Enrico Melen 83, Genoa, 16152, Italy, Ludwig Maximilian University, Großhaderner Str. 2, Planegg, 82152, Germany; Ciardo, F., Istituto Italiano di Tecnologia, Social Cognition in Human-Robot Interaction, Centre for Human Technologies, Via Enrico Melen 83, Genoa, 16152, Italy; Tikhanoff, V., Istituto Italiano di Tecnologia, iCub Facility, Via San Quirico 19, Genoa, 16163, Italy; Metta, G., Istituto Italiano di Tecnologia, iCub Facility, Via San Quirico 19, Genoa, 16163, Italy, University of Plymouth, Drake Circus, Plymouth, PL4 8AA, United Kingdom; Wykowska, A., Istituto Italiano di Tecnologia, Social Cognition in Human-Robot Interaction, Centre for Human Technologies, Via Enrico Melen 83, Genoa, 16152, Italy, Luleå University of Technology, Luleå, 97187, Sweden","This paper reports a study where we examined how a humanoid robot was evaluated by users, dependent on established eye contact. In two experiments, the robot was programmed to either establish eye contact with the user, or to look elsewhere. Across the experiments, we altered the level of predictiveness of the robot’s gaze direction with respect to a subsequent target stimulus (in Exp.1 the gaze direction was non-predictive, in Exp. 2 it was counter-predictive). Results of subjective reports showed that participants were sensitive to eye contact. Moreover, participants felt more engaged with the robot when it established eye contact, and the majority attributed higher degree of human-likeness in the eye contact condition, relative to no eye contact. This was independent of predictiveness of the gaze cue. Our results suggest that establishing eye contact by embodied humanoid robots has a positive impact on perceived socialness of the robot, and on the quality of human–robot interaction (HRI). Therefore, establishing eye contact should be considered in designing robot behaviors for social HRI. © 2019, Springer Nature B.V.","Eye contact; iCub; Social attention; Social human–robot interaction","Agricultural robots; Anthropomorphic robots; Economic and social effects; Eye contact; Gaze direction; Human likeness; iCub; Robot behavior; Robot interactions; Social attention; Target stimulus; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 715058; European Research Council, ERC","This Project has received funding from the European Research Council (ERC) under the European Union?s Horizon 2020 research and innovation program (Grant awarded to A. Wykowska, titled ?InStance: Intentional Stance for Social Attunement. Grant agreement No: 715058).",,,,,,,,,,"Beyond dirty, dangerous and dull: What everyday people think robots should do. In: Proceedings of the 3rd ACM/IEEE, international conference on human robot interaction. 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(2014) Int J of Soc Robot, 6 (3), pp. 357-366","Kompatsiari, K.; Istituto Italiano di Tecnologia, Via Enrico Melen 83, Italy; email: Kyveli.kompatsiari@iit.it",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85067251508
"Banjanović-Mehmedović L.","9335512600;","Artificial intelligence advancement in Service Robots Applications",2021,"Service Robots: Advances in Research and Applications",,,,"55","75",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114848785&partnerID=40&md5=cf3adddbe3299fe997470e159a85ca2b","Department of Control Systems, Automation and Robotics, Faculty of Electrical Engineering, University of Tuzla, Tuzla, Bosnia and Herzegovina","Banjanović-Mehmedović, L., Department of Control Systems, Automation and Robotics, Faculty of Electrical Engineering, University of Tuzla, Tuzla, Bosnia and Herzegovina","Service robots have recently attracted a lot of attention of the public. Integrating with artificial intelligence and new technological issues like as embedded systems and virtual reality, modern service robots have great potential as they are capable of performing many sophisticated works of the human. The recent advances in an artificial intelligence have opened up new possibilities for technological progress in implementation of service robots in medical, field, defense, logistics, construction and demolition,rescue and security,underwater systems, inspection and maintenance, professional cleaning, production processes as well as in other branches of industry and human environment. This paper presents a comprehensive survey of artificial intelligence techniques, new challenges with explainable intelligence and examples of service robots applications. Advanced approaches of AI for service robot applications are reinforcement learning for imitation based robot learning, deep learning based human-robot interaction in home environment, optimal design of rehabilitation exoskeleton robots or explainable robotics in assistive robotics. AI with soft robotics and virtual reality have led to a new era of service robotics, which can adapt to the new needs of humans. © 2021 by Nova Science Publishers, Inc. All rights reserved.","Artificial intelligence; Collaborative robots; Deep learning; Explainable robotics; Fuzzy logic; Machine learning; Optimization algorithms; Soft robotics; Virtual reality",,,,,,,,,,,,,,,,,"Arrieta, A.B., Diaz-Rodriguez, N., Ser, J.D., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Herrera, F., (2019) Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges Toward Responsible AI, pp. 10-22. , DeepAI; Attolico, C., Reno, V., Guaragnella, C., D'orazio, T., Cicirelli, G., A Review of Gesture Recognition Approaches for HRI (2014) Conference: Workshop On Real- Time Gesture Recognition For Human Robot Interaction; Banjanović-Mehmedović, L., Artificial Intelligence Drives Advances in Human Robot Collaboration (2020) Book: Industrial Robots: Design, Applications and Technology, pp. 201-224. , (Editors: Isak Karabegović, Lejla Banjanović-Mehmedović). 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Robot.: Adv. in Res. and Appl.",Book Chapter,"Final","",Scopus,2-s2.0-85114848785
"Tian L., Carreno-Medrano P., Allen A., Sumartojo S., Mintrom M., Coronado Zuniga E., Venture G., Croft E., Kulic D.","56151869000;56336138700;57219633402;55548214000;6602830007;57188864138;15623574000;7003817722;10244652900;","Redesigning Human-Robot Interaction in Response to Robot Failures: A Participatory Design Methodology",2021,"Conference on Human Factors in Computing Systems - Proceedings",,,,"","",,2,"10.1145/3411763.3443440","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105772870&doi=10.1145%2f3411763.3443440&partnerID=40&md5=321ef5a42afb04c1078b182908d0caa3","Department of Electrical and Computer Systems Engineering, Monash University, Australia; Monash Art Design and Architecture, Monash University, Australia; Faculty of Art Monash University, Australia; Gvlab Tuat, Japan","Tian, L., Department of Electrical and Computer Systems Engineering, Monash University, Australia; Carreno-Medrano, P., Department of Electrical and Computer Systems Engineering, Monash University, Australia; Allen, A., Department of Electrical and Computer Systems Engineering, Monash University, Australia; Sumartojo, S., Monash Art Design and Architecture, Monash University, Australia; Mintrom, M., Faculty of Art Monash University, Australia; Coronado Zuniga, E., Gvlab Tuat, Japan; Venture, G., Gvlab Tuat, Japan; Croft, E., Department of Electrical and Computer Systems Engineering, Monash University, Australia; Kulic, D., Department of Electrical and Computer Systems Engineering, Monash University, Australia","People are increasingly encountering robots in public spaces. To increase the robustness of such in-the-wild robotic applications and to achieve their designed outcomes, existing research focuses on improving the technical reliability of robots and identifying effective strategies to prevent or recover from technical failures. However, in human-robot interaction (HRI), a user's perception of a robot failure may not necessarily relate to technical issues. We focus on understanding users' perception of robot behaviours and interactions within the context of a public space. In our exploratory study using a novel participatory design methodology, participants designed robot behaviours for applications in public spaces, and tested their design both in a simulator and on the physical robot. We investigate how participants' perception and expectations change during this iterative participatory prototyping process, especially when the robot exhibits erratic or unexpected behaviours. Our work provides insights on users' perception of robot failures, and how users' design of robot behaviours shifts as they observe the robot within the spatial and social context. © 2021 Owner/Author.","Human-Robot Interaction; Participatory Prototyping; Social Robotics","Human engineering; Iterative methods; Machine design; Man machine systems; Exploratory studies; Human robot Interaction (HRI); Participatory design; Prototyping process; Robotic applications; Technical failures; User's perceptions; Users' perception; Human robot interaction",,,,,,"We would like to thank all our research participants. This project is supported by the Monash University Data Futures Institute Interdisciplinary Research Seed Grant. Aimee Allen is supported by the Australian Government Research Training Program PhD Scholarship.",,,,,,,,,,"Azenkot, S., Feng, C., Cakmak, M., Enabling building service robots to guide blind people a participatory design approach (2016) HRI'16, IEEE, pp. 3-10; Bartneck, C., Kuli, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81. , 2009; Ben-Youssef, A., Varni, G., Essid, S., Clavel, C., On-the-fly detection of user engagement decrease in spontaneous human-robot interaction using recurrent and deep neural networks (2019) International Journal of Social Robotics, 11 (5), pp. 815-828. , 2019; Björling, E.A., Rose, E., Davidson, A., Ren, R., Wong, D., Can we keep him forever? 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Relation of previous experiences with social robots in real life or in the media and expectancies based on qualitative and quantitative assessment (2019) Frontiers in Psychology, 10, p. 939. , 2019; Izui, T., Venture, G., Correlation analysis for predictive models of robot user's impression: A study on visual medium and mechanical noise (2019) International Journal of Social Robotics, pp. 1-15. , 2019; Benigno Latupeirissa, A., Claudio, P., Bresin, R., Exploring emotion perception in sonic hri (2020) Sound and Music Computing Conference, Torino, pp. 434-441. , 24-26 June 2020. Zenodo; Rin Lee, H., Riek, L.D., Reframing assistive robots to promote successful aging (2018) ACM Trans. On HRI, 7 (1), pp. 1-23. , 2018; Rin Lee, H., Šabanovi, S., Chang, W., Nagata, S., Piatt, J., Bennett, C., Hakken, D., Steps toward participatory design of social robots: Mutual learning with older adults with depression (2017) HRI'17, pp. 244-253; Marres, N., Guggenheim, M., Wilkie, A., (2018) Inventing the Social, , Mattering Press; Metatla, O., Bardot, S., Cullen, C., Serrano, M., Joufrais, C., Robots for inclusive play: Co-designing an educational game with visually impaired and sighted children (2020) Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1-13; Muir, B.M., Trust in automation: Part i. theoretical issues in the study of trust and human intervention in automated systems (1994) Ergonomics, 37 (11), pp. 1905-1922. , 1994; Kumar Pandey, A., Gelin, R., A mass-produced sociable humanoid robot: Pepper: The frst machine of its kind (2018) IEEE Robotics&Automation Magazine, 25 (3), pp. 40-48. , 2018; Pink, S., Santiago Caminha, C., De Porto, C.S.A., Zilse, R., Sandro Gomes, A., Design ethnography for screenless interaction style: Hands-on and no-hands in early morning routines (2017) Proceedings of the XVI Brazilian Symposium on Human Factors in Computing Systems, pp. 1-10; Pink, S., Fors, V., Glöss, M., The contingent futures of the mobile present: Automation as possibility (2018) Mobilities, 13 (5), pp. 615-631. , 2018; Pot, E., Monceaux, J., Gelin, R., Maisonnier, B., Choregraphe: A graphical tool for humanoid robot programming (2009) RO-MAN'09, IEEE, pp. 46-51; Maria Sabelli, A., Kanda, T., Robovie as a mascot: A qualitative study for long-term presence of robots in a shopping mall (2016) International Journal of Social Robotics, 8 (2), pp. 211-221. , 2016; Schramm, L.T., Dufault, D., Young, J.E., Warning: This robot is not what it seems! exploring expectation discrepancy resulting from robot design (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 439-441; Shi, C., Satake, S., Kanda, T., Ishiguro, H., A robot that distributes fyers to pedestrians in a shopping mall (2018) International Journal of Social Robotics, 10 (4), pp. 421-437. , 2018; Sidnera, C.L., Leea, C., Kiddb, C.D., Lesha, N., Richa, C., Explorations in engagement for humans and robots (2005) Artifcial Intelligence, 166, pp. 140-164. , 2005; Sumartojo, S., Lugli, D., Kuli, D., Tian, L., Carreno-Medrano, P., Mintrom, M., Allen, A., (2020) Robotic Logics of Public Space in the COVID Pandemic, , https://www.mediapolisjournal.com/2020/08/robotic-logics-of-public-space/, Retrieved Sep 17 2020 from; Tian, L., Carreno-Medrano, P., Sumartojo, S., Mintrom, M., Coronado, E., Venture, G., Kuli, D., User expectations of robots in public spaces: A co-design methodology (2020) International Conference on Social Robotics, pp. 259-270. , Springer; Tian, L., Oviatt, S., A taxonomy of social errors in human-robot interaction (2020) ACM Transactions on Human-Robot Interaction (To Appear), , 2020; Tiddi, I., Bastianelli, E., Daga, E., D'Aquin, M., Motta, E., Robot-city interaction: Mapping the research landscape-a survey of the interactions between robots and modern cities (2020) International Journal of Social Robotics, 12 (2), pp. 299-324. , 2020; Tolmeijer, S., Weiss, A., Hanheide, M., Lindner, F., Powers, T.M., Dixon, C., Tielman, M.L., Taxonomy of trust-relevant failures and mitigation strategies (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 3-12; Weiss, A., Bernhaupt, R., Tscheligi, M., The usus evaluation framework for user-centered hri (2011) New Frontiers in Human-Robot Interaction, 2, pp. 89-110. , 2011; Winkle, K., Caleb-Solly, P., Turton, A., Bremner, P., Mutual shaping in the design of socially assistive robots: A case study on social robots for therapy (2019) International Journal of Social Robotics, pp. 1-20. , 2019",,,"ACM Special Interest Group on Computer-Human Interaction (SIGCHI)","Association for Computing Machinery","2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI EA 2021","8 May 2021 through 13 May 2021",,168787,,9781450380959,,,"English","Conf Hum Fact Comput Syst Proc",Conference Paper,"Final","",Scopus,2-s2.0-85105772870
"Jung Y., Cho E., Kim S.","24778544300;57223800561;55718730600;","Users' Affective and Cognitive Responses to Humanoid Robots in Different Expertise Service Contexts",2021,"Cyberpsychology, Behavior, and Social Networking","24","5",,"300","306",,1,"10.1089/cyber.2020.0170","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106145204&doi=10.1089%2fcyber.2020.0170&partnerID=40&md5=50d6ff44c928735ff9fb7df124c12c8c","School of Media and Communication, Korea University, Seoul, South Korea; SK Holdings CandC, Seoul, South Korea","Jung, Y., School of Media and Communication, Korea University, Seoul, South Korea; Cho, E., SK Holdings CandC, Seoul, South Korea; Kim, S., School of Media and Communication, Korea University, Seoul, South Korea","The uncanny valley (UCV) model is an influential human-robot interaction theory that explains the relationship between the resemblance that robots have to humans and attitudes toward robots. Despite its extraordinary worth, this model remains untested in certain respects. One current limitation is that the model has only been examined in general or context-free situations. Given that humanoids function in the world beyond laboratories, investigating the UCV in specific and actual situations is critical. Additionally, few studies have examined the impact of affective responses presented in the UCV to other appraisals of humanoids. To address these issues, this study explored affective and cognitive responses to humanoids in specific service situations. In particular, we examined the effect of affective responses on trust, which is regarded as a critical cognitive factor influencing technology adoption, in two service contexts: hotel reception (low expertise) and tutoring (high expertise). By providing a richer understanding of human both affective and cognitive reactions to humanoids, our findings expand the UCV theory and ultimately contribute to research regarding user adoption of service robots. © Yoonhyuk Jung et al. 2021; Published by Mary Ann Liebert, Inc. 2021.","human-robot interaction; humanoid; trust; uncanny valley","attitude; human; procedures; psychology; robotics; trust; Attitude; Humans; Robotics; Trust",,,,,"Ministry of Education, MOE; Ministry of Science, ICT and Future Planning, MSIP: IITP-2020-0-01749; National Research Foundation of Korea, NRF: NRF-2019S1A3A2099973; Institute for Information and Communications Technology Promotion, IITP","This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A3A2099973) and the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-0-01749) supervised by the IITP (Institute of Information & Communications Technology Planning & Evaluation).",,,,,,,,,,"Nichols, G., Robot fired from grocery store for utter incompetence (2018) ZDNet, , https://www.zdnet.com/article/robot-fired-from-grocery-store-for-utter-incompetence/, (Jan. 22). 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"Ji Y., Yang Y., Shen H.T., Harada T.","36677523000;57222954946;7404523209;7403055831;","View-invariant action recognition via Unsupervised AttentioN Transfer (UANT)",2021,"Pattern Recognition","113",,"107807","","",,2,"10.1016/j.patcog.2020.107807","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100038616&doi=10.1016%2fj.patcog.2020.107807&partnerID=40&md5=dd436d43bab5eae4905b220744d92fa9","Center for Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China; Research Center for Advanced Science and Technology, The University of Tokyo, Japan","Ji, Y., Center for Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China; Yang, Y., Center for Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China; Shen, H.T., Center for Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China; Harada, T., Research Center for Advanced Science and Technology, The University of Tokyo, Japan","With wide applications in surveillance and human-robot interaction, view-invariant human action recognition is critical, however, challenging, due to the action occlusion and information loss caused by view change. Current methods mainly seek for a common feature space for different views. However, such solutions become invalid when there exist few common features, e.g. large view change. To tackle the problem, we propose an Unsupervised AttentioN Transfer (UANT) approach for view-invariant action recognition. Other than transferring feature knowledge, UANT transfers attention from one selected reference view to arbitrary views, which correctly emphasizes crucial body joints and their relations for view-invariant representation. In addition, the attention calculation method taking into account both recognition contribution and reliability of skeleton joints generates effective attention. Experiments showed its effectiveness for correctly locating crucial body joints in action sequences. We exhaustively evaluate our approach on the UESTC and the NTU dataset, performing unsupervised view-invariant evaluations, i.e. X-view and Arbitrary-view recognition. Experiment results demonstrate its superiority in view-invariant representation and recognition. © 2020","Attention learning; Cross-view evaluation; Transfer learning; View-invariant recognition","Pattern recognition; Software engineering; Action sequences; Common features; Human-action recognition; Information loss; Skeleton joints; View invariants; View-invariant action recognition; Social robots",,,,,"National Natural Science Foundation of China, NSFC: 61673088","This research is supported by the Natural Science Foundation of China (NSFC) under grant no. 61673088.",,,,,,,,,,"Weinland, D., Ronfard, R., Boyer, E., Free viewpoint action recognition using motion history volumes (2006) Comput. Vis. 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"Mucchiani C., Yim M.","57200142286;7005922938;","Dynamic Grasping for Object Picking Using Passive Zero-DOF End-Effectors",2021,"IEEE Robotics and Automation Letters","6","2","9363578","3089","3096",,1,"10.1109/LRA.2021.3062296","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101803083&doi=10.1109%2fLRA.2021.3062296&partnerID=40&md5=a5bd1e41ede026c17039edfe9582e3bc","Deparment of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA  19139, United States","Mucchiani, C., Deparment of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA  19139, United States; Yim, M., Deparment of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA  19139, United States","This letter presents a new grasping approach that utilizes object inertia for picking objects using end-effectors that have no actuated degrees of freedom. The end-effector provides two contacts at a fixed distance, not necessarily antipodal, on two opposing edges of a convex object. The object is dynamically swept off of a position at rest to a goal position at stable rest while maintaining stability throughout the motion by assuring accelerations and torques from the path provide stabilizing forces in the passive gripper. Since the end-effector lacks the ability to close on the object, and the same is only subjected to the force of gravity and the applied external acceleration and torque, no internal forces are applied to the object. Practical applications for this method include very low complexity, affordable end-effectors for pick-and-place applications, and also sensorless transferring of objects in human-robot interaction. Implementation and experimentation using a conventional manipulator with three passive gripper geometries with 11 different common household objects demonstrate usefulness of the proposed method. © 2016 IEEE.","Grasping; grippers and other end-effectors; manipulation planning","Agricultural robots; Degrees of freedom (mechanics); Human robot interaction; Manipulators; Desired position; Different geometry; Frictional properties; Internal forces; Pick and place; Planar convex objects; Sensorless; Very low complexity; End effectors",,,,,"National Science Foundation, NSF: TT 1827673","Manuscript received October 15, 2020; accepted February 7, 2021. Date of publication February 25, 2021; date of current version March 19, 2021. This letter was recommended for publication by Associate Editor Y. Zheng and Editor H. Liu upon evaluation of the reviewers’ comments. This work was funded in part by the NSF Grant PFI:TT 1827673. (Corresponding author: Caio Mucchiani.) The authors are with the Deparment of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA 19139 USA (e-mail: caio@seas.upenn.edu; yim@grasp.upenn.edu).",,,,,,,,,,"Mucchiani, C., Kennedy, M., Yim, M., Seo, J., Object picking through in-hand manipulation using passive end-effectors with zero mobility (2018) Ieee Robot. Automat. Lett., 3 (2), pp. 1096-1103. , Apr; Nguyen, V.-D., Constructing force-closure grasps (1988) Int. J. Robot. Res., 7 (3), pp. 3-16; Siciliano, B., Khatib, O., (2016) Springer Handbook of Robotics, , Berlin, Germany: Springer; Howard, W.S., Kumar, V., On the stability of grasped objects (1996) Ieee Trans. Robot. Automat., 12 (6), pp. 904-917. , Dec; Trinkle, J.C., On the stability and instantaneous velocity of grasped fric-tionless objects (1992) Ieee Trans. Robot. Automat., 8 (5), pp. 560-572. , Oct; Stappen Der Van, A.F., Wentink, C., Overmars, M.H., Computing form-closure configurations (1999) Proc. Ieee Int. Conf. Robot. Automat., 3, pp. 1837-1842; Pollard, N.S., Closure and quality equivalence for efficient synthesis of grasps from examples (2004) Int. J. Robot. Res., 23 (6), pp. 595-613; Mason, M.T., (2001) Mechanics of Robotic Manipulation, , MIT Press; Vahedi, M., Stappen Der, A.Van, Caging polygons with two and three fingers (2008) Int. J. Robot. Res., 27 (11-12), pp. 1308-1324; Rodriguez, A., Mason, M., Ferry, S., From caging to grasping (2012) Int. J. Robot. Res., 31 (7), pp. 886-900; Dogar, M., Srinivasa, S., A framework for push-grasping in clutter (2011) Robot.: Sci. Syst. Vii; Lynch, K.M., Mason, M.T., Stable pushing: Mechanics, controllability, and planning (1996) Int. J. Robot. Res., 15 (6), pp. 533-556; Meriçli, T., Veloso, M., Akin, H.L., Push-manipulation of complex passive mobile objects using experimentally acquired motion models (2015) Auton. Robots, 38 (3), pp. 317-329; Mericli, T.A., Veloso, M., Akin, H.L., Achievable push-manipulation for complex passive mobile objects using past experience (2013) Proc. Int. Conf. Auton. Agents Multi-Agent Syst., pp. 71-78; Lynch, K.M., Toppling manipulation (1999) Proc. Ieee Int. Conf. Robot. Automat., 4, pp. 2551-2557; Shi, J., Woodruff, J.Z., Umbanhowar, P.B., Lynch, K.M., Dynamic in-hand sliding manipulation (2017) Ieee Trans. Robot., 33 (4), pp. 778-795. , Aug; Woodruff, J.Z., Lynch, K.M., Planning and control for dynamic, nonprehensile, and hybrid manipulation tasks (2017) Proc. Ieee Int. Conf. Robot. Automat., pp. 4066-4073; Lynch, K.M., Mason, M.T., Dynamic nonprehensile manipulation: Controllability, planning, and experiments (1999) Int. J. Robot. Res., 18 (1), pp. 64-92; Abell, T., Erdmann, M., Stably supported rotations of a planar polygon with two frictionless contacts (1995) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. 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Res., 7 (3), pp. 3-16; Roa, M.A., Suárez, R., Grasp quality measures: Review and performance (2015) Auton. Robots, 38 (1), pp. 65-88; Roa, M.A., Suárez, R., Rosell, J., Grasp space generation using sampling and computation of independent regions (2008) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 2258-2263; Jeong, H., Cheong, J., Independent contact region (icr) based in-hand motion planning algorithm with guaranteed grasp quality margin (2012) Proc. Ieee Int. Conf. Automat. Sci. Eng., pp. 1089-1094; Lynch, K.M., (1996) Nonprehensile Robotic Manipulation: Controllability and Planning, , Citeseer; Hunt, K.H., (1978) Kinematic Geometry of Mechanisms, 7. , Oxford Univ. Press, USA; Murray, R.M., Li, Z., Sastry, S.S., (1994) A Mathematical Introduction to Robotic Manipulation, , CRC; Nguyen, V.-D., Constructing force-closure grasps (1988) Int. J. Robot. Res., 7 (3), pp. 3-16; Lynch, K., Park, F., (2017) Modern Robotics, , Cambridge U.K.: Cambridge Univ. Press","Mucchiani, C.; Deparment of Mechanical Engineering and Applied Mechanics, United States; email: caio@seas.upenn.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85101803083
"Ivanovic B., Leung K., Schmerling E., Pavone M.","57205429330;57207825897;56738104900;12240587200;","Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach",2021,"IEEE Robotics and Automation Letters","6","2","9286482","295","302",,7,"10.1109/LRA.2020.3043163","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097955251&doi=10.1109%2fLRA.2020.3043163&partnerID=40&md5=4587e3f167e9e8d9645adbaf0dc80f49","Department of Aeronautics and Astronautics, Stanford University, Stanford, CA  94305, United States; Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA  94305, United States","Ivanovic, B., Department of Aeronautics and Astronautics, Stanford University, Stanford, CA  94305, United States; Leung, K., Department of Aeronautics and Astronautics, Stanford University, Stanford, CA  94305, United States; Schmerling, E., Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA  94305, United States; Pavone, M., Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA  94305, United States","Human behavior prediction models enable robots to anticipate how humans may react to their actions, and hence are instrumental to devising safe and proactive robot planning algorithms. However, modeling complex interaction dynamics and capturing the possibility of many possible outcomes in such interactive settings is very challenging, which has recently prompted the study of several different approaches. In this work, we provide a self-contained tutorial on a conditional variational autoencoder (CVAE) approach to human behavior prediction which, at its core, can produce a multimodal probability distribution over future human trajectories conditioned on past interactions and candidate robot future actions. Specifically, the goals of this tutorial paper are to review and build a taxonomy of state-of-the-art methods in human behavior prediction, from physics-based to purely data-driven methods, provide a rigorous yet easily accessible description of a data-driven, CVAE-based approach, highlight important design characteristics that make this an attractive model to use in the context of model-based planning for human-robot interactions, and provide important design considerations when using this class of models. © 2016 IEEE.","Autonomous vehicle navigation; deep learning methods; social HRI","Agricultural robots; Behavioral research; Forecasting; Learning systems; Machine design; Predictive analytics; Probability distributions; Robot programming; Data-driven methods; Design characteristics; Design considerations; Generative model; Interaction dynamics; Model complexes; State-of-the-art methods; Trajectory prediction; Human robot interaction",,,,,,,,,,,,,,,,"Rudenko, A., Palmieri, L., Herman, M., Kitani, K.M., Gavrila, D.M., Arras, K.O., Human motion trajectory prediction: A survey (2020) Int. 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Learn., pp. 663-670; Abbeel, P., Ng, A.Y., Apprenticeship learning via inverse reinforcement learning (2004) Proc. Int. Conf. Mach. Learn., pp. 1-8; Ziebart, B.D., Maas, A., Bagnell, J.A., Dey, A.K., Maximum entropy inverse reinforcement learning (2008) Proc. Aaai Conf. Artif. Intell.; Sadigh, D., Sastry, S., Seshia, S.A., Dragan, A.D., Planning for autonomous cars that leverage effects on human actions (2016) Robot.: Sci. Syst.; Kretzschmar, H., Spies, M., Sprunk, C., Burgard, W., Socially compliant mobile robot navigation via inverse reinforcement learning (2016) Int. J. Robot. Res., 35 (11), pp. 1289-1307; Choudhury, R., Swamy, G., Hadfield-Menell, D., Dragan, A.D., On the utility of model learning in HRI (2019) Proc. Ieee Int. Conf. Human-Robot Interact., pp. 317-325; Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S., Social LSTM:Human trajectory prediction in crowded spaces (2016) Proc. Ieee Conf. Comput. Vis. Pattern Recognit., pp. 961-971; Jain, A., Zamir, A.R., Savarese, S., Saxena, A., Structural-RNN: Deep learning on spatio-temporal graphs (2016) Ieee Conf. Comput. Visi. Pattern Recognit., pp. 5308-5317; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Comput.; Goodfellow, I., Generative adversarial nets (2014) Proc. Conf. Neural Inform. Process. Syst., pp. 2672-2680; Mirza, M., Osindero, S., (2014) Conditional Generative Adversarial Nets, , https://arxiv.org/abs/1411.1784; Kingma, D.P., Welling, M., (2013) Auto-encoding Variational Bayes, , https://arxiv.org/abs/1312.6114; Gupta, A., Johnson, J., Li, F., Savarese, S., Alahi, A., Social GAN: Socially acceptable trajectories with generative adversarial networks (2018) Ieee Conf. Comput. Vis. Pattern Recognit., pp. 2255-2264; Lee, N., Choi, W., Vernaza, P., Choy, C.B., Torr, P.H.S., Chandraker, M., DESIRE: Distant future prediction in dynamic scenes with interacting agents (2017) Proc. Ieee Conf. Comput. Vis. Patt. 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Learn., pp. 214-223; Jang, E., Gu, S., Poole, B., Categorial reparameterization with gumbelsoftmax (2017) Proc. Int. Conf. Learn. Representations, , https://openreview.net/forum?id=rkE3y85ee; Maddison, C.J., Mnih, A., Teh, Y.W., The concrete distribution: A continuous relaxation of discrete random variables (2017) Proc. Int. Conf. Learn. Representations; Adel, T., Ghahramani, Z., Weller, A., Discovering interpretable representations for both deep generative and discriminative models (2018) Proc. Int. Conf. Mach. Learn., pp. 50-59; Kong, J., Pfeifer, M., Schildbach, G., Borrelli, F., Kinematic and dynamic vehicle models for autonomous driving control design (2015) Ieee Intell. Vehicles Symp., pp. 1094-1099; LaValle, S.M., Better unicycle models (2006) Planning Algorithms, p. 743. , Cambridge, U.K.: Cambridge Univ. Press; Casas, S., Gulino, C., Liao, R., Urtasun, R., SpAGNN: Spatially-aware graph neural networks for relational behavior forecasting from sensor data (2020) Ieee Int. Conf. Robotics Automation, Paris, France, pp. 9491-9497; Sadeghian, A., Legros, F., Voisin, M., Vesel, R., Alahi, A., Savarese, S., CAR-Net: Clairvoyant attentive recurrent network (2018) Proc. Eur. Conf. Comput. Vis.; Pellegrini, S., Ess, A., Schindler, K., Gool, L.V., You'll never walk alone: Modeling social behavior for multi-target tracking (2009) Ieee Int. Conf. Comput. Vis., pp. 261-268; Lerner, A., Chrysanthou, Y., Lischinski, D., Crowds by example (2007) Comput. Graph. Forum, 26 (3), pp. 655-664; Caesar, H., NuScenes: A multimodal dataset for autonomous driving (2020) IEEE/CVF Conf. Comput. Vision Pattern Recognit, pp. 11618-11628. , Seattle, WA, USA; Itkina, M., Ivanovic, B., Senanayake, R., Kochenderfer, M.J., Pavone, M., Evidential sparsification of multimodal latent spaces in conditional variational autoencoders (2020) Proc. Conf. Neural Inform. Process. Syst., , in Press","Ivanovic, B.; Department of Aeronautics and Astronautics, United States; email: borisi@stanford.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85097955251
"Guidolin M., Menegatti E., Reggiani M., Tagliapietra L.","57212468487;7007056708;56246028800;56938576600;","A ROS Driver for Xsens Wireless Inertial Measurement Unit Systems",2021,"Proceedings of the IEEE International Conference on Industrial Technology","2021-March",,"9453640","677","683",,1,"10.1109/ICIT46573.2021.9453640","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104105677&doi=10.1109%2fICIT46573.2021.9453640&partnerID=40&md5=358301fc57256637718e4f64179576d9","University of Padova, Department of Management and Engineering, Padova, Italy; University of Padova, Department of Information Engineering, Padova, Italy","Guidolin, M., University of Padova, Department of Management and Engineering, Padova, Italy; Menegatti, E., University of Padova, Department of Information Engineering, Padova, Italy; Reggiani, M., University of Padova, Department of Management and Engineering, Padova, Italy; Tagliapietra, L., University of Padova, Department of Information Engineering, Padova, Italy","This paper presents an efficient open-source driver for interfacing Xsens inertial measurement systems (in particular the Xsens MTw Awinda wireless motion trackers) with the Robot Operating System (ROS). The driver supports the simultaneous connection of up to 20 trackers, limit fixed by the Xsens software, to a master PC, and directly streams sensors data (linear accelerations, angular velocities, magnetic fields, orientations) up to 120 Hz to the ROS network through one or multiple configurable topics. Moreover, a synchronization procedure is implemented to avoid possible partial frames where the readings from one (or multiple) trackers are missing. The proposed messages are based on ROS standard ones and comply with the ROS developer guidelines. This guarantees the compatibility of any ROS package requiring as input ROS standard messages with the proposed driver, thus effectively integrating Xsens inertial measurement systems with the ROS ecosystem. This work aims to push forward the development of a large variety of human-robot interaction applications where accurate real-time knowledge of human motion is crucial. © 2021 IEEE.","Hi-ROS; Human-Robot Interaction; IMU; Inertial Measurement Units; ROS; Xsens","Automobile drivers; Data streams; Open source software; Open systems; Personal computers; Driver support; Human motions; Inertial measurement system; Inertial measurement unit; Linear accelerations; Motion tracker; Push forwards; Robot operating systems (ROS); Human robot interaction",,,,,"Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR","Part of this work was supported by MIUR (Italian Minister for Education) under the initiative ”Departments of Excellence” (Law 232/2016).",,,,,,,,,,"Madgwick, S.O., Harrison, A.J., Vaidyanathan, R., Estimation of imu and marg orientation using a gradient descent algorithm (2011) 2011 IEEE Int. Conf. On Rehabilitation Robotics. IEEE, pp. 1-7; Balatti, P., Kanoulas, D., Tsagarakis, N.G., Ajoudani, A., Towards robot interaction autonomy: Explore, identify, and interact (2019) 2019 Int. Conf. On Robotics and Automation (ICRA). IEEE, pp. 9523-9529; Sandau, M., Koblauch, H., Moeslund, T.B., Aanæs, H., Alkjær, T., Simonsen, E.B., Markerless motion capture can provide reliable 3d gait kinematics in the sagittal and frontal plane (2014) Medical Engineering & Physics, 36 (9), pp. 1168-1175; Cappozzo, A., Della Croce, U., Leardini, A., Chiari, L., Human movement analysis using stereophotogrammetry: Part 1: Theoretical background (2005) Gait & Posture, 21 (2), pp. 186-196; Von Marcard, T., Rosenhahn, B., Black, M.J., Pons-Moll, G., Sparse inertial poser: Automatic 3d human pose estimation from sparse imus (2017) Computer Graphics Forum, 36 (2), pp. 349-360. , Wiley Online Library; Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., Sheikh, Y., (2018) Openpose: Realtime Multi-person 2d Pose Estimation Using Part Affinity Fields; Zhang, L., Sturm, J., Cremers, D., Lee, D., Real-time human motion tracking using multiple depth cameras (2012) 2012 IEEE/RSJ Int. Conf. On Intelligent Robots and Systems. IEEE, pp. 2389-2395; Asteriadis, S., Chatzitofis, A., Zarpalas, D., Alexiadis, D.S., Daras, P., Estimating human motion from multiple kinect sensors (2013) 6th Int. Conf. On Computer Vision/Computer Graphics Collaboration Techniques and Applications, pp. 1-6; Munaro, M., Basso, F., Menegatti, E., Openptrack: Open source multi-camera calibration and people tracking for rgb-d camera networks (2016) Robotics and Autonomous Systems, 75, pp. 525-538; Malaguti, A., Carraro, M., Guidolin, M., Tagliapietra, L., Menegatti, E., Ghidoni, S., Real-time tracking-by-detection of human motion in rgb-d camera networks (2019) 2019 IEEE Int. Conf. On Systems, Man and Cybernetics (SMC). IEEE, pp. 3198-3204; Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., Black, M.J., Keep it smpl: Automatic estimation of 3d human pose and shape from a single image (2016) Eur. Conf. On Computer Vision, pp. 561-578. , Springer; Windolf, M., Götzen, N., Morlock, M., Systematic accuracy and precision analysis of video motion capturing systems-exemplified on the vicon-460 system (2008) J. Of Biomechanics, 41 (12), pp. 2776-2780; Munaro, M., Lewis, C., Chambers, D., Hvass, P., Menegatti, E., Rgb-d human detection and tracking for industrial environments (2016) Intelligent Autonomous Systems 13, pp. 1655-1668. , Springer; Filippeschi, A., Schmitz, N., Miezal, M., Bleser, G., Ruffaldi, E., Stricker, D., Survey of motion tracking methods based on inertial sensors: A focus on upper limb human motion (2017) Sensors, 17 (6), p. 1257; Joukov, V., Cesic, J., Westermann, K., Markovic, I., Kulic, D., Petrovíc, I., Human motion estimation on lie groups using imu measurements (2017) 2017 IEEE/RSJ Int. Conf. On Intelligent Robots and Systems (IROS). IEEE, pp. 1965-1972; Jurman, D., Jankovec, M., Kamnik, R., Topic, M., Calibration and data fusion solution for the miniature attitude and heading reference system (2007) Sensors and Actuators A: Physical, 138 (2), pp. 411-420; Lou, L., Xu, X., Cao, J., Chen, Z., Xu, Y., Sensor fusion-based attitude estimation using low-cost mems-imu for mobile robot navigation (2011) 2011 6th IEEE Int. Conf. On Information Technology and Artificial Intelligence, 2, pp. 465-468; Hyyti, H., Visala, A., A DCM based attitude estimation algorithm for low-cost mems imus (2015) Int. J. Of Navigation and Observation, 2015; (2018) MTw Awinda User Manual, , https://www.xsens.com/hubfs/Downloads/Manuals/MTwAwindaUserManual.pdf, XSens; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., Ros: An open-source robot operating system (2009) ICRA Workshop on Open Source Software, 3 (2-3), p. 5. , Kobe, Japan; (2019) Github Repository, , https://github.com/qleonardolp/xsensmtwdriver-release; (2017) Github Repository, , https://github.com/Raffa87/xsense-awinda; (2018) Best Practices, , http://wiki.ros.org/BestPractices; (2020) Xsens MTi Products, , https://www.xsens.com/mti-products; Guidolin, M., Budau Petrea, R.A., Oboe, R., Reggiani, M., Menegatti, E., Tagliapietra, L., On the accuracy of imus for human motion tracking: A comparative evaluation (2021) 2021 IEEE International Conference on Mechatronics (ICM). IEEE, , to be published; Pizzolato, C., Lloyd, D.G., Sartori, M., Ceseracciu, E., Besier, T.F., Fregly, B.J., Reggiani, M., Ceinms: A toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks (2015) J. Of Biomechanics, 48 (14), pp. 3929-3936; Tonin, L., Beraldo, G., Tortora, S., Tagliapietra, L., Millán J, D.R., Menegatti, E., Ros-neuro: A common middleware for bmi and robotics. The acquisition and recorder packages (2019) 2019 IEEE Int. Conf. On Systems, Man and Cybernetics (SMC). IEEE, pp. 2767-2772; Tagliapietra, L., Modenese, L., Ceseracciu, E., Mazzà, C., Reggiani, M., Validation of a model-based inverse kinematics approach based on wearable inertial sensors (2018) Computer Methods in Biomechanics and Biomedical Engineering, 21 (16), pp. 834-844; Delp, S.L., Anderson, F.C., Arnold, A.S., Loan, P., Habib, A., John, C.T., Guendelman, E., Thelen, D.G., Opensim: Open-source software to create and analyze dynamic simulations of movement (2007) IEEE Trans. On Biomedical Engineering, 54 (11), pp. 1940-1950",,,"IEEE Industrial Electronics Society (IES);The Institute of Electrical and Electronics Engineers (IEEE)","Institute of Electrical and Electronics Engineers Inc.","22nd IEEE International Conference on Industrial Technology, ICIT 2021","10 March 2021 through 12 March 2021",,170894,,9781728157306,85RSA,,"English","Proc IEEE Int Conf Ind Technol",Conference Paper,"Final","",Scopus,2-s2.0-85104105677
"Saleh K., Yu K., Chen F.","57193135096;37036489800;55714370100;","Improving users engagement detection using end-to-end spatio-temporal convolutional neural networks",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447157","190","194",,,"10.1145/3434074.3447157","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102770336&doi=10.1145%2f3434074.3447157&partnerID=40&md5=0918763ad7527900ed7062e72f8b304f","University of Technology Sydney, Sydney, NSW, Australia","Saleh, K., University of Technology Sydney, Sydney, NSW, Australia; Yu, K., University of Technology Sydney, Sydney, NSW, Australia; Chen, F., University of Technology Sydney, Sydney, NSW, Australia","The ability to infer latent behaviours such as the degree of engagement of humans interacting with social robots is still considered one challenging task in the human-robot interaction (HRI) field. Data-driven techniques based on machine learning were recently shown to be a promising approach for tackling the users' engagement detection problem, however, the resolution often involves multiple consecutive stages. This in return makes these techniques either incapable of capturing the users' engagement especially in a dynamic environment or un-deployable because of their inability to track engagement in real-time. This study is based on a data-driven framework, and we propose an end-to-end technique based on a unique 3D convolutional neural network architecture. Our proposed framework was trained and evaluated using a real-life dataset of users interacting spontaneously with a social robot in a dynamic environment. The framework has shown promising results over three different evaluation metrics when compared against three baseline approaches from the literature with an F1-score of 76.72. Additionally, our framework has achieved a resilient real-time performance of 25 Hz. © 2021 ACM.","3D convnet; Engagement prediction; Spatio-temporal modelling","Agricultural robots; Convolution; Convolutional neural networks; Man machine systems; Network architecture; Data driven technique; Detection problems; Dynamic environments; Evaluation metrics; Human robot Interaction (HRI); On-machines; Real time performance; Spatio temporal; Social robots",,,,,,,,,,,,,,,,"Baltrusaitis, T., Zadeh, A., Chong Lim, Y., Morency, L., Openface 2. 0: Facial behavior analysis toolkit (2018) 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pp. 59-66; Ben-Youssef, A., Clavel, C., Essid, S., Bilac, M., Chamoux, M., Lim, A., Ue-hri: A new dataset for the study of user engagement in spontaneous human-robot interactions (2017) Proceedings of the 19th ACM International Conference on Multimodal Interaction, pp. 464-472; Ben-Youssef, A., Varni, G., Essid, S., Clavel, C., On-the-fly detection of user engagement decrease in spontaneous human-robot interaction using recurrent and deep neural networks (2019) International Journal of Social Robotics, 11 (5), pp. 815-828. , 2019; Breazeal, C., Dautenhahn, K., Kanda, T., Social robotics (2016) Springer Handbook of Robotics, pp. 1935-1972. , Springer; Carreira, J., Zisserman, A., Quo vadis, action recognition? A new model and the kinetics dataset (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299-6308; Castellano, G., Pereira, A., Leite, I., Paiva, A., McOwan, P.W., Detecting user engagement with a robot companion using task and social interaction-based features (2009) Proceedings of the 2009 International Conference on Multimodal Interfaces, pp. 119-126; Del Duchetto, F., Baxter, P., Hanheide, M., (2020) Are You Still with Me? Continuous Engagement Assessment from A Robot's Point of View, , 2020; Hara, K., Kataoka, H., Satoh, Y., Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet? (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6546-6555; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778; Ioffe, S., Szegedy, C., (2015) Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, , 2015; Kingma, D.P., Ba, J., (2014) Adam: A Method for Stochastic Optimization, , 2014; Liu, T., Kappas, A., Predicting engagement breakdown in hri using thin-slices of facial expressions (2018) Thirty-second AAAI Conference on Artificial Intelligence; Rudovic, O., Zhang, M., Schuller, B., Picard, R., Multimodal active learning from human data: A deep reinforcement learning approach (2019) 2019 International Conference on Multimodal Interaction, pp. 6-15; Saleh, K., Hossny, M., Nahavandi, S., Real-time intent prediction of pedestrians for autonomous ground vehicles via spatio-temporal densenet (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 9704-9710; Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Rabinovich, A., Going deeper with convolutions (2015) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9; Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., Rethinking the inception architecture for computer vision (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818-2826; Tonkin, M., Vitale, J., Ojha, S., Williams, M., Fuller, P., Judge, W., Wang, X., Would you like to sample? Robot engagement in a shopping centre (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 42-49; Tran, D., Bourdev, L.D., Fergus, R., Torresani, L., Paluri, M., (2014) C3D: Generic Features for Video Analysis, 2 (7), p. 8. , CoRR, abs/1412 0767 2014; Vaufreydaz, D., Johal, W., Combe, C., Starting engagement detection towards a companion robot using multimodal features (2016) Robotics and Autonomous Systems, 75, pp. 4-16. , 2016; Yao, Y., Rosasco, L., Caponnetto, A., On early stopping in gradient descent learning (2007) Constructive Approximation, 26 (2), pp. 289-315. , 2007; Ben Youssef, A., Clavel, C., Essid, S., Early detection of user engagement breakdown in spontaneous human-humanoid interaction (2019) IEEE Transactions on Affective Computing, , 2019",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102770336
"Marvel J.A., Bagchi S., Zimmerman M., Aksu M., Antonishek B., Li X., Wang Y., Mead R., Fong T., Ben Amor H.","35074587400;57208164628;57208165783;57195229251;8620417100;57222473338;36549975900;23009115600;7102098734;23388323100;","Novel and emerging test methods and metrics for effective HRI",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444880","730","732",,,"10.1145/3434074.3444880","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102737029&doi=10.1145%2f3434074.3444880&partnerID=40&md5=2cc085a7d10332f6d26c64a4635c988f","U.S. National Institute of Standards and Technology, Gaithersburg, MD, United States; Clemson University, Clemson, SC, United States; Semio, Los Angels, CA, United States; U.S. National Aeronautics and Space Administration, United States; Arizona State University, Tempe, AZ, United States","Marvel, J.A., U.S. National Institute of Standards and Technology, Gaithersburg, MD, United States; Bagchi, S., U.S. National Institute of Standards and Technology, Gaithersburg, MD, United States; Zimmerman, M., U.S. National Institute of Standards and Technology, Gaithersburg, MD, United States; Aksu, M., U.S. National Institute of Standards and Technology, Gaithersburg, MD, United States; Antonishek, B., U.S. National Institute of Standards and Technology, Gaithersburg, MD, United States; Li, X., U.S. National Institute of Standards and Technology, Gaithersburg, MD, United States; Wang, Y., Clemson University, Clemson, SC, United States; Mead, R., Semio, Los Angels, CA, United States; Fong, T., U.S. National Aeronautics and Space Administration, United States; Ben Amor, H., Arizona State University, Tempe, AZ, United States","This is the third annual, full-day workshop that aims to explore the state-of-practice in the metrology necessary for repeatably and independently assessing the performance of robotic systems in realworld human-robot interaction (HRI) scenarios. This workshop continues the aims of shortening the lead time between between the theory and applications of HRI, enabling reproducible studies, and accelerating the adoption of cutting edge technologies as the industry state-of-practice. This third installment of the annual workshop, ""Test Methods and Metrics for Effective HRI,' seeks to identify novel and emerging test methods and metrics for the holistic assessment and assurance of HRI performance. The focus is on identifying innovative metrics and test methods for the evaluation of HRI performance, and to advance the growth of the HRI community based on the principles of collaboration, data sharing, and repeatability. The goal of this workshop is to aid in the advancement of HRI technologies through the development of experimental design, test methods, and metrics for assessing interaction and interface designs. © 2021 Owner/Author.","Benchmarking; Data sets; Performance measures; Repeatability studies; Test methods and metrics; Use cases","Agricultural robots; Data Sharing; Man machine systems; Social robots; Cutting edge technology; Hri communities; Human robot Interaction (HRI); Interaction and interface design; Real-world; Robotic systems; State of practice; Test method; Testing",,,,,,,,,,,,,,,,,,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102737029
"Hart J.W., Depalma N., Pryor M.W., Hayes B., Kruusamäe K., Mirsky R., Xiao X.","25031145200;35193719800;7004159251;55638866400;34771687000;57192390008;56742595000;","Exploring applications for autonomous nonverbal human-robot interaction",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444882","728","729",,1,"10.1145/3434074.3444882","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102732152&doi=10.1145%2f3434074.3444882&partnerID=40&md5=3ecba1a5a2fe5733b61a6d79c90bcf0c","The University of Texas at Austin, Austin, TX, United States; Facebook AI Research (FAIR), Pittsburgh, PA, United States; The University of Colorado Boulder, Boulder, CO, United States; The University of Tartu, Tartu, Estonia","Hart, J.W., The University of Texas at Austin, Austin, TX, United States; Depalma, N., Facebook AI Research (FAIR), Pittsburgh, PA, United States; Pryor, M.W., The University of Texas at Austin, Austin, TX, United States; Hayes, B., The University of Colorado Boulder, Boulder, CO, United States; Kruusamäe, K., The University of Tartu, Tartu, Estonia; Mirsky, R., The University of Texas at Austin, Austin, TX, United States; Xiao, X., The University of Texas at Austin, Austin, TX, United States","Non-verbal Human-Robot Interaction (nHRI) encompasses the study of the exchange of human-robot gaze, gesture, touch, body language, paralinguistic, facial and affect expression. nHRI has advanced beyond theoretical and computational contributions. Progress has been made through a variety of user studies and laboratory experiments as well as practical efforts such as integration of nonverbal inputs with other HRI modalities including domain specific implementations. This workshop seeks to promote collaboration between two threads of research: experimental nHRI, and application domains that can benefit from its use. The workshop will link researchers working on new approaches to nHRI in the laboratory to applied roboticists who present challenges in specific domains, such as: service robots, field robotics, socially-assistive robotics, and human-robot collaborative work that could benefit from richer nHRI. This workshop will draw participation from diverse areas to evaluate best practices and integration efforts across different research domains. We will target a broad, cross-disciplinary audience, and provide a venue for recent efforts related to multimodal interaction, system integration, data collection, and user studies. © 2021 Owner/Author.","Human-robot interaction; Nonverbal hri","Agricultural robots; Man machine systems; Robotics; Assistive robotics; Collaborative Work; Cross-disciplinary; Laboratory experiments; Multi-Modal Interactions; Non-verbal human; Research domains; System integration; Social robots",,,,,,,,,,,,,,,,,,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102732152
"Dede M.I.C., Kiper G., Ayav T., Ozdemirel B., Tatlıcıoglu E., Hanalioglu S., Işıkay I., Berker M.","55561029700;23985110500;13408184500;56712818400;14034778100;55801129800;25936413500;55920860500;","Human–robot interfaces of the neuroboscope: A minimally invasive endoscopic pituitary tumor surgery robotic assistance system",2021,"Journal of Medical Devices, Transactions of the ASME","15","1","011106","","",,,"10.1115/1.4049394","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107958529&doi=10.1115%2f1.4049394&partnerID=40&md5=975e1687eb1d91a0fe45def8948da24f","Department of Mechanical Engineering, Izmir Institute of Technology, Urla, Izmir, 35430, Turkey; Department of Computer Engineering, Izmir Institute of Technology, Urla, Izmir, 35430, Turkey; Department of Electrical and Electronics Engineering, Izmir Institute of Technology, Urla, Izmir, 35430, Turkey; Department of Electrical and Electronics Engineering, Ege University, Bornova, Izmir, 35040, Turkey; Department of Neurosurgery, Faculty of Medicine, Hacettepe University, Altındag, Ankara, 06230, Turkey","Dede, M.I.C., Department of Mechanical Engineering, Izmir Institute of Technology, Urla, Izmir, 35430, Turkey; Kiper, G., Department of Mechanical Engineering, Izmir Institute of Technology, Urla, Izmir, 35430, Turkey; Ayav, T., Department of Computer Engineering, Izmir Institute of Technology, Urla, Izmir, 35430, Turkey; Ozdemirel, B., Department of Electrical and Electronics Engineering, Izmir Institute of Technology, Urla, Izmir, 35430, Turkey; Tatlıcıoglu, E., Department of Electrical and Electronics Engineering, Ege University, Bornova, Izmir, 35040, Turkey; Hanalioglu, S., Department of Neurosurgery, Faculty of Medicine, Hacettepe University, Altındag, Ankara, 06230, Turkey; Işıkay, I., Department of Neurosurgery, Faculty of Medicine, Hacettepe University, Altındag, Ankara, 06230, Turkey; Berker, M., Department of Neurosurgery, Faculty of Medicine, Hacettepe University, Altındag, Ankara, 06230, Turkey","Endoscopic endonasal surgery is a commonly practiced minimally invasive neurosurgical operation for the treatment of a wide range of skull base pathologies including pituitary tumors. A common shortcoming of this surgery is the necessity of a third hand when the endoscope has to be handled to allow active use of both hands of the main surgeon. The robot surgery assistant NeuRoboScope system has been developed to take over the endoscope from the main surgeon’s hand while providing the surgeon with the necessary means of controlling the location and direction of the endoscope. One of the main novelties of the NeuRoboScope system is its human–robot interface designs which regulate and facilitate the interaction between the surgeon and the robot assistant. The human–robot interaction design of the NeuRoboScope system is investigated in two domains: direct physical interaction (DPI) and master–slave teleoperation (MST). The user study indicating the learning curve and ease of use of the MST is given and this paper is concluded via providing the reader with an outlook of possible new human–robot interfaces for the robot assisted surgery systems. Copyright VC 2021 by ASME",,"Endoscopy; Machine design; Robotics; Social robots; Surgery; Surgical equipment; Tumors; Assistance system; Learning curves; Minimally invasive; Physical interactions; Robot interactions; Robot interface; Robot surgery; Robot-assisted surgery; Robotic surgery; Article; endoscopic pituitary surgery; human; hypophysis tumor; learning curve; minimally invasive surgery; robot assisted surgery",,,,,,,,,,,,,,,,"Ezzat, S., Asa, S. L., Couldwell, W. T., Barr, C. E., Dodge, W. E., Vance, M. L., McCutcheon, I. E., The Prevalence of Pituitary Adenomas (2004) Cancer, 101 (3), pp. 613-619; Jane, J. A., Sulton, L. D., Laws, E. R., Surgery for Primary Brain Tumors at United States Academic Training Centers: Results From the Residency Review Committee for Neurological Surgery (2005) J. Neurosurg, 103 (5), pp. 789-793; Cappabianca, P., Cavallo, L. M., de Divitiis, E., Endoscopic Endonasal Transsphenoidal Surgery (2004) Neurosurgery, 55 (4), pp. 933-941; Berker, M., Hazer, D. B., Y€ucel, T., G€urlek, A., Cila, A., Aldur, M., Onerci, M., Complications of Endoscopic Surgery of the Pituitary Adenomas: Analysis of 570 Patients and Review of the Literature (2012) Pituitary, 15 (3), pp. 288-300; Rotenberg, B., Tam, S., Ryu, W. H., Duggal, N., Microscopic Versus Endoscopic Pituitary Surgery: A Systematic Review (2010) Laryngoscope, 120 (7), pp. 1292-1297; Gao, Y., Zhong, C., Wang, Y., Xu, S., Guo, Y., Dai, C., Zheng, Y., Jiang, J., Endoscopic Versus Microscopic Transsphenoidal Pituitary Adenoma Surgery: A Meta-Analysis (2017) World J. Surg. Oncol, 101, pp. 236-246; Rudmik, L., Starreveld, Y. P., Vandergrift, W. A., Banglawala, S. M., Soler, Z. M., Cost-Effectiveness of the Endoscopic Versus Microscopic Approach for Pituitary Adenoma Resection (2015) Laryngoscope, 125 (1), pp. 16-24; Nathan, C. A. O., Chakradeo, V., Malhotra, K., D’Agostino, H., Patwardhan, R., The Voice-Controlled Robotic Assist Scope Holder AESOP for the Endoscopic Approach to the Sella (2006) Skull Base, 16 (3), pp. 123-131; Kim, K. C., (2014) Robotics in General Surgery, , Springer, New York; Taniguchi, K., Nishikawa, A., Sekimoto, M., Kobayashi, T., Kazuhara, K., Ichihara, T., Kurashita, N., Miyazaki, F., (2010) Robot Surgery (Classification, Design and Evaluation of Endoscope Robots), , Intech Open Access Book, London, UK; Wang, Y., Laby, K. P., Uecker, D. R., Mangaser, A. A., Ghodoussi, M., (1999) Automated Endoscope System for Optimal Positioning, , Patent No. U.S.5878193; Nishikawa, A., Hosoi, T., Koara, K., Daiji, N., Hikita, A., Asano, S., Miyazaki, F., Monden, M., Real-Time Visual Tracking of the Surgeon’s Face for Laparoscopic Surgery (2001) Medical Image Computing and Computer-Assisted Intervention—MICCAI 2001, , Springer, Berlin; Aiono, S., Gilbert, J. M., Soin, B., Finlay, P. A., Gordan, A., Controlled Trial of the Introduction of a Robotic Camera Assistant (EndoAssist) for Laparoscopic Cholecystectomy (2002) Surg. Endoscopy, 16 (9), pp. 1267-1270; Hourley, P., How to Maintain the Quality of Laparoscopic Surgery in the Era of Lack of Hands? (2006) Acta Chir. Belg, 106, pp. 22-26; Kobayashi, E., Masamune, K., Sakuma, I., Dohi, T., Hashimoto, D., A New Safe Laparoscopic Manipulator System With a Five-Bar Linkage Mechanism and an Optimal Zoom (1999) Comput. Aided Surg, 4 (4), pp. 182-192; Taniguchi, K., Nishikawa, A., Yohda, T., Ito, K., Sekimoto, M., Yasui, M., Takiguchi, S., Miyazaki, F., Development of a Compact Oblique-viewing Endoscope Robot for Laparoscopic Surgery (2007) Trans. Jpn. Soc. Med. Biol. Eng, 45 (1), pp. 36-47; Nishikawa, A., Sekimoto, M., Taniguchi, K., Yamada, Y., Miyoshi, N., Takiguchi, S., Doki, Y., Miyazaki, F., (2010) Advanced Strategies for Robot Manipulators (Design and Control of a Compact Endoscope Manipulator: A Biologically Inspired Approach), , InTech Open Access Book, London, UK; Wurm, J., Steinhart, H., Bumm, K., Vogele, M., Nimsky, C., Iro, H., A Novel Robot System for Fully Automated Paranasal Sinus Surgery (2003) Int. Congr. Ser, 1256, pp. 633-638; Zimmermann, M., Krishnan, R., Raabe, A., Seifert, V., Robot-Assisted Navigated Neuroendoscopy (2002) Neurosurgery, 51 (6), pp. 1446-1452; Nimsky, C., Rachinger, J., Iro, H., Fahlbusch, R., Adaptation of a Hexapod-Based Robotic System for Extended Endoscope-Assisted Transsphenoidal Skull Base Surgery (2004) Minimally Invasive Neurosurg, 47 (1), pp. 41-46; iDede, M., Kiper, C., Ayav, G., Tatlıcıoglu, T., Ozdemirel, € E. B., Maaroof, O., Ateş, W., Hanalioglu, Ş., Cerrahın Anlık Y€onlendirilebildigi Robot Yardımlı Endoskop Kontrol Sistemi Mimarisi—NeuRoboScope (2018) T€urkiye Robotbilim Konferansı, pp. 1-6. , and, Istanbul, _ Turkey, Apr. 12–14; Merlet, J. P., (2006) Parallel Robots, , Springer, Dordrecht, The Netherlands; Yaşır, A., Kiper, G., Structural Synthesis of 2R1T Type Mechanisms for Minimally Invasive Surgery Applications (2017) Proceedings of the Fourth MeTrApp Conference, pp. 31-38. , Trabzon, Turkey, July 3–5; Yaşır, A., Kiper, G., Dede, M. I., Kinematic Design of a Non-Parasitic 2R1T Parallel Mechanism With Remote Center of Motion to Be Used in Minimally Invasive Surgery Applications (2020) Mech. Mach. Theory, 153, p. 104013. , C; Ateş, G., (2018) Teleoperation System Design of a Robot Assisted Endoscopic Pituitary Surgery, , M.Sc. thesis, Izmir _ Institute of Technology, Izmir, _ Turkey; Gogu, G., (2012) Structural Synthesis of Parallel Robots - Part 4: Other Topologies With Two and Three Degrees of Freedom, , Springer, Amsterdam, The Netherlands; Ateş, G., Majani, R., Dede, M. I., Design of a Teleoperation Scheme With a Wearable Master for Minimally Invasive Surgery (2018) Proceedings of the Sixth International Workshop on Medical and Service Robots, pp. 45-53. , C., Cassino, Italy, July 4–6; DAI14583 IoT Sensor Development Kit (2020) Dialog Semiconductor, , https://www.dialogsemiconductor.com/iotsensor, Dialog Semiconductor, Reading, UK, accessed Sept. 13, 2020; Dede, M. I., Maaroof, O., Ateş, W., Berker, G., Işıkay, M., Hanalioglu, I., Unilateral Teleoperation Design for a Robotic Endoscopic Pituitary Surgery System (2016) Proceedings of the Fifth International Workshop on Medical and Service Robots, pp. 101-115. , C., and, _ Ş., Graz, Austria, July 4–8","Dede, M.I.C.; Department of Mechanical Engineering, Turkey; email: candede@iyte.edu.tr",,,"American Society of Mechanical Engineers (ASME)",,,,,19326181,,,,"English","J. Med. Devices Trans. ASME",Article,"Final","",Scopus,2-s2.0-85107958529
"Sarda Gou M., Webb T.L., Prescott T.","56303539600;7202490578;6603749094;","The effect of direct and extended contact on attitudes towards social robots",2021,"Heliyon","7","3","e06418","","",,1,"10.1016/j.heliyon.2021.e06418","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103422844&doi=10.1016%2fj.heliyon.2021.e06418&partnerID=40&md5=c77d7f8d111305faf155644d12756a3d","Department of Psychology, The University of Sheffield, United Kingdom; Department of Computer Science, The University of Sheffield, United Kingdom","Sarda Gou, M., Department of Psychology, The University of Sheffield, United Kingdom; Webb, T.L., Department of Psychology, The University of Sheffield, United Kingdom; Prescott, T., Department of Computer Science, The University of Sheffield, United Kingdom","The development of social robots has the potential to address significant societal concerns, however, most people have limited experience of such technology. The present research investigated whether techniques borrowed from the psychology of intergroup relations – namely direct and extended contact – affect people's attitudes towards robots. Participants were provided with either direct contact with a social robot or extended contact (these participants watched a video recorded by a friend who had met the robot) before their explicit and implicit attitudes towards robots were measured. Results indicated that direct contact affected both explicit and implicit attitudes, while extended contact affected implicit attitudes. The implication of these findings is that contact with a robot, direct or indirect, can change attitudes; much as previous research has shown that contact with a person who is a member of an out-group can change attitudes towards that group. We conclude that methods and theories from the study of human intergroup relationships can be usefully applied to understand attitudes toward social robots. © 2021 The AuthorsHuman-robot interaction; Contact theory; Extended contact; Attitudes; Social robotics © 2021 The Authors","Attitudes; Contact theory; Extended contact; Human-robot interaction; Social robotics",,,,,,"Wellcome Trust, WT: 214963/A/18/Z; Horizon 2020 Framework Programme, H2020: 785907, 945539, SGA-2, SGA-3","This work was supported by Wellcome Trust ( 214963/A/18/Z ) and Horizon 2020 Framework Programme (Human Brain Project SGA-2, 785907 ; Human Brain Project SGA-3, 945539 ).",,,,,,,,,,"Allport, G.W., (1954) The Nature of Prejudice, , Addison-Wesley Cambridge, MA; Aron, A., Aron, E.N., Smollan, D., Inclusion of other in the self scale and the structure of interpersonal closeness (1992) J. Pers. Soc. 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Rev., 23 (2), pp. 132-160","Prescott, T.; Department of Computer Science, United Kingdom; email: t.j.prescott@sheffield.ac.uk",,,"Elsevier Ltd",,,,,24058440,,,,"English","Heliyon",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85103422844
"Grau A., Indri M., Lo Bello L., Sauter T.","35760811100;6603713226;57200419171;55753172700;","Robots in Industry: The Past, Present, and Future of a Growing Collaboration with Humans",2021,"IEEE Industrial Electronics Magazine","15","1","9305203","50","61",,4,"10.1109/MIE.2020.3008136","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098776023&doi=10.1109%2fMIE.2020.3008136&partnerID=40&md5=efd0b0864382451424fd725f489e69cf","Automatic Control, Technical Univ of Catalonia, Barcelona, Barcelona, Spain; Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Piemonte, Italy; Department of Electrical, Electronic and Computer Engineering, University of Catania, Catania, Sicilia, Italy; Center for Micro and Nano Sensors, Donau-Universitat Krems, Wien, Austria","Grau, A., Automatic Control, Technical Univ of Catalonia, Barcelona, Barcelona, Spain; Indri, M., Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Piemonte, Italy; Lo Bello, L., Department of Electrical, Electronic and Computer Engineering, University of Catania, Catania, Sicilia, Italy; Sauter, T., Center for Micro and Nano Sensors, Donau-Universitat Krems, Wien, Austria","Robots have been part of automation systems for a very long time, and in public perception, they are often synonymous with automation and industrial revolution perse. Fueled by Industry 4.0 and Internet of Things (IoT) concepts as well as by new software technologies, the field of robotics in industry is currently undergoing a revolution on its own. This article gives an overview of the evolution of robotics from its beginnings to recent trends like collaborative robotics, autonomous robots, and human-robot interaction. Particular attention is devoted to the deep changes of the last decades, from the traditional industrial scenario based on isolated robotic cells up to the most recent coworking and collaborative robots. The role of robotics in the Industry 4.0 framework is analyzed, and the relationships with industrial communications and software technologies are also discussed. Some future directions for robotics are envisaged, focusing on the contributions coming from new materials, sensors, actuators, and technologies. Open issues are highlighted as well as the main barriers that currently limit the deployment of industrial robots in the small and medium enterprise (SME) world. © 2007-2011 IEEE.",,"Industrial robots; Industry 4.0; Internet of things; Robotics; Automation systems; Collaborative robots; Industrial communications; Industrial revolutions; Industrial scenarios; Internet of Things (IOT); Small and medium enterprise; Software technology; Social robots",,,,,"European Commission, EC; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR; Università di Catania","Lucia Lo Bello (lobello@unict.it) earned her Ph.D. degree in 1998. She is a tenured associate professor at the University of Catania, Catania, Italy. Her research interests include industrial and automotive communications, real-time embedded systems, and wireless networks for robotics. She has published more than 170 technical papers. She is responsible for the University of Catania research contracts with STMi-crolectronics on wireless robotics communications as well as several research projects funded by the Italian Ministry of Research and by the European Commission. She is a Senior Member of IEEE and received the Industrial Electronics Society Early Career Award in 2008.",,,,,,,,,,"Grau, A., Indri, M., Lo Bello, L., Sauter, T., Industrial robotics in factory automation: From the early stage to the Internet of Things (2017) Proc. IECON 2017-43rd Annu. Conf IEEE Ind. Electron. Soc., Beijing, pp. 6159-6164; Zheng, P., Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives (2018) Front. Mech. 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"Zeng C., Yang C., Cheng H., Li Y., Dai S.-L.","57195945568;57204881979;56083885000;35174564100;36877968100;","Simultaneously Encoding Movement and sEMG-Based Stiffness for Robotic Skill Learning",2021,"IEEE Transactions on Industrial Informatics","17","2","9055202","1244","1252",,14,"10.1109/TII.2020.2984482","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096550487&doi=10.1109%2fTII.2020.2984482&partnerID=40&md5=728ceba4fae055338c00801dcc502ba9","School of Automation Science and Engineering, South China University of Technology, Guangzhou, China; Center for Robotics, University of Electronic Science and Technology of China, Chengdu, China; Department of Engineering and Design, University of Sussex, Brighton, United Kingdom","Zeng, C., School of Automation Science and Engineering, South China University of Technology, Guangzhou, China; Yang, C., School of Automation Science and Engineering, South China University of Technology, Guangzhou, China; Cheng, H., Center for Robotics, University of Electronic Science and Technology of China, Chengdu, China; Li, Y., Department of Engineering and Design, University of Sussex, Brighton, United Kingdom; Dai, S.-L., School of Automation Science and Engineering, South China University of Technology, Guangzhou, China","Transferring human stiffness regulation strategies to robots enables them to effectively and efficiently acquire adaptive impedance control policies to deal with uncertainties during the accomplishment of physical contact tasks in an unstructured environment. In this article, we develop such a physical human-robot interaction system which allows robots to learn variable impedance skills from human demonstrations. Specifically, the biological signals, i.e., surface electromyography are utilized for the extraction of human arm stiffness features during the task demonstration. The estimated human arm stiffness is then mapped into a robot impedance controller. The dynamics of both movement and stiffness are simultaneously modeled by using a model combining the hidden semi-Markov model and the Gaussian mixture regression. More importantly, the correlation between the movement information and the stiffness information is encoded in a systematic manner. This approach enables capturing uncertainties over time and space and allows the robot to satisfy both position and stiffness requirements in a task with modulation of the impedance controller. The experimental study validated the proposed approach. © 2005-2012 IEEE.","Adaptive impedance control; human-robot interaction systems; multimodality","Markov processes; Robotics; Signal encoding; Stiffness; Biological signals; Hidden semi-Markov modeling; Human demonstrations; Impedance controllers; Physical human-robot interactions; Surface electromyography; Unstructured environments; Variable impedance; Social robots",,,,,"National Natural Science Foundation of China, NSFC: 61811530281, 61861136009, 61973129; Fundamental Research Funds for the Central Universities: TII-20-0301","Manuscript received January 21, 2020; revised March 6, 2020; accepted March 25, 2020. Date of publication April 2, 2020; date of current version November 18, 2020. This work was supported in part by National Nature Science Foundation (NSFC) under Grants 61861136009, 61811530281, and 61973129, and the Fundamental Research for the Central Universities. Paper no. TII-20-0301. (Corresponding author: Chenguang Yang.) Chao Zeng, Chenguang Yang, and Shi-Lu Dai are with the School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China (e-mail: mjzengchao@163.com; cyang@ieee.org; audaisl@scut.edu.cn).",,,,,,,,,,"Billard, A., Calinon, S., Dillmann, R., Schaal, S., Robot programming by demonstration (2008) Springer Handbook of Robotics, pp. 1371-1394. , Berlin, Germany: Springer; Duan, J., Ou, Y., Hu, J., Wang, Z., Jin, S., Xu, C., Fast and stable learning of dynamical systems based on extreme learning machine (2019) Ieee Trans. Syst. Man Cybernet.: Syst., 49 (6), pp. 1175-1185. , Jun; Ti, B., Gao, Y., Li, Q., Zhao, J., Human intention understanding from multiple demonstrations and behavior generalization in dynamic movement primitives framework (2019) Ieee Access, 7, pp. 36186-36194. , Mar; Racca, M., Pajarinen, J., Montebelli, A., Kyrki, V., Learning in-contact control strategies from demonstration (2016) Proc. 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"Xue T., Wang Z., Zhang T., Bai O., Zhang M., Han B.","57208213913;57190881030;55547106760;57205912472;57208209572;54788899500;","Continuous Finite-Time Torque Control for Flexible Assistance Exoskeleton with Delay Variation Input",2021,"Robotica","39","2",,"291","316",,2,"10.1017/S0263574720000375","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095728972&doi=10.1017%2fS0263574720000375&partnerID=40&md5=14230d228714182ddc987e9f8575d91e","Department of Automation, Tsinghua University, Beijing, 100084, China; Department of Electrical and Computer Engineering, Florida International University, Miami, FL  33174, United States; Move Robotics Technology Company Ltd., Shanghai, 201306, China; School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China","Xue, T., Department of Automation, Tsinghua University, Beijing, 100084, China; Wang, Z., Department of Automation, Tsinghua University, Beijing, 100084, China; Zhang, T., Department of Automation, Tsinghua University, Beijing, 100084, China; Bai, O., Department of Electrical and Computer Engineering, Florida International University, Miami, FL  33174, United States; Zhang, M., Department of Automation, Tsinghua University, Beijing, 100084, China, Move Robotics Technology Company Ltd., Shanghai, 201306, China; Han, B., School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China","Accurate torque control is a critical issue in the compliant human-robot interaction scenario, which is, however, challenging due to the ever-changing human intentions, input delay, and various disturbances. Even worse, the performances of existing control strategies are limited on account of the compromise between precision and stability. To this end, this paper presents a novel high-performance torque control scheme without compromise. In this scheme, a new nonlinear disturbance observer incorporated with equivalent control concept is proposed, where the faster convergence and stronger anti-noise capability can be obtained simultaneously. Meanwhile, a continuous fractional power control law is designed with an iteration method to address the matched/unmatched disturbance rejection and global finite-time convergence. Moreover, the finite-time stability proof and prescribed control performance are guaranteed using constructed Lyapunov function with adding power integrator technique. Both the simulation and experiments demonstrate enhanced control accuracy, faster convergence rate, perfect disturbance rejection capability, and stronger robustness of the proposed control scheme. Furthermore, the evaluated assistance effects present improved gait patterns and reduced muscle efforts during walking and upstair activity. Copyright © The Author(s), 2020. Published by Cambridge University Press.","Compliant human-robot interaction; Finite-time control; Fluctuated delay input; Force control; Robotic exoskeleton; Series elastic actuator","Agricultural robots; Disturbance rejection; Exoskeleton (Robotics); Human robot interaction; Iterative methods; Lyapunov functions; Torque control; Walking aids; Antinoise capability; Control performance; Equivalent control; Finite time stability; Finite-time convergence; Nonlinear disturbance observer; Power integrator technique; Torque control scheme; Power control",,,,,,,,,,,,,,,,"Xue, T., Wang, Z., Zhang, T., Zhang, M., Adaptive oscillator-based robust control for flexible hip assistive exoskeleton (2019) IEEE Robot. Autom. 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"Dang J., Liu L.","57190682614;57119771700;","Robots are friends as well as foes: Ambivalent attitudes toward mindful and mindless AI robots in the United States and China",2021,"Computers in Human Behavior","115",,"106612","","",,9,"10.1016/j.chb.2020.106612","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094324067&doi=10.1016%2fj.chb.2020.106612&partnerID=40&md5=b718e931a2bad545d912d0fb709f5b63","Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, China","Dang, J., Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, China; Liu, L., Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, China","In light of the ongoing and rapid development of innovative technologies, two intriguing issues arise: do people have more positive or more negative attitudes toward robots with high (versus low) mental capabilities, and do attitudes toward robots differ between Western and East Asian cultures? Past work on these topics has produced contradictory results. Inspired by the perspective that attitudes are ambivalent rather than bipolar, we argue that these controversial findings stem from people's ambivalent attitudes toward robots. To test the assumption that ambivalent attitudes toward robots differ by type of robots and by cultural background, we conducted an experimental study. By manipulating the level of robot mind and recruiting both American and Chinese participants, we examined how robot mind and culture influence ambivalent attitudes toward robots. We simultaneously measured participants' perceptions of robots as “ally” or “enemy”. The results revealed that robots with high (versus low) mental abilities elicited more ambivalent attitudes and that American participants reported more ambivalence toward robots than Chinese participants. These findings enhance our understanding of human–robot interaction and provide guidance for modulating people's attitudes toward robots. © 2020 Elsevier Ltd","Ambivalent attitudes; Artificial intelligence; Culture; Human–robot interaction; Mind","Industrial manipulators; Cultural backgrounds; Innovative technology; Provide guidances; Robot interactions; Human robot interaction; adult; ambivalence; American; article; artificial intelligence; attitude; China; experimental study; female; friend; human; human experiment; male; mental capacity; perception; robotics",,,,,"China Postdoctoral Science Foundation: 2020M670188, 2020T130064; Fundamental Research Funds for the Central Universities: 2019NTSS30; National Office for Philosophy and Social Sciences, NPOPSS: 18ZDA332","The authors gratefully acknowledge the financial support provided by the Major Project of the National Social Science Foundation of China ( 18ZDA332 ), the Fundamental Research Funds for the Central University ( 2019NTSS30 ) and the China Postdoctoral Science Foundation ( 2020M670188 ; 2020T130064 ). The funders had no role in study design, data collection, decision to publish, or preparation of the manuscript. We thank Anita Harman, PhD, from Liwen Bianji, Edanz Editing China ( www.liwenbianji.cn/ac ), for editing the English text of a draft of this manuscript.",,,,,,,,,,"Alexander, M.G., Brewer, M.B., Herrmann, R.K., Images and affect: A functional analysis of out-group stereotype (1999) Journal of Personality and Social Psychology, 77, pp. 78-93; Armitage, C.J., Conner, M., The effects of attitudinal ambivalence on attitude-intention behavior relations (2004) Contemporary perspectives on the psychology of attitudes, pp. 121-143. , G. Haddock G.R. Maio Psychology Press; Bartneck, C., Hu, J., Rapid prototyping for interactive robots (2004) Proceedings of the 8th conference on intelligent autonomous systems (IAS-8), pp. 136-145. , F. Groen N. Amato A. Bonarini E. Yoshida B. 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The stability and importance of individual differences in anthropomorphism (2010) Perspectives on Psychological Science, 5 (3), pp. 219-232; Waytz, A., Heafner, J., Epley, N., The mind in the machine: Anthropomorphism increases trust in an autonomous vehicle (2014) Journal of Experimental Social Psychology, 52 (3), pp. 113-117; Winkielman, P., Halberstadt, J., Fazendeiro, T., Catty, S., Prototypes are attractive because they are easy on the mind (2006) Psychological Science, 17 (9), pp. 799-806; Yogeeswaran, K., Złotowski, J., Livingstone, M., Bartneck, C., Sumioka, H., Ishiguro, H., The interactive effects of robot anthropomorphism and robot ability on perceived threat and support for robotics research (2016) Journal on Human Robot Interaction, 5 (2), pp. 29-47","Liu, L.; Faculty of Psychology, 19 Xinjiekouwai Street, China; email: l.liu@bnu.edu.cn",,,"Elsevier Ltd",,,,,07475632,,CHBEE,,"English","Comput. Hum. Behav.",Article,"Final","",Scopus,2-s2.0-85094324067
"Shata E.H., Nguyen K.-D., Acharya P., Doom J.","57211159062;25928614200;57211157505;16645547400;","A Series-elastic Robot for Back-pain Rehabilitation",2021,"International Journal of Control, Automation and Systems","19","2",,"1054","1064",,,"10.1007/s12555-019-0859-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093076586&doi=10.1007%2fs12555-019-0859-x&partnerID=40&md5=e17fb9b17a8bff594ff0eb4f4378975c","Department of Mechanical Engineering, South Dakota State University, Brookings, SD, United States","Shata, E.H., Department of Mechanical Engineering, South Dakota State University, Brookings, SD, United States; Nguyen, K.-D., Department of Mechanical Engineering, South Dakota State University, Brookings, SD, United States; Acharya, P., Department of Mechanical Engineering, South Dakota State University, Brookings, SD, United States; Doom, J., Department of Mechanical Engineering, South Dakota State University, Brookings, SD, United States","This paper addresses the robot-assisted rehabilitation of back pain, an epidemic health problem affecting a large portion of the population. The design is composed of two springs in series connected to an end-effector via a pair of antagonistic cables. The spring and cable arrangement forms an elastic coupling from the actuator to the output shaft. An input-output torque model of the series-elastic mechanism is established and studied numerically. The study also illustrates the variation of the mechanism’s effective stiffness by changing the springs’ position. In addition, we built a prototype of the robotic mechanism and design experiments with a robotic manipulator to experimentally investigate its dynamic characteristics. The experimental results confirm the predicted elasticity between the input motion and the output torque at the end-effector. We also observe an agreement between the data generated by the torque model and data collected from the experiments. An experiment with a full-scale robot and a human subject is carried out to investigate the human-robot interaction and the mechanism behavior. © 2020, ICROS, KIEE and Springer.","Back-pain rehabilitation; experimental validation; torque models; variable stiffness actuators","Cables; End effectors; Health; Human robot interaction; Industrial manipulators; Robotics; Springs (components); Cable arrangements; Dynamic characteristics; Effective stiffness; Elastic coupling; Elastic mechanism; Robot-assisted rehabilitation; Robotic manipulators; Robotic mechanism and designs; Manipulators",,,,,"South Dakota Board of Regents, SDBOR","This work is supported by the FY20 Competitive Research Grant Program of South Dakota Board of Regents.",,,,,,,,,,"Friedly, J., Standaert, C., Chan, L., Epidemiology of spine care: the back pain dilemma (2010) Physical Medicine and Rehabilitation Clinics, 21 (4), pp. 659-677; van den Berg, R., de Hooge, M., van Gaalen, F., Reijnierse, M., Huizinga, T., van der Heijde, D., Percentage of patients with Spondylarthritis in patients referred because of chronic back pain and performance of classification criteria: experience from the spondyloarthritis caught early (space) cohort (2013) Rheumatology, 52 (8), pp. 1492-1499; 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Lum, P., Reinkensmeyer, D., Mahoney, R., Robotic devices for movement therapy after stroke: current status and challenges to clinical acceptance (2002) Topics in Stroke Rehabilitation, 8 (4), pp. 40-53; Stanger, C.A., Anglin, C., Harwin, W.S., Devices for assisting manipulation: a summary of user task priorities (1994) IEEE Transactions on Rehabilitation Engineering, 2 (4), pp. 256-265; Shen, Z., Zhuang, Y., Zhou, J., Gao, J., Song, R., Design and test of admittance control with inner adaptive robust position control for a lower limb rehabilitation robot (2020) International Journal of Control, Automation and Systems, 18 (1), pp. 134-142; Lee, M., Rittenhouse, M., Abdullah, H.A., Design issues for therapeutic robot systems: results from a survey of physiotherapists (2005) Journal of Intelligent and Robotic Systems, 42 (3), pp. 239-252; Kim, B., Analysis on effective rehabilitation of human arms using compliant strap (2018) Int’l Journal of Control, Automation and Systems, 16 (6), pp. 2958-2965; 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Electromyography and Kinesiology, 13 (3), pp. 239-252; Li, Z., Su, C.Y., Li, G., Su, H., Fuzzy approximation-based adaptive backstepping control of an exoskeleton for human upper limbs (2014) IEEE Transactions on Fuzzy Systems, 23 (3), pp. 555-566; Su, H., Sandoval, J., Vieyres, P., Poisson, G., Ferrigno, G., de Momi, E., Safety-enhanced collaborative framework for tele-operated minimally invasive surgery using a 7-DoF torque-controlled robot (2018) International Journal of Control, Automation and Systems, 16 (6), pp. 2915-2923; Nguyen, K.D., Dankowicz, H., Cooperative control of networked robots on a dynamic platform in the presence of communication delays (2017) Journal of Robust and Nonlinear Control, 27 (9), pp. 1433-1461; Nguyen, K.D., Li, Y., Dankowicz, H., Delay robustness of an L1 adaptive controller for a class of systems with unknown matched nonlinearities (2017) IEEE Trans. on Automatic Control, 63 (10), pp. 5485-5491; Nguyen, K.D., A predictor-based model reference adaptive controller for time-delay systems (2018) IEEE Trans. on Automatic Control, 63 (12), pp. 4375-4382; Nguyen, K.D., Dankowicz, H., Input-delay compensation in a robust adaptive control framework (2019) IET Control Theory & Applications, 13 (11), pp. 1718-1727; Nguyen, K.D., Uniform continuity and delay robustness of an adaptive controller for Lagrangian systems (2020) IMA J. of Mathematical Control and Information, 37 (2), pp. 555-584; Nguyen, K.D., An adaptive compensator for uncertain nonlinear systems with multiple time delays (2020) Automatica, 117, p. 108976","Nguyen, K.-D.; Department of Mechanical Engineering, United States; email: Doang.Nguyen@sdstate.edu",,,"Institute of Control, Robotics and Systems",,,,,15986446,,,,"English","Int. J. Control Autom. Syst.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85093076586
"Du Z., Liang Y., Yan Z., Sun L., Chen W.","8698649300;57195986873;55414380400;57204292698;57198583932;","Human-robot interaction control of a haptic master manipulator used in laparoscopic minimally invasive surgical robot system",2021,"Mechanism and Machine Theory","156",,"104132","","",,1,"10.1016/j.mechmachtheory.2020.104132","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092351200&doi=10.1016%2fj.mechmachtheory.2020.104132&partnerID=40&md5=b1f7579d99f7fd37e5484a1d7af4bced","State Key Laboratory of Robotics and System, Harbin Institute of Technology, 2 Yikuang Street, Harbin, 150080, China; Shenzhen polytechnic, Shenzhen, Guangdong, China","Du, Z., State Key Laboratory of Robotics and System, Harbin Institute of Technology, 2 Yikuang Street, Harbin, 150080, China; Liang, Y., State Key Laboratory of Robotics and System, Harbin Institute of Technology, 2 Yikuang Street, Harbin, 150080, China; Yan, Z., State Key Laboratory of Robotics and System, Harbin Institute of Technology, 2 Yikuang Street, Harbin, 150080, China; Sun, L., State Key Laboratory of Robotics and System, Harbin Institute of Technology, 2 Yikuang Street, Harbin, 150080, China; Chen, W., Shenzhen polytechnic, Shenzhen, Guangdong, China","As an interface for physical human-robot interaction (pHRI), the master manipulator of robot-assisted surgery system is critical to the success rate of surgical procedures. In this paper, a pHRI control scheme of the haptic master manipulator used in laparoscopic surgical robots is presented for enhancing the precision and comfort of operations. Firstly, the inverse dynamic equations of the master manipulator are derived and a modified friction model is proposed to improve the accuracy of friction torque calculation. Then a torque observer based on generalized momentum is designed. The proposed observer can precisely track the joint driving torque needed by the motion state and comply with the operator's intentions. Thirdly, the compensator based on time-delay neural network (TDNN) is proposed to improve the performance of pHRI. Finally, the pHRI control strategy which can adjust the degree of operation compliance actively is introduced. The results of theoretical analysis and comparative experiments indicate that the proposed strategy can substantially reduce the force and torque applied to surgeons by the master manipulator during the operation. © 2020","Generalized momentum; Master manipulator; Physical human-robot interaction; Surgical robots; Torque observer","Compliance control; Delay control systems; Friction; Laparoscopy; Man machine systems; Manipulators; Social robots; Surgical equipment; Torque; Transplantation (surgical); Comparative experiments; Human-robot interaction control; Inverse dynamic equations; Laparoscopic surgical robots; Minimally invasive surgical robot; Physical humanrobot interaction (phri); Robot-assisted surgery; Time delay neural networks; Robotic surgery",,,,,"National Key Research and Development Program of China, NKRDPC: 2017YFB1304202","This work was supported by the National Key R&D Program of China [grant number 2017YFB1304202 ].",,,,,,,,,,"Liang, Y., Du, Z., Wang, W., Sun, L., A novel position compensation scheme for cable-pulley mechanisms used in laparoscopic surgical robots (2017) Sensors, 17, p. 2257; Liang, Y., Du, Z., Wang, W., Yan, Z., Sun, L., An improved scheme for eliminating the coupled motion of surgical instruments used in laparoscopic surgical robots (2019) Rob. Autom. Syst., 112, pp. 49-59; Xue, R., Du, Z., Yan, Z., Ren, B., An estimation method of grasping force for laparoscope surgical robot based on the model of a cable-pulley system (2019) Mech. Mach. Theory, 134, pp. 440-454; Nisar, S., Endo, T., Matsuno, F., Design and optimization of a 2-degree-of-freedom planar remote center of motion mechanism for surgical manipulators with smaller footprint (2018) Mech. Mach. Theory, 129, pp. 148-161; Liang, Y., Sun, L., Du, Z., Yan, Z., Wang, W., Mechanism design and optimization of a haptic master manipulator for laparoscopic surgical robots (2019) IEEE Access, 7, pp. 147808-147824; Xue, R., Ren, B., Yan, Z., Du, Z., A cable-pulley system modeling based position compensation control for a laparoscope surgical robot (2017) Mech. Mach. Theory, 118, pp. 283-299; Yaşır, A., Kiper, G., Dede, M.İ.C., Kinematic design of a non-parasitic 2R1T parallel mechanism with remote center of motion to be used in minimally invasive surgery applications (2020) Mech. Mach. Theory, 153; Kim, W., Choi, Y.R., Cho, J.Y., Yoon, Y.S., Han, H.S., Robot single incision left lateral sectionectomy via da Vinci Xi Single Site & vaginal extraction of the specimen (2020) Surg. Oncol., 33, pp. 254-255; Agarwal, D.K., Sharma, V., Toussi, A., Viers, B.R., Tollefson, M.K., Gettman, M.T., Frank, I., Initial experience with da Vinci single-port robot-assisted radical prostatectomies (2019) Eur. Urol.; Freschi, C., Ferrari, V., Melfi, F., Ferrari, M., Mosca, F., Cuschieri, A., Technical review of the da Vinci surgical telemanipulator (2013) Int. J. Med. Robot. Comput. Assist. Surgery, 9, pp. 396-406; Ballantyne, G.H., Moll, F., The da Vinci telerobotic surgical system: the virtual operative field and telepresence surgery (2003) Surgical Clinics, 83, pp. 1293-1304; Du, Z., Wang, W., Yan, Z., Dong, W., Wang, W., Variable admittance control based on fuzzy reinforcement learning for minimally invasive surgery manipulator (2017) Sensors, 17, p. 844; Li, Z., Bo, H., Ye, Z., Deng, M., Yang, C., Physical human-robot interaction of a robotic exoskeleton by admittance control (2018) IEEE Trans. Indust. Electron., p. 1. , -1; Chandrasekaran, K., Sivaraman, S., Thondiyath, A., (2015), Static balancing and inertia compensation of a master manipulator for tele-operated surgical robot application; Ugurlu, B., Oshima, H., Sariyildiz, E., Narikiyo, T., Babic, J., Active compliance control reduces upper body effort in exoskeleton-supported walking (2020) IEEE Trans. Human Mach. 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Surgery, 6, pp. 334-342; Riviere, C.N., Gangloff, J., Mathelin, M.D., Robotic compensation of biological motion to enhance surgical accuracy (2006) Proc. IEEE, 94, pp. 1705-1716","Liang, Y.; State Key Laboratory of Robotics and System, 2 Yikuang Street, China; email: liang_yun_lei@163.com",,,"Elsevier Ltd",,,,,0094114X,,MHMTA,,"English","Mech Mach Theory",Article,"Final","",Scopus,2-s2.0-85092351200
"Cappuccio M.L., Sandoval E.B., Mubin O., Obaid M., Velonaki M.","56524371200;37057915200;25960231600;16245637200;24073920300;","Can Robots Make us Better Humans?: Virtuous Robotics and the Good Life with Artificial Agents",2021,"International Journal of Social Robotics","13","1",,"7","22",,3,"10.1007/s12369-020-00700-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092252680&doi=10.1007%2fs12369-020-00700-6&partnerID=40&md5=f334d1f988ca722c2cf9c51bc0763e28","School of Engineering and IT, University of New South Wales Canberra, Canberra, Australia; School of Art and Design, University of New South Wales, Sydney, Australia; Western Sydney University, Sydney, Australia; Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden","Cappuccio, M.L., School of Engineering and IT, University of New South Wales Canberra, Canberra, Australia; Sandoval, E.B., School of Art and Design, University of New South Wales, Sydney, Australia; Mubin, O., Western Sydney University, Sydney, Australia; Obaid, M., Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden; Velonaki, M., School of Art and Design, University of New South Wales, Sydney, Australia","This position paper proposes a novel approach to the ethical design of social robots. We coin the term “Virtuous Robotics” to describe Human–Robot Interaction (HRI) designed to help humans reach a higher level of moral development. Our approach contrasts with mainstream approaches to robot design inspired by the other normative theories, Consequentialism and Deontology. In the paper we theoretically justify our proposal, illustrating how the Virtuous Robotics approach allows us to discriminate between positive and negative applications of robotics systems, of which we provide examples. From an ethical perspective, our proposal is theoretically robust because it is based on the assistive role played by the robot rather than the robot’s moral agency. From a designer’s perspective, Virtuous Robotics is technically feasible because it transfers the cognitive burden of HRI from the robot to the user, bypassing the need for complex decision-making abilities. From the user’s perspective, it is concretely advantageous, because it envisions a realistic way to make robots morally desirable in our lives, as supports for personal betterment and fulfilment. © 2020, Springer Nature B.V.","Aristotle; Character formation; Consequentialism; Deontology; Human–Robot interaction; Machine Ethics; Recognition theory; Self-improvement; Virtue Ethics; Virtuous Robotics","Agricultural robots; Behavioral research; Decision making; Philosophical aspects; Robotics; Social robots; Artificial agents; Complex decision; Ethical perspectives; Moral development; Normative theory; Position papers; Robot interactions; Robotics systems; Machine design",,,,,,,,,,,,,,,,"Robotic tortoise helps kids to learn that robot abuse is a bad thing—IEEE Spectrum (2018) Library Catalog: Spectrum.Ieee.Org, , https://spectrum.ieee.org/automaton/robotics/robotics-hardware/shelly-robotic-tortoise-helps-kids-learn-that-robot-abuse-is-a-bad-thing; Addison, A., Bartneck, C., Yogeeswaran, K., Robots can be more than Black and White: Examining racial bias towards robots (2019) In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. 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J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85092252680
"Jang I., Niu H., Collins E.C., Weightman A., Carrasco J., Lennox B.","57196086367;56654773500;55948963600;7004346861;24721275700;7007135718;","Virtual Kinesthetic Teaching for Bimanual Telemanipulation",2021,"2021 IEEE/SICE International Symposium on System Integration, SII 2021",,,"9382763","120","125",,3,"10.1109/IEEECONF49454.2021.9382763","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103742452&doi=10.1109%2fIEEECONF49454.2021.9382763&partnerID=40&md5=1bf7a2f264f19dc8f0af151108ac5cc3","University of Manchester, Robotics for Extreme Environment Group, Department of Electrical and Electronic EngineeringM13 9PL, United Kingdom; University of Manchester, Department of Computer ScienceM13 9PL, United Kingdom; University of Manchester, Department of Mechanical, Aerospace and Civil EngineeringM13 9PL, United Kingdom","Jang, I., University of Manchester, Robotics for Extreme Environment Group, Department of Electrical and Electronic EngineeringM13 9PL, United Kingdom; Niu, H., University of Manchester, Robotics for Extreme Environment Group, Department of Electrical and Electronic EngineeringM13 9PL, United Kingdom; Collins, E.C., University of Manchester, Department of Computer ScienceM13 9PL, United Kingdom; Weightman, A., University of Manchester, Department of Mechanical, Aerospace and Civil EngineeringM13 9PL, United Kingdom; Carrasco, J., University of Manchester, Robotics for Extreme Environment Group, Department of Electrical and Electronic EngineeringM13 9PL, United Kingdom; Lennox, B., University of Manchester, Robotics for Extreme Environment Group, Department of Electrical and Electronic EngineeringM13 9PL, United Kingdom","This paper proposes a novel telemanipulation system that enables a human operator to control a dual-arm robot. The operation provides kinesthetic teaching via a digital twin of the robot which the operator cyber-physically guides to perform a task. Its key enabler is the concept of a virtual reality interactive marker, which serves as a simplified end effector of the digital twin robot. In virtual reality, the operator can interact with the marker using bare hands, which are sensed by the Leap Motion on top of a virtual reality headset. Then, the status (e.g. position/orientation) of the marker is transformed to the corresponding joint space command to the remote robot so that its end effector can follow the marker. We provide the details of the system architecture, and implement the system based on commercial robots/devices (i.e. UR5, Robotiq gripper, Leap Motion), virtual reality, ROS, and Unity3D. Moreover, the paper discusses the technical challenges that we had to address, and the system's potential benefits from a human-robot interaction perspective. © 2021 IEEE.",,"Digital twin; End effectors; Manipulators; Social robots; Virtual reality; Commercial robots; Kinesthetic teachings; Potential benefits; System architectures; Technical challenges; Telemanipulation; Telemanipulation systems; Virtual-reality headsets; E-learning",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/R026084/1; University of Manchester: M13 9PL","*This work was supported by EPSRC RAIN project No. EP/R026084/1 and EPSRC project No.EP/S03286X/1 I. Jang, H. Niu, J. Carrasco and B. Lennox are with Robotics for Extreme Environment Group, Department of Electrical & Electronic Engineering, The University of Manchester, M13 9PL, United Kingdom (e-mail: inmo.jang@manchester.ac.uk). E. C. Collins is with Department of Computer Science, The University of Manchester, M13 9PL, United Kingdom A. Weightman is with Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, M13 9PL, United Kingdom Fig. 1. Virtual kinesthetic teaching for telemanipulation: this work proposes a novel teleoperation system that enables control of a remote robot by providing kinesthetic teaching to its digital twin via virtual reality.",,,,,,,,,,"Liu, Y., Zhang, Y., Toward welding robot with human knowledge: A remotely-controlled approach (2015) IEEE Transactions on Au Tomation Science and Engineering, 12 (2), pp. 769-774; Marturi, N., Rastegarpanah, A., Takahashi, C., Adjigble, M., Stolkin, R., Zurek, S., Kopicki, M., Bekiroglu, Y., Towards advanced robotic manipulation for nuclear decommissioning: A pilot study on tele-operation and autonomy (2016) International Con Ference on Robotics and Automation for Humanitarian Applications (RAHA), pp. 1-8; Artigas, J., Balachandran, R., De Stefano, M., Panzirsch, M., Lampariello, R., Albu-Schaeffer, A., Harder, J., Letschnik, J., Teleoperation for on-orbit servicing missions through the astra geostationary satellite (2016) 2016 IEEE Aerospace Conference, pp. 1-12; Hebert, P., Ma, J., Borders, J., Aydemir, A., Bajracharya, M., Hudson, N., Shankar, K., Burdick, J., Supervised remote robot with guided autonomy and teleoperation (surrogate): A framework for whole-body manipulation (2015) IEEE International Conference on Robotics and Automation (ICRA), pp. 5509-5516; 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Rakita, D., Mutlu, B., Gleicher, M., Hiatt, L.M., Shared control-based bimanual robot manipulation (2019) Science Robotics, 4 (30); MacPherson, I., Dunlop, A., Development of a systematic approach to post-operation clean out at sellafield (2016) Interna Tional Symposium on Preparation Fo R Decommissioning 2016 (PREDEC2016)",,,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE/SICE International Symposium on System Integration, SII 2021","11 January 2021 through 14 January 2021",,168053,,9781728176581,,,"English","IEEE/SICE Int. Symp. Syst. Integr., SII",Conference Paper,"Final","",Scopus,2-s2.0-85103742452
"Beckers T., Hirche S.","57193098211;15725278100;","Prediction with Approximated Gaussian Process Dynamical Models",2021,"IEEE Transactions on Automatic Control",,,,"","",,,"10.1109/TAC.2021.3131988","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120884076&doi=10.1109%2fTAC.2021.3131988&partnerID=40&md5=3e3c0a80336b748554322c5c075d95b9","Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, United States of America, 19104 (e-mail: tbeckers@seas.upenn.edu); Chair of Information-oriented Control, Technische Universitt Mnchen, Munich, Germany, 80290 (e-mail: hirche@tum.de)","Beckers, T., Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, United States of America, 19104 (e-mail: tbeckers@seas.upenn.edu); Hirche, S., Chair of Information-oriented Control, Technische Universitt Mnchen, Munich, Germany, 80290 (e-mail: hirche@tum.de)","The modeling and simulation of dynamical systems is a necessary step for many control approaches. Using classical, parameter-based techniques for modeling of modern systems, e.g., soft robotics or human-robot interaction, is often challenging or even infeasible due to the complexity of the system dynamics. In contrast, data-driven approaches need only a minimum of prior knowledge and scale with the complexity of the system. In particular, Gaussian process dynamical models (GPDMs) provide very promising results for the modeling of complex dynamics. However, the control properties of these GP models are just sparsely researched, which leads to a ""blackbox"" treatment in modeling and control scenarios. In addition, the sampling of GPDMs for prediction purpose respecting their non-parametric nature results in non-Markovian dynamics making the theoretical analysis challenging. In this article, we present approximated GPDMs which are Markov and analyze their control theoretical properties. Among others, the approximated error is analyzed and conditions for boundedness of the trajectories are provided. The outcomes are illustrated with numerical examples that show the powe IEEE","Adaptation models; Computational modeling; Data models; data-based control; Gaussian processes; Gaussian processes; learning systems; Mathematical models; Modeling; nonparametric methods; Predictive models; Probabilistic models; probabilistic simulation; stochastic modeling","Dynamical systems; Gaussian noise (electronic); Human robot interaction; Process control; Stochastic control systems; Stochastic models; Stochastic systems; Adaptation models; Computational modelling; Data based controls; Gaussian Processes; Modeling; Nonparametric methods; Predictive models; Probabilistic models; Probabilistic simulation; Stochastic-modeling; Gaussian distribution",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,00189286,,IETAA,,"English","IEEE Trans Autom Control",Article,"Article in Press","All Open Access, Green",Scopus,2-s2.0-85120884076
"Oyelude A.A.","25650299600;","AI and libraries: trends and projections",2021,"Library Hi Tech News","38","10",,"1","4",,,"10.1108/LHTN-10-2021-0079","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119980425&doi=10.1108%2fLHTN-10-2021-0079&partnerID=40&md5=f2b92098f1bcc5fc01c2aab24a6182e2","Kenneth Dike Library, University of Ibadan, Ibadan, Nigeria","Oyelude, A.A., Kenneth Dike Library, University of Ibadan, Ibadan, Nigeria","Purpose: This paper aims to focus on the trends and projection for future use of artificial intelligence (AI) in libraries. AI technologies is the latest among the technologies being used in libraries. The technology has systems that have natural language processing, machine learning and pattern recognition capabilities that make service provision easier for libraries. Design/methodology/approach: Systematic literature review is done, exploring blogs and wikis, to collect information on the ways in which AI is used and can be futuristically used in libraries. Findings: This paper found that uses of AI in libraries entailed enhanced services such as content indexing, document matching, content mapping content summarization and many others. AI possibilities were also found to include improving the technology of gripping, localizing and human–robot interaction and also having artificial superintelligence, the hypothetical AI that surpasses human intelligence and abilities. Originality/value: It is concluded that advanced technologies that AI are, will help librarians to open up new horizons and solve challenges that crop up in library service delivery. © 2021, Emerald Publishing Limited.","Artificial intelligence; Computer software; Information systems; Libraries; Library services; Technology",,,,,,,,,,,,,,,,,"Asemi, A., Ko, A., Nowkarizi, M., Intelligent libraries: a review on expert systems, artificial intelligence, and robot (2021) Library Hi Tech, 39 (2), pp. 412-434; Shruthi, S.V., Chandru, B.G., Methodological role of artificial intelligence in the bailiwick of library management system (2021) Journal of Scholastic Engineering Science and Management, 1 (2), pp. 27-34","Oyelude, A.A.; Kenneth Dike Library, Nigeria; email: toyelude@yahoo.com",,,"Emerald Group Holdings Ltd.",,,,,07419058,,,,"English","Libr. Hi Tech News",Review,"Final","",Scopus,2-s2.0-85119980425
"Wu W., Lin C., Lin G., Cai S., Xie L.","57216627291;57217101292;57217102959;57190013652;8218534100;","Motion Intention Recognition Based on Air Bladders",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"586","595",,,"10.1007/978-3-030-90525-5_51","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119855503&doi=10.1007%2f978-3-030-90525-5_51&partnerID=40&md5=eae2405ee0ea402050646523fab7c304","Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangdong, Guangzhou, China; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore","Wu, W., Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangdong, Guangzhou, China; Lin, C., Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangdong, Guangzhou, China; Lin, G., Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangdong, Guangzhou, China; Cai, S., Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; Xie, L., Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangdong, Guangzhou, China","Recognition of human motion intention plays an important role in many robotic applications, such as human-assistive exoskeletons and rehabilitation robots. Motion intention recognition (MIR) based on physiological signals is one of the most common and intuitive methods. However, physiological signals are sensitive to environmental disturbances and suffer from complex preparation. In this paper, we proposed a novel air bladder-based MIR method, in which the human-robot interaction (HRI) force is measured directly by four air bladders. The air bladders can be installed at the end of a robot to interact with the user’s arm. We validate the linearity and repeatability of the air bladders through comprehensive experiments. In addition, we compare the performance of the proposed air bladder-based MIR method with the conventional method based on force sensors and surface electromyography (sEMG) signals. Experiments show that the proposed method can capture the change of the external force, even when the force changes rapidly. Moreover, the performance of our method is more comparative and robust in caparison with the sEMG-based MIR method. © 2021, Springer Nature Switzerland AG.","Air bladder; Human-robot interaction; Motion intention recognition","Exoskeleton (Robotics); Man machine systems; Motion estimation; Physiology; Air bladder; Assistive; Exoskeleton robots; Human motion intention; Humans-robot interactions; Motion intention recognition; Performance; Physiological signals; Recognition methods; Robotics applications; Human robot interaction",,,,,"201903010028, 202002030324; 6141A02033124; AC01202005011; 2020B2020; National Natural Science Foundation of China, NSFC: 52075177; Guangdong Medical Research Foundation: 2018KZDXM002, 2019A050505001","Acknowledgements. This work was supported in part by the National Natural Science Foundation of China (Grant No. 52075177), Joint Fund of the Ministry of Education for Equipment Pre-Research (Grant No. 6141A02033124), Research Foundation of Guangdong Province (Grant No. 2019A050505001 and 2018KZDXM002), Guangzhou Research Foundation (Grant No. 202002030324 and 201903010028), Zhongshan Research Foundation (Grant No.2020B2020), and Shenzhen Institute of Artificial Intelligence and Robotics for Society (Grant No. AC01202005011).",,,,,,,,,,"Bi, L., Feleke, A., Guan, C., A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration (2019) Biomed. Signal Process. Control, 51, pp. 113-127; Naik, G.R., Al-Timemy, A.H., Nguyen, H.T., Transradial amputee gesture classification using an optimal number of sEMG sensors: An approach using ICA clustering (2016) IEEE Trans. Neural Syst. Rehabil. Eng., 24, pp. 837-846; Siqi, C., SVM-based classification of sEMG signals for upper-limb self-rehabilitation training (2019) Front. Neurorobotics, 13 (31); Liu, J., Kang, S.H., Xu, D., Ren, Y., Lee, S.J., Zhang, L.-Q., EMG-based continuous and simultaneous estimation of arm kinematics in able-bodied individuals and stroke survivors (2017) Front. Neurosci., 11 (480); Bu, D., Guo, S., Gao, W., Continuous estimation of a sEMG-based upper limb joint (2019) IEEE International Conference on Mechatronics and Automation (ICMA), pp. 904-909. , pp., 2019; Han, J., Ding, Q., Xiong, A., Zhao, X., A state-space EMG model for the estimation of continuous joint movements (2015) IEEE Trans. Industr. Electron., 62, pp. 4267-4275; Kiguchi, K., Hayashi, Y., An EMG-based control for an upper-limb power-assist exoskeleton robot (2012) IEEE Trans. Syst. Man Cybern. Part B Cybern. Publ. IEEE Syst. Man Cybern. Soc., 42 (4), pp. 1064-1071; Calado, A., Soares, F., Matos, D., A review on commercially available anthropomorphic myoelectric prosthetic hands, pattern-recognition-based microcontrollers and sEMG sensors used for prosthetic control (2019) IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 1-6. , pp., 2019; Kong, K., Jeon, D., Design and control of an exoskeleton for the elderly and patients (2006) IEEE/ASME Trans. Mechatron., 11, pp. 428-432; Yamamoto, K., Ishii, M., Noborisaka, H., Hyodo, K., Stand alone wearable power assisting suit-sensing and control systems (2004) RO-MAN 2004. 13Th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No.04Th8759), pp. 661-666. , pp; Kim, B., Deshpande, A., An upper-body rehabilitation exoskeleton harmony with an anatomical shoulder mechanism: Design, modeling, control, and performance evaluation (2017) Int. J. Robot. Res., 36, pp. 414-435; Wang, X., Lu, T., Wang, S., Gu, J., Yuan, K., (2016) A Patient-Driven Control Method for Lower-Limb Rehabilitation Robot, pp. 908-913. , pp; Siqi, C., Wu, W., Xie, L., (2020) Dual-Arm Upper Limb Rehabilitation Robot: Mechanism Design and Preliminary Experiments, pp. 80-86. , pp; Huang, J., Huo, W., Xu, W., Mohammed, S., Amirat, Y., Control of upper-limb power-assist exoskeleton using a human-robot interface based on motion intention recognition (2015) IEEE Trans. Autom. Sci. Eng., 12, pp. 1257-1270; Moubarak, S., Pham, M., Pajdla, T., Redarce, T., (2010) Design and Modeling of an Upper Extremity Exoskeleton, , https://doi.org/10.1007/978-3-642-03889-1_127; Choi, H., Jung, P.-G., Jung, K., Kong, K., Design and fabrication of a soft three-axis force sensor based on radially symmetric pneumatic chambers (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 5519-5524. , pp; York, D., Least-squares fitting of a straight line (2011) Can. J. Phys., 44, pp. 1079-1086; Gong, D., He, R., Yu, J., Zuo, G., A pneumatic tactile sensor for co-operative robots (2017) Sensors, 17 (11), pp. 2592-2606","Xie, L.; Shien-Ming Wu School of Intelligent Engineering, Guangdong, China; email: melhxie@scut.edu.cn","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119855503
"Morillo-Mendez L., Schrooten M.G.S., Loutfi A., Mozos O.M.","57211823245;55409661000;7004383993;55663022800;","Age-Related Differences in the Perception of Eye-Gaze from a Social Robot",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"350","361",,,"10.1007/978-3-030-90525-5_30","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119849431&doi=10.1007%2f978-3-030-90525-5_30&partnerID=40&md5=f56a01558d36fcc10bffd0c28a3681d7","Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, 702 81, Sweden; Center for Health and Medical Psychology, Örebro University, Örebro, 702 81, Sweden","Morillo-Mendez, L., Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, 702 81, Sweden; Schrooten, M.G.S., Center for Health and Medical Psychology, Örebro University, Örebro, 702 81, Sweden; Loutfi, A., Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, 702 81, Sweden; Mozos, O.M., Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, 702 81, Sweden","The sensibility to deictic gaze declines naturally with age and often results in reduced social perception. Thus, the increasing efforts in developing social robots that assist older adults during daily life tasks need to consider the effects of aging. In this context, as non-verbal cues such as deictic gaze are important in natural communication in human-robot interaction, this paper investigates the performance of older adults, as compared to younger adults, during a controlled, online (visual search) task inspired by daily life activities, while assisted by a social robot. This paper also examines age-related differences in social perception. Our results showed a significant facilitation effect of head movement representing deictic gaze from a Pepper robot on task performance. This facilitation effect was not significantly different between the age groups. However, social perception of the robot was less influenced by its deictic gaze behavior in older adults, as compared to younger adults. This line of research may ultimately help informing the design of adaptive non-verbal cues from social robots for a wide range of end users. © 2021, Springer Nature Switzerland AG.","Human-robot interaction; Non-verbal cues; Older adults","Economic and social effects; Machine design; Man machine systems; Social behavior; Age-related differences; Daily lives; Eye-gaze; Facilitation effects; Humans-robot interactions; Non-verbal cue; Older adults; Social perception; Social robots; Young adults; Human robot interaction",,,,,"Universidad Complutense de Madrid, UCM; Knut och Alice Wallenbergs Stiftelse: RTI2018-095599-A-C22; Horizon 2020: 754285","This work is funded by the EU Horizon 2020 research and innovation programme under the Marie Sk?lodowska-Curie grant agreement No 754285, by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, and by the RobWellproject (No RTI2018-095599-A-C22) funded by the Spanish Ministerio de Ciencia, Innovaci?n y Univer-sidades. We want to thank the universities that helped us with the sample recruiting process: Complutense University of Madrid, University Carlos III of Madrid, University of Murcia and University of Alicante. The authors thank Neziha Akalin and Estefan?a S?nchez-Pastor for their support in the initial stages of this study.","Acknowledgments. This work is funded by the EU Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie grant agreement No 754285, by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, and by the RobWellproject (No RTI2018-095599-A-C22) funded by the Spanish Ministerio de Ciencia, Innovación y Univer-sidades. We want to thank the universities that helped us with the sample recruiting process: Complutense University of Madrid, University Carlos III of Madrid, University of Murcia and University of Alicante. The authors thank Neziha Akalin and Estefańıa Sánchez-Pastor for their support in the initial stages of this study.",,,,,,,,,"Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: A review (2017) J. Hum. Rob. Interact., 6 (1), pp. 25-63; Admoni, H., Weng, T., Hayes, B., Scassellati, B., Robot nonverbal behavior improves task performance in difficult collaborations (2016) Proceedings ACM/IEEE International Conference HRI (HRI 2016), pp. 51-58. , Christchurch, New Zealand, pp; Andrist, S., Tan, X.Z., Gleicher, M., Mutlu, B., Conversational gaze aversion for humanlike robots (2014) Proceedings ACM/IEEE International Conference HRI (HRI 2014), pp. 25-32. , Bielefeld, Germany, pp; Baron-Cohen, S., (1995) Mindblindness: An Essay on Autism and Theory of Mind, , The MIT Press, Cambridge; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot., 1 (1), pp. 71-81; Carpinella, C.M., Wyman, A.B., Perez, M.A., Stroessner, S.J., The robotic social attributes scale (RoSAS): Development and validation (2017) Proceedings ACM/IEEE International Conference HRI (HRI 2017), pp. 254-262. , New York, USA, pp; Cañigueral, R., Hamilton, A.F.C., The role of eye gaze during natural social interactions in typical and autistic people (2019) Frontiers Psychol, 10; Faul, F., Erdfelder, E., Lang, A.G., Buchner, A., G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences (2007) Behav. Res. Methods, 39 (2), pp. 175-191; Feingold-Polak, R., Elishay, A., Shahar, Y., Stein, M., Edan, Y., Levy-Tzedek, S., Differences between young and old users when interacting with a humanoid robot: A qualitative usability study (2018) Paladyn, 9 (1), pp. 183-192; Finger, H., Goeke, C., Diekamp, D., Standvoß, K., König, P., LabVanced: A unified Javascript framework for online studies (2017) Proceedings International Conference IC2S2 (IC2S2, p. 2017. , Cologne, Germany; Harada, C.N., Love, M.C.N., Triebel, K., Normal cognitive aging (2013) Clin. Geriatr. Med., 29, p. 737; Hart, S.G., Staveland, L.E., Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research (1988) Adv. Psychol., 52, pp. 139-183; Kiilavuori, H., Sariola, V., Peltola, M.J., Hietanen, J.K., Making eye contact with a robot: Psychophysiological responses to eye contact with a human and with a humanoid robot (2021) Biolog. Psychol., 158; Kontogiorgos, D., The effects of anthropomorphism and non-verbal social behaviour in virtual assistants (2019) Proceedings ACM International Conference IVA (IVA 2019), pp. 133-140. , Paris, France, pp; Morillo-Mendez, L., Mozos, O.M., Towards human-based models of behaviour in social robots: Exploring age-related differences in the processing of gaze cues in human-robot interaction (2020) Starting Artificial Intelligence Researchers Symposium (STAIRS 2020), , Santiago de Compostela, Spain; Mutlu, B., Forlizzi, J., Hodgins, J., A storytelling robot: Modeling and evaluation of human-like gaze behavior (2006) Proceedings IEEE-RAS International Conference HUMANOIDS, Genoa, Italy, pp. 518-523. , pp; Mutlu, B., Shiwa, T., Kanda, T., Ishiguro, H., Hagita, N., Footing in human-robot conversations (2009) Proceedings ACM/IEEE International Conference HRI (HRI 2009), p. 61. , La Jolla, California, USA, p; Mwangi, E., Barakova, E.I., Díaz-Boladeras, M., Mallofré, A.C., Rauterberg, M., Directing attention through gaze hints improves task solving in human-humanoid interaction (2018) Int. J. Soc. Robot., 10 (3), pp. 343-355; Pino, M., Boulay, M., Jouen, F., Rigaud, A.S., “Are we ready for robots that care for us?” Attitudes and opinions of older adults toward socially assistive robots (2015) Frontiers Aging Neurosci, 7; Pu, L., Moyle, W., Jones, C., Todorovic, M., The effectiveness of social robots for older adults: A systematic review and meta-analysis of randomized controlled studies (2019) Gerontologist, 59 (37-51); Slessor, G., Venturini, C., Bonny, E.J., Insch, P.M., Rokaszewicz, A., Finnerty, A.N., Specificity of age-related differences in eye-gaze following: Evidence from social and nonsocial stimuli (2016) J. Gerontol. Series B, 71, pp. 11-22; Zafrani, O., Nimrod, G., Towards a holistic approach to studying human-robot interaction in later life (2019) Gerontologist, 59 (26-36)","Morillo-Mendez, L.; Centre for Applied Autonomous Sensor Systems, Sweden; email: lucas.morillo@oru.se","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85119849431
"Yu X., Li B., He W., Feng Y., Cheng L., Silvestre C.","57203040999;57209826253;57218621820;35114710600;7403337296;56962772800;","Adaptive-Constrained Impedance Control for Human-Robot Co-Transportation",2021,"IEEE Transactions on Cybernetics",,,,"","",,,"10.1109/TCYB.2021.3107357","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118686483&doi=10.1109%2fTCYB.2021.3107357&partnerID=40&md5=f1e2609b833cc7761cf1bbc634289bfe","Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China.; Institute of Artificial Intelligence and the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.; Institute of Artificial Intelligence and the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China (e-mail: weihe@ieee.org); College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China, and also with the School of Artificial Intelligence, University of the Chinese Academy of Sciences, Beijing 100049, China.; Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China, on leave from the Instituto Superior T&#x00E9;cnico, University of Lisbon, 1649-004 Lisbon, Portugal.","Yu, X., Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China.; Li, B., Institute of Artificial Intelligence and the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.; He, W., Institute of Artificial Intelligence and the School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China (e-mail: weihe@ieee.org); Feng, Y., College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.; Cheng, L., State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China, and also with the School of Artificial Intelligence, University of the Chinese Academy of Sciences, Beijing 100049, China.; Silvestre, C., Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China, on leave from the Instituto Superior T&#x00E9;cnico, University of Lisbon, 1649-004 Lisbon, Portugal.","Human-robot co-transportation allows for a human and a robot to perform an object transportation task cooperatively on a shared environment. This range of applications raises a great number of theoretical and practical challenges arising mainly from the unknown human-robot interaction model as well as from the difficulty of accurately model the robot dynamics. In this article, an adaptive impedance controller for human-robot co-transportation is put forward in task space. Vision and force sensing are employed to obtain the human hand position, and to measure the interaction force between the human and the robot. Using the latest developments in nonlinear control theory, we propose a robot end-effector controller to track the motion of the human partner under actuators' input constraints, unknown initial conditions, and unknown robot dynamics. The proposed adaptive impedance control algorithm offers a safe interaction between the human and the robot and achieves a smooth control behavior along the different phases of the co-transportation task. Simulations and experiments are conducted to illustrate the performance of the proposed techniques in a co-transportation task. IEEE","Collaboration; Error constraint; Force; human-robot co-transportation; Impedance; input constraint; neural networks (NNs); Robot sensing systems; Robots; Sensors; Task analysis; vision and force sensing",,,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21682267,,,,"English","IEEE Trans. Cybern.",Article,"Article in Press","",Scopus,2-s2.0-85118686483
"Liu Y., Yao Y., Peng H., Liu Y.","57315754700;7403567957;57315453100;56014658300;","An MRF-Based Intention Recognition Framework for WMRA with Selected Objects as Contextual Clues",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13015 LNAI",,,"345","356",,,"10.1007/978-3-030-89134-3_32","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118132979&doi=10.1007%2f978-3-030-89134-3_32&partnerID=40&md5=18c07381ceb0f50b210856f17f3d8b33","Industrial Research Institute of Robotics and Intelligent Equipment, Harbin Institute of Technology (Weihai), Shandong, 264209, China","Liu, Y., Industrial Research Institute of Robotics and Intelligent Equipment, Harbin Institute of Technology (Weihai), Shandong, 264209, China; Yao, Y., Industrial Research Institute of Robotics and Intelligent Equipment, Harbin Institute of Technology (Weihai), Shandong, 264209, China; Peng, H., Industrial Research Institute of Robotics and Intelligent Equipment, Harbin Institute of Technology (Weihai), Shandong, 264209, China; Liu, Y., Industrial Research Institute of Robotics and Intelligent Equipment, Harbin Institute of Technology (Weihai), Shandong, 264209, China","To mitigate the physical burden of disabled people, we propose an approach that a robot could perceive the implied action intentions of disabled people by their selected objects. This article presents a framework for recognizing and learning human intentions based on selected household objects and interaction history. First, the intention network is modeled based on Markov random field (MRF) to connect the selected objects and daily activities. Second, the q-learning algorithm is added to provide the intention network with the function of adapting to the user’s intention preference. Then, we build the wheelchair-mounted robotic arms (WMRA) with a green laser pointer as human-robot interaction (HRI). Finally, we demonstrate the feasibility of the intention recognition framework by evaluating a scene comprised of objects from 11 categories, along with 7 possible actions, 36 single-object intentions, and 24 multiple-object intentions. We achieve approximately 70% reduction in fewer sessions than the Recursive Bayesian Incremental Learning and achieve approximately 87.5% and 86.2% reduction in interactions overall on recognizing multiple-object intention, respectively. © 2021, Springer Nature Switzerland AG.","Human-robot interaction; Intention recognition; Markov random field; Wheel mounted robotic arms","Human robot interaction; Learning algorithms; Magnetorheological fluids; Man machine systems; Markov processes; Reinforcement learning; Robotics; % reductions; Action intentions; Disabled people; Humans-robot interactions; Intention recognition; Markov Random Fields; Multiple objects; Wheel mounted; Wheel mounted robotic arm; Wheelchair-mounted robotic arms; Robotic arms",,,,,"2016GGX101013; 2016DXGJMS04; National Key Research and Development Program of China, NKRDPC: 2018YFB1309400","Acknowledgment. This work is supported by the National Key R&D Program of China (Grant No. 2018YFB1309400), the Key R&D Program of Shandong Province (Grant No. 2016GGX101013), and the University Common Construction Project of Weihai (Grant No. 2016DXGJMS04).",,,,,,,,,,"Alqasemi, R.M., McCaffrey, E.J., Edwards, K.D., Analysis, evaluation and development of wheelchair-mounted robotic arms (2005) Proceedings of the 9Th International Conference on Rehabilitation Robotics, 2005 ICORR 2005, Pp. 469–472; Jiang, H., Zhang, T., Wachs, J.P., Enhanced control of a wheelchair-mounted robotic manipulator using 3-D vision and multimodal interaction (2016) Comput. Vision Image Understand., 149, pp. 21-31; Graser, A., Heyer, T., Fotoohi, L., A supportive friend at work: Robotic workplace assistance for the disabled (2013) IEEE Rob. Autom. Mag, 20 (4), pp. 148-159; Bien, Z., Chung, M.-J., Chang, P.-H., Integration of a rehabilitation robotic system (KARES II) with human-friendly man-machine interaction units (2004) Auton. Robot., 16 (2), pp. 165-191; Shishehgar, M., Kerr, D., Blake, J., The effectiveness of various robotic technologies in assisting older adults (2019) Health Inf. 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Surv., 54 (3), pp. 1-35; Kuhlmann, T., ICF (International Classification of Functioning, Disability and Health) (2011) Suchttherapie, 12 (1), pp. 7-7; Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M., Yolov4: Optimal speed and accuracy of object detection (2020) Arxiv Preprint Arxiv:200410934; Schaal, S., Dynamic movement primitives – a framework for motor control in humans and humanoid robotics (2006) Adaptive Motion of Animals and Machines, , Kimura, H., Tsuchiya, K., Ishiguro, A., Witte, H. (eds.), Springer, Tokyo; Hoffmann, H., Pastor, P., Park, D.-H., Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance (2009) Proceedings of the 2009 IEEE International Conference on Robotics and Automation, pp. 2587-2592. , pp; Chi, M., Yao, Y., Liu, Y., Learning, generalization, and obstacle avoidance with dynamic movement primitives and dynamic potential fields (2019) Appl. Sci., 9 (8), p. 1535; Ravichandar, H., Polydoros, A.S., Chernova, S., Recent Advances in Robot Learning from Demonstration (2020) Annu. Rev. Control Robot. Auton. Syst., 3 (1), pp. 297-330","Liu, Y.; Industrial Research Institute of Robotics and Intelligent Equipment, China; email: liuyaxin@hit.edu.cn","Liu X.Nie Z.Yu J.Xie F.Song R.",,"Springer Science and Business Media Deutschland GmbH","14th International Conference on Intelligent Robotics and Applications, ICIRA 2021","22 October 2021 through 25 October 2021",,267169,03029743,9783030891336,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85118132979
"Wittmann M., Cash P., Mariani M., Maier A., Hansen J.P.","57311428700;47661004600;57310342800;10040829600;57310877000;","Sustaining behaviour change through immersive technologies: Trends, perspectives, and approaches",2021,"Proceedings of the Design Society","1",,,"2891","2900",,,"10.1017/pds.2021.550","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117795442&doi=10.1017%2fpds.2021.550&partnerID=40&md5=01817f6e7de1df1493a8544829fb395e","DTU","Wittmann, M., DTU; Cash, P., DTU; Mariani, M., DTU; Maier, A., DTU; Hansen, J.P., DTU","Long-term behaviour changes are critical to answering societal and individual challenges surrounding areas such as sustainability and health. Current understanding of how to bring about sustained behaviour is focused on the identification of Behaviour Change Techniques (BCTs) without explicit guidance on how these should be matched with technological solutions. Based on this gap we set out to answer the research question: What is the relationship between BCTs and interactive immersive technologies with respect to long-term, sustainable behaviour? To this end, we report a literature review on technology trends in the fields of human computer interaction, human robot interaction, and game design. Based on this review we develop three main contributions with implications for design theory and practice. First, we propose a number of characteristics and mechanisms in emerging immersive technologies. Second, we highlight technological pathways connected to specific BCT clusters likely to be disrupted: technology as a conveyor of information, an augmenter of feedback, and as an embodiment of empathy. Third, we explore these connections between BCT clusters and the actual technological interventions. © ICED 2021.All right reserved.","Design for interfaces; Healthcare Technology; Human behaviour in design; Long-term Adherence; Virtual reality","Behavioral research; Computer games; Human computer interaction; Human robot interaction; Machine design; Virtual reality; 'current; Behaviour changes; Design for interface; Healthcare technology; Human behaviour in designs; Immersive technologies; Individual challenges; Long-term adherence; Long-term behavior; Technology trends; Game design",,,,,,"This work has received funding from the Horizon 2020 research and innovation programme of the European Union under grant agreement no. 871767 of the project ReHyb: Rehabilitation based on hybrid neuroprosthesis.",,,,,,,,,,"Ahn, S.J., Fox, J., Bailenson, J.N., Avatars (2012) Leadership in Science and Technology: A Reference Handbook, , Bainbridge, W.S. Ed, SAGE Publications, Inc; Behavior depends on consequences Behavioural Principles and Practices, (1). , BEHAVE. n.d 1.1; Bohlen, L.C., Michie, S., de Bruin, M., Rothman, A.J., Kelly, M.P., Groarke, H.N.K., Carey, R.N., Do combinations of behavior change techniques that occur frequently in interventions reflect underlying theory? (2020) Annals of Behavioral Medicine, 54 (11), pp. 827-842; Cash, P., Khadilkar, P., Jensen, J., Dusterdich, C., Mugge, R., Designing behaviour change: A behavioural problem/solution (BPS) matrix (2020) International Journal of Design, 14 (2), pp. 65-83; Cash, P.J., Hartlev, C.G., Durazo, C.B., Behavioural design: A process for integrating behaviour change and design (2017) Design Studies, 48, pp. 96-128; Cavazza, M., Charles, F., Mead, S.J., Interactive storytelling: From AI experiment to new media (2003) Proceedings of the International Conference on Entertainment Computing (ICEC’03), pp. 1-8; Chen, Y., Wang, Q., Chen, H., Song, X., Tang, H., Tian, M., An overview of augmented reality technology (2019) Journal of Physics: Conference Series, 1237 (2); Cheng Chia, G.L., Anderson, A., McLean, L.A., Behavior change techniques incorporated in fitness trackers: Content analysis (2019) JMIR MHealth and UHealth, 7 (7). , https://doi.org/10.2196/12768; Cicek, M., Wearable technologies and its future applications (2015) International Journal of Electrical, Electronics and Data Communication, 3 (4), pp. 45-50; Davis, F.D., Perceived usefulness, perceived ease of use, and user acceptance of information technology (1989) MIS Quarterly: Management Information Systems, 13 (3), pp. 319-340; Dirksen, J., DiTommaso, D., Plunkett, C., Augmented and virtual reality for behavior change (2019) The ELearning Guild; Egenfeldt-Nielsen, S., Smith, J.H., Tosca, S.P., (2019) Understanding Video Games: The Essential Introduction, , Routledge; Ersner-Hershfield, H., Bailenson, J., Carstensen, L., Feeling more connected to your future self (2008) Association for Psychological Science Annual Convention, , Chicago, IL; Fogg, B., Creating persuasive technologies (2009) Persuasive ‘09: Proceedings of the 4th International Conference on Persuasive Technology, pp. 1-6; Fogg, B., A behavior model for persuasive design (2009) ACM International Conference Proceeding Series; Fogg, B.J., Hreha, J., Behavior Wizard: A method for matching target behaviors with solutions (2010) PERSUASIVE 2010: Persuasive Technology, pp. 117-131; Glanz, K., Bishop, D.B., The role of behavioral science theory in development and implementation of public health interventions (2010) Annual Review of Public Health, 31, pp. 399-418; Gupta, A., Hathwar, D., Vijayakumar, A., Introduction to AI chatbots (2020) International Journal of Engineering Research and Technology, 9 (7), pp. 255-258; Haag, S., Anderl, R., Digital twin – Proof of concept (2018) Manufacturing Letters, pp. 64-66; Halaweh, M., Emerging technology: What is it? 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Mehmood, F., Saqib, K., An overview on speech recognition system and comparative study of its approaches (2014) International Conference on Engineering & Emerging Technologies (ICEET-2014), , University, Superior, Lahore; Michie, S., Atkins, L., West, R., (2014) The Behaviour Change Wheel: A Guide to Designing Interventions, the Behavior Change Wheel, , Book Launch Event; Michie, S., Richardson, M., Johnston, M., Abraham, C., Francis, J., Hardeman, W., Eccles, M.P., The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: Building an international consensus for the reporting of behavior change interventions (2013) Annals of Behavioral Medicine, 46 (1), pp. 81-95; Milgram, P., Kishino, F., Taxonomy of mixed reality visual displays (1994) IEICE Transactions on Information and Systems, pp. 1321-1329; Naslund, J.A., Aschbrenner, K.A., Kim, S.J., McHugo, G.J., Unützer, J., Bartels, S.J., Marsch, L.A., Health behavior models for informing digital technology interventions for individuals with mental illness (2017) Psychiatric Rehabilitation Journal, 40 (3), pp. 325-335; 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Prochaska, J.O., Johnson, S., Lee, P., The transtheoretical model of behavoir change (1998) Handbook of Health Behavior Change, pp. 59-84. , Shumaker, S.A., Schron, E.B., Ockene, J.K. and McBee, W.L; Rapp, A., Tirassa, M., Tirabeni, L., Rethinking technologies for behavior change: A view from the inside of Human change (2019) ACM Transactions on Computer-Human Interaction, 26 (4); Shih, J.J., Krusienski, D.J., Wolpaw, J.R., Brain-computer interfaces in medicine (2012) Mayo Clinic Proceedings, 87 (3), pp. 268-279; Snyder, H., Literature review as a research methodology: An overview and guidelines (2019) Journal of Business Research, 104, pp. 333-339; Sony, M., Mekoth, N., A qualitative study on electricity energy-saving behaviour (2018) Management of Environmental Quality: An International Journal, 29 (5); Steuer, J., Defining virtual reality: Dimensions determining telepresence (1992) Journal of Communication, 42 (4), pp. 73-83; Thorpe, J.R., Forchhammer, B.H., Maier, A.M., Development of a sensor-based behavioral monitoring solution to support dementia care (2019) Journal of Medical Internet Research, 7 (6). , https://doi.org/10.2196/12013; Thorpe, J.R., Rønn-Andersen, K.V.H., Bień, P., Özkil, A.G., Forchhammer, B.H., Maier, A.M., Pervasive assistive technology for people with dementia: A UCD case (2016) Healthcare Technology Letters, 3 (4), pp. 297-302; Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D., User acceptance of information technology: Toward a unified view (2003) MIS Quarterly: Management Information Systems, 27 (3), pp. 425-478; Wang, Y., Fadhil, A., Lange, J.P., Reiterer, H., Integrating taxonomies into theory-based digital health interventions for behavior change: A holistic framework (2019) Journal of Medical Internet Research, 8 (1). , https://doi.org/10.2196/resprot.8055; Yao, M., Chen, J., Jing, J., Sheng, H., Tan, X., Jin, J., Defining the rehabilitation adherence curve and adherence phases of stroke patients: An observational study (2017) Patient Preference and Adherence, 11, pp. 1435-1441","Cash, P.; DTU Technical University of Denmark DTU ManagementDenmark; email: pcas@dtu.dk",,,"Cambridge University Press","23rd International Conference on Engineering Design, ICED 2021","16 August 2021 through 20 August 2021",,172546,2732527X,,,,"English","Proc. Des. Soc.",Conference Paper,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85117795442
"Konijn E.A., Jansen B., Mondaca Bustos V., Hobbelink V.L.N.F., Preciado Vanegas D.","8362947700;57219484815;57292075600;57291168400;57292301000;","Social Robots for (Second) Language Learning in (Migrant) Primary School Children",2021,"International Journal of Social Robotics",,,,"","",,1,"10.1007/s12369-021-00824-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116861601&doi=10.1007%2fs12369-021-00824-3&partnerID=40&md5=0bf52a6e13d3f965ddc6f98337d1f440","Department of Communication Science, Media Psychology Program, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Department of Psychobiology, University of Amsterdam, Amsterdam, Netherlands","Konijn, E.A., Department of Communication Science, Media Psychology Program, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Jansen, B., Department of Communication Science, Media Psychology Program, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Mondaca Bustos, V., Department of Communication Science, Media Psychology Program, Vrije Universiteit Amsterdam, Amsterdam, Netherlands; Hobbelink, V.L.N.F., Department of Psychobiology, University of Amsterdam, Amsterdam, Netherlands; Preciado Vanegas, D., Department of Communication Science, Media Psychology Program, Vrije Universiteit Amsterdam, Amsterdam, Netherlands","Especially these days, innovation and support from technology to relieve pressure in education is highly urgent. This study tested the potential advantage of a social robot over a tablet in (second) language learning on performance, engagement, and enjoyment. Shortages in primary education call for new technology solutions. Previous studies combined robots with tablets, to compensate for robot’s limitations, however, this study applied direct human–robot interaction. Primary school children (N = 63, aged 4–6) participated in a 3-wave field experiment with story-telling exercises, either with a semi-autonomous robot (without tablet, using WOz) or a tablet. Results showed increased learning gains over time when training with a social robot, compared to the tablet. Children who trained with a robot were more engaged in the story-telling task and enjoyed it more. Robot’s behavioral style (social or neutral) hardly differed overall, however, seems to vary for high versus low educational abilities. While social robots need sophistication before being implemented in schools, our study shows the potential of social robots as tutors in (second) language learning. © 2021, The Author(s).","Field experiment; Longitudinal; Migrant children; Primary school; Robot tutor; Robot-child interaction; Second language learning","Social robots; Field experiment; Language learning; Longitudinal; Migrant child; Primary schools; Robot tutor; Robot-child interaction; Second language; Second language learning; Social robots; Educational robots",,,,,"Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO: 406, DI.19.005","This study is funded by the Netherlands Organization for Scientific Research (NWO Open Competition–Digitalization, grant number: 406.DI.19.005).",,,,,,,,,,"Ahmad, M.I., Mubin, O., Shahid, S., Orlando, J., Robot’s adaptive emotional feedback sustains children’s social engagement and promotes their vocabulary learning: a long-term child–robot interaction study (2019) Adapt Behav, 27, pp. 243-266; Ali, H., Bhansali, S., Köksal, I., Möller, M., Pekarek-Rosin, T., Sharma, S., Thebille, A.-K., Wermter, S., Virtual or physical? social robots teaching a fictional language through a role-playing game inspired by game of thrones (2019) Soc Robot ICSR, 2019 (11876), pp. 358-367; (2013) Diagnostic and statistical manual of mental disorders: DSM-5, , 5, DC, Autor, Washington; van Baars, L., De groeiende greep van big tech op het digitale onderwijs [The growing grip of big tech on digital education] (2020) Trouw, , https://www.trouw.nl/onderwijs/de-groeiendegreep-van-big-tech-op-het-digitale-onderwijs~b4cbc7be/, Retrieved November 12, 2020, from; Bartneck, C., Forlizzi, J., A design-centred framework for social human-robot interaction (2004) RO-MAN 2004, pp. 591-594. , 13th IEEE international workshop on robot and human interactive communication (IEEE Catalog No.04TH8759); Baxter, P., Ashurst, E., Read, R., Kennedy, J., Belpaeme, T., Robot education peers in a situated primary school study: personalisation promotes child learning (2017) PLoS ONE; Belpaeme, T., Kennedy, J., Ramachandran, A., Scasselati, B., Tanaka, F., Social robots for education: a review (2018) Sci Robot, 3, pp. 1-9; van den Berghe, R., Verhagen, J., Oudgenoeg-Paz, O., van der Ven, S., Leseman, P.P.M., Social robots for language learning: a review (2018) Rev Educ Res; van den Berghe, R., de Haas, M., Oudgenoeg-Paz, O., Krahmer, E., Verhagen, J., Vogt, P., Willemsen, B., Leseman, P., A toy or a friend? 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(2010) Methodology, 6, pp. 147-151; Skinner, E.A., Belmont, M.J., Motivation in the classroom: Reciprocal effects of teacher behavior and student engagement across the school year (1993) J Educ Psychol, 85, p. 571; Smakman, M.H.J., Konijn, E.A., Vogt, P., Pankowska, P., Attitudes towards social robots in education: enthusiast, practical, troubled, sceptic, and mindfully positive (2021) Robotics, 10, p. 24; Tolksdorf, N.F., Siebert, S., Zorn, I., Horwath, I., Rohlfing, K.J., Ethical considerations of applying robots in kindergarten settings: towards an approach from a macroperspective (2020) Int J Soc Robot","Konijn, E.A.; Department of Communication Science, Netherlands; email: elly.konijn@vu.nl",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85116861601
"Wilkinson A., Gonzales M., Hoey P., Kontak D., Wang D., Torname N., Laderoute S., Han Z., Allspaw J., Platt R., Yanco H.","57219645725;57263443500;57263811900;35727048600;57221155274;57263812400;57263630400;57208162674;56072809000;7102023517;6601994484;","Design guidelines for human-robot interaction with assistive robot manipulation systems",2021,"Paladyn","12","1",,"392","401",,,"10.1515/pjbr-2021-0023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115127943&doi=10.1515%2fpjbr-2021-0023&partnerID=40&md5=191efe1aceabc72623a6733f67896f1a","Department of Computer Science, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; Department of Electrical and Computer Engineering, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; NERVE Center, University of Massachusetts Lowell (UML), Lowell, MA  01852, United States; Khoury College of Computer Sciences, Northeastern University, Boston, MA  02115, United States","Wilkinson, A., Department of Computer Science, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; Gonzales, M., Department of Computer Science, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; Hoey, P., Department of Electrical and Computer Engineering, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; Kontak, D., NERVE Center, University of Massachusetts Lowell (UML), Lowell, MA  01852, United States; Wang, D., Khoury College of Computer Sciences, Northeastern University, Boston, MA  02115, United States; Torname, N., Department of Electrical and Computer Engineering, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; Laderoute, S., Department of Computer Science, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; Han, Z., Department of Computer Science, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; Allspaw, J., Department of Computer Science, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States; Platt, R., Khoury College of Computer Sciences, Northeastern University, Boston, MA  02115, United States; Yanco, H., Department of Computer Science, University of Massachusetts Lowell (UML), Lowell, MA  01854, United States","The design of user interfaces (UIs) for assistive robot systems can be improved through the use of a set of design guidelines presented in this article. As an example, the article presents two different UI designs for an assistive manipulation robot system. We explore the design considerations from these two contrasting UIs. The first is referred to as the graphical user interface (GUI), which the user operates entirely through a touchscreen as a representation of the state of the art. The second is a type of novel UI referred to as the tangible user interface (TUI). The TUI makes use of devices in the real world, such as laser pointers and a projector-camera system that enables augmented reality. Each of these interfaces is designed to allow the system to be operated by an untrained user in an open environment such as a grocery store. Our goal is for these guidelines to aid researchers in the design of human-robot interaction for assistive robot systems, particularly when designing multiple interaction methods for direct comparison. © 2021 Alexander Wilkinson et al., published by De Gruyter.","assistive robotics; augmented reality; design guidelines; graphical user interface; human-robot interaction; tangible user interface",,,,,,"National Science Foundation, NSF: IIS-1426968, IIS-1427081, IIS-1724191, IIS-1724257, IIS-1763469; Office of Naval Research, ONR: N000141410047; National Aeronautics and Space Administration, NASA: NNX13AQ85G, NNX16AC48A; Google","Funding information : This work has been supported in part by the National Science Foundation (IIS-1426968, IIS-1427081, IIS-1724191, IIS-1724257, IIS-1763469), NASA (NNX16AC48A, NNX13AQ85G), ONR (N000141410047), Amazon through an ARA to Platt, and Google through an FRA to Platt.",,,,,,,,,,"Graf, B., Hans, M., Schraft, R.D., ""care-O-bot II-development of a next generation robotic home assistant,"" (2004) Autonom. Robot., 16 (2), pp. 193-205; Kemp, C.C., Anderson, C.D., Nguyen, H., Trevor, A.J., Xu, Z., ""a point-and-click interface for the real world: Laser designation of objects for mobile manipulation,"" (2008) 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 241-248. , in IEEE pp; Gelšvartas, J., Simutis, R., Maskeliūnas, R., ""projection mapping user interface for disabled people,"" (2018) J. Healthcare Eng., 2018. , 6916204; Wang, D., Kohler, C., Pas, A.T., Wilkinson, A., Liu, M., Yanco, H., ""towards assistive robotic pick and place in open world environments,"" (2019) Proceedings of the International Symposium on Robotics Research (ISRR), , http://arXiv.org/abs/arXiv:1809.09541, in arXiv; Martens, C., Ruchel, N., Lang, O., Ivlev, O., Graser, A., ""a friend for assisting handicapped people,"" (2001) IEEE Robot. Automat. 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Robot., 10 (1), pp. 128-139; Cofre, J.P., Moraga, G., Rusu, C., Mercado, I., Inostroza, R., Jimenez, C., ""developing a touchscreen-based domotic tool for users with motor disabilities,"" (2012) 2012 Ninth International Conference on Information Technology-New Generations, pp. 696-701. , in pp; Choi, Y.S., Anderson, C.D., Glass, J.D., Kemp, C.C., ""laser pointers and a touch screen: Intuitive interfaces for autonomous mobile manipulation for the motor impaired,"" (2008) Proceedings of the 10th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 225-232. , in ACM pp; Gualtieri, M., Kuczynski, J., Shultz, A.M., Ten Pas, A., Platt, R., Yanco, H., ""open world assistive grasping using laser selection,"" (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 4052-4057. , in IEEE pp; Quintero, C.P., Tatsambon, R., Gridseth, M., Jägersand, M., ""visual pointing gestures for bi-directional human robot interaction in a pick-and-place task,"" (2015) 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 349-354; Rouhollahi, A., Azmoun, M., Masouleh, M.T., ""experimental study on the visual servoing of a 4-DOF parallel robot for pick-and-place purpose,"" (2018) 2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), pp. 27-30. , in pp; Chacko, S.M., Kapila, V., ""an augmented reality interface for human-robot interaction in unconstrained environments,"" (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3222-3228. , in pp; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., ""rOS: An open-source robot operating system,"" (2009) ICRA Workshop on Open Source Software, 3, p. 5. , in Kobe, Japan p; Ten Pas, A., Gualtieri, M., Saenko, K., Platt, R., ""grasp pose detection in point clouds,"" (2017) Int. J. Robot. Res., 36 (NO. 13-14), pp. 1455-1473. , pp; Gualtieri, M., Ten Pas, A., Saenko, K., Platt, R., ""high precision grasp pose detection in dense clutter,"" (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 598-605. , in pp; (2019) National Eye Institute, ""types of Color Blindness,"", , http://www.nei.nih.gov/learn-about-eye-health/eye-conditions-and-diseases/color-blindness/types-color-blindness, [Accessed: May 12, 2021]","Wilkinson, A.; Department of Computer Science, United States; email: Alexander_Wilkinson@student.uml.edu",,,"De Gruyter Open Ltd",,,,,20814836,,,,"English","Paladyn",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85115127943
"Zabidi S.A.M., Yusof H.M., Sidek S.N., Rashidan M.A., Ghazali A.S.","57222169813;56862078000;55560554800;56539931300;55888486800;","Rethinking Autism: A Review on the Use of HRI Platform to Improve Joint Attention and Imitation Skill for ASD Children",2021,"Lecture Notes in Mechanical Engineering",,,,"214","231",,,"10.1007/978-981-16-4803-8_23","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113793115&doi=10.1007%2f978-981-16-4803-8_23&partnerID=40&md5=d537214ebf994d7b4d72376c125ae35e","International Islamic University of Malaysia, Kuala Lumpur, Malaysia","Zabidi, S.A.M., International Islamic University of Malaysia, Kuala Lumpur, Malaysia; Yusof, H.M., International Islamic University of Malaysia, Kuala Lumpur, Malaysia; Sidek, S.N., International Islamic University of Malaysia, Kuala Lumpur, Malaysia; Rashidan, M.A., International Islamic University of Malaysia, Kuala Lumpur, Malaysia; Ghazali, A.S., International Islamic University of Malaysia, Kuala Lumpur, Malaysia","The growing interest in the field of Human-Robot Interaction (HRI) brings about the prospect of utilizing robots in the intervention therapies for Autism Spectrum Disorder (ASD) children. This paper reviews the topic that relates to the use of HRI in improving joint attention and imitation skill for these children. The review limits papers from the past 10 years in which the earlier findings on the experimental use and design of HRI platforms are highlighted. Also, the methodological steps including the selection of test subjects and methods for outcome measures are elaborated. As results, studies suggest that the use of HRI contributes to positive implications, especially in gaining attention from ASD subjects during therapy sessions. That is, ASD children initiated more social interactions with a robotic partner than a human partner. Thus, it can be concluded that robots have a huge potential to be used as a tool for a therapist to connect with ASD children easily. Future studies might focus on developing a more autonomous HRI so that the robots are capable to operate independently with minor intervention by humans. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.","Autism Spectrum Disorder (ASD); Human-Robot Interaction (HRI); Joint attention; Social skill; Socially assistive robots (SAR)",,,,,,"Ministry of Higher Education, Malaysia, MOHE: TRGS/1/2019/UIAM/02/4/3","Acknowledgement. The authors gratefully acknowledge the Ministry of Education Malaysia (MOE) for funding the research project through the Transdisciplinary Research Grant Scheme (TRGS) [Ref. No TRGS/1/2019/UIAM/02/4/3].",,,,,,,,,,"Defilippis, M., Wagner, K.D., Treatment of autism spectrum disorder in children and adolescents (2010) J. Autism Dev. 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Autism, 9 (1). , https://doi.org/10.1186/s13229-018-0230-8; Anzalone, S.M., Quantifying patterns of joint attention during human-robot interactions: An application for autism spectrum disorder assessment (2019) Pattern Recogn. Lett., 118, pp. 42-50. , https://doi.org/10.1016/j.patrec.2018.03.007; Zheng, Z., Nie, G., Swanson, A., Weitlauf, A., Warren, Z., Sarkar, N., A randomized controlled trial of an intelligent robotic response to joint attention intervention system (2020) J. Autism Dev. Disord., 50 (8), pp. 2819-2831. , https://doi.org/10.1007/s10803-020-04388-5; Lord, C., Rutter, M., DiLavore, P.C., Risi, S., Gotham, K., Bishop, S.L.: Autism diagnostic observation schedule, 2; Schopler, E., Lansing, M.D., Reichler, R.J., Marcus, L.M., (2005) Psychoeducational Profile: TEACCH Individualized Psychoeducational Assessment for Children with Autism Spectrum Disorders, , 3rd edn. Pro-Ed, Austin; Bruininks, R., Bruininks, B., (2005) Bruininks-Oseretsky Test of Motor Proficiency, , 2nd edn. NCS Pearson, Minneapolis; Kaufman, A., Kaufman, N., (1983) Kaufman Assessment Battery for Children: Administration and Scoring Manual, , American Guidance Service, Circle Pines; Kurt, O., Tekin-Iftar, E., A comparison of constant time delay and simultaneous prompting within embedded instruction on teaching leisure skills to children with autism (2008) Top. Early Child. Spec. Educ., 28 (1), pp. 53-64. , https://doi.org/10.1177/0271121408316046; Cengher, M., Shamoun, K., Moss, P., Roll, D., Feliciano, G., Fienup, D.M., A comparison of the effects of two prompt-fading strategies on skill acquisition in children with autism spectrum disorders (2015) Behav. Anal. Pract., 9 (2), pp. 115-125. , https://doi.org/10.1007/s40 617-015-0096-6; Madipakkam, A.R., Rothkirch, M., Dziobek, I., Sterzer, P., Unconscious avoidance of eye contact in autism spectrum disorder (2017) Sci. Rep., 7; Qidwai, U., Kashem, S.B.A., Conor, O., Humanoid robot as a teacher’s assistant: Helping children with autism to learn social and academic skills (2019) J. Intell. Rob. Syst., 98 (3-4), pp. 759-770. , https://doi.org/10.1007/s10846-019-01075-1; Alcorn, A.M., Educators’ views on using humanoid robots with autistic learners in special education settings in England (2019) Front. Robot. AI, 6 (1-15). , https://doi.org/10. 3389/frobt.2019.00107; Meltzoff, A.N., Moore, M.K., Newborn infants imitate adult facial gestures (1983) Child Dev, 54 (3), pp. 702-709; (2005) Perspectives on Imitation: From Neuroscience to Social Science, , Hurley, S., Chater, N. (eds.), MIT Press, Cambridge; Heimann, M., Nordqvist, E., Strid, K., Connant Almrot, J., Tjus, T., Children with autism respond differently to spontaneous, elicited and deferred imitation (2016) J. Intellect. Disabil. Res., 60 (5), pp. 491-501. , https://doi.org/10.1111/jir.12272; Vivanti, G., Trembath, D., Dissanayake, C., Mechanisms of imitation impairment in autism spectrum disorder (2014) J. Abnorm. Child Psychol., 42 (8), pp. 1395-1405. , https://doi.org/10. 1007/s10802-014-9874-9; Meltzoff, A.N., ‘Like me’: A foundation for social cognition (2007) Dev. Sci., 10 (1), pp. 126-134. , https://doi.org/10.1111/j.1467-7687.2007.00574.x; Meltzoff, A.N., Elements of a developmental theory of imitation (2002) Meltzoff, A.N., Prinz, W. (Eds.) Cambridge Studies in Cognitive Perceptual Development. the Imitative Mind: Development, Evolution, and Brain Bases, Pp. 19–41. Cambridge University Press; Call, J., Carpenter, M., Tomasello, M., Copying results and copying actions in the process of social learning: Chimpanzees (Pan troglodytes) and human children (Homo sapiens) (2005) Anim. Cogn., 8, pp. 151-163; Mundy, P., Sigman, M., Kasari, C., A longitudinal study of joint attention and language development in autistic children (1990) J. Autism Dev. Disord., 20, pp. 115-128; Bruinsma, Y., Koegel, R.L., Koegel, L.K., Joint attention and children with autism: A review of the literature (2004) Ment. Retard. Dev. Disabil. Res. Rev., 10, pp. 169-175; Charman, T., Why is joint attention a pivotal skill in autism (2003) Philos. Trans. R. Soc. Lond., 358, pp. 315-324; Loveland, K.A., Landry, S.H., Joint attention and language in autism and development language delay (1986) J. Autism Dev. Disord., 16, pp. 335-349; Whalen, C., Schreibman, L., Ingersoll, B., The collateral effects of joint attention training on social initiations, positive affect, imitation, and spontaneous speech for young children with autism (2006) J. Autism Dev. Disord., 36, pp. 655-664; Jones, E.A., Carr, E.G., Feeley, K.M., Multiple effects of joint attention intervention for children with autism (2006) Behav. Modif., 30, pp. 782-834; Ingersoll, B., Schreibman, L., Teaching reciprocal imitation skills to young children with autism using a naturalistic behavioral approach: Effects on language, pretend play, and joint attention (2006) J. Autism Dev. Disord., 36, pp. 487-505; Martin, N., (2006) Test of Visual Perceptual Skills, , 3rd edn. Academic Therapy Publications, Novato; Sundberg, M.L., (2008) Verbal Behavior Milestones Assessment and Placement Program: The VB-MAPP, , AVB Press, Concord; Rueda, M.R., Development of attentional networks in childhood (2004) Neuropsychologia, , https://doi.org/10.1016/j.neuropsychologia.2003.12.012; Dawson, G., Toth, K., Abbott, R., Osterling, J., Munson, J., Estes: Early social attention impairments in autism: Social orienting, joint attention, and attention to distress (2004) Dev. Psychol., 40, pp. 271-283; Kitchenham, B., (2004) Procedures for Performing Systematic Reviews; Undertaking Systematic Review of Research on Effectiveness (2001) CRD Report, 4. , Khan, K.S., ter Riet, G., Glanville, J., Sowden, A.J., Kleijnen, J. (eds.), 2nd edn. NHS Centre for Reviews and Dissemination, University of York; (2000) American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, p. 84. , 4th edn. (rev.), p. , Washington DC; Shamsuddin, S., Yussof, H., Ismail, L.I., Mohamed, S., Hanapiah, F.A., Zahari, N., Humanoid robot NAO interacting with autistic children of moderately impaired intelligence to augment communication skills (2012) Procedia Eng, 41, pp. 1533-1538; Wong, H.Y.A., Zhong, Z.W., Assessment of robot training for social cognitive learning (2016) 16Th International Conference on Control, Automation and Systems (ICCAS), Gyeongju, pp. 893-898. , https://doi.org/10.1109/ICCAS.2016.7832420, , pp. , 2016; Benitti, F.B.V., Exploring the educational potential of robotics in schools: A systematic review (2012) Comput. Educ., 58 (3), pp. 978-988. , https://doi.org/10.1016/j.compedu.2011.10.006; Bedaf, S., Gelderblom, G.J., de Witte, L., Overview and categorization of robots supporting independent living of elderly people: What activities do they support and how far have they developed (2015) Assist. Technol., 27 (2), pp. 88-100. , https://doi.org/10.1080/10400435.2014. 978916, 26132353; Linstead, E., Dixon, D.R., Hong, E., et al.: An evaluation of the effects of intensity and duration on outcomes across treatment domains for children with autism spectrum disorder. Transl. Psychiatry 7(9), e1234 (2017). Published 19 Sept 2017. https://doi.org/10.1038/tp. 2017.207; Grynszpan, O., Weiss, P.L., Perez-Diaz, F., Gal, E.: Innovative technology-based interventions for autism spectrum disorders: a meta-analysis. Autism 18(4), 346–361 (2014). https://doi. org/10.1177/1362361313476767; Colombi, C., Liebal, K., Tomasello, M., Young, G., Warneken, F., Rogers, S.J., Examining correlates of cooperation in autism: Imitation, joint attention, and understanding intentions (2009) Autism, 13 (2), pp. 143-163. , https://doi.org/10.1177/1362361308098514, 19261685; Kirkpatrick, D.L., Kirkpatrick, J.D., (2006) Evaluating Training Porgrams: The Four Levels, , Berrett-Koehler, San Francisco","Zabidi, S.A.M.; International Islamic University of MalaysiaMalaysia; email: sarahafiqah.zabidi@live.iium.edu.my","Chew E.P. P. Abdul Majeed A.Liu P.Platts J.Myung H.Kim J.Kim J.",,"Springer Science and Business Media Deutschland GmbH","8th International Conference on Robot Intelligence Technology and Applications, RiTA 2020","11 December 2020 through 13 December 2020",,263529,21954356,9789811648021,,,"English","Lect. Notes Mech. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85113793115
"Olowo O.O., Zhang R., Yang Z., Goulet B., Popa D.O.","57193411592;57195069295;57195062852;57226758190;57226691709;","Organic piezoresistive robotic skin sensor fabrication, integration and characterization",2021,"Proceedings of the ASME 2021 16th International Manufacturing Science and Engineering Conference, MSEC 2021","2",,,"","",,,"10.1115/MSEC2021-63942","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112550475&doi=10.1115%2fMSEC2021-63942&partnerID=40&md5=f1642f8e084860278adb4cfe9a088479","Louisville Automation and Robotics Research Institute, University of Louisville, Kentucky, United States","Olowo, O.O., Louisville Automation and Robotics Research Institute, University of Louisville, Kentucky, United States; Zhang, R., Louisville Automation and Robotics Research Institute, University of Louisville, Kentucky, United States; Yang, Z., Louisville Automation and Robotics Research Institute, University of Louisville, Kentucky, United States; Goulet, B., Louisville Automation and Robotics Research Institute, University of Louisville, Kentucky, United States; Popa, D.O., Louisville Automation and Robotics Research Institute, University of Louisville, Kentucky, United States","Advanced applications for human-robot interaction require perception of physical touch in a manner that imitates the human tactile perception. Feedback generated from tactile sensor arrays can be used to control the interaction of a robot with their environment and other humans. In this paper, we present our efforts to fabricate piezoresistive organic polymer sensor arrays using PEDOT: PSS or poly (3,4-ethylenedioxythiophene)-poly(styrenesulfonate). Sensors are realized as strain-gauges on Kapton substrates with thermal and electrical response characteristics to human touch. In this paper, we detail fabrication processes associated with a Gold etching technique combined with a wet lift-off photolithographic process to implement a circular tree designed sensor microstructure in our cleanroom. The testing of this microstructure is done on a load testing apparatus facilitated by an integrated circuit design. Furthermore, a lamination process is employed to compensate for temperature drift while measuring pressure for double-sided sensor substrates. Experiments carried out to evaluate the performance of the fabricated structure, indicates 100% sensor yields with the updated technique implemented. Copyright © 2021 by ASME","Gold etching; Lamination; Lift-off; Microfabrication; PEDOT: PSS; Tactile sensor","Biofeedback; Conducting polymers; Etching; Human robot interaction; Integrated circuit testing; Load testing; Microstructure; Photolithography; Robotics; Substrates; Advanced applications; Double-sided sensors; Fabricated structures; Photolithographic process; Poly(styrene sulfonate); Poly-3 ,4-ethylenedioxythiophene; Polymer sensor array; Tactile sensor array; Fabrication",,,,,"National Science Foundation, NSF: 1828355; Office of Experimental Program to Stimulate Competitive Research, EPSCoR: 1849213; University of Louisville, UL","This study was supported by the National Science Foundation awards MRI #1828355 and EPSCOR #1849213. We would like to thank the University of Louisville's Micro Nano Technology Center (MNTC) staffs, Julia Aebersold and Curtis Mckenna for their dedicated assistance in our fabrication process. Our appreciation also goes to Jacob Berdichevsky and Danming Wei for their assistance in setting up the testing station.","This study was supported by the National Science Foundation awards MRI #1828355 and EPSCOR #1849213. We would like to thank the University of Louisville’s Micro Nano Technology Center (MNTC) staffs, Julia Aebersold and Curtis Mckenna for their dedicated assistance in our fabrication process. Our appreciation also goes to Jacob Berdichevsky and Danming Wei for their assistance in setting up the testing station.",,,,,,,,,"Crowder, R., Towards robots that can sense texture by touch (2006) Science, pp. 1479-1479; Linshuai Zhang, S. G., Huadong Yu, Y. S., Performance evaluation of a strain-gauge force sensor for a haptic robot-assisted catheter operating system (2017) Microsyst Technol, pp. 5041-5050; Lin, C. H., Erickson, T. W., Fishel, J. A., Wettels, N., Loeb, G. E., Signal processing and fabrication of a biomimetic tactile sensor array with thermal, force and microvibration modalities (2009) IEEE International Conference on Robotics and Biomimetics (ROBIO), , Guilin; Hongbo Wang, M. T., Beccai, Lucia, Development of fully shielded soft inductive tactile sensors, , Center for Micro-BioRobotics, Istituto Italiano di Tecnologia (IIT), Italy2019; Mickael Pruvost, W. J. S., Monteux, Cecile, Poulin, Philippe, Colin, Annie, (2019) npj Flexible Electronics, , Polymeric foams for flexible and highly sensitive low-pressure capacitive sensors; Olowo, O. O., Abdulkareem, A., Modeling and Analysis of Powerline Temperature Surveillance with Optisystem Simulation (2017) The international Journal of Engineering and science, pp. 14-20; Danming Wei, R. Z., Saadatzi, Mohammad N., Popa, Dan O., Organic Piezoresistive Pressure Sensitive Robotic Skin For Physical Human-Robot Interaction (2020) International Manufacturing Science and Engineering Conference, , Cincinnati; Mohammad Saadatzi, Z. Y., Baptist, Joshua R., Popa, Dan O., Parametric Investigation of Scalable Tactile Sensor Arrays (2017) SPIE Commercial + Scientific Sensing and Imaging; Saadatzi, M. N., Joshua, D. O. P., Baptist Zhong Yang, R., Modeling and fabrication of scalable Tactile Sensor (2019) IEEE SENSORS JOURNAL, 19 (17), pp. 1558-1748; Joe Sanford, I. R., Popa, Dan O., Physical Human-Robot interaction with a Mobile Manipulator through Pressure Sensitive Robot Skin (2013) International Conference on Pervasive Technologies Related to Assistive Environments, pp. 1-6. , Greece, Association of Computing Machinery; Alexanda El Zein, C. H., Cochrane, Cedric, Development of a Flexible Strain Sensor Based on PEDOT:PSS for Thin Film Structures (2017) MDPI Sensors, pp. 1-14; Harish Devaraj, T. G., Petit, Adeline, Assadian, Mahtab, Aw, Kean, The Development of Highly Flexible Stretch Sensors for a Robotic Hand (2018) MDPI, 7 (54), pp. 1-13; Baptist, J. R., Zhang, R., Wei, D., Saadatzi, M. N., Popa, D. O., Fabrication of strain gauge based sensors for tactile skins (2017) SPIE Commercial + Scientific Sensing and Imaging, , Anaheim; Nothnagle, C., Baptist, J. R., Sanford, J., Lee, W. H., Popa, D. O., Wijesundara, M. B. J., EHD printing of PEDOT: PSS inks for fabricating pressure and strain sensor arrays on flexible substrates (2015) SPIE Sensing Technology + Applications, , Baltimore; Mirza, F., (2016) Piezoresistive pressure sensor array for robotic skin; (2020) MIllipore Sigma, , https://www.sigmaaldrich.com/catalog/product/aldrich/655201?lang=en&region=US, ed: Sigma-Aldrich; Mohammad Saadatzi, J. R. B., Wijayasinghe, Indika, Popa, Dan O., Characterization of Large-area Pressure Sensitive Robot Skin (2017) SPIE Commercial + Scientific Sensing and Imaging","Olowo, O.O.; Louisville Automation and Robotics Research Institute, United States; email: olalekanolakitan.olowo@louisville.edu",,"Manufacturing Engineering Division","American Society of Mechanical Engineers","ASME 2021 16th International Manufacturing Science and Engineering Conference, MSEC 2021","21 June 2021 through 25 June 2021",,170865,,9780791885079,,,"English","Proc. ASME Int. Manuf. Sci. Engi. Conf., MSEC",Conference Paper,"Final","",Scopus,2-s2.0-85112550475
"Divya R., Inchara N., Muskaan Z.A., Honnavalli P.B., Charanraj B.R.","57226653283;57226638875;57226639830;57211807480;57222511184;","Autonomous Sailing Boat",2021,"Lecture Notes in Networks and Systems","190",,,"153","164",,,"10.1007/978-981-16-0882-7_11","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112237046&doi=10.1007%2f978-981-16-0882-7_11&partnerID=40&md5=9dda569de2fe6d6241e8b98e85c08de7","PES University, Bangalore, India","Divya, R., PES University, Bangalore, India; Inchara, N., PES University, Bangalore, India; Muskaan, Z.A., PES University, Bangalore, India; Honnavalli, P.B., PES University, Bangalore, India; Charanraj, B.R., PES University, Bangalore, India","Among several self-sufficient surface vehicles, cruising mechanical autonomy is a successful innovation for lengthy haul missions and semi-persistent nearness in the seas. Self-governance of these vehicles as far as vitality will be accomplished by inexhaustible sunlight-based and wind power sources. The fundamental commitment of this paper is to design an autonomous sailing robot that explores the water surface and sends live video footage to the base station, the identification of the behavior of sailing, human–robot interaction, robot control, estimation of position, and obstacle detection. Major key applications for this vessel are the assessment of the nation’s ocean boundary and fishermen’s activity. The project presented is with a robotic vehicle which works automatically and manually and controls the moving object in the water the robot will capture and sends the information to the base system which helps to keep track of fisherman who cross the nation’s boundary unknowingly. Here, Arduino processor is inbuilt with interfacing a wireless Raspberry Pi camera. The servo motors are used to actuate in the ocean and provide movement in all directions. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.","Arduino Uno; Autonomous sailing robot; Bluetooth; Live video streaming; Obstacle detection; Raspberry Pi; Robot; Robotic vehicle; Sailboat; Sailing robot; Servo motors; Solar panel; Ultrasonic sensors and batteries",,,,,,,"The authors would like to thank the faculty Prof. Prasad B Honnavalli and Assistant Prof. Charanraj BR for helping and guiding us throughout the project.",,,,,,,,,,"Neal, M., (2006) Regular Issue Peer-Reviewed Technical Communication a Hardware Proof of Concept of a Sailing Robot for Ocean Observation; Naveau, M., Anthierensy, C., Paulyz, E., Courmontagnez, P., (2013) MARIUS Project: Design of a Sailing Robot for Oceanographic Missions; Guo, V., Romero, M., Ieng, S.-H., Plumet, F., Benosman, R., Gas, B., (2011) Reactive Path Planning for Autonomous Sailboat Using and Omni-Directional Camera for Obstacle Detection; Zhi, L., Xuesong, M., Navigation and Control System of Mobile Robot Based on ROS; Plumet, F., Pêtrès, C., Romero-Ramirez, M.-A., Gas, B., Ieng, S.-H., (2014) Toward an Autonomous Sailing Boat","Divya, R.; PES UniversityIndia; email: divyarajshekar123@gmail.com","Kaiser M.S.Xie J.Rathore V.S.",,"Springer Science and Business Media Deutschland GmbH","5th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2020","11 December 2020 through 12 December 2020",,262459,23673370,9789811608810,,,"English","Lect. Notes Networks Syst.",Conference Paper,"Final","",Scopus,2-s2.0-85112237046
"Marge M., Espy-Wilson C., Ward N.G., Alwan A., Artzi Y., Bansal M., Blankenship G., Chai J., Daumé H., III, Dey D., Harper M., Howard T., Kennington C., Kruijff-Korbayová I., Manocha D., Matuszek C., Mead R., Mooney R., Moore R.K., Ostendorf M., Pon-Barry H., Rudnicky A.I., Scheutz M., Amant R.S., Sun T., Tellex S., Traum D., Yu Z.","23009418600;6701329317;7102178443;7006698761;51664667500;16466939600;57226283621;8842227500;57210198346;53863310500;57226273842;57203090257;55792290300;55894415700;35452853100;10239806100;23009115600;7102791999;55484218200;7006018763;6508002927;6602574360;6603548841;56877306100;57219699023;7801643708;6603622906;57307463200;","Spoken language interaction with robots: Recommendations for future research",2021,"Computer Speech and Language","71",,"101255","","",,,"10.1016/j.csl.2021.101255","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111001903&doi=10.1016%2fj.csl.2021.101255&partnerID=40&md5=ca0aa8190b8b757c56c4436279537bf3","DEVCOM Army Research Laboratory, United States; University of Maryland, College Park, United States; University of Texas at El Paso, United States; University of California at Los Angeles, United States; Cornell University, United States; University of North Carolina at Chapel Hill, United States; University of Michigan, United States; Microsoft, United States; University of Rochester, United States; Boise State University, United States; DFKI, Germany; University of Maryland, Baltimore County, United States; Semio, United States; University of Texas at Austin, United States; University of Sheffield, United Kingdom; University of Washington, United States; Mount Holyoke College, United States; Carnegie Mellon University, United States; Tufts University, United States; Adobe, United States; Brown University, United States; USC Institute for Creative Technologies, United States; University of California at Davis, United States","Marge, M., DEVCOM Army Research Laboratory, United States; Espy-Wilson, C., University of Maryland, College Park, United States; Ward, N.G., University of Texas at El Paso, United States; Alwan, A., University of California at Los Angeles, United States; Artzi, Y., Cornell University, United States; Bansal, M., University of North Carolina at Chapel Hill, United States; Blankenship, G., University of Maryland, College Park, United States; Chai, J., University of Michigan, United States; Daumé, H., III, University of Maryland, College Park, United States; Dey, D., Microsoft, United States; Harper, M., DEVCOM Army Research Laboratory, United States; Howard, T., University of Rochester, United States; Kennington, C., Boise State University, United States; Kruijff-Korbayová, I., DFKI, Germany; Manocha, D., University of Maryland, College Park, United States; Matuszek, C., University of Maryland, Baltimore County, United States; Mead, R., Semio, United States; Mooney, R., University of Texas at Austin, United States; Moore, R.K., University of Sheffield, United Kingdom; Ostendorf, M., University of Washington, United States; Pon-Barry, H., Mount Holyoke College, United States; Rudnicky, A.I., Carnegie Mellon University, United States; Scheutz, M., Tufts University, United States; Amant, R.S., DEVCOM Army Research Laboratory, United States; Sun, T., Adobe, United States; Tellex, S., Brown University, United States; Traum, D., USC Institute for Creative Technologies, United States; Yu, Z., University of California at Davis, United States","With robotics rapidly advancing, more effective human–robot interaction is increasingly needed to realize the full potential of robots for society. While spoken language must be part of the solution, our ability to provide spoken language interaction capabilities is still very limited. In this article, based on the report of an interdisciplinary workshop convened by the National Science Foundation, we identify key scientific and engineering advances needed to enable effective spoken language interaction with robotics. We make 25 recommendations, involving eight general themes: putting human needs first, better modeling the social and interactive aspects of language, improving robustness, creating new methods for rapid adaptation, better integrating speech and language with other communication modalities, giving speech and language components access to rich representations of the robot's current knowledge and state, making all components operate in real time, and improving research infrastructure and resources. Research and development that prioritizes these topics will, we believe, provide a solid foundation for the creation of speech-capable robots that are easy and effective for humans to work with. © 2021","Challenges; Issues; Priorities; Research agenda; Users","Engineering research; Modeling languages; Robotics; Speech communication; Communication modalities; Language component; National Science Foundations; Rapid adaptation; Research and development; Research infrastructure; Robot interactions; Spoken languages; Social robots",,,,,"National Science Foundation, NSF: -1941541","We thank Tanya Korelsky and the National Science Foundation, United States of America for initiating our discussions and for support, via Grant #IIS-1941541. We also thank the government observers at the workshop for their contributions to the discussions: Jonathan Fiscus, Susan G. Hill, Nia Peters, Erion Plaku, Christopher Reardon, and Clare Voss. We also thank Erin Zaroukian for further discussion.","We thank Tanya Korelsky and the National Science Foundation, United States of America for initiating our discussions and for support, via Grant #IIS-1941541 . We also thank the government observers at the workshop for their contributions to the discussions: Jonathan Fiscus, Susan G. Hill, Nia Peters, Erion Plaku, Christopher Reardon, and Clare Voss. We also thank Erin Zaroukian for further discussion.",,,,,,,,,"Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: A review (2017) J. Hum.-Robot Interact., 6 (1), pp. 25-63; Al Moubayed, S., Beskow, J., Skantze, G., Granström, B., Furhat: A back-projected human-like robot head for multiparty human-machine interaction (2012) Cognitive Behavioural Systems, pp. 114-130. , Springer; Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Horvitz, E., (2019), pp. 1-13. , Guidelines for human-AI interaction. In: Proceedings of CHI; Bainbridge, W.A., Hart, J.W., Kim, E.S., Scassellati, B., The benefits of interactions with physically present robots over video-displayed agents (2011) Int. J. Soc. Robot., 3 (1), pp. 41-52; Balentine, B., It's Better To Be a Good Machine than a Bad Person: Speech Recognition and Other Exotic User Interfaces At the Twilight of the Jetsonian Age (2007), ICMI Press; Beckerle, P., Castellini, C., Lenggenhager, B., Robotic interfaces for cognitive psychology and embodiment research: A research roadmap (2019) Wiley Interdiscip. Rev. Cogn. Sci., 10 (2); Bohus, D., Andrist, S., Jalobeanu, M., (2017), pp. 493-494. , Rapid development of multimodal interactive systems: A demonstration of platform for situated intelligence. In: Proceedings of ICMI; Bohus, D., Horvitz, E., (2010), On the challenges and opportunities of physically situated dialog. In: Proceedings of the AAAI Fall Symposium on Dialog with Robots; Bohus, D., Saw, C.W., Horvitz, E., (2014), pp. 637-644. , Directions Robot: In-the-wild experiences and lessons learned. In: Proceedings of AAMAS; Boltz, M.G., Temporal dimensions of conversational interaction: The role of response latencies and pauses in social impression formation (2005) J. Lang. Soc. Psychol., 24 (2), pp. 103-138; Bulyko, I., Ostendorf, M., (2001), 2, pp. 781-784. , Joint prosody prediction and unit selection for concatenative speech synthesis. In: Proceedings of ICASSP; Chai, J.Y., She, L., Fang, R., Ottarson, S., Littley, C., Liu, C., Hanson, K., (2014), pp. 33-40. , Collaborative effort towards common ground in situated human-robot dialogue. 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In: Proceedings of AAMAS; Devillers, L., Kawahara, T., Moore, R.K., Scheutz, M., Spoken language interaction with virtual agents and robots (SLIVAR): Towards effective and ethical interaction (dagstuhl seminar 20021) (2020) Dagstuhl Reports: Tech. Rep., pp. 1-51. , Schloss Dagstuhl: Leibniz-Zentrum für Informatik; Eskenazi, M., Zhao, T., Report from the NSF future directions workshop, toward user-oriented agents: Research directions and challenges (2020), arXiv preprint; Fang, R., Doering, M., (2015), pp. 271-278. , Chai, J.Y. Embodied collaborative referring expression generation in situated human-robot dialogue. In: Proceedings of HRI; Fusaroli, R., Tylén, K., Investigating conversational dynamics: Interactive alignment, interpersonal synergy, and collective task performance (2016) Cogn. Sci., 40 (1), pp. 145-171; Gaschler, A., Jentzsch, S., Giuliani, M., Huth, K., (2012), pp. 2128-2133. , de Ruiter, J., Knoll, A. 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In: Proceedings of SIGdial","Marge, M.; DEVCOM Army Research LaboratoryUnited States; email: matthew.r.marge.civ@mail.mil",,,"Academic Press",,,,,08852308,,CSPLE,,"English","Comput Speech Lang",Review,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85111001903
"Fang Z., Lou L., Tang K., Wang W., Chen B., Wang Y., Zheng Y.","57201897537;56540874400;7401589663;37006077200;55723067700;57201910511;7404837363;","A CMOS-integrated radar-assisted cognitive sensing platform for seamless human-robot interactions",2021,"Proceedings - IEEE International Symposium on Circuits and Systems","2021-May",,"9401535","","",,1,"10.1109/ISCAS51556.2021.9401535","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109040585&doi=10.1109%2fISCAS51556.2021.9401535&partnerID=40&md5=867773951e2b7695c41ad2c0a3b424fd","School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore; School of Electrical Engineering, Shanghai Jiaotong University, Shanghai, China; Singapore University of Technology and Design, Singapore, Singapore","Fang, Z., School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore; Lou, L., School of Electrical Engineering, Shanghai Jiaotong University, Shanghai, China; Tang, K., School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore; Wang, W., School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore; Chen, B., School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore; Wang, Y., Singapore University of Technology and Design, Singapore, Singapore; Zheng, Y., School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","With the rapid development of the internet of things (IoT), industry 4.0, smart manufacturing, intelligent building techniques, robots are becoming more and more demanding for emerging new applications. For robots used in complex environments, precise sensing of its surroundings is essential to enable safe and robust operation. Comprehensive and cognitive perception capability is needed to recognize human subjects in a complex and dynamic environment to avoid the possible collisions. Moreover, comprehensive sensing is required to enable accurate simultaneous localization and mapping (SLAM) under scenarios with clutters and moving human subjects. To achieve the goal, a CMOS-integrated radar-assisted robot sensing platform is proposed. By leveraging the phased-array radar techniques and time-phase processing techniques, high accuracy can be achieved for localization and recognition of human subjects. Fabricated in a 65-nm CMOS process, the chip-scale radar sensing platform is with compact size. A series of experiments have been carried out on verifying the capabilities of the radar platform for ranging and human recognition based on vital signs, exploring the potential for seamless human-robot interaction applications. © 2021 IEEE","CMOS; Cognitive sensing; Edge devices; Human/robot interaction; Internet of things (IoT); Mixed-signal IC; Radar; Robots; Sensor platform","CMOS integrated circuits; Industrial robots; Intelligent buildings; Internet of things; Man machine systems; Radar; Cognitive Sensing; Complex environments; Dynamic environments; Internet of thing (IOT); Perception capability; Phased array radars; Simultaneous localization and mapping; Smart manufacturing; Social robots",,,,,,"ACKNOWLEDGMENT The authors would like to thank the funding supported by Singapore MOE AcRF Tier 2: MOE2019-T2-2-179.",,,,,,,,,,"Zaffar, M., Sensors, slam and long-term autonomy: A review (2018) NASA/ESA Conference on Adaptive Hardware and Systems (AHS); Durrant-Whyte, H., Bailey, T., Simultaneous localization and mapping: Part I (2006) IEEE Robotics & Automation Magazine, 13 (2), pp. 99-110. , June; Cao, Y., Yang, C., Li, R., Knoll, A., Beltrame, G., Accurate position tracking with a single UWB anchor (2020) IEEE International Conference on Robotics and Automation (ICRA); Atanasov, N., Decentralized active information acquisition: Theory and application to multi-robot SLAM (2015) IEEE International Conference on Robotics and Automation (ICRA); Tang, K., A 4Tx/4RX pulsed chirping phased-array radar transceiver in 65-nm CMOS for X-band synthetic aperture radar application (2020) IEEE J. Solid-State Circuits, 55 (11), pp. 2970-2983. , Nov; Koo, V.C., A new unmanned aerial vehicle synthetic aperture RADAR for environmental monitoring (2012) Proc. In Electromagnetics Research, 122, pp. 245-268; Ansari, M.A.H., Law, C.L., Grating lobe suppression of multicycle IR-UWB collaborative radar sensor in wireless sensor network system (2020) IEEE Sens. Letters, 4 (1), pp. 1-4. , Jan; Park, P., Kim, S., Woo, S., Kim, C., A centimeter resolution, 10 m range CMOS impulse radio radar for human motion monitoring (2014) IEEE J. Solid-State Circuits, 49 (5), pp. 1125-1134. , May; Peng, Z., A portable FMCW interferometry radar with programmable low-IF architecture for localization, ISAR imaging, and vital sign tracking (2017) IEEE Trans. Microw. Theory Techn., 65 (4), pp. 1334-1344. , Apr; Nosrati, M., Tavassolian, N., High-accuracy heart rate variability monitoring using Doppler radar based on Gaussian pulse train modeling and FTPR algorithm (2018) IEEE Trans. Microw. Theory Techn., 66 (1), pp. 556-567. , Jan; Lou, L., A 253mW/channel 4TX/4RX pulsed chirping phased-array radar TRX in 65nm CMOS for X-band synthetic-aperture radar imaging (2018) IEEE Int. Solid-State Circuits Conf. Dig. Tech. Papers, pp. 160-162. , Feb; Lou, L., Radar Transceivers for Inverse Synthetic Aperture Radar (ISAR) Imaging of Human Activity in 65nm CMOS (2019) IEEE Int. System-on-Chip Conf., pp. 471-474. , Singapore,. Sep; Fang, Z., A Ku-band FMCW Radar on Chip for Wireless Micro Physiological Signal Monitoring by Interferometry Phase Analysis (2018) IEEE Int. Symp. Circuits Syst., 1, pp. 1-4. , May; Fang, Z., Wide field-of-view locating and multimodal vital sign monitoring based on X -band CMOS-integrated phased-array radar sensor (2020) IEEE Trans. Microw. Theory Techn., 68 (9), pp. 4054-4065. , Sept",,,,"Institute of Electrical and Electronics Engineers Inc.","53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021","22 May 2021 through 28 May 2021",,169837,02714310,9781728192017,PICSD,,"English","Proc IEEE Int Symp Circuits Syst",Conference Paper,"Final","",Scopus,2-s2.0-85109040585
"Dimeas F.","55762845500;","Singularity Avoidance in Human-Robot Collaboration with Performance Constraints",2021,"Springer Proceedings in Advanced Robotics","18",,,"89","100",,,"10.1007/978-3-030-71356-0_7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107078033&doi=10.1007%2f978-3-030-71356-0_7&partnerID=40&md5=9bb31c9b4abbb128434cc348625309d0","Automation and Robotics Lab, Aristotle University of Thessaloniki, Thessaloniki, Greece","Dimeas, F., Automation and Robotics Lab, Aristotle University of Thessaloniki, Thessaloniki, Greece","Avoidance of low performance configurations for robotic manipulators such as a singularity during physical human-robot interaction, is a crucial issue for effective cooperation. Performance constraints is a framework for online calculation of repulsive forces in the task space so that the robot does not allow the human to guide it to singularities. However, this repulsive field is configuration dependent and non-conservative, so energy can be injected to the system. In this paper we build upon performance constraints, utilizing a handling mechanism to monitor the energy flow of the system and dissipate the excessive energy. Moreover, the equivalent task space stiffness of performance constraints is determined and the appropriate damping is calculated for the desired dynamic behavior of the robot, so that no oscillations appear that can have a negative effect in the haptic feeling of the operator. This damping is the required minimum so that no over-damped behavior is observed, making the robot cumbersome to manipulate. The proposed method is verified experimentally in a redundant manipulator during physical interaction with a human. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.",,,,,,,"European Commission, EC; European Social Fund, ESF: MIS-5033021","Acknowledgment. This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme “Human Resources Development, Education and Lifelong Learning” in the context of the project “Reinforcement of Postdoctoral Researchers - 2nd Cycle” (MIS-5033021), implemented by the State Scholarships Foundation (IKY).",,,,,,,,,,"Carmichael, M.G., Liu, D., Waldron, K.J., A framework for singularity-robust manipulator control during physical human-robot interaction (2017) Int. J. Robot. Res. P.; Chiaverini, S., Singularity-robust task-priority redundancy resolution for real-time kinematic control of robot manipulators (1997) IEEE Trans. Robot. Autom., 13 (3), pp. 398-410; Di Vito, D., Natale, C., Antonelli, G., A Comparison of Damped Least Squares Algorithms for Inverse Kinematics of Robot Manipulators (2017) Ifac-Papersonline, 50 (1), pp. 6869-6874; Dietrich, A., Wu, X., Bussmann, K., Ott, C., Albu-Schaffer, A., Stramigioli, S., Passive Hierarchical Impedance Control Via Energy Tanks (2017) IEEE Robot. Autom. Lett., 2 (2), pp. 522-529; Dimeas, F., Moulianitis, V.C., Aspragathos, N., Manipulator performance constraints in human-robot cooperation (2018) Robot. Comput.-Integrated Manuf., 50, pp. 222-233; Dimeas, F., Moulianitis, V.C., Papakonstantinou, C., Aspragathos, N., Manipulator performance constraints in cartesian admittance control for human-robot cooperation (2016) IEEE International Conference on Robotics and Automation, Stockholm; Ferraguti, F., Preda, N., Manurung, A., Bonfe, M., Lambercy, O., Gassert, R., Muradore, R., Secchi, C., An energy tank-based interactive control architecture for autonomous and teleoperated robotic surgery (2015) IEEE Trans. Robot., 31 (5), pp. 1073-1088; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) IEEE Trans. Robot., 31 (4), pp. 850-863; Klein, C.A., Blaho, B.E., Dexterity measures for the design and control of kine-matically redundant manipulators (1987) Int. J. Robot. Res., 6 (2), pp. 72-83; Krastev, E., Passing through Jacobian singularities in motion path control of redundant robot arms (2019) Mech. Mach. Sci., 67, pp. 447-455; Kronander, K., Billard, A., Passive interaction control with dynamical systems (2016) IEEE Robot. Autom. Lett., 1 (1), pp. 106-113; Lee, D., Lee, W., Park, J., Chung, W.K., Task space control of articulated robot near kinematic singularity: Forward dynamics approach (2020) IEEE Robot. Autom. Lett., 5 (2), pp. 752-759; Long, P., Kelestemur, T., Onol, A.O., Padir, T., Optimization-based human-in-the-loop manipulation using joint space polytopes (2019) Proceedings-Ieee International Conference on Robotics and Automation 2019-May, pp. 204-210. , , pp; Manavalan, J., Howard, M., Learning singularity avoidance (2019) IEEE/RSJ International Conference on Intelligent Robots and System, Pp. 6849–6854. No. 1; Ott, C., Cartesian impedance control of redundant and flexible-joint robots (2008) Springer Tracts in Advanced Robotics, 49. , Siciliano, B., Khatib, O., Groen, F. (eds.), vol; Ott, C., Mukherjee, R., Nakamura, Y., A hybrid system framework for unified impedance and admittance control (2015) J. Intell. Robot. Syst., 78 (3-4), pp. 359-375; Patel, S., Sobh, T., Manipulator performance measures-a comprehensive literature survey (2015) J. Intell. Robot. Syst., 77 (3-4), pp. 547-570; Salisbury, J.K., Craig, J.J., Articulated hands: Force control and kinematic issues (1982) Int. J. Robot. Res., 1 (1), pp. 4-17; Staffetti, E., Bruyninckx, H., de Schutter, J., On the Invariance of Manipulability Indices (2002) Advances in Robot Kinematics, Pp. 57–66. No, p. 1. , Lenarcic, J., Thomas, F. (eds.), Springer Netherlands, Dordrecht; Talignani Landi, C., Ferraguti, F., Fantuzzi, C., Secchi, C., A passivity-based strategy for coaching in human-robot interaction (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 3279-3284. , , pp. , IEEE, May; Wampler, C., Manipulator inverse kinematic solutions based on vector formulations and damped least-squares methods (1986) IEEE Trans. Syst. Man Cybern., 16 (1), pp. 93-101; Yoshikawa, T., Manipulability of robotic mechanisms (1985) Int. J. Robot. Res., 4 (2), pp. 3-9","Dimeas, F.; Automation and Robotics Lab, Greece; email: dimeasf@ece.auth.gr",,,"Springer Science and Business Media B.V.",,,,,25111256,,,,"English","Springer. Proc. Adv. Robot.",Book Chapter,"Final","",Scopus,2-s2.0-85107078033
"Cousin C.A., Deptula P., Rouse C.A., Dixon W.E.","57193090588;57201257269;57193091381;57202484614;","A Switched Lyapunov-Passivity Approach to Motorized FES Cycling Using Adaptive Admittance Control",2021,"IEEE Transactions on Control Systems Technology",,,,"","",,,"10.1109/TCST.2021.3076934","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105888868&doi=10.1109%2fTCST.2021.3076934&partnerID=40&md5=fa1422f498ce22fb857f80a795dd7a54","Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL 35401 USA (e-mail: cacousin@eng.ua.edu); The Charles Stark Draper Laboratory, Inc., Cambridge, MA 02139 USA.; Southwest Research Institute, San Antonio, TX 78238 USA.; Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611 USA.","Cousin, C.A., Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL 35401 USA (e-mail: cacousin@eng.ua.edu); Deptula, P., The Charles Stark Draper Laboratory, Inc., Cambridge, MA 02139 USA.; Rouse, C.A., Southwest Research Institute, San Antonio, TX 78238 USA.; Dixon, W.E., Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611 USA.","For individuals with neuromuscular disorders (NDs) affecting the coordination and control of their legs, motorized functional electrical stimulation (FES) cycling serves as a rehabilitation strategy and offers numerous health benefits. A motorized FES cycle is an example of a hybrid exoskeleton involving cooperative physical human-robot interaction where both the cycle's motor and rider's muscles (through electrical stimulation) must be controlled to achieve desirable performance. A robust sliding-mode cadence controller is developed for the rider's muscles, while an adaptive admittance controller is employed on the cycle's motor to preserve rider comfort and safety. A switched systems stability analysis using Lyapunov and passivity-based methods is conducted to ensure global asymptotic stability of the admittance error system and that the cadence error system remains passive. Experiments are conducted on one able-bodied participant and four participants with NDs to illustrate the performance of the control design. For the participants with NDs, the controller achieved an average admittance tracking error of -0.08&#x00B1;1.05 rpm at an average cadence of 47.85&#x00B1;1.13 rpm for the desired cadence of 50 rpm. IEEE","Admittance; functional electrical stimulation (FES); Lyapunov; nonlinear control; rehabilitation.","Asymptotic stability; Errors; Exoskeleton (Robotics); Functional electric stimulation; Human robot interaction; Muscle; System stability; Coordination and Control; Electrical stimulations; Functional electrical stimulation; Global asymptotic stability; Neuromuscular disorders; Physical human-robot interactions; Rehabilitation strategy; Robust sliding mode; Controllers",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10636536,,IETTE,,"English","IEEE Trans Control Syst Technol",Article,"Article in Press","",Scopus,2-s2.0-85105888868
"Gao Q., Chen Y., Ju Z., Liang Y.","57219899863;36571325400;25655056800;57223431842;","Dynamic Hand Gesture Recognition Based on 3D Hand Pose Estimation for Human-Robot Interaction",2021,"IEEE Sensors Journal",,,,"","",,1,"10.1109/JSEN.2021.3059685","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105875311&doi=10.1109%2fJSEN.2021.3059685&partnerID=40&md5=e7ec80492b9a9c81ae2fc5e566301a9e","Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518035 China and Institute of Robotics and Intelligent Manufacturing &#x0026; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172 China.; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518035 China and Institute of Robotics and Intelligent Manufacturing &#x0026; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172 China. (e-mail: yqchen@cuhk.edu.cn); School of Computing, University of Portsmouth, Portsmouth, PO1 3HE U.K.; Institute of Robotics and Intelligent Manufacturing &#x0026; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172 China.","Gao, Q., Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518035 China and Institute of Robotics and Intelligent Manufacturing &#x0026; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172 China.; Chen, Y., Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518035 China and Institute of Robotics and Intelligent Manufacturing &#x0026; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172 China. (e-mail: yqchen@cuhk.edu.cn); Ju, Z., School of Computing, University of Portsmouth, Portsmouth, PO1 3HE U.K.; Liang, Y., Institute of Robotics and Intelligent Manufacturing &#x0026; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172 China.","Dynamic hand gesture recognition is a challenging problem in the area of hand-based human-robot interaction (HRI), such as issues of a complex environment and dynamic perception. In the context of this problem, we learn from the principle of the data-glove-based hand gesture recognition method and propose a dynamic hand gesture recognition method based on 3D hand pose estimation. This method uses 3D hand pose estimation, data fusion and deep neural network to improve the recognition accuracy of dynamic hand gestures. First, a 2D hand pose estimation method based on OpenPose is improved to obtain a fast 3D hand pose estimation method. Second, the weighted sum fusion method is utilized to combine the RGB, depth and 3D skeleton data of hand gestures. Finally, a 3DCNN + ConvLSTM framework is used to identify and classify the combined dynamic hand gesture data. In the experiment, the proposed method is verified on a developed dynamic hand gesture database for HRI and gets 92.4% accuracy. Comparative experiment results verify the reliability and efficiency of the proposed method. IEEE","Data gloves; Databases; dynamic hand gestures; Feature extraction; Gesture recognition; hand pose estimation; neural network; Pose estimation; Skeleton; Three-dimensional displays","Data fusion; Deep neural networks; Man machine systems; Palmprint recognition; Social robots; 3D hand pose estimations; Comparative experiments; Complex environments; Fusion methods; Hand pose estimations; Hand-gesture recognition; Human robot Interaction (HRI); Recognition accuracy; Gesture recognition",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,1530437X,,,,"English","IEEE Sensors J.",Article,"Article in Press","",Scopus,2-s2.0-85105875311
"Hernández Ó.G., Morell V., Ramon J.L., Jara C.A.","57223340622;39061902900;57208387518;35875768500;","Human pose detection for robotic-assisted and rehabilitation environments",2021,"Applied Sciences (Switzerland)","11","9","4183","","",,,"10.3390/app11094183","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105607989&doi=10.3390%2fapp11094183&partnerID=40&md5=5462229dfb63f12a30913ecba4feda8e","Human Robotics Group, University of Alicante, San Vicente del Raspeig, Alicante, 03690, Spain","Hernández, Ó.G., Human Robotics Group, University of Alicante, San Vicente del Raspeig, Alicante, 03690, Spain; Morell, V., Human Robotics Group, University of Alicante, San Vicente del Raspeig, Alicante, 03690, Spain; Ramon, J.L., Human Robotics Group, University of Alicante, San Vicente del Raspeig, Alicante, 03690, Spain; Jara, C.A., Human Robotics Group, University of Alicante, San Vicente del Raspeig, Alicante, 03690, Spain","Assistance and rehabilitation robotic platforms must have precise sensory systems for human–robot interaction. Therefore, human pose estimation is a current topic of research, especially for the safety of human–robot collaboration and the evaluation of human biomarkers. Within this field of research, the evaluation of the low-cost marker-less human pose estimators of OpenPose and Detectron 2 has received much attention for their diversity of applications, such as surveillance, sports, videogames, and assessment in human motor rehabilitation. This work aimed to evaluate and compare the angles in the elbow and shoulder joints estimated by OpenPose and Detectron 2 during four typical upper-limb rehabilitation exercises: Elbow side flexion, elbow flexion, shoulder extension, and shoulder abduction. A setup of two Kinect 2 RGBD cameras was used to obtain the ground truth of the joint and skeleton estimations during the different exercises. Finally, we provided a numerical comparison (RMSE and MAE) among the angle measurements obtained with OpenPose, Detectron 2, and the ground truth. The results showed how OpenPose outperforms Detectron 2 in these types of applications. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Human pose estimation; Human–robot interaction; Robotic rehabilitation",,,,,,,,,,,,,,,,,"Olaronke, I., Oluwaseun, O., Rhoda, I., State of The Art: A Study of Human-Robot Interaction in Healthcare (2017) Int. J. Inf. Eng. Electron. Bus, 9, pp. 43-55; Claflin, E.S., Krishnan, C., Khot, S.P., Emerging Treatments for Motor Rehabilitation after Stroke (2015) Neurohospitalist, 5, pp. 77-88; Krebs, H.I., Volpe, B.T., Ferraro, M., Fasoli, S., Palazzolo, J., Rohrer, B., Edelstein, L., Hogan, N., Robot-Aided Neuro-Rehabilitation: From Evidence-Based to Science-Based Rehabilitation (2002) Topics in Stroke Rehabilitation, pp. 54-70. , Thomas Land Publishers Inc.: St. Louis, MO, USA; Burgar, C.G., Lum, P.S., Shor, P.C., Van Der Loos, H.F.M., Development of Robots for Rehabilitation Therapy: The Palo Alto VA/Stanford Experience (2000) J. Rehabil. Res. 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Rehabil, 3, p. 6; Smisek, J., Jancosek, M., Pajdla, T., 3D with Kinect (2013) Consumer Depth Cameras for Computer Vision, pp. 3-25. , Springer: London, UK; Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A., Real-Time Human Pose Recognition in Parts from Single Depth Images Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1297-1304. , Colorado Springs, CO, USA, 20–25 June 2011; Ganapathi, V., Plagemann, C., Koller, D., Thrun, S., Real Time Motion Capture Using a Single Time-of-Flight Camera Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 755-762. , San Francisco, CA, USA, 13–18 June 2010; Baak, A., Müller, M., Bharaj, G., Seidel, H.-P., Theobalt, C., (2013) A Data-Driven Approach for Real-Time Full Body Pose Reconstruction from a Depth Camera, pp. 71-98. , Springer: London, UK; Pavllo, D., Zürich, E., Feichtenhofer, C., Grangier, D., Brain, G., Auli, M., 3D Human Pose Estimation in Video with Temporal Convolutions and Semi-Supervised Training (2019) Proceedings of the 2019 Conference on Computer Vision and Pattern Recognition, , Long Beach, CA, USA, 16–20 June; Moon, G., Chang, J.Y., Lee, K.M., Camera Distance-Aware Top-down Approach for 3D Multi-Person Pose Estimation from a Single RGB Image (2019) Proceedings of the International Conference on Computer Vision, , Seoul, Korea, 27 October–2 November; Seethapathi, N., Wang, S., Saluja, R., Blohm, G., Kording, K.P., (2019) Movement Science Needs Different Pose Tracking Algorithms, , arXiv arXiv:1907.10226; Wang, Q., Kurillo, G., Ofli, F., Bajcsy, R., Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect Proceedings of the 2015 IEEE International Conference on Healthcare Informatics, ICHI 2015, pp. 380-389. , Dallas, TX, USA, 21–23 October 2015; Ma, M., Proffitt, R., Skubic, M., Validation of a Kinect V2 Based Rehabilitation Game (2018) PLoS ONE, 13, p. e0202338; GitHub-CMU-Perceptual-Computing-Lab/Openpose: OpenPose: Real-Time Multi-Person Keypoint Detection Library for Body, Face, Hands, and Foot Estimation, , Https://github.com/CMU-Perceptual-Computing-Lab/openpose, (accessed on 24 March 2020); Wu, Y., Kirillov, A., Massa, F., Lo, W.-Y., Girshick, R., Detectron2, , Https://github.com/facebookresearch/detectron2, (accessed on 21 November 2020); Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y., Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields (2017) Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), , Honolulu, HI, USA, 21–26 July; Viswakumar, A., Rajagopalan, V., Ray, T., Parimi, C., Human Gait Analysis Using OpenPose Proceedings of the 2019 Fifth International Conference on Image Information Processing (ICIIP), pp. 310-314. , Shimla, India, 15–17 November 2019; Pasinetti, S., Muneeb Hassan, M., Eberhardt, J., Lancini, M., Docchio, F., Sansoni, G., Performance Analysis of the PMD Camboard Picoflexx Time-of-Flight Camera for Markerless Motion Capture Applications (2019) IEEE Trans. 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Arthroplast, 23, pp. 85-91","Jara, C.A.; Human Robotics Group, San Vicente del Raspeig, Spain; email: carlos.jara@ua.es",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85105607989
"Weiss A., Spiel K.","23089739100;56266706100;","Robots beyond Science Fiction: mutual learning in human–robot interaction on the way to participatory approaches",2021,"AI and Society",,,,"","",,1,"10.1007/s00146-021-01209-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105377875&doi=10.1007%2fs00146-021-01209-w&partnerID=40&md5=5455915adf17d271939ed25e7cd64f23","Human-Computer Interaction (HCI) Group, Institute of Visual Computing and Human-Centered Technology, Faculty of Informatics, TU Wien, Argentinierstr. 8, Wien, 1040, Austria","Weiss, A., Human-Computer Interaction (HCI) Group, Institute of Visual Computing and Human-Centered Technology, Faculty of Informatics, TU Wien, Argentinierstr. 8, Wien, 1040, Austria; Spiel, K., Human-Computer Interaction (HCI) Group, Institute of Visual Computing and Human-Centered Technology, Faculty of Informatics, TU Wien, Argentinierstr. 8, Wien, 1040, Austria","Putting laypeople in an active role as direct expert contributors in the design of service robots becomes more and more prominent in the research fields of human–robot interaction (HRI) and social robotics (SR). Currently, though, HRI is caught in a dilemma of how to create meaningful service robots for human social environments, combining expectations shaped by popular media with technology readiness. We recapitulate traditional stakeholder involvement, including two cases in which new intelligent robots were conceptualized and realized for close interaction with humans. Thereby, we show how the robot narrative (impacted by science fiction, the term robot itself, and assumptions on human-like intelligence) together with aspects of power balancing stakeholders, such as hardware constraints and missing perspectives beyond primary users, and the adaptivity of robots through machine learning that creates unpredictability, pose specific challenges for participatory design processes in HRI. We conclude with thoughts on a way forward for the HRI community in developing a culture of participation that considers humans when conceptualizing, building, and using robots. © 2021, The Author(s).","Human–robot interaction; Methodology; Participatory design; Service robots","Balancing; Intelligent robots; Machine design; Mobile robots; Hardware constraints; Human-like intelligence; Participatory approach; Participatory design; Robot interactions; Social environment; Stakeholder involvement; Technology readiness; Social robots",,,,,"Austrian Science Fund, FWF: V587-G29","We greatly acknowledge the financial support from the Austrian Science Foundation (FWF) under Grant Agreement No. V587-G29 (SharedSpace) and T 1146-G (Exceptional Norms).",,,,,,,,,,"Alissandrakis, A., Syrdal’r, D.S., Miyake, Y., Helping robots imitate acknowledgmentof, and adaptation to, the robot’s feedback to a human task demonstration (2011) New Front Hum Robot Interact, 2, pp. 9-34; Argall, B.D., Chernova, S., Veloso, M., Browning, B., A survey of robot learning from demonstration (2009) Robot Auton Syst, 57 (5), pp. 469-483; Azenkot, S., Feng, C., Cakmak, M., Enabling building service robots to guide blind people a participatory design approach (2016) In 11Th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 3-10; Bajones, M., Huber, A., Weiss, A., Vincze, M., Towards more flexible hri: How to adapt to the user? (2014) Workshop on Cognitive Architectures for Human-Robot Interaction at 9Th ACM/IEEE International Conference on Human-Robot Interaction, , Citeseer; Bajones, M., Fischinger, D., Weiss, A., Wolf, D., Vincze, M., de la Puente, P., Körtner, T., Michel, D., Hobbit: providing fall detection and prevention for the elderly in the real world (2018) J Robot; Bajones, M., Fischinger, D., Weiss, A., Puente, P.D., Wolf, D., Vincze, M., Körtner, T., Frennert, S., Results of field trials with a mobile service robot for older adults in 16 private households (2019) J. Hum. 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"Sorrentino A., Fiorini L., Mancioppi G., Nocentini O., Cavallo F.","57194094756;56902334800;57205157293;57211132860;14628324200;","Preliminary Study of Brain-Inspired Model for Multimodal Human Behavior Detection in Social Context",2021,"Lecture Notes in Electrical Engineering","725",,,"177","190",,,"10.1007/978-3-030-63107-9_14","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102734624&doi=10.1007%2f978-3-030-63107-9_14&partnerID=40&md5=62543cb297341a15431059d569e37a0a","The BioRobotics Institute, Scuola Superiore Sant’Anna, Viale Rinaldo Piaggio, 34, Pontedera (PI), 56025, Italy; Department of Industrial Engineering, University of Florence, Via Santa Marta 3, Florence (FI), 50139, Italy","Sorrentino, A., The BioRobotics Institute, Scuola Superiore Sant’Anna, Viale Rinaldo Piaggio, 34, Pontedera (PI), 56025, Italy; Fiorini, L., The BioRobotics Institute, Scuola Superiore Sant’Anna, Viale Rinaldo Piaggio, 34, Pontedera (PI), 56025, Italy; Mancioppi, G., The BioRobotics Institute, Scuola Superiore Sant’Anna, Viale Rinaldo Piaggio, 34, Pontedera (PI), 56025, Italy; Nocentini, O., The BioRobotics Institute, Scuola Superiore Sant’Anna, Viale Rinaldo Piaggio, 34, Pontedera (PI), 56025, Italy; Cavallo, F., The BioRobotics Institute, Scuola Superiore Sant’Anna, Viale Rinaldo Piaggio, 34, Pontedera (PI), 56025, Italy, Department of Industrial Engineering, University of Florence, Via Santa Marta 3, Florence (FI), 50139, Italy","In a near future, robots will permeate our daily life; indeed, they have the potential to proactively support the senior citizen in tedious and different daily tasks (i.e. cleaning, gaming, walking activity, promote socialization). However, to efficiently cooperate with human-beings, robots should have enhanced human–robot interaction capabilities. This work addresses the challenge of designing a robotic model by simulating the modality human beings interact with each other. The first objective of this work concerns the identification of the social cues which correctly describe the user’s emotional and engagement state during the interaction. Based on selected descriptors, a perceptual system has been proposed to detect elderly’s behavior in a social context. The proposed architecture is inspired to the human brain structure as concern the functionalities modules and analogies in the modules’ location. The proposed perceptual system aims at transforming raw data coming from three kinds of sensors (camera, microphone, and laser) into behavioral patterns by mimicking the abstraction evolution which characterizes the consciousness process of the human brain. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.",,"Assisted living; Behavioral research; Brain; Metadata; Behavioral patterns; Human behavior detection; Perceptual system; Proposed architectures; Robot interactions; Robotic modeling; Senior citizens; Walking activity; Human robot interaction",,,,,,,,,,,,,,,,"United Nations Department of Economic and Social Affairs Population Division: E02 World Population Prospects The 2017 Revision: Key Findings and Advance Tables (2017) World Popul. 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BMC Neurosci 4. https://doi. org/10.1186/1471-2202-4-31; Sporns, O., What neuro-robotic models can teach us about neural and cognitive development (2007) Neuroconstructivism Perspect Prospect, 2, pp. 179-204. , https://doi.org/10.1093/acprof:oso/9780198529934.003.0008; Balkenius, C., Morén, J., Emotional learning: A computational model of the amygdala (2001) Cybern Syst, 32, pp. 611-636; Raymundo, C.R., Johnson, C.G., Vargas, P.A., (2015) An Architecture for Emotional and Context-Aware Associative Learning for Robot Companions, , https://doi.org/10.1109/ROMAN.2015.7333699; Dağlarlı, E., Dağlarlı, S.F., Günel, G.Ö., Köse, H., Improving human-robot interaction based on joint attention (2017) Appl Intell, 47, pp. 62-82. , https://doi.org/10.1007/s10489-016-0876-x; Al-Qaderi, M.K., Rad, A.B., A brain-inspired multi-modal perceptual system for social robots: An experimental realization (2018) IEEE Access, 6, pp. 35402-35424. , https://doi.org/10.1109/ACCESS.2018.2851841; Premack, D., Woodruff, G., Does the chimpanzee have a theory of mind? 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J Nonverbal Behav 25, 225–240. https://doi.org/10.1023/A:101254 4204986; Ekman P (1993) Facial expression and emotion Am Psychol, , https://doi.org/10.1037/0003-066X.48.4.384; Emery, N.J., The eyes have it: The neuroethology, function and evolution of social gaze (2000) Neurosci Biobehav Rev, 24, pp. 581-604. , https://doi.org/10.1016/S0149-7634(00)00025-7; Mason, M.F., Tatkow, E.P., Macrae, C.N., The look of love: Gaze shifts and person perception (2005) Psychol Sci, 16, pp. 236-239. , https://doi.org/10.1111/j.0956-7976.2005.00809.x; Scherer, K.R., Vocal communication of emotion: A review of research paradigms (2003) Speech Commun, 40, pp. 227-256. , https://doi.org/10.1016/S0167-6393(02)00084-5; Scherer, K.R., (1979) Personality Markers in Speech, , In: Social markers in speech. Cambridge University Press; Glass, C.R., Merluzzi, T.V., Biever, J.L., Larsen, K.H., Cognitive assessment of social anxiety: Development and validation of a self-statement questionnaire (1982) Cognit Ther Res, 6, pp. 37-55. , https://doi.org/10.1007/BF01185725; Cavallo, F., Limosani, R., Fiorini, L., Esposito, R., Furferi, R., Governi, L., Carfagni, M., Design impact of acceptability and dependability in assisted living robotic applications (2018) Int J Interact Des Manuf, 12. , https://doi.org/10.1007/s12008-018-0467-7; D’Onofrio, G., Fiorini, L., de Mul, M., Fabbricotti, I., Okabe, Y., Hoshino, H., Limosani, R., Greco, A., Agile Co-creation for robots and aging (ACCRA) project: New technological solutions for older people (2018) Eur Geriatr Med, pp. 1-6. , https://doi.org/10.1007/s41999-018-0106-7; Cao, Z., Simon, T., Wei, S.E., Sheikh, Y., Realtime multi-person 2D pose estimation using part affinity fields (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7291-7299. , https://doi.org/10.1109/CVPR.2017.143, pp; Viola, P., Jones, M., Robust real-time object detection (2001) Int J Comput Vis, 4, pp. 34-47; Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: A review (2017) J Human-Robot Interact, 6, pp. 25-63. , https://doi.org/10.5898/jhri.6.1.admoni; Vrigkas, M., Nikou, C., Kakadiaris, I.A., A review of human activity recognition methods (2015) Front Robot AI, 2. , https://doi.org/10.3389/frobt.2015.00028; Baltrušaitis, T., Ahuja, C., Morency, L.P., (2019) Multimodal Machine Learning: A Survey and Taxonomy, , https://doi.org/10.1109/TPAMI.2018.2798607; Srivastava, N., Salakhutdinov, R.R., Multimodal learning with deep boltzmann machines (2012) Adv Neural Inf Process Syst, pp. 2222-2230. , https://doi.org/10.1109/CVPR.2013.49; Lan T, Sigal L, Mori G (2012) Social roles in hierarchical models for human activity recognition. In: IEEE conference on computer vision and pattern recognition. pp 1354–1361. https://doi. org/10.1109/CVPR.2012.6247821","Sorrentino, A.; The BioRobotics Institute, Scuola Superiore Sant’Anna, Viale Rinaldo Piaggio, 34, Italy; email: alessandra.sorrentino@santannapisa.it","Monteriu A.Freddi A.Longhi S.",,"Springer Science and Business Media Deutschland GmbH","10th Italian Forum on Ambient Assisted Living, ForItAAL 2019","19 June 2019 through 21 June 2019",,255649,18761100,9783030631062,,,"English","Lect. Notes Electr. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85102734624
"Yi Z., Xu T., Shang W., Wu X.","57191875528;55325569800;23089242300;7407065301;","Touch Modality Identification With Tensorial Tactile Signals: A Kernel-Based Approach",2021,"IEEE Transactions on Automation Science and Engineering",,,,"","",,2,"10.1109/TASE.2021.3055251","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100847783&doi=10.1109%2fTASE.2021.3055251&partnerID=40&md5=e7bf1c53245f97d5b603d46c0508dc3d","CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China, and also with the Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.; CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China.; Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China, and also with the SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518055, China (e-mail: xy.wu@siat.ac.cn)","Yi, Z., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China, and also with the Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.; Xu, T., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China.; Shang, W., CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China.; Wu, X., Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China, and also with the SIAT Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518055, China (e-mail: xy.wu@siat.ac.cn)","Touch modality identification has attracted increasing attention due to its importance in human-robot interactions. There are three issues involved in the tactile perception for the touch modality identification, including the high dimensionality of tactile signals, complex tensor morphology of tactile sensing units, and the misalignment among different tactile time-series samples. In this article, we propose a novel kernel-based approach to deal with these three issues in a unified framework. Specifically, the techniques, including sparse principal component analysis and subsampling, are employed to reduce the feature dimension. Then, a singular value decomposition (SVD)-based kernel is proposed to preserve the spatial information of the tactile sensing elements. The sample misalignment issue is addressed via the employment of a global alignment kernel. Moreover, the merits of these two kernels are fused through an ideal regularized composite kernel, which simultaneously takes the label information of the training set into consideration. The effectiveness of the proposed kernel-based approach is verified on a public touch modality data set with a comprehensive comparison with the competing methods. IEEE","Composite kernels (CKs); global alignment kernel; Kernel; Robot sensing systems; Robots; Sensors; singular value decomposition (SVD); Task analysis; tensor representation; Tensors; Time measurement; touch modality classification.","Alignment; Human robot interaction; Comprehensive comparisons; Feature dimensions; High dimensionality; Kernel based approach; Label information; Sparse principal component analysis; Spatial informations; Tactile perception; Singular value decomposition",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15455955,,,,"English","IEEE Trans. Autom. Sci. Eng.",Article,"Article in Press","",Scopus,2-s2.0-85100847783
"Thakur N., Han C.Y.","57202222098;7403379456;","A multilayered contextually intelligent activity recognition framework for smart home",2021,"Advances in Intelligent Systems and Computing","1253 AISC",,,"278","283",,1,"10.1007/978-3-030-55307-4_42","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089619273&doi=10.1007%2f978-3-030-55307-4_42&partnerID=40&md5=6a7121ddd3db37f9bf53be6783d34e20","Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH  45221-0030, United States","Thakur, N., Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH  45221-0030, United States; Han, C.Y., Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH  45221-0030, United States","The future of Internet of Things (IoT)-based living spaces would involve interaction, coordination, collaboration and communication between humans, machines, robots and other technology-laden systems in the context of users performing their daily routine tasks. Through this Contextually Intelligent Activity Recognition framework, this work proposes to develop a long-term, robust, feasible, easily implementable, sustainable and economic solution for Activity Recognition and Activity Monitoring, that would be able to track, monitor, evaluate, analyze and access human behavior in the context of the multimodal aspects of these user interactions including the spatial and temporal features. This Multilayered Contextually Intelligent Activity Recognition Framework envisions to take a holistic approach to improve the quality of life, enhance the user experience, acceptance and trust on technology during Activities of Daily Living (ADLs), in the context of human-computer, human-machine and human-robot interactions. The results presented uphold the relevance and demonstrate the feasibility for practical implementation of this framework in the future of IoT-based living spaces, for instance Smart Homes and Smart Cities. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.","Activities of daily living; Big data; Human-computer interaction; Smart cities; Smart homes; Ubiquitous systems","Ambient intelligence; Automation; Behavioral research; Human computer interaction; Intelligent buildings; Intelligent robots; Internet of things; Pattern recognition; User experience; Activities of daily living (ADLs); Activity monitoring; Activity recognition; Economic solutions; Holistic approach; Internet of Things (IOT); Temporal features; User interaction; Social robots",,,,,,,,,,,,,,,,"World Population Prospects 2019: Highlights, , https://www.un.org/development/desa/publications/world-population-prospects-2019-highlights.html, United Nations Report; Kaptelinin, V., Nardi, B., Activity theory in HCI fundamentals and reflections (2012) Synth. Lect. Hum.-Centered Inform, 5, pp. 1-105. , Morgan and Claypool Publishers. ISBN: 9781608457045, eISBN: 9781608457052; Davies, N., Siewiorek, D.P., Sukthankar, R., Activity-based computing (2008) Pervasive Comput. IEEE, 7, pp. 20-21; Azkune, G., Almeida, A., López-de-Ipiña, D., Chen, L., Extending knowledge driven activity models through data-driven learning techniques (2015) Expert Syst. Appl, 42. , https://doi.org/10.1016/j.eswa.2014.11.063; Cheng, Z., Qin, L., Huang, Q., Jiang, S., Yan, S., Tian, Q., Human group activity analysis with fusion of motion and appearance information (2011) Proceedings of the 19th ACM International Conference on Multimedia, pp. 1401-1404. , Scottsdale, Arizona, USA, 28 November–01 December (2011); Skocir, P., Krivic, P., Tomeljak, M., Kusek, M., Jezic, G., Activity detection in smart home environment (2016) Proceedings of the 20th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, , 5–7 September; Doryab, A., Bardram, J.E., Designing activity-aware recommender systems for operating rooms (2011) Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation, , 13 February; Abascal, J., Bonail, B., Marco, A., Sevillano, J.L., AmbienNet: an intelligent environment to support people with disabilities and elderly people (2008) Proceedings of ASSETS 2008, , 13–15 October Halifax, Nova Scotia, Canada (2008); Chan, M., Campo, E., Bourennane, W., Bettahar, F., Charlon, Y., Mobility behavior assessment using a smart-monitoring system to care for the elderly in a hospital environment Proceedings of PETRA 2014, , 27–30 May 2014, Island of Rhodes, Greece (2014); Yared, R., Abdulrazak, B., Tessier, T., Mabilleau, P., Cooking risk analysis to enhance safety of elderly people in smart kitchen (2015) Proceedings of PETRA 2015, , 01–03 July Corfu, Greece (2015); Jamal Deen, M., Information and communications technologies for elderly ubiquitous healthcare in a smart home (2015) Pers. Ubiquit. Comput, 19, pp. 573-599; Civitarese, G., Bettini, C., Belfiore, S., Let the objects tell what you are doing Proceedings of Ubicomp/ISWC 2016 Adjunct, 12–16 September 2016, Heidelberg, Germany, , (2016); Iglesias, R., Gomez de Segura, N., Iturburu, M., the elderly interacting with a digital agenda through an RFID pen and a touch screen (2009) Proceedings of MSIADU 2009, , 23 October Beijing, China (2009); Angelini, L., Nyffeler, N., Caon, M., Jean-Mairet, M., Carrino, S., Mugellini, E., Bergeron, L., Designing a desirable smart bracelet for older adults Proceedings of UbiComp 2013, 8–12 September 2013, Zurich, Switzerland, , (2013); Khosla, R., Chu, M.-T., Kachouie, R., Yamada, K., Yoshihiro, F., Yamaguchi, T., Interactive multimodal social robot for improving quality of care of elderly in Australian nursing homes (2012) Proceedings of MM 2012, , 29 October–2 November Nara, Japan (2012); Sarkar, D.P., A nurse bot for elderly people (2018) Proceedings of UbiComp/ISWC 2018 Adjunct, , 8–12 October Singapore (2018); Thakur, N., Han, C.Y., An improved approach for complex activity recognition in smart homes (2019) Reuse in the Big Data Era. Lecture Notes in Computer Science, 11602, pp. 220-231. , Springer, Cham; Thakur, N., Han, C.Y., Framework for a personalized intelligent assistant to elderly people for activities of daily living (2019) Int. J. Recent Trends Hum. Comput. Interact. (IJHCI), 9 (1), pp. 1-22; Thakur, N., Han, C.Y., Framework for an intelligent affect aware smart home environment for elderly people (2019) Int. J. Recent Trends Hum. Comput. Interact. (IJHCI), 9 (1), pp. 23-43; Thakur, N., Han, C.Y., A context-driven complex activity framework for smart home (2018) Proceedings of the 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) 2018, , Vancouver, Canada, 1–3 November (2018); Thakur, N., Han, C.Y., A hierarchical model for analyzing user experiences in affect aware systems (2018) Proceedings of the 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) 2018, , Vancouver, Canada, 1–3 November (2018); Thakur, N., Han, C.Y., An approach to analyze the social acceptance of virtual assistants by elderly people (2018) Proceedings of the 8th International Conference on the Internet of Things (IoT) 2018, , Santa Barbara, California, 15–18 October (2018); Thakur, N., Han, C.Y., Methodology for forecasting user experience for smart and assisted living in affect aware systems (2018) Proceedings of the 8th International Conference on the Internet of Things (IoT) 2018, , Santa Barbara, California, 15–18 October (2018); Thakur, N., Han, C.Y., An activity analysis model for enhancing user experiences in affect aware systems (2018) Proceedings of the IEEE 5G World Forum Conference (IEEE 5GWF) 2018, , Santa Clara, California, 09–11 July (2018); Thakur, N., Han, C.Y., A virtual wisdom mining ‘pan’ for connecting retired experts with currently active professionals (2018) Proceedings of IT Research Symposium, , University of Cincinnati, 10 April (2018); Thakur, N., Han, C.Y., A complex activity based emotion recognition algorithm for affect aware systems (2018) Proceedings of IEEE 8th Annual Computing and Communication Workshop and Conference (IEEE CCWC) 2018, , Las Vegas, 08–10 January; Saguna, S., Zaslavsky, A., Chakraborty, D., Complex activity recognition using contextdriven activity theory and activity signatures (2013) ACM Trans. Comput. Hum. Interact, 20 (6), pp. 1-34. , Article 32; Ordóñez, F.J., de Toledo, P., Sanchis, A., Activity recognition using hybrid generative/discriminative models on home environments using binary sensors (2013) Sensors, 13, pp. 5460-5477; Ritthoff, O., Klinkenberg, R., Fischer, S., Mierswa, I., Felske, S., (2001) YALE: yet another learning environment, , https://doi.org/10.17877/de290r-15309","Thakur, N.; Department of Electrical Engineering and Computer Science, United States; email: thakurna@mail.uc.edu","Ahram T.Taiar R.Langlois K.Choplin A.",,"Springer","3rd International Conference on Human Interaction and Emerging Technologies: Future Applications, IHIET 2020","27 August 2020 through 29 August 2020",,243259,21945357,9783030553067,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85089619273
[No author name available],[No author id available],"AHFE Virtual Conference on Human Factors in Robots, Drones and Unmanned Systems, and the International Conference on Human Factors in Cybersecurity, 2020",2021,"Advances in Intelligent Systems and Computing","1210 AISC",,,"","",120,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088273367&partnerID=40&md5=1095d465eb3433f48c9c80e079228eeb",,"","The proceedings contain 16 papers. The special focus in this conference is on Human Factors in Robots, Drones and Unmanned Systems. The topics include: Use a UAV System to Enhance Port Security in Unconstrained Environment; Humanoid Robotics: A UCD Review; influence of Two Industrial Overhead Exoskeletons on Perceived Strain – A Field Study in the Automotive Industry; Human-Robot Interaction via a Virtual Twin and OPC UA; self-assessment of Proficiency of Intelligent Systems: Challenges and Opportunities; behavioral-Based Autonomous Robot Operation Under Robot-Central Base Loss of Communication; an Interaction Taxonomy of Human–Agent Teaming in Next Generation Combat Vehicle Systems; human-Autonomy Teaming for Unmanned Vehicle Control: Examination of the Role of Operator Individual Differences; The New Science of Autonomous Human-Machine Teams (a-HMT): Interdependence Theory Scales; verifying Automation Trust in Automated Driving System from Potential Users’ Perspective; Differences Between Manned and Unmanned Pilots Flying a UAV in the Terminal Area; Obtaining Public Opinion About sUAS Activity in an Urban Environment; learn on the Fly; Boeing 737 MAX: Expectation of Human Capability in Highly Automated Systems.",,,,,,,,,,,,,,,,,,,,"Zallio M.",,"Springer","AHFE Virtual Conference on Human Factors in Robots, Drones and Unmanned Systems, and the International Conference on Human Factors in Cybersecurity, 2020","16 July 2020 through 20 July 2020",,241779,21945357,9783030517571,,,"English","Adv. Intell. Sys. Comput.",Conference Review,"Final","",Scopus,2-s2.0-85088273367
"Franco E., Garriga-Casanovas A.","57188844859;57200512886;","Energy-shaping control of soft continuum manipulators with in-plane disturbances",2021,"International Journal of Robotics Research","40","1",,"236","255",,8,"10.1177/0278364920907679","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082191709&doi=10.1177%2f0278364920907679&partnerID=40&md5=9fe53fadcaca265161a66110cccc10bc","Mechatronics in Medicine Laboratory, Mechanical Engineering Department, Imperial College London, United Kingdom","Franco, E., Mechatronics in Medicine Laboratory, Mechanical Engineering Department, Imperial College London, United Kingdom; Garriga-Casanovas, A., Mechatronics in Medicine Laboratory, Mechanical Engineering Department, Imperial College London, United Kingdom","Soft continuum manipulators offer levels of compliance and inherent safety that can render them a superior alternative to conventional rigid robots for a variety of tasks, such as medical interventions or human–robot interaction. However, the ability of soft continuum manipulators to compensate for external disturbances needs to be further enhanced to meet the stringent requirements of many practical applications. In this paper, we investigate the control problem for soft continuum manipulators that consist of one inextensible segment of constant section, which bends under the effect of the internal pressure and is subject to unknown disturbances acting in the plane of bending. A rigid-link model of the manipulator with a single input pressure is employed for control purposes and an energy-shaping approach is proposed to derive the control law. A method for the adaptive estimation of disturbances is detailed and a disturbance compensation strategy is proposed. Finally, the effectiveness of the controller is demonstrated with simulations and with experiments on an inextensible soft continuum manipulator that employs pneumatic actuation. © The Author(s) 2020.","adaptive control; energy shaping; nonlinear control; Soft continuum manipulators; underactuated mechanical systems","Adaptive control systems; Agricultural robots; Human robot interaction; Medical robotics; Adaptive Control; Disturbance compensation; Energy shaping control; Energy-shaping; External disturbances; Non linear control; Stringent requirement; Under-actuated mechanical systems; Manipulators",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/R009708/1; Royal Commission for the Exhibition of 1851","The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Engineering and Physical Sciences Research Council (grant number EP/R009708/1). Arnau Garriga Casanovas was also supported by an industrial fellowship from the Royal Commission for the Exhibition of 1851.",,,,,,,,,,"Abe, R., Takemura, K., Edamura, K., Concept of a micro finger using electro-conjugate fluid and fabrication of a large model prototype (2007) Sensors and Actuators, A: Physical, 136 (2), pp. 629-637; Alqumsan, A.A., Khoo, S., Norton, M., Robust control of continuum robots using Cosserat rod theory (2019) Mechanism and Machine Theory, 131, pp. 48-61; Astolfi, A., Karagiannis, D., Ortega, R., (2007) Nonlinear and Adaptive Control with Applications, , Berlin, Springer; Astolfi, A., Ortega, R., Immersion and invariance: A new tool for stabilization and adaptive control of nonlinear systems (2003) IEEE Transactions on Automatic Control, 48 (4), pp. 590-606; Bieze, T.M., Largilliere, F., Kruszewski, A., Finite element method-based kinematics and closed-loop control of soft, continuum manipulators (2018) Soft Robotics, 5 (3), pp. 348-364; Bloch, A.M., Leonard, N.E., Marsden, J.E., Controlled Lagrangians and the stabilization of mechanical systems. 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In","Franco, E.; Mechatronics in Medicine Laboratory, United Kingdom; email: e.franco11@imperial.ac.uk",,,"SAGE Publications Inc.",,,,,02783649,,IJRRE,,"English","Int J Rob Res",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85082191709
"Rodriguez-Tirado A., Magallan-Ramirez D., Martinez-Aguilar J.D., Moreno-Garcia C.F., Balderas D., Lopez-Caudana E.","57218552287;57226741597;57226746048;56332688500;55312134500;6504082224;","A pipeline framework for robot maze navigation using computer vision, path planning and communication protocols",2020,"Proceedings - International Conference on Developments in eSystems Engineering, DeSE","2020-December",,"9450731","152","157",,,"10.1109/DeSE51703.2020.9450731","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112571992&doi=10.1109%2fDeSE51703.2020.9450731&partnerID=40&md5=faaaa6fc9f9d81c6cd5d8cf7573b06ef","Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City, Mexico; Robert Gordon University, School of Computing, Aberdeen, United Kingdom","Rodriguez-Tirado, A., Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City, Mexico; Magallan-Ramirez, D., Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City, Mexico; Martinez-Aguilar, J.D., Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City, Mexico; Moreno-Garcia, C.F., Robert Gordon University, School of Computing, Aberdeen, United Kingdom; Balderas, D., Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City, Mexico; Lopez-Caudana, E., Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City, Mexico","Maze navigation is a recurring challenge in robotics competitions, where the aim is to design a strategy for one or several entities to traverse the optimal path in a fast and efficient way. To do so, numerous alternatives exist, relying on different sensing systems. Recently, camera-based approaches are becoming increasingly popular to address this scenario due to their reliability and given the possibility of migrating the resulting technologies to other application areas, mostly related to human-robot interaction. The aim of this paper is to present a pipeline methodology towards enabling a robot solving maze autonomously, by means of computer vision and path planning. Afterwards, the robot is capable of communicating the learned experience to a second robot, which then will solve the same challenge considering its own mechanical characteristics which may differ from the first robot. The pipeline is divided into four steps: (1) camera calibration (2) maze mapping (3) path planning and (4) communication. Experimental validation shows the efficiency of each step towards building this pipeline. © 2020 IEEE.","camera calibration; communication; computer vision; educational innovation; higher education; mapping; NAO robot; path planning; Robot navigation","Cameras; Human robot interaction; Pipelines; Robot programming; Application area; Camera calibration; Camera-based; Experimental validations; Mechanical characteristics; Optimal paths; Robotics competitions; Sensing systems; Computer vision",,,,,"Instituto Tecnológico y de Estudios Superiores de Monterrey, ITESM","ACKNOWLEDGMENT The authors would like to acknowledge the financial and technical support of Writing Lab, TecLabs, Tecnologico de Monterrey, Mexico, in the production of this work.",,,,,,,,,,"Cortés, X., Serratosa, F., Moreno-Garca, C.F., Semiautomatic pose estimation of a fleet of robots with embedded stereoscopic cameras (2016) Emerging Technologies and Factory Automation; Respall, V.M., Sellami, S., Afanasyev, I., (2019) Implementation of Autonomous Visual Detection, Tracking and Landing for Ar. Drone 2. 0 Quadcopter; Kumar, A., Biplab, P., Parhi, D., (2018) Intelligent Navigation of Humanoids in Cluttered Environments Using Regression Analysis and Genetic Algorithm, , https://doi.org/10.1007/s13369-018-3157-7, Mar; Schranz, M., Umlauft, M., Sende, M., Elmenreich, W., (2020) Swarm Robotic Behaviors and Current Applications, , file : / / /C : /Users / Areli / Downloads / Swarm Robotic Behaviors and Current Applications. pdf; Zhang, Z., A flexible new technique for camera calibration (2000) IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (11), pp. 1330-1334; Matas, J., Galambos, C., Kittler, J., (1998) Progressive Probabilistic Hough Transform; Anureev, I., Context machines (2009) Proceedings of XVIIIth Concurrency, Specification and Programming, pp. 1-12",,,,"Institute of Electrical and Electronics Engineers Inc.","13th International Conference on Developments in eSystems Engineering, DeSE 2020","13 December 2020 through 17 December 2020",,170860,21611343,9781665422383,,,"English","Proc. - Int. Conf. Dev. eSystems Eng., DeSE",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85112571992
"Gulletta G., Erlhagen W., Bicho E.","56790459300;6602126228;6602434613;","Human-like arm motion generation: A review",2020,"Robotics","9","4","102","1","48",,5,"10.3390/robotics9040102","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097260617&doi=10.3390%2frobotics9040102&partnerID=40&md5=fdf692518ac707e83b1f604db44931fc","Center Algoritmi, Department of Industrial Electronics, University of Minho, Guimarães, 4800-058, Portugal; Centre of Mathematics, Department of Mathematics, University of Minho, Guimarães, 4800-058, Portugal","Gulletta, G., Center Algoritmi, Department of Industrial Electronics, University of Minho, Guimarães, 4800-058, Portugal; Erlhagen, W., Centre of Mathematics, Department of Mathematics, University of Minho, Guimarães, 4800-058, Portugal; Bicho, E., Center Algoritmi, Department of Industrial Electronics, University of Minho, Guimarães, 4800-058, Portugal","In the last decade, the objectives outlined by the needs of personal robotics have led to the rise of new biologically-inspired techniques for arm motion planning. This paper presents a literature review of the most recent research on the generation of human-like arm movements in humanoid and manipulation robotic systems. Search methods and inclusion criteria are described. The studies are analyzed taking into consideration the sources of publication, the experimental settings, the type of movements, the technical approach, and the human motor principles that have been used to inspire and assess human-likeness. Results show that there is a strong focus on the generation of single-arm reaching movements and biomimetic-based methods. However, there has been poor attention to manipulation, obstacle-avoidance mechanisms, and dual-arm motion generation. For these reasons, human-like arm motion generation may not fully respect human behavioral and neurological key features and may result restricted to specific tasks of human-robot interaction. Limitations and challenges are discussed to provide meaningful directions for future investigations. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Arm motion planning; Human-like motion; Humanoid robots; Literature review",,,,,,"UIDB/00319/2020; Seventh Framework Programme, FP7: 289146; European Commission, EC; Fundação para a Ciência e a Tecnologia, FCT: SFRH/BD/114923/2016, UID/MAT/00013/2013","This work was funded by the EU Project FP7 Marie Curie NETT-Neural Engineering and Transformative Technologies (ID 289146), the FCT PhD grant (ref. SFRH/BD/114923/2016), the FCT Project UID/MAT/00013/2013, and the FCT?Funda??o para a Ci?ncia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.",,,,,,,,,,"Sheridan, T.B., Human-Robot Interaction: Status and Challenges (2016) Hum. Factors J. Hum. Factors Ergon. Soc, 58, pp. 525-532. , [CrossRef]; Gladden, M.E., Who Will Be the Members of Society 5.0? 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Robot, 26, pp. 576-584. , [CrossRef]","Gulletta, G.; Center Algoritmi, Portugal; email: d6468@dei.uminho.pt",,,"MDPI AG",,,,,22186581,,,,"English","Robotics",Review,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85097260617
"Liu S., Zhang J., Zhang Y., Zhu R.","57094270100;57209619459;57218215917;7202446527;","A wearable motion capture device able to detect dynamic motion of human limbs",2020,"Nature Communications","11","1","5615","","",,18,"10.1038/s41467-020-19424-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095435236&doi=10.1038%2fs41467-020-19424-2&partnerID=40&md5=1b4896009f283ebf1066787aebf8476f","State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China","Liu, S., State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China; Zhang, J., State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China; Zhang, Y., State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China; Zhu, R., State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing, 100084, China","Limb motion capture is essential in human motion-recognition, motor-function assessment and dexterous human-robot interaction for assistive robots. Due to highly dynamic nature of limb activities, conventional inertial methods of limb motion capture suffer from serious drift and instability problems. Here, a motion capture method with integral-free velocity detection is proposed and a wearable device is developed by incorporating micro tri-axis flow sensors with micro tri-axis inertial sensors. The device allows accurate measurement of three-dimensional motion velocity, acceleration, and attitude angle of human limbs in daily activities, strenuous, and prolonged exercises. Additionally, we verify an intra-limb coordination relationship exists between thigh and shank in human walking and running, and establish a neural network model for it. Using the intra-limb coordination model, dynamic motion capture of human lower limbs including thigh and shank is tactfully implemented by a single shank-worn device, which simplifies the capture device and reduces cost. Experiments in strenuous activities and long-time running validate excellent performance and robustness of the wearable device in dynamic motion recognition and reconstruction of human limbs. © 2020, The Author(s).",,"activity pattern; detection method; equipment; experimental study; reconstruction; sensor; walking; acceleration; Article; artificial neural network; coordination; exercise; limb movement; lower limb; running; thigh; velocity; walking; biomechanics; devices; electronic device; equipment design; human; leg; limb; motion; physiologic monitoring; physiology; reproducibility; Biomechanical Phenomena; Equipment Design; Extremities; Humans; Leg; Monitoring, Physiologic; Motion; Neural Networks, Computer; Reproducibility of Results; Thigh; Wearable Electronic Devices",,,,,"National Natural Science Foundation of China, NSFC; Tsinghua University, THU: 2019GQG1009, 62003184; National Natural Science Foundation of China-Yunnan Joint Fund, NSFC-Yunnan Joint Fund: 51735007","We thank Q. Zhao, K. Chen, and D. Zhong (iCenter, Tsinghua University) for preparation of the optical VICON system. We also thank T. Zheng, G. Li, and Z. Zhang (Department of Precision Instruments, Tsinghua University) for helpful discussions. This work is supported by National Natural Science Foundation of China (#51735007), a grant from the Institute for Guo Qiang, Tsinghua University (#2019GQG1009) and National Natural Science Foundation of China (#62003184).",,,,,,,,,,"Yin, J., Yang, Q., Pan, J.J., Sensor-based abnormal human-activity detection (2008) IEEE Trans. Knowl. Data Eng., 20, pp. 1082-1090; Kim, J., Wearable smart sensor systems integrated on soft contact lenses for wireless ocular diagnostics (2017) Nat. Commun., 8; Mukhopadhyay, S.C., Wearable sensors for human activity monitoring: a review (2015) IEEE Sens. J., 15, pp. 1321-1330; Jones, S.E., Genetic studies of accelerometer-based sleep measures yield new insights into human sleep behaviour (2019) Nat. 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"Zhang Z., Chen G., Fan W., Yan W., Kong L., Wang H.","57195411291;25654723300;57219624973;57219712150;56711541800;55869083400;","A Stiffness Variable Passive Compliance Device with Reconfigurable Elastic Inner Skeleton and Origami Shell",2020,"Chinese Journal of Mechanical Engineering (English Edition)","33","1","75","","",,3,"10.1186/s10033-020-00490-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094899405&doi=10.1186%2fs10033-020-00490-y&partnerID=40&md5=d3d6aabfd8545e2e84bc33c6753cb0fc","Shanghai Key Laboratory of Digital Manufacture for Thin-walled Structures, Shanghai Jiao Tong University, Shanghai, 200240, China; State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, China; Intelligent Robot Research Center, Zhejiang Lab, Hangzhou, 311100, China","Zhang, Z., Shanghai Key Laboratory of Digital Manufacture for Thin-walled Structures, Shanghai Jiao Tong University, Shanghai, 200240, China, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, China; Chen, G., Shanghai Key Laboratory of Digital Manufacture for Thin-walled Structures, Shanghai Jiao Tong University, Shanghai, 200240, China, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, China; Fan, W., Shanghai Key Laboratory of Digital Manufacture for Thin-walled Structures, Shanghai Jiao Tong University, Shanghai, 200240, China; Yan, W., Shanghai Key Laboratory of Digital Manufacture for Thin-walled Structures, Shanghai Jiao Tong University, Shanghai, 200240, China; Kong, L., Intelligent Robot Research Center, Zhejiang Lab, Hangzhou, 311100, China; Wang, H., Shanghai Key Laboratory of Digital Manufacture for Thin-walled Structures, Shanghai Jiao Tong University, Shanghai, 200240, China, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, China","Devices with variable stiffness are drawing more and more attention with the growing interests of human-robot interaction, wearable robotics, rehabilitation robotics, etc. In this paper, the authors report on the design, analysis and experiments of a stiffness variable passive compliant device whose structure is a combination of a reconfigurable elastic inner skeleton and an origami shell. The main concept of the reconfigurable skeleton is to have two elastic trapezoid four-bar linkages arranged in orthogonal. The stiffness variation generates from the passive deflection of the elastic limbs and is realized by actively switching the arrangement of the leaf springs and the passive joints in a fast, simple and straightforward manner. The kinetostatics and the compliance of the device are analyzed based on an efficient approach to the large deflection problem of the elastic links. A prototype is fabricated to conduct experiments for the assessment of the proposed concept. The results show that the prototype possesses relatively low stiffness under the compliant status and high stiffness under the stiff status with a status switching speed around 80 ms. © 2020, The Author(s).","Origami shell; Passive compliance device; Reconfigurable skeleton; Variable stiffness","Human robot interaction; Leaf springs; Machine design; Musculoskeletal system; Robotics; Wearable technology; Compliant devices; Large deflection; Passive compliance; Rehabilitation robotics; Status switching; Stiffness variations; Variable stiffness; Wearable robotics; Stiffness",,,,,"National Natural Science Foundation of China, NSFC: 51875334; Huazhong University of Science and Technology, HUST: DMETKF2019007; State Key Lab of Digital Manufacturing Equipment and Technology; National Key Research and Development Program of China, NKRDPC: 2017YFE0111300","Supported in part by National Key Research and Development Program of China (Grant No. 2017YFE0111300), National Natural Science Foundation of China (Grant No. 51875334), and State Key Lab of Digital Manufacturing Equipment and Technology (Huazhong University of Science and Technology) (Grant No. DMETKF2019007). 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Proceedings 2002 IEEE International Conference on Robotics and Automation (2002) IEEE, 1, pp. 248-253; Bamotra, A., Walia, P., Prituja, A.V., Layer-jamming suction grippers with variable stiffness (2019) Journal of Mechanisms and Robotics, 11 (3), p. 035003; Wang, T., Zhang, J., Li, Y., Electrostatic layer jamming variable stiffness for soft robotics (2019) IEEE/ASME Transactions on Mechatronics, 24 (2), pp. 424-433; Kim, Y.J., Cheng, S., Kim, S., A novel layer jamming mechanism with tunable stiffness capability for minimally invasive surgery (2013) IEEE Transactions on Robotics, 29 (4), pp. 1031-1042; Tao, Y., Wang, T., Wang, Y., A new variable stiffness robot joint (2015) Industrial Robot: An International Journal, 42 (4), pp. 371-378; Bi, S., Liu, C., Zhao, H., Design and analysis of a novel variable stiffness actuator based on parallel-assembled-folded serial leaf springs (2017) Advanced Robotics, 31 (18), pp. 990-1001; Chen, G., Zhang, Z., Wang, H., A general approach to the large deflection problems of spatial flexible rods using principal axes decomposition of compliance matrices (2018) Journal of Mechanisms and Robotics, 10 (3); Chen, G., Zhang, Z., Kong, L., Analysis and validation of a flexible planar two degrees-of-freedom parallel manipulator with structural passive compliance (2020) Journal of Mechanisms and Robotics, 12 (1); Dai, J.S., Zoppi, M., Kong, X., (2012) Advances in Reconfigurable Mechanisms and Robots I, , London, Springer; Wei, G., Dai, J.S., Wang, S., Kinematic analysis and prototype of a metamorphic anthropomorphic hand with a reconfigurable palm (2011) International Journal of Humanoid Robotics, 8 (3), pp. 459-479; Wei, G., Dai, J.S., (2014) Advances in Robot Kinematics, , Cham, Springer; Yoshimura, Y., On the mechanism of buckling of a circular cylindrical shell under axial compression (1955) Reports of the Institute of Science and Technology of the University of Tokyo; Rus, D., Sung, C., Spotlight on origami robots (2018) Science Robotics, 3 (15), p. eaat0938; Wei, G., Dai, J.S., Origami-inspired integrated planar-spherical overconstrained mechanisms (2014) Journal of Mechanical Design, 136 (5), p. 051003; Chen, Y., Lv, W., Peng, R., Mobile assemblies of four-spherical-4R-integrated linkages and the associated four-crease-integrated rigid origami patterns (2019) Mechanism and Machine Theory, 142, p. 103613; Zhang, Z., Chen, G., Wu, H., A pneumatic/cable-driven hybrid linear actuator with combined structure of origami chambers and deployable mechanism (2020) IEEE Robotics and Automation Letters, 5 (2), pp. 3564-3571; Paez, L., Agarwal, G., Paik, J., Design and analysis of a soft pneumatic actuator with origami shell reinforcement (2016) Soft Robotics, 3 (3), pp. 109-119; Martinez, R.V., Fish, C.R., Chen, X., Elastomeric origami: programmable paper-elastomer composites as pneumatic actuators (2012) Advanced Functional Materials, 22 (7), pp. 1376-1384; Luo, M., Yan, R., Wan, Z., OriSnake: Design, fabrication, and experimental analysis of a 3-D origami snake robot (2018) IEEE Robotics and Automation Letters, 3 (3), pp. 1993-1999; Santoso, J., Skorina, E.H., Luo, M., Design and analysis of an origami continuum manipulation module with torsional strength 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2017, pp. 2098-2104; Miura, K., Tachi, T., (2010) Synthesis of rigid-foldable cylindrical polyhedra, pp. 204-213. , Art and Science, Symmetry; Chen, G., Wang, H., Lin, Z., The principal axes decomposition of spatial stiffness matrices (2015) IEEE Transactions on Robotics, 31 (1), pp. 191-207; Howell, L., (2001) Compliant mechanisms, , John Wiley & Sons; Chen, G., Wang, H., Lin, Z., Determination of the identifiable parameters in robot calibration based on the POE formula (2014) IEEE Transactions on Robotics, 30 (5), pp. 1066-1077; Bicchi, A., Tonietti, G., Fast and"" soft-arm"" tactics [robot arm design] (2004) IEEE Robotics & Automation Magazine, 11 (2), pp. 22-33","Chen, G.; Shanghai Key Laboratory of Digital Manufacture for Thin-walled Structures, China; email: leungchan@sjtu.edu.cn",,,"Springer",,,,,10009345,,CJMEE,,"English","Chin J Mech Eng Engl Ed",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85094899405
"Al-Makhadmeh Z., Tolba A.","57196260251;56442428300;","Dependable information processing method for reliable human-robot interactions in smart city applications",2020,"Image and Vision Computing","104",,"104045","","",,1,"10.1016/j.imavis.2020.104045","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092662310&doi=10.1016%2fj.imavis.2020.104045&partnerID=40&md5=50cba219a706cd56d0b86d0000a7fef1","Computer Science Department, Community College, King Saud University, Riyadh, 11437, Saudi Arabia; Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin-El-Kom, 32511, Egypt","Al-Makhadmeh, Z., Computer Science Department, Community College, King Saud University, Riyadh, 11437, Saudi Arabia; Tolba, A., Computer Science Department, Community College, King Saud University, Riyadh, 11437, Saudi Arabia, Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Shebin-El-Kom, 32511, Egypt","Human-robot interaction (HRI) is a multidisciplinary area that consists of several technologies that are used to create various smart city applications. The knowledge gain and analysis of the smart city environment improves response time. This paper introduces the dependable information processing (DIP) method for handling multi-attribute environmental information in a smart city application. Information sensed from the environment is categorized in the initial stage regarding how it meets application requirements. It helps to identify the need and response of the application through different interacting spans and previous trials. For attribute categorization and span validation, learning via K-means clustering is exploited to identify similar and dissimilar information attributes. This identification speeds up the process of successive responses with improved interaction sessions. The accuracy of user responses and latency-free assessment improves the reliability of DIP in smart city applications. The efficiency of the system is then evaluated using detection accuracy (97.34%), the response time (4.3 s), and interaction time (12.3 s), which is compared with other methods. © 2020 Elsevier B.V.","Attribute categorization; Human-robot interaction; Information processing; K-means clustering; Smart city","K-means clustering; Man machine systems; Processing; Smart city; Application requirements; Detection accuracy; Environmental information; Human robot Interaction (HRI); Interaction time; Multi-attributes; Human robot interaction",,,,,"Deanship of Scientific Research, King Saud University: RG-1439-088","The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through Research Group No. RG-1439-088 .",,,,,,,,,,"Onnasch, L., Roesler, E., A Taxonomy to Structure and Analyze Human–Robot Interaction (2020), International Journal of Social Robotics; Tiddi, I., Bastianelli, E., Daga, E., D'Aquin, M., Motta, E., Robot–City interaction: mapping the research landscape—a survey of the interactions between robots and modern cities (2019) Int. J. Soc. Robot., 12 (2), pp. 299-324; Erol, B.A., Majumdar, A., Benavidez, P., Rad, P., Choo, K.-K.R., Jamshidi, M., Toward artificial emotional intelligence for cooperative social human–machine interaction (2020) IEEE Trans. Comput. Soc. Syst., 7 (1), pp. 234-246; Berg, J., Lottermoser, A., Richter, C., Reinhart, G., Human-robot-interaction for mobile industrial robot teams (2019) Proced. CIRP, 79, pp. 614-619; Gustavsson, P., Holm, M., Syberfeldt, A., Wang, L., Human-robot collaboration – towards new metrics for selection of communication technologies (2018) Proced. CIRP, 72, pp. 123-128; Nyholm, S., Attributing agency to automated systems: reflections on human–robot collaborations and responsibility-loci (2017) Sci. Eng. Ethics, 24 (4), pp. 1201-1219; Arun, K.K., Mydhili, S.K., Baskar, S., Shakeel, P.M., Fuzzy rule-based environment-aware autonomous mobile robots for actuated touring (2020) Intell. Serv. Robot.; Lin, Y., Min, H., Zhou, H., Pei, F., A human–robot-environment interactive reasoning mechanism for object sorting robot (2018) IEEE Trans. Cognitive Develop. Syst., 10 (3), pp. 611-623; Quintas, J., Martins, G.S., Santos, L., Menezes, P., Dias, J., Toward a context-aware human–robot interaction framework based on cognitive development (2019) IEEE Trans. Syst. Man Cybernetics., 49 (1), pp. 227-237; Garcia, C.A., Montalvo-Lopez, W., Garcia, M.V., Human-robot collaboration based on cyber-physical production system and MQTT (2020) Procedia Manufacturing, 42, pp. 315-321; Gomathi, P., Baskar, S., Shakeel, P.M., Concurrent service access and management framework for user-centric future internet of things in smart cities (2020) Complex Intell. Syst.; Liu, Y., Li, Z., Liu, H., Kan, Z., Skill transfer learning for autonomous robots and human–robot cooperation: a survey (2020) Robot. Auton. Syst., 128, p. 103515; Dziergwa, M., Kaczmarek, M., Kaczmarek, P., Kędzierski, J., Wadas-Szydłowska, K., Long-term cohabitation with a social robot: a case study of the influence of human attachment patterns (2017) Int. J. Soc. Robot., 10 (1), pp. 163-176; Zhu, T., Xia, Z., Dong, J., Zhao, Q., A sociable human-robot interaction scheme based on body emotion analysis (2019) Int. J. Control. Autom. Syst., 17 (2), pp. 474-485; Savić, S.Ž., Gnjatović, M., Stefanović, D., Lalić, B., Maček, N., Automatic domain modeling for human–robot interaction (2019) Intell. Serv. Robot., 13 (1), pp. 99-111; Neto, P., Simão, M., Mendes, N., Safeea, M., Gesture-based human-robot interaction for human assistance in manufacturing (2018) Int. J. Adv. Manuf. Technol., 101 (1-4), pp. 119-135; Żarkowski, M., Multi-party turn-taking in repeated human–robot interactions: an interdisciplinary evaluation (2019) Int. J. Soc. 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Comput. Integr. Manuf., 63, p. 101891; Rückert, P., Tracht, K., Herfs, W., Roggendorf, S., Schubert, V., Schneider, M., Consolidation of product lifecycle information within human-robot collaboration for assembly of multi-variant products (2020) Procedia Manufacturing, 49, pp. 217-221; Dong, J., Xu, J., Zhou, Q., Hu, S., Physical human–robot interaction force control method based on adaptive variable impedance (2020), Journal of the Franklin Institute; Yongda, D., Fang, L., Huang, X., Research on multimodal human-robot interaction based on speech and gesture (2018) Comput. Electr. Eng., 72, pp. 443-454; Oliff, H., Liu, Y., Kumar, M., Williams, M., Ryan, M., Reinforcement learning for facilitating human-robot-interaction in manufacturing (2020) J. Manuf. Syst., 56, pp. 326-340; Mi, J., Tang, S., Deng, Z., Goerner, M., Zhang, J., Object affordance based multimodal fusion for natural human-robot interaction (2019) Cogn. Syst. Res., 54, pp. 128-137","Al-Makhadmeh, Z.; Computer Science Department, Saudi Arabia; email: zalmakhadmee@ksu.edu.sa",,,"Elsevier Ltd",,,,,02628856,,IVCOD,,"English","Image Vision Comput",Article,"Final","",Scopus,2-s2.0-85092662310
"Byun G., Kikuuwe R.","55919408600;8718896900;","Stiff and safe task-space position and attitude controller for robotic manipulators",2020,"ROBOMECH Journal","7","1","18","","",,1,"10.1186/s40648-020-00166-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082799642&doi=10.1186%2fs40648-020-00166-1&partnerID=40&md5=cfe60fe5ddd7914234d6fd47c1fd5454","Department of Mechanical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka  819-0395, Japan; Department of Mechanical Systems Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima  739-8527, Japan","Byun, G., Department of Mechanical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka  819-0395, Japan; Kikuuwe, R., Department of Mechanical Systems Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima  739-8527, Japan","This paper proposes a stiff and safe task-space position and attitude control scheme for robotic manipulators. This study extends the work of Kikuuwe et al’s. (2006) velocity-bounding proxy-based sliding mode control by explicitly addressing the attitude part. The proposed controller has a Jacobian-based structure, which realizes smooth trajectories when the desired attitude is far rotated from the actual attitude. It also imposes arbitrary magnitude limits on the end-effector velocity, angular velocity, and each actuator force without sacrificing a stiffness, which is the same level as a high-gain PID position control below the limits. The benefit of the proposed controller becomes apparent after the robot yields to external forces due to force saturations, when the robot makes contact with obstacles. In such a situation, if the external forces disappear, the controller generates overdamped resuming motion from large tracking errors. The proposed controller can be expected to enhance the safety of robotic applications for the human–robot interaction. The proposed method is validated by experiments employing a six-degree of freedom industrial manipulator. © 2020, The Author(s).","Attitude control; Human–robot interaction; Multi-dimensional task space; Proportional-integral-derivative (PID) position control; Proxy-based sliding mode control (PSMC); Quaternion representation; Safety regulation","Attitude control; Degrees of freedom (mechanics); Flexible manipulators; Human robot interaction; Industrial manipulators; Motion tracking; Proportional control systems; Robotics; Sliding mode control; Two term control systems; Proportional integral derivatives; Proxy-based sliding mode controls; Quaternion representation; Robot interactions; Safety regulations; Task space; Controllers",,,,,,,,,,,,,,,,"Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., (2010) Robotics: modelling, planning and control, , Springer, Berlin; Buss, S.R., Introduction to inverse kinematics with jacobian transpose, pseudoinverse and damped least squares methods (2004) IEEE J Robot Autom, 17 (1-19), p. 16; Arimoto, S., Miyazaki, F., Stability and robustness of pid feedback control for robot manipulators of sensory capability (1984) Robotics Research, First International Symposium, pp. 783-799. , MIT Press; Chiaverini, S., Siciliano, B., Villani, L., A survey of robot interaction control schemes with experimental comparison (1999) IEEE/ASME Trans Mechatron, 4 (3), pp. 273-285; Caccavale, F., Natale, C., Siciliano, B., Villani, L., Integration for the next generation: embedding force control into industrial robots (2005) IEEE Robot Autom Mag, 12 (3), pp. 53-64; Luh, J., Walker, M., Paul, R., Resolved-acceleration control of mechanical manipulators (1980) IEEE Trans Autom Control, 25 (3), pp. 468-474; Khatib, O., A unified approach for motion and force control of robot manipulators: the operational space formulation (1987) IEEE J Robot Autom, 3 (1), pp. 43-53; Diebel, J., (2006) Representing Attitude: Euler Angles, Unit Quaternions, and Rotation Vectors, , http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.5134; Caccavale, F., Chiacchio, P., Chiaverini, S., Task-space regulation of cooperative manipulators (2000) Automatica, 36 (6), pp. 879-887; Caccavale, F., Chiacchio, P., Marino, A., Villani, L., Six-dof impedance control of dual-arm cooperative manipulators (2008) IEEE/ASME Trans Mechatron, 13 (5), pp. 576-586; Kikuuwe, R., Fujimoto, H., Proxy-based sliding mode control for accurate and safe position control (2006) Proceedings of the 2006 IEEE International Conference on Robotics and Automation, pp. 25-30; Kikuuwe, R., Yasukouchi, S., Fujimoto, H., Yamamoto, M., Proxy-based sliding mode control: a safer extension of PID position control (2010) IEEE Trans Robot, 26 (4), pp. 670-683; Kikuuwe, R., Some stability proofs on proxy-based sliding mode control (2018) IMA J Math Control Inform, 35 (4), pp. 1319-1341; Watanabe, T., Yano, K., Aoki, T., Nishimoto, Y., Extension motion assistance for upper limb using proxy-based sliding mode control (2011) IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2885-2890; Liao, Y., Zhou, Z., Wang, Q., BioKEX: A bionic knee exoskeleton with proxy-based sliding mode control (2015) Proceedings of 2015 IEEE International Conference on Industrial Technology, pp. 125-130; Chen, G., Zhou, Z., Vanderborght, B., Wang, N., Wang, Q., Proxy-based sliding mode control of a robotic ankle-foot system for post-stroke rehabilitation (2016) Adv Robot, 30 (15), pp. 992-1003; Kashiri, N., Lee, J., Tsagarakis, N.G., Van Damme, M., Vanderborght, B., Caldwell, D.G., Proxy-based position control of manipulators with passive compliant actuators: stability analysis and experiments (2016) Robot Auton Syst, 75, pp. 398-408; Van Damme, M., Beyl, P., Vanderborght, B., Versluys, R., Van Ham, R., Vanderniepen, I., Daerden, F., Lefeber, D., The safety of a robot actuated by pneumatic muscles—a case study (2010) Int J Soc Robot, 2 (3), pp. 289-303; Hastürk, Ö., Erkmen, A.M., Erkmen, İ., Proxy-based sliding mode stabilization of a two-axis gimbaled platform (2011) Proceedings of the World Congress on Engineering and Computer Science, pp. 370-376; Prieto, P.J., Rubio, E., Hernández, L., Urquijo, O., Proxy-based sliding mode control on platform of 3 degree of freedom (3-DOF) (2013) Adv Robot, 27 (10), pp. 773-784; Gu, G.-Y., Zhu, L.-M., Su, C.-Y., Ding, H., Fatikow, S., Proxy-based sliding-mode tracking control of piezoelectric-actuated nanopositioning stages (2015) IEEE/ASME Trans Mechatron, 20 (4), pp. 1956-1965; Nishi, F., Katsura, S., Ultrafine manipulation considering input saturation using proxy-based sliding mode control (2015) Proceedings of 2015 IEEE International Conference on Mechatronics, pp. 547-552; Kikuuwe, R., Yamamoto, T., Fujimoto, H., Velocity-bounding stiff position controller (2006) Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3050-3055; Hogan, N., Impedance control: an approach to manipulation: part i-theory (1985) Trans ASME J Dyn Syst Meas Control, 107 (1), pp. 1-7; Hogan, N., Impedance control: an approach to manipulation: part ii-implementation (1985) Trans ASME J Dyn Syst Meas Control, 107 (1), pp. 8-16; Hogan, N., Impedance control: an approach to manipulation: part iii-applications (1985) Trans ASME J Dyn Syst Meas Control, 107 (1), pp. 17-24; Slotine, J.-J.E., Sastry, S.S., Tracking control of non-linear systems using sliding surfaces, with application to robot manipulators (1983) Int J Control, 38 (2), pp. 465-492; Lange, F., Hirzinger, G., Adaptive minimization of the maximal path deviations of industrial robots (1999) 1999 European Control Conference (ECC). IEEE, pp. 1914-1919; Xie, Y., Jin, J., Tang, X., Ye, B., Tao, J., Robust cascade path-tracking control of networked industrial robot using constrained iterative feedback tuning (2018) IEEE Access, 7, pp. 8470-8482","Byun, G.; Department of Mechanical Engineering, 744 Motooka, Japan; email: byun@ctrl.mech.kyushu-u.ac.jp",,,"Springer Science and Business Media Deutschland GmbH",,,,,21974225,,,,"English","ROBOMECH J.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85082799642
"Wörgötter F., Ziaeetabar F., Pfeiffer S., Kaya O., Kulvicius T., Tamosiunaite M.","7003438923;57195425011;57215587737;57215577865;10540924300;10540148500;","Humans Predict Action using Grammar-like Structures",2020,"Scientific Reports","10","1","3999","","",,4,"10.1038/s41598-020-60923-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081222163&doi=10.1038%2fs41598-020-60923-5&partnerID=40&md5=8907b3c4c4a223576477396c114e5c17","Universität Göttingen, Department for Computational Neuroscience at the Bernstein Center Göttingen, Inst. of Physics 3 and Leibniz Science Campus for Primate Cognition, Göttingen, Germany; Vytautas Magnus University, Faculty of Informatics, Kaunas, Lithuania","Wörgötter, F., Universität Göttingen, Department for Computational Neuroscience at the Bernstein Center Göttingen, Inst. of Physics 3 and Leibniz Science Campus for Primate Cognition, Göttingen, Germany; Ziaeetabar, F., Universität Göttingen, Department for Computational Neuroscience at the Bernstein Center Göttingen, Inst. of Physics 3 and Leibniz Science Campus for Primate Cognition, Göttingen, Germany; Pfeiffer, S., Universität Göttingen, Department for Computational Neuroscience at the Bernstein Center Göttingen, Inst. of Physics 3 and Leibniz Science Campus for Primate Cognition, Göttingen, Germany; Kaya, O., Universität Göttingen, Department for Computational Neuroscience at the Bernstein Center Göttingen, Inst. of Physics 3 and Leibniz Science Campus for Primate Cognition, Göttingen, Germany; Kulvicius, T., Universität Göttingen, Department for Computational Neuroscience at the Bernstein Center Göttingen, Inst. of Physics 3 and Leibniz Science Campus for Primate Cognition, Göttingen, Germany; Tamosiunaite, M., Universität Göttingen, Department for Computational Neuroscience at the Bernstein Center Göttingen, Inst. of Physics 3 and Leibniz Science Campus for Primate Cognition, Göttingen, Germany, Vytautas Magnus University, Faculty of Informatics, Kaunas, Lithuania","Efficient action prediction is of central importance for the fluent workflow between humans and equally so for human-robot interaction. To achieve prediction, actions can be algorithmically encoded by a series of events, where every event corresponds to a change in a (static or dynamic) relation between some of the objects in the scene. These structures are similar to a context-free grammar and, importantly, within this framework the actual objects are irrelevant for prediction, only their relational changes matter. Manipulation actions and others can be uniquely encoded this way. Using a virtual reality setup and testing several different manipulation actions, here we show that humans predict actions in an event-based manner following the sequence of relational changes. Testing this with chained actions, we measure the percentage predictive temporal gain for humans and compare it to action-chains performed by robots showing that the gain is approximately equal. Event-based and, thus, object independent action recognition and prediction may be important for cognitively deducing properties of unknown objects seen in action, helping to address bootstrapping of object knowledge especially in infants. © 2020, The Author(s).",,"article; bootstrapping; grammar; human; infant; prediction; robotics; virtual reality; clinical trial; female; knowledge; linguistics; male; physiology; virtual reality; vision; Female; Humans; Knowledge; Linguistics; Male; Recognition, Psychology; Virtual Reality; Visual Perception",,,,,"Horizon 2020 Framework Programme, H2020: 732266; Deutsche Forschungsgemeinschaft, DFG: WO388/13-1","The authors are especially grateful to Ricarda Schubotz and Nadiya El-Sourani for their help at various stages in the design of the experiments and the interpretation of the results. We wish to thanks N. Krüger for lending us one of his Schunk Hands after one of ours broke down. Without this, finalizing the experiments would have been delayed by more than 3 months. The research leading to these results has received funding from the German Research Foundation (DFG) grant WO388/13-1 and the European Community’s H2020 Programme (Future and Emerging Technologies, FET) under grant agreement no. 732266, Plan4Act.",,,,,,,,,,"Kappeler, P., Schaik, C., (2006) Cooperation in Primates and Humans: Mechanisms and Evolution, , Springer; Cao, Y., Recognize human activities from partially observed videos (2013) In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2658-2665; Lan, T., Chen, T.-C., Savarese, S., A hierarchical representation for future action prediction (2014) European Conference on Computer Vision, pp. 689-704. , Springer; Ryoo, M.S., Human activity prediction: Early recognition of ongoing activities from streaming videos (2011) 2011 IEEE International Conference On, pp. 1036-1043. , Computer Vision (ICCV; Gupta, A., Kembhavi, A., Davis, L.S., Observing human-object interactions: Using spatial and functional compatibility for recognition (2009) IEEE Transactions on Pattern Analysis and Machine Intelligence, 31 (10), pp. 1775-1789; Kjellström, H., Romero, J., Kragic, D., Visual object-action recognition: Inferring object affordances from human demonstration (2011) Computer Vision and Image Understanding, 115 (1), pp. 81-90; Yao, B., Fei-Fei, L., Modeling mutual context of object and human pose in human-object interaction activities (2010) Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference On, pp. 17-24; Zhu, Y., Nayak, N.M., Roy-Chowdhury, A.K., Context-aware modeling and recognition of activities in video (2013) In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2491-2498; Koppula, H.S., Saxena, A., Anticipating human activities using object affordances for reactive robotic response (2016) IEEE Transactions on Pattern Analysis and Machine Intelligence, 38 (1), pp. 14-29; Morariu, V.I., Davis, L.S., Multi-agent event recognition in structured scenarios (2011) Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference On, pp. 3289-3296; Demiris, Y., Khadhouri, B., Hierarchical attentive multiple models for execution and recognition of actions (2006) Robotics and Autonomous Systems, 54 (5), pp. 361-369; Ognibene, D., Demiris, Y., Towards active event recognition (2013) In Twenty-Third International Joint Conference on Artificial Intelligence; Baker, C.L., Tenenbaum, J.B., Modeling human plan recognition using Bayesian theory of mind (2014) Plan, Activity, and Intent Recognition: Theory and Practice, pp. 177-204; Charniak, E., Goldman, R.P., A Bayesian model of plan recognition (1993) Artificial Intelligence, 64 (1), pp. 53-79. , pp; Demeester, E., User-adapted plan recognition and user-adapted shared control: A Bayesian approach to semi-autonomous wheelchair driving (2008) Autonomous Robots., 24 (2), pp. 193-211; Levine, S.J., Williams, B.C., Concurrent plan recognition and execution for human-robot teams (2014) International Conference on Automated Planning and Scheduling, ICAPS; Pastra, K., Aloimonos, Y., The minimalist grammar of action (2012) Philosophical Transactions of the Royal Society of London B: Biological Sciences., 367 (1585), pp. 103-117; Rosani, A., Conci, N., De Natale, F.G., Human behavior recognition using a context-free grammar (2014) Journal of Electronic Imaging, 23 (3), p. 033016; Ryoo, M.S., Aggarwal, J.K., Recognition of composite human activities through context-free grammar based representation (2006) In Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, 2, pp. 1709-1718; Yang, Y., Guha, A., Fermüller, C., Aloimonos, Y., A cognitive system for understanding human manipulation actions (2014) Advances in Cognitive Sysytems, 3, pp. 67-86; Feichtenhofer, C., Pinz, A., Zisserman, A., Convolutional two-stream network fusion for video action recognition (2016) In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1933-1941; Simonyan, K., Zisserman, A., Two-stream convolutional networks for action recognition in videos (2014) Advances in Neural Information Processing Systems, pp. 568-576; Sun, L., Jia, K., Yeung, D.-Y., Shi, B.E., Human action recognition using factorized spatio-temporal convolutional networks (2015) Proceedings of the IEEE International Conference on Computer Vision, pp. 4597-4605; Wang, L., Qiao, Y., Tang, X., Action recognition with trajectory-pooled deep-convolutional descriptors (2015) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4305-4314; Aksoy, E.E., Learning the semantics of object–action relations by observation (2011) The International Journal of Robotics Research, 30 (10), pp. 1229-1249; Aksoy, E.E., Tamosiunaite, M., Wörgötter, F., Model-free incremental learning of the semantics of manipulation actions (2015) Robotics and Autonomous Systems, 71, pp. 118-133; Aksoy, E.E., Orhan, A., Wörgötter, F., Semantic decomposition and recognition of long and complex manipulation action sequences (2017) International Journal of Computer Vision, 122 (1), pp. 84-115; Borràs, J., Mandery, C., Asfour, T., A whole-body support pose taxonomy for multi-contact humanoid robot motions (2017) Science Robotics, 2 (13), p. eaaq0560; Summers-Stay, D., Teo, C.L., Yang, Y., Fermüller, C., Aloimonos, Y., Using a minimal action grammar for activity understanding in the real world (2012) Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference On, pp. 4104-4111; Wörgötter, F., A simple ontology of manipulation actions based on hand-object relations (2013) IEEE Transactions on Autonomous Mental Development, 5 (2), pp. 117-134; Ziaeetabar, F., Kulvicius, T., Tamosiunaite, M., Wörgötter, F., Recognition and prediction of manipulation actions using enriched semantic event chains (2018) Robotics and Autonomous Systems, 110, pp. 173-188; Krüger, N., Object-action complexes: Grounded abstractions of sensory-motor processes (2011) Robotics and Autonomous Systems., 59 (10), pp. 740-757; Wörgötter, F., Agostini, A., Krüger, N., Shylo, N., Porr, B., Cognitive agents – a procedural perspective relying on the predictability of Object-Action-Complexes (OACs) (2009) Robotics and Autonomous Systems, 57 (4), pp. 420-432; Ijspeert, A.J., Nakanishi, J., Schaal, S., Movement imitation with nonlinear dynamical systems in humanoid robots (2002) Robotics and Automation (ICRA’02), IEEE International Conference On, 2, pp. 1398-1403; Kulvicius, T., Ning, K., Tamosiunaite, M., Wörgötte, F., Joining movement sequences: Modified dynamic movement primitives for robotics applications exemplified on handwriting (2012) IEEE Transactions on Robotics, 28 (1), pp. 145-157","Wörgötter, F.; Universität Göttingen, Germany; email: worgott@gwdg.de",,,"Nature Research",,,,,20452322,,,"32132602","English","Sci. Rep.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85081222163
"Zhao J., Parry C.J., Anjos R.D., Anslow C., Rhee T.","57218766572;57221559664;57221562234;16306402300;14619599800;","Voice Interaction for Augmented Reality Navigation Interfaces with Natural Language Understanding",2020,"International Conference Image and Vision Computing New Zealand","2020-November",,"9290643","","",,,"10.1109/IVCNZ51579.2020.9290643","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099432441&doi=10.1109%2fIVCNZ51579.2020.9290643&partnerID=40&md5=80b444be5583b10a3b0d5ea72caf5dcf","Victoria University of Wellington, Computational Media Innovation Centre (CMIC), New Zealand; Victoria University of Wellington, School of Engineering and Computer Science, New Zealand","Zhao, J., Victoria University of Wellington, Computational Media Innovation Centre (CMIC), New Zealand; Parry, C.J., Victoria University of Wellington, Computational Media Innovation Centre (CMIC), New Zealand; Anjos, R.D., Victoria University of Wellington, Computational Media Innovation Centre (CMIC), New Zealand; Anslow, C., Victoria University of Wellington, School of Engineering and Computer Science, New Zealand; Rhee, T., Victoria University of Wellington, Computational Media Innovation Centre (CMIC), New Zealand","Voice interaction with natural language understanding (NLU) has been extensively explored in desktop computers, handheld devices, and human-robot interaction. However, there is limited research into voice interaction with NLU in augmented reality (AR). There are benefits of using voice interaction in AR, such as high naturalness and being hands-free. In this project, we introduce VOARLA, an NLU-powered AR voice interface, which navigate courier driver delivery a package. A user study was completed to evaluate VOARLA against an AR voice interface without NLU to investigate the effectiveness of NLU in the navigation interface in AR. We evaluated from three aspects: accuracy, productivity, and commands learning curve. Results found that using NLU in AR increases the accuracy of the interface by 15%. However, higher accuracy did not correlate to an increase in productivity. Results suggest that NLU helped users remember the commands on the first run when they were unfamiliar with the system. This suggests that using NLU in an AR hands-free application can make the learning curve easier for new users. © 2020 IEEE.","artificial intelligence; Augmented Reality; intelligent interface; natural language understanding (NLU); speech interaction; speech recognition","Augmented reality; Personal computers; Productivity; Hand held device; Hands-free; Learning curves; Natural language understanding; Navigation interface; User study; Voice interaction; Voice interfaces; Human robot interaction",,,,,"Ministry of Business, Innovation and Employment, MBIE","This project was funded by the Smart Ideas Endeavour Fund from MBIE and in part by the Entrepreneurial University Programme from TEC in New Zealand",,,,,,,,,,"LaViola, J.J., Jr., Kruijff, E., McMahan, R.P., Bowman, D., Poupyrev, I.P., (2017) 3D User Interfaces: Theory and Practice, , Addison-Wesley Professional; Billinghurst, M., Kato, H., Myojin, S., Advanced interaction techniques for augmented reality applications (2009) International Conference on Virtual and Mixed Reality, pp. 13-22. , Springer; Wang, D., Wang, X., Lv, S., An overview of end-to-end automatic speech recognition (2019) Symmetry, 11 (8), p. 1018; McGlashan, S., Axling, T., A speech interface to virtual environments (1996) Swedish Institute of Computer Science; Lai, J., Vergo, J., Medspeak: Report creation with continuous speech recognition (1997) Proceedings of the Acm Sigchi Conference on Human Factors in Computing Systems, pp. 431-438; Pascoal, R., Ribeiro, R., Batista, F., De Almeida, A., Adapting speech recognition in augmented reality for mobile devices in outdoor environments (2017) 6th Symposium on Languages, Applications and Technologies (SLATE 2017), , Schloss Dagstuhl-Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing; Yu, D., Deng, L., (2016) Automatic Speech Recognition, , Springer; Westphal, M., Waibel, A., Towards spontaneous speech recognition for on-board car navigation and information systems (1999) Sixth European Conference on Speech Communication and Technology; Graves, A., Jaitly, N., Mohamed, A.-R., Hybrid speech recognition with deep bidirectional lstm (2013) 2013 Ieee Workshop on Automatic Speech Recognition and Understanding, pp. 273-278; Shaharin, R., Prodhan, U.K., Rahman, M., Performance study of tdnn training algorithm for speech recognition (2014) International Journal of Advanced Research in Computer Science & Technology, 2 (4), pp. 90-95; Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I., Attention is all you need (2017) Advances in Neural Information Processing Systems, pp. 5998-6008; Ibm Watson, , https://www.ibm.com/watson; Dragon-World's Best-Selling Speech Recognition Software, , https://www.nuance.com/en-nz/dragon.html, Nuance; Use Voice Recognition in Windows 10, , https://support.microsoft.com/en-us/help/4027176/windows-10-use-voice-recognition; Trott, S., Eppe, M., Feldman, J., Recognizing intention from natural language: Clarification dialog and construction grammar (2016) Workshop on Communicating Intentions in Human-Robot Interaction; Hamroun, M., Gouider, M.S., A survey on intention analysis: Successful approaches and open challenges Journal of Intelligent Information Systems, 2020; Li, C., Du, Y., Wang, S., Mining implicit intention using attention-based rnn encoder-decoder model (2017) International Conference on Intelligent Computing, pp. 413-424. , Springer; Kato, T., Nagai, A., Noda, N., Sumitomo, R., Wu, J., Yamamoto, S., Utterance intent classification of a spoken dialogue system with efficiently untied recursive autoencoders (2017) Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, pp. 60-64; (2017) Watson Assistant, , https://www.ibm.com/cloud/watson-Assistant, IBM, Oct; Zhou, F., Duh, H.B.-L., Billinghurst, M., Trends in augmented reality tracking, interaction and display: A review of ten years of ismar (2008) 2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality, pp. 193-202; Clark, L., Doyle, P., Garaialde, D., Gilmartin, E., Schlögl, S., Edlund, J., Aylett, M., Edwards, J., The state of speech in hci: Trends, themes and challenges (2019) Interacting with Computers, 31 (4), pp. 349-371; Chan, Z.Y., Shum, P., Smart office: A voice-controlled workplace for everyone (2018) Proceedings of the 2nd International Symposium on Computer Science and Intelligent Control, pp. 1-5; Irawati, S., Green, S., Billinghurst, M., Duenser, A., Ko, H., An evaluation of an augmented reality multimodal interface using speech and paddle gestures (2006) International Conference on Artificial Reality and Telexistence, pp. 272-283. , Springer; Majewski, M., Kacalak, W., Conceptual design of innovative speech interfaces with augmented reality and interactive systems for controlling loader cranes (2016) Artificial Intelligence Perspectives in Intelligent Systems, pp. 237-247. , Springer; Majewski, M., Human-machine speech-based interfaces with augmented reality and interactive systems for controlling mobile cranes (2016) International Conference on Interactive Collaborative Robotics, pp. 89-98",,,,"IEEE Computer Society","35th International Conference on Image and Vision Computing New Zealand, IVCNZ 2020","25 November 2020 through 27 November 2020",,165983,21512191,9781728185798,,,"English","Int. Conf. Image Vis. Comput. New Zealand",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85099432441
"Xu Y., Hu B., Yan J., Chen L., Zhang D.-X., Guan Z.-H.","57222004598;57207150639;57211535238;57209903288;8852967100;7202542255;","Robots Learn to Write via Human-Robot Interaction",2020,"Proceedings - 2020 Chinese Automation Congress, CAC 2020",,,"9326574","3653","3658",,,"10.1109/CAC51589.2020.9326574","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100934412&doi=10.1109%2fCAC51589.2020.9326574&partnerID=40&md5=9aaa1c61f1b8e0bfc0d63a3eaba67ef0","Huazhong University of Science and Technology, School of Artificial Intelligence and Automation, Wuhan, China","Xu, Y., Huazhong University of Science and Technology, School of Artificial Intelligence and Automation, Wuhan, China; Hu, B., Huazhong University of Science and Technology, School of Artificial Intelligence and Automation, Wuhan, China; Yan, J., Huazhong University of Science and Technology, School of Artificial Intelligence and Automation, Wuhan, China; Chen, L., Huazhong University of Science and Technology, School of Artificial Intelligence and Automation, Wuhan, China; Zhang, D.-X., Huazhong University of Science and Technology, School of Artificial Intelligence and Automation, Wuhan, China; Guan, Z.-H., Huazhong University of Science and Technology, School of Artificial Intelligence and Automation, Wuhan, China","Human-Robot Interaction (HRI) has been extensively investigated in academia, industries, and technology companies. How to teach a robot to write is recognized as a difficult task, involving perception, learning and control of the overall robotic system. This paper develops a robotic handwriting system on a NAO robot by using HRI and Q-learning. In the simulator setup, the human demonstrator first writes to form a desired writing content, with the coordinates of each stroke writing saved; then, by applying the Q-learning algorithm, the virtual NAO automatically learns to find the correct writing order and to optimize the strokes. With the learned knowledge, the real NAO uses its inverse kinematics for generating the joint values from the stroke trajectories, and thus can quickly replicate the letters or words written by the human demonstrator. It is shown that by using the proposed HRI-based handwriting system, the human can naturally and conveniently teach NAO to write many characters and NAO can also have an acceptable writing quality close to the human. © 2020 IEEE.","handwriting; Human-Robot Interaction (HRI); NAO robot; Q-learning","Industrial robots; Inverse kinematics; Learning algorithms; Learning systems; Man machine systems; Reinforcement learning; Robotics; Human robot Interaction (HRI); Q-learning; Q-learning algorithms; Robotic handwriting; Robotic systems; Technology companies; Writing quality; Human robot interaction",,,,,,,,,,,,,,,,"Stiefelhagen, R., Fugen, C., Gieselmann, R., Holzapfel, H., Nickel, K., Waibel, A., Natural human-robot interaction using speech, head pose and gestures 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3, pp. 2422-2427; Du, G.L., Chen, M.X., Liu, C.B., Zhang, B., Zhang, P., Online robot teaching with natural human-robot interaction (2018) IEEE Transactions on Industrial Electronics, 65 (12), pp. 9571-9581; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Social robots for education: A review (2018) Science Robotics, 3 (21), p. eaat5954; Han, J., Kim, D., R-learning services for elementary school students with a teaching assistant robot[c] 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI)., pp. 255-256; Liu, T.C., Chang, M., Human-robot interaction research issues of educational robots 2008 Second IEEE International Conference on Digital Game and Intelligent Toy Enhanced Learning, pp. 209-210; Medwell, J., Wray, D., HandwritingCA forgotten language skill[J] (2008) Language and Education, 1 (22), pp. 34-47; Hood, D., Lemaignan, S., Dillenbourg, P., When children teach a robot to write: An autonomous teachable humanoid which uses simulated handwriting[c] (2015) Proceedings of the Tenth Annual IEEE International Conference on Human-Robot Interaction. ACM, pp. 83-90; Christensen, C.A., The role of orthographic motor integration in the production of creative and well-structured written text for students in secondary school (2005) Educ. Psychol., p. 441C453; Potkonjak, V., Popovic, M., Lazarevic, M., Sinanovic, J., Redundancy problem in writing: From human to anthropomorphic robot arm (1998) IEEE Trans. Syst. Man Cybern. Part B Cybern., p. 790C805; Huebel, N., Mueggler, E., Waibel, M., D'Andrea, R., Towards robotic calligraphy (2012) Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura-Algarve, p. 5165C5166. , Portugal, 7C12 October; Chao, F., Huang, Y., Lin, C., Yang, L., Hu, H., Zhou, C., Use of automatic Chinese character decomposition and human gestures for Chinese calligraphy robots (2019) IEEE Transactions on Human-Machine Systems, 49 (1), pp. 47-58. , Feb; Chang, C.W., Lee, J.H., Chao, P.Y., Wang, C.Y., Exploring the possibility of using humanoid robots as instructional tools for teaching a second language in primary school (2010) Educ. Technol. Soc., p. 13C24; Hood, D., Lemaignan, S., Dillenbourg, P., The cowriter project: Teaching a robot how to write (2015) Proceedings of the 10th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI'15 Extended Abstracts, p. 269. , 2C5 March; Johal, W., Jacq, A., Paiva, A., Dillenbourg, P., Child-robot spatial arrangement in a learning by teaching activity (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 533-538. , New York, USA, 26-31 August; Jacq, A., Lemaignan, S., Garcia, F., Dillenbourg, P., Building successful long child-robot interaction in a learning context (2016) 11th ACM/IEEE International Conference on Human-Robot Interaction, pp. 239-246; Chandra, S., Dillenbourg, P., Paiva, A., Classification of children's handwriting errors for the design of an educational co-writer robotic peer (2017) Proceedings of the 2017 Conference on Interaction Design and Children, pp. 215-225; Chandra, S., Paradeda, R., Yin, H., Dillenbourg, P., Prada, R., Paiva, A., Do children perceive whether a robotic peer is learning or not (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 41-49; Chandra, S., Dillenbourg, P., Paiva, A., Children teach handwriting to a social robot with different learning competencies (2019) International Journal of Social Robotics., pp. 1-28; Kucuk, S., Bingul, Z., Robot kinematics: Forward and inverse kinematics (2006) Industrial Robotics: Theory, Modelling and Control. IntechOpen; Watkins, C.J., Dayan, P., Michalski, R.S., Tecuci, G., Carbonell, J.G., Mitchell, T.M., Q-learning (1992) Machine Learning, 8, pp. 279-292",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 Chinese Automation Congress, CAC 2020","6 November 2020 through 8 November 2020",,166835,,9781728176871,,,"English","Proc. - Chin. Autom. Congr., CAC",Conference Paper,"Final","",Scopus,2-s2.0-85100934412
"Chen Y., Xie J., Gong Y.","36597797200;57222002441;26221682800;","Design, Analysis and Modeling of Flexible Arrayless Tactile Sensor under Sharp Contact",2020,"Proceedings - 2020 Chinese Automation Congress, CAC 2020",,,"9326994","3642","3647",,,"10.1109/CAC51589.2020.9326994","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100929456&doi=10.1109%2fCAC51589.2020.9326994&partnerID=40&md5=98084eb95ec79cf92efc43e2c2dcfbbc","Dalian Maritime University, Naval Architecture and Ocean Engineering College, Dalian, China","Chen, Y., Dalian Maritime University, Naval Architecture and Ocean Engineering College, Dalian, China; Xie, J., Dalian Maritime University, Naval Architecture and Ocean Engineering College, Dalian, China; Gong, Y., Dalian Maritime University, Naval Architecture and Ocean Engineering College, Dalian, China","The flexible tactile sensor, as the important medium of human-robot interaction, its accuracy of the contact position and force has a vital impact on the intelligence and security of the system. For now, the existing traditional flexible capacitive pressure sensors mostly adopt array structure, however, which is still unable to meet all the requirements of easy manufacture, low cost, and simple structure. This paper innovatively introduces a flexible array-less capacitive sensor which can recognize both contact position and contact force under sharp contact. The position information is identified by measuring voltage change in real time and the contact force under sharp contact is identified by measuring the change of capacitance using the theory of elastic contact mechanics. The simulation and verification results show that the proposed flexible array-less capacitive sensor can accurately identify the contact position and force under sharp contact, which also verify the effeteness of the proposed measuring method. The proposed flexible array-less capacitive sensor is simple and robust, which has broad application prospects in wearable devices, biomedical devices, artificial intelligence robots and other related fields. © 2020 IEEE.","Array-less; Capacitance; Flexible tactile sensor; Position identification; Sharp contact","Artificial intelligence; Capacitance; Deformation; End effectors; Intelligent robots; Social robots; Wearable sensors; Analysis and modeling; Biomedical devices; Broad application; Capacitive pressure sensors; Flexible tactile sensors; Position information; Simple structures; Verification results; Capacitive sensors",,,,,,,,,,,,,,,,"Lamba, M., Mittal, N., Chaudhary, H., Design analysis of polysilicon piezoresistors pdms (polydimethylsiloxane) microcantilever based mems force sensor[j] (2020) International Journal of Modern Physics B, 34 (9); Stefan, C.B.M., Benjamin, C.K.T., Zhe-Nan, B., Highly sensitive flexible pressure sensors with microstructured rubber dielectric layers[j] (2010) Nature Materials, 9 (10), pp. 859-864; Wang, Y.-C., Chen, T.-Y., Chen, R.-S., Mutual capacitive flexible tactile sensor for 3-d image control[j] (2013) Journal of Microelectromechanical Systems, 22 (3), pp. 804-814; Li, X., Guo, J.-J., Ma, X.-X., Flexible capacitive curvature sensor with one-time calibration for amphibious gait monitoring[j] (2020) Soft Robotics, , May; Yang, X.-F., Wang, Y.-S., Qing, X.-L., A flexible capacitive pressure sensor based on ionic liquid[j] (2018) Sensors, 18 (7); Le, T.H.L., Maslyczyk, A., Duchaine, V., A highly sensitive multimodal capacitive tactile sensor[j] (2017) IEEE International Conference on Robotics and Automation (ICRA), , Singapore, May 29-June 3; Wei, P.-Q., Guo, X.-L., Qiu, X.-B., (2019) Flexible Capacitive Pressure Sensor with Sensitivity and Linear Measuring Range Enhanced Based on Porous Composite of Carbon Conductive Paste and Polydimethylsiloxane[J], 30 (45); Attar, I., Altintig, K.S., Bozyel, I., Design of a highly sensitive, flexible and stretchable tactile sensor for electronic skin applications[c] (2019) IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS), , Glasgow, United Kingdom; Zhang, Y.-Q., Liu, S.-D., Nie, B.-Q., Highly stretchable and sensitive pressure sensor array based on icicle-shaped liquid metal film electrodes[j] (2020) ACS APPLIED MATERIALS & INTERFACES, 12 (25), pp. 27961-27970; Jiang, Y., Liu, Z.-Y., Yin, Z.-G., Sandwich structured dielectrics for air-stable and flexible low-voltage organic transistors in ultrasensitive pressure sensing[j] (2020) Materials Chemistry Frontiers, 4 (5), pp. 1459-1470; Lo, C.-Y., Advancements in polymeric capacitive tactile sensors[c] (2019) IEEE 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII, , Berlin, Germany; Dinh, T.H.N., Martincic, E., Joubert, P.Y., Capacitive flexible pressure sensor: Microfabrication process and experimental characterization[j] (2015) Microsystem Technologies-Micro-and Nanosystems-Information Storage and Processing Systems, 22 (3), pp. 465-471; Li, T., Luo, H., Zhang, T., Flexible capacitive tactile sensor based on micropatterned dielectric layer[j] (2016) Small, 12 (36), pp. 5042-5048; He, Z.-F., Chen, W.-J., Gui, X.-C., Capacitive pressure sensor with high sensitivity and fast response to dynamic interaction based on graphene and porous nylon networks[j] (2018) Acs Applied Materials & Interfaces, 10 (15), pp. 12816-12823; Kim, K., Song, G., Yun, K.S., Multifunctional woven structure operating as triboelectric energy harvester, capacitive tactile sensor array, and piezoresistive strain sensor array[j] (2017) Sensors, 17 (11); Park, S.W., Das, P.S., Park, J.Y., A flexible capacitive pressure sensor for wearable respiration monitoring system[j] (2017) IEEE Sensors Journal, 17 (20), pp. 6558-6564; Liu, J.-X., Wang, P., Guo, S.-J., Array-less touch position identification based on a flexible capacitive tactile sensor for human-robot interactions[c] (2019) IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 458-462; Liu, J.-X., Na, L., Guo, S.-J., Touch position identification based on a flexible array-less supercapacitive tactile sensor[j] (2019) AIP Advances, 9 (1), p. 015026; Luo, S., Yang, J., Song, X.-F., Tunable-sensitivity flexible pressure sensor based on graphene transparent electrode[j] (2018) Solid-state Electronics, 145, pp. 29-33; Johnson, K.L., (1985) Contact Mechanics[M], , Combridge : Combridge University Press",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 Chinese Automation Congress, CAC 2020","6 November 2020 through 8 November 2020",,166835,,9781728176871,,,"English","Proc. - Chin. Autom. Congr., CAC",Conference Paper,"Final","",Scopus,2-s2.0-85100929456
"Poeppel A., Hoffmann A., Siehler M., Reif W.","57191589978;14826982600;57221532140;7005971156;","Robust Distance Estimation of Capacitive Proximity Sensors in HRI using Neural Networks",2020,"Proceedings - 4th IEEE International Conference on Robotic Computing, IRC 2020",,,"9287901","344","351",,1,"10.1109/IRC.2020.00061","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099403445&doi=10.1109%2fIRC.2020.00061&partnerID=40&md5=decd6aab9e07ff5d62a7e6bc9d1ba927","Institute for Software Systems Engineering, University of Augsburg, Germany","Poeppel, A., Institute for Software Systems Engineering, University of Augsburg, Germany; Hoffmann, A., Institute for Software Systems Engineering, University of Augsburg, Germany; Siehler, M., Institute for Software Systems Engineering, University of Augsburg, Germany; Reif, W., Institute for Software Systems Engineering, University of Augsburg, Germany","With Industry 4.0 and the idea of flexible and hybrid manufacturing systems, the need for safe human-robot-interaction has become increasingly important. For this purpose, it is necessary to reliably detect persons in the workspace of a robot. Capacitive sensors mounted to the robot structure can be used to measure the presence of conductive objects and, hence, allow the detection of persons. However, for a reliable detection in an adequate range, it is necessary to compensate for various additional influences on capacitive sensors. In this paper, we propose a segmentation of the world model and the use of machine learning to compensate for the self-influence of the robot. Moreover, a correlation between the measured capacitance and the distance of a human hand can be calculated using machine learning, as well. Finally, it is possible to estimate the distance between capacitive sensors at the robot structure and a human hand while the robot is in motion. A motion capturing system is used as ground truth for the distance. © 2020 IEEE.","capacitive proximity sensors; human-robot interaction; neural networks","Agricultural robots; Industrial robots; Machine learning; Manufacture; Neural networks; Robotics; Social robots; Capacitive proximity sensors; Ground truth; Hybrid manufacturing systems; Motion capturing; Reliable detection; Robot structures; Robust distance; Safe human-robot interaction; Capacitive sensors",,,,,"Bundesministerium für Bildung und Forschung, BMBF; Bundesministerium für Bildung und Forschung, BMBF","This work partly presents results of the research project SINA which was funded by the German Federal Ministry of Education and Research (BMBF).",,,,,,,,,,"Kagermann, H., Wahlster, W., Helbig, J., Recommendations for implementing the strategic initiative industrie 4.0 (2013) Acatech-National Academy of Science and Engineering, , Final report of the Industrie 4.0 Working Group; Richert, A., Shehadeh, M., Müller, S., Schröder, S., Jeschke, S., Robotic workmates-hybrid human-robot-teams in the industry 4.0 (2016) Proc. 11th Intl. 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On Robotics, 27 (3), pp. 401-410; Mittendorfer, P., Yoshida, E., Cheng, G., Realizing whole-body tactile interactions with a self-organizing, multi-modal artificial skin on a humanoid robot (2015) Advanced Robotics, 29 (1), pp. 51-67; Dahiya, R.S., Mittendorfer, P., Valle, M., Cheng, G., Lumelsky, V.J., Directions toward effective utilization of tactile skin: A review (2013) Ieee Sensors Journal, 13 (11), pp. 4121-4138; Gecks, T., Henrich, D., Human-robot cooperation: Safe pick-And-place operations (2005) 14th Ieee Intl. Works. On Robot and Human Interactive Comm., Nashville, Usa; Wang, L., Schmidt, B., Nee, A.Y., Vision-guided active collision avoidance for human-robot collaborations (2013) Manufacturing Letters, 1 (1), pp. 5-8; De Luca, A., Flacco, F., Integrated control for phri: Collision avoidance, detection, reaction and collaboration (2012) Proc. 4th Ieee Ras & Embs Intl. Cond. On Biomedical Robotics and Biomechatronics (BioRob). Ieee, pp. 288-295; Ostermann, B., Huelke, M., Kahl, A., Freed from fences: Safeguarding industrial robots with ultrasound (2013) Safety Science Monitor, 17 (1); Escaida Navarro, S., Hein, B., Wörn, H., Capacitive tactile proximity sensing: From signal processing to applications in manipulation and safe human-robot interaction (2015) Soft Robotics, pp. 54-65; Novak, J., Feddema, J., A capacitance-based proximity sensor for whole arm obstacle avoidance (1992) Proc. 1992 Ieee Intl. Conf. On Robot. & Autom, pp. 1307-1314. , Nice, France; Feddema, J.T., Novak, J.L., Whole arm obstacle avoidance for teleoperated robots (1994) Proc. 1994 Ieee Intl. Conf. On Robot. & Autom, 4, pp. 3303-3309. , San Diego, CA, USA; Koeppe, R., Engelhardt, D., Hagenauer, A., Heiligensetzer, P., Kneifel, B., Knipfer, A., Stoddard, K., Robot-robot and human-robot cooperation in commercial robotics applications (2005) 11th Intl. 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On Robot. & Autom, , Seattle; Hoffmann, A., Poeppel, A., Schierl, A., Reif, W., Environment-Aware proximity detection with capacitive sensors for human-robot-interaction (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 145-150. , Oct; Patra, J.C., Kot, A.C., Panda, G., An intelligent pressure sensor using neural networks (2000) Ieee Transactions on Instrumentation and Measurement, 49 (4), pp. 829-834; Merriaux, P., Dupuis, Y., Boutteau, R., Vasseur, P., Savatier, X., A study of vicon system positioning performance (2017) Sensors, 17 (7), p. 1591; (2015) AD7147: CapTouch Programmable Controller for Single-Electrode Capacitance Sensors, Analog Devices, , Norwood, MA, USA; (2012) ATtiny24A/44A/84A, Atmel Corporation, , San Jose, CA, USA; (2017) Motion Capture Systems, , https://www.vicon.com, Vicon Motion Systems Ltd, [online]; (2017) Open Neural Networks Library, , http://www.opennn.net, Artificial Intelligence Techniques, [online]; Hoffmann, A., Angerer, A., Ortmeier, F., Vistein, M., Reif, W., Hiding real-time: A new approach for the software development of industrial robots (2009) Proc. 2009 IEEE/RSJ Intl. 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On Robotic Computing, , Taichung, Taiwan; (2016) APAS-Intelligent Systems for Man-Machine Collaboration, , http://www.bosch-Apas.com/media/en/apas/bosch-Apas/produkte/bosch-Apas-broschure-english-lores.pdf, Robert Bosch GmbH, Sep, [online]",,,,"Institute of Electrical and Electronics Engineers Inc.","4th IEEE International Conference on Robotic Computing, IRC 2020","9 November 2020 through 11 November 2020",,166083,,9781728152370,,,"English","Proc. - IEEE Int. Conf. Robot. Comput., IRC",Conference Paper,"Final","",Scopus,2-s2.0-85099403445
"Setiono F.Y., Elibol A., Chong N.-Y.","53264907100;24733903500;7007012034;","Human Emotional State Estimation Evaluation using Heart Rate Variability and Activity Data",2020,"Proceedings - 4th IEEE International Conference on Robotic Computing, IRC 2020",,,"9287948","178","182",,1,"10.1109/IRC.2020.00035","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099383484&doi=10.1109%2fIRC.2020.00035&partnerID=40&md5=d8148ec775c11bcd3f2cbfcc26be4eef","School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Ishikawa, Japan","Setiono, F.Y., School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Ishikawa, Japan; Elibol, A., School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Ishikawa, Japan; Chong, N.-Y., School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Ishikawa, Japan","Human-Robot Interaction (HRI) is one of the most rapidly emerging fields in robotic applications over the years. One direction of the improvements in the HRI field is by adding the capability of emotional understanding as a fundamental part of human-human interaction necessities. Human emotion understanding has been studied through the well-known Heart Rate Variability (HRV) analysis recently. In this paper, two different methods of classification are proposed to find the relations between activity, heart rate, and emotional states. Two individual k-Nearest Neighborhood (kNN)-based classifications used in the first method and implemented for each dataset of pre-processed accelerometer data and HRV data where both aim to estimate the user's emotion and activity data at the same time. The features of the frequency domain-based HRV data and the user's activity data are combined into a new dataset and two different classifiers of Multilayer Perceptron (MLP) and Support Vector Machines (SVM) were used in the experimental evaluations. Performance comparisons are presented to show the efficiency. Results from both methods are analyzed and reported in this paper. © 2020 IEEE.","accelerometer; activity estimation; emotional state estimation; heart rate variability; HRV; IBI; inter beat intervals; k nearest neighbors; multilayer perceptron; support vector machines","Agricultural robots; Classification (of information); Frequency domain analysis; Heart; Multilayer neural networks; Nearest neighbor search; Robotics; Support vector machines; Experimental evaluation; Heart rate variability; Human robot Interaction (HRI); Human-human interactions; K-nearest neighborhoods; Multi layer perceptron; Performance comparison; Robotic applications; Human robot interaction",,,,,"European Commission, EC; Ministry of Internal Affairs and Communications, MIC","This work is supported under the CARESSES (Culture-Aware Robots and Environmental Sensor Systems for Elderly Support) project; funded by the European Union and the Ministry of Internal Affairs and Communications of Japan. For more information about the project; please look up at http://caressesrobot.org/en/project/ or email: info@caressesrobot.org All authors are with the School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), Nomi, Ishikawa, Japan",,,,,,,,,,"J., Heart rate variability analysis as an index of emotion regulation processes: Interest of the analgesia nociception index (ani) (2012) Proc. 34th Annual International Conference of the Ieee Embs, pp. 3432-3435; Y., Neural network-based emotion using heart rate variability and skin resistance (2005) Proc. Of Advanced in Natural Computation Part 1, Icnc 2005, pp. 818-824; Yu, S.-N., Chen, S.-F., Emotion state identification based on heart rate variability and genetic algorithm (2015) Proc. 37th Annual International Conference of the Ieee Engineering in Medicine and Biology Society, pp. 538-541; J., Emotion recognition through cardiovascular response in daily life using knn classifier, ser (2018) Advances in Computer Science and Ubiquitous Computing. Cute 2017, Csa 2017. Lecture Notes in Electrical Engineering, 474. , Singapore: Springer; G., Heart rate variability signal features for emotion recognition by using principal component analysis and support vector machine (2012) Proc. 16th International Conference on Bioinformatics and Bioengineering, pp. 274-277; N., A potential approach for emotion prediction using heart rate signal (2017) Proc 9th International Conference on Knowledge and System Engineering, pp. 221-226; Ménard, M., Richard, P., Hamdi, H., Daucé, B., Yamaguchi, T., Emotion recognition based on heart rate and skin conductance (2015) PhyCS, pp. 26-32; Brouwer, A.-M., Van Dam, E., Van Erp, J.B., Spangler, D.P., Brooks, J.R., Improving real-life estimates of emotion based on heart rate: A perspective on taking metabolic heart rate into account (2018) Frontiers in Human Neuroscience, 12; Rakshit, R., Reddy, V.R., Deshpande, P., Emotion detection and recognition using hrv features derived from photoplethysmogram signals (2016) Proceedings of the 2nd Workshop on Emotion Representations and Modelling for Companion Systems, p. 2; K., (2010) Ganong's Review of Medical Physiology, , 23rd ed. Mc-GrawHill Medical; Cui, T., (2013) Spectrum Analysis of Heart Rate Variability (HRV), , student paper, September; Heart rate variability: Standards of measurement, physiological interpretation, and clinical use (1996) European Heart Journal, 17, pp. 354-381. , T. F. of the European Society of Cardiology, the North American Society of Pacing, and Electrophysiology; Von Rosenberg, W., Resolving ambiguities in the lf/hf ratio: Lf-hf scatter plots for the categorization of mental and physical stress from hrv (2017) Frontiers in Physiology, 8. , June; Fred, S., Ginsberg, J., An overview of heart rate variability metrics and norms (2017) Frontiers in Public Health, 5, p. 258. , September; Tom, K., Methodological aspects of heart rate variability analysis: Heart rate variability (hrv) signal analysis, ser (2012) Heart Rate Variability (HRV) Signal Analysis, , CRC Press, October; Clifford, G.D., (2002) Signal Processing Methods for Heart Rate Variability, , Ph.D. dissertation, Oxford University; (2019) Empatica E4 Product Page, , https://www.empatica.com/research/e4, January; (2019) SiliconLabs BlueGiga BLED112-v1 Product Page, , https://www.silabs.com/products/wireless/bluetooth/bluetooth-low-energy-modules/bled112-bluetooth-smart-dongle, January; (2019) Empatica Ble Server for Windows (Beta) Product Page, , http://developer.empatica.com/windows-ble-server.html, January; Bartels, R., (2019) Pythonic Package for Heart Rate Variability Analysis Version 0.2.3, , https://github.com/rhenanbartels/hrv, January",,,,"Institute of Electrical and Electronics Engineers Inc.","4th IEEE International Conference on Robotic Computing, IRC 2020","9 November 2020 through 11 November 2020",,166083,,9781728152370,,,"English","Proc. - IEEE Int. Conf. Robot. Comput., IRC",Conference Paper,"Final","",Scopus,2-s2.0-85099383484
"Bagheri E., Esteban P.G., Cao H.-L., Beir A.D., Lefeber D., Vanderborght B.","57208165169;55328694600;56650447400;56203275100;55161380600;57191490554;","An autonomous cognitive empathy model responsive to users' facial emotion expressions",2020,"ACM Transactions on Interactive Intelligent Systems","10","3","3341198","","",,4,"10.1145/3341198","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097225306&doi=10.1145%2f3341198&partnerID=40&md5=6b3056773f580546273a9ec06bea4681","Robotics and Multibody Mechanics Research Group, Vrije Universiteit Brussel and Flanders Make, Pleinlaan 2, Brussels, 1050, Belgium","Bagheri, E., Robotics and Multibody Mechanics Research Group, Vrije Universiteit Brussel and Flanders Make, Pleinlaan 2, Brussels, 1050, Belgium; Esteban, P.G., Robotics and Multibody Mechanics Research Group, Vrije Universiteit Brussel and Flanders Make, Pleinlaan 2, Brussels, 1050, Belgium; Cao, H.-L., Robotics and Multibody Mechanics Research Group, Vrije Universiteit Brussel and Flanders Make, Pleinlaan 2, Brussels, 1050, Belgium; Beir, A.D., Robotics and Multibody Mechanics Research Group, Vrije Universiteit Brussel and Flanders Make, Pleinlaan 2, Brussels, 1050, Belgium; Lefeber, D., Robotics and Multibody Mechanics Research Group, Vrije Universiteit Brussel and Flanders Make, Pleinlaan 2, Brussels, 1050, Belgium; Vanderborght, B., Robotics and Multibody Mechanics Research Group, Vrije Universiteit Brussel and Flanders Make, Pleinlaan 2, Brussels, 1050, Belgium","Successful social robot services depend on how robots can interact with users. The effective service can be obtained through smooth, engaged, and humanoid interactions in which robots react properly to a user's affective state. This article proposes a novel Automatic Cognitive Empathy Model, ACEM, for humanoid robots to achieve longer and more engaged human-robot interactions (HRI) by considering humans' emotions and replying to them appropriately. The proposed model continuously detects the affective states of a user based on facial expressions and generates desired, either parallel or reactive, empathic behaviors that are already adapted to the user's personality. Users' affective states are detected using a stacked autoencoder network that is trained and tested on the RAVDESS dataset. The overall proposed empathic model is verified throughout an experiment, where different emotions are triggered in participants and then empathic behaviors are applied based on proposed hypothesis. The results confirm the effectiveness of the proposed model in terms of related social and friendship concepts that participants perceived during interaction with the robot. © 2020 ACM.","adaptive interaction; Empathy; facial emotion detection; human robot interaction; non-verbal behavior; social robots","Anthropomorphic robots; Affective state; Auto encoders; Facial emotions; Facial Expressions; Human robot Interaction (HRI); Humanoid interaction; Humanoid robot; Users' affective state; Human robot interaction",,,,,"611391; Vlaamse regering","The reviewing of this article was managed by special issue associate editors Shimei Pan, Oliver Brdiczka, Andrea Klein-smith, and Yangqiu Song. The work leading to these results has received funding from the European Commission 7th Framework Program as a part of the DREAM project (grant no. 611391), the ICON project ROBO-CURE, and was partly supported by the Flemish Government under the program “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen.” Authors’ addresses: E. Bagheri, P. G. Esteban, H.-L. Cao, A. De Beir, D. Lefeber, and B. Vanderborght, Robotics and Multi-body Mechanics Research Group, Vrije Universiteit Brussel, Pleinlaan 2, Brussels-1050, Belgium; emails: {elahe.bagheri, pablo.gomez.esteban, hoang.long.cao}@vub.be, albert@debeir.com, dlefeber@vub.ac.be, bram.vanderborght@vub.be. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2020 Association for Computing Machinery. 2160-6455/2020/11-ART20 $15.00 https://doi.org/10.1145/3341198",,,,,,,,,,"Adams, R.G., Blieszner, R., Vries, B.D, Definitions of friendship in the third age: Age, gender, and study location effects. (2000) J. Aging Stud, 14 (1), pp. 117-133. , 2000; Aly, A., Tapus, A., A model for synthesizing a combined verbal and nonverbal behavior based on personality traits in human-robot interaction (2013) Proceedings of the 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI'13). 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"Brahmi B., Saad M., Rahman M.H., Brahmi A.","57193153269;7202075533;57210856870;54945428500;","Adaptive Force and Position Control Based on Quasi-Time Delay Estimation of Exoskeleton Robot for Rehabilitation",2020,"IEEE Transactions on Control Systems Technology","28","6","8795591","2152","2163",,2,"10.1109/TCST.2019.2931522","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092696323&doi=10.1109%2fTCST.2019.2931522&partnerID=40&md5=af7b25ba369825cdc9e2042f549e9d5a","Department of Electrical Engineering, Ecole de Technologie Superieure, Montreal, QC, Canada; Department of Mechanical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, United States","Brahmi, B., Department of Electrical Engineering, Ecole de Technologie Superieure, Montreal, QC, Canada; Saad, M., Department of Electrical Engineering, Ecole de Technologie Superieure, Montreal, QC, Canada; Rahman, M.H., Department of Mechanical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, United States; Brahmi, A., Department of Electrical Engineering, Ecole de Technologie Superieure, Montreal, QC, Canada","Rehabilitation robots have become an influential tool in physical therapy treatment since they are able to provide an intensive rehabilitation treatment for a long period of time. However, this technology still suffers from various problems such as dynamics uncertainties, external disturbances, and human-robot interaction. In this paper, we present a new integral second-order terminal sliding mode control incorporating quasi-time delay estimation (Q-TDE) applied to an exoskeleton robot with dynamics uncertainties and unknown bounded disturbances. Unlike the conventional TDE approach, the proposed Q-TDE uses delayed one step only of the control input of the system to approximate the uncertain dynamics while avoiding the delays on all states of the system. The proposed controller aims to perform passive and active rehabilitation protocols without the need for velocity and acceleration measurements of the robot system. A finite time of both selected sliding surface and estimation error simultaneous is achieved using an appropriate Lyapunov function. Experimental results with healthy subjects found using a virtual reality environment confirm the effectiveness of the proposed control. © 1993-2012 IEEE.","Lyapunov function; passive and active assistive motion; quasi-time delay estimation (Q-TDE); rehabilitation robots; second-order sliding mode","Acceleration measurement; Bioelectric phenomena; Delay control systems; Dynamics; Exoskeleton (Robotics); Lyapunov functions; Position control; Sliding mode control; Time delay; Timing circuits; Active rehabilitations; Bounded disturbances; Dynamics uncertainties; External disturbances; Rehabilitation robot; Terminal sliding mode control; Time delay estimation; Virtual-reality environment; Human robot interaction",,,,,,"Manuscript received May 9, 2019; revised July 6, 2019; accepted July 12, 2019. Date of publication August 13, 2019; date of current version October 9, 2020. Manuscript received in final form July 23, 2019. This work was supported by the Power Electrics and Industrial Control Research Group (GREPCI), École de technologie supérieure, Montreal, Canada. Recommended by Associate Editor G. Rovithakis. (Corresponding author: Brahim Brahmi.) B. Brahmi, M. Saad, and A. Brahmi are with the Department of Electrical Engineering, École de technologie supérieure, Montreal, QC H3C 1K3, Canada (e-mail: brahim.brahmi.1@ens.etsmtl.ca; maarouf.saad@etsmtl.ca; abdelkrim.brahmi.1@ens.etsmtl.ca).",,,,,,,,,,"Lundström, E., Terént, A., Borg, J., Prevalence of disabling spasticity 1 year after first-ever stroke (2008) Eur. J. 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Brigham Health Women's Hospital; Craig, J.J., (2009) Introduction to Robotics: Mechanics and Control, 3rd Ed, , New Delhi, India: Pearson; Brahmi, B., Saad, M., Luna, C.O., Archambault, P.S., Rahman, M.H., Passive and active rehabilitation control of human upper-limb exoskeleton robot with dynamic uncertainties (2018) Robotica, 36 (11), pp. 1757-1779","Brahmi, B.; Department of Electrical Engineering, Canada; email: brahim.brahmi.1@ens.etsmtl.ca",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10636536,,IETTE,,"English","IEEE Trans Control Syst Technol",Article,"Final","",Scopus,2-s2.0-85092696323
"Morrison T., Su H.-J.","57208405912;8379272100;","Stiffness modeling of a variable stiffness compliant link",2020,"Mechanism and Machine Theory","153",,"104021","","",,8,"10.1016/j.mechmachtheory.2020.104021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087984163&doi=10.1016%2fj.mechmachtheory.2020.104021&partnerID=40&md5=12d568cb55890259bf05a0b56f0d27c2","Department of Mechanical and Aerospace Engineering, The Ohio State University, 201 W. 19th Avenue, Columbus, OH  43210, United States","Morrison, T., Department of Mechanical and Aerospace Engineering, The Ohio State University, 201 W. 19th Avenue, Columbus, OH  43210, United States; Su, H.-J., Department of Mechanical and Aerospace Engineering, The Ohio State University, 201 W. 19th Avenue, Columbus, OH  43210, United States","Recently, a variable stiffness robotic link based on the rotating beam concept has been developed for applications in physical human robot interaction. A substantial challenge for design of such links is the modeling of stiffness behavior to permit stiffness control. In this paper we present a general 3D model of the link stiffness using screw theory and compliance matrices as well as a planar model for the lateral and torsional stiffness. Since axial buckling is a major failure mode, we also derive an analytical model for predicting axial buckling behavior. The analytical models are compared to the finite element method and experimental results. One of the challenges involved in design and analysis of variable stiffness links is the parasitic compliance of the mechanical elements that support and drive the active portion of the mechanism. For the design analyzed in this paper, we use the models we derive to identify the major sources of parasitic compliance and suggest optimizations to minimize their effects. These results can be used as guidelines for designing variable stiffness links. © 2020","Compliance matrix; Compliant mechanisms; Mechanics modeling; Screw theory; Variable stiffness","3D modeling; Analytical models; Buckling; Human robot interaction; Axial buckling behaviors; Design and analysis; Mechanical elements; Physical human-robot interactions; Stiffness control; Stiffness model; Torsional stiffness; Variable stiffness; Stiffness",,,,,"National Science Foundation, NSF: 1637656, CMMI-1637656","This material is based upon work supported by the National Science Foundation under Grant No: CMMI-1637656 . Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.",,,,,,,,,,"She, Y., Su, H.-J., Meng, D., Lai, C., Design and modeling of a continuously tunable stiffness arm for safe physical human robot interaction (2020) J. Mech. 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McGraw-Hill Education New York, NY, USA; Timoshenko, S.P., Gere, J.M., Theory of Elastic Stability (2009), second ed. Dover Publications","Morrison, T.; Department of Mechanical and Aerospace Engineering, 201 W. 19th Avenue, United States; email: morrison.730@osu.edu",,,"Elsevier Ltd",,,,,0094114X,,MHMTA,,"English","Mech Mach Theory",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85087984163
"Chacon-Quesada R., Demiris Y.","57194648737;6506125343;","Augmented reality user interfaces for heterogeneous multirobot control",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341422","11439","11444",,,"10.1109/IROS45743.2020.9341422","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102402887&doi=10.1109%2fIROS45743.2020.9341422&partnerID=40&md5=27adb9862f898d573d3858a55e871f70","Imperial College Lon-don, Personal Robotics Laboratory, Department of Electrical and Electronic Engineering, London, SW7 2AZ, United Kingdom","Chacon-Quesada, R., Imperial College Lon-don, Personal Robotics Laboratory, Department of Electrical and Electronic Engineering, London, SW7 2AZ, United Kingdom; Demiris, Y., Imperial College Lon-don, Personal Robotics Laboratory, Department of Electrical and Electronic Engineering, London, SW7 2AZ, United Kingdom","Recent advances in the design of head-mounted augmented reality (AR) interfaces for assistive human-robot interaction (HRI) have allowed untrained users to rapidly and fluently control single-robot platforms. In this paper, we investigate how such interfaces transfer onto multirobot architectures, as several assistive robotics applications need to be distributed among robots that are different both physically and in terms of software. As part of this investigation, we introduce a novel head-mounted AR interface for heterogeneous multirobot control. This interface generates and displays dynamic joint-affordance signifiers, i.e. signifiers that combine and show multiple actions from different robots that can be applied simultaneously to an object. We present a user study with 15 participants analysing the effects of our approach on their perceived fluency. Participants were given the task of filling-out a cup with water making use of a multirobot platform. Our results show a clear improvement in standard HRI fluency metrics when users applied dynamic joint-affordance signifiers, as opposed to a sequence of independent actions. © 2020 IEEE.",,"Agricultural robots; Application programs; Augmented reality; Industrial robots; Intelligent robots; Machine design; User interfaces; Affordances; Assistive; Assistive robotics; Human robot Interaction (HRI); Independent action; Multirobots; Single robots; User study; Human robot interaction",,,,,,,,,,,,,,,,"Chacon, R., Demiris, Y., Augmented reality control of smart wheelchair using eye-gaze-enabled selection of affordances (2018) IROS 2018 Workshop on Robots for Assisted Living, , https://spiral.imperial.ac.uk:8443/handle/10044/1/71986; Chacon, R., Demiris, Y., Augmented reality controlled smart wheelchair using dynamic signifiers for affordance representation (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), pp. 4812-4818; Zolotas, M., Elsdon, J., Demiris, Y., Head-mounted augmented reality for explainable robotic wheelchair assistance (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1823-1829. , Oct; Zolotas, M., Demiris, Y., Towards explainable shared control using augmented reality (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3020-3026; Parker, L.E., Alliance: An architecture for fault tolerant multirobot cooperation (1998) IEEE Transactions on Robotics and Automation, 14 (2), pp. 220-240; Balch, T., Parker, L.E., (2002) Robot Teams: From Diversity to Polymor-phism, , AK Peters/CRC Press; Hoffman, G., Evaluating flouency in human-robot collaboration (2019) IEEE Transactions on Human-Machine Systems, 49 (3), pp. 209-218. , June; Gibson, J.J., (1966) The Senses Considered As Perceptual Systems, , Boston, MA, USA: Houghton Mifin; Norman, D., (2013) The Design of Everyday Things: Revised and Expanded Edition, , Constellation; Ardón, P., Pairet, E., Lohan, K.S., Ramamoorthy, S., Petrick, R., (2020) Affordances in Robotic Tasks-A Survey; McMahon, S., Sünderhauf, N., Upcroft, B., Milford, M., Multimodal trip hazard affordance detection on construction sites (2017) IEEE Robotics and Automation Letters, 3 (1), pp. 1-8; Min, H., Luo, R., Affordance matching from the shared information in multi-robot (2015) 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 66-71; Koppula, H.S., Saxena, A., Anticipating human activities using object affordances for reactive robotic response (2016) IEEE Transactions on Pattern Analysis and Machine Intelligence, 38 (1), pp. 14-29; Chuang, C.-Y., Li, J., Torralba, A., Fidler, S., Learning to act properly: Predicting and explaining affordances from images (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 975-983; Nagarajan, T., Feichtenhofer, C., Grauman, K., Grounded humanobject interaction hotspots from video (2019) Proceedings of the IEEE International Conference on Computer Vision, pp. 8688-8697; Li, M., Alvarez, A., De Pellegrini, F., Prabhakaran, B., Chlamtac, I., Robotrak: A centralized real-time monitoring, control, and coordination system for robot swarms (2007) Proceedings of the 1st International Conference on Robot Communication and Coordination, pp. 1-4; Bashyal, S., Venayagamoorthy, G.K., Human swarm interaction for radiation source search and localization (2008) 2008 IEEE Swarm Intelligence Symposium, pp. 1-8; Ding, X.C., Powers, M., Egerstedt, M., Young, R., An optimal timing approach to controlling multiple uavs (2009) 2009 American Control Conference, pp. 5374-5379; Martins, M.F., Demiris, Y., Learning multirobot joint action plans from simultaneous task execution demonstrations (2010) AAMAS, pp. 931-938; Kira, Z., Potter, M.A., Exerting human control over decentralized robot swarms (2009) 2009 4th International Conference on Autonomous Robots and Agents, pp. 566-571; Kolling, A., Nunnally, S., Lewis, M., Towards human control of robot swarms (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-robot Interaction, pp. 89-96; Patel, J., Xu, Y., Pinciroli, C., Mixed-granularity human-swarm interaction (2019) 2019 International Conference on Robotics and Au-tomation (ICRA), pp. 1059-1065. , May; Daily, M., Cho, Y., Martin, K., Payton, D., World embedded interfaces for human-robot interaction (2003) 36th Annual Hawaii In-ternational Conference on System Sciences, 2003. Proceedings of the, p. 6; Sahin, E., Cakmak, M., Dogar, M.R., Ugur, E., Üçoluk, G., To afford or not to afford: A new formalization of affordances toward affordance-based robot control (2007) Adaptive Behavior, 15 (4), pp. 447-472",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102402887
"Qiu S., Liu H., Zhang Z., Zhu Y., Zhu S.-C.","57219797230;57200626348;57211250285;57140988800;22236080700;","Human-robot interaction in a shared augmented reality workspace",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340781","11413","11418",,1,"10.1109/IROS45743.2020.9340781","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102400989&doi=10.1109%2fIROS45743.2020.9340781&partnerID=40&md5=ef60f1810b91c6d84531f62cc044759d","UCLA Center for Vision, Cognition, Learning, and Autonomy (VCLA), Statistics Department","Qiu, S., UCLA Center for Vision, Cognition, Learning, and Autonomy (VCLA), Statistics Department; Liu, H., UCLA Center for Vision, Cognition, Learning, and Autonomy (VCLA), Statistics Department; Zhang, Z., UCLA Center for Vision, Cognition, Learning, and Autonomy (VCLA), Statistics Department; Zhu, Y., UCLA Center for Vision, Cognition, Learning, and Autonomy (VCLA), Statistics Department; Zhu, S.-C., UCLA Center for Vision, Cognition, Learning, and Autonomy (VCLA), Statistics Department","We design and develop a new shared Augmented Reality (AR) workspace for Human-Robot Interaction (HRI), which establishes a bi-directional communication between human agents and robots. In a prototype system, the shared AR workspace enables a shared perception, so that a physical robot not only perceives the virtual elements in its own view but also infers the utility of the human agent - the cost needed to perceive and interact in AR - by sensing the human agent's gaze and pose. Such a new HRI design also affords a shared manipulation, wherein the physical robot can control and alter virtual objects in AR as an active agent; crucially, a robot can proactively interact with human agents, instead of purely passively executing received commands. In experiments, we design a resource collection game that qualitatively demonstrates how a robot perceives, processes, and manipulates in AR and quantitatively evaluates the efficacy of HRI using the shared AR workspace. We further discuss how the system can potentially benefit future HRI studies that are otherwise challenging. © 2020 IEEE.",,"Agricultural robots; Augmented reality; Intelligent robots; Machine design; Man machine systems; Active agents; Bi-directional communication; Human robot Interaction (HRI); Physical robots; Prototype system; Resource collections; Virtual elements; Virtual objects; Social robots",,,,,"Defense Advanced Research Projects Agency, DARPA: XAI N66001-17-2-4029; Office of Naval Research, ONR: MURI N00014-16-1-2007, N00014-19-1-2153","‹ Shuwen Qiu and Hangxin Liu contributed equally to this work. UCLA Center for Vision, Cognition, Learning, and Autonomy (VCLA) at Statistics Department. Emails: {s.qiu, hx.liu, zeyuzhang, yixin.zhu}@ucla.edu, sczhu@stat.ucla.edu. 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(2009) The Royal Society B, 276 (1655), pp. 247-254; Hunt, G.R., Manufacture and use of hook-tools by new caledonian crows (1996) Nature, 379 (6562), pp. 249-251; Deng, E., Mutlu, B., Mataric, M.J., Embodiment in socially interactive robots (2019) Foundations and TrendsR in Robotics, 7 (4), pp. 251-356; Zhu, Y., Gao, T., Fan, L., Huang, S., Edmonds, M., Liu, H., Gao, F., Zhu, S.-C., Dark, beyond deep: A paradigm shift to cognitive ai with humanlike common sense (2020) Engineering, 6 (3), pp. 310-345",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102400989
"Kartmann R., Zhou Y., Liu D., Paus F., Asfour T.","57207942783;57192426317;57222349028;57200626049;15724268700;","Representing spatial object relations as parametric polar distribution for scene manipulation based on verbal commands",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340925","8373","8380",,,"10.1109/IROS45743.2020.9340925","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102395514&doi=10.1109%2fIROS45743.2020.9340925&partnerID=40&md5=5e8254f7ef778fd0396cfa00d05ccc87","Karlsruhe Institute of Technology, Institute for Anthropomatics and Robotics, Karlsruhe, Germany","Kartmann, R., Karlsruhe Institute of Technology, Institute for Anthropomatics and Robotics, Karlsruhe, Germany; Zhou, Y., Karlsruhe Institute of Technology, Institute for Anthropomatics and Robotics, Karlsruhe, Germany; Liu, D., Karlsruhe Institute of Technology, Institute for Anthropomatics and Robotics, Karlsruhe, Germany; Paus, F., Karlsruhe Institute of Technology, Institute for Anthropomatics and Robotics, Karlsruhe, Germany; Asfour, T., Karlsruhe Institute of Technology, Institute for Anthropomatics and Robotics, Karlsruhe, Germany","Understanding spatial relations is a key element for natural human-robot interaction. Especially, a robot must be able to manipulate a given scene according to a human verbal command specifying desired spatial relations between objects. To endow robots with this ability, a suitable representation of spatial relations is necessary, which should be derivable from human demonstrations. We claim that polar coordinates can capture the underlying structure of spatial relations better than Cartesian coordinates and propose a parametric probability distribution defined in polar coordinates to represent spatial relations. We consider static spatial relations such as left of, behind, and near, as well as dynamic ones such as closer to and other side of, and take into account verbal modifiers such as roughly and a lot. We show that adequate distributions can be derived for various combinations of spatial relations and modifiers in a sample-efficient way using Maximum Likelihood Estimation, evaluate the effects of modifiers on the distribution parameters, and demonstrate our representation's usefulness in a pick-and-place task on a real robot. © 2020 IEEE.",,"Agricultural robots; Human robot interaction; Intelligent robots; Maximum likelihood estimation; Cartesian coordinate; Distribution parameters; Human demonstrations; Key elements; Pick and place; Polar coordinate; Spatial objects; Spatial relations; Spatial distribution",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 01IS18040A; Bundesministerium für Bildung und Forschung, BMBF","This work has been supported by the German Federal Ministry of Education and Research (BMBF) under the project OML (01IS18040A).",,,,,,,,,,"Krüger, N., Geib, C., Piater, J., Petrick, R., Steedman, M., Wörgötter, F., Ude, A., Dillmann, R., Object-action complexes: Grounded abstractions of sensorimotor processes (2011) Robotics and Autonomous Systems, 59, pp. 740-757; Rosman, B., Ramamoorthy, S., Learning spatial relationships between objects (2011) International Journal of Robotics Research, 30 (11), pp. 1328-1342; Mees, O., Abdo, N., Mazuran, M., Burgard, W., Metric learning for generalizing spatial relations to new objects (2017) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3175-3182. , Sept; Ziaeetabar, F., Kulvicius, T., Tamosiunaite, M., Wörgötter, F., Recognition and prediction of manipulation actions using enriched semantic event chains (2018) Robotics and Autonomous Systems (RAS), 110, pp. 173-188. , Dec; Dreher, C.R.G., Wächter, M., Asfour, T., Learning object-action relations from bimanual human demonstration using graph networks (2019) IEEE Robotics and Automation Letters, , Oct; Mees, O., Emek, A., Vertens, J., Burgard, W., Learning object placements for relational instructions by hallucinating scene representations (2020) IEEE International Conference on Robotics and Automation (ICRA); Tellex, S., Kollar, T., Dickerson, S., Walter, M.R., Banerjee, A.G., Teller, S., Roy, N., Understanding natural language commands for robotic navigation and mobile manipulation (2011) Twenty-Fifth AAAI Conference on Artificial Intelligence, , Aug; Shridhar, M., Mittal, D., Hsu, D., INGRESS: Interactive visual grounding of referring expressions (2020) The International Journal of Robotics Research, 39 (2-3), pp. 217-232; Muthugala, M.A.V.J., Jayasekara, A.G.B.P., A review of service robots coping with uncertain information in natural language instructions (2018) IEEE Access, 6, pp. 12913-12928; Sjöö, K., Jensfelt, P., Learning spatial relations from functional simulation (2011) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1513-1519. , Sept; Fichtl, S., McManus, A., Mustafa, W., Kraft, D., Krüger, N., Guerin, F., Learning spatial relationships from 3D vision using histograms (2014) IEEE International Conference on Robotics and Automation (ICRA), pp. 501-508. , May; Zampogiannis, K., Yang, Y., Fermuller, C., Aloimonos, Y., Learning the spatial semantics of manipulation actions through preposition grounding (2015) IEEE International Conference on Robotics and Automation (ICRA), pp. 1389-1396; Aksoy, E.E., Zhou, Y., Wächter, M., Asfour, T., Enriched manipulation action semantics for robot execution of time constrained tasks (2016) IEEE/RAS International Conference on Humanoid Robots (Humanoids), pp. 109-116. , Nov; Ziaeetabar, F., Kulvicius, T., Tamosiunaite, M., Wörgötter, F., Prediction of manipulation action classes using semantic spatial reasoning (2018) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3350-3357. , Oct; Savarimuthu, T.R., Buch, A.G., Schlette, C., Wantia, N., Roßmann, J., Martínez, D., Alenyà, G., Krüger, N., Teaching a robot the semantics of assembly tasks (2018) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48 (5), pp. 670-692. , May; Stopp, E., Gapp, K.-P., Herzog, G., Laengle, T., Lueth, T.C., Utilizing spatial relations for natural language access to an autonomous mobile robot (1994) KI-94: Advances in Artificial Intelligence, Ser. Lecture Notes in Computer Science, pp. 39-50. , Springer; Tan, J., Ju, Z., Liu, H., Grounding spatial relations in natural language by fuzzy representation for human-robot interaction (2014) IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1743-1750. , July; Bao, J., Hong, Z., Tang, H., Cheng, Y., Jia, Y., Xi, N., Teach robots understanding new object types and attributes through natural language instructions (2016) International Conference on Sensing Technology (ICST), 10, pp. 1-6. , Nov; Forbes, M., Rao, R.P.N., Zettlemoyer, L., Cakmak, M., Robot programming by demonstration with situated spatial language understanding (2015) IEEE International Conference on Robotics and Automation (ICRA), pp. 2014-2020. , May; Paul, R., Arkin, J., Roy, N., Howard, T.M., Efficient grounding of abstract spatial concepts for natural language interaction with robot manipulators (2016) Robotics: Science and Systems (RSS), 12. , June; Shridhar, M., Hsu, D., Interactive visual grounding of referring expressions for human-robot interaction (2018) Robotics: Science & Systems (RSS); Jund, P., Eitel, A., Abdo, N., Burgard, W., Optimization beyond the convolution: Generalizing spatial relations with end-to-end metric learning (2018) IEEE International Conference on Robotics and Automation (ICRA), pp. 4510-4516. , May; Nicolescu, M., Arnold, N., Blankenburg, J., Feil-Seifer, D., Banisetty, S.B., Nicolescu, M., Palmer, A., Monteverde, T., Learning of complex-structured tasks from verbal instruction (2019) IEEE/RAS International Conference on Humanoid Robots (Humanoids), p. 8. , Oct; Guadarrama, S., Riano, L., Golland, D., Göhring, D., Jia, Y., Klein, D., Abbeel, P., Darrell, T., Grounding spatial relations for human-robot interaction (2013) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1640-1647. , Nov; Skubic, M., Perzanowski, D., Blisard, S., Schultz, A., Adams, W., Bugajska, M., Brock, D., Spatial language for human-robot dialogs (2004) IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 34 (2), pp. 154-167. , May; Jiang, Y., Zheng, C., Lim, M., Saxena, A., Learning to place new objects (2012) IEEE International Conference on Robotics and Automation (ICRA), pp. 3088-3095. , May; Jiang, Y., Lim, M., Saxena, A., Learning object arrangements in 3d scenes using human context (2012) International Conference on Machine Learning (ICML), , June; Chang, A., Savva, M., Manning, C.D., Learning spatial knowledge for text to 3d scene generation (2014) Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, pp. 2028-2038. , Oct; Azad, P., Asfour, T., Dillmann, R., Combining Harris interest points and the SIFT descriptor for fast scale-invariant object recognition (2009) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4275-4280. , Oct; Zhou, Y., Gao, J., Asfour, T., Learning via-point movement primitives with inter-and extrapolation capabilities (2019) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Bhattacharyya, A.K., On a measure of divergence between two statistical populations defined by their probability distributions (1943) Bulletin of the Calcutta Mathematical Society, 35, pp. 99-109; Coleman, G.B., Andrews, H.C., Image segmentation by clustering (1979) Proceedings of the IEEE, 67 (5), pp. 773-785. , May",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102395514
"Ma C., Tan Q., Xu C., Zhao J., Wang X., Sun J.","56941211200;36816552800;57215777029;57210474184;57219095264;56352307300;","Comparative Analysis on Few Typical Algorithms of Gesture Recognition for Human-robot Interaction",2020,"PervasiveHealth: Pervasive Computing Technologies for Healthcare",,,"3425047","","",,,"10.1145/3424978.3425047","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094922400&doi=10.1145%2f3424978.3425047&partnerID=40&md5=be06b5b4dc6d74286d4e4391a95846c4","Beijing Key Laboratory of Intelligent Space Robotic Systems, Technology and Applications, Beijing Institute of Spacecraft System Engineering, Beijing, China; School of Instrument Science and Optoelectronic Engineering, Beijing Information Science and Technology University, Beijing, China","Ma, C., Beijing Key Laboratory of Intelligent Space Robotic Systems, Technology and Applications, Beijing Institute of Spacecraft System Engineering, Beijing, China; Tan, Q., Beijing Key Laboratory of Intelligent Space Robotic Systems, Technology and Applications, Beijing Institute of Spacecraft System Engineering, Beijing, China; Xu, C., Beijing Key Laboratory of Intelligent Space Robotic Systems, Technology and Applications, Beijing Institute of Spacecraft System Engineering, Beijing, China; Zhao, J., School of Instrument Science and Optoelectronic Engineering, Beijing Information Science and Technology University, Beijing, China; Wang, X., School of Instrument Science and Optoelectronic Engineering, Beijing Information Science and Technology University, Beijing, China; Sun, J., Beijing Key Laboratory of Intelligent Space Robotic Systems, Technology and Applications, Beijing Institute of Spacecraft System Engineering, Beijing, China","Human-robot interaction (HRI) is considered as one of the key techniques of space intelligence robots. Few typical features of complicated human actions need to be captured and understood accurately by intelligent robot to ensure free communication and interaction between both above in real-time, especially for identifying and tracking hand state. There are four approaches for gesture recognition, including algorithm based on Kinect V2 SDK, model-based particle swarm optimization algorithm (PSO), deep learning algorithm and gesture module based on Baidu AI platform. These have been selected to compare in the form of principle, calculating time, robustness, range, environmental adaptability and correct rate respectively. The comparative results have generalized that both the second and the third algorithm have better performance than other algorithms in the above aspects such as calculating efficiency, robustness, detecting range, external disturbance and correct ratio. Particularly, the second algorithm is not only suitable for close range, but also suitable for multi-view cases. However, the third algorithm can have better performance, but depends on precise network model and weights by introducing lots of positive and negative gesture samples. © 2020 ACM.","Deep learning; Gesture recognition; Human-robot interaction; Particle swarm optimization","Deep learning; Gesture recognition; Intelligent robots; Man machine systems; Particle swarm optimization (PSO); Social robots; Calculating efficiencies; Comparative analysis; Environmental adaptability; External disturbances; Free communications; Human robot Interaction (HRI); Intelligence robots; Particle swarm optimization algorithm; Learning algorithms",,,,,"030601, HTKJ2019KL502016; National Natural Science Foundation of China, NSFC: 51975044; Beijing Information Science and Technology University, BISTU","This research was supported, in part, by the National Natural Science Foundation of China (No. 51975044), the Science and Technology on Space Intelligent Control Laboratory (No. HTKJ2019KL502016), the China Advance Research for Manned Space Project (No. 030601) and the 2019 Practice Training Program of Beijing Information Science and Technology University.",,,,,,,,,,"Xuefeng, C., Research on robot control technology based on human posture recognition (2014) Wuhan University of Science and Technology, pp. 2-5; Zhihua, H., Dawei, T., Qijie, Z., A human-robot-interaction-based navigation system for mobile service robot (2009) Robot, 31 (3), pp. 248-250; Mehrabian, A., Nonverbal communication (1977) Transaction Publishers, America; Jiewang, C., Chunxia, G., Somatosensory information: Concept, characteristics and management (2014) Journal of Intelligence, 33 (11), pp. 150-151; Lei, L., Jinling, Z., A static gesture recognition method based on data glove (2015) Journal of Computer-Aided Design & Computer Graphics, 27 (12), pp. 2410-2418; Uddin, Z.M.Z., Thang, N.D., Human activity recognition using body joint-anglefeatures and hidden markov model (2011) Etri. Journal, 33 (4), pp. 569-579; Erol, A., Bebis, G., Nicolescu, M., Vision-based Hand Pose Estimation: A review (2007) Computer Vision and Image Understanding, 108 (1-2), pp. 52-73; Romero, J., Kjellstrm, H., Kragic, D., Monocular real-time 3d articulated hand pose estimation (2009) 9th IEEE-RAS International Conference on Humanoid Robots, pp. 87-92; Shotton, J., Fitzgibbon, A., Cook, M., Real-time human pose recognition in parts from single depth images (2011) IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2011, pp. 1297-1304; Oikonomidis, I., Kyriazis, N., Markerless and effificient 26-dof hand pose recovery (2010) 10th Asian Conference on Computer Vision, ACCV, 2010, pp. 744-757; De La Gorce, M., Paragios, N., Fleet, D.J., Model-based hand tracking with texture, shading and self-occlusions (2008) IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2008, pp. 1-8; Kerdvibulvech, C., Human hand motion recognition using an extended particle filter[j] (2014) AMDO, pp. 71-80; Siriborvornratanakul, T., Enhancing user experiences of mobile-based augmented reality via spatial augmented reality: Designs and architectures of projector-camera devices (2018) Advances in Multimedia, pp. 81947261-819472617; Kerdvibulvech, C., A review of augmented reality-based human-computer interaction applications of gesture-based interaction (2019) International Conference on Human-Computer Interaction, Springer, Cham, pp. 233-242; Pan, W., Wei, G., A gesture recognition research and application based on kinect (2018) Computer and Digital Engineering, 46 (8), pp. 1660-1661; Antonis, A.A., Lourakis, M., Real-time tracking of multiple skincolored objects with a possibly moving camera (2004) Lecture Notes in Computer Science, pp. 368-379; Oikonomidis, I., Kyriazis, N., Full dof tracking of a hand interacting with an object by modeling occlusions and physical constraints (2011) IEEE International Conference on Computer Vision, ICCV 2011, 1, pp. 2088-2095; Kennedy, J., Eberhart, R., Particle swarm optimization (1995) IEEE International Conference on Neural Networks, 4, pp. 1942-1948; Hongling, Y., Shibin, X., Yuanbin, M., Hand gesture recognition based on convolutional neural network (2016) Computer Technology and Development, 28 (7), pp. 13-14; https://ai.baidu.com/, Baidu AI Homepage",,"Emrouznejad A.","Association for Science and Engineering (ASciE)","ICST","4th International Conference on Computer Science and Application Engineering, CSAE 2020","20 October 2020 through 22 October 2020",,163982,21531633,9781450377720,,,"English","PervasiveHealth: Pervasive Computing Technol Healthc.",Conference Paper,"Final","",Scopus,2-s2.0-85094922400
"Li H., Liu C., Yan L., Zhang B., Li D., Zhang Q.","36461195800;57235357900;23981745100;57190679647;8450346800;9636699000;","Research on Impendence Control of an Upper Limb Exoskeleton Robot and Joint Experiments [上肢外骨骼机器人的阻抗控制与关节试验研究]",2020,"Jixie Gongcheng Xuebao/Journal of Mechanical Engineering","56","19",,"200","209",,,"10.3901/JME.2020.19.200","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097732877&doi=10.3901%2fJME.2020.19.200&partnerID=40&md5=8e029056f7fdb50b8236a1901567c617","School of Automation, Beijing University of Posts and Telecommunications, Beijing, 100876, China; School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, China; School of Electromechanical Engineering, Beijing Information Science and Technology University, Beijing, 100192, China","Li, H., School of Automation, Beijing University of Posts and Telecommunications, Beijing, 100876, China; Liu, C., School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, China; Yan, L., School of Automation, Beijing University of Posts and Telecommunications, Beijing, 100876, China; Zhang, B., School of Automation, Beijing University of Posts and Telecommunications, Beijing, 100876, China; Li, D., School of Automation, Beijing University of Posts and Telecommunications, Beijing, 100876, China; Zhang, Q., School of Electromechanical Engineering, Beijing Information Science and Technology University, Beijing, 100192, China","The exoskeleton robot is worn by the human operator, which can be designed as an assistance, augmentation or interaction device for rehabilitation or teleoperation field. Human-robot interaction control is important for an upper limb exoskeleton robot. An impendence control model for an upper limb exoskeleton robot is proposed and the dynamics model of an integrated joint and the impendence control approach is designed in detail. A position-based impedance control method and a force-based impedance control method are presented to deal with the following task in exoskeleton robot interaction. Parameter improvement method of impedance control is discussed based on characteristics of a secondary system. The numerical simulation model of a single joint's impedance control is established, and the influence of inertia, damping, stiffness and human-robot stiffness parameter on impedance control performance is respectively analyzed by simulation experiments. The physical experiments of a single joint's impedance control are set up. The experimental results show that the collision safety function, gravity compensation, and impedance control are available and the human-robot cooperative movement is verified. © 2020 Journal of Mechanical Engineering.","Human-robot interaction; Impendence control; Robot; Teleoperation; Upper limb exoskeleton","Exoskeleton (Robotics); Joints (anatomy); Stiffness; Cooperative movement; Gravity compensation; Human-robot interaction control; Impedance control methods; Improvement methods; Interaction devices; Physical experiments; Stiffness parameters; Human robot interaction",,,,,,,,,,,,,,,,"PONS, J L., (2008) Wearable robots: Biomechatronic exoskeletons, , [M]. England: John Wiley and Sons; ZHAO, Xingang, TAN, Xiaowei, ZHANG, Bi, Development of soft lower extremity exoskeleton and its key technologies: A survey (2020) Robot, 42 (3), pp. 365-384. , 赵新刚, 谈晓伟, 张弼. 柔性下肢外骨骼机器人研究进展及关键技术分析[J]. 机器人, 2020, 42(3): 365-384. [J]; BOGUE, R., Robotic exoskeletons: A review of recent progress (2015) Industrial Robot: An International Journal, 42 (1), pp. 5-10. , [J]; LOOZE DE, M P, BOSCH, T, KRAUSE, F, Exoskeletons for industrial application and their potential effects on physical work load (2015) Ergonomics, 59 (5), pp. 671-681. , [J]; GIJBELS, D, LAMERS, I, KERKHOFS, L, The Armeo Spring as training tool to improve upper limb functionality in multiple sclerosis: A pilot study (2011) Journal of Neuroengineering and Rehabilitation, 8 (1), p. 5. , [J]; KIM, B, DESHPANDE, A D., An upper-body rehabilitation exoskeleton harmony with an anatomical shoulder mechanism: Design, modeling, control, and performance evaluation (2017) The International Journal of Robotics Research, 36 (4), pp. 414-435. , [J]; TALATY, M, ESQUENAZI, A, BRICENO, J E., Differentiating ability in users of the ReWalk(TM) powered exoskeleton: an analysis of walking kinematics (2013) Proceedings of IEEE 13th International Conference on Rehabilitation Robotics (ICORR), p. 6650469. , [C] Piscataway: IEEE; TUCKER, M R, OLIVIER, J, PAGEL, A, Control strategies for active lower extremity prosthetics and orthotics: A review (2015) Journal of Neuroengineering and Rehabilitation, 12 (1), p. 1. , [J]; LI, Jian, CHEN, Diansheng, TAO, Chunjing, Synthesis and experiment of a lower limb exoskeleton rehabilitation robot (2017) Industrial Robot-An International Journal, 44 (3), pp. 264-274. , [J]; CHEN, Weihai, LI, Zhongyi, CUI, Xiang, Mechanical design and kinematic modeling of a cable-driven arm exoskeleton incorporating inaccurate human limb anthropomorphic parameters (2019) Sensors, 19 (20), p. 4461. , [J]; ZHANG, Leiyu, LI, Jianfeng, LIU, Junhui, Design and human-machine compatibility analysis of co-exos for upper-limb rehabilitation (2018) Journal of Mechanical Engineering, 54 (5), pp. 19-28. , 张雷雨, 李剑锋, 刘钧辉, 等. 上肢康复外骨骼的设计与人机相容性分析[J]. 机械工程学报, 2018, 54(5): 19-28. [J]; HUANG, Rui, WU, Qian, QIU, Jing, Adaptive gait planning with dynamic movement primitives for walking assistance lower exoskeleton in uphill slopes (2020) Sensors and Materials, 32 (4), pp. 1279-1291. , [J]; ZHANG, Guoan, LIU, Gangfeng, MA, Sun, Biomechanical design of escalading lower limb exoskeleton with novel linkage joints (2017) Technology & Health Care, 25 (S1), pp. 267-273. , [J]; REBELO, J, SEDNAOUI, T, DEN EXTER, E B, Bilateral robot teleoperation: A wearable arm exoskeleton featuring an intuitive user interface (2014) IEEE Robotics & Automation Magazine, 21 (4), pp. 62-69. , [J]; SAGARDIA, M, HERTKORN, K, HULIN, T, VR-OOS: The DLR's virtual reality simulator for telerobotic on-orbit servicing with haptic feedback (2015) IEEE Aerospace Conference, pp. 1-17. , [C] Piscataway: IEEE; NAGATANI, K, KIRIBAYASHI, S, OKADA, Y, Emergency response to the nuclear accident at the Fukushima Daiichi nuclear power plants using mobile rescue robots (2013) Journal of Robotic Systems, 30 (1), pp. 44-63. , [J]; SUTTER, B, LELEVE, A, PHAM, M T, A semi-autonomous mobile robot for bridge inspection (2018) Automation in Construction, 91 (7), pp. 111-119. , [J]; MADANI, M, MOALLEM, M., Hybrid position/force control of a flexible parallel manipulator (2011) Journal of the Franklin Institute, 348 (6), pp. 999-1012. , [J]; OTT, C, MUKHERJEE, R, NAKAMURA, Y., Unified impedance and admittance control (2010) IEEE International Conference on Robotics and Automation, pp. 554-561. , [C] Piscataway: IEEE; LIU, Chang, LI, Haiyuan, ZHANG, Qinjian, Configuration comparison and design of an upper limb exoskeleton for robot teleoperation (2019) Proceedings of IEEE International Conference on Mechatronics and Automation (ICMA), pp. 921-926. , [C] Piscataway: IEEE; SICILIANO, B, SCIAVICCO, L, VILLANI, L, (2009) Robotics: modeling, planning and control, , [M]. London: Springer","Zhang, Q.; School of Electromechanical Engineering, China; email: 13671245929@126.com",,,"Chinese Mechanical Engineering Society",,,,,05776686,,CHHKA,,"Chinese","Jixie Gongcheng Xuebao",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85097732877
"Li W., Han Y., Wu J., Xiong Z.","57219602837;57102211300;56984638700;7202956027;","Collision Detection of Robots Based on a Force/Torque Sensor at the Bedplate",2020,"IEEE/ASME Transactions on Mechatronics","25","5","9097380","2565","2573",,7,"10.1109/TMECH.2020.2995904","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094170257&doi=10.1109%2fTMECH.2020.2995904&partnerID=40&md5=99b0c3ea39196f56254aa3289e19d94d","School of Mechanical Engineering, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, China","Li, W., School of Mechanical Engineering, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, China; Han, Y., School of Mechanical Engineering, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, China; Wu, J., School of Mechanical Engineering, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, China; Xiong, Z., School of Mechanical Engineering, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, China","For a collaborative robot that shares the workspaces with humans, physical human-robot interactions are unavoidable. Therefore, it must be capable to detect collisions. Usual collision detection algorithms are based on the robot's dynamic model that frequently suffers from complex joint frictions. The uncertainty of commonly modeled frictions could lead to low sensitivity of the collision detection algorithm. This article presents a novel collision detection scheme based on the robot's dynamic model that calculates the reaction force/torque (F/T) at the bedplate. This dynamic model could avoid modeling joint frictions. To identify the dynamic model, a systematic procedure by measuring the force and torque at the robot's bedplate with an F/T sensor is introduced. Moreover, a compensation method for the F/T sensor signal coupling is developed. The proposed identification procedure has been demonstrated on a six-degree-of-freedom collaborative robot. Finally, collision detection experiments are conducted on a test-bed and humans. The results show that the proposed scheme is reliable and can effectively reduce the impact force with simple reaction strategies. © 2020 IEEE.","Collision detection; force/torque sensor; robot dynamics","Chemical detection; Degrees of freedom (mechanics); Dynamic models; Friction; Signal detection; Collaborative robots; Collision detection; Collision detection algorithm; Compensation method; Force/torque sensor; Identification procedure; Physical human-robot interactions; Six degree-of-freedom; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 91848106; Program of Shanghai Academic Research Leader: 18XD1401700","Manuscript received January 6, 2020; revised March 23, 2020; accepted April 29, 2020. Date of publication May 20, 2020; date of current version October 14, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 91848106 and in part by the Program of Shanghai Academic/Technology Research Leader under Grant 18XD1401700. Recommended by Technical Editor J. Ueda. (Corresponding author: Jianhua Wu.) The authors are with the State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China (e-mail: 05liwang@sjtu.edu.cn; ramoflaple@sjtu.edu.cn; wujh@sjtu.edu.cn; mexiong@sjtu.edu.cn).",,,,,,,,,,"Kuhn, S., Henrich, D., Fast vision-basedminimum distance determination between known and unknown objects (2007) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 2186-2191; Leite, A.C., Almeida-Antonio, T.B., From, P.J., Lizarralde, F., Hsu, L., Control and obstacle collision avoidance method applied to human-robot interaction (2015) Proc. IEEE Int. Workshop Adv. Robot. 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Control Autom., pp. 6878-6882; Yao, J., Zhu, J., Wang, Z., Xu, Y., Zhao, Y., Measurement theory and experimental study of fault-tolerant fully pre-stressed parallel six-component force sensor (2013) IEEE Sensors J., 13 (9), pp. 3472-3482. , Sep; Khalil, W., Dombre, E., (2004) Modeling, Identification and Control of Robots, , Oxford, UK: Butterworth-Heinemann; Gautier, M., Numerical calculation of the base inertial parameters of robots (1990) Proc. IEEE Int. Conf. Robot. Autom., 2, pp. 1020-1025; Swevers, J., Ganseman, C., Tukel, D.B., Schutter J, D., Brussel, H.V., Optimal robot excitation and identification (1997) IEEE Trans. Robot. Autom., 13 (5), pp. 730-740. , Oct; Ayusawa, K., Rioux, A., Yoshida, E., Venture, G., Gautier, M., Generating persistently exciting trajectory based on condition number optimization (2017) Proc. IEEE Int. Conf. Robot. Autom., pp. 6518-6524; Haddadin, S., Albu-Schaffer, A., De Luca, A., Hirzinger, G., Collision detection and reaction: A contribution to safe physical human-robot interaction (2008) IEEE/RSJ Int. Conf. Intell. Robot. Syst., pp. 3356-3363; Hu, G., Gao, Q., Cao, H., Pan, H., Shuang, F.J.I.A., Decoupling analysis of a six-dimensional force sensor bridge fault (2017) IEEE Access, 6, pp. 7029-7036. , Dec; Gautier, M., Dynamic identification of robotswith powermodel (1997) Proc. Int. Conf. Robot. Autom., 3, pp. 1922-1927; Pham, M.T., Gautier, M., Poignet, P., Identification of joint stiffness with bandpass filtering (2001) Proc. ICRA IEEE Int. Conf. Robot. Autom., 3, pp. 2867-2872; (2016) Robotic Devices-Collaborative Robots, ISO 15066","Wu, J.; School of Mechanical Engineering, China; email: wujh@sjtu.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10834435,,IATEF,,"English","IEEE ASME Trans Mechatron",Article,"Final","",Scopus,2-s2.0-85094170257
"Yorozu A., Takahashi M.","51261534300;7406846479;","Estimation of body direction based on gait for service robot applications",2020,"Robotics and Autonomous Systems","132",,"103603","","",,2,"10.1016/j.robot.2020.103603","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089481621&doi=10.1016%2fj.robot.2020.103603&partnerID=40&md5=8cf6bd8956f3da7523712d079cbd013a","Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan; Department of System Design Engineering, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan","Yorozu, A., Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan; Takahashi, M., Department of System Design Engineering, Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan","Recently, there have been several studies on the research and development of service robots, such as reception or waiter robots for facilities and companion robots for support of baggage transportation or guidance in public spaces. Several experimental results in real environments have been reported. To realize socially acceptable human–robot interaction for service robots, human recognition, including not only position but also body direction, around the robot is important. Using an RGB-D camera, it is possible to detect the posture of a person. However, because the viewing angle of the camera is narrow, it is difficult to recognize the environment around the robot with a single device. This study proposes the estimation of the body direction based on the gait, that is, not only the position and velocity, but also the state of the legs (stance or swing phase), using laser range sensors installed at shin height. We verify the effectiveness of the proposed method for several patterns of movement, which are seen when a person interacts with the service robot and evaluate measurement accuracy. © 2020 Elsevier B.V.","Human–robot interaction; Kalman filter; Laser range sensor; Pedestrian tracking; Service robots","Agricultural robots; Cameras; Mobile robots; Robot applications; Baggage transportation; Companion robot; Human recognition; Laser range sensors; Measurement accuracy; Real environments; Research and development; Robot interactions; Social robots",,,,,"Japan Society for the Promotion of Science, KAKEN: 17K14619; Japan Science and Technology Agency, JST: JPMJCR14E3; Core Research for Evolutional Science and Technology, CREST","This study was supported by JSPS KAKENHI Grant Number 17K14619 and “A Framework PRINTEPS to Develop Practical Artificial Intelligence” of the Core Research for Evolutional Science and Technology (CREST) of the Japan Science and Technology Agency (JST) under Grant Number JPMJCR14E3 .",,,,,,,,,,"https://www.tokyo-airport-bldg.co.jp/hanedaroboticslab/en.html, Haneda Robotics Lab. Homepage; https://www.softbank.jp/robot/biz/case/hamazushi/, SoftBank Corp. Pepper for Biz; Nakamura, K., Morita, T., Yamaguchi, T., PRINTEPS for development integrated intelligent applications and application to robot teahouse (2017), pp. 1199-1206. , International Conference on Web Intelligence; Yamaguchi, T., PRINTEPS - A Framework PRINTEPS to develop practical artificial intelligence (2018) Impact, 2018 (11), pp. 19-21; Satake, S., Kanda, T., Glas, D.F., Imai, M., Ishiguro, H., Hagita, N., A robot that approaches pedestrians (2013) IEEE Trans. Robot., 29 (2); Hu, J.S., Wang, J.J., Ho, D.M., Design of sensing system and anticipative behavior for human following of mobile robots (2014) IEEE Trans. Ind. Electron., 61 (4), pp. 1916-1927; Karunarathne, D., Morales, Y., Kanda, T., Ishiguro, H., Model of side-by-side walking without the robot knowing the goal (2017) Int. J. Soc. Robot., pp. 1-20; Ferrer, G., Zulueta, A.G., Cotarelo, F.H., Sanfeliu, A., Robot social-aware navigation framework to accompany people walking side-by-side (2017) Autono. Robots, 41 (4), pp. 775-793; Repiso, E., Garrell, A., Sanfeliu, A., Robot approaching and engaging people in a human-robot companion framework (2018), pp. 8200-8205. , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Oishi, S., Kohari, Y., Miura, J., Toward a robotic attendant adaptively behaving according to human state (2016), pp. 1038-1043. , IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN); Choi, J., Lee, B.J., Zhang, B.T., Human body orientation estimation using convolutional neural network (2016), arXiv preprint; Glas, D.F., Miyashita, T., Ishiguro, H., Hagita, N., Laser-based tracking of human position and orientation using parametric shape modeling (2009) Adv. Robot., 23 (4), pp. 405-428; Shimizu, M., Koide, K., Ardiyanto, I., Miura, J., Oishi, S., LIDAR-based body orientation estimation by integrating shape and motion information (2016), pp. 1948-1953. , 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO); Chen, C., Heili, A., Odobez, J., Combined estimation of location and body pose in surveillance video (2011), pp. 5-10. , 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance; Cao, Z., Simon, T., Wei, S.E., Sheikh, Y., Realtime multi-person 2d pose estimation using part affinity fields (2017), pp. 7291-7299. , IEEE Conference on Computer Vision and Pattern Recognition; Carballo, A., Ohya, A., Yuta, S., Reliable people detection using range and intensity data from multiple layers of laser range finders on a mobile robot (2011) Int. J. Soc. Robot., 3 (2), pp. 167-186; Yorozu, A., Moriguchi, T., Takahashi, M., Improved leg tracking considering gait phase and spline-based interpolation during turning motion in walk tests (2015) Sensors, 15 (9), pp. 22451-22472; Yorozu, A., Takahashi, M., Navigation for gait measurement robot evaluating dual-task performance considering following human in living space (2016), Workshop on Assistance and Service Robotics in a Human Environment in conjunction with IEEE/RSJ International Conference on Intelligent Robots and Systems; Svenstrup, M., Tranberg, S., Andersen, H.J., Bak, T., Pose estimation and adaptive robot behaviour for human-robot interaction (2009), pp. 3571-3576. , 2009 IEEE International Conference on Robotics and Automation; Yorozu, A., Takahshi, M., Service robot using estimation of body direction based on gait for human robot interaction (2018), pp. 199-209. , 15th International Conference on Intelligent Autonomous Systems; Matsumura, T., Moriguchi, T., Yamada, M., Uemura, K., Nishiguchi, S., Aoyama, T., Takahashi, M., Development of measurement system for task oriented step tracking using laser range finder (2013) J. NeuroEng. Rehabil., (10); Sreenivasa, M.N., Frissen, I., Souman, J.L., Ernst, M.O., Walking along curved paths of different angles: the relationship between head and trunk turning (2008) Exp. Brain Res., 191 (3), pp. 313-320; Imai, T., Moore, S.T., Raphan, T., Cohen, B., Interaction of the body, head, and eyes during walking and turning (2001) Exp. Brain Res., 136 (1), pp. 1-18; Shorter, K.A., Wu, A., Kuo, A.D., The high cost of swing leg circumduction during human walking (2017) Gait Posture, 54, pp. 265-270; http://www.hokuyo-aut.jp/, Hokuyo Automatic Co. Ltd. homepage; Hall, E.T., The Hidden Dimension (1966), Doubleday NY; Walters, M.L., Syrdal, D.S., Koay, K.L., Dautenhahn, R., Human approach distances to a mechanical-looking robot with different robot voice styles (2008), pp. 707-712. , The 17th IEEE International Symposium on Robot and Human Interactive Communication; Walters, M.L., Dautenhahn, K., Koay, K.L., Kaouri, C., Boekhorst, R., Nehaniv, C., Werry, I., Lee, D., Close encounters: Spatial distances between people and a robot of mechanistic appearance (2005), pp. 450-455. , 5th IEEE-RAS International Conference on Humanoid Robots","Yorozu, A.; Graduate School of Science and Technology, 3-14-1 Hiyoshi, Kohoku-ku, Japan; email: ayanoriyorozu@keio.jp",,,"Elsevier B.V.",,,,,09218890,,RASOE,,"English","Rob Autom Syst",Article,"Final","",Scopus,2-s2.0-85089481621
"Xu J., Xu L., Li Y., Cheng G., Shi J., Liu J., Chen S.","57203993574;35270512100;8589964900;57210413948;57217010491;57203989310;57210415711;","A Multi-Channel Reinforcement Learning Framework for Robotic Mirror Therapy",2020,"IEEE Robotics and Automation Letters","5","4","9134883","5385","5392",,5,"10.1109/LRA.2020.3007408","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088703097&doi=10.1109%2fLRA.2020.3007408&partnerID=40&md5=e8b928634f3f51cd6bc552307a8cfce9","Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230026, China; Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, 999077, Hong Kong; Hefei Institutes and Physical Science, Chinese Academy of Sciences, Changzhou, 213164, China","Xu, J., Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230026, China, Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, 999077, Hong Kong; Xu, L., Hefei Institutes and Physical Science, Chinese Academy of Sciences, Changzhou, 213164, China; Li, Y., Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, 999077, Hong Kong; Cheng, G., Hefei Institutes and Physical Science, Chinese Academy of Sciences, Changzhou, 213164, China; Shi, J., Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230026, China; Liu, J., Hefei Institutes and Physical Science, Chinese Academy of Sciences, Changzhou, 213164, China; Chen, S., Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230026, China","In the letter, a robotic framework is proposed for hemiparesis rehabilitation. Mirror therapy is applied to transfer therapeutic training from the patient's function limb (FL) to the impaired limb (IL). The IL mimics the action prescribed by the FL with the assistance of the wearable robot, stimulating and strengthening the injured muscles through repetitive exercise. A master-slave robotic system is presented to implement the mirror therapy. Especially, the reinforcement learning is involved in the human-robot interaction control to enhance the rehabilitation efficacy and guarantee safety. Multi-channel sensed information, including the motion trajectory, muscle activation and the user's emotion, are incorporated in the learning algorithm. The muscle activation is expressed via the skin surface electromyography (EMG) signals, and the emotion is shown as the facial expression. The reinforcement learning approach is realized by the normalized advantage functions (NAF) algorithm. Then, a lower extremity rehabilitation robot with magnetorheological (MR) actuators is specially developed. The clinical experiments are carried out using the robot to verify the performance of the framework. © 2016 IEEE.","physical human-robot interaction; Rehabilitation robotics; reinforcement learning","Agricultural robots; Chemical activation; Human robot interaction; Mirrors; Muscle; Patient treatment; Reinforcement learning; Robotics; Clinical experiments; Facial Expressions; Human-robot interaction control; Lower extremity rehabilitations; Magneto-rheological; Master-slave robotic systems; Motion trajectories; Reinforcement learning approach; Learning algorithms",,,,,"BE2017007-1; National Key Research and Development Program of China, NKRDPC: 2017YFB1303200; Science and Technology Major Project of Guangxi: 17030901034","Thisworkwas supported in part by the National Key Research and Development Plan under Grant 2017YFB1303200, in part by the Anhui Science and Technology Major Project under Grant 17030901034","Manuscript received February 22, 2020; accepted June 15, 2020. Date of publication July 7, 2020; date of current version July 16, 2020. This letter was recommended for publication by Associate Editor J. D. Brown and Editor A. M. Okamura upon evaluation of the reviewers’ comments. This work was supported in part by the National Key Research and Development Plan under Grant 2017YFB1303200, in part by the Anhui Science and Technology Major Project under Grant 17030901034, and in part by the Key Research and Development Plan of Jiangsu Province under Grant BE2017007-1. (Corresponding author: Linsen Xu.) Jiajun Xu is with the Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China, and also with the Department of Mechanical Engineering, City University of Hong Kong, Hong Kong 999077, China (e-mail: jiajun@mail.ustc.edu.cn).",,,,,,,,,"Shahbazi, M., Atashzar, S.F., Tavakoli, M., Patel, R.V., Roboticsassisted mirror rehabilitation therapy: A therapist-in-theloop assist-asneeded architecture (2016) IEEE/ASME Trans. Mechatronics, 21 (4), pp. 1954-1965. , Aug; Sharifi, M., Cooperative modalities in robotic tele-rehabilitation using nonlinear bilateral impedance control (2017) Control Eng. Pract., 67, pp. 52-63; Hamaya, M., Matsubara, T., Noda, T., Teramae, T., Morimoto, J., Learning assistive strategies from a few user-robot interactions: Model-based reinforcement learning approach (2016) Proc. IEEE Int. Conf. Robot. Autom., pp. 3346-3351. , Stockholm, Sweden, May 16-21; Modares, H., Ranatunga, I., Lewis, F.L., Popa, D.O., Optimized assistive human-robot interaction using reinforcement learning (2016) IEEE Trans. Cybern., 46 (3), pp. 655-667. , Mar; Gu, S., Lillicrap, T., Sutskever, I., Levine, S., Continuous deep Qlearning with model-based acceleration (2016) Proc. Mach. Learn. Res., Int. Conf. Mach. Learn., 48, pp. 2829-2838. , http://proceedings.mlr.press/v48/gu16.pdf, New York, USA, 20-22 Jun; Filntisis, P.P., Efthymiou, N., Koutras, P., Potamianos, G., Maragos, P., Fusing body posture with facial expressions for joint recognition of affect in child-robot interaction (2019) IEEE Robot. Autom. Lett., 4 (4), pp. 4011-4018. , Oct; Sharifi, M., Salarieh, H., Behzadipour, S., Tavakoli, M., Impedance control of nonlinear multi-dof teleoperation systems with time delay: Absolute stability (2018) IET Control Theory Appl., 12 (12), pp. 1722-1729. , Aug. 14; Côté-Allard, U., Deep learning for electromyographic hand gesture signal classification using transfer learning (2019) IEEE Trans. Neural Syst. Rehab. Eng., 27 (4), pp. 760-771. , Apr; Hidler, J.M., Carroll, M., Federovich, E.H., Strength and coordination in the paretic leg of individuals following acute stroke (2007) IEEE Trans. Neural Syst. Rehab. Eng., 15 (4), pp. 526-534. , Dec; Ang, W.S., Geyer, H., Chen, I., Ang, W.T., Objective assessment of spasticity with a method based on human upper limb model (2018) IEEE Trans. Neural Syst. Rehab. Eng., 26 (7), pp. 1414-1423. , Jul; Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I., The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression (2010) Proc. IEEE Comput. Vision Pattern Recogn. Workshops, pp. 94-101; Hasani, B., Mahoor, M.H., Facial expression recognition using enhanced deep 3D convolutional neural networks (2017) Proc. IEEE Comput. Vision Pattern Recogn. Workshops, pp. 2278-2288; Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A., Inception-v4, Inception-ResNet and the impact of residual connections on learning (2017) Proc. Thirty-First AAAI Conf. Artif. Intell., pp. 4278-4284. , https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14806, San Francisco, California, USA, 4-9 Feb; Boularias, A., Kober, J., Peters, J., Relative entropy inverse reinforcement learning (2011) Proc. Artif. Intell. Statist., pp. 20-27; Xu, J., A multi-mode rehabilitation robot with magnetorheological actuators based on human motion intention estimation (2019) IEEE Trans. Neural Syst. Rehab. Eng., 27 (10), pp. 2216-2228. , Oct; Xu, J., Design and implementation of the lower extremity robotic exoskeleton with magnetorheological actuators (2019) Proc. IEEE Int. Conf. Mechatronics Autom., pp. 1294-1299. , Tianjin, China, Aug. 4-7; Xu, J., Xu, L., Li, Y., Liu, J., Li, X., Lu, L., Design of lower extremity rehabilitation robots with magnetorheological dampers and wire-driven system (2018) Proc. IEEE Int. Conf. Inf. Autom., pp. 395-400. , Wuyi Moutain, China; Schutte, L.M., Rodgers, M.M., Zajac, F.E., Glaser, R.M., Improving the efficacy of electrical stimulation-induced leg cycle ergometry: An analysis based on a dynamic musculoskeletal model (1993) IEEE Trans. Rehab. Eng., 1 (2), pp. 109-125. , Jul","Xu, L.; Hefei Institutes and Physical Science, China; email: lsxu@iamt.ac.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85088703097
"Zhu Q., Williams T., Jackson B., Wen R.","56300893800;7404172627;57205545565;57210420554;","Blame-Laden Moral Rebukes and the Morally Competent Robot: A Confucian Ethical Perspective",2020,"Science and Engineering Ethics","26","5",,"2511","2526",,8,"10.1007/s11948-020-00246-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087647812&doi=10.1007%2fs11948-020-00246-w&partnerID=40&md5=d16c030885e809e911cf4b896be2adb9","Division of Humanities, Arts and Social Sciences, Colorado School of Mines, Golden, United States; Department of Computer Science, Colorado School of Mines, Golden, United States","Zhu, Q., Division of Humanities, Arts and Social Sciences, Colorado School of Mines, Golden, United States; Williams, T., Department of Computer Science, Colorado School of Mines, Golden, United States; Jackson, B., Department of Computer Science, Colorado School of Mines, Golden, United States; Wen, R., Department of Computer Science, Colorado School of Mines, Golden, United States","Empirical studies have suggested that language-capable robots have the persuasive power to shape the shared moral norms based on how they respond to human norm violations. This persuasive power presents cause for concern, but also the opportunity to persuade humans to cultivate their own moral development. We argue that a truly socially integrated and morally competent robot must be willing to communicate its objection to humans’ proposed violations of shared norms by using strategies such as blame-laden rebukes, even if doing so may violate other standing norms, such as politeness. By drawing on Confucian ethics, we argue that a robot’s ability to employ blame-laden moral rebukes to respond to unethical human requests is crucial for cultivating a flourishing “moral ecology” of human–robot interaction. Such positive moral ecology allows human teammates to develop their own moral reflection skills and grow their own virtues. Furthermore, this ability can and should be considered as one criterion for assessing artificial moral agency. Finally, this paper discusses potential implications of the Confucian theories for designing socially integrated and morally competent robots. © 2020, Springer Nature B.V.","Blame-laden moral rebukes; Confucian ethics; Moral cultivation; Morally competent robots; Robot ethics; Role ethics","human; morality; robotics; Humans; Moral Development; Morals; Robotics; Virtues",,,,,"National Science Foundation, NSF: 1909847, IIS-1909847","This work was funded in part by National Science Foundation grant IIS-1909847.",,,,,,,,,,"Ames, R.T., (2011) Confucian role ethics: A vocabulary, , The Chinese University of Hong Kong Press, Hong Kong; Ames, R.T., Theorizing ""person"" in Confucian ethics: A good place to start (2016) Sungkyun Journal of East Asian Studies, 16 (2), pp. 141-162; Bartneck, C., Bleeker, T., Bun, J., Fens, P., Riet, L., The influence of robot anthropomorphism on the feelings of embarrassment when interacting with robots (2010) Paladyn, Journal of Behavioral Robotics, 1 (2), pp. 109-115; Bell, D., Metz, T., Confucianism and Ubuntu: Reflections on a dialogue between Chinese and African traditions (2011) Journal of Chinese Philosophy, 38 (s1), pp. 78-95; Brandstetter, J., Beckner, C., Sandoval, E.B., Bartneck, C., (2017) Persistent Lexical Entrainment in HRI, , https://dl.acm.org/citation.cfm?id=3020257, March, Vienna, Austria. 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Paper Presented at the IEEE 3Rd Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL), , https://ieeexplore.ieee.org/document/6652521, August, Osaka, Japan, Retrieved from; Williams, T., Acharya, S., Schreitter, S., Scheutz, M., Situated open world reference resolution for human–robot dialogue (2016) Paper Presented at the 11Th ACM/IEEE International Conference on human–robot Interaction, , https://ieeexplore.ieee.org/document/7451767, March, Christchurch, New Zealand, Retrieved from; Williams, T., Jackson, R.B., Lockshin, J., A Bayesian analysis of moral norm malleability during clarification dialogues (2018) Paper Presented at the 40Th Annual Meeting of the Cognitive Science Society, , https://inside.mines.edu/~twilliams/pdfs/williams2018cogsci.pdf, July, Madison, WI, Retrieved from; Williams, T., Scheutz, M., Reference in robotics: A givenness hierarchy theoretic approach (2019) Oxford handbook of reference, , Gundel J, Abbott B, (eds), Oxford University Press, Oxford, UK; Wong, D.B., Cultivating the self in concert with others (2014) Dao companion to the Analects, pp. 171-198. , Olberding A, (ed), Springer, Dordrecht, Netherlands; Wong, P.-H., Dao, harmony and personhood: Towards a Confucian ethics of technology (2012) Philosophy and Technology, 25 (1), pp. 67-86","Zhu, Q.; Division of Humanities, United States; email: qzhu@mines.edu",,,"Springer Science and Business Media B.V.",,,,,13533452,,,"32632786","English","Sci. Eng. Ethics",Article,"Final","",Scopus,2-s2.0-85087647812
"Duan S., Wang C., Li Y., Zhang L., Yuan Y., Wu X.","57214751664;57223792573;57211034281;57196129982;57212019056;7407065301;","A Quantifiable Muscle Fatigue Method Based on sEMG during Dynamic Contractions for Lower Limb Exoskeleton",2020,"2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020",,,"9303284","20","25",,1,"10.1109/RCAR49640.2020.9303284","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099380849&doi=10.1109%2fRCAR49640.2020.9303284&partnerID=40&md5=04f35386872d88e48c22343ee08e0619","Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen, 518055, China; Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, China; University of Chinese Academy of Sciences, China; Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, Hong Kong","Duan, S., Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen, 518055, China, Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, China, University of Chinese Academy of Sciences, China; Wang, C., Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen, 518055, China, Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; Li, Y., Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen, 518055, China, Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, China, University of Chinese Academy of Sciences, China; Zhang, L., Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen, 518055, China, Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, China, University of Chinese Academy of Sciences, China; Yuan, Y., Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen, 518055, China, Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, China; Wu, X., Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen, 518055, China, Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, China, Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China, Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, Hong Kong","Advanced interactive methods between users and lower-limb exoskeletons are gradually drawing attention. Muscle fatigue is regarded as a significant contributory factor in muscle injuries and low work efficiency which is harmful to human-robot interactions. The detection and quantification of muscle fatigue during dynamic contractions remain as a challenging research. In this research, we introduce a novel method called Wavelet Packet Energy Entropy (WPEE) to quantify the muscle fatigue based on sEMG during dynamic contractions. Fatigue experiments and exoskeleton experiments are executed and three healthy subjects complete these experiments. sEMG signals from six muscles of arms are selected and analyzed. The results derived from fatigue experiments validate that the introduced method is better at quantifying the muscle fatigue compared with traditional method mean power frequency (MNF). WPEE is applied in exoskeleton experiments to investigate the arms' muscle fatigue under three conditions. The quantitative results detailly express the user's muscle fatigue condition and fatigue trends when the user is using an exoskeleton with crutches. Considering the muscle fatigue will contribute to ergonomic design and high-quality interactive control of exoskeleton. © 2020 IEEE.","fatigue-based feedback; lower limb exoskeleton; multiple muscles.; Muscle fatigue based-sEMG; wavelet packet energy entropy","Agricultural robots; Human robot interaction; Muscle; Contributory factors; Detection and quantifications; Fatigue experiments; Interactive control; Interactive methods; Mean power frequency; Quantitative result; Wavelet packet energies; Exoskeleton (Robotics)",,,,,"National Key Research and Development Program of China, NKRDPC: 2017YFB1302303; U1613219","This work in the paper is partially supported by the National Key Research and Development Program of China (2017YFB1302303) and the NSFC-Shenzhen Robotics Research Center Project (U1613219). The authors would also like to thank all subjects and researchers who participate in this research.",,,,,,,,,,"He, Y., BrainCmachine interfaces for controlling lower-limb powered robotic systems (2018) Journal of Neural Engineering, 15 (2), p. 021004; Wu, X.Y., Ma, Y., A REVIEW on HUMAN-EXOSKELETON COORDINATION TOWARDS LOWER LIMB ROBOTIC EXOSKELETON SYSTEMS (2019) International Journal of Robotics and Automation 34, 4; Wu, D.X., Liu, X.Y., Individualized gait pattern generation for sharing lower limb exoskeleton robot (2018) Ieee Transactions on Automation Science and Engineering, 15 (4), pp. 1459-1470; Zhang, L., Li, J., Design and human-machine compatibility analysis of Co-Exos II for upper-limb rehabilitation (2019) Assembly Automation, 39 (4), pp. 715-726; Zhang, X., Dai, J., EMG-based shoulder-elbow composite motion pattern recognition and control methods for upper limb rehabilitation robot (2019) Assembly Automation, 39 (3), pp. 394-400; Liu, Y., Cui, S., Robotic hand-Arm system for on-orbit servicing missions in Tiangong-2 Space Laboratory (2019) Assembly Automation, 39 (5), pp. 999-1012; Guo, Z.M., Wang, C., Stable Control Gait Planning Strategy for A Rehabilitation Exoskeleton Robot (2019) 2019 Ieee International Conference on Mechatronics and Automation (ICMA), Ieee, pp. 1265-1270; Vollestad, N.K., Measurement of human muscle fatigue (1997) Journal of Neuroscience Methods, 742, pp. 219-227; Montoya, M.F., Munoz, J.E., Enhancing Virtual Rehabilitation in Upper Limbs with Biocybernetic Adaptation: The Effects of Virtual Reality on Perceived Muscle Fatigue, Game Performance and User Experience (2020) Ieee Transactions on Neural Systems and Rehabilitation Engineering; Troiano, A., Naddeo, F., Assessment of force and fatigue in isometric contractions of the upper trapezius muscle by surface EMG signal and perceived exertion scale (2018) Gait and Posture, 282, pp. 179-186; Zhou, Q.X., Chen, Y.H., Evaluation of upper limb muscle fatigue based on surface electromyography (2011) Science China Life Sciences, 5410, pp. 939-944; Lin, T.C., Krishnan, A.U., Li, Z., Physical Fatigue Analysis of Assistive Robot Teleoperation via Whole-body Motion Mapping (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Ieee, pp. 2240-2245; Cifrek, M., Medved, V., Tonkovic, S., Ostojic, S., Surface emg based muscle fatigue evaluation in biomechanics (2009) Clinical Biomechanics, 244, pp. 327-340; Nakashima, K., Kushida, D., Estimation of Muscle Fatigue Time Based on Electromyography during Isotonic Contraction (2019) 2019 Ieee 1st Global Conference on Life Sciences and Technologies (LifeTech Ieee, pp. 65-69; Guan, S.Y., Sugi, M., Quantification of Muscle Fatigue Using Surface Electromyography for Isometric Handgrip Task (2017) Journal of Telecommunication, Electronic and Computer Engineering (JTEC, 93, pp. 205-209; Gonzlez-Izal, M., EMG wavelet-based indices predicts muscle power loss during dynamic contractions (2010) Journal of Electromyography and Kinesiology, 206, pp. 1097-1106; Liu, D.X., Xu, J., Vision-Assisted Autonomous Lower-Limb Exoskeleton Robot (2019) Ieee Transactions on Systems, Man, and Cybernetics: Systems; Williams, N., The Borg Rating of Perceived Exertion (RPE) scale (2017) Occupational Medicine, 675, pp. 404-405; Merletti, R., Farina, D., Myoelectric manifestations of muscle fatigue (2006) Wiley Encyclopedia of Biomedical Engineering","Wang, C.; Shenzhen Institutes of Advanced Technology, China; email: can.wang@siat.ac.cn",,"Harbin Institute of Technology;IEEE Robotics and Automation Society (RA);Shanghai Jiao Tong University","Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020","28 September 2020 through 29 September 2020",,166261,,9781728172927,,,"English","IEEE Int. Conf. Real-Time Comput. Robot., RCAR",Conference Paper,"Final","",Scopus,2-s2.0-85099380849
"Uddin M.Z., Noori F.M., Torresen J.","24482836700;57189591227;6602518300;","In-Home Emergency Detection Using an Ambient Ultra-Wideband Radar Sensor and Deep Learning",2020,"2020 IEEE Ukrainian Microwave Week, UkrMW 2020 - Proceedings",,,"9252708","1089","1093",,,"10.1109/UkrMW49653.2020.9252708","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097713423&doi=10.1109%2fUkrMW49653.2020.9252708&partnerID=40&md5=c9ea944ef7fe17a759d6fb3d1a24abc4","Univ. of Oslo, Software and Service Innovation, Sintef Digital Dept. of Informatics, Oslo, Norway; University of Oslo, Dept. of Informatics, Oslo, Norway; University of Oslo, Dept. of Informatics and Ritmo, Oslo, Norway","Uddin, M.Z., Univ. of Oslo, Software and Service Innovation, Sintef Digital Dept. of Informatics, Oslo, Norway; Noori, F.M., University of Oslo, Dept. of Informatics, Oslo, Norway; Torresen, J., University of Oslo, Dept. of Informatics and Ritmo, Oslo, Norway","Human behavior analysis in indoor environments from ambient sensor data is one of the most challenging human-machine interaction research topics. In this paper, we present a deep learning-based novel approach for human posture recognition and emergency detection using an ultra-wideband radar sensor. The strength of this sensor is that it collects less privacy-related information than a regular camera. The sensor is installed on a mobile robot to observe a subject from a short distance. At first, raw data is collected from the sensor. Then, the data is used to train a Recurrent Neural Network (RNN) for modeling of different human activities and conditions, including normal and emergency conditions. Finally, the trained model is used for testing the model on new inputs. The experiments were performed using two datasets recorded in lab environments, and the proposed approach produced better recognition performance than the conventional ones. The proposed method can be applied in many prominent research fields (e.g., human-robot interaction) for different practical applications such as mobile robots for eldercare. © 2020 IEEE.","Heart Rate; Posture; RNN; XeThru Ultra-Wideband Sensor","Human robot interaction; Man machine systems; Mobile robots; Radar; Radar equipment; Recurrent neural networks; Ultra-wideband (UWB); Emergency conditions; Emergency detection; Human behavior analysis; Human machine interaction; Human posture recognition; Indoor environment; Recurrent neural network (RNN); Ultra wideband radars; Behavioral research",,,,,"247697; Norges Forskningsråd","This work is partially supported by The Research Council of Norway as a part of the Multimodal Elderly Care Systems (MECS) project, under grant agreement 247697.",,,,,,,,,,"Attal, F., Mohammed, S., Dedabrishvili, M., Chamroukhi, F., Oukhellou, L., Amirat, Y., Physical human activity recognition using wearable sensors (2015) Sensors, 15 (12), pp. 31314-31338; Tasoulis, S.K., Doukas, N., Plagianakos, V.P., Maglogiannis, I., Statistical data mining of streaming motion data for activity and fall recognition in assistive environments (2013) Neurocomputing, 107, pp. 87-96; Sjöman, H., Soares, N., Suijkerbuijk, M., Blindheim, J., Steinert, M., Wisland, D.T., The breathing room (2018) Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems-CHI '18; Andersen, N., Granhaug, K., Michaelsen, J.A., Bagga, S., Hjortland, H.A., Knutsen, M.R., Lande, T.S., Wisland, D.T., A 118-mw pulse-based radar soc in 55-nm CMOS for non-contact human vital signs detection (2017) IEEE Journal of Solid-State Circuits, 52 (12), pp. 3421-3433. , Dec; Wisland, D.T., Granhaug, K., Pleym, J.R., Andersen, N., Stoa, S., Hjortland, H.A., Remote monitoring of vital signs using a CMOS UWB radar transceiver (2016) 2016 14th IEEE International New Circuits and Systems Conference (NEWCAS), , Jun; Helal, S., Mann, W., El-Zabadani, H., King, J., Kaddoura, Y., Jansen, E., The Gator Tech Smart House: A programmable pervasive space (2005) Computer, 38 (3), pp. 50-60. , Mar; Cook, D.J., Crandall, A.S., Thomas, B.L., Krishnan, N.C., Casas: A smart home in a box (2013) Computer, 46 (7), pp. 62-69. , Jul; Ni, Q., Hernando, A.G., De La Cruz, I., The elderly's independent living in smart homes: A characterization of activities and sensing infrastructure survey to facilitate services development (2015) Sensors, 15 (5), pp. 11312-11362. , May; Billis, A.S., Papageorgiou, E.I., Frantzidis, C., Konstantinidis, E.I., Bamidis, P.D., Towards a hierarchically-structured decision support tool for improving seniors' independent living: The USEFIL decision support systemthe (2013) 6th Int. Conf. Pervasive Technol. Related Assistive Environ, pp. 1-4; Zhang, Q., Su, Y., Yu, P., Assisting an elderly with early dementia using wireless sensors data in smarter safer home (2014) Service Science and Knowledge Innovation, Springer Berlin Heidelberg, pp. 398-404; Uddin, M.Z., Torresen, J., A deep learning-based human activity recognition in darkness (2018) 2018 Colour and Visual Computing Symposium (CVCS), Gjøvik, pp. 1-5; Uddin, M.Z., A wearable sensor-based activity prediction system to facilitate edge computing in smart healthcare system (2019) Journal of Parallel and Distributed Computing, 123, pp. 46-53. , Jan; Noori, F.M., Wallace, B., Uddin, M.Z., Torresen, J., A robust human activity recognition approach using openpose, motion features, and deep recurrent neural network (2019) Scandinavian Conference on Image Analysis, pp. 299-310. , June; Curry, G., (2005) Radar System Performance Modeling, , 2nd ed. Norwood, MA, USA: Artech House",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE Ukrainian Microwave Week, UkrMW 2020","21 September 2020 through 25 September 2020",,164964,,9781728173139,,,"English","IEEE Ukrainian Microw. Week, UkrMW - Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85097713423
"Amadori P.V., Fischer T., Wang R., Demiris Y.","56703112800;57190126084;57189039099;6506125343;","Decision Anticipation for Driving Assistance Systems",2020,"2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020",,,"9294216","","",,2,"10.1109/ITSC45102.2020.9294216","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099667660&doi=10.1109%2fITSC45102.2020.9294216&partnerID=40&md5=0167e13a074b83f53d015c6c28c6e443","Imperial College London, Personal Robotics Laboratory, Department of Electrical and Electronic EngineeringSW7 2BT, United Kingdom","Amadori, P.V., Imperial College London, Personal Robotics Laboratory, Department of Electrical and Electronic EngineeringSW7 2BT, United Kingdom; Fischer, T., Imperial College London, Personal Robotics Laboratory, Department of Electrical and Electronic EngineeringSW7 2BT, United Kingdom; Wang, R., Imperial College London, Personal Robotics Laboratory, Department of Electrical and Electronic EngineeringSW7 2BT, United Kingdom; Demiris, Y., Imperial College London, Personal Robotics Laboratory, Department of Electrical and Electronic EngineeringSW7 2BT, United Kingdom","Anticipating the correctness of imminent driver decisions is a crucial challenge in advanced driving assistance systems and has the potential to lead to more reliable and safer human-robot interactions. In this paper, we address the task of decision correctness prediction in a driver-in-the-loop simulated environment using unobtrusive physiological signals, namely, eye gaze and head pose. We introduce a sequence-to-sequence based deep learning model to infer the driver's likelihood of making correct/wrong decisions based on the corresponding cognitive state. We provide extensive experimental studies over multiple baseline classification models on an eye gaze pattern and head pose dataset collected from simulated driving. Our results show strong correlates between the physiological data and decision correctness, and that the proposed sequential model reliably predicts decision correctness from the driver with 80% precision and 72% recall. We also demonstrate that our sequential model performs well in scenarios where early anticipation of correctness is critical, with accurate predictions up to two seconds before a decision is performed. © 2020 IEEE.",,"Behavioral research; Classification (of information); Deep learning; Intelligent systems; Man machine systems; Physiological models; Physiology; Social robots; Accurate prediction; Classification models; Driving assistance systems; Physiological data; Physiological signals; Sequential model; Simulated driving; Simulated environment; Advanced driver assistance systems",,,,,"EP/P008461/1; Royal Academy of Engineering, RAENG","ACKNOWLEDGMENTS This work was supported in part by UK DSTL/EPSRC Grant EP/P008461/1, and a Royal Academy of Engineering Chair in Emerging Technologies to Yiannis Demiris.",,,,,,,,,,"Demiris, Y., Prediction of intention in robotics and multiagent systems (2007) Cognitive Processing, 8 (3), pp. 151-158; Kuderer, M., Kretzschmar, H., Sprunk, C., Burgard, W., Featurebased prediction of trajectories for socially compliant navigation (2012) Robotics: Science and Systems; Huang, X., McGill, S., Williams, B.C., Fletcher, L., Rosman, G., Uncertainty-aware driver trajectory prediction at urban intersections (2019) IEEE International Conference on Robotics and Automation, pp. 9718-9724; Tawari, A., Sivaraman, S., Trivedi, M.M., Shannon, T., Tippelhofer, M., Looking-in and looking-out vision for urban intelligent assistance: Estimation of driver attentive state and dynamic surround for safe merging and braking (2014) IEEE Intelligent Vehicles Symposium, pp. 115-120; Healey, J.A., Picard, R.W., Detecting stress during real-world driving tasks using physiological sensors (2005) IEEE Transactions on Intelligent Transportation Systems, 6 (2), pp. 156-166; Reimer, B., Gulash, C., Mehler, B., Foley, J.P., Arredondo, S., Waldmann, A., The mit agelab n-back: A multi-modal android application implementation (2014) International Conference on Automotive User Interfaces and Interactive Vehicular Applications; Solovey, E.T., Zec, M., Garcia Perez, E.A., Reimer, B., Mehler, B., Classifying driver workload using physiological and driving performance data: Two field studies (2014) SIGCHI Conference on Human Factors in Computing Systems, pp. 4057-4066; Liang, Y., Reyes, M.L., Lee, J.D., Real-time detection of driver cognitive distraction using support vector machines (2007) IEEE Transactions on Intelligent Transportation Systems, 8 (2), pp. 340-350; Wang, R., Amadori, P.V., Demiris, Y., Real-time workload classification during driving using hypernetworks (2018) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3060-3065; Zhang, X., Sugano, Y., Fritz, M., Bulling, A., MPIIGaze: Real-world dataset and deep appearance-based gaze estimation (2017) IEEE Transactions on Pattern Analysis and Machine Intelligence, 41 (1), pp. 162-175; Fischer, T., Chang, H.J., Demiris, Y., RT-gene: Real-time eye gaze estimation in natural environments (2018) European Conference on Computer Vision, pp. 339-357; Jain, A., Singh, A., Koppula, H.S., Soh, S., Saxena, A., Recurrent neural networks for driver activity anticipation via sensory-fusion architecture (2016) IEEE International Conference on Robotics and Automation, pp. 3118-3125; Morris, B., Doshi, A., Trivedi, M.M., Lane change intent prediction for driver assistance: On-road design and evaluation (2011) IEEE Intelligent Vehicles Symposium, pp. 895-901; Kumar, P., Perrollaz, M., Lefevre, S., Laugier, C., Learningbased approach for online lane change intention prediction (2013) IEEE Intelligent Vehicles Symposium, pp. 797-802; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Computation, 9 (8), pp. 1735-1780; Ohn-Bar, E., Trivedi, M.M., Looking at humans in the age of self-driving and highly automated vehicles (2016) IEEE Transactions on Intelligent Vehicles, 1 (1), pp. 90-104; Martin, S., Vora, S., Yuen, K., Trivedi, M.M., Dynamics of driver's gaze: Explorations in behavior modeling and maneuver prediction (2018) IEEE Transactions on Intelligent Vehicles, 3 (2), pp. 141-150; Billah, T., Rahman, S.M., Ahmad, M.O., Swamy, M., Recognizing distractions for assistive driving by tracking body parts (2018) IEEE Transactions on Circuits and Systems for Video Technology, 29 (4), pp. 1048-1062; Palazzi, A., Abati, D., Solera, F., Cucchiara, R., Predicting the driver's focus of attention: The dr (eye)ve project (2018) IEEE Transactions on Pattern Analysis and Machine Intelligence, 41 (7), pp. 1720-1733; Wollmer, M., Blaschke, C., Schindl, T., Schuller, B., Farber, B., Mayer, S., Trefflich, B., Online driver distraction detection using long short-term memory (2011) IEEE Transactions on Intelligent Transportation Systems, 12 (2), pp. 574-582; Rastgoo, M.N., Nakisa, B., Maire, F., Rakotonirainy, A., Chandran, V., Automatic driver stress level classification using multimodal deep learning (2019) Expert Systems with Applications, 138, p. 112793; Fridman, L., Reimer, B., Mehler, B., Freeman, W.T., Cognitive load estimation in the wild (2018) CHI Conference on Human Factors in Computing Systems, pp. 6521-6529; Peláez, G.A., Garcia, F., De La Escalera, A., Armingol, J.M., Driver monitoring based on low-cost 3-d sensors (2014) IEEE Transactions on Intelligent Transportation Systems, 15 (4), pp. 1855-1860; Vora, S., Rangesh, A., Trivedi, M.M., Driver gaze zone estimation using convolutional neural networks: A general framework and ablative analysis (2018) IEEE Transactions on Intelligent Vehicles, 3 (3), pp. 254-265; Fridman, L., Langhans, P., Lee, J., Reimer, B., Driver gaze region estimation without use of eye movement (2016) IEEE Intelligent Systems, 31 (3), pp. 49-56; Zhang, Q., Wu, Q., Zhou, Y., Wu, X., Ou, Y., Zhou, H., Webcambased, non-contact, real-time measurement for the physiological parameters of drivers (2017) Measurement, 100, pp. 311-321; Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Rabinovich, A., Going deeper with convolutions (2015) IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9; Mehler, B., Reimer, B., How demanding is just driving? A cognitive workload-psychophysiological reference evaluation (2019) International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, pp. 363-369; Grimes, D., Tan, D.S., Hudson, S.E., Shenoy, P., Rao, R.P., Feasibility and pragmatics of classifying working memory load with an electroencephalograph (2008) SIGCHI Conference on Human Factors in Computing Systems, pp. 835-844",,,,"Institute of Electrical and Electronics Engineers Inc.","23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020","20 September 2020 through 23 September 2020",,166105,,9781728141497,,,"English","IEEE Int. Conf. Intell. Transp. Syst., ITSC",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85099667660
"Conti C.J., Varde A.S., Wang W.","57219973528;15124504600;57201554862;","Robot action planning by commonsense knowledge in human-robot collaborative tasks",2020,"IEMTRONICS 2020 - International IOT, Electronics and Mechatronics Conference, Proceedings",,,"9216410","","",,7,"10.1109/IEMTRONICS51293.2020.9216410","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096364752&doi=10.1109%2fIEMTRONICS51293.2020.9216410&partnerID=40&md5=4b6a475563e143811fce90398833b88c","Montclair State University, Deparment of Computer Science, Montclair, NJ, United States","Conti, C.J., Montclair State University, Deparment of Computer Science, Montclair, NJ, United States; Varde, A.S., Montclair State University, Deparment of Computer Science, Montclair, NJ, United States; Wang, W., Montclair State University, Deparment of Computer Science, Montclair, NJ, United States","Robotics and artificial intelligence (AI) span the broad realm of mechatronics in general. Humans and robots can collaborate with each other to enhance various tasks, e.g. in the vehicle industry. Facets of AI such as commonsense knowledge can play a significant role here. In this paper, we propose an approach for human-robot collaboration such that it leverages commonsense knowledge to develop models for the simulation of assembly tasks in real-world applications. We consider modeling based on relevant attributes, e.g. weight and stability of parts. The proposed approach thereby entails human-robot interaction, mathematical modeling, both semantics and pragmatics in commonsense knowledge, as well as challenges specific to the application domain. We describe our approach, focusing on the mathematical modeling, and conduct our experimental evaluation using simulation tasks. Experimental results indicate that the proposed approach yields better outcomes than humans or robots working alone, which is in line with other such claims in the field of human-robot collaboration. This work sets the stage for real robot applications based on the results of our simulation tasks. © 2020 IEEE.","Artificial intelligence; Commonsense knowledge; Human-robot interaction; Mathematical modeling; Robotics; Simulation studies","Artificial intelligence; Industrial robots; Robot applications; Robot programming; Semantics; Assembly tasks; Collaborative tasks; Commonsense knowledge; Experimental evaluation; Human-robot collaboration; Model-based OPC; Robots working; Vehicle industry; Social robots",,,,,"Montclair State University","This work entails financial support from Montclair State University. Christopher J. Conti is funded by a graduate assistantship from the Department of Computer Science. Aparna S. Varde is supported by a doctoral faculty program fomrthe Gaduatre School and a faculty scholashirp program from Compuer Stcience. Weitian Wang has startup funds from the College of Science and Mathematics.",,,,,,,,,,"Thoben, K.-D., Wiesner, S., Wuest, T., 'industrie 4. 0' and smart manufacturing-a review of research issues and application examples (2017) International Journal of Automation Technology, 11 (1), pp. 4-16; Suchan, J., Bhatt, M., (2017) Commonsense Scene Semantics for Cognitive Robotics: Towards Grounding Embodied Visuo-Locomotive Interactions; Chen, Y., Wang, W., Abdollahi, Z., Wang, Z., Shulte, J., Krowi, V., Jia, Y., A robotic lift assister: A smart companion for heavy payload transport and manipulation in automotive assembly (2018) IEEE Robotics and Automation Magazine, 25 (2), pp. 107-119; Whitney, D.E., Lozinski, C.A., Rourke, J.M., Industrial robot forward calibration method and results (1986) Journal of Dynamic Systems, Measurement and Control, 108 (1), pp. 1-8; Pettersen, T., Pretlove, J., Skourup, C., Engedal, T., Lokstad, T., Augmented reality for programming industrial robots (2003) The Second IEEE and ACM International Symposium on Mixed and Augmented Reality, pp. 319-320; Iqbal, J., Islam, R.U., Abbas, S.Z., Khan, A.A., Ajwad, S.A., Automating industrial tasks through mechatronic systems-a review of robotics in industrial perspective (2016) Teh. Vjesn., 23 (3), pp. 917-924; Tandon, N., Varde, A.S., De Melo, G., Commonsense knowledge in machine intelligence (2017) ACM SIGMOD Record, 46 (4), pp. 49-52; Tandon, N., De Melo, G., Weikum, G., Webchild 2. 0: Finegrained commonsense knowledge distillation (2017) The Annual Meeting of the Association for Computational Linguistics (ACL) Conference, pp. 115-120; Pandey, P., Puri, M., Varde, A., Object detection with neural models, deep learning and common sense to aid smart mobility (2018) IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 859-863; Persaud, P., Varde, A.S., Robila, S., Enhancing autonomous vehicles with commonsense: Smart mobility in smart cities (2017) IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1008-1012; Davis, E., Marcus, G., Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence, , http://cs.nyu.edu/faculty/davise/papers/WS.html; Wang, W., Li, R., Chen, Y., Diekel, Z.M., Jia, Y., Facilitating human-robot collaborative tasks by teaching-learning-collaboration from human demonstrations (2018) IEEE Transactions on Automation Science and Engineering, 16 (2), pp. 640-653; Matheson, E., Minto, R., Zampieri, E.G.G., Faccio, M., Rosati, G., Human-robot collaboration in manufacturing applications: A review (2019) Robotics Journal, MDPI, 6 (4), p. 100; Vasconez, J.P., Kantor, G.A., Auat Cheein, F.A., Human-robot interaction in agriculture: A survey and current challenges (2019) Biosystems Engineering Journal, pp. 35-48; Chen, M., Nikolaidis, S., Soh, H., Hsu, D., Srinivasa, S., Planning with trust for human-robot collaboration (2018) Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, pp. 307-315; Kasparov, G., Greengard, M., (2017) Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins, , Public Affairs Publishers, Perseus Books, LLC, USA",,"Chakrabarti S.Paul R.Gill B.Gangopadhyay M.Poddar S.","IEEE Vancouver Section;Institute of Engineering and Management (IEM);SMART;University of Engineering and Management (UEM)","Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International IOT, Electronics and Mechatronics Conference, IEMTRONICS 2020","9 September 2020 through 12 September 2020",,163882,,9781728196152,,,"English","IEMTRONICS - Int. IOT, Electron. Mechatronics Conf., Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85096364752
"Alimuradov A.K., Tychkov A.Y., Mezhina V.A., Fokina E.A., Zhurina A.E., Ageykin A.V., Gorbunov V.N., Reva E.K.","56595170300;36731632400;57219973215;57219972070;57219970957;57194621365;57202754023;57219971241;","Development of Natural Emotional Speech Database for Training Automatic Recognition Systems of Stressful Emotions in Human-Robot Interaction",2020,"Conference Proceedings - 4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics, DCNAIR 2020",,,"9216940","11","16",,,"10.1109/DCNAIR50402.2020.9216940","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096359588&doi=10.1109%2fDCNAIR50402.2020.9216940&partnerID=40&md5=b77a498ca0c4cd09e7a9c8c4da7d5996","Penza State University, Student Research and Production Business Incubator, Penza, Russian Federation; Penza State University, Research Institute for Fundamental and Applied Studies, Penza, Russian Federation; Department of Instrument Engineering, Information Technology and Electronics, Penza State University, Penza, Russian Federation; Department of Microbiology, Epidemiology and Infectious Diseases, Penza State University, Penza, Russian Federation; Technology Transfer Center, Penza State University, Penza, Russian Federation; Penza State University, Department of Journalism, Penza, Russian Federation","Alimuradov, A.K., Penza State University, Student Research and Production Business Incubator, Penza, Russian Federation; Tychkov, A.Y., Penza State University, Research Institute for Fundamental and Applied Studies, Penza, Russian Federation; Mezhina, V.A., Penza State University, Research Institute for Fundamental and Applied Studies, Penza, Russian Federation; Fokina, E.A., Department of Instrument Engineering, Information Technology and Electronics, Penza State University, Penza, Russian Federation; Zhurina, A.E., Department of Instrument Engineering, Information Technology and Electronics, Penza State University, Penza, Russian Federation; Ageykin, A.V., Department of Microbiology, Epidemiology and Infectious Diseases, Penza State University, Penza, Russian Federation; Gorbunov, V.N., Technology Transfer Center, Penza State University, Penza, Russian Federation; Reva, E.K., Penza State University, Department of Journalism, Penza, Russian Federation","A comparative analysis of 18 popular speech databases used in the field of affective computing was carried out. A database containing unique audio recordings of Russian speech (words, phrases, and sentences), registered in natural experience of emotions by eyewitnesses and/or participants in adverse events under stress, has been developed. Voice recordings were taken from video materials of disasters and emergencies occurred on the territory of Russia in 2015-2019. Examination results of psychological and psychiatric reactions of 293 eyewitnesses and/or participants (119 men and 174 women, aged 15 to 65 years) to traumatic factors of adverse events are presented. In accordance with the development results, the following key conclusions have been made: In the future, the database will allow researchers to objectively assess the capabilities of existing methods and approaches for analyzing emotional speech, as well as to assess the need for new mathematical devices in speech signal processing; the voice database can be successfully used as training or test sample, necessary for effective training of systems for automatic recognition of stressful emotions in human-robot interaction. © 2020 IEEE.","affective computing; artificial intelligence systems; emotion recognition; emotional speech database; speech signal; stressful emotions","Agricultural robots; Audio recordings; Audio signal processing; Complex networks; Database systems; Educational robots; Man machine systems; Robotics; Speech; Speech analysis; Speech recognition; Affective Computing; Automatic recognition; Automatic recognition system; Comparative analysis; Development results; Emotional speech; Speech database; Speech signal processing; Social robots",,,,,"Russian Foundation for Basic Research, РФФИ: 18-37-00256 mol_a; Council on grants of the President of the Russian Federation: MK-490.2020.8","The article is a continuation of previous studies by the authors [2-4], carried out under the Russian Foundation for Basic Research grant, and obtained as a result of 2018 contest of projects for fundamental scientific research conducted by young scientists (My First Grant), No. 18-37-00256 mol_a.","Council for Grants of the President of the Russian Federation, project No. MK-490.2020.8.",,,,,,,,,"Schuller, B.W., Batliner, A.M., (2013) Computational Paralinguistics: Emotion Affect and Personality in Speech and Language Processing, , New York: Wiley; Alimuradov, A.K., Fokina, E.A., Zhurina, A.E., A brief review of known emotional speech databases: Current state, problems and development prospects (2020) Proc. 12th Int. Sc. Conf. Methods, Means and Technologies of Obtaining and Processing Data (Shlyandin Readings 2020), pp. 292-294. , Penza, March 16- 18, 2020, E.A. Pecherskaya, Ed. Penza: PSU Publishing House; Alimuradov, A.K., Tychkov, A.Yu., Churakov, P.P., A method for noise-robust speech signal processing to assess human psychoemotional state, "" (2019) 2019 3rd School on Dynamics of Complex Networks and Their Application in Intellectual Robotics (DCNAIR), pp. 6-8. , Sept. 9-11, Innopolis, Russia; Alimuradov, A.K., Tychkov, A.Yu., Application of improved complete ensemble empirical mode decomposition with adaptive noise in speech signal processing 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 1351-1355. , Jan. 27-30 2020, Saint Petersburg and Moscow, Russia; Engberg, I.S., Hansen, A.V., (1996) Documentation of the Danish Emotional Speech Database (DES). Internal AAU Report, , Center for Person Kommunikation, Department of Communication Technology, Institute of Electronic Systems, Aalborg University, Denmark; Hansen, L.J.H., Bou-Ghazale, S.E., Getting started with susas: A speech under simulated and actual stress database 5th European Conference on Speech Communication and Technology, pp. 1743-1746. , September 22-25 1997, Rhodes, Greece; Berlin Database of Emotional Speech. Official Page, , http://emodb.bilderbar.info/docu/, [Online] Available, accessed April 20 2020); Makarova, V., Petrushin, V.A., Ruslana: A database of russian emotional utterances 7th International Conference on Spoken Language Processing (ICSLP2002-INTERSPEECH 2002), pp. 2041-2044. , September 16-20, 2002, Denver, Colorado, USA; Hozjan, V., Kacic, Z., Moreno, A., Bonafonte, A., Nogueiras, A., Interface databases: Design and collection of a multilingual emotional speech database (2002) Third International Conference on Language Resources and Evaluation (LREC02), pp. 2024-2048. , May 29-31, Las Palmas, Canary Islands-Spain; Batliner, A., Steidl, S., Nöth, E., Releasing a thoroughly annotated and processed spontaneous emotional database: The fau aibo emotion corpus International Conference on Language Resources and Evaluation (LREC 2008), pp. 28-31. , 26 May-1 June, 2008, Marrakech, Morocco; Grimm, M., Kroschel, K., Narayanan, Sh., The vera am mittag german audio-visual emotional speech database 2008 IEEE International Conference on Multimedia and Expo, pp. 865-868. , June 23-26, 2008, Hannover, Germany; Gajšek, R., Štruc, V., Miheli, F., Podlesek, A., Komidar, L., Soan, G., Bajec, B., Multi-modal emotional database: Avid (2009) Informatica (Ljubljana, 33 (1), pp. 101-106; Database. Official Page, , http://kahlan.eps.surrey.ac.uk/savee, Surrey Audio-Visual Expressed Emotion (SAVEE), [Online] Available, (accessed April 20 2020); TURkish Emotional Speech Database (TURES). Official Page, , http://www.turesdatabase.com, [Online] Available, (accessed April 20 2020); Arimoto, Y., Kawatsu, H., Ohno, S., Iida, H., Naturalistic emotional speech collection paradigm with online game and its psychological and acoustical assessment (2012) Acoustical Science and Technology, 33 (6), pp. 359-369; Wang, K.X., Zhang, Q.L., Liao, S.Y., A database of elderly emotional speech (2013) 2013 International Symposium on Signal Processing, Biomedical Engineering and Informatics (SPBEI 2013), pp. 549-553. , December 16- 18, Hangzou, China; Moriyama, T., Mori, Sh., Ozawa, Sh., A synthesis method of emotional speech using subspace constraints in prosody (2009) Journal of Information Processing, 50 (3), pp. 1181-1191; Zhang, H., Lopez, G., Shuzo, M., Omiya, Y., Mistuyoshi, Sh., Warisawa, Sh., Yamada, I., A database of japanese emotional signals elicited by real experiences (2014) Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Book Series (LNICST), Pervasive Computing Paradigms for Mental Health (4th International Symposium, MindCare, pp. 3-12. , Tokyo, Japan, May 8-9, 2014). Eds. P. Cipresso, A. Matic, G. Lopez 2014 100. Cham: Springer; Livingstone, S.R., Russo, F.A., The ryerson audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english (2018) PLoS ONE, 13 (5); Geethashree, A., Ravi, D.J., Kannada emotional speech database: Design, development and evaluation (2017) Lecture Notes in Networks and Systems Book Series (LNNS), Proceedings of International Conference on Cognition and Recognition, 14, pp. 135-143. , Eds. D.S. Guru, T. Vasudev, H.K. Chethan, Y.H. Sharath Kumar, Singapore: Springer; Sapinski, T., Kaminska, D., Pelikant, A., Avots, E., Ozcinar, C., Anbarjafari, Gh., Multimodal database of emotional speech, video and gestures (2018) International Journal of Information, Control and Computer Sciences, 12 (10), pp. 809-814; Lassalle, A., Pigat, D., OReilly, H., Berggen, S., Fridenson-Hayo, S., Tal, S., Elfström, S., Lundqvist, D., The eu-emotion voice database (2019) Behavior Research Methods, 51 (2), pp. 493-506; The Ministry of the Russian Federation for Civil Defence, Emergencies and Elimination of Consequences of Natural Disasters: Official Page, , https://www.mchs.gov.ru, [Online] Available, accessed April 20 2020)",,,,"Institute of Electrical and Electronics Engineers Inc.","4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics, DCNAIR 2020","7 September 2020 through 9 September 2020",,163883,,9781728172866,,,"English","Conf. Proc. - Sci. Sch. Dyn. Complex Networks Appl. Intellect. Robot., DCNAIR",Conference Paper,"Final","",Scopus,2-s2.0-85096359588
"Lee B.K., Han C.S.","56079678600;56298150500;","Design of a 7-DOF upper-limb exoskeleton robot based on performance index",2020,"Journal of the Korean Society for Precision Engineering","37","9",,"653","658",,,"10.7736/JKSPE.020.022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094829192&doi=10.7736%2fJKSPE.020.022&partnerID=40&md5=f1f8853046be075aa01e3f15d161eaea","Department of Mechanical Engineering, Graduate School, Hanyang University, South Korea; Department of Robotics, Hanyang University, South Korea","Lee, B.K., Department of Mechanical Engineering, Graduate School, Hanyang University, South Korea; Han, C.S., Department of Robotics, Hanyang University, South Korea","For the teleoperation of dual-arm robots with various tasks, the existence of a controller with a high degree of freedom is indispensable. Especially when precise work is required, additional information such as force feedback is very helpful for the operator. In transmitting such force information, a control device of exoskeleton-type with many points of contact with the human body can be one of the solutions. This paper proposes an optimal design method for the 7 degrees of freedom (DOF) exoskeleton systems. The proposed method optimizes the kinematic parameters by using kinematic performance indices related to the dexterity of the human and exoskeleton system. The manipulability ellipsoid is a representative index that can confirm the dexterity of the robot. In this study, we derived the objective function considering the human body model and then optimized it using a genetic algorithm. Unlike other HRI (Human-Robot Interaction) systems, exoskeleton robots share the end-effector as well as the base of the robot with the wearer. Therefore, it is hypothesized that the proposed performance index will be highly suitable for exoskeleton systems. © 2020 The Korean Society for Precision Engineering.","Exoskeleton robot; Human-robot interaction; Kinematic performance index; Manipulability ellipsoid; Optimal design",,,,,,,,,,,,,,,,,"Frisoli, A., Salsedo, F., Bergamasco, M., Rossi, B., Carboncini, M. C., A Force-Feedback Exoskeleton for Upper-Limb Rehabilitation in Virtual Reality (2009) Applied Bionics and Biomechanics, 6 (2), pp. 115-126; Folgheraiter, M., Jordan, M., Straube, S., Seeland, A., Kim, S. K., Measuring the Improvement of the Interaction Comfort of a Wearable Exoskeleton (2012) International Journal of Social Robotics, 4 (3), pp. 285-302; Sone, J., Inoue, R., Yamada, K., Nagae, T., Fujita, K., Development of a Wearable Exoskeleton Haptic Interface Device (2008) Journal of Computing and Information Science in Engineering, 8 (4), pp. 1-12; Kim, Y. S., Lee, J., Lee, S., Kim, M., A Force Reflected Exoskeleton-Type Masterarm for Human-Robot Interaction (2005) IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 35 (2), pp. 198-212; Letier, P., Motard, E., Verschueren, J. P., Exostation: Haptic Exoskeleton Based Control Station (2010) Proc. of the IEEE International Conference on Robotics and Automation, pp. 1840-1845; Roderick, S., Liszka, M., Carignan, C., Design of an Arm exoskeleton with Scapula Motion for Shoulder Rehabilitation (2005) Proc. of the 12th International Conference on Advanced Robotics, pp. 524-531; Gupta, A., O'Malley, M. K., Design of a Haptic Arm Exoskeleton for Training and Rehabilitation (2006) IEEE/ASME Transactions on Mechatronics, 11 (3), pp. 280-289; Perry, J. C., Rosen, J., Burns, S., Upper-Limb Powered Exoskeleton Design (2007) IEEE/ASME Transactions on Mechatronics, 12 (4), pp. 408-417; Lo, H. S., Xie, S. S., Optimization of a Redundant 4R Robot for a Shoulder Exoskeleton (2013) Proc. of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 798-803; Zeiaee, A., Soltani-Zarrin, R., Langari, R., Tafreshi, R., Kinematic Design Optimization of an Eight Degree-of-Freedom Upper-Limb Exoskeleton (2019) Robotica, 37 (12), pp. 2073-2086; Klein, J., Spencer, S., Allington, J., Bobrow, J. E., Reinkensmeyer, D. J., Optimization of a Parallel Shoulder Mechanism to Achieve a High-Force, Low-Mass, Robotic-Arm Exoskeleton (2010) IEEE Transactions on Robotics, 26 (4), pp. 710-715; 7th Human Dimensional Survey, , https://sizekorea.kr/page/report/1, Korea Institute of Design Promotion (Size Korea), (Accessed 7 August 2020); Letier, P, A vraam, M, eillerette, S, H orodinca, e Bartolomei, M., SAM: A 7-DOF Portable Arm Exoskeleton with Local Joint Control (2008) Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3501-3506. , M; Yu, Y., Liang, W., Design Optimization for Lower Limb Assistive Mechanism based on Manipulability Inclusive Principle (2012) Proc. of the IEEE International Conference on Robotics and Biomimetics, pp. 174-180","Han, C.S.; Department of Robotics, South Korea; email: cshan@hanyang.ac.kr",,,"Korean Society for Precision Engineeing",,,,,12259071,,,,"Korean","J. Korean Soc. Precis. Eng.",Article,"Final","",Scopus,2-s2.0-85094829192
"Weiming Y., Xiaohua W., Nan W.","57219165987;55901745800;57219164572;","Sewing gesture image detection method based on improved SSD Model",2020,"Laser and Optoelectronics Progress","57","18","181017-1","","",,2,"10.3788/LOP57.181017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091538068&doi=10.3788%2fLOP57.181017&partnerID=40&md5=72a203022bd7d496504f795a9a709ba4","College of Electronics and Information, Xi'An Polytechnic University, Xi'an, Shaanxi, 710048, China; College of Information Engineering, Shaanxi Xueqian Normal University, Xi'an, Shaanxi, 710048, China","Weiming, Y., College of Electronics and Information, Xi'An Polytechnic University, Xi'an, Shaanxi, 710048, China; Xiaohua, W., College of Electronics and Information, Xi'An Polytechnic University, Xi'an, Shaanxi, 710048, China; Nan, W., College of Information Engineering, Shaanxi Xueqian Normal University, Xi'an, Shaanxi, 710048, China","In human-robot collaborative sewing the premise of realizing human-robot interaction is based on the detection and understanding of robot sewing gestures The traditional algorithm is characterized by a low gesturerecognition rate and poor target-gesture detection Therefore a method based on an improved single-shot multibox detector SSD model for recognizing sewing gestures is proposed in this work First a deeper Resnet50 residual network was introduced to replace the VGG16 basic network in the original SSD model to improve the network feature extraction capability Subsequently a feature base pyramid FPN -based network structure was used to perform a fusion of high- and low-level features thereby further improving the detection accuracy Experiment results reveal that for the constructed sewing gesture dataset the improved model exhibited higher detection accuracy than the original SSD algorithm and other algorithms Furthermore the residual connection in the network improved accuracy without increasing the number of parameters and complexity of the model In our method the average detection speed is 52 frame s which can fully meet the requirements for real-time detection of sewing gestures. © 2020 Universitat zu Koln. All rights reserved.","Feature pyrami; Human-robot collaboration; Image processing; Sewing gesture; SSD; Target detection",,,,,,,,,,,,,,,,,"Research on the basic theory and key technologies of communicative robots"" (2019) J Pattern Recognition and Artificial Intelligence, 32 (3), pp. 193-203. , The National Natural Science Foundation of China's major research project; Qi, J, Xu, K, Ding, X L, Vision-based hand gesture recognition for human-robot interaction a review (2017) J Robot, 39 (4), pp. 565-584. , J 2017 39 4 565-584; Mitra, S, Acharya, T, Gesture recognition a survey (2007) J IEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews, 37 (3), pp. 311-324; Berman, S, Stern, H, Sensors for gesture recognition systems (2012) J IEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews, 42 (3), pp. 277-290; Pisharady, P K, Vadakkepat, P, Loh, A P, Attention based detection and recognition of hand postures against complex backgrounds (2013) J International Journal of Computer Vision, 101 (3), pp. 403-419; Grimes, G J, Digital data entry glove interface device US4414537 P 1983-11-08; Su, B Y, Zheng, D D, Sheng, M, Daily behave recognition with single sensor based on functional time series data modeling (2018) J Pattern Recognition and Artificial Intelligence, 31 (7), p. 653. , 661 J 2018 31 7 653-661; Oyedotun, O K, Khashman, A, Deep learning in vision-based static hand gesture recognition (2017) J Neural Computing and Applications, 28 (12), pp. 3941-3951; Asaari, M S M, Suandi, S A, Rosdi, B A, Fusion of band limited phase only correlation and width centroid contour distance for finger based biometrics (2014) J Expert Systems With Applications, 41 (7), pp. 3367-3382; Sangi, P, Matilainen, M, Silvén, O, Rotation tolerant hand pose recognition using aggregation of gradient orientations (2016), pp. 257-267. , M Lecture Notes in Computer Science Cham Springer International Publishing; Mohanty, A, Rambhatla, S S, Sahay, R R, Deep gesture static hand gesture recognition using CNN M Advances (2016), pp. 449-461. , Intelligent Systems and Computing Singapore Springer Singapore; Fang, B, Sun, F C, Liu, H P, 3D human gesture capturing and recognition by the IMMU-based data glove (2018) J Neurocomputing, 277, pp. 198-207; Meng, B, Liu, X J, Wang, X L, Human body action recognition based on quaternion spatial-temporal convolutional neural network (2017) J Chinese Journal of Scientific Instrument, (38 11), p. 2643. , 2017 2650 J 38 11 2643-2650; Wu, X F, Zhang, J X, Xu, X C, Hand gesture recognition algorithm based on faster R-CNN (2018) J Journal of Computer-Aided Design & Computer Graphics, 30 (3), pp. 468-476; He, K M, Gkioxari, G, Dollár, P, Mask, R-CNN C, (2017) IEEE International Conference on Computer Vision ICCV, pp. 2980-2988. , et al, 2017 October 22-29 2017 Venice Italy New York IEEE Press; Redmon, J, Divvala, S, Girshick, R, You only look once unified real-time object detection C 2016 (2016) IEEE Conference on Computer Vision and Pattern Recognition CVPR, pp. 779-788. , June 27-30 2016 Las Vegas NV USA New York IEEE Press; Peng, Y Q, Zhao, X S, Tao, H F, Hand gesture recognition against complex background based on deep learning (2019) J Robot, 41 (4), pp. 534-542; Fu, C Y, Liu, W, Ranga, A, DSSD deconvolutional single shot detector EB OL 2018-12-14 2019-12-12, , httpsarxivorgabs170106659htm; Ren, S Q, He, K M, Girshick, R, Faster RCNN towards real-time object detection with region proposal networks (2017) J IEEE Transactions on Pattern Analysis and Machine Intelligence, 39 (6), pp. 1137-1149; Chen, H J, Wang, Q Q, Yang, G W, SSD object detection algorithm with multi-scale convolution feature fusion (2019) J Journal of Frontiers of Computer Science and Technology, (13 6), pp. 1049-1061. , 2019 SSD J 13 6 1049-1061; He, K M, Zhang, X Y, Ren, S Q, Deep residual learning for image recognition (2016) J 2016 IEEE Conference on Computer Vision and Pattern Recognition CVPR, pp. 770-778; Simonyan, K, Zisserman, A, Very deep convolutional networks for large-scale image recognition EB OL 2015-04-10 2019-12-12 https arxiv org abs 1409 1556",,,,"Universitat zu Koln",,,,,10064125,,,,"Chinese","Laser Optoelectron. Prog.",Article,"Final","",Scopus,2-s2.0-85091538068
"Su H., Ovur S.E., Zhou X., Qi W., Ferrigno G., De Momi E.","57203702628;57204781654;57190885661;57194568340;7005615860;57203951877;","Depth vision guided hand gesture recognition using electromyographic signals",2020,"Advanced Robotics","34","15",,"985","997",,29,"10.1080/01691864.2020.1713886","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078276080&doi=10.1080%2f01691864.2020.1713886&partnerID=40&md5=22c701c7f8b253a1d65ff710e4f39416","Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy; State Key Laboratory of High Performance Complicated, Central South University, Changsha, China","Su, H., Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy; Ovur, S.E., Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy; Zhou, X., Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy, State Key Laboratory of High Performance Complicated, Central South University, Changsha, China; Qi, W., Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy; Ferrigno, G., Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy; De Momi, E., Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milano, Italy","Hand gesture recognition has been applied to many research fields and has shown its prominent advantages in increasing the practicality of Human-Robot Interaction (HRI). The development of advanced techniques in data science, such as big data and machine learning, facilitate the accurate classification of the hand gestures using electromyography (EMG) signals. However, the processing of the collection and label of the large data set imposes a high work burden and results in time-consuming implementations. Therefore, a novel method is proposed to combine the benefits of depth vision learning and EMG based hand gesture recognition. It is capable of labeling the class of the collected EMG data under the guidance of depth vision automatically, without consideration of the hand motion sequence. Finally, we demonstrated the proposed method for recognizing the ten hand gestures using a Myo armband. The experiment is set in a supervised learning way to evaluate the performance of the designed Hk-means algorithm. It shows that the proposed method can succeed in hand gesture recognition without labeling the data in advance. © 2020 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","classification; clustering; Depth vision; electromyographic signals; hand gesture recognition","Biomedical signal processing; Classification (of information); Human robot interaction; Machine learning; Palmprint recognition; clustering; Electromyographic signal; Hand gesture; Hand motion; Hand-gesture recognition; Large datasets; Research fields; Vision learning; Gesture recognition",,,,,"732515","This work was supported by the European Unions Horizon 2020 - H2020 European Institute of Innovation and Technology research and innovation program under SMARTsurg project grant agreement No. 732515.",,,,,,,,,,"Li, Z., Xia, Y., Su, C.Y., (2015) Intelligent networked teleoperation control, , Berlin Heidelberg: Springer; Qi, W., Su, H., Yang, C., A fast and robust deep convolutional neural networks for complex human activity recognition using smartphone (2019) Sensors, 19 (17), p. 3731. , et al; Wen, R., Tay, W.L., Nguyen, B.P., Hand gesture guided robot-assisted surgery based on a direct augmented reality interface (2014) Comput Methods Programs Biomed, 116 (2), pp. 68-80. , et al; Sathiyanarayanan, M., Rajan, S., Myo armband for physiotherapy healthcare: a case study using gesture recognition application. 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"Dromnelle R., Girard B., Renaudo E., Chatila R., Khamassi M.","57211624716;36945382400;56303861600;7003451196;6508136456;","Coping with the variability in humans reward during simulated human-robot interactions through the coordination of multiple learning strategies",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223451","612","617",,2,"10.1109/RO-MAN47096.2020.9223451","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095773376&doi=10.1109%2fRO-MAN47096.2020.9223451&partnerID=40&md5=1ee991132786f5880fe4f37eb974c489","Sorbonne Université, Institute of Intelligent Systems and Robotics, Paris, France; Universitat Innsbruck, Intelligent and Interactive Systems Group, Innsbruck, Austria","Dromnelle, R., Sorbonne Université, Institute of Intelligent Systems and Robotics, Paris, France; Girard, B., Sorbonne Université, Institute of Intelligent Systems and Robotics, Paris, France; Renaudo, E., Universitat Innsbruck, Intelligent and Interactive Systems Group, Innsbruck, Austria; Chatila, R., Sorbonne Université, Institute of Intelligent Systems and Robotics, Paris, France; Khamassi, M., Sorbonne Université, Institute of Intelligent Systems and Robotics, Paris, France","An important current challenge in Human-Robot Interaction (HRI) is to enable robots to learn on-the-fly from human feedback. However, humans show a great variability in the way they reward robots. We propose to address this issue by enabling the robot to combine different learning strategies, namely model-based (MB) and model-free (MF) reinforcement learning. We simulate two HRI scenarios: a simple task where the human congratulates the robot for putting the right cubes in the right boxes, and a more complicated version of this task where cubes have to be placed in a specific order. We show that our existing MB-MF coordination algorithm previously tested in robot navigation works well here without retuning parameters. It leads to the maximal performance while producing the same minimal computational cost as MF alone. Moreover, the algorithm gives a robust performance no matter the variability of the simulated human feedback, while each strategy alone is impacted by this variability. Overall, the results suggest a promising way to promote robot learning flexibility when facing variable human feedback. © 2020 IEEE.",,"Agricultural robots; Biofeedback; Educational robots; Learning systems; Man machine systems; Reinforcement learning; Computational costs; Coordination algorithms; Human robot Interaction (HRI); Learning strategy; Model-based OPC; Robot navigation; Robust performance; Specific ordering; Social robots",,,,,"ANR11 LABX65; Horizon 2020 Framework Programme, H2020: 731761; Délégation Générale pour l'Armement, DGA","* This work has been funded by a DGA (French National Defence Armaments Procurement Agency) scholarships (RD) and by Labex SMART (ANR11 LABX65). The research leading to these results has received funding from the European Unions Horizon 2020 research and innovation programme (grant agreement number 731761, IMAGINE).",,,,,,,,,,"Najar, A., (2017) Shaping Robot Behaviour with Unlabeled Human Instructions, , PhD thesis, Universit de Cercy Pontoise, Paris, FR; Ho, M.K., Littman, M.L., Cushman, F., Austerweil, J.L., Teaching with rewards and punishments: Reinforcement or communication? (2015) CogSci; Roe, A., Siegelman, M., A parent-child relations questionnaire (1963) Child Development, pp. 355-369; Palminteri, S., Khamassi, M., Joffily, M., Coricelli, G., Contextual modulation of value signals in reward and punishment learning (2015) Nature Communications, 6 (1), pp. 1-14; Knox, W.B., Stone, P., Combining manual feedback with subsequent mdp reward signals for reinforcement learning (2010) Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: Volume 1-Volume 1, pp. 5-12. , Citeseer; Griffith, S., Subramanian, K., Scholz, J., Isbell, C.L., Thomaz, A.L., Policy shaping: Integrating human feedback with reinforcement learning (2013) Advances in Neural Information Processing Systems, pp. 2625-2633; Fournier, P., (2019) Intrinsically Motivated and Interactive Reinforcement Learning: A Developmental Approach, , PhD thesis, EDITE de Paris; Sutton, R.S., Barto, A.G., (1998) Introduction to Reinforcement Learning, , Cambridge, MA, USA: MIT Press, 1st ed; Dromnelle, R., Renaudo, E., Pourcel, G., Chatila, R., Girard, B., Khamassi, M., How to reduce computation time while sparing performance during robot navigation? A neuro-inspired architecture for autonomous shifting between model-based and model-free learning (2020) Living Machines 2020 : The 9th International Conference on Biomimetic and Biohybrid Systems.; Gat, E., On three-layer architectures (1998) Artificial Intelligence and Mobile Robots, , MIT Press; Alami, R., Chatila, R., Fleury, S., Ghallab, M., Ingrand, F., An architecture for autonomy (1998) IJRR Journal, 17, pp. 315-337; Daw, N., Niv, Y., Dayan, P., Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control (2005) Nat. Neurosci., 8 (12), pp. 1704-1711; Viejo, G., Khamassi, M., Brovelli, A., Girard, B., Modelling choice and reaction time during arbitrary visuomotor learning through the coordination of adaptive working memory and reinforcement learning (2015) Frontiers in Behavioral Neuroscience, 9 (225); Powell, T., Sammut-Bonnici, T., (2015) Pareto Analysis. 01; Dzeroski, S., De Raedt, L., Driessens, K., Relational reinforcement learning (2001) Machine Learning, 43 (1-2), pp. 7-52; Knox, W.B., Stone, P., Reinforcement learning from simultaneous human and mdp reward (2012) AAMAS",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85095773376
"Li W., Liu J., Ni F., Rovetta A., Guo C.","57219423905;57221548822;57219413601;35609139600;49861146400;","A Virtual Reality-Based Framework for Enhancing Walking abilities Using Exoskeleton",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223569","1374","1379",,,"10.1109/RO-MAN47096.2020.9223569","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095762121&doi=10.1109%2fRO-MAN47096.2020.9223569&partnerID=40&md5=c6f79e5808bb89d898ae1bfa920fc7b8","Beijing Microelectronics Technology Institute, Beijing, 100076, China; Beijing Institute of Radio Measurement, Beijing, 1000854, China; Beihang University, Beijing, China; Northwestern Polytechnical University, School of Electronics and Information, Xian, Shaanxi, 710072, China","Li, W., Beijing Microelectronics Technology Institute, Beijing, 100076, China; Liu, J., Beijing Institute of Radio Measurement, Beijing, 1000854, China; Ni, F., Beijing Institute of Radio Measurement, Beijing, 1000854, China; Rovetta, A., Beihang University, Beijing, China; Guo, C., Northwestern Polytechnical University, School of Electronics and Information, Xian, Shaanxi, 710072, China","This article is to develop virtual reality-based framework for enhancing walking abilities using exoskeleton robotics. On one hand, China will face an ever-increasing and unprecedented demand for rehabilitation treatment and training for a long period of time in the future; on the other hand, backward and shortage of rehabilitation training resources urgently need to be rapidly upgraded through scientific and technological progress. A lightweight and intelligent system for indoor walking abilities rehabilitation training that combines virtual reality and exoskeleton robotics will effectively improve the rehabilitation training conditions, efficiency and effects through scientific and technological means. Through in-depth analysis and tracking of the training plan, combined with the research results of gait theory, the article describes a customized exoskeleton with VR glass and local cloud, and the sensing process, virtual scene establishment and presentation, human-robot interaction mechanism, virtual training and performance evaluation, and constraints are elaborated. Large-scale system testing and data collection and analysis, and the heterogeneity of rehabilitation training with this method will be the focus of future research. © 2020 IEEE.",,"Agricultural robots; E-learning; Educational robots; Exoskeleton (Robotics); Intelligent systems; Large scale systems; Virtual reality; In-depth analysis; Rehabilitation training; Research results; Sensing process; Technological progress; Unprecedented demand; Virtual training; Walking ability; Social robots",,,,,,"*Research supported by China Scholarship Council Foundation.",,,,,,,,,,"China 2010 Census Data, , http://www.stats.gov.cn/tjsj/pcsj/rkpc/6rp/indexce.htm; Annual Data Report of China Disabled Persons' Federation, , http://www.cdpf.org.cn/sjzx/sjcx/ndsj/; Su, H., Qi, W., Hu, Y., Sandoval, A., Juan, S., Zhang, L., Schmirander, Y., Momi, E., Towards model-free tool dynamic identification and calibration using multi-layer neural network (2019) Sensors, 19 (17), p. 3636; Perry, J., Burnfield, J., (1992) Gait Analysis: Normal and Pathological Function, , Slack Inc; Berenpas, F., Surplus value of implanted peroneal functional electrical stimulation over ankle-foot orthosis for gait adaptability in people with foot drop after stroke (2019) Gait & Posture, 71, pp. 157-162; Zandian, R., Witkowski, U., Robot self-localization in ultra-wideband large scale multi-node setups (2017) 2017 14th Workshop on Positioning, Navigation and Communications (WPNC), pp. 1-6; Zangiacomi, A., Architecture of a virtual reality and semantics-based framework for the return to work of wheelchair user (2017) Augmented Reality, Virtual Reality, and Computer Graphics: 4th International Conference, AVR 2017, p. 74. , 10325; Su, H., Yang, C., Mdeihly, H., Rizzo, A., Ferrigno, G., Momi, E., Neural network enhanced robot tool identification and calibration for bilateral teleoperation (2019) IEEE Access, 7, pp. 12241-12251; Su, H., Depth vision guided hand gesture recognition using electromyographic signals (2020) Advanced Robotics, pp. 1-13; Biffi, E., An immersive virtual reality platform to enhance walking ability of children with acquired brain injuries (2017) Methods of Information in Medicine, 56 (2), pp. 119-126; Arlati, S., A social virtual reality-based application for the physical and cognitive training of the elderly at home (2019) Sensors, 19 (2), p. 261; Bayón-Calatayud, M., Virtual rehabilitation (2016) Emerging Therapies in Neurorehabilitation II, pp. 303-318. , Springer","Guo, C.; Beijing Microelectronics Technology InstituteChina; email: nvs_2008@126.com",,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095762121
"Zahray L., Savery R., Syrkett L., Weinberg G.","57219789886;57190783994;57219852995;8601346400;","Robot Gesture Sonification to Enhance Awareness of Robot Status and Enjoyment of Interaction",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223452","978","985",,1,"10.1109/RO-MAN47096.2020.9223452","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095746398&doi=10.1109%2fRO-MAN47096.2020.9223452&partnerID=40&md5=6392ec8d288dff6b1bf5f9a656ed8f48","Georgia Tech Center for Music Technology, Atlanta, GA  30332, United States","Zahray, L., Georgia Tech Center for Music Technology, Atlanta, GA  30332, United States; Savery, R., Georgia Tech Center for Music Technology, Atlanta, GA  30332, United States; Syrkett, L., Georgia Tech Center for Music Technology, Atlanta, GA  30332, United States; Weinberg, G., Georgia Tech Center for Music Technology, Atlanta, GA  30332, United States","We present a divergent approach to robotic sonification with the goal of improving the quality and safety of human-robot interactions. Sonification (turning data into sound) has been underutilized in robotics, and has broad potential to convey robotic movement and intentions to users without requiring visual engagement. We design and evaluate six different sonifications of movements for a robot with four degrees of freedom. Our sonification techniques include a direct mapping from each degree of freedom to pitch and timbre changes, emotion-based sound mappings, and velocity-based mappings using different types of sounds such as motors and music. We evaluate these sonifications using metrics for ease of use, enjoyment/appeal, and conveyance of movement information. Based on our results, we make recommendations to inform decisions for future robot sonification design. We suggest that when using sonification to improve safety of human-robot collaboration, it is necessary not only to convey sufficient information about movements, but also to convey that information in a pleasing and even social way to to enhance the human-robot relationship. © 2020 IEEE.",,"Agricultural robots; Degrees of freedom (mechanics); Machine design; Mapping; Robotics; Degree of freedom; Direct mapping; Divergent approach; Four-degrees-of-freedom; Human-robot collaboration; Quality and safeties; Robot gestures; Robotic movements; Social robots",,,,,"National Science Foundation, NSF: 1925178",,,,,,,,,,,"Robla-Gómez, S., Becerra, V.M., Llata, J.R., Gonzalez-Sarabia, E., Torre-Ferrero, C., Perez-Oria, J., Working together: A review on safe human-robot collaboration in industrial environments (2017) IEEE Access, 5, pp. 26754-26773; Tadele, T.S., De Vries, T., Stramigioli, S., The safety of domestic robotics: A survey of various safety-related publications (2014) IEEE Robotics Automation Magazine, 21 (3), pp. 134-142; You, S., Kim, J.-H., Lee, S., Kamat, V., Robert, L.P., Jr., Enhancing perceived safety in human-robot collaborative construction using immersive virtual environments (2018) Automation in Construction, 96, pp. 161-170; Kramer, G., Walker, B., Bonebright, T., Cook, P., Flowers, J., Miner, N., Neuhoff, J., Berger, J., The sonification report: Status of the field and research agenda. report prepared for the national science foundation by members of the international community for auditory display (1999) International Community for Auditory Display (ICAD), , Santa Fe, NM; Bartneck, C., Kulíc, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81; Herrmann, G., Melhuish, C., Towards safety in human robot interaction (2010) International Journal of Social Robotics, 2 (3), pp. 217-219; Dhillon, B.S., Fashandi, A., Safety and reliability assessment techniques in robotics (1997) Robotica, 15 (6), pp. 701-708; Papanastasiou, S., Kousi, N., Karagiannis, P., Gkournelos, C., Papavasileiou, A., Dimoulas, K., Baris, K., Makris, S., Towards seamless human robot collaboration: Integrating multimodal interaction (2019) The International Journal of Advanced Manufacturing Technology, 105 (9), pp. 3881-3897; Savery, R., Rose, R., Weinberg, G., Establishing human-robot trust through music-driven robotic emotion prosody and gesture (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 1-7; Young, J., Hawkins, R., Sharlin, E., Igarashi, T., Toward acceptable domestic robots: Applying insights from social psychology (2009) International Journal of Social Robotics, 1 (95); Denning, T., Matuszek, C., Koscher, K., Smith, J.R., Kohno, T., A spotlight on security and privacy risks with future household robots: Attacks and lessons (2009) Proceedings of the 11th International Conference on Ubiquitous Computing, Ser. UbiComp '09, pp. 105-114. , New York, NY, USA: Association for Computing Machinery; Breazeal, C., Dautenhahn, K., Kanda, T., Social robotics (2016) Springer Handbook of Robotics, pp. 1935-1972. , Springer; Saunderson, S., Nejat, G., How robots influence humans: A survey of nonverbal communication in social human-robot interaction (2019) International Journal of Social Robotics, 11 (4), pp. 575-608; Hermann, T., Neumann, A., Zehe, S., Head gesture sonification for supporting social interaction (2012) Proceedings of the 7th Audio Mostly Conference: A Conference on Interaction with Sound, pp. 82-89; Oh, U., Kane, S.K., Findlater, L., Follow that sound: Using sonification and corrective verbal feedback to teach touchscreen gestures (2013) Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility, Ser. ASSETS '13, , New York, NY, USA: Association for Computing Machinery; Landry, S., Jeon, M., Participatory design research methodologies: A case study in dancer sonification (2017) The 23rd International Conference on Auditory Display (ICAD 2017), , State College, PA, USA; Schaffert, N., Mattes, K., Effenberg, A.O., A sound design for the purposes of movement optimisation in elite sport (using the example of rowing) (2009) Georgia Institute of Technology; Moroni, A., Manzolli, J., From evolutionary composition to robotic sonification (2010) European Conference on the Applications of Evolutionary Computation, pp. 401-410. , Springer; Zhang, R., Barnes, J., Ryan, J., Jeon, M., Park, C.H., Howard, A., Musical robots for children with asd using a client-server architecture (2016) International Conference on Auditory Display; Sterkenburg, J., Jeon, M., Plummer, C., Auditory emoticons: Iterative design and acoustic characteristics of emotional auditory icons and earcons (2014) Human-Computer Interaction. Advanced Interaction Modalities and Techniques, pp. 633-640. , M. Kurosu, Ed. Cham: Springer International Publishing; Frid, E., Bresin, R., Alexanderson, S., (2018) Perception of Mechanical Sounds Inherent to Expressive Gestures of A Nao Robot-implications for Movement Sonification of Humanoids, , 07; Walker, B.N., Nees, M.A., Theory of sonification (2011) The Sonification Handbook, pp. 9-39; Likert, R., A technique for the measurement of attitudes (1932) Archives of Psychology, 22 (140), pp. 1-55; Tomlinson, B.J., Noah, B.E., Walker, B.N., Buzz: An auditory interface user experience scale (2018) Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1-6",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095746398
"Sato D., Sasagawa M., Niijima A.","57219846388;57188731284;55211717200;","Affective Touch Robots with Changing Textures and Movements",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223481","1","6",,,"10.1109/RO-MAN47096.2020.9223481","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095740304&doi=10.1109%2fRO-MAN47096.2020.9223481&partnerID=40&md5=b9de4de127e16614ba2e9390a851c086","Ntt Service Evolution Laboratories, Kanagawa, 239-0847, Japan","Sato, D., Ntt Service Evolution Laboratories, Kanagawa, 239-0847, Japan; Sasagawa, M., Ntt Service Evolution Laboratories, Kanagawa, 239-0847, Japan; Niijima, A., Ntt Service Evolution Laboratories, Kanagawa, 239-0847, Japan","We explore how to design emotional expression using tabletop social robots with multiple texture modules. Previous studies in human-robot interaction have presented various designs for emotionally expressive robots without using anthropomorphic forms or cues. They revealed that haptic stimulation based on the textures and movements of the robots could evoke some emotions in users, although these were limited. In this work, we propose using a combination of textures and movements for richer emotional expression. We implemented tabletop robots equipped with detachable texture modules made of five different materials (plastic resin, aluminum, clay, Velcro, and cotton) and performed a user study with 13 participants to investigate how they would map the combinations of textures and movements to nine emotions chosen from Russell's circumplex model. The results indicated that the robots could express various emotions such as excited, happy, calm, and sad. Deeper analysis of these results revealed some interesting relationships between emotional valence/arousal and texture/movement: for example, cold texture played an important role in expressing negative valence, and controlling the frequency of the movements could change the expression of arousal. © 2020 IEEE.",,"Agricultural robots; Anthropomorphic robots; Machine design; Textures; Circumplex models; Emotional expressions; Emotional valences; Multiple texture; Plastic resins; User study; Social robots",,,,,,,,,,,,,,,,"Breazeal, C., Brooks, R., Robot emotion: A functional perspective (2005) Who Needs Emotions, pp. 271-310; Hu, Y., Hoffman, G., Using skin texture change to design emotion expression in social robots (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 2-10; Kim, L.H., Follmer, S., Swarmhaptics: Haptic display with swarm robots (2019) Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1-13; Van Waveren, S., Björklund, L., Carter, E.J., Leite, I., Knock on wood: The effects of material choice on the perception of social robots (2019) International Conference on Social Robotics. Springer, pp. 211-221; Nakata, T., Sato, T., Mori, T., Mizoguchi, H., Expression of emotion and intention by robot body movement (1998) Proceedings of the 5th International Conference on Autonomous Systems; Bartneck, C., Reichenbach, J., Breemen, V.A., (2004) In Your Face, Robot! the Influence of A Character S Embodiment on How Users Perceive Its Emotional Expressions; Miwa, H., Umetsu, T., Takanishi, A., Takanohu, H., Human-like robot head that has olfactory sensation and facial color expression (2001) Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164), 1, pp. 459-464. , IEEE; Kalegina, A., Schroeder, G., Allchin, A., Berlin, K., Cakmak, M., Characterizing the design space of rendered robot faces (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 96-104; Breazeal, C., Emotive qualities in robot speech (2001) Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No. 01CH37180), 3, pp. 1388-1394. , IEEE; Barnes, J., Richie, E., Lin, Q., Jeon, M., Park, C.H., Emotive voice acceptance in human-robot interaction (2018) Proceedings of the 24th International Conference on Auditory Display; Miwa, H., Itoh, K., Matsumoto, M., Zecca, M., Takanobu, H., Rocella, S., Carrozza, M.C., Takanishi, A., Effective emotional expressions with expression humanoid robot we-4rii: Integration of humanoid robot hand rch-1 (2004) 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No. 04CH37566), 3, pp. 2203-2208. , IEEE; Li, J., Chignell, M., Communication of emotion in social robots through simple head and arm movements (2011) International Journal of Social Robotics, 3 (2), pp. 125-142; Lim, A., Ogata, T., Okuno, H.G., Converting emotional voice to motion for robot telepresence (2011) 2011 11th IEEE-RAS International Conference on Humanoid Robots. IEEE, pp. 472-479; Tielman, M., Neerincx, M., Meyer, J.-J., Looije, R., Adaptive emotional expression in robot-child interaction (2014) 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 407-414; Eid, M.A., Al Osman, H., Affective haptics: Current research and future directions (2015) IEEE Access, 4, pp. 26-40; Smith, J., MacLean, K., Communicating emotion through a haptic link: Design space and methodology (2007) International Journal of Human-Computer Studies, 65 (4), pp. 376-387; Salminen, K., Surakka, V., Lylykangas, J., Raisamo, J., Saarinen, R., Raisamo, R., Rantala, J., Evreinov, G., Emotional and behavioral responses to haptic stimulation (2008) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1555-1562; Karana, E., Hekkert, P., Kandachar, P., Meanings of materials through sensorial properties and manufacturing processes (2009) Materials & Design, 30 (7), pp. 2778-2784; Hu, Y., Zhao, Z., Vimal, A., Hoffman, G., Soft skin texture modulation for social robotics (2018) 2018 IEEE International Conference on Soft Robotics (RoboSoft). IEEE, pp. 182-187; Russell, J.A., A circumplex model of affect (1980) Journal of Personality and Social Psychology, 39 (6), p. 1161; Berns, K., Hirth, J., Control of facial expressions of the humanoid robot head roman (2006) 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 3119-3124; Hashimoto, T., Hiramatsu, S., Kobayashi, H., Development of face robot for emotional communication between human and robot (2006) 2006 International Conference on Mechatronics and Automation. IEEE, pp. 25-30; Sosnowski, S., Bittermann, A., Kuhnlenz, K., Buss, M., Design and evaluation of emotion-display eddie (2006) 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 3113-3118; Breazeal, C., Emotion and sociable humanoid robots (2003) International Journal of Human-computer Studies, 59 (1-2), pp. 119-155; Yonezawa, T., Yamazoe, H., Utsumi, A., Abe, S., Gazecommunicative behavior of stuffed-toy robot with joint attention and eye contact based on ambient gaze-tracking (2007) Proceedings of the 9th International Conference on Multimodal Interfaces, pp. 140-145; Kanda, T., Ishiguro, H., Ono, T., Imai, M., Nakatsu, R., Development and evaluation of an interactive humanoid robot robovie (2002) Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292), 2, pp. 1848-1855. , IEEE; Miyauchi, D., Nakamura, A., Kuno, Y., Bidirectional eye contact for human-robot communication (2005) IEICE Transactions on Information and Systems, 88 (11), pp. 2509-2516; Ito, A., Terada, K., Producing intentionality in eye-contact robot (2005) 11th International Conference on Human-Computer Interaction, HCI International, pp. 22-27; Niculescu, A., Van Dijk, B., Nijholt, A., Li, H., See, S.L., Making social robots more attractive: The effects of voice pitch, humor and empathy (2013) International Journal of Social Robotics, 5 (2), pp. 171-191; Le, Q.A., Hanoune, S., Pelachaud, C., Design and implementation of an expressive gesture model for a humanoid robot (2011) 2011 11th IEEE-RAS International Conference on Humanoid Robots. IEEE, pp. 134-140; Cabibihan, J.-J., So, W.-C., Pramanik, S., Human-recognizable robotic gestures (2012) IEEE Transactions on Autonomous Mental Development, 4 (4), pp. 305-314; Embgen, S., Luber, M., Becker-Asano, C., Ragni, M., Evers, V., Arras, K.O., Robot-specific social cues in emotional body language (2012) 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 1019-1025; Stiehl, W.D., Breazeal, C., Han, K.-H., Lieberman, J., Lalla, L., Maymin, A., Salinas, J., Tong, C.H., The huggable: A therapeutic robotic companion for relational, affective touch (2006) ACM SIGGRAPH 2006 Emerging Technologies, p. 15; Wada, K., Shibata, T., Saito, T., Tanie, K., Effects of robotassisted activity for elderly people and nurses at a day service center (2004) Proceedings of the IEEE, 92 (11), pp. 1780-1788; Arafsha, F., Alam, K.M., El Saddik, A., Design and development of a user centric affective haptic jacket (2015) Multimedia Tools and Applications, 74 (9), pp. 3035-3052; Huisman, G., Darriba Frederiks, A., Towards tactile expressions of emotion through mediated touch (2013) CHI'13 Extended Abstracts on Human Factors in Computing Systems, pp. 1575-1580; Yohanan, S., MacLean, K.E., The role of affective touch in humanrobot interaction: Human intent and expectations in touching the haptic creature (2012) International Journal of Social Robotics, 4 (2), pp. 163-180; Holliins, M., Faldowski, R., Rao, S., Young, F., Perceptual dimensions of tactile surface texture: A multidimensional scaling analysis (1993) Perception & Psychophysics, 54 (6), pp. 697-705; Whitmire, E., Benko, H., Holz, C., Ofek, E., Sinclair, M., Haptic revolver: Touch, shear, texture, and shape rendering on a reconfigurable virtual reality controller (2018) Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1-12",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095740304
"Mitsuno S., Yoshikawa Y., Ishiguro H.","57219856405;7201413552;26643316500;","Robot-on-Robot Gossiping to Improve Sense of Human-Robot Conversation",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223442","653","658",,1,"10.1109/RO-MAN47096.2020.9223442","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095730396&doi=10.1109%2fRO-MAN47096.2020.9223442&partnerID=40&md5=ac2ec563cda67bf4704bb20f32ef0b07","Graduate School of Engineering Science, Osaka University, Intelligent Robotics Lab, Japan","Mitsuno, S., Graduate School of Engineering Science, Osaka University, Intelligent Robotics Lab, Japan; Yoshikawa, Y., Graduate School of Engineering Science, Osaka University, Intelligent Robotics Lab, Japan; Ishiguro, H., Graduate School of Engineering Science, Osaka University, Intelligent Robotics Lab, Japan","In recent years, a substantial amount of research has been aimed at realizing a social robot that can maintain long-term user interest. One approach is using a dialogue strategy in which the robot makes a remark based on previous dialogues with users. However, privacy problems may occur owing to private information of the user being mentioned. We propose a novel dialogue strategy whereby a robot mentions another robot in the form of gossiping. This dialogue strategy can improve the sense of conversation, which results in increased interest while avoiding the privacy issue. We examined our proposal by conducting a conversation experiment evaluated by subject impressions. The results demonstrated that the proposed method could help the robot to obtain higher evaluations. In particular, the perceived mind was improved in the Likert scale evaluation, whereas the robot empathy and intention to use were improved in the binary comparison evaluation. Our dialogue strategy may contribute to understanding the factors regarding the sense of conversation, thereby adding value to the field of human-robot interaction. © 2020 IEEE.",,"Agricultural robots; Dialogue strategy; Human robots; Intention to use; Likert scale; Privacy issue; Privacy problems; Private information; User interests; Social robots",,,,,"Japan Society for the Promotion of Science, KAKEN: JP19H05691; Exploratory Research for Advanced Technology, ERATO","This work was supported by JSPS KAKENHI Grant Number JP19H05691 and JST ERATO, Japan.",,,,,,,,,,"Gockley, R., Bruce, A., Forlizzi, J., Michalowski, M., Mundell, A., Rosenthal, S., Sellner, B., Designing robots for long-term social interaction (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, , 133843. ieeexplore. ieee. org; Broekens, J., Heerink, M., Rosendal, H., Assistive social robots in elderly care: A review (2009) Gerontechnology: International Journal on the Fundamental Aspects of Technology to Serve the Ageing Society, 8 (2), p. 94103; Kanda, T., Sato, R., Saiwaki, N., Ishiguro, H., A two-month field trial in an elementary school for long-term human-robot interaction (2007) IEEE Transactions on Robotics, 23 (5), p. 96271; Higashinaka, R., Sakai, K., Sugiyama, H., Narimatsu, H., Arimoto, T., Fukutomi, T., Matsui, K., Argumentative dialogue system based on argumentation structures (2017) SEMDIAL 2017 SaarDial, pp. 154-155; Uchida, T., Minato, T., Ishiguro, H., A values-based dialogue strategy to build motivation for conversation with autonomous conversational robots (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), , 20611. ieeexplore. ieee. org; Salter, T., Dautenhahn, K., Bockhorst, R., Robots moving out of the laboratory-detecting interaction levels and human contact in noisy school environments (2004) RO-MAN 2004. 13th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No. 04TH8759), , 56368. ieeexplore. ieee. org; Leite, I., Martinho, C., Pereira, A., Paiva, A., As time goes by: Long-term evaluation of social presence in robotic companions (2009) RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, , 66974. ieeexplore. ieee. org; Fernaeus, Y., Hakansson, M., Jacobsson, M., Ljungblad, S., How do you play with a robotic toy animal? A long-term study of pleo (2010) Proceedings of the 9th International Conference on Interaction Design and Children, , 3948. dl. acm. org; Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., Hagita, N., A communication robot in a shopping mall (2010) IEEE Transactions on Robotics, 26 (5), p. 897913; Lee, M.K., Forlizzi, J., Kiesler, S., Rybski, P., Antanitis, J., Savetsila, S., Personalization in hri: A longitudinal field experiment (2012) 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI), , 31926. ieeexplore. ieee. org; Mahzoon, H., Ogawa, K., Yoshikawa, Y., Tanaka, M., Ogawa, K., Miyazaki, R., Ota, Y., Ishiguro, H., Effect of self-representation of interaction history by the robot on perceptions of mind and positive relationship: A case study on a home-use robot (2019) Advanced Robotics: The International Journal of the Robotics Society of Japan, 33 (21), p. 111228; Changzeng, F., Yoshikawa, Y., Iio, T., Ishiguro, H., Sharing experiences to help a robot present its mind and sociability (2020) International Journal of Social Robotics; Goldfarb, A., Tucker, C., Shifts in privacy concerns (2012) The American Economic Review, 102 (3), p. 34953; Benjafield, J., Adams-Webber, J., THE golden section hypothesis (1976) British Journal of Psychology, 67 (1), p. 1115; Yoshikawa, Y., Iio, T., Arimoto, T., Sugiyama, H., Ishiguro, H., Proactive conversation between multiple robots to improve the sense of human-robot conversation (2017) 2017 AAAI Fall Symposium Series, , aaai. org; Uchida, T., Minato, T., Ishiguro, H., (2019) The Relationship between Dialogue Motivation and Attribution of Subjective Opinions to Conversational Androids Transactions of the Japanese Society for Artificial Intelligence, 34 (1), pp. B-I6218. , (in Japanese); Gray, M., Gray, K., Wegner, D., Dimensions of mind perception (2007) Science, 315 (5812), p. 619; Mishima, N., Shinkoda, H., Hukumoto, H., (1999) Kaigo Ni Ikasu Sekkyokuteki Keichohou [Actively Listening for Nursing Care], , Medicus Shuppan Publisshers. (in Japanese); Heerink, M., Krose, B., Evers, V., Wielinga, B., The influence of social presence on enjoyment and intention to use of a robot and screen agent by elderly users (2008) RO-MAN 2008-The 17th IEEE International Symposium on Robot and Human Interactive Communication, , 695700. ieeexplore. ieee. org; Sytsma, J., Machery, E., Two conceptions of subjective experience (2010) Philosophical Studies, 151 (2), p. 299327",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85095730396
"Lindblom J., Alenljung B.","7006805787;24402835300;","The anemone: Theoretical foundations for UX evaluation of action and intention recognition in human-robot interaction",2020,"Sensors (Switzerland)","20","15","4284","1","49",,9,"10.3390/s20154284","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088948958&doi=10.3390%2fs20154284&partnerID=40&md5=3df110e0ea1f318acc3e80ab4acb0001","School of Informatics, University of Skövde, Box 408, Skövde, 541 28, Sweden","Lindblom, J., School of Informatics, University of Skövde, Box 408, Skövde, 541 28, Sweden; Alenljung, B., School of Informatics, University of Skövde, Box 408, Skövde, 541 28, Sweden","The coexistence of robots and humans in shared physical and social spaces is expected to increase. A key enabler of high-quality interaction is a mutual understanding of each other’s actions and intentions. In this paper, we motivate and present a systematic user experience (UX) evaluation framework of action and intention recognition between humans and robots from a UX perspective, because there is an identified lack of this kind of evaluation methodology. The evaluation framework is packaged into a methodological approach called ANEMONE (action and intention recognition in human robot interaction). ANEMONE has its foundation in cultural-historical activity theory (AT) as the theoretical lens, the seven stages of action model, and user experience (UX) evaluation methodology, which together are useful in motivating and framing the work presented in this paper. The proposed methodological approach of ANEMONE provides guidance on how to measure, assess, and evaluate the mutual recognition of actions and intentions between humans and robots for investigators of UX evaluation. The paper ends with a discussion, addresses future work, and some concluding remarks. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Action recognition; Activity theory; Evaluation; Human-robot collaboration; Human-robot interaction; Intention recognition; Seven stages of action; User experience (UX); User-centered","Air navigation; Man machine systems; User experience; Action and intention; Cultural-historical; Evaluation framework; Evaluation methodologies; Methodological approach; Mutual understanding; Theoretical foundations; User experiences (ux); Social robots; behavior; human; robotics; Humans; Intention; Recognition, Psychology; Robotics",,,,,"Horizon 2020 Framework Programme, H2020: 637107; European Commission, EC; Stiftelsen för Kunskaps- och Kompetensutveckling, KKS: 20140220","Funding: This work was financially supported by the Knowledge Foundation, Stockholm, under SIDUS grant agreement no. 20140220 (AIR, action and intention recognition in human interaction with autonomous systems) as well as the European Commission under the Horizon 2020 Programme under grant agreement no. 637107 (SYMBIO-TIC).",,,,,,,,,,"Stephanidis, C., Salvendy, G., Antona, M., Chen, J.Y., Dong, J., Duffy, V.G., Guo, Y., Seven HCI Grand Challenges (2019) Int. 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"Wu X., Li Z.","57209362380;56266709300;","Cooperative Manipulation of Wearable Dual-Arm Exoskeletons Using Force Communication between Partners",2020,"IEEE Transactions on Industrial Electronics","67","8","8818671","6629","6638",,7,"10.1109/TIE.2019.2937036","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083233802&doi=10.1109%2fTIE.2019.2937036&partnerID=40&md5=7fe90e6186ef613b845606fb036bbd4c","Department of Automation, University of Science and Technology of China, Hefei, China","Wu, X., Department of Automation, University of Science and Technology of China, Hefei, China; Li, Z., Department of Automation, University of Science and Technology of China, Hefei, China","In this article, physical assistance using haptic interaction is investigated to improve the motor ability of human, in which the interaction force from a partner adapts to the physical behavior of the human interaction. A theoretical framework of interactive tasks, including the 'no computation' model and the 'multisensory integration' model, has been established on wearable dual-arm exoskeletons. Based on these models, two region-based controllers, i.e., motion-coupled control and force-coupled control, have been proposed, and the stability of the closed-loop system by using these controllers has also been analyzed under the consideration of system nonlinearities, uncertainties, and the time-varying human-robot interactions. In addition, the proposed controllers have reduced the motion conflicts between the human and the robot in a steady manner. Finally, we experimentally verify the proposed models during an interactive target tracking task and the experimental results demonstrate that collaborative exoskeleton robots using the proposed controllers can provide humanlike assistance, and indicate that movement goal exchange is the key to physical assistance. © 2019 IEEE.","Cooperative manipulation; force communication; region-based controllers; wearable dual-arm exoskeletons","Behavioral research; Closed loop systems; Control nonlinearities; Controllers; Cooperative communication; Exoskeleton (Robotics); Robotic arms; Target tracking; Uncertainty analysis; Wearable technology; Cooperative manipulations; Exoskeleton robots; Haptic interactions; Human interactions; Multisensory integration; Physical behaviors; System nonlinearities; Theoretical framework; Human robot interaction",,,,,"17030901029; National Natural Science Foundation of China, NSFC: 61573147, 61625303, 61751310; National Key Research and Development Program of China, NKRDPC: 2018YFC2001600","Manuscript received November 2, 2018; revised April 25, 2019; accepted August 5, 2019. Date of publication August 28, 2019; date of current version March 31, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61573147, Grant 61625303, and Grant 61751310, in part by the National Key Research and Development Program of China under Grant 2018YFC2001600, and in part by the Anhui Science and Technology Major Program under Grant 17030901029. (Corresponding author: Zhijun Li.) The authors are with the Department of Automation, University of Science and Technology of China, Hefei 230060, China (e-mail:,xiaoyuw@ mail.ustc.edu.cn; zjli@ieee.org).",,,,,,,,,,"Li, Z., Huang, B., Ajoudani, A., Yang, C., Su, C.-Y., Bicchi, A., Asymmetric bimanual coordinate control of dual-arm exoskeleton robots for human cooperative manipulations (2018) IEEE Trans. Robot., 34 (1), pp. 264-271. , Feb; Sebanz, N., Bekkering, H., Knoblich, G., Joint action: Bodies andminds moving together (2006) Trends Cogn. 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Eng., 20 (1), pp. 68-77. , Jan; Haddadin, S., Albu-Schffer, A., Hirzinger, G., Safety analysis for a human-friendly manipulator (2010) Int. J. Social Robot., 2 (3), pp. 235-252","Li, Z.; Department of Automation, China; email: zjli@ieee.org",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,02780046,,ITIED,,"English","IEEE Trans Ind Electron",Article,"Final","",Scopus,2-s2.0-85083233802
"Yang J., Peng C.","56549263100;57201132375;","Adaptive neural impedance control with extended state observer for human–robot interactions by output feedback through tracking differentiator",2020,"Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering","234","7",,"820","833",,1,"10.1177/0959651819898936","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079156959&doi=10.1177%2f0959651819898936&partnerID=40&md5=933fcc3f0a0e94ddabc62a22c5a969f2","State Key Laboratory of Mechanical System and Vibration, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China","Yang, J., State Key Laboratory of Mechanical System and Vibration, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China; Peng, C., State Key Laboratory of Mechanical System and Vibration, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China","Although impedance control has huge application potential in human–robot cooperation, its engineering application is still quite limited, owing to the high nonlinearity of the human–robot dynamics and disturbances. This article presents a novel adaptive neural network controller with extended state observer for the human–robot interaction using output feedback. The adaptive neural network with extended state observer integrates the adaptive neural network and extended state observer to combine their advantages. The proposed algorithm can address the challenges encountered in human–machine systems, for example, slow convergence of neural networks, internal and external disturbances. Output feedback is realized using tracking differentiator to avoid the costly measurements of certain states. The errors of the closed-loop system are proven to converge to a small compact set containing 0 by Lyapunov theory. Simulations and experiments were conducted to verify the effectiveness of the proposed controller. Results show that the proposed strategy offers superior convergence and better tracking performance compared with the adaptive neural network. The proposed controller can be widely applied in various human–machine interactions to enhance productivity and efficiency. © IMechE 2020.","Adaptive neural network; extended state observer; human–robot interaction; impedance control; output feedback","Adaptive control systems; Closed loop systems; Controllers; Human robot interaction; Neural networks; State estimation; Adaptive neural networks; Extended state observer; Impedance control; Output feedback; Robot interactions; Feedback",,,,,,,,,,,,,,,,"Kazerooni, H., Human-robot interaction via the transfer of power and information signals (1990) IEEE T Syst Man Cyb, 20 (2), pp. 450-463; Xing, C., Yan, Z., Yin, Y., Improving the transparency of an exoskeleton knee joint based on the understanding of motor intent using energy kernel method of EMG (2017) IEEE T Neur Sys Reh, 25 (6), pp. 577-588; Kosuge, K., Hirata, Y., Human-robot interaction (2003) IEEE Robot Autom Mag, 17 (2), pp. 85-89; Fan, Y., Yin, Y., Active and progressive exoskeleton rehabilitation using multisource information fusion from EMG and force-position EPP (2013) IEEE T Bio-Med Eng, 60 (12), pp. 3314-3321; Hogan, N., Impedance control: an approach to manipulation: part II—implementation (1985) J Dyn Syst-T ASME, 107 (1), pp. 8-16; Lu, Z., Goldenberg, A.A., Robust impedance control and force regulation: theory and experiments (1995) Int J Robot Res, 14, pp. 225-254; Carelli, R., Kelly, R., An adaptive impedance/force controller for robot manipulators (1991) IEEE T Automat Contr, 36 (8), pp. 967-971; Sharifi, M., Behzadipour, S., Vossoughi, G., Nonlinear model reference adaptive impedance control for human–robot interactions (2014) Control Eng Pract, 32, pp. 9-27; Liu, Y.J., Lu, S., Tong, S., Adaptive control-based Barrier Lyapunov Functions for a class of stochastic nonlinear systems with full state constraints (2018) Automatica, 87, pp. 83-93; Yao, B., (1996) Adaptive robust control of nonlinear systems with application to control of mechanical systems, , Berkeley, CA, University of California, Berkeley; Yao, B., Tomizuka, M., Adaptive robust control of SISO nonlinear systems in a semi-strict feedback form (1997) Automatica, 33 (5), pp. 893-900; Sun, T., Pei, H., Pan, Y., Neural network-based sliding mode adaptive control for robot manipulators (2011) Neurocomputing, 74 (14-15), pp. 2377-2384; Fei, J., Ding, H., Adaptive sliding mode control of dynamic system using RBF neural network (2012) Nonlinear Dynam, 70 (2), pp. 1563-1573; He, W., Chen, Y., Zhao, Y., Adaptive neural network control of an uncertain robot with full-state constraints (2016) IEEE T Cybernetics, 46 (3), pp. 620-629; Li, D., Chen, C.L.P., Liu, Y.J., Neural network controller design for a class of nonlinear delayed systems with time-varying full-state constraints (2019) IEEE T Neur Net Lear, 30, pp. 2625-2636; Li, D., Liu, L., Liu, Y.J., Adaptive NN control without feasibility conditions for nonlinear state constrained stochastic systems with unknown time delays (2019) IEEE T Cybernetics, 49, pp. 4485-4494; He, W., Dong, Y., Adaptive fuzzy neural network control for a constrained robot using impedance learning (2018) IEEE T Neur Net Lear, 29 (4), pp. 1174-1186; Liu, L., Liu, Y.J., Tong, S., Fuzzy-based multierror constraint control for switched nonlinear systems and its applications (2019) IEEE T Fuzzy Syst, 27, pp. 1519-1531; Ge, S.S., Hang, C.C., Woon, L.C., Impedance control of robot manipulators using adaptive neutral network (1998) Int J Intell Control Syst, 2 (3), pp. 433-452; Yang, C., Peng, G., Li, Y., Neural networks enhanced adaptive admittance control of optimized robot-environment interaction (2019) IEEE T Cybernetics, 49, pp. 2568-2579; He, W., Ge, S.S., Li, Y., Neural network control of a rehabilitation robot by state and output feedback (2015) J Intell Robot Syst, 80 (1), pp. 15-31; Chen, M., Ge, S.S., Adaptive neural output feedback control of uncertain nonlinear systems with unknown hysteresis using disturbance observer (2015) IEEE T Ind Electron, 62 (12), pp. 7706-7716; Ren, B., Ge, S.S., Tee, K.P., Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function (2010) IEEE T Neural Networ, 21 (8), pp. 1339-1345; Guo, B.Z., Zhao, Z.L., On the convergence of an extended state observer for nonlinear systems with uncertainty (2011) Syst Control Lett, 60 (6), pp. 420-430; Wang, Y., Shi, R., Wang, H., ESO-Based fuzzy sliding-mode control for a 3-DOF serial-parallel hybrid humanoid arm (2014) J Control Sci Eng, 2014, p. 304590; Han, J.Q., Nonlinear tracking-differentiator (1994) J Syst Sci Math Sci, 14 (2), pp. 177-183. , (,):, –, (in Chinese; Meng, W., Yang, Q., Pan, D., NN-based asymptotic tracking control for a class of strict-feedback uncertain nonlinear systems with output constraints, pp. 5410-5415. , Proceedings of the 51st IEEE conference on decision and control (CDC), Maui, HI, 10–13 December 2012, New York, IEEE, In; Slotine, J.J.E., Li, W., (1991) Applied nonlinear control, , Englewood Cliffs, NJ, Prentice Hall; Wilson, M., Spong, M.W., Robot modeling and control (2006) Ind Robot Int J, 17 (5), pp. 709-737; Wang, J., Li, S., Yang, J., Extended state observer-based sliding mode control for PWM-based DC–DC buck power converter systems with mismatched disturbances (2015) IET Control Theory A, 9 (4), pp. 579-586; Yao, J., Jiao, Z., Ma, D., Adaptive robust control of DC motors with extended state observer (2014) IEEE T Ind Electron, 61, pp. 3630-3637; Tan, C.P., Yu, X., Man, Z., Terminal sliding mode observers for a class of nonlinear systems (2010) Automatica, 46 (8), pp. 1401-1404","Yang, J.; State Key Laboratory of Mechanical System and Vibration, China; email: JTYang@sjtu.edu.cn",,,"SAGE Publications Ltd",,,,,09596518,,PMJEE,,"English","Proc Inst Mech Eng Part I J Syst Control Eng",Article,"Final","",Scopus,2-s2.0-85079156959
"Pedrelli L., Hinaut X.","56902536700;53063834600;","Hierarchical-Task Reservoir for Anytime POS Tagging from Continuous Speech",2020,"Proceedings of the International Joint Conference on Neural Networks",,,"9207285","","",,2,"10.1109/IJCNN48605.2020.9207285","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093827673&doi=10.1109%2fIJCNN48605.2020.9207285&partnerID=40&md5=2210c10c34508e0c168d97737fe7e38e","INRIA Bordeaux Sud-Ouest, France; LaBRI, Bordeaux INP, CNRS, UMR 5800, France; Institut des Maladies Neurodégénératives, Université de Bordeaux, CNRS, UMR 5293, Bordeaux, France","Pedrelli, L., INRIA Bordeaux Sud-Ouest, France, LaBRI, Bordeaux INP, CNRS, UMR 5800, France, Institut des Maladies Neurodégénératives, Université de Bordeaux, CNRS, UMR 5293, Bordeaux, France; Hinaut, X., INRIA Bordeaux Sud-Ouest, France, LaBRI, Bordeaux INP, CNRS, UMR 5800, France, Institut des Maladies Neurodégénératives, Université de Bordeaux, CNRS, UMR 5293, Bordeaux, France","We propose a novel architecture called Hierarchical-Task Reservoir (HTR) suitable for real-time sentence parsing from continuous speech. Accordingly, we introduce a novel task that consists in performing anytime Part-of-Speech (POS) tagging from continuous speech. This HTR architecture is designed to address three sub-tasks (phone, word and POS tag estimation) with increasing levels of abstraction. These tasks are performed by the consecutive layers of the HTR architecture. Interestingly, the qualitative results show that the learning of sub-tasks en-forces low frequency dynamics (i.e. with longer timescales) in the more abstract layers. We compared HTR with a baseline hierarchical reservoir architecture (in which each layer is an ESN that addresses the same POS tag estimation). Moreover, we also performed a thorough experimental comparison with several architectural variants. Finally, the HTR obtained the best performance in all experimental comparisons. Overall, the proposed approach will be a useful tool for further studies regarding both the modeling of language comprehension in a neuroscience context and for real-time implementations in Human-Robot Interaction (HRI) context. © 2020 IEEE.","Anytime Process; Hierarchical Processing; Hierarchical Reservoir Computing; Natural Language Processing; Part-of-Speech; POS tagging; Recurrent Neural Networks; Speech Recognition","Computational linguistics; Computational neuroscience; End effectors; Human robot interaction; Modeling languages; Network architecture; Real time control; Syntactics; Experimental comparison; Human robot Interaction (HRI); Language comprehensions; Levels of abstraction; Low frequency dynamics; Part of speech tagging; Real-time implementations; Reservoir architecture; Neural networks",,,,,,,,,,,,,,,,"Christiansen, M.H., (2016) Creating Language: Integrating Evolution, Acquisition, and Processing, , MIT Press; Sutskever, I., Sequence to sequence learning with neural networks (2014) NIPS, pp. 3104-3112; Bahdanau, D., (2014) Neural Machine Translation by Jointly Learning to Align and Translate; Vaswani, A., Attention is all you need (2017) NIPS, pp. 5998-6008; Hinaut, X., Exploring the acquisition and production of grammatical constructions through human-robot interaction with echo state networks (2014) Frontiers in Neurorobotics, p. 8; Twiefel, J., Using natural language feedback in a neuro-inspired integrated multimodal robotic architecture (2016) Proc. of RO-MAN, , New York City, USA; Hinaut, X., Twiefel, J., Teach your robot your language! trainable neural parser for modelling human sentence processing: Examples for 15 languages (2019) IEEE TCDS; Hatori, J., Interactively picking real-world objects with unconstrained spoken language instructions (2018) IEEE ICRA, , May; Triefenbach, F., Acoustic modeling with hierarchical reservoirs (2013) IEEE Transactions on Audio, Speech, and Language Processing, 21 (11), pp. 2439-2450. , November; Graves, A., Speech recognition with deep recurrent neural networks (2013) IEEE ICASSP, pp. 6645-6649; Chorowski, J.K., Attention-based models for speech recognition (2015) NIPS, pp. 577-585; Markov, N.T., Kennedy, H., The importance of being hierarchical (2013) Current Opinion in Neurobiology, 23 (2), pp. 187-194. , April; Schmidhuber, J., Learning complex, extended sequences using the principle of history compression (1992) Neural Computation, 4 (2), pp. 234-242; Hihi, S.E., Bengio, Y., Hierarchical recurrent neural networks for long-term dependencies (1995) NIPS, pp. 493-499; Hermans, M., Schrauwen, B., Training and analysing deep recurrent neural networks (2013) NIPS, pp. 190-198; Gallicchio, C., Deep reservoir computing: A critical experimental analysis (2017) Neurocomputing, 268, pp. 87-99; Gallicchio, C., Design of deep echo state networks (2018) Neural Networks, 108, pp. 33-47; Verstraeten, D., An experimental unification of reservoir computing methods (2007) Neural Networks, 20 (3), pp. 391-403; Lukosevicius, M., Jaeger, H., Reservoir computing approaches to recurrent neural network training (2009) Computer Science Review, 3 (3), pp. 127-149; Triefenbach, F., Phoneme recognition with large hierarchical reservoirs (2010) NIPS, pp. 2307-2315; Garofolo, J., Timit acoustic-phonetic continuous speech corpus (1993) Linguistic Data Consortium LDC93S1; Jaeger, H., (2001) The Echo State Approach to Analysing and Training Recurrent Neural Networks, , Technical Report 148, German National Research Center for Information Technology GMD, Bonn, Germany; Jaeger, H., Optimization and applications of echo state networks with leaky-integrator neurons (2007) Neural Networks, 20 (3), pp. 335-352; Jaeger, H., Haas, H., Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication (2004) Science, 304 (5667), pp. 78-80; Gallicchio, C., Chasing the echo state property (2018) ESANN; Gallicchio, C., Micheli, A., Echo state property of deep reservoir computing networks (2017) Cognitive Computation, 9 (3), pp. 337-350. , May; Twiefel, J., Syntactic reanalysis in language models for speech recognition (2017) IEEE ICDL-EpiRob; Hinaut, X., Dominey, P., Real-time parallel processing of grammatical structure in the fronto-striatal system: A recurrent network simulation study using reservoir computing (2013) PLoS ONE, 8 (2), p. e52946; Hinaut, X., Which input abstraction is better for a robot syntax acquisition model phonemes, words or grammatical constructions (2018) IEEE ICDL-EpiRob, , September; Hinaut, X., Spranger, M., Learning to parse grounded language using reservoir computing (2019) IEEE ICDL-EpiRob, , August; Hanuschkin, A., A hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models (2013) Frontiers in Neural Circuits, 7; Huang, Z., (2015) Bidirectional LSTM-CRF Models for Sequence Tagging; Twiefel, J., Semantic role labelling for robot instructions using echo state networks (2016) ESANN",,,"IEEE Computational Intelligence Society (CIS)","Institute of Electrical and Electronics Engineers Inc.","2020 International Joint Conference on Neural Networks, IJCNN 2020","19 July 2020 through 24 July 2020",,163566,,9781728169262,85OFA,,"English","Proc Int Jt Conf Neural Networks",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85093827673
"Choi J., Park J., Kim J.","57221178198;57212529942;13905404900;","A theoretical model for an inflatable wrinkle bending actuator",2020,"Journal of the Korean Society for Precision Engineering","37","7",,"503","508",,1,"10.7736/JKSPE.020.014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091659361&doi=10.7736%2fJKSPE.020.014&partnerID=40&md5=2269852000ddfc73233f7166e482d712","Department of Mechanical Engineering, KAIST, South Korea","Choi, J., Department of Mechanical Engineering, KAIST, South Korea; Park, J., Department of Mechanical Engineering, KAIST, South Korea; Kim, J., Department of Mechanical Engineering, KAIST, South Korea","In recent years, many soft wearable robots have been developed to overcome the limitations of conventional rigid wearable robots. Among the types of soft robots, soft pneumatic actuators (SPA) have been developed because of compliant characteristics that can guarantee safe human-robot interaction to improve one of the rigid wearable robot limitations. Especially, among various SPAs, inflatable actuators have been developed because they can be easily manufactured with various types of structures. However, the theoretical modelings proposed in the inflatable actuators are specific to apply to other joints, because their purpose is performance analysis. In this paper, we improve the theoretical modeling for the design of wrinkled inflatable actuators. The actuator’s design parameters such as height and number of layers were determined by the proposed theoretical model to provide the target torque. The soft actuator was manufactured with determined design parameters and then measure the torque for the various angles and pressures. The theoretical torque values acquired through the proposed theoretical model have an error of < 8% from the experimental torque values and showed higher accuracy than the previously proposed model. Copyright © The Korean Society for Precision Engineering","Soft actuator; Theoretical model; Wrinkle inflatable actuator",,,,,,,,,,,,,,,,,"Yan, T., Cempini, M., Oddo, C. M., Vitiello, N., Review of Assistive Strategies in Powered Lower-Limb Orthoses and Exoskeletons (2015) Robotics and Autonomous Systems, 64, pp. 120-136; Gopura, R., Bandara, D., Kiguchi, K., Mann, G. K., Developments in Hardware Systems of Active Upper-Limb Exoskeleton Robots: A Review (2016) Robotics and Autonomous Systems, 75, pp. 203-220; Asbeck, A. T., De Rossi, S. M., Galiana, I., Ding, Y., Walsh, C. J., Stronger, Smarter, Softer: Next-Generation Wearable Robots (2014) IEEE Robotics & Automation Magazine, 21 (4), pp. 22-33; Yap, H. K., Khin, P. M., Koh, T. H., Sun, Y., Liang, X., A Fully Fabric-Based Bidirectional Soft Robotic Glove for Assistance and Rehabilitation of Hand Impaired Patients (2017) IEEE Robotics and Automation Letters, 2 (3), pp. 1383-1390; Felt, W., Folded-Tube Soft Pneumatic Actuators for Bending (2019) Soft Robotics, 6 (2), pp. 174-183; Chou, C. P., Hannaford, B., Measurement and Modeling of Mckibben Pneumatic Artificial Muscles (1996) IEEE Transactions on Robotics and Automation, 12 (1), pp. 90-102; Andrikopoulos, G., Nikolakopoulos, G., Manesis, S., A Survey on Applications of Pneumatic Artificial Muscles (2011) Proc. of the 19th Mediterranean Conference on Control & Automation, pp. 1439-1446; Park, Y. L., Chen, B. R., Majidi, C., Wood, R. J., Nagpal, R., Active Modular Elastomer Sleeve for Soft Wearable Assistance Robots (2012) Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1595-1602; Agarwal, G., Besuchet, N., Audergon, B., Paik, J., Stretchable Materials for Robust Soft Actuators towards Assistive Wearable Devices (2016) Scientific Reports, 6 (1), pp. 1-8; Thalman, C. M., Lam, Q. P., Nguyen, P. H., Sridar, S., Polygerinos, P., A Novel Soft Elbow Exosuit to Supplement Bicep Lifting Capacity (2018) Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 6965-6971; Natividad, R. F., Del Rosario, M. R., Chen, P. C., Yeow, C. H., A Hybrid Plastic-Fabric Soft Bending Actuator with Reconfigurable Bending Profiles (2017) Proc. of the IEEE International Conference on Robotics and Automation, pp. 6700-6705; Sridar, S., Qiao, Z., Muthukrishnan, N., Zhang, W., Polygerinos, P., A Soft-Inflatable Exosuit for Knee Rehabilitation: Assisting Swing Phase during Walking (2018) Frontiers in Robotics and AI, 5, p. 44; Chung, J., Heimgartner, R., O'Neill, C. T., Phipps, N. S., Walsh, C. J., ExoBoot, a Soft Inflatable Robotic Boot to Assist Ankle during Walking: Design, Characterization and Preliminary Tests (2018) Proc. of the 7th IEEE International Conference on Biomedical Robotics and Biomechatronics, pp. 509-516; Wilke, H. J., Neef, P., Hinz, B., Seidel, H., Claes, L., Intradiscal Pressure Together with Anthropometric Data-A Data Set for the Validation of Models (2001) Clinical Biomechanics, 16, pp. S111-S126; Zelik, K. E., Kuo, A. D., Human Walking Isn’t All Hard Work: Evidence of Soft Tissue Contributions to Energy Dissipation and Return (2010) Journal of Experimental Biology, 213 (24), pp. 4257-4264","Kim, J.; Department of Mechanical Engineering, South Korea; email: jungkim@kaist.ac.kr",,,"Korean Society for Precision Engineeing",,,,,12259071,,,,"Korean","J. Korean Soc. Precis. Eng.",Article,"Final","",Scopus,2-s2.0-85091659361
"Raza F., Owaki D., Hayashibe M.","57218836923;16550460300;6602744530;","Modeling and control of a hybrid wheeled legged robot: Disturbance analysis",2020,"IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM","2020-July",,"9158833","466","473",,2,"10.1109/AIM43001.2020.9158833","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090384584&doi=10.1109%2fAIM43001.2020.9158833&partnerID=40&md5=2858fab09f3e7703447510bd55a121c1","Tohoku University, Department of Robotics, Graduate School of Engineering, Sendai, 980-8579, Japan; Tohoku University, Graduate School of Biomedical Engineering, Graduate School of Engineering, Sendai, 980-8579, Japan","Raza, F., Tohoku University, Department of Robotics, Graduate School of Engineering, Sendai, 980-8579, Japan; Owaki, D., Tohoku University, Department of Robotics, Graduate School of Engineering, Sendai, 980-8579, Japan; Hayashibe, M., Tohoku University, Graduate School of Biomedical Engineering, Graduate School of Engineering, Sendai, 980-8579, Japan","The most common cause of injuries among older adults is falling. Recently, there have been numerous developments in assistive and exoskeleton systems. However, comparatively little work is being done on systems that may help people to keep an upright position and avoid falling over. In this preliminary work, we investigate the feasibility of the wheel-legged robot as a balance-assist system for the people who cannot maintain balance and walk because of an injury, old age, or neurological or physical disorder. We perform motion stability analyses of the wheel-legged robot under different conditions such as system modeling errors, sensor noise, and external disturbances. The linear quadratic regulator (LQR) control approach is adopted for balancing, steering, and translational position control of the robot. To validate our control framework and visualize results, the robot is modeled and tested in the Gazebo simulator using ROS (Robot Operating System). Subsequently, the simulation results demonstrate the effectiveness of the LQR control method under the translational and rotational pushes of the wheel-legged system for human-robot interaction. © 2020 IEEE.",,"Exoskeleton (Robotics); Intelligent mechatronics; Linear control systems; Position control; Wheels; Disturbance analysis; Exoskeleton systems; External disturbances; Linear quadratic regulator control; Modeling and control; Physical disorders; Robot operating system; Wheel-legged robots; Social robots",,,,,"Japan Society for the Promotion of Science, KAKEN: Grant18H01399","*This work was supported by the JSPS Grant-in-Aid for Scientific Research (B) under Grant18H01399.",,,,,,,,,,"Burns, E.R., Stevens, J.A., Lee, R., The direct costs of fatal and non-fatal falls among older adults-United States (2016) Journal of Safety Research, 58, pp. 99-103; Kannus, P., Sievänen, H., Palvanen, M., Järvinen, T., Parkkari, J., Prevention of falls and consequent injuries in elderly people (2005) The Lancet, 366 (9500), pp. 1885-1893; Pons, J.L., (2008) Wearable Robots: Biomechatronic Exoskeletons, , Wiley; Mooney, L.M., Rouse, E.J., Herr, H.M., Autonomous exoskeleton reduces metabolic cost of human walking during load carriage (2014) Journal of NeuroEngineering and Rehabilitation; Ma, Q., Ji, L., Wang, R., The development and preliminary test of a powered alternately walking exoskeleton with the wheeled foot for paraplegic patients (2018) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26 (2), pp. 451-459; Hirata, Y., Hara, A., Kosuge, K., Motion control of passive intelligent walker using servo brakes (2007) IEEE Transactions on Robotics, 23 (5), pp. 981-990; Zoss, A.B., Kazerooni, H., Chu, A., Biomechanical design of the Berkeley lower extremity exoskeleton (bleex) (2006) IEEE/ASME Transactions on Mechatronics, 11 (2), pp. 128-138; Zhang, T., Tran, M., Huang, H., Design and experimental verification of hip exoskeleton with balance capacities for walking assistance (2018) IEEE/ASME Transactions on Mechatronics, 23 (1), pp. 274-285; Introducing handle Boston Dynamics., , https://www.youtube.com/watch?v=-7xvqQeoA8c; Jeong, S., Takahashi, T., Wheeled inverted pendulum type assistant robot: Inverted mobile, standing, and sitting motions (2007) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1932-1937; Smith, J.A., Sharf, I., Trentini, M., Paw: A hybrid wheeled-leg robot (2006) IEEE International Conference on Robotics and Automation (ICRA), pp. 4043-4048; Suzumura, A., Fujimoto, Y., Real-time motion generation and control systems for high wheel-legged robot mobility (2014) IEEE Transactions on Industrial Electronics, 61 (7), pp. 3648-3659; Kamedula, M., Kashiri, N., Tsagarakis, N.G., On the kinematics of wheeled motion control of a hybrid wheeled-legged centauro robot (2018) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2426-2433; De Viragh, Y., Bjelonic, M., Bellicoso, C.D., Jenelten, F., Hutter, M., Trajectory optimization for wheeled-legged quadrupedal robots using linearized zmp constraints (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 1633-1640; Hebi Robotics., , https://www.hebirobotics.com/robotic-kits, Igor; Sarkar, N., Yun, X., Vijay Kumar, R., Control of mechanical systems with rolling constraints: Application to dynamic control of mobile robots (1994) The International Journal of Robotics Research, 13 (1), pp. 55-69; Kelly, A., (2010) A Vector Algebra Formulation of Kinematics of Wheeled Mobile Robots, , August Carnegie Mellon University, Pittsburgh, PA, Tech. Rep. CMU-RI-TR-10-33; Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., (2009) Robotics: Modelling, Planning and Control, , Springer; Astrom, K.J., Murray, R.M., (2008) Feedback Systems: An Introduction for Scientists and Engineers, , Princeton University Press; Bryson, A.E., (1975) Applied Optimal Control: Optimization, Estimation and Control, , Hemisphere Publishing Corporation; Open source robotics foundation Gazebo, , http://gazebosim.org/; Russell, S., ODE-open Dynamics Engine, , http://www.ode.org",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020","6 July 2020 through 9 July 2020",,162314,,9781728167947,,,"English","IEEE ASME Int Conf Adv Intellig Mechatron AIM",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85090384584
"Zhao W., Sun L., Liu C., Tomizuka M.","57211026224;56007948700;56642121000;35567325500;","Experimental Evaluation of Human Motion Prediction Toward Safe and Efficient Human Robot Collaboration",2020,"Proceedings of the American Control Conference","2020-July",,"9147277","4349","4354",,,"10.23919/ACC45564.2020.9147277","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089594236&doi=10.23919%2fACC45564.2020.9147277&partnerID=40&md5=acca6bc80d8afcfb82ad2c5b091a391d","Carnegie Mellon University, Robotic Institute, Pittsburgh, PA  15213, United States; University of California, Department of Mechanical Engineering, Berkeley, CA  94720, United States","Zhao, W., Carnegie Mellon University, Robotic Institute, Pittsburgh, PA  15213, United States; Sun, L., University of California, Department of Mechanical Engineering, Berkeley, CA  94720, United States; Liu, C., Carnegie Mellon University, Robotic Institute, Pittsburgh, PA  15213, United States; Tomizuka, M., University of California, Department of Mechanical Engineering, Berkeley, CA  94720, United States","Human motion prediction is non-trivial in modern industrial settings. Accurate prediction of human motion can not only improve efficiency in human-robot collaboration, but also enhance human safety in close proximity to robots. Although many prediction models have been proposed with various parameterization and identification approaches, some fundamental questions remain unclear: what is the necessary parameterization of a prediction model? Is online adaptation of models necessary? Can a prediction model help improve safety and efficiency during human-robot collaboration? These unaddressed questions result from the difficulty of quantitatively evaluating different prediction models in a closed-loop fashion in real human-robot interaction. This paper develops a method to evaluate the closed-loop performance of different prediction models. In particular, we compare models with different parameterizations and models with or without online parameter adaptation. Extensive experiments were conducted on a human-robot collaboration platform. The experimental results demonstrate that human motion prediction significantly enhance the collaboration efficiency and human safety. Adaptable prediction models that are parameterized by neural networks achieve better performance. © 2020 AACC.",,"Efficiency; Forecasting; Motion estimation; Parameterization; Predictive analytics; Accurate prediction; Closed-loop performance; Experimental evaluation; Human-robot collaboration; Identification approach; Industrial settings; Online parameter adaptation; Safety and efficiencies; Social robots",,,,,"National Science Foundation, NSF; Directorate for Engineering, ENG: 1734109; Carnegie Mellon University, CMU","This work was supported by National Science Foundation (Award #1734109) W. Zhao and C. Liu are with the Robotic Institute, Carnegie Mellon University, Pittsburgh, PA 15213 USA (e-mail: weiyezha, cliu6@andrew.cmu.edu).",,,,,,,,,,"Lasota, P.A., Shah, J.A., Analyzing the effects of human-aware motion planning on close-proximity human-robot collaboration (2015) Human Factors, 57 (1), pp. 21-33; Cheng, Y., Zhao, W., Liu, C., Tomizuka, M., Human motion prediction using semi-adaptable neural networks (2019) 2019 American Control Conference (ACC). 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IEEE, pp. 238-243",,,"International Federation of Automatic Control (IFAC)","Institute of Electrical and Electronics Engineers Inc.","2020 American Control Conference, ACC 2020","1 July 2020 through 3 July 2020",,162073,07431619,9781538682661,PRACE,,"English","Proc Am Control Conf",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85089594236
"Lin Y., Chen Z., Yao B.","57215774429;55705729600;57203461849;","Decoupled Torque Control of Series Elastic Actuator with Adaptive Robust Compensation of Time-Varying Load-Side Dynamics",2020,"IEEE Transactions on Industrial Electronics","67","7","8798969","5604","5614",,10,"10.1109/TIE.2019.2934023","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081920971&doi=10.1109%2fTIE.2019.2934023&partnerID=40&md5=a947c7df405cb5cb79f3d25d533a17b2","State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China; Ocean College, Zhejiang University, Zhoushan, 316021, China; School of Mechanical Engineering, Purdue University, West Lafayette, IN  47907, United States","Lin, Y., State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China; Chen, Z., State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China, Ocean College, Zhejiang University, Zhoushan, 316021, China; Yao, B., State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China, School of Mechanical Engineering, Purdue University, West Lafayette, IN  47907, United States","Modern robots are increasingly being designed to collaborate with human, and the safety of human-robot interaction has become more significant than ever. Series elastic actuator (SEA) is a potential actuator system in force control for various robotic applications with inherent safe property. However, achieving the good performances of SEA torque control especially with unknown load-side dynamics is still challenging. In this article, a high performance decoupled torque controller is proposed to control the SEA as an ideal torque source that is suitable for various application scenarios. A more general decoupled dynamics of the SEA system is first established to fit the various scenarios by considering the large parametric uncertainties, nonlinearities, and unmodeled uncertainties. The adaptive robust control algorithm with an effective compensation mechanism is then developed to attenuate the uncertainties. Theoretically, the proposed approach guarantees the stability and high performance of the SEA system. Comparative experiments are carried out on a SEA testbed, and the experimental results demonstrate that the proposed approach has significant performance improvements over previous approaches. © 1982-2012 IEEE.","Adaptive compensation; adaptive robust control (ARC); decoupled control; series elastic actuators (SEAs); time-varying dynamics; torque mode control","Actuators; Dynamics; Human robot interaction; Robust control; Torque; Torque control; Adaptive compensation; Adaptive robust control; Decoupled control; Series elastic actuators; Time-varying dynamics; Torque modes; Time varying control systems",,,,,"National Natural Science Foundation of China, NSFC: 51821093, 51875508, 61603332","Manuscript received January 28, 2019; revised May 27, 2019; accepted July 17, 2019. Date of publication August 14, 2019; date of current version March 4, 2020. This work is supported in part by National Natural Science Foundation of China under Grant 51875508 and Grant 61603332, and in part by Science Fund for Creative Research Groups of National Natural Science Foundation of China under Grant 51821093. (Corresponding author: Zheng Chen.) Y. Lin is with the State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China (e-mail:, yjlin@zju.edu.cn).",,,,,,,,,,"Wang, Y., Li, B., Yan, F., Chen, B., Practical adaptive fractional-order nonsingular terminal slidingmode control for a cable-driven manipulator (2019) Int. J. Robust Nonlinear Control, 29 (5), pp. 1396-1417; Wang, Y., Yan, F., Chen, J., Ju, F., Chen, B., A new adaptive time-delay control scheme for cable-driven manipulators (2019) Ieee Trans. Ind. Inform., 15 (6), pp. 3469-3481. , Jun; Sun, W., Zhang, J., Liu, Z., Two-time-scale redesign for antilock braking systems of ground vehicles (2019) Ieee Trans. Ind. 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"Ji Y., Yang Y., Shen F., Shen H.T., Li X.","36677523000;57208586877;55272525500;7404523209;55936260100;","A Survey of Human Action Analysis in HRI Applications",2020,"IEEE Transactions on Circuits and Systems for Video Technology","30","7","8698327","2114","2128",,11,"10.1109/TCSVT.2019.2912988","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080463699&doi=10.1109%2fTCSVT.2019.2912988&partnerID=40&md5=2bce4ebaaa8f008851f93b86978a0b04","Center for Future Media, School of Computer Science Engineering, University of Electronic Science and Technology of China, Chengdu, China; State Key Laboratory of Transient Optics and Photonics, Center for OPTical IMagery Analysis and Learning, Xi'An Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China","Ji, Y., Center for Future Media, School of Computer Science Engineering, University of Electronic Science and Technology of China, Chengdu, China; Yang, Y., Center for Future Media, School of Computer Science Engineering, University of Electronic Science and Technology of China, Chengdu, China; Shen, F., Center for Future Media, School of Computer Science Engineering, University of Electronic Science and Technology of China, Chengdu, China; Shen, H.T., Center for Future Media, School of Computer Science Engineering, University of Electronic Science and Technology of China, Chengdu, China; Li, X., State Key Laboratory of Transient Optics and Photonics, Center for OPTical IMagery Analysis and Learning, Xi'An Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China","The human action is an important information source for human social interaction, and it simultaneously plays a crucial role in human-robot interaction (HRI). For a natural and fluent interaction, robots are required to understand human actions and have the capacity to predict action intentions and to imitate human actions for an appropriate response. Currently, existing survey papers for the action recognition mainly summarize algorithms that perform action recognition in experimental scenarios, and survey papers of the HRI mainly introduced various interaction interfaces in the HRI. Different from these surveys, we focus on the human action analysis on robot platforms for the HRI application, including the body motion and gestures. We review the existing HRI related references involving the action recognition, prediction, and the robot imitation of the human action. Moreover, we give a summary of robot platforms and action datasets that are frequently used in the study of HRI. Finally, we give an analysis on the development trend and future research directions of action analysis for the HRI applications. © 1991-2012 IEEE.","action imitation; action prediction; action recognition; datasets; Human-robot interaction; robots","Surveys; Action recognition; Development trends; Future research directions; Human action analysis; Human robot Interaction (HRI); Human social interactions; Information sources; Interaction interface; Social robots",,,,,"National Natural Science Foundation of China, NSFC: 61673088; Higher Education Discipline Innovation Project: B17008","Manuscript received March 13, 2019; accepted April 21, 2019. Date of publication April 24, 2019; date of current version July 2, 2020. This work was supported in part by the Natural Science Foundation of China (NSFC) under Grant 61673088 and in part by the 111 Project under Grant B17008. This paper was recommended by Associate Editor G.-J. Qi. (Corresponding author: Heng Tao Shen.) Y. Ji, Y. Yang, F. 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AI, 2, p. 28. , Nov","Shen, H.T.; Center for Future Media, China; email: shenhengtao@hotmail.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10518215,,ITCTE,,"English","IEEE Trans Circuits Syst Video Technol",Article,"Final","",Scopus,2-s2.0-85080463699
"Mi J., Lyu J., Tang S., Li Q., Zhang J.","56352257300;57215558841;55366202900;8561667000;56039580400;","Interactive Natural Language Grounding via Referring Expression Comprehension and Scene Graph Parsing",2020,"Frontiers in Neurorobotics","14",,"43","","",,3,"10.3389/fnbot.2020.00043","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087708760&doi=10.3389%2ffnbot.2020.00043&partnerID=40&md5=67e5f6fb67c774e6afeaa5fe588199d7","Institute of Machine Intelligence (IMI), University of Shanghai for Science and Technology, Shanghai, China; Technical Aspects of Multimodal Systems, Department of Informatics, University of Hamburg, Hamburg, Germany","Mi, J., Institute of Machine Intelligence (IMI), University of Shanghai for Science and Technology, Shanghai, China, Technical Aspects of Multimodal Systems, Department of Informatics, University of Hamburg, Hamburg, Germany; Lyu, J., Technical Aspects of Multimodal Systems, Department of Informatics, University of Hamburg, Hamburg, Germany; Tang, S., Institute of Machine Intelligence (IMI), University of Shanghai for Science and Technology, Shanghai, China, Technical Aspects of Multimodal Systems, Department of Informatics, University of Hamburg, Hamburg, Germany; Li, Q., Institute of Machine Intelligence (IMI), University of Shanghai for Science and Technology, Shanghai, China; Zhang, J., Technical Aspects of Multimodal Systems, Department of Informatics, University of Hamburg, Hamburg, Germany","Natural language provides an intuitive and effective interaction interface between human beings and robots. Currently, multiple approaches are presented to address natural language visual grounding for human-robot interaction. However, most of the existing approaches handle the ambiguity of natural language queries and achieve target objects grounding via dialogue systems, which make the interactions cumbersome and time-consuming. In contrast, we address interactive natural language grounding without auxiliary information. Specifically, we first propose a referring expression comprehension network to ground natural referring expressions. The referring expression comprehension network excavates the visual semantics via a visual semantic-aware network, and exploits the rich linguistic contexts in expressions by a language attention network. Furthermore, we combine the referring expression comprehension network with scene graph parsing to achieve unrestricted and complicated natural language grounding. Finally, we validate the performance of the referring expression comprehension network on three public datasets, and we also evaluate the effectiveness of the interactive natural language grounding architecture by conducting extensive natural language query groundings in different household scenarios. © Copyright © 2020 Mi, Lyu, Tang, Li and Zhang.","human-robot interaction; interactive natural language grounding; referring expression comprehension; scene graph; visual and textual semantics","Electric grounding; Human robot interaction; Natural language processing systems; Semantics; Speech processing; Auxiliary information; Dialogue systems; Effective interactions; Household scenarios; Natural language queries; Natural languages; Referring expressions; Visual semantics; Visual languages; article; attention network; comparative effectiveness; comprehension; Excavata; household; human; human experiment; language; nonhuman; robotics; semantics",,,,,"National Science Foundation, NSF; German Academic Exchange Service London, DAAD London; Deutsche Forschungsgemeinschaft, DFG; National Natural Science Foundation of China, NSFC","Funding. This work was partly funded by the German Research Foundation (DFG) and National Science Foundation (NSFC) in project Crossmodal Learning under contract Sonderforschungsbereich Transregio 169, and the DAAD German Academic Exchange Service under CASY project.",,,,,,,,,,"Ahn, H., Choi, S., Kim, N., Cha, G., Oh, S., Interactive text2pickup networks for natural language-based human-robot collaboration (2018) IEEE Robot. Automat. 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Tang, S.; Technical Aspects of Multimodal Systems, Germany; email: tang@informatik.uni-hamburg.de",,,"Frontiers Media S.A.",,,,,16625218,,,,"English","Front. Neurorobotics",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85087708760
"Shao M., Snyder M., Nejat G., Benhabib B.","57212463954;57217688019;12789569200;7007071051;","User affect elicitation with a socially emotional robot",2020,"Robotics","9","2","44","","",,4,"10.3390/ROBOTICS9020044","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087503430&doi=10.3390%2fROBOTICS9020044&partnerID=40&md5=ee33f2ea18ecf1bf5179eec25983f444","Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON  M5S 3G8, Canada; Yee Hong Centre for Geriatric Care, Mississauga, ON  L5V 2X5, Canada","Shao, M., Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON  M5S 3G8, Canada; Snyder, M., Yee Hong Centre for Geriatric Care, Mississauga, ON  L5V 2X5, Canada; Nejat, G., Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON  M5S 3G8, Canada; Benhabib, B., Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON  M5S 3G8, Canada","To effectively communicate with people, social robots must be capable of detecting, interpreting, and responding to human affect during human-robot interactions (HRIs). In order to accurately detect user affect during HRIs, affect elicitation techniques need to be developed to create and train appropriate affect detection models. In this paper, we present such a novel affect elicitation and detection method for social robots in HRIs. Non-verbal emotional behaviors of the social robot were designed to elicit user affect, which was directly measured through electroencephalography (EEG) signals. HRI experiments with both younger and older adults were conducted to evaluate our affect elicitation technique and compare the two types of affect detection models we developed and trained utilizing multilayer perceptron neural networks (NNs) and support vector machines (SVMs). The results showed that; on average, the self-reported valence and arousal were consistent with the intended elicited affect. Furthermore, it was also noted that the EEG data obtained could be used to train affect detection models with the NN models achieving higher classification rates. © 2020 by the authors.","Affective computing; Assistive and social robotics; Human-robot interaction",,,,,,"Canadian Institute for Advanced Research, CIFAR; Natural Sciences and Engineering Research Council of Canada, NSERC; Canada Research Chairs","This research is supported by AGE-WELL Inc., the Natural Sciences and Engineering Council of Canada (NSERC), the Canadian Institute for Advanced Research (CIFAR), and the Canada Research Chairs Program.",,,,,,,,,,"Fortunati, L., Esposito, A., Lugano, G., Introduction to the Special Issue ""Beyond Industrial Robotics: Social Robots Entering Public and Domestic Spheres"" (2015) Inf. Soc., 31, pp. 229-236; Saunderson, S., Nejat, G., How Robots Influence Humans: A Survey of Nonverbal Communication in Social Human-Robot Interaction (2019) Int. J. Soc. Robot. 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Comput., 6, pp. 209-222; Bradley, A.E., The Use of the Area Under the ROC Curve in the Evaluation of Machine Learning Algorithms (1997) Pattern Recognit., 30, pp. 1145-1159","Shao, M.; Department of Mechanical and Industrial Engineering, Canada; email: mingyang.shao@mail.utoronto.ca",,,"MDPI AG",,,,,22186581,,,,"English","Robotics",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85087503430
"Huang J.-Y., Lee W.-P., Chen C.-C., Dong B.-W.","55945102500;55567492800;57216953862;57211254940;","Developing emotion-aware human-robot dialogues for domain-specific and goal-oriented tasks",2020,"Robotics","9","2","31","","",,6,"10.3390/ROBOTICS9020031","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085518040&doi=10.3390%2fROBOTICS9020031&partnerID=40&md5=435d877b810dd4e8e182406ec7062d47","Department of Information Management, National Sun Yat-sen University, Kaohsiung, 80424, Taiwan","Huang, J.-Y., Department of Information Management, National Sun Yat-sen University, Kaohsiung, 80424, Taiwan; Lee, W.-P., Department of Information Management, National Sun Yat-sen University, Kaohsiung, 80424, Taiwan; Chen, C.-C., Department of Information Management, National Sun Yat-sen University, Kaohsiung, 80424, Taiwan; Dong, B.-W., Department of Information Management, National Sun Yat-sen University, Kaohsiung, 80424, Taiwan","Developing dialogue services for robots has been promoted nowadays for providing natural human-robot interactions to enhance user experiences. In this study, we adopted a service-oriented framework to develop emotion-aware dialogues for service robots. Considering the importance of the contexts and contents of dialogues in delivering robot services, our framework employed deep learning methods to develop emotion classifiers and two types of dialogue models of dialogue services. In the first type of dialogue service, the robot works as a consultant, able to provide domain-specific knowledge to users. We trained different neural models for mapping questions and answering sentences, tracking the human emotion during the human-robot dialogue, and using the emotion information to decide the responses. In the second type of dialogue service, the robot continuously asks the user questions related to a task with a specific goal, tracks the user's intention through the interactions and provides suggestions accordingly. A series of experiments and performance comparisons were conducted to evaluate the major components of the presented framework and the results showed the promise of our approach. © 2020 by the authors.","Deep learning; Dialogue modeling; Emotion recognition; Human-machine interaction; Service robot",,,,,,"Ministry of Science and Technology, Taiwan, MOST: MOST-108-2221-E-110-054","Funding: This research was supported in part by the Ministry of Science and Technology of Taiwan, under Contract MOST-108-2221-E-110-054.",,,,,,,,,,"Erl, T., (2005) Service-Oriented Architecture, 8. , Prentice Hall: New York, NY, USA; Yang, T.-H., Lee, W.-P., A service-oriented framework for developing home robots (2013) Int. J. Adv. Robot. 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"Skrickij V., Šabanovič E., Žuraulis V.","36696914800;57188752995;50263512000;","Autonomous road vehicles: Recent issues and expectations",2020,"IET Intelligent Transport Systems","14","6",,"471","479",,11,"10.1049/iet-its.2018.5513","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083587176&doi=10.1049%2fiet-its.2018.5513&partnerID=40&md5=32e067064196f9f3f9c33fb4e2d08b66","Faculty of Transport Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, Vilnius, 10223, Lithuania","Skrickij, V., Faculty of Transport Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, Vilnius, 10223, Lithuania; Šabanovič, E., Faculty of Transport Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, Vilnius, 10223, Lithuania; Žuraulis, V., Faculty of Transport Engineering, Vilnius Gediminas Technical University, Saulėtekio al. 11, Vilnius, 10223, Lithuania","The automotive industry is currently facing an automated driving revolution. This technology is tightly linked to societal and economic challenges: minimisation of traffic accidents, fuel consumption, traffic congestion, parking demand, and providing mobility for an ageing population, as well as to customer needs toward more personalised services. This study aims at the analysis and presentation of the current state of the art and prospects of automated vehicles (AVs) from various perspectives. This study concentrates on revision of the critical technologies, estimation of the impact on social aspects, identification of legal issues, consideration of factors in commercial success through user acceptance, and foresight carried out by other researchers. The primary material was prepared by review and analysis of research papers, standards, regulations, roadmaps, and projects in the field of AVs technology and its implementation worldwide. A SWOT analysis was performed, and it was found that for rapid AV spread, technological solutions need to be made taking into account law and regulation; user acceptance and human-robot interaction need to be solved together as part of one system. © The Institution of Engineering and Technology 2020",,"Automotive industry; Autonomous vehicles; Human robot interaction; Laws and legislation; Social aspects; Traffic congestion; Ageing population; Automated driving; Automated vehicles; Critical technologies; Economic challenges; Primary materials; State of the art; Technological solution; Road vehicles",,,,,,,,,,,,,,,,"(2013) Preliminary Statement of Policy concerning Automated Vehicles, pp. 1-14. , National Highway Traffic Safety Administration NHTSA; (2018) Surface Vehicle Recommended Practices: Taxonomy and Definitions for Terms Related to Driving Automation Systems for on-Road Motor Vehicles (J3016), pp. 1-35. , Society of Automotive Engineers (SAE) International; Tsugawa, S., Yatabe, T., Hirose, T., An automobile with artificial intelligence (1979) Proc. 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A, 124, pp. 587-610; Naujoks, F., Höfling, S., Puruckera, C., From partial and high automation to manual driving: Relationship between non-driving related tasks, drowsiness and take-over performance (2018) Accid. Anal. Prev., 121, pp. 28-42; Choi, J.K., Ji, Y.G., Investigating the importance of trust on adopting an autonomous vehicle (2015) Int. J. Human-Comput. Interact., 31 (10), pp. 692-702; McDonald, S.S., Rodier, C., Envisioning automated vehicles within the built environment: 2020, 2035, and 2050 (2015) Road Veh. Autom., 2, pp. 225-233; (2019) Automated Driving Roadmap, p. 55. , The European Road Transport Research Advisory Council ERTRAC","Skrickij, V.; Faculty of Transport Engineering, Saulėtekio al. 11, Lithuania; email: viktor.skrickij@vgtu.lt",,,"Institution of Engineering and Technology",,,,,1751956X,,,,"English","IET Intel. Transport Syst.",Review,"Final","All Open Access, Bronze",Scopus,2-s2.0-85083587176
"Yu X., Li Y., Zhang S., Xue C., Wang Y.","57203040999;35174564100;45761577400;57203136027;55969140000;","Estimation of human impedance and motion intention for constrained human–robot interaction",2020,"Neurocomputing","390",,,"268","279",,6,"10.1016/j.neucom.2019.07.104","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075464952&doi=10.1016%2fj.neucom.2019.07.104&partnerID=40&md5=583775a889972d3f4a02bed7d2700ad0","School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, 100083, China; Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, University of Science and Technology Beijing, Beijing, 100083, China; Department of Engineering and Design, University of Sussex, Brighton, BN1 9RH, United Kingdom; Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China","Yu, X., School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China, Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, 100083, China, Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, University of Science and Technology Beijing, Beijing, 100083, China; Li, Y., Department of Engineering and Design, University of Sussex, Brighton, BN1 9RH, United Kingdom; Zhang, S., School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China, Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, 100083, China, Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, University of Science and Technology Beijing, Beijing, 100083, China; Xue, C., School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China, Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, 100083, China, Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, University of Science and Technology Beijing, Beijing, 100083, China; Wang, Y., Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China","In this paper, a complete framework for safe and efficient physical human-robot interaction (pHRI) is developed for robot by considering both issues of adaptation to the human partner and ensuring the motion constraints during the interaction. We consider the robot's learning of not only human motion intention, but also the human impedance. We employ radial basis function neural networks (RBFNNs) to estimate human motion intention in real time, and least square method is utilized in robot learning of human impedance. When robot has learned the impedance information about human, it can adjust its desired impedance parameters by a simple tuning law for operative compliance. An adaptive impedance control integrated with RBFNNs and full-state constraints is also proposed in our work. We employ RBFNNs to compensate for uncertainties in the dynamics model of robot and barrier Lyapunov functions are chosen to ensure that full-state constraints are not violated in pHRI. Results in simulations and experiments show the better performance of our proposed framework compared with traditional methods. © 2019","Adaptive neural network control; Barrier Lyapunov functions; Full-state constraints; Human motion intention estimation; Impedance learning","Adaptive control systems; Least squares approximations; Lyapunov functions; Motion estimation; Radial basis function networks; Adaptive neural network control; Human motion intention; Impedance parameters; Least square methods; Motion constraints; Physical humanrobot interaction (phri); Radial basis function neural networks; State constraints; Human robot interaction; article; human; human experiment; impedance; learning; least square analysis; motion; radial basis function neural network; robotics; simulation; uncertainty",,,,,"National Natural Science Foundation of China, NSFC: 61873297; China Postdoctoral Science Foundation: 2018M630074, 2019T120048; Fundamental Research Funds for the Central Universities: FRF-GF-18-027B; Beijing Science and Technology Planning Project: Z181100003118006","The authors would like to thank the Editor-in-Chief, the Associate Editor, and the anonymous reviewers for their constructive comments which helped to improve the quality and presentation of this paper. This work was supported by the National Natural Science Foundation of China under Grant No. 61873297 , the China Postdoctoral Science Foundation under Grant No. 2019T120048 , 2018M630074 , the Fundamental Research Funds for the Central Universities under grant No. FRF-GF-18-027B and Beijing Science and Technology Project (Grant Z181100003118006 ).",,,,,,,,,,"He, W., Li, Z., Chen, C.P., A survey of human-centered intelligent robots: issues and challenges (2017) IEEE/CAA J. Autom. Sin., 4 (4), pp. 602-609; Medina, J.R., Lawitzky, M., Mörtl, A., Lee, D., Hirche, S., An experience-driven robotic assistant acquiring human knowledge to improve haptic cooperation (2011) Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2416-2422. , IEEE; Yang, C., Huang, K., Cheng, H., Li, Y., Su, C.-Y., Haptic identification by ELM-controlled uncertain manipulator (2017) IEEE Trans. Syst. 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"Jiang Y., Yang C., Wang Y., Ju Z., Li Y., Su C.-Y.","56603393700;57204881979;55998880600;25655056800;35174564100;7402820075;","Multi-hierarchy interaction control of a redundant robot using impedance learning",2020,"Mechatronics","67",,"102348","","",,6,"10.1016/j.mechatronics.2020.102348","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083692185&doi=10.1016%2fj.mechatronics.2020.102348&partnerID=40&md5=6906973a64adf6b1607a5de665851d21","College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China; Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom; School of Computing, University of Portsmouth, Portsmouth, PO1 3HE, United Kingdom; School of Engineering and Informatics, University of Sussex, Brighton, BN1 9RH, United Kingdom; Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, Quebec  H3G 1M8, Canada","Jiang, Y., College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China, School of Computing, University of Portsmouth, Portsmouth, PO1 3HE, United Kingdom; Yang, C., Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom; Wang, Y., College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China; Ju, Z., School of Computing, University of Portsmouth, Portsmouth, PO1 3HE, United Kingdom; Li, Y., School of Engineering and Informatics, University of Sussex, Brighton, BN1 9RH, United Kingdom; Su, C.-Y., Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, Quebec  H3G 1M8, Canada","The control of robots with a compliant joint motion is important for reducing collision forces and improving safety during human robot interactions. In this paper, a multi-hierarchy control framework is proposed for the redundant robot to enable the robot end-effector to physically interact with the unknown environment, while providing compliance to the joint space motion. To this end, an impedance learning method is designed to iteratively update the stiffness and damping parameters of the end-effector with desired performance. In addition, based on a null space projection technique, an extra low stiffness impedance controller is included to improve compliant joint motion behaviour when interaction forces are acted on the robot body. With an adaptive disturbance observer, the proposed controller can achieve satisfactory performance of the end-effector control even with the external disturbances in the joint space. Experimental studies on a 7 DOF Sawyer robot show that the learning framework can not only update the target impedance model according to a given cost function, but also enhance the task performance when interaction forces are applied on the robot body. © 2020","Disturbance observer; Impedance control; Impedance parameters learning; Null space; Robot interaction control","Compliance control; Controllers; Cost functions; Human robot interaction; Iterative methods; Learning systems; Redundant manipulators; Stiffness; Universal joints; Disturbance observer; End effector control; External disturbances; Impedance controllers; Interaction controls; Projection techniques; Stiffness and damping; Unknown environments; End effectors",,,,,"2020SK3007; Engineering and Physical Sciences Research Council, EPSRC: EP/S001913; National Natural Science Foundation of China, NSFC: 51575412, 61733004, 61971071; State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, SKLDMVB; National Key Research and Development Program of China, NKRDPC: 2018YFB1308200; Science and Technology Program of Hunan Province: 2017XK2102, 2018GK2022, 2018JJ3079","This work was supported in part by National Nature Science Foundation (NSFC) under Grants 61733004, 61971071 and 51575412, Science and Technology Project of Hunan Province under Grants 2017XK2102, 2018GK2022 and 2018JJ3079, National Key Research and Development Program of China Under Grant 2018YFB1308200, Emergency Project Against the Epidemic of Novel Coronavirus Pneumonia from Special Fund of Hunan Province under Grant 2020SK3007, Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/S001913 , and Independent Research Subject Funding from Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body.",,,,,,,,,,"Yang, C., Ganesh, G., Haddadin, S., Parusel, S., Albu-Schaeffer, A., Burdet, E., Human-like adaptation of force and impedance in stable and unstable interactions (2011) IEEE Trans Rob, 27 (5), pp. 918-930; 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"Batista M.R., Macharet D.G., Romero R.A.F.","56027380100;33568044800;24465478300;","Socially Acceptable Navigation of People with Multi-robot Teams",2020,"Journal of Intelligent and Robotic Systems: Theory and Applications","98","2",,"481","510",,2,"10.1007/s10846-019-01080-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074489396&doi=10.1007%2fs10846-019-01080-4&partnerID=40&md5=95f88e2fdad578c82ef0a6155a471a93","Institute of Mathematics and Computer Sciences, São Paulo University, São Carlos, SP, Brazil; Computer Vision and Robotics Laboratory (VeRLab), Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil","Batista, M.R., Institute of Mathematics and Computer Sciences, São Paulo University, São Carlos, SP, Brazil; Macharet, D.G., Computer Vision and Robotics Laboratory (VeRLab), Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil; Romero, R.A.F., Institute of Mathematics and Computer Sciences, São Paulo University, São Carlos, SP, Brazil","Socially acceptable navigation is a subject that involves developments regarding Human-Robot Interaction (HRI) and autonomous mobile robot navigation. In this context, there is little research considering people interacting with a robot team, and even fewer considering multi-robot teams with people. In this paper, a study on socially acceptable navigation involving a human and a robot team is presented. Four navigation strategies that consider social aspects are presented and compared in simulated environment by terms as the average number of robots invading the personal space and the number of robots to the person’s side, with two of them using Asymmetric Gaussian Functions (AGFs) as the person’s social zone model; a navigation perception comparison is made involving people to investigate their view on navigating with three robots in contrast to a single robot. Simulated and real-world experiments were performed showing that the proposed methods have advantages over each other on different aspects. The follow-up techniques for interaction between humans and a team of robots suggest that people’s perception may not be affected significantly when interacting with more than one robot. © 2019, Springer Nature B.V.","Human-robot interaction; Multi-robot systems","Industrial robots; Man machine systems; Mobile robots; Multipurpose robots; Navigation; Social aspects; Autonomous Mobile Robot; Gaussian functions; Human robot Interaction (HRI); Multi-robot systems; Multi-robot teams; Navigation strategies; Real world experiment; Simulated environment; Human robot interaction",,,,,"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES","This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.","This study was financed in part by the Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - Brasil (CAPES) - Finance Code 001.",,,,,,,,,"Arai, M., Sato, Y., Suzuki, R., Kobayashi, Y., Kuno, Y., Miyazawa, S., Fukushima, M., Yamazaki, A., Robotic wheelchair moving with multiple companions (2014) IEEE International Symposium on Robot and Human Interactive Communication, pp. 513-518; Batista, M.R., Macharet, D.G., Romero, R.A., A study on the effect of human proxemics rules in human following by a robot team (2017) Latin American Robotics Symposium (LARS) and Brazilian Symposium on Robotics (SBR), pp. 1-6; Chang, W.L., White, J.P., Park, J., Holm, A., Šabanović, S., The effect of group size on people’s attitudes and cooperative behaviors toward robots in interactive gameplay (2012) IEEE RO-MAN, pp. 845-850; Cosgun, A., Florencio, D.A., Christensen, H.I., Autonomous person following for telepresence robots (2013) Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 4335-4342. , https://doi.org/10.1109/ICRA.2013.6631191; Ferryman, J., Hogg, D., Sochman, J., Behera, A., Rodriguez-Serrano, J.A., Worgan, S., Li, L., Cornic, P., Robust abandoned object detection integrating wide area visual surveillance and social context (2013) Pattern Recogn. 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Electron., 61 (4), pp. 1916-1927; van Houwelingen-Snippe, J., Vroon, J., Englebienne, G., Haselager, P., Blame my telepresence robot: Joint effect of proxemics and attribution on interpersonal attraction (2017) 2017 26Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN, pp. 162-168; Kendon, A., Conducting interaction: Patterns of behavior in focused encounters (1990) CUP Archive, 7; Kendon, A., Spacing and orientation in co-present interaction (2010) Development of Multimodal Interfaces: Active Listening and Synchrony, pp. 1-15. , Springer; Kirby, R., (2010) Social Robot Navigation, , Ph.D. thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh PA; Laga, H., Amaoka, T., Modeling the spatial behavior of virtual agents in groups for non-verbal communication in virtual worlds (2009) Proceedings of the 3Rd International Universal Communication Symposium, IUCS’09, pp. 154-159. , https://doi.org/10.1145/1667780.1667811, ACM, New York; Marquardt, N., Hinckley, K., Greenberg, S., Cross-device interaction via micro-mobility and f-formations (2012) Proceedings of the 25Th Annual ACM Symposium on User Interface Software and Technology, pp. 13-22; Mead, R., Matarić, M.J., Robots have needs too: How and why people adapt their proxemic behavior to improve robot social signal understanding (2016) J. 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Robot., 7 (2), pp. 137-153. , https://doi.org/10.1007/s12369-014-0251-1; Satake, S., Kanda, T., Glas, D.F., Imai, M., Ishiguro, H., Hagita, N., How to approach humans?-strategies for social robots to initiate interaction (2009) Proceedings of 4Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 109-116. , https://doi.org/10.1145/1514095.1514117; Sato, Y., Suzuki, R., Arai, M., Kobayashi, Y., Kuno, Y., Fukushima, M., Yamazaki, K., Yamazaki, A., Multiple robotic wheelchair system able to move with a companion using map information (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction, pp. 286-287. , ACM; Suzuki, R., Yamada, T., Arai, M., Sato, Y., Kobayashi, Y., Kuno, Y., Multiple robotic wheelchair system considering group communication (2014) International Symposium on Visual Computing, pp. 805-814; Vasquez, D., Stein, P., Rios-Martinez, J., Escobedo, A., Spalanzani, A., Laugier, C., (2013) Human aware navigation for assistive robotics, pp. 449-462. , Springer International Publishing, Heidelberg; Yeh, A., Ratsamee, P., Kiyokawa, K., Uranishi, Y., Mashita, T., Takemura, H., Fjeld, M., Obaid, M., Exploring proxemics for human-drone interaction (2017) Proceedings of the 5Th International Conference on Human Agent Interaction, pp. 81-88","Batista, M.R.; Institute of Mathematics and Computer Sciences, Brazil; email: rehderbatista@gmail.com",,,"Springer",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","",Scopus,2-s2.0-85074489396
"Park J.-Y., Kim J.-H.","57192667984;36484629700;","Online Incremental Classification Resonance Network and Its Application to Human-Robot Interaction",2020,"IEEE Transactions on Neural Networks and Learning Systems","31","5","8744499","1426","1436",,4,"10.1109/TNNLS.2019.2920158","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068105633&doi=10.1109%2fTNNLS.2019.2920158&partnerID=40&md5=0ec69ba5f4a2ac178f16c2d2c7dc516b","School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea","Park, J.-Y., School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea; Kim, J.-H., School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea","In human-robot interaction (HRI), classification is one of the most important problems, and it is essential particularly when the robot recognizes the surroundings and chooses a reaction based on a certain situation. Each interaction is different since new people appear or the environment changes, and the robot should be able to adapt to different situations during a brief interaction. Thus, it is imperative that the classification is performed incrementally in real time. In this sense, we propose an online incremental classification resonance network (OICRN) that enables incremental class learning in multi-class classification with high performance online. In OICRN, a scale-preserving projection process is introduced to use the raw input vectors online without a normalization process in advance. The integrated network of the convolutional neural network (CNN) for feature extraction and the OICRN for classification is applied to a robotic system that learns human identities through HRIs. To demonstrate the effectiveness of our network, experiments are carried out on benchmark data sets and on a humanoid robot, Mybot, developed in the Robot Intelligence Technology Laboratory, KAIST. © 2012 IEEE.","Concept vectors; face identification; human-robot interaction (HRI); incremental class learning; online incremental classification resonance network (OICRN)","Anthropomorphic robots; Classification (of information); Intelligent robots; Man machine systems; Neural networks; Resonance; Convolutional neural network; Face identification; Human robot Interaction (HRI); incremental class learning; Multi-class classification; Normalization process; Resonance networks; Robot intelligences; Human robot interaction; artificial intelligence; classification; factual database; human; procedures; robotics; Artificial Intelligence; Automated Facial Recognition; Databases, Factual; Humans; Neural Networks, Computer; Robotics",,,,,"National Research Foundation of Korea, NRF; Ministry of Science and ICT, South Korea, MSIT: NRF-2017R1A2A1A17069837","Manuscript received December 13, 2017; revised May 31, 2018, November 6, 2018, and March 6, 2019; accepted May 24, 2019. Date of publication June 24, 2019; date of current version May 1, 2020. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (No. NRF-2017R1A2A1A17069837). (Corresponding author: Jong-Hwan Kim.) The authors are with the School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, South Korea (e-mail: johkim@rit.kaist.ac.kr).",,,,,,,,,,"Cauwenberghs, G., Poggio, T., Incremental and decremental support vector machine learning (2001) Proc. Adv. Neural Inf. Process. Syst., pp. 409-415; Zhang, B.-F., Su, J.-S., Xu, X., A class-incremental learning method for multi-class support vector machines in text classification (2006) Proc. Int. Conf. Mach. Learn. 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Massachusetts, , Amherst, MA, USA, Tech. Rep. 07-49; Best-Rowden, L., Han, H., Otto, C., Klare, B.F., Jain, A.K., Unconstrained face recognition: Identifying a person of interest from a media collection (2014) IEEE Trans. Inf. Forensics Security, 9 (12), pp. 2144-2157. , Dec; Taigman, Y., Yang, M., Ranzato, M., Wolf, L., DeepFace: Closing the gap to human-level performance in face verification (2014) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 1701-1708. , Jun; Taigman, Y., Yang, M., Ranzato, M., Wolf, L., Web-scale training for face identification (2015) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 2746-2754. , Jun; Sun, Y., Wang, X., Tang, X., Deeply learned face representations are sparse, selective, and robust (2015) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 2892-2900. , Jun; Liu, X., Kan, M., Wu, W., Shan, S., Chen, X., VIPLFaceNet: An open source deep face recognition SDK (2017) Frontiers Comput. Sci., 11 (2), pp. 208-218; Parkhi, O.M., Vedaldi, A., Zisserman, A., Deep face recognition (2015) Proc. Brit. Mach. Vis. Conf. (BMVC), pp. 1-12; Wen, Y., Zhang, K., Li, Z., Qiao, Y., A discriminative feature learning approach for deep face recognition (2016) Proc. Eur. Conf. Comput. Vis. (ECCV)., pp. 499-515. , Berlin, Germany: Springer; Crosswhite, N., Byrne, J., Stauffer, C., Parkhi, O., Cao, Q., Zisserman, A., Template adaptation for face verification and identification (2018) Image Vis. Comput., 79, pp. 35-48. , Nov; Sohn, K., Liu, S., Zhong, G., Yu, X., Yang, M.-H., Chandraker, M., Unsupervised domain adaptation for face recognition in unlabeled videos (2017) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 3210-3218. , Oct; Yang, J., Neural aggregation network for video face recognition (2017) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 4362-4371. , Jul; Klare, B.F., Pushing the frontiers of unconstrained face detection and recognition: IARPA janus benchmark A (2015) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 1931-1939. , Jun","Kim, J.-H.; School of Electrical Engineering, South Korea; email: johkim@rit.kaist.ac.kr",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,2162237X,,,"31247580","English","IEEE Trans. Neural Networks Learn. Sys.",Article,"Final","",Scopus,2-s2.0-85068105633
"Abel M., Kuz S., Patel H.J., Petruck H., Schlick C.M., Pellicano A., Binkofski F.C.","57217705883;37102065900;57199391428;57191077187;6603813336;24492258300;7003382482;","Gender Effects in Observation of Robotic and Humanoid Actions",2020,"Frontiers in Psychology","11",,"797","","",,1,"10.3389/fpsyg.2020.00797","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084738541&doi=10.3389%2ffpsyg.2020.00797&partnerID=40&md5=1d05030e3745616596eb63861b1dd63c","Division for Clinical and Cognitive Sciences, Department of Neurology Medical Faculty, RWTH Aachen University, Aachen, Germany; Institute of Industrial Engineering and Ergonomics, RWTH Aachen University, Aachen, Germany; Institute of Neuroscience and Medicine (INM-4), Research Center Jülich GmbH, Jülich, Germany","Abel, M., Division for Clinical and Cognitive Sciences, Department of Neurology Medical Faculty, RWTH Aachen University, Aachen, Germany; Kuz, S., Institute of Industrial Engineering and Ergonomics, RWTH Aachen University, Aachen, Germany; Patel, H.J., Division for Clinical and Cognitive Sciences, Department of Neurology Medical Faculty, RWTH Aachen University, Aachen, Germany; Petruck, H., Institute of Industrial Engineering and Ergonomics, RWTH Aachen University, Aachen, Germany; Schlick, C.M., Institute of Industrial Engineering and Ergonomics, RWTH Aachen University, Aachen, Germany; Pellicano, A., Division for Clinical and Cognitive Sciences, Department of Neurology Medical Faculty, RWTH Aachen University, Aachen, Germany; Binkofski, F.C., Division for Clinical and Cognitive Sciences, Department of Neurology Medical Faculty, RWTH Aachen University, Aachen, Germany, Institute of Neuroscience and Medicine (INM-4), Research Center Jülich GmbH, Jülich, Germany","Robots are gaining an increasingly important role in industrial production. Notably, a high level of acceptance is an important factor for co-working situation between human and robot. The aim of the present study was to investigate the differences in the perception of anthropomorphic and robotic movements using models consisting of a virtual robot and a digital human. Videos of each model displayed different degrees of human likeness or robot likeness in speed and trajectories of placing movements. Female and male participants were asked to rate on a Likert scale the perceived levels of human likeness or robot likeness in the two models. Overall, results suggest that males were sensitive to the differences between robotic and anthropomorphic movements, whereas females showed no difference between them. However, compared to males, female participants attributed more anthropomorphic features to robotic movements. The study is a first step toward a more comprehensive understanding of the human ability to differentiate between anthropomorphic and robotic movements and suggests a crucial role of gender in the human-robot interaction. © Copyright © 2020 Abel, Kuz, Patel, Petruck, Schlick, Pellicano and Binkofski.","anthropomorphism; digital human model; gantry robot model; gender effect; human-robot interaction; mirror neurons system; motion perception",,,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV7013","This work was supported by the Federal Ministry of Education and Research (BMBF) under Grant 16SV7013.",,,,,,,,,,"Billings, D.R., Schaefer, K.E., Chen, J.Y.C., Hancock, P.A., Human-robot interaction: developing trust in robots (2012) Proceedings of the 7th ACM/IEEE International Conference on Human-Robot Interaction, pp. 109-110. , Boston, MA; Choi, M., Kornfield, R., Takayama, L., Mutlu, B., Movement matters: effects of motion and mimicry on perception of similarity and closeness in robot-mediated communication (2017) Proceedings of the 2017 CHI conference on human factors in computing systems, pp. 325-335. , Denver, ACM; Di Pellegrino, G., Fadiga, L., Fogassi, L., Gallese, V., Rizzolatti, G., Understanding motor events: a neurophysiological study (1992) Exp. Brain Res, 91, pp. 176-180. , 1301372; Duffy, B.R., Anthropomorphism and The Social Robot (2003) Spec. Issue Soc. Interact. Robots Anthropomor. Auton. 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Skills, 77, pp. 1267-1273. , 8170777; Luczak, H., Rötting, M., Schmidt, L., Let’s talk: anthropomorphization as means to cope with stress of interacting with technical devices (2003) Ergonomics, 46, pp. 1361-1374. , 14612325; Oleson, K.E., Billings, B.R., Kocsis, V., Chen, J.Y.C., Hancock, P.A., Antescendents of trust in human-robot collaboration (2011) Proceedings of the 2011 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), pp. 175-178. , Piscataway, NJ, IEEE; Piek, J.P., Baynam, G.B., Barrett, N.C., The relationship between fine and gross motor ability, self-perceptions and self-worth in children and adolescents (2006) Hum. Mov. Sci, 25, pp. 65-75. , 16442171; Rizzolatti, G., Fadiga, L., Gallese, V., Fogassi, L., Premotor cortex and the recognition of motor actions (1996) Cogn. Brain Res, 3, pp. 131-141; Roberton, M.A., Konczak, J., Predicting children’s overarm throw ball velocities from their developmental levels in throwing (2001) Res. Q. Exerc. Sport, 2, pp. 91-103. , 11393884; (2019) Berichte: Blickpunkt Arbeitsmarkt – MINT – Berufe, , Nürnberg, Statistik der Bundesagentur für Arbeit; Zanchettin, A.M., (2012) Human-centric behavior of redundant manipulators under kinematic control, [dissertation], , Milano, Politecnico di Milano","Binkofski, F.C.; Division for Clinical and Cognitive Sciences, Germany; email: f.binkofski@fz-juelich.de",,,"Frontiers Media S.A.",,,,,16641078,,,,"English","Front. Psychol.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85084738541
"Agrigoroaie R., Ciocirlan S.-D., Tapus A.","57189047432;57197872892;23010519200;","In the Wild HRI Scenario: Influence of Regulatory Focus Theory",2020,"Frontiers in Robotics and AI","7",,"58","","",,2,"10.3389/frobt.2020.00058","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084563556&doi=10.3389%2ffrobt.2020.00058&partnerID=40&md5=973ff7d938856c49a67af50af2c25daf","Autonomous Systems and Robotics Laboratory, U2IS, ENSTA-Paris, Institut Polytechnique de Paris, Palaiseau, France","Agrigoroaie, R., Autonomous Systems and Robotics Laboratory, U2IS, ENSTA-Paris, Institut Polytechnique de Paris, Palaiseau, France; Ciocirlan, S.-D., Autonomous Systems and Robotics Laboratory, U2IS, ENSTA-Paris, Institut Polytechnique de Paris, Palaiseau, France; Tapus, A., Autonomous Systems and Robotics Laboratory, U2IS, ENSTA-Paris, Institut Polytechnique de Paris, Palaiseau, France","Research related to regulatory focus theory has shown that the way in which a message is conveyed can increase the effectiveness of the message. While different research fields have used this theory, in human-robot interaction (HRI), no real attention has been given to this theory. In this paper, we investigate it in an in the wild scenario. More specifically, we are interested in how individuals react when a robot suddenly appears at their office doors. Will they interact with it or will they ignore it? We report the results from our experimental study in which the robot approaches 42 individuals. Twenty-nine of them interacted with the robot, while the others either ignored it or avoided any interaction with it. The robot displayed two types of behavior (i.e., promotion or prevention). Our results show that individuals that interacted with a robot that matched their regulatory focus type interacted with it significantly longer than individuals that did not experience regulatory fit. Other qualitative results are also reported, together with some reactions from the participants. © Copyright © 2020 Agrigoroaie, Ciocirlan and Tapus.","acceptance; HRI; in the wild; regulatory focus; social robotics",,,,,,,,,,,,,,,,,"Andrist, S., Mutlu, B., Tapus, A., “Look like me: matching robot personality via gaze to increase motivation,” (2015) Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (ACM), pp. 3603-3612. , Seoul; Barrick, M.R., Mount, M.K., The big five personality dimensions and job performance: a meta-analysis (1991) Pers. Psychol, 44, pp. 1-26; Breazeal, C., “Social robots: from research to commercialization,” (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (ACM), p. 1. , Vienna, in; Cesario, J., Higgins, E.T., Making message recipients “feel right” how nonverbal cues can increase persuasion (2008) Psychol. Sci, 19, pp. 415-420. , 18466399; Cohen, J., (2013) Statistical Power Analysis for the Behavioral Sciences, , Cambridge, MA, Routledge; Crowe, E., Higgins, E.T., Regulatory focus and strategic inclinations: promotion and prevention in decision-making (1997) Organ. Behav. Hum. Decis. Process, 69, pp. 117-132; Cruz-Maya, A., Agrigoroaie, R., Tapus, A., “Improving user's performance by motivation: matching robot interaction strategy with user's regulatory state,” (2017) International Conference on Social Robotics, pp. 464-473. , Tsukuba; Cruz-Maya, A., Tapus, A., “Negotiating with a robot: analysis of regulatory focus behavior,” (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), , Brisbane, in; Faur, C., Martin, J.-C., Clavel, C., “Matching artificial agents' and users' personalities: designing agents with regulatory-focus and testing the regulatory fit effect,” (2015) Annual Meeting of the Cognitive Science Society (CogSci 2015), , Pasadena, CA, in; Faur, C., Martin, J.-C., Clavel, C., Measuring chronic regulatory focus with proverbs: The developmental and psychometric properties of a french scale (2017) Pers. Indiv. Differ, 107, pp. 137-145; Goldberg, L., An alternative ‘description of personality': the big-five factor structure (1990) J. Pers. Soc. Psychol, 59, pp. 1216-1229. , 2283588; Higgins, E.T., Beyond pleasure and pain (1997) Am. Psychol, 52, pp. 1280-1300. , 9414606; Higgins, E.T., Making a good decision: value from fit (2000) Am. Psychol, 55, p. 1217. , 11280936; Higgins, E.T., Value from regulatory fit (2005) Curr. Direct. Psychol. Sci, 14, pp. 209-213; King, D., Dlib-ml: a machine learning toolkit (2009) J. Mach. Learn. Res, 10, pp. 1755-1758; Kristensen, T., Hannerz, H., Hgh, A., Borg, V., The Copenhagen psychosocial questionnaire-a tool for the assessment and improvement of the psychosocial work environment (2005) Scand. J. Work Environ. Health, 31, pp. 438-449. , 16425585; Lee, A.Y., Aaker, J.L., Bringing the frame into focus: the influence of regulatory fit on processing fluency and persuasion (2004) J. Pers. Soc. Psychol, 86, p. 205. , 14769079; Nakagawa, K., Shiomi, M., Shinozawa, K., Matsumura, R., Ishiguro, H., Hagita, N., “Effect of robot's active touch on people's motivation,” (2011) Proceedings of the 6th International Conference on Human-Robot Interaction (ACM), pp. 465-472. , Lausanne; Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., “ROS: an open-source robot operating system,” (2009) ICRA Workshop on Open Source Software, 3, p. 5. , Kobe, in; Rezai, L.S., Chin, J., Bassett-Gunter, R., Burns, C., “Investigating efficacy of regulatory fit theory in design of persuasive systems that promote physical activity,” (2017) 2nd International Workshop on Personalization in Persuasive Technology, PPT 2017, pp. 21-25. , Amsterdam; Tazhigaliyeva, N., Diyas, Y., Brakk, D., Aimambetov, Y., Sandygulova, A., “Learning with or from the robot: exploring robot roles in educational context with children,” (2016) International Conference on Social Robotics, pp. 650-659. , Kansas City, MO, Springer","Tapus, A.; Autonomous Systems and Robotics Laboratory, France; email: adriana.tapus@ensta-paris.fr",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85084563556
"Laksono P.W., Sasaki M., Matsushita K., Suhaimi M.S.A.B., Muguro J.","56071532500;56664979800;14052460900;57211298796;57216633405;","Preliminary research of surface electromyogram (sEMG) signal analysis for robotic arm control",2020,"AIP Conference Proceedings","2217",,"030034","","",,1,"10.1063/5.0000542","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084125688&doi=10.1063%2f5.0000542&partnerID=40&md5=8cc33dd6971e3bdd634370e2efa3521b","Department of Mechanical Engineering, Gifu University, Yanagido 1-1, Gifu, Japan; Department of Industrial Engineering, Universitas Sebelas Maret, Surakarta, Indonesia; Department of Electrical Engineering, Dedan Kimanthi University of Technology, Nyeri, Kenya","Laksono, P.W., Department of Mechanical Engineering, Gifu University, Yanagido 1-1, Gifu, Japan, Department of Industrial Engineering, Universitas Sebelas Maret, Surakarta, Indonesia; Sasaki, M., Department of Industrial Engineering, Universitas Sebelas Maret, Surakarta, Indonesia; Matsushita, K., Department of Industrial Engineering, Universitas Sebelas Maret, Surakarta, Indonesia; Suhaimi, M.S.A.B., Department of Industrial Engineering, Universitas Sebelas Maret, Surakarta, Indonesia; Muguro, J., Department of Industrial Engineering, Universitas Sebelas Maret, Surakarta, Indonesia, Department of Electrical Engineering, Dedan Kimanthi University of Technology, Nyeri, Kenya","Human-robot interactions have gained popularity in the recent past, particularly in the advent and revolution of industry 4.0 era. There are still open research issues to be addressed, e.g., human-machine interaction, especially in robotic operation using bio-signal. This paper presents surface electromyography (sEMG) signal analysis of the motion of upper limb muscles to control the robotic arm. The objective is to use as few EMG channels to estimate joint angles for application in a robotic arm. The overall target is how this information can be applied to a robot control scheme. Three motions are proposed based on the 2 degrees of freedom (DOF) between joint elbow and shoulder. Three sEMG channel signal is captured using a DAQ unit comprising of pre-amplifier and NI USB 6008 and a laptop running LabVIEW software. The analysis is conducted using Matlab software. The result indicates that the sEMG from three muscles of the upper limb gives useful information and the performance of the sEMG synergy-based model has a good prospect for the controlling robot arm. © 2020 Author(s).",,,,,,,,,,,,,,,,,,"(2018) Executive Summary World Robotics 2018 Industrial Robots, , International Federation of Robotics; Antuvan, C.W., Ison, M., Artemiadis, P., Embedded human control of robots using myoelectric interfaces (2014) IEEE Trans. Neural Syst. Rehabil. Eng., 22 (4), pp. 820-827; Feleke, A.G., Bi, L., Guan, C., A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration (2019) Biomed. Signal Process. Control, 51, pp. 1-17; Artemiadis, P.K., Kyriakopoulos, K.J., A switching regime model for the emg-based control of a robot arm (2011) IEEE Trans. Syst. Man, Cybern. Part B Cybern., 41 (1), pp. 53-63; Kiguchi, K., Hayashi, Y., An EMG-Based Control for an Upper-Limb (2012) IEEE Trans. Syst. Man, Cybern. Part B Cybern., 42 (4), pp. 1064-1071; Rojas-Martínez, M., Mañanas, M.A., Alonso, J.F., High-density surface EMG maps from upper-arm and forearm muscles (2012) J. Neuroeng. Rehabil., 9 (1), pp. 1-17; Triwiyanto, T., Wahyunggoro, O., Nugroho, H.A., Herianto, H., Evaluating the performance of Kalman filter on elbow joint angle prediction based on electromyography (2017) Int. J. Precis. Eng. Manuf.; Ma, J., Thakor, N.V., Matsuno, F., Hand and wrist movement control of myoelectric prosthesis based on synergy (2015) IEEE Trans. Human-Machine Syst., 45 (1), pp. 74-83; Hermens, H.J., Freriks, B., Disselhorst-Klug, C., Rau, G., Development of recommendations for SEMG sensors and sensor placement procedures (2000) J. Electromyogr. Kinesiol., 10 (5), pp. 361-374. , Oct","Laksono, P.W.; Department of Mechanical Engineering, Yanagido 1-1, Japan; email: x3912008@edu.gifu-u.ac.jp","Sutopo W.Anwar M.Ibrahim M.H.Maghfiroh H.Apribowo C.H.B.Ibrahim S.Hisjam M.",,"American Institute of Physics Inc.","5th International Conference on Industrial, Mechanical Electrical, and Chemical Engineering 2019, ICIMECE 2019","17 September 2019 through 18 September 2019",,159176,0094243X,9780735419711,,,"English","AIP Conf. Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85084125688
"Wang R., Wang S., Du S., Xiao E., Yuan W., Feng C.","57210792853;57215871383;57215871213;57215871738;56423022900;51461205400;","Real-Time Soft Body 3D Proprioception via Deep Vision-Based Sensing",2020,"IEEE Robotics and Automation Letters","5","2","9006921","3382","3389",,5,"10.1109/LRA.2020.2975709","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082175677&doi=10.1109%2fLRA.2020.2975709&partnerID=40&md5=fa1d05c72af1621e78aca7425aca3c83","Tandon School of Engineering, New York University, Brooklyn, NY, United States; Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States","Wang, R., Tandon School of Engineering, New York University, Brooklyn, NY, United States; Wang, S., Tandon School of Engineering, New York University, Brooklyn, NY, United States; Du, S., Tandon School of Engineering, New York University, Brooklyn, NY, United States; Xiao, E., Tandon School of Engineering, New York University, Brooklyn, NY, United States; Yuan, W., Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States; Feng, C., Tandon School of Engineering, New York University, Brooklyn, NY, United States","Soft bodies made from flexible and deformable materials are popular in many robotics applications, but their proprioceptive sensing has been a long-standing challenge. In other words, there has hardly been a method to measure and model the high-dimensional 3D shapes of soft bodies with internal sensors. We propose a framework to measure the high-resolution 3D shapes of soft bodies in real-time with embedded cameras. The cameras capture visual patterns inside a soft body, and a convolutional neural network (CNN) produces a latent code representing the deformation state, which can then be used to reconstruct the body's 3D shape using another neural network. We test the framework on various soft bodies, such as a Baymax-shaped toy, a latex balloon, and some soft robot fingers, and achieve real-time computation (\leq2.5 ms/frame) for robust shape estimation with high precision (\leq1% relative error) and high resolution. We believe the method could be applied to soft robotics and human-robot interaction for proprioceptive shape sensing. Our code is available at: https://ai4ce.github.io/DeepSoRo/. © 2016 IEEE.","3D deep learning; and learning for soft robots; control; deep learning in robotics and automation; Modeling","Cameras; Control engineering; Convolutional neural networks; Deep learning; Deformation; Models; Robotics; Deformable material; Deformation state; High-dimensional; Internal sensors; Real-time computations; Robotics applications; Soft robot; Vision based sensing; Human robot interaction",,,,,,,,,,,,,,,,"Harnett, C., Flexible circuits with integrated switches for robotic shape sensing (2016) Sensors Next-Gener. Robot. III, International Society for Optics and Photonics, 9859; Jin, H., Wang, J., Yang, Z., Kumar, S., Hong, J., Wish: Towards a wireless shape-aware world using passive rfids (2018) Proc. 16th Annu. Int. Conf. Mobile Syst., Appl., Services., pp. 428-441; Wessely, M., Tsandilas, T., Mackay, W.E., Shape-aware material: Interactive fabrication with shapeme (2018) Proc. 31st Annu. ACM Symp. User Interface Softw. Technol., pp. 127-139; Runge, G., Wiese, M., Gunther, L., Raatz, A., A framework for the kinematic modeling of soft material robots combining finite element analysis and piecewise constant curvature kinematics (2017) Proc. IEEE Control, Autom. Robot.3rd Int. Conf., pp. 7-14; Zhang, Z., Dequidt, J., Kruszewski, A., Largilliere, F., Duriez, C., Kinematic modeling and observer based control of soft robot using real-time finite element method (2016) Proc. IEEE Intell. Robots Syst. /RSJ Int. Conf., pp. 5509-5514; Martinez, R.V., Fish, C.R., Chen, X., Whitesides, G.M., Elastomeric origami: Programmable letter-elastomer composites as pneumatic actuators (2012) Adv. Functional Mater., 22 (7), pp. 1376-1384; Mosadegh, B., Pneumatic networks for soft robotics that actuate rapidly (2014) Adv. Functional Mater., 24 (15), pp. 2163-2170; Polygerinos, P., Modeling of soft fiber-reinforced bending actuators (2015) IEEE Trans. Robot., 31 (3), pp. 778-789. , Jun; Glauser, O., Panozzo, D., Hilliges, O., Sorkine-Hornung, O., Deformation capture via self-sensing capacitive arrays (2019) ACM Trans. Graph., 38 (2), pp. 1-16; Van Meerbeek, I., De Sa, C., Shepherd, R., Soft optoelectronic sensory foams with proprioception (2018) Sci. Robot., 3 (24); Molnar, J.L., Cheng -, A.C., Tiziani, L.O., Boots, B., Hammond, F.L., Optical sensing and control methods for soft pneumatically actuated robotic manipulators (2018) Proc. IEEE Int. Conf. Robot. Autom., pp. 1-8; Thuruthel, T.G., Shih, B., Laschi, C., Tolley, A.T., Soft robot perception using embedded soft sensors and recurrent neural networks (2019) Sci. Robot., 4 (26); Scimeca, L., Hughes, J., Maiolino, P., Iida, F., Model-free soft-structure reconstruction for proprioception using tactile arrays (2019) IEEE Robot. Autom. Lett., 4 (3), pp. 2479-2484. , Jul; Wang, H., Totaro, M., Beccai, L., Toward perceptive soft robots: Progress and challenges (2018) Adv. Sci., 5 (9); Ferrier, R.N., Brockett, W.R., Reconstructing the shape of a deformable membrane from image data (2000) T. J. Robot.Res., 19 (9), pp. 795-816; Kamiyama, K., Vlack, K., Mizota, T., Kajimoto, H., Kawakami, K., Tachi, S., Vision-based sensor for real-time measuring of surface traction fields (2005) IEEE Comput. Graph. And Appl., 25 (1), pp. 68-75. , Jan./Feb; Chorley, C., Melhuish, C., Pipe, T., Rossiter, J., Tactile edge detection (2010) Proc. IEEE Sensors., pp. 2593-2598; Yamaguchi, A., Atkeson, C.G., Implementing tactile behaviors using fingervision (2017) Proc. IEEE-RAS 17th Int. Conf. Humanoid Robot., pp. 241-248; Yuan, W., Dong, S., Adelson, E.H., Gelsight: High-resolution robot tactile sensors for estimating geometry and force (2017) Sensors, 17 (12); Choy, C.B., Xu, D., Gwak, J., Chen, K., Savarese, S., 3d-r2n2:Aunified approach for single and multi-view 3d object reconstruction (2016) Proc. Eur. Conf. Comput. Vision., pp. 628-644; Sun, X., Pix3d: Dataset and methods for single-image 3d shape modeling (2018) Proc. IEEE Conf. Comput. Vision Pattern Recognit., pp. 2974-2983; Fan, H., Su, H., Guibas, L.J., A point set generation network for 3d object reconstruction from a single image (2017) Proc. IEEE Conf. Comput. Vision Pattern Recognit., pp. 605-613; Alp Guler, R., Neverova, N., Kokkinos, I., Densepose: Dense human pose estimation in the wild (2018) Proc. IEEE Conf. Comput. Vision Pattern Recognit., pp. 7297-7306; Wu, J., Zhang, C., Xue, T., Freeman, W.T., Tenenbaum, J.B., Learning a probabilistic latent space of object shapes via 3d generativeadversarial modeling (2016) Proc. Adv. Neural Inf. Process. Syst., pp. 82-90; Yang, Y., Feng, C., Shen, Y., Tian, D., Foldingnet: Interpretable unsupervised learning on 3d point clouds (2018) Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 206-215; Zhuang, W., Sun, G., Li, H., Lou, X., Dong, M., Zhu, L., Fbg based shape sensing of a silicone octopus tentacle model for soft robotics (2018) Optik, 165, pp. 7-15; Qi, C.R., Su, H., Mo, K., Guibas, L.J., Pointnet: Deep learning on point sets for 3d classification and segmentation (2017) Proc. IEEE Conf. Comput. Vision Pattern Recognit., pp. 77-85; Huang, A.S., Olson, E., Moore, D.C., Lcm: Lightweight communications and marshalling (2010) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 4057-4062; Schulman, J., Lee, A., Ho, J., Abbeel, P., Tracking deformable objects with point clouds (2013) Proc. IEEE Int. Conf. Robot. Autom., pp. 1130-1137; Glauser, O., Wu, S., Panozzo, D., Hilliges, O., Sorkine-Hornung, O., Interactive hand pose estimation using a stretch-sensing soft glove (2019) ACM Trans. Graph., 38 (4); Olson, E., Apriltag: A robust and flexible visual fiducial system (2011) Proc. IEEE Int. Conf. Robot. Autom., pp. 3400-3407; Paszke, A., PyTorch: An imperative style, high-performance deep learning library (2019) Proc. Adv. Neural Inf. Process. Syst., pp. 8024-8035; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2016) Proc. IEEE Conf. Comput. Vision Pattern Recognit., pp. 770-778","Wang, R.; Tandon School of Engineering, United States; email: ruoyuwang@nyu.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85082175677
"Zhang D., Liu J., Gao A., Yang G.-Z.","57207943701;55252402400;42261544700;57211728432;","An Ergonomic Shared Workspace Analysis Framework for the Optimal Placement of a Compact Master Control Console",2020,"IEEE Robotics and Automation Letters","5","2","9000520","2995","3002",,4,"10.1109/LRA.2020.2974428","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081611815&doi=10.1109%2fLRA.2020.2974428&partnerID=40&md5=72fd0941501abc068710246f15ce1307","Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China","Zhang, D., Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom; Liu, J., Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom; Gao, A., Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China; Yang, G.-Z., Hamlyn Centre for Robotic Surgery, Imperial College London, London, United Kingdom, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China","Master-Slave control is commonly used for Robot-Assisted Minimally Invasive Surgery (RAMIS). The configuration, as well as the placement of the master manipulators, can influence the remote control performance. An ergonomic shared workspace analysis framework is proposed in this letter. Combined with the workspace of the master manipulators and the human arms, the human-robot interaction workspace can be generated. The optimal master robot placement can be determined based on three criteria: 1) interaction workspace volume, 2) interaction workspace quality, and 3) intuitiveness for slave robot control. Experimental verification of the platform is conducted on a da Vinci Research Kit (dVRK). An in-house compact master manipulator (Hamlyn CRM) is used as the master robot and the da Vinci robot is used as the slave robot. Comparisons are made between with and without using design optimization to validate the effectiveness of the ergonomic shared workspace analysis technique. Results indicate that the proposed ergonomic shared workspace analysis can improve the performance of teleoperation in terms of task completion time and the number of clutching required during operation. © 2020 IEEE.","Learning and adaptive systems; medical robots and systems; telerobotics and teleoperation","Ergonomics; Human robot interaction; Manipulators; Remote control; Design optimization; Experimental verification; Learning and adaptive system; Master slave control; Medical robots and systems; Minimally invasive surgery; Task completion time; Tele-robotics; Quality control",,,,,,,,,,,,,,,,"Christos, B., Guang-Zhong, Y., From passive tool holders to microsurgeodns: Safer, smaller, smarter surgical robots (2014) IEEE Trans. Biomed. Eng., 61 (5), pp. 1565-1576. , May; Vitiello, V., Lee, S.-L., Cundy, T.P., Yang, G.-Z., Emerging robotic platforms forminimally invasive surgery (2012) IEEE Rev. Biomed. Eng., 6, pp. 111-126. , Dec; Payne, C.J., Yang, G.-Z., Hand-held medical robots (2014) Ann. Biomed. Eng., 42 (8), pp. 1594-1605; Thant, Z., Low, S., Tang, S., Phee, L., Ho, K., Chung, S., Ergonomic master controller for flexible endoscopic gastrointestinal robot manipulator (2006) Proc. Int. Conf. Biomed. Pharmaceutical Eng., pp. 575-579. , IEEE; Kamei, Y., Study on master manipulator design parameters for robotic microsurgery (2012) Proc. Biomed. Robot. Biomechatronics 4th IEEE RAS & EMBS Int. Conf., pp. 847-852; Abdel-Malek, K., Yu, W., Yang, J., Placement of robot manipulators to maximize dexterity (2004) Int. J. Robot. Autom., 19 (1), pp. 6-14; Vahrenkamp, N., Asfour, T., Dillmann, R., Robot placement based on reachability inversion (2013) Proc. Robot. Autom. IEEE Int. Conf., pp. 1970-1975; Venema, S.C., Hannaford, B., A probabilistic representation of human workspace for use in the design of human interface mechanisms (2001) IEEE/ASME Trans. Mechatronics, 6 (3), pp. 286-294. , Sep; Terlemez, O., Ulbrich, S., Mandery, C., Do, M., Vahrenkamp, N., Asfour, T., Master motormap (mmm)-framework and toolkit for capturing, representing, and reproducing human motion on humanoid robots (2014) Proc. 14th IEEE-RAS Int. Conf. Humanoid Robot., pp. 894-901; Van'T Hullenaar, C.D., Hermans, B., Broeders, I.A., Ergonomic assessment of the da vinci console in robot-assisted surgery (2017) Innovative Surgical Sci., 2 (2), pp. 97-104; Papanikolaidi, I., Synodinos, A., Moulianitis, V., Aspragathos, N., Xidias, E., Optimal base placement of the da vinci system based on the manipulability index (2013) Proc. Int. Conf. Intell. Robot. Syst., p. 3; Takahashi, H., Master manipulator with higher operability designed for micro neuro surgical system (2008) Proc. Robot. Autom., IEEE Int. Conf., pp. 3902-3907; Mitsuishi, M., Master-slave robotic platform and its feasibility study for micro-neurosurgery (2013) Int. J. Med. Robot. Comput. Assisted Surgery, 9 (2), pp. 180-189; Bagheri, M., Ajoudani, A., Lee, J., Caldwell, D.G., Tsagarakis, N.G., Kinematic analysis and design considerations for optimal base frame arrangement of humanoid shoulders (2015) Proc. IEEE Int. Conf. Robot. Autom., pp. 2710-2715; Cloutier, G.M., Jutard, A., Bétemps, M., A robottask conformance index for the design of robotized cells (1994) Robot. Auton. Syst., 13 (4), pp. 233-243; Zhang, D., Liu, J., Zhang, L., Yang, G.-Z., Design and verification of a portable master manipulator based on an effective workspace analysis framework (2019) Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst., pp. 417-424. , Macau, China; Bohigas, O., Manubens, M., Ros, L., A complete method forworkspace boundary determination on general structure manipulators (2012) IEEE Trans. Robot., 28 (5), pp. 993-1006. , Oct; Zhang, D., Cursi, F., Yang, G.-Z., Wsrender: A workspace analysis and visualization toolbox for robotic manipulator design and verification (2019) IEEE Robot. Autom. Lett., 4 (4), pp. 3836-3843. , Oct; Yoshikawa, T., Manipulability of robotic mechanisms (1985) Int. J. Robot. Res., 4 (2), pp. 3-9; Kim, J.-O., Khosla, K., Dexterity measures for design and control of manipulators (1991) Proc. IEEE/RSJ Int. Workshop Intell. Robot. Syst. Intell. Mech. Syst., pp. 758-763; Azad, P., Asfour, T., Dillmann, R., Toward an unified representation for imitation of human motion on humanoids (2007) Proc. IEEE Int. Conf. Robot. Autom., pp. 2558-2563; Direkwatana, C., Wilasrusmee, C., Workspace analysis for a newdesign laparoscopic robotic manipulator, ""mu-laparobot"" (2011) 20th National Grad Research Conf., pp. 2-3; Zahedi, F., The analytic hierarchy process-A survey of the method and its applications (1986) Interfaces, 16 (4), pp. 96-108; Chicea, A.-L., Breaz, R.-E., Bologa, O., Racz, S.-G., Using serial industrial robots and CAM techniques for manufacturing prosthetic devices (2015) Appl. Mech. Mater., 762, pp. 313-318; Alonso, J.A., Lamata, M.T., Consistency in the analytic hierarchy process: A new approach (2006) Int. J. Uncertainty, Fuzziness Knowl.-Based Syst., 14 (4), pp. 445-459; Matern, U., Waller, P., Giebmeyer, C., Rückauer, K.D., Farthmann, E.H., Ergonomics: Requirements for adjusting the height of laparoscopic operating tables (2001) J. Soc. Laparoendos. Surg., 5 (1), p. 7; Kuo, C.-H., Dai, J.S., Dasgupta, P., Kinematic design considerations for minimally invasive surgical robots: An overview (2012) Int. J. Med. Robot. Comput. Assisted Surg., 8 (2), pp. 127-145; Nagyné-Elek, R., Haidegger, T., Robot-assisted minimally invasive surgical skill assessment-manual and automated platforms (2019) Acta Polytech. Hung., 16 (8), pp. 141-169; Trejos, A.L., Patel, R.V., Malthaner, R.A., Schlachta, C.M., Development of force-based metrics for skills assessment in minimally invasive surgery (2014) Surg. Endos., 28 (7), pp. 2106-2119; Zhang, D., Xiao, B., Huang, B., Zhang, L., Liu, J., Yang, G.-Z., A selfadaptive motion scaling framework for surgical robot remote control (2018) IEEE Robot. Autom. Lett., 4 (2), pp. 359-366; Zhang, D., Chen, J., Li, W., Salinas, D.B., Yang, G.-Z., A microsurgical robot research platform for robot-assisted microsurgery research and training (2020) Int. J. Comput. Assisted Radiol. Surgery, 15 (1), pp. 15-25. , Jan. 1; Judkins, T.N., Oleynikov, D., Stergiou, N., Objective evaluation of expert and novice performance during robotic surgical training tasks (2009) Surg. Endos., 23 (3), p. 590","Yang, G.-Z.; Hamlyn Centre for Robotic Surgery, United Kingdom; email: gzyang@sjtu.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85081611815
"Aller F., Pinto-Fernandez D., Torricelli D., Pons J.L., Mombaur K.","57191261774;57204241527;24479212400;7202123192;55885381400;","From the State of the Art of Assessment Metrics Toward Novel Concepts for Humanoid Robot Locomotion Benchmarking",2020,"IEEE Robotics and Automation Letters","5","2","8894403","914","920",,,"10.1109/LRA.2019.2952291","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079803802&doi=10.1109%2fLRA.2019.2952291&partnerID=40&md5=1d8c6cb63f7e2b80bae09e409d03d18e","Optimization Robotics and Biomechanics Chair, Institute of Computer Engineering, Heidelberg University, Heidelberg, 69117, Germany; Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council, Madrid, Spain; Legs and Walking Ability Lab, Shirley Ryan AbilityLab, Chicago, IL, United States","Aller, F., Optimization Robotics and Biomechanics Chair, Institute of Computer Engineering, Heidelberg University, Heidelberg, 69117, Germany; Pinto-Fernandez, D., Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council, Madrid, Spain; Torricelli, D., Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council, Madrid, Spain; Pons, J.L., Legs and Walking Ability Lab, Shirley Ryan AbilityLab, Chicago, IL, United States; Mombaur, K., Optimization Robotics and Biomechanics Chair, Institute of Computer Engineering, Heidelberg University, Heidelberg, 69117, Germany","In order to prepare humanoid robots for real-world applications, it is necessary to assess the robots' locomotion performance and to determine their readiness level. We have carried out an extensive statistical literature review of 204 publications evaluating what kind of benchmarking scenarios and performance indicators are used and if they are sufficient to take a robot from prototype to production. We report the results of our analysis and discuss the most relevant findings such as the overall increase in the number of publications and motion tasks considered. Previous benchmarking efforts have been devoted to the functional assessment of the robotic system itself. A trend towards goal level oriented performance indicators such as robustness to external disturbance and dependability can be observed. We also identify a deficiency in benchmarking of non-functional aspects like safety or human-robot interaction. Based on our results, we outline the creation of a benchmarking framework with respect to current benchmarking approaches also introducing testbeds considering currently neglected motion tasks with a focus on a high degree of standardization. © 2016 IEEE.","humanoid and bipedal locomotion; Humanoid robots; performance evaluation and benchmarking","Anthropomorphic robots; Human robot interaction; Bipedal locomotion; External disturbances; Humanoid robot; Humanoid robot locomotion; Non-functional aspects; Performance evaluation and benchmarking; Performance indicators; Statistical literature; Benchmarking",,,,,"Horizon 2020 Framework Programme, H2020: 779963","Manuscript received June 7, 2019; accepted October 17, 2019. Date of publication November 8, 2019; date of current version January 28, 2020. This letter was recommended for publication by Associate Editor B. Ugurlu and Editor N. Tsagarakis upon evaluation of the reviewers’ comments. This work was supported by the project EUROBENCH (European Robotic Framework for Bipedal Locomotion Benchmarking) through H2020 Topic ICT 27-2017 under Grant 779963. (Corresponding author: Felix Aller.) F. Aller and K. Mombaur are with the Optimization, Robotics and Biomechanics Chair, Institute of Computer Engineering, Heidelberg University, 69117 Heidelberg, Germany (e-mail: felix.aller@ziti.uni-heidelberg; katja.mombaur@ziti.uni-heidelberg.de).",,,,,,,,,,"Del Pobil, A.P., Madhavan, R., Messina, E., (2006) Benchmarks in Robotics Research, , https://pdfs.semanticscholar.org/9722/ba36480ee26d1e40194ead61371aeea024e3.pdf; DARPA Robotics Challenge, , https://www.darpa.mil/program/darpa-robotics-challenge, Accessed: Jan. 23 2020; Eu ROBOTICS, , https://www.eu-robotics.net; Behnke, S., Robot competitions-Ideal benchmarks for robotics research (2006) Proc. IROS Workshop Benchmarks Robot. Res.; Torricelli, D., Human-like compliant locomotion: State of the art of robotic implementations (2016) Bioinspiration Biomimetics, 11 (5); Torricelli, D., Benchmarking bipedal locomotion: A unified scheme for humanoids, wearable robots, and humans (2015) IEEE Robot. Autom. 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Therapy, 84 (10), pp. 906-918; Morat, T., Kroeger, D., Mechling, H., The multisurface obstacle test for older adults (MSOT): Development and reliability of a novel test for older adults (2013) Eur. Rev. Aging Phys. Activity, 10 (2), pp. 117-125; Kinzey, S.J., Armstrong, C.W., The reliability of the star-excursion test in assessing dynamic balance (1998) J. Orthopaedic Sports Phys. Therapy, 27 (5), pp. 356-360; Virk, G., Cameron, S., ISO-IEC standardization efforts in robotics (2014) Proc. IROS Workshop Standardized Knowl. Represent. Ontologies Robot. Autom., pp. 5-6; Aisbl, E.R., Robotics 2020 multi-annual roadmap for robotics in Europe (2014) Initial Release B, 15 (1), pp. 178-228; SciRoc-Smart CIty RObotic Challenge, , https://sciroc.eu/","Aller, F.; Optimization Robotics and Biomechanics Chair, Germany; email: felix.aller@ziti.uni-heidelberg",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85079803802
"Chevalier P., Kompatsiari K., Ciardo F., Wykowska A.","57016293400;55904188300;57164931800;15924284200;","Examining joint attention with the use of humanoid robots-A new approach to study fundamental mechanisms of social cognition",2020,"Psychonomic Bulletin and Review","27","2",,"217","236",,6,"10.3758/s13423-019-01689-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076883105&doi=10.3758%2fs13423-019-01689-4&partnerID=40&md5=8741a04fda51c7dcf1948533c03e3280","Social Cognition in Human–Robot Interaction Unit, Istituto Italiano di Tecnologia, Genoa, Italy","Chevalier, P., Social Cognition in Human–Robot Interaction Unit, Istituto Italiano di Tecnologia, Genoa, Italy; Kompatsiari, K., Social Cognition in Human–Robot Interaction Unit, Istituto Italiano di Tecnologia, Genoa, Italy; Ciardo, F., Social Cognition in Human–Robot Interaction Unit, Istituto Italiano di Tecnologia, Genoa, Italy; Wykowska, A., Social Cognition in Human–Robot Interaction Unit, Istituto Italiano di Tecnologia, Genoa, Italy","This article reviews methods to investigate joint attention and highlights the benefits of new methodological approaches that make use of the most recent technological developments, such as humanoid robots for studying social cognition. After reviewing classical approaches that address joint attention mechanisms with the use of controlled screen-based stimuli, we describe recent accounts that have proposed the need for more natural and interactive experimental protocols. Although the recent approaches allow for more ecological validity, they often face the challenges of experimental control in more natural social interaction protocols. In this context, we propose that the use of humanoid robots in interactive protocols is a particularly promising avenue for targeting the mechanisms of joint attention. Using humanoid robots to interact with humans in naturalistic experimental setups has the advantage of both excellent experimental control and ecological validity. In clinical applications, it offers new techniques for both diagnosis and therapy, especially for children with autism spectrum disorder. The review concludes with indications for future research, in the domains of healthcare applications and human–robot interaction in general. © 2019, The Author(s).","Autism; Healthy and clinical populations; Human–robot interaction; Joint attention; Review","attention; autism; human; human relation; perception; robotics; Attention; Autism Spectrum Disorder; Humans; Interpersonal Relations; Robotics; Social Perception",,,,,"Horizon 2020 Framework Programme, H2020: 715058, 754490; European Research Council, ERC","This work is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant awarded to A.W., titled “InStance: Intentional Stance for Social Attunement,” grant agreement no. 715058) and by Minded Program–Marie Skłodowska-Curie grant agreement no. 754490, a fellowship awarded to P.C.",,,,,,,,,,"Admoni, H., Bank, C., Tan, J., Toneva, M., Scassellati, B., Robot gaze does not reflexively cue human attention (2011) Expanding the space of cognitive science: Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, pp. 1983-1988. , https://escholarship.org/uc/item/3pq1v9b0, Carlson L, Hölscher C, Shipley TF, (eds), Cognitive Science Society, Austin, TX: Retrieved from https://escholarship.org/uc/item/3pq1v9b0; Admoni, H., Scassellati, B., Social eye gaze in human–robot interaction: A review (2017) Journal of Human–Robot Interaction, 6, pp. 25-63; Akiyama, T., Kato, M., Muramatsu, T., Maeda, T., Hara, T., Kashima, H., Gaze-triggered orienting is reduced in chronic schizophrenia (2008) Psychiatry Research, 158, pp. 287-296; Allison, T., Puce, A., McCarthy, G., Social perception from visual cues: Role of the STS region (2000) Trends in Cognitive Sciences, 4, pp. 267-278; Anzalone, S.M., Tilmont, E., Boucenna, S., Xavier, J., Jouen, A.-L., Bodeau, N., Cohen, D., How children with autism spectrum disorder behave and explore the 4-dimensional (spatial 3D + time) environment during a joint attention induction task with a robot (2014) Research in Autism Spectrum Disorders, 8, pp. 814-826. , (,).,. doi:, https://doi.org/; Anzalone, S.M., Xavier, J., Boucenna, S., Billeci, L., Narzisi, A., Muratori, F., Chetouani, M., Quantifying patterns of joint attention during human–robot interactions: An application for autism spectrum disorder assessment (2019) Pattern Recognition Letters, 118, pp. 42-50. , (,).,. doi:, https://doi.org/; (2013) Diagnostic and statistical manual of mental disorders: DSM-5, , American Psychiatric Association, Arlington, VA; Baron-Cohen, S., Empathizing, systemizing, and the extreme male brain theory of autism (2010) Sex differences in the human brain, their underpinnings and implications (Progress in Brain Research), 186, pp. 167-175. , Savic I, (ed), Elsevier, Amsterdam, The Netherlands; Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., Clubley, E., The Autism-Spectrum Quotient (AQ): Evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians (2001) Journal of Autism and Developmental Disorders, 31, pp. 5-17; Bayliss, A.P., Murphy, E., Naughtin, C.K., Kriticos, A., Schilbach, L., Becker, S.I., Gaze leading”: Initiating simulated joint attention influences eye movements and choice behavior (2013) Journal of Experimental Psychology: General, 142, pp. 76-92; Bayliss, A.P., di Pellegrino, G., Tipper, S.P., Sex differences in eye gaze and symbolic cueing of attention (2005) Quarterly Journal of Experimental Psychology, 58A, pp. 631-650; Bayliss, A.P., Tipper, S.P., Predictive gaze cues and personality judgments: Should eye trust you? 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"Mucchiani C., Cacchione P., Torres W., Johnson M.J., Yim M.","57200142286;6602728456;57197799256;57203731750;7005922938;","Exploring Low-Cost Mobile Manipulation for Elder Care Within a Community Based Setting",2020,"Journal of Intelligent and Robotic Systems: Theory and Applications","98","1",,"59","70",,1,"10.1007/s10846-019-01041-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067244245&doi=10.1007%2fs10846-019-01041-x&partnerID=40&md5=d74999a696aac92e2976aa2303fead2c","Philadelphia, United States","Mucchiani, C., Philadelphia, United States; Cacchione, P., Philadelphia, United States; Torres, W., Philadelphia, United States; Johnson, M.J., Philadelphia, United States; Yim, M., Philadelphia, United States","This paper identifies tasks an affordable mobile manipulator service robot could do to benefit older adults’ independence in a supportive apartment living facility, and a series of tests validating the highest ranked tasks. Previous deployments considered a mobile only robotic base, performing exercises through walking encouragement and hydration by water delivery, both followed by pain assessment and are briefly described. Current tests investigated the efficacy of mobile manipulation tasks by adapting a novel, low-cost telescopic robotic arm to the same mobile base, with aspects of human-robot interaction investigated through a physical interactive game with the older adults. All deployments took place at a Program of All-inclusive Care (PACE) center and interactions were evaluated by two observers, along with post-interaction surveys with the older adults. Previous work on elder care robotics is discussed. Results of the mobile manipulation deployments, along with design guidelines are presented. Future work includes the development of a new mobile manipulator capable of performing the investigated tasks with a greater level of autonomy and efficiency. © 2019, Springer Nature B.V.","Elder care robotics; Human robot interaction; Mobile manipulation","Costs; Man machine systems; Manipulators; Robotics; Community-based; Elder care; Interactive games; Level of autonomies; Mobile manipulation; Mobile manipulator; Service robots; Water delivery; Human robot interaction",,,,,,,,,,,,,,,,"Spijker, J., MacInnes, J., Population ageing: the timebomb that isn’t? (2013) Bmj, 347, p. f6598; Robinson, H., MacDonald, B., Kerse, N., Broadbent, E., The psychosocial effects of a companion robot: a randomized controlled trial (2013) J. Am. Med. Dir. Assoc., 14 (9), pp. 661-667; Banks, M.R., Willoughby, L.M., Banks, W.A., Animal-assisted therapy and loneliness in nursing homes: use of robotic versus living dogs (2008) J. Am. Med. Dir. 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Res., 12 (3), pp. 189-198; Taggart, W., Turkle, S., Kidd, C.D., An interactive robot in a nursing home: Preliminary remarks (2005) Towards Social Mechanisms of Android Science, 2005, pp. 56-61; Abdollahi, H., Mollahosseini, A., Lane, J.T., Mahoor, M.H., A pilot study on using an intelligent life-like robot as a companion for elderly individuals with dementia and depression (2017) 2017 IEEE-RAS 17Th International Conference on Humanoid Robotics (Humanoids), pp. 541-546. , IEEE; Shiomi, M., Iio, T., Kamei, K., Sharma, C., Hagita, N., Effectiveness of social behaviors for autonomous wheelchair robot to support elderly people in japan (2015) PLOS ONE, 5, pp. 1-15","Mucchiani, C.United States; email: caio@seas.upenn.edu",,,"Springer",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","",Scopus,2-s2.0-85067244245
"Barker N., Jewitt C., Price S.","57216333639;15220569600;16234800800;","Becoming in touch with industrial robots through ethnography",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"128","130",,1,"10.1145/3371382.3378246","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083282523&doi=10.1145%2f3371382.3378246&partnerID=40&md5=dcf40d503bb81b5d10cec017d66d1fbb","Institute of Education, University College, London, United Kingdom","Barker, N., Institute of Education, University College, London, United Kingdom; Jewitt, C., Institute of Education, University College, London, United Kingdom; Price, S., Institute of Education, University College, London, United Kingdom","Touch is central to communication and social interaction. For both humans and robots touch is a mode through which they sense the world. A second wave of industrial robots is reshaping how touch operates within the labor process. Recent studies have turned their attention to the role of touch in Human-Robot Interaction (HRI). While these studies have produced useful knowledge in relation to the affective capacities of robotic touch, methods remain restrictive. This paper contributes to expanding research methods for the study of robotic touch. It reports on the design of an ongoing ethnography that forms part of the InTouch project. The interdisciplinary project takes forward a socially orientated stance and is concerned with how technologies shape the semiotic and sensory dimensions of touch in the 'real world'. We contend that these dimensions are key factors in shaping how humans and robots interact, yet are currently overlooked in the HRI community. This multi-sited sensory ethnography research has been designed to explore the social implications of robotic touch within industrial settings (e.g. manufacturing and construction). © 2020 ACM.","Industrial robots; Multi-sited sensory ethnography; Robotic touch","Agricultural robots; Industrial research; Industrial robots; Man machine systems; Robotics; Hri communities; Human robot Interaction (HRI); Industrial settings; Interdisciplinary project; InTouch; Real-world; Social implication; Social interactions; Human robot interaction",,,,,"European Research Council, ERC: 681489","This research is part of the IN-TOUCH project, funded by a European Research Council Consolidator Award (No: 681489).",,,,,,,,,,"Cramer, H., Kemper, N., Amin, A., Evers, V., The effects of robot touch and proactive behaviour on perceptions of human-robot interactions (2009) Proc. 4th ACM/IEEE Int. Conf. Human-Robot Interact. HRI'09, pp. 275-276; Royakkers, L., Van Est, R., A literature review on new robotics: Automation from love to war (2015) Int. J. Soc. Robot, 7 (5), pp. 549-570; Yohanan, S., MacLean, K.E., The role of affective touch in human-robot interaction: Human intent and expectations in touching the haptic creature (2012) Int. J. Soc. Robot, 4 (2), pp. 163-180; Arnold, T., Scheutz, M., Observing robot touch in context: How does touch and attitude affect perceptions of a robot's social qualities (2018) ACM/IEEE Int. Conf. Human-Robot Interact, pp. 352-360; Chen, T.L., King, C.H.A., Thomaz, A.L., Kemp, C.C., An investigation of responses to robot-initiated touch in a nursing context (2014) Int. J. Soc. Robot, 6 (1), pp. 141-161; Jewitt, C., Price, S., Mackley, K., Giannoutsou, N., Atkinson, D., (2020) Interdisciplinary Insights for Digital Touch Communication, , Springer International Publishing; Hasse, C., Trentemøller, S., Sorenson, J., Special issue on ethnography in human-robot interaction research (2019) J. Behav. Robot, 10 (1), pp. 180-181; Blond, L., Studying robots outside the lab: HRI as ethnography (2019) J. Behav. Robot, 10 (1), pp. 117-127; Chun, B., Doing autoethnography of social robots: Ethnographic reflexivity in HRI (2019) J. Behav. Robot, 10 (1), pp. 228-236; Jewitt, C., Leder Mackley, K., Methodological dialogues across multimodality and sensory ethnography: Digital touch communication (2019) Qual. Res, 19 (1), pp. 90-110; Falzon, M., Multi-sited ethnography: Theory, praxis and locality in contemporary research (2016) Ashgate: Ashgate Publishing Company; Pink, S., (2015) Doing Sensory Ethnography, 2nd Ed, , London: SAGE Publications Ltd",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85083282523
"Augustine A.C., Ryusuke M., Liu C., Ishi C.T., Ishiguro H.","57216607735;57216341866;35956240500;6506878555;26643316500;","Generation and evaluation of audio-visual anger emotional expression for android robot",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"96","98",,,"10.1145/3371382.3378282","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083229690&doi=10.1145%2f3371382.3378282&partnerID=40&md5=fd9274d435c3045bc2dfd41139f55171","ATR Hiroshi Ishiguro Laboratory, Kyoto, Japan","Augustine, A.C., ATR Hiroshi Ishiguro Laboratory, Kyoto, Japan; Ryusuke, M., ATR Hiroshi Ishiguro Laboratory, Kyoto, Japan; Liu, C., ATR Hiroshi Ishiguro Laboratory, Kyoto, Japan; Ishi, C.T., ATR Hiroshi Ishiguro Laboratory, Kyoto, Japan; Ishiguro, H., ATR Hiroshi Ishiguro Laboratory, Kyoto, Japan","Recent studies in human-human interaction (HHI) have revealed the propensity of negative emotional expression to initiate affiliating functions that are beneficial to the expresser and also help to foster cordiality and closeness amongst interlocutors. However, efforts in human-robot interaction (HRI) have not attempted to investigate the consequences of expression of negative emotion by robots on HRI. Thus, the background of this study as a first step is to furnish humanoid robots with natural audio-visual anger expression for HRI. Based on the analysis results from a multimodal HHI corpus, we implemented different types of gestures related to anger expressions for humanoid robots and carried-out subjective evaluation of the generated anger expressions. Findings from this study revealed that the semantic context and functional content of anger-based utterances play a significant role in the choice of gesture to accompany such utterance. Our current result shows that ""Pointing"" gesture is adjudged more appropriate for utterances with ""you"" and anger-based ""questioning"" utterances; while ""both arms spread"" and ""both arm swing"" gestures were evaluated more appropriated for ""declarative"" and ""disagreement"" utterances respectively. © 2020 ACM.","Anger; Emotion; Hhi; HRI; Multimodal","Agricultural robots; Anthropomorphic robots; Man machine systems; Semantics; Arm swing; Emotional expressions; Human robot Interaction (HRI); Human-human interactions; Humanoid robot; Multi-modal; Semantic context; Subjective evaluations; Human robot interaction",,,,,,,,,,,,,,,,"Van Kleef, G.A., Understanding the positive and negative effects of emotional expressions in organizations: Easi does it (2014) Human Relations, 67 (9), pp. 1145-1164; Van Kleef, G.A., De Dreu, C.K.W., Manstead, A.S.R., The interpersonal effects of emotions in negotiations: A motivated information processing approach (2004) Journal of Personality and Social Psychology, 87 (4), p. 510; Campellone, T.R., Kring, A.M., Who do you trust the impact of facial emotion and behaviour on decision making (2013) Cognition & Emotion, 27 (4), pp. 603-620; Von Billerbeck, S.B.K., (2016) Whose Peace: Local Ownership and United Nations Peacekeeping; Rimé, B., Emotion elicits the social sharing of emotion: Theory and empirical review (2009) Emotion Review, 1 (1), pp. 60-85; Zeelenberg, M., Pligt Der J.Van, Manstead, A.S.R., Undoing regret on Dutch television: Apologizing for interpersonal regrets involving actions or inactions (1998) Personality and Social Psychology Bulletin, 24 (10), pp. 1113-1119; Song, S., Yamada, S., Expressing emotions through color, sound, and vibration with an appearance-constrained social robot (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 2-11; Liu, C., Ishi, C.T., Ishiguro, H., Hagita, N., Generation of nodding, head tilting and gazing for human-robot speech interaction (2013) International Journal of Humanoid Robotics, 10 (1), p. 1350009; Ishi, C.T., Funayama, T., Minato, T., Ishiguro, H., Motion generation in android robots during laughing speech (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3327-3332; Ishi, C.T., Minato, T., Ishiguro, H., Motion analysis in vocalized surprise expressions and motion generation in android robots (2017) IEEE Robotics and Automation Letters, 2 (3), pp. 1748-1754; Zecca, M., Endo, N., Momoki, S., Itoh, K., Takanishi, A., Design of the humanoid robot kobian-preliminary analysis of facial and whole body emotion expression capabilities (2008) Humanoids 2008-8th IEEE-RAS International Conference on Humanoid Robots, pp. 487-492; Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria, E., Mihalcea, R., (2018) Meld: A Multimodal Multi-party Dataset for Emotion Recognition in Conversations, , arXiv preprint arXiv:1810.02508; Senowarsito, S., Samiati Tarjana, S., Nurkamto, J., Politeness of non-verbal interaction in efl classrooms (2016) PROSIDING PRASASTI, pp. 682-688; Bremner, P., Leonards, U., Iconic gestures for robot avatars, recognition and integration with speech (2016) Frontiers in Psychology, 7, p. 183; Salem, M., Ziadee, M., Sakr, M., Effects of politeness and interaction context on perception and experience of hri (2013) International Conference on Social Robotics, pp. 531-541; Ishi, C.T., Machiyashiki, D., Mikata, R., Ishiguro, H., A speech-driven hand gesture generation method and evaluation in android robots (2018) IEEE Robotics and Automation Letters, 3 (4), pp. 3757-3764",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083229690
"Kuhn M., Pollmann K., Papadopoulos J.","57204656365;57128090300;57194589014;","I'm your partner -i'm your boss: Framing human-robot collaboration with conceptual metaphors",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"322","324",,2,"10.1145/3371382.3378250","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083207687&doi=10.1145%2f3371382.3378250&partnerID=40&md5=40568674ee0b28cf45c8df4b8ec6f25d","Fraunhofer Institute for Industrial Engineering IAO, Stuttgart, Germany; Stuttgart Media University, Stuttgart, Germany","Kuhn, M., Fraunhofer Institute for Industrial Engineering IAO, Stuttgart, Germany; Pollmann, K., Fraunhofer Institute for Industrial Engineering IAO, Stuttgart, Germany; Papadopoulos, J., Stuttgart Media University, Stuttgart, Germany","Using speech as an effective communication modality in human-robot interaction (HRI) allows designers to implement conceptual metaphors as a linguistic device. Metaphors appear to instantiate psychological framing effects and can influence the useŕs reasoning significantly. The present paper presents two consecutive studies that explore how metaphors can be used in speech output for social robots to deliberately influence the user's perception of their relationship with the robot. As a first step, metaphoric expressions for framing cooperation or delegation in HRI settings were identified in a workshop. Effects of those metaphors on peoplés strategies for organizing tasks in human-robot collaboration were then evaluated in an online survey. We found that metaphors suitable for encouraging users to do a task together with a robot are metaphors like ""partners on a journey"" or ""allies"". In contrast, metaphors that can be used to encourage handing over the task to a robot are metaphors like ""boss"" or ""ruler"". Further research needs to test whether these metaphors also have an effect on users' actions in HRI settings. © 2020 ACM.","Conceptual metaphors; Human-robot collaboration; Human-robot interaction","Agricultural robots; Man machine systems; Speech communication; Effective communication; Framing effects; Human robot Interaction (HRI); Human-robot collaboration; Linguistic devices; Online surveys; Research needs; User's perceptions; Social robots",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV7941","This research is part of the NIKA project and was funded by the German Federal Ministry of Education and Research (BMBF 16SV7941).",,,,,,,,,,"Forceville, C., Non-verbal and multimodal metaphor in a cognitivist framework: Agendas for research (2006) Applications of Cognitive Linguistics, 1 (379); Mark, J., Lakoff, G., (2013) Metaphors We Live by, , University of Chicago Press, Chicago, Illinois; Mark, T., Lakoff, G., More than Cool Reason: A Field Guide to Poetic Metaphor (1989) University of Chicago Press; Raymond, W.G., Jr., Walking the walk while thinking about the talk: Embodied interpretation of metaphorical narratives (2013) Journal of Psycholinguistic Research, 42 (4), pp. 363-378; Raymond, W., Gibbs, P., Jr., Lima, L.C., Francozo, E., Metaphor is grounded in embodied experience (2004) Journal of Pragmatics, 36 (7), pp. 1189-1210; Hurtienne, J., (2011) Image Schemas and Design for Intuitive Use, , Doctoral Thesis. Technical University Berlin; Fan, L.P., Literature review on the cognitive approach to metaphor (2018) Procedia Comput. Sci, 131 (C), pp. 925-928. , https://doi.org/10.1016/j.procs.2018.04.224; Shaikh, N., Deshmukh, R.R., Speech recognition system- A review (2016) IOSR Journal of Computer Engineering, 18, pp. 1-9; Thibodeau, P.H., Boroditsky, L., Metaphors we think with: The role of metaphor in reasoning (2011) PloS One, 6 (2), p. e16782; Thibodeau, P.H., Hendricks, R.K., Boroditsky, L., How linguistic metaphor scaffolds reasoning (2017) Trends in Cognitive Sciences, 21 (11), pp. 852-863",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083207687
"Pollmann K., Ziegler D.","57128090300;56236807100;","Personal HRI 2020: Workshop on behavioral patterns and interaction modelling for personalized HRI",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"654","655",,,"10.1145/3371382.3374846","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083182044&doi=10.1145%2f3371382.3374846&partnerID=40&md5=00e2d98208b961b2884907838a32feb8","Fraunhofer IAO, Institute for Industrial Engineering, Stuttgart, Germany","Pollmann, K., Fraunhofer IAO, Institute for Industrial Engineering, Stuttgart, Germany; Ziegler, D., Fraunhofer IAO, Institute for Industrial Engineering, Stuttgart, Germany","It has been argued that people are more inclined to accept a robot in their daily lives and even their home, if they have the feeling that the robot's behavior and interaction style matches their characteristics, needs and abilities. The concept of personalization has been introduced in order to create such personal, tailored human-robot interactions (HRI). Personalizing HRI means that robot takes into account individual user characteristics and can adjust its behavior to the situation and the human interaction partner. Previous HRI research indicates that personalization can have positive effects on the user experience during HRI as well as the user's attitudes towards and perceptions of the robot. This workshop is aimed at discussing the value of and exchanging ideas about the concept of personalization for HRI design. More concretely, we want to explore tools, methods and processes for appropriate robot behavior design and interaction modelling for personalized HRI. The workshop uses interactive, creative activities inspired by design thinking and participatory design to promote collaboration and co-creation between the participants and to inspire new, innovative perspectives on personalized HRI. © 2020 ACM.","Design patterns; Interaction models; Personalization; Social robots","Agricultural robots; Machine design; Man machine systems; User experience; Behavioral patterns; Human interactions; Human robot Interaction (HRI); Interaction modelling; Interaction styles; Methods and process; Participatory design; User characteristics; Human robot interaction",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV7941","The NIKA project was funded by the German Federal Ministry of Education and Research (BMBF 16SV7941).",,,,,,,,,,"https://dschool.stanford.edu/resources/the-bootcampbootleg, Thomas Both and Dave Baggereor. 09.08.2017. The Bootcamp Bootleg: Design Artifacts, Virtual Crash Course; Churamani, N., Anton, P., Brügger, M., Fliewasser, E., Hummel, T., Mayer, J., Mustafa, W., Wermter, S., The impact of personalisation on human-robot interaction in learning scenarios (2017) Proceedings of the 5th International Conference on Human Agent Interaction (HAI '17), pp. 171-180. , https://doi.org/10.1145/3125739.3125756, ACM, New York, NY, USA; Dautenhahn, K., Robots we like to live with!-A developmental perspective on a personalized, life-long robot companion (2004) RO-MAN 2004. 13th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No. 04TH8759), pp. 17-22. , https://doi.org/10.1109/ROMAN.2004.1374720; Lamers, M.H., Verbeek, F.J., Human-robot personal relationships-Third international conference, hrpr 2010, leiden, the netherlands, June 23-24, 2010, revised selected papers (2011) Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 59. , https://doi.org/10.1007/978-3-642-19385-9, Springer; Syrdal, D.S., Koay, K.L., Walters, M.L., Dautenhahn, K., A personalized robot companion-The role of individual differences on spatial preferences in HRI scenarios (2007) RO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication, pp. 1143-1148. , https://doi.org/10.1109/ROMAN.2007.4415252",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083182044
"Tufek N., Yalcin M., Altintas M., Kalaoglu F., Li Y., Bahadir S.K.","57203004752;57203010573;57211821236;6602825810;56262537800;45861070800;","Human Action Recognition Using Deep Learning Methods on Limited Sensory Data",2020,"IEEE Sensors Journal","20","6","8918509","3101","3112",,23,"10.1109/JSEN.2019.2956901","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079739124&doi=10.1109%2fJSEN.2019.2956901&partnerID=40&md5=07e974da3b2432c8dc383a73ec851d6d","Department of Computer Engineering, Istanbul Technical University (ITU), Istanbul, 34467, Turkey; Department of Textile Engineering, Istanbul Technical University (ITU), Istanbul, 344437, Turkey; School of Materials, University of Manchester, Manchester, M13 9PL, United Kingdom; Faculty of Mechanical Engineering, Istanbul Technical University (ITU), Istanbul, 34437, Turkey","Tufek, N., Department of Computer Engineering, Istanbul Technical University (ITU), Istanbul, 34467, Turkey; Yalcin, M., Department of Computer Engineering, Istanbul Technical University (ITU), Istanbul, 34467, Turkey; Altintas, M., Department of Computer Engineering, Istanbul Technical University (ITU), Istanbul, 34467, Turkey; Kalaoglu, F., Department of Textile Engineering, Istanbul Technical University (ITU), Istanbul, 344437, Turkey; Li, Y., School of Materials, University of Manchester, Manchester, M13 9PL, United Kingdom; Bahadir, S.K., Faculty of Mechanical Engineering, Istanbul Technical University (ITU), Istanbul, 34437, Turkey","In recent years, due to the widespread usage of various sensors action recognition is becoming more popular in many fields such as person surveillance, human-robot interaction etc. In this study, we aimed to develop an action recognition system by using only limited accelerometer and gyroscope data. Several deep learning methods like Convolutional Neural Network(CNN), Long-Short Term Memory (LSTM) with classical machine learning algorithms and their combinations were implemented and a performance analysis was carried out. Data balancing and data augmentation methods were applied and accuracy rates were increased noticeably. We achieved new state-of-the-art result on the UCI HAR dataset by 97.4% accuracy rate with using 3 layer LSTM model. Also, we implemented same model on collected dataset (ETEXWELD) and 99.0% accuracy rate was obtained which means a solid contribution. Moreover, the performance analysis is not only based on accuracy results, but also includes precision, recall and f1-score metrics. Additionally, a real-time application was developed by using 3 layer LSTM network for evaluating how the best model classifies activities robustly. © 2001-2012 IEEE.","Activity recognition; CNN; data augmentation; data balancing; deep learning; LSTM","Balancing; Convolutional neural networks; Deep learning; Human robot interaction; Learning algorithms; Learning systems; Network layers; Action recognition; Action recognition systems; Activity recognition; Data augmentation; Human-action recognition; LSTM; Performance analysis; Real-time application; Long short-term memory",,,,,"Horizon 2020 Framework Programme, H2020: 644268","Manuscript received October 12, 2019; accepted November 13, 2019. Date of publication December 2, 2019; date of current version February 14, 2020. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 644268. Welding of E-Textiles for Interactive Clothing (ETEXWELD) Project. The associate editor coordinating the review of this article and approving it for publication was Prof. Chang-Hee Won. (Corresponding author: Senem Kursun Bahadir.) N.Tufek,M.Yalcinand M.AltintasarewiththeDepartmentofComputer Engineering, Istanbul Technical University (ITU), 34467 Istanbul, Turkey (e-mail: ntufek@gmail.com; yalcinmur@itu.edu.tr; maltintas@itu.edu.tr).",,,,,,,,,,"Rodriguez, M.D., Ahmed, J., Shah, M., Action Mach a spatiotemporal maximum average correlation height filter for action recognition (2008) Proc. Ieee Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 1-8. , Anchorage, AK, USA, Jun; Salvo, P., Di Francesco, F., Costanzo, D., Ferrari, C., Trivella, M.G., De Rossi, D., A wearable sensor for measuring sweat rate (2010) Ieee Sensors J., 10 (10), pp. 1557-1558. , Oct; Strohrmann, C., Harms, H., Kappeler-Setz, C., Troster, G., Monitoring kinematic changes with fatigue in running using bodyworn sensors (2012) Ieee Trans. Inf. Technol. Biomed., 16 (5), pp. 983-990. , Sep; Strohrmann, C., Labruyère, R.E., Gerber, C.N., Van Hedel, H.J.A., Arnrich, B., Tröster, G., Monitoring motor capacity changes of children during rehabilitation using body-worn sensors (2013) J. Neuroeng. Rehabil., 10, p. 83. , Dec; Jin, H., Shibli Abu-Raya, Y., Haick, H., Advanced materials for health monitoring with skin-based wearable devices (2017) Adv. Healthcare Mater., 6 (11). , Mar; Kursun Bahadir, S., Koncar, V., Kalaoglu, F., Wearable obstacle detection system fully integrated to textile structures for visually impaired people (2012) Sens. Actuators A, Phys., 179, pp. 297-311. , Jun; Rincon, F.O., Tommasini, E., Rainoldi, M., Egger, R., The future of wearable devices on-site: A scenario technique approach (2017) Proc. Int. Conf. Inf. Commun. Technol. Tourism, pp. 285-299. , Rome, Italy, Springer, Jan; Krizhevsky, A., Sutskever, I., Hinton, G.E., ImageNet classification with deep convolutional neural networks (2012) Proc. Nips, 1, pp. 1097-1105; Graves, A., (2012) Supervised Sequence Labelling with Recurrent Neural Networks, , https://cds.cern.ch/record/1503877, (Studies in Computational Intelligence). Berlin, Germany: Springer, [Online]; Zhang, M., Sawchuk, A.A., Usc-had: A daily activity dataset for ubiquitous activity recognition using wearable sensors (2012) Proc. Acm Conf. Ubiquitous Comput., pp. 1036-1043. , Sep; Bahadir, S.K., Thomassey, S., Koncar, V., Kalaoglu, F., An algorithm based on neuro-fuzzy controller implemented in a smart clothing system for obstacle avoidance (2013) Int. J. Comput. Intell. Syst., 6, pp. 503-517. , Mar; Bahadir, S.K., Decision of sensor location and best classification method for entrail and muscle disease detection in healthcare smart clothing based on acceleration measurements (2015) Trans. Inst. Meas. Control, 37 (8), pp. 999-1008. , Sep; Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L., A public domain dataset for human activity recognition using smartphones (2013) Proc. Esann, pp. 1-6. , Apr; Reyes-Ortiz, J.-L., Oneto, L., Ghio, A., Samá, A., Anguita, D., Parra, X., Human activity recognition on smartphones with awareness of basic activities and postural transitions (2014) Artificial Neural Networks and Machine Learning-ICANN, 8681, pp. 177-184. , (Lecture Notes in Computer Science), S. Wermter et al., Eds. Cham, Switzerland: Springer; Zhu, J., San-Segundo, R., Pardo, J.M., Feature extraction for robust physical activity recognition (2017) Hum.-Centric Comput. Inf. Sci., 7 (1), p. 16. , Dec; Iosifidis, A., Tefas, A., Nikolaidis, N., Pitas, I., Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis (2012) Comput. Vis. Image Understand., 116 (3), pp. 347-360; Baek, J., Lee, G., Park, W., Yun, B.-J., Accelerometer signal processing for user activity detection (2004) Proc. Kes, pp. 610-617; Lara, O.D., Labrador, M.A., A survey on human activity recognition using wearable sensors (2013) Ieee Commun. Surveys Tuts., 15 (3), pp. 1192-1209. , Jul; Hayashi, T., Nishida, M., Kitaoka, N., Takeda, K., Daily activity recognition based on DNN using environmental sound and acceleration signals (2015) Proc. 23rd Eur. Signal Process. Conf. (EUSIPCO), pp. 2306-2310. , Aug; Mukhopadhyay, S.C., Wearable sensors for human activity monitoring: A review (2015) Ieee Sensors J., 15 (3), pp. 1321-1330. , Mar; Lu, W., Fan, F., Chu, J., Jing, P., Yuting, S., Wearable computing for Internet of Things: A discriminant approach for human activity recognition (2019) Ieee Internet Things J., 6 (2), pp. 2749-2759. , Apr; Wang, A., Chen, G., Yang, J., Zhao, S., Chang, C.-Y., A comparative study on human activity recognition using inertial sensors in a smartphone (2016) Ieee Sensors J., 16 (11), pp. 4566-4578. , Jun; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Comput., 9 (8), pp. 1735-1780. , Nov; Ronao, C.A., Cho, S.-B., Human activity recognition with smartphone sensors using deep learning neural networks (2016) Expert Syst. Appl., 59, pp. 235-244. , Oct; Jain, A., Kanhangad, V., Human activity classification in smartphones using accelerometer and gyroscope sensors (2018) Ieee Sensors J., 18 (3), pp. 1169-1177. , Feb; Burges, C.J.C., A tutorial on support vector machines for pattern recognition (1998) Data Mining Knowl. Discovery, 2 (2), pp. 121-167; Ha, S., Yun, J.-M., Choi, S., Multi-modal convolutional neural networks for activity recognition (2015) Proc. Ieee Int. Conf. Syst., Man, Cybern., pp. 3017-3022. , Oct; Wang, X., Gao, L., Song, J., Shen, H., Beyond frame-level CNN: Saliency-aware 3-D CNN with LSTM for video action recognition (2017) Ieee Signal Process. 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Kdd Workshop, pp. 359-370; Eyobu, O.S., Han, D.S., Feature representation and data augmentation for human activity classification based on wearable IMU sensor data using a deep LSTM neural network (2018) Sensors, 18 (9), p. 2892; Fullerton, E., Heller, B., Munoz-Organero, M., Recognizing human activity in free-living using multiple body-worn accelerometers (2017) Ieee Sensors J., 17 (16), pp. 5290-5297. , Aug","Bahadir, S.K.; Faculty of Mechanical Engineering, Turkey; email: kursuns@itu.edu.tr",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,1530437X,,,,"English","IEEE Sensors J.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85079739124
"Pereira A., Oertel C., Fermoselle L., Mendelson J., Gustafson J.","25031890200;50561763400;57215560678;57200084338;24480882100;","Effects of different interaction contexts when evaluating gaze models in HRI",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"131","138",,2,"10.1145/3319502.3374810","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082024451&doi=10.1145%2f3319502.3374810&partnerID=40&md5=ecf40a24f78011e701d777b52c2d19c1","KTH Royal Institute of Technology, Stockholm, Sweden; TU Delft, Delft, Netherlands; TNO, Den Haag, Netherlands; Furhat Robotics, Stockholm, Sweden","Pereira, A., KTH Royal Institute of Technology, Stockholm, Sweden; Oertel, C., TU Delft, Delft, Netherlands; Fermoselle, L., TNO, Den Haag, Netherlands; Mendelson, J., Furhat Robotics, Stockholm, Sweden; Gustafson, J., KTH Royal Institute of Technology, Stockholm, Sweden","We previously introduced a responsive joint attention system that uses multimodal information from users engaged in a spatial reasoning task with a robot and communicates joint attention via the robot's gaze behavior [25]. An initial evaluation of our system with adults showed it to improve users' perceptions of the robot's social presence. To investigate the repeatability of our prior findings across settings and populations, here we conducted two further studies employing the same gaze system with the same robot and task but in different contexts: Evaluation of the system with external observers and evaluation with children. The external observer study suggests that third-person perspectives over videos of gaze manipulations can be used either as a manipulation check before committing to costly real-time experiments or to further establish previous findings. However, the replication of our original adults study with children in school did not confirm the effectiveness of our gaze manipulation, suggesting that different interaction contexts can affect the generalizability of results in human-robot interaction gaze studies. © 2020 Association for Computing Machinery.","Joint attention; Mutual gaze; Social presence; Social robots","Man machine systems; External observer; Gaze manipulations; Interaction context; Joint attention; Multi-modal information; Mutual gazes; Real-time experiment; Social presence; Social robots",,,,,,,,,,,,,,,,"Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: A review (2017) Journal of Human-Robot Interaction, 6 (1), pp. 25-63. , 2017; Admoni, H., Bradley Hayes, T., Scassellati, B., Robot nonverbal behavior improves task performance in difficult collaborations (2016) The Eleventh ACM/IEEE International Conference on Human Robot Interaction, pp. 51-58. , IEEE Press; Al Moubayed, S., Beskow, J., Skantze, G., Granström, B., Furhat: A back-projected human-like robot head for multiparty human-machine interaction (2012) Cognitive Behavioural Systems, pp. 114-130. , Springer; Belpaeme, T., Baxter, P., De Greeff, J., Kennedy, J., Read, R., Looije, R., Neerincx, M., Coti Zelati, M., Child-robot interaction: Perspectives and challenges (2013) International Conference on Social Robotics, pp. 452-459. , Springer; Biocca, F., Harms, C., Gregg, J., The networked minds measure of social presence: Pilot test of the factor structure and concurrent validity (2001) 4th Annual International Workshop on Presence, pp. 1-9. , Philadelphia, PA; Bohus, D., Horvitz, E., Facilitating multiparty dialog with gaze, gesture, and speech (2010) International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction, 5. , ACM; Science Collaboration, O., Estimating the reproducibility of psychological science (2015) Science, 349 (6251), p. 4716. , 2015; Dautenhahn, K., Methodology & themes of human-robot interaction: A growing research field (2007) International Journal of Advanced Robotic Systems, 4 (1), p. 15. , 2007; Delaunay, F., De Greeff, J., Belpaeme, T., A study of a retro-projected robotic face and its effectiveness for gaze reading by humans (2010) Proceedings of the 5th ACM/IEEE International Conference on Human-robot Interaction, pp. 39-44. , IEEE Press; Feinberg, J.M., Aiello, J.R., The effect of challenge and threat appraisals under evaluative presence (2010) Journal of Applied Social Psychology, 40 (8), pp. 2071-2104. , 2010; Harms, C., Biocca, F., (2004) Internal Consistency and Reliability of the Networked Minds Measure of Social Presence, , 2004; Heerink, M., Kröse, B., Evers, V., Wielinga, B., (2008) The Influence of Social Presence on Acceptance of a Companion Robot by Older People, , 2008; Hoffmann, L., Krämer, N.C., Investigating the effects of physical and virtual embodiment in task-oriented and conversational contexts (2013) International Journal of Human-Computer Studies, 71 (7-8), pp. 763-774. , 2013; Huang, C., Lockerd Thomaz, A., Joint attention in human-robot interaction (2010) AAAI Fall Symposium: Dialog with Robots; Irfan, B., Kennedy, J., Lemaignan, S., Papadopoulos, F., Senft, E., Belpaeme, T., Social psychology and human-robot interaction: An uneasy marriage (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 13-20. , ACM; 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Strait, M.K., Floerke, V.A., Ju, W., Maddox, K., Remedios, J.D., Jung, M.F., Urry, H.L., Understanding the uncanny: Both atypical features and category ambiguity provoke aversion toward humanlike robots (2017) Frontiers in Psychology, 8, p. 1366. , 2017; Tu, C., McIsaac, M., The relationship of social presence and interaction in online classes (2002) The American Journal of Distance Education, 16 (3), pp. 131-150. , 2002; Vogt, P., Van Den Berghe, R., De Haas, M., Hoffman, L., Kanero, J., Mamus, E., Montanier, J., Hernández Garciá, D., Second language tutoring using social robots: A large-scale study (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 497-505. , Ieee; Wang, X., Krumhuber, E.G., Mind perception of robots varies with their economic versus social function (2018) Frontiers in Psychology, 9, p. 1230. , 2018; Patrick Ehrenbrink, I., Schleicher, R., Möller, S., Investigating the social facilitation effect in human-robot interaction (2014) Natural Interaction with Robots, Knowbots and Smartphones, pp. 167-177. , Springer; 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Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85082024451
"Dobrosovestnova A., Hannibal G.","57199645448;56512012200;","Teachers' disappointment: Theoretical perspective on the inclusion of ambivalent emotions in human-robot interactions in education",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"471","480",,,"10.1145/3319502.3374816","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082021189&doi=10.1145%2f3319502.3374816&partnerID=40&md5=de151492edf988bc9f14487cf171c721","Austrian Institute for Artificial Intelligence (OFAI), Vienna, Austria; Human Computer Interaction, Institute of Visual Computing and Human-Centered Technology, TU Wien, Vienna, Austria","Dobrosovestnova, A., Austrian Institute for Artificial Intelligence (OFAI), Vienna, Austria; Hannibal, G., Human Computer Interaction, Institute of Visual Computing and Human-Centered Technology, TU Wien, Vienna, Austria","Following affective turn in cognitive science, recent decades have witnessed an increasing interest toward the role of emotions in education. Ample evidence suggests that learners and teachers experience a variety of emotions, ranging from joy and pride to anger and frustration. However, when it comes to the design of affective behavior in robotic systems for education purposes, the emphasis has been predominantly on communication of positive emotions. While we recognize that positive emotions are fundamental to successful learning, in this paper we wish to make the case for the consideration of ambivalent emotions for the design of social robots for tutoring. To ground this proposal, we focus on the emotion of teachers' disappointment. First, we discuss under which conditions communicated teachers' disappointment, while it may be experienced as emotionally ambivalent by teachers and students, functions as an affiliating pedagogical strategy. We proceed to sketch out the methodological suggestions we consider relevant for future studies of communicated disappointment in human-robot interactions within learning contexts. We conclude with critical reflections about the ethics of responsible designs of such studies. © 2020 Association for Computing Machinery.","Complex emotions; Disappointment; Human-robot interaction; Learning; Robotic tutors; Socio-cognitive paradigm; Teaching","Human robot interaction; Man machine systems; Robotics; Social robots; Teaching; Affective behaviors; Complex emotions; Critical reflections; Disappointment; Learning; Pedagogical strategies; Positive emotions; Socio-cognitive; Machine design",,,,,,,,,,,,,,,,"Alves-Oliveira, P., Sequeira, P., Paiva, A., The role that an educational robot plays (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 817-822. , IEEE; Arroyo, I., Beal, C., Murray, T., Walles, R., Woolf, B.P., Web-based intelligent multimedia tutoring for high stakes achievement tests (2004) International Conference on Intelligent Tutoring Systems, pp. 468-477. , Springer; James, R., Averill, A constructivist view of emotion (1980) Theories of Emotion, pp. 305-339. , Elsevier; Bandura, A., (1986) Social Foundations of Thought and Action, , Englewood Cliffs, NJ 19861986); Susann Becker, E., Goetz, T., Morger, V., Ranellucci, J., The importance of teachers' emotions and instructional behavior for their students' emotions-an experience sampling analysis (2014) Teaching and Teacher Education, 43, pp. 15-26. , 2014; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Science Robotics, 3 (21), p. 5954. , 2018; Beran, T.N., Ramirez-Serrano, A., Kuzyk, R., Fior, M., Nugent, S., Understanding how children understand robots: Perceived animism in child-robot interaction (2011) International Journal of Human-Computer Studies, 69 (7-8), pp. 539-550. , 2011; Brogaard Bertel, L., Hannibal, G., Tema 2: The nao robot as a persuasive educational and entertainment robot (peer)-a case study on children's articulation, categorization and interaction with a social robot for learning (2016) Tidsskriftet Læring Og Medier (LOM), 8 (14). , 2016; Brogaard Bertel, L., Malig Rasmussen, D., Christiansen, E., Robots for real: Developing a participatory design framework for implementing educational robots in real-world learning environments (2013) IFIP Conference on Human-Computer Interaction, pp. 437-444. , Springer; Björling, E.A., Rose, E., Participatory research principles in humancentered design: Engaging teens in the co-design of a social robot (2019) Multimodal Technologies and Interaction, 3 (1), p. 8. , 2019; Breazeal, C., (2002) Designing Sociable Robots, , MIT Press, Cambridge, MA, USA; Breazeal, C., Toward sociable robots (2003) Robotics and Autonomous Systems, 42 (3), pp. 167-175. , http://doi.org/10.1016/S0921-8890(02)00373-1, 2003; Brown, B., (2015) Daring Greatly: How the Courage to Be Vulnerable Transforms the Way We Live, Love, Parent, and Lead, , Penguin; Butler, J., (2002) Gender Trouble, , routledge; Chase, C.C., Chin, D.B., Oppezzo, M.A., Schwartz, D.L., Teachable agents and the protégé effect: Increasing the effort towards learning (2009) Journal of Science Education and Technology, 18 (4), pp. 334-352; Cho, E., Lee, K., Cherniak, S., Eun Jung, S., Heterogeneous associations of second-graders' learning in robotics class (2017) Technology, Knowledge and Learning, 22 (3), pp. 465-483. , 2017; Coeckelbergh, M., Are emotional robots deceptive (2011) IEEE Transactions on Affective Computing, 3 (4), pp. 388-393. , 2011; Coeckelbergh, M., (2013) Human Being@ Risk: Enhancement, Technology, and the Evaluation of Vulnerability Transformations., 12. , Springer Science & Business Media; Coeckelbergh, M., Care robots and the future of ict-mediated elderly care: A response to doom scenarios (2016) AI & Society, 31 (4), pp. 455-462. , 2016; Correia, F., Alves-Oliveira, P., Maia, N., Ribeiro, T., Petisca, S., Melo, F.S., Paiva, A., Just follow the suit! 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Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85082021189
"Zanatto D., Patacchiola M., Goslin J., Thill S., Cangelosi A.","55463260500;56537094300;55915556900;36624312800;6701387796;","Do humans imitate robots? An investigation of strategic social learning in human-robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"449","457",,5,"10.1145/3319502.3374776","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082011166&doi=10.1145%2f3319502.3374776&partnerID=40&md5=a0ab5afdadcc7aa152619a46fd591147","School of Computer Science, University of Plymouth, Plymouth, United Kingdom; School of Psychology, University of Plymouth, Plymouth, United Kingdom; Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands; Department of Computer Science, University of Manchester, Manchester, United Kingdom; Department of Computer Science, University of Bristol, Bristol, United Kingdom; School of Informatics, University of Edinburgh, Edinburgh, The chameleon effect: The perception-behavior link and social interaction, United Kingdom","Zanatto, D., School of Computer Science, University of Plymouth, Plymouth, United Kingdom, Department of Computer Science, University of Bristol, Bristol, United Kingdom; Patacchiola, M., School of Computer Science, University of Plymouth, Plymouth, United Kingdom, School of Informatics, University of Edinburgh, Edinburgh, The chameleon effect: The perception-behavior link and social interaction, United Kingdom; Goslin, J., School of Psychology, University of Plymouth, Plymouth, United Kingdom; Thill, S., Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands; Cangelosi, A., Department of Computer Science, University of Manchester, Manchester, United Kingdom","Theories on social learning indicate that imitative choices are usually performed whenever copying the others' behaviour has no additional cost. Here, we extended such investigations of social learning to Human-Robot Interaction (HRI). Participants played the Economic Investment Game with a robot banker while observing another robot player also investing in the robot banker. By manipulating the robot banker payoff, three conditions of unfairness were created: (1) unfair payoff for the participants, (2) unfair payoff for the robot player and (3) unfair payoff for both. Results showed that when the payoff was low for the participants and high for the robot player, participants invested more money in the robot banker than when both parties received a low return. Also, for this specific condition, participants' investments increased further with a more interactive robot player (defined as demonstrating increased attention, congruent movements and speech) This suggests that social and cognitive human competencies can be used and transposed to non-human agents. Further, imitation can potentially be extended to HRI, with interactivity likely having a key role in increasing this effect. © 2020 Association for Computing Machinery.","Human-Robot Interaction; Imitation; Investment Game; Social Learning; Strategy","Human robot interaction; Investments; Man machine systems; Additional costs; Human competencies; Human robot Interaction (HRI); Imitation; Interactive robot; Interactivity; Social learning; Strategy; Social robots",,,,,"Air Force Office of Scientific Research, AFOSR; Air Force Materiel Command, AFMC; U.S. Air Force, USAF: FA9550-19-1-7002","This material is based upon work supported by the Air Force Office of Scientific Research, Air Force Materiel Command, USAF under Award No, FA9550-19-1-7002 (THRIVE++).",,,,,,,,,,"Ali, S., Moroso, T., Breazeal, C., Can children learn creativity from a social robot (2019) Proceedings of the 2019 on Creativity and Cognition, pp. 359-368. , ACM; Bandura, A., (1962) Social Learning Through Imitation, , Univer. 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Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85082011166
"Paetzel M., Perugia G., Castellano G.","57191708013;57193757433;23007528900;","The persistence of first impressions: The effect of repeated interactions on the perception of a social robot",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"73","82",,18,"10.1145/3319502.3374786","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082007347&doi=10.1145%2f3319502.3374786&partnerID=40&md5=30835d316b895d6270560f05ad17cce0","Social Robotics Lab, Uppsala University, Sweden","Paetzel, M., Social Robotics Lab, Uppsala University, Sweden; Perugia, G., Social Robotics Lab, Uppsala University, Sweden; Castellano, G., Social Robotics Lab, Uppsala University, Sweden","Numerous studies in social psychology have shown that familiarization across repeated interactions improves people's perception of the other. If and how these findings relate to human-robot interaction (HRI) is not well understood, even though such knowledge is crucial when pursuing long-term interactions. In our work, we investigate the persistence of first impressions by asking 49 participants to play a geography game with a robot.We measure how their perception of the robot changes over three sessions with three to ten days of zero exposure in between. Our results show that different perceptual dimensions stabilize within different time frames, with the robot's competence being the fastest to stabilize and perceived threat the most fluctuating over time. We also found evidence that perceptual differences between robots with varying levels of humanlikeness persist across repeated interactions. This study has important implications for HRI design as it sheds new light on the influence of robots' embodiment and interaction abilities. Moreover, it also impacts HRI theory as it presents novel findings contributing to research on the uncanny valley and robot perception in general. © 2020 Association for Computing Machinery.","Embodiment; First Impressions; Robot Perception; Uncanny Valley","Machine design; Man machine systems; Embodiment; First impressions; Human robot Interaction (HRI); Long-term interaction; Perceptual difference; Perceptual dimensions; Robot perception; Uncanny valley; Social robots",,,,,"Stiftelsen för Strategisk Forskning, SSF","This work was supported by the COIN project (RIT15-0133) funded by the Swedish Foundation for Strategic Research. Thanks to S. Walkötter for Figure 2, R. Kessler for assisting with the technical setup and R. Manuvinakurike for his work on the game dynamics.",,,,,,,,,,"Imtiaz Ahmad, M., Mubin, O., Orlando, J., Adaptive social robot for sustaining social engagement during long-term children-robot interaction (2017) International Journal of Human-Computer Interaction, 33 (12), pp. 943-962. , (2018); Imtiaz Ahmad, M., Mubin, O., Shahid, S., Orlando, J., Robot's adaptive emotional feedback sustains children's social engagement and promotes their vocabulary learning: A long-term child-robot interaction study (2019) Adaptive Behavior, pp. 243-266. , (2019); Al Moubayed, S., Beskow, J., Skantze, G., Granström, B., Furhat: A back-projected human-like robot head for multiparty human-machine interaction (2012) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7403 LNCS, pp. 114-130. , (2012); Albright, L., Kenny, D.A., Malloy, T.E., Consensus in personality judgments at zero acquaintance (1988) Journal of Personality and Social Psychology, 55 (3), pp. 387-395. , 1988; Ambady, N., Bernieri, F.J., Richeson, J.A., Toward a histology of social behavior: Judgmental accuracy from thin slices of the behavioral stream (2000) Advances in Experimental Social Psychology, 32, pp. 201-271. , Elsevier; Bar, M., Neta, M., Linz, H., (2006) Very First Impressions. 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Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85082007347
"Natarajan M., Gombolay M.","57215830081;55806238000;","Effects of anthropomorphism and accountability on trust in human robot interaction",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"33","42",,21,"10.1145/3319502.3374839","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082003650&doi=10.1145%2f3319502.3374839&partnerID=40&md5=ea79419a97d8ba6db2f7852c216535f4","Georgia Institute of Technology, Atlanta, GA, United States","Natarajan, M., Georgia Institute of Technology, Atlanta, GA, United States; Gombolay, M., Georgia Institute of Technology, Atlanta, GA, United States","This paper examines how people's trust and dependence on robot teammates providing decision support varies as a function of different attributes of the robot, such as perceived anthropomorphism, type of support provided by the robot, and its physical presence.We conduct a mixed-design user study with multiple robots to investigate trust, inappropriate reliance, and compliance measures in the context of a time-constrained game.We also examine how the effect of human accountability addresses errors due to over-compliance in the context of human robot interaction (HRI). This study is novel as it involves examining multiple attributes at once, thus enabling us to perform multi-way comparisons between different attributes on trust and compliance with the agent. Results from the 4x4x2x2 study show that behavior and anthropomorphism of the agent are the most significant factors in predicting the trust and compliance with the robot. Furthermore, adding a coalition-building preface, where the agent provides context to why it might make errors while giving advice, leads to an increase in trust for specific behaviors of the agent. © 2020 Association for Computing Machinery.","Accountability; Coalition-building; Compliance; HRI; Inappropriate Reliance; Trust","Decision support systems; Machine design; Man machine systems; Accountability; Compliance; Decision supports; Human robot Interaction (HRI); Inappropriate Reliance; Multiple attributes; Multiple robot; Trust; Human robot interaction",,,,,"Georgia Institute of Technology, Georgia Tech","We would like to thank Woradorn Kamolopornwijit for his support in designing and developing the game leveraged for this user study. 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Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85082003650
"Farokh Atashzar S., Tavakoli M., Farina D., Patel R.V.","57222138990;8668966400;7004908025;7403736805;","Editorial: Autonomy and intelligence in neurorehabilitation robotic and prosthetic technologies",2020,"Journal of Medical Robotics Research","5","1-2","2002001","","",,,"10.1142/S2424905X20020018","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120539672&doi=10.1142%2fS2424905X20020018&partnerID=40&md5=1ce11f3d754df3e52280075d95c65651","Dept. of Electrical and Computer Engineering and Department of Mechanical and Aerospace Engineering, New York University, United States; Department of Electrical and Computer Engineering, University of Alberta, Canada; Department of Bioengineering, Imperial College London, United Kingdom; Department of Electrical and Computer Engineering, University of Western Ontario, Canada","Farokh Atashzar, S., Dept. of Electrical and Computer Engineering and Department of Mechanical and Aerospace Engineering, New York University, United States; Tavakoli, M., Department of Electrical and Computer Engineering, University of Alberta, Canada; Farina, D., Department of Bioengineering, Imperial College London, United Kingdom; Patel, R.V., Department of Electrical and Computer Engineering, University of Western Ontario, Canada","Neurorehabilitation robotic technologies and powered assistive prosthetic devices have shown great potential for accelerating motor recovery or compensating for the lost motor functions of disabled users. The functioning of these technologies relies on a highly-interactive bidirectional flow of information and physical energy between a human user and a robotic system. Thus, key factors are integrity, intelligence and quality of the interaction loops. As a result, research in this field has focused on (a) enhancing the quality and safety of the physical interaction between disabled users and robotic systems while providing a high level of intelligence and adaptability for generating assistive and therapeutic force fields; (b) detecting the user's motor intention with high spatiotemporal resolution to provide bidirectional human-machine interfacing; (c) promoting mental engagement through designing multimodal interactive interfaces and various sensory manipulation strategies. This Special Issue has collected papers that contribute to these three research areas, highlighting the importance of different aspects in human-robot interaction loops for augmenting the performance of neurorehabilitation robotic systems and prosthetic devices. © 2020 World Scientific Publishing Company.",,"algorithm; artificial intelligence; biomechanics; human; motor control; motor learning; motor performance; neurorehabilitation; Review; robotics; sensory feedback",,,,,,,,,,,,,,,,"Rahimian, E., Zabihi, S., Atashzar, S. F., Asif, A., Mohammadi, A., Surface EMG-based hand gesture recognition via hybrid and dilated deep neural network architectures for neurorobotic prostheses (2020) J. Med. Robot. Res, , https://doi.org/10.1142/S2424905X20410019; Dalrymple, A. N., Mushahwar, V. K., Intelligent control of a spinal prosthesis to restore walking after neural injury: Recent work and future possibilities (2020) J. Med. Robot. Res, , https://doi.org/10.1142/S2424905X20410032; Piwowarczyk, J., Carriere, J., Adams, K., Tavakoli, M., An admittancecontrolled force-scaling dexterous assistive robotic system (2020) J. Med. Robot. Res, , https://doi.org/10.1142/S2424905X20410020; Manjunatha, H., Pareek, S., Memar, A. H., Kesavadas, T., Esfahani, E. T., Effect of haptic assistance strategy on mental engagement in fine motor tasks (2020) J. Med. Robot. Res, , https://doi.org/10.1142/S2424905X20410044",,,,"World Scientific",,,,,24249068,,,,"English","J. Med. Robotics Res.",Review,"Final","",Scopus,2-s2.0-85120539672
"Sherwani F., Asad M.M., Ibrahim B.S.K.K.","56618807100;57203360700;36165092800;","Collaborative Robots and Industrial Revolution 4.0 (IR 4.0)",2020,"2020 International Conference on Emerging Trends in Smart Technologies, ICETST 2020",,,"9080724","","",,7,"10.1109/ICETST49965.2020.9080724","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084948565&doi=10.1109%2fICETST49965.2020.9080724&partnerID=40&md5=a2bec967b07184dcbb557fc1fa4ebc65","NationalUniversity of Computer and Emerging Sciences, Department of Computer Science, Karachi, Pakistan; Sukkur IBA University, Department of Education, Sukkur, Pakistan; Faculty of Engineering Environment and Computing, Coventry University, Coventry, United Kingdom","Sherwani, F., NationalUniversity of Computer and Emerging Sciences, Department of Computer Science, Karachi, Pakistan; Asad, M.M., Sukkur IBA University, Department of Education, Sukkur, Pakistan; Ibrahim, B.S.K.K., Faculty of Engineering Environment and Computing, Coventry University, Coventry, United Kingdom","Collaborative robots have lately been extremely relevant to the domain of production and manufacturing industry after the arrival of Fourth Industrial Revolution or Industry 4.0 (IR 4.0). Collaborative robots have evolved as one of the key drivers in Industry 4.0 and they have advanced substantially within the last couple of decades. In comparison to the industrial robots, collaborative robots offer increased productivity, flexibility, versatility and safety. Collaborative robots are designed to execute tasks alongside the human workforce while sharing the same working space as colleagues, offering greater mobility and flexibility. Collaborative robots allow for a physical interaction with humans in a shared workspace to execute production, manufacturing and assembly tasks. These machines are designed to be reprogrammed easily, even by personnel without any programming background. Human-Robot Interaction (HRI) between humans and collaborative robots provides promising methods to achieve increased productivity with reduced production costs by combining the decision-making ability of humans along with the repeatability and strength of robots. This article reviews the significance of the collaborative robots today and also presents an insight into their future potential. © 2020 IEEE.","Artificial Intelligence; Collaborative Robots; Human-Robot Interaction; Industrial Revolution 4.0","Accident prevention; Decision making; Industrial robots; Industry 4.0; Personnel; Productivity; Collaborative robots; Human robot Interaction (HRI); Increased productivity; Industrial revolutions; Manufacturing industries; Physical interactions; Reduced production; Shared-workspace; Human robot interaction",,,,,,,,,,,,,,,,"Ostergaard, E.H., (2017) The Role of Cobots in Industry 4.0, , Universal Robots, White paper; https://www.mirai-lab.co.jp, Retrieved from url; Karabegovic, I., Comparative analysis of automation of production process with industrial robots in asia/australia and Europe (2017) International Journal of Human Capital in Urban Management, 2 (1), pp. 29-38; Lu, Y., Industry 4.0: A survey on technologies, applications and open research issues (2017) J. Of Industrial Information Integration, 6, pp. 1-10; Hagele, M., Nilsson, K., Pires, J., Bischoff, R., (2016) Industrial Robotics in Handbook of Robotics (2nd Ed.), pp. 1385-1421. , B. Siciliano and O. Khatib, Eds Springer; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human-robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266; Landi, C.T., Ferraguti, F., Secchi, C., Fantuzzi, C., Tool compensation in walk-through programming for admittance-controlled robots (2016) Proceedings of the 42nd Annual Conference of the IEEE Industrial Electronics Society, , Firenze, Italy; Ferraguti, F., Landi, C.T., Secchi, C., Fantuzzi, C., Nolli, M., Pesamosca, M., Walk-through programming for industrial applications (2017) Procedia Manufacturing, 11, pp. 31-38; Platbrood, F., Gornemann, O., (2017) Safe Robotics-Safety in Collaborative Robot Systems, Sick Ag, , Waldkirch, Germany; Mihelj, M., Collaborative robots (2019) Robotics, , Springer, Cham; Matheson, E., Minto, R., Zampieri, E.G., Faccio, M., Rosati, G., Human-robot collaboration in manufacturing applications: A review (2019) Robotics, 8 (4), p. 100; Siciliano, B., Khatib, O., (2016) Springer Handbook of Robotics, , 2nd ed. Springer; Tsarouchi, P., Makris, S., Chryssolouris, G., Human-robot interaction review and challenges on task planning and programming (2016) Int J Comput Integr Manufact, 29 (8), pp. 916-931; Heyer, C., Human-robot interaction and future industrial robotics applications (2010) Proceedings of the Ieee/Rsj International Conference Intelligent Robots and Systems (Iros), pp. 4749-4754. , ieee; Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P., Collaborative manufacturing with physical human-robot interaction (2016) Robot Comput Integr Manuf, 40, pp. 1-13; Ding, H., Schipper, M., Matthias, B., Optimized task distribution for industrial assembly in mixed human-robot environments-case study on io module assembly (2014) Proceedings of the IEEE International Conference on Automation Science and Engineering (CASE), pp. 19-24; Pellegrinelli, S., Moro, F.L., Pedrocchi, N., Molinari Tosatti, L., Tolio, T., A probabilistic approach to workspace sharing for human-robot cooperation in assembly tasks (2016) CIRP Ann Manuf Technol, 65 (1), pp. 57-60; Pellegrinelli, S., Orlandini, A., Pedrocchi, N., Umbirco, A., Tolio, T., Motion planning and scheduling for human and industrial-robot collaboration (2017) CIRP Ann Manuf Technol, 66 (1), pp. 1-4; Keller, S., Hausmann, R., Kressner, L., Koenig, A., An approach of a computerized planning assistant to the system design of collaborative robot installations (2016) Proceedings of the IEEE Twenty-First International Conference Emerging Technologies and Factory Automation (ETFA, pp. 1-4; Pini, F., Leali, F., Ansaloni, M., A systematic approach to the engineering design of a hrc workcell for bio-medical product assembly (2015) Proceedings of the IEEE Twentyfirth International Conference Emerging Technologies and Factory Automation (ETFA; Guerin, K.R., Lea, C., Paxton, C., Hager, G.D., A framework for end-user instruction of a robot assistant for manufacturing Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 6167-6174. , Seattle, WA, USA, 26-30 May 2015; Global Collaborative Robot (Cobot) Market: Focus on Payload Application and Industry-Analysis and Forecast 2019-2024, , https://www.researchandmarkets.com/reports/4833504/global-collaborative-robot-cobot-market-focus, Retrieved from; Application and Geography-Global Forecast to 2025, , https://www.marketsandmarkets.com/Market-Reports/collaborative-robot-market-194541294.html, Collaborative Robot Market by Payload",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 International Conference on Emerging Trends in Smart Technologies, ICETST 2020","26 March 2020 through 27 March 2020",,159553,,9781728171135,,,"English","Int. Conf. Emerg. Trends Smart Technol., ICETST",Conference Paper,"Final","",Scopus,2-s2.0-85084948565
"Dzedzickis A., Kaklauskas A., Bucinskas V.","57189035357;55926913900;55954183100;","Human emotion recognition: Review of sensors and methods",2020,"Sensors (Switzerland)","20","3","592","","",,67,"10.3390/s20030592","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078264416&doi=10.3390%2fs20030592&partnerID=40&md5=5a082be34352464ca0ec5ffbbe9172c8","Faculty of Mechanics, Vilnius Gediminas Technical University, J. Basanaviciaus g. 28, Vilnius, LT-03224, Lithuania; Faculty of Civil engineering, Vilnius Gediminas Technical University, Sauletekio ave. 11, Vilnius, LT-10223, Lithuania","Dzedzickis, A., Faculty of Mechanics, Vilnius Gediminas Technical University, J. Basanaviciaus g. 28, Vilnius, LT-03224, Lithuania; Kaklauskas, A., Faculty of Civil engineering, Vilnius Gediminas Technical University, Sauletekio ave. 11, Vilnius, LT-10223, Lithuania; Bucinskas, V., Faculty of Mechanics, Vilnius Gediminas Technical University, J. Basanaviciaus g. 28, Vilnius, LT-03224, Lithuania","Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human– robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Emotion perception; Human emotions; Physiologic sensors","Human robot interaction; Internet of things; Man machine systems; Speech recognition; Affective Computing; Classification of emotions; Evaluation and analysis; Human emotion; Human emotion recognition; Internet of thing (IOT); Scientific researches; Technical equipments; Behavioral research; electrode; emotion; genetic procedures; human; perception; physiology; procedures; Biosensing Techniques; Electrodes; Emotions; Humans; Perception",,,,,"Lietuvos Mokslo Taryba; European Regional Development Fund, ERDF: 01.2.2-LMT-K-718-01-0073","Funding: This project has received funding from European Regional Development Fund (project No 01.2.2-LMT-K-718-01-0073) under grant agreement with the Research Council of Lithuania (LMTLT).",,,,,,,,,,"Rattanyu, K., Ohkura, M., Mizukawa, M., (2010) Emotion Monitoring from Physiological Signals for Service Robots in the Living Space, pp. 580-583; Byron, K., Terranova, S., Nowicki, S., Nonverbal Emotion Recognition and Salespersons: Linking Ability to Perceived and Actual Success (2007) J. Appl. Soc. Psychol., 37, pp. 2600-2619; Feidakis, M., Daradoumis, T., Caballe, S., Emotion Measurement in Intelligent Tutoring Systems: What, When and How to Measure (2011) Proceedings of the 2011 Third International Conference on Intelligent Networking and Collaborative Systems, pp. 807-812. , IEEE, Fukuoka, Japan, 30 November–2 December; Mandryk, R.L., Atkins, M.S., Inkpen, K.M., A continuous and objective evaluation of emotional experience with interactive play environments (2006) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’06), 2006, p. 1027. , Montréal, QC, CanadaApril, ACM Press: New York, NY, USA; Sosnowski, S., Bittermann, A., Kuhnlenz, K., Buss, M., Design and Evaluation of Emotion-Display EDDIE (2006) Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3113-3118. , Beijing, China, 9–15 October; Ogata, T., Sugano, S., Emotional communication between humans and the autonomous robot which has the emotion model (1999) Proceedings of the 1999 IEEE International Conference on Robotics and Automation (Cat. No.99Ch36288c), 4, pp. 3177-3182. , Detroit, MI, USA, 10–15 May; Malfaz, M., Salichs, M.A., A new architecture for autonomous robots based on emotions (2004) IFAC, 37, pp. 805-809; Delkhoon, M.A., Lotfizadeh, F., An Investigation on the Effect of Gender on Emotional Responses and Purchasing Intention Due to Advertisements (2014) UCT J. Soc. Sci. Humanit. Res., 2, pp. 6-11; Singh, J., Goyal, G., Gill, R., Use of neurometrics to choose optimal advertisement method for omnichannel business (2019) Enterp. Inf. Syst., pp. 1-23; Chung, W.J., Patwa, P., Markov, M.M., (2012) Targeting Advertisements Based on Emotion, , U.S. Patent Application No 12/958,775, 7 June; D’Mello, S.K., Craig, S.D., Gholson, B., Franklin, S., Picard, R.W., Graesser, A.C., Integrating Affect Sensors in an Intelligent Tutoring System (2005) Proceedings of the 2005 International Conference on Intelligent User Interfaces, pp. 10-13. , San Diego, CA, USA, January; Woolf, B.P., Arroyo, I., Cooper, D., Burleson, W., Muldner, K., (2010) Affective Tutors: Automatic Detection of and Response to Student Emotion, pp. 207-227. , Springer: Berlin/Heidelberg, Germany; Scotti, S., Mauri, M., Barbieri, R., Jawad, B., Cerutti, S., Mainardi, L., Brown, E.N., Villamira, M.A., Automatic Quantitative Evaluation of Emotions in E-learning Applications (2006) Proceedings of the 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1359-1362. , New York, NY, USA, 30 August–3 Septemebr; Kolakowska, A., Landowska, A., Szwoch, M., Szwoch, W., Wrobel, M.R., Emotion recognition and its application in software engineering Proceedings of the 2013 6Th International Conference on Human System Interactions (HSI), pp. 532-539. , Gdansk, Sopot, Poland; Guo, F., Liu, W.L., Cao, Y., Liu, F.T., Li, M.L., Optimization Design of a Webpage Based on Kansei Engineering (2016) Hum. 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Brain Sci., 5, pp. 431-432; Feidakis, M., Daradoumis, T., Caballe, S., Endowing e-Learning Systems with Emotion Awareness (2011) Proceedings of the 2011 Third International Conference on Intelligent Networking and Collaborative Systems, pp. 68-75. , Fukuoka, Japan, 30 November–2 December; (1966) Université De Montréal; Presses De l’Université De Montréal, 52. , Interaction of Emotion and Cognition in the Processing of Textual Materia; Presses de l’Université de Montréal: Québec, QC, Canada; Russell, J.A., A circumplex model of affect (1980) J. Pers. Soc. 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"Mohammed S.N., Hassan A.K.A.","57213186106;55293743800;","A Survey on Emotion Recognition for Human Robot Interaction",2020,"Journal of Computing and Information Technology","28","2",,"125","146",,1,"10.20532/cit.2020.1004841","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108645378&doi=10.20532%2fcit.2020.1004841&partnerID=40&md5=5c0186ba32c6757a708d86c0ae14849a","Department of Computer Science, University of Technology, Baghdad, Iraq; Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq","Mohammed, S.N., Department of Computer Science, University of Technology, Baghdad, Iraq, Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq; Hassan, A.K.A., Department of Computer Science, University of Technology, Baghdad, Iraq","With the recent developments of technology and the advances in artificial intelligent and machine learning techniques, it becomes possible for the robot to acquire and show the emotions as a part of Human-Robot Interaction (HRI). An emotional robot can recognize the emotional states of humans so that it will be able to interact more naturally with its human counterpart in different environments. In this article, a survey on emotion recognition for HRI systems has been presented. The survey aims to achieve two objectives. Firstly, it aims to discuss the main challenges that face researchers when building emotional HRI systems. Secondly, it seeks to identify sensing channels that can be used to detect emotions and provides a literature review about recent researches published within each channel, along with the used methodologies and achieved results. Finally, some of the existing emotion recognition issues and recommendations for future works have been outlined. ACM CCS (2012) Classification: Human-centered computing → Human computer interaction (HCI) → HCI theory, concepts and models Human-centered computing → Human computer interaction (HCI) → Interaction techniques © 2020. All Rights Reserved.","affective computing; emotional HRI challenges; social robotics","Computation theory; Human computer interaction; Learning systems; Speech recognition; Surveys; Artificial intelligent; Emotion recognition; Human computer interaction (HCI); Human robot Interaction (HRI); Human-centered computing; Interaction techniques; Literature reviews; Machine learning techniques; Social robots",,,,,,,,,,,,,,,,"Ahmad, M. 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A., Visuo-Auditory Multimodal Emotional Structure to Improve Human-Robot- Interaction (2012) International Journal of Social Robotics, 4 (1), pp. 29-51. , http://dx.doi.org/10.1007/s12369-011-0134-7, [88]; Fadil, C., Multimodal Emotion Recognition Using Deep Networks (2014) VI Latin American Congress on Biomedical Engineering CLAIB, pp. 813-816. , http://dx.doi.org/10.1007/978-3-319-13117-7_207, [89] 2014; Tzirakis, P., End-to-End Multimodal Emotion Recognition Using Deep Neural Networks (2017) IEEE Journal of Selected Topics in Signal Processing, 11 (8), pp. 1301-1309. , http://dx.doi.org/10.1109/JSTSP.2017.2764438, [90]; Perez, A. K., Identification of Multimodal Signals for Emotion Recognition in the Context of Human-Robot Interaction (2018) International Symposium on Intelligent Computing Systems, pp. 67-80. , http://dx.doi.org/10.1007/978-3-319-76261-6_6, [91]; Lee, C., Convolutional Attention Networks for Multimodal Emotion Recognition from Speech and Text Data (2018) Challenge-HML, pp. 28-34. , [92]; Yoon, S., Multimodal Speech Emotion Recognition Using Audio and Text (2018) 2018 IEEE Spoken Language Technology Workshop (SLT), pp. 112-118. , http://dx.doi.org/10.1109/SLT.2018.8639583, [93]; Liu, Z. T., A Multimodal Emotional Communication Based Humans-Robots Interaction System (2016) Proc. of the 35th Chinese Control Conference, pp. 6363-6368. , http://dx.doi.org/10.1109/ChiCC.2016.7554357, [94] in","Mohammed, S.N.; Department of Computer Science, Iraq; email: suhailan.mo@sc.uobaghdad.edu.iq
Hassan, A.K.A.; Department of Computer Science, Iraq; email: 110018@uotechnology.edu.iq",,,"University of Zagreb, Faculty of Political Sciences",,,,,13301136,,,,"English","J. Compt. Inf. Technol.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85108645378
"Escalante F.M., Pérez-Ibarra J.C., Jaimes J.C., Siqueira A.A.G., Terra M.H.","57195420315;56940822100;57195319020;7003788864;7003287549;","Pplied to a modular knee-exoskeleton applied to a modular knee-exoskeleton",2020,"IFAC-PapersOnLine","53",,,"10141","10147",,,"10.1016/j.ifacol.2020.12.2740","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107774699&doi=10.1016%2fj.ifacol.2020.12.2740&partnerID=40&md5=378b7eeb8d83434b978931fdb3c1b19d","Department of Electrical Engineering, University of São Paulo, São Carlos, Brazil; Department of Mechanical Engineering, University of São Paulo, São Carlos, Brazil","Escalante, F.M., Department of Electrical Engineering, University of São Paulo, São Carlos, Brazil, Department of Mechanical Engineering, University of São Paulo, São Carlos, Brazil; Pérez-Ibarra, J.C., Department of Electrical Engineering, University of São Paulo, São Carlos, Brazil, Department of Mechanical Engineering, University of São Paulo, São Carlos, Brazil; Jaimes, J.C., Department of Mechanical Engineering, University of São Paulo, São Carlos, Brazil; Siqueira, A.A.G., Department of Mechanical Engineering, University of São Paulo, São Carlos, Brazil; Terra, M.H., Department of Electrical Engineering, University of São Paulo, São Carlos, Brazil","Lower limb exoskeletons have improved mobility and safety during gait rehabilitation. Joint actuators can be programmed to produce sufficient joint torque to promote human movement. However, the mechanical impedance of the human joints changes constantly to maintain a stable interaction with the environment during walking. These continuous changes introduce nonlinearities and uncertainties that alter abruptly the dynamics of the humanrobot interaction, which can destabilize the control system. In this paper, an impedance control approach under explicit Markovian torque control architecture is developed, considering the variable human impedance parameters as parametric uncertainties. As the time-varying human dynamics during walking depends on the quasi-cyclic gait phase transitions, we defined five Markovian operation modes to describe the human-robot interaction during walking. Additionally, impedance parameters of the human knee joint were estimated using an ensemblebased method. Experimental results of the proposed control scheme on a knee-exoskeleton driven by a series elastic actuator show that our proposal guarantees stability and high performance despite the stochastic uncertain human impedance behavior throughout the gait cycle. © 2020 Elsevier B.V.. All rights reserved.","Human-robot interaction; Impedance control; Markovian control; Robust control","Actuators; Behavioral research; Control nonlinearities; Human robot interaction; Joints (anatomy); Man machine systems; Markov processes; Stochastic systems; Control architecture; Ensemble-based method; Gait rehabilitation; Impedance control; Impedance parameters; Mechanical impedances; Parametric uncertainties; Series elastic actuators; Exoskeleton (Robotics)",,,,,"Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS); Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq","This work is supported by Pro-Rectory of Research of University of S?o Paulo, Coordination for the Improvement of Higher Education Personnel (CAPES) - Finance Code 001, Colciencias (Colombia), National Council for Scientific and Technological Development (CNPq), and S?o Paulo Research Foundation (FAPESP).",,,,,,,,,,"Anam, K., Al-Jumaily, A., Active exoskeleton control systems: State of the art (2012) Procedia Engineering, 41, pp. 988-994; Calanca, A., Fiorini, P., Impedance control of series elastic actuators based on well-defined force dynamics (2017) Robotics and Autonomous Systems, 96, pp. 81-92; Calanca, A., Muradore, R., Fiorini, P., A review of algorithms for compliant control of stiff and fixedcompliance robots (2016) IEEE/ASME Transactions on Mechatronics, 21 (2), pp. 613-624; Cerri, J., Terra, M., Recursive robust regulator for discrete-time Markovian jump linear systems (2017) IEEE Trans. On Automatic Control, 62 (11), pp. 6004-6011; Dos Santos, W.M., Siqueira, A.A.G., Design and control of a transparent lower limb exoskeleton (2019) Wearable Robotics: Challenges and Trends, pp. 175-179. , Springer; Dos Santos, W.M., Caurin, G.A., Siqueira, A.A.G., Design and control of an active knee orthosis driven by a rotary series elastic actuator (2017) Control Engineering Practice, 58, pp. 307-318; Hogan, N., Impedance control: An approach to manipulation: Parts I-III (1985) Journal of Dynamic Systems, Measurement, and Control, 107 (1), pp. 17-24. , March; Jutinico, A.L., Jaimes, J.C., Escalante, F.M., Perezibarra, J.C., Terra, M.H., Siqueira, A.A.G., Impedance control for robotic rehabilitation: A robust Markovian approach (2017) Frontiers in Neurorobotics, 11, p. 43; Kong, K., Bae, J., Tomizuka, M., Control of rotary series elastic actuator for ideal force-mode actuation in human-robot interaction applications (2009) IEEE/ASME Transactions on Mechatronics, 14 (1), pp. 105-118; Lasota, P.A., Fong, T., Shah, J.A., A survey of methods for safe human-robot interaction (2017) Foundations and Trends in Robotics, 5 (4), pp. 261-349; Lee, H., Hogan, N., Time-varying ankle mechanical impedance during human locomotion (2015) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 23 (5), pp. 755-764; Li, X., Pan, Y., Chen, G., Yu, H., Adaptive human-robot interaction control for robots driven by series elastic actuators (2017) IEEE Transactions on Robotics, 33 (1), pp. 169-182. , February; Ma, H., Liao, W., Human gait modeling and analysis using a semi-Markov process with ground reaction forces (2017) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (6), pp. 597-607; Mehling, J., Holley, J., O'Malley, M., Leveraging disturbance observer based torque control for improved impedance rendering with series elastic actuators (2015) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1646-1651. , IEEE; Oh, S., Kong, K., High-precision robust force control of a series elastic actuator (2016) IEEE/ASME Transactions on Mechatronics, 22 (1), pp. 71-80; Paine, N., Mehling, J., Holley, J., Radford, N., Johnson, G., Fok, C., Sentis, L., Actuator control for the nasajsc valkyrie humanoid robot: A decoupled dynamics approach for torque control of series elastic robots (2015) Journal of Field Robotics, 32 (3), pp. 378-396; Pérez-Ibarra, J., Alarcón, A., Jaimes, J.C., Ortega, F., Terra, M.H., Siqueira, A., Design and analysis of H8 force control of a series elastic actuator for impedance control of an ankle rehabilitation robotic platform (2017) 2017 American Control Conference, pp. 2423-2428; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Transactions on Robotics, 31 (5), pp. 1089-1100. , October","Escalante, F.M.; Department of Electrical Engineering, Brazil; email: maurinho707@usp.br
Pérez-Ibarra, J.C.; Department of Electrical Engineering, Brazil; email: jcperezibarra@sc.usp.br
Terra, M.H.; Department of Electrical Engineering, Brazil; email: terra@sc.usp.br",,,"Elsevier B.V.","21st IFAC World Congress 2020","12 July 2020 through 17 July 2020",,145388,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85107774699
"Krmer M., Muster F.I., Rsmann C., Bertram T.","57199753083;57209274072;56022584400;56262850600;","Elasticity-aware online motion optimization for link-elastic manipulators",2020,"IFAC-PapersOnLine","53",,,"9980","9985",,,"10.1016/j.ifacol.2020.12.2715","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107768750&doi=10.1016%2fj.ifacol.2020.12.2715&partnerID=40&md5=19dd48b4d90a4629ebf53dc79c98874a","Institute of Control Theory and Systems Engineering, TU Dortmund University, Dortmund, Germany","Krmer, M., Institute of Control Theory and Systems Engineering, TU Dortmund University, Dortmund, Germany; Muster, F.I., Institute of Control Theory and Systems Engineering, TU Dortmund University, Dortmund, Germany; Rsmann, C., Institute of Control Theory and Systems Engineering, TU Dortmund University, Dortmund, Germany; Bertram, T., Institute of Control Theory and Systems Engineering, TU Dortmund University, Dortmund, Germany","The field of human-robot interaction is a typical application of elastic robots, as they reduce the risk of injuries and physical damage in case of a collision. Elasticities, however, also impose high demands on underlying joint controllers to guarantee minimal vibration during regular operation. Numerous control concepts assume a sufficiently high ability to control vibrations, by e.g., dedicated actuators or special kinematic structures. This work presents an online, optimization-based trajectory planning approach that concentrates on maximizing this ability for elastic manipulators without additional damping actuators or certain kinematic structures. The planning algorithm utilizes a modified quadratic objective function to incorporate the controllability of vibrations as a secondary goal. The effectiveness of the approach is demonstrated on a real 3-DOF, link-elastic robot for different set-points subject to disturbances. The results show that the approach successfully generates elasticityaware motions and improves the vibration damping capabilities of the underlying controllers. Especially for critical configurations in which the controllers usually have little or no influence on the vibrations, vibration damping is improved or even made possible. Copyright © 2020 The Authors.","Elastic Links; Model Predictive Control; Online Trajectory Planning; Robotic Manipulators; Vibration Damping","Actuators; Controllers; Damping; Elasticity; Human robot interaction; Kinematics; Manipulators; Robot programming; Critical configurations; Elastic manipulator; Kinematic structures; Motion optimization; Planning algorithms; Quadratic objective functions; Trajectory Planning; Typical application; Vibration control",,,,,,,,,,,,,,,,"Abdolvand, M., Fatehi, M.H., Model-base predictive control for vibration suppression of a flexible manipulator (2012) International Conference on Control, pp. 562-567; Boscariol, P., Gasparetto, A., Vidoni, R., Romano, A., A model-based trajectory planning approach for flexible-link mechanisms (2013) International Conference on Mechatronics, pp. 219-224; Cao, F., Liu, J., Optimal trajectory control for a two-link rigid-flexible manipulator with ODE-PDE model (2018) Optimal Control Applications and Methods, 39 (4), pp. 1515-1529; Dubay, R., Hassan, M., Li, C., Charest, M., Finite element based model predictive control for active vibration suppression of a one-link flexible manipulator (2014) ISA Transactions, 53 (5), pp. 1609-1619; Elliott, J.R., Dubay, R., Mohany, A., Hassan, M., Model predictive control of vibration in a two flexible link manipulator - Part i (2014) Journal of Low Frequency Noise, Vibration and Active Control, 33 (4), pp. 455-468; Esfandiar, H., Korayem, M.H., Optimal point to point path planning of flexible manipulator under large deformation by using harmony search method (2016) Journal of Theoretical and Applied Mechanics, pp. 179-193; Fareh, R., Al-Shabi, M., Bettayeb, M., Ghommam, J., Robust active disturbance rejection control for flexible link manipulator (2019) Robotica, pp. 1-18; Grüne, L., Pannek, J., (2017) Nonlinear Model Predictive Control: Theory and Algorithms. Communications and Control Engineering., , Springer, 2nd edition. Harwell; (2018) A Collection of Fortran Codes for Large Scale Scientific Computation., , http://www.hsl.rl.ac.uk/; John, F.I., Malzahn, J., Bertram, T., Controllability and accessibility of vibrations in multiple planes on link-elastic robot arms (2017) ROBIO, pp. 1491-1496; Krämer, M., Rösmann, C., Hoffmann, F., Bertram, T., Model predictive control of a collaborative manipulator considering dynamic obstacles (2020) Optimal Control Applications and Methods.; Lambeck, S., Sawodny, O., Trajectory generation and oscillation damping control for a flexible link robot (2007) Annual Conference of the Industrial Electronics Society, pp. 2748-2753; Malzahn, J., (2014) Modeling and Control of Multi-elasticlink Robots under Gravity., , Ph.D. thesis, TU Dortmund University; Muster, F.I., Malzahn, J., Bertram, T., Exploiting controllability for vibration damping in multiple planes of link-elastic robot arms (2019) International Conference on Mechatronics, pp. 187-192; Nocedal, J., Wright, S.J., (2006) Numerical Optimization. Springer Series in Operations Research and Financial Engineering, , Springer Science, New York, NY, 2nd edition; Pradhan, S.K., Subudhi, B., Nonlinear adaptive model predictive controller for a flexible manipulator: An experimental study (2014) Transactions on Control Systems Technology, 22 (5), pp. 1754-1768; Reyhanoglu, M., Hoffman, D., Modeling and control of a flexible-structure-mounted manipulator (2016) International Conference on Advanced Intelligent Mechatronics, pp. 953-957; Rösmann, C., Krämer, M., Makarow, A., Hoffmann, F., Bertram, T., Exploiting sparse structures in nonlinear model predictive control with hypergraphs (2018) International Conference on Advanced Intelligent Mechatronics, pp. 1255-1260; Schnelle, F., Eberhard, P., Adaptive nonlinear model predictive control design of a flexible-link manipulator with uncertain parameters (2017) Acta Mechanica Sinica, 33 (3), pp. 529-542; Song, B.J., Koivo, A.J., Nonlinear predictive control with application to manipulator with flexible forearm (1999) Transactions on Industrial Electronics, 46 (5), pp. 923-932; Tosunoglu, S., Lin, S.H., Tesar, D., Accessibility and controllability of flexible robotic manipulators (1992) Journal of Dynamic Systems, Measurement, and Control, 114 (1), pp. 50-58; Wächter, A., Biegler, L.T., On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming (2006) Mathematical Programming, pp. 25-57","Krmer, M.; Institute of Control Theory and Systems Engineering, Germany; email: maximilian.kraemer@tu-dortmund.de
Muster, F.I.; Institute of Control Theory and Systems Engineering, Germany
Rsmann, C.; Institute of Control Theory and Systems Engineering, Germany
Bertram, T.; Institute of Control Theory and Systems Engineering, Germany",,,"Elsevier B.V.","21st IFAC World Congress 2020","12 July 2020 through 17 July 2020",,145388,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85107768750
"Yang Q., Xie C., Tang R., Liu H., Song R.","55572578800;57219420301;57222495146;56148904600;26431599600;","Hybrid active control with human intention detection of an upper-limb cable-driven rehabilitation robot",2020,"IEEE Access","8",,,"195206","195215",,,"10.1109/ACCESS.2020.3033301","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102903048&doi=10.1109%2fACCESS.2020.3033301&partnerID=40&md5=bfbc9dc050f6e939059b048323a2ccf0","Key Laboratory of Sensing Technology and Biomedical, Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, 510006, China; Department of Rehabilitation Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China","Yang, Q., Key Laboratory of Sensing Technology and Biomedical, Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, 510006, China; Xie, C., Key Laboratory of Sensing Technology and Biomedical, Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, 510006, China; Tang, R., Key Laboratory of Sensing Technology and Biomedical, Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, 510006, China; Liu, H., Department of Rehabilitation Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China; Song, R., Key Laboratory of Sensing Technology and Biomedical, Instrument of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, 510006, China","Rehabilitation robots play an increasingly important role in the recovery of motor function for stroke. To ensure a natural physical human-robot interaction (pHRI) and enhance the active participation of subjects, it is necessary for the robots to understand the human intention and cooperate actively with humanlike characteristics. This study proposed a hybrid active control algorithm with human motion intention detection. The motion intention was defined as the desired position and velocity, which were continuously estimated according to the human upper-limb model and minimum jerk model, respectively. The motion intention was then fed into a hybrid force and position controller of an upper-limb cable driven rehabilitation robot (CDRR). And a three-dimensional reaching task without predefined trajectory was employed to validate the effectiveness of the proposed control algorithm. Experimental results showed that the control algorithm could continuously recognize the human motion intention and enabled the robot better movement performance indicated as smaller offset error, smoother trajectory, and lower impact. The proposed method could guarantee a natural pHRI and improve the engagement of the subjects, which has great potential in clinical applications. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.","Cable-driven; Hybrid control; Motion intention detection; Rehabilitation robot","Cables; Neuromuscular rehabilitation; Clinical application; Desired position; Human motion intention; Minimum jerk model; Movement performance; Physical humanrobot interaction (phri); Position controller; Rehabilitation robot; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 61803395; Natural Science Foundation of Guangdong Province: 2018A030310280; Fundamental Research Funds for the Central Universities: 18zxxt27; Science and Technology Planning Project of Guangdong Province: 2017B020210011; Science and Technology Planning Project of Shenzhen Municipality: JCYJ20170818155028942","This work was supported in part by the National Natural Science Foundation of China under Grant 61803395, in part by the Guangdong Natural Science Foundation under Grant 2018A030310280, in part by the Fundamental Research Funds for the Central Universities under Grant 18zxxt27, in part by the Guangdong Science and Technology Planning Project under Grant 2017B020210011, and in part by the Science and Technology Project of Shenzhen under Grant JCYJ20170818155028942.",,,,,,,,,,"Kisa, A., Lorkowski, S., Castro, F., Global, regional, and national burden of stroke, 1990-2016: A systematic analysis for the Global Burden of Disease Study 2016 (2019) Lancet Neurol, 18, pp. 439-458. , May; Lo, A.C., Guarino, P.D., Richards, L., Haselkorn, J., Wittenberg, G., Federman, D.G., Ringer, R.J., Bever, C.T., Jr., Robot-assisted therapy for long-term upper-limb impairment after stroke (2010) New. 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Rehabil., 83 (7), pp. 952-959. , Jul; Riener, R., Guidali, M., Keller, U., Duschau-Wicke, A., Klamroth, V., Nef, T., Transferring ARMin to the clinics and industry (2011) Topics Spinal Cord Injury Rehabil., 17 (1), pp. 54-59. , Jul; Proietti, T., Crocher, V., Roby-Brami, A., Jarrasse, N., Upper-limb robotic exoskeletons for neurorehabilitation: A review on control strategies (2016) IEEE Rev. Biomed. Eng., 9, pp. 4-14; Hogan, N., Impedance control - An approach to manipulation. I-Theory. II-Implementation. III-Applications (1985) J. Dyn. Syst.-Trans. ASME, 107 (1), pp. 304-313; Otten, A., Voort, C., Stienen, A., Aarts, R., Van Asseldonk, E., Kooij Der, H.Van, LIMPACT:A hydraulically powered self-aligning upper limb exoskeleton (2015) IEEE/ASME Trans. Mechatronics, 20 (5), pp. 2285-2298. , Oct; Li, H.-Y., Dharmawan, A.G., Paranawithana, I., Yang, L., Tan, U.-X., A control scheme for physical human-robot interaction coupled with an environment of unknown stiffness (2020) J. Intel. Robot. 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Rehabil., 6 (1), p. 20. , Dec; Kang, G., Oh, H.S., Seo, J.K., Kim, U., Choi, H.R., Variable admittance control of robot manipulators based on human intention (2019) IEEE/ASME Trans. Mechatronics, 24 (3), pp. 1023-1032. , Jun; Sun, L., An, H., Ma, H., Gao, J., Real-time human intention recognition of multi-joints based on MYO (2020) IEEE Access, 8, pp. 4235-4243","Song, R.; Key Laboratory of Sensing Technology and Biomedical, China; email: songrong@mail.sysu.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85102903048
"Deuerlein C., Langer M., Seßner J., Heß P., Franke J.","57205293231;57221979822;57021762100;56400248900;57169710000;","Human-robot-interaction using cloud-based speech recognition systems",2020,"Procedia CIRP","97",,,"130","135",,1,"10.1016/j.procir.2020.05.214","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100856364&doi=10.1016%2fj.procir.2020.05.214&partnerID=40&md5=bab74d2476ec7da6c1785ca928320ec6","Technische Hochschule Nürnberg, Keßlerplatz 12, Nürnberg, 90489, Germany; Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Factory Automation and Production Systems, Egerlandstr. 7, Erlangen, 91058, Germany","Deuerlein, C., Technische Hochschule Nürnberg, Keßlerplatz 12, Nürnberg, 90489, Germany; Langer, M., Technische Hochschule Nürnberg, Keßlerplatz 12, Nürnberg, 90489, Germany; Seßner, J., Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Factory Automation and Production Systems, Egerlandstr. 7, Erlangen, 91058, Germany; Heß, P., Technische Hochschule Nürnberg, Keßlerplatz 12, Nürnberg, 90489, Germany; Franke, J., Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Factory Automation and Production Systems, Egerlandstr. 7, Erlangen, 91058, Germany","Progress in natural speech processing has enabled significantly more powerful speech processing systems, primarily due to the use of machine learning technologies. In order to integrate cloud-based speech recognition systems for human-robot interaction, an interface for the voice control of a lightweight robot was developed. The main contribution of this work is the design and implementation of a software interface to recognize commands via cloud-based speech processing and the subsequently conversion into machine-readable code. Requirements for the evaluation of different cloud-services for the control of robots are determined. Furthermore, the control architecture for the robot is modeled and implemented. An example application, which enables users to control robot movements via speech, is realized as a proof of concept and for additional studies. This application includes the basic features of cloud-based speech processing: intent recognition from utterances, slot filling and dialogue-based interaction. Lastly, the influence of background noise on process safety was examined within an experiment. It turns out that a feasible process reliability can be achieved with the system despite the presence of background noises. © 2020 The Author(s).","Cloud-service; Human-robot-interaction; Industrial robot; Speech control","Man machine systems; Social robots; Speech processing; Control architecture; Design and implementations; Intent recognition; Machine learning technology; Process reliability; Processing systems; Software interfaces; Speech recognition systems; Speech recognition",,,,,"Bundesministerium für Bildung und Forschung, BMBF; Bundesministerium für Bildung und Forschung, BMBF","This work was conducted within the project MRK&MoCap4Robots (13FH008IX6), funded by the German Federal Ministry of Education and Research (BMBF). We are thankful for the support and funding from the BMBF.",,,,,,,,,,"Almansor, E.H., Hussain, F.K., Survey on intelligent chatbots: State-of-the-art and future research directions (2019) Proceedings of the 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS), 13-15, p. 534; (2019), https://developer.amazon.com/en-GB/docs/alexa/ask-overviews/build-skills-with-the-alexa-skills-kit.html, Amazon, Alexa Skill Kit:, accessed: 2019-11-24; Assefi, M., Wittie, M., Knight, A., Impact of network performance on cloud speech recognition (2015) Proceedings of the 24th International Conference, p. 1; Asseffi, M., Liu, G., Wittie, M.P., Izurieta, C., (2020) An Experimental Evaluation of Apple Siri and Google Speech Recognition, , Department of Computer Science, Montana State University; Braun, D., Hernandez, A., Matthes, F., Langen, M., Evaluating Natural Language Understanding Services for Conversational Question Answering Systems (2017) Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue, p. 174. , Association for Computational Linguistics; Google, , https://dialogflow.com/docs/reference/agent, 2019. 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(2013) Proceedings of the SIGDIAL Conference, pp. 394-403. , Metz, France; Park, S., Kim, Y., Matson, E.T., Jang, H., An intuitive interaction system for fire safety using a speech recognition technology (2015) Proceedings of the 6th International Conference, p. 388; Sharan, S., Nguyen, T.Q., Nauth, P., Araujo, R., Implementation and testing of voice control in a mobile robot for navigation (2019) Proceedings of the IEEE/ASME, p. 145; Skraba, A., Kolozvari, A., Kofjac, D., Stojanovic, R., Development of cyber-physical speech-controlled wheelchair for disabled persons (2019) Proceedings of the 22nd Euromicro Conference, p. 456; Wang, L., Tong, R., Leung, C.C., Ni, C., Cloud-based automatic speech recognition systems for southeast Asian (2017) Proceedings of the Languages Proceedings of the International Conference on Orange Technologies (ICOT), p. 147. , Singapore Kent Ridge Campus, National University of Singapore; Wölfel, K., Henrich, D., Grounding of uncertain force parameters in spoken robot commands (2020) Advances in Service and Industrial Robotics, p. 194. , Springer International Publishing; Zinchenko, K., Wu, C.Y., Song, K.T., A study on speech recognition control for a surgical robot (2017) IEEE Transactions on Industrial Informatics, 13 (2), p. 607","Deuerlein, C.; Technische Hochschule Nürnberg, Keßlerplatz 12, Germany; email: christian.deuerlein@th-nuernberg.de","Makris S.",,"Elsevier B.V.","8th CIRP Conference of Assembly Technology and Systems, CATS 2020","29 September 2020 through 1 October 2020",,167017,22128271,,,,"English",,Conference Paper,"Final","All Open Access, Gold",Scopus,2-s2.0-85100856364
"Mousa M.A., Soliman M., Saleh M.A., Radwan A.G.","57215287388;57208177012;57203917895;7103379659;","Biohybrid soft robots, E-skin, and bioimpedance potential to build up their applications: A review",2020,"IEEE Access","8",,,"184524","184539",,3,"10.1109/ACCESS.2020.3030098","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100556204&doi=10.1109%2fACCESS.2020.3030098&partnerID=40&md5=f89cb9f1a308dafc68e0e4fde030748c","Nanoelectronics Integrated Systems Center (NISC), Nile University, Sheikh Zayed City, 12588, Egypt; Mechanical Engineering Program, School of Engineering and Applied Sciences, Nile University, Sheikh Zayed City, 12588, Egypt; Department of Engineering Mathematics and Physics, Cairo University, Giza, 12613, Egypt; School of Engineering and Applied Sciences, Nile University, Sheikh Zayed City, 12588, Egypt","Mousa, M.A., Nanoelectronics Integrated Systems Center (NISC), Nile University, Sheikh Zayed City, 12588, Egypt; Soliman, M., Mechanical Engineering Program, School of Engineering and Applied Sciences, Nile University, Sheikh Zayed City, 12588, Egypt; Saleh, M.A., Mechanical Engineering Program, School of Engineering and Applied Sciences, Nile University, Sheikh Zayed City, 12588, Egypt; Radwan, A.G., Department of Engineering Mathematics and Physics, Cairo University, Giza, 12613, Egypt, School of Engineering and Applied Sciences, Nile University, Sheikh Zayed City, 12588, Egypt","Soft Robotics is a new approach towards better human-robot interaction and biomimicry in the robotics field. Its integration with biological materials (Biohybrid soft robotics) is one of the topics being focused on in the soft robotics research in the last fifteen years. The motive for this approach is to combine the best of biological and artificial systems. In this article, Biohybrid soft robots and Electronic Skin (E-skin), which is considered one of the advances of soft robotics, are reviewed. Their most significant milestones and the highlights of their most researched applications are listed. Bioimpedance, which is the impedance of biological tissue to an applied AC signal, has been used recently in many agriculture and medical applications for its non-destructive nature. It has been used in many bio-related applications for its ability to assess the state of living tissues. However, it has not been applied in biohybrid soft robots yet. Bioimpedance equivalent electrical models are surveyed showing both integer and fractional order based models. Finally, its potential to enhance biohybrid soft robotics and e-skin applications is discussed and validated. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.","Biohybrid robots; Bioimpedance; E-skin; Fractional order calculus; Soft robotics","Agricultural robots; Biomimetics; Medical applications; Robotics; Tissue; Artificial systems; Biological tissues; Electronic skin; Equivalent electrical models; Fractional order; Living tissues; New approaches; Non destructive; Human robot interaction",,,,,"Academy of Scientific Research and Technology, ASRT: 4779","This work was supported by the Academy of Scientific Research and Technology (ASRT) through the Project ‘‘Development and Manufacturing of Soft Actuated Under Water Robots (SUWR)’’ under Grant 4779.",,,,,,,,,,"Rus, D., Tolley, M.T., Design, fabrication and control of soft robots (2015) Nature, 521 (7553), pp. 467-475. , May; Trivedi, D., Rahn, C.D., Kier, W.M., Walker, I.D., Soft robotics: Biological inspiration, state of the art, and future research (2008) Appl. 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Actuators A, Phys., 314. , Oct; Al-Ali, A., Elwakil, A., Ahmad, A., Maundy, B., Design of a portable low-cost impedance analyzer (2017) Proc. 10th Int. Joint Conf. Biomed. Eng. Syst. Technol., pp. 104-109; Katzschmann, R.K., DelPreto, J., Maccurdy, R., Rus, D., Exploration of underwater life with an acoustically controlled soft robotic fish (2018) Sci. Robot., 3 (16). , Mar","Mousa, M.A.; Nanoelectronics Integrated Systems Center (NISC), Egypt; email: mabdelrahman@nu.edu.eg",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Review,"Final","All Open Access, Gold",Scopus,2-s2.0-85100556204
"Zhou H., Wei D., Chen Y., Wu F.","55574208996;57219626342;36597797200;57221783721;","Promoting mutual adaptation in haptic negotiation using adaptive virtual fixture",2020,"Industrial Robot","48","2",,"313","326",,1,"10.1108/IR-07-2020-0142","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100180061&doi=10.1108%2fIR-07-2020-0142&partnerID=40&md5=cb4298f55e2b60ac6d782b605fb3f746","State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, China; Dalian Maritime University, Dalian, China; Algorithm Department, Zhejiang Demetics Medical Technology, Hangzhou, China","Zhou, H., State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, China; Wei, D., State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, China; Chen, Y., Dalian Maritime University, Dalian, China; Wu, F., Algorithm Department, Zhejiang Demetics Medical Technology, Hangzhou, China","Purpose: To promote the intuitiveness of collaborative tasks, the negotiation ability of humans with each other has inspired a large amount of studies aimed at reproducing the capacity in physical human-robot interaction (pHRI). This paper aims to promote mutual adaptation in negotiation when both parties possess incomplete information. Design/methodology/approach: This paper introduces virtual fixtures into the traditional negotiation mechanism, locally regulating tracking trajectory and impedance parameters in the negotiating phase until the final plan integrates bilateral intentions well. In the strategy, robots convey its task information to humans and offer groups of guide plans for them to choose, on the premise of maximizing the robot’s own profits. Findings: Compared with traditional negotiation strategies, humans adapt to robots easily and show lower cognitive load in the method, while the satisfied plan shows better performance for the whole human-robot system. Originality/value: In this study, this paper proposes a novel negotiation strategy to facilitate the mutual adaptation of humans and robots in complicated shared tasks, especially when both parties possess incomplete information of tasks. © 2020, Emerald Publishing Limited.","Cooperative robots; Haptic negotiation","Agricultural robots; Fixtures (tooling); Collaborative tasks; Design/methodology/approach; Human-robot systems; Impedance parameters; Incomplete information; Negotiation mechanism; Negotiation strategy; Physical humanrobot interaction (phri); Social robots",,,,,"51821093","The authors thank Zhejiang Demetics Medical Technology for providing UR5 robot and technical assistance.Funding: This research is supported by the third phase of the innovation group “Basic Research on Electro-Hydraulic System” (Grant No. 51821093).",,,,,,,,,,"Ajoudani, A., Zanchettin, A., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human–robot collaboration (2017) Autonomous Robots, 42 (5), pp. 957-975; 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Robot",Article,"Final","",Scopus,2-s2.0-85100180061
"Mahajan K., Groechel T., Pakkar R., Cordero J., Lee H., Matarić M.J.","57219438287;57215285175;57209640009;57220179414;57220180694;7005611984;","Adapting Usability Metrics for a Socially Assistive, Kinesthetic, Mixed Reality Robot Tutoring Environment",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"381","391",,,"10.1007/978-3-030-62056-1_32","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097190778&doi=10.1007%2f978-3-030-62056-1_32&partnerID=40&md5=560350bed1a41c51f6e5089c8cbf6cd6","Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States","Mahajan, K., Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States; Groechel, T., Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States; Pakkar, R., Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States; Cordero, J., Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States; Lee, H., Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States; Matarić, M.J., Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States","The field of Socially Assistive Robot (SAR) tutoring has extensively explored both subjective and objective usability metrics for seated tablet-based human-robot interactions. As SAR tutoring introduces kinesthetic mixed reality environments where students can move around and physically manipulate virtual objects, usability metrics for such interactions need to be re-evaluated. This paper applies standard usability metrics from seated 2D interactions to a kinesthetic mixed reality environment and validates those metrics with post-interaction survey data. Using data from a pilot study (n= 9 ) conducted with a mixed reality SAR tutor, three commonly-used metrics of usability for seated 2D tutoring interfaces were collected: performance, manipulation time, and gaze. The strength of each usability metric was compared to subjective survey-based scores measured with the System Usability Scale (SUS). The results show that usability scores were correlated with the gaze metric but not with the manipulation time or performance metrics. The findings provide interesting implications for the design and evaluation of kinesthetic mixed reality robot tutoring environments. © 2020, Springer Nature Switzerland AG.","Mixed reality; Socially assistive robot tutor; Usability","Machine design; Robotics; Social robots; Surveys; Usability engineering; Design and evaluations; Mixed-reality environment; Performance metrics; Pilot studies; Socially assistive robots; System Usability Scale (SUS); Usability metrics; Virtual objects; Mixed reality",,,,,"National Science Foundation, NSF: IIS-1925083","Acknowledgements. This work was supported by NSF NRI 2.0 grant for “Communicate, Share, Adapt: A Mixed Reality Framework for Facilitating Robot Integration and Customization” (NSF IIS-1925083). 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IEEE","Mahajan, K.; Viterbi School of Engineering, United States; email: kmahajan@usc.edu","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097190778
"Buisan G., Sarthou G., Alami R.","57204733130;57212393872;7003803131;","Human Aware Task Planning Using Verbal Communication Feasibility and Costs",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"554","565",,1,"10.1007/978-3-030-62056-1_46","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097143961&doi=10.1007%2f978-3-030-62056-1_46&partnerID=40&md5=1361a90a2a7db7d5346fe2468f90d68f","LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France; Artificial and Natural Intelligence Toulouse Institute (ANITI), Toulouse, France","Buisan, G., LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France; Sarthou, G., LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France; Alami, R., LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France, Artificial and Natural Intelligence Toulouse Institute (ANITI), Toulouse, France","This paper addresses the problem of taking into account the feasibility and cost of verbal communication actions at the task planning level in the context of human-robot interaction. By determining the human and robot context in which the referring expressions to entities can be effectively achieved and at which cost, our planner can 1) prevent potential deadlocked situations where it is not possible to produce a feasible communication act 2) find plans reducing the overall communication complexity 3) evaluate different communications strategies and select the most suitable one. Our approach is based on the extension of a multi agent hierarchical task planner capable of maintaining, throughout the planning process, an estimate of the human partner knowledge about the environment using a semantic representation. A Referring Expression Generation (REG) can then be executed for each communication action allowing the task planner to be informed about its feasibility and cost during the plan elaboration process. Three scenarios are presented to validate the method, where a robot drives the human actions to perform a cube arrangement task. © 2020, Springer Nature Switzerland AG.","Hierarchical task planner; Human-Robot collaboration; Referring expression generation; Verbal communication planning","Multi agent systems; Robot programming; Robotics; Semantics; Communication act; Communication actions; Communication complexity; Communications strategies; Planning process; Referring expressions; Semantic representation; Verbal communications; Social robots",,,,,"Agence Nationale de la Recherche, ANR: ANR-16-CE33-0017","Acknowledgements. We thank Amandine Mayima for her contribution to the implementation of the integrated scenarios. This work has been funded by the French Agence Nationale de la Recherche JointAction4HRI project ANR-16-CE33-0017.","We thank Amandine Mayima for her contribution to the implementation of the integrated scenarios. This work has been funded by the French Agence Nationale de la Recherche JointAction4HRI project ANR-16-CE33-0017.",,,,,,,,,"Brawer, J., Mangin, O., Roncone, A., Widder, S., Scassellati, B., Situated human-robot collaboration: Predicting intent from grounded natural language (2018) IEEE/RSJ IROS; Buisan, G., Sarthou, G., Bit-Monnot, A., Clodic, A., Alami, R., Efficient, situated and ontology based referring expression generation for human-robot collaboration (2020) RO-MAN. IEEE; Devin, S., Alami, R., An implemented theory of mind to improve human-robot shared plans execution (2016) ACM/IEEE HRI; Fokoue, A., Kershenbaum, A., Ma, L., Schonberg, E., Srinivas, K., The summary abox: Cutting ontologies down to size (2006) ISWC 2006. LNCS, 4273, pp. 343-356. , https://doi.org/10.1007/1192607825, Cruz, I., et al. (eds.), Springer, Heidelberg; Gharbi, M., Lallement, R., Alami, R., Combining symbolic and geometric planning to synthesize human-aware plans: Toward more efficient combined search (2015) RSJ IROS. IEEE; Guitton, J., Warnier, M., Alami, R., Belief management for HRI planning (2012) European Conference on Artificial Intelligence; Lallement, R., (2016) Symbolic and Geometric Planning for Teams of Robots and Humans, , Theses, INSA de Toulouse; Lallement, R., de Silva, L., Alami, R., HATP: An HTN planner for robotics (2014) ICAPS Workshop on Planning and Robotics; Mavridis, N., A review of verbal and non-verbal human-robot interactive communication (2015) Robot. Auton. Syst., 63, pp. 22-35; Nikolaidis, S., Kwon, M., Forlizzi, J., Srinivasa, S., Planning with verbal communication for human-robot collaboration (2018) ACM Trans. Hum.-Robot Interact. (THRI), 7 (3), pp. 1-21; Roncone, A., Mangin, O., Scassellati, B., Transparent role assignment and task allocation in human robot collaboration (2017) ICRA. IEEE; Sanelli, V., Cashmore, M., Magazzeni, D., Iocchi, L., (2017) Short-Term Human-Robot Interaction through Conditional Planning and Execution, p. ICAPS; Sarthou, G., Clodic, A., Alami, R., Ontologenius: A long-term semantic memory for robotic agents (2019) RO-MAN. IEEE; Schaefer, K.E., Straub, E.R., Chen, J.Y., Putney, J., Evans, A.W., III., Communicating intent to develop shared situation awareness and engender trust in human-agent teams (2017) Cogn. Syst. Res., 46, pp. 26-39; Sebastiani, E., Lallement, R., Alami, R., Iocchi, L., (2017) Dealing with On-Line Human-Robot Negotiations in Hierarchical Agent-Based Task Planner, p. ICAPS; Shah, J., Wiken, J., Williams, B., Breazeal, C., Improved human-robot team performance using Chaski, a human-inspired plan execution system (2011) ACM/IEEE HRI; Tellex, S., Gopalan, N., Kress-Gazit, H., Matuszek, C., Robots that use language (2020) Ann. Rev. Control Robot. Auton. Syst., 3 (1), pp. 25-55; Tellex, S., Knepper, R., Li, A., Rus, D., Roy, N., Asking for help using inverse semantics (2014) Robot. Sci. Syst. X; Unhelkar, V.V., Li, S., Shah, J.A., Decision-making for bidirectional communication in sequential human-robot collaborative tasks (2020) ACM/IEEE HRI; Unhelkar, V.V., Yang, X.J., Shah, J.A., (2017) Challenges for Communication Decision-Making in Sequential Human-Robot Collaborative Tasks, , Workshop on Mathematical Models, Algorithms, and Human-Robot Interaction at RSS","Buisan, G.; LAAS-CNRS, France; email: guilhem.buisan@laas.fr","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85097143961
"Trainer T., Taylor J.R., Stanton C.J.","57220176815;57195346873;23092202700;","Choosing the Best Robot for the Job: Affinity Bias in Human-Robot Interaction",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"490","501",,,"10.1007/978-3-030-62056-1_41","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097133781&doi=10.1007%2f978-3-030-62056-1_41&partnerID=40&md5=ac0f44637a7a8408b48c16042f72fdc7","MARCS Institute, Western Sydney University, Sydney, NSW  2174, Australia","Trainer, T., MARCS Institute, Western Sydney University, Sydney, NSW  2174, Australia; Taylor, J.R., MARCS Institute, Western Sydney University, Sydney, NSW  2174, Australia; Stanton, C.J., MARCS Institute, Western Sydney University, Sydney, NSW  2174, Australia","Humans subconsciously judge others as being either similar or dissimilar to themselves, manifesting as an unconscious preference, or affinity bias, for those who are perceived to be similar. In human-to-human interaction, affinity bias can significantly influence trust formation and lead to discrimination, for example, in decisions related to recruitment and team selection. We investigate whether affinity bias is observed in human-robot interaction during team formation with social agents that differ in gender and skin tone. In this study, we asked 61 participants to order the resumés of 24 different avatars that varied in gender, skin tone, and competency under the pretext of choosing the “best” avatars to be the participant’s teammate. Then, using a wizard-of-oz style experiment, participants performed a task with two avatar teammates (one most preferred and one least preferred) to measure trust. Results showed that while avatars were predominantly chosen based upon competency, avatar appearance generated an affinity bias in resumé sorting, and participants were more likely to trust their preferred teammate. © 2020, Springer Nature Switzerland AG.","Affinity bias; Human-robot interaction; Trust","Man machine systems; Robotics; Human-to-human interactions; Skin tone; Social agents; Team formation; Wizard of Oz; Social robots",,,,,,,,,,,,,,,,"Louis, C., (2019) Unconscious Bias: The Affinity Bias, , https://podojo.com/unconscious-bias-the-affinity-bias/, Retrieved from, Accessed 20 May 2020; Turnbull, H., (2014) The Affinity Bias Conundrum: The Illusion of Inclusion Part III, , https://diversityjournal.com/13763-affinity-bias-conundrum-illusion-inclusion-part-iii/.Accessed20May2020, Retrieved from; Turnbull, H., The Illusion of Inclusion: Global Inclusion, Unconscious Bias, and the Bottom Line, , Business Expert Press, NY, USA (2016); Eyssel, F., Kuchenbrandt, D., Social categorization of social robots: Anthropomorphism as a function of robot group membership (2012) Br. 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Psychol., 66, pp. 519-545; Ghazali, A., Ham, J., Barakova, E., Markopoulos, P., Effects of robot facial characteristics and gender in persuasive human-robot interaction (2018) Front. Robot. AI, 5 (73), pp. 1-15; Cameron, D., Framing factors: The importance of context and the individual in understanding trust in human-robot interaction (2015) IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), , Hamburg, Germany; Bendick, M., Nunes, A., Developing the research basis for controlling bias in hiring (2012) J. Soc. Issues, 68 (2), pp. 238-262; Wirts, A., Discriminatory intent and implicit bias: Title VII liability and unwitting discrimination (2017) Boston Coll. Law Rev., 58 (2), pp. 809-856; Broadbent, E., Interactions with robots: The truths we reveal about ourselves (2017) Ann. Rev. Psychol., 68 (1), pp. 627-652; Avatarmaker Homepage. https://avatarmaker.com/. Accessed 20 May 2020; TTSMP3 Homepage, , https://ttsmp3.com, Accessed 20 May 2020","Stanton, C.J.; MARCS Institute, Australia; email: c.stanton@westernsydney.edu.au","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097133781
"Kont M., Alimardani M.","57220182371;55836794100;","Engagement and Mind Perception Within Human-Robot Interaction: A Comparison Between Elderly and Young Adults",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"344","356",,3,"10.1007/978-3-030-62056-1_29","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097131336&doi=10.1007%2f978-3-030-62056-1_29&partnerID=40&md5=96dce9bb75007811b4b182932dd88c5a","Department of Cognitive Science and AI, Tilburg University, Tilburg, Netherlands","Kont, M., Department of Cognitive Science and AI, Tilburg University, Tilburg, Netherlands; Alimardani, M., Department of Cognitive Science and AI, Tilburg University, Tilburg, Netherlands","People can feel engaged and attribute human-like traits when interacting with a social robot and reveal this unconsciously to observers. Studies have suggested that behavioral signals such as facial expressions, posture, speech and laughter play an important role in identifying engagement in Human-Robot Interaction (HRI), however the effect of these factors in different age groups, as well as their relationship with mind attribution towards robots remains unclear. This study examined 24 elderly people and 24 university students on facial expressions, laughter and speech during an interaction with a NAO-robot. In addition, self-reported engagement level and mind perception scores were collected after the interaction and analyzed. Results showed that elderly had a significantly lower report of engagement with the robot, which was positively correlated with their perception of mind capacity in the robot. Furthermore, for both elderly and students, there was a negative trend between self-reported mind perception and observed behavioral engagement with the robot. Findings of this study could be employed in the design and evaluation of future HRI scenarios. © 2020, Springer Nature Switzerland AG.","Behavior analysis; Elderly; Engagement; Human-Robot Interaction (HRI); Mind perception; Social robots; Young adults","Man machine systems; Robotics; Design and evaluations; Elderly people; Engagement levels; Facial Expressions; Human like; Human robot Interaction (HRI); University students; Young adults; Social robots",,,,,,,,,,,,,,,,"Abdi, J., Al-Hindawi, A., Ng, T., Vizcaychipi, M.P., Scoping review on the use of socially assistive robot technology in elderly care (2018) BMJ Open, 8 (2); Fasola, J., Mataric, M.J., Using socially assistive human–robot interaction to motivate physical exercise for older adults (2012) Proc. 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Commun., 56 (4), pp. 754-772; Drejing, K., Thill, S., Hemeren, P., Engagement: A traceable motivational concept in human-robot interaction (2015) 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 956-961. , IEEE; Hall, J., Tritton, T., Rowe, A., Pipe, A., Melhuish, C., Leonards, U., Perception of own and robot engagement in human–robot interactions and their dependence on robotics knowledge (2014) Robot. Auton. Syst., 62 (3), pp. 392-399; Ivaldi, S., Lefort, S., Peters, J., Chetouani, M., Provasi, J., Zibetti, E., Towards engagement models that consider individual factors in HRI: On the relation of extroversion and negative attitude towards robots to gaze and speech during a human–robot assembly task (2017) Int. J. Social Robot., 9 (1), pp. 63-86. , https://doi.org/10.1007/s12369-016-0357-8; Biswas, M., Romeo, M., Cangelosi, A., Jones, R.B., Are older people any different from younger people in the way they want to interact with robots? Scenario based survey (2019) J. Multimod. User Interfaces, 14 (1), pp. 61-72. , https://doi.org/10.1007/s12193-019-00306-x; Kachouie, R., Sedighadeli, S., Khosla, R., Chu, M.T., Socially assistive robots in elderly care: A mixed-method systematic literature review (2014) Int. J. Hum.-Comput. Interact., 30 (5), pp. 369-393; Bartneck, C., Nomura, T., Kanda, T., Suzuki, T., Kato, K., Cultural differences in attitudes towards robots (2005) Proceedings of the AISB Symposium on Robot Companions: Hard Problems and Open Challenges in Human-Robot Interaction, pp. 1-4; Evers, V., Maldonado, H., Brodecki, T., Hinds, P., Relational vs. Group self-construal: Untangling the role of national culture in HRI (2008) 2008 3Rd ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 255-262; Joosse, M., Lohse, M., Evers, V., Lost in proxemics: Spatial behavior for cross-cultural HRI (2014) HRI 2014 Workshop on Culture-Aware Robotics, pp. 1-6","Alimardani, M.; Department of Cognitive Science and AI, Netherlands; email: m.alimardani@uvt.nl","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097131336
"Silva I.R.R., Barbosa G.B.N., Ledebour C.C.D., Filho A.T.O., Kelner J., Sadok D., Lins S., Souza R.","57203715541;55575024600;57219604585;57219594957;6603724483;6603634824;55925398500;36060244600;","Assessing Deep Learning Models for Human-Robot Collaboration Collision Detection in Industrial Environments",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12319 LNAI",,,"240","255",,1,"10.1007/978-3-030-61377-8_17","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094109103&doi=10.1007%2f978-3-030-61377-8_17&partnerID=40&md5=de51083d08979e6106f52493efaea84e","Universidade Federal de Pernambuco, Recife, Brazil; Centro de Inovações, Ericsson, Brazil","Silva, I.R.R., Universidade Federal de Pernambuco, Recife, Brazil; Barbosa, G.B.N., Universidade Federal de Pernambuco, Recife, Brazil; Ledebour, C.C.D., Universidade Federal de Pernambuco, Recife, Brazil; Filho, A.T.O., Universidade Federal de Pernambuco, Recife, Brazil; Kelner, J., Universidade Federal de Pernambuco, Recife, Brazil; Sadok, D., Universidade Federal de Pernambuco, Recife, Brazil; Lins, S., Centro de Inovações, Ericsson, Brazil; Souza, R., Centro de Inovações, Ericsson, Brazil","The increasing adoption of industrial robots to boost production efficiency is turning human-robot collaborative scenarios much more frequent. In this context, technical factory workers need to be safe at all times from collisions and prepare for emergencies and potential accidents. Another trend in industrial automation is the usage of machine learning techniques - specifically, deep learning algorithms - for image classification. Following these tendencies, this work evaluates the application of deep learning models to detect physical collision in human-robot interactions. Security camera images are used as the primary information source for intelligent collision detection. Unlike other proposed approaches in the literature that apply sensors like Light Detection And Ranging (LIDAR), Laser Range Finder (LRF), or torque sensors from robots, this work does not consider extra sensors, using only 2D cameras. Results show more than 99% of accuracy in the evaluated scenarios, revealing that approaches adopting deep learning algorithms could be promising for human-robot collision avoidance in industrial scenarios. The proposed models may support safety in industrial environments and reduce the impact of collision accidents. © 2020, Springer Nature Switzerland AG.","Collision detection; Deep learning; Human-robot collaboration","Accident prevention; Accidents; Cameras; Deep learning; Industrial robots; Intelligent systems; Learning systems; Optical radar; Range finders; Social robots; Human-robot collaboration; Industrial automation; Industrial environments; Industrial scenarios; Laser range finders; Light detection and ranging; Machine learning techniques; Production efficiency; Learning algorithms",,,,,"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco, FACEPE","This work was supported by the Ericsson Research (Brazil), Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico (CNPq), Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES), and the Funda??o de Amparo a Ci?ncia e Tecnologia de Pernambuco (FACEPE).","Acknowledgments. This work was supported by the Ericsson Research (Brazil), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Coordena¸cão de Aperfei¸coamento de Pessoal de Ńıvel Superior (CAPES), and the Funda¸cão de Amparo a Ciência e Tecnologia de Pernambuco (FACEPE).",,,,,,,,,"(2019) 365 Microsoft Dynamics: 2019 Manufacturing Trends Report, , Accessed 09 Sep 2019; Das, N., Yip, M., Learning-Based Proxy Collision Detection for Robot Motion Planning Applications; Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L., ImageNet: A large-scale hierarchical image database (2009) CVPR 2009; Ferreira, J.L.C., Aloise, A.F., Matter, V.K., Barbosa, J.L.V., Rigo, S.J., de Oliveira, K.S.F., A model for predicting disapproval of apprentices in distance education using decision tree (2019) Proceedings of the XV Brazilian Symposium on Information Systems, SBSI 2019, Aracaju, Brazil, 20–24 May 2019, pp. 1-14. , https://doi.org/10.1145/3330204.3330223; Gecks, T., Henrich, D., Multi-camera collision detection allowing for object occlusions (2006) 37Th International Symposium on Robotics (ISR 2006)/4Th German Conference on Robotics (Robotik 2006); Henrich, D., Gecks, T., Multi-camera collision detection between known and unknown objects (2008) 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras, pp. 1-10. , IEEE; Howard, A.G., (2017) Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications.; Kingma, D.P., Ba, J., Adam: A method for stochastic optimization (2015) 3Rd International Conference on Learning Representations, , http://arxiv.org/abs/1412.6980, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings; (2018) Labor Statistics: Employer-Reported Workplace Injuries and Illnesses-2017; Lecun, Y., Bottou, L., Bengio, Y., Haffner, P., Gradient-based learning applied to document recognition (1998) Proc. IEEE, 86 (11), pp. 2278-2324; Lee, S., Kim, M., Song, J., Sensorless collision detection for safe human-robot collaboration (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2392-2397. , https://doi.org/10.1109/IROS.2015.7353701, September; Lee, S., Song, J., Collision detection for safe human-robot cooperation of a redundant manipulator (2014) 2014 14Th International Conference on Control, Automation and Systems (ICCAS 2014), Pp. 591–593, , https://doi.org/10.1109/ICCAS.2014.6987848, October; Pan, J., Chitta, S., Manocha, D., Probabilistic collision detection between noisy point clouds using robust classification (2017) Robotics Research. STAR, 100, pp. 77-94. , https://doi.org/10.1007/978-3-319-29363-95, Christensen, H.I., Khatib, O. (eds.) , Springer, Cham; Sharkawy, A.N., Aspragathos, N., Human-robot collision detection based on neural networks (2018) Int. J. Mech. Eng. Robot. Res, 7 (2), pp. 150-157; Sharkawy, A.-N., Koustoumpardis, P.N., Aspragathos, N.A., Manipulator collision detection and collided link identification based on neural networks (2019) RAAD 2018. MMS, 67, pp. 3-12. , https://doi.org/10.1007/978-3-030-00232-91, Aspragathos, N.A., Koustoumpardis, P.N., Moulianitis, V.C. (eds.), Springer, Cham; Silva, I.R.R., Silva, G.S.L., Souza, R.G., Santos, W.P., Fagundes, R.A.A., Model based on deep feature extraction for diagnosis of Alzheimer’s disease (2019) Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), 1; Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition (2015) 3Rd International Conference on Learning Representations, , http://arxiv.org/abs/1409.1556, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings; Takiguchi, T., Lee, J.H., Okamoto, S., Collision avoidance algorithm using deep learning type artificial intelligence for a mobile robot (2018) Proceedings of the International Multiconference of Engineers and Computer Scientists, 1","Silva, I.R.R.; Universidade Federal de PernambucoBrazil; email: iago.silva@gprt.ufpe.br","Cerri R.Prati R.C.",,"Springer Science and Business Media Deutschland GmbH","9th Brazilian Conference on Intelligent Systems, BRACIS 2020","20 October 2020 through 23 October 2020",,250329,03029743,9783030613761,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85094109103
"Steffen L., Liebert A., Ulbrich S., Roennau A., Dillmannn R.","57204180499;57219602747;57219101526;35273332500;57219605215;","Adaptive, Neural Robot Control – Path Planning on 3D Spiking Neural Networks",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12397 LNCS",,,"509","520",,,"10.1007/978-3-030-61616-8_41","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094108150&doi=10.1007%2f978-3-030-61616-8_41&partnerID=40&md5=97f01abe43b13af52c99976ee549fb8b","FZI Research Center for Information Technology, Karlsruhe, Germany","Steffen, L., FZI Research Center for Information Technology, Karlsruhe, Germany; Liebert, A., FZI Research Center for Information Technology, Karlsruhe, Germany; Ulbrich, S., FZI Research Center for Information Technology, Karlsruhe, Germany; Roennau, A., FZI Research Center for Information Technology, Karlsruhe, Germany; Dillmannn, R., FZI Research Center for Information Technology, Karlsruhe, Germany","Safe, yet efficient, Human-robot interaction requires real-time-capable and flexible algorithms for robot control including the human as a dynamic obstacle. Even today, methods for collision-free motion planning are often computationally expensive, preventing real-time control. This leads to unnecessary standstills due to safety requirements. As nature solves navigation and motion control sophisticatedly, biologically motivated techniques based on the Wavefront algorithm have been previously applied successfully to path planning problems in 2D. In this work, we present an extension thereof using Spiking Neural Networks. The proposed network equals a topologically organized map of the work space, allowing an execution in 3D space. We tested our work on simulated environments with increasing complexity in 2D with different connection types. Subsequently, the application is extended to 3D spaces and the effectiveness and efficiency of the used approach are attested by simulations and comparison studies. Thereby, a foundation is set to control a robot arm flexibly in a workspace with a human co-worker. In combination with neuromorphic hardware this method will likely achieve real-time capability. © 2020, Springer Nature Switzerland AG.","Cognitive robotics; Neural motion control; Spiking Neural Networks; Wavefront algorithm","Adaptive control systems; Human robot interaction; Real time control; Robot programming; Effectiveness and efficiencies; Neuromorphic hardwares; Path planning problems; Real time capability; Safety requirements; Simulated environment; Spiking neural networks; Wavefront algorithms; Neural networks",,,,,"Baden-Württemberg Stiftung","Acknowledgments. The research leading to this paper received funding as the project NeuroReact from the Baden-Württemberg Stiftung under the research program Neurorobotik.",,,,,,,,,,"Bing, Z., Meschede, C., Röhrbein, F., Huang, K., Knoll, A., A survey of robotics control based on learning-inspired spiking neural networks (2018) Front. Neurorobot., 12, p. 35; Davies, M., Loihi: A neuromorphic manycore processor with on-chip learning (2018) IEEE Micro, 38 (1), pp. 82-99; de Momi, E., Kranendonk, L., Valenti, M., Enayati, N., Ferrigno, G., A neural network-based approach for trajectory planning in robot-human handover tasks (2016) Front. Robot. AI, 3, p. 34. , JUN; Ewerton, M., Learning trajectory distributions for assisted teleoperation and path planning (2019) Front. Robot. AI, 6, p. 89; Feldman, D., The spike-timing dependence of plasticity (2012) Neuron, 75 (4), pp. 556-571; Furber, S., Galluppi, F., Temple, S., Plana, L., The SpiNNaker project (2014) Proc. IEEE, 102 (5), pp. 652-665; Glasius, R., Komoda, A., Gielen, S.C., Neural network dynamics for path planning and obstacle avoidance (1995) Neural Netw, 8 (1), pp. 125-133; Qu, H., Yang, S., Willms, A., Yi, Z., Real-time robot path planning based on a modified pulse-coupled neural network model (2009) Trans. NN, 20 (11), pp. 1724-1739; Janglová, D., Neural networks in mobile robot motion (2004) Int. J. Adv. Robot. Syst., 1 (1), pp. 15-22; Jost, J., Temporal correlation based learning in neuron models (2006) Theory Biosci, 125 (1), pp. 37-53; Khajeh-Alijani, A., Urbanczik, R., Senn, W., Scale-free navigational planning by neuronal traveling waves (2015) Plos ONE, 10 (7); Koenig, S., Likhachev, M., (2002) Incremental A*. NIPS2001, pp. 1539-1546; Merolla, P.A., A million spiking-neuron integrated circuit with a scalable communication network and interface (2014) Science, 345 (6197), pp. 668-673; O’Keefe, J., Dostrovsky, J., The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat (1971) Brain Res, 34 (1), pp. 171-175; Pal, A., Tiwari, R., Shukla, A., A focused wave front algorithm for mobile robot path planning (2011) HAIS 2011. LNCS (LNAI), 6678, pp. 190-197. , https://doi.org/10.1007/978-3-642-21219-2_25, Corchado, E., Kurzyński, M., Woźniak, M. (eds.), Springer, Heidelberg; Polyakova, M., Rubin, G., Danilova, Y., (2018) Method for Matching Customer and Manufacturer Positions for Metal Product Parameters Standardization, 1946. , AIP; Ponulak, F., Hopfield, J., Rapid, parallel path planning by propagating wavefronts of spiking neural activity (2013) Front. Comp. Neurosci., 7, p. 98; Qureshi, A., Simeonov, A., Bency, M., Yip, M., (2019) Motion Planning Networks; Raković, M., Savić, S., Santos-Victor, J., Nikolić, M., Borovac, B., Human-inspired online path planning and biped walking realization in unknown environment (2019) Front. Neurorobot., 13, p. 36; Subashini, M., Kumarsahoo, S., Pulse coupled neural networks and its applications (2014) Expert Syst. Appl., 41 (8), pp. 3965-3974; Tolman, E.C., Cognitive maps in rats and men (1948) Psychol. Rev., 55 (4), p. 189; Weber, C., Triesch, J., From exploration to planning (2008) ICANN 2008. LNCS, 5163, pp. 740-749. , https://doi.org/10.1007/978-3-540-87536-9_76, Kůrková, V., Neruda, R., Koutník, J. (eds.), Springer, Heidelberg; Zeller, M., Sharma, R., Schulten, K., Motion planning of a pneumatic robot using a neural network (1997) IEEE Control Syst, 17 (3), pp. 89-98; Zennir, M., Benmohammed, M., Boudjadja, R., (2015) Spike-Time Dependant Plasticity in a Spiking Neural Network for Robot Path Planning, 1539. , AIAI","Steffen, L.; FZI Research Center for Information TechnologyGermany; email: steffen@fzi.de","Farkas I.Masulli P.Wermter S.",,"Springer Science and Business Media Deutschland GmbH","29th International Conference on Artificial Neural Networks, ICANN 2020","15 September 2020 through 18 September 2020",,250349,03029743,9783030616151,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85094108150
"Guda V.K., Chablat D., Chevallereau C.","57218271992;6602989627;6603913141;","Safety in a human robot interactive: application to haptic perception",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12190 LNCS",,,"562","574",,1,"10.1007/978-3-030-49695-1_38","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088502499&doi=10.1007%2f978-3-030-49695-1_38&partnerID=40&md5=ffdbc9668e3a0faf6e97607c07fd2529","Laboratoire des Sciences du Numérique de Nantes (LS2N), UMR CNRS 6004, Nantes, 44300, France","Guda, V.K., Laboratoire des Sciences du Numérique de Nantes (LS2N), UMR CNRS 6004, Nantes, 44300, France; Chablat, D., Laboratoire des Sciences du Numérique de Nantes (LS2N), UMR CNRS 6004, Nantes, 44300, France; Chevallereau, C., Laboratoire des Sciences du Numérique de Nantes (LS2N), UMR CNRS 6004, Nantes, 44300, France","Usually, traditional haptic interfaces, such as Virtuose 6DOF [1], are used in the design phases by engineers [2]. Such interfaces are safe. However, the user can apply a force/torque but cannot really feel textures and appreciate material quality. These interfaces have a limited workspace, low stiffness and are very expensive. New haptic interfaces using an industrial robots or a cobot (robots specially designed to work in Human-Robot environments) can be used as a haptic interface with intermittent contacts [3, 4]. For application considered in this paper, the cobot carries several specimens of texture on its end-effector, to allow contact between a finger of the user and the robot. Safety is an important aspect in Human Robot Interactions (HRI) [5], even with the use of cobots, because contacts are expected. The purpose of this paper is to introduce a new methodology to define the basic placement of the robot in relation to the human body and for the planning and control of movements during HRIs to ensure safety. © Springer Nature Switzerland AG 2020.","Haptic interface; Human safety; Motion capture; Robot base location; Workspace","End effectors; Haptic interfaces; Human computer interaction; Industrial robots; Mice (computer peripherals); Mixed reality; Robot programming; Textures; Design phasis; Haptic perception; Human bodies; Human robot Interaction (HRI); Intermittent-contacts; Limited workspaces; Material quality; Planning and control; Human robot interaction",,,,,,,,,,,,,,,,"Perret, J., Vercruysse, P., Advantages of mechanical backdrivability for medical applications of force control (2014) Conference on Computer/Robot Assisted Surgery (CRAS), pp. 84-86; McNeely, W.A., Robotic graphics: a new approach to force feedback for virtual reality (1993) Proceedings of IEEE Virtual Reality Annual International Symposium, pp. 336-341. , IEEE; Araujo, B., Jota, R., Perumal, V., Yao, J.X., Singh, K., Wigdor, D., Snake charmer: physically enabling virtual objects (2016) Proceedings of the TEI 2016: Tenth International Conference on Tangible, Embedded, and Embodied Interaction, pp. 218-226. , ACM; Kim, Y., Kim, Y., Versatile encountered-type haptic display for VR environment using a 7-DOF manipulator (2016) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016); Goodrich, M.A., Schultz, A.C., Human-robot interaction: a survey (2008) Found. Trends R○ Hum.-Comput. Interact, 1 (3), pp. 203-275; Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P., Collaborative manufacturing with physical human-robot interaction (2016) Robot. Comput.-Integr. Manuf, 40, pp. 1-13; Long, P., Chevallereau, C., Chablat, D., Girin, A., An industrial security system for human-robot coexistence (2018) Ind. Robot Int. J, 45 (2), pp. 220-226; De La Cruz, O., Gosselin, F., Bachta, W., Morel, G., Contributions to the design of a 6 DOF contactless sensor intended for intermittent contact haptic interfaces (2018) 2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 130-135. , IEEE; Ban, Y., Kajinami, T., Narumi, T., Tanikawa, T., Hirose, M., Modifying an identified curved surface shape using pseudo-haptic effect (2012) 2012 IEEE Haptics Symposium (HAPTICS), pp. 211-216. , IEEE; Lécuyer, A., Simulating haptic feedback using vision: a survey of research and applications of pseudo-haptic feedback (2009) Presence Teleoperators Virtual Environ, 18 (1), pp. 39-53; Mercado, V.R., Marchal, M., Lécuyer, A., ENTROPiA: towards infinite surface haptic displays in virtual reality using encountered-type rotating props (2019) IEEE Trans. Vis. Comput. Graph, , https://doi.org/10.1109/TVCG.2019.2963190; Salazar, S.V., Pacchierotti, C., de Tinguy, X., Maciel, A., Marchal, M., Altering the stiffness, friction, and shape perception of tangible objects in virtual reality using wearable haptics (2020) IEEE Trans. Haptics (ToH), 13 (1), pp. 167-174; Tomić, M., Chevallereau, C., Jovanović, K., Potkonjak, V., Rodić, A., Human to humanoid motion conversion for dual-arm manipulation tasks (2018) Robotica, 36 (8), pp. 1167-1187; Ostergaard, E.H., Lightweight robot for everybody [industrial activities] (2012) IEEE Robot. Autom. Mag, 19 (4), pp. 17-18; Wenger, P., Cuspidal and noncuspidal robot manipulators (2007) Robotica, 25 (6), pp. 677-689; Zacharias, F., Borst, C., Hirzinger, G., Capturing robot workspace structure: representing robot capabilities (2007) 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3229-3236. , IEEE; Makhal, A., Goins, A.K., (2017) Reuleaux: robot base placement by reachability analysis, , ArXiv e-prints, October; Quigley, M., ROS: an open-source robot operating system (2009) ICRA Workshop on Open Source Software, Kobe, Japan, 3 (3.2), p. 5; (2019) KDL kinematics and dynamics library (KDL), , http://wiki.ros.org/kdl, June; (2019), https://github.com/flexible-collision-library/fcl, FCL flexible collision library, June; https://www.lobbybot.fr/, Lobbybot project","Chablat, D.; Laboratoire des Sciences du Numérique de Nantes (LS2N), France; email: Damien.Chablat@cnrs.fr","Chen J.Y.C.Fragomeni G.",,"Springer","12th International Conference on Virtual, Augmented and Mixed Reality, VAMR 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242159,03029743,9783030496944,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85088502499
"Petrič T., Žlajpah L.","36447813900;6603769815;","Combining Virtual and Physical Guides for Autonomous In-Contact Path Adaptation",2020,"Mechanisms and Machine Science","84",,,"181","189",,,"10.1007/978-3-030-48989-2_20","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087025874&doi=10.1007%2f978-3-030-48989-2_20&partnerID=40&md5=c83e5ae7f251e7b1306fab85e9614f3f","Department for Automation, Biocybernetics and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia","Petrič, T., Department for Automation, Biocybernetics and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia; Žlajpah, L., Department for Automation, Biocybernetics and Robotics, Jožef Stefan Institute, Ljubljana, Slovenia","Several approaches exist for learning and control of robot behaviors in physical human-robot interaction (PHRI) scenarios. One of these is the approach based on virtual guides which actively helps to guide the user. Such a system enables guiding users towards preferred movement directions or prevents them to enter into a prohibited zone. Despite being shown that such a framework works well in physical contact with humans, the efficient interaction with the environment is still limited. Within the virtual guide framework, the environment is considered as a physical guide, for example, a table is a plane that prevents the robot to penetrate through. To mitigate these limits we introduce and evaluate the means of autonomous path adaptation through interaction with physical guides, which essentially means merging virtual and physical guides. The virtual guide framework was extended by introducing an algorithm which partially modifies the virtual guides online. The path updates are now based on the interactive force measurements and essentially improves the virtual guides to match them with the actual physical guides. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.","Human-robot physical cooperation; On-line path adaptation; Virtual guides","Behavioral research; A-plane; Contact paths; Efficient interaction; Physical contacts; Physical humanrobot interaction (phri); Prohibited zones; Robot behavior; Virtual guides; Human robot interaction",,,,,,,,,,,,,,,,"Billard, A., Calinon, S., Dillmann, R., Schaal, S., (2008) Robot Programming by Demonstration, pp. 1371-1394. , Springer, Heidelberg; Gams, A., Petrič, T., Do, M., Nemec, B., Morimoto, J., Asfour, T., Ude, A., Adaptation and coaching of periodic motion primitives through physical and visual interaction (2015) Robot. Auton. Syst, , https://doi.org/10.1016/j.robot.2015.09.011; Gruebler, A., Berenz, V., Suzuki, K., Coaching robot behavior using continuous physiological affective feedback (2011) 2011 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Bled, Slovenia, pp. 466-471; Lee, D., Ott, C., Incremental kinesthetic teaching of motion primitives using the motion refinement tube (2011) Auton. Robots, 31 (2–3), pp. 115-131; Nicolescu, M.N., Mataric, M.J., Natural methods for robot task learning: instructive demonstrations, generalization and practice (2003) Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 241-248; Ott, C., Mukherjee, R., Nakamura, Y., Unified impedance and admittance control (2010) Proceedings-IEEE International Conference on Robotics and Automation, pp. 554-561; Petrič, T., Žlajpah, L., On-line adaption of virtual guides through physical interaction (2020) Advances in Service and Industrial Robotics, pp. 293-300. , Berns, K., Görges, D. (eds) Springer, Cham; Ranatunga, I., Lewis, F.L., Popa, D.O., Tousif, S.M., Adaptive admittance control for human-robot interaction using model reference design and adaptive inverse filtering (2017) IEEE Trans. Control Syst. Technol, 25 (1), pp. 278-285. , https://doi.org/10.1109/TCST.2016.2523901; Restrepo, S.S., Raiola, G., Chevalier, P., Lamy, X., Sidobre, D., Iterative virtual guides programming for human-robot comanipulation (2017) 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pp. 219-226. , IEEE, July; Riley, M., Ude, A., Atkeson, C., Cheng, G., Coaching: an approach to efficiently and intuitively create humanoid robot behaviors (2006) 2006 6th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Genoa, Italy, pp. 567-574; Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., (2009) Robotics-Modelling. Planning and Control, , Springer, London; Žlajpah, L., Petrič, T., Unified virtual guides framework for path tracking tasks Robotica, pp. 1-17. , https://doi.org/10.1017/S0263574719000973; Žlajpah, L., Petrič, T., Virtual guides for redundant robots using admittance control for path tracking tasks (2019) Advances in Service and Industrial Robotics: Proceedings of the 27th International Conference on Robotics in Alpe-Adria Danube Region (RAAD 2018), pp. 13-23. , Aspragathos, N.A., Koustoumpardis, P.N., Mou-lianitis, V.C. (eds) Springer","Petrič, T.; Department for Automation, Slovenia; email: tadej.petric@ijs.si",,,"Springer Science and Business Media B.V.",,,,,22110984,,,,"English","Mech. Mach. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85087025874
"Messeri C., Zanchettin A.M., Rocco P., Gianotti E., Chirico A., Magoni S., Gaggioli A.","57207943011;27268100300;57216880234;57216871963;56755080200;57205090175;6603138127;","On the effects of leader-follower roles in dyadic human-robot synchronisation",2020,"IEEE Transactions on Cognitive and Developmental Systems",,,,"","",,1,"10.1109/TCDS.2020.2991864","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085093985&doi=10.1109%2fTCDS.2020.2991864&partnerID=40&md5=c623186e4577a94c48d425af7f2edbfb","Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza Leonardo Da Vinci 32, 20133, Milano, Italy. (e-mail: costanza.messeri@polimi.it); Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza Leonardo Da Vinci 32, 20133, Milano, Italy.; Universit&#x00E0; Cattolica del Sacro Cuore, Dipartimento di Psicologia, Largo Gemelli 1, 20123, Milano, Italy.","Messeri, C., Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza Leonardo Da Vinci 32, 20133, Milano, Italy. (e-mail: costanza.messeri@polimi.it); Zanchettin, A.M., Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza Leonardo Da Vinci 32, 20133, Milano, Italy.; Rocco, P., Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Piazza Leonardo Da Vinci 32, 20133, Milano, Italy.; Gianotti, E., Universit&#x00E0; Cattolica del Sacro Cuore, Dipartimento di Psicologia, Largo Gemelli 1, 20123, Milano, Italy.; Chirico, A., Universit&#x00E0; Cattolica del Sacro Cuore, Dipartimento di Psicologia, Largo Gemelli 1, 20123, Milano, Italy.; Magoni, S., Universit&#x00E0; Cattolica del Sacro Cuore, Dipartimento di Psicologia, Largo Gemelli 1, 20123, Milano, Italy.; Gaggioli, A., Universit&#x00E0; Cattolica del Sacro Cuore, Dipartimento di Psicologia, Largo Gemelli 1, 20123, Milano, Italy.","The spread of cobots working side-by-side to humans has recently drawn attention to the psychological aspects of human-robot interaction. We propose an exploratory study that investigates whether and how the role taken on by the robot during a collaborative task influences the human&#x2019;s psycho-physiological response and production rate. We assume the cobot can either take the lead with respect to the human operator or comply with the partner&#x2019;s decision; namely, being the leader or the follower within the dyad. Against this background, we examined the effects of the leader-follower paradigm on a collaborative tower-building task. We evaluated the stress induced on the subject by the cooperation with the robot, based on both the ECG measurements and on PANAS and STAI questionnaires. Moreover, based on the measured cycle-time, we estimated the user&#x2019;s production rate. The results highlighted that when the human takes the lead, he/she is subject to a lower physiological stress and is less productive compared to the case where he/she follows the robot strategy. IEEE","Cognitive Human-Robot Interaction; Collaboration; Collaborative robotics; Collision avoidance; Leader-follower paradigm.; Physiological stress; Psychology; Robot kinematics; Stress; Task analysis","Physiological models; Physiology; Surveys; Collaborative tasks; Ecg measurements; Exploratory studies; Leader-follower; Physiological stress; Production rates; Psycho-physiological; Psychological Aspects; Human robot interaction",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23798920,,,,"English","IEEE Trans. Cogn. Dev. Syst.",Article,"Article in Press","",Scopus,2-s2.0-85085093985
"Di Dio C., Manzi F., Peretti G., Cangelosi A., Harris P.L., Massaro D., Marchetti A.","14069963000;57201195835;57216431805;6701387796;7403636494;36495047000;36927793400;","How children think about the robot's mind. The role of attachment and theory of mind in the attribution of mental states to a robotic agent [Come i bambini pensano alla mente del robot: Il ruolo dell'attaccamento e della teoria della mente nell'attribuzione di stati mentali ad un agente robotico]",2020,"Sistemi Intelligenti","1",,,"41","56",,10,"10.1422/96279","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083518613&doi=10.1422%2f96279&partnerID=40&md5=d56f5e26139edd7639ae365678265636","Dipartimento di Psicologia, Università Cattolica del Sacro Cuore, Largo Gemelli 1, Milano, 20123, Italy; The University of Manchester, Oxford Rd, Manchester, M13 9PL, United Kingdom; Graduate School of Education, Harvard University, Cambridge, MA  02138, United States","Di Dio, C., Dipartimento di Psicologia, Università Cattolica del Sacro Cuore, Largo Gemelli 1, Milano, 20123, Italy; Manzi, F., Dipartimento di Psicologia, Università Cattolica del Sacro Cuore, Largo Gemelli 1, Milano, 20123, Italy; Peretti, G., Dipartimento di Psicologia, Università Cattolica del Sacro Cuore, Largo Gemelli 1, Milano, 20123, Italy; Cangelosi, A., The University of Manchester, Oxford Rd, Manchester, M13 9PL, United Kingdom; Harris, P.L., Graduate School of Education, Harvard University, Cambridge, MA  02138, United States; Massaro, D., Dipartimento di Psicologia, Università Cattolica del Sacro Cuore, Largo Gemelli 1, Milano, 20123, Italy; Marchetti, A., Dipartimento di Psicologia, Università Cattolica del Sacro Cuore, Largo Gemelli 1, Milano, 20123, Italy","Human-robot interaction requires socially competent robots to encompass a variety of human features. At the basis of social interactions are our representations of the others' psychological abilities. In this perspective, we asked the following question: what is the human representation of the ""mind"" of a robot? To address this question, we asked children aged 3 to 9 years to attribute mental states to a robot and a human before and after children interacted with them. In addition, to explore the influence of children's psychological competencies on the attribution of mental states to robots and humans, we evaluated the child's quality of attachment relationships and Theory of Mind competences. Against expectations, the results showed that the interaction with the robot did not influence children's attribution of its psychological competences. The results also showed that, particularly in children aged 3 and 5 years, attachment is significantly associated with children's attribution of mental states to the robot. The development of Theory Mind ultimately exerts a moderation effect on the relationship between attachment and attribution of states. © 2020 Societa Editrice il Mulino.","Attachment; Attribution of mental states (AMS); Child; Development; Human-robot interaction (HRI); Theory of mind (ToM)",,,,,,,,,,,,,,,,,"Astington, J.W., Pelletier, J., Homer, B., Theory of mind and epistemo-logical development: The relation between children's second-order false-belief understanding and their ability to reason about evidence (2002) New Ideas in Psychology, 20 (2-3), pp. 131-144; Baron, R.M., Kenny, D.A., The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations (1986) Journal of personality and social psychology, 51 (6), p. 1173; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Science Robotics, 3 (21). , eaat5954; Bowlby, J., (1969) Attachment. Attachment and loss, (1). , New York: Basic Books; Bowlby, J., (1973) Separation: Anxiety & Anger. Attachment and Loss, (2). , London: Hogarth Press; Bowlby, J., (1980) Loss: Sadness & Depression. Attachment and Loss, (3). , London: Hogarth Press; Camisasca, E., Procaccia, R., Miragoli, S., Valtolina, G.G., Di Blasio, P., Maternal mind-mindedness as a linking mechanism between childbirth-related posttraumatic stress symptoms and parenting stress (2017) Health Care for Women International, 38 (6), pp. 593-612; Cangelosi, A., Schlesinger, M., (2015) Developmental robotics: From babies to robots, , MIT Press; Di Dio, C., Manzi, F., Itakura, S., Kanda, T., Ishiguro, H., Massaro, D., It Does Not Matter Who You Are: Fairness in Pre-schoolers Interacting with Human and Robotic Partners (2019) International Journal of Social Robotics, pp. 1-15; Di Dio, C., Isernia, S., Ceolaro, C., Marchetti, A., Massaro, D., Growing up thinking of God's beliefs: Theory of mind and ontological knowledge (2018) Sage Open, 8 (4), pp. 1-14; Fonagy, P., Attachment theory approach to treatment of the difficult patient (1998) Bulletin of the Menninger Clinic, 62 (2), pp. 147-169; Katayama, N., Katayama, J.I., Kitazaki, M., Itakura, S., Young children's folk knowledge of robots (2010) Asian Culture and History, 2 (2), p. 111; Klagsbrun, M., Bowlby, J., Responses to separation from parents: A clinical test for young children (1976) British Journal of Projective Psychology & Personality Study, 21 (2), pp. 7-27; Liverta Sempio, O., Marchetti, A., Lecciso, F., (2001) Il SAT famiglia e il SAT scuola: Strumenti di Misura dell'Ansia da Separazione da Genitori ed Insegnanti, , Milano: Università Cattolica del Sacro Cuore, ISU; Lombardi, E., Di Dio, C., Castelli, I., Massaro, D., Marchetti, A., Prospective thinking and decision making in primary school age children (2017) Heliyon, 3 (6); Main, M., Kaplan, N., Cassidy, J., Security in infancy, childhood, and adulthood: A move to the level of representation (1985) Monographs of the society for research in child development.; Manzi, F., Di Dio, C., Itakura, S., Kanda, T., Ishiguro, H., Massaro, D., Moral evaluation of Human and Robot interactions in Japanese preschoolers (2020) ACM IUI Workshop Proceedings, , Cagliari, Italy; Marchetti, A., Manzi, F., Itakura, S., Massaro, D., Theory of mind and humanoid robots from a lifespan perspective (2018) Zeitschrift für Psychologie, 226, pp. 98-109; Martini, M.C., Gonzalez, C.A., Wiese, E., Seeing minds in others-Can agents with robotic appearance have human-like preferences (2016) PloS One, 11 (1); Perner, J., Wimmer, H., ""John thinks that Mary thinks that. "" attribution of second-order beliefs by 5-to 10-year-old children (1985) Journal of Experimental Child Psychology, 39 (3), pp. 437-471; Piaget, J., (2017) The child's conception of physical causality, , London: Routledge; Premack, D., Woodruff, G., Does the chimpanzee have a theory of mind (1978) Behavioral and Brain Sciences, 1 (4), pp. 515-526; Slaughter, V., Peterson, C.C., Carpenter, M., Maternal mental state talk and infants' early gestural communication (2009) Journal of Child Language, 36 (5), pp. 1053-1074; Torta, E., van Dijk, E., Ruijten, P.A., Cuijpers, R.H., The ultimatum game as measurement tool for anthropomorphism in human-robot interaction (2013) International Conference on Social Robotics, pp. 209-217. , Springer: Cham; Vinanzi, S., Patacchiola, M., Chella, A., Cangelosi, A., Would a robot trust you? Developmental robotics model of trust and theory of mind (2019) Philosophical Transactions of the Royal Society B, 374 (1771); Wellman, H.M., Cross, D., Watson, J., Meta-analysis of theory-of-mind development: The truth about false belief (2001) Child Development, 72 (3), pp. 655-684; Wimmer, H., Perner, J., Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children's understanding of deception (1983) Cognition, 13 (1), pp. 103-128",,,,"Societa Editrice il Mulino",,,,,11209550,,,,"Italian","Sist. Intelligenti",Article,"Final","",Scopus,2-s2.0-85083518613
"Li Z., Ye J., Wu H.","57072300600;56447569000;57214357856;","Collision Detection Algorithm for Robots with Unknown Payload [机器人带未知负载条件下的碰撞检测算法]",2020,"Jiqiren/Robot","42","1",,"29","38",,2,"10.13973/j.cnki.robot.190169","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083036470&doi=10.13973%2fj.cnki.robot.190169&partnerID=40&md5=328948497d789ff4fe3c69a62191bcf6","School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350116, China","Li, Z., School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350116, China; Ye, J., School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350116, China; Wu, H., School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350116, China","The unknown payload will increase the error of the robot model, which will cause serious interference to the collision detection and even lead to algorithm failure. Therefore, a collision detection algorithm for robots working with payloads is proposed in this paper. Firstly, a novel torque observer with band-pass filtering characteristics is designed based on the generalized momentum of the robot. Through the analysis of the frequency domain characteristics of the robot dynamics, the maximum frequency threshold of the robot dynamics model corresponding to the joint speed is obtained, and the parameters of the band-pass torque observer are determined. Then, the band-pass torque observer is used to filter the joint torque signal to improve the tolerance of the collision detection algorithm to the robot model error. Finally, the effectiveness of the proposed collision detection algorithm is further validated by human-robot interaction experiments. The experimental results show that the proposed algorithm is more robust than existing algorithms, and can meet the needs of the robot with 0~2 kg unknown payload. © 2020, Science Press. All right reserved.","Band-pass torque observer; Collision detection; Frequency domain analysis; Robot safety; Unknown payload","Bandpass filters; Chemical detection; Error detection; Frequency domain analysis; Signal detection; Torque; Band pass filtering; Collision detection; Collision detection algorithm; Frequency domains; Maximum frequency; Robot dynamics; Robots working; Torque observer; Human robot interaction",,,,,,,,,,,,,,,,"Robla-Gomez, S., Becerra, V.M., Llata, J.R., Working together: A review on safe human-robot collaboration in industrial environments (2017) IEEE Access, 5, pp. 26754-26773; Flacco, F., Kroeger, T., de Luca, A., A depth space approach for evaluating distance to objects (2015) Journal of Intelligent and Robotic Systems, 80 (1), pp. 7-22; Villani, V., Pini, F., Leali, F., Survey on human-robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266; Wu, H.B., Chen, J.P., Su, Y.X., New tactile sensor for position detection based on distributed planar electric field (2016) Sensors and Actuators A: Physical, 242, pp. 146-161; Haddadin, S., de Luca, A., Albu-Schaffer, A., Robot collisions: A survey on detection, isolation, and identification (2017) IEEE Transactions on Robotics, 33 (6), pp. 1292-1312; Xie, Z.W., Li, C., Liu, H., An approach of integrating force sensing and safety operation for manipulator (2013) International Journal of Intelligent Unmanned Systems, 1 (1), pp. 62-75; Dimeas, F., Avendavalencia, L.D., Aspragathos, N., Human-robot collision detection and identification based on fuzzy and time series modelling (2015) Robotica, 33 (9), pp. 1886-1898; Heo, Y.J., Kim, D., Lee, W., Collision detection for industrial collaborative robots: A deep learning approach (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 740-746; Li, Z.J., Wu, H.B., Yang, J.M., A position and torque switching control method for robot collision safety (2018) International Journal of Automation and Computing, 15 (2), pp. 156-168; Je, H.W., Baek, J.Y., Min, C.L., Current based compliance control method for minimizing an impact force at collision of service robot arm (2011) International Journal of Precision Engineering and Manufacturing, 12 (2), pp. 251-258; Yamada, Y., Suita, K., Imai, K., A failure-to-safety robot system for human-robot coexistence (1996) Robotics and Autonomous Systems, 18 (1-2), pp. 283-291; Jung, B.J., Koo, J.C., Choi, H.R., Human-robot collision detection under modeling uncertainty using frequency boundary of manipulator dynamics (2014) Journal of Mechanical Science and Technology, 28 (11), pp. 4389-4395; de Luca, A., Albu-Schaffer, A., Haddadin, S., Collision detection and safe reaction with the DLR-III lightweight manipulator arm (2006) IEEE International Conference on Intelligent Robots and Systems, pp. 1623-1630. , Piscataway, USA: IEEE; Zhong, C.W., Xiang, J., Wei, W., Collision detection and safe reaction of manipulator based on disturbance observer (2012) Journal of Zhejiang University: Engineering Science, 46 (6), pp. 1115-1121. , 钟琮玮, 项基, 韦巍, 等. 基于扰动观测器的机械手碰撞检测与安全响应[J]. 浙江大学学报: 工学版, 2012, 46(6): 1115-1121; Wu, H.B., Li, S.Y., Wu, G.K., Collision detection algorithm for robot manipulator based on momentum deviation observer (2015) Electric Machines and Control, 19 (5), pp. 97-104. , 吴海彬, 李实懿, 吴国魁. 基于动量偏差观测器的机器人碰撞检测算法[J]. 电机与控制学报, 2015, 19(5): 97-104; Lee, S.D., Song, J.B., Sensorless collision based on friction model for a robot manipulator (2016) International Journal of Precision Engineering and Manufacturing, 17 (1), pp. 11-17; Makarov, M., Caldas, A., Grossard, M., Adaptive filtering for robust proprioceptive robot impact detection under model uncertainties (2014) IEEE/ASME Transactions on Mechatronics, 19 (6), pp. 1917-1928; Sotoudehnejad, V., Kermani, M.R., Velocity-based variable thresholds for improving collision detection in manipulators (2014) IEEE International Conference on Robotics and Automation, pp. 3364-3369. , Piscataway, USA: IEEE; Gaz, C., de Luca, A., Payload estimation based on identified coefficients of robot dynamics-With an application to collision detection (2017) IEEE International Conference on Intelligent Robots and Systems, pp. 3033-3040. , Piscataway, USA: IEEE","Wu, H.; School of Mechanical Engineering and Automation, China; email: wuhb@fzu.edu.cn",,,"Chinese Academy of Sciences",,,,,10020446,,JIQIE,,"Chinese","Jiqiren",Article,"Final","",Scopus,2-s2.0-85083036470
"Gao Y., Chang H.J., Demiris Y.","57116746000;35168664400;6506125343;","User Modelling Using Multimodal Information for Personalised Dressing Assistance",2020,"IEEE Access","8",,"9024050","45700","45714",,,"10.1109/ACCESS.2020.2978207","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082066456&doi=10.1109%2fACCESS.2020.2978207&partnerID=40&md5=1811a0adfeca53be730fe1f730b9c9bb","Department of Electrical and Electronic Engineering, Personal Robotics Laboratory, Imperial College London, London, SW7 2BT, United Kingdom","Gao, Y., Department of Electrical and Electronic Engineering, Personal Robotics Laboratory, Imperial College London, London, SW7 2BT, United Kingdom; Chang, H.J., Department of Electrical and Electronic Engineering, Personal Robotics Laboratory, Imperial College London, London, SW7 2BT, United Kingdom; Demiris, Y., Department of Electrical and Electronic Engineering, Personal Robotics Laboratory, Imperial College London, London, SW7 2BT, United Kingdom","Assistive robots in home environments are steadily increasing in popularity. Due to significant variabilities in human behaviour, as well as physical characteristics and individual preferences, personalising assistance poses a challenging problem. In this paper, we focus on an assistive dressing task that involves physical contact with a human's upper body, in which the goal is to improve the comfort level of the individual. Two aspects are considered to be significant in improving a user's comfort level: having more natural postures and exerting less effort. However, a dressing path that fulfils these two criteria may not be found at one time. Therefore, we propose a user modelling method that combines vision and force data to enable the robot to search for an optimised dressing path for each user and improve as the human-robot interaction progresses. We compare the proposed method against two single-modality state-of-the-art user modelling methods designed for personalised assistive dressing by user studies (31 subjects). Experimental results show that the proposed method provides personalised assistance that results in more natural postures and less effort for human users. © 2013 IEEE.","Assistive dressing; Human-robot interaction; Multimodal user modelling; Vision and force fusion","Behavioral research; Man machine systems; Assistive; Assistive robots; Human behaviours; Individual preference; Multi-modal information; Physical characteristics; Physical contacts; User Modelling; Human robot interaction",,,,,"Royal Academy of Engineering, RAENG","This research was supported by a Royal Academy of Engineering Chair in Emerging Technologies to Yiannis Demiris.",,,,,,,,,,"Flandorfer, P., Population ageing and socially assistive robots for elderly persons: The importance of sociodemographic factors for user acceptance (2012) Int. J. Population Res., 2012, pp. 1-13. , Apr; Tapus, A., Maja, M., Scassellatti, B., Socially assistive robotics [grand challenges of robotics] (2007) IEEE Robot. Autom. Mag., 14 (1), pp. 35-42. , Mar; Broekens, J., Heerink, M., Rosendal, H., Assistive social robots in elderly care: A review (2009) Gerontechnology, 8 (2), pp. 94-103. , Apr; Kostavelis, I., Giakoumis, D., Peleka, G., Kargakos, A., Skartados, E., Vasileiadis, M., Tzovaras, D., RAMCIP robot: A personal robotic assistant; Demonstration of a complete framework (2018) Proc. Eur. Conf. Comput. Vis., pp. 96-111. , Munich, Germany: Springer; Kostavelis, I., Giakoumis, D., Malassiotis, S., Tzovaras, D., A POMDP design framework for decision making in assistive robots (2017) Proc. Int. Conf. Hum.-Comput. Interact., pp. 467-479. , Vancouver, BC, Canada: Springer; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Robot. Auto. Syst., 42 (3-4), pp. 143-166. , Mar; Gao, Y., Jin Chang, H., Demiris, Y., User modelling for personalised dressing assistance by humanoid robots (2015) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), pp. 1840-1845. , Sep; Gao, Y., Chang, H.J., Demiris, Y., Iterative path optimisation for personalised dressing assistance using vision and force information (2016) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), pp. 4398-4403. , Oct; Fischer, G., User modeling in human-computer interaction (2001) User Model. User-Adapted Interact., 11 (1-2), pp. 65-86. , Mar; Hothi, J., Hall, W., An evaluation of adapted hypermedia techniques using static user modelling (1998) Proc. 2nd Workshop Adapt. Hypertext Hypermedia, pp. 45-50; McTear, M.F., User modelling for adaptive computer systems: A survey of recent developments (1993) Artif. Intell. Rev., 7 (3-4), pp. 157-184. , Aug; Rich, E., User modeling via stereotypes (2010) Cognit. Sci., 3 (4), pp. 329-354. , Feb; Langley, P., User modeling in adaptive interface (1999) UM99 User Model-ing., pp. 357-370. , Banff, AB, Canada: Springer; Canal, G., Alenyà, G., Torras, C., Personalization framework for adaptive robotic feeding assistance (2016) Proc. Int. Conf. Social Robot., pp. 22-31. , Kansas City, MO, USA: Springer; Tapus, A., Pu, C., Matari, M.J., User_Robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy (2008) Intell. Service Robot., 1 (2), pp. 169-183. , Feb; Ewen, R., (2014) An Introduction to Theories of Personality, , East Sussex, U.K.: Psychology Press; Dautenhahn, K., Walters, M., Woods, S., Koay, K.L., Nehaniv, C.L., Sisbot, A., Alami, R., Siméon, T., How may i serve you?: A robot companion approaching a seated person in a helping context (2006) Proc. 1st ACM SIGCHI/SIGART Conf. Hum.-Robot Interact., pp. 172-179; Cakmak, M., Srinivasa, S.S., Kyung Lee, M., Forlizzi, J., Kiesler, S., Human preferences for robot-human hand-over configurations (2011) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 1986-1993. , Sep; Sisbot, E.A., Marin, L.F., Alami, R., Spatial reasoning for human robot interaction (2007) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 2281-2287. , Oct; Kostavelis, I., Vasileiadis, M., Skartados, E., Kargakos, A., Giakoumis, D., Bouganis, C.-S., Tzovaras, D., Understanding of human behavior with a robotic agent through daily activity analysis (2019) Int. J. Social Robot., 11 (3), pp. 437-462. , Jan; Tamei, T., Matsubara, T., Rai, A., Shibata, T., Reinforcement learning of clothing assistance with a dual-arm robot (2011) Proc. 11th IEEE-RAS Int. Conf. Humanoid Robots, pp. 733-738. , Oct; Matsubara, T., Shinohara, D., Kidode, M., Reinforcement learning of a motor skill for wearing a T-shirt using topology coordinates (2013) Adv. Robot., 27 (7), pp. 513-524. , May; Colome, A., Planells, A., Torras, C., A friction-model-based framework for reinforcement learning of robotic tasks in non-rigid environments (2015) Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 5649-5654. , May; Pignat, E., Calinon, S., Learning adaptive dressing assistance from human demonstration (2017) Robot. Auto. Syst., 93, pp. 61-75. , Jul; Koganti, N., Ngeo, J.G., Tomoya, T., Ikeda, K., Shibata, T., Cloth dynamics modeling in latent spaces and its application to robotic clothing assistance (2015) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), pp. 3464-3469. , Sep; Koganti, N., Tamei, T., Ikeda, K., Shibata, T., Bayesian nonparametric learning of cloth models for real-time state estimation (2017) IEEE Trans. Robot., 33 (4), pp. 916-931. , Aug; Kapusta, A., Yu, W., Bhattacharjee, T., Liu, C.K., Turk, G., Kemp, C.C., Data-driven haptic perception for robot-assisted dressing (2016) Proc. 25th IEEE Int. Symp. Robot Hum. Interact. Commun. 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Healthcare Conf., pp. 420-439","Gao, Y.; Department of Electrical and Electronic Engineering, United Kingdom; email: y.gao12@alumni.imperial.ac.uk",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85082066456
"Hsieh W.-F., Sato-Shimokawara E., Yamaguchi T.","57202001489;25031564100;56501037800;","Investigation of robot expression style in human-robot interaction",2020,"Journal of Robotics and Mechatronics","32","1",,"224","235",,,"10.20965/jrm.2020.p0224","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079877371&doi=10.20965%2fjrm.2020.p0224&partnerID=40&md5=77e567b592bd9823ccd363dac2fb5269","Department of Information and Communications Systems Engineering, Faculty of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Tokyo, Hino, 191-0065, Japan; Faculty of System Design, Tokyo Metropolitan Technology, 6-6 Asahigaoka, Tokyo, Hino, 191-0065, Japan; Faculty of System design, Tokyo Metropolitan University, 6-6 Asahigaoka, Tokyo, Hino, 191-0065, Japan","Hsieh, W.-F., Department of Information and Communications Systems Engineering, Faculty of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Tokyo, Hino, 191-0065, Japan; Sato-Shimokawara, E., Faculty of System Design, Tokyo Metropolitan Technology, 6-6 Asahigaoka, Tokyo, Hino, 191-0065, Japan; Yamaguchi, T., Faculty of System design, Tokyo Metropolitan University, 6-6 Asahigaoka, Tokyo, Hino, 191-0065, Japan","In our daily conversation, we obtain considerable information from our interlocutor’s non-verbal behaviors, such as gaze and gestures. Several studies have shown that nonverbal messages are prominent factors in smoothing the process of human-robot interaction. Our previous studies have shown that not only a robot’s appearance but also its gestures, tone, and other nonverbal factors influence a person’s impression of it. The paper presented an analysis of the impressions made when human motions are implemented on a humanoid robot, and experiments were conducted to evaluate impressions made by robot expressions to analyze the sensations. The results showed the relation between robot expression patterns and human preferences. To further investigate biofeedback elicited by different robot styles of expression, a scenario-based experiment was done. The results revealed that people’s emotions can definitely be affected by robot behavior, and the robot’s way of expressing itself is what most influences whether or not it is perceived as friendly. The results show that it is potentially useful to combine our concept into a robot system to meet individual needs. © 2020, Fuji Technology Press. All rights reserved.","Human-robot interaction; Nonverbal communication; Robot expression style; Robot impression analysis","Anthropomorphic robots; Behavioral research; Biofeedback; Man machine systems; Expression patterns; Expression styles; Humanoid robot; Impression analysis; Non-verbal communications; Nonverbal behavior; Robot behavior; Scenario-based; Human robot interaction",,,,,,,,,,,,,,,,"Borja, R., de La Pinta, J.R., Lvarez, A., Maestre, J.M., Inte-gration of service robots in the smart home by means of UPnP: A surveillance robot case study (2013) Robotics and Autonomous Systems, 61 (2), pp. 153-160; Huijnen, C., Badii, A., van den Heuvel, H., Caleb-Solly, P., Thiemert, D., Maybe it becomes a buddy, but do not call it a robot” – seamless cooperation between companion robotics and smart homes (2011) Ambient Intelligence,” Pp. 324-329, , D. V. Keyson et al. 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"Martínez del Horno M., Romero-González C., Orozco-Barbosa L., García-Varea I.","57190977818;56326193000;7006046164;23100071600;","A Novel Inter-device Calibration for Wi-Fi-aided Indoor Localization Systems",2020,"Advances in Intelligent Systems and Computing","1093 AISC",,,"610","619",,,"10.1007/978-3-030-36150-1_50","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079085623&doi=10.1007%2f978-3-030-36150-1_50&partnerID=40&md5=f060bfd750707365ec1c256f54cdd39a","Instituto de Investigación en Informática de Albacete (I3A), Universidad de Castilla-La Mancha, Campus Univ. s/n, Albacete, 02071, Spain","Martínez del Horno, M., Instituto de Investigación en Informática de Albacete (I3A), Universidad de Castilla-La Mancha, Campus Univ. s/n, Albacete, 02071, Spain; Romero-González, C., Instituto de Investigación en Informática de Albacete (I3A), Universidad de Castilla-La Mancha, Campus Univ. s/n, Albacete, 02071, Spain; Orozco-Barbosa, L., Instituto de Investigación en Informática de Albacete (I3A), Universidad de Castilla-La Mancha, Campus Univ. s/n, Albacete, 02071, Spain; García-Varea, I., Instituto de Investigación en Informática de Albacete (I3A), Universidad de Castilla-La Mancha, Campus Univ. s/n, Albacete, 02071, Spain","Wi-Fi-based indoor localization mechanisms have attracted many research efforts in recent years due to the widespread use of this technology. All robots in indoor scenarios use this technology to provide Internet connection for Cloud services in speech understanding or human-robot interaction. However, this technology can also be used to provide localization services based on the Received Signal Strength Indicator (RSSI). Nevertheless, the majority of the current proposed indoor localization systems spend huge amounts of time in order to set-up the system in the target environment. In addition, given that the IEEE 802.11 standards leave the RSSI computation up to the manufacturers, each device which needs to be located has to survey the wireless platform to correctly calibrate the localization system. To overcome these drawbacks, this paper presents a novel inter-device calibration procedure for new potential devices which makes use of a previous calibration carried out by a different device. The proposed calibration procedure enables an on-the-fly configuration of any new device with a negligible loss of localization accuracy. © 2020, Springer Nature Switzerland AG.","Indoor robot localization; Inter-device calibration procedure; Wi-Fi based","Calibration; Human robot interaction; IEEE Standards; Robot applications; Robotics; Wireless local area networks (WLAN); Calibration procedure; Device calibration; IEEE 802.11 standards; Indoor localization systems; Indoor robots; Localization accuracy; Localization services; Received signal strength indicators; Indoor positioning systems",,,,,"SBPLY/17/180501/000493; Ministerio de Economía y Competitividad, MINECO: RTI2018-098156-B-C52; Universidad de Castilla-La Mancha, UCLM: 2016/14100; European Regional Development Fund, FEDER","Acknowledgments. This work has been partially funded by the Spanish Ministry of Economy and Competitiveness under Grant number RTI2018-098156-B-C52, and by the Regional Council of Education, Culture and Sports of Castilla-La Mancha under grant number SBPLY/17/180501/000493, supported with FEDER funds. Miguel Martínez del Horno is also funded by the Universidad de Castilla-La Mancha grant 2016/14100.","This work has been partially funded by the Spanish Ministry of Economy and Competitiveness under Grant number RTI2018-098156-B-C52, and by the Regional Council of Education, Culture and Sports of Castilla-La Mancha under grant number SBPLY/17/180501/000493, supported with FEDER funds. Miguel Mart?nez del Horno is also funded by the Universidad de Castilla-La Mancha grant 2016/14100.",,,,,,,,,"BCM4339 Preliminary Data Sheet, , http://www.mouser.com/ds/2/100/Radio%20with%20Integrated%20Bluetooth%204.1%20and%20FM%20Receive-961626.pdf; Castillo-Cara, M., Lovón-Melgarejo, J., Bravo-Rocca, G., Orozco-Barbosa, L., García-Varea, I., An empirical study of the transmission power setting for Bluetooth-based indoor localization mechanisms (2017) Sensors, 17 (6), p. 1318; Chen, Z., Zou, H., Jiang, H., Zhu, Q., Soh, Y., Xie, L., Fusion of WiFi, smartphone sensors and landmarks using the Kalman filter for indoor localization (2015) Sensors, 15 (1), pp. 715-732; Dong, F., Chen, Y., Liu, J., Ning, Q., Piao, S., A calibration-free localization solution for handling signal strength variance (2009) International Workshop on Mobile Entity Localization and Tracking in Gps-Less Environments, pp. 79-90. , Springer, Heidelberg; Fang, S.H., Wang, C.H., Chiou, S.M., Lin, P., Calibration-free approaches for robust Wi-Fi positioning against device diversity: A performance comparison (2012) 2012 IEEE 75Th Vehicular Technology Conference (VTC Spring), pp. 1-5. , IEEE; Martínez Del Horno, M., García-Varea, I., Orozco Barbosa, L., Calibration of Wi-Fi-based indoor tracking systems for Android-based smartphones (2019) Remote Sens, 11 (9), p. 1072; Kim, Y., Shin, H., Chon, Y., Cha, H., Smartphone-based Wi-Fi tracking system exploiting the RSS peak to overcome the RSS variance problem (2013) Perv. 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IEEE Press, Piscataway; Romero-González, C., Villena, Á., González-Medina, D., Martínez-Gómez, J., Rodríguez-Ruiz, L., García-Varea, I., InLiDa: A 3D lidar dataset for people detection and tracking in indoor environments (2017) 12Th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), VISAPP, 6, pp. 484-491. , Porto, Portugal; Sun, G., Chen, J., Guo, W., Liu, K., Signal processing techniques in network-aided positioning: A survey of state-of-the-art positioning designs (2005) IEEE Sig. Process. Mag., 22 (4), pp. 12-23; Tsui, A.W., Chuang, Y.H., Chu, H.H., Unsupervised learning for solving RSS hardware variance problem in WiFi localization (2009) Mob. Netw. Appl., 14 (5), pp. 677-691; Tuta, J., Juric, M.B., A self-adaptive model-based Wi-Fi indoor localization method (2016) Sensors, 16, p. 2074; Yassin, A., Nasser, Y., Awad, M., Al-Dubai, A., Liu, R., Yuen, C., Raulefs, R., Aboutanios, E., Recent advances in indoor localization: A survey on theoretical approaches and applications (2016) IEEE Commun. Surv. Tutorials, 19 (2), pp. 1327-1346","Martínez del Horno, M.; Instituto de Investigación en Informática de Albacete (I3A), Campus Univ. s/n, Spain; email: Miguel.MHorno@uclm.es","Silva M.F.Luis Lima J.Reis L.P.Sanfeliu A.Tardioli D.",,"Springer","4th Iberian Robotics Conference, ROBOT 2019","20 November 2019 through 22 November 2019",,236649,21945357,9783030361495,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85079085623
"Belanche D., Casaló L.V., Flavián C.","36337572600;15847695300;6505957239;","Customer’s Acceptance of Humanoid Robots in Services: The Moderating Role of Risk Aversion",2020,"Smart Innovation, Systems and Technologies","167",,,"449","458",,14,"10.1007/978-981-15-1564-4_42","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076984285&doi=10.1007%2f978-981-15-1564-4_42&partnerID=40&md5=de17bba86c8f764204f16bf0e37ffbf7","Faculty of Economy and Business, University of Zaragoza (Spain), Gran Vía 2, 50.005, Saragossa, Spain; Faculty of Business and Public Administration, University of Zaragoza (Spain), Plaza Constitución s/n, Huesca, 22.001, Spain","Belanche, D., Faculty of Economy and Business, University of Zaragoza (Spain), Gran Vía 2, 50.005, Saragossa, Spain; Casaló, L.V., Faculty of Business and Public Administration, University of Zaragoza (Spain), Plaza Constitución s/n, Huesca, 22.001, Spain; Flavián, C., Faculty of Economy and Business, University of Zaragoza (Spain), Gran Vía 2, 50.005, Saragossa, Spain","The emerging introduction of humanoid robots in service encounters is becoming a reality in the present and the short-term. Owing to this unstoppable advance, there is a need to better understand customers’ perceptions and reactions toward humanoid agents in service encounters. To shed some light on this underexplored phenomenon, this research investigates how the interaction between robot and customer’s features may contribute to a successful introduction of this disruptive innovation. Results of an empirical study with a sample of 168 US customers reveal that customer’s perceptions of robot’s human-likeness increase the intentions to use humanoid service robots. Interestingly, customers’ risk aversion moderates this relationship. Specifically, the study found that highly risk-averse customers tend to avoid using humanoids when they are perceived as highly mechanical-like. The discussion highlights the main contributions of the research, which combine previous knowledge on human–robot interaction and risk aversion from a marketing approach. Managerial implications derived from the research findings and the avenues opened for further research are described at the end. © 2020, Springer Nature Singapore Pte Ltd.","Humanoid; Risk aversion; Robot","Anthropomorphic robots; Commerce; Robots; Sales; Disruptive innovations; Empirical studies; Humanoid; Humanoid service robot; Managerial implications; Risk aversion; Robot interactions; Service encounter; Human robot interaction",,,,,,,,,,,,,,,,"Mende, M., Scott, M.L., van Doorn, J., Grewal, D., Shanks, I., Service robots rising: How humanoid robots influence service experiences and elicit compensatory consumer responses (2019) J. Mark. Res., 56 (4), pp. 1-22; Negahban, A., Smith, J.S., Simulation for manufacturing system design and operation: Literature review and analysis (2014) J. Manuf. Syst., 33, pp. 241-261; Wirtz, J., Patterson, P.G., Kunz, W.H., Gruber, T., Lu, V.N., Paluch, S., Martins, A., Brave new world: Service robots in the frontline (2018) J. Serv. Manag., 29 (5), pp. 907-931; (2018) International Federation of Robotics, , https://ifr.org/ifr-press-releases/news/robot-density-rises-globally, Robot density rises globally, Accessed 03 June 2019; Huang, M.H., Rust, R.T., Artificial intelligence in service (2018) J. Serv. Res., 21 (2), pp. 155-172; https://www.abc.es/espana/comunidad-valenciana/abci-como-seran-hoteles-futuro-sin-recepcionista-robots-camarero-o-cocinero-y-comida-sana-201906112007_noticia.html, Annual Report,, https://www.elmundo.es/viajes/el-baul/2019/07/27/5d3ac4defdddff83558b45b7.html. Accessed 03 June 2019; van Doorn, J., Mende, M., Noble, S.M., Hulland, J., Ostrom, A.L., Grewal, D., Petersen, J.A., Domo arigato Mr. Roboto: Emergence of automated social presence in organizational frontlines and customers’ service experiences (2017) J. Serv. Res., 20 (1), pp. 43-58; Rosenthal-Von der Püthen, A.M., Kramer, N.C., How design characteristics of robots determine evaluation and uncanny valley related responses (2014) Computers in Human Behavior, 36, pp. 422-439; Allen, D.G., Weeks, K.P., Moffitt, K.R., Turnover intentions and voluntary turnover: The moderating roles of self-monitoring, locus of control, proactive personality, and risk aversion (2005) J. Appl. Psychol., 90 (5), pp. 980-996; Walters, M.L., Syrdal, D.S., Dautenhahn, K., Te Boekhorst, R., Koay, K.L., Avoiding the uncanny valley: Robot appearance, personality and consistency of behavior in an attention-seeking home scenario for a robot companion (2008) Auton. Robots, 24 (2), pp. 159-178; Nass, C., Moon, Y., Fogg, B.J., Reeves, B., Dryer, D.C., Can computer personalities be human personalities? (1995) Int. J. Hum Comput Stud., 43, pp. 223-239; Luczak, H., Roetting, M., Schmidt, L., Let’s talk: Anthropomorphization as means to cope with stress of interacting with technical devices (2003) Ergonomics, 46 (13-14), pp. 1361-1374; Mori, M., The uncanny valley (1970) Energy, 7 (4), pp. 33-35; Tussyadiah, I.P., Park, S., Consumer evaluation of hotel service robots (2018) Information and Communication Technologies in Tourism 2018, pp. 308-320. , Stangl, B., Pesonen, J. (eds.), Springer; Gray, K., Wegner, D.M., Feeling robots and human zombies: Mind perception and the uncanny valley (2012) Cognition, 125 (1), pp. 125-130; Pratt, J.W., Risk aversion in the small and in the large (1978) Uncertainty in Economics, pp. 59-79. , Academic Press; Hofstede, G., Bond, M.H., Hofstede’s culture dimensions: An independent validation using Rokeach’s value survey (1984) J. Cross Cult. Psychol., 15, pp. 417-433; Bao, Y., Zhou, K.Z., Su, C., Face consciousness and risk aversion: Do they affect consumer decision-making? (2003) Psychol. Market., 20 (8), pp. 733-755; Sun, J., How risky are services? An empirical investigation on the antecedents and consequences of perceived risk for hotel service (2014) Int. J. Hosp. Manag., 37, pp. 171-179; Meroño-Cerdán, A.L., López-Nicolás, C., Molina-Castillo, F.J., Risk aversion, innovation and performance in family firms (2017) Econ. Innov. New Technol., 27 (2), pp. 189-203; Wu, C.H.J., Liao, H.C., Hung, K.P., Ho, Y.H., Service guarantees in the hotel industry: Their effects on consumer risk and service quality perceptions (2012) Int. J. Hosp. Manag., 31 (3), pp. 757-763; Bauer, K., Hein, S.E., The effect of heterogeneous risk on the early adoption of Internet banking technologies (2006) J. Bank. Finance, 30 (6), pp. 1713-1725; Tan, S.J., Strategies for reducing consumers’ risk aversion in Internet shopping (1999) J. Consum. Market., 16 (2), pp. 163-180; Schleich, J., Gassmann, X., Meissner, T., Faure, C., A large-scale test of the effects of time discounting, risk aversion, loss aversion, and present bias on household adoption of energy-efficient technologies (2019) Energy Econ, 80, pp. 377-393; Dedeke, A., Service quality: A fulfilment-oriented and interactions-centered approach (2003) Manag. Serv. Qual. Int. J., 13 (4), pp. 276-289; de Jong, A., de Ruyter, K., Lemmink, J., The adoption of information technology by self-managing service teams (2003) J. Serv. Res., 6 (2), pp. 162-179; Cauberghe, V., de Pelsmacker, P., The impact of banners on digital television: The role of program interactivity and product involvement (2008) Cyberpsychol. Behav., 11 (1), pp. 91-94; Steenkamp, J.B., Geyskens, I., How country characteristics affect the perceived value of a Website (2006) J. Market., 70 (3), pp. 136-150; Wu, L., Chen, J.L., An extension of trust and TAM model with TPB in the initial adoption of on-line tax: An empirical study (2005) Int. J. Hum Comput Stud., 62 (6), pp. 784-808; Jaccard, J., Turrisi, R., Interaction effects in multiple regression, 72 (2003) Sage; Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D., User acceptance of information technology: Toward a unified view (2003) MIS Q, 27 (3), pp. 425-478; Jaccard, J., Wan, C., (1996) LISREL Approaches to Interaction Effects in Multiple Regressions, , Sage Publications, Thousand Oaks, CA","Belanche, D.; Faculty of Economy and Business, Gran Vía 2, 50.005, Spain; email: belan@unizar.es","Rocha A.Reis J.L.Peter M.K.Bogdanovic Z.",,"Springer","International Conference on Marketing and Technologies, ICMarkTech 2019","27 November 2019 through 29 November 2019",,234839,21903018,9789811515637,,,"English","Smart Innov. Syst. Technol.",Conference Paper,"Final","",Scopus,2-s2.0-85076984285
"Gwóźdź M.","57192387771;","Figurative language grounding in humanoid robots",2020,"Advances in Intelligent Systems and Computing","1038",,,"347","362",,,"10.1007/978-3-030-29513-4_25","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072844719&doi=10.1007%2f978-3-030-29513-4_25&partnerID=40&md5=482c9a9c58d3914f4a8a9c28be8ed4e8","Ludwig Maximilian University of Munich, Geschwister-Scholl-Platz 1, Munich, 80539, Germany","Gwóźdź, M., Ludwig Maximilian University of Munich, Geschwister-Scholl-Platz 1, Munich, 80539, Germany","The paper proposes a method for representing idioms (in particular, colour-related idioms) within the framework of Fluid Construction Grammar (FCG). The main purpose of this endeavour is to facilitate (figurative) language grounding in humanoid robots in the near future. There is sufficient evidence in the cognitive robotics literature that figurative language plays (or, will play) an important role in humanoid robots. Idioms pose a real challenge to satisfactory human-robot interaction due to the plethora of linguistic and contextual subtleties. In my approach, the starting point for encoding idioms within FCG was the selection of several colour-related idioms due to the fact that they could be well-integrated in current robotics experiments. Humanoid robots (for instance, the iCub) are frequently subjected to simple colour-related experiments (such as, learning how to lift and manipulate colourful objects, etc.). Introducing much more difficult tasks, such as recognising colour-idioms as abstract, is needed to achieve further robotic development. The paper presents some techniques on the theoretical encoding of idioms, as well as practical guidelines on how to implement custom figurative constructions within FCG. © Springer Nature Switzerland AG 2020.","Cognitive robotics; Figurative language; Fluid construction grammar idioms; Language grounding; Linguistics","Anthropomorphic robots; Color; Encoding (symbols); Intelligent systems; Linguistics; Robotics; Signal encoding; Cognitive robotics; Figurative language; Fluid construction grammars; Humanoid robot; Language grounding; Practical guidelines; Human robot interaction",,,,,,,,,,,,,,,,"Cacciari, C., Tabossi, P., (1993) Idioms: Processing, Structure, and Interpretation, , (eds.), Lawrence Erlbaum Associates, Hillsdale; Cangelosi, A., Tikhanoff, V., Fontanari, J.F., Integrating language and cognition: A cognitive robotics approach (2007) IEEE Comput. Intell. Mag., 2, pp. 65-70; Cangelosi, A., Grounding language in action and perception: From cognitive agents to humanoid robots (2010) Phys. Life Rev., 7, pp. 139-151; Flasiński, M., (2011) Wstep Do Sztucznej Inteligencji [Introduction to Artificial Intelli-Gence], , Wydawnictwo Naukowe PWN, Warsaw; Gibbs, R.W., Jr., (2005) Embodiment and Cognitive Science, , Cambridge University Press, Cambridge; Giora, R., On Our Mind: Salience, Context, and Figurative Language. Oxford UP (2003) Oxford; Glucksberg, S., (2001) Understanding Figurative Language: From Metaphors to Idioms, , Oxford UP, Oxford; Indurkhya, B., (1992) Metaphor and Cognition: An Interactionist Approach, , Kluwer, Dor-drecht; Kanda, T., Ishiguro, H., (2013) Human-Robot Interaction in Social Robotics, , CRC Press, London; Kemp, C.C., Fitzpatrick, P., Hirukawa, H., Yokoi, K., Harada, K., Matsumoto, Y., Humanoids (2008) Springer Handbook of Robotics, pp. 1307-1329. , Siciliano, B., Khatib, O. (eds.), pp., Springer, Heidelberg; Marocco, D., Cangelosi, A., Fischer, K., Belpaeme, T., Grounding action words in the sensorimotor interaction with the world: Experiments with a simulated iCub humanoid robot (2010) Front. Neurorobotics, 4 (7), p. 7; Metta, G., Cangelosi, A., Cognitive robotics (2012) Encyclopedia of the Sciences of Learning, pp. 613-616. , Seel, N.M. (ed.), pp., Springer, London; Parisi, D., Robots with language (2010) Front. Neurorobotics, 4 (10), pp. 1-8; Samani, H., (2016) Cognitive Robotics, , (ed.), CRC Press, London; Sinclair, J., (1995) The Cognitive Dictionary of Idioms, , Harper Collins, Glasgow; Steels, L., (2004) Fluid Construction Grammar, , https://www.fcg-net.org/wp-content/uploads/papers/tutorial-2.pdf, A Brief Tutorial; Steels, L., (2012) Language Grounding in Robots, , Springer, New York; Steels, L., (2015) The Talking Heads Experiment: Origins of Words and Meanings, , Language Sciences Press, Berlin; Ziemke, T., Zlatev, J., Frank, R.M., (2007) Body, Language and Mind, Vol. 1. Embodiment. Mouton De Gruyter, Berlin","Gwóźdź, M.; Ludwig Maximilian University of Munich, Geschwister-Scholl-Platz 1, Germany; email: mkg33@cantab.net","Bi Y.Bhatia R.Kapoor S.",,"Springer Verlag","Intelligent Systems Conference, IntelliSys 2019","5 September 2019 through 6 September 2019",,231009,21945357,9783030295127,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85072844719
"Alvarez J., Zapata M., Paillacho D.","57209416558;56818703100;56084736200;","Mechanical Design of a Spatial Mechanism for the Robot Head Configuration in Social Robotics",2020,"Advances in Intelligent Systems and Computing","1026",,,"160","165",,,"10.1007/978-3-030-27928-8_25","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071487795&doi=10.1007%2f978-3-030-27928-8_25&partnerID=40&md5=51c3d0d0fa4a59e87b2d7f04b21cc9ee","Center for Knowledge and Technology Transfer, Universidad Indoamérica, Machala y Sabanilla, Quito, 172103, Ecuador; Research Center of Mechatronics and Interactive Systems, Universidad Indoamérica, Machala y Sabanilla, Quito, 172103, Ecuador; ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador","Alvarez, J., Center for Knowledge and Technology Transfer, Universidad Indoamérica, Machala y Sabanilla, Quito, 172103, Ecuador; Zapata, M., Research Center of Mechatronics and Interactive Systems, Universidad Indoamérica, Machala y Sabanilla, Quito, 172103, Ecuador; Paillacho, D., ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, Campus Gustavo Galindo Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador","The manuscript presents the mechanical design of the head configuration in the Human Robot Interaction (HRI) used for the message transmission of emotions through nonverbal communications styles. The evolution of this structure results on a natural movement reproduction for the implementation of non-verbal communication strategies in a normal behavior, achieve the main patterns to evaluate the social interaction with the robotic platform. The mechanical design result from a biomechanical evaluation of the Pitch, Roll, and Yaw trajectories of the human head and neck. The spatial mechanisms, according to the Grübber formula for Spatial Robots, allows 4 degrees of freedom. The spatial chain has universal, prismatic, spiral and revolute joins of the mechanical model-ling. This CAD model permit the 3D print of cardan elements to performance the structure of the mechanisms. The appearance is friendly and the interface reach similar capabilities than a human would have for communication. Finally, human interaction through the head movement gives the opportunity in the future for the evaluation of more parameters of the social robotic interaction between robots-humans and robots-robots. © Springer Nature Switzerland AG 2020.","Additive manufacturing; Biomechanics; Mechanical design; Social robotics; Spatial mechanism; Structural design","3D modeling; 3D printers; Biomechanics; Cell proliferation; Computer aided design; Degrees of freedom (mechanics); Facsimile; Machine design; Mechanisms; Robotics; Structural design; Systems engineering; 4 degrees of freedom; Biomechanical evaluation; Human robot Interaction (HRI); Mechanical design; Message transmissions; Non-verbal communications; Social robotics; Spatial mechanism; Human robot interaction",,,,,,,,,,,,,,,,"Paillacho, D., (2019) Designing a Robot to Evaluate Group Formations, , Doctoral Thesis, Universitat Politécnica de Catalunya; Nordin, A.I., Hudson, M., Denisova, A., Beeston, J., (2016) Perceptions of Telepresence Robot Form, 4; Nuñez, V., Modelo vrml interactivo de un robot humanoide Bioloid (2013) México, L Congreso Interdisciplinario De Cuerpos Académicos, p. 2013; Nourbakhsh, I.R., (2000) Robots and Education in the Classroom and in the Museum: On the Study of Robots, , and robots for study; Edirisinghe, E.A.N.S., Design and simulation of a human-like robot neck mechanism (2015) 2015 Electrical Engineering Conference [Eecon], 1893; Scheer, J.K., Cervical spine alignment, sagittal deformity, and clinical implications (2013) J. Neurosurg. Spine, 19, pp. 141-159; Ölçücüoğlu, O., (2007) Human-Like Robot Head Design a Thesis Submitted to the Graduate School of Natural and Applied Sciences of Middle East Technical University; Danev, L., Hamann, M., Fricke, N., Hollarek, T., Paillacho, D., Development of animated facial expressions to express emotions in a robot: RobotIcon (2018) 2017 IEEE 2Nd Ecuador Technical Chapters Meeting, ETCM 2017, pp. 1-6; Hernández, X.R., (2017) Rediscovering the Experimental Robotic Platform MASHI. Thesis, p. 49. , January","Alvarez, J.; Center for Knowledge and Technology Transfer, Machala y Sabanilla, Ecuador; email: jorgealvarez@uti.edu.ec","Ahram T.Karwowski W.Pickl S.Taiar R.",,"Springer Verlag","2nd International Conference on Human Systems Engineering and Design: Future Trends and Applications, IHSED 2019","16 September 2019 through 18 September 2019",,230069,21945357,9783030279271,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85071487795
"Petrič T., Žlajpah L.","36447813900;6603769815;","On-line Adaption of Virtual Guides Through Physical Interaction",2020,"Advances in Intelligent Systems and Computing","980",,,"293","300",,1,"10.1007/978-3-030-19648-6_34","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065959936&doi=10.1007%2f978-3-030-19648-6_34&partnerID=40&md5=058658acfcfad64fd6cc1841d36aa25d","Jožef Stefan Institute, Ljubljana, Slovenia","Petrič, T., Jožef Stefan Institute, Ljubljana, Slovenia; Žlajpah, L., Jožef Stefan Institute, Ljubljana, Slovenia","Virtual guide framework allows efficient learning and control of complex robot behaviors in human-robot interaction scenarios. The framework can help to guide users to move in a predefined direction or prevent them to enter a forbidden-region. As such, the framework also allows efficient modulation of regions by changing of parameters. In this paper, we introduce and evaluate the means of adapting path parameters through physical interaction. The main goal was to introduce an algorithm into a virtual guide framework which can partially modify the path trajectories. The path updates are based on physical interaction and allow human intervention to improve the task performance. This enables to update the path trajectory only where needed and hence, to bypass the need to re-learn the whole trajectory from scratch. Since virtual guides are also active while learning, the required effort from the user is lower compared to the required effort when the user is teaching the robot with kinesthetic guidance. The effectiveness of the proposed algorithm has been demonstrated with simulation results and experiments on a KUKA LWR robot. © Springer Nature Switzerland AG 2020.","Human-robot physical cooperation; On-line path adaptation; Virtual guides","Behavioral research; Robotics; Trajectories; Virtual reality; Efficient learning; Forbidden region; Human intervention; Human robots; On-line path adaptation; Physical interactions; Task performance; Virtual guides; Human robot interaction",,,,,,"This work was supported by IJS Director’s found grand CoBoTaT.",,,,,,,,,,"Abbott, J.J., Marayong, P., Okamura, A.M., Haptic virtual fixtures for robot-assisted manipulation (2007) Robotics Research, pp. 49-64. , pp; Billard, A., Calinon, S., Dillmann, R., Schaal, S., (2008) Robot Programming by Demonstration, pp. 1371-1394. , https://doi.org/10.1007/978-3-540-30301-560, pp., Springer, Heidelberg; Bowyer, S.A., Davies, B.L., Rodriguez y Baena, F.: Active constraints/virtual fixtures: A survey (2014) IEEE Trans. Robot, 30 (1), pp. 138-157; Colgate, J., Peshkin, M., Klostermeyer, S., Intelligent assist devices in industrial applications: A review (2003) Proceedings 2003 IEEE/RSJ IROS, 3, pp. 2516-2521. , vol., pp., October; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) IEEE Trans. Robot., 31 (4), pp. 850-863; Gams, A., Petrič, T., Do, M., Nemec, B., Morimoto, J., Asfour, T., Ude, A., Adaptation and coaching of periodic motion primitives through physical and visual interaction (2015) Robot. Auton. Syst., , http://linkinghub.elsevier.com/retrieve/pii/S0921889015001992; Gruebler, A., Berenz, V., Suzuki, K., Coaching robot behavior using continuous physiological affective (2011) Feedback, pp. 466-471. , pp; Kragic, D., Marayong, P., Li, M., Okamura, A.M., Hager, G.D., Human-machine collaborative systems for microsurgical applications (2005) Int. J. Robot. Res., 24 (9), pp. 731-741; Lecours, A., Mayer-St-Onge, B., Gosselin, C., Variable admittance control of a four-degree-of-freedom intelligent assist device (2012) Proceedings-Ieee International Conference on Robotics and Automation, 2, pp. 3903-3908. , vol., pp; Lee, D., Ott, C., Incremental kinesthetic teaching of motion primitives using the motion refinement tube (2011) Auton. Robots, 31 (2-3), pp. 115-131; Nicolescu, M.N., Mataric, M.J., Natural methods for robot task learning: Instructive demonstrations, generalization and practice (2003) Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 241-248. , pp; Niemeyer, G., Preusche, C., Hirzinger, G., Telerobotics (2008) Handbook of Robotics, pp. 741-757. , Springer, pp., Springer, Heidelberg; Ott, C., Mukherjee, R., Nakamura, Y., Unified impedance and admittance control (2010) Proceedings-Ieee International Conference on Robotics and Automation, pp. 554-561. , pp; Petrič, T., Cevzar, M., Babič, J., Utilizing speed-accuracy trade-off models for human-robot coadaptation during cooperative groove fitting task (2017) 2017 IEEE-RAS 17Th International Conference on Humanoid Robotics (Humanoids), pp. 107-112. , pp., November; Ranatunga, I., Lewis, F., Popa, D.O., Tousif, S.M., Adaptive admittance control for human-robot interaction using model reference design and adaptive inverse filtering (2016) IEEE Trans. Control Syst. Technol., pp. 1-10; Riley, M., Ude, A., Atkeson, C., Cheng, G., Coaching: An approach to efficiently and intuitively create humanoid robot behaviors (2006) 2006 6Th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Genoa, Italy, pp. 567-574. , pp; Rosenberg, L., Virtual fixtures: Perceptual tools for telerobotic manipulation (1993) Proceedings of IEEE Virtual Reality Annual International Symposium, pp. 76-82. , pp; Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., (2009) Robotics-Modelling. Planning and Control, , Springer, London; Ude, A., Nemec, B., Petrič, T., Morimoto, J., Orientation in cartesian space dynamic movement primitives (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 2997-3004. , pp., May; Žlajpah, L., Petrič, T., Virtual guides for redundant robots using admittance control for path tracking tasks (2019) Advances in Service and Industrial Robotics: Proceedings of the 27Th International Conference on Robotics in Alpe-Adria Danube Region (RAAD 2018), pp. 13-23. , Aspragathos, N.A., Koustoumpardis, P.N., Moulianitis, V.C. (eds.), pp., Springer, Cham","Petrič, T.; Jožef Stefan InstituteSlovenia; email: tadej.petric@ijs.si","Berns K.Gorges D.","Deutsche Forschungsgemeinschaft;Deutsches Forschungszentrum für Künstliche Intelligenz;Fraunhofer Zentrum Kaiserslautern;Technische Universität Kaiserslautern","Springer Verlag","28th International Conference on Robotics in Alpe-Adria-Danube Region, RAAD 2019","19 June 2019 through 21 June 2019",,225939,21945357,9783030196479,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85065959936
"Reich-Stiebert N., Eyssel F., Hohnemann C.","56960645000;9846264800;57204531223;","Exploring University Students’ Preferences for Educational Robot Design by Means of a User-Centered Design Approach",2020,"International Journal of Social Robotics","12","1",,"227","237",,7,"10.1007/s12369-019-00554-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065731237&doi=10.1007%2fs12369-019-00554-7&partnerID=40&md5=50ab9e2c2a770c6e8f8a67c12251c4d2","Applied Social Psychology and Gender Research, CITEC, Bielefeld University, Inspiration 1, Bielefeld, 33619, Germany; Department of Psychology, Bielefeld University, Bielefeld, Germany","Reich-Stiebert, N., Applied Social Psychology and Gender Research, CITEC, Bielefeld University, Inspiration 1, Bielefeld, 33619, Germany; Eyssel, F., Applied Social Psychology and Gender Research, CITEC, Bielefeld University, Inspiration 1, Bielefeld, 33619, Germany; Hohnemann, C., Department of Psychology, Bielefeld University, Bielefeld, Germany","User-centered design approaches have become more prominent in various domains and have slowly been adopted in robotics research. Previous research on user-centered design highlights the beneficial effects of actively involving end users into the design process. Results further imply that end users have different notions about ideal robot design, placing special demands on social robots depending on the context and personal factors. In the present exploratory research, we applied a user-centered design method and investigated university students’ (N = 116) preferences regarding the design of educational robots. With regard to robot design, university students prefer a medium-sized machinelike robot with human characteristics and minimal facial features. Our results further suggest that a robot should primarily interact via speech and be able to display basic emotions, especially the positive ones. Additionally, from a university students’ perspective, an ideal educational robot should display behavior that is marked by conscientiousness, agreeableness, and openness. We discuss implications of our results for educational robot design, and highlight the gains of user involvement in design decisions for human–robot interaction research. © 2019, Springer Nature B.V.","Educational robots; Robot prototyping; User involvement; User-centered design","Agricultural robots; Machine design; Social robots; Students; User centered design; Beneficial effects; Exploratory research; Robot interactions; Robotics research; University students; User centered design methods; User involvement; User-centered design approaches; Educational robots",,,,,,,,,,,,,,,,"Benitti, F.B.V., Exploring the educational potential of robotics in schools: a systematic review (2012) Comp Educ, 58, pp. 978-988; Mubin, O., Stevens, C.J., Shahid, S., Al Mahmud, A., Dong, J.J., A review of the applicability of robots in education (2013) Technol Educ Learn, 1, pp. 1-7; Reich-Stiebert, N., Eyssel, F.A., Learning with educational companion robots? 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The broaden-and-build theory of positive emotions (2001) Am Psychol, 56, pp. 218-226; Breazeal, C.L., (2002) Designing sociable robots, , MIT Press, Cambridge; Calo, R., Robots and privacy (2010) Robot ethics: the ethical and social implications of robotics, pp. 187-202. , Lin P, Bekey G, Abney K, (eds), MIT Press, Cambridge; Ingram, B., Jones, D., Lewis, A., Richards, M., Rich, C., Schachterle, L., A code of ethics for robotics engineers (2010) Proceedings of the 5Th International Conference on human–robot Interaction, pp. 103-104. , ACM Press; Pekrun, R., The impact of emotions on learning and achievement: towards a theory of cognitive/motivational mediators (1992) Appl Psychol, 42, pp. 359-376; Gumora, G., Arsenio, W.F., Emotionality, emotion regulation, and school performance in middle school children (2002) J School Psychol, 40, pp. 395-413; O’Regan, J.K., How to build a robot that is conscious and feels (2012) Mind Mach, 22, pp. 117-136","Reich-Stiebert, N.; Applied Social Psychology and Gender Research, Inspiration 1, Germany; email: nreich@cit-ec.uni-bielefeld.de",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85065731237
"Fitter N.T., Kuchenbecker K.J.","55921515500;8349364500;","How Does It Feel to Clap Hands with a Robot?",2020,"International Journal of Social Robotics","12","1",,"113","127",,3,"10.1007/s12369-019-00542-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064445770&doi=10.1007%2fs12369-019-00542-x&partnerID=40&md5=3392fc9cbbda9e46af1ae5e8594ea3e7","Collaborative Robotics and Intelligent Systems Institute, Oregon State University, Corvallis, OR  97331, United States; Haptic Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, 70569, Germany","Fitter, N.T., Collaborative Robotics and Intelligent Systems Institute, Oregon State University, Corvallis, OR  97331, United States; Kuchenbecker, K.J., Haptic Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, 70569, Germany","Future robots may need lighthearted physical interaction capabilities to connect with people in meaningful ways. To begin exploring how users perceive playful human-robot hand-to-hand interaction, we conducted a study with 20 participants. Each user played simple hand-clapping games with the Rethink Robotics Baxter Research Robot during a 1-h-long session involving 24 randomly ordered conditions that varied in facial reactivity, physical reactivity, arm stiffness, and clapping tempo. Survey data and experiment recordings demonstrate that this interaction is viable: all users successfully completed the experiment and mentioned enjoying at least one game without prompting. Hand-clapping tempo was highly salient to users, and human-like robot errors were more widely accepted than mechanical errors. Furthermore, perceptions of Baxter varied in the following statistically significant ways: facial reactivity increased the robot’s perceived pleasantness and energeticness; physical reactivity decreased pleasantness, energeticness, and dominance; higher arm stiffness increased safety and decreased dominance; and faster tempo increased energeticness and increased dominance. These findings can motivate and guide roboticists who want to design social-physical human-robot interactions. © 2019, The Author(s).","Robot affect; Social motor coordination; Social-physical human-robot interaction","Agricultural robots; Machine design; Man machine systems; Stiffness; Hand-clapping; Human like robots; Increased safety; Mechanical errors; Motor co-ordination; Physical human-robot interactions; Physical interactions; Research robots; Social robots",,,,,"National Science Foundation, NSF: 0966142, 1156366, DGE-0822; Max-Planck-Gesellschaft, MPG","Open access funding provided by Max Planck Society. The first author was supported by a US National Science Foundation (NSF) Graduate Research Fellowship under Grant No. DGE-0822 and the University of Pennsylvania?s NSF Integrative Graduate Education and Research Traineeship under Grant No. 0966142. We thank Michelle Neuburger, a past summer Research Experiences for Undergraduates (REU) student, for her help with this work; she was supported by NSF Grant No. 1156366. We additionally thank Alex Burka for his design advice and Kostas Daniilidis for the use of his Baxter robot.",,,,,,,,,,"Ammi, M., Demulier, V., Caillou, S., Gaffary, Y., Tsalamlal, Y., Martin, J.C., Tapus, A., Haptic human-robot affective interaction in a handshaking social protocol (2015) Proceedings ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 263-270; Argall, B.D., Billard, A.G., A survey of tactile human-robot interactions (2010) Robot Auton Syst, 58 (10), pp. 1159-1176; Avraham, G., Nisky, I., Fernandes, H.L., Acuna, D.E., Kording, K.P., Loeb, G.E., Karniel, A., Toward perceiving robots as humans: three handshake models face the Turing-like handshake test (2012) IEEE Trans Haptics, 5 (3), pp. 196-207; Baba, T., Ushiama, T., Tsuruno, R., Tomimatsu, K., Video game that uses skin contact as controller input (2007) Proceedings ACM SIGGRAPH Emerging Technologies, p. 4; Baltrušaitis, T., Mahmoud, M., Robinson, P., Cross-dataset learning and person-specific normalisation for automatic action unit detection (2015) Proceedings IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 6, pp. 1-6; Bandura, A., Social cognitive theory of social referencing (1992) Social referencing and the social construction of reality in infancy, pp. 175-208. , Feinman S, (ed), Springer, Boston; Bhattacharjee, T., Niemeyer, G., Antagonistic muscle based robot control for physical interactions (2015) Proceedings IEEE International Conference on Robotics and Automation (ICRA), pp. 298-303; Chan, W.P., Parker, C.A.C., Van der Loos, H.F.M., Croft, E.A., A human-inspired object handover controller (2013) Int J Robot Res (IJRR), 32 (8), pp. 971-983; Cooney, M.D., Becker-Asano, C., Kanda, T., Alissandrakis, A., Ishiguro, H., Full-body gesture recognition using inertial sensors for playful interaction with small humanoid robot (2010) Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2276-2282; Disalvo, C.F., Gemperle, F., Forlizzi, J., Kiesler, S., All robots are not created equal: The design and perception of humanoid robot heads (2002) Proceedings ACM Conference on Designing Interactive Systems, pp. 321-326; Fiene, J.P., Kuchenbecker, K.J., Shaping event-based haptic transients via an improved understanding of real contact dynamics (2007) Proceedings IEEE World Haptics Conference, pp. 170-175; Fitter, N.T., Hawkes, D.T., Johnson, M.J., Kuchenbecker, K.J., Designing human-robot exercise games for Baxter (2016) Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3434-3435. , work in progress; Fitter, N.T., Huang, Y.E., Mayer, J.P., Kuchenbecker, K.J., IMU-mediated real-time human-Baxter hand-clapping interaction (2016) Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3438-3439. , work in progress; Fitter, N.T., Kuchenbecker, K.J., Designing and assessing expressive open-source faces for the Baxter robot (2016) Proceedings International Conference on Social Robotics (ICSR), pp. 340-350; Fitter, N.T., Kuchenbecker, K.J., Equipping the Baxter robot with human-inspired hand-clapping skills (2016) Proceedings IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 105-112; Fitter, N.T., Kuchenbecker, K.J., Qualitative user reactions to a hand-clapping humanoid robot (2016) Proceedings International Conference on Social Robotics (ICSR), pp. 317-327; Heerink, M., Krose, B., Evers, V., Wielinga, B., Measuring acceptance of an assistive social robot: A suggested toolkit (2009) Proceedings IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 528-533; Huber, M., Rickert, M., Knoll, A., Brandt, T., Glasauer, S., Human-robot interaction in handing-over tasks (2008) Proceedings IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 107-112; Iwata, H., Sugano, S., Human-robot-contact-state identification based on tactile recognition (2005) IEEE Trans Ind Electron, 52 (6), pp. 1468-1477; Kanda, T., Sato, R., Saiwaki, N., Ishiguro, H., A two-month field trial in an elementary school for long-term human-robot interaction (2007) IEEE Trans Robot, 23 (5), pp. 962-971; Kim, Y., Lee, S., Hwang, I., Ro, H., Lee, Y., Mo On, M., Song, J., High5: Promoting interpersonal hand-to-hand touch for vibrant workplace with electrodermal sensor watches (2014) Proceedings ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp), pp. 15-19; Klemmer, S.R., Hartmann, B., Takayama, L., How bodies matter: Five themes for interaction design (2006) Proceedings ACM Conference on Designing Interactive Systems, pp. 140-149; Mehrabian, A., (1980) Basic dimensions for a general psychological theory, , Oelgeschlager, Gunn & Hain, Cambridge; Noy, L., Dekel, E., Alon, U., The mirror game as a paradigm for studying the dynamics of two people improvising motion together (2011) Proc Natl Acad Sci, 108 (52), pp. 20947-20952; Rabbitt, S.M., Kazdin, A.E., Scassellati, B., Integrating socially assistive robotics into mental healthcare interventions: applications and recommendations for expanded use (2015) Clin Psychol Rev, 35, pp. 35-46; Robins, B., Dautenhahn, K., Te Boekhorst, R., Billard, A., Robotic assistants in therapy and education of children with autism: can a small humanoid robot help encourage social interaction skills? 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In; Stiehl, W.D., Breazeal, C., Han, K.H., Lieberman, J., Lalla, L., Maymin, A., Salinas, J., Tong, C.H., The Huggable: A therapeutic robotic companion for relational, affective touch (2006) Proceedings ACM SIGGRAPH Conference, Emerging Technologies, p. 15; Visell, Y., Okamoto, S., Vibrotactile sensation and softness perception (2014) Multisensory softness, pp. 31-47. , Luca M, (ed), Springer, London; Weiss, A., Bernhaupt, R., Tscheligi, M., Wollherr, D., Kuhnlenz, K., Buss, M., A methodological variation for acceptance evaluation of human-robot interaction in public places (2008) Proceedings IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 713-718; Yohanan, S., Maclean, K., A tool to study affective touch: Goals & design of the haptic creature (2009) Proceedings ACM CHI Conference on Human Factors in Computing Systems (CHI), Spotlight on Works in Progress, pp. 4153-4158; Yohanan, S., MacLean, K.E., The role of affective touch in human-robot interaction: human intent and expectations in touching the haptic creature (2012) Int J Soc Robot (SORO), 4 (2), pp. 163-180; Zhou, A., Hadfield-Menell, D., Nagabandi, A., Dragan, A.D., Expressive robot motion timing (2017) Proceedings ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 22-31","Fitter, N.T.; Collaborative Robotics and Intelligent Systems Institute, United States; email: naomi.fitter@oregonstate.edu",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85064445770
"Canal G., Torras C., Alenyà G.","57015106200;7003421114;6508265237;","Are Preferences Useful for Better Assistance?: A Physically Assistive Robotics User Study",2021,"ACM Transactions on Human-Robot Interaction","10","4","3472208","","",,1,"10.1145/3472208","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115156061&doi=10.1145%2f3472208&partnerID=40&md5=9af402abfa326ad77c760bc2c33672c6","Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Department of Informatics, King's College London, Bush House, 30 Aldwych, London, WC2B 4BG, United Kingdom; Institut de Robòtica i Informàtica Industrial, CSIC-UPC, C/Llorens i Artigas 4-6, Barcelona, Spain","Canal, G., Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Department of Informatics, King's College London, Bush House, 30 Aldwych, London, WC2B 4BG, United Kingdom; Torras, C., Institut de Robòtica i Informàtica Industrial, CSIC-UPC, C/Llorens i Artigas 4-6, Barcelona, Spain; Alenyà, G., Institut de Robòtica i Informàtica Industrial, CSIC-UPC, C/Llorens i Artigas 4-6, Barcelona, Spain","Assistive Robots have an inherent need of adapting to the user they are assisting. This is crucial for the correct development of the task, user safety, and comfort. However, adaptation can be performed in several manners. We believe user preferences are key to this adaptation. In this article, we evaluate the use of preferences for Physically Assistive Robotics tasks in a Human-Robot Interaction user evaluation. Three assistive tasks have been implemented consisting of assisted feeding, shoe-fitting, and jacket dressing, where the robot performs each task in a different manner based on user preferences. We assess the ability of the users to determine which execution of the task used their chosen preferences (if any). The obtained results show that most of the users were able to successfully guess the cases where their preferences were used even when they had not seen the task before. We also observe that their satisfaction with the task increases when the chosen preferences are employed. Finally, we also analyze the user's opinions regarding assistive tasks and preferences, showing promising expectations as to the benefits of adapting the robot behavior to the user through preferences. © 2021 Owner/Author.","assistive dressing; assistive feeding; HRI; physically assistive robotics; shoe fitting; user preferences","Agricultural robots; Human robot interaction; Robotics; Assistive; Assistive robotics; Assistive robots; Robot behavior; User evaluations; User study; Behavioral research",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/V062506/1; Ministerio de Educación, Cultura y Deporte, MECD: FPU15/00504; Agencia Estatal de Investigación, AEI: MDM-2016-0656","This work has been supported by the ERC project Clothilde (ERC-2016-ADG-741930), the HuMoUR project (Spanish Ministry of Science and Innovation TIN2017-90086-R) and by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). Gerard Canal has also been supported by the Spanish Ministry of Education, Culture and Sport by the FPU15/00504 doctoral grant and the CHIST-ERA project COHERENT (EPSRC EP/V062506/1). Authors’ addresses: G. Canal, Institut de Robòtica i Informàtica Industrial, CSIC-UPC, and Department of Informatics, King’s College London, Bush House, 30 Aldwych, London, WC2B 4BG, United Kingdom; email: gcanal@iri.upc.edu; C. Torras and G. Alenyà, Institut de Robòtica i Informàtica Industrial, CSIC-UPC, C/Llorens i Artigas 4-6, Barcelona, Spain; emails: {torras, galenya}@iri.upc.edu. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). © 2021 Copyright held by the owner/author(s). 2573-9522/2021/09-ART39 $15.00 https://doi.org/10.1145/3472208",,,,,,,,,,"Isabel Aldinhas Ferreira, M., Silva Sequeira, J., Robots in Ageing Societies (2017) Springer International Publishing, Cham, pp. 217-223. , https://doi.org/10.1007/978-3-319-46667-5\T1\textbackslash17; Baraka, K., Veloso, M., Adaptive interaction of persistent robots to user temporal preferences (2015) Proceedings of the International Conference on Social Robotics. Springer, pp. 61-71; Bhattacharjee, T., Lee, G., Song, H., Srinivasa, S.S., Towards robotic feeding: Role of haptics in fork-based food manipulation (2019) Ieee Robot. Autom. Lett., 4 (2), pp. 1485-1492. , 2019; Biswas, M., Romeo, M., Cangelosi, A., Jones, R.B., Are older people any different from younger people in the way they want to interact with robots? (2019) Scenario Based Survey. J. Multimodal User Interfaces, 14, pp. 61-72. , https://doi.org/10.1007/s12193-019-00306-x, 2020; Cakmak, M., Srinivasa, S.S., Kyung Lee, M., Forlizzi, J., Kiesler, S., Human preferences for robot-human hand-over configurations (2011) Proceedings of the 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems., pp. 1986-1993. , https://doi.org/10.1109/IROS.2011.6094735; Canal, G., Alenya, G., Torras, C., Personalization framework for adaptive robotic feeding assistance (2016) Proceedings of the International Conference on Social Robotics (ICSR). 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Robot., 35 (4), pp. 873-888. , https://doi.org/10.1109/TRO.2019.2904461, 2019",,,,"Association for Computing Machinery",,,,,25739522,,,,"English","ACM Trans. Hum. Robot Interact",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85115156061
"Kim U., Jo G., Jeong H., Park C.H., Koh J.-S., Park D.I., Do H., Choi T., Kim H.-S., Park C.","54791071900;57193006848;57218766391;57204032511;35491056200;35743433100;24448129400;9133482400;57209188138;57218766795;","A Novel Intrinsic Force Sensing Method for Robot Manipulators during Human-Robot Interaction",2021,"IEEE Transactions on Robotics","37","6",,"2218","2225",,2,"10.1109/TRO.2021.3072736","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105882679&doi=10.1109%2fTRO.2021.3072736&partnerID=40&md5=99fcc4fcc711e5cc4fa65dfc49c2e966","Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea; Department of Mathematics, Kunsan National University, Gunsan, South Korea; Department of Mechanical Engineering, Ajou University, Suwon, South Korea","Kim, U., Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea; Jo, G., Department of Mathematics, Kunsan National University, Gunsan, South Korea; Jeong, H., Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea; Park, C.H., Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea; Koh, J.-S., Department of Mechanical Engineering, Ajou University, Suwon, South Korea; Park, D.I., Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea; Do, H., Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea; Choi, T., Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea; Kim, H.-S., Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea; Park, C., Department of Robotics and Mechatronics, Korea Institute of Machinery and Materials, Daejeon, South Korea","Robotic manipulators require contact force sensing capabilities to sense the contact force between the manipulator and an object. Specifically, for humans and robots to coexist in the work environment, the robot must be able to detect an external force applied by a human. This study presents a new intrinsic force sensing method for robot manipulators that can obtain accurate information of the external force applied by a human during human-robot interaction. The method employs a robot cover, which is typically utilized in robot manipulators. Unlike conventional force sensing methods, a six-axis force/torque sensor is placed between the cover and the link of the robot manipulator. As a result, the proposed method provides information of the three-axis contact force applied to the cover surface and its contact location. Therefore, the cover itself becomes a sensorized cover based on the intrinsic force sensing method. To evaluate its sensing performance, the sensorized cover is experimentally validated using reference sensors. Finally, an experiment is performed in which the robot successfully recognizes letters written on the cover, indicating a high level of contact force sensing performance. © 2004-2012 IEEE.","Contact location; human-robot interaction (HRI); intrinsic force sensing; six-axis force/torque (F/T) sensor; three-axis force","Agricultural robots; Flexible manipulators; Industrial manipulators; Modular robots; Robot applications; Contact location; Reference sensors; Robot interactions; Robot manipulator; Robotic manipulators; Sensing performance; Six-axis force/torque sensor; Work environments; Human robot interaction",,,,,"Korea Institute of Machinery and Materials, KIMM: NK224C, NK232F","This work was supported by the Basic Research Program funded by the Korea Institute of Machinery and Materials under Grant NK224C and Grant NK232F.",,,,,,,,,,"Santis, A.D., Siciliano, B., Luca, A.D., Bicchi, A., An atlas of physical human-robot interaction (2008) Mechanism Mach. Theory, 43, pp. 253-270; Bartolozzi, C., Natale, L., Nori, F., Metta, G., Robots with a sense of touch (2016) Nature Mater., 15, pp. 921-925; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Trans. Robot., 31 (5), pp. 1089-1100. , Oct; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) IEEE Trans. Robot., 31 (4), pp. 850-863. , Aug; Lecours, A., Otis, M.J., Gosselin, C., Modeling of physical humanrobot interaction: Admittance controllers applied to intelligent assist devices with large payload (2016) Int. J. Adv. Robot. Syst., 13 (5), pp. 1-12; Park, Y.-L., Ryu, S.C., Black, R.J., Chau, K.K., Moslehi, B., Cutkosky, M.R., Exoskeletal force-sensing end-effectors with embedded optical fiber-Bragg-grating sensors (2009) IEEE Trans. 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Res., 12, pp. 249-262; Boucher, G., Laliberte, T., Gosselin, C., A parallel low-impedance sensing approach for highly responsive physical human-robot interaction (2019) Proc. IEEE Int. Conf. Robot. Automat., pp. 3754-3760; Kim, U., Lee, D.-H., Kim, Y.B., Seok, D.-Y., Choi, H.R., A novel 6-axis force/torque sensor for robotic applications (2017) IEEE/ASME Trans. Mechatronics, 22 (3), pp. 1381-1391. , Jun; (2017), https://www.robotous.com/forcetorquesensor, Robotous RFT60-HA. Accessed: Jul. 20; Kim, U., Jeong, H., Do, H., Park, J., Park, C., Six-axis force/torque fingertip sensor for an anthropomorphic robot hand (2020) IEEE Trans. Robot. Autom., 5 (4), pp. 5566-5572. , Oct","Kim, U.; Department of Robotics and Mechatronics, South Korea; email: ukkim@kimm.re.kr",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15523098,,,,"English","IEEE Trans. Rob.",Article,"Final","",Scopus,2-s2.0-85105882679
"Ruocco M., Mou W., Cangelosi A., Jay C., Zanatto D.","57201548087;56684766800;6701387796;16642730800;55463260500;","Theory of mind improves human’s trust in an iterative human-robot game",2021,"HAI 2021 - Proceedings of the 9th International User Modeling, Adaptation and Personalization Human-Agent Interaction",,,,"227","234",,,"10.1145/3472307.3484176","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119369708&doi=10.1145%2f3472307.3484176&partnerID=40&md5=b3d309e003147dd5ebd64cbb13572863","Department of Computer Science, University of Manchester, United Kingdom; School of Computer Science, University of Bristol, United Kingdom","Ruocco, M., Department of Computer Science, University of Manchester, United Kingdom; Mou, W., Department of Computer Science, University of Manchester, United Kingdom; Cangelosi, A., Department of Computer Science, University of Manchester, United Kingdom; Jay, C., Department of Computer Science, University of Manchester, United Kingdom; Zanatto, D., School of Computer Science, University of Bristol, United Kingdom","Trust is a critical issue in human-robot interactions as it is at the base of the establishment of solid relationships. Theory of Mind (ToM) is the cognitive skill that allows us to understand what others think and believe. Several studies in HRI and psychology suggest that trust and ToM are interdependent concepts since we trust another agent based on our representation of its actions, beliefs, and intentions. However, very few works take ToM of the robot into consideration while studying trust in HRI. In this paper, we aim to examine whether the perception of ToM abilities on a robotic agent influences human-robot trust over time in an iterative game scenario. To this end, participants played an Investment Game with a humanoid robot (Pepper) that was presented as having either low-level ToM or high-level ToM. During the game, the participants were asked to pick a sum of money to invest in the robot. The amount invested was used as the main measurement of human-robot trust. Our experimental results show that robots possessing a high-level of ToM abilities were trusted more than the robots presented with low-level ToM skills. © 2021 ACM.","human-robot interaction; theory of mind; trust","Anthropomorphic robots; Man machine systems; Agent based; Cognitive skill; Critical issues; Game scenario; Human robots; Humanoid robot; Humans-robot interactions; Robotic agents; Theory of minds; Trust; Human robot interaction",,,,,"Air Force Office of Scientific Research, AFOSR; U.S. Air Force, USAF: FA9550-19-1-7002; Engineering and Physical Sciences Research Council, EPSRC: EP/R004757/1; University of Bristol; Thales Group, Thales","This material is based upon work supported by the Air Force Office of Scientific Research, USAF under Award No. FA9550-19-1-7002. The work of Debora Zanatto was funded and delivered in partnership between the Thales Group and the University of Bristol, and with the support of the UK Engineering and Physical Sciences Research Council Grant Award EP/R004757/1 entitled ‘Thales-Bristol Partnership in Hybrid Autonomous Systems Engineering (T-B PHASE)’.",,,,,,,,,,"Arts, E., Zörner, S., Bhatia, K., Mir, G., Schmalzl, F., Srivastava, A., Vasiljevic, B., Strahl, E., Exploring Human-Robot Trust through the Investment Game: An Immersive Space Mission Scenario (2020) Proceedings of the International Conference on Human-Agent Interaction (HAI, pp. 121-130; Augello, A., Cipolla, E., Infantino, I., Manfré, A., Pilato, G., Vella, F., 2018. Social signs processing in a cognitive architecture for an humanoid robot (2018) Procedia Computer Science, 123, pp. 63-68; Baron-Cohen, S., Leslie, A.M., Frith, U., 1985. Does the autistic child have a theory of mind""? 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Trust in humans and robots: Economically similar but emotionally different (2020) Journal of Economic Psychology, 78, p. 102253; Shu, P., Min, C., Bodala, I., Nikolaidis, S., Hsu, D., Soh, H., Human trust in robot capabilities across tasks (2018) Companion of ACM/ IEEE International Conference on Human-Robot Interaction, pp. 241-242; Sturgeon, S., Palmer, A., Blankenburg, J., Feil-Seifer, D., Perception of Social Intelligence in Robots Performing False-Belief Tasks (2019) IEEE International Conference on Robot and Human Interactive Communication (RO-MAN, pp. 1-7; Vanderbilt, K.E., Liu, D., Heyman, G.D., 2011 the development of distrust (2011) Child Development, 82 (5), pp. 1372-1380; Warneken, F., Tomasello, M., 2006. 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An investigation of strategic social learning in human-robot interaction (2020) Proceedings of the ACM/ IEEE International Conference on Human-Robot Interaction (HRI, pp. 449-457",,,"CyberAgent","Association for Computing Machinery, Inc","9th International User Modeling, Adaptation and Personalization Human-Agent Interaction, HAI 2021","9 November 2021 through 11 November 2021",,173654,,9781450386203,,,"English","HAI - Proc. Int. User Model., Adapt. Pers. Human-Agent Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85119369708
"Bevilacqua R., Felici E., Cavallo F., Amabili G., Maranesi E.","55079342600;56009466100;14628324200;57290714700;55859858500;","Designing acceptable robots for assisting older adults: A pilot study on the willingness to interact",2021,"International Journal of Environmental Research and Public Health","18","20","10686","","",,,"10.3390/ijerph182010686","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116852567&doi=10.3390%2fijerph182010686&partnerID=40&md5=fc2368b22ed627aa89eccddbbe42b3ad","IRCCS INRCA Scientific Direction, Ancona, 60124, Italy; Dipartimento Ingegneria Industriale, Università degli Studi di Firenze, Firenze, 50139, Italy; Istituto di BioRobotica, Scuola Superiore Sant’Anna, Pontedera, 56025, Italy","Bevilacqua, R., IRCCS INRCA Scientific Direction, Ancona, 60124, Italy; Felici, E., IRCCS INRCA Scientific Direction, Ancona, 60124, Italy; Cavallo, F., Dipartimento Ingegneria Industriale, Università degli Studi di Firenze, Firenze, 50139, Italy, Istituto di BioRobotica, Scuola Superiore Sant’Anna, Pontedera, 56025, Italy; Amabili, G., IRCCS INRCA Scientific Direction, Ancona, 60124, Italy; Maranesi, E., IRCCS INRCA Scientific Direction, Ancona, 60124, Italy","The aim of this paper was to explore the psychosocial determinants that lead to acceptability and willingness to interact with a service robot, starting with an analysis of older users’ behaviors toward the Robot-Era platform, in order to provide strategies for the promotion of social assistive robotics. A mixed-method approach was used to collect information on acceptability, usability, and human–robot interaction, by analyzing nonverbal behaviors, emotional expressions, and verbal communication. The study involved 35 older adults. Twenty-two were women and thirteen were men, aged 73.8 (±6) years old. Video interaction analysis was conducted to capture the users’ gestures, statements, and expressions. A coded scheme was designed on the basis of the literature in the field. Percentages of time and frequency of the selected events are reported. The statements of the users were collected and analyzed. The results of the behavioral analysis reveal a largely positive attitude, inferred from nonverbal clues and nonverbal emotional expressions. The results highlight the need to provide robotic solutions that respect the tasks they offer to the users It is necessary to give older consumers dedicated training in technological literacy to guarantee proper, long-lasting, and successful use. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Emotional design; Human-centered design; Human–robot interaction; Older people; Qualitative study; Technology acceptance","elderly population; frequency analysis; psychology; qualitative analysis; technology adoption; aged; female; human; interpersonal communication; male; pilot study; robotics; self help device; technology; Aged; Communication; Female; Humans; Male; Pilot Projects; Robotics; Self-Help Devices; Technology",,,,,"Seventh Framework Programme, FP7: 288899","Funding: The Robot-Era project received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 288899 FP7-ICT-Challenge 5: ICT for Health, Ageing Well, Inclusion and Governance.",,,,,,,,,,"Robinson, H., MacDonald, B., Broadbent, E., The role of healthcare robots for older adults at home: A review (2014) Int. J. Soc. Rob, 6, pp. 575-591; Robinson, H., MacDonald, B.A., Kerse, N., Broadbent, E., Suitability of healthcare robots for a dementia unit and suggested improvements (2013) J. Am. Med. Dir. Assoc, 14, pp. 34-40; Broekens, J., Heerink, M., Rosendal, H., Assistive social robots in older adults care: A review (2009) Gerontes, 8, pp. 94-103; Maranesi, E., Fiorett, S., Ghetti, G.G., Rabini, R.A., Burattini, L., Mercante, O., Di Nardo, F., The surface electromyographic evaluation of the Functional Reach in elderly subjects (2016) J. Electromyogr. Kinesiol, 26, pp. 102-110; Feil-Seifer, D., Mataric, M.J., Defining socially assistive robotics. 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Aging, 21, p. 2; Czaja, S.J., Charness, N., Fisk, A.D., Hertzog, C., Nair, S.N., Rogers, W.A., Sharit, J., Factors predicting the use of technology: Findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE) (2006) Psychol. Aging, 21, pp. 333-352; Bevilacqua, R., Casaccia, S., Cortellessa, G., Astell, A., Lattanzio, F., Corsonello, A., D’ascoli, P., Rossi, L., Coaching through technology: A systematic review into efficacy and effectiveness for the ageing population (2020) Int. J. Environ. Res. Public Health, 17, pp. 1-14; Lee, C.C., Czaja, S.J., Moxley, J.H., Sharit, J., Boot, W.R., Charness, N., Rogers, W.A., Attitudes Toward Computers Across Adulthood From 1994 to 2013 (2019) Gerontologist, 59, pp. 22-33; Beer, J.M., Prakash, A., Smarr, C.A., Chen, T.L., Hawkins, K., Nguyen, H., Deyle, T., Rogers, W.A., Older Users’ Acceptance of an Assistive Robot: Attitudinal Changes Following Brief Exposure (2017) Gerontechnology, 16, pp. 21-36; Smarr, C.A., Mitzner, T.L., Beer, J.M., Prakash, A., Chen, T.L., Kemp, C.C., Rogers, W.A., Domestic Robots for Older Adults: Attitudes, Preferences, and Potential (2014) Int. J. Soc. Robot, 6, pp. 229-247; Casaccia, S., Revel, G.M., Scalise, L., Bevilacqua, R., Rossi, L., Paauwe, R.A., Karkowsky, I., Suijkerbuijk, S., Social Robot and Sensor Network in Support of Activity of Daily Living for People with Dementia (2019) Commun. Comput. Inf. Sci, 1117, pp. 128-135; Smarr, C.A., Prakash, A., Beer, J.M., Mitzner, T.L., Kemp, C.C., Rogers, W.A., Older Adults’ Preferences for and Acceptance of Robot Assistance for Everyday Living Tasks (2012) Proc. Hum. Factors Ergon. Soc. Annu. Meet, 56, pp. 153-157; Papetti, A., Iualé, M., Ceccacci, S., Bevilacqua, R., Germani, M., Mengoni, M., Smart Objects: An Evaluation of the Present State Based on User Needs Proceesings of the 2nd International Conference on Distributed, Ambient and Pervasive Interactions, DAPI 2014, pp. 359-368. , Heraklion, Greece, 22–27 June 2014; Meng, Q., Lee, M.H., Design issues for assistive robotics for the older adults (2006) Adv. Eng. Inform, 20, pp. 171-186; Morley, J., Caplan, G.A., Cesari, M., Dong, B., Flaherty, J.H., Grossberg, G.T., Holmerova, I., Little, M.O., International survey of nursing home research priorities (2014) J. Am. Med. Dir. Assoc, 15, pp. 309-312; Hersh, M., Overcoming Barriers and Increasing Independence: Service Robots for Older adults and Disabled Adults (2015) Int. J. Adv. Rob. Sys, 12, p. 114; Ferri, G., Manzi, A., Salvini, P., Mazzolai, B., Laschi, C., Dario, P., DustCart, an autonomous robot for door-to-door garbage collection: From DustBot project to the experimentation in the small town of Peccioli Proceedings of the IEEE International Conference on Robotics and Automation, pp. 655-660. , Shanghai, China, 9–13 May 2011; Cavallo, F., Esposito, R., Limosani, R., Manzi, A., Bevilacqua, R., Felici, E., di Nuovo, A., Dario, P., Robotic Services Acceptance in Smart Environments with Older Adults: User Satisfaction and Acceptability Study (2018) J. Med. Int. Res, 20, p. e264; Bevilacqua, R., Felici, E., Marcellini, F., Glende, S., Klemcke, S., Conrad, I., Esposito, R., Dario, P., Robot-Era Project: Preliminary Results on the System Usability (2015) Design, User Experience, and Usability: Interactive Experience Design. DUXU 2015. Lecture Notes in Computer Science, 9188. , https://doi.org/10.1007/978-3-319-20889-3_51, Marcus, A., Ed.; Springer: Cham, Switzerland; Knoblauch, H., Schnettler, B., Raab, J., Soeffner, H.G., Video analysis. Methodology and Methods (2006) Qualitative Audiovisual Data Analysis in Sociology 2006, , Frankfurt am Main; Peter Lang: Bern, Switzerland; Giuliani, M., Mirnig, N., Stollnberger, G., Stadler, S., Buchner, R., Tscheligi, M., Systematic analysis of video data from different human–robot interaction studies: A categorization of social signals during error situations (2015) Front. Psychol, 6, p. 931; Bethel, C.L., Murphy, R.R., Use of Large Sample Sizes and Multiple Evaluation Methods in Human-Robot Interaction Experimentation Proceedings of Experimental Design for Real-World Systems, Papers from the 2009 AAAI Spring Symposium, pp. 9-16. , Stanford, CA, USA, 23–25 March 2009; Nabe, S., Cowley, S.J., Kanda, T., Hiraki, K., Ishiguro, H., Hagita, N., Robots as Social Mediators: Coding for engineering (2006) Robot and Human Interactive Communication, pp. 384-390; Andrès, A., Pardo, D.E., Diaz, M., Angulo, C., New Instrumentation for human robot interaction assessment based on observational methods (2015) JAISE, 7, pp. 397-413; Bartneck, C., Forlizzi, J., A design centred framework for social human-robot interaction (2004) Proceedings of the Ro-Man, pp. 591-594. , Kurashiki, Japan, 22 September; Huang, C.M., Thomaz, A., Effects of responding to, initiating and ensuring joint attention in human-robot interaction Proceedings of the RO-MAN, pp. 65-71. , Atlanta, GA, USA, 31 July 31–3August 2011; Bonaccorsi, M., Fiorini, L., Cavallo, F., Saffiotti, A., Dario, P., A cloud robotics solution to improve social assistive robots for active and healthy aging (2016) Int. J. Soc. Robot, 8, pp. 393-408; Cavallo, F., Limosani, R., Manzi, A., Bonaccorsi, M., Esposito, R., Di Rocco, M., Pecora, F., Dario, P., Development of a socially believable multi-robot solution from town to home (2014) Cognit. Comput, 6, pp. 954-967; Di Nuovo, A., Broz, F., Wang, N, Belpaeme, T., Cangelosi, A., Jones, R., Esposito, R., Dario, P., The multi-modal interface of Robot-Era multi-robot services tailored for the older adults (2017) Intell. Serv. Robot, 11, pp. 1-18; Standards for Technological Literacy: Content for the Study of Technology, , https://www.iteea.org/42511.aspx, Reston, VA. (accessed on 6 October 2021)","Amabili, G.; IRCCS INRCA Scientific DirectionItaly; email: g.amabili@inrca.it",,,"MDPI",,,,,16617827,,,"34682433","English","Int. J. Environ. Res. Public Health",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85116852567
"Li W.-B., Zhang W.-M., Gao Q.-H., Guo Q., Wu S., Zou H.-X., Peng Z.-K., Meng G.","57189631938;8615158400;57195588371;57221017276;57209180532;57248863300;7401790868;7202900098;","Electrically Activated Soft Robots: Speed up by Rolling",2021,"Soft Robotics","8","5",,"611","624",,,"10.1089/soro.2020.0012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117931693&doi=10.1089%2fsoro.2020.0012&partnerID=40&md5=c24a5d7a10db4de40bff0c2e89eb48aa","State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Shanghai Institute of Aerospace Systems Engineering, Shanghai, China","Li, W.-B., State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Zhang, W.-M., State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Gao, Q.-H., State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Guo, Q., Shanghai Institute of Aerospace Systems Engineering, Shanghai, China; Wu, S., Shanghai Institute of Aerospace Systems Engineering, Shanghai, China; Zou, H.-X., State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Peng, Z.-K., State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Meng, G., State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China","Soft robots show excellent body compliance, adaptability, and mobility when coping with unstructured environments and human-robot interactions. However, the moving speed for soft locomotion robots is far from that of their rigid partners. Rolling locomotion can provide a promising solution for developing high-speed robots. Based on different rolling mechanisms, three rolling soft robot (RSR) prototypes with advantages of simplicity, lightweight, fast rolling speed, good compliance, and shock resistance are fabricated by using dielectric elastomer actuators. The experimental results demonstrate that the impulse-based and gravity-based RSRs can move both stably and continuously on the ground with a maximum speed higher than 1 blps (body length per second). The ballistic RSR exhibits a high rolling speed of ∼4.59 blps. And during its accelerating rolling process, the instantaneous rolling speed of the robot prototype reaches about 0.65 m/s (13.21 blps), which is much faster than most of the previously reported locomotion robots driven by soft responsive materials. The structure design and implementation methods based on different rolling mechanisms presented can provide guidance and inspiration for creating new, fast-moving, and hybrid mobility soft robots. © 2021, Mary Ann Liebert, Inc., publishers.","DEMES; dielectric elastomer actuators; rolling mechanisms; soft locomotion robots","Elastomers; Electroactive polymer actuators; Gravitation; Machine design; Plastics; Speed; DEMES; Dielectric elastomer actuators; Humans-robot interactions; Robot prototypes; Rolling mechanism; Rolling speed; Soft locomotion robot; Soft robot; Speed up; Unstructured environments; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 11625208, 91748118; National Postdoctoral Program for Innovative Talents: BX20190201","This work was funded by the National Natural Science Foundation of China under grant 91748118, the National Postdoctoral Program for Innovative Talents under grant BX20190201, the Program of Shanghai Key Laboratory of Spacecraft Mechanism, and the National Natural Science Foundation for Distinguished Young Scholars of China under grant 11625208.","The authors gratefully acknowledge the supports by the National Natural Science Foundation of China under grant 91748118, the National Postdoctoral Program for Innovative Talents under grant BX20190201, the Program of Shanghai Key Laboratory of Spacecraft Mechanism, and the National Natural Science Foundation for Distinguished Young Scholars of China under grant 11625208.",,,,,,,,,"Armour, RH, Vincent, JFV., Rolling in nature and robotics: a review (2006) J Bionic Eng, 3, pp. 195-208. , 1; Floyd, M, Minor, MA, Impulse based dynamic rolling in the rolling disk biped (2010) IEEE International Conference on Bio-medical Robotics and Biomechatronics, BioRob), pp. 498-503. , 2. 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(Orlando, May; Li, Y, Yao, Y, He, Y., Design and analysis of a multi-mode mobile robot based on a parallel mechanism with branch variation (2018) Mech Mach Theory, 130, pp. 276-300. , 36; Firouzeh, A, Ozmaeian, M, Alasty, A, An IPMC-made deformable-ring-like robot (2012) Smart Mater Struct, 21, p. 065011. , 37; Li, WB, Zhang, WM, Zou, HX, Asymmetry bistability for a coupled dielectric elastomer minimum energy structure (2016) Smart Mater Struct, 25, p. 115023. , 38","Zhang, W.-M.; State Key Laboratory of Mechanical System and Vibration, 800 Dongchuan Road, China; email: wenmingz@sjtu.edu.cn",,,"Mary Ann Liebert Inc.",,,,,21695172,,,"33180656","English","Soft Robotics",Article,"Final","",Scopus,2-s2.0-85117931693
"Xiong J., Sun Y., Zheng J., Dong D., Bai L.","57264502600;56957366900;56957418800;56957675700;57265067600;","Design and experiment of a SMA-based continuous-stiffness-adjustment torsional elastic component for variable stiffness actuators",2021,"Smart Materials and Structures","30","10","105021","","",,,"10.1088/1361-665X/ac1eae","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115237897&doi=10.1088%2f1361-665X%2fac1eae&partnerID=40&md5=9c2a44546b709fb6c9109a87b2a6c348","State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China; School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, China","Xiong, J., State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China; Sun, Y., State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China; Zheng, J., School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; Dong, D., School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, China; Bai, L., State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, 400044, China","The variable stiffness actuator (VSA) is widely applied in robotics and human-robot interactions, generating controllable torque with sufficient compliance to ensure safety and robustness. At present, typical VSA design usually adopted pure mechanical structures that integrated an additional motor for stiffness adjustment, making it hard to meet the demand of lightweight joint actuation due to large actuator size and weight. As a new functional material, shape memory alloy (SMA) shows the prospect of lightweight actuation due to its high power-to-weight ratio. In this paper, a compact SMA-based torsional elastic component (SMA-TE) for the VSA is proposed. The SMA-TE with torsional stiffness is implemented by the confrontational arrangement of paired spiral SMA springs. The ceramic heating plate and semiconductor refrigeration plate are installed in the elastic component shell, and the thermally conductive medium is filled between the internal gap. The torsional stiffness of the SMA-TE can be continuously adjusted via a feed-forward cascade controller. Repeated experimental results show that the SMA-TE can precisely maintain constant stiffness and continuously adjust its stiffness between 2.6 Nm rad-1 and 6.8 Nm rad-1 when performing precise square wave stiffness tracking under variable frequencies. © 2021 IOP Publishing Ltd.","shape memory alloy; stiffness control; variable stiffness actuator; variable stiffness component","Functional materials; Human robot interaction; Mechanical actuators; Plates (structural components); Shape-memory alloy; Cascade controller; Elastic components; Mechanical structures; Semiconductor refrigerations; Shape memory alloys(SMA); Stiffness adjustments; Torsional stiffness; Variable frequencies; Stiffness",,,,,,,,,,,,,,,,"Wolf, S, Variable stiffness actuators: review on design and components (2016) IEEE/ASME Trans. Mechatron, 21, pp. 2418-2430; Tsagarakis, N G, Laffranchi, M, Vanderborght, B, Caldwell, D G, A compact soft actuator unit for small scale human friendly robots (2009) 2009 IEEE Int. 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Autom, 26, pp. 597-606; Eiberger, O, Haddadin, S, Weis, M, Albu-Schäffer, A, Hirzinger, G, On joint design with intrinsic variable compliance: derivation of the DLR QA-Joint 2010 (2010) IEEE Int. Conf. on Robotics and Automation, pp. 1687-1694; Jafari, A, Tsagarakis, N G, Caldwell, D G, A novel intrinsically energy efficient actuator with adjustable stiffness (AwAS) (2013) IEEE/ASME Trans. Mechatron, 18, pp. 355-365; Tsagarakis, N G, Sardellitti, I, Caldwell, D G, A new variable stiffness actuator (CompAct-VSA): design and modelling (2011) 2011 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 378-383; Jafari, A, Tsagarakis, N G, Caldwell, D G, AwAS-II: a new actuator with adjustable stiffness based on the novel principle of adaptable pivot point and variable lever ratio (2011) 2011 IEEE Int. Conf. on Robotics and Automation, pp. 4638-4643; Cheng, Q, Liu, Y, Liu, X, A design and modeling of the bionic variable stiffness unit based on the elbow joint 2018 3rd Int (2018) Conf. on Robotics and Automation Engineering (ICRAE), pp. 150-154; Wolf, S, Hirzinger, G, A new variable stiffness design: matching requirements of the next robot generation 2008 (2008) IEEE Int. Conf. on Robotics and Automation, pp. 1741-1746; Vanderborght, B, Tsagarakis, N G, Semini, C, Ham, R V, Caldwell, D G, MACCEPA 2.0: adjustable compliant actuator with stiffening characteristic for energy efficient hopping 2009 (2009) IEEE Int. Conf. on Robotics and Automation, pp. 544-549; Ham, R V, Vanderborght, B, Damme, M V, Verrelst, B, Lefeber, D, MACCEPA: the mechanically adjustable compliance and controllable equilibrium position actuator for 'controlled passive walking' (2006) Proc. 2006 IEEE Int. Conf. on Robotics and Automation, 2006 ICRA 2006, pp. 2195-2200; Sun, Y, Tang, P, Zheng, J, Dong, D, Chen, X, Bai, L, Ge, W, Optimal design of a nonlinear series elastic actuator for the prosthetic knee joint based on the conjugate cylindrical cam (2019) IEEE ACCES S, 7, pp. 140846-140859; Li, X, Chen, W, Lin, W, Design of a structure-controlled variable stiffness actuator based on rotary flexure hinges 2017 (2017) IEEE Int. Conf. on Robotics and Automation (ICRA), pp. 45-50; Negrello, F, Catalano, M G, Garabini, M, Poggiani, M, Caldwell, D G, Tsagarakis, N G, Bicchi, A, Design and characterization of a novel high-compliance spring for robots with soft joints 2017 (2017) IEEE Int. Conf. on Advanced Intelligent Mechatronics (AIM), pp. 271-278; Folgheraiter, M, Aubakir, B, Varol, H A, Thermally-controlled coiled polymeric wire as a novel variable elastic element 2017 (2017) IEEE Int. Conf. on Advanced Intelligent Mechatronics (AIM), pp. 466-471; Sun, L, Huang, W M, Ding, Z, Zhao, Y, Wang, C C, Purnawali, H, Tang, C, Stimulus-responsive shape memory materials: a review (2012) Mater. Des, 33, pp. 577-640; Gepreel, M A H, Niinomi, M, Biocompatibility of Ti-alloys for long-term implantation (2013) J. Mech. Behav. Biomed. Mater, 20, pp. 407-415; Jani, J M, Leary, M, Subic, A, Gibson, M A, A review of shape memory alloy research, applications and opportunities (2014) Mater. Des, 56, pp. 1078-1113; Li, Y H, Yang, C, Zhao, H D, Qu, S G, Li, X Q, Li, Y Y, New developments of Ti-based alloys for biomedical applications (2014) Materials, 7, pp. 1709-1800; Seok, S, Onal, C D, Cho, K, Wood, R J, Rus, D, Kim, S, Meshworm: a peristaltic soft robot with antagonistic nickel titanium coil actuators (2013) IEEE/ASME Trans. 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Struct, 21, p. 035010; Nalini, D, Ruth, D J S, Dhanalakshmi, K, Design of a variable stiffness actuator using shape memory alloy wire 2016 (2016) IEEE 7th Power India Int. Conf. (PIICON), pp. 1-5; Nalini, D, Dhanalakshmi, K, Synergistically configured shape memory alloy for variable stiffness translational actuation (2019) J. Intell. Mater. Syst. Struct, 30, pp. 844-854; Golgouneh, A, Holschuh, B, Dunne, L, A controllable biomimetic SMA-actuated robotic arm 2020 (2020) 8th IEEE RAS/EMBS Int. Conf. for Biomedical Robotics and Biomechatronics (Biorob), pp. 152-157; Nalini, D, Nandakumar, A, Sampath, A, Dhanalakshmi, K, Fuzzy based active stiffness control of a synergistically compliant variable stiffness shape memory actuator 2019 (2019) IEEE 5th Int. Conf. for Convergence in Technology (I2CT), pp. 1-5; Mozhi, G T, Sundareswari, M B, Kaliaperumal, D, The influencing parameters of variable stiffness actuation of shape memory spring for self-sensing 2020 (2020) IEEE 15th Int. Conf. on Industrial and Information Systems (ICIIS), pp. 306-311; Xu, Y, Guo, K, Sun, J, Li, J, Design and analysis of a linear digital variable stiffness actuator (2021) IEEE ACCES S, 9, pp. 13992-14004; Liang, C, Rogers, C A, One-dimensional thermomechanical constitutive relations for shape memory materials (Reprinted from Journal of Intelligent Material Systems and Structures, vol 1, pg 207-234, 1990) (1997) J. Intell. Mater. Syst. Struct, 8, pp. 285-302; Cisse, C, Zaki, W, Ben Zineb, T, A review of constitutive models and modeling techniques for shape memory alloys (2016) Int. J. Plast, 76, pp. 244-284; Madill, D R, Wang, D, Modeling and L2-stability of a shape memory alloy position control system (1998) IEEE Trans. Control Syst. Technol, 6, pp. 473-481; Zhang, X D, Rogers, C A, Liang, C, Modelling of the two-way shape memory effect (1997) J. Intell. Mater. Syst. Struct, 8, pp. 353-362; Luo, H Y, Abel, E W, A comparison of methods for the training of NiTi two-way shape memory alloy (2007) Smart Mater. Struct, 16, pp. 2543-2549; Shakiba, S, Yousefi-Koma, A, Ayati, M, Development of a frequency-dependent constitutive model for hysteresis of shape memory alloys (2020) Proc. Inst. Mech. Eng. L, 234, pp. 1535-1549; Achitei, D C, Abdullah, M M A B, Sandu, A V, Vizureanu, P, Abdullah, A, On the fatigue of shape memory alloys (2014) Key Eng. Mater, 594-595, pp. 133-139","Sun, Y.; State Key Laboratory of Mechanical Transmission, China; email: sunyuanxi@cqu.edu.cn",,,"IOP Publishing Ltd",,,,,09641726,,SMSTE,,"English","Smart Mater Struct",Article,"Final","",Scopus,2-s2.0-85115237897
"Mayetin U., Kucuk S.","57188922279;6603257433;","A low cost 3-DOF force sensing unit design for wrist rehabilitation robots",2021,"Mechatronics","78",,"102623","","",,1,"10.1016/j.mechatronics.2021.102623","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111605581&doi=10.1016%2fj.mechatronics.2021.102623&partnerID=40&md5=f897d82f8371d481247a0dab43130bb2","Kocaeli University, Department of Biomedical Engineering, Turkey","Mayetin, U., Kocaeli University, Department of Biomedical Engineering, Turkey; Kucuk, S., Kocaeli University, Department of Biomedical Engineering, Turkey","In this study, a low cost and modular three degree-of-freedom force sensor design is developed for the purposes of wrist rehabilitation. The proposed sensor in this study and the existing sensors in the literature are compared to each other considering nine main topics namely, measurement capability, design purpose, measurement approach, force limits, dimensions, producing & assembly complexity, producing cost, modularity and electronics. The comparison results are given as a table. Considering the sensors developed especially for the purpose of wrist rehabilitation in the literature, the proposed sensor has some advantages as follows: The proposed sensor has i) modular structure, ii) less producing & assembly complexity, iii) low cost and iv) integrated electronic and mechanical structures. A specific experimental setup is also developed for both performing force measurements and control of flexion/extension movements. Linearity, hysteresis and repeatability errors of proposed multi-axis force sensor unit are given as table. A set of force measurements is carried out using this experimental setup. Measurements are illustrated as figures. It can be concluded that the proposed force sensor can be used for human-robot interaction in wrist rehabilitation. © 2021","Admittance control; Force sensors; Human-Robot interaction; Modular structure; Rehabilitation robots; Sensor design; Wrist rehabilitation","Costs; Degrees of freedom (mechanics); Force measurement; Human robot interaction; Patient rehabilitation; Comparison result; Force sensing; Force sensor; Integrated electronics; Mechanical structures; Modular structures; Three degree of freedoms; Wrist Rehabilitation; Machine design",,,,,,,,,,,,,,,,"Izzo, C., Carrizzo, A., Alfano, A., Virtuoso, N., Capunzo, M., The impact of aging on cardio and cerebrovascular diseases (2018) Int J Mol Sci, 19 (481), pp. 1-31; Johnson, C.O., Nguyen, M., Roth, G.A., Nichols, E., Alam, T., Global, regional, and national burden of stroke, 1990–2016: a systematic analysis for the global burden of disease study 2016 (2019) Lancet Neurol, 18 (5), pp. 439-458; Katan, M., Luft, A., Global burden of stroke (2018) Semin Neurol, 38 (2), pp. 208-211; Oña, E.D., Garcia-Haro, J.M., Jardón, A., Balaguer, C., Robotics in health care perspectives of robot-aided interventions in clinical practice for rehabilitation of upper limbs (2019) Appl Sci, 9 (2586), pp. 1-27; Knecht, S., Hesse, S., Oster, P., Rehabilitation after stroke (2011) Deutsches Arzteblatt Int, 108 (36), pp. 600-606; Basteris, A., Nijenhuis, S.M., Stienen, A.H.A., Buurke, J.H., Prange, G.B., Amirabdollahian, F., Training modalities in robot-mediated upper limb rehabilitation in stroke: a framework for classification based on a systematic review (2014) J NeuroEng Rehabil, 11 (111), pp. 1-15; Blank, A.A., French, J.A., Pehlivan, A.U., O'Malley, M.K., Current trends in robot-assisted upper-limb stroke rehabilitation promoting patient engagement in therapy (2014) Curr Phys Med Rehabil Rep, 2 (3), pp. 184-195; Li, M., Xu, G., Xie, J., Chen, C., A review: motor rehabilitation after stroke with control based on human intent (2018) J Eng Med, pp. 1-17; Tagliamonte, N.L., Formica, D., Scorcia, M., Campolo, D., Guglielmelli, E., Force control of a robot for wrist rehabilitation: towards coping with human intrinsic constraints (2010) IEEE/RSJ International conference on intelligent robots and systems, Taiwan; Proietti, T., Crocher, V., Roby-Brami, A., Jarrass´e, N., Upper-limb robotic exoskeletons for neurorehabilitation a review on control strategies (2016) IEEE Rev Biomed Eng, 9, pp. 4-14; Keemink, A.Q., van der Kooij, H., Stienen, A.H., Admittance control for physical human–robot interaction (2018) Int J Robot Res, pp. 1-24; Zeng, G., Hemami, A., An overview of robot force control (1997) Robotica, 15, pp. 473-482; Hogan, N., Impedance control: an approach to manipulation: part II (1985) J Dyn Syst Meas Control, 107 (1), pp. 8-16; Akdoğan, E., Aktan, M., Koru, E., A., T., Arslan, S., Atlıhan, M., Kuran, B., Hybrid impedance control of a robot manipulator for wrist and forearm rehabilitation: Performance analysis and clinical results (2018) Mechatronics, 49, pp. 77-91; Celestino, J., (2003), “Characterization and control of a robot for wrist rehabilitation”, Master thesis, Cambridge: Dept. Mech. Eng., Massachusetts Institute of Technology; Takaiwa, M., Noritsugu, T., Sasaki, D., Wrist rehabilitation using pneumatic parallel manipulator (2012) Trans Jpn Fluid Power Syst Soc, 43 (3), pp. 85-91; Gopura, R.A.R.C., Kiguchi, K., Development of an exoskeleton robot for human wrist and forearm motion assist (2007) International conference on industrial and information systems, Penadeniye, Sri Lanka, , 9-11 August; Atlihan, M., Akdogan, E., Arslan, M.S., Development of a therapeutic exercise robot for wrist and forearm rehabilitation (2014) 19th international conference on methods and models in automation and robotics (MMAR), Miedzyzdroje, Poland; Bian, H., Chen, Z., Wang, H., Zhao, T., Mechanical design of EFW Exo II: a hybrid exoskeleton for elbow-forearm-wrist rehabilitation (2017) International conference on rehabilitation robotics (ICORR), London, UK; Buongiorno, D., Sotgiu, E., Leonardis, D., Marcheschi, S., Solazzi, M., Frisoli, A., WRES: a novel 3 DOF wrist exoskeleton with tendon-driven differential transmission for neuro-rehabilitation and teleoperation (2018) IEEE Robot Autom Lett, 3 (3), pp. 2152-2159; Pehlivan, A., Sergi, F.U., Erwin, A., Yozbatiran, N., Francisco, G.E., O'Malley, M.K., Design and validation of the RiceWrist-S exoskeleton for robotic rehabilitation after incomplete spinal cord injury (2014) Robotica, 32 (8), pp. 1415-1431; Ruiz, A.F., Rocon, E., Forner-Cordero, A., Exoskeleton-based robotic platform applied in biomechanical modelling of the human upper limb (2009) Appl Bionics Biomech, 6 (2), pp. 205-216; Song, Z., Guo, S., Pang, M., Zhang, S., ULERD-based active training for upper limb rehabilitation (2012) IEEE international conference on mechatronics and automation, Chengdu, China; Beekhuis, J., Westerveld, H., A., J., Van Der Kooij, H., Stienen, A., H., A., Design of a self-aligning 3-DOF actuated exoskeleton for diagnosis and training of wrist and forearm after stroke (2013) IEEE 13th international conference on rehabilitation robotics (ICORR), Seattle/WA, USA; Hope, J., McDaid, A., Development of wearable wrist and forearm exoskeleton with shape memory alloy actuators (2017) J Intell Robot Syst, 86, pp. 397-417; Kim, U., Lee, D.H., Kim, Y.B., Seok, D.Y., Choi, H.R., A novel six-axis force torque sensor for robotic applications (2017) IEEE/ASME Trans Mechatron, 22 (3), pp. 1381-1391; https://www.ati-ia.com/products/ft/ft_models.aspx?id=Nano25, ATI industrial automation, force /torque sensors, model Nano25, ””, Last Access (30.10.2020); Kim, G.S., The design of a six-component force moment sensor and evaluation of its uncertainty (2001) Meas Sci Technol, 12, pp. 1445-1455; Song, A., Wu, J., Gang, Q., Huang, W., A novel self-decoupled four degree-of-freedom wrist force-torque sensor (2007) Measurement, 40, pp. 883-891; Kim, G.S., Design of a six-axis wrist force moment sensor using FEM and its fabrication for an intelligent robot (2007) Sens Actuators, 133, pp. 27-34; Jacq, C., Lüthi, B., Th, M., Lambercy, O., Gassert, R., Ryser, P., Thick-film multi-DOF force-torque sensor for wrist rehabilitation (2010) Sens Actuators, 162, pp. 361-366; Kim, G.S., Kim, H., Kim, H., Pio, M., Shin, H., Yoon, J., Development of 6-axis force/moment sensor for measuring the fingers' muscular strength of human (2010) 2010 IEEE international symposium on industrial electronics, Bari, pp. 428-433; Kim, H.M., Yoon, J., Kim, G.S., Development of a six-axis force-moment sensor for a spherical-type force measuring system (2012) Meas Technol, 6 (2), pp. 96-104; Wu, B., Cai, P., Decoupling analysis of a sliding structure six-axis force torque sensor (2013) Meas Sci Rev, 13 (4), pp. 187-193; Ma, J., Song, A., Fast estimation of strains for cross-beams six-axis force-torque sensors by mechanical modeling (2013) Sensors, 13, pp. 6669-6686; Wang, Z., Li, H.J., Yao, J., Zhao, Y., Optimal design and experiment research of a fully pre-stressed six-axis force torque sensor (2013) Measurement, 46, pp. 2103-21021; Jacq, C., Maeder, T., Emery, S., Simoncini, M., Meurville, E., Ryser, P., Investigation of polymer thick-film piezoresistors for medical wrist rehabilitation and artificial knee load sensors (2014) Procedia Eng, 87, pp. 1194-1197; Yuan, C., Luo, L.P., Yuan, Q., Wu, J., Yan, R.J., Kim, H., Shin, K.S., Han, C.S., Development and evaluation of a compact 6-axis forcemoment sensor with a serial structure for the humanoid robot foot (2015) Measurement, 70, pp. 110-122; Liang, Q., Zhang, D., Coppola, G., Mao, J., Sun, W., Wang, Y., Ge, Y., Design and Analysis of a Sensor System for Cutting Force Measurement in Machining Processes (2016) Sensors, 16 (70), pp. 1-17; Guggenheim, J.W., Jentoft, L.P., Tenzer, Y., Howe, R.D., Robust and Inexpensive 6-Axis Force-Torque Sensors using MEMS Barometers (2016) IEEE Trans Mechatron, 22 (2), pp. 838-844; Grosu, V., Grosu, S., Vanderborght, B., Lefeber, D., Rodriguez-Guerrero, C., Multi-axis force sensor for human–robot interaction sensing in a rehabilitation robotic device (2017) Sensors, 17, pp. 1294-1306; Xiong, L., Jiang, G., Guo, Y., Liu, H., A three-dimensional fiber bragg grating force sensor for robot (2018) IEEE Sens J, 18 (9), pp. 3632-3639; Kebede, G.A., Ahmad, A.R., Lee, S.-C., Lin, C.-Y., Decoupled six-axis force–moment sensor with a novel strain gauge arrangement and error reduction techniques (2019) Sensors, 19 (3012), pp. 1-14","Kucuk, S.; Kocaeli University, Turkey; email: skucuk@kocaeli.edu.tr",,,"Elsevier Ltd",,,,,09574158,,MECHE,,"English","Mechatronics",Article,"Final","",Scopus,2-s2.0-85111605581
"Ajibo C.A., Ishi C.T., Ishiguro H.","57220578619;6506878555;26643316500;","Advocating Attitudinal Change through Android Robot's Intention-Based Expressive Behaviors: Toward WHO COVID-19 Guidelines Adherence",2021,"IEEE Robotics and Automation Letters","6","4","9476976","6521","6528",,,"10.1109/LRA.2021.3094783","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111103751&doi=10.1109%2fLRA.2021.3094783&partnerID=40&md5=679695b872178288319ac48547e3866b","Graduate School of Engineering Science, Osaka University, Osaka, Japan; Guardian Robot Project, Riken Hiroshi Ishiguro Labs., Atr, Kyoto, Japan","Ajibo, C.A., Graduate School of Engineering Science, Osaka University, Osaka, Japan; Ishi, C.T., Guardian Robot Project, Riken Hiroshi Ishiguro Labs., Atr, Kyoto, Japan; Ishiguro, H., Graduate School of Engineering Science, Osaka University, Osaka, Japan","Motivated by the fact that some human emotional expressions promote affiliating functions such as signaling, social change, and support, all of which have been established as providing social benefits, we investigated how these behaviors can be extended to Human-Robot Interaction (HRI) scenarios. We explored how to furnish an android robot with socially motivated expressions geared toward eliciting adherence to COVID-19 guidelines. We analyzed how different behaviors associated with social expressions in such situations occur in Human-Human Interaction (HHI) and designed a scenario where a robot utilizes context-inspired behaviors (polite, gentle, displeased, and angry) to enforce social compliance. We then implemented these behaviors in an android robot and subjectively evaluated how effectively it expressed them and how they were perceived in terms of their appropriateness, effectiveness, and tendency to enforce social compliance to COVID-19 guidelines. We also considered how the subjects' sense of values regarding compliance awareness would affect the robot's behavior impressions. Our evaluation results indicated that participants generally preferred polite behaviors by a robot, although participants with different levels of compliance awareness manifested different trends toward appropriateness and effectiveness for social compliance enforcement through negative expressions by the robot. © 2016 IEEE.","Android robot; attitudinal behavior; compliance; COVID-19; human-robot interaction","Agricultural robots; Social robots; Emotional expressions; Evaluation results; Human robot Interaction (HRI); Human-human interactions; Social benefits; Social changes; Economic and social effects",,,,,"Japan Science and Technology Agency, JST: JPMJMS2011","Manuscript received February 24, 2021; accepted June 10, 2021. Date of publication July 7, 2021; date of current version July 19, 2021. This letter was recommended for publication by Associate Editor Amy LaViers and Editor Prof. Gentiane Venture upon evaluation of the reviewers’ comments. This work was supported in part by the Grant-in-Aid for Scientific Research on Innovative Areas JP20H05576, and in part by JST, Moonshot R&D under Grant JPMJMS2011. (Corresponding author: Carlos Toshinori Ishi.) Chinenye Augustine Ajibo and Hiroshi Ishiguro are with the Graduate School of Engineering Science, Osaka University, Osaka 565-0871, Japan, and also with the Hiroshi Ishiguro Labs., ATR, Kyoto 619-0288, Japan (e-mail: ajibo.augustine@irl.sys.es.osaka-u.ac.jp; ishiguro@sys.es.osaka-u.ac.jp).",,,,,,,,,,"Wong, S., Bond, M.H., Rodriguez Mosquera, P.M., The influence of cultural value orientations on self-reported emotional expression across cultures (2008) J. Cross-Cultural Psychol., 39 (2), pp. 1224-1229; Van Oorsouw, W.M., Embregts, P.J., Sohier, J., Verbal and nonverbal emotional behavior of staff: A first attempt in the development of an observation instrument (2011) Res. Devlop. 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Interactive Commun., pp. 22-29","Ajibo, C.A.; Graduate School of Engineering Science, Japan; email: ajibo.augustine@irl.sys.es.osaka-u.ac.jp",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85111103751
"Zhi J., Yu L.-F., Lien J.-M.","57210796082;49862480500;7102773542;","Designing Human-Robot Coexistence Space",2021,"IEEE Robotics and Automation Letters","6","4","9484808","7161","7168",,,"10.1109/LRA.2021.3097061","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110903609&doi=10.1109%2fLRA.2021.3097061&partnerID=40&md5=9393e603d98f84db59c21b52a4784912","Department of Computer Science, George Mason University, Fairfax, VA, United States","Zhi, J., Department of Computer Science, George Mason University, Fairfax, VA, United States; Yu, L.-F., Department of Computer Science, George Mason University, Fairfax, VA, United States; Lien, J.-M., Department of Computer Science, George Mason University, Fairfax, VA, United States","When the human-robot interactions become ubiquitous, the environment surrounding these interactions will have significant impact on the safety and comfort of the human and the effectiveness and efficiency of the robot. Although most robots are designed to work in the spaces created for humans, many environments, such as living rooms and offices, can be and should be redesigned to enhance and improve human-robot collaboration and interactions. This letter uses autonomous wheelchair as an example and investigates the computational design in the human-robot coexistence spaces. Given the room size and the objects $O$ in the room, the proposed framework computes the optimal layouts of $O$ that satisfy both human preferences and navigation constraints of the wheelchair. The key enabling technique is a motion planner that can efficiently evaluate hundreds of similar motion planning problems. Our implementation shows that the proposed framework can produce a design around three to five minutes on average comparing to 10 to 20 minutes without the proposed motion planner. Our results also show that the proposed method produces reasonable designs even for tight spaces and for users with different preferences. © 2016 IEEE.","human-aware motion planning; Motion and path planning; nonholonomic motion planning","Agricultural robots; Machine design; Wheelchairs; Autonomous wheelchair; Computational design; Effectiveness and efficiencies; Enabling techniques; Human-robot collaboration; Motion planners; Motion planning problems; Wheelchair robot; Social robots",,,,,"National Science Foundation, NSF: 1942531","Manuscript received February 1, 2021; accepted June 22, 2021. Date of publication July 14, 2021; date of current version July 28, 2021. This letter was recommended for publication by Associate Editor J. Godoy and Editor N. Amato upon evaluation of the reviewers’ comments. The work of Lap-Fai Yu was supported the NSF CAREER under Grant 1942531. (Corresponding author: Jixuan Zhi.) The authors are with the Department of Computer Science, George Mason University, Fairfax, VA 22030 USA (e-mail: jzhi@gmu.edu; craigyu@gmu.edu; jmlien@cs.gmu.edu).",,,,,,,,,,"Fu, Q., Chen, X., Wang, X., Wen, S., Zhou, B., Fu, H., Adaptive synthesis of indoor scenes via activity-associated object relation graphs (2017) ACM Trans. Graph., 36 (6), pp. 1-13; Dolgov, D., Thrun, S., Montemerlo, M., Diebel, J., Practical search techniques in path planning for autonomous driving (2008) Ann Arbor, 1001 (48105), pp. 18-80; Yu, L.F., Yeung, S.K., Tang, C.K., Terzopoulos, D., Chan, T.F., Osher, S.J., Make it home: Automatic optimization of furniture arrangement (2011) ACM Trans. Graph.-Proc. ACM SIGGRAPH, 30 (4); Li, W., Talavera, J., Samayoa, A.G., Lien, J.-M., Yu, L.-F., Automatic synthesis of virtual wheelchair training scenarios (2020) Proc. IEEE Conf. Virtual Reality 3D User Interfaces, pp. 539-547; Fisher, M., Ritchie, D., Savva, M., Funkhouser, T., Hanrahan, P., Example-based synthesis of 3D object arrangements (2012) ACM Trans. Graph., 31 (6), pp. 1-11; Li, M., Grains: Generative recursive autoencoders for indoor scenes (2019) ACM Trans. Graph., 38 (2), pp. 1-16; Fisher, M., Savva, M., Li, Y., Hanrahan, P., Nießner, M., Activity-centric scene synthesis for functional 3D scene modeling (2015) ACM Trans. Graph., 34 (6), pp. 1-13; Savva, M., Chang, A.X., Hanrahan, P., Fisher, M., Nießner, M., Scene-Grok: Inferring action maps in 3D environments (2014) ACM Trans. Graph., 33 (6), pp. 1-10; Ma, R., Li, H., Zou, C., Liao, Z., Tong, X., Zhang, H., Action-driven 3D indoor scene evolution (2016) ACM Trans. Graph., 35 (6), pp. 1-13; Lien, J.-M., Lu, Y., Planning motion in environments with similar obstacles (2009) Proc. Robot., Sci. 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Syst., 34 (4), pp. 179-202","Zhi, J.; Department of Computer Science, United States; email: jzhi@gmu.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85110903609
"Pollmann K., Ziegler D.","57128090300;56236807100;","A pattern approach to comprehensible and pleasant human–robot interaction",2021,"Multimodal Technologies and Interaction","5","9","49","","",,,"10.3390/mti5090049","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114261840&doi=10.3390%2fmti5090049&partnerID=40&md5=ccdc2b4cb5ab81898503d9897dc79068","Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, Stuttgart, 70569, Germany","Pollmann, K., Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, Stuttgart, 70569, Germany; Ziegler, D., Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, Stuttgart, 70569, Germany","HRI designers are faced with the task of creating robots that are easy and pleasant to use for the users. The growing body of research in human–robot interaction (HRI) is still mainly focused on technical aspects of the interaction. It lacks defined guidelines that describe how behavioral expressions for social robots need to be designed to promote high usability and positive user experi-ence. To achieve this goal, we propose to apply the concept of design patterns to HRI. We present a design process that provides step-by-step guidance and methods for HRI designers to generate high quality behavioral patterns for social robots that can be used for different robots and use cases. To document the resulting patterns, we developed a documentation format that provides a clear, standardized structure to note down all relevant aspects of a pattern so that others can understand its design recommendations and apply them to their own robot and use cases. In the present paper, we demonstrate our pattern approach based on an example and describe how we arrived at a pattern language of 40 behavioral patterns that found the basis for future social robot design and related research activities. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Behavioral design patterns; Human-centered design; Human–robot interaction; Pattern language; User experience",,,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV7941","Funding: This research was conducted as part of the NIKA project and funded by the German Federal Ministry of Education and Research (BMBF 16SV7941).",,,,,,,,,,"Forlizzi, J., How robotic products become social products: An ethnographic study of cleaning in the home Proceedings of the 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 129-136. , Arlington, VA, USA, 10–12 March 2007; Tanaka, F., Ghosh, M., The implementation of care-receiving robot at an English learning school for children Proceedings of the 6th International Conference on Human-Robot Interaction (HRI), pp. 265-266. , Lausanne, Switzerland, 6–9 March 2011; Wada, K., Shibata, T., Living with seal robots—Its sociopsychological and physiological influences on the elderly at a care house (2007) IEEE Trans. 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Interact, 21, pp. 49-102; Tidwell, J., (1999) Common Ground: A Pattern Language for Human-Computer Interface Design, , http://www.mit.edu/~jtidwell/interaction_patterns.html, (accessed on 16 August 2021); Conley, K., (2010) REP Purpose and Guidelines, , https://www.ros.org/reps/rep-0001.html, (accessed on 16 August 2021); Dautenhahn, K., Robots we like to live with?!—A developmental perspective on a personalized, life-long robot companion Proceedings of the RO-MAN 2004. 13th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No.04TH8759), pp. 17-22. , Kurashiki, Japan, 20–22 September 2004; Syrdal, D.S., Lee Koay, K., Walters, M.L., Dautenhahn, K., A personalized robot companion?—The role of individual differences on spatial preferences in HRI scenarios Proceedings of the RO-MAN 2007—The 16th IEEE International Symposium on Robot and Human Interactive Communication, pp. 1143-1148. , Jeju Island, Korea, 26–29 August 2007; Pollmann, K., Ziegler, D., Personal Quizmaster: A Pattern Approach to Personalized Interaction Experiences with the MiRo Robot Proceedings of the Conference on Mensch und Computer (MuC ’20), pp. 485-489. , Magdeburg, Germany, 6–9 September 2020; Association for Computing Machinery: New York, NY, USA, 2020; Mizoguchi, H., Takagi, K., Hatamura, Y., Nakao, M., Sato, T., Behavioral expression by an expressive mobile robot-expressing vividness, mental distance, and attention (1997) Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. 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Robot, 10, pp. 65-92; Choi, Y., Kim, J., Pan, P., Jeung, J., The Considerable Elements of the Emotion Expression Using Lights in Apparel Types (2007) Proceedings of the 4th International Conference on Mobile Technology, Applications, and Systems and the 1st International Symposium on Computer Human Interaction in Mobile Technology (Mobility ’07), pp. 662-666. , Singapore, 10–12 September Association for Computing Machinery: New York, NY, USA, 2007; Goffman, E., (1963) Behavior in Public Places: Notes on the Social Organization of Gatherings, , The Free Press: New York, NY, USA; Jan, D., Traum, D.R., Dynamic Movement and Positioning of Embodied Agents in Multiparty Conversations (2007) Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS ’07), , Honolulu, HI, USA, 14–18 May Association for Computing Machinery: New York, NY, USA, 2007; Cassell, J., Bickmore, T., Billinghurst, M., Campbell, L., Chang, K., Vilhjálmsson, H., Yan, H., Embodiment in Conversational Interfaces: Rea (1999) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’99), pp. 520-527. , Pittsburgh, PA, USA, 15–20 May Association for Computing Machinery: New York, NY, USA, 1999; Cassell, J., Thorisson, K.R., The power of a nod and a glance: Envelope vs. emotional feedback in animated conversational agents (1999) Appl. Artif. Intell, 13, pp. 519-538","Pollmann, K.; Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, Germany; email: kathrin.pollmann@iao.fraunhofer.de",,,"MDPI",,,,,24144088,,,,"English","Multimodal Tech. Inter.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85114261840
"Soares I., Petry M., Moreira A.P.","57243157800;25641541900;55971638300;","Programming robots by demonstration using augmented reality",2021,"Sensors","21","17","5976","","",,,"10.3390/s21175976","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114207357&doi=10.3390%2fs21175976&partnerID=40&md5=414fa000bb839d508ffe2b15413a0a8b","Department of Electrical and Computer Engineering, Faculdade de Engenharia da Universidade do Porto, FEUP, Porto, 4200-465, Portugal; INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Porto, 4200-465, Portugal","Soares, I., Department of Electrical and Computer Engineering, Faculdade de Engenharia da Universidade do Porto, FEUP, Porto, 4200-465, Portugal, INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Porto, 4200-465, Portugal; Petry, M., INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Porto, 4200-465, Portugal; Moreira, A.P., Department of Electrical and Computer Engineering, Faculdade de Engenharia da Universidade do Porto, FEUP, Porto, 4200-465, Portugal, INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Porto, 4200-465, Portugal","The world is living the fourth industrial revolution, marked by the increasing intelligence and automation of manufacturing systems. Nevertheless, there are types of tasks that are too complex or too expensive to be fully automated, it would be more efficient if the machines were able to work with the human, not only by sharing the same workspace but also as useful collaborators. A possible solution to that problem is on human–robot interaction systems, understanding the applications where they can be helpful to implement and what are the challenges they face. This work proposes the development of an industrial prototype of a human–machine interaction system through Augmented Reality, in which the objective is to enable an industrial operator without any programming experience to program a robot. The system itself is divided into two different parts: the tracking system, which records the operator’s hand movement, and the translator system, which writes the program to be sent to the robot that will execute the task. To demonstrate the concept, the user drew geometric figures, and the robot was able to replicate the operator’s path recorded. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Augmented reality; Collaborative robots; Industrial robots; Programming by demonstration","Augmented reality; Industrial robots; Manufacture; Program translators; Robot programming; Fully automated; Hand movement; Industrial revolutions; Interaction systems; Programming experience; Programming robots; Robot interactions; Tracking system; Social robots; automation; human; industry; movement (physiology); robotics; Augmented Reality; Automation; Humans; Industry; Movement; Robotics",,,,,"European Commission, EC; European Institute of Innovation and Technology, EIT","Funding: Work realized within the scope of the project MirrorLabs2. KIC MirrorLabs2-21086 is supported by the European Institute of Innovation and Technology (EIT), a body of the European Union.",,,,,,,,,,"Isma, A., Brahim, B., Time-dependant trajectory generation for tele-operated mobile manipulator (2015) Proceedings of the 3rd International Conference on Control, Engineering and Information Technology, CEIT 2015, , Tlemcen, Algeria, 25–27 May [CrossRef]; Masood, T., Egger, J., Augmented reality in support of Industry 4.0—Implementation challenges and success factors (2019) Robot. Comput. Integr. Manuf, 58, pp. 181-195. , [CrossRef]; Egger, J., Masood, T., Augmented reality in support of intelligent manufacturing—A systematic literature review (2020) Comput. Ind. Eng, 140, p. 106195. , [CrossRef]; Blankemeyer, S., Wiemann, R., Posniak, L., Pregizer, C., Raatz, A., Intuitive robot programming using augmented reality (2018) Procedia CIRP, 76, pp. 155-160. , [CrossRef]; Duffy, B.R., Anthropomorphism and the social robot (2003) Robot. Auton. Syst, 42, pp. 177-190. , [CrossRef]; Argall, B.D., Chernova, S., Veloso, M., Browning, B., A survey of robot learning from demonstration (2009) Robot. Auton. Syst, 57, pp. 469-483. , [CrossRef]; De Pace, F., Manuri, F., Sanna, A., Augmented Reality in Industry 4.0 (2018) Am. J. Comput. Sci. Inf. Technol, 6, p. 17. , [CrossRef]; Bottani, E., Vignali, G., Augmented reality technology in the manufacturing industry: A review of the last decade (2019) IISE Trans, 51, pp. 284-310. , [CrossRef]; Shirer, M., Worldwide Spending on Augmented and Virtual Reality Forecast to Deliver Strong Growth through 2024, According to a New IDC Spending Guide, , https://www.idc.com/getdoc.jsp?containerId=prUS47012020, IDC, 2020. (accessed on 12 January 2021); Aleotti, J., Micconi, G., Caselli, S., Object interaction and task programming by demonstration in visuo-haptic augmented reality (2016) Multimed. Syst, 22, pp. 675-691. , [CrossRef]; Araiza-Illan, D., De San Bernabe, A., Hongchao, F., Shin, L.Y., Augmented Reality for Quick and Intuitive Robotic Packing Re-Programming (2019) Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, , Daegu, Korea, 11–14 March [CrossRef]; Rudorfer, M., Guhl, J., Hoffmann, P., Kruger, J., Holo Pick’n’Place (2018) Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), , Torino, Italy, 4–7 September [CrossRef]; Soares, I., Sousa, R., Petry, M.R., Moreira, A.P., Accuracy and Repeatability Tests on HoloLens 2 and HTC Vive (2021) Multimodal Technol. Interact, 5, p. 8. , [CrossRef]; HoloLens 2 Gestures for Navigating a Guide in Dynamics 365 Guides, , https://docs.microsoft.com/en-us/dynamics365/mixed-reality/guides/operator-gestures-hl2, (accessed on 18 August 2021); (2021), https://github.com/siemens/ros-sharp, ROS (accessed on 18 August 2021); (2019) The URScript Programming Language, , https://www.siemens-pro.ru/docs/ur/scriptManual.pdf, Universal Robots. For version 3.10. (accessed on 18 August 2021); (2010) Technical Reference Manual—RAPID Instructions, Functions and Data Types, , ABB Robotics. ABB Robotics: Västeras, Sweden","Petry, M.; INESC TEC—Institute for Systems and Computer Engineering, Portugal; email: marcelo.petry@inesctec.pt",,,"MDPI",,,,,14248220,,,"34502864","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85114207357
"Tsamis G., Chantziaras G., Giakoumis D., Kostavelis I., Kargakos A., Tsakiris A., Tzovaras D.","57226120890;57223012415;35147896000;35484641300;56641721700;55251139200;13105681700;","Intuitive and safe interaction in multi-user human robot collaboration environments through augmented reality displays",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"520","526",,,"10.1109/RO-MAN50785.2021.9515474","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115112112&doi=10.1109%2fRO-MAN50785.2021.9515474&partnerID=40&md5=6107785296590e654b0324efd35eedc2","Information Technologies Institute (CERTH / ITI), Centre for Research and Technology-Hellas, 6th Km Charilaou-Thermi Road, Thessaloniki, 57001, Greece","Tsamis, G., Information Technologies Institute (CERTH / ITI), Centre for Research and Technology-Hellas, 6th Km Charilaou-Thermi Road, Thessaloniki, 57001, Greece; Chantziaras, G., Information Technologies Institute (CERTH / ITI), Centre for Research and Technology-Hellas, 6th Km Charilaou-Thermi Road, Thessaloniki, 57001, Greece; Giakoumis, D., Information Technologies Institute (CERTH / ITI), Centre for Research and Technology-Hellas, 6th Km Charilaou-Thermi Road, Thessaloniki, 57001, Greece; Kostavelis, I., Information Technologies Institute (CERTH / ITI), Centre for Research and Technology-Hellas, 6th Km Charilaou-Thermi Road, Thessaloniki, 57001, Greece; Kargakos, A., Information Technologies Institute (CERTH / ITI), Centre for Research and Technology-Hellas, 6th Km Charilaou-Thermi Road, Thessaloniki, 57001, Greece; Tsakiris, A., Information Technologies Institute (CERTH / ITI), Centre for Research and Technology-Hellas, 6th Km Charilaou-Thermi Road, Thessaloniki, 57001, Greece; Tzovaras, D., Information Technologies Institute (CERTH / ITI), Centre for Research and Technology-Hellas, 6th Km Charilaou-Thermi Road, Thessaloniki, 57001, Greece","As autonomous collaborative robots are more widely used in work environments alongside humans it is of great importance to facilitate the communication between people and robotic systems, in a way that promotes safety and productivity. To this end, we propose an Augmented Reality (AR) based system that allows workers in a human-robot collaborative environment to interact with a robot while also receiving information regarding the robot state and plans that relate to the human's safety and trust, such as the intended movement of the robotic arm or the navigation plan of the mobile platform. To evaluate the effectiveness of the proposed system we conducted experiments with 13 participants, where two users had to work in the same workspace while being assisted by a mobile manipulator. We measured the task completion time as well as the robot idle time using our AR-based human-robot interaction system and compared them to a conventional setup without the use of augmented reality. Additional, subjective evaluations related to user satisfaction, system usability, perceived safety and trust showed that users assessed the system in a positive way and preferred AR visualization over more traditional interfaces. © 2021 IEEE.",,"Agricultural robots; Augmented reality; Manipulators; Robotics; Collaborative environments; Collaborative robots; Human-robot collaboration; Mobile manipulator; Subjective evaluations; Task completion time; User satisfaction; Work environments; Social robots",,,,,"Horizon 2020: 820742","This work has been supported by the EU Horizon 2020 funded project Hybrid Human-Robot RECYcling plant for electriCal and eLEctRonic Equipment (HR-RECYCLER) under the grant agreement with no: 820742","This work has been supported by the EU Horizon 2020 funded project ”Hybrid Human-Robot RECYcling plant for electriCal and eLEctRonic Equipment (HR-RECYCLER)” under the grant agreement with no: 820742.",,,,,,,,,"Bragança, S., Costa, E., Castellucci, I., Arezes, P.M., A brief overview of the use of collaborative robots in industry 4. 0: Human role and safety (2019) Occupational and Environmental Safety and Health, pp. 641-650; Sherwani, F., Asad, M.M., Ibrahim, B., Collaborative robots and industrial revolution 4. 0 (ir 4. 0) (2020) 2020 International Conference on Emerging Trends in Smart Technologies (ICETST). IEEE, pp. 1-5; Azuma, R.T., A survey of augmented reality (1997) Presence: Teleoperators & Virtual Environments, 6 (4), pp. 355-385; Makhataeva, Z., Zhakatayev, A., Varol, H.A., Safety aura visualization for variable impedance actuated robots (2019) 2019 IEEE/SICE International Symposium on System Integration (SII). IEEE, pp. 805-810; Pereira, A., Carter, E.J., Leite, I., Mars, J., Lehman, J.F., Augmented reality dialog interface for multimodal teleoperation (2017) 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 764-771; Adadi, A., Berrada, M., Peeking inside the black-box: A survey on explainable artificial intelligence (xai) (2018) IEEE Access, 6, pp. 52138-52160; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human-robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266; Frank, J.A., Moorhead, M., Kapila, V., Realizing mixed-reality environments with tablets for intuitive human-robot collaboration for object manipulation tasks (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 302-307; Gkournelos, C., Karagiannis, P., Kousi, N., Michalos, G., Koukas, S., Makris, S., Application of wearable devices for supporting operators in human-robot cooperative assembly tasks (2018) Procedia CIRP, 76, pp. 177-182; Makris, S., Karagiannis, P., Koukas, S., Matthaiakis, A.-S., Augmented reality system for operator support in human-robot collaborative assembly (2016) CIRP Annals, 65 (1), pp. 61-64; Lotsaris, K., Fousekis, N., Koukas, S., Aivaliotis, S., Kousi, N., Michalos, G., Makris, S., Augmented reality (ar) based framework for supporting human workers in flexible manufacturing (2021) Procedia CIRP, 96, pp. 301-306; Hernández, J.D., Sobti, S., Sciola, A., Moll, M., Kavraki, L.E., Increasing robot autonomy via motion planning and an augmented reality interface (2020) IEEE Robotics and Automation Letters, 5 (2), pp. 1017-1023; Hietanen, A., Pieters, R., Lanz, M., Latokartano, J., Kämäräinen, J.-K., Ar-based interaction for human-robot collaborative manufacturing (2020) Robotics and Computer-Integrated Manufacturing, 63, p. 101891; Kapinus, M., Beran, V., Materna, Z., BambuSek, D., Spatially situated end-user robot programming in augmented reality (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 1-8; Bambûsek, D., Materna, Z., Kapinus, M., Beran, V., Smrz, P., Combining interactive spatial augmented reality with head-mounted display for end-user collaborative robot programming (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 1-8; Puljiz, D., Krebs, F., Bösing, F., Hein, B., (2020) What the Hololens Maps Is Your Workspace: Fast Mapping and Set-up of Robot Cells Via Head Mounted Displays and Augmented Reality; Evans, G., Miller, J., Pena, M.I., MacAllister, A., Winer, E., Evaluating the microsoft hololens through an augmented reality assembly application (2017) Degraded Environments: Sensing, Processing, and Display 2017, p. 101970V. , 10197. International Society for Optics and Photonics; Abdelrasoul, Y., Saman, A.B.S.H., Sebastian, P., A quantitative study of tuning ros gmapping parameters and their effect on performing indoor 2D slam (2016) 2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA). IEEE, pp. 1-6; Guimarães, R.L., De Oliveira, A.S., Fabro, J.A., Becker, T., Brenner, V.A., Ros navigation: Concepts and tutorial (2016) Robot Operating System (ROS), pp. 121-160. , Springer; Tremblay, J., To, T., Sundaralingam, B., Xiang, Y., Fox, D., Birchfield, S., (2018) Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects; Chitta, S., Moveit!: An introduction (2016) Robot Operating System (ROS), pp. 3-27. , Springer; Gammell, J.D., Srinivasa, S.S., Barfoot, T.D., Informed rrt: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic (2014) International Conference on Intelligent Robots and Systems. IEEE, pp. 2997-3004; Chantziaras, G., Triantafyllidis, A., Papaprodromou, A., Chatzikonstantinou, I., Giakoumis, D., Tsakiris, A., Votis, K., Tzovaras, D., An augmented reality-based remote collaboration platform for worker assistance (2021) Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, pp. 404-416. , January 10-15 Proceedings, Part VII. Springer International Publishing, 2021; Bangor, A., Kortum, P., Miller, J., Determining what individual sus scores mean: Adding an adjective rating scale (2009) Journal of Usability Studies, 4 (3), pp. 114-123",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85115112112
"Gurung N., Grant J.B., Herath D.","57222468411;18036903700;8366358100;","What's in a face? The effect of faces in human robot interaction",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"1024","1030",,,"10.1109/RO-MAN50785.2021.9515378","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115101122&doi=10.1109%2fRO-MAN50785.2021.9515378&partnerID=40&md5=a11b2f87d426adf7b38efdf516f79087","University of Canberra, Faculty of Science and Technology, Canberra, ACT  2617, Australia; University of Canberra, Faculty of Health, Canberra, ACT  2617, Australia","Gurung, N., University of Canberra, Faculty of Science and Technology, Canberra, ACT  2617, Australia; Grant, J.B., University of Canberra, Faculty of Health, Canberra, ACT  2617, Australia; Herath, D., University of Canberra, Faculty of Science and Technology, Canberra, ACT  2617, Australia","The face is the most influential feature in any interaction. This study investigated the effect of robot faces in embodied interactions on a range of subjective and objective factors. The platform used to answer the research question was an interactive robotic art installation, incorporating a robot arm that could be presented with or without a face displayed on an attached screen. Participants were exposed to one of three conditions - the robot arm only, the robot arm with a static face displayed, and the robot arm with a dynamic face displayed. We used the Godspeed Questionnaire to determine whether the different embodiments would be perceived differently on measures of likeability, animacy, and safety before and after the interaction. We also measured how close participants stood to the robot and how much time they spent interacting with the robot. We found that the three embodiments did not significantly differ in time spent, distance, animacy, likeability, or safety before and after the interaction. This surprising result hints at other possible reasons that influence the success of a robot interaction and advances the understanding of the effect of faces in human-robot interaction. © 2021 IEEE.",,"Agricultural robots; Man machine systems; Robotic arms; Embodied interaction; Interactive robotics; Likeability; Research questions; Robot arms; Robot face; Robot interactions; Time spent; Social robots",,,,,,,,,,,,,,,,"Jack, R.E., Schyns, P.G., The human face as a dynamic tool for social communication (2015) Current Biology, 25 (14), pp. 621-634; Todorov, A., Pakrashi, M., Oosterhof, N.N., Evaluating faces on trustworthiness after minimal time exposure (2009) Social Cognition, 27 (6), pp. 813-833; Zebrowitz, L.A., Montepare, J.M., Social psychological face perception: Why appearance matters (2008) Social and Personality Psychology Compass, 2 (3), pp. 1497-1517; Frith, C., Role of facial expressions in social interactions (2009) Philosophical Transactions of the Royal Society B: Biological Sciences, 364 (1535), pp. 3453-3458; Thornhill, R., Gangestad, S.W., Facial attractiveness (1999) Trends in Cognitive Sciences, 3 (12), pp. 452-460; Norman, D., (2013) The Design of Everyday Things: Revised and Expanded Edition, , Basic books; Blow, M., Dautenhahn, K., Appleby, A., Nehaniv, C.L., Lee, D., The art of designing robot faces: Dimensions for human-robot interaction (2006) Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-robot Interaction, pp. 331-332; Breazeal, C., Social interactions in hri: The robot view (2004) IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 34 (2), pp. 181-186; Mirnig, N., Strasser, E., Weiss, A., Kühnlenz, B., Wollherr, D., Tscheligi, M., Can you read my face? (2015) International Journal of Social Robotics, 7 (1), pp. 63-76; Mori, M., The uncanny valley (1970) Energy, 7 (4), pp. 33-35; McCloud, S., (1993) Understanding Comics: The Invisible Art, , Northampton, Mass; Kalegina, A., Schroeder, G., Allchin, A., Berlin, K., Cakmak, M., Characterizing the design space of rendered robot faces (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 96-104; Rothstein, N., Kounios, J., Ayaz, H., De Visser, E.J., Assessment of human-likeness and anthropomorphism of robots: A literature review (2020) International Conference on Applied Human Factors and Ergonomics, pp. 190-196. , Springer; Reeves, B., Nass, C., (1996) The Media Equation: How People Treat Computers, Television, and New Media like Real People, , Cambridge university press Cambridge, UK; Broadbent, E., Kumar, V., Li, X., Sollers, J., III, Stafford, R.Q., MacDonald, B.A., Wegner, D.M., Robots with display screens: A robot with a more humanlike face display is perceived to have more mind and a better personality (2013) PloS One, 8 (8); Phillips, E., Zhao, X., Ullman, D., Malle, B.F., What is humanlike? decomposing robots' human-like appearance using the anthropomorphic robot (abot) database (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 105-113; Bartneck, C., Kanda, T., Ishiguro, H., Hagita, N., Is the uncanny valley an uncanny cliff? (2007) RO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication, pp. 368-373; Schwind, V., Wolf, K., Henze, N., Korn, O., Determining the characteristics of preferred virtual faces using an avatar generator (2015) Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play, pp. 221-230; Benitez, J., Wyman, A.B., Carpinella, C.M., Stroessner, S.J., The authority of appearance: How robot features influence trait inferences and evaluative responses (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 397-404; Bruce, A., Nourbakhsh, I., Simmons, R., The role of expressiveness and attention in human-robot interaction (2002) Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292), 4, pp. 4138-4142; Cavedon, L., Kroos, C., Herath, D., Burnham, D., Bishop, L., Leung, Y., Stevens, C.J., C'mon dude!"": Users adapt their behaviour to a robotic agent with an attention model (2015) International Journal of Human-Computer Studies, 80, pp. 14-23; Argyle, M., Dean, J., Eye-contact, distance and affiliation (1965) Sociometry, American Sociological Association, pp. 289-304; Zibrek, K., Martin, S., McDonnell, R., Is photorealism important for perception of expressive virtual humans in virtual reality? (2019) ACM Transactions on Applied Perception (TAP), 16 (3), pp. 1-19; Zibrek, K., Kokkinara, E., McDonnell, R., The effect of realistic appearance of virtual characters in immersive environments-does the character's personality play a role? (2018) IEEE Transactions on Visualization and Computer Graphics, 24 (4), pp. 1681-1690; Gurung, N., Herath, D., Grant, J.B., Feeling safe: A study on trust with an interactive robotic art installation (2021) Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, pp. 447-451; Rajamohan, V., Scully-Allison, C., Dascalu, S., Feil-Seifer, D., Factors influencing the human preferred interaction distance (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 1-7; Koay, K.L., Syrdal, D.S., Walters, M.L., Dautenhahn, K., Living with robots: Investigating the habituation effect in participants' preferences during a longitudinal human-robot interaction study (2007) RO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 564-569; Siebert, F.W., Klein, J., Rötting, M., Roesler, E., (2020) The Influence of Distance and Lateral Offset of Follow Me Robots on User Perception; Siebert, F.W., Pickl, J., Klein, J., Rötting, M., Roesler, E., Let's not get too personal-distance regulation for follow me robots (2020) International Conference on Human-Computer Interaction, pp. 459-467. , Springer; Shanee, H.S., Dror, K., Tal, O.-G., Yael, E., The influence of following angle on performance metrics of a human-following robot (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 593-598; Patterson, M.L., An arousal model of interpersonal intimacy (1976) Psychological Review, 83 (3), p. 235; Mumm, J., Mutlu, B., Human-robot proxemics: Physical and psychological distancing in human-robot interaction (2011) Proceedings of the 6th International Conference on Human-robot Interaction, pp. 331-338; Herath, D.C., Kroos, C., Stevens, C., Burnham, D., Adopt-a-robot: A story of attachment (or the lack thereof) (2013) 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 135-136; Gockley, R., Bruce, A., Forlizzi, J., Michalowski, M., Mundell, A., Rosenthal, S., Sellner, B., Schultz, A.C., Designing robots for long-term social interaction (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1338-1343; Bartneck, C., Kulíc, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81; Kroos, C., Herath, D.C., Evoking agency: Attention model and behavior control in a robotic art installation (2012) Leonardo, 45 (5), pp. 401-407; Clarke, J., (2021) View of Stelarc's Prosthetic Head | Ctheory, , https://journals.uvic.ca/index.php/ctheory/article/view/14458/5300; Kroos, C., Herath, D., Stelarc ""the articulated head: An intelligent interactive agent as an artistic installation (2009) Proceedings of International Conference on Intelligent Robots and Systems; (2020) Innovation in Hardware, Design and Interfaces for Social Robots and Hri, , https://sites.psu.edu/icsr2020/awards/, I. Conference; Lazaroo, S., (2021) Talk to the (Articulated) Head 2. 0, , https://www.canberra.edu.au/uncover/newsarchive/2019/september/talk-to-the-articulated-head-2.0; McDonnell, R., Breidt, M., Bülthoff, H.H., Render me real? investigating the effect of render style on the perception of animated virtual humans (2012) ACM Transactions on Graphics (TOG), 31 (4), pp. 1-11",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115101122
"Rossi A., Raiano M., Rossi S.","57189499587;57215275463;55619320887;","Affective, cognitive and behavioural engagement detection for human-robot interaction in a bartending scenario",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"208","213",,,"10.1109/RO-MAN50785.2021.9515435","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115087121&doi=10.1109%2fRO-MAN50785.2021.9515435&partnerID=40&md5=2cb2f4c3854549683cd7dcd34cc81e6b","University of Naples Federico II, Interdepartmental Center for Advances in Robotic Surgery, Italy; University of Naples Federico II, Department of Electrical Engineering and Information Technologies, Italy","Rossi, A., University of Naples Federico II, Interdepartmental Center for Advances in Robotic Surgery, Italy; Raiano, M., University of Naples Federico II, Department of Electrical Engineering and Information Technologies, Italy; Rossi, S., University of Naples Federico II, Department of Electrical Engineering and Information Technologies, Italy","Guaranteeing people's engagement during an interaction is very important to elicit positive and effective emotions in public service scenarios. A robot should be able to detect its interlocutor's level and mode of engagement to accordingly modulate its behaviours. However, there is not a generally accepted model to annotate and classify engagement during an interaction. In this work, we consider engagement as a multidimensional construct with three relevant dimensions: affective, cognitive and behavioural. To be automatically evaluated by a robot, such a complex construct requires the selection of the proper interaction features among a large set of possibilities. Moreover, manually collecting and annotating large datasets of real interactions are extremely time-consuming and costly. In this study, we collected the recordings of human-robot interactions in a bartending scenario, and we compared different feature selection and regression models to find the features that characterise a user's engagement in the interaction, and the model that can efficiently detect them. Results showed that the characterisation of each dimension separately in terms of features and regression obtains better results with respect to a model that directly combines the three dimensions. © 2021 IEEE.",,"Agricultural robots; Feature extraction; Large dataset; Man machine systems; Regression analysis; Bartending; Interaction features; Large datasets; Public services; Real interactions; Regression model; Three dimensions; Social robots",,,,,,"ACKNOWLEDGMENT This work has been supported by Italian PON I&C 2014-2020 within the BRILLO research project “Bartending Robot for Interactive Long-Lasting Operations”, no. F/190066/01-02/X44.",,,,,,,,,,"Berezina, K., Ciftci, O., Cobanoglu, C., (2019) Robots, Artificial Intelligence, and Service Automation in Restaurants, 10, pp. 185-219; Masuda, T., Misaki, D., Development of Japanese green tea serving robot ""t-bartender"" (2005) IEEE International Conference Mechatronics and Automation, 2, pp. 1069-1074. , 2005; Rossi, A., Staffa, M., Origlia, A., Di Maro, M., Rossi, S., Brillo: A robotic architecture for personalised long-lasting interactions in a bartending domain (2021) Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, Ser. HRI '21 Companion, pp. 426-429. , New York, NY, USA: Association for Computing Machinery; Stikic, M., Berka, C., Levendowski, D.J., Rubio, R.F., Tan, V., Korszen, S., Barba, D., Wurzer, D., Modeling temporal sequences of cognitive state changes based on a combination of EEG-engagement, EEG-workload, and heart rate metrics (2014) Frontiers in Neuroscience, 8, p. 342; Ben-Eliyahu, A., Moore, D., Dorph, R., Schunn, C.D., Investigating the multidimensionality of engagement: Affective, behavioral, and cognitive engagement across science activities and contexts (2018) Contemporary Educational Psychology, 53, pp. 87-105. , https://www.sciencedirect.com/science/article/pii/S0361476X17300334; Dell'Aquila, E., Maggi, G., Conti, D., Rossi, S., A preparatory study for measuring engagement in pediatric virtual and robotics rehabilitation settings (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, Ser. HRI '20, pp. 183-185. , New York, NY, USA: Association for Computing Machinery; Rossi, S., Dell'Aquila, E., Maggi, G., Russo, D., What would you like to drink? engagement and interaction styles in hri (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, Ser. HRI '20, pp. 415-417. , New York, NY, USA: Association for Computing Machinery; Staffa, M., Rossi, S., Recommender interfaces: The more humanlike, the more humans like (2016) Social Robotics, pp. 200-210. , Cham: Springer International Publishing; Oertel, C., Castellano, G., Chetouani, M., Nasir, J., Obaid, M., Pelachaud, C., Peters, C., Engagement in human-agent interaction: An overview (2020) Frontiers in Robotics and AI, 7, p. 92; Sidner, C.L., Lee, C., Lesh, N., Engagement when looking: Behaviors for robots when collaborating with people (2003) Diabruck: Proceedings of the 7th Workshop on the Semantic and Pragmatics of Dialogue, pp. 123-130. , University of Saarland; Bosch, N., Detecting student engagement: Human versus machine (2016) Ser. UMAP '16, pp. 317-320. , New York, NY, USA: Association for Computing Machinery; Sharma, P., Joshi, S., Gautam, S., Maharjan, S., Filipe, V., Reis, M.J.C.S., (2020) Student Engagement Detection Using Emotion Analysis, Eye Tracking and Head Movement with Machine Learning; Foster, M.E., Keizer, S., Lemon, O., Towards action selection under uncertainty for a socially aware robot bartender (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction, Ser. HRI '14, pp. 158-159. , New York, NY, USA: Association for Computing Machinery; Giuliani, M., Petrick, R.P., Foster, M.E., Gaschler, A., Isard, A., Pateraki, M., Sigalas, M., Comparing task-based and socially intelligent behaviour in a robot bartender (2013) Proceedings of the 15th ACM on International Conference on Multimodal Interaction, Ser. ICMI '13, pp. 263-270. , New York, NY, USA: Association for Computing Machinery; Petrick, R.P.A., Foster, M.E., Planning for social interaction in a robot bartender domain (2013) Proceedings of the Twenty-Third International Conference on International Conference on Automated Planning and Scheduling, Ser. ICAPS'13, pp. 389-397. , AAAI Press; Ishii, R., Nakano, Y.I., Nishida, T., Gaze awareness in conversational agents: Estimating a user's conversational engagement from eye gaze (2013) ACM Trans. Interact. Intell. Syst., 3 (2). , Aug; Brown, L., Kerwin, R., Howard, A.M., Applying behavioral strategies for student engagement using a robotic educational agent (2013) 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 4360-4365; Rich, C., Ponsleur, B., Holroyd, A., Sidner, C., (2010) Recognizing Engagement in Human-robot Interaction, 3, pp. 375-382; Castellano, G., Leite, I., Pereira, A., Martinho, C., Paiva, A., Mcowan, P.W., Context-sensitive affect recognition for a robotic game companion (2014) ACM Trans. Interact. Intell. Syst., 4 (2). , Jun; Foster, M.E., Gaschler, A., Giuliani, M., Automatically classifying user engagement for dynamic multi-party human-robot interaction (2017) International Journal of Social Robotics, 9, p. 11; Anzalone, S.M., Boucenna, S., Ivaldi, S., Chetouani, M., Evaluating the engagement with social robots (2015) International Journal of Social Robotics, 7 (4), pp. 465-478; Niculescu, A., Dijk, B., Nijholt, A., Li, H., Lan, S.S., Making social robots more attractive: The effects of voice pitch, humor and empathy (2013) International Journal of Social Robotics, 5, pp. 171-191; Cao, Z., Hidalgo Martinez, G., Simon, T., Wei, S., Sheikh, Y.A., Openpose: Realtime multi-person 2D pose estimation using part affinity fields (2019) IEEE Transactions on Pattern Analysis and Machine Intelligence; Subramainan, L., Mahmoud, M., Ahmad, M., Yusoff, M., An emotion-based model for improving students' engagement using agentbased social simulator (2016) International Journal on Advanced Science, Engineering and Information Technology, 6 (12), p. 952",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115087121
"Hannibal G., Weiss A., Charisi V.","56512012200;23089739100;56766259400;","""The robot may not notice my discomfort""- Examining the experience of vulnerability for trust in human-robot interaction",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"704","711",,,"10.1109/RO-MAN50785.2021.9515513","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115062842&doi=10.1109%2fRO-MAN50785.2021.9515513&partnerID=40&md5=1aa6620f7b38ece3857672daeb5edbe0","TU Wien, Institute of Visual Computing and Human-Centered Technology, Austria; EU Commission, Joint Research Center (JRC)","Hannibal, G., TU Wien, Institute of Visual Computing and Human-Centered Technology, Austria; Weiss, A., TU Wien, Institute of Visual Computing and Human-Centered Technology, Austria; Charisi, V., EU Commission, Joint Research Center (JRC)","Ensuring trust in human-robot interaction (HRI) is considered essential for widespread use of robots in society and everyday life. While the majority of studies use game-based and high-risk scenarios with low familiarity to gain a deeper understanding of human trust in robots, scenarios with more subtle trust violations that could happen in everyday life situations are less often considered. In this paper, we present a theory-driven approach to studying the situated trust in HRI by focusing on the experience of vulnerability. Focusing on vulnerability not only challenges previous work on trust in HRI from a theoretical perspective, but is also useful for guiding empirical investigations. As a first proof-of-concept study, we conducted an interactive online survey that demonstrates that it is possible to measure human experience of vulnerability in the ordinary, mundane, and familiar situation of clothes shopping. We conclude that the inclusion of subtle trust violation scenarios occurring in the everyday life situation of clothes shopping enables a better understanding of situated trust in HRI, which is of special importance when considering more near-future applications of robots. © 2021 IEEE.",,"Agricultural robots; Clothes; Man machine systems; Risk perception; Empirical investigation; Future applications; Game-Based; Human robot Interaction (HRI); Online surveys; Proof of concept; Risk scenarios; Theory driven approaches; Social robots",,,,,"E056-05; Technische Universitat Wien; European Commission, EC","This work has been supported by the Trust Robot Doctoral College (E056-05) at TU Wien and the Human Behaviour and Machine Intelligence (HUMAINT) project at the EU Commission.",,,,,,,,,,"Lee, J.D., See, K.A., Trust in automation: Designing for appropriate reliance (2004) Human Factors: The Journal of the Human Factors and Ergonomics Society, 46 (1), pp. 50-80; Hoffman, R.R., Cognition, M., Lee, J.D., Madison, W., Trust in automation (2013) IEEE Intelligent Systems, 28 (1), pp. 84-88; Lewis, M., Sycara, K., Walker, P., The role of trust in human-robot interaction (2018) Foundations of Trusted Autonomy (Studies in Systems, Decision and Control, 177, pp. 135-159. , H. 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Thousand Oaks, CA: SAGE Publications Ltd; Mota, R.C.R., Rea, D.J., Le Tran, A., Young, J.E., Sharlin, E., Sousa, M.C., Playing the 'trust game' with robots: Social strategies and experiences (2016) Proceedings of the 25th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN), pp. 519-524. , New York, NY: IEEE; Aroyo, A.M., Rea, F., Sandini, G., Sciutti, A., Trust and social engineering in human robot interaction: Will a robot make you disclose sensitive information, conform to its recommendations or gamble? (2018) IEEE Robotics and Automation Letters, 3 (4), pp. 3701-3708. , 10; Zörner, S., Arts, E., Vasiljevic, B., Srivastava, A., Schmalzl, F., Mir, G., Bhatia, K., Wermter, S., An immersive investment game to study human-robot trust (2021) Frontiers in Robotics and AI, 8, pp. 1-16. , June; Correia, F., Alves-Oliveira, P., Maia, N., Ribeiro, T., Petisca, S., Melo, F.S., Paiva, A., Just follow the suit! trust in human-robot interactions during card game playing (2016) 25th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN), 8, pp. 507-512. , New York, USA: IEEE; Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K., Would you trust a (faulty) robot?: Effects of error, task type and personality on human-robot cooperation and trust (2015) Proceedings of the 10th Annual ACM/IEEE International Conference on Human-Robot Interaction (CHII), pp. 141-148. , Portland, USA: ACM; Rossi, A., Dautenhahn, K., Koay, K.L., Walters, M.L., Holthaus, P., Evaluating people's perceptions of trust in a robot in a repeated interactions study (2020) 12th International Conference on Social Robotics. Golden (CO), 11, pp. 453-465. , USA: Springer, Cham; Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K., Towards safe and trustworthy social robots: Ethical challenges and practical issues (2015) 7th International Conference on Social Robotics (ICSR). Lecture Notes in Artificial Intelligence, 9388, pp. 584-593. , A. Tapus, E. André, J. Martin, F. Ferland, and M. Ammi, Eds. Paris, France: Springer, Cham; Luhmann, N., Familiarity, confidence, trust: Problems and alternatives (1988) Trust: Making and Breaking Cooperative Relations, pp. 94-107. , D. Gambetta, Ed. Oxford, UK: Basil Blackwell",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115062842
"Chiou M., McCabe F., Grigoriou M., Stolkin R.","57188977049;57208836780;57210442620;16025500800;","Trust, shared understanding and locus of control in mixed-initiative robotic systems",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"684","691",,,"10.1109/RO-MAN50785.2021.9515476","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115061300&doi=10.1109%2fRO-MAN50785.2021.9515476&partnerID=40&md5=c4501e0d1927266a168037b034875356","University of Birmingham, Extreme Robotics Lab (ERL), National Center for Nuclear Robotics (NCNR), United Kingdom","Chiou, M., University of Birmingham, Extreme Robotics Lab (ERL), National Center for Nuclear Robotics (NCNR), United Kingdom; McCabe, F., University of Birmingham, Extreme Robotics Lab (ERL), National Center for Nuclear Robotics (NCNR), United Kingdom; Grigoriou, M., University of Birmingham, Extreme Robotics Lab (ERL), National Center for Nuclear Robotics (NCNR), United Kingdom; Stolkin, R., University of Birmingham, Extreme Robotics Lab (ERL), National Center for Nuclear Robotics (NCNR), United Kingdom","This paper investigates how trust, shared under-standing between a human operator and a robot, and the Locus of Control (LoC) personality trait, evolve and affect Human-Robot Interaction (HRI) in mixed-initiative robotic systems. As such systems become more advanced and able to instigate actions alongside human operators, there is a shift from robots being perceived as a tool to being a team-mate. Hence, the team-oriented human factors investigated in this paper (i.e. trust, shared understanding, and LoC) can play a crucial role in efficient HRI. Here, we present the results from an experiment inspired by a disaster response scenario in which operators remotely controlled a mobile robot in navigation tasks, with either human-initiative or mixed-initiative control, switching dynamically between two different levels of autonomy: teleoperation and autonomous navigation. Evidence suggests that operators trusted and developed an understanding of the robotic systems, especially in mixed-initiative control, where trust and understanding increased over time, as operators became more familiar with the system and more capable of performing the task. Lastly, evidence and insights are presented on how LoC affects HRI. © 2021 IEEE.","Human-Robot Interaction; Human-Robot Teaming; Locus of Control; Mixed-Initiative; Trust","Agricultural robots; End effectors; Mobile robots; Robotics; Autonomous navigation; Disaster response; Human robot Interaction (HRI); Locus of control; Mixed initiative; Navigation tasks; Personality traits; Shared understanding; Social robots",,,,,"EP/R02572X/1","This work was supported by the UKRI-EPSRC grand EP/R02572X/1. 1Extreme Robotics Lab (ERL) and National Center for Nuclear Robotics (NCNR), University of Birmingham, UK",,,,,,,,,,"Bainbridge, L., Ironies of automation (1983) Automatica, 19 (6), pp. 775-779; Parasuraman, R., Sheridan, T.B., Wickens, C.D., A model for types and levels of human interaction with automation (2000) IEEE Transactions on Systems, Man, and Cybernetics. Part A, Systems and Humans, 30 (3), pp. 286-297; De Visser, E.J., Pak, R., Shaw, T.H., From 'automation' to 'autonomy': The importance of trust repair in human-machine interaction (2018) Ergonomics, 61 (10), pp. 1409-1427; Chiou, M., Stolkin, R., Bieksaite, G., Hawes, N., Shapiro, K.L., Harrison, T.S., Experimental analysis of a variable autonomy framework for controlling a remotely operating mobile robot (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3581-3588; Chiou, M., Hawes, N., Stolkin, R., Mixed-initiative variable autonomy for remotely operated mobile robots (2021) ACM Transactions on Human-Robot Interaction, 10 (4); Chen, J.Y.C., Barnes, M.J., Human-agent teaming for multirobot control: A review of human factors issues (2014) IEEE Transactions on Human-Machine Systems, 44 (1), pp. 13-29; Lee, J.D., See, K.A., Trust in automation: Designing for appropriate reliance (2004) Human Factors: The Journal of the Human Factors and Ergonomics Society, 46 (50), pp. 50-80; Hoff, K.A., Bashir, M., Trust in automation: Integrating empirical evidence on factors that influence trust (2015) Human Factors, 57 (3), pp. 407-434; Schaefer, K.E., Chen, J.Y., Szalma, J.L., Hancock, P.A., A meta-analysis of factors influencing the development of trust in automation: Implications for understanding autonomy in future systems (2016) Human Factors, 58 (3), pp. 377-400; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Matthews, G., Lin, J., Panganiban, A.R., Long, M.D., Individual differences in trust in autonomous robots: Implications for transparency (2020) IEEE Transactions on Human-Machine Systems, 50 (3), pp. 234-244; Chien, S.-Y., Lewis, M., Sycara, K., Kumru, A., Liu, J.S., Influence of culture, transparency, trust, and degree of automation on automation use (2020) IEEE Transactions on Human-Machine Systems, 50 (3), pp. 205-214; Wright, J.L., Chen, J.Y., Lakhmani, S.G., Agent transparency and reliability in human-robot interaction: The influence on user confidence and perceived reliability (2020) IEEE Transactions on Human-Machine Systems, 50 (3), pp. 254-263; Yang, X.J., Unhelkar, V.V., Li, K., Shah, J.A., Evaluating effects of user experience and system transparency on trust in automation (2017) ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 408-416; Desai, M., Medvedev, M., Vázquez, M., McSheehy, S., Gadea-Omelchenko, S., Bruggeman, C., Steinfeld, A., Yanco, H., Effects of changing reliability on trust of robot systems (2012) ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 73-80; Nam, C., Walker, P., Li, H., Lewis, M., Sycara, K., Models of trust in human control of swarms with varied levels of autonomy (2020) IEEE Transactions on Human-Machine Systems, 50 (3), pp. 194-204. , https://ieeexplore.ieee.org/document/8651317/; Wang, N., Pynadath, D.V., Hill, S.G., Trust calibration within a human-robot team: Comparing automatically generated explanations (2016) ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 109-116; Soh, H., Xie, Y., Chen, M., Hsu, D., Multi-task trust transfer for human-robot interaction (2018) International Journal of Robotics Research, 39 (2-3), pp. 233-249; Rotter, J.B., Generalized expectancies of internal versus external control of reinforcements (1966) Psychological Monographs: General and Applied, 80 (1), pp. 1-28; Furnham, A., Steele, H., Measuring locus of control: A critique of general, children's, health-and work-related locus of control questionnaires (1993) British Journal of Psychology, 84 (4), pp. 443-479; Takayama, L., Marder-Eppstein, E., Harris, H., Beer, J.M., Assisted driving of a mobile remote presence system: System design and controlled user evaluation (2011) IEEE International Conference on Robotics and Automation, pp. 1883-1889; Acharya, U., Kunde, S., Hall, L., Duncan, B.A., Bradley, J.M., Inference of user qualities in shared control (2018) IEEE International Conference on Robotics and Automation (ICRA), pp. 588-595; Jiang, S., Arkin, R.C., Mixed-initiative human-robot interaction: Definition, taxonomy, and survey (2015) 2015 IEEE International Conference on Systems, Man, and Cybernetics. IEEE, pp. 954-961; Chiou, M., Talha, M., Stolkin, R., Learning effects in variable autonomy human-robot systems: How much training is enough? (2019) IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 720-727; Marder-Eppstein, E., Berger, E., Foote, T., Gerkey, B., Konolige, K., The office marathon: Robust navigation in an indoor office environment (2010) IEEE International Conference on Robotics and Automation (ICRA), pp. 300-307; Mathôt, S., Schreij, D., Theeuwes, J., Opensesame: An opensource, graphical experiment builder for the social sciences (2012) Behavior Research Methods, 44 (2), pp. 314-324; Ganis, G., Kievit, R., A new set of three-dimensional shapes for investigating mental rotation processes: Validation data and stimulus set (2015) Journal of Open Psychology Data, 3 (1); Murphy, R.R., Human-robot interaction in rescue robotics (2004) IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 34 (2), pp. 138-153; Hoffman, G., Evaluating fluency in human-robot collaboration (2013) International Conference on Human-robot Interaction (HRI), , workshop on human robot collaboration; Nikolaidis, S., Hsu, D., Srinivasa, S., Human-robot mutual adaptation in collaborative tasks: Models and experiments (2017) The International Journal of Robotics Research, 36 (5-7), pp. 618-634; Duttweiler, P.C., The internal control index: A newly developed measure of locus of control (1984) Educational and Psychological Measurement, 44 (2), pp. 209-221; Meyers, L.S., Wong, D.T., Validation of a new test of locus of control: The internal control index (1988) Educational and Psychological Measurement, 48 (3), pp. 753-761; Maltby, J., Cope, C.D., Reliability estimates of the internal control index among UK samples (1996) Psychological Reports, 79 (2), pp. 595-598; Sharek, D., A useable, online nasa-tlx tool (2011) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 55 (1), pp. 1375-1379; Twenge, J.M., Zhang, L., Im, C., It's beyond my control: A crosstemporal meta-analysis of increasing externality in locus of control, 1960-2002 (2004) Personality and Social Psychology Review, 8 (3), pp. 308-319; Chancey, E.T., Bliss, J.P., Yamani, Y., Handley, H.A., Trust and the compliance-reliance paradigm: The effects of risk, error bias, and reliability on trust and dependence (2017) Human Factors, 59 (3), pp. 333-345; Chen, M., Nikolaidis, S., Soh, H., Hsu, D., Srinivasa, S., Planning with trust for human-robot collaboration (2018) ACM/IEEE International Conference on Human-Robot Interaction, pp. 307-315",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85115061300
"Try P., Schöllmann S., Wöhle L., Gebhard M.","57226631649;57226632110;57203154747;56785513800;","Visual sensor fusion based autonomous robotic system for assistive drinking",2021,"Sensors","21","16","5419","","",,,"10.3390/s21165419","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112128990&doi=10.3390%2fs21165419&partnerID=40&md5=9ed1fb98fd5bc1a8287fe38f1361f549","Group of Sensors and Actuators, Department of Electrical Engineering and Applied Sciences, Westphalian University of Applied Sciences, Gelsenkirchen, 45877, Germany","Try, P., Group of Sensors and Actuators, Department of Electrical Engineering and Applied Sciences, Westphalian University of Applied Sciences, Gelsenkirchen, 45877, Germany; Schöllmann, S., Group of Sensors and Actuators, Department of Electrical Engineering and Applied Sciences, Westphalian University of Applied Sciences, Gelsenkirchen, 45877, Germany; Wöhle, L., Group of Sensors and Actuators, Department of Electrical Engineering and Applied Sciences, Westphalian University of Applied Sciences, Gelsenkirchen, 45877, Germany; Gebhard, M., Group of Sensors and Actuators, Department of Electrical Engineering and Applied Sciences, Westphalian University of Applied Sciences, Gelsenkirchen, 45877, Germany","People with severe motor impairments like tetraplegia are restricted in activities of daily living (ADL) and are dependent on continuous human assistance. Assistive robots perform physical tasks in the context of ADLs to support people in need of assistance. In this work a sensor fusion algorithm and a robot control algorithm for localizing the user’s mouth and autonomously navigating a robot arm are proposed for the assistive drinking task. The sensor fusion algorithm is implemented in a visual tracking system which consists of a 2-D camera and a single point time-of-flight distance sensor. The sensor fusion algorithm utilizes computer vision to combine camera images and distance measurements to achieve reliable localization of the user’s mouth. The robot control algorithm uses visual servoing to navigate a robot-handled drinking cup to the mouth and establish physical contact with the lips. This system features an abort command that is triggered by turning the head and unambiguous tracking of multiple faces which enable safe human robot interaction. A study with nine able-bodied test subjects shows that the proposed system reliably localizes the mouth and is able to autonomously navigate the cup to establish physical contact with the mouth. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Assistive robotics; Autonomous robotic system; Computer vision; Human robot interaction; Localization; Sensor fusion; Visual servoing","Cameras; Navigation; Robotics; Social robots; Activities of Daily Living; Autonomous robotic systems; Physical contacts; Robot control algorithm; Safe human-robot interaction; Sensor fusion algorithms; Unambiguous tracking; Visual tracking systems; Visual servoing; algorithm; daily life activity; human; mouth; quadriplegia; robot assisted surgery; Activities of Daily Living; Algorithms; Humans; Mouth; Quadriplegia; Robotic Surgical Procedures",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV7866K","Funding: This work was funded by the Federal Ministry of Education and Research of Germany grant number 16SV7866K. We acknowledge support by the Open Access Publication Fund of the Westphalian University of Applied Sciences.",,,,,,,,,,"(2011) World Report on Disability 2011, , World Health Organization. World Health Organization: Geneva, Switzerland; Chung, C.S., Wang, H., Cooper, R.A., Functional assessment and performance evaluation for assistive robotic manipulators: Literature review (2013) J. Spinal Cord Med, 36, pp. 273-289. , [CrossRef] [PubMed]; Rudigkeit, N., Gebhard, M., AMiCUS 2.0-System Presentation and Demonstration of Adaptability to Personal Needs by the Example of an Individual with Progressed Multiple Sclerosis (2020) Sensors, 20, p. 1194. , [CrossRef] [PubMed]; Rudigkeit, N., Gebhard, M., AMiCUS-A Head Motion-Based Interface for Control of an Assistive Robot (2019) Sensors, 19, p. 2836. , [CrossRef] [PubMed]; Kim, D.J., Lovelett, R., Behal, A., An empirical study with simulated ADL tasks using a vision-guided assistive robot arm Proceedings of the IEEE International Conference on Rehabilitation Robotics, pp. 504-509. , Kyoto, Japan, 23–26 June 2009; [CrossRef]; Graser, A., Heyer, T., Fotoohi, L., Lange, U., Kampe, H., Enjarini, B., Heyer, S., Ristic-Durrant, D., A Supportive FRIEND at Work: Robotic Workplace Assistance for the Disabled (2013) IEEE Robot. Autom. Mag, 20, pp. 148-159. , [CrossRef]; Volosyak, I., Ivlev, O., Graser, A., Rehabilitation Robot FRIEND II-the General Concept and Current Implementation (2005) Proceedings of the 9th International Conference on Rehabilitation Robotics, pp. 540-544. , Chicago, IL, USA, 28 June–1 July [CrossRef]; Schroer, S., Killmann, I., Frank, B., Volker, M., Fiederer, L., Ball, T., Burgard, W., An autonomous robotic assistant for drinking Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 6482-6487. , Washington, DC, USA, 26–30 May 2015; [CrossRef]; Fujii, K., Gras, G., Salerno, A., Yang, G.Z., Gaze gesture based human robot interaction for laparoscopic surgery (2018) Med Image Anal, 44, pp. 196-214. , [CrossRef] [PubMed]; Zhang, Z., Huang, Y., Chen, S., Qu, J., Pan, X., Yu, T., Li, Y., An Intention-Driven Semi-autonomous Intelligent Robotic System for Drinking (2017) Front. Neurorobotics, 11, p. 48. , [CrossRef] [PubMed]; Looned, R., Webb, J., Xiao, Z.G., Menon, C., Assisting drinking with an affordable BCI-controlled wearable robot and electrical stimulation: A preliminary investigation (2014) J. Neuroeng. Rehabil, 11, p. 51. , [CrossRef] [PubMed]; Goldau, F.F., Shastha, T.K., Kyrarini, M., Graser, A., Autonomous Multi-Sensory Robotic Assistant for a Drinking Task (2019) IEEE Int. Conf. Rehabil. Robot, 2019, pp. 210-216. , [CrossRef] [PubMed]; Fang, Q., Kyrarini, M., Ristic-Durrant, D., Gräser, A., RGB-D Camera Based 3D Human Mouth Detection and Tracking towards Robotic Feeding Assistance Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference, pp. 391-396. , Corfu, Greece, 26–29 June 2018; [CrossRef]; Koller, T.L., Kyrarini, M., Gräser, A., Towards robotic drinking assistance Proceedings of the PETRA ’19: 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments, pp. 243-246. , Rhodes, Greece, 5–7 June 2019; [CrossRef]; Schöllmann, S., Try, P., Wöhle, L., Gebhard, M., Sensors for Assistive Robotic Drinking with Physical Contact Proceedings of the IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1-6. , Lausanne, Switzerland, 23–25 June 2021; [CrossRef]; Castrillón, M., Déniz, O., Guerra, C., Hernández, M., ENCARA2: Real-time detection of multiple faces at different resolutions in video streams (2007) J. 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Europa-Lehrmittel Nourney Vollmer: Haan-Gruiten, Germany; LoPresti, E., Brienza, D.M., Angelo, J., Gilbertson, L., Sakai, J., Neck range of motion and use of computer head controls (2000) Proceedings of the Fourth International ACM Conference on Assistive Technologies, pp. 121-128. , Arlington, VA, USA, 13–15 November [CrossRef]; Sagonas, C., Antonakos, E., Tzimiropoulos, G., Zafeiriou, S., Pantic, M., 300 Faces In-The-Wild Challenge: Database and results (2016) Image Vis. Comput, 47, pp. 3-18. , [CrossRef]; Brandon, A., The 68 Landmarks Model Created by Brandon Amos of CMU Who Works on OpenFace, , https://github.com/cmusatyalab/openface/blob/master/images/dlib-landmark-mean.png, (accessed on 3 August 2021)","Try, P.; Group of Sensors and Actuators, Germany; email: pieter.try@w-hs.de",,,"MDPI AG",,,,,14248220,,,"34450861","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85112128990
"Jaramillo J., Lin A., Sung E., Richter I.J.H., Petersen K.","57226723168;57226708329;57226718623;57226728565;57188685048;","Mobile, inflatable interface to support human robot interaction studies",2021,"2021 18th International Conference on Ubiquitous Robots, UR 2021",,,"9494648","320","325",,,"10.1109/UR52253.2021.9494648","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112429195&doi=10.1109%2fUR52253.2021.9494648&partnerID=40&md5=aae4d7bda682e465fea5b760aafc7dbd","Cornell Engineering, Ithaca, NY  14853, United States","Jaramillo, J., Cornell Engineering, Ithaca, NY  14853, United States; Lin, A., Cornell Engineering, Ithaca, NY  14853, United States; Sung, E., Cornell Engineering, Ithaca, NY  14853, United States; Richter, I.J.H., Cornell Engineering, Ithaca, NY  14853, United States; Petersen, K., Cornell Engineering, Ithaca, NY  14853, United States","Robots capable of engaging with individuals in large crowds can be of use in diverse scenarios such as festivals, conventions, and during building evacuation. Such situations pose a unique combination of hardware constraints involving safety for human contact, durability to withstand physical inter-action with novice users, agility to keep up with moving crowds in non-trivial terrains, versatility to adapt to the situation and audience, and low cost to permit mass deployment. Here, we present a new mobile robot platform composed of a small rover base and a soft human-scale inflatable interface, capable of visual, tactile, and audible human interaction. The inflatable interface allows the robot to maneuver discretely or in confined spaces when deflated, yet grow to encourage interaction; it combines an internal projector, a camera, speakers, and a microphone to emit and receive user information. The rover base is designed to keep up with humans at jogging speeds over relatively uneven terrain. Low weight further permits easy handling and transport. The entire robot costs less than 1.2K USD, and can serve as a general purpose, open source test platform for future human-robot interaction research. © 2021 IEEE.",,"Agricultural robots; Man machine systems; Building evacuation; Confined space; Hardware constraints; Human interactions; Mobile robot platforms; Test platforms; Uneven terrain; User information; Human robot interaction",,,,,"National Science Foundation, NSF: 1830471","Funding for this research was provided by NSF #1830471, GETTYLABS, and a Packard Fellowship for Science and Engineering. 1 Cornell Engineering, Ithaca, NY 14853, USA. jdj78, yl656, hs699, ijr25, kirstin @cornell.edu",,,,,,,,,,"Breazeal, C., Dautenhahn, K., Kanda, T., Social robotics (2016) Springer Handbook of Robotics. Springer, pp. 1935-1972; Kragic, D., Gustafson, J., Karaoguz, H., Jensfelt, P., Krug, R., Interactive, collaborative robots: Challenges and opportunities (2018) IJCAI, pp. 18-25; Tsarouchi, P., Makris, S., Chryssolouris, G., Human-robot interaction review and challenges on task planning and programming (2016) International Journal of Computer Integrated Manufacturing, 29 (8), pp. 916-931; Wirtz, J., Patterson, P.G., Kunz, W.H., Gruber, T., Lu, V.N., Paluch, S., Martins, A., Brave new world: Service robots in the frontline (2018) Journal of Service Management; Schulte, J., Rosenberg, C., Thrun, S., Spontaneous, short-term interaction with mobile robots (1999) Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No. 99CH36288C), 1, pp. 658-663; Shibata, T., Irie, R., Artificial emotional creature for human-robot interaction-A new direction for intelligent systems (1997) Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics. IEEE, p. 47; Polygerinos, P., Correll, N., Morin, S.A., Mosadegh, B., Onal, C.D., Petersen, K., Cianchetti, M., Shepherd, R.F., Soft robotics: Review of fluid-driven intrinsically soft devices; Manufacturing, sensing, control, and applications in human-robot interaction Advanced Engineering Materials, pp. 1700016-n/a. , http://dx.doi.org/10.1002/adem.201700016; Bartneck, C., Forlizzi, J., A design-centred framework for social human-robot interaction (2004) RO-MAN 2004. 13th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No. 04TH8759). IEEE, pp. 591-594; Rossini, N., Esposito, A., Modeling human behaviour in emergency: A research agenda for the creation of a rescue robot (2011) WIRN, pp. 235-246; Garzotto, F., Gelsomini, M., Kinoe, Y., Puffy: A mobile inflatable interactive companion for children with neurodevelopmental disorder (2017) IFIP Conference on Human-Computer Interaction. Springer, pp. 467-492; Borkowski, S., Sabry, S., Crowley, J.L., Projector-camera pair: An universal io device for human machine interaction (2004) Polish National Robotics Conference KKR VIII; Sareen, H., Umapathi, U., Shin, P., Kakehi, Y., Ou, J., Ishii, H., Maes, P., Printflatables: Printing human-scale, functional and dynamic inflatable objects (2017) Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, pp. 3669-3680; Penney, C.G., Modality effects in short-term verbal memory (1975) Psychological Bulletin, 82 (1), p. 68; Bradski, G., Kaehler, A., Learning OpenCV: Computer vision with the OpenCV library (2008) O'Reilly Media, Inc.; Suzuki, S., Abe, K., Topological structural analysis of digitized binary images by border following (1985) Computer Vision, Graphics, and Image Processing, 30 (1), pp. 32-46; Hu, Y., Bejarano, S.M., Hoffman, G., Shadowsense: Detecting human touch in a social robot using shadow image classification (2020) Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4 (4), pp. 1-24",,,,"Institute of Electrical and Electronics Engineers Inc.","18th International Conference on Ubiquitous Robots, UR 2021","12 July 2021 through 14 July 2021",,170755,,9781665438995,,,"English","Int. Conf. Ubiquitous Robot., UR",Conference Paper,"Final","",Scopus,2-s2.0-85112429195
"Ling Z.-Q., Cao G.-Z., Zhang Y.-P., Cheng H.-R., He B.-B., Cao S.-B.","57226709330;57226725256;57226730080;57219625792;57226710621;57226716863;","Real-time knee joint angle estimation based on surface electromyograph and back propagation neural network",2021,"2021 18th International Conference on Ubiquitous Robots, UR 2021",,,"9494639","256","263",,,"10.1109/UR52253.2021.9494639","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112424053&doi=10.1109%2fUR52253.2021.9494639&partnerID=40&md5=982d305c0c6207dccad17232462b9038","Shenzhen University, Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen Key Laboratory of Electromagnetic Control, College of Mechatronics and Control Engineering, Shenzhen, 518060, China","Ling, Z.-Q., Shenzhen University, Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen Key Laboratory of Electromagnetic Control, College of Mechatronics and Control Engineering, Shenzhen, 518060, China; Cao, G.-Z., Shenzhen University, Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen Key Laboratory of Electromagnetic Control, College of Mechatronics and Control Engineering, Shenzhen, 518060, China; Zhang, Y.-P., Shenzhen University, Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen Key Laboratory of Electromagnetic Control, College of Mechatronics and Control Engineering, Shenzhen, 518060, China; Cheng, H.-R., Shenzhen University, Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen Key Laboratory of Electromagnetic Control, College of Mechatronics and Control Engineering, Shenzhen, 518060, China; He, B.-B., Shenzhen University, Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen Key Laboratory of Electromagnetic Control, College of Mechatronics and Control Engineering, Shenzhen, 518060, China; Cao, S.-B., Shenzhen University, Laboratory of Electromagnetic Control and Intelligent Robots, Shenzhen Key Laboratory of Electromagnetic Control, College of Mechatronics and Control Engineering, Shenzhen, 518060, China","Aiming at the human-robot interaction and continuous motion control of lower limb rehabilitation exoskeleton robot, a real-time knee joint angle estimation method based on Surface Electromyograph (sEMG) and Back Propagation Neural Network (BPNN) is proposed. BPNN is used to train and analyze the mapping relationship between sEMG and knee joint angle, upon which a real-time knee joint angle estimation system is built. Moreover, the real-time continuous motion estimation of knee joint angle under normal walking condition is realized. By designing two test environments- offline test and online test, the estimation accuracy, rapidity and robustness of the proposed method are proved. In the offline test, the root mean square error (RMSE) between estimated knee joint angle and true knee joint angle is 5.945° at 1km/h walking speed, 5.486 ° at 2km/h, 4.406° at 3km/h, respectively. In the online test, the RMSE is 6.622° at 1km/h, 5.748° at 2km/h, 5.219° at 3km/h, respectively. By comparing the results with Support vector Regression (SVR) and Long Short-Term Memory Network (LSTM) in both offline and online test, the superiority of the proposed method in continuous motion estimation of knee joint angle is proved. © 2021 IEEE.","Back Propagation Neural Network; Real-time Knee joint angle estimation; Surface Electromyograph","Agricultural robots; Backpropagation; Exoskeleton (Robotics); Human robot interaction; Long short-term memory; Mean square error; Motion estimation; Support vector regression; Testing; Torsional stress; Back-propagation neural networks; Continuous motions; Exoskeleton robots; Mapping relationships; Root mean square errors; Short term memory; Support vector regression (SVR); Test Environment; Joints (anatomy)",,,,,"National Natural Science Foundation of China, NSFC: JSGG20200701095406010, NSFC U1813212","* Research supported in part by the National Natural Science Foundation of China under Grant NSFC U1813212, in part by the Shenzhen government fund under Grant JSGG20200701095406010.",,,,,,,,,,"Li, W.Z., Cao, G.Z., Wang, Y., Lyu, X., Wavelet-based electromyographic feature selection method for real-time ankle movement recognition (2020) 10th IEEE Int. Conf. Cyber Technol. Autom. Control Intell. Syst. CYBER 2020, pp. 24-30; Luo, R., Member, S., Sun, S., Zhang, X., Tang, Z., Wang, W., (2020) A Low-Cost End-to-End SEMG-Based Gait Sub-Phase Recognition System, 28 (1), pp. 267-276; Gui, K., Liu, H., Zhang, D., A Practical and Adaptive Method to Achieve EMG-Based Torque Estimation for a Robotic Exoskeleton (2019) IEEE/ASME Trans. Mechatronics, 24 (2), pp. 483-494; Huang, Y., Chen, K., Zhang, X., Wang, K., Ota, J., Joint torque estimation for the human arm from sEMG using backpropagation neural networks and autoencoders (2020) Biomed. Signal Process. Control, 62; Huang, Y., Real-Time intended knee joint motion prediction by deep-recurrent neural networks (2019) IEEE Sens. J., 19 (23), pp. 11503-11509; Electromyography, S., (2020) Continuous Estimation of Knee Joint Angle Based on; Phinyomark, A., Quaine, F., Charbonnier, S., Serviere, C., Tarpin-Bernard, F., Laurillau, Y., EMG feature evaluation for improving myoelectric pattern recognition robustness (2013) Expert Systems with Applications, 40 (12), pp. 4832-4840; Wang, J., Wang, L., Miran, S.M., Xi, X., Xue, A., Surface electromyography based estimation of knee joint angle by using correlation dimension of wavelet coefficient (2019) IEEE Access, 7, pp. 60522-60531; Hogan, N., Adaptive control of mechanical impedance by coactivation of antagonist muscles (1984) IEEE Trans. Automat. Contr., 29 (8), pp. 681-690; Bateman, H.V., Surgery simulation-A computer graphics system to analyze and design musculoskeletal reconstructions of the lower limb (1998) Dissertation, p. 274; Li, Q.L., Song, Y., Hou, Z.G., Estimation of lower limb periodic motions from SEMG using least squares support vector regression (2015) Neural Process. Lett., 41 (3), pp. 371-388; Zhang, F., SEMG-based continuous estimation of joint angles of human legs by using BP neural network (2012) Neurocomputing, 78 (1), pp. 139-148; Chen, J., Zhang, X., Cheng, Y., Xi, N., Surface EMG based continuous estimation of human lower limb joint angles by using deep belief networks (2018) Biomed. Signal Process. Control, 40, pp. 335-342; Li, K., Zhang, J., Wang, L., Zhang, M., Li, J., Bao, S., A review of the key technologies for sEMG-based human-robot interaction systems (2020) Biomed. Signal Process. Control, 62; Phinyomark, A., Phukpattaranont, P., Limsakul, C., Feature reduction and selection for EMG signal classification (2012) Expert Syst. Appl., 39 (8), pp. 7420-7431; Wu, Q., Classification of EMG signals by BFA-Optimized GSVCM for diagnosis of fatigue status (2017) IEEE Trans. Autom. Sci. Eng., 14 (2), pp. 915-930; Lecun, Y., Bengio, Y., Hinton, G., Deep learning (2015) Nature, 521 (7553), pp. 436-444; Hornik, K., Stinchcombe, M., White, H., Multilayer feedforward networks are universal approximators (1989) Neural Networks, 2 (5), pp. 359-366; Cybenkot, G., Mathematics of Control, Signals, and Systems Approximation by Superpositions of a Sigmoidal Function∗ (1989) Math. Control Signals Syst., 2, pp. 303-314","Cao, G.-Z.; Shenzhen University, China; email: gzcao@szu.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.","18th International Conference on Ubiquitous Robots, UR 2021","12 July 2021 through 14 July 2021",,170755,,9781665438995,,,"English","Int. Conf. Ubiquitous Robot., UR",Conference Paper,"Final","",Scopus,2-s2.0-85112424053
"Aguirre A., Sierra M. S.D., Munera M., Cifuentes C.A.","57203047265;57210015527;6505875786;36717243800;","Online System for Gait Parameters Estimation Using a LRF Sensor for Assistive Devices",2021,"IEEE Sensors Journal","21","13","9210510","14272","14280",,1,"10.1109/JSEN.2020.3028279","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112719143&doi=10.1109%2fJSEN.2020.3028279&partnerID=40&md5=7385575e0881cf9bcc1deebd863ef38c","Biomedical Engineering Department, Colombian School of Engineering Julio Garavito (ECI), Bogotá, Colombia","Aguirre, A., Biomedical Engineering Department, Colombian School of Engineering Julio Garavito (ECI), Bogotá, Colombia; Sierra M., S.D., Biomedical Engineering Department, Colombian School of Engineering Julio Garavito (ECI), Bogotá, Colombia; Munera, M., Biomedical Engineering Department, Colombian School of Engineering Julio Garavito (ECI), Bogotá, Colombia; Cifuentes, C.A., Biomedical Engineering Department, Colombian School of Engineering Julio Garavito (ECI), Bogotá, Colombia","Recent implementations of sensory systems have addressed gait characterization in several assistive, rehabilitation and human-robot interaction scenarios. Sensors such as laser rangefinders, force platforms and motion tracking systems have been widely used to achieve legs' position tracking, as well as to estimate gait spatio-temporal parameters. However, the validation of those measurements with a gold standard system is still lacking. In this sense, this work is aimed at proposing an online system for the estimation of gait parameters for walker-assisted gait with smart or robotic devices. Moreover, a validation study with an optoelectronic system was carried out. A group of 30 healthy volunteers was recruited. The trials were performed on a treadmill, where the subjects were asked to walk at 4 different speeds. The proposed system is equipped with a laser rangefinder to calculate the users' legs position. Additionally, two adaptive filters, as well as a linear mathematical model were used to adjust the estimations of the users' gait parameters. Results show that our proposed system is able to estimate the stride cadence and the step length with an error lower than 5% compared with the gold standard system. © 2001-2012 IEEE.","ambulatory sensors; human-robot interaction; laser rangefinder; spatio-temporal gait parameters; Walker-assisted gait","Adaptive filtering; Adaptive filters; Motion tracking; Optoelectronic devices; Range finders; Social robots; Tracking (position); Assistive devices; Gait parameters; Healthy volunteers; Laser range finders; Motion tracking system; Optoelectronic systems; Validation study; Walker-assisted gait; Parameter estimation",,,,,,"Manuscript received September 4, 2020; accepted September 25, 2020. Date of publication October 1, 2020; date of current version June 30, 2021. This work was supported in part by the Minciencias under Grant 801-2017 and in part by the Colombian School of Engineering Julio Garavito (ECI). The associate editor coordinating the review of this article and approving it for publication was Dr. Emiliano Schena. (Corresponding author: Carlos A. Cifuentes.) The authors are with the Biomedical Engineering Department, Colombian School of Engineering Julio Garavito (ECI), Bogotá 111166, Colombia (e-mail: andres.aguirre@escuelaing.edu.co; sergio.sierra@ escuelaing.edu.co; marcela.munera@escuelaing.edu.co; carlos. cifuentes@escuelaing.edu.co). Digital Object Identifier 10.1109/JSEN.2020.3028279",,,,,,,,,,"Marín, J., Blanco, T., Marín, J.J., Moreno, A., Martitegui, E., Arages, J.C., Integrating a gait analysis test in hospital rehabilitation: A service design approach (2019) PLoS ONE, 14 (10). , Oct, Art. no. e0224409; Whittle, M.W., Clinical gait analysis: A review (1996) Human Movement Sci, 15, pp. 369-387; Stolze, H., Gait analysis during treadmill and overground locomotion in children and adults (1997) Electroencephalogr. Clin. Neurophysiol./Electromyography Motor Control, 105 (6), pp. 490-497. , Dec; Cifuentes, C.A., Frizera, A., (2016) Human-Robot Interact. Strategies for Walker-Assisted Locomotion, 115. , http://link.springer.com/10.1007/978-3-319-34063-, Cham, Switzerland Springer; Casas, J., Cespedes, N., Múnera, M., Cifuentes, C.A., Human-robot interaction for rehabilitation scenarios (2020) Control Systems Design of Bio-Robotics and Bio-mechatronics with Advanced Applications, pp. 1-31. , https://linkinghub.elsevier.com/retrieve/pii/B9780128174630000010, Amsterdam, The Netherlands Elsevier; Sierra M, S.D.S., Garzón, M., Mánera, M., Cifuentes, C.A., Human-robot-environment interaction interface for smart walker assisted gait: AGoRA walker (2019) Sensors, 19 (13), p. 2897. , Jun; Scheidegger, W.M., A novel multimodal cognitive interaction for walker-Assisted rehabilitation therapies (2019) Proc IEEE 16th Int. Conf. Rehabil. Robot. ICORR, pp. 905-910. , https://ieeexplore.ieee.org/document/8779469/, Jun; Ballesteros, J., Urdiales, C., Martinez, A.B., Tirado, M., Online estimation of rollator user condition using spatiotemporal gait parameters (2016) Proc IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS, pp. 3180-3185. , Oct; Cifuentes, C.A., Rodriguez, C., Frizera, A., Bastos, T., Sensor fusion to control a robotic walker based on upper-limbs reaction forces and gait kinematics (2014) Proc. 5th IEEE RAS/EMBS Int. Conf. Biomed. Robot. Biomechatron, pp. 1098-1103. , Aug; Muro-De-La-Herran, A., Garcia-Zapirain, B., Mendez-Zorrilla, A., Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications (2014) Sensors, 14 (2), pp. 3362-3394. , Feb; Ferrari, A., Ginis, P., Hardegger, M., Casamassima, F., Rocchi, L., Chiari, L., A mobile Kalman-filter based solution for the real-Time estimation of spatio-Temporal newline gait parameters (2016) IEEE Trans. Neural Syst. Rehabil. Eng, 24 (7), pp. 764-773. , Jul; Rueterbories, J., Spaich, E.G., Larsen, B., Andersen, O.K., Methods for gait event detection and analysis in ambulatory systems (2010) Med. Eng. Phys, 32 (6), pp. 545-552. , Jul; Taborri, J., Palermo, E., Rossi, S., Cappa, P., Gait partitioning methods: A systematic review (2016) Sensors, 16, pp. 1-5. , Dec; Qi, Y., Soh, C.B., Gunawan, E., Low, K.-S., Thomas, R., Assessment of foot trajectory for human gait phase detection using wireless ultrasonic sensor network (2016) IEEE Trans. Neural Syst. Rehabil. Eng, 24 (1), pp. 88-97. , Jan; Caldas, R., Mundt, M., Potthast, W., Neto Lima, D.F.Buarque, Markert, B., A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms (2017) Gait Posture, 57, pp. 204-210. , Sep; Sánchez Manchola, M.D.S., Pinto Bernal, M.J.P., Munera, M., Cifuentes, C.A., Gait phase detection for lower-limb exoskeletons using foot motion data from a single inertial measurement unit in hemiparetic individuals (2019) Sensors, 19 (13), p. 2988. , Jul; Ho, N.-H., Truong, P., Jeong, G.-M., Step-detection and adaptive step-length estimation for pedestrian dead-reckoning at various walking speeds using a smartphone (2016) Sensors, 16 (9), p. 1423. , Sep; Brodie, M., Wearable pendant device monitoring using new wavelet-based methods shows daily life and laboratory gaits are different (2016) Med. Biol. Eng, 54, pp. 663-674. , Dec; Iwai, M., The validity of spatiotemporal gait analysis using dual laser range sensors: A cross-sectional study (2019) Arch. Physiotherapy, 9 (1). , Dec; Fudickar, S., Stolle, C., Volkening, N., Hein, A., Scanning laser rangefinders for the unobtrusive monitoring of gait parameters in unsupervised settings (2018) Sensors, 18 (10), p. 3424. , http://www.mdpi.com/1424-8220/18/10/3424, Oct; (2020) Scanning Rangefinder Distance Data URG-04LX-UG01 Product Details, , https://www.hokuyo-Aut.jp/search/single.php?serial=166, Accessed Feb. 3 Hokuyo Automatic CO; Bioengineering, B., (2020) SMART-DX|Motion Capture System, , http://www.btsbioengineering.com/products/smart-dx/, Accessed Feb. 3; Davis, R.B., Ounpuu, S., Tyburski, D., Gage, J.R., A gait analysis data collection and reduction technique (1991) Hum. Movement Sci, 10 (5), pp. 575-587. , Oct; Sekiya, N., Nagasaki, H., Ito Taketo, H.F., The invariant relationship between step length and step rate during free walking (1996) J. Hum. Movement Stud, 30 (6), pp. 241-257; Latt, W.T., Tan, U.-X., Veluvolu, K.C., Shee, C.Y., Ang, W.T., Real-Time estimation and prediction of periodic signals from attenuated and phase-shifted sensed signals (2009) Proc IEEE/ASME Int. Conf. Adv. Intell. Mechatronics, pp. 1643-1648. , http://ieeexplore.ieee.org/document/5229825/, Jul; Bonnet, V., Mazzá, C., McCamley, J., Cappozzo, A., Use of weighted Fourier linear combiner filters to estimate lower trunk 3D orientation from gyroscope sensors data (2013) J. NeuroEng. Rehabil, 10 (1), p. 29. , http://jneuroengrehab.biomedcentral.com/articles/10.1186/1743-0003-10-2%9; Bonnet, V., Mazzà, C., McCamley, K., Cappozzo, J.A., Use of weighted Fourier linear combiner filters to gyroscope sensors data (2013) J. Neuroeng. Rehabil, 10 (1), p. 29; Gallego, J., Rocon, E., Roa, J.O., Moreno, J., Pons, J.L., Realtime estimation of pathological tremor parameters from gyroscope data (2010) Sensors, 10 (3), pp. 2129-2149. , Mar; Neto, A.F., Gallego, J.A., Rocon, E., Pons, J.L., Ceres, R., Extraction of user 's navigation commands from upper body force interaction in walker assisted gait (2010) Biomed. Eng. OnLine, 8, pp. 1-16. , Oct; Item-Glatthorn, J.F., Casartelli, N.C., Maffiuletti, N.A., Reproducibility of gait parameters at different surface inclinations and speeds using an instrumented treadmill system (2016) Gait Posture, 44, pp. 259-264. , Feb; Pallej, T., Teixidó, M., Tresanchez, M., Palacín, J., Measuring gait using a ground laser range sensor (2009) Sensors, 9 (11), pp. 9133-9146. , http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3260635&to%ol=pmcentrez&rendertype=abstract, Nov; Teixidó, M., Pallejâ, T., Tresanchez, M., Nogués, M., Palacín, J., Measuring oscillating walking paths with a LIDAR (2011) Sensors, 11 (5), pp. 5071-5086. , May; Alton, F., Baldey, L., Caplan, S., Morrissey, M.C., A kinematic comparison of overground and treadmill walking (1998) Clin. Biomechanics, 13 (6), pp. 434-440. , Sep","Cifuentes, C.A.; Biomedical Engineering Department, Colombia; email: carlos.cifuentes@escuelaing.edu.co",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,1530437X,,,,"English","IEEE Sensors J.",Article,"Final","",Scopus,2-s2.0-85112719143
"Qi W., Ovur S.E., Li Z., Marzullo A., Song R.","57194568340;57204781654;57218308705;57194212378;57226643030;","Multi-Sensor Guided Hand Gesture Recognition for a Teleoperated Robot Using a Recurrent Neural Network",2021,"IEEE Robotics and Automation Letters","6","3","9457168","6039","6045",,5,"10.1109/LRA.2021.3089999","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112224447&doi=10.1109%2fLRA.2021.3089999&partnerID=40&md5=fbdc3f185a6970ac9b9657474827e3fc","Institute of Advanced Technology, University of Science and Technology of China, Hefei, 5089, China; Department of Electrical and Electronic Engineering, Imperial College London, United Kingdom; Department of Automation, University of Science and Technology of China, Hefei, China; Department of Mathematics and Computer Science, University of Calabria, Rende, Italy; School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, 510000, China","Qi, W., Institute of Advanced Technology, University of Science and Technology of China, Hefei, 5089, China; Ovur, S.E., Department of Electrical and Electronic Engineering, Imperial College London, United Kingdom; Li, Z., Department of Automation, University of Science and Technology of China, Hefei, China; Marzullo, A., Department of Mathematics and Computer Science, University of Calabria, Rende, Italy; Song, R., School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, 510000, China","Touch-free guided hand gesture recognition for human-robot interactions plays an increasingly significant role in teleoperated surgical robot systems. Indeed, despite depth cameras provide more practical information for recognition accuracy enhancement, the instability and computational burden of depth data represent a tricky problem. In this letter, we propose a novel multi-sensor guided hand gesture recognition system for surgical robot teleoperation. A multi-sensor data fusion model is designed for performing interference in the presence of occlusions. A multilayer Recurrent Neural Network (RNN) consisting of a Long Short-Term Memory (LSTM) module and a dropout layer (LSTM-RNN) is proposed for multiple hand gestures classification. Detected hand gestures are used to perform a set of human-robot collaboration tasks on a surgical robot platform. Classification performance and prediction time is compared among the LSTM-RNN model and several traditional Machine Learning (ML) algorithms, such as k-Nearest Neighbor (k-NN) and Support Vector Machines (SVM). Results show that the proposed LSTM-RNN classifier is able to achieve a higher recognition rate and faster inference speed. In addition, the present adaptive data fusion system shows a strong anti-interference capability for hand gesture recognition in real-time. © 2016 IEEE.","deep learning; hand gesture recognition; Human-Robot interaction; sensor fusion; teleoperation","Agricultural robots; Gesture recognition; Multilayer neural networks; Nearest neighbor search; Palmprint recognition; Robotic surgery; Sensor data fusion; Social robots; Support vector machines; Surgery; Antiinterference capability; Classification performance; Computational burden; Hand-gesture recognition; Human-robot collaboration; Multilayer recurrent neural networks; Multisensor data fusion; Recognition accuracy; Long short-term memory",,,,,"National Natural Science Foundation of China, NSFC: 61625303, U1913601; National Key Research and Development Program of China, NKRDPC: 2020YFC2007900","Manuscript received February 10, 2021; accepted May 23, 2021. Date of publication June 16, 2021; date of current version June 29, 2021. This work was supported in part by the National Natural Science Foundation of China under Grants U1913601, 61625303 and by the National Key Research and Development Program of China under Grant 2020YFC2007900. This work was also supported by the Opening Project of Shanghai Robot R&D and Transformation Functional Platform. This letter was recommended for publication by Associate Editor H. Su and Editor P. Valdastri upon evaluation of the reviewers’ comments. (*Wen Qi and Salih Ertug Ovur contributed equally to this work.) (Corresponding author: Zhijun Li.) Wen Qi is with the Institute of Advanced Technology, University of Science and Technology of China, 5089 Hefei, China (e-mail: wen.qi@ieee.org).",,,,,,,,,,"Qi, W., Su, H., Aliverti, A., A smartphone-based adaptive recognition and real-time monitoring system for human activities (2020) IEEE Trans. Human-Mach. 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"Thellman S., Ziemke T.","57191892409;6603792948;","The Perceptual Belief Problem: Why Explainability Is a Tough Challenge in Social Robotics",2021,"ACM Transactions on Human-Robot Interaction","10","3","29","","",,,"10.1145/3461781","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111601983&doi=10.1145%2f3461781&partnerID=40&md5=fcb16dd923f7fb7c3e8ee87747437245","Linköping University, Sweden","Thellman, S., Linköping University, Sweden; Ziemke, T., Linköping University, Sweden","The explainability of robotic systems depends on people's ability to reliably attribute perceptual beliefs to robots, i.e., what robots know (or believe) about objects and events in the world based on their perception. However, the perceptual systems of robots are not necessarily well understood by the majority of people interacting with them. In this article, we explain why this is a significant, difficult, and unique problem in social robotics. The inability to judge what a robot knows (and does not know) about the physical environment it shares with people gives rise to a host of communicative and interactive issues, including difficulties to communicate about objects or adapt to events in the environment. The challenge faced by social robotics researchers or designers who want to facilitate appropriate attributions of perceptual beliefs to robots is to shape human-robot interactions so that people understand what robots know about objects and events in the environment. To meet this challenge, we argue, it is necessary to advance our knowledge of when and why people form incorrect or inadequate mental models of robots' perceptual and cognitive mechanisms. We outline a general approach to studying this empirically and discuss potential solutions to the problem. © 2021 ACM.","belief attribution; common ground; explainability; Human-robot interaction; intentional stance; intentionality; mental state attribution; predictability; social robotics; understandability","Agricultural robots; Robotics; Cognitive mechanisms; Mental model; Perceptual system; Physical environments; Robotic systems; Social robotics; Social robots",,,,,,"This work was supported by ELLIIT, the Excellence Center at Linköping-Lund in Information Technology. Authors’ addresses: S. Thellman and T. Ziemke, Linköping University, Sweden; emails: {sam.thellman, tom.ziemke}@liu.se. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. 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"Vianello L., Mouret J.-B., Dalin E., Aubry A., Ivaldi S.","57224317245;23019477600;57224323899;23033362000;26643860200;","Human Posture Prediction during Physical Human-Robot Interaction",2021,"IEEE Robotics and Automation Letters","6","3","9447230","6046","6053",,1,"10.1109/LRA.2021.3086666","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107369930&doi=10.1109%2fLRA.2021.3086666&partnerID=40&md5=88fe4912ac44ca8be9fa518ebfa9a6fe","University of Lorraine, Cnrs Inria, France","Vianello, L., University of Lorraine, Cnrs Inria, France; Mouret, J.-B., University of Lorraine, Cnrs Inria, France; Dalin, E., University of Lorraine, Cnrs Inria, France; Aubry, A., University of Lorraine, Cnrs Inria, France; Ivaldi, S., University of Lorraine, Cnrs Inria, France","When a human is interacting physically with a robot to accomplish a task, his/her posture is inevitably influenced by the robot movement. Since the human is not controllable, an active robot imposing a collaborative trajectory should predict the most likely human posture. This prediction should consider individual differences and preferences of movement execution, and it is necessary to evaluate the impact of the robot's action from an ergonomics standpoint. Here, we propose a method to predict, in probabilistic terms, the human postures of an individual for a given robot trajectory executed in a collaborative scenario. We formalize the problem as the prediction of the human joints velocity given the current posture and robot end-effector velocity. The key idea of our approach is to learn the distribution of the null space of the Jacobian and the weights of the weighted pseudo-inverse from demonstrated human movements: both carry information about human postural preferences, to leverage redundancy and ensure that the predicted posture will be coherent with the end-effector position. We validate our approach in a simulated toy problem and on two real human-robot interaction experiments where a human is physically interacting with a Franka robot. © 2016 IEEE.","ergonomics; pHRI; posture prediction","Agricultural robots; End effectors; Ergonomics; Forecasting; Inverse problems; Man machine systems; Human movements; Human postures; Individual Differences; Physical human-robot interactions; Robot end effector; Robot movements; Robot trajectory; Weighted pseudo inverse; Human robot interaction",,,,,"ANR-15-IDEX04-LUE; Horizon 2020: 731540; European Regional Development Fund, ERDF","Manuscript received December 21, 2020; accepted April 24, 2021. Date of publication June 4, 2021; date of current version June 30, 2021. This letter was recommended for publication by Associate Editor K. Ramirez-Amaro and Editor T. Asfour upon evaluation of the reviewers’ comments. This work was partly supported by the European Union Horizon 2020 Research Program under Grant 731540 (project AnDy), the CHIST-ERA project HEAP, the European FEDER in the context of the CPER Sciarat, and the Lorraine University of Excellence (LUE) project C-SHIFT under Grant ANR-15-IDEX04-LUE. (Corresponding author: Serena Ivaldi.) Lorenzo Vianello is with the University of Lorraine, CNRS, Inria, LORIA F-54000, France, and also with the Université de Lorraine, CNRS, CRAN F-54000, France (e-mail: lorenzo.vianello@univ-lorraine.fr).",,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human-robot collaboration (2018) Auton. Robots, 42 (5), pp. 957-975; Malaisé, A., Maurice, P., Colas, F., Ivaldi, S., Activity recognition for ergonomics assessment of industrial tasks with automatic feature selection (2019) IEEE Robot. Automat. 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Robot, pp. 25-34. , Springer; Dietrich, A., Ott, C., Albu-Schäffer, A., An overview of null space projections for redundant, torque-controlled robots (2015) Int. J. Robot. Res, 34 (11), pp. 1385-1400; Siciliano, B., Kinematic control of redundant robot manipulators: A tutorial (1990) J. Intell. Robotic Syst, 3 (3), pp. 201-212; Huang, M.Z., Varma, H., Optimal rate allocation in kinematicallyredundant manipulators-the dual projection method (1991) Proc. IEEE Int. Conf. Robot. Automat, pp. 702-707; Chatzilygeroudis, K., Mouret, J.-B., Using parameterized black-box priors to scale up model-based policy search for robotics (2018) Proc. Int. Conf. Robot. Automat, pp. 5121-5128; Hansen, N., Benchmarking a bi-population CMA-ES on the BBOB-2009 function testbed (2009) Proc. 11th Annu. Conf. Companion Genet. Evol. Comput. Conf. : Late Breaking Letters, pp. 2389-2396; Chatzilygeroudis, K., Rama, R., Kaushik, R., Goepp, D., Vassiliades, V., Mouret, J.-B., Black-box data-efficient policy search for robotics (2017) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst, pp. 51-58; Carpentier, J., Valenza, F., Mansard, N., (2015) Pinocchio: Fast Forward and Inverse Dynamics for Poly-Articulated Systems, , https://stack-of-tasks.github.io/pinocchio","Vianello, L.; University of Lorraine, France; email: lorenzo.vianello@univ-lorraine.fr",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85107369930
"Betriana F., Tanioka T., Osaka K., Kawai C., Yasuhara Y., Locsin R.C.","57204071741;7003753763;8279753000;57222190194;36635266600;7003940978;","Interactions between healthcare robots and older people in Japan: A qualitative descriptive analysis study",2021,"Japan Journal of Nursing Science","18","3","e12409","","",,,"10.1111/jjns.12409","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101833499&doi=10.1111%2fjjns.12409&partnerID=40&md5=e02e705cda532fa6182157c63ca708b9","Graduate School of Health Sciences, Tokushima University, Tokushima, Japan; Department of Nursing Outcome Management, Institute of Biomedical Sciences, Tokushima University, Graduate School, Tokushima, Japan; Department of Psychiatric Nursing, Graduate School of Integrated and Sciences, Kochi University, Kochi, Japan; Department of Nursing, Institute of Biomedical Sciences, Tokushima University, Graduate School, Tokushima, Japan; Institute of Biomedical Sciences, Tokushima University, Tokushima, Japan; Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL, United States","Betriana, F., Graduate School of Health Sciences, Tokushima University, Tokushima, Japan; Tanioka, T., Department of Nursing Outcome Management, Institute of Biomedical Sciences, Tokushima University, Graduate School, Tokushima, Japan; Osaka, K., Department of Psychiatric Nursing, Graduate School of Integrated and Sciences, Kochi University, Kochi, Japan; Kawai, C., Graduate School of Health Sciences, Tokushima University, Tokushima, Japan; Yasuhara, Y., Department of Nursing, Institute of Biomedical Sciences, Tokushima University, Graduate School, Tokushima, Japan; Locsin, R.C., Institute of Biomedical Sciences, Tokushima University, Tokushima, Japan, Christine E. Lynn College of Nursing, Florida Atlantic University, Boca Raton, FL, United States","Aim: This study aimed to describe the interactions between healthcare robots and older persons in two selected institutions. Methods: A qualitative descriptive analysis study using simultaneous observations of healthcare robot and older person interactions was conducted at two healthcare institutions in Japan. The Pepper robot equipped with Care Prevention Gymnastics Exercise programs was used in two situations: (a) exercise regimen led by Pepper; and (b) interactive dialogue between Pepper and older persons during the exercise process. These interactions were recorded using audiovisual equipment and analyzed using content analysis thereby revealing four thematic categories. Interpretation and discussion were structured based on assumptions of the Transactive Relationship Theory of Nursing. Results: The analysis revealed four thematic categories: (a) intermediary participation of healthcare practitioners; (b) technologies of care for practice; (c) human-robot interactions involving artificial intelligence; and (d) valuing safety in nursing through technology. Conclusion: Interactions between healthcare robots and older persons may improve quality of care. However, issues regarding fidelity and functionality of dialogic conversations, and ensuring safety and security of using healthcare robots are recognized as critical nursing practice concerns in healthcare robot use in institutional settings. © 2021 Japan Academy of Nursing Science.","aging; healthcare robots; older people; Transactive Relationship Theory of Nursing",,,,,,"Japan Society for the Promotion of Science, KAKEN: JP17H01609","JSPS KAKENHI Grant, Grant/Award Number: JP17H01609 Funding information",,,,,,,,,,"Boykin, A., Schoenhofer, S.O., (2001) A model for transforming practice, , Sudbury, CT, Jones and Bartlett; (2018) The ageing society: Current situation and implementation measures FY 2017, , https://www8.cao.go.jp/kourei/english/annualreport/2018/pdf/c1-1.pdf; Colorafi, K.J., Evans, B., Qualitative descriptive methods in health science research (2016) Health Environments Research & Design Journal, 9 (4), pp. 16-25. , https://doi.org/10.1177/1937586715614171; Cruz-Sandoval, D., Favela, J., Incorporating conversational strategies in a social robot to interact with people with dementia (2019) Dementia and Geriatric Cognitive Disorders, 47 (3), pp. 140-148. , https://doi.org/10.1159/000497801; Dautenhahn, K., Socially intelligent robots: Dimensions of human-robot interaction (2007) Philosophical Transactions of the Royal Society B, 362, pp. 679-704. , https://doi.org/10.1098/rstb.2006.2004; Elo, S., Kyngäs, H., The qualitative content analysis process (2008) Journal of Advanced Nursing, 62 (1), pp. 107-115. , https://doi.org/10.1111/j.1365-2648.2007.04569.x; Eyssel, F., Kuchenbrandt, D., Social categorization of social robots: Anthropomorphism as a function of robot group membership (2012) British Journal of Social Psychology, 51 (4), pp. 724-731. , https://doi.org/10.1111/j.2044-8309.2011.02082.x; Grobbel, C., van Wynsberghe, A., Davis, R., Poly-Droulard, L., Designing nursing care practices complemented by robots: Ethical implications and application of caring frameworks (2019) International Journal for Human Caring, 23 (2), pp. 132-140. , https://doi.org/10.20467/1091-5710.23.2.132; Imai, Y., Hasegawa, K., The Revised Hasegawaʼs Dementia Scale (HDS-R)—Evaluation of its usefulness as a screening test for dementia (1994) Hong Kong Journal of Psychiatry, 4, pp. 20-24; Kim, Y., Sundar, S., Anthropomorphism of computers: Is it mindful or mindless? 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J. Nurs. Sci.",Article,"Final","",Scopus,2-s2.0-85101833499
"Xia Y., LeTendre G.","57203969826;6603189983;","Robots for Future Classrooms: A Cross-Cultural Validation Study of “Negative Attitudes Toward Robots Scale” in the U.S. Context",2021,"International Journal of Social Robotics","13","4",,"703","714",,3,"10.1007/s12369-020-00669-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087498602&doi=10.1007%2fs12369-020-00669-2&partnerID=40&md5=ebca69881623f6290fe34d346eb7dc7d","The Pennsylvania State University, 310G Rackley, University Park, PA  16802, United States","Xia, Y., The Pennsylvania State University, 310G Rackley, University Park, PA  16802, United States; LeTendre, G., The Pennsylvania State University, 310G Rackley, University Park, PA  16802, United States","Social robots are increasingly used in educational settings, but research on human–robot interaction in schools has largely focused on robotic functions and effects. Teacher attitudes towards robots are an important factor in determining the effectiveness of robot-related classroom interventions. In this paper we assessed the usefulness of the Negative Attitudes Toward Robots Scale (NARS) for use in U.S. populations. We reviewed research on the use of NARS and on teacher attitudes toward social robots, showing the potential of this scale for use in the field of education. Using data from 54 undergraduate students in a pre-service teacher education course, we found slightly different subscale loadings on the NARS than previous studies. We identified items on the scale that needed clarification and noted that the paucity of items related to emotional responses may limit the effectiveness of the scale as an international tool for assessing attitudes given cultural differences in emotion. We also found that lack of prior experience with robots was the strongest predictor of negative attitudes towards robots, suggesting that increased exposure to social robots in teacher education might be an effective way to improve educator attitudes toward robots. © 2020, Springer Nature B.V.","Cross-cultural validation; Human–robotic interaction; Social robots in education; Teacher attitudes","Agricultural robots; Economic and social effects; Educational robots; Students; Teaching; Cultural difference; Educational settings; Emotional response; Pre-service teacher education; Robot interactions; Teacher education; Undergraduate students; Validation study; Social robots",,,,,,,,,,,,,,,,"Ahmad, M.I., Mubin, O., Orlando, J., Understanding behaviours and roles for social and adaptive robots in education (2016) teacher’s Perspective. In: Proceedings of the Fourth International Conference on Human Agent Interaction. 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"Delgado Bellamy D., Chance G., Caleb-Solly P., Dogramadzi S.","57209878256;57190841301;16244225300;6507900733;","Safety Assessment Review of a Dressing Assistance Robot",2021,"Frontiers in Robotics and AI","8",,"667316","","",,,"10.3389/frobt.2021.667316","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109032248&doi=10.3389%2ffrobt.2021.667316&partnerID=40&md5=888672aa3df2f364c03fa5f856fcbd8b","University of the West of England, Bristol, United Kingdom","Delgado Bellamy, D., University of the West of England, Bristol, United Kingdom; Chance, G., University of the West of England, Bristol, United Kingdom; Caleb-Solly, P., University of the West of England, Bristol, United Kingdom; Dogramadzi, S., University of the West of England, Bristol, United Kingdom","Hazard analysis methods such as HAZOP and STPA have proven to be effective methods for assurance of system safety for years. However, the dimensionality and human factors uncertainty of many assistive robotic applications challenges the capability of these methods to provide comprehensive coverage of safety issues from interdisciplinary perspectives in a timely and cost-effective manner. Physically assistive tasks in which a range of dynamic contexts require continuous human–robot physical interaction such as e.g., robot-assisted dressing or sit-to-stand pose a new paradigm for safe design and safety analysis methodology. For these types of tasks, considerations have to be made for a range of dynamic contexts where the robot-assistance requires close and continuous physical contact with users. Current regulations mainly cover industrial collaborative robotics regarding physical human–robot interaction (pHRI) but largely neglects direct and continuous physical human contact. In this paper, we explore limitations of commonly used safety analysis techniques when applied to robot-assisted dressing scenarios. We provide a detailed analysis of the system requirements from the user perspective and consider user-bounded hazards that can compromise safety of this complex pHRI. © Copyright © 2021 Delgado Bellamy, Chance, Caleb-Solly and Dogramadzi.","assistive robotics; collision avoidance; industry standards; pHRI; safety; SHARD; system-theoretic process analysis; unified modeling language",,,,,,"Seventh Framework Programme, FP7; Engineering and Physical Sciences Research Council, EPSRC; European Commission, EC; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; Ministerio de Economía y Competitividad, MINECO","I-DRESS is a CHIST-ERA project funded by the next National Funding Organisations: Spain: Ministerio de Economía y Competitividad (MINECO) United Kingdom: Engineering and Physical Sciences Research Council (EPSRC) Switzerland: Swiss National Science Foundation (SNSF).","De Luca, A. (2015). Safe and Autonomous Physical Human-Aware Robot Interaction. Project Supported by the European Commission under the 7th Framework Programme. Available at: http://www.saphari.eu/.","The application considered in this paper is robot assisted dressing. It was developed as part of the project ‘Assistive interactive robotic system for support in dressing—I-DRESS’ (i-dress-project.iri.upc.edu) funded by CHIST-ERA (chistera.eu) and EPSRC to develop a multi-modal interaction framework and intuitive user interfaces to facilitate safe physical interaction and cognitive robot behavior. The project considered proactive assistance for dressing tasks (shoes, jackets) to support people with disabilities, or high risk healthcare workers (personal protective equipment such as surgical gowns). A key objective for the project was ensuring safety by using multi-modal inputs accounting for environment dynamics, human errors and ergonomic limits. We used a Baxter robot to investigate dressing dynamics thanks to its similarity with the human arms (bi-manual operation) (Chance et al., 2016; Chance et al., 2017). 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Robotics, 14, pp. 1-16; Zube, A., Combined Workspace Monitoring and Collision Avoidance for mobile Manipulators, (2015) IEEE 20th Conference on Emerging Technologies and Factory Automation (ETFA) (IEEE), pp. 1-8","Delgado Bellamy, D.; University of the West of EnglandUnited Kingdom; email: daniel.delgadobellamy@uwe.ac.uk",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85109032248
"González L., Álvarez J.C., López A.M., Álvarez D.","57219925928;57209074060;16468551500;22984500800;","Metrological evaluation of human–robot collaborative environments based on optical motion capture systems†",2021,"Sensors","21","11","3748","","",,2,"10.3390/s21113748","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106592178&doi=10.3390%2fs21113748&partnerID=40&md5=f2cd3dce5ac1673e2f978f1c9213d275","Multisensor Systems and Robotics Group (SiMuR), Department of Electrical, Electronic, Computer and Systems Engineering, University of Oviedo, C/Pedro Puig Adam, Gijoń, 33203, Spain","González, L., Multisensor Systems and Robotics Group (SiMuR), Department of Electrical, Electronic, Computer and Systems Engineering, University of Oviedo, C/Pedro Puig Adam, Gijoń, 33203, Spain; Álvarez, J.C., Multisensor Systems and Robotics Group (SiMuR), Department of Electrical, Electronic, Computer and Systems Engineering, University of Oviedo, C/Pedro Puig Adam, Gijoń, 33203, Spain; López, A.M., Multisensor Systems and Robotics Group (SiMuR), Department of Electrical, Electronic, Computer and Systems Engineering, University of Oviedo, C/Pedro Puig Adam, Gijoń, 33203, Spain; Álvarez, D., Multisensor Systems and Robotics Group (SiMuR), Department of Electrical, Electronic, Computer and Systems Engineering, University of Oviedo, C/Pedro Puig Adam, Gijoń, 33203, Spain","In the context of human–robot collaborative shared environments, there has been an in-crease in the use of optical motion capture (OMC) systems for human motion tracking. The accuracy and precision of OMC technology need to be assessed in order to ensure safe human–robot interactions, but the accuracy specifications provided by manufacturers are easily influenced by various factors affecting the measurements. This article describes a new methodology for the met-rological evaluation of a human–robot collaborative environment based on optical motion capture (OMC) systems. Inspired by the ASTM E3064 test guide, and taking advantage of an existing industrial robot in the production cell, the system is evaluated for mean error, error spread, and re-peatability. A detailed statistical study of the error distribution across the capture area is carried out, supported by a Mann–Whitney U-test for median comparisons. Based on the results, optimal capture areas for the use of the capture system are suggested. The results of the proposed method show that the metrological characteristics obtained are compatible and comparable in quality to other methods that do not require the intervention of an industrial robot. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Calibration; Groupware; Human–robot interaction; Industrial robots; Optical tracking","Errors; Industrial robots; Motion capture; Motion tracking; Accuracy and precision; Collaborative environments; Error distributions; Human motion tracking; Metrological characteristics; Optical motion capture; Robot interactions; Statistical study; Social robots",,,,,"Universidad de Oviedo: PAPI-20-GR-2017-0002","Funding: This research was partly funded by I.U.T.A., grant number SV-18-GIJÓ N-1-09 and University of Oviedo, grant number PAPI-20-GR-2017-0002.",,,,,,,,,,"Bi, Z.M., Luo, M., Miao, Z., Zhang, B., Zhang, W.J., Wang, L., Safety assurance mechanisms of collaborative robotic systems in manufacturing (2021) Robot. Comput. Integr. 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Une En Iso 92831998; International Organization for Standardization: Geneva, Switzerland; Gregory, W., Corder, D.I.F., (2014) Nonparametric Statistics: A Step-by-Step Approach, , Wiley: Hoboken, NJ, USA; Myles, H., Douglas, A., Wolfe, E.C., (2014) Nonparametric Statistical Methods, , Wiley: Hoboken, NJ, USA; Huynh, D.Q., Metrics for 3D rotations: Comparison and analysis (2009) J. Math. Imaging Vis, 35, pp. 155-164; Skurowski, P., Pawlyta, M., On the noise complexity in an optical motion capture facility (2019) Sensors, 19, p. 4435","González, L.; Multisensor Systems and Robotics Group (SiMuR), C/Pedro Puig Adam, Spain; email: leti@dieecs.com",,,"MDPI AG",,,,,14248220,,,"34071352","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85106592178
"Tognon M., Alami R., Siciliano B.","56742951400;7003803131;7006263661;","Physical Human-Robot Interaction with a Tethered Aerial Vehicle: Application to a Force-Based Human Guiding Problem",2021,"IEEE Transactions on Robotics","37","3","9353246","723","734",,1,"10.1109/TRO.2020.3038700","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101466642&doi=10.1109%2fTRO.2020.3038700&partnerID=40&md5=fed7d46c6ddf39d84b49ce728987a0d2","Autonomous Systems Lab, Department of Mechanical and Process Engineering, ETH Zürich, Zürich, Switzerland; LAAS-CNRS, Universit DeToulouse, CNRS and Artificial and Natural Intelligence Toulouse Institute, Toulouse, France; Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy","Tognon, M., Autonomous Systems Lab, Department of Mechanical and Process Engineering, ETH Zürich, Zürich, Switzerland; Alami, R., LAAS-CNRS, Universit DeToulouse, CNRS and Artificial and Natural Intelligence Toulouse Institute, Toulouse, France; Siciliano, B., Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy","Today, physical human-robot interaction (pHRI) is a very popular topic in the field of ground manipulation. At the same time, aerial physical interaction is also developing very fast. Nevertheless, pHRI with aerial vehicles has not been addressed so far. In this work, we present the study of one of the first systems in which a human is physically connected to an aerial vehicle by a cable. We want the robot to be able to pull the human toward a desired position (or along a path) only using forces as an indirect communication-channel. We propose an admittance-based approach with a controller, inspired by the literature on flexible manipulators, that computes the desired interaction forces that properly guide the human. The stability of the system is formally proved with a Lyapunov-based argument. The system is also shown to be passive, and thus robust to nonidealities like model and tracking errors, additional human forces, time-varying inputs, and other external disturbances. We also design a maneuver regulation policy to simplify the path following problem. The global method has been experimentally validated on a group of four subjects, showing a reliable and safe pHRI. © 2004-2012 IEEE.","Aerial systems; force control; mechanics and control; motion control; physical human-robot interaction (pHRI)","Agricultural robots; Antennas; Man machine systems; System stability; Vehicles; External disturbances; Indirect communication; Interaction forces; Path-following problems; Physical human-robot interactions; Physical humanrobot interaction (phri); Physical interactions; Time varying input; Social robots",,,,,,,,,,,,,,,,"Ruggiero, F., Lippiello, V., Ollero, A., Aerial manipulation: Aliterature review (2018) IEEE Robot. Autom. Lett., 3 (3), pp. 1957-1964. , Jul; Bartelds, T., Capra, A., Hamaza, S., Stramigioli, S., Fumagalli, M., Compliant aerial manipulators: Toward a new generation of aerial robotic workers (2016) IEEE Robot. Autom,. 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Autom., pp. 5203-5208. , Singapore, May; Lee, T., Leoky, M., McClamroch, N.H., Geometric tracking control of a quadrotor UAV on SE(3) (2010) Proc. 49th IEEE Conf. Decis. Control, pp. 5420-5425. , Atlanta, GA, USA, Dec; Tognon, M., Dash, S.S., Franchi, A., Observer-based control of position and tension for an aerial robot tethered to amoving platform (2016) IEEE Robot. Autom. Lett., 1 (2), pp. 732-737. , Jul; Khambhaita, H., Alami, R., Viewing robot navigation in human environment as a cooperative activity (2020) Robotics Research, pp. 285-300. , Cham, Switzerland: Springer; Fiore, M., Khambhaita, H., Milliez, G., Alami, R., An adaptive and proactive human-aware robot guide (2015) Proc. 7th Int. Conf. Social Robot., pp. 194-203. , Paris, France: Springer, Oct","Tognon, M.; Autonomous Systems Lab, Switzerland; email: mtognon@ethz.ch",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15523098,,,,"English","IEEE Trans. Rob.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85101466642
"Li D., Dornadula V., Lin K., Wehner M.","57223267797;57215828632;57223258422;55388694300;","Position control for soft actuators, next steps toward inherently safe interaction",2021,"Electronics (Switzerland)","10","9","1116","","",,1,"10.3390/electronics10091116","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105413120&doi=10.3390%2felectronics10091116&partnerID=40&md5=ccdea2119ed79d4ababe8a1d7b327aaf","Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA  95064, United States","Li, D., Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA  95064, United States; Dornadula, V., Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA  95064, United States; Lin, K., Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA  95064, United States; Wehner, M., Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA  95064, United States","Soft robots present an avenue toward unprecedented societal acceptance, utility in populated environments, and direct interaction with humans. However, the compliance that makes them attractive also makes soft robots difficult to control. We present two low-cost approaches to control the motion of soft actuators in applications common in human-interaction tasks. First, we present a passive impedance approach, which employs restriction to pneumatic channels to regulate the inflation/deflation rate of a pneumatic actuator and eliminate the overshoot/oscillation seen in many underdamped silicone-based soft actuators. Second, we present a visual servoing feedback control approach. We present an elastomeric pneumatic finger as an example system on which both methods are evaluated and compared to an uncontrolled underdamped actuator. We perturb the actuator and demonstrate its ability to increase distal curvature around the obstacle and maintain the desired end position. In this approach, we use the continuum deformation characteristic of soft actuators as an advantage for control rather than a problem to be minimized. With their low cost and complexity, these techniques present great opportunity for soft robots to improve human–robot interaction. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Control; Human–robot interaction; Soft robot",,,,,,,,,,,,,,,,,"Accident Search Results Page | Occupational Safety and Health Administration, , https://www.osha.gov/pls/eferencesimis/accidentsearch.search?sic=&sicgroup=&naics=&acc_description=&acc_abstract=&acc_keyword=%22Robot%22&inspnr=Accident&fatal=&officetype=&office=&startmonth=&startday=&startyear=&endmonth=&endday=&endyear=&keyword_list=on&p_SearchResultsPage|OccupationalSafetyandHealthAdministrationAvahttps://www.osha.gov/pls/imis/accidentsearch.search?sic=&sicgroup=&naics=&acc_description=&acc_abstract=start=&p_finish=40&p_sort=&p_desc=DESC&p_direction=Next&p_show=20 (accessed on 9 April 2021); Michaelis, J.E., Siebert-Evenstone, A., Shaffer, D.W., Mutlu, B., Collaborative or Simply Uncaged? Understanding Human-Cobot d=%22Robot%22&inspnr=&fatal=&officetype=&office=&startmonth=&startday=&startyear=&endmonth=&endInteractions in Automation Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1-12. , keyword_list=on&p_start=&p_finish=40&p_sort=&p_desc=DESC&p_direction=Next&p_show=20USA, Honolulu, HI, =&, 21 April 2020; (accessed; Lawrence, C., Requiem for Rethink Robotics (2019) Mech. Eng, 141, pp. 40-45. , [CrossRef]; Kim, S., Laschi, C., Trimmer, B., Soft Robotics: A Bioinspired Evolution in Robotics (2013) Trends Biotechnol, 31, pp. 287-294. , [CrossRef] [PubMed]; Li, H., Go, G., Ko, S.Y., Park, J.-O., Park, S., Magnetic Actuated PH-Responsive Hydrogel-Based Soft Micro-Robot for Targeted Drug Delivery (2016) Smart Mater. Struct, 25, p. 027001. , [CrossRef]; Shepherd, R.F., Ilievski, F., Choi, W., Morin, S.A., Stokes, A.A., Mazzeo, A.D., Chen, X., Whitesides, G.M., Multigait Soft Robot (2011) Proc. Natl. Acad. Sci. 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Technol, 7, pp. 396-401; Morrow, J., Shin, H.-S., Phillips-Grafflin, C., Jang, S.-H., Torrey, J., Larkins, R., Dang, S., Berenson, D., Improving Soft Pneumatic Actuator Fingers through Integration of Soft Sensors, Position and Force Control, and Rigid Fingernails Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 5024-5031. , Stockholm, Sweden, 16–21 May 2016; Nasab, A.M., Sabzehzar, A., Tatari, M., Majidi, C., Shan, W., A Soft Gripper with Rigidity Tunable Elastomer Strips as Ligaments (2017) Soft Robot, 4, pp. 411-420. , [CrossRef] [PubMed]; Murray, D., Basu, A., Motion Tracking with an Active Camera (1994) IEEE Trans. Pattern Anal. Mach. Intell, 16, pp. 449-459. , [CrossRef]","Wehner, M.; Department of Electrical and Computer Engineering, United States; email: miwehner@ucsc.edu",,,"MDPI AG",,,,,20799292,,,,"English","Electronics (Switzerland)",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85105413120
"Kaur M., Kim T.-H., Kim W.S.","57192410548;56168759700;17136903100;","New Frontiers in 3D Structural Sensing Robots",2021,"Advanced Materials","33","19","2002534","","",,6,"10.1002/adma.202002534","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100159592&doi=10.1002%2fadma.202002534&partnerID=40&md5=e9bf33a1d853aec5865f9c90d4635439","Additive Manufacturing Laboratory, School of Mechatronics System Engineering, Simon Fraser University, Surrey, BC  V3T 0A3, Canada","Kaur, M., Additive Manufacturing Laboratory, School of Mechatronics System Engineering, Simon Fraser University, Surrey, BC  V3T 0A3, Canada; Kim, T.-H., Additive Manufacturing Laboratory, School of Mechatronics System Engineering, Simon Fraser University, Surrey, BC  V3T 0A3, Canada; Kim, W.S., Additive Manufacturing Laboratory, School of Mechatronics System Engineering, Simon Fraser University, Surrey, BC  V3T 0A3, Canada","Advanced robotics is the result of various contributions from complex fields of science and engineering and has tremendous value in human society. Sensing robots are highly desirable in practical settings such as healthcare and manufacturing sectors through sensing activities from human–robot interaction. However, there are still ongoing research and technical challenges in the development of ideal sensing robot systems. The sensing robot should synergically merge sensors and robotics. Geometrical difficulty in the sensor positioning caused by the structural complexity of sensing robots and their corresponding processing have been the main challenges in the production of sensing robots. 3D electronics integrated into 3D objects prepared by the 3D printing process can be the potential solution for designing realistic sensing robot systems. 3D printing provides the advantage to manufacture complex 3D structures in electronics in a single setup, allowing the ease of design flexibility, and customized functions. Therefore, the platform of 3D sensing systems is investigated and their expansion into sensing robots is studied further. The progress toward sensing robots from 3D electronics integrated into 3D objects and the advanced material strategies, used to overcome the challenges, are discussed. © 2021 Wiley-VCH GmbH","3D printing; 3D sensors; sensing robots; structural electronics","Electronics industry; Human robot interaction; Robotics; 3D printing process; Advanced materials; Manufacturing sector; Robot interactions; Science and engineering; Sensor positioning; Structural complexity; Technical challenges; 3D printers",,,,,,,,,,,,,,,,"Brogårdh, T., (2007) Annu. Rev. Control, 31, p. 69; Zhang, Z., Wang, X., Liu, J., Dai, C., Sun, Y., (2019) Annu. Rev. Control. Robot. Auton. Syst., 2, p. 181; Hentout, A., Aouache, M., Maoudj, A., Akli, I., (2019) Adv. Robotics, 33, p. 764; Flores-Abad, A., Ma, O., Pham, K., Ulrich, S., (2014) Prog. Aerosp. Sci., 68, p. 1; Chu, W.-S., Lee, K.-T., Song, S.-H., Han, M.-W., Lee, J.-Y., Kim, H.-S., Kim, M.-S., Ahn, S.-H., (2012) Int. J. Pr. Eng. 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Actuators, A, 174, p. 96; Guo, S., Lin, R., Wang, L., Lau, S., Wang, Q., Liu, R., (2019) Mater. Sci. Eng., C, 99, p. 735; Tong, Y., Kucukdeger, E., Halper, J., Cesewski, E., Karakozoff, E., Haring, A.P., McIlvain, D., Johnson, B.N., (2019) PLoS One, 14; Leal-Junior, A.G., Díaz, C.R., Pontes, M.J., Marques, C., Frizera, A., (2019) Opt. Laser Technol., 112, p. 323","Kim, W.S.; Additive Manufacturing Laboratory, Canada; email: woosook@sfu.ca",,,"John Wiley and Sons Inc",,,,,09359648,,ADVME,"33458908","English","Adv Mater",Article,"Final","",Scopus,2-s2.0-85100159592
"Jia M., Yu J., Liu Y., Guo P., Lei Y., Wang W., Yu A., Zhu Y., Sun Q., Zhai J., Wang Z.L.","57210420098;57205224025;56341876300;57216205490;57206637193;57214050249;55203874700;57202548780;57217271057;57059835700;57217204735;","Multibit tribotronic nonvolatile memory based on van der Waals heterostructures",2021,"Nano Energy","83",,"105785","","",,4,"10.1016/j.nanoen.2021.105785","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099864187&doi=10.1016%2fj.nanoen.2021.105785&partnerID=40&md5=9278483c965f678b63ea214a0b1cd27b","Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China; College of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning, 530004, China; Department of Electrical and Electronic Engineering, Graduate School of Engineering, Kobe University, 1-1 Rokkodai, Nada Ward, Kobe, Hyogo, 657-8501, Japan; School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA  30332, United States","Jia, M., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, College of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; Yu, J., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, College of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; Liu, Y., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, College of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; Guo, P., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, College of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; Lei, Y., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, College of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; Wang, W., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, College of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; Yu, A., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, College of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China, Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning, 530004, China; Zhu, Y., Department of Electrical and Electronic Engineering, Graduate School of Engineering, Kobe University, 1-1 Rokkodai, Nada Ward, Kobe, Hyogo, 657-8501, Japan; Sun, Q., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, College of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China, Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning, 530004, China; Zhai, J., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, College of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China, Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning, 530004, China; Wang, Z.L., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, College of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China, School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA  30332, United States","Low power and multifunctional nonvolatile memories are promising candidates for processing massive data in the Internet of Things era. However, the storage states in conventional memory devices are under the restricted control by electrical or optical signals. Herein, a new multibit tribotronic nonvolatile memory (T-NVM) based on a graphene/hexagonal boron nitride/molybdenum disulfide van der Waals heterostructure and triboelectric nanogenerator (TENG) is proposed. The programming/erasing states can be modulated by the triboelectric potential, which is determined by changing the distance between the two triboelectrification layers. Under the modulation of external mechanical actions, the device exhibits a high on/off ratio of 105 via manipulating mechanical distance from − 0.2 mm to + 0.2 mm, a long retention time up to 6000 s, a stable switching behavior for over 100 cycles, and a multilevel data storage capability of 14 stages by different external stimuli. Furthermore, a memory inverter circuit employing the triboelectric potential as input signals can serve as the conversion of logical signals. This work proves the great potential of tribotronic devices for direct interaction with external environment in lower power and broadening diverse applications of human-robot interactions, self-powered wearable devices, and intelligent instrumentation. © 2021","Logic inverter; Multibit tribotronic nonvolatile memory; Triboelectric nanogenerator; Tribotronics; van der Waals heterostructures","Human robot interaction; III-V semiconductors; Nanogenerators; Nonvolatile storage; Sulfur compounds; Van der Waals forces; Wearable technology; Conventional memory; Direct interactions; Diverse applications; External environments; Intelligent instrumentation; Non-volatile memory; Switching behaviors; Triboelectrification; Data handling",,,,,"2016YFA0202703; National Natural Science Foundation of China, NSFC: 51872031, 52073032, 61904013; Beijing Nova Program: Z191100001119047; University of Chinese Academy of Sciences, UCAS: E0E48909","M. J. and J. Y. contributed equally to this work. This work was supported by China National Key Research and Development Plan Project from Minister of Science and Technology, China ( 2016YFA0202703 ), the National Natural Science Foundation of China (Grant No. 51872031 , 61904013 and 52073032 ), the University of Chinese Academy of Sciences (Grant no. E0E48909 ), and Beijing Nova Program ( Z191100001119047 ).",,,,,,,,,,"Meena, J.S., Sze, S.M., Chand, U., Tseng, T.-Y., Overview of emerging nonvolatile memory technologies (2014) Nanoscale Res. Lett., 9, p. 526; Wang, Z., Zhang, S.-R., Zhou, L., Mao, J.-Y., Han, S.-T., Ren, Y., Yang, J.-Q., Zhou, Y., Functional non-volatile memory devices: from fundamentals to photo-tunable properties (2019) Phys. Status Solidi RRL, 13; Bertolazzi, S., Gobbi, M., Zhao, Y., Backes, C., Samori, P., Molecular chemistry approaches for tuning the properties of two-dimensional transition metal dichalcogenides (2018) Chem. Soc. 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Sci., 3; Zhang, C., Li, J., Han, C.B., Zhang, L.M., Chen, X.Y., Wang, L.D., Dong, G.F., Wang, Z.L., Organic tribotronic transistor for contact-electrification-gated light-emitting diode (2015) Adv. Funct. Mater., 25, pp. 5625-5632; Li, J., Zhang, C., Duan, L., Zhang, L.M., Wang, L.D., Dong, G.F., Wang, Z.L., Flexible organic tribotronic transistor memory for a visible and wearable touch monitoring system (2016) Adv. Mater., 28. , (106-+); Sun, Y., Zheng, X., Yan, X., Liao, Q., Liu, S., Zhang, G., Li, Y., Zhang, Y., Bioinspired tribotronic resistive switching memory for self-powered memorizing mechanical stimuli (2017) ACS Appl. Mater. Interfaces, 9, pp. 43822-43829; Ferrari, A.C., Meyer, J.C., Scardaci, V., Casiraghi, C., Lazzeri, M., Mauri, F., Piscanec, S., Geim, A.K., Raman spectrum of graphene and graphene layers (2006) Phys. Rev. Lett., 97; Ganatra, R., Zhang, Q., Few-layer MoS2: a promising layered semiconductor (2014) ACS Nano, 8, pp. 4074-4099; Li, H., Zhang, Q., Yap, C.C.R., Tay, B.K., Edwin, T.H.T., Olivier, A., Baillargeat, D., From bulk to monolayer MoS2: evolution of raman scattering (2012) Adv. Funct. Mater., 22, pp. 1385-1390; Lenzlinger, M., Snow, E.H., Fowler-Nordheim tunneling into thermally grown SiO2 (1969) J. Appl. Phys., 40, pp. 278-283. , (278-+); Castellanos-Gomez, A., Buscema, M., Molenaar, R., Singh, V., Janssen, L., van der Zant, H.S.J., Steele, G.A., Deterministic transfer of two-dimensional materials by all-dry viscoelastic stamping (2014) 2D Mater., 1","Sun, Q.; Beijing Institute of Nanoenergy and Nanosystems, China; email: sunqijun@binn.cas.cn",,,"Elsevier Ltd",,,,,22112855,,,,"English","Nano Energy",Article,"Final","",Scopus,2-s2.0-85099864187
"Jiang J., Nan Z., Chen H., Chen S., Zheng N.","57204879760;57193017397;57144842800;57193352008;56725835000;","Predicting short-term next-active-object through visual attention and hand position",2021,"Neurocomputing","433",,,"212","222",,,"10.1016/j.neucom.2020.12.069","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100195467&doi=10.1016%2fj.neucom.2020.12.069&partnerID=40&md5=af1164dcdb8b06d943277f018273d536","Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shaanxi  710049, China","Jiang, J., Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shaanxi  710049, China; Nan, Z., Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shaanxi  710049, China; Chen, H., Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shaanxi  710049, China; Chen, S., Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shaanxi  710049, China; Zheng, N., Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, Shaanxi  710049, China","Human intention prediction is of great significance in many applications, such as human-robot interaction, intelligent rehabilitation robots. This paper studies the problem of short-term next-active-object prediction in egocentric images. The short-term next-active-object refers to the object that a human is going to interact with in the short-term future, which is an embodiment of human intention. Most current methods usually use object-centered cues, such as the deviation of object appearance change and the unique shape of the egocentric object trajectory, to predict the next-active-object. In this paper, inspired by the fact that human intention is also revealed by human-centered cues, we propose a deep neural network model that integrates the cues from visual attention and hand positions to predict the next-active-object. Firstly, the probability maps of visual attention and hand positions are constructed, and then the probability distribution of next-active-object is generated. We experimentally compare our method with several baseline methods using two datasets and confirm its effectiveness. In addition, ablation experiments are conducted, and crucial points concerning the next-active-object are discussed. © 2021 Elsevier B.V.","Human attention; Human intention; Human robot interaction; Next-active-object prediction","Behavioral research; Deep neural networks; Forecasting; Probability distributions; Ablation experiments; Baseline methods; Human intentions; Neural network model; Object appearance; Object trajectories; Rehabilitation robot; Visual Attention; Human robot interaction; article; deep neural network; human; human experiment; prediction; probability; robotics; visual attention",,,,,"National Natural Science Foundation of China, NSFC: 61773312, 61790562, 61790563, 62006180","This work is supported by the National Science Foundation of China (Nos. 61790562 , 62006180 , 61790563 , 61773312 ).",,,,,,,,,,"Furnari, A., Battiato, S., Grauman, K., Farinella, G.M., Next-active-object prediction from egocentric videos (2017) Journal of Visual Communication and Image Representation, 49, pp. 401-411; Damen, D., Doughty, H., Maria Farinella, G., Fidler, S., Furnari, A., Kazakos, E., Moltisanti, D., Price, W., Scaling egocentric vision: The epic-kitchens dataset (2018) Proceedings of the European Conference on Computer Vision (ECCV), pp. 720-736; Pirsiavash, H., Ramanan, D., Detecting activities of daily living in first-person camera views (2012) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2847-2854; Dickinson, S.J., Christensen, H.I., Tsotsos, J.K., Olofsson, G., Active object recognition integrating attention and viewpoint control (1997) Computer Vision and Image Understanding, pp. 239-260; González Díaz, I., Buso, V., Benois-Pineau, J., Bourmaud, G., Megret, R., Modeling instrumental activities of daily living in egocentric vision as sequences of active objects and context for alzheimer disease research (2013) Proceedings of the ACM International Workshop on Multimedia Indexing and Information Retrieval for Healthcare, pp. 11-14; González-Díaz, I., Benois-Pineau, J., Domenger, J.-P., Cattaert, D., de Rugy, A., Perceptually-guided deep neural networks for ego-action prediction: object grasping (2019) Pattern Recognition, 88, pp. 223-235; Lee, Y.J., Grauman, K., Predicting important objects for egocentric video summarization (2015) International Journal of Computer Vision, 114 (1), pp. 38-55; González-Díaz, I., Buso, V., Benois-Pineau, J., Perceptual modeling in the problem of active object recognition in visual scenes (2016) Pattern Recognition, 56, pp. 129-141; Bertasius, G., Park, H.S., Yu, S.X., Shi, J., First person action-object detection with egonet, arXiv preprint arXiv:1603.04908; Bertasius, G., Soo Park, H., Yu, S.X., Shi, J., Unsupervised learning of important objects from first-person videos (2017) Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1956-1964; Ren, X., Philipose, M., Egocentric recognition of handled objects: benchmark and analysis (2009) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1-8; Ren, X., Gu, C., Figure-ground segmentation improves handled object recognition in egocentric video (2010) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3137-3144; Goertzel, B., Pennachin, C., (2007) Artificial General Intelligence, 2. , Springer; He, H., She, Y., Xiahou, J., Yao, J., Li, J., Hong, Q., Ji, Y., Real-time eye-gaze based interaction for human intention prediction and emotion analysis (2018), pp. 185-194. , Proceedings of the Computer Graphics International; Huang, C.-M., Mutlu, B., Anticipatory robot control for efficient human-robot collaboration (2016) Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 83-90; Xu, B., Li, J., Wong, Y., Zhao, Q., Kankanhalli, M.S., Interact as you intend: Intention-driven human-object interaction detection, IEEE Transactions on Multimedia; Abuduweili, A., Li, S., Liu, C., (1909), Adaptable human intention and trajectory prediction for human-robot collaboration, arXiv preprint arXiv05089; Wang, Z., Mülling, K., Deisenroth, M.P., Ben Amor, H., Vogt, D., Schölkopf, B., Peters, J., Probabilistic movement modeling for intention inference in human–robot interaction (2013) The International Journal of Robotics Research, 32 (7), pp. 841-858; Zhang, L., Diao, X., Ma, O., A preliminary study on a robot's prediction of human intention (2017) Proceedings of the IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 1446-1450; Hudson, M., Nicholson, T., Ellis, R., Bach, P., I see what you say: prior knowledge of other’ goals automatically biases the perception of their actions (2016) Cognition, 146, pp. 245-250; Ryoo, M., Fuchs, T.J., Xia, L., Aggarwal, J.K., Matthies, L., Robot-centric activity prediction from first-person videos: What will they do to me? 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"Escobedo P., Ntagios M., Shakthivel D., Navaraj W.T., Dahiya R.","57188743232;57205626715;55346628000;56766192600;24331529200;","Energy Generating Electronic Skin with Intrinsic Tactile Sensing without Touch Sensors",2021,"IEEE Transactions on Robotics","37","2","9246727","683","690",,15,"10.1109/TRO.2020.3031264","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103941994&doi=10.1109%2fTRO.2020.3031264&partnerID=40&md5=da4051841d7d5a0887013a4131a93761","Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, United Kingdom","Escobedo, P., Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, United Kingdom; Ntagios, M., Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, United Kingdom; Shakthivel, D., Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, United Kingdom; Navaraj, W.T., Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, United Kingdom; Dahiya, R., Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, United Kingdom","Electronic skin (eSkin) with various types of sensors over large conformable substrates has received considerable interest in robotics. The continuous operation of large number of sensors and the readout electronics make it challenging to meet the energy requirements of eSkin. In this article, we present the first energy generating eSkin with intrinsic tactile sensing without any touch sensor. The eSkin comprises a distributed array of miniaturized solar cells and infrared light emitting diodes (IRLEDs) on soft elastomeric substrate. By innovatively reading the variations in the energy output of the solar cells and IRLEDs, the eSkin could sense multiple parameters (proximity, object location, edge detection, etc.). As a proof of concept, the eSkin has been attached to a 3-D-printed hand. With an energy surplus of 383.6 mW from the palm area alone, the eSkin could generate more than 100 W if present over the whole body (area ∼1.5 m2). Further, with an industrial robot arm, the presented eSkin is shown to enable safe human-robot interaction. The novel paradigm presented in this article for the development of a flexible eSkin extends the application of solar cell from energy generation alone to simultaneously acting as touch sensors. © 2004-2012 IEEE.","Electronic skin (eSkin); energy harvesting; human-robot interaction (HRI); proximity sensing; solar cell; touch sensing","Agricultural robots; Contact sensors; Edge detection; Human robot interaction; Industrial robots; Object detection; Solar cells; Continuous operation; Distributed arrays; Elastomeric substrates; Energy requirements; Infrared light emitting diodes; Multiple parameters; Readout Electronics; Safe human-robot interaction; Substrates",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/M002527/1, EP/R029644/1","Manuscript received May 22, 2020; accepted September 3, 2020. Date of publication November 2, 2020; date of current version April 2, 2021. This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) through engineering fellowship for growth under Grant EP/R029644/1 and Grant EP/M002527/1. This article was recommended for publication by Associate Editor J. Paik and Editor E. Yoshida upon evaluation of the reviewers’ comments. (Corresponding author: Ravinder Dahiya.) The authors are with the Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, U.K. (e-mail: ravinder.dahiya@glasgow.ac.uk).",,,,,,,,,,"Dahiya, R., E-Skin: From humanoids to humans [point of view] (2019) Proc. IEEE, 107 (2), pp. 247-252. , Feb; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Sci. Robot., 3 (21); Leite, I., Martinho, C., Paiva, A., Social robots for long-term interaction: A survey (2013) Int. J. Soc. 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Robot., 27 (3), pp. 389-400. , Jun; Yang, K.-J., Flexible Cu2ZnSn(S, Se)4 solar cells with over 10% efficiency and methods of enlarging the cell area (2019) Nat. Commun., 10 (1); Hashemi, S.A., Ramakrishna, S., Aberle, A.G., Recent progress in flexible-wearable solar cells for self-powered electronic devices (2020) Energy Environ. Sci., 13 (3), pp. 685-743; Manjakkal, L., Pullanchiyodan, A., Yogeswaran, N., Hosseini, E.S., Dahiya, R., A wearable supercapacitor based on conductive PEDOT: PSScoated cloth and a sweat electrolyte (2020) Adv. Mater.","Dahiya, R.; Bendable Electronics and Sensing Technologies (BEST) Group, United Kingdom; email: ravinder.dahiya@glasgow.ac.uk",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15523098,,,,"English","IEEE Trans. Rob.",Article,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85103941994
"Saunderson S., Nejat G.","16029308800;12789569200;","Robots Asking for Favors: The Effects of Directness and Familiarity on Persuasive HRI",2021,"IEEE Robotics and Automation Letters","6","2","9357914","1793","1800",,1,"10.1109/LRA.2021.3060369","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101775990&doi=10.1109%2fLRA.2021.3060369&partnerID=40&md5=68eb1907f34c45721b9dbd08389814a0","Department of Mechanical and Industrial Engineering, Autonomous Systems and Biomechatronics Lab, University of Toronto, Toronto, ON, Canada","Saunderson, S., Department of Mechanical and Industrial Engineering, Autonomous Systems and Biomechatronics Lab, University of Toronto, Toronto, ON, Canada; Nejat, G., Department of Mechanical and Industrial Engineering, Autonomous Systems and Biomechatronics Lab, University of Toronto, Toronto, ON, Canada","As robots work in increasingly collaborative settings with humans, scenarios will arise where robots need to successfully request favors to be effective. Communication directness and familiarity have been shown to be important factors to persuasion. However, these two critical factors have not yet been jointly investigated in human-robot interaction (HRI). This letter explores how they can be used by social robots to request favors from people. We present a social HRI study that uniquely investigates the effects of a robot's communication directness and its familiarity on persuasiveness, trustworthiness, and a person's willingness to help the robot. In the study, we present participants with scenarios where two different robots (one familiar, one unfamiliar) ask participants for favors using either direct or indirect requests. Our results show that a familiar robot is more persuasive, trustworthy, and people are more willing to help it. Furthermore, indirect requests are perceived to be more trustworthy and encourage greater willingness to help than direct requests, regardless of the robot's familiarity. Further discussions of these results highlight key considerations for collaborative social robots, particularly when robots request assistance or favors from people. © 2016 IEEE.","communication directness; persuasive robotics; robot companions; Social HRI","Agricultural robots; Social robots; Collaborative settings; Critical factors; Design considerations; Human robot Interaction (HRI); Indirect communication; Social hri; Machine design",,,,,"AGE-WELL; Natural Sciences and Engineering Research Council of Canada, NSERC; Canada Research Chairs","Manuscript received September 17, 2020; accepted January 28, 2021. Date of publication February 18, 2021; date of current version March 8, 2021. This letter was recommended for publication by Associate Editor D. Losey and Editor G. Venture upon evaluation of the reviewers’ comments. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC), Vanier Canada Graduate Scholarships (Vanier CGS), AGE-WELL, and in part by the Canada Research Chairs (CRC) program. (Corresponding author: Shane Saunderson.) The authors are with the Autonomous Systems and Biomechatronics Lab, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada (e-mail: shane.saunderson@mail.utoronto.ca; nejat@mie.utoronto.ca). Digital Object Identifier 10.1109/LRA.2021.3060369",,,,,,,,,,"Waldherr, S., Romero, R., Thrun, S., Gesture based interface for humanrobot interaction (2000) Auton. Robots, 9 (2), pp. 151-173; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y.C., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Hum. Factors J. Hum. Factors Ergon. 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Robot., 11 (4), pp. 575-608; Li, J., Ju, W., Nass, C., Observer perception of dominance andmirroring behavior in human-robot relationships (2015) Proc. Int. Conf. Hum.-Robot Interaction, pp. 133-140; Roubroeks, M.A.J., Ham, J.R.C., Midden, C.J.H., The dominant robot: Threatening robots cause psychological reactance, especially when they have incongruent goals (2010) Persuasive Technology, 6137, pp. 174-184. , T. Ploug, P. Hasle, and H. Oinas-Kukkonen, eds. Berlin, Germany: Springer; Beer, J., Older users' acceptance of an assistive robot: Attitudinal changes following brief exposure (2017) Gerontechnology, 16 (1), pp. 21-36","Saunderson, S.; Department of Mechanical and Industrial Engineering, Canada; email: shane.saunderson@mail.utoronto.ca",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85101775990
"Garcia-Rosas R., Yu T., Oetomo D., Manzie C., Tan Y., Choong P.","57193827178;57219687057;57220353202;12791337200;57221822926;7005534751;","Exploiting Inherent Human Motor Behaviour in the Online Personalisation of Human-Prosthetic Interfaces",2021,"IEEE Robotics and Automation Letters","6","2","9361165","1973","1980",,,"10.1109/LRA.2021.3061351","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101746239&doi=10.1109%2fLRA.2021.3061351&partnerID=40&md5=a1ace815270b91c5c60171b04310dc0e","School of Electrical, Mechanical and Infrastructure Engineering, University of Melbourne, Melbourne, VIC, Australia; Department of Surgery, University of Melbourne, Fitzroy, VIC, Australia","Garcia-Rosas, R., School of Electrical, Mechanical and Infrastructure Engineering, University of Melbourne, Melbourne, VIC, Australia; Yu, T., School of Electrical, Mechanical and Infrastructure Engineering, University of Melbourne, Melbourne, VIC, Australia; Oetomo, D., School of Electrical, Mechanical and Infrastructure Engineering, University of Melbourne, Melbourne, VIC, Australia; Manzie, C., School of Electrical, Mechanical and Infrastructure Engineering, University of Melbourne, Melbourne, VIC, Australia; Tan, Y., School of Electrical, Mechanical and Infrastructure Engineering, University of Melbourne, Melbourne, VIC, Australia; Choong, P., Department of Surgery, University of Melbourne, Fitzroy, VIC, Australia","Human-prosthetic interfaces require their settings to be tuned to individual users. This can potentially be done autonomously while the prosthesis user performs a task by using online personalisation algorithms. These online personalisation algorithms adjust the interface parameters to optimise a given measure of performance. For convergence to be reached, both the human and the personalisation algorithm need to optimise towards the same objective. To date, task-oriented measures of performance have been utilised as the objective, requiring explicit feedback of the measure of performance to the prosthesis user, which is not practical. In this letter, the use of inherent human motor behaviour as the measure of performance for online personalisation algorithms is proposed and investigated. This allows the personalisation procedure to occur without the prosthesis user needing explicit knowledge of the measure of performance. The methodology for formulating inherent human motor behaviour within the framework of online personalisation of human-prosthetic interfaces is presented and validated through an experiment with nine able-bodied subjects. Experimental results demonstrate the efficacy of inherent human motor behaviour-based measures of performance in the design of an intuitive human-prosthetic interface specifically, applicable to human-robot interaction in general. © 2016 IEEE.","human-robot collaboration; optimization and optimal control; Prosthetics and exoskeletons","Agricultural robots; Behavioral research; Biofeedback; Machine design; Prosthetics; Explicit feedback; Explicit knowledge; Human motor; Interface parameters; Measure of performance; Measures of performance; Personalisation; Task-oriented; Human robot interaction",,,,,,"This work was supported by the Valma Angliss Trust.",,,,,,,,,,"Merad, M., De Montalivet, É., Touillet, A., Martinet, N., Roby-Brami, A., Jarrassé, N., Can we achieve intuitive prosthetic elbow control based on healthy upper limb motor strategies? (2018) Front. 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Neurosci., 7 (9), pp. 907-915; Carey, S.L., Highsmith, M.J., Maitland, M.E., Dubey, R.V., Compensatory movements of transradial prosthesis users during common tasks (2008) Clin. Biomech., 23 (9), pp. 1128-1135; Metzger, A.J., Dromerick, A.W., Holley, R.J., Lum, P.S., Characterization of compensatory trunk movements during prosthetic upper limb reaching tasks (2012) Arch. Phys. Med. Rehabil., 93 (11), pp. 2029-2034; Alshammary, N.A., Bennett, D.A., Goldfarb, M., Synergistic elbow control for a myoelectric transhumeral prosthesis (2018) IEEE Trans. Neural Syst. Rehabil. Eng., 26 (2), pp. 468-476. , Feb; Latash, M.L., Scholz, J.P., Schöner, G., Toward a new theory of motor synergies (2007) Motor Control, 11, pp. 276-308. , Jul; Santello, M., Baud-Bovy, G., Jörntell, H., Neural bases of hand synergies (2013) Front. Comput. 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Prosthetics Orthot., 20 (3), pp. 126-132; Stlie, K., Franklin, R.J., Skjeldal, O.H., Skrondal, A., Magnus, P., Musculoskeletal pain and overuse syndromes in adult acquired major upper-limb amputees (2011) Arch. Phys. Med. Rehabil., 92 (12), pp. 1967-1973; Anderson, F.C., Pandy, M.G., Dynamic optimization of human walking (2001) J. Biomech. Eng., 123 (5), pp. 381-390; Westermann, K., Lin, J.F.S., Kulić, D., Inverse optimal control with time-varying objectives: Application to human jumping movement analysis (2020) Sci. Rep., 10 (1)","Garcia-Rosas, R.; School of Electrical, Australia; email: garcia.r@unimelb.edu.au",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85101746239
"Ning Y., Huang H., Xu W., Zhang W., Li B.","57193811149;55637358900;14038527700;57209049575;55628528575;","Design and Implementation of a Novel Variable Stiffness Actuator with Cam-Based Relocation Mechanism",2021,"Journal of Mechanisms and Robotics","13","2","021009","","",,2,"10.1115/1.4049150","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099498029&doi=10.1115%2f1.4049150&partnerID=40&md5=13957609a4ad1ae2ec2920fba651d1ff","Harbin Institute of Technology, HIT Campus of University Town, Shenzhen, 518055, China; State Key Laboratory of Robotics and System (HIT), Nangang District, Harbin, 150001, China; Beijing Institute of Technology, Beijing Haidian District, Beijing, 100081, China","Ning, Y., Harbin Institute of Technology, HIT Campus of University Town, Shenzhen, 518055, China; Huang, H., Harbin Institute of Technology, HIT Campus of University Town, Shenzhen, 518055, China; Xu, W., Harbin Institute of Technology, HIT Campus of University Town, Shenzhen, 518055, China, State Key Laboratory of Robotics and System (HIT), Nangang District, Harbin, 150001, China; Zhang, W., Beijing Institute of Technology, Beijing Haidian District, Beijing, 100081, China; Li, B., Harbin Institute of Technology, HIT Campus of University Town, Shenzhen, 518055, China, State Key Laboratory of Robotics and System (HIT), Nangang District, Harbin, 150001, China","Variable stiffness actuators (VSAs) are widely explored as they could improve the safe performance for human-robot interaction and make the system torque controllable based on the internal compliance. This article presents a novel VSA based on the cam-based relocation mechanism (CRM-VSA), which is utilized to change the locations of pivot and spring of a lever mechanism simultaneously. Consequently, such structure makes the actuator compacted and the stiffness regulation designable, which could help engineers to pursue different demands of stiffness regulation. The simultaneous relocations of the pivot and spring also permit a wide range of adjustable stiffness. By introducing linear guide pairs, the internal friction of the relocations of pivot and spring could be greatly reduced, thus enhancing the energy efficiency. To evaluate the performance of the proposed CRM-VSA, the point-to-point control strategy is developed, which contributes to a higher tracking accuracy and oscillation attenuation at both the start and end points of the trajectory. In addition, the performance of torque controllability is also verified through experiments. These excellent capabilities enable the proposed CRM-VSA to be qualified for constructing a robotic arm toward service applications. © 2021 by ASME.","actuators and transmissions; cam-based relocation mechanism; oscillation attenuation; point-to-point control; torque controllability; variable stiffness actuator","Actuators; Agricultural robots; Cams; Energy efficiency; Social robots; Springs (components); Adjustable stiffness; Design and implementations; Lever mechanism; Oscillation attenuation; Point to point control; Service applications; Tracking accuracy; Variable stiffness; Stiffness",,,,,"2019B090915001; National Key Research and Development Program of China, NKRDPC: 2017YFB1302200; Shenzhen Research and Development Program: JSGG20180504170011639","This work was supported by the National Key Research and Development Program of China (Grant No. 2017YFB1302200), the Key-Area Research and Development Program of Guangdong Province (Grant No. 2019B090915001), and the Shenzhen Research and Development Program of China (Grant No. JSGG20180504170011639).",,,,,,,,,,"Yu, H., Spenko, M., Dubowsky, S., 1/2An Adaptive Shared Control System for an Intelligent Mobility Aid for the Elderly, 1/2 Auton (2003) Robot, 15 (1), p. 53. , 1/266; Zhu, C., Oda, M., Yoshioka, M., Nishikawa, T., Luo, X., 1/2Admittance Control Based Walking Support and Power Assistance of an Omnidirectional Wheelchair Typed Robot (2010) 1/2 IEEE International Conference on Robotics and Biomimetics (ROBIO), p. 381. , Tianjin, China, Dec. 14 1/218, 1/2386; Park, K. 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Xu, W.; State Key Laboratory of Robotics and System (HIT)China; email: wfxu@hit.edu.cn",,,"American Society of Mechanical Engineers (ASME)",,,,,19424302,,,,"English","J. Mech. Robot.",Article,"Final","",Scopus,2-s2.0-85099498029
"Wu Q., Chen Y.","55811062400;57220962706;","Development of an Intention-Based Adaptive Neural Cooperative Control Strategy for Upper-Limb Robotic Rehabilitation",2021,"IEEE Robotics and Automation Letters","6","2","9286495","335","342",,3,"10.1109/LRA.2020.3043197","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097954726&doi=10.1109%2fLRA.2020.3043197&partnerID=40&md5=5ee318818e133fe7e30a60253ef4cb13","College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; College of Continuing Education, Nanjing University of Aeronautics and Astronautics, Nanjing, China","Wu, Q., College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China; Chen, Y., College of Continuing Education, Nanjing University of Aeronautics and Astronautics, Nanjing, China","Robotic rehabilitation therapy has become an important technology to recover the motor ability of disabled individuals. Clinical studies indicate that involving the active intention of patient into rehabilitation training contributes to promoting the performance of therapies. An adaptive neural cooperative control strategy is developed in this letter to realize intention-based human-cooperative rehabilitation training. The human motion intention is estimated by fusing the human-robot interaction forces and the muscular forces into a Gaussian radial basis function network. A biological force estimation method is proposed to obtain the muscular forces of biceps and triceps based on surface electromyography signals and Kalman filter. A robust adaptive sliding mode controller is integrated into the cooperative control scheme to ensure the accuracy and stability of inner position control loop with uncertainties. The minimum jerk cost principle is used to improve the smoothness and continuity of trajectory. To evaluate the effectiveness of the proposed control scheme, further experimental investigations are conducted on a planar upper-limb rehabilitation robot with ten volunteers. The results indicate that the proposed control strategy has significant potential to modulate the interaction compliance and cooperation process during training. © 2016 IEEE.","Cooperative control; intention recognition; Kalman filter; rehabilitation robotics","Agricultural robots; Compliance control; Control systems; Human robot interaction; Position control; Radial basis function networks; Robotics; Signal processing; Sliding mode control; Experimental investigations; Gaussian-radial-basis-function networks; Human motion intention; Position control loop; Rehabilitation training; Robotic rehabilitation; Surface electromyography signals; Upper-limb rehabilitation; Neuromuscular rehabilitation",,,,,"National Natural Science Foundation of China, NSFC: 51705240; China Postdoctoral Science Foundation: 2019T120425","This work was supported in part by the National Natural Science Foundation of China under Grant 51705240, and in part by the China Postdoctoral Science Foundation under Grant 2019T120425.",,,,,,,,,,"Keller, U., Hedel, H.J., Marganska, V.K., Rieneret, R., ChARMin: The first actuated exoskeleton robot for pediatric arm rehabilitation (2016) IEEE/ASME Trans. Mech., 21 (5), pp. 2201-2213. , Oct; Zimmermann, Y., Forino, A., Riener, R., Hutter, M., ANYexo: A versatile and dynamic upper-limb rehabilitation robot (2019) Ieee Robot Autom. Lett., 4 (4), pp. 3649-3656. , Oct; Clemente, L., Cordella, F., Guglielmelli, E., Zollo, L., Learning by demonstration for planning activities of daily living in rehabilitation and assistive robotics (2017) Ieee Robot Autom. Lett., 2 (3), pp. 1375-1382. , Jul; Kwakkel, G., Kollen, B.J., Krebs, H.I., Effects of robot-assisted therapy on upper limb recovery after stroke: A systematic review (2008) Neurorehabil Neural Repair, 22 (2), pp. 111-121; Miao, Q., Reviewing high-level control techniques on robot-assisted upper-limb rehabilitation (2018) Adv. 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Basic Eng., 82 (1), pp. 35-45; Hogan, N., Adaptive control of mechanical impedance by coactivation of antagonist muscles (1984) Ieee Trans. Automat. Contr., AC-29 (8), pp. 681-690. , Aug; Lenzi, T., Proportional EMG control for upper-limb powered exoskeletons (2011) Proc. Annu. Int. Conf. Ieee Eng. Medicine Biol. Soc. Embs, pp. 628-631; Ma, Y., A patient-specific muscle force estimation model for the potential use of human-inspired swing-assist rehabilitation robots (2016) Adv. Robot., 30, pp. 948-964","Wu, Q.; College of Mechanical and Electrical Engineering, China; email: wuqc@nuaa.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85097954726
"Betriana F., Osaka K., Matsumoto K., Tanioka T., Locsin R.C.","57204071741;8279753000;24295342800;7003753763;7003940978;","Relating Mori’s Uncanny Valley in generating conversations with artificial affective communication and natural language processing",2021,"Nursing Philosophy","22","2","e12322","","",,,"10.1111/nup.12322","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089456003&doi=10.1111%2fnup.12322&partnerID=40&md5=2adb661a2cd57962d3f006fb5ec87d28","Graduate School of Health Sciences, Tokushima University Graduate School, Tokushima, Japan; Kochi University Graduate School of Integrated and Sciences, Kochi, Japan; Tokushima University Graduate School of Technology, Industrial and Social Sciences, Tokushima, Japan; Tokushima University Graduate School, Tokushima, Japan; Institute of Biomedical Sciences, Tokushima University Graduate School Tokushima, Tokushima, Japan; Florida Atlantic University, Boca Raton, FL, United States","Betriana, F., Graduate School of Health Sciences, Tokushima University Graduate School, Tokushima, Japan; Osaka, K., Kochi University Graduate School of Integrated and Sciences, Kochi, Japan; Matsumoto, K., Tokushima University Graduate School of Technology, Industrial and Social Sciences, Tokushima, Japan; Tanioka, T., Tokushima University Graduate School, Tokushima, Japan; Locsin, R.C., Institute of Biomedical Sciences, Tokushima University Graduate School Tokushima, Tokushima, Japan, Florida Atlantic University, Boca Raton, FL, United States","Human beings express affinity (Shinwa-kan in Japanese language) in communicating transactive engagements among healthcare providers, patients and healthcare robots. The appearance of healthcare robots and their language capabilities often feature characteristic and appropriate compassionate dialogical functions in human–robot interactions. Elements of healthcare robot configurations comprising its physiognomy and communication properties are founded on the positivist philosophical perspective of being the summation of composite parts, thereby mimicking human persons. This article reviews Mori's theory of the Uncanny Valley and its consequent debates, and examines ""Uncanny"" relations with generating healthcare robot conversational content with artificial affective communication (AAC) using natural language processing. With healthcare robots provoking influential physical composition and sensory expressions, the relations in human–healthcare robot transactive engagements are argued as supportive of the design and development in natural language processing. This implies that maintaining human–healthcare robot interaction and assessing the eeriness situations explained in the Uncanny Valley theory are crucial positions for healthcare robot functioning as a valuable commodity in health care. As such, physical features, language capabilities and mobility of healthcare robots establish the primacy of the AAC with natural language processing as integral to healthcare robot–human healthcare practice. © 2020 John Wiley & Sons Ltd","artificial affective communication; healthcare robots; Masahiro Mori; natural language processing; nursing; Uncanny Valley","adult; article; conversation; facial expression; health care personnel; health care practice; human; natural language processing; nursing; robotics; artificial intelligence; human relation; Artificial Intelligence; Humans; Interpersonal Relations; Natural Language Processing",,,,,,,,,,,,,,,,"Pepper is a healthcare robot, , https://developer.softbankrobotics.com/pepper-qisdk/design/pepper-humanoid-robot, (n.d)., Retrieved from; Asada, M., How can artificial empathy follow the developmental pathway of natural empathy? 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Locsin, R.C.; Florida Atlantic UniversityUnited States; email: locsin@tokushima-u.ac.jp",,,"Blackwell Publishing Ltd",,,,,14667681,,,"32803785","English","Nurs. Philos.",Article,"Final","",Scopus,2-s2.0-85089456003
"Robinson F.A., Velonaki M., Bown O.","57215966842;24073920300;27867522200;","Smooth operator: Tuning robot perception through artificial movement sound",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444658","53","62",,1,"10.1145/3434073.3444658","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102780630&doi=10.1145%2f3434073.3444658&partnerID=40&md5=4264111819658ea18be6a13c264abd23","Creative Robotics Lab, University of New South Wales, Sydney, Australia; Interactive Media Lab, University of New South Wales, Sydney, Australia","Robinson, F.A., Creative Robotics Lab, University of New South Wales, Sydney, Australia; Velonaki, M., Creative Robotics Lab, University of New South Wales, Sydney, Australia; Bown, O., Interactive Media Lab, University of New South Wales, Sydney, Australia","Canwe influence howa robot is perceived by designing the sound of its movement? Drawing from practices in film sound, we overlaid a video depicting a robot's movement routine with three types of artificial movement sound. In a between-subject study design, participants saw either one of the three designs or a quiet control condition and rated the robot's movement quality, safety, capability, and attractiveness. We found that, compared to our control, the sound designs both increased and decreased perceived movement quality. Coupling the same robotic movement with different sounds lead to the motions being rated as more or less precise, elegant, jerky, or uncontrolled, among others. We further found that the sound conditions decreased perceived safety, and did not affect perceived capability and attractiveness. More unrealistic sound conditions led to larger differences in ratings, while the subtle addition of harmonic material was not rated differently to the control condition in any of the measures. Based on these findings, we discuss the challenges and opportunities regarding the use of artificial movement sound as an implicit channel of communication that may eventually be able to selectively target specific characteristics, helping designers in creating more refined and nuanced human-robot interactions. © 2021 IEEE Computer Society. All rights reserved.","Human-robot interaction; Movement sonification; Sonic interaction design","Agricultural robots; Machine design; Man machine systems; Quality control; Harmonic materials; Perceived capabilities; Robot perception; Robotic movements; Smooth operator; Sound designs; Study design; Social robots",,,,,,,,,,,,,,,,"Abdi, H., Williams, L.J., Tukey's honestly significant difference (HSD) test (2010) Encyclopedia of Research Design, 3; Albastaki, A., Hoggenmueller, M., Anthony Robinson, F., Hespanhol, L., Augmenting Remote Interviews through Virtual Experience Prototypes (2020) 32nd Australian Conference on Human-Computer Interaction (OzCHI '20), p. 9. , https://doi.org/10.1145/3441000.3441057ZSCC:0000000, Sydney, NSW, Australia., ACM, New York, NY, USA; Arons, B., A review of the cocktail party effect (1992) Journal of the American Voice I/O Society, 12, p. 7; Bellona, J., Bai, L., Dahl, L., LaViers, A., Empirically informed sound synthesis application for enhancing the perception of expressive robotic movement (2017) Proceedings of the 23rd International Conference on Auditory Display-ICAD 2017. 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Zenodo; Latupeirissa, A.B., Frid, E., Bresin, R., Sonic characteristics of robots in films (2019) Sound and Music Computing Conference; Luria, M., Reig, S., Zhi Tan, X., Steinfeld, A., Forlizzi, J., Zimmerman, J., Re-Embodiment and Co-Embodiment: Exploration of social presence for robots and conversational agents (2019) Proceedings of the 2019 on Designing Interactive Systems Conference-DIS '19., pp. 633-644. , https://doi.org/10.1145/3322276.3322340, ACM Press, San Diego, CA, USA; Luria, M., Zimmerman, J., Forlizzi, J., (2019) Championing Research through Design in HRI, 2019; Mason, W., Suri, S., Conducting behavioral research on Amazon's mechanical turk (2012) Behavior Research Methods, 44, p. 1; Moore, D., Currano, R., Sirkin, D., Sound decisions: How synthetic motor sounds improve autonomous vehicle-pedestrian interactions (2020) 12th International Conference on Automotive User Interfaces and Interactive Vehicular Applications; Moore, D., Dahl, T., Varela, P., Ju, W., Naes, T., Berget, I., Unintended consonances: Methods to understand robot motor sound perception (2019) Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems-CHI '19., , https://doi.org/10.1145/3290605.3300730, ACM Press, Glasgow, Scotland Uk; Moore, D., Tennent, H., Martelaro, N., Ju, W., Making noise intentional: A study of servo sound perception (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction-HRI '17, , https://doi.org/10.1145/2909824.3020238, ACM Press, Vienna, Austria; Ocnarescu, I., Cossin, I., The contribution of art and design to robotics (2019) International Conference on Social Robotics., , Springer; Palan, S., Schitter, C., Prolific. Ac-A subject pool for online experiments (2018) Journal of Behavioral and Experimental Finance, 17; Pelikan, H.R.M., Broth, M., Keevallik, L., Are you sad, cozmo?"" How humans make sense of a home robot's emotion displays (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction; Read, R., Belpaeme, T., Situational context directs how people affectively interpret robotic non-linguistic utterances (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-robot Interaction-HRI '14, , https://doi.org/10.1145/2559636.2559680, ACM Press, Bielefeld, Germany; Anthony Robinson, F., Bown, O., Velonaki, M., Implicit communication through distributed sound design: Exploring a new modality in human-robot interaction (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI '20 Companion), , https://doi.org/10.1145/3371382.3377431, ACM, Cambridge, United Kingdom; Schwenk, M., Arras, K.O., R2-D2 Reloaded: A flexible sound synthesis system for sonic human-robot interaction design (2014) The 23rd IEEE International Symposium on Robot and Human Interactive Communication. IEEE, Edinburgh, UK, , https://doi.org/10.1109/ROMAN.2014.6926247; Spence, C., Wang, Q., Sensory expectations elicited by the sounds of opening the packaging and pouring a beverage (2015) Flavour, 4 (1), p. 35. , https://doi.org/10.1186/s13411-015-0044-y, Dec; Tennent, H., Moore, D., Jung, M., Ju, W., Good vibrations: How consequential sounds affect perception of robotic arms (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN), , https://doi.org/10.1109/ROMAN.2017.8172414, IEEE, Lisbon; Thiessen, R., Rea, D.J., Garcha, D.S., Cheng, C., Young, J.E., Infrasound for HRI: A robot using low-frequency vibrations to impact how people perceive its actions (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI); Trovato, G., Do, M., Terlemez, Ö., Mandery, C., Ishii, H., Bianchi-Berthouze, N., Asfour, T., Takanishi, A., Is hugging a robot weird? Investigating the influence of robot appearance on users' perception of hugging (2016) 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids). IEEE; Trovato, G., Paredes, R., Balvin, J., Cuellar, F., Thomsen, N.B., Bech, S., Tan, Z.-H., The sound or silence: Investigating the influence of robot noise on proxemics (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)., , https://doi.org/10.1109/ROMAN.2018.8525795, IEEE, Nanjing; Tünnermann, R., Hammerschmidt, J., Hermann, T., Blended sonification-Sonification for casual information interaction (2013) Georgia Institute of Technology; Van Egmond, R., The experience of product sounds (2008) Product Experience., , Elsevier; Watanabe, K., Shimojo, S., When sound affects vision: Effects of auditory grouping on visual motion perception (2001) Psychological Science, 12 (2), pp. 109-116. , https://doi.org/10.1111/1467-9280.00319, March; Wickham, H., (2016) Ggplot2: Elegant Graphics for Data Analysis., , springer; Wolford, J., Gabaldon, B., Rivas, J., Min, B., (2019) Condition-Based Robot Audio Techniques., , Google Patents; Woods, K.J.P., Siegel, M.H., Traer, J., McDermott, J.H., Headphone screening to facilitate web-based auditory experiments (2017) Attention, Perception, & Psychophysics, 79, p. 7. , https://doi.org/10.3758/s13414-017-1361-2, Oct; Yilmazyildiz, S., Read, R., Belpeame, T., Verhelst, W., Review of semantic-free utterances in social human-robot interaction (2016) International Journal of Human-Computer Interaction, 32, p. 1. , https://doi.org/10.1080/10447318.2015.1093856, Jan; Zimmerman, J., Forlizzi, J., Evenson, S., Research through design as a method for interaction design research in HCI (2007) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems-CHI '07., , https://doi.org/10.1145/1240624.1240704, ACM Press, San Jose, California, USA; Zölzer, U., (2011) DAFX: Digital Audio Effects, , https://www.dafx.de/DAFX-Book-Page-2nd-edition/index.html, John Wiley & Sons; Özcan, E., Van Egmond, R., Product sound design and application: An overview (2006) Proceedings of the Fifth International Conference on Design and Emotion, Gothenburg",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS)","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167644,21672148,9781450382892,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102780630
"Goedicke D., Tennent H., Moore D., Ju W.","57202043646;57189046152;57194722639;7005898423;","Acoustically aware robots: Detecting and evaluating sounds robots make and hear",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444876","697","699",,,"10.1145/3434074.3444876","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102775508&doi=10.1145%2f3434074.3444876&partnerID=40&md5=49d06079e27c7d8152623edd4a1dbb37","Cornell Tech, New York, NY, United States; Independent Researcher, Oakland, CA, United States; Stanford University, Stanford, CA, United States","Goedicke, D., Cornell Tech, New York, NY, United States; Tennent, H., Independent Researcher, Oakland, CA, United States; Moore, D., Stanford University, Stanford, CA, United States; Ju, W., Cornell Tech, New York, NY, United States","The sound a robot or automated system makes and the sounds it listens for in our shared acoustic environment can greatly expand its contextual understanding and to shape its behaviors to the interactions it is trying to perform. People convey significant information with sound in interpersonal communication in social contexts. Para-linguistic information about where we are, how loud we're speaking, or if we sound happy, sad or upset are relevant to understand for a robot that looks to adapt its interactions to be socially appropriate. Similarly, the qualities of the sound an object makes can change how people perceive that object and can alter whether or not it attracts attention, interrupts other interactions, reinforces or contradicts an emotional expression, and as such should be aligned with the designer's intention for the object. In this tutorial, we will introduce the participants to software and design methods to help robots recognize and generate sound for human-robot interaction (HRI). Using open-source tools and methods designers can apply to their own robots, we seek to increase the application of sound to robot design and stimulate HRI research in robot sound. © 2021 Owner/Author.","Acoustic scene analysis; Audio; Audio, sound recognition; Consequential sounds; Human robot interaction; Non-verbal","Agricultural robots; Automation; Behavioral research; End effectors; Machine design; Man machine systems; Open source software; Acoustic environment; Automated systems; Contextual understanding; Emotional expressions; Human robot Interaction (HRI); Inter-personal communications; Linguistic information; Open source tools; Human robot interaction",,,,,,,,,,,,,,,,"Ju, W., Goedicke, D., (2020) Neural Nets for Music, , https://ccrma.stanford.edu/workshops/neural-nets-music; Langeveld, L., Van Egmond, R., Jansen, R., Özcan, E., Product sound design: Intentional and consequential sounds (2013) Advances in Industrial Design Engineering, 47 (3). , 2013; Martelaro, N., Shiloh, M., Ju, W., The interaction engine: Tools for prototyping connected devices (2016) Proceedings of the TEI'16: Tenth International Conference on Tangible, Embedded, and Embodied Interaction, pp. 762-765; Martelaro, N., Zamfirescu-Pereria, J.D., Goedicke, D., Ju, W., Make this! introduction to electronics prototyping using arduino (2020) Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI EA '20), pp. 1-4. , https://doi.org/10.1145/3334480.3375052, Association for Computing Machinery, New York, NY, USA; Moore, D., Dahl, T., Varela, P., Ju, W., Næs, T., Berget, I., Unintended consonances: Methods to understand robot motor sound perception (2019) Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland UK) (CHI '19), pp. 1-12. , https://doi.org/10.1145/3290605.3300730, Association for Computing Machinery, New York, NY, USA; Moore, D., Tennent, H., Martelaro, N., Ju, W., Making noise intentional: A study of servo sound perception (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (Vienna, Austria) (HRI '17), pp. 12-21. , https://doi.org/10.1145/2909824.3020238, Association for Computing Machinery, New York, NY, USA; Salamon, J., Jacoby, C., Pablo Bello, J., A dataset and taxonomy for urban sound research (2014) Proceedings of the 22nd ACM International Conference on Multimedia, pp. 1041-1044; Seetala, K., Birdsong, W., Reddy, Y.B., Image classification using tensorflow (2019) 16th International Conference on Information Technology-New Generations (ITNG 2019), pp. 485-488. , Springer; Tennent, H., Moore, D., Jung, M., Ju, W., Good vibrations: How consequential sounds affect perception of robotic arms (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 928-935; Tünnermann, R., Hammerschmidt, J., Hermann, T., (2013) Blended Sonification-sonification for Casual Information Interaction, , Georgia Institute of Technology",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102775508
"Kirschner R.J., Mansfeld N., Pena G.G., Abdolshah S., Haddadin S.","57223928721;55556621500;57223923817;37071846300;23008499000;","Notion on the correct use of the robot effective mass in the safety context and comments on ISO/TS 15066",2021,"ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics",,,"9419495","6","9",,,"10.1109/ISR50024.2021.9419495","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106497882&doi=10.1109%2fISR50024.2021.9419495&partnerID=40&md5=d655e5327866b287842e0d84fc1a7c6b","Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, Technical University of Munich, Munich, 80797, Germany","Kirschner, R.J., Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, Technical University of Munich, Munich, 80797, Germany; Mansfeld, N., Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, Technical University of Munich, Munich, 80797, Germany; Pena, G.G., Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, Technical University of Munich, Munich, 80797, Germany; Abdolshah, S., Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, Technical University of Munich, Munich, 80797, Germany; Haddadin, S., Institute for Robotics and System Intelligence, Munich School of Robotics and Machine Intelligence, Technical University of Munich, Munich, 80797, Germany","Collision experiments in the human-robot interaction (HRI) context showed that the effective robot mass is one of the main parameters that influence human injury probability during a collision. Also the current standard ISO/TS 15066 highlights the importance of this parameter and provides a method to determine the maximum safe robot velocity based on the effective mass. To enable both safe and efficient robot applications, it is crucial to derive the robot's instantaneous effective mass sufficiently accurate based on either a), a kinematic and dynamic model or b), a suitable collision experiment. In this paper, we describe and quantitatively compare the well-established reflected mass model by Khatib and the simplified model provided in the ISO/TS 15066 for the KUKA LWR IV + and the Franka Emika Panda robot. Furthermore, we propose a method to practically determine the effective mass using a passive mechanical pendulum setup. Our results show that the simplified ISO/TS model can lead to a significant safety-relevant error. With our preliminary experimental setup, however, we can verify that the reflected mass obtained by the dynamics model only differs 1.1-7.8 % from the measured value. © 2021 IEEE.",,"Agricultural robots; Light water reactors; Robot applications; Robotics; Collision experiments; Dynamics modeling; Effective mass; Human injury; Human robot Interaction (HRI); Main parameters; Mass modeling; Measured values; Human robot interaction",,,,,"5140951; 5140953; Horizon 2020 Framework Programme, H2020: 732737","ACKNOWLEDGMENT The authors would like to thank Johanna Wolff for her preparatory work. This work was supported by the European Union’s Horizon 2020 research and innovation programme as part of the project ILIAD under grant no. 732737 and partially supported by the Research Project I.AM. through the European Union H2020 program under GA 871899. We gratefully acknowledge the funding of the Lighthouse Initiative Geriatronics by StMWi Bayern (Project X, grant no. 5140951) and LongLeif GaPa gGmbH (Project Y, grant no. 5140953). Please note that S. Haddadin has a potential conflict of interest as shareholder of Franka Emika GmbH.",,,,,,,,,,"Bicchi, A., Tonietti, G., Fast and ""soft-arm"" tactics (2004) IEEE Robotics & Automation Magazine, 11 (2), pp. 22-33. , June; Behrens, R., Elkmann, N., Study on meaningful and verified Thresholds for minimizing the consequences of human-robot collisions (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 3378-3383. , Hong Kong; Haddadin, S., Albu-Schffer, A., Eiberger, O., Hirzinger, G., New insights concerning intrinsic joint elasticity for safety (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2181-2187. , Taipei; Suita, K., Yamada, Y., Tsuchida, N., Imai, K., Ikeda, H., Sugimoto, N., A failure-to-safety ""Kyozon"" system with simple contact detection and stop capabilities for safe human-autonomous robot coexistence (1995) Proceedings of 1995 IEEE International Conference on Robotics and Automation, 3, pp. 3089-3096. , Nagoya, Japan; Haddadin, S., Albu-Schaffer, A., Hirzinger, G., Safety evaluation of physical human-robot interaction via crash-testing (2007) Robotics: Science and Systems, 3; Ikuta, K., Ishii, H., Nokata, M., Safety evaluation method of design and control for human-care robots (2003) The International Journal of Robotics Research, 22 (5), pp. 281-297; Khatib, O., A unified approach for motion and force control of robot manipulators: The operational space formulation (1987) IEEE Journal on Robotics and Automation, 3 (1), pp. 43-53. , February; Khatib, O., Inertial properties in robotic manipulation: An object-level framework (1995) The International Journal of Robotics Research, 14 (1), pp. 19-36; Walker, D., The use of kinematic redundancy in reducing impact and contact effects in manipulation (1990) Robotics and Automation 1990. Proceedings. 1990 IEEE International Conference on, pp. 434-439; Haddadin, S., Haddadin, S., Khoury, A., Rokahr, T., Parusel, S., Burgkart, R., Albu-Schaffer, A., On making robots understand safety: Embedding injury knowledge into control (2012) The International Journal of Robotics Research, 31, pp. 1578-1602. , 13th ed; Kulic, D., Croft, E., Pre-collision safety strategies for human-robot interaction (2007) Auton Robot, 22, p. 149164; Mansfeld, N., Djellab, B., Veuthey, J.R., Beck, F., Ott, C., Haddadin, S., Improving the performance of biomechanically safe velocity control for redundant robots through reflected mass minimization (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5390-5397. , Vancouver, BC; (2017) Robots and Robotic Devices-Collaborative Robots, , ISO/TS 15066. Beuth-Verlag, Berlin; Lens, T., Von Stryk, O., Investigation of safety in humanrobot-interaction for a series elastic, tendon-driven robot arm (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4309-4314. , Vilamoura; Haddadin, S., Requirements for safe robots: Measurements, analysis and new insights (2009) The International Journal of Robotics Research, 28 (1112), p. 15071527. , Nov; Albu-Schaffer, A., Fischer, M., Schreiber, G., Schoeppe, F., Hirzinger, G., Soft robotics: What Cartesian stiffness can obtain with passively compliant, uncoupled joints? (2004) 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), 4, pp. 3295-3301. , Sendai; Corke, P., (2017) Robotics, Vision and Control: Fundamental Algorithms in MATLAB Second, Completely Revised, 118. , Springer; Stronge, W.J., (2018) Impact Mechanics, , Cambridge university press; Gaz, C., Cognetti, M., Oliva, A., Robuffo Giordano, P., De Luca, A., Dynamic identification of the franka emika panda robot with retrieval of feasible parameters using penalty-based optimization (2019) IEEE Robotics and Automation Letters, 4 (4), pp. 4147-4154. , Oct; Konietschke, R., (2008) Planning of Workplaces with Multiple Kinematically Redundant Robots, , Technical University of Munich","Kirschner, R.J.; Institute for Robotics and System Intelligence, Germany; email: robin-jeanne.kirschner@tum.de",,"IEEE Robotics and Automation Society;Nagoya University","Institute of Electrical and Electronics Engineers Inc.","2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021","4 March 2021 through 6 March 2021",,168860,,9781665438629,,,"English","ISR - IEEE Int. Conf. Intell. Saf. Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85106497882
"Kim D., Han S., Kim T., Kim C., Lee D., Kang D., Koh J.-S.","57208222058;24337917700;57201124770;57221217109;57222432566;36682500000;35491056200;","Design of a sensitive balloon sensor for safe human–robot interaction",2021,"Sensors","21","6","2163","1","12",,1,"10.3390/s21062163","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102705064&doi=10.3390%2fs21062163&partnerID=40&md5=96090d257ec39377a2e95886dcbf951a","Department of Mechanical Engineering, Ajou University, San 5, Woncheon‐dong, Yeongtong‐gu, Suwon, 443‐749, South Korea","Kim, D., Department of Mechanical Engineering, Ajou University, San 5, Woncheon‐dong, Yeongtong‐gu, Suwon, 443‐749, South Korea; Han, S., Department of Mechanical Engineering, Ajou University, San 5, Woncheon‐dong, Yeongtong‐gu, Suwon, 443‐749, South Korea; Kim, T., Department of Mechanical Engineering, Ajou University, San 5, Woncheon‐dong, Yeongtong‐gu, Suwon, 443‐749, South Korea; Kim, C., Department of Mechanical Engineering, Ajou University, San 5, Woncheon‐dong, Yeongtong‐gu, Suwon, 443‐749, South Korea; Lee, D., Department of Mechanical Engineering, Ajou University, San 5, Woncheon‐dong, Yeongtong‐gu, Suwon, 443‐749, South Korea; Kang, D., Department of Mechanical Engineering, Ajou University, San 5, Woncheon‐dong, Yeongtong‐gu, Suwon, 443‐749, South Korea; Koh, J.-S., Department of Mechanical Engineering, Ajou University, San 5, Woncheon‐dong, Yeongtong‐gu, Suwon, 443‐749, South Korea","As the safety of a human body is the main priority while interacting with robots, the field of tactile sensors has expanded for acquiring tactile information and ensuring safe human–robot interaction (HRI). Existing lightweight and thin tactile sensors exhibit high performance in detecting their surroundings. However, unexpected collisions caused by malfunctions or sudden external collisions can still cause injuries to rigid robots with thin tactile sensors. In this study, we present a sensitive balloon sensor for contact sensing and alleviating physical collisions over a large area of rigid robots. The balloon sensor is a pressure sensor composed of an inflatable body of low‐density polyethylene (LDPE), and a highly sensitive and flexible strain sensor laminated onto it. The mechanical crack‐based strain sensor with high sensitivity enables the detection of extremely small changes in the strain of the balloon. Adjusting the geometric parameters of the balloon allows for a large and easily customizable sensing area. The weight of the balloon sensor was approximately 2 g. The sensor is employed with a servo motor and detects a finger or a sheet of rolled paper gently touching it, without being damaged. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Human–robot interaction; Pressure sensor; Soft robot","Balloons; Machine design; Contact sensing; High sensitivity; Inflatable bodies; Robot interactions; Strain sensors; Tactile information; Tactile sensors; Without being damaged; Human robot interaction; aircraft; finger; human; robotics; touch; Aircraft; Fingers; Humans; Robotics; Touch",,,,,"Ajou University; Ministry of Trade, Industry and Energy, MOTIE: 20000512; National Research Foundation of Korea, NRF: 2019R1F1A1063066, 2021R1C1C1011872","Funding: This work is supported in parts by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program. No. 20000512, Basic Science Research Pro‐ gram through the National Research Foundation of Korea (NRF) funded by the Korea government (2019R1F1A1063066, 2021R1C1C1011872). Daeshik Kang, Seungyong Han, and Je‐sung Koh acknowledge financial support by the new faculty research fund of Ajou University and the Ajou University research fund.",,,,,,,,,,"Heinzmann, J., Zelinsky, A., Quantitative Safety Guarantees for Physical Human‐Robot Interaction (2003) Int. J. Rob. Res, 22, pp. 479-504; Haddadin, S., Albu‐Schäffer, A., De Luca, A., Hirzinger, G., Collision detection and reaction: A contribution to safe physical human‐robot interaction (2008) IEEE RSJ Int. Conf. Intell. Robot. Syst. Iros, pp. 3356-3363. , doi.org; Michalos, G., Makris, S., Tsarouchi, P., Guasch, T., Kontovrakis, D., Chryssolouris, G., Design considerations for safe human-robot collaborative workplaces (2015) Proc. CiRP, 37, pp. 248-253; Lasota, P.A., Fong, T., Shah, J.A., A Survey of Methods for Safe Human‐Robot Interaction (2017) Found. Trends Robot, 5, pp. 261-349; Zou, L., Ge, C., Wang, Z.J., Cretu, E., Li, X., Novel tactile sensor technology and smart tactile sensing systems: A review (2017) Sensors, 17, pp. 1-24; Argall, B.D., Billard, A.G., A survey of Tactile Human‐Robot Interactions (2010) Robot. Auton. Syst, 58, pp. 1159-1176; Dahiya, R.S., Mittendorfer, P., Valle, M., Cheng, G., Lumelsky, V.J., Directions toward effective utilization of tactile skin: A review (2013) IEEE Sens. J, 13, pp. 4121-4138; Luo, S., Bimbo, J., Dahiya, R., Liu, H., Robotic tactile perception of object properties: A review (2017) Mechatronics, 48, pp. 54-67; Huet, L.A., Rudnicki, J.W., Hartmann, M.J.Z., Tactile sensing with whiskers of various shapes: Determining the three‐dimen-sional location of object contact based on mechanical signals at the whisker base (2017) Soft Robot, 4, pp. 88-102; Wu, Y., Liu, Y., Zhou, Y., Man, Q., Hu, C., Asghar, W., Li, F., Liu, G., A skin‐inspired tactile sensor for smart prosthetics (2018) Sci. Robot, 3, p. eaat0429; Xie, H., Liu, H., Luo, S., Seneviratne, L.D., Althoefer, K., Fiber optics tactile array probe for tissue palpation during minimally invasive surgery (2013) IEEE Int. Conf. Intell. Robot. Syst, pp. 2539-2544. , doi.org; Cirillo, A., Ficuciello, F., Natale, C., Pirozzi, S., Villani, L., A conformable force/tactile skin for physical human‐robot interaction (2016) IEEE Robot. Autom. Lett, 1, pp. 41-48; Kaltenbrunner, M., Sekitani, T., Reeder, J., Yokota, T., Kuribara, K., Tokuhara, T., Drack, M., Bauer‐ Gogonea, S., An ultra‐lightweight design for imperceptible plastic electronics (2013) Nature, 499, pp. 458-463; Ohmura, Y., Kuniyoshi, Y., Nagakubo, A., Conformable and scalable tactile sensor skin for a curved surfaces (2006) Proc. IEEE Int. Conf. Robot. Autom, 2006, pp. 1348-1353; Mittendorfer, P., Cheng, G., Humanoid multimodal tactile‐sensing modules (2011) IEEE Trans. Robot, 27, pp. 401-410; O’Neill, J., Lu, J., Dockter, R., Kowalewski, T., Practical, stretchable smart skin sensors for contact‐aware robots in safe and collaborative interactions (2015) Proc. IEEE Int. Conf. Robot. Autom, 2015, pp. 624-629; Kim, T., Park, J., Yoon, S.J., Kong, D.H., Park, H.W., Park, Y.L., Design of a lightweight inflatable sensing sleeve for increased adaptability and safety of legged robots (2019) IEEE Int. Conf. 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Rep, 7, pp. 1-8; Kang, D., Pikhitsa, P.V., Choi, Y.W., Lee, C., Shin, S.S., Piao, L., Park, B., Choi, M., Ultrasensitive mechanical crack‐based sensor inspired by the spider sensory system (2014) Nature, 516, pp. 222-226; Lee, E., Kim, T., Suh, H., Kim, M., Pikhitsa, P.V., Han, S., Koh, J.S., Kang, D., Effect of metal thickness on the sensitivity of crack-based sensors (2018) Sensors, 18, pp. 1-8; Kim, T., Lee, T., Lee, G., Choi, Y.W., Kim, S.M., Kang, D., Choi, M., Polyimide encapsulation of spider‐inspired crack‐based sensors for durability improvement (2018) Appl. Sci, 8, p. 367","Kang, D.; Department of Mechanical Engineering, San 5, Woncheon‐dong, Yeongtong‐gu, South Korea; email: dskang@ajou.ac.kr
Koh, J.-S.; Department of Mechanical Engineering, San 5, Woncheon‐dong, Yeongtong‐gu, South Korea; email: jskoh@ajou.ac.kr",,,"MDPI AG",,,,,14248220,,,"33808860","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85102705064
"Tuyen N.T.V., Elibol A., Chong N.Y.","57197734080;24733903500;7007012034;","Learning Bodily Expression of Emotion for Social Robots through Human Interaction",2021,"IEEE Transactions on Cognitive and Developmental Systems","13","1","9129823","16","30",,,"10.1109/TCDS.2020.3005907","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102738945&doi=10.1109%2fTCDS.2020.3005907&partnerID=40&md5=28b270e6f9b46240dd97a4f5a77009c1","School of Information Science, Japan Advanced Institute of Science and Technology, Nomi, Japan","Tuyen, N.T.V., School of Information Science, Japan Advanced Institute of Science and Technology, Nomi, Japan; Elibol, A., School of Information Science, Japan Advanced Institute of Science and Technology, Nomi, Japan; Chong, N.Y., School of Information Science, Japan Advanced Institute of Science and Technology, Nomi, Japan","Human facial and bodily expressions play a crucial role in human-human interaction to convey the communicator's feelings. Being echoed by the influence of human social behavior, recent studies in human-robot interaction (HRI) have investigated how to generate emotional behaviors for social robots. Emotional behaviors can enhance user engagement, allowing the user to interact with robots in a transparent manner. However, they are ambiguous and affected by many factors, such as personality traits, cultures, and environments. This article focuses on developing the robot's emotional bodily expressions adopting the user's affective gestures. We propose the behavior selection and transformation model, enabling the robots to incrementally learn from the user's gestures, to select the user's habitual behaviors, and to transform the selected behaviors into robot motions. The experimental results under several scenarios showed that the proposed incremental learning model endows a social robot with the capability of entering into a positive, long-lasting HRI. We have also confirmed that the robot can express emotions through the imitated motions of the user. The robot's emotional gestures that reflected the interacting partner's traits were widely accepted within the same cultural group, and perceptible across different cultural groups in different ways. © 2016 IEEE.","Affective behaviors; cross-cultural evaluation; human-robot interaction (HRI); imitation learning","Behavioral research; Economic and social effects; Behavior selection; Emotional behavior; Human interactions; Human robot Interaction (HRI); Human-human interactions; Incremental learning; Personality traits; Transformation model; Social robots",,,,,"Air Force Office of Scientific Research, AFOSR: AFOSR-AOARD/FA2386-19-1-4015; Horizon 2020: 737858; Ministry of Internal Affairs and Communications, MIC","Manuscript received July 31, 2019; revised December 26, 2019, March 17, 2020, May 9, 2020, and June 17, 2020; accepted June 18, 2020. Date of publication June 30, 2020; date of current version March 11, 2021. This work was supported in part by the EU-Japan coordinated Research and Development Project on “Culture Aware Robots and Environmental Sensor Systems for Elderly Support,” commissioned by the Ministry of Internal Affairs and Communications of Japan and EC Horizon 2020 Research and Innovation Programme under Grant 737858, and in part by the Air Force Office of Scientific Research under Grant AFOSR-AOARD/FA2386-19-1-4015. (Corresponding author: Nguyen Tan Viet Tuyen.) The authors are with the School of Information Science, Japan Advanced Institute of Science and Technology, Nomi 923-1211, Japan (e-mail: ngtvtuyen@jaist.ac.jp; aelibol@jaist.ac.jp; nakyoung@jaist.ac.jp).",,,,,,,,,,"Darwin, C., Prodger, P., (1998) The Expression of the Emotions in Man and Animals. Oxford, U.K.: Oxford Univ. Press; De Meijer, M., The contribution of general features of body movement to the attribution of emotions (1989) J. 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Human Agent Interact, pp. 213-222; Fothergill, S., Mentis, H., Kohli, P., Nowozin, S., Instructing people for training gestural interactive systems (2012) Proc. ACM SIGCHI Conf. Human Factors Comput. Syst, pp. 1737-1746; Noroozi, F., Corneanu, C.A., Kaminska, D., Sapinski, T., Escalera, S., Anbarjafari, G., (2018) Survey on Emotional Body Gesture Recognition, , http://arxiv.org/abs/1801.07481","Tuyen, N.T.V.; School of Information Science, Japan; email: ngtvtuyen@jaist.ac.jp",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23798920,,,,"English","IEEE Trans. Cogn. Dev. Syst.",Article,"Final","",Scopus,2-s2.0-85102738945
"Strathearn C., Ma E.M.","57204127919;57222241081;","A Novel Speech to Mouth Articulation System for Realistic Humanoid Robots",2021,"Journal of Intelligent and Robotic Systems: Theory and Applications","101","3","54","","",,,"10.1007/s10846-021-01332-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101980978&doi=10.1007%2fs10846-021-01332-2&partnerID=40&md5=9ecee650f9b98cab9810ea146e0dd543","Edinburgh Napier University, Edinburgh, United Kingdom; Falmouth University, Falmouth, United Kingdom","Strathearn, C., Edinburgh Napier University, Edinburgh, United Kingdom; Ma, E.M., Falmouth University, Falmouth, United Kingdom","A significant ongoing issue in realistic humanoid robotics (RHRs) is inaccurate speech to mouth synchronisation. Even the most advanced robotic systems cannot authentically emulate the natural movements of the human jaw, lips and tongue during verbal communication. These visual and functional irregularities have the potential to propagate the Uncanny Valley Effect (UVE) and reduce speech understanding in human-robot interaction (HRI). This paper outlines the development and testing of a novel Computer Aided Design (CAD) robotic mouth prototype with buccinator actuators for emulating the fluidic movements of the human mouth. The robotic mouth system incorporates a custom Machine Learning (ML) application that measures the acoustic qualities of speech synthesis (SS) and translates this data into servomotor triangulation for triggering jaw, lip and tongue positions. The objective of this study is to improve current robotic mouth design and provide engineers with a framework for increasing the authenticity, accuracy and communication capabilities of RHRs for HRI. The primary contributions of this study are the engineering of a robotic mouth prototype and the programming of a speech processing application that achieved a 79.4% syllable accuracy, 86.7% lip synchronisation accuracy and 0.1s speech to mouth articulation differential. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.","Human robot interaction; Lip synchronisation; Machine learning; Realistic humanoid robotics","Agricultural robots; Anthropomorphic robots; Computer aided design; Robot programming; Robotics; Software prototyping; Speech processing; Speech synthesis; Articulation systems; Communication capabilities; Development and testing; Human robot Interaction (HRI); Primary contribution; Processing applications; Speech understanding; Verbal communications; Social robots",,,,,"Staffordshire University","This project was funded by Staffordshire University as a PhD Scholarship.",,,,,,,,,,"Dang, S., (2019) Artificial Intelligence in Humanoid Robots, , https://www.forbes.com/sites/cognitiveworld/2019/02/25/artificial-intelligence-inhumanoid-robots/87a95fb24c72, Accessed 17 Nov 2019; Urbi, J., The Complicated Truth about Sophia the Robot, an Almost Human Robot Or a PR Stunt, , https://www.cnbc.com/2018/06/05/hanson-roboticssophia-the-robot-pr-stunt-artificial-intelligence.html, . 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"Gómez Rivas J., Toribio-Vázquez C., Taratkin M., Marenco J.L., Grossmann R.","55912543700;57218570563;57196184959;57206260381;57222220423;","Autonomous robots: a new reality in healthcare? A project by European Association of Urology-Young Academic Urologist group",2021,"Current opinion in urology","31","2",,"155","159",,,"10.1097/MOU.0000000000000842","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101896416&doi=10.1097%2fMOU.0000000000000842&partnerID=40&md5=5aeb05c410ec4b8ac69fa9865e098c7b","Department of Urology, Clinico San Carlos University HospitalMadrid, Spain; Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, Netherlands; Department of Urology, La Paz University HospitalMadrid, Spain; Institute for Urology and Reproductive Health, Sechenov UniversityMoscow, Russian Federation; Department of Urology, Instituto Valenciano De OncologiaValencia, Spain; Eastern Maine Medical Center, Bangor, ME, United States","Gómez Rivas, J., Department of Urology, Clinico San Carlos University HospitalMadrid, Spain, Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, Netherlands; Toribio-Vázquez, C., Department of Urology, La Paz University HospitalMadrid, Spain; Taratkin, M., Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, Netherlands, Institute for Urology and Reproductive Health, Sechenov UniversityMoscow, Russian Federation; Marenco, J.L., Young Academic Urologist-Urotechnology Working Party (ESUT-YAU), European Association of Urology, Arnhem, Netherlands, Department of Urology, Instituto Valenciano De OncologiaValencia, Spain; Grossmann, R., Eastern Maine Medical Center, Bangor, ME, United States","PURPOSE OF REVIEW: Artificial intelligence appears as a potential revolution in the general process of medical training, disease diagnosis and treatment. A novel disruptive technology of the 21st century will be 'learner' robots from artificial intelligence systems able to use all the combination of the available knowledge in medical repositories to give the best standard of care. RECENT FINDINGS: The autonomy level of robots depends on three factors: the complexity of the task; the environment in which the robot operates, and the required level of human-robot interaction. Autonomous robots in healthcare may be classified in delivery, nurse, and surgical robots. The increasing capability of robots to perform independent actions and complex tasks raises responsibility and accountability issues in a wide variety of application domains. Ethical analyses of these issues are underway and are mostly oriented toward the development of ethical policies requiring a law frame on robotic autonomous behaviors. SUMMARY: Autonomous robots have the potential to improve current medical practice offering a more secure, reliable, and reproducible medicine. Many advancements are required for these new technologies to be fully integrated. Furthermore, the ethical implications of these technologies are yet to be evaluated. Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.",,"artificial intelligence; health care delivery; human; robotics; urologist; urology; Artificial Intelligence; Delivery of Health Care; Humans; Robotics; Urologists; Urology",,,,,,,,,,,,,,,,,,,,"NLM (Medline)",,,,,14736586,,,"33332877","English","Curr Opin Urol",Review,"Final","",Scopus,2-s2.0-85101896416
"Nguyen P.D.H., Georgie Y.K., Kayhan E., Eppe M., Hafner V.V., Wermter S.","57192431670;57211372926;57204543147;55734653100;55910634600;7003826680;","Sensorimotor Representation Learning for an “Active Self” in Robots: A Model Survey",2021,"KI - Kunstliche Intelligenz","35","1",,"9","35",,4,"10.1007/s13218-021-00703-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100844361&doi=10.1007%2fs13218-021-00703-z&partnerID=40&md5=5a8245ae00c63619425ea193f89b0b38","Department of Informatics, University of Hamburg, Vogt-Koelln-Str. 30, Hamburg, 22527, Germany; Department of Computer Science, Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10019, Germany; Department of Developmental Psychology, University of Potsdam, Potsdam, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany","Nguyen, P.D.H., Department of Informatics, University of Hamburg, Vogt-Koelln-Str. 30, Hamburg, 22527, Germany; Georgie, Y.K., Department of Computer Science, Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10019, Germany; Kayhan, E., Department of Developmental Psychology, University of Potsdam, Potsdam, Germany, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Eppe, M., Department of Informatics, University of Hamburg, Vogt-Koelln-Str. 30, Hamburg, 22527, Germany; Hafner, V.V., Department of Computer Science, Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10019, Germany; Wermter, S., Department of Informatics, University of Hamburg, Vogt-Koelln-Str. 30, Hamburg, 22527, Germany","Safe human-robot interactions require robots to be able to learn how to behave appropriately in spaces populated by people and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations. In humans, these capabilities are thought to be related to our ability to perceive our body in space, sensing the location of our limbs during movement, being aware of other objects and agents, and controlling our body parts to interact with them intentionally. Toward the next generation of robots with bio-inspired capacities, in this paper, we first review the developmental processes of underlying mechanisms of these abilities: The sensory representations of body schema, peripersonal space, and the active self in humans. Second, we provide a survey of robotics models of these sensory representations and robotics models of the self; and we compare these models with the human counterparts. Finally, we analyze what is missing from these robotics models and propose a theoretical computational framework, which aims to allow the emergence of the sense of self in artificial agents by developing sensory representations through self-exploration. © 2021, The Author(s).","Agency; Body schema; Developmental robotics; Peripersonal space; Robot learning","Biomimetics; Human robot interaction; Robotics; Agency; Body schema; Developmental robotics; Dynamic environments; Learn+; Peripersonal space; Robotic modeling; Safe human-robot interaction; Sensorimotors; Unstructured environments; Surveys",,,,,"Deutsche Forschungsgemeinschaft, DFG: 402790442, KA 4926/1-1; Max-Planck-Gesellschaft, MPG; Institute of Development and Economic Alternatives, IDEAS: 402776968, 433323019","EK was supported by the Max Planck Society and by a grant from the Deutsche Forschungsgemeinschaft (DFG) (grant numbers: KA 4926/1-1). YKG and VVH were funded by the DFG - “Prerequisites for the Development of an Artificial Self” (402790442). PDHN, ME, SW acknowledge funding by the DFG through the IDEAS (402776968) and LeCAREbot (433323019) projects.",,,,,,,,,,"Abrossimoff, J., Pitti, A., Gaussier, P., Visual learning for reaching and body-schema with gain-field networks (2018) 2018 Joint IEEE 8Th International Conference on Development and Learning and Epigenetic Robotics, Icdl-Epirob 2018. 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Intell.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85100844361
"Yan Y., Hu Z., Yang Z., Yuan W., Song C., Pan J., Shen Y.","57222112374;57207686442;56297892100;56423022900;41662155000;35786627600;23089768400;","Soft magnetic skin for super-resolution tactile sensing with force self-decoupling",2021,"Science Robotics","6","51","eabc8801","","",,7,"10.1126/SCIROBOTICS.ABC8801","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102661411&doi=10.1126%2fSCIROBOTICS.ABC8801&partnerID=40&md5=59c43deed65ed31e50bb16557e2cb5b2","Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, Hong Kong; Department of Computer Science, University of Hong Kong, Hong Kong, Hong Kong; Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, Hong Kong; Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States; Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China; City University of Hong Kong Shenzhen Research Institute, Shenzhen, 518057, China","Yan, Y., Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, Hong Kong, Department of Computer Science, University of Hong Kong, Hong Kong, Hong Kong; Hu, Z., Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, Hong Kong, Department of Computer Science, University of Hong Kong, Hong Kong, Hong Kong; Yang, Z., Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, Hong Kong; Yuan, W., Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States; Song, C., Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China; Pan, J., Department of Computer Science, University of Hong Kong, Hong Kong, Hong Kong; Shen, Y., Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, Hong Kong, City University of Hong Kong Shenzhen Research Institute, Shenzhen, 518057, China","Human skin can sense subtle changes of both normal and shear forces (i.e., self-decoupled) and perceive stimuli with finer resolution than the average spacing between mechanoreceptors (i.e., super-resolved). By contrast, existing tactile sensors for robotic applications are inferior, lacking accurate force decoupling and proper spatial resolution at the same time. Here, we present a soft tactile sensor with self-decoupling and super-resolution abilities by designing a sinusoidally magnetized flexible film (with the thickness ~0.5 millimeters), whose deformation can be detected by a Hall sensor according to the change of magnetic flux densities under external forces. The sensor can accurately measure the normal force and the shear force (demonstrated in one dimension) with a single unit and achieve a 60-fold super-resolved accuracy enhanced by deep learning. By mounting our sensor at the fingertip of a robotic gripper, we show that robots can accomplish challenging tasks such as stably grasping fragile objects under external disturbance and threading a needle via teleoperation. 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Shen, Y.; Department of Biomedical Engineering, Hong Kong; email: yajishen@cityu.edu.hk",,,"American Association for the Advancement of Science",,,,,24709476,,,"34043530","English","Sci. Robotics",Article,"Final","",Scopus,2-s2.0-85102661411
"Han L., Xu W., Kang P., Yuan H.","57188989478;14038527700;57211032309;56427336800;","Unified Neural Adaptive Control for Multiple Human-Robot-Environment Interactions",2021,"IEEE Transactions on Industrial Informatics","17","2","9018090","1166","1175",,,"10.1109/TII.2020.2977051","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096777050&doi=10.1109%2fTII.2020.2977051&partnerID=40&md5=6b43785d98f70925aa14fe1234af737c","School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, China","Han, L., School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, China; Xu, W., School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, China; Kang, P., School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, China; Yuan, H., School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, China","To go from human demonstration to robot independent operation, there are generally three phases of interaction to undergo, including human-robot interaction (HRI), human-robot-environment interaction (HREI), and robot-environment interaction (REI). Most existing methods address problems of a single stage. In this article, a unified neural adaptive control method that organically fuses multiple interactions is proposed. HRI, REI, and their coupling effects in HREI are comprehensively considered. First, the iterative least squares method is used for robot dynamics identification based on the linearized momentum observer. The accuracy of external force observation is improved to deal with dynamic uncertainties. The human force and the environmental force are achieved and decoupled by using only a force sensor, a momentum observer, and a selection matrix S. Next, the neural adaptive control method compensating position errors caused by the model uncertainty is addressed. The control system is proved to be stable based on the Lyapunov theorem. The trajectory tracking error under the model uncertainties is reduced. Then, the adaptive admittance control method is introduced. The interaction force of HRI is minimized and the interaction force control of REI is realized. Finally, the proposed method is verified by simulations and experiments. © 2005-2012 IEEE.","Admittance control; human-robot-environment interaction (HREI); iterative least squares (ILS) method; momentum observer; neural adaptive control","Iterative methods; Least squares approximations; Man machine systems; Uncertainty analysis; Environmental forces; Human demonstrations; Human robot Interaction (HRI); Iterative least squares; Multiple interactions; Neural adaptive controls; Robot-environment interaction; Trajectory tracking errors; Human robot interaction",,,,,"JCYJ20170811155308246, JCYJ20180507183610564, JCYJ20180507183644237, TII-20-0190; National Natural Science Foundation of China, NSFC: 61803125; National Key Research and Development Program of China, NKRDPC: 2018YFB1304600; Special Project for Research and Development in Key areas of Guangdong Province: 2019B090915001","Manuscript received January 14, 2020; accepted February 21, 2020. Date of publication February 28, 2020; date of current version November 18, 2020. This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1304600, in part by the National Natural Science Foundation of China under Grant 61803125, in part by the Key Research and Development Program of Guangdong Province under Grant 2019B090915001, and in part by the Basic Research Program of Shenzhen under Grant JCYJ20180507183610564, Grant JCYJ20180507183644237, and Grant JCYJ20170811155308246. Paper no. TII-20-0190. (Corresponding author: Han Yuan.) The authors are with the School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China (e-mail: lianghanhit_edu@163.com; wfxu@hit.edu.cn; kangpeng@stu.hit.edu.cn; yuanhan@hit.edu.cn).",,,,,,,,,,"Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) Ieee Trans. Robot., 31 (4), pp. 850-863. , Aug; Li, Z., Huang, B., Ye, Z., Deng, M., Yang, C., Physical human-robot interaction of a robotic exoskeleton by admittance control (2018) Ieee Trans. Ind. Electron., 65 (12), pp. 9614-9624. , Dec; Matinfar, M., Hashtrudi-Zaad, K., Optimization-based robot compliance control: Geometric and linear quadratic approaches (2005) Int. J. Robot. Res., 24 (8), pp. 645-656; Dimeas, F., Aspragathos, N., Learning optimal variable admittance control for rotational motion in human-robot co-manipulation (2015) IFACPapersOnLine, 48 (19), pp. 124-129; Sharifi, M., Behzadipour, S., Vossoughi, G., Nonlinear model reference adaptive impedance control for human-robot interactions (2014) Control Eng. Pract., 32, pp. 9-27; Duan, J., Gan, Y., Chen, M., Dai, X., Adaptive variable impedance control for dynamic contact force tracking in uncertain environment (2018) Robot. Auton. Syst., 102, pp. 54-65; Yang, C., Zeng, C., Cong, Y., Wang, N., Wang, M., A learning framework of adaptive manipulative skills from human to robot (2019) Ieee Trans. Ind. Informat., 15 (2), pp. 1153-1161. , Feb; Zhuang, Y., Yao, S., Ma, C., Song, R., Admittance control based on EMG-driven musculoskeletal model improves the human-robot synchronization (2019) Ieee Trans. Ind. Informat., 15 (2), pp. 1211-1218. , Feb; Zanchettin, A., Casalino, A., Piroddi, L., Rocco, P., Prediction of human activity patterns for human-robot collaborative assembly tasks (2019) Ieee Trans. Ind. Informat., 15 (7), pp. 3934-3942. , Jul; Labrecque, P.D., Gosselin, C., Variable admittance for pHRI: From intuitive unilateral interaction to optimal bilateral force amplification (2018) Robot. Comput.-Integr. Manuf., 52, pp. 1-8; Li, H., Stable and compliant motion of physical human-robot interaction coupled with a moving environment using variable admittance and adaptive control (2018) Ieee Robot. Autom. 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Autom., 16 (6), pp. 689-699. , Dec; Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., (2010) Robotics: Modelling Planning and Control, , London, U.K.: Springer-Verlag","Yuan, H.; School of Mechanical Engineering and Automation, China; email: yuanhan@hit.edu.cn",,,"IEEE Computer Society",,,,,15513203,,,,"English","IEEE Trans. Ind. Inf.",Article,"Final","",Scopus,2-s2.0-85096777050
"Kang S.-H., Han J.-H.","57220190890;37048749200;","Video Captioning Based on Both Egocentric and Exocentric Views of Robot Vision for Human-Robot Interaction",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00842-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120323280&doi=10.1007%2fs12369-021-00842-1&partnerID=40&md5=c8a25d0bfdfa98eab0ff45d8649142a0","Seoul National University of Science and Technology, Seoul, South Korea","Kang, S.-H., Seoul National University of Science and Technology, Seoul, South Korea; Han, J.-H., Seoul National University of Science and Technology, Seoul, South Korea","Robot vision provides the most important information to robots so that they can read the context and interact with human partners successfully. Moreover, to allow humans recognize the robot’s visual understanding during human-robot interaction (HRI), the best way is for the robot to provide an explanation of its understanding in natural language. In this paper, we propose a new approach by which to interpret robot vision from an egocentric standpoint and generate descriptions to explain egocentric videos particularly for HRI. Because robot vision equals to egocentric video on the robot’s side, it contains as much egocentric view information as exocentric view information. Thus, we propose a new dataset, referred to as the global, action, and interaction (GAI) dataset, which consists of egocentric video clips and GAI descriptions in natural language to represent both egocentric and exocentric information. The encoder-decoder based deep learning model is trained based on the GAI dataset and its performance on description generation assessments is evaluated. We also conduct experiments in actual environments to verify whether the GAI dataset and the trained deep learning model can improve a robot vision system © 2021, The Author(s).","Deep learning; Egocentric video; Human robot interaction; Video captioning","Computer vision; Deep learning; Man machine systems; Visual languages; Deep learning; Egocentric video; Global actions; Global interaction; Humans-robot interactions; Learning models; Natural languages; New approaches; Video captioning; Video-clips; Human robot interaction",,,,,"National Research Foundation of Korea, NRF; Ministry of Science and ICT, South Korea, MSIT: 2018R1C1B6007230","This work was supported by a grant from the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) (No. 2018R1C1B6007230).",,,,,,,,,,"Kongyun, Y., (2018) Human Action Recognition and Prediction: A Survey, , arXiv preprint arXiv:1806.11230; McColl, D., Hong, A., Hatakeyama, N., Nejat, G., Benhabib, B., A survey of autonomous human affect detection methods for social robots engaged in natural hri (2016) J Intell Robot Syst, 82 (1), pp. 101-133; Ji, Y., Yangfumin, S., (2019) A Survey of Human Action Analysis in Hri Applications, IEEE Transactions on Circuits and Systems for Video Technology, , Heng Tao Shen, and Xuelong Li; Lunghi, G., Marin, R., Di Castro, M., Masi, A., Sanz Pedro, J., Multimodal human-robot interface for accessible remote robotic interventions in hazardous environments (2019) IEEE Access, 7, pp. 127290-127319; Ramos, R.A.Y., Figueroa, R.L.J., Balasubramaniyan, C., A sensor fusion based robotic system architecture using human interaction for motion control (2019) In: 2019 IEEE 9Th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0095-0100. , IEEE; Dizan, V., Procópio, S., Jorge, R.-M., Arturo, E., Anne, S., Christian, L., Human aware navigation for assistive robotics (2013) Experimental Robotics, pp. 449-462. , Springer; Gonçalves Duarte, M.F., Santana Pedro, B.J., Human-aware navigation for autonomous mobile robots for intra-factory logistics (2017) International Workshop on Symbiotic Interaction, pp. 79-85. , Springer; Moghadas, M., Moradi, H., Analyzing human-robot interaction using machine vision for autism screening (2018) 2018 6Th RSI International Conference on Robotics and Mechatronics (Icrom), pp. 572-576. , IEEE; Miao, L., Siyuli, T., Rehg James M (2020) Forecasting human-object interaction: Joint prediction of motor attention and actions in first person video European Conference on Computer Vision, pp. 704-721. , In, Springer; Anh, N., Dimitrios, K., Lucacaldwell Darwin, M., Tsagarakis Nikos, G.G., Translating videos to commands for robotic manipulation with deep recurrent neural networks (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1-9. , IEEE; Silvia, C., Gabriele, C., Ciarfuglia Thomas, A., Paolo, V., Fravolini Mario, L., Full-gru natural language video description for service robotics applications (2018) IEEE Robot Autom Lett, 3 (2), pp. 841-848; Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R., Darrell, T., Saenko, K., Sequence to sequence-video to text (2015) Proceedings of the IEEE International Conference on Computer Vision, Pages, pp. 4534-4542; Ashish, V., Noam, S., Niki, P., Jakob, U., Lliongomez Aidan, J., Łukasz, N., Polosukhin Illia (2017) Attention is all you need Advances in Neural Information Processing Systems, pp. 5998-6008; Kishore, P., Salim, R., Todd, W., Wei-Jing, Z., Bleu: A method for automatic evaluation of machine translation (2002) Proceedings of the 40Th Annual Meeting of the Association for Computational Linguistics, pp. 311-318. , In; Yi, B., Yang, Y., Fumin, S., Ning, X., Tao, S.H., Xuelong, L., Describing video with attention-based bidirectional lstm (2018) IEEE Trans Cybernet, 49 (7), pp. 2631-2641; Zhao Bin, L.X., Xiaoqiang, L., Mam-rnn: Multi-level attention model based rnn for video captioning (2017) IJCAI, pp. 2208-2214; Yang Yang, B.Y., Xu Xing, S.F., Tao, S.H., Bidirectional long-short term memory for video description (2016) Proceedings of the 24Th ACM International Conference on Multimedia, pp. 436-440; Fang, K., Zhou, L., Jin, C., Zhang, Y., Weng, K., Zhang, T., Fan, W., Fully convolutional video captioning with coarse-to-fine and inherited attention (2019) proceedings of the AAAI conference on artificial intelligence, 33, pp. 8271-8278. , (,),. 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Rob.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85120323280
"Caccavale R., Finzi A.","56085967900;8711150100;","A Robotic Cognitive Control Framework for Collaborative Task Execution and Learning",2021,"Topics in Cognitive Science",,,,"","",,,"10.1111/tops.12587","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119827688&doi=10.1111%2ftops.12587&partnerID=40&md5=98f0ac4dbe963472515a4796d1a1245e","Dipartimento di Ingegneria Elettrica e Tecnologie dell'Informazione (DIETI), Università degli Studi di Napoli “Federico II”, Italy","Caccavale, R., Dipartimento di Ingegneria Elettrica e Tecnologie dell'Informazione (DIETI), Università degli Studi di Napoli “Federico II”, Italy; Finzi, A., Dipartimento di Ingegneria Elettrica e Tecnologie dell'Informazione (DIETI), Università degli Studi di Napoli “Federico II”, Italy","In social and service robotics, complex collaborative tasks are expected to be executed while interacting with humans in a natural and fluent manner. In this scenario, the robotic system is typically provided with structured tasks to be accomplished, but must also continuously adapt to human activities, commands, and interventions. We propose to tackle these issues by exploiting the concept of cognitive control, introduced in cognitive psychology and neuroscience to describe the executive mechanisms needed to support adaptive responses and complex goal-directed behaviors. Specifically, we rely on a supervisory attentional system to orchestrate the execution of hierarchically organized robotic behaviors. This paradigm seems particularly effective not only for flexible plan execution but also for human–robot interaction, because it directly provides attention mechanisms considered as pivotal for implicit, non-verbal human–human communication. Following this approach, we are currently developing a robotic cognitive control framework enabling collaborative task execution and incremental task learning. In this paper, we provide a uniform overview of the framework illustrating its main features and discussing the potential of the supervisory attentional system paradigm in different scenarios where humans and robots have to collaborate for learning and executing everyday activities. © 2021 Cognitive Science Society LLC.","Attention; Cognitive architecture; Cognitive control; Cognitive robotics; Human–robot collaboration",,,,,,"101017008","The research leading to these results has been partially supported by the projects ICOSAF (PON R&I 2014‐2020) and HARMONY by EU H2020 R&I under agreement No. 101017008.",,,,,,,,,,"Anderson, J.R., Matessa, M., Lebiere, C., Act-r: A theory of higher level cognition and its relation to visual attention (1997) Human-Computer Interaction, 12 (4), pp. 439-462; Baars, B., (1997) In the theated of consciousness: The workspace of the mind, , Oxford, England, Oxford University Press; Baddeley, A., (1993) Working memory or working attention, pp. 152-170. , Oxford, England, Oxford University Press; Baddeley, A., Exploring the central executive (1996) The Quarterly Journal of Experimental Psychology: Section A, 49 (1), pp. 5-28; Baxter, P., de Greeff, J., Belpaeme, T., Cognitive architecture for human-robot interaction: Towards behavioural alignment (2013) Journal of Biologically Inspired Cognitive Architectures, 6, pp. 30-39; Bengio, Y., (2019) The consciousness prior; Botvinick, M.M., Braver, T.S., Barch, D.M., Carter, C.S., Cohen, J.D., Conflict monitoring and cognitive control (2001) Psychological Review, 108 (3), pp. 624-652; Breazeal, C., Edsinger, A., Fitzpatrick, P., Scassellati, B., Active vision for sociable robots (2001) IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 31 (5), pp. 443-453; Breazeal, C., Kidd, C.D., Thomaz, A.L., Hoffman, G., Berlin, M., (2005) Effects of nonverbal communication on efficiency and robustness in human-robot teamwork, pp. 708-713. , 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS-05;,). 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Cognitive Sci.",Article,"Article in Press","",Scopus,2-s2.0-85119827688
"Sun B., Zhao M.","57224706098;12794040000;","Simple Light-Weight Network for Human Pose Estimation",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13033 LNAI",,,"279","292",,,"10.1007/978-3-030-89370-5_21","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119293094&doi=10.1007%2f978-3-030-89370-5_21&partnerID=40&md5=124a059edd2f42bb15b5cf9d1e51091a","Beijing Research Institute of UBTECH, Beijing, China; Department of Automation, Center for Brain-Inspired Computing Research (CBICR), Tsinghua University, Beijing, China","Sun, B., Beijing Research Institute of UBTECH, Beijing, China; Zhao, M., Department of Automation, Center for Brain-Inspired Computing Research (CBICR), Tsinghua University, Beijing, China","Human pose estimation has achieved significant improvement. However, most existing methods mainly consider how to improve the model performance using complex architecture or computationally expensive model, ignoring the deployment costs in practice, especially in human-robot interaction. In this paper, we investigate a highly efficient pose estimation model with comparable accuracy. We propose an adaptive convolution, which can adaptively generate one or more feature maps with desired channels. Since redundant information in the feature map is an important characteristic, to preserve the redundant information while taking only a few numbers of FLOPs and parameters, we propose a light-weight block based on adaptive convolution, which is performed with two parallel convolution operations. And then, to further reduce the FLOPs, we propose heterogeneous filters based light-weight block, which contains two different kinds of filters in each layer. Finally, three light-weight units are designed to stack light-weight block, and a simple light-weight pose estimation network (SLPE) can be easily established. Extensive evaluations demonstrate the advantages of SLPE over state-of-the-art methods in terms of model cost-effectiveness on the standard benchmark datasets, MPII and COCO dataset. © 2021, Springer Nature Switzerland AG.","Adaptive convolution; Light-weight network; Pose estimation","Computer vision; Cost effectiveness; Human robot interaction; Adaptive convolution; Complex architectures; Expensive models; Feature map; Human pose estimations; Light weight; Light-weight network; Modeling performance; Pose-estimation; Simple++; Convolution",,,,,,,,,,,,,,,,"Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B., 2d human pose estimation: New benchmark and state of the art analysis (2014) CVPR, pp. 3686-3693. , pp; Bulat, A., Tzimiropoulos, G., Binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources (2017) ICCV, pp. 3706-3714. , pp; Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y., Realtime multi-person 2d pose estimation using part affinity fields (2017) CVPR, pp. 7291-7299. , pp; Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J., Cascaded pyramid network for multi-person pose estimation (2018) CVPR, pp. 7103-7112. , pp; Chen, Y., Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution (2019) ICCV, pp. 3435-3444. , pp; Chéron, G., Laptev, I., Schmid, C., P-CNN: Pose-based CNN features for action recognition (2015) ICCV, pp. 3218-3226. , pp; Chollet, F., (2017) Xception: Deep Learning with Depthwise Separable Convolutions, pp. 1251-1258. , CVPR, pp; Fang, H.-S., Xie, S., Tai, Y.-W., Lu, C., RMPE: Regional multi-person pose estimation (2017) ICCV, pp. 2334-2343. , pp; Han, S., Mao, H., Dally, W.J., Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding (2016) ICLR, pp. 1-14. , pp; He, K., Gkioxari, G., Dollár, P., Girshick, R., (2017) Mask R-CNN. In: ICCV, pp. 2961-2969. , pp; He, Y., Kang, G., Dong, X., Fu, Y., Yang, Y., Soft filter pruning for accelerating deep convolutional neural networks (2018) IJCAI, pp. 2234-2240. , pp; Howard, A.G., (2017) Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications. Arxiv Preprint Arxiv, 1704, p. 04861; Huang, S., Gong, M., Tao, D., A coarse-fine network for keypoint localization (2017) ICCV, pp. 3028-3037. , pp; Kingma, D.P., Ba, J., Adam: A method for stochastic optimization (2015) ICLR, pp. 1-15. , pp; Kocabas, M., Karagoz, S., Akbas, E.: MultiPoseNet: fast multi-person pose estimation using pose residual network. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 437–453. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6 26; Krizhevsky, A., Sutskever, I., Hinton, G.E., (2012) Imagenet Classification with Deep Convolutional Neural Networks. 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In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12364, pp. 351–367. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58529-7 21","Zhao, M.; Department of Automation, China; email: mgzhao@mail.tsinghua.edu.cn","Pham D.N.Theeramunkong T.Governatori G.Liu F.",,"Springer Science and Business Media Deutschland GmbH","18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021","8 November 2021 through 12 November 2021",,267459,03029743,9783030893699,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119293094
"Nie Y., Ma X.","57287553500;27171307900;","Gaze Based Implicit Intention Inference with Historical Information of Visual Attention for Human-Robot Interaction",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13015 LNAI",,,"293","303",,,"10.1007/978-3-030-89134-3_27","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118131809&doi=10.1007%2f978-3-030-89134-3_27&partnerID=40&md5=3707a2dd7c96ddfdbd3cb2afff0a0744","School of Control Science and Engineering, Shandong University, Jinan, 250061, China","Nie, Y., School of Control Science and Engineering, Shandong University, Jinan, 250061, China; Ma, X., School of Control Science and Engineering, Shandong University, Jinan, 250061, China","Human-robot interaction (HRI) is the key capability for assistive robots to provide support for the elders and impaired in daily activities. Implicit intention understanding is a challenge problem for natural and intuitive HRI. In this paper, an implicit intention inference framework based on gaze behavior is proposed. First, support vector machine classifier is used to classify human’s gaze behavior as intentional and unintentional visual behavior. Then, a Naive Bayes model based on implicit intention inference is presented with taking historical visual attention into accounts. The advantage of this model is that the human’s implicit intention could be inferred based on his/her gaze at multiple objects by dealing with the historical information of visual behavior. Finally, experiments are conducted in a scenario of “home care in the kitchen”. People are free to look at one or more objects among 14 kinds of objects provided in experiment. With the proposed model, four kinds of intentions could be inferred based on people’s historical gaze on objects. The experiment results validate that the proposed model, which considers the historical information of visual behavior, outperforms the previous gaze-based intention inference methods. © 2021, Springer Nature Switzerland AG.","Gaze behavior; Human-robot interaction; Implicit intention recognition","Bayesian networks; Behavioral research; Man machine systems; Robotics; Support vector machines; Assistive robots; Daily activity; Gaze behaviours; Historical information; Humans-robot interactions; Implicit intention recognition; Implicit intentions; Intention recognition; Visual Attention; Visual behavior; Human robot interaction",,,,,"Key Technology Research and Development Program of Shandong: ZR2019ZD07; National Key Research and Development Program of China, NKRDPC: 2018YFB1305803","Acknowledgement. This work was supported in part by the National Key Research and Development Plan of China under Grant 2018YFB1305803 and the Key Development Program for Basic Research of Shandong Province under Grant ZR2019ZD07.",,,,,,,,,,"Louie, W.Y.G., McColl, D., Nejat, G., Acceptance and attitudes toward a human-like socially assistive robot by older adults (2014) Assist. Technol., 26 (3), pp. 140-150; Trick, S., Koert, D., Peters, J., (2019) Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction. Arxiv Preprint Arxiv, 1907, p. 02426; Bingol, M.C., Aydogmus, O., Performing predefined tasks using the human–robot interaction on speech recognition for an industrial robot (2020) Eng. Appl. Artif. Intell., 95; Pleshkova, S., Bekiarski, A., Algorithm for motion management by interaction between mobile robot and human (2019) 2019 II International Conference on High Technology for Sustainable Development (Hitech), Pp. 1–4. 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Learn., 20 (3), pp. 273-297","Ma, X.; School of Control Science and Engineering, China; email: maxin@sdu.edu.cn","Liu X.Nie Z.Yu J.Xie F.Song R.",,"Springer Science and Business Media Deutschland GmbH","14th International Conference on Intelligent Robotics and Applications, ICIRA 2021","22 October 2021 through 25 October 2021",,267169,03029743,9783030891336,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85118131809
"Duan E., Bryant M.","57314492900;57203196529;","Design of pennate topology fluidic artificial muscle bundles under spatial constraints",2021,"Proceedings of ASME 2021 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, SMASIS 2021",,,"V001T01A011","","",,,"10.1115/SMASIS2021-68183","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118127019&doi=10.1115%2fSMASIS2021-68183&partnerID=40&md5=5bd670676052fc22f5660084fe6d7961","North Carolina State University, Raleigh, NC, United States","Duan, E., North Carolina State University, Raleigh, NC, United States; Bryant, M., North Carolina State University, Raleigh, NC, United States","In this paper, we investigate the design of pennate topology fluidic artificial muscle bundles under spatial and operating constraints. Soft fluidic actuators are of great interest to roboticists and engineers due to their potential for inherent compliance and safe human-robot interaction. McKibben fluidic artificial muscles (FAMs) are soft fluidic actuators that are especially attractive due to their high force-to-weight ratio, inherent flexibility, relatively inexpensive construction, and muscle-like force-contraction behavior. Observations of natural muscles of equivalent cross-sectional area have indicated that muscles with a pennate fiber configuration can achieve higher output forces as compared to the parallel configuration due to larger physiological cross-sectional area (PCSA). However, this is not universally true because the contraction and rotation behavior of individual actuator units (fibers) are both key factors contributing to situations where bipennate muscle configurations are advantageous as compared to parallel muscle configurations. This paper analytically explores a design case for pennate topology artificial muscle bundles that maximize fiber radius. The findings can provide insights on optimizing artificial muscle topologies under spatial constraints. Furthermore, the study can be extended to evaluate muscle topology implications on work capacity and efficiency for tracking a desired dynamic motion. © 2021 by ASME.","Biomimetic; Fluidic artificial muscles; Muscle topology; Pennate; Soft actuators","Actuators; Biomimetics; Human robot interaction; Muscle; Artificial muscle; Fluidic actuators; Fluidic artificial muscles; Muscle topology; Operating constraints; Pennate; Safe human-robot interaction; Soft actuators; Spatial constraints; Weight ratios; Topology",,,,,"National Science Foundation, NSF: 1845203","This work was supported primarily by the Faculty Early Career Development Program (CAREER) of the National Science Foundation under NSF Award Number 1845203 and Program Manager Irina Dolinskaya. A NC Space Grant Graduate Research Fellowship also supported this research.",,,,,,,,,,"Ueda, J., Schultz, J. A., Asada, H. H., Structure of cellular actuators (2017) Cellular Actuators, pp. 1-44; Huston, D., Esser, B., Spencer, G., Burns, D., Kahn, E., Hierarchical actuator systems (2005) Smart Structures and Materials 2005: Industrial and Commercial Applications of Smart Structures Technologies; Mathijssen, G., Lefeber, D., Vanderborght, B., Variable Recruitment of Parallel Elastic Elements: Series-Parallel Elastic Actuators (SPEA) With Dephased Mutilated Gears (2015) IEEE/ASME Transactions on Mechatronics, 20 (2), pp. 594-602; Bryant, M., Meller, M. A., Garcia, E., Variable recruitment fluidic artificial muscles: Modeling and experiments (2014) Smart Materials and Structures, 23 (7), p. 074009; Meller, M., Chipka, J., Volkov, A., Bryant, M., Garcia, E., Improving actuation efficiency through variable recruitment hydraulic McKibben muscles: Modeling, orderly recruitment control, and experiments (2016) Bioinspiration & Biomimetics, 11 (6), p. 065004; Azizi, E., Brainerd, E. L., Roberts, T. J., Variable gearing in pennate muscles (2008) Proceedings of the National Academy of Sciences, 105 (5), pp. 1745-1750; Holt, N. C., Danos, N., Roberts, T. J., Azizi, E., Stuck in gear: Age-related loss of variable gearing in skeletal muscle (2016) Journal of Experimental Biology, 219 (7), pp. 998-1003; Azizi, E., Roberts, T. J., Geared up to stretch: Pennate muscle behavior during active lengthening (2014) Journal of Experimental Biology, 217 (3), pp. 376-381; Azizi, E., Roberts, T. J., Variable gearing in a biologically inspired pneumatic actuator array (2013) Bioinspiration & Biomimetics, 8 (2), p. 026002; Kianzad, S., Pandit, M., Lewis, J. D., Berlingeri, A. R., Haebler, K. J., Madden, J. D., Variable stiffness structure using nylon actuators arranged in a pennate muscle configuration (2015) Electroactive Polymer Actuators and Devices (EAPAD), , 2015; Jenkins, T., Bryant, M., Pennate actuators: Force, contraction and stiffness (2020) Bioinspiration & Biomimetics, 15 (4), p. 046005; Jenkins, T. E., Bryant, M., Variable stiffness soft robotics using pennate muscle architecture (2019) Bioinspiration, Biomimetics, and Bioreplication IX; Tondu, B., Lopez, P., Modeling and control of McKibben artificial muscle robot actuators (2000) IEEE Control Systems, 20 (2), pp. 15-38",,,,"American Society of Mechanical Engineers (ASME)","ASME 2021 Conference on Smart Materials, Adaptive Structures and Intelligent Systems, SMASIS 2021","14 September 2021 through 15 September 2021",,172826,,9780791885499,,,"English","Proc. ASME Conf. Smart Mater., Adapt. Struct. Intel. Syst., SMASIS",Conference Paper,"Final","",Scopus,2-s2.0-85118127019
"Spencer J., Choudhury S., Barnes M., Schmittle M., Chiang M., Ramadge P., Srinivasa S.","57202507905;55842232500;57218717862;57203913426;7102873398;7003731705;57292613100;","Expert Intervention Learning: An online framework for robot learning from explicit and implicit human feedback",2021,"Autonomous Robots",,,,"","",,,"10.1007/s10514-021-10006-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117255015&doi=10.1007%2fs10514-021-10006-9&partnerID=40&md5=eff711f621677ea8018baeb30b00695f","Princeton UniversityNJ, United States; University of Washington, Washington, United States","Spencer, J., Princeton UniversityNJ, United States; Choudhury, S., University of Washington, Washington, United States; Barnes, M., University of Washington, Washington, United States; Schmittle, M., University of Washington, Washington, United States; Chiang, M., Princeton UniversityNJ, United States; Ramadge, P., Princeton UniversityNJ, United States; Srinivasa, S., University of Washington, Washington, United States","Scalable robot learning from human-robot interaction is critical if robots are to solve a multitude of tasks in the real world. Current approaches to imitation learning suffer from one of two drawbacks. On the one hand, they rely solely on off-policy human demonstration, which in some cases leads to a mismatch in train-test distribution. On the other, they burden the human to label every state the learner visits, rendering it impractical in many applications. We argue that learning interactively from expert interventions enjoys the best of both worlds. Our key insight is that any amount of expert feedback, whether by intervention or non-intervention, provides information about the quality of the current state, the quality of the action, or both. We formalize this as a constraint on the learner’s value function, which we can efficiently learn using no regret, online learning techniques. We call our approach Expert Intervention Learning (EIL), and evaluate it on a real and simulated driving task with a human expert, where it learns collision avoidance from scratch with just a few hundred samples (about one minute) of expert control. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.",,"E-learning; Feedback; Learning systems; 'current; Expert feedback; Human demonstrations; Humans-robot interactions; Imitation learning; Learn+; Online learning; Real-world; S values; Value functions; Human robot interaction",,,,,"1544797; 1637748; National Institutes of Health, NIH: R01EB019335; Office of Naval Research, ONR; Defense Advanced Research Projects Agency, DARPA","This work was (partially) funded by the DARPA Dispersed Computing program, NIH R01 (R01EB019335), NSF CPS (#1544797), NSF NRI (#1637748), the Office of Naval Research, RCTA, Amazon, and Honda Research Institute USA.",,,,,,,,,,"Abbeel, P., Ng, A.Y., (2004) Apprenticeship Learning via Inverse Reinforcement Learning; Alt, H., Godau, M., Computing the Fréchet distance between two polygonal curves (1995) International Journal of Computational Geometry & Applications, 5, pp. 75-91; Amershi, S., Cakmak, M., Knox, W.B., Kulesza, T., Power to the people: The role of humans in interactive machine learning (2014) AI Magazine, 35, pp. 105-120; Argall, B.D., Chernova, S., Veloso, M., Browning, B., A survey of robot learning from demonstration (2009) Robotics and Autonomous Systems; Bajcsy, A., Losey, D.P., O’Malley, M.K., Dragan, A.D., Learning from physical human corrections, one feature at a time (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (HRI); Bajcsy, A., Losey, D.P., O’Malley, M.K., Dragan, A.D., Learning robot objectives from physical human interaction (2017) Proceedings of the 1St Annual Conference on Robot Learning (Corl); Bi, J., Dhiman, V., Xiao, T., Xu, C., Learning from interventions using hierarchical policies for safe learning (2020) Proceedings of the AAAI Conference on Artificial Intelligence, 34-6, pp. 10352-10360; Bi, J., Xiao, T., Sun, Q., Xu, C., (2018) Navigation by imitation in a pedestrian-rich environment, , . arXiv preprint arXiv:1811.00506; Celemin, C., Ruiz-del Solar, J., An interactive framework for learning continuous actions policies based on corrective feedback (2019) Journal of Intelligent & Robotic Systems, 95, pp. 77-97; Chen, M., Nikolaidis, S., Soh, H., Hsu, D., Srinivasa, S., Planning with trust for human-robot collaboration (2018) In: Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (HRI); Chernova, S., Veloso, M., Interactive policy learning through confidence-based autonomy (2009) Journal of Artificial Intelligence Research, 34, pp. 1-25. , &; Choudhury, S., Dugar, V., Maeta, S., Macallister, B., Arora, S., Althoff, D., Scherer, S., High performance and safe flight of full-scale helicopters from takeoff to landing with an ensemble of planners (2019) Journal of Field Robotics (JFR), 36 (8), pp. 1275-1332; Daumé, I.I.I.H.J.D., (2009) Search-Based Structured Prediction, 75 (3), pp. 297-325; Dhariwal, P., Hesse, C., Klimov, O., Nichol, A., Plappert, M., Radford, A., Schulman, J., Wu, Y., (2017), https://github.com/openai/baselines, & Zhokhov, P, Openai baselines; Fisac, J.F., Gates, M.A., Hamrick, J.B., Liu, C., Hadfield-Menell, D., Palaniappan, M., Malik, D., Dragan, A.D., (2019) Pragmatic-Pedagogic Value Alignment, pp. 49-57; Goecks, V.G., Gremillion, G.M., Lawhern, V.J., Valasek, J., Waytowich, N.R., Efficiently combining human demonstrations and interventions for safe training of autonomous systems in real-time (2019) Proceedings of the AAAI Conference on Artificial Intelligence, 33, pp. 2462-2470; Grollman, D.H., Jenkins, O.C., (2007) Dogged Learning for Robots; Gupta, S., Davidson, J., Levine, S., Sukthankar, R., Malik, J., Cognitive mapping and planning for visual navigation (2017) In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).; Hadfield-Menell, D., Russell, S.J., Abbeel, P., Dragan, A., Cooperative inverse reinforcement learning (2016) In: Advances in Neural Information Processing Systems (Neurips).; Jain, A., Wojcik, B., Joachims, T., Saxena, A., (2013) Advances in Neural Information Processing Systems (Neurips)., , . Learning trajectory preferences for manipulators via iterative improvement. In; Judah, K., Fern, A.P., Dietterich, T.G., Active imitation learning via reduction to iid active learning (2012) 2012 AAAI Fall Symposium Series; Kelly, M., Sidrane, C., Driggs-Campbell, K., Kochenderfer, M.J., Hg-dagger: Interactive imitation learning with human experts (2019) 2019 International Conference on Robotics and Automation (ICRA); Kim, B., Farahmand, A., Pineau, J., Precup, D., Learning from limited demonstrations (2013) Advances in Neural Information Processing Systems (Neurips); Kim, B., Pineau, J., (2013) In: Robotics: Science and Systems (RSS), , Maximum mean discrepancy imitation learning; Kollmitz, M., Koller, T., Boedecker, J., Burgard, W., (2020) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 11025-11031. , Learning human-aware robot navigation from physical interaction via inverse reinforcement learning. In, pp; Laskey, M., Chuck, C., Lee, J., Mahler, J., Krishnan, S., Jamieson, K., Dragan, A., Goldberg, K., Comparing human-centric and robot-centric sampling for robot deep learning from demonstrations (2017) . In: IEEE International Conference on Robotics and Automation (ICRA).; Laskey, M., Lee, J., Hsieh, W., Liaw, R., Mahler, J., Fox, R., Goldberg, K., (2017), Iterative noise injection for scalable imitation learning. arXiv preprint arXiv:1703.09327; Laskey, M., Staszak, S., Hsieh, W.Y.S., Mahler, J., Pokorny, F.T., Dragan, A.D., Goldberg, K., SHIV: Reducing supervisor burden in dagger using support vectors for efficient learning from demonstrations in high dimensional state spaces (2016) . In; Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J., Quillen, D., Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection (2018) The International Journal of Robotics Research (IJRR).; Loftin, R., Peng, B., Macglashan, J., Littman, M.L., Taylor, M.E., Huang, J., Roberts, D.L., Learning behaviors via human-delivered discrete feedback: Modeling implicit feedback strategies to speed up learning (2016) Autonomous Agents and Multi-Agent Systems; Macglashan, J., Ho, M.K., Loftin, R., Peng, B., Wang, G., Roberts, D.L., Taylor, M.E., Littman, M.L., Interactive learning from policy-dependent human feedback (2017) In: Proceedings of the 34Th International Conference on Machine Learning (ICML); McPherson, D.L., Scobee, D.R., Menke, J., Yang, A.Y., Sastry, S.S., Modeling supervisor safe sets for improving collaboration in human-robot teams (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 861-868. , IEEE; Menda, K., Driggs-Campbell, K.R., Kochenderfer, M.J., (2018) EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning, , . arXiv preprint arXiv:1807.08364; Osa, T., Pajarinen, J., Neumann, G., Bagnell, J.A., Abbeel, P., Peters, J., An algorithmic perspective on imitation learning (2018) Foundations and Trends in Robotics, 7 (1-2), pp. 1-179. , &; Packard, B., Ontañón, S., (2017) Policies for Active Learning from Demonstration, , In: 2017 AAAI Spring Symposium Series; Pomerleau, D.A., Alvinn: An autonomous land vehicle in a neural network (1989) Advances in Neural Information Processing Systems (Neurips); Ross, S., Gordon, G., Bagnell, D., A reduction of imitation learning and structured prediction to no-regret online learning (2011) Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (Aistats); Ross, S., Melik-Barkhudarov, N., Shankar, K.S., Wendel, A., Dey, D., Bagnell, J.A., Hebert, M., Learning monocular reactive UAV control in cluttered natural environments (2013) IEEE International Conference on Robotics and Automation (ICRA); Sadat, A., Ren, M., Pokrovsky, A., Lin, Y.C., Yumer, E., Urtasun, R., (2019) Jointly learnable behavior and trajectory planning for self-driving vehicles, , arXiv preprint; Sadigh, D., Sastry, S.S., Seshia, S.A., Dragan, A., Information gathering actions over human internal state (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Saunders, W., Sastry, G., Stuhlmueller, A., Evans, O., (2017) Trial without error: Towards safe reinforcement learning via human intervention, , arXiv preprint; Shalev-Shwartz, S., Online learning and online convex optimization (2012) Foundations and Trends in Machine Learning, 4 (2), pp. 107-194; Spencer, J., Choudhury, S., Barnes, M., Schmittle, M., Chiang, M., Ramadge, P., Srinivasa, S., Learning from interventions: Human-robot interaction as both explicit and implicit feedback (2020) . In: Robotics: Science and Systems (RSS).; Spencer, J., Choudhury, S., Venkatraman, A., Ziebart, B., Bagnell, J.A., (2021) Feedback in imitation learning: The three regimes of covariate shift, , . arXiv preprint arXiv:2102.02872; Srinivasa, S.S., Lancaster, P., Michalove, J., Schmittle, M., Summers, C., Rockett, M., Smith, J.R., Sadeghi, F., (2019), MuSHR: A Low-Cost, Open-Source Robotic Racecar for Education and Research. arXiv preprint arXiv:1908.08031; Sun, W., Venkatraman, A., Gordon, G.J., Boots, B., Bagnell, J.A., Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction (2017) In: Proceedings of the 34Th International Conference on Machine Learning (ICML).; Zinkevich, M., Online convex programming and generalized infinitesimal gradient ascent (2003) In: Proceedings of the 20Th International Conference on Machine Learning (ICML).","Spencer, J.; Princeton UniversityUnited States; email: j.spencer@princeton.edu",,,"Springer",,,,,09295593,,AUROF,,"English","Auton. Robots",Article,"Article in Press","",Scopus,2-s2.0-85117255015
"Lestingi L., Askarpour M., Bersani M.M., Rossi M.","57219181705;57191286316;35730853100;7403708768;","A Deployment Framework for Formally Verified Human-Robot Interactions",2021,"IEEE Access","9",,,"136616","136635",,,"10.1109/ACCESS.2021.3117852","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117238639&doi=10.1109%2fACCESS.2021.3117852&partnerID=40&md5=fca69e9276e263611216c286eceb481d","Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, 20133, Italy; Department of Computing and Software, McMaster University, Hamilton, ON  L8S 4L8, Canada; Dipartimento di Meccanica, Politecnico di Milano, Milan, 20156, Italy","Lestingi, L., Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, 20133, Italy; Askarpour, M., Department of Computing and Software, McMaster University, Hamilton, ON  L8S 4L8, Canada; Bersani, M.M., Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, 20133, Italy; Rossi, M., Dipartimento di Meccanica, Politecnico di Milano, Milan, 20156, Italy","In the future, assistive robots will spread to everyday settings and regularly interact with humans. This paper introduces a deployment approach for assistive robotic applications where human-robot interaction is the main element. The deployment infrastructure hinges on a model-to-code transformation technique and a ROS-based middleware layer and enables deployment in real life or simulation in a virtual environment. The approach fits into a model-driven framework for the formal verification of interactive scenarios. At design-time, the application analyst estimates the most likely outcome of the robotic mission through Statistical Model Checking of a Stochastic Hybrid Automata network modeling the scenario. We introduce an innovative approach to convert a specific subset of Stochastic Hybrid Automata into executable code to control the robot and respond to human actions. Deploying or simulating the application allows analysts to validate the results obtained at design time or to refine the formal model based on runs in the real or the virtual scene. The methodology's effectiveness is tested via simulation of use cases from the healthcare setting, which can significantly benefit from this kind of approach thanks to its innovative features related to human physiology and autonomous behavior. © 2013 IEEE.","Human-robot interaction; model-driven approach; robot deployment; service robots","Automata theory; Behavioral research; Man machine systems; Middleware; Model checking; Physiology; Robotics; Stochastic models; Stochastic systems; Virtual reality; Assistive robotics; Assistive robots; Design time; Humans-robot interactions; Hybrid automatons; Model driven approach; Robot deployment; Robotics applications; Service robots; Stochastics; Human robot interaction",,,,,,,,,,,,,,,,"Gwon, T., Park, H., Seo, D., Lee, S., Kim, D., Jeon, S., A study on safety evaluation criteria of the personal carrier robot based on ISO 13482 (2019) Proc. 3rd IEEE Int. Conf. Robotic Comput. 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"Zeng H., Hu R., Huang X., Peng Z.","56528785100;57289657700;55963783200;57289486600;","Robot Navigation in Crowd Based on Dual Social Attention Deep Reinforcement Learning",2021,"Mathematical Problems in Engineering","2021",,"7114981","","",,,"10.1155/2021/7114981","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116662051&doi=10.1155%2f2021%2f7114981&partnerID=40&md5=75dbb72e2b0992a6aebb3bce4df9819d","Department of Information Engineering, East China Jiaotong University, Nanchang, 330013, China","Zeng, H., Department of Information Engineering, East China Jiaotong University, Nanchang, 330013, China; Hu, R., Department of Information Engineering, East China Jiaotong University, Nanchang, 330013, China; Huang, X., Department of Information Engineering, East China Jiaotong University, Nanchang, 330013, China; Peng, Z., Department of Information Engineering, East China Jiaotong University, Nanchang, 330013, China","Finding a feasible, collision-free path in line with social activities is an important and challenging task for robots working in dense crowds. In recent years, many studies have used deep reinforcement learning techniques to solve this problem. In particular, it is necessary to find an efficient path in a short time which often requires predicting the interaction with neighboring agents. However, as the crowd grows and the scene becomes more and more complex, researchers usually simplify the problem to a one-way human-robot interaction problem. But, in fact, we have to consider not only the interaction between humans and robots but also the influence of human-human interactions on the movement trajectory of the robot. Therefore, this article proposes a method based on deep reinforcement learning to enable the robot to avoid obstacles in the crowd and navigate smoothly from the starting point to the target point. We use a dual social attention mechanism to jointly model human-robot and human-human interaction. All sorts of experiments demonstrate that our model can make robots navigate in dense crowds more efficiently compared with other algorithms. © 2021 Hui Zeng et al.",,"Human robot interaction; Man machine systems; Navigation; Reinforcement learning; Collision-free paths; Efficient path; Human-human interactions; Humans-robot interactions; Interaction problems; Movement trajectories; Reinforcement learning techniques; Robot navigation; Robots working; Social activities; Deep learning",,,,,,,,,,,,,,,,"Zhu, C.Z., Chen, P., Navigation for indoor robot: Straight line movement via navigator (2018) Mathematical Problems in Engineering, 2018, p. 10. , 8419384 2-s2.0-85057405366; Repiso, E., Garrell, A., Sanfeliu, A., Adaptive side-by-side social robot navigation to approach and interact with people (2020) International Journal of Social Robotics, 12 (2), pp. 1-23. , 2-s2.0-85066901876; Tai, L., Zhang, J.W., Liu, M., Burgard, W., Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning, pp. 1111-1117. , Proceedings-IEEE International Conference on Robotics and Automation May 2018 Guangzhou, China 2-s2.0-85063157228; 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Chen, C., Liu, Y., Kreiss, S., Alahi, A., Crowd-robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning, pp. 6015-6022. , Proceedings of International Conference on Robotics and Automation May 2019 Montreal, Canada 2-s2.0-85071424250; Douthwaite, J.A., Zhao, S., Mihaylova, L.S., Velocity obstacle approaches for multi-agent collision avoidance (2019) Unmanned Systems, pp. 1-10. , 2-s2.0-85063295529","Hu, R.; Department of Information Engineering, China; email: hrdyx971013@163.com",,,"Hindawi Limited",,,,,1024123X,,,,"English","Math. Probl. Eng.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85116662051
"Ghafurian M., Akgun S.A., Crowley M., Dautenhahn K.","57190254083;57209982972;16318327500;7003305185;","Recognition of a Robot’s Affective Expressions Under Conditions with Limited Visibility",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12934 LNCS",,,"448","469",,,"10.1007/978-3-030-85613-7_31","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115236223&doi=10.1007%2f978-3-030-85613-7_31&partnerID=40&md5=6cb4a8295749a4b171928fa8f2754a3b","Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada; Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada","Ghafurian, M., Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada; Akgun, S.A., Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada; Crowley, M., Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada; Dautenhahn, K., Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada","The capability of showing affective expressions is important for the design of social robots in many contexts, where the robot is designed to communicate with humans. It is reasonable to expect that, similar to all other interaction modalities, communicating with affective expressions is not without limitations. In this paper, we present two online video studies (72 and 50 participants) and investigate if/to what extent the recognition of affective displays of a zoomorphic robot is affected under situations with different levels of visibility. Recognition of five affective expressions under five visibility effects were studied. The intensity of the effects was more pronounced in the second experiment. While visual constraints affected recognition of expressions, our results showed that affective displays of the robot conveyed through its head and body motions can be robust and recognition rates can still be high even under severe visibility constraints. This study supported the effectiveness of using affective displays as a complementary communication modality in human-robot interaction in situations with visibility constraints, e.g. in the case of older users with visual impairments, or in outdoor scenarios such as search-and-rescue. © 2021, IFIP International Federation for Information Processing.","Affective displays; Impaired visibility; Miro; Social robots; Zoomorphic robots","Human computer interaction; Machine design; Visibility; Body motions; Communication modalities; Limited visibility; Older users; Online video; Search and rescue; Visibility constraint; Visual impairment; Social robots",,,,,"Canada Research Chairs; University of Waterloo, UW","Acknowledgment. This research was undertaken, in part, thanks to funding from the Canada 150 Research Chairs Program and funding from the Network for Aging Research at the University of Waterloo. We would like to thank the members of the Social and Intelligent Robotics Research Laboratory (SIRRL) at the University of Waterloo for their comments on the video effects.",,,,,,,,,,"Akgun, S.A., Ghafurian, M., Crowley, M., Dautenhahn, K., Using emotions to complement multimodal humanrobot interaction in urban search and rescue scenarios (2020) Proceedings of the 2020 International Conference on Multimodal Interaction, pp. 575-584. , pp; Baker, M., Casey, R., Keyes, B., Yanco, H.A., Improved interfaces for humanrobot interaction in urban search and rescue (2004) 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583), Vol. 3, Pp. 2960–2965. 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Neurosci., 5 (6), pp. 598-604; Woods, S.N., Walters, M.L., Koay, K.L., Dautenhahn, K., Methodological issues in HRI: A comparison of live and videobased methods in robot to human approach direction trials (2006) ROMAN 2006 the 15Th IEEE International Symposium on Robot and Human Interactive Communication, pp. 51-58. , pp., IEEE; Zhao, J., Gao, J., Zhao, F., Liu, Y., A searchandrescue robot system for remotely sensing the underground coal mine environment (2017) Sensors, 17 (10), p. 2426","Ghafurian, M.; Department of Electrical and Computer Engineering, Canada; email: moojan@uwaterloo.ca","Ardito C.Lanzilotti R.Malizia A.Malizia A.Petrie H.Piccinno A.Desolda G.Inkpen K.",,"Springer Science and Business Media Deutschland GmbH","18th IFIP TC 13 International Conference on Human-Computer Interaction, INTERACT 2021","30 August 2021 through 3 September 2021",,264499,03029743,9783030856120,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85115236223
"Yan F., Yang X., Li N., Yu X., Zhai H.","51061778800;57239626200;57219761664;55535815700;35767021400;","Emotion Generation and Transition of Companion Robots Based on Plutchik's Model and Quantum Circuit Schemes",2021,"Security and Communication Networks","2021",,"6802521","","",,,"10.1155/2021/6802521","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113966417&doi=10.1155%2f2021%2f6802521&partnerID=40&md5=570877b9658c1130a8318995e5587279","School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China; College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao266061, China","Yan, F., School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China; Yang, X., School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China; Li, N., School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China; Yu, X., College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao266061, China; Zhai, H., School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China","Loneliness and isolation are on the rise worldwide, threatening human well-being and the wellness of different age groups and backgrounds. Notably, global social distancing measures during the COVID-19 crisis have exacerbated this problem, resulting in various psychological and physiological ailments. Within both the categories of social and medical robots, companion robots are capable of engaging emotionally with users and providing continuous monitoring and assessment of their health. In this study, we propose a framework for modeling the emotion space of companion robots to facilitate their emotion generation and transition based on Plutchik's wheel of emotions and reversible quantum circuit schemes. Superposition encodings allow fewer computing resources for the generation and storage of emotional states, and by using unitary operations, they facilitate easier emotion transition and recovery over different intervals. Further, an encryption strategy is designed based on the emotion communication architecture to secure the emotion-related data in human-robot interaction. It is hoped that such an integrative framework and research agenda exploring the role of companion robots will be useful to care for users' social health by mitigating their negative emotions, especially during difficult times. © 2021 Fei Yan et al.",,"Cryptography; Digital storage; Medical robotics; Timing circuits; Communication architectures; Computing resource; Continuous monitoring; Emotion generation; Encryption strategy; Integrative framework; Research agenda; Unitary operation; Social robots",,,,,,,,,,,,,,,,"Swarte, T.D., Boufous, O., Escalle, P., Artificial intelligence, ethics and human values: The cases of military drones and companion robots (2019) Artificial Life and Robotics, 24 (3), pp. 291-296. , 2-s2.0-85061050791; Liu, Z., Wu, M., Cao, W., A facial expression emotion recognition based human-robot interaction system (2017) IEEE/CAA Journal of Automatica Sinica, 4 (4), pp. 668-676. , 2-s2.0-85029781916; Miwa, H., Umetsu, T., Takanishi, A., Takanobu, H., Robot Personality Based on the Equations of Emotion Defined in the 3D Mental Space, pp. 2602-2607. , Proceedings of the IEEE International Conference On Robotics And Automation May 2001 Seoul, South Korea; Yan, F., Iliyasu, A.M., Hirota, K., Emotion space modelling for social robots (2021) Engineering Applications of Artificial Intelligence, 100. , 104178; Russell, J.A., A circumplex model of affect (1980) Journal of Personality and Social Psychology, 39 (6), pp. 1161-1178. , 2-s2.0-4644280844; Mehrabian, A., Russell, J.A., (1974) An Approach to Environmental Psychology, , Cambridge, UK The MIT Press; Plutchik, R., (1980) ""a General Psychoevolutionary Theory of Emotion,"" Theories of Emotion, , New York, NY, USA Academic Press; Dunjko, V., Briegel, H.J., Machine learning & artificial intelligence in the quantum domain: A review of recent progress (2018) Reports on Progress in Physics, 81 (7). , 074001 2-s2.0-85045331288; Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., Lloyd, S., Quantum machine learning (2017) Nature, 549 (7671), pp. 195-202. , 2-s2.0-85030752361; Lukac, M., Perkowski, M., Quantum Mechanical Model of Emotional Robot Behaviors, p. 19. , Proceedings of the 37th International Symposium On Multiple-Valued Logic May 2007 Oslo, Norway 2-s2.0-34548279823; Raghuvanshi, A., Perkowski, M., Fuzzy Quantum Circuits to Model Emotional Behaviors of Humanoid Robots, pp. 1-8. , Proceedings of the IEEE Congress on Evolutionary Computation July 2010 Barcelona, Spain 2-s2.0-79959451486; Yan, F., Iliyasu, A.M., Iliyasu, A.M., Bloch sphere-based representation for quantum emotion space (2015) Journal of Advanced Computational Intelligence and Intelligent Informatics, 19 (1), pp. 134-142. , 2-s2.0-84921684890; Hoorn, J.F., Ho, J.K., (2019) Robot Affect: The Amygdala As Bloch Sphere, , https://arxiv.org/abs/1911.12128; Nielsen, M., Chuang, I., (2000) Quantum Computation and Quantum Information, , Cambridge, UK Cambridge University Press; Vedral, V., Barenco, A., Ekert, A., Quantum networks for elementary arithmetic operations (1996) Physical Review A, 54 (1), pp. 147-153. , 2-s2.0-0000442097; Chen, K., Yan, F., Iliyasu, A.M., Zhao, J., Dual quantum audio watermarking schemes based on quantum discrete cosine transform (2019) International Journal of Theoretical Physics, 58 (2), pp. 502-521. , 2-s2.0-85056993921; Zhang, Y., Qian, Y.-F., Wu, D., Hossain, M.S., Ghoneim, A., Chen, M., Emotion-Aware multimedia systems security (2018) IEEE Transactions on Multimedia, 21 (3), pp. 617-624; Hong, A., Lunscher, N., Hu, T., A multimodal emotional human-robot interaction architecture for social robots engaged in bidirectional Communication\vspace ∗ 7pt (2020) IEEE Transactions on Cybernetics, pp. 1-15; Ravì, D., Wong, C., Deligianni, F., Deep learning for health informatics (2016) IEEE Journal of Biomedical and Health Informatics, 21 (1), pp. 4-21; Chen, M., Zhou, P., Fortino, G., Emotion communication system (2016) IEEE Access, 5 (16), pp. 326-337; Yan, F., Iliyasu, A.M., Guo, Y., Yang, H., Flexible representation and manipulation of audio signals on quantum computers (2018) Theoretical Computer Science, 752, pp. 71-85. , 2-s2.0-85038827663; Zhou, N., Hu, Y., Gong, L., Li, G., Quantum image encryption scheme with iterative generalized Arnold transforms and quantum image cycle shift operations (2017) Quantum Information Processing, 16 (6), p. 164. , 2-s2.0-85019210723; Le, P.Q., Iliyasu, A.M., Dong, F.-Y., Hirota, K., Fast geometric transformations on quantum images (2010) International Journal of Applied Mathematics, 40 (3), pp. 113-123; Weinstein, Y.S., Pravia, M.A., Fortunato, E.M., Lloyd, S., Cory, D.G., Implementation of the quantum Fourier transform (2001) Physical Review Letters, 86 (9), p. 1889; Busemeyer, J.R., Bruza, P.D., (2012) Quantum Models of Cognition and Decision: Can Quantum Systems Learn? Quantum Updating, , Cambridge, UK Cambridge University Press","Yu, X.; School of Computer Science and Technology, China; email: yuxu0532@qust.edu.cn",,,"Hindawi Limited",,,,,19390114,,,,"English","Secur. Commun. Networks",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85113966417
"Mahzoon H., Okazaki M., Yoshikawa Y., Ishiguro H.","57193626184;57236506200;7201413552;26643316500;","Effect of the projection of robot’s talk information on the perception of communicating human",2021,"Advanced Robotics","35","20",,"1209","1222",,,"10.1080/01691864.2021.1964597","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113731452&doi=10.1080%2f01691864.2021.1964597&partnerID=40&md5=5ee20791994abb2f2530271a605a7559","Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Osaka, Japan; Graduate School of Engineering Science, Osaka University, Osaka, Japan","Mahzoon, H., Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Osaka, Japan; Okazaki, M., Graduate School of Engineering Science, Osaka University, Osaka, Japan; Yoshikawa, Y., Graduate School of Engineering Science, Osaka University, Osaka, Japan; Ishiguro, H., Graduate School of Engineering Science, Osaka University, Osaka, Japan","For a conversational robot, conveying its intention explicitly to the communicating human is one of the most important but challenging tasks in the field of human–robot interaction (HRI). Expressing the robot’s current mental state or feeling while conversing may help achieve a more effective communication. In this study, a robot equipped with a projecting system is implemented, in which the mental state of the robot is projected onto the backside wall to better convey its intention. To verify the effectiveness of the implemented system, as a first step, the effect of projecting only the text of the robot’s speech on the subjective evaluation of the communicating human was examined from different aspects of the robot. To this end, two experiments were conducted: a video watching experiment, in which several human subjects watched a video of a speaking robot equipped with the projecting system, and a participating experiment, in which a limited number of human subjects participated in listening to the speech of such a robot in a real-world environment. The experimental results revealed that the projection of the speech information can improve some elements of the human perception of the robot’s speech and mind. © 2021 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","conversational robots; Human–robot interaction; mind perception","Agricultural robots; Effective communication; Human perception; Human subjects; Mental state; Real world environments; Robot interactions; Speech information; Subjective evaluations; Social robots",,,,,"Japan Society for the Promotion of Science, KAKEN: JP19H05691; Ministry of Education, Culture, Sports, Science and Technology, MEXT","This study was supported by Innovation Platform for Society 5.0 at MEXT Japan as well as JSPS KAKENHI [grant number JP19H05691].",,,,,,,,,,"Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Rob Auton Syst, 42, pp. 143-166; Breazeal, C.L., (2002) Designing sociable robots, , Cambridge: MIT press; Klingspor, V., Demiris, J., Kaiser, M., Human-robot communication and machine learning (1997) Appl Artif Intell, 11, pp. 719-746; Ikemoto, S., Amor, H.B., Minato, T., Physical human-robot interaction: mutual learning and adaptation (2012) IEEE Robot Autom Mag, 19, pp. 24-35; Rani, P., Liu, C., Sarkar, N., An empirical study of machine learning techniques for affect recognition in human–robot interaction (2006) Pattern Anal Appl, 9, pp. 58-69; Chen, M., Zhou, P., Fortino, G., Emotion communication system (2016) IEEE Access, 5, pp. 326-337; Endo, N., Momoki, S., Zecca, M., Development of whole-body emotion expression humanoid robot. 2008 IEEE International Conference on Robotics and Automation, Pasadena, CA, USA. 2008. p. 2140–2145; Miwa, H., Itoh, K., Matsumoto, M., Effective emotional expressions with expression humanoid robot we-4rii: integration of humanoid robot hand rch-1. 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat No 04CH37566), Sendai, Japan. 2004. p. 2203–2208; Bruce, A., Nourbakhsh, I., Simmons, R., The role of expressiveness and attention human-robot interaction. 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Proceedings of 5th International Conference on Multimodal Interaction Vancouver, British Columbia, Canada. 2003. p. 28–35; Li, L., Yu, X., Li, J., Vision-based attention estimation and selection for social robot to perform natural interaction the open world. 2012 7th ACM/IEEE International Conference on Human-Robot Interaction, Boston, MA, USA. 2012. p. 183–184; Mavridis, N., A review of verbal and non-verbal human–robot interactive communication (2015) Rob Auton Syst, 63, pp. 22-35; Alonso-Martin, F., Salichs, M.A., Integration of a voice recognition system in a social robot (2011) Cybern Syst An Int J, 42, pp. 215-245; Miwa, H., Okuchi, T., Itoh, K., A new mental model for humanoid robots for human friendly communication introduction of learning system, mood vector and second order equations of emotion. 2003 IEEE International Conference on Robotics and Automation (Cat No 03CH37422), Taipei, Taiwan. 2003. p. 3588–3593; Terada, K., Yamauchi, A., Ito, A., Artificial emotion expression for a robot by dynamic color change. 2012 IEEE RO-MAN 21st IEEE International Symposium on Robot and Human Interactive Communication, Paris, France. 2012. p. 314–321; Jaimes, A., Sebe, N., Multimodal human–computer interaction: a survey (2007) Comput Vis Image Underst, 108, pp. 116-134; Ishiguro, H., Ono, T., Imai, M., Robovie: an interactive humanoid robot (2001) Ind Robot An Int J, , 28:498–504; Watanabe, T., Okubo, M., Ogawa, H., An embodied interaction robots system based on speech. 8th IEEE International Workshop on Robot and Human Interaction RO-MAN’99 (Cat No 99TH8483), Pisa, Italy. 1999. p. 225–230; Hashimoto, S., Narita, S., Kasahara, H., Humanoid robots in Waseda University–hadaly-2 and wabian (2002) Auton Robots, 12, pp. 25-38; Kato, S., Ohshiro, S., Itoh, H., Development of a communication robot ifbot. 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A pilot study (2018) Front Psychiatry, 9, p. 36; Arimoto, T., Yoshikawa, Y., Ishiguro, H., Multiple-robot conversational patterns for concealing incoherent responses (2018) Int J Soc Robot, 10, pp. 583-593; Hall, J.A., Gender effects in decoding nonverbal cues (1978) Psychol Bull, 85, p. 845; Katsikitis, M., Pilowsky, I., Innes, J.M., Encoding and decoding of facial expression (1997) J Gen Psychol, 124, pp. 357-370; Hall, J.A., Matsumoto, D., Gender differences in judgments of multiple emotions from facial expressions (2004) Emotion, 4, p. 201; Hall, J.K., Hutton, S.B., Morgan, M.J., Sex differences in scanning faces: does attention to the eyes explain female superiority in facial expression recognition? (2010) Cogn Emot, 24, pp. 629-637; Schermerhorn, P., Scheutz, M., Crowell, C.R., Robot social presence and gender: do females view robots differently than males? Proceedings of 3rd ACM/IEEE International Conference on Human-Robot Interaction, Amsterdam, Netherlands. 2008. p. 263–270; Chung-En, Y., Humanlike robot and human staff service: age and gender differences perceiving smiling behaviors. 2018 7th International Conference on Industrial Technology and Management, Oxford, UK. 2018. p. 99–103; Dautenhahn, K., Walters, M., Woods, S., How may I serve you? A robot companion approaching a seated person a helping context. Proceedings of 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction, Salt Lake City, Utah, USA. 2006. p. 172–179; Nomura, T., Kanda, T., Suzuki, T., Experimental investigation into influence of negative attitudes toward robots on human–robot interaction (2006) Ai Soc, 20, pp. 138-150; Kamide, H., Takashima, K., Arai, T., Development of Japanese version of the psychological scale of anthropomorphism (2016) Jpn J Personal, 25, pp. 218-225; Bartneck, C., Kulić, D., Croft, E., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int J Soc Robot, 1, pp. 71-81; Heerink, M., Kröse, B., Evers, V., The influence of social presence on acceptance of a companion robot by older people. 2008","Mahzoon, H.; Institute for Open and Transdisciplinary Research Initiatives (OTRI), 1–1 Yamadaoka, Japan; email: hamed.mahzoon@irl.sys.es.osaka-u.ac.jp",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Final","",Scopus,2-s2.0-85113731452
"Gasteiger N., Hellou M., Ahn H.S.","57204357716;57216352354;56218250500;","Factors for Personalization and Localization to Optimize Human–Robot Interaction: A Literature Review",2021,"International Journal of Social Robotics",,,,"","",,2,"10.1007/s12369-021-00811-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113164391&doi=10.1007%2fs12369-021-00811-8&partnerID=40&md5=ecf4dfd367b8fbae8dc42db93f37742a","Department of Psychological Medicine, The University of Auckland, Auckland, New Zealand; School of Health Sciences, The University of Manchester, Manchester, United Kingdom; Département d’Informatique, Facultés des Sciences et Ingénieries, Sorbonne Université, Paris, France; Department of Electrical, Computer and Software Engineering, CARES, The University of Auckland, Auckland, New Zealand","Gasteiger, N., Department of Psychological Medicine, The University of Auckland, Auckland, New Zealand, School of Health Sciences, The University of Manchester, Manchester, United Kingdom; Hellou, M., Département d’Informatique, Facultés des Sciences et Ingénieries, Sorbonne Université, Paris, France, Department of Electrical, Computer and Software Engineering, CARES, The University of Auckland, Auckland, New Zealand; Ahn, H.S., Department of Electrical, Computer and Software Engineering, CARES, The University of Auckland, Auckland, New Zealand","Social service robots are becoming increasingly pervasive in our everyday lives, including in healthcare, education and customer service settings. It is known that different cultures and individuals have an array of diverse expectations when interacting with robots. These expectations influence acceptability and willingness to engage with them. However, previous research in this field mostly focuses on a sole human-related factor that may impact interaction and the acceptability of robots both within and across groups of people. This review aims to synthesize the existing literature on human factors to consider when designing robots that can be personalized or localized (transferred to other cultures). The literature review highlights key studies in this area and synthesizes them into four overarching factors: (1) communication and language, (2) behavior and service, (3) proxemics, and (4) interface design. The review shows that personalization and localization in robotics needs to move beyond catering to simple language preferences or accents. Instead, this encompasses the intricate details of interface design, service expectations, proxemics and individual and cultural communication styles and cultural values that users may possess. This study consequently highlights key considerations when attempting to optimize human–robot interaction across individuals and cultures. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Culturalization; Design; Human–robot interaction; Localization; Personalization","Agricultural robots; Machine design; Social robots; Communication styles; Customer services; Impact interaction; Interface designs; Literature reviews; Personalizations; Robot interactions; Service expectations; Educational robots",,,,,"Ministry of Science, ICT and Future Planning, MSIP: 2020-0-00842; Institute for Information and Communications Technology Promotion, IITP","This work was supported by the Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. 2020-0-00842, Development of Cloud Robot Intelligence for Continual Adaptation to User Reactions in Real Service Environments).",,,,,,,,,,"(2020) International Federation of Robotics. 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J. Soc. Rob.",Article,"Article in Press","",Scopus,2-s2.0-85113164391
"Bhardwaj S., Khan A.A., Muzammil M.","57194685397;36599954000;15725883900;","Lower limb rehabilitation robotics: The current understanding and technology",2021,"Work","69","3",,"775","793",,,"10.3233/WOR-205012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111084171&doi=10.3233%2fWOR-205012&partnerID=40&md5=104f5679d50266cae9a937b73bf3840a","Department of Mechanical Engineering, Aligarh Muslim University, UP, Aligarh, India","Bhardwaj, S., Department of Mechanical Engineering, Aligarh Muslim University, UP, Aligarh, India; Khan, A.A., Department of Mechanical Engineering, Aligarh Muslim University, UP, Aligarh, India; Muzammil, M., Department of Mechanical Engineering, Aligarh Muslim University, UP, Aligarh, India","With the increasing rate of ambulatory disabilities and rise in the elderly population, advance methods to deliver the rehabilitation and assistive services to patients have become important. Lower limb robotic therapeutic and assistive aids have been found to improve the rehabilitation outcome. The article aims to present the updated understanding in the field of lower limb rehabilitation robotics and identify future research avenues. METHODS: Groups of keywords relating to assistive technology, rehabilitation robotics, and lower limb were combined and searched in EMBASE, IEEE Xplore Digital Library, Scopus, Web of Science and Google Scholar database. RESULTS: Based on the literature collected from the databases we provide an overview of the understanding of robotics in rehabilitation and state of the art devices for lower limb rehabilitation. Technological advancements in rehabilitation robotic architecture (sensing, actuation and control) and biomechanical considerations in design have been discussed. Finally, a discussion on the major advances, research directions, and challenges is presented. CONCLUSIONS: Although the use of robotics has shown a promising approach to rehabilitation and reducing the burden on caregivers, extensive and innovative research is still required in both cognitive and physical human-robot interaction to achieve treatment efficacy and efficiency. © 2021 - IOS Press. All rights reserved.","assistive devices; Biomechatronic; lower limb; rehabilitation robotics; robot architecture.","aged; human; lower limb; robotics; self help device; stroke rehabilitation; technology; Aged; Humans; Lower Extremity; Robotics; Self-Help Devices; Stroke Rehabilitation; Technology",,,,,"Council of Scientific and Industrial Research, India, CSIR: 09/112(0554)2K17","This work was supported by the Council of Scientific and Industrial Research (CSIR), New Delhi under the Senior Research Fellow (SRF) scheme. 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Grimmer, M., Riener, R., Walsh, C.J., Seyfarth, A., Mobility related physical and functional losses due to aging and disease-a motivation for lower limb exoskeletons (2019) Journal of NeuroEngineering and Rehabilitation., 16, pp. 1-21","Khan, A.A.; Department of Mechanical Engineering, UP, India; email: abid.khan.me@amu.ac.in",,,"IOS Press BV",,,,,10519815,,WORKF,"34180443","English","Work",Review,"Final","",Scopus,2-s2.0-85111084171
"Sano T., Horii T., Abe K., Nagai T.","57219850259;55842337200;55555739800;55624470947;","Temperament estimation of toddlers from child–robot interaction with explainable artificial intelligence",2021,"Advanced Robotics","35","17",,"1068","1077",,,"10.1080/01691864.2021.1955001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111053600&doi=10.1080%2f01691864.2021.1955001&partnerID=40&md5=0f81539c03ac16acbc40afac4add878d","Graduate School of Engineering Science, Osaka University, Osaka, Japan; Artificial Intelligence Exploration Research Center, The University of Electro-Communications, Tokyo, Japan","Sano, T., Graduate School of Engineering Science, Osaka University, Osaka, Japan; Horii, T., Graduate School of Engineering Science, Osaka University, Osaka, Japan; Abe, K., Artificial Intelligence Exploration Research Center, The University of Electro-Communications, Tokyo, Japan; Nagai, T., Graduate School of Engineering Science, Osaka University, Osaka, Japan, Artificial Intelligence Exploration Research Center, The University of Electro-Communications, Tokyo, Japan","Personality estimation is a vital ability for communicating with others. It can help robots that interact with humans to model various human behaviors with a few parameters. Numerous studies have proposed models for estimating human personality from human–robot interaction. However, a limited number of methods have focused on the personalities of toddlers, which are dominated by innate temperaments. In this study, we propose a regression model that estimates the toddler temperament from images acquired by a teleoperated childcare robot named ChiCaRo. We gather a dataset from actual interactions between toddlers and ChiCaRo, and extract features from the data to train the regression model. Moreover, an explainable Artificial Intelligence model known as Shapley additive explanations (SHAP) is employed to understand the estimation tendency of the trained model and to compare the tendency with the temperament definition. The proposed model achieved a mean squared error of 0.024 for the average of all temperament factors. The analysis of SHAP confirmed that the model could reasonably learn the tendency compared to the definition in most temperament factors and suggested the possibility of data bias under a specific temperament factor. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.","Child–robot interaction; explainable artificial intelligence; human–robot interaction","Agricultural robots; Artificial intelligence; Behavioral research; Intelligent robots; Mean square error; Regression analysis; Human behaviors; Mean squared error; Number of methods; Regression model; Robot interactions; Shapley; Teleoperated; Social robots",,,,,"Japan Society for the Promotion of Science, KAKEN: JP17J40236; New Energy and Industrial Technology Development Organization, NEDO; Japan Science and Technology Agency, JST: JPMJCR18A1","This work was supported by the New Energy and Industrial Technology Development Organization (NEDO), JST CREST Grant Number JPMJCR18A1, and Grant-in-Aid for JSPS Fellows JP17J40236.",,,,,,,,,,"Goldberg, L.R., An alternative “description of personality”: the big-five factor structure (1990) J Pers Soc Psychol, 59 (6), pp. 1216-1229; Cloninger, C.R., Svrakic, D.M., Przybeck, T.R., A psychobiological model of temperament and character (1993) Arch Gen Psychiatry, 50 (12), pp. 975-990. , Dec; Tapus, A., Ţăpuş, C., Matarić, M.J., User-robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy (2008) Intel Serv Robot, 1 (2), pp. 169-183. , Apr; Yorita, A., Egerton, S., Oakman, J., et al. 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In: Proceedings of the Second International Conference on Human-Agent Interaction; Oct; Tsukuba, Japan; 2014. p. 165–168; Thomas, A., Chess, S., Birch, H.G., The origin of personality (1970) Sci Am, 223 (2), pp. 102-109; Arrieta, A.B., Díaz-Rodríguez, N., Del Ser, J., Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI (2020) Inform Fusion, 58, pp. 82-115. , et al; Abe, K., Nagai, T., Hieida, C., Estimating children's personalities through their interaction activities with a tele-operated robot (2020) J Robot Mechatron, 32 (1), pp. 21-31. , Feb, et al; Sano, T., Horii, T., Abe, K., et al. Explainable temperament estimation of toddlers by a childcare robot. In: 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Naples, Italy; 2020 Aug 31–Sep 4. p. 159–164; Lundberg, S.M., Lee, S.I., A unified approach to interpreting model predictions. In: Advances Neural Information Processing Systems; Long Beach (CA); 2017 Dec 4–9. p. 4765–4774; Takei, Y., Terasaki, M., Kadota, M., Construction of new toddler temperament questionnaire (2007) Jpn J Personality, 16 (1), pp. 80-91. , Oct; Robert, L.P., Alahmad, R., Esterwood, C., et al. A review of personality human robot interactions. arXiv e-prints, 2020 Jan; Celiktutan, O., Skordos, E., Gunes, H., Multimodal human-human-robot interactions (mhhri) dataset for studying personality and engagement (2017) IEEE Trans Affect Comput, 10 (4), pp. 484-497; Devillers, L., Rosset, S., Duplessis, G.D., et al. Multimodal data collection of human-robot humorous interactions the joker project. In: 2015 International Conference on Affective Computing and Intelligent Interaction (ACII). 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Available from; Pedregosa, F., Varoquaux, G., Gramfort, A., Scikit-learn: machine learning in Python (2011) J Mach Learn Res, 12, pp. 2825-2830. , et al","Sano, T.; Graduate School of Engineering Science, Japan; email: t.sano@rlg.sys.es.osaka-u.ac.jp",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85111053600
"Neggers M.M.E., Cuijpers R.H., Ruijten P.A.M., IJsselsteijn W.A.","57205018969;6602683032;57211197783;6603812096;","Determining Shape and Size of Personal Space of a Human when Passed by a Robot",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00805-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109325193&doi=10.1007%2fs12369-021-00805-6&partnerID=40&md5=9fd4afc44cf1d0ff7f6d4800864282e6","Eindhoven University of Technology, P.O Box 513, Eindhoven, 5600MB, Netherlands","Neggers, M.M.E., Eindhoven University of Technology, P.O Box 513, Eindhoven, 5600MB, Netherlands; Cuijpers, R.H., Eindhoven University of Technology, P.O Box 513, Eindhoven, 5600MB, Netherlands; Ruijten, P.A.M., Eindhoven University of Technology, P.O Box 513, Eindhoven, 5600MB, Netherlands; IJsselsteijn, W.A., Eindhoven University of Technology, P.O Box 513, Eindhoven, 5600MB, Netherlands","Autonomous mobile robots that operate in environments with people are expected to be able to deal with human proxemics and social distances. Previous research investigated how robots can approach persons or how to implement human-aware navigation algorithms. However, experimental research on how robots can avoid a person in a comfortable way is largely missing. The aim of the current work is to experimentally determine the shape and size of personal space of a human passed by a robot. In two studies, both a humanoid as well as a non-humanoid robot were used to pass a person at different sides and distances, after which they were asked to rate their perceived comfort. As expected, perceived comfort increases with distance. However, the shape was not circular: passing at the back of a person is more uncomfortable compared to passing at the front, especially in the case of the humanoid robot. These results give us more insight into the shape and size of personal space in human–robot interaction. Furthermore, they can serve as necessary input to human-aware navigation algorithms for autonomous mobile robots in which human comfort is traded off with efficiency goals. © 2021, The Author(s).","Human-Aware Navigation; Human–Robot Proxemics; Personal Space","Agricultural robots; Air navigation; Anthropomorphic robots; Mechanical variables measurement; Mobile robots; Navigation; Autonomous Mobile Robot; Experimental research; Humanoid robot; Navigation algorithms; Personal spaces; Robot interactions; Shape and size; Social distance; Social robots",,,,,,,,,,,,,,,,"Aiello, J.R., A further look at equilibrium theory: visual interaction as a function of interpersonal distance (1977) Environ Psychol Nonverbal Behav, 1 (2), pp. 122-140; Argyle, M., Dean, J., Eye-contact, distance and affiliation (1965) Sociometry, pp. 289-304; Bailenson, J.N., Blascovich, J., Beall, A.C., Loomis, J.M., Equilibrium theory revisited: mutual gaze and personal space in virtual environments (2001) Presence Teleoperators Virtual Environ, 10 (6), pp. 583-598; Beck, S.J., Ollendick, T.H., Personal space, sex of experimenter, and locus of control in normal and delinquent adolescents (1976) Psychol Rep, 38 (2), pp. 383-387; Bemelmans, R., Gelderblom, G.J., Jonker, P., De Witte, L., Socially assistive robots in elderly care: a systematic review into effects and effectiveness (2012) J Am Med Dir Assoc, 13 (2), pp. 114-120; Bitgood, S., Dukes, S., Not another step! economy of movement and pedestrian choice point behavior in shopping malls (2006) Environ Behav, 38 (3), pp. 394-405; Charalampous, K., Kostavelis, I., Gasteratos, A., Robot navigation in large-scale social maps: an action recognition approach (2016) Expert Syst Appl, 66, pp. 261-273; Cuijpers, R.H., Kappers, A.M., Koenderink, J.J., On the role of external reference frames on visual judgements of parallelity (2001) Acta Psychol, 108 (3), pp. 283-302; Elkmann, N., Hortig, J., Fritzsche, M., Cleaning automation (2009) Springer Handbook of Automation, Springer, pp. 1253-1264; Gérin-Lajoie, M., Richards, C.L., Fung, J., McFadyen, B.J., Characteristics of personal space during obstacle circumvention in physical and virtual environments (2008) Gait Posture, 27 (2), pp. 239-247; Hall, E.T., (1966) The hidden dimension, , Anchor Books, New York; Hayduk, L.A., Personal space: an evaluative and orienting overview (1978) Psychol Bull, 85 (1), p. 117; Hayduk, L.A., The shape of personal space: an experimental investigation (1981) Can J Behav Sci Rev Canadienne Des Sciences du Comportement, 13 (1), p. 87; Hecht, H., Welsch, R., Viehoff, J., Longo, M.R., The shape of personal space (2019) Acta Psychol, 193, pp. 113-122; Helbing, D., Molnar, P., Social force model for pedestrian dynamics (1995) Phys Rev, 51 (5), p. 4282; Henry, P., Vollmer, C., Ferris, B., Fox, D., Learning to navigate through crowded environments (2010) 2010 IEEE International Conference on Robotics and Automation, IEEE, pp. 981-986; Kanda, A., Arai, M., Suzuki, R., Kobayashi, Y., Kuno, Y., Recognizing groups of visitors for a robot museum guide tour (2014) 2014 7Th International Conference on Human System Interactions (HSI), IEEE, pp. 123-128; Kirby, R., Simmons, R., Forlizzi, J., Companion: A constraint-optimizing method for person-acceptable navigation (2009) RO-MAN 2009-The 18Th IEEE International Symposium on Robot and Human Interactive Communication, IEEE, pp. 607-612; Koay, K.L., Syrdal, D., Bormann, R., Saunders, J., Walters, M.L., Dautenhahn, K., Initial design, implementation and technical evaluation of a context-aware proxemics planner for a social robot (2017) International Conference on Social Robotics, pp. 12-22. , Springer; Krueger, J., Extended cognition and the space of social interaction (2011) Conscious Cogn, 20 (3), pp. 643-657; Kruse, T., Pandey, A.K., Alami, R., Kirsch, A., Human-aware robot navigation: a survey (2013) Robot Autono Syst, 61 (12), pp. 1726-1743; Lichtenthäeler, C., Peters, A., Griffiths, S., Kirsch, A., Be a robot!: Robot navigation patterns in a path crossing scenario (2013) Proceedings of the 8Th ACM/IEEE International Conference on Human-Robot Interaction, pp. 181-182. , IEEE Press; Lo, S.Y., Yamaneki, K., Perception of pedestrian avoidance strategies of a self-balancing mobile robot (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 1243-1250; Luber, M., Spinello, L., Silva, J., Arras, K.O., Socially-aware robot navigation: A learning approach. 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In: 5th IEEE-RAS international conference on humanoid robots, 2005 (2005) IEEE, pp. 181-188; Sisbot, E.A., Marin-Urias, L.F., Alami, R., Simeon, T., A human aware mobile robot motion planner (2007) IEEE Trans Rob, 23 (5), pp. 874-883; Su, H., Qi, W., Hu, Y., Karimi, H.R., Ferrigno, G., de Momi, E., An incremental learning framework for human-like redundancy optimization of anthropomorphic manipulators (2020) IEEE Trans Industr Inf; Torta, E., Cuijpers, R.H., Juola, J.F., Design of a parametric model of personal space for robotic social navigation (2013) Int J Social Robot, 5 (3), pp. 357-365; Vasic, M., Billard, A., Safety issues in human-robot interactions. In: 2013 IEEE international conference on robotics and automation (2013) IEEE, pp. 197-204; Walters, M.L., Dautenhahn, K., Te Boekhorst, R., Koay, K.L., Kaouri, C., Woods, S., Nehaniv, C., Werry, I., (2005) The influence of subjects’ personality traits on personal spatial zones in a human-robot interaction experiment. Robot and Human Interactive Communication, pp. 347-352. , (2005) ROMAN 2005. IEEE International Workshop, IEEE; Walters, M.L., Dautenhahn, K., Te Boekhorst, R., Koay, K.L., Syrdal, D.S., Nehaniv, C.L., An empirical framework for human-robot proxemics (2009) New Front Hum Robot Interact","Neggers, M.M.E.; Eindhoven University of Technology, P.O Box 513, Netherlands; email: m.m.e.neggers@tue.nl",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85109325193
"Starke G., van den Brule R., Elger B.S., Haselager P.","57218095131;55599300400;7003895817;24448040000;","Intentional machines: A defence of trust in medical artificial intelligence",2021,"Bioethics",,,,"","",,1,"10.1111/bioe.12891","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108120400&doi=10.1111%2fbioe.12891&partnerID=40&md5=d6228794c4123953a25637106fc8aa71","Institute for Biomedical Ethics, University of Basel, Basel, Switzerland; Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands; Behavioral Science Institute, Radboud University, Nijmegen, Netherlands; University Center of Legal Medicine, University of Geneva, Geneva, Switzerland","Starke, G., Institute for Biomedical Ethics, University of Basel, Basel, Switzerland; van den Brule, R., Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands, Behavioral Science Institute, Radboud University, Nijmegen, Netherlands; Elger, B.S., Institute for Biomedical Ethics, University of Basel, Basel, Switzerland, University Center of Legal Medicine, University of Geneva, Geneva, Switzerland; Haselager, P., Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands","Trust constitutes a fundamental strategy to deal with risks and uncertainty in complex societies. In line with the vast literature stressing the importance of trust in doctor–patient relationships, trust is therefore regularly suggested as a way of dealing with the risks of medical artificial intelligence (AI). Yet, this approach has come under charge from different angles. At least two lines of thought can be distinguished: (1) that trusting AI is conceptually confused, that is, that we cannot trust AI; and (2) that it is also dangerous, that is, that we should not trust AI—particularly if the stakes are as high as they routinely are in medicine. In this paper, we aim to defend a notion of trust in the context of medical AI against both charges. To do so, we highlight the technically mediated intentions manifest in AI systems, rendering trust a conceptually plausible stance for dealing with them. Based on literature from human–robot interactions, psychology and sociology, we then propose a novel model to analyse notions of trust, distinguishing between three aspects: reliability, competence, and intentions. We discuss each aspect and make suggestions regarding how medical AI may become worthy of our trust. © 2021 John Wiley & Sons Ltd","artificial intelligence; healthcare; trust; trustworthiness",,,,,,,,,,,,,,,,,,"Starke, G.; Institute for Biomedical Ethics, Switzerland; email: georg.starke@unibas.ch",,,"Blackwell Publishing Ltd",,,,,02699702,,BIETE,,"English","Bioethics",Article,"Article in Press","",Scopus,2-s2.0-85108120400
"Groechel T., Pakkar R., Dasgupta R., Kuo C., Lee H., Cordero J., Mahajan K., Matarić M.J.","57215285175;57209640009;57224126281;57224127777;57220180694;57220179414;57219438287;7005611984;","Kinesthetic Curiosity: Towards Personalized Embodied Learning with a Robot Tutor Teaching Programming in Mixed Reality",2021,"Springer Proceedings in Advanced Robotics","19",,,"245","252",,,"10.1007/978-3-030-71151-1_22","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107055571&doi=10.1007%2f978-3-030-71151-1_22&partnerID=40&md5=1dcb419c7af21e87689d3e781256a550","University of Southern California, Los Angeles, CA  90089, United States","Groechel, T., University of Southern California, Los Angeles, CA  90089, United States; Pakkar, R., University of Southern California, Los Angeles, CA  90089, United States; Dasgupta, R., University of Southern California, Los Angeles, CA  90089, United States; Kuo, C., University of Southern California, Los Angeles, CA  90089, United States; Lee, H., University of Southern California, Los Angeles, CA  90089, United States; Cordero, J., University of Southern California, Los Angeles, CA  90089, United States; Mahajan, K., University of Southern California, Los Angeles, CA  90089, United States; Matarić, M.J., University of Southern California, Los Angeles, CA  90089, United States","Personalizing interactions in socially assistive robot (SAR) tutoring has shown promise with a wide variety of learners, especially when using multiple interaction modalities. Many of those interactions, however, focus on seated learning contexts, creating a need for multimodal personalization measures in kinesthetic (i.e., embodied) learning contexts. This paper proposes a multimodal measure of student kinesthetic curiosity (KCS ) that combines a student’s movement and curiosity measures into a single, personalized measure. This work evaluates the efficacy of KCS in a SAR tutor interaction by conducting a within-subjects (n= 9 ) pilot study where participants completed kinesthetic mixed reality coding exercises alongside a curious robot tutor whose actions were determined by KCS. The study results indicate that the stationarity assumptions needed for KCS were met and that the robot tutor was able to successfully use KCS to personalize its action policy, thereby positively affecting short term KCS. However, no significant results were found for longer state changes for each student. The mixed reality visual programming language (MoveToCode) created for this work has been made open-source. This work aims to inform future online features and measures for mixed reality human-robot interactions. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Augmented and mixed reality; Embodied learning; Socially assistive robotics; Visual programming languages",,,,,,"National Science Foundation, NSF: IIS-1925083","Acknowledgement. This research was supported by National Science Foundation National Robotics Initiative 2.0 grant for “Communicate, Share, Adapt: A Mixed Reality Framework for Facilitating Robot Integration and Customization” (NSF IIS-1925083).",,,,,,,,,,"Bloom, B.S., The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring (1984) Educ. Researcher, 13 (6), pp. 4-16; Clabaugh, C., Matarić, M., Escaping Oz: Autonomy in socially assistive robotics (2019) Ann. Rev. Control Rob. Auton. Syst., 2, pp. 33-61; Deng, E., Mutlu, B., Mataric, M.J., Embodiment in socially interactive robots. Found (2019) Trends R○ Rob, 7 (4), pp. 251-356; Groechel, T., Kuo, C., Dasgupta, R.: Interaction-lab/movetocode: Doi release (2020). https://doi.org/10.5281/zenodo.3924514; Groechel, T., Shi, Z., Pakkar, R., Mataric, M.J., Using socially expressive mixed reality arms for enhancing low-expressivity robots (2019) 2019 28Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE; Jain, S., Thiagarajan, B., Shi, Z., Clabaugh, C., Matarić, M.J., Modeling engagement in long-term, in-home socially assistive robot interventions for children with autism spectrum disorders (2020) Sci. Rob., 5 (39); Lipton, J.I., Fay, A.J., Rus, D., Baxter’s homunculus: Virtual reality spaces for teleoperation in manufacturing (2018) IEEE Rob. Autom. Lett., 3 (1), pp. 179-186; Macedonia, M., Embodied learning: Why at school the mind needs the body (2019) Front. Psychol., 10, p. 2098; Mariéthoz, J., Bengio, S., A unified framework for score normalization techniques applied to text-independent speaker verification (2005) IEEE Signal Process. Lett., 12 (7), pp. 532-535; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., (2009) Ros: An Open-Source Robot Operating System. In: ICRA Workshop on Open Source Software, 3, p. 5. , Kobe, Japan, vol., p; Schodde, T., Bergmann, K., Kopp, S., Adaptive robot language tutoring based on bayesian knowledge tracing and predictive decision-making (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 128-136. , , pp; Spaulding, S., Breazeal, C., Frustratingly easy personalization for real-time affect interpretation of facial expression (2019) 2019 8Th International Conference on Affective Computing and Intelligent Interaction (ACII), Pp. 531–537. IEEE; Walker, M., Hedayati, H., Lee, J., Szafir, D., Communicating robot motion intent with augmented reality (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 316-324. , , pp., ACM; Williams, T., Hirshfield, L., Tran, N., Grant, T., Woodward, N., Using augmented reality to better study human-robot interaction (2020) HCII Conference on Virtual, Augmented, and Mixed Reality; Williams, T., Szafir, D., Chakraborti, T., Soh Khim, O., Rosen, E., Booth, S., Groechel, T., Virtual, augmented, and mixed reality for human-robot interaction (Vam-hri) (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2020, Pp. 663–664","Groechel, T.; University of Southern CaliforniaUnited States; email: groechel@usc.edu",,,"Springer Science and Business Media B.V.",,,,,25111256,,,,"English","Springer. Proc. Adv. Robot.",Book Chapter,"Final","",Scopus,2-s2.0-85107055571
"De Oliveira Schultz Ascari R.E., Silva L., Pereira R.","57212466264;8668018600;7201455819;","Computer Vision applied to improve interaction and communication of people with motor disabilities: A systematic mapping",2021,"Technology and Disability","33","1",,"11","28",,2,"10.3233/TAD-200308","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106667025&doi=10.3233%2fTAD-200308&partnerID=40&md5=c46e0554a9164b4c153f156accb0c3b4","Department of Informatics, Federal University of Technology - Paraná, Pato Branco, PR, Brazil; Department of Informatics, Federal University of Parana, Curitiba, PR, Brazil","De Oliveira Schultz Ascari, R.E., Department of Informatics, Federal University of Technology - Paraná, Pato Branco, PR, Brazil; Silva, L., Department of Informatics, Federal University of Parana, Curitiba, PR, Brazil; Pereira, R., Department of Informatics, Federal University of Parana, Curitiba, PR, Brazil","BACKGROUND: The use of computers as a communication tool by people with disabilities can serve as an alternative effective to promote social interactions and the more inclusive and active participation of people in society. OBJECTIVE: This paper presents a systematic mapping of the literature that provides a survey of scientific contributions where Computer Vision is applied to enable users with motor and speech impairments to access computers easily, allowing them to exert their communicative abilities. METHODS: The mapping was conducted employing searches that identified 221 potentially eligible scientific articles published between 2009 and 2019, indexed by ACM, IEEE, Science Direct, and Springer databases. RESULTS: From the retrieved papers, 33 were selected and categorized into themes of this research interest: Human-Computer Interaction, Human-Machine Interaction, Human-Robot Interaction, Recreation, and surveys. Most of the chosen studies use sets of predefined gestures, low-cost cameras, and tracking a specific body region for gestural interaction. CONCLUSION: The results offer an overview of the Computer Vision techniques used in applied research on Assistive Technology for people with motor and speech disabilities, pointing out opportunities and challenges in this research domain. © 2021 - IOS Press. All rights reserved.","Assistive Technology; Communication; Computer Vision; Human-Computer Interaction; systematic mapping","applied research; Article; assistive technology; computer vision; controlled study; eye-tracking technology; gesture; human computer interaction; interpersonal communication; leisure; motor dysfunction; ScienceDirect; speech disorder",,,"Kinect, Microsoft","Microsoft","Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES","The authors thank Capes and CNPq by the support to this research.",,,,,,,,,,"Ascari, R.E.O.S., Silva, L., Pereira, R., Personalized gestural interaction applied in a gesture interactive gamebased approach for people with disabilities (2020) Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 100-110; Wang, R.H., Viswanathan, P., Czarnuch, S., Boger, J., Nejat, G., Mihailidis, A., Developing advanced assistive technologies for older adults with dementia: Lessons learned (2013) Proceedings of Rehabilitation Engineering Society of North America (RESNA)., pp. 22-24; Ascari, R.E.O.S., Pereira, R., Silva, L., Computer vision-based methodology to improve interaction for people with motor and speech impairmen (2020) ACM Transactions on Accessible Computing (ACM TACCESS)., 13 (4), pp. 1-33; Betke, M., Gips, J., Fleming, P., The Camera Mouse: Visual tracking of body features to provide computer access for people with severe disabilities (2002) IEEE Transactions on Neural Systems and Rehabilitation Engineering., 10 (1), pp. 1-10; Marnik, J., BlinkMouse-On-Screen Mouse Controlled by Eye Blinks (2014) Information Technologies in Biomedicine. Springer, 4, pp. 237-248; Ascari, R.E.O.S., Pereira, R., Silva, L., Towards a Methodology to Support Augmentative and Alternative Communication by Means of Personalized Gestural Interaction (2018) Proceedings of the 17th Brazilian Symposium on Human Factors in Computing Systems. ACM, p. 38; Montanini, L., Cippitelli, E., Gambi, E., Spinsante, S., Low complexity head tracking on portable android devices for real time message composition (2015) Journal on Multimodal User Interfaces., 9 (2), pp. 141-151; Ascari, R.E.O.S., Pereira, R., Silva, L., Mobile interaction for augmentative and alternative communication: A systematic mapping (2018) SBC Journal on 3D Interactive Systems., 9 (2), pp. 105-118; Mauri, C., Granollers Saltiveri, I.T., Lorés Vidal, J., García, M., Computer vision interaction for people with severe movement restrictions (2006) Human Technology: An Interdisciplinary Journal on Humans in ICT Environments., 2 (1), pp. 38-54; Morikawa, C., Lyons, M.J., Design and evaluation of vision-based head and face tracking interfaces for assistive input (2018) Smart Technologies: Breakthroughs in Research and Practice. 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Springer, pp. 1-60; Graham-Knight, K., Tzanetakis, G., Adaptive music technology using the Kinect (2015) Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, pp. 1-4; Kurauchi, A., Feng, W., Morimoto, C., Betke, M., HMAGIC: Head movement and gaze input cascaded pointing (2015) Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, pp. 1-4; Saleh, S., Berns, K., Nonverbal communication with a humanoid robot via head gestures (2015) Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments, pp. 1-8; Missimer, E., Betke, M., Blink and wink detection for mouse pointer control (2010) Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments, pp. 1-8; Paquette, I., Kwan, C., Betke, M., Menu controller: Making existing software more accessible for people with motor impairments (2011) Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 1-8; McMurrough, C., Rich, J., Metsis, V., Nguyen, A., Makedon, F., Low-cost head position tracking for gaze point estimation (2012) Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 1-4; Feng, W., Chen, M., Betke, M., Target reverse crossing: A selection method for camera-based mousereplacement systems (2014) Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 1-4; Ghanem, S., Conly, C., Athitsos, V., A survey on sign language recognition using smartphones (2017) Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 171-176; Schrammel, J., Leitner, M., Tscheligi, M., Semantically structured tag clouds: An empirical evaluation of clustered presentation approaches (2009) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2037-2040; Pal, S., Mangal, N.K., Khosla, A., Development of assistive application for patients with communication disability (2017) 2017 International Conference on Innovations in Green Energy and Healthcare Technologies (IGEHT). IEEE, pp. 1-4; Fine, G., Tsotsos, J.K., Examining the feasibility of face gesture detection using a wheelchair mounted camera (2009) Proceedings of the 1st ACM SIGMM International Workshop on Media Studies and Implementations That Help Improving Access to Disabled Users, pp. 19-28; Gao, G.W., Duan, X.Y., An overview of human-computer interaction based on the camera for disabled people (2011) Advanced Materials Research. 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Springer, pp. 1411-1419; Manresa-Yee, C., Varona, J., Perales, F.J., Salinas, I., Design recommendations for camera-based head-controlled interfaces that replace the mouse for motion-impaired users (2014) Universal Access in the Information Society., 13 (4), pp. 471-482; Pauly, L., Sankar, D., Non intrusive eye blink detection from low resolution images using HOG-SVM classifier (2016) International Journal of Image, Graphics & Signal Processing., 8 (10); Rosenberg, D., Scott, K., (1999) Use Case Driven Object Modeling with UML, , Springer; Kintsch, A., Depaula, R., A framework for the adoption of assistive technology (2002) SWAAAC 2002: Supporting Learning through Assistive Technology, pp. 1-10; Bhattacharya, S., Samanta, D., Basu, A., Performance models for automatic evaluation of virtual scanning keyboards (2008) IEEE Transactions on Neural Systems and Rehabilitation Engineering., 16 (5), pp. 510-519; Hanke, H., Knees, D., A phase-field damage model based on evolving microstructure (2017) Asymptotic Analysis., 101, pp. 149-180; Lefever, E., A hybrid approach to domainindependent taxonomy learning (2016) Applied Ontology., 11 (3), pp. 255-278; Meltzer, P.S., Kallioniemi, A., Trent, J.M., Chromosome alterations in human solid tumors (2002) The Genetic Basis of Human Cancer, pp. 93-113. , Vogelstein B, Kinzler KW, eds, New York: McGraw-Hill; Murray, P.R., Rosenthal, K.S., Kobayashi, G.S., Pfaller, M.A., (2002) Medical Microbiology, , 4th ed. 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Acta Medica Transilvanica., 22 (4); Hemmingsson, H., Ahlsten, G., Wandin, H., Rytterström, P., Borgestig, M., Eye-gaze control technology as early intervention for a non-verbal young child with high spinal cord injury: A case report (2018) Technologies., 6 (1), p. 12","De Oliveira Schultz Ascari, R.E.; Department of Informatics, Brazil; email: rubia@utfpr.edu.br",,,"IOS Press BV",,,,,10554181,,TEDIF,,"English","Technol. Disabil.",Article,"Final","",Scopus,2-s2.0-85106667025
"Cousin C., Duenas V., Dixon W.","57193090588;57193098845;57202484614;","Fes cycling and closed-loop feedback control for rehabilitative human–robot interaction",2021,"Robotics","10","2","61","","",,,"10.3390/robotics10020061","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105733512&doi=10.3390%2frobotics10020061&partnerID=40&md5=db311b84c101c2e2f7ec987853f20004","Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL  35487, United States; Department of Mechanical and Aerospace Engineering, Syracuse University, Syracuse, NY  13244, United States; Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL  32611, United States","Cousin, C., Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL  35487, United States; Duenas, V., Department of Mechanical and Aerospace Engineering, Syracuse University, Syracuse, NY  13244, United States; Dixon, W., Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL  32611, United States","For individuals with movement impairments due to neurological injuries, rehabilitative therapies such as functional electrical stimulation (FES) and rehabilitation robots hold vast potential to improve their mobility and activities of daily living. Combining FES with rehabilitation robots results in intimately coordinated human–robot interaction. An example of such interaction is FES cycling, where motorized assistance can provide high-intensity and repetitive practice of coordinated limb motion, resulting in physiological and functional benefits. In this paper, the development of multiple FES cycling testbeds and safeguards is described, along with the switched nonlinear dynamics of the cycle–rider system. Closed-loop FES cycling control designs are described for cadence and torque tracking. For each tracking objective, the authors’ past work on robust and adaptive controllers used to compute muscle stimulation and motor current inputs is presented and discussed. Experimental results involving both able-bodied individuals and participants with neurological injuries are provided for each combination of controller and tracking objective. Tradeoffs for the control algorithms are discussed based on the requirements for implementation, desired rehabilitation outcomes and resulting rider performance. Lastly, future works and the applicability of the developed methods to additional technologies including teleoperated robotics are outlined. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Adaptive control; Cycling; FES; Human–robot interaction; NMES; Nonlinear control; Rehabilitation; Robotics",,,,,,"National Science Foundation, NSF: 1762829","Funding: This research is supported in part by NSF award number 1762829. 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Control, 18, pp. 73-76; Szecsi, J., Straube, A., Fornusek, C., Comparison of the pedalling performance induced by magnetic and electrical stimulation cycle ergometry in able-bodied subjects (2014) Med. Eng. Phys, 36, pp. 484-489; Van der Schaft, A., (1996) L2-Gain and Passivity Techniques in Nonlinear Control, , Springer-Verlag London Ltd.: London, UK; Krstic, M., Kanellakopoulos, I., Kokotovic, P.V., (1995) Nonlinear and Adaptive Control Design, , John Wiley & Sons: New York, NY, USA; Liberzon, D., (2003) Switching in Systems and Control, , Birkhauser: Boston, MA, USA; Stegman, P., Crawford, C.S., Andujar, M., Nijholt, A., Gilbert, J.E., Brain-Computer Interface Software: A Review and Discussion (2020) IEEE Trans. Hum. Mach. Syst, 50, pp. 101-115","Dixon, W.; Department of Mechanical and Aerospace Engineering, United States; email: wdixon@ufl.edu",,,"MDPI AG",,,,,22186581,,,,"English","Robotics",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85105733512
"Zhang G., Jing W., Tao H., Rahman M.A., Salih S.Q., Al-Saffar A., Zhang R.","24333592000;55546532300;36350315600;55457938100;57203978808;57201725434;57208779192;","ADA-SR: Activity detection and analysis using security robots for reliable workplace safety",2021,"Work","68","3",,"935","943",,,"10.3233/WOR-203427","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103475585&doi=10.3233%2fWOR-203427&partnerID=40&md5=e02b2cc0098beeb79cfb38d3a5dea6ca","School of Computer Science, Baoji University of Arts and Sciences, Baoji, China; Faculty of Computing, Ibm CoE, and Earth Resources and Sustainability Center, Universiti Malaysia Pahang, Pahang, Malaysia; Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam; School of Electronics Engineering and Computer Science, Peking University, Beijing, China; Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi Mara, Shah Alam, Malaysia","Zhang, G., School of Computer Science, Baoji University of Arts and Sciences, Baoji, China; Jing, W., School of Computer Science, Baoji University of Arts and Sciences, Baoji, China; Tao, H., School of Computer Science, Baoji University of Arts and Sciences, Baoji, China, Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi Mara, Shah Alam, Malaysia; Rahman, M.A., Faculty of Computing, Ibm CoE, and Earth Resources and Sustainability Center, Universiti Malaysia Pahang, Pahang, Malaysia; Salih, S.Q., Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam; Al-Saffar, A., Faculty of Computing, Ibm CoE, and Earth Resources and Sustainability Center, Universiti Malaysia Pahang, Pahang, Malaysia; Zhang, R., School of Electronics Engineering and Computer Science, Peking University, Beijing, China","BACKGROUND: Human-Robot Interaction (HRI) has become a prominent solution to improve the robustness of real-time service provisioning through assisted functions for day-to-day activities. The application of the robotic system in security services helps to improve the precision of event detection and environmental monitoring with ease. OBJECTIVES: This paper discusses activity detection and analysis (ADA) using security robots in workplaces. The application scenario of this method relies on processing image and sensor data for event and activity detection. The events that are detected are classified for its abnormality based on the analysis performed using the sensor and image data operated using a convolution neural network. This method aims to improve the accuracy of detection by mitigating the deviations that are classified in different levels of the convolution process. RESULTS: The differences are identified based on independent data correlation and information processing. The performance of the proposed method is verified for the three human activities, such as standing, walking, and running, as detected using the images and sensor dataset. CONCLUSION: The results are compared with the existing method for metrics accuracy, classification time, and recall. © 2021 - IOS Press. All rights reserved.","convolution neural networks; event detection; HRI; Monitoring; sensor data processing","human; robotics; workplace; Humans; Neural Networks, Computer; Robotics; Workplace",,,,,"2019GY-131, 2020GY-078; Baoji University of Arts And Sciences, BUAS: ZK2018062","This work is supported by the doctoral scientific research initial funding project of Baoji University of Arts and Sciences (ZK2018062), the Key Research and Development Program in Shaanxi Province (2019GY-131) and Key Research and Development Program in Shaanxi Province(2020GY-078).",,,,,,,,,,"Lemaignan, S., Warnier, M., Sisbot, E.A., Clodic, A., Alami, R., Artificial cognition for social human-robot interaction: An implementation (2017) Artificial Intelligence., 247, pp. 45-69; Mead, R., Matarić, M.J., Autonomous human-robot proxemics: Socially aware navigation based on interaction potential (2017) Autonomous Robots., 41 (5), pp. 1189-1201; Peternel, L., Tsagarakis, N., Ajoudani, A., A human-robot co-manipulation approach based on human sensorimotor information (2017) Ieee Transactions on Neural Systems and Rehabilitation Engineering., 25 (7), pp. 811-822; 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"Allan D.D., Vonasch A.J., Bartneck C.","57222556024;16425076400;6602256988;","The Doors of Social Robot Perception: The Influence of Implicit Self-theories",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00767-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103173147&doi=10.1007%2fs12369-021-00767-9&partnerID=40&md5=f0f207502b3177a113e90c2c9dc15a36","Human Interface Technology Lab NZ, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand; Department of Psychology, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand","Allan, D.D., Human Interface Technology Lab NZ, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand; Vonasch, A.J., Department of Psychology, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand; Bartneck, C., Human Interface Technology Lab NZ, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand","Understanding people’s perceptions and inferences about social robots and, thus, their responses toward them, constitutes one of the most pervasive research themes in the field of Human–Robot interaction today. We herein augment and extend this line of work by investigating, for the first time, the novel proposition that one’s implicit self-theory orientation (underlying beliefs about the malleability of self-attributes, such as one’s intelligence), can influence one’s perceptions of emerging social robots developed for everyday use. We show that those who view self-attributes as fixed (entity theorists) express greater robot anxiety than those who view self-attributes as malleable (incremental theorists). This result holds even when controlling for well-known covariate influences, like prior robot experience, media exposure to science fiction, technology commitment, and certain demographic factors. However, only marginal effects were obtained for both attitudinal and intentional robot acceptance, respectively. In addition, we show that incremental theorists respond more favorably to social robots, compared to entity theorists. Furthermore, we find evidence indicating that entity theorists exhibit more favorable responses to a social robot positioned as a servant. We conclude with a discussion about our findings. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Human–Robot interaction; Implicit self-theories; Mindset; Robot anxiety; Social robots","Agricultural robots; Economic and social effects; Covariates; Demographic factors; Marginal effects; Robot acceptances; Robot interactions; Robot perception; Science fictions; Technology commitment; Social robots",,,,,,"This research was supported by the University of Canterbury, New Zealand.",,,,,,,,,,"ACM/IEEE International Conference on Human–Robot Interaction (2020) ISBN 9781450367462, , Association for Computing Machinery, Cambridge, UK; Aiken, L.S., West, S.G., Reno, R.R., (1991) Multiple regression: testing and interpreting interactions, , Sage, Thousand Oaks; Aldahdouh, T.Z., Nokelainen, P., Korhonen, V., Innovativeness of staff in higher education: Do implicit theories and goal orientations matter? 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In: 2008 IEEE/RSJ international conference on intelligent robots and systems (2008) IEEE, pp. 3816-3821; Reich-Stiebert, N., Eyssel, F., Learning with educational companion robots? toward attitudes on education robots, predictors of attitudes, and application potentials for education robots (2015) Int J Social Robot, 7 (5), pp. 875-888; Reich-Stiebert, N., Eyssel, F., Hohnemann, C., Involve the user! changing attitudes toward robots by user participation in a robot prototyping process (2019) Comput Hum Behav, 91, pp. 290-296; Robb, D.A., Ahmad, M.I., Tiseo, C., Aracri, S., McConnell, A.C., Page, V., Dondrup, C., Pairet, E., Robots in the danger zone: Exploring public perception through engagement (2020) Proceedings of the 2020 ACM/IEEE International Conference on human–robot Interaction, pp. 93-102; Robert, L., Alahmad, R., Esterwood, C., Kim, S., You, S., Zhang, Q., A review of personality in humanrobot interactions (2020) Available at SSRN; Robinette, P., Howard, A.M., Wagner, A.R., Effect of robot performance on humanrobot trust in time-critical situations (2017) IEEE Trans Hum Mach Syst, 47 (4), pp. 425-436; Robins, R.W., Pals, J.L., Implicit self-theories in the academic domain: implications for goal orientation, attributions, affect, and self-esteem change (2002) Self Identity, 1 (4), pp. 313-336; Robinson, J., Rosenzweig, C., Moss, A.J., Litman, L., Tapped out or barely tapped? recommendations for how to harness the vast and largely unused potential of the mechanical turk participant pool (2019) PLoS ONE, 14 (12); Rossi, A., Dautenhahn, K., Koay, K.L., Saunders, J., Investigating human perceptions of trust in robots for safe hri in home environments (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on human–robot Interaction, pp. 375-376; Rossi, A., Dautenhahn, K., Koay, K.L., Walters, M.L., The impact of peoples personal dispositions and personalities on their trust of robots in an emergency scenario (2018) Paladyn J Behav Robot, 9 (1), pp. 137-154; Rossi, A., Dautenhahn, K., Koay, K.L., Walters, M.L., Holthaus, P., Evaluating peoples perceptions of trust in a robot in a repeated interactions study (2020) International Conference on Social Robotics, pp. 453-465. , Springer; Rucker, D.D., Galinsky, A.D., Growing beyond growth: Why multiple mindsets matter for consumer behavior (2016) J Consum Psychol, 26 (1), pp. 161-164; Sandoval, E.B., Mubin, O., Obaid, M., Human robot interaction and fiction: A contradiction (2014) International Conference on Social Robotics, pp. 54-63. , Springer; Santamaria, T., Nathan-Roberts, D., Personality measurement and design in Human-Robot interaction: A systematic and critical review (2017) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61, pp. 853-857. , SAGE Publications Sage CA, Los Angeles, CA; Schaefer, K.E., (2016) Measuring Trust in Human Robot Interactions: Development of the Trust Perception Scale-Hri, pp. 191-218. , Springer; Schermerhorn, P., Scheutz, M., Crowell, C.R., Robot social presence and gender: Do females view robots differently than males? In: Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction, ACM (2008) Pp 263–270; Severson, R.L., Carlson, S.M., Behaving as or behaving as if? Childrens conceptions of personified robots and the emergence of a new ontological category (2010) Neural Netw, 23 (8-9), pp. 1099-1103; Sevincer, A.T., Kluge, L., Oettingen, G., Implicit theories and motivational focus: desired future versus present reality (2014) Motiv Emot, 38 (1), pp. 36-46; Sharifi, S.S., Palmeira, M., Customers’ reactions to technological products: The impact of implicit theories of intelligence (2017) Comput Hum Behav, 77, pp. 309-316; Solberg, E., Traavik, L.E., Wong, S.I., Digital mindsets: recognizing and leveraging individual beliefs for digital transformation (2020) California Management Review, , (,),., p; Song, Y.A., Lee, S.Y., Kim, Y., Does mindset matter for using social networking sites?: Understanding motivations for and uses of instagram with growth versus fixed mindset (2019) International Journal of Advertising, pp. 1-19; Strait, M., Urry, H.L., Muentener, P., Children’s responding to humanlike agents reflects an uncanny valley (2019) 2019 14Th ACM/IEEE International Conference on human–robot Interaction (HRI), IEEE, pp. 506-515; Strait, M.K., Aguillon, C., Contreras, V., Garcia, N., The public’s perception of humanlike robots: Online social commentary reflects an appearance-based uncanny valley, a general fear of a technology takeover and the unabashed sexualization of female-gendered robots. In: 2017 26th IEEE international symposium on robot and human interactive communication (RO-MAN) (2017) IEEE, pp. 1418-1423; Takayama, L., Ju, W., Nass, C., Beyond dirty, dangerous and dull: What everyday people think robots should do (2008) 2008 3Rd ACM/IEEE International Conference on human–robot Interaction (HRI), IEEE, pp. 25-32; Tamir, M., John, O.P., Srivastava, S., Gross, J.J., Implicit theories of emotion: Affective and social outcomes across a major life transition (2007) J Pers Soc Psychol, 92 (4), p. 731; Walters, M.L., Koay, K.L., Syrdal, D.S., Dautenhahn, K., Te Boekhorst, R., Preferences and perceptions of robot appearance and embodiment in Human–Robot interaction trials (2009) Procs of New Frontiers in Human–Robot Interaction; Wheeler, S.C., Omair, A., Potential growth areas for implicit theories research (2016) J Consumer Psychol, 26 (1), pp. 137-141; Wullenkord, R., Eyssel, F., The influence of robot number on robot group perceptiona call for action (2020) ACM THRI, 9 (4), pp. 1-14; Xia, Y., Letendre, G., Robots for future classrooms: A cross-cultural validation study of negative attitudes toward robots scale in the us context (2020) Int J Soc Robot, pp. 1-12; Xu, K., First encounter with robot alpha: How individual differences interact with vocal and kinetic cues in users social responses (2019) New Media Society P; Yogeeswaran, K., Zotowski, J., Livingstone, M., Bartneck, C., Sumioka, H., Ishiguro, H., The interactive effects of robot anthropomorphism and robot ability on perceived threat and support for robotics research (2016) J Hum Robot Interact, 5 (2), pp. 29-47; Yorkston, E.A., Nunes, J.C., Matta, S., The malleable brand: the role of implicit theories in evaluating brand extensions (2010) J Market, 74 (1), pp. 80-93; Zotowski, J., Yogeeswaran, K., Bartneck, C., Can we control it? autonomous robots threaten human identity, uniqueness, safety, and resources (2017) Int J Hum Comput Stud, 100, pp. 48-54","Allan, D.D.; Human Interface Technology Lab NZ, Private Bag 4800, New Zealand; email: dwain.allan@pg.canterbury.ac.nz",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Article in Press","",Scopus,2-s2.0-85103173147
"Lee J.-Y., Cho S.J., Seo Y.-S., Park C.-H., Park D.-I., Kim B.-I., Kim H.-S., Song S.-H.","57211630772;37025610000;57211629568;57218766795;35743433100;57211975380;57209188138;55241747000;","Shape-adaptive stiffness variable soft gripper using porous structure",2021,"Journal of Institute of Control, Robotics and Systems","27","3",,"238","246",,,"10.5302/J.CROS.2021.20.0203","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103051420&doi=10.5302%2fJ.CROS.2021.20.0203&partnerID=40&md5=696ccd2b28d79316ce7d1ed0dbb07fc5","Department of Robotics & Mechatronics, Korea Institute of Machinery & Materials, Daejeon, South Korea; School of Mechanical Engineering, Chungnam National University, Daejeon, South Korea","Lee, J.-Y., Department of Robotics & Mechatronics, Korea Institute of Machinery & Materials, Daejeon, South Korea, School of Mechanical Engineering, Chungnam National University, Daejeon, South Korea; Cho, S.J., School of Mechanical Engineering, Chungnam National University, Daejeon, South Korea; Seo, Y.-S., Department of Robotics & Mechatronics, Korea Institute of Machinery & Materials, Daejeon, South Korea, School of Mechanical Engineering, Chungnam National University, Daejeon, South Korea; Park, C.-H., Department of Robotics & Mechatronics, Korea Institute of Machinery & Materials, Daejeon, South Korea; Park, D.-I., Department of Robotics & Mechatronics, Korea Institute of Machinery & Materials, Daejeon, South Korea; Kim, B.-I., Department of Robotics & Mechatronics, Korea Institute of Machinery & Materials, Daejeon, South Korea; Kim, H.-S., Department of Robotics & Mechatronics, Korea Institute of Machinery & Materials, Daejeon, South Korea; Song, S.-H., Department of Robotics & Mechatronics, Korea Institute of Machinery & Materials, Daejeon, South Korea","Robotic grippers are essential components used in robots to perform certain tasks. Effective gripping capabilities are important for industrial and service robots. However, the parallel-type gripper, which is one of the most widely used robotic grippers, has limitations in terms of the objects that it can handle as well as applying the correct handling force, especially when handling fragile objects. Hence, a soft gripper fabricated using soft materials is proposed as a new alternative, with the advantage of preventing injuries when human-robot interaction is required. However, the limited holding force and uncontrollable movement of a soft gripper, due to its softness, could limit its use in real-life conditions. Therefore, in this article, we propose a shape-adaptive tip using a variable stiffness mechanism. To decrease the initial stiffness of the gripper tip, the reticulated polyurethane foam structure was embedded in the tip as the soft supporting layer to support the external force. The flexible mesh structure was applied as the jamming layer to change the stiffness of the tip. The prototype of the gripping system was tested, and the gripper could transfer the grape effectively without damage using the jamming mechanism. © ICROS 2021.","Parallel gripper; Soft robotics; Universal gripper; Variable stiffness","Agricultural robots; Human robot interaction; Industrial robots; Jamming; Robotics; Stiffness; Fragile objects; Holding forces; Initial stiffness; Mesh structures; Porous structures; Reticulated polyurethane foams; Robotic grippers; Variable stiffness mechanism; Grippers",,,,,,,,,,,,,,,,"Han, M.G., Koo, J.C., DMP Accuracy improvement to facilitate learning from demonstration for industrial cooperative robots (2020) Journal of institute of control, robotics and systems (in korean), 26 (12), pp. 1062-1066. , Dec; Bicchi, A., Kumar, V., Robotic grasping and contact: A review (2000) IEEE International Conference on Robotics and Automation., 1, pp. 348-353. , Apr; Laschi, C., Cianchetti, M., Soft robotics: New perspectives for robot bodyware and control (2014) Frontiers in bioengineering and biotechnology, 2 (3). , Jan; Sangbae, K., Laschi, C., Trimmer, B., Soft robotics: A bioinspired evolution in robotics (2013) Trends in biotechnology, 31 (5), pp. 287-294. , May; Arachchige, D.D., Chen, Y., Walker, I.D., Godage, I.S., A novel variable stiffness soft robotic gripper (2020) arXiv preprint arXiv:2010.11473, , Oct; Balak, R., Mazumdar, Y.C., Multi-modal pneumatic actuator for twisting, extension, and bending (2020) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 25 (29). , Oct; Pizza, C., Cosimo, D.S., Manuel, C., Giorgio, G., Manolo, G., Antonio, B., Softhand pro-d: Matching dynamic content of natural user commands with hand embodiment for enhanced prosthesis control (2016) IEEE International Conference on Robotics and Automation (ICRA), pp. 3516-3523. , May; Matcei, C., Fernando, F.M., Holmberg, R., Hawke, J., Schlicht, M., Jeff, G., Scott, S., Bahadur, R., The Velo gripper: A versatile single-actuator design for enveloping, parallel and fingertip grasps (2014) International Journal of Robotics Research, 33 (5), pp. 753-767. , Feb; Backus, S.B., Dollar, A.M., Dollar, Aaron M. An adaptive three-fingered prismatic gripper with passive rotational joints (2016) IEEE Robotics and Automation Letters, 1 (2), pp. 668-675. , Jul; Tavakoli, M., de Almeida, A.T., Adaptive under-actuated anthropomorphic hand: ISR-SoftHand (2014) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1629-1634. , Sep; Zhou, J., Chen, S., Wang, Z., A soft-robotic gripper with enhanced object adaptation and grasping reliability (2017) IEEE Robotics and automation letters, 2 (4), pp. 2287-2293. , Jun; Chang, C.-M., Gerez, L., Elangovan, N., Zisimatos, A., Liarokapis, M., On alternative uses of structural compliance for the development of adaptive robot grippers and hands (2019) Frontiers in neurorobotics, 13, pp. 1-16. , Nov; Choi, D.S., Lee, J.E., Hwang, M.J., Design of flexible gripper for unstructured object manipulation (2020) Journal of institute of control, robotics and systems (in Korean), 26 (3), pp. 184-190. , Jul; Galloway, K.C., Becker, K.P., Phillips, B., Kirby, J., Licht, S., Tchernov, D., Robert, J.W., Gruber, D.F., Soft robotic grippers for biological sampling on deep reefs (2016) Soft robotics, 3 (1), pp. 23-33. , Mar; Tsugami, Y., Barbié, T., Tadakuma, K., Nishida, T., Development of universal parallel gripper using reformed magnetorheological fluid (2017) 2017 11th Asian Control Conference (ASCC). IEEE, pp. 778-783. , Dec; Pettersson, A., Davis, S., Gray, J.O., Dodd, T.J., Ohlsson, T., Design of a magnetorheological robot gripper for handling of delicate food products with varying shapes (2010) Journal of Food Engineering, 98 (3), pp. 332-338. , Jun; Wang, W., Ahn, S.H., Shape memory alloy-based soft gripper with variable stiffness for compliant and effective grasping (2017) Soft Robot, 4 (4), pp. 379-389. , Dec; Zhu, M., Mori, Y., Wakayama, T., Wada, A., Kawamura, S., A fully multi-material three-dimensional printed soft gripper with variable stiffness for robust grasping (2019) Soft Robot, 6 (4), pp. 507-519. , Aug; Brown, E., Rodenberg, N., Amend, J., Mozeika, A., Steltz, E., Zakin, M.R., Hod, L., Heinrich, M.J., Universal robotic gripper based on the jamming of granular material (2010) Proc. Natl. Acad. Sci, 107 (44), pp. 18809-18814. , Nov; Amend, J., Lipson, H., The JamHand: Dexterous manipulation with minimal actuation (2017) Soft Robot, 4 (1), pp. 70-80. , Mar","Song, S.-H.; Department of Robotics & Mechatronics, South Korea; email: shsong@kmm.re.kr",,,"Institute of Control, Robotics and Systems",,,,,19765622,,,,"Korean","J. Inst. Control Rob. Syst.",Article,"Final","",Scopus,2-s2.0-85103051420
"Strübing T., Ziaeetabar F., Wörgötter F.","57218890015;57195425011;7003438923;","A summarized semantic structure to represent manipulation actions",2021,"VISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","4",,,"370","379",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102995580&partnerID=40&md5=d0d3aabb00c3e2d57fe464e032a9c393","Göttingen University, Institute for Physics 3, Biophysics and Bernstein Center for Computational Neuroscience, Friedrich-Hund-Platz 1, Göttingen, 37077, Germany","Strübing, T., Göttingen University, Institute for Physics 3, Biophysics and Bernstein Center for Computational Neuroscience, Friedrich-Hund-Platz 1, Göttingen, 37077, Germany; Ziaeetabar, F., Göttingen University, Institute for Physics 3, Biophysics and Bernstein Center for Computational Neuroscience, Friedrich-Hund-Platz 1, Göttingen, 37077, Germany; Wörgötter, F., Göttingen University, Institute for Physics 3, Biophysics and Bernstein Center for Computational Neuroscience, Friedrich-Hund-Platz 1, Göttingen, 37077, Germany","To represent human manipulation actions in a simple and understandable way, we had proposed a framework called enriched semantic event chains (eSEC) which creates a temporal sequence of static and dynamic spatial relations between objects in a manipulation. The eSEC framework has so far only been used in manipulation actions consisting of one hand. As the eSECs descriptors are in the form of huge matrices, we need to have a concise version of them. Here, we want to extend this framework to interactions which involve more hands. Therefore, we applied statistical and semantic analyses to summarize the current eSEC while preserving its important features and introducing an enhanced eSEC (e2SEC). This summarization is done by reducing the number of rows in an eSEC matrix and merging semantic spatial relations between manipulated objects. Eventually, we presented the new e2SEC framework which has 20% fewer rows, 16.7% less static spatial and 11.1% less dynamic spatial relations while still maintaining the eSEC efficiency in recognition and differentiation of manipulation actions. This simplification paves the way for a simpler recognition and predicting complex actions and interactions in a shorter time and is beneficial in real time applications such as human-robot interactions. Copyright © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.","Enriched Semantic Event Chain; Semantic Action Representation; Spatial Relations; Statistical Analysis","Computer graphics; Semantics; Social robots; Human manipulation; Important features; Manipulated objects; Real-time application; Recognition and predicting; Semantic analysis; Semantic structures; Temporal sequences; Computer vision",,,,,,,,,,,,,,,,"Aksoy, E., Abramov, A., Dörr, J., Ning, K., Dellen, B., Wörgötter, F., Learning the semantics of object-action relations by observation (2011) I. J. Robotic Res; Avola, D., Bernardi, M., Foresti, G. L., Fusing depth and colour information for human action recognition (2019) Multimedia Tools and Applications; Borràs, J., Mandery, C., Asfour, T., A wholebody support pose taxonomy for multi-contact humanoid robot motions (2017) Science Robotics; Contero, M., Naya, F., Company, P., Saorín, J., Learning support tools for developing spatial abilities in engineering design (2006) International Journal of Engineering Education; Crockett, T. M., Powell, M. W., Shams, K. S., Spatial planning for robotics operations (2009) 2009 IEEE Aerospace conference; Fei-Fei, L., Perona, P., A bayesian hierarchical model for learning natural scene categories (2005) 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05); Khan, M., Javed, K., Saba, T., Habib, U., Human action recognition using fusion of multiview and deep features: An application to video surveillance (2020) Multimedia Tools and Applications; Kuncheva, L. I., (2004) Combining Pattern Classifiers: Methods and Algorithms, , Wiley-Interscience; Mandery, C., Terlemez, O., Do, M., Vahrenkamp, N., Asfour, T., The kit whole-body human motion database (2015) International Conference on Advanced Robotics (ICAR); Park, J. A., Kim, Y. S., Cho, J. Y., Visual reasoning as a critical attribute in design creativity (2006) Proceedings of International Design Research Symposium; Qi, M., Wang, Y., Qin, J., Li, A., Luo, J., Van Gool, L., stagnet: An attentive semantic rnn for group activity and individual action recognition (2019) IEEE Transactions on Circuits and Systems for Video Technology; Sridhar, M., Cohn, A., Hogg, D. C., Learning functional object-categories from a relational spatiotemporal representation (2008) ECAI; Wei, Y., Brunskill, E., Kollar, T., Roy, N., Where to go: Interpreting natural directions using global inference (2009) 2009 IEEE International Conference on Robotics and Automation; Wörgötter, F., Ziaeetabar, F., Pfeiffer, S., Kaya, O., Kulvicius, T., Tamosiunaite, M., Humans predict action using grammar-like structures (2020) Scientific reports; Ziaeetabar, F., Aksoy, E. E., Wörgötter, F., Tamosiunaite, M., Semantic analysis of manipulation actions using spatial relations (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA); Ziaeetabar, F., Kulvicius, T., Tamosiunaite, M., Wörgötter, F., Prediction of manipulation action classes using semantic spatial reasoning (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Ziaeetabar, F., Kulvicius, T., Tamosiunaite, M., Wörgötter, F., Recognition and prediction of manipulation actions using enriched semantic event chains (2018) Robotics and Autonomous Systems",,"Farinella G.M.Radeva P.Braz J.Bouatouch K.","Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","SciTePress","16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2021","8 February 2021 through 10 February 2021",,167535,,9789897584886,,,"English","VISIGRAPP - Proc. Int. Jt. Conf. Comput. Vis., Imaging Comput. Graph. Theory Appl.",Conference Paper,"Final","",Scopus,2-s2.0-85102995580
"Aqdus Ilyas C.M., Nunes R., Nasrollahi K., Rehm M., Moeslund T.B.","57202323833;57222517071;24476795100;10039896700;6507267791;","Deep emotion recognition through upper body movements and facial expression",2021,"VISIGRAPP 2021 - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","5",,,"669","679",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102970170&partnerID=40&md5=2c1005429d4e1dc007d0706537338d2b","Department of Electronic Systems, Aalborg University, Aalborg, Denmark; Visual Analysis of People Lab., Aalborg University, Aalborg, Denmark; Human-Robot Interaction Lab., Aalborg University, Aalborg, Denmark","Aqdus Ilyas, C.M., Visual Analysis of People Lab., Aalborg University, Aalborg, Denmark, Human-Robot Interaction Lab., Aalborg University, Aalborg, Denmark; Nunes, R., Department of Electronic Systems, Aalborg University, Aalborg, Denmark; Nasrollahi, K., Visual Analysis of People Lab., Aalborg University, Aalborg, Denmark; Rehm, M., Human-Robot Interaction Lab., Aalborg University, Aalborg, Denmark; Moeslund, T.B., Visual Analysis of People Lab., Aalborg University, Aalborg, Denmark","Despite recent significant advancements in the field of human emotion recognition, applying upper body movements along with facial expressions present severe challenges in the field of human-robot interaction. This article presents a model that learns emotions through upper body movements and corresponds with facial expressions. Once this correspondence is mapped, tasks such as emotion and gesture recognition can easily be identified using facial features and movement vectors. Our method uses a deep convolution neural network trained on benchmark datasets exhibiting various emotions and corresponding body movements. Features obtained through facial movements and body motion are fused to get emotion recognition performance. We have implemented various fusion methodologies to integrate multimodal features for non-verbal emotion identification. Our system achieves 76.8% accuracy of emotion recognition through upper body movements only, surpassing 73.1% on the FABO dataset. In addition, employing multimodal compact bilinear pooling with temporal information surpassed the state-of-the-art method with an accuracy of 94.41% on the FABO dataset. This system can lead to better human-machine interaction by enabling robots to recognize emotions and body actions and react according to their emotions, thus enriching the user experience. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.","Body movements; Convolutional neural networks; Deep learning; Emotion recognition; Facial expressions","Computer graphics; Computer vision; Deep neural networks; Face recognition; Man machine systems; Speech recognition; User experience; Convolution neural network; Emotion identifications; Emotion recognition; Human emotion recognition; Human machine interaction; Multimodal features; State-of-the-art methods; Temporal information; Social robots",,,,,,,,,,,,,,,,"Agrafioti, F., Hatzinakos, D., Anderson, A. K., Ecg pattern analysis for emotion detection (2011) IEEE Transactions on Affective Computing, 3 (1), pp. 102-115; Arriaga, O., Valdenegro-Toro, M., Plöger, P., (2017) Real-time convolutional neural networks for emotion and gender classification, , arXiv preprint arXiv:1710.07557; Augello, A., Dignum, F., Gentile, M., Infantino, I., Maniscalco, U., Pilato, G., Vella, F., A social practice oriented signs detection for human-humanoid interaction (2018) Biologically inspired cognitive architectures, 25, pp. 8-16; Bänziger, T., Grandjean, D., Scherer, K. R., Emotion recognition from expressions in face, voice, and body: the multimodal emotion recognition test (mert) (2009) Emotion, 9 (5), p. 691; Bänziger, T., Mortillaro, M., Scherer, K. R., Introducing the geneva multimodal expression corpus for experimental research on emotion perception (2012) Emotion, 12 (5), p. 1161; Barros, P., Jirak, D., Weber, C., Wermter, S., Multimodal emotional state recognition using sequence-dependent deep hierarchical features (2015) Neural Networks, 72, pp. 140-151; Barros, P., Parisi, G. I., Jirak, D., Wermter, S., Real-time gesture recognition using a humanoid robot with a deep neural architecture (2014) 2014 IEEE-RAS International Conference on Humanoid Robots, pp. 646-651. , IEEE; Barros, P., Wermter, S., Developing crossmodal expression recognition based on a deep neural model (2016) Adaptive behavior, 24 (5), pp. 373-396; Breazeal, C., Emotion and sociable humanoid robots (2003) International journal of human-computer studies, 59 (1-2), pp. 119-155; Chen, S., Tian, Y., Liu, Q., Metaxas, D. N., Recognizing expressions from face and body gesture by temporal normalized motion and appearance features (2013) Image and Vision Computing, 31 (2), pp. 175-185; Chollet, F., (2016) Xception: deep learning with depthwise separable convolutions, , corr abs/1610.02357 (2016). arXiv preprint arXiv:1610.02357; de Gelder, B., De Borst, A., Watson, R., The perception of emotion in body expressions (2015) Wiley Interdisciplinary Reviews: Cognitive Science, 6 (2), pp. 149-158; Ekman, P., Friesen, W. V., Ellsworth, P., (2013) Emotion in the human face: Guidelines for research and an integration of findings, 11. , Elsevier; Fukui, A., Park, D. H., Yang, D., Rohrbach, A., Darrell, T., Rohrbach, M., (2016) Multimodal compact bilinear pooling for visual question answering and visual grounding, , arXiv preprint arXiv:1606.01847; Glowinski, D., Dael, N., Camurri, A., Volpe, G., Mortillaro, M., Scherer, K., Toward a minimal representation of affective gestures (2011) IEEE Transactions on Affective Computing, 2 (2), pp. 106-118; Gunes, H., Piccardi, M., A bimodal face and body gesture database for automatic analysis of human nonverbal affective behavior (2006) 18th International Conference on Pattern Recognition (ICPR’06), 1, pp. 1148-1153. , pages IEEE; Gunes, H., Piccardi, M., Bi-modal emotion recognition from expressive face and body gestures (2007) Journal of Network and Computer Applications, 30 (4), pp. 1334-1345; Gunes, H., Piccardi, M., Automatic temporal segment detection and affect recognition from face and body display (2008) IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39 (1), pp. 64-84; Ilyas, C. M. A., Haque, M. A., Rehm, M., Nasrollahi, K., Moeslund, T. B., Facial expression recognition for traumatic brain injured patients (2018) VISIGRAPP (4: VISAPP), pp. 522-530. , pages; Ilyas, C. M. A., Nasrollahi, K., Rehm, M., Moeslund, T. B., Rehabilitation of traumatic brain injured patients: Patient mood analysis from multimodal video (2018) 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 2291-2295. , pages IEEE; Jerritta, S., Murugappan, M., Nagarajan, R., Wan, K., Physiological signals based human emotion recognition: a review (2011) 2011 IEEE 7th International Colloquium on Signal Processing and its Applications, pp. 410-415. , IEEE; Karpouzis, K., Caridakis, G., Kessous, L., Amir, N., Raouzaiou, A., Malatesta, L., Kollias, S., Modeling naturalistic affective states via facial, vocal, and bodily expressions recognition (2007) Artifical intelligence for human computing, pp. 91-112. , Springer; Khorrami, P., Le Paine, T., Brady, K., Dagli, C., Huang, T. 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Int. Jt. Conf. Comput. Vis., Imaging Comput. Graph. Theory Appl.",Conference Paper,"Final","",Scopus,2-s2.0-85102970170
"Cappuccio M.L., Galliott J.C., Sandoval E.B.","56524371200;55200353100;37057915200;","Saving Private Robot: Risks and Advantages of Anthropomorphism in Agent-Soldier Teams",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00755-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101048758&doi=10.1007%2fs12369-021-00755-z&partnerID=40&md5=b3d6c76ee0a02221e73b3246105fbd99","Values in Defence and Security Technology Group, School of Engineering and Information Technology, University of New South Wales, Kensington, Australia; AI & Robotics Lab, United Arab Emirates University, Abu Dhabi, United Arab Emirates; Creative Robotics Lab, University of New South Wales, Kensington, Australia","Cappuccio, M.L., Values in Defence and Security Technology Group, School of Engineering and Information Technology, University of New South Wales, Kensington, Australia, AI & Robotics Lab, United Arab Emirates University, Abu Dhabi, United Arab Emirates; Galliott, J.C., Values in Defence and Security Technology Group, School of Engineering and Information Technology, University of New South Wales, Kensington, Australia; Sandoval, E.B., Creative Robotics Lab, University of New South Wales, Kensington, Australia","Hybrid military teams, formed by human warfighters and autonomous artificial agents, represent the technological future of Defence operations. Both the potential and the inherent limitations of current technology are well-known, but the cognitive–behavioral and motivational aspects of human–robot interaction on the battlefield have yet to be systematically investigated. To lay the theoretical and methodological foundation of this scientific investigation, our position paper critically examines how the military personnel’s spontaneous tendency to anthropomorphize artificial autonomous agents can affect operations of hybrid military teams in multiple ways. We will argue that the psychological impact of anthropomorphism on military personnel is neither easily avoidable nor necessarily detrimental. Correctly identifying the multi-level cognitive mechanisms that underpin implicit and explicit forms of anthropomorphism allows us to increase the efficacy of human–agent interaction. We will argue that, within hybrid teams, the capability to communicate with teammates, allies, civilians, and adversaries relies on embodied social cognition processes that are inherently geared toward anthropomorphism and leverage its effects. By updating both the design of autonomous artificial agents and the training of human troops to account for these processes, their reciprocal coordination can be augmented. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.","Autonomous artificial agents; Autonomous vehicle; Autonomy; Human–agent interaction; Hybrid military teams; Military drone; Situational awareness; Social cognition; Social psychology; Social robotics; Value-sensitive design","Agricultural robots; Autonomous agents; Artificial agents; Cognitive mechanisms; Current technology; Defence operations; Inherent limitations; Military personnels; Robot interactions; Scientific investigation; Social robots",,,,,"Air Force Office of Scientific Research, AFOSR: FA9550-18-1-0039","This material is based upon work supported by the Air Force Office of Scientific Research and the Minerva Initiative under Award Number FA9550-18-1-0039. The work has also received the support of the Australian Government.",,,,,,,,,,"Barnes, M.J., Jentsch, F.J., (2010) Human–robot interactions in future military operations, , (eds), Ashgate Publishing, Farnham; Galliott, J.C., Unmanned systems and war’s end: prospects for lasting peace (2013) Dyn Int, 8, pp. 1-24; Scharre, P., (2018) Army of none: autonomous weapons and the future of war, , WW Norton & Company, New York; Singer, P.W., (2009) Wired for war: the robotics revolution and conflict in the 21st century, , Penguin, London; Swiderska, A., Küster, D., Avatars in pain: visible harm enhances mind perception in humans and robots (2018) Perception; Abbass, H.A., Scholz, J., Reid, D.J., Foundations of trusted autonomy: an introduction (2018) Foundations of trusted autonomy, , Abbass HA, Scholz J, Reid DJ, (eds), Springer, Berlin; Breazeal, C., Dautenhahn, K., Kanda, T., Social robotics (2016) Springer handbook of robotics, pp. 1935-1972. , Sicialian B, Khatib O, (eds), Springer International, Cham; Cummings, M.L., (2017) Artificial Intelligence and the Future of Warfare, , . 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(2015) Synth Philos, 58 (2), pp. 253-272; Cappuccio, M.L., Peeters, A., McDonald, W., Sympathy for Dolores: moral consideration for robots based on virtue and recognition (2019) Philos Technol (February), 33 (1), pp. 1-23; Wodehouse, A., Brisco, R., Broussard, E., Duffy, A., Pareidolia: characterizing facial anthropomorphism and its implications for product design (2018) J Des Res, 16 (2), pp. 83-98; Chen, J.Y., Quinn, S., Wright, J., Barnes, M.J., Barber, D., Adams, D., Human-agent teaming for robot management in multitasking environments (2013) In: 2013 8Th ACM/IEEE International Conference on human–robot Interaction (HRI); Chérif, L., Wood, V., Marois, A., Labonté, K., Vachon, F., Multitasking in the military: cognitive consequences and potential solutions (1918) Appl Cogn Psychol, 32 (4), pp. 429-439; Cauchard, J.R., Zhai, K.Y., Spadafora, M., Landay, J.A., Emotion encoding in human–drone interaction (2016) 2016 11Th ACM/IEEE International Conference on human–robot Interaction (HRI), Christchurch, 2016, pp. 263-270","Cappuccio, M.L.; Values in Defence and Security Technology Group, Australia; email: m.cappuccio@unsw.edu.au",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Article in Press","",Scopus,2-s2.0-85101048758
"Yu Y., Shi R., Lou Y.","57221950520;57191585152;7202582017;","Bias Estimation and Gravity Compensation for Wrist-mounted Force/Torque Sensor",2021,"IEEE Sensors Journal",,,,"","",,,"10.1109/JSEN.2021.3056943","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100713345&doi=10.1109%2fJSEN.2021.3056943&partnerID=40&md5=ee5c86353231bbf2497bd335995d3475","School of Mechatronics Engineering and Automation, Harbin Institute of Technology Shenzhen, HIT Campus, University Town of Shenzhen, Shenzhen, China ((Lou)).; School of Mechatronics Engineering and Automation, Harbin Institute of Technology Shenzhen, HIT Campus, University Town of Shenzhen, Shenzhen, China ((Lou)). (e-mail: louyj@hitsz.edu.cn)","Yu, Y., School of Mechatronics Engineering and Automation, Harbin Institute of Technology Shenzhen, HIT Campus, University Town of Shenzhen, Shenzhen, China ((Lou)).; Shi, R., School of Mechatronics Engineering and Automation, Harbin Institute of Technology Shenzhen, HIT Campus, University Town of Shenzhen, Shenzhen, China ((Lou)).; Lou, Y., School of Mechatronics Engineering and Automation, Harbin Institute of Technology Shenzhen, HIT Campus, University Town of Shenzhen, Shenzhen, China ((Lou)). (e-mail: louyj@hitsz.edu.cn)","Robot force-controlled task executions require an accurate perception of the contact force/torque. When a six-axis force/torque (F/T) sensor is attached to the robot wrist, the compensation precision is affected by many non-contact features, such as the end-effector gravity, inertial, centrifugal, Coriolis forces, and the mechanical errors of the sensor. However, it is complicated to identify all parameters at the same time. Some features will be ignored in practice applications, e.g., surface finishing, human-robot interaction, and haptic applications. In this paper, a novel end-effector gravity compensation method for the F/T sensor is proposed, which identifies the end-effector gravity, its center, and the biases (the rotation between the sensor and robot, the robot installation declination, the bias of sensor). Our key is the rotation calibration while the transformation between the F/T sensor and robot is unknown. Simulation and experimental results show that the proposed method works effectively. In a wrist-mounted F/T sensor system, compared to the previous limited method, the compensation error in common force control channel Fz by our method diminishes 1.0% of the end-effector gravity (i.e., 21.7% relative improvement). IEEE","calibration; End effectors; force sensor; Gravity; gravity compensation; Robot kinematics; Robot sensing systems; robotics; Robots; Sensors; Torque","Error compensation; Human robot interaction; Surface treatment; Compensation precision; Force/torque sensor; Gravity compensation; Haptic applications; Mechanical errors; Robot installation; Six-axis force/torque (F/T) sensor; Surface finishing; End effectors",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,1530437X,,,,"English","IEEE Sensors J.",Article,"Article in Press","",Scopus,2-s2.0-85100713345
"Li T., Zheng K., Liu J., Jia X., Feng J.","55728930300;57220593437;35603814200;24080342400;57220011532;","Research on Operation Intention Based on Flexible Tactile Sensing Handle",2021,"IEEE Access","9",,"9321478","12362","12373",,,"10.1109/ACCESS.2021.3050991","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099603635&doi=10.1109%2fACCESS.2021.3050991&partnerID=40&md5=046ddc5202932f477960be8a0b1558c7","School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300131, China","Li, T., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300131, China; Zheng, K., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300131, China; Liu, J., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300131, China; Jia, X., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300131, China; Feng, J., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300131, China","In order to obtain and analyze the operator's intention comprehensively and accurately in human-robot interaction, an array-type flexible tactile sensor was designed. The sensor was encapsulated into a tactile handle to sense the grasping state of the human hand in real time. According to the analysis of different operators' grasping posture and grasping habits, the grasping state was defined as 5 modes. Based on Harris feature point positioning and extraction, a method of grasping posture conversion was proposed to ensure the completeness and standard of the extracted grasping features. A set of Convolutional Neural Networks (CNN) suitable for the real-time classification of the grasping intention was built to distinguish the grasping state sensed by the handle in real time, accurately determine the operator's intention, and complete the interaction with the robot. Using a UR collaborative robot as the experimental platform and the haptic handle as the intent sensing device, the intent-behaviour mapping relationship was constructed to control the motion of the UR collaborative robot. The experimental results show that the classification accuracy of operation intention is as high as 97.87%. © 2013 IEEE.","grasp recognition; Human-robot interaction; intention understanding; tactile perception","Convolutional neural networks; Classification accuracy; Collaborative robots; Experimental platform; Flexible tactile sensors; Mapping relationships; Point positioning; Sensing devices; Tactile sensing; Human robot interaction",,,,,"2019YFB1302702; National Natural Science Foundation of China, NSFC: U1813222","This work was supported in part by the National Natural Science Foundation of China under Grant U1813222, and in part by the National Key Research and Development Project of China under Grant 2019YFB1302702.",,,,,,,,,,"(2015) Special Eurobarometer 427 'Autonomous Systems', , TNS Opinion & Social, Eur. Commission, Brussels, Belgium, Tech. Rep. 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"Ginés Clavero J., Martín Rico F., Rodríguez-Lera F.J., Guerrero Hernández J.M., Matellán Olivera V.","57215366132;57199976970;56545842100;57220590288;57218173737;","Defining Adaptive Proxemic Zones for Activity-Aware Navigation",2021,"Advances in Intelligent Systems and Computing","1285",,,"3","17",,1,"10.1007/978-3-030-62579-5_1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097385489&doi=10.1007%2f978-3-030-62579-5_1&partnerID=40&md5=4f73c7afbec41ac4947a59677a7c5ba7","Escuela Internacional de Doctorado, Rey Juan Carlos University, Móstoles, Spain; Intelligent Robotics Lab, Rey Juan Carlos University, Fuenlabrada, Spain; Escuela de Ingenierías Industrial e Informática, Universidad de León, León, Spain; Supercomputación Castilla y León, SCAYLE, León, Spain","Ginés Clavero, J., Escuela Internacional de Doctorado, Rey Juan Carlos University, Móstoles, Spain; Martín Rico, F., Intelligent Robotics Lab, Rey Juan Carlos University, Fuenlabrada, Spain; Rodríguez-Lera, F.J., Escuela de Ingenierías Industrial e Informática, Universidad de León, León, Spain; Guerrero Hernández, J.M., Intelligent Robotics Lab, Rey Juan Carlos University, Fuenlabrada, Spain; Matellán Olivera, V., Supercomputación Castilla y León, SCAYLE, León, Spain","Many of the tasks that a service robot can perform at home involve navigation skills. In a real world scenario, the navigation system should consider individuals beyond just objects, theses days it is necessary to offer particular and dynamic representation in the scenario in order to enhance the HRI experience. In this paper, we use the proxemic theory to do this representation. The proxemic zones are not static. The culture or the context influences them and, if we have this influence into account, we can increase humans’ comfort. Moreover, there are collaborative tasks in which these zones take different shapes to allow the task’s best performance. This research develops a layer, the social layer, to represent and distribute the proxemics zones’ information in a standard way, through a cost map and using it to perform a social navigate task. We have evaluated these components in a simulated scenario, performing different collaborative and human-robot interaction tasks and reducing the personal area invasion in a 32%. The material developed during this research can be found in a public repository (https://github.com/IntelligentRoboticsLabs/social_navigation2_WAF), as well as instructions to facilitate the reproducibility of the results. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.","Activity-aware; Collaborative navigation; Proxemics; Social navigation; Social robot","Navigation systems; Collaborative tasks; Cost maps; Different shapes; Dynamic representation; Public repositories; Real-world scenario; Reproducibilities; Service robots; Human robot interaction",,,,,,,,,,,,,,,,"Bera, A., Randhavane, T., Prinja, R., Manocha, D., Sociosense: Robot navigation amongst pedestrians with social and psychological constraints (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7018-7025. , https://doi.org/10.1109/IROS.2017.8206628; Billy Okal, T.L., (2016) Pedsim Ros, , https://github.com/jginesclavero/pedsimros/tree/eloquent-dev; Breazeal, C.L., (2004), Designing Sociable Robots. 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Res., 20, pp. 61-124; Galvan, M., Repiso, E., Sanfeliu, A., Robot navigation to approach people using G2-spline path planning and extended social force model (2020) Advances in Intelligent Systems and Computing, 1093, pp. 15-27. , https://doi.org/10.1007/978-3-030-36150-12, AISC, Springer, Cham; Ginés, J., Martín, F., Vargas, D., Rodríguez, F.J., Matellán, V., Social navigation in a cognitive architecture using dynamic proxemic zones (2019) Sensors, 19 (23). , https://doi.org/10.3390/s19235189; Gloor, C., (2016) Pedsim: Pedestrian Crowd Simulation, 5 (1). , http://pedsim.silmaril.org; Hall, E.T., (1910) The Hidden Dimension, 609. , Doubleday, Garden City; Helbing, D., Molnár, P., Social force model for pedestrian dynamics (1995) Phys. Rev. E, 51 (5), pp. 4282-4286. , https://doi.org/10.1103/physreve.51.4282; Kirby, R., Social robot navigation (2010) Proquest Dissertations and Theses, 347, p. 232; Kirby, R., Simmons, R., Forlizzi, J., Companion: A constraint-optimizing method for person-acceptable navigation (2009) RO-MAN 2009-The 18Th IEEE International Symposium on Robot and Human Interactive Communication, pp. 607-612. , https://doi.org/10.1109/ROMAN.2009.5326271; Koay, K.L., Syrdal, D., Bormann, R., Saunders, J., Walters, M.L., Dautenhahn, K., Initial design, implementation and technical evaluation of a context-aware proxemics planner for a social robot (2017) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinfor-Matics). 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Syst, 61 (12), pp. 1726-1743. , https://doi.org/10.1016/j.robot.2013.05.007; Lu, D.V., Hershberger, D., Smart, W.D., Layered cost maps for context-sensitive navigation (2014) IEEE International Conference on Intelligent Robots and Systems, pp. 709-715. , https://doi.org/10.1109/IROS.2014.6942636, Institute of Electrical and Electronics Engineers Inc; Lu, D.V., Smart, W.D., Towards more efficient navigation for robots and humans (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1707-1713. , https://doi.org/10.1109/IROS.2013.6696579; Marder-Eppstein, E., Berger, E., Foote, T., Gerkey, B., Konolige, K., The office marathon: Robust navigation in an indoor office environment (2010) IEEE International Conference on Robotics and Automation, pp. 300-307. , https://doi.org/10.1109/ROBOT.2010.5509725, 2010; Mead, R., Mataric, M.J., Robots have needs too: People adapt their proxemic preferences to improve autonomous robot recognition of human social signals (2015) AISB Convention 2015; Okal, B., Arras, K.O., Learning socially normative robot navigation behaviors with Bayesian inverse reinforcement learning (2016) 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 2889-2895. , IEEE; Pacchierotti, E., Christensen, H.I., Jensfelt, P., Evaluation of passing distance for social robots (2006) ROMAN 2006-The 15Th IEEE International Symposium on Robot and Human Interactive Communication, pp. 315-320. , https://doi.org/10.1109/ROMAN.2006.314436; Repiso, E., Garrell, A., Sanfeliu, A., Adaptive side-by-side social robot navigation to approach and interact with people (2019) Int. J. Soc. Robot., pp. 1-22. , https://doi.org/10.1007/s12369-019-00559-2; Rico, F.M., (2019) Ros2 Planning System, , https://github.com/IntelligentRoboticsLabs/ros2planningsystem; Vega, A., Manso, L.J., Macharet, D.G., Bustos, P., Núñez, P., Socially aware robot navigation system in human-populated and interactive environments based on an adaptive spatial density function and space affordances (2019) Pattern Recogn. Lett., 118, pp. 72-84. , https://doi.org/10.1016/j.patrec.2018.07.015; Vega-Magro, A., Manso, L.J., Bustos, P., Núñez, P., A flexible and adaptive spatial density model for context-aware social mapping: Towards a more realistic social navigation (2018) Proceedings of 15Th International Conference on Control, Automation, Robotics and Vision, pp. 1727-1732; Walters, M.L., Dautenhahn, K., Boekhorst, R., Koay, K.L., Syrdal, D.S., Nehaniv, C.L., An empirical framework for Human-Robot proxemics (2009) Adaptive and Emergent Behaviour and Complex Systems-Proceedings of the 23Rd Convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour, AISB 2009, pp. 144-149","Ginés Clavero, J.; Escuela Internacional de Doctorado, Spain; email: j.gines@alumnos.urjc.es","Bergasa L.M.Ocana M.Barea R.Lopez-Guillen E.Revenga P.",,"Springer Science and Business Media Deutschland GmbH","21st International Workshop of Physical Agents, WAF 2020","19 November 2020 through 20 November 2020",,251329,21945357,9783030625788,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85097385489
"Wengefeld T., Lewandowski B., Seichter D., Pfennig L., Müller S., Gross H.-M.","56731144600;57201701924;57189598177;57211553152;57199400995;7202668837;","Real-time person orientation estimation and tracking using colored point clouds",2021,"Robotics and Autonomous Systems","135",,"103665","","",,,"10.1016/j.robot.2020.103665","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094587565&doi=10.1016%2fj.robot.2020.103665&partnerID=40&md5=0f096cc39fe25e68c0112ad4d4506c61","Ilmenau University of Technology, Germany","Wengefeld, T., Ilmenau University of Technology, Germany; Lewandowski, B., Ilmenau University of Technology, Germany; Seichter, D., Ilmenau University of Technology, Germany; Pfennig, L., Ilmenau University of Technology, Germany; Müller, S., Ilmenau University of Technology, Germany; Gross, H.-M., Ilmenau University of Technology, Germany","Robustly estimating the orientations of people is a crucial precondition for a wide range of applications. Especially for autonomous systems operating in populated environments, the orientation of a person can give valuable information to increase their acceptance. Given people's orientations, mobile systems can apply navigation strategies which take people's proxemics into account or approach them in a human like manner to perform human robot interaction (HRI) tasks. In this paper, we present an approach for person orientation estimation based on computationally efficient features extracted from colored point clouds, formerly used for a two-class person attribute classification. The classification approach has been extended to the continuous domain while treating the problem of orientation estimation in real time. Furthermore, we present an approach for tracking estimated orientations over time using a Bayesian filter. We will show that tracking can increase the accuracy of orientations by up to 3.69° on a dataset recorded with a mobile robot. Best results on this highly challenging dataset are achieved with a regression approach for orientation estimation in combination with tracking. The mean angular error of just 16.49° proofs the applicability in real-world scenarios. © 2020 Elsevier B.V.","Mobile robotics; Person orientation estimation; Person perception; Real time","Agricultural robots; Air navigation; Autonomous systems; Classification approach; Computationally efficient; Continuous domain; Human robot Interaction (HRI); Navigation strategies; Orientation estimation; Real-world scenario; Social robots",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 03ZZ0437D, 03ZZ0460, 16SV8426","This work has received funding from the German Federal Ministry of Education and Research (BMBF) to the project 3D-PersA2 (grant agreement no. 03ZZ0460 ), the project ROTATOR, Germany (grant agreement no. 03ZZ0437D ) and the project MORPHIA, Germany (grant agreement no. 16SV8426 ).","This work has received funding from the German Federal Ministry of Education and Research (BMBF) to the project 3D-PersA2 (grant agreement no. 03ZZ0460), the project ROTATOR, Germany (grant agreement no. 03ZZ0437D) and the project MORPHIA, Germany (grant agreement no. 16SV8426).",,,,,,,,,"Papadakis, P., Spalanzani, A., Laugier, C., Social mapping of human-populated environments by implicit function learning (2013), pp. 1701-1706. , International Conference on Intelligent Robots and Systems, IROS; Amaoka, T., Laga, H., Saito, S., Nakajima, M., Personal space modeling for human-computer interaction (2009), pp. 60-72. , Entertainment Computing, ICEC; Truong, X., Ngo, T., “To approach humans?”: A unified framework for approaching pose prediction and socially aware robot navigation (2018) IEEE Trans. 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Tools; Chen, T., Guestrin, C., Xgboost: A scalable tree boosting system (2016) Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794","Wengefeld, T.; Ilmenau University of TechnologyGermany; email: tim.wengefeld@tu-ilmenau.de",,,"Elsevier B.V.",,,,,09218890,,RASOE,,"English","Rob Autom Syst",Article,"Final","",Scopus,2-s2.0-85094587565
"Di Marino C., Rega A., Vitolo F., Patalano S., Lanzotti A.","57210820455;57209912700;55745342500;8355841900;15063012200;","A new approach to the anthropocentric design of human-robot collaborative environments",2020,"Acta IMEKO","9","4",,"80","87",,,"10.21014/acta_imeko.v9i4.743","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098137247&doi=10.21014%2facta_imeko.v9i4.743&partnerID=40&md5=5bbdb145fa32bb7c6e5663e25ddb8980","Department of Management, Information and Production Engineering, University of Bergamo, Bergamo, Italy; Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples 'Federico II', Naples, Italy; Fraunhofer J-Lab IDEAS, Deptartment of Industrial Engineering, University of Naples 'Federico II', Naples, Italy","Di Marino, C., Department of Management, Information and Production Engineering, University of Bergamo, Bergamo, Italy; Rega, A., Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples 'Federico II', Naples, Italy; Vitolo, F., Fraunhofer J-Lab IDEAS, Deptartment of Industrial Engineering, University of Naples 'Federico II', Naples, Italy; Patalano, S., Fraunhofer J-Lab IDEAS, Deptartment of Industrial Engineering, University of Naples 'Federico II', Naples, Italy; Lanzotti, A., Fraunhofer J-Lab IDEAS, Deptartment of Industrial Engineering, University of Naples 'Federico II', Naples, Italy","This paper deals with collaborative robotics by highlighting the main issues linked to the interaction between humans and robots. A critical study of the standards in force on human-robot interaction and the current principles on workplace design for human-robot collaboration (HRC) are presented. The paper focuses on an anthropocentric paradigm in which the human becomes the core of the workplace in combination with the robot, and it presents a basis for designing workplaces through two key concepts: (i) the introduction of human and robot spaces as elementary spaces and (ii) the dynamic variations of the elementary spaces in shape, size and position. According to this paradigm, the limitations of a safety-based approach, introduced by the standards, are overcome by positioning the human and the robot inside the workplace and managing their interaction through the elementary spaces. The introduced concepts, in combination with the safety prescriptions, have been organised by means of a multi-level graph for driving the HRC design phase. The collaborative workplace is separated into sublevels. The main elements of a collaborative workplace are identified and their relationships presented by means of digraphs. © 2020 International Measurement Confederation (IMEKO). All rights reserved.","Anthropocentric approach; Collaborative environment; Digraph; Graph theory; Human-robot collaboration",,,,,,"ARS01_00861; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR","This study was developed with the economic support of MIUR (Italian Ministry of Universities and Research) under the remit of project ARS01_00861, 'Integrated collaborative systems for smart factory - ICOSAF'.",,,,,,,,,,"Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human-robot collaboration in industrial settings: safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266. , https://doi.org/10.1016/j.mechatronics.2018.02.009; Labate, C., Di Gironimo, G., Renno, F., Plasma facing components: a conceptual design strategy for the first wall in FAST tokamak (2015) Nuclear Fusion, 55, p. 113013. , https://doi.org/10.1088/0029-5515/55/11/113013; Di Gironimo, G., Carfora, D., Esposito, G., Lanzotti, A., Marzullo, D., Siuko, M., Concept design of the DEMO divertor cassette-to-vacuum vessel locking system adopting a systems engineering approach (2015) Fusion Engineering and Design, 94 (1), pp. 72-81. , https://doi.org/10.1016/j.fusengdes.2015.03.039; Di Gironimo, G., Lanzotti, A., Marzullo, D., Esposito, G., Carfora, D., Siuko, M., Iterative and participative axiomatic design process in complex mechanical assemblies: case study on fusion engineering (2015) International Journal on Interactive Design and Manufacturing, 9, pp. 325-338. , https://doi.org/10.1007/s12008-015-0270-7; Patalano, S., Lanzotti, A., Del Giudice, D. M., Vitolo, F., Gerbino, S., On the usability assessment of the graphical user interface related to a digital pattern software tool (2017) International Journal on Interactive Design and Manufacturing, 11 (3), pp. 457-469. , https://doi.org/10.1007/s12008-015-0287-y; Franciosa, P., Palit, A., Vitolo, F., Ceglarek, D., Rapid response diagnosis of multi-stage assembly process with compliant non-ideal parts using self-evolving measurement system (2017) Procidia CIRP, 60, pp. 38-43. , https://doi.org/10.1016/j.procir.2017.01.035; Tan, Q., Tong, Y., Wu, S., Li, D., Anthropocentric approach for smart assembly: integration and collaboration (2019) Journal of Robotics, 2019, pp. 1-8. , https://doi.org/10.1155/2019/3146782; Aaltonen, I., Salmi, T., Marstio, I., Refining levels of collaboration to support the design and evaluation of human-robot interaction in the manufacturing industry (2018) Proc. of 51st CIRP Conference on Manufacturing Systems, pp. 93-98. , https://doi.org/10.1016/j.procir.2018.03.214, Stockholm, Sweden, 15-18 May; Krüger, J., Nickolay, B., Heyer, P., Seliger, G., Image based 3D surveillance for flexible man-robot-cooperation (2005) CIRP Annals, 54, pp. 19-22. , https://doi.org/10.1016/S0007-8506(07)60040-7; Helms, E., Schraft, R. D., Hagele, M., Robot assistant in industrial environments (2002) Proc. of the 11th IEEE International Workshop on Robot and Human Interactive Communication, pp. 399-404. , https://doi.org/10.1109/ROMAN.2002.1045655, Berlin, Germany; Krüger, J., Lien, T. K., Verl, A., Cooperation of human and machines in assembly lines (2009) CIRP Annals - Manufacturing Technology, 58, pp. 628-646. , https://doi.org/10.1016/j.cirp.2009.09.009; Bortot, D., Born, M., Bengler, K., Directly or on detours? How should industrial robots approximate humans? (2013) Journal of Experimental Psychology, 55, pp. 352-358. , https://doi.org/10.1109/HRI.2013.6483515; (2016) ISO/TS 15066: 2016: Robots and robotic devices - Collaborative robots, , ISO, Geneva, Switzerland: International Organization for Standardization; (2011) Robots and robotic devices - Safety requirements for industrial robots - Part 1: Robots, , ISO, ISO 10218-1: 2011: Geneva, Switzerland: International Organization for Standardization; (2011) Robots and robotic devices - Safety requirements for industrial robots - Part 2: Robot systems and integration, , ISO, ISO 10218-2: 2011: Geneva, Switzerland: International Organization for Standardization; Gualtieri, L., Rauch, E., Rojas, R., Vidoni, R., Matt, D. T., Application of axiomatic design for the design of a safe collaborative human-robot assembly workplace (2018) MATEC Web of Conferences, 223. , https://doi.org/10.1051/matecconf/201822301003, EDP Sciences, 01003, 8 pages; Oyekan, J. O., Hutabarat, W., Tiwari, A., Grech, R., Aung, M. H., Mariani, M. P., López-Dávalos, L., Dupuis, C., The effectiveness of virtual environments in developing collaborative strategies between industrial robots and humans (2019) Robotics and Computer-Integrated Manufacturing, 55, pp. 41-54. , https://doi.org/10.1016/j.rcim.2018.07.006; Arai, T., Kato, R., Fujita, M., Assessment of operator stress induced by robot collaboration in assembly (2010) Robotics and Computer-Integrated Manufacturing, 59, pp. 5-8. , https://doi.org/10.1016/j.cirp.2010.03.043; Michalos, G., Makris, S., Tsarouchi, P., Guasch, T., Kontovrakis, D., Chryssolouris, G., Design considerations for safe human-robot collaborative workplaces (2015) Procedia CIRP, 37, pp. 248-253. , https://doi.org/10.1016/j.procir.2015.08.014; Bdiwi, M., Pfeifer, M., Sterzing, A., A new strategy for ensuring human safety during various levels of interaction with industrial robots (2017) CIRP Annals, 66, pp. 453-456. , https://doi.org/10.1016/j.cirp.2017.04.009; Ogorodnikova, O., Human weaknesses and strengths in collaboration with robots (2008) Periodica Polytechnica, Mechanical Engineering, 52, pp. 25-33. , https://doi.org/10.3311/pp.me.2008-1.05; Rega, A., Patalano, S., Vitolo, F., Gerbino, S., A sensor data fusion-based locating method for reverse engineering scanning systems (2019) II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4. 0&IoT), pp. 123-126. , https://doi.org/10.1109/METROI4.2019.8792864, Naples, Italy; Michalos, G., Makris, S., Spiliotopoulos, J., Misios, I., Tsarouchi, P., Chryssolouris, G., ROBO-PARTNER: Seamless human-robot cooperation for intelligent, flexible and safe operations in the assembly factories of the future (2014) Conference on Assembly Technologies and Systems, 23, pp. 71-76. , https://doi.org/10.1016/j.procir.2014.10.079, Procedia CIRP, Dresden, Germany, 13-14 November; Patalano, S., Vitolo, F., Lanzotti, A., A digital pattern approach to 3D CAD modelling of automotive car door assembly by using directed graphs (2017) Graph-Based Modelling in Engineering, Mechanisms and Machine Science, 42, pp. 175-185. , https://doi.org/10.1007/978-3-319-39020-8_13; Deo, DNarsingh, (2017) Graph theory with applications to engineering and computer science, , Courier Dover Publications, Englewood Cliffs, New Jersey, ISBN 0486820815, 9780486820811, 496 pages; Koutrouli, M., Karatzas, E., Paez-Espino, D., Pavlopoulos, G. A., A guide to conquer the biological network era using graph theory (2020) Frontiers in Bioengineering and Biotechnology, 8, p. 34. , https://doi.org/10.3389/fbioe.2020.00034, article; Majeed, A., Rauf, I., Graph theory: a comprehensive survey about graph theory applications in computer science and social networks (2020) Inventions, 5 (1), p. 10. , https://doi.org/10.3390/inventions5010010, article; Schaeffer, S. E., Graph clustering (2007) Computer Science Review, 1 (1), pp. 27-64. , https://doi.org/10.1016/j.cosrev.2007.05.001","Di Marino, C.; Department of Management, Italy; email: castrese.dimarino@unibg.it",,,"International Measurement Confederation (IMEKO)",,,,,0237028X,,,,"English","Acta IMEKO",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85098137247
"Dimitrievska V., Ackovska N.","57224370919;18433777900;","Behavior Models of Emotion-Featured Robots: A Survey",2020,"Journal of Intelligent and Robotic Systems: Theory and Applications","100","3-4",,"1031","1053",,1,"10.1007/s10846-020-01219-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087701686&doi=10.1007%2fs10846-020-01219-8&partnerID=40&md5=159d55a88b1de3adeacd59bc3e90df19","Faculty of Computer Science and Engineering, Rugjer Boshkovikj 16, Skopje, North Macedonia","Dimitrievska, V., Faculty of Computer Science and Engineering, Rugjer Boshkovikj 16, Skopje, North Macedonia; Ackovska, N., Faculty of Computer Science and Engineering, Rugjer Boshkovikj 16, Skopje, North Macedonia","Emotions are important in many aspects of human behavior. Emotions are displayed on human faces, they are reflected in human memory, and they even influence human intelligence. The creation of robots that try to mimic humans arises the question of how the concept of emotion can be transferred to robots. There is no unique answer to the question, however many robots that leverage emotions exist. By summarizing the work done on these robots we try to enlighten the relations between robots and emotions from several perspectives. We first identify how artificial emotion can be defined in a robotic system. Next, we investigate the possible roles of emotions in robotic behavior models and analyze different implementations of the concept of emotion in these models. Finally, we elaborate on the evaluation of how emotions influence human-robot interaction. For this purpose, we qualitatively analyzed a selected set of robots that include emotions in their model. Considering the diversity of state-of-the-art approaches to using emotions in robots, we try to present the findings in a structured and comprehensive way that could be valuable for future researchers. © 2020, Springer Nature B.V.","Behavior model; Emotions; Human-robot interaction; Social robots; Survey","Agricultural robots; Behavioral research; Robotics; Artificial emotions; Behavior model; Human behaviors; Human intelligence; Human memory; Robotic behavior; Robotic systems; State-of-the-art approach; Human robot interaction",,,,,,"This paper is partially financed by the Faculty of Computer Science and Engineering.",,,,,,,,,,"Cherry, K., (2019) Overview of the 6 Major Theories of Emotion, , https://www.verywellmind.com/theories-of-emotion-2795717, Verywellmind, Accessed May 2019; Xin, L., Lun, X., Zhi-Iang, W., Dong-mei, F., Robot emotion and performance regulation based on HMM (2013) Int. J. of Adv. Robot. 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Mobile robots (2006) Towards New Appl, pp. 269-286","Dimitrievska, V.; Faculty of Computer Science and Engineering, Rugjer Boshkovikj 16, North Macedonia; email: vesna.kirandziska@finki.ukim.mk",,,"Springer Science and Business Media B.V.",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","",Scopus,2-s2.0-85087701686
"McBride N.","36458687800;","Developing socially inspired robotics through the application of human analogy: capabilities and social practice",2020,"AI and Society","35","4",,"857","868",,1,"10.1007/s00146-020-00948-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079398684&doi=10.1007%2fs00146-020-00948-6&partnerID=40&md5=fe79273a13c98b7bc2f793a886e73025","Centre for Computing and Social Responsibility, De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom","McBride, N., Centre for Computing and Social Responsibility, De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom","Socially inspired robotics involves drawing on the observation and study of human social interactions to apply them to the design of sociable robots. As there is increasing expectation that robots may participate in social care and provide some relief for the increasing shortage of human care workers, social interaction with robots becomes of increasing importance. This paper demonstrates the potential of socially inspired robotics through the exploration of a case study of the interaction of a partially sighted social worker with a support worker. This is framed within the capability approach in which the interaction of a human and a sociable robot is understood as resulting in a collaborative capability which is grounded the relationship between the human and the robot rather than the autonomous capabilities of the robot. The implications of applying the case study as an analogy for human–robot interaction are expressed through a discussion of capabilities and social practice and policy. The study is attenuated by a discussion of the technical limits of robots and the extensive complexity of the social context in which it is envisaged sociable robots may be employed. © 2020, Springer-Verlag London Ltd., part of Springer Nature.","Capability approach; Sociable robots; Socially inspired robotics; Trusting robots","Machine design; Robotics; Autonomous capability; Capability approach; Human social interactions; Robot interactions; Sociable robots; Social interactions; Social practices; Technical limits; Social robots",,,,,,,,,,,,,,,,"Alkire, S., Why the capability approach? (2005) J Hum Dev, 6 (1), pp. 115-135; Anderson, M., Anderson, S.L., Robot be good: A call for ethical autonomous robots (2010) Sci Am, 303, pp. 72-77; Barlas, Z., When robots tell you what to do: sense of agency in human- and robot-guided actions (2019) Conscious Cogn, 75, p. 102819; Bradshaw, J.M., Hoffman, R.R., Johnson, M., Woods, D.D., The seven deadly myths of autonomous systems (2013) IEEE Intell Syst, 2013, pp. 54-61. , May/June; Breazeal, C.L., (2002) Designing sociable robots, , MIT Press, Cambridge; (2016) BS 8611—Robots and robotic devices: guide to the ethical design and application of robots and robotic systems (No. BS 8611:2016), , BSI Standards Publication, ‎London; Byagowi, A., Kopacek, P., Using natural human gait for a humanoid robot (2010) 19Th International Workshop on Robotics in Alpe-Adria-Danube Region—RAAD 2010, , June 23–25, 2010 Budapest Hungary; Chen, Y., Wu, F., Shuai, W., Chen, X., Robots serve humans in public places—Kejia robot as a shopping assistant (2017) Int J Adv Robot Syst, 2017, pp. 1-20; Coeckelbergh, M., How i learned to love the robot” capabilities, information technologies and elderly care (2008) The capability approach, technology and design, philosophy of engineering and technology, 5. , Oosterlaken I, Hoven, (eds), Springer, Berlin; Collins, H., (2010) Tacit and explicit knowledge, , University of Chicago Press, Chicago; Danaher, D., Robot Betrayal: A guide to the ethics of robot deception (2020) Ethics Inf Technol; Deng, E., Mutlu, B., Mataric, M.J., Embodiment in socially interactive robots (2019) Found Trends Robot, 7 (4), pp. 251-356; Déniz, O., Hernández, M., Lorenzo, J., Castrillón, M., An engineering approach to sociable robots (2007) J Exp Theor Artif Intell, 19 (4), pp. 285-306; Denzin, N.K., (2001) Interpretive Interactionism, , Sage Publications, Thousand Oaks; (2019) Raspberry Pi robot that reads emotions, , https://www.dexterindustries.com/projects/raspberry-pi-robot-that-reads-emotions/, Accessed 3 Aug 2019; (2019) Why the world’s first robot hotel was a disaster, , https://www.economist.com/gulliver/2019/03/27/why-the-worlds-first-robot-hotel-was-a-disaster, Accessed 3 Aug 2019; Fellous, J.-M., (2004) From Human Emotions to Robot Emotions, , https://www.aaai.org/Papers/Symposia/Spring/2004/SS-04-02/SS04-02-008.pdf, American Association for Artificial Intelligence, Accessed 3 Aug 2019; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Robot Auton Syst, 42, pp. 143-166; Foster, M.E., Natural language generation for social robotics: Opportunities and challenges (2019) Phil Trans R Soc B, 374; Garnier, S., From ants to robots and back: how robotics can contribute to the study of collective animal behavior (2011) Bio-inspired self-organizing robotic systems. 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Proceedings of the 2003 IEEE Changsha, China October 2003 International Conference on Robotics, Intelligent Systems and Signal Processing","McBride, N.; Centre for Computing and Social Responsibility, The Gateway, United Kingdom; email: nkm@dmu.ac.uk",,,"Springer Science and Business Media Deutschland GmbH",,,,,09515666,,,,"English","AI Soc.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85079398684
"Ayounis H., Jamaludin R., Wahab M.N.A., Mohamed A.S.A.","57210408507;54401335900;36471236100;57190968285;","The review of NAO robotics in Educational 2014-2020 in COVID-19 Virus (Pandemic Era): Technologies, type of application, advantage, disadvantage and motivation",2020,"IOP Conference Series: Materials Science and Engineering","928","3","032014","","",,1,"10.1088/1757-899X/928/3/032014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097147382&doi=10.1088%2f1757-899X%2f928%2f3%2f032014&partnerID=40&md5=14d86cccb2dcdfbbe6ff53d583eef4ef","College of Education for Women, University of Basrah, Basrah, Iraq; School of Computer Sciences, Universiti Sains Malaysia, Pulau, Pinang, 11800, Malaysia","Ayounis, H., College of Education for Women, University of Basrah, Basrah, Iraq, School of Computer Sciences, Universiti Sains Malaysia, Pulau, Pinang, 11800, Malaysia; Jamaludin, R., School of Computer Sciences, Universiti Sains Malaysia, Pulau, Pinang, 11800, Malaysia; Wahab, M.N.A., School of Computer Sciences, Universiti Sains Malaysia, Pulau, Pinang, 11800, Malaysia; Mohamed, A.S.A., School of Computer Sciences, Universiti Sains Malaysia, Pulau, Pinang, 11800, Malaysia","The use of robotics in education is a very important issue for disposal and galaxies in this era of the pandemic (COVID-19). Where this study examines the topic of robotics in education (RIE) by using modern and specific query methods extracted from different research sites and based on judicious scholar's standards. These sites are Web of Science, Taylor and Francis and Science Direct. After careful investigation and deep research, the following titles should be taken which are (a)Educational robots, (b)education in robots, (c) human-robot interaction, (d) Higher Education, (e) academic, (f) smart pedagogy, (j)student, and (h) tutors. The retrieved articles were filtered according to the Use of robotics in Education. A total of 98 articles were selected and examined. Finally, we examined the taxonomy of these articles of robotics in Education base on faith and guidance, according to specific criteria, into six groups, which include Faith and Guidance, Concepts, Device, Application, Manufacturing, Studies Analysis and educational. Therefore, this work will be the platform and the guide for student, researcher, educators, anyone how interest in this field. The current focus in this area is on employing papers containing NAO robots and that 17 articles. © 2020 Published under licence by IOP Publishing Ltd.","academic; Education in robots; NAO robot; smart pedagogy; student",,,,,,,,,,,,,,,,,"Rodriguez, I., Astigarraga, A., Jauregi, E., Ruiz, T., Lazkano, E., Humanizing NAO robot teleoperation using ROS (2015) IEEE-RAS Int. Conf. Humanoid Robot, pp. 179-186. , 2015-Febru; Ali, S., (2020) Hand gesture based control of NAO robot using myo armband 953, , (Springer International Publishing); Mubin, O., Cappuccio, M., Alnajjar, F., Ahmad, M. I., Shahid, S., Can a robot invigilator prevent cheating? (2020) AI Soc; Hameed, I. A., Strazdins, G., Hatlemark, H. A. M., Jakobsen, I. S., Damdam, J. O., Robots that can mix serious with fun (2018) International Conference on Advanced Machine Learning Technologies and Applications, pp. 595-604; Caudana, E. L., Baltazar Reyes, G., Acevedo, R. G., Ponce, P., Mazon, N., Hernandez, J. M., RoboTICs: Implementation of a Robotic Assistive Platform in a Mathematics High School Class (2019) IEEE Int. Symp. Ind. Electron, pp. 1589-1594. , 2019-June; Wan, Z., Qin, W., Song, K., Wang, B., (2018) Design and Implementation of Virtual Instructor Based on NAO Robot, 690, pp. 1207-1212; Mubin, O., Ahmad, M. I., Kaur, S., Shi, W., Khan, A., Social Robots in Public Spaces: A Meta-review (2018) International Conference on Social Robotics, pp. 213-220; Pöhner, N., Hennecke, M., Evaluation of a robotics course with the humanoid Robot NAO in CS teacher education (2018) ACM Int. Conf. Proceeding Ser, pp. 2-3; Michieletto, S., Tosello, E., Pagello, E., Menegatti, E., Teaching humanoid robotics by means of human teleoperation through RGB-D sensors (2016) Rob. Auton. Syst, 75, pp. 671-678; Hawley, L., Suleiman, W., Control framework for cooperative object transportation by two humanoid robots (2019) Rob. Auton. Syst, 115, pp. 1-16; Rosanda, V., Starčič, A. I., A review of social robots in classrooms: Emerging educational technology and teacher education (2019) Educ. Self Dev, 14, pp. 93-106; Hong, N. W. W., Chew, E., Sze-Meng, J. W., The review of educational robotics research and the need for real-world interaction analysis 2016 (2016) 14th Int. Conf. Control. Autom. Robot. Vision, ICARCV 2016; Kossewska, J., Kłosowska, J., Acceptance of Robot-Mediated Teaching and Therapy for Children With Atypical Development by Polish Professionals (2020) J. Policy Pract. Intellect. Disabil, 17, pp. 21-30; Keller, L., John, I., Motivating female students for computer science by means of robot workshops (2020) Int. J. Eng. Pedagog, 10, pp. 94-108; Santos, C. B., Ferreira, D. J., De Souza, M. C. B. D. N. R., Martins, A. R., Robotics and programming: Attracting girls to technology 2016 (2016) Int. Conf. Adv. Comput. Commun. Informatics, ICACCI, 2016, pp. 2052-2056; Wright, J. R., Ginter, E. S., David, B. G., Kilbourne, B. J., Wells, J. R., Intermediate programming methodologies for manipulating Modern Humanoid Robots Univers (2019) J. Electr. Electron. Eng, 6, pp. 214-222; The Next Wave of Learning with Humanoid Robot: Learning Innovation Design starts with 'Hello NAO,' (2019) Accid. Anal. Prev, 19, pp. 501-502; Giaretta, A., De Donno, M., Dragoni, N., Secure Coding and Ethical Hacking Workshops with NAO for Engaging K-12 Female Students (2018) CS ACM Int. Conf. Proceeding Ser; Alex Polishuk, I. V., (2017) Student-Robot Interactions in Museum Workshops: Learning Activities and Outcomes","Ayounis, H.; College of Education for Women, Iraq; email: hussain.younis@uobasrah.edu.iq","Shafik S.S.Roomi A.B.Sharrad F.I.",,"IOP Publishing Ltd","2nd International Scientific Conference of Al-Ayen University, ISCAU 2020","15 July 2020 through 17 July 2020",,165187,17578981,,,,"English","IOP Conf. Ser. Mater. Sci. Eng.",Conference Paper,"Final","All Open Access, Gold",Scopus,2-s2.0-85097147382
"Ye Z., Yang G., Pang G., Huang X., Yang H.","57220960565;54961736000;57202024082;55264255400;7406558218;","Design and Implementation of Robot Skin Using Highly Sensitive Sponge Sensor",2020,"IEEE Transactions on Medical Robotics and Bionics","2","4","9180323","670","680",,1,"10.1109/TMRB.2020.3020320","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115866572&doi=10.1109%2fTMRB.2020.3020320&partnerID=40&md5=3125b82d4dff62b05ddafa19bf053c0f","State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China; College of Electrical Engineering, Zhejiang University, Hangzhou, 310027, China","Ye, Z., State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China; Yang, G., State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China; Pang, G., State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China; Huang, X., College of Electrical Engineering, Zhejiang University, Hangzhou, 310027, China; Yang, H., State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China","Robot skin, as a key component to collect tactile information, plays an important role in human-robot interaction (HRI). Conventional robot skin is mainly made of rigid components and bendable films, which cannot provide a fully soft physical interface in HRI. In addition, the sensitivity of robot skin is still to be enhanced so that robot could perceive touch behavior more precisely from a human. Here, a novel robot skin with features of high sensitivity and softness is designed in this article. The proposed robot skin is composed of a matrix of carbon black-polyurethane sponge sensor units through a foaming process. The sensitivity of the sensor is further enhanced by optimizing its geometric structure, based on the finite element method and physical experimental tests. The excellent cycling stability and transient responsiveness of the sponge sensor embedded into the robot skin endow host robots with an accurate long-term perception of both transient and quasi-static force. Finally, the validation of the robot skin is carried out by installing it on the back of a collaborative robot to perceive four touch modalities, illustrating great application prospects in affective HRI. © 2018 IEEE.","geometric structure optimization; Robot skin; sponge sensor","Agricultural robots; Carbon black; Machine design; Application prospect; Collaborative robots; Conventional robots; Design and implementations; Human robot Interaction (HRI); Polyurethane sponge; Quasi static forces; Tactile information; Human robot interaction",,,,,"SKLoFP-ZZ-2002; K18-508116-008-03; 2020XGZX017; National Natural Science Foundation of China, NSFC: 51821093, 51890884, 51975513; Natural Science Foundation of Zhejiang Province, ZJNSF: LR20E050003","This work was supported in part by the National Natural Science Foundation of China under Grant 51975513, Grant 51890884, and Grant 51821093; in part by the Natural Science Foundation of Zhejiang Province, China, under Grant LR20E050003; in part by Worldwide Universities Network Special Grant Scheme; in part by Zhejiang University Special Scientific Research Fund for COVID- 19 Prevention and Control under Grant 2020XGZX017; in part by Director Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems under Grant SKLoFP-ZZ-2002; in part by the China's Thousand Talents Plan Young Professionals Program; and in part by Robotics Institute of Zhejiang University under Grant K18-508116-008-03.",,,,,,,,,,"Sefidgar, Y.S., MacLean, K.E., Yohanan, S., Van Der Loos, M.H.F., Croft, E.A., Garland, E.J., Design and evaluation of a touchcentered calming interaction with a social robot (2016) Ieee Trans. Affect. Comput, 7 (2), pp. 108-121. , Apr.-Jun; Cooney, M.D., Nishio, S., Ishiguro, H., Recognizing affection for a touch-based interaction with a humanoid robot (2012) Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst, pp. 1420-1427. , Vilamoura, Portugal, Oct; Wada, K., Shibata, T., Asada, T., Musha, T., Robot therapy for prevention of dementia at home (2007) J. Robot. Mechatronics, 19 (6), pp. 691-692. , Apr; Pang, Z., Yang, G., Khedri, R., Zhang, Y., Introduction to the special section: Convergence of automation technology, biomedical engineering, and health informatics toward the healthcare 4.0 (2018) Ieee Rev. Biomed. Eng, 11, pp. 249-259; Taichi, T., Takahiro, M., Hiroshi, I., Norihiro, H., Automatic categorization of haptic interactions-what are the typical haptic interactions between a human and a robot? (2006) Proc. IEEE-RAS Int. Conf. Hum. Robots, pp. 490-496. , Genova, Italy, Dec; Heng, W., Pang, G., Xu, F., Huang, X., Pang, Z., Yang, G., Flexible insole sensors with stably connected electrodes for gait phase detection (2019) Sensors, 19 (23), p. 5197. , Nov; Andreasson, R., Alenljun, B., Billing, E., Lowe, R., Affective touch in human-robot interaction: Conveying emotion to the nao robot (2018) Int. J. Soc. Robot, 10 (4), pp. 473-491. , Dec; Kimura, S., Nakatsuma, K., Ohshima, Y., Tanabe, M., Kobayashi, M., Torigoe, I., Fabrication and evaluation of an uniform piezoelectric film for a robot skin sensor (2018) Proc. Ieee Sens., New Delhi, India, pp. 1-4. , Oct; Cirillo, A., Ficuciello, F., Natale, C., Pirozzi, S., Villani, L., A conformable force/tactile skin for physical human-robot interaction (2016) Ieee Robot. Autom. Lett, 1 (1), pp. 41-48. , Jan; Cataldi, P., Carbon nanofiber versus graphene-based stretchable capacitive touch sensors for artificial electronic skin (2018) Adv. Sci, 5 (2). , Dec; Massari, L., A machine-learning-based approach to solve both contact location and force in soft material tactile sensors (2020) Soft Robot, 7, pp. 409-420. , Aug; Ventrelli, L., Beccai, L., Mattoli, V., Menciassi, A., Dario, P., Development of a stretchable skin-like tactile sensor based on polymeric composites (2019) Proc. Ieee Int. Conf. Robot. Biomimetics, pp. 123-128. , Guilin, China, Dec; Silvera-Tawil, D., Rye, D., Velonaki, M., Interpretation of social touch on an artificial arm covered with an eit-based sensitive skin (2014) Int. J. Soc. Robot, 6 (4), pp. 489-505. , Jan; Silvera-Tawil, D., Rye, D., Velonaki, M., Interpretation of the modality of touch on an artificial arm covered with an eit-based sensitive skin (2012) Int. J. Robot. Res, 31 (13), pp. 1627-1641. , May; Argall, B.D., Billard, A.G., A survey of tactile human-robot interactions (2010) Robot. Auton. Syst, 58 (10), pp. 1159-1176. , Oct; Tawil, D.S., Rye, D., Velonaki, M., Touch modality interpretation for an eit-based sensitive skin (2011) Proc. Ieee Int. Conf. Robot. Autom, pp. 3770-3776. , Shanghai, China, May; Wu, X., Han, Y., Zhang, X., Zhou, Z., Lu, C., Large-area compliant, low-cost, and versatile pressure-sensing platform based on microcrack-designed carbon black@ polyurethane sponge for human-machine interfacing (2016) Adv. Funct. Mater, 26 (34), pp. 6246-6256. , Jun; Heng, W., Facile fabrication of highly soft tactile sensor based on porous sponge with geometry effect on sensing characteristics (2019) Proc. Int. Flexible Electron. Technol. Conf, pp. 1-3. , Vancouver, BC, Canada; Han, J., Kim, B., Li, J., Meyyappan, M., Flexible, compressible, hydrophobic, floatable, and conductive carbon nanotube-polymer sponge (2013) Appl. Phys. Lett, 102 (5). , Feb; Yin, X., Vinod, T.P., Jelinek, R., A flexible high-sensitivity piezoresistive sensor comprising a au nanoribbon-coated polymer sponge (2015) J. Mater. Chem. C, 3 (35), pp. 9247-9252. , Jul; Zhong, Y., Tunable wrinkled graphene foams for highly reliable piezoresistive sensor (2018) Sens. Actuat. A, Phys, 281, pp. 141-149. , Oct; Yang, G., Pang, G., Pang, Z., Gu, Y., Mantysalo, M., Yang, H., Non-invasive flexible and stretchable wearable sensors with nano-based enhancement for chronic disease care (2018) Ieee Rev. Biomed. Eng, 12, pp. 34-71; Zuruzi, A.S., Haffiz, T.M., Affidah, D., Amirul, A., Norfatriah, A., Nurmawati, M.H., Towards wearable pressure sensors using multiwall carbon nanotube/polydimethylsiloxane nanocomposite foams (2017) Mater. des, 132, pp. 449-458. , Oct; Zhao, S., Binary synergistic sensitivity strengthening of bioinspired hierarchical architectures based on fragmentized reduced graphene oxide sponge and silver nanoparticles for strain sensors and beyond (2017) Small, 13 (28). , Jul; (2018) Flexible Cellular Polymeric Materials-Determination of Compression Set, , ISO Standard 1856: 2018; Mei, Z., Zhou, X., Wu, F., Deformation mechanism and energy dissipation of solid buoyant material with different ratio of height to diameter under uniaxial compression loading (2017) J. Mater. Res, 32 (8), pp. 591-598. , Aug; (2009) Abaqus Analysis User's Manual, Dassault Systems Simulia Corp, , https://classes.engineering.wustl.edu/2009/spring/mase5513/abaqus/docs/v6.6/books/usb/default.htm?startat=pt05ch18s03abm32.html, FEA Abaqus, Johnston, RI, USA; Kim, T., Yoon, S.J., Park, Y.-L., Soft inflatable sensing modules for safe and interactive robots (2018) Ieee Robot. Autom. Lett, 3 (4), pp. 3216-3223. , Oct; Yang, L., Development of a highly sensitive, broad-range hierarchically structured reduced graphene oxide/polyhipe foam for pressure sensing (2019) Acs Appl. Mater. Interfaces, 11 (4), pp. 4318-4327. , Jan; Wu, W., Wen, X., Wang, Z., Taxel-addressable matrix of verticalnanowire piezotronic transistors for active and adaptive tactile imaging (2013) Science, 340 (6135), pp. 952-957. , May; Pang, G., Deng, J., Wang, F., Zhang, J., Pang, Z., Yang, G., Development of flexible robot skin for safe and natural human-robot collaboration (2018) Micromachines, 9 (11), p. 576. , Nov; Qin, Y., Lightweight, superelastic, and mechanically flexible graphene/polyimide nanocomposite foam for strain sensor application (2015) Acs Nano, 9 (9), pp. 8933-8941. , Aug; Wu, Y., Channel crack-designed gold@ pu sponge for highly elastic piezoresistive sensor with excellent detectability (2017) Acs Appl. Mater. Interfaces, 9 (23), pp. 20098-20105. , May; Tewari, A., Gandla, S., Bohm, S., McNeil, C.R., Gupta, D., Highly exfoliated mwnt-rgo ink-wrapped polyurethane foam for piezoresistive pressure sensor applications (2018) Acs Appl. Mater. Interfaces, 10 (6), pp. 5185-5195. , Jan; Yang, J., Ye, Y., Li, X., Lu, X., Chen, R., Flexible, conductive, and highly pressure-sensitive graphene-polyimide foam for pressure sensor application (2018) Compos. Sci. Technol, 164, pp. 187-194. , May; Alemán-Domínguez, M.E., Polycaprolactone-carboxymethyl cellulose composites for manufacturing porous scaffolds by material extrusion (2018) Bio Design Manuf, 1 (4), pp. 245-253. , Oct; Cai, Z., Liu, Z., Hu, X., Kuang, H., Zhai, J., The effect of porosity on the mechanical properties of 3d-printed triply periodic minimal surface (tpms) bioscaffold (2019) Bio Design Manuf, 2 (4), pp. 242-255. , Nov","Yang, G.; State Key Laboratory of Fluid Power and Mechatronic Systems, China; email: yanggeng@zju.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,25763202,,,,"English","IEEE Trans. Med. Rob. Bion.",Article,"Final","",Scopus,2-s2.0-85115866572
"Vasylkovskyi V., Guerreiro S., Sequeira J.S.","56784404400;24536769600;7006523955;","BlockRobot: Increasing Privacy in Human Robot Interaction by Using Blockchain",2020,"Proceedings - 2020 IEEE International Conference on Blockchain, Blockchain 2020",,,"9284787","106","115",,1,"10.1109/Blockchain50366.2020.00021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099235907&doi=10.1109%2fBlockchain50366.2020.00021&partnerID=40&md5=240bfbb50a7c72ef4e71bc77fab0cb56","University of Lisbon, INESC-ID Instituto Superior Técnico, Lisbon, Portugal; University of Lisbon, Institute for Systems and Robotics Instituto Superior Técnico, Lisbon, Portugal","Vasylkovskyi, V., University of Lisbon, INESC-ID Instituto Superior Técnico, Lisbon, Portugal; Guerreiro, S., University of Lisbon, INESC-ID Instituto Superior Técnico, Lisbon, Portugal; Sequeira, J.S., University of Lisbon, Institute for Systems and Robotics Instituto Superior Técnico, Lisbon, Portugal","Social robots can cause privacy concerns for humans, both because they collect personal data while moving around globally, and because humans can perceive them as social actors. Nowadays, robots can record and transmit (private) data in a human-readable format, which is identified as a problem in human-robot interaction (HRI). Privacy is an individual's right to have control over its data. With this problem in mind, we present BlockRobot- A new privacy-by-design conceptual model for access control of personal data based on Blockchain (BC) technology. With this solution, we ensure data privacy rights by giving each user control over its data in a decentralized manner without the need for an intermediate data controller. This BC solution grants confidentiality, integrity, and non-repudiation of data transparently and fairly to every user. As proof of concept, we demonstrate the initial implementation of a Decentralized Application (DAP) based on EOS Blockchain integrated with robotic events that contain private data. This paper details the experiments conducted with a Social Robot (SR) in a non-lab environment, and the collected data is analyzed using process mining techniques. © 2020 IEEE.","Blockchain; Confidentiality; Evolution of Scalability; Healthcare; Human Robot Interaction; Integrity; Privacy; Social robot","Access control; Blockchain; Man machine systems; Privacy by design; Conceptual model; Data controllers; Human robot Interaction (HRI); Human-readable; Non repudiation; Privacy concerns; Privacy rights; Proof of concept; Social robots",,,,,"822404; 2020-2023; Fundação para a Ciência e a Tecnologia, FCT: UIDB/50021/2020","The first and second authors state that this work was supported by the European Commission program H2020 under the grant agreement 822404 (project QualiChain) and by national funds through Fundac¸ão para a Ciência e a Tecnologia (FCT) with reference UIDB/50021/2020 (INESC-ID). The third author state that this work was supported by LARSyS – FCT Plurianual funding 2020-2023.",,,,,,,,,,"Cucco, E., Fisher, M., Dennis, L., Dixon, C., Webster, M., Broecker, B., Williams, R., Tuyls, K., (2017) Towards Robots for Social Engagement, , [Department of Computer Science, University of Liverpool, UK; Lutz, C., Tamò, A., Privacy and healthcare robots-an ant analysis (2016) We Robot 2016: The Fifth Annual Conference on Legal and Policy Issues Relating to Robotics; Finck, M., Blockchains and data protection in the european union (2018) Eur. Data Prot. L. Rev., 4, p. 17; Ball, J., Nsa's prism surveillance program: How it works and what it can do (2013) The Guardian, 8; Goel, V., Facebook tinkers with users' emotions in news feed experiment, stirring outcry (2014) The New York Times, 29; Jay, R., Malcolm, W., Parry, E., Townsend, L., Bapat, A., (2017) Guide to the General Data Protection Regulation (GDPR), , Sweet & Maxwell; Finck, M., (2019) Blockchain and the General Data Protection Regulation: Can Distributed Ledgers Be Squared with European Data Protection Law?: Study, , European Parliament; Kruijff, J.D., Weigand, H., Understanding the blockchain using enterprise ontology (2017) International Conference on Advanced Information Systems Engineering, pp. 29-43. , Springer; Weber, I., Xu, X., Riveret, R., Governatori, G., Ponomarev, A., Mendling, J., Untrusted business process monitoring and execution using blockchain (2016) International Conference on Business Process Management, pp. 329-347. , Springer; López-Pintado, O., García-Bañuelos, L., Dumas, M., Weber, I., Caterpillar: A blockchain-based business process management system (2017) Proceedings of the Bpm Demo Track and Bpm Dissertation Award Co-located with 15th International Conference on Business Process Modeling (BPM 2017), , Barcelona, Spain; Mendling, J., Weber, I., Aalst, W., Brocke, J., Cabanillas, C., Daniel, F., Debois, S., Dustdar, S., Blockchains for business process management-challenges and opportunities (2018) Acm Transactions on Management Information Systems (TMIS), 9 (1), p. 4; Zyskind, G., Nathan, O., Decentralizing privacy: Using blockchain to protect personal data (2015) 2015 Ieee Security and Privacy Workshops, pp. 180-184; Truong, N.B., Sun, K., Lee, G.M., Guo, Y., Gdpr-compliant personal data management: A blockchain-based solution (2019) Ieee Transactions on Information Forensics and Security, 15, pp. 1746-1761; Faber, B., Michelet, G.C., Weidmann, N., Mukkamala, R.R., Vatrapu, R., Bpdims: A blockchain-based personal data and identity management system (2019) Proceedings of the 52nd Hawaii International Conference on System Sciences; Ferrer, E.C., Rudovic, O., Hardjono, T., Pentland, A., (2018) Robochain: A Secure Data-sharing Framework for Human-robot Interaction, , arXiv preprint arXiv:1802.04480; Degardin, B., (2018) Blockchain for Robotic Event Recognition, , Covilhã: Universidade da Beira Interior; Fernandes, M., Alexandre, L.A., Robotchain: Using tezos technology for robot event management (2019) Ledger; Brown, A., Rise of the machines? Amazon's army of more than 100, 000 warehouse robots still can't replace humans because they lack 'common sense' (2018) Daily Mail, , https://www.dailymail.co.uk/sciencetech/article-5808319/Amazon-100-000-warehouserobots-company-insists-replace-humans.html, accessed 9 March 2019; Carter, J.L., Wegman, M.N., Universal classes of hash functions (1979) Journal of Computer and System Sciences, 18 (2), pp. 143-154; Rivest, R.L., Shamir, A., Adleman, L., A method for obtaining digital signatures and public-key cryptosystems (1978) Communications of the Acm, 21 (2), pp. 120-126; Ajami, S., Rajabzadeh, A., Radio frequency identification (RFID) technology and patient safety (2013) Journal of Research in Medical Sciences: The Official Journal of Isfahan University of Medical Sciences, 18 (9), p. 809; Benet, J., Ipfs-content addressed (2014) Versioned, P2P File System, 2; Izaguirre, J., Furgeson, J., Ma, Q., (2000) Proceedings of cse 331, data structures, department of computer science and engineering, university of notre dame, notre dame, Indiana, pp. 63-67; Merkle, R.C., Protocols for public key cryptosystems (1980) 1980 Ieee Symposium on Security and Privacy, pp. 122-122; Grigg, I., (2017) Eos-an Introduction, , https://whitepaperdatabase.com/eos-whitepaper, White paper; Kanda, T., Hirano, T., Eaton, D., Ishiguro, H., Person identification and interaction of social robots by using wireless tags (2003) Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003), 2, pp. 1657-1664. , (Cat. No. 03CH37453), IEEE; Sequeira, J., Lima, P., Saffiotti, A., Gonzalez-Pacheco, V., Salichs, M.A., Monarch: Multi-robot cognitive systems operating in hospitals (2013) Icra 2013 Workshop on Many Robot Systems; Vitale, J., Tonkin, M., Herse, S., Ojha, S., Clark, J., Williams, M.-A., Wang, X., Judge, W., (2018) Be More Transparent and Users Will like You:a Robot Privacy and User Experience Design Experiment; Garriga, M., Arias, M., De Renzis, A., (2018) Blockchain and Cryptocurrency: A Comparative Framework of the Main Architectural Drivers, , arXiv preprint arXiv:1812.08806; (2018) Aitheon Whitepaper, , https://www.allcryptowhitepapers.com/aitheon-whitepaper/, Aitheon, "", "", [Online]; Belchior, R., Vasconcelos, A., Guerreiro, S., Correia, M., (2020) A Survey on Blockchain Interoperability: Past, Present, and Future Trends, , arXiv preprint arXiv:2005.14282",,,,"Institute of Electrical and Electronics Engineers Inc.","3rd IEEE International Conference on Blockchain, Blockchain 2020","2 November 2020 through 6 November 2020",,165802,,9780738104959,,,"English","Proc. - IEEE Int. Conf. Blockchain, Blockchain",Conference Paper,"Final","",Scopus,2-s2.0-85099235907
"Pan M.K.X.J., Choi S., Kennedy J., McIntosh K., Zamora D.C., Niemeyer G., Kim J., Wieland A., Christensen D.","39062157200;55954115000;55638956200;56963514200;57209273491;7006936424;57191681488;57222347294;55819493000;","Realistic and interactive robot gaze",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341297","11072","11078",,1,"10.1109/IROS45743.2020.9341297","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102400362&doi=10.1109%2fIROS45743.2020.9341297&partnerID=40&md5=cb47f92b59f92d95f584f9cd2c7cfa06","Disney Research, Glendale, CA, United States; California Institute of Technology, Pasadena, CA, United States; University of Illinois at Urbana-Champaign, Urbana, IL, United States; Walt Disney Imagineering - Advanced Development, Glendale, CA, United States","Pan, M.K.X.J., Disney Research, Glendale, CA, United States; Choi, S., Disney Research, Glendale, CA, United States; Kennedy, J., Disney Research, Glendale, CA, United States; McIntosh, K., Disney Research, Glendale, CA, United States; Zamora, D.C., Disney Research, Glendale, CA, United States; Niemeyer, G., California Institute of Technology, Pasadena, CA, United States; Kim, J., University of Illinois at Urbana-Champaign, Urbana, IL, United States; Wieland, A., Walt Disney Imagineering - Advanced Development, Glendale, CA, United States; Christensen, D., Disney Research, Glendale, CA, United States","This paper describes the development of a system for lifelike gaze in human-robot interactions using a humanoid Audio-Animatronics® bust. Previous work examining mutual gaze between robots and humans has focused on technical implementation. We present a general architecture that seeks not only to create gaze interactions from a technological standpoint, but also through the lens of character animation where the fidelity and believability of motion is paramount; that is, we seek to create an interaction which demonstrates the illusion of life. A complete system is described that perceives persons in the environment, identifies persons-of-interest based on salient actions, selects an appropriate gaze behavior, and executes high fidelity motions to respond to the stimuli. We use mechanisms that mimic motor and attention behaviors analogous to those observed in biological systems including attention habituation, saccades, and differences in motion bandwidth for actuators. Additionally, a subsumption architecture allows layering of simple motor movements to create increasingly complex behaviors which are able to interactively and realistically react to salient stimuli in the environment through subsuming lower levels of behavior. The result of this system is an interactive human-robot experience capable of human-like gaze behaviors. © 2020 IEEE.",,"Agricultural robots; Animation; Behavioral research; Intelligent robots; Character animation; Gaze interaction; General architectures; Interactive robot; Persons of interests; Subsumption architecture; Technical implementation; Through the lens; Social robots",,,,,,,,,,,,,,,,"Kreysa, H., Kessler, L., Schweinberger, S.R., Direct speaker gaze promotes trust in truth-ambiguous statements (2016) Plos One, 11, p. 162291. , Sep; Murphy, N.A., Hall, J.A., Colvin, C.R., Accurate intelligence assessments in social interactions: Mediators and gender effects (2003) Journal of Personality, 71, pp. 465-493. , Jun; Zhou, C., Jiang, T., Zhu, L., Direct gaze blurs self-other boundaries (2018) Journal of General Psychology, 145, pp. 280-295. , Jul; Kleinke, C.L., Pohlen, P.D., Affective and emotional responses as a function of other person's gaze and cooperativeness in a two-person game (1971) Journal of Personality and Social Psychology, 17 (3), p. 308; 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Zaraki, A., Mazzei, D., Giuliani, M., De Rossi, D., Designing and evaluating a social gaze-control system for a humanoid robot (2014) Ieee Transactions on Human-Machine Systems, 44 (2), pp. 157-168; Siagian, C., Itti, L., Biologically inspired mobile robot vision localization (2009) Ieee Transactions on Robotics, 25 (4), pp. 861-873; Vijayakumar, S., Conradt, J., Shibata, T., Schaal, S., Overt visual attention for a humanoid robot (2001) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 4, pp. 2332-2337; Mota, P., Paetzel, M., Fox, A., Amini, A., Srinivasan, S., Kennedy, J., Lehman, J.F., Expressing coherent personality with incremental acquisition of multimodal behaviors (2018) Proceedings of the 27th Ieee International Symposium on Robot and Human Interactive Communication (RO-MAN); Kucherenko, T., Hasegawa, D., Henter, G.E., Kaneko, N., Kjellstr ¨om, H., Analyzing input and output representations for speech-driven gesture generation (2019) Proceedings of the 19th Acm International Conference on Intelligent Virtual Agents, pp. 97-104; Yoon, Y., Ko, W.-R., Jang, M., Lee, J., Kim, J., Lee, G., Robots learn social skills: End-to-end learning of co-speech gesture generation for humanoid robots (2019) Proceedings of the International Conference on Robotics and Automation (ICRA), pp. 4303-4309; Breazeal, C., Edsinger, A., Fitzpatrick, P., Scassellati, B., Active vision for sociable robots (2001) Ieee Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 31 (5), pp. 443-453; Brooks, R.A., A robust layered control system for a mobile robot (1986) Ieee Journal on Robotics and Automation, 2 (1), pp. 14-23; Choi, S., Kim, J., Towards a natural motion generator: A pipeline to control a humanoid based on motion data (2020) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4373-4380. , IEEE, Jan; Ude, A., Wyart, V., Lin, L.H., Cheng, G., Distributed visual attention on a humanoid robot (2005) Proceedings of 2005 5th IEEE-RAS International Conference on Humanoid Robots, 2005, pp. 381-386; Roncone, A., Pattacini, U., Metta, G., Natale, L., A cartesian 6-dof gaze controller for humanoid robots (2016) Robotics: Science and Systems, , Jun; Nishio, S., Ishiguro, H., Hagita, N., Geminoid: Teleoperated android of an existing person (2007) Humanoid Robots: New Developments, 14, pp. 343-352; Mura, D., Knoop, E., Catalano, M.G., Grioli, G., B¨acher, M., Bicchi, A., On the role of stiffness and synchronization in human-robot handshaking (2020) The International Journal of Robotics Research; Paetzel, M., Kennedy, J., Castellano, G., Lehman, J.F., Incremental acquisition and reuse of multimodal affective behaviors in a conversational agent (2018) Proceedings of the 6th International Conference on Human-Agent Interaction (HAI), pp. 92-100","Pan, M.K.X.J.; Disney ResearchUnited States
Choi, S.; Disney ResearchUnited States
Kennedy, J.; Disney ResearchUnited States
McIntosh, K.; Disney ResearchUnited States
Zamora, D.C.; Disney ResearchUnited States
Christensen, D.; Disney ResearchUnited States",,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102400362
"Jiang C., Dehghan M., Jagersand M.","57210792958;51461138500;6602596558;","Understanding contexts inside robot and human manipulation tasks through vision-language model and ontology system in video streams",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340905","8366","8372",,1,"10.1109/IROS45743.2020.9340905","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094109055&doi=10.1109%2fIROS45743.2020.9340905&partnerID=40&md5=ce9c7a8c7d61899d482443c8994f21c7","University of Alberta, Department of Computing Science, Edmonton, T6G 2E8, Canada","Jiang, C., University of Alberta, Department of Computing Science, Edmonton, T6G 2E8, Canada; Dehghan, M., University of Alberta, Department of Computing Science, Edmonton, T6G 2E8, Canada; Jagersand, M., University of Alberta, Department of Computing Science, Edmonton, T6G 2E8, Canada","Manipulation tasks in daily life, such as pouring water, unfold through human intentions. Being able to process contextual knowledge from these Activities of Daily Living (ADLs) over time can help us understand manipulation intentions, which are essential for an intelligent robot to transition smoothly between various manipulation actions. In this paper, to model the intended concepts of manipulation, we present a vision dataset under a strictly constrained knowledge domain for both robot and human manipulations, where manipulation concepts and relations are stored by an ontology system in a taxonomic manner. Furthermore, we propose a scheme to generate a combination of visual attentions and an evolving knowledge graph filled with commonsense knowledge. Our scheme works with real-world camera streams and fuses an attention-based Vision-Language model with the ontology system. The experimental results demonstrate that the proposed scheme can successfully represent the evolution of an intended object manipulation procedure for both robots and humans. The proposed scheme allows the robot to mimic human-like intentional behaviors by watching real-time videos. We aim to develop this scheme further for real-world robot intelligence in Human-Robot Interaction. © 2020 IEEE.",,"Agricultural robots; Behavioral research; Computational linguistics; Computer hardware description languages; Intelligent robots; Knowledge representation; Ontology; Video streaming; Activities of daily living (ADLs); Commonsense knowledge; Contextual knowledge; Human manipulation; Knowledge domains; Manipulation task; Object manipulation; Robot intelligences; Social robots",,,,,,,,,,,,,,,,"Yang, Y., Li, Y., Fermuller, C., Aloimonos, Y., Robot learning manipulation action plans by"" watching"" unconstrained videos from the world wide web (2015) AAAI; Yang, S., Zhang, W., Lu, W., Wang, Y.L.H., Learning actions from human demonstration video for robotic manipulation (2019) International Conference on Intelligent Robots and Systems (IROS), pp. 1805-1811; Nguyen, A., Kanoulas, D., Muratore, L., Caldwell, D.G., Tsagarakis, N.G., Translating videos to commands for robotic manipulation with deep recurrent neural networks (2018) International Conference on Robotics and Automation (ICRA), pp. 1-9; Zhang, H., Nikolaidis, S., (2019) Robot Learning and Execution of Collaborative Manipulation Plans from Youtube Videos; Nguyen, A., Do, T.-T., Reid, I., Caldwell, D.G., Tsagarakis, N.G., (2019) V2cnet: A Deep Learning Framework to Translate Videos to Commands for Robotic Manipulation; Sudhakaran, S., Lanz, O., Attention is all we need: Nailing down object-centric attention for egocentric activity recognition (2018) British Machine Vision Conference, (BMVC), p. 229; Lu, M., Li, Z.-N., Wang, Y., Pan, G., Deep attention network for egocentric action recognition (2019) IEEE Transactions on Image Processing, 28 (8), pp. 3703-3713; Li, Z., Huang, Y., Cai, M., Sato, Y., Manipulation-skill assessment from videos with spatial attention network (2019) Proceedings of the IEEE International Conference on Computer Vision Workshops; Sutskever, I., Vinyals, O., Le, Q.V., Sequence to sequence learning with neural networks (2014) NIPS; Aditya, S., Yang, Y., Baral, C., Fermuller, C., Aloimonos, Y., Visual commonsense for scene understanding using perception, semantic parsing and reasoning (2015) AAAI Spring Symposium Series; Ye, C., Yang, Y., Mao, R., Fermüller, C., Aloimonos, Y., What can i do around here? deep functional scene understanding for cognitive robots (2017) International Conference on Robotics and Automation (ICRA), pp. 4604-4611; Aditya, S., Yang, Y., Baral, C., Aloimonos, Y., Fermüller, C., Image understanding using vision and reasoning through scene description graph (2018) Computer Vision and Image Understanding, 173, pp. 33-45; Beetz, M., Beßler, D., Haidu, A., Pomarlan, M., Bozcuoglu, A.K., Bartels, G., Know rob 2. 0-a 2nd generation knowledge processing framework for cognition-enabled robotic agents (2018) International Conference on Robotics and Automation (ICRA), pp. 512-519; Aditya, S., Yang, Y., Baral, C., (2019) Integrating Knowledge and Reasoning in Image Understanding; Yang, Y., Guha, A., Fermüller, C., Aloimonos, Y., Manipulation action tree bank: A knowledge resource for humanoids (2014) International Conference on Humanoid Robots, pp. 987-992; Zhang, H., Lan, X., Bai, S., Wan, L., Zhou, X., Zheng, N., A multitask convolutional neural network for autonomous robotic grasping in object stacking scenes (2018) International Conference on Intelligent Robots and Systems (IROS), pp. 6435-6442; Fox, R., Berenstein, R., Stoica, I., Goldberg, K., Multi-task hierarchical imitation learning for home automation (2019) International Conference on Automation Science and Engineering (CASE), pp. 1-8; French, K., Wu, S., Pan, T., Zhou, Z., Jenkins, O.C., Learning behavior trees from demonstration (2019) International Conference on Robotics and Automation (ICRA), pp. 7791-7797; Colledanchise, M., Almeida, D., Gren, P.Ö., Towards blended reactive planning and acting using behavior trees (2016) International Conference on Robotics and Automation (ICRA), pp. 8839-8845; Hatori, J., Kikuchi, Y., Kobayashi, S., Takahashi, K., Tsuboi, Y., Unno, Y., Ko, W., Tan, J., Interactively picking real-world objects with unconstrained spoken language instructions (2017) International Conference on Robotics and Automation (ICRA), pp. 3774-3781; Shridhar, M., Hsu, D., Interactive visual grounding of referring expressions for human-robot interaction (2018) Proceedings of Robotics: Science and Systems; Dehghan, M., Zhang, Z., Siam, M., Jin, J., Petrich, L., Jagersand, M., Online object and task learning via human robot interaction (2019) International Conference on Robotics and Automation (ICRA), pp. 2132-2138; Paxton, C., Bisk, Y., Thomason, J., Byravan, A., Fox, D., Prospection: Interpretable plans from language by predicting the future (2019) International Conference on Robotics and Automation (ICRA), pp. 6942-6948; Zhang, Q., Chen, J.-H., Liang, D., Liu, H., Zhou, X., Ye, Z., Liu, W., An object attribute guided framework for robot learning manipulations from human demonstration videos (2019) International Conference on Intelligent Robots and Systems (IROS), pp. 6113-6119; Mahdisoltani, F., Berger, G., Gharbieh, W., Fleet, D., Memisevic, R., (2018) On the Effectiveness of Task Granularity for Transfer Learning; Damen, D., Doughty, H., Maria Farinella, G., Fidler, S., Furnari, A., Kazakos, E., Moltisanti, D., Price, W., Scaling egocentric vision: The epic-kitchens dataset (2018) Proceedings of the European Conference on Computer Vision (ECCV), pp. 720-736; Li, Y., Liu, M., Rehg, J.M., In the eye of beholder: Joint learning of gaze and actions in first person video (2018) Proceedings of the European Conference on Computer Vision (ECCV), pp. 619-635; Siam, M., Jiang, C., Lu, S., Petrich, L., Gamal, M., Elhoseiny, M., Jagersand, M., Video object segmentation using teacher-student adaptation in a human robot interaction (hri) setting (2019) International Conference on Robotics and Automation (ICRA), pp. 50-56; Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., Bengio, Y., Show, attend and tell: Neural image caption generation with visual attention (2015) International Conference on Machine Learning, pp. 2048-2057; Bahdanau, D., Cho, K., Bengio, Y., (2014) Neural Machine Translation by Jointly Learning to Align and Translate; Musen, M.A., The protégé project: A look back and a look forward (2015) AI Matters, 1 (4), pp. 4-12; Lamy, J.-B., Owlready: Ontology-oriented programming in python with automatic classification and high level constructs for biomedical ontologies (2017) Artificial Intelligence in Medicine, 80, pp. 11-28; Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R.J., Darrell, T., Saenko, K., (2015) Sequence to Sequence-video to Text, pp. 4534-4542; Ramanishka, V., Das, A., Zhang, J., Saenko, K., Top-down visual saliency guided by captions (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7206-7215; Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollár, P., Zitnick, C.L., (2015) Microsoft Coco Captions: Data Collection and Evaluation Server",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85094109055
"Zhang T., Lowmanstone L., Wang X., Glassman E.L.","57020216700;57220059574;57026634100;23975987900;","Interactive program synthesis by augmented examples",2020,"UIST 2020 - Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology",,,"3415900","627","648",,4,"10.1145/3379337.3415900","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096997416&doi=10.1145%2f3379337.3415900&partnerID=40&md5=f496cbb3574b9789f9312b07f4e0f84c","Harvard UniversityMA, United States; University of Michigan, Ann Arbor, MI, United States","Zhang, T., Harvard UniversityMA, United States; Lowmanstone, L., Harvard UniversityMA, United States; Wang, X., University of Michigan, Ann Arbor, MI, United States; Glassman, E.L., Harvard UniversityMA, United States","Programming-by-example (PBE) has become an increasingly popular component in software development tools, human-robot interaction, and end-user programming. A long-standing challenge in PBE is the inherent ambiguity in user-provided examples. This paper presents an interaction model to disambiguate user intent and reduce the cognitive load of understanding and validating synthesized programs. Our model provides two types of augmentations to user-given examples: 1) semantic augmentation where a user can specify how different aspects of an example should be treated by a synthesizer via light-weight annotations, and 2) data augmentation where the synthesizer generates additional examples to help the user understand and validate synthesized programs. We implement and demonstrate this interaction model in the domain of regular expressions, which is a popular mechanism for text processing and data wrangling and is often considered hard to master even for experienced programmers. A within-subjects user study with twelve participants shows that, compared with only inspecting and annotating synthesized programs, interacting with augmented examples significantly increases the success rate of finishing a programming task with less time and increases users? confidence of synthesized programs. © 2020 ACM.","Disambiguation; Example augmentation; Program synthesis","Data handling; End effectors; Human robot interaction; Robot programming; Semantics; Software design; Text processing; Data augmentation; End user programming; Interaction model; Interactive programs; Programming by Example; Regular expressions; Semantic augmentations; Software development tools; User interfaces",,,,,,,,,,,,,,,,"Regular Expression to Validate the Country Code of Mobile Number in Javascript, , https://stackoverflow.com/questions/41487596, Accessed: 2020-04-04; Bastani, O., Sharma, R., Aiken, A., Liang, P., Synthesizing program input grammars (2017) ACM SIGPLAN Notices, 52 (6), pp. 95-110. , 2017; Berry, G., Sethi, R., From regular expressions to deterministic automata (1986) Theoretical Computer Science, 48, pp. 117-126. , 1986; Chapman, C., Stolee, K.T., Exploring regular expression usage and context in python (2016) Proceedings of the 25th International Symposium on Software Testing and Analysis, pp. 282-293; Chapman, C., Wang, P., Stolee, K.T., Exploring regular expression comprehension (2017) 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 405-416; Chasins, S.E., Mueller, M., Bodik, R., Rousillon: Scraping distributed hierarchical web data (2018) Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology, pp. 963-975; Chen, Q., Wang, X., Ye, X., Durrett, G., Dillig, I., Multi-modal synthesis of regular expressions (2020) Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI); Drachsler-Cohen, D., Shoham, S., Yahav, E., Synthesis with abstract examples (2017) International Conference on Computer Aided Verification, pp. 254-278. , Springer; Drews, S., Albarghouthi, A., D'Antoni, L., Efficient synthesis with probabilistic constraints (2019) International Conference on Computer Aided Verification, pp. 278-296. , Springer; Drosos, I., Barik, T., Guo, P.J., DeLine, R., Gulwani, S., Wrex: A unified programming-by-example interaction for synthesizing readable code for data scientists (2020) Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 6038-6049; D'Antoni, L., Samanta, R., Singh, R., Qlose: Program repair with quantitative objectives (2016) International Conference on Computer Aided Verification, pp. 383-401. , Springer; D'Antoni, L., Singh, R., Vaughn, M., Nofaq: Synthesizing command repairs from examples (2017) Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (ESEC/FSE 2017), pp. 582-592. , http://dx.doi.org/10.1145/3106237.3106241, Association for Computing Machinery, New York, NY, USA; Ellis, K., Solar-Lezama, A., Tenenbaum, J., Sampling for Bayesian program learning (2016) Advances in Neural Information Processing Systems, pp. 1297-1305; Feng, Y., Martins, R., Van Geffen, J., Dillig, I., Chaudhuri, S., Component-based synthesis of table consolidation and transformation tasks from examples (2017) ACM SIGPLAN Notices, 52 (6), pp. 422-436. , 2017; Feng, Y., Martins, R., Wang, Y., Dillig, I., Reps, T.W., Component-based synthesis for complex apis (2017) Proceedings of the 44th ACM SIGPLAN Symposium on Principles of Programming Languages, pp. 599-612; Feser, J.K., Chaudhuri, S., Dillig, I., Synthesizing data structure transformations from input-output examples (2015) ACM SIGPLAN Notices, 50 (6), pp. 229-239. , 2015; Fraser, G., Arcuri, A., Evosuite: Automatic test suite generation for object-oriented software (2011) Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering, pp. 416-419; Gentner, D., Loewenstein, J., Thompson, L., Learning and transfer: A general role for analogical encoding (2003) Journal of Educational Psychology, 95 (2), p. 393. , 2003; Godefroid, P., Kiezun, A., Levin, M.Y., Grammar-based whitebox fuzzing (2008) Proceedings of the 29th ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 206-215; Godefroid, P., Peleg, H., Singh, R., Learn&fuzz: Machine learning for input fuzzing (2017) 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 50-59; Green, C., Application of theorem proving to problem solving (1981) Readings in Artificial Intelligence, pp. 202-222. , Elsevier; Gulwani, S., Automating string processing in spreadsheets using input-output examples (2011) ACM Sigplan Notices, 46 (1), pp. 317-330. , 2011; Gulwani, S., Harris, W.R., Singh, R., Spreadsheet data manipulation using examples (2012) Commun. 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ACM Program. Lang, p. 4. , http://dx.doi.org/10.1145/3371117, POPL Dec. 2019; Wang, P., Stolee, K.T., How well are regular expressions tested in the wild (2018) Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp. 668-678; Wang, X., Gulwani, S., Singh, R., Fidex: Filtering spreadsheet data using examples (2016) ACM SIGPLAN Notices, 51 (10), pp. 195-213. , 2016; Wang, Y., Dong, J., Shah, R., Dillig, I., Synthesizing database programs for schema refactoring (2019) Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, pp. 286-300; Yaghmazadeh, N., Wang, Y., Dillig, I., Dillig, T., Sqlizer: Query synthesis from natural language (2017) Proceedings of the ACM on Programming Languages, 1, pp. 1-26. , OOPSLA (2017); Yessenov, K., Tulsiani, S., Menon, A., Miller, R.C., Gulwani, S., Lampson, B., Kalai, A., A colorful approach to text processing by example (2013) Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology, pp. 495-504; Yuan, Y., Alur, R., Thau Loo, B., Netegg: Programming network policies by examples (2014) Proceedings of the 13th ACM Workshop on Hot Topics in Networks (HotNets-XIII), pp. 1-7. , http://dx.doi.org/10.1145/2670518.2673879, Association for Computing Machinery, New York, NY, USA",,,"ACM SIGCHI;ACM SIGGRAPH","Association for Computing Machinery, Inc","33rd Annual ACM Symposium on User Interface Software and Technology, UIST 2020","20 October 2020 through 23 October 2020",,164266,,9781450375146,,,"English","UIST - Proc. Annu. ACM Symp. User Interface Softw. Technol.",Conference Paper,"Final","",Scopus,2-s2.0-85096997416
"Petisca S., Paiva A., Esteves F.","57188997854;7102683774;7004375639;","The effect of a robotic agent on dishonest behavior",2020,"Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents, IVA 2020",,,,"","",,2,"10.1145/3383652.3423953","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096959299&doi=10.1145%2f3383652.3423953&partnerID=40&md5=9fcb86608a327304536f3fabac9faa30","Lisbon University Institute (ISCTE), CIS-IUL, INESC-ID, Portugal; Instituto Superior Tecnico (IST), INESC-ID, Portugal; Mid Sweden University, Iscte","Petisca, S., Lisbon University Institute (ISCTE), CIS-IUL, INESC-ID, Portugal; Paiva, A., Instituto Superior Tecnico (IST), INESC-ID, Portugal; Esteves, F., Mid Sweden University, Iscte","Future human-robot interactions will have to consider different human traits. One human feature that may be affected by the presence of virtual agents or robots is human honesty. Will people try to take advantage in the presence of a robot/virtual agent? Some previous studies have shown that the physical presence of a robot can decrease cheating in humans. In this paper, we investigated if merely a simple video of a robot looking at the user was enough to affect human's cheating behavior. Further, we also investigated if the Honesty-Humility personality trait predicted cheating. We conducted a study with 160 participants that were randomly allocated to one of two conditions: (1) performing the task with a video of a robot looking at them, or (2) doing the task alone. Results showed that being alone or with a video of a robot produced equal levels of cheating and the Honesty-Humility dimension predicted cheating, particularly the fairness sub-domain was responsible for predicting cheating behavior. This study has implications for future scenarios where dishonesty might be tempting, and physical presence of an observer might not be possible. © 2020 ACM.","cheating behavior; dishonesty; human-agent interaction; Human-robot interaction","Behavioral research; Intelligent virtual agents; Robotics; Human traits; Implications for futures; Personality traits; Robotic agents; Sub-domains; Virtual agent; Human robot interaction",,,,,"Faculty of Science and Engineering, University of Manchester, FSE; Fundação para a Ciência e a Tecnologia, FCT; European Social Fund, ESF; Fundació Catalana de Trasplantament, FCT: Ref.SFRH/BD/118013/2016","The authors thank the help and support of Joana Campos in creating the game task for the study. This work was supported by the Social European Fund (FSE) and the Foundation for Science and Technology (FCT) with reference UIDB/50021/2020, Sofia Petisca acknowledges an FCT Grant (Ref.SFRH/BD/118013/2016).",,,,,,,,,,"Ashton, M.C., Lee, K., Empirical, theoretical, and practical advantages of the hexaco model of personality structure (2007) Personality and Social Psychology Review, 11 (2), pp. 150-166. , 2007; Ashton, M.C., Lee, K., The hexaco-60: A short measure of the major dimensions of personality (2009) Journal of Personality Assessment, 91 (4), pp. 340-345. , 2009; Ashton, M.C., Lee, K., De Vries, R.E., The hexaco honesty-humility, agreeableness, and emotionality factors: A review of research and theory (2014) Personality and Social Psychology Review, 18 (2), pp. 139-152. , 2014; Bateson, M., Nettle, D., Roberts, G., Cues of being watched enhance cooperation in a real-world setting (2006) Biology Letters, 2 (3), pp. 412-414. , 2006; Békir, I., Harbi, S.E., Grolleau, G., Mzoughi, N., Sutan, A., The impact of monitoring and sanctions on cheating: Experimental evidence from tunisia (2016) Managerial and Decision Economics, 37 (7), pp. 461-473. , 2016; Bersof, D.M., Why good people sometimes do bad things: Motivated reasoning and unethical behavior (2001) The Next Phase of Business Ethics: Integrating Psychology and Ethics, pp. 239-262. , Emerald Group Publishing Limited; Böckler, A., Van Der Wel, R., Welsh, T.N., Catching eyes: Efects of social and nonsocial cues on attention capture (2014) Psychological Science, 25 (3), pp. 720-727. , 2014; Covey, M.K., Saladin, S., Killen, P.J., Self-monitoring, surveillance, and incentive efects on cheating (1989) The Journal of Social Psychology, 129 (5), pp. 673-679. , 1989; De Melo, C.M., Carnevale, P., Gratch, J., The infuence of emotions in embodied agents on human decision-making (2010) International Conference on Intelligent Virtual Agents, pp. 357-370. , Springer; De Melo, C.M., Zheng, L., Gratch, J., Expression of moral emotions in cooperating agents (2009) International Workshop on Intelligent Virtual Agents, pp. 301-307. , Springer; Dear, K., Dutton, K., Fox, E., Do 'watching eyes' infuence antisocial behavior? A systematic review & meta-Analysis (2019) Evolution and Human Behavior, 40 (3), pp. 269-280. , 2019; Diener, E., Wallbom, M., Efects of self-Awareness on antinor-mative behavior (1976) Journal of Research in Personality, 10 (1), pp. 107-111. , 1976; Ernest-Jones, M., Nettle, D., Bateson, M., Efects of eye images on everyday cooperative behavior: A feld experiment (2011) Evolution and Human Behavior, 32 (3), pp. 172-178. , 2011; Forlizzi, J., Saensuksopa, T., Salaets, N., Shomin, M., Mericli, T., Hofman, G., Let's be honest: A controlled feld study of ethical behavior in the presence of a robot (2016) 2016 25th Ieee International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 769-774; Hilbig, B.E., Zettler, I., When the cat's away, some mice will play: A basic trait account of dishonest behavior (2015) Journal of Research in Personality, 57, pp. 72-88. , 2015; Hofman, G., Forlizzi, J., Ayal, S., Steinfeld, A., Antanitis, J., Hochman, G., Hochendoner, E., Finkenaur, J., Robot presence and human honesty: Experimental evidence (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 181-188; Hood, B.M., MacRae, C.N., Cole-Davies, V., Dias, M., Eye remember you: The efects of gaze direction on face recognition in children and adults (2003) Developmental Science, 6 (1), pp. 67-71. , 2003; Jiang, T., (2012) The Mind Game: Invisible Cheating and Inferable Intentions, , 2012); Kedzierski, J., Muszynski, R., Zoll, C., Oleksy, A., Fron-Tkiewicz, M., Emys-Emotive head of a social robot (2013) International Journal of Social Robotics, 5 (2), pp. 237-249. , 2013; Markus, H., The efect of mere presence on social facilitation: An unobtrusive test (1978) Journal of Experimental Social Psychology, 14 (4), pp. 389-397. , 1978; Mazar, N., Amir, O., Ariely, D., The dishonesty of honest people: A theory of self-concept maintenance (2008) Journal of Marketing Research, 45 (6), pp. 633-644. , 2008; Mead, N.L., Baumeister, R.F., Gino, F., Schweitzer, M.E., Ariely, D., Too tired to tell the truth: Self-control resource depletion and dishonesty (2009) Journal of Experimental Social Psychology, 45 (3), pp. 594-597. , 2009; Mol, J.M., Heijden Der, E.Van, Potters, J.J.M., Not) alone in the world: Cheating in the presence of a virtual observer (2020) Experimental Economics, (2020), pp. 1-18; Mubin, O., Cappuccio, M., Alnajjar, F., Ahmad, M.I., Shahid, S., Can a robot invigilator prevent cheating? (2020) Ai & Society, (2020), pp. 1-9; Northover, S.B., Pedersen, W.C., Cohen, A.B., Andrews, P.W., Artifcial surveillance cues do not increase generosity: Two meta-Analyses (2017) Evolution and Human Behavior, 38 (1), pp. 144-153. , 2017; Petisca, S., Esteves, F., Paiva, A., Cheating with robots: How at ease do they make us feel? (2019) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)., pp. 2102-2107; Petisca, S., Leite, I., Paiva, A., Esteves, F., (2020) Human Dishonesty in the Presence of a Robot: The Efects of Situation Awareness and Relational Priming, , Manuscript submitted for publication (2020); Pfattheicher, S., Keller, J., The watching eyes phenomenon: The role of a sense of being seen and public self-Awareness (2015) European Journal of Social Psychology, 45 (5), pp. 560-566. , 2015; Pfattheicher, S., Schindler, S., Nockur, L., On the impact of honesty-humility and a cue of being watched on cheating behavior (2019) Journal of Economic Psychology, 71, pp. 159-174. , 2019; Pfattheicher, S., Strauch, C., Diefenbacher, S., Schnuerch, R., A feld study on watching eyes and hand hygiene compliance in a public restroom (2018) Journal of Applied Social Psychology, 48 (4), pp. 188-194. , 2018; Shalvi, S., Eldar, O., Bereby-Meyer, Y., Honesty requires time (and lack of justifcations) (2012) Psychological Science, 23 (10), pp. 1264-1270. , 2012; Tomasello, M., Carpenter, M., Shared intentionality (2007) Developmental Science, 10 (1), pp. 121-125. , 2007; Zhong, C.-B., Bohns, V.K., Gino, F., Good lamps are the best police: Darkness increases dishonesty and self-interested behavior (2010) Psychological Science, 21 (3), pp. 311-314. , 2010",,,"ACM Special Interest Group on Artificial Intelligence (SIGAI)","Association for Computing Machinery, Inc","20th ACM International Conference on Intelligent Virtual Agents, IVA 2020","20 October 2020 through 22 October 2020",,164271,,9781450375863,,,"English","Proc. ACM Int. Conf. Intell. Virtual Agents, IVA",Conference Paper,"Final","",Scopus,2-s2.0-85096959299
"Muhammad U., Sipra K.A., Waqas M., Tu S.","57224873171;57221500060;57215416666;36642846800;","Applications of Myo Armband Using EMG and IMU Signals",2020,"2020 3rd International Conference on Mechatronics, Robotics and Automation, ICMRA 2020",,,"9398375","6","11",,,"10.1109/ICMRA51221.2020.9398375","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104584064&doi=10.1109%2fICMRA51221.2020.9398375&partnerID=40&md5=3d2b0ab846a7b1b096bc0b162f86f292","Faculty of Computer Science Engineering, GIK Institute of Engineering Sciences Technology, Topi, 23460, Pakistan; Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China","Muhammad, U., Faculty of Computer Science Engineering, GIK Institute of Engineering Sciences Technology, Topi, 23460, Pakistan; Sipra, K.A., Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China; Waqas, M., Faculty of Computer Science Engineering, GIK Institute of Engineering Sciences Technology, Topi, 23460, Pakistan; Tu, S., Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China","Human-Robot Interaction (HRI) plays an important role in a wide range of applications in healthcare, entertainment, and industries. Many ways are being devised to improve the interaction between humans and machines in the field of robotics. For instance, the existing approaches claim precise control over the robotic arm using many different controllers like the joystick, keypad control, or electroMyographic (EMG) sensors attached to the human arm as separate units. However, it requires a non-practical amount of calibration time and high operational expertise. Therefore, our goal is to mimic the human arm movements on the 6-degree of freedom (DOF) robotic arm in real-time, providing minimum latency. We practically show the interfacing of Myo armband with robots that provide tremendously low calibration time without compromising efficiency. We also allow a 6-DOF using EMG and inertial measurement unit (IMU) signals for Myo armband and extract the values of EMG and IMU sensors. The extracted raw EMG and IMU values are calibrated using Arduino as a standard micro-controller. Due to the extraction of raw values, Myo armband can also be integrated with other users' peripheral, which in our case is a 6-DOF robotic arm. © 2020 IEEE.","6-DOF; calibration time; component; EMG and IMU sensors; myo armband","Agricultural robots; Calibration; Controllers; Entertainment industry; Industrial robots; Robotic arms; Robotics; Calibration time; Electromyographic; Human arm; Human arm movements; Human robot Interaction (HRI); Inertial measurement unit; Precise control; Separate unit; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 61801008; Beijing Municipal Commission of Education: KM201910005025; Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, GIKI; Beijing Municipal Natural Science Foundation: L172049; National Key Research and Development Program of China, NKRDPC: 2018YFB0803600","This work is also supported by Faculty of Computer Science and Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Pakistan. The authors are thankful for the continuous support and guidance of Dr. Muhammad Waqas and Dr. Shanshan Tu.","ACKNOWLEDGMENT This work was partially supported by the National Key R&D Program of China (No.2018YFB0803600), National Natural Science Foundation of China (No. 61801008), Beijing Municipal Natural Science Foundation (No. L172049), Scientific Research Common Program of Beijing Municipal Commission of Education (No. KM201910005025).",,,,,,,,,"Visconti, P., Gaetani, F., Zappatonre, G., Primiceri, P., Technical features and functionalities of myo armband: An overview on related literature and advanced applications of myoelectricl armbands mainly focused on arm prostheses (2018) International Journal on Smart Sensing and Intelligent Systems, 11 (1), pp. 1-25; (2020) Robotic Arm, 250. , Jia et al Feb. 6 US Patent App. 16/528; Yamamoto, H., Inaba, H., Morioka, M., Iwayama, T., (2019) Industrial Robot, 590. , Feb. 26 US Patent App. 29/581; Garriz, C., Domingo, R., Development of trajectories through the kalman algorithm and application to an industrial robot in the automotive industry (2019) IEEE Access, 7, pp. 23570-23578; Bakri, A.B., Adnan, R., Ruslan, F.A., Wireless hand gesture controlled robotic arm via NRF24101 transceiver (2019) IEEE 9th Symposium on Computer Applications & Industrial Electronics, pp. 16-22; De Souza, G.A.M., Da Silve, M.G.N.M., Da Gama, A.E.F., Upper limb muscle activition: An EMG analysis using myo armband (2019) XXVI Brazilian Congress on Biomedical Engineering, pp. 397-4040. , Springer; Fan, Y., Yang, C., Wu, X., Improved teleoperation of an industrial robot arm system using leap motion and myo armband (2019) IEEE International Conference on Robotics and Biomimetics, pp. 1670-1675; A customizable wearable robust 3D printed bionic arm: Muscle controlled (2019) IEEE 3rd International Conference on Bioengineering for Smart Technologies, pp. 1-6. , Said et al; Hassan, H.F., Abou-Loukh, S.J., Ibraheem, I.K., Teleoperated robotic arm movement using electromyography signal with wearable myo armband (2019) Journal of King Saud University-Engineering Sciences; Using EMG signals to remotely control a 3D industrial robotic arm (2019) ASME International Mechanical Engineering Congress and Exposition, p. 59414. , Batayneh et al; Kristof, R., Moldovan, C., Ciupe, V., Maniu, I., Banda, M., Applicatons based on electomyography sensors (2019) ITM Web of Conferences 29, EDP Sciences, p. 02007; Said, S., Kork, S.A., Nait-Ali, A., Wearable technologies in biomedical and biometric applications (2019) Biometrics under Biomedical Considerations, p. 02007. , Springer; Schabrom, B., Reust, A., Desai, J., Yihun, Y., Intergration of forearm sEMG singals with IMU sensors for trajectory planning and control of assistive robotic arm (2019) 41st Annual International Conference of the IEEE Enginering in Medicine and Biology Society, pp. 5274-5277; Liu, M., Zhou, H., Pang, A., Research on motion control system of 6-DoF robotic arm (2020) 11th International Conference on Modelling, Identificaiton and Control, pp. 53-61. , Springer; Patel, A., Ramsay, J., Imtiaz, M., Lu, Y., EMG-based human machine interface control (2019) 12th International Conference on Human System Interaction, pp. 127-131; Kim, D., Jorgensen, S.J., Stone, P., Sentis, L., Dynamic behaviors on the NAO robot with closed-loop whole body operational space control (2016) 16th IEEE International Conference on Humanoid Robots, pp. 1121-1128; Prasek, M., Kotyrba, M., How to progrma NAO robot using NET (2019) AIP Conference Processdings, 2116 (1), p. 060010; Wu, H., Zhang, S., Liu, J., Qiu, J., Zhang, X., The gesture disagreement problem in free-hand gesture interaction (2019) International Journal of Human-Computer Interaction, 35 (12), pp. 1102-1114; Ren, Z., Yuan, J., Meng, J., Zhang, Z., Robust part-based hand gesture recognition using kinect sensor (2013) IEEE Transactions on Multimedia, 15 (5), pp. 1110-1120; Xiong, W., Carlsson, P., Lagerstrom, R., Re-using enterprise architecture repositories for agile threat modeling (2019) IEEE 23rd International Enterprise Distributed Object Computing Workshop, pp. 118-127",,,"Beijing Institute of Control Robots and Intelligent Technology (BICRI);Shanghai University of Engineering Science (SUES)","Institute of Electrical and Electronics Engineers Inc.","3rd International Conference on Mechatronics, Robotics and Automation, ICMRA 2020","16 October 2020 through 18 October 2020",,168402,,9781728190327,,,"English","Int. Conf. Mechatronics, Robot. Autom., ICMRA",Conference Paper,"Final","",Scopus,2-s2.0-85104584064
"Zahn M.","57223051733;","Development of an underwater hand gesture recognition system",2020,"2020 Global Oceans 2020: Singapore - U.S. Gulf Coast",,,"9389313","","",,,"10.1109/IEEECONF38699.2020.9389313","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104609788&doi=10.1109%2fIEEECONF38699.2020.9389313&partnerID=40&md5=147c8efb457ebf2ae8770afd9cf752cc","Chair for Data Processing, Technical University of Munich, Munich, Germany","Zahn, M., Chair for Data Processing, Technical University of Munich, Munich, Germany","In the maritime environment, proper diver-robot communication is a bottleneck to enable collaborative work underwater. Many applications like companion or safety robots require a robust system to communicate underwater. In this paper, we propose a four-step vision-based underwater hand gesture recognition system to address this need. First, the input images are enhanced to account for the frequency-dependent loss underwater and made scale and rotation invariant. This allows the segmentation and extraction of the arm region. In a second step, a wrist line normalization is performed to extract the visible hand region. Then a hand posture detection is performed. Therefore, a new underwater posture dataset is created. Two detection algorithms are compared. A convex hull defect-based approach achieves 92.22% accuracy and a finger segmentation-based algorithm achieves an accuracy of 94.81%. In the last step, a dynamic hand gesture recognition is implemented. The presented system is based on the creation of generalizable posture parameters and can be adapted to any given posture. This makes the system easy to customize and therefore enables a wide range of new applications for human-robot interaction underwater. © 2020 IEEE.","hand gesture recognition; hand posture recognition; human-robot interaction; underwater robotics","Image enhancement; Marine communication; Palmprint recognition; Social robots; Collaborative Work; Detection algorithm; Frequency-dependent loss; Hand-gesture recognition; Maritime environment; New applications; Robot communication; Scale and rotation; Gesture recognition",,,,,,,,,,,,,,,,"Dudek, G., Sattar, J., Xu, A., A visual language for robot control and programming: A human-interface study (2007) Proceedings-IEEE International Conference on Robotics and Automation, pp. 2507-2513; Sattar, J., Giguere, P., Dudek, G., Sensor-based behavior control for an autonomous underwater vehicle (2009) Int. J. Rob. Res., 28 (6), pp. 701-713; Chiarella, D., Gesture-based language for diver-robot underwater interaction (2015) MTS/IEEE Ocean. 2015-Genova Discov. Sustain. Ocean Energy A New World; Chavez, A.G., Mueller, C.A., Doernbach, T., Chiarella, D., Birk, A., (2018) Robust Gesture-Based Communication for Underwater Human-Robot Interaction in the Context of Search and Rescue Diver Missions, 2018, pp. 2-5; Chavez, A.G., Ranieri, A., Chiarella, D., Zereik, E., Babi, A., Birk, A., CADDY underwater stereo-vision dataset for human-robot interaction (hri) in the context of diver activities (2018) J. Mar. Sci. Eng., 7 (1). , Jul; Xia, Y., Sattar, J., Visual diver recognition for underwater human robot collaboration (2018) Proc.-IEEE Int. Conf. Robot. Autom., 2019, pp. 6839-6845. , Sep.-May; Xu, P., (2017) Gesture-based Human-robot Interaction for Field Programmable Autonomous Underwater Robots; Zhang, X.-N., Jiang, J., Liang, Z., Liu, C.-L., Skin color enhancement based on favorite skin color in hsv color space (2010) Idw'10, pp. 947-950; Chen, Z., Kim, J.-T., Liang, J., Zhang, J., Yuan, Y.-B., Real-time hand gesture recognition using finger segmentation (2014) Sci. World J., 2014, pp. 1-9; Xu, Y., Park, D.-W., Pok, G., Hand gesture recognition based on convex defect detection (2017) Int. J. Appl. Eng. Res., 12 (18), pp. 7075-7079; Grzejszczak, T., Kawulok, M., Galuszka, A., Hand landmarks detection and localization in color images (2016) Multimed. Tools Appl., 75 (23), pp. 16363-16387; Kawulok, M., Kawulok, J., Nalepa, J., Smolka, B., Self-adaptive algorithm for segmenting skin regions (2014) EURASIP J. Adv. Signal Process., 2014 (170), pp. 1-22; Nalepa, J., Kawulok, M., Fast and accurate hand shape classification (2014) Beyond Databases, Architectures, and Structures, 424, pp. 364-373. , S. Kozielski, D. Mrozek, P. Kasprowski, B. Malysiak-Mrozek, and D. Kostrzewa, Eds. Springer",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 Global Oceans: Singapore - U.S. Gulf Coast, OCEANS 2020","5 October 2020 through 30 October 2020",,168366,,9781728154466,,,"English","Glob. Oceans: Singapore - U.S. Gulf Coast",Conference Paper,"Final","",Scopus,2-s2.0-85104609788
"Liu H., Chen Z., Shi W.","55769747023;57221267199;57207096415;","Robust Audio-Visual Mandarin Speech Recognition Based on Adaptive Decision Fusion and Tone Features",2020,"Proceedings - International Conference on Image Processing, ICIP","2020-October",,"9190894","1381","1385",,,"10.1109/ICIP40778.2020.9190894","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098666470&doi=10.1109%2fICIP40778.2020.9190894&partnerID=40&md5=68ac0039b870af263e052fec8d68e91d","Peking University, Key Laboratory of Machine Perception, Shenzhen Graduate School, China","Liu, H., Peking University, Key Laboratory of Machine Perception, Shenzhen Graduate School, China; Chen, Z., Peking University, Key Laboratory of Machine Perception, Shenzhen Graduate School, China; Shi, W., Peking University, Key Laboratory of Machine Perception, Shenzhen Graduate School, China","Audio-visual speech recognition (AVSR) integrates both audio and visual information to perform automatic speech recognition (ASR), which improves the robustness of human-robot interaction systems especially in noise environments. However, few methods and applications have paid attention to AVSR in tonal languages, in which the linguistic feature can play an important role as well as visual information. In this work, we propose a method for AVSR in Mandarin based on adaptive decision fusion as well as making full use of tone features. Firstly, we introduce tone features calculated by Constant Q trasform (CQT) and put them into a CNN-based audio network together with Mel-Frequency Cepstral Coefficient (MFCC) audio features. Then, the visual features are extracted by Discrete Cosine Transform (DCT) from mouth regions in video frames and modeled by an LSTM-based visual network. Finally, an adaptive decision fusion network combines the outputs from both streams to make final predictions. Experimental results on the PKU-AV2 dataset show that the tone features can significantly improve the robustness of Mandarin speech recognition systems, and the adaptability of the proposed method to various noise environments. © 2020 IEEE.","Audio-visual speech recognition; decision fusion; tone feature extraction","Acoustic noise; Audio acoustics; Audio systems; Discrete cosine transforms; Image processing; Linguistics; Long short-term memory; Social robots; Visual languages; Audio and visual information; Audio visual speech recognition; Automatic speech recognition; Discrete Cosine Transform(DCT); Linguistic features; Mandarin speech recognition; Mandarin speech recognition system; Mel-frequency cepstral coefficients; Speech recognition",,,,,"National Natural Science Foundation of China, NSFC: 61673030, U1613209","This work is supported by National Natural Science Foundation of China (NSFC No.61673030, U1613209).",,,,,,,,,,"Dupont, S., Luettin, J., Audio-visual speech modeling for continuous speech recognition (2000) IEEE Transactions on Multimedia, 2 (3), pp. 141-151; Potamianos, G., Neti, C., Gravier, G., Garg, A., Senior, A.W., Recent advances in the automatic recognition of audiovisual speech (2003) Proceedings of the IEEE, 91 (9), pp. 1306-1326; Petridis, S., Pantic, M., Prediction-based audiovisual fusion for classification of non-linguistic vocalisations (2015) IEEE Transactions on Affective Computing, 7 (1), pp. 45-58; Feng, W., Guan, N., Li, Y., Zhang, X., Luo, Z., Audio visual speech recognition with multimodal recurrent neural networks (2017) 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 681-688; Takashima, Y., Aihara, R., Takiguchi, T., Ariki, Y., Mitani, N., Omori, K., Nakazono, K., Audio-visual speech recognition using bimodal-trained bottleneck features for a person with severe hearing loss (2016) Interspeech, pp. 277-281; Baltrusaitis, T., Ahuja, C., Morency, L., Multimodal machine learning: A survey and taxonomy (2018) IEEE Transactions on Pattern Analysis and Machine Intelligence, 41 (2), pp. 423-443; Hu, D., Li, X., Temporal multimodal learning in audiovisual speech recognition (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3574-3582; Son Chung, J., Senior, A., Vinyals, O., Zisserman, A., Lip reading sentences in the wild (2017) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 3444-3453; Petridis, S., Stafylakis, T., Ma, P., Cai, F., Tzimiropoulos, G., Pantic, M., End-to-end audiovisual speech recognition (2018) 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 6548-6552; Ding, R., Pang, C., Liu, H., Audiovisual keyword spotting based on multidimensional convolutional neural network (2018) 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, pp. 4138-4142; Wu, P., Liu, H., Li, X., Fan, T., Zhang, X., A novel lip descriptor for audio-visual keyword spotting based on adaptive decision fusion (2016) IEEE Transactions on Multimedia, 18 (3), pp. 326-338; Ghahremani, P., BabaAli, B., Povey, D., Riedhammer, K., Trmal, J., Khudanpur, S., A pitch extraction algorithm tuned for automatic speech recognition (2014) 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 2494-2498; Thang Vu, N., Schultz, T., Vietnamese large vocabulary continuous speech recognition (2009) 2009 IEEE Workshop on Automatic Speech Recognition & Understanding. IEEE, pp. 333-338; Chang-Han Huang, H., Seide, F., Pitch tracking and tone features for Mandarin speech recognition (2000) 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). IEEE, 3, pp. 1523-1526; Zou, W., Jiang, D., Zhao, S., Yang, G., Li, X., Comparable study of modeling units for end-to-end Mandarin speech recognition (2018) 2018 11th International Symposium on Chinese Spoken Language Processing (ISCSLP). IEEE, pp. 369-373; Brown, J.C., Calculation of a constant q spectral transform (1991) The Journal of the Acoustical Society of America, 89 (1), pp. 425-434; Viola, P., Jones, M., Rapid object detection using a boosted cascade of simple features (2001) IEEE Conference on Computer Vision and Pattern Recognition, pp. 511-518; Kazemi, V., Sullivan, J., One millisecond face alignment with an ensemble of regression trees (2014) IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867-1874; McFee, B., Raffel, C., Liang, D., Ellis, D.P.W., McVicar, M., Battenberg, E., Nieto, O., Librosa: Audio and music signal analysis in python (2015) Proceedings of the 14th Python in Science Conference, 8; Varga, A., Steeneken, H.J.M., Assessment for automatic speech recognition: II. noisex-92: A database and an experiment to study the effect of additive noise on speech recognition systems (1993) Speech Communication, 12 (3), pp. 247-251",,,"The Institute of Electrical and Electronics Engineers Signal Processing Society","IEEE Computer Society","2020 IEEE International Conference on Image Processing, ICIP 2020","25 September 2020 through 28 September 2020",,165772,15224880,9781728163956,,,"English","Proc. Int. Conf. Image Process. ICIP",Conference Paper,"Final","",Scopus,2-s2.0-85098666470
"Permana A.S., Djamal E.C., Nugraha F., Kasyidi F.","57212347783;56204479000;57215054500;57201686575;","Hand movement identification using single-stream spatial convolutional neural networks",2020,"International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","2020-October",,,"172","176",,,"10.23919/EECSI50503.2020.9251896","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097806404&doi=10.23919%2fEECSI50503.2020.9251896&partnerID=40&md5=f20893ba0a67fd3e42f42c97aee69bae","Department of Informatics, Universitas Jenderal Achmad Yani, Cimahi, Indonesia","Permana, A.S., Department of Informatics, Universitas Jenderal Achmad Yani, Cimahi, Indonesia; Djamal, E.C., Department of Informatics, Universitas Jenderal Achmad Yani, Cimahi, Indonesia; Nugraha, F., Department of Informatics, Universitas Jenderal Achmad Yani, Cimahi, Indonesia; Kasyidi, F., Department of Informatics, Universitas Jenderal Achmad Yani, Cimahi, Indonesia","Human-robot interaction can be through several ways, such as through device control, sounds, brain, and body, or hand gesture. There are two main issues: the ability to adapt to extreme settings and the number of frames processed concerning memory capabilities. Although it is necessary to be careful with the selection of the number of frames so as not to burden the memory, this paper proposed identifying hand gesture of video using Spatial Convolutional Neural Networks (CNN). The sequential image's spatial arrangement is extracted from the frames contained in the video so that each frame can be identified as part of one of the hand movements. The research used VGG16, as CNN architecture is concerned with the depth of learning where there are 13 layers of convolution and three layers of identification. Hand gestures can only be identified into four movements, namely ‘right’, ’left’, ’grab’, and ’phone’. Hand gesture identification on the video using Spatial CNN with an initial accuracy of 87.97%, then the second training increased to 98.05%. Accuracy was obtained after training using 5600 training data and 1120 test data, and the improvement occurred after manual noise reduction was performed. © 2020 Institute of Advanced Engineering and Science (IAES). All Rights Reserved.","Convolutional neural networks; Hand gesture identification; Spatial; Video",,,,,,"B/87/E3/RA.00/2020","ACKNOWLEDGMENT Thanks to the Ministry of Technology Research for the financial support provided for this research in the “Penelitian Terapan Unggulan Perguruan Tinggi” 2020 grant with number B/87/E3/RA.00/2020.",,,,,,,,,,"Oyedotun, O. K., Khashman, A., Deep learning in vision-based static hand gesture recognition (2017) Neural Computing and Applications, 28 (12), pp. 3941-3951; Zhao, Z., Song, L., Xie, R., Yang, X., GPU accelerated high-quality video/image super-resolution (2016) IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, pp. 2-5; Zhao, W., Du, S., Emery, W. J., Object-Based Convolutional Neural Network for High-Resolution Imagery Classification (2017) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10 (7), pp. 3386-3396; Zafra, J. E., Hernández, R. D., Comparison between Backpropagation and CNN for the Recognition of Traffic Signs (2017) International Journal of Applied Engineering Research, 12 (17), pp. 6814-6820; Agarap, A. F., An Architecture Combining Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for Image Classification (2017) Computer Science, Computer Vision and Pattern Recognition, pp. 5-8; Zhang, J., Shao, K., Luo, X., Small sample image recognition using improved Convolutional Neural Network (2018) Journal of Visual Communication and Image Representation, 55 (July), pp. 640-647; Pan, X., Shi, J., Luo, P., Wang, X., Tang, X., Spatial as deep: Spatial CNN for traffic scene understanding (2018) 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, pp. 7276-7283; Jing, L., Yang, X., Tian, Y., Video you only look once: Overall temporal convolutions for action recognition (2018) Journal of Visual Communication and Image Representation, 52 (January), pp. 58-65; Yan, S., Xiong, Y., Lin, D., Spatial-temporal graph convolutional networks for skeleton-based action recognition (2018) 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, pp. 7444-7452; Alani, A. A., Cosma, G., Taherkhani, A., McGinnity, T. M., Hand gesture recognition using an adapted convolutional neural network with data augmentation (2018) 2018 4th International Conference on Information Management, ICIM 2018, pp. 5-12; Pham, H.-H., Khoudour, L., Crouzil, A., Zegers, P., Velastin, S. A., Video-based human action recognition using deep learning: a review (2015) Archivo preprint, pp. 1-34; Islam, M. R., Mitu, U. K., Bhuiyan, R. A., Shin, J., Hand gesture feature extraction using deep convolutional neural network for recognizing American sign language (2018) 2018 4th International Conference on Frontiers of Signal Processing, ICFSP 2018, pp. 115-119. , September; Nugraha, F., Djamal, E. C., Video Recognition of American Sign Language Using Two-Stream Convolution Neural Networks (2019) International Conference on Electrical Engineering and Informatics; Kishore, P. V. V., Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks (2018) Advances in Multimedia, , January 2018; Cao, C., Zhang, Y., Wu, Y., Lu, H., Cheng, J., Egocentric Gesture Recognition Using Recurrent 3D Convolutional Neural Networks with Spatiotemporal Transformer Modules (2017) Proceedings of the IEEE International Conference on Computer Vision, pp. 3783-3791. , 2017-Octob; Patel, R., Dhakad, J., Desai, K., Gupta, T., Correia, S., Hand gesture recognition system using convolutional neural networks (2018) 2018 4th International Conference on Computing Communication and Automation, ICCCA 2018, pp. 1-6; Ren, L., Dai, J. S., Static Hand Gesture Recognition with Parallel CNNs for Space Human-Robot Interaction (2017) International Conference on Intelligent Robotics and Applications, 1, pp. 71-83. , G. W. B, October; Qassim, H., Verma, A., Feinzimer, D., Compressed residual-VGG16 CNN model for big data places image recognition (2018) 2018 IEEE 8th Annual Computing and Communication Workshop and Conference, CCWC 2018, 2018, pp. 169-175. , Janua; Munasinghe, M. I. N. P., Dynamic Hand Gesture Recognition Using Computer Vision and Neural Networks (2018) 2018 3rd International Conference for Convergence in Technology, I2CT 2018, , January; Hu, Y., Lu, M., Lu, X., Spatial-Temporal Fusion Convolutional Neural Network for Simulated Driving Behavior Recognition (2018) 2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018, pp. 1271-1277; Dyrmann, M., Karstoft, H., Midtiby, H. S., Plant species classification using deep convolutional neural network (2016) Biosystems Engineering, 151, pp. 72-80. , 2005; Wang, Y., Long, M., Wang, J., Yu, P. S., Spatiotemporal pyramid network for video action recognition (2017) Proceedings-30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, pp. 2097-2106. , Janua","Djamal, E.C.; Department of Informatics, Indonesia; email: esmeralda.contessa@lecture.unjani.ac.id",,,"Institute of Advanced Engineering and Science","7th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2020","1 October 2020 through 2 October 2020",,164986,2407439X,9786020737614,,,"English","Int. Conf. Electr. Eng. Comput. Sci. Informatics",Conference Paper,"Final","",Scopus,2-s2.0-85097806404
"Saadatzi M., Saadatzi M.N., Banerjee S.","57190884956;36642836100;56518718500;","Modeling and Fabrication of a Piezoelectric Artificial Cochlea Electrode Array with Longitudinal Coupling",2020,"IEEE Sensors Journal","20","19","9097896","11163","11172",,4,"10.1109/JSEN.2020.2996192","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091010644&doi=10.1109%2fJSEN.2020.2996192&partnerID=40&md5=14e928c2c3d155f46ec8d345e45b65ae","Mechanical Engineering Department, University of South Carolina, Columbia, SC  29208, United States; Electrical Engineering Department, University of Louisville, Louisville, KY  40292, United States","Saadatzi, M., Mechanical Engineering Department, University of South Carolina, Columbia, SC  29208, United States; Saadatzi, M.N., Electrical Engineering Department, University of Louisville, Louisville, KY  40292, United States; Banerjee, S., Mechanical Engineering Department, University of South Carolina, Columbia, SC  29208, United States","In this study, inspired from human cochlea, we propose an artificial basilar membrane structure based on an array of gold electrodes microfabricated along the opposite surfaces of a piezoelectric continuum. The proposed piezoelectric continuum features an isosceles trapezoidal geometry and is situated atop an elastomer matrix with embedded electrode grooves and a duct. At first, a detailed analytical model is presented, which formulates the continuum's planar deflection equation, governing its elastodynamic behavior. Subsequently, in order to verify the analytical outcomes, a multiphysics finite-element analysis is performed in COMSOL®. Both analytical and numerical models demonstrate desired frequency selectivity akin to the capability of a biological basilar membrane. Subsequently, a proof-of-concept model of the proposed structure is custom-fabricated through standard photo-and soft lithography techniques using Polyvinylidene Difluoride (PVDF), gold, and Polydimethylsiloxane (PDMS) materials. Finally, the fabricated artificial basilar membrane that is intended to be used as a broad band sensor, and artificial cochlea for seamless human-robot interaction is experimentally characterized using a custom-built acoustic exciter, and its effective performance is validated within the frequency range of 3 kHz to 8 kHz. © 2001-2012 IEEE.","analytical modeling; artificial basilar membrane; Cochlea implant; FEA; finite-element analysis; photolithography; piezoelectric; PVDF; soft lithography","Electrodes; Fabrication; Gold; Lithography; Piezoelectricity; Polydimethylsiloxane; Silicones; Analytical and numerical models; Effective performance; Elastodynamic behavior; Frequency selectivity; Longitudinal coupling; Piezoelectric continuum; Polydimethylsiloxane PDMS; Polyvinylidene difluoride; Human robot interaction",,,,,"Langley Research Center, LaRC: NNL15AA16C; University of South Carolina, USC","Manuscript received April 18, 2020; accepted May 10, 2020. Date of publication May 21, 2020; date of current version September 3, 2020. This work was supported in part by the University of South Carolina and in part by the NASA Langley Research Center (LaRC), USA, under Contract NNL15AA16C. The associate editor coordinating the review of this article and approving it for publication was Dr. Ioannis Raptis. (Corresponding author: Sourav Banerjee.) Mohammadsadegh Saadatzi and Sourav Banerjee are with the Mechanical Engineering Department, University of South Carolina, Columbia, SC 29208 USA (e-mail: saadatzi@email.sc.edu; banerjes@ cec.sc.edu).",,,,,,,,,,"Hoffman, H.J., Dobie, R.A., Losonczy, K.G., Themann, C.L., Flamme, G.A., Declining prevalence of hearing loss in US adults aged 20 to 69 years (2017) Jama Otolaryngol.-Head Neck Surg., 143 (3), pp. 274-285; Lin, F.R., Niparko, J.K., Ferrucci, L., Hearing loss prevalence in the United States (2011) Arch. Internal Med., 171 (20), pp. 1851-1853; Saadatzi, M., Saadatzi, M.N., Tavaf, V., Banerjee, S., Finite element analysis of smart structural implications of a beam-type artificial basilar membrane inspired sensor (2019) Proc. Spie 10965, , Mar; Saadatzi, M., Saadatzi, M.N., Tavaf, V., Banerjee, S., Development of a PVDF based artificial basilar membrane (2018) Proc. Spie 10593, , Mar; (2019) Everything about Noise-Induced Hearing Loss, , https://www.makatimed.net.ph/news-and-exhibits/news/are-you-going-deaf, Makati Medical Center. Aug. 13, [Online]; Hajioff, D., Cochlear implantation: A review of current clinical practice (2016) Brit. J. Hospital Med., 77 (12), pp. 680-684; Harada, M., Ikeuchi, N., Fukui, S., Toshiyoshi, H., Fujita, H., Ando, S., Micro mechanical acoustic sensor toward artificial basilar membrane modeling (1999) Ieej Trans. Sensors Micromach., 119 (3), pp. 125-130; Shen, S.-H., Young, S.-T., Fang, W., Design and fabrication of a MEMS filter bank for hearing aids applications (2002) Proc. 2nd Annu. Int. IEEE-EMBS Special Topic Conf. Microtechnol. Med. Biol, pp. 352-355. , May; Tanaka, K., Abe, M., Ando, S., A novel mechanical cochlea 'fishbone' with dual sensor/actuator characteristics (1998) IEEE/ASME Trans. Mechatronics, 3 (2), pp. 98-105. , Jun; Xu, T., Bachman, M., Zeng, F.-G., Li, G.-P., Polymeric micro-cantilever array for auditory front-end processing (2004) Sens. Actuators A, Phys., 114 (2-3), pp. 176-182. , Sep; Battiston, F.M., A chemical sensor based on a microfabricated cantilever array with simultaneous resonance-frequency and bending readout (2001) Sens. Actuators B, Chem., 77 (1-2), pp. 122-131. , Jun; Moulin, A.M., O'Shea, S.J., Welland, M.E., Microcantilever-based biosensors (2000) Ultramicroscopy, 82 (1-4), pp. 23-31. , Feb; Chen, F., A hydromechanical biomimetic cochlea: Experiments and models (2006) J. Acoust. Soc. Amer., 119 (1), pp. 394-405. , Jan; Shintaku, H., Kobayashi, T., Zusho, K., Kotera, H., Kawano, S., Wide-range frequency selectivity in an acoustic sensor fabricated using a microbeam array with non-uniform thickness (2013) J. Micromech. Microeng., 23 (11). , Nov; Jang, J., A microelectromechanical system artificial basilar membrane based on a piezoelectric cantilever array and its characterization using an animal model (2015) Sci. Rep., 5 (1), p. 12447. , Dec; White, R.D., Grosh, K., Microengineered hydromechanical cochlear model (2005) Proc. Nat. Acad. Sci. Usa, 102 (5), pp. 1296-1301. , Feb; Jang, J., Kim, S., Sly, D.J., O'Leary, S.J., Choi, H., MEMS piezoelectric artificial basilar membrane with passive frequency selectivity for short pulse width signal modulation (2013) Sens. Actuators A, Phys., 203, pp. 6-10. , Dec; Kim, S., Song, W.J., Jang, J., Jang, J.H., Choi, H., Mechanical frequency selectivity of an artificial basilar membrane using a beam array with narrow supports (2013) J. Micromech. Microeng., 23 (9). , Sep; Shahruz, S.M., Design of mechanical band-pass filters with large frequency bands for energy scavenging (2006) Mechatronics, 16 (9), pp. 523-531. , Nov; Shahruz, S.M., Limits of performance of mechanical band-pass filters used in energy scavenging (2006) J. Sound Vibrat., 293 (1-2), pp. 449-461. , May; Saadatzi, M., Saadatzi, M.N., Tavaf, V., Banerjee, S., AEVE 3D: Acousto electrodynamic three-dimensional vibration exciter for engineering testing (2018) IEEE/ASME Trans. Mechatronics, 23 (4), pp. 1897-1906. , Aug; Ghafoori, E., Kargarnovin, M.H., Ghahremani, A.R., Dynamic responses of a rectangular plate under motion of an oscillator using a semi-analytical method (2011) J. Vibrat. Control, 17 (9), pp. 1310-1324. , Aug; Mauritsson, K., Folkow, P.D., Boström, A., Dynamic equations for a fully anisotropic elastic plate (2011) J. Sound Vibrat., 330 (11), pp. 2640-2654. , May; Mauritsson, K., Folkow, P.D., Dynamic equations for a fully anisotropic piezoelectric rectangular plate (2015) Comput. Struct., 153, pp. 112-125. , Jun; Ahmed, R., Banerjee, S., An articulated predictive model for fluid-free artificial basilar membrane as broadband frequency sensor (2018) Mech. Syst. Signal Process., 100, pp. 766-781. , Feb; Dovgilovich, L., Sofronov, I., High-accuracy finite-difference schemes for solving elastodynamic problems in curvilinear coordinates within multiblock approach (2015) Appl. Numer. Math., 93, pp. 176-194. , Jul; Ahmed, R.U., Banerjee, S., A predictive model for biomimetic plate type broadband frequency sensor (2016) Proc. Spie, 9797. , Apr; Ahmed, M.R.U., (2015) Bio-Inspired Design of Mechanical Band Pass Sensor with the Ability to Scavenge Energy, , Ph.D. dissertation Univ. South Carolina, Columbia, SC, USA; Saadatzi, M., Saadatzi, M.N., Ahmed, R., Banerjee, S., An electro-dynamic 3-dimensional vibration test bed for engineering testing (2017) Proc. Spie 10166, , Apr; Wever, E.G., II the width of the basilar membrane in Man (1938) Ann. Otol., Rhinol. Laryngol., 47 (1), pp. 37-47. , Mar","Banerjee, S.; Mechanical Engineering Department, United States; email: banerjes@cec.sc.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,1530437X,,,,"English","IEEE Sensors J.",Article,"Final","",Scopus,2-s2.0-85091010644
"Bingol M.C., Aydogmus O.","57195613088;23088079600;","Performing predefined tasks using the human–robot interaction on speech recognition for an industrial robot",2020,"Engineering Applications of Artificial Intelligence","95",,"103903","","",,7,"10.1016/j.engappai.2020.103903","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089847163&doi=10.1016%2fj.engappai.2020.103903&partnerID=40&md5=411b89ad0e9cb959565949c0d11cff36","Fırat University, Technology Faculty, Mechatronics Engineering, Elazig, Turkey","Bingol, M.C., Fırat University, Technology Faculty, Mechatronics Engineering, Elazig, Turkey; Aydogmus, O., Fırat University, Technology Faculty, Mechatronics Engineering, Elazig, Turkey","People who are not experts in robotics can easily implement complex robotic applications by using human–robot interaction (HRI). HRI systems require many complex operations such as robot control, image processing, natural speech recognition, and decision making. In this study, interactive control with an industrial robot was performed by using speech recognition software in the Turkish language. The collected voice data were converted to text data by using automatic speech recognition module based on deep neural networks (DNN). The proposed DNN (p-DNN) was compared to classic classification algorithms. Converted text data was improved in another module to select the process to be applied. According to selected process, position data were defined using image processing. The determined position information was sent to the robot using a fuzzy controller. The developed HRI system was implemented on a KUKA KR Agilus KR6 R900 sixx robot manipulator. The word accuracy rate of the p-DNN model was measured as 90.37%. The developed image processing module and fuzzy controller worked with minimal errors. The contribution of this study is that an industrial robot is easily programming using this software by people who are not experts in robotics and know Turkish. © 2020 Elsevier Ltd","Deep neural networks; Human–robot interaction; Intelligent robots; Robotic vision; Turkish speech recognition","Behavioral research; Character recognition; Complex networks; Controllers; Decision making; Deep neural networks; Image processing; Industrial robots; Manipulators; Robot applications; Robot programming; Robotics; Social robots; Automatic speech recognition; Classification algorithm; Complex operations; Image Processing Module; Interactive control; Position information; Robotic applications; Speech recognition softwares; Speech recognition",,,,,,,,,,,,,,,,"Amrouche, A., Debyeche, M., Taleb-Ahmed, A., Michel Rouvaen, J., Yagoub, M.C.E., An efficient speech recognition system in adverse conditions using the nonparametric regression (2010) Eng. Appl. Artif. 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"Seok D.-Y., Kim Y.B., Lee S.Y., Kim J., Choi H.R.","57191675082;56125838200;57207576695;9437375400;7404339840;","Ultra-Thin Joint Torque Sensor with Enhanced Sensitivity for Robotic Application",2020,"IEEE Robotics and Automation Letters","5","4","9144393","5873","5880",,6,"10.1109/LRA.2020.3010442","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089308275&doi=10.1109%2fLRA.2020.3010442&partnerID=40&md5=d2c4a9f0d71f38a926b0bb1353c753e8","School of Mechanical Engineering, Sungkyunkwan University, Suwon, South Korea","Seok, D.-Y., School of Mechanical Engineering, Sungkyunkwan University, Suwon, South Korea; Kim, Y.B., School of Mechanical Engineering, Sungkyunkwan University, Suwon, South Korea; Lee, S.Y., School of Mechanical Engineering, Sungkyunkwan University, Suwon, South Korea; Kim, J., School of Mechanical Engineering, Sungkyunkwan University, Suwon, South Korea; Choi, H.R., School of Mechanical Engineering, Sungkyunkwan University, Suwon, South Korea","As advanced robotic technologies such as human-robot interaction and automatic assembly processes have emerged, torque sensors have become an essential component for robots. However, commercial torque sensors are not suitable for robotic applications because of their large sizes, heavy weights, narrow options, and high prices. In this letter, we develop a novel capacitive joint torque sensor with ultra-thin structure, high performance, but low cost. To achieve these goals, novel designs have been applied to both the sensing and deformable parts, which are the most important elements of the torque sensor. To obtain high sensitivity, a novel electrode structure called wedge electrode was applied to the sensing part, and a new deformable structure was designed to be ultra-thin and easy to manufacture. Then, the electrode and deformable structures were implemented in a single torque sensor. The developed torque sensor was calibrated based on an artificial neural network (ANN) model and verified to perform high accuracy and sensitivity, and low crosstalk by comparing it with a commercial torque sensor. Finally, a high-performance torque sensor was implemented in ultra-thin size with a diameter of 108 mm and thickness of 13 mm. © 2016 IEEE.","cooperating robots; Force and tactile sensing","Agricultural robots; Costs; Deformation; Electrodes; Human robot interaction; Robotic assembly; Robotics; Torque; Torque meters; Artificial neural network models; Deformable structures; Electrode structure; Enhanced sensitivity; Joint torque sensors; Robotic applications; Robotic technologies; Ultra-thin structures; Capacitive sensors",,,,,"Ministry of Trade, Industry and Energy, MOTIE","Manuscript received February 23, 2020; accepted June 26, 2020. Date of publication July 20, 2020; date of current version July 30, 2020. This letter was recommended for publication by Associate Editor J. Falco and Editor D. Popa upon evaluation of the reviewers’ comments. This work was supported by the Robot Industry Core Technology Development Program under Grnat 20000944, in part by the Development of skin type proximity sensing technology for detecting human at 20 cm distance to prevent collision, funded by the Ministry of Trade, Industry and Energy (MI, Korea). (Corresponding author: Hyouk Ryeol Choi.) The authors are with the School of Mechanical Engineering, Sungkyunkwan University, Suwon 440-746, South Korea (e-mail: dy.seok@skku.edu; yongbum87@me.skku.ac.kr; sylee6051@skku.edu; kjywodbs@skku.edu; choihyoukryeol@gmail.com). Digital Object Identifier 10.1109/LRA.2020.3010442",,,,,,,,,,"Ott, C., Albu-Schaffer, A., Kugi, A., Hirzinger, G., On the passivitybased impedance control of flexible joint robots (2008) IEEE Trans. Robot., 24 (2), pp. 416-429. , Apr; Bluethmann, W., Robonaut: A robot designed to work with humans in space (2003) Auton. 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Electrostat., 66, pp. 366-368; Ugural, A.C., Fenster, S.K., (2003) Advanced Strength and Applied Elasticity, 4th Ed, , Englewood Cliffs, NJ, USA: Prentice Hall","Choi, H.R.; School of Mechanical Engineering, South Korea; email: choihyoukryeol@gmail.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85089308275
"Li H.-Y., Dharmawan A.G., Paranawithana I., Yang L., Tan U.-X.","57191343038;56595047500;57188987711;57214359135;22235331000;","A Control Scheme for Physical Human-Robot Interaction Coupled with an Environment of Unknown Stiffness",2020,"Journal of Intelligent and Robotic Systems: Theory and Applications","100","1",,"165","182",,5,"10.1007/s10846-020-01176-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081936407&doi=10.1007%2fs10846-020-01176-2&partnerID=40&md5=6c1b6120fb160518cd64761b0ad122f5","Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore; Zhejiang University/University of Illinois at Urbana-Champaign Institute, Hangzhou, China","Li, H.-Y., Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore; Dharmawan, A.G., Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore; Paranawithana, I., Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore; Yang, L., Zhejiang University/University of Illinois at Urbana-Champaign Institute, Hangzhou, China; Tan, U.-X., Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore","Variable admittance control is commonly used for a collaborative robot to achieve the compliant or accurate cooperation according to human’s intention. However, existing research seldom investigates such a human-robot collaboration coupled with an extra environment with unknown stiffness. If the end-effector that is guided by a human with various intended motion contacts the unknown environment, the interaction might become unstable. Additionally, current research for this physical human-robot-environment interaction use two force sensors to address the issue, and hence the cost of the robot is likely to increase and it reduces the flexibility to many applications. Therefore, in this paper, we address the issue of physical human-robot interaction coupled with an extra environment whose stiffness is unknown. To achieve this, the condition of robot admittance is rigorously proved in accordance with different human intended motion and environmental stiffness. Moreover, a variable admittance control scheme is proposed based on human intention, environmental force and environment stiffness using the combination of a force sensor and a force observer. Simulation and experiments are conducted to demonstrate the effectiveness of the proposed control scheme. © 2020, Springer Nature B.V.","Collaborative manipulator; Physical human-robot interaction (pHRI); Variable admittance control","Man machine systems; Manipulators; Physical addresses; Stiffness; Admittance control; Collaborative robots; Environmental forces; Human intentions; Human-robot collaboration; Physical human-robot interactions; Physical humanrobot interaction (phri); Unknown environments; Human robot interaction",,,,,,,,,,,,,,,,"Colgate, E., Wannasuphoprasit, W., Peshkin, M., Cobots: Robots for collaboration with human operators (1996) Proceedings of the ASME Dynamic Systems and Control Division, 58, pp. 433-439; Khatib, O., Yokoi, K., Brock, O., Chang, K., Casal, A., Robots in human environments: Basic autonomous capabilities (1999) Int. J. Robot. 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Robot., 30 (2), pp. 493-506; Sun, K., Mou, S., Qiu, J., Wang, T., Gao, H., Adaptive fuzzy control for non-triangular structural stochastic switched nonlinear systems with full state constraints (2018) IEEE Transactions on Fuzzy Systems., , https://doi.org/10.1109/TFUZZ.2018.2883374; Qiu, J., Sun, K., Wang, T., Gao, H., Observer-based fuzzy adaptive event-triggered control for pure-feedback nonlinear systems with prescribed performance (2019) IEEE Transactions on Fuzzy Systems, , https://doi.org/10.1109/TFUZZ.2019.2895560; Qiu, J., Sun, K., Rudas, I.J., Gao, H., Command filter-based adaptive NN control for MIMO nonlinear systems with full-state constraints and actuator hysteresis (2019) IEEE Transactions on Cybernetics, , https://doi.org/10.1109/TCYB.2019.2944761; Aldini, S., Akella, A., Singh, A.K., Wang, Y.K., Carmichael, M., Liu, D., Lin, C.T., Effect of mechanical resistance on cognitive conflict in physical human-robot collaboration (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 6137-6143","Li, H.-Y.; Pillar of Engineering Product Development, Singapore; email: hsiehyu_li@sutd.edu.sg",,,"Springer",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","",Scopus,2-s2.0-85081936407
"Xue Er Shamaine C., Qiao Y., Henry J., McNevin K., Murray N.","57220024635;22036417400;57225745331;57221462121;7201513152;","RoSTAR: ROS-based Telerobotic Control via Augmented Reality",2020,"IEEE 22nd International Workshop on Multimedia Signal Processing, MMSP 2020",,,"9287100","","",,,"10.1109/MMSP48831.2020.9287100","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099221994&doi=10.1109%2fMMSP48831.2020.9287100&partnerID=40&md5=54aae460217659e13817fa731553b779","Athlone Institute of Technology, Department of Computer and Software Engineering, Athlone, Ireland; Robotics and Drives Mullingar Business Park, Mullingar, Ireland","Xue Er Shamaine, C., Athlone Institute of Technology, Department of Computer and Software Engineering, Athlone, Ireland; Qiao, Y., Athlone Institute of Technology, Department of Computer and Software Engineering, Athlone, Ireland; Henry, J., Robotics and Drives Mullingar Business Park, Mullingar, Ireland; McNevin, K., Robotics and Drives Mullingar Business Park, Mullingar, Ireland; Murray, N., Athlone Institute of Technology, Department of Computer and Software Engineering, Athlone, Ireland","Real world virtual world communication and interaction will be a cornerstone of future intelligent manufacturing ecosystems. Human robotic interaction is considered to be the basic element of factories of the future. Despite the advancement of different technologies such as wearables and Augmented Reality (AR), human-robot interaction (HRI) is still extremely challenging. Whilst progress has been made in the development of different mechanisms to support HRI, there are issues with cost, naturalistic and intuitive interaction, and communication across heterogeneous systems. To mitigate these limitations, RoSTAR is proposed. RoSTAR is a novel open-source HRI system based on the Robot Operating System (ROS) and Augmented Reality. An AR Head Mounted Display (HMD) is deployed. It enables the user to interact and communicate through a ROS powered robotic arm. A model of the robot arm is imported directly into the Unity Game engine, and any interactions with this virtual robotic arm are communicated to the ROS robotic arm. This system has the potential to be used for different process tasks, such as robotic gluing, dispensing and arc welding as part of an interoperable, low cost, portable and naturalistically interactive experience. © 2020 IEEE.","Augmented Reality; Human-Robot Interaction; Robot Operating System; Unity3D","Augmented reality; Electric welding; Helmet mounted displays; Interoperability; Multimedia signal processing; Robotic arms; Robotics; Wearable technology; Communication and interaction; Factories of the futures; Heterogeneous systems; Human robot Interaction (HRI); Human-Robotic interaction; Intelligent Manufacturing; Intuitive interaction; Robot operating systems (ROS); Human robot interaction",,,,,"Science Foundation Ireland, SFI: SFI/16/RC/3918","ACKNOWLEDGEMENT This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under grant number SFI/16/RC/3918, co-funded by CONFIRM Smart Manufacturing and Robotics & Drives Ltd.",,,,,,,,,,"Feil-Seifer, D., Mataric, M.J., (2009) Human-Robot Interaction, , https://www.researchgate.net/publication/236324876Human-RobotInteraction, Unknown: Springer; Goodrich, M.A., Schultz, A.C., (2010) Human-Robot Interaction, Rijeka, Croatia: BoD-Books on Demand, , https://www.researchgate.net/publication/220613473Human-RobotInteractionASurvey; Williams, T., Szafir, D., Chakraborti, T., Phillips, E., Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Daegu, Korea (South), pp. 671-672. , https://ieeexplore-ieee-org.ezproxy.ait.ie/stamp/stamp.jsp?tp=&arnumber=8673207; Chacko, S.M., Kapila, V., An Augmented Reality Interface for Human-Robot Interaction in Unconstrained Environments (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, pp. 3222-3228. , https://ieeexplore.ieee.org/document/8967973; Quintero, C.P., Li, S., Pan, M.K., Chan, W.P., Machiel Loos, H.F., Croft, E., Robot Programming through Augmented Trajectories in Augmented Reality 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, 2018, pp. 1838-1844. , https://ieeexplore-ieee-org.ezproxy.ait.ie/document/8593700; Ni, D., Yew, A., Ong, S.K., Andrew, N., Haptic and visual augmented reality interface for programming welding robots 2017 Advances in Manufacturing, , https://www.researchgate.net/publication/319189649Hapticandvisualaugmentedrealityinterfaceforprogrammingweldingrobots; Dima, E., View Position Impact on QoE in an Immersive Telepresence System for Remote Operation (2019) 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), Berlin, Germany, pp. 1-3. , https://ieeexplore.ieee.org/document/8743147; Kortekamp, S., Werning, S., Thomas, O., Ickerott, I., THE FUTURE of DIGITAL WORK-USE CASES for AUGMENTED REALITY GLASSES"". In Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm & Uppsala, Sweden (2019), https://aisel.aisnet.org/ecis2019rp/165, June; Monferrer, A., Bonyuet, D., Cooperative robot teleoperation through virtual reality interfaces (2002) Proceedings Sixth International Conference on Information Visualisation, London, UK, pp. 243-248. , https://www.researchgate.net/publication/3964856Cooperativerobotteleoperationthroughvirtualrealityinterfaces; Green, S., Billinghurst, M., Chen, X., Chase, J., Human Robot Collaboration: An Augmented Reality Approach A Literature Review and Analysis (2007) Proceeding of MESA 07:3RD International Conference on Mechatronics and Embedded System and Applications, Nevada,USA, , https://www.researchgate.net/publication/29488268HumanRobotCollaborationAnAugmentedRealityApproachALiteratureReviewAndAnalysis; Möller, S., Raake, A., Quality and Quality of Experience (2014) Quality of Experience: Advanced Concepts Applications and Methods, Springer, p. 19; 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(2020) ""moveIt Tutorials-Moveittutorials Kinetic Documentation"", , http://docs.ros.org/melodic/api/moveittutorials/html/index.html.[Accessed:19Jan2020], Docs.ros.org; (2020) ""gazebo : Tutorial : URDF in Gazebo"", Gazebosim.org, , http://gazebosim.org/tutorials?tut=rosurdf.[Accessed:11Jan2020]",,,,"Institute of Electrical and Electronics Engineers Inc.","22nd IEEE International Workshop on Multimedia Signal Processing, MMSP 2020","21 September 2020 through 24 September 2020",,165866,,9781728193205,,,"English","IEEE Int. Workshop Multimed. Signal Process., MMSP",Conference Paper,"Final","",Scopus,2-s2.0-85099221994
"Wu Y., Zhao F., Kim W., Ajoudani A.","57202714727;57195553684;55978740100;23388667500;","An intuitive formulation of the human arm active endpoint stiffness",2020,"Sensors (Switzerland)","20","18","5357","1","15",,,"10.3390/s20185357","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091066661&doi=10.3390%2fs20185357&partnerID=40&md5=fc5b261bd22fa2e9e6718dd3af487fd8","State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an, 710049, China; Shaanxi Key Laboratory of Intelligent Robots and School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, China; Human Robot Interfaces and physical Interaction lab (HRI2), Istituto Italiano di Tecnologia (IIT), Genova, 16163, Italy","Wu, Y., State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an, 710049, China, Shaanxi Key Laboratory of Intelligent Robots and School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, China, Human Robot Interfaces and physical Interaction lab (HRI2), Istituto Italiano di Tecnologia (IIT), Genova, 16163, Italy; Zhao, F., State Key Laboratory for Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an, 710049, China, Shaanxi Key Laboratory of Intelligent Robots and School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, China; Kim, W., Human Robot Interfaces and physical Interaction lab (HRI2), Istituto Italiano di Tecnologia (IIT), Genova, 16163, Italy; Ajoudani, A., Human Robot Interfaces and physical Interaction lab (HRI2), Istituto Italiano di Tecnologia (IIT), Genova, 16163, Italy","In this work, we propose an intuitive and real-time model of the human arm active endpoint stiffness. In our model, the symmetric and positive-definite stiffness matrix is constructed through the eigendecomposition Kc = V DVT, where V is an orthonormal matrix whose columns are the normalized eigenvectors of Kc, and D is a diagonal matrix whose entries are the eigenvalues of Kc . In this formulation, we propose to construct V and D directly by exploiting the geometric information from a reduced human arm skeleton structure in 3D and from the assumption that human arm muscles work synergistically when co-contracted. Through the perturbation experiments across multiple subjects under different arm configurations and muscle activation states, we identified the model parameters and examined the modeling accuracy. In comparison to our previous models for predicting human active arm endpoint stiffness, the new model offers significant advantages such as fast identification and personalization due to its principled simplicity. The proposed model is suitable for applications such as teleoperation, human–robot interaction and collaboration, and human ergonomic assessments, where a personalizable and real-time human kinodynamic model is a crucial requirement. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Human factors; Physical human–robot collaboration; Robot adaptation and learning","Eigenvalues and eigenfunctions; Muscle; Stiffness; Stiffness matrix; Eigen decomposition; Ergonomic assessment; Geometric information; Muscle activation; Positive definite; Real time modeling; Robot interactions; Skeleton structure; Social robots; arm; human; joint characteristics and functions; movement (physiology); skeleton; theoretical model; Arm; Humans; Models, Theoretical; Movement; Range of Motion, Articular; Skeleton",,,,,"2017ZDL-G-3-1; 850932; National Natural Science Foundation of China, NSFC: 91748208; China Scholarship Council, CSC; Istituto Italiano di Tecnologia, IIT","Funding: This work was carried out in HRI2 Lab, Istituto Italiano di Tecnologia, Genoa, Italy. This work was supported in part by the ERC-StG Ergo-Lean (grant agreement number 850932), in part by the National Natural Science Foundation of China under grant 91748208 and in part by the Department of Science and Technology of Shaanxi Province under grant 2017ZDL-G-3-1. In this work, Y. Wu was also supported by the China Scholarship Council.","This work was carried out in HRI2 Lab, Istituto Italiano di Tecnologia, Genoa, Italy. This work was supported in part by the ERC-StG Ergo-Lean (grant agreement number 850932), in part by the National Natural Science Foundation of China under grant 91748208 and in part by the Department of Science and Technology of Shaanxi Province under grant 2017ZDL-G-3-1. In this work, Y. Wu was also supported by the China Scholarship Council.",,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human–robot collaboration (2018) Auton. Robot, 42, pp. 957-975. , [CrossRef]; Hogan, N., Impedance control: An approach to manipulation: Part I—Theory (1985) J. Dyn. Syst. Meas. Control, 107, pp. 1-7. , [CrossRef]; Ajoudani, A., Tsagarakis, N.G., Bicchi, A., Tele-impedance: Preliminary results on measuring and replicating human arm impedance in tele operated robots Proceedings of the 2011 IEEE international conference on robotics and biomimetics, pp. 216-222. , Karon Beach, Phuket, Thailand, 7–11 December 2011; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human–robot physical interaction (2015) IEEE Trans. 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Res, 38, pp. 833-852. , [CrossRef]; Trumbower, R.D., Krutky, M.A., Yang, B.S., Perreault, E.J., Use of self-selected postures to regulate multi-joint stiffness during unconstrained tasks (2009) PLoS ONE, 4, p. e5411. , [CrossRef]; Franklin, D.W., Liaw, G., Milner, T.E., Osu, R., Burdet, E., Kawato, M., Endpoint stiffness of the arm is directionally tuned to instability in the environment (2007) J. Neurosci, 27, pp. 7705-7716. , [CrossRef] [PubMed]; Tsuji, T., Takeda, Y., Tanaka, Y., Analysis of mechanical impedance in human arm movements using a virtual tennis system (2004) Biol. Cybern, 91, pp. 295-305. , [CrossRef] [PubMed]; Erden, M.S., Billard, A., Hand impedance measurements during interactive manual welding with a robot (2015) IEEE Trans. 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No. 02CH37292), 1, pp. 544-549. , Washington, DC, USA, 11–15 May; Ajoudani, A., Tsagarakis, N., Bicchi, A., Tele-impedance: Teleoperation with impedance regulation using a body–machine interface (2012) Int. J. Robot. Res, 31, pp. 1642-1656. , [CrossRef]; Ajoudani, A., Fang, C., Tsagarakis, N.G., Bicchi, A., A reduced-complexity description of arm endpoint stiffness with applications to teleimpedance control Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1017-1023. , Hamburg, Germany, 28 September–2 October 2015; Ajoudani, A., Fang, C., Tsagarakis, N., Bicchi, A., Reduced-complexity representation of the human arm active endpoint stiffness for supervisory control of remote manipulation (2018) Int. J. Robot. Res, 37, pp. 155-167. , [CrossRef]; Zatsiorsky, V.M., Zaciorskij, V.M., (2002) Kinetics of Human Motion, , Human Kinetics: Champaign, IL, USA; Ding, X., Fang, C., A novel method of motion planning for an anthropomorphic arm based on movement primitives (2012) IEEE/ASME Trans. Mechatron, 18, pp. 624-636. , [CrossRef]; Perreault, E.J., Kirsch, R.F., Crago, P.E., Voluntary control of static endpoint stiffness during force regulation tasks (2002) J. Neurophysiol, 87, pp. 2808-2816. , [CrossRef]; Pennec, X., Fillard, P., Ayache, N., A Riemannian framework for tensor computing (2006) Int. J. Comput. Vis, 66, pp. 41-66. , [CrossRef]; Vemulapalli, R., Jacobs, D.W., (2015) Riemannian metric learning for symmetric positive definite matrices, , arXiv arXiv:1501.02393; Perreault, E.J., Kirsch, R.F., Crago, P.E., Effects of shoulder stability on endpoint stiffness (2000) IEEE Eng. Med. Biol. Mag, 19, pp. 53-58. , [PubMed]; Inouye, J.M., Valero-Cuevas, F.J., Muscle synergies heavily influence the neural control of arm endpoint stiffness and energy consumption (2016) PLoS Comput. Biol, 12, p. e1004737. , [CrossRef]","Wu, Y.; State Key Laboratory for Manufacturing System Engineering, China; email: wuyuqiang@stu.xjtu.edu.cn
Wu, Y.; Shaanxi Key Laboratory of Intelligent Robots and School of Mechanical Engineering, China; email: wuyuqiang@stu.xjtu.edu.cn
Wu, Y.; Human Robot Interfaces and physical Interaction lab (HRI2), Italy; email: wuyuqiang@stu.xjtu.edu.cn",,,"MDPI AG",,,,,14248220,,,"32962084","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85091066661
"Alimuradov A.K., Tychkov A.Y., Makarova N.A.","56595170300;36731632400;57219971857;","Study of Voiced Speech Using Empirical Mode Decomposition to Detect Stressful Emotions in Human-Robot Interaction",2020,"Conference Proceedings - 4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics, DCNAIR 2020",,,"9216904","97","100",,,"10.1109/DCNAIR50402.2020.9216904","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096358447&doi=10.1109%2fDCNAIR50402.2020.9216904&partnerID=40&md5=4747f138a04ef30dac41b6f14edede10","Penza State University, Student Research and Production Business Incubator, Penza, Russian Federation; Penza State University, Research Institute for Fundamental and Applied Studies, Penza, Russian Federation; K.R. Evgrafov Regional Psychiatric Hospital, Twelfth Children's Department, Penza, Russian Federation","Alimuradov, A.K., Penza State University, Student Research and Production Business Incubator, Penza, Russian Federation; Tychkov, A.Y., Penza State University, Research Institute for Fundamental and Applied Studies, Penza, Russian Federation; Makarova, N.A., K.R. Evgrafov Regional Psychiatric Hospital, Twelfth Children's Department, Penza, Russian Federation","A method for studying voiced speech by means of improved complete ensemble empirical mode decomposition with adaptive noise to detect stressful emotions in human-robot interaction is presented. The informative parameters of voiced speech fully reflect the violation in functioning of organs of human speech apparatus due to emotional excitation. The key feature of the research is to decompose a signal into empirical modes, to detect modes containing periodic information on the excitation source of the vocal tract, and to construct a composite signal reflecting information on glottal activity. The research results of voiced speech, being computation of the fundamental frequency of 100 multi-harmonic signals, are presented. The pitch frequency modulation of multi-harmonic signals in the frequency range of 0-2.5 Hz/ms in 0.5 Hz/ms increments simulated the irregularity in vibration of the vocal folds (30-40 % of the nominal value) arising from the emotional excitation of a person. The results obtained allow us to conclude that the presented method for the study of voiced speech, using the improved decomposition, can be successfully tested to detect stressful emotions in human interaction with robotic devices. © 2020 IEEE.","affective computing; emotional speech; empirical mode decomposition; speech signal processing; voiced speech","Agricultural robots; Complex networks; Man machine systems; Robotics; Signal processing; Speech; Speech analysis; Speech recognition; Empirical Mode Decomposition; Ensemble empirical mode decomposition; Excitation sources; Frequency ranges; Fundamental frequencies; Human interactions; Informative parameters; Research results; Human robot interaction",,,,,"Council on grants of the President of the Russian Federation: MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8; Council on grants of the President of the Russian Federation: MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8; Council on grants of the President of the Russian Federation: MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8, MK-490.2020.8","Council for Grants of the President of the Russian Federation, project No. MK-490.2020.8.","The research described in this article is devoted to the study of human voiced speech to detect stressful emotions in human interaction with robotic devices. Within the framework of the research, a unique technology for adaptive analysis of non-stationary signals, namely, the method of empirical mode decomposition (EMD) is used. The research was carried out with the financial support of the Council for Grants of the President of the Russian Federation, project No. MK-490.2020.8 “Development of methods and virtual means for adaptive noise-robust processing and detection of clinically significant parameters of medical electrical and acoustic signals in patients with borderline mental disorders.”","ACKNOWLEDGEMENTS The authors are grateful to the Council for Grants of the President of the Russian Federation for support in implementation of the project “Development of methods and virtual means for adaptive noise-robust processing and detection of clinically significant parameters of medical electrical and acoustic signals in patients with borderline mental disorders” No. MK-490.2020.8.",,,,,,,,"Schuller, B.W., Batliner, A.M., (2013) Computational Paralinguistics: Emotion Affect and Personality in Speech and Language Processing, , New York: Wiley; Emotion AI Hub R&D Neurodata Lab: Official Page, , https://neurodatalab.com, [Online] Available, (accessed April 20, 2020); Alimuradov, A.K., Fokina, E.A., Zhurina, A.E., A brief review of known emotional speech databases: Current state, problems and development prospects (2020) Proc. 12th Int. Sc. Conf. Methods, Means and Technologies of Obtaining and Processing Data (Shlyandin Readings 2020), pp. 292-294. , Penza, March 16-18, 2020, E.A. Pecherskaya, Ed. Penza: PSU Publishing House; Alimuradov, A.K., Tychkov, A.Y., Ageykin, A.V., An adaptive speech segmentation algorithm to determine temporal patterns of human psycho-emotional states (2020) Advances in Automation. Lecture Notes in Electrical Engineering, 641, pp. 879-890; Huang, N.E., Zheng, Sh., Steven, R.L., The empirical mode decomposition and the hilbert spectrum for nonlinear and nonstationary time series analysis (1998) Proc. Of the Royal Society of London, 454, pp. 903-995; Zhaohua, W., Huang, N.E., Ensemble empirical mode decomposition: A noise-assisted data analysis method (2009) Advances in Adaptive Data Analysis, 1 (1), pp. 1-41; Yeh, J.-R., Shieh, J.-S., Huang, N.E., Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method (2010) Advances in Adaptive Data Analysis, 2 (2), pp. 135-156; Torres, M.E., Colominas, M.A., Schlotthauer, G., Flandrin, P., A complete ensemble empirical mode decomposition with adaptive noise 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (ICASSP-11), pp. 4144-4147. , May 22-27, 2011, Prague, Czech Republic; Colominasa, M.A., Schlotthauera, G., Torres, M.E., Improved complete ensemble emd: A suitable tool for biomedical signal processing (2014) Biomedical Signal Processing and Control, 14, pp. 19-29; Lee, H.S., Improvement of decomposing results of empirical mode decomposition and its variations for sea-level records analysis (2018) Journal of Coastal Research, Special: Proceedings of the 15th International Coastal Symposium, (85), pp. 526-530; Sharma, R., Prasanna, S.R.M., Characterizing glottal activity from speech using empirical mode decomposition 2015 21st National Conference on Communicati",,,,"Institute of Electrical and Electronics Engineers Inc.","4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual Robotics, DCNAIR 2020","7 September 2020 through 9 September 2020",,163883,,9781728172866,,,"English","Conf. Proc. - Sci. Sch. Dyn. Complex Networks Appl. Intellect. Robot., DCNAIR",Conference Paper,"Final","",Scopus,2-s2.0-85096358447
"Bazzi S., Sternad D.","55990405400;7003842039;","Human control of complex objects: towards more dexterous robots",2020,"Advanced Robotics","34","17",,"1137","1155",,2,"10.1080/01691864.2020.1777198","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086928211&doi=10.1080%2f01691864.2020.1777198&partnerID=40&md5=666b8cfbfa5edd3dcc6e406a974be0d6","Department of Biology, Northeastern University, Boston, MA, United States; Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States; Department of Physics, Northeastern University, Boston, MA, United States","Bazzi, S., Department of Biology, Northeastern University, Boston, MA, United States, Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States; Sternad, D., Department of Biology, Northeastern University, Boston, MA, United States, Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States, Department of Physics, Northeastern University, Boston, MA, United States","Manipulation of objects with underactuated dynamics remains a challenge for robots. In contrast, humans excel at ‘tool use’ and more insight into human control strategies may inform robotic control architectures. We examined human control of objects that exhibit complex–underactuated, nonlinear, and potentially chaotic dynamics, such as transporting a cup of coffee. Simple control strategies appropriate for unconstrained movements, such as maximizing smoothness, fail as interaction forces have to be compensated or preempted. However, predictive control based on internal models appears daunting when the objects have nonlinear and unpredictable dynamics. We hypothesized that humans learn strategies that make these interactions predictable. Using a virtual environment subjects interacted with a virtual cup and rolling ball using a robotic visual and haptic interface. Two different metrics quantified predictability: stability or contraction, and mutual information between controller and object. In point-to-point displacements subjects exploited the contracting regions of the object dynamics to safely navigate perturbations. Control contraction metrics showed that subjects used a controller that exponentially stabilized trajectories. During continuous cup-and-ball displacements subjects developed predictable solutions sacrificing smoothness and energy efficiency. These results may stimulate control strategies for dexterous robotic manipulators and human–robot interaction. © 2020 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","chaos; Complex object manipulation; human motor control; stability; underactuated","Agricultural robots; Controllers; Dynamics; Energy efficiency; Haptic interfaces; Human robot interaction; Manipulators; Robotics; Control strategies; Human control strategies; Interaction forces; Mutual informations; Predictive control; Robot interactions; Robotic manipulators; Stabilized trajectory; Visual servoing",,,,,"1723998; National Science Foundation, NSF: 1637854, 1825942; National Institutes of Health, NIH: R01-HD081346, R01-HD087089","This research was supported by NIH [grant numbers R01-HD087089, R01-HD081346], NSF-NRI [grant number 1637854], NSF-CRCNS [grant number 1723998] and NSF-M3X [grant number 1825942], awarded to D. S. S. B. was supported by NSF-NRI [grant number 1637854] and NIH [grant number R01-HD087089].",,,,,,,,,,"Mayer, H.C., Krechetnikov, R., Walking with coffee: why does it spill? 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Hoboken (NJ; Nasseroleslami, B., Hasson, C.J., Sternad, D., Rhythmic manipulation of objects with complex dynamics: predictability over chaos (2014) PLoS Comput Biol, 10 (10), p. e1003900; Maurice, P., Hogan, N., Sternad, D., Predictability, force and (anti-) resonance in complex object control (2018) J Neurophysiol, 120 (2), pp. 765-780; Shaw, R., Strange attractors, chaotic behavior, and information flow (1981) Z Naturforsch A, 36 (1), pp. 80-112; Shaw, R., (1984) The dripping faucet as a model chaotic system, , Aerial Press,. Santa Cruz (CA","Bazzi, S.; Department of Biology, United States; email: s.bazzi@northeastern.edu",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85086928211
"Sun Q.-J., Zhao X.-H., Yeung C.-C., Tian Q., Kong K.-W., Wu W., Venkatesh S., Li W.-J., Roy V.A.L.","55467951500;57195425687;57200109304;57215891049;57191587523;56732152600;57190581530;27172170000;7005870324;","Bioinspired, self-powered, and highly sensitive electronic skin for sensing static and dynamic pressures",2020,"ACS Applied Materials and Interfaces","12","33",,"37239","37247",,6,"10.1021/acsami.0c10788","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089714245&doi=10.1021%2facsami.0c10788&partnerID=40&md5=30b25b1d6e0892a2a3ce9007f533323a","State Key Laboratory of Terahertz and Millimeter Waves, Department of Materials Science & Engineering, City University of Hong Kong, Kowloon, 999077, Hong Kong; Department of Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China; Department of Chemistry, City University of Hong Kong, Kowloon, 999077, Hong Kong; Department of Mechanical Engineering, City University of Hong Kong, Kowloon, 999077, Hong Kong","Sun, Q.-J., State Key Laboratory of Terahertz and Millimeter Waves, Department of Materials Science & Engineering, City University of Hong Kong, Kowloon, 999077, Hong Kong; Zhao, X.-H., Department of Chemistry, Southern University of Science and Technology, Shenzhen, 518055, China; Yeung, C.-C., Department of Chemistry, City University of Hong Kong, Kowloon, 999077, Hong Kong; Tian, Q., State Key Laboratory of Terahertz and Millimeter Waves, Department of Materials Science & Engineering, City University of Hong Kong, Kowloon, 999077, Hong Kong; Kong, K.-W., Department of Mechanical Engineering, City University of Hong Kong, Kowloon, 999077, Hong Kong; Wu, W., State Key Laboratory of Terahertz and Millimeter Waves, Department of Materials Science & Engineering, City University of Hong Kong, Kowloon, 999077, Hong Kong; Venkatesh, S., State Key Laboratory of Terahertz and Millimeter Waves, Department of Materials Science & Engineering, City University of Hong Kong, Kowloon, 999077, Hong Kong; Li, W.-J., Department of Mechanical Engineering, City University of Hong Kong, Kowloon, 999077, Hong Kong; Roy, V.A.L., State Key Laboratory of Terahertz and Millimeter Waves, Department of Materials Science & Engineering, City University of Hong Kong, Kowloon, 999077, Hong Kong","Flexible piezoresistive pressure sensors obtain global research interest owing to their potential applications in healthcare, human-robot interaction, and artificial nerves. However, an additional power supply is usually required to drive the sensors, which results in increased complexity of the pressure sensing system. Despite the great efforts in pursuing self-powered pressure sensors, most of the self-powered devices can merely detect the dynamic pressure and the reliable static pressure detection is still challenging. With the help of redox-induced electricity, a bioinspired graphite/polydimethylsiloxane piezoresistive composite film acting both as the cathode and pressure sensing layer, a neoteric electronic skin sensor is presented here to detect not only the dynamic forces but also the static forces without an external power supply. Additionally, the sensor exhibits a fascinating pressure sensitivity of ∼103 kPa-1 over a broad sensing range from 0.02 to 30 kPa. Benefiting from the advanced performance of the device, various potential applications including arterial pulse monitoring, human motion detecting, and Morse code generation are successfully demonstrated. This new strategy could pave a way for the development of next-generation self-powered wearable devices. © 2020 American Chemical Society.","Bioinspired; Electronic skin; Health monitoring; Redox-induced electricity; Self-powered","Composite films; Electric power systems; Human robot interaction; Pressure sensors; Signaling; Artificial nerves; Dynamic pressures; External power supplies; Piezoresistive composites; Piezoresistive pressure sensors; Pressure sensitivities; Research interests; Wearable devices; Morse potential; baysilon; dimeticone; graphite; chemistry; electric conductivity; electrochemical analysis; electrode; electronic device; genetic procedures; human; oxidation reduction reaction; physiologic monitoring; power supply; pressure; pulse rate; Biosensing Techniques; Dimethylpolysiloxanes; Electric Conductivity; Electric Power Supplies; Electrochemical Techniques; Electrodes; Graphite; Humans; Monitoring, Physiologic; Oxidation-Reduction; Pressure; Pulse; Wearable Electronic Devices",,"dimeticone, 32028-95-8, 68248-27-1, 9004-73-3, 9006-65-9; graphite, 7782-42-5; baysilon; Dimethylpolysiloxanes; Graphite",,,"CityU 11210819","The authors acknowledge the grant from the Research Grant Council of HKSAR (General Research Fund grant no. 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Interfaces, 9, pp. 17499-17507; Zhang, C., Liu, S., Huang, X., Guo, W., Li, Y., Wu, H., A stretchable dual-mode sensor array for multifunctional robotic electronic skin (2019) Nano Energy, 62, pp. 164-170; Park, J., Lee, Y., Hong, J., Ha, M., Jung, Y.-D., Lim, H., Kim, S.Y., Ko, H., Giant tunneling piezoresistance of composite elastomers with interlocked microdome arrays for ultrasensitive and multimodal electronic skins (2014) ACS Nano, 8, pp. 4689-4697; Huang, W., Dai, K., Zhai, Y., Liu, H., Zhan, P., Gao, J., Zheng, G., Shen, C., Flexible and lightweight pressure sensor based on carbon nanotube/thermoplastic polyurethane-aligned conductive foam with superior compressibility and stability (2017) ACS Appl. Mater. Interfaces, 9, pp. 42266-42277; Yang, Y., Han, J., Huang, J., Sun, J., Wang, Z.L., Seo, S., Sun, Q., Stretchable energy-harvesting tactile interactive interface with liquid-metal-nanoparticle-based electrodes (2020) Adv. Funct. 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Interfaces, 11, pp. 10380-10388; Dos Santos, A., Pinela, N., Alves, P., Santos, R., Fortunato, E., Martins, R., Águas, H., Igreja, R., Piezoresistive e-skin sensors produced with laser engraved molds (2018) Adv. Electron. Mater., 4, p. 1800182; Liu, W., Liu, N., Yue, Y., Rao, J., Luo, C., Zhang, H., Yang, C., Gao, Y., A flexible and highly sensitive pressure sensor based on elastic carbon foam (2018) J. Mater. Chem. C, 6, pp. 1451-1458; Roh, E., Lee, H.-B., Kim, D.-I., Lee, N.-E., A solution-processable, omnidirectionally stretchable, and high-pressure-sensitive piezoresistive device (2017) Adv. Mater., 29, p. 1703004; Bae, G.Y., Pak, S.W., Kim, D., Lee, G., Kim, D.H., Chung, Y., Cho, K., Linearly and highly pressure-sensitive electronic skin based on a bioinspired hierarchical structural array (2016) Adv. Mater., 28, pp. 5300-5306; Kohara, K., Tabara, Y., Oshiumi, A., Miyawaki, Y., Kobayashi, T., Miki, T., Radial augmentation index: A useful and easily obtainable parameter for vascular aging (2005) Am. J. Hypertens., 18, pp. 11S-14S","Roy, V.A.L.; State Key Laboratory of Terahertz and Millimeter Waves, Hong Kong; email: Roy.Vellaisamy@glasgow.ac.uk",,,"American Chemical Society",,,,,19448244,,,"32814376","English","ACS Appl. Mater. Interfaces",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85089714245
"Zargarbashi F., Harandi H.N., Hamed A., Masouleh M.T.","57221133326;57221133661;57208001686;16550342100;","An experimental study on the design construction of a torque measurement unit with tactile sensors",2020,"2020 28th Iranian Conference on Electrical Engineering, ICEE 2020",,,"9260632","","",,,"10.1109/ICEE50131.2020.9260632","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098323838&doi=10.1109%2fICEE50131.2020.9260632&partnerID=40&md5=42453f0d23f35f70e7bfa9dadbb10761","School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran; School of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran; Human and Robot Interaction Laboratory, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran","Zargarbashi, F., School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran; Harandi, H.N., School of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran; Hamed, A., Human and Robot Interaction Laboratory, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran; Masouleh, M.T., Human and Robot Interaction Laboratory, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran","This paper presents the state of the art of the design and construction of a torque measurement unit based on a new modification in balancing method. This unit consists of six soft tactile sensors that each include a MEMS barometer covered by silicone rubber. Sensors are mounted in an array under the body of a servo motor to read the force from which motor torque can be obtained. Correct performance of sensor was validated in experimental tests by applying different loads on a Kinco servo motor and comparison of sensor output with value of actual torque obtained from motor driver software. The sensor was able to measure torque with less than 0.1 N.m uncertainty. The main advantage of this torque measurement method is its accuracy, simple construction, precision, real-time measurement and low cost compared to expensive strain gauge based sensors. Due to its soft surface, this torque sensor can be safely employed in human-robot interaction purposes and one can instantly find the force exerted on the environment by the end effector of robot due to the low delay time of the sensors. © 2020 IEEE.","Balancing Method; MEMS barometer; Tactile sensor; Torque sensor","Balancing; Costs; End effectors; Human robot interaction; Silicones; Software testing; Strain gages; Torque measurement; Design and construction; Design construction; Experimental test; Real time measurements; Sensor output; State of the art; Tactile sensors; Torque sensors; Torque",,,,,"334","ACKNOWLEDGMENT The authors would like to acknowledge the financial support of the Iranian National Elites Foundation, research group number 334.",,,,,,,,,,"Sun, L., Gao, H., Song, Q., Nei, J., Measurement of torque ripple in pm brushless motors (2002) Conference Record of the 2002 IEEE Industry Applications Conference. 37th IAS Annual Meeting (Cat. No.02CH37344), 4, pp. 2567-2571. , IEEE; Firoozian, R., (2014) Servo Motors and Industrial Control Theory, , Springer; Tahar, L., (2011) Torque Control/Edited by Prof. Moulay Tahar Lamchich, 292p. , Croatia: Publisher InTech, 2011; Pfister, P.-D., Perriard, Y., Very-high-speed slotless permanent-magnet motors: Analytical modeling, optimization, design, and torque measurement methods (2009) IEEE Transactions on Industrial Electronics, 57 (1), pp. 296-303; Jansson, J., Radahl, C., Torque Estimation Algorithms for Stepper Motor.; Kashiri, N., Malzahn, J., Tsagarakis, N.G., On the sensor design of torque controlled actuators: A comparison study of strain gauge and encoder-based principles (2017) IEEE Robotics and Automation Letters, 2 (2), pp. 1186-1194; Kang, S.H., Meneveau, C., Direct mechanical torque sensor for model wind turbines (2010) Measurement Science and Technology, 21 (10), p. 105206; Muscolo, G.G., Cannata, G., A novel tactile sensor for underwater applications: Limits and perspectives (2015) InOCEANS 2015-Genova, pp. 1-7. , May 18. IEEE; Uribe, D.O., Schoukens, J., Stroop, R., Improved tactile resonance sensor for robotic assisted surgery (2018) Mechanical Systems and Signal Processing, 99, pp. 600-610. , Jan 15; Jung, Y., Lee, D.G., Park, J., Ko, H., Lim, H., Piezoresistive tactile sensor discriminating multidirectional forces (2015) Sensors., 15 (10), pp. 25463-25473. , Oct 2; Zillich, M., Feiten, W., A versatile tactile sensor system for covering large and curved surface areas (2012) InIntelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, pp. 20-24. , Oct 7. IEEE; Hamed, A., Tale Masouleh, M., Kalhor, A., Design & characterization of a bio-inspired 3-dof tactile/force sensor for human-robot interaction purposes (2018) 2018 6th RSI International Conference on Robotics and Mechatronics (IcRoM), pp. 488-493. , IEEE; Shayan, M.A., Khazaei, A., Hamed, A., Amralizadeh, A., Tale Masouleh, M., Shrewdshoe, a smart pressure sensitive wearable platform (2018) 2018 6th RSI International Conference on Robotics and Mechatronics (IcRoM), pp. 458-463. , IEEE",,,,"Institute of Electrical and Electronics Engineers Inc.","28th Iranian Conference on Electrical Engineering, ICEE 2020","4 August 2020 through 6 August 2020",,165457,,9781728172965,,,"English","Iranian Conf. Electr. Eng., ICEE",Conference Paper,"Final","",Scopus,2-s2.0-85098323838
"Sirintuna D., Ozdamar I., Aydin Y., Basdogan C.","57218282723;57215790455;56084865200;6603706568;","Detecting Human Motion Intention during pHRI Using Artificial Neural Networks Trained by EMG Signals",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223438","1280","1287",,1,"10.1109/RO-MAN47096.2020.9223438","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095797289&doi=10.1109%2fRO-MAN47096.2020.9223438&partnerID=40&md5=ac0a589f877a1c39aded807138f8365a","Koc University, College of Engineering, Istanbul, 34450, Turkey","Sirintuna, D., Koc University, College of Engineering, Istanbul, 34450, Turkey; Ozdamar, I., Koc University, College of Engineering, Istanbul, 34450, Turkey; Aydin, Y., Koc University, College of Engineering, Istanbul, 34450, Turkey; Basdogan, C., Koc University, College of Engineering, Istanbul, 34450, Turkey","With the recent advances in cobot (collaborative robot) technology, we can now work with a robot side by side in manufacturing environments. The collaboration between human and cobot can be enhanced by detecting the intentions of human to make the production more flexible and effective in future factories. In this regard, interpreting human intention and then adjusting the controller of cobot accordingly to assist human is a core challenge in physical human-robot interaction (pHRI). In this study, we propose a classifier based on Artificial Neural Networks (ANN) that predicts intended direction of human movement by utilizing electromyography (EMG) signals acquired from human arm muscles. We employ this classifier in an admittance control architecture to constrain human arm motion to the intended direction and prevent undesired movements along other directions. The proposed classifier and the control architecture have been validated through a path following task by utilizing a KUKA LBR iiwa 7 R800 cobot. The results of our experimental study with 6 participants show that the proposed architecture provides an effective assistance to human during the execution of task and reduces undesired motion errors, while not sacrificing from the task completion time. © 2020 IEEE.",,"Agricultural robots; Motion analysis; Network architecture; Neural networks; Time and motion study; Admittance control; Collaborative robots; Control architecture; Human motion intention; Manufacturing environments; Physical humanrobot interaction (phri); Proposed architectures; Task completion time; Social robots",,,,,"EEEAG-117E645; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK","The Scientific and Technological Research Council of Turkey (TUBITAK) supported this work under contract EEEAG-117E645. ∗The first two authors contributed equally to this work. 1Doganay Sirintuna, Idil Ozdamar, Yusuf Aydin, and Cagatay Basdogan are with College of Engineering, Koc University, 34450, Istanbul, Turkey #Corresponding author: cbasdogan@ku.edu.tr",,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human-robot collaboration (2018) Autonomous Robots, 42, pp. 957-975. , Oct; Losey, D.P., McDonald, C.G., Battaglia, E., O'Malley, M.K., A review of intent detection, arbitration, and communication aspects of shared control for physical human-robot interaction (2018) Applied Mechanics Reviews, 70 (1). , Jan; Sirintuna, D., Aydin, Y., Caldiran, O., Tokatli, O., Patoglu, V., Basdogan, C., A variable-fractional order admittance controller for phri (2020) IEEE International Conference on Robotics and Automation (ICRA), pp. 10162-10168. , May; Ikeura, R., Moriguchi, T., Mizutani, K., Optimal variable impedance control for a robot and its application to lifting an object with a human (2002) IEEE International Workshop on Robot and Human Interactive Communication, pp. 500-505. , Sep; Kang, G., Oh, H.S., Seo, J.K., Kim, U., Choi, H.R., Variable admittance control of robot manipulators based on human intention (2019) IEEE/ASME Transactions on Mechatronics, 24 (3), pp. 1023-1032. , June; Li, H., Paranawithana, I., Yang, L., Lim, T.S.K., Foong, S., Ng, F.C., Tan, U., Stable and compliant motion of physical human-robot interaction coupled with a moving environment using variable admittance and adaptive control (2018) IEEE Robotics and Automation Letters, 3 (3), pp. 2493-2500. , July; Li, H., Nuradha, T., Xavier, S.A., Tan, U., Towards a compliant and accurate cooperative micromanipulator using variable admittance control (2018) International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 230-235. , July; Wakita, K., Huang, J., Di, P., Sekiyama, K., Fukuda, T., Humanwalking-intention-based motion control of an omnidirectional-type cane robot (2013) IEEE/ASME Transactions on Mechatronics, 18 (1), pp. 285-296. , Feb; Duchaine, V., Gosselin, C.M., General model of human-robot cooperation using a novel velocity based variable impedance control (2007) Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (WHC'07), pp. 446-451. , March; Duchaine, V., Mayer St-Onge, B., Gao, D., Gosselin, C., Stable and intuitive control of an intelligent assist device (2012) IEEE Transactions on Haptics, 5 (2), pp. 148-159. , April; Artemiadis, P., Emg-based robot control interfaces: Past, present and future (2012) Advances in Robotics & Automation, 1. , Jan; Cavanagh, P., Komi, P., Electromechanical delay in human skeletal muscle under concentric and eccentric contractions (1979) European Journal of Applied Physiology and Occupational Physiology, 42 (3), pp. 159-163. , Nov; Tamei, T., Ishii, S., Shibata, T., Virtual force/tactile sensors for interactive machines using the user's biological signals (2008) Advanced Robotics, 22 (8), pp. 893-911. , Aug; Bi, L., Genetu, F.A., Guan, C., A review on emg-based motor intention prediction of continuous human upper limb motion for humanrobot collaboration (2019) Biomed. Signal Proc. and Control, 51, pp. 113-127. , May; Li, Z., Wang, B., Sun, F., Yang, C., Xie, Q., Zhang, W., SEMGbased joint force controls for an upper-limb power-assist exoskeleton robot (2014) IEEE Journal of Biomedical and Health Informatics, 18, pp. 1043-1050. , May; Karavas, N., Ajoudani, A., Tsagarakis, N., Saglia, J., Bicchi, A., Caldwell, D., Tele-impedance based assistive control for a compliant knee exoskeleton (2015) Robotics and Autonomous Systems, 73, pp. 78-90. , Nov; Tang, Z., Zhang, K., Sun, S., Gao, Z., Zhang, L., Yang, Z., An upperlimb power-assist exoskeleton using proportional myoelectric control (2014) Sensors, 14, pp. 6677-6694. , Apr; DelPreto, J., Rus, D., Sharing the load: Human-robot team lifting using muscle activity (2019) International Conference on Robotics and Automation (ICRA), pp. 7906-7912. , May; Grafakos, S., Dimeas, F., Aspragathos, N., Variable admittance control in phri using emg-based arm muscles co-activation (2016) IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1900-1905. , Oct; Wu, R., Zhang, H., Peng, T., Fu, L., Zhao, J., Variable impedance interaction and demonstration interface design based on measurement of arm muscle co-activation for demonstration learning (2019) Biomedical Signal Processing and Control, 51, pp. 8-18. , May; Aydin, Y., Tokatli, O., Patoglu, V., Basdogan, C., Fractional order admittance control for physical human-robot interaction (2017) IEEE World Haptics Conference (WHC), pp. 257-262. , June; Aydin, Y., Tokatli, O., Patoglu, V., Basdogan, C., Stable physical human-robot interaction using fractional order admittance control (2018) IEEE Transactions on Haptics, 11 (3), pp. 464-475. , July; Aydin, Y., Tokatli, O., Patoglu, V., Basdogan, C., A computational multi-criteria optimization approach to controller design for physical human-robot interaction IEEE Transactions on Robotics; Oskoei, M.A., Hu, H., Myoelectric control systems-a survey (2007) Biomedical Signal Processing and Control, 2 (4), pp. 275-294. , Oct; Aydin, Y., Sirintuna, D., Basdogan, C., Towards collaborative drilling with a cobot using admittance controller (2020) Transactions of the Institute of Measurement and Control, , July; Faria, C., Ferreira, F., Erlhagen, W., Monteiro, S., Bicho, E., Position-based kinematics for 7-dof serial manipulators with global configuration control, joint limit and singularity avoidance (2018) Mechanism and Machine Theory, 121, pp. 317-334. , March; Hermens, H.J., Freriks, B., Disselhorst-Klug, C., Rau, G., Development of recommendations for semg sensors and sensor placement procedures (2000) Journal of Electromyography and Kinesiology, 10 (5), pp. 361-374. , Oct; Luca, C.J.D., Surface electromyography: Detection and recording (2002) DelSys Incorporated, 10 (2); Mon, Y., Al-Jumaily, A., Estimation of upper limb joint angle using surface emg signal (2013) International Journal of Advanced Robotic Systems, 10, p. 1. , Oct; Dimitrova, N., Arabadzhiev, T., Hogrel, J.-Y., Dimitrov, G., Fatigue analysis of interference emg signals obtained from biceps brachii during isometric voluntary contraction at various force levels (2009) Journal of Electromyography and Kinesiology, 19 (2), pp. 252-258. , Apr; Luca, C.J.D., Gilmore, L.D., Kuznetsov, M., Roy, S.H., Filtering the surface emg signal: Movement artifact and baseline noise contamination (2010) Journal of Biomechanics, 43 (8), pp. 1573-1579. , May; Brown, S.H., Brookham, R.L., Dickerson, C.R., High-pass filtering surface emg in an attempt to better represent the signals detected at the intramuscular level (2010) Muscle & Nerve, 41 (2), pp. 234-239. , Feb; Luca, C.J.D., The use of surface electromyography in biomechanics (1997) Journal of Applied Biomechanics, 13 (2), pp. 135-163. , Jan; Soderberg, G.L., Knutson, L.M., A guide for use and interpretation of kinesiologic electromyographic data (2000) Physical Therapy, 80 (5), pp. 485-498. , May; Yang, J.F., Winter, D.A., Electromyographic amplitude normalization methods: Improving their sensitivity as diagnostic tools in gait analysis (1984) Archives of Physical Medicine and Rehabilitation, 65, pp. 517-521. , Sep; Bolgla, L., Uhl, T., Reliability of electromyographic normalization methods for evaluating the hip musculature (2007) Journal of Electromyography and Kinesiology, 17, pp. 102-111. , March; Burden, A., Trew, M., Baltzopoulos, V., Normalisation of gait emgs: A re-examination (2003) Journal of Electromyography and Kinesiology, 13, pp. 519-532. , Dec; Burden, A., Bartlett, R., Normalisation of emg amplitude: An evaluation and comparison of old and new methods (1999) Medical Engineering & Physics, 21 (4), pp. 247-257. , May; Graichen, H., Englmeier, K.-H., Reiser, M., Eckstein, F., An in vivo technique for determining 3d muscular moment arms in different joint positions and during muscular activation-application to the supraspinatus (2001) Clinical Biomechanics, 16 (5), pp. 389-394. , Jun; Murray, W.M., Delp, S.L., Buchanan, T.S., Variation of muscle moment arms with elbow and forearm position (1995) Journal of Biomechanics, 28 (5), pp. 513-525. , May; Aung, Y.M., Al-Jumaily, A., SEMG based ann for shoulder angle prediction (2012) Procedia Engineering, 41, pp. 1009-1015. , Dec","Basdogan, C.; Koc University, Turkey; email: cbasdogan@ku.edu.tr",,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85095797289
"Ueno A., Hlavac V., Mizuuchi I., Hoffmann M.","57214596049;55897406200;6603185295;34971244000;","Touching a Human or a Robot? Investigating Human-likeness of a Soft Warm Artificial Hand",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223523","14","20",,1,"10.1109/RO-MAN47096.2020.9223523","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095769891&doi=10.1109%2fRO-MAN47096.2020.9223523&partnerID=40&md5=eeb7e3001f103bff49165c16449ec5e0","Tokyo University of Agriculture and Technology, Department of Mechanical Systems Engineering, Koganei, Japan; Czech Technical University in Prague, Czech Institute of Informatics, Robotics, Cybernetics, Czech Republic","Ueno, A., Tokyo University of Agriculture and Technology, Department of Mechanical Systems Engineering, Koganei, Japan; Hlavac, V., Czech Technical University in Prague, Czech Institute of Informatics, Robotics, Cybernetics, Czech Republic; Mizuuchi, I., Tokyo University of Agriculture and Technology, Department of Mechanical Systems Engineering, Koganei, Japan; Hoffmann, M., Czech Technical University in Prague, Czech Institute of Informatics, Robotics, Cybernetics, Czech Republic","With the advent of different electronic skins sensitive to touch and robots composed of soft materials, tactile or haptic human-robot interaction is gaining importance. We designed a highly realistic artificial hand aiming to reproduce human-to-human physical contact through a special morphology imitating flesh and bones and a heating system imitating human body temperature. The mechanical response properties of different finger designs were analyzed and the most mimetic one came very close to a human finger. We designed three experiments with participants using haptic exploration to evaluate the human-likeness of: (1) finger morphologies; (2) complete hands: real human vs. soft and warm artificial hand vs. rubber hand (3) the hand mounted on a manipulator with fixed vs. passive compliant wrist in a handshake scenario. First, participants find the mimetic finger morphology most humanlike. Second, people can reliably distinguish the real human hand, the artificial one, and a rubber hand. In terms of humanlikeness (Anthropomorphism, Animacy, and Likeability), the human hand scores better than the artificial hand which in turn clearly outperforms the rubber hand. The temperature, or warmth, was rated as the most human-like feature of the artificial hand. © 2020 IEEE.",,"Agricultural robots; Morphology; Rubber; Artificial hand; Electronic skin; Haptic explorations; Heating system; Human body temperature; Human likeness; Mechanical response; Physical contacts; Social robots",,,,,"20-24186X; Grantová Agentura České Republiky, GA ČR","M.H. was supported by the Czech Science Foundation (GA Cˇ R), project EXPRO (nr. 20-24186X).",,,,,,,,,,"Argall, B.D., Billard, A.G., A survey of tactile human-robot interactions (2010) Robotics and Autonomous Systems, 58 (10), pp. 1159-1176; Breazeal, C., Dautenhahn, K., Kanda, T., Social robotics (2016) Springer Handbook of Robotics, pp. 1935-1972. , Springer; Dan Stiehl, W., Breazeal, C., Han, K., Lieberman, J., Evi Lalla, L., Maymin, A., Salinas, J., Gray, J., The huggable: A therapeutic robotic companion for relational, affective touch (2006) ACM SIGGRAPH 2006 Emerging Technologies, SIGGRAPH '06; Bartneck, C., Kulíc, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81; DiSalvo, C., Gemperle, F., Forlizzi, J., Montgomery, E., The hug: An exploration of robotic form for intimate communication (2003) The 12th IEEE International Workshop on Robot and Human Interactive Communication, 2003. Proceedings., pp. 403-408; Endo, N., Kojima, T., Endo, K., Iida, F., Hashimoto, K., Takanishi, A., Development of anthropomorphic soft robotic hand wsh-1rii (2013) Romansy 19-Robot Design, Dynamics and Control, pp. 175-182; Mizuno, K., Kawasetsu, T., Ishihara, H., Horii, T., Asada, M., Prototyping of a child-type android hand with tactile sensors and bones (2019) Proceedings of the 2019 JSME Conference on Robotics and Mechatronics; Kitamura, Y., (2019) Development of Flexible Hand with Temperature Controller and Passive Mechanismfor Touch Care Robot, , Master's thesis, NARA INSTITUTE of SCIENCE and TECHNOLOGY; Ueno, A., Miura, Y., Mizuuchi, I., Development of human mimetic hand aiming to achieve both active touch functions and passive touch reproducibility (2019) Transactions of the Society of Instrument and Control Engineers, 55 (11), pp. 739-744; Ueno, A., Mizuuchi, I., Morooka, Y., Proposal for robot hand and forearm design to reproduce human-to-human physical contact (2019) 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 971-976; Mori, M., MacDorman, K.F., Kageki, N., The uncanny valley (2012) IEEE Robotics & Automation Magazine, 19 (2), pp. 98-100; MacDorman, K.F., Ishiguro, H., The uncanny advantage of using androids in cognitive and social science research (2006) Interaction Studies, 7 (3), pp. 297-337; Pütten Der Rosenthal-Von, A.M., Krämer, N.C., Maderwald, S., Brand, M., Grabenhorst, F., (2019) Neural Mechanisms for Accepting and Rejecting Artificial Social Partners in the Uncanny Valley, 39 (33), pp. 6555-6570; Berger, C.C., Gonzalez-Franco, M., Ofek, E., Hinckley, K., The uncanny valley of haptics (2018) Science Robotics, 3 (17); Shirado, H., Nonomura, Y., Maeno, T., Development of artificial skin having human skin-like texture (2007) Transactions of the Japan Society of Mechanical Engineers Series C, 73 (726), pp. 541-546; Jindai, M., Watanabe, T., Fukuta, T., Development of a shake-motion transit model for a handshake robot system (mechanical systems) (2010) Transactions of the Japan Society of Mechanical Engineers Series C, 76 (766), pp. 1539-1546; Nagano, H., Okamoto, S., Yamada, Y., Research directions in structuring of tactile dimensions of material textures (2011) Transactions of the Virtual Reality Society of Japan, 16 (3), pp. 343-353; Montgomery, L.D., Williams, B.A., Effect of ambient temperature on the thermal profile of the human forearm, hand, and fingers (1976) ANNALS of BIOMEDICAL ENGINEERING, 4, pp. 209-219",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095769891
"Prajod P., Hindriks K.","57212552269;55904124900;","On the Expressivity of a Parametric Humanoid Emotion Model",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223459","926","931",,,"10.1109/RO-MAN47096.2020.9223459","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095763859&doi=10.1109%2fRO-MAN47096.2020.9223459&partnerID=40&md5=fc07eb9f028c3c9127f93e30706bcd6b","University of Augsburg, Human-Centered Multimedia, Germany; Vrije Universiteit, Artificial Intelligence, Amsterdam, Netherlands","Prajod, P., University of Augsburg, Human-Centered Multimedia, Germany; Hindriks, K., Vrije Universiteit, Artificial Intelligence, Amsterdam, Netherlands","Emotion expression is an important part of human-robot interaction. Previous studies typically focused on a small set of emotions and a single channel to express them. We developed an emotion expression model that modulates motion, poses and LED features parametrically, using valence and arousal values. This model does not interrupt the task or gesture being performed and hence can be used in combination with functional behavioural expressions. Even though our model is relatively simple, it is just as capable of expressing emotions as other more complicated models that have been proposed in the literature. We systematically explored the expressivity of our model and found that a parametric model using 5 key motion and pose features can be used to effectively express emotions in the two quadrants where valence and arousal have the same sign. As paradigmatic examples, we tested for happy, excited, sad and tired. By adding a second channel (eye LEDs), the model is also able to express high arousal (anger) and low arousal (relaxed) emotions in the two other quadrants. Our work supports other findings that it remains hard to express moderate arousal emotions in these quadrants for both negative (fear) and positive (content) valence. © 2020 IEEE.",,"Agricultural robots; Light emitting diodes; Behavioural expressions; Emotion expression; Emotion modeling; Express emotions; Parametric modeling; Single channels; Work support; Social robots",,,,,,,,,,,,,,,,"Xu, J., Broekens, J., Hindriks, K., Neerincx, M.A., Mood expression through parameterized functional behavior of robots 2013 IEEE RO-MAN; Castillo, G., Neff, M., What do we express without knowing?: Emotion in gesture (2019) Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, Ser. AAMAS '19, pp. 702-710. , International Foundation for Autonomous Agents and Multiagent Systems; Xu, J., Broekens, J., Hindriks, K., Neerincx, M.A., Mood contagion of robot body language in human robot interaction (2015) Autonomous Agents and Multi-Agent Systems, 29 (6), pp. 1216-1248; Cohen, I., Looije, R., Neerincx, M.A., Child's recognition of emotions in robot's face and body (2011) Proceedings of the 6th International Conference on Human-robot Interaction. ACM, pp. 123-124; Yamaguchi, A., Yano, Y., Doki, S., Okuma, S., A study of emotional motion description by motion modification rules using adjectival expressions (2006) 2006 IEEE International Conference on Systems, Man and Cybernetics, 4, pp. 2837-2842. , IEEE; Xu, J., Broekens, J., Hindriks, K., Neerincx, M.A., Bodily mood expression: Recognize moods from functional behaviors of humanoid robots (2013) International Conference on Social Robotics, pp. 511-520. , Springer; Johnson, D.O., Cuijpers, R.H., Investigating the effect of a humanoid robot's head position on imitating human emotions (2019) International Journal of Social Robotics, pp. 1-10; Johnson, D.O., Cuijpers, R.H., Pol Der D.Van, Imitating human emotions with artificial facial expressions (2013) International Journal of Social Robotics, 5 (4), pp. 503-513; Terada, K., Yamauchi, A., Ito, A., Artificial emotion expression for a robot by dynamic color change (2012) 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication. IEEE, pp. 314-321; Beck, A., Cañamero, L., Bard, K.A., Towards an affect space for robots to display emotional body language (2010) 19th International Symposium in Robot and Human Interactive Communication. IEEE, pp. 464-469; Novikova, J., Watts, L., A design model of emotional body expressions in non-humanoid robots (2014) Proceedings of the Second International Conference on Human-agent Interaction, Ser. HAI '14. ACM, pp. 353-360; Russell, J.A., A circumplex model of affect (1980) Journal of Personality and Social Psychology, 39 (6), p. 1161; Xu, J., Broekens, J., Hindriks, K., Neerincx, M.A., The relative importance and interrelations between behavior parameters for robots' mood expression (2013) Affective Computing and Intelligent Interaction (ACII). IEEE, pp. 558-563; Fagerberg, P., Ståhl, A., Höök, K., Emoto: Emotionally engaging interaction (2004) Personal and Ubiquitous Computing, 8 (5), pp. 377-381; Amaya, K., Bruderlin, A., Calvert, T., Emotion from motion (1996) Graphics Interface, 96, pp. 222-229. , Toronto, Canada; Castellano, G., Villalba, S.D., Camurri, A., Recognising human emotions from body movement and gesture dynamics (2007) International Conference on Affective Computing and Intelligent Interaction. Springer, pp. 71-82; Naz, K., Epps, H., Relationship between color and emotion: A study of college students (2004) College Student J, 38 (3); Prajod, P., (2019) Affect Expression in Social Robots: Combining Non-verbal Affect Expression Techniques, , Master's thesis, Delft University of Technology; Li, W., Pasquier, P., Automatic affect classification of human motion capture sequences in the valence-arousal model (2016) Proceedings of the 3rd International Symposium on Movement and Computing, pp. 1-8",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095763859
"Lagerstedt E., Thill S.","57210616605;36624312800;","Benchmarks for evaluating human-robot interaction: Lessons learned from human-animal interactions",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223347","137","143",,1,"10.1109/RO-MAN47096.2020.9223347","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095758350&doi=10.1109%2fRO-MAN47096.2020.9223347&partnerID=40&md5=fcc0e3f1950b1efd6a0a3ff852bf3b82","University of Skövde, School of Informatics, Skövde, Sweden; Radboud University, Donders Institute for Brain Cognition, and Behaviour, Nijmegen, Netherlands","Lagerstedt, E., University of Skövde, School of Informatics, Skövde, Sweden; Thill, S., Radboud University, Donders Institute for Brain Cognition, and Behaviour, Nijmegen, Netherlands","Human-robot interaction (HRI) is fundamentally concerned with studying the interaction between humans and robots. While it is still a relatively young field, it can draw inspiration from other disciplines studying human interaction with other types of agents. Often, such inspiration is sought from the study of human-computer interaction (HCI) and the social sciences studying human-human interaction (HHI). More rarely, the field also turns to human-animal interaction (HAI).In this paper, we identify two distinct underlying motivations for making such comparisons: to form a target to recreate or to obtain a benchmark (or baseline) for evaluation. We further highlight relevant (existing) overlap between HRI and HAI, and identify specific themes that are of particular interest for further trans-disciplinary exploration. At the same time, since robots and animals are clearly not the same, we also discuss important differences between HRI and HAI, their complementarity notwithstanding. The overall purpose of this discussion is thus to create an awareness of the potential mutual benefit between the two disciplines and to describe opportunities that exist for future work, both in terms of new domains to explore, and existing results to learn from. © 2020 IEEE.",,"Agricultural robots; Animals; Human computer interaction; Human computer interaction (HCI); Human interactions; Human robot Interaction (HRI); Human-animal interactions; Human-human interactions; Mutual benefit; Social robots",,,,,,,,,,,,,,,,"Han, Z., Yanco, H., The effects of proactive release behaviors during human-robot handovers (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 440-448; Moosaei, M., Das, S.K., Popa, D.O., Riek, L.D., Using facially expressive robots to calibrate clinical pain perception (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 32-41; Horie, T., Takashio, K., Handling conversation interruption in many-to-many hr interaction considering emotional behaviors and human relationships (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 528-533; Fraune, M.R., Sherrin, S., Sabanovíc, S., Smith, E.R., Is humanrobot interaction more competitive between groups than between individuals? (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 104-113; Hoenen, M., Lübke, K.T., Pause, B.M., Non-anthropomorphic robots as social entities on a neurophysiological level (2016) Computers in Human Behavior, 57, pp. 182-186; Sportillo, D., Paljic, A., Ojeda, L., On-road evaluation of autonomous driving training (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 182-190; Ramachandran, A., Huang, C.-M., Scassellati, B., Give me a break!: Personalized timing strategies to promote learning in robotchild tutoring (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 146-155; Tonkin, M., Vitale, J., Herse, S., Williams, M.-A., Judge, W., Wang, X., Design methodology for the ux of hri: A field study of a commercial social robot at an airport (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 407-415; Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., Hoffmann, M., Industry 4. 0 (2014) Business & Information Systems Engineering, 6 (4), pp. 239-242; Epley, N., Waytz, A., Cacioppo, J.T., On seeing human: A threefactor theory of anthropomorphism (2007) Psychological Review, 114 (4), pp. 864-886; Heider, F., Simmel, M., An experimental study of apparent behavior (1944) The American Journal of Psychology, 57 (2), pp. 243-259; Phillips, E., Zhao, X., Ullman, D., Malle, B.F., What is humanlike?: Decomposing robots' human-like appearance using the anthropomorphic robot (abot) database (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 105-113; Kwon, M., Huang, S.H., Dragan, A.D., Expressing robot incapability (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 87-95; Chita-Tegmark, M., Lohani, M., Scheutz, M., Gender effects in perceptions of robots and humans with varying emotional intelligence (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 230-238; Tabrez, A., Agrawal, S., Hayes, B., Explanation-based reward coaching to improve human performance via reinforcement learning (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 249-257; Cheon, E., Su, N.M., Futuristic autobiographies: Weaving participant narratives to elicit values around robots (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 388-397; Henrich, J., Heine, S.J., Norenzayan, A., The weirdest people in the world? (2010) Behavioral and Brain Sciences, 33, pp. 61-135; Eberts, R.E., Four approaches to human-computer interaction (1994) User Interface Design, pp. 47-60. , Englewood Cliffs, New Jersey: Prentice Hall; Reeves, B., Nass, C.I., (1996) The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places, , Cambridge: Cambridge university press; Rea, D.J., Seo, S.H., Bruce, N., Young, J.E., Movers, shakers, and those who stand still: Visual attention-grabbing techniques in robot teleoperation (2017) 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI. IEEE, pp. 398-407; Perera, V., Veloso, M., Learning to understand questions on the task history of a service robot (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN). IEEE, pp. 304-309; Reich-Stiebert, N., Eyssel, F., Robots in the classroom: What teachers think about teaching and learning with education robots (2016) International Conference on Social Robotics, pp. 671-680. , Springer; Ziemke, T., Sharkey, N.E., A stroll through the worlds of robots and animals: Applying jakob von uexküll's theory of meaning to adaptive robots and artificial life (2001) Semiotica, 134 (1-4), pp. 701-746; Stensson, P., Jansson, A., Autonomous technology-sources of confusion: A model for explanation and prediction of conceptual shifts (2014) Ergonomics, 57 (3), pp. 455-470; Von Uexküll, J., (1928) Teoretische Biologie, , Berlin: Springer; Maturana, H.R., Varela, F.J., (1987) The Tree of Knowledge: The Biological Roots of Human Understanding, , Boston, Massachusetts: New Science Library/Shambhala Publications; Vernon, D., (2014) Artificial Cognitive Systems: A Primer, , Cambridge, Massachusetts: MIT press; Ziemke, T., Are robots embodied? 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ACM, pp. 224-232; Hu, Y., Hoffman, G., Using skin texture change to design emotion expression in social robots (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 2-10; McDonald, S.J., Colton, M.B., Alder, C.K., Goodrich, M.A., Haptic shape-based management of robot teams in cordon and patrol (2017) 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI. IEEE, pp. 380-388; Joshi, S., Sabanovíc, S., Robots for inter-generational interactions: Implications for nonfamilial community settings (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 478-486; Hosey, G., Melfi, V., (2019) Anthrozoology: Human-Animal Interactions in Domesticated and Wild Animals, , Oxford, United Kingdom: Oxford University Press; DeMello, M., (2012) Animals and Society: An Introduction to Human-Animal Studies, , New York, USA: Columbia University Press; (2019) Executive Summary World Robotics 2019 Industrial Robots, , IFR Statistical Department; Demello, M., (2019) Executive Summary World Robotics 2019 Service Robots; Hosey, G., Melfi, V., Human-animal interactions, relationships and bonds: A review and analysis of the literature (2014) International Journal of Comparative Psychology, 27 (1), pp. 117-142; Boivin, X., Lensink, J., Tallet, C., Veissier, I., Stockmanship and farm animal welfare (2003) Animal Welfare, 12 (4), pp. 479-492; Anthony, R., The ethical implications of the human-animal bond on the farm (2003) Animal Welfare, 12 (4), pp. 505-512; Montebelli, A., Billing, E., Lindblom, J., Dahlberg, G.M., Reframing hri education: A dialogic reformulation of hri education to promote diverse thinking and scientific progress (2017) Journal of Human-Robot Interaction, 6 (2), pp. 3-26; Lacey, C., Caudwell, C., Cuteness as a 'dark pattern' in home robots (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 374-381; Del Duchetto, F., Baxter, P., Hanheide, M., Lindsey the tour guide robot-usage patterns in a museum long-term deployment (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 1-8; Skantze, G., Predicting and regulating participation equality in humanrobot conversations: Effects of age and gender (2017) 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI. IEEE, pp. 196-204; Shibata, T., Inoue, K., Irie, R., Emotional robot for intelligent system - Artificial emotional creature project (1996) 5th IEEE International Workshop on Robot and Human Communication, pp. 466-471. , Tsukuba, Japan: IEEE; Groom, V., Nass, C., Can robots be teammates?: Benchmarks in human-robot teams (2007) Interaction Studies, 8 (3), pp. 483-500; Dautenhahn, K., Socially intelligent robots: Dimensions of human-robot interaction (2007) Philosophical Transactions of the Royal Society B: Biological Sciences, 362 (1480), pp. 679-704; Moore, R.K., Marxer, R., Thill, S., Vocal interactivity in-andbetween humans, animals, and robots (2016) Frontiers in Robotics and AI, 3, pp. 1-17; McCune, S., Esposito, L., Griffin, J.A., Introduction to a thematic series on animal assisted interventions in special populations (2017) Applied Developmental Science, 21 (2), pp. 136-138; Jegatheesan, B., Beetz, A., Ormerod, E., Johnson, R., Fine, A., Yamazaki, K., Dudzik, C., Choi, G., The iahaio definitions for animal assisted intervention and guidelines for wellness of animals involved in aai (2018) International Association of Human-Animal Interaction Organizations, Tech. Rep.; Charry-Sánchez, J.D., Pradilla, I., Talero-Gutíerrez, C., Animalassisted therapy in adults: A systematic review (2018) Complementary Therapies in Clinical Practice, 32, pp. 169-180; Guneysu Ozgur, A., Wessel, M.J., Johal, W., Sharma, K., Özgür, A., Vuadens, P., Mondada, F., Dillenbourg, P., Iterative design of an upper limb rehabilitation game with tangible robots (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 241-250; Veerbeek, J.M., Langbroek-Amersfoort, A.C., Van Wegen, E.E., Meskers, C.G., Kwakkel, G., Effects of robot-assisted therapy for the upper limb after stroke: A systematic review and meta-analysis (2017) Neurorehabilitation and Neural Repair, 31 (2), pp. 107-121; Dolbow, J.D., Mehler, C., Stevens, S.L., Hinojosa, J., Roboticassisted gait training therapies for pediatric cerebral palsy: A review (2016) Journal of Rehabilitation Robotics, 4, pp. 14-21; Bharatharaj, J., Huang, L., Mohan, R., Al-Jumaily, A., Krägeloh, C., Robot-assisted therapy for learning and social interaction of children with autism spectrum disorder (2017) Robotics, 6 (1), pp. 4-14; Abdi, J., Al-Hindawi, A., Ng, T., Vizcaychipi, M.P., Scoping review on the use of socially assistive robot technology in elderly care (2018) BMJ Open, 8 (2), p. e018815; Kolbeinsson, A., Lagerstedt, E., Lindblom, J., Foundation for a classification of collaboration levels for human-robot cooperation in manufacturing (2019) Production & Manufacturing Research, 7 (1), pp. 448-471; Zambon, I., Cecchini, M., Egidi, G., Saporito, M.G., Colantoni, A., Revolution 4. 0: Industry vs. Agriculture in a future development for smes (2019) Processes, 7 (1), pp. 36-51; Klerkx, L., Jakku, E., Labarthe, P., A review of social science on digital agriculture, smart farming and agriculture 4. 0: New contributions and a future research agenda (2019) NJAS-Wageningen Journal of Life Sciences, p. 100315; Robb, S.S., Stegman, C.E., Companion animals and elderly people: A challenge for evaluators of social support (1983) The Gerontologist, 23 (3), pp. 277-282; Zeisel, H., The significance of insignificant differences (1955) Public Opinion Quarterly, 19 (3), pp. 319-321; Beck, A.M., Katcher, A.H., A new look at pet-facilitated therapy (1984) Journal of the American Veterinary Medical Association, 184 (4), pp. 414-421; Baxter, P., Kennedy, J., Senft, E., Lemaignan, S., Belpaeme, T., From characterising three years of hri to methodology and reporting recommendations (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 391-398. , Christchurch, New Zealand: IEEE; Smedegaard, C.V., Reframing the role of novelty within social hri: From noise to information (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 411-420. , Daegu, South Korea: IEEE; Thellman, S., Ziemke, T., The intentional stance toward robots: Conceptual and methodological considerations (2019) The 41st Annual Conference of the Cognitive Science Society, pp. 1097-1103. , Montreal, Canada, July 24-26; Ziemke, T., Thill, S., Vernon, D., Embodiment is a doubleedged sword in human-robot interaction: Ascribed vs. intrinsic intentionality (2015) Cognition: A Bridge between Robotics and Interaction. Workshop at HRI2015, pp. 1-2; Morgan, L., (1894) An Introduction to Comparative Psychology, , Fellingon-Tyne: The Walter Scott Publishing Co., Ltd., 1903 original work published; Karin-D'Arcy, M., The modern role of morgan's canon in comparative psychology (2005) International Journal of Comparative Psychology, 18 (3), pp. 179-201",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095758350
"Torre I., Latupeirissa A.B., McGinn C.","56188105900;57216733848;36651232500;","How context shapes the appropriateness of a robot's voice",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223449","215","222",,1,"10.1109/RO-MAN47096.2020.9223449","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095726891&doi=10.1109%2fRO-MAN47096.2020.9223449&partnerID=40&md5=877ce6d2dd61ce04f8d7add3a3b962a4","Kth Royal Institute of Technology, Ilaria Torre Is with the Division of Robotics Perception and Learning, Stockholm, Sweden; Trinity College Dublin, Conor McGinn Is with the School of Engineering, Dublin, Ireland","Torre, I., Kth Royal Institute of Technology, Ilaria Torre Is with the Division of Robotics Perception and Learning, Stockholm, Sweden; Latupeirissa, A.B., Kth Royal Institute of Technology, Ilaria Torre Is with the Division of Robotics Perception and Learning, Stockholm, Sweden; McGinn, C., Trinity College Dublin, Conor McGinn Is with the School of Engineering, Dublin, Ireland","Social robots have a recognizable physical appearance, a distinct voice, and interact with users in specific contexts. Previous research has suggested a 'matching hypothesis', which seeks to rationalise how people judge a robot's appropriateness for a task by its appearance. Other research has extended this to cover combinations of robot voices and appearances. In this paper, we examine the missing connection between robot voice, robot appearance, and deployment context. In so doing, we asked participants to match a robot image to a voice within a defined interaction context. We selected widely available social robots, identified task contexts they are used in, and manipulated the voices in terms of gender, naturalness, and accent. We found that the task context mediates the 'matching hypothesis'. People consistently selected a robot based on a vocal feature for a certain context, and a different robot based on the same vocal feature for another context. We suggest that robot voice design should take advantage of current technology that enables the creation and tuning of custom voices. They are a flexible tool to increase perception of appropriateness, which has a positive influence on Human-Robot Interaction. © 2020 IEEE.",,"Agricultural robots; End effectors; Machine design; Current technology; Flexible tool; Interaction context; Robot appearance; Robot images; Social robots",,,,,"Horizon 2020 Framework Programme, H2020; H2020 Marie Skłodowska-Curie Actions, MSCA: 713567; Science Foundation Ireland, SFI: 13/RC/2016; Trinity College Dublin, TCD; Kungliga Tekniska Högskolan, KTH; European Regional Development Fund, FEDER; ADAPT - Centre for Digital Content Technology","*The research was funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713567, and by the ADAPT Centre for Digital Content Technology, which is funded under the SFI Research Centres Programme (Grant 13/RC/2016) and is co-funded by the European Regional Development Fund. The first author is funded by a WASP Expedition Project on Correct-by-design and Socially Acceptable Autonomy (CorSA) 1Ilaria Torre is with the Division of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden ilariat@kth.se 2Adrian Benigno Latupeirissa is with the Division of Media Technology and Interaction Design, KTH Royal Institute of Technology, Stockholm, Sweden ablat@kth.se 3Conor McGinn is with the School of Engineering, Trinity College Dublin, Dublin, Ireland mcginnco@tcd.ie",,,,,,,,,,"Hegel, F., Eyssel, F., Wrede, B., The social robot 'flobi': Key concepts of industrial design (2010) Proceedings of the 19th International Workshop on Robot and Human Interactive Communication, Ser. ROMAN '10. 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Personality impressions from brief novel voices (2014) PLoS ONE, 9 (3), p. e90779; Torre, I., Goslin, J., White, L., Zanatto, D., Trust in artificial voices: A congruency effect of first impressions and behavioural experience Proceedings of APAScience '18: Technology, Mind, and Society (TechMindSociety '18); Gobl, C., Chasaide, A.N., The role of voice quality in communicating emotion, mood and attitude (2003) Speech Communication, 40, pp. 189-212; Laver, J.D.M., Voice quality and indexical information (1968) British Journal of Disorders of Communication, 3 (1), pp. 43-54; Brown, B.L., Strong, W.J., Rencher, A.C., Acoustic determinants of perceptions of personality from speech (1975) Linguistics, 13 (166), pp. 11-32; Tigue, C.C., Borak, D.J., O'Connor, J.J.M., Schandl, C., Feinberg, D.R., Voice Pitch Influences Voting Behaviour, 33, pp. 210-216; Moore, R.K., Appropriate voices for artefacts: Some key insights (2017) Proceedings of the 1st International Workshop on Vocal Interactivity In-and-between Humans, Animals and Robots, Ser. 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Springer International Publishing; Crowell, C.R., Scheutz, M., Schermerhorn, P., Villano, M., Gendered voice and robot entities: Perceptions and reactions of Male and feMale subjects (2009) Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, Ser. IROS '09. IEEE, pp. 3735-3741; Bryant, D., Borenstein, J., Howard, A., Why should we gender? The effect of robot gendering and occupational stereotypes on human trust and perceived competency (2020) Proceedings of the 15th ACM/IEEE International Conference on Human-Robot Interaction, Ser. HRI '20, pp. 13-21; Walters, M.L., Syrdal, D.S., Koay, K.L., Dautenhahn, K., Te Boekhorst, R., Human approach distances to a mechanical-looking robot with different robot voice styles Proceedings of the 17th International Workshop on Robot and Human Interactive Communication, Ser. RO-MAN '08. IEEE, pp. 707-712; Tamagawa, R., Watson, C.I., Kuo, I.H., MacDonald, B.A., Broadbent, E., The effects of synthesized voice accents on user perceptions of robots (2011) International Journal of Social Robotics, 3 (3), pp. 253-262; Bestelmeyer, P.E.G., Belin, P., Ladd, D.R., A neural marker for social bias toward in-group accents (2014) Cerebral Cortex, 25 (10), pp. 3953-3961; Kuchenbrandt, D., Eyssel, F., Bobinger, S., Neufeld, M., When a robot's group membership matters (2013) International Journal of Social Robotics, 5 (3), pp. 409-417; Andrist, S., Ziadee, M., Boukaram, H., Mutlu, B., Sakr, M., Effects of culture on the credibility of robot speech Proceedings of the 10th ACM/IEEE International Conference on Human-Robot Interaction, Ser. HRI '15, pp. 157-164. , ACM. 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HRI '16, pp. 543-544; Short, E.S., Chang, M.L., Thomaz, A., Detecting contingency for hri in open-world environments (2018) Proceedings of the 13th ACM/IEEE International Conference on Human-Robot Interaction, Ser. HRI '18, pp. 425-433; Cakmak, M., Takayama, L., Teaching people how to teach robots: The effect of instructional materials and dialog design (2014) Proceedings of the 9th ACM/IEEE International Conference on Human-Robot Interaction, Ser. HRI '14, pp. 431-438; Harrison, A.M., Xu, W.M., Trafton, J.G., User-centered robot head design: A sensing computing interaction platform for robotics research (sciprr) (2018) Proceedings of the 13th ACM/IEEE International Conference on Human-Robot Interaction, Ser. HRI '18, pp. 215-223; McGinn, C., Bourke, E., Murtagh, A., Cullinan, M.F., Kelly, K., Exploring the application of design thinking to the development of service robot technology (2018) ICRA2018 Workshop on Elderly Care Robotics-Technology and Ethics (WELCARO); J. N. Fuertes, W. 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"Machová K., Mikula M., Gao X., Mach M.","17435396000;56583901000;8701636400;22735085000;","Lexicon-based sentiment analysis using particle swarm optimization",2020,"Electronics (Switzerland)","9","8","1317","1","22",,4,"10.3390/electronics9081317","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090255861&doi=10.3390%2felectronics9081317&partnerID=40&md5=c4186d4b880fbf0496a176eedc4dc166","Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letna 9, Košice, 04200, Slovakia; School of Engineering and Computer Science, Victoria University of Wellington, P.O. Box 600, Wellington, 6140, New Zealand","Machová, K., Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letna 9, Košice, 04200, Slovakia; Mikula, M., Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letna 9, Košice, 04200, Slovakia; Gao, X., School of Engineering and Computer Science, Victoria University of Wellington, P.O. Box 600, Wellington, 6140, New Zealand; Mach, M., Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letna 9, Košice, 04200, Slovakia","This work belongs to the field of sentiment analysis; in particular, to opinion and emotion classification using a lexicon-based approach. It solves several problems related to increasing the effectiveness of opinion classification. The first problem is related to lexicon labelling. Human labelling in the field of emotions is often too subjective and ambiguous, and so the possibility of replacement by automatic labelling is examined. This paper offers experimental results using a nature-inspired algorithm—particle swarm optimization—for labelling. This optimization method repeatedly labels all words in a lexicon and evaluates the effectiveness of opinion classification using the lexicon until the optimal labels for words in the lexicon are found. The second problem is that the opinion classification of texts which do not contain words from the lexicon cannot be successfully done using the lexicon-based approach. Therefore, an auxiliary approach, based on a machine learning method, is integrated into the method. This hybrid approach is able to classify more than 99% of texts and achieves better results than the original lexicon-based approach. The final hybrid model can be used for emotion analysis in human–robot interactions. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Hybrid approach; Lexicon generation; Lexicon labelling; Lexicon-based approach; Opinion classification; Particle swarm optimization; Sentiment analysis",,,,,,"Agentúra na Podporu Výskumu a Vývoja, APVV: APVV-16-0213, APVV-17-0267","This work was supported by the Slovak Research and Development Agency under the contract No. APVV-17-0267 ?Automated Recognition of Antisocial Behavior in Online Communities? and the contract No. APVV-16-0213 ?Knowledge-based Approaches for Intelligent Analysis of Big Data?.",,,,,,,,,,"Chaturvedi, I., Ragusa, E., Gastaldo, P., Zunino, R., Cambria, E., Bayesian network based extreme learning machine for subjectivity detection (2018) J. Frankl. Inst, 355, pp. 1780-1797. , [CrossRef]; Pang, B., Lee, L., Vaithyanathan, S., Thumbs up? Sentiment classification using machine learning technique Proceedings of the EMNLP, pp. 79-86. , Philadelphia, PA, USA, 6–7 July 2002; Ahmad, M., Aftab, S., Muhamad, S.S., Ahmad, S., Machine learning methods for sentiment analysis: A Review (2013) Int. J. Multidiscip. Sci. Eng, 8, pp. 27-32; Tan, S., Zhang, J., An empirical study of sentiment analysis for Chinese documents (2008) Expert Syst. Appl, 34, pp. 2622-2629. , [CrossRef]; Dos Santos, C.N., Gatti, M., Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts Proceedings of the COLING 2014—25th International Conference on Computational Linguistics, pp. 69-78. , Dublin, Ireland, 23–29 August 2014; Tang, D., Wei, F., Qin, B., Liu, T., Zhou, M., Coooolll: A Deep Learning System for Twitter Sentiment Classification Proceedings of the SemEval 2014—8th International Workshop on Semantic Evaluation, pp. 208-212. , Dublin, Ireland, 23–24 August 2014; Rebiai, Z., Andersen, S., Debrenne, A., Lafargue, V., SCIA at SemEval-2019 Task 3: Sentiment analysis in textualconversations using deep learning Proceedings of the SemEval-2019—13th International Workshop on SemanticEvaluation, pp. 297-301. , Minneapolis, MN, USA, 6–7 June 2019; Mikula, M., Gao, X., Machová, K., Adapting sentiment analysis system from English to Slovak (2017) Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI), , Honolulu, HI, USA, 27 November 1 December; Stone, P.J., Dunphy, D.C., Smith, M.S., Ogilvie, D.M., (1966) The General Inquirer: A Computer Approach to Content Analysis, p. 704. , The MIT Press: Cambridge, MA, USA; Hu, M., Liu, B., Mining and Summarizing Customer Reviews Proceedings of the KDD 04—10th International Conference on Knowledge Discovery and Data Mining, pp. 168-177. , Seattle, WA, USA, 22–25 August 2004; Warriner, A.B., Kuperman, V., Brysbaert, M., Norms of valence, arousal, and dominance for 13,915 English lemmas (2013) Behav. Res. Methods, 45, pp. 1191-1207. , [CrossRef] [PubMed]; Mohammad, S.M., Turney, P.D., Crowdsourcing a word-emotion association Lexicon (2012) Comput. Intell, 29, pp. 436-465. , [CrossRef]; Baccianella, S., Esuli, A., Sebastiani, F., SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining Proceedings of the LREC, pp. 2200-2204. , Valletta, Malta, 17–18 May 2010; Strapparava, C., Valittutii, A., WordNetAffect: An Affective Extension of WordNet Proceedings of the LREC 2004—4th International Conference on Language Resources and Evaluation, pp. 39-46. , Lisbon, Portugal, 26–28 May 2004; Bajpai, R., Cambria, E., Poria, S., Schuller, B.W., SenticNet 4: A semantic resource for sentiment analysis based on conceptual primitives Proceedings of the COLING, pp. 2666-2677. , Osaka, Japan, 11–16 December 2016; Yang, X., Zhang, Z., Zhang, Z., Mo, Y., Li, L., Yu, L., Zhu, P., Automatic construction and global optimization of a multi-sentiment lexicon (2016) Comput. Intell. Neurosci, 2016, p. 2093406. , [CrossRef] [PubMed]; Gupta, D.K., Reddy, K.S., Sôhweta, A., Ekbal, A., PSO-ASent: Feature Selection Using Particle Swarm Optimization for Aspect Based Sentiment Analysis (2002) Proceedings of the NLDB, 9103, pp. 79-86. , Passau, Germany, 13–15 June; Basari, S., Hussin, B., Anantaa, L.G.P., Zeniarja, J., Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization (2013) Procedia Eng, 53, pp. 453-462. , [CrossRef]; Escalante, H.J., Garcia-Limon, M.A., Morales-Reyes, A., Graff, M., Montes-y-Gomez, M., Morales, E.F., Martinez-Carranza, J., Term-weighting learning via genetic programming for text classification (2015) Knowl.-Based Syst, 83, pp. 176-189; Xing, F.Z., Pallucchini, F., Cambria, E., Cognitive-inspired domain adaptation of sentiment lexicons (2019) Inf. Process. Manag, 56, pp. 554-564. , [CrossRef]; Kacprzyk, J., Pedrycz, J., (2015) Handbook of Computational Intelligence, p. 1663. , Springer: Berlin/Heidelberg, Germany, p, ISBN 978-3-662-43504-5; Van den Bergh, F., Engelbrecht, A.P., A study of particle swarm optimization particle trajectories (2006) Inf. Sci. Inform. Comput. Sci. Intell. Syst. Appl. Int. J, 176, pp. 937-971; Kennedy, K., Bare bones particle swarms Proceedings of the SIS 03—IEEE Swarm Intelligence Symposium, pp. 80-87. , Indianapolis USA, 8–10 June 2003; Mohammad, S.M., Kiritchenko, S., Zhu, X., NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets Proceedings of the SemEval 13—7th International Workshop on Semantic Evaluation Exercises, pp. 321-327. , Atlanta, GA, USA, 14–15 June 2013; Nielsen, F.A., A new ANEW: Evaluation of a word list for sentiment analysis in microblogs (2011) Proceedings of the ESWC2011 Workshop on ‘Making Sense of Microposts’: Big Things Come in Small Packages, pp. 93-98. , Heraklion, Crete, Greece, 30 May; Taboada, M., Brooke, J., Tofiloski, M., Voli, K., Stede, M., Lexicon-based Methods for Sentiment Analysis (2011) Comput. Linguist, 38, pp. 267-307. , [CrossRef]; Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Sentiment strength detection in short informal text (2010) J. Am. Soc. Inf. Sci. Technol, 61, pp. 2544-2558. , [CrossRef]; Pang, B., Lee, L., A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, pp. 271-278. , Barcelona, Spain, 21–26 July 2004; Kennedy, A., Inkpen, D., Sentiment classification of movie reviewsusing contextual valence shifters (2006) Comput. Intell, 22, p. 110. , [CrossRef]; Martineau, J., Finin, T., Joshi, A., Patel, S., Improving binary classificationon text problems using differential word features Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 2019-2024. , Hong Kong, China, 2–6 November 2009; Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C., Learning wordvectors for sentiment analysis (2011) Proceedings of the 49th Annual Meetingof the Association for Computational Linguistics: Human Language Technologies, 1, pp. 142-150. , Portland, OR, USA, 19–24 June; Tu, Z., He, Y., Foster, J., Van Genabith, J., Liu, Q., Lin, S., Identifyinghigh-impact sub-structures for convolution kernels in document-levelsentiment classification (2012) Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, 2, pp. 338-343. , Jeju Island, Korea, 8–14 July; Nguyen, D.Q., Nguyen, D.Q., Pham, S.B., A two-stage classifier forsentiment analysis Proceedings of the Sixth International Joint Conference on Natural Language Processing, Asian Federation of Natural Language Processing, pp. 897-901. , Nagoya, Japan, 14–18 October 2013; Ghorbani, M., Bahaghighad, M., Xin, Q., Ozen, F., ConvLSTMConv network: A deep learning approach for sentiment analysis in cloud computing (2020) J. Cloud Comput. Adv. Syst. Appl, 9, p. 16. , [CrossRef]; Dang, N.C., Moreno-Garcia, M.N., De la Prieta, F., Sentiment Analysis Based on Deep Learning: A Comparative Study (2020) Electronics, 9, p. 483. , [CrossRef]; Alamo, T., Reina, D.G., Mammarella, M., Abella, A., Covid-19: Open-data Resources for Monitoring, Modeling, and Forecasting the Epidemic (2020) Electronics, 9, p. 827. , [CrossRef]","Mach, M.; Department of Cybernetics and Artificial Intelligence, Letna 9, Slovakia; email: marian.mach@tuke.sk",,,"MDPI AG",,,,,20799292,,,,"English","Electronics (Switzerland)",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85090255861
"Wu X., Li Z., Kan Z., Gao H.","57209362380;56266709300;36548225700;57195674051;","Reference Trajectory Reshaping Optimization and Control of Robotic Exoskeletons for Human-Robot Co-Manipulation",2020,"IEEE Transactions on Cybernetics","50","8","8820131","3740","3751",,15,"10.1109/TCYB.2019.2933019","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088202590&doi=10.1109%2fTCYB.2019.2933019&partnerID=40&md5=75f21f8e571d8800b16aa038f359a8f0","Department of Automation, University of Science and Technology of China, Hefei, China","Wu, X., Department of Automation, University of Science and Technology of China, Hefei, China; Li, Z., Department of Automation, University of Science and Technology of China, Hefei, China; Kan, Z., Department of Automation, University of Science and Technology of China, Hefei, China; Gao, H., Department of Automation, University of Science and Technology of China, Hefei, China","For human-robot co-manipulation by robotic exoskeletons, the interaction forces provide a communication channel through which the human and the robot can coordinate their actions. In this article, an optimization approach for reshaping the physical interactive trajectory is presented in the co-manipulation tasks, which combines impedance control to enable the human to adjust both the desired and the actual trajectories of the robot. Different from previous studies, the proposed method significantly reshapes the desired trajectory during physical human-robot interaction (pHRI) based on force feedback, without requiring constant human guidance. The proposed scheme first formulates a quadratically constrained programming problem, which is then solved by neural dynamics optimization to obtain a smooth and minimal-energy trajectory similar to the natural human movement. Then, we propose an adaptive neural-network controller based on the barrier Lyapunov function (BLF), which enables the robot to handle the uncertain dynamics and the joint space constraints directly. To validate the proposed method, we perform experiments on the exoskeleton robot with human operators for co-manipulation tasks. The experimental results demonstrate that the proposed controller could complete the co-manipulation tasks effectively. © 2013 IEEE.","Optimized trajectory; physical human-robot interaction (pHRI); reference trajectory reshaping; robotic exoskeleton","Adaptive control systems; Computer programming; Controllers; Exoskeleton (Robotics); Lyapunov functions; Trajectories; Adaptive neural network controller; Desired trajectories; Optimization and control; Optimization approach; Physical humanrobot interaction (phri); Quadratically constrained programming; Reference trajectories; Robotic exoskeletons; Social robots; algorithm; arm; exoskeleton (rehabilitation); human; man machine interaction; physiology; signal processing; Algorithms; Arm; Exoskeleton Device; Humans; Man-Machine Systems; Neural Networks, Computer; Signal Processing, Computer-Assisted",,,,,"17030901029; National Natural Science Foundation of China, NSFC: 61573147, 61625303, 61751310; National Key Research and Development Program of China, NKRDPC: 2018YFC2001600","Manuscript received February 3, 2019; revised May 19, 2019 and July 20, 2019; accepted July 24, 2019. Date of publication August 30, 2019; date of current version July 10, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61573147, Grant 61625303, and Grant 61751310, in part by the National Key Research and Development Program of China under Grant 2018YFC2001600, and in part by the Anhui Science and Technology Major Program under Grant 17030901029. This article was recommended by Associate Editor Z. Ju. (Corresponding author: Zhijun Li.) The authors are with the Department of Automation, University of Science and Technology of China, Hefei 230060, China (e-mail: zjli@ieee.org).",,,,,,,,,,"Jarrasse, N., A methodology to quantify alterations in human upper limb movement during co-manipulation with an exoskeleton (2010) IEEE Trans. Neural Syst. Rehab. 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"Fortunati L., Cavallo F., Sarrica M.","16244589600;14628324200;22952040200;","Multiple Communication Roles in Human–Robot Interactions in Public Space",2020,"International Journal of Social Robotics","12","4",,"931","944",,4,"10.1007/s12369-018-0509-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064480199&doi=10.1007%2fs12369-018-0509-0&partnerID=40&md5=050df40d3835884a4fb28453a0104efe","Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze, 206, Udine, 33100, Italy; The BioRobotics Institute, Scuola Superiore Sant’Anna, Viale Rinaldo Piaggio, 34, Pontedera, Pisa, 56025, Italy; Department of Communication and Social Research, Sapienza, University of Rome, Via Salaria 113, Rome, 00198, Italy","Fortunati, L., Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze, 206, Udine, 33100, Italy; Cavallo, F., The BioRobotics Institute, Scuola Superiore Sant’Anna, Viale Rinaldo Piaggio, 34, Pontedera, Pisa, 56025, Italy; Sarrica, M., Department of Communication and Social Research, Sapienza, University of Rome, Via Salaria 113, Rome, 00198, Italy","This study looks at robots as media and aims to explore the multiple communication roles that they can play in public space. We have analyzed three scenarios: European Researchers’ Night in Pisa; the inauguration of the University of Udine’s 39th academic year; and the official video of the inauguration in Udine. These three scenarios represent three types of media interactions. The first is a human–robot interaction based on a one-to-one or circular communication model; the second is a robot–human interaction based on a one-to-many communication model in-presence; and the third is a robot–human interaction, based on the classical one-to-many communication model mediated through a television screen. Results show that public patterns of behavior toward the robot tend to replicate the ritualization of encounters between humans in the one-to-one model, and audience rituals in public events toward human characters in the one-to-many model. Second, greater proximity and familiarity with the robot increases the respondents’ positive evaluations of all the aspects of the interactions. These results are in line with results coming from research on Information and Communication Technologies (ICT) use. © 2018, Springer Nature B.V.","Human–robot communication; Public interaction with social robots; Robots as public figures; Robots in public space; Robots’ multiple communication roles; Robot–humans communication","Agricultural robots; Information theory; Communication modeling; Human interactions; Information and Communication Technologies; Public figure; Public interactions; Public space; Robot communication; Robot interactions; Social robots",,,,,,,,,,,,,,,,"de Graaf, M.M.A., Ben Allouch, S., Jan van Dijk, J.A.G.M., Long-term evaluation of a social robot in real homes (2017) Interact Stud, 17 (3), pp. 462-491; Coeckelbergh, M., Humans, animals, and robots: a phenomenological approach to human–robot relations (2010) Int J Soc Robot, 3 (2), pp. 197-204; Cabibihan, J.J., Williams, M.A., Simmons, R., When robots engage humans (2014) Int J Soc Robot, 6 (3), pp. 311-313; Wolbring, G., Yumakulov, S., Social robots: views of staff of a disability service organization (2014) Int J Soc Robot, 6 (3), pp. 457-468; Stafford, R.Q., MacDonald, B.A., Jayawardena, C., Wegner, D.M., Broadbent, E., Does the robot have a mind? Mind perception and attitudes towards robots predict use of an eldercare robot (2014) Int J Soc Robot, 6 (1), pp. 17-32; Fortunati, L., Esposito, A., Sarrica, M., Ferrin, G., Children’s knowledge and imaginary about robots (2015) Int J Soc Robot, 7, pp. 685-695; Zhao, S., Humanoid social robots as a medium of communication (2006) New Media Soc, 8, pp. 401-419; Jensen, B., Tomatis, N., Mayor, L., Drygajlo, A., Siegwart, R., Robots meet humans—interaction in public spaces (2005) IEEE Trans Industr Electron, 52, pp. 1-17; Sone, Y., (2016) Japanese robot culture, , Springer, New York; Niemelä, M., Heikkilä, P., Lammi, H., A social service robot in a shopping mall: Expectations of the management, retailers and consumers (2017) Proceeding HRI ‘17. Proceedings of the Companion of the 2017 ACM/IEEE International Conference on human–robot Interaction, pp. 227-228; Salvini, P., Urban robotics: towards responsible innovations for our cities (2017) Robot Auton Syst, 100, pp. 278-286; Cavallo, F., Limosani, R., Manzi, A., Bonaccorsi, M., Esposito, R., Di Rocco, M., Dario, P., Development of a socially believable multi-robot solution from town to home (2014) Cogn Comput, 6, pp. 954-967; Höflich, J.R., Relationships to social robots (2013) Intervalla Platf Intellect Exchange, 1, pp. 35-48; Krotz, F., (2007) Mediatisierung: Fallstudien zum Wandel von Kommunikation, , VS-Verlag, Wiesbaden; Reeves, B., Nass, C., (1996) The media equation: how people treat computers, television, and the new media like real people, , Cambridge University Press, Cambridge; Kanda, T., Hirano, T., Eaton, D., Ishiguro, H., Interactive robots as social partners and peer tutors for children: a field trial (2004) Hum Comput Interact, 19, pp. 61-84; European Parliament Resolution of 16 February 2017 with Recommendations to the Commission on Civil Law Rules on Robotics (2015/2103(INL)), , http://www.europarl.europa.eu/sides/getDoc.do?pubRef=-//EP//NONSGML+TA+P8-TA-2017-0051+0+DOC+PDF+V0//EN; Delvaux, M., (2016) REPORT with Recommendations to the Commission on Civil Law Rules on Robotics, , http://www.europarl.europa.eu/sides/getDoc.do?pubRef=-//EP//TEXT+REPORT+A8-2017-0005+0+DOC+XML+V0//EN; Gittleson, B., Saudi Arabia criticized for giving female robot citizenship, while it restricts women’s rights (2017) ABC News, , October 26. Retrieved October 28, 2017; Maza, C., (2017) Saudi Arabia Gives Citizenship to a Non-Muslim, , English-speaking robot, Newsweek, Retrieved October 26, 2017; Fong, T., Nourbakhsh, I., Dautenbaun, K., A survey of socially interactive robots (2003) Robot Auton Syst, 42, pp. 143-166; Dautenhahn, K., Billard, A., Bringing up robots or—the psychology of socially intelligent robots: From theory to implementation (1999) 3Rd Annual Conference on Autonomous Agents, pp. 366-367; Jovchelovitch, S., Priego-Hernandez, J., Cognitive polyphasia, knowledge encounters and public spheres (2015) The Cambridge handbook of social representations, pp. 163-178. , Sammut G, Andreouli E, Gaskell G, Valsiner J, (eds), Cambridge University Press, Cambridge; Goffman, E., (1959) The presentation of self in everyday life, , Doubleday Anchor Books, New York: (It. transl.: La vita quotidiana come rappresentazione, Bologna 1969; Bagnasco, A., (2001) Organizzazione sociale dello spazio. 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The Eleventh ACM/IEEE International Conference on Human Robot Interaction, 2016, pp. 483-484. , IEEE Press, March 2016; Ferrari, G.A., Meccanica ‘allargata’ (1984) La scienza ellenistica, pp. 225-296. , Giannantoni G, Vegetti M, (eds), Bibliopolis, Napoli; Parlato, E., Il Volto dell’Utopia: modi e significato dell’automa rinascimentale (1991) Il mito dell’automa, pp. 26-30. , Artioli U, Bartoli F, (eds), Artificio, Firenze; Rinaldi, A., La ricerca della ‘terza’ natura: artificialia e naturalia nel giardino toscano del ‘500 (1979) Natura e artificio, pp. 154-175. , Fagiolo M, (ed), Officina Edizioni, Roma; Zanca, A., Il mondo degli automi tra manierismo e secolo dei lumi (1991) Il mito dell’automa, pp. 31-39. , Artioli U, Bartoli F, (eds), Artificio, Firenze; Baron, N., Authenticity, emotions, and ICTs (2013) Intervalla Platf Intellect Exchange, 1, pp. 7-16; Fortunati, L., Manganelli, A., The social representations of telecommunications (2008) Pers Ubiquit Comput, 12, pp. 421-431; Elster, J., (1989) Nuts and bolts for the social sciences, , Cambridge University Press, Cambridge; Contarello, A., Fortunati, L., Sarrica, M., Social thinking and the mobile phone: a study of social change with the diffusion of mobile phones, using a social representations framework (2007) Mob Phone Cult Special Issue Contin J Media Cult, 21, pp. 149-163. , (edited by Gerard Goggin","Fortunati, L.; Department of Mathematics, Via delle Scienze, 206, Italy; email: Leopoldina.Fortunati@uniud.it",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85064480199
"Dong W., Yang L., Fortino G.","56083913500;57215656715;6602895297;","Stretchable Human Machine Interface Based on Smart Glove Embedded with PDMS-CB Strain Sensors",2020,"IEEE Sensors Journal","20","14","9042856","8073","8081",,14,"10.1109/JSEN.2020.2982070","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090145648&doi=10.1109%2fJSEN.2020.2982070&partnerID=40&md5=99df5536434e140fa54615474ddd2938","School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, China; Department of Mechanical and Electrical Engineering, Huazhong Agricultural University, Wuhan, 430070, China; Department of Informatics, Modeling, Electronics and Systems, University of Calabria, Rende, Italy","Dong, W., School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, China; Yang, L., Department of Mechanical and Electrical Engineering, Huazhong Agricultural University, Wuhan, 430070, China; Fortino, G., Department of Informatics, Modeling, Electronics and Systems, University of Calabria, Rende, Italy","Human machine interface (HMI) has recently received more and more attentions as it would provide a media for human to interact with outside environments to promote people's lifestyles. Flexible and stretchable electronics have been widely laminated onto the surface of human skin, gloves, or other soft objects for collecting the deformation signal. The paper proposes an HMI based on stretchable smart glove embedded with soft piezoresistive strain sensors for signal classification and controlling the motion of robot fingers. The stretchable polydimethylsiloxane-carbon black (PDMS-CB) strain sensors satisfy larger deformation (>30%) which is validated by FEM simulation and experiments. The morphology of the soft and complex objects is detected by the smart glove when fingers touch the objects. Feature signals are extracted with the different finger gestures by the soft strain sensors laminated onto different fingers. Human robot interaction (HRI) algorithm is proposed to analyze and recognize the finger gestures for controlling the motion of the robot fingers. Experiments demonstrate that the motion of robot finger is successfully controlled remotely by the smart glove embedded with PDMS-CB strain sensors. Exploring HRI capabilities into the stretchable smart glove represents promising research directions in the future. © 2001-2012 IEEE.","human machine interface; PDMS-CB strain sensor; sensor arrays; smart glove; Smart sensor systems; stretchable electronics","Carbon black; Deformation; Flexible electronics; Laminating; Man machine systems; Microchannels; Morphology; Object detection; Robotic arms; Silicones; Complex objects; Deformation signal; Finger gestures; Human Machine Interface; Human robot Interaction (HRI); Larger deformations; Signal classification; Stretchable electronics; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 51705376; Natural Science Foundation of Jiangxi Province: 20192BAB216025; Fundamental Research Funds for the Central Universities: 2662019QD002","Manuscript received January 23, 2020; revised March 6, 2020; accepted March 12, 2020. Date of publication March 19, 2020; date of current version June 18, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 51705376, in part by the Fundamental Research Funds for the Central Universities under Grant 2662019QD002, and in part by the Natural Science Foundation of Jiangxi province under Grant 20192BAB216025. The associate editor coordinating the review of this article and approving it for publication was Prof. Aime Lay-Ekuakille. (Corresponding author: Lin Yang.) Wentao Dong is with the School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, China (e-mail: wentao_dong@163.com).",,,,,,,,,,"Guo, Q., Di, Z., Lagally, M.G., Mei, Y., Strain engineering and mechanical assembly of silicon/germanium nanomembranes (2018) Mater. Sci. 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Robots Syst, pp. 4000-4007. , Sep; Chouhan, T., Panse, A., Voona, A.K., Sameer, S.M., Smart glove with gesture recognition ability for the hearing and speech impaired (2014) Proc Ieee Global Humanitarian Technol. Conf.-South Asia Satell. (GHTC-SAS), pp. 105-110. , Sep; Ali, A.M.M., Ambar, R., Jamil, M.M.A., Wahi, A.J.M., Salim, S., Artificial hand gripper controller via smart glove for rehabilitation process (2012) Proc. Int. Conf. Biomed. Eng. (ICoBE, pp. 300-304. , Feb; Dong, W., Cheng, X., Xiong, T., Wang, X., Stretchable bio-potential electrode with self-similar serpentine structure for continuous, longterm, stable ECG recordings (2019) Biomed. Microdevices, 21 (1). , Mar, Art. no 6; Jung, S., Reverse-micelle-induced porous pressure-sensitive rubber for wearable human-machine interfaces (2014) Adv. Mater, 26 (28), pp. 4825-4830. , Jul; Shin, Y.-E., Lee, J.-E., Park, Y., Hwang, S.-H., Chae, H.G., Ko, H., Sewing machine stitching of polyvinylidene fluoride fibers: Programmable textile patterns for wearable triboelectric sensors (2018) J. Mater. Chem. A, 6 (45), pp. 22879-22888. , Nov; O'Mahony, K., Liang, J., Delaney, K., A real-Time system for sensor information interactive visualization with multi-Touch technology (2010) Proc Ieee Int. Conf. Virtual Environ. , Hum.-Comput. Interfaces Meas. Syst, pp. 141-145. , Sep; Ji, X., Liu, H., Li, Y., Brown, D., Visual-based view-invariant human motion analysis: A review (2008) Proc. Int. Conf. Knowl.-Based Intell. Inf. Eng. Syst, 5177, pp. 741-748; Gravina, R., Alinia, P., Ghasemzadeh, H., Fortino, G., Multi-sensor fusion in body sensor networks: State-of-The-Art and research challenges (2017) Inf. Fusion, 35, pp. 68-80. , May; Wu, Y., Piezoresistive stretchable strain sensors with human machine interface demonstrations (2018) Sens. Actuators A, Phys, 279, pp. 46-52. , Aug; Li, Y.W., Shen, L.Y., A sensor used to detect the thigh humanmachine coupling force in lower limb rehabilitation robot (2013) Appl. Mech. Mater, 310, pp. 444-447. , Feb; Rodríguez, S., De Paz, J.F., Villarrubia, G., Zato, C., Bajo, J., Corchado, J.M., Multi-Agent information fusion system to manage data from a WSN in a residential home (2015) Inf. Fusion, 23, pp. 43-57. , May; Lee, Y.-D., Chung, W.-Y., Wireless sensor network based wearable smart shirt for ubiquitous health and activity monitoring (2009) Sens. Actuators B, Chem, 140 (2), pp. 390-395. , Jul; García-Macías, E., Rodríguez-Tembleque, L., Sáez, A., Ubertini, F., Crack detection and localization in RC beams through smart MWCNT/epoxy strip-like strain sensors (2018) Smart Mater. Struct, 27 (11). , Nov, Art. no 115022; Lu, N., Lu, C., Yang, S., Rogers, J., Highly sensitive skin-mountable strain gauges based entirely on elastomers (2012) Adv. Funct. Mater, 22 (19), pp. 4044-4050. , Oct; Xiao, X., High-strain sensors based on ZnO nanowire/polystyrene hybridized flexible films (2011) Adv. Mater, 23 (45), pp. 5440-5444. , Dec; Lu, B., Ultra-flexible piezoelectric devices integrated with heart to harvest the biomechanical energy (2015) Sci. Rep, 5 (1), p. 16065. , Dec; Roh, E., Hwang, B.-U., Kim, D., Kim, B.-Y., Lee, N.-E., Stretchable, transparent, ultrasensitive, and patchable strain sensor for human-machine interfaces comprising a nanohybrid of carbon nanotubes and conductive elastomers (2015) Acs Nano, 9 (6), pp. 6252-6261. , Jun; Wu, S., Peng, S., Wang, C.H., Stretchable strain sensors based on PDMS composites with cellulose sponges containing one-And two-dimensional nanocarbons (2018) Sens. Actuators A, Phys, 279, pp. 90-100. , Aug; Ying, M., Silicon nanomembranes for fingertip electronics (2012) Nanotechnology, 23 (34). , Aug, Art. no 344004; Suh, J.-H., Amjadi, M., Park, I., Yoo, H.-J., Finger motion detection glove toward human-machine interface (2015) Proc Ieee Sensors, pp. 1-4. , Nov; Flynn, B.O., Sanchez, J., Angove, P., Connolly, J., Condell, J., Curran, K., Novel smart sensor glove for arthritis rehabilitation (2014) Proc. EPoSS Gen. Assem. Annu. Forum, pp. 1-6; Wajahat, M., Flexible strain sensors fabricated by meniscus-guided printing of carbon nanotube-polymer composites (2018) Acs Appl. Mater. Interfaces, 10 (23), pp. 19999-20005. , Jun; Li, X., Stretchable and highly sensitive graphene-on-polymer strain sensors (2012) Sci. Rep, 2 (1), p. 870. , Dec; Akshay, D.R., Rao, K.U., Low cost smart glove for remote control by the physically challenged (2013) Proc Ieee Global Humanitarian Technol. Conf. , South Asia Satell. (GHTC-SAS), pp. 36-39. , Aug; Zhu, M., Tuning electro-optic susceptibity via strain engineering in artificial PZT multilayer films for high-performance broadband modulator (2017) Appl. Surf. Sci, 425, pp. 1059-1065. , Dec; Jason, N.N., Shen, W., Cheng, W., Copper nanowires as conductive ink for low-cost draw-on electronics (2015) Acs Appl. Mater. Interfaces, 7 (30), pp. 16760-16766. , Aug; Souri, H., Bhattacharyya, D., Highly stretchable and wearable strain sensors using conductive wool yarns with controllable sensitivity (2019) Sens. Actuators A, Phys, 285, pp. 142-148. , Jan; Atalay, O., Atalay, A., Gafford, J., Wang, H., Wood, R., Walsh, C., A highly stretchable capacitive-based strain sensor based on metal deposition and laser rastering (2017) Adv. Mater. Technol, 2 (9). , Sep, Art. no 1700081; Li, S., A flexible piezoresistive sensor with highly elastic weave pattern for motion detection (2019) Smart Mater. Struct, 28 (3). , Mar, Art. no 035020; Jeong, Y.R., A skin-Attachable, stretchable integrated system based on liquid GaInSn for wireless human motion monitoring with multi-site sensing capabilities (2017) Npg Asia Mater, 9 (10), p. e443. , Oct; Jeong, J.-W., Materials and optimized designs for human-machine interfaces via epidermal electronics (2013) Adv. Mater, 25 (47), pp. 6839-6846. , Dec; Lim, S., Transparent and stretchable interactive human machine interface based on patterned graphene heterostructures (2015) Adv. Funct. Mater, 25 (3), pp. 375-383. , Jan; Huang, Y., Self-similar design for stretchable wireless LC strain sensors (2015) Sens. Actuators A, Phys, 224, pp. 36-42. , Apr; Dong, W., Cheng, X., Wang, X., Zhang, H., Fractal serpentine-shaped design for stretchable wireless strain sensors (2018) Appl. Phys. A, Solids Surf, 124 (7), p. 478. , Jul; Yin, F., Ye, D., Zhu, C., Qiu, L., Huang, Y., Stretchable, highly durable ternary nanocomposite strain sensor for structural health monitoring of flexible aircraft (2017) Sensors, 17 (11), p. 2677","Yang, L.; Department of Mechanical and Electrical Engineering, China; email: yl@live.de",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,1530437X,,,,"English","IEEE Sensors J.",Article,"Final","",Scopus,2-s2.0-85090145648
"Panda A.S., Prakash R., Behera L., Dutta A.","57219551100;57197201102;6701522952;57190397627;","Combined Online and Offline Inverse Dynamics Learning for a Robot Manipulator",2020,"Proceedings of the International Joint Conference on Neural Networks",,,"9206604","","",,,"10.1109/IJCNN48605.2020.9206604","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093822821&doi=10.1109%2fIJCNN48605.2020.9206604&partnerID=40&md5=ab8f21e25198235944174e789e5816c1","Indian Institute of Technology, Department of Mechanical Engineering, Kanpur, India","Panda, A.S., Indian Institute of Technology, Department of Mechanical Engineering, Kanpur, India; Prakash, R., Indian Institute of Technology, Department of Mechanical Engineering, Kanpur, India; Behera, L., Indian Institute of Technology, Department of Mechanical Engineering, Kanpur, India; Dutta, A., Indian Institute of Technology, Department of Mechanical Engineering, Kanpur, India","Due to the approximation errors in dynamic model, changing payloads and dynamic disturbances acting on the system, the model based tracking is not satisfactory. Hence the application of real-time machine learning techniques in inverse dynamics learning has gained prominence for collaborative human robot interaction. In this paper, we propose a novel combined online and offline Neural Network based learning technique in conjunction with an acceleration tracker for inverse dynamics learning of a robot manipulator. This eliminates the need for explicit reliance on the approximate analytical robot model while controlling the robotic systems. The proposed approach can even capture the system dynamics accurately at higher acceleration where non-linear forces such as non-linear friction and damping play a prominent role. The performance of the proposed inverse dynamic model has been verified using extensive simulations on control of a 6 DOF UR5 robot manipulator in an accurate physics based Pybullet Simulator. The efficacy of the proposed method has been validated by comparing the control performance using model based backstepping controller. The results show that the inverse dynamics learning based controller outperforms significantly its counterpart in real world scenarios where uncertainties are ubiquitous. © 2020 IEEE.",,"Backstepping; Controllers; Dynamic models; E-learning; Flexible manipulators; Industrial manipulators; Inverse problems; Learning systems; Modular robots; Neural networks; Robot applications; Approximate analytical; Approximation errors; Backstepping controller; Dynamic disturbances; Extensive simulations; Inverse dynamic model; Model-based tracking; Offline neural networks; Human robot interaction",,,,,,,,,,,,,,,,"Duy, N.-T., Jan, P., Model learning for robot control: A survey (2011) Cognitive processing, 12 (4), pp. 319-340; Bcsi, B., Csat, L., Peters, J., Alignment-based transfer learning for robot models (2013) The 2013 International Joint Conference on Neural Networks (IJCNN, pp. 1-7. , Aug; Sünderhauf, N., Brock, O., Scheirer, W.J., Hadsell, R., Fox, D., Leitner, J., Upcroft, B., Corke, P., The limits and potentials of deep learning for robotics (2018) CoRR, , abs/1804.06557; Gijsberts, A., Metta, G., Real-time model learning using incremental sparse spectrum Gaussian process regression (2013) Neural Networks, 41, pp. 59-69. , Special Issue on Autonomous Learning; Sigaud, O., Salan, C., Padois, V., On-line regression algorithms for learning mechanical models of robots: A survey (2011) Robotics and Autonomous Systems, 59 (12), pp. 1115-1129; Nguyen-Tuong, D., Peters, J.R., Seeger, M., Local Gaussian process regression for real time online model learning (2009) Advances in Neural Information Processing Systems, 21, pp. 1193-1200. , In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Curran Associates, Inc; Meier, F., Schaal, S., Drifting Gaussian processes with varying neighborhood sizes for online model learning (2016) 2016 IEEE International Conference on Robotics and Automation (ICRA, pp. 264-269. , IEEE; Meier, F., Kappler, D., Ratliff, N., Schaal, S., Towards robust online inverse dynamics learning (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS, pp. 4034-4039. , Oct; Kappler, D., Meier, F., Ratliff, N., Schaal, S., A new data source for inverse dynamics learning (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS, pp. 4723-4730. , Sep; Meier, F., Schaal, S., Drifting Gaussian processes with varying neighborhood sizes for online model learning (2016) Proceedings of the IEEE International Conference on Robotics and Automation (ICRA, , 2016. IEEE, May; Ratliff, N., Meier, F., Kappler, D., Schaal, S., (2016) Doomed: Direct online optimization of modeling errors in dynamics, , arXiv preprint arXiv:1608.00309 August; Todorov, E., Erez, T., Tassa, Y., Mujoco: A physics engine for model-based control (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5026-5033. , IEEE; Corke, P.I., (2017) Robotics, Vision & Control: Fundamental Algorithms in MATLAB, , Springer, second edition; Freese, M., Rohmer, E., Singh, S.P.N., Coppeliasim (formerly v-rep): A versatile and scalable robot simulation framework (2013) Proc. of The International Conference on Intelligent Robots and Systems (IROS, , www.coppeliarobotics.com; Gupta, V., Chittawadigi, R.G., Saha, S.K., Roboanalyzer: Robot visualization software for robot technicians (2017) Proceedings of the Advances in Robotics, , AIR 17, New York, NY, USA. Association for Computing Machinery; Coumans, E., Bai, Y., Pybullet, a python module for physics simulation for games, robotics and machine learning, , http://pybullet.org, 2016-2019; (1955) Introduction to robotics : Mechanics & control, , Craig./John J. Craig. Addison-Wesley Pub. Co., , Reading, Mass., 1986. Includes bibliographies and index",,,"IEEE Computational Intelligence Society (CIS)","Institute of Electrical and Electronics Engineers Inc.","2020 International Joint Conference on Neural Networks, IJCNN 2020","19 July 2020 through 24 July 2020",,163566,,9781728169262,85OFA,,"English","Proc Int Jt Conf Neural Networks",Conference Paper,"Final","",Scopus,2-s2.0-85093822821
"Kyeong S., Kim J.","57038555500;13905404900;","Enhanced torque estimation method from multi-channel surface electromyography compensating electrode location variation",2020,"Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS","2020-July",,"9176498","3965","3968",,,"10.1109/EMBC44109.2020.9176498","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091030926&doi=10.1109%2fEMBC44109.2020.9176498&partnerID=40&md5=32b99807a43aae3a6edcb529289f21f1","Korea Advanced Institute of Science and Technology (KAIST), Department of Mechanical Engineering, Daejeon, 305-701, South Korea","Kyeong, S., Korea Advanced Institute of Science and Technology (KAIST), Department of Mechanical Engineering, Daejeon, 305-701, South Korea; Kim, J., Korea Advanced Institute of Science and Technology (KAIST), Department of Mechanical Engineering, Daejeon, 305-701, South Korea","Recognizing human intentions from the human counterpart is very important in human-robot interaction applications. Surface electromyography(sEMG) has been considered as a potential source for motion intention because the signal represents the on-set timing and amplitude of muscle activation. It is also reported that sEMG has the advantage of knowing body movements ahead of actual movement. However, sEMG based applications suffer from electrode location variation because sEMG shows different characteristics whenever the skin condition is different. They need to recreate the estimation model if electrodes are attached to different locations or conditions. In this paper, we developed a sEMG torque estimation model for electrode location variation. A decomposition model of sEMG signals was developed to discriminate the muscle source signals for electrode location variation, and we verified this model without making a new torque estimation model. Torque estimation accuracy using the proposed method was increased by 24.8% and torque prediction accuracy was increased by 47.7% for the electrode location variation in comparison with the method without decomposition. Therefore, the proposed sEMG decomposition method showed an enhancement in torque estimation for electrode location variation. © 2020 IEEE.",,"Electrodes; Human robot interaction; Muscle; Torque; Decomposition methods; Decomposition model; Estimation models; Location variation; Muscle activation; Potential sources; Surface electromyography; Torque prediction; Location",,,,,,,,,,,,,,,,"He, H., Kiguchi, K., A study on EMG-based control of exoskeleton robots for human lower-limb motion assist (2007) 2007 6th International Special Topic Conference on Information Technology Applications in Biomedicine. IEEE; Aguirre-Ollinger, G., Learning muscle activation patterns via nonlinear oscillators: Application to lower-limb assistance (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE; Castellini, C., Fiorilla, A.E., Sandini, G., Multi-subject/dailylife activity EMG-based control of mechanical hands (2009) J. Neuroeng. Rehabil., 6 (1), p. 41; Boschmann, A., Platzner, M., Towards robust HD EMG pattern recognition: Reducing electrode displacement effect using structural similarity (2014) Proc. 36th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), pp. 4547-4550. , Aug; Pan, L., Zhang, D., Jiang, N., Sheng, X., Zhu, X., Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns (2015) J. Neuroeng. Rehabil., 12 (1), p. 110; Young, A.J., Hargrove, L.J., Kuiken, T.A., Improving myoelectric pattern recognition robustness to electrode shift by changing interelectrode distance and electrode configuration (2012) IEEE Trans. Biomed. Eng., 59 (3), pp. 645-652; Gui, K., Liu, H., Zhang, D., A Practical and Adaptive Method to Achieve EMG-Based Torque Estimation for a Robotic Exoskeleton (2019) IEEE/ASME Trans. Mechatronics, 24 (2), pp. 483-494; Staudenmann, D., Methodological aspects of SEMG recordings for force estimation-a tutorial and review (2010) Journal of Electromyography and Kinesiology, 20 (3), pp. 375-387; Sherwood, L., Human physiology: From cells to systems (2015) Cengage Learning; Hogan, N., Mann, R.W., Myoelectric signal processing: Optimal estimation applied to electromyographyPart I: Derivation of the optimal myoprocessor (1980) IEEE Transactions on Biomedical Engineering, 7, pp. 382-395; Ziai, A., Menon, C., Comparison of regression models for estimation of isometric wrist joint torques using surface electromyography (2011) Journal of Neuroengineering and Rehabilitation, 8 (1), p. 56",,,,"Institute of Electrical and Electronics Engineers Inc.","42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020","20 July 2020 through 24 July 2020",,162693,1557170X,9781728119908,,,"English","Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS",Conference Paper,"Final","",Scopus,2-s2.0-85091030926
"Mizanoor Rahman S.M.","37039570500;","Admittance-based bio-inspired cognitive pid control to optimize human-robot interaction in power-assisted object manipulation",2020,"IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM","2020-July",,"9158811","679","684",,2,"10.1109/AIM43001.2020.9158811","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090387278&doi=10.1109%2fAIM43001.2020.9158811&partnerID=40&md5=b6e6b1606e8de5c983b4b6c18bee85f3","Hal Marcus College of Science and Engineering, University of West Florida, Dept. of Intelligent Systems and Robotics, Pensacola, FL  32514, United States; Dept. of Mechanical Engineering, Mie University, Tsu, Mie, Japan","Mizanoor Rahman, S.M., Hal Marcus College of Science and Engineering, University of West Florida, Dept. of Intelligent Systems and Robotics, Pensacola, FL  32514, United States, Dept. of Mechanical Engineering, Mie University, Tsu, Mie, Japan","A novel approach to PID control for optimizing human-robot interaction (HRI) in manipulating objects with a power assist robotic system (PARS) was presented. The PID control was developed in the form of the admittance control. To take bio-inspiration and fit the control with human cognition, a novel method of inclusion of weight perception in the dynamics and control of the system was proposed. Then, a comprehensive evaluation and optimization scheme was proposed to determine the optimum HRI and co-manipulation performance. A 1-DOF testbed PARS was developed to experimentally verify the proposed control in a pilot study. During the experiments, human subjects lifted objects with the system and the HRI and system characteristics were investigated and analyzed. The results showed that the proposed PID control aligned with human cognition through inclusion of weight perception in the dynamics and control was effective to optimize the HRI and co-manipulation performance. The results can be used to design human-centered control methods for PARSs to assist humans manipulating heavy objects in industries and at homes that can provide optimum HRI and manipulation performance. © 2020 IEEE.",,"Biomimetics; Intelligent mechatronics; Man machine systems; Three term control systems; Admittance control; Comprehensive evaluation; Dynamics and control; Human robot Interaction (HRI); Human-centered control; Object manipulation; Optimization scheme; System characteristics; Social robots",,,,,,,,,,,,,,,,"Rahman, S.M.M., Ikeura, R., Improving interactions between a power assist robot system and its human user in horizontal transfer of objects using a novel adaptive control method (2012) Advances in Human-Computer Interaction, 2012; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., Study on optimum maneuverability in horizontal manipulation of objects with power-assist based on weight perception (2010) Proc. of SPIE, 7500, p. 75000P; Rahman, S.M.M., Ikeura, R., Hayakawa, S., Novel human-centric force control methods of power assist robots for object manipulation (2013) Proc. of 2013 IEEE Int. Conf. on Robotics and Biomimetics (IEEE ROBIO 2013), pp. 340-345. , Dec 12-14, Shenzhen, China; Gosselin, C., Laliberte, T., Mayer-St-Onge, B., Foucault, S., Lecours, A., Duchaine, V., Paradis, N., Menassa, R., A friendly beast of burden: A human-assistive robot for handling large payloads (2013) IEEE Robotics & Automation Magazine, 20 (4), pp. 139-147; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., Unimanual and bimanual weight discrimination in lifting objects with a power assist system (2009) Proc. of ICROS-SICE Int. Joint Conf., pp. 4787-4792; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., A psychophysical model of the power assist system for lifting objects Proc. of 2009 IEEE Int. Conf. on Systems, Man, and Cybernetics, pp. 4125-4130; Hara, H., Sankai, Y., Hal equipped with passive mechanism (2012) Proc. of IEEE Int. Symp. on System Integration, pp. 1-6; Doi, T., Yamada, H., Ikemoto, T., Naratani, H., Simulation of a pneumatic hand crane power-assist system (2008) Journal of Robotics and Mechatronics, 20 (6), pp. 896-902; Rahman, S.M.M., Ikeura, R., Weight-perception-based fixed and variable admittance control algorithms for unimanual and bimanual lifting of objects with a power assist robotic system (2018) Int. Journal of Advanced Robotic Systems, 15 (4), pp. 1-15; Rahman, S.M.M., Ikeura, R., Mpc to optimise performance in power-assisted manipulation of industrial objects (2017) IET Electric Power Applications, 11 (7), pp. 1235-1244; Rahman, S.M.M., Ikeura, R., Weight-prediction-based predictive optimal position and force controls of a power assist robotic system for object manipulation (2016) IEEE Transactions on Industrial Electronics, 63 (9), pp. 5964-5975. , September; Sheng, G., Gao, G., Research on the attitude control of civil quadrotor uav based on fuzzy pid control (2019) Proc. of 2019 Chinese Control and Decision Conference, pp. 4566-4569. , China; Zhou, X., A rolling pid control approach and its applications (2019) Proc. of 2019 Chinese Control Conference, pp. 2863-2867. , China; Rahman, S.M.M., Ikeura, R., Optimizing perceived heaviness and motion for lifting objects with a power assist robot system considering change in time constant (2012) International Journal on Smart Sensing and Intelligent Systems, 5 (2), pp. 458-486. , June; Rahman, S.M.M., Ikeura, R., Cognition-based control and optimization algorithms for optimizing human-robot interactions in power assisted object manipulation (2016) Journal of Information Science and Engineering, 32 (5), pp. 1325-1344; Rahman, S.M.M., Trustworthy power assistance in object manipulation with a power assist robotic system (2019) Proc. of IEEE SoutheastCon 2019, pp. 1-6. , April 11-14, USA; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., Controlling a power assist robot for lifting objects considering human's unimanual, bimanual and cooperative weight perception (2010) Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 2356-2362. , May 3-8","Mizanoor Rahman, S.M.; Hal Marcus College of Science and Engineering, United States; email: smrahman@uwf.edu",,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020","6 July 2020 through 9 July 2020",,162314,,9781728167947,,,"English","IEEE ASME Int Conf Adv Intellig Mechatron AIM",Conference Paper,"Final","",Scopus,2-s2.0-85090387278
"Hainsworth T., Smith L., Alexander S., MacCurdy R.","57217387857;57217392541;57217384261;24335714000;","A Fabrication Free, 3D Printed, Multi-Material, Self-Sensing Soft Actuator",2020,"IEEE Robotics and Automation Letters","5","3","9068247","4118","4125",,7,"10.1109/LRA.2020.2986760","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087179118&doi=10.1109%2fLRA.2020.2986760&partnerID=40&md5=a536a0226f7617f8e15133608024ad24","Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO  80309, United States","Hainsworth, T., Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO  80309, United States; Smith, L., Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO  80309, United States; Alexander, S., Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO  80309, United States; MacCurdy, R., Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO  80309, United States","Soft robotics offers a range of attractive features relative to traditional rigid robots, including inherently safer human-robot interactions, and continuous passive dynamics that enable morphological computation. Here we present an actuator with an integrated print-in-place strain sensor which is produced entirely via multi-material additive manufacturing and requires no post processing or manual fabrication steps. One natural application of this technology is the end effector of robotic arms; incorporating deformable actuators into a gripping mechanism allows for the safe integration of robotic assistance in human-occupied settings. While numerous soft robot actuators have been implemented without feedback, force sensing and proprioception are valuable signals to leverage in extending the capabilities of these systems. Prior approaches to integrating sensors into soft robot components have relied on manual processes, or specialized fabrication tools. Our work shows a novel method for automatically manufacturing soft pneumatically-driven actuators with embedded sensors through readily available 3D printing tools with no human fabrication required. Automatically manufacturing these sensor-actuator systems enables more complex, capable, and integrate-able designs, because the labor of assembly is eliminated; actuator-sensor designs that would be tedious or impossible to manufacture become tractable with our approach. © 2016 IEEE.","additive manufacturing; grippers and other end-effectors; hydraulic/pneumatic actuators; soft robot materials and design; Soft sensors and actuators","Actuators; Agricultural robots; End effectors; Fabrication; Human robot interaction; Pneumatic drives; Robotics; Actuator sensors; Embedded sensors; Fabrication tool; Gripping mechanisms; Inherently safer; Integrating sensors; Morphological computation; Passive dynamics; 3D printers",,,,,,"Manuscript received October 15, 2019; accepted March 16, 2020. Date of publication April 15, 2020; date of current version May 10, 2020. This letter was recommended for publication by Associate Editor Dr. Marcello Calisti and Editor Kyu-Jin Cho upon evaluation of the reviewers’ comments. This work was supported by CU Boulder Autonomous Systems Interdisciplinary Research Theme. (Travis Hainsworth and Lawrence Smith contributed equally to this work.) (Corresponding author: Robert MacCurdy.) The authors are with the Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO 80309 USA (e-mail: travis.hainsworth @gmail.com; lasm4254@colorado.edu; sebastian.alexander@colorado. edu; maccurdy@colorado.edu). Digital Object Identifier 10.1109/LRA.2020.2986760 Fig. 1. An example gripper made with two of our print-in-place, self-sensing, soft actuators. These actuators are more compliant, yet can exert higher forces than related approaches (Fig. 3) and incorporate a bend-angle sensor suitable for feedback control. In particular, the ability to exert higher forces, which expands the application envelope, can be enhanced by feedback position and force control in order to maintain the ability to interact with delicate objects. The entire sensor-actuator assembly is printed in a single step via multi-material 3D printing, requiring no manual fabrication steps.",,,,,,,,,,"Mosadegh, B., Pneumatic networks for soft robotics that actuate rapidly (2014) Adv. Functional Mater., 24 (15), pp. 2163-2170; Zolfagharian, A., Kouzani, A.Z., Khoo, S.Y., Moghadam, A.A.A., Gibson, I., Kaynak, A., Evolution of 3d printed soft actuators (2016) Sensors Actuators, A: Physical, 250, pp. 258-272; Pelrine, R., Kornbluh, R., Joseph, J., Heydt, R., Pei, Q., Chiba, S., High-field deformation of elastomeric dielectrics for actuators (2000) Mater. Sci. Eng. C, 11 (2), pp. 89-100; Acome, E., Hydraulically amplified self-healing electrostatic actuators with muscle-like performance (2018) Science, 359 (6371), pp. 61-65; Tolley, M.T., An untethered jumping soft robot (2014) Proc. IEEE Int. Conf. Intell. Robots Syst., pp. 561-566; Shepherd, R.F., Using explosionstopowerasoft robot (2013) Angewandte Chemie-Int. Ed., 52 (10), pp. 2892-2896; Ilievski, F., Mazzeo, A.D., Shepherd, R.F., Chen, X., Whitesides, G.M., Soft robotics for chemists (2011) Angewandte Chemie-Int. Ed., 50 (8), pp. 1890-1895; Joshi, S., Paik, J., Multi-DoF force characterization of soft actuators (2019) IEEE Robot. Autom. Lett., 4 (4), pp. 3679-3686. , Oct; Lipson, H., Challenges and opportunities for design, simulation, and fabrication of soft robots (2014) Soft Robot., 1 (1), pp. 21-27; Zhang, H., Wang, M., Chen, F., Wang, Y., Kumar, A., Fuh, J., Design and development of a soft gripper with topology optimization (2017) Proc. IEEE Int. Conf. Intell. 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AI, 6, pp. 1-12; Mutlu, R., Alici, G., Inhet Panhuis, M., Spinks, G.M., 3D printed flexure hinges for soft monolithic prosthetic fingers (2016) Soft Robot., 3 (3), pp. 120-133; MacCurdy, R., Katzschmann, R., Kim, Y., Rus, D., Printable hydraulics: A method for fabricating robots by 3D co-printing solids and liquids (2016) Proc. IEEE Int. Conf. Robot. Autom., pp. 3878-3885; Patel, D.K., Sakhaei, A.H., Layani, M., Zhang, B., Ge, Q., Magdassi, S., Highly stretchable and UV curable elastomers for digital light processing based 3D printing (2017) Adv. Mater., 29 (15), pp. 1-7; Peele, B.N., Wallin, T.J., Zhao, H., Shepherd, R.F., 3D printing antagonistic systems of artificial muscle using projection stereolithography (2015) Bioinspiration Biomimetics, 10 (5); Manion, C.A., Fuge, M., Bergbrieter, S., Modeling and evaluation of additive manufactured HASEL actuators ∗ (2018) Proc. Int. Conf. Intell. 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Syst., 1 (1), pp. 1900-2002; (2015) Standard Test Method for Peel Resistance of Adhesives (T-Peel Test), ASTM D1876-08, , ASTM Book of Standards, West Conshohocken, PA, USA May; Cacucciolo, V., Shintake, J., Kuwajima, Y., Maeda, S., Floreano, D., Shea, H., Stretchable pumps for soft machines (2019) Nature, 572 (7770), pp. 516-519; Ding, L., Design of soft multi-material pneumatic actuators based on principal strain field (2019) Mater. Design, 182; Fras, J., Noh, Y., Wurdemann, H., Althoefer, K., Soft fluidic rotary actuator with improved actuation properties (2017) Proc. IEEE Int. Conf. Intell. Robots Syst., pp. 5610-5615","Maccurdy, R.; Department of Mechanical Engineering, United States; email: maccurdy@colorado.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85087179118
"Li Y.-L., Zhao Y.-W., Wang Z., Zhang D.-H., Zhao X.-G.","57208762156;35180540200;56780598100;46061681400;16551339800;","Environment modelling method for manipulator robot based offline-mapping between depth image and OctoMap [基于深度图像与三维栅格离线映射的机械臂环境建模方法]",2020,"Kongzhi yu Juece/Control and Decision","35","7",,"1537","1546",,,"10.13195/j.kzyjc.2018.1356","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086502893&doi=10.13195%2fj.kzyjc.2018.1356&partnerID=40&md5=3ea015a32959e2ead08d75584832b5cb","State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China; University of Chinese Academy of Sciences, Beijing, 100049, China","Li, Y.-L., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China, University of Chinese Academy of Sciences, Beijing, 100049, China; Zhao, Y.-W., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China; Wang, Z., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China; Zhang, D.-H., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China; Zhao, X.-G., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China","In human-robot interaction process, to ensure the safety of human, robots and environment in a dynamic non-structured environment, this paper presents an environment modelling method for the manipulator robot using offline-mapping between the depth image and OctoMap. This method includes a two-stage procedure: offline mapping and online updating. In the first stage, we discretize the workspace of the robot into cells and save it as a OctoMap, then get and save the offline-mapping between pixels of depth image and cells of OctoMap using intrinsic and extrinsic matrixes of the RGB-D camera. In the second stage, the OctoMap is updated by the adjacent frame substraction and lookup to the offline-mapping. Furthermore, to fully describe the robot's workspace, data fusion of multi-RGB-D cameras and robot-body-filtering based on this mehtod are also proposed. The proposed method has the advantages of powerful description ability, consistent and high accuracy, multi-sensor data fusion ability and easily online updating. Finally, the validity of this approach is demonstrated using simulations, modelling and the obstacle avoidance experiment for the flange, which is conducted by using a 7-DOF KUKA IIWA manipulator and the Kinect sensor. © 2020, Editorial Office of Control and Decision. All right reserved.","Depth image; Environment modeling; Human-robot safety; Multi-sensor data fusion; OctoMap; Offline-mapping","Cameras; Human robot interaction; Mapping; Sensor data fusion; Adjacent frames; Description abilities; Environment modelling; Manipulator robots; Multisensor data fusion; Robot's workspace; Structured environment; Two stage procedure; Manipulators",,,,,,,,,,,,,,,,"Thrun, S., (2003) Robotic mapping: A survey, pp. 1-35. , [M]. San Francisco: Morgan Kaufmann Publishers Inc; Pan, J, Sucan, I A, Chitta, S, Real-time collision detection and distance computation on point cloud sensor data (2013) IEEE International Conference on Robotics and Automation, pp. 3593-3599. , [C]. 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Big Sky: IEEE; Cant, R, Langensiepen, C, Foster, L., A composite convex hull collision detector for the open dynamics engine (2008) International Conference on Computer Modeling and Simulation, pp. 122-125. , [C]. Cambridge: IEEE; Elfes, A., Using occupancy grids for mobile robot perception and navigation (1989) Computer, 22 (6), pp. 46-57. , [J]; Yguel, M, Aycard, O, Laugier, C., (2006) Field and service robotics, pp. 219-230. , [M]. Berlin: Springer; Triebel, R, Pfaff, P, Burgard, W., Multi-level surface maps for outdoor terrain mapping and loop-closing (2006) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2276-2282. , [C]. Beijing: IEEE; Rivadeneyra, C, Miller, I, Schoenberg, J R, Probabilistic estimation of multi-level terrain maps (2009) IEEE International Conference on Robotics and Automation, pp. 1643-1648. , [C]. 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Qingdao: IEEE; Chitta, S, Sucan, I, Cousins, S., MoveIt![ROS Topics] (2012) IEEE Robotics & Automation Magazine, 19 (1), pp. 18-19. , [J]; Wei, H, Huang, Z, Yu, Q, RGMP-ROS: A real-time ROS architecture of hybrid RTOS and GPOS on multi-core processor (2014) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2482-2487. , [C]. Hong Kong: IEEE; Du, H B, Song, G L, Zhao, Y W, Hand-eye calibration method for manipulator and RGB-D camera using 3D-printed ball (2018) Robot, 40 (6), pp. 835-842. , 杜惠斌, 宋国立, 赵忆文, 等.利用3D打印标定球的机械臂与RGB-D相机手眼标定方法[J].机器人, 2018, 40(6): 835-842. [J]","Zhao, Y.-W.; State Key Laboratory of Robotics, China; email: zhaoyw@sia.cn",,,"Northeast University",,,,,10010920,,KYJUE,,"Chinese","Kongzhi yu Juece Control Decis",Article,"Final","",Scopus,2-s2.0-85086502893
"Chien S.-H., Wang J.-H., Cheng M.-Y.","57221287244;57221294493;56326622700;","Performance Comparisons of Different Observer-Based Force-Sensorless Approaches for Impedance Control of Collaborative Robot Manipulators",2020,"Proceedings - 2020 IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020",,,"9274790","326","331",,3,"10.1109/ICPS48405.2020.9274790","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098725281&doi=10.1109%2fICPS48405.2020.9274790&partnerID=40&md5=fde05ed17ed8879450282e6cc24afcc8","National Cheng Kung University, Department of Electrical Engineering, Tainan, Taiwan","Chien, S.-H., National Cheng Kung University, Department of Electrical Engineering, Tainan, Taiwan; Wang, J.-H., National Cheng Kung University, Department of Electrical Engineering, Tainan, Taiwan; Cheng, M.-Y., National Cheng Kung University, Department of Electrical Engineering, Tainan, Taiwan","Human robot interaction is commonly seen in the application of collaborative robots. In order to ensure the safety of the human operator, most existing approaches exploit the idea of compliance control in the design of suitable controllers. In particular, force sensors are often used to estimate the contact force that is essential in the design of the compliance control scheme. However, force sensor-based approaches are not cost-effective. In order to cope with this problem so as to facilitate the implementation of compliance control, this paper aims to compare various observer-based force-sensorless approaches for implementing impedance control of collaborative robot manipulators. Different observer-based approaches, such as the generalized momentum approach, the extended state observer, the modified extended state observer, and the closed-loop disturbance observer are used in this paper to estimate the contact force. Several impedance control experiments conducted on a 6-DOF robot manipulator are used to assess the performances of the aforementioned observer-based approaches. Experimental results indicate that the modified extended state observer exhibits the best performance on contact force estimation among all the tested observer-based approaches. © 2020 IEEE.","disturbance observer; human-robot interaction; impedance control; robot manipulator","Cost effectiveness; Embedded systems; Flexible manipulators; Human robot interaction; Industrial manipulators; Modular robots; Robot applications; State estimation; Collaborative robots; Contact force estimations; Contact forces; Disturbance observer; Extended state observer; Force sensorless; Impedance control; Performance comparison; Compliance control",,,,,"Industrial Technology Research Institute, ITRI; Ministry of Science and Technology, Taiwan, MOST: 105-2221-E-006-105-MY3","ACKNOWLEDGMENT The authors would like to thank the Industrial Technology Research Institute and the Ministry of Science and Technology, Taiwan, for support of this research under Grant No. MOST 105-2221-E-006-105-MY3.",,,,,,,,,,"Li, Y., Ge, S.S., Human-robot collaboration based on motion intention estimation (2014) IEEE/ASME Trans. On Mechatronics, 19 (3), pp. 1007-1014; Li, Z., Huang, B., Ye, Z., Deng, M., Yang, C., Physical human-robot interaction of a robotic exoskeleton by admittance control (2018) Ieee Trans. On Industrial Electronics, 65 (12), pp. 9614-9624; Campeau-Lecours, A., Otis, M.J.D., Gosselin, C., Modeling of physical human-robot interaction (2016) International Journal of Advanced Robotic Systems, 13 (5); Labrecque, P.D., Laliberte, T., Foucault, S., Abdallah, M.E., Gosselin, C., Uman: A low-impedance manipulator for human-robot cooperation based on underactuated redundancy (2017) IEEE/ASME Trans. On Mechatronics, 22 (3), pp. 1401-1411; Erden, M.S., Billard, A., End-point impedance measurements across dominant and nondominant hands and robotic assistance with directional damping (2015) Ieee Trans Cybern, 45 (6), pp. 1146-1157. , Jun; Kang, G., Oh, H.S., Seo, J.K., Kim, U., Choi, H.R., Variable admittance control of robot manipulators based on human intention (2019) IEEE/ASME Trans. On Mechatronics, 24 (3), pp. 1023-1032; Hogan, N., Impedance control: An approach to manipulation: Part i: Theory (1985) J. Dyn. Sys., Meas., Control, 107 (1), pp. 304-313; Hogan, N., Stable execution of contact tasks using impedance control Proc. 1987 Ieee International Conference on Robotics and Automation, pp. 1047-1054; Johansson, R., Robertsson, A., Robotic force control using observer-based strict positive real impedance control Proc. Of the 2003 Ieee International Conference on Robotics and Automation, 3, pp. 3686-3691; Stolt, A., Linderoth, M., Robertsson, A., Johansson, R., Force controlled robotic assembly without a force sensor Proc. Of the 2012 Ieee International Conference on Robotics and Automation, pp. 1538-1543; Capurso, M., Ardakani, M.M.G., Johansson, R., Robertsson, A., Rocco, P., Sensorless kinesthetic teaching of robotic manipulators assisted by observer-based force control Proc. Of the 2017 Ieee International Conference on Robotics and Automation, pp. 945-950; Chen, W.-H., Yang, J., Guo, L., Li, S., Disturbance-observer-based control and related methods-an overview (2016) Ieee Trans. On Industrial Electronics, 63 (2), pp. 1083-1095; Ren, T., Dong, Y., Wu, D., Chen, K., Collision detection and identification for robot manipulators based on extended state observer (2018) Control Engineering Practice, 79, pp. 144-153; Haddadin, S., De Luca, A., Albu-Schaffer, A., Robot collisions: A survey on detection, isolation, and identification (2017) Ieee Trans. On Robotics, 33 (6), pp. 1292-1312; Lee, S.D., Kim, M.C., Song, J.B., Sensorless collision detection for safe human-robot collaboration (2015) IEEE/RSJ International Conference on Intelligent Robots and Systems IROS 2015, pp. 2392-2397. , Sept; Zhao, L., Liu, X., Wang, T., Trajectory tracking control for double-joint manipulator systems driven by pneumatic artificial muscles based on a nonlinear extended state observer (2019) Mechanical Systems and Signal Processing, 122, pp. 307-320. , 2019/05/01; Cui, R., Chen, L., Yang, C., Chen, M., Extended state observer-based integral sliding mode control for an underwater robot with unknown disturbances and uncertain nonlinearities (2017) Ieee Trans. On Industrial Electronics, 64 (8), pp. 6785-6795; Chen, C., Zhang, C., Hu, T., Ni, H., Luo, W., Model-Assisted extended state observer-based computed torque control for trajectory tracking of uncertain robotic manipulator systems (2018) International Journal of Advanced Robotic Systems, 15 (5). , 2018/09/01; Chen, C.-J., Cheng, M.-Y., Su, K.-H., Observer-based impedance control and passive velocity control of power assisting devices for exercise and rehabilitation (2013) Proc. Of the 39th Annual Conference of the Ieee Industrial Electronics Society, pp. 6502-6507; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) Ieee Trans. On Robotics, 31 (4), pp. 850-863; Khatib, O., A unified approach for motion and force control of robot manipulators: The operational space formulation (1987) Ieee Journal on Robotics and Automation, 3 (1), pp. 43-53; Sebastian, G., Li, Z., Crocher, V., Kremers, D., Tan, Y., Oetomo, D., Interaction force estimation using extended state observers: An application to impedance-based assistive and rehabilitation robotics (2019) Ieee Robotics and Automation Letters, 4 (2), pp. 1156-1161; Kim, M.J., Chung, W.K., Disturbance-observer-based pd control of flexible joint robots for asymptotic convergence (2015) Ieee Trans. On Robotics, 31 (6), pp. 1508-1516",,,"IEEE Industrial Electronics Society (IES);IEEE Systems Council;IEEE Technology and Engineering Management Society (TEMS);The Institute of Electrical and Electronics Engineers (IEEE)","Institute of Electrical and Electronics Engineers Inc.","3rd IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020","10 June 2020 through 12 June 2020",,165643,,9781728163895,,,"English","Proc. - IEEE Conf. Ind. Cyberphysical Syst., ICPS",Conference Paper,"Final","",Scopus,2-s2.0-85098725281
"She Y., Song S., Su H.-J., Wang J.","56545216500;57200117296;8379272100;7701333092;","A Comparative Study on the Effect of Mechanical Compliance for a Safe Physical Human-Robot Interaction",2020,"Journal of Mechanical Design, Transactions of the ASME","142","6","4046068","","",,5,"10.1115/1.4046068","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087990633&doi=10.1115%2f1.4046068&partnerID=40&md5=9451f39fcc6966265766a728bdc60601","Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States; Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States","She, Y., Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States; Song, S., Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States; Su, H.-J., Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States; Wang, J., Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States","In this paper, we study the effects of mechanical compliance on safety in physical human-robot interaction (pHRI). More specifically, we compare the effect of joint compliance and link compliance on the impact force assuming a contact occurred between a robot and a human head. We first establish pHRI system models that are composed of robot dynamics, an impact contact model, and head dynamics. These models are validated by Simscape simulation. By comparing impact results with a robotic arm made of a compliant link (CL) and compliant joint (CJ), we conclude that the CL design produces a smaller maximum impact force given the same lateral stiffness as well as other physical and geometric parameters. Furthermore, we compare the variable stiffness joint (VSJ) with the variable stiffness link (VSL) for various actuation parameters and design parameters. While decreasing stiffness of CJs cannot effectively reduce the maximum impact force, CL design is more effective in reducing impact force by varying the link stiffness. We conclude that the CL design potentially outperforms the CJ design in addressing safety in pHRI and can be used as a promising alternative solution to address the safety constraints in pHRI. © 2020 American Society of Mechanical Engineers (ASME). All rights reserved.","compliant mechanisms, design theory and methodology, variable stiffness design, robot design, robotic systems","Man machine systems; Stiffness; Universal joints; Alternative solutions; Comparative studies; Design parameters; Mechanical compliance; Physical human-robot interactions; Physical humanrobot interaction (phri); Safety constraint; Variable stiffness; Human robot interaction",,,,,"National Science Foundation, NSF: 1637656, CMMI-1637656","This material is based upon the work supported by the National Science Foundation under Grant No: CMMI-1637656. 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L., Johnson, K. L., (1987) Contact Mechanics, , Cambridge University Press, Cambridge, UK; López-Martínez, J., García-Vallejo, D., Giménez-Fernández, A., Torres-Moreno, J., A Flexible Multibody Model of a Safety Robot Arm for Experimental Validation and Analysis of Design Parameters (2014) ASME J. Comput. Nonlinear Dyn, 9 (1), p. 011003; Book, W. J., Recursive Lagrangian Dynamics of Flexible Manipulator Arms (1984) Int. J. Rob. Res, 3 (3), pp. 87-101; Wensing, P. M., Wang, A., Seok, S., Otten, D., Lang, J., Kim, S., Proprioceptive Actuator Design in the MIT Cheetah: Impact Mitigation and High-Bandwidth Physical Interaction for Dynamic Legged Robots (2017) IEEE Trans. Rob, 33 (3), pp. 509-522","She, Y.; Department of Mechanical and Aerospace Engineering, United States; email: yushe@mit.edu",,,"American Society of Mechanical Engineers (ASME)",,,,,10500472,,JMDEE,,"English","J Mech Des, Trans ASME",Article,"Final","",Scopus,2-s2.0-85087990633
"Goenaga S., Navarro L., Quintero C.G.M., Pardo M.","57193234774;57208123403;57218951642;56871310900;","Imitating human emotions with a nao robot as interviewer playing the role of vocational tutor",2020,"Electronics (Switzerland)","9","6","971","1","25",,1,"10.3390/electronics9060971","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086441913&doi=10.3390%2felectronics9060971&partnerID=40&md5=95e8d35dcb0d69ac8661e8babbfd44da","Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla, 081007, Colombia","Goenaga, S., Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla, 081007, Colombia; Navarro, L., Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla, 081007, Colombia; Quintero, C.G.M., Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla, 081007, Colombia; Pardo, M., Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla, 081007, Colombia","This paper proposes an intelligent system that can hold an interview, using a NAO robot as interviewer playing the role of vocational tutor. For that, twenty behaviors within five personality profiles are classified and categorized into NAO. Five basic emotions are considered: anger, boredom, interest, surprise, and joy. Selected behaviors are grouped according to these five different emotions. Common behaviors (e.g., movements or body postures) used by the robot during vocational guidance sessions are based on a theory of personality traits called the “Five-Factor Model”. In this context, a predefined set of questions is asked by the robot—according to a theoretical model called the “Orientation Model”—about the person’s vocational preferences. Therefore, NAO could react as conveniently as possible during the interview, according to the score of the answer given by the person to the question posed and its personality type. Additionally, based on the answers to these questions, a vocational profile is established, and the robot could provide a recommendation about the person’s vocation. The results show how the intelligent selection of behaviors can be successfully achieved through the proposed approach, making the Human–Robot Interaction friendlier. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Computational intelligence; Human; Imitating emotions; NAO robot; Robot interaction",,,,,,"Universidad del Norte","Acknowledgments: This work was supported by Universidad del Norte, Barranquilla—Colombia.",,,,,,,,,,"Hayashi, K., Sakamoto, D., Humanoid robots as a passive-social medium-a field experiment at a train station (2007) Proceedings of the 2Nd ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 137-144. , Arlington, VA, USA, 9–11 March; Złotowski, J., Weiss, A., Tscheligi, M., Interaction scenarios for HRI in public space (2011) International Conference on Social Robotics, pp. 1-10. , Springer: Berlin/Heidelberg, Germany; Finke, A., Knoblauch, A., Koesling, H., Ritter, H., A hybrid brain interface for a humanoid robot assistant (2011) Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 7421-7424. , Boston, MA, USA, 30 August–3 September; Shamsuddin, S., Yussof, H., Ismail, L., Hanapiah, F.A., Mohamed, S., Piah, H.A., Zahari, N.I., Initial response of autistic children in human-robot interaction therapy with humanoid robot NAO (2012) Proceedings of the IEEE 8Th International Colloquium on Signal Processing and Its Applications, pp. 188-193. , Melaka, Malaysa, 23–25 March; Robinson, W., Rescue-robot show-down (2014) IEEE Spectr, 51, pp. 52-55; Gouaillier, D., Hugel, V., Blazevic, P., Kilner, C., Monceaux, J., Lafourcade, P., Marnier, B., Maisonnier, B., Mechatronic design of NAO humanoid (2009) Proceedings of the IEEE International Conference on Robotics and Automation, pp. 769-774. , Kobe, Japan, 12–17 May; Quintero, M., Goenaga, S., Vásquez, J., Intelligent management of hierarchical behaviors using a nao robot as a vocational tutor (2016) In Proceedings of the IEEE Colombian Conference on Robotics and Automation (CCRA), pp. 1-6. , Bogotá, Colombia, 29–30 September; Pot, E., Monceaux, J., Gelin, R., Maisonnier, B., Robotics, A., Choregraphe: A graphical tool for humanoid robot programming (2009) Proceedings of the 18Th IEEE International Symposium on Robot and Human Interactive Communication, pp. 46-51. , Toyama, Japan, 27 September–2 October; Zhao, H., Seibert, S.E., The big five personality dimensions and entrepreneurial status: A meta-analytical review (2006) J. Appl. Psychol., 91, pp. 259-271; Gottfredson, G.D., John, L., Holland (1919–2008) (2009) Am. Psychol, 64, p. 561; Holland, J.L., Personality types (1997) Making Vocational Choices, , 3th ed.; Psychological Assessment Resources: Odessa, FL, USA; Larsen, R.J., Traits and trait taxonomies (2005) Personality Psychology: Domains of Knowledge about Human Nature, pp. 62-93. , McGraw-Hil: New York, NY, USA; Ashton, M.C., Lee, K., Goldberg, L.R., A hierarchical analysis of 1710 English personality-descriptive adjectives (2004) J. Personal. Soc. Psychol., 87, pp. 707-721; Saucier, G., Goldberg, L.R., Evidence for the Big Five in analyses of familiar English personality adjectives (1996) Eur. J. Personal., 10, pp. 61-77; Nauta, M.M., The development, evolution, and status of Holland’s theory of vocational personalities: Reflections and future directions for counseling psychology (2010) J. Couns. Psychol., 57, pp. 11-22; Einarsdóttir, S., Rounds, J., Gerstein, L.H., The structure of vocational interests in Iceland: Examining Holland’s and Gati’s RIASEC models (2002) Eur. J. Psychol. Assess., 18, pp. 85-95; Armstrong, P.I., Day, S.X., McVay, J.P., Rounds, J., Holland’s RIASEC model as an integrative framework for individual differences (2008) J. Couns. Psychol., 55, pp. 1-18; Darcy, M.U., Tracey, T.J.G., Circumplex structure of Holland’s RIASEC Interests across gender and time (2007) J. Couns. Psychol., 54, pp. 17-31; Le, Q.A., Hanoune, S., Pelachaud, C., Design and implementation of an expressive gesture model for a humanoid robot (2011) Proceedings of the 11Th IEEE-RAS International Conference on Humanoid Robot, pp. 134-140. , Bled, Slovenia, 26–28 October; Lim, A., Ogata, T., Okuno, H.G., Converting emotional voice to motion for robot telepresence (2011) Proceedings of the 11Th IEEE-RAS International Conference on Humanoid Robots, pp. 472-479. , Bled, Slovenia, 26–28 October 2011; Cohen, I., Looije, R., Neerincx, M., Child’s recognition of emotions in robot’s face and body (2011) Proceedings of the 6Th International Conference on Human-Robot Interaction, pp. 123-124. , Lausanne, Switzerland, 6–9 March; Shen, J., Rudovic, O., Cheng, S., Pantic, M., Sentiment apprehension in human-robot interaction with NAO In Proceedings of the International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 867-872. , Xian, China, 21–24 September 2015; Miskam, M.A., Shamsuddin, S., Yussof, H., Ariffin, I.M., Omar, A.R., A questionnaire-based survey: Therapist’s response on emotions gestures using humanoid robot for autism In Proceedings of the International Symposium on Micro-Nano Mechatronics and Human Science (MHS), pp. 1-7. , Nagoya, Japan, 23–25 November 2015; Keizer, S., Foster, M.E., Lemon, O., Gaschler, A., Giuliani, M., Training and evaluation of an MDP model for social multi-user human-robot interaction (2013) Proceedings of the 14Th Annual Sigdial Meeting on Discourse and Dialogue, pp. 22-24. , Metz, France, August; Klotz, D., (2010) Modeling Engagement in a Multi-Party Human-Robot Dialog, , Master Thesis, Bielefeld University, Faculty of Technology, Bielefeld, Germany; Meena, R., Jokinen, K., Wilcock, G., Integration of gestures and speech in human-robot interaction (2012) In Proceedings of the IEEE 3Rd International Conference on Cognitive Infocommunications (Coginfocom), Kosice, Slovakia, 2–5 December, pp. 673-678; Tapus, A., Tapus, C., Mataric, M., User-Robot Personality Matching and Robot Behavior Adaptation for Post-Stroke Rehabilitation Therapy Maja Mataric. User-Robot Personality Matching and Robot Behavior Adaptation for Post-Stroke Rehabilitation Therapy (2008) Intell. Serv. Robot., 1, pp. 169-183; Lee, K.M., Peng, W., Jin, S., Yan, C., Can Robots Manifest Personality?: An Empirical Test of Personality Recognition, Social Responses, and Social Presence in Human–Robot Interaction (2006) J. Commun., 56, pp. 754-772; Andrist, S., Mutlu, B., Tapus, A., Look like me: Matching robot personality via gaze to increase motivation (2015) Proceedings of the 33Rd Annual ACM Conference on Human Factors in Computing Systems, pp. 3603-3612. , Seoul, Korea, 18–23 April","Quintero, C.G.M.; Department of Electrical and Electronics Engineering, Colombia; email: christianq@uninorte.edu.co",,,"MDPI AG",,,,,20799292,,,,"English","Electronics (Switzerland)",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85086441913
"Baser O., Kizilhan H., Kilic E.","25521416500;56896023300;36598410100;","Employing variable impedance (stiffness/damping) hybrid actuators on lower limb exoskeleton robots for stable and safe walking trajectory tracking",2020,"Journal of Mechanical Science and Technology","34","6",,"2597","2607",,,"10.1007/s12206-020-0534-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085549275&doi=10.1007%2fs12206-020-0534-4&partnerID=40&md5=cf5c13f4a62679fd9de0cd19fe3105b0","Department of Mechanical Engineering, Suleyman Demirel University, Isparta, Turkey","Baser, O., Department of Mechanical Engineering, Suleyman Demirel University, Isparta, Turkey; Kizilhan, H., Department of Mechanical Engineering, Suleyman Demirel University, Isparta, Turkey; Kilic, E., Department of Mechanical Engineering, Suleyman Demirel University, Isparta, Turkey","Compliant actuators are employed in exoskeleton robots instead of stiff actuators for safe human-robot interaction. In parallel with this idea, we previously constructed a biomimetic compliant exoskeleton robot (BioComEx). In this study, to provide more stable and safe trajectory tracking even under disturbances, magneto-rheological (MR) brakes were added to all joints of BioComEx as variable damping actuators and a PID+D controller was proposed. To evaluate the robot and controller, first, BioComEx was hung on a platform and the controller was applied without device user under external forces. This primary test results showed that the proposed design and controller can effectively minimize disturbance effects and consequently reduce trajectory tracking oscillations. In the rest of the study, the similar control experiments were repeated with a user who has unilateral lower limb movement disorders. In these experiments, the movements of the user’s healthy leg were detected by force feedback impedance control algorithm and then were used as reference for the impaired leg with walking cycle delay in real time. The secondary test results showed that the variable impedance exoskeleton robot design with PID+D controller can ensure effective walking assistance for the impaired human legs. © 2020, The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature.","Hybrid actuator; Lower limb exoskeleton; Magneto-rheological brake; Variable damping actuator; Variable stiffness actuator; Walking trajectory tracking","Actuators; Biomimetics; Controllers; Exoskeleton (Robotics); Machine design; Trajectories; Walking aids; Compliant actuator; Control experiments; Disturbance effects; Exoskeleton robots; Magnetorheological brakes; Safe human-robot interaction; Trajectory tracking; Walking assistance; Human robot interaction",,,,,"Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK: 213M297","The authors thank the Scientific and Technological Research Council of Turkey for the financial support of a research project titled as “Design and Control of a Biomimetic Exoskeleton Robot” and numbered 213M297.",,,,,,,,,,"Lim, D., Kim, W., Lee, H., Kim, H., Shin, K., Park, T., Lee, J., Han, C., Development of a lower extremity exoskeleton robot with a quasi-anthropomorphic design approach for load car riage (2015) IEEE/RSJ Int. 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Shamaei, K., Sawicki, G.S., Dollar, A.M., Estimation of quasi-stiffness and propulsive work of the human ankle in the stance phase of walking (2013) PLoS ONE, 8 (3); Shamaei, K., Sawicki, G.S., Dollar, A.M., Estimation of quasi-stiffness of the human knee in the stance phase of walking (2013) PloS ONE, 8 (3); Shamaei, K., Sawicki, G.S., Dollar, A.M., Estimation of quasi-stiffness of the human hip in the stance phase of walking (2013) PloS ONE, 8 (12); Baser, O., Kizilhan, H., Mechanical design and preliminary tests of VS-AnkleExo (2018) J. of the Brazilian Society of Mechanical Sciences and Engineering, 40 (9), p. 442; Baser, O., Kizilhan, H., Kilic, E., Mechanical design of a biomimetic compliant lower limb exoskeleton (BioComEx) (2016) Autonomous Robot Systems and Competitions, pp. 60-65; Zhang, C., Liu, G., Li, C., Zhao, J., Yu, H., Zhu, Y., Development of a lower limb rehabilitation exoskeleton based on realtime gait detection and gait tracking (2016) Advances in Mechanical Engineering, 8 (1), pp. 1-9; Homaeinezhad, M.R., Yaqubi, S., Discrete-time sliding-surface based control of parametrically uncertain nonlinear systems with unknown time-delay and inaccessible switching mode detection (2019) International J. of Control, pp. 1-20; Bishop, R.H., (2007) Mechatronic System Control, Logic, and Data Acquisition, , 2nd Ed","Baser, O.; Department of Mechanical Engineering, Turkey; email: ozgurbaser@sdu.edu.tr",,,"Korean Society of Mechanical Engineers",,,,,1738494X,,,,"English","J. Mech. Sci. Technol.",Article,"Final","",Scopus,2-s2.0-85085549275
"Jiménez M.F., Mello R.C., Bastos T., Frizera A.","57201595264;57204660804;6602380721;57195401878;","Assistive locomotion device with haptic feedback for guiding visually impaired people",2020,"Medical Engineering and Physics","80",,,"18","25",,4,"10.1016/j.medengphy.2020.04.002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085048632&doi=10.1016%2fj.medengphy.2020.04.002&partnerID=40&md5=8f9a05c9358473f26d41a07f1fd062f9","Department of Bioengineering, Faculty of Engineering, Universidad El Bosque, Bogotá D.C., Colombia; Postgraduate Program in Electrical Engineering, Federal University of Espıirito Santo, 29075-910, Vitoria, Brazil","Jiménez, M.F., Department of Bioengineering, Faculty of Engineering, Universidad El Bosque, Bogotá D.C., Colombia; Mello, R.C., Postgraduate Program in Electrical Engineering, Federal University of Espıirito Santo, 29075-910, Vitoria, Brazil; Bastos, T., Postgraduate Program in Electrical Engineering, Federal University of Espıirito Santo, 29075-910, Vitoria, Brazil; Frizera, A., Postgraduate Program in Electrical Engineering, Federal University of Espıirito Santo, 29075-910, Vitoria, Brazil","Robotic assistive devices are able to enhance physical stability and balance. Smart walkers, in particular, are also capable of offering cognitive support for individuals whom conventional walkers are unsuitable. However, visually impaired individuals often need additional sensorial assistance from those devices. This work proposes a smart walker with an admittance controller for guiding visually impaired individuals along a desired path. The controller uses as inputs the physical interaction between the user and the walker to provide haptic feedback hinting the path to be followed. Such controller is validated in a set of experiments with healthy individuals. At first, users were blindfolded during navigation to assess the capacity of the smart walker in providing guidance without visual input. Then, the blindfold is removed and the focus is on evaluating the human-robot interaction when the user had visual information during navigation. The results indicate that the admittance controller design and the design of the guidance path were factors impacting on the level of comfort reported by users. In addition, when the user was blindfolded, the linear velocity assumed lower values than when did not wear it, from a mean value of 0.19 m/s to 0.21 m/s. © 2020 IPEM","Admittance control; Haptic feedback; Smart walker; Visual impairment","Controllers; Feedback; Human robot interaction; Walking aids; Assistive devices; Controller designs; Healthy individuals; Physical interactions; Physical stability; Visual information; Visually impaired; Visually impaired people; Visual servoing; Article; comfort; equipment design; gait disorder; human; man machine interaction; priority journal; robotics; simulation; tactile feedback; validation study; velocity; visual impairment; visual information; feedback system; locomotion; self help device; walker; walking; Feedback; Humans; Locomotion; Self-Help Devices; Walkers; Walking",,,"Smart Walker",,"64797816.7.0000.5542; Horizon 2020 Framework Programme, H2020: 688941; European Commission, EC; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES: 88887.095626 / 2015-01; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq: 304192 / 2016-3; Fundação de Amparo à Pesquisa e Inovação do Espírito Santo, FAPES: 80709036; Ministério da Ciência, Tecnologia, Inovações e Comunicações, MCTIC","This research is supported by CAPES (grant number 88887.095626 / 2015-01 ), FAPES (grant number 80709036 ) and CNPq (grant number 304192 / 2016-3 ). The research leading to these results also received funding from the European Commission H2020 program under grant agreement no. 688941 (FUTEBOL), as well from the Brazilian Ministry of Science, Technology, Innovation, and Communication (MCTIC) through RNP and CTIC.","Federal University of Esp?rito Santo, UFES/Brazil. Protocol number - CAAE: 64797816.7.0000.5542, This research is supported by CAPES (grant number 88887.095626 / 2015-01), FAPES (grant number 80709036) and CNPq (grant number 304192 / 2016-3). The research leading to these results also received funding from the European Commission H2020 program under grant agreement no. 688941 (FUTEBOL), as well from the Brazilian Ministry of Science, Technology, Innovation, and Communication (MCTIC) through RNP and CTIC.",,,,,,,,,"World Health Organization, Universal eye health. A global action plan 2014–2019 (2014) Tech. Rep.; United Nations, Department of Economic and Social Affiars Population Division, World Population Ageing 2017 - Highlights (2017), http://www.un.org/en/development/desa/population/publications/pdf/ageing/WPA2017_Highlights.pdf; Pirker, W., Katzenschlager, R., Gait disorders in adults and the elderly: a clinical guide (2017) Wien Klin Wochenschr, 129 (3-4), pp. 81-95; Alexander, N.B., Goldberg, A., Gait disorders : search for multiple causes (2005) Cleve Clin J Med, 72 (7), pp. 586-600; Crews, J.E., A.Campbell, V., Vision impairment and hearing loss among community dwelling older americans: implications for health and functioning (2004) Am J Public Health, 94 (5), pp. 823-829; Deshen, S., Deshen, H., On social aspects of the usage of guidedogs and longcanes1 (1989) Sociol Rev, 37 (1), pp. 89-103; Wachaja, A., Agarwal, P., Zink, M., Adame, M.R., Möller, K., Burgard, W., Navigating blind people with walking impairments using a smart walker (2016) Auton Robots, 2015–Decem, pp. 1-19; Bradley, S.M., Hernandez, C.R., Geriatric assistive devices (2011) Am Fam Physician, 84 (4), pp. 405-411; Martins, M.M., Santos, C.P., Frizera-Neto, A., Ceres, R., Assistive mobility devices focusing on smart walkers: classification and review (2011) Rob Auton Syst, 60 (4), pp. 548-562; Jiménez, M.F., Monllor, M., Frizera, A., Bastos, T., Roberti, F., Carelli, R., Admittance controller with spatial modulation for assisted locomotion using a smart walker (2018) J Intell Robot Syst, pp. 1-17; Spenko, M., Yu, H., Dubowsky, S., Robotic personal aids for mobility and monitoring for the elderly (2006) IEEE Trans Neural SystRehabil Eng, 14 (3), pp. 344-351; Bousbia-Salah, M., Bettayeb, M., Larbi, A., A navigation aid for blind people (2011) J Intell Robot Syst, 64 (3-4), pp. 387-400; Geravand, M., Werner, C., Hauer, K., Peer, A., An integrated decision making approach for adaptive shared control of mobility assistance robots (2016) Int J Soc Robot, 8 (5), pp. 631-648; Andò, B., Graziani, S., Multisensor strategies to assist blind people: aclear-path indicator (2009) IEEE Trans Instrum Meas, 58 (8), pp. 2488-2494; Pons (CSIC), J.L., Wearable robots: biomechatronic exoskeletons (2008), John Wiley & Sons Ltd Madrid; Werner, C., Ullrich, P., Geravand, M., Peer, A., Bauer, J.M., Hauer, K., A systematic review of study results reported for the evaluation of robotic rollators from the perspective of users (2017) Disabil Rehabil, 0 (0), pp. 1-12; Lin, Q., Han, Y., A context-aware-based audio guidance system for blind people using a multimodal profile model (2014) Sensors (Switzerland), 14 (10), pp. 18670-18700; Motta, G., Mobility of visually impaired people: fundamentals and ICT assistive technologies (2018), Springer Cham; Lacey, G., Context-Aware shared control of a robot mobility aid for the elderly blind (2000) Int J Rob Res, 19 (11), pp. 1054-1065; Lacey, G.J., Rodriguez-losada, D., The evolution of guido (2008) IEEE Robot Autom Mag, 15 (December), pp. 75-83; Lu, C.K., Huang, Y.C., Lee, C.J., Adaptive guidance system design for the assistive robotic walker (2015) Neurocomputing, 170, pp. 152-160; Cifuentes, C.A., Frizera, A., Human-Robot interaction strategies for locomotion (2016), Springer International Publishing Switzerland; Andaluz, V.H., Roberti, F., Toibero, J.M., Carelli, R., Wagner, B., Adaptive dynamic path following control of an unicycle like mobile robot (2011) Intelligent robotics and applications; chap. 56, pp. 563-574. , Springer Berlin Heidelberg; Peel, N.M., Kuys, S.S., Klein, K., Gait speed as a measure in geriatric assessment in clinical settings: a systematic review (2012) J Gerontol, 68 (1), pp. 39-46; Clifford, R., A Comparison of the Power of Wilcoxon ’ s Rank-Sum Statistic to That of Student ’ s t Statistic under Various Nonnormal Distributions Author (s): R. Clifford Blair and James J. Higgins Published by : American Educational Research Association and Ameri (1980) Journal of Educational Statistics, 5 (4), pp. 309-335. , http://www.jstor.org/stable/1164905; de Winter, J.C., Dodou, D., Five-point likert items: t test versus Mann-Whitney-Wilcoxon (2010) Pract Assess Res Eval, 15 (11)","Jiménez, M.F.; Department of Bioengineering, Colombia; email: mjimenezh@unbosque.edu.co",,,"Elsevier Ltd",,,,,13504533,,MEPHE,"32446757","English","Med. Eng. Phys.",Article,"Final","",Scopus,2-s2.0-85085048632
"Tosun F.E., Tosun F.E., Patoglu V.","57217782548;57217782548;19640643500;","Necessary and Sufficient Conditions for the Passivity of Impedance Rendering with Velocity-Sourced Series Elastic Actuation",2020,"IEEE Transactions on Robotics","36","3","8963769","757","772",,8,"10.1109/TRO.2019.2962332","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082099137&doi=10.1109%2fTRO.2019.2962332&partnerID=40&md5=073ec14250827450125aa26507fbf680","Faculty of Engineering and Natural Sciences, Sabancı University, İstanbul, 34956, Turkey; Uppsala University, Uppsala, 75236, Sweden","Tosun, F.E., Faculty of Engineering and Natural Sciences, Sabancı University, İstanbul, 34956, Turkey, Uppsala University, Uppsala, 75236, Sweden; Tosun, F.E., Faculty of Engineering and Natural Sciences, Sabancı University, İstanbul, 34956, Turkey, Uppsala University, Uppsala, 75236, Sweden; Patoglu, V., Faculty of Engineering and Natural Sciences, Sabancı University, İstanbul, 34956, Turkey","Series elastic actuation (SEA) has become prevalent in applications involving physical human-robot interaction, as it provides considerable advantages over traditional stiff actuators in terms of stability robustness and force control fidelity. Several impedance control architectures have been proposed for SEA. Among these alternatives, the cascaded controller with an innermost velocity loop, an intermediate torque loop, and an outermost impedance loop is particularly favored for its simplicity, robustness, and performance. In this article, we derive the necessary and sufficient conditions for passively rendering null impedance and virtual springs with this cascade-controller architecture. Based on the newly established conditions, we provide nonconservative passivity design guidelines to haptically display these two impedance models, which serve as the basic building blocks of various virtual environments, while ensuring the safety of interaction. We also demonstrate the importance of including physical damping in the actuator model for deriving the passivity conditions, when integrators are utilized. In particular, we prove the unintuitive adversary effect of physical damping on the passivity of the system by noting that the damping term reduces the system Z-width, as well as introducing an extra passivity constraint. Finally, we experimentally validate our theoretical results using an SEA brake pedal. © 2004-2012 IEEE.","Compliance and impedance control; coupled stability; haptics and haptic interfaces; physical human-robot interaction (pHRI); series elastic actuation (SEA)","Actuators; Agricultural robots; Controllers; Damping; Robustness (control systems); System stability; Basic building block; Cascade controller; Impedance control; Impedance models; Nonconservative; Physical human-robot interactions; Series elastic actuations; Stability robustness; Human robot interaction",,,,,,,,,,,,,,,,"Colgate, J.E., (1988) The Control of Dynamically Interacting Systems, , Ph.D. dissertation Dept. Mech. Eng., Massachusetts Inst. 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Conf., pp. 833-840; Vallery, H., Ekkelenkamp, R., Kooij Der, H., Buss, M., Passive and accurate torque control of series elastic actuators (2007) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 3534-3538; Vallery, H., Veneman, J., Asseldonk, E.V., Compliant actuation of rehabilitation robots (2008) IEEE Robot. Autom. Mag., 15 (3), pp. 60-69. , Sep; Tagliamonte, N.L., Accoto, D., Passivity constraints for the impedance control of series elastic actuators (2014) J. Syst. Control Eng., 228 (3), pp. 138-153; Calanca, A., Muradore, R., Fiorini, P., Impedance control of series elastic actuators: Passivity and acceleration-based control (2017) Mechatronics, 47, pp. 37-48; Ogata, K., (2009) Modern Control Engineering, , 5th ed. London U.K.: Pearson; Haykin, S., (1970) Active Network Theory, , Reading MA USA: Addison-Wesley; Losey, D.P., O'Malley, M.K., Effects of discretization on the k-width of series elastic actuators (2017) Proc. IEEE Int. Conf. Robot. Autom, pp. 421-426. , May; Treadway, E., Yang, Y., Gillespie, R.B., Decomposing the performance of admittance and series elastic haptic rendering architectures (2017) Proc. IEEE World Haptics Conf, pp. 346-351. , Jun; Dohring, M., Newman, W., The passivity of natural admittance control implementations (2003) Proc. IEEE Int. Conf. Robot. Autom., 3, pp. 3710-3715; Newman, W., Stability and performance limits of interaction controllers (1992) ASME Trans. J. Dyn. Syst. Meas. Control, 114, pp. 563-570. , Dec; Willaert, B., Franken, M., Brussel, H.V., Poorten, E.B.V., On the use of shunt impedances versus bounded environment passivity for teleoperation systems (2011) Proc. IEEE Int. Conf. Robot. Autom., pp. 2111-2117; Caliskan, U., Patoglu, V., (2019) Efficacy of Haptic Pedal Feel Compensation on Driving with Regenerative Braking, , https://arxiv.org/abs/1910.02440, [online]; Weir, D.W., Colgate, J.E., Stability of haptic displays (2008) Haptic Rendering: Foundations, Algorithms and Applications, pp. 123-156. , Boca Raton, FL, USA: CRC Press; Hannaford, B., Ryu, J.-H., Time-domain passivity control of haptic interfaces (2002) IEEE Trans. Robot. Autom., 18 (1), pp. 1-10. , Feb; Ryu, J.-H., Kwon, D.-S., Hannaford, B., Stability guaranteed control: Time domain passivity approach (2004) IEEE Trans. Control Syst. Technol., 12 (6), pp. 860-868. , Nov; Buerger, S.P., Hogan, N., Complementary stability and loop shaping for improved human-robot interaction (2007) IEEE Trans. Robot., 23 (2), pp. 232-244. , Apr; Haddadi, A., Hashtrudi-Zaad, K., Bounded-impedance absolute stability of bilateral teleoperation control systems (2010) IEEE Trans. Haptics, 3 (1), pp. 15-27. , Jan.-Mar; Tokatli, O., Patoglu, V., Stabilityof haptic systems with fractional order controllers (2015) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst, pp. 1172-1177. , Sep; Aydin, Y., Tokatli, O., Patoglu, V., Basdogan, C., Stable physical human-robot interaction using fractional order admittance control (2018) IEEE Trans. Haptics, 11 (3), pp. 464-475. , Jul.-Sep","Tosun, F.E.; Faculty of Engineering and Natural Sciences, Turkey; email: femretosun@sabanciuniv.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15523098,,,,"English","IEEE Trans. Rob.",Article,"Final","",Scopus,2-s2.0-85082099137
"Zeng X., Hurd C., Su H.-J., Song S., Wang J.","56763155100;57117395300;8379272100;57200117296;7701333092;","A parallel-guided compliant mechanism with variable stiffness based on layer jamming",2020,"Mechanism and Machine Theory","148",,"103791","","",,8,"10.1016/j.mechmachtheory.2020.103791","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078813233&doi=10.1016%2fj.mechmachtheory.2020.103791&partnerID=40&md5=78914a89c86308973199b2273b880533","Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States; Mechanical Engineering, University of Texas at AustinTX  78712, United States","Zeng, X., Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States; Hurd, C., Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States; Su, H.-J., Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH  43210, United States; Song, S., Mechanical Engineering, University of Texas at AustinTX  78712, United States; Wang, J., Mechanical Engineering, University of Texas at AustinTX  78712, United States","This article introduces a parallel-guided compliant mechanism, which can achieve a high stiffness ratio (maximum over minimum) of 75 times through pneumatic actuated layer jamming. The compliant mechanism is composed of two flexible beams sandwiched with thin plastic friction layers. With a novel beam cross-section, the beams have large thickness, but still retain high flexibility. The effect of layer jamming is augmented by this large thickness due to the increased leverage of friction force. The beams in parallel-guided configuration have higher vertical and torsional stability compared to a single beam setup. The functionalities of the compliant mechanism have been validated experimentally: stiffness is measured as a function of the applied vacuum pressure. This paper describes the design concept, FEA validation of the design concept, prototyping, experiment results, analytical model, and analysis of vertical and torsional stability. The proposed concept of the compliant mechanism provides a potential solution for design of variable stiffness robotic links for addressing safety concerns in physical human robot interaction. The analytical model for identifying critical design parameters for maximum stiffness-variation effect provides a guideline for high stiffness-variation design on similar structures. © 2020 Elsevier Ltd","Compliant Mechanisms; Layer Jamming; Parallel-Guided; Variable Stiffness","Analytical models; Compliant mechanisms; Flexible structures; Friction; Human robot interaction; Jamming; Machine design; Mechanisms; Beam cross section; Critical design parameters; High flexibility; Maximum stiffness; Parallel-Guided; Physical human-robot interactions; Torsional stability; Variable stiffness; Stiffness",,,,,"National Science Foundation, NSF: 1637656, CMMI-1637656","This material is based upon work supported by the National Science Foundation under Grant No. CMMI-1637656 . Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.",,,,,,,,,,"Rus, D., Tolley, M.T., Design, fabrication and control of soft robots (2015) Nature, 521 (7553), pp. 467-475; Trivedi, D., Rahn, C.D., Kier, W.M., Walker, I.D., Soft robotics: biological inspiration, state of the art, and future research (2008) Appl. Bionic. Biomech., 5 (3), pp. 99-117; Wood, R., Walsh, C., Smaller, Softer, Safer, Smarter Robots (2013) Sci. Transl. 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Struct., 23 (7), p. 075009; Hauser, S., Robertson, M., Ijspeert, A., Paik, J., JammJoint: a variable stiffness Device Based on Granular Jamming for Wearable Joint Support (2017) IEEE Robot. Automat. Lett., 2 (2), pp. 849-855; Brown, E., Rodenberg, N., Amend, J., Mozeika, A., Steltz, E., Zakin, M.R., Lipson, H., Jaeger, H.M., Universal robotic gripper based on the jamming of granular material (2010) Proc. Natl. Acad. Sci., 107 (44), pp. 18809-18814; Cheng, N.G., Lobovsky, M.B., Keating, S.J., Setapen, A.M., Gero, K.I., Hosoi, A.E., Iagnemma, K.D., Design and analysis of a robust, low-cost, highly articulated manipulator enabled by jamming of granular media (2012) 2012 IEEE International Conference on Robotics and Automation, pp. 4328-4333. , IEEE St Paul, MN, USA; Jiang, A., Xynogalas, G., Dasgupta, P., Althoefer, K., Nanayakkara, T., Design of a variable stiffness flexible manipulator with composite granular jamming and membrane coupling (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2922-2927. , IEEE Vilamoura-Algarve, Portugal; Ranzani, T., Cianchetti, M., Gerboni, G., Falco, I.D., Petroni, G., Menciassi, A., A modular soft manipulator with variable stiffness4; Ranzani, T., Cianchetti, M., Gerboni, G., Falco, I.D., Menciassi, A., A soft modular manipulator for minimally invasive surgery: design and characterization of a single Module (2016) IEEE Trans. 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Kubota X. Zhu R. Dillmann D. Zhou Springer International Publishing Cham; Tognarelli, S., Brancadoro, M., Dolosor, V., Menciassi, A., Soft tool for gallbladder retraction in minimally invasive surgery based on layer jamming (2018) 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), pp. 67-72. , IEEE Enschede; Amanov, E., Nguyen, T.-D., Markmann, S., Imkamp, F., Burgner-Kahrs, J., Toward a flexible variable stiffness endoport for single-site partial nephrectomy (2018) Annal. Biomed. Eng., 46 (10), pp. 1498-1510; Narang, Y.S., Vlassak, J.J., Howe, R.D., Mechanically versatile soft machines through laminar jamming (2018) Adv. Funct. Mater., 28 (17), p. 1707136; Narang, Y.S., Degirmenci, A., Vlassak, J.J., Howe, R.D., Transforming the dynamic response of robotic structures and systems through laminar jamming (2018) IEEE Robot. Automat. Lett., 3 (2), pp. 688-695; Roark, R.J., Young, W.C., Budynas, R.G., Roark's formulas for stress and strain (2002), 7th ed McGraw-Hill New York","Su, H.-J.; Mechanical and Aerospace Engineering, United States; email: su.298@osu.edu",,,"Elsevier Ltd",,,,,0094114X,,MHMTA,,"English","Mech Mach Theory",Article,"Final","",Scopus,2-s2.0-85078813233
"Hwang E.J., Kyu Ahn B., MacDonald B.A., Seok Ahn H.","57192197750;57219439544;7101734705;57219436979;","Demonstration of Hospital Receptionist Robot with Extended Hybrid Code Network to Select Responses and Gestures",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9197160","8013","8018",,4,"10.1109/ICRA40945.2020.9197160","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092739745&doi=10.1109%2fICRA40945.2020.9197160&partnerID=40&md5=9a7a7c3d0aa89d77d228b40db85fc817","University of Auckland, Department of Electrical Computer, and Software Engineering, Auckland, 1142, New Zealand","Hwang, E.J., University of Auckland, Department of Electrical Computer, and Software Engineering, Auckland, 1142, New Zealand; Kyu Ahn, B., University of Auckland, Department of Electrical Computer, and Software Engineering, Auckland, 1142, New Zealand; MacDonald, B.A., University of Auckland, Department of Electrical Computer, and Software Engineering, Auckland, 1142, New Zealand; Seok Ahn, H., University of Auckland, Department of Electrical Computer, and Software Engineering, Auckland, 1142, New Zealand","Task-oriented dialogue system has a vital role in Human-Robot Interaction (HRI). However, it has been developed based on conventional pipeline approach which has several drawbacks; expensive, time-consuming, and so on. Based on this approach, developers manually define a robot's behaviour such as gestures and facial expressions on the corresponding dialogue states. Recently, end-to-end learning of Recurrent Neural Networks (RNNs) is an attractive solution for the dialogue system. In this paper, we proposed a social robot system using end-to-end dialogue system in the context of hospital receptionist. We utilized Hybrid Code Network (HCN) as an end-to-end dialogue system and extended to select both response and gesture using RNN based gesture selector. We evaluate its performance with human users and compare the results with one of the conventional methods. Empirical result shows that the proposed method has benefits in terms of dialogue efficiency, which indicates how efficient users were in performing the given tasks with the help of the robot. Moreover, we achieved the same performance regarding the robot's gesture with the proposed method compared to manually defined gestures. © 2020 IEEE.",,"Agricultural robots; Hospitals; Network coding; Recurrent neural networks; Robotics; Speech processing; Attractive solutions; Conventional methods; Dialogue systems; Facial Expressions; Human robot Interaction (HRI); Receptionist robots; Recurrent neural network (RNNs); Task-oriented; Social robots",,,,,"Ministry of Trade, Industry and Energy, MOTIE","ACKNOWLEDGMENT This work was supported by the Technology Innovation Program (10077553, Development of Social Robot Intelligence for Social Human-Robot Interaction of Service Robots) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea).",,,,,,,,,,"(2018) Developing Service for Ai Era, , https://www.slideshare.net/ifkakao/ai-113145560, KaKao, LinkedIn SlideShare, [Online]., [Accessed: 11-Sep-2018]; Yan, Z., Docchat: An information retrieval approach for chatbot engines using unstructured documents (2016) Proceedings Ofthe 54th Annual Meeting Ofthe Association for Computational Linguistics, pp. 516-525; Banchs, R.E., Li, H., Iris: A chat-oriented dialogue system based on the vector space model (2012) Proceedings Ofthe 50th Annual Meeting Ofthe Association for Computational Linguistics, pp. 37-42; Vinyals, O., Le, Q., A neural conversational model (2015) CoRR, , vol. abs/1506.0; Ram, A., (2018) Conversational AI: The Science behind the Alexa Prize; McTear, D., Michael, Callejas, Zoraida, Griol, (2016) The Conversational Interface: Talking to Smart Devices., , Springer; Faber, F., The humanoid museum tour guide robotinho (2009) Robot and Human Interactive Communication, 2009. RO-MAN 2009. The 18th Ieee International Symposium on, pp. 891-896; Fong, T., Thorpe, C., Baur, C., Collaboration, dialogue, and human-robot interaction (2001) Proceedings of the 10th International Symposium of Robotics Research, , Lorne, Victoria, Australia; Spiliotopoulos, D., Spyropoulos, C.D., Human-robot interaction based on spoken natural language dialogue (2001) Proceedings of the European Workshop on Service and Humanoid Robots, pp. 25-27; Lucignano, L., Cutugno, F., Rossi, S., Finzi, A., A dialogue system for multimodal human-robot interaction (2013) Proceedings of the 15th Acm on International Conference on Multimodal Interaction, pp. 197-204; Stiefelhagen, R., Enabling multimodal human-robot interaction for the karlsruhe humanoid robot (2007) Ieee Trans. Robot., 23 (5), pp. 840-851; Papaioannou, I., Dondrup, C., Novikova, J., Lemon, O., Hybrid chat and task dialogue for more engaging hri using reinforcement learning (2017) RO-MAN 2017-26th Ieee International Symposium on Robot and Human Interactive Communication, pp. 593-598; Williams, J.D., Asadi, K., Zweig, G., Hybrid code networks: Practical and efficient end-To-end dialog control with supervised and reinforcement learning (2017) Proceedings Ofthe 55th Annual Meeting Ofthe Association for Computational Linguistics, pp. 665-677; Eric, M., Manning, C.D., A copy-Augmented sequence-To-sequence architecture gives good performance on task-oriented dialogue (2017) The Association for Computational Linguistics, 2, pp. 468-473; Shang, L., Lu, Z., Li, H., Neural responding machine for short-Text conversation (2015) Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 1577-1586; Bordes, A., Boureau, Y.-L., Weston, J., Learning end-To-end goal-oriented dialog (2017) Iclr 2017; Sukhbaatar, S., Szlam, A., Weston, J., Fergus, R., End-To-end memory networks (2015) Advances in Neural Information Processing Systems., 2015; Liu, F., Perez, J., Gated end-To-end memory networks (2017) Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, 1, pp. 1-10; Seo, M., Min, S., Farhadi, A., Hajishirzi, H., Query-reduction networks for question answering (2017) International Conference on Learning Representations; Zhou, P., Attention-based bidirectional long short-Term memory networks for relation classification (2016) Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 207-212; Karpov, N., Demidovskij, A., Malafeev, A., Development of a model to predict intention using deep learning (2017) Proceedings of the 6th International Conference ""analysis of Images, Social Networks and Texts""(AIST 2017), pp. 27-29; Purohit, H., Dong, G., Shalin, V., Thirunarayan, K., Sheth, A., Intent classification of short-Text on social media (2015) Proc. -2015 Ieee Int. Conf. Smart City, SmartCity 2015 Held Jointly with 8th Ieee Int. Conf. Soc. Comput. Networking, Soc. 2015, 5th Ieee Int. Conf. Sustain. Comput. Communic, pp. 222-228; Jang, M., Kim, J., Ahn, B.K., A software framework design for social human-robot interaction (2015) 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence, Urai 2015, pp. 411-412; Hugel, V., Mechatronic design of nao humanoid (2009) Ieee International Conference on Robotics and Automation, pp. 769-774; Google Cloud Speech, , https://cloud.google.com/speech-To-Text/, Google, [Online]., [Accessed: 30-Sep-2018]; Hwang, E.J., MacDonald, B.A., Ahn, H.S., End-To-end dialogue system with multi languages for hospital receptionist robot (2019) 2019 16th Int. Conf. Ubiquitous Robot. Ur 2019, pp. 278-283; Li, X., Wang, Y., Sun, S., Panda, S., Liu, J., Gao, J., (2018) Microsoft Dialogue Challenge: Building End-To-end Task-completion Dialogue Systems; Hochreiter, S., Urgen Schmidhuber, J., Long short-Term memory (1997) Neural Comput., 9 (8), pp. 1735-1780; Dialogflow, , https://dialogflow.com/, Google, [Online]., [Accessed: 28-Oct-2018]; Walker, M.A., Litman, D.J., Kamm, C.A., Abella, A., Paradise: A framework for evaluating spoken dialogue agents (1997) Proceedings of the Eighth Conference on European Chapter of the Association for Computational Linguistics, pp. 271-280; Walker, M.A., An application of reinforcement learning to dialogue strategy selection in a spoken dialogue system for email (2000) J. Artif. Intell. Res., 12, p. 387; Bartneck, C., Kulic, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot., 1 (1), pp. 71-81",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","",Scopus,2-s2.0-85092739745
"Ding Y., Thomas U.","56031700400;7004226574;","Collision Avoidance with Proximity Servoing for Redundant Serial Robot Manipulators",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9196759","10249","10255",,3,"10.1109/ICRA40945.2020.9196759","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092703939&doi=10.1109%2fICRA40945.2020.9196759&partnerID=40&md5=9bdc02741cc2eb62507a41657f949fe6","Chemnitz University of Technology, Lab of Robotics and Human Machine-Interaction, Chemnitz, SN, 09126, Germany","Ding, Y., Chemnitz University of Technology, Lab of Robotics and Human Machine-Interaction, Chemnitz, SN, 09126, Germany; Thomas, U., Chemnitz University of Technology, Lab of Robotics and Human Machine-Interaction, Chemnitz, SN, 09126, Germany","Collision avoidance is a key technology towards safe human-robot interaction, especially on-line and fastreacting motions are required. Skins with proximity sensors mounted on the robot's outer shell provide an interesting approach to occlusion-free and low-latency perception. However, collision avoidance algorithms which make extensive use of these properties for fast-reacting motions have not yet been fully investigated. We present an improved collision avoidance algorithm for proximity sensing skins by formulating a quadratic optimization problem with inequality constraints to compute instantaneous optimal joint velocities. Compared to common repulsive force methods, our algorithm confines the approach velocity to obstacles and keeps motions pointing away from obstacles unrestricted. Since with repulsive motions the robot only moves in one direction, opposite to obstacles, our approach has better exploitation of the redundancy space to maintain the task motion and gets stuck less likely in local minima. Furthermore, our method incorporates an active behaviour for avoiding obstacles and evaluates all potentially colliding obstacles for the whole arm, rather than just the single nearest obstacle. We demonstrate the effectiveness of our method with simulations and on real robot manipulators in comparison with commonly used repulsive force methods and our prior proposed approach. © 2020 IEEE.",,"Agricultural robots; Collision avoidance; Constraint theory; Flexible manipulators; Industrial robots; Modular robots; Quadratic programming; Robot applications; Robotics; Active behaviours; Approach velocities; Avoiding obstacle; Inequality constraint; Proximity sensing; Quadratic optimization problems; Repulsive forces; Safe human-robot interaction; Human robot interaction",,,,,,,,,,,,,,,,"Cheung, E., Lumelsky, V.J., Proximity sensing in robot manipulator motion planning: System and implementation issues (1989) IEEE Transactions on Robotics and Automation, 5 (6), pp. 740-751. , Dec; Bergner, F., Dean-Leon, E., Cheng, G., Efficient distributed torque computation for large scale robot skin (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1593-1599. , Oct; Bascetta, L., Magnani, G., Rocco, P., Migliorini, R., Pelagatti, M., Anti-collision systems for robotic applications based on laser timeof-flight sensors (2010) 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 278-284. , July; Ding, Y., Wilhelm, F., Faulhammer, L., Thomas, U., With proximity servoing towards safe human-robot-interaction (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4907-4912. , Nov; Brandstötter, M., Mühlbacher-Karrer, S., Schett, D., Zangl, H., Virtual compliance control of a kinematically redundant serial manipulator with 9 dof (2017) Advances in Robot Design and Intelligent Control, pp. 38-46. , A. Rodíc and T. Borangiu, Eds. Cham: Springer International Publishing; Navarro, S.E., Koch, S., Hein, B., 3d contour following for a cylindrical end-effector using capacitive proximity sensors (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 82-89. , Oct; FOGALE Robotics, Sensitive Surfaces for Human/Robot Interaction & Cooperation, , http://www.fogale-robotics.com/pdf/Sensitivesurfaces.pdf, online: Accessed 1-March-2019; Schlegl, T., Kröger, T., Gaschler, A., Khatib, O., Zangl, H., Virtual whiskers-highly responsive robot collision avoidance (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5373-5379. , Nov; Buizza Avanzini, G., Ceriani, N.M., Zanchettin, A.M., Rocco, P., Bascetta, L., Safety control of industrial robots based on a distributed distance sensor (2014) IEEE Transactions on Control Systems Technology, 22 (6), pp. 2127-2140. , Nov; Ceriani, N.M., Zanchettin, A.M., Rocco, P., Stolt, A., Robertsson, A., Reactive task adaptation based on hierarchical constraints classification for safe industrial robots (2015) IEEE/ASME Transactions on Mechatronics, 20 (6), pp. 2935-2949. , Dec; Lacevic, B., Rocco, P., Kinetostatic danger field-a novel safety assessment for human-robot interaction (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2169-2174. , Oct; Bosscher, P., Hedman, D., Real-time collision avoidance algorithm for robotic manipulators (2009) 2009 IEEE International Conference on Technologies for Practical Robot Applications, pp. 113-122. , Nov; Vergara, C., Borghesan, G., Aertbelien, E., De Schutter, J., Incorporating artificial skin signals in the constraint-based reactive control of human-robot collaborative manipulation tasks (2018) 2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 280-287. , July; Ding, Y., Zhang, H., Thomas, U., Capacitive proximity sensor skin for contactless material detection (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7179-7184. , Oct; Nakamura, Y., Hanafusa, H., Inverse kinematic solutions with singularity robustness for robot manipulator control (1986) Journal of Dynamic Systems, Measurement, and Control, 108 (3), pp. 163-171. , 09; Flacco, F., De Luca, A., Khatib, O., Motion control of redundant robots under joint constraints: Saturation in the null space (2012) 2012 IEEE International Conference on Robotics and Automation, pp. 285-292. , May; Yoshikawa, T., Dynamic manipulability of robot manipulators (1985) Proceedings. 1985 IEEE International Conference on Robotics and Automation, 2, pp. 1033-1038. , March; Rohmer, E., Singh, S.P.N., Freese, M., V-rep: A versatile and scalable robot simulation framework (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1321-1326. , Nov",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","",Scopus,2-s2.0-85092703939
"Wu X., Liu H., Liu Z., Chen M., Wan F., Fu C., Asada H., Wang Z., Song C.","57218169924;57205643389;57218162814;57218164370;57195419628;57208278421;55722793800;35216891400;41662155000;","Robotic Cane as a Soft SuperLimb for Elderly Sit-to-Stand Assistance∗",2020,"2020 3rd IEEE International Conference on Soft Robotics, RoboSoft 2020",,,"9116028","599","606",,2,"10.1109/RoboSoft48309.2020.9116028","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088094475&doi=10.1109%2fRoboSoft48309.2020.9116028&partnerID=40&md5=d3d6b049732b148a4c232e4588262598","Southern University of Science and Technology, Department of Mechanical and Energy Engineering, Shenzhen, Guangdong, 518055, China; SUSTech Institute of Robotics, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China; MIT Cambridge, Department of Mechanical EngineeringMA  02138, United States","Wu, X., Southern University of Science and Technology, Department of Mechanical and Energy Engineering, Shenzhen, Guangdong, 518055, China; Liu, H., Southern University of Science and Technology, Department of Mechanical and Energy Engineering, Shenzhen, Guangdong, 518055, China; Liu, Z., Southern University of Science and Technology, Department of Mechanical and Energy Engineering, Shenzhen, Guangdong, 518055, China; Chen, M., Southern University of Science and Technology, Department of Mechanical and Energy Engineering, Shenzhen, Guangdong, 518055, China; Wan, F., SUSTech Institute of Robotics, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China; Fu, C., Southern University of Science and Technology, Department of Mechanical and Energy Engineering, Shenzhen, Guangdong, 518055, China; Asada, H., MIT Cambridge, Department of Mechanical EngineeringMA  02138, United States; Wang, Z., Southern University of Science and Technology, Department of Mechanical and Energy Engineering, Shenzhen, Guangdong, 518055, China; Song, C., Southern University of Science and Technology, Department of Mechanical and Energy Engineering, Shenzhen, Guangdong, 518055, China","Many researchers have identified robotics as a potential solution to the aging population faced by many developed and developing countries. If so, how should we address the cognitive acceptance and ambient control of elderly assistive robots through design? In this paper, we proposed an explorative design of an ambient SuperLimb (Supernumerary Robotic Limb) system that involves a pneumatically-driven robotic cane for at-home motion assistance, an inflatable vest for compliant human-robot interaction, and a depth sensor for ambient intention detection. The proposed system aims at providing active assistance during the sit-to-stand transition for at-home usage by the elderly at the bedside, in the chair, and on the toilet. We proposed a modified biomechanical model with a linear cane robot for closed-loop control implementation. We validated the design feasibility of the proposed ambient SuperLimb system including the biomechanical model, our result showed the advantages in reducing lower limb efforts and elderly fall risks, yet the detection accuracy using depth sensing and adjustments on the model still require further research in the future. Nevertheless, we summarized empirical guidelines to support the ambient design of elderly-assistive SuperLimb systems for lower limb functional augmentation. © 2020 IEEE.","Design-for-the-Elderly; Physical Human-Robot Interaction; Soft Robot Materials and Design; Supernumerary Robotic Limbs; Wearable Robots","Agricultural robots; Biomechanics; Closed loop control systems; Developing countries; Machine design; Pneumatic drives; Robotics; Aging population; Assistive robots; Bio-mechanical models; Closed-loop control; Depth sensing; Design feasibility; Detection accuracy; Intention detection; Social robots",,,,,"Southern University of Science and Technology, SUSTech; National College Students Innovation and Entrepreneurship Training Program: 201914325006","*This work was supported by Southern University of Science and Technology, National Students’ Innovation and Entrepreneurship Training Program (No. 201914325006), and SUSTech-MIT Joint Center for Mechanical Engineering Education and Research.",,,,,,,,,,"(2019) Population Ages 65 and above (% of Total Population), , https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS?end=2017&start=1960&view=map, [online]; Góngora Alonso, S., Hamrioui, S., Van De Díez Torre, I., Motta Cruz, E., López-Coronado, M., Franco, M., Social robots for people with aging and dementia: A systematic review of literature (2019) Telemedicine and E-Health, 25 (7), pp. 533-540; Cynthia L Breazeal, N.S., Ostrowski, A.K., Park, H.W., Designing social robots for older adults (2019) The Bridge, 49 (1), pp. 22-31; Tinetti, M.E., Preventing falls in elderly persons (2003) New England Journal of Medicine, 348 (1), pp. 42-49; Podsiadlo, D., Richardson, S., The timed ""up & go"": A test of basic functional mobility for frail elderly persons (1991) Journal of the American Geriatrics Society, 39 (2), pp. 142-148; Beauchet, O., Fantino, B., Allali, G., Muir, S.W., Montero-Odasso, M., Annweiler, C., Timed up and go test and risk of falls in older adults: A systematic review (2011) The Journal of Nutrition, Health Aging, 15 (10), pp. 933-938; Isaacs, B., Clinical and laboratory studies of falls in old people. Prospects for prevention (1985) Clin Geriatr Med, 1 (3), pp. 513-524; Parietti, F., Asada, H.H., Independent, voluntary control of extra robotic limbs (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 5954-5961. , May; Gonzalez, D.J., Asada, H.H., Design of extra robotic legs for augmenting human payload capabilities by exploiting singularity and torque redistribution 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Conference Proceedings, pp. 4348-4354; Gonzalez, D.J., Asada, H.H., Hybrid open-loop closed-loop control of coupled human-robot balance during assisted stance transition with extra robotic legs (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 1676-1683. , April; Millington, P.J., Myklebust, B.M., Shambes, G.M., Biomechan-ical analysis of the sit-to-stand motion in elderly persons (1992) Archives of Physical Medicine and Rehabilitation, 73 (7), pp. 609-617; Hughes, M.A., Weiner, D.K., Schenkman, M.L., Long, R.M., Studenski, S.A., Chair rise strategies in the elderly (1994) Clinical Biomechanics, 9 (3), pp. 187-192; Scarborough, D., McGibbon, C., Krebs, D., Chair rise strategies in older adults with functional limitations (2007) Journal of Rehabilitation Research and Development, 44 (2), pp. 33-42; Broekens, J., Heerink, M., Rosendal, H., Assistive social robots in elderly care: A review (2009) Gerontechnology, 8 (2), pp. 94-103; Pape, T.L.-B., Kim, J., Weiner, B., The shaping of individual meanings assigned to assistive technology: A review of personal factors (2002) Disability and Rehabilitation, 24 (1-3), pp. 5-20; Pantelopoulos, A., Bourbakis, N.G., A survey on wearable sensor-based systems for health monitoring and prognosis (2009) IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40 (1), pp. 1-12; Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., Havinga, P., Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey (2010) 23th International Conference on Architecture of Computing Systems 2010. VDE, pp. 1-10; Chen, C., Jafari, R., Kehtarnavaz, N., A survey of depth and inertial sensor fusion for human action recognition (2017) Multimedia Tools and Applications, 76 (3), pp. 4405-4425; Foroughi, H., Aski, B.S., Pourreza, H., Intelligent video surveillance for monitoring fall detection of elderly in home environments (2008) 2008 11th International Conference on Computer and Information Technology, pp. 219-224; Rougier, C., Auvinet, E., Rousseau, J., Mignotte, M., Meunier, J., Fall detection from depth map video sequences (2011) International Conference on Smart Homes and Health Telematics, pp. 121-128. , Springer; Presti, L.L., La Cascia, M., 3d skeleton-based human action classification: A survey (2016) Pattern Recognition, 53, pp. 130-147; Yeung, S., Rinaldo, F., Jopling, J., Liu, B., Mehra, R., Downing, N.L., Guo, M., Lee, J.A., A computer vision system for deep learning-based detection of patient mobilization activities in the icu (2019) Nature Partner Journals Digital Medicine, 2 (1); Liu, B., Guo, M., Chou, E., Mehra, R., Yeung, S., Downing, N.L., Salipur, F., Fei-Fei, L., 3d point cloud-based visual prediction of icu mobility care activities (2018) Proceedings of the 3rd Machine Learning for Healthcare Conference, 85, pp. 17-29. , F. Doshi-Velez, J. Fackler, K. Jung, D. Kale, R. Ranganath, B. Wallace, and J. Wiens, Eds. 17-18 Aug; Sibella, F., Galli, M., Romei, M., Montesano, A., Crivellini, M., Biomechanical analysis of sit-to-stand movement in normal and obese subjects (2003) Clinical Biomechanics, 18 (8), pp. 745-750; De Leva, P., Adjustments to zatsiorsky-seluyanov's segment inertia parameters (1996) Journal of Biomechanics, 29 (9), pp. 1223-1230; Kaye, H., Kang, T., Laplante, M., (2000) Mobility Device Use in the United States, Disability Statistics, , Washington, DC: National Institute on Disability and Rehabilitation Research, U.S. Department of Education, Tech. Rep; Bradley, S.M., Hernandez, C.R., Geriatric assistive devices (2011) American Family Physician, 84 (4); Fryar, C., Kruszon-Moran, D., Gu, Q., Ogden, C., Mean body weight, height, waist circumference, and body mass index among adults: United States, 1999-2000 through 2015-2016 (2018) National Health Statistics Reports, 2018, p. 12; Shotton, J., Sharp, T., Kipman, A.A., Fitzgibbon, A., Finocchio, M.J., Blake, A., Cook, M., Moore, R., Real-time human pose recognition in parts from single depth images (2013) Communications of the ACM, 56 (1), pp. 116-124; Yamada, T., Demura, S., Instruction in reliability and magnitude of evaluation parameters at each phase of a sit-to-stand movement (2005) Perceptual and Motor Skills, 101 (3), pp. 695-706; Yamada, T., Demura, S.-I., Relationships between ground reaction force parameters during a sit-to-stand movement and physical activity and falling risk of the elderly and a comparison of the movement characteristics between the young and the elderly (2009) Archives of Gerontology and Geriatrics, 48 (1), pp. 73-77; Gross, M.M., Stevenson, P.J., Charette, S.L., Pyka, G., Marcus, R., Effect of muscle strength and movement speed on the biomechanics of rising from a chair in healthy elderly and young women (1998) Gait Posture, 8 (3), pp. 175-185; Schenkman, M., Riley, P.O., Pieper, C., Sit to stand from progressively lower seat heights-alterations in angular velocity (1996) Clinical Biomechanics, 11 (3), pp. 153-158","Song, C.; Southern University of Science and Technology, China; email: songcy@sustech.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.","3rd IEEE International Conference on Soft Robotics, RoboSoft 2020","15 May 2020 through 15 July 2020",,161217,,9781728165707,,,"English","IEEE Int. Conf. Soft Robot., RoboSoft",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85088094475
"Averta G., Caporale D., Santina C.D., Bicchi A., Bianchi M.","57195416332;56060787300;57188680141;7004326697;57202798446;","A technical framework for human-like motion generation with autonomous anthropomorphic redundant manipulators",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9196937","3853","3859",,7,"10.1109/ICRA40945.2020.9196937","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085062361&doi=10.1109%2fICRA40945.2020.9196937&partnerID=40&md5=6248c182f0fb5dcb844bdaca07b88395","Universita' di Pisa, Centro di Ricerca Enrico Piaggio, Pisa, 56126, Italy; Massachusetts Institute of Technology, Mit Computer Science Artificial Intelligence Lab, Cambridge, MA, United States","Averta, G., Universita' di Pisa, Centro di Ricerca Enrico Piaggio, Pisa, 56126, Italy; Caporale, D., Universita' di Pisa, Centro di Ricerca Enrico Piaggio, Pisa, 56126, Italy; Santina, C.D., Massachusetts Institute of Technology, Mit Computer Science Artificial Intelligence Lab, Cambridge, MA, United States; Bicchi, A., Universita' di Pisa, Centro di Ricerca Enrico Piaggio, Pisa, 56126, Italy; Bianchi, M., Universita' di Pisa, Centro di Ricerca Enrico Piaggio, Pisa, 56126, Italy","The need for users' safety and technology accept-ability has incredibly increased with the deployment of co-bots physically interacting with humans in industrial settings, and for people assistance. A well-studied approach to meet these requirements is to ensure human-like robot motions. Classic solutions for anthropomorphic movement generation usually rely on optimization procedures, which build upon hypotheses devised from neuroscientific literature, or capitalize on learning methods. However, these approaches come with limitations, e.g. limited motion variability or the need for high dimensional datasets. In this work, we present a technique to directly embed human upper limb principal motion modes computed through functional analysis in the robot trajectory optimization. We report on the implementation with manipulators with redundant anthropomorphic kinematic architectures - although dissimilar with respect to the human model used for functional mode extraction - via Cartesian impedance control. In our experiments, we show how human trajectories mapped onto a robotic manipulator still exhibit the main characteristics of human-likeness, e.g. low jerk values. We discuss the results with respect to the state of the art, and their implications for advanced human-robot interaction in industrial co-botics and for human assistance. © 2020 IEEE.",,"Accident prevention; Agricultural robots; Learning systems; Manipulators; Neurophysiology; Robotics; Cartesian impedance control; High dimensional datasets; Human like robots; Human-like motion; Industrial settings; Optimization procedures; Robotic manipulators; Technical frameworks; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020: 688857, 810346, 871237; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR","* This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 688857 (SoftPro), 810346 (Natural BionicS) and 871237 (Sophia), and and by the Italian Ministry of Education and Research (MIUR) in the framework of the CrossLab project (Departments of Excellence).",,,,,,,,,,"Wischniewski, S., Ajoudani, A., How can assistive robotics improve personal and work life? 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Learning Syst., 26 (12), pp. 3251-3262; Mordatch, I., Lowrey, K., Andrew, G., Popovic, Z., Todorov, E.V., Interactive control of diverse complex characters with neural networks (2015) Advances in Neural Information Processing Systems, pp. 3132-3140; Peng, X.B., Berseth, G., De Van, P.M., Terrain-adaptive locomotion skills using deep reinforcement learning (2016) ACM Transactions on Graphics (TOG), 35 (4), p. 81; Liarokapis, M.V., Artemiadis, P.K., Katsiaris, P.T., Kyriakopoulos, K.J., Manolakos, E.S., Learning human reach-to-grasp strategies: Towards emg-based control of robotic arm-hand systems (2012) 2012 IEEE International Conference on Robotics and Automation, pp. 2287-2292. , May; Figueroa Fernandez, N.B., Billard, A., Modeling compositions of impedance-based primitives via dynamical systems (2018) In Proceedings of the Workshop on Cognitive Whole-Body Control for Compliant Robot Manipulation (COWB-COMP), , CONF; Mordatch, I., Wang, J.M., Todorov, E., Koltun, V., Animating human lower limbs using contact-invariant optimization (2013) ACM Transactions on Graphics (TOG), 32 (6), p. 203; Miossec, S., Kheddar, A., Human motion in cooperative tasks: Moving object case study (2009) Robotics and Biomimetics, 2008. 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III-applications (1985) ASME Transactions Journal of Dynamic Systems and Measurement Control B, 107, pp. 1-24; Khatib, O., A unified approach for motion and force control of robot manipulators: The operational space formulation (1987) IEEE Journal on Robotics and Automation, 3 (1), pp. 43-53; Khatib, O., Inertial properties in robotic manipulation: An object-level framework (1995) The International Journal of Robotics Research, 14 (1), pp. 19-36; Dietrich, A., Ott, C., Albu-Schäffer, A., An overview of null space projections for redundant, torque-controlled robots (2015) The International Journal of Robotics Research, 34 (11), pp. 1385-1400; Zanchettin, A.M., Bascetta, L., Rocco, P., Acceptability of robotic manipulators in shared working environments through human-like redundancy resolution (2013) Applied Ergonomics, 44 (6), pp. 982-989; Flash, T., The control of hand equilibrium trajectories in multi-joint arm movements (1987) Biological Cybernetics, 57 (4-5), pp. 257-274; Peternel, L., Fang, C., Tsagarakis, N., Ajoudani, A., A selective muscle fatigue management approach to ergonomic human-robot co-manipulation (2019) Robotics and Computer-Integrated Manufacturing, 58, pp. 69-79. , http://www.sciencedirect.com/science/article/pii/S0736584518303132",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85085062361
"Wilde N., Blidaru A., Smith S.L., Kulić D.","57207876111;57201703369;55574212853;10244652900;","Improving user specifications for robot behavior through active preference learning: Framework and evaluation",2020,"International Journal of Robotics Research","39","6",,"651","667",,4,"10.1177/0278364920910802","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082112880&doi=10.1177%2f0278364920910802&partnerID=40&md5=ab74271cb93464c3ef54482bacf1cfe2","Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada; Monash University, Melbourne, VIC, Australia","Wilde, N., Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada; Blidaru, A., Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada; Smith, S.L., Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada; Kulić, D., Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada, Monash University, Melbourne, VIC, Australia","An important challenge in human–robot interaction (HRI) is enabling non-expert users to specify complex tasks for autonomous robots. Recently, active preference learning has been applied in HRI to interactively shape a robot’s behavior. We study a framework where users specify constraints on allowable robot movements on a graphical interface, yielding a robot task specification. However, users may not be able to accurately assess the impact of such constraints on the performance of a robot. Thus, we revise the specification by iteratively presenting users with alternative solutions where some constraints might be violated, and learn about the importance of the constraints from the users’ choices between these alternatives. We demonstrate our framework in a user study with a material transport task in an industrial facility. We show that nearly all users accept alternative solutions and thus obtain a revised specification through the learning process, and that the revision leads to a substantial improvement in robot performance. Further, the learning process reduces the variances between the specifications from different users and, thus, makes the specifications more similar. As a result, the users whose initial specifications had the largest impact on performance benefit the most from the interactive learning. © The Author(s) 2020.","Cognitive HRI; motion planning","Learning systems; Motion planning; Specifications; Alternative solutions; Cognitive HRI; Graphical interface; Industrial facilities; Interactive learning; Performance benefits; Preference learning; Robot interactions; Human robot interaction",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC","This research was partially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and OTTO Motors.",,,,,,,,,,"Abbeel, P., Ng, A.Y., (2004) Apprenticeship learning via inverse reinforcement learning, p. 1. , Proceedings of the 21st International Conference on Machine Learning, New York, ACM Press, In:, :, p; Akgun, B., Cakmak, M., Yoo, J.W., Thomaz, A.L., (2012) Trajectories and keyframes for kinesthetic teaching: A human–robot interaction perspective, pp. 391-398. , Proceedings of the 7th Annual ACM/IEEE International Conference on Human–Robot Interaction, New York, ACM Press, In; Akrour, R., Schoenauer, M., Sebag, M., (2012) Active preference learning-based reinforcement learning, pp. 116-131. , April, Joint European Conference on Machine Learning and Knowledge Discovery Databases, Berlin, Springer, :, In; Anderson, G.T., Yang, G., (2007) A proposed measure of environmental complexity for robotic applications, pp. 2461-2466. , 2007 IEEE International Conference on Systems, Man and Cybernetics, In; Argall, B.D., Chernova, S., Veloso, M., Browning, B., A survey of robot learning from demonstration (2009) Robotics and Autonomous Systems, 57 (5), pp. 469-483; Bangor, A., Kortum, P.T., Miller, J.T., An empirical evaluation of the system usability scale (2008) International Journal of Human–Computer Interaction, 24 (6), pp. 574-594; Basu, C., Singhal, M., Dragan, A.D., (2018) Learning from richer human guidance: Augmenting comparison-based learning with feature queries, pp. 132-140. , Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’18), New York, ACM Press, In; Bhatia, A., Kavraki, L.E., Vardi, M.Y., (2010) Sampling-based motion planning with temporal goals, pp. 2689-2696. , 2010 IEEE International Conference on Robotics and Automation (ICRA), IEEE, In; Billard, A.G., Calinon, S., Dillmann, R., (2016) Learning From Humans, pp. 1995-2014. , New York, Springer; Blidaru, A., Smith, S.L., Kulić, D., (2018) Assessing user specifications for robot task planning, pp. 72-79. , 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), In; Christiano, P.F., Leike, J., Brown, T., Martic, M., Legg, S., Amodei, D., Deep reinforcement learning from human preferences (2017) Advances in Neural Information Processing Systems, pp. 4299-4307. , In; Crandall, J.W., (2003) Towards Developing Effective Human–Robot Systems, , Brigham Young University. 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The development of a human–robot interaction trust scale (2012) International Journal of Social Robotics, 4 (3), pp. 235-248; Yanco, H.A., Norton, A., Ober, W., Shane, D., Skinner, A., Vice, J., Analysis of Human–robot interaction at the DARPA Robotics Challenge Trials (2015) Journal of Field Robotics, 32 (3), pp. 420-444; Yang, G., Anderson, G.T., (2011) An experimental study of environmental complexity as seen by robots, pp. 3102-3106. , 2011 IEEE International Conference on Systems, Man, and Cybernetics, In; Young, S.H., Mazzuchi, T.A., Sarkani, S., A framework for predicting future system performance in autonomous unmanned ground vehicles (2017) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47 (7), pp. 1192-1206; Ziebart, B.D., Maas, A.L., Bagnell, J.A., Dey, A.K., (2008) Maximum entropy inverse reinforcement learning, 8, pp. 1433-1438. , Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, In","Wilde, N.; Department of Electrical and Computer Engineering, Canada; email: nwilde@uwaterloo.ca",,,"SAGE Publications Inc.",,,,,02783649,,IJRRE,,"English","Int J Rob Res",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85082112880
"Candello H., Munteanu C., Clark L., Sin J., Torres M.I., Porcheron M., Myers C.M., Cowan B., Fischer J., Schlögl S., Murad C., Reeves S.","35182771600;14036143000;56437237000;57203323930;15756450800;56157070100;56890451900;39061243700;36095705300;34882202200;57204703940;7102635630;","CUI@CHI: Mapping grand challenges for the conversational user interface community",2020,"Conference on Human Factors in Computing Systems - Proceedings",,,"3375152","","",,12,"10.1145/3334480.3375152","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090157721&doi=10.1145%2f3334480.3375152&partnerID=40&md5=14dd243a6ebbdff6581c3bdc79064bcf","IBM Research, Sao Paulo, Brazil; University of Toronto, Toronto, ON, Canada; Swansea University, Dublin, Ireland; Universidad Del Pais Vasco, BIlbao, Spain; University of Nottingham, Nottinghamshire, Nottingham, United Kingdom; Drexel University, Philadelphia, United States; University College Dublin, Dublin, Ireland; Univ. of Nottingham, Nottinghamshire, Nottingham, United Kingdom; Management Center Innsbruck, Innsbruck, Austria; University of Toronto, Toronto, Canada","Candello, H., IBM Research, Sao Paulo, Brazil; Munteanu, C., University of Toronto, Toronto, ON, Canada; Clark, L., Swansea University, Dublin, Ireland; Sin, J., University of Toronto, Toronto, ON, Canada; Torres, M.I., Universidad Del Pais Vasco, BIlbao, Spain; Porcheron, M., University of Nottingham, Nottinghamshire, Nottingham, United Kingdom; Myers, C.M., Drexel University, Philadelphia, United States; Cowan, B., University College Dublin, Dublin, Ireland; Fischer, J., Univ. of Nottingham, Nottinghamshire, Nottingham, United Kingdom; Schlögl, S., Management Center Innsbruck, Innsbruck, Austria; Murad, C., University of Toronto, Toronto, Canada; Reeves, S., Univ. of Nottingham, Nottinghamshire, Nottingham, United Kingdom","The aim of this workshop is twofold. First, it aims to grow critical mass in Conversational User Interfaces (CUI) research by mapping the grand challenges in designing and researching these interactions. Second, this workshop is intended to further build the CUI community with these challenges in mind, whilst also growing CUI research presence at CHI. In particular, the workshop will survey and map topics such as: interaction design for text and voice-based CUI; the interplay between engineering efforts such as in Natural language Processing (NLP) and the design of CUI; practical CUI applications (e.g. human-robot interaction, public spaces, hands-free and wearables); and social, contextual, and cultural aspects of CUI design (e.g. ethics, privacy, trust, information exploration, persuasion, well-being, or decision-making, marginalized users). By drawing from the diverse interdisciplinary expertise that defines CHI, we are proposing this workshop as a platform on which to build a community that is better equipped to tackle an emerging field that is rapidly-evolving, yet is under-studied - - especially as the commercial advances seem to outpace the scholarly research in this space. © 2020 Owner/Author.","Conversational user interface; CUI; Intelligent personal assistants; Speech interface; Voice user interface","Decision making; Human engineering; Machine design; Mapping; Natural language processing systems; Social robots; Critical mass; Cultural aspects; Grand Challenge; Information exploration; Interaction design; NAtural language processing; Public space; Scholarly research; User interfaces",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/M02315X/1, EP/N014243/1","This work is supported by the Engineering and Physical Sciences Research Council [grant numbers EP/N014243/1, EP/M02315X/1].",,,,,,,,,,,,,"ACM SIGCHI","Association for Computing Machinery","2020 ACM CHI Conference on Human Factors in Computing Systems, CHI EA 2020","25 April 2020 through 30 April 2020",,160405,,9781450368193,,,"English","Conf Hum Fact Comput Syst Proc",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85090157721
"Xin G., Wolfslag W., Lin H.-C., Tiseo C., Mistry M.","56425442400;56032215100;56318164000;56648036000;12646109500;","An Optimization-Based Locomotion Controller for Quadruped Robots Leveraging Cartesian Impedance Control",2020,"Frontiers in Robotics and AI","7",,"48","","",,6,"10.3389/frobt.2020.00048","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084342262&doi=10.3389%2ffrobt.2020.00048&partnerID=40&md5=8e377a440bba687de78f3cf01b4384de","School of Informatics, Institute of Perception, Action and Behaviour, The University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Centre for Robotics, The University of Edinburgh, Edinburgh, United Kingdom; School of Computer Science, Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada","Xin, G., School of Informatics, Institute of Perception, Action and Behaviour, The University of Edinburgh, Edinburgh, United Kingdom, Edinburgh Centre for Robotics, The University of Edinburgh, Edinburgh, United Kingdom; Wolfslag, W., School of Informatics, Institute of Perception, Action and Behaviour, The University of Edinburgh, Edinburgh, United Kingdom, Edinburgh Centre for Robotics, The University of Edinburgh, Edinburgh, United Kingdom; Lin, H.-C., School of Computer Science, Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada; Tiseo, C., School of Informatics, Institute of Perception, Action and Behaviour, The University of Edinburgh, Edinburgh, United Kingdom, Edinburgh Centre for Robotics, The University of Edinburgh, Edinburgh, United Kingdom; Mistry, M., School of Informatics, Institute of Perception, Action and Behaviour, The University of Edinburgh, Edinburgh, United Kingdom, Edinburgh Centre for Robotics, The University of Edinburgh, Edinburgh, United Kingdom","Quadruped robots require compliance to handle unexpected external forces, such as impulsive contact forces from rough terrain, or from physical human-robot interaction. This paper presents a locomotion controller using Cartesian impedance control to coordinate tracking performance and desired compliance, along with Quadratic Programming (QP) to satisfy friction cone constraints, unilateral constraints, and torque limits. First, we resort to projected inverse-dynamics to derive an analytical control law of Cartesian impedance control for constrained and underactuated systems (typically a quadruped robot). Second, we formulate a QP to compute the optimal torques that are as close as possible to the desired values resulting from Cartesian impedance control while satisfying all of the physical constraints. When the desired motion torques lead to violation of physical constraints, the QP will result in a trade-off solution that sacrifices motion performance to ensure physical constraints. The proposed algorithm gives us more insight into the system that benefits from an analytical derivation and more efficient computation compared to hierarchical QP (HQP) controllers that typically require a solution of three QPs or more. Experiments applied on the ANYmal robot with various challenging terrains show the efficiency and performance of our controller. © Copyright © 2020 Xin, Wolfslag, Lin, Tiseo and Mistry.","impedance control; locomotion controller; projected inverse-dynamics; quadratic programming; quadruped robots",,,,,,"EP/R026173/1, EPR02572X/1; Horizon 2020: ICT-2017-1","This work has been supported by the following grants: EPSRC UK RAI Hub NCNR (EPR02572X/1) and ORCA (EP/R026173/1), THING project in the EU Horizon 2020 (ICT-2017-1).",,,,,,,,,,"Aghili, F., A unified approach for inverse and direct dynamics of constrained multibody systems based on linear projection operator: applications to control and simulation (2005) IEEE Trans. Robot, 21, pp. 834-849; Albu-Schaffer, A., Ott, C., Frese, U., Hirzinger, G., Cartesian impedance control of redundant robots: recent results with the DLR-light-weight-arms (2003) IEEE Int. Conf. Robot. Autom, 3, pp. 3704-3709; Angelini, F., Xin, G., Wolfslag, W.J., Tiseo, C., Mistry, M., Garabini, M., Online optimal impedance planning for legged robots (2019) IEEE International Conference on Intelligent Robots and Systems, , Macau, in; Bellicoso, C.D., Gehring, C., Hwangbo, J., Fankhauser, P., Hutter, M., Perception-less terrain adaptation through whole body control and hierarchical optimization (2016) IEEE-RAS International Conference on Humanoid Robots, pp. 558-564. , Cancun; Bellicoso, C.D., Kramer, K., Stäuble, M., Sako, D., Jenelten, F., Bjelonic, F., Alma-articulated locomotion and manipulation for a torque-controllable robot (2019) International Conference on Robotics and Automation (ICRA 2019), , Montreal, in; Boaventura, T., Semini, C., Buchli, J., Frigerio, M., Focchi, M., Caldwell, D.G., Dynamic torque control of a hydraulic quadruped robot (2012) 2012 IEEE International Conference on Robotics and Automation, pp. 1889-1894. , Saint Paul, MN, IEEE; De Lasa, M., Mordatch, I., Hertzmann, A., Feature-based locomotion controllers (2010) ACM Trans. Graph, 29, p. 131; Escande, A., Mansard, N., Wieber, P.-B., Hierarchical quadratic programming: fast online humanoid-robot motion generation (2014) Int. J. Robot. Res, 33, pp. 1006-1028; Gehring, C., Coros, S., Hutter, M., Bloesch, M., Hoepflinger, M.A., Siegwart, R., Control of dynamic gaits for a quadrupedal robot (2013) 2013 IEEE International Conference on Robotics and Automation, pp. 3287-3292. , Karlsruhe, IEEE; Goldfarb, D., Idnani, A., A numerically stable dual method for solving strictly convex quadratic programs (1983) Math. Program, 27, pp. 1-33; Herzog, A., Rotella, N., Mason, S., Grimminger, F., Schaal, S., Righetti, L., Momentum control with hierarchical inverse dynamics on a torque-controlled humanoid (2016) Auton. Robots, 40, pp. 473-491; Hutter, M., Sommer, H., Gehring, C., Hoepflinger, M., Bloesch, M., Siegwart, R., Quadrupedal locomotion using hierarchical operational space control (2014) Int. J. Robot. Res, 33, pp. 1047-1062; Khatib, O., A unified approach for motion and force control of robot manipulators: the operational space formulation (1987) IEEE J. Robot. Autom, 3, pp. 43-53; Lee, Y., Hwang, S., Park, J., Balancing of humanoid robot using contact force/moment control by task-oriented whole body control framework (2016) Auton. Robots, 40, pp. 457-472; Lin, H.-C., Smith, J., Babarahmati, K.K., Dehio, N., Mistry, M., A projected inverse dynamics approach for multi-arm cartesian impedance control (2018) IEEE International Conference on Robotics and Automation, , Brisbane, in; Mansard, N., A dedicated solver for fast operational-space inverse dynamics (2012) 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 4943-4949. , Saint Paul, MN, IEEE; Mistry, M., Buchli, J., Schaal, S., Inverse dynamics control of floating base systems using orthogonal decomposition (2010) 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 3406-3412. , Anchorage, AK, Citeseer; Mistry, M., Nakanishi, J., Cheng, G., Schaal, S., Inverse kinematics with floating base and constraints for full body humanoid robot control (2008) Humanoids 2008-8th IEEE-RAS International Conference on Humanoid Robots, pp. 22-27. , Daejeon, IEEE; Mistry, M., Righetti, L., Operational space control of constrained and underactuated systems (2012) Robotics: Science and Systems VII, pp. 225-232. , Sydney; Pratt, J., Chew, C.-M., Torres, A., Dilworth, P., Pratt, G., Virtual model control: an intuitive approach for bipedal locomotion (2001) Int. J. Robot. Res, 20, pp. 129-143; Saab, L., Ramos, O.E., Keith, F., Mansard, N., Soueres, P., Fourquet, J.Y., Dynamic whole-body motion generation under rigid contacts and other unilateral constraints (2013) IEEE Trans. Robot, 29, pp. 346-362; Sentis, L., Khatib, O., A whole-body control framework for humanoids operating in human environments (2006) ICRA 2006. Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006, pp. 2641-2648. , Orlando, FL, IEEE; Sentis, L., Park, J., Khatib, O., Compliant control of multicontact and center-of-mass behaviors in humanoid robots (2010) IEEE Trans. Robot, 26, pp. 483-501; Xin, G., Lin, H.-C., Smith, J., Cebe, O., Mistry, M., A model-based hierarchical controller for legged systems subject to external disturbances (2018) IEEE/RSJ International Conference on Robotics and Automation, pp. 4375-4382. , Brisbane","Xin, G.; School of Informatics, United Kingdom; email: guiyang.xin@ed.ac.uk",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85084342262
"Uppalapati N.K., Krishnan G.","57203147988;7005413403;","VaLeNS: Design of a Novel Variable Length Nested Soft Arm",2020,"IEEE Robotics and Automation Letters","5","2","8961979","1135","1142",,2,"10.1109/LRA.2020.2967303","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079272782&doi=10.1109%2fLRA.2020.2967303&partnerID=40&md5=90c37eeff6ef9024585602cbcdc0f6c2","Department of Industrial and Enterprise Systems Engineering, University of Illinois Urbana-Champaign (UIUC), Urbana, IL  61801, United States","Uppalapati, N.K., Department of Industrial and Enterprise Systems Engineering, University of Illinois Urbana-Champaign (UIUC), Urbana, IL  61801, United States; Krishnan, G., Department of Industrial and Enterprise Systems Engineering, University of Illinois Urbana-Champaign (UIUC), Urbana, IL  61801, United States","Over the last decade, soft continuum arms (SCAs) have successfully demonstrated the compliance and dexterity needed to operate in unstructured environments and handle fragile objects. However, their inherent soft compliance limits their performance in situations where stiffness and force transfer is required. In this letter, we present a compact design architecture, which is a hybrid between soft arms and rigid links known as Variable Length Nested Soft (VaLeNS) arm. The design architecture involves a novel SCA nested inside a concentric rigid tube. The SCA can undergo a combination of spatial bending ($B$) and bidirectional axial twist ($R^2$), and can extrude out or retract back into the rigid tube with varying length. The resulting configuration is shown to modulate stiffness up to a factor of ten and exhibits enhanced workspace and dexterity. Furthermore, the VaLeNS arm mounted on a rigid robotic platform allows for bifurcation of the overall workspace into rigid and soft, and can achieve high reachability in constrained environments. The letter demonstrates the effectiveness of the VaLeNS arm system in manipulation tasks that require both the rigid and soft attributes. This design architecture is deemed useful in agricultural applications and in physical human robot interaction. © 2016 IEEE.","Soft robot applications; soft robot materials and design","Agricultural robots; Human robot interaction; Robot applications; Stiffness; Compact designs; Design architecture; Manipulation task; Physical human-robot interactions; Robotic platforms; Soft robot; Unstructured environments; Variable length; Machine design",,,,,"U.S. Department of Agriculture, USDA: 2019-67021-28989; National Institute of Food and Agriculture, NIFA","Manuscript received September 10, 2019; accepted January 9, 2020. Date of publication January 17, 2020; date of current version January 30, 2020. This letter was recommended for publication by Associate Editor A. A. Stokes and Editor K.-J. Cho upon evaluation of the reviewers’ comments. This work was jointly funded by NIFA through the NIFA-NSF National Robotics Initiative (USDA 2019-67021-28989). (Corresponding author: Naveen Kumar Uppalap-ati.) The authors are with the Department of Industrial and Enterprise Systems Engineering, University of Illinois Urbana-Champaign (UIUC), Urbana, IL 61801 USA (e-mail: uppalap2@illinois.edu; gkrishna@illinois.edu).",,,,,,,,,,"Trivedi, D., Dienno, D., Rahn, C.D., Optimal, model-based design of soft robotic manipulators (2008) J. Mech. Des., 130 (9); Rus, D., Tolley, M.T., Design, fabrication and control of soft robots (2015) Nature, 521 (7553), pp. 467-475; Mazzolai, B., Margheri, L., Cianchetti, M., Dario, P., Laschi, C., Soft-robotic arm inspired by the octopus: II. From artificial requirements to innovative technological solutions (2012) Bioinspiration & Biomimetics, 7 (2); Laschi, C., Cianchetti, M., Mazzolai, B., Margheri, L., Follador, M., Dario, P., Soft robot arm inspired by the octopus (2012) Adv. Robot., 26 (7), pp. 709-727; Giorelli, M., Renda, F., Calisti, M., Arienti, A., Ferri, G., Laschi, C., Learning the inverse kinetics of an octopus-like manipulator in three-dimensional space (2015) Bioinspiration & Biomimetics, 10 (3). , May; McMahan, W., Field trials and testing of the OctArm con-tinuum manipulator (2006) Proc. IEEE Int. Conf. Robot. Autom., pp. 2336-2341; Uppalapati, N.K., Singh, G., Krishnan, G., Parameter estimation and modeling of a pneumatic continuum manipulator with asymmetric building blocks (2018) Proc. IEEE Int. Conf. Soft Robot., pp. 528-533; Connolly, F., Walsh, C.J., Bertoldi, K., Automatic design of fiber-reinforced soft actuators for trajectory matching (2017) Proc. Nat. Acad. Sci., 114 (1), pp. 51-56; Li, J., Xiao, J., Progressive generation of force-closure grasps for an n-section continuum manipulator (2013) Proc.-IEEE Int. Conf. Robot. Autom., pp. 4016-4022; Manti, M., Exploiting morphology of a soft manipulator for assistive tasks (2017) Proc. 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"Hernandez J.D., Sobti S., Sciola A., Moll M., Kavraki L.E.","56742631800;57214805654;57190740334;15832207500;7004243865;","Increasing robot autonomy via motion planning and an augmented reality interface",2020,"IEEE Robotics and Automation Letters","5","2","8962156","1017","1023",,5,"10.1109/LRA.2020.2967280","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079142724&doi=10.1109%2fLRA.2020.2967280&partnerID=40&md5=8c50d323aaa135363273a2f9a9969d40","Department of Computer Science, Rice University, Houston, TX, United States; Department of Physics and Astronomy, Rice University, Houston, TX, United States","Hernandez, J.D., Department of Computer Science, Rice University, Houston, TX, United States; Sobti, S., Department of Computer Science, Rice University, Houston, TX, United States; Sciola, A., Department of Physics and Astronomy, Rice University, Houston, TX, United States; Moll, M., Department of Computer Science, Rice University, Houston, TX, United States; Kavraki, L.E., Department of Computer Science, Rice University, Houston, TX, United States","Recently, there has been a growing interest in robotic systems that are able to share workspaces and collaborate with humans. Such collaborative scenarios require efficient mechanisms to communicate human requests to a robot, as well as to transmit robot interpretations and intents to humans. Recent advances in augmented reality (AR) technologies have provided an alternative for such communication. Nonetheless, most of the existing work in human-robot interaction with AR devices is still limited to robot motion programming or teleoperation. In this paper, we present an alternative approach to command and collaborate with robots. Our approach uses an AR interface that allows a user to specify high-level requests to a robot, to preview, approve or modify the computed robot motions. The proposed approach exploits the robot's decision-making capabilities instead of requiring low-level motion specifications provided by the user. The latter is achieved by using a motion planner that can deal with high-level goals corresponding to regions in the robot configuration space. We present a proof of concept to validate our approach in different test scenarios, and we present a discussion of its applicability in collaborative environments. © 2016 IEEE.","Motion and path planning; virtual reality and interfaces","Augmented reality; Decision making; Human robot interaction; Motion planning; Virtual reality; Collaborative environments; High-level goals; Low-level motions; Motion and path planning; Motion planners; Proof of concept; Robot configurations; Virtual reality and interfaces; Robot programming",,,,,"National Science Foundation, NSF: 1514372; Rice University","Manuscript received September 10, 2019; accepted December 30, 2019. Date of publication January 17, 2020; date of current version January 30, 2020. This letter was recommended for publication by Associate Editor N. Amato and Editor S. Thomas upon evaluation of the reviewers’ comments. This work has been supported in part by the NSF under Grant 1514372 and in part by Rice University funds. (Juan David Hernández and Shlok Sobti contributed equally to this work.) (Corresponding author: Lydia E. Kavraki.) J. D. Hernández, S. Sobti, M. Moll, and L. E. Kavraki are with the Department of Computer Science, Rice University, Houston, TX 77005 USA (e-mail: juandhv@rice.edu; sobti@rice.edu; mmoll@rice.edu; kavraki@rice.edu).",,,,,,,,,,"Argall, B.D., Chernova, S., Veloso, M., Browning, B., A survey of robot learning from demonstration (2009) Robot. Auton. Syst, 57 (5), pp. 469-483; Burger, B., Ferrané, I., Lerasle, F., Infantes, G., Two-handed gesture recognition and fusion with speech to command a robot (2012) Auton. Robots, 32 (2), pp. 129-147. , Feb; Green, S.A., Billinghurst, M., Chen, X., Chase, J.G., Human-robot collaboration: A literature review and augmented reality approach in design (2008) Int. J. Adv. Robot. Syst, 5 (1), pp. 1-18. , Mar; Charoenseang, S., Tonggoed, T., Human robot collaboration with augmented reality (2011) HCI International. 2011 Posters Extended Abstracts. HCI 2011. Commun. In Comput. In Inf. Sci, 174, pp. 93-97. , C. Stephanidis, Eds. Berlin, Heidelberg: Springer-Verlag; Perez Quintero, C., Li, S., Pan, M.K., Chan, W.P., Van Der Loos, M.H., Croft, E., Robot programming through augmented trajectories in augmented reality (2018) Proc IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 1838-1844. , Oct; Ong, S., Yew, A., Thanigaivel, N., Nee, A., Augmented reality-Assisted robot programming system for industrial applications Robot. Comput.-Integr. Manuf, 61, p. 2020. , Art 101820; Whitney, D., Rosen, E., Phillips, E., Konidaris, G., Tellex, S., Comparing robot grasping teleoperation across desktop and virtual reality with ROS reality Proc. Int. Symp. Robot. 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Autom, pp. 1006-1013. , May 2017; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on humanrobot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266. , Nov; Sibirtseva, E., A comparison of visualisation methods for disambiguating verbal requests in human-robot interaction Proc IEEE Int. Symp. Robot Human Interactive Commun., 2018, pp. 43-50. , Aug; Hedayati, H., Walker, M., Szafir, D., Improving collocated robot teleoperation with augmented reality Proc. ACM/ IEEE Int. Conf. Human-Robot Interaction, 2018, pp. 78-86; Fang, H., Ong, S., Nee, A., Interactive robot trajectory planning and simulation using augmented reality (2012) Robot. Comput.-Integrat. Manuf, 28 (2), pp. 227-237. , Apr; Gaschler, A., Springer, M., Rickert, M., Knoll, A., Intuitive robot tasks with augmented reality and virtual obstacles (2014) Proc. IEEE Int. Conf. Robot. 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Accessed, 2019. , https://unity.com/, Sept; Sucan, I.A., Chitta, S., (2012) MoveIt!, , http://moveit.ros.org; Sucan, I.A., Moll, M., Kavraki, L.E., The open motion planning library (2012) IEEE Robot. Autom. Mag, 19 (4), pp. 72-82. , Dec","Sciola, A.; Department of Physics and Astronomy, United States; email: ams16@rice.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85079142724
"Wan Z., Jiang C., Fahad M., Ni Z., Guo Y., He H.","57196021321;56422994300;57210222946;38862477100;35205046400;24802077000;","Robot-Assisted Pedestrian Regulation Based on Deep Reinforcement Learning",2020,"IEEE Transactions on Cybernetics","50","4","8540942","1669","1682",,12,"10.1109/TCYB.2018.2878977","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057148773&doi=10.1109%2fTCYB.2018.2878977&partnerID=40&md5=d13387edb95b2324fb031d6ce138ed2a","Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI  02881, United States; Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ  07030, United States; Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD  07030, United States","Wan, Z., Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI  02881, United States; Jiang, C., Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ  07030, United States; Fahad, M., Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ  07030, United States; Ni, Z., Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, SD  07030, United States; Guo, Y., Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ  07030, United States; He, H., Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI  02881, United States","Pedestrian regulation can prevent crowd accidents and improve crowd safety in densely populated areas. Recent studies use mobile robots to regulate pedestrian flows for desired collective motion through the effect of passive human-robot interaction (HRI). This paper formulates a robot motion planning problem for the optimization of two merging pedestrian flows moving through a bottleneck exit. To address the challenge of feature representation of complex human motion dynamics under the effect of HRI, we propose using a deep neural network to model the mapping from the image input of pedestrian environments to the output of robot motion decisions. The robot motion planner is trained end-to-end using a deep reinforcement learning algorithm, which avoids hand-crafted feature detection and extraction, thus improving the learning capability for complex dynamic problems. Our proposed approach is validated in simulated experiments, and its performance is evaluated. The results demonstrate that the robot is able to find optimal motion decisions that maximize the pedestrian outflow in different flow conditions, and the pedestrian-accumulated outflow increases significantly compared to cases without robot regulation and with random robot motion. © 2013 IEEE.","Deep reinforcement learning (DRL); human-robot interaction (HRI); pedestrian flow regulation","Collision avoidance; Complex networks; Deep neural networks; Extraction; Feature extraction; Human robot interaction; Learning algorithms; Machine learning; Man machine systems; Motion planning; Navigation; Pedestrian safety; Planning; Reinforcement learning; Deep reinforcement learning (DRL); Human robot Interaction (HRI); Pedestrian flow; Robot motion; Robot sensing system; Robot programming; algorithm; crowding (area); human; image processing; pedestrian; procedures; robotics; Algorithms; Crowding; Deep Learning; Humans; Image Processing, Computer-Assisted; Pedestrians; Robotics",,,,,"National Science Foundation, NSF: 1527016, 1833005, CMMI-1526835, CMMI-1527016, OIA-1833005","Manuscript received April 10, 2018; revised August 24, 2018 and October 16, 2018; accepted October 17, 2018. Date of publication November 20, 2018; date of current version February 25, 2020. The work of Z. Wan and H. He was supported by the U.S. National Science Foundation under Grant CMMI-1526835. The work of C. Jiang, M. Fahad, and Y. Guo was supported by the U.S. National Science Foundation under Grant CMMI-1527016. The work of Z. Ni was supported by the U.S. National Science Foundation under Grant OIA-1833005. This paper was recommended by Associate Editor D. Zhao. (Corresponding author: Haibo He.) Z. Wan and H. He are with the Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881 USA (e-mail: zwan@ele.uri.edu; he@ele.uri.edu).",,,,,,,,,,"Luh, P.B., Wilkie, C.T., Chang, S.-C., Marsh, K.L., Olderman, N., Modeling and optimization of building emergency evacuation considering blocking effects on crowd movement (2012) IEEE Trans. Autom. Sci. 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Autom., pp. 3357-3364; Martinez-Gil, F., Lozano, M., Fernández, F., MARL-Ped: A multiagent reinforcement learning based framework to simulate pedestrian groups (2014) Simulat. Modell. Pract. Theory, 47, pp. 259-275. , Sep; Torrey, L., Crowd simulation via multi-agent reinforcement learning (2010) Proc. 6th AAAI Conf. Artif. Intell. Interact. Digit. Entertainment, pp. 89-94; Helbing, D., Johansson, A., Al-Abideen, H.Z., Dynamics of crowd disasters: An empirical study (2007) Phys. Rev. E, Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top, 75 (4). , Art. no. 046109; Johansson, A., Helbing, D., Al-Abideen, H.Z., Al-Bosta, S., From crowd dynamics to crowd safety: A video-based analysis (2008) Adv. Complex Syst., 11 (4), pp. 497-527; Tajima, Y., Nagatani, T., Clogging transition of pedestrian flow in T-shaped channel (2002) Physica A Stat. Mech. Appl., 303 (1-2), pp. 239-250; Zhang, J., Klingsch, W., Schadschneider, A., Seyfried, A., Transitions in pedestrian fundamental diagrams of straight corridors and T-junctions (2011) J. Stat. Mech. Theory Exp., 2011 (6). , Art. no. P06004; Haworth, B., Using synthetic crowds to inform building pillar placements (2016) Proc. IEEE Virtual Humans Crowds Immersive Environ., pp. 7-11; Chen, Z., Jiang, C., Guo, Y., Pedestrian-robot interaction experiments in an exit corridor (2018) Proc. 15th Int. Conf. Ubiquitous Robots, Honolulu, HI, USA, pp. 29-34. , Jun; Dumoulin, V., Visin, F., (2018) A Guide to Convolution Arithmetic for Deep Learning, , arXiv:1603.07285, Jan; Cybenko, G., Approximation by superpositions of a sigmoidal function (1989) Math. Control Signals Syst., 2 (4), pp. 303-314; Van Hasselt, H., Guez, A., Silver, D., Deep reinforcement learning with double Q-learning (2016) Proc. Assoc. Adv. Artif. Intell., pp. 2094-2100; Sutton, R.S., Barto, A.G., (1998) Reinforcement Learning: An Introduction, 1. , Cambridge, MA, USA: MIT Press; Ferrer, G., Garrell, A., Sanfeliu, A., Robot companion: A social-force based approach with human awareness-navigation in crowded environments (2013) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 1688-1694. , Nov; Vasquez, D., Okal, B., Arras, K.O., Inverse reinforcement learning algorithms and features for robot navigation in crowds: An experimental comparison (2014) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 1341-1346. , Sep","He, H.; Department of Electrical, United States; email: he@ele.uri.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21682267,,,"30475740","English","IEEE Trans. Cybern.",Article,"Final","",Scopus,2-s2.0-85057148773
"Kundu J.N., Buckchash H., Mandikal P., Rahul M.V., Jamkhandi A., Babu R.V.","57191858622;57196062798;57206484940;57216944905;57216950759;57202410887;","Cross-conditioned recurrent networks for long-term synthesis of inter-person human motion interactions",2020,"Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020",,,"9093627","2713","2722",,1,"10.1109/WACV45572.2020.9093627","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085497715&doi=10.1109%2fWACV45572.2020.9093627&partnerID=40&md5=010d15b036207a63a2afa496eef6a4d6","Video Analytics Lab, CDS, Indian Institute of Science, Bangalore, India","Kundu, J.N., Video Analytics Lab, CDS, Indian Institute of Science, Bangalore, India; Buckchash, H., Video Analytics Lab, CDS, Indian Institute of Science, Bangalore, India; Mandikal, P., Video Analytics Lab, CDS, Indian Institute of Science, Bangalore, India; Rahul, M.V., Video Analytics Lab, CDS, Indian Institute of Science, Bangalore, India; Jamkhandi, A., Video Analytics Lab, CDS, Indian Institute of Science, Bangalore, India; Babu, R.V., Video Analytics Lab, CDS, Indian Institute of Science, Bangalore, India","Modeling dynamics of human motion is one of the most challenging sequence modeling problem, with diverse applications in animation industry, human-robot interaction, motion-based surveillance, etc. Available attempts to use auto-regressive techniques for long-term single-person motion generation usually fails, resulting in stagnated motion or divergence to unrealistic pose patterns. In this paper, we propose a novel cross-conditioned recurrent framework targeting long-term synthesis of inter-person interactions beyond several minutes. We carefully integrate positive implications of both auto-regressive and encoder-decoder recurrent architecture, by interchangeably utilizing two separate fixed-length cross person motion prediction models for long-term generation in a novel hierarchical fashion. As opposed to prior approaches, we guarantee structural plausibility of 3D pose by training the recurrent model to regress latent representation of a separately trained generative pose embedding network. Different variants of the proposed frameworks are evaluated through extensive experiments on SBU-interaction, CMU-MoCAP and an inhouse collection of duet-dance dataset. Qualitative and quantitative evaluation on several tasks, such as Short-term motion prediction, Long-term motion synthesis and Interaction-based motion retrieval against prior state-of-the-art approaches clearly highlight superiority of the proposed framework. © 2020 IEEE.",,"Automotive industry; Computer vision; Human robot interaction; Recurrent neural networks; Diverse applications; Embedding network; Motion generation; Quantitative evaluation; Recurrent networks; Sequence modeling; Short term motion predictions; State-of-the-art approach; Motion estimation",,,,,"Indian Institute of Science, IISc","Acknowledgements. This work was supported by a Wipro PhD Fellowship (Jogendra), and a project grant from Robert Bosch Centre for Cyber-Physical Systems, IISc.",,,,,,,,,,"Akhter, I., Black, M.J., Pose-conditioned joint angle limits for 3d human pose reconstruction (2015) Proceedings of the IEEE Conference on Computer Vision and Pattern Recogni-tion, pp. 1446-1455; Bahdanau, D., Cho, K., Bengio, Y., (2014) Neural Machine Translation by Jointly Learning to Align and Translate; Bengio, S., Vinyals, O., Jaitly, N., Shazeer, N., Scheduled sampling for sequence prediction with recurrent neural networks (2015) Advances in Neural Information Processing Sys-tems, pp. 1171-1179; Butepage, J., Black, M.J., Kragic, D., Kjellstrom, H., Deep representation learning for human motion prediction and classification (2017) The IEEE Conference on Computer VI-sion and Pattern Recognition, , July; Butepage, J., Black, M.J., Kragic, D., Kjellström, H., Deep representation learning for human motion prediction and classification (2017) 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition, JUL 21-26, 2016, Honolulu, HI, pp. 1591-1599. , IEEE; Chen, X., Grossman, T., Wigdor, D.J., Fitzmaurice, G., Duet: Exploring joint interactions on a smart phone and a smart watch (2014) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 159-168. , ACM; Chung, J., Gulcehre, C., Cho, K., Bengio, Y., Gated feedback recurrent neural networks (2015) International Conference on Machine Learning, pp. 2067-2075; Du, Y., Wang, W., Wang, L., Hierarchical recurrent neural network for skeleton based action recognition (2015) Proceed-ings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1110-1118; Fragkiadaki, K., Levine, S., Felsen, P., Malik, J., Recurrent network models for human dynamics (2015) Proceedings of the IEEE International Conference on Computer Vision, pp. 4346-4354; Ghosh, P., Song, J., Aksan, E., Hilliges, O., Learning human motion models for long-term predictions (2017) 3D Vision, 2017 International Conference On. IEEE; Graves, A., (2013) Generating Sequences with Recurrent Neural Networks; Graves, A., Mohamed, A.-R., Hinton, G., Speech recognition with deep recurrent neural networks (2013) Acoustics, Speech and Signal Processing, IEEE International Confer-ence on, pp. 6645-6649. , IEEE; Gregor, K., Danihelka, I., Graves, A., Rezende, D.J., Wierstra, D., (2015) Draw: A Recurrent Neural Network for Image Generation; Ho, E.S., Komura, T., Character motion synthesis by topology coordinates (2009) Computer Graphics Forum, 28, pp. 299-308. , Wiley Online Library; Ho, E.S., Komura, T., Tai, C.-L., Spatial relationship preserving character motion adaptation (2010) ACM Transactions on Graphics, 29, p. 33. , ACM; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Computation, 9 (8), pp. 1735-1780; Holden, D., Komura, T., Saito, J., Phase-functioned neural networks for character control (2017) ACM Transactions on Graph-ics, 36 (4), p. 42; Holden, D., Saito, J., Komura, T., A deep learning framework for character motion synthesis and editing (2016) ACM Trans-actions on Graphics, 35 (4), p. 138; Holden, D., Saito, J., Komura, T., Joyce, T., Learning motion manifolds with convolutional autoencoders (2015) SIG-GRAPH Asia 2015 Technical Briefs, p. 18. , ACM; Jain, A., Zamir, A.R., Savarese, S., Saxena, A., Structuralrnn: Deep learning on spatio-temporal graphs (2016) Proceed-ings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5308-5317; Kingma, D.P., Ba, J., (2014) Adam: A Method for Stochastic Optimization; Kundu, J.N., Gor, M., Babu, R.V., Bihmp-gan: Bidirectional 3d human motion prediction gan (2019) AAAI Conference on Artificial Intelligence; Kundu, J.N., Gor, M., Uppala, P.K., Radhakrishnan, V.B., Unsupervised feature learning of human actions as trajectories in pose embedding manifold (2019) 2019 IEEE Win-ter Conference on Applications of Computer Vision, pp. 1459-1467. , IEEE; Lee, K.H., Choi, M.G., Lee, J., Motion patches: Building blocks for virtual environments annotated with motion data (2006) ACM Transactions on Graphics, 25, pp. 898-906. , ACM; Li, Z., Zhou, Y., Xiao, S., He, C., Huang, Z., Li, H., Autoconditioned recurrent networks for extended complex human motion synthesis (2018) International Conference on Learning Representations; Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., Frey, B., (2015) Adversarial Autoencoders; Martinez, J., Black, M.J., Romero, J., On human motion prediction using recurrent neural networks (2017) 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4674-4683. , IEEE; Pawar, M.M., Pradhan, G.N., Zhang, K., Prabhakaran, B., Content based querying and searching for 3d human motions (2008) International Conference on Multimedia Modeling, pp. 446-455. , Springer; Pradhan, G.N., Li, C., Prabhakaran, B., Hierarchical indexing structure for 3d human motions (2007) Interna-tional Conference on Multimedia Modeling, pp. 386-396. , Springer; Raghuraman, S., Venkatraman, K., Wang, Z., Prabhakaran, B., Guo, X., A 3d tele-immersion streaming approach using skeleton-based prediction (2013) Proceedings of the 21st ACM International Conference on Multimedia, pp. 721-724. , ACM; Rogez, G., Weinzaepfel, P., Schmid, C., Lcr-net++: Multiperson 2d and 3d pose detection in natural images (2019) IEEE Transactions on Pattern Analysis and Machine Intelligence; Savitzky, A., Golay, M.J., Smoothing and differentiation of data by simplified least squares procedures (1964) Analytical Chemistry, 36 (8), pp. 1627-1639; Shahroudy, A., Liu, J., Ng, T.-T., Wang, G., Ntu rgb+ d: A large scale dataset for 3d human activity analysis (2016) Pro-ceedings of the IEEE Conference on Computer Vision and Pat-tern Recognition, pp. 1010-1019; Sutskever, I., Martens, J., Hinton, G.E., Generating text with recurrent neural networks (2011) Proceedings of the 28th International Conference on Machine Learning, pp. 1017-1024; Tian, Y., Ruan, Q., An, G., Fu, Y., Action recognition using local consistent group sparse coding with spatio-temporal structure (2016) Proceedings of the 24th ACM International Con-ference on Multimedia, pp. 317-321. , ACM; Vinyals, O., Toshev, A., Bengio, S., Erhan, D., Show and tell: A neural image caption generator (2015) Proceedings of the IEEE Conference on Computer Vision and Pattern Recog-nition, pp. 3156-3164; Won, J., Lee, K., O'Sullivan, C., Hodgins, J.K., Lee, J., Generating and ranking diverse multi-character interactions (2014) ACM Transactions on Graphics, 33 (6), p. 219; Yun, K., Honorio, J., Chattopadhyay, D., Berg, T.L., Samaras, D., Two-person interaction detection using bodypose features and multiple instance learning (2012) 2012 IEEE Computer Society Conference on Computer Vision and Pat-tern Recognition Workshops, pp. 28-35. , IEEE; Zunino, A., Cavazza, J., Koul, A., Cavallo, A., Becchio, C., Murino, V., Predicting human intentions from motion cues only: A 2d+ 3d fusion approach (2017) Proceedings of the 25th ACM International Conference on Multimedia, pp. 591-599. , ACM",,,"CVF;IEEE Computer Society","Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020","1 March 2020 through 5 March 2020",,159803,,9781728165530,,,"English","Proc. - IEEE Winter Conf. Appl. Comput. Vis., WACV",Conference Paper,"Final","",Scopus,2-s2.0-85085497715
"Ramos A., Hashtrudi-Zaad K.","57216857142;6701420299;","Estimation of Upper-Limb Energy Absorption Capabilities for Stable Human-Robot Interactions",2020,"IEEE Haptics Symposium, HAPTICS","2020-March",,"9086305","115","120",,1,"10.1109/HAPTICS45997.2020.ras.HAP20.20.95bee409","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085045366&doi=10.1109%2fHAPTICS45997.2020.ras.HAP20.20.95bee409&partnerID=40&md5=b545c1f2d8b8cc9de417c871f9a77924","Queen's University, Department of Electrical and Computer Engineering, Kingston, ON  K7L3N6, Canada","Ramos, A., Queen's University, Department of Electrical and Computer Engineering, Kingston, ON  K7L3N6, Canada; Hashtrudi-Zaad, K., Queen's University, Department of Electrical and Computer Engineering, Kingston, ON  K7L3N6, Canada","Human-robot interactions are becoming more and more prevalent in various aspects of life, enhancing humans' mobility, accessibility and health. However, safety measures need to be addressed when applying robotic-generated forces that put human users at risk. One way to improve safety and performance in robotic tasks is to include physiological information, such as damping properties of the arm, in the control system to help regulate the energy that is delivered to the user. In this work, we estimated the energy absorbing capabilities of the human arm, based on the metric Excess of Passivity (EOP), originally defined in [1]. We used data from healthy subjects to generate models that fit different levels of safety and stability. Variability in subjects' EOP was a major finding in this study. For demanding applications such as robotic rehabilitation therapy, we suggest using a linear model with two EOP points. Such points are the mean values of EOP estimations at relaxed and rigid levels of hand-grasp forces. Two standard deviations were subtracted from each EOP point to consider the variability due to the neuromuscular changes in the human arm. © 2020 IEEE.","Control systems; excess of passivity; haptic interfaces; human-robot interaction; rehabilitation robotics; stability; teleoperation; upper-limb; variability","Energy absorption; Man machine systems; Risk assessment; Robotics; Safety engineering; Damping property; Energy absorbing capability; Energy absorption capability; Healthy subjects; Physiological informations; Robotic rehabilitation; Safety and stabilities; Standard deviation; Human robot interaction",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC; Consejo Nacional de Ciencia y Tecnología, CONACYT: 439840","This paper was first submitted on October 25th, 2019, then it was accepted on December 30th, 2019, and it was revised and resubmitted as final version on January 27th, 2020. This research was supported by Consejo Nacional de Ciencia y Tecnología (CONACYT) México, under the ‘Becas al Extranjero’ program, grant number 439840, and Natural Sciences and Engineering Research Council of Canada (NSERC).",,,,,,,,,,"Atashzar, S.F., Shahbazi, M., Tavakoli, M., Patel, R.V., A passivity-based approach for stable patient-robot interaction in haptics-enabled rehabilitation systems: Modulated time-domain passivity control (2017) IEEE Transactions on Control Systems Technology, 25 (3), pp. 991-1006; Coles, T.R., Meglan, D., John, N.W., The role of haptics in medical training simulators: A survey of the state of the art (2011) IEEE Transactions on Haptics, 4 (1), pp. 51-66; Hung, V.M., Mihai, V., Ion, I., Paraschiv, N., Dragana, C., Improvements of force tracking performances for a minimal surgery training system using haptic and robot devices (2017) IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, , Bucharest, Romania; Wu, A.R., Dzeladini, F., Brug, T.J.H., Federica Tamburella, N.L.T., Asseldonk, E.H.F.V., Kooij, H.V.D., Ijspeert, A.J., An adaptive neuromuscular controller for assistive lower-limb exoskeletons: A preliminary study on subjects with spinal cord injury (2017) Frontiers in Neurorobotics, 11 (30); Dehem, S., Montedoro, V., Brouwers, I., Edwards, M.G., Detrembleur, C., Stoquart, G., Renders, A., Lejeune, T., Validation of a robot serious game assessment protocol for upper limb motor impairment in children with cerebral palsy (2019) NeuroRehabilitation, 45 (2), pp. 137-149; Dyck, M., Jazayeri, A., Tavakoli, M., Is the human operator in a teleoperation system passive? (2013) IEEE World Haptics Conference, , Daejeon, South Korea; Li, H., Zhang, L., Kawashima, K., Operator dynamics for stability condition in haptic and teleoperation system: A survey (2018) International Journal of Medical Robotics and Computer Assisted Surgery, 14 (2); Colgate, J.E., Schenkel, G., Passivity of a class of sampled-data systems: Application to haptic interfaces (1997) Field Robotics, 14, pp. 37-47; Yin, S., Koti, A., Haddadi, A., Hashtrudi-Zaad, K., Uncoupled stability analysis of haptic simulation systems for various kinematic sampled data and discretization methods (2014) IEEE Haptics Symposium, , Houston, USA; Cleveland, D.M., (2017) Digital Control Techniques for High Fidelity Haptic Simulation Systems, , Master's Dissertation, Queen's University; Hashtrudi-Zaad, K., Salcudean, S.E., Analysis of control architectures for teleoperation systems with impedance-admittance master and slave manipulators (2001) The International Journal of Robotics Research, 20 (6), pp. 419-445; Colgate, J.E., Robust impedance shaping telemanipulation (1993) IEEE Transactions on Robotics and Automation, 9 (4), pp. 374-384; Burdet, E., Tee, K.P., Mareels, I., Milner, T.E., Chew, C.M., Franklin, D.W., Osu, R., Kawato, M., Stability and motor adaptation in human arm movements (2006) Biological Cybernetics, 94 (1), pp. 20-32; Atashzar, S.F., Shahbazi, M., Tavakoli, M., Patel, R.V., A grasp-based passivity signature for haptics-enabled human-robot interaction: Application to design of a new safety mechanism for robotic rehabilitation (2017) The International Journal of Robotics Research, 36 (5-7), pp. 778-799; Emerson, I., Potgieter, J., Xu, W., Control implementation for an integrated robotic and virtual mirror therapy system for stroke rehabilitation (2016) IEEE International Workshop on Advanced Motion Control, , Auckland New Zealand",,,"Facebook;Immersion;Reality Labs","IEEE Computer Society","26th IEEE Haptics Symposium, HAPTICS 2020","28 March 2020 through 31 March 2020",,159714,23247347,9781728102344,,,"English","IEEE Haptics Symp., HAPTICS",Conference Paper,"Final","",Scopus,2-s2.0-85085045366
"Zardiashvili L., Fosch-Villaronga E.","57213153797;57189046483;","“Oh, Dignity too?” Said the Robot: Human Dignity as the Basis for the Governance of Robotics",2020,"Minds and Machines","30","1",,"121","143",,9,"10.1007/s11023-019-09514-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077626833&doi=10.1007%2fs11023-019-09514-6&partnerID=40&md5=1cef8b2626eba92df09da45a254cd441","eLaw Center for Law and Digital Technologies, Leiden University, Leiden, Netherlands","Zardiashvili, L., eLaw Center for Law and Digital Technologies, Leiden University, Leiden, Netherlands; Fosch-Villaronga, E., eLaw Center for Law and Digital Technologies, Leiden University, Leiden, Netherlands","Healthcare robots enable practices that seemed far-fetched in the past. Robots might be the solution to bridge the loneliness that the elderly often experience; they may help wheelchair users walk again, or may help navigate the blind. European Institutions, however, acknowledge that human contact is an essential aspect of personal care and that the insertion of robots could dehumanize caring practices. Such instances of human–robot interactions raise the question to what extent the use and development of robots for healthcare applications can challenge the dignity of users. In this article, therefore, we explore how different robot applications in the healthcare domain support individuals in achieving ‘dignity’ or pressure it. We argue that since healthcare robot applications are novel, their associated risks and impacts may be unprecedented and unknown, thus triggering the need for a conceptual instrument that is binding and remains flexible at the same time. In this respect, as safety rules and data protection are often criticized to lack flexibility, and technology ethics to lack enforceability, we suggest human dignity as the overarching governance instrument for robotics, which is the inviolable value upon which all fundamental rights are grounded. © 2020, The Author(s).","Governance; Healthcare robots; Human dignity; Human–robot interaction; Robotics","Health care; Robot applications; Robotics; European institutions; Governance; Health care application; Healthcare domains; Human dignity; Personal care; Robot interactions; Wheelchair users; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020: 707404; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO","The research of the first author is part of the SCALES project in the research programme Responsible Innovation (MVI) with Project Number 313-99-315. This programme is financed by the Netherlands Organisation for Scientific Research (NWO). Second author acknowledges that part of this project is funded by the LEaDing Fellows Marie Curie COFUND fellowship, a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 707404. 1 Concept vs. conception: concept—what exists; conception—why it exists (what exists) (see McCrudden 2008 ).","The research of the first author is part of the SCALES project in the research programme Responsible Innovation (MVI) with Project Number 313-99-315. This programme is financed by the Netherlands Organisation for Scientific Research (NWO). Second author acknowledges that part of this project is funded by the LEaDing Fellows Marie Curie COFUND fellowship, a project that has received funding from the European Union?s Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie Grant Agreement No. 707404.",,,,,,,,,"Allsop, J., Values in law: How they influence and shape rules and the application of law (2016) Lawasia J, p. 125; Universal declaration of human rights (1948) UN General Assembly, 302 (2); Athlin, E., Norberg, A., Asplund, K., Caregivers’ perceptions and interpretations of severely demented patients during feeding in a task assignment system (1990) Scandinavian Journal of Caring Sciences, 4 (4), pp. 147-156; Beck, U., Beck-Gernsheim, E., (2002) Institutionalized Individualism and Its Social and Political Consequences, , https://pdfs.semanticscholar.org/587d/7897bf2629a4e6a0fba2c48b87a55166fa6e.pdf, Ann Arbor, University of Michigan Press, Retrieved from; Bremhorst, A., Mongillo, P., Howell, T., Marinelli, L., Spotlight on assistance dogs—legislation, welfare and research (2018) Animals, 8 (8), p. 129; Broom, D.M., Animal Welfare in the European Union (2017) DG for Internal Policies, , https://www.europarl.europa.eu/RegData/etudes/STUD/2017/583114/IPOL_STU(2017)583114_EN.pdf, Retrieved from:, Accessed 8 Oct 2019; Canal, G., Alenyà, G., Torras, C., Personalization framework for adaptive robotic feeding assistance (2016) International Conference on Social Robotics, pp. 22-31. , November, Cham, Springer; Carr, N., (2010) The shallows. 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"Jain S., Thiagarajan B., Shi Z., Clabaugh C., Matarić M.J.","57216313091;57216314625;57215277563;57188667737;7005611984;","Modeling engagement in long-term, in-home socially assistive robot interventions for children with autism spectrum disorders",2020,"Science Robotics","5","39","aaz3791","","",,18,"10.1126/scirobotics.aaz3791","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083078309&doi=10.1126%2fscirobotics.aaz3791&partnerID=40&md5=0f7b2b738855c91738e6440c72a0c842","Interaction Lab, University of Southern California, Los Angeles, CA  90089, United States","Jain, S., Interaction Lab, University of Southern California, Los Angeles, CA  90089, United States; Thiagarajan, B., Interaction Lab, University of Southern California, Los Angeles, CA  90089, United States; Shi, Z., Interaction Lab, University of Southern California, Los Angeles, CA  90089, United States; Clabaugh, C., Interaction Lab, University of Southern California, Los Angeles, CA  90089, United States; Matarić, M.J., Interaction Lab, University of Southern California, Los Angeles, CA  90089, United States","Socially assistive robotics (SAR) has great potential to provide accessible, affordable, and personalized therapeutic interventions for children with autism spectrum disorders (ASD). However, human-robot interaction (HRI) methods are still limited in their ability to autonomously recognize and respond to behavioral cues, especially in atypical users and everyday settings. This work applies supervised machine-learning algorithms to model user engagement in the context of long-term, in-home SAR interventions for children with ASD. Specifically, we present two types of engagement models for each user: (i) generalized models trained on data from different users and (ii) individualized models trained on an early subset of the user's data. The models achieved about 90% accuracy (AUROC) for post hoc binary classification of engagement, despite the high variance in data observed across users, sessions, and engagement states. Moreover, temporal patterns in model predictions could be used to reliably initiate reengagement actions at appropriate times. These results validate the feasibility and challenges of recognition and response to user disengagement in long-term, real-world HRI settings. The contributions of this work also inform the design of engaging and personalized HRI, especially for the ASD community. © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works",,"Agricultural robots; Diseases; Learning algorithms; Supervised learning; Binary classification; Children with autisms; Generalized models; Human robot Interaction (HRI); Socially assistive robotics (SAR); Socially assistive robots; Supervised machine learning; Therapeutic intervention; Human robot interaction; algorithm; association; autism; child; child behavior; communication aid; computer interface; devices; feasibility study; home care; human; personalized medicine; psychological model; psychology; robotics; self help device; social behavior; supervised machine learning; theoretical model; Algorithms; Autism Spectrum Disorder; Child; Child Behavior; Communication Aids for Disabled; Cues; Feasibility Studies; Home Care Services; Humans; Models, Psychological; Models, Theoretical; Precision Medicine; Robotics; Self-Help Devices; Social Behavior; Supervised Machine Learning; User-Computer Interface",,,,,"National Science Foundation, NSF; Directorate for Computer and Information Science and Engineering, CISE: 1139148",,,,,,,,,,,"Feil-Seifer, D., Matarić, M.J., Defining socially assistive robotics (2005) Proceedings of the 2005 IEEE 9th International Conference on Rehabilitation Robotics, pp. 465-468. , IEEE; Matarić, M.J., Scassellati, B., (2016) Springer Handbook of Robotics, pp. 1973-1994. , Springer; Matarić, M.J., Socially assistive robotics: Human augmentation versus automation (2017) Sci. 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Res., 12, pp. 2825-2830; (2015) Hmmlearn: Hidden Markov Models in Python; Okazaki, N., (2007) CRFsuite: A Fast Implementation of Conditional Random Fields; Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Zheng, X., (2015) TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, , software tensorflow.org; Chollet, F., (2015) Keras, , https://keras.io; O'Brien, M., Tobin, K., (2018) Science Nation: Socially Assistive Robots for Children on the Autism Spectrum, , National Science Foundation, 29 October; Baltrušaitis, T., Mahmoud, M., Robinson, P., Cross-dataset learning and person-specific normalisation for automatic action unit detection (2015) 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 6, pp. 1-6. , IEEE","Jain, S.; Interaction Lab, United States; email: shomikja@usc.edu",,,"American Association for the Advancement of Science",,,,,24709476,,,"33022604","English","Sci. Robotics",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85083078309
"Zhang T.","57220035158;","Co-creating tourism experiences through a traveler’s journey: a perspective article",2020,"Tourism Review","75","1",,"56","60",,20,"10.1108/TR-06-2019-0251","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073812587&doi=10.1108%2fTR-06-2019-0251&partnerID=40&md5=e6250dd2c9ee20df187713be1cf3a8e6","Department of Hospitality Services, University of Central Florida Rosen, Orlando, FL, United States","Zhang, T., Department of Hospitality Services, University of Central Florida Rosen, Orlando, FL, United States","Purpose: This perspective article aims to summarize research on value co-creation and tourism experience management from the past decades and to propose several future research priorities related to the topic. Design/methodology/approach: This perspective article reviews the previous work and offers a process model on co-creating tourism experiences through a traveler’s journey. In addition, the article proposes five future research priorities related to value co-creation strategies in tourism experience management. Findings: This perspective article depicts value co-creation activities during the pre-, mid-, and post-travel stages and proposes five research priorities: (1) big data and real-time co-creation will become ubiquitous in future travel experiences, (2) social media will continue to empower travelers throughout their travels, (3) human–robot interactions will increase for travelers’ co-creation activities, and (4) the diversity of travelers and younger generations (Generation Z) will impact co-created travel journeys. Originality/value: Co-creation of tourism experiences has been increasing in the travel industry. It is necessary to review what has been said in the past and to project future research priorities and directions for travel academia and practitioners. © 2019, Emerald Publishing Limited.","Big data; Co-creation; Diversity of travelers; Real time; Tourism experience",,,,,,,,,,,,,,,,,"Buhalis, D., Foerste, M., SoCoMo marketing for travel and tourism: empowering co-creation of value (2015) Journal of Destination Marketing & Management, 4 (3), pp. 151-161; Buhalis, D., Sinarta, Y., Real-time co-creation and nowness service: lessons from tourism and hospitality (2019) Journal of Travel & Tourism Marketing, 36 (5), pp. 563-582; Carlson, J., Rahman, M., Voola, R., De Vries, N., Customer engagement behaviours in social media: capturing innovation opportunities (2018) Journal of Services Marketing, 32 (1), pp. 83-94; Grissemann, U.S., Stokburger-Sauer, N.E., Customer co-creation of travel services: the role of company support and customer satisfaction with the co-creation performance (2012) Tourism Management, 33 (6), pp. 1483-1492; Grönroos, C., Value co-creation in service logic: a critical analysis (2011) Marketing Theory, 11 (3), pp. 279-301; Grönroos, C., Voima, P., Critical service logic: making sense of value creation and co-creation (2013) Journal of the Academy of Marketing Science, 41 (2), pp. 133-150; Hwang, Y.-H., Jani, D., Jeong, H.K., Analyzing international tourists’ functional information needs: a comparative analysis of inquiries in an on-line travel forum (2013) Journal of Business Research, 66 (6), pp. 700-705; Laesser, C., Luo, J., Beritelli, P., The SOMOAR operationalization: a holistic concept to travel decision modelling (2019) Tourism Review, 74 (3), pp. 613-631; Leung, X.Y., Xue, L., Wen, H., Framing the sharing economy: toward a sustainable ecosystem (2019) Tourism Management, 71, pp. 44-53; Prahalad, C.K., Ramaswamy, V., Co-creating unique value with customers (2004) Strategy & Leadership, 32 (3), pp. 4-9; Prahalad, C.K., Ramaswamy, V., Co-creation experiences: the next practice in value creation (2004) Journal of Interactive Marketing, 18 (3), pp. 5-14; Ray, A., Dhir, A., Bala, P.K., Kaur, P., Why do people use food delivery apps (FDA)? A uses and gratification theory perspective (2019) Journal of Retailing and Consumer Services, 51, pp. 221-230; Rihova, I., Buhalis, D., Gouthro, M.B., Moital, M., Customer-to-customer co-creation practices in tourism: lessons from customer-dominant logic (2018) Tourism Management, 67, pp. 362-375; Rihova, I., Buhalis, D., Moital, M., Gouthro, M.-B., Conceptualising customer-to-customer value co-creation in tourism (2015) International Journal of Tourism Research, 17 (4), pp. 356-363; Weigert, M., Jumia travel in Africa: expanding the boundaries of the online travel agency business model (2018) Tourism Review, , https://doi.org/10.1108/TR-04-2017-0073; Zhang, T., Abound Omran, B., Cobanoglu, C., Generation Y’s positive and negative eWOM: use of social media and mobile technology (2017) International Journal of Contemporary Hospitality Management, 29 (2), pp. 732-761; Zhang, T., Jahromi, M.F., Kizildag, M., Value co-creation in a sharing economy: the end of price wars? (2018) International Journal of Hospitality Management, 71, pp. 51-58; Zhang, T., Lu, C., Kizildag, M., Engaging generation Y to co-create through mobile technology (2017) International Journal of Electronic Commerce, 21 (4), pp. 489-516","Zhang, T.; Department of Hospitality Services, United States; email: Tingting.Zhang@ucf.edu",,,"Emerald Group Holdings Ltd.",,,,,16605373,,,,"English","Tour. Rev.",Article,"Final","",Scopus,2-s2.0-85073812587
"Mizuchi Y., Inamura T.","36809967300;7102940240;","Optimization of criterion for objective evaluation of HRI performance that approximates subjective evaluation: a case study in robot competition",2020,"Advanced Robotics","34","3-4",,"142","156",,5,"10.1080/01691864.2019.1698462","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076043284&doi=10.1080%2f01691864.2019.1698462&partnerID=40&md5=666a30c947fb2bebdcb423ae32322995","National Institute of Informatics, Chiyoda-ku, Japan; The Graduate University for Advanced Studies (SOKENDAI), Chiyoda-ku, Japan","Mizuchi, Y., National Institute of Informatics, Chiyoda-ku, Japan; Inamura, T., National Institute of Informatics, Chiyoda-ku, Japan, The Graduate University for Advanced Studies (SOKENDAI), Chiyoda-ku, Japan","The standard approach to evaluate the performance of human-robot interaction (HRI) is subjective evaluation, for example, using questionnaires. Because such subjective evaluation is time-consuming, an alternative evaluation method based on only objective factors (i.e. human reaction behavior) is required for autonomous learning by robots and for scoring in robot competitions, etc. To this end, we aimed to investigate the extent to which subjective evaluation results can be approximated using objective factors. We observed and stored HRI history data through a robot-competition task in which the robot was required to generate comprehensible and unambiguous natural language expressions and gestures to guide inexpert users in virtual everyday environments. In addition, to acquire subjective evaluation results, we asked third-parties to evaluate the HRI performance by reviewing the stored interaction histories. From the results of a case study of robot-competition, we demonstrate the necessity for an objective method for the evaluation of HRI performance and a determination process for an effective evaluation criterion. The results of a multiple linear regression analysis to estimate the subjective evaluation results reveal that the subjective evaluation of HRI can indeed be reasonably approximated base on objective factors. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","evaluation criteria; Human-robot interaction; robot competition; virtual reality","Consumer behavior; Linear regression; Man machine systems; Surveys; Virtual reality; Evaluation criteria; Human robot Interaction (HRI); Interaction history; Multiple linear regression analysis; Natural language expressions; Objective evaluation; Robot competitions; Subjective evaluations; Human robot interaction",,,,,"Japan Society for the Promotion of Science, KAKEN: 19K20379; New Energy and Industrial Technology Development Organization, NEDO; Core Research for Evolutional Science and Technology, CREST: JPMJCR15E3","This work was supported by the New Energy and Industrial Technology Development Organization (NEDO), JST CREST-Core Research for Evolutional Science and Technology under Grant Number JPMJCR15E3 and Japan Society for the Promotion of Science (KAKENHI) under Grant Number 19K20379, Japan.",,,,,,,,,,"Iocchi, L., Holz, D., Ruiz-Del-Solar, J., RoboCup@Home: analysis and results of evolving competitions for domestic and service robots (2015) Artif Intell, 229, pp. 258-281. , et al; Mizuchi, Y., Inamura, T., Cloud-based multimodal human-robot interaction simulator utilizing ROS and unity frameworks. In: IEEE/SICE International Symposium on System Integration; 2017. p. 948–955; Inamura, T., Mizuchi, Y., Competition design to evaluate cognitive functions human-robot interaction based on immersive VR. In: RoboCup 2017: Robot World Cup XXI, Lecture Notes Artificial Intelligence, 11175; 2018. p. 84–94; Tellex, S., Kollar, T., Dickerson, S., Understanding Natural Language Commands for Robotic Navigation and Mobile Manipulation. 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In: World Automation Congress; 2010. p. 1–6; Shi, C., Shiomi, M., Kanda, T., Measuring communication participation to initiate conversation in Human–Robot interaction (2015) Int J Soc Robot, 7 (5), pp. 889-910. , et al; Dragan, A.D., Bauman, S., Forlizzi, J., Effects of robot motion on human-robot collaboration. In: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction; 2015. p. 51–58, et al; Baraglia, J., Cakmak, M., Nagai, Y., Efficient human-robot collaboration: when should a robot take initiative? (2017) Int J Rob Res, 36 (7), pp. 563-579. , et al; Rossi, S., Staffa, M., Bove, L., Users personality and activity influence on hri comfortable distances. In: Lecture Notes Computer Science, 10652; 2017. p. 167–177, et al; Knepper, R.A., Tellex, S., Li, A., Recovering from failure by asking for help (2015) Auton Robots, 39 (3), pp. 347-362. , et al; Knepper, R.A., Mavrogiannis, C.I., Proft, J., Implicit communication a joint action. In: ACM/IEEE International Conference on Human-Robot Interaction; 2017. p. 283–292, et al; Kamide, H., Kawabe, K., Shigemi, S., Anshin as a concept of subjective well-being between humans and robots in Japan (2015) Adv Robot, 29 (24), pp. 1624-1636. , et al; Nomura, T., Kanda, T., Rapport–Expectation with a robot scale (2016) Int J Soc Robot, 8 (1), pp. 21-30; Bartneck, C., Kulić, D., Croft, E., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int J Soc Robot, 1 (1), pp. 71-81. , et al; Iocchi, L., Kraetzschmar, G.K., Nardi, D., RoCKIn@Home: domestic robots challenge. In: Rockin–Benchmarking Through Robot Competitions. IntechOpen; 2017. p. 21–46, et al; Fontana, G., Matteucci, M., Amigoni, F., Rockin benchmarking and scoring system. In: Rockin–Benchmarking Through Robot Competitions. IntechOpen; 2017. p. 75–98, et al; Brščić, D., Kidokoro, H., Suehiro, Y., Escaping from childrens abuse of social robots. In: Proceedings of ACM/IEEE International Conference on Human-Robot Interaction; 2015. p. 59–66, et al; Orkin, J., Roy, D., The restaurant game: learning social behavior and language from thousands of players online (2007) J Game Develop, 3 (1), pp. 39-60; Breazeal, C., DePalma, N., Orkin, J., Crowdsourcing human-Robot interaction: new methods and system evaluation in a public environment (2013) J Human-Robot Int, 2 (1), pp. 82-111. , et al; Striegnitz, K., Denis, A., Gargett, A., Report on the second second challenge on generating instructions virtual environments (GIVE-2.5). In: Proc. of european workshop on natural language generation; 2011. p. 270–279, et al; Inamura, T., Tan, J.T.C., Sugiura, K., Development of robocup@home simulation towards long-term large scale hri. In: Lecture Notes Computer Science: Robot World Cup XVII; 2014. p. 672–680, et al; Kanda, T., Ishiguro, H., Imai, M., Development and evaluation of interactive humanoid robots (2004) Proc IEEE, 92 (11), pp. 1839-1850. , et al; Gargett, A., Garoufi, K., Koller, A., The GIVE-2 corpus of giving instructions virtual environments. In: Proceedings of the Seventh conference on International Language Resources and Evaluation; 2010. p. 2401–2406, et al; de Vries, H., Shuster, K., Batra, D., Talk the walk: navigating New York city through grounded dialogue. 2018, et al; Mizuchi, Y., Inamura, T., Estimation of subjective evaluation of HRI performance based on objective behaviors of human and robots. In: RoboCup International Symposium; 2019","Mizuchi, Y.; National Institute of Informatics, 2-1-2 Hitotsubashi, Japan; email: mizuchi@nii.ac.jp",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Final","",Scopus,2-s2.0-85076043284
"Ye J., Lin Z., You J., Huang S., Wu H.","56447569000;57205178543;57215583365;57205118815;57214357856;","Inconsistency calibrating algorithms for large scale piezoresistive electronic skin",2020,"Micromachines","11","2","162","","",,2,"10.3390/mi11020162","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081277291&doi=10.3390%2fmi11020162&partnerID=40&md5=39f3f05135732ebe49d593995e641ed0","School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350116, China","Ye, J., School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350116, China; Lin, Z., School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350116, China; You, J., School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350116, China; Huang, S., School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350116, China; Wu, H., School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350116, China","In the field of safety and communication of human-robot interaction (HRI), using large-scale electronic skin will be the tendency in the future. The force-sensitive piezoresistive material is the key for piezoresistive electronic skin. In this paper, a non-array large scale piezoresistive tactile sensor and its corresponding calibration methods were presented. Because of the creep inconsistency of large scale piezoresistive material, a creep tracking compensation method based on K-means clustering and fuzzy pattern recognition was proposed to improve the detection accuracy. With the compensated data, the inconsistency and nonlinearity of the sensor was calibrated. The calibration process was divided into two parts. The hierarchical clustering algorithm was utilized firstly to classify and fuse piezoresistive property of different regions over the whole sensor. Then, combining the position information, the force detection model was constructed by Back-Propagation (BP) neural network. At last, a novel flexible tactile sensor for detecting contact position and force was designed as an example and tested after being calibrated. The experimental results showed that the calibration methods proposed were effective in detecting force, and the detection accuracy was improved. © 2020 by the authors.","Calibration; Electronic skin; Inconsistency; Large scale; Piezoresistive","Backpropagation; Calibration; Creep; Hierarchical clustering; Human robot interaction; Pattern recognition; Back propagation neural networks; Electronic skin; Fuzzy pattern recognition; Human robot Interaction (HRI); Inconsistency; Large scale; Piezo-resistive; Piezoresistive properties; K-means clustering",,,,,"National Natural Science Foundation of China, NSFC: 51575111; National Key Research and Development Program of China, NKRDPC: 2018YFB1308603","This research was funded by the National Key Research and Development Project of China, grant number 2018YFB1308603, and the National Natural Science Foundation of China, grant number 51575111.","Funding: This research was funded by the National Key Research and Development Project of China, grant number 2018YFB1308603, and the National Natural Science Foundation of China, grant number 51575111.",,,,,,,,,"Shan, L., Bimbo, J., Dahiya, R., Liu, H., Robotic tactile perception of object properties: A review (2017) Mechatronics, 48, pp. 54-67; Lederman, S., Klatzky, R., Haptic perception: A tutorial (2009) Atten. Percept. Psychophys, 71, pp. 1439-1459; Kang, D., Kwak, S., In Feel Me If You Can: The Effect of Robot Types and Robot's Tactility Types on Users' Perception toward a Robot (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, , Vienna, Austria, 3-4 March; Shimojo, M., Namiki, A., Ishikawa, M., Makino, R., Mabuchi, K., A Tactile Sensor Sheet Using Pressure Conductive Rubber with Electrical-Wires Stitched Method (2004) IEEE Sens. J, 4, pp. 589-596; Bugmann, G., Pan, Z., Zhu, Z., Flexible full-body tactile sensor of low cost and minimal output connections for service robot (2005) Ind. Robot, 32, pp. 485-491; Yang, Y.J., Cheng, M.Y., Chang, W.Y., Tsao, L.C., Yang, S.A., Shih, W.P., Chang, F.Y., Fan, K.C., An integrated flexible temperature and tactile sensing array using PI-copper films (2008) Sens. Actuators A Phys, 143, pp. 143-153; Engel, J., Chen, N., Tucker, C., Liu, C., Kim, S.H., Jones, D., Flexible Multimodal Tactile Sensing System for Object Identification (2016) Proceedings of the 2016 IEEE Conference on Sensors, , Daegu, Korea, 22-25 October; Luo, Y., Xiao, Q., Li, B., A Stretchable Pressure-Sensitive Array Based on Polymer Matrix (2017) Sensors, 17, p. 1571; Wang, H., Zhou, D., Cao, J., Development of a Skin-Like Tactile Sensor Array for Curved Surface (2013) IEEE Sens. J, 14, pp. 55-61; Cheng, M., Tsao, C., Yang, Y., An anthropomorphic robotic skin using highly twistable tactile sensing array (2010) Proceedings of the 2010 IEEE Conference on Industrial Electronics & Applications, , Taichung, Taiwan, 15-17 June; Yao, A., Yang, C., Seo, K., Soleimani, M., EIT-based fabric pressure sensing (2013) Comput. Math. Methods Med, 4, p. 405325; Lee, H., Kwon, D., Cho, H., Park, I., Kim, J., Soft Nanocomposite Based Multi-point, Multi-directional Strain Mapping Sensor Using Anisotropic Electrical Impedance Tomography (2017) Sci. Rep, 7, p. 39837; Vidhate, S., Chung, J., Vaidyanathan, V., Souza, N., Time dependent piezoresistive behavior of polyvinylidene fluoride/carbon nanotube conductive composite (2009) Mater. Lett, 63, pp. 1771-1773; Ying, H., Xiulan, F., Min, W., Panfeng, H., Yunjian, G., Research on Nano-SiÜ2/Carbon Black Composite for Flexible Tactile Sensing (2007) Proceedings of the 2007 IEEE International Conference on Information Acquisition (ICIA07), pp. 260-264. , Seogwipo-si, Korea, 8-11 July; Wang, P., Ding, T., Creep of electrical resistance under uniaxial pressures for carbon black-silicone rubber composite (2010) J. Mater. Sci, 45, pp. 3595-3601; Almassr, A., Wan, Z., Hasan, W., Ahmad, S., Self-Calibration Algorithm for a Pressure Sensor with a Real-Time Approach Based on an Artificial Neural Network (2018) Sensors, 18, p. 2561; Ding, T., Wang, L., Peng, W., Changes in electrical resistance of carbon-black-filled silicone rubber composite during compression (2010) J. Polym. Sci. Part B Polym. Phys, 45, pp. 2700-2706; Phan, K.L., Methods to correct for creep in elastomer-based sensors (2008) Proceedings of the 2008 IEEE Sensors, pp. 1119-1122. , Lecce, Italy, 26-29 October; Hecht, N., Theory of the backpropagation neural network (1989) Proceedings of the International Joint Conference on Neural Networks, , Washington, DC, USA, 18-22 June; Wu, H., Chen, J., Su, Y., Li, Z., Ye, J., New tactile sensor for position detection based on distributed planar electric field (2016) Sens. Actuators A Phys, 242, pp. 146-161; Ye, J., Chen, J., Wang, H., Wu, H., A New Flexible Tactile Sensor for Contact Position Detection Based on Refraction of Electric Field Lines (2018) Sens. Mater, 30, pp. 1953-1975; Wu, H., Wang, H., Huang, J., Zhang, Y., Ye, J., A flexible annular sectorial sensor for detecting contact position based on constant electric field (2018) Micromachines, 9, p. 309; Zhang, Y., Ye, J., Wang, H., Huang, S., Wu, H., A Flexible Tactile Sensor with Irregular Planar Shape Based on Uniform Electric Field (2018) Sensors, 18, p. 4445","Wu, H.; School of Mechanical Engineering and Automation, China; email: wuhb@fzu.edu.cn",,,"MDPI AG",,,,,2072666X,,,,"English","Micromachines",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85081277291
"Andronas D., Apostolopoulos G., Fourtakas N., Makris S.","57209693252;7004169705;57235211800;12241673400;","Multi-modal interfaces for natural Human-Robot Interaction",2020,"Procedia Manufacturing","54",,,"197","202",,1,"10.1016/j.promfg.2021.07.030","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113713445&doi=10.1016%2fj.promfg.2021.07.030&partnerID=40&md5=d1386940dc9c90c30627181d3ea59610","Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, 26504, Greece","Andronas, D., Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, 26504, Greece; Apostolopoulos, G., Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, 26504, Greece; Fourtakas, N., Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, 26504, Greece; Makris, S., Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, 26504, Greece","Recent years, manufacturing aims on increasing flexibility while maintaining productivity for satisfying emerging market needs for higher product customization. Human Robot Collaboration (HRC) is able to bring about this balance by combining the benefits of manual assembly and robotic automation. When introducing a hybrid concept, safety and human acceptance are of vital importance for achieving implementation. Fenceless coexistence may lead to discomfort of operators especially in cases where close Human Robot Interaction (HRI) occurs. This work aims at designing and implementing a natural Human-System and System-Human interaction framework that enables seamless interaction between operators and their ""robot colleagues"". This natural interaction will strengthen hybrid implementation through increased: a) operator's and system's awareness, b) operator's trust to the system, and through the decrease of: a) human errors and b) safety incidents. The overall architecture of the proposed system makes it scalable, flexible, and applicable in different collaborative scenarios by enabling the connectivity of multiple interfaces with customizable environments according to operator's needs. The performance of the system is evaluated on a scenario originating from the automotive industry proving that an intuitive interaction framework can increase acceptance and performance of both robots and operators. © 2021 The Author(s).","Augmented Reality; Human Robot Collaboration; Human Robot Interaction; Interface; Robotic cells","Competition; Industrial robots; Man machine systems; Robotic assembly; Sustainable development; Human robot Interaction (HRI); Human-robot collaboration; Hybrid implementation; Intuitive interaction; Multi-modal interfaces; Multiple interfaces; Natural interactions; Product customization; Social robots",,,,,"European Commission, EC: 820807","This work has been partially funded by the EC research project “SHEWARK OR - Safe and effective HumAn-Robot coopEration toWards a better cOmpetiveness on current automation lacK manufacturing processes” ( Grant Agreement: 820807).",,,,,,,,,,"Chryssolouris, G., (2006) Manufacturing Systems: Theory and Practice, , Springer; Makris, S., (2021) Cooperating Robots for Flexible Manufacturing, , Springer International Publishing Cham; Khojasteh, Y., (2017) Production Managementâ€»: Advanced Tools, Models, and Applications for Pull Systems; Paletta, L., Pszeida, M., Ganster, H., Fuhrmann, F., Weiss, W., Ladstatter, S., Dini, A., Reiterer, B., (2019) Gaze-based Human Factors Measurements for the Evaluation of Intuitive Human-Robot Collaboration in Real-time., , IEEE International Conference on Emerging Technologies and Factory Automation, ETFA. Institute of Electrical and Electronics Engineers Inc; Heyer, C., Human-robot interaction and future industrial robotics applications. (2010) IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, Iros 2010-Conference Proceedings.; Tsarouchi, P., Makris, S., Chryssolouris, G., Human-robot interaction review and challenges on task planning and programming (2016) International Journal of Computer Integrated Manufacturing, 29, pp. 916-931; Kardos, C., Kemény, Z., Kovács, A., Pataki, B.E., Váncza, J., Context-dependent multimodal communication in human-robot collaboration (2018) Procedia Cirp, , Elsevier B.V; Rossi, S., Leone, E., Fiore, M., Finzi, A., Cutugno, F., An extensible architecture for robust multimodal human-robot communication. (2013) Ieee International Conference on Intelligent Robots and Systems.; Abich, J., Daniel, Barber, J., Barber, D.J., The impact of human-robot multimodal communication on mental workload, usability preference, and expectations of robot behavior (2017) J Multimodal User Interfaces, 11, pp. 211-225; Ong, S.K., Yuan, M.L., Nee, A.Y.C., Augmented reality applications in manufacturing: A survey (2008) International Journal of Production Research, 46, pp. 2707-2742; Chang, C.-C., New Approach for Static Gesture Recognition (2006) J. Inf. Sci. Eng., 22, pp. 1047-1057; Pires, J.N., Robot-by-voice: Experiments on commanding an industrial robot using the human voice (2005) Industrial Robot, 32, pp. 505-511; Nee, A.Y.C., Ong, S.K., Virtual and augmented reality applications in manufacturing. (2013) Ifac Proceedings Volumes (IFAC-PapersOnline). Ifac Secretariat; Murar, M., Brad, S., Monitoring and controlling of smart equipments using Android compatible devices towards IoT applications and services in manufacturing industry. (2014) Proceedings of 2014 Ieee International Conference on Automation, Quality and Testing, Robotics, Aqtr 2014., , IEEE Computer Society; Kühnlenz, B., Erhart, M., Kainert, M., Wang, Z.Q., Wilm, J., Kühnlenz, K., Impact of trajectory profiles on user stress in close human-robot interaction (2018) At-Automatisierungstechnik, 66, pp. 483-491; Andronas, D., Argyrou, A., Fourtakas, K., Paraskevopoulos, P., Makris, S., Design of Human Robot Collaboration workstations-Two automotive case studies (2020) Procedia Manufacturing, , Elsevier B.V; Brooke, J., (1995) SUS-A Quick and Dirty Usability Scale","Makris, S.; Laboratory for Manufacturing Systems and Automation, Greece; email: makris@lms.mech.upatras.gr",,,"Elsevier B.V.","10th CIRP Sponsored Conference on Digital Enterprise Technologies - Digital Technologies as Enablers of Industrial Competitiveness and Sustainability, DET 2020","12 October 2020 through 14 October 2020",,146288,23519789,,,,"English","Procedia Manuf.",Conference Paper,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85113713445
"Camporese G., Coscia P., Furnari A., Farinella G.M., Ballan L.","57219739880;57190073520;56348910300;57191972987;23134291400;","Knowledge distillation for action anticipation via label smoothing",2020,"Proceedings - International Conference on Pattern Recognition",,,"9412660","3312","3319",,1,"10.1109/ICPR48806.2021.9412660","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110505567&doi=10.1109%2fICPR48806.2021.9412660&partnerID=40&md5=7dfd6c7b916ae614c7e4f4871ddbf59a","Department of Mathematics “Tullio Levi-Civita”, University of Padova, Italy; Department of Mathematics and Computer Science, University of Catania, Italy","Camporese, G., Department of Mathematics “Tullio Levi-Civita”, University of Padova, Italy; Coscia, P., Department of Mathematics “Tullio Levi-Civita”, University of Padova, Italy; Furnari, A., Department of Mathematics and Computer Science, University of Catania, Italy; Farinella, G.M., Department of Mathematics and Computer Science, University of Catania, Italy; Ballan, L., Department of Mathematics “Tullio Levi-Civita”, University of Padova, Italy","Human capability to anticipate near future from visual observations and non-verbal cues is essential for developing intelligent systems that need to interact with people. Several research areas, such as human-robot interaction (HRI), assisted living or autonomous driving need to foresee future events to avoid crashes or help people. Egocentric scenarios are classic examples where action anticipation is applied due to their numerous applications. Such challenging task demands to capture and model domain's hidden structure to reduce prediction uncertainty. Since multiple actions may equally occur in the future, we treat action anticipation as a multi-label problem with missing labels extending the concept of label smoothing. This idea resembles the knowledge distillation process since useful information is injected into the model during training. We implement a multi-modal framework based on long short-term memory (LSTM) networks to summarize past observations and make predictions at different time steps. We perform extensive experiments on EPIC-Kitchens and EGTEA Gaze+ datasets including more than 2500 and 100 action classes, respectively. The experiments show that label smoothing systematically improves performance of state-of-the-art models for action anticipation. © 2020 IEEE",,"Distillation; Distilleries; Intelligent systems; Long short-term memory; Pattern recognition; Action anticipations; Autonomous driving; Different time steps; Distillation process; Human robot Interaction (HRI); Multi-label problems; Prediction uncertainty; Visual observations; Human robot interaction",,,,,"Nvidia; Dairy Management, DMI: AIM1893589 - CUP E64118002540007; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR","Acknowledgements: Research at the University of Padova is partially supported by MIUR PRIN-2017 PREVUE grant. Authors from the Univ. of Padova gratefully acknowledge the support of NVIDIA for their donation of GPUs, and the UNIPD CAPRI Consortium for its support and access to computing resources. Research at the University of Catania is supported by PTR 2016-2018 Linea 2 of DMI and MIUR AIM - Linea 1 - AIM1893589 - CUP E64118002540007.",,,,,,,,,,"Ballan, L., Bertini, M., Del Bimbo, A., Seidenari, L., Serra, G., Event detection and recognition for semantic annotation of video (2011) Multimedia Tools and Applications, 51 (1), pp. 279-302; Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L., Large-scale video classification with convolutional neural networks (2014) IEEE Computer Vision and Pattern Recognition, , Jun; Ji, S., Xu, W., Yang, M., Yu, K., 3D convolutional neural networks for human action recognition (2013) IEEE Trans. On Pattern Analysis and Machine Intelligence, 35 (1), pp. 221-231. , Jan; Sultani, W., Chen, C., Shah, M., Real-world anomaly detection in surveillance videos (2018) IEEE Conf. On Computer Vision and Pattern Recognition, , June; Bhattacharyya, A., Fritz, M., Schiele, B., Long-term on-board prediction of people in traffic scenes under uncertainty (2018) The IEEE Conf. On Computer Vision and Pattern Recognition; Maqueda, A.I., Loquercio, A., Gallego, G., García, N., Scaramuzza, D., Event-based vision meets deep learning on steering prediction for self-driving cars (2018) The IEEE Conf. On Computer Vision and Pattern Recognition, , June; Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B., Learning realistic human actions from movies (2008) IEEE Conf. On Computer Vision and Pattern Recognition; Simonyan, K., Zisserman, A., Two-stream convolutional networks for action recognition in videos (2014) Advances in Neural Information Processing Systems (NeurIPS); Feichtenhofer, C., Pinz, A., Zisserman, A., Convolutional two-stream network fusion for video action recognition (2016) IEEE Computer Vision and Pattern Recognition; Ryoo, M.S., Human activity prediction: Early recognition of ongoing activities from streaming videos (2011) IEEE Int. Conf. On Computer Vision; Cao, Y., Barrett, D., Barbu, A., Narayanaswamy, S., Yu, H., Michaux, A., Lin, Y., Wang, S., Recognize human activities from partially observed videos (2013) IEEE Computer Vision and Pattern Recognition; de Geest, R., Gavves, E., Ghodrati, A., Li, Z., Snoek, C., Tuytelaars, T., Online action detection (2016) European Conf. On Computer Vision; Damen, D., Doughty, H., Farinella, G.M., Fidler, S., Furnari, A., Kazakos, E., Moltisanti, D., Wray, M., The epic-kitchens dataset: Collection, challenges and baselines (2020) IEEE Trans. On Pattern Analysis and Machine Intelligence (TPAMI); Furnari, A., Farinella, G.M., Rolling-unrolling lstms for action anticipation from first-person video (2020) IEEE Trans. On Pattern Analysis and Machine Intelligence (PAMI), , https://iplab.dmi.unict.it/rulstm, Online; Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., Rethinking the inception architecture for computer vision (2016) IEEE Conf. On Computer Vision and Pattern Recognition, , June; Wang, H., Kläser, A., Schmid, C., Liu, C.-L., Dense trajectories and motion boundary descriptors for action recognition (2013) Int. Journal of Computer Vision, 103 (1), pp. 60-79; Wang, H., Schmid, C., Action recognition with improved trajectories (2013) IEEE Int. Conf. On Computer Vision; Feichtenhofer, C., Pinz, A., Wildes, R.P., Spatiotemporal multiplier networks for video action recognition (2017) IEEE Computer Vision and Pattern Recognition; Wang, L., Xiong, Y., Wang, Z., Qiao, Y., Lin, D., Tang, X., van Gool, L., Temporal segment networks: Towards good practices for deep action recognition (2016) European Conf. On Computer Vision; Zhou, B., Andonian, A., Oliva, A., Torralba, A., Temporal relational reasoning in videos (2018) European Conf. On Computer Vision; Lin, J., Gan, C., Han, S., TSM: Temporal shift module for efficient video understanding (2019) IEEE Int. Conf. On Computer Vision; Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M., Learning spatiotemporal features with 3d convolutional networks (2015) IEEE Int. Conf. On Computer Vision; Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M., A closer look at spatiotemporal convolutions for action recognition (2018) IEEE Conf. On Computer Vision and Pattern Recognition; Donahue, J., Hendricks, L.A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T., Long-term recurrent convolutional networks for visual recognition and description (2015) IEEE Conf. On Computer Vision and Pattern Recognition; Sudhakaran, S., Escalera, S., Lanz, O., LSTA: Long short-term attention for egocentric action recognition (2018) IEEE Computer Vision and Pattern Recognition; Hoai, M., de la Torre, F., Max-margin early event detectors (2014) Int. Journal of Computer Vision, 107 (2), pp. 191-202; Huang, D., Yao, S., Wang, Y., de la Torre, F., Sequential max-margin event detectors (2014) European Conf. On Computer Vision; Aliakbarian, M.S., Saleh, F.S., Salzmann, M., Fernando, B., Petersson, L., Andersson, L., Encouraging LSTMs to anticipate actions very early (2017) IEEE Int. Conf. On Computer Vision; Becattini, F., Uricchio, T., Seidenari, L., Ballan, L., Del Bimbo, A., Am I done? Predicting action progress in videos ACM Trans. On Multimedia Computing, Communications, and Applications (TOMM), p. 2020. , press; de Geest, R., Tuytelaars, T., Modeling temporal structure with lstm for online action detection (2018) IEEE Winter Conf. On Applications in Computer Vision; Lan, T., Chen, T.-C., Savarese, S., A hierarchical representation for future action prediction (2014) European Conf. On Computer Vision; Jain, A., Koppula, H.S., Raghavan, B., Soh, S., Saxena, A., Car that knows before you do: Anticipating maneuvers via learning temporal driving models (2015) IEEE Int. Conf. On Computer Vision; Vondrick, C., Pirsiavash, H., Torralba, A., Anticipating visual representations from unlabeled video (2016) IEEE Conf. On Computer Vision and Pattern Recognition; Gao, J., Yang, Z., Nevatia, R., Red: Reinforced encoder-decoder networks for action anticipation (2017) British Machine Vision Conf; Zeng, K.-H., Shen, W.B., Huang, D.-A., Sun, M., Niebles, J.C., Visual forecasting by imitating dynamics in natural sequences (2017) IEEE Int. Conf. On Computer Vision, , Oct; Mahmud, T., Hasan, M., Roy-Chowdhury, A.K., Joint prediction of activity labels and starting times in untrimmed videos (2017) IEEE Int. Conf. On Computer Vision, , Oct; Farha, Y.A., Richard, A., Gall, J., When will you do what? - Anticipating temporal occurrences of activities (2018) IEEE Conf. On Computer Vision and Pattern Recognition, , June; Hinton, G., Vinyals, O., Dean, J., Distilling the knowledge in a neural network (2015) NIPS Deep Learning and Representation Learning Workshop; Ioffe, S., Szegedy, C., Batch normalization: Accelerating deep network training by reducing internal covariate shift (2015) Int. Conf. On Machine Learning; Girshick, R.B., Fast R-CNn (2015) IEEE Int. Conf. On Computer Vision; Furnari, A., Battiato, S., Farinella, G.M., Leveraging uncertainty to rethink loss functions and evaluation measures for egocentric action anticipation (2018) European Conf. On Computer Vision Workshops; Pennington, J., Socher, R., Manning, C.D., Glove: Global vectors for word representation (2014) Empirical Methods in Natural Language Processing; Li, Y., Liu, M., Rehg, J.M., In the eye of beholder: Joint learning of gaze and actions in first person video (2018) ECCV; Koppula, H.S., Saxena, A., Anticipating human activities using object affordances for reactive robotic response (2016) IEEE Trans. On Pattern Analysis and Machine Intelligence, 38 (1), pp. 14-29; Miech, A., Laptev, I., Sivic, J., Wang, H., Torresani, L., Tran, D., Leveraging the present to anticipate the future in videos (2019) IEEE Conf. On Computer Vision and Pattern Recognition Workshops","Camporese, G.; Department of Mathematics “Tullio Levi-Civita”, Italy
Coscia, P.; Department of Mathematics “Tullio Levi-Civita”, Italy
Ballan, L.; Department of Mathematics “Tullio Levi-Civita”, Italy",,,"Institute of Electrical and Electronics Engineers Inc.","25th International Conference on Pattern Recognition, ICPR 2020","10 January 2021 through 15 January 2021",,169954,10514651,9781728188089,PICRE,,"English","Proc. Int. Conf. Pattern Recognit.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85110505567
"Guerra-Zubiaga D.A., Nasajpour-Esfahani N., Siddiqui B., Kamperman K., II","23060428300;57222062476;57222071401;57222072710;","Implementing a novel framework to create tacit knowledge models to support human-robot interactions",2020,"ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)","2B-2020",,,"","",,,"10.1115/IMECE2020-23803","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101270241&doi=10.1115%2fIMECE2020-23803&partnerID=40&md5=003f9cc2858d067a6b58f91e7a6968da","Kennesaw State University, Kennesaw, Georgia, United States; Kennesaw State University, Marietta, GA, United States","Guerra-Zubiaga, D.A., Kennesaw State University, Kennesaw, Georgia, United States; Nasajpour-Esfahani, N., Kennesaw State University, Marietta, GA, United States; Siddiqui, B., Kennesaw State University, Marietta, GA, United States; Kamperman, K., II, Kennesaw State University, Marietta, GA, United States","While it is not only important to synthesize instruments, controls, and robotics, it is also essential to connect these elements to people to achieve the future of automation. Whether in an operating room with surgical robots or in an earthquake disaster zone where an operator is aided by search and rescue drones, interaction between machines and humans is becoming central to increasing productivity. Industry 4.0 trends such as Internet of Things (IoT) and digital manufacturing are the early adopters of human-machine interfaces that support manufacturing automation. Such models must consider various aspects of process implementation such as explicit, implicit, and tacit knowledge to properly mimic a human's performance. However, most inquiries in this field use expressed information instead of tacit knowledge due to an unfulfilled need for an industrial tacit knowledge framework. Tacit knowledge is difficult to learn and transfer if an operator's logic is never revealed. In response, this research provides a knowledge model to structure, categorize, and reuse tacit knowledge for advanced manufacturing operations. The model is implemented in a human-robot interaction by capturing valuable experiences using digital tools such as Tecnomatix for further reuse in a variety of industrial applications. © 2020 The Author(s). This is an Open Access article under the CC BY license.","Collaborative robots; Digital manufacturing; Next generation automation systems; Tacit knowledge framework","Digital devices; End effectors; Industrial research; Industrial robots; Internet of things; Man machine systems; Manufacture; User experience; Advanced manufacturing; Digital manufacturing; Earthquake disaster; Human Machine Interface; Internet of Things (IOT); Manufacturing Automation; Process implementation; Search and rescue; Human robot interaction",,,,,,,,,,,,,,,,"Guerra-Zubiaga, David A., Briceno, Simon, Product Lifecycle Management Tools to Support Next Generation of Mechatronic Systems at Aerospace Industry (2017) Proceedings of the ASME 2017 International Mechanical Engineering Congress & Exposition, IMECE2017, , November 3-9, 2017, Tampa, Florida, USA; Djuric, A., Urbanic, R., Rickli, J., A Framework for Collaborative Robot (CoBot) Integration in Advanced Manufacturing Systems SAE Int. J. Mater. Manf, 9 (2), p. 2016; Guerra-Zubiaga, David A., Tacit Knowledge Elicitation Techniques Applied to Complex Manufacturing Processes (2017) Proceedings of the ASME 2017 International Mechanical Engineering Congress & Exposition, IMECE2017, , November 3-9, 2017, Tampa, Florida, USA; Cai, Y., Starly, B., Cohen, P., Lee, Y., Sensor Data and Information Fusion to Construct Digital-twins Virtual Machine Tools for Cyber-physical Manufacturing (2017) Procedia Manufacturing, 10, pp. 1031-1042; Bassi, L., Industry 4.0: Hope, hype or revolution? (2017) 2017 IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI); Rosen, R., Wichert, G. V., Lo, G., Bettenhausen, K. D., About the Importance of Autonomy and Digital Twins for the Future of Manufacturing (2015) IFAC-PapersOnLine, 48 (3), pp. 567-572; Mourtzis, Dimitris, The Role of Simulation in Digital Manufacturing: Applications and Outlook (2015) International Journal of Computer Integrated Manufacturing, 28 (1), pp. 3-24. , Jan. EBSCOhost; Guerra-Zubiaga, D.A., Young, R.I.M., Design of a Manufacturing Knowledge Model (2008) International Journal of Computer Integrated Manufacturing, 12 (5), pp. 526-539; Guerra-Zubiaga, David A., Tacit Knowledge Elicitation Techniques Applied to Complex Manufacturing Processes (2017) Proceedings of the ASME 2017 International Mechanical Engineering Congress & Exposition, IMECE2017, , November 3-9, 2017, Tampa, Florida, USA; Guerra-Zubiaga, David A., Newman, Stephen, Creating A New Energy Efficiency Tool for Manufacturing Automation to Support Next Generation Sustainable Eco-Factories (2017) Proceedings of the ASME 2017 International Mechanical Engineering Congress & Exposition, IMECE2017, , November 3-9, 2017, Tampa, Florida, USA; Norton, A., Ober, W., Baraniecki, L., Shane, D., Skinner, A., Yanco, H., Perspectives on Human-Robot Team Performance from an Evaluation of the DARPA Robotics Challenge (2018) the DARPA Robotics Challenge Finals: Humanoid Robots to The Rescue, pp. 631-666. , Springer, Cham; Yamane, Katsu, Anderson, Stuart O., Hodgins, Jessica K., Controlling Humanoid Robots with Human Motion Data: Experimental Validation (2010) IEEE-RAS International Conference on Humanoid Robots, , Nashville, TN, USA, December 6-8, 2010; Lu, Y., Cecil, J., An Internet of Things (IoT)based collaborative framework for advanced manufacturing (2016) International Journal Of Advanced Manufacturing Technology, 84 (5-8), p. 1141; Calitz, A. P., Poisat, P., Cullen, M., The future African workplace: The use of collaborative robots in manufacturing (2017) South African Journal of Human Resource Management, 15 (1), pp. 1-11; Maurice, P., Padois, V., Measson, Y., Bidaud, P., Human-Oriented Design of Collaborative Robots (2017) International Journal of Industrial Ergonomics, 57, pp. 88-102; Ball, A., Patel, M., Mcmahon, C., Green, S., Clarkson, J., Culley, S., A Grand Challenge:Immortal Information and Through-Life Knowledge Management (KIM) (2008) International Journal of Digital Curation, 1, pp. 53-59; Mulder, U., Whiteley, A., Emerging and capturing tacit knowledge:a methodology for a bounded environment (2007) Journal of Knowledge Management; Augustsson, S., Olsson, J., Christiernin, L., Bolmsjö, G., (2014) How to Transfer Information between Collaborating Human Operators and Industrial Robots in an Assembly; Esmaeili, H., Thwaites, H., Woods, P., (2017) Immersive Virtual Environments for Tacit Knowledge Transfer Focusing on Gestures: A Workflow",,,"American Society of Mechanical Engineers (ASME)","American Society of Mechanical Engineers (ASME)","ASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020","16 November 2020 through 19 November 2020",,167181,,9780791884492,,,"English","ASME Int Mech Eng Congress Expos Proc",Conference Paper,"Final","",Scopus,2-s2.0-85101270241
"Rojas R.A., Garcia M.A.R., Gualtieri L., Rauch E.","57195804043;57206406204;57204731285;32467549600;","Combining safety and speed in collaborative assembly systems - An approach to time optimal trajectories for collaborative robots",2020,"Procedia CIRP","97",,,"308","312",,1,"10.1016/j.procir.2020.08.003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100831212&doi=10.1016%2fj.procir.2020.08.003&partnerID=40&md5=0241003720e866157e6d4133c0dc3e33","Free University of Bozen-Bolzano, Faculty of Science and Technology, Piazza Unversità 1, Bozen-Bolzano, 39100, Italy","Rojas, R.A., Free University of Bozen-Bolzano, Faculty of Science and Technology, Piazza Unversità 1, Bozen-Bolzano, 39100, Italy; Garcia, M.A.R., Free University of Bozen-Bolzano, Faculty of Science and Technology, Piazza Unversità 1, Bozen-Bolzano, 39100, Italy; Gualtieri, L., Free University of Bozen-Bolzano, Faculty of Science and Technology, Piazza Unversità 1, Bozen-Bolzano, 39100, Italy; Rauch, E., Free University of Bozen-Bolzano, Faculty of Science and Technology, Piazza Unversità 1, Bozen-Bolzano, 39100, Italy","Human-robot interaction is a key enabling technology of Industry 4.0 and the prospected pervasiveness of robotics in industrial environments will be not possible without enabling them to safety interact with humans. Such a fact imposes a relevant constraints because the limits imposed by the actual technical deliverables entail strong requirements in the operational velocity of robots when sharing their workspace with humans. In this paper, we address the theoretical limits of velocity under the light of current state-of-the-art trajectory planning and normative requirements. The main goal is to find a methodology to plan safe trajectories without neglecting cognitive ergonomics and production efficiency aspects. We start by considering the set of trajectories which are optimal with respect to a cognitive criteria and give a suitable parametrization to it. Then we are able to formulate the safety requirements in terms of constraints in an optimization problem. Finally, experimental results are provided. This allow the identification of the preferable sets of possible motions which satisfy the operator psychological wellbeing and the assembly process performance by complying the safety requirements in terms of mechanical risk prevention. © 2020 Elsevier B.V.. All rights reserved.","Assembly; Collaborative robotics; Ergonomics; Industry 4.0; Safety; Trajectory planning","Accident prevention; Assembly; Constrained optimization; Ergonomics; Industrial robots; Trajectories; Collaborative assembly; Collaborative robots; Enabling technologies; Industrial environments; Optimization problems; Production efficiency; Psychological well-being; Time optimal trajectory; Human robot interaction",,,,,,,,,,,,,,,,"Abb, Operating Manual IRB 14000 (2018) Technical Report, , ABB; Arai, T., Kato, R., Fujita, M., Assessment of operator stress induced by robot collaboration in assembly (2010) CIRP Ann., 59 (1), pp. 5-8; De Luca, A., Farina, R., Dynamic scaling of trajectories for robots with elastic joints (2002) Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No. 02CH37292), 3, pp. 2436-2442. , IEEE; Flash, T., Hogan, N., The coordination of arm movements: An experimentally confirmed mathematical model (1985) J. Neurosci., 5 (7), pp. 1688-1703; Garcia, M.A.R., Rojas, R.A., Pirri, F., Object-centered teleoperation of mobile manipulators with remote center of motion constraint (2019) IEEE Robot. Autom. Lett., 4 (2), pp. 1745-1752; Gasparetto, A., Boscariol, P., Lanzutti, A., Vidoni, R., Trajectory planning in robotics (2012) Math. Comput. Sci., 6 (3), pp. 269-279; Gasparetto, A., Zanotto, V., A new method for smooth trajectory planning of robot manipulators (2007) Mech. Mach. Theory, 42 (4), pp. 455-471; Gualtieri, L., Rauch, E., Rojas, R., Vidoni, R., Matt, D.T., Application of axiomatic design for the design of a safe collaborative human-robot assembly workplace (2018) MATEC Web of Conferences, 223, p. 01003. , EDP Sciences; Gualtieri, L., Rojas, R., Carabin, G., Palomba, I., Rauch, E., Vidoni, R., Matt, D.T., Advanced automation for SMES in the i4. 0 revolution: Engineering education and employees training in the smart mini factory laboratory (2018) 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 1111-1115. , IEEE; Gualtieri, L., Rojas, R.A., Garcia, M.A.R., Rauch, E., Vidoni, R., Implementation of a laboratory case study for intuitive collaboration between man and machine in SME assembly (2020) Industry 4.0 for SMEs, p. 335; Hollerbach, J.M., Dynamic scaling of manipulator trajectories (1984) J. Dyn. Syst. Meas.Control, 106 (1), pp. 102-106; Robots and Robotic Devices - Safety Requirements for Industrial Robots - Part 1: Robots (2011) Standard, , International Organization for Standardization Geneva, CH; Robots and Robotic Devices - Safety Requirements for Industrial Robots - Part 2: Robot Systems and Integration (2011) Standard, , International Organization for Standardization Geneva, CH; Safety of machinery - Risk assessment - Part 2: Practical guidance and examples of methods (2012) Standard, , International Organization for Standardization Geneva, CH; Robots and Robotic Devices - Collaborative Robots (2016) Technical Specification, , International Organization for Standardization Geneva, CH; Kokabe, M., Shibata, S., Yamamoto, T., Modeling of handling motion reflecting emotional state and its application to robots (2008) Proceedings of the SICE Annual Conference, pp. 495-501. , IEEE; Lasota, P.A., Shah, J.A., Analyzing the effects of human-aware motion planning on close-proximity human-robot collaboration (2015) Hum. Factors, 57 (1), pp. 21-33; Meirovitch, Y., Bennequin, D., Flash, T., Geometrical invariance and smoothness maximization for task-space movement generation (2016) IEEE Trans. Robot., 32 (4), pp. 837-853; Oguz, O.S., Zhou, Z., Glasauer, S., Wollherr, D., An inverse optimal control approach to explain human arm reaching control based on multiple internal models (2018) Sci. Rep., 8 (1), p. 5583; Or, C.K., Duffy, V.G., Cheung, C.C., Perception of safe robot idle time in virtual reality and real industrial environments (2009) Int. J. Ind. Ergon., 39 (5), pp. 807-812; Robots, U., User Manual UR3/CB3 Original Instructions (2017) Technical Report, , Universal Robots; Rojas, R.A., Carcaterra, A., An approach to optimal semi-active control of vibration energy harvesting based on mems (2018) Mech. Syst. Signal Process., 107, pp. 291-316; Rojas, R.A., Garcia, M.A.R., Implementation of industrial internet of things and cyber-physical systems in SMES for distributed and service-oriented control (2020) Industry 4.0 for SMEs, pp. 73-103. , Palgrave Macmillan, Cham; Rojas, R.A., Rauch, E., Dallasega, P., Matt, D.T., Safe human-machine centered design of an assembly station in a learning factory environment (2018) Proceedings of the 8th International Conference on Industrial Engineering and Operations Management; Rojas, R.A., Rauch, E., Vidoni, R., Matt, D.T., Enabling connectivity of cyber-physical production systems: A conceptual framework (2017) Procedia Manuf., 11, pp. 822-829; Rojas, R.A., Ruiz Garcia, M.A., Wehrle, E., Vidoni, R., A variational approach to minimum-jerk trajectories for psychological safety in collaborative assembly stations (2019) IEEE Robot. Autom. Lett., 4 (2), pp. 823-829; Rojas, R.A., Wehrle, E., Vidoni, R., A multicriteria motion planning approach for combining smoothness and speed in collaborative assembly systems (2020) Appl. Sci., 10 (15), p. 5086","Rojas, R.A.; Free University of Bozen-Bolzano, Piazza Unversità 1, Italy; email: rafael.rojas@unibz.it","Makris S.",,"Elsevier B.V.","8th CIRP Conference of Assembly Technology and Systems, CATS 2020","29 September 2020 through 1 October 2020",,167017,22128271,,,,"English",,Conference Paper,"Final","All Open Access, Gold",Scopus,2-s2.0-85100831212
"Bonial C., Donatelli L., Abrams M., Lukin S.M., Tratz S., Marge M., Artstein R., Traum D., Voss C.R.","54392575400;57193625450;57219176963;56031320700;56287697600;23009418600;56062888900;6603622906;7004570831;","Dialogue-AMR: Abstract meaning representation for dialogue",2020,"LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings",,,,"684","695",,7,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096605839&partnerID=40&md5=b4b24303f4d63aed40e8ddf0f5a56c85","U.S. Army Research Lab, Adelphi, MD  20783, United States; Saarland University, Saarbrücken, Germany; USC Institute for Creative Technologies, Playa VistaCA  90094, United States","Bonial, C., U.S. Army Research Lab, Adelphi, MD  20783, United States; Donatelli, L., Saarland University, Saarbrücken, Germany; Abrams, M., U.S. Army Research Lab, Adelphi, MD  20783, United States; Lukin, S.M., U.S. Army Research Lab, Adelphi, MD  20783, United States; Tratz, S., U.S. Army Research Lab, Adelphi, MD  20783, United States; Marge, M., U.S. Army Research Lab, Adelphi, MD  20783, United States; Artstein, R., USC Institute for Creative Technologies, Playa VistaCA  90094, United States; Traum, D., USC Institute for Creative Technologies, Playa VistaCA  90094, United States; Voss, C.R., U.S. Army Research Lab, Adelphi, MD  20783, United States","This paper describes a schema that enriches Abstract Meaning Representation (AMR) in order to provide a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems. AMR offers a valuable level of abstraction of the propositional content of an utterance; however, it does not capture the illocutionary force or speaker's intended contribution in the broader dialogue context (e.g., make a request or ask a question), nor does it capture tense or aspect. We explore dialogue in the domain of human-robot interaction, where a conversational robot is engaged in search and navigation tasks with a human partner. To address the limitations of standard AMR, we develop an inventory of speech acts suitable for our domain, and present “Dialogue-AMR”, an enhanced AMR that represents not only the content of an utterance, but the illocutionary force behind it, as well as tense and aspect. To showcase the coverage of the schema, we use both manual and automatic methods to construct the “DialAMR” corpus-a corpus of human-robot dialogue annotated with standard AMR and our enriched Dialogue-AMR schema. Our automated methods can be used to incorporate AMR into a larger NLU pipeline supporting human-robot dialogue. © European Language Resources Association (ELRA), licensed under CC-BY-NC","Abstract Meaning Representation; Dialogue; Illocutionary Force","Human robot interaction; Semantics; Speech processing; Automated methods; Automatic method; Dialogue systems; Human-robot dialogue; Level of abstraction; Natural language understanding; Navigation tasks; Semantic representation; Speech communication",,,,,"W911NF-14-D-0005, W911QX-18-F-0096; Association of Research Libraries, ARL; Army Research Laboratory, ARL","We are grateful to anonymous reviewersfor their feedback. The third author was sponsored by the U.S. Army Research Laboratory (ARL) under the AdvancedResearch Technology, Inc. contract number W911QX-18-F-0096; the seventh and eighth authors were sponsored by ARL under contract number W911NF-14-D-0005.","We are grateful to anonymous reviewers for their feedback. The third author was sponsored by the U.S. Army Research Laboratory (ARL) under the Advanced Research Technology, Inc. contract number W911QX-18-F-0096; the seventh and eighth authors were sponsored by ARL under contract number W911NF-14-D-0005.",,,,,,,,,"Abrams, M., Bonial, C., Donatelli, L., Graph-to-graph meaning representation transformations for human-robot dialogue (2020) Proceedings of the Society for Computation in Linguistics, 3 (1), pp. 459-462; Allen, J. F., Perrault, C. R., Analyzing intention in utterances (1980) Artificial Intelligence, 15 (3), pp. 143-178; Austin, J. L., (1962) How to Do Things with Words, , Harvard University Press and Oxford University Press; Banarescu, L., Bonial, C., Cai, S., Georgescu, M., Griffitt, K., Hermjakob, U., Knight, K., Schneider, N., Abstract Meaning Representation for sembanking (2013) Proceedings of the 7th Linguistic Annotation Workshop and Interoperability with Discourse, pp. 178-186; Bastianelli, E., Castellucci, G., Croce, D., Iocchi, L., Basili, R., Nardi, D., HuRIC: a human robot interaction corpus (2014) LREC, pp. 4519-4526; Bonial, C., Donatelli, L., Lukin, S. M., Tratz, S., Artstein, R., Traum, D., Voss, C. R., Augmenting Abstract Meaning Representation for human-robot dialogue (2019) Proceedings of the First International Workshop on Designing Meaning Representations (DMR), pp. 199-210. , pages Florence, Italy, August; Bonial, C. N., Donatelli, L., Ervin, J., Voss, C. R., Abstract Meaning Representation for human-robot dialogue (2019) Proceedings of the Society for Computation in Linguistics, 2 (1), pp. 236-246; Bruce, B. C., Generation as a social action (1975) Theoretical Issues in Natural Language Processing, pp. 64-67; Bunt, H., Alexandersson, J., Choe, J.-W., Fang, A. C., Hasida, K., Petukhova, V., Popescu-Belis, A., Traum, D., ISO 24617-2: A semantically-based standard for dialogue annotation (2012) Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pp. 430-437; Cai, S., Knight, K., Smatch: An evaluation metric for semantic feature structures (2013) Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2, pp. 748-752; Cohen, P. R., Perrault, C. R., Elements of a plan-based theory of speech acts (1979) Cognitive science, 3 (3), pp. 177-212; Donatelli, L., Regan, M., Croft, W., Schneider, N., Annotation of tense and aspect semantics for sentential AMR (2018) Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018), pp. 96-108; Donatelli, L., Schneider, N., Croft, W., Regan, M., Tense and aspect semantics for sentential AMR (2019) Proceedings of the Society for Computation in Linguistics, 2 (1), pp. 346-348; Flanigan, J., Thomson, S., Carbonell, J., Dyer, C., Smith, N. A., A discriminative graph-based parser for the Abstract Meaning Representation (2014) Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 1, pp. 1426-1436. , Long Papers), pages; Garg, S., Galstyan, A., Hermjakob, U., Marcu, D., Extracting biomolecular interactions using semantic parsing of biomedical text (2016) Proc. of AAAI, , Phoenix, Arizona, USA, February; Hajicova, E., A plea for information structure as a part of meaning representation (2019) Proceedings of the First International Workshop on Designing Meaning Representations, pp. 66-72; He, D., Xia, Y., Qin, T., Wang, L., Yu, N., Liu, T.-Y., Ma, W.-Y., Dual learning for machine translation (2016) Advances in Neural Information Processing Systems, pp. 820-828; Kim, G., Kane, B., Duong, V., Mendiratta, M., McGuire, G., Sackstein, S., Platonov, G., Schubert, L., Generating discourse inferences from unscoped episodic logical formulas (2019) Proceedings of the First International Workshop on Designing Meaning Representations, pp. 56-65; Langkilde, I., Knight, K., Generation that exploits corpus-based statistical knowledge (1998) 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, 1, pp. 704-710. , pages Montreal, Quebec, Canada, August. 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G., Voss, C., Applying the Wizard-of-Oz technique to multimodal human-robot dialogue (2016) RO-MAN 2016: IEEE International Symposium on Robot and Human Interactive Communication; Marge, M., Bonial, C., Foots, A., Hayes, C., Henry, C., Pollard, K., Artstein, R., Traum, D., Exploring variation of natural human commands to a robot in a collaborative navigation task (2017) Proceedings of the First Workshop on Language Grounding for Robotics, pp. 58-66; McClosky, D., Charniak, E., Johnson, M., Automatic domain adaptation for parsing (2010) Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 28-36. , Association for Computational Linguistics; Miller, G. 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Technical report, Information Sciences Institute; Poesio, M., Traum, D., Towards an axiomatization of dialogue acts (1998) Proceedings of the Twente Workshop on the Formal Semantics and Pragmatics of Dialogues (13th Twente Workshop on Language Technology, , Citeseer; Pourdamghani, N., Knight, K., Hermjakob, U., Generating English from Abstract Meaning Representations (2016) Proc. of INLG, pp. 21-25; Rao, S., Marcu, D., Knight, K., Daumé, H., Biomedical event extraction using Abstract Meaning Representation (2017) Proc. of BioNLP, pp. 126-135. , Vancouver, Canada, August; Riek, L., Wizard of Oz Studies in HRI: A Systematic Review and New Reporting Guidelines (2012) Journal of Human-Robot Interaction, 1 (1); Schuler, K. K., (2005) VerbNet: A broad-coverage, comprehensive verb lexicon, , Ph.D. thesis, University of Pennsylvania; Searle, J. R., (1969) Speech acts: An essay in the philosophy of language, , Cambridge University Press; Shen, H., (2018) Semantic Parsing in Spoken Language Understanding using Abstract Meaning Representation, , Ph.D. thesis, Brandeis University; Traum, D. R., Larsson, S., The information state approach to dialogue management (2003) Current and new directions in discourse and dialogue, pp. 325-353. , Jan van Kuppevelt et al., editors, pages Springer; Traum, D., Henry, C., Lukin, S., Artstein, R., Gervits, F., Pollard, K., Bonial, C., Hill, S., Dialogue structure annotation for multi-floor interaction (2018) Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), pp. 104-111. , Nicoletta Calzolari (Conference chair), et al., editors, pages Miyazaki, Japan, May 7-12, 2018. European Language Resources Association (ELRA); Traum, D. R., Speech acts for dialogue agents (1999) Foundations of Rational Agency, pp. 169-201. , Anand Rao et al., editors, pages Kluwer; Wang, C., Xue, N., Pradhan, S., A transition-based algorithm for AMR parsing (2015) Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 366-375; Wang, Y., Liu, S., Rastegar-Mojarad, M., Wang, L., Shen, F., Liu, F., Liu, H., Dependency and AMR embeddings for drug-drug interaction extraction from biomedical literature (2017) Proc. of ACM-BCB, pp. 36-43. , New York, NY, USA; Xu, W., Rudnicky, A. I., Task-based dialog management using an agenda (2000) Proceedings of the 2000 ANLP/NAACL Workshop on Conversational systems, 3, pp. 42-47; Zhang, S., Ma, X., Duh, K., Van Durme, B., (2019) AMR parsing as sequence-to-graph transduction, , arXiv preprint arXiv:1905.08704; Ziser, Y., Reichart, R., (2016) Neural structural correspondence learning for domain adaptation, , arXiv preprint arXiv:1610.01588","Bonial, C.; U.S. Army Research LabUnited States; email: claire.n.bonial.civ@mail.mil","Calzolari N.Bechet F.Blache P.Choukri K.Cieri C.Declerck T.Goggi S.Isahara H.Maegaard B.Mariani J.Mazo H.Moreno A.Odijk J.Piperidis S.","Amazon AWS;Bertin;Lenovo;Ontotex;Vecsys;Vocapia","European Language Resources Association (ELRA)","12th International Conference on Language Resources and Evaluation, LREC 2020","11 May 2020 through 16 May 2020",,164155,,9791095546344,,,"English","LREC - Int. Conf. Lang. Resour. Eval., Conf. Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85096605839
"Guatibonza A., Solaque L., Velasco A.","57205600808;22942186900;57196953191;","Low-gain feedback controller for a real soft actuated knee rehabilitation device using a state observer",2020,"International Journal of Mechatronics and Automation","7","4",,"192","200",,,"10.1504/IJMA.2020.110858","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095775836&doi=10.1504%2fIJMA.2020.110858&partnerID=40&md5=4e08f4540c01d3ee2dfb77f9b3139fc8","Mechatronics Engineering Department, Militar Nueva Granada University, Bogotá, Colombia","Guatibonza, A., Mechatronics Engineering Department, Militar Nueva Granada University, Bogotá, Colombia; Solaque, L., Mechatronics Engineering Department, Militar Nueva Granada University, Bogotá, Colombia; Velasco, A., Mechatronics Engineering Department, Militar Nueva Granada University, Bogotá, Colombia","Rehabilitation devices using soft actuation have been designed recently due to their characteristics in the human-robot interaction. Though, the analysis and control of these kinds of systems are challenging. We have designed a five-link knee rehabilitation device actuated in two of the five joints. This system can be reconfigured to attend a wide range of patients according to their height. We use elastic actuation to help the motion at the knee joint. In this work, we simplify the rehabilitation device modelling into a one-degree of freedom (DoF) soft actuated system. We present the design and implementation of a dynamic feedback controller to track a desired reference. For the proposed controller, we require an estimation of the system stiffness and some of the states, so we propose a state observer as part of the design. As a result, we present the design and implementation of the controller with a state observer that follows a desired angular joint trajectory with the desired stiffness. We demonstrate in simulation, by performing different tests oriented to carry out rehabilitation routines, the effectiveness, and stability of the controlled system which responds effectively to disturbances. Copyright © 2020 Inderscience Enterprises Ltd.","Assistive device; Estimator; Knee rehabilitation.; Low-gain feedback controller; Soft actuation; State observer",,,,,,"Universidad Militar Nueva Granada, UMNG: IMP-ING-2291","This work is funded by the Universidad Militar Nueva Granada – Vicerrectoría de Investigaciones, under research grant for project IMP-ING-2291, entitled ‘Diseño de un prototipo para rehabilitación de rodilla mediante el uso de actuadores flexibles’.",,,,,,,,,,"Allen, M., Zhong, Q., Kirsch, N., Dani, A., Clark, W.W., Sharma, N., A nonlinear dynamics-based estimator for functional electrical stimulation: preliminary results from lower-leg extension experiments (2017) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (12), pp. 2365-2374; Bartenbach, V., Schmidt, K., Naef, M., Wyss, D., Riener, R., Concept of a soft exosuit for the support of leg function in rehabilitation (2015) 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 125-130; Chen, C., Hsieh, M., Lee, F., Lin, Y., Intelligent control for lower limb rehabilitation system (2018) 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), pp. 1-5; Della-Santina, C., Bianchi, M., Grioli, G., Angelini, F., Catalano, M.G., Garabini, M., Bicchi, A., Controlling soft robots: balancing feedback and feedforward elements (2017) IEEE Robot. Automat. Mag, 24 (3), pp. 75-83. , https://doi.org/10.1109/MRA.2016.2636360, [online]; Greenfield, J.R.J., Hwang, H.F., Davies, C., McDaid, A.J., Soft-stop knee brace for rehabilitation from ligament injuries: design and pilot trial (2017) 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 352-357; Guatibonza, A., Solaque, L., Velasco, A., Kinematic and dynamic modeling of a 5-bar assistive device for knee rehabilitation (2018) Proceedings ETCM; Guo, S., Zhao, F., Wei, W., Guo, J., Zhao, X., Zhang, W., Soft actuator for hand rehabilitation (2015) 2015 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 2197-2202; Htoon, Z.L., Sidek, S.N., Fatai, S., Rashid, M.M., Estimation of upper limb impedance parameters using recursive least square estimator (2016) 2016 International Conference on Computer and Communication Engineering (ICCCE), pp. 144-148; Huo, W., Mohammed, S., Moreno, J.C., Amirat, Y., Lower limb wearable robots for assistance and rehabilitation: a state of the art (2016) IEEE Systems Journal, 10 (3), pp. 1068-1081; (2013) Ingeniería de Rehabilitación, , https://www.nibib.nih.gov/sites/default/files/2019-2/Ingenieria-de-Rehabilitacion.pdf, Instituto Nacional de Bioingeniería e Imágenes Biomédicas. [online] (accessed 10 August 2019); Katzschmann, R.K., Santina, C.D., Toshimitsu, Y., Bicchi, A., Rus, D., Dynamic motion control of multisegment soft robots using piecewise constant curvature matched with an augmented rigid body model (2019) 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft), pp. 454-461; Koller-Hodac, A., Leonardo, D., Walpen, S., Felder, D., A novel robotic device for knee rehabilitation improved physical therapy through automated process (2010) 2010 3rd IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 820-824; Low, F., Lim, J.H., Kapur, J., Yeow, R.C., Effect of a soft robotic sock device on lower extremity rehabilitation following stroke: a preliminary clinical study with focus on deep vein thrombosis prevention (2019) IEEE Journal of Translational Engineering in Health and Medicine, 7, pp. 1-6; Luenberger, D.G., An introduction to observers (1971) IEEE Transactions on Automatic Control, 16 (6), pp. 596-602; Mancisidor, A., Zubizarreta, A., Cabanes, I., Bengoa, P., Jung, J.H., Interaction force and motion estimators facilitating impedance control of the upper limb rehabilitation robot (2017) 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 561-566; Ogata, K., (1996) Discrete Time Control Systems, , https://books.google.com.cobooks?id=aYFUs17m0YQC, Pearson Education, USA [online]; Ogata, K., (2010) Modern Control Engineering, , Pearson Education, USA; Park, Y., Chen, B., Young, D., Stirling, L., Wood, R.J., Goldfield, E., Nagpal, R., Bio-inspired active soft orthotic device for ankle foot pathologies (2011) 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4488-4495; (2018) qb Move Kit User Manual, , https://qbrobotics.com/wpcontent/uploads/2018/05/qbmove-KIT-231008.pdf, qb robotics [online] (accessed 12 March 2020); (2019) Qbmove Advanced, , https://qbrobotics.com/products/qbmove-advanced/, qb robotics [online] (accessed 6 August 2019); Schrade, S.O., Nager, Y., Wu, A.R., Gassert, R., Ijspeert, A., Bio-inspired control of joint torque and knee stiffness in a robotic lower limb exoskeleton using a central pattern generator (2017) 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 1387-1394; Solaque, L., Velasco, A., Control strategy for a soft actuated knee rehabilitation device (2019) Proceedings of ICMRE; Solaque, L., Romero, M., Velasco, A., Knee rehabilitation device with soft actuation: an approach to the motion control (2018) Proceedings of ICINCO; Umivale, P.S., (2011) Patología de la Rodilla: Guía de Manejo Clínico; Wilkening, A., Baiden, D., Ivlev, O., Assistive control of motion therapy devices based on pneumatic softactuators with rotary elastic chambers (2011) 2011 IEEE International Conference on Rehabilitation Robotics, pp. 1-6; Wu, Q., Wang, X., Chen, B., Wu, H., Design and fuzzy sliding mode admittance control of a soft wearable exoskeleton for elbow rehabilitation (2018) IEEE Access, 6, pp. 60249-60263; Yousefi, F., Alipour, K., Tarvirdizadeh, B., Hadi, A., Knee rehabilitation robot control by sliding-backstepping and admittance control (2017) 2017 Artificial Intelligence and Robotics (IRANOPEN), pp. 51-57; Zhang, G., Lin, M., Design of a soft robot using pneumatic muscles for elbow rehabilitation (2018) 2018 3rd International Conference on Robotics and Automation Engineering (ICRAE), pp. 14-18","Guatibonza, A.; Mechatronics Engineering Department, Colombia; email: u1802425@unimilitar.edu.co",,,"Inderscience Publishers",,,,,20451059,,,,"English","Int. J. Mechatronics Autom.",Article,"Final","",Scopus,2-s2.0-85095775836
"Ko S., Liu X., Mamros J., Lawson E., Swaim H., Yao C., Jeon M.","57211673991;57219605904;57219604645;57219596568;57216345163;57219593979;35113142300;","The Effects of Robot Appearances, Voice Types, and Emotions on Emotion Perception Accuracy and Subjective Perception on Robots",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12424 LNCS",,,"174","193",,,"10.1007/978-3-030-60117-1_13","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094125461&doi=10.1007%2f978-3-030-60117-1_13&partnerID=40&md5=41c3aa8a65195f428fcc4dc8abb7eeba","Department of Industrial and Systems Engineering, Mind Music Machine Lab, Virginia Tech, Blacksburg, VA, United States","Ko, S., Department of Industrial and Systems Engineering, Mind Music Machine Lab, Virginia Tech, Blacksburg, VA, United States; Liu, X., Department of Industrial and Systems Engineering, Mind Music Machine Lab, Virginia Tech, Blacksburg, VA, United States; Mamros, J., Department of Industrial and Systems Engineering, Mind Music Machine Lab, Virginia Tech, Blacksburg, VA, United States; Lawson, E., Department of Industrial and Systems Engineering, Mind Music Machine Lab, Virginia Tech, Blacksburg, VA, United States; Swaim, H., Department of Industrial and Systems Engineering, Mind Music Machine Lab, Virginia Tech, Blacksburg, VA, United States; Yao, C., Department of Industrial and Systems Engineering, Mind Music Machine Lab, Virginia Tech, Blacksburg, VA, United States; Jeon, M., Department of Industrial and Systems Engineering, Mind Music Machine Lab, Virginia Tech, Blacksburg, VA, United States","In human-robot interaction, natural and intuitive communication between robot and human is one of the most important research topics. Emotion plays a crucial role to make natural and social interactions. Research has focused more on robots’ appearances and facial emotional expressions, but little research has investigated robots’ voices and their mixed effects with robot types and different emotions for users to perceive robots’ emotional states. In this study, anthropomorphic and zoomorphic robots, four different voice types, and seven different emotional voices were used as mixed factors to discuss how these influence users’ perception on robots’ emotional expression and other characteristics. Sixteen participants were asked to read fairy tales to robots and determine robots’ emotional states when the robots verbally responded. Overall, the anthropomorphic robot (Nao) was preferred over the zoomorphic robot (Pleo), but this appearance did not influence emotion recognition accuracy or other robot characteristics. Participants showed lower accuracy in recognizing negative emotions with high arousal: anger, fear, and disgust. TTS was rated lower than other human voices in all robot characteristics, such as warmth, honesty, trustworthiness, and naturalness. Implications and design directions are discussed with the results. © 2020, Springer Nature Switzerland AG.","Emotion recognition; Human-robot interaction; Robot appearance; Robot voice","Anthropomorphic robots; Behavioral research; Human computer interaction; Emotion recognition; Emotional expressions; Emotional state; Emotional voices; Research topics; Robot appearance; Social interactions; Subjective perceptions; Social robots",,,,,,,,,,,,,,,,"Dautenhahn, K., Woods, S., Kaouri, C., Walters, M.L., Koay, K.L., Werry, I., What is a robot companion-friend, assistant or butler? 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Robot., 2 (2), pp. 175-186; Hosseini, F., Hilliger, S., Barnes, J., Jeon, M., Park, H., Howard, M., Love at first sight: Mere exposure to robot appearance leaves impressions similar to interactions with physical robots (2017) Proceedings of the 26Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 615-620. , IEEE; Barnes, J., Fakhrhosseini, M., Jeon, M., Park, H., Howard, A., The influence of robot design on acceptance of social robots (2017) Proceedings of the 14Th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 51-55. , IEEE; Hegel, F., Krach, S., Kircher, T., Wrede, B., Sagerer, G., Understanding social robots: A user study on anthropomorphism (2008) Proceedings of the 17Th IEEE International Symposium on Robot and Human Interactive Communication, pp. 574-579. , Roman, IEEE; Bachorowski, J., Owren, M., Sounds of emotion: Production and perception of affect-related vocal acoustics (2003) Ann. New York Acad. Sci., 1000 (1), pp. 244-265; Fischer, K., Jung, M., Jensen, L., Emotion expression in HRI – when and why (2019) Proceedings of the 14Th ACM/IEEE International Conference on Human-Robot Interaction (HRI); Frith, D., Frith, U., The neural basis of mentalizing (2006) Neuron, 50 (4), pp. 531-534; Calvo, A., D’Mello, S., Affect detection: An interdisciplinary review of models, methods, and their applications (2010) IEEE Trans. Affect. Comput., 1 (1), pp. 18-37; Schirmer, A., Adolphs, R., Emotion perception from face, voice, and touch: Comparisons and convergence (2017) Trends Cogn. Sci, 21 (3), pp. 216-228; Williams, G., Watts, N., Macleod, C., Mathews, A., (1988) Cognitive Psychology and Emotional Disorders, , John Wiley & Sons, Oxford; Barnes, J., Richie, E., Lin, Q., Jeon, M., Park, H., Emotive voice acceptance in human-robot interaction (2018) Proceedings of the 24Th International Conference on Auditory Display; Jeon, M., Rayan, I.A., The effect of physical embodiment of an animal robot on affective prosody recognition (2011) HCI 2011. LNCS, 6762, pp. 523-532. , https://doi.org/10.1007/978-3-642-21605-3_57, Jacko, Julie A. (ed.) , Springer, Heidelberg","Jeon, M.; Department of Industrial and Systems Engineering, United States; email: myounghoonjeon@vt.edu","Stephanidis C.Kurosu M.Degen H.Reinerman-Jones L.",,"Springer Science and Business Media Deutschland GmbH","22nd International Conference on Human Computer Interaction,HCII 2020","19 July 2020 through 24 July 2020",,250389,03029743,9783030601164,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85094125461
"Aleksandrovich A., Gomes L.M.","57219126086;57200123373;","Shared multisensory sexual arousal in virtual reality (VR) environments",2020,"Paladyn","11","1",,"379","389",,1,"10.1515/pjbr-2020-0018","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091373982&doi=10.1515%2fpjbr-2020-0018&partnerID=40&md5=0b6f8a08f0f0e387d9c2f10d63c9474b","Extended Reality, Raspberry Dream Labs Ltd., London, United Kingdom; Department of Computer and Electrical Engineering, São Carlos School of Engineering (EESC), University of São Paulo, SP, São Carlos, Brazil","Aleksandrovich, A., Extended Reality, Raspberry Dream Labs Ltd., London, United Kingdom; Gomes, L.M., Department of Computer and Electrical Engineering, São Carlos School of Engineering (EESC), University of São Paulo, SP, São Carlos, Brazil","This research explores multisensory sexual arousal in men and women, and how it can be implemented and shared between multiple individuals in Virtual Reality (VR). This is achieved through the stimulation of human senses with immersive technology including visual, olfactory, auditory, and haptic triggers. Participants are invited to VR to test various sensory triggers and assess them as sexually arousing or not. A literature review on VR experiments related to sexuality, the concepts of perception and multisensory experiments, and data collected from self-reports was used to conclude. The goal of this research is to establish that sexual arousal is a multisensory event that may or may not be linked to the presence or thought of the intended object of desire (sexual partner). By examining what stimulates arousal, we better understand the multisensory capacity of humans, leading not only to richer sexual experiences but also to the further development of wearable sextech products, soft robotics, and multisensory learning machines. This understanding helps with other research related to human-robot interaction, affection, detection, and transmission in both physical and virtual realities, and how VR technology can help to design a new generation of sex robots. © 2020 Angelina Aleksandrovich and Leonardo Mariano Gomes, published by De Gruyter 2020.","Cybersex; Immersive environments; Multisensory sexual arousal; Sextech; Virtual reality",,,,,,,,,,,,,,,,,"Parisi, L., (2004) Abstract Sex: Philosophy, Bio-Technology and the Mutations of Desire, , Continuum London, UK; McLuhan, M., (1964) Understanding Media, , Routledge & K.Paul London; Boring, E., (1950) A History of Experimental Psychology, , Prentice Hall Englewood Cliffs, NJ; Mather, G., (2009) Foundations of Sensation and Perception, 2nd Ed., , Psychology Press Ltd East Sussex; Jutte, R., (2005) A History of the Senses: From Antiquity to Cyberspace, , Polity Press Cambridge, UK; Nager, W., Estorf, K., Münte, T.F., Crossmodal attention effects on brain responses to different stimulus classes (2006) BMC Neurosci., 7; Smith, I., (2010) Freud - Complete Works, , Digital Publishing; Huxley, A., (1932) Brave New World, , Chatto & Windus London, UK; Gibson, G., (1984) Neuromancer, , Ace Books New York, USA; ""barbarella,"" Directed by Roger Vadim, the Excessive Machine, Marianne Productions, Dino de Laurentiis, Cinematografica, 1968; ""demolition Man,"" Directed by Marco Brambilla, Silver Pictures, 1993; ""lawnmower Man,"" Directed by Brett Leonard, Allied Vision, Fuji Eight Company Ltd., Lane Pringle Productions, Angel Studios, 1992; ""sleeper,"" Directed by Woody Allen, Orgasmotron, 1973; ""strange Days,"" Directed by Kathryn Bigelow, SQUID, Lightstorm Entertainment, 1995; Grimshaw, M., (2014) The Oxford Handbook of Virtuality, , Oxford University Press New York, USA; Stenslie, S., (1993) The CyberCM, , https://stensliehome.files.wordpress.com/2014/07/the-cybersm-pdf.pdf, [Accessed 5 May 2019]; (2019) Cortical Systematics S.L. 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Reprod. Health, 44 (3), pp. 161-164","Aleksandrovich, A.; Extended Reality, United Kingdom; email: info@raspberrydreamlabs.com",,,"De Gruyter Open Ltd",,,,,20814836,,,,"English","Paladyn",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85091373982
"Peng Z., Li X., Zhu Z., Unoki M., Dang J., Akagi M.","57197828642;57188756632;57191372680;6604048482;7006574053;16318490800;","Speech Emotion Recognition Using 3D Convolutions and Attention-Based Sliding Recurrent Networks with Auditory Front-Ends",2020,"IEEE Access","8",,"8963694","16560","16572",,17,"10.1109/ACCESS.2020.2967791","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081108992&doi=10.1109%2fACCESS.2020.2967791&partnerID=40&md5=912d996ce40e77b82fb986fc53e4d038","Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa, 9231292, Japan; Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China; Peng Cheng Laboratory, Shenzhen, 518055, China","Peng, Z., Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa, 9231292, Japan, Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China; Li, X., Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa, 9231292, Japan; Zhu, Z., Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa, 9231292, Japan; Unoki, M., Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa, 9231292, Japan; Dang, J., Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa, 9231292, Japan, Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China, Peng Cheng Laboratory, Shenzhen, 518055, China; Akagi, M., Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa, 9231292, Japan","Emotion information from speech can effectively help robots understand speaker's intentions in natural human-robot interaction. The human auditory system can easily track temporal dynamics of emotion by perceiving the intensity and fundamental frequency of speech, and focus on the salient emotion regions. Therefore, speech emotion recognition combined with the auditory mechanism and attention mechanism may be an effective way. Some previous studies used auditory-based static features to identify emotion while ignoring the emotion dynamics. Some other studies used attention models to capture the salient regions of emotion while ignoring cognitive continuity. To fully utilize the auditory and attention mechanism, we first investigate temporal modulation cues from auditory front-ends and then propose a joint deep learning model that combines 3D convolutions and attention-based sliding recurrent neural networks (ASRNNs) for emotion recognition. Our experiments on the IEMOCAP and MSP-IMPROV datasets indicate that the proposed method can be effectively used to recognize the emotions of speech from temporal modulation cues. The subjective evaluation shows that the attention patterns of the attention model are basically consistent with human behaviors in recognizing the emotions. © 2020 IEEE.","3D convolutions; attention-based sliding recurrent networks; Auditory front-ends; joint spectral-temporal representations; speech emotion recognition","3D modeling; Acoustic intensity; Audition; Behavioral research; Convolution; Human robot interaction; Modulation; Recurrent neural networks; Speech; Front end; Fundamental frequencies; Human auditory system; Recurrent networks; Speech emotion recognition; Subjective evaluations; Temporal modulations; Temporal representations; Speech recognition",,,,,"18A414; Japan Society for the Promotion of Science, JSPS: 16K00297","This work was supported in part by the JSPS KAKENHI Grant under Grant 16K00297, and in part by the Research Foundation of Education Bureau of Hunan Province, China, under Grant 18A414.",,,,,,,,,,"El Ayadi, M., Kamel, M.S., Karray, F., Survey on speech emotion recognition: Features, classification schemes, and databases (2011) Pattern Recognit., 44 (3), pp. 572-587. , Mar; Atlas, L., Shamma, S.A., Joint acoustic and modulation frequency (2003) EURASIP J. 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(ICASSP), pp. 7390-7394. , May; Kell, A.J., Yamins, D.L., Shook, E.N., Norman-Haignere, S.V., McDermott, J.H., A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy (2018) Neuron, 98 (3), pp. 630e16-644e16. , May; Latif, S., Rana, R., Khalifa, S., Jurdak, R., Epps, J., (2019) Direct Modelling of Speech Emotion from Raw Speech, , https://arxiv.org/abs/1904.03833, [Online]; Aldeneh, Z., Provost, E.M., Using regional saliency for speech emotion recognition (2017) Proc. IEEE Int. Conf. Acoust., Speech Signal Process. (ICASSP), (3), pp. 2741-2745. , Mar; Xia, R., Liu, Y., A multi-task learning framework for emotion recognition using 2D continuous space (2017) IEEE Trans. Affect. Comput., 8 (1), pp. 3-14. , Jan; Ghosh, S., Laksana, E., Morency, L.-P., Scherer, S., Representation learning for speech emotion recognition (2016) Proc. Interspeech, pp. 3603-3607; Zhao, Z., Zheng, Y., Zhang, Z., Wang, H., Zhao, Y., Li, C., Exploring spatio-temporal representations by integrating attention-based bidirectional-LSTM-RNNs and FCNs for speech emotion recognition (2018) Proc. Interspeech, pp. 272-276. , Aug; Sainath, T.N., Kingsbury, B., Mohamed, A.-R., Ramabhadran, B., Learning filter banks within a deep neural network framework (2013) Proc. IEEE Workshop Autom. Speech Recognit. Understand., pp. 297-302. , Dec","Peng, Z.; Graduate School of Advanced Science and Technology, Japan; email: zcpeng@jaist.ac.jp",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85081108992
"Ghiglino D., Willemse C., Tommaso D.D., Bossi F., Wykowska A.","57195956118;57188686151;57215212104;56963595100;15924284200;","At first sight: Robots' subtle eye movement parameters affect human attentional engagement, spontaneous attunement and perceived human-likeness",2020,"Paladyn","11","1",,"31","39",,5,"10.1515/pjbr-2020-0004","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080137980&doi=10.1515%2fpjbr-2020-0004&partnerID=40&md5=90bdf1fb85cea9c64fc2cb143a56b36f","Istituto Italiano di Tecnologia, Via Morego 30, Genova, 16163, Italy; Dibris, Università Degli Studi di Genova, Via Opera Pia 13, Genova, 16145, Italy; Imt School, Lucca, Italy","Ghiglino, D., Istituto Italiano di Tecnologia, Via Morego 30, Genova, 16163, Italy, Dibris, Università Degli Studi di Genova, Via Opera Pia 13, Genova, 16145, Italy; Willemse, C., Istituto Italiano di Tecnologia, Via Morego 30, Genova, 16163, Italy; Tommaso, D.D., Istituto Italiano di Tecnologia, Via Morego 30, Genova, 16163, Italy; Bossi, F., Istituto Italiano di Tecnologia, Via Morego 30, Genova, 16163, Italy, Imt School, Lucca, Italy; Wykowska, A., Istituto Italiano di Tecnologia, Via Morego 30, Genova, 16163, Italy","Human-robot interaction research could benefit from knowing how various parameters of robotic eye movement control affect specific cognitive mechanisms of the user, such as attention or perception. In the present study, we systematically teased apart control parameters of Trajectory Time of robot eye movements (rTT) between two joint positions and Fixation Duration (rFD) on each of these positions of the iCub robot. We showed recordings of these behaviors to participants and asked them to rate each video on how human-like the robot's behavior appeared. Additionally, we recorded participants' eye movements to examine whether the different control parameters evoked different effects on cognition and attention. We found that slow but variable robot eye movements yielded relatively higher human-likeness ratings. On the other hand, the eye-tracking data suggest that the human range of rTT is most engaging and evoked spontaneous involvement in joint attention. The pattern observed in subjective ratings was paralleled only by one measure in the implicit objective metrics, namely the frequency of spontaneous attentional following. These findings provide significant clues for controller design to improve the interaction between humans and artificial agents. © 2020 Davide Ghiglino et al., published by De Gruyter 2020.","behavioral sciences; biological control systems; eye movements; humanoid robots; robot control",,,,,,"European Research Council, ERC","This project has received funding from the European Research Council (ERC) under the EuropeanUnion’sHorizon 2020 research and innovation programme (grant awarded to A.Wykowska, titled “InStance: Intentional Stance for Social Attunement. Grant agreement No: 715058).",,,,,,,,,,"Kobayashi, H., Kohshima, S., ""unique morphology of the human eye and its adaptive meaning: Comparative studies on external morphology of the primate eye,"" (2001) J. Hum. Evol, 40 (5), pp. 419-435. , May; Emery, N.J., ""the eyes have it: The neuroethology, function and evolution of social gaze,"" (2000) Neurosci. Biobehav. 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Rep, 8 (1), p. 17842. , http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000453224400003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=b7bc2757938ac7a7a821505f8243d9f3","Ghiglino, D.; Istituto Italiano di Tecnologia, Via Morego 30, Italy; email: davide.ghiglino@iit.it",,,"De Gruyter",,,,,20814836,,,,"English","Paladyn",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85080137980
"Zheng X., Shiomi M., Minato T., Ishiguro H.","57211989348;8397042200;7005390640;26643316500;","How can robots make people feel intimacy through touch?",2020,"Journal of Robotics and Mechatronics","32","1",,"51","58",,5,"10.20965/jrm.2020.p0051","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079874549&doi=10.20965%2fjrm.2020.p0051&partnerID=40&md5=5065483d72aa4264572ff43dea09dc04","Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai Seika-cho, Sorakugun, Kyoto, 619-0288, Japan; Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyamacho, Osaka, Toyonaka, 560-0043, Japan","Zheng, X., Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai Seika-cho, Sorakugun, Kyoto, 619-0288, Japan, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyamacho, Osaka, Toyonaka, 560-0043, Japan; Shiomi, M., Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai Seika-cho, Sorakugun, Kyoto, 619-0288, Japan; Minato, T., Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai Seika-cho, Sorakugun, Kyoto, 619-0288, Japan; Ishiguro, H., Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai Seika-cho, Sorakugun, Kyoto, 619-0288, Japan, Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyamacho, Osaka, Toyonaka, 560-0043, Japan","This study investigates the effects of the touch characteristics that change the intimacy perceived by humans in human-robot touch interaction with an android robot having a human-like feminine appearance. Past studies on human-robot touch interaction focused on understanding which types of human touches are used to express emotions to robots. However, they less focused on how a robot’s touch characteristics can affect humans’ perceived intimacy. In this study, first, we concentrated on two types of touch characteristics (type and place) and their effects on the perceived intimacy of a commonly used emotion in human-robot interaction, namely happiness. The results showed that the touch types are useful for changing the perceived intimacy, although the touched place did not exhibit any significant effects. Based on the results of our first experiment, we investigated the effects of different touch characteristics (length and part). We concluded that the touch part is useful to change the perceived intimacy, although the touch length did not exhibit any significant effects. Finally, the results suggested that a pat (type) by the fingers (part) is a better combination to express intimacy with our robot. © 2020, Fuji Technology Press. All rights reserved.","Haptic interaction; Human-robot interaction; Perceived intimacy","Behavioral research; Human computer interaction; Man machine systems; Express emotions; Haptic interactions; Human like; Human robots; Human touches; Perceived intimacy; Touch interaction; Human robot interaction",,,,,"JPMJER1401; Japan Society for the Promotion of Science, KAKEN: JP17K00293; Core Research for Evolutional Science and Technology, CREST: JPMJCR18A1","This research work was partly supported by JST CREST Grant Number JPMJCR18A1, Japan, the JST ERATO Ishi-guro Symbiotic Human Robot Interaction Project (Grant Number: JPMJER1401), and JSPS KAKENHI Grant Numbers JP17K00293.","This research work was partly supported by JST CREST Grant Number JPMJCR18A1, Japan, the JST ERATO Ishiguro Symbiotic Human Robot Interaction Project (Grant Number: JPMJER1401), and JSPS KAKENHI Grant Numbers JP17K00293.",,,,,,,,,"Fisher, J.D., Rytting, M., Heslin, R., Hands Touching Hands: Affective and Evaluative Effects of an Interpersonal Touch (1976) So-Ciometry, 39 (4), pp. 416-421; Deethardt, J.F., Hines, D.G., Tactile communication and personality differences (1983) J. of Nonverbal Behavior, 8 (2), pp. 143-156; Willis, F.N., Jr., Dodds, R.A., Age, relationship, and touch ini-tiation (1998) The J. of Social Psychology, 138 (1), pp. 115-123; Lee, J.W., Guerrero, L.K., Types of touch in cross-sex relationships between coworkers: Perceptions of relational and emotional messages, inappropriateness, and sexual harassment (2001) J. of Applied Communication Research, 29 (3), pp. 197-220; Field, T., Touch for socioemotional and physical well-being: A re-view (2010) Developmental Review, 30 (4), pp. 367-383; Hertenstein, M.J., Holmes, R., McCullough, M., Keltner, D., The communication of emotion via touch (2009) Emotion, 9 (4), p. 566; Cekaite, A., Holm, M.K., The Comforting Touch: Tactile Intimacy and Talk in Managing Children’s Distress (2017) Research on Language and Social Interaction, 50 (2), pp. 109-127; O’Lynn, C., Krautscheid, L., How should I touch you?’: A qualitative study of attitudes on intimate touch in nursing care (2011) AJN the American J. of Nursing, 111 (3), pp. 24-31; Jakubiak, B.K., Feeney, B.C., Interpersonal touch as a resource to facilitate positive personal and relational outcomes during stress discussions (2019) J. of Social and Personal Relationships, 36 (9), pp. 2918-2936; van Erp, J.B., Toet, A., “How to touch humans: Guidelines for social agents and robots that can touch (2013) 2013 Humaine Association Conf. on Affective Computing and Intelligent Interaction (ACII), pp. 780-785. , pp.; van Erp, J.B.F., Toet, A., Social Touch in Human-Computer In-teraction (2015) Frontiers in Digital Humanities, 2 (2), p. 2; Huisman, G., Social Touch Technology: A Survey of Haptic Technology for Social Touch (2017) IEEE Trans. on Haptics, 10 (3), pp. 391-408; Shiomi, M., Shatani, K., Minato, T., Ishiguro, H., How should a Robot React before People’s Touch?: Modeling a Pre-Touch Reaction Distance for a Robot’s Face (2018) IEEE Robotics and Automation Letters, 3 (4), pp. 3773-3780; Block, A.E., Kuchenbecker, K.J., Softness, Warmth, and Responsiveness Improve Robot Hugs (2019) Int. J. of Social Robotics, 11 (1), pp. 49-64; Yu, R., Use of a Therapeutic, Socially Assistive Pet Robot (PARO) in Improving Mood and Stimulating Social Interaction and Communication for People With Dementia: Study Protocol for a Randomized Controlled Trial (2015) JMIR Research Protocols, 4 (2), p. e45; Sumioka, H., Nakae, A., Kanai, R., Ishiguro, H., Huggable communication medium decreases cortisol levels (2013) Scientific Reports, 3; Shiomi, M., Hagita, N., Audio-Visual Stimuli Change not Only Robot’s Hug Impressions but Also Its Stress-Buffering Effects (2019) Int. J. of Social Robotics, pp. 1-8; Shiomi, M., Nakata, A., Kanbara, M., Hagita, N., A Robot that Encourages Self-disclosure by Hug (2017) Proc. of Int. 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Symp. on Robot and Human Interactive Communication (RO-MAN), pp. 22-29; Roberson, D., Damjanovic, L., Kikutani, M., Show and tell: The role of language in categorizing facial expression of emotion (2010) Emotion Review, 2 (3), pp. 255-260; Bickmore, T.W., Fernando, R., Ring, L., Schulman, D., Em-pathic touch by relational agents (2010) IEEE Trans. on Affective Computing, 1 (1), pp. 60-71; Gallace, A., Spence, C., The science of interpersonal touch: An overview (2010) Neuroscience & Biobehavioral Reviews, 34 (2), pp. 246-259; Ijzerman, H., Gallucci, M., Pouw, W.T., Weißgerber, S.C., van Doesum, N.J., Williams, K.D., Cold-blooded loneliness: Social exclusion leads to lower skin temperatures (2012) Acta Psychologica, 140 (3), pp. 283-288; Suvilehto, J.T., Topography of social touching depends on emotional bonds between humans (2015) Proc. of the National Academy of Sciences, 112 (45), pp. 13811-13816; Safdar, S., Variations of emotional display rules within and across cultures: A comparison between Canada, USA, and Japan (2009) Canadian J. of Behavioural Science, 41 (1), pp. 1-10; Zheng, X., Shiomi, M., Minato, T., Ishiguro, H., What Kinds of Robot’s Touch Will Match Expressed Emotions? (2020) IEEE Robotics and Automation Letters, 5 (1), pp. 127-134","Zheng, X.; Advanced Telecommunications Research Institute International (ATR), 2-2-2 Hikaridai Seika-cho, Sorakugun, Japan; email: zheng.xiqian@irl.sys.es.osaka-u.ca.jp",,,"Fuji Technology Press",,,,,09153942,,,,"English","J. Rob. Mechatronics",Article,"Final","",Scopus,2-s2.0-85079874549
"Vogan A.A., Alnajjar F., Gochoo M., Khalid S.","57212061191;14526943700;56038574900;57212061480;","Robots, AI, and cognitive training in an era of mass age-related cognitive decline: A systematic review",2020,"IEEE Access","8",,"8960353","18284","18304",,11,"10.1109/ACCESS.2020.2966819","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079828697&doi=10.1109%2fACCESS.2020.2966819&partnerID=40&md5=e5ea354aaf2835ba7f2c3daac9d02b13","College of Information Technology, United Arab Emirates University, Al Ain, 15551, United Arab Emirates; Riken Center for Brain Science (CBS), Wako, 463-0003, Japan; School of Information and Communication Technology, Mongolian University of Science and Technology, Ulaanbaatar, 13341, Mongolia","Vogan, A.A., College of Information Technology, United Arab Emirates University, Al Ain, 15551, United Arab Emirates; Alnajjar, F., College of Information Technology, United Arab Emirates University, Al Ain, 15551, United Arab Emirates, Riken Center for Brain Science (CBS), Wako, 463-0003, Japan; Gochoo, M., College of Information Technology, United Arab Emirates University, Al Ain, 15551, United Arab Emirates, School of Information and Communication Technology, Mongolian University of Science and Technology, Ulaanbaatar, 13341, Mongolia; Khalid, S., College of Information Technology, United Arab Emirates University, Al Ain, 15551, United Arab Emirates","Developing countries world-wide are witnessing historical growth in their elderly populations, and with it, importantly, a steady rise in the number of people experiencing age-related cognitive decline. This reality has the potential to produce an unprecedented strain on affected families, healthcare systems and taxpayers in the very near future. This study: a) examines the present limits and predicted capacity of Artificial Intelligence (AI) as they relate to the various and complex needs of those hoping to optimize the positive benefits of cognitive training, and b) systematically reviews the efficacy of Human Robot Interaction (HRI) as an intervention strategy for elderly individuals confronting cognitive challenges along the spectrum from Mild Cognitive Impairment (MCI) to Advanced Cognitive Impairment (ACI). The results of this systematic review suggest that, overall, the utilization of humanoid and pet robots, such as NAO and PARO, respectively, produce improvements in cognition and markers of social and emotional health and engagement; however, when embedded with AI with the capacity for Deep Learning the potential of robotic technology to aggressively meet the needs of individuals experiencing age-related cognitive decline will be significant. © 2013 IEEE.","Artificial intelligence; cognitive decline; elderly cognitive training; robotics","Artificial intelligence; Deep learning; Developing countries; Intelligent robots; Robotics; Social robots; Taxation; Cognitive decline; Cognitive impairment; Cognitive training; Elderly populations; Human robot Interaction (HRI); Intervention strategy; Mild cognitive impairments (MCI); Robotic technologies; Population statistics",,,,,"31R178","This work was supported by the Zayed Center for Health Science under Grant 31R178.",,,,,,,,,,"Pinker, S., (2018) Enlightenment Now: The Case for Science, Reason, Humanism, and Progress, , New York, NY, USA: Viking; Abdi, J., Al-Hindawi, A., Ng, T., Vizcaychipi, M.P., Scoping review on the use of socially assistive robot technology in elderly care (2018) BMJ Open, 8 (2). , Feb; National Institution of Population and Social Security, National Inst. 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Aging Res., 2012, pp. 1-10; Pu, L., Moyle, W., Jones, C., Todorovic, M., The effectiveness of social robots for older adults: A systematic review and meta-analysis of randomized controlled studies (2019) Gerontologist, 59 (1), pp. e37-e51. , Jan; https://www.softbankrobotics.com/emea/index.php/en/nao, Accessed: Jan. 19, 2020; (2020), http://www.parorobots.com/, Accessed: Jan. 19; (2020), https://www.softbankrobotics.com/emea/en/pepper, Accessed: Jan. 19; (2020), https://www.robotlab.com/store/nao-power-v6-standardedition""https://www.robotlab.com/store/nao-power-v6-standard-edition, Accessed: Jan. 19; (2020), https://www.marketsandmarkets.com/Market-Reports/humanoid-robot-market-99567653.html, Accessed: Jan. 19","Alnajjar, F.; College of Information Technology, United Arab Emirates; email: fady.alnajjar@uaeu.ac.ae",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85079828697
"Kong H., Yang C., Li G., Dai S.-L.","57205766018;57204881979;57188641035;36877968100;","A sEMG-Based Shared Control System with No-Target Obstacle Avoidance for Omnidirectional Mobile Robots",2020,"IEEE Access","8",,"8976090","26030","26040",,10,"10.1109/ACCESS.2020.2970468","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079640426&doi=10.1109%2fACCESS.2020.2970468&partnerID=40&md5=91e0e982e948238368e016f7b2e2d19b","School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China; Bristol Robotics Laboratory, University of the West of England, Bristol, BS16 1QY, United Kingdom; School of Engineering and Materials Science, Queen Mary University of London, London, E1 4NS, United Kingdom","Kong, H., School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China; Yang, C., Bristol Robotics Laboratory, University of the West of England, Bristol, BS16 1QY, United Kingdom; Li, G., School of Engineering and Materials Science, Queen Mary University of London, London, E1 4NS, United Kingdom; Dai, S.-L., School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China","We propose a novel shared control strategy for mobile robots in a human-robot interaction manner based on surface eletromyography (sEMG) signals. For security reasons, an obstacle avoidance scheme is introduced to the shared control system as collision avoidance guidance. The motion of the mobile robot is a resultant of compliant motion control and obstacle avoidance. In the mode of compliant motion, the sEMG signals obtained from the operator's forearms are transformed into human commands to control the moving direction and linear velocity of the mobile robot, respectively. When the mobile robot is blocked by obstacles, the motion mode is converted into obstacle avoidance. Aimed at the obstacle avoidance problem without a specific target, we develop a no-target Bug (NT-Bug) algorithm to guide the mobile robot to avoid obstacles and return to the command line. Besides, the command moving direction given by the operator is taken into consideration in the obstacle avoidance process to plan a smoother and safer path for the mobile robot. A model predictive controller is exploited to minimize the tracking errors. Experiments have been implemented to demonstrate the effectiveness of the proposed shared control strategy and the NT-Bug algorithm. © 2013 IEEE.","model predictive control; no-target Bug algorithm; Shared control system; surface electromyography","Control systems; Man machine systems; Mobile robots; Model predictive control; Bug algorithms; Compliant motion; Compliant motion control; Model predictive controllers; Moving direction; Omnidirectional mobile robot; Shared-control systems; Surface electromyography; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 61473120; Shaanxi Key Science and Technology Innovation Team Project: 2018IT100322","This work was supported in part by the Foshan Science and Technology Innovation Team Special Project under Grant 2018IT100322, and in part by the National Nature Science Foundation (NSFC) under Grant 61473120.",,,,,,,,,,"Zhang, L., Liu, R., Yue, M., Shared control of robot based on fuzzy discrete event system (2014) Proc. 11th World Congr. 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Eng., 118 (3), pp. 367-376. , Aug","Yang, C.; Bristol Robotics Laboratory, United Kingdom; email: cyang@ieee.org",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85079640426
"Mi J., Takahashi Y.","57014390000;55705381800;","Whole-body joint angle estimation for real-time humanoid robot imitation based on gaussian process dynamical model and particle filter",2020,"Applied Sciences (Switzerland)","10","1","5","","",,1,"10.3390/app10010005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079161483&doi=10.3390%2fapp10010005&partnerID=40&md5=78458e9fdecb3144e6d81cd344417a1a","Department of Human and Artificial Intelligent Systems, Graduate School of Engineering, University of Fukui, 3-9-1, Bunkyo, Fukui, 910-8507, Japan","Mi, J., Department of Human and Artificial Intelligent Systems, Graduate School of Engineering, University of Fukui, 3-9-1, Bunkyo, Fukui, 910-8507, Japan; Takahashi, Y., Department of Human and Artificial Intelligent Systems, Graduate School of Engineering, University of Fukui, 3-9-1, Bunkyo, Fukui, 910-8507, Japan","Real-time imitation enables a humanoid robot to mirror the behavior of humans, being important for applications of human-robot interaction. For imitation, the corresponding joint angles of the humanoid robot should be estimated. Generally, a humanoid robot comprises dozens of joints that construct a high-dimensional exploration space for estimating the joint angles. Although a particle filter can estimate the robot state and provides a solution for estimating joint angles, the computational cost becomes prohibitive given the high dimension of the exploration space. Furthermore, a particle filter can only estimate the joint angles accurately using a motion model. To realize accurate joint angle estimation at low computational cost, Gaussian process dynamical models (GPDMs) can be adopted. Specifically, a compact state space can be constructed through the GPDM learning of high-dimensional time-series motion data to obtain a suitable motion model. We propose a GPDM-based particle filter using a compact state space from the learned motion models to realize efficient estimation of joint angles for robot imitation. Simulations and real experiments demonstrate that the proposed method efficiently estimates humanoid robot joint angles at low computational cost, enabling real-time imitation. © 2019 by the authors.","3D motion model; Gaussian process dynamical model (GPDM); Humanoid robot; Particle filter; Prediction; State estimation",,,,,,,,,,,,,,,,,"Thrun, S., Burgard, W., Fox, D., Probabilistic Robotics (2006), The MIT Press: Cambridge, MA, USA; Dong, H., Cao, B., Yang, Y., Application of Particle Filter for Target Tracking in Wireless Sensor Networks In Proceedings of the 2010 International Conference on Communications and Mobile Computing, 3, pp. 504-508. , Shenzhen, China, 12-14 April 2010; Xu, Y., Xu, K., Wan, J., Xiong, Z., Li, Y., Research on Particle Filter Tracking Method Based on Kalman Filter (2018) In Proceedings of the 2018 2nd IEEE Advanced Information Management,Communicates, pp. 1564-1568. , Electronic and Automation Control Conference (IMCEC), Xi'an, China 25-27 May; Mi, J., Takahashi, Y., Low cost design of HF-band RFID system for mobile robot self-localization based on multiple readers and tags (2015) In Proceedings of the 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 194-199. , Zhuhai, China, 6-9 December; Mi, J., Takahashi, Y., Design of an HF-Band RFID System with Multiple Readers and Passive Tags for Indoor Mobile Robot Self-Localization (2016) Sensors, 16, p. 1200; Su, Y., Zhao, Q., Zhao, L., Gu, D., Abrupt motion tracking using a visual saliency embedded particle filter (2014) Pattern Recognit., 47, pp. 1826-1834; Zhang, T., Liu, S., Xu, C., Liu, B., Yang, M., Correlation Particle Filter for Visual Tracking (2018) IEEE Trans. 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Inform., 22, pp. 965-977; Mi, J., Takahashi, Y., Performance analysis of mobile robot self-localization based on different configurations of RFID system (2015) In Proceedings of the 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pp. 1591-1596. , Busan, Korea, 7-11 July; Cao, Z., Simon, T., Wei, S.E., Sheikh, Y., Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields (2017) In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), , Honolulu, HI, USA 21-26 July; Mehta, D., Sridhar, S., Sotnychenko, O., Rhodin, H., Shafiei, M., Seidel, H.P., Xu, W., Theobalt, C., VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera (2017) ACM Trans. Graph., 36 (44), pp. 1-44 and 14; Capecci, M., Ceravolo, M.G., Ferracuti, F., Iarlori, S., Kyrki, V., Monteriu, A., Romeo, L., Verdini, F., A Hidden Semi-Markov Model based approach for rehabilitation exercise assessment (2018) J. Biomed. Inform., 78, pp. 1-11; Takahashi, Y., Sakakibara, K., Real-time joint angle estimation for a humanoid robot to imitate human motion using particle filter (2015) The Japanese Society for Artificial Intelligence(JSAI), SIG-Challenge, Proceedings of the 42nd Meeting of Special Interest Group on AI Challenges, pp. 21-22. , Fukui, Japan, 3 May; Wang, J., Hertzmann, A., Fleet, D.J., Gaussian Process Dynamical Models (2006) In Advances in Neural Information Processing Systems 18, pp. 1441-1448. , The MIT Press: Cambridge, MA, USA; Mackay, D.J., Information Theory, Inference, and Learning Algorithms (2003), Cambridge University Press: Cambridge, UK","Mi, J.; Department of Human and Artificial Intelligent Systems, 3-9-1, Bunkyo, Japan; email: jmi@ir.his.u-fukui.ac.jp",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85079161483
"Lee H., Piao M., Lee J., Byun A., Kim J.","57197736696;55617690000;56734179800;57190252282;57209737019;","The purpose of bedside robots: Exploring the needs of inpatients and healthcare professionals",2020,"CIN - Computers Informatics Nursing","38","1",,"8","17",,2,"10.1097/CIN.0000000000000558","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078424629&doi=10.1097%2fCIN.0000000000000558&partnerID=40&md5=ad9a1078ef24a0f4c61779def731652a","Research Institute of Nursing Science, South Korea; College of Nursing, Seoul National University, Research Institute of Nursing Science, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea; College of Life and Health Sciences, Hoseo University, South Korea","Lee, H., College of Nursing, Seoul National University, Research Institute of Nursing Science, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea; Piao, M., College of Nursing, Seoul National University, Research Institute of Nursing Science, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea; Lee, J., College of Life and Health Sciences, Hoseo University, South Korea; Byun, A., College of Nursing, Seoul National University, Research Institute of Nursing Science, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea; Kim, J., Research Institute of Nursing Science, South Korea, College of Nursing, Seoul National University, Research Institute of Nursing Science, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea","Robotic systems are used to support inpatients and healthcare professionals and to improve the efficiency and quality of nursing. There is a lack of scientific literature on how applied robotic systems can be used to support inpatients. This study uses surveys and focus group interviews to identify the necessary aspects and functions of bedside robots for inpatients. A total of 90 healthcare professionals and 108 inpatients completed the questionnaire, and four physicians and five nurses participated in the focus group interviews. The most highly desired functionalities were related to patient care and monitoring, including alerting staff, measuring vital signs, and sensing falls. Nurses and physicians reported different needs for human-robot interaction. Nurses valued robotic functions such as nonverbal expression recognition, automatic movement, content suggestion, and emotional expressions. The results of the patients' open-ended questions and healthcare professionals' focus groups indicate that the purpose of the robots should primarily be treatment and nursing. Participants believe bedside robots would be helpful but have concerns regarding safety and utility. This study attempts to determine which aspects of robots may increase their acceptance. Our findings suggest that if robots are used in healthcare institutions, they may improve the effectiveness of care. Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.","Artificial intelligence; Inpatients; Needs assessment; Point-of-care systems; Robotics","adult; devices; falling; female; health care personnel; hospital patient; human; information processing; Internet; male; nursing informatics; patient satisfaction; physiologic monitoring; point of care system; prevention and control; questionnaire; robotics; Accidental Falls; Adult; Female; Focus Groups; Health Personnel; Humans; Inpatients; Internet; Male; Monitoring, Physiologic; Nursing Informatics; Patient Satisfaction; Point-of-Care Systems; Robotics; Surveys and Questionnaires",,,,,"Ministry of Trade, Industry and Energy, MOTIE: 10063098","Author Affiliations: Research Institute of Nursing Science (Dr Kim) and College of Nursing, Seoul National University (Drs Kim, H. Lee, and Piao, and Ms Byun); and College of Life & Health Sciences, Hoseo University (Dr J. Lee), South Korea. This material is based upon work supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program (no. 10063098, “Telepresence Robot System Development for the Support of POC (Point of Care) Service Associated With ICT Technology”). The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Jeongeun Kim, PhD, RN, College of Nursing, Seoul National University Research Institute of Nursing Science, 103 Daehak-ro, Jongno-gu, Seoul 03080, Korea (kim0424@snu.ac.kr). Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.","The authors greatly appreciate Prof Sukwha Kim and Prof Hyoun-Joong Kong for valuable guidance on this project. They also thank Hyeongju Ryu, HyunJin Joo, and EunJin Hwang for support of this study.",,,,,,,,,"Erikson, H., Salzmann-Erikson, M., Future challenges of robotics and artificial intelligence in nursing: What can we learn from monsters in popular culture? (2016) The Permanente Journal, 20 (3), pp. 127-129; Ebell, M., Information at the point of care: Answering clinical questions (1999) Journal of American Board of Family Medicine, 12, pp. 225-235; McQueen, A.C., Emotional intelligence in nursing work (2004) Journal of Advanced Nursing, 47 (1), pp. 101-108; Yen, P.Y., Walker, D.M., Smith, J.M.G., Zhou, M.P., Menser, T.L., McAlearney, A.S., Usability evaluation of a commercial inpatient portal (2018) International Journal of Medical Informatics, 110, pp. 10-18; Yoo, S., Lee, K.H., Baek, H., Development and user research of a smart bedside station system toward patient-centered healthcare system (2015) Journal of Medical Systems, 39 (9), p. 86; Baird, A.H., The new Royal Adelaide Hospital: The age of the digital hospital dawns in South Australia (2013) Proceedings of the Sixth Australasian Workshop on Health Informatics and Knowledge Management, 142. , Adelaide, Australia: Australian Computer Society; Beer, J.M., Prakash, A., Mitzner, T.L., Rogers, W.A., (2011) Understanding Robot Acceptance, , https://smartech.gatech.edu/bitstream/handle/1853/39672/HFA-TR-1103-RobotAcceptance.pdf, Accessed August 22, 2018; Broadbent, E., Stafford, R., MacDonald, B., Acceptance of healthcare robots for the older population: Review and future directions (2009) International Journal of Social Robotics, 1 (4), p. 319; Deneke, J., Lehane, D., Kandler, A., Menchini, T., Laaksoharju, M., Obaid, M., Using rapid prototyping to explore design implications for a pill-dispensing social agent (2017) The Proceedings of the 5th International Conference on Human Agent Interaction, , Bielefeld, Germany: Association for Computing Machinery; Stewart, D.W., Shamdasani, P.N., (2014) Focus Groups: Theory and Practice, , Thousand Oaks, CA: Sage Publications; 2014; Dale, Ø., Boysen, E.S., Svagård, I., One size does not fit all (2016) Proceedings of the International Conference on Computers Helping People with Special Needs, , Linz, Austria: Springer; Kilian, J., (2012) Method and Mobile Device for Automatic Activation of Applications: Google Patents, , https://patents.google.com/patent/US20110230209A1/en, Accessed August 22, 2018; Speziale, H.S., Streubert, H.J., Carpenter, D.R., (2011) Qualitative Research in Nursing: Advancing the Humanistic Imperative, , Philadelphia, PA: Lippincott Williams & Wilkins; Khan, Z., Interaction and presentation laboratory (1998) Attitudes towards Intelligent Service Robots, , Stockholm, Sweden; Shore, J.H., Telepsychiatry: Videoconferencing in the delivery of psychiatric care (2013) The American Journal of Psychiatry, 170 (3), pp. 256-262; Copleston, S., Bugmann, G., Personal robot user expectations (2008) Technical Report 08-01, Robotics and Intelligent Systems, p. 230. , Plymouth, UK: University of Plymouth; Lee, J.Y., Song, Y.A., Jung, J.Y., Nurses' needs for care robots in integrated nursing care services (2018) Journal of Advanced Nursing, 74, pp. 2094-2105; Mundher, Z.A., Zhong, J., A real-time fall detection system in elderly care using mobile robot and kinect sensor (2014) International Journal of Materials, Mechanics and Manufacturing, 2 (2), pp. 133-138; de Graaf, M.M., Allouch, S.B., Klamer, T., Sharing a life with Harvey: Exploring the acceptance of and relationship-building with a social robot (2015) Computers in Human Behavior, 43, pp. 1-14; Heerink, M., Kröse, B., Evers, V., Wielinga, B., Assessing acceptance of assistive social agent technology by older adults: The Almere model (2010) International Journal of Social Robotics, 2 (4), pp. 361-375; Whelan, S., Murphy, K., Barrett, E., Krusche, C., Santorelli, A., Casey, D., Factors affecting the acceptability of social robots by older adults including people with dementia or cognitive impairment: A literature review (2018) International Journal of Social Robotics, p. e26; Lee, H.S., Kim, J., Scenario-based assessment of user needs for point-of-care robots (2018) Healthcare Informatics Research, 24 (1), pp. 12-21; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., de Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Sun, H., Zhang, P., Causal relationships between perceived enjoyment and perceived ease of use: An alternative approach (2006) Journal of the Association for Information Systems, 7 (9), p. 24; Czaja, S.J., Charness, N., Fisk, A.D., Factors predicting the use of technology: Findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE) (2006) Psychology and Aging, 21 (2), p. 333; Di Nuovo, A., Broz, F., Wang, N., The multi-modal interface of Robot-Era multi-robot services tailored for the elderly (2018) Intelligent Service Robotics, 11 (1), pp. 109-126; Goher, K., Mansouri, N., Fadlallah, S., Assessment of personal care and medical robots from older adults' perspective (2017) Robotics and Biomimetics, 4 (1), p. 5; Vlachos, E., Jochum, E., Demers, L.P., The effects of exposure to different social robots on attitudes toward preferences (2017) Interaction Studies, 17 (3), pp. 390-404; Hosseini, S.M.F., Hilliger, S., Barnes, J., Jeon, M., Park, C.H., Howard, A.M., (2017) Love at First Sight: Mere Exposure to Robot Appearance Leaves Impressions Similar to Interactions with Physical Robots, , Lisbon, Portugal: IEEE; de Ruyter, B., Saini, P., Markopoulos, P., van Breemen, A., Assessing the effects of building social intelligence in a robotic interface for the home (2005) Interacting with Computers, 17 (5), pp. 522-541; McColl, D., (2015) Affective Body Language Detection and Generation for Socially Assistive Robots, , Toronto, ON, Canada: University of Toronto; Kim, M., You, N.K., Kim, Y., Hahn, S., Yun, M.H., Effects of ICT ability of the elderly people for the acceptability of conversational agent (2017) The Proceedings of the Conference of Ergonomics Society of Korea, , Jeju, Korea: Ergonomics Society of Korea; Park, E., Kwon, S.J., The adoption of teaching assistant robots: A technology acceptance model approach (2016) Program, 50 (4), pp. 354-366; Broadbent, E., Kuo, I.H., Lee, Y.I., Attitudes and reactions to a healthcare robot (2010) Telemedicine Journal and E-Health, 16 (5), pp. 608-613; Hiroi, Y., Ito, A., (2008) Are Bigger Robots Scary? The Relationship between Robot Size and Psychological Threat, , Xian, China: IEEE; Dautenhahn, K., Socially intelligent robots: Dimensions of human-robot interaction (2007) Philosophical Transactions of the Royal Society of London, Series B, Biological Sciences, 362 (1480), pp. 679-704; Salzmann-Erikson, M., Eriksson, H., Tech-resistance: The complexity of implementing nursing robots in healthcare workplaces (2016) Contemporary Nurse, 52 (5), p. 567; Bettinelli, M., Lei, Y., Beane, M., Mackey, C., Liesching, T.N., Does robotic telerounding enhance nurse-physician collaboration satisfaction about care decisions? (2015) Telemedicine Journal and E-Health, 21 (8), pp. 637-643","Kim, J.; Research Institute of Nursing ScienceSouth Korea; email: kim0424@snu.ac.kr",,,"Lippincott Williams and Wilkins",,,,,15382931,,,"31524689","English","CIN Comput. Informatics Nurs.",Article,"Final","",Scopus,2-s2.0-85078424629
"Jin Q., Zhang Z., Jin W.","57211160394;57211159694;56264333600;","Design and implementation of the IMU human body motion tracking system",2020,"Advances in Intelligent Systems and Computing","1037",,,"1034","1043",,,"10.1007/978-3-030-29516-5_77","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072845494&doi=10.1007%2f978-3-030-29516-5_77&partnerID=40&md5=88edb2c9776707f733e62c7f20bb6d15","College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China","Jin, Q., College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China; Zhang, Z., College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China; Jin, W., College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China","Real-time tracking of human motion has been applied in human-robot interaction, training and others. A new data acquisition and transfer method of full-body motion supporting cloud fusion, and realizing reliable and natural interaction is studied. A wearable device for the motion tracking is designed using the inertial measurement unit (IMU). This device adopts a tree-bus structure, and is divided into three levels: sensor node, sink node and mobile terminal. The sensor node is applied for motion acquisition of each key parts of the body skeleton, and establishes communication connection with the sink node by RS485 bus mode. The sink node not only plays the role of network routing and keeping the time synchronization of the system, but also controls and manages the entire network. Apart from the aforementioned functions, the sink node is also responsible for data collection and filter processing. It receives the motion data of sensor nodes connecting to it, then sends the data to a mobile terminal or PC by WIFI. The human body tracking system supports no more than 24 sensor nodes, whose data updating rate is up to 120 Hz, and the delay is lower than 20 ms. © Springer Nature Switzerland AG 2020.","IMU; Motion tracking system; Sensor network; Time synchronization","Computer terminals; Data acquisition; Human robot interaction; Intelligent systems; Mobile telecommunication systems; Motion analysis; Motion compensation; Sensor networks; Sensor nodes; Tracking (position); Communication connection; Design and implementations; Human body motion tracking; Human body tracking; Inertial measurement unit; Motion tracking system; Natural interactions; Time synchronization; Wearable sensors",,,,,"National Basic Research Program of China (973 Program): 2016YFB1001300, 2016YFB1001302","This project is supported by the National Key Research and Development Program of China (NO. 2016YFB1001302, NO. 2016YFB1001300).",,,,,,,,,,"Yun, X., Bachmann, E.R., Kavousanos-Kavousanakis, A., Yildiz, F., McGhee, R.B., Design and implementation of the MARG human body motion tracking system (2004) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 625-630. , pp., IEEE, Sendai; Tao, G., Sun, S., Huang, S., Huang, Z., Wu, J., Human modeling and real-time motion reconstruction for micro-sensor motion tracking (2011) IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems Proceedings, Ottawa, ON, Canada, pp. 1-5; Jung, H.-H., Kim, M.-K., Lyou, J., Realization of a hybrid human motion capture system (2017) 17Th International Conference on Control, Automation and Systems, pp. 1581-1585. , pp., IEEE, Jeju; Xu, W., Ortega-Sanchez, C., Murray, I., Measuring human joint movement with IMUs (2017) 15Th Student Conference on Research and Development, pp. 172-177. , pp., IEEE, Putrajaya; Sakata, R., Kobayashi, F., Nakamoto, H., Development of motion capture system using multiple depth sensors (2017) International Symposium on Micro-Nanomechatronics and Human Science, pp. 1-7. , pp., IEEE, Nagoya; Vicon, , https://www.vicon.com/; Motion Analysis Inc, , http://motionanalysisinc.com/wordpress1/; Xsens, , https://www.xsens.com/; Innalabs, Inc., , http://www.3dsuit.com/; Valenti, R.G., Dryanovski, I., Xiao, J., A linear Kalman filter for MARG orientation estimation using the algebraic quaternion algorithm (2016) IEEE Trans. Instrum. Meas., 65, pp. 467-481; Zihajehzadeh, S., Loh, D., Lee, M., Hoskinson, R., Park, E.J., A cascaded two-step Kalman filter for estimation of human body segment orientation using MEMS-IMU (2014) IEEE/RSJ International Conference on Engineering in Medicine and Biology Society, pp. 6270-6273. , pp","Jin, W.; College of Information Science and Electronic Engineering, China; email: jinguang@zju.edu.cn","Bi Y.Bhatia R.Kapoor S.",,"Springer Verlag","Intelligent Systems Conference, IntelliSys 2019","5 September 2019 through 6 September 2019",,231009,21945357,9783030295158,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85072845494
"Kang J., Sundar S.S.","57202771321;7103328070;","Social robots with a theory of mind (ToM): Are we threatened when they can read our emotions?",2020,"Advances in Intelligent Systems and Computing","1006",,,"80","88",,1,"10.1007/978-3-030-24097-4_10","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068609467&doi=10.1007%2f978-3-030-24097-4_10&partnerID=40&md5=6be332ca673c91e4a10d5d072370dc87","Media Effects Research Laboratory, Donald P. Bellisario College of Communications, The Pennsylvania State University, University Park, PA  16802, United States","Kang, J., Media Effects Research Laboratory, Donald P. Bellisario College of Communications, The Pennsylvania State University, University Park, PA  16802, United States; Sundar, S.S., Media Effects Research Laboratory, Donald P. Bellisario College of Communications, The Pennsylvania State University, University Park, PA  16802, United States","How would human users react to social robots that possess a theory of mind (ToM)? Would robots that can infer their users’ cognitions and emotions threaten their sense of uniqueness and evoke other negative reactions because ToM is a uniquely human trait? If so, can we alleviate these negative user reactions by framing robots as members of our ingroup? We addressed these questions with a 3 (robot’s affective ToM: correct vs. incorrect vs. control) × 2 (robot’s group membership: ingroup vs. outgroup) × 2 (user gender: female vs. male) between-subjects online experiment. Participants were asked to complete an online task with a robot named Pepper that was identified as an ingroup member or outgroup member. They first read a passage describing a past user’s interaction with Pepper, in which the user expressed sarcasm and Pepper correctly or incorrectly identified the user’s sarcasm or made a neutral comment. Males reacted more negatively to Pepper that correctly identified sarcasm and reported lower expected enjoyment with Pepper than females. Ingroup Pepper made participants feel closer to the robot but also threatened their sense of uniqueness than the outgroup Pepper. Design implications for fostering better human-robot interaction (HRI) are discussed. © Springer Nature Switzerland AG 2020.","Ingroup; Social robots; Theory of mind; Threat to uniqueness","Ambient intelligence; Application programs; Artificial intelligence; Machine design; Design implications; Group memberships; Human robot Interaction (HRI); Ingroup; On-line experiments; Social robots; Theory of minds; Threat to uniqueness; Human robot interaction",,,,,,,,,,,,,,,,"Shamay-Tsoory, S.G., Aharon-Peretz, J., Dissociable prefrontal networks for cognitive and affective theory of mind: A lesion study (2007) Neuropsychologia, 45 (13), pp. 3054-3067; Call, J., Tomasello, M., Does the chimpanzee have a theory of mind? 30 years later (2008) Trends Cogn. Sci., 12 (5), pp. 187-192; Hong, A., (2016) Human-Robot Interactions for Single Robots and Multi-Robot Teams, , MA Dissertation. University of Toronto, CA; Stephan, A., Empathy for artificial agents (2015) Int. J. Soc. Robot., 7 (1), pp. 111-116; Mori, M., (1970) The Uncanny Valley. Energy, 7, pp. 33-35; Gray, K., Wegner, D.M., Feeling robots and human zombies: Mind perception and the uncanny valley (2012) Cognition, 125 (1), pp. 125-130; Macdoman, K.F., Vasudevan, S.K., Ho, C.C., Does Japan really have robot mania? Comparing attitudes by implicit and explicit measures (2009) AI Soc, 23 (4), pp. 485-510; Kaplan, F., Who is afraid of the humanoid? Investigating cultural differences in the acceptance of robots (2004) Int. J. Hum. Robot., 1 (3), pp. 465-480; Ferrari, F., Paladino, M.P., Jetten, J., Blurring human–machine distinctions: Anthropomorphic appearance in social robots as a threat to human distinctiveness (2016) Int. J. Soc. Robot., 8 (2), pp. 287-302; Stein, J.P., Liebold, B., Ohler, P., Stay back, clever thing! Linking situational control and human uniqueness concerns to the aversion against autonomous technology (2019) Comput. Hum. Behav., 95, pp. 73-82; Hogg, M.A., (2016) Understanding Peace and Conflict through Social Identity Theory, pp. 3-17. , https://doi.org/10.1007/978-3-319-29869-6, Social identity theory. In: McKeown, S., Haji, R., Ferguson, N. (eds.), pp., Springer, Switzerland; Howard, J.W., Rothbart, M., Social categorization and memory for in-group and out-group behavior (1980) J. Pers. Soc. Psychol., 38 (2), pp. 301-310; Levine, M., Prosser, A., Evans, D., Reicher, S., Identity and emergency intervention: How social group membership and inclusiveness of group boundaries shape helping behavior (2005) Pers. Soc. Psychol. Bull., 31 (4), pp. 443-453; Häring, M., Kuchenbrandt, D., André, E., Would you like to play with me?: How robots’ group membership and task features influence human-robot interaction (2014) Proceedings of the 2014 International Conference on Human-Robot Interaction, pp. 9-16. , pp., ACM, Germany; Eyssel, F., Kuchenbrandt, D., Social categorization of social robots: Anthropomorphism as a function of robot group membership (2012) Br. J. Soc. Psychol., 51 (4), pp. 724-731; Heerink, M., Exploring the influence of age, gender, education and computer experience on robot acceptance by older adults (2011) Proceedings of the 6Th International Conference on Human-Robot Interaction (HRI’11), pp. 147-148. , pp., ACM, New York; Arras, K.O., Cerqui, D., (2005) Do We Want to Share Our Lives and Bodies with Robots? A 2000 People Survey: A 2000-People Survey, pp. 1-41. , Technical Report, pp; Nass, C., Fogg, B.J., Moon, Y., Can computers be teammates? (1996) Int. J. Hum. Comput. Stud., 45 (6), pp. 669-678; Shamay-Tsoory, S.G., Tomer, R., Aharon-Peretz, J., The neuroanatomical basis of understanding sarcasm and its relationship to social cognition (2005) Neuropsychology, 19 (3), p. 288; Buhrmester, M., Kwang, T., Gosling, S.D., Amazon’s mechanical turk: A new source of inexpensive, yet high-quality, data? (2011) Persp. Psychol. Sci, 6 (1), pp. 3-5","Kang, J.; Media Effects Research Laboratory, United States; email: jbk5361@psu.edu","Novais P.Carneiro D.Lloret J.Chamoso P.Navarro E.Omatu S.",,"Springer Verlag","10th International Symposium on Ambient Intelligence, ISAmI 2019","26 June 2019 through 28 June 2019",,227779,21945357,9783030240967,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85068609467
"Cho S., Park J.-M., Song T.-J., Kim J.-H.","55686250200;57207895799;57209412902;36484629700;","Human-Robot Full-Sentence VQA Interaction System with Highway Memory Network",2020,"Lecture Notes in Mechanical Engineering",,,,"131","148",,,"10.1007/978-981-13-8323-6_12","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067686741&doi=10.1007%2f978-981-13-8323-6_12&partnerID=40&md5=ae2e994f593a8539f8a4253a05d2187f","School of Electrical Engineering, KAIST, 291 Daehangno, Yuseong-gu, Daejeon, 305-701, South Korea","Cho, S., School of Electrical Engineering, KAIST, 291 Daehangno, Yuseong-gu, Daejeon, 305-701, South Korea; Park, J.-M., School of Electrical Engineering, KAIST, 291 Daehangno, Yuseong-gu, Daejeon, 305-701, South Korea; Song, T.-J., School of Electrical Engineering, KAIST, 291 Daehangno, Yuseong-gu, Daejeon, 305-701, South Korea; Kim, J.-H., School of Electrical Engineering, KAIST, 291 Daehangno, Yuseong-gu, Daejeon, 305-701, South Korea","Intelligent robots such as social robots and home service robots that are being developed nowadays are required to have the ability to naturally communicate with users in natural language. Beyond simple data retrieval and simple dialogue level, we propose a new Human-Robot Interaction (HRI) system that enables a robot to understand and reason the environment around a user and present information about them in natural language, whenever the user asks a question to the robot. For its intelligent HRI, based on Dynamic Memory Networks (DMN), a neural network for Visual Question Answering (VQA), we propose a new full sentence VQA network model called Full-Sentence Highway Memory Network (FSHMN). For its robot platform, a three DOF robotic head was used which has a neck with three motors and a tablet PC head. To verify the feasibility of the proposed system, an experiment is performed in which a user and a robot interact with each other in a way of question answering in a customized kitchen environment. Through the experiment, we not only demonstrated the effectiveness of applying deep learning to HRI applications in real environments but also presented a new insight into HRI. © Springer Nature Singapore Pte Ltd. 2020.","Full-Sentence Highway Memory Network (FSHMN); Full-Sentence Visual Question Answering (FSVQA); Human-Robot Interaction (HRI)",,,,,,"National Research Foundation of Korea, NRF; Ministry of Science and ICT, South Korea, MSIT","Acknowledgements This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2017R1A2A1A17069837), and in part by the ICT R&D program of MSIT/IITP [R7124-16-0005, Research on adaptive machine learning technology development for intelligent autonomous digital companion].",,,,,,,,,,"Breazeal, C.L., (2004) Designing Sociable Robots, , MIT press; Park, G.-M., Kim, D.-H., Jeong, I.-B., Ko, W.-R., Yoo, Y.-H., Kim, J.-H., Taskintelligence of robots: Neural model-based mechanism of thought and online motion planning (2017) IEEE Trans Emerg Top Comput Intell, 1 (1), pp. 41-50; Lin, P., Abney, K., Bekey, G.A., (2011) Robot Ethics: The Ethical and Social Implications of Robotics, , MIT press; Matthias, S., What is robot ethics?[tc spotlight] (2013) IEEE Robot Autom Mag, 20 (4), pp. 20-165; Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C., Recursive deep models for semantic compositionality over a sentiment treebank (2013) Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 1631, p. 1642. , Citeseer; Ma, L., Lu, Z., Li, H., (2015) Learning to Answer Questions from Imageusing Convolutional Neural Network; Yang, Z., He, X., Gao, J., Deng, L., Smola, A., Stacked attention networks for image question answering (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 21-29; Xiong, C., Merity, S., Socher, R., (2016) Dynamic Memory Networks for Visual and Textual Question Answering. Arxiv, p. 1603; Srivastava, R.K., Greff, K., Schmidhuber, J., Training very deepnetworks (2015) Advances in Neural Information Processing Systems, pp. 2377-2385; Zilly, J.G., Srivastava, R.K., Koutnık, J., Schmidhuber, J., (2016) Recurrent Highway Networks. Arxiv Preprint Arxiv, 1607, p. 03474; Sutskever, I., Vinyals, O., Le, Q.V., Sequence to sequence learning withneural networks (2014) Advances in Neural Information Processing Systems, pp. 3104-3112; Cho, S.-H., Lee, W.-H., Kim, J.-H., Implementation of human-robot vqa interaction system with dynamic memory networks (2017) 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 495-500; Kingma, D., Adam, J.B., (2014) A Method for Stochastic Optimization. Arxiv Preprint Arxiv, 1412, p. 6980; Shin, A., Ushiku, Y., Harada, T., (2016) The Color of the Cat Isgray: 1 Million Full-Sentences Visual Question Answering (Fsvqa)","Kim, J.-H.; School of Electrical Engineering, 291 Daehangno, Yuseong-gu, South Korea; email: johkim@rit.kaist.ac.kr",,,"Pleiades Publishing",,,,,21954356,,,,"English","Lect. Notes Mech. Eng.",Book Chapter,"Final","",Scopus,2-s2.0-85067686741
"Guo L., Li Z., Hu W., Liu T., Zheng Y., Yuan M., Dai Y., Ning R., Zhu Y., Tao K., Zhang M., Du T., Zhang L., Su C., Haick H., Wu W.","57221976880;57369299800;57195412794;57369659700;55582770400;57226893959;57370213900;57310741900;57221977424;57225192108;57369843900;57214892091;57369844000;57196459164;6603256731;55707535200;","A flexible dual-structured MXene for ultra-sensitive and ultra-wide monitoring of anatomical and physiological movements",2021,"Journal of Materials Chemistry A","9","47",,"26867","26874",,,"10.1039/d1ta08727d","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120978593&doi=10.1039%2fd1ta08727d&partnerID=40&md5=767ad305c97be13ce37e1b7a37081683","School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi'an, 712000, China; School of Aerospace Science and Technology, Xidian University, Xi'an, 712000, China; Interdisciplinary Research Center of Smart Sensors, Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an, 712000, China; Department of Chemical Engineering, Technion-Israel Institute of Technology, Israel; Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518033, China; School of Chemistry and Molecular Engineering, Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, East China Normal University, Xi'an, 712000, China","Guo, L., School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi'an, 712000, China; Li, Z., School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi'an, 712000, China; Hu, W., School of Aerospace Science and Technology, Xidian University, Xi'an, 712000, China; Liu, T., Interdisciplinary Research Center of Smart Sensors, Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an, 712000, China; Zheng, Y., Department of Chemical Engineering, Technion-Israel Institute of Technology, Israel; Yuan, M., Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518033, China; Dai, Y., School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi'an, 712000, China; Ning, R., Interdisciplinary Research Center of Smart Sensors, Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an, 712000, China; Zhu, Y., School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi'an, 712000, China; Tao, K., School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi'an, 712000, China; Zhang, M., School of Chemistry and Molecular Engineering, Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, East China Normal University, Xi'an, 712000, China; Du, T., School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi'an, 712000, China; Zhang, L., School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi'an, 712000, China; Su, C., School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi'an, 712000, China; Haick, H., Department of Chemical Engineering, Technion-Israel Institute of Technology, Israel; Wu, W., School of Advanced Materials and Nanotechnology, Interdisciplinary Research Center of Smart Sensors, Xidian University, Xi'an, 712000, China","Flexible devices for capturing anatomical and physiological movements are essential for improving the quality of life in, e.g., disease monitoring, physical rehabilitation, and assistance for people with cognitive disorders. They require high sensitivity, wide detection range, multi-functional applicability, etc. Nevertheless, the current devices and technologies face the challenge of simultaneous achievement of these features, mainly sensitivity and detection range, and thus their utility and applications are limited. Herein we report on the design and production of dual-microstructures of surface micro-bumps and internal hollow pores in a conductive material, an MXene, for obtaining a multifunctional high-performance pressure sensor. The designed sensor has ultra-high sensitivity (401.01 kPa-1, 0-12 kPa), a wide detection range (1.96 Pa to 100 kPa), and stability in a wide range of human physiological and anatomical movement types, including wide range movement (joint movement and gesture), slight movement (muscle movement and wrist pulse), and synchronous movement (respiration, carotid artery, and head movement). With data-mining methods, we show an ultra-sensitive ability to extract gesture behavioral information and physiological information from the sensor signals, and its implications for human health. These performances could be used as a shuttling pad for motor function assessment and dexterous human-robot interaction for rehabilitation robots and intelligent prosthetics. © The Royal Society of Chemistry.",,"Cognitive systems; Data mining; Human computer interaction; Human robot interaction; Intelligent robots; Detection range; Disease monitoring; Flexible device; High sensitivity; Multi-functional; Performance; Physical rehabilitation; Quality of life; Ultra-wide; Ultrasensitive; Conductive materials",,,,,"2021MC0AB02; National Natural Science Foundation of China, NSFC: 51903197, 61904134; Fundamental Research Funds for the Central Universities: JB211305, JC2110","This work was nancially supported by the National Natural Science Foundation of China (51903197, 61904134), Fundamental Research Funds for the Central Universities (JC2110, JB211305), and Open Fund of Zhejiang Lab (2021MC0AB02).",,,,,,,,,,"Jie, Y., Qiang, Y., Pan, J.J., (2008) IEEE Trans Knowl Data Eng., 20, p. 1082; Kim, J., Kim, M., Lee, M.S., Kim, K., Ji, S., Kim, Y.T., Park, J.U., (2017) Nat. 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Mater., 28, p. 9364; Tao, L.Q., Zhang, K.N., Tian, H., Liu, Y., Wang, D.Y., Chen, Y.Q., Ren, T.L., (2017) ACS Nano, 11, p. 8790; Moin, A., Zhou, A., Rahimi, A., Menon, A., Benatti, S., Alexandrov, G., Rabaey, J.M., (2020) Nat. Electron., 4, p. 54; Kim, K.K., Ha, I., Kim, M., Choi, J., Won, P., Jo, S., Ko, S.H., (2020) Nat. Commun., 11, p. 2149; Wu, W., Haick, H., (2018) Adv. Mater., 30, p. 1705024","Su, C.; School of Advanced Materials and Nanotechnology, China; email: csu@xidian.edu.cn
Wu, W.; School of Advanced Materials and Nanotechnology, China; email: wwwu@xidian.edu.cn
Haick, H.; Department of Chemical Engineering, Israel; email: hhossam@technion.ac.il",,,"Royal Society of Chemistry",,,,,20507488,,JMCAE,,"English","J. Mater. Chem. A",Article,"Final","",Scopus,2-s2.0-85120978593
"Khawaja F.I., Kanazawa A., Kinugawa J., Kosuge K.","55933890400;57191519700;36810067100;55821756500;","A human-following motion planning and control scheme for collaborative robots based on human motion prediction",2021,"Sensors","21","24","8229","","",,,"10.3390/s21248229","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120829449&doi=10.3390%2fs21248229&partnerID=40&md5=d7815f5e71df26d3609bef3710371ad1","Center for Transformative AI and Robotics, Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan; Robotics and Intelligent Systems Engineering (RISE) Laboratory, Department of Robotics and Artificial Intelligence, School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Sector H-12, Islamabad, 44000, Pakistan; Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong","Khawaja, F.I., Center for Transformative AI and Robotics, Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan, Robotics and Intelligent Systems Engineering (RISE) Laboratory, Department of Robotics and Artificial Intelligence, School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Sector H-12, Islamabad, 44000, Pakistan; Kanazawa, A., Center for Transformative AI and Robotics, Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan; Kinugawa, J., Center for Transformative AI and Robotics, Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan; Kosuge, K., Center for Transformative AI and Robotics, Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan, Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong","Human–Robot Interaction (HRI) for collaborative robots has become an active research topic recently. Collaborative robots assist human workers in their tasks and improve their efficiency. However, the worker should also feel safe and comfortable while interacting with the robot. In this paper, we propose a human-following motion planning and control scheme for a collaborative robot which supplies the necessary parts and tools to a worker in an assembly process in a factory. In our proposed scheme, a 3-D sensing system is employed to measure the skeletal data of the worker. At each sampling time of the sensing system, an optimal delivery position is estimated using the real-time worker data. At the same time, the future positions of the worker are predicted as probabilistic distributions. A Model Predictive Control (MPC)-based trajectory planner is used to calculate a robot trajectory that supplies the required parts and tools to the worker and follows the predicted future positions of the worker. We have installed our proposed scheme in a collaborative robot system with a 2-DOF planar manipulator. Experimental results show that the proposed scheme enables the robot to provide anytime assistance to a worker who is moving around in the workspace while ensuring the safety and comfort of the worker. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Collaborative robots; Human motion prediction; Human-following robots; Human–robot collaboration; Human–robot interaction; Motion planning; Robot control","Collaborative robots; Industrial manipulators; Model predictive control; Motion estimation; Motion planning; Probability distributions; Robot programming; Collaborative robots; Human following; Human motion prediction; Human motions; Human-following robot; Human-robot collaboration; Humans-robot interactions; Motion prediction; Motion-planning; Robots control; Predictive control systems",,,,,,,,,,,,,,,,"Troccaz, J., Lavallee, S., Hellion, E., A passive arm with dynamic constraints: A solution to safety problems in medical robotics (1993) Proceedings of the IEEE Systems Man and Cybernetics Conference—SMC, 3, pp. 166-171. , Le Touquet, France, 17–20 October; Colgate, J., Wannasuphoprasit, W., Peshkin, M., Cobots: Robots for collaboration with human operators (1996) Proceedings of the 1996 ASME International Mechanical Engineering Congress and Exposition, pp. 433-439. , Atlanta, Georgia, 17–22 November; Yamada, Y., Konosu, H., Morizono, T., Umetani, Y., Proposal of Skill-Assist: A system of assisting human workers by reflecting their skills in positioning tasks (1999) Proceedings of the IEEE SMC’99 Conference Proceedings 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. 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Autom, 8, pp. 42-52; Jaillet, L., Cortes, J., Simeon, T., Transition-based RRT for path planning in continuous cost spaces (2008) Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2145-2150. , Nice, France, 22–26 September; Kinugawa, J., Kanazawa, A., Arai, S., Kosuge, K., Adaptive Task Scheduling for an Assembly Task Coworker Robot Based on Incremental Learning of Human’s Motion Patterns (2017) IEEE Robot. Autom. Lett, 2, pp. 856-863; Calinon, S., Bruno, D., Caldwell, D.G., A task-parameterized probabilistic model with minimal intervention control (2014) Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 3339-3344. , Hong Kong, China, 31 May–7 June; Du Toit, N.E., Burdick, J.W., Robot Motion Planning in Dynamic, Uncertain Environments (2012) IEEE Trans. Robot, 28, pp. 101-115; Sung, H.G., (2004) Gaussian Mixture Regression and Classification, , Ph.D. Thesis, Rice University, Houstion, TX, USA","Khawaja, F.I.; Center for Transformative AI and Robotics, Japan; email: fahad.iqbal@smme.nust.edu.pk",,,"MDPI",,,,,14248220,,,,"English","Sensors",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85120829449
"Rashid F., Burns D., Song Y.S.","57326548600;57189574906;57211646767;","Sensing small interaction forces through proprioception",2021,"Scientific Reports","11","1","21829","","",,,"10.1038/s41598-021-01112-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118681525&doi=10.1038%2fs41598-021-01112-w&partnerID=40&md5=c25a38efa694976d01b7f01929a62f51","Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO  65401, United States; Department of Psychological Science, Missouri University of Science and Technology, Rolla, MO  65401, United States","Rashid, F., Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO  65401, United States; Burns, D., Department of Psychological Science, Missouri University of Science and Technology, Rolla, MO  65401, United States; Song, Y.S., Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO  65401, United States","Understanding the human motor control strategy during physical interaction tasks is crucial for developing future robots for physical human–robot interaction (pHRI). In physical human–human interaction (pHHI), small interaction forces are known to convey their intent between the partners for effective motor communication. The aim of this work is to investigate what affects the human’s sensitivity to the externally applied interaction forces. The hypothesis is that one way the small interaction forces are sensed is through the movement of the arm and the resulting proprioceptive signals. A pHRI setup was used to provide small interaction forces to the hand of seated participants in one of four directions, while the participants were asked to identify the direction of the push while blindfolded. The result shows that participants’ ability to correctly report the direction of the interaction force was lower with low interaction force as well as with high muscle contraction. The sensitivity to the interaction force direction increased with the radial displacement of the participant’s hand from the initial position: the further they moved the more correct their responses were. It was also observed that the estimated stiffness of the arm varies with the level of muscle contraction and robot interaction force. © 2021, The Author(s).",,,,,,,"National Science Foundation, NSF: 1843892; Center for Selective C-H Functionalization, National Science Foundation; Center for Hierarchical Manufacturing, National Science Foundation, CHM, NSF","This work is supported by the National Science Foundation #1843892.",,,,,,,,,,"Heyer, C., Human–robot interaction and future industrial robotics applications (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, , In, IEEE; Kosuge, K., Hirata, Y., Human–robot interaction (2004) 2004 IEEE International Conference on Robotics and Biomimetics, , IEEE; Wang, Z.-D., Hirata, Y., Takano, Y., Kosuge, K., (2004) From Human to Pushing Leader Robot: Leading a Decentralized Multirobot System for Object Handling, , IEEE; Glende, S., Conrad, I., Krezdorn, L., Klemcke, S., Krätzel, C., Increasing the acceptance of assistive robots for older people through marketing strategies based on stakeholder needs (2016) Int. 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"Zheng G.","57212037873;","A Novel Attention-Based Convolution Neural Network for Human Activity Recognition",2021,"IEEE Sensors Journal","21","23",,"27015","27025",,,"10.1109/JSEN.2021.3122258","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118553773&doi=10.1109%2fJSEN.2021.3122258&partnerID=40&md5=97fed3d71faac2169f8f592d14db2a70","Department of Computing and Informatics, Bournemouth University, Dorset, United Kingdom","Zheng, G., Department of Computing and Informatics, Bournemouth University, Dorset, United Kingdom","Human Activity Recognition (HAR) has been widely used for various applications, such as smart homes, healthcare, security and human-robot interaction. In this paper, a novel deep learning model is proposed based on different Convolution Neural Networks (CNN) and the attention mechanism for addressing the HAR problem by using sensory data from accelerometers and gyroscopes and providing high recognition accuracy. The architecture of the proposed model (named LGSTNet) is designed to be able to analyse and extract Local and Global Spatial-Temporal features from sensory data for HAR. The idea of LGSTNet is to segment an activity window into several sub-windows and join the Attention mechanism into an 2D CNN (named Attention-based 2D-CNN module) to precisely learn local spatial-temporal features from those sub-windows. Meanwhile, an 3D CNN (named 3D-CNN module) is designed to learn global spatial-temporal features from the whole window. Both comparison and ablation experiments are conducted on two real-world datasets, named WISDM and UCI-HAR. By comparing LGSTNet to eight other well-known existing models and its variants on both datasets, LGSTNet achieves the best performance (over 0.8064 and $0.9569\,\,{F}_{w}-\textit {Meansure}$ on WISDM and UCI-HAR, respectively) and is also more robust. © 2001-2012 IEEE.","attention mechanism; convolution neural network; Human activity recognition; spatial-temporal features","Automation; Convolution; Deep learning; Human computer interaction; Human robot interaction; Intelligent buildings; Neural networks; Pattern recognition; Activity recognition; Attention mechanisms; Convolution neural network; Convolutional neural network; Deep learning; Features extraction; Human activity recognition; Neural-networks; Sensory data; Spatial-temporal features; Data mining",,,,,,,,,,,,,,,,"(2019) World Population Ageing, , https://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2019-Highlights.pdf; Golestani, N., Moghaddam, M., Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks (2020) Nature Commun., 11 (1), pp. 1-11. , Dec; Liu, T., Wang, J., Yang, B., Wang, X., NGDNet: Nonuniform Gaussian-label distribution learning for infrared head pose estimation and on-task behavior understanding in the classroom (2021) Neurocomputing, 436, pp. 210-220. , May; Liu, T., Liu, H., Li, Y.-F., Chen, Z., Zhang, Z., Liu, S., Flexible FTIR spectral imaging enhancement for industrial robot infrared vision sensing (2020) IEEE Trans. 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"Bustamante S., Peters J., Scholkopf B., Grosse-Wentrup M., Jayaram V.","57189325365;35248912800;7004460308;11939406800;56626757500;","ArmSym: A Virtual Human-Robot Interaction Laboratory for Assistive Robotics",2021,"IEEE Transactions on Human-Machine Systems","51","6",,"568","577",,,"10.1109/THMS.2021.3106865","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115703911&doi=10.1109%2fTHMS.2021.3106865&partnerID=40&md5=a0a96255d618aea3b7551102c3cb45d5","Empirical Inference Department, The Max Planck Institute for Intelligent Systems, Tübingen, Germany; Graduate School of Neural Information Processing, University of Tübingen, Tübingen, 72074, Germany; Centre for Robotics and Mechatronics, German Aeroespace Center, Weßling, 82234, Germany; Computer Science Department, Technische Universität, Darmstadt, 01069, Germany; Faculty of Computer Science, University of Vienna, Vienna, 1010, Austria","Bustamante, S., Empirical Inference Department, The Max Planck Institute for Intelligent Systems, Tübingen, Germany, Graduate School of Neural Information Processing, University of Tübingen, Tübingen, 72074, Germany, Centre for Robotics and Mechatronics, German Aeroespace Center, Weßling, 82234, Germany; Peters, J., Empirical Inference Department, The Max Planck Institute for Intelligent Systems, Tübingen, Germany, Computer Science Department, Technische Universität, Darmstadt, 01069, Germany; Scholkopf, B., Empirical Inference Department, The Max Planck Institute for Intelligent Systems, Tübingen, Germany; Grosse-Wentrup, M., Empirical Inference Department, The Max Planck Institute for Intelligent Systems, Tübingen, Germany, Faculty of Computer Science, University of Vienna, Vienna, 1010, Austria; Jayaram, V., Empirical Inference Department, The Max Planck Institute for Intelligent Systems, Tübingen, Germany","Research in human-robot interaction for assistive robotics usually presents many technical challenges for experimenters, forcing researchers to split their time between solving technical problems and conducting experiments. In addition, previous work in virtual reality setups tends to focus on a single assistive robotics application. In order to alleviate these problems, we present ArmSym, a virtual reality laboratory with a fully simulated and developer-friendly robot arm. The system is intended as a testbed to run many sorts of experiments on human control of a robotic arm in a realistic environment, ranging from an upper limb prosthesis to a wheelchair-mounted robotic manipulator. To highlight the possibilities of this system, we perform a study comparing different sorts of prosthetic control types. Looking at nonimpaired subjects, we study different psychological metrics that evaluate the interaction of the user with the robot under different control conditions. Subjects report a perception of embodiment in the absence of realistic cutaneous touch, supporting previous studies in the topic. We also find interesting correlations between control and perceived ease of use. Overall our results confirm that ArmSym can be used to gather data from immersive experiences prosthetics, opening the door to closer collaboration between device engineers and experience designers in the future. © 2013 IEEE.","ArmSym; human-robot interaction; robot arm; simulated laboratory; virtual reality","Human robot interaction; Job analysis; Man machine systems; Manipulators; Prosthetics; Robotics; Virtual reality; Armsym; Assistive robotics; Humans-robot interactions; Psychology; Robot arms; Robot interactions; Shoulder; Simulated laboratory; Task analysis; Tracking; Robotic arms",,,,,,,,,,,,,,,,"Montagnani, F., Controzzi, M., Cipriani, C., Is it finger orwrist dexterity that is missing in current hand prostheses? (2015) IEEE Trans. Neural Syst. Rehabil. Eng., 23 (4), pp. 600-609. , Jul; Mathiowetz, V., Volland, G., Kashman, N., Weber, K., Adult norms for the box and block test of manual dexterity (1985) Amer. J. Occup. 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Eng., 16 (1), pp. 46-50. , Feb; Haverkate, L., Smit, G., Plettenburg, D.H., Assessment of bodypowered upper limb prostheses by able-bodied subjects, using the box and blocks test and the nine-hole peg test (2016) Prosthetics Orthotics Int., 40, pp. 109-116; Marasco, P.D., Kim, K., Colgate, J.E., Peshkin, M.A., Kuiken, T.A., Robotic touch shifts perception of embodiment to a prosthesis in targeted reinnervation amputees (2011) Brain, 134 (3), pp. 747-758","Bustamante, S.; Empirical Inference Department, Germany; email: samuel.bustamante@dlr.de",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21682291,,,,"English","IEEE Trans. Human Mach. Syst.",Article,"Final","",Scopus,2-s2.0-85115703911
"Muñoz J.E., Dautenhahn K.","56645651500;7003305185;","Robo Ludens: A Game Design Taxonomy for Multiplayer Games Using Socially Interactive Robots",2021,"ACM Transactions on Human-Robot Interaction","10","4","3451343","","",,,"10.1145/3451343","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115152132&doi=10.1145%2f3451343&partnerID=40&md5=ec5afbb384e800d3da314bd2b156b239","Systems Design Engineering Department, University of Waterloo, Waterloo, ON, Canada; Electrical and Computer Engineering, Systems Design Engineering Departments, University of Waterloo, Waterloo, ON, Canada","Muñoz, J.E., Systems Design Engineering Department, University of Waterloo, Waterloo, ON, Canada; Dautenhahn, K., Electrical and Computer Engineering, Systems Design Engineering Departments, University of Waterloo, Waterloo, ON, Canada","The use of games as vehicles to study human-robot interaction (HRI) has been established as a suitable solution to create more realistic and naturalistic opportunities to investigate human behavior. In particular, multiplayer games that involve at least two human players and one or more robots have raised the attention of the research community. This article proposes a scoping review to qualitatively examine the literature on the use of multiplayer games in HRI scenarios employing embodied robots aiming to find experimental patterns and common game design elements. We find that researchers have been using multiplayer games in a wide variety of applications in HRI, including training, entertainment and education, allowing robots to take different roles. Moreover, robots have included different capabilities and sensing technologies, and elements such as external screens or motion controllers were used to foster gameplay. Based on our findings, we propose a design taxonomy called Robo Ludens, which identifies HRI elements and game design fundamentals and classifies important components used in multiplayer HRI scenarios. The Robo Ludens taxonomy covers considerations from a robot-oriented perspective as well as game design aspects to provide a comprehensive list of elements that can foster gameplay and bring enjoyable experiences in HRI scenarios. © 2021 Owner/Author.","cognitive stimulation; education; embodiment; Game design; multiplayer; social interaction; social robotics; therapy","Agricultural robots; Behavioral research; Machine design; Social robots; Taxonomies; Human behaviors; Human robot Interaction (HRI); Interactive robot; Motion controller; Multiplayer games; Research communities; Sensing technology; Suitable solutions; Educational robots",,,,,,"This research was undertaken, in part, thanks to funding from the Canada 150 Research Chairs Program. Authors’ addresses: J. E. Muñoz and K. 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"Zhang D., Shen J., Li S., Gao K., Gu R.","55717539100;57212169668;57248301400;57226885206;36188070700;","I, robot: depression plays different roles in human–human and human–robot interactions",2021,"Translational Psychiatry","11","1","438","","",,,"10.1038/s41398-021-01567-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113257230&doi=10.1038%2fs41398-021-01567-5&partnerID=40&md5=a13b765127526a9328456ea95799f286","School of Psychology, Shenzhen University, Shenzhen, 518060, China; Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, 518060, China; CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China","Zhang, D., School of Psychology, Shenzhen University, Shenzhen, 518060, China, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, 518060, China; Shen, J., School of Psychology, Shenzhen University, Shenzhen, 518060, China; Li, S., School of Psychology, Shenzhen University, Shenzhen, 518060, China; Gao, K., School of Psychology, Shenzhen University, Shenzhen, 518060, China; Gu, R., CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, China, Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China","Socially engaging robots have been increasingly applied to alleviate depressive symptoms and to improve the quality of social life among different populations. Seeing that depression negatively influences social reward processing in everyday interaction, we investigate this influence during simulated interactions with humans or robots. In this study, 35 participants with mild depression and 35 controls (all from nonclinical populations) finished the social incentive delay task with event-related potential recording, in which they received performance feedback from other persons or from a robot. Compared to the controls, the mild depressive symptom (MDS) group represented abnormalities of social reward processing in the human feedback condition: first, the MDS group showed a lower hit rate and a smaller contingent-negative variation (correlated with each other) during reward anticipation; second, depression level modulated both the early phase (indexed by the feedback-related negativity (FRN)) and the late phase (indexed by the P3) of reward consumption. In contrast, the effect of depression was evident only on FRN amplitude in the robot feedback condition. We suggest that compared to human–human interaction, the rewarding properties of human–robot interaction are less likely to be affected by depression. These findings have implications for the utilization of robot-assisted intervention in clinical practice. © 2021, The Author(s).",,"adult; Article; association; clinical article; comparative study; contingent negative variation; controlled study; depression; event related potential; false discovery rate; feedback system; female; human; male; punishment; reward; social incentive; social interaction; task performance; young adult; depression; electroencephalography; evoked response; robotics; Depression; Electroencephalography; Evoked Potentials; Humans; Reward; Robotics",,,,,"2021SHIBS0003; National Outstanding Youth Science Fund Project of National Natural Science Foundation of China, IUSS: 31970980, 32020103008, 32071083; National Natural Science Foundation of China, NSFC: 17ZDA324; Youth Innovation Promotion Association of the Chinese Academy of Sciences: 2019088; Science and Technology Planning Project of Guangdong Province: 2019A050510048","This study was funded by the National Natural Science Foundation of China (31970980, 32071083, 32020103008), Shenzhen-Hong Kong Institute of Brain Science (2021SHIBS0003), Science and Technology Planning Project of Guangdong Province of China (2019A050510048), Major Program of the Chinese National Social Science Foundation (17ZDA324), and Youth Innovation Promotion Association CAS (2019088).",,,,,,,,,,"Hill, C.A., Affiliation motivation: people who need people… but in different ways (1987) J Personal Soc Psychol, 52, pp. 1008-1018. , COI: 1:STN:280:DyaL2s3isFKmsg%3D%3D; Kawamichi, H., Sugawara, S.K., Hamano, Y.H., Makita, K., Kochiyama, T., Sadato, N., Increased frequency of social interaction is associated with enjoyment enhancement and reward system activation (2016) Sci Rep., 6, pp. 1-11. , COI: 1:CAS:528:DC%2BC28XmsFaks7w%3D; Bishop, S.J., Gagne, C., Anxiety, depression, and decision making: a computational perspective (2018) Annu Rev Neurosci, 41, pp. 371-388. , COI: 1:CAS:528:DC%2BC1cXosFyrtLk%3D, PID: 29709209; Kupferberg, A., Bicks, L., Hasler, G., Social functioning in major depressive disorder (2016) Neurosci Biobehav Rev, 69, pp. 313-332. , PID: 27395342; Forbes, E.E., Dahl, R.E., Research review: altered reward function in adolescent depression: what, when and how? 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Gu, R.; CAS Key Laboratory of Behavioral Science, China; email: gurl@psych.ac.cn",,,"Springer Nature",,,,,21583188,,,"34420040","English","Transl. Psychiatry",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85113257230
"Johanson D.L., Ahn H.S., Broadbent E.","57200649858;56218250500;7003284294;","Improving Interactions with Healthcare Robots: A Review of Communication Behaviours in Social and Healthcare Contexts",2021,"International Journal of Social Robotics","13","8",,"1835","1850",,,"10.1007/s12369-020-00719-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095992051&doi=10.1007%2fs12369-020-00719-9&partnerID=40&md5=f3f195ead2052764e3a972a247dbf24f","Department of Psychological Medicine, Faculty of Health Psychology, The University of Auckland, Auckland, New Zealand; Department of Electrical and Computer Engineering, Faculty of Engineering, The University of Auckland, Auckland, New Zealand","Johanson, D.L., Department of Psychological Medicine, Faculty of Health Psychology, The University of Auckland, Auckland, New Zealand; Ahn, H.S., Department of Electrical and Computer Engineering, Faculty of Engineering, The University of Auckland, Auckland, New Zealand; Broadbent, E., Department of Psychological Medicine, Faculty of Health Psychology, The University of Auckland, Auckland, New Zealand","A growing shortfall exists between the number of older individuals who require healthcare support and the number of qualified healthcare professionals who can provide this. Robots offer the potential to provide healthcare support to patients both at home and in healthcare settings. However, in order for robots to be successfully implemented in these environments, they need to behave in ways that are appropriate and acceptable to human users. One way to identify appropriate social behaviours for healthcare robots is to model their behaviour on interactions between healthcare professionals and patients. This literature review aimed to inform healthcare robotics research by highlighting communication behaviours that are important within the context of healthcare. The review focussed on relevant research in human clinical interactions, followed by a review of similar factors in social robotics research. Three databases were searched for terms relating to healthcare professional communication behaviours associated with patient outcomes. The results identified key communication behaviours that can convey clinical empathy, including humour, self-disclosure, facial expressions, eye gaze, body posture, and gestures. A further search was conducted to identify research examining these key behaviours within the context of social and healthcare robotics. Research into these factors in human–robot interaction in healthcare is limited to date, and this review provides a useful guide for future research. © 2020, Springer Nature B.V.","Communication; Human–robot interactions; Non-verbal behaviours; Physician–patient interactions; Verbal behaviours","Agricultural robots; Clinical research; Health care; Robotics; Facial Expressions; Health care professionals; Healthcare robotics; Healthcare support; Literature reviews; Robot interactions; Similar factors; Social behaviour; Social robots",,,,,"Ministry of Trade, Industry and Energy, MOTIE","This study was supported by the Technology Innovation Program (10077553, Development of Social Robot Intelligence for Social Human–Robot Interaction of Service Robots) funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea).",,,,,,,,,,"(2015) World Population Prospects., , https://esa.un.org/unpd/wpp/Publications/Files/WPP2015_Volume-I_Comprehensive-Tables.pdf, United Nations, Accessed 27 Mar 2019; Third global forum on human resources for health report—a universal truth: No health without a work force (2013) World Health Organization, , https://www.who.int/workforcealliance/knowledge/resources//hrhreport2013/en/; Balasubramanian, S., Klein, J., Burdet, E., Robot-assisted rehabilitation of hand function (2010) Curr Opin Neurol, 23, pp. 661-670; Mukai, T., Hirano, S., Nakashima, H., Kato, Y., Sakaida, Y., Guo, S., Hosoe, S., Development of a nursing-care assistance robot RIBA that can lift a human in its arms (2010) In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, , https://doi.org/10.1109/iros.2010.5651735; Yanco, H.A., A robotic wheelchair system: Indoor navigation and user interface (1998) Assistive technology and artificial intelligence, pp. 256-268. , Mittal VO, Yanco HA, Simpson R, (eds), Springer, Berlin; Guizzo, E., Goldstein, H., The rise of the body bots (2005) IEEE Spectrum, 42, pp. 51-56; Blanson Henkemans, O.A., Bierman, B.P.B., Janssen, J., Neerinx, M.A., Looije, R., van der Bosch, H., van der Giessen, J.A.M., Using a robot to personalist health education for children with diabetes type 1: a pilot study (2013) Patient Educ Couns, 92, pp. 174-181; Broadbent, E., Peri, K., Kerse, N., Jayawardena, C., Kuo, I., Datta, C., Macdonald, B., Robots in older people’s homes to improve medication adherence and quality of life: A randomised cross-over trial (2014) Proceedings of the 6Th International Conference in Social Robotics (ICSR), pp. 64-73. , https://doi.org/10.1007/978-3-319-11973-1_7; Broadbent, E., Orejana, J.R., Ahn, H.S., Xie, J., Rouse, P., Macdonald, B.A., The cost-effectiveness of a robot measuring vital signs in a rural medical practice (2015) Proceedings of the Robot and Human Interactive Communication (RO-MAN) Conference, pp. 577-581. , https://doi.org/10.1109/roman.2015.7333668; Eriksson, J., Mataric, M.J., Winstein, C.J., Hands-off assistive robotics for post-stroke arm rehabilitation (2005) Proceedings of the 2005 IEEE 9Th International Conference on Rehabilitation Robotics, pp. 21-24. , https://doi.org/10.1109/icorr.2005.1501042; Tapus, A., Tapus, C., Mataric, M.J., User-robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy (2008) Intell Serv Robot, 1, pp. 169-183; Fasola, J., Mataric, M.J., A socially assistive robot exercise coach for the elderly (2013) J Hum Robot Interact, 2, pp. 3-32; Lara, S.J., Casas, J., Aguirre, A., Munera, M., Rincon-Roncancio, M., Irfan, B., Senft, E., Cifentes, C.A., Human–robot sensor interface for cardia rehabilitation (2017) Proceedings of the 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 1013-1018. , https://doi.org/10.1109/icorr.2017.8009382; Kachouie, R., Sedighadeli, S., Khosla, R., Chu, M., Socially assistive robots in elderly care: a mixed-method systematic literature review (2014) Int J Hum Comput Interact, 5, pp. 369-393; Dawe, J., Sutherland, C., Barco, A., Broadbent, E., Can social robots help children in healthcare contexts? 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J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85095992051
"James J., Balamurali B.T., Watson C.I., MacDonald B.","57211243483;57204209428;57193916673;7101734705;","Empathetic Speech Synthesis and Testing for Healthcare Robots",2021,"International Journal of Social Robotics","13","8",,"2119","2137",,1,"10.1007/s12369-020-00691-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090755646&doi=10.1007%2fs12369-020-00691-4&partnerID=40&md5=e6d2fbb614c296ba7d803a31715e7357","Centre for Automation and Robotic Engineering Science, Department of Electrical, Computer, and Software Engineering, The University of Auckland, Auckland, New Zealand; Singapore University of Technology & Design, Singapore, Singapore","James, J., Centre for Automation and Robotic Engineering Science, Department of Electrical, Computer, and Software Engineering, The University of Auckland, Auckland, New Zealand; Balamurali, B.T., Singapore University of Technology & Design, Singapore, Singapore; Watson, C.I., Centre for Automation and Robotic Engineering Science, Department of Electrical, Computer, and Software Engineering, The University of Auckland, Auckland, New Zealand; MacDonald, B., Centre for Automation and Robotic Engineering Science, Department of Electrical, Computer, and Software Engineering, The University of Auckland, Auckland, New Zealand","One of the major factors that affect the acceptance of robots in Human-Robot Interaction applications is the type of voice with which they interact with humans. The robot’s voice can be used to express empathy, which is an affective response of the robot to the human user. In this study, the aim is to find out if social robots with empathetic voice are acceptable for users in healthcare applications. A pilot study using an empathetic voice spoken by a voice actor was conducted. Only prosody in speech is used to express empathy here, without any visual cues. Also, the emotions needed for an empathetic voice are identified. It was found that the emotions needed are not only the stronger primary emotions, but also the nuanced secondary emotions. These emotions are then synthesised using prosody modelling. A second study, replicating the pilot test is conducted using the synthesised voices to investigate if empathy is perceived from the synthetic voice as well. This paper reports the modelling and synthesises of an empathetic voice, and experimentally shows that people prefer empathetic voice for healthcare robots. The results can be further used to develop empathetic social robots, that can improve people’s acceptance of social robots. © 2020, Springer Nature B.V.","Artificial empathy; Emotional speech synthesis; Healthcare; Prosody modelling; Social robots","Agricultural robots; Health care; Speech synthesis; Affective response; Health care application; Human users; Major factors; Pilot studies; Pilot tests; Prosody modelling; Synthetic voices; Social robots",,,,,"University of Auckland","This research was supported by the Centre for Automation and Robotic Engineering Science, University of Auckland Seed funding. The authors would like to thank the professional actors who recorded their voices for the JLCorpus and the perception test participants for their time and effort.",,,,,,,,,,"Tapus, A., Assistive robotics for healthcare and rehabilitation (2009) . In: Int. 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An exploratory study on the role of voice in the perception of robots (2019) 2019 14Th ACM/IEEE Int. Conf. on human–robot Interaction (HRI), pp. 211-221. , IEEE; Anzalone, S.M., Boucenna, S., Ivaldi, S., Chetouani, M., Evaluating the engagement with social robots (2015) Int J Social Robot, 7 (4), pp. 465-478; Leite, I., Castellano, G., Pereira, A., Martinho, C., Paiva, A., Empathic robots for long-term interaction (2014) Int J Social Robot, 6 (3), pp. 329-341; Tamagawa, R., Watson, C.I., Han Kuo, I., MacDonald, B.A., Broadbent, E., The effects of synthesized voice accents on user perceptions of robots (2011) Int J Social Robot, 3 (3), pp. 253-262","James, J.; Centre for Automation and Robotic Engineering Science, New Zealand; email: jesin.james@auckland.ac.nz",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85090755646
"Pisla D., Tarnita D., Tucan P., Tohanean N., Vaida C., Geonea I.D., Bogdan G., Abrudan C., Carbone G., Plitea N.","14067935700;14051138200;57190377695;36115091400;24377283500;42061278800;57316359600;57210106233;7005370456;6602657211;","A parallel robot with torque monitoring for brachial monoparesis rehabilitation tasks",2021,"Applied Sciences (Switzerland)","11","21","9932","","",,2,"10.3390/app11219932","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118209521&doi=10.3390%2fapp11219932&partnerID=40&md5=9e50fe8e720834a2c674f5973ddf6194","CESTER, Technical University of Cluj-Napoca, Cluj-Napoca, 400641, Romania; Department of Applied Mechanics and Civil Construction, Faculty of Mechanics, University of Craiova, Craiova, 200585, Romania; Department of Neurology, University of Medicine and Pharmacy “Iuliu Hatieganu”, Cluj-Napoca, 400012, Romania; DIMEG, University of Calabria, Cosenza, 87036, Italy","Pisla, D., CESTER, Technical University of Cluj-Napoca, Cluj-Napoca, 400641, Romania; Tarnita, D., Department of Applied Mechanics and Civil Construction, Faculty of Mechanics, University of Craiova, Craiova, 200585, Romania; Tucan, P., CESTER, Technical University of Cluj-Napoca, Cluj-Napoca, 400641, Romania; Tohanean, N., Department of Neurology, University of Medicine and Pharmacy “Iuliu Hatieganu”, Cluj-Napoca, 400012, Romania; Vaida, C., CESTER, Technical University of Cluj-Napoca, Cluj-Napoca, 400641, Romania; Geonea, I.D., Department of Applied Mechanics and Civil Construction, Faculty of Mechanics, University of Craiova, Craiova, 200585, Romania; Bogdan, G., CESTER, Technical University of Cluj-Napoca, Cluj-Napoca, 400641, Romania; Abrudan, C., Department of Neurology, University of Medicine and Pharmacy “Iuliu Hatieganu”, Cluj-Napoca, 400012, Romania; Carbone, G., DIMEG, University of Calabria, Cosenza, 87036, Italy; Plitea, N., CESTER, Technical University of Cluj-Napoca, Cluj-Napoca, 400641, Romania","Robots for rehabilitation tasks require a high degree of safety for the interaction with both the patients and for the operators. In particular, high safety is a stable and intuitive control of the moving elements of the system combined with an external system of sensors able to monitor the position of every aspect of the rehabilitation system (operator, robot, and patient) and overcome in a certain measure all the events that may occur during the robotic rehabilitation procedure. This paper presents the development of an internal torque monitoring system for ASPIRE. This is a parallel robot designed for shoulder rehabilitation, which enables the use of strategies towards developing a HRI (human–robot interaction) system for the therapy. A complete analysis regarding the components of the robotic system is carried out with the purpose of determining the dynamic be-havior of the system. Next, the proposed torque monitoring system is developed with respect to the previously obtained data. Several experimental tests are performed using healthy subjects being equipped with a series of biomedical sensors with the purpose of validating the proposed torque monitoring strategy and, at the same time, to satisfy the degree of safety that is requested by the medical procedure. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Brachial monoparesis; Human–robot interaction; Robotic rehabilitation; Torque monitoring",,,,,,"POCU/380/6/13/123927; Ontario Ministry of Research, Innovation and Science, MRIS; European Social Fund, ESF; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii, UEFISCDI: PN-III-P2-2.1-PED-2019-3022/546PED/2020, POCU/380/6/13/123927—ANTREDOC; Colegiul Consultativ pentru Cercetare-Dezvoltare şi Inovare, CCCDI","Funding: This work was supported by a grant from the Romanian Ministry of Research and Innovation, CCCDI—UEFISCDI, project number PN-III-P2-2.1-PED-2019-3022/546PED/2020 (Neu-roAssist) within PNCDI III, and by the project POCU/380/6/13/123927—ANTREDOC, “Entrepreneurial competencies and excellence research in doctoral and postdoctoral studies programs”, co-funded from the European Social Fund through the Human Capital Operational Program 2014– 2020.","This work was supported by a grant from the Romanian Ministry of Research and Inno-vation, CCCDI?UEFISCDI, project number PN-III-P2-2.1-PED-2019-3022/546PED/2020 (Neu-roAssist) within PNCDI III, and by the project POCU/380/6/13/123927?ANTREDOC, ?Entrepreneurial competencies and excellence research in doctoral and postdoctoral studies programs?, co-funded from the European Social Fund through the Human Capital Operational Program 2014? 2020.",,,,,,,,,"https://www.healthline.com/health/monoplegia, Healthline. 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"Manzoor S., Joo S.-H., Kim E.-J., Bae S.-H., In G.-G., Pyo J.-W., Kuc T.-Y.","57211621136;57202507954;57202505127;57202503473;57221045435;57192190627;35607090700;","3d recognition based on sensor modalities for robotic systems: A survey",2021,"Sensors","21","21","7120","","",,,"10.3390/s21217120","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117882285&doi=10.3390%2fs21217120&partnerID=40&md5=98dae5916d7463202aef25a4599b1afe","Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon, 16419, South Korea","Manzoor, S., Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon, 16419, South Korea; Joo, S.-H., Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon, 16419, South Korea; Kim, E.-J., Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon, 16419, South Korea; Bae, S.-H., Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon, 16419, South Korea; In, G.-G., Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon, 16419, South Korea; Pyo, J.-W., Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon, 16419, South Korea; Kuc, T.-Y., Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon, 16419, South Korea","3D visual recognition is a prerequisite for most autonomous robotic systems operating in the real world. It empowers robots to perform a variety of tasks, such as tracking, understanding the environment, and human–robot interaction. Autonomous robots equipped with 3D recognition capability can better perform their social roles through supportive task assistance in professional jobs and effective domestic services. For active assistance, social robots must recognize their surroundings, including objects and places to perform the task more efficiently. This article first highlights the value-centric role of social robots in society by presenting recently developed robots and describes their main features. Instigated by the recognition capability of social robots, we present the analysis of data representation methods based on sensor modalities for 3D object and place recognition using deep learning models. In this direction, we delineate the research gaps that need to be addressed, summarize 3D recognition datasets, and present performance comparisons. Finally, a discussion of future research directions concludes the article. This survey is intended to show how recent developments in 3D visual recognition based on sensor modalities using deep-learning-based approaches can lay the groundwork to inspire further research and serves as a guide to those who are interested in vision-based robotics applications. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","3D detection dataset; 3D visual recognition; Autonomous vehicles; Camera; Deep learning; LiDAR; Object detection; Place recognition; Robotic systems; Sensor fusion; Sensors","Autonomous vehicles; Deep learning; Human robot interaction; Object recognition; Robotics; Surveys; 3d detection dataset; 3D recognition; 3d visual recognition; Autonomous Vehicles; Deep learning; LiDAR; Place recognition; Robotic systems; Sensor fusion; Visual recognition; Object detection",,,,,"Ministry of Trade, Industry and Energy, MOTIE: 1415168187; Korea Evaluation Institute of Industrial Technology, KEIT","Funding: This research was supported by the Korea Evaluation Institute of Industrial Technology (KEIT) funded by the Ministry of Trade, Industry & Energy (MOTIE) (No. 1415168187).",,,,,,,,,,"Manzoor, S., Rocha, Y.G., Joo, S.H., Bae, S.H., Kim, E.J., Joo, K.J., Kuc, T.Y., Ontology-Based Knowledge Representation in Robotic Systems: A Survey Oriented toward Applications (2021) Appl. 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"Digiacomo F., Bologna F., Inglese F., Stefanini C., Milazzo M.","57218598683;57218116473;57191339400;13605957700;56073318800;","MechaTag: A Mechanical Fiducial Marker and the Detection Algorithm",2021,"Journal of Intelligent and Robotic Systems: Theory and Applications","103","3","46","","",,,"10.1007/s10846-021-01507-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117806570&doi=10.1007%2fs10846-021-01507-x&partnerID=40&md5=9b490e058439f78a6f01655b230d078a","The Biorobotics Institute of Sant’Anna School of Advanced Studies, Viale Rinaldo Piaggio, 34, Pontedera, PI  56025, Italy; Nuovo Pignone Tecnologie S.r.l, Baker Hughes Company, Florence, Italy","Digiacomo, F., The Biorobotics Institute of Sant’Anna School of Advanced Studies, Viale Rinaldo Piaggio, 34, Pontedera, PI  56025, Italy; Bologna, F., The Biorobotics Institute of Sant’Anna School of Advanced Studies, Viale Rinaldo Piaggio, 34, Pontedera, PI  56025, Italy, Nuovo Pignone Tecnologie S.r.l, Baker Hughes Company, Florence, Italy; Inglese, F., The Biorobotics Institute of Sant’Anna School of Advanced Studies, Viale Rinaldo Piaggio, 34, Pontedera, PI  56025, Italy; Stefanini, C., The Biorobotics Institute of Sant’Anna School of Advanced Studies, Viale Rinaldo Piaggio, 34, Pontedera, PI  56025, Italy; Milazzo, M., The Biorobotics Institute of Sant’Anna School of Advanced Studies, Viale Rinaldo Piaggio, 34, Pontedera, PI  56025, Italy","Fiducial markers are fundamental components of many computer vision systems that help, through their unique features (e.g., shape, color), a fast localization of spatial objects in unstructured scenarios. They find applications in many scientific and industrial fields, such as augmented reality, human-robot interaction, and robot navigation. In order to overcome the limitations of traditional paper-printed fiducial markers (i.e. deformability of the paper surface, incompatibility with industrial and harsh environments, complexity of the shape to reproduce directly on the piece), we aim at exploiting existing, or additionally fabricated, structural features on rigid bodies (e.g., holes), developing a fiducial mechanical marker system called MechaTag. Our system, endowed with a dedicated algorithm, is able to minimize recognition errors and to improve repeatability also in case of ill boundary conditions (e.g., partial illumination). We assess MechaTag in a pilot study, achieving a robustness of fiducial marker recognition above 95% in different environment conditions and position configurations. The pilot study was conducted by guiding a robotic platform in different poses in order to experiment with a wide range of working conditions. Our results make MechaTag a reliable fiducial marker system for a wide range of robotic applications in harsh industrial environments without losing accuracy of recognition due to the shape and material. © 2021, The Author(s).","Computer vision; Detection algorithm; Fiducial marker; Robot navigation","Augmented reality; Human robot interaction; Robotics; Signal detection; Computer vision system; Detection algorithm; Fiducial marker; Fundamental component; Industrial environments; Localisation; Mechanical; Pilot studies; Robot navigation; Unique features; Computer vision",,,,,"Ministero dello Sviluppo Economico, MiSE","This work was possible thanks to the fruitful joint action between the BioRobotics Institute of Scuola Superiore Sant?Anna, and Baker Hughes Company, which has strongly believed in the collaboration between industrial companies and research centers.","Funding This project has received funding from the Italian Ministry of Economic Development (MISE) under the program “GALILEO”.",,,,,,,,,"(2008) LOCALIZATION OF DETECTED OBJECTS IN MULTI-CAMERA NETWORK Roland Miezianko Honeywell Labs Minneapolis, pp. 2376-2379. , MN 55418 Dragoljub Pokrajac Delaware State University; Khan, D., Ullah, S., Yan, D.M., Rabbi, I., Richard, P., Hoang, T., Billinghurst, M., Zhang, X., Robust tracking through the Design of High Quality Fiducial Markers: an optimization tool for ARToolKit (2018) IEEE Access, 6, pp. 22421-22433; Aaaar, O., Mokhtari, I.E., Bengherabi, M., PDCAT: a framework for fast, robust, and occlusion resilient fiducial marker tracking (2020) J. 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Syst., 134, p. 103669; Wu, Y., Tang, F., Li, H., Image BASED CAMERA LOCALIZATION: AN OVERVIEW INVITED PAPER BY VISUAL COMPUTING FOR INDUSTRY, BIOMEDICINE AND ART, 2018 (2018) Visual Computing for Industry, pp. 1-21. , Biomedicine and Art; Calvet, L., Gurdjos, P., Griwodz, C., Gasparini, S., Detection and Accurate Localization of Circular Fiducials under Highly Challenging Conditions (2016) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2016-Decem, pp. 562-570; Bergamasco, F., Albarelli, A., Rodolà, E., Torsello, A., RUNE-Tag: A High Accuracy Fiducial Marker with Strong Occlusion Resilience (2011) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 113-120; Bartindale, T., Harrison, C., Stacks on the Surface: Resolving Physical Order Using Fiducial Markers with Structured Transparency (2009) ITS 2009 - the ACM International Conference on Interactive Tabletops and Surfaces, Proceedings, pp. 57-60; Putta, R., Shinde, G., Lohani, P., Object detection using image processing for an industrial robot (2013) Int. J. Adv. Comput. Eng. Netw., 17, pp. 21-26; Rege, S., Memane, R., Phatak, M., Agarwal, P., 2D Geometric Shape and Color Recognition Using (2013) Int. J. Adv. Res. Electr. Electron. Instrum. Eng., 2 (6), pp. 2479-2487; Wang, W.C., Chen, S.L., Chen, L.B., Chang, W.J., A machine vision based automatic optical inspection system for measuring drilling quality of printed circuit boards (2017) IEEE Access, 5, pp. 10817-10833; Ali, U., Karim, K.J.B.A., Buang, N.A., A review of the properties and applications of poly (methyl methacrylate) (PMMA) (2015) Polym. Rev., 55 (4), pp. 678-705; Milazzo, M., Negrini, N.C., Scialla, S., Marelli, B., Farè, S., Danti, S., Buehler, M.J., Additive manufacturing approaches for hydroxyapatite-reinforced composites (2019) Adv. Funct. Mater., 29, p. 1903055; Smith, B.H., Szyniszewski, S., Hajjar, J.F., Schafer, B.W., Arwade, S.R., Steel foam for structures: a review of applications, manufacturing and material properties (2012) J. Constr. Steel Res., 71, pp. 1-10; Bhargavi, K., Jyothi, S., A survey on threshold based segmentation technique in image processing (2014) Int. J. Innov. Res. Dev., 3 (12), pp. 234-239; Muralidharan, R., Chandrasekar, C., (2014) Edge Detection Using the Canny’s Edge Detector with User Defined Filter to Obtain Good Feature for Object Recognition, , August; Teoh, S.H., Ibrahim, H., Median filtering frameworks for reducing impulse noise from grayscale digital images: a literature survey (2012) Int. J. Futur. Comput. Commun., 1 (4), pp. 323-326; Fitzgibbon, A.W., (2013) Fisher, R.B.: A Buyer’s Guide to Conic Fitting, 51 (1-51), p. 10; Suzuki, S., Abe, K., Topological structural analysis of digital binary image by border following (1985) Cvgip, 30, pp. 32-46; Nekrasov, R., Putilova, U., Tempel, Y., Precision CNC machining and ways to achieve it (2018) MATEC Web of Conferences, 224","Digiacomo, F.; The Biorobotics Institute of Sant’Anna School of Advanced Studies, Viale Rinaldo Piaggio, 34, Italy; email: francesca.digiacomo@santannapisa.it",,,"Springer Science and Business Media B.V.",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85117806570
"Wang S., Sun Z., Zhao Y., Zuo L.","57190308100;57226633995;56118325600;57226633511;","A highly stretchable hydrogel sensor for soft robot multi-modal perception",2021,"Sensors and Actuators A: Physical","331",,"113006","","",,1,"10.1016/j.sna.2021.113006","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112127970&doi=10.1016%2fj.sna.2021.113006&partnerID=40&md5=201f47a6f94b0c06144dd15fb32b4cb0","Department of Control Engineering, Northeastern University, Qinhuangdao, Hebei  066001, China; Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA  24061, United States","Wang, S., Department of Control Engineering, Northeastern University, Qinhuangdao, Hebei  066001, China; Sun, Z., Department of Control Engineering, Northeastern University, Qinhuangdao, Hebei  066001, China; Zhao, Y., Department of Control Engineering, Northeastern University, Qinhuangdao, Hebei  066001, China; Zuo, L., Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA  24061, United States","Soft robots attracted surging interests in recent years due to its enhanced safety, and it significantly contributes to its human-robot interaction application. However, the compliant system has infinite dimensions, which remained challenging for accurate state perception and control. Embedded soft sensors are the potential solutions to this problem as they can trigger electrical or optical signals even when stretched. Yet current conductive material's upper strain limit are not large enough. Here we design a highly stretchable hydrogel resistive sensor for soft fingers’ multi-modal perception with a simple and low cost fabrication method. The hydrogel's maximum tensile strain is up to 1200 %, which ensures to tolerate the pneumatic actuator's large deformation, and its water loss can be as low as 0.0625 % within 30 days. We demonstrated the sensor's multi-modal perception for the actuator's state estimation, as it can effectively monitor the soft fingers’ bending, twisting, and external forces with high sensitivity, low hysteresis and high reliability. In addition, we also explore the impact of the hydrogel sensor on the pneumatic actuator's dynamics, and it provides insights into how the sensor's placement location influence the perception. This study could lead to improved soft robot system's kinematics study and potentially more accurate close-loop control. © 2021 Elsevier B.V.","Hydrogel; Multi-modal perception; Soft robot; Strain sensing","Conductive materials; Human robot interaction; Hydrogels; Pneumatic equipment; Pneumatics; Tensile strain; Electrical signal; Humans-robot interactions; Hydrogel sensors; Infinite dimensions; Multi modal perceptions; Optical signals; Soft robot; Soft sensors; Soft-finger; Strain sensing; Pneumatic actuators",,,,,"North Carolina Central University, NCCU: 2023012; Natural Science Foundation of Hebei Province: F2020501033","The author, Shuyu Wang, thanks the Fundamental Research Fund from Central University (No. 2023012 ) and Natural Science Foundation of Hebei Province (No. F2020501033 ).",,,,,,,,,,"Lee, Y., Song, W.J., Sun, J.Y., Hydrogel soft robotics (2020) Mater. Today Phys., 15, p. 100258; Mosadegh, B., Pneumatic networks for soft robotics that actuate rapidly (2014) Adv. Funct. Mater., 24 (15), pp. 2163-2170; Bilodeau, R.A., Design for control of a soft bidirectional bending actuator (2018) Intelligent Robots and Systems, , IEEE; Polygerinos, P., Soft robotics: review of fluid-driven intrinsically soft devices; manufacturing, sensing, control, and applications in human-robot interaction (2017) Adv. Eng. Mater., 19 (12), p. 1700016; Thuruthel, T.G., Soft robot perception using embedded soft sensors and recurrent neural networks (2019) Sci. Robot., 4 (26), p. eaav1488; Georgopoulou, A., Piezoresistive sensor fiber composites based on silicone elastomers for the monitoring of the position of a robot arm (2021) Sens. Actuators A Phys., 318, p. 112433; Coevoet, E., Software toolkit for modeling, simulation, and control of soft robots (2017) Adv. Robot., 31 (22), pp. 1208-1224; Chin, K., Hellebrekers, T., Majidi, C., Machine learning for soft robotic sensing and control (2020) Adv. Intell. Syst., 2 (6), p. 1900171; Boland, J.J., Within touch of artificial skin (2010) Nat. Mater., 9 (10), pp. 790-792; Yang, J.C., Electronic skin: recent progress and future prospects for skin‐attachable devices for health monitoring, robotics, and prosthetics (2019) Adv. Mater., 31 (48), p. 1904765; Woon-Hong, Y., Multi-functional electronics: multifunctional epidermal electronics printed directly onto the skin (adv. Mater. 20/2013) (2013) Adv. Mater., 25 (20), pp. 2773-2778; Park, Y., Chen, B., Wood, R.J., Design and fabrication of soft artificial skin using embedded microchannels and liquid conductors (2012) IEEE Sens. J., 12 (8), pp. 2711-2718; Park, Y., Exoskeletal force-sensing end-effectors with embedded optical fiber-Bragg-grating sensors (2009) Ieee Trans. Robot., 25 (6), pp. 1319-1331; Sareh, S., Noh, Y., Low Profile Stretch Sensor for Soft Wearable Robotics (2018), IEEE; Tian, K., 3D printing of transparent and conductive heterogeneous hydrogel–elastomer systems (2017) Adv. Mater., 29 (10), p. 1604827; Cuttaz, E., Conductive elastomer composites for fully polymeric, flexible bioelectronics (2019) Biomater. Sci., 7 (4), pp. 1372-1385; Wang, L., Study on compressive resistance creep and recovery of flexible pressure sensitive material based on carbon black filled silicone rubber composite (2011) Sens. Actuators A Phys., 165 (2), pp. 207-215; Zheng, Q., Zhou, J.F., Song, Y.H., Time-dependent uniaxial piezoresistive behavior of high-density polyethylene/short carbon fiber conductive composites (2004) J. Mater. Res., 19 (9), pp. 2625-2634; Yamada, T., A stretchable carbon nanotube strain sensor for human-motion detection (2011) Nat. Nanotechnol., 6 (5), pp. 296-301; Park, Y., Hyperelastic pressure sensing with a liquid-embedded elastomer (2010) J. Micromech. Microeng., 20 (12), p. 125029; Sun, B., Fabrication of curled conducting polymer microfibrous arrays via a novel electrospinning method for stretchable strain sensors (2013) Nanoscale, 5 (15), p. 7041; Li, M., Stretchable conductive polypyrrole/polyurethane (PPy/PU) strain sensor with netlike microcracks for human breath detection (2014) ACS Appl. Mater. Interfaces, 6 (2), pp. 1313-1319; Seyedin, M.Z., Strain-responsive Polyurethane/PEDOT:PSS elastomeric composite fibers with high electrical conductivity (2014) Adv. Funct. Mater., 24 (20), pp. 2957-2966; Amjadi, M., Highly stretchable and sensitive strain sensor based on silver nanowire–elastomer nanocomposite (2014) ACS Nano, 8 (5), pp. 5154-5163; Chen, S., Synthesis of Cu-nanoparticle hydrogel with self-healing and photothermal properties (2017) ACS Appl. Mater. Interfaces, 9 (24), pp. 20895-20903; Shin, S., Ion-conductive self-healing hydrogels based on an interpenetrating polymer network for a multimodal sensor (2019) Chem. Eng. J., 371, pp. 452-460; Jeong-Yun, S., Highly stretchable and tough hydrogels (2012) Nature, 489 (7414), pp. 133-136; Lu, Y., Highly stretchable, elastic, and sensitive MXene-based hydrogel for flexible strain and pressure sensors (2020) Research, 2020, pp. 1-13; Zhang, Q., Ultra-stretchable wearable strain sensors based on skin-inspired adhesive, tough and conductive hydrogels (2019) Chem. Eng. J., 365, pp. 10-19; Ge, G., Stretchable, transparent, and self-patterned hydrogel-based pressure sensor for human motions detection (2018) Adv. Funct. Mater., 28 (32), p. 1802576; Lei, Z., A bioinspired mineral hydrogel as a self-healable, mechanically adaptable ionic skin for highly sensitive pressure sensing (2017) Adv. Mater., 29 (22), p. 1700321; Wang, Z., Cong, Y., Fu, J., Stretchable and tough conductive hydrogels for flexible pressure and strain sensors (2020) J. Mater. Chem. B, 8 (16), pp. 3437-3459; Lee, H., Kim, C., Sun, J., Stretchable ionics – a promising candidate for upcoming wearable devices (2018) Adv. Mater., 30 (42), p. 1704403; Zheng, W.J., Tough Al-alginate/poly(N -isopropylacrylamide) hydrogel with tunable LCST for soft robotics (2015) ACS Appl. Mater. Interfaces, 7 (3), pp. 1758-1764; Ge, Q., 3D printing of highly stretchable hydrogel with diverse UV curable polymers (2021) Sci. Adv., 7 (2), p. eaba4261; Thuruthel, T.G., Learning dynamic models for open loop predictive control of soft robotic manipulators (2017) Bioinspir. Biomim., 12 (6), p. 066003; Yamada, T., A stretchable carbon nanotube strain sensor for human-motion detection (2011) Nat. Nanotechnol., 6 (5), pp. 296-301; Frutiger, A., Capacitive soft strain sensors via multicore–shell fiber printing (2015) Adv. Mater., 27 (15), pp. 2440-2446; Cai, G., Extremely stretchable strain sensors based on conductive self-healing dynamic cross-links hydrogels for human-motion detection (2017) Adv. Sci., 4 (2). , p. 1600190-1600190; Gu, G., Integrated soft ionotronic skin with stretchable and transparent hydrogel–elastomer ionic sensors for hand-motion monitoring (2019) Soft Eobotics, 6 (3), pp. 368-376; Cheng, S., Stick‐on large‐strain sensors for soft robots (2019) Adv. Mater. Interfaces, 6 (20); Liu, Y., Ultrasensitive wearable soft strain sensors of conductive, self-healing, and elastic hydrogels with synergistic “soft and hard” hybrid networks (2017) ACS Appl. Mater. Interfaces, 9 (30), pp. 25559-25570","Wang, S.; Department of Control Engineering, China; email: wangshuyu@neuq.edu.cn",,,"Elsevier B.V.",,,,,09244247,,SAAPE,,"English","Sens Actuators A Phys",Article,"Final","",Scopus,2-s2.0-85112127970
"Petrea R.A.B., Bertoni M., Oboe R.","57219743778;57347448700;6701579829;","On the Interaction Force Sensing Accuracy of Franka Emika Panda Robot",2021,"IECON Proceedings (Industrial Electronics Conference)","2021-October",,,"","",,,"10.1109/IECON48115.2021.9589424","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119480012&doi=10.1109%2fIECON48115.2021.9589424&partnerID=40&md5=8b0504b581235e5b973cc1680bd27cad","University of Padova, Deparment of Management and Eng., Vicenza, Italy","Petrea, R.A.B., University of Padova, Deparment of Management and Eng., Vicenza, Italy; Bertoni, M., University of Padova, Deparment of Management and Eng., Vicenza, Italy; Oboe, R., University of Padova, Deparment of Management and Eng., Vicenza, Italy","The precise and reliable interaction force knowledge is among the main aspects of human-robot interaction applications. Since many robots are equipped with joint torque sensors, relying on these measurements avoids increasing cost and complexity, brought by additional sensors. In this paper, we investigate the sensing capabilities of the Franka Emika Panda Robot for interaction force estimation, focusing on the available filtered force estimates provided by the robot controller. We perform experiments involving contact interaction between the robot and an external environment, implemented by using an impedance controlled servomotor. In this way, it is possible to reproduce forces typical of the human behaviour, in controlled and repeatable conditions. The evaluation is based on the identification of the motor impedance parameters, comparing the results obtained by the Panda interaction force estimate with a ground truth value, obtained by load cell measurements. Since joint torque measurements are heavily affected by disturbance torques (e.g. friction) a simple compensation procedure is performed, through data collected without contact. The delay introduced on the external force measurements by the filtering action is compensated, allowing an online usage of the proposed method. Results show that the Panda Robot provides a fairly good estimate of the interaction force. © 2021 IEEE.","Human-Robot Interaction; Impedance Identification; Interaction Force Estimation",,,,,,,,,,,,,,,,,"Sariyildiz, E., Oboe, R., Ohnishi, K., Disturbance observer-based robust control and its applications: 35th anniversary overview (2020) IEEE Transactions on Industrial Electronics, 67 (3), pp. 2042-2053; Chen, W.-H., Ballance, D., Gawthrop, P., O'Reilly, J., A nonlinear disturbance observer for robotic manipulators (2000) IEEE Transactions on Industrial Electronics, 47 (4), pp. 932-938; Mitsantisuk, C., Katsura, S., Ohishi, K., Force control of human-robot interaction using twin direct-drive motor system based on modal space design (2010) IEEE Transactions on Industrial Electronics, 57 (4), pp. 1383-1392; Colomé, A., Pardo, D., Alenyà, G., Torras, C., External force estimation during compliant robot manipulation (2013) 2013 Ieee International Conference on Robotics and Automation, pp. 3535-3540; Garofalo, G., Mansfeld, N., Jankowski, J., Ott, C., Sliding mode momentum observers for estimation of external torques and joint acceleration (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 6117-6123; Márton, L., Lantos, B., Control of robotic systems with unknown friction and payload (2011) IEEE Transactions on Control Systems Technology, 19, pp. 1534-1539; Simpson, J.L., Cook, C., Li, Z., (2002) Sensorless Force Estimation for Robots with Friction; Wahrburg, A., Zeiss, S., Matthias, B., Ding, H., Contact force estimation for robotic assembly using motor torques (2014) 2014 Ieee International Conference on Automation Science and Engineering (CASE), pp. 1252-1257; Chan, L., Naghdy, F., Stirling, D., Extended active observer for force estimation and disturbance rejection of robotic manipulators (2013) Robotics and Autonomous Systems, 61 (12), pp. 1277-1287; An improved extended active observer for adaptive control of a n-dof robot manipulator (2017) Journal of Intelligent & Robotic Systems, 85, p. 03; Kawasaki, H., Ueki, S., Disturbance observer estimating frictions and external forces for robot manipulators (2016) 2016 Ieee Andescon, pp. 1-4; Yao, B., Zhou, Z., Wang, L., Xu, W., Liu, Q., Sensor-less external force detection for industrial manipulators to facilitate physical humanrobot interaction (2018) Journal of Mechanical Science and Technology, 32 (10), pp. 4909-4923; Yokoyama, M., Budau Petrea, R.A., Oboe, R., Shimono, T., External force estimation in linear series elastic actuator without load-side encoder (2021) IEEE Transactions on Industrial Electronics, 68 (1), pp. 861-870; Yang, C., Zeng, C., Liang, P., Li, Z., Li, R., Su, C.-Y., Interface design of a physical human-robot interaction system for human impedance adaptive skill transfer (2018) IEEE Transactions on Automation Science and Engineering, 15 (1), pp. 329-340; Yu, X., Li, Y., Zhang, S., Xue, C., Wang, Y., Estimation of human impedance and motion intention for constrained human-robot interaction (2019) Neurocomputing, 390, p. 10; Corrá, L., Oboe, R., Shimono, T., Adaptive optimal control for rehabilitation systems (2017) Iecon 2017-43rd Annual Conference of the Ieee Industrial Electronics Society, pp. 5197-5202; Wahrburg, A., Robertsson, A., Matthias, B., Dai, F., Ding, H., Improving contact force estimation accuracy by optimal redundancy resolution (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3735-3741; Robot and Interface Specifications, , https://frankaemika.github.io/docs/controlparameters.html, Franka Emika GmbH; Hogan, N., Impedance control of industrial robots (2000) Robotics and Computer-Integrated Manufacturing, 1 (1), pp. 97-113",,,,"IEEE Computer Society","47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021","13 October 2021 through 16 October 2021",,173927,,9781665435543,IEPRE,,"English","IECON Proc",Conference Paper,"Final","",Scopus,2-s2.0-85119480012
"Huang Z., Shen Y., Li J., Fey M., Brecher C.","57267456400;57203599877;57219757948;36521055200;55947597300;","A survey on AI-driven digital twins in industry 4.0: Smart manufacturing and advanced robotics",2021,"Sensors","21","19","6340","","",,1,"10.3390/s21196340","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115401810&doi=10.3390%2fs21196340&partnerID=40&md5=f51367a041d8780a984e611ab6582310","Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Aachen, D-52074, Germany; UBTECH North America Research and Development Center, Pasadena, CA  91101-4858, United States; Department of Statistics, University of California Los Angeles, Los Angeles, CA  90095-1554, United States","Huang, Z., Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Aachen, D-52074, Germany; Shen, Y., UBTECH North America Research and Development Center, Pasadena, CA  91101-4858, United States; Li, J., Department of Statistics, University of California Los Angeles, Los Angeles, CA  90095-1554, United States; Fey, M., Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Aachen, D-52074, Germany; Brecher, C., Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Aachen, D-52074, Germany","Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Artificial intelligence; Deep learning; Digital shadow; Digital twin; Industry 4.0; Machine learning; Review; Robotics; Sustainability; Sustainable smart manufacturing","3D printers; Application programs; Deep learning; E-learning; Human robot interaction; Industry 4.0; Integration; Reinforcement learning; Robotics; Sustainable development; Artificial intelligence technologies; Deep learning; Depth integrations; Digital replicas; Digital shadow; Domain specific; Industrial sector; Intelligence integration; Smart manufacturing; Sustainable smart manufacturing; Surveys; algorithm; artificial intelligence; automation; human; industry; robotics; Algorithms; Artificial Intelligence; Automation; Humans; Industry; Robotics",,,,,"Deutsche Forschungsgemeinschaft, DFG: 390621612","Acknowledgments: The authors would like to thank the German Research Foundation DFG for the support within the Cluster of Excellence “Internet of Production”—390621612. The authors would also like to thank Altair Engineering, Inc., Fraunhofer IPT, as well as the Chair of Production Metrology and Quality Management, and Production Engineering of the Laboratory for Machine Tools and Production Engineering (WZL) for their permission to reuse the images.","Funding: This research was partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through the Cluster of Excellence “Internet of Production”—390621612.",,,,,,,,,"Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui, F., Digital twin-driven product design, manufacturing and service with big data (2018) Int. J. Adv. Manuf. Technol, 94, pp. 3563-3576. , [CrossRef]; Scharl, S., Praktiknjo, A., The Role of a Digital Industry 4.0 in a Renewable Energy System (2019) Int. J. 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Shen, Y.; UBTECH North America Research and Development CenterUnited States; email: yangshen@ieee.org",,,"MDPI",,,,,14248220,,,"34640660","English","Sensors",Review,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85115401810
"Rodin I., Furnari A., Mavroeidis D., Farinella G.M.","57220389077;56348910300;13405059000;57191972987;","Predicting the future from first person (egocentric) vision: A survey",2021,"Computer Vision and Image Understanding","211",,"103252","","",,,"10.1016/j.cviu.2021.103252","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113278797&doi=10.1016%2fj.cviu.2021.103252&partnerID=40&md5=57171b9c4468b6e81dda06ef77337674","University of Catania, Viale Andrea Dorea 6, Catania, 95125, Italy; Philips Research Eindhoven, High Tech Campus 34, Eindhoven, 5656 AE, Netherlands","Rodin, I., University of Catania, Viale Andrea Dorea 6, Catania, 95125, Italy; Furnari, A., University of Catania, Viale Andrea Dorea 6, Catania, 95125, Italy; Mavroeidis, D., Philips Research Eindhoven, High Tech Campus 34, Eindhoven, 5656 AE, Netherlands; Farinella, G.M., University of Catania, Viale Andrea Dorea 6, Catania, 95125, Italy","Egocentric videos can bring a lot of information about how humans perceive the world and interact with the environment, which can be beneficial for the analysis of human behaviour. The research in egocentric video analysis is developing rapidly thanks to the increasing availability of wearable devices and the opportunities offered by new large-scale egocentric datasets. As computer vision techniques continue to develop at an increasing pace, the tasks related to the prediction of future are starting to evolve from the need of understanding the present. Predicting future human activities, trajectories and interactions with objects is crucial in applications such as human–robot interaction, assistive wearable technologies for both industrial and daily living scenarios, entertainment and virtual or augmented reality. This survey summarizes the evolution of studies in the context of future prediction from egocentric vision making an overview of applications, devices, existing problems, commonly used datasets, models and input modalities. Our analysis highlights that methods for future prediction from egocentric vision can have a significant impact in a range of applications and that further research efforts should be devoted to the standardization of tasks and the proposal of datasets considering real-world scenarios such as the ones with an industrial vocation. © 2021 Elsevier Inc.","Anticipation; Egocentric vision; First person vision; Future prediction","Augmented reality; Behavioral research; Employment; Forecasting; Industrial research; Industrial robots; Large dataset; Surveys; Wearable computers; Computer vision techniques; Existing problems; Future predictions; Human activities; Human behaviours; Real-world scenario; Robot interactions; Wearable devices; Human robot interaction",,,,,"European Commission, EC: 812882; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR: AIM1893589, E64118002540007, F/190050/02/X44 - CUP: B61B19000520008","This research has been supported by Marie Sk?odowska-Curie Innovative Training Networks - European Industrial Doctorates - PhilHumans Project, European Union - Grant agreement 812882 (http://www.philhumans.eu), MIUR AIM - Attrazione e Mobilita Internazionale Linea1, Italy - AIM1893589 - CUP: E64118002540007 and by MISE - PON I&C 2014-2020 - Progetto ENIGMA, Italy - Prog n. F/190050/02/X44 - CUP: B61B19000520008.","This research has been supported by Marie Skłodowska-Curie Innovative Training Networks - European Industrial Doctorates - PhilHumans Project, European Union - Grant agreement 812882 ( http://www.philhumans.eu ), MIUR AIM - Attrazione e Mobilita Internazionale Linea1, Italy - AIM1893589 - CUP: E64118002540007 and by MISE - PON I&C 2014-2020 - Progetto ENIGMA, Italy - Prog n. 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"Mehr J.K., Sharifi M., Mushahwar V.K., Tavakoli M.","57221662636;55224489900;6603751651;8668966400;","Intelligent Locomotion Planning with Enhanced Postural Stability for Lower-Limb Exoskeletons",2021,"IEEE Robotics and Automation Letters","6","4","9495154","7588","7595",,1,"10.1109/LRA.2021.3098915","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112626622&doi=10.1109%2fLRA.2021.3098915&partnerID=40&md5=cc31b7e619e683d5050c5f3c3046de74","Department of Electrical and Computer Engineering, Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, AB  T6G 1H9, Canada; Department of Medicine, Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, AB  T6G 1H9, Canada","Mehr, J.K., Department of Electrical and Computer Engineering, Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, AB  T6G 1H9, Canada; Sharifi, M., Department of Electrical and Computer Engineering, Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, AB  T6G 1H9, Canada; Mushahwar, V.K., Department of Medicine, Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, AB  T6G 1H9, Canada; Tavakoli, M., Department of Electrical and Computer Engineering, Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, AB  T6G 1H9, Canada","In this letter, an integrated control strategy is developed for both locomotion trajectory planning and postural stability, enabling shared autonomy between the human and lower-limb exoskeleton. Divergent component of motion (DCM) analysis was employed previously based on the linear inverted pendulum flywheel (LIPF) model to regulate the position of the center of mass (CoM) for humanoid robots. In this study, a new extended model is investigated for the DCM analysis by replacing the previous LIPF model, which is tailored for multi-degree-of-freedom (DOF) exoskeletons. This new model is designed to be personalized for each specific user's body by relaxing the assumption of having the total CoM at the hip joint in the previous LIPF model. Accordingly, the exoskeleton has the authority to ensure the postural stability and viability of locomotion in this human-robot interaction (HRI) by adjusting the upper body position using a DCM-based hip correction strategy. Integrating adaptive central pattern generators (CPGs), the human has enough authority to modify the gait trajectories in real-time, while the amplitude and frequency of walking are constrained to their feasible ranges. The effectiveness of this intelligent controller for safe and stable locomotion is investigated through experimental studies on a lower-limb exoskeleton. © 2016 IEEE.","adaptive motion planning; central pattern generator (CPG); divergent component of motion (DCM); human-robot interaction; Lower-limb exoskeletons","Agricultural robots; Anthropomorphic robots; Degrees of freedom (mechanics); Human robot interaction; Central pattern generator; Human robot Interaction (HRI); Integrated control strategy; Locomotion planning; Locomotion trajectory; Multi degree-of-freedom; Postural stability; Stable locomotion; Exoskeleton (Robotics)",,,,,"Canadian Institutes of Health Research, CIHR; Natural Sciences and Engineering Research Council of Canada, NSERC; Canada Foundation for Innovation, CFI","Manuscript received February 25, 2021; accepted June 30, 2021. Date of publication July 26, 2021; date of current version August 13, 2021. This work was supported in part by the Canada Foundation for Innovation (CFI), in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada, in part by the Canadian Institutes of Health Research (CIHR), and in part by the Alberta Jobs, Economy, and Innovation Ministry’s Major Initiatives Fund to the Center for Autonomous Systems in Strengthening Future Communities. This letter was recommended for publication by Associate Editor S. Ivaldi and Jee-Hwan Ryu upon evaluation of the reviewers’ comments. (Corresponding author: Javad K. Mehr.) Javad K. Mehr, Mojtaba Sharifi, and Mahdi Tavakoli are with the Department of Electrical and Computer Engineering, and Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, Alberta T6G 1H9, Canada (e-mail: mahdi.tavakoli@ualberta.ca, M.Sharifi@ualberta.ca, Mahdi.Tavakoli@ualberta.ca).",,,,,,,,,,"Martínez, A., Lawson, B., Durrough, C., Goldfarb, M., A velocity-fieldbased controller for assisting leg movement duringwalkingwith a bilateral hip and knee lower limb exoskeleton (2019) IEEE Trans. Robot., 35 (2), pp. 307-316. , Apr; Zhou, L., Chen, W., Wang, J., Bai, S., Yu, H., Zhang, Y., A novel precision measuring parallel mechanism for the closed-loop control of a biologically inspired lower limb exoskeleton (2018) IEEE/ASME Trans. 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Soc., pp. 2788-2792; Winter, D.A., (2009) Biomechanics and Motor Control of Human Movement, , Hoboken, NJ, USA: Wiley","Mehr, J.K.; Department of Electrical and Computer Engineering, Canada; email: khodaeim@ualberta.ca",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85112626622
"Mazhar O., Babuska R., Kober J.","56103590300;24723543100;22940629000;","GEM: Glare or Gloom, i Can Still See You - End-to-End Multi-Modal Object Detection",2021,"IEEE Robotics and Automation Letters","6","4","9468959","6321","6328",,,"10.1109/LRA.2021.3093871","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111096213&doi=10.1109%2fLRA.2021.3093871&partnerID=40&md5=cd67d451e1256d4c17e62e2e492923bd","Cognitive Robotics Department, Delft University of Technology, Delft, Netherlands","Mazhar, O., Cognitive Robotics Department, Delft University of Technology, Delft, Netherlands; Babuska, R., Cognitive Robotics Department, Delft University of Technology, Delft, Netherlands; Kober, J., Cognitive Robotics Department, Delft University of Technology, Delft, Netherlands","Deep neural networks designed for vision tasks are often prone to failure when they encounter environmental conditions not covered by the training data. Single-modal strategies are insufficient when the sensor fails to acquire information due to malfunction or its design limitations. Multi-sensor configurations are known to provide redundancy, increase reliability, and are crucial in achieving robustness against asymmetric sensor failures. To address the issue of changing lighting conditions and asymmetric sensor degradation in object detection, we develop a multi-modal 2D object detector, and propose deterministic and stochastic sensor-aware feature fusion strategies. The proposed fusion mechanisms are driven by the estimated sensor measurement reliability values/weights. Reliable object detection in harsh lighting conditions is essential for applications such as self-driving vehicles and human-robot interaction. We also propose a new 'r-blended' hybrid depth modality for RGB-D sensors. Through extensive experimentation, we show that the proposed strategies outperform the existing state-of-the-art methods on the FLIR-Thermal dataset, and obtain promising results on the SUNRGB-D dataset. We additionally record a new RGB-Infra indoor dataset, namely L515-Indoors, and demonstrate that the proposed object detection methodologies are highly effective for a variety of lighting conditions. © 2016 IEEE.","computer vision for automation; deep learning for visual perception; object detection; RGB-D perception; segmentation and categorization; sensor fusion","Agricultural robots; Deep neural networks; Human robot interaction; Lighting; Object recognition; Redundancy; Stochastic systems; Design limitations; Environmental conditions; Fusion mechanism; Lighting conditions; Sensor degradation; Sensor measurements; State-of-the-art methods; Stochastic sensors; Object detection",,,,,"Horizon 2020: 871449","Manuscript received February 24, 2021; accepted June 8, 2021. Date of publication June 30, 2021; date of current version July 15, 2021. The research presented in this article was carried out as part of the OpenDR project, which has received funding from the European Unions Horizon 2020 Research and Innovation Programme under Grant Agreement 871449. (Corresponding author: Osama Mazhar.) Osama Mazhar and Jens Kober are with the Cognitive Robotics Department, Delft University of Technology, Delft, CD 2628, The Netherlands (e-mail: o.mazhar@tudelft.nl; j.kober@tudelft.nl).",,,,,,,,,,"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P., Focal loss for dense object detection (2017) Proc. IEEE Intl. Conf. Comput. Vis., pp. 2980-2988; Liu, W., Ssd: Single shot multibox detector (2016) Proc. Eur. Conf. Comput. Vis., pp. 21-37; Redmon, J., Farhadi, A., (2018) Yolov3: An Incremental Improvement; Ren, S., He, K., Girshick, R., Sun, J., Faster r-cnn: Towards real-time object detection with region proposal networks (2015) Adv. Neural Inf. Process. 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On Learning, pp. 1-13; Song, S., Lichtenberg, S.P., Xiao, J., Sun rgb-d: A rgb-d scene understanding benchmark suite (2015) Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 567-576; Gupta, S., Girshick, R., Arbeláez, P., Malik, J., Learning rich features from rgb-d images for object detection and segmentation (2014) Proc. Eur. Conf. Comput. Vis. Springer, pp. 345-360; Howard, A.G., (2017) Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications","Mazhar, O.; Cognitive Robotics Department, Netherlands; email: o.mazhar@tudelft.nl",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85111096213
"Veselic S., Zito C., Farina D.","57208599560;55556640700;7004908025;","Human-Robot Interaction With Robust Prediction of Movement Intention Surpasses Manual Control",2021,"Frontiers in Neurorobotics","15",,"695022","","",,,"10.3389/fnbot.2021.695022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117135870&doi=10.3389%2ffnbot.2021.695022&partnerID=40&md5=86ba8ba660f376ca6dbd94c887561272","Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom; Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom; School of Computer Science, University of Birmingham, Birmingham, United Kingdom; Autonomous Robotics Research Centre, Technology Innovation Institute, Abu Dhabi, United Arab Emirates; Department of Bioengineering, Imperial College London, London, United Kingdom","Veselic, S., Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom, Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom, School of Computer Science, University of Birmingham, Birmingham, United Kingdom; Zito, C., School of Computer Science, University of Birmingham, Birmingham, United Kingdom, Autonomous Robotics Research Centre, Technology Innovation Institute, Abu Dhabi, United Arab Emirates; Farina, D., Department of Bioengineering, Imperial College London, London, United Kingdom","Physical human-robot interaction (pHRI) enables a user to interact with a physical robotic device to advance beyond the current capabilities of high-payload and high-precision industrial robots. This paradigm opens up novel applications where a the cognitive capability of a user is combined with the precision and strength of robots. Yet, current pHRI interfaces suffer from low take-up and a high cognitive burden for the user. We propose a novel framework that robustly and efficiently assists users by reacting proactively to their commands. The key insight is to include context- and user-awareness in the controller, improving decision-making on how to assist the user. Context-awareness is achieved by inferring the candidate objects to be grasped in a task or scene and automatically computing plans for reaching them. User-awareness is implemented by facilitating the motion toward the most likely object that the user wants to grasp, as well as dynamically recovering from incorrect predictions. Experimental results in a virtual environment of two degrees of freedom control show the capability of this approach to outperform manual control. By robustly predicting user intention, the proposed controller allows subjects to achieve superhuman performance in terms of accuracy and, thereby, usability. © Copyright © 2021 Veselic, Zito and Farina.","AI assistance; motion intention estimation; motion prediction; physical human robot interaction; reach and grasp","Controllers; Decision making; Degrees of freedom (mechanics); Forecasting; Human robot interaction; Industrial robots; Man machine systems; AI assistance; Current capability; Humans-robot interactions; Motion intention estimations; Motion prediction; Movement intentions; Physical humanrobot interaction (phri); Reach and grasp; Robotic devices; Robust predictions; Motion estimation",,,,,"810346; H2020 European Research Council, ERC; es:CEI; fr:CER; pl:ERBN","This study was supported by the European Research Council Synergy Project Natural BionicS (contract # 810346 to DF). 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Healthc Eng, 2017, pp. 1-11. , 29093805; Van Den Berg, J., Miller, S., Duckworth, D., Hu, H., Wan, A., Fu, X.Y., “Superhuman performance of surgical tasks by robots using iterative learning from human-guided demonstrations,” (2010) Proceedings IEEE International Conference on Robotics and Automation, pp. 2074-2081. , Anchorage, AK, IEEE; Zito, C., Ortienzi, V., Adjigble, M., Kopicki, M.S., Stolkin, R., Wyatt, Hypothesis-based belief planning for dexterous grasping (2019) arXiv, , [cs.RO] (cs.AI","Zito, C.; School of Computer Science, United Kingdom; email: claudio.zito@tii.ae",,,"Frontiers Media S.A.",,,,,16625218,,,,"English","Front. Neurorobotics",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85117135870
"Roesler E., Manzey D., Onnasch L.","57217215081;6604041254;35111634600;","A meta-Analysis on the effectiveness of anthropomorphism in human-robot interaction",2021,"Science Robotics","6","58","5425","","",,1,"10.1126/scirobotics.abj5425","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114805229&doi=10.1126%2fscirobotics.abj5425&partnerID=40&md5=0f0a83cafb8c6f24e3ca381c7961b104","Technische Universitat Berlin, Berlin, Germany; Humboldt-Universitat zu Berlin, Berlin, Germany","Roesler, E., Technische Universitat Berlin, Berlin, Germany; Manzey, D., Technische Universitat Berlin, Berlin, Germany; Onnasch, L., Humboldt-Universitat zu Berlin, Berlin, Germany","The application of anthropomorphic design features is widely assumed to facilitate human-robot interaction (HRI). However, a considerable number of study results point in the opposite direction. There is currently no comprehensive common ground on the circumstances under which anthropomorphism promotes interaction with robots. Our meta-Analysis aims to close this gap. A total of 4856 abstracts were scanned. After an extensive evaluation, 78 studies involving around 6000 participants and 187 effect sizes were included in this meta-Analysis. The majority of the studies addressed effects on perceptual aspects of robots. In addition, effects on attitudinal, affective, and behavioral aspects were also investigated. Overall, a medium positive effect size was found, indicating a beneficial effect of anthropomorphic design features on human-related outcomes. However, closer scrutiny of the lowest variable level revealed no positive effect for perceived safety, empathy, and task performance. Moreover, the analysis suggests that positive effects of anthropomorphism depend heavily on various moderators. For example, anthropomorphism was in contrast to other fields of application, constantly facilitating social HRI. The results of this analysis provide insights into how design features can be used to improve the quality of HRI. Moreover, they reveal areas in which more research is needed before any clear conclusions about the effects of anthropomorphic robot design can be drawn. © 2021 American Association for the Advancement of Science. All rights reserved.",,"Agricultural robots; Anthropomorphic robots; Machine design; Man machine systems; Quality control; Anthropomorphic design; Beneficial effects; Common ground; Design features; Human robot Interaction (HRI); Meta analysis; Perceptual aspects; Task performance; Human robot interaction",,,,,,,,,,,,,,,,"(2020) World Robotics 2020: Industrial Robots, , International Federation of Robotics, in 13 16; (2020) World Robotics 2020: Service Robots, , International Federation of Robotics, in 11 12; Onnasch, L., Roesler, E., A taxonomy to structure and analyze human robot interaction (2021) Int. J. Soc. Robot, 13, p. 833849; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Rob. Auton. Syst, 42, p. 143166; Breazeal, C., Toward sociable robots (2003) Rob. Auton. 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"Abadia I., Naveros F., Ros E., Carrillo R.R., Luque N.R.","57219588780;56335393100;7102531020;8924397600;36652768800;","A cerebellar-based solution to the nondeterministic time delay problem in robotic control",2021,"Science Robotics","6","58","2756","","",,1,"10.1126/scirobotics.abf2756","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114804296&doi=10.1126%2fscirobotics.abf2756&partnerID=40&md5=10e1e4cebaee0421fcca947f1242a9a2","Research Centre for Information and Communication Technologies (CITIC), Department of Computer Architecture and Technology, University of Granada, Granada, Spain; Computer School, Department of Architecture and Technology of Informatics Systems, Polytechnic University of Madrid, Madrid, Spain","Abadia, I., Research Centre for Information and Communication Technologies (CITIC), Department of Computer Architecture and Technology, University of Granada, Granada, Spain; Naveros, F., Research Centre for Information and Communication Technologies (CITIC), Department of Computer Architecture and Technology, University of Granada, Granada, Spain, Computer School, Department of Architecture and Technology of Informatics Systems, Polytechnic University of Madrid, Madrid, Spain; Ros, E., Research Centre for Information and Communication Technologies (CITIC), Department of Computer Architecture and Technology, University of Granada, Granada, Spain; Carrillo, R.R., Research Centre for Information and Communication Technologies (CITIC), Department of Computer Architecture and Technology, University of Granada, Granada, Spain; Luque, N.R., Research Centre for Information and Communication Technologies (CITIC), Department of Computer Architecture and Technology, University of Granada, Granada, Spain","The presence of computation and transmission-variable time delays within a robotic control loop is a major cause of instability, hindering safe human-robot interaction (HRI) under these circumstances. Classical control theory has been adapted to counteract the presence of such variable delays; however, the solutions provided to date cannot cope with HRI robotics inherent features. The highly nonlinear dynamics of HRI cobots (robots intended for human interaction in collaborative tasks), together with the growing use of flexible joints and elastic materials providing passive compliance, prevent traditional control solutions from being applied. Conversely, human motor control natively deals with low power actuators, nonlinear dynamics, and variable transmission time delays. The cerebellum, pivotal to human motor control, is able to predict motor commands by correlating current and past sensorimotor signals, and to ultimately compensate for the existing sensorimotor human delay (tens of milliseconds). This work aims at bridging those inherent features of cerebellar motor control and current robotic challenges namely, compliant control in the presence of variable sensorimotor delays. We implement a cerebellar-like spiking neural network (SNN) controller that is adaptive, compliant, and robust to variable sensorimotor delays by replicating the cerebellar mechanisms that embrace the presence of biological delays and allow motor learning and adaptation. © 2021 American Association for the Advancement of Science. All rights reserved.",,"Adaptive control systems; Agricultural robots; Brain; Compliance control; Computation theory; Delay control systems; Dynamics; Robotics; Time delay; Timing circuits; Classical control theory; Collaborative tasks; Human motor control; Non-deterministic time; Safe human-robot interaction; Spiking neural network(SNN); Variable time delay; Variable transmission; Human robot interaction; article; cerebellum",,,,,,,,,,,,,,,,"Vysocky, A., Novak, P., Human-robot collaboration in industry (2016) MM Sci. J, 9, p. 903906; Van der Loos, H. M., Reinkensmeyer, D. 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Neurosci, 6 (297 311)","Luque, N.R.; Research Centre for Information and Communication Technologies (CITIC), Spain; email: nluque@ugr.es",,,"American Association for the Advancement of Science",,,,,24709476,,,"34516748","English","Sci. Robotics",Article,"Final","",Scopus,2-s2.0-85114804296
"Belkaid M., Kompatsiari K., Tommaso D.D., Zablith I., Wykowska A.","55975421600;55904188300;57215212104;57225283411;15924284200;","Mutual gaze with a robot affects human neural activity and delays decision-making processes",2021,"Science Robotics","6","58","5044","","",,,"10.1126/scirobotics.abc5044","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114777452&doi=10.1126%2fscirobotics.abc5044&partnerID=40&md5=3646fb1cfe33fd3215eee45986a3449c","Istituto Italiano di Tecnologia (IIT), Genova, Italy","Belkaid, M., Istituto Italiano di Tecnologia (IIT), Genova, Italy; Kompatsiari, K., Istituto Italiano di Tecnologia (IIT), Genova, Italy; Tommaso, D.D., Istituto Italiano di Tecnologia (IIT), Genova, Italy; Zablith, I., Istituto Italiano di Tecnologia (IIT), Genova, Italy; Wykowska, A., Istituto Italiano di Tecnologia (IIT), Genova, Italy","In most everyday life situations, the brain needs to engage not only in making decisions but also in anticipating and predicting the behavior of others. In such contexts, gaze can be highly informative about others intentions, goals, and upcoming decisions. Here, we investigated whether a humanoid robot s gaze (mutual or averted) influences the way people strategically reason in a social decision-making context. Specifically, participants played a strategic game with the robot iCub while we measured their behavior and neural activity by means of electroencephalography (EEG). Participants were slower to respond when iCub established mutual gaze before their decision, relative to averted gaze. This was associated with a higher decision threshold in the drift diffusion model and accompanied by more synchronized EEG alpha activity. In addition, we found that participants reasoned about the robot s actions in both conditions. However, those who mostly experienced the averted gaze were more likely to adopt a self-oriented strategy, and their neural activity showed higher sensitivity to outcomes. Together, these findings suggest that robot gaze acts as a strong social signal for humans, modulating response times, decision threshold, neural synchronization, as well as choice strategies and sensitivity to outcomes. This has strong implications for all contexts involving human-robot interaction, from robotics to clinical applications. © 2021 American Association for the Advancement of Science. All rights reserved.",,"Agricultural robots; Anthropomorphic robots; Behavioral research; Brain; Decision making; Electroencephalography; Electrophysiology; Neurons; Alpha activities; Clinical application; Decision making process; Decision threshold; Drift-diffusion model; Making decision; Neural activity; Neural synchronization; Social robots",,,,,,,,,,,,,,,,"Kleinke, C. L., Gaze and eye contact: A research review (1986) Psychol. Bull, 100, p. 78100; Emery, N. J., The eyes have it: The neuroethology, function and evolution of social gaze (2000) Neurosci. Biobehav. 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"Liang X., Wang W., Su T., Hou Z., He G., Ren S., Shi W.","57201802748;55904592500;57194449138;57218664616;7401955572;57201793633;57211080015;","Active Compliant and Adaptive Interaction Control for a Lower Limb Rehabilitation Robot [下肢康复机器人的主动柔顺自适应交互控制]",2021,"Jiqiren/Robot","43","5",,"547","556",,,"10.13973/j.cnki.robot.210029","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117733400&doi=10.13973%2fj.cnki.robot.210029&partnerID=40&md5=818da4c6d4dc409695e2e1e3e303a7c2","North China University of Technology, Beijing, 100144, China; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, 100190, China","Liang, X., North China University of Technology, Beijing, 100144, China, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Wang, W., State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Su, T., North China University of Technology, Beijing, 100144, China; Hou, Z., State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China, University of Chinese Academy of Sciences, Beijing, 100049, China, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, 100190, China; He, G., North China University of Technology, Beijing, 100144, China; Ren, S., State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China, University of Chinese Academy of Sciences, Beijing, 100049, China; Shi, W., State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China, University of Chinese Academy of Sciences, Beijing, 100049, China","In order to provide patients with a stable, comfortable and active compliant rehabilitation training environment, an active compliant interactive control scheme for a lower limb rehabilitation robot based on adaptive adjustment of impedance parameters is proposed. It consists of two parts: the outer loop for impedance parameter adjustment and the inner loop for trajectory tracking. Firstly, a fuzzy adaptive impedance parameter adjuster is proposed to solve the problem caused by the dynamic change of impedance parameters of human body during rehabilitation training. It takes the human-robot interaction force, the position error and the speed error as inputs, and uses the fuzzy inference to adjust the damping and stiffness coefficients in real time to realize the self-adaptation to human body impedance. Secondly, an indirect adaptive fuzzy controller is designed, in which the fuzzy system is reasonably constructed to approximate the unknown nonlinear system, and the set trajectory reflecting the patient's motion intention is tracked stably. The system stability is proved by Lyapunov method. Finally, the simulation results show that the maximum deviations of the hip and knee trajectories under the proposed method are reduced by 53.43% and 66.87% respectively in comparison with the general method of fixed expected impedance parameters, which verifies the feasibility and effectiveness of the proposed method. © 2021, Science Press. All right reserved.","Compliance; Impedance control; Interaction control; Lower limb rehabilitation robot","Adaptive control systems; Compliance control; Fuzzy inference; Human robot interaction; Lyapunov functions; Lyapunov methods; Active compliant; Adaptive interaction; Compliance; Compliant interaction; Human bodies; Impedance control; Impedance parameters; Interaction controls; Lower limbs rehabilitation robots; Rehabilitation training; Trajectories",,,,,,,,,,,,,,,,"Wolfe, C D A., The impact of stroke (2000) British Medical Bulletin, 56 (2), pp. 275-286. , [J]; Brief report on stroke prevention and treatment in China"" writing group. Brief report on stroke prevention and treatment in China, 2019 (2020) Chinese Journal of Cerebrovascular Diseases, 17 (5), pp. 272-281. , 中国脑卒中防治报告》编写组.《中国脑卒中防治报告2019》概要[J]. 中国脑血管病杂志, 2020, 17(5): 272-281. ""[J]; Hou, Z G, Zhao, X G, Cheng, L, Recent advances in rehabilitation robots and intelligent assistance systems (2016) Acta Automatica Sinica, 42 (12), pp. 1765-1779. , 侯增广, 赵新刚, 程龙, 等. 康复机器人与智能辅助系统的研究进展[J]. 自动化学报, 2016, 42(12): 1765-1779. [J]; Klamroth-Marganska, V, Blanco, J, Campen, K, Threedimensional, task-specific robot therapy of the arm after stroke: A multicentre, parallel-group randomised trial (2014) The Lancet Neurology, 13 (2), pp. 159-166. , [J]; Connell, L A, McMahon, N E, Harris, J E, A formative evaluation of the implementation of an upper limb stroke rehabilitation intervention in clinical practice: A qualitative interview study (2014) Implementation Science, 9. , [J]; Dai, H., (2019) Rehabilitation medicine, , 戴红. 康复医学[M]. 北京: 北京大学医学出版社, 2019. [M]. Beijing: Peking University Medical Press; Yang, Y, Wang, X F, Wang, L P., Neuroplasticity and motor function recovery after stroke (2016) Chinese Journal of Medicine, 51 (6), pp. 15-19. , 杨颐, 王雪飞, 王麟鹏. 神经可塑性与脑卒中后运动功能恢复[J]. 中国医刊, 2016, 51(6): 15-19. [J]; Liang, X, Wang, W Q, Hou, Z G, Interactive control methods for rehabilitation robot (2018) Scientia Sinica: Informationis, 48 (1), pp. 24-46. , 梁旭, 王卫群, 侯增广, 等. 康复机器人的人机交互控制方法[J]. 中国科学: 信息科学, 2018, 48(1): 24-46. [J]; Wang, X F, Li, X, Wang, J H., Active interaction exercise control of exoskeleton upper limb rehabilitation robot using model-free adaptive methods (2016) Acta Automatica Sinica, 42 (12), pp. 1899-1914. , 王晓峰, 李醒, 王建辉. 基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法[J]. 自动化学报, 2016, 42(12): 1899-1914. [J]; Hu, J., Exercise training and interactive control with a horizontal exoskeleton for lower limb rehabilitation (2014), 胡进. 坐卧式外骨骼下肢康复机器人的运动训练策略和交互控制方法[D]. 北京: 中国科学院大学, 2014. [D]. Beijing: University of Chinese Academy of Sciences; Yu, X B, He, W, Li, Y N, Bayesian estimation of human impedance and motion intention for human-robot collaboration (2021) IEEE Transactions on Cybernetics, 51 (4), pp. 1822-1834. , [J]; Ibarra, J C P, Siqueira, A A G., Impedance control of rehabilitation robots for lower limbs, review (2014) Joint Conference on Robotics: SBR-LARS Robotics Symposium and Robocontrol, pp. 235-240. , [C] Piscataway, USA: IEEE; Huo, W G, Mohammed, S, Amirat, Y, Active impedance control of a lower limb exoskeleton to assist sit-to-stand movement (2016) IEEE International Conference on Robotics and Automation, pp. 3530-3536. , [C] Piscataway, USA: IEEE; Jamwal, P K, Hussain, S, Ghayesh, M H, Impedance control of an intrinsically compliant parallel ankle rehabilitation robot (2016) IEEE Transactions on Industrial Electronics, 63 (6), pp. 3638-3647. , [J]; Koopman, B, van Asseldonk, E H, van der Kooij, H., Selective control of gait subtasks in robotic gait training: Foot clearance support in stroke survivors with a powered exoskeleton (2013) Journal of NeuroEngineering and Rehabilitation, 10. , [J]; Du, Y H, Qiu, S, Xie, P, Adaptive interaction control for lower limb rehabilitation robots (2018) Acta Automatica Sinica, 44 (4), pp. 743-750. , 杜义浩, 邱石, 谢平, 等. 下肢康复机器人的自适应人机交互控制策略[J]. 自动化学报, 2018, 44(4): 743-750. [J]; Meng, W, Liu, Q, Zhou, Z D, Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation (2015) Mechatronics, 31, pp. 132-145. , [J]; Miranda, A B W, Forner-Cordero, A., Upper limb exoskeleton control based on sliding mode control and feedback linearization (2013) ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living, , [C] Piscataway, USA: IEEE; Rahman, M H, Ochoa-Luna, C, Rahman, M J, Forceposition control of a robotic exoskeleton to provide upper extremity movement assistance (2014) International Journal of Modelling, Identification and Control, 21 (4), pp. 390-400. , [J]; Li, Z J, Su, C Y, Li, G L, Fuzzy approximation-based adaptive backstepping control of an exoskeleton for human upper limbs (2015) IEEE Transactions on Fuzzy Systems, 23 (3), pp. 555-566. , [J]; Huang, Z C, Liu, J Q, Li, Z J, Adaptive impedance control of robotic exoskeletons using reinforcement learning (2016) International Conference on Advanced Robotics and Mechatronics, pp. 243-248. , [C] Piscataway, USA: IEEE; Wang, W Q, Hou, Z G, Cheng, L, Toward patients' motion intention recognition: Dynamics modeling and identification of iLeg-An LLRR under motion constraints (2016) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46 (7), pp. 980-992. , [J]; Wang, W Q, Shi, W G, Hou, Z G, Prediction of human voluntary torques based on collaborative neuromusculoskeletal modeling and adaptive learning (2021) IEEE Transactions on Industrial Electronics, 68 (6), pp. 5217-5226. , [J]; Liu, J K., (2008) Design of robot control system and MATLAB simulation, , 刘金琨. 机器人控制系统设计与MATLAB仿真[M]. 北京: 清华大学出版社, 2008. [M]. Beijing: Tsinghua University Press; Yoo, B K, Ham, W C., Adaptive control of robot manipulator using fuzzy compensator (2000) IEEE Transactions on Fuzzy Systems, 8 (2), pp. 186-199. , [J]; Narendra, K S, Annaswamy, A M., (1989) Stable adaptive systems, , [M]. Englewood Cliffs, USA: Prentice Hall","Wang, W.; State Key Laboratory of Management and Control for Complex Systems, China; email: weiqun.wang@ia.ac.cn",,,"Chinese Academy of Sciences",,,,,10020446,,JIQIE,,"Chinese","Jiqiren",Article,"Final","",Scopus,2-s2.0-85117733400
"Laubscher C.A., Sawicki J.T.","57197800670;7102846145;","A robust impedance controller for improved safety in human-robot interaction",2021,"Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME","143","9","091004","","",,,"10.1115/1.4050504","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104421939&doi=10.1115%2f1.4050504&partnerID=40&md5=9e72ab0418162054ce1c24de8628691f","Center for Rotating Machinery Dynamics and Control (RoMaDyC), Cleveland State University, Cleveland, OH  44115, United States","Laubscher, C.A., Center for Rotating Machinery Dynamics and Control (RoMaDyC), Cleveland State University, Cleveland, OH  44115, United States; Sawicki, J.T., Center for Rotating Machinery Dynamics and Control (RoMaDyC), Cleveland State University, Cleveland, OH  44115, United States","This paper presents a novel impedance controller modified with a switching strategy for the purpose of improving safety in human-robot interactions. Under normal operating conditions, an impedance controller is enabled when adequate tracking performance is maintained in the presence of bounded disturbances. However, if disturbances are greater than anticipated such that tracking performance is degraded, the proposed controller temporarily switches modes to a control strategy better apt to limit control inputs. With disturbances returning to the prescribed bounds, tracking performance will be restored and the impedance controller will resume for nominal operation. The control parameters are constrained by a few conditions necessary for smooth operation. First, a pair of equality constraints is required for the control signal to be continuous when switching control modes. Second, a Lyapunov analysis is performed to formulate an equality constraint on the control parameters to ensure only a single switch occurs when changing control modes to avert control chatter. Third, a matrix inequality constraint is necessary to ensure a robust positive invariant set is formed for when impedance control is active. Numerical simulations are provided to illustrate the controller and conditions. The simulation results successfully validate the presented theory, demonstrating how the constraints yield a continuous control signal, eliminate switching chatter, and permit robustness to disturbances. © 2021 by ASME","Impedance control; Robotics; Robust control; Sliding mode control; Switching control","Constraint theory; Control system analysis; Human robot interaction; Man machine systems; Bounded disturbances; Continuous control; Equality constraints; Impedance controllers; Normal operating conditions; Positive invariant set; Switching strategies; Tracking performance; Controllers",,,,,,,,,,,,,,,,"Hogan, N., Impedance Control: An Approach to Manipulation: Part I-Theory (1985) ASME J. Dyn. Syst., Meas., Control, 107 (1), pp. 1-7; Asada, H., Slotine, J.-J. E., (1986) Robot Analysis and Control, , Wiley, New York; Ibarra, J. C. P., Siqueira, A. A. G., Impedance Control of Rehabilitation Robots for Lower Limbs, Review (2014) Joint Conference on Robotics: SBR-LARS Robotics Symposium and Robocontrol, pp. 235-240. , IEEE, Sao Carlos, Sao Paulo, Brazil, Oct. 18-23; Raiola, G., Cardenas, C. A., Tadele, T. S., de Vries, T., Stramigioli, S., Development of a Safety- and Energy-Aware Impedance Controller for Collaborative Robots (2018) IEEE Rob. Autom. Lett, 3 (2), pp. 1237-1244; Mohammadi, H., Richter, H., Robust Tracking/Impedance Control: Application to Prosthetics (2015) American Control Conference, pp. 2673-2678. , IEEE, Chicago, IL, July 1-3; Edwards, C., Spurgeon, S., (1998) Sliding Mode Control: Theory and Applications, , CRC Press, London, UK; Kurfess, T. R., (2005) Robotics and Automation Handbook, , CRC Press, Boca Raton, FL; Zheng, E.-H., Xiong, J.-J., Luo, J.-L., Second Order Sliding Mode Control for a Quadrotor UAV (2014) ISA Trans, 53 (4), pp. 1350-1356; Altintas, Y., Erkorkmaz, K., Zhu, W.-H., Sliding Mode Controller Design for High Speed Feed Drives (2000) CIRP Ann, 49 (1), pp. 265-270; Madani, T., Daachi, B., Djouani, K., Non-Singular Terminal Sliding Mode Controller: Application to an Actuated Exoskeleton (2016) Mechatronics, 33, pp. 136-145; Utkin, V., Guldner, J., Shijun, M., (1999) Sliding Mode Control in Electromechanical Systems, , CRC Press, London, UK; Bartolini, G., Ferrara, A., Usai, E., Chattering Avoidance by Second-Order Sliding Mode Control (1998) IEEE Trans. Autom. Control, 43 (2), pp. 241-246; Hung, J. Y., Gao, W., Hung, J. C., Variable Structure Control: A Survey (1993) IEEE Trans. Ind. Electron, 40 (1), pp. 2-22; Piltan, F., Sulaiman, N. B., Review of Sliding Mode Control of Robotic Manipulator (2012) World Appl. Sci. J, 18 (12), pp. 1855-1869; Kachroo, P., Tomizuka, M., Chattering Reduction and Error Convergence in the Sliding-Mode Control of a Class of Nonlinear Systems (1996) IEEE Trans. Autom. Control, 41 (7), pp. 1063-1068; Garcıa-Valdovinos, L.-G., Parra-Vega, V., Arteaga, M. A., Observer-Based Sliding Mode Impedance Control of Bilateral Teleoperation Under Constant Unknown Time Delay (2007) Rob. Auton. Syst, 55 (8), pp. 609-617; Cho, H. C., Park, J. H., Kyunghwan, K., Park, J.-O., Sliding-Mode-Based Impedance Controller for Bilateral Teleoperation Under Varying Time-Delay (2001) International Conference on Robotics & Automation, pp. 1025-1030. , Seoul, South Korea, May 21-26; Xu, Q., Adaptive Discrete-Time Sliding Mode Impedance Control of a Piezoelectric Microgripper (2013) IEEE Trans. Rob, 29 (3), pp. 663-673; Azimi, V., Abolfazl Fakoorian, S., Tien Nguyen, T., Simon, D., Robust Adaptive Impedance Control With Application to a Transfemoral Prosthesis and Test Robot (2018) ASME J. Dyn. Syst., Meas., Control, 140 (12), p. 121002; Mohammadi, A., Marquez, H. J., Tavakoli, M., Nonlinear Disturbance Observers: Design and Applications to Euler-Lagrange Systems (2017) IEEE Control Syst. Mag, 37 (4), pp. 50-72; Radke, A., Zhiqiang, G., A Survey of State and Disturbance Observers for Practitioners (2006) American Control Conference, pp. 5183-5188. , Minneapolis, MN, June 14-16; Bernstein, D. S., Matrix Mathematics: Theory (2009) Facts, and Formulas, , Princeton University Press, Princeton, NJ; Gardiner, J. D., Stabilizing Control for Second-Order Models and Positive Real Systems (1992) J. Guid., Control, Dyn, 15 (1), pp. 280-282; Blanchini, F., Set Invariance in Control (1999) Automatica, 35 (11), pp. 1747-1767; Horn, R. A., Mathias, R., Block-Matrix Generalizations of Schur's Basic Theorems on Hadamard Products (1992) Linear Algebra Appl, 172, pp. 337-346; Slotine, J.-J. E., Coetsee, J. A., Adaptive Sliding Controller Synthesis for Non-Linear Systems (1986) Int. J. Control, 43 (6), pp. 1631-1651","Sawicki, J.T.; Center for Rotating Machinery Dynamics and Control (RoMaDyC), United States; email: j.sawicki@csuohio.edu",,,"American Society of Mechanical Engineers (ASME)",,,,,00220434,,JDSMA,,"English","J Dyn Syst Meas Control Trans ASME",Article,"Final","",Scopus,2-s2.0-85104421939
"Smith L., Hainsworth T., Jordan Z., Bell X., MacCurdy R.","57217392541;57217387857;57295305500;57295412700;24335714000;","A Seamless Workflow for Design and Fabrication of Multimaterial Pneumatic Soft Actuators",2021,"IEEE International Conference on Automation Science and Engineering","2021-August",,,"718","723",,,"10.1109/CASE49439.2021.9551668","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117060793&doi=10.1109%2fCASE49439.2021.9551668&partnerID=40&md5=2f8dda3d321ab262cc5b84d1375555fc","University of Colorado Boulder, Department of Mechanical Engineering, Boulder, CO, United States","Smith, L., University of Colorado Boulder, Department of Mechanical Engineering, Boulder, CO, United States; Hainsworth, T., University of Colorado Boulder, Department of Mechanical Engineering, Boulder, CO, United States; Jordan, Z., University of Colorado Boulder, Department of Mechanical Engineering, Boulder, CO, United States; Bell, X., University of Colorado Boulder, Department of Mechanical Engineering, Boulder, CO, United States; MacCurdy, R., University of Colorado Boulder, Department of Mechanical Engineering, Boulder, CO, United States","Soft robotic actuators offer a range of attractive features relative to traditional rigid robots including inherently safer human-robot interaction and robustness to unexpected or extreme loading conditions. Soft robots are challenging to design and fabricate, and most actuators are designed by trial and error and fabricated using labor-intensive multi-step casting processes. We present an integrated collection of software tools that address several limitations in the existing design and fabrication workflow for pneumatic soft actuators. We use implicit geometry functions to specify geometry and material distribution, a GUI-based software tool for interactive exploration of computational network representations of these implicit functions, and an automated tool for generating rapid simulation results of candidate designs. We prioritize seamless connectivity between all stages of the design and fabrication process, and elimination of steps that require human intervention. The software tools presented here integrate with existing capabilities for multimaterial additive manufacturing, and are also forward-compatible with emerging automated design techniques. The workflow presented here is intended as a community resource, and aimed at lowering barriers for the discovery of novel soft actuators by experts and novice users. The data gathered from human-interaction with this tool will be used by future automation tools to enable fully-automated soft actuator design based on high-level specifications. © 2021 IEEE.","Additive Manufacturing; Compliant Joints and Mechanisms; Hydraulic/Pneumatic Actuators","3D printers; Additives; Automation; Computational geometry; Computer software; Human robot interaction; Machine design; Pneumatic actuators; Pneumatic equipment; Pneumatics; Compliant joint and mechanism; Compliant joints; Hydraulic/pneumatic actuator; Inherently safer; Multi materials; Rigid robots; Robotic actuators; Soft actuators; Soft robotics; Work-flows; Fabrication",,,,,,"We would like to thank Festo for the donation of pneumatic components that allowed us to collect the test data presented in this report.",,,,,,,,,,"Rus, D., Tolley, M., Design, fabrication and control of soft robots (2015) Nature; Holland, D., The soft robotics toolkit: Shared resources for research and design (2014) Soft Robotics; Pozzi, M., Efficient fem-based simulation of soft robots modeled as kinematic chains (2018) Proceedings-IEEE Intl. Conference on Robotics and Automation; Pasquier, C., Design and computational modeling of a 3d printed pneumatic toolkit for soft robotics (2019) Soft Robotics; Zolfagharian, A., Evolution of 3d printed soft actuators (2016) Sensors and Actuators A: Physical; Yap, H.K., High-force soft printable pneumatics for soft robotic applications (2016) Soft Robotics; Yirmibesoglu, O., Direct 3d printing of silicone elastomer soft robots and their performance comparison with molded counterparts (2018) 2018 IEEE International Conference on Soft Robotics, RoboSoft; Byrne, O., Additive manufacture of composite soft pneumatic actuators (2018) Soft Robotics; Truby, R., Soft somatosensitive actuators via embedded 3d printing (2018) Advanced Materials; Zhang, H., Design and development of a soft gripper with topology optimization (2017) IEEE International Conference on Intelligent Robots and Systems; MacCurdy, R., Printable hydraulics: A method for fabricating robots by 3d co-printing solids and liquids (2016) IEEE Intl. Conference on Robotics and Automation; Peele, B., 3D printing antagonistic systems of artificial muscle using projection stereolithography (2015) Bioinspiration and Biomimetics; Hainsworth, T., A fabrication free, 3d printed, multi-material, self-sensing soft actuator (2020) IEEE Robotics and Automation Letters; Mosadegh, B., Pneumatic networks for soft robotics that actuate rapidly (2014) Advanced Functional Materials; Li, Q., Towards additive manufacturing oriented geometric modeling using implicit functions (2018) Visual Computing for Industry, Biomedicine, and Art; Allen, G., (2019) NTopology Modeling Technology; Hiller, J., Lipson, H., Automatic design and manufacture of soft robots (2012) IEEE Transactions on Robotics; Cheney, N., Unshackling evolution (2014) ACM SIGEVOlution; Kicinger, R., Evolutionary computation and structural design : A survey of the state of the art (2005) Computers Structures; Joldes, G., (2019) Suite of Meshless Algorithms for Accurate Computation of Soft Tissue Deformation for Surgical Simulation; Fang, Q., Boas, D., Tetrahedral mesh generation from volumetric binary and grayscale images (2009) IEEE International Symposium on Biomedical Imaging; Shepherd, R., Multigait soft robot (2011) Proceedings, Natl, , Academy of Sciences, United States of America; Connolly, F., Sew-free anisotropic textile composites for rapid design and manufacturing of soft wearable robots (2019) Extreme Mechanics Letters","Maccurdy, R.; University of Colorado Boulder, United States; email: maccurdy@colorado.edu",,,"IEEE Computer Society","17th IEEE International Conference on Automation Science and Engineering, CASE 2021","23 August 2021 through 27 August 2021",,172370,21618070,9781665418737,,,"English","IEEE Int. Conf. Autom. Sci. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85117060793
"Samantha Rajapaksha U.U., Jayawardena C., MacDonald B.A.","26431124400;15848351900;7101734705;","ROS Based Multiple Service Robots Control and Communication with High Level User Instruction with Ontology",2021,"2021 10th International Conference on Information and Automation for Sustainability, ICIAfS 2021",,,,"381","386",,,"10.1109/ICIAfS52090.2021.9606062","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119972201&doi=10.1109%2fICIAfS52090.2021.9606062&partnerID=40&md5=0483d27eda86c2d4750beb86b5f90c04","Sri Lanka Institute of Information Technology, Department of Information Technology, Malabe, Sri Lanka; Sri Lanka Institute of Information Technology, Department of Computer System and Engineering, Malabe, Sri Lanka; The University of Auckland, Department of Electrical, Computer and Software Engineering, Auckland, New Zealand","Samantha Rajapaksha, U.U., Sri Lanka Institute of Information Technology, Department of Information Technology, Malabe, Sri Lanka; Jayawardena, C., Sri Lanka Institute of Information Technology, Department of Computer System and Engineering, Malabe, Sri Lanka; MacDonald, B.A., The University of Auckland, Department of Electrical, Computer and Software Engineering, Auckland, New Zealand","Human Robot Interaction (HRI) is one of the biggest research field in the Robotics research world. Understanding the semantic meaning of the user instruction is very important to establish the communication between user and robot. When a user instructs to all heterogeneous service Robots with high level instructions who are working at different locations in smart house, all robots need to operate by understanding the semantic of the instruction and complete the task uniformly without considering underline software and hardware implementations. Ontology is used to extract the semantic meaning of the instructions. Each robot is assigned a specific task for specific time period for each day. User can issue commands to all robots at different time slots but the same command can be issued with different sentence by different users. We have used ontology to represents the semantic of the sentence with different commands. We have used three robots (TurtleBot, TiaGo and Husky) for our experiment in Gazebo simulator. Robot Operating System (ROS) is the middleware which is hiding more complex implementation of different functions for different robots and provide the interoperability for heterogeneous robot operations. The ROS nodes and topics can be different for different robots, therefore our implementation can solve this issue by autonomous robot registration algorithm which is working with Robot ontology. © 2021 IEEE.","Instruction; Ontology; ROS; Semantic; Service Robots","Human robot interaction; Interoperability; Middleware; Ontology; Semantics; Humans-robot interactions; Instruction; Multiple services; Ontology's; Research fields; Robot communication; Robot operating system; Robotics research; Robots control; Service robots; Mobile robots",,,,,,,,,,,,,,,,"Kim, J., Jang, M., Sohn, J., An ontological approach for natural language command interpretation and its application in robotics (2006) 2006 SICE-ICASE International Joint Conference, pp. 3874-3878; Martinez, A., Yannuzzi, M., De Vergara, J.E.L., Serral-Gracià, R., Ramrez, W., An ontology-based information extraction system for bridging the configuration gap in hybrid SDN environments (2015) 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM), pp. 441-449; Mazuel, L., Sabouret, N., Generic command interpretation algorithms for conversational agents (2006) 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, pp. 146-153; Jeon, S., Jang, M., Lee, D., Lee, C.-E., Cho, Y., Control architecture for heterogeneous multiple robots with human-in-the-loop (2012) 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 274-278; Alberri, M., Hegazy, S., Badra, M., Nasr, M., Shehata, O.M., Morgan, E.I., Generic ros-based architecture for heterogeneous multiautonomous systems development (2018) 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES), pp. 1-6; Hu, C., Hu, C., He, D., Gu, Q., A new ros-based hybrid architecture for heterogeneous multi-robot systems (2015) The 27th Chinese Control and Decision Conference (2015 CCDC), pp. 4721-4726; Msala, Y., Hamlich, M., Mouchtachi, A., A new robust heterogeneous multi-robot approach based on cloud for task allocation (2019) 2019 5th International Conference on Optimization and Applications (ICOA), pp. 1-4; Zheng, S., Lin, Z., Zeng, Q., Zheng, R., Liu, C., Xiong, H., Iapcloud: A cloud control platform for heterogeneous robots (2018) IEEE Access, 6, pp. 30577-30591; Costa, L.F., Gonçalves, L.M.G., Roboserv: A ros based approach towards providing heterogeneous robots as a service (2016) 2016 XIII Latin American Robotics Symposium and IV Brazilian Robotics Symposium (LARS/SBR), pp. 169-174; Rajapaksha, U.U.S., Jayawardena, C., Ontology based optimized algorithms to communicate with a service robot using a user command with unknown terms (2020) 2020 2nd International Conference on Advancements in Computing (ICAC), 1, pp. 258-262; Tiddi, I., Bastianelli, E., Bardaro, G., D'Aquin, M., Motta, E., An ontology-based approach to improve the accessibility of ros-based robotic systems (2017) Proceedings of the Knowledge Capture Conference, K-CAP, , December, 2017; Pomarlan, M., Bateman, J., Robot program construction via grounded natural language semantics & simulation robotics track (2018) Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, 2, pp. 857-864; Rajapaksha, S., Illankoon, V., Halloluwa, N.D., Satharana, M., Umayanganie, D., Responsive drone autopilot system for uncertain natural language commands (2019) 2019 International Conference on Advancements in Computing (ICAC), pp. 232-237; Waruna Bandara, H.L., Wijesekera, D.S., Bandara Herath, H.D., Kodagoda, D.L., Rajapaksha, S., Methodology for coping with uncertain information contained in natural language instructions in a robotic system (2020) 2020 2nd International Conference on Advancements in Computing (ICAC), 1, pp. 234-239; Han, R., Chen, S., Hao, Q., Cooperative multi-robot navigation in dynamic environment with deep reinforcement learning (2020) 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 448-454; Muthugala, M.A.V.J., Jayasekara, A.G.B.P., A review of service robots coping with uncertain information in natural language instructions (2018) IEEE Access, 6, pp. 12913-12928; Müllers, F., Holz, D., Behnke, S., Rxdeveloper: Gui-aided software developmemt in ros (2009) Proc. Of ICRA Workshop on Software Development and Integration in Robotics (SDIR), p. 2009; Sutherland, C.J., MacDonald, B., Robolang: A simple domain specific language to script robot interactions (2019) 2019 16th International Conference on Ubiquitous Robots, pp. 265-270. , UR 2019; Jayawardena, C., Kuo, I., Broadbent, E., MacDonald, B.A., Socially assistive robot healthbot: Design, implementation, and field trials (2016) IEEE Systems Journal, 10 (3), pp. 1056-1067; Datta, C., Jayawardena, C., Kuo, I.H., MacDonald, B.A., Robostudio: A visual programming environment for rapid authoring and customization of complex services on a personal service robot (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2352-2357; Rajapaksha, S.K., Kodagoda, N., Internal structure and semantic web link structure based ontology ranking (2008) 2008 4th International Conference on Information and Automation for Sustainability, pp. 86-90; Rajapaksha, U., Fernando, H., Ontology matching and ranking: Issues and research challenges in semantic web application development (2009) ITRU Research Symposium",,,,"Institute of Electrical and Electronics Engineers Inc.","10th International Conference on Information and Automation for Sustainability, ICIAfS 2021","11 August 2021 through 13 August 2021",,173956,,9781665444293,,,"English","Int. Conf. Inf. Autom. Sustain., ICIAfS",Conference Paper,"Final","",Scopus,2-s2.0-85119972201
"Patrona F., Mademlis I., Pitas I.","57090843300;56436769200;35569279700;","Self-Supervised Convolutional Neural Networks for Fast Gesture Recognition in Human-Robot Interaction",2021,"2021 10th International Conference on Information and Automation for Sustainability, ICIAfS 2021",,,,"88","93",,,"10.1109/ICIAfS52090.2021.9605915","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119958973&doi=10.1109%2fICIAfS52090.2021.9605915&partnerID=40&md5=ad9bbfb3d199fb60f06f0b3af019369d","Aristotle University of Thessaloniki, Department of Informatics, Thessaloniki, Greece","Patrona, F., Aristotle University of Thessaloniki, Department of Informatics, Thessaloniki, Greece; Mademlis, I., Aristotle University of Thessaloniki, Department of Informatics, Thessaloniki, Greece; Pitas, I., Aristotle University of Thessaloniki, Department of Informatics, Thessaloniki, Greece","Current autonomous systems (e.g., self-driving cars, autonomous drones, consumer robots, etc.) can already perform a wide variety of tasks and are predicted to be able to collaboratively assist humans in the near future. Thus, the need for efficient human-robot interaction (HRI) methods is greatly increasing. Gesture recognition is an effective HRI approach, since many robots are equipped with cameras and computer vision algorithms have progressed significantly in recent years, with advanced Deep Neural Networks (DNNs) being able to be executed on-board an autonomous system. However, computational/memory limitations are still significant for embedded AI methods, rendering the increase of DNN accuracy without imposing a penalty on runtime requirements a very important research priority. This paper investigates self-supervised DNN pretraining for a novel pretext task, relying on spatiotemporal video frame compression via tensor decomposition and low-rank approximation, as a means to augment gesture recognition performance, without inducing any runtime overhead during the inference stage. Thus, the method permits the use of less complex and much faster neural architectures that are well-suited to robotics applications and HRI. Quantitative evaluation on a gesture recognition dataset for autonomous Unmanned Aerial Vehicle (UAV) handling demonstrates the effectiveness and real-time performance of the proposed method on embedded AI compute hardware. © 2021 IEEE.","Autonomous Unmanned Aerial Vehicles; Convolutional Neural Networks; Gesture Recognition; Human-Robot Interaction; Low-Rank Approximation; Self-Supervised Learning; Tensor Decomposition","Aircraft detection; Antennas; Autonomous vehicles; Convolution; Convolutional neural networks; Deep neural networks; Drones; Gesture recognition; Man machine systems; Tensors; 'current; Autonomous unmanned aerial vehicles; Computer vision algorithms; Convolutional neural network; Gestures recognition; Humans-robot interactions; Interaction methods; Low rank approximations; Self-supervised learning; Tensor decomposition; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020","This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No871479 (AERIAL-CORE).",,,,,,,,,,"Ahsan, U., Madhok, R., Essa, I., Video jigsaw: Unsupervised learning of spatiotemporal context for video action recognition (2019) Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV). 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Zhang, R., Isola, P., Efros, A.A., Split-brain autoencoders: Unsupervised learning by cross-channel prediction (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",,,,"Institute of Electrical and Electronics Engineers Inc.","10th International Conference on Information and Automation for Sustainability, ICIAfS 2021","11 August 2021 through 13 August 2021",,173956,,9781665444293,,,"English","Int. Conf. Inf. Autom. Sustain., ICIAfS",Conference Paper,"Final","",Scopus,2-s2.0-85119958973
"Yang Z., Guo S., Liu Y.","57211042370;57212331621;57191517612;","Preliminary Evaluation of a Performance-based Stiffness Control for Upper Limb Elbow Joints Rehabilitation",2021,"2021 IEEE International Conference on Mechatronics and Automation, ICMA 2021",,,,"1280","1285",,1,"10.1109/ICMA52036.2021.9512573","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115167648&doi=10.1109%2fICMA52036.2021.9512573&partnerID=40&md5=14a291a1aa83e1114f18cdf489b2afd2","Graduate School of Engineering; Kagawa University, Department of Intelligent Mechanical Systems Engineering, Hayashi-cho, Takamatsu, 761-0396, Japan; Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Haidian District, Beijing, 100081, China","Yang, Z., Graduate School of Engineering; Guo, S., Kagawa University, Department of Intelligent Mechanical Systems Engineering, Hayashi-cho, Takamatsu, 761-0396, Japan, Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Haidian District, Beijing, 100081, China; Liu, Y., Graduate School of Engineering","For post-stroke patient, the interaction force between the impaired limb and the rehabilitation robotic system is an important issue for patient safety in clinical treatment. The compliance control scheme can be achieved by the rehabilitation robotics with variable stiffness actuator (VSA) for the safe physical human robot interaction. The output stiffness would be adjusted independently via variable stiffness actuator during the rehabilitation training processing. In addition, when the stiffness changing, the different impedance characteristic can be realized according to the environmental applied force. The compliance control of VSA can be utilized into the rehabilitation application scenario for improving the training safety. In this paper, a performance-based stiffness control strategy for maximum the rehabilitation effect and increasing the patient participant was proposed utilizing the compliance characteristic of VSA. The elbow joint output stiffness would be regulated according the patient's training performance which will be determined using the real-time position tracking error. It is noted that the training performance should be different to adapt the patient individual-specific. Therefore, an acceptable position tracking error range should be set in advance for the suitable training plan. The experimental results show that the proposed method can adjust the elbow joint stiffness for patients according to the real-time training performance. © 2021 IEEE.","Interaction Force; Performance-based control strategy; physical human robot interaction; Stiffness Control; Upper limb elbow joint","Actuators; Compliance control; Human robot interaction; Joints (anatomy); Patient treatment; Robotics; Stiffness; Tracking (position); Application scenario; Impedance characteristics; Physical human-robot interactions; Position tracking errors; Rehabilitation effects; Rehabilitation robotic systems; Rehabilitation robotics; Rehabilitation training; Patient rehabilitation",,,,,,,,,,,,,,,,"Benjamin, E.J., Heart disease and stroke statistics-2019 update: A report from the American Heart Association (2019) Circulation, 139 (10), pp. 56-528; Bao, G., Pan, L., Fang, H., Wu, X., Yu, H., Academic review and perspectives on robotic exoskeletons (2019) IEEE Trans. Neural Syst. Rehabil. 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Eng., 67 (12), pp. 3438-3451; Song, Z., Guo, S., Xiao, N., Gao, B., Shi, L., Implementation of human-machine synchronization control for active rehabilitation using an inertia sensor (2012) Sensors, 12 (12), pp. 16046-16059; Rudenko, A., Palmieri, L., Herman, M., Kitani, K.M., Gavrila, D.M., Arras, K.O., Human motion trajectory prediction : A survey (2020) The International Journal of Robotics Research, 8, pp. 895-935; Bessler, J., Prange-Lasonder, G.B., Schaake, L., Saenz, J.F., (2021) Safety Assessment of Rehabilitation Robots : A Review Identifying Safety Skills and Current Knowledge Gaps, 8, pp. 1-18. , March; Bessler, J., Prange-Lasonder, G.B., Schaake, L., Saenz, J.F., (2021) Safety Assessment of Rehabilitation Robots : A Review Identifying Safety Skills and Current Knowledge Gaps, 8, pp. 1-18. , March; Xiong, D., Zhang, D., Zhao, X., Zhao, Y., Deep learning for emg-based human-machine interaction : A review (2021) IEEE/CAA Journal of Automatica Sinica, 8, pp. 512-533; Van Kooij Der, H., Admittance control for physical human-robot interaction (2018) The International Journal of Robotics Research, 37, pp. 1421-1444; Li, S., Li, J., Tian, G., Shang, H., Variable stiffness control for seas in rehabilitation training (2019) Adv. 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ICMA 2019, pp. 2168-2173; Zhang, S., Fu, Q., Guo, S., Fu, Y., Coordinative motion-based bilateral rehabilitation training system with exoskeleton and haptic devices for biomedical application (2018) Micromachines, 10 (1); Zhang, S., Fu, Q., Guo, S., Fu, Y., A telepresence system for therapist-in-the-loop training for elbow joint rehabilitation (2019) Appl. Sci., 9 (8); Zhang, S., Fu, Q., Guo, S., Fu, Y., Coordinative motion-based bilateral rehabilitation training system with exoskeleton and haptic devices for biomedical application (2019) Micromechines; Yang, Z., Guo, S., Liu, Y., Comparison of isometric force estimation methods for upper limb elbow joints (2020) 2020 IEEE Int. Conf. Mechatronics Autom. ICMA 2020, pp. 1558-1563; Song, Z., Implementation of resistance training using an upper-limb exoskeleton rehabilitation device for elbow joint (2014) J. Med. Biol. Eng., 34 (2), pp. 188-196; Liu, Y., Guo, S., Yang, Z., Performance evaluation of a powered variable-stiffness exoskeleton device for bilateral training (2019) Proc. 2019 IEEE Int. Conf. Mechatronics Autom. ICMA 2019, p. 21632167; Asl, H.J., Yamashita, M., Narikiyo, T., Kawanishi, M., Field-based assist-as-needed control schemes for rehabilitation robots (2020) IEEE/ASME Transactions on Mechantronics, 4435 (C); Liu, Y., Guo, S., Yang, Z., Hirata, H., Tamiya, T., A home-based bilateral rehabilitation system with semg-based real-time variable stiffness (2020) IEEE J. Biomed. Heal. Informatics, (C), p. 11; Liu, L., Misgeld, B.J.E., Pomprapa, A., Leonhardt, S., A testable robust stability framework for the variable impedance control of 1-dof exoskeleton with variable stiffness actuator (2021) IEEE Trans. Control Syst. Technol., , January; Liu, L., Leonhardt, S., Ngo, C., Misgeld, B.J.E., Impedance-controlled variable stiffness actuator for lower limb robot applications (2020) IEEE Trans. Autom. Sci. Eng., 17 (2), pp. 991-1004",,,,"Institute of Electrical and Electronics Engineers Inc.","18th IEEE International Conference on Mechatronics and Automation, ICMA 2021","8 August 2021 through 11 August 2021",,171466,,9781665441001,,,"English","IEEE Int. Conf. Mechatronics Autom., ICMA",Conference Paper,"Final","",Scopus,2-s2.0-85115167648
"Paulo J.R., Garrote L., Peixoto P., Nunes U.J.","56730362400;56841438900;7003452669;6603870766;","Spatiotemporal 2D skeleton-based image for dynamic gesture recognition using convolutional neural networks",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"1138","1144",,,"10.1109/RO-MAN50785.2021.9515418","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115068014&doi=10.1109%2fRO-MAN50785.2021.9515418&partnerID=40&md5=b3db4658f13ab946f1819c7cd2c657bc","University of Coimbra, Institute of Systems and Robotics, Portugal","Paulo, J.R., University of Coimbra, Institute of Systems and Robotics, Portugal; Garrote, L., University of Coimbra, Institute of Systems and Robotics, Portugal; Peixoto, P., University of Coimbra, Institute of Systems and Robotics, Portugal; Nunes, U.J., University of Coimbra, Institute of Systems and Robotics, Portugal","This paper presents a dynamic gesture recognition approach using a novel spatiotemporal 2D skeleton image representation that can be fed to computationally efficient deep convolutional neural networks, for applications on human-robot interaction. Gestures are a seamless modality of human interaction and represent a potentially natural way to interact with the smart devices around us, like robots. The contribution of this paper is the proposal of a visually interpretable representation of dynamic gestures, which has a two-fold advantage: (i) conveys both spatial and temporal characteristics relying on a technique inspired in computer graphics, (ii) and can be used with simple and efficient architectures of convolutional neural networks. In our representation, a 3D skeleton model is projected to a 2D camera's point-of-view, preserving spatial relations, and through a sliding window the temporal domain is encoded in a fused image of consecutive frames, through a shading motion effect achieved by manipulating a transparency coefficient. The result is a 2D image that when fed to simple custom-designed convolutional neural networks, it is achieved accurate classification of dynamic gestures. Experimmental reuslts obtained with a purposely captured 6 gesture dataset of 11 subjects, and also 2 public datasets, give evidence of a strong performance of our approach, when compared to other methods. © 2021 IEEE.",,"3D modeling; Agricultural robots; Computer graphics; Convolution; Convolutional neural networks; Deep neural networks; Musculoskeletal system; Social robots; Computationally efficient; Efficient architecture; Human interactions; Image representations; Interpretable representation; Spatial relations; Temporal characteristics; Temporal domain; Gesture recognition",,,,,"UIDB/00048/2020; European Commission, EC; Fundação para a Ciência e a Tecnologia, FCT","This work was supported by the project CENTRO-01-0247-FEDER-047148 INPACT - ”Intelligent Platform for Autonomous Collaborative Telerehabilitation” financed by the Portugal2020 program and European Union’s structural funds. It was also partially supported by the Portuguese Foundation for Science and Technology (FCT) under the ISR-UC FCT grant UIDB/00048/2020, Portugal.",,,,,,,,,,"Turaga, P., Chellappa, R., Subrahmanian, V.S., Udrea, O., Machine recognition of human activities: A survey (2008) IEEE Transactions on Circuits and Systems for Video Technology, 18 (11), p. 1473; Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Darrell, T., Long-term recurrent convolutional networks for visual recognition and description (2015) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2625-2634; Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M., Learning spatiotemporal features with 3D convolutional networks (2015) Proceed-ings of the IEEE International Conference on Computer Vision, pp. 4489-4497; Qiu, Z., Yao, T., Mei, T., Learning spatio-temporal representation with pseudo-3d residual networks (2017) 2017 IEEE International Con-ference on Computer Vision (ICCV). 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IEEE, pp. 579-583; Huang, Z., Wan, C., Probst, T., Van Gool, L., Deep learning on lie groups for skeleton-based action recognition (2017) Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1243-1252. , IEEE computer Society; Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y., Realtime multi-person 2D pose estimation using part affinity fields (2017) CVPR; Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L., Microsoft coco: Common objects in context (2014) European Conference on Computer Vision, pp. 740-755. , Springer; Corke, P., (2017) Robotics, Vision and Control: Fundamental Algorithms in MATLABR Second, Completely Revised, 118. , Springer; Worboys, M.F., Duckham, M., (2004) GIS: A Computing Perspective, , CRC press; Kingma, D.P., Ba, J., Adam: A method for stochastic optimization (2014) CoRR","Paulo, J.R.; University of Coimbra, Portugal; email: jpaulo@isr.uc.pt",,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115068014
"Ribeiro T., Gonçalves F., Garcia I.S., Lopes G., Ribeiro A.F.","57203502292;57209689381;57195303638;42861976400;57226657281;","CHARMIE: A collaborative healthcare and home service and assistant robot for elderly care",2021,"Applied Sciences (Switzerland)","11","16","7248","","",,,"10.3390/app11167248","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112307386&doi=10.3390%2fapp11167248&partnerID=40&md5=fb152fe3a62991c44ec0c61b40f21917","Industrial Electronics Department, University of Minho, Guimarães, 4800-058, Portugal; Centro ALGORITMI, University of Minho, Campus Azurém, Guimarães, 4800-058, Portugal; Mechanical Engineering Department, University of Minho, Guimarães, 4800-058, Portugal; CMEMS, University of Minho, Campus Azurém, Guimarães, 4800-058, Portugal; International Iberian Nanotechnology Laboratory (INL), Braga, 4715-330, Portugal; University Institute of Maia—ISMAI, Maia, 4475-690, Portugal","Ribeiro, T., Industrial Electronics Department, University of Minho, Guimarães, 4800-058, Portugal, Centro ALGORITMI, University of Minho, Campus Azurém, Guimarães, 4800-058, Portugal; Gonçalves, F., Mechanical Engineering Department, University of Minho, Guimarães, 4800-058, Portugal, CMEMS, University of Minho, Campus Azurém, Guimarães, 4800-058, Portugal; Garcia, I.S., Industrial Electronics Department, University of Minho, Guimarães, 4800-058, Portugal, Centro ALGORITMI, University of Minho, Campus Azurém, Guimarães, 4800-058, Portugal, International Iberian Nanotechnology Laboratory (INL), Braga, 4715-330, Portugal; Lopes, G., University Institute of Maia—ISMAI, Maia, 4475-690, Portugal; Ribeiro, A.F., Industrial Electronics Department, University of Minho, Guimarães, 4800-058, Portugal, Centro ALGORITMI, University of Minho, Campus Azurém, Guimarães, 4800-058, Portugal","The global population is ageing at an unprecedented rate. With changes in life expectancy across the world, three major issues arise: an increasing proportion of senior citizens; cognitive and physical problems progressively affecting the elderly; and a growing number of single-person households. The available data proves the ever-increasing necessity for efficient elderly care solutions such as healthcare service and assistive robots. Additionally, such robotic solutions provide safe healthcare assistance in public health emergencies such as the SARS-CoV-2 virus (COVID-19). CHARMIE is an anthropomorphic collaborative healthcare and domestic assistant robot capable of performing generic service tasks in non-standardised healthcare and domestic environment settings. The combination of its hardware and software solutions demonstrates map building and self-localisation, safe navigation through dynamic obstacle detection and avoidance, different human-robot interaction systems, speech and hearing, pose/gesture estimation and household object manipulation. Moreover, CHARMIE performs end-to-end chores in nursing homes, domestic houses, and healthcare facilities. Some examples of these chores are to help users transport items, fall detection, tidying up rooms, user following, and set up a table. The robot can perform a wide range of chores, either independently or collaboratively. CHARMIE provides a generic robotic solution such that older people can live longer, more independent, and healthier lives. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Assistant robot; Collaborative robot; COVID-19; Elderly care; Healthcare; Intelligent systems; Service robot",,,,,,"UIDB/00319/2020; Fundação para a Ciência e a Tecnologia, FCT: SFRH/BD/06944/2020; European Social Fund, ESF: SFRH/BD/145993/2019; Universidade do Minho, Uminho","This work has been supported by FCT?Funda??o para a Ci?ncia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. The author T.R. received funding through a doctoral scholarship from the Portuguese Foundation for Science and Technology (Funda??o para a Ci?ncia e a Tecnologia) [grant number SFRH/BD/06944/2020], with funds from the Portuguese Ministry of Science, Technology and Higher Education and the European Social Fund through the Programa Operacional do Capital Humano (POCH). The author F.G. received funding through a doctoral scholarship from the Portuguese Foundation for Science and Technology (Funda??o para a Ci?ncia e a Tecnologia) [grant number SFRH/BD/145993/2019], with funds from the Portuguese Ministry of Science, Technology and Higher Education and the European Social Fund through the Programa Operacional do Capital Humano (POCH).The authors of this work would like to thank the members of the Laboratory of Automation and Robotics from the University of Minho as well as all former CHARMIE project members.","Funding: This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. The author T.R. received funding through a doctoral scholarship from the Portuguese Foundation for Science and Technology (Fundação para a Ciência e a Tecnologia) [grant number SFRH/BD/06944/2020], with funds from the Portuguese Ministry of Science, Technology and Higher Education and the European Social Fund through the Programa Operacional do Capital Humano (POCH). The author F.G. received funding through a doctoral scholarship from the Portuguese Foundation for Science and Technology (Fundação para a Ciência e a Tecnologia) [grant number SFRH/BD/145993/2019], with funds from the Portuguese Ministry of Science, Technology and Higher Education and the European Social Fund through the Programa Operacional do Capital Humano (POCH).",,,,,,,,,"(2019) World Population Prospects 2019, , United Nations Department of Economic and Social Affairs. 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Sci.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85112307386
"García A., Gracia L., Solanes J.E., Girbés-Juan V., Perez-Vidal C., Tornero J.","57218877607;57198068796;50362016600;57217682767;26657631300;56162306600;","Robotic assistance for industrial sanding with a smooth approach to the surface and boundary constraints",2021,"Computers and Industrial Engineering","158",,"107366","","",,1,"10.1016/j.cie.2021.107366","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107060688&doi=10.1016%2fj.cie.2021.107366&partnerID=40&md5=e46aac954908b3b3419135069cc94d82","Instituto de Diseño y Fabricación, Universitat Politècnica de València, Camino de Vera s/n, Valencia, 46022, Spain; Departament d'Enginyeria Electrònica, Universitat de València, Avda de la Universitat s/n, Burjassot, 46100, Spain; Departamento de Ingeniería de Sistemas y Automática, Universidad Miguel Hernández, Avda de la Universidad s/n, Elche, 03202, Spain","García, A., Instituto de Diseño y Fabricación, Universitat Politècnica de València, Camino de Vera s/n, Valencia, 46022, Spain; Gracia, L., Instituto de Diseño y Fabricación, Universitat Politècnica de València, Camino de Vera s/n, Valencia, 46022, Spain; Solanes, J.E., Instituto de Diseño y Fabricación, Universitat Politècnica de València, Camino de Vera s/n, Valencia, 46022, Spain; Girbés-Juan, V., Departament d'Enginyeria Electrònica, Universitat de València, Avda de la Universitat s/n, Burjassot, 46100, Spain; Perez-Vidal, C., Departamento de Ingeniería de Sistemas y Automática, Universidad Miguel Hernández, Avda de la Universidad s/n, Elche, 03202, Spain; Tornero, J., Instituto de Diseño y Fabricación, Universitat Politècnica de València, Camino de Vera s/n, Valencia, 46022, Spain","Surface treatment operations, such as sanding, deburring, finishing, grinding, polishing, etc. are progressively becoming more automated using robotic systems. However, previous research in this field used a completely automatic operation of the robot system or considered a low degree of human-robot interaction. Therefore, to overcome this issue, this work develops a truly synergistic cooperation between the human operator and the robot system to get the best from both. In particular, in the application developed in this work the human operator provides flexibility, guiding the tool of the robot system to treat arbitrary regions of the workpiece surface; while the robot system provides strength, accuracy and security, not only holding the tool and keeping the right tool orientation, but also guaranteeing a smooth approach to the workpiece and confining the tool within the allowed area close to the workpiece. Moreover, to add more flexibility to the proposed method, when the user is not guiding the robot tool, a robot automatic operation is activated to perform the treatment in prior established regions. Furthermore, a camera network is used to get a global view of the robot workspace in order to obtain the workpiece location accurately and in real-time. The effectiveness of the proposed approach is shown with several experiments using a 6R robotic arm. © 2021 Elsevier Ltd","Boundary constraints; Human-robot cooperation; Smooth approach",,,,,,"Generalitat Valenciana, GVA: ACIF/2019/007, GV/2021/181, PID2020-117421RB-C21","This research was founded by the Spanish Government and the Generalitat Valenciana under Grants PID2020-117421RB-C21, GV/2021/181 and ACIF/2019/007.",,,,,,,,,,"Stavropoulos, P., Papacharalampopoulos, A., Petridis, D., A vision-based system for real-time defect detection: a rubber compound part case study (2020) Procedia CIRP, 93, pp. 1230-1235; Kieselbach, K.K., Nöthen, M., Heuer, H., Development of a visual inspection system and the corresponding algorithm for the detection and subsequent classification of paint defects on car bodies in the automotive industry (2019) Journal of Coatings Technology and Research, 16, pp. 1033-1042; https://www.ferrobotics.com/en/technologie-produkte/produkte/acf/, tool from FerRobotics, (Accessed 2020/06/10); Kuo, Y., Huang, S., Lan, C., Sensorless force control of automated grinding/deburring using an adjustable force regulation mechanism (2019) in: 2019 Int. Conference on Robotics and Automation (ICRA), pp. 9489-9495; Mohammad, A.E.K., Hong, J., Wang, D., Design of a force-controlled end-effector with low-inertia effect for robotic polishing using macro-mini robot approach (2018) Robotics and Computer-Integrated Manuf., 49, pp. 54-65; Villagrossi, E., Cenati, C., Pedrocchi, N., Beschi, M., Molinari Tosatti, L., Flexible robot-based cast iron deburring cell for small batch production using single-point laser sensor (2017) The International Journal of Advanced Manufacturing Technology, 92, pp. 1425-1438; Diao, S., Chen, X., Luo, J., Development and experimental evaluation of a 3D vision system for grinding robot (2018) Sensors, 18, pp. 1-20; Mohsin, I., He, K., Cai, J., Chen, H., Du, R., Robotic polishing with force controlled end effector and multi-step path planning (2017) IEEE Int. Conference on Information and Automation (ICIA), 2017, pp. 344-348; Mohsin, I., He, K., Li, Z., Du, R., Path planning under force control in robotic polishing of the complex curved surfaces (2019) Applied Sciences, 9, p. 5489; Oba, Y., Kakinuma, Y., Simultaneous tool posture and polishing force control of unknown curved surface using serial-parallel mechanism polishing machine (2017) Precision Engineering, 49, pp. 24-32; Tian, F., Li, Z., Lv, C., Liu, G., Polishing pressure investigations of robot automatic polishing on curved surfaces (2016) The International Journal of Advanced Manufacturing Technology, 87, pp. 639-646; Sato, A., Shen, K., Minami, M., Matsuno, T., Improvement of force-sensorless grinding accuracy with resistance compensation (2017) Artificial Life and Robotics, 22, pp. 509-514; Segreto, T., Teti, R., Machine learning for in-process end-point detection in robot-assisted polishing using multiple sensor monitoring (2019) Int. J. Adv. Manuf. Technol., 103, pp. 4173-4187; Huang, S., Zhu, Z., Chen, J., Zhou, X., Yu, J., Gao, P., Wang, H., Target force tracking and automatic contour surface processing in grinding of industrial robots (2020), pp. 188-195. , 2020 6th International Conference on Control, Automation and Robotics (ICCAR); Kharidege, A., Ting, D., Yajun, Z., A practical approach for automated polishing system of free-form surface path generation based on industrial arm robot (2017) International Journal of Advanced Manufacturing Technology, 93, pp. 3921-3934; Ochoa, H., Cortesão, R., Control architecture for robotic-assisted polishing tasks based on human skills (2019), 1, pp. 630-637. , IECON 2019–45th Annual Conference of the IEEE Industrial Electronics Society; Nemec, B., Yasuda, K., Mullennix, N., Likar, N., Ude, A., Learning by demonstration and adaptation of finishing operations using virtual mechanism approach (2018) IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 7219-7225; Gaz, C., Magrini, E., De Luca, A., A model-based residual approach for human-robot collaboration during manual polishing operations (2018) Mechatronics, 55, pp. 234-247; Perez-Vidal, C., Gracia, L., Sanchez-Caballero, S., Solanes, J.E., Saccon, A., Tornero, J., Design of a polishing tool for collaborative robotics using minimum viable product approach (2019) International Journal of Computer Integrated Manufacturing, 32, pp. 848-857; Gracia, L., Solanes, J., Muñoz-Benavent, P., Valls Miro, J., Perez-Vidal, C., Tornero, J., Human-robot collaboration for surface treatment tasks (2019) Interaction Studies, 20, pp. 148-184; Santos, L., Cortesão, R., Computed-torque control for robotic-assisted tele-echography based on perceived stiffness estimation (2018) IEEE Transactions on Automation Science and Engineering, 15, pp. 1337-1354; Cui, L., Wang, H., Liang, X., Wang, J., Chen, W., Visual servoing of a flexible aerial refueling boom with an eye-in-hand camera (2021) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 1-11. , in pres; Taryudi, Wang, M.-S., Eye to hand calibration using anfis for stereo vision-based object manipulation system (2018) Microsystem Technologies, 24, pp. 305-317; Lai, S.-H., Fu, C.-W., Chang, S., A generalized depth estimation algorithm with a single image (1992) IEEE transactions on pattern analysis and machine intelligence, 14, pp. 405-411; Kent, D., Saldanha, C., Chernova, S., Leveraging depth data in remote robot teleoperation interfaces for general object manipulation (2020) The International Journal of Robotics Research, 39, pp. 39-53; Gong, Y., Seibel, E.J., Feature-based three-dimensional registration for repetitive geometry in machine vision (2016) Journal of information technology & software engineering, 6, pp. 184-1-184-5; He, Y., Liang, B., Yang, J., Li, S., He, J., An iterative closest points algorithm for registration of 3D laser scanner point clouds with geometric features (2017) Sensors, 17, pp. 1862-1-1862-16; Angeles, J., Fundamentals of Robotic Mechanical Systems: Theory, Methods, and Algorithms (2007), 3rd ed. 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Springer-Verlag Berlin, Germany; (2021), https://media.upv.es/player/?id=accfd130-5599-11eb-ba71-19e9cf6f3c04, Video of the first experiment. Accessed 2021/05/16; (2021), https://media.upv.es/player/?id=6ef90bd0-5597-11eb-ba71-19e9cf6f3c04, Video ofthe second experiment. Accessed 2021/05/16; (2021), https://media.upv.es/player/?id=d426bab0-5598-11eb-ba71-19e9cf6f3c04, Video of the third experiment. Accessed 2021/05/16","Solanes, J.E.; Instituto de Diseño y Fabricación, Camino de Vera s/n, Spain; email: esolanes@idf.upv.es",,,"Elsevier Ltd",,,,,03608352,,CINDD,,"English","Comput Ind Eng",Article,"Final","",Scopus,2-s2.0-85107060688
"Coronado E., Venture G., Yamanobe N.","57188864138;15623574000;6507370973;","Applying Kansei/Affective Engineering Methodologies in the Design of Social and Service Robots: A Systematic Review",2021,"International Journal of Social Robotics","13","5",,"1161","1171",,2,"10.1007/s12369-020-00709-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093943634&doi=10.1007%2fs12369-020-00709-x&partnerID=40&md5=d34a27141c758b923e394828678c1c82","Tokyo University of Agriculture and Technology, Tokyo, Japan; National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan","Coronado, E., Tokyo University of Agriculture and Technology, Tokyo, Japan; Venture, G., Tokyo University of Agriculture and Technology, Tokyo, Japan; Yamanobe, N., National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan","This article provides a systematic review of research articles applying Kansei / Affective Engineering methodologies for the development of Social and Service Robots. We describe relevant concepts and main types of Kansei Engineering methodologies to assist new researchers and practitioners interested in the area. We also summarize the main objectives and findings of eleven peer-reviewed research articles published in relevant conferences and journals. We selected these articles after performing a systematic search in relevant databases for robotics research (Science Direct, Web of Science, IEEE Xplore, ACM digital library, and Springer Link) and Kansei Engineering (J-STAGE). This search includes studies published in the English language between 1999 and 2019. Findings from observed articles indicate that Kansei Engineering is a suitable paradigm for robot design. Moreover, developers can use Kansei Engineering methodologies to identify and better understand the design aspects enabling social and service robots to be accepted and desired by users. Contributions of articles reviewed include (i) the use of novel approaches for grasping emotional reactions in Human–Robot Interaction experiments and (ii) the proposal of guidelines for designing social and service robots. However, the use of Kansei Engineering methodologies in robotics is still a poorly explored area. Therefore, we describe possible future directions and open challenges. © 2020, Springer Nature B.V.","Human-centered design; Human–robot interaction; Kansei engineering; Service robots; Social robots","Agricultural robots; Digital libraries; Economic and social effects; Machine design; Mobile robots; Robotics; Emotional reactions; Engineering methodology; English languages; Kansei Engineering; Robot interactions; Robotics research; Systematic Review; Systematic searches; Social robots",,,,,,,,,,,,,,,,"Al-Hindawe, J., Considerations when constructing a semantic differential scale (1996) La Trobe Pap Linguist, 9 (7), pp. 1-9; Avots, E., Sapiński, T., Bachmann, M., Kamińska, D., Audiovisual emotion recognition in wild (2019) Mach Vis Appl, 30 (5), pp. 975-985; Aziz, A.A., Moganan, F.F.M., Ismail, A., Lokman, A.M., Autistic children’s kansei responses towards humanoid-robot as teaching mediator (2015) Proc Comput Sci, 76, pp. 488-493; Azmin, A.F., Shamsuddin, S., Yussof, H., Hri observation with my keepon robot using kansei engineering approach. In: 2016 2nd IEEE international symposium on robotics and manufacturing automation (ROMA) (2016) IEEE, pp. 1-6; Bannon, L., Reimagining hci: toward a more human-centered perspective (2011) Interactions, 18 (4), pp. 50-57; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int J Soc Robot, 1 (1), pp. 71-81; Bidin, S.A.H., Lokman, A.M., Enriching the comfortability emotion on website interface design using kansei engineering approach (2018) International Conference on Kansei Engineering and Emotion Research, Springer, pp. 792-800; Bidin, S.A.H., Lokman, A.M., Mohd, W.A.R.W., Tsuchiya, T., Initial intervention study of kansei robotic implementation for elderly (2017) Proc Comput Sci, 105, pp. 87-92; Bongard-Blanchy, K., Bouchard, C., Aoussat, A., Limits of kansei–kansei unlimited (2013) Int J Affect Eng, 12 (2), pp. 145-153; Breazeal, C., Emotion and sociable humanoid robots (2003) Int J Human Comput Stud, 59 (1-2), pp. 119-155; Budgen, D., Brereton, P., Performing systematic literature reviews in software engineering (2006) Proceedings of the 28Th International Conference on Software Engineering, pp. 1051-1052. , ACM; Carpinella, C.M., Wyman, A.B., Perez, M.A., Stroessner, S.J., The robotic social attributes scale (Rosas) development and validation (2017) ACM/IEEE International Conference on Human-Robot Interaction, pp. 254-262; Cavallo, F., Semeraro, F., Fiorini, L., Magyar, G., Sinčák, P., Dario, P., Emotion modelling for social robotics applications: a review (2018) J Bion Eng, 15 (2), pp. 185-203; Dandıl, E., Özdemir, R., Real-time facial emotion classification using deep learning (2019) Data Sci Appl, 2 (1), pp. 13-17; Duffy, V.G., Modern human-robot interaction in smart services and value co-creation (2016) International Conference on Digital Human Modeling and Applications in Health. Safety, Ergonomics and Risk Management, pp. 399-408. , Springer; Grimsaeth, K., Gangwar, M., Baggerud, B., Narain, R., Kansei engineering based emotional product design: an illustration by a battery drill case-study (2010) Kansei Eng Int J, 9 (2), pp. 67-79; Hashizume, A., Kurosu, M., Kansei engineering as an indigenous research field originated in Japan (2016) International Conference on human–computer Interaction, Springer, pp. 46-52; Hoffman, G., (2019) Anki, jibo, and kuri: What we can learn from social robots that didn’t make it, , IEEE Spectrum; Hsiao, S.W., Wu, C.N., A ke, dsm and ism based approach for patrol robot development (2018) 2018 International Conference on Control and Robots (ICCR), pp. 30-34; Ishihara, S., Nagamachi, M., Schütte, S., Eklund, J., (2008) Affective meaning: the kansei engineering approach in product experience, pp. 477-496. , Elsevier, Amsterdam; Ismail, N.N.N.N., Lokman, A.M., Redzuan, F., Kansei-spiritual therapeutic robot interaction design (2018) International Conference on Kansei Engineering and Emotion Research, Springer, pp. 580-591; Jafar, F.A., Rosli, N.A., Rashid, S.A., Ali, M.M., Shamsuddin, S., Kassim, A.M., Towards the development of kansei haptic sensing technology for robot application—exploring human haptic emotion (2018) International Conference on Kansei Engineering and Emotion Research, Springer, pp. 622-630; Jung, M., Hinds, P., Robots in the wild: a time for more robust theories of human–robot interaction (2018) ACM Trans Human Robot Interact (THRI), 7 (1), p. 2; Keele, S., (2007); Kitchenham, B., Procedures for performing systematic reviews (2004) Keele UK Keele Univ, 33 (2004), pp. 1-26; Lin, K.C., Wu, C.F., Practicing universal design to actual hand tool design process (2015) Appl Ergon, 50, pp. 8-18; Lo, C.H., Liao, G.Y., Tang, Z.X., Chen, X.W., Xu, H.F., Hu, J., A study of iphone’s attractiveness factors by the miryoku engineering approach (2018) Kne Soc Sci, 3, pp. 1680-1690; Lokman, A.M., Design and emotion: the kansei engineering methodology (2010) Malays J Comput, 1 (1), pp. 1-11; Lokman, A.M., Haron, M.B.C., Abidin, S.Z.Z., Khalid, N.E.A., Ishihara, S., Prelude to natphoric kansei engineering framework (2013) J Softw Eng Appl, 6 (12), p. 638; 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J. Soc. Rob.",Review,"Final","",Scopus,2-s2.0-85093943634
"Kontogiorgos D., Gustafson J.","57126340100;24480882100;","Measuring Collaboration Load With Pupillary Responses - Implications for the Design of Instructions in Task-Oriented HRI",2021,"Frontiers in Psychology","12",,"623657","","",,1,"10.3389/fpsyg.2021.623657","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111926686&doi=10.3389%2ffpsyg.2021.623657&partnerID=40&md5=38fade1388400f3c301c61da03dedefc","Division of Speech, Music and Hearing, Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden","Kontogiorgos, D., Division of Speech, Music and Hearing, Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden; Gustafson, J., Division of Speech, Music and Hearing, Department of Intelligent Systems, KTH Royal Institute of Technology, Stockholm, Sweden","In face-to-face interaction, speakers establish common ground incrementally, the mutual belief of understanding. Instead of constructing “one-shot” complete utterances, speakers tend to package pieces of information in smaller fragments (what Clark calls “installments”). The aim of this paper was to investigate how speakers' fragmented construction of utterances affect the cognitive load of the conversational partners during utterance production and comprehension. In a collaborative furniture assembly, participants instructed each other how to build an IKEA stool. Pupil diameter was measured as an outcome of effort and cognitive processing in the collaborative task. Pupillometry data and eye-gaze behaviour indicated that more cognitive resources were required by speakers to construct fragmented rather than non-fragmented utterances. Such construction of utterances by audience design was associated with higher cognitive load for speakers. We also found that listeners' cognitive resources were decreased in each new speaker utterance, suggesting that speakers' efforts in the fragmented construction of utterances were successful to resolve ambiguities. The results indicated that speaking in fragments is beneficial for minimising collaboration load, however, adapting to listeners is a demanding task. We discuss implications for future empirical research on the design of task-oriented human-robot interactions, and how assistive social robots may benefit from the production of fragmented instructions. © Copyright © 2021 Kontogiorgos and Gustafson.","collaboration; common ground; dialogue and discourse; least-collaborative-effort; pupillometry; referential communication; situated interaction; social signal processing",,,,,,"Stiftelsen för Strategisk Forskning, SSF: GMT14-0082","We would like to thank Elena Sibirtseva for helping in the data collection of the experiment. We also thank Hannah Pelikan, Andre Pereira, Jana G?tze and David Schlangen for helpful comments and discussion, and the reviewers for their useful comments in earlier versions of this manuscript. Funding. This work was supported by the Swedish Foundation for Strategic Research project FACT (GMT14-0082).","This work was supported by the Swedish Foundation for Strategic Research project FACT (GMT14-0082).",,,,,,,,,"Ahern, S., Beatty, J., Pupillary responses during information processing vary with scholastic aptitude test scores (1979) Science, 205, pp. 1289-1292; Bates, D., Mächler, M., Bolker, B., Walker, S., Fitting linear mixed-effects models using lme4 (2015) J. Stat. Softw, 67, pp. 1-48; Beatty, J., Task-evoked pupillary responses, processing load, and the structure of processing resources (1982) Psychol. 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J. Psychophysiol, 81, pp. 133-141. , 21679730","Kontogiorgos, D.; Division of Speech, Sweden; email: diko@kth.se",,,"Frontiers Media S.A.",,,,,16641078,,,,"English","Front. Psychol.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85111926686
"Wu Z., Liu Z., Wang D.","57214751955;57224926433;55713277700;","Multi-Grounding Navigator for Self-Supervised Vision-and-Language Navigation",2021,"Proceedings of the International Joint Conference on Neural Networks","2021-July",,,"","",,,"10.1109/IJCNN52387.2021.9533628","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116485690&doi=10.1109%2fIJCNN52387.2021.9533628&partnerID=40&md5=737566722a8e7a23fbe6a4f46c7b6232","School of Engineering, Westlake University, Hangzhou, China; Westlake Institute for Advanced Study, Hangzhou, China","Wu, Z., School of Engineering, Westlake University, Hangzhou, China, Westlake Institute for Advanced Study, Hangzhou, China; Liu, Z., School of Engineering, Westlake University, Hangzhou, China, Westlake Institute for Advanced Study, Hangzhou, China; Wang, D., School of Engineering, Westlake University, Hangzhou, China, Westlake Institute for Advanced Study, Hangzhou, China","Vision-and-language navigation (VLN) based on human-robot interaction has become increasingly attractive in recent years for flexible robot navigation. As key metrics, the success rate and path length are equally important in VLN, where the former represents the accuracy of navigation and the latter indicates the efficiency. However, the most current approaches aim to improve the accuracy of navigation while rarely consider the path efficiency. For this situation, we propose a progress predictor module, where the progress predictor is used to predict the future navigation progress to be attained by taking action. Using the progress predictor, we design a multi-grounding navigator (MGN) to generate the distribution of actions by adding the predicted future progress for improving the accuracy and efficiency simultaneously. After action distribution is given, a path-shortening (PS) method is presented to further improve the path efficiency. On the other hand, by considering an extra progress monitor, we design a self-supervised (SS) module to further improve the navigation performance. Experiments using Room-to-Room (R2R) dataset are conducted to demonstrate the effectiveness of our MGN with SS and/or PS. Experiment results show that our scheme enhances accuracy and efficiency than baselines, especially in unseen environments. © 2021 IEEE.","multi-grounding navigator; progress predictor; self-supervised; VLN","Agricultural robots; Efficiency; Human robot interaction; 'current; Flexible robots; Humans-robot interactions; Multi-grounding navigator; Navigation performance; Pathlengths; Progress predictor; Robot navigation; Self-supervised; Vision-and-language navigation; Navigation",,,,,"Natural Science Foundation of Zhejiang Province, ZJNSF: LZ19F020001; Jiangsu Postdoctoral Research Foundation: 103126582001","ACKNOWLEDGMENT This project is supported by the Key Program of Natural Science Foundation of Zhejiang province, China (Grant No. LZ19F020001) and the Hangzhou Postdoctoral Research Foundation (Grant No. 103126582001).",,,,,,,,,,"Chen, Y., Li, T., Cui, Y., Li, Z., Gao, M., Zhou, L., Research on indoor and outdoor integrated location service technology (2018) 2018 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), pp. 117-120; Ma, R., Guo, Q., Hu, C., Xue, J., An improved wifi indoor positioning algorithm by weighted fusion (2015) Sensors, 15 (9), pp. 21824-21843; Mendelson, E., (2015) Indoor and Outdoor Mapping and Navigation Utilizing Rf Bluetooth Beacons, , December 1, US Patent 9, 204, 257; Rosinol Vidal, A., Rebecq, H., Horstschaefer, T., Scaramuzza, D., Ultimate slam? Combining events, images, and imu for robust visual slam in hdr and high-speed scenarios (2018) Ieee Robotics and Automation Letters, 3 (2), pp. 994-1001; Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sünderhauf, N., Reid, I., Van Den Hengel, A., Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments (2018) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 3674-3683; Alomari, M., Duckworth, P., Hogg, D.C., Cohn, A.G., Natural language acquisition and grounding for embodied robotic systems (2017) Thirty-First Aaai Conference on Artificial Intelligence; Mittal, M., Mohan, R., Burgard, W., Valada, A., (2019) Vision-based Autonomous Uav Navigation and Landing for Urban Search and Rescue; Wortsman, M., Ehsani, K., Rastegari, M., Farhadi, A., Mottaghi, R., Learning to learn how to learn: Self-adaptive visual navigation using meta-learning (2019) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 6750-6759; Zhu, Y., Mottaghi, R., Kolve, E., Lim, J.J., Gupta, A., Fei-Fei, L., Farhadi, A., Target-driven visual navigation in indoor scenes using deep reinforcement learning (2017) 2017 Ieee International Conference on Robotics and Automation (ICRA), pp. 3357-3364; Fried, D., Hu, R., Cirik, V., Rohrbach, A., Andreas, J., Morency, L., Berg-Kirkpatrick, T., Darrell, T., Speaker-follower models for vision-andlanguage navigation (2018) Advances in Neural Information Processing Systems, pp. 3314-3325; Wang, X., Xiong, W., Wang, H., Yang Wang, W., Look before you leap: Bridging model-free and model-based reinforcement learning for planned-ahead vision-and-language navigation (2018) Proceedings of the European Conference on Computer Vision (ECCV), pp. 37-53; Wang, X., Huang, Q., Celikyilmaz, A., Gao, J., Shen, D., William YangWang, Y., Zhang, L., Reinforced cross-modal matching and self-supervised imitation learning for visionlanguage navigation (2019) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 6629-6638; Ma, C., Lu, J., Wu, Z., AlRegib, G., Kira, Z., Socher, R., Xiong, C., (2019) Self-monitoring Navigation Agent Via Auxiliary Progress Estimation; Ma, C., Wu, Z., AlRegib, G., Xiong, C., Kira, Z., The regretful agent: Heuristic-aided navigation through progress estimation (2019) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 6732-6740; Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Lawrence Zitnick, C., Parikh, D., Vqa: Visual question answering (2015) Proceedings of the Ieee International Conference on Computer Vision, pp. 2425-2433; Lu, J., Yang, J., Batra, D., Parikh, D., Hierarchical question-image co-attention for visual question answering (2016) Advances in Neural Information Processing Systems, pp. 289-297; Zhuang, B., Wu, Q., Shen, C., Reid, I., Van Den Hengel, A., Parallel attention: A unified framework for visual object discovery through dialogs and queries (2018) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 4252-4261; Deng, C., Wu, Q., Wu, Q., Hu, F., Lyu, F., Tan, M., Visual grounding via accumulated attention (2018) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 7746-7755; Yu, Z., Cui, Y., Yu, J., Tao, D., Tian, Q., (2019) Multimodal Unified Attention Networks for Vision-and-language Interactions; Das, A., Datta, S., Gkioxari, G., Lee, S., Parikh, D., Batra, D., Embodied question answering (2018) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition Workshops, pp. 2054-2063; Wu, Y., Wu, Y., Gkioxari, G., Tian, Y., (2018) Building Generalizable Agents with a Realistic and Rich 3d Environment; Das, A., Gkioxari, G., Lee, S., Parikh, D., Batra, D., (2018) Neural Modular Control for Embodied Question Answering; Yu, L., Chen, X., Gkioxari, G., Bansal, M., Berg, T.L., Batra, D., Multi-target embodied question answering (2019) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition, pp. 6309-6318","Wang, D.; School of Engineering, China; email: wangdonglin@westlake.edu.cn",,"IEEE Computational Intelligence Society;International Neural Network Society","Institute of Electrical and Electronics Engineers Inc.","2021 International Joint Conference on Neural Networks, IJCNN 2021","18 July 2021 through 22 July 2021",,171891,,9780738133669,85OFA,,"English","Proc Int Jt Conf Neural Networks",Conference Paper,"Final","",Scopus,2-s2.0-85116485690
"Romero A., Bellas F., Duro R.J.","57202612161;57223032812;7003592275;","Exploring the Effect of Dynamic Drive Balancing in Open-ended Learning Robots",2021,"Proceedings of the International Joint Conference on Neural Networks","2021-July",,,"","",,,"10.1109/IJCNN52387.2021.9534137","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116428846&doi=10.1109%2fIJCNN52387.2021.9534137&partnerID=40&md5=cec573edd58490d9be6c7152b1bcc220","Universidade da Coruña, Gii, Citic Research Center, A Coruña, Spain","Romero, A., Universidade da Coruña, Gii, Citic Research Center, A Coruña, Spain; Bellas, F., Universidade da Coruña, Gii, Citic Research Center, A Coruña, Spain; Duro, R.J., Universidade da Coruña, Gii, Citic Research Center, A Coruña, Spain","This paper seeks to explore the effect and possibilities of autonomously balancing drives in a motivational architecture aimed at open-ended learning robots. These types of robots are very useful in unconstrained human robot interaction settings or when uncontrolled dynamic scenarios that are unknown at design time must be addressed. Designing a robot under these conditions implies that it must be endowed with some primary operational purpose and some additional self-preservation objectives whose fulfillment depend on the characteristics of the particular domain it is facing each moment in time. Domains that are not known beforehand and for which no a priori goal or skill structure can be designed in. Thus, an approach to the design and engineering of motivational structures to endow robots with specific purposes is proposed and tested here. We concentrate on the drive structure of a motivational system and the effects of its autonomous adaptation to changing circumstances. To provide for this adaptation, a simple evolutionary strategy is defined for the autonomous regulation of multiple drives seeking to optimize long-term operation. The proposal is tested on a Baxter robot performing an industrial task and the results confirm the potential of autonomous dynamic drive balancing as a tool in open-ended settings. © 2021 IEEE.","autonomous robotics; drives; motivation; Open-ended learning","Drives; Evolutionary algorithms; Human robot interaction; Machine design; Autonomous robotics; Condition; Design time; Drive structures; Dynamic drives; Dynamic scenarios; Humans-robot interactions; Open-ended learning; Self preservation; Uncontrolled dynamics; Balancing",,,,,"Ministerio de Ciencia, Innovación y Universidades, MCIU; Ministerio de Educación, Cultura y Deporte, MECD; European Regional Development Fund, ERDF: ED431G 2019/01, RTI2018-101114-B-I00; Xunta de Galicia: ED431C 2017/12","This work has been partially funded by Ministerio de Ciencia, Innovación y Universidades of Spain/FEDER (grant RTI2018-101114-B-I00), Xunta de Galicia and FEDER (grant ED431C 2017/12), and by the Spanish Ministry of Education, Culture and Sports for the FPU grant of Alejandro Romero Montero. 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Rev, 66 (5), p. 297; Baldassarre, G., Mirolli, M., Intrinsically motivated learning in natural and artificial systems (2013) 2013. Intrinsically Motivated Learning Systems: An Overview, pp. 1-14. , Springer Berlin Heidelberg; Oudeyer, P., Baranes, A., Kaplan, F., Intrinsically motivated learning of real-world sensorimotor skills with developmental constraints (2013) Intrinsically Motiv. Learn. Nat. Artif. Syst., pp. 303-365; Starzyk, J.A., Motivated learning for computational intelligence (2011) Computational Modeling and Simulation of Intellect: Current State and Future Perspectives, IGI Global, pp. 265-292; Singh, S.P., Lewis, R.L., Barto, A.G., Sorg, J., Intrinsically motivated reinforcement learning: An evolutionary perspective (2010) IEEE Trans. Auton. Ment. Dev, 2 (2), pp. 70-82; Oudeyer, P., Hafner, V.V., Whyte, A., The playground experiment : task-independent development of a curious robot (2005) Proc. AAAI Spring Symposium on Developmental Robotics, pp. 42-47; Nehmzow, U., Gatsoulis, Y., Kerr, E., Condell, J., Siddique, N., Mcginnity, T.M., (2013) Intrinsically Motivated Learning in Natural and Artificial Systems, , Springer Berlin Heidelberg; Ugur, E., Piater, J., Emergent structuring of interdependent affordance learning tasks using intrinsic motivation and empirical feature selection (2016) IEEE Trans. Cogn. Dev. Syst, 9 (4), pp. 328-340; Santucci, V.G., Baldassarre, G., Mirolli, M., Which is the best intrinsic motivation signal for learning multiple skills (2013) Front. Neurorobot, 7; Wigfield, A., Eccles, J.S., Expectancy-value theory of achievement motivation (2000) Contemp. Educ. Psychol, 25 (1), pp. 68-81; Merrick, K., Value systems for developmental cognitive robotics: A survey (2017) Cogn. Syst. Res, 41, pp. 38-55; Prieto, A., Romero, A., Bellas, F., Salgado, R., Duro, R.J., Introducing separable utility regions in a motivational engine for cognitive developmental robotics (2019) Integr. Comput. Aided. Eng, 26 (1), pp. 3-20; Duro, R.J., Becerra, J.A., Monroy, J., Bellas, F., Perceptual generalization and context in a network memory inspired long term memory for artificial cognition (2019) Int. J. Neural Syst, 29 (6); Dietterich, T.G., Hierarchical reinforcement learning with the maxq value function decomposition (2000) J. Artif. Intell. Res, 13, pp. 227-303; Taylor, M.E., Stone, P., Transfer learning for reinforcement learning domains : a survey (2009) J. Mach. Learn. Res, 10, pp. 1633-1685; Hawes, N., A survey of motivation frameworks for intelligent systems (2011) Artif Intell, 175 (5-6), pp. 1020-1036; Rolf, M., Asada, M., What are goals and if so, how many (2015) Proc. Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pp. 332-339; Berlyne, D.E., A theory of human curiosity (1954) Br. J. Psychol. Gen. Sect, 45 (3), pp. 180-191; Deci, E.L., Ryan, R.M., Cognitive evaluation theory (1985) Intrinsic Motivation and Self-Determination in Human Behavior, pp. 43-85. , Springer, Boston, MA; Ryan, R., Deci, E., Intrinsic and extrinsic motivations: Classic definitions and new directions (2000) Contemp. Educ. Psychol, 25 (1), pp. 54-67; Di Nocera, D., Finzi, A., Rossi, S., Staffa, M., The role of intrinsic motivations in attention allocation and shifting (2014) Front. Psychol, 5, p. 273; Bellas, F., Duro, R.J., Faiña, A., Souto, D., Multilevel darwinist brain (mdb): Artificial evolution in a cognitive architecture for real robots (2010) IEEE Trans. Auton. Ment. Dev, 2 (4), pp. 340-354; Huang, X., Weng, J., Novelty and reinforcement learning in the value system of developmental robots (2002) Proceedings Second International Workshop on Epigenetic Robotics, pp. 47-55; Burda, Y., Edwards, H., Pathak, D., Storkey, A., Darrel, T., Efros, A., (2018) Large-scale Study of Curiosity-driven Learning; Baldassarre, G., Stafford, T., Mirolli, M., Redgrave, P., Ryan, R., Barto, A., Intrinsic motivations and open-ended development in animals, humans, and robots: An overview (2014) Front. Psychol, 5, p. 985; Romero, A., Bellas, F., Becerra, J.A., Duro, R.J., Motivation as a tool for designing lifelong learning robots (2020) Integr. Comput. Aided. Eng, 27 (4), pp. 353-372",,,"IEEE Computational Intelligence Society;International Neural Network Society","Institute of Electrical and Electronics Engineers Inc.","2021 International Joint Conference on Neural Networks, IJCNN 2021","18 July 2021 through 22 July 2021",,171891,,9780738133669,85OFA,,"English","Proc Int Jt Conf Neural Networks",Conference Paper,"Final","",Scopus,2-s2.0-85116428846
"Xu D., Xu H., Sun Y., Li Y., Zhang X., Li X., Fu J.","57203985480;57304975100;57203987295;57203994429;57203995925;14015806700;57220930595;","Robot-assisted quantitative assessment of axis misalignment of wrist joint",2021,"2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021",,,,"1397","1401",,,"10.1109/RCAR52367.2021.9517487","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115355583&doi=10.1109%2fRCAR52367.2021.9517487&partnerID=40&md5=7c434faf602fbc67dd681d010cc08f24","Auckland Tongji Medical Rehabilitation Equipment Research Centre, Tongji Zhejiang College, Jiaxing, China; Rehabilitation Medical Centre, Second Affiliated Hospital of Jiaxing University, Jiaxing, China","Xu, D., Auckland Tongji Medical Rehabilitation Equipment Research Centre, Tongji Zhejiang College, Jiaxing, China; Xu, H., Auckland Tongji Medical Rehabilitation Equipment Research Centre, Tongji Zhejiang College, Jiaxing, China; Sun, Y., Rehabilitation Medical Centre, Second Affiliated Hospital of Jiaxing University, Jiaxing, China; Li, Y., Auckland Tongji Medical Rehabilitation Equipment Research Centre, Tongji Zhejiang College, Jiaxing, China; Zhang, X., Auckland Tongji Medical Rehabilitation Equipment Research Centre, Tongji Zhejiang College, Jiaxing, China; Li, X., Auckland Tongji Medical Rehabilitation Equipment Research Centre, Tongji Zhejiang College, Jiaxing, China; Fu, J., Rehabilitation Medical Centre, Second Affiliated Hospital of Jiaxing University, Jiaxing, China","Axis misalignment between the human wrist joint and the robot joint could result in undesired excessive interaction force or torque. This can bring discomfort and even injuries to patients during training. According to the state of the art, few studies have focused on axis misalignment quantitative assessment of axis misalignment of wrist joint. This paper proposed a quantitative assessment method of axis misalignment of wrist joint by introducing a self-aligning mechanism (SAM) based on the wrist rehabilitation device (WReD II). The SAM can mimic the axis variation of wrist joint naturally during human-robot interaction, and the quantitative values can be recorded to assess axis misalignment. Experiments were conducted on 10 healthy subjects to measure passive wrist extension/flexion (E/F) and radial/ulnar deviation (RD/UD). The maximums, minimums and the Linear Range of Motion (LROM) of axis misalignment were all obtained. Surface Electromyography (sEMG) was also collected to ensure the consistency of passive exercises. Preliminary data indicated high reliability of the proposed assessment method with intraclass correlation coefficient ICC (2, 1) more than 0.866, and standard error of measurement (SEM) of LROM less than 1.34 mm. Future work will focus on its validity validation, paving the way for clinical applications. © 2021 IEEE.",,"Human robot interaction; Axis misalignments; Interaction forces; Linear range; On-axis; Quantitative assessments; Range-of-motion; Robot joint; Self-aligning; State of the art; Wrist joints; Alignment",,,,,"Bureau of Science and Technology of Jiangmen Municipality: 2019AY32021, 2020AY30013","*Resrach supported by Jiaxing Science and Technology Bureau (Science and Technology Plan Project Nos. 2019AY32021 and 2020AY30013), China.",,,,,,,,,,"Duncan, W.P., Stroke disability (1994) Physical Therapy, 5, p. 399; Charles, S.K., Wrist rehabilitation following stroke: Initial clinical results (2005) International Conference on Rehabilitation Robotics.; Krebs, H.I., Rehabilitation robotics: Performance-based progressive robot-assisted therapy (2003) Autonomous Robots, 15 (1), pp. 7-20; Miao, Q., Performance-based iterative learning control for task-oriented rehabilitation: A pilot study in robot-assisted bilateral training (2021) IEEE Transactions on Cognitive and Developmental Systems, (99), pp. 1-1; Masia, L., Whole-Arm rehabilitation following stroke: Hand module (2016) IEEE/RAS-EMBS International Conference on Biomedical Robotics & Biomechatronics.; Zhong, B., Synchronous position and compliance regulation on a bi-joint gait exoskeleton driven by pneumatic muscles (2020) IEEE Transactions on Automation Science and Engineering, (99), pp. 1-5; O'Malley, M.K., The ricewrist: A distal upper extremity rehabilitation robot for stroke therapy (2006) American Society of Mechanical Engineers; Krebs, H.I., Robot-aided neurorehabilitation: A robot for wrist rehabilitation (2007) IEEE Transactions on Neural Systems & Rehabilitation Engineering A Publication of the IEEE Engineering in Medicine & Biology Society, 15 (3), pp. 327-335; Nef, T., Guidali, M., Riener, R., ARMin III - Arm Therapy Exoskeleton with An Ergonomic Shoulder Actuation, , IOS Press; Higuma, T., Kiguchi, K., Arata, J., Low-profile Two-degree-of-freedom Wrist Exoskeleton Device using Multiple Spring Blade (2017) IEEE Robotics & Automation Letters, (99), pp. 1-1; Stienen, A., Self-Aligning exoskeleton axes through decoupling of joint rotations and translations (2009) IEEE Transactions on Robotics, 25 (3), pp. 628-633; Näf, M., Misalignment compensation as a way to ensure full human-exoskeleton kinematic compatibility: State of the art and evaluation (2019) Applied Mechanics Reviews; Moltedo, M., Mechanical design of a lightweight compliant and adaptable active ankle foot orthosis (2016) IEEE International Conference on Biomedical Robotics & Biomechatronics.; Beekhuis, J.H., Design of a self-aligning 3-DOF actuated exoskeleton for diagnosis and training of wrist and forearm after stroke (2013) IEEE International Conference on Rehabilitation Robotics : Proceedings, pp. 1-5. , 2013; Sarkisian, S.V., Ishmael, M.K., Lenzi, T., Self-Aligning Mechanism Improves Comfort and Performance with a Powered Knee Exoskeleton (2021) IEEE Transactions on Neural Systems and Rehabilitation Engineering, (99), pp. 1-1; Xu, D., Zhang, M., Sun, Y., Development of a reconfigurable wrist rehabilitation device with an adaptive forearm holder (2018) 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 454-459; Zhang, M., Automated robot-assisted assessment for wrist active ranges of motion (2019) Medical Engineering & Physics, 71, pp. 98-101; Zhang, Automated objective robot-assisted assessment of wrist passive ranges of motion (2018) Journal of Biomechanics; Williams, D.J., Krebs, H.I., Hogan, N., A robot for wrist rehabilitation (2001) International Conference of the IEEE Engineering in Medicine & Biology Society.; Weir, J.P., Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM (2005) J Strength Cond Res, 19 (1), pp. 231-240; Munro, B.H., (1995) Statistical Methods for Health Care Research, , Statistical Methods for Health Care Research; Ju-Hwan, Evaluation and verification of a novel wrist rehabilitation robot employing safety-related mechanism (2019) 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR).; Eini, D.S., (2010) A Simple Camera Tracking Virtual Reality System for Evaluation of Wrist Range of Motion.; Manto, M., A new myohaptic instrument to assess wrist motion dynamically (2010) Sensors, 10 (4). , Basel, Switzerland",,,"IEEE Robotics and Automation Society (RA);Shanghai Jiao Tong University;Shenzhen Institute of Advanced Technology (SIAT)","Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021","15 July 2021 through 19 July 2021",,171512,,9781665436786,,,"English","IEEE Int. Conf. Real-Time Comput. Robot., RCAR",Conference Paper,"Final","",Scopus,2-s2.0-85115355583
"Yousaf S.N., Esmatloo P., Ghonasgi K., Deshpande A.D.","57212314873;57195415034;57190752341;7202168327;","A method for the analysis of physical human-robot interaction",2021,"IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM","2021-July",,,"1249","1254",,,"10.1109/AIM46487.2021.9517358","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114963409&doi=10.1109%2fAIM46487.2021.9517358&partnerID=40&md5=d61d739292b0b0bcdf8272da23b1b935","The University of Texas, Department of Mechanical Engineering, Austin, TX  78712, United States","Yousaf, S.N., The University of Texas, Department of Mechanical Engineering, Austin, TX  78712, United States; Esmatloo, P., The University of Texas, Department of Mechanical Engineering, Austin, TX  78712, United States; Ghonasgi, K., The University of Texas, Department of Mechanical Engineering, Austin, TX  78712, United States; Deshpande, A.D., The University of Texas, Department of Mechanical Engineering, Austin, TX  78712, United States","Physical human-robot interaction (pHRI) interfaces are responsible for ensuring safe, comfortable, and effective force transfer between wearable devices and their users. However, analysis is often oversimplified by treating the human-robot attachment as a rigid connection and using gross load measurements. As a result, information about the distribution of forces across the human-robot contact surface is lost. In this paper, we present an analysis method to predict distributed loading across a pHRI interface based on a model with discretized elastic elements that account for compliance from human soft tissue and the robot attachment. Stiffness properties of a proxy upper arm are measured with an indenter and used in the pHRI interface model. The analysis is performed assuming a rigid arm model, consistent with the underlying assumption in literature, and repeated using the proposed compliant arm model with measured elastic properties. The distributed loading predicted by the pHRI interface model is validated with measurements from a sensorized upper arm cuff on the Harmony exoskeleton. Our results reveal that a model incorporating compliance at the human-robot attachment is necessary to improve prediction of distributed interface loads. This motivates the need for human-centered analysis which can enable finer control of interaction forces and help design more ergonomic attachment interfaces. © 2021 IEEE.",,"Elasticity; Exoskeleton (Robotics); Intelligent mechatronics; Man machine systems; Distributed interfaces; Distributed loading; Elastic properties; Interaction forces; Load measurements; Physical human-robot interactions; Physical humanrobot interaction (phri); Stiffness properties; Human robot interaction",,,,,"National Science Foundation, NSF: 1941260, 2019704, DGE-1610403","This work was funded, in part, by the National Science Foundation (NSF) grant numbers 2019704 and 1941260, as well as the NSF Graduate Research Fellowship Program (GRFP) grant number DGE-1610403.",,,,,,,,,,"Viteckova, S., Kutilek, P., Jirina, M., Wearable lower limb robotics: A review (2013) Biocybernetics and Biomedical Engineering, 33 (2), pp. 96-105; MacIejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Troy, A., Leonhardt, S., A survey on robotic devices for upper limb rehabilitation (2014) Journal of Neuroengineering and Rehabilitation, 11 (1), p. 3; Dollar, A.M., Herr, H., Lower extremity exoskeletons and active orthoses: Challenges and state-of-the-art (2008) IEEE Transactions on Robotics, 24 (1), pp. 144-158; De Santis, A., Siciliano, B., De Luca, A., Bicchi, A., An atlas of physical human-robot interaction (2008) Mechanism and Machine Theory, 43 (3), pp. 253-270; Rocon, E., Ruiz, A., Raya, R., Schiele, A., Pons, J.L., Belda-Lois, J., Poveda, R., Moreno, J., Human-robot physical interaction (2008) Wearable Robots: Biomechatronic Exoskeletons, pp. 127-163; Biddiss, E., Chau, T., Upper-limb prosthetics: Critical factors in device abandonment (2007) American Journal of Physical Medicine & Rehabilitation, 86 (12), pp. 977-987; Pezent, E., Fani, S., Clark, J., Bianchi, M., O'Malley, M.K., Spatially separating haptic guidance from task dynamics through wearable devices (2019) IEEE Transactions on Haptics, 12 (4), pp. 581-593; Kung, P.-C., Lin, C.-C.K., Ju, M.-S., Neuro-rehabilitation robotassisted assessments of synergy patterns of forearm, elbow and shoulder joints in chronic stroke patients (2010) Clinical Biomechanics, 25 (7), pp. 647-654; Schiele, A., Ergonomics of exoskeletons: Objective performance metrics (2009) World Haptics 2009-Third Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems., pp. 103-108; Jarrassé, N., Morel, G., Connecting a human limb to an exoskeleton (2011) IEEE Transactions on Robotics, 28 (3), pp. 697-709; Schiele, A., Helm Der Van, F.C., Kinematic design to improve ergonomics in human machine interaction (2006) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14 (4), pp. 456-469; Accoto, D., Sergi, F., Tagliamonte, N.L., Carpino, G., Sudano, A., Guglielmelli, E., Robomorphism: A nonanthropomorphic wearable robot (2014) IEEE Robotics & Automation Magazine, 21 (4), pp. 45-55; Sengeh, D.M., Herr, H., A variable-impedance prosthetic socket for a transtibial amputee designed from magnetic resonance imaging data (2013) JPO: Journal of Prosthetics and Orthotics, 25 (3), pp. 129-137; Varghese, R.J., Mukherjee, G., King, R., Keller, S., Deshpande, A.D., Designing variable stiffness profiles to optimize the physical human robot interface of hand exoskeletons (2018) 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), pp. 1101-1108; Donati, M., Vitiello, N., De Rossi, S.M.M., Lenzi, T., Crea, S., Persichetti, A., Giovacchini, F., Munih, M., A flexible sensor technology for the distributed measurement of interaction pressure (2013) Sensors, 13 (1), pp. 1021-1045; Tamez-Duque, J., Cobian-Ugalde, R., Kilicarslan, A., Venkatakrishnan, A., Soto, R., Contreras-Vidal, J.L., Real-time strap pressure sensor system for powered exoskeletons (2015) Sensors, 15 (2), pp. 4550-4563; Rathore, A., Wilcox, M., Ramirez, D.Z.M., Loureiro, R., Carlson, T., Quantifying the human-robot interaction forces between a lower limb exoskeleton and healthy users (2016) 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 586-589; Kim, B., Deshpande, A.D., An upper-body rehabilitation exoskeleton harmony with an anatomical shoulder mechanism: Design, modeling, control, and performance evaluation (2017) The International Journal of Robotics Research, 36 (4), pp. 414-435; De Oliveira, A.C., Rose, C.G., Warburton, K., Ogden, E.M., Whitford, B., Lee, R.K., Deshpande, A.D., Exploring the capabilities of harmony for upper-limb stroke therapy (2019) 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), pp. 637-643; Ghonasgi, K., Yousaf, S.N., Esmatloo, P., Deshpande, A.D., A modular design for distributed measurement of human-robot interaction forces in wearable devices (2021) Sensors, 21 (4), p. 1445; Petron, A., Duval, J.-F., Herr, H., Multi-indenter device forin vivobiomechanical tissue measurement (2016) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (5), pp. 426-435; Shih, R., (2014) Introduction to Finite Element Analysis Using Solidworks Simulation 2014, , SDC publications; Ambrose, A.B., Hammond, F.L., Soft pneumatic system for interface pressure regulation and automated hands-free donning in robotic prostheses (2020) 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 4848-4854; Yousaf, S.N., Joshi, V.S., Britt, J.E., Rose, C.G., O'Malley, M.K., Design and characterization of a passive instrumented hand ASME Letters in Dynamic Systems and Control, 1 (1); Jones, D.H., Kilgour, R.D., Comtois, A.S., Test-retest reliability of pressure pain threshold measurements of the upper limb and torso in young healthy women (2007) The Journal of Pain, 8 (8), pp. 650-656","Deshpande, A.D.; The University of Texas, United States; email: ashish@austin.utexas.edu",,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021","12 July 2021 through 16 July 2021",,171450,,9781665441391,,,"English","IEEE ASME Int Conf Adv Intellig Mechatron AIM",Conference Paper,"Final","",Scopus,2-s2.0-85114963409
"Kramer M., Velasco-Guillen R.J., Beckerle P., Bertram T.","57199753083;57219802929;36964076200;56262850600;","Comparing online robot joint space trajectory optimization for task space applications",2021,"IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM","2021-July",,,"223","230",,,"10.1109/AIM46487.2021.9517608","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114960954&doi=10.1109%2fAIM46487.2021.9517608&partnerID=40&md5=be56c7c25bff0d50cc7e4457bbbbe118","Institute of Control Theory and Systems Engineering, TU Dortmund University, Dortmund, Germany; Friedrich-Alexander University Erlangen-Nürnberg, Chair of Autonomous Systems and Mechatronics, Erlangen, Germany","Kramer, M., Institute of Control Theory and Systems Engineering, TU Dortmund University, Dortmund, Germany; Velasco-Guillen, R.J., Friedrich-Alexander University Erlangen-Nürnberg, Chair of Autonomous Systems and Mechatronics, Erlangen, Germany; Beckerle, P., Friedrich-Alexander University Erlangen-Nürnberg, Chair of Autonomous Systems and Mechatronics, Erlangen, Germany; Bertram, T., Institute of Control Theory and Systems Engineering, TU Dortmund University, Dortmund, Germany","Online trajectory planning allows robotic manipulators to adapt to changing environments and dynamic tasks, which becomes increasingly relevant through human-robot interaction. optimizing motion objectives is a common way of planning but is usually conducted in joint space, whereas many planning objectives are rather defined in task space. Joint Space Planning (JSP) first transforms target poses into joint space and then performs conventional optimization. Implicit Task Space Planning (ITSP) uses a special objective function to directly consider task quantities and is similar to economic model predictive control. Remarkably, comparative studies of both approaches with respect to their performance are scarce. This contribution aims at filling this gap by providing a systematic analysis of both methods using a 6-DoF collaborative robot in practical experiments. Results show that the first method is less sensitive to local minima and profits from active reconfiguration abilities while it generates less straight-lined motions and is not able to account for target manifolds in the task space. The second method can easily consider target manifolds and performs more straight-lined motions while it is more sensitive to local minima and provides active reconfiguration only partially. Although the cost function of the second method is significantly more complex, the computational effort relativizes during planning. © 2021 IEEE.","Human-Robot-Collaboration; Online Trajectory optimization; Task Space Motion Planning","Cost functions; Human robot interaction; Industrial manipulators; Intelligent mechatronics; Manipulators; Model predictive control; Space applications; Space flight; Changing environment; Collaborative robots; Comparative studies; Computational effort; Conventional optimization; Online trajectory planning; Reconfiguration ability; Robotic manipulators; Robot programming",,,,,,,,,,,,,,,,"Han, Z., Yanco, H., The effects of proactive release behaviors during human-robot handovers (2019) HRI'19, pp. 440-448. , Piscataway, NJ: IEEE; Ragaglia, M., Zanchettin, A.M., Rocco, P., Safety-aware trajectory scaling for human-robot collaboration with prediction of human occupancy (2015) International Conference on Advanced Robotics, pp. 85-90. , S. Kalkan, Ed. IEEE; Beckert, D., Pereira, A., Althoff, M., Online verification of multiple safety criteria for a robot trajectory (2017) Conference on Decision and Control., pp. 6454-6461; Lange, F., Albu-Schaffer, A., Path-accurate online trajectory generation for jerk-limited industrial robots (2016) Robotics and Automation Letters, 1 (1), pp. 82-89; Betts, J.T., Survey of numerical methods for trajectory optimization (1998) Journal of Guidance, Control, and Dynamics, 21 (2), pp. 193-207; Krämer, M., Muster, F.I., Rösmann, C., Bertram, T., An optimizationbased approach for elasticity-aware trajectory planning of link-elastic manipulators (2021) Mechatronics, , (accepted); Park, C., Pan, J., Manocha, D., ITOMP: Incremental trajectory optimization for real-time replanning in dynamic environments (2013) International Conference on International Conference on Automated Planning and Scheduling, pp. 207-215. , AAAI Press; Krämer, M., Rösmann, C., Hoffmann, F., Bertram, T., Model predictive control of a collaborative manipulator considering dynamic obstacles (2020) Optimal Control Applications and Methods; Buizza Avanzini, G., Zanchettin, A.M., Rocco, P., Constrained model predictive control for mobile robotic manipulators (2018) Robotica, 36 (1), pp. 19-38; Zube, A., Cartesian nonlinear model predictive control of redundant manipulators considering obstacles (2015) International Conference on Industrial Technology., pp. 137-142; Wang, Y., Ye, X., Yang, Y., Zhang, W., Collision-free trajectory planning in human-robot interaction through hand movement prediction from vision (2017) International Conference on Humanoid Robotics., pp. 305-310; Pavlichenko, D., Behnke, S., Efficient stochastic multicriteria arm trajectory optimization (2017) International Conference on Intelligent Robots and Systems., pp. 4018-4025; Jamaludin, M.Z., Swartz, C.L., Approximation of closed-loop prediction for dynamic real-time optimization calculations (2017) Computers & Chemical Engineering, 103, pp. 23-38; Amrit, R., Rawlings, J.B., Angeli, D., Economic optimization using model predictive control with a terminal cost (2011) Annual Reviews in Control, 35 (2), pp. 178-186; Lenarcic, J., Bajd, T., Stanišic, M.M., (2013) Robot Mechanisms Ser. International Series on Intelligent Systems Control and Automation, 60. , Dordrecht: Springer; Grüne, L., Pannek, J., (2017) Nonlinear Model Predictive Control: Theory and Algorithms, 2nd Ed., Ser. Communications and Control Engineering., , Springer; Siciliano, B., (2010) Robotics: Modelling Planning and Control Ser. Advanced Textbooks in Control and Signal Processing., , London: Springer; Lumelsky, V.J., On fast computation of distance between line segments (1985) Information Processing Letters, 21 (2), pp. 55-61; Eberly, D., Geometric Tools Engine, Version 5.4, , github.com/davideberly/GeometricTools, last visited 18.11.2020; Rösmann, C., Krämer, M., Makarow, A., Hoffmann, F., Bertram, T., Exploiting sparse structures in nonlinear model predictive control with hypergraphs (2018) International Conference on Advanced Intelligent Mechatronics, pp. 1255-1260; Wächter, A., Biegler, L.T., On the implementation of an interiorpoint filter line-search algorithm for large-scale nonlinear programming (2006) Mathematical Programming, pp. 25-57; (2018) A Collection of Fortran Codes for Large Scale Scientific Computation., , http://www.hsl.rl.ac.uk/; Nocedal, J., Wright, S.J., (2006) Numerical Optimization, 2nd Ed., Ser. Springer Series in Operations Research and Financial Engineering, , New York, NY: Springer Science","Kramer, M.; Institute of Control Theory and Systems Engineering, Germany; email: maximilian.kraemer@tu-dortmund.de
Beckerle, P.; Friedrich-Alexander University Erlangen-Nürnberg, Germany; email: philipp.beckerle@fau.de",,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021","12 July 2021 through 16 July 2021",,171450,,9781665441391,,,"English","IEEE ASME Int Conf Adv Intellig Mechatron AIM",Conference Paper,"Final","",Scopus,2-s2.0-85114960954
"Ben-Youssef A., Clavel C., Essid S.","57217901148;22033255800;16033218700;","Early Detection of User Engagement Breakdown in Spontaneous Human-Humanoid Interaction",2021,"IEEE Transactions on Affective Computing","12","3","8637822","776","787",,2,"10.1109/TAFFC.2019.2898399","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114475091&doi=10.1109%2fTAFFC.2019.2898399&partnerID=40&md5=ee765cac4b2b3849fb0508b8879d9843","Ltci, Telecom ParisTech, Paris, 75013, France","Ben-Youssef, A., Ltci, Telecom ParisTech, Paris, 75013, France; Clavel, C., Ltci, Telecom ParisTech, Paris, 75013, France; Essid, S., Ltci, Telecom ParisTech, Paris, 75013, France","This paper presents a supervised classification system for forecasting a potential user engagement breakdown in human-robot interaction. We define engagement breakdown as a failure to successfully complete a predefined interaction scenario, where the user leaves before the expected end. The goal is thus to detect as early as possible such a potential engagement breakdown during the interaction between a human and a humanoid robot. To this end, we exploit a dataset that we have collected in real-world conditions where a set of participants were left to spontaneously engage in an interaction with the robot. The dataset is labeled according to the presence/absence of engagement breakdown. This study investigates the use of a multimodal approach to this problem, where a set of non-verbal features is considered to characterize the users' behavior. The use of combined multimodal features is found to effectively improve the performance of the system. The optimal set of data streams useful for this task is the combination of the distance to the robot, gaze and head motion, as well as facial expressions and speech. We study the time extent over which a user's departure can be anticipated. We find that this ability to anticipate the departure depends on the window during which we observe the user behavior. © 2010-2012 IEEE.","HRI; prediction of engagement breakdown; real-time prediction; spontaneous interaction; User engagement","Anthropomorphic robots; Behavioral research; Data streams; Facial Expressions; Humanoid interaction; Multi-modal approach; Multimodal features; Non-verbal features; Presence/absence; Supervised classification; User engagement; Social robots",,,,,"ITN 7659552","This work was supported by SoftBank Robotics and European projects H2020 ANIMATAS (ITN 7659552). The authors would like to thank Nicolas Rollet and Christian Licoppe for useful discussion on pre-closing and Rodolphe Gelin, Angelica Lim, Marine Chanoux and Myriam Bilac from Softbank robotics for their help in the recording of UE-HRI dataset.",,,,,,,,,,"Giuliani, M., Petrick, R., Robots in public spaces: Towards multi-party, short-term, dynamic human-robot interaction (2013) Proc. Int. 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"Schadenberg B.R., Reidsma D., Heylen D.K.J., Evers V.","57191965928;14007846800;6602878289;55890823500;","I See What You Did There"": Understanding People's Social Perception of a Robot and Its Predictability",2021,"ACM Transactions on Human-Robot Interaction","10","3","28","","",,2,"10.1145/3461534","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111562019&doi=10.1145%2f3461534&partnerID=40&md5=df891c0777e4fda506b17c0caa3668db","University of Twente, Netherlands; Nanyang Technological University, Singapore, Singapore","Schadenberg, B.R., University of Twente, Netherlands; Reidsma, D., University of Twente, Netherlands; Heylen, D.K.J., University of Twente, Netherlands; Evers, V., University of Twente, Netherlands, Nanyang Technological University, Singapore, Singapore","Unpredictability in robot behaviour can cause difficulties in interacting with robots. However, for social interactions with robots, a degree of unpredictability in robot behaviour may be desirable for facilitating engagement and increasing the attribution of mental states to the robot. To generate a better conceptual understanding of predictability, we looked at two facets of predictability, namely, the ability to predict robot actions and the association of predictability as an attribute of the robot. We carried out a video human-robot interaction study where we manipulated whether participants could either see the cause of a robot's responsive action or could not see this, because there was no cause, or because we obstructed the visual cues. Our results indicate that when the cause of the robot's responsive actions was not visible, participants rated the robot as more unpredictable and less competent, compared to when it was visible. The relationship between seeing the cause of the responsive actions and the attribution of competence was partially mediated by the attribution of unpredictability to the robot. We argue that the effects of unpredictability may be mitigated when the robot identifies when a person may not be aware of what the robot wants to respond to and uses additional actions to make its response predictable. © 2021 Owner/Author.","human-robot interaction; Predictability; responsive actions; social perception","Agricultural robots; Behavioral research; Conceptual understanding; Mental state; Robot actions; Social interactions; Social perception; Visual cues; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 688835","This work has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 688835 (DE-ENIGMA). Authors’ addresses: B. R. Schadenberg, D. Reidsma, and D. K. J. 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Evers, University of Twente, Drienerlolaan 5, 7522 NB Enschede, the Netherlands; email: v.evers@utwente.nl.",,,,,,,,,,"Alami, R., Albu-Schaeffer, A., Bicchi, A., Bischoff, R., Chatila, R., De Luca, A., De Santis, A., Villani, L., Safe and dependable physical human-robot interaction in anthropic domains: State of the art and challenges (2006) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1-16. , https://doi.org/10.1109/IROS.2006.6936985, IEEE, Beijing, China; Alcorn, A.M., Pain, H., Good, J., Motivating children's initiations with novelty and surprise (2014) Proceedings of the Conference on Interaction Design and Children (IDC'14), pp. 225-228. , https://doi.org/10.1145/2593968.2610458, ACM Press, Aarhus, Denmark; Ballard, D.H., Hinton, G.E., Sejnowski, T.J., Parallel visual computation (1983) Nature, 306 (5938), pp. 21-26. , https://doi.org/10.1038/306021a0, Nov. 1983; Basili, P., Huber, M., Kourakos, O., Lorenz, T., Brandt, T., Hirche, S., Glasauer, S., Inferring the goal of an approaching agent: A human-robot study (2012) Proceedings of the 21st IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN'12), pp. 527-532. , https://doi.org/10.1109/ROMAN.2012.6343805, IEEE, Paris, France; Berenbaum, H., Bredemeier, K., Thompson, R.J., Intolerance of uncertainty: Exploring its dimensionality and associations with need for cognitive closure, psychopathology, and personality (2008) J. 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"Zhong H., Li X., Gao L., Dong H.","57208718405;56021323400;56406738100;57205333952;","Development of Admittance Control Method with Parameter Self-optimization for Hydraulic Series Elastic Actuator",2021,"International Journal of Control, Automation and Systems","19","7",,"2357","2372",,,"10.1007/s12555-020-0465-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105398061&doi=10.1007%2fs12555-020-0465-y&partnerID=40&md5=acfd91e6307e76d701fb3182b1d79050","State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology (HUST), 1037, Luoyu Road, Hubei, Wuhan, China; State Key Laboratory of Digital Manufacturing Equipment and Technology, the School of Mechanical Science and Engineering, Huazhong University of Science and Technology (HUST), 1037 Luoyu Road, Hubei, Wuhan, China","Zhong, H., State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology (HUST), 1037, Luoyu Road, Hubei, Wuhan, China; Li, X., State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology (HUST), 1037, Luoyu Road, Hubei, Wuhan, China; Gao, L., State Key Laboratory of Digital Manufacturing Equipment and Technology, the School of Mechanical Science and Engineering, Huazhong University of Science and Technology (HUST), 1037 Luoyu Road, Hubei, Wuhan, China; Dong, H., State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology (HUST), 1037, Luoyu Road, Hubei, Wuhan, China","Series elastic actuators (SEAs) have emerged as promising devices to enhance the safety of human-robot interactions in the manufacturing industry. However, the control of a hydraulic SEA under disturbance remains an unexplored issue. To address this problem, an admittance control method with parameter self-optimization is developed in this study. The hydraulic SEA and its dynamic model are first developed, and then, an admittance controller that combines a passive disturbance observer (DOB) and a feedback compensator is developed based on load movement dynamics. The control law of the framework is made independent of the hydraulic dynamics by considering the uncertainty and tracking error as disturbances. This simplifies the controller computation, enhances system robustness, and facilitates practical application. Next, the control performance is further improved by optimizing the control parameters using an improved crowding-based dynamic population size differential evolution (crowding-based dynNP-DE) algorithm. Benchmark and optimization experiments are performed to verify the superiority of the modified algorithm and obtain the control parameters. Finally, the optimized parameters are applied to practical experiments to validate the improved performance of the proposed admittance control scheme. The results show that the proposed method effectively reduces the SEA stiffness tracking error, with respect to the external contact force. © 2021, ICROS, KIEE and Springer.","Admittance control; compliance control; human-robot interaction; hydraulic series elastic actuator; parameter optimization","Accident prevention; Biofeedback; Controllers; Evolutionary algorithms; Human robot interaction; Industrial robots; Optimization; Parameter estimation; Population statistics; Control performance; Differential Evolution; Disturbance observer; Dynamic population sizes; Feedback compensators; Manufacturing industries; Optimized parameter; Series elastic actuators; Hydraulic actuators",,,,,"2017QYTD04; National Natural Science Foundation of China, NSFC: 51721092, 51825502","This study was supported by the National Natural Science Foundation of China (grant number 51721092), and the program for the HUST Academic Frontier Youth Team (grant number 2017QYTD04).","Liang Gao received his Ph.D. degree in mechatronic engineering from the Huazhong University of Science and Technology (HUST), Wuhan, China, in 2002. He is a Professor with the School of Mechanical Science & Engineering, HUST. He received the National Natural Science Foundation for Distinguished Young Scholars of China award under Grant No. 51825502. He has authored or co-authored more than 140 refereed papers. Prof. Gao is an Associate Editor of Swarm and Evolutionary Computation, Journal of Industrial and Production Engineering, and Swarm Intelligence and Numerical Method. He is also an editorial board member of the European Journal of Industrial Engineering.",,,,,,,,,"Ba, K., Ma, G., Yu, B., Jin, Z., Huang, Z., Zhang, J., Kong, X., A nonlinear model-based variable impedance parameters control for position-based impedance control system of hydraulic drive unit (2020) International Journal of Control, Automation and Systems, 18 (7), pp. 1806-1817. , February; Shi, Y., Han, Q., Shen, W., Zhang, H., Potential applications of 5G communication technologies in collaborative intelligent manufacturing (2019) IET Collaborative Intelligent Manufacturing, 1 (4), pp. 109-116. , December; Lee, H.D., Moon, J.I., Kang, T.H., Design of a series elastic tendon actuator based on gait analysis for a walking assistance exosuit (2019) International Journal of Control, Automation and Systems, 17 (11), pp. 2940-2947. , November; Park, Y., Paine, N., Oh, S., Development of force observer in series elastic actuator for dynamic control (2018) IEEE Transactions on Industrial Electronics, 65 (3), pp. 2398-2407. , March; Yildirim, M.C., Kansizoglu, A.T., Sendur, P., Ugurlu, B., High power series elastic actuator development for torque-controlled exoskeletons Wearable Robotics: Challenges and Trends. 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"Paetzel-Prüsmann M., Perugia G., Castellano G.","57222066404;57193757433;23007528900;","The Influence of robot personality on the development of uncanny feelings",2021,"Computers in Human Behavior","120",,"106756","","",,3,"10.1016/j.chb.2021.106756","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101290289&doi=10.1016%2fj.chb.2021.106756&partnerID=40&md5=626e7299cce978bf31343833df6aa740","Uppsala Social Robotics Lab, Division of Visual Information and Interaction, Department of Information Technology, Box 337, Uppsala, 75105, Sweden; Computational Linguistics, Department of Linguistics, University of Potsdam, Karl-Liebknecht-Straße 24-25, Potsdam, 14476, Germany","Paetzel-Prüsmann, M., Uppsala Social Robotics Lab, Division of Visual Information and Interaction, Department of Information Technology, Box 337, Uppsala, 75105, Sweden, Computational Linguistics, Department of Linguistics, University of Potsdam, Karl-Liebknecht-Straße 24-25, Potsdam, 14476, Germany; Perugia, G., Uppsala Social Robotics Lab, Division of Visual Information and Interaction, Department of Information Technology, Box 337, Uppsala, 75105, Sweden; Castellano, G., Uppsala Social Robotics Lab, Division of Visual Information and Interaction, Department of Information Technology, Box 337, Uppsala, 75105, Sweden","Empirical investigations on the uncanny valley have almost solely focused on the analysis of people's non-interactive perception of a robot at first sight. Recent studies suggest, however, that these uncanny first impressions may be significantly altered over an interaction. What is yet to discover is whether certain interaction patterns can lead to a faster decline in uncanny feelings. In this paper, we present a study in which participants with limited expertise in Computer Science played a collaborative geography game with a Furhat robot. During the game, Furhat displayed one of two personalities, which corresponded to two different interaction strategies. The robot was either optimistic and encouraging, or impatient and provocative. We performed the study in a science museum and recruited participants among the visitors. Our findings suggest that a robot that is rated high on agreeableness, emotional stability, and conscientiousness can indeed weaken uncanny feelings. This study has important implications for human-robot interaction design as it further highlights that a first impression, merely based on a robot's appearance, is not indicative of the affinity people might develop towards it throughout an interaction. We thus argue that future work should emphasize investigations on exact interaction patterns that can help to overcome uncanny feelings. © 2021 Elsevier Ltd","Crowd-sourcing; Human perception of robots; Human-robot interaction; Multimodal behavior; Robot personality; Uncanny valley","Behavioral research; Human computer interaction; Machine design; Man machine systems; Crowd sourcing; Empirical investigation; First impressions; Human perception of robot; Humans-robot interactions; Interaction pattern; Interaction strategy; Multimodal behaviour; Robot personalities; Uncanny valley; Human robot interaction",,,,,"Stiftelsen för Strategisk Forskning, SSF: RIT15-0133","This work was supported by the Swedish Foundation for Strategic Research under the COIN project ( RIT15-0133 ). The research study was conducted at the Tekniska Museet in Stockholm. We would like to kindly thank all the staff for their support and, in particular, P. Brudin and C. 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Manuvinakurike for his work on the game dynamics.",,,,,,,,,,"Al Moubayed, S., Beskow, J., Skantze, G., Granström, B., Furhat: A back-projected human-like robot head for multiparty human-machine interaction (2012) International training school on cognitive behavioural systems, pp. 114-130. , Springer; Aly, A., Tapus, A., A model for synthesizing a combined verbal and nonverbal behavior based on personality traits in human-robot interaction (2013) 8th ACM/IEEE international conference on human-robot interaction (HRI), pp. 325-332. , IEEE; Ambady, N., Bernieri, F.J., Richeson, J.A., Toward a histology of social behavior: Judgmental accuracy from thin slices of the behavioral stream (2000) Advances in experimental social psychology, 32, pp. 201-271. , Elsevier; Andrist, S., Mutlu, B., Tapus, A., Look like me: Matching robot personality via gaze to increase motivation (2015) 33rd annual ACM conference on human factors in computing systems, pp. 3603-3612; Ball, G., Breese, J., Emotion and personality in a conversational agent (2000) Embodied Conversational Agents, pp. 189-219; 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Role and demeanor congruity as predictors of robot attraction and use intentions among senior citizens (2017) International Journal of Human-Computer Studies, 97, pp. 88-97; Tapus, A., Ţăpuş, C., Matarić, M.J., Userâ€”robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy (2008) Intelligent Service Robotics, 1, p. 169; Tinwell, A., Nabi, D.A., Charlton, J.P., Perception of psychopathy and the Uncanny Valley in virtual characters (2013) Computers in Human Behavior, 29, pp. 1617-1625; Venture, G., Kulić, D., Robot expressive motions: A survey of generation and evaluation methods (2019) ACM Transactions on Human-Robot Interaction (THRI), 8, pp. 1-17; Williams, T., Briggs, P., Pelz, N., Scheutz, M., Is robot telepathy acceptable? Investigating effects of NonverbalRobot-robot communication on human-robot interaction (2014) 23rd IEEE international symposium on robot and human interactive communication (RO-MAN), pp. 886-891. , IEEE; Yamashita, Y., Ishihara, H., Ikeda, T., Asada, M., Path analysis for the halo effect of touch sensations of robots on their personality impressions (2016) International conference on social robotics, pp. 502-512. , Springer; Złotowski, J., Sumioka, H., Nishio, S., Glas, D.F., Bartneck, C., Ishiguro, H., Persistence of the uncanny valley: The influence of repeated interactions and a robot's attitude on its perception (2015) Frontiers in Psychology, 6, p. 883; Łupkowski, P., Gierszewska, M., Attitude towards humanoid robots and the uncanny valley hypothesis (2019) Foundations of Computing and Decision Sciences, 44, pp. 101-119","Paetzel-Prüsmann, M.; Uppsala Social Robotics Lab, Box 337, Sweden; email: paetzel-pruesmann@uni-potsdam.de",,,"Elsevier Ltd",,,,,07475632,,CHBEE,,"English","Comput. Hum. Behav.",Article,"Final","",Scopus,2-s2.0-85101290289
"Innes J.M., W. Morrison B.","57193917288;35867834000;","Experimental Studies of Human–Robot Interaction: Threats to Valid Interpretation from Methodological Constraints Associated with Experimental Manipulations",2021,"International Journal of Social Robotics","13","4",,"765","773",,2,"10.1007/s12369-020-00671-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087668679&doi=10.1007%2fs12369-020-00671-8&partnerID=40&md5=9c1bd72caf5ee15b13e2fddebc081834","Australian College of Applied Psychology, 16 Coglin Street, Adelaide, SA  5000, Australia; University of South Australia, Adelaide, Australia; Charles Sturt University, Bathurst, Australia; Macquarie University, Sydney, Australia","Innes, J.M., Australian College of Applied Psychology, 16 Coglin Street, Adelaide, SA  5000, Australia, University of South Australia, Adelaide, Australia; W. Morrison, B., Charles Sturt University, Bathurst, Australia, Macquarie University, Sydney, Australia","The rapid development of artificial intelligence brings with it the increasing likelihood of ubiquitous interaction between humans and robots. A significant contribution to studying human–robot interactions (HRI) comes from experimental studies, whereby humans and robots interact in controlled conditions and researchers observe and measure the reactions of humans (and robots). The use of experiments to understand human interactions has long been a central source of information in the field of experimental social psychology. These studies have yielded numerous major insights into the causes and outcomes of interaction. The methodology of experiments, however, including the demands made upon human participants to behave in predictable ways and the impact of experimenters’ expectancies upon results, has been a focus of much critical analysis. We examined a sample of 100 high impact HRI studies for evidence of potentially contaminating experimental artefacts and/or authors’ awareness of such factors. In our conclusions we highlight several methodological issues that appeared frequently in our sample, which may impede generalisations from laboratory experiments to real-world settings. Ultimately, we suggest that researchers may need to reformulate the methodologies used to study the unique features of HRI, and offer a number of recommendations for researchers designing HRI experiments. © 2020, Springer Nature B.V.","Demand characteristics; Experiment artefacts; Experimenter bias; Human–robot interaction","Agricultural robots; Intelligent robots; Controlled conditions; Critical analysis; Human interactions; Laboratory experiments; Real world setting; Robot interactions; Social psychology; Unique features; Social robots",,,,,,,,,,,,,,,,"Barber, T.X., Silver, M.J., Fact, fiction and the experimenter-bias effect (1968) Psychol Bull, 70, pp. 1-29; Bargh, J.A., Chen, M., Burrows, L., Automaticity of social behaviour: direct effect of trait construct and stereotype activation in action (1996) J Pers Soc Psychol, 71, pp. 230-244; Baumeister, R.F., Vohs, K.D., Funder, D.C., Psychology as the science of self-reports and finger movements: What happened to actual behavior? 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Shadish, W.R., Cook, T.D., Campbell, D.T., (2002) Experimental and quasi-experimental designs for generalised causal inference, , Houghton-Mifflin, Boston; Sherwood, J.J., Nataupsky, M., Predicting the conclusions of negro-white intelligence research from biographical characteristics of the investigator (1968) J Pers Soc Psychol, 8, pp. 53-58; St. Claire, L., Turner, J.C., The role of demand characteristics in the social categorization paradigm (1982) Eur J Soc Psychol, 12, pp. 307-314; Toomela, A., Valsiner, J., (2010) Methodological thinking in psychology: 60 years gone astray?, , (eds), Information Age Publishing, Charlotte; Wagner, A.J., Borenstein, J., Howard, A., Overtrust in the robotic age: a contemporary ethical challenge (2018) Commun ACM, 61 (9), pp. 22-24; Walsh, T., (2018) 2062, , LaTrobe University Press, Melbourne; Wilson, T.D., Aronson, E., Carlsmith, K., The art of laboratory experimentation (2010) Handbook of Social Psychology, pp. 51-81. , Fiske ST, Gilbert DT, Lindzey G, Wiley, Hoboken; Zajonc, R.B., One hundred years of rationality assumptions in social psychology (1999) Reflections on 100 years of experimental social psychology, pp. 200-214. , Rodrigues A, Levine R, (eds), Basic Books, New York; Zimbardo, P.G., Experimental social psychology: Behaviorism with minds and matters (1999) Reflections on 100 years of experimental social psychology, pp. 135-157. , Rodrigues A, Levine R, (eds), Basic Books, New York","Innes, J.M.; Australian College of Applied Psychology, 16 Coglin Street, Australia; email: mike.innes@navitas.com",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85087668679
"Badr A.A., Abdul-Hassan A.K.","57223101011;55293743800;","CatBoost Machine Learning Based Feature Selection for Age and Gender Recognition in Short Speech Utterances",2021,"International Journal of Intelligent Engineering and Systems","14","3",,"150","159",,,"10.22266/ijies2021.0630.14","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105821737&doi=10.22266%2fijies2021.0630.14&partnerID=40&md5=8a37e05aeda2f0f2348e6b3534f7b535","Collage of Managerial and Financial Sciences, Imam Ja’afar Al-Sadiq University, Salahaddin, Iraq; Department of Computer Science, University of Technology, Baghdad, Iraq","Badr, A.A., Collage of Managerial and Financial Sciences, Imam Ja’afar Al-Sadiq University, Salahaddin, Iraq, Department of Computer Science, University of Technology, Baghdad, Iraq; Abdul-Hassan, A.K., Department of Computer Science, University of Technology, Baghdad, Iraq","Lately, with the rapid growth of various technologies, identifying the information of gender and age give short speech utterances has become a necessity for many applications in daily life like human-robot interaction, targeted marketing, identifying suspects in criminal cases, etc. Despite the comprehensive studies carried out to extract descriptive features, the recognition accuracy is still not satisfactory. In this study, an automatic system is proposed to classify age and gender in short speech utterances without depending on the text. Firstly, two groups of features are extracted from each utterance frame, followed by measuring 10 statistical functionals for each extracted feature dimension. After that, the extracted features dimensions are normalized using the Quantile technique. Then, the CatBoost machine is utilized as an important features detection to select the most discriminatory features for speaker age and gender recognition tasks. For classification purposes, the selected feature dimensions are fed into the Support Vector Machine (SVM). Experiments are conducted on the aGender data-set for measuring the suggested system's performance. The unweighted accuracies (UA) of the proposed system for gender, age, in addition to gender & age is 89.62%, 72.29%, and 71.96%, respectively. The achieved results outperform recent results on the same data-set. © 2021. All Rights Reserved.","aGender data-set; CatBoost machine; Quantile technique; Speaker age and gender recognition; Statistical functionals",,,,,,,,,,,,,,,,,"Markitantov, M., Verkholyak, O., Automatic Recognition of Speaker Age and Gender Based on Deep Neural Networks (2019) Proc. of International Conf. on Speech and Computer, pp. 327-336. , [1] Istanbul, Turkey; Markitantov, M., Transfer Learning in Speaker’s Age and Gender Recognition (2020) Proc. of International Conf. on Speech and Computer, pp. 326-335. , [2] St. Petersburg, Russia; Mallouh, A., Qawaqneh, Z., Barkana, B., New transformed features generated by deep bottleneck extractor and a GMm_UBM classifier for speaker age and gender classification (2017) Neural Comput. Appl, 30, pp. 2581-2593. , [3]; Barkana, B., Zhou, J., A new pitch-range based feature set for a speaker’s age and gender classification (2015) Appl. Acoust, 98, pp. 52-61. , [4]; Yücesoy, E., Nabiyev, V., A new approach with score-level fusion for the classification of a speaker age and gender (2016) Comput. Electr. Eng, 53, pp. 29-39. , [5]; Grzybowska, J., Kacprzak, S., Speaker age classification and regression using i-vectors (2016) Proc. of Annu. Conf. Int. Speech Commun. Assoc. INTERSPEECH, pp. 1402-1406. , [6] San Francisco; Al-Sarem, M., Saeed, F., Boulila, W., Emara, A., Al-Mohaimeed, M., Errais, M., Feature Selection and Classification Using CatBoost Method for Improving the Performance of Predicting Parkinson’s Disease (2020) Proc. of First International Conf. of Advanced Computing and Informatics ICACIn, pp. 189-199. , [7] Casablanca, Morocco; Hancock, J., Khoshgoftaar, T., CatBoost for big data: an interdisciplinary review (2020) J. big data, 7 (1), p. 94. , [8]; Ghori, K., Ayaz, A., Awais, M., Imran, M., Ullah, A., Szathmary, L., Impact of Feature Selection on Non-technical Loss Detection (2020) Proc. of 6th Conf. on Data Science and Machine Learning Applications (CDMA), pp. 19-24. , [9] Riyadh, KSA; Zhang, F., Fleyeh, H., Short Term Electricity Spot Price Forecasting Using CatBoost and Bidirectional Long Short Term Memory Neural Network (2019) Proc. of 16th International Conf. on the European Energy Market (EEM), pp. 1-6. , [10] Ljubljana, Slovenia; Qawaqneh, Z., Mallouh, A., Barkana, B., Deep neural network framework and transformed MFCCs for speaker’s age and gender classification (2017) Knowledge-Based Syst, 115, pp. 5-14. , [11]; Kockmann, M., Burget, L., Cernocký, J., Brno University of Technology system for Interspeech 2010 Paralinguistic Challenge (2010) Proc. of 11thAnnual Conf. of the International Speech Communication Association, pp. 2822-2825. , [12] Makuhari, Chiba, Japan; Schuller, B., Steidl, S., Batliner, A., Burkhardt, F., Devillers, L., Müller, C., Narayanan, S., The INTERSPEECH 2010 paralinguistic challenge (2010) Proc. of 11th Annual Conf. of the International Speech Communication Association, pp. 2794-2797. , [13] Makuhari, Chiba, Japan; Burkhardt, F., Eckert, M., Johannsen, W., Stegmann, J., A Database of Age and Gender Annotated Telephone Speech (2010) Proc. of the Seventh International Conf. on Language Resources and Evaluation (LREC'10), pp. 1562-1565. , [14] Valletta, Malta; Li, M., Han, K. J., Narayanan, S., Automatic speaker age and gender recognition using acoustic and prosodic level information fusion (2013) Comput. Speech Lang, 27 (1), pp. 151-167. , [15]; Sharma, G., Umapathy, K., Krishnan, S., Trends in audio signal feature extraction methods (2020) Appl. Acoust, 158, p. 107020. , [16]; Badr, A., Abdul-Hassan, A., A review on voice-based interface for human-robot interaction (2020) Iraqi Journal for Electrical And Electronic Engineering, 16 (2), pp. 91-102. , [17]; Rao, K., Manjunath, K., (2017) Speech recognition using Articulatory and Excitation Source Features, , [18] Cham, Switzerland, Springer International Publishing AG; Paliwal, K., Spectral subband centroid features for speech recognition (1998) Proc. of the 1998 IEEE International Conf. on Acoustics, Speech and Signal Processing, 2, pp. 617-620. , [19] Seattle, Washington, USA; Chougule, S., Chavan, M., Speaker Recognition in Mismatch Conditions: A Feature Level Approach (2017) Int. J. Image, Graph. Signal Process, 9 (4), pp. 37-43. , [20]; Bolstad, B., Irizarry, R., Åstrand, M., Speed, T., A comparison of normalization methods for high density oligonucleotide array data based on variance and bias (2003) Bioinformatics, 19 (2), pp. 185-193. , [21]; Pan, M., Zhang, J., Quantile normalization for combining gene-expression datasets (2018) Biotechnol. Biotechnol. Equip, 32 (3), pp. 751-758. , [22]; Dorogush, A., Ershov, V., Gulin, A., (2018) CatBoost: Gradient boosting with categorical features support, pp. 1-11. , [23] arXiv Section 4; Liu, W., Deng, K., Zhang, X., Cheng, Y., Zhang, Z., Jiang, F., Peng, J., A Semi-Supervised Tri-CatBoost Method for Driving Style Recognition (2020) Symmetry, 12 (3). , [24]; Wu, X., Kumar, V., Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G., Steinberg, D., Top 10 algorithms in data mining (2008) Knowl. Inf. Syst, 14 (1), pp. 1-37. , [25]","Badr, A.A.; Collage of Managerial and Financial Sciences, Iraq; email: cs.19.53@grad.uotechnology.edu.iq",,,"Intelligent Network and Systems Society",,,,,2185310X,,,,"English","Int. J. Intelligent Eng. Syst.",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85105821737
"Lee K.-S., Pyo J.-B., Kim T.-S., Park H.-S.","56580653000;57189306353;57210850540;55703108500;","Tubular Hygromechanical Polymeric Brake for Soft and Compact Wearable Robots",2021,"ACS Applied Polymer Materials","3","6",,"3206","3213",,,"10.1021/acsapm.1c00413","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108501832&doi=10.1021%2facsapm.1c00413&partnerID=40&md5=dc31463a51820337c5fec67715f0434f","Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea","Lee, K.-S., Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea; Pyo, J.-B., Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea; Kim, T.-S., Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea; Park, H.-S., Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea","Designing a robot with soft materials that are lightweight and compact allows it to enable safe human-robot interaction and portable design. While most research has been concentrated on various external stimuli to trigger soft actuators and sensors, less work has been conducted on stimuli for soft brakes that lock and release the motion. Brakes are essential components for robotic systems to achieve stable positioning and increase safety against unintended movements. In this study, a tubular brake is proposed that utilized the hygromechanical behavior of polymers for compact soft wearable robots. The inner diameter of the tube changes quantitatively with varying moisture content in the tube. This change adjusts the contact pressure between the tube and its inner wire, to control the friction applied to the force transmission wire. The brake can generate high frictional braking force per unit mass (∼774 N g-1) and unit area (∼0.5 MPa), which is 2.5 times higher than other soft brakes. It is also lightweight, flexible, and easily adaptable to the existing wire-driven mechanisms by replacing the conventional wire sheath with the hygroscopic polymer tube. The effectiveness of the brake was demonstrated by implementing it into an electrical-power-free wearable rehabilitation glove for a stroke survivor. © 2021 American Chemical Society.","hygromechanical polymer; Nafion; rehabilitation; soft actuator; soft brake; swelling behavior; wearable robot","Friction; Human robot interaction; Machine design; Polymers; Wearable technology; Wire; Contact pressures; Electrical power; External stimulus; Force transmission; Frictional braking; Hygroscopic polymers; Safe human-robot interaction; Stroke survivors; Brakes",,,,,"National Research Foundation of Korea, NRF; Ministry of Science and ICT, South Korea, MSIT: 2016R1A5A1009926","This paper is based on research that has been conducted as part of the Korea Advanced Institute of Science and Technology-funded Global Singularity Research Program (2020) and supported by the Wearable Platform Materials Technology Center (WMC) funded by the National Research Foundation of Korea (NRF) Grant by the Korean Government (MSIT) (No. 2016R1A5A1009926).",,,,,,,,,,"Gorissen, B., Reynaerts, D., Konishi, S., Yoshida, K., Kim, J.W., De Volder, M., Elastic inflatable actuators for soft robotic applications (2017) Adv. 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A, 291, pp. 186-207; Park, S., Bishop, L., Post, T., Xiao, Y., Stein, J., Ciocarlie, M., On the feasibility of wearable exotendon networks for whole-hand movement patterns in stroke patients (2016) 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 3729-3735. , IEEE; Zasler, N., Katz, D., Zafonte, R., (2007) Brain Injury Medicine: Principles and Practice, , Demos Medical; Ward, N., Brown, M., Thompson, A., Frackowiak, R., Neural correlates of motor recovery after stroke: A longitudinal fMRI study (2003) Brain, 126, pp. 2476-2496; Khanicheh, A., Muto, A., Triantafyllou, C., Weinberg, B., Astrakas, L., Tzika, A., Mavroidis, C., FMRI-compatible rehabilitation hand device (2006) J. Neuroeng. Rehabil., 3, p. 24; Yap, H.K., Kamaldin, N., Lim, J.H., Nasrallah, F.A., Goh, J.C.H., Yeow, C.-H., A magnetic resonance compatible soft wearable robotic glove for hand rehabilitation and brain imaging (2016) IEEE Trans. Neural Syst. Rehab. Eng., 25, pp. 782-793; Majsztrik, P.W., Satterfield, M.B., Bocarsly, A.B., Benziger, J.B., Water sorption, desorption and transport in Nafion membranes (2007) J. Membr. Sci., 301, pp. 93-106; Xiang, C., Wang, W., Zhu, Q., Xue, D., Zhao, X., Li, M., Wang, D., Flexible and Super-Sensitive Moisture-Responsive Actuators by Dispersing Graphene Oxide into Three-Dimensional Structures of Nanofibers and Silver Nanowires (2019) ACS Appl. Mater. Interfaces, 12, pp. 3245-3253","Kim, T.-S.; Department of Mechanical Engineering, South Korea; email: tskim1@kaist.ac.kr
Park, H.-S.; Department of Mechanical Engineering, South Korea; email: hyungspark@kaist.ac.kr",,,"American Chemical Society",,,,,26376105,,,,"English","ACS Appl. Polymer Mat.",Article,"Final","",Scopus,2-s2.0-85108501832
"Tölgyessy M., Dekan M., Chovanec L.","55986819900;55316973500;55356258700;","Skeleton tracking accuracy and precision evaluation of Kinect V1, Kinect V2, and the azure kinect",2021,"Applied Sciences (Switzerland)","11","12","5756","","",,1,"10.3390/app11125756","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109114460&doi=10.3390%2fapp11125756&partnerID=40&md5=397af626c32a8738a391ff69f073665d","Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology STU in Bratislava, Ilkovičova 3, Bratislava, 812 19, Slovakia","Tölgyessy, M., Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology STU in Bratislava, Ilkovičova 3, Bratislava, 812 19, Slovakia; Dekan, M., Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology STU in Bratislava, Ilkovičova 3, Bratislava, 812 19, Slovakia; Chovanec, L., Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology STU in Bratislava, Ilkovičova 3, Bratislava, 812 19, Slovakia","The Azure Kinect, the successor of Kinect v1 and Kinect v2, is a depth sensor. In this paper we evaluate the skeleton tracking abilities of the new sensor, namely accuracy and precision (repeatability). Firstly, we state the technical features of all three sensors, since we want to put the new Azure Kinect in the context of its previous versions. Then, we present the experimental results of general accuracy and precision obtained by measuring a plate mounted to a robotic manipulator end effector which was moved along the depth axis of each sensor and compare them. In the second experiment, we mounted a human-sized figurine to the end effector and placed it in the same positions as the test plate. Positions were located 400 mm from each other. In each position, we measured relative accuracy and precision (repeatability) of the detected figurine body joints. We compared the results and concluded that the Azure Kinect surpasses its discontinued predecessors, both in accuracy and precision. It is a suitable sensor for human–robot interaction, body-motion analysis, and other gesture-based applications. Our analysis serves as a pilot study for future HMI (human–machine interaction) designs and applications using the new Kinect Azure and puts it in the context of its successful predecessors. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Azure Kinect; Depth sensor; Kinect; Pose tracking; Skeletal tracking; Skeleton tracking",,,,,,"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV, VEGA: 1/0754/19, 1/0775/20","Funding: This work was funded by APVV-17-0214, VEGA 1/0775/20 and VEGA 1/0754/19.",,,,,,,,,,"Elaraby, A.F., Hamdy, A., Rehan, M., A Kinect-Based 3D Object Detection and Recognition System with Enhanced Depth Estimation Algorithm Proceedings of the 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 247-252. , Vancouver, BC, Canada, 1–3 November 2018; [CrossRef]; Tanabe, R., Cao, M., Murao, T., Hashimoto, H., Vision based object recognition of mobile robot with Kinect 3D sensor in indoor environment Proceedings of the 2012 Proceedings of SICE Annual Conference (SICE), pp. 2203-2206. , Akita, Japan, 20–23 August 2012; Manap, M.S.A., Sahak, R., Zabidi, A., Yassin, I., Tahir, N.M., Object Detection using Depth Information from Kinect Sensor Proceedings of the 2015 IEEE 11th International Colloquium on Signal Processing & Its Applications (CSPA), pp. 160-163. , Kuala Lumpur, Malaysia, 6–8 March 2015; Xin, G.X., Zhang, X.T., Wang, X., Song, J., A RGBD SLAM algorithm combining ORB with PROSAC for indoor mobile robot Proceedings of the 2015 4th International Conference on Computer Science and Network Technology (ICCSNT), pp. 71-74. , Harbin, China, 19–20 December 2015; Henry, P., Krainin, M., Herbst, E., Ren, X., Fox, D., RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments (2012) Int. J. Robot. Res, 31, pp. 647-663. , [CrossRef]; Ibragimov, I.Z., Afanasyev, I.M., Comparison of ROS-based visualslam methods in homogeneous indoor environment (2017) Proceedings of the 2017 14th Workshop on Positioning, Navigation and Communications (WPNC), , Bremen, Germany, 25–26 October; Plouffe, G., Cretu, A., Static and dynamic hand gesture recognition in depthdata using dynamic time warping (2016) IEEE Trans. Instrum. Meas, 65, pp. 305-316. , [CrossRef]; Wang, C., Liu, Z., Chan, S., Superpixel-based hand gesture recognition withKinect depth camera (2015) IEEE Trans. Multimed, 17, pp. 29-39. , [CrossRef]; Ren, Z., Yuan, J., Meng, J., Zhang, Z., Robust part-based hand gesture recognition using kinect sensor (2013) IEEE Trans. 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J. Soc. Robot, 9, pp. 509-523. , [CrossRef]; Tölgyessy, M., Dekan, M., Chovanec, L’., Hubinský, P., Evaluation of the Azure Kinect and Its Comparison to Kinect V1 and Kinect V2 (2021) Sensors, 21, p. 413. , [CrossRef] [PubMed]; Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kipman, A., Efficient human pose estimation from single depth images (2012) IEEE Trans. Pattern Anal. Mach. Intell, 35, pp. 2821-2840. , [CrossRef] [PubMed]; Sarbolandi, H., Lefloch, D., Kolb, A., Kinect range sensing: Structured-light versus Time-of-Flight Kinect (2015) Comput. Vis. Image Underst, 139, pp. 1-20. , [CrossRef]; Bamji, C.S., Mehta, S., Thompson, B., Elkhatib, T., Wurster, S., Akkaya, O., Payne, A., Rajasekaran, V., IMpixel 65nm BSI 320MHz demodulated TOF Image sensor with 3 µm global shutter pixels and analog binning Proceedings of the 2018 IEEE International Solid-State Circuits Conference-(ISSCC), pp. 94-96. , San Francisco, CA, USA, 11–15 February 2018","Tölgyessy, M.; Institute of Robotics and Cybernetics, Ilkovičova 3, Slovakia; email: michal.tolgyessy@stuba.sk",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85109114460
"Toan N.-V., Khoi P.-B., Yi S.-Y.","57216717021;57201260426;7201404755;","A mlp-hedge-algebras admittance controller for physical human–robot interaction",2021,"Applied Sciences (Switzerland)","11","12","5459","","",,,"10.3390/app11125459","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108624560&doi=10.3390%2fapp11125459&partnerID=40&md5=df3557aa99da74f8e4b83b962fa2ee88","Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul, 01811, South Korea; School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, 10000, Viet Nam","Toan, N.-V., Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul, 01811, South Korea; Khoi, P.-B., School of Mechanical Engineering, Hanoi University of Science and Technology, Hanoi, 10000, Viet Nam; Yi, S.-Y., Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul, 01811, South Korea","Recently, the identification of inertia and damping matrices (IIDM) and safety issues, as well as natural cooperation, are interestingly considered to enhance the quality of the physical human–robot interaction (pHRI). To cover all of these issues, advanced admittance controllers, such as those based on fuzzy logic or hedge algebras, have been formulated and successfully applied in several industrial problems. However, the inference mechanism of those kinds of controllers causes the discreteness of the super surface describing the input–output relationship in the Cartesian coordinates. As a consequence, the quality of the safe-natural cooperation between humans and robots is negatively affected. This paper presents an alternative admittance controller for pHRI by using a combination of hedge algebras and multilayer perceptron neural network (MLP), whose purpose is to create a more accurate inference mechanism for the admittance controller. To our best knowledge, this is the first time that such a neural network is considered for the inference mechanism of hedge algebras and also the first time that such an admittance controller is used for pHRI. The proposed admittance controller is verified on a teaching task using a 6-DOF manipulator. Experimental results have shown that the proposed method provides better cooperation compared with previous methods. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Fuzzy control; Hedge algebras; MLP neural network; Natural linguistic semantics; Physical human–robot interaction",,,,,,"Seoul National University of Science and Technology","Funding: This study was supported by the Research Program funded by the SeoulTech (Seoul National University of Science and Technology).",,,,,,,,,,"Navarro, B., Cherubini, A., Fonte, A., Passama, R., Poisson, G., Fraisse, P., An ISO10218-compliant adaptive damping controller for safe Physical Human-Robot Interaction Proceedings of the 2016 IEEE International Conference on Robotics and Automation(ICRA), pp. 3043-3048. , Stockholm, Sweden, 16–21 May 2016; Jindai, M., Watanabe, T., A Handshake Robot System Based on a Shake-Motion Leading Model Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3330-3335. , Nice, France, 22–26 September 2008; Kovács, S., Vincze, D., Gacsi, M., Miklósi, A., Korondi, P., Fuzzy Automaton based Human-Robot Interaction Proceedings of the SAMI 2010 8th IEEE International Symposium on Applied Machine Intelligence and Informatics, pp. 165-169. , Herlany, Slovakia, 28–30 January 2010; Kovács, S., Vincze, D., Gácsi, M., Korondi, P., Interpolation based Fuzzy Automaton for Human-Robot Interaction (2009) Proceedings of the 9th IFAC Symposium on Robot Control, 42, pp. 317-322. , Gifu, Japan, 9–12 September; Dimeas, F., Moulianitis, V.C., Papakonstantinou, C., Aspragathos, N., Manipulator Performance Constraints in Cartesian Admittance Control for Human-Robot Cooperation Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 3049-3054. , Stockholm, Sweden, 16–21 May 2016; Papageorgiou, D., Doulgeri, Z., A Kinematic Controller for Human-Robot Handshaking using Internal Motion Adaption Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 5622-5627. , Seattle, WA, USA, 26–30 May 2015; Zhong, H., Li, X., Gao, L., Li, C., Toward Safe Human-Robot Interaction: A Fast-Response Admittance Control Method for Series Elastic Actuator (2021) IEEE Trans. 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Appl, 4, pp. 2-9. , [CrossRef]; Daniela, A., Fuzzy Rule Base Complexity Reduction: A Survey Proceedings of the 15th International Conference on Control Systems and Computer Science, pp. 33-37. , Politehnica University of Bucharest, Bucharest, Romania, 25–27 May 2005; Hsuan-Shih, L., An optimal algorithm for computing the max-min transitive closure of a fuzzy similarity matrix (2001) Fuzzy Sets Syst, 123, pp. 129-136; Feilong, L., An Efficient Centroid Type Reduction Strategy for General Type-2 Fuzzy Logic System (2008) Inf. Sci, 178, pp. 2224-2236; Toraichi, K., Katagishi, K., Sekita, I., Mori, R., Computational complexity of spline interpolation (1987) Int. J. Syst. Sci, 18, pp. 945-954. , [CrossRef]; Acharya, T., Tsai, P.S., Computational Foundations of Image Interpolation Algorithms (2007) ACM Ubiquity, 8, pp. 1-17. , [CrossRef]; Serpen, G., Gao, Z., Complexity Analysis of Multilayer Perceptron Neural Network Embedded into a Wireless Sensor Network (2014) Procedia Comput. 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Yi, S.-Y.; Department of Electrical and Information Engineering, South Korea; email: suylee@seoultech.ac.kr",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85108624560
"Pulloquinga J.L., Escarabajal R.J., Ferrándiz J., Vallés M., Mata V., Urízar M.","57196247449;57219801021;57224476330;7007099897;6701432459;35185764300;","Vision-based hybrid controller to release a 4-dof parallel robot from a type ii singularity",2021,"Sensors","21","12","4080","","",,3,"10.3390/s21124080","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107645213&doi=10.3390%2fs21124080&partnerID=40&md5=caeb8e4da27bb0aa6bc6639ada16964f","Departamento de Ingeniería de Sistemas y Automática, Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, 46022, Spain; Departamento de Ingeniería Mecánica y de Materiales, Instituto Universitario de Investigación Concertado de Ingeniería Mecánica y Biomecánica, Universitat Politècnica de València, Valencia, 46022, Spain; Department of Mechanical Engineering, Faculty of Engineering in Bilbao, University of the Basque Country, Bilbao, 48013, Spain","Pulloquinga, J.L., Departamento de Ingeniería de Sistemas y Automática, Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, 46022, Spain; Escarabajal, R.J., Departamento de Ingeniería de Sistemas y Automática, Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, 46022, Spain; Ferrándiz, J., Departamento de Ingeniería de Sistemas y Automática, Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, 46022, Spain; Vallés, M., Departamento de Ingeniería de Sistemas y Automática, Instituto de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, 46022, Spain; Mata, V., Departamento de Ingeniería Mecánica y de Materiales, Instituto Universitario de Investigación Concertado de Ingeniería Mecánica y Biomecánica, Universitat Politècnica de València, Valencia, 46022, Spain; Urízar, M., Department of Mechanical Engineering, Faculty of Engineering in Bilbao, University of the Basque Country, Bilbao, 48013, Spain","The high accuracy and dynamic performance of parallel robots (PRs) make them suitable to ensure safe operation in human–robot interaction. However, these advantages come at the ex-pense of a reduced workspace and the possible appearance of type II singularities. The latter is due to the loss of control of the PR and requires further analysis to keep the stiffness of the PR even after a singular configuration is reached. All or a subset of the limbs could be responsible for a type II singularity, and they can be detected by using the angle between two output twist screws (OTSs). However, this angle has not been applied in control because it requires an accurate measure of the pose of the PR. This paper proposes a new hybrid controller to release a 4-DOF PR from a type II singularity based on a real time vision system. The vision system data are used to automatically readapt the configuration of the PR by moving the limbs identified by the angle between two OTSs. This controller is intended for a knee rehabilitation PR, and the results show how this release is accomplished with smooth controlled movements where the patient’s safety is not compromised. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","3D tracking; Motion control; Parallel robot; Screw theory; Singular configuration","Human robot interaction; Patient rehabilitation; Dynamic performance; Hybrid controller; Loss of control; Parallel robots; Real time vision system; Robot interactions; Safe operation; Singular configurations; Controllers; human; knee; robotics; vision; Humans; Knee Joint; Robotics; Vision, Ocular",,,,,"PID2020-119522RBI00","Funding: This research was funded by the FEDER-CICYT project with reference PID2020-119522RBI00 (ROBOTS PARALELOS DE REHABILITACION: DETECCION Y CONTROL DE SINGULAR-IDADES EN PRESENCIA DE ERRORES DE MANUFACTURA), Spain.",,,,,,,,,,"Briot, S., Khalil, W., (2015) Dynamics of Parallel Robots—From Rigid Links to Flexible Elements, , Springer: Berlin/Heidelberg, Germany, ISBN 978-3-319-19787-6; Patel, Y.D., George, P.M., Parallel Manipulators Applications—A Survey (2012) Mod. Mech. 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Parallel Program, 48, pp. 119-136","Pulloquinga, J.L.; Departamento de Ingeniería de Sistemas y Automática, Spain; email: jopulza@doctor.upv.es",,,"MDPI AG",,,,,14248220,,,"34199313","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85107645213
"Björling E.A., Ling H., Bhatia S., Matarrese J.","55301519700;57220180970;57205199791;57222082640;","Sharing stressors with a social robot prototype: What embodiment do adolescents prefer?",2021,"International Journal of Child-Computer Interaction","28",,"100252","","",,,"10.1016/j.ijcci.2021.100252","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101331049&doi=10.1016%2fj.ijcci.2021.100252&partnerID=40&md5=72eae34b2904cee0e3402b6134abc1ec","3960 Benton Lane NE, Seattle, WA  98195, United States; 1851 NE Grant Ln, Seattle, WA  98195, United States","Björling, E.A., 3960 Benton Lane NE, Seattle, WA  98195, United States; Ling, H., 3960 Benton Lane NE, Seattle, WA  98195, United States; Bhatia, S., 1851 NE Grant Ln, Seattle, WA  98195, United States; Matarrese, J., 3960 Benton Lane NE, Seattle, WA  98195, United States","Social robots may be an effective and appropriate technology to help reduce stress in adolescents, thereby improving mental health. Designing appropriate and engaging human–robot interactions must take into consideration the effect of the robot embodiment. Therefore, we conducted an exploratory, within-person study with 66 US adolescents (ages 14-19) in local high schools and a university setting. We compared stress disclosure interactions with a social robot prototype in three distinct embodiments:​ physically present, digitally rendered on a computer screen, and in immersive virtual reality. We compared participants’ responses to attribute ratings as well as their verbal engagement for all embodiments. After interacting with all three embodiments, participants completed exit surveys and interviews in which they identified and discussed their interaction experiences and their preferred robot embodiment. Participants suggested that sharing stress with the robots was helpful and they clearly articulated their individual views on the benefits and detriments of each robot embodiment. Overall, the physical robot was rated most comfortable. However, more adolescents preferred the interaction with the virtual embodiment than the digital or physical. Statistically significant gender differences were found in verbal engagement. Both quantitative and qualitative data from this study provide a preliminary understanding of how adolescents experience interactions with social robots as well as their preferences for certain embodiments. © 2021 Elsevier B.V.","Adolescents; Human–robot interaction; Virtual reality",,,,,,"National Science Foundation, NSF: SES-1734100","This work was supported by the National Science Foundation, United States award SES-1734100 “NRI: INT: Design and Development of a Social Robot for Gathering Ecological Momentary Stress Data from Teens”.",,,,,,,,,,"Alderman, E.M., Breuner, C.C., Unique needs of the adolescent (2019) Pediatrics, 144; Aminuddin, R., Sharkey, A., (2017), A paro robot reduces the stressful effects of environmental noise. 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"Aguirre-Ollinger G., Yu H.","24482974600;7405853761;","Lower-Limb Exoskeleton with Variable-Structure Series Elastic Actuators: Phase-Synchronized Force Control for Gait Asymmetry Correction",2021,"IEEE Transactions on Robotics","37","3","9258991","763","779",,2,"10.1109/TRO.2020.3034017","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098791012&doi=10.1109%2fTRO.2020.3034017&partnerID=40&md5=b9d764c28c63490ae94789e00c822bf1","Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore","Aguirre-Ollinger, G., Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore; Yu, H., Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore","Series elastic actuators (SEAs) can provide accurate force control and backdrivability in physical human-robot interaction. Control of SEA-generated forces or torques makes allowance for the user's own volitional control and allows implementing a wide variety of assistive strategies. A novel force control method for a SEA-driven lower-limb assistive exoskeleton is presented. The device features variable-structure SEAs coupled via Bowden cables. The actuator alternates between two discrete levels of stiffness depending on the amplitude of the commanded force. The algorithm features a switching force-tracking control based on the forward-propagating Riccati equation. A disturbance-rejection component increases the device's transparency in zero assistance mode. The force control was used to implement an assistive strategy that aims to correct the asymmetric gait typical of stroke survivors. Assistive joint torques synchronize with the user's gait by means of an adaptive frequency oscillator, which extracts the continuous phase and frequency of the patient's gait using data from both the paretic and the healthy sides. The control was tested with healthy subjects wearing the exoskeleton while subject to a simulated knee flexion impairment. The control proved effective in restoring spatial and temporal knee flexion symmetry to levels comparable to unobstructed gait. © 2004-2012 IEEE.","Exoskeleton; force control; legged locomotion; medical robotics; rehabilitation robotics","Actuators; Agricultural robots; Disturbance rejection; Force control; Human robot interaction; Physiological models; Riccati equations; Robot applications; Adaptive frequency; Continuous phase; Discrete levels; Healthy subjects; Robot interactions; Series elastic actuators; Stroke survivors; Variable structures; Exoskeleton (Robotics)",,,,,"192 25 00045; National University of Singapore, NUS; Fédération pour la Recherche sur le Cerveau, FRC: R-397-000-328-114, R397-000-302-114","Manuscript received June 18, 2020; revised August 17, 2020; accepted September 4, 2020. Date of publication November 16, 2020; date of current version June 4, 2021. This work was supported in part by the Agency for Science, Technology and Research, Singapore, under the National Robotics Program, with A*Star SERC Grant 192 25 00045, and in part by the FRC Tier 1 under Grant R397-000-302-114 and Grant R-397-000-328-114, National University of Singapore. This article was recommended for publication by Associate Editor F. Ficuciello and Editor E. Yoshida upon evaluation of the reviewers’ comments. (Corresponding author: Gabriel Aguirre-Ollinger.) The authors are with the Department of Biomedical Engineering, National University of Singapore, 117583, Singapore (e-mail: elgao68@gmail.com; bieyhy@nus.edu.sg).",,,,,,,,,,"Yan, T., Cempini, M., Oddo, C.M., Vitiello, N., Review of assistive strategies in powered lower-limb orthoses and exoskeletons (2015) Robot. Auton. 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IEEE Int. Conf. Syst., Man, Cybern., pp. 2535-2541; Chan, S., Goodwin, G., Sin, K., Convergence properties of the Riccati difference equation in optimal filtering of nonstabilizable systems (1984) IEEE Trans. Autom. Control, AC-29 (2), pp. 110-118. , Feb; De Souza, C., Monotonicity and stabilizability results for the solutions of the Riccati difference equation (1989) Proc. Workshop Riccati Equ. Control, Syst., Signals, pp. 38-41. , Como, Italy","Aguirre-Ollinger, G.; Department of Biomedical Engineering, Singapore; email: elgao68@gmail.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15523098,,,,"English","IEEE Trans. Rob.",Article,"Final","",Scopus,2-s2.0-85098791012
"Liu Y., Dai W., Lu H., Liu Y., Zhou Z.","57193067554;57188988094;55729801700;56502573400;8974315300;","Brain-computer interface for human-multirobot strategic consensus with a differential world model",2021,"Applied Intelligence","51","6",,"3645","3663",,1,"10.1007/s10489-020-01963-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096106275&doi=10.1007%2fs10489-020-01963-2&partnerID=40&md5=aa10a04ef2243033463aca5ac2a26627","College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China","Liu, Y., College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China; Dai, W., College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China; Lu, H., College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China; Liu, Y., College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China; Zhou, Z., College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China","In a distributed multi-robot system, the world model maintained by each robot is inconsistent due to measurement errors from onboard sensors, which will produce different and even incorrect strategies. In this paper, we propose an advanced interaction approach for human-multirobot strategic consensus. First, an opinion dynamics model is used to find the consistent multi-robot strategy, which is not necessarily the best choice due to the inaccurate world model. When the human receives the strategy from the robots, he/she can accept or reject it and reselect the strategy via a brain-computer interface (BCI). Of course, human judgment may be incorrect, and the BCI has false detections. Thus, the robots do not directly accept the human strategy but add it to the opinion dynamics model as a new node and recalculate the final consistent strategy. In addition, we developed a custom-designed simulation system based on the Robot Operating System and Gazebo to realize and evaluate the human-multirobot interaction. The extensive simulation results show that the proposed approach can significantly improve the correct rate of strategy selection compared with robot-only or human-only control, as well as the traditional human-robot interaction methods and other strategic consensus models. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.","Brain-computer interface; Distributed multi-robot system; Human-multirobot interaction; Opinion dynamics model","Industrial robots; Multipurpose robots; Social robots; Consensus models; Distributed multi-robot systems; Extensive simulations; Human multi-robot interactions; Multi-robot strategy; Robot operating system; Simulation systems; Strategy selection; Brain computer interface",,,,,"National Natural Science Foundation of China, NSFC: U1813205, U1913202, U19A2083; Hunan Provincial Innovation Foundation for Postgraduate: CX2018B010; National Key Research and Development Program of China, NKRDPC: 2017YFB1002505","This work was supported by the National Natural Science Foundation of China [grant numbers U19A2083, U1813205, U1913202]; the National Key Research and Development Program [grant number 2017YFB1002505]; and the Hunan Provincial Innovation Foundation For Postgraduate [grant number CX2018B010]. 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"Zibafar A., Saffari E., Alemi M., Meghdari A., Faryan L., Ghorbandaei Pour A., RezaSoltani A., Taheri A.","43161591300;57225282338;55312745000;7003828407;57197873650;57197874666;57197870451;56513500500;","State-of-the-Art Visual Merchandising Using a Fashionable Social Robot: RoMa",2021,"International Journal of Social Robotics","13","3",,"509","523",,9,"10.1007/s12369-019-00566-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066508945&doi=10.1007%2fs12369-019-00566-3&partnerID=40&md5=b68843fb83d43de5e7983f6b22ec2538","Humanities Department, West-Tehran Branch, Islamic Azad University, Tehran, Iran; Social and Cognitive Robotics Laboratory, Sharif University of Technology, Tehran, Iran; Ava and Nima Social Robotics Co. (Dr. Robot), Tehran, Iran","Zibafar, A., Social and Cognitive Robotics Laboratory, Sharif University of Technology, Tehran, Iran; Saffari, E., Social and Cognitive Robotics Laboratory, Sharif University of Technology, Tehran, Iran, Ava and Nima Social Robotics Co. (Dr. Robot), Tehran, Iran; Alemi, M., Humanities Department, West-Tehran Branch, Islamic Azad University, Tehran, Iran, Social and Cognitive Robotics Laboratory, Sharif University of Technology, Tehran, Iran, Ava and Nima Social Robotics Co. (Dr. Robot), Tehran, Iran; Meghdari, A., Social and Cognitive Robotics Laboratory, Sharif University of Technology, Tehran, Iran, Ava and Nima Social Robotics Co. (Dr. Robot), Tehran, Iran; Faryan, L., Humanities Department, West-Tehran Branch, Islamic Azad University, Tehran, Iran; Ghorbandaei Pour, A., Ava and Nima Social Robotics Co. (Dr. Robot), Tehran, Iran; RezaSoltani, A., Ava and Nima Social Robotics Co. (Dr. Robot), Tehran, Iran; Taheri, A., Social and Cognitive Robotics Laboratory, Sharif University of Technology, Tehran, Iran, Ava and Nima Social Robotics Co. (Dr. Robot), Tehran, Iran","Social Robots have the potential to attract people and this capability can be used to develop robots that can come into the retail market and enhance visual merchandising. This paper investigates important criteria to use in the design of a robot that can be utilized to promote customer appeal in the fashion industry. RoMa is a novel robotic mannequin that has been developed with the consideration of professional showcase characteristics and a primary feature of interaction with people. Since real and perceptive human–robot interaction relies on the social robot’s sensitivity to its environmental information, RoMa is able to localize the people around itself. An empirical test was conducted to demonstrate the robot’s acceptability and performance. In this test the robot was outfitted and placed in a showcase at an apparel store where prospective customer behavior was studied via a camera, microphone and questionnaire. The results indicate that RoMa was able to increase the number of people who looked at the showcase by 280% during the day, which validates the idea of visual merchandising and sales enhancement. In addition, with its design and adequate affinity RoMa was able to attract customer attention from the viewpoints of anthropology, movements, and likeability. © 2019, Springer Nature B.V.","Fashion industry; Human–robot interaction; Robotic Mannequin; Social robot; Visual merchandising","Agricultural robots; Machine design; Robotics; Sales; Empirical test; Environmental information; Fashion industry; Number of peoples; Prospective customers; Robot interactions; State of the art; Visual merchandising; Social robots",,,,,"Iran National Science Foundation, INSF","This project was funded in part by the Iranian National Science Foundation (INSF: http://www.insf.org) and the Office of the Vice-President in Science and Technology, Iran. We also appreciate the assistance of Mr. Amirmahdi Hassani during the practical tests of RoMa performing in the clothing store.",,,,,,,,,,"Alemi, M., Meghdari, A., Ghazisaedy, M., The impact of social robotics on L2 learners’ anxiety and attitude in English vocabulary acquisition (2015) Int J Social Robot, 7 (4), pp. 523-535; Taheri, A., Meghdari, A., Alemi, M., Pouretemad, H., Clinical interventions of social humanoid robots in the treatment of a set of high-and low-functioning autistic Iranian twins (2017) Sci Iran Trans B Mech Eng, (6), p. 24; Taheri, A., Meghdari, A., Alemi, M., Pouretemad, H., Human–robot interaction in autism treatment: a case study on three pairs of autistic children as twins, siblings, and classmates (2018) Int J Social Robot, 10 (1), pp. 93-113; Zibafar, A., Ghaffari, S., Vossoughi, G., Achieving transparency in series elastic actuator of sharif lower limb exoskeleton using LLNF-NARX model (2016) Robotics and Mechatronics (ICROM), 4Th International Conference On. 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IEEE, pp. 1147-1152; Tsagarakis, N.G., Metta, G., iCub: the design and realization of an open humanoid platform for cognitive and neuroscience research (2007) Adv Robot, 21 (10), pp. 1151-1175; Kim, U.-H., Nakadai, K., Okuno HG (2013) Improved sound source localization and front-back disambiguation for humanoid robots with two ears International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 282-291. , Springer, Berlin; Shafiee, S., Almasganj, F., Vazirnezhad, B., Jafari, A., A two-stage speech activity detection system considering fractal aspects of prosody (2010) Pattern Recognit Lett, 31 (9), pp. 936-948; Alemi, M., Meghdari, A., Saffari, E., RoMa: A hi-tech robotic mannequin for the fashion industry (2017) International Conference on Social Robotics, pp. 209-219. , Springer, Berlin; https://www.salehoo.com","Alemi, M.; Ava and Nima Social Robotics Co. (Dr. Robot)Iran; email: alemi@sharif.edu
Meghdari, A.; Ava and Nima Social Robotics Co. (Dr. Robot)Iran; email: meghdari@sharif.edu",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85066508945
"Wang K.-J., Sugaya M.","57188662549;7004276282;","Focus and concentrate! exploring the use of conversational robot to improve self-learning performance during pandemic isolation by closed-loop brainwave neurofeedback",2021,"International IEEE/EMBS Conference on Neural Engineering, NER","2021-May",,"9441469","928","932",,,"10.1109/NER49283.2021.9441469","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107466800&doi=10.1109%2fNER49283.2021.9441469&partnerID=40&md5=8e277625f7c0e829858a5f8943fe7506","University of Pittsburgh, Department of Bioengineering, Pittsburgh, PA  15213, United States; Shibaura Institute of Technology, Department of Information Science, Tokyo, Japan","Wang, K.-J., University of Pittsburgh, Department of Bioengineering, Pittsburgh, PA  15213, United States; Sugaya, M., Shibaura Institute of Technology, Department of Information Science, Tokyo, Japan","Recently, Human-Robot Interaction (HRI) researchers have paid lots of attentions on the conversational robots that can provide didactic or pedagogy teachings, especially for the children and young adolescents with cognitive disabilities, such as autism and epilepsy. The research and developments of such robots for educational purposes are investigated intensively. Above all the difficult challenges, how to evaluate the effectiveness of a conversational robot, which mimics a teacher communicating with a student, to improve the performance of learning and studying, is the key factor to deploy such robots in our society and be widely adopted. However, we haven't seen much investigation in previous literatures so far. In order to bridge the gap, this preliminary study had explored the use of conversational robot with electroencephalogram (EEG) biosignals as evidence measurements to improve the self-learning performance during COVID-19 pandemic crisis, while the schools are forced to close, and the students are inevitably segregated in social isolations. We had collected 10 student participants' EEG data, which were calculated to find concentration levels, and then the robot had conversations with the students adaptively according to his/her concentration levels. The result showed that the conversations between robot and humans, who are constantly not concentrating on the learning tasks, could effectively increase his/her level of concentrations. © 2021 IEEE.",,"Electroencephalography; Human robot interaction; Students; Cognitive disability; Concentration levels; Electro-encephalogram (EEG); Human robot Interaction (HRI); Learning tasks; Research and development; Self-learning; Social isolation; Educational robots",,,,,,,,,,,,,,,,"Ehrlich, S.K., Agres, K.R., Guan, C., Cheng, G., A closed-loop, music-based brain-computer interface for emotion mediation (2019) PloS One, 14 (3), p. 0213516; Kinoshita, Y., (2004) Teachers' Motivation to Enhance Learning Motivation: Words That Improve Children's Motivation in Mathematics Class, 3, pp. 203-211. , Tokyo Gakugei University Graduate School Annual Report; Okazaki, K., Abe, T., Effect of teacher's language on students' motivation to study (2018) Bulletin of Joetsu University of Education, 6, pp. 43-50; Kimura, M., Improvement or loss of learning motivation by teachers' voice 1-looking back on junior high school era (2014) Bulletin of Nagoya University of Arts, 35, pp. 115-139; Yoshikawa, M., Sannomiya, M., Effect of teacher's wording on students' motivation to learn (2007) Naruto University of Education, 4, pp. 19-27; Ito, T., Tobe, Y., Sugaya, M., Supportive voice-casting robots using bio-estimated emotion for rehabilitation (2019) The 15th International Conference on Intelligent Environments, pp. 24-27. , June, Rabat, Morocco, 2019; Heger, D., Putze, F., Schultz, T., An EEG adaptive information system for an empathic robot (2011) International Journal of Social Robotics, 3 (4), pp. 415-425. , Nov 1; Kitajo, K., Yamaguchi, Y., Study of visual perception by EEG phase synchronization analysis (2007) Vision, 19 (4), pp. 193-200; Sakamoto, Y., Yoshida, K., Miyachi, I., Comparison of analysis of learning state with simple EEG (2012) Multimedia, Distributed, Cooperative and Mobile (DICOMO) 2012 Symposium, pp. 724-729","Wang, K.-J.; University of Pittsburgh, United States; email: kew88@pitt.edu",,"Blackrock Microsystems;et al.;Instituto Italiano di Tecnologia;Medtronic;The Biorobotics Institute, Sant'Anna School of Advanced Studies - Pisa;University of Houston, Cullen College of Engineering, Department of Biomedical Engineering","IEEE Computer Society","10th International IEEE/EMBS Conference on Neural Engineering, NER 2021","4 May 2021 through 6 May 2021",,169316,19483546,9781728143378,,,"English","Int. IEEE/EMBS Conf. Neural Eng., NER",Conference Paper,"Final","",Scopus,2-s2.0-85107466800
"Zhang P., Xu J., Hao Y., Xu B.","57221083945;55898934000;57211167276;37022633100;","Online Audio-Visual Speech Separation with Generative Adversarial Training",2021,"ACM International Conference Proceeding Series",,,,"379","385",,,"10.1145/3467707.3467764","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116215568&doi=10.1145%2f3467707.3467764&partnerID=40&md5=2aee1011b591c1531989fba5bd3a89dd","Institute of Automation, Chinese Academy of Sciences, School of Artificial Intelligence, University of Chinese Academy of Science, China; Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Science, China; Institute of Automation, Chinese Academy of Sciences, School of Artificial Intelligence, University of Chinese Academy of Science, Center for Excellence in Brain Science and Intelligence Technology, China","Zhang, P., Institute of Automation, Chinese Academy of Sciences, School of Artificial Intelligence, University of Chinese Academy of Science, China; Xu, J., Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Science, China; Hao, Y., Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Science, China; Xu, B., Institute of Automation, Chinese Academy of Sciences, School of Artificial Intelligence, University of Chinese Academy of Science, Center for Excellence in Brain Science and Intelligence Technology, China","Audio-visual speech separation has been demonstrated to be effective in solving the cocktail party problem. However, most of the models cannot meet online processing, which limits their application in video communication and human-robot interaction. Besides, SI-SNR, the most popular training loss function in speech separation, results in some artifacts in the separated audio, which would harm downstream applications, such as automatic speech recognition (ASR). In this paper, we propose an online audio-visual speech separation model with generative adversarial training to solve the two problems mentioned above. We build our generator (i.e., audio-visual speech separator) with causal temporal convolutional network block and propose a streaming inference strategy, which allows our model to do speech separation in an online manner. The discriminator is involved in optimizing the generator, which can reduce the negative effects of SI-SNR. Experiments on simulated 2-speaker mixtures based on challenging audio-visual dataset LRS2 show that our model outperforms the state-of-the-art audio-only model Conv-TasNet and audio-visual model advr-AVSS under the same model size. We test the running time of our model on GPU and CPU, and results show that our model meets online processing. The demo and code can be found at https://github.com/aispeech-lab/oavss. © 2021 ACM.","Audio-visual speech separation; causal temporal convolutional network; generative adversarial training; online processing","Audio acoustics; Convolution; E-learning; Human robot interaction; Source separation; Speech; Speech analysis; Speech communication; Speech recognition; Audio-visual; Audio-visual speech; Audio-visual speech separation; Causal temporal convolutional network; Cocktail party problems; Convolutional networks; Generative adversarial training; Online processing; Speech separation; Video communications; Separation",,,,,"Chinese Academy of Sciences, CAS: XDB32070000","This work was supported by the Major Project for New Generation of AI (Grant No. 2018AAA0100400), and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB32070000).",,,,,,,,,,"Kinoshita, K., Ochiai, T., Delcroix, M., Nakatani, T., Improving noise robust automatic speech recognition with single channel timedomain enhancement network (2020) ICASSP, pp. 7009-7013; Ephrat, A., Looking to listen at the cocktail party: A speakerindependent audio-visual model for speech separation (2018) ACM Transactions on Graphics, 37 (4 CD), pp. 1121-11211; Lu, R., Duan, Z., Zhang, C., Audio-visual deep clustering for speech separation (2019) IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27 (11), pp. 1697-1712; Afouras, T., Son Chung, J., Zisserman, A., The conversation: Deep audio-visual speech enhancement (2018) Interspeech; Zhang, P., Xu, J., Shi, J., Hao, Y., Xu, B., (2020) Audio-visual Speech Separation with Adversarially Disentangled Visual Representation; Afouras, T., Son Chung, J., Senior, A., Vinyals, O., Zisserman, A., Deep audiovisual speech recognition (2018) IEEE Transactions on Pattern Analysis and Machine Intelligence; King, D.E., Dlib-ml: A machine learning toolkit (2009) Journal of Machine Learning Research, 10, pp. 1755-1758; Lucas, B.D., Kanade, T., An iterative image registration technique with an application to stereo vision (1981) IJCAI; Sunohara, M., Haruta, C., Ono, N., Low-latency realtime blind source separation for hearing AIDS based on time-domain implementation of online independent vector analysis with truncation of non-causal components (2017) ICASSP. IEEE, pp. 216-220; Wang, Y., Wang, D., Hu, K., (2017) Real-time Method for Implementing Deep Neural Network Based Speech Separation, , uS Patent App; Han, C., Luo, Y., Mesgarani, N., Online deep attractor network for real-time single-channel speech separation (2019) ICASSP; Li, Z.-X., Song, Y., Dai, L.-R., McLoughlin, I., Source-aware context network for single-channel multi-speaker speech separation (2018) ICASSP. IEEE, pp. 681-685; Luo, Y., Mesgarani, N., Tasnet: Time-domain audio separation network for real-time, single-channel speech separation (2018) 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 696-700; Luo, Y., Mesgarani, N., Conv-tasnet: Surpassing ideal time-frequency magnitude masking for speech separation (2019) IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27 (8), pp. 1256-1266; Goodfellow, I., Generative adversarial nets (2014) International Conference on Neural Information Processing Systems, pp. 2672-2680; Mao, X., Li, Q., Xie, H., Lau, K.R.Y., Wang, Z., Paul Smolley, S., (2017) Least Squares Generative Adversarial Networks; Hao, Y., Xu, J., Shi, J., Zhang, P., Qin, L., Xu, B., A unified of framework for low-latency speaker extraction in cocktail party environments (2020) Interspeech, pp. 1431-1435; Xu, C., Rao, W., Siong Chng, E., Li, H., (2020) Spex: Multiscale Time Domain Speaker Extraction Network; Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y., (2018) Spectral Normalization for Generative Adversarial Networks; Zhang, K., Zhang, Z., Li, Z., Qiao, Y., Joint face detection and alignment using multitask cascaded convolutional networks (2016) IEEE Signal Processing Letters, 23 (10), pp. 1499-1503; Kingma, D.P., Ba, J., Adam: A method for stochastic optimization (2014) Proc. Int. Conf. Lear. Represent; Vincent, E., Gribonval, R., Févotte, C., Performance measurement in blind audio source separation (2006) IEEE Transactions on Audio, Speech, and Language Processing, 14 (4), pp. 1462-1469; Rix, A.W., Beerends, J.G., Hollier, M.P., Hekstra, A.P., Perceptual evaluation of speech quality (pesq)-a new method for speech quality assessment of telephone networks and codecs (2001) ICASSP, pp. 749-752; Taal, C.H., Hendriks, R.C., Heusdens, R., Jensen, J., An algorithm for intelligibility prediction of time-frequency weighted noisy speech (2011) IEEE Transactions on Audio, Speech, and Language Processing, 19 (7), pp. 2125-2136; Harriott, C.E., Adams, J.A., Towards reaction and response time metrics for real-world human-robot interaction (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 799-804","Xu, B.; Institute of Automation, China; email: xubo@ia.ac.cn",,,"Association for Computing Machinery","7th International Conference on Computing and Artificial Intelligence, ICCAI 2021","23 April 2021 through 26 April 2021",,171905,,9781450389501,,,"English","ACM Int. Conf. Proc. Ser.",Conference Paper,"Final","",Scopus,2-s2.0-85116215568
"Miller L., Kraus J., Babel F., Baumann M.","57218917919;57158564500;57204529534;7202494638;","More Than a Feeling—Interrelation of Trust Layers in Human-Robot Interaction and the Role of User Dispositions and State Anxiety",2021,"Frontiers in Psychology","12",,"592711","","",,,"10.3389/fpsyg.2021.592711","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104972690&doi=10.3389%2ffpsyg.2021.592711&partnerID=40&md5=e2987ec89a868231bc90edc7e5ec1716","Department Human Factors, Institute of Psychology and Education, Ulm University, Ulm, Germany","Miller, L., Department Human Factors, Institute of Psychology and Education, Ulm University, Ulm, Germany; Kraus, J., Department Human Factors, Institute of Psychology and Education, Ulm University, Ulm, Germany; Babel, F., Department Human Factors, Institute of Psychology and Education, Ulm University, Ulm, Germany; Baumann, M., Department Human Factors, Institute of Psychology and Education, Ulm University, Ulm, Germany","With service robots becoming more ubiquitous in social life, interaction design needs to adapt to novice users and the associated uncertainty in the first encounter with this technology in new emerging environments. Trust in robots is an essential psychological prerequisite to achieve safe and convenient cooperation between users and robots. This research focuses on psychological processes in which user dispositions and states affect trust in robots, which in turn is expected to impact the behavior and reactions in the interaction with robotic systems. In a laboratory experiment, the influence of propensity to trust in automation and negative attitudes toward robots on state anxiety, trust, and comfort distance toward a robot were explored. Participants were approached by a humanoid domestic robot two times and indicated their comfort distance and trust. The results favor the differentiation and interdependence of dispositional, initial, and dynamic learned trust layers. A mediation from the propensity to trust to initial learned trust by state anxiety provides an insight into the psychological processes through which personality traits might affect interindividual outcomes in human-robot interaction (HRI). The findings underline the meaningfulness of user characteristics as predictors for the initial approach to robots and the importance of considering users’ individual learning history regarding technology and robots in particular. © Copyright © 2021 Miller, Kraus, Babel and Baumann.","affect; comfort distance; human-robot interaction; state anxiety; trust in automation; trust in robots; trust layers; user dispositions",,,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV7967","This research has been conducted within the interdisciplinary research project “RobotKoop,” which was funded by the German Ministry of Education and Research (Grant No. 16SV7967).",,,,,,,,,,"Aiello, J.R., Human spatial behavior (1987) Handbook of Environmental Psychology, pp. 389-504. , Stokols D., Altman I., (eds), New York, NY, Wiley; Ajzen, I., Attitude structure and behavior (1989) Attitude Structure and Function, pp. 241-274. , Pratkanis A.R., Breckler S.J., Greenwald A.G., (eds), New Jersey, NJ, Lawrence Erlbaum; Ajzen, I., The theory of planned behavior (1991) Organ. 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Autonomous robots threaten human identity, uniqueness, safety, and resources (2017) Int. J. Hum. Comput. Stud, 100, pp. 48-54","Miller, L.; Department Human Factors, Germany; email: linda.miller@uni-ulm.de
Kraus, J.; Department Human Factors, Germany; email: johannes.kraus@uni-ulm.de",,,"Frontiers Media S.A.",,,,,16641078,,,,"English","Front. Psychol.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85104972690
"Lee Y.-S., Chiao K.-W., Lan C.-C.","57218837076;57221660725;7202621008;","A Hybrid Impedance Controller for Series Elastic Actuators to Render a Wide Range of Stable Stiffness in Uncertain Environments",2021,"IEEE Robotics and Automation Letters","6","2","9325010","739","746",,1,"10.1109/LRA.2021.3051615","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099728273&doi=10.1109%2fLRA.2021.3051615&partnerID=40&md5=b9a403ff299d59f9f16e901e032cac30","Department of Mechanical Engineering, National Cheng Kung University, Tainan, 701, Taiwan","Lee, Y.-S., Department of Mechanical Engineering, National Cheng Kung University, Tainan, 701, Taiwan; Chiao, K.-W., Department of Mechanical Engineering, National Cheng Kung University, Tainan, 701, Taiwan; Lan, C.-C., Department of Mechanical Engineering, National Cheng Kung University, Tainan, 701, Taiwan","Accurate and wide-range stiffness control is important for safe human-robot interaction. Accurate stiffness control can be better achieved using series elastic actuators (SEAs) than conventional rigid actuators. However, the stable range of virtual stiffness rendered by SEAs is limited by the stiffness of the actual spring, which cannot be too high in order to ensure good force control accuracy. Adding a virtual damper or derivative gain can increase the stable range of virtual stiffness, but the stable range would highly depend on the environmental stiffness. To relax the stiffness limitation in uncertain environments and explore more merits of SEAs, this letter proposes a hybrid impedance controller. This new controller linearly combines the spring force feedback and inertia force feedback. The stable range of virtual stiffness can be easily increased to ten times the actual spring stiffness with minimum effect on the force control accuracy. Unlike typical impedance controllers, the environmental stiffness can be used to raise the stable range of stiffness and hence the robustness of the controller can be ensured. Experiments will be provided to verify the hybrid impedance controller. We expect that the hybrid impedance controller can be used for SEAs in unstructured environments to provide a wide range of virtual stiffness. © 2016 IEEE.","environmental stiffness; high-stiffness control; Impedance controller; series elastic actuator; virtual damping; virtual stiffness","Actuators; Agricultural robots; Biofeedback; Controllers; Force control; Human robot interaction; Springs (components); Stiffness; Control accuracy; Impedance controllers; Safe human-robot interaction; Series elastic actuators; Stiffness control; Uncertain environments; Unstructured environments; Virtual stiffness; Feedback",,,,,"Ministry of Science and Technology, Taiwan, MOST: MOST 109-2218-E-006-033","Manuscript received October 15, 2020; accepted December 23, 2020. Date of publication January 14, 2021; date of current version January 25, 2021. This letter was recommended for publication by Associate Editor K. Tahara and Editor C. Gosselin upon evaluation of the reviewers’ comments. This work was supported by the Ministry of Science and Technology, Taiwan (with Project No. MOST 109-2218-E-006-033). (Corresponding author: Chao-Chieh Lan.) The authors are with the Department of Mechanical Engineering, National Cheng Kung University, Tainan 701, Taiwan (e-mail: zxcv2248664@ gmail.com; buiooiu44@gmail.com; cclan@mail.ncku.edu.tw).","This work was supported by theMinistry of Science and Technology, Taiwan (with Project No. MOST 109-2218-E-006-033).",,,,,,,,,"Morbi, A., Ahmadi, M., Safely rendering small impedances in admittance-controlled haptic devices (2015) IEEE/ASME Trans. Mechatronics, 21 (3), pp. 1272-1280. , Jun; Tsumugiwa, T., Stability analysis for impedance control of robot in human-robot cooperative task system (2007) J. Adv. Mech. Des., Syst., Manuf., 1 (1), pp. 113-121; Aung, M.T.S., Kikuuwe, R., Stability enhancement of admittance control with acceleration feedback and friction compensation (2017) Mechatronics, 45, pp. 110-118; Keemink, A.Q., Kooij, H.V.D., Stienen, A.H., Admittance control for physical human-robot interaction (2018) Int. J. Robot. Res., 37 (11), pp. 1421-1444; Convens, B., Modeling, design and test-bench validation of a semiactive propulsive ankle prosthesis with a clutched series elastic actuator (2019) IEEE Robot. Automat. Lett., 4 (2), pp. 1823-1830. , Apr; Rampeltshammer, A.K.W.F., Kooij, H.V.D., An improved force controller with low and passive apparent impedance for series elastic actuators (2020) IEEE/ASME Trans.Mechatronics, 25 (3), pp. 1220-1230. , Jun; Yu, Y.-L., Lan, C.-C., Design of a miniature series elastic actuator for bilateral teleoperations requiring accurate torque sensing and control (2019) IEEE Robot. Automat. Lett., 4 (2), pp. 500-507. , Apr; Lin, C.-H., A spatial-motion assist-as-needed controller for the passive, active, and resistive robot-aided rehabilitation of the wrist (2020) IEEE Access, 8, pp. 133951-133960; Lin, K.-Y., Chung, C.-C., Lan, C.-C., Improving the dynamic force control of series elastic actuation using motors of high torque-to-inertia ratios (2020) IEEE Access, 8, pp. 6968-6977; Isik, K., Thomas, G.C., Sentis, L., A fixed structure gain selection strategy for high impedance series elastic actuator behavior (2019) J. Dyn. Syst., Meas., Control, 141 (2); Roozing, W., On the stiffness selection for torque-controlled serieselastic actuators (2017) IEEE Robot.Automat. Lett., 2 (4), pp. 2255-2262. , Oct; Haninger, K., Asignacion, A., Oh, S., Safe high impedance control of a series-elastic actuator with a disturbance observer (2020) Proc. IEEE Int. Conf. Robot. Automat., pp. 921-927; Landi, C.T., Admittance control parameter adaptation for physical human-robot interaction (2017) Proc. IEEE Int. Conf. Robot. Automat., pp. 2911-2916; Gallagher, W., Gao, D., Ueda, J., Improved stability of haptic human-robot interfaces using measurement of human arm stiffness (2014) Adv. Robot., 28 (13), pp. 869-882; Calanca, A., Fiorini, P., A rationale for acceleration feedback in force control of series elastic actuators (2018) IEEE Trans. Robot., 34 (1), pp. 48-61. , Feb; Sariyildiz, E., Chen, G., Yu, H., An acceleration-based robust motion controller design for a novel series elastic actuator (2016) IEEE Trans. Ind. Electron., 63 (3), pp. 1900-1910. , Mar; Ott, C., On the passivity-based impedance control of flexible joint robots (2008) IEEE Trans. Robot., 24 (2), pp. 416-429. , Apr; Lee, H., Relaxing the conservatism of passivity condition for impedance controlled series elastic actuators (2019) Proc. IEEE/RSJ Int.Conf. Intell. Robots Syst., pp. 7610-7615; Lee, S.-H., Song, J.-B., Acceleration estimator for low-velocity and low-acceleration regions based on encoder position data (2001) IEEE/ASME Trans. Mechatronics, 6 (1), pp. 58-64. , Mar; Lee, Y.-S., Lan, C.-C., Rendering of arbitrary and stable stiffness using a series elastic actuator (2020) Proc. IEEE/ASME Int. Conf. Adv. Intell. Mechatronics, pp. 960-965; Semjon, J., Verification of the UR5 robot's properties after a crash caused by a fall of a transferred load from a crane (2020) Int. J. Adv. Robotic Syst., 17 (1). , https://doi.org/10.1177/1729881420904209","Lan, C.-C.; Department of Mechanical Engineering, Taiwan; email: cclan@mail.ncku.edu.tw",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85099728273
"Chen C.-S., Chen S.-K., Lai C.-C., Lin C.-T.","56118484400;57188754141;16549930300;8942403600;","Sequential Motion Primitives Recognition of Robotic Arm Task via Human Demonstration Using Hierarchical BiLSTM Classifier",2021,"IEEE Robotics and Automation Letters","6","2","9309330","502","509",,1,"10.1109/LRA.2020.3047772","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099100940&doi=10.1109%2fLRA.2020.3047772&partnerID=40&md5=5b6fb753a7d1209b10b9db4a8287eef2","Institute of Automation Technology, National Taipei University of Technology, Taipei, Taiwan; Graduate Institute of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan; Department of Electrical Engineering, National Yunlin University of Science and Technology, Taiwan; Cibci Lab, Centre for AI, Feit, University of Technology Sydney, Sydney, Australia","Chen, C.-S., Institute of Automation Technology, National Taipei University of Technology, Taipei, Taiwan; Chen, S.-K., Graduate Institute of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan; Lai, C.-C., Department of Electrical Engineering, National Yunlin University of Science and Technology, Taiwan; Lin, C.-T., Cibci Lab, Centre for AI, Feit, University of Technology Sydney, Sydney, Australia","Learning from demonstration (LfD) is an intuitive teaching technology without extensive programming for an operator. In recent LfD research, machine vision is usually used to capture the human-robot interaction. However, it's not reliable during the machining process. In this letter, a novel intuitive high-level kinesthetic teaching technology is proposed by reconstructing the motion information recorded from human-guided demonstrations. A hierarchical BiLSTM-based machine learning algorithm is proposed in this letter to recognize and segment motion primitives according to the therblig definition. A hybrid sensing interface is used to record and extract the motion features, consisting of the velocity profile, force/torque, and gripper information. The motion features are then used to classify into the target motion primitive by the proposed classifier. The experimental results and comparisons with the state-of-the-art algorithm show that the proposed method can correctly and efficiently synthesize the recorded motion features into a motion primitive sequence. Finally, the recognition results of real-world tasks show that the proposed algorithm can be used to reconstruct the human-guided task and further used to command a KUKA robot. The experimental results of the reconstructed trajectory show that a real-world task can represent and maintain the accuracy in 2.37 mm using the proposed algorithm. © 2016 IEEE.","deep learning in grasping and manipulation; industrial robots; Learning from demonstration; machine learning for robot control","Agricultural robots; Machine learning; Machining; Motion estimation; Robotic arms; Robotics; Social robots; Human demonstrations; Kinesthetic teachings; Learning from demonstration; Machining Process; Motion information; Reconstructed trajectory; State-of-the-art algorithms; Teaching technologies; Learning algorithms",,,,,"Ministry of Science and Technology, Taiwan, MOST: 108-2221-E-027-094","Manuscript received August 19, 2020; accepted December 13, 2020. Date of publication December 28, 2020; date of current version January 12, 2021. This letter was recommended for publication by Associate Editor M. Khoramshahi and Editor T. Asfour upon evaluation of the reviewers’ comments. This work was supported in part by the Ministry of Science and Technology, Taiwan, R.O.C., under Grant MOST 108-2221-E-027-094. (Corresponding author: Chin-Sheng Chen.) Chin-Sheng Chen is with the Institute of Automation Technology, National Taipei University of Technology, Taipei 10608, Taiwan (e-mail: saint@ntut.edu.tw).",,,,,,,,,,"Schreiber, G., Hirzinger, G., An intuitive interface for nullspace teaching of redundant robots (2000) Advances in Robot Kinematics, pp. 209-216. , J. Lenarcic and M. M. Stanišíc, eds., DordrechtNetherlands: Springer; Spong, M.W., Hutchinson, S., Vidyasagar, M., (2006) Robot Modeling and Control, , Hoboken, NJ, USA: Wiley; Hogan, N., Impedance control: An approach to manipulation (1985) J. 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Robots Syst., , Madrid, Spain, Oct; Chen, C.-S., Chen, S.-K., Synchronization of tool tip trajectory and attitude based on the surface characteristics of workpiece for 6-DOF robot manipulator (2019) Robot. Comput.-Integr. Manuf., 59 C, pp. 13-27","Chen, C.-S.; Institute of Automation Technology, Taiwan; email: saint@ntut.edu.tw",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85099100940
"Xu P., Chen G., Ji J.","57218702115;57281493500;34869653800;","Applying Soft Attention in Deep Reinforcement Learning for Robot Collision Avoidance",2021,"ACM International Conference Proceeding Series",,,,"34","38",,,"10.1145/3460569.3460573","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114282769&doi=10.1145%2f3460569.3460573&partnerID=40&md5=1ffc8d94f2a1eb05fb420ada51847723","University of Science and Technology of China, China","Xu, P., University of Science and Technology of China, China; Chen, G., University of Science and Technology of China, China; Ji, J., University of Science and Technology of China, China","Attention models have been used in many applications, like playing ATARI games and human-robot interaction, with promising results. In this paper, we integrate soft attention with deep reinforcement learning for robot collision avoidance, which provides an end-to-end approach for learning an obstacle avoidance policy for robot navigation. In specific, using experience collected from various simulation environments, a convolutional neural network (CNN) is first trained to extract feature maps from egocentric local occupancy maps of the robot. Then the attention model weighs these low-level CNN features before feeding them into the recurrent module. At last, the trained model that specifies a collision avoidance policy is deployed to the robot to guide its local path planning. We evaluate the new approach in both simulation and real-world robot experiments. The results show that the integration of soft attention can greatly improve the performance of the deep reinforcement learning algorithm, i.e., the vanilla DQN. In addition, the attention mechanism adds the intuitive interpretability to the model regarding where and what the agent's attention is focusing on while making decisions. Extensive experiments on a real-world robot show that the learned navigation model is easy to deploy to the robotic platform. © 2021 ACM.","Attention mechanism; Deep reinforcement learning; Robot navigation","Air navigation; Collision avoidance; Convolutional neural networks; Human robot interaction; Recurrent neural networks; Reinforcement learning; Robot programming; User experience; Attention mechanisms; Interpretability; Local path-planning; Navigation model; Robot collisions; Robot navigation; Robotic platforms; Simulation environment; Learning algorithms",,,,,,"This work is supported by the National Major Program for Technological Innovation 2030—New Generation Artificial Intelligence (Grant no: 2018AAA0100500).",,,,,,,,,,"Zhang, W., Wei, S., Teng, Y., Dynamic obstacle avoidance for unmanned underwater vehicles based on an improved velocity obstacle method[j] (2017) Sensors, 17 (12), p. 2742; Zhou, D., Wang, Z., Bandyopadhyay, S., Fast, on-line collision avoidance for dynamic vehicles using buffered voronoi cells[j] (2017) IEEE Robotics and Automation Letters, 2 (2), pp. 1047-1054; Rösmann, C., Hoffmann, F., Bertram, T., Integrated online trajectory planning and optimization in distinctive topologies[j] (2017) Robotics and Autonomous Systems, 88, pp. 142-153; Kahn, G., Villaflor, A., Ding, B., Self-supervised deep reinforcement learning with generalized computation graphs for robot navigation[c] (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 1-8; Mnih, V., Kavukcuoglu, K., Silver, D., Human-level control through deep reinforcement learning[j] Nature; Mnih, V., Badia, A.P., Mirza, M., Asynchronous methods for deep reinforcement learning[c] (2016) International Conference on Machine Learning, pp. 1928-1937; Xie, L., Wang, S., Rosa, S., Learning with training wheels: Speeding up training with a simple controller for deep reinforcement learning[c] (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 6276-6283; Hausknecht, M., Stone, P., (2015) Deep Recurrent Q-learning for Partially Observable Mdps[J]; Bahdanau, D., Cho, K., Bengio, Y., (2014) Neural Machine Translation by Jointly Learning to Align and Translate[J]; Xu, K., Ba, J., Kiros, R., Show, attend and tell: Neural image caption generation with visual attention[c] (2015) International Conference on Machine Learning, pp. 2048-2057; Chen, C., Liu, Y., Kreiss, S., Crowd-robot interaction: Crowd-aware robot navigation with attention-based deep reinforcement learning[c] (2019) 2019 International Conference on Robotics and Automation (ICRA). IEEE, pp. 6015-6022; Sorokin, I., Seleznev, A., Pavlov, M., (2015) Deep Attention Recurrent Q-network[J]; Hochreiter, S., Schmidhuber, J., Long short-term memory[j] (1997) Neural Computation, 9 (8), pp. 1735-1780; Lu, D.V., Hershberger, D., Smart, W.D., Layered costmaps for context-sensitive navigation[c] (2014) 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 709-715; Mott, A., Zoran, D., Chrzanowski, M., Towards interpretable reinforcement learning using attention augmented agents[c] (2019) Advances in Neural Information Processing Systems, pp. 12350-12359; Bengio, Y., Louradour, J., Collobert, R., Curriculum learning[c] (2009) Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41-48; Koenig, N., Howard, A., Design and use paradigms for gazebo, an open-source multi-robot simulator[c] (2004) 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat No. 04CH37566). IEEE, 3, pp. 2149-2154; Fan, T., Long, P., Liu, W., Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios[j] (2020) The International Journal of Robotics Research, pp. 0278364920916531","Ji, J.; University of Science and Technology of ChinaChina; email: jianmin@ustc.edu.cn",,,"Association for Computing Machinery","6th International Conference on Mathematics and Artificial Intelligence, ICMAI 2021","19 March 2021 through 21 March 2021",,171507,,9781450389464,,,"English","ACM Int. Conf. Proc. Ser.",Conference Paper,"Final","",Scopus,2-s2.0-85114282769
"Muñoz Cardona J.E., Dautenhahn K.","56645651500;7003305185;","Robo ludens: Game design techniques applied in HRI experiments",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444883","691","693",,1,"10.1145/3434074.3444883","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102772068&doi=10.1145%2f3434074.3444883&partnerID=40&md5=569aa6693f896b40395b710be9761ddd","Systems Design Engineering Department, University of Waterloo, Waterloo, ON, Canada; Electrical and Computer Engineering System, Design Engineering Departments, University of Waterloo, Waterloo, ON, Canada","Muñoz Cardona, J.E., Systems Design Engineering Department, University of Waterloo, Waterloo, ON, Canada; Dautenhahn, K., Electrical and Computer Engineering System, Design Engineering Departments, University of Waterloo, Waterloo, ON, Canada","Games have been used extensively to study human behavior. Researchers in the field of human-robot interaction (HRI) are becoming more aware of the importance of designing compelling and playful games to study interrelationships among players. Despite the growing interest, the use of game design techniques in the creation of playful experiences for HRI experiments is still in its infancy and more multidisciplinary activities should be promoted to foster the convergence between game research and HRI. This workshop aims at discussing the value of using iterative game design techniques to integrate playful experiences using social robots for HRI experiments. More concretely, we want to explore tools, approaches and methods used in previous experiences for appropriate design of interactive games in HRI. Furthermore, based on previous research, a taxonomy for game design using social robots will be presented and attendees will have access to hands-on material created to facilitate the design of interactive games considering important aspects of the robotic systems to maximize the fun experience. We hope this workshop will bring to HRI researchers, game designers, roboticists, and technology enthusiasts enlightening thoughts and ideas to confront the often complicated and time-demanding process of designing compelling games for HRI experiments. © 2021 Owner/Author.","Game design; Gameplay; Human-robot interaction; Playful; Social robots; Taxonomy","Agricultural robots; Behavioral research; Iterative methods; Man machine systems; Social robots; User experience; Appropriate designs; Game designers; Game research; Human behaviors; Human robot Interaction (HRI); Interactive games; Multi-disciplinary activities; Robotic systems; Machine design",,,,,,"This research was undertaken, in part, thanks to funding from the Canada 150 Research Chairs Program.",,,,,,,,,,"Hodent, C., (2017) The Gamer's Brain: How Neuroscience and UX Can Impact Video Game Design, , CRC Press; Kim, M.-G., Suzuki, K., Comparative study of human behavior in card playing with a humanoid playmate (2014) International Journal of Social Robotics., 6 (1), pp. 5-15. , 2014; Mora, A., Riera, D., Gonzalez, C., Arnedo-Moreno, J., A literature review of gamification design frameworks (2015) 2015 7th International Conference on Games and Virtual Worlds for Serious Applications (VS-Games), pp. 1-8; Munoz, J.E., Dautenhahn, K., Robo ludens: A game design taxonomy for multiplayer games using socially interactive robots (2020) Under Review; Robins, B., Dautenhahn, K., Ferrari, E., Kronreif, G., Prazak-Aram, B., Marti, P., Iacono, I., Caprino, F., Scenarios of robot-assisted play for children with cognitive and physical disabilities (2012) Interaction Studies., 13 (2), pp. 189-234. , 2012; Schell, J., (2014) The Art of Game Design: A Book of Lenses, , AK Peters/CRC Press; Thompson, C., Mohamed, S., Louie, G.W.-Y., He, J.C., Li, J., Nejat, G., The robot tangy facilitating trivia games: A team-based user-study with long-term care residents (2017) 2017 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), pp. 173-178. , 2017",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102772068
"Bennett C.C.","57221704213;","Emergent robotic personality traits via agent-based simulation of abstract social environments",2021,"Information (Switzerland)","12","3","103","1","22",,1,"10.3390/info12030103","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102561980&doi=10.3390%2finfo12030103&partnerID=40&md5=5ad10bcb454fe10b3ad4406f3f9de1b6","School of Intelligence Computing, Hanyang University, Seoul, 04763, South Korea; College of Computing, DePaul University, Chicago, IL  60614, United States","Bennett, C.C., School of Intelligence Computing, Hanyang University, Seoul, 04763, South Korea, College of Computing, DePaul University, Chicago, IL  60614, United States","This paper discusses the creation of an agent-based simulation model for interactive robotic faces, built based on data from physical human–robot interaction experiments, to explore hypotheses around how we might create emergent robotic personality traits, rather than pre-scripted ones based on programmatic rules. If an agent/robot can visually attend and behaviorally respond to social cues in its environment, and that environment varies, then idiosyncratic behavior that forms the basis of what we call a “personality” should theoretically be emergent. Here, we evaluate the stability of behavioral learning convergence in such social environments to test this idea. We conduct over 2000 separate simulations of an agent-based model in scaled-down, abstracted forms of the environment, each one representing an “experiment”, to see how different parameters interact to affect this process. Our findings suggest that there may be systematic dynamics in the learning patterns of an agent/robot in social environments, as well as significant interaction effects between the environmental setup and agent perceptual model. Furthermore, learning from deltas (Markovian approach) was more effective than only considering the current state space. We discuss the implications for HRI research, the design of interactive robotic faces, and the development of more robust theoretical frameworks of social interaction. © 2021 by the author. Licensee MDPI, Basel, Switzerland.","Agent-based modeling; Agent-based simulation; Human–robot interaction; Personality; Robotic face; Social interaction","Autonomous agents; Computational methods; Markov processes; Social robots; Agent based simulation; Agent-based simulation models; Interaction effect; Interactive robotics; Learning convergence; Robot interactions; Social interactions; Theoretical framework; Robotics",,,,,"National Science Foundation, NSF: -1143712; Society of Instrument and Control Engineers, SICE","Funding: The data from the physical experiments was funded by NSF grant #IIS-1143712, as well as the NSF/IU IGERT summer research award in cognitive science and Indiana University’s School of Informatics, Computing, and Engineering (SICE).",,,,,,,,,,"Dexter, J.P., Prabakaran, S., Gunawardena, J., A complex hierarchy of avoidance behaviors in a single-cell eukaryote (2019) Curr. Biol, 29, pp. 4323-4329; Robert, L., Personality in the human robot interaction literature: A review and brief critique Proceedings of the 24th Americas Conference on Information Systems, pp. 16-18. , New Orleans, LA, USA, 16–18 August 2018; Calvo, R., D’Mello, S., Gratch, J., Kappas, A., Lisetti, C., Hudlicka, E., Why and how to build emotion-based agent architectures (2015) The Oxford Handbook of Affective Computing, p. 94. , Oxford University Press: Oxford, UK; Bennett, C.C., Šabanović, S., Deriving minimal features for human-like facial expressions in robotic faces (2014) Int. J. Soc. Robot, 6, pp. 367-381; Bennett, C.C., Šabanović, S., The effects of culture and context on perceptions of robotic facial expressions (2015) Interact. Stud, 16, pp. 272-302; Bennett, C.C., (2015) Robotic Faces: Exploring Dynamical Patterns of Social Interaction Between Humans and Robots, , Ph.D.; Thesis, Indiana University, Bloomington USA, May; Bennett, C., Wagner, P., Sabanovic, S., Audio-visual synthesized attitudes presented by the German speaking robot SMiRAE (2019) Proceedings of the 15th International Conference on Auditory-Visual Speech Processing, , Hönemann1A Melbourne, Australia, 10–11 August; Brooks, R.A., From earwigs to humans (1997) Robot. Auton. Syst, 20, pp. 291-304; Sun, R., (2006) Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation, , Cambridge University Press: Cambridge, UK; Masterson, J.F., (1988) The Search for the Real Self: Unmasking the Personality Disorders of Our Age, , Taylor & Francis: New York, NY, USA; Lieb, K., Zanarini, M.C., Schmahl, C., Linehan, M.M., Bohus, M., Borderline personality disorder (2004) Lancet, 364, pp. 453-461; Ortony, A., Clore, G.L., Collins, A.J., (1988) The Cognitive Structure of Emotions, , Cambridge University Press: Cambridge, UK; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Comput, 9, pp. 1735-1780; Scherer, K.R., Schorr, A., Johnstone, T., (2001) Appraisal Processes in Emotion: Theory, Methods, Research, , Oxford University Press: Oxford, UK; Murphy, R., Lisetti, C., Tardif, R., Irish, L., Gage, A., Emotion-based control of cooperating heterogeneous mobile robots (2002) IEEE Trans. Robot. Autom, 18, pp. 744-757; Tonkin, M., Vitale, J., Herse, S., Williams, M.A., Judge, W., Wang, X., Design methodology for the UX of HRI: A field study of a commercial social robot at an airport ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 407-415. , Chicago, IL, USA, 5–8 March 2018; Cruz-Maya, A., Tapus, A., Influence of user’s personality on task execution when reminded by a robot (2016) Proceedings of the International Conference on Social Robotics (ICSR), pp. 829-838. , Kansas City, MO, USA, 1–3 November; Gockley, R., Mataric, M.J., Encouraging physical therapy compliance with a hands-Off mobile robot Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 150-155. , Salt Lake City, UT, USA, 2–3 March 2006; Kimoto, M., Iio, T., Shiomi, M., Tanev, I., Shimohara, K., Hagita, N., Relationship between personality and robots’ interaction strategies in object reference conversations Proceedings of the Second International Conference on Electronics and Software Science (ICESS2016), pp. 128-136. , Takamatsu, Japan, 14–16 November 2016; Lee, K.M., Peng, W., Jin, S.-A., Yan, C., Can robots manifest personality?: An empirical test of personality recognition, social responses, and social presence in human–robot interaction (2006) J. Commun, 56, pp. 754-772; Looije, R., Neerincx, M.A., Cnossen, F., Persuasive robotic assistant for health self-management of older adults: Design and evaluation of social behaviors (2010) Int. J. Hum. Comput. Stud, 68, pp. 386-397; Park, E., Jin, D., Del Pobil, A.P., The law of attraction in human-robot interaction (2012) Int. J. Adv. Robot. Syst, 9, p. 35; Sandoval, E.B., Brandstetter, J., Obaid, M., Bartneck, C., Reciprocity in human-robot interaction: A quantitative approach through the prisoner’s dilemma and the ultimatum game (2016) Int. J. Soc. Robot, 8, pp. 303-317; Bartneck, D., Kulic, E., Croft, M., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, per-ceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot, 1, pp. 71-81; Santamaria, T., Nathan-Roberts, D., Personality measurement and design in human-robot interaction: A systematic and critical review (2017) Proc. Hum. Factors Ergon. Soc. Annu. Meet, 61, pp. 853-857; Grollman, D.H., Avoiding the content treadmill for robot personalities (2017) Int. J. Soc. Robot, 10, pp. 225-234; Ekman, P., Friesen, W.V., (2003) Unmasking the Face: A Guide to Recognizing Emotions from Facial Clues, , Malor Books: Los Altos, CA, USA; Russell, J.A., Fernández-Dols, J.M., (1997) The Psychology of Facial Expression, , Cambridge University Press: Cambridge, UK; Breazeal, C., Emotion and sociable humanoid robots (2003) Int. J. Hum. Comput. Stud, 59, pp. 119-155; Han, M.-J., Lin, C.-H., Song, K.-T., Robotic emotional expression generation based on mood transition and personality model (2012) IEEE Trans. Cybern, 43, pp. 1290-1303; Ilies, R., Judge, T.A., Understanding the dynamic relationships among personality, mood, and job satisfaction: A field experience sampling study (2002) Organ. Behav. Hum. Decis. Process, 89, pp. 1119-1139; Meyer, G.J., Shack, J.R., Structural convergence of mood and personality: Evidence for old and new directions (1989) J. Personal. Soc. Psychol, 57, p. 691; Van Steenbergen, H., Band, G.P., Hommel, B., In the mood for adaptation: How affect regulates conflict-driven control (2010) Psychol. Sci, 21, pp. 1629-1634; Ivanovic, M., Budimac, Z., Radovanovic, M., Kurbalija, V., Dai, W., Bădică, C., Colhon, M., Mitrovic, D., Emotional agents—state of the art and applications (2015) Comput. Sci. Inf. Syst, 12, pp. 1121-1148; Bosse, T., Duell, R., Memon, Z.A., Treur, J., Van Der Wal, C.N., Agent-based modeling of emotion contagion in groups (2014) Cogn. Comput, 7, pp. 111-136; Evers, E., De Vries, H., Spruijt, B.M., Sterck, E.H.M., The EMO-Model: An agent-based model of primate social behavior regulated by two emotional dimensions, anxiety-FEAR and satisfaction-LIKE (2014) PLoS ONE, 9, p. e87955; Shen, Z., Miao, C., An Emotional Agent in Virtual Learning Environment (2010) Transactions on Edutainment IV, pp. 22-33. , Springer: Berlin/Heidelberg, Germany; Puică, M.A., Florea, A.M., Emotional belief-desire-intention agent model: Previous work and proposed architecture (2013) Int. J. Adv. Res. Artif. Intell, 2, pp. 1-8; Bosse, T., De Lange, F.P., Development of Virtual Agents with A Theory of Emotion Regulation (2008) Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, 2, pp. 461-468. , Sydney, Australia, 9–12 December; Gratch, J., The social psychology of human-agent interaction Proceedings of the 7th International Conference on Human-Agent Interaction (HAI), pp. 1-1. , Kyoto, Japan, 6–10 October 2019; MacDorman, K.F., Chattopadhyay, D., Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not (2016) Cognition, 146, pp. 190-205; Dang, T.H.H., Hutzler, G., Hoppenot, P., Emotion Modeling for Intelligent Agents—Towards A Unifying Framework (2011) Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 3, pp. 70-73. , Lyon, France, 22–27 August; Laird, J.E., (2012) The Soar Cognitive Architecture, , MIT Press: Cambridge MA, USA; Picard, R.W., (2000) Affective Computing, , MIT Press: Cambridge MA, USA; Šabanović, S., Robots in society, society in robots (2010) Int. J. Soc. Robot, 2, pp. 439-450; Sabanovic, S., Michalowski, M., Simmons, R., Robots in the wild: Observing human-robot social interaction outside the lab Proceedings of the 9th IEEE International Workshop on Advanced Motion Control, pp. 596-601. , Istanbul, Turkey, 27–29 March 2006; Jung, M., Hinds, P., Robots in the wild: A time for more robust theories of human-robot interaction (2018) ACM Trans. Hum. Robot. Interact, 7, p. 2; Cass, A.G., Striegnitz, K., Webb, N., Yu, V., Exposing real-world challenges using HRI in the wild (2018) Proceedings of the 4th Workshop on Public Space Human-Robot Interaction at the Intl. Conf. on Human-Computer Interaction with Mobile Devices and Services, , Barcelona, Spain, 3 September; Šabanović, S., Bennett, C.C., Lee, H.R., Towards culturally robust robots: A critical social perspective on robotics and culture (2014) Proceedings of the HRI Workshop on Culture-Aware Robotics, , Bielefeld, Germany, 3–6 March; Schneider, D., Lam, R., Bayliss, A.P., Dux, P.E., Cognitive load disrupts implicit theory-of-mind processing (2012) Psychol. Sci, 23, pp. 842-847; Spunt, R.P., Lieberman, M.D., The busy social brain: Evidence for automaticity and control in the neural systems supporting social cognition and action understanding (2013) Psychol. Sci, 24, pp. 80-86; Paas, F., Van Gog, T., Sweller, J., Cognitive load theory: New conceptualizations, specifications, and integrated research perspectives (2010) Educ. Psychol. Rev, 22, pp. 115-121; Smith, S.M., Petty, R.E., Personality moderators of mood congruency effects on cognition: The role of self-esteem and negative mood regulation (1995) J. Personal. Soc. Psychol, 68, p. 1092; Robins, B., Dautenhahn, K., Te Boekhorst, R., Nehaniv, C.L., Behaviour delay and robot expressiveness in child-robot inter-actions: A user study on interaction kinesics Proceedings of the ACM/IEEE international conference on Human robot Iinteraction (HRI), pp. 17-24. , Amsterdam, The Netherlands, 12–15 March 2008; Barsalou, L.W., Breazeal, C., Smith, L.B., Cognition as coordinated non-cognition (2007) Cogn. Process, 8, pp. 79-91; Beer, R.D., Dynamical approaches to cognitive science (2000) Trends Cogn. Sci, 4, pp. 91-99; Vallacher, R.R., Read, S.J., Nowak, A., The dynamical perspective in personality and social psychology (2002) Pers. Soc. Psychol. Rev, 6, pp. 264-273; Kastner, S., De Weerd, P., Desimone, R., Ungerleider, L.G., Mechanisms of directed attention in the human extrastriate cortex as revealed by functional MRI (1998) Science, 282, pp. 108-111; Land, M.F., Nilsson, D.E., (2012) Animal Eyes, , Oxford University Press: Oxford, UK; Movellan, J.R., Tanaka, F., Fortenberry, B., Aisaka, K., The RUBI/QRIO project: Origins, principles, and first steps Proceedings of the IEEE International Conference on Development and Learning (ICDL), pp. 80-86. , Osaka, Japan, 19–21 July 2005; Bennett, C.C., Hauser, K., Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach (2013) Artif. Intell. Med, 57, pp. 9-19; Cover, T.M., Thomas, J.A., (2006) Elements of Information Theory, , John Wiley and Sons: Hoboken, NJ, USA; Shannon, C.E., The mathematical theory of communication (1948) Bell Syst. Tech. J, 27, pp. 379-423; Clark, A., Whatever next? Predictive brains, situated agents, and the future of cognitive science (2013) Behav. Brain Sci, 36, pp. 181-204; Hosoya, T., Baccus, S.A., Meister, M., Dynamic predictive coding by the retina (2005) Nature, 436, pp. 71-77; Broz, F., Nehaniv, C.L., Kose, H., Dautenhahn, K., Interaction histories and short-term memory: Enactive development of turn-taking behaviours in a childlike humanoid robot (2019) Philosophies, 4, p. 26; Bashir, F.I., Khokhar, A.A., Schonfeld, D., Object trajectory-based activity classification and recognition using hidden Markov Models (2007) IEEE Trans. Image Process, 16, pp. 1912-1919; Xia, L., Chen, C.-C., Aggarwal, J.K., View invariant human action recognition using histograms of 3D joints Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20-27. , Providence, RI, USA, 16–21 June 2012; Lones, J., Lewis, M., Cañamero, L., Lewis, M., Hormonal modulation of interaction between autonomous agents Proceedings of the 4th International Conference on Development and Learning and on Epigenetic Robotics, pp. 402-407. , Genoa, Italy, 13–16 October 2014; Belkaid, M., Cuperlier, N., Gaussier, P., Emotional modulation of peripersonal space as a way to represent reachable and comfort areas Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 353-359. , Hamburg, Germany, 28 September–2 October 2015; Grant, B.F., Chou, S.P., Goldstein, R.B., Huang, B., Stinson, F.S., Saha, T.D., Smith, S.M., Pickering, R.P., Prevalence, correlates, disability, and comorbidity of DSM-IV Borderline Personality Disorder: Results from the Wave 2 National Epidemiologic Survey on Alcohol and Related Conditions (2008) J. Clin. Psychiatry, 69, pp. 533-545; Fonagy, P., Attachment and Borderline Personality Disorder (2000) J. Am. Psychoanal. Assoc, 48, pp. 1129-1146; Honi, S., Oron-Gilad, T., Understanding and resolving failures in human-robot interaction: Literature review and model development (2018) Front. Psychol, 9, p. 861","Bennett, C.C.; School of Intelligence Computing, South Korea; email: cabennet@hanyang.ac.kr
Bennett, C.C.; College of Computing, United States; email: cabennet@hanyang.ac.kr",,,"MDPI AG",,,,,20782489,,,,"English","Information",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85102561980
"Göttler C., Elflein K., Siegwart R., Sitti M.","57195419613;57221781064;35926876800;7003431959;","Spider Origami: Folding Principle of Jumping Spider Leg Joints for Bioinspired Fluidic Actuators",2021,"Advanced Science","8","5","2003890","","",,4,"10.1002/advs.202003890","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100169235&doi=10.1002%2fadvs.202003890&partnerID=40&md5=ad8b0066fb68649df981de263925f5e0","Physical Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, 70569, Germany; Autonomous Systems Laboratory, ETH Zurich, Zürich, 8092, Switzerland; Institute for Biomedical Engineering, ETH Zurich, Zürich, 8092, Switzerland","Göttler, C., Physical Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, 70569, Germany, Autonomous Systems Laboratory, ETH Zurich, Zürich, 8092, Switzerland; Elflein, K., Physical Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, 70569, Germany; Siegwart, R., Autonomous Systems Laboratory, ETH Zurich, Zürich, 8092, Switzerland; Sitti, M., Physical Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, 70569, Germany, Institute for Biomedical Engineering, ETH Zurich, Zürich, 8092, Switzerland","Jumping spiders (Phidippus regius) are known for their ability to traverse various terrains and have targeted jumps within the fraction of a second to catch flying preys. Different from humans and insects, spiders use muscles to flex their legs, and hydraulic actuation for extension. By pressurizing their inner body fluid, they can achieve fast leg extensions for running and jumping. Here, the working principle of the articular membrane covering the spider leg joint pit is investigated. This membrane is highly involved in walking, grasping, and jumping motions. Hardness and stiffness of the articular membrane is studied using nanoindentation tests and preparation methods for scanning electron microscopy and histology are developed to give detailed information about the inner and outer structure of the leg joint and its membrane. Inspired by the stroller umbrella-like folding mechanism of the articular membrane, a robust thermoplastic polyurethane-based rotary semifluidic actuator is demonstrated, which shows increased durability, achieves working angles over 120°, produces high torques which allows lifts over 100 times of its own weight and jumping abilities. The developed actuator can be used for future grasping tasks, safe human–robot interactions and multilocomotion ground robot applications, and it can shed light into spider locomotion-related questions. © 2021 The Authors. Advanced Science published by Wiley-VCH GmbH","bioinspired robotics; fluidic actuators; hydraulics; jumping spiders","Actuators; Body fluids; Human robot interaction; Robot applications; Scanning electron microscopy; Fluidic actuators; Folding mechanism; Hydraulic actuation; Inner and outer structures; Nanoindentation tests; Preparation method; Robot interactions; Thermoplastic polyurethanes; Membranes",,,,,"Eidgenössische Technische Hochschule Zürich, ETH; Max-Planck-Gesellschaft, MPG: ETH Z?rich","The authors would like to thank A. Diem for supervising the nanoindentation device, T. Sims for assistance in histology preparation and fixation procedures, J. Spatz for usage of SEM, L. B?hler for assistance in high-speed camera recording, Y. Yue for assisting in UV-light photography, V. Oehmig for spider breeding advices, J. Nieto, M. Yunusa, J. Chen, N. Lawrence, and L. Ott for discussions and feedback on the manuscript, H. G?ttler for 3D printing maintenance, A. G?ttler for image editing and T. G?ttler as assistance for the robotic experiments. This work was funded by the Max Planck Society, ETH Z?rich and the Max Planck ETH Center for Learning Systems.","The authors would like to thank A. Diem for supervising the nanoindentation device, T. Sims for assistance in histology preparation and fixation procedures, J. Spatz for usage of SEM, L. Böhler for assistance in high‐speed camera recording, Y. Yue for assisting in UV‐light photography, V. Oehmig for spider breeding advices, J. Nieto, M. Yunusa, J. Chen, N. Lawrence, and L. Ott for discussions and feedback on the manuscript, H. Göttler for 3D printing maintenance, A. Göttler for image editing and T. Göttler as assistance for the robotic experiments. This work was funded by the Max Planck Society, ETH Zürich and the Max Planck ETH Center for Learning Systems.",,,,,,,,,"Trivedi, D., Rahn, C.D., Kier, W.M., Walker, I.D., (2008) Appl. Bionics Biomech., 5, p. 99; Deimel, R., Brock, O., (2016) Int. 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Congr., p. 3669; Nemiroski, A., Shevchenko, Y.Y., Stokes, A.A., Unal, B., Ainla, A., Albert, S., Compton, G., Whitesides, G.M., (2017) Arthrobots Soft Robot, 4, p. 183; Spröwitz, A., Göttler, C., Sinha, A., Caer, C., Öztekin, M.U., Petersen, K., Sitti, M., (2017) Proc. IEEE Int. Conf. Robot. Autom., pp. 64-70. , IEEE, Piscataway, NJ, p; Petrunkevitch, A., (1909) Ann. Entomol. Soc. Am., 2, p. 11; Foelix, R.F., (1992) Biologie der Spinnen, , G. Thieme, Stuttgart; Reußenzehn, S., (2010) M.Sc. Thesis, , University of Otago, New Zealand; Manton, S.M., Harding, J.P., (1958) J. Nat. Hist., 1, p. 161; Barth, F.G., (1973) Z. Zellforsch. Mikrosk. Anat., 144, p. 409; Elvin, C.M., Carr, A.G., Huson, M.G., Maxwell, J.M., Pearson, R.D., Vuocolo, T., Liyou, N.E., Dixon, N.E., (2005) Nature, 437, p. 999; Wiesenborn, W.D., (2011) Psyche: J. Entomol., 2011; Burrows, M., Shaw, S.R., Sutton, G.P., (2008) BMC Biol., 6, p. 41; Chen, S., Liu, L., Wang, T., (2005) Surf. Coatings Technol., 191, p. 25; Yang, G.-Z., Bellingham, J., Dupont, P.E., Fischer, P., Floridi, L., Full, R., Jacobstein, N., Wood, R., (2018) Sci. Robot., 3; Hines, L., Petersen, K., Lum, G.Z., Sitti, M., (2017) Adv. Mater., 29; Kilper, S., Facey, S.J., Burghard, Z., Hauer, B., Rothenstein, D., Bill, J., (2018) Adv. Funct. Mater., 28; Oliver, W.C., Pharr, G.M., (1992) J. Mater. Res., 7, p. 1564; Song, F., Xiao, K.W., Bai, K., Bai, Y.L., (2007) Mater. Sci. Eng. A, 457, p. 254; Weihmann, T., Karner, M., Full, R.J., Blickhan, R., (2010) J. Comp. Physiol. A, 196, p. 421; Nabawy, M.R.A., Sivalingam, G., Garwood, R.J., Crowther, W.J., Sellers, W.I., (2018) Sci. Rep., 8, p. 7142; Sensenig, A.T., Shultz, J.W., (2003) J. Exp. Biol., 206, p. 771; Arndt, R.E.A., (1981) Annu. Rev. Fluid Mech., 13, p. 273; Merrill, E.W., Pelletier, G.A., (1967) J. Appl. Physiol., 23, p. 178; Kim, Y., Yuk, H., Zhao, R., Chester, S.A., Zhao, X., (2018) Nature, 558, p. 274; Rich, S.I., Wood, R.J., Majidi, C., (2018) Nat. Electron., 1, p. 102; Hu, W., Lum, G.Z., Mastrangeli, M., Sitti, M., (2018) Nature, 554, p. 81; Shepherd, R.F., Ilievski, F., Choi, W., Morin, S.A., Stokes, A.A., Mazzeo, A.D., Chen, X., Whitesides, G.M., (2011) Proc. Natl. Acad. Sci. USA, 108; Yuk, H., Lin, S., Ma, C., Takaffoli, M., Fang, N.X., Zhao, X., (2017) Nat. Commun., 8; Mosadegh, B., Polygerinos, P., Keplinger, C., Wennstedt, S., Shepherd, R.F., Gupta, U., Shim, J., Whitesides, G.M., (2014) Adv. Funct. Mater., 24, p. 2163; Machałowski, T., Wysokowski, M., Tsurkan, M.V., Galli, R., Schimpf, C., Rafaja, D., Brendler, E., Ehrlich, H., (2019) Molecules, 24, p. 3736; Neff, D., Frazier, S.F., Quimby, L., Wang, R.-T., Zill, S., (2000) Arthropod Struct. Dev., 29, p. 75; Li, X., Bhushan, B., (2002) Mater. Charact., 48, p. 11; Pharr, G.M., Oliver, W.C., (1992) MRS Bull., 17, p. 28","Sitti, M.; Physical Intelligence Department, Germany; email: sitti@is.mpg.de
Sitti, M.; Institute for Biomedical Engineering, Switzerland; email: sitti@is.mpg.de",,,"John Wiley and Sons Inc",,,,,21983844,,,,"English","Adv. Sci.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85100169235
"Hafi L.E., Nakamura H., Taniguchi A., Hagiwara Y., Taniguchi T.","57188836029;57212144620;57189575562;26648361300;35585520600;","Teaching System for Multimodal Object Categorization by Human-Robot Interaction in Mixed Reality",2021,"2021 IEEE/SICE International Symposium on System Integration, SII 2021",,,"9382607","320","324",,1,"10.1109/IEEECONF49454.2021.9382607","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103740027&doi=10.1109%2fIEEECONF49454.2021.9382607&partnerID=40&md5=62c344047ca18cda822a04837d3503e0","Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, 525-8577, Japan","Hafi, L.E., Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, 525-8577, Japan; Nakamura, H., Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, 525-8577, Japan; Taniguchi, A., Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, 525-8577, Japan; Hagiwara, Y., Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, 525-8577, Japan; Taniguchi, T., Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, 525-8577, Japan","As service robots are becoming essential to support aging societies, teaching them how to perform general service tasks is still a major challenge preventing their deployment in daily-life environments. In addition, developing an artificial intelligence for general service tasks requires bottom-up, unsupervised approaches to let the robots learn from their own observations and interactions with the users. However, compared to the top-down, supervised approaches such as deep learning where the extent of the learning is directly related to the amount and variety of the pre-existing data provided to the robots, and thus relatively easy to understand from a human perspective, the learning status in bottom-up approaches is by their nature much harder to appreciate and visualize. To address these issues, we propose a teaching system for multimodal object categorization by human-robot interaction through Mixed Reality (MR) visualization. In particular, our proposed system enables a user to monitor and intervene in the robot's object categorization process based on Multimodal Latent Dirichlet Allocation (MLDA) to solve unexpected results and accelerate the learning. Our contribution is twofold by 1) describing the integration of a service robot, MR interactions, and MLDA object categorization in a unified system, and 2) proposing an MR user interface to teach robots through intuitive visualization and interactions. © 2021 IEEE.",,"Deep learning; Man machine systems; Mixed reality; Mobile robots; Statistics; User interfaces; Visualization; Aging societies; Bottom up approach; Human perspectives; Latent Dirichlet allocation; Object categorization; Teaching systems; Unified system; Unsupervised approaches; Social robots",,,,,"Ministry of Education, Culture, Sports, Science and Technology, Monbusho: 17H06379, 4903","This study was supported by the Japan Ministry of Education, Culture, Sports, Science and Technology (MEXT) KAKENHI Grant-in-Aid for Scientific Research on Innovative Areas “Integrative Studies of Language Evolution for Co-Creative Human Communication” (Area no. 4903, Task no. 17H06379). 1Lotf El Hafi, Hitoshi Nakamura, Akira Taniguchi, Yoshinobu Hagiwara, and Tadahiro Taniguchi are with Ritsumeikan University; 1-1-1 Noji-Higashi, Kusatsu, Shiga 525-8577, Japan. { "" l o t f i . e l h a f i , nakam ura.hitoshi, a.taniguchi, yhagiwara, t a n i g u c h i .Z @ e m . c i . r i t s u m e i . a c . j p",,,,,,,,,,"Nakamura, T., Nagai, T., Iwahashi, N., Grounding of word meanings in multimodal concepts using lda (2009) Proceedings of 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), p. 39433948. , St. Louis, United States, Oct; Araki, T., Nakamura, T., Nagai, T., Long-term learning of concept and word by robots: Interactive learning framework and preliminary results (2013) Proceedings of 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), pp. 2280-2287. , Tokyo, Japan, Nov; Liu, H., Zhang, Y., Si, W., Xie, X., Zhu, Y., Zhu, S.-C., Interactive robot knowledge patching using augmented reality (2018) Proceedings of 2018 IEEE International Conference on Robotics and Automation (ICRA 2018), pp. 1947-1954. , Brisbane, Australia, May; (2016) HoloLens, , https://www.microsoft.com/en-us/hololens, Microsoft; El Hafi, L., Isobe, S., Tabuchi, Y., Katsumata, Y., Nakamura, H., Fukui, T., Matsuo, T., Taniguchi, T., System for augmented human-robot interaction through mixed reality and robot training by non-experts in customer service environments (2020) RSJ Advanced Robotics, 34 (3-4), pp. 157-172. , Feb; Wada, K., New robot technology challenge for convenience store (2017) Proceedings of 2017 IEEE/SICE International Symposium on System Integration (SII2017), pp. 1086-1091. , Taipei, Taiwan, Dec; (2005) Unity Technologies, , https://unity3d.com/, Unity; (2018) Sciurus17, , https://rt-net.jp/products/sciurus17/, RT Corporation; Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Ng, A., Ros: An open-source robot operating system (2009) Proceedings of 2009 IEEE Workshop on Open Source Software, , Kobe, Japan, May; Lamb, P., (2017) ARToolKt5, , https://github.com/artoolkit/ARToolKit5; Bischoff, M., (2017) ROS#, , https://github.com/siemens/ros-sharp; (2000) OpenCV: Open Source Computer Vision Library, , https://github.com/opencv/opencv, Intel Corporation; Alcantarilla, P.F., Nuevo, J., Bartoli, A., Fast explicit diffusion for accelerated features in nonlinear scale spaces (2013) Proceedings of 24th British Machine Vision Conference (BMVC 2013), 13, pp. 1-11. , Bristol, United Kingdoms, Sept",,,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE/SICE International Symposium on System Integration, SII 2021","11 January 2021 through 14 January 2021",,168053,,9781728176581,,,"English","IEEE/SICE Int. Symp. Syst. Integr., SII",Conference Paper,"Final","",Scopus,2-s2.0-85103740027
"Hussain K., Wang X., Omar Z., Elnour M., Ming Y.","57221701442;35254291600;57221696032;57219110471;57222153996;","Robotics and Artificial Intelligence Applications in Manage and Control of COVID-19 Pandemic",2021,"2021 International Conference on Computer, Control and Robotics, ICCCR 2021",,,"9349386","66","69",,2,"10.1109/ICCCR49711.2021.9349386","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101709426&doi=10.1109%2fICCCR49711.2021.9349386&partnerID=40&md5=10cfb2fcec495fa2e50de036f5831308","Southeast University, School of Mechanical Engineering, Nanjing, China; Southeast University, School of Automation, Nanjing, China","Hussain, K., Southeast University, School of Mechanical Engineering, Nanjing, China; Wang, X., Southeast University, School of Mechanical Engineering, Nanjing, China; Omar, Z., Southeast University, School of Mechanical Engineering, Nanjing, China; Elnour, M., Southeast University, School of Automation, Nanjing, China; Ming, Y., Southeast University, School of Mechanical Engineering, Nanjing, China","Robotics and Artificial Intelligence (A.I.), applications have played an essential role in managing and controlling COVID-19 pandemic specifically in hospitals, hotels, restaurants, airports, transportation systems, schools, and others community levels. Artificial intelligence technologies, autonomous vehicles, drones, mobile robots, humanoid robots, manipulator robots and other intelligent robots are used potentially during the outbreak to control the transmission network from person to person. Due to the highly uses, we consider A.I. and robotics in health will likely continue after the COVID-19 pandemic and development of these technologies is needed for fighting against infectious diseases. Furthermore, the uses of these systems in different fields like industry, sports, ergonomics, distribution of goods, and social life will keep going in the coming years. In this article, the artificial intelligent devices and robots helped in fighting, and managing covid19 are explored, including the new trends. These new trends are led by artificial intelligence, autonomous driving, network communication, cooperative work, nanorobotics, friendly human-robot interfaces, safe human-robot interaction, and emotion expression and perception. Furthermore, these news trends are applied to different fields such as medicine, health care, sports, ergonomics, industry, distribution of goods, and service robot. These tendencies will keep going their evolution in the coming years. © 2021 IEEE.","artificial intelligence (A.I.); autonomous systems; COVID-19; drones; robotics; wearable technologies","Agricultural robots; Anthropomorphic robots; Autonomous vehicles; Behavioral research; Cooperative communication; Distribution of goods; Ergonomics; Industrial manipulators; Industrial robots; Intelligent robots; Manipulators; Mobile robots; Robotics; Service industry; Sports; Sports medicine; Vehicle transmissions; Artificial intelligence technologies; Artificial intelligent; Human-Robot Interface; Infectious disease; Manipulator robots; Network communications; Safe human-robot interaction; Transportation system; Social robots",,,,,,,,,,,,,,,,"Coronavirus Graphs: Worldwide Cases and Deaths ?Worldometer, , https://www.worldometers.info/coronavirus/worldwide-graphs/#totalcases, [Accessed: 10-Nov-2020]; Colombo, M.G., Piva, E., Quas, A., Rossi-Lamastra, C., How hightech entrepreneurial ventures cope with the global crisis: Changes in product innovation and internationalization strategies (2016) Ind. Innov; AI-Enabled Technologies That Fight the Coronavirus Outbreak, pp. 23-45. , I. R. Identification; Robotics, S., (2020) Combating COVID-19-The Role of Robotics in Managing Public Health and Infectious Diseases, pp. 1-3. , no. March; Tsikala Vafea, M., Emerging Technologies for Use in the Study, Diagnosis, and Treatment of Patients with COVID-19 (2020) Cell. Mol. Bioeng; Culp, W.C., Coronavirus Disease 2019 (2020) A A Pract, 14 (6), p. e01218; Yang, G.Z., Combating COVID-19-The role of robotics in managing public health and infectious diseases (2020) Science Robotics; Tavakoli, M., Carriere, J., Torabi, A., Robotics, Smart Wearable Technologies, and Autonomous Intelligent Systems for Healthcare during the COVID-19 Pandemic: An Analysis of the State of the Art and Future Vision (2020) Adv. Intell. Syst; OMeara, S., Coronavirus: Hospital Ward Staffed Entirely by Robots Opens in China, , https://www.newscientist.com/article/2236777-coronavirus-hospitalward-staffed-entirely-by-robots-opens-in-china/, 2020, March 09 [Accessed: 16-Aug-2020]; Any, T., (2020) Healthcare Applications of Mobile Robotics during the COVID-19 Pandemic Response, , no. April; Falden, S., (2016) Evaluation of the UV-Disinfection Robot; Skorup, B., Haaland, C., How Drones Can Help Fight the Coronavirus (2020) Ssrn Electron. J; Stojanovic, R., (2020) A Headset like Wearable Device to Track COVID-19 Symptoms, pp. 8-11; Qureshi, D.M.S., Singh, P., Swarnkar, P., Goud, H., Robotics Solutions to Combat Novel Corona Virus Disease-2019 (COVID-19) Ssrn Electron. J, 2019, p. 2020; Shuangyi, H.L.Z.H., Kehao Wang, W., Design of a Low-cost Miniature Robot to Assist the COVID-19 Nasopharyngeal Swab Sampling; Malik, A.A., Masood, T., Kousar, R., Repurposing factories with robotics in the face of COVID-19: Movie 1 (2020) Sci. Robot, 5 (43), p. eabc2782; Custom-made 3D-printed face masks in case of pandemic crisis situations with a lack of commercially available FFP2/3 masks (2020) Int. J. Oral Maxillofac. Surg; Coronavirus: Singapore Develops Smartphone App for Efficient Contact Tracing, Singapore News & Top Stories-The Straits Times, , https://www.straitstimes.com/singapore/coronavirus-singaporedevelops-smartphone-App-for-efficient-contact-Tracing, [Accessed: 17-Dec-2020]; Mei, X., Artificial intelligence-enabled rapid diagnosis of patients with COVID-19 (2020) Nat. Med","Wang, X.; Southeast University, China; email: xswang@seu.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.","2021 International Conference on Computer, Control and Robotics, ICCCR 2021","8 January 2021 through 10 January 2021",,167105,,9781728190358,,,"English","Int. Conf. Comput., Control Robot., ICCCR",Conference Paper,"Final","",Scopus,2-s2.0-85101709426
"Liu J., Yamada Y., Akiyama Y., Okamoto S., Iki Y.","57218350116;35410760400;55516094900;7403338059;57222145553;","A Novel Safety-Oriented Control Strategy for Manipulators Based on the Observation and Adjustment of the External Momentum",2021,"2021 International Conference on Computer, Control and Robotics, ICCCR 2021",,,"9349377","80","86",,,"10.1109/ICCCR49711.2021.9349377","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101699154&doi=10.1109%2fICCCR49711.2021.9349377&partnerID=40&md5=00ab9b01bb82270ecee409d61adb29d6","Nagoya University, School of Engineering, Nagoya, Japan","Liu, J., Nagoya University, School of Engineering, Nagoya, Japan; Yamada, Y., Nagoya University, School of Engineering, Nagoya, Japan; Akiyama, Y., Nagoya University, School of Engineering, Nagoya, Japan; Okamoto, S., Nagoya University, School of Engineering, Nagoya, Japan; Iki, Y., Nagoya University, School of Engineering, Nagoya, Japan","Human safety assurance in physical human-robot interactions (pHRIs) has become a challenge for current robotic applications. Considering the safety assurance of a pHRI validation experiment, the viscoelastic properties of a human body part were investigated in a previous study and were added in a pHRI simulation. This study focuses on a threshold momentum value as an injury criterion for momentum control strategy design. A novel safety-oriented control strategy is proposed, which consists of a conventional proportional-derivative (PD) controller, an external momentum observer-based compensator for monitoring the external momentum, and an adjustment loop for shaping the trajectory motion command to bind the external momentum within the injury criterion. The proposed control strategy can be designed without needing to obtain the mechanical properties of the environment. The strategy can also be combined with the related injury criterion. In addition, it shows the effectiveness of ensuring safety even in the worst pHRI clamping situation, where a human body part can escape from such a situation. Simulation results show that the proposed control strategy performs well in the trajectory tracking task, and the adjustment loop successfully shapes the predetermined trajectory motion command to bind the external momentum within the injury criterion. © 2021 IEEE.","control strategy; human-robot interaction; in-vivo experiment; momentum injury criteria; safety; viscoelasticity","Agricultural robots; Human robot interaction; Motion planning; Motion tracking; Robotics; Safety engineering; Trajectories; Viscoelasticity; Control strategies; Observer-based compensators; Physical human-robot interactions; Proportional-derivative controllers; Robotic applications; Safety assurance; Trajectory tracking; Viscoelastic properties; Momentum",,,,,,,,,,,,,,,,"Ogure, T., Nakabo, Y., Jeong, S., Yamada, Y., Hazard analysis of an industrial upper-body humanoid (2009) Ind. Rob, 36 (5), pp. 469-476; Siciliano, B., Khatib, O., (2009) Springer Handbook of Robotics, 46 (6); Xia, J., Jiang, Z., Liu, H., Cai, H., Wu, G., A Novel hybrid safetycontrol strategy for a manipulator (2014) Int. J. Adv. Robot. Syst, 11 (1), pp. 1-10; Yamada, Y., Hirasawa, Y., Huang, S.Y., Umetani, Y., Fail-safe human/robot contact in the safety space (1996) Proceedings 5th Ieee International Workshop on Robot and Human Communication. ROMAN? 96 Tsukuba, pp. 59-64; Suita, K., Yamada, Y., Tsuchida, N., Imai, K., Ikeda, H., Sugimoto, N., Failure-To-safety ?Kyozon? system with simple contact detection and stop capabilities for safe human-Autonomous robot coexistence (1995) Proceedings-IEEE International Conference on Robotics and Automation, 3, pp. 3089-3096; IEC (2009) 60204-5.1: Safety of Machinery-Electrical Equipment of Machines-Part 1: General Requirements; Robots and robotic devices-Collaborative robots (2016) Iso/Ts 15066 2016, pp. 1-33; Haddadin, S., Albu-Schaffer, A., Albu-Schaffer, A., Albu-Sch, A., Evaluation of collision detection and reaction for a human-friendly robot on biological tissues (2008) 6th IARP/ IEEE-RAS/EURON Work. Tech. Challenges Dependable Robot. Hum. Environ, , http://www.phriends.eu/Stanford-08a.pdf; Salisbury, J.K., Active Stiffness Control of a Manipulator in Cartesian Coordinates (1980) Proceedings of the Ieee Conference on Decision and Control, 1, pp. 95-100; Ajoudani, A., Tsagarakis, N.G., Bicchi, A., On the role of robot configuration in Cartesian stiffness control (2015) Proceedings-IEEE International Conference on Robotics and Automation, 2015, pp. 1010-1016. , June, no. June; Hogan, N., Impedance Control: An Approach to Manipulation 1984 American Control Conference, Jul. 1984, pp. 304-313; Morinaga, S., Kosuge, K., Collision detection system for manipulator based on adaptive impedance control law 2003 Ieee International Conference on Robotics and Automation (Cat. No.03CH37422), 2003, 1, pp. 1080-1085; Cheah, C.C., Wang, D., Learning impedance control for robotic manipulators (1998) Ieee Trans. Robot. Autom, 14 (3), pp. 452-465; Raibert, M.H., Craig, J.J., Hybrid position/force control of manipulators (1981) J. Dyn. Syst. Meas. Control. Trans. Asme, 103 (2), pp. 126-133; De Luca, A., Mattone, R., Sensorless robot collision detection and hybrid force/motion control (2005) Proceedings-IEEE International Conference on Robotics and Automation, 2005, pp. 999-1004. , April; Haddadin, S., Albu-Schaffer, A., De Luca, A., Hirzinger, G., Collision detection and reaction: A contribution to safe physical human-robot interaction 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Iros, 2008, pp. 3356-3363; Haddadin, S., (2014) Towards Safe Robots: Approaching Asimov?s 1st Law, 90; Fujikawa, T., Sugiura, R., Nishikata, R., Nishimoto, T., Critical contact pressure and transferred energy for soft tissue injury by blunt impact in human-robot interaction (2017) International Conference on Control, Automation and Systems, 2017, pp. 867-872. , Octob; Haddadin, S., On making robots understand safety: Embedding injury knowledge into control (2012) Int. J. Rob. Res, 31 (13), pp. 1578-1602; Sakaino, S., Ohnishi, K., Dynamic force control having no singular point on momentum control system Proceedings of the 2007 4th Ieee International Conference on Mechatronics, Icm 2007, 2007, pp. 8-10. , May; Alcocer, A., Robertsson, A., Valera, A., Johansson, R., Force estimation and control in robot manipulators (2003) Ifac Proc. Vol, 36 (17), pp. 55-60; Ohishi, K., Miyazaki, M., Fujita, M., Ogino, Y., H/sup infinity /observer based force control without force sensor Proceedings Iecon ?91 1991 International Conference on Industrial Electronics, Control and Instrumentation, 1991, 2 (4), pp. 1049-1054; Sakaino, S., Ohnishi, K., A composition of decoupling motion controller based on momentum and its application for singular configurations (2007) Iecon Proc. (Industrial Electron. Conf, pp. 2331-2336; Lewis, F.L., Dawson, D.M., Abdallah, C.T., (2004) Robot Manipulator Control: Theory and Practice, , CRC Press; Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., (2009) Robotics: Modelling, Planning and Control 9781846286414, , Springer Science & Business Media; Iki, Y., Yamada, Y., Akiyama, Y., Okamoto, S., Niwa, K., Liu, J., Evaluation of contact between a human hand and a robot end tip for designing a dummy (2019) Proc. Jsme Annu. Conf. Robot. Mechatronics, 2019, pp. 2A1-F08; Kelly, R., A Tuning Procedure for Stable PID Control of Robot Manipulators (1995) Robotica, 13 (2), pp. 141-148","Liu, J.; Nagoya University, Japan; email: liu.jian@k.mbox.nagoya-u.ac.jp",,,"Institute of Electrical and Electronics Engineers Inc.","2021 International Conference on Computer, Control and Robotics, ICCCR 2021","8 January 2021 through 10 January 2021",,167105,,9781728190358,,,"English","Int. Conf. Comput., Control Robot., ICCCR",Conference Paper,"Final","",Scopus,2-s2.0-85101699154
"Kim D.H., Lee D., Kim Y., Kim S., Shin D.","57207193276;57205745198;55662738300;57221790286;55449807100;","A power assistant algorithm based on human–robot interaction analysis for improving system efficiency and riding experience of e‐bikes",2021,"Sustainability (Switzerland)","13","2","768","1","19",,1,"10.3390/su13020768","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100187764&doi=10.3390%2fsu13020768&partnerID=40&md5=b41818b342bad304fc9a29228d37e62a","Department of Mechanical Engineering, Human‐Centered Robotics Lab, Chung‐Ang University, Seoul, 06974, South Korea","Kim, D.H., Department of Mechanical Engineering, Human‐Centered Robotics Lab, Chung‐Ang University, Seoul, 06974, South Korea; Lee, D., Department of Mechanical Engineering, Human‐Centered Robotics Lab, Chung‐Ang University, Seoul, 06974, South Korea; Kim, Y., Department of Mechanical Engineering, Human‐Centered Robotics Lab, Chung‐Ang University, Seoul, 06974, South Korea; Kim, S., Department of Mechanical Engineering, Human‐Centered Robotics Lab, Chung‐Ang University, Seoul, 06974, South Korea; Shin, D., Department of Mechanical Engineering, Human‐Centered Robotics Lab, Chung‐Ang University, Seoul, 06974, South Korea","As robots are becoming more accessible in our daily lives, the interest in physical human– robot interaction (HRI) is rapidly increasing. An electric bicycle (E‐bike) is one of the best examples of HRI, because a rider simultaneously actuates the rear wheel of the E‐bike in close proximity. Most commercially available E‐bikes employ a control methodology known as a power assistant system (PAS). However, this type of system cannot offer fully efficient power assistance for E‐bikes since it does not account for the biomechanics of riders. In order to address this issue, we propose a control algorithm to increase the efficiency and enhance the riding experience of E‐bikes by implementing the control parameters acquired from analyses of human leg kinematics and muscular dynamics. To validate the proposed algorithm, we have evaluated and compared the performance of E‐bikes in three different conditions: (1) without power assistance, (2) assistance with a PAS algorithm, and (3) assistance with the proposed algorithm. Our algorithm required 5.09% less human energy consumption than the PAS algorithm and 11.01% less energy consumption than a bicycle operated without power assistance. Our algorithm also increased velocity stability by 11.89% and acceleration stability by 27.28%, and decreased jerk by 12.36% in comparison to the PAS algorithm. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Electric bicycles (E‐bikes); Energy consumption; Human analysis; Leg kinematics; Muscular dynamics; Riding experience","algorithm; biomechanics; detection method; efficiency measurement; electric vehicle; methodology; parameter estimation",,,,,"Ministry of Trade, Industry and Energy, MOTIE; Ministry of Science, ICT and Future Planning, MSIP: 20007058, 2018R1C1B6008549; National Research Foundation of Korea, NRF","Funding: This study was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No.NRF‐2018R1C1B6008549). Also, this study was supported by the Industrial Technology Innovation Program (No. 20007058, Development of safe and comfortable human augmentation hybrid robot suit) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea). And, this study was supported by the Chung‐Ang University Graduate Research Scholarship in 2017.","This study was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No.NRF?2018R1C1B6008549). Also, this study was supported by the Industrial Technology Innovation Program (No. 20007058, Development of safe and comfortable human augmentation hybrid robot suit) funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea). And, this study was supported by the Chung?Ang University Graduate Research Scholarship in 2017.The authors would like to thank people in the Human?Centered Robotics Laboratory at Chung?Ang University for their valuable comments and feedback.",,,,,,,,,"Shin, D., Sardellitti, I., Khatib, O., Cutkosky, M., Design and control of a bio‐inspired human‐friendly robot (2010) Int. J. Robot. Res, 29, pp. 571-584; Wang, P., (2018) Dynamics and control of rider‐bicycle systems, , Ph.D. dissertation. Rutgers University, Piscataway, NJ, USA, October; Wang, P., Gong, Y., Yi, J., Liu, T., An integrated stationary/moving balance control of an autonomous bikebot Proceedings of the IEEE American Control Conference, pp. 3273-3278. , Philadelphia, PA, USA, 10–12 July 2019; Kim, N., Yun, S., Shin, D., A bio‐inspired lightweight wrist for high‐dof robotic prosthetic arms (2019) Ieee/Asme Trans. Mechatron, 24, pp. 2674-2683; Lee, D., Kim, D.H., Che, C.H., In, J.B., Shin, D., Highly durable bidirectional joint with twisted string actuators and variable radius pulley (2020) Ieee/Asme Trans. Mechatron, 25, pp. 2674-2683; Goodrich, M.A., Schultz, A.C., Human–robot interaction: A survey (2007) Found. Trends® Hum.–Comput. Interact, 1, pp. 203-275; Kim, N. H., Kim, J. M., Khatib, O., Shin, D., Design optimization of hybrid actuation combining macro‐mini actuators (2017) Int. J. Precis. Eng. Manuf, 18, pp. 519-527; Hansen, K. B., Nielsen, T. A. S., Exploring characteristics and motives of long distance commuter cyclists (2014) Transp. Policy, 35, pp. 57-63; Ton, D., Duives, D. C., Cats, O., Hoogendoorn‐Lanser, S., Hoogendoorn, S. P., Cycling or walking? Determinants of mode choice in the Netherlands (2019) Transp. Res. Part A: Policy Pract, 123, pp. 7-23; Koszowski, C., Gerike, R., Hubrich, S., Götschi, T., Pohle, M., Wittwer, R., Active mobility: bringing together transport planning, urban planning, and public health (2019) Towards User‐Cent. Transp. Eur. Springer: Chamswitzerland, pp. 149-171; Hatada, K., Hirata, K., Energy‐efficient power assisting methods for periodic motions and its experimental verification (2012) Proceedings of the 2012 IEEE International Conference on Industrial Technology, pp. 858-863. , Athens, Greece, 19–21 March; Spagnol, P., Alli, G., Spelta, C., Lisanti, P., Todeschini, F., Savaresi, S.M., Morelli, A., A full hybrid electric bike: How to increase human efficiency (2012) Proceedings of the 2012 American Control Conference (ACC), pp. 2761-2766. , Montreal, QC, Canada. 27–29 June; Hull, M.L., Gonzalez, H., Bivariate optimization of pedaling rate and crank arm length in cycling (1988) J. Biomech, 21, pp. 839-849; Too, D., Williams, C., Determination of the optimal crank arm length to maximize peak power production in an upright cycling position (2018) Hum. Power Ej, 25, pp. 1-26; Cafolla, D., Chen, I, Ceccarelli, M., An experimental characterization of human torso motion (2015) Front. Mech. Eng, 10, pp. 311-325. , M; Gorenflo, C., Rios, I., Golab, L., Keshav, S., Usage patterns of electric bicycles: an analysis of the WeBike project (2017) J. Adv. Transp, pp. 1-14; Hsu, R. C., Liu, C. T., Chan, D. Y., A reinforcement‐learning‐based assisted power management with QoR provisioning for human–electric hybrid bicycle (2012) Trans. Ind. Electronics, 59, pp. 3350-3359; Lee, J. S., Jiang, J. W., Enhanced fuzzy‐logic‐based power‐assisted control with user‐adaptive systems for human‐electric bikes (2019) Iet Intell. Transp. Systems, 13, pp. 1492-1498; Liu, C. T., Hsu, R. C., A fuzzy Q‐learning based assisted power management method for comfortable riding of pedelec (2015) IEEE International Conference on Automation, Robotics and Applications, pp. 580-585. , Queenstown, New Zealand. 17–19 February; Bhargava, L. J., Pandy, M. G., Anderson, F. C., A phenomenological model for estimating metabolic energy consumption in muscle contraction (2004) J. Biomechanics, 37, pp. 81-88; Marks, R., The effect of isometric quadriceps strength training in mid‐range for osteoarthritis of the knee (1993) Arthritis Rheum.: Off. J. Am. Coll. Rheumatology, 6, pp. 52-56; Thelen, D. G., Adjustment of muscle mechanics model parameters to simulate dynamic contractions in older adults (2003) J. Biomechanics. Eng, 125, pp. 70-77; Ting, L. H., Kautz, S. A., Brown, D. A., Zajac, F. E., Phase reversal of biomechanical functions and muscle activity in backward cycling (1999) J. Neurophysiology, 81, pp. 544-551; Childers, W. L., Kistenberg, R. S., Gregor, R. J., The biomechanics of cycling with a transtibial amputation: recommendations for prosthetic design and direction for future research (2009) Prosthet. Orthot. Intl, 33, pp. 256-271; Lai, A. K., Arnold, A. S., Wakeling, J. M., Why are antagonist muscles co‐activated in my simulation? A musculoskeletal model for analysing human locomotor tasks (2017) Ann. Biomed. Engineering, 45, pp. 2762-2774; Shi, F., Improving urban non‐motorized mobility for public affairs trips: a survey and analysis of innovative official bicycles in Nanjing city, China (2019) J. Urban Management, 8, pp. 396-407; Jensen, P., Rouquier, J. B., Ovtracht, N., Robardet, C., Characterizing the speed and paths of shared bicycle use in Lyon (2010) Transp. Res. Part D: Transp. Environment, 15, pp. 522-524; Rossi, R., Mantuano, A., Pascucci, F., Rupi, F., Fitting time headway and speed distributions for bicycles on separate bicycle lanes (2017) Transp. Res. Procedia, 27, pp. 19-26; Szecsi, J., Krause, P., Krafczyk, S., Brandt, T., Straube, A., Functional output improvement in FES cycling by means of forced smooth cycling (2007) Med. Sci. Sports Exercise, 39, pp. 764-780; Zhang, L., Diraneyya, M. M., Ryu, J., Haas, C. T., Abdel‐Rahman, E. M., Jerk as an indicator of physical exertion and fatigue (2019) Autom. Construction, 104, pp. 120-128","Shin, D.; Department of Mechanical Engineering, South Korea; email: djshin@cau.ac.kr",,,"MDPI",,,,,20711050,,,,"English","Sustainability",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85100187764
"Baltanas S.-F., Ruiz-Sarmiento J.-R., Gonzalez-Jimenez J.","57215534255;50861829000;6603553203;","Improving the head pose variation problem in face recognition for mobile robots",2021,"Sensors (Switzerland)","21","2","659","1","18",,,"10.3390/s21020659","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100183877&doi=10.3390%2fs21020659&partnerID=40&md5=8244e19e6f5bde43c42ddb281bebed05","Machine Perception and Intelligent Robotics Group (MAPIR), Department of System Engineering and Automation, Biomedical Research Institute of Malaga (IBIMA), University of Malaga, Málaga, 29071, Spain","Baltanas, S.-F., Machine Perception and Intelligent Robotics Group (MAPIR), Department of System Engineering and Automation, Biomedical Research Institute of Malaga (IBIMA), University of Malaga, Málaga, 29071, Spain; Ruiz-Sarmiento, J.-R., Machine Perception and Intelligent Robotics Group (MAPIR), Department of System Engineering and Automation, Biomedical Research Institute of Malaga (IBIMA), University of Malaga, Málaga, 29071, Spain; Gonzalez-Jimenez, J., Machine Perception and Intelligent Robotics Group (MAPIR), Department of System Engineering and Automation, Biomedical Research Institute of Malaga (IBIMA), University of Malaga, Málaga, 29071, Spain","Face recognition is a technology with great potential in the field of robotics, due to its prominent role in human-robot interaction (HRI). This interaction is a keystone for the successful deployment of robots in areas requiring a customized assistance like education and healthcare, or assisting humans in everyday tasks. These unconstrained environments present additional difficulties for face recognition, extreme head pose variability being one of the most challenging. In this paper, we address this issue and make a fourfold contribution. First, it has been designed a tool for gathering an uniform distribution of head pose images from a person, which has been used to collect a new dataset of faces, both presented in this work. Then, the dataset has served as a testbed for analyzing the detrimental effects this problem has on a number of state-of-the-art methods, showing their decreased effectiveness outside a limited range of poses. Finally, we propose an optimization method to mitigate said negative effects by considering key pose samples in the recognition system’s set of known faces. The conducted experiments demonstrate that this optimized set of poses significantly improves the performance of a state-of-the-art, cutting-edge system based on Multitask Cascaded Convolutional Neural Networks (MTCNNs) and ArcFace. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Assistant mobile robots; Cross-pose face recognition; Face recognition; Human-robot interaction; MAPIR Faces","Convolutional neural networks; Educational robots; Social robots; Cutting-edge systems; Human robot Interaction (HRI); Number of state; Optimization method; Recognition systems; State of the art; Unconstrained environments; Uniform distribution; Face recognition; face; facial recognition; head; human; robotics; Face; Facial Recognition; Head; Humans; Neural Networks, Computer; Robotics",,,,,"Universidad de Málaga, UMA; Ministry of Economy, Trade and Industry, METI","Work partially funded by the WISER project ([DPI2014-55826-R]), financed by the Spanish Ministry of Economy, Industry and Competitiveness, and by a postdoc contract from the I-PPIT-UMA program, financed by the University of M?laga.","Funding: Work partially funded by the WISER project ([DPI2014-55826-R]), financed by the Spanish Ministry of Economy, Industry and Competitiveness, and by a postdoc contract from the I-PPIT-UMA program, financed by the University of Málaga.",,,,,,,,,"Orlandini, A., Kristoffersson, A., Almquist, L., Björkman, P., Cesta, A., Cortellessa, G., Galindo, C., Kiselev, A., ExCITE Project: A review of forty-two months of robotic telepresence technology evolution (2016) Presence Teleoperators Virtual Environ, 25, pp. 204-221. , [CrossRef]; Borghese, N.A., Bulgheroni, M., Miralles, F., Savanovic, A., Ferrante, S., Kounoudes, T., Cid Gala, M., Gonzalez-Jimenez, J., Heterogeneous Non Obtrusive Platform to Monitor, Assist and Provide Recommendations to Elders at Home: The MoveCare Platform (2019) Ambient Assisted Living; Lecture Notes in Electrical Engineering, pp. 53-69. , Casiddu, N., Porfirione, C., Monteriù, A., Cavallo, F., Eds.; Springer International Publishing: Cham, Switzerland, [CrossRef]; Sabhanayagam, T., Venkatesan, V.P., Senthamaraikannan, K., A Comprehensive Survey on Various Biometric Systems (2018) Int. 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Res, 13, pp. 2276-2297; Krizhevsky, A., Sutskever, I., Hinton, G.E., ImageNet Classification with Deep Convolutional Neural Networks (2012) Advances in Neural Information Processing Systems 25, pp. 1097-1105. , Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q., Eds.; Curran Associates, Inc.: Red Hook, NY, USA; Taigman, Y., Yang, M., Ranzato, M., Wolf, L., DeepFace: Closing the Gap to Human-Level Performance in Face Verification Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701-1708. , Columbus, OH, USA, 23–28 June 2014; Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E., (2007) Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments, , Technical report; University of Massachusetts: Amherst, MA, USA; Klare, B.F., Klein, B., Taborsky, E., Blanton, A., Cheney, J., Allen, K., Grother, P., Jain, A.K., Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1931-1939. , Boston, MA, USA, 7–12 June 2015; ISSN: 1063-6919. 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Appl, 23, pp. 479-490. , [CrossRef]; Bansal, A., Nanduri, A., Castillo, C., Ranjan, R., Chellappa, R., (2017) UMDFaces: An Annotated Face Dataset for Training Deep Networks, , arXiv arXiv:1611.01484; Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., Pantic, M., 300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 397-403. , Sydney, Australia, 2–8 December 2013; Savran, A., Alyüz, N., Dibeklioğlu, H., Çeliktutan, O., Gökberk, B., Sankur, B., Akarun, L., Bosphorus Database for 3D Face Analysis (2008) Biometrics and Identity Management; Lecture Notes in Computer Science, pp. 47-56. , Schouten, B., Juul, N.C., Drygajlo, A., Tistarelli, M., Eds.; Springer: Berlin/Heidelberg, Germany, [CrossRef]; Goodfellow, I., Bengio, Y., Courville, A., (2016) Deep Learning, , http://www.deeplearningbook.org, MIT Press: Cambridge, MA, USA, (accessed on 19 January 2021); Zhang, K., Zhang, Z., Li, Z., Qiao, Y., Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks (2016) IEEE Signal Process. Lett, 23, pp. 1499-1503. , [CrossRef]; Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L., Sphereface: Deep hypersphere embedding for face recognition Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 212-220. , Honolulu, HI, USA, 21–26 July 2017; Shi, Y., Jain, A.K., Probabilistic Face Embeddings (2019) Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), , Seoul, Korea, 27 October–2 November; Goldberg, D.E., (1989) Genetic Algorithms in Search, Optimization and Machine Learning, , 1st ed.; Addison-Wesley Longman Publishing Co., Inc.: Reading, MA, USA","Baltanas, S.-F.; Machine Perception and Intelligent Robotics Group (MAPIR), Spain; email: sambalmol@uma.es",,,"MDPI AG",,,,,14248220,,,"33477884","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85100183877
"Fu J., Gan D.","57354533200;23060288900;","A reconfigurable variable-stiffness parallel beam for compliant robotic mechanisms towards safe human interaction",2021,"Proceedings of the ASME Design Engineering Technical Conference","8A-2021",,"V08AT08A020","","",,,"10.1115/DETC2021-70226","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119972446&doi=10.1115%2fDETC2021-70226&partnerID=40&md5=23b7eeb8a85c11b225c702975969c727","Polytechnic Institute, Purdue University, West Lafayette, IN  47907, United States","Fu, J., Polytechnic Institute, Purdue University, West Lafayette, IN  47907, United States; Gan, D., Polytechnic Institute, Purdue University, West Lafayette, IN  47907, United States","To co-work with humans, robotic mechanisms need to have variable stiffness with high rigidity for performance and low compliance for safe interactions. This paper introduces a reconfigurable variable-stiffness parallel beam (VSPB) which can be used in both robotic joints and links for variable compliance. The VSPB is a compliant cantilever mechanism with hollow parallel beams in the middle and solid connections at both ends. Stiffness adjusting can be realized by changing the cross-sectional area property of the hollow beam segment discretely through a bistable mechanism block or continuously by the block sliding. Detailed stiffness models of the two VSPB stiffness modes with the block on and off are derived using the approach of serially connected beam modeling and superposition combination. The developed model not only works for thin-walled flexure beams but also general thick beam models. The stiffness change relationship with various design parameters is investigated using the developed model and validated by finite element analysis (FEA) results. The correlation between parameters and errors between FEA and theoretical values is observed and analyzed to optimize the model. These methods and results provide a new concept and theoretical basis for developing new variable stiffness robotic mechanisms towards safe human-robot interaction applications. © 2021 by ASME","Compliant mechanisms; Discrete variable stiffness; Parallel beam; Reconfigurable beam; Stiffness modeling","Human robot interaction; Mechanisms; Robotics; Stiffness; Thin walled structures; Beam modeling; Developed model; Discrete variable stiffness; Discrete variables; Parallel beams; Reconfigurable; Reconfigurable beam; Robotic mechanisms; Stiffness modeling; Variable stiffness; Compliant mechanisms",,,,,,,,,,,,,,,,"Keller, S., An approach of a computerized planning assistant to the system design of collaborative robot installations (2016) IEEE Int. Conf. Emerg. Technol. Fact. Autom. ETFA, Institute of Electrical and Electronics Engineers Inc; Jin, Yan, Price, Mark, (2019) Advances in Manufacturing Technology XXXIII, , ed., Ios Pr Inc; Bdiwi, M., Pfeifer, M., Sterzing, A., A new strategy for ensuring human safety during various levels of interaction with industrial robots (2017) CIRP Ann. - Manuf. Technol, 66 (1), pp. 453-456; Schiavi, R., Bicchi, A., Flacco, F., Integration of active and passive compliance control for safe human-robot coexistence (2009) Proc. - IEEE Int. Conf. Robot. Autom, pp. 259-264; Song, K.T., Chang, Y.H., Chen, J.H., 3D vision for object grasp and obstacle avoidance of a collaborative robot (2019) IEEE/ASME Int. Conf. Adv. Intell. Mechatronics, AIM, Institute of Electrical and Electronics Engineers Inc, pp. 254-258; Popov, D., Klimchik, A., Mavridis, N., Collision detection, localization & classification for industrial robots with joint torque sensors (2017) RO-MAN 2017 - 26th IEEE Int. Symp. Robot Hum. Interact. Commun., Institute of Electrical and Electronics Engineers Inc, pp. 838-843; Pang, G., Development of Flexible Robot Skin for Safe and Natural Human-Robot Collaboration (2018) Micromachines, 9 (11), p. 576; She, Y., Dynamic modeling of a 2D compliant link for safety evaluation in human-robot interactions (2015) IEEE Int. Conf. Intell. Robot. Syst., Institute of Electrical and Electronics Engineers Inc, pp. 3759-3764; Gan, D., Stiffness design for a spatial three degrees of freedom serial compliant manipulator based on impact configuration decomposition (2013) J. Mech. Robot, 5 (1), p. 11002. , 1-11002-10; Awad, M.I., Modeling, design & characterization of a novel Passive Variable Stiffness Joint (pVSJ) (2016) IEEE Int. Conf. Intell. Robot. Syst., Institute of Electrical and Electronics Engineers Inc, pp. 323-329; Awad, M.I., Design of a novel passive binary-controlled variable stiffness joint (BpVSJ) towards passive haptic interface application (2018) IEEE Access, 6, pp. 63045-63057; Schimmels, J.M., Garces, D.R., The Arched Flexure VSA: A compact variable stiffness actuator with large stiffness range (2015) Proc. - IEEE Int. Conf. Robot. Autom, pp. 220-225. , 2015-June (June); Li, X., Chen, W., Lin, W., Design of a structure-controlled variable stiffness actuator based on rotary flexure hinges (2017) Proc. - IEEE Int. Conf. Robot. Autom, pp. 45-50; Wu, K., (2020) Design of a Controlled-Stiffness Flexure Mechanism Based on the FACT Method, , Springer Singapore; Liu, L., Impedance-Controlled Variable Stiffness Actuator for Lower Limb Robot Applications (2020) IEEE Trans. Autom. Sci. Eng, 17 (2), pp. 991-1004; Murat, R., Ebrahimi, N., Jafari, A., Elastic actuator design based on bending of cylindrical beam for robotic applications (2020) Actuators, 9 (3), pp. 1-12; Xie, Z., Qiu, L., Yang, D., Analysis of a novel variable stiffness filleted leaf hinge (2020) Mech. Mach. Theory, 144, p. 103673; Carl, N., Laurence, N., Gerard, P., Variable Stiffness Mechanism for Robotic Rehabilitation (2019) Adv. Mech. Mach. Sci, pp. 1761-1769. , A., and, Springer International Publishing; Braun, D.J., Variable Stiffness Spring Actuators for Low-Energy-Cost Human Augmentation (2019) IEEE Trans. Robot, 35 (6), pp. 1435-1449; Wu, Y.S., Lan, C.C., Design of a linear variable-stiffness mechanism using preloaded bistable beams (2014) IEEE/ASME Int. Conf. Adv. Intell. Mechatronics, AIM, pp. 605-610; Guo, J., Tian, G., Conceptual design and analysis of four types of variable stiffness actuators based on spring pretension (2015) Int. J. Adv. Robot. Syst, 12; McPherson, A., Design of a Passive, Variable Stiffness Exoskeleton for Triceps Deficiency Mitigation (2020) Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS 2020-July, pp. 4921-4925; Li, W.B., Bioinspired Variable Stiffness Dielectric Elastomer Actuators with Large and Tunable Load Capacity (2019) Soft Robot, 6 (5), pp. 631-643; Allen, D.P., Mechanical simplification of variable-stiffness actuators using dielectric elastomer transducers (2019) Actuators, 8 (2), pp. 1-19; Howell, L.L., Compliant Mechanisms (2013) 21st Century Kinemat, pp. 189-216. , Springer London; Morrison, T., Su, H.J., Stiffness modeling of a variable stiffness compliant link (2020) Mech. Mach. Theory, 153, p. 104021; Yeshmukhametov, Koganezawa, Yamamoto, A, Novel Discrete Wire-Driven Continuum Robot Arm with Passive Sliding Disc: Design, Kinematics and Passive Tension Control (2019) Robotics, 8 (3), p. 51; Hu, R., Venkiteswaran, V., Su, H.J., A variable stiffness robotic arm using linearly actuated compliant parallel guided mechanism (2019) Mech. Mach. Sci, pp. 33-40. , Springer Netherlands; Smith, Stuart T., (1994) Foundations of Ultra-Precision Mechanism Design, , 1st edition, CRC Press; Hussain, I., Modeling, identification, and control of a discrete variable stiffness actuator (DVSA) (2019) Actuators, 8 (3); Awad, M.I., Passive discrete variable stiffness joint (PDVSJ-II): Modeling, design, characterization, and testing toward passive haptic interface (2019) J. Mech. Robot, 11 (1); Singhal, D., Narayanamurthy, V., Large and Small Deflection Analysis of a Cantilever Beam (2019) J. Inst. Eng. Ser. A, 100 (1), pp. 83-96; Yuanqiang, L., Wangyu, L., Analysis of the displacement of distributed compliant parallel-guiding mechanism considering parasitic rotation and deflection on the guiding plate (2014) Mech. Mach. Theory, 80, pp. 151-165; Zirbel, S.A., Bistable Mechanisms for Space Applications (2016) PLoS One, 11 (12), p. e0168218; Howell, L.L., Magleby, S.P., Olsen, B.M., (2013) Handbook of Compliant Mechanisms, , Wiley; Awtar, S., Slocum, A.H., Sevincer, E., Characteristics of beam-based flexure modules (2007) J. Mech. Des. Trans. ASME, 129 (6), pp. 625-639; Awtar, S., Sevincer, E., Elastic Averaging in Flexure Mechanisms: A Muliti-Beam Parallelogram Flexure Case-Study (2006) ASME International, pp. 251-257; Awtar, S., Shimotsu, K., Sen, S., Elastic averaging in flexure mechanisms: A three-beam parallelogram flexure case study (2010) J. Mech. Robot, 2 (4)","Gan, D.; Polytechnic Institute, United States; email: dgan@purdue.edu",,"Computers and Information in Engineering Division;Design Engineering Division","American Society of Mechanical Engineers (ASME)","45th Mechanisms and Robotics Conference, MR 2021, Held as Part of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2021","17 August 2021 through 19 August 2021",,174204,,9780791885444,,,"English","Proc. ASME Des. Eng. Tech. Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85119972446
"Alhaddad A.Y., Mecheter A., Wadood M.A., Alsaari A.S., Mohammed H., Cabibihan J.-J.","57190946524;57210599422;55924926900;57351518400;57351973000;8386919800;","Anthropomorphism and Its Negative Attitudes, Sociability, Animacy, Agency, and Disturbance Requirements for Social Robots: A Pilot Study",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"791","796",,,"10.1007/978-3-030-90525-5_73","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119880396&doi=10.1007%2f978-3-030-90525-5_73&partnerID=40&md5=5603e003c44ad04ce1e14b30fda64e6b","Mechanical and Industrial Engineering Department, Qatar University, Doha, 2713, Qatar","Alhaddad, A.Y., Mechanical and Industrial Engineering Department, Qatar University, Doha, 2713, Qatar; Mecheter, A., Mechanical and Industrial Engineering Department, Qatar University, Doha, 2713, Qatar; Wadood, M.A., Mechanical and Industrial Engineering Department, Qatar University, Doha, 2713, Qatar; Alsaari, A.S., Mechanical and Industrial Engineering Department, Qatar University, Doha, 2713, Qatar; Mohammed, H., Mechanical and Industrial Engineering Department, Qatar University, Doha, 2713, Qatar; Cabibihan, J.-J., Mechanical and Industrial Engineering Department, Qatar University, Doha, 2713, Qatar","A social robot that meets the acceptability requirements of the target end-users presents a significant challenge to robot designers. The design process is often iterative and requires continuous improvements and optimization over time. One key aspect in designing an acceptable social robot is anthropomorphism. Social roboticists have developed assessment tools to evaluate different aspects for the perception of the observer. In this study, we evaluated the attitude of children toward four robots with different degrees of anthropomorphic traits. Questionnaires based on the Negative Attitude toward Robots Scale (NARS) and the Human-Robot Interaction Evaluation Scale (HRIES) were used to acquire the responses of 33 participants. To identify any changes due to interactions, a pre-test questionnaire was given prior to the interaction with a robot. It was then followed by a post-test questionnaire. Statistical tests were used to analyze the effects of gender, test (i.e., pre-test vs post-test), and the four robots, on the observers’ perception. Statistical differences were found between the four robots in the subscales of HRIES, namely, Sociability, Animacy, and Disturbance. The preferences of the children were leaning toward the humanoid robot (i.e., Alpha) with the moderate anthropomorphic traits in the Disturbance subscale. Low to moderate correlations were found between the subscales of NARS and HRIES. © 2021, Springer Nature Switzerland AG.","Acceptability; Anthropomorphism; Negative attitudes; Social robots","Anthropomorphic robots; Surveys; Testing; Acceptability; Animacy; Anthropomorphism; Humans-robot interactions; Interaction evaluations; Negative attitude; Negative attitude towards robots scale; Pilot studies; Post test; Social robots; Human robot interaction",,,,,"M-CTP-CENG-2020-4","Acknowledgments. The work was supported by a research grant from QU Marubeni Concept to Prototype Grant under the grant number M-CTP-CENG-2020-4. The statements made herein are solely the responsibility of the authors. The authors declare that they have no conflict of interest.",,,,,,,,,,"Alban, A.Q., Detection of challenging behaviours of children with autism using wearable sensors during interactions with social robots (2021) IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), Pp. 852–857. IEEE; Alhaddad, A.Y., Cabibihan, J.J., Bonarini, A., Recognition of aggressive interactions of children toward robotic toys (2019) IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1-8. , pp., IEEE; Alhaddad, A.Y., Cabibihan, J.-J., Bonarini, A., Influence of reaction time in the emotional response of a companion robot to a child’s aggressive interaction (2020) Int. J. Soc. Robot., 12 (6), pp. 1279-1291. , https://doi.org/10.1007/s12369-020-00626-z; Cabibihan, J.J., Pattofatto, S., Jomâa, M., Benallal, A., Carrozza, M.C., Dario, P., The conformance test for robotic/prosthetic fingertip skins (2006) International Conference on Biomedical Robotics and Biomechatronics, pp. 561-566. , pp., IEEE; Cabibihan, J.J., Carrozza, M.C., Dario, P., Pattofatto, S., Jomaa, M., Benallal, A., The uncanny valley and the search for human skin-like materials for a prosthetic fingertip (2006) IEEE-RAS International Conference on Humanoid Robots, pp. 474-477. , pp., IEEE; Cabibihan, J.J., So, W.C., Saj, S., Zhang, Z., Telerobotic pointing gestures shape human spatial cognition (2012) Int. J. Soc. Robot., 4 (3), pp. 263-272; Charisi, V., Davison, D., Reidsma, D., Evers, V., Evaluation methods for user-centered child-robot interaction (2016) 2016 25Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Pp. 545–550. IEEE; Krägeloh, C.U., Bharatharaj, J., Sasthan Kutty, S.K., Nirmala, P.R., Huang, L., Questionnaires to measure acceptability of social robots: A critical review (2019) Robotics, 8 (4), p. 88; Li, H., Cabibihan, J.J., Tan, Y.K., Towards an effective design of social robots (2011) Int. J. Soc. Robot., 3 (4), pp. 333-335; Martín, F., Ginés, J.: Practical aspects of deploying robotherapy systems. In: Ollero, A., Sanfeliu, A., Montano, L., Lau, N., Cardeira, C. (eds.) ROBOT 2017. AISC, vol. 693, pp. 367–378. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-70833-1 30; Mori, M., Macdorman, K.F., Kageki, N., The uncanny valley (2012) IEEE Robot. Autom. Mag., 19 (2), pp. 98-100. , from the field; Nomura, T., Kanda, T., Suzuki, T., Kato, K., Psychology in human-robot communication: An attempt through investigation of negative attitudes and anxiety toward robots (2004) IEEE International Workshop on Robot and Human Interactive Communication, pp. 35-40. , pp., IEEE; Spatola, N., Kühnlenz, B., Cheng, G., Perception and evaluation in human-robot interaction: The human-robot interaction evaluation scale (HRIES)—a multicomponent approach of anthropomorphism (2021) Int. J. Soc. Robot., pp. 1-23. , https://doi.org/10.1007/s12369-020-00667-4","Cabibihan, J.-J.; Mechanical and Industrial Engineering Department, Qatar; email: john.cabibihan@qu.edu.qa","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119880396
"Gholipour A., Taheri A., Mohammadzade H.","57352579800;56513500500;21833960600;","Automated Lip-Reading Robotic System Based on Convolutional Neural Network and Long Short-Term Memory",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"73","84",,,"10.1007/978-3-030-90525-5_7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119837267&doi=10.1007%2f978-3-030-90525-5_7&partnerID=40&md5=9e7018148377da01908b0af6221dfcd9","Social and Cognitive Robotics Laboratory, Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran; Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran","Gholipour, A., Social and Cognitive Robotics Laboratory, Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran; Taheri, A., Social and Cognitive Robotics Laboratory, Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran; Mohammadzade, H., Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran","In Iranian Sign Language (ISL), alongside the movement of fingers/arms, the dynamic movement of lips is also essential to perform/recognize a sign completely and correctly. In a follow up of our previous studies in empowering the RASA social robot to interact with individuals with hearing problems via sign language, we have proposed two automated lip-reading systems based on DNN architectures, a CNN-LSTM and a 3D-CNN, on the robotic system to recognize OuluVS2 database words. In the first network, CNN was used to extract static features, and LSTM was used to model temporal dynamics. In the second one, a 3D-CNN network was used to extract appropriate visual and temporal features from the videos. The accuracy rate of 89.44% and 86.39% were obtained for the presented CNN-LSTM and 3D-CNN networks, respectively; which were fairly promising for our automated lip-reading robotic system. Although the proposed non-complex networks did not provide the highest accuracy for this database (based on the literature), 1) they were able to provide better results than some of the more complex and even pre-trained networks in the literature, 2) they are trained very fast, and 3) they are quite appropriate and acceptable for the robotic system during Human-Robot Interactions (HRI) via sign language. © 2021, Springer Nature Switzerland AG.","Convolutional Neural Network (CNN); Deep learning; Lip-Reading; Long Short-Term Memory (LSTM); Social robot","Audition; Brain; Complex networks; Convolution; Human robot interaction; Robotics; Automated lip readings; Convolutional neural network; Deep learning; Dynamic movements; Lip reading; Long short-term memory; Robotic systems; Sign language; Social robots; Long short-term memory",,,,,"Sharif University of Technology: G980517","Acknowledgement. This research was funded by Sharif University of Technology (Grant No. G980517). The complementary and continues support of the Social & Cognitive Robotics Laboratory by Dr. Ali Akbar Siassi Memorial Grant is also greatly appreciated.",,,,,,,,,,"McGurk, H., Macdonald, J., Hearing lips and seeing voices (1976) Nature, 264 (5588), pp. 746-748; Erber, N.P., Auditory visual perception of speech (1975) J. Speech Hear. Disorders, 40 (4), pp. 481-492; Chiţu, A., Rothkrantz, L.J., Automatic visual speech recognition (2012) Speech Enhancement Model. Recogn. Algorithms Appl., pp. 95-120; Antonakos, E., Roussos, A., Zafeiriou, S.:. A survey on mouth modeling and analysis for sign language recognition (2015) 2015 11Th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015; Howell, D., Cox, S., Theobald, B., Visual units and confusion modelling for automatic lipreading (2016) Image Vis. Comput., 51 (1-12); Hassanat, A., Visual passwords using automatic lip reading (2014) Int. J. Sci. Basic Appl. Res (IJSBAR), 13, pp. 218-231; Biswas, A., Sahu, P.K., Chandra, M., Multiple cameras audio visual speech recognition using active appearance model visual features in car environment (2016) Int. J. Speech Technol., 19 (1), pp. 159-171. , https://doi.org/10.1007/s10772-016-9332-x; Basiri, S., Taheri, A., Meghdari, A., Alemi, M., Design and Implementation of a robotic architecture for adaptive teaching: A case study on iranian sign language (2021) J. Intell. Rob. Syst., 102 (2), pp. 1-19. , https://doi.org/10.1007/s10846-021-01413-2; Hosseini, S.R., Taheri, A., Meghdari, A., Alemi, M., Teaching Persian Sign Language to a Social Robot via the Learning from Demonstrations Approach (2019) ICSR 2019. LNCS (LNAI), Vol. 11876, pp. 655-665. , https://doi.org/10.1007/978-3-030-35888-4_61, Salichs, M.A., et al. (eds.), pp., Springer, Cham; Hosseini, S.R., Taheri, A., Meghdari, A., Alemi, M., Let there be intelligence!-A novel cognitive architecture for teaching assistant social robots (2018) Lecture Notes in Computer Science including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 275-285. , pp; Matthews, I., Cootes, T.F., Bangham, J.A., Cox, S., Harvey, R., Extraction of visual features for lipreading (2002) IEEE Trans. Pattern Anal. Mach. Intell., 24 (2), pp. 198-213; Zhao, G., Barnard, M., Pietikäinen, M., Lipreading with local spatiotemporal descriptors (2009) IEEE Trans. Multimed., 11 (7), pp. 1254-1265; Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.:. Multimodal deep learning (2011) Proceedings of the 28Th International Conference on Machine Learning, ICML 2011; Li, Y., Takashima, Y., Takiguchi, T., Ariki, Y.:. Lip reading using a dynamic feature of lip images and convolutional neural networks (2016) 2016 IEEE/ACIS 15Th International Conference on Computer and Information Science, ICIS 2016-Proceedings; Petridis, S., Li, Z., Pantic, M., End-to-end visual speech recognition with LSTMS (2017) ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings; Fernandez-Lopez, A., Sukno, F.M., Lip-reading with limited-data network (2019) European Signal Processing Conference; Anina, I., Zhou, Z., Zhao, G., Pietikainen, M.:. OuluVS2: A multi-view audiovisual database for non-rigid mouth motion analysis (2015) 2015 11Th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015; Hochreiter, S., Schmidhuber, J., LSTM can solve hard long time lag problems (1997) Adv. Neural Inf. Process. Syst.; Petridis, S., Wang, Y., Ma, P., Li, Z., Pantic, M., End-to-end visual speech recognition for small-scale datasets (2020) Pattern Recogn. Lett., 131, pp. 421-427. , http://www.ee.oulu.fi/res earch/imag/OuluVS2/ACCVW.html; Saitoh, T., Zhou, Z., Zhao, G., Pietikäinen, M., Concatenated frame image based CNN for visual speech recognition (2017) ACCV 2016. LNCS, Vol. 10117, pp. 277-289. , https://doi.org/10.1007/978-3-319-54427-4_21, Chen, C.S., Lu, J., Ma, K.K. (eds.), pp., Springer, Cham; Chung, J.S., Zisserman, A., Out of time: Automated lip sync in the wild (2017) ACCV 2016. LNCS, Vol. 10117, pp. 251-263. , https://doi.org/10.1007/978-3-319-54427-4_19, Chen, C.S., Lu, J., Ma, K. (eds.), pp., Springer, Cham; Petridis, S., Wang, Y., Ma, P., Li, Z., Pantic, M., End-to-end visual speech recognition for small-scale datasets (2020) Pattern Recogn. Lett., 131, pp. 421-427; Basiri, S., Taheri, A., Meghdari, A.F., Boroushaki, M., Alemi, M., Dynamic iranian sign language recognition using an optimized deep neural network: An implementation via a robotic-based architecture (2021) Int. J. Social Robot.; Hosseini, S.R., Taheri, A., Alemi, M., Meghdari, A., One-shot learning from demonstration approach toward a reciprocal sign language-based HRI (2021) Int. J. Soc. Robot.","Taheri, A.; Social and Cognitive Robotics Laboratory, Iran; email: artaheri@sharif.edu","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119837267
"Zhang Q., Fang L., Zhang Q., Xiong C.","56888078500;57346156400;57346485000;57211738191;","Simultaneous estimation of joint angle and interaction force towards sEMG-driven human-robot interaction during constrained tasks",2021,"Neurocomputing",,,,"","",,,"10.1016/j.neucom.2021.05.113","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119412427&doi=10.1016%2fj.neucom.2021.05.113&partnerID=40&md5=f6abefe45936a89e349d435c464747dd","State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China","Zhang, Q., State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China; Fang, L., State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China; Zhang, Q., State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China; Xiong, C., State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China","Human has excellent motor capability and performance in conducting various manipulation tasks. During some tasks such as tightening/loosening a screw with a screwdriver, the motion is accompanied by force exertion to the environment (that is, constrained motion). To obtain natural human-robot interaction (HRI) as human interacts/collaborates with the environment, decoding the human's movement intention in a way of motion and interaction force is meaningful for robots to carry out such constrained tasks. This paper proposes a long shortterm memory (LSTM) -based decoding method for the simultaneous estimation of human motion and interactive force from muscle activities represented by surface electromyography (sEMG) signals. The sEMG recorded from the muscles of forearm is used to decode human's movement intention. In order to extract smooth features from non-stationary sEMG signals, Bayesian filter is applied instead of traditional time-domain feature extraction method. From the real-time experiments on eight subjects, the LSTM-based decoding method represents high accuracy of motion estimation (91.7%) and force estimation (96.1%) despite of the existence of muscle coupling and non-stationary mapping between muscle activities and motion/interaction force during such constrained tasks. It indicates that the estimated motion and interaction force can be further applied for HRI in accomplishing constrained tasks. © 2021 Elsevier B.V.","Constrained motions; Human-robot interaction (HRI); Long short-term memory (LSTM); Simultaneous estimation; Surface electromyography (sEMG)","Decoding; Human robot interaction; Long short-term memory; Muscle; Signal encoding; Time domain analysis; Constrained motion; Constrained tasks; Human movements; Human-robot interaction; Humans-robot interactions; Interaction forces; Long short-term memory; Simultaneous estimation; Surface electromyography; Motion estimation",,,,,"National Natural Science Foundation of China, NSFC: 52027806, 52175268, U1913601, U2013213","This research was funded by the National Natural Science Foundation of China (Grant No. 52175268, U2013213, 52027806, U1913601) in part.",,,,,,,,,,"Karimi, H.R., A sliding mode approach to h∞ synchronization of master-slave time-delay systems with markovian jumping parameters and nonlinear uncertainties (2012) J. Franklin Inst., 349 (4), pp. 1480-1496; Sheridan, T.B., Human–robot interaction: status and challenges (2016) Human Factors, 58 (4), pp. 525-532; Zhu, J., Navarro, B., Passama, R., Fraisse, P., Crosnier, A., Cherubini, A., Robotic manipulation planning for shaping deformable linear objects withenvironmental contacts (2020) IEEE Robotics Automation Letters, 5 (1), pp. 16-23; Gu, N., Peng, Z., Wang, D., Zhang, F., Path-guided containment maneuvering of mobile robots: Theory and experiments (2020) IEEE Trans. Industr. Electron., p. 1; Wang, Z., Yang, S., Xiang, X., Vasilijevic, A., Miskovic, N., Nad, D., Cloud-based mission control of USV fleet: Architecture, implementation and experiments (2021) Control Eng. Practice, 106. , 104657; Karimi, H.R., Gao, H., New delay-dependent exponential h∞ synchronization for uncertain neural networks with mixed time delays (2010) IEEE Trans. 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Control, 61; Wang, C., Guo, W., Zhang, H., Guo, L., Huang, C., Lin, C., semg-based continuous estimation of grasp movements by long-short term memory network (2020) Biomed. Signal Process. Control, 59; Phinyomark, A., Phukpattaranont, P., Limsakul, C., Feature reduction and selection for emg signal classification (2012) Expert Systems Appl., 39 (8), pp. 7420-7431; Artemiadis, P.K., Kyriakopoulos, K.J., Emg-based control of a robot arm using low-dimensional embeddings (2010) IEEE Trans. Rob., 26 (2), pp. 393-398","Zhang, Q.; State Key Lab of Digital Manufacturing Equipment and Technology, China; email: qin.zhang@hust.edu.cn",,,"Elsevier B.V.",,,,,09252312,,NRCGE,,"English","Neurocomputing",Article,"Article in Press","",Scopus,2-s2.0-85119412427
"Lima M.R., Wairagkar M., Gupta M., Baena F.R.Y., Barnaghi P., Sharp D.J., Vaidyanathan R.","57219193243;56938674800;57325673900;57205122194;9337843600;35559514400;35570517600;","Conversational Affective Social Robots for Ageing and Dementia Support",2021,"IEEE Transactions on Cognitive and Developmental Systems",,,,"","",,,"10.1109/TCDS.2021.3115228","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118609025&doi=10.1109%2fTCDS.2021.3115228&partnerID=40&md5=29ecfa89c27adf952e117a2714bfefe8","Department of Mechanical Engineering, Imperial College London and UK Dementia Research Institute Care Research &#x0026; Technology Centre, London, UK.; Google Research India, Bangalore, India.; Department of Mechanical Engineering and the Hamlyn Centre for Robotic Surgery, Imperial College London, London, UK.; Department of Brain Sciences, Imperial College London and UK Dementia Research Institute Care Research &#x0026; Technology Centre, London, UK.","Lima, M.R., Department of Mechanical Engineering, Imperial College London and UK Dementia Research Institute Care Research &#x0026; Technology Centre, London, UK.; Wairagkar, M., Department of Mechanical Engineering, Imperial College London and UK Dementia Research Institute Care Research &#x0026; Technology Centre, London, UK.; Gupta, M., Google Research India, Bangalore, India.; Baena, F.R.Y., Department of Mechanical Engineering and the Hamlyn Centre for Robotic Surgery, Imperial College London, London, UK.; Barnaghi, P., Department of Brain Sciences, Imperial College London and UK Dementia Research Institute Care Research &#x0026; Technology Centre, London, UK.; Sharp, D.J., Department of Brain Sciences, Imperial College London and UK Dementia Research Institute Care Research &#x0026; Technology Centre, London, UK.; Vaidyanathan, R., Department of Mechanical Engineering, Imperial College London and UK Dementia Research Institute Care Research &#x0026; Technology Centre, London, UK.","Socially assistive robots (SAR) hold significant potential to assist older adults and people with dementia in human engagement and clinical contexts by supporting mental health and independence at home. While SAR research has recently experienced prolific growth, long-term trust, clinical translation and patient benefit remain immature. Affective human-robot interactions are unresolved and the deployment of robots with conversational abilities is fundamental for robustness and humanrobot engagement. In this paper, we review the state of the art within the past two decades, design trends, and current applications of conversational affective SAR for ageing and dementia support. A horizon scanning of AI voice technology for healthcare, including ubiquitous smart speakers, is further introduced to address current gaps inhibiting home use. We discuss the role of user-centred approaches in the design of voice systems, including the capacity to handle communication breakdowns for effective use by target populations. We summarise the state of development in interactions using speech and natural language processing, which forms a baseline for longitudinal health monitoring and cognitive assessment. Drawing from this foundation, we identify open challenges and propose future directions to advance conversational affective social robots for: 1) user engagement, 2) deployment in real-world settings, and 3) clinical translation. Author","ageing; Aging; Artificial intelligence; Cognitive robotics; conversational AI; Dementia; dementia.; human-robot interaction (HRI); Monitoring; Robots; socially assistive robots (SAR); Sociology; Statistics; voice technology",,,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23798920,,,,"English","IEEE Trans. Cogn. Dev. Syst.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85118609025
"Vasiliu L., Cortis K., McDermott R., Kerr A., Peters A., Hesse M., Hagemeyer J., Belpaeme T., McDonald J., Villing R., Mileo A., Capulto A., Scriney M., Griffiths S., Koumpis A., Davis B.","15060820100;55481022500;57191664237;57303662100;57205284418;56684399400;24479336700;13103663500;13308745400;8450195000;57198982108;57303969100;57188979102;53879604400;6602999236;57213738778;","CASIE - Computing affect and social intelligence for healthcare in an ethical and trustworthy manner",2021,"Paladyn","12","1",,"437","453",,,"10.1515/pjbr-2021-0026","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117601933&doi=10.1515%2fpjbr-2021-0026&partnerID=40&md5=8248ff5d7dbb43f8afeed02b938fe498","Peracton Ltd., Dublin, Ireland; School of Computing, Dublin City University, Dublin, Ireland; Department of Sociology, Maynooth University, Kildare, Ireland; Informatik 6 - Lehrstuhl für Robotik, Künstliche Intelligenz und Echtzeitsysteme Fakultät für Informatik, Technische Universität München, Munich, Germany; Cognitronics and Sensor Systems Group, Center for Cognitive Interaction Technology (CITEC), Universität Bielefeld, Bielefeld, Germany; IDLab, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium; NoosWare Bv, Amsterdam, Netherlands; Berner Fachhochschule, Business School, Institute Digital Enabling, Bern, Switzerland; Department of Computer Science, Maynooth University, Kildare, Ireland","Vasiliu, L., Peracton Ltd., Dublin, Ireland; Cortis, K., School of Computing, Dublin City University, Dublin, Ireland; McDermott, R., School of Computing, Dublin City University, Dublin, Ireland; Kerr, A., Department of Sociology, Maynooth University, Kildare, Ireland; Peters, A., Informatik 6 - Lehrstuhl für Robotik, Künstliche Intelligenz und Echtzeitsysteme Fakultät für Informatik, Technische Universität München, Munich, Germany; Hesse, M., Cognitronics and Sensor Systems Group, Center for Cognitive Interaction Technology (CITEC), Universität Bielefeld, Bielefeld, Germany; Hagemeyer, J., Cognitronics and Sensor Systems Group, Center for Cognitive Interaction Technology (CITEC), Universität Bielefeld, Bielefeld, Germany; Belpaeme, T., IDLab, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium; McDonald, J., Department of Computer Science, Maynooth University, Kildare, Ireland; Villing, R., Department of Computer Science, Maynooth University, Kildare, Ireland; Mileo, A., School of Computing, Dublin City University, Dublin, Ireland; Capulto, A., School of Computing, Dublin City University, Dublin, Ireland; Scriney, M., School of Computing, Dublin City University, Dublin, Ireland; Griffiths, S., NoosWare Bv, Amsterdam, Netherlands; Koumpis, A., Berner Fachhochschule, Business School, Institute Digital Enabling, Bern, Switzerland; Davis, B., School of Computing, Dublin City University, Dublin, Ireland","This article explores the rapidly advancing innovation to endow robots with social intelligence capabilities in the form of multilingual and multimodal emotion recognition, and emotion-aware decision-making capabilities, for contextually appropriate robot behaviours and cooperative social human-robot interaction for the healthcare domain. The objective is to enable robots to become trustworthy and versatile social robots capable of having human-friendly and human assistive interactions, utilised to better assist human users' needs by enabling the robot to sense, adapt, and respond appropriately to their requirements while taking into consideration their wider affective, motivational states, and behaviour. We propose an innovative approach to the difficult research challenge of endowing robots with social intelligence capabilities for human assistive interactions, going beyond the conventional robotic sense-think-act loop. We propose an architecture that addresses a wide range of social cooperation skills and features required for real human-robot social interaction, which includes language and vision analysis, dynamic emotional analysis (long-term affect and mood), semantic mapping to improve the robot's knowledge of the local context, situational knowledge representation, and emotion-aware decision-making. Fundamental to this architecture is a normative ethical and social framework adapted to the specific challenges of robots engaging with caregivers and care-receivers. © 2021 Laurentiu Vasiliu et al., published by De Gruyter.","computing affect; emotion analysis; healthcare robots; robot ethics; robot-assisted care; sHRI; social human-robot interaction",,,,,,"SFI/12/RC/2289 P2; 13/RC/2106; Science Foundation Ireland, SFI: 13/RC/2094; European Regional Development Fund, ERDF; ADAPT - Centre for Digital Content Technology","Funding information : This work was supported, in part, by Science Foundation Ireland Grant 13/RC/2094 and co-funded under the European Regional Development Fund through the Southern and Eastern Regional Operational Programme to Lero – the Science Foundation Ireland Research Centre for Software (http://www.lero.ie), the Insight Centre for Data Analytics which is funded by Science Foundation Ireland (SFI/12/RC/2289 P2), and the ADAPT Centre for Digital Content Technology (https://www.adaptcentre.ie) which is funded under the Science Foundation Ireland Research Centres Programme (Grant 13/RC/2106).",,,,,,,,,,"Murad, C., Munteanu, C., ""i don't know what you're talking about, HALexa,"" (2019) Proceedings of the 1st International Conference on Conversational User Interfaces - Cui '19, , in ACM Press; Hoffman, G., ""anki, Jibo, and Kuri: What we can learn from social robots that didn't make it,"" (2019) Ieee Spectrum, , http://spectrum.ieee.org/automaton/robotics/home-robots/anki-jibo-and-kuri-what-we-can-learn-from-social-robotics-failures; Tian, L., Oviatt, S., ""a taxonomy of social errors in human-robot interaction,"" (2021) Acm Trans. 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Sci., 164, pp. 618-628. , 10.1016/j.procs.2019.12.228, pp","Davis, B.; School of Computing, Ireland; email: brian.davis@dcu.ie",,,"De Gruyter Open Ltd",,,,,20814836,,,,"English","Paladyn",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85117601933
"Moon H., Seo J.","57192929320;56092498500;","Fast User Adaptation for Human Motion Prediction in Physical HumanRobot Interaction",2021,"IEEE Robotics and Automation Letters",,,,"","",,,"10.1109/LRA.2021.3116319","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116893992&doi=10.1109%2fLRA.2021.3116319&partnerID=40&md5=f2db2305f0718faeaebbfcba86837719","School of Integrated Technology, Yonsei University, Incheon, Korea, South, 21983 (e-mail: hs.moon@yonsei.ac.kr); School of Integrated Technology, Yonsei University, Incheon, Korea, South, 21983 (e-mail: jiwon.seo@yonsei.ac.kr)","Moon, H., School of Integrated Technology, Yonsei University, Incheon, Korea, South, 21983 (e-mail: hs.moon@yonsei.ac.kr); Seo, J., School of Integrated Technology, Yonsei University, Incheon, Korea, South, 21983 (e-mail: jiwon.seo@yonsei.ac.kr)","Accurate prediction of human movements is required to enhance the efficiency of physical human-robot interaction. Behavioral differences across various users are crucial factors that limit the prediction of human motion. Although recent neural network-based modeling methods have improved their prediction accuracy, most did not consider an effective adaptations to different users, thereby employing the same model parameters for all users. To deal with this insufficiently addressed challenge, we introduce a meta-learning framework to facilitate the rapid adaptation of the model to unseen users. In this study, we propose a model structure and a meta-learning algorithm specialized to enable fast user adaptation in predicting human movements in cooperative situations with robots. The proposed prediction model comprises shared and adaptive parameters, each addressing the user's general and individual movements. Using only a small amount of data from an individual user, the adaptive parameters are adjusted to enable user-specific prediction through a two-step process: initialization via a separate network and adaptation via a few gradient steps. Regarding the motion dataset that has 20 users collaborating with a robotic device, the proposed method outperforms existing meta-learning and non-meta-learning baselines in predicting the movements of unseen users. IEEE","Adaptation models; Adaptive systems; deep learning methods; fast user adaptation; Hidden Markov models; human motion prediction; meta-learning; Physical human-robot interaction; Prediction algorithms; Predictive models; Robots; Task analysis","Deep learning; Forecasting; Hidden Markov models; Human robot interaction; Job analysis; Learning algorithms; Man machine systems; Adaptation models; Deep learning method; Fast user adaptation; Hidden-Markov models; Human motion prediction; Human motions; Learning methods; Metalearning; Motion prediction; Physical humanrobot interaction (phri); Prediction algorithms; Predictive models; Task analysis; User adaptation; Motion estimation",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Article in Press","All Open Access, Green",Scopus,2-s2.0-85116893992
"Yang J., Chew E., Liu P.","57220006927;23989981600;56428216700;","Service humanoid robotics: a novel interactive system based on bioniccompanionship framework",2021,"PeerJ Computer Science","7",,,"1","20",,,"10.7717/peerj-cs.674","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116447669&doi=10.7717%2fpeerj-cs.674&partnerID=40&md5=564fa57f5181df8752112af08e1c3de1","Cardiff School of Technologies, Cardiff Metropolitan University,Cardiff, Cardiff, United Kingdom; The Department of Computer Science, University of York, York, United Kingdom","Yang, J., Cardiff School of Technologies, Cardiff Metropolitan University,Cardiff, Cardiff, United Kingdom; Chew, E., Cardiff School of Technologies, Cardiff Metropolitan University,Cardiff, Cardiff, United Kingdom; Liu, P., The Department of Computer Science, University of York, York, United Kingdom","At present, industrial robotics focuses more on motion control and vision, whereas humanoid service robotics (HSRs) are increasingly being investigated and researched in the field of speech interaction. The problem and quality of human-robot interaction (HRI) has become a widely debated topic in academia. Especially when HSRs are applied in the hospitality industry, some researchers believe that the current HRI model is not well adapted to the complex social environment. HSRs generally lack the ability to accurately recognize human intentions and understand social scenarios. This study proposes a novel interactive framework suitable for HSRs. The proposed framework is grounded on the novel integration of Trevarthen’s (2001) companionship theory and neural image captioning (NIC) generation algorithm. By integrating image-to-natural interactivity generation and communicating with the environment to better interact with the stakeholder, thereby changing from interaction to a bionic-companionship. Compared to previous research a novel interactive system is developed based on the bionic-companionship framework. The humanoid service robot was integrated with the system to conduct preliminary tests. The results show that the interactive system based on the bionic-companionship framework can help the service humanoid robot to effectively respond to changes in the interactive environment, for example give different responses to the same character in different scenes. © Copyright 2021 Yang et al. Distributed under Creative Commons CC-BY 4.0","Human-robot interaction; Humanoid robotics; Social robotics","Anthropomorphic robots; Man machine systems; Robotics; 'current; Hospitality industry; Humanoid robotics; Humans-robot interactions; Industrial robotics; Interaction modeling; Interactive system; Service robotics; Social robotics; Speech interaction; Human robot interaction",,,,,,"The authors received no funding for this work",,,,,,,,,,"(2020) Intelligent Robot AI Makers Laboratory, , https://www.canbotrobots.com/html/yy-, CANBOT; Caleb-Solly, P, Dogramadzi, S, Huijnen, CA, Heuvel, HVD., Exploiting ability for human adaptation to facilitate improved human-robot interaction and acceptance (2018) The Information Society, 34 (3), pp. 153-165; Cascianelli, S, Costante, G, Ciarfuglia, TA, Valigi, P, Fravolini, ML., Full-GRU natural language video description for service robotics applications (2018) IEEE Robotics and Automation Letters, 3 (2), pp. 841-848; Chen, L, He, Y, Fan, L., Let the robot tell: describe car image with natural language via LSTM (2017) Pattern Recognition Letters, 98 (8), pp. 75-82; Chew, E, Lee, PL, Hu, S, Yang, J., Investigating the first robotic nurses: humanoid robot nightingale and partners for COVID-19 preventive design (2021) The Seventh Edition of the International Workshop on New Trends in Medical and Service Robots Conference, , Basel, Switzerland: University Hospital Basel; Cho, K, van Merrienboer, B, Gulcehre, C, Bougares, F, Schwenk, H., Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014) EMNLP; Chung, MJY, Cakmak, M., How was your stay?: Exploring the use of robots for gathering customer feedback in the hospitality industry (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 947-954. , Piscataway: IEEE; Dautenhahn, K, Woods, S, Kaouri, C, Walters, ML, Koay, KL, Werry, I., What is a robot companion-friend, assistant or butler? 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Yang, J, Chew, E., A systematic review for service humanoid robotics model in hospitality, springer international journal for social robotics (2020) International Journal of Social Robotics, 22 (2), p. 253; Yang, J, Chew, E., The Design Model for Robotic Waitress (2021) International Journal of Social Robotics, pp. 1-11; Yamada, T, Murata, S, Arie, H, Ogata, T., Dynamical integration of language and behavior in a recurrent neural network for human-robot interaction (2016) Frontiers in Neurorobotics, 10, p. 5; Zhang, QC, Hu, L, Gow, J., Output feedback stabilization for mimo semi-linear stochastic systems with transient optimisation (2020) International Journal of Automation and Computing, 17 (1), pp. 83-95","Yang, J.; Cardiff School of Technologies, United Kingdom; email: JYang@cardiffmet.ac.uk",,,"PeerJ Inc.",,,,,23765992,,,,"English","PeerJ Comput. Sci.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85116447669
"Ji Z., Liu Q., Xu W., Yao B., Liu J., Zhou Z.","57214917794;55577742400;57221159460;56405052900;57191589441;55538054700;","A closed-loop brain-computer interface with augmented reality feedback for industrial human-robot collaboration",2021,"International Journal of Advanced Manufacturing Technology",,,,"","",,,"10.1007/s00170-021-07937-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114681965&doi=10.1007%2fs00170-021-07937-z&partnerID=40&md5=c2b32900558cc814a3f42528c8e19b12","School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China; Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan, 430070, China; School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, 430070, China","Ji, Z., School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China, Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan, 430070, China; Liu, Q., School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China, Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan, 430070, China; Xu, W., School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China, Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan, 430070, China; Yao, B., Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan, 430070, China, School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, 430070, China; Liu, J., School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China, Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan, 430070, China; Zhou, Z., School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China, Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan, 430070, China","Industrial human-robot collaboration (HRC) aims to combine human intelligence and robotic capability to achieve higher productiveness. In industrial HRC, the communication between humans and robots is essential to enhance the understanding of the intent of each other to make a more fluent collaboration. Brain-computer interface (BCI) is a technology that could record the user’s brain activity that can be translated into interaction messages (e.g., control commands) to the outside world, which can build a direct and efficient communication channel between human and robot. However, due to lacking information feedback mechanisms, it is challenging for BCI to control robots with a high degree of freedom with a limited number of classifiable mental states. To address this problem, this paper proposes a closed-loop BCI with contextual visual feedback by an augmented reality (AR) headset. In such BCI, the electroencephalogram (EEG) patterns from the multiple voluntary eye blinks are considered the input and its online detection algorithm is proposed whose average accuracy can reach 94.31%. Moreover, an AR-enable information feedback interface is designed to achieve an interactive robotic path planning. A case study of an industrial HRC assembly task is also developed to show that the proposed closed-up BCI could shorten the time of user input in human-robot interaction. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.","Augmented reality; Brain-computer interface; Industrial human-robot collaboration; Interactive robotic path planning","Augmented reality; Biofeedback; Brain; Degrees of freedom (mechanics); Electroencephalography; Feedback; Human computer interaction; Industrial robots; Robot programming; Robotics; Social robots; Visual communication; Visual servoing; Efficient communications; Electro-encephalogram (EEG); High Degree of Freedom; Human intelligence; Human-robot collaboration; Information feedback; Interactive robotics; Voluntary eye blink; Brain computer interface",,,,,"National Natural Science Foundation of China, NNSFC: 51775399; Fundamental Research Funds for the Central Universities: 2020III047","This research is supported by the National Natural Science Foundation of China (Grant No. 51775399) and the Fundamental Research Funds for the Central Universities (WUT: 2020III047).",,,,,,,,,,"Matsumoto, M., Ijomah, W., Remanufacturing (2013) Handbook of Sustainable Engineering, pp. 389-408. , Springer; Xu, W., Tang, Q., Liu, J., Liu, Z., Zhou, Z., Pham, D.T., Disassembly sequence planning using discrete Bees algorithm for human-robot collaboration in remanufacturing (2020) Robot Comput Integr Manuf, 62, p. 101860; Özceylan, E., Kalayci, C.B., Güngör, A., Gupta, S.M., Disassembly line balancing problem: a review of the state of the art and future directions (2019) Int J Prod Res, 57 (15-16), pp. 4805-4827; Parsa, S., Saadat, M., Intelligent selective disassembly planning based on disassemblability characteristics of product components (2019) Int J Adv Manuf Technol, 104 (5), pp. 1769-1783; Liu, H., Wang, L., Gesture recognition for human-robot collaboration: a review (2018) Int J Ind Ergon, 68, pp. 355-367; Cirillo, A., Ficuciello, F., Natale, C., Pirozzi, S., Villani, L., A conformable force/tactile skin for physical human–robot interaction (2015) IEEE Robot Autom Lett, 1 (1), pp. 41-48; Hollmann, R., Hägele, M., The use of voice control for industrial robots in noisy manufacturing environments (2008) 39Th International Symposium on Robotics, ISR, 2008, pp. 14-18; Mohammed, A., Wang, L., Brainwaves driven human-robot collaborative assembly (2018) CIRP Ann, 67 (1), pp. 13-16; Mohammed, A., Wang, L., Advanced human-robot collaborative assembly using electroencephalogram signals of human brains (2020) Proc CIRP, 93, pp. 1200-1205; Wolpaw, J.R., Birbaumer, N., Heetderks, W.J., McFarland, D.J., Peckham, P.H., Schalk, G., Donchin, E., Vaughan, T.M., Brain-computer interface technology: a review of the first international meeting (2000) IEEE Trans Rehab Eng, 8 (2), pp. 164-173; Nijholt, A., Tan, D., Pfurtscheller, G., Brunner, C., Millán, J.R., Allison, B., Graimann, B., Müller, K.-R., Brain-computer interfacing for intelligent systems (2008) IEEE Intell Syst, 23 (3), pp. 72-79; Ji, Z., Liu, Q., Xu, W., Yao, B., Hu, Y., Feng, H., Zhou, Z., Augmented reality-enabled intuitive interaction for industrial human-robot collaboration (2019) 49Th International Conference on Computers and Industrial Engineering (CIE 2019); Agarwal, M., Sivakumar, R., Blink: A fully automated unsupervised algorithm for eye-blink detection in EEG signals (2019) 2019 57Th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp. 1113-1121; Carlson, T., Millan, J.R., Brain-controlled wheelchairs: a robotic architecture (2013) IEEE Robot Autom Mag, 20 (1), pp. 65-73; Carabalona, R., Grossi, F., Tessadri, A., Castiglioni, P., Caracciolo, A., de Munari, I., Light on! 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"Story M., Jaksic C., Fletcher S.R., Webb P., Tang G., Carberry J.","57247500800;57208129424;35362138100;7202522411;56767460800;57248150100;","Evaluating the use of human aware navigation in industrial robot arms",2021,"Paladyn","12","1",,"379","391",,,"10.1515/pjbr-2021-0024","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114376644&doi=10.1515%2fpjbr-2021-0024&partnerID=40&md5=0abe0d51bb53647c4a93a9e1ef317515","School of Aerospace, Transport and Manufacturing, Cranfield University, College Rd, Bedford, MK43 0AL, United Kingdom; Assembly Development, BAE Systems-Air, Samlesbury Aerodrome, Myerscough Smithy Rd, Balderstone, Blackburn, S420C, United Kingdom","Story, M., School of Aerospace, Transport and Manufacturing, Cranfield University, College Rd, Bedford, MK43 0AL, United Kingdom; Jaksic, C., School of Aerospace, Transport and Manufacturing, Cranfield University, College Rd, Bedford, MK43 0AL, United Kingdom; Fletcher, S.R., School of Aerospace, Transport and Manufacturing, Cranfield University, College Rd, Bedford, MK43 0AL, United Kingdom; Webb, P., School of Aerospace, Transport and Manufacturing, Cranfield University, College Rd, Bedford, MK43 0AL, United Kingdom; Tang, G., School of Aerospace, Transport and Manufacturing, Cranfield University, College Rd, Bedford, MK43 0AL, United Kingdom; Carberry, J., Assembly Development, BAE Systems-Air, Samlesbury Aerodrome, Myerscough Smithy Rd, Balderstone, Blackburn, S420C, United Kingdom","Although the principles followed by modern standards for interaction between humans and robots follow the First Law of Robotics popularized in science fiction in the 1960s, the current standards regulating the interaction between humans and robots emphasize the importance of physical safety. However, they are less developed in another key dimension: psychological safety. As sales of industrial robots have been increasing over recent years, so has the frequency of human-robot interaction (HRI). The present article looks at the current safety guidelines for HRI in an industrial setting and assesses their suitability. This article then presents a means to improve current standards utilizing lessons learned from studies into human aware navigation (HAN), which has seen increasing use in mobile robotics. This article highlights limitations in current research, where the relationships established in mobile robotics have not been carried over to industrial robot arms. To understand this, it is necessary to focus less on how a robot arm avoids humans and more on how humans react when a robot is within the same space. Currently, the safety guidelines are behind the technological advance, however, with further studies aimed at understanding HRI and applying it to newly developed path finding and obstacle avoidance methods, science fiction can become science fact. © 2021 Matthew Story et al., published by De Gruyter.","HAN; HRI; human factors in robots; human-robot collaboration; industrial robotics; robot ethics; robotics safety",,,,,,"Engineering and Physical Sciences Research Council, EPSRC","Funding information: This study was funded an EPSRC BAE Systems Case Award.",,,,,,,,,,"Asimov, I., (1967) I, Robot, , Great Britain Dobson Books Ltd; (2017) ""executive Summary World Robotics 2017 Industrial Robots,"", , IFR; (2018) ""executive Summary World Robotics 2018 Industrial Robots,"", , IFR; (2019) ""executive Summary World Robotics 2019 Industrial Robots,"", , IFR; Charalambous, G., Fletcher, S.R., Webb, P., ""the development of a human factors readiness level tool for implementing industrial human-robot collaboration,"" (2017) Int. 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Factors, 57 (NO. 1), pp. 21-33. , https://doi.org/10.1177/0018720814565188; Unhelkar, V.V., Lasota, P.A., Tyroller, Q., Buhai, R.-D., Marceau, L., Deml, B., Human-Aware robotic assistant for collaborative assembly: Integrating human motion prediction with planning in time (2018) IEEE Robot. Autom. Lett., 3 (NO. 3), pp. 2394-2401. , https://doi.org/10.1109/LRA.2018.2812906","Story, M.; School of Aerospace, College Rd, United Kingdom; email: m.story@cranfield.ac.uk",,,"De Gruyter Open Ltd",,,,,20814836,,,,"English","Paladyn",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85114376644
"Yao C., Liu C., Liu M., Chen Q.","57224941784;35148210000;55512872300;36571318600;","Navigation in Multi-Agent System with Side Preference Path Optimizer",2021,"IEEE Access","9",,"9512081","113944","113953",,,"10.1109/ACCESS.2021.3104470","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113313377&doi=10.1109%2fACCESS.2021.3104470&partnerID=40&md5=0745f5eeef756489b7a3c0391488f973","Department of Control Science and Engineering, Tongji University, Shanghai, China; Tongji Artificial Intelligence (Suzhou) Research Institute, Suzhou, 215000, China; Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong","Yao, C., Department of Control Science and Engineering, Tongji University, Shanghai, China, Tongji Artificial Intelligence (Suzhou) Research Institute, Suzhou, 215000, China; Liu, C., Department of Control Science and Engineering, Tongji University, Shanghai, China, Tongji Artificial Intelligence (Suzhou) Research Institute, Suzhou, 215000, China; Liu, M., Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong; Chen, Q., Department of Control Science and Engineering, Tongji University, Shanghai, China","Multi-agent path planning aims to find a set of collision-free global path in the joint configuration space to move each agent from the given start states to the goal states and keep the total cost of the path to a minimum. However, the multi-agent systems in the real-world turn out to be a non-communication and partially observable system where the goal states and the planning strategy of other agents are usually unknown. In order to solve the multi-agent path planning problem in a more general environment, a social-aware navigation method is proposed in this paper. By imitating the most basic human road rules, left/right-handed rules, we add side preference to the global path planning by using a path optimizer so that each agent only needs to consider its own planning while the system is approximately optimal. The proposed path optimizer is tested in benchmark maps, robot-robot and robot-human interaction environment, respectively. With the side preference path optimizer, the success rate of multi-agent navigation with 6 agents increase from 0% to 78% at the cost of only extra 0.1 seconds planning time. The proposed method makes the navigation in human-robot interaction scenes become easier. © 2013 IEEE.","Multi-agent systems; navigation; path planning; social-aware","Human robot interaction; Navigation; Global path planning; Joint configuration; Multi-agent navigations; Navigation methods; Partially observable systems; Path planning problems; Planning strategies; Robot-human interaction; Multi agent systems",,,,,"BE2020101; PKX2019-R18; National Natural Science Foundation of China, NNSFC: 61733013, 62073245, U1713211","This work was supported in part by the National Natural Science Foundation of China under Grant U1713211, Grant 62073245, and Grant 61733013; in part by Pudong New Area Science and Technology Development Fund under Grant PKX2019-R18; and in part by Jiangsu Key Research and Development Project under Grant BE2020101.",,,,,,,,,,"Truong, X.-T., Ngo, T.D., Toward socially aware robot navigation in dynamic and crowded environments: A proactive social motion model (2017) IEEE Trans. Autom. Sci. Eng., 14 (4), pp. 1743-1760. , Oct; Desaraju, V.R., How, J.P., Decentralized path planning for multi-agent teams in complex environments using rapidly-exploring random trees (2011) Proc. IEEE Int. Conf. Robot. Autom., pp. 4956-4961. , May; Yu, J., LaValle, S.M., Planning optimal paths for multiple robots on graphs (2013) Proc. IEEE Int. Conf. Robot. Autom., pp. 3612-3617. , May; Vedder, K., Biswas, J., X: Anytime multi-agent path finding for sparse domains using window-based iterative repairs (2021) Artif. Intell., 291. , Feb; Wagner, G., Subdimensional expansion: A framework for computationally tractable multirobot path planning (2015) Tech. Rep.; Cohen, L., Greco, M., Ma, H., Hernández, C., Felner, A., Kumar, T.K.S., Koenig, S., Anytime focal search with applications (2018) Proc. Ijcai, pp. 1434-1441. , Jul; Sharon, G., Stern, R., Felner, A., Sturtevant, N.R., Conflict-based search for optimal multi-agent pathfinding (2015) Artif. 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Conf. Mobile Robots (ECMR), pp. 1-6. , Sep; Pandey, A.K., Alami, R., A framework towards a socially aware mobile robot motion in human-centered dynamic environment (2010) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 5855-5860. , Oct; Knepper, R.A., Rus, D., Pedestrian-inspired sampling-based multirobot collision avoidance (2012) Proc. 21st IEEE Int. Symp. Robot Hum. Interact. Commun. (IEEE RO-MAN), pp. 94-100. , Sep; Chen, Y.F., Everett, M., Liu, M., How, J.P., Socially aware motion planning with deep reinforcement learning (2017) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), pp. 1343-1350. , Sep; Dijkstra, E.W., A note on two problems in connexion with graphs (1959) Numerische Math., 1 (1), pp. 269-271. , Dec; Hart, P.E., Nilsson, N.J., Raphael, B., A formal basis for the heuristic determination of minimum cost paths (1968) IEEE Trans. Syst. Sci. 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(ICRA), pp. 7055-7062. , May; Harabor, D.D., Grastien, A., Online graph pruning for pathfinding on grid maps (2011) Proc. 25th Aaai Conf. Artif. Intell., pp. 1-6; Karaman, S., Frazzoli, E., Sampling-based algorithms for optimal motion planning (2011) Int. J. Robot. Res., 30 (7), pp. 846-894; Gammell, J.D., Srinivasa, S.S., Barfoot, T.D., Informed RRT: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic (2014) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 2997-3004. , Sep; Hall, E., (1969) The Hidden Dimension: Man's Use of Space in Public and Private; Hayduk, L.A., The shape of personal space: An experimental investigation (1981) Can. J. Behav. Sci./Revue Canadienne des Sci. du Comportement, 13 (1), p. 87; Gérin-Lajoie, M., Richards, C.L., Fung, J., McFadyen, B.J., Characteristics of personal space during obstacle circumvention in physical and virtual environments (2008) Gait Posture, 27 (2), pp. 239-247. , Feb; Helbing, D., Molnár, P., Social force model for pedestrian dynamics (1995) Phys. Rev. E, Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top., 51 (5), p. 4282; Nissoux, C., Simeon, T., Laumond, J.-P., Visibility based probabilistic roadmaps (1999) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. Hum. Envi-ron. Friendly Robots High Intell. Emotional Quotients, 3, pp. 1316-1321. , Oct","Liu, C.; Department of Control Science and Engineering, China; email: liuchengju@tongji.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85113313377
"Xiao J., Dou S., Zhao W., Liu H.","8836539200;57226638364;57226718758;56437274400;","Sensorless Human-Robot Collaborative Assembly Considering Load and Friction Compensation",2021,"IEEE Robotics and Automation Letters",,,,"","",,,"10.1109/LRA.2021.3088789","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112165794&doi=10.1109%2fLRA.2021.3088789&partnerID=40&md5=177f794e02226df4b65d1e172aace7ef","Key Laboratory of Modern Mechanisms and Equipment Design of The State Ministry of Education, Tianjin University, Tianjin, China, 300072 (e-mail: tjxjl@tju.edu.cn); Mechanical engineering, Tianjin University, Tianjin, China, 300354 (e-mail: dousx_0101@tju.edu.cn); Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China, 300350 (e-mail: Kent@tju.edu.cn); School of mechanical engineering, Tianjin University, Tianjin, China, 300072 (e-mail: liuht@tju.edu.cn)","Xiao, J., Key Laboratory of Modern Mechanisms and Equipment Design of The State Ministry of Education, Tianjin University, Tianjin, China, 300072 (e-mail: tjxjl@tju.edu.cn); Dou, S., Mechanical engineering, Tianjin University, Tianjin, China, 300354 (e-mail: dousx_0101@tju.edu.cn); Zhao, W., Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China, 300350 (e-mail: Kent@tju.edu.cn); Liu, H., School of mechanical engineering, Tianjin University, Tianjin, China, 300072 (e-mail: liuht@tju.edu.cn)","The collaboration of humans and robots can combine human flexibility with the efficiency of robots, so that robots can accomplish a variety of complex tasks, such as handling and assembly. However, most robots have poor collaboration capabilities and rely on expensive sensors to obtain the force interaction information of limited space range. To solve this problem, a human-robot collaborative assembly scheme without using additional force/torque sensors is proposed in this paper. First, a load identification and compensation method that does not require the load model is proposed to realize the decoupling of load force and human-robot interaction force. Next, in order to realize a compliant dragging effect of the robots, an improved LuGre friction model is proposed based on the experimental discovery that the motion of the load has a non-negligible effect on the friction torque of robot joints. Finally, the kinesthetic teaching experiments of compliant assembly using a universal 6-DOF collaborative robot have been completed by designing a task-determined adaptive impedance control algorithm. The experimental results prove that the proposed scheme can effectively improve the safety, compliance, and flexibility of the loaded robots compared with the existing position-encoded robots, indicating its great potential for industry application. IEEE","Collaboration; compliance and impedance control; Compliant assembly; Force; Friction; human-robot collaboration; Load modeling; Robot sensing systems; Robots; Torque","Accident prevention; Agricultural robots; Friction; Industrial robots; Loads (forces); Collaboration capabilities; Collaborative assembly; Collaborative robots; Compensation method; Friction compensation; Industry applications; Kinesthetic teachings; LuGre friction model; Social robots",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Article in Press","",Scopus,2-s2.0-85112165794
"Bellarbi A., Mouaddib A.-I., Achour N., Ouadah N.","57193158921;6701745605;7004112248;24725147100;","A new approach for social navigation and interaction using a dynamic proxemia modeling",2021,"Evolutionary Intelligence",,,,"","",,,"10.1007/s12065-021-00633-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110777034&doi=10.1007%2fs12065-021-00633-7&partnerID=40&md5=fdca81a4670ab30126ae4685cba7bda7","USTHB: Universite des Sciences et de la Technologie Houari Boumediene, Algiers, Algeria; CDTA: Centre de Developpement des Technologies Avancées, Algiers, Algeria; GREYC: Groupe de Recherche en Informatique, Image, et Instrumentation de Caen, Caen, France","Bellarbi, A., USTHB: Universite des Sciences et de la Technologie Houari Boumediene, Algiers, Algeria, CDTA: Centre de Developpement des Technologies Avancées, Algiers, Algeria, GREYC: Groupe de Recherche en Informatique, Image, et Instrumentation de Caen, Caen, France; Mouaddib, A.-I., GREYC: Groupe de Recherche en Informatique, Image, et Instrumentation de Caen, Caen, France; Achour, N., USTHB: Universite des Sciences et de la Technologie Houari Boumediene, Algiers, Algeria; Ouadah, N., CDTA: Centre de Developpement des Technologies Avancées, Algiers, Algeria","This paper treats the problem of social navigation and human–robot interaction. In most previous works addressing this issue, the proxemia concept has been considered static with regards to the activity’s nature. Furthermore, the F-Formation type is not considered. The different activities of people can not be considered similarly. For example, the way to navigate socially without disturbing a group drinking coffee or discussing a poster is different, just like the interaction location when serving coffee or explaining a poster. The main contribution of this paper is to propose a dynamic proxemia modeling approach “DPMA” for social navigation and interaction. It is based on proxemia and spatial modeling, which allow the robot to navigate socially by considering the comfort of people, and to interact in appropriate interaction conditions. It considers the F-Formation type and the nature of the activity, additionally to the different social spaces and the navigation and interaction constraints. Then, it uses a social map to encode them. The performance and the efficiency DPMA are evaluated both in three simulation environments using the ROS Stage simulator and on a real robot used as a service robot in a mediation event example. Promising results are given in this paper. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.","Dynamic proxemia; Human–robot interaction; Social navigation","Air navigation; Navigation; Model approach; New approaches; Robot interactions; Service robots; Simulation environment; Social navigation; Social spaces; Spatial modeling; Social robots",,,,,,,,,,,,,,,,"Hall, E., (1966) The hidden dimension, vol 6, no 1, , Doubleday, Garden City; Karami, A.B., (2011) Modèles Décisionnels d’Interaction Homme-Robot, , PhD Thesis report; Goodrich, M.A., Schultz, A.C., Human–robot interaction: a survey (2007) Found Trends Hum Comput Interact, 1 (3), pp. 203-275; Social robot navigation. PhD Thesis (2010) Robotics Institute, , Carnegie Mellon University, Pittsburgh, PA; Yoda, M., Shiota, Y., The mobile robot which passes a man (1997) Procs of the 6Th IEEE International Workshop on Robot and Human Communication, pp. 112-117. , RO-MAN; Pandey, A.K., Alami, R., A framework towards a socially aware mobile robot motion in human-centered dynamic environment (2010) IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems IROS 2010—conference Proceeding, pp. 5855-5860; Chen, W., Zhang, T., Zou, Y., Mobile robot path planning based on social interaction space in social environment (2018) Int J Adv Robot Syst, 15 (3), pp. 1-10; Kuderer, M., Kretzschmar, H., Sprunk, C., Burgard, W., Featurebased prediction of trajectories for socially compliant navigation (2012) Robotics Science and Systems (RSS), , Sydney, Australia; Ferrer, G., (2013) Social Robot Navigation, , PhD Thesis; Ramirez, O.A.I., Khambhaita, H., Chatila, R., Chetouani, M., Alami, R., Robots learning how and where to approach people (2016) IEEE 25Th International Symposium on Robot and Human Interactive Communication, pp. 347-353. , RO-MAN 2016. New York, NY, USA; Satake, S., Kanda, T., Glas, D.F., Imai, M., Ishiguro, H., Hagita, N., How to approach humans?: Strategies for social robots to initiate interaction (2009) 4Th ACM/IEEE International Conference on human–robot Interaction, , HRI 2009. La Jolla, California, USA; Lindner, F., A conceptual model of personal space for human-aware robot activity placement (2015) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Congress Center Hamburg; Rios-Martinez, J., Spalanzani, A., Laugier, C., Understanding human interaction for probabilistic autonomous navigation using Risk-RRT approach (2011) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2014-2019; Michalowski, M.P., Sabanovic, S., Simmons, R., A spatial model of engagement for a social robot (2006) 9Th IEEE International Workshop on Advanced Motion Control, pp. 762-767; Truong, X.T., Ngo, T.D., Dynamic social zone based mobile robot navigation for human comfortable safety in social environments (2016) Int J Soc Robot, 8 (5), pp. 663-684; Charalampous, K., Kostavelis, I., Gasteratos, A., Robot navigation in large-scale social maps: an action recognition approach (2016) Expert Syst Appl, 66, pp. 261-273; Dijkstra, E.W., A note on two problems in connection with graphs (1959) Numer Math, 1, pp. 269-271","Bellarbi, A.; USTHB: Universite des Sciences et de la Technologie Houari BoumedieneAlgeria; email: a_bellarbi@cdta.dz",,,"Springer Science and Business Media Deutschland GmbH",,,,,18645909,,,,"English","Evol. Intelligence",Article,"Article in Press","",Scopus,2-s2.0-85110777034
"Xie Q., Meng Q., Dai Y., Zeng Q., Fan Y., Yu H.","57210424487;55811973700;57222961905;57222965494;35931784100;57223021211;","Human-robot coupling dynamic modeling and analysis for upper limb rehabilitation robots",2021,"Technology and Health Care","29","4",,"709","723",,,"10.3233/THC-202424","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110522537&doi=10.3233%2fTHC-202424&partnerID=40&md5=55c3e8726e55d4cb0c1ee53f33cc37b3","Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai, China; Shanghai Engineering Research Center of Assistive Devices, Shanghai, China; Key Lab. of Neural-Funct. Info. and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, China; Department of Rehabilitation Robot Product, Shanghai Electric Group Central Academe, Shanghai, China","Xie, Q., Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai, China, Shanghai Engineering Research Center of Assistive Devices, Shanghai, China, Key Lab. of Neural-Funct. Info. and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, China; Meng, Q., Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai, China, Shanghai Engineering Research Center of Assistive Devices, Shanghai, China, Key Lab. of Neural-Funct. Info. and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, China; Dai, Y., Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai, China, Shanghai Engineering Research Center of Assistive Devices, Shanghai, China, Key Lab. of Neural-Funct. Info. and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, China; Zeng, Q., Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai, China, Shanghai Engineering Research Center of Assistive Devices, Shanghai, China, Key Lab. of Neural-Funct. Info. and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, China; Fan, Y., Department of Rehabilitation Robot Product, Shanghai Electric Group Central Academe, Shanghai, China; Yu, H., Rehabilitation Engineering and Technology Institute, University of Shanghai for Science and Technology, Shanghai, China, Shanghai Engineering Research Center of Assistive Devices, Shanghai, China, Key Lab. of Neural-Funct. Info. and Rehabilitation Engineering of the Ministry of Civil Affairs, Shanghai, China","BACKGROUND: Upper limb rehabilitation robots have become an important piece of equipment in stroke rehabilitation. Human-robot coupling (HRC) dynamics play a key role in the control of rehabilitation robots to improve human-robot interaction. OBJECTIVE: This study aims to study the methods of modeling and analysis of HRC dynamics to realize more accurate dynamic control of upper limb rehabilitation robots. METHODS: By the analysis of force interaction between the human arm and the upper limb rehabilitation robot, the HRC torque is achieved by summing up the robot torque and the human arm torque. The HRC torque and robot torque of a 2-DOF upper limb rehabilitation robot (FLEXO-Arm) are solved by Lagrangian equation and step-by-step dynamic parameters identification method. RESULTS: The root mean square (RMS) is used to evaluate the accuracy of the HRC torque and the robot torque calculated by the parameter identification, and the error of both is about 10%. Moreover, the HRC torque and the robot torque are compared with the actual torque measured by torque sensors. The error of the robot torque is more than twice the HRC. Therefore, the HRC torque is more accurate than the actual torque. CONCLUSIONS: The proposed HRC dynamics effectively achieves more accurate dynamic control of upper limb rehabilitation robots. © 2021 - IOS Press. All rights reserved.","dynamic model; Human-robot coupling (HRC) dynamics; parameter identification; rehabilitation robotics","arm; Article; calculation; cerebrovascular accident; control strategy; controlled study; degree of freedom; dynamics; force; human; human computer interaction; human robot coupling dynamics; Lagrangian equation; mathematical analysis; measurement accuracy; neurorehabilitation; passive movement; robotics; root mean square; stroke patient; torque; upper limb",,,"FLEXO-Arm",,"National Natural Science Foundation of China, NSFC: 61803265; National Key Research and Development Program of China, NKRDPC: 2018YFB1307200","The authors gratefully acknowledge the financial support by th National Key R&D Program of China (2018YFB1307200), and the National Natural Science Foundation of China (61803265).",,,,,,,,,,"Islam, M.R., Spiewak, C., Rahman, M.H., Fareh, R., A brief review on robotic exoskeletons for upper extremity rehabilitation to find the gap between research porotype and commercial type (2017) Advances in Robotics & Automation., 6, pp. 1-12; 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Christensen, S., Bai, S., Kinematic analysis and design of a novel shoulder exoskeleton using a double parallelogram linkage (2018) Journal of Mechanisms and Robotics., 10, p. 041008; Kim, B., Deshpande, A.D., An upper-body rehabilitation exoskeleton harmony with an anatomical shoulder mechanism: Design, modeling, control, and performance evaluation (2017) The International Journal of Robotics Research., 36, pp. 414-435; Cui, X., Chen, W., Jin, X., Agrawal, S.K., Design of a 7-DOF cable-driven arm exoskeleton (CAREX-7) and a controller for dexterous motion training or assistance (2017) IEEE/ASME Transactions on Mechatronics., 22, pp. 161-172; Klein, J., Spencer, S., Allington, J., Bobrow, J.E., Reinkensmeyer, D.J., Optimization of a parallel shoulder mechanism to achieve a high-force, low-mass, robotic-arm exoskeleton (2010) IEEE Transactions on Robotics., 26, pp. 710-715; Li, X., Wang, X., Wang, J., Rocha, A., A kind of lagrange dynamic simplified modeling method for multi-DOF robot1 (2016) Journal of Intelligent & Fuzzy Systems., 31, pp. 2393-2401; Li, X., Wang, X., Wang, J., Fang, X., Dynamics modeling and simulation of five-degree-of-freedom upper limb rehabilitation robot (2012) Control Engineering., 5, pp. 90-93+98; Wang, L., Hu, X., Cao, W., Zhang, F., Yu, H., Design and motion simulation and dynamics analysis of wearable upper limb rehabilitation robot (2017) Beijing Biomedical Engineering., 2, pp. 177-185; Pina-Martinez, E., Roberts, R., Leal-Merlo, S., Rodriguez-Leal, E., Vision system-based design and assessment of a novel shoulder joint mechanism for an enhanced workspace upper limb exoskeleton (2018) Appl Bionics Biomech., 2018, p. 6019381; Lioulemes, A., Theofanidis, M., Kanal, V., Tsiakas, K., Abujelala, M., Collander, C., Townsend, W.B., Makedon, F., MAGNI dynamics: A vision-based kinematic and dynamic upper-limb model for intelligent robotic rehabilitation (2017) International Journal of Biomedical and Biological Engineering,World Academy of Science, Engineering and Technology., 11, pp. 158-167; Rosen, J., Perry, J., Manning, N., Burns, S., Hannaford, B., The human arm kinematics and dynamics during daily activities-toward a 7 DOF upper limb powered exoskeleton (2005) 12th International Conference on Advanced Robotics., pp. 532-539; Laitenberger, M., Raison, M., Périé, D., Begon, M., Refinement of the upper limb joint kinematics and dynamics using a subject-specific closed-loop forearm model (2014) Multibody System Dynamics., 33, pp. 413-438; Song, A., Pan, L., Xu, G., Li, H., Impedance identification and adaptive control of rehabilitation robot for upper-limb passive training (2014) Foundations and Applications of Intelligent Systems., 213, pp. 691-710; Zheng, G., Song, A., Li, H., Experimental study on fuzzy-logic-based impedance control for upper-limb rehabilitation robot (2010) Robot., 6, pp. 90-96; Zhang, J., Cheah, C.C., Passivity and stability of human-robot interaction control for upper-limb rehabilitation robots (2015) IEEE Transactions on Robotics., 31, pp. 233-245; Wang, J., Zuo, G., Zhang, J., Shi, C., Song, T., Guo, S., Research on assist-as-needed control strategy of wrist functionrehabilitation robot (2020) Sheng Wu Yi Xue Gong Cheng Xue Za Zhi., 37, pp. 129-135; Chen, Y., Hu, L., Identification method of payload dynamic parameters of industrial robot (2020) Robot., 42 (3), pp. 325-335; 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Health Care",Article,"Final","",Scopus,2-s2.0-85110522537
"Domingo J.D., Gómez-García-Bermejo J., Zalama E.","57190025605;57217391062;56018068000;","Optimization and improvement of a robotics gaze control system using LSTM networks",2021,"Multimedia Tools and Applications",,,,"","",,,"10.1007/s11042-021-11112-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109367095&doi=10.1007%2fs11042-021-11112-7&partnerID=40&md5=0fff40db841589f2b3d1bc05756db39d","CARTIF Foundation, División de Sistemas Industriales y Digitales, Parque Tecnológico de Boecillo, Valladolid, 47151, Spain; ITAP-DISA, University of Valladolid, Pl. Santa Cruz 8, Valladolid, 47002, Spain","Domingo, J.D., CARTIF Foundation, División de Sistemas Industriales y Digitales, Parque Tecnológico de Boecillo, Valladolid, 47151, Spain; Gómez-García-Bermejo, J., CARTIF Foundation, División de Sistemas Industriales y Digitales, Parque Tecnológico de Boecillo, Valladolid, 47151, Spain, ITAP-DISA, University of Valladolid, Pl. Santa Cruz 8, Valladolid, 47002, Spain; Zalama, E., CARTIF Foundation, División de Sistemas Industriales y Digitales, Parque Tecnológico de Boecillo, Valladolid, 47151, Spain, ITAP-DISA, University of Valladolid, Pl. Santa Cruz 8, Valladolid, 47002, Spain","Gaze control represents an important issue in the interaction between a robot and humans. Specifically, deciding who to pay attention to in a multi-party conversation is one way to improve the naturalness of a robot in human-robot interaction. This control can be carried out by means of two different models that receive the stimuli produced by the participants in an interaction, either an on-center off-surround competitive network or a recurrent neural network. A system based on a competitive neural network is able to decide who to look at with a smooth transition in the focus of attention when significant changes in stimuli occur. An important aspect in this process is the configuration of the different parameters of such neural network. The weights of the different stimuli have to be computed to achieve human-like behavior. This article explains how these weights can be obtained by solving an optimization problem. In addition, a new model using a recurrent neural network with LSTM layers is presented. This model uses the same set of stimuli but does not require its weighting. This new model is easier to train, avoiding manual configurations, and offers promising results in robot gaze control. The experiments carried out and some results are also presented. © 2021, The Author(s).","Competitive network; Computer vision; Deep neural network; Gaze control; Gaze engagement; HRI; Humanoid robot; LSTM; Recurrent neural network; RNN; ROS","Behavioral research; Human robot interaction; Man machine systems; Multilayer neural networks; Network layers; Robotics; Competitive network; Competitive neural network; Focus of Attention; Gaze control; Human like; Multi-party conversations; Optimization problems; Smooth transitions; Long short-term memory",,,,,"Ministerio de Ciencia, Innovación y Universidades, MCIU: RTI2018-096652-B-I00; European Regional Development Fund, ERDF; Junta de Castilla y León: VA233P18","The present research has been partially financed by “Programa Retos Investigación del Ministerio de Ciencia, Innovación y Universidades (Ref. RTI2018-096652-B-I00)” and by “Programa de Apoyo a Proyectos de Investigación de la Junta de Castilla y León (Ref. VA233P18)”, cofinancied with FEDER funds.",,,,,,,,,,"Abd El-Moneim, S., Nassar, M., Dessouky, M.I., Ismail, N.A., El-Fishawy, A.S., Abd El-Samie, F.E., Text-independent speaker recognition using lstm-rnn and speech enhancement (2020) Mult Tools Appl, 79 (33), pp. 24,013-24,028; Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: a review (2017) J Human Robot Interact, 6 (1), pp. 25-63; Alonso-Martín, F., Gorostiza, J.F., Malfaz, M., Salichs, M.A., User localization during human-robot interaction (2012) Sensors, 12 (7), pp. 9913-9935; Andrist, S., Mutlu, B., Tapus, A., Look like me: Matching robot personality via gaze to increase motivation (2015) Proceedings of the 33Rd Annual ACM Conference on Human Factors in Computing Systems, pp. 3603-3612. , ACM; Bendris, M., Charlet, D., Chollet, G., Lip activity detection for talking faces classification in tv-content (2010) International Conference on Machine Vision, pp. 187-190; Benrachou, D.E., Dos Santos, F.N., Boulebtateche, B., Bensaoula, S., Online vision-based eye detection: Lbp/svm vs lbp/lstm-rnn (2015) CONTROLO’2014–proceedings of the 11Th Portuguese Conference on Automatic Control, pp. 659-668. , Springer; Carrara, F., Elias, P., Sedmidubsky, J., Zezula, P., Lstm-based real-time action detection and prediction in human motion streams (2019) Multimed Tools Appl, 78 (19), pp. 27,309-27,331; Chen, Y., Liu, C., Shi, B.E., Liu, M., Robot navigation in crowds by graph convolutional networks with attention learned from human gaze (2020) IEEE Robot Auto Lett, 5 (2), pp. 2754-2761; Dalal, N., Triggs, B., (2005) Histograms of Oriented Gradients for Human Detection; Domingo, J.D., Gómez-García-bermejo, J., Zalama, E., Optimization of a robotics gaze control system (2020) Workshop of Physical Agents, pp. 213-226. , Springer; Duque-Domingo, J., Gómez-García-Bermejo, J., Zalama, E., Gaze control of a robotic head for realistic interaction with humans (2020) Front Neurorobot, 14, p. 34; King, E., (2015) D.: Max-Margin Object Detection. 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"Rabb N., Law T., Chita-Tegmark M., Scheutz M.","57222470528;57212389268;56700303500;6603548841;","An Attachment Framework for Human-Robot Interaction",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00802-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109345996&doi=10.1007%2fs12369-021-00802-9&partnerID=40&md5=e1d08f1754866e83b8281d7b2dd862c4","HRI Lab, Tufts University, 200 Boston Avenue, Medford, MA  02155, United States","Rabb, N., HRI Lab, Tufts University, 200 Boston Avenue, Medford, MA  02155, United States; Law, T., HRI Lab, Tufts University, 200 Boston Avenue, Medford, MA  02155, United States; Chita-Tegmark, M., HRI Lab, Tufts University, 200 Boston Avenue, Medford, MA  02155, United States; Scheutz, M., HRI Lab, Tufts University, 200 Boston Avenue, Medford, MA  02155, United States","Attachment theory is a research area in psychology that has enjoyed decades of successful study, and has subsequently become explored in realms beyond that of the original infant-caregiver bonds. Now, attachment is studied in relation to pets, symbols (such as deities), objects, technologies, and notably for our purposes, robots. When we discuss attachment in Human-Robot Interaction (HRI), is “attachment” to a robot the same as being attached to a pet? Or does it more closely resemble attachment to a technology device such as a smartphone? Through untangling the concept of attachment in HRI, we summarize a breadth of the existing attachment literature in a unified spectrum. We present a notion of weak attachment, and strong attachment before setting both as distinct ends of a spectrum of attachment. We motivate this spectrum by teasing out the underlying theoretical basis for strong attachment, and how capabilities of the attachment figure could lead to stronger or weaker attachment. This more nuanced, multi-dimensional representation of attachment allows us to present a clarified categorization of where various human-robot bonds explored in HRI studies fit on the spectrum, where robots in general could place, and how a clearer definition of human-robot attachment can benefit future HRI studies. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Attachment; Emotional bond; Human-robot interaction; Social bond","Agricultural robots; Social robots; Human robot Interaction (HRI); Human robots; Multi dimensional; Man machine systems",,,,,,,,,,,,,,,,"Ainsworth Mary, D.S., Mary, B., Sally, W., (1978) Patterns of Attachment, , Everett Waters; Ainsworth Mary, S., Attachments beyond infancy (1989) Am Psychol, 44 (4), p. 709; Ali, S., Moroso, T., Breazeal, C., Can children learn creativity from a social robot? 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"Li T., Wang L., Liu J., Yuan J., Liu P.","57224812932;55721469500;57211952772;57225041521;55574230503;","Sharing the Roads: Robot Drivers (Vs. Human Drivers) Might Provoke Greater Driving Anger When They Perform Identical Annoying Driving Behaviors",2021,"International Journal of Human-Computer Interaction",,,,"","",,2,"10.1080/10447318.2021.1938392","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109047377&doi=10.1080%2f10447318.2021.1938392&partnerID=40&md5=066d718740c86385f820adbee1929b15","College of Management and Economics, Tianjin University, Tianjin, China; Department of Library and Information Science, Incheon National University, Incheon, South Korea; Center for Psychological Sciences, Zhejiang University, Hangzhou, China","Li, T., College of Management and Economics, Tianjin University, Tianjin, China; Wang, L., Department of Library and Information Science, Incheon National University, Incheon, South Korea; Liu, J., College of Management and Economics, Tianjin University, Tianjin, China; Yuan, J., College of Management and Economics, Tianjin University, Tianjin, China; Liu, P., Center for Psychological Sciences, Zhejiang University, Hangzhou, China","Automated vehicles (AVs) are ready to share public roads with humans in mixed traffic. How human drivers will interact with AVs is unclear. We assumed that AVs’ aberrant behaviors (e.g., driving slowly) provoke higher driving anger than identical ones by human drivers do and examined this assumption from the perspective of mind perception (i.e., perception of whether AVs have human-like minds or lack thereof). Mind perception has two dimensions: agency (capable of doing and planning) and experience (capable of feeling and sensing). Our survey (N = 622) supported this assumption. Agency attribution to AVs is negatively correlated with driving anger, suggesting that perceiving AVs as lacking agency might be associated with greater anger toward AVs’ aberrant behaviors. Attributing high agency to AVs eliminated the human-AV difference. The relationship between driving anger and experience attribution was U-shaped: Participants attributing “middle” experience to AVs reported lower anger than those attributing no or “high” experience to AVs. We speculate that perceiving AV as having high capability to feel and sense might induce the “uncanny valley” effect documented in the literature on human-robot interaction, enhancing more negative reactions. Our findings suggest AVs’ aberrant behaviors and their induced anger as emerging risks to future mixed-traffic flow. © 2021 Taylor & Francis Group, LLC.",,"Traffic control; Automated vehicles; Driving anger; Driving behavior; High capabilities; Mixed traffic; Mixed traffic flow; Two-dimension; Uncanny valley; Human robot interaction",,,,,,,,,,,,,,,,"Ābele, L., Haustein, S., Møller, M., Zettler, I., Links between observed and self-reported driving anger, observed and self-reported aggressive driving, and personality traits (2020) Accident Analysis & Prevention, 140, p. 105516. , https://doi.org/10.1016/j.aap.2020.105516; Ajzen, I., The theory of planned behavior (1991) Organizational Behavior and Human Decision Processes, 50 (2), pp. 179-211. , https://doi.org/10.1016/0749-5978(91)90020-T; Ajzen, I., Fishbein, M., Attitude-behavior relations: A theoretical analysis of review of empirical research (1977) Psychological Bulletin, 84 (5), pp. 888-918. , https://doi.org/10.1037/0033-2909.84.5.888; Alambeigi, H., McDonald, A.D., Tankasala, S.R., (2020) Crash themes in automated vehicles: A topic modeling analysis of the California department of motor vehicles automated vehicle crash database, , https://arxiv.org/abs/2001.11087, Retrieved July6 2020, from; Banerjee, S.S., Jha, S., Cyriac, J., Kalbarczyk, Z.T., Iyer, R.K., Hands off the wheel in autonomous vehicles? 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J. Hum.-Comput. Interact.",Article,"Article in Press","",Scopus,2-s2.0-85109047377
"Colombel J., Daney D., Bonnet V., Charpillet F.","57216849384;36867137800;26427555600;6603910626;","Markerless 3D Human Pose Tracking in the Wild with Fusion of Multiple Depth Cameras: Comparative Experimental Study with Kinect 2 and 3",2021,"Smart Innovation, Systems and Technologies","204",,,"119","134",,1,"10.1007/978-981-15-8944-7_8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107347008&doi=10.1007%2f978-981-15-8944-7_8&partnerID=40&md5=008dd127a709372dc63b412b659cbaed","Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France; Inria Bordeaux Sud Ouest - IMS (UMR 5218), Talence, 33405, France; Univ. Paris Est Creteil, LISSI, Vitry, 94400, France","Colombel, J., Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France; Daney, D., Inria Bordeaux Sud Ouest - IMS (UMR 5218), Talence, 33405, France; Bonnet, V., Univ. Paris Est Creteil, LISSI, Vitry, 94400, France; Charpillet, F., Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France","Human–robot interaction requires a robust estimate of human motion in real time. This work presents a fusion algorithm for joint center positions tracking from multiple depth cameras to improve human motion analysis accuracy. The main contribution is the proposed algorithm based on body tracking measurement fusion with an extended Kalman filter and anthropomorphic constraints, independent of sensors. As an illustration of the use of this algorithm, this paper presents the direct comparison of joint center positions estimated with a reference stereophotogrammetric system and the ones estimated with the new Kinect 3 (Azure Kinect) sensor and its older version the Kinect 2 (Kinect for Windows). The experiment was made in two parts, one for each model of Kinect, by comparing raw and merging body tracking data of a two-sided Kinect with the proposed algorithm. The proposed approach improves body tracker data for Kinect 3 which does not have the same characteristics as Kinect 2. This study shows also the importance of defining good heuristics to merge data depending on how the body tracking works. Thus, with proper heuristics, the joint center position estimates are improved by at least 14.6 %. Finally, we propose an additional comparison between Kinect 2 and Kinect 3 exhibiting the pros and cons of the two sensors. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.",,"Cameras; Gesture recognition; Human robot interaction; Kalman filters; Windows operating system; Fusion algorithms; Human motion analysis; Human pose tracking; Measurement fusion; Multiple depth cameras; Position estimates; Robot interactions; Robust estimate; Motion tracking",,,,,,,,,,,,,,,,"Microsoft Kinect—Windows app development. [Online]. Available: https://developer. microsoft.com/en-us/windows/kinect; Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., Sheikh, Y.: OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields (2018). arXiv:1812.08008 [cs]; Moon, S., Park, Y., Ko, D.W., Suh, I.H., Multiple kinect sensor fusion for human skeleton tracking using kalman filtering (2016) Int. J. Adv. Robot. Syst., 13 (2), p. 65; Feng, S., Murray-Smith, R., Fusing Kinect sensor and inertial sensors with multi-rate Kalman filter (2014) IET Conference on Data Fusion Target Tracking 2014: Algorithms and Applications (DF TT 2014), pp. 1-8. , pp; Colombel, J., Bonnet, V., Daney, D., Dumas, R., Seilles, A., Charpillet, F.: Physically consistent whole-body kinematics assessment based on an rgb-d sensor. Application to simple rehabilita-tion exercises. Sensors 20(10), 2848 (2020). Number: 10 Publisher: Multidisciplinary Digital Publishing Institute. [Online]. Available: https://www.mdpi.com/1424-8220/20/10/2848; Wang, Q., Kurillo, G., Ofli, F., Bajcsy, R., Evaluation of pose tracking accuracy in the first and second generations of microsoft kinect (2015) International Conference on Healthcare Informatics, pp. 380-389. , pp., 2015; Pagliari, D., Pinto, L., Calibration of kinect for xbox one and comparison between the two generations of microsoft sensors (2015) Sensors, 15 (11), pp. 27569-27589; Davis, R.B., Õunpuu, S., Tyburski, D., Gage, J.R., A gait analysis data collection and reduction technique (1991) Hum. Mov. Sci., 10 (5), pp. 575-587; Bell, A.L., Brand, R.A., Pedersen, D.R., Prediction of hip joint centre location from external landmarks (1989) Hum. Mov. Sci., 8 (1), pp. 3-16; Rab, G., Petuskey, K., Bagley, A., A method for determination of upper extremity kinematics (2002) Gait Posture, 15 (2), pp. 113-119; Wu, G., Siegler, S., Allard, P., Kirtley, C., Leardini, A., Rosenbaum, D., Whittle, M., Stokes, I., Standardization and Terminology Committee of the International Society of Biomechanics, “ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion–part I: Ankle, hip, and spine. International society of biomechanics (2002) J. Biomech., 35 (4), pp. 543-548; Wu, G., van der Helm, F.C.T., (DirkJan) Veeger (2005) H.E.J., Makhsous, M., Van Roy, P., Anglin, C., Nagels, J., Karduna, A.R., Mcquade, K., Wang, X., Werner, F.W., Buchholz, B.: ISB Recommendation on Definitions of Joint Coordinate Systems of Various Joints for the Reporting of Human Joint motion–Part II: Shoulder, Elbow, Wrist and Hand. J. Biomech, 38 (5), pp. 981-992; Khalil, W., Creusot, D., SYMORO+: A system for the symbolic modelling of robots (1997) Robotica, 15 (2), pp. 153-161; Roecker, J.A., McGillem, C.D., Comparison of two-sensor tracking methods based on state vector fusion and measurement fusion (1988) IEEE Trans. Aerosp. Electron. Syst., 24 (4), pp. 447-449; Gan, Q., Harris, C.J., Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion (2001) IEEE Trans. Aerosp. Electron. Syst., 37 (1), pp. 273-279; Yeung, K.-Y., Kwok, T.H., Wang, C., Improved skeleton tracking by duplex kinects: A practical approach for real-time applications (2013) J. Comput. Inf. Sci. Eng., 13; Otto, M.M., Agethen, P., Geiselhart, F., Rietzler, M., Gaisbauer, F., Rukzio, E., Presenting a holistic framework for scalable, marker-less motion capturing: Skeletal tracking performance analysis, sensor fusion algorithms and usage in automotive industry (2017) J. Virtual Reality Broadcast., 13 (3); Haralick, R.M., Joo, H., Lee, C., Zhuang, X., Vaidya, V.G., Kim, M.B., Pose estimation from corresponding point data (1989) IEEE Trans. Syst. Man Cybern., 19 (6), pp. 1426-1446; Tripathy, S.R., Chakravarty, K., Sinha, A., Constrained particle filter for improving kinect based measurements (2018) 26Th European Signal Processing Conference (EUSIPCO), pp. 306-310. , pp., 2018; Skals, S., Rasmussen, K.P., Bendtsen, K.M., Yang, J., Andersen, M.S., A musculoskeletal model driven by dual microsoft kinect sensor data (2017) Multibody Syst. Dyn., 41 (4), pp. 297-316; Naeemabadi, M., Dinesen, B., Andersen, O.K., Hansen, J., Investigating the impact of a motion capture system on Microsoft Kinect v2 recordings: A caution for using the technologies together (2018) PLOS ONE, 13 (9); Banos, O., Calatroni, A., Damas, M., Pomares, H., Rojas, I., Sagha, H., del, J., Millán, R., Troster, G., Chavarriaga, R., Roggen, D.: Kinect=IMU? Learning MIMO signal mappings to automatically translate activity recognition systems across sensor modalities. In: 2012 16th International Symposium on Wearable Computers, pp. 92–99 (2012). iSSN: 2376-8541","Colombel, J.; Université de Lorraine, France; email: jessica.colombel@inria.fr","Ahad M.A.R.Ahad M.A.R.Inoue S.Roggen D.Fujinami K.",,"Springer Science and Business Media Deutschland GmbH","Activity and Behavior Computing conference, ABC 2020","26 August 2020 through 29 August 2020",,257149,21903018,9789811589430,,,"English","Smart Innov. Syst. Technol.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85107347008
"Otaran A., Tokatli O., Patoglu V.","57190182363;36667722400;19640643500;","Physical Human-Robot Interaction Using HandsOn-SEA: An Educational Robotic Platform with Series Elastic Actuation",2021,"IEEE Transactions on Haptics",,,,"","",,,"10.1109/TOH.2021.3081982","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107195500&doi=10.1109%2fTOH.2021.3081982&partnerID=40&md5=b22332c78f7d7052a9f7d69a401d6d19","Mechatronics Engineering, Sabanci University Faculty of Engineering and Natural Sciences, 395780 Istanbul, Istanbul, Turkey, (e-mail: ataotaran@sabanciuniv.edu); Mechatronics Engineering, Sabanci University Faculty of Engineering and Natural Sciences, 395780 Istanbul, Istanbul, Turkey, (e-mail: otokatli@sabanciuniv.edu); Mechatronics Engineering, Sabanci University Faculty of Engineering and Natural Sciences, 395780 Istanbul, Istanbul, Turkey, (e-mail: vpatoglu@sabanciuniv.edu)","Otaran, A., Mechatronics Engineering, Sabanci University Faculty of Engineering and Natural Sciences, 395780 Istanbul, Istanbul, Turkey, (e-mail: ataotaran@sabanciuniv.edu); Tokatli, O., Mechatronics Engineering, Sabanci University Faculty of Engineering and Natural Sciences, 395780 Istanbul, Istanbul, Turkey, (e-mail: otokatli@sabanciuniv.edu); Patoglu, V., Mechatronics Engineering, Sabanci University Faculty of Engineering and Natural Sciences, 395780 Istanbul, Istanbul, Turkey, (e-mail: vpatoglu@sabanciuniv.edu)","For gaining proficiency in physical human-robot interactions, it is crucial for engineering students to be provided with the opportunity to gain hands-on experience with robotic devices that feature kinesthetic feedback. We propose HandsOn-SEA, a low-cost, single degree-of-freedom, force-controlled educational robot with series elastic actuation and introduce educational modules for the use of the device to allow students to experience the fundamental performance trade-offs inherent in robotic systems. The novelty of the proposed robot is due to the deliberate introduction of a compliant joint between the actuator and the handle, whose deflections are measured to perform closed-loop force control. As an admittance-type robot, HandsOn-SEA relies on force feedback to achieve the desired level of safety and transparency and complements the existing impedance-type educational robots. We present the integration of HandsOn-SEA into the robotics curriculum, by providing guidelines for its use in a senior-level robotics course, to help students experience the challenges involved in the synergistic design and control of robotic devices. We systematically evaluate the efficacy of the device in a robotics course delivered for five semesters and provide evidence that HandsOn-SEA is effective in instilling fundamental concepts and trade-offs in the design and control of robotic devices. IEEE","Educational Robotics; Educational robots; Force; Haptic interfaces; Interaction Control; Performance evaluation; Physical Human-Robot Interaction (pHRI); Robot sensing systems; Robots; Robustness; Series Elastic Actuation (SEA)","Biofeedback; Commerce; Costs; Curricula; Degrees of freedom (mechanics); Economic and social effects; Feedback; Human robot interaction; Man machine systems; Robotics; Students; Universal joints; Educational robotics; Fundamental concepts; Kinesthetic feedback; Performance trade-off; Physical human-robot interactions; Robotics curriculums; Series elastic actuations; Single degree of freedoms; Educational robots",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,19391412,,,,"English","IEEE Trans. Haptic",Article,"Article in Press","",Scopus,2-s2.0-85107195500
"Gorbenko A., Popov V.","46861096400;8988704500;","Prediction of Changes of Intentions and Avoidance Strategies for the Way Crossing Scenarios",2021,"Lecture Notes in Electrical Engineering","729 LNEE",,,"811","821",,,"10.1007/978-3-030-71119-1_79","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104842679&doi=10.1007%2f978-3-030-71119-1_79&partnerID=40&md5=3b848ad4e3a5ae4ccc98e98db15de1eb","Ural Federal University, 51 Lenina Street, Ekaterinburg, 620083, Russian Federation","Gorbenko, A., Ural Federal University, 51 Lenina Street, Ekaterinburg, 620083, Russian Federation; Popov, V., Ural Federal University, 51 Lenina Street, Ekaterinburg, 620083, Russian Federation","Finding effective solutions to the problem of avoiding collisions plays a critical role in ensuring safety and increasing the efficiency of production processes. The problem of avoiding collisions is very hard. In the general case, the problem is still very far from its final solution. The main attention of researchers is directed to the consideration of various specific important scenarios for avoiding collisions and finding effective solutions for them. In this paper, we consider the way crossing scenario. Despite its simplicity, the scenario is crucial because it is extensively used for a large number of different interaction tasks. We consider both the interaction of two robots and the interaction of a human and a robot for the scenario. We propose an approach to solve the collision avoidance problem for the scenario. Our approach is based on a method of predicting changes in the intentions of another agent. To predict changes in intentions, we use an agent trajectory analysis based on models of motor primitives. The case of human-robot interaction is significantly more difficult. It requires a much more thorough analysis. In particular, to predict changes in the intentions of a human subject, we propose an approach to modeling human avoidance strategies for the way crossing scenario. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Collision avoidance; Human-robot interaction; Intention prediction; Mobile robot; Motor primitives","Automation; Forecasting; Effective solution; Human subjects; Motor primitives; Production process; Trajectory analysis; Human robot interaction",,,,,,,,,,,,,,,,"Graham, J.H., Meagher, J.F., Derby, S.J., A safety and collision avoidance system for industrial robots (1986) IEEE T Ind. Appl., pp. 195-203. , IA-22; Winkler, A., Suchy, J., Vision based collision avoidance of industrial robots (2011) IFAC Proc, 44, pp. 9452-9457; Saveriano, M., Lee, D., Distance based dynamical system modulation for reactive avoidance of moving obstacles (2014) IEEE International Conference on Robotics and Automation, , Hong Kong, 31 May–7 June 2014; Zeng, I., Bone, G.M., Mobile robot collision avoidance in human environments (2013) Int. J. Adv. Rob. Syst., 10, pp. 1-14; Hamasaki, S., Tamura, Y., Yamashita, A., Asama, H., Prediction of human’s movement for collision avoidance of mobile robot (2011) 2011 IEEE International Conference on Robotics and Biomimetics, , Karon Beach, 7–11 December 2011; Liu, S.B., Roehm, H., Heinzemann, C., Lütkebohle, I., Oehlerking, J., Althoff, M., Provably safe motion of mobile robots in human environments (2017) IEEE/RSJ International Conference on Intelligent Robots and Systems, , Vancouver, 24–28 September 2017; Muramatsu, S., Matsuda, H., Nakamoto, S., Chugo, D., Suehiro, T., Hashimoto, H., Consideration of human movement prediction for human friendly mobile robot’s path planning – 1st report analysis of pedestrians movement trajectory in a real world (2013) Proceedings of the 4Th IEEE International Conference on Cognitive Infocommunications, , Budapest, 2–5 December 2013; Dachner, G.C., Warren, W.H., Behavioral dynamics of heading alignment in pedestrian following (2014) Transp. Res. Procedia, 2, pp. 69-76; Lamb, M., Kallen, R.W., Harrison, S.J., Di Bernardo, M., Minai, A., Richardson, M.J., To pass or not to pass: Modeling the movement and affordance dynamics of a pick and place task (2017) Front. Psychol., 8, p. 1061; Richardson, M.J., Self-organized complementary joint action: Behavioral dynamics of an interpersonal collision-avoidance task (2015) J. Exp. Psychol. Hum. Percept. Perform., 41, pp. 665-679; Vassallo, C., Olivier, A.H., Soueres, P., Crétual, A., Stasse, O., Pettré, J., How do walkers avoid a mobile robot crossing their way? (2017) Gait & Posture, 51, pp. 97-103; Kober, J., Peters, J., Learning Motor Skills: From Algorithms to Robot Experiments (2014) Springer International Publishing, Cham; Tieck, J., Schnell, T., Kaiser, J., Mauch, F., Roennau, A., Dillmann, R., Generating pointing motions for a humanoid robot by combining motor primitives (2019) Front. Neurorobot., 13, pp. 1-9; Seker, M., Imre, M., Piater, J., Ugur, E., Conditional neural movement primitives (2019) Robot. Sci. Syst., 15, pp. 1-9; Mitrevski, A., Padalkar, A., Nguyen, M., Plöger, Paul G.: “Lucy, take the noodle box!”: Domestic object manipulation using movement primitives and whole body motion (2019) Robocup 2019. LNCS (LNAI), 11531, pp. 189-200. , https://doi.org/10.1007/978-3-030-35699-6_15, Chalup, S., Niemueller, T., Suthakorn, J., Williams, M.-A. (eds.), pp. , Springer, Cham; Sanzari, M., Ntouskos, V., Pirri, F., Discovery and recognition of motion primitives in human activities (2019) Plos ONE, 14, pp. 1-39; Chi, M., Yao, Y., Liu, Y., Learning, generalization, and obstacle avoidance with dynamic movement primitives and dynamic potential fields (2019) Appl. Sci., 9, pp. 1-16; Ugur, E., Girgin, H., Compliant parametric dynamic movement primitives (2019) Robotica, 38, pp. 1-20; Dermy, O., Paraschos, A., Ewerton, M., Prediction of intention during interaction with iCub with probabilistic movement primitives (2017) Front. Robot. AI, 4, pp. 1-27; Dindo, H., Chella, A., What will you do next? A cognitive model for understanding others’ intentions based on shared representations (2013) VAMR 2013. LNCS, 8021, pp. 253-266. , https://doi.org/10.1007/978-3-642-39405-8_29, Shumaker, R. (ed.), pp. , Springer, Heidelberg; Gorbenko, A., Popov, V., Description of sequences of rhythmic motor primitives (2014) Adv. Mater. Res., 1016, pp. 612-616; Dawson, S., Wellman, B.L., Anderson, M., The effect of multiple robots on simulation accuracy (2010) CD Proceedings of the Workshop on the Role of Experiments in Robotics Research – ICRA, , Anchorage, May 2010; Suomalainen, M., Nilles, A., Lavalle, S., (2019) Virtual Reality for Robots; Pellegrini, S., Ess, A., Schindler, K., van Gool, L., You’ll never walk alone: Modeling social behavior for multi-target tracking (2010) 2009 IEEE 12Th International Conference on Computer Vision, , Kyoto, 29 September–2 October 2009; Hacohen, S., Shvalb, N., Shoval, S., Dynamic model for pedestrian crossing in congested traffic based on probabilistic navigation function (2018) Transp. Res. C, 86, pp. 78-96; Usher, J.M., McCool, R., Strawderman, L., Simulation modeling of pedestrian behavior in the presence of unmanned mobile robots (2017) Simul. Model. Pract. Theory, 75, pp. 96-112; Zhou, M., Dong, H., Wang, F.Y., Modeling and simulation of pedestrian dynamical behavior based on a fuzzy logic approach (2016) Inf. Sci., 360, pp. 112-130; Kruse, T., Pandey, A.K., Alami, R., Human-aware robot navigation: A survey (2013) Robot. Auton. Syst., 61, pp. 1726-1743; Claes, D., Tuyls, K., Multi robot collision avoidance in a shared workspace (2018) Auton. Robot., 42, pp. 1749-1770; Gorbenko, A., Mornev, M., Popov, V., Planning a typical working day for indoor service robots (2011) IAENG Int. J. Comput. Sci., 38, pp. 176-182","Popov, V.; Ural Federal University, 51 Lenina Street, Russian Federation; email: popovvvv@gmail.com","Radionov A.A.Gasiyarov V.R.",,"Springer Science and Business Media Deutschland GmbH","International Russian Automation Conference, RusAutoConf 2020","6 September 2020 through 12 September 2020",,256749,18761100,9783030711184,,,"English","Lect. Notes Electr. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85104842679
"Young S.N., Lanciloti R.J., Peschel J.M.","57194775342;57222811783;8628042500;","The Effects of Interface Views on Performing Aerial Telemanipulation Tasks Using Small UAVs",2021,"International Journal of Social Robotics",,,,"","",,1,"10.1007/s12369-021-00783-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104058444&doi=10.1007%2fs12369-021-00783-9&partnerID=40&md5=89494b4e27a9cd37da5dc95bf5a91e8b","Biological and Agricultural Engineering, North Carolina State University, Campus Box 7625, Raleigh, NC  27695, United States; Computer Engineering, Iowa State University, 2520 Osborn Dr., Ames, IA  50011, United States; Agricultural and Biosystems Engineering, Iowa State University, 605 Bissell Rd., Ames, IA  50011, United States","Young, S.N., Biological and Agricultural Engineering, North Carolina State University, Campus Box 7625, Raleigh, NC  27695, United States; Lanciloti, R.J., Computer Engineering, Iowa State University, 2520 Osborn Dr., Ames, IA  50011, United States; Peschel, J.M., Agricultural and Biosystems Engineering, Iowa State University, 605 Bissell Rd., Ames, IA  50011, United States","This paper presents a human-robot interaction (HRI) study of a dedicated Mission Specialist interface for performing telemanipulation tasks using a small unoccupied aerial vehicle (UAV). Current literature suggests that the successful completion of aerial manipulation tasks in real-world environments requires human input due to challenges in autonomous perception and control. Visual information of the remote environment in a telemanipulation interface can significantly affect performance under direct control; however, the effects of interface visualizations on task performance have not been studied for UAV telemanipulation. This work evaluated the effects of interface viewpoint on aerial manipulation task performance. The interfaces evaluated in this study included video streams from cameras located onboard the UAV, including: (i) a manipulator egocentric view, (ii) a manipulator exocentric view, and (iii) a combination of egocentric and exocentric views. A total of 36 participants completed three different manipulation tasks using all three interface conditions. The observations and results showed that both the exocentric and mixed view configurations contributed to improved task performance over an egocentric-only interface. Further, this study resulted in data regarding view use, view effectiveness, and task type that can be used for further developing interfacing for aerial manipulators that change and adapt to the environment and task. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Human robot interaction; Manipulation; Tablet interface; UAV","Agricultural robots; Manipulators; Social robots; Unmanned aerial vehicles (UAV); Human robot Interaction (HRI); Interface conditions; Manipulation task; Real world environments; Remote environment; Task performance; Telemanipulation; Visual information; Antennas",,,,,"National Science Foundation, NSF: 1925262; U.S. Department of Defense, DOD; Air Force Office of Scientific Research, AFOSR; National Defense Science and Engineering Graduate, NDSEG: 32 CFR 168a","S. Young acknowledges Government support under and awarded by DoD, Air Force Office of Scientific Research, National Defense Science and Engineering Graduate (NDSEG) Fellowship, 32 CFR 168a. This material is based upon work supported by the National Science Foundation under Grant No. 1925262. The authors would like to thank Jacob Riesen and Karun Koppula for assistance with developing this study, in addition to the 36 people who participated in the experiments.",,,,,,,,,,"Construction of cubic structures with quadrotor teams (2011) Proc. Robotics: Science & Systems, 7, pp. 177-184. , IEEE, Los Angeles, CA, USA; Werfel, J., Bar-Yam, Y., Rus, D., Nagpal, R., Distributed construction by mobile robots with enhanced building blocks (2006) Proceedings of the 2006 IEEE International Conference on Robotics Robotics and Automation (ICRA, pp. 2787-2794. , IEEE, Orlando, FL, USA; Pounds, P., Bersak, D., Dollar, A., Grasping from the air: Hovering capture and load stability (2011) Proc. 2011 International Conference on Robotics and Automation (ICRA), IEEE, Shanghai, China, pp. 2491-2498; Tognon, M., Chávez, H.A.T., Gasparin, E., Sablé, Q., Bicego, D., Mallet, A., Lany, M., Cortés, J., A truly-redundant aerial manipulator system with application to push-and-slide inspection in industrial plants (2019) IEEE Robot Autom Lett, 4 (2), pp. 1846-1851; Ruggiero, F., Lippiello, V., Ollero, A., Aerial manipulation: a literature review (2018) IEEE Robot Autom Lett, 3 (3), pp. 1957-1964; Xilun, D., Pin, G., Kun, X., Yushu, Y., A review of aerial manipulation of small-scale rotorcraft unmanned robotic systems (2019) Chin J Aeronaut, 32 (1), pp. 200-214; Murphy, R.R., Burke, J.L., From remote tool to shared roles (2008) IEEE Robot Autom, 15 (4), pp. 39-49; Young, S.N., Peschel, J.M., Review of human-machine interfaces for small unmanned systems with robotic manipulators (2020) IEEE Trans Hum Mach Syst, 50 (2), pp. 131-143; Burke, J.L., Murphy, R.R., (2004) Situation Awareness and Task Performance in Robot-Assisted Technical Search: Bujold Goes to Bridgeport, , Tech. 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ARL-00-14/FED-LAB-00-4, University of Illinois at Urbana-Champaign Savoy Aviation Research Lab; Witmer, B.G., Sadowski, W.J., Jr., Nonvisually guided locomotion to a previously viewed target in real and virtual environments (1998) Hum Factors, 40 (3), pp. 478-488; Lee Pazuchanics, S., The effects of camera perspective and field of view on performance in teleoperated navigation (2006) Proc 2006 Human Factors and Ergonomics Society Annual Meeting, 50 (16), pp. 1528-1532; Woods, D.D., Tittle, J., Feil, M., Roesler, A., Envisioning human-robot coordination in future operations (2004) IEEE Trans Syst Man Cybern Part C (Appl Rev), 34 (2), pp. 210-218; Casper, J., Murphy, R.R., Human-robot interactions during the robot-assisted urban search and rescue response at the World Trade Center (2003) IEEE Trans Syst Man Cybern Part B (Cybernetics), 33 (3), pp. 367-385; Rudolphkempster, D.P.K., Peterson B (2001) Effects of streaming video quality of service on spatial comprehension in a reconnaissance task Proc. 2011 Interservice/Industry Training, Simulation and Education Conf. 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In: 2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) (2018) IEEE, pp. 1-7; Wu, M., Dai, S.L., Yang, C., Mixed reality enhanced user interactive path planning for omnidirectional mobile robot (2020) Appl Sci; Vozar, S.E., (2013) A Framework for Improving the Speed and Performance of Teleoperated Mobile Manipulators, , PhD thesis, University of Michigan; Chintamani, K., Cao, A., Ellis, R.D., Pandya, A.K., Improved telemanipulator navigation during display-control misalignments using augmented reality cues (2010) IEEE Trans Syst Mand Cybern Part A (Systems and Humans), 40 (1), pp. 29-39; Thomas, L.C., Wickens, C.D., Visual displays and cognitive tunneling: Frames of reference effects on spatial judgments and change detection (2001) Proc. 2001 Human Factors and Ergonomics Society Annual Meeting, 45, pp. 336-340. , SAGE Publications Sage CA, Los Angeles, CA, Minneapolis, MN, USA; Fanni, M., Khalifa, A., A new 6-DOF quadrotor manipulation system: design, kinematics, dynamics, and control (2017) IEEE/ASME Trans Mechatron, 22 (3), pp. 1315-1326; Orsag, M., Korpela, C., Oh, P., Modeling and control of MM-UAV: Mobile manipulating unmanned aerial vehicle (2012) J Intell Robot Syst, 65 (1-4), pp. 93-101; Sa, I., Kamel, M., Khanna, R., Popović, M., Nieto, J., Siegwart, R., Dynamic system identification, and control for a cost-effective and open-source multi-rotor mav (2018) Field and Service Robotics, pp. 605-620. , Hutter M, Siegwart R, (eds), Springer International Publishing, Cham; Sa, I., Hrabar, S., Corke, P., Inspection of pole-like structures using a vision-controlled VTOL UAV and shared autonomy (2014) Proc. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4819-4826. , https://doi.org/10.1109/IROS.2014.6943247, IEEE, Chicago, IL, USA; Sa, I., Kamel, M., Khanna, R., Popovic, M., Nieto, J., Siegwart, R., (2017) Dynamic System Identification, and Control for a Cost Effective Open-Source VTOL MAV.; Kamel, M., Stastny, T., Alexis, K., Siegwart, R., (2017) Model Predictive Control for Trajectory Tracking of Unmanned Aerial Vehicles Using Robot Operating System, pp. 3-39. , https://doi.org/10.1007/978-3-319-54927-9_1, Springer International Publishing, Cham; Weiss, S., Scaramuzza, D., Siegwart, R., Monocular-SLAM-based navigation for autonomous micro helicopters in GPS-denied environments (2011) J Field Robot, 28 (6), pp. 854-874; Ogura, T., (2016), rqt\_ez\_publisher. https://github.com/OTL/rqt_ez_publisher; Teammco, R., (2016), https://github.com/teammcr192/streaming-udp-video, Streaming UDP Video; Bethel, C.L., Murphy, R.R., Review of human studies methods in HRI and recommendations (2010) Int J Soc Robot, 2 (4), pp. 347-359; Jewitt, C., (2012) An Introduction to Using Video for Research, , National Centre for Research Methods (NCRM) Working Paper; Olmos, O., Wickens, C.D., Chudy, A., Tactical displays for combat awareness: an examination of dimensionality and frame of reference concepts and the application of cognitive engineering (2000) Int J Aviation Psychol, 10 (3), pp. 247-271; Thomas, L.C., Wickens, C.D., Immersion and battlefield visualization: Frame of reference effects on navigation tasks and cognitive tunneling (1999) Proc. 1999 Human Factors and Ergonomics Society Annual Meeting, 43, pp. 153-157. , SAGE Publications Sage CA, Los Angeles, CA, Houston, TX, USA; Wickens, C.D., Carswell, C.M., The proximity compatibility principle: Its psychological foundation and relevance to display design (1995) Hum Factors, 37 (3), pp. 473-494","Young, S.N.; Biological and Agricultural Engineering, Campus Box 7625, United States; email: syoung2@ncsu.edu",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Article in Press","",Scopus,2-s2.0-85104058444
"Raza F., Zhu W., Hayashibe M.","57218836923;57222734353;6602744530;","Balance Stability Augmentation for Wheel-Legged Biped Robot through Arm Acceleration Control",2021,"IEEE Access","9",,"9395085","54022","54031",,1,"10.1109/ACCESS.2021.3071055","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103876007&doi=10.1109%2fACCESS.2021.3071055&partnerID=40&md5=150deac68e436ae670e23c9d21fa400a","Department of Robotics, School of Engineering, Tohoku University, Sendai, Japan","Raza, F., Department of Robotics, School of Engineering, Tohoku University, Sendai, Japan; Zhu, W., Department of Robotics, School of Engineering, Tohoku University, Sendai, Japan; Hayashibe, M., Department of Robotics, School of Engineering, Tohoku University, Sendai, Japan","A self-balancing wheel-legged robot provides higher maneuverability and mobility than legged biped robots. For this reason, wheel-legged systems have attracted enormous interest from academia and commercial sectors in recent years. Most of the past works in this field mainly focused on lower body stabilization. Motivated by the human ability to maintain balance in laborious activities by articulating the arm actively, we explore and analyze the active arm control on top of the wheel-legged system to assist in its balance recovery during external pushes and disturbances. This paper presents a control framework to improve the stability and robustness of an underactuated self-balancing wheel-legged robot using its upper limb arm. Furthermore, we use the centroidal moment pivot (CMP) as a key metric to quantitatively evaluate the effect of the active arm usage on the balance stability improvement of the robot in the ROS-Gazebo environment. The difference from the case of nonusage of arm is verified to clarify the impact of the active arm quantitatively. This concept would lead to the wheel-legged biped robot with an active arm for dual purposes, one is for carrying objects, another is for increasing the balance stability. This point is important for future application in a real-world environment with human-robot interactions. © 2013 IEEE.","balance recovery; disturbance rejection; motion control; stability analysis; underactuated robots; Wheel-legged robots; wheeled inverted pendulum (WIP)","Acceleration control; Stability; Wheels; Balance recoveries; Centroidal moment pivot; Future applications; Real world environments; Stability and robustness; Stability augmentations; Stability improvement; Wheel-legged robots; Human robot interaction",,,,,"Japan Society for the Promotion of Science, KAKEN: 18H01399","This work was supported by the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (B) under Grant 18H01399.",,,,,,,,,,"Dynamics, B., (2017) Introducing Handle, , https://www.youtube.com/watch?v=-7xvqQeoA8c, Accessed Feb. 28; Koyanagi, E., Iida, S., Kimoto, K., Yuta, S., A wheeled inverse pendulum type self-contained mobile robot and its two-dimensional trajectory control (1992) Proc. 2nd Int. Symp., Meas. Control Robot, pp. 891-898; Ha, Y., Yuta, S., Trajectory tracking control for navigation of selfcontained mobile inverse pendulum (1994) Proc IEEE/RSJ Int. Conf. Intell. Robots Syst. IROS, pp. 1875-1882. , Sep; Shiroma, N., Matsumoto, O., Tani, K., Cooperative behavior of a mechanically unstable mobile robot for object transportation (1999) JSME Int. J. Ser. C, 42 (4), pp. 965-973; Grasser, F., D'Arrigo, A., Colombi, S., Rufer, A.C., JOE: A mobile, inverted pendulum (2002) IEEE Trans. Ind. Electron, 49 (1), pp. 107-114. , Feb; Baloh, M., Parent, M., Modeling and model veri-cation of an intelligent self-balancing two-wheeled vehicle for an autonomous urban transportation system (2003) Proc. Conf. Comput. Intell., Robot., Autonomous Syst, pp. 1-7; Pathak, K., Franch, J., Agrawal, S.K., Velocity control of a wheeled inverted pendulum by partial feedback linearization (2004) Proc. 43rd IEEE Conf. Decis. Control CDC, pp. 3962-3967. , Dec; Pathak, K., Franch, J., Agrawal, S.K., Velocity and position control of a wheeled inverted pendulum by partial feedback linearization (2005) IEEE Trans. Robot, 21 (3), pp. 505-513. , Jun; Jeong, S., Takahashi, T., Wheeled inverted pendulum type assistant robot: Inverted mobile, standing, and sitting motions (2007) Proc IEEE/RSJ Int. Conf. Intell. Robots Syst, pp. 1932-1937. , Oct; Jeong, S., Takahashi, T., Wheeled inverted pendulum type assistant robot: Design concept and mobile control (2008) Intell. Service Robot, 1 (4), pp. 313-320. , Oct; Stilman, M., Olson, J., Gloss, W., Golem Krang: Dynamically stable humanoid robot for mobile manipulation (2010) Proc IEEE Int. Conf. Robot. Autom, pp. 3304-3309. , May; Huang, J., Guan, Z.-H., Matsuno, T., Fukuda, T., Sekiyama, K., Slidingmode velocity control of mobile-wheeled inverted-pendulum systems (2010) IEEE Trans. Robot, 26 (4), pp. 750-758. , Aug; Xu, J.-X., Guo, Z.-Q., Lee, T.H., Design and implementation of integral sliding-mode control on an underactuated two-wheeled mobile robot (2014) IEEE Trans. Ind. Electron, 61 (7), pp. 3671-3681. , Jul; Dai, F., Gao, X., Jiang, S., Guo, W., Liu, Y., A two-wheeled inverted pendulum robot with friction compensation (2015) Mechatronics, 30, pp. 116-125. , Sep; Zafar, M., Christensen, H.I., Whole body control of a wheeled inverted pendulum humanoid (2016) Proc IEEE-RAS 16th Int. Conf. Humanoid Robots Humanoids, pp. 89-94. , Nov; Zafar, M., Hutchinson, S., Theodorou, E.A., Hierarchical optimization for whole-body control of wheeled inverted pendulum humanoids (2019) Proc. Int. Conf. Robot. Autom. (ICRA, pp. 7535-7542. , May; Xin, Y., Chai, H., Li, Y., Rong, X., Li, B., Li, Y., Speed and acceleration control for a two wheel-leg robot based on distributed dynamic model and whole-body control (2019) IEEE Access, 7, pp. 180630-180639; Xin, S., You, Y., Zhou, C., Fang, C., Tsagarakis, N., A torque-controlled humanoid robot riding on a two-wheeled mobile platform (2017) Proc IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS, pp. 1435-1442. , Sep; Klemm, V., Morra, A., Salzmann, C., Tschopp, F., Bodie, K., Gulich, L., Kung, N., Siegwart, R., Ascento: A two-wheeled jumping robot (2019) Proc. Int. Conf. Robot. Autom. (ICRA, pp. 7515-7521. , May; Klemm, V., Morra, A., Gulich, L., Mannhart, D., Rohr, D., Kamel, M., Viragh, Y.D., Siegwart, R., LQR-Assisted whole-body control of a wheeled bipedal robot with kinematic loops (2020) IEEE Robot. Autom. Lett, 5 (2), pp. 3745-3752. , Apr; Zambella, G., Lentini, G., Garabini, M., Grioli, G., Catalano, M.G., Palleschi, A., Pallottino, L., Caporale, D., Dynamic whole-body control of unstable wheeled humanoid robots (2019) IEEE Robot. Autom. Lett, 4 (4), pp. 3489-3496. , Oct; Kim, S., Kwon, S., Nonlinear optimal control design for underactuated two-wheeled inverted pendulum mobile platform (2017) IEEE/ASME Trans. Mechatronics, 22 (6), pp. 2803-2808. , Dec; Lin, L.-G., Xin, M., Nonlinear control of two-wheeled robot based on novel analysis and design of SDRE scheme (2020) IEEE Trans. Control Syst. Technol, 28 (3), pp. 1140-1148. , May; Boström, K.J., Dirksen, T., Zentgraf, K., Wagner, H., The contribution of upper body movements to dynamic balance regulation during challenged locomotion (2018) Frontiers Hum. Neurosci, 12, p. 8. , Jan; Popovic, M.B., Goswami, A., Herr, H., Ground reference points in legged locomotion: De-nitions biological trajectories and control implications (2005) Int. J. Robot. Res, 24 (12), pp. 1013-1032. , Dec; Hayashibe, M., Gonzalez, A., Tournier, M., Personalized balance and fall risk visualization with Kinect two (2020) Proc. 42nd Annu. Int. Conf IEEE Eng. Med. Biol. Soc. EMBC, pp. 4863-4866. , Jul; Raza, F., Owaki, D., Hayashibe, M., Modeling and control of a hybrid wheeled legged robot: Disturbance analysis (2020) Proc IEEE/ASME Int. Conf. Adv. Intell. Mechatronics (AIM, pp. 466-473. , Jul; Raza, F., Hayashibe, M., Towards robust wheel-legged biped robot system: Combining feedforward and feedback control (2021) Proc IEEE/SICE Int. Symp. Syst. Integr. (SII), pp. 606-612. , Jan; Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., (2009) Robotics: Modelling, Planning and Control, , London, U.K Springer; Luh, J., Walker, M., Paul, R., Resolved-Acceleration control of mechanical manipulators (1980) IEEE Trans. Autom. Control, 25 (3), pp. 468-474. , Jun; Goswami, A., Kallem, V., Rate of change of angular momentum and balance maintenance of biped robots (2004) Proc IEEE Int. Conf. Robot. Autom. (ICRA), pp. 3785-3790. , Apr./May; Popovic, M., Hofmann, A., Herr, H., Angular momentum regulation during human walking: Biomechanics and control (2004) Proc IEEE Int. Conf. Robot. Autom. (ICRA), pp. 2405-2411. , Apr./May; Orin, D., (1976) Interactive Control of A Six-legged Vehicle with Optimization of Both Stability and Energy, , Ph.D. dissertation, Dept. Elect. Eng., Ohio State Univ., Columbus, OH, Canada; Lin, B.-S., Song, S.-M., Dynamic modeling, stability and energy ef-ciency of a quadrupedal walking machine (1993) Proc IEEE Int. Conf. Robot. Autom, pp. 367-373. , May; Open source robotics foundation (2014) Gazebo, , http://gazebosim.org/, Accessed Jul; Open Source Robotics Foundation (2014) ROS-Robot Operating System, , https://www.ros.org/, Accessed Jul","Raza, F.; Department of Robotics, Japan; email: fahadraza@dc.tohoku.ac.jp",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85103876007
"Tao H., Rahman M.A., Jing W., Li Y., Li J., Al-Saffar A., Zhang R., Salih S.Q.","36350315600;55457938100;55546532300;55719081300;56769465100;57201725434;57208779192;57203978808;","Interaction modeling and classification scheme for augmenting the response accuracy of human-robot interaction systems",2021,"Work","68","3",,"903","912",,,"10.3233/WOR-203424","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103516005&doi=10.3233%2fWOR-203424&partnerID=40&md5=998725ee816d2ae9c0e031899bcd5b85","School of Computer Science, Baoji University of Arts and Sciences, Baoji, China; Faculty of Computing, Ibm CoE, and Earth Resources and Sustainability Center, Universiti Malaysia Pahang, Pahang, Malaysia; Business School, Lanzhou City University, Lanzhou, China; School of Electronics Engineering and Computer Science, Peking University, Beijing, China; Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam","Tao, H., School of Computer Science, Baoji University of Arts and Sciences, Baoji, China; Rahman, M.A., Faculty of Computing, Ibm CoE, and Earth Resources and Sustainability Center, Universiti Malaysia Pahang, Pahang, Malaysia; Jing, W., School of Computer Science, Baoji University of Arts and Sciences, Baoji, China; Li, Y., School of Computer Science, Baoji University of Arts and Sciences, Baoji, China; Li, J., Business School, Lanzhou City University, Lanzhou, China; Al-Saffar, A., Faculty of Computing, Ibm CoE, and Earth Resources and Sustainability Center, Universiti Malaysia Pahang, Pahang, Malaysia; Zhang, R., School of Electronics Engineering and Computer Science, Peking University, Beijing, China; Salih, S.Q., Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam","BACKGROUND: Human-robot interaction (HRI) is becoming a current research field for providing granular real-time applications and services through physical observation. Robotic systems are designed to handle the roles of humans and assist them through intrinsic sensing and commutative interactions. These systems handle inputs from multiple sources, process them, and deliver reliable responses to the users without delay. Input analysis and processing is the prime concern for the robotic systems to understand and resolve the queries of the users. OBJECTIVES: In this manuscript, the Interaction Modeling and Classification Scheme (IMCS) is introduced to improve the accuracy of HRI. This scheme consists of two phases, namely error classification and input mapping. In the error classification process, the input is analyzed for its events and conditional discrepancies to assign appropriate responses in the input mapping phase. The joint process is aided by a linear learning model to analyze the different conditions in the event and input detection. RESULTS: The performance of the proposed scheme shows that it is capable of improving the interaction accuracy by reducing the ratio of errors and interaction response by leveraging the information extraction from the discrete and successive human inputs. CONCLUSION: The fetched data are analyzed by classifying the errors at the initial stage to achieve reliable responses. © 2021 - IOS Press. All rights reserved.","input mapping; interaction response; Linear processing; recognition; robotic systems","article; extraction; human; learning; robotics; Humans; Learning; Robotics",,,,,"YB111; 2019GY-131; 2018F-33; Baoji University of Arts And Sciences, BUAS: ZK2018062; Education Department of Shaanxi Province: 19JK0040","In this manuscript, interaction modeling and classification scheme is introduced for improving the response accuracy of HRI systems. The fetched This work is supported by Shaanxi Provincial Department of Education Scientific Research Project (19JK0040), the doctoral scientific research initial funding project of Baoji University of Arts and Sciences (ZK2018062), the Key Research and Development Program in Shaanxi Province (2019GY-131), Gansu social science planning program in 2017 (YB111) and Strategic Project of Colleges and Universities in Gansu Province (2018F-33).",,,,,,,,,,"Cao, H.L., Esteban, P.G., Simut, R., Van De Perre, G., Lefeber, D., Vanderborght, B., A collaborative homeostatic-based behavior controller for social robots in human-robot interaction experiments (2017) International Journal of Social Robotics., 9 (5), pp. 675-690; Johannsmeier, L., Haddadin, S., A hierarchical human-robot interaction-planning framework for task allocation in collaborative industrial assembly processes (2016) Ieee Robotics and Automation Letters., 2 (1), pp. 41-48; Lemaignan, S., Warnier, M., Sisbot, E.A., Clodic, A., Alami, R., Artificial cognition for social human-robot interaction: An implementation (2017) Artificial Intelligence., 247, pp. 45-69; Leica, P., Roberti, F., Monllor, M., Toibero, J.M., Carelli, R., Control of bidirectional physical human-robot interaction based on the human intention (2017) Intelligent Service Robotics., 10 (1), pp. 31-40; Zaraki, A., Pieroni, M., De Rossi, D., Mazzei, D., Garofalo, R., Cominelli, L., Dehkordi, M.B., Design and Evaluation of a Unique Social Perception System for Human-robot Interaction.; Wiltshire, T.J., Warta, S.F., Barber, D., Fiore, S.M., Enabling robotic social intelligence by engineering human socialcognitive mechanisms (2017) Cognitive Systems Research., 43, pp. 190-207; Daǧlarli, E., Daǧlarli, S.F., Günel, G.O., Köse, H., Improving human-robot interaction based on joint attention (2017) Applied Intelligence., 47 (1), pp. 62-82; Aly, A., Griffiths, S., Stramandinoli, F., Metrics and benchmarks in human-robot interaction: Recent advances in cognitive robotics (2017) Cognitive Systems Research., 43, pp. 313-323; Whitsell, B., Artemiadis, P., Physical human-robot interaction (pHRI) in 6 DOF with asymmetric cooperation (2017) Ieee Access., 5, pp. 10834-10845; Maeda, G.J., Neumann, G., Ewerton, M., Lioutikov, R., Kroemer, O., Peters, J., Probabilistic movement primitives for coordination of multiple human-robot collaborative tasks (2017) Autonomous Robots., 41 (3), pp. 593-612; Truong, X.T., Yoong, V.N., Ngo, T.D., Socially aware robot navigation system in human interactive environments (2017) Intelligent Service Robotics., 10 (4), pp. 287-295; Haage, M., Piperagkas, G., Papadopoulos, C., Mariolis, I., Malec, J., Bekiroglu, Y., Hedelind, M., Tzovaras, D., Teaching assembly by demonstration using advanced human robot interaction and a knowledge integration framework (2017) Procedia Manufacturing., 11, pp. 164-173; Gui, K., Liu, H., Zhang, D., Toward multi-modal human-robot interaction to enhance active participation of users in gait rehabilitation (2017) Ieee Transactions on Neural Systems and Rehabilitation Engineering., 25 (11), pp. 2054-2066; Losey, D.P., O'Malley, M.K., Enabling robots to infer how end-users teach and learn through human-robot interaction (2019) Ieee Robotics and Automation Letters., 4 (2), pp. 1956-1963; Khamassi, M., Velentzas, G., Tsitsimis, T., Tzafestas, C., Robot fast adaptation to changes in human engagement during simulated dynamic social interaction with active exploration in parameterized reinforcement learning (2018) Ieee Transactions on Cognitive and Developmental Systems., 10 (4), pp. 881-893; Chen, L., Zhou, M., Wu, M., She, J., Liu, Z., Dong, F., Hirota, K., Three-layer weighted fuzzy support vector regression for emotional intention understanding in human-robot interaction (2018) Ieee Transactions on Fuzzy Systems., 26 (5), pp. 2524-2538; Zhang, T., Xia, J., Interconnection and damping assignment passivity-based impedance control of a compliant assistive robot for physical human-robot interactions (2019) Ieee Robotics and Automation Letters., 4 (2), pp. 538-545; Qureshi, A.H., Nakamura, Y., Yoshikawa, Y., Ishiguro, H., Intrinsically motivated reinforcement learning for human-robot interaction in the real-world (2018) Neural Networks., 107, pp. 23-33; Maram, B., Gnanasekar, J.M., Manogaran, G., Intelligent security algorithm for UNICODE data privacy and security in IOT (2019) Service Oriented Computing and Applications., 13, p. 3; Malik, A.A., Bilberg, A., Developing a reference model for human-robot interaction (2019) International Journal on Interactive Design and Manufacturing (IJIDeM)., pp. 1-7; Gao, X., Zhou, C., Chao, F., Yang, L., Lin, C.M., Shang, C., A Robotic Writing Framework-Learning Human Aesthetic Preferences via Human-Machine Interactions (2019) Ieee Access., 7, pp. 144043-144053; Wang, L., Du, Z., Dong, W., Shen, Y., Zhao, G., Hierarchical human machine interaction learning for a lower extremity augmentation device (2019) International Journal of Social Robotics., 11 (1), pp. 123-139; Wang, Z., Lou, X., Yu, Z., Guo, B., Zhou, X., Enabling noninvasive and real-time human-machine interactions based on wireless sensing and fog computing (2019) Personal and Ubiquitous Computing., 23 (1), pp. 29-41; Dröder, K., Bobka, P., Germann, T., Gabriel, F., Dietrich, F., A machine learning-enhanced digital twin approach for human-robot-collaboration (2018) Procedia CIRP., 76, pp. 187-192; Segreto, T., Teti, R., Machine learning for in-process endpoint detection in robot-assisted polishing using multiple sensor monitoring (2019) The International Journal of Advanced Manufacturing Technology., pp. 1-5; Zarkowski, M., Multi-party turn-taking in repeated human-robot interactions: An interdisciplinary evaluation (2019) International Journal of Social Robotics., pp. 1-5; Mi, J., Tang, S., Deng, Z., Goerner, M., Zhang, J., Object affordance based multi-modal fusion for natural Human-Robot interaction (2019) Cognitive Systems Research., 54, pp. 128-137","Li, J.; Business School, China; email: jinglilzu@163.com",,,"IOS Press BV",,,,,10519815,,WORKF,"33720867","English","Work",Article,"Final","",Scopus,2-s2.0-85103516005
"Chen M., Wei W., Chao H., Li Y.","57211856395;57198566861;7202973858;57222581516;","Robotic Musicianship Based on Least Squares and Sequence Generative Adversarial Networks",2021,"IEEE Sensors Journal",,,,"","",,1,"10.1109/JSEN.2021.3066200","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103243771&doi=10.1109%2fJSEN.2021.3066200&partnerID=40&md5=4411d14b1c5e43a032adcd1700e9e06d","Department of Engineering Science, National Cheng Kung University, Tainan 70101, Taiwan.; School of Computer Science and Engineering, Xi&#x2019;an University of Technology, Xi&#x2019;an 710048, China.; Department of Electrical Engineering, National Dong Hwa University, Hualien 97401, Taiwan. (e-mail: hcc@niu.edu.tw); Department of Information Management, National Taichung University of Science and Technology, Taichung 404, Taiwan.","Chen, M., Department of Engineering Science, National Cheng Kung University, Tainan 70101, Taiwan.; Wei, W., School of Computer Science and Engineering, Xi&#x2019;an University of Technology, Xi&#x2019;an 710048, China.; Chao, H., Department of Electrical Engineering, National Dong Hwa University, Hualien 97401, Taiwan. (e-mail: hcc@niu.edu.tw); Li, Y., Department of Information Management, National Taichung University of Science and Technology, Taichung 404, Taiwan.","Robotic musicianship research aims to the configuration of robots can analyze, reason, and generate music autonomous. The goal of this research is to achieve the inspiring and meaningful musical interactions between humans and artificially creative robots. This research presents a new model of automatic music generation which is based on least squares and generative adversarial networks (GANs). This research specifies classical piano as the music source and uses the sensors as the context-awareness technology to sense and receive the input audio in human-robot interaction. Therefore, this research applies sequence generation adversarial network (SeqGAN) techniques that are better able to address discrete issues in generating samples of classical piano melodies and datasets. Modifying the SeqGAN approach, this research presented the Least Squares SeqGAN (LS-SeqGAN) method to create melody units on different chords and generates a set of music pieces as testing dataset. In this research, we implement the original method and use the least squares method to stabilize the training of GANs. The performance evaluation shows that proposed LS-SeqGAN method can fulfill the need both of music quality and creativity. It offers a robust infrastructure for the human-robotic interaction that can be used to promote the related robotic applications. IEEE","Data models; Deep Learning; Generative adversarial networks; Generative Adversarial Networks; Music; Music Generation; Neural networks; Robot sensing systems; Sensors; Sensors; Sequence Generative Adversarial Networks; Training","Audio acoustics; Human robot interaction; Robotics; Statistical tests; Adversarial networks; Context- awareness; Generating samples; Human-Robotic interaction; Least squares methods; Music quality; Robotic applications; Sequence generation; Least squares approximations",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,1530437X,,,,"English","IEEE Sensors J.",Article,"Article in Press","",Scopus,2-s2.0-85103243771
"Burns R.B., Seifi H., Lee H., Kuchenbecker K.J.","57200602056;35520245800;48361392900;8349364500;","Getting in touch with children with autism: Specialist guidelines for a touch-perceiving robot",2021,"Paladyn","12","1",,"115","135",,3,"10.1515/pjbr-2021-0010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099091267&doi=10.1515%2fpjbr-2021-0010&partnerID=40&md5=3ced62b85f685957eebd2daaf18c8b81","Haptic Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, 70569, Germany","Burns, R.B., Haptic Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, 70569, Germany; Seifi, H., Haptic Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, 70569, Germany; Lee, H., Haptic Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, 70569, Germany; Kuchenbecker, K.J., Haptic Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, 70569, Germany","Children with autism need innovative solutions that help them learn to master everyday experiences and cope with stressful situations. We propose that socially assistive robot companions could better understand and react to a child's needs if they utilized tactile sensing. We examined the existing relevant literature to create an initial set of six tactile-perception requirements, and we then evaluated these requirements through interviews with 11 experienced autism specialists from a variety of backgrounds. Thematic analysis of the comments shared by the specialists revealed three overarching themes: the touch-seeking and touch-avoiding behavior of autistic children, their individual differences and customization needs, and the roles that a touch-perceiving robot could play in such interactions. Using the interview study feedback, we refined our initial list into seven qualitative requirements that describe robustness and maintainability, sensing range, feel, gesture identification, spatial, temporal, and adaptation attributes for the touch-perception system of a robot companion for children with autism. Finally, by utilizing the literature and current best practices in tactile sensor development and signal processing, we transformed these qualitative requirements into quantitative specifications. We discuss the implications of these requirements for future human-robot interaction research in the sensing, computing, and user research communities. © 2021 Rachael Bevill Burns et al., published by De Gruyter 2021.","autism spectrum disorder; human-robot interaction; robot-assisted therapy; robot-mediated intervention; socially assistive robotics; tactile perception; tactile sensors",,,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC; International Max Planck Research School for Advanced Methods in Process and Systems Engineering, IMPRS","The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting Rachael Burns and the Natural Sciences and Engineering Research Council (NSERC) of Canada for providing funding for Hasti Seifi. The authors also thank all the participants who contributed their insights and time to this study and the reviewers who provided constructive comments on this manuscript. 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"Ji Y., Yang Y., Shen F., Shen H.T., Zheng W.-S.","36677523000;57222954946;55272525500;7404523209;25928152800;","Arbitrary-View Human Action Recognition: A Varying-View RGB-D Action Dataset",2021,"IEEE Transactions on Circuits and Systems for Video Technology","31","1","9007412","289","300",,1,"10.1109/TCSVT.2020.2975845","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098778085&doi=10.1109%2fTCSVT.2020.2975845&partnerID=40&md5=f88632ef46b9d64c8252d32f30a9bc91","Center for Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 611731, China","Ji, Y., Center for Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Yang, Y., Center for Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Shen, F., Center for Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Shen, H.T., Center for Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Zheng, W.-S., School of Data and Computer Science, Sun Yat-sen University, Guangzhou, 611731, China","Current researches of action recognition which focus on single-view and multi-view recognition can hardly satisfy the requirements of human-robot interaction (HRI) applications for recognizing human actions from arbitrary views. Arbitrary-view recognition is still a challenging issue due to view changes and visual occlusions. In addition, the lack of datasets also sets up barriers. To provide data for arbitrary-view action recognition, we collect a new large-scale RGB-D action dataset for arbitrary-view action analysis, including RGB videos, depth and skeleton sequences. The dataset includes action samples captured in 8 fixed viewpoints and varying-view sequences which cover the entire 360° view angles. In total, 118 persons are invited to act 40 action categories. Our dataset involves more participants, more viewpoints and a large number of samples. More importantly, it is the first dataset containing the entire 360° varying-view sequences. The dataset provides sufficient data for multi-view, cross-view and arbitrary-view action analysis. Besides, we propose a View-guided Skeleton CNN (VS-CNN) to tackle the problem of arbitrary-view action recognition. Experiment results show that the VS-CNN achieves superior performance, and our dataset provides valuable but challenging data for the evaluation of arbitrary-view recognition. © 1991-2012 IEEE.","arbitrary-view recognition; cross-view recognition; HRI; Human action recognition; varying-view RGB-D action dataset","Large dataset; Musculoskeletal system; Action analysis; Action recognition; Human actions; Human robot Interaction (HRI); Human-action recognition; Number of samples; View sequence; Visual occlusions; Social robots",,,,,"National Natural Science Foundation of China, NSFC: 61673088; Higher Education Discipline Innovation Project: B17008","Manuscript received October 3, 2019; revised December 20, 2019, January 17, 2020, and February 19, 2020; accepted February 19, 2020. Date of publication February 24, 2020; date of current version January 7, 2021. This work was supported in part by the Natural Science Foundation of China (NSFC) under Grant 61673088 and in part by the 111 Project under Grant B17008. This article was recommended by Associate Editor H. Zeng. (Corresponding author: Heng Tao Shen.) Yanli Ji, Yang Yang, Fumin Shen, and Heng Tao Shen are with the Center for Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: yanliji@uestc.edu.cn; shenhengtao@hotmail.com).",,,,,,,,,,"Li, C., Huang, Z., Yang, Y., Cao, J., Sun, X., Shen, H.T., Hierarchical latent concept discovery for video event detection (2017) Ieee Trans. Image Process., 26 (5), pp. 2149-2162. , May; Shen, Y., Ji, R., Zhang, S., Zuo, W., Wang, Y., Generative adversarial learning towards fast weakly supervised detection (2018) Proc. IEEE/CVF Conf. Comput. Vis. 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"Althnian A., Aloboud N., Alkharashi N., Alduwaish F., Alrshoud M., Kurdi H.","57188700633;57218312102;57211430676;57221287651;57221281887;24071124200;","Face gender recognition in the wild: An extensive performance comparison of deep-learned, hand-crafted, and fused features with deep and traditional models",2021,"Applied Sciences (Switzerland)","11","1","89","1","16",,2,"10.3390/app11010089","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098721061&doi=10.3390%2fapp11010089&partnerID=40&md5=2d36ca233da638d17bf793de7e2026a5","Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11451, Saudi Arabia; Center of Excellence of Decision Support Center, King Abdulaziz City for Science and Technology, Riyadh, 12354, Saudi Arabia; Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11451, Saudi Arabia; Saudi Information Technology Company, Riyadh, 12382, Saudi Arabia; Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11451, Saudi Arabia; Mechanical Engineering Department, Massachusetts Institute of Technology (MIT), Cambridge, MA  02142-1308, United States","Althnian, A., Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11451, Saudi Arabia; Aloboud, N., Center of Excellence of Decision Support Center, King Abdulaziz City for Science and Technology, Riyadh, 12354, Saudi Arabia; Alkharashi, N., Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11451, Saudi Arabia; Alduwaish, F., Saudi Information Technology Company, Riyadh, 12382, Saudi Arabia; Alrshoud, M., Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11451, Saudi Arabia; Kurdi, H., Computer Science Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11451, Saudi Arabia, Mechanical Engineering Department, Massachusetts Institute of Technology (MIT), Cambridge, MA  02142-1308, United States","Face gender recognition has many useful applications in human–robot interactions as it can improve the overall user experience. Support vector machines (SVM) and convolutional neural networks (CNNs) have been used successfully in this domain. Researchers have shown an increased interest in comparing and combining different feature extraction paradigms, including deep-learned features, hand-crafted features, and the fusion of both features. Related research in face gender recognition has been mostly restricted to limited comparisons of the deep-learned and fused features with the CNN model or only deep-learned features with the CNN and SVM models. In this work, we perform a comprehensive comparative study to analyze the classification performance of two widely used learning models (i.e., CNN and SVM), when they are combined with seven features that include hand-crafted, deep-learned, and fused features. The experiments were performed using two challenging unconstrained datasets, namely, Adience and Labeled Faces in the Wild. Further, we used T-tests to assess the statistical significance of the differences in performances with respect to the accuracy, f-score, and area under the curve. Our results proved that SVMs showed best performance with fused features, whereas CNN showed the best performance with deep-learned features. CNN outperformed SVM significantly at p < 0.05. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","CNN; Deep learning; Deep-learned features; Feature fusion; Gender recognition; Hand-crafted features; SVM",,,,,,"King Saud University, KSU: RSP-2020/204","Funding: This research was funded by Researchers Supporting Unit, King Saud University, Riyadh, Saudi Arabia, grant number RSP-2020/204.",,,,,,,,,,"Scheuerman, M.K., Paul, J.M., Brubaker, J.R., How computers see gender: An evaluation of gender classification in commercial facial analysis services (2019) Proc. ACM Hum. Comput. 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Kurdi, H.; Mechanical Engineering Department, United States; email: hkurdi@ksu.edu.sa",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85098721061
"Zhang W., Wang J., Lan F.","57209797909;55890357200;57220183016;","Dynamic hand gesture recognition based on short-term sampling neural networks",2021,"IEEE/CAA Journal of Automatica Sinica","8","1","9272702","110","120",,6,"10.1109/JAS.2020.1003465","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097202777&doi=10.1109%2fJAS.2020.1003465&partnerID=40&md5=acedb36650509b21e72cf9e6a9ef0bc9","Department of Computer Science and Software Engineering, Monmouth University, New Jersey, 07740, United States","Zhang, W., Department of Computer Science and Software Engineering, Monmouth University, New Jersey, 07740, United States; Wang, J., Department of Computer Science and Software Engineering, Monmouth University, New Jersey, 07740, United States; Lan, F., Department of Computer Science and Software Engineering, Monmouth University, New Jersey, 07740, United States","Hand gestures are a natural way for human-robot interaction. Vision based dynamic hand gesture recognition has become a hot research topic due to its various applications. This paper presents a novel deep learning network for hand gesture recognition. The network integrates several well-proved modules together to learn both short-term and long-term features from video inputs and meanwhile avoid intensive computation. To learn short-term features, each video input is segmented into a fixed number of frame groups. A frame is randomly selected from each group and represented as an RGB image as well as an optical flow snapshot. These two entities are fused and fed into a convolutional neural network ConvNet for feature extraction. The ConvNets for all groups share parameters. To learn long-term features, outputs from all ConvNets are fed into a long short-term memory LSTM network, by which a final classification result is predicted. The new model has been tested with two popular hand gesture datasets, namely the Jester dataset and Nvidia dataset. Comparing with other models, our model produced very competitive results. The robustness of the new model has also been proved with an augmented dataset with enhanced diversity of hand gestures. © 2014 Chinese Association of Automation.",,"Convolutional neural networks; Deep learning; Gesture recognition; Human robot interaction; Optical flows; Palmprint recognition; Classification results; Fixed numbers; Hand gesture; Hand-gesture recognition; Hot research topics; Learning network; RGB images; Vision based; Long short-term memory",,,,,,,,,,,,,,,,"Starrier, T., Weaver, J., Pentland, A., Real-time American sign language recognition using desk and wearable computer based video (1998) Ieee Trans. Pattern Anal. Mach. Intell., 20 (12), pp. 1371-1375. , Dec; Cooper, H., Holt, B., Bowden, R., Sign language recognition (2011) Visual Analysis of Humans: Looking at People, pp. 539-562. , T. B. Moeslund, A. Hilton, V. Kruger, and L. Sigal, Eds. 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Autom. Sinica, 6 (1), pp. 247-257. , Jan","Zhang, W.; Department of Computer Science and Software Engineering, United States; email: s1260807@monmouth.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23299266,,,,"English","IEEE CAA J. Autom. Sin.",Article,"Final","",Scopus,2-s2.0-85097202777
"Biermann H., Brauner P., Ziefle M.","57203129232;35228685600;6602475572;","How context and design shape human-robot trust and attributions",2021,"Paladyn","12","1",,"74","86",,,"10.1515/pjbr-2021-0008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097184876&doi=10.1515%2fpjbr-2021-0008&partnerID=40&md5=93f3abd5042397874da1db1075e8778e","Chair for Communication Science and Human-Computer Interaction Center, RWTH Aachen University, Campus-Boulevard 57, Aachen, 52074, Germany","Biermann, H., Chair for Communication Science and Human-Computer Interaction Center, RWTH Aachen University, Campus-Boulevard 57, Aachen, 52074, Germany; Brauner, P., Chair for Communication Science and Human-Computer Interaction Center, RWTH Aachen University, Campus-Boulevard 57, Aachen, 52074, Germany; Ziefle, M., Chair for Communication Science and Human-Computer Interaction Center, RWTH Aachen University, Campus-Boulevard 57, Aachen, 52074, Germany","In increasingly digitized working and living environments, human-robot collaboration is growing fast with human trust toward robotic collaboration as a key factor for the innovative teamwork tosucceed. This article explores the impact of design factors of the robotic interface (anthropomorphic vs functional) and usage context (production vs care) on human-robot trust and attributions. Theresults of a scenario-based survey with N = 228 N=228 participants showed a higher willingness to collaborate with production robots compared to care. Context and design influenced the trust attributed to the robots: robots with a technical appearance in production were trusted more than anthropomorphic robots or robots in the care context. The evaluation of attributions by means of a semanticdifferential showed that differences in robot design were less pronounced for the production context in comparison to the care context. In the latter, anthropomorphic robots were associated with positive attributes. The results contribute to a better understanding of the complex nature of trust in automation and can be used to identify and shape use case-specific risk perceptions as well as perceived opportunities to interacting with collaborative robots. Findings of this study are pertinent to research (e.g., experts in human-robot interaction) and industry, with special regard given tothe technical development and design. © 2021 Hannah Biermann et al., published by De Gruyter.","assistance robotics; attribution; collaborative robotics; human-robot collaboration; human-robot interaction; semantic differential; technology acceptance; trust in automation",,,,,,"Deutsche Forschungsgemeinschaft, DFG: 390621612, EXC-2023","The authors thank all participants for their patience and openness to share opinions on future human-robot interaction. Special thanks to Semih Yildirim and Imke Haverkämper for research assistance. This study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2023 Internet of Production – 390621612.",,,,,,,,,,"Chandrasekaran, B., Conrad, J.M., ""human-robot collaboration: A survey,"" (2015) Proceedings of the IEEE SoutheastCon, Fort Lauderdale, Florida, USA, pp. 1-8. , in; ""world Population Prospects 2019. Highlights,"" (2019) Tech. Rep., , United Nations, Department of Economic and Social Affairs, New York; ""digitalisation research and innovation - Transforming European industry and services,"" (2017) Tech. Rep, , European Commission European Commission; Schmidtler, J., Knott, V.C., Hoelzel, C., Bengler, K., ""human centered assistance applications for the working environment of the future,"" (2015) Occup. Ergonomics, 12 (NO. 3), pp. 83-95; Sheridan, T.B., ""human-robot interaction: Status and challenges Thomas,"" (2016) Hum. 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Robot., 11 (NO. 4), pp. 679-689","Biermann, H.; Chair for Communication Science and Human-Computer Interaction Center, Campus-Boulevard 57, Germany; email: biermann@comm.rwth-aachen.de",,,"De Gruyter Open Ltd",,,,,20814836,,,,"English","Paladyn",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85097184876
"Itadera S., Kobayashi T., Nakanishi J., Aoyama T., Hasegawa Y.","57050273900;56654561400;7005280340;24722658700;7403042933;","Towards physical interaction-based sequential mobility assistance using latent generative model of movement state",2021,"Advanced Robotics","35","1",,"64","79",,1,"10.1080/01691864.2020.1844797","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096314608&doi=10.1080%2f01691864.2020.1844797&partnerID=40&md5=33f0e31427e4f397a5646bf86b12c7b8","Department of Micro, Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan; Division of Information Science, Nara Institute of Science and Technology, Nara, Japan; Department of Mechanical Engineering, Meijo University, Nagoya, Japan","Itadera, S., Department of Micro, Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan; Kobayashi, T., Division of Information Science, Nara Institute of Science and Technology, Nara, Japan; Nakanishi, J., Department of Mechanical Engineering, Meijo University, Nagoya, Japan; Aoyama, T., Department of Micro, Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan; Hasegawa, Y., Department of Micro, Nano Mechanical Science and Engineering, Nagoya University, Nagoya, Japan","In this paper, we address sequential mobility assistance for daily elderly care through physical human–robot interaction. The goal of this work is to develop a robotic assistive system to provide physical support in daily life such as movement transition, e.g. sit-to-stand and walking. Using a mobile human support robotic platform, we propose an unsupervised learning-based approach to providing desirable physical support through an adaptive impedance parameter selection strategy according to the recognized user's movement state in an online manner. Using a latent generative model with a long short-term memory-based variational autoencoder, we first estimate the probability of the user's current movement state based on the sensory information in a low dimensional latent space. Then, the desired impedance parameters are selected adaptively according to the estimated movement state. One of the benefits of such an unsupervised learning approach is that no labeling is necessary in the training phase. Furthermore, our proposed framework is capable of detecting possible novel states such as falling over based on the obtained latent space. In order to demonstrate the proof of concept of our proposed approach, we present the experimental results of performance evaluations of online movement state recognition as well as novel movement detection. © 2020 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","latent generative model; mobility assistance; movement detection; Physical human–robot interaction; Variational autoencoder","Agricultural robots; Human robot interaction; Robotics; Unsupervised learning; Walking aids; Impedance parameters; Learning-based approach; Movement detection; Physical interactions; Robot interactions; Sensory information; Sequential mobility; State recognition; Motion estimation",,,,,,,,,,,,,,,,"http://www.un.org/esa/population/publications/worldageing19502050, World population ageing: 1950–2050. 2001. 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In: International Conference on Artificial Neural Networks. 2019; p. 443–455","Itadera, S.; Department of Micro, Japan; email: itadera@mein.nagoya-u.ac.jp",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Final","",Scopus,2-s2.0-85096314608
"Cameron D., de Saille S., Collins E.C., Aitken J.M., Cheung H., Chua A., Loh E.J., Law J.","35067867800;56412160000;55948963600;24722521600;57190277195;56959992800;57200620797;36675177700;","The effect of social-cognitive recovery strategies on likability, capability and trust in social robots",2021,"Computers in Human Behavior","114",,"106561","","",,8,"10.1016/j.chb.2020.106561","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091006704&doi=10.1016%2fj.chb.2020.106561&partnerID=40&md5=99f5804cfc36d9b8929499ce3521fcef","Information School, The University of Sheffield, Sheffield, United Kingdom; Institute for the Study of the Human, The University of Sheffield, Sheffield, United Kingdom; Department of Computer Science, The University of Liverpool, Liverpool, United Kingdom; Department for Automatic Control & Systems Engineering, The University of Sheffield, Sheffield, United Kingdom; Department of Psychology, The University of Sheffield, Sheffield, United Kingdom; Sheffield Robotics, The University of Sheffield, Sheffield, United Kingdom","Cameron, D., Information School, The University of Sheffield, Sheffield, United Kingdom; de Saille, S., Institute for the Study of the Human, The University of Sheffield, Sheffield, United Kingdom; Collins, E.C., Department of Computer Science, The University of Liverpool, Liverpool, United Kingdom; Aitken, J.M., Department for Automatic Control & Systems Engineering, The University of Sheffield, Sheffield, United Kingdom; Cheung, H., Department of Psychology, The University of Sheffield, Sheffield, United Kingdom; Chua, A., Department of Psychology, The University of Sheffield, Sheffield, United Kingdom; Loh, E.J., Department of Psychology, The University of Sheffield, Sheffield, United Kingdom; Law, J., Sheffield Robotics, The University of Sheffield, Sheffield, United Kingdom","As robots become more prevalent, particularly in complex public and domestic settings, they will be increasingly challenged by dynamic situations that could result in performance errors. Such errors can have a harmful impact on a user's trust and confidence in the technology, potentially reducing use and preventing full realization of its benefits. A potential countermeasure, based on social psychological concepts of trust, is for robots to demonstrate self-awareness and ownership of their mistakes to mitigate the impact of errors and increase users’ affinity towards the robot. We describe an experiment examining 326 people's perceptions of a mobile guide robot that employs synthetic social behaviours to elicit trust in its use after error. We find that a robot that identifies its mistake, and communicates its intention to rectify the situation, is considered by observers to be more capable than one that simply apologizes for its mistake. However, the latter is considered more likeable and, uniquely, increases people's intention to use the robot. These outcomes highlight that the complex and multifaceted nature of trust in human–robot interaction may extend beyond established approaches considering robots’ capability in performance and indicate that social cognitive models are valuable in developing trustworthy synthetic social agents. © 2020 Elsevier Ltd","Human–robot interaction; Social robotics; Trust; User experience","Errors; Social robots; Intention to use; Mobile guide robots; Performance error; Recovery strategies; Robot interactions; Self awareness; Social behaviour; Social cognitive models; Economic and social effects; article; cognitive model; human; human experiment; perception; robotics; trust",,,,,"Seventh Framework Programme, FP7: 611971, FP7-ICT-2013-10","Funding: This work is supported by the European Union Seventh Framework Programme (FP7-ICT-2013-10) under grant agreement no. 611971 .",,,,,,,,,,"Adubor, O., St John, R., Steinfeld, A., Personal safety is more important than cost of damage during robot failure (2017) Proceedings of the companion of the 2017 ACM/IEEE international conference on human-robot interaction, p. 403. , ACM; Baker, A.L., Phillips, E.K., Ullman, D., Keebler, J.R., Toward an understanding of trust repair in human-robot interaction: Current research and future directions (2018) ACM Transactions on Interactive Intelligent Systems, 8 (4), pp. 1-30; Barczak, G., Lassk, F., Mulki, J., Antecedents of team creativity: An examination of team emotional intelligence, team trust and collaborative culture (2010) Creativity and Innovation Management, 19 (4), pp. 332-345; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81; Brookfield, S.D., The skillful teacher: On technique, trust, and responsiveness in the classroom (2015), John Wiley & Sons; Brooks, D.J., Begum, M., Yanco, H.A., Analysis of reactions towards failures and recovery strategies for autonomous robots (2016) 25th IEEE international symposium on robot and human interactive communication, pp. 487-492. , IEEE; Cameron, D., Aitken, J.M., Collins, E.C., Boorman, L., Chua, A., Law, J., (2015), Framing factors: The importance of context and the individual in understanding trust in human-robot interaction. 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Hum. Behav.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85091006704
"Jaiswal S., Jain A., Nandi G.C.","57206253502;57222416479;6602138539;","Image based Emotional State Prediction from Multiparty Audio Conversation",2020,"2020 IEEE Pune Section International Conference, PuneCon 2020",,,"9362475","77","82",,,"10.1109/PuneCon50868.2020.9362475","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102659399&doi=10.1109%2fPuneCon50868.2020.9362475&partnerID=40&md5=1c8a0df2c0a5094cc5d07b62fc5a0423","Indian Institute of Information Technology, Allahabad, India","Jaiswal, S., Indian Institute of Information Technology, Allahabad, India; Jain, A., Indian Institute of Information Technology, Allahabad, India; Nandi, G.C., Indian Institute of Information Technology, Allahabad, India","Recognizing human emotion is a complex task and is being researched upon since couple of decades. The problem has still gained popularity because of its need in various domains, when it comes to human computer interaction or human robot interaction. As per researchers, human predict other persons state of mind by observing various parameters, 70% of them being non-verbal. Human have emotions embedded in their speech, pose, gesture, context, facial expressions, and even the past history of conversation or situation. These all sub problems can be beautifully solved using learning based techniques. Predicting emotion in multi party audio based conversation aids complexity to the problem, which needs to predict intent of speech, culture, accent of talking, gender and many other diversities. There are various attempts made by researchers to classify human audio into required classes, using Support Vector Machine model, Long Short Term Memeory (LSTM) and bi-LSTM on audio input. We propose an image based emotional classification approach for an audio conversation. Spectrogram of an audio signal plotted as an image is used as a input to Convolutional Neural Network model obtaining the pattern for classification. The proposed approach is able to provide an accuracy of around 86% on test dataset, which is considerable improvement over state of the art models. © 2020 IEEE.","Audio Classification; Convolutional Neural Network; Emotion Recognition; Spectrograms","Complex networks; Convolutional neural networks; Forecasting; Human computer interaction; Human robot interaction; Statistical tests; Support vector machines; Audio signal; Complex task; Emotional classification; Emotional state; Facial Expressions; Recognizing Human Emotion; State of the art; Support vector machine models; Long short-term memory",,,,,,,,,,,,,,,,"Lalitha, S., Madhavan, A., Bhushan, B., Saketh, S., Speech emotion recognition (2014) 2014 International Conference on Advances in Electronics Computers and Communications, pp. 1-4. , Bangalore; Umamaheswari, J., Akila, A., An enhanced human speech emotion recognition using hybrid of prnn and knn (2019) 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), pp. 177-183. , Faridabad, India; Luoh, L., Huang, C.-C., Liu, H.-Y., Image processing based emotion recognition (2010) 2010 International Conference on System Science and Engineering, pp. 491-494. , Taipei; Jaiswal, S., Nandi, G.C., Robust real-time emotion detection system using cnn architecture 2019 Neural Computing and Applications, pp. 1-10. , Springer; Wu, C., Liang, W., Emotion recognition of affective speech based on multiple classifiers using acoustic-prosodic information and semantic labels (2011) Ieee Transactions on Affective Computing, 2 (1), pp. 10-21. , Jan.-June; Becker, S., Ackermann, M., Lapuschkin, S., Müller, K., Samek, W., (2019) Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals; Khamparia, A., Gupta, D., Nguyen, N.G., Khanna, A., Pandey, B., Tiwari, P., Sound classification using convolutional neural network and tensor deep stacking network (2019) Ieee Access, 7, pp. 7717-7727; Poria, S., Hazarika, D., Majumder, N., Naik, G., Cambria, E., Mihalcea, R., (2019) Meld: a Multimodal Multi-Party Dataset for Emotion Recognition in Conversations; Lu, K., Jia, Y., Audio-visual emotion recognition using boltzmann zippers 2012 19th Ieee International Conference on Image Processing; Sebe, N., Cohen, I., Gevers, T., Huang, T.S., Emotion recognition based on joint visual and audio cues 2006, 18th International Conference on Pattern Recognition (icpr'06; Prasetya R., M.M., Harjoko, A., Supriyanto, C., Speech emotion recognition of indonesian movie audio tracks based on mfcc and svm 2019 International Conference on Contemporary Computing and Informatics (ic3i; Rajak, R., Mall, R., Emotion recognition from audio, dimensional and discrete categorization using cnns Tencon 2019-2019 Ieee Region 10 Conference (Tencon; Erdem, E.S., Sert, M., Efficient recognition of human emotional states from audio signals 2014 Ieee International Symposium on Multimedia; Ooi, C.S., Seng, K.P., Ang, L., Chew, L.W., (2014) A New Approach of Audio Emotion Recognition, , Elsevier",,,,"Institute of Electrical and Electronics Engineers Inc.","3rd IEEE Pune Section International Conference, PuneCon 2020","16 December 2020 through 18 December 2020",,167617,,9781728196008,,,"English","IEEE Pune Sect. Int. Conf., PuneCon",Conference Paper,"Final","",Scopus,2-s2.0-85102659399
"Palestra G., Pino O.","55859663600;8433960200;","Detecting emotions during a memory training assisted by a social robot for individuals with Mild Cognitive Impairment (MCI)",2020,"Multimedia Tools and Applications","79","47-48",,"35829","35844",,3,"10.1007/s11042-020-10092-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096138762&doi=10.1007%2fs11042-020-10092-4&partnerID=40&md5=4bad7b9a7f465f12c86ae78df3c389f8","Research Department, HERO srl, Bari, Italy; Department of Medicine and Surgery, University of Parma, Parma, Italy","Palestra, G., Research Department, HERO srl, Bari, Italy; Pino, O., Department of Medicine and Surgery, University of Parma, Parma, Italy","The attention towards robot-assisted therapies (RAT) had grown steadily in recent years particularly for patients with dementia. However, rehabilitation practice using humanoid robots for individuals with Mild Cognitive Impairment (MCI) is still a novel method for which the adherence mechanisms, indications and outcomes remain unclear. An effective computing represents a wide range of technological opportunities towards the employment of emotions to improve human-computer interaction. Therefore, the present study addresses the effectiveness of a system in automatically decode facial expression from video-recorded sessions of a robot-assisted memory training lasted two months involving twenty-one participants. We explored the robot’s potential to engage participants in the intervention and its effects on their emotional state. Our analysis revealed that the system is able to recognize facial expressions from robot-assisted group therapy sessions handling partially occluded faces. Results indicated reliable facial expressiveness recognition for the proposed software adding new evidence base to factors involved in Human-Robot Interaction (HRI). The use of a humanoid robot as a mediating tool appeared to promote the engagement of participants in the training program. Our findings showed positive emotional responses for females. Tasks affects differentially affective involvement. Further studies should investigate the training components and robot responsiveness. © 2020, The Author(s).","Facial expression recognition; Memory training; Mild Cognitive Impairment; Social robot","Anthropomorphic robots; Educational robots; End effectors; Face recognition; Human computer interaction; Emotional response; Facial Expressions; Facial expressiveness; Human robot Interaction (HRI); Mild cognitive impairments (MCI); Patients with dementia; Robot-assisted therapies; Technological opportunity; Social robots",,,,,"Università degli Studi di Parma, UNIPR","Open access funding provided by Università degli Studi di Parma within the CRUI-CARE Agreement.",,,,,,,,,,"Albert, M.S., Cohen, C., Koff, E., Perception of affect in patients with dementia of the alzheimer type (1991) Arch Neurol, 48 (8), pp. 791-795; Bäck, I., Makela, K., Kallio, J., Robot-guided exercise program for the rehabilitation of older nursing home residents (2013) Annals of Long Term Care, 21 (6), pp. 38-41; Bailey, P.E., Brady, B., Ebner, N.C., Ruffman, T., Effects of age on emotion regulation, emotional empathy, and prosocial behavior (2020) The Journals of Gerontology B: Psychological Science and Social Sciences, 75 (4), pp. 802-810; Beadle, J.N., Christine, E., Impact of aging on empathy: Review of psychological and neural mechanisms (2019) Frontiers in Psychiatry, 10, p. 331; Beck, A., Cañamero, L., Hiolle, A., Damiano, L., Cosi, P., Tesser, F., Sommavilla, G., Interpretation of emotional body language displayed by a humanoid robot: A case study with children (2013) International Journal of Social Robotics, 5 (3), pp. 325-334; Boissy, P., Corriveau, H., Michaud, F., Labonté, D., Royer, M.P., A qualitative study of in-home robotic telepresence for home care of community-living elderly subjects (2007) J Telemed Telecare, 13 (2), pp. 79-84; Corcella, L., Manca, M., Nordvik, J.E., Paterǹo, F., Sanders, A.M., Santoro, C., En-abling personalisation of remote elderly assistance (2019) Multimedia Tools and Applications, 78 (15), pp. 21557-21583; Dahl, T.S., Boulos, M.N.K., Robots in health and social care: a complementary technology to home care and telehealthcare? (2014) Robotics, 3 (1), pp. 1-21; Descartes’error: Emotion, reason (1995) And the Human Brain, , Penguin Book, New York (NY; Dautenhahn, K., Ogden, B., Quick, T., From embodied to socially embedded agents–implications for interaction-aware robots (2002) Cogn Syst Res, 3 (3), pp. 397-428. , https://doi.org/10.1016/S1389-0417(02)00050-5; de Carolis, B., Ferilli, S., Palestra, G., Improving speech-based human robot interaction with emotion recognition (2015) International Symposium on Methodologies for Intelligent Systems, pp. 273-279. , Springer; De Carolis, B., Ferilli, S., Palestra, G., Simulating empathic behavior in a social as-sistive robot (2017) Multimededia, Tools and Applications, 76 (4), pp. 5073-5094; de Carolis, B., Macchiarulo, N., Palestra, G., Soft biometrics for social adaptive robots (2019) International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pp. 687-699. , Springer; de Carolis, B., Palestra, G., Pino, O., Facial expression recognition from nao robot within a memory training program for individuals with mild cognitive impairment (2019) Proceedings of the Workshop Socio-Affective Technologies: An Interdisciplinary Approach Co-Located with IEEE SMC 2019 (Systems, Man and Cybernetics), pp. 42-46; de Carolis, B.N., Ferilli, S., Palestra, G., Carofiglio, V., Towards an empathic social robot for ambient assisted living (2015) ESSEM@ AAMAS, pp. 19-34. , https://dl.acm.org/citation.cfm?id=3054107; Ekman, P., (2006) Darwin and facial expression: A century of research in review, , Malor Books, Cambridge, MA Los Altos, CA; Elferink, M.W.O., van Tilborg, I., Kessels, R.P., Perception of emotions in mild cognitive impairment and alzheimer’s dementia: does intensity matter? (2015) Translational Neuroscience, 6 (1), pp. 139-149; Farina, E., Baglio, F., Pomati, S., d’Amico, A., Campini, I.C., Di Tella, S., Belloni, G., Pozzo, T., The mirror neurons network in aging, mild cognitive impairment, and alzheimer disease: A functional MRI study (2017) Frontiers in Aging Neuroscience, 9, p. 371; Fincannon, T., Barnes, L.E., Murphy, R.R., Riddle, D.L., Evidence of the need for social intelligence in rescue robots (2004) 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)(IEEE Cat. No. 04CH37566), 2, pp. 1089-1095. , IEEE; Gauthier, S., Reisberg, B., Zaudig, M., Petersen, R.C., Ritchie, K., Broich, K., Belleville, S., Winblad, B., Mild cognitive impairment (2006) Lancet, 367 (9518), pp. 1262-1270. , (,),., (,):., https://doi.org/10.1016/S0140-6736(06)68542-5; Gazzola, V., Rizzolatti, G., Wicker, B., Keysers, C., The anthropomorphic brain: the mirror neuron system responds to human and robotic actions (2007) Neuroimage, 35 (4), pp. 1674-1684; Gollin, D., Ferrari, A., Peruzzi, A., (2007) Una Palestra per La Mente, , Stimolazione cognitiva per l’invecchiamento cerebrale e le demenze, Edizioni Erickson, Trento (IT); Gross, H.M., Schroeter, C., Mueller, S., Volkhardt, M., Einhorn, E., Bley, A., Langner, T., Merten, M., I’ll keep an eye on you: Home robot companion for elderly people with cognitive impairment (2011) Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference On, pp. 2481-2488. , IEEE; Gross, H.M., Schroeter, C., Mueller, S., Volkhardt, M., Einhorn, E., Bley, A., Langner, T., van Den Heuvel, H., Further progress towards a home robot companion for people with mild cognitive impairment (2012) Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference On, pp. 637-644. , IEEE; Karas, G., Scheltens, P., Rombouts, S., Visser, P., Van Schijndel, R., Fox, N., Barkhof, F., Global and local gray matter loss in mild cognitive impairment and alzheimer’s disease (2004) Neuroimage, 23 (2), pp. 708-716; Kasper, S., Bancher, C., Eckert, A., Förstl, H., Frölich, L., Hort, J., Korczyn, A.D., Paloma, M.S.M., Management of Mild Cognitive Impairment (mci): The need for national and international guidelines (2020) World Journal of Biology Psychiatry, 21 (8), pp. 579-594; Knapp, M.L., Hall, J.A., Horgan, T.G., (2013) Nonverbal Communication in Human Interaction, , Cengage Learning Inc, Boston (MA); Kory, J., Breazeal, C., Storytelling with robots: Learning companions for preschool children’s language development (2014) Robot and Human Interactive Communication, 2014 RO-MAN: The 23Rd IEEE International Symposium On, pp. 643-648. , IEEE; Law, M., Sutherland, C., Ahn, H.S., MacDonald, B.A., Peri, K., Johanson, D.L., Vajsakovic, D.S., Broadbent, E., Developing assistive robots for people with mild cognitive impairment and mild dementia: a qualitative study with older adults and experts in aged care (2019) BMJ Open, 9 (9). , (,),., (,):,., https://doi.org/10.1136/bmjopen-2019-031937; Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I., The extended cohn-kanade dataset (Ck+): A complete dataset for action unit and emotion-specified expression (2010) 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, pp. 94-101. , IEEE; Martín, F., Agüero, C., Cañas, J.M., Abella, G., Beńıtez, R., Rivero, S., Valenti, M., Martínez-Martín, P., Robots in therapy for dementia patients (2013) Journal of Physical Agents, 7 (1), pp. 48-55; Martín, F., Agüero, C.E., Cañas, J.M., Valenti, M., Martínez-Martín, P., Robotherapy with dementia patients (2013) International Journal of Advanced Robotic Systems, 10 (1), p. 10; McCade, D., Savage, G., Naismith, S.L., Review of emotion recognition in Mild Cognitive Impairment (2011) Dementia and Geriatric Cognitive Disorders, 32 (4), pp. 257-266. , (,),., (,):., https://doi.org/10.1159/000335009; Moyle, W., Jones, C., Cooke, M., O’Dwyer, S., Sung, B., Drummond, S., Connecting the person with dementia and family: a feasibility study of a telepresence robot (2014) BMC Geriatr, 14 (1), p. 7; Nourbakhsh, I.R., Kunz, C., Willeke, T., The mobot museum robot installations: A five year experiment (2003) Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No. 03CH37453), 4, pp. 3636-3641. , IEEE; Palestra, G., Pettinicchio, A., Del Coco, M., Carcagni, P., Leo, M., Distante, C., Improved performance in facial expression recognition using 32 geometric features (2015) International Conference on Image Analysis and Processing, pp. 518-528. , Springer; Palestra, G., de Carolis, B., Esposito, F., Artificial intelligence for robot-assisted treatment of autism (2017) WAIAH@ AI* IA, pp. 17-24; Perugia, G., van Berkel, R., Díaz-Boladeras, M., Català-Mallofŕe, A., Rauterberg, M., Barakova, E., Understanding engagement in dementia through behavior. The ethographic and laban-inspired coding system of engagement (elicse) and the evidence-based model of engagement-related behavior (emodeb) (2018) Front Psychol, 9, p. 690. , https://doi.org/10.3389/fpsyg.2018.00690; Petersen, R.C., Caracciolo, B., Brayne, C., Gauthier, S., Jelic, V., Fratiglioni, L., Mild cognitive impairment: A concept in evolution (2014) J Intern Med, 275 (3), pp. 214-228. , (,),., (,):., https://doi.org/10.1111/joim.12190; Petersen, R.C., Lopez, O., Armstrong, M.J., Getchius, T.S., Ganguli, M., Gloss, D., Gronseth, G.S., Day, G.S., Practice guide line update summary: Mild cognitive impairment: Report of the guideline development, dissemination, and implementation subcommittee of the american academy of (2018) Neurology, 90 (3), pp. 126-135. , https://www.overleaf.com/project/neurology; Picard, R.W., Affective computing for hci (1999) HCI (1), Citeseer, pp. 829-833; Pino, O., Memory impairments and rehabilitation: Evidence-based effects of approaches and training programs (2015) The Open Rehabilitation Journal, 8 (1), pp. 25-33; Pino, O., Palestra, G., Trevino, R., De Carolis, B., The humanoid robot nao as trainer in a memory program for elderly people with mild cognitive impairment (2020) International Journal of Social Robotics, 12 (1), pp. 21-33. , (,),., (,):., https://doi.org/10.1007/s12369-019-00533-y; Posner, J., Russell, J.A., Peterson, B.S., The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology (2005) Developmental Psychopathology, 17 (3), pp. 715-734; Reyes, M.E., Meza, I.V., Pineda, L.A., Robotics facial expression of anger in collaborative human–robot interaction (2019) International Int J Adv Robot Syst, 16 (1). , https://doi.org/10.1177/1729881418817972; Rouaix, N., Retru-Chavastel, L., Rigaud, A.S., Monnet, C., Lenoir, H., Pino, M., Affective and engagement issues in the conception and assessment of a robot-assisted psychomotor therapy for persons with dementia (2017) Front Psychol, 8, p. 950. , https://doi.org/10.3389/fpsyg.2017.00950; Sabanovic, S., Bennett, C.C., Chang, W.L., Huber, L., Paro robot affects diverse interaction modalities in group sensory therapy for older adults with dementia (2013) Rehabilitation Robotics (ICORR), 2013, pp. 1-6. , IEEE International Conference on, IEEE; Spoletini, I., Marra, C., Di Iulio, F., Gianni, W., Sancesario, G., Giubilei, F., Trequattrini, A., Spalletta, G., Facial emotion recognition deficit in amnestic mild cognitive impairment and alzheimer disease (2008) Am J Geriatr Psychiatr, 16 (5), pp. 389-398; Valentí Soler, M., Agüera-Ortiz, L., Olazarán Rodŕıguez, J., Mendoza Rebolledo, C., Ṕerez Muñoz, A., Rodŕıguez Ṕerez, I., Osa Ruiz, E., Carrasco Chillón, L., Social robots in advanced dementia (2015) Front Aging Neurosci, 7, p. 133. , https://doi.org/10.3389/fnagi.2015.00133; Vital, J.P., Couceiro, M.S., Rodrigues, N.M., Figueiredo, C.M., Ferreira, N.M., Fostering the nao platform as an elderly care robot (2013) 2013 IEEE 2Nd International Conference on Serious Games and Applications for Health (Segah), pp. 1-5. , IEEE","Pino, O.; Department of Medicine and Surgery, Italy; email: olimpia.pino@unipr.it",,,"Springer",,,,,13807501,,MTAPF,,"English","Multimedia Tools Appl",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85096138762
"Roveda L., Bussolan A., Braghin F., Piga D.","56031943800;57219666351;6701856637;35791353300;","6d virtual sensor for wrench estimation in robotized interaction tasks exploiting extended kalman filter",2020,"Machines","8","4","67","1","18",,2,"10.3390/machines8040067","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094649354&doi=10.3390%2fmachines8040067&partnerID=40&md5=495a83b6b5a42da40c4f60f25da50520","Istituto Dalle Molle di studi sull’Intelligenza Artificiale (IDSIA), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Università della Svizzera Italiana (USI), Manno, 6928, Switzerland; Department of Mechanical Engineering, Politecnico di Milano, Milano, 20156, Italy","Roveda, L., Istituto Dalle Molle di studi sull’Intelligenza Artificiale (IDSIA), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Università della Svizzera Italiana (USI), Manno, 6928, Switzerland; Bussolan, A., Department of Mechanical Engineering, Politecnico di Milano, Milano, 20156, Italy; Braghin, F., Department of Mechanical Engineering, Politecnico di Milano, Milano, 20156, Italy; Piga, D., Istituto Dalle Molle di studi sull’Intelligenza Artificiale (IDSIA), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Università della Svizzera Italiana (USI), Manno, 6928, Switzerland","Industrial robots are commonly used to perform interaction tasks (such as assemblies or polishing), requiring the robot to be in contact with the surrounding environment. Such environments are (partially) unknown to the robot controller. Therefore, there is the need to implement interaction controllers capable of suitably reacting to the established contacts. Although standard force controllers require force/torque measurements to close the loop, most of the industrial manipulators do not have installed force/torque sensor(s). In addition, the integration of external sensors results in additional costs and implementation effort, not affordable in many contexts/applications. To extend the use of compliant controllers to sensorless interaction control, a model-based methodology is presented in this paper for the online estimation of the interaction wrench, implementing a 6D virtual sensor. Relying on sensorless Cartesian impedance control, an Extended Kalman Filter (EKF) is proposed for the interaction wrench estimation. The described approach has been validated in simulations, taking into account four different scenarios. In addition, experimental validation has been performed employing a Franka EMIKA panda robot. A human–robot interaction scenario and an assembly task have been considered to show the capabilities of the developed EKF, which is able to perform the estimation with high bandwidth, achieving convergence with limited errors. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This articl.","6D virtual sensor; Extended kalman filter; Industrial robots; Interaction robotized tasks; Sensorless cartesian impedance control; Wrench estimation",,,,,,,,,,,,,,,,,"Ben-Ari, M., Mondada, F., Robots and their applications (2018) Elements of Robotics, pp. 1-20. , Springer: Berlino, Germany; Yang, G.Z., Bellingham, J., Dupont, P.E., Fischer, P., Floridi, L., Full, R., Jacobstein, N., Merrifield, R., The grand challenges of Science Robotics (2018) Science robotics, 3, p. eaar7650; Polverini, M.P., Rossi, R., Morandi, G., Bascetta, L., Zanchettin, A.M., Rocco, P., Performance improvement of implicit integral robot force control through constraint-based optimization (2016) Proceedings of the 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp. 3368-3373. , Daejeon, South Korea, 9–14 October; Mohamed, Z.M., (2018) Flexible Manufacturing Systems: Planning Issues and Solutions, , Taylor & Francis: Oxfordshire, UK; Dattaprasad, S., Rao, Y.V., A Survey of Various Robot Learning Techniques (2018) Int. J. Pure Appl. Math, 118, pp. 3823-3831; Hogan, N., Impedance control: An approach to manipulation (1984) Proceedings of the 1984 American control conference, pp. 304-313. , San Diego, CA, USA, 6–8 June; Vukobratovic, M., Robot-environment dynamic interaction survey and future trends (2010) J. Comput. Syst. Sci. Int, 49, pp. 329-342; Roveda, L., Pedrocchi, N., Tosatti, L.M., Exploiting impedance shaping approaches to overcome force overshoots in delicate interaction tasks (2016) Int. J. Adv. Robot. Syst, 13, p. 1729881416662771; Roveda, L., Pedrocchi, N., Beschi, M., Tosatti, L.M., High-accuracy robotized industrial assembly task control schema with force overshoots avoidance (2018) Control. Eng. 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Electron, 47, pp. 932-938; Colomé, A., Pardo, D., Alenya, G., Torras, C., External force estimation during compliant robot manipulation Proceedings of the 2013 IEEE International Conference on Robotics and Automation, pp. 3535-3540. , Karlsruhe, Germany, 6–10 May 2013; Hu, J., Xiong, R., Contact force estimation for robot manipulator using semiparametric model and disturbance Kalman filter (2017) IEEE Trans. Ind. Electron, 65, pp. 3365-3375; Peng, G., Yang, C., He, W., Chen, C.L.P., Force Sensorless Admittance Control With Neural Learning for Robots With Actuator Saturation (2020) IEEE Trans. Ind. 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Automatic Control in Aerospace, pp. 147-152. , Toulouse, France, 25–29, June","Roveda, L.; Istituto Dalle Molle di studi sull’Intelligenza Artificiale (IDSIA), Switzerland; email: loris.roveda@idsia.ch",,,"MDPI AG",,,,,20751702,,,,"English","Mach.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85094649354
"Salichs M.A., Castro-González Á., Salichs E., Fernández-Rodicio E., Maroto-Gómez M., Gamboa-Montero J.J., Marques-Villarroya S., Castillo J.C., Alonso-Martín F., Malfaz M.","6603829011;36655483500;57103536200;57195252678;57203536791;57194276023;57204553474;7202601719;55215683700;8613265500;","Mini: A New Social Robot for the Elderly",2020,"International Journal of Social Robotics","12","6",,"1231","1249",,6,"10.1007/s12369-020-00687-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090243773&doi=10.1007%2fs12369-020-00687-0&partnerID=40&md5=05bcd31eab4202695c74d5c04ea13578","Department of Systems Engineering and Automation, Robotics Lab, Carlos III University of Madrid Avda. Universidad 30, Leganés, Madrid  28911, Spain","Salichs, M.A., Department of Systems Engineering and Automation, Robotics Lab, Carlos III University of Madrid Avda. Universidad 30, Leganés, Madrid  28911, Spain; Castro-González, Á., Department of Systems Engineering and Automation, Robotics Lab, Carlos III University of Madrid Avda. Universidad 30, Leganés, Madrid  28911, Spain; Salichs, E., Department of Systems Engineering and Automation, Robotics Lab, Carlos III University of Madrid Avda. Universidad 30, Leganés, Madrid  28911, Spain; Fernández-Rodicio, E., Department of Systems Engineering and Automation, Robotics Lab, Carlos III University of Madrid Avda. Universidad 30, Leganés, Madrid  28911, Spain; Maroto-Gómez, M., Department of Systems Engineering and Automation, Robotics Lab, Carlos III University of Madrid Avda. Universidad 30, Leganés, Madrid  28911, Spain; Gamboa-Montero, J.J., Department of Systems Engineering and Automation, Robotics Lab, Carlos III University of Madrid Avda. Universidad 30, Leganés, Madrid  28911, Spain; Marques-Villarroya, S., Department of Systems Engineering and Automation, Robotics Lab, Carlos III University of Madrid Avda. Universidad 30, Leganés, Madrid  28911, Spain; Castillo, J.C., Department of Systems Engineering and Automation, Robotics Lab, Carlos III University of Madrid Avda. Universidad 30, Leganés, Madrid  28911, Spain; Alonso-Martín, F., Department of Systems Engineering and Automation, Robotics Lab, Carlos III University of Madrid Avda. Universidad 30, Leganés, Madrid  28911, Spain; Malfaz, M., Department of Systems Engineering and Automation, Robotics Lab, Carlos III University of Madrid Avda. Universidad 30, Leganés, Madrid  28911, Spain","The unceasing aging of the population is leading to new problems in developed countries. Robots represent an opportunity to extend the period of independent living of the elderly as well as to ameliorate their economic burden and social problems. We present a new social robot, Mini, specifically designed to assist and accompany the elderly in their daily life either at home or in a nursing facility. Based on the results of several meetings with experts in this field, we have built a robot able to provide services in the areas of safety, entertainment, personal assistance and stimulation. Mini supports elders and caregivers in cognitive and mental tasks. We present the robot platform and describe the software architecture, particularly focussing on the human–robot interaction. We give in detail how the robot operates and the interrelation of the different modules of the robot in a real use case. In the last part of the paper, we evaluated how users perceive the robot. Participants reported interesting results in terms of usability, appearance, and satisfaction. This paper describes all aspects of the design and development of a new social robot that can be used by other researchers who face the multiple challenges of creating a new robotic platform for older people. © 2020, Springer Nature B.V.","Assistive robotics; Healthcare robotics; HRI; Human–robot interaction; Robots for elderly; Social robotics","Agricultural robots; Machine design; Design and Development; Developed countries; Economic burden; Independent living; Robot interactions; Robot platform; Robotic platforms; Social problems; Social robots",,,,,"Ministerio de Ciencia, Innovación y Universidades, es:MCIU: RTI2018-096338-B-I00; Ministerio de Economía y Competitividad, en:MINECO, MEC","The research leading to these results has received funding from the projects: Development of social robots to help seniors with cognitive impairment (ROBSEN), funded by the Ministerio de Economia y Competitividad; and Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES), funded by the Ministerio de Ciencia, Innovación y Universidades.",,,,,,,,,,"Abdollahi, H., Mollahosseini, A., Lane, J.T., Mahoor, M.H., A pilot study on using an intelligent life-like robot as a companion for elderly individuals with dementia and depression (2017) 2017 IEEE-RAS 17Th International Conference on Humanoid Robotics (Humanoids). 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Springer, Berlin, pp. 94-102; Velazquez Navarro, E., Gonzalez-Diaz, S., Alonso-Martin, F., Castillo, J., Castro-Gonzalez, A., Malfaz, M., Salichs, M., Social robot Mini as a platform for developing multimodal interaction games (2019) Medina FT, pp. 214-220. , Garcia OR, Actas de las Jornadas Nacionales de Robótica, Spanish National Robotics Conference; Viola, P., Jones, M.J., Robust real-time face detection (2004) Int J Comput Vis, 57 (2), pp. 137-154; Yang, C.Y., Lu, M.J., Tseng, S.H., Fu, L.C., A companion robot for daily care of elders based on homeostasis (2017) 2017 56Th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). IEEE, New York, pp. 1401-1406","Castro-González, Á.; Department of Systems Engineering and Automation, Spain; email: acgonzal@ing.uc3m.es",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85090243773
"Westermann K., Lin J.F.-S., Kulić D.","57198860117;55891993000;10244652900;","Inverse optimal control with time-varying objectives: application to human jumping movement analysis",2020,"Scientific Reports","10","1","11174","","",,2,"10.1038/s41598-020-67901-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087610982&doi=10.1038%2fs41598-020-67901-x&partnerID=40&md5=b965f6c2dfd86b697fd468e2c9f2afdd","University of Waterloo, Waterloo, Canada; Monash University, Melbourne, Australia","Westermann, K., University of Waterloo, Waterloo, Canada; Lin, J.F.-S., University of Waterloo, Waterloo, Canada; Kulić, D., University of Waterloo, Waterloo, Canada, Monash University, Melbourne, Australia","Analysis of complex human movements can provide valuable insights for movement rehabilitation, sports training, humanoid robot design and control, and human–robot interaction. To accomplish complex movement, the central nervous system must coordinate the musculo-skeletal system to achieve task and internal (e.g., effort minimisation) objectives. This paper proposes an inverse optimal control approach for analysing complex human movement that does not assume that the control objective(s) remains constant throughout the movement. The movement trajectory is assumed to be optimal with respect to a cost function composed of the sum of weighted basis cost functions, which may be time varying. The weights of the cost function are recovered using a sliding window. To illustrate the proposed approach, a dataset consisting of standing broad jump to targets at three different distances is collected. The method can be used to extract control objectives that influence task success, identify different motion strategies/styles, as well as to observe how control strategy changes during the motor learning process. Kinematic analysis confirms that the identified control objectives, including centre-of-mass takeoff vector and foot placement upon landing are important to ensure that a given participant lands on the target. The dataset, including nearly 800 jump trajectories from 22 participants is also provided. © 2020, The Author(s).",,"adult; article; control strategy; controlled study; female; foot; human; human experiment; jumping; major clinical study; male; motion; motor learning; acceleration; biomechanics; leg; movement (physiology); physiology; skeletal muscle; theoretical model; time; torque; trunk; Acceleration; Adult; Biomechanical Phenomena; Humans; Leg; Male; Models, Theoretical; Movement; Muscle, Skeletal; Time; Torque; Torso",,,,,"Mitacs","The authors would like to thank Vladimir Joukov for helping with data collection and providing assistance with the EKF pose estimation process, as well as Pamela Carreno for help editing this manuscript, testing the sample code, and statistical support. We would also like to acknowledge the assistance of Trang Bui of the University of Waterloo Statistical Consulting and Collaborative Research Unit for providing guidance on the statistical analysis. This work was supported by the Mitacs Accelerate program.",,,,,,,,,,"Zhou, H., Hu, H., Human motion tracking for rehabilitation—A survey (2008) Biomed. Signal Process. Control, 3, pp. 1-18. , https://doi.org/10.1016/j.bspc.2007.09.001; Wulf, G., Self-controlled practice enhances motor learning: Implications for physiotherapy (2007) Physiotherapy, 93, pp. 96-101; Berret, B., Chiovetto, E., Nori, F., Pozzo, T., Evidence for composite cost functions in arm movement planning: An inverse optimal control approach (2011) PLoS Computat. Biol., 7; Mombaur, K., Truong, A., Laumond, J.-P., From human to humanoid locomotion—An inverse optimal control approach (2010) Autonom. Robot., 28, pp. 369-383; Clever, D., Mombaur, K.D., An inverse optimal control approach for the transfer of human walking motions in constrained environment to humanoid robots (2016) Robot. Sci. Syst.; Bauer, A., Wollherr, D., Buss, M., Human-robot collaboration: A survey (2008) Int. J. Human. 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"Korcsok B., Faragó T., Ferdinandy B., Miklósi Á., Korondi P., Gácsi M.","56333246500;6602434494;54079646400;7003466993;55809786600;6603300873;","Artificial sounds following biological rules: A novel approach for non-verbal communication in HRI",2020,"Scientific Reports","10","1","7080","","",,,"10.1038/s41598-020-63504-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083997448&doi=10.1038%2fs41598-020-63504-8&partnerID=40&md5=a9d9b49e6373a355aa2f4904f4641417","Department of Mechatronics, Optics and Mechanical Engineering Informatics, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary; Department of Ethology, Eötvös Loránd University, Budapest, Hungary; MTA-ELTE Comparative Ethology Research Group, Budapest, Hungary","Korcsok, B., Department of Mechatronics, Optics and Mechanical Engineering Informatics, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary; Faragó, T., Department of Ethology, Eötvös Loránd University, Budapest, Hungary; Ferdinandy, B., MTA-ELTE Comparative Ethology Research Group, Budapest, Hungary; Miklósi, Á., Department of Ethology, Eötvös Loránd University, Budapest, Hungary, MTA-ELTE Comparative Ethology Research Group, Budapest, Hungary; Korondi, P., Department of Mechatronics, Optics and Mechanical Engineering Informatics, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary; Gácsi, M., Department of Ethology, Eötvös Loránd University, Budapest, Hungary, MTA-ELTE Comparative Ethology Research Group, Budapest, Hungary","Emotionally expressive non-verbal vocalizations can play a major role in human-robot interactions. Humans can assess the intensity and emotional valence of animal vocalizations based on simple acoustic features such as call length and fundamental frequency. These simple encoding rules are suggested to be general across terrestrial vertebrates. To test the degree of this generalizability, our aim was to synthesize a set of artificial sounds by systematically changing the call length and fundamental frequency, and examine how emotional valence and intensity is attributed to them by humans. Based on sine wave sounds, we generated sound samples in seven categories by increasing complexity via incorporating different characteristics of animal vocalizations. We used an online questionnaire to measure the perceived emotional valence and intensity of the sounds in a two-dimensional model of emotions. The results show that sounds with low fundamental frequency and shorter call lengths were considered to have a more positive valence, and samples with high fundamental frequency were rated as more intense across all categories, regardless of the sound complexity. We conclude that applying the basic rules of vocal emotion encoding can be a good starting point for the development of novel non-verbal vocalizations for artificial agents. © 2020, The Author(s).",,"article; human; human experiment; human tissue; nonverbal communication; questionnaire; sound; vocalization; adult; clinical trial; emotion; female; hearing; interpersonal communication; male; middle aged; physiology; Adult; Auditory Perception; Communication; Emotions; Female; Humans; Male; Middle Aged; Sound",,,,,"K115862, K120501; Budapesti Műszaki és Gazdaságtudományi Egyetem, BME; Magyar Tudományos Akadémia, MTA: 460002, MTA 01 031; Emberi Eroforrások Minisztériuma, EMMI; Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NKFIA: TUDFO/51757/2019-ITM","We would like to thank Viktor Devecseri for creating the first version of the online interactive questionnaire, and for providing access to the source code. The research was supported by the ÚNKP-16-3-I. New National Excellence Program of the Ministry of Human Capacities; the Hungarian Academy of Sciences (MTA 01 031); the Premium Postdoctoral Grant (460002) by the Office for Research Groups Attached to Universities and Other Institutions of the Hungarian Academy of Sciences in Hungary; and the National Research, Development, and Innovation Office grant (K115862) and (K120501); and by the National Research, Development and Innovation Fund (TUDFO/51757/2019-ITM, Thematic Excellence Program). The research reported in this paper was supported by the Higher Education Excellence Program of the Ministry of Human Capacities within the Artificial Intelligence research area of Budapest University of Technology and Economics (BME FIKP-MI).",,,,,,,,,,"Mavridis, N., A review of verbal and non-verbal human-robot interactive communication (2015) Rob. Auton. 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"Dolganov A.G., Letnev K.Y.","57217210596;57194207357;","Informative modeling of subjective reality for intellectual anthropomorphic robots",2020,"IOP Conference Series: Materials Science and Engineering","966","1","012084","","",,1,"10.1088/1757-899X/966/1/012084","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097087542&doi=10.1088%2f1757-899X%2f966%2f1%2f012084&partnerID=40&md5=980d9de279c7dbbf17be75bf29839587","Ural Federal University Named after the First President of Russia B N Yeltsin, 19 Mira str., Yekaterinburg, 620000, Russian Federation","Dolganov, A.G., Ural Federal University Named after the First President of Russia B N Yeltsin, 19 Mira str., Yekaterinburg, 620000, Russian Federation; Letnev, K.Y., Ural Federal University Named after the First President of Russia B N Yeltsin, 19 Mira str., Yekaterinburg, 620000, Russian Federation","This research is aimed at solving the problem of safeguarding robot operation by modeling the subjective reality of an intellectual anthropomorphic robot. Analysis has been carried out on trends in designing advanced robot control systems and methods of improving the safety of robot usage. The following conclusions are drawn: the risk of causing damage to human life, health or property increases when people interact with robots; it is necessary to combine neural network control systems with expert control systems in order to enhance the intellect of a robot and raise the level of trust held by humans towards robots; developing norms and regulations, designing collaborative robots, drawing visualization diagrams for safety zones cannot really provide the depth of robot socialization sufficient for the human society; an informative model of the subjective reality could be integrated into an intellectual anthropomorphic robot to provide a safer 'human-robot' interaction. Such an approach should result in achieving the greatest trust of humans and their safety due to anthropomorphic conversion of both the external design of a robot and its internal control structure. By its nature, the research is interdisciplinary - it is run in the fields of artificial intelligence and philosophy of subjective reality. © Published under licence by IOP Publishing Ltd.",,,,,,,,,,,,,,,,,,"The national program 'Digital Economy of the Russian Federation', , http://government.ru/rugovclassifier/614/events/, (accessed on 01.06.2020); (2019) Analytic review of the world robotics market, , http://www.sberbank.ru/common/img/uploaded/pdf/sberbank_robotics_review_2019_17.07.2019_m.pdf, Sberbank. (accessed on 01.06.2020); (2018), https://ifr.org/downloads/press2018/WR_Presentation_Industry_and_Service_Robots_rev_5_12_18.pdf, International Federation of Robotics-Representing the global robotics industry, (accessed on 01.06.2020); Terminology and definitions, , Russian state standard GOST R ISO 8373-2014 Robots and robot devices; Russian state standard GOST R 60.0.0.2-2016 Robots and robot devices, , Classification; Requirements for robot safety and maintenance, , Russian state standard GOST R 60.2.2.1-2016 Robots and robot devices; Russian state standard GOST R 60.0.2.1-2016 Robots and robot devices, , General requirements for safety; https://robo-hunter.com/news/kollaborativnaya-revolyciya-chego-jdat-i-stoit-li-opasatsya9045, Collaborative revolution: what to expect and whether to fear. (accessed on 01.06.2020); https://www.fanuc.eu/ru/, FANUC Corporation. (accessed on 01.06.2020); http://moralmachine.mit.edu/, Moral Machine. (accessed on 01.06.2020); https://schunk.com/ru_ru/kompania/o-kompanii/, Schunk Company. (accessed on 01.06.2020); (2009) Encyclopedia of Epistemology and Philosophy of Science, , (Moscow: 'Canon-plus ROOI 'Rehabilitation') 1248; Nichols, S, Stich, S, (2003) How to read your own mind: A cognitive theory of self-consciousness Consciousness, , New philosophical perspectives ed Q Smith and A Jokic (Oxford: Oxford University Press); Descartes, R, (1950) Discourse on Method and Metaphysical Meditations Selected works, p. 250. , (Moscow: GIPL); Kant, I, (1965) Prolegomena to Any Future Metaphysics Collected works, 4. , (in 6 (Moscow: GIPL) 320 part 1; Fichte, J G, (1993) Collected works (in 2 vols), , (Saint-Petersburg: Mifril) 1485; Husserl, E, (2001) Cartesian Meditations, p. 264. , (Saint-Petersburg: Science); https://sites.google.com/site/problemyznania/home, On self-examination as a method of human self-identification. (accessed on 01.06.2020)","Letnev, K.Y.; Ural Federal University Named after the First President of Russia B N Yeltsin, 19 Mira str., Russian Federation; email: ptmir@inbox.ru","Goman V.Mironova M.",,"IOP Publishing Ltd","15th International Conference on Industrial Manufacturing and Metallurgy, ICIMM 2020","18 June 2020 through 19 June 2020",,165176,17578981,,,,"English","IOP Conf. Ser. Mater. Sci. Eng.",Conference Paper,"Final","All Open Access, Gold",Scopus,2-s2.0-85097087542
"Aronsson A., Holm F.","57216163410;57191693632;","Conceptualizing Robotic Agency Social Robots in Elder Care in Contemporary Japan",2020,"Relations","8",,,"15","35",,,"10.7358/rela-2020-0102-arho","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108572117&doi=10.7358%2frela-2020-0102-arho&partnerID=40&md5=bdfc67fa4767f4680eb97836307d3bfd","Universität Zürich","Aronsson, A., Universität Zürich; Holm, F., Universität Zürich","Japan is a hyper-aging society, and its government is encouraging robotic solutions to address elder care labor shortage. Therefore, authorities have adopted an agenda of introducing social robots. However, increasing numbers of people in Japan are becoming emotionally attached to anthropomorphic machines, and their introduction into elder care may thus be perceived as contentious. By exploring human engagement with social robots in the care context, this paper argues that rapid technological advances in the twenty-first century will see robots achieve some level of agency, contributing to human society by carving out unique roles for themselves and by bonding with humans. Nevertheless, the questions remain of whether there should be a difference between humans attributing agency to a being and those beings having the inherent ability to produce agency and how we might understand that difference if unable to access the minds of other humans, let alone nonhumans, some of which are not even alive in the classical sense. Using the example of an interaction between an elderly woman and a social robot, we engage with these questions; discuss linguistic, attributed, and inherent agencies; and suggest that a processual type of agency might be most appropriate for understanding human-robot interaction. © 2020. All Rights Reserved.","attributed agency; emotional attachment; inherent agency; Japan; Kohn Eduardo; linguistic agency; machine learning; nursing home; Pepper; social robots",,,,,,,"The authors would like to express their gratitude to Marco Motta and Paul Dumouchel for their detailed comments.",,,,,,,,,,"Ahearn, Laura, Language and Agency (2001) Annual Review of Anthropology, 30, pp. 109-137; Aronsson, Anne S., Social Robots in Elderly Care: The Turn Toward Emotional Machines in Contemporary Japan (2020) Japanese Review of Cultural Anthropology, 21 (1), pp. 421-456. , Special Issue: Relations, Entanglements, and Enmeshmentsof Humans and Things: A Materiality Perspective; Aronsson, Anne S., (2021) Social Robots in Elder Care in Contemporary Japan: Rethinking the Human Ethnographic Experience in Terms of the Nonhuman, , Forthcoming; Barker, Chris, Jane, Emma, (2016) Cultural Studies: Theory and Practice, , London: Sage Publications; Berger, Arthur Asa, (2010) Objects of Affections: Semiotics and Consumer Culture, , New York: Palgrave Macmillan; Bourdieu, Pierre, (1984) Distinction: A Social Critique of the Judgement of Taste, , translated by Richard Nice. Cambridge: Harvard University Press; Bourdieu, Pierre, (1991) Language and Symbolic Power, , translated by Gino Raymond and Matthew Adamson. Cambridge (MA): Harvard University Press; Breazeal, Cynthia, (2002) Designing Sociable Robots, , Cambridge: MIT Press; Cavell, Stanley, (1999) The Claim of Reason: Wittgenstein, Skepticism, Morality, and Tragedy, , New York: Oxford University Press; (2020) Japanese Town Deploys ‘Monster Wolf’ Robots to Deter Wild Bears, , https://edition.cnn.com/2020/11/12/asia/japanese-monster-wolf-robots-intl-hnk/index.html, CNN. Last modified November 12; Damiano, Luisa, Dumouchel, Paul, Anthropomorphism in Human-Robot Co-Evolution (2018) Frontiers in Psychology, 9, p. 468. , https://doi.org/10.3389/fpsyg.2018.00468; Das, Veena, Being together with Animals: Death, Violence and Noncruelty in Hindu Imagination (2013) Living Beings: Perspectives on Interspecies Engagements, pp. 1-16. , edited by Penelope Dransart, London: Bloomsbury; Despret, Vinciane, (2016) What Would Animals Say If We Asked the Right Questions?, , translated by Brett Buchanan. Minneapolis: University of Minnesota Press; Devlin, Kate, (2018) Turned on: Science, Sex and Robots, , London: Bloomsbury Sigma; Dumouchel, Paul, Damiano, Luisa, (2017) Living with Robots, , translated by Malcolm DeBevoise. Cambridge: Harvard University Press; Dumouchel, Paul, Damiano, Luisa, (2021), January 14, Personal communication; Frumer, Yulia, Cognition and Emotions in Japanese Humanoid Robotics (2018) History of Technology, 34 (2), pp. 157-183; Giddens, Anthony, (1984) The Constitution of Society: Outline of the Theory of Structuration, , Berkeley: University of California Press; Goffman, Erving, (1959) The Presentation of Self in Everyday Life, , New York: Doubleday Anchor; Govindrajan, Radhika, (2018) Animal Intimacies: Interspecies Relatedness in India’s Central Himalayas, , Chicago: University of Chicago Press; Hamaguchi, Keiichiro, How Have Japanese Policies Changed in Accepting Foreign Workers? (2019) Japan Labor Issues, 3 (14), pp. 2-7; Haraway, Donna, Speculative Fabulations for Technoculture’s Generations: Taking Care of Unexpected Country (2014) The Multispecies Salon, pp. 242-262. , edited by Eben Kirksey, London: Duke University Press; Helmreich, Stefan, What Was Life: Answers from Three Limit Biologies (2011) Critical Inquiry, 37 (4), pp. 671-696; Jones, Raya, What Makes a Robot ‘Social’? (2016) Social Studies of Science, 47 (4), pp. 556-579; Kohn, Eduardo, (2013) How Forests Think: Toward an Anthropology beyond the Human, , Berkeley: University of California Press; Latour, Bruno, (2005) Reassembling the Social: An Introduction to Actor-Network- Theory, , Oxford: Oxford University Press; Levesque, Hector, (2017) Common Sense, the Turing Test, and the Quest for Real AI, , Cambridge: MIT Press; Lukács, Gabriella, (2020) Invisibility by Design: Women and Labor in Japan’s Digital Economy, , Durham: Duke University Press; Fukushi Yōgu Kaigo Robotto no Kaihatsu to Fukyū (2018) Development and Spread of Welfare Equipment and Care Robots, , http://www.techno-aids.or.jp/robot/file30/01kaihatu.pdf, Ministry of Labor, Health, and Welfare; Motta, Marco, The Bewitchment of Our Intelligence: Skepticism about Other Minds in Anthropology (2021), Forthcoming; Nolan, Jonathan, Joy, Lisa, (2017) Westworld: Season One, , Kilter Films, Jerry Weintraub Productions. Los Angeles: HBO; Ortner, Sherry, (1989) High Religion: A Cultural and Political History of Sherpa Buddhism, , Princeton: Princeton University Press; Pelkmans, Mathjis, The Restlessness of Doubt, and the Tenacity of Belief (2016) HAU: Journal of Ethnographic Theory, 6 (1), pp. 499-503; Rambelli, Fabio, (2007) Buddhist Materiality: A Cultural History of Objects in Japanese Buddhism, , Stanford: Stanford University Press; Rambelli, Fabio, (2019) Spirits and Animism in Contemporary Japan: The Invisible Empire, , London: Bloomsbury Academic; Robertson, Jennifer, (2018) Robo Sapiens Japanicus: Robots, Gender, Family, and the Japanese Nation, , Oakland: University of California Press; Seligman, Adam, Weller, Robert, Puett, Michael, Simon, Bennett, (2008) Ritual and Its Consequences: An Essay on the Limits of Sincerity, , Oxford: Oxford University Press; Stengers, Isabelle, Including Nonhumans in Political Theory: Opening the Pandora’s Box? (2010) Political Matter: Technoscience, Democracy, and Public Life, pp. 3-33. , edited by Bruce Braun and Sarah J. Whatmore, Minneapolis: University of Minnesota Press; Taussig, Michael, Viscerality, Faith, and Skepticism: Another Theory of Magic (2003) Magic and Modernity: Interfaces of Revelation and Concealment, pp. 272-306. , edited by Birgit Meyer and Peter Pels, Stanford: Stanford University Press; Tsing, Anna Lowenhaupt, Unruly Edges: Mushrooms as Companion Species (2012) Environmental Humanities, 1 (1), pp. 141-154; Turkle, Sherry, Relational Artifacts/Children/Elders: The Complexities of CyberCompanions (2005) Cognitive Science Society, pp. 62-73; Turkle, Sherry, (2017) Alone together: Why We Expect More from Technology and Less from Each Other, , New York: Basic Books; van Dooren, Thom, Kirksey, Eben, Münster, Ursula, Multispecies Studies: Cultivating Arts of Attentiveness (2016) Environmental Humanities, 8 (1), pp. 1-23; Verheggen, Claudine, (2017) Wittgenstein and Davidson on Language, Thought, and Action, , Cambridge: Cambridge University Press; White, Daniel, Contemplating the Robotic Embrace: Introspection for Affective Anthropology (2018) More-Than-Human-Worlds: A NatureCulture Blog Series, , https://www.natcult.net/contemplatingthe-robotic-embrace/, Last modified June 20; White, Daniel, Model Emotion: Android Perspectives on Affect in Japan (2020), Presentation at u:japan lectures, University of Wien, November 19; White, Daniel, Galbraith, Patrick W., Japan’s Emerging Emotional Tech (2019) Anthropology News, , http://www.anthropology-news.org/index.php/2019/01/25/japans-emerging-emotionaltech/, Last modified January 25; White, Daniel, Katsuno, Hirofumi, Toward and Affective Sense of Life: Artificial Intelligence, Animacy, and Amusement at a Robot Pet Memorial Service in Japan (2021) Cultural Anthropology, 36 (2). , May, forthcoming","Aronsson, A.; Universität Zürichemail: anne.aronsson@aoi.uzh.ch
Holm, F.; Universität Zürichemail: fynn.holm@uzh.ch",,,"Edizioni Universitarie di Lettere Economia Diritto",,,,,22833196,,,,"English","Relat.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85108572117
"Steffen L., Silva R.K.D., Ulbrich S., Tieck J.C.V., Roennau A., Dillmann R.","57204180499;57221017710;57219101526;57193327343;35273332500;35599949300;","Networks of Place Cells for Representing 3D Environments and Path Planning",2020,"Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics","2020-November",,"9224441","1158","1165",,1,"10.1109/BioRob49111.2020.9224441","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095613414&doi=10.1109%2fBioRob49111.2020.9224441&partnerID=40&md5=6894f67568c80f162270b1e5654fcd6d","Fzi Research Center for Information Technology, Haid-und-Neu-Straße 10-14, Karlsruhe, 76131, Germany","Steffen, L., Fzi Research Center for Information Technology, Haid-und-Neu-Straße 10-14, Karlsruhe, 76131, Germany; Silva, R.K.D., Fzi Research Center for Information Technology, Haid-und-Neu-Straße 10-14, Karlsruhe, 76131, Germany; Ulbrich, S., Fzi Research Center for Information Technology, Haid-und-Neu-Straße 10-14, Karlsruhe, 76131, Germany; Tieck, J.C.V., Fzi Research Center for Information Technology, Haid-und-Neu-Straße 10-14, Karlsruhe, 76131, Germany; Roennau, A., Fzi Research Center for Information Technology, Haid-und-Neu-Straße 10-14, Karlsruhe, 76131, Germany; Dillmann, R., Fzi Research Center for Information Technology, Haid-und-Neu-Straße 10-14, Karlsruhe, 76131, Germany","Conventional methods for motion control and path planning in robots are nowhere near as reactive and flexible as in nature. Brains solve navigation using place cells - neurons that provide a cognitive representation of a specific environment. Neural techniques for path planning in 2D have been developed for years, however, to allow their application for robotic tasks beyond locomotion, a transmission to 3D is required. We present an implementation for path planning via a propagating wavefront on 3D environments. The algorithm operates on a Spiking Neural Network of excitatory place cells structured as a grid. A wavefront travelling through the network is initiated by activating the goal place cell. The wave strengthens synapses in the direction of the propagation using STDP, as a synaptic learning rule. By interpreting the synaptic weights as a vector field, a path can be derived from any place cell, reached by the wave, to the destination. We demonstrate, using a neural simulator, that our algorithm works well on maps with multiple obstacles. Our method allows fast simulation and query times and we expect to considerably improve the network creation time by using dedicated hardware, allowing massive parallelism. Our algorithm applies bio-inspired techniques and is especially interesting for Human-robot interaction, which requires reactive flexible motion planning. © 2020 IEEE.",,"Agricultural robots; Backpropagation; Biomimetics; Cells; Cytology; Human robot interaction; Neurons; Robotics; Wavefronts; Bio-inspired techniques; Conventional methods; Dedicated hardware; Massive parallelism; Network creation; Neural simulators; Neural techniques; Spiking neural networks; Robot programming",,,,,"Baden-Württemberg Stiftung","ACKNOWLEDGMENT The research leading to this paper has received funding as the project NeuroReact from the Baden-Württemberg Stiftung under the research program Neurorobotik.",,,,,,,,,,"Dijkstra, E.W., A note on two problems in connexion with graphs (1959) Numerische Mathematik, 1 (1), pp. 269-271; Koenig, S., Likhachev, M., (2002) Incremental A, pp. 1539-1546; Polyakova, M., Rubin, G., Danilova, Y., Method for matching customer and manufacturer positions for metal product parameters standardization (2018) AIP, 1946; Cheng, L., Liu, C., Yan, B., Improved hierarchical a-star algorithm for optimal parking path planning of the large parking lot (2014) 2014 IEEE International Conference on Information and Automation (ICIA), pp. 695-698; Guruji, A.K., Agarwal, H., Parsediya, D., Time-efficient a algorithm for robot path planning (2016) Procedia Technology, pp. 144-149; Kuffner, J., LaValle, S., RRT-connect: An efficient approach to single-query path planning Proceedings 2000 ICRA. 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Biomechatronics, BioRob",Conference Paper,"Final","",Scopus,2-s2.0-85095613414
"Mayer R.M., Mohammadi A., Tan Y., Alici G., Choong P., Oetomo D.","57210635259;57197104501;35086948600;7003543070;7005534751;57220353202;","Psychometric Evaluation of Multi-Point Bone-Conducted Tactile Stimulation on the Three Bony Landmarks of the Elbow",2020,"Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics","2020-November",,"9224406","648","653",,,"10.1109/BioRob49111.2020.9224406","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095606343&doi=10.1109%2fBioRob49111.2020.9224406&partnerID=40&md5=ca27597f240002d902d5ba00c870cd1a","Human Robotics Laboratory, Department of Mechanical Engineering, University of Melbourne, Australia; Department of Surgery, St Vincent's Hospital, University of Melbourne, Australia; School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Australia; ARC Centre of Excellence for Electromaterials Science, United States","Mayer, R.M., Human Robotics Laboratory, Department of Mechanical Engineering, University of Melbourne, Australia; Mohammadi, A., Human Robotics Laboratory, Department of Mechanical Engineering, University of Melbourne, Australia; Tan, Y., Human Robotics Laboratory, Department of Mechanical Engineering, University of Melbourne, Australia; Alici, G., School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Australia, ARC Centre of Excellence for Electromaterials Science, United States; Choong, P., Department of Surgery, St Vincent's Hospital, University of Melbourne, Australia, ARC Centre of Excellence for Electromaterials Science, United States; Oetomo, D., Human Robotics Laboratory, Department of Mechanical Engineering, University of Melbourne, Australia, ARC Centre of Excellence for Electromaterials Science, United States","Sensory feedback is highly desirable in upper limb prostheses as well as in human robot interaction and other human machine interfaces. Bone conduction as sensory feedback interface is a recently studied approach showing promising properties. A combination of different feedback information is often necessary for prosthetic grasping, thus multiple feedback channels are required for effective sensory feedback. The use of multiple bone conduction stimulation sites simultaneously has not yet been studied. In this paper, the psychometric evaluation of multiple stimulation sites on the physiologically given bony landmarks on the elbow is investigated. The proposed approach is evaluated on human-subject experiments with six able-bodied subjects and one subject with transradial amputation. Vibrotactile transducers are placed on the bony landmarks of the elbow to determine the identification rate of each stimulation point separately as well as the identification rate of the number of active stimulation points for different frequencies. The outcomes show high identification rates for a frequency range from 100 to 750 Hz whilst performance deteriorates to at chance level at higher frequencies. A decreasing performance in identifying the number of active stimulation sites for an increasing number of simultaneous active transducers was observed. The obtained good performance in location identification suggests that information can be encoded via the location of the stimulation. © 2020 IEEE.",,"Agricultural robots; Artificial limbs; Biofeedback; Human robot interaction; Man machine systems; Physiological models; Robotics; Sensory feedback; Transducers; Different frequency; Feed back information; Human Machine Interface; Human subject experiments; Identification rates; Location identification; Tactile stimulation; Upper limb prosthesis; Well stimulation",,,,,"University of Melbourne, UNIMELB","This project is funded by the Valma Angliss Trust and the University of Melbourne.",,,,,,,,,,"Farina, D., Amsüss, S., Reflections on the present and future of upper limb prostheses (2016) Expert Review of Medical Devices, 13 (4), pp. 321-324; Cordella, F., Ciancio, A.L., Sacchetti, R., Davalli, A., Cutti, A.G., Guglielmelli, E., Zollo, L., Literature review on needs of upper limb prosthesis users (2016) Frontiers in Neuroscience, 10, pp. 1-14. , 5; Stephens-Fripp, B., Alici, G., Mutlu, R., A review of non-invasive sensory feedback methods for transradial prosthetic hands (2018) IEEE Access, 6; Svensson, P., Wijk, U., Björkman, A., Antfolk, C., A review of invasive and non-invasive sensory feedback in upper limb prostheses (2017) Expert Review of Medical Devices, 14 (6), p. 6; Schofield, J.S., Evans, K.R., Carey, J.P., Hebert, J.S., Applications of sensory feedback in motorized upper extremity prosthesis: A review (2014) Expert Review of Medical Devices, 11 (5), pp. 499-511. , 9; Rossi, M., Bianchi, M., Battaglia, E., Catalano, M.G., Bicchi, A., HapPro: A wearable haptic device for proprioceptive feedback (2019) IEEE Transactions on Biomedical Engineering, 66 (1), pp. 138-149. , 1; Clemente, F., Håkansson, B., Cipriani, C., Wessberg, J., Kulbacka-Ortiz, K., Brånemark, R., Fredén Jansson, K.-J., Ortiz-Catalan, M., Touch and hearing mediate osseoperception (2017) Scientific Reports, 7, p. 12; Mayer, R.M., Mohammadi, A., Alici, G., Choong, P., Oetomo, D., Bone conduction as sensory feedback interface : A preliminary study (2019) Engineering in Medicine and Biology (EMBC), , Berlin; Mayer, R.M., Mohammadi, A., Alici, G., Choong, P., Oetomo, D., Static force dependency of bone conduction transducer as sensory feedback for stump-socket based prosthesis (2018) ACRA 2018 Proceedings, , Lincoln; Kim, J., Lee, M., Shim, H.J., Ghaffari, R., Cho, H.R., Son, D., Jung, Y.H., Kim, D.H., Stretchable silicon nanoribbon electronics for skin prosthesis (2014) Nature Communications, 5; Mohammadi, A., Xu, Y., Tan, Y., Choong, P., Oetomo, D., Magneticbased soft tactile sensors with deformable continuous force transfer medium for resolving contact locations in robotic grasping and manipulation (2019) Sensors, 19 (22), p. 4925; Westling, G., Johansson, R.S., (1984) Factors Influencing the Force Control during Precision Grip, pp. 277-284; Augurelle, A.S., Smith, A.M., Lejeune, T., Thonnard, J.L., Importance of cutaneous feedback in maintaining a secure grip during manipulation of hand-held objects (2003) Journal of Neurophysiology, 89 (2), pp. 665-671; Johansson, R., Westling, G., Signals in tactile afferents from the fingers eliciting adaptive motor responses during precision grip (1987) Experimental Brain Research, 66 (3), pp. 141-154; Sanders, J.E., Allyn, K.J., Harrison, D.S., Myers, T.R., Ciol, M.A., Tsai, E.C., Preliminary investigation of residual-limb fluid volume changes within one day (2012) Journal of Rehabilitation Research and Development, 49 (10); Paterno, L., Ibrahimi, M., Gruppioni, E., Menciassi, A., Ricotti, L., Sockets for limb prostheses: A review of existing technologies and open challenges (2018) IEEE Transactions on Biomedical Engineering; Mayer, R.M., Garcia-Rosas, R., Mohammadi, A., Tan, Y., Alici, G., Choong, P., Oetomo, D., Tactile feedback in closed-loop control of myoelectric hand grasping: Conveying information of multiple sensors simultaneously via a single feedback channel (2020) Frontiers in Neuroscience, 14 (348). , 4; Lederman, S.J., Klatzky, R.L., Hand movements : A window into haptic object recognition (1987) Cognitive Psychology, 19, pp. 342-368; Weber, E.H., (1996) E. H. Weber on the Tactile Senses, , 2nd ed. Hove : Erlbaum (UK) Taylor & Francis; Purves, D., Augustine, G.J., Fitzpatrick, D., Katz, L.C., LaMantia, A.-S., McNamara, J.O., Williams, S.M., Differences in mechanosensory discrimination across the body surface (2001) Neuroscience, , 2nd ed., S. M. S. Associates, Ed; Solomonow, M., Lyman, J., Freedy, A., Electrotactile two-point discrimination as a function of frequency, body site, laterality, and stimulation codes (1977) Annals of Biomedical Engineering, 5 (1), pp. 47-60; Zhang, Z., Tannan, V., Holden, J.K., Dennis, R.G., Tommerdahl, M., A quantitative method for determining spatial discriminative capacity (2008) BioMedical Engineering Online, 7, pp. 1-8; Tong, J., Mao, O., Goldreich, D., Two-point orientation discrimination versus the traditional two-point test for tactile spatial acuity assessment (2013) Frontiers in Human Neuroscience, 7, pp. 1-11; Jansson, K.J.F., Håkansson, B., Johannsen, L., Tengstrand, T., Electro-acoustic performance of the new bone vibrator radioear b81: A comparison with the conventional radioear b71 (2015) International Journal of Audiology, 54 (5), pp. 334-340; Daniel, W.W., (1990) Applied Nonparametric Statistics, , 2nd ed. Boston: PWS-KENT Pub; Wilcoxon, F., Individual comparisons by ranking methods (1945) Biometrics Bulletin, 1 (6), pp. 80-83; Stephens-Fripp, B., Mutlu, R., Alici, G., Using vibration motors to create tactile apparent movement for transradial prosthetic sensory feedback (2018) Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, 2018, pp. 213-218. , Augus",,,,"IEEE Computer Society","8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020","29 November 2020 through 1 December 2020",,164150,21551774,9781728159072,,,"English","Proc. IEEE RAS EMBS Int. Conf. Biomed. Rob. Biomechatronics, BioRob",Conference Paper,"Final","",Scopus,2-s2.0-85095606343
"Ding H., Esfahani M.I.M., Shen Y., Tan H., Zhang C.","56888224500;57219798488;57203599877;57219800992;57219801728;","Hybrid Humanoid Robotic Head Mechanism: Design, Modeling, and Experiments with Object Tracking",2020,"Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics","2020-November",,"9224326","124","131",,2,"10.1109/BioRob49111.2020.9224326","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095603959&doi=10.1109%2fBioRob49111.2020.9224326&partnerID=40&md5=f8faa057f18e07db09951019ee5bd2a6","Ubtech North America Research and Development Center, Pasadena, CA  91101, United States","Ding, H., Ubtech North America Research and Development Center, Pasadena, CA  91101, United States; Esfahani, M.I.M., Ubtech North America Research and Development Center, Pasadena, CA  91101, United States; Shen, Y., Ubtech North America Research and Development Center, Pasadena, CA  91101, United States; Tan, H., Ubtech North America Research and Development Center, Pasadena, CA  91101, United States; Zhang, C., Ubtech North America Research and Development Center, Pasadena, CA  91101, United States","The head mechanism is a critical part of a humanoid healthcare robot for potential healthcare or elderly care. The head structure of a humanoid robot should provide both motion and perception capabilities to mimic head function (e.g. object tracking) for Human-robot interaction (HRI) at the head level. In this paper, an innovative hybrid head mechanism consisting of one rotational servo motor and two linear servos has been proposed. Structural design, kinematic analysis, and automation have been studied to advance locomotive capability as well as functionality. Additionally, a visual servoing object tracking system was implemented as a perception unit to achieve real-time object tracking function. For the structural design, a parallel configuration of two linear actuators was selected to simplify the kinematic analysis, which enables precise control of joint angles. For the perception part, a vision-based real-time Aruco maker tracking system with proportional-integral-derivative (PID) control was set as a test scenario to mimic human head function with joint control of 3 DOFs (yaw, pitch, and roll). The functionality of this head mechanism has been validated by tests on potential use case (visual servoing object tracking). This head mechanism can servo as a valuable platform for broader HRI applications with humanoid healthcare robots. © 2020 IEEE.","head design; Humanoid robot","Agricultural robots; Anthropomorphic robots; Health care; Human robot interaction; Kinematics; Linear actuators; Motion tracking; Proportional control systems; Robotics; Structural design; Two term control systems; Visual servoing; Human robot Interaction (HRI); Humanoid robotics; Kinematic Analysis; Parallel configuration; Perception capability; Perception unit; Proportional integral derivative control; Real-time object tracking; Object tracking",,,,,,,,,,,,,,,,"Suzman, R., Beard, J., Global health and aging (2011) NIH Publ, 1 (4), pp. 273-277; Murray, M.K., The nursing shortage: Past, present, and future (2002) JONA J. Nurs. Adm., 32 (2), pp. 79-84; Curtis, L.A., Burns, A., (2009) Unit Costs of Health and Social Care 2015. Personal Social Services Research Unit, , The University of Kent; Robinson, H., MacDonald, B., Broadbent, E., The role of healthcare robots for older people at home: A review (2014) Int. J. Soc. Robot., 6, pp. 575-591. , nov; Khosravi, P., Ghapanchi, A.H., Investigating the effectiveness of technologies applied to assist seniors: A systematic literature review (2016) Int. J. Med. Inform., 85, pp. 17-26. , jan; Nwosu, A.C., Sturgeon, B., McGlinchey, T., Goodwin, C.D., Behera, A., Mason, S., Stanley, S., Payne, T.R., Robotic technology for palliative and supportive care: Strengths, weaknesses, opportunities and threats (2019) Palliat. Med., 33, pp. 1106-1113. , sep; Shishehgar, M., Kerr, D., Blake, J., A systematic review of research into how robotic technology can help older people (2018) Smart Heal., 7-8, pp. 1-18. , jun; Armstrong, H.G., (1988) Anthropometry and Mass Distribution for Human Analogues Volume I: Military Male Aviators Brooks Air Force Base, Texas 78235-5301, , March; Panero, J., Zelnik, M., (1979) Human Dimension & Interior Space: A Source Book of Design Reference Standards, , Watson-Guptill; Fitzpatrick, R., (2010) Designing and Constructing An Animatronic Head Capable of Human Motion Programmed Using Face-Tracking Software Table of Contents, , tech. rep., Worcester Polytechnic Institute; Merlet, J.-P., (2006) Parallel Robots, , Springer Science & Business Media; Beira, R., Lopes, M., Praca, M., Santos-Victor, J., Bernardino, A., Metta, G., Becchi, F., Saltaren, R., Design of the robot-cub (icub) head (2006) Proc. 2006 IEEE Int. Conf. Robot. Autom. 2006. ICRA, 2006, pp. 94-100. , IEEE, 2006; Alfayad, S., El Asswad, M., Abdellatif, A., Ouezdou, F.B., Blanchard, A., Beaussé, N., Gaussier, P., HYDRö?D humanoid robot head with perception and emotion capabilities: Modeling, design, and experimental results (2016) Front. Robot. AI, 3, pp. 1-16. , apr; Gao, B., Zhu, Z., Zhao, J., Jiang, L., Inverse kinematics and workspace analysis of a 3 dof flexible parallel humanoid neck robot (2017) J. Intell. Robot. Syst., 87, pp. 211-229. , aug; Zhou, Y., Cheng, H., Jiang, H., Hao, L., Design of a pma-driven bionic neck robot with a spring spine (2017) 2017 IEEE Int. Conf. Cyborg Bionic Syst., pp. 35-40. , Beijing; Nagua, L., Munoz, J., Monje, C.A., Balaguer, C., A first approach to a proposal of a soft robotic link acting as a neck (2018) Actas Las XXXIX Jornadas Automática, pp. 522-529. , Badajoz; Nori, F., Jamone, L., Sandini, G., Metta, G., Accurate control of a human-like tendon-driven neck (2007) 2007 7th IEEE-RAS Int. Conf. Humanoid Robot., pp. 371-378. , IEEE, nov; Tlusty, J., Ziegert, J., Ridgeway, S., Fundamental comparison of the use of serial and parallel kinematics for machines tools (1999) CIRP Ann., 48 (1), pp. 351-356",,,,"IEEE Computer Society","8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020","29 November 2020 through 1 December 2020",,164150,21551774,9781728159072,,,"English","Proc. IEEE RAS EMBS Int. Conf. Biomed. Rob. Biomechatronics, BioRob",Conference Paper,"Final","",Scopus,2-s2.0-85095603959
"Lotti N., Xiloyannis M., Missiroli F., Chiaradia D., Frisoli A., Sanguineti V., Masia L.","57197795349;56809490700;57210646038;56287840000;6603070041;7003348473;8900790600;","Intention-detection strategies for upper limb exosuits: Model-based myoelectric vs dynamic-based control",2020,"Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics","2020-November",,"9224284","410","415",,2,"10.1109/BioRob49111.2020.9224284","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095586426&doi=10.1109%2fBioRob49111.2020.9224284&partnerID=40&md5=85c178bcd24653495beedc9ff171e5f1","Institut für Technische Informatik (ZITI), Heidelberg University, Heidelberg, 69120, Germany; Institute of Robotics and Intelligent Systems, Eth Zürich, Zürich, 8092, Switzerland; TeCIP Institute, Percro Laboratory, Scuola Superiore Sant'Anna, Pisa, 56127, Italy; University of Genoa, Dept. Informatics, Bioengineering, Robotics and Systems Engineering, Genova, 16145, Italy","Lotti, N., Institut für Technische Informatik (ZITI), Heidelberg University, Heidelberg, 69120, Germany; Xiloyannis, M., Institute of Robotics and Intelligent Systems, Eth Zürich, Zürich, 8092, Switzerland; Missiroli, F., Institut für Technische Informatik (ZITI), Heidelberg University, Heidelberg, 69120, Germany; Chiaradia, D., TeCIP Institute, Percro Laboratory, Scuola Superiore Sant'Anna, Pisa, 56127, Italy; Frisoli, A., TeCIP Institute, Percro Laboratory, Scuola Superiore Sant'Anna, Pisa, 56127, Italy; Sanguineti, V., University of Genoa, Dept. Informatics, Bioengineering, Robotics and Systems Engineering, Genova, 16145, Italy; Masia, L., Institut für Technische Informatik (ZITI), Heidelberg University, Heidelberg, 69120, Germany","The cognitive human-robot interaction between an exosuit and its wearer plays a key role in determining both the biomechanical effects of the device on movements and its perceived effectiveness. There is a lack of evidence, however, on the comparative performance of different control methods, implemented on the same device. Here, we compare two different control approaches on the same robotic suit: a model-based myoelectric control (myoprocessor), which estimates the joint torque from the activation of target muscles, and a dynamic-based control that provides support against gravity using an inverse dynamic model. Tested on a cohort of four healthy participants, assistance from the exosuit results in a marked reduction in the effort of muscles working against gravity with both control approaches (peak reduction of 68.6±18.8%, for the dynamic arm model and 62.4±25.1% for the myoprocessor), when compared to an unpowered condition. Neither of the two controllers had an affect on the performance of their users in a joint-angle tracking task (peak errors of 15.4° and 16.4° for the dynamic arm model and myoprocessor, respectively, compared to 13.1o in the unpowered condition). However, our results highlight the remarkable adaptability of the myoprocessor to seamlessly adapt to changing external dynamics. © 2020 IEEE.","Human-machine interfaces; Soft robotics; Wearable technologies","Agricultural robots; Joints (anatomy); Metal drawing; Muscle; Robotics; Biomechanical effects; Cognitive human-robot interactions; Comparative performance; Control approach; External dynamics; Intention detection; Inverse dynamic model; Myoelectric control; Human robot interaction",,,,,,,,,,,,,,,,"Guizzo, E., Goldstein, H., The rise of the body bots (2005) IEEE Spectrum, 42 (10), pp. 42-48. , oct; Asbeck, A.T., De Rossi, S.M., Galiana, I., Ding, Y., Walsh, C.J., Stronger, smarter, softer: Next-generation wearable robots (2014) IEEE Robotics and Automation Magazine, 21 (4), pp. 22-33; Koller, J.R., Jacobs, D.A., Ferris, D.P., Remy, C.D., Learning to walk with an adaptive gain proportional myoelectric controller for a robotic ankle exoskeleton (2015) Journal of NeuroEngineering and Rehabilitation, 12 (1), pp. 1-14. , http://dx.doi.org/10.1186/s12984-015-0086-5, nov; Kazerooni, H., Steger, R., Huang, L., Hybrid control of the Berkeley lower extremity exoskeleton (bleex) (2006) The International Journal of Robotics Research, 25 (5-6), pp. 561-573; Fougner, A., Stavdahl, O., Kyberd, P.J., Losier, Y.G., Parker, P.A., Control of upper limb prostheses: Terminology and proportional myoelectric controla review (2012) IEEE Transactions on Neural Systems and Rehabilitation Engineering; Merletti, R., Parker, P., (2004) Electromyography : Physiology, Engineering, and Noninvasive Applications, , 11th ed. Wiley-IEEE Press; Sartori, M., Reggiani, M., Farina, D., Lloyd, D.G., EMG-driven forward-dynamic estimation of muscle force and joint moment about multiple degrees of freedom in the human lower extremity (2012) PLoS ONE; Durandau, G., Farina, D., Asín-Prieto, G., Dimbwadyo-Terrer, I., Lerma-Lara, S., Pons, J.L., Moreno, J.C., Sartori, M., Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling (2019) Journal of NeuroEngineering and Rehabilitation; Lotti, N., Sanguineti, V., Estimation of muscle torques from emg and kinematics during planar arm movements (2018) 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob). IEEE, pp. 948-953; Lotti, N., Sanguineti, V., EMG-driven force fields: Toward a myoprocessor for 'virtual biomechanics' (2018) Biosystems and Biorobotics; Lotti, N., Xiloyannis, M., Durandau, G., Galofaro, E., Sanguineti, V., Masia, L., Sartori, M., Adaptive model-based myoelectric control for a soft wearable arm exosuit: A new generation of wearable robot control (2020) IEEE Robotics Automation Magazine; Sartori, M., Llyod, D.G., Farina, D., Neural data-driven musculoskeletal modeling for personalized neurorehabilitation technologies (2016) IEEE Transactions on Biomedical Engineering, 63 (5), pp. 879-893. , May; Cain, S.M., Gordon, K.E., Ferris, D.P., Locomotor adaptation to a powered ankle-foot orthosis depends on control method (2007) Journal of NeuroEngineering and Rehabilitation, 4, pp. 1-13; Xiloyannis, M., Chiaradia, D., Frisoli, A., Masia, L., Physiological and kinematic effects of a soft exosuit on arm movements (2019) Journal of NeuroEngineering and Rehabilitation, 16 (1), p. 29. , https://doi.org/10.1186/s12984-019-0495-y, Feb; Chiaradia, D., Xiloyannis, M., Antuvan, C.W., Frisoli, A., Masia, L., Design and embedded control of a soft elbow exosuit (2018) 2018 IEEE International Conference on Soft Robotics (RoboSoft), pp. 565-571. , April; Lloyd, D.G., Besier, T.F., An emg-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo (2003) Journal of Biomechanics, 36 (6), pp. 765-776; Holzbaur, K.R., Murray, W.M., Delp, S.L., A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control (2005) Annals of Biomedical Engineering, 33 (6), pp. 829-840; Sartori, M., Reggiani, M., Bogert Den Van, A.J., Lloyd, D.G., Estimation of musculotendon kinematics in large musculoskeletal models using multidimensional b-splines (2012) Journal of Biomechanics, 45 (3), pp. 595-601; Yu, W., Rosen, J., Li, X., Pid admittance control for an upper limb exoskeleton (2011) Proceedings of the 2011 American Control Conference. IEEE, pp. 1124-1129; Hermens, H.J., Freriks, B., Disselhorst-Klug, C., Rau, G., Development of recommendations for semg sensors and sensor placement procedures (2000) Journal of Electromyography and Kinesiology, 10 (5), pp. 361-374; Flash, T., Hogan, N., The coordination of arm movements: An experimentally confirmed mathematical model (1985) Journal of Neuroscience, 5 (7), pp. 1688-1703",,,,"IEEE Computer Society","8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020","29 November 2020 through 1 December 2020",,164150,21551774,9781728159072,,,"English","Proc. IEEE RAS EMBS Int. Conf. Biomed. Rob. Biomechatronics, BioRob",Conference Paper,"Final","",Scopus,2-s2.0-85095586426
"Ho D.H., Han J., Huang J., Choi Y.Y., Cheon S., Sun J., Lei Y., Park G.S., Wang Z.L., Sun Q., Cho J.H.","57102643100;57212552461;57200122180;57215490745;57191201785;55963777700;57216177607;57218657213;57217204735;57217271057;35241694100;","β-Phase-Preferential blow-spun fabrics for wearable triboelectric nanogenerators and textile interactive interface",2020,"Nano Energy","77",,"105262","","",,22,"10.1016/j.nanoen.2020.105262","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089945430&doi=10.1016%2fj.nanoen.2020.105262&partnerID=40&md5=0e70151d0229e8ed352e829bea620b82","Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China; SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 440-746, South Korea; School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; School of Physics and Electronics, Central South University, Changsha, 410083, China; Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 03722, South Korea; School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA  30332-0245, United States; Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning, 530004, China","Ho, D.H., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 440-746, South Korea; Han, J., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; Huang, J., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; Choi, Y.Y., SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 440-746, South Korea; Cheon, S., SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 440-746, South Korea; Sun, J., School of Physics and Electronics, Central South University, Changsha, 410083, China; Lei, Y., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China; Park, G.S., Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 03722, South Korea; Wang, Z.L., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China, School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA  30332-0245, United States; Sun, Q., Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 100083, China, School of Nanoscience and Technology, University of Chinese Academy of Sciences, Beijing, 100049, China, Center on Nanoenergy Research, School of Physical Science and Technology, Guangxi University, Nanning, 530004, China; Cho, J.H., Department of Chemical and Biomolecular Engineering, Yonsei University, Seoul, 03722, South Korea","Wearable textile electronics have been extensively developed with versatile functionality and self-powered autonomy. Here, we demonstrated β-phase-preferential solution-blow-spun fabrics for application to wearable TENGs and a textile interactive interface. The nonwoven fabric fabricated by solution blow spinning (SBS) was successfully applied as the triboelectric layer in TENGs. The uniaxial elongation of the polymer chains along the fiber axis during the SBS process promoted the formation of polar β-phase crystals in the solution-blow-spun nanofibers, which led to a negative shift in the surface potential and enhancement of the TENG performances. The relationship between the surface charge potential and the crystalline phase of the fabricated nonwoven fabric was comprehensively investigated. The constructed fabric TENG delivered excellent output performances such as a high open-circuit voltage of 260 V, short-circuit current of 27 μA, and high output power of 7 mW; these values were satisfactory for its utilization in the harvesting of biomechanical energy and subsequent driving of commercial portable electronic devices. The fabric TENG was further successfully utilized as a wireless fabric interactive interface for controlling domestic appliances and video playback. The proposed β-phase-preferential blow-spun fabric TENGs are highly promising for application to next-generation intelligent textronics and self-powered human–robot interaction interfaces. © 2020 Elsevier Ltd","Nonwoven fabric; Solution blow spinning; Textile interactive interface; Triboelectric nanogenerator; β-phase","Domestic appliances; Human robot interaction; Intelligent robots; Nanogenerators; Nonwoven fabrics; Open circuit voltage; Triboelectricity; Wearable technology; Charge potentials; Crystalline phase; High output power; Interactive interfaces; Output performance; Portable electronic devices; Robot interactions; Uniaxial elongation; Spinning (fibers)",,,,,"20SCIP-B146646-03; National Natural Science Foundation of China, NSFC: 51605034, 51711540300; Chinese Academy of Sciences, CAS; Ministry of Land, Infrastructure and Transport, MOLIT; Beijing Nova Program: Z191100001119047; National Key Research and Development Program of China, NKRDPC: 2016YFA0202703, 2016YFA0202704; Fundamental Research Funds for the Central Universities: E0EG6801X2","This work is financially supported by the National Key Research and Development Program of China ( 2016YFA0202703 , 2016YFA0202704 ), the Fundamental Research Funds for the Central Universities ( E0EG6801X2 ), Beijing Nova Program ( Z191100001119047 ), the “Hundred Talents Program” of the Chinese Academy of Science , and the National Natural Science Foundation of China ( 51605034 , 51711540300 ). Prof. J. H. Cho was financially supported by a grant from the Construction Technology Research Project (Grant No. 20SCIP-B146646-03 ) funded by the Ministry of Land, Infrastructure and Transport, Korea .","This work is financially supported by the National Key Research and Development Program of China (2016YFA0202703, 2016YFA0202704), the Fundamental Research Funds for the Central Universities (E0EG6801X2), Beijing Nova Program (Z191100001119047), the ?Hundred Talents Program? of the Chinese Academy of Science, and the National Natural Science Foundation of China (51605034, 51711540300). Prof. J. H. Cho was financially supported by a grant from the Construction Technology Research Project (Grant No. 20SCIP-B146646-03) funded by the Ministry of Land, Infrastructure and Transport, Korea.",,,,,,,,,"Wang, Z.L., On Maxwell's displacement current for energy and sensors: the origin of nanogenerators (2017) Mater. 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Mater., 26, pp. 5310-5336; Kang, H., Zhao, C., Huang, J., Ho, D.H., Megra, Y.T., Suk, J.W., Sun, J., Cho, J.H., Fingerprint-inspired conducting hierarchical wrinkles for energy-harvesting E-skin (2019) Adv. Funct. Mater., 29, p. 1903580; Yang, Y., Han, J., Huang, J., Sun, J., Wang, Z.L., Seo, S., Sun, Q., Stretchable energy-harvesting tactile interactive interface with liquid-metal-nanoparticle-based electrodes (2020) Adv. Funct. Mater., p. 1909652; Chun, J., Ye, B.U., Lee, J.W., Choi, D., Kang, C.Y., Kim, S.W., Wang, Z.L., Baik, J.M., Boosted output performance of triboelectric nanogenerator via electric double layer effect (2016) Nat. Commun., 7, p. 12985; Guo, Z., Nilsson, E., Rigdahl, M., Hagström, B., Melt spinning of PVDF fibers with enhanced β phase structure (2013) J. Appl. Polym. Sci., 130, pp. 2603-2609; Yoon, J., Yang, H.S., Lee, B.S., Yu, W.R., Recent progress in coaxial electrospinning: new parameters, various structures, and wide applications (2018) Adv. 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Mater., 29, p. 1807837; Sun, B., Long, Y.Z., Liu, S.L., Huang, Y.Y., Ma, J., Zhang, H.D., Shen, G., Xu, S., Fabrication of curled conducting polymer microfibrous arrays via A novel electrospinning method for stretchable strain sensors (2013) Nanoscale, 5, p. 7041; Ho, D.H., Cheon, S., Hong, P., Park, J.H., Suk, J.W., Kim, D.H., Han, J.T., Cho, J.H., Multifunctional Smart textronics with blow-spun nonwoven fabrics (2019) Adv. Funct. Mater., 29, p. 1900025; Liu, R.-Q., Wang, X.-X., Fu, J., Zhang, Q.-Q., Song, W.-Z., Xu, Y., Chen, Y.-Q., Long, Y.-Z., Preparation of nanofibrous PVDF membrane by solution blow spinning for mechanical energy harvesting (2019) Nanomaterials, 9, p. 1090; Cheon, S., Kang, H., Kim, H., Son, Y., Lee, J.Y., Shin, H.-J., Kim, S.-W., Cho, J.H., High-performance triboelectric nanogenerators based on electrospun polyvinylidene fluoride-silver nanowire composite nanofibers (2018) Adv. Funct. 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Cho, J.H.; Department of Chemical and Biomolecular Engineering, South Korea; email: jhcho94@yonsei.ac.kr",,,"Elsevier Ltd",,,,,22112855,,,,"English","Nano Energy",Article,"Final","",Scopus,2-s2.0-85089945430
"Rocha R.P., De Almeida A.T., Tavakoli M.","55934289300;7102581109;56256952600;","Water based magnification of capacitive proximity sensors: Water containers as passive human detectors",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340877","11058","11064",,,"10.1109/IROS45743.2020.9340877","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102410955&doi=10.1109%2fIROS45743.2020.9340877&partnerID=40&md5=6e79192e36da1b0dce078e7a0bcf4d6a","University of Coimbra, Institute of Systems and Robotics, Portugal","Rocha, R.P., University of Coimbra, Institute of Systems and Robotics, Portugal; De Almeida, A.T., University of Coimbra, Institute of Systems and Robotics, Portugal; Tavakoli, M., University of Coimbra, Institute of Systems and Robotics, Portugal","Sensors that detect human presence received an increasing attention due to the recent advances in smart homes, collaborative fabrication cells, and human robot interaction. These sensors can be used in collaborative robot cells and mobile robots, in order to increase the robot awareness about the presence of humans, in order to increase safety during their operation. Among proximity detection systems, capacitive sensors are interesting, since they are low cost and simple human proximity detectors, however their detection range is limited. In this article, we show that the proximity detection range of a capacitive sensor can be enhanced, when the sensor is placed near a water container. In addition, the signal can pass trough several adjacent water containers, even if they are separated by a few centimeters. This phenomenon has an important implication in establishing low cost sensor networks. For instance, a limited number of active capacitive sensor nodes can be linked with several simple passive nodes, i.e. water containers, to detect human or animal proximity in a large area such as a farm, a factory or home. Analysis on the change of the maximum proximity range with sensor dimension, container size and liquid filler was performed in order to study this effect. Examples of application are also demonstrated. © 2020 IEEE.",,"Agricultural robots; Automation; Containers; Costs; Human robot interaction; Intelligent buildings; Intelligent robots; Sensor networks; Sensor nodes; Capacitive proximity sensors; Collaborative robots; Low-cost sensors; Proximity detection; Proximity detection systems; Proximity detectors; Sensor dimensions; Water containers; Capacitive sensors",,,,,"CENTRO-01-0145-FEDER-022158; European Regional Development Fund, FEDER: CENTRO2020","VII. ACKNOWLEDGMENT This work was partially supported by the national funds of the Foundation of Science and Technology of Portugal through the CMU-Portugal project Stretchtron-ics (Nr. CMUP-ERI/TIC/0021/2014), and Dermotronic (02/SAICT/2017 - 31784). Funding also came from PAMI (Nr. CENTRO-01-0145-FEDER-022158), co-financed by the European Regional Development Fund (FEDER) through “Programa Operacional Regional do Centro” (CENTRO2020).",,,,,,,,,,"Eren, H., Sandor, L., Fringe-effect capacitive proximity sensors for tamper proof enclosures (2005) 2005 IEEE Conference on Sensors for Industry, pp. 22-26; Hoffmann, A., Poeppel, A., Schierl, A., Reif, W., Environmentaware proximity detection with capacitive sensors for human-robotinteraction (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 145-150. , Oct; Xia, F., Bahreyni, B., Campi, F., Multi-functional capacitive proximity sensing system for industrial safety applications (2016) 2016 IEEE Conference; Bavall, L., Karlsson, N., Capacitive detection of humans for safety in industry-a numerical and experimental investigation (1998) Measurement Science and Technology, 9 (3), p. 505. , http://stacks.iop.org/0957-0233/9/i=3/a=027; Navarro, S.E., Koch, S., Hein, B., 3d contour following for a cylindrical end-effector using capacitive proximity sensors (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 82-89. , Oct; Tavakoli, M., Lopes, P., Lourenço, J., Rocha, R.P., Giliberto, L., De Almeida, A.T., Majidi, C., Autonomous selection of closing posture of a robotic hand through embodied soft matter capacitive sensors (2017) IEEE Sensors Journal, 17 (17), pp. 5669-5677. , Sept; Rocha, R.P., Lopes, P.A., De Almeida, A.T., Tavakoli, M., Majidi, C., Fabrication and characterization of bending and pressure sensors for a soft prosthetic hand (2018) Journal of Micromechanics and Microengineering, 28 (3), p. 034001; Osoinach, B., Proximity capacitive sensor technology for touch sensing applications (2007) Freescale White Paper 12; Sekoranja, B., Basíc, D., Svaco, M., Suligoj, F., Jerbíc, B., Humanrobot interaction based on use of capacitive sensors (2014) Procedia Engineering, 69, pp. 464-468; Rocha, R., Lopes, P., De Almeida, A.T., Tavakoli, M., Majidi, C., Soft-matter sensor for proximity, tactile and pressure detection (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , Sept; George, B., Zangl, H., Bretterklieber, T., Brasseur, G., A combined inductive & capacitive proximity sensor for seat occupancy detection (2010) IEEE Transactions on Instrumentation and Measurement, 59 (5), pp. 1463-1470. , May; Tavakoli, M., Malakooti, M.H., Paisana, H., Ohm, Y., Green Marques, D., Alhais Lopes, P., Piedade, A.P., Majidi, C., Egain-assisted room-temperature sintering of silver nanoparticles for stretchable, inkjet-printed, thin-film electronics (2018) Advanced Materials, 30 (29), p. 1801852; Tavakoli, M., Rocha, R., Osorio, L., Almeida, M., De Almeida, A., Ramachandran, V., Tabatabai, A., Majidi, C., Carbon doped pdms: Conductance stability over time and implications for additive manufacturing of stretchable electronics (2017) Journal of Micromechanics and Microengineering, 27 (3), p. 035010; Marques, D.G., Lopes, P.A., De Almeida, A.T., Majidi, C., Tavakoli, M., Reliable interfaces for egain multi-layer stretchable circuits and microelectronics (2019) Lab on A Chip, 19 (5), pp. 897-906; Lopes, P.A., Paisana, H., De Almeida, A.T., Majidi, C., Tavakoli, M., Hydroprinted electronics: Ultrathin stretchable ag-in-ga e-skin for bioelectronics and human-machine interaction (2018) ACS Applied Materials & Interfaces, 10 (45), pp. 38760-38768; Silva, A.F., Paisana, H., Fernandes, T., Góis, J., Serra, A., Coelho, J.F., De Almeida, A.T., Tavakoli, M., High resolution soft and stretchable circuits with pva/liquid-metal mediated printing (2020) Advanced Materials Technologies, p. 2000343; Navarro, S.E., Marufo, M., Ding, Y., Puls, S., Göger, D., Hein, B., Wörn, H., Methods for safe human-robot-interaction using capacitive tactile proximity sensors (2013) Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference On. IEEE, pp. 1149-1154; Schlegl, T., Kröger, T., Gaschler, A., Khatib, O., Zangl, H., Virtual whiskers-highly responsive robot collision avoidance (2013) Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference On. IEEE, pp. 5373-5379; Lee, H.-K., Chang, S.-I., Yoon, E., Dual-mode capacitive proximity sensor for robot application: Implementation of tactile and proximity sensing capability on a single polymer platform using shared electrodes (2009) IEEE Sensors Journal, 9 (12), pp. 1748-1755; (2015) AN64846-Getting Started with CapSense, , C. SemiConductors; Lide, D.R., (2003) Handbook of Chemistry and Physics, , 84th ed. CRC Press","Tavakoli, M.; University of Coimbra, Portugal; email: mahmoud@isr.uc.pt",,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102410955
"Karulkar R.M., Wensing P.M.","56644901100;36714486200;","Application of interacting models to estimate the gait speed of an exoskeleton user",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341110","3452","3458",,1,"10.1109/IROS45743.2020.9341110","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102406734&doi=10.1109%2fIROS45743.2020.9341110&partnerID=40&md5=af29d551b46cb8c660ebeba2789b5a2b","University of Notre Dame, Department of Aerospace and Mechanical Engineering, Notre Dame, IN, United States","Karulkar, R.M., University of Notre Dame, Department of Aerospace and Mechanical Engineering, Notre Dame, IN, United States; Wensing, P.M., University of Notre Dame, Department of Aerospace and Mechanical Engineering, Notre Dame, IN, United States","This paper outlines steps toward a framework for model-based user intent detection to enable fluent human-robot interaction in assistive exoskeletons. An interacting multi-model (IMM) estimation scheme is presented to address state estimation for lower-extremity exoskeletons and to handle their hybrid dynamics. The proposed IMM scheme includes new approaches that enable it to estimate states of hybrid systems with dynamics that are unique to each phase. Traditional IMMs only consider the probabilistic likelihood of being in each phase, while the implementation in this work has been modified to consider physical likelihood as well. The IMM compares exoskeleton sensor readings to multiple candidate gaits from a template model of walking. Candidate gaits are generated using a numerical optimization procedure applied to a Bipedal Spring-Loaded Inverted Pendulum (B-SLIP) model. The framework was tested with sensor data acquired from walking trials in an Ekso GT exoskeleton, and was used to estimate gait phase and center of mass velocity. It is shown that the standard IMM filtering approach results in incorrect estimates of gait phase, while the proposed addition to the IMM estimator using physical likelihood improves the estimates. Results with human subject data further show the ability to estimate gait phase and speed in experimental settings. © 2020 IEEE.",,"Agricultural robots; Human robot interaction; Hybrid systems; Intelligent robots; Optimization; Estimation schemes; Intent detection; Lower extremity exoskeletons; Model-based OPC; Numerical optimizations; Sensor readings; Spring loaded inverted pendulums; Template models; Exoskeleton (Robotics)",,,,,"National Science Foundation, NSF: 1734532",,,,,,,,,,,"https://www.ninds.nih.gov/Disorders/Patient-Caregiver-Education/Hope-Through-Research/Spinal-Cord-Injury-Hope-Through-Research, Spinal cord injury: Hope through research; https://rewalk.com/rewalk-robotics-announces-expansion-to-indiawith-saimed-innovations-2-2-2-2-3-2-3/, , Rewalk personal exoskeleton system cleared by FDA for home use; Sup, F., Bohara, A., Goldfarb, M., Design and control of a powered transfemoral prosthesis (2008) The International Journal of Robotics Research, 27 (2), pp. 263-273; Hidler, J., Sainburg, R., Role of robotics in neurorehabilitation (2011) Topics in Spinal Cord Injury Rehabilitation, 17 (1), pp. 42-49; Huang, H., Zhang, F., Hargrove, L.J., Dou, Z., Rogers, D.R., Englehart, K.B., Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion (2011) Ieee Transactions on Biomedical Engineering, 58 (10), pp. 2867-2875; Jung, J., Jang, I., Riener, R., Park, H., Walking intent detection algorithm for paraplegic patients using a robotic exoskeleton walking assistant with crutches (2012) International Journal of Control, Automation and Systems, 10 (5), pp. 954-962; Gambon, T., Schmiedeler, J.P., Wensing, P.M., Exoskeleton user intent identification via the mahalanobis distance (2020) Ieee RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics (To Appear); Kulic, D., Croft, E.A., Affective state estimation for human-robot interaction (2007) Ieee Transactions on Robotics, 23 (5), pp. 991-1000; Schmiedeler, J.P., Wensing, P.M., Discussion of a review of intent detection, arbitration, and communication aspects of shared control for physical human-robot interaction Asme Appl. 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Chapman &Hall/CRC; Gambon, T.M., Schmiedeler, J.P., Wensing, P.M., Characterizing intent changes in exoskeleton-assisted walking through onboard sensors (2019) Ieee International Conference on Rehabilitation Robotics, pp. 471-476; Agostini, V., Balestra, G., Knaflitz, M., Segmentation and classification of gait cycles (2013) Ieee Transactions on Neural Systems and Rehabilitation Engineering, 22 (5), pp. 946-952; De Rossi, S.M., Crea, S., Donati, M., Rebersek, P., Novak, D., Vitiello, N., Lenzi, T., Carrozza, M.C., Gait segmentation using bipedal foot pressure patterns (2012) Ieee RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 361-366; Kalinowska, A., Berrueta, T.A., Zoss, A., Murphey, T., Datadriven gait segmentation for walking assistance in a lower-limb assistive device (2019) Ieee International Conference on Robotics and Automation, pp. 1390-1396",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102406734
"Weber D., Santini T., Zell A., Kasneci E.","57217014046;54881866000;7003785870;56059892600;","Distilling location proposals of unknown objects through gaze information for human-robot interaction",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340893","11086","11093",,,"10.1109/IROS45743.2020.9340893","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102404452&doi=10.1109%2fIROS45743.2020.9340893&partnerID=40&md5=8033b0eb4a87c9bd3a699ff89ada11a7","University of Tübingen, Tübingen, 72076, Germany","Weber, D., University of Tübingen, Tübingen, 72076, Germany; Santini, T., University of Tübingen, Tübingen, 72076, Germany; Zell, A., University of Tübingen, Tübingen, 72076, Germany; Kasneci, E., University of Tübingen, Tübingen, 72076, Germany","Successful and meaningful human-robot interaction requires robots to have knowledge about the interaction context - e.g., which objects should be interacted with. Unfortunately, the corpora of interactive objects is - for all practical purposes - infinite. This fact hinders the deployment of robots with pre-trained object-detection neural networks other than in pre-defined scenarios. A more flexible alternative to pre-training is to let a human teach the robot about new objects after deployment. However, doing so manually presents significant usability issues as the user must manipulate the object and communicate the object's boundaries to the robot. In this work, we propose streamlining this process by using automatic object location proposal methods in combination with human gaze to distill pertinent object location proposals. Experiments show that the proposed method 1) increased the precision by a factor of approximately 21 compared to location proposal alone, 2) is able to locate objects sufficiently similar to a state-of-the-art pre-trained deep-learning method (FCOS) without any training, and 3) detected objects that were completely missed by FCOS. Furthermore, the method is able to locate objects for which FCOS was not trained on, which are undetectable for FCOS by definition. © 2020 IEEE.",,"Agricultural robots; Deep learning; Educational robots; Intelligent robots; Learning systems; Location; Man machine systems; Object detection; Object recognition; Interaction context; Interactive objects; Learning methods; Object location; Pre-training; State of the art; Unknown objects; Human robot interaction",,,,,,,,,,,,,,,,"Nee, A.Y., Ong, S., Chryssolouris, G., Mourtzis, D., Augmented reality applications in design and manufacturing (2012) CIRP Annals, 61 (2), pp. 657-679; Rentzos, L., Papanastasiou, S., Papakostas, N., Chryssolouris, G., Augmented reality for human-based assembly: Using product and process semantics (2013) IFAC Proceedings Volumes, 46 (15), pp. 98-101; DeVries, T., Misra, I., Wang, C., Maaten Der, L.Van, Does object recognition work for everyone? 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Springer, pp. 391-405",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102404452
"Nascimento H., Mujica M., Benoussaad M.","57219534655;57208247021;34167551000;","Collision avoidance in human-robot interaction using kinect vision system combined with robot's model and data",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341248","10293","10298",,,"10.1109/IROS45743.2020.9341248","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102396790&doi=10.1109%2fIROS45743.2020.9341248&partnerID=40&md5=b8bffc55a6a8fc65a208455233670ac4","University of Pernambuco, Polytechnic School, Automation and Control Department50720-001, Brazil; University of Toulouse, LGP-ENIT, Tarbes, France","Nascimento, H., University of Pernambuco, Polytechnic School, Automation and Control Department50720-001, Brazil, University of Toulouse, LGP-ENIT, Tarbes, France; Mujica, M., University of Toulouse, LGP-ENIT, Tarbes, France; Benoussaad, M., University of Toulouse, LGP-ENIT, Tarbes, France","Human-Robot Interaction (HRI) is a largely ad-dressed subject today. Collision avoidance is one of main strategies that allow space sharing and interaction without contact between human and robot. It is thus usual to use a 3D depth camera sensor which may involves issues related to occluded robot in camera view. While several works overcame this issue by applying infinite depth principle or increasing the number of cameras, we developed in the current work a new and an original approach based on the combination of a 3D depth sensor (Microsoft® Kinect V2) and the proprioceptive robot position sensors. This method uses a principle of limited safety contour around the obstacle to dynamically estimate the robot-obstacle distance, and then generate the repulsive force that controls the robot. For validation, our approach is applied in real time to avoid collision between dynamical obstacles (humans or objects) and the end-effector of a real 7-dof Kuka LBR iiwa collaborative robot.Several strategies based on distancing and its combination with dodging were tested. Results have shown a reactive and efficient collision avoidance, by ensuring a minimum obstacle-robot distance (of ≈ 240mm), even when the robot is in an occluded zone in the Kinect camera view. © 2020 IEEE.",,"Agricultural robots; Cameras; Collision avoidance; Intelligent robots; Man machine systems; Depth sensors; Human robot Interaction (HRI); Kinect cameras; Obstacle distance; Repulsive forces; Robot positions; Space-sharing; Vision systems; Social robots",,,,,,,,,,,,,,,,"Tan, J.T.C., Duan, F., Zhang, Y., Watanabe, K., Kato, R., Arai, T., Human-robot collaboration in cellular manufacturing: Design and development (2009) 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, Iros 2009, pp. 29-34; Kr¨uger, J., Lien, T.K., Verl, A., Cooperation of human and machines in assembly lines (2009) Cirp Annals-Manufacturing Technology, 58 (2), pp. 628-646; Santis, A.D., Siciliano, B., Luca, A.D., Bicchi, A., An atlas of physical human . robot interaction (2008) Mechanism and Machine Theory, 43, pp. 253-270; Alami, R., Albu-Schaeffer, A., Bicchi, A., Bischoff, R., Chatila, R., De Luca, A., De Santis, A., Villani, L., Safe and dependable physical human-robot interaction in anthropic domains: State of the art and challenges (2006) 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1-16. , Oct; Popov, D., Klimchik, A., Mavridis, N., Collision detection, localization &classification for industrial robots with joint torque sensors (2017) RO-MAN 2017-26th Ieee International Symposium on Robot and Human Interactive Communication, 2017, pp. 838-843. , May 2018-Janua; De Luca, A., Flacco, F., Integrated control for pHRI: Collision avoidance, detection, reaction and collaboration (2012) Proceedings of the Ieee Ras and Embs International Conference on Biomedical Robotics and Biomechatronics, pp. 288-295; Flacco, F., Kr¨oger, T., De Luca, A., Khatib, O., A depth space approach to human-robot collision avoidance (2012) Proceedings-IEEE International Conference on Robotics and Automation, pp. 338-345; Schmidt, B., Wang, L., Depth camera based collision avoidance via active robot control (2014) Journal of Manufacturing Systems, 33 (4), pp. 711-718. , http://dx.doi.org/10.1016/j.jmsy.2014.04.004; Chen, J.-H., Song, K.-T., Collision-free motion planning for human-robot collaborative safety under cartesian constraint (2018) 2018 Ieee International Conference on Robotics and Automation (ICRA), pp. 1-7; Safeea, M., Neto, P., Kuka sunrise toolbox: Interfacing collaborative robots with matlab (2019) Ieee Robotics and Automation Magazine, 26 (1), pp. 91-96; Flacco, F., Kroeger, T., De Luca, A., Khatib, O., A depth space approach for evaluating distance to objects: With application to human-robot collision avoidance (2015) Journal of Intelligent and Robotic Systems: Theory and Applications, 80, pp. 7-22; Wasenm¨uller, O., Stricker, D., Comparison of kinect v1 and v2 depth images in terms of accuracy and precision (2017) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 34-45. , LNCS 10117; (2015) Kuka Sunrise.Connectivity Fri 1.7. Augsburg, pp. 1-59. , Germany, 1st edition Group K.R; Gaz, C., De Luca, A., Payload estimation based on identified coefficients of robot dynamics-With an application to collision detection (2017) Ieee International Conference on Intelligent Robots and Systems, 2017, pp. 3033-3040. , Septe; Zhang, W., Ma, X., Cui, L., Chen, Q., 3 points calibration method of part coordinates for arc welding robot (2008) Intelligent Robotics and Applications, C. Xiong, Y. Huang, Y. Xiong, and H. Liu, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 216-224; Chen, S.Y., Kalman filter for robot vision: A survey (2012) Ieee Transac-tions on Industrial Electronics, 59 (11), pp. 4409-4420. , Nov; Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots (1985) Proceedings. 1985 Ieee International Conference on Robotics and Automation, 2, pp. 500-505. , March; Albu-Sch¨affer, A., Haddadin, S., Ott, C., Stemmer, A., Wimb¨ock, T., Hirzinger, G., The DLR lightweight robot: Design and control concepts for robots in human environments (2007) Industrial Robot, 34 (5), pp. 376-385",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102396790
"Peddi R., Franco C.D., Gao S., Bezzo N.","57210919123;57215526338;57210916055;36547454800;","A data-driven framework for proactive intention-aware motion planning of a robot in a human environment",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341210","5738","5744",,1,"10.1109/IROS45743.2020.9341210","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101951070&doi=10.1109%2fIROS45743.2020.9341210&partnerID=40&md5=4aee33aba75d4a72f5ce0625c0296b52","University of Virginia, Department of Systems and Information Engineering, The Charles L. Brown Department of Electrical and Computer Engineering, Charlottesville, VA  22904, United States","Peddi, R., University of Virginia, Department of Systems and Information Engineering, The Charles L. Brown Department of Electrical and Computer Engineering, Charlottesville, VA  22904, United States; Franco, C.D., University of Virginia, Department of Systems and Information Engineering, The Charles L. Brown Department of Electrical and Computer Engineering, Charlottesville, VA  22904, United States; Gao, S., University of Virginia, Department of Systems and Information Engineering, The Charles L. Brown Department of Electrical and Computer Engineering, Charlottesville, VA  22904, United States; Bezzo, N., University of Virginia, Department of Systems and Information Engineering, The Charles L. Brown Department of Electrical and Computer Engineering, Charlottesville, VA  22904, United States","For safe and efficient human-robot interaction, a robot needs to predict and understand the intentions of humans who share the same space. Mobile robots are traditionally built to be reactive, moving in unnatural ways without following social protocol, hence forcing people to behave very differently from human-human interaction rules, which can be overcome if robots instead were proactive. In this paper, we build an intention-aware proactive motion planning strategy for mobile robots that coexist with multiple humans. We propose a framework that uses Hidden Markov Model (HMM) theory with a history of observations to: i) predict future states and estimate the likelihood that humans will cross the path of a robot, and ii) concurrently learn, update, and improve the predictive model with new observations at run-time. Stochastic reachability analysis is proposed to identify multiple possibilities of future states and a control scheme that leverages temporal virtual physics inspired by spring-mass systems is proposed to enable safe proactive motion planning. The proposed approach is validated with simulations and experiments involving an unmanned ground vehicle (UGV) performing go-to-goal operations in the presence of multiple humans, demonstrating improved performance and effectiveness of online learning when compared to reactive obstacle avoidance approaches. © 2020 IEEE.",,"Agricultural robots; Hidden Markov models; Intelligent robots; Intelligent vehicle highway systems; Mobile robots; Motion planning; Predictive analytics; Social robots; Stochastic systems; Hidden Markov model theories; Human environment; Human-human interactions; Predictive modeling; Reachability analysis; Reactive obstacle avoidance; Spring-mass system; Unmanned ground vehicles; Robot programming",,,,,,,,,,,,,,,,"Trautman, P., Krause, A., Unfreezing the robot: Navigation in dense, interacting crowds (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 797-803. , Oct; Fiorini, P., Shiller, Z., Motion planning in dynamic environments using velocity obstacles (1998) The International Journal of Robotics Research, 17 (7), pp. 760-772; Phillips, M., Likhachev, M., Sipp: Safe interval path planning for dynamic environments (2011) 2011 Ieee International Conference on Robotics and Automation, pp. 5628-5635. , May; Hashim, M.S.M., Lu, T., Basri, H.H., Dynamic obstacle avoidance approach for car-like robots in dynamic environments (2012) 2012 International Symposium on Computer Applications and Industrial Electronics (ISCAIE), pp. 130-135. , Dec; Gao, Y., Luh, P.B., Zhang, H., Chen, T., A modified social force model considering relative velocity of pedestrians (2013) 2013 Ieee International Conference on Automation Science and Engineering (CASE), pp. 747-751. , Aug; Bansal, S., Bajcsy, A., Ratner, E., Dragan, A.D., Tomlin, C.J., (2019) A Hamilton-jacobi Reachability-based Framework for Predicting and Analyzing Human Motion for Safe Planning; Fisac, J.F., Bajcsy, A., Herbert, S.L., Fridovich-Keil, D., Wang, S., Tomlin, C.J., Dragan, A.D., Probabilistically safe robot planning with confidence-based human predictions (2018) CoRR, , http://arxiv.org/abs/1806.00109, abs/1806.00109; Martinez, J., Black, M.J., Romero, J., On human motion prediction using recurrent neural networks (2017) 2017 Ieee Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4674-4683. , July; Chen, Y.F., Everett, M., Liu, M., How, J.P., Socially aware motion planning with deep reinforcement learning (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1343-1350; Xu, Y., Piao, Z., Gao, S., Encoding crowd interaction with deep neural network for pedestrian trajectory prediction (2018) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5275-5284. , June; Wang, Z., Jensfelt, P., Folkesson, J., Multi-scale conditional transition map: Modeling spatial-temporal dynamics of human movements with local and long-term correlations (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6244-6251. , Sep; Bansal, S., Chen, M., Herbert, S., Tomlin, C.J., Hamilton-jacobi reachability: A brief overview and recent advances (2017) 2017 Ieee 56th Annual Conference on Decision and Control (CDC), pp. 2242-2253. , Dec; Liebenwein, L., Baykal, C., Gilitschenski, I., Karaman, S., Rus, D., Sampling-based approximation algorithms for reachability analysis with provable guarantees (2018) Robotics: Science and Systems; Lin, T.X., Yel, E., Bezzo, N., Energy-aware persistent control of heterogeneous robotic systems (2018) 2018 Annual American Control Conference (ACC), pp. 2782-2787. , June; Nasraoui, L., Atallah, L.N., Siala, M., Performance study of a reduced complexity time synchronization approach for OFDM systems (2012) Third International Conference on Communications and Networking, pp. 1-5. , March; Dong, Y., Su, H., Zhu, J., Zhang, B., Improving interpretability of deep neural networks with semantic information (2017) 2017 Ieee Conference on Computer Vision and Pattern Recognition (CVPR), pp. 975-983. , July; Alonso-Mora, J., Breitenmoser, A., Beardsley, P., Siegwart, R., Reciprocal collision avoidance for multiple car-like robots (2012) 2012 Ieee International Conference on Robotics and Automation, pp. 360-366. , May; Linder, T., Breuers, S., Leibe, B., Arras, K.O., On multi-modal people tracking from mobile platforms in very crowded and dynamic environments (2016) 2016 Ieee International Conference on Robotics and Automation (ICRA). Ieee, pp. 5512-5519",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85101951070
"Zhang Z.","57210362788;","Towards a Multimodal and Context-Aware Framework for Human Navigational Intent Inference",2020,"ICMI 2020 - Proceedings of the 2020 International Conference on Multimodal Interaction",,,,"738","742",,1,"10.1145/3382507.3421156","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096694345&doi=10.1145%2f3382507.3421156&partnerID=40&md5=813340eb9aca4f7f1b6a70096fbe356d","Simon Fraser University, Burnaby, BC, Canada","Zhang, Z., Simon Fraser University, Burnaby, BC, Canada","A socially acceptable robot needs to make correct decisions and be able to understand human intent in order to interact with and navigate around humans safely. Although research in computer vision and robotics has made huge advance in recent years, today's robotics systems still need better understanding of human intent to be more effective and widely accepted. Currently such inference is typically done using only one mode of perception such as vision, or human movement trajectory. In this extended abstract, I describe my PhD research plan of developing a novel multimodal and context-aware framework, in which a robot infers human navigational intentions through multimodal perception comprised of human temporal facial, body pose and gaze features, human motion feature as well as environmental context. To facility this framework, a data collection experiment is designed to acquire multimodal human-robot interaction data. Our initial design of the framework is based on a temporal neural network model with human motion, body pose and head orientation features as input. And we will increase the complexity of the neural network model as well as the input features along the way. In the long term, this framework can benefit a variety of settings such as autonomous driving, service and household robots. © 2020 ACM.","human-robot interaction; intent inference; machine learning; multimodal perception","Computer vision; Interactive computer systems; Neural networks; Robot programming; Robotics; Autonomous driving; Environmental contexts; Extended abstracts; Household robots; Multimodal perception; Neural network model; Orientation features; Temporal neural networks; Social robots",,,,,"R569337","The author would like to thank his PhD advisors Dr. Angelica Lim and Dr. Mo Chen for their support and guidance. The author would also like to thank Dr. Jimin Rhim for helping design the experiment, and all the participants who participant the experiment. This work was supported under the Huawei-SFU Joint Lab Project (R569337).",,,,,,,,,,"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S., Social lstm: Human trajectory prediction in crowded spaces (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 961-971; Cao, Y., Barrett, D., Barbu, A., Narayanaswamy, S., Yu, H., Michaux, A., Lin, Y., Wang, S., Recognize human activities from partially observed videos (2013) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2658-2665; Cao, Z., Simon, T., Wei, S., Sheikh, Y., Realtime multiperson 2d pose estimation using part affinity fields (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7291-7299; Chandra, R., Bhattacharya, U., Bera, A., Manocha, D., Traphic: Trajectory prediction in dense and heterogeneous traffic using weighted interactions (2019) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8483-8492; Chen, M., Tomlin, C.J., Hamilton-Jacobi reachability: Some recent theoretical advances and applications in unmanned airspace management (2018) Annual Review of Control, Robotics, and Autonomous Systems, 1, pp. 333-358. , 2018; Djuric, N., Radosavljevic, V., Cui, H., Nguyen, T., Chou, F., Lin, T., Schneider, J., (2018) Short-term Motion Prediction of Traffic Actors for Autonomous Driving Using Deep Convolutional Networks, , arXiv preprint arXiv:1808.05819 2018; Fridovich-Keil, D., Herbert, S.L., Fisac, J.F., Deglurkar, S., Tomlin, C.J., Planning, fast and slow: A framework for adaptive real-time safe trajectory planning (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 387-394; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Computation, 9 (8), pp. 1735-1780. , 1997; Huang, C., Wu, B., Nevatia, R., Robust object tracking by hierarchical association of detection responses (2008) European Conference on Computer Vision. Springer, pp. 788-801; Huang, S., Li, X., Zhang, Z., He, Z., Wu, F., Liu, W., Tang, J., Zhuang, Y., Deep learning driven visual path prediction from a single image (2016) IEEE Transactions on Image Processing, 25 (12), pp. 5892-5904. , 2016; Jaderberg, M., Simonyan, K., Zisserman, A., Spatial transformer networks (2015) Advances in Neural Information Processing Systems. 2017-2025; Jain, A., Singh, A., Koppula, H.S., Soh, S., Saxena, A., Recurrent neural networks for driver activity anticipation via sensory-fusion architecture (2016) Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 3118-3125; Kingma, D.P., Mohamed, S., Rezende, D.J., Welling, M., Semi-supervised learning with deep generative models (2014) Advances in Neural Information Processing Systems, pp. 3581-3589; Kitani, K.M., Ziebart, B.D., Bagnell, J.A., Hebert, M., Activity forecasting (2012) European Conference on Computer Vision. Springer, pp. 201-214; Pieter Kooij, J.F., Schneider, N., Flohr, F., Gavrila, D.M., Context-based pedestrian path prediction (2014) European Conference on Computer Vision. Springer, pp. 618-633; Lerner, A., Chrysanthou, Y., Lischinski, D., Crowds by example (2007) Computer Graphics Forum, 26, pp. 655-664. , Wiley Online Library; Liang, J., Jiang, L., Niebles, J.C., Hauptmann, A.G., Fei-Fei, L., Peeking into the future: Predicting future person activities and locations in videos (2019) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5725-5734; Linou, K., (2016) NBA-Player-Movements, , https://github.com/linouk23/NBAPlayer-Movements; Ma, S., Sigal, L., Sclaroff, S., Learning activity progression in lstms for activity detection and early detection (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1942-1950; Ma, W., Huang, D., Lee, N., Kitani, K.M., Forecasting interactive dynamics of pedestrians with fictitious play (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 774-782; Pellegrini, S., Ess, A., Schindler, K., Van Gool, L., You'll never walk alone: Modeling social behavior for multi-target tracking (2009) Proceedings of the 12th IEEE International Conference on Computer Vision. IEEE, pp. 261-268; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., ROS: An open-source robot operating system (2009) ICRA Workshop on Open Source Software, 3. , Kobe, Japan, 5; Rivoire, C., Lim, A., Habit detection within a long-term interaction with a social robot: An exploratory study (2016) Proceedings of the International Workshop on Social Learning and Multimodal Interaction for Designing Artificial Agents, pp. 1-6; Robicquet, A., Sadeghian, A., Alahi, A., Savarese, S., Learning social etiquette: Human trajectory understanding in crowded scenes (2016) European Conference on Computer Vision. Springer, pp. 549-565; Ryoo, M.S., Human activity prediction: Early recognition of ongoing activities from streaming videos (2011) 2011 International Conference on Computer Vision. IEEE, pp. 1036-1043; Schneider, N., Gavrila, D.M., Pedestrian path prediction with recursive Bayesian filters: A comparative study (2013) German Conference on Pattern Recognition. Springer, pp. 174-183; Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, U., Polosukhin, I., Attention is all you need (2017) Advances in Neural Information Processing Systems, pp. 5998-6008; Xie, D., Shu, T., Todorovic, S., Zhu, S., Learning and inferring"" dark matter"" and predicting human intents and trajectories in videos (2017) IEEE Transactions on Pattern Analysis and Machine Intelligence, 40 (7), pp. 1639-1652. , 2017; Yagi, T., Mangalam, K., Yonetani, R., Sato, Y., Future person localization in first-person videos (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7593-7602",,,"ACM SIGCHI","Association for Computing Machinery, Inc","22nd ACM International Conference on Multimodal Interaction, ICMI 2020","25 October 2020 through 29 October 2020",,164395,,9781450375818,,,"English","ICMI - Proc. Int. Conf. Multimodal Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85096694345
"Ortenzi V., Cini F., Pardi T., Marturi N., Stolkin R., Corke P., Controzzi M.","55414623500;57219714439;57207190523;55178519000;16025500800;7005306124;26422782200;","The Grasp Strategy of a Robot Passer Influences Performance and Quality of the Robot-Human Object Handover",2020,"Frontiers in Robotics and AI","7",,"542406","","",,3,"10.3389/frobt.2020.542406","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094924909&doi=10.3389%2ffrobt.2020.542406&partnerID=40&md5=0f674ff7b93aefb0d1383f4f2487a95e","Extreme Robotics Laboratory, School of Metallurgy and Materials, University of Birmingham, Birmingham, United Kingdom; The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy; Department of Excellence in Robotics and Artificial Intelligence (AI), Scuola Superiore Sant'Anna, Pisa, Italy; Australian Research Council (ARC) Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, QLD, Australia","Ortenzi, V., Extreme Robotics Laboratory, School of Metallurgy and Materials, University of Birmingham, Birmingham, United Kingdom; Cini, F., The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy, Department of Excellence in Robotics and Artificial Intelligence (AI), Scuola Superiore Sant'Anna, Pisa, Italy; Pardi, T., Extreme Robotics Laboratory, School of Metallurgy and Materials, University of Birmingham, Birmingham, United Kingdom; Marturi, N., Extreme Robotics Laboratory, School of Metallurgy and Materials, University of Birmingham, Birmingham, United Kingdom; Stolkin, R., Extreme Robotics Laboratory, School of Metallurgy and Materials, University of Birmingham, Birmingham, United Kingdom; Corke, P., Australian Research Council (ARC) Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, QLD, Australia; Controzzi, M., The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy, Department of Excellence in Robotics and Artificial Intelligence (AI), Scuola Superiore Sant'Anna, Pisa, Italy","Task-aware robotic grasping is critical if robots are to successfully cooperate with humans. The choice of a grasp is multi-faceted; however, the task to perform primes this choice in terms of hand shaping and placement on the object. This grasping strategy is particularly important for a robot companion, as it can potentially hinder the success of the collaboration with humans. In this work, we investigate how different grasping strategies of a robot passer influence the performance and the perceptions of the interaction of a human receiver. Our findings suggest that a grasping strategy that accounts for the subsequent task of the receiver improves substantially the performance of the human receiver in executing the subsequent task. The time to complete the task is reduced by eliminating the need of a post-handover re-adjustment of the object. Furthermore, the human perceptions of the interaction improve when a task-oriented grasping strategy is adopted. The influence of the robotic grasp strategy increases as the constraints induced by the object's affordances become more restrictive. The results of this work can benefit the wider robotics community, with application ranging from industrial to household human-robot interaction for cooperative and collaborative object manipulation. © Copyright © 2020 Ortenzi, Cini, Pardi, Marturi, Stolkin, Corke and Controzzi.","human-robot collaboration (HRC); human-robot interaction (HRI); object handover; seamless interaction; task-oriented grasping",,,,,,"CE140100016; Nuclear Decommissioning Authority, NDA; Horizon 2020 Framework Programme, H2020: 870142; Engineering and Physical Sciences Research Council, EPSRC: EP/R02572X/1; European Commission, EC; Horizon 2020","VO and MC initiated this work, oversaw, and advised the research. MC, FC, and VO designed the experiments. VO, NM, and TP built the experimental setup. VO, TP, FC, and MC performed the experiments. RS and MC provided the financial support. All authors contributed to the article and approved the submitted version.","The authors want to thank all the subjects for taking the time to participate to the experiment. A big thank-you goes to Anita, Chris, Maxime, and Peter for their support during the setting up of the experiment. Funding. VO, NM, and RS were supported by the UK National Centre for Nuclear Robotics initiative, funded by EPSRC EP/R02572X/1. TP was supported by an NDA Ph.D. bursary. PC was supported by the Australian Research Council Centre of Excellence for Robotic Vision (project number CE140100016). FC and MC were supported by the European Commission under the Horizon 2020 framework program for Research and Innovation (project acronym: APRIL, project number: 870142).",,,,,,,,,"Adjigble, M., Marturi, N., Ortenzi, V., Rajasekaran, V., Corke, P., Stolkin, R., Model-free and learning-free grasping by local contact moment matching, (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2933-2940. , Madrid, IEEE; Aleotti, J., Micelli, V., Caselli, S., An affordance sensitive system for robot to human object handover (2014) Int. J. Soc. Robot, 6, pp. 653-666; Ansuini, C., Giosa, L., Turella, L., Altoé, G., Castiello, U., An object for an action, the same object for other actions: effects on hand shaping (2008) Exp. Brain Res, 185, pp. 111-119. , 17909766; Ansuini, C., Santello, M., Massaccesi, S., Castiello, U., Effects of end-goal on hand shaping (2006) J. 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Autom, 5, pp. 269-279; Detry, R., Papon, J., Matthies, L., Task-oriented grasping with semantic and geometric scene understanding, (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3266-3273. , Vancouver, BC, IEEE; Do, T.T., Nguyen, A., Reid, I., AffordanceNet: an end-to-end deep learning approach for object affordance detection, (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1-5. , Brisbane, QLD, IEEE; Dragan, A.D., Bauman, S., Forlizzi, J., Srinivasa, S.S., Effects of robot motion on human-robot collaboration, (2015) HRI '15 Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 51-58. , Portland, OR, ACM Press; Ellis, R., Tucker, M., Micro-affordance: the potentiation of components of action by seen objects (2000) Br. J. 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Controzzi, M.; The BioRobotics Institute, Italy; email: marco.controzzi@santannapisa.it
Controzzi, M.; Department of Excellence in Robotics and Artificial Intelligence (AI), Italy; email: marco.controzzi@santannapisa.it",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85094924909
"Sudo Y., Itoyama K., Nishida K., Nakadai K.","57215558246;18042499100;57204950175;6603685669;","Sound event aware environmental sound segmentation with Mask U-Net",2020,"Advanced Robotics","34","20",,"1280","1290",,3,"10.1080/01691864.2020.1829040","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092088760&doi=10.1080%2f01691864.2020.1829040&partnerID=40&md5=2ff4d5bebdc73d16ec76326795f73f6a","Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Tokyo, Japan; Honda Research Institute Japan Co. Ltd., Saitama, Japan","Sudo, Y., Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Tokyo, Japan; Itoyama, K., Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Tokyo, Japan; Nishida, K., Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Tokyo, Japan; Nakadai, K., Department of Systems and Control Engineering, School of Engineering, Tokyo Institute of Technology, Tokyo, Japan, Honda Research Institute Japan Co. Ltd., Saitama, Japan","This paper proposes an environmental sound segmentation method using Mask U-Net. In recent years, human–robot interactions, especially speech dialogue, have been assessed by auditory scene analysis. Methods, such as noise reduction, section detection, and sound source separation have been proposed for robot audition, acoustic signal processing, and machine learning. However, such conventional approaches have three drawbacks: (1) Many studies have analyzed individual functions, which are regarded as being a cascade. Cascade systems can, however, result in the accumulation of errors generated at each functional block. (2) Unlike conventional cascade systems, deep learning-based methods that simultaneously detect sections, separate sound sources, and identify classes have also been proposed for speech separation. These techniques can be extended for multiple classes of environmental sounds, but their performance becomes degraded with large variations in sound event lengths among classes. (3) In addition, these methods have recurrent neural network layers making it difficult to process calculations in parallel. This paper proposes an environmental sound segmentation method called Mask U-Net, which robustly differentiates sound event lengths among classes. Simulation experiments using a developed 75-class environmental sound data set showed that the proposed method was faster than conventional methods and showed high segmentation performance. © 2020 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","auditory scene analysis; environmental sound segmentation; Human–robot interaction; robot audition; sound event detection","Acoustic generators; Agricultural robots; Audition; Human robot interaction; Multilayer neural networks; Network layers; Recurrent neural networks; Separation; Source separation; Auditory Scene Analysis; Conventional approach; Conventional methods; Environmental sounds; Learning-based methods; Process calculation; Segmentation performance; Sound source separation; Learning systems",,,,,"Japan Society for the Promotion of Science, JSPS: 16H02884, 17K00365, 19K12017","This work was supported by Japan Society for the Promotion of Science (JSPS KAKENHI) [grant number 16H02884], [grant number 17K00365], [grant number 19K12017].",,,,,,,,,,"Kennedy, J., Lemaignan, S., Montassier, C., Child speech recognition human-robot interaction: evaluations and recommendations. 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Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP; Brisbane. 2015. p. 5610–5614; Rascon, C., Meza, I., Localization of sound sources in robotics: A review (2017) Rob Auton Syst, 96, pp. 184-210","Sudo, Y.; Department of Systems and Control Engineering, Japan; email: sudo@ra.sc.e.titech.ac.jp",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Final","",Scopus,2-s2.0-85092088760
"Xu J., Howard A.","57201781642;26643170000;","How much do you Trust your Self-Driving Car? Exploring Human-Robot Trust in High-Risk Scenarios",2020,"Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics","2020-October",,"9282866","4273","4280",,2,"10.1109/SMC42975.2020.9282866","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098886170&doi=10.1109%2fSMC42975.2020.9282866&partnerID=40&md5=1d813f39e39ccd66bbb13fc5a3c29750","Georgia Institute of Technology, Institute for Robotics and Intelligent Machines, Atlanta, GA, United States; Georgia Institute of Technology, School of Interactive Computing, Atlanta, GA, United States","Xu, J., Georgia Institute of Technology, Institute for Robotics and Intelligent Machines, Atlanta, GA, United States; Howard, A., Georgia Institute of Technology, School of Interactive Computing, Atlanta, GA, United States","Trust is an important characteristic of successful interactions between humans and agents in many scenarios. Self-driving scenarios are of particular relevance when discussing the issue of trust due to the high-risk nature of erroneous decisions being made. The present study aims to investigate decision-making and aspects of trust in a realistic driving scenario in which an autonomous agent provides guidance to humans. To this end, a simulated driving environment based on a college campus was developed and presented. An online and an in-person experiment were conducted to examine the impacts of mistakes made by the self-driving AI agent on participants' decisions and trust. During the experiments, participants were asked to complete a series of driving tasks and make a sequence of decisions in a time-limited situation. Behavior analysis indicated a similar relative trend in the decisions across these two experiments. Survey results revealed that a mistake made by the self-driving AI agent at the beginning had a significant impact on participants' trust. In addition, similar overall experience and feelings across the two experimental conditions were reported. The findings in this study add to our understanding of trust in human-robot interaction scenarios and provide valuable insights for future research work in the field of human-robot trust. © 2020 IEEE.","human-robot interaction; human-robot trust; self-driving car","Automobile drivers; Autonomous agents; Autonomous vehicles; Behavioral research; Risk perception; Surveys; Behavior analysis; College campus; Driving tasks; Experimental conditions; Risk scenarios; Self drivings; Simulated driving; Time limited; Social robots",,,,,"Air Force Office of Scientific Research, AFOSR","This work was partially supported by funding from the Linda J. and Mark C. Smith Endowed Chair and by award #FA9550-17-1-0017 from the Air Force Office of Sponsored Research. The 3D models of Georgia Tech campus were provided by the IMAGINE Lab in the College of Design at the Georgia Institute of Technology.",,,,,,,,,,"Gaudiello, I., Zibetti, E., Lefort, S., Chetouani, M., Ivaldi, S., Trust as indicator of robot functional and social acceptance. An experimental study on user conformation to iCub answers (2016) Computers in Human Behavior, 61, pp. 633-655; Robinette, P., Wagner, A., Howard, A., Building and maintaining trust between humans and guidance robots in an emergency (2013) 2013 Aaai Spring Symposium: Trust and Autonomous Systems, , Stanford, CA, March; Xu, J., Howard, A., The impact of first impressions on human-robot trust during problem-solving scenarios (2018) 2018 27th Ieee International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 435-441. , IEEE; Ye, S., Neville, G., Schrum, M., Gombolay, M., Chernova, S., Howard, A., Human trust after robot mistakes: Study of the effects of different forms of robot communication (2019) 2019 28th Ieee International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1-7. , IEEE; Borenstein, J., Mahajan, H.P., Wagner, A.R., Howard, A., Trust and pediatric exoskeletons: A comparative study of clinician and parental perspectives (2020) Ieee Transactions on Technology and Society, p. 1; Xu, J., De'Aira, G.B., Howard, A., Would You trust a robot therapist? Validating the equivalency of trust in human-robot healthcare scenarios (2018) 2018 27th Ieee International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 442-447. , IEEE; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Robotics and Autonomous Systems, 42 (3-4), pp. 143-166; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y.C., Visser, E.J.D., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Winkle, T., Safety benefits of automated vehicles: Extended findings from accident research for development, validation and testing (2016) Autonomous Driving, pp. 335-364. , Springer; Chen, M., Nikolaidis, S., Soh, H., Hsu, D., Srinivasa, S., Planning with trust for human-robot collaboration (2018) Proceedings of the 2018 ACM/ Ieee International Conference on Human-Robot Interaction, pp. 307-315; Gombolay, M., Robotic assistance in the coordination of patient care (2018) The International Journal of Robotics Research, 37 (10), pp. 1300-1316; Howard, D., Dai, D., Public perceptions of self-driving cars: The case of Berkeley, California (2014) Transportation Research Board 93rd Annual Meeting, 14 (4502), pp. 1-16; Carlson, M.S., Drury, J.L., Desai, M., Kwak, H., Yanco, H.A., Identifying factors that influence trust in automated cars and medical diagnosis systems (2014) 2014 Aaai Spring Symposium Series; Hengstler, M., Enkel, E., Duelli, S., Applied artificial intelligence and trust-The case of autonomous vehicles and medical assistance devices (2016) Technological Forecasting and Social Change, 105, pp. 105-120; Dikmen, M., Burns, C., Trust in autonomous vehicles: The case of tesla autopilot and summon (2017) 2017 Ieee International Conference on Systems, Man, and Cybernetics (SMC), pp. 1093-1098. , IEEE; Raats, K., Fors, V., Pink, S., Understanding trust in automated vehicles (2019) Proceedings of the 31st Australian Conference on Human-Computer-Interaction, pp. 352-358; Rasouli, A., Tsotsos, J.K., Autonomous vehicles that interact with pedestrians: A survey of theory and practice (2019) Ieee Transactions on Intelligent Transportation Systems, 21 (3), pp. 900-918; Helldin, T., Falkman, G., Riveiro, M., Davidsson, S., Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving (2013) Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 210-217; Payre, W., Cestac, J., Delhomme, P., Fully automated driving: Impact of trust and practice on manual control recovery (2016) Human Factors, 58 (2), pp. 229-241; Shahrdar, S., Park, C., Nojoumian, M., Human Trust measurement using an immersive virtual reality autonomous vehicle simulator (2019) Proceedings of the 2019 AAAI/ Acm Conference on AI, Ethics, and Society, pp. 515-520; Jayaraman, S., Pedestrian trust in automated vehicles: Role of traffic signal and AV driving behavior (2019) Frontiers in Robotics and Ai, , Forthcoming; Sheng, S., A case study of trust on autonomous driving (2019) 2019 Ieee Intelligent Transportation Systems Conference (ITSC), pp. 4368-4373. , IEEE; Tenhundfeld, N.L., De Visser, E.J., Ries, A.J., Finomore, V.S., Tossell, C.C., Trust and distrust of automated parking in a Tesla Model X (2020) Human Factors, 62 (2), pp. 194-210; Robinette, P., Howard, A.M., Wagner, A.R., Effect of robot performance on human-robot trust in time-critical situations (2017) Ieee Transactions on Human-Machine Systems; Jian, J.-Y., Bisantz, A.M., Drury, C.G., Foundations for an empirically determined scale of trust in automated systems (2000) International Journal of Cognitive Ergonomics, 4 (1), pp. 53-71. , 03/01 2000",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020","11 October 2020 through 14 October 2020",,165855,1062922X,9781728185262,PICYE,,"English","Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.",Conference Paper,"Final","",Scopus,2-s2.0-85098886170
"Jo W., Kannan S.S., Cha G.-E., Lee A., Min B.-C.","56152510500;57214751493;57219691264;55662789800;39161762500;","ROSbag-based Multimodal Affective Dataset for Emotional and Cognitive States",2020,"Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics","2020-October",,"9283320","226","233",,,"10.1109/SMC42975.2020.9283320","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098872678&doi=10.1109%2fSMC42975.2020.9283320&partnerID=40&md5=285d3ab4b3e4c59ea0ebf650ff307e6b","Purdue University, Smart Lab, Department of Computer and Information Technology, West Lafayette, IN  47907, United States","Jo, W., Purdue University, Smart Lab, Department of Computer and Information Technology, West Lafayette, IN  47907, United States; Kannan, S.S., Purdue University, Smart Lab, Department of Computer and Information Technology, West Lafayette, IN  47907, United States; Cha, G.-E., Purdue University, Smart Lab, Department of Computer and Information Technology, West Lafayette, IN  47907, United States; Lee, A., Purdue University, Smart Lab, Department of Computer and Information Technology, West Lafayette, IN  47907, United States; Min, B.-C., Purdue University, Smart Lab, Department of Computer and Information Technology, West Lafayette, IN  47907, United States","This paper introduces a new ROSbag-based multimodal affective dataset for emotional and cognitive states generated using the Robot Operating System (ROS). We utilized images and sounds from the International Affective Pictures System (IAPS) and the International Affective Digitized Sounds (IADS) to stimulate targeted emotions (happiness, sadness, anger, fear, surprise, disgust, and neutral), and a dual N-back game to stimulate different levels of cognitive workload. 30 human subjects participated in the user study; their physiological data were collected using the latest commercial wearable sensors, behavioral data were collected using hardware devices such as cameras, and subjective assessments were carried out through questionnaires. All data were stored in single ROSbag files rather than in conventional Comma-Separated Values (CSV) files. This not only ensures synchronization of signals and videos in a data set, but also allows researchers to easily analyze and verify their algorithms by connecting directly to this dataset through ROS. The generated affective dataset consists of 1,602 ROSbag files, and the size of the dataset is about 787GB. The dataset is made publicly available. We expect that our dataset can be a great resource for many researchers in the fields of affective computing, Human-Computer Interaction (HCI), and Human-Robot Interaction (HRI). © 2020 IEEE.",,"Human computer interaction; Surveys; Affective Computing; Cognitive workloads; Hardware devices; Human computer interaction (HCI); Human robot Interaction (HRI); Physiological data; Robot operating systems (ROS); Subjective assessments; Human robot interaction",,,,,"National Science Foundation, NSF: 1846221, IIS-1846221","This material is based upon work supported by the National Science Foundation under Grant No. IIS-1846221.",,,,,,,,,,"Kowalczuk, Z., Czubenko, M., Merta, T., Emotion monitoring system for drivers (2019) IFAC-PapersOnLine, 52 (8), pp. 200-205; Bera, A., Randhavane, T., Prinja, R., Manocha, D., Sociosense: Robot navigation amongst pedestrians with social and psychological constraints (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 7018-7025; Bera, A., Randhavane, T., Prinja, R., Kapsaskis, K., Wang, A., Gray, K., Manocha, D., (2019) The Emotionally Intelligent Robot: Improving Social Navigation in Crowded Environments; Tzirakis, P., Trigeorgis, G., Nicolaou, M.A., Schuller, B.W., Zafeiriou, S., End-to-end multimodal emotion recognition using deep neural networks (2017) IEEE Journal of Selected Topics in Signal Processing, 11 (8), pp. 1301-1309; Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Patras, I., Deap: A database for emotion analysis; Using physiological signals (2011) IEEE Transactions on Affective Computing, 3 (1), pp. 18-31; Abadi, M.K., Subramanian, R., Kia, S.M., Avesani, P., Patras, I., Sebe, N., Decaf: Meg-based multimodal database for decoding affective physiological responses (2015) IEEE Transactions on Affective Computing, 6 (3), pp. 209-222; Shan, C., (2012) Learning Human Emotion from Body Gesture, pp. 1887-1889. , https://doi.org/10.1007/978-1-4419-1428-61905, Boston, MA: Springer US; Correa, J.A.M., Abadi, M.K., Sebe, N., Patras, I., Amigos: A dataset for affect, personality and mood research on individuals and groups (2018) IEEE Transactions on Affective Computing; Subramanian, R., Wache, J., Abadi, M.K., Vieriu, R.L., Winkler, S., Sebe, N., Ascertain: Emotion and personality recognition using commercial sensors (2016) IEEE Transactions on Affective Computing, 9 (2), pp. 147-160; Noroozi, F., Kaminska, D., Corneanu, C., Sapinski, T., Escalera, S., Anbarjafari, G., Survey on emotional body gesture recognition (2018) IEEE Transactions on Affective Computing; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., Ros: An open-source robot operating system (2009) ICRA Workshop on Open Source Software, 3 (3), p. 5. , 2. Kobe, Japan; Santos, O.C., Emotions and personality in adaptive e-learning systems: An affective computing perspective (2016) Emotions and Personality in Personalized Services, pp. 263-285. , Springer; Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., Yang, X., A review of emotion recognition using physiological signals (2018) Sensors, 18 (7), p. 2074; Poria, S., Cambria, E., Bajpai, R., Hussain, A., A review of affective computing: From unimodal analysis to multimodal fusion (2017) Information Fusion, 37, pp. 98-125; Mollahosseini, A., Hasani, B., Mahoor, M.H., Affectnet: A database for facial expression, valence, and arousal computing in the wild (2017) IEEE Transactions on Affective Computing, 10 (1), pp. 18-31; Leventhal, H., Scherer, K., The relationship of emotion to cognition: A functional approach to a semantic controversy (1987) Cognition and Emotion, 1 (1), pp. 3-28; Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., Van Laerhoven, K., Introducing wesad, a multimodal dataset for wearable stress and affect detection (2018) Proceedings of the 20th ACM International Conference on Multimodal Interaction, pp. 400-408; Real-time Physiological signals-e4 Eda/gsr Sensor, , https://e4.empatica.com/e4-wristband; Myo Armband, , https://support.getmyo.com/hc/en-us; Polar h10: Heart Rate Monitor Chest Strap, , https://www.polar.com/us-en/products/accessories/h10heartratesensor; Shimmer3 Gsr Unit, , https://www.shimmersensing.com/products/shimmer3-wireless-gsr-sensor; Intel® Realsense™ Depth and Tracking Cameras, , https://www.intelrealsense.com/; Sánchez-Reolid, R., García, A.S., Vicente-Querol, M.A., Fernández-Aguilar, L., López, M.T., Fernández-Caballero, A., González, P., Artificial neural networks to assess emotional states from braincomputer interface (2018) Electronics, 7 (12), p. 384; Hsu, J.-L., Zhen, Y.-L., Lin, T.-C., Chiu, Y.-S., Affective content analysis of music emotion through EEG (2018) Multimedia Systems, 24 (2), pp. 195-210; Lang, P.J., Bradley, M.M., Cuthbert, B.N., International affective picture system (iaps): Technical manual and affective ratings (1997) NIMH Center for the Study of Emotion and Attention, 1, pp. 39-58; Bradley, M., Lang, P.J., The international affective digitized sounds (iads): Stimuli, instruction manual and affective ratings (1999) NIMH Center for the Study of Emotion and Attention; Hampson, M., Driesen, N.R., Skudlarski, P., Gore, J.C., Constable, R.T., Brain connectivity related to working memory performance (2006) Journal of Neuroscience, 26 (51), pp. 13338-13343; Bradley, M.M., Lang, P.J., Measuring emotion: The selfassessment manikin and the semantic differential (1994) Journal of Behavior Therapy and Experimental Psychiatry, 25 (1), pp. 49-59; Hart, S.G., Staveland, L.E., Development of nasa-tlx (task load index): Results of empirical and theoretical research (1988) Advances in Psychology, 52, pp. 139-183. , Elsevier; Lang, P., Bradley, M., Cuthbert, B., (2008) Technical Report a-8. International Affective Picture System (Iaps): Affective Ratings of Pictures and Instruction Manual (University of Florida, Gainesville, Fl); Bradley, M.M., Lang, P.J., (2007) The International Affective Digitized Sounds (; iads-2): Affective Ratings of Sounds and Instruction Manual, , University of Florida, Gainesville, FL, Tech. Rep. B-3; https://pypi.org/project/fer/",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020","11 October 2020 through 14 October 2020",,165855,1062922X,9781728185262,PICYE,,"English","Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85098872678
"Madjid N.A., Liatsis P.","57215333901;8410551000;","Object Segmentation in Electrical Impedance Tomography for Tactile Sensing",2020,"Proceedings - International Conference on Image Processing, ICIP","2020-October",,"9191036","3050","3054",,1,"10.1109/ICIP40778.2020.9191036","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098635255&doi=10.1109%2fICIP40778.2020.9191036&partnerID=40&md5=206361f1ff1948cfdc5079da1a1210ae","Khalifa University of Science and Technology, Department of Electrical Engineering and Computer Science, Abu Dhabi, United Arab Emirates","Madjid, N.A., Khalifa University of Science and Technology, Department of Electrical Engineering and Computer Science, Abu Dhabi, United Arab Emirates; Liatsis, P., Khalifa University of Science and Technology, Department of Electrical Engineering and Computer Science, Abu Dhabi, United Arab Emirates","Over the last decade, robotics has experienced a rapid increase in research related to human-robot interaction. Developments in artificial skin research can equip robots with tactile sensing in a similar manner to the human sense of touch. This capability will make human-robot communication more natural and safer since an important part of perception indeed relies on tactile sensing. Electrical impedance tomography (EIT)-based sensors are considered as a potentially promising alternative for tactile sensing. These sensors can reconstruct images of the conductivity variation, which appear as a response to the applied pressure. However, due to the ill-posedness of the EIT inverse problem, reconstructed images have low spatial resolution and object boundaries are not preserved. In this research, we explore the hypothesis that performing image segmentation in conjunction with preserving the object boundaries may increase the accuracy of a subsequent classification of the reconstructed images. We compare the quality of EIT images segmented by a splitting and merging segmentation algorithm, Morphological Active Contours without Edges, Random Walker and transfer learning. While the explored classical techniques appear to have predilection towards over-segmentation, the deep learning approach results to a remarkable improvement of approximately 118% in terms of the similarity index. © 2020 IEEE.","Active contours; Electrical Impedance Tomography; Random Walker; segmentation; tactile sensing","Artificial organs; Deep learning; Edge detection; Electric impedance; Electric impedance measurement; Electric impedance tomography; Image reconstruction; Inverse problems; Social robots; Transfer learning; Active contours without edges; Classical techniques; Conductivity variation; Electrical impe dance tomography (EIT); Electrical impedance tomography; Human-robot communication; Segmentation algorithms; Splitting and merging; Image segmentation",,,,,"Khalifa University of Science, Technology and Research, KU: CIRA-2018-59","This research is funded by the Khalifa University of Science and Technology, under grant number CIRA-2018-59.",,,,,,,,,,"Duan, X., Taurand, S., Soleimani, M., Artificial skin through super-sensing method and electrical impedance data from conductive fabric with aid of deep learning (2019) Scientific Reports, 9 (1), p. 12; Zifan, A., Liatsis, P., Chapman, B.E., The use of the kalman filter in the automated segmentation of eit lung images (2013) Physiological Measurement, 34 (6), pp. 671-694. , 6; Dunne, E., Santorelli, A., McGinley, B., Leader, G., O'Halloran, M., Porter, E., Image-based classification of bladder state using electrical impedance tomography (2018) Physiological Measurement, 39 (12), p. 124001; Borcea, L., Electrical impedance tomography (2002) Inverse Problems, 18 (6), p. R99. , 10; Borcea, L., (2002) Electrical Impedance Tomography, p. 12; Hestenes, M.R., Stiefel, E., Methods of conjugate gradients for solving linear systems (1952) Journal of Research of the National Bureau of Standards, 49 (6), p. 409. , 12; Silvera-Tawil, D., Rye, D., Soleimani, M., Velonaki, M., Electrical impedance tomography for artificial sensitive robotic skin: A review (2015) IEEE Sensors Journal, 15 (4), pp. 2001-2016; Golub, G.H., Christian Hansen, P., O'Leary, D.P., Tikhonov regularization and total least squares (1999) SIAM Journal on Matrix Analysis and Applications, 21 (1), pp. 185-194; EIDORS: Software Algorithms for Forward and Inverse Modelling for Electrical Impedance Tomography (EIT) and Diffusion Based Optical Tomography; Grady, L., Random walks for image segmentation (2006) IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (11), pp. 1768-1783; Bezdek, J.C., (1981) Pattern Recognition with Fuzzy Objective Function Algorithms, , Springer US; Chan, T.F., Vese, L.A., Active contours without edges (2001) IEEE Transactions on Image Processing, 10 (2), pp. 266-277; Van Der Walt, S., Schönberger, J.L., Nunez-Iglesias, J., Boulogne, F., Warner, J.D., Yager, N., Gouillart, E., Yu, T., Scikit-image: Image processing in python (2014) PeerJ, 2014 (1), p. e453. , 6; Marquez-Neila, P., Baumela, L., Alvarez, L., A morphological approach to curvature-based evolution of curves and surfaces (2014) IEEE Transactions on Pattern Analysis and Machine Intelligence, 36 (1), pp. 2-17; Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chieh Chen, L., Mobilenetv2: Inverted residuals and linear bottlenecks (2018) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 12, pp. 4510-4520. , IEEE Computer Society; Zijdenbos, A.P., Dawant, B.M., Margolin, R.A., Palmer, A.C., Morphometric analysis of white matter lesions in mr images: Method and validation (1994) IEEE Transactions on Medical Imaging, 13 (4), pp. 716-724; Wang, Y., Liatsis, P., Automatic segmentation of coronary arteries in ct imaging in the presence of kissing vessel artifacts (2012) IEEE Transactions on Information Technology in Biomedicine, 16 (4), pp. 782-788",,,"The Institute of Electrical and Electronics Engineers Signal Processing Society","IEEE Computer Society","2020 IEEE International Conference on Image Processing, ICIP 2020","25 September 2020 through 28 September 2020",,165772,15224880,9781728163956,,,"English","Proc. Int. Conf. Image Process. ICIP",Conference Paper,"Final","",Scopus,2-s2.0-85098635255
"Ficht G., Farazi H., Rodriguez D., Pavlichenko D., Allgeuer P., Brandenburger A., Behnke S.","57197736313;55747109800;57203182781;57197735182;56311266900;57197727040;13007277900;","Nimbro-OP2X: Affordable Adult-Sized 3D-Printed Open-Source Humanoid Robot for Research",2020,"International Journal of Humanoid Robotics","17","5","2050021","","",,,"10.1142/S0219843620500218","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093518583&doi=10.1142%2fS0219843620500218&partnerID=40&md5=1fd4a49d62a78b7b3e771550152cfb28","Institute for Computer Science Vi, University of Bonn, Endenicher Allee 19a, Bonn, 53115, Germany","Ficht, G., Institute for Computer Science Vi, University of Bonn, Endenicher Allee 19a, Bonn, 53115, Germany; Farazi, H., Institute for Computer Science Vi, University of Bonn, Endenicher Allee 19a, Bonn, 53115, Germany; Rodriguez, D., Institute for Computer Science Vi, University of Bonn, Endenicher Allee 19a, Bonn, 53115, Germany; Pavlichenko, D., Institute for Computer Science Vi, University of Bonn, Endenicher Allee 19a, Bonn, 53115, Germany; Allgeuer, P., Institute for Computer Science Vi, University of Bonn, Endenicher Allee 19a, Bonn, 53115, Germany; Brandenburger, A., Institute for Computer Science Vi, University of Bonn, Endenicher Allee 19a, Bonn, 53115, Germany; Behnke, S., Institute for Computer Science Vi, University of Bonn, Endenicher Allee 19a, Bonn, 53115, Germany","For several years, high development and production costs of humanoid robots restricted researchers interested in working in the field. To overcome this problem, several research groups have opted to work with simulated or smaller robots, whose acquisition costs are significantly lower. However, due to scale differences and imperfect simulation replicability, results may not be directly reproducible on real, adult-sized robots. In this paper, we present the NimbRo-OP2X, a capable and affordable adult-sized humanoid platform aiming to significantly lower the entry barrier for humanoid robot research. With a height of 135cm and weight of only 19kg, the robot can interact in an unmodified, human environment without special safety equipment. Modularity in hardware and software allows this platform enough flexibility to operate in different scenarios and applications with minimal effort. The robot is equipped with an on-board computer with GPU, which enables the implementation of state-of-The-Art approaches for object detection and human perception demanded by areas such as manipulation and human-robot interaction. Finally, the capabilities of the NimbRo-OP2X, especially in terms of locomotion stability and visual perception, are evaluated. This includes the performance at RoboCup 2018, where NimbRo-OP2X won all possible awards in the AdultSize class. © 2020 World Scientific Publishing Company.","3D-printed; bipedal gait; hardware design; Humanoid robot; visual perception","3D printers; Agricultural robots; Anthropomorphic robots; Application programs; Human computer interaction; Object detection; Open source software; Acquisition costs; Hardware and software; Human environment; Locomotion stability; Onboard computers; Safety equipments; State-of-the-art approach; Visual perception; Human robot interaction",,,,,"Deutsche Forschungsgemeinschaft, DFG","This work was partially funded by grant BE 2556/13 of the German Research Foundation (DFG).",,,,,,,,,,"Kato, I., Development of WABOT 1 (1973) Biomechanism, 2, pp. 173-214; Hirai, K., Hirose, M., Haikawa, Y., Takenaka, T., The Development of Honda Humanoid Robot (1998) IEEE Int. Conf. Robotics and Automation (ICRA), pp. 1321-1326. , (IEEE Press, Leuven, Belgium); Sakagami, Y., Watanabe, R., Aoyama, C., Matsunaga, S., Higaki, N., Fujimura, K., The intelligent ASIMO: System overview and integration (2002) IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), pp. 2478-2483. , (IEEE Press, Lausanne, Switzerland); Yokoi, K., Kanehiro, F., Kaneko, K., Kajita, S., Fujiwara, K., Hirukawa, H., Experimental study of humanoid robot HRP-1S (2004) Robot. Res, 23 (4-5); Hirukawa, H., Humanoid robotics platforms developed in HRP (2004) Robot. Autonom. Syst, 48 (4), pp. 165-175; Kaneko, K., Harada, K., Kanehiro, F., Miyamori, G., Akachi, K., Humanoid robot HRP-3 (2008) IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), pp. 2471-2478. , (IEEE Press, Nice, France); Kaneko, K., Kanehiro, F., Morisawa, M., Akachi, K., Miyamori, G., Hayashi, A., Kanehira, N., Humanoid robot HRP-4 humanoid robotics platform with lightweight and slim body (2011) IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), pp. 4400-4407. , (IEEE Press, San Francisco, California, USA); Kaneko, K., Kanehiro, F., Morisawa, M., Miura, K., Nakaoka, S., Kajita, S., Cybernetic Human HRP-4C (2009) IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), pp. 7-14. , (IEEE Press, Paris, France); Englsberger, J., Overview of the torque-controlled humanoid robot TORO (2014) IEEERAS Int. Conf. on Humanoid Robots (Humanoids), pp. 916-923. , (IEEE Press, Madrid, Spain); Radford, N., Valkyrie: NASA's first bipedal humanoid robot (2015) Field Robot, 32 (3), pp. 397-419; Stasse, O., TALOS: A new humanoid research platform targeted for industrial applications (2017) IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), pp. 689-695. , (IEEE Press, Birmingham, UK); Schwarz, M., Schreiber, M., Schueller, S., Missura, M., Behnke, S., NimbRo-OP Humanoid TeenSize Open Platform (2012) Workshop on Humanoid Soccer Robots, IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), , (IEEE Press, Osaka, Japan); Allgeuer, P., Farazi, H., Schreiber, M., Behnke, S., Child-sized 3D Printed igus Humanoid Open Platform (2015) IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), pp. 33-40. , (IEEE Press, Seoul, Korea); Ficht, G., Allgeuer, P., Farazi, H., Behnke, S., NimbRo-OP2: Grown-up 3D printed open humanoid platform for research (2017) IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), pp. 669-675. , (IEEE Press, Birmingham, UK); Ficht, G., Pavlichenko, D., Allgeuer, P., Farazi, H., Rodriguez, D., brandenburger, A., Kuersch, J., Behnke, S., Grown-up NimbRo robots winning RoboCup 2017 Humanoid AdultSize soccer competitions (2017) RoboCup 2017: Robot World Cup XXI, pp. 448-460. , (Springer); Ficht, G., Farazi, H., brandenburger, A., Rodriguez, D., Pavlichenko, D., Allgeuer, P., Hosseini, M., Behnke, S., NimbRo-OP2X: Adult-sized open-source 3D printed humanoid robot (2018) IEEE-RAS Int. Conf. on Humanoid Robots (Humanoids), pp. 1-9. , (IEEE Press, Beijing, China); NimbRo-OP2(X) Hardware CAD Data, , https://github.com/NimbRo/nimbroop2, NimbRo, (Sep, 2017); igus Humanoid Open Platform ROS Software, , https://github.com/AIS-Bonn/humanoidopros, NimbRo, (Oct, 2015); Gopinath, K., Mayuram, M., (2010) Lecture notes in Machine Design, , (Indian Institute of Technology Madras); Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheelr, R., Ng, A., ROS: An open-source Robot Operating System (2009) Workshop on Open Source Software, IEEE Int. Conf. Robotics and Automation (ICRA), , (IEEE Press, Kobe, Japan); Schwarz, M., Pastrana, J., Allgeuer, P., Schreiber, M., Schueller, S., Missura, M., Behnke, S., Grown-up NimbRo robots winning RoboCup 2017 Humanoid AdultSize soccer competitions (2013) RoboCup 2013: Robot World Cup XVII, pp. 568-575. , (Springer); Chen, X., Zhang, H., Lu, H., Xiao, J., Wang, P., Qiu, Q., Yan, R., Li, Y., (2017) RoboCup Rescue Team Description Paper NuBot, , University of Newcastle; Biddulph, A., Houliston, T., Mendes, A., Chalup, S., Comparing computing platforms for deep learning on a humanoid robot (2018) Int. 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"Barchard K.A., Lapping-Carr L., Westfall R.S., Fink-Armold A., Banisetty S.B., Feil-Seifer D.","6603132752;56041830300;56900966900;57217487137;57195150511;14033850500;","Measuring the perceived social intelligence of robots",2020,"ACM Transactions on Human-Robot Interaction","9","4","24","","",,5,"10.1145/3415139","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092724959&doi=10.1145%2f3415139&partnerID=40&md5=2756a6b588510e50b57f3d42553c1aa3","Department of Psychology, University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, NV  89154-5030, United States; Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, 446 E Ontario St, 1576, Chicago, IL  60611, United States; Psychology Department, Western Wyoming College, 2500 College Drive, Rock Spring, WY  82901, United States; Department of Computer Science and Engineering, University of Nevada Reno, Mail Stop 0171, 1664 N Virginia St, Reno, NV  89557, United States","Barchard, K.A., Department of Psychology, University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, NV  89154-5030, United States; Lapping-Carr, L., Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, 446 E Ontario St, 1576, Chicago, IL  60611, United States; Westfall, R.S., Psychology Department, Western Wyoming College, 2500 College Drive, Rock Spring, WY  82901, United States; Fink-Armold, A., Department of Psychology, University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, NV  89154-5030, United States; Banisetty, S.B., Department of Computer Science and Engineering, University of Nevada Reno, Mail Stop 0171, 1664 N Virginia St, Reno, NV  89557, United States; Feil-Seifer, D., Department of Computer Science and Engineering, University of Nevada Reno, Mail Stop 0171, 1664 N Virginia St, Reno, NV  89557, United States","Robotic social intelligence is increasingly important. However, measures of human social intelligence omit basic skills, and robot-specific scales do not focus on social intelligence. We combined human robot interaction concepts of beliefs, desires, and intentions with psychology concepts of behaviors, cognitions, and emotions to create 20 Perceived Social Intelligence (PSI) Scales to comprehensively measure perceptions of robots with a wide range of embodiments and behaviors. Participants rated humanoid and non-humanoid robots interacting with people in five videos. Each scale had one factor and high internal consistency, indicating each measures a coherent construct. Scales capturing perceived social information processing skills (appearing to recognize, adapt to, and predict behaviors, cognitions, and emotions) and scales capturing perceived skills for identifying people (appearing to identify humans, individuals, and groups) correlated strongly with social competence and constituted the Mind and Behavior factors. Social presentation scales (appearing friendly, caring, helpful, trustworthy, and not rude, conceited, or hostile) relate more to Social Response to Robots Scales and Godspeed Indices, form a separate factor, and predict positive feelings about robots and wanting social interaction with them. For a comprehensive measure, researchers can use all PSI 20 scales for free. Alternatively, they can select the most relevant scales for their projects. © 2020 ACM.","human-computer interaction; human-robot interaction; Social intelligence; socially assistive robotics","Agricultural robots; Anthropomorphic robots; Behavioral research; Economic and social effects; Intelligent robots; Behavior factor; Humanoid robot; Internal consistency; Positive feelings; Social competences; Social information processing; Social intelligence; Social interactions; Social robots",,,,,"P20GM103650; National Science Foundation, NSF: IIS-1719027; Nevada Space Grant Consortium, NVSGC: NNX15AK48A","The authors thank the Nevada Space Grant Consortium (NNX15AK48A), the National Institute for Health (P20GM103650), and the National Science Foundation (IIS-1719027) for their support of this research. Authors’ addresses: K. A. Barchard and A. Fink-Armold, Department of Psychology, University of Nevada, Las Vegas, 4505 Maryland Parkway, Las Vegas, NV, 89154-5030; emails: {kim.barchard, andrea.fink-armold}@unlv.edu; L. Lapping-Carr, Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, 446 E Ontario St, 1576, Chicago, IL 60611; email: leiszle.lapping-carr@northwestern.edu; R. S. Westfall, Psychology Department, Western Wyoming College, 2500 College Drive, Rock Spring, WY, 82901; email: rwestfall@westernwyoming.edu; S. B. Banisetty and D. Feil-Seifer, Department of Computer Science & Engineering, University of Nevada Reno, Mail Stop 0171, 1664 N Virginia St, Reno, NV 89557; emails: santoshbanisetty@nevada.unr.edu, dave@cse.unr.edu.",,,,,,,,,,"Agran, M., Hughes, C., Thoma, C.A., Scott, L.A., (2016) Social Skills Survey, , http://dx.doi.org/10.1037/t50828-000, PsycTests; Albrecht, K., (2004) Social Intelligence: The New Science of Success, , Pfeiffer, San Francisco, CA; Barchard, K.A., Lapping-Carr, L., Westfall, R.S., Banisetty, S.B., Feil-Seifer, D., (2018) Perceived Social Intelligence (PSI). Scales Test Manual, , https://ipip.ori.org/newMultipleconstructs.htm, Unpublished psychological test and test manual. 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"Li H.-Y., Yang L., Tan U.-X.","57191343038;57211687537;22235331000;","A Control Scheme for Smooth Transition in Physical Human-Robot-Environment between Two Modes: Augmentation and Autonomous",2020,"IEEE Robotics and Automation Letters","5","4","9144376","5803","5810",,1,"10.1109/LRA.2020.3010450","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089913439&doi=10.1109%2fLRA.2020.3010450&partnerID=40&md5=7486cc8ad535640193c034fb1ed8d9d0","Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore; Zhejiang University, University of Illinois at Urbana-Champaign Institute, Haining, China","Li, H.-Y., Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore; Yang, L., Zhejiang University, University of Illinois at Urbana-Champaign Institute, Haining, China; Tan, U.-X., Pillar of Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore","There has been an increasing demand for physical human-robot collaboration during the design prototyping phase. For example, users would like to maneuver the end-effector compliantly in free space followed by supplying a contact force to obtain a firm adhesive connection. The technical challenges is the design of the controller, especially during the switching from human-robot interaction (human guides robot) to robot-environment interaction for the robot to continuously maintain the contact force even after the human lets go. Traditional controllers often result in unstable interaction during the switches of the controllers. Therefore, this letter proposes a control scheme that unifies impedance and admittance in the outer loop, and unifies the adaptive position and velocity control in the inner loop to address this issue. The cooperation of the cobot is divided into two modes, namely, an augmentation mode where the human force is the desired input to guide the motion of the cobot, and an autonomous mode where predefined position and force commands are used (e.g., to maintain a desired holding force). With the proposed control scheme, the physical interaction between the robot, human and environment can be smoothly and stably transited from augmentation mode to autonomous mode. Experiments are then conducted to validate the proposed approach. © 2016 IEEE.","adaptive position/velocity control; Human-robot collaboration; impedance/admittance control","Adhesives; Agricultural robots; Controllers; Machine design; Autonomous mode; Human-robot collaboration; Physical interactions; Position and velocity control; Pre-defined position; Robot-environment interaction; Smooth transitions; Technical challenges; Social robots",,,,,,,,,,,,,,,,"Landi, C.T., Ferraguti, F., Sabattini, L., Secchi, C., Fantuzzi, C., Admittance control parameter adaptation for physical human-robot interaction (2017) Proc. Ieee Int. Conf. Robot. Autom., pp. 2911-2916; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) Ieee Trans. Robot., 31 (4), pp. 850-863. , Aug; Krüger, J., Surdilovic, D., Robust control of force-coupled humanrobot-interaction in assembly processes (2008) Cirp Ann. Manuf. 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Mechatron., 22 (3), pp. 1401-1411. , Jun; Labrecque, P.D., Gosselin, C., Variable admittance for pHRI: From intuitive unilateral interaction to optimal bilateral force amplification (2018) Robot. Comput. Integr. Manuf., 52 (2018), pp. 1-8; Li, H., Stable and compliant motion of physical human-robot interaction coupledWith a moving environment using variable admittance and adaptive control (2018) Ieee Robot. Autom. Lett., 3 (3), pp. 2493-2500. , Jul; Li, H.-Y., Towards to a robotic assisted system for percutaneous nephrolithotomy (2018) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 791-797; Hogan, N., Impedance control: An approach to manipulation: Part I-Theory (1985) J. Dyn. Syst., Meas., Control, 107, pp. 1-7; Li, Y., Ge, S.S., Impedance learning for robots interacting with unknownenvironments (2014) Ieee Trans.Control Syst. Technol., 22 (4), pp. 1422-1432. , Jul; Jung, S., Hsia, T.C., Bonitz, R.G., Force tracking impedance control of robot manipulators under unknown environment (2004) Ieee Trans. Control Syst. Technol., 12 (3), pp. 474-483. , May; Tarn, T.-J., Wu, Y., Xi, N., Isidori, A., Force regulation and contact transition control (1996) Ieee Control Syst. Mag., 16 (1), pp. 32-40. , Feb; Ott, C., Mukherjee, R., Nakamura, Y., Unified impedance and admittance Control (2010) Proc. Ieee Int. Conf. Robot. Autom., pp. 554-561; Wu, J., Smooth transition adaptive hybrid impedance control for connector assembly (2018) Ind. Robot: An Int. J., 45 (2), pp. 287-299; Raibert, M.H., Craig, J.J., Hybrid position/force control of manipulators (1981) J. Dyn. Syst. Meas. Control, 103 (2), pp. 126-133; Li, H.-Y., A control scheme for physical human-robot interaction coupled with an environment with unknown stiffness (2020) J. Intell. Robot. Syst., , Mar; Li, X., Chi, G., Vidas, S., Cheah, C.C., Human-guided robotic comanipulation: Two illustrative scenarios (2016) Ieee Trans.Control Syst. Technol., 24 (5), pp. 1751-1763. , Sep; Zhang, J., Cheah, C.C., Passivity and stability of human-robot interaction control for upper-limb rehabilitation robots (2015) Ieee Trans. Robot., 31 (2), pp. 233-245. , Apr; Luca, A.D., Albu-Schaffer, A., Haddadin, S., Hirzinger, G., Collision detection and safe reactionwith theDLR-III lightweight manipulator arm (2006) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 1623-1630; Cheah, C.-C., Wang, D., Learning impedance control for robotic manipulators (1998) Ieee Trans. Robot. Autom., 14 (3), pp. 452-465. , Jun; Byrnes, C.I., Isidori, A., Willems, J.C., Passivity, feedback equivalence, and the global stabilization of minimum phase nonlinear systems (1991) Ieee Trans. Autom. 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Robot., 30 (2), pp. 493-506. , Apr; Li, X., Liu, Y.-H., Yu, H., Iterative learning impedance control for rehabilitation robots driven by series elastic actuators (2018) Automatica, 90, pp. 1-7; Garofalo, G., Mansfeld, N., Jankowski, J., Ott, C., Slidingmode momentum observers for estimation of external torques and joint acceleration (2019) Proc. Ieee Int. Conf. Robot. Autom., pp. 6117-6123; Jinno, M., Development of a force controlled robot for grinding, chamfering and polishing (1995) Proc. Ieee Int. Conf. Robot. Autom., 2, pp. 1455-1460; Losey, D.P., Erwin, A., McDonald, C.G., Sergi, F., O'Malley, M.K., A time-domain approach to control of series elastic actuators: Adaptive torque and passivity-based impedance control (2016) IEEE/ASME Trans. Mechatronics, 21 (4), pp. 2085-2096. , Aug; Karami, A., Sadeghian, H., Keshmiri, M., Oriolo, G., Hierarchical tracking task control in redundant manipulators with compliance control in the null-space (2018) Mechatronics, 55, pp. 171-179","Li, H.-Y.; Pillar of Engineering Product Development, Singapore; email: hsiehyu_li@sutd.edu.sg",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85089913439
"Bilancia P., Berselli G., Palli G.","57195804520;24483038000;23009761100;","Virtual and physical prototyping of a beam-based variable stiffness actuator for safe human-machine interaction",2020,"Robotics and Computer-Integrated Manufacturing","65",,"101886","","",,11,"10.1016/j.rcim.2019.101886","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083508500&doi=10.1016%2fj.rcim.2019.101886&partnerID=40&md5=6ef9dc32405b1246a6ce0114f3fad173","Dept. of Mechanical, Energy, Management and Transportation Engineering, University of Genova, Genova, 16145, Italy; Dept. of Electrical Electronic and Information Engineering ”Guglielmo Marconi”, University of Bologna, Bologna, 40136, Italy","Bilancia, P., Dept. of Mechanical, Energy, Management and Transportation Engineering, University of Genova, Genova, 16145, Italy; Berselli, G., Dept. of Mechanical, Energy, Management and Transportation Engineering, University of Genova, Genova, 16145, Italy; Palli, G., Dept. of Electrical Electronic and Information Engineering ”Guglielmo Marconi”, University of Bologna, Bologna, 40136, Italy","This paper reports about the virtual and physical prototyping of an antagonistic Variable Stiffness Actuator (VSA) to be used on robotic arms specifically realized for physical human-robot interaction. Such antagonistic actuation system, which comprises purposely conceived Compliant Transmission Elements (CTEs) characterized by a nonlinear relation between the deflection and the applied torque, allows to simultaneously control both the joint's position and stiffness. The CTE's beams geometry, namely slender spline beams, has been defined by means of an automatic routine leveraging on Matlab and ANSYS and allowing for the shape optimization of complex flexures. The synthesized springs are characterized by a predefined quadratic torque-deflection characteristic, which is shown to guarantee a precise stiffness modulation while avoiding the need for a joint's position sensor. After shape optimization, the CTE is fabricated via additive manufacturing and subsequently tested. The acquired data show a very good consistency with the numerical results, although highlighting a non-negligible hysteresis due to material damping. Therefore, in order to cope with such unavoidable effect along with other parameter uncertainties and unmodeled effects (e.g. static friction), a robust feedback controller is proposed, allowing for the simultaneous and decoupled regulation of joint position and stiffness. Finally, a VSA prototype is produced and tested. Experimental results confirm that the VSA behaves as expected. © 2019 Elsevier Ltd","Compliant mechanisms; Multi software framework; Shape optimization; Sliding mode controller; Variable stiffness actuators","Actuators; Agricultural robots; Man machine systems; Shape optimization; Stiction; Stiffness; Virtual prototyping; Human machine interaction; Nonlinear relations; Parameter uncertainty; Physical human-robot interactions; Physical prototyping; Robust feedback controllers; Stiffness modulation; Variable stiffness; Human robot interaction",,,,,"Università degli Studi di Genova","The research has received funding from University of Genova grant - COSMET, COmpliant Shel-based mechanisms for MEdical Technologies.",,,,,,,,,,"Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human–robot collaboration in industrial settings: safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266; Heyer, C., Human-robot interaction and future industrial robotics applications (2010) IEEE IROS International Conference on Intelligent Robots and Systems, pp. 4749-4754; Brogårdh, T., Present and future robot control development-an industrial perspective (2007) Annu. 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"Schürmann T., Beckerle P.","56019085000;36964076200;","Personalizing Human-Agent Interaction Through Cognitive Models",2020,"Frontiers in Psychology","11",,"561510","","",,2,"10.3389/fpsyg.2020.561510","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092539050&doi=10.3389%2ffpsyg.2020.561510&partnerID=40&md5=196e13f05cf03e0f25e471b6e34faa37","Work and Engineering Psychology Research Group, Department of Human Sciences, Technical University of Darmstadt, Darmstadt, Germany; Elastic Lightweight Robotics, Department of Electrical Engineering and Information Technology, Robotics Research Institute, Technische Universität Dortmund, Dortmund, Germany; Institute for Mechatronic Systems, Mechanical Engineering, Technical University of Darmstadt, Darmstadt, Germany","Schürmann, T., Work and Engineering Psychology Research Group, Department of Human Sciences, Technical University of Darmstadt, Darmstadt, Germany; Beckerle, P., Elastic Lightweight Robotics, Department of Electrical Engineering and Information Technology, Robotics Research Institute, Technische Universität Dortmund, Dortmund, Germany, Institute for Mechatronic Systems, Mechanical Engineering, Technical University of Darmstadt, Darmstadt, Germany","Cognitive modeling of human behavior has advanced the understanding of underlying processes in several domains of psychology and cognitive science. In this article, we outline how we expect cognitive modeling to improve comprehension of individual cognitive processes in human-agent interaction and, particularly, human-robot interaction (HRI). We argue that cognitive models offer advantages compared to data-analytical models, specifically for research questions with expressed interest in theories of cognitive functions. However, the implementation of cognitive models is arguably more complex than common statistical procedures. Additionally, cognitive modeling paradigms typically have an explicit commitment to an underlying computational theory. We propose a conceptual framework for designing cognitive models that aims to identify whether the use of cognitive modeling is applicable to a given research question. The framework consists of five external and internal aspects related to the modeling process: research question, level of analysis, modeling paradigms, computational properties, and iterative model development. In addition to deriving our framework from a concise literature analysis, we discuss challenges and potentials of cognitive modeling. We expect cognitive models to leverage personalized human behavior prediction, agent behavior generation, and interaction pretraining as well as adaptation, which we outline with application examples from personalized HRI. © Copyright © 2020 Schürmann and Beckerle.","behavior prediction/generation; cognitive modeling; human-agent interaction; interaction adaption; personalization",,,,,,"Deutsche Forschungsgemeinschaft, DFG; Technische Universität Dortmund, TUDO","Funding. We acknowledge financial support by Deutsche Forschungsgemeinschaft and Technische Universit?t Dortmund/TU Dortmund University within the funding programme Open Access Publishing.","We acknowledge financial support by Deutsche Forschungsgemeinschaft and Technische Universität Dortmund/ TU Dortmund University within the funding programme Open Access Publishing.",,,,,,,,,"Adams, W.J., A common light-prior for visual search, shape, and reflectance judgments (2007) J. Vis, 7, p. 11. , 17997666, :,, PMID; Addyman, C., French, R.M., Computational modeling in cognitive science: a manifesto for change (2012) Top. Cogn. Sci, 4, pp. 332-341. , 22711233, PMID; Alatalo, T., Siponen, M.T., (2001), Addressing the personalization paradox the development of electronic commerce systems. Tampere, Finland; Asada, M., Hosoda, K., Kuniyoshi, Y., Ishiguro, H., Inui, T., Yoshikawa, Y., Cognitive developmental robotics: a survey (2009) IEEE Trans. Auton. Ment. 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Beckerle, P.; Institute for Mechatronic Systems, Germany; email: philipp.beckerle@tu-dortmund.de",,,"Frontiers Media S.A.",,,,,16641078,,,,"English","Front. Psychol.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85092539050
"Li S., Ni S., Peng K.","57211287193;55908625400;57031295600;","The Role of Good Human Uniqueness in Social Robot Anthropomorphism Influenced by Chronic Loneliness",2020,"Proceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020",,,"9209343","","",,1,"10.1109/ICHMS49158.2020.9209343","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093982925&doi=10.1109%2fICHMS49158.2020.9209343&partnerID=40&md5=11ab35a6c27023e2e57ab9898724974c","Tsinghua-Berkeley Shenzhen Institute Tsinghua University, Shenzhen, China; Tsinghua Shenzhen International Graduate School Tsinghua University, Department of Social Science and Management, Shenzhen, China; Tsinghua University, Department of Psychology, Beijing, China","Li, S., Tsinghua-Berkeley Shenzhen Institute Tsinghua University, Shenzhen, China; Ni, S., Tsinghua Shenzhen International Graduate School Tsinghua University, Department of Social Science and Management, Shenzhen, China; Peng, K., Tsinghua University, Department of Psychology, Beijing, China","Social robot companion will become pervasive in the near future as a result of technological development and social work robotization to confront long-Time loneliness. Chronic loneliness is not only a painful individual experience but an increasingly serious social problem. It is thus crucial to drill down the relationship between people's chronic loneliness and how they see humans in this artificial intelligent agent. Based on the designed human-robot interactions, we measured participants' anthropomorphic tendency on our social robot prototype as well as their chronic loneliness, and found that chronic loneliness-permanent disposition caused by person, not easily relieved-deterred participants from making anthropomorphic inference of the robot (i.e., to perceive human essence, warmth and competence, from the robot). In particular, mediation analysis revealed that reduced attribution of positive human uniqueness traits (i.e., Humble, Thorough, Organized, Broadminded, and Polite) to the robot explained the negative relation between being chronically lonely and anthropomorphizing the robot. We further corroborated this result by enabling the robot to conversationally manifest the five traits and successfully verified a substantial reduction in the negative effect of chronic loneliness on robot anthropomorphism. The findings suggest good human uniqueness, exemplifying secondary emotions, is vital not only in interpersonal contexts but in establishing connections with social robots. © 2020 IEEE.","chronic loneliness; human uniqueness; robot anthropomorphism; social robot","Anthropomorphic robots; Drilling machines (machine tools); Economic and social effects; Man machine systems; Artificial intelligent; Mediation analysis; Robot companion; Robot prototypes; Social problems; Social work; Substantial reduction; Technological development; Social robots",,,,,,,,,,,,,,,,"Hegel, F., Muhl, C., Wrede, B., Hielscher-Fastabend, M., Sagerer, G., Understanding Social Robots (2009) 2009 Second International Conferences on Advances in Computer-Human Interactions, pp. 169-174. , Cancun, Mexico; Darling, K., Extending Legal Rights to Social Robots (2012) SSRN Electron. J; Campa, R., The rise of social robots: A review of the recent literature (2016) J. Evol. 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Neurosci, 9 (9), pp. 1276-1280; Li, M., Leidner, B., Castano, E., Toward a comprehensive taxonomy of dehumanization: Integrating two senses of humanness, mind perception theory, and stereotype content model (2014) Test. Psychom. Methodol. Appl. Psychol, (3), pp. 285-300",,"Fortino G.Wang F.-Y.Nurnberger A.Kaber D.Falcone R.Mendonca D.Yu Z.Guerrieri A.",,"Institute of Electrical and Electronics Engineers Inc.","1st IEEE International Conference on Human-Machine Systems, ICHMS 2020","7 September 2020 through 9 September 2020",,163564,,9781728158716,,,"English","Proc. IEEE Int. Conf. Hum.-Mach. Syst., ICHMS",Conference Paper,"Final","",Scopus,2-s2.0-85093982925
"Laudante E., Greco A., Caterino M., Fera M.","57197770710;57192181305;57209547668;23484700200;","Human-robot interaction for improving fuselage assembly tasks: A case study",2020,"Applied Sciences (Switzerland)","10","17","5757","","",,6,"10.3390/app10175757","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090577773&doi=10.3390%2fapp10175757&partnerID=40&md5=8c57f4dfb572ec23036720f8370e9071","Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma, Aversa, 29-81031, Italy","Laudante, E., Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma, Aversa, 29-81031, Italy; Greco, A., Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma, Aversa, 29-81031, Italy; Caterino, M., Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma, Aversa, 29-81031, Italy; Fera, M., Department of Engineering, University of Campania Luigi Vanvitelli, Via Roma, Aversa, 29-81031, Italy","In current industrial systems, automation is a very important aspect for assessing manufacturing production performance related to working times, accuracy of operations and quality. In particular, the introduction of a robotic system in the working area should guarantee some improvements, such as risks reduction for human operators, better quality results and a speed increase for production processes. In this context, human action remains still necessary to carry out part of the subtasks, as in the case of composites assembly processes. This study aims at presenting a case study regarding the reorganization of the working activity carried out in workstation in which a composite fuselage panel is assembled in order to demonstrate, by means of simulation tool, that some of the advantages previously listed can be achieved also in aerospace industry. In particular, an entire working process for composite fuselage panel assembling will be simulated and analyzed in order to demonstrate and verify the applicability and effectiveness of human-robot interaction (HRI), focusing on working times and ergonomics and respecting the constraints imposed by standards ISO 10218 and ISO TS 15066. Results show the effectiveness of HRI both in terms of assembly performance, by reducing working times and ergonomics-for which the simulation provides a very low risk index. © 2020 by the authors.","Aerospace production; Ergonomics; Human-robot interaction; Simulation",,,,,,"785419; European Commission, EC","Acknowledgments: This work was supported by the European Commission under the H2020 Framework Clean Sky JTI–project LABOR, ID no. 785419, topic manager: Leonardo SpA.","This work was supported by the European Commission under the H2020 Framework Clean Sky JTI-project LABOR, ID no. 785419, topic manager: Leonardo SpA.",,,,,,,,,"Burgess-Limerick, R., Steiner, L.J., Torma-Krajewsky, J., (2009) Ergonomics Processes: Implementation Guide and Tools for the Mining Industry, , US Department of Health and Human Services: Washington, DC, USA; Centers for Disease Control and Prevention: Atlanta, GA, USA; National Institute for Occupational Safety and Health: Washington, DC, USA; DHHS (NIOSH): Pittsburg, PA, USA; Seri Rahayu, K., Norhidayah, H., Shahrizan, O.M., A Study on Muscle Fatigue Associated with Awkward Posture among Workers in Aerospace Industry (2013) Adv. 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"Adiyatov O., Rakhim B., Zhakatayev A., Varol H.A.","55933930800;57188876842;57155301300;24484134500;","Sensor reduction of variable stiffness actuated robots using moving horizon estimation",2020,"IEEE Transactions on Control Systems Technology","28","5","8759937","1757","1769",,2,"10.1109/TCST.2019.2924601","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089818495&doi=10.1109%2fTCST.2019.2924601&partnerID=40&md5=577edbf22be19e0186cc8e434ed9ba12","Department of Robotics and Mechatronics, Nazarbayev University, Astana, Z05H0P9, Kazakhstan","Adiyatov, O., Department of Robotics and Mechatronics, Nazarbayev University, Astana, Z05H0P9, Kazakhstan; Rakhim, B., Department of Robotics and Mechatronics, Nazarbayev University, Astana, Z05H0P9, Kazakhstan; Zhakatayev, A., Department of Robotics and Mechatronics, Nazarbayev University, Astana, Z05H0P9, Kazakhstan; Varol, H.A., Department of Robotics and Mechatronics, Nazarbayev University, Astana, Z05H0P9, Kazakhstan","Variable stiffness actuated (VSA) robots are expected to play an important role in physical human-robot interaction, thanks to their inherent safety features. These systems can control the position and stiffness concurrently by incorporating two or more actuators for each joint. Unfortunately, the need for extra sensors to measure the state of these actuators decreases the reliability of these systems. In this paper, we present a sensor reduction scheme for VSA robots. Specifically, we utilize moving horizon estimation (MHE) to estimate the unmeasured states of the system. Due to its ability to handle constraints, MHE is chosen as the estimation algorithm. The estimated states are then used by a nonlinear model predictive controller to implement a closed-loop control system. In order to show the efficacy of our framework, we conducted extensive simulation and real-world experiments with a reaction wheel augmented VSA system. The objective of these experiments was to compare the control performance of the sensor reduced system (from four encoders to two encoders) with the system using the full set of states for control. The results of these experiments show the feasibility of the MHE-based sensor reduction. Sensor reduction might increase the reliability of VSA robots and might facilitate their earlier introduction to the industrial environments. © 1993-2012 IEEE.","Model predictive control (MPC); moving horizon estimation (MHE); sensor reduction; variable stiffness actuation (VSA)","Actuators; Closed loop control systems; Man machine systems; Signal encoding; Stiffness; Control performance; Estimation algorithm; Extensive simulations; Industrial environments; Moving horizon estimation; Physical human-robot interactions; Real world experiment; Variable stiffness; Human robot interaction",,,,,"Ministry of Education and Science of the Republic of Kazakhstan; Nazarbayev University, NU","Manuscript received October 2, 2018; revised March 22, 2019; accepted June 12, 2019. Date of publication July 11, 2019; date of current version August 6, 2020. Manuscript received in final form June 18, 2019. This work was supported in part by the Grant “Methods for Safe Human Robot Interaction with VIA Robots” from the Ministry of Education and Science of the Republic of Kazakhstan and in part by the Nazarbayev University Faculty Development Program Grant “Hardware and Software-Based Methods for Safe Human-Robot Interaction with Variable Impedance Robots.” Recommended by Associate Editor S. Di Cairano. (Corresponding author: Huseyin Atakan Varol.) The authors are with the Department of Robotics and Mechatronics, Nazarbayev University, Astana Z05H0P9, Kazakhstan (e-mail: oadiyatov@nu.edu.kz; bexultan.rakhim@nu.edu.kz; azhakatayev@nu.edu.kz; ahvarol@nu.edu.kz).",,,,,,,,,,"Kalman, R.E., A new approach to linear filtering and prediction problems (1960) Trans. ASME, D, J. Basic Eng., 82, pp. 35-45; Bar-Shalom, Y., Li, X.-R., (1993) Estimation and Tracking: Principles, , Techniques, and Software. 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"Hussein A., Elsawah S., Abbass H.A.","57204830026;36682211400;6701824380;","The reliability and transparency bases of trust in human-swarm interaction: principles and implications",2020,"Ergonomics","63","9",,"1116","1132",,4,"10.1080/00140139.2020.1764112","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084816702&doi=10.1080%2f00140139.2020.1764112&partnerID=40&md5=36b16d0e16bf36d2633d41eca7b593c1","School of Engineering and Information Technology, University of New South Wales, Canberra, Australia","Hussein, A., School of Engineering and Information Technology, University of New South Wales, Canberra, Australia; Elsawah, S., School of Engineering and Information Technology, University of New South Wales, Canberra, Australia; Abbass, H.A., School of Engineering and Information Technology, University of New South Wales, Canberra, Australia","Automation reliability and transparency are key factors for trust calibration and as such can have distinct effects on human reliance behaviour and mission performance. One question that remains unexplored is: what are the implications of reliability and transparency on trust calibration for human-swarm interaction? We investigate this research question in the context of human-swarm interaction, as swarm systems are becoming more popular for their robustness and versatility. Thirty-two participants performed swarm-based tasks under different reliability and transparency conditions. The results indicate that trust, whether it is reliability- or transparency-based, indicates high reliance rates and shorter response times. Reliability-based trust is negatively correlated with correct rejection rates while transparency-based trust is positively correlated with these rates. We conclude that reliability and transparency have distinct effects on trust calibration. Practitioner Summary: Reliability and transparency have distinct effects on trust calibration. Findings from our human experiments suggest that transparency is a necessary design requirement if and when humans need to be involved in the decision-loop of human-swarm systems, especially when swarm reliability is high. Abbreviations: HRI: human-robot interaction; IOS: inter-organisational systems; LMM: liner mixed models; MANOVA: multivariate analysis of variance; UxV: heterogeneous unmanned vehicles; UAV: unmanned aerial vehicle. © 2020 Informa UK Limited, trading as Taylor & Francis Group.","Complacency; human-swarm teaming; reliability; reliance","Antennas; Behavioral research; Calibration; Multivariant analysis; Reliability; Transparency; Unmanned aerial vehicles (UAV); Automation reliabilities; Inter-organisational systems; Mission performance; Multivariate analysis of variances; Rejection rates; Reliability-based; Research questions; Transparency conditions; Human robot interaction; adult; article; calibration; clinical article; controlled study; female; human; human experiment; male; multivariate analysis of variance; physician; reaction time; reliability; robotics; trust; unmanned aerial vehicle; automation; computer interface; man machine interaction; reproducibility; task performance; Automation; Humans; Man-Machine Systems; Reproducibility of Results; Task Performance and Analysis; Trust; User-Computer Interface",,,,,"Australian Research Council, ARC: DP160102037","This work was funded by the Australian Research Council Discovery Grant number [DP160102037].",,,,,,,,,,"Adams, J.A., Chen, J.Y., Goodrich, M.A., (2018), and,. 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HRI, 408–416","Hussein, A.; School of Engineering and Information Technology, Australia; email: a.hussein@student.adfa.edu.au",,,"Taylor and Francis Ltd.",,,,,00140139,,ERGOA,"32370651","English","Ergonomics",Article,"Final","",Scopus,2-s2.0-85084816702
"Martins R., Bersan D., Campos M.F.M., Nascimento E.R.","56742715200;57205730344;57201919592;26423977100;","Extending Maps with Semantic and Contextual Object Information for Robot Navigation: a Learning-Based Framework Using Visual and Depth Cues",2020,"Journal of Intelligent and Robotic Systems: Theory and Applications","99","3-4",,"555","569",,1,"10.1007/s10846-019-01136-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081375515&doi=10.1007%2fs10846-019-01136-5&partnerID=40&md5=c228a4e8f010b2b2953b5e6845eda1d4","Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil; Inria, Sophia Antipolis, France","Martins, R., Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil, Inria, Sophia Antipolis, France; Bersan, D., Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil; Campos, M.F.M., Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil; Nascimento, E.R., Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil","This paper addresses the problem of building augmented metric representations of scenes with semantic information from RGB-D images. We propose a complete framework to create an enhanced map representation of the environment with object-level information to be used in several applications such as human-robot interaction, assistive robotics, visual navigation, or in manipulation tasks. Our formulation leverages a CNN-based object detector (Yolo) with a 3D model-based segmentation technique to perform instance semantic segmentation, and to localize, identify, and track different classes of objects in the scene. The tracking and positioning of semantic classes is done with a dictionary of Kalman filters in order to combine sensor measurements over time and then providing more accurate maps. The formulation is designed to identify and to disregard dynamic objects in order to obtain a medium-term invariant map representation. The proposed method was evaluated with collected and publicly available RGB-D data sequences acquired in different indoor scenes. Experimental results show the potential of the technique to produce augmented semantic maps containing several objects (notably doors). We also provide to the community a dataset composed of annotated object classes (doors, fire extinguishers, benches, water fountains) and their positioning, as well as the source code as ROS packages. © 2020, Springer Nature B.V.","Object detection; RGB-D localization and mapping; Semantic object mapping; Visual robot navigation","3D modeling; Mapping; Navigation; Object detection; Object recognition; Semantics; Localization and mappings; Map representations; Model-based segmentation; Robot navigation; Semantic information; Semantic objects; Semantic segmentation; Sensor measurements; Human robot interaction",,,,,"Nvidia; Fundação de Amparo à Pesquisa do Estado de Minas Gerais, FAPEMIG","The authors thank PNPD-CAPES and FAPEMIG for financial support during this research. We also gratefully acknowledge NVIDIA for the donation of the Jetson TX2 GPU used in the online experiments of this research.","The authors thank PNPD-CAPES and FAPEMIG for financial support during this research. We also gratefully acknowledge NVIDIA for the donation of the Jetson TX2 GPU used in the online experiments of this research.",,,,,,,,,"Armeni, I., Sax, A., Zamir, A.R., Savarese, S., (2017) Joint 2D-3D-Semantic Data for Indoor Scene Understanding, , ArXiv e-prints; Bersan, D., Martins, R., Campos, M., Nascimento, E.R., Semantic Map Augmentation for Robot Navigation: A Learning Approach Based on Visual and Depth Data (2018) IEEE LARS; Bolya, D., Zhou, C., Xiao, F., Lee, Y.J., Yolact: Real-time Instance Segmentation (2019) ICCV; Bozhinoski, D., Di Ruscio, D., Malavolta, I., Pelliccione, P., Crnkovic, I., Safety for mobile robotic systems: a systematic mapping study from a software engineering perspective (2019) J. Syst. 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"Yu S., Huang T.-H., Yang X., Jiao C., Yang J., Chen Y., Yi J., Su H.","57202865965;57210576050;56359230900;57218657172;57215348162;56151326200;7402736580;55059793600;","Quasi-Direct Drive Actuation for a Lightweight Hip Exoskeleton with High Backdrivability and High Bandwidth",2020,"IEEE/ASME Transactions on Mechatronics","25","4","9095261","1794","1802",,9,"10.1109/TMECH.2020.2995134","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089955756&doi=10.1109%2fTMECH.2020.2995134&partnerID=40&md5=63ad7d015430aa35a4384d71d773edff","Lab of Biomechatronics and Intelligent Robotics (BIRO), Department of Mechanical Engineering, City University of New York, City College, NY  10023, United States; Department of Mechanical Engineering, University of Arkansas, Fayetteville, AR  72701, United States; Department of Mechanical and Aerospace Engineering, Rutgers, State University of New Jersey, Piscataway, NJ  08854, United States","Yu, S., Lab of Biomechatronics and Intelligent Robotics (BIRO), Department of Mechanical Engineering, City University of New York, City College, NY  10023, United States; Huang, T.-H., Lab of Biomechatronics and Intelligent Robotics (BIRO), Department of Mechanical Engineering, City University of New York, City College, NY  10023, United States; Yang, X., Lab of Biomechatronics and Intelligent Robotics (BIRO), Department of Mechanical Engineering, City University of New York, City College, NY  10023, United States; Jiao, C., Lab of Biomechatronics and Intelligent Robotics (BIRO), Department of Mechanical Engineering, City University of New York, City College, NY  10023, United States; Yang, J., Lab of Biomechatronics and Intelligent Robotics (BIRO), Department of Mechanical Engineering, City University of New York, City College, NY  10023, United States; Chen, Y., Department of Mechanical Engineering, University of Arkansas, Fayetteville, AR  72701, United States; Yi, J., Department of Mechanical and Aerospace Engineering, Rutgers, State University of New Jersey, Piscataway, NJ  08854, United States; Su, H., Lab of Biomechatronics and Intelligent Robotics (BIRO), Department of Mechanical Engineering, City University of New York, City College, NY  10023, United States","High-performance actuators are crucial to enable mechanical versatility of wearable robots, which are required to be lightweight, highly backdrivable, and with high bandwidth. State-of-the-art actuators, e.g., series elastic actuators, have to compromise bandwidth to improve compliance (i.e., backdrivability). In this article, we describe the design and human-robot interaction modeling of a portable hip exoskeleton based on our custom quasi-direct drive actuation (i.e., a high torque density motor with low ratio gear). We also present a model-based performance benchmark comparison of representative actuators in terms of torque capability, control bandwidth, backdrivability, and force tracking accuracy. This article aims to corroborate the underlying philosophy of 'design for control,' namely meticulous robot design can simplify control algorithms while ensuring high performance. Following this idea, we create a lightweight bilateral hip exoskeleton to reduce joint loadings during normal activities, including walking and squatting. Experiments indicate that the exoskeleton is able to produce high nominal torque (17.5 Nm), high backdrivability (0.4 Nm backdrive torque), high bandwidth (62.4 Hz), and high control accuracy (1.09 Nm root mean square tracking error, 5.4% of the desired peak torque). Its controller is versatile to assist walking at different speeds and squatting. This article demonstrates performance improvement compared with state-of-the-art exoskeletons. © 1996-2012 IEEE.","Exoskeleton; high-torque actuator; human augmentation; quasi-direct drive (QDD) actuation; wearable robots","Bandwidth; Benchmarking; Human robot interaction; Machine design; Mechanical actuators; Torque; Walking aids; Benchmark comparison; Control bandwidth; Design for control; High torque density; Root mean square tracking error; Series elastic actuators; State of the art; Torque capability; Exoskeleton (Robotics)",,,,,"National Science Foundation, NSF: 1830613, CMMI 1944655, IIS 1830613; National Institutes of Health, NIH: R01EB029765; City College of New York, CCNY","Manuscript received January 9, 2020; revised April 3, 2020; accepted April 29, 2020. Date of publication May 18, 2020; date of current version August 13, 2020. This work was supported in part by the National Science Foundation Grant IIS 1830613, in part by CMMI 1944655, in part by NIH R01EB029765, and in part by Grove School of Engineering, The City University of New York, City College. Recommended by Technical Editor G. M. Clayton and Senior Editor X. Chen. (Shuangyue Yu and Tzu-Hao Huang contributed equally to this work.) (Corresponding author: Hao Su.) Shuangyue Yu, Tzu-Hao Huang, Xiaolong Yang, Chunhai Jiao, Jianfu Yang, and Hao Su are with the Lab of Biomechatronics and Intelligent Robotics (BIRO), Department of Mechanical Engineering,The City University of New York, City College, NY 10023 USA (e-mail: syu003@citymail.cuny.edu; thuang@ccny.cuny.edu; xyang1@ccny. cuny.edu; cjiao000@citymail.cuny.edu; jy2863@columbia.edu; hao. su@ccny.cuny.edu).",,,,,,,,,,"Crea, S., Controlling a robotic hip exoskeleton with noncontact capacitive sensors (2019) IEEE/ASME Trans. 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Biomechanics, 39 (13), pp. 2491-2502","Su, H.; Lab of Biomechatronics and Intelligent Robotics (BIRO), United States; email: hao.su@ccny.cuny.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10834435,,IATEF,,"English","IEEE ASME Trans Mechatron",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85089955756
"Jaiswal S., Nandi G.C.","57206253502;6602138539;","Robust real-time emotion detection system using CNN architecture",2020,"Neural Computing and Applications","32","15",,"11253","11262",,9,"10.1007/s00521-019-04564-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074615771&doi=10.1007%2fs00521-019-04564-4&partnerID=40&md5=02bf9427a6f35fef1f87ca9c9fb61e50","IIIT Allahabad, Allahabad, India","Jaiswal, S., IIIT Allahabad, Allahabad, India; Nandi, G.C., IIIT Allahabad, Allahabad, India","As the human–robot interaction is catching eye day by day with the increase in need of automation in every field, personal robots are increasing in every area which may be coping needs of elderly people, treating autistic patients or child therapy, even in the area of babysitting the child. As robots are helping human being in all such cases, robots need to understand human emotion in order to treat human in a more customized manner. Predicting human emotion has been a difficult problem which is being solved over a decade’s time. In this paper, we have built a model which can predict human emotion from an image in real time. The network build is based on convolutional neural network which has reduced parameters by 90× from that of Vanilla CNN and also 50× from the latest state-of-the-art research carried out to the best of our knowledge. The network build is tested robustly on 8 different datasets, namely Fer2013, CK and CK+, Chicago Face Database, JAFFE Dataset, FEI face dataset, IMFDB, TFEID and custom dataset build in our laboratory having different angles, faces, backgrounds and age groups. The network achieves 74% accuracy which is an improved accuracy from the state-of-the-art accuracy with reduced computation complexity. © 2019, Springer-Verlag London Ltd., part of Springer Nature.","Convolution neural network; Deep learning; Emotion recognition; Inception; Real-time network","Convolution; Deep learning; Deep neural networks; Man machine systems; Neural networks; Patient treatment; Computation complexity; Convolution neural network; Convolutional neural network; Emotion recognition; Inception; Real time network; Real-time emotion detection; Robot interactions; Human robot interaction",,,,,,,,,,,,,,,,"Goodfellow, I.J., (2013) Challenges in Representation Learning: A Report on Three Machine Learning Contests, , e-prints; Wu, X., Bartram, L., (2018) Social robots for people with developmental disabilities: A user study on design features of a graphical user interface; Jaiswal, S., Mishra, P., Nandi, G.C., Deep learning based command pointing direction estimation using a single RGB Camera (2018) 2018 5Th IEEE Uttar Pradesh Section International Conference on Electrical, , Electronics and Computer Engineering (UPCON); Szegedy, C., (2014) Going Deeper with Convolutions, , arXiv e-prints; Semwal, V.B., Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach (2017) Neural Comput Appl, 28 (3), pp. 565-574; Chen, L., Zhou, C., Shen, L., Facial expression recognition based on SVM in E-learning (2012) IERI Procedia, 2, pp. 781-787; Chen, X., Convolution neural network for automatic facial expression recognition (2017) 2017 International Conference on Applied System Innovation (ICASI); Alizadeh, S., Fazel, A., (2017) Convolutional Neural Networks for Facial Expression Recognition; Ren, X., Convolutional neural network based on principal component analysis initialization for image classification (2016) 2016 IEEE First International Conference on Data Science in Cyberspace (DSC; Song, T., Zheng, W., Song, P., Cui, Z., EEG emotion recognition using dynamical graph convolutional neural networks (2018) IEEE Trans Affect Comput; Samara, A., Menezes, M.L.R., Galway, L., Feature extraction for emotion recognition and modelling using neurophysiological data (2016) 2016 15Th International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace and Security (IUCC-CSS); Lopes, A.T., Facial expression recognition with convolutional neural networks: coping with few data and the training sample order (2017) Pattern Recogn, 61, pp. 610-628; Bawa, V.S., Kumar, V., Emotional sentiment analysis for a group of people based on transfer learning with a multi-modal system (2018) Neural Comput Appl; Mohammadpour, M., Facial emotion recognition using deep convolutional networks (2017) 2017 IEEE 4Th International Conference on Knowledge-Based Engineering and Innovation (KBEI); Anwar, I., Islam, N.U., (2017) Learned Features are Better for Ethnicity Classification, , arXiv e-prints; Dachapally, P.R., (2017) Facial Emotion Detection Using Convolutional Neural Networks and Representational Autoencoder Units, , e-prints; Tripathi, S., (2017) Using deep and convolutional neural networks for accurate emotion classification on DEAP dataset, p. 2017; Pilla, V., Zanellato, A., Bortolini, C., Gamba, H.R., Borba, G.B., Medeiros, H., Facial expression classification using convolutional neural network and support vector machine (2016) Conference Proceedings; Ko, B.C., A brief review of facial emotion recognition based on visual information. Sensors, 2018 (2018) Conference Proceedings; Arriaga, O., Valdenegro-Toro, M., Plöger, P., (2017) Real-Time Convolutional Neural Networks for Emotion and Gender Classification, , arXiv e-prints; Iqbal, M.T.B., Wadud, M.A., Ryu, B., Makhmudkhujaev, F., Chae, O., Facial expression recognition with neighborhood-aware edge directional pattern (NEDP) (2018) IEEE Trans Affect Comput; Dibeklioglu, H., Gevers, T., Automatic estimation of taste liking through facial expression dynamics (2018) IEEE Trans Affect Comput; Al Chanti, D.A., Caplier, A., Deep learning for spatio-temporal modeling of dynamic spontaneous emotions (2018) IEEE Trans Affect Comput; Ding, S., Extreme learning machine with kernel model based on deep learning (2017) Neural Comput Appl, 28 (8), pp. 1975-1984; Williams, R.J., Zipser, D., A learning algorithm for continually running fully recurrent neural networks (1989) Neural Comput, 1 (2), pp. 270-280; Pierre-Luc Carrier, A.C., (2013) Challenges in Representation Learning: Facial Expression Recognition Challenge; Lucey, P., The extended Cohn-Kanade dataset (CK +): A complete dataset for action unit and emotion-specified expression (2010) 2010 IEEE Computer Society Conference on Computer Vision and Pattern recognition—workshops; Ma, C., Wittenbrink, The Chicago face database: a free stimulus set of faces and norming data (2015) Behav Res Methods, 1122-1135, p. 47; Lyons, M.J., Akamatsu, S., Kamachi, M., Gyoba, J., Coding facial expressions with gabor wavelets (1998) 3Rd IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200-205; Thomaz, C.E., FEI face database; Setty, S., Indian movie face database: A benchmark for face recognition under wide variations (2013) 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG); Li-Fen, C., Taiwanese, Y.-S., (2007) Facial Expression Image Database, , Brain Mapping Laboratory, Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan; Wang, C.-F., (2018) A Basic Introduction to Separable Convolutions; He, K., Sun, J., (2015) Convolutional Neural Networks at Constrained Time Cost","Jaiswal, S.; IIIT AllahabadIndia; email: shruti.jaiswal123@gmail.com",,,"Springer",,,,,09410643,,,,"English","Neural Comput. Appl.",Article,"Final","",Scopus,2-s2.0-85074615771
"Fosch-Villaronga E., Özcan B.","57189046483;57191040360;","The Progressive Intertwinement Between Design, Human Needs and the Regulation of Care Technology: The Case of Lower-Limb Exoskeletons",2020,"International Journal of Social Robotics","12","4",,"959","972",,10,"10.1007/s12369-019-00537-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071461970&doi=10.1007%2fs12369-019-00537-8&partnerID=40&md5=50707edce1fcbbcf0281c9b8bd6885e6","Marie Skłodowska-Curie Postdoctoral Researcher at the eLaw, Center for Law and Digital Technologies, Leiden University, Leiden, Netherlands; Postdoctoral Research Fellow at the Institute of Cognitive Sciences and Technologies, ISTC-CNR, Rome, Italy","Fosch-Villaronga, E., Marie Skłodowska-Curie Postdoctoral Researcher at the eLaw, Center for Law and Digital Technologies, Leiden University, Leiden, Netherlands; Özcan, B., Postdoctoral Research Fellow at the Institute of Cognitive Sciences and Technologies, ISTC-CNR, Rome, Italy","The adoption of robot technology is accelerating in healthcare settings. Care robots can support and extend the work of caregivers in assisting patients, elderly or children. Typical examples of such systems are ‘cognitive therapeutic robots,’ ‘physical rehabilitation robots,’ ‘assistive and lifting robots.’ Although these robots might reduce the workload of care workers, and be a cost-efficient solution against healthcare system cuts, the insertion of such technologies may also raise ethical, legal and societal concerns concerning users. In this article, we describe some of these concerns, including cognitive safety, prospective liability, and privacy. We argue that the current regulatory framework for care robot technology is ill-prepared to address such multidisciplinary concerns because it only focuses on physical safety requirements, whereas it disregards other issues arising from the human–robot interaction. We support the idea that design plays a significant role in shaping the technology to meet the needs of the users and the goals set by the regulation. To illustrate practical challenges, in this article we consider as an example the case of lower-limb exoskeletons. This example helps illuminate the overarching idea of the article, that is, that regulation, design, and human needs need to intertwine and mutually shape each other to serve the solutions these technologies proclaim. © 2019, The Author(s).","Care robots; Cognitive; Design; Human–robot interaction; Law; Privacy; Regulation; Safety; Technology; User-centered approach","Accident prevention; Agricultural robots; Cognitive systems; Data privacy; Design; Exoskeleton (Robotics); Health care; Laws and legislation; Social robots; Cognitive; Health-care system; Physical rehabilitation; Regulation; Regulatory frameworks; Robot interactions; Safety requirements; User-centered approach; Machine design",,,,,"Horizon 2020 Framework Programme, H2020; H2020 Marie Skłodowska-Curie Actions, MSCA;MSCM;AMSC: 707404","Part of this project was funded by the LEaDing Fellows Marie Curie COFUND fellowship, a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 707404.",,,,,,,,,,"Roadmap robotics for healthcare (2008) European Foresight Monitoring Network, , http://www.foresight-platform.eu/wp-content/uploads/2011/02/EFMN-Brief-No.-157_Robotics-for-Healthcare.pdf, last modified; Feil-Seifer, D., Mataric, M.J., Defining socially assistive robotics (2005) ICORR 2005, 9Th International Conference on Rehabilitation Robotics, pp. 465-468. , . 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Essays, , http://www.kurzweilai.net/human-body-version-20, Accessed 12 April 2013; Mallin, S.S.V., de Carvalho, H.G., Assistive technology and user-centered design: emotion as element for innovation (2015) Procedia Manuf, 3, pp. 5570-5578; Shedroff, N., Lavín, C., Martín, R.S., Rosales, P., Mondragón, S., Vergara, M., Las emociones están en camino a la innovación significativa (2008) Revista Faz, 2 (Julio), p. 98. , (,); Desmet, P., Dijkhuis, E., A wheelchair can be fun: A case of emotion-driven design (2003) Proceedings of the 2003 International Conference on Designing Pleasurable Products and Interfaces, pp. 22-27. , https://doi.org/10.1145/782896.782903, ACM, New York; Nicolás, J.C.O., Aurisicchio, M., Desmet, P.M.A., How users experience great products (2013) Presented at the 5Th International Congress of International Association of Societies of Design Research, p. 12. , p; Desmet, P.M., Faces of product pleasure: 25 positive emotions in human-product interactions (2012) Int J Des, 6 (2); Jordan, P.W., Human factors for pleasure in product use (1998) Appl Ergon, 29 (1), pp. 25-33; Federici, S., Scherer, M., (2017) Assistive technology assessment handbook, , CRC Press, Boca Raton","Fosch-Villaronga, E.; Marie Skłodowska-Curie Postdoctoral Researcher at the eLaw, Netherlands; email: e.fosch.villaronga@law.leidenuniv.nl",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85071461970
"Repiso E., Garrell A., Sanfeliu A.","57028516500;35589794700;7003840083;","Adaptive Side-by-Side Social Robot Navigation to Approach and Interact with People",2020,"International Journal of Social Robotics","12","4",,"909","930",,6,"10.1007/s12369-019-00559-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066901876&doi=10.1007%2fs12369-019-00559-2&partnerID=40&md5=311e087fd04ff2381a2caf6a1c9d8889","Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Llorens Artigas 4-6, Barcelona, 08028, Spain","Repiso, E., Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Llorens Artigas 4-6, Barcelona, 08028, Spain; Garrell, A., Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Llorens Artigas 4-6, Barcelona, 08028, Spain; Sanfeliu, A., Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Llorens Artigas 4-6, Barcelona, 08028, Spain","This paper presents a new framework for how autonomous social robots approach and accompany people in urban environments. The method discussed allows the robot to accompany a person and approach to other one, by adapting its own navigation in anticipation of future interactions with other people or contact with static obstacles. The contributions of the paper are manifold: firstly, we extended the Social Force model and the Anticipative Kinodynamic Planner (Ferrer and Sanfeliu, in: IEEE/RSJ international conference on intelligent robots and systems. IEEE, 2014) to the case of an adaptive side-by-side navigation; secondly, we enhance side-by-side navigation with an approaching task and a final positioning that allows the robot to interact with both people; and finally, we use findings from experiments of real-life observations of people walking in pairs to define the parameters of the human–robot interaction in our case of adaptive side-by-side. The method was validated by a large set of simulations; we also conducted real-life experiments with our robot, Tibi, to validate the framework described for the interaction process. In addition, we carried out various surveys and user studies to indicate the social acceptability of the robots performance of the accompanying, approaching and positioning tasks. © 2019, Springer Nature B.V.","Human–robot approaching; Human–robot collaboration; Human–robot companion side-by-side; Human–robot interaction; Robot navigation","Agricultural robots; Air navigation; Intelligent robots; Navigation; Interaction process; Real-life observations; Robot interactions; Robot navigation; Side by sides; Social acceptability; Social force models; Urban environments; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 730994; Ministerio de Ciencia e Innovación, MICINN: BES-2014-067713, DPI2016-78957- R; Agencia Estatal de Investigación, AEI: MDM-2016-0656",,,,,,,,,,,"Ferrer, G., Sanfeliu, A., Proactive kinodynamic planning using the extended social force model and human motion prediction in urban environments (2014) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1730-1735. , In, IEEE; Leite, I., Martinho, C., Paiva, A., Social robots for long-term interaction: a survey (2013) Int J Soc Robot, 5 (2), pp. 291-308; Bauer, A., Wollherr, D., Buss, M., Human–robot collaboration: a survey (2008) Int J Humanoid Robot, 5 (1), pp. 47-66; Goodrich, M.A., Schultz, A.C., Human–robot interaction: a survey (2007) Found Trends Hum Comput Interact, 1 (3), pp. 203-275; Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., Hagita, N., An affective guide robot in a shopping mall (2009) Proceedings of the 4Th ACM/IEEE International Conference on Human Robot Interaction, pp. 173-180. , In; Gross, H.-M., Boehme, H., Schroeter, C., Müller, S., König, A., Einhorn, E., Martin, C., Bley, A., Toomas: Interactive shopping guide robots in everyday use-final implementation and experiences from long-term field trials (2009) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2005-2012; Garrell, A., Sanfeliu, A., Cooperative social robots to accompany groups of people (2012) Int J Robot Res, 31 (13), pp. 1675-1701; Kuderer, M., Burgard, W., An approach to socially compliant leader following for mobile robots (2014) International Conference on Social Robotics, pp. 239-248. , In,. 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J. Soc. Rob.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85066901876
"Tian Y., Wang G., Li L., Jin T., Xi F., Yuan G.","15836338500;57218704714;57188975448;57208208649;7102360062;55990982500;","A Universal Self-Adaption Workspace Mapping Method for Human-Robot Interaction Using Kinect Sensor Data",2020,"IEEE Sensors Journal","20","14","9042278","7918","7928",,1,"10.1109/JSEN.2020.2981091","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090151555&doi=10.1109%2fJSEN.2020.2981091&partnerID=40&md5=936c59776949c5d533d475652901ab8b","Laboratory of Intelligent Mechanism and Advanced Robotics, School of Mechanical Manufacture and Automation, Shanghai University, Shanghai, 200072, China; Department of Mechanical, Aerospace and Industrial Engineering, Ryerson University, Toronto, Canada","Tian, Y., Laboratory of Intelligent Mechanism and Advanced Robotics, School of Mechanical Manufacture and Automation, Shanghai University, Shanghai, 200072, China; Wang, G., Department of Mechanical, Aerospace and Industrial Engineering, Ryerson University, Toronto, Canada; Li, L., Laboratory of Intelligent Mechanism and Advanced Robotics, School of Mechanical Manufacture and Automation, Shanghai University, Shanghai, 200072, China; Jin, T., Department of Mechanical, Aerospace and Industrial Engineering, Ryerson University, Toronto, Canada; Xi, F., Laboratory of Intelligent Mechanism and Advanced Robotics, School of Mechanical Manufacture and Automation, Shanghai University, Shanghai, 200072, China; Yuan, G., Laboratory of Intelligent Mechanism and Advanced Robotics, School of Mechanical Manufacture and Automation, Shanghai University, Shanghai, 200072, China","Human-robot interactions (HRIs) are a key technology to combine human and robot capabilities. Though many feasible methods have been proposed in this field, the current workspace mapping methods are deficient in terms of generality. In this paper, a universal control system based on the self-adaption workspace mapping method for HRI is designed. In normal circumstances, human movement is adjusted adaptively in a safe, obedient, stable and efficient way to the external environment. The universal control system could naturally carry out an object manipulation task by allowing a human operator to communicate his motions to the robot manipulators. More importantly, by applying this system, people no longer need to find parameters by trial-and-error because they are not affected by changes in the human and robot model. The function of similarity that is used to evaluate the similarity between a human arm and the robot is introduced. Moreover, experiments are carried out, and an analysis of results verifies the feasibility and excellent performance of the universal control system. Finally, a demonstration is applied to display the flexible implementation of the object manipulation task that is collaboratively done by the universal robot through this system, and the method is evaluated by comparing it with other method. © 2001-2012 IEEE.","Gesture recognition; human-robot interaction; robotics; similarity; skeleton data; workspace mapping","Control systems; Flexible displays; Man machine systems; Manipulators; Mapping; Robot applications; External environments; Human movements; Key technologies; Kinect sensors; Object manipulation; Robot manipulator; Trial and error; Universal control; Social robots",,,,,"National Natural Science Foundation of China, NSFC: 51775322, 61603237, 61704102","Manuscript received December 16, 2019; revised March 9, 2020 and March 10, 2020; accepted March 10, 2020. Date of publication March 19, 2020; date of current version June 18, 2020. This work was supported in part by the Special Plan of Major Scientific Instruments and Equipment of the State under Grant 2018YFF01013101, in part by the National Natural Science Foundation of China under Grant 51775322, Grant 61704102, and Grant 61603237, and in part by the Project Key Technology Research and Demonstration Line Construction of Advanced Laser Intelligent Manufacturing Equipment from Shanghai Lingang Area Development Administration. The associate editor coordinating the review of this article and approving it for publication was Dr. Marco J. Da Silva. (Corresponding author: Guangjie Yuan.) Yingzhong Tian, Guopeng Wang, Long Li, Tao Jin, and Guangjie Yuan are with the Laboratory of Intelligent Mechanism and Advanced Robotics, School of Mechanical Manufacture and Automation, Shanghai University, Shanghai 200072, China (e-mail: troytian@shu.edu.cn; 18721018328@163.com; lil@shu.edu.cn; jt233@shu.edu.cn; guangjie@shu.edu.cn).",,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human-robot collaboration (2018) Auto. Robots, 42 (5), pp. 957-975. , Jun; Liang, P., Ge, L., Liu, Y., Zhao, L., Li, R., Wang, K., An augmented discrete-Time approach for human-robot collaboration (2016) Discrete Dyn. Nature Soc, 2016, pp. 1-13; Tian, Y., Research on layout and operational pose optimization of robot grinding system based on optimal stiffness performance (2017) J. Adv. Mech. Design, Syst. , Manuf, 11 (2). , Art. no. 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"Xiong X., Yu Z., Ma T., Luo N., Wang H., Lu X., Fan H.","57217427202;7404347135;55490554800;57218248199;55878609000;55687439900;36647626600;","Weighted Brain Network Metrics for Decoding Action Intention Understanding Based on EEG",2020,"Frontiers in Human Neuroscience","14",,"232","","",,4,"10.3389/fnhum.2020.00232","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088423948&doi=10.3389%2ffnhum.2020.00232&partnerID=40&md5=c4f7170b9c9e9fa5c0122c3925a92e5f","Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science Medical Engineering, Southeast University, Nanjing, China; College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an, China; Institute of Software, Chinese Academy of Sciences, Beijing, China; Department of Rehabilitation, Zhongda Hospital, Southeast University, Nanjing, China; Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Shandong Technology and Business University, Yantai, China","Xiong, X., Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science Medical Engineering, Southeast University, Nanjing, China; Yu, Z., College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an, China; Ma, T., College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an, China; Luo, N., Institute of Software, Chinese Academy of Sciences, Beijing, China; Wang, H., Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science Medical Engineering, Southeast University, Nanjing, China; Lu, X., Department of Rehabilitation, Zhongda Hospital, Southeast University, Nanjing, China; Fan, H., Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Shandong Technology and Business University, Yantai, China","Background: Understanding the action intentions of others is important for social and human-robot interactions. Recently, many state-of-the-art approaches have been proposed for decoding action intention understanding. Although these methods have some advantages, it is still necessary to design other tools that can more efficiently classify the action intention understanding signals. New Method: Based on EEG, we first applied phase lag index (PLI) and weighted phase lag index (WPLI) to construct functional connectivity matrices in five frequency bands and 63 micro-time windows, then calculated nine graph metrics from these matrices and subsequently used the network metrics as features to classify different brain signals related to action intention understanding. Results: Compared with the single methods (PLI or WPLI), the combination method (PLI+WPLI) demonstrates some overwhelming victories. Most of the average classification accuracies exceed 70%, and some of them approach 80%. In statistical tests of brain network, many significantly different edges appear in the frontal, occipital, parietal, and temporal regions. Conclusions: Weighted brain networks can effectively retain data information. The integrated method proposed in this study is extremely effective for investigating action intention understanding. Both the mirror neuron and mentalizing systems participate as collaborators in the process of action intention understanding. © Copyright © 2020 Xiong, Yu, Ma, Luo, Wang, Lu and Fan.","action intention understanding; classification accuracy; mentalizing system; mirror neuron system; phase lag index; weighted network metric","article; electroencephalogram; frontal cortex; functional connectivity; human; human cell; human experiment; mentalization; mirror neuron system; occipital cortex; temporal lobe",,,,,"H2018042; National Natural Science Foundation of China, NSFC: 61773114; Government of Jiangsu Province: BE2017007-03","We thank Huibin Jia, Weixing Yang, Lingyun Gu, and numerous helpers for their suggestions in implementing experiments. Funding. This work was supported in part by the National Nature Science Foundation of China under Grant 61773114, the Foundation of Hygiene and Health of Jiangsu Province under Grant H2018042, and the Key Research and Development Plan (Industry Fore-sight and Common Key Technology) of Jiangsu Province under Grant BE2017007-03.",,,,,,,,,,"Ahmadlou, M., Adeli, H., Complexity of weighted graph: a new technique to investigate structural complexity of brain activities with applications to aging and autism (2017) Neurosci. Lett, 650, pp. 103-108; Amodio, D.M., Frith, C.D., Meeting of minds: the medial frontal cortex and social cognition (2006) Nat. Rev. Neurosci, 7, pp. 268-277. , 16552413; Arnaud, D., Scott, M., EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis (2004) J. Neurosci. 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Lu, X.; Department of Rehabilitation, China; email: luxue_001@163.com",,,"Frontiers Media S.A.",,,,,16625161,,,,"English","Front. Human Neurosci.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85088423948
"Brščić D., Evans R.W., Rehm M., Kanda T.","12795389900;57198969556;10039896700;7403134618;","Using a rotating 3d lidar on a mobile robot for estimation of person’s body angle and gender",2020,"Sensors (Switzerland)","20","14","3964","1","17",,2,"10.3390/s20143964","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088028313&doi=10.3390%2fs20143964&partnerID=40&md5=57c0bb10f25c431bbe11e0cd8541c515","Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan; Technical Faculty of IT and Design, Aalborg University, Aalborg, 9220, Denmark","Brščić, D., Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan; Evans, R.W., Technical Faculty of IT and Design, Aalborg University, Aalborg, 9220, Denmark; Rehm, M., Technical Faculty of IT and Design, Aalborg University, Aalborg, 9220, Denmark; Kanda, T., Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan","We studied the use of a rotating multi-layer 3D Light Detection And Ranging (LiDAR) sensor (specifically the Velodyne HDL-32E) mounted on a social robot for the estimation of features of people around the robot. While LiDARs are often used for robot self-localization and people tracking, we were interested in the possibility of using them to estimate the people’s features (states or attributes), which are important in human–robot interaction. In particular, we tested the estimation of the person’s body orientation and their gender. As collecting data in the real world and labeling them is laborious and time consuming, we also looked into other ways for obtaining data for training the estimators: using simulations, or using LiDAR data collected in the lab. We trained convolutional neural network-based estimators and tested their performance on actual LiDAR measurements of people in a public space. The results show that with a rotating 3D LiDAR a usable estimate of the body angle can indeed be achieved (mean absolute error 33.5◦ ), and that using simulated data for training the estimators is effective. For estimating gender, the results are satisfactory (accuracy above 80%) when the person is close enough; however, simulated data do not work well and training needs to be done on actual people measurements. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Body angle estimation; Gender recognition; LiDAR; Social robotics","Convolutional neural networks; Human robot interaction; Body orientation; Lidar measurements; Light detection and ranging; Mean absolute error; People tracking; Robot interactions; Self localization; Training needs; Optical radar; biometry; body position; devices; human; laser; robotics; sexual characteristics; Biometry; Humans; Lasers; Neural Networks, Computer; Posture; Robotics; Sex Characteristics",,,,,"JPMJCR17A2; Aalborg Universitet, AAU","Funding: This research was funded by JST CREST, Grant Number JPMJCR17A2, Japan.","Acknowledgments: The authors would like to thank Kanako Tomita for help with the data collection. The Aalborg University Internationalisation Pool provided financial support for Rhys Wyn Evans during his stay at Kyoto University.",,,,,,,,,"Karunarathne, D., Morales, Y., Kanda, T., Ishiguro, H., Understanding a public environment via continuous robot observations (2020) Robot. Auton. Syst, 126, p. 103443; Mizumaru, K., Satake, S., Kanda, T., Ono, T., Stop doing it! Approaching strategy for a robot to admonish pedestrians Proceedings of the 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 449-457. , Daegu, Korea, 11–14 March 2019; Munaro, M., Menegatti, E., Fast RGB-D people tracking for service robots (2014) Auton. Robot, 37, pp. 227-242; Liu, K., Li, Y., New patent maps to visualize worldwide patent strength of competitors on LiDAR system (2020) J. Multidiscip. Eng. Sci. Stud, 6, pp. 3094-3098; Li, Y., Ibanez-Guzman, J., (2020) Lidar for autonomous driving: The principles, challenges, and trends for automotive lidar and perception systems, , arXiv arXiv:2004.08467; Yoo, H.W., Druml, N., Brunner, D., Schwarzl, C., Thurner, T., Hennecke, M., Schitter, G., MEMS-based lidar for autonomous driving (2018) e & i Elektrotechnik Und Informationstechnik, 135, pp. 408-415; Hokuyo YVT-35LX 3D Scanning Range Finder, , https://www.hokuyo-aut.jp/search/single.php?serial=224, (accessed on 12 July 2020); Amzajerdian, F., Roback, V.E., Bulyshev, A., Brewster, P.F., Hines, G.D., Imaging flash lidar for autonomous safe landing and spacecraft proximity operation (2016) Proceedings of the AIAA Space and Astronautics Forum and Exposition (SPACE 2016), p. 5591. , Long Beach, CA, USA, 12–15 September; Eldada, L., Yu, T., Pacala, A., (2018) Optical Phased Array Lidar System and Method of Using Same, , US Patent 10,126,412, 13 November; Zhang, J., Singh, S., LOAM: Lidar odometry and mapping in real-time (2014) Proceedings of the Robotics: Science and Systems Conference (RSS), , Berkeley, CA, USA, 12–16 July; Moosmann, F., Stiller, C., Velodyne slam Proceedings of the 2011 IEEE Intelligent Vehicles Symposium (IV), pp. 393-398. , Baden-Baden, Germany, 5–9 June 2011; Nüchter, A., Lingemann, K., Hertzberg, J., Surmann, H., 6D SLAM—3D mapping outdoor environments (2007) J. 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Syst, 20, pp. 3782-3795; Li, Y., Ma, L., Zhong, Z., Liu, F., Cao, D., Li, J., Chapman, M.A., (2020) Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review, , arXiv arXiv:2005.09830; Chen, X., Ma, H., Wan, J., Li, B., Xia, T., Multi-view 3d object detection network for autonomous driving Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1907-1915. , Honolulu, HI, USA, 21–26 July 2017; Oh, S.I., Kang, H.B., Object detection and classification by decision©level fusion for intelligent vehicle systems (2017) Sensors, 17, p. 207; Wang, Z., Zhan, W., Tomizuka, M., Fusing bird’s eye view lidar point cloud and front view camera image for 3d object detection Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 1-6. , Changshu, China, 26–30 June 2018; Kidono, K., Miyasaka, T., Watanabe, A., Naito, T., Miura, J., Pedestrian recognition using high-definition LIDAR Proceedings of the 2011 IEEE Intelligent Vehicles Symposium (IV), pp. 405-410. , Baden-Baden, Germany, 5–9 June 2011; Li, K., Wang, X., Xu, Y., Wang, J., Density enhancement-based long-range pedestrian detection using 3-D range data (2015) IEEE Trans. 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Syst, 114, pp. 29-40; Zimmermann, C., Welschehold, T., Dornhege, C., Burgard, W., Brox, T., 3d human pose estimation in rgbd images for robotic task learning Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1986-1992. , Brisbane, QLD, Australia, 21–25 May 2018; Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A., Real-time human pose recognition in parts from single depth images Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1297-1304. , Providence, RI, USA, 20–25 June 2011; Lewandowski, B., Seichter, D., Wengefeld, T., Pfennig, L., Drumm, H., Gross, H.M., Deep orientation: Fast and robust upper body orientation estimation for mobile robotic applications Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 441-448. , Venetian Macao, 3–8 November 2019; Teichman, A., Levinson, J., Thrun, S., Towards 3D object recognition via classification of arbitrary object tracks Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 4034-4041. , Shanghai, China, 9–13 May2011; Wang, D.Z., Posner, I., Newman, P., What could move? Finding cars, pedestrians and bicyclists in 3D laser data Proceedings of the 2012 IEEE International Conference on Robotics and Automation (ICRA), pp. 4038-4044. , Saint Paul, MN, USA, 14–18 May 2012; Zhao, J., Xu, H., Liu, H., Wu, J., Zheng, Y., Wu, D., Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors (2019) Transp. Res. Part Emerg. Technol, 100, pp. 68-87; Varol, G., Romero, J., Martin, X., Mahmood, N., Black, M.J., Laptev, I., Schmid, C., Learning from synthetic humans Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 109-117. , Honolulu, HI, USA, 21–26 July 2017; Malik, J., Elhayek, A., Nunnari, F., Varanasi, K., Tamaddon, K., Heloir, A., Stricker, D., Deephps: End-to-end estimation of 3d hand pose and shape by learning from synthetic depth Proceedings of the 2018 International Conference on 3D Vision (3DV), pp. 110-119. , Verona, Italy, 5–8 September 2018; https://velodynelidar.com/products/hdl-32e/, Velodyne HDL-32E. (accessed on 28 May 2020); Satake, S., Kaczmarek, T., Brščić, D., Kanda, T., Facilitating software development for mobile social robots by simulating interactions between a robot and pedestrians Proceedings of the 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 626-627. , Daegu, Korea, 11–14 March 2019; https://www.openrobots.org/morse/, The MORSE Simulator. (accessed on 28 May 2020); Willems, J., Corbetta, A., Menkovski, V., Toschi, F., (2020) Pedestrian orientation dynamics from high-fidelity measurements, , arXiv arXiv:2001.04646; Goodfellow, I., Bengio, Y., Courville, A., (2016) Deep Learning, , MIT Press: Cambridge, MA, USA; Mirnig, N., Stollnberger, G., Miksch, M., Stadler, S., Giuliani, M., Tscheligi, M., To err is robot: How humans assess and act toward an erroneous social robot (2017) Front. Robot. AI, 4, p. 21","Brščić, D.; Graduate School of Informatics, Japan; email: drazen@i.kyoto-u.ac.jp",,,"MDPI AG",,,,,14248220,,,"32708707","English","Sensors",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85088028313
"Ashley A.L., Arvaneh M.","57209889481;37078815700;","Improving EEG-based error detection using relative peak features",2020,"Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS","2020-July",,"9176376","272","275",,1,"10.1109/EMBC44109.2020.9176376","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090996462&doi=10.1109%2fEMBC44109.2020.9176376&partnerID=40&md5=0c5240f054ea08b4007f2e5cef8f9635","University of Sheffield, Automatic Control and System Engineering Department, Sheffield, United Kingdom","Ashley, A.L., University of Sheffield, Automatic Control and System Engineering Department, Sheffield, United Kingdom; Arvaneh, M., University of Sheffield, Automatic Control and System Engineering Department, Sheffield, United Kingdom","A brain-computer interface (BCI) potentially enables a severely disabled person to communicate using brain signals. Automatic detection of error-related potentials (ErrPs) in electroencephalograph (EEG) could improve BCI performance by allowing to correct the erroneous action made by the machine. However, the current low accuracy in detecting ErrPs, particularly in some users, can reduce its potential benefits. The paper addresses this problem by proposing a novel relative peak feature (RPF) selection method to improve performance and accuracy for recognising an ErrP in the EEG. Using data collected from 29 participants with a mean age of 24.14 years the relative peak features yielded an average across all classifiers of 81.63% accuracy in detecting the erroneous events and an average 78.87 % accuracy in detecting the correct events, using KNN, SVM and LDA classifiers. In comparison to the temporal feature selection, there was a gain in performance in all classifiers of 17.85% for error accuracy and a reduction of -6.16% for correct accuracy Specifically; our proposed RPF used significantly reduced the number of features by 91.7% when compared with the state of the art temporal features.In the future, this work will improve the human-robot interaction by improving the accuracy of detecting errors that enable the BCI to correct any mistakes. © 2020 IEEE.","BCI; ERP; Error detection; ErrP; KNN; LDA; Relative peak features; RPF; SVM","Brain computer interface; Disabled persons; Electroencephalography; Error detection; Human robot interaction; Automatic Detection; Brain signals; Error related potentials; Improve performance; LDA classifiers; Potential benefits; Selection methods; State of the art; Feature extraction",,,,,,,,,,,,,,,,"Falkenstein, M., Hoormann, J., Christ, S., Hohnsbein, J., (2000) ERP Components on Reaction Errors and Their Functional Significance: A Tutorial, pp. 87-107; Debener, S., Ullsperger, M., Siegel, M., Fiehler, K., Von Cramon, D.Y., Engel, A.K., Trial-bytrial coupling of concurrent electroencephalogram and functional magnetic resonance imaging Identifies the Dynamics of Performance Monitoring (2005) The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 25 (50), pp. 11730-11737; Frank, M.J., Woroch, B.S., Curran, T., Error-related negativity predicts reinforcement learning and conflict biases (2005) Neuron, 47 (4), pp. 495-501; Themanson, J.R., Rosen, P.J., Pontifex, M.B., Hillman, C.H., McAuley, E., Alterations in error-related brain activity and post-error behavior over time (2012) Brain and Cognition, 80 (2), pp. 257-265. , http://dx.doi.org/10.1016/j.bandc.2012.07.003; Holroyd, C.B., Coles, M.G.H., The neural basis. of human error processing: Reinforcement learning, dopamine, and the error-related negativity (2002) Psychological Review-New York, 109 (4), pp. 679-709. , papers3://publication/doi/10.1037//0033-295X.109.4.679; Taylor, S.F., Stern, E.R., Gehring, W.J., Neural systems for error monitoring (2007) The Neuroscientist, 13 (2), pp. 160-172; Falkenstein, M., Hohnsbein, J., Hoormann, J., Blanke, L., Effects of crossmodal divided attention on ERP components: Error processing in choice reaction tasks (1991) Electroencephalography and Clinical Neurophysiology, 78 (6), pp. 447-455; Gehring, W.J., Goss, B., Coles, M.G.H., Meyer, D.E., Donchin, E., A neural system eor error detection and compensation (1993) Psychological Science, 4 (6), pp. 385-391; Ferrez, P.W., You are wrong!-automatic detection of interaction errors from brain waves (2005) Proceedings of the 19th International Joint Conference on Artificial Intelligence; Ferrez, P.W., Del Millan, R.J., Error-related EEG potentials generated during simulated brain-computer interaction (2008) IEEE Transactions on Biomedical Engineering, 55 (3), pp. 923-929; Bi, L., Fan, X.A., Liu, Y., EEG-based brain-controlled mobile robots: A survey (2013) IEEE Transactions on HumanMachine Systems, 43 (2), pp. 161-176; World medical association declaration of helsinki (2001) Bulletin of the World Health Organization., 79 (4), pp. 373-374. , World Medical Association; Lacey, D.E.A., Error Awareness Dot Task (EADT) (2015) Trinity College Dublin, School of Psychology, Tech. Rep; Hester, R., Foxe, J.J., Molholm, S., Shpaner, M., Garavan, H., (2005) Neural Mechanisms Involved in Error Processing : A Comparison of Errors Made with and Without Awareness, 27, pp. 602-608; Ashley, A.L., Mihaylova, L.S., Arvaneh, M., A Novel Adaptive Decision Surface Classifier for Improving Error Detection Using Brain Signals in BCI.""",,,,"Institute of Electrical and Electronics Engineers Inc.","42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020","20 July 2020 through 24 July 2020",,162693,1557170X,9781728119908,,,"English","Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS",Conference Paper,"Final","",Scopus,2-s2.0-85090996462
"Pinto-Fernandez D., Torricelli D., Sanchez-Villamanan M.D.C., Aller F., Mombaur K., Conti R., Vitiello N., Moreno J.C., Pons J.L.","57204241527;24479212400;57195493650;57191261774;55885381400;57204245126;14055167900;35299717000;7202123192;","Performance Evaluation of Lower Limb Exoskeletons: A Systematic Review",2020,"IEEE Transactions on Neural Systems and Rehabilitation Engineering","28","7","9103122","1573","1583",,21,"10.1109/TNSRE.2020.2989481","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087667090&doi=10.1109%2fTNSRE.2020.2989481&partnerID=40&md5=7f79669f2f6519bc3886a67f0ab64110","Neural Rehabilitation Group, Cajal Institute, Spanish Research Council, Madrid, 28002, Spain; Chair Optimization, Robotics and Biomechanics (ORB), Institute of Computer Engineering (ZITI), Heidelberg University, Heidelberg, 69120, Germany; Iuvo S.R.L., Pontedera, 56025, Italy; Scuola Superiore Sant'Anna, BioRobotics Institute, Pisa, 56127, Italy; Scientific Chair, Legs and Walking AbilityLab, Shirley Ryan AbilityLab (Formerly Rehabilitation Institute of Chicago), Chicago, IL  60611, United States","Pinto-Fernandez, D., Neural Rehabilitation Group, Cajal Institute, Spanish Research Council, Madrid, 28002, Spain; Torricelli, D., Neural Rehabilitation Group, Cajal Institute, Spanish Research Council, Madrid, 28002, Spain; Sanchez-Villamanan, M.D.C., Neural Rehabilitation Group, Cajal Institute, Spanish Research Council, Madrid, 28002, Spain; Aller, F., Chair Optimization, Robotics and Biomechanics (ORB), Institute of Computer Engineering (ZITI), Heidelberg University, Heidelberg, 69120, Germany; Mombaur, K., Chair Optimization, Robotics and Biomechanics (ORB), Institute of Computer Engineering (ZITI), Heidelberg University, Heidelberg, 69120, Germany; Conti, R., Iuvo S.R.L., Pontedera, 56025, Italy; Vitiello, N., Scuola Superiore Sant'Anna, BioRobotics Institute, Pisa, 56127, Italy; Moreno, J.C., Scuola Superiore Sant'Anna, BioRobotics Institute, Pisa, 56127, Italy; Pons, J.L., Scientific Chair, Legs and Walking AbilityLab, Shirley Ryan AbilityLab (Formerly Rehabilitation Institute of Chicago), Chicago, IL  60611, United States","Benchmarks have long been used to verify and compare the readiness level of different technologies in many application domains. In the field of wearable robots, the lack of a recognized benchmarking methodology is one important impediment that may hamper the efficient translation of research prototypes into actual products. At the same time, an exponentially growing number of research studies are addressing the problem of quantifying the performance of robotic exoskeletons, resulting in a rich and highly heterogeneous picture of methods, variables and protocols. This review aims to organize this information, and identify the most promising performance indicators that can be converted into practical benchmarks. We focus our analysis on lower limb functions, including a wide spectrum of motor skills and performance indicators. We found that, in general, the evaluation of lower limb exoskeletons is still largely focused on straight walking, with poor coverage of most of the basic motor skills that make up the activities of daily life. Our analysis also reveals a clear bias towards generic kinematics and kinetic indicators, in spite of the metrics of human-robot interaction. Based on these results, we identify and discuss a number of promising research directions that may help the community to attain a comprehensive benchmarking methodology for robot-assisted locomotion more efficiently. © 2001-2011 IEEE.","assessment; Benchmarking; locomotion; orthoses; posture; walking; wearable robots","Exoskeleton (Robotics); Human robot interaction; Walking aids; Activities of Daily Life; Benchmarking methodology; Lower-limb function; Performance indicators; Readiness levels; Research prototype; Robotic exoskeletons; Systematic Review; Benchmarking; Article; cognition; data base; electromyography; endurance; ergonomics; human; kinematics; motor performance; muscle contraction; musculoskeletal system parameters; nonhuman; physical performance; range of motion; six minute walk test; spatiotemporal analysis; systematic review; walking; weight bearing; biomechanics; exoskeleton (rehabilitation); locomotion; lower limb; Biomechanical Phenomena; Exoskeleton Device; Humans; Locomotion; Lower Extremity; Walking",,,,,"Horizon 2020 Framework Programme, H2020: 779963; European Cooperation in Science and Technology, COST: CA16116","Manuscript received May 31, 2019; revised November 21, 2019; accepted January 3, 2020. Date of publication May 28, 2020; date of current version July 8, 2020. This work was supported in part by the H2020 Project EUROBENCH under Grant 779963, and in part by the COST Action under Grant CA16116. (David Pinto-Fernandez and Diego Torricelli contributed equally to this work.) (Corresponding author: Diego Torricelli.) David Pinto-Fernandez, Diego Torricelli, Maria del Carmen Sanchez-Villamanan, and Juan C. Moreno are with the Neural Rehabilitation Group, Cajal Institute, Spanish Research Council, 28002 Madrid, Spain (e-mail: diego.torricelli@csic.es).",,,,,,,,,,"Lajeunesse, V., Vincent, C., Routhier, F., Careau, E., Michaud, F., Exoskeletons' design and usefulness evidence according to a systematic review of lower limb exoskeletons used for functional mobility by people with spinal cord injury (2016) Disab. Rehabil., Assistive Technol., 11 (7), pp. 535-547. , Oct; Dijkers, M.P., Akers, K.G., Dieffenbach, S., Galen, S.S., Systematic reviews of clinical benefits of exoskeleton use for gait and mobility in neurologic disorders: A tertiary study (2019) Arch. Phys. Med. Rehabil, , Mar; Torricelli, D., Del Ama, A., Gonzalez, J., Moreno, J., Gil, A., Pons, J., Benchmarking lower limb wearable robots: Emerging approaches and technologies (2015) Proc. 8th Acm Int. Conf. PErvasive Technol. Related Assistive Environ., pp. 1-4; He, Y., Eguren, D., Luu, T.P., Contreras-Vidal, J.L., Risk management and regulations for lower limb medical exoskeletons: A review (2017) Med. Devices: Evidence Res., 10, pp. 89-107. , May; Riener, R., The cybathlon promotes the development of assistive technology for people with physical disabilities (2016) J. Neuroeng. 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"Feng Y., Barakova E.I., Yu S., Hu J., Matthias Rauterberg G.W.","57196468593;35614959800;13906567800;56442891500;6701479494;","Effects of the level of interactivity of a social robot and the response of the augmented reality display in contextual interactions of people with dementia",2020,"Sensors (Switzerland)","20","13","3771","1","12",,4,"10.3390/s20133771","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087435746&doi=10.3390%2fs20133771&partnerID=40&md5=0c42851ce04ed89f951500f06f106064","Department of Industrial Design, Eindhoven University of Technology, Eindhoven, 5600 MB, Netherlands; Department of Industrial Design, Northwestern Polytechnical University, Xi’an, 710072, China","Feng, Y., Department of Industrial Design, Eindhoven University of Technology, Eindhoven, 5600 MB, Netherlands, Department of Industrial Design, Northwestern Polytechnical University, Xi’an, 710072, China; Barakova, E.I., Department of Industrial Design, Eindhoven University of Technology, Eindhoven, 5600 MB, Netherlands; Yu, S., Department of Industrial Design, Northwestern Polytechnical University, Xi’an, 710072, China; Hu, J., Department of Industrial Design, Eindhoven University of Technology, Eindhoven, 5600 MB, Netherlands; Matthias Rauterberg, G.W., Department of Industrial Design, Eindhoven University of Technology, Eindhoven, 5600 MB, Netherlands","The well-being of people with dementia (PWD) living in long-term care facilities is hindered due to disengagement and social isolation. Animal-like social robots are increasingly used in dementia care as they can provide companionship and engage PWD in meaningful activities. While most previous human–robot interaction (HRI) research studied engagement independent from the context, recent findings indicate that the context of HRI sessions has an impact on user engagement. This study aims to explore the effects of contextual interactions between PWD and a social robot embedded in the augmented responsive environment. Three experimental conditions were compared: reactive context-enhanced robot interaction; dynamic context-enhanced interaction with a static robot; a control condition with only the dynamic context presented. Effectiveness evaluations were performed with 16 participants using four observational rating scales on observed engagement, affective states, and apathy related behaviors. Findings suggested that the higher level of interactivity of a social robot and the interactive contextualized feedback helped capture and maintain users’ attention during engagement; however, it did not significantly improve their positive affective states. Additionally, the presence of either a static or a proactive robot reduced apathy-related behaviors by facilitating purposeful activities, thus, motivating behavioral engagement. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Apathy; Context; Dementia; Engagement; Human–robot interaction; Interactivity; Social robot","Augmented reality; Economic and social effects; Neurodegenerative diseases; Patient rehabilitation; Affective state; Dynamic contexts; Effectiveness evaluation; Experimental conditions; Responsive environments; Robot interactions; Social isolation; User engagement; Social robots; aged; dementia; emotion; female; human; human relation; male; psychology; robotics; very elderly; Aged; Aged, 80 and over; Augmented Reality; Dementia; Emotions; Female; Humans; Interpersonal Relations; Male; Robotics",,,,,,,,,,,,,,,,"Cadieux, M.-A., Garcia, L.J., Patrick, J., Needs of people with dementia in long-term care: A systematic review (2013) Am. J. Alzheimer Dis. Dement, 28, pp. 723-733. , [CrossRef] [PubMed]; Hancock, G.A., Woods, B., Challis, D., Orrell, M., The needs of older people with dementia in residential care (2006) Int. J. Geriatr. Psychiatry J. 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Psychogeriatr, , [CrossRef] [PubMed]; Lazar, A., Thompson, H.J., Piper, A.M., Demiris, G., Rethinking the design of robotic pets for older adults Proceedings of the 2016 ACM Conference on Designing Interactive Systems, pp. 1034-1046. , Brisbane, Australia, 4–8 June 2016; Cohen-Mansfield, J., Marx, M.S., Freedman, L.S., Murad, H., Regier, N.G., Thein, K., Dakheel-Ali, M., The comprehensive process model of engagement (2011) Am. J. Geriatr. Psychiatry, 19, pp. 859-870. , [CrossRef]; Jones, C., Sung, B., Moyle, W., Engagement of a Person with Dementia Scale: Establishing content validity and psychometric properties (2018) J. Adv. Nurs, 74, pp. 2227-2240. , [CrossRef]; Lawton, M.P., Van Haitsma, K., Klapper, J., Observed affect in nursing home residents with Alzheimer’s disease (1996) J. Gerontol. Ser. B Psychol. Sci. Soc. Sci, 51, pp. P3-P14. , [CrossRef] [PubMed]; Jao, Y.-L., Algase, D.L., Specht, J.K., Williams, K., Developing the Person–Environment Apathy Rating for persons with dementia (2016) Aging Ment. Health, 20, pp. 861-870. , [CrossRef] [PubMed]; Kolanowski, A., Litaker, M., Buettner, L., Moeller, J., Costa, J., Paul, T., A randomized clinical trial of theory-based activities for the behavioral symptoms of dementia in nursing home residents (2011) J. Am. Geriatr. Soc, 59, pp. 1032-1041. , [CrossRef]; Fleiss, J.L., Levin, B., Paik, M.C., (2013) Statistical Methods for Rates and Proportions, , John Wiley & Sons: Hoboken, NJ, USA, [CrossRef]; Cohen, J., Citation-classic-a coefficient of agreement for nominal scales (1986) Curr. Contents Soc. Behav. Sci, 18. , [CrossRef]; Cohen-Mansfield, J., Dakheel-Ali, M., Jensen, B., Marx, M.S., Thein, K., An analysis of the relationships among engagement, agitated behavior, and affect in nursing home residents with dementia (2012) Int. Psychogeriatr, 24, pp. 742-752. , [CrossRef]; Perugia, G., Díaz-Boladeras, M., Català-Mallofré, A., Barakova, E.I., Rauterberg, M., ENGAGE-DEM: A model of engagement of people with dementia (2020) IEEE Trans. Affect. Comput, , [CrossRef]","Feng, Y.; Department of Industrial Design, Netherlands; email: Y.Feng@tue.nl
Feng, Y.; Department of Industrial Design, China; email: Y.Feng@tue.nl
Barakova, E.I.; Department of Industrial Design, Netherlands; email: E.I.Barakova@tue.nl",,,"MDPI AG",,,,,14248220,,,"32635640","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85087435746
"Castellanos-Ramos J., Trujillo-Leon A., Navas-Gonzalez R., Barbero-Recio F., Sanchez-Duran J.A., Oballe-Peinado O., Vidal-Verdu F.","27267493400;56029085400;6507797243;57217196789;36093366000;27267867200;6602727727;","Adding proximity sensing capability to tactile array based on off-the-shelf FSR and PSoC",2020,"IEEE Transactions on Instrumentation and Measurement","69","7","8853265","4238","4250",,2,"10.1109/TIM.2019.2944555","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086703150&doi=10.1109%2fTIM.2019.2944555&partnerID=40&md5=1916b02696c9072ae77efb88497430be","Departamento de Electrónica, Universidad de Málaga, Málaga, 29071, Spain; Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, 29010, Spain; Institute for Intelligent Systems and Robotics (ISIR), Sorbonne University, Paris, 75005, France; TDK Group, Madrid, 28906, Spain","Castellanos-Ramos, J., Departamento de Electrónica, Universidad de Málaga, Málaga, 29071, Spain, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, 29010, Spain; Trujillo-Leon, A., Institute for Intelligent Systems and Robotics (ISIR), Sorbonne University, Paris, 75005, France; Navas-Gonzalez, R., Departamento de Electrónica, Universidad de Málaga, Málaga, 29071, Spain, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, 29010, Spain; Barbero-Recio, F., TDK Group, Madrid, 28906, Spain; Sanchez-Duran, J.A., Departamento de Electrónica, Universidad de Málaga, Málaga, 29071, Spain, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, 29010, Spain; Oballe-Peinado, O., Departamento de Electrónica, Universidad de Málaga, Málaga, 29071, Spain, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, 29010, Spain; Vidal-Verdu, F., Departamento de Electrónica, Universidad de Málaga, Málaga, 29071, Spain, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga, 29010, Spain","Tactile sensors have been incorporated into robots to help in many tasks, such as preventing damage to humans in collaborative works or assistance or in dexterous manipulation. Proximity sensors are also common in robotics and are proposed to implement pretouch in multimodal realizations. Many tactile sensors are based on off-the-shelf force-sensing resistors (FSRs). The parasitic capacitance associated with the electrodes of the FSR can be exploited to measure proximity. Moreover, it is possible to implement both force and proximity signal acquisition with a single chip. This article presents a multimodal proximity and tactile patch based on commercial FSR plus a programmable system on chip (PSoC). The ability to measure proximity is achieved without adding any extra element. Discussions about key design issues as well as results that show the performance of the sensor patch are presented. As proximity sensing array, the range is in the order of the size of the electrodes, and it depends on the properties of the object nearby, as expected. The best performance is observed for conductive objects. Therefore, this approach is especially interesting in human-robot interaction tasks, such as shaping robotic hands or grippers around the human body. Furthermore, dielectric objects are also detected, so it can also be used to implement pretouch in manipulation tasks. © 1963-2012 IEEE.","Multimodal sensors; pretouch; programmable system on chip (PSoC); proximity sensors; tactile sensors","Capacitance; Electrodes; Object detection; Robotics; System-on-chip; Collaborative Work; Dexterous manipulation; Dielectric objects; Force sensing resistor; Parasitic capacitance; Programmable system on chips; Proximity sensing; Signal acquisitions; Human robot interaction",,,,,"European Regional Development Fund, FEDER: TEC2015-67642-R","Manuscript received February 12, 2019; revised September 10, 2019; accepted September 16, 2019. Date of publication September 30, 2019; date of current version June 9, 2020. This work was supported in part by the Spanish Government and in part by the European ERDF Program Funds under Contract TEC2015-67642-R. The Associate Editor coordinating the review process was Bruno Ando. (Corresponding author: Julián Castellanos-Ramos.) J. Castellanos-Ramos, R. Navas-González, J. A. Sánchez-Durán, Ó. Oballe-Peinado, and F. Vidal-Verdú are with the Departamento de Electrónica, Universidad de Málaga, 29071 Málaga, Spain, and also with the Instituto de Investigación Biomédica de Málaga (IBIMA), 29010 Málaga, Spain (e-mail: jcramos@uma.es).",,,,,,,,,,"Lumelsky, V.J., Shur, M.S., Wagner, S., Sensitive skin (2001) IEEE Sensors J., 1 (1), pp. 41-51. , Jun; Novak, J.L., Feddema, I.T., A capacitance-based proximity sensor for whole arm obstacle avoidance (1992) Proc. IEEE Int. Conf. Robot. Automat., pp. 1307-1314. , May; Patel, R., Correll, N., Integrated force and distance sensing using elastomer-embedded commodity proximity sensors (2016) Proc. Robot., 12. , Sci. Syst., Ann Arbor, MI, USA Jun; Koyama, K., Suzuki, Y., Ming, A., Shimojo, M., Integrated control of a multi-fingered hand and arm using proximity sensors on the fingertips (2016) Proc. IEEE Int. Conf. Robot. Automat. (ICRA), pp. 4282-4288. , May; Navarro, S.E., Schonert, M., Hein, B., Wörn, H., 6D proximity servoing for preshaping and haptic exploration using capacitive tactile proximity sensors (2014) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 7-14. , Sep; Mayton, B., LeGrand, L., Smith, J.R., An electric field pretouch system for grasping and co-manipulation (2010) Proc. IEEE Int. Conf. Robot. Automat., pp. 831-838. , May; Tsai, J.-R., Lin, P.-C., A low-computation object grasping method by using primitive shapes and in-hand proximity sensing (2017) Proc. IEEE Int. Conf. Adv. Intell. Mechatronics (AIM), pp. 497-502. , Jul; Hsiao, K., Nangeroni, P., Huber, M., Saxena, A., Ng, A.Y., Reactive grasping using optical proximity sensors (2009) Proc. IEEE Int. Conf. Robot. Automat., pp. 2098-2105. , May; Konstantinova, J., Stilli, A., Faragasso, A., Althoefer, K., Fingertip proximity sensor with realtime visual-based calibration (2016) Proc. IEEE Int. Conf. Intell. Robots Syst., pp. 170-175. , Oct; Maldonado, A., Alvarez, H., Beetz, M., Improving robot manipulation through fingertip perception (2012) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 2947-2954. , Oct; Shimonomura, K., Nakashima, H., Nozu, K., Robotic grasp control with high-resolution combined tactile and proximity sensing (2016) Proc. IEEE Int. Conf. Robot. Automat. (ICRA), pp. 138-143. , May; Yang, B., Lancaster, P., Smith, J.R., Pre-touch sensing for sequential manipulation (2017) Proc. IEEE Int. Conf. Robot. Automat. (ICRA), pp. 5088-5095. , May/Jun; Koyama, K., Shimojo, M., Senoo, T., Ishikawa, M., High-speed highprecision proximity sensor for detection of tilt, distance, and contact (2018) IEEE Robot. Autom. Lett., 3 (4), pp. 3224-3231. , Oct; Mittendorfer, P., Yoshida, E., Cheng, G., Realizing whole-body tactile interactions with a self-organizing, multi-modal artificial skin on a humanoid robot (2015) Adv. Robot., 29 (1), pp. 51-67; Yousef, H., Boukallel, M., Althoefer, K., Tactile sensing for dexterous in-hand manipulation in robotics-A review (2011) Sens. Actuators A, Phys., 167 (2), pp. 171-187; Göger, D., Alagi, H., Wörn, H., Tactile proximity sensors for robotic applications (2013) Proc. IEEE Int. Conf. Ind. Technol. (ICIT), pp. 978-983. , Feb; Lee, H.-K., Chang, S.-I., Yoon, E., Dual-mode capacitive proximity sensor for robot application: Implementation of tactile and proximity sensing capability on a single polymer platform using shared electrodes (2009) IEEE Sensors J., 9 (12), pp. 1748-1755. , Dec; Kim, J.-O., Highly ordered 3D microstructure-based electronic skin capable of differentiating pressure, temperature, and proximity (2018) ACS Appl. Mater. Interfaces, 11, pp. 1503-1511. , Dec; Vidal-Verdú, F., A large area tactile sensor patch based on commercial force sensors (2011) Sensors, 11 (5), pp. 5489-5507; Saudabayev, A., Varol, H.A., Sensors for robotic hands: A survey of state of the art (2015) IEEE Access, 3, pp. 1765-1782; (2019), https://www.cypress.com/part/cy8c28445-24pvxi, Cypress Semiconductor Corporation. CY8C28445-24PVXI. Accessed: Jul. 1; (2019), http://www.produktinfo.conrad.com/datenblaetter/500000-524999/503368-an-01-en-DRUCKSENSOR_FSR_400.pdf, Interlink Electronics. (2017). FSR Integration Guide. Accessed: Feb. 4; Kalantari, M., Dargahi, J., Kövecses, J., Mardasi, M.G., Nouri, S., A new approach for modeling piezoresistive force sensors based on semiconductive polymer composites (2012) IEEE/ASME Trans. Mechatronics, 17 (3), pp. 572-581. , Jun; Barber, J.R., Bounds on the electrical resistance between contacting elastic rough bodies (2003) Proc. Roy. Soc. London. A, Math., Phys. Eng. Sci., 459 (2029), pp. 53-66; Pastewka, L., Prodanov, N., Lorenz, B., Müser, M.H., Robbins, M.O., Persson, B.N.J., Finite-size scaling in the interfacial stiffness of rough elastic contacts (2013) Phys. Rev. E, Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top., 87 (6). , Jun; Hu, X., Yang, W., Planar capacitive sensors-designs and applications (2010) Sensor Rev., 30 (1), pp. 24-39; Chen, Z., Luo, R.C., Design and implementation of capacitive proximity sensor using microelectromechanical systems technology (1998) IEEE Trans. Ind. Electron., 45 (6), pp. 886-894. , Dec; (2019), https://goo.gl/bM4sjl, Cypress Semiconductor Corporation. (2016). Getting Started With CapSense. Accessed: Feb. 4","Castellanos-Ramos, J.; Departamento de Electrónica, Spain; email: jcramos@uma.es",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,00189456,,IEIMA,,"English","IEEE Trans. Instrum. Meas.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85086703150
"Chen X., Shao J., Tian H., Li X., Wang C., Luo Y., Li S.","56883949500;15060602900;40662167600;40661581400;56263985400;57208784912;57216873912;","Scalable Imprinting of Flexible Multiplexed Sensor Arrays with Distributed Piezoelectricity-Enhanced Micropillars for Dynamic Tactile Sensing",2020,"Advanced Materials Technologies","5","7","2000046","","",,8,"10.1002/admt.202000046","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085119741&doi=10.1002%2fadmt.202000046&partnerID=40&md5=a3dc7d676e92ae026bd437c0daea7a4f","State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi  710049, China","Chen, X., State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi  710049, China; Shao, J., State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi  710049, China; Tian, H., State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi  710049, China; Li, X., State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi  710049, China; Wang, C., State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi  710049, China; Luo, Y., State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi  710049, China; Li, S., State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi  710049, China","The ongoing revolution of human–robot interactions and electronic skins has created new requirements for tactile sensors, including good mechanical flexibility, high sensitivity, and the availability of distributed pixels for detecting force distribution. Here, a highly sensitive flexible sensor array based on piezoelectricity-enhanced vertically aligned P(VDF-TrFE) micropillars is developed for dynamic tactile sensing. The core piezoelectric sensing micropillars are fabricated using a straightforward and scalable nanoimprinting technology and then sandwiched between a pair of cross electrode arrays to construct multiplexed sensor arrays. Due to the proposed structural design and nanoimprinting methodology, the sensor pixels exhibit uniform output generation, robust output stability, and scalable fabrication ability. In addition, taking advantage of the high compressibility and enhanced strain of the piezoelectric micropillars compared to planar films, the microstructured sensors show an enhanced sensitivity of 228.2 mV N−1 and a highly linear response to loads. By integrating the flexible sensor with a portable signal processing circuit, a complete tactile sensing system is successfully developed to provide clear intuitive user interfaces. The good flexibility and robust stability of the sensor arrays enable them to be attached onto curved surface for real-time tracking of dynamic force and imaging the force distribution. © 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim","flexible sensor arrays; micropillars; nanoimprinting; piezoelectricity; tactile sensors","Crystallography; Human robot interaction; Piezoelectric devices; Piezoelectricity; Pixels; Structural design; User interfaces; Enhanced sensitivity; Flexible sensor array; Intuitive user interface; Mechanical flexibility; Multiplexed sensor array; Nanoimprinting technology; Signal processing circuits; Tactile sensing system; Flexible electronics",,,,,"2017YFB1102900; National Natural Science Foundation of China, NSFC: 51522508, 51905415, 91323303; China Postdoctoral Science Foundation: 2019M653588; National Postdoctoral Program for Innovative Talents: BX20180251","This work was supported by the National Natural Science Foundation of China (Grant Nos. 91323303, 51905415, and 51522508), China Gas Turbine Establishment of Aero Engine Corporation of China, National Key Research and Development Projects (2017YFB1102900), National Postdoctoral Program for Innovative Talents (No. BX20180251), and China Postdoctoral Science Foundation (No. 2019M653588). 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Kim, IEEE, Jeju, Korea (South), June; Vuorinen, T., Zakrzewski, M., Rajala, S., Lupo, D., Vanhala, J., Palovuori, K., Tuukkanen, S., (2014) Adv. Funct. Mater., 24, p. 6340; Yu, P., Liu, W., Gu, C., Cheng, X., Fu, X., (2016) Sensors, 16, p. 819; Chunyan, L., Pei-Ming, W., Soohyun, L., Gorton, A., Schulz, M.J., Ahn, C.H., (2008) J. Microelectromech. Syst., 17, p. 334; Kim, M.-S., Ahn, H.-R., Lee, S., Kim, C., Kim, Y.-J., (2014) Sens. Actuators, A, 212, p. 151; Park, M., Bok, B.-G., Ahn, J.-H., Kim, M.-S., (2018) Micromachines, 9, p. 321; Choi, Y.Y., Yun, T.G., Qaiser, N., Paik, H., Roh, H.S., Hong, J., Hong, S., No, K., (2015) Sci. Rep., 5","Shao, J.; State Key Laboratory for Manufacturing Systems Engineering, China; email: jyshao@mail.xjtu.edu.cn",,,"Wiley-Blackwell",,,,,2365709X,,,,"English","Adv. Mater. Technol.",Article,"Final","",Scopus,2-s2.0-85085119741
"Andriella A., Torras C., Alenyà G.","57202547412;7003421114;6508265237;","Short-Term Human–Robot Interaction Adaptability in Real-World Environments",2020,"International Journal of Social Robotics","12","3",,"639","657",,4,"10.1007/s12369-019-00606-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076511966&doi=10.1007%2fs12369-019-00606-y&partnerID=40&md5=43421a4de20a33f733ff8ef60f8b44fe","CSIC-UPC, Institut de Robòtica i Informàtica Industrial, C/Llorens i Artigas 4-6, Barcelona, 08028, Spain","Andriella, A., CSIC-UPC, Institut de Robòtica i Informàtica Industrial, C/Llorens i Artigas 4-6, Barcelona, 08028, Spain; Torras, C., CSIC-UPC, Institut de Robòtica i Informàtica Industrial, C/Llorens i Artigas 4-6, Barcelona, 08028, Spain; Alenyà, G., CSIC-UPC, Institut de Robòtica i Informàtica Industrial, C/Llorens i Artigas 4-6, Barcelona, 08028, Spain","In recent years there has been an increasing interest in deploying robotic systems in public environments able to effectively interact with people. To properly work in the wild, such systems should be robust and be able to deal with complex and unpredictable events that seldom happen in controlled laboratory conditions. Moreover, having to deal with untrained users adds further complexity to the problem and makes the task of defining effective interactions especially difficult. In this work, a Cognitive System that relies on planning is extended with adaptive capabilities and embedded in a Tiago robot. The result is a system able to help a person to complete a predefined game by offering various degrees of assistance. The robot may decide to change the level of assistance depending on factors such as the state of the game or the user performance at a given time. We conducted two days of experiments during a public fair. We selected random users to interact with the robot and only for one time. We show that, despite the short-term nature of human–robot interactions, the robot can effectively adapt its way of providing help, leading to better user performances as compared to a robot not providing this degree of flexibility. © 2019, Springer Nature B.V.","Adaptive Behaviour; Cognitive System; Human–Robot Interaction; Short-Term Human–Robot Interaction; Social Robotics","Agricultural robots; Cognitive systems; Social robots; Adaptive capabilities; Controlled laboratories; Degree of flexibility; Effective interactions; Public environment; Real world environments; Robot interactions; User performance; Robot programming",,,,,"Horizon 2020 Framework Programme, H2020; H2020 Marie Skłodowska-Curie Actions, MSCA: 721619; Ministerio de Ciencia e Innovación, MICINN: TIN2017-90086-R; Agencia Estatal de Investigación, AEI: MDM-2016-0656","This project has been partially funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement SOCRATES (No 721619), by the Spanish Ministry of Science and Innovation HuMoUR (TIN2017-90086-R), and by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656).",,,,,,,,,,"Aaltonen, I., Arvola, A., Heikkilä, P., Lammi, H., Hello pepper, may i tickle you?: Children’s and adults’ responses to an entertainment robot at a shopping mall (2017) Proceedings of the Companion of the 12Th ACM/IEEE International Conference on human–robot Interaction, pp. 53-54. , NY, USA; Alami, R., Clodic, A., Montreuil, V., Sisbot, E., Chatila, R., Task planning for human–robot interaction (2005) Proceedings of the Joint Conference on Smart Objects and Ambient Intelligence: Innovative Context-Aware Services: Usages and Technologies, pp. 81-85; Andriella, A., Alenyà, G., Hernández-Farigola, J., Torras, C., Deciding the different robot roles for patient cognitive training (2018) Int J Hum Comput Stud, 117, pp. 20-29; Bennewitz, M., Faber, F., Joho, D., Schreiber, M., Behnke, S., Towards a humanoid museum guide robot that interacts with multiple persons (2005) Proceedings of the 5Th IEEE-RAS International Conference on Humanoid Robots, pp. 418-423; Bensch, S., Jevtić, A., Hellström, T., On interaction quality in human-robot interaction (2017) Proceedings of the 9Th International Conference on Agents and Artificial Intelligence, 2, pp. 182-189; Breazeal, C., Dautenhahn, K., Kanda, T., Social Robotics (2016) Springer handbook of robotics, pp. 1935-1972. , Siciliano B, Khatib O, (eds), Springer, Cham; Cashmore, M., Fox, M., Long, D., Magazzeni, D., Ridder, B., Carrera, A., Palomeras, N., Carreras, M., ROSPlan: Planning in the robot operating system (2015) Proceedings of International Conference on Ai Planning and Scheduling, pp. 333-341; Chan, J., Nejat, G., Social intelligence for a robot engaging people in cognitive training activities (2012) Int J Adv Robot Syst, 9, pp. 1-13; Chen, Y., Wu, F., Shuai, W., Chen, X., Robots serve humans in public places-KeJia robot as a shopping assistant (2017) Int J Adv Robot Syst, 14 (3), pp. 1-20; Coles, A.J., Coles, A., Fox, M., Long, D., Forward-chaining partial-order planning (2010) 20Th International Conference on Automated Planning & Scheduling, pp. 42-49; Dayoub, F., Morris, T., Corke, P., Rubbing shoulders with mobile service robots (2015) IEEE Access, 3, pp. 333-342; Fox, M., Long, D., PDDL2.1: An extension to PDDL for expressing temporal planning domains (2003) J Artif Intell Res, 20, pp. 61-124; Gao, Y., Barendregt, W., Obaid, M., Castellano, G., When robot personalisation does not help: Insights from a robot-supported learning study (2018) RO-MAN 2018 - 27Th IEEE International Symposium on Robot and Human Interactive Communication, 1, pp. 705-712; Gordon, G., Spaulding, S., Westlund, J.K., Lee, J.J., Plummer, L., Martinez, M., Das, M., Breazeal, C., Affective personalization of a social robot tutor for children’s second language skills (2016) Proceedings of the 30Th Conference on Artificial Intelligence, pp. 3951-3957; Hemminghaus, J., Kopp, S., Towards adaptive social behavior generation for assistive robots using reinforcement learning (2017) Proceedings of the 12Th ACM/IEEE International Conference on human–robot Interaction, pp. 332-340. , ACM Press, New York, USA; Hoffmann, J., The metric-FF planning system: translating “ignoring delete lists” to numeric state variables (2003) J Artif Intell Res, 20, pp. 291-341; Hong, A., Igharoro, O., Liu, Y., Niroui, F., Nejat, G., Benhabib, B., Investigating human–robot teams for learning-based semi-autonomous control in urban search and rescue environments (2019) J Intell Robot Syst, 94 (3), pp. 669-686; Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., Hagita, N., An affective guide robot in a shopping mall (2009) Proceedings of the 4Th ACM/IEEE International Conference on Human Robot Interaction, pp. 173-180; Lallement, R., de Silva, L., Alami, R., (2014) HATP: An HTN Planner for Robotics.; Leite, I., Castellano, G., Pereira, A., Martinho, C., Paiva, A., Empathic robots for long-term interaction: evaluating social presence, engagement and perceived support in children (2014) Int J Soc Robot, 6 (3), pp. 329-341; Matarić, M.J., Scassellati, B., Socially assistive robotics (2016) Springer Handb Robot, 6, pp. 1973-1994; Nardi, L., Iocchi, L., Representation and execution of social plans through human–robot collaboration (2014) Fifth International Conference on Social Robotics, pp. 266-275; Sabanovic, S., Michalowski, M.P., Simmons, R., Robots in the wild: Observing human–robot social interaction outside the lab (2006) 9Th IEEE International Workshop on Advanced Motion Control, pp. 596-601; Sanelli, V., Cashmore, M., Magazzeni, D., Iocchi, L., Short-term human–robot interaction through conditional planning and execution (2017) Proceedings of International Conference on Automated Planning and Scheduling, pp. 540-548; Tapus, A., Ţapus, C., Matarić, M.J., The use of socially assistive robots in the design of intelligent cognitive therapies for people with dementia (2009) IEEE International Conference on Rehabilitation Robotics, pp. 924-929; Tomic, S., Pecora, F., Saffiotti, A., Too cool for school—adding social constraints in human aware planning (2014) Proceedings of the 9Th International Workshop on Cognitive Robotics, Cogrob (ECAI); Tonkin, M., Vitale, J., Ojha, S., Williams, M.A., Fuller, P., Judge, W., Wang, X., Would you like to sample’l Robot engagement in a shopping centre (2017) 26Th IEEE International Symposium on Robot and Human Interactive Communication, pp. 42-49; Tsiakas, K., Abujelala, M., Makedon, F., Task engagement as personalization feedback for socially-assistive robots and cognitive training (2018) Technologies, 6 (2), p. 49; Yuen, H.K., Illingworth, J., Kittler, J., Ellipse detection using the hough transform (1988) Procedings of the Alvey Vision Conference, 8, pp. 1-41","Andriella, A.; CSIC-UPC, C/Llorens i Artigas 4-6, Spain; email: aandriella@iri.upc.edu",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85076511966
"Bian F., Ren D., Li R., Liang P., Wang K., Zhao L.","57195946919;57204532133;57219000160;56341925400;57157579400;57191521088;","Dynamical system based variable admittance control for physical human-robot interaction",2020,"Industrial Robot","47","4",,"623","635",,1,"10.1108/IR-12-2019-0258","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084550792&doi=10.1108%2fIR-12-2019-0258&partnerID=40&md5=4c0e71511a4552f92b6ee63ef18ea1e2","Harbin Institute of Technology, Harbin, China; Fujian (Quanzhou) HIT Institute of Engineering and TechnologyQuanzhou, China; State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China","Bian, F., Harbin Institute of Technology, Harbin, China; Ren, D., Fujian (Quanzhou) HIT Institute of Engineering and TechnologyQuanzhou, China; Li, R., State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China; Liang, P., Fujian (Quanzhou) HIT Institute of Engineering and TechnologyQuanzhou, China; Wang, K., Harbin Institute of Technology, Harbin, China; Zhao, L., Harbin Institute of Technology, Harbin, China","Purpose: The purpose of this paper is to enable robots to intelligently adapt their damping characteristics and motions in a reactive fashion toward human inputs and task requirements during physical human–robot interaction. Design/methodology/approach: This paper exploits a combination of the dynamical system and the admittance model to create robot behaviors. The reference trajectories are generated by dynamical systems while the admittance control enables robots to compliantly follow the reference trajectories. To determine how control is divided between the two models, a collaborative arbitration algorithm is presented to change their contributions to the robot motion based on the contact forces. In addition, the authors investigate to model the robot’s impedance characteristics as a function of the task requirements and build a novel artificial damping field (ADF) to represent the virtual damping at arbitrary robot states. Findings: The authors evaluate their methods through experiments on an UR10 robot. The result shows promising performances for the robot to achieve complex tasks in collaboration with human partners. Originality/value: The proposed method extends the dynamical system approach with an admittance control law to allow a robot motion being adjusted in real time. Besides, the authors propose a novel ADF method to model the robot’s impedance characteristics as a function of the task requirements. © 2020, Emerald Publishing Limited.","Artificial damping field; Compliant control; Compliant mechanisms; Cooperative robots; Dynamical systems; Haptic devices; Man machine interface (MMI); Physical human–robot interaction; Robotics","Agricultural robots; Compliance control; Damping; Dynamical systems; Machine design; Arbitration algorithm; Damping characteristics; Design/methodology/approach; Dynamical system approaches; Impedance characteristics; Physical human-robot interactions; Reference trajectories; Robot interactions; Social robots",,,,,"2017ZT015; National Natural Science Foundation of China, NSFC: 61673136; National Key Research and Development Program of China, NKRDPC: 2017YFB1301600","This work was partially supported by National Key R&D Program of China (2017YFB1301600), the National Natural Science Foundation of China (61673136) and the High Level Talent Project of Quanzhou City under Grant No.2017ZT015.",,,,,,,,,,"Abut, T., Soyguder, S., Real-time control of bilateral teleoperation system with adaptive computed torque method (2017) Industrial Robot: An International Journal, 44 (3); 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Yang, C., Zeng, C., Liang, P., Li, Z., Li, R., Su, C.-Y., Interface design of a physical human–robot interaction system for human impedance adaptive skill transfer (2017) IEEE Transactions on Automation Science and Engineering, 15 (1), pp. 329-340; Zhu, Y., Yang, C., Wei, Q., Wu, X., Yang, W., Human–robot shared control for humanoid manipulator trajectory planning (2020) Industrial Robot: The International Journal of Robotics Research and Application; Khoramshahi, M., Billard, A., A dynamical system approach to task-adaptation in physical human–robot interaction (2019) Autonomous Robots, 43 (4), pp. 927-946","Li, R.; State Key Laboratory of Robotics and System, China; email: lrf100@hit.edu.cn",,,"Emerald Group Holdings Ltd.",,,,,0143991X,,IDRBA,,"English","Ind. Robot",Article,"Final","",Scopus,2-s2.0-85084550792
"Gerłowska J., Furtak-Niczyporuk M., Rejdak K.","56180398000;57202388196;8284992700;","Robotic assistance for people with dementia: a viable option for the future?",2020,"Expert Review of Medical Devices","17","6",,"507","518",,1,"10.1080/17434440.2020.1770592","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086792831&doi=10.1080%2f17434440.2020.1770592&partnerID=40&md5=46058a0db4d2626b36c210abd1755dd7","Faculty of Education and Psychology, UMCS, Lublin, Poland; Department of Public Health, Medical University of Lublin, Lublin, Poland; Department of Neurology, Medical University of Lublin, Lublin, Poland","Gerłowska, J., Faculty of Education and Psychology, UMCS, Lublin, Poland; Furtak-Niczyporuk, M., Department of Public Health, Medical University of Lublin, Lublin, Poland; Rejdak, K., Department of Neurology, Medical University of Lublin, Lublin, Poland","Introduction: Demographic changes in society and fewer personnel working in healthcare services have resulted in an increase in the speed of development of safe, reliable robotic assistance technologies for patients with neurological diseases. This paper aims to advocate for the frailty of patients in light of the economic need for robotic assistance, discuss potential hazards, and outline related factors that influence positive outcomes. Areas covered: This article reviews the state of the art and perspectives regarding the use of robotics in older adults with dementia. We focus on current trends in the development of robotic technologies for these patients and discuss the potential hazards associated with the implementation of such cutting-edge technology in daily practice. Expert opinion: We envisage a gradual increase in the usage of robot-based devices for the management and support of patients with cognitive deficits. In particular, the introduction of artificial intelligence will enhance the functionality of these technologies, but also increase potential hazards resulting from human-robot interactions. The development of such technology must consider whether neurological syndromes are static or progressive. Progressive syndromes pose the biggest challenge since the functionality of robotic devices must adapt to patients changing cognitive and motor performance profiles. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.","Brain disorders; cognitive impairment; dementia; human-robot interaction; robots","Hazards; Intelligent robots; Neurodegenerative diseases; Neurology; Robotics; Cognitive deficits; Cutting edge technology; Demographic changes; Healthcare services; Motor performance; Neurological disease; Potential hazards; Robotic technologies; Social robots; artificial intelligence; caregiver; cognition; cognitive defect; dementia; human; human dignity; legal aspect; motor performance; Review; robotics; social bonding; clinical trial (topic); dementia; devices; robotics; Artificial Intelligence; Clinical Trials as Topic; Dementia; Humans; Robotics",,,,,"643433, PHC-19-2014; European Commission, EC; Horizon 2020","This study was supported by the European Commission under Horizon 2020 (European Union Research and Innovation program PHC-19-2014: Advancing active and healthy aging with ICT: Service robotics within assisted living environments; Grant number: 643433).",,,,,,,,,,"http://ec.europa.eu/eurostat/statistics-explained/index.php/Population_structure_and_ageing, cited 2019 Sep1; https://ec.europa.eu/health/indicators/echi/list_en, cited 2019 Sep1; https://population.un.org/wpp/Publications/Files/WPP2017_Volume-I_Comprehensive-Tables.pdf, cited 2019 Sep1; Dogangil, G., Davies, B.L., Rodriguez Y Baena, F., A review of medical robotics for minimally invasive soft tissue surgery (2010) Proc Inst Mech Eng H, 224 (5), pp. 653-679; Rosiek, A., Leksowski, K., Technology advances in hospital practices: robotics in treatment of patients (2015) Technol Cancer Res Treat, 14 (3), pp. 270-276. , Jun, Epub 2014 Sep 10; https://ifr.org/downloads/press2018/Executive_Summary_WR_Service_Robots_2018.pdf, cited 2019 Sep1; Abdi, J., Al-Hindawi, A., Ng, T., Scoping review on the use of socially assistive robot technology in elderly care (2018) BMJ Open, 8 (2), p. e018815. , Feb, 12; Kim, G.H., Jeon, S., Im, K., Structural brain changes after traditional and robot-assisted multi-domain cognitive training in community-dwelling healthy elderly (2015) PLoS ONE, 10 (4); Takayanagi, K., Kirita, T., Shibata, T., Comparison of verbal and emotional responses of elderly people with mild/moderate dementia and those with severe dementia in responses to seal robot, PARO (2014) Front Aging Neurosci, 6, p. 257; Tanaka, M., Ishii, A., Yamano, E., Effect of a human-type communication robot on cognitive function in elderly women living alone (2012) Med Sci Monit, 18 (9), pp. CR550-CR557; Moyle, W., The promise of technology in the future of dementia care (2019) Nat Rev Neurol, 15 (6), pp. 353-359; Vogan, A.A., Alnajjar, F., Gochoo, M., Robots, AI, and cognitive training in an era of mass age-related cognitive decline: a systematic review (2020) IEEE Access, 8, pp. 18284-18304; Savela, N., Turja, T., Oksanen, A., Social acceptance of robots in different occupational fields: a systematic literature review (2018) Int J Soc Robot, 10 (4), pp. 493-502; van Maris, A., Zook, N., Caleb-Solly, P., Designing ethical social robots—a longitudinal field study with older adults (2020) Front Robot, AI 7, p. 1; (2018), https://cordis.europa.eu/project/rcn/194090/results/en, [cited 2019 Sep 1]. ; Vandemeulebroucke, T., Dierckx de Casterlé, B., Gastmans, C., The use of care robots in aged care: A systematic review of argument-based ethics literature (2018) Arch Gerontol Geriatr, 74, pp. 15-25; Frennert, S., Östlund, B., Review: seven matters of concern of social robots and older people (2014) Int J of Soc Robotics, 6 (2), p. 299; Wachsmuth, I., Robots like me: challenges and ethical issues in aged care (2018) Front Psychol, 9, p. 432. , ISSN=1664-1078; Sun, W., Wilson, M.G., Schreiber, D., Ethical challenges related to assistive product access for older adults and adults living with a disability: a scoping review protocol (2017) Syst Rev, 6 (1), p. 24. , Sun; Reed, S., Building bridges between AI and cognitive psychology (2019) AI Mag, 40 (2), pp. 17-28. , Jun; Torresen, J., A review of future and ethical perspectives of robotics and AI (2018) Front Rob AI, 4. , Jan; (2019) Robotics—application of ISO 13482—part 2: application guideline, , International Organization for Standardization ISO Geneva, Switzerland; (2018) A vision for prioritizing human well-being with autonomous and intelligent systems (A/IS), , IEEE; Goodrich, M.A., Schultz, A.C., Human–Robot Interaction: A Survey (2007) Found Trends Hum-Comput Interact, 1 (3), pp. 203-275; Everitt, T., Hutter, M., Practical agents and fundamental challenges Foundations of trusted autonomy studies in systems, decision and control, p. 117. , Abbass H.A., Scholz J., Reid D.J., (eds),. 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Conference on Legal and Policy Issues Relating to Robotics; Holder, C., Khurana, V., Harrison, F., Robotics and law: key legal and regulatory implications of the robotics age (Part I of II) (2016) Comput Law Secur Rev, 32; Holder, C., Khurana, V., Hook, J., Robotics and law: key legal and regulatory implications of the robotics age (part II of II) (2016) Comput Law Secur Rev, 32; Chlopecki, A., (2018) Artificial intelligence - legal and futurological sketches, Beck, Warsaw, p. 4,6. , Warsaw Poland, Wydawnictwo C.H. Beck.,. p; https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52017IP0051,  [cited 2019 Sep 1]; https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52018DC0237, cited 2019 Sep1","Rejdak, K.; Department of Neurology, Poland; email: k.rejdak@umlub.pl",,,"Taylor and Francis Ltd",,,,,17434440,,,"32511027","English","Expert Rev. Med. Devices",Review,"Final","",Scopus,2-s2.0-85086792831
"Woo J., Inoue S., Ohyama Y., Kubota N.","36603728900;57219131376;8358930000;7202158579;","Physical contact interaction based on touch sensory information for robot partners",2020,"International Conference on Human System Interaction, HSI","2020-June",,"9142642","100","105",,1,"10.1109/HSI49210.2020.9142642","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091401027&doi=10.1109%2fHSI49210.2020.9142642&partnerID=40&md5=38a52a080d28d62aa36c2e36aa648c1d","Tokyo University of Technology, Department of Mechanical Engineering, Tokyo, Japan; Graduate School of Systems Design, Tokyo Metropolitan University, Tokyo, Japan","Woo, J., Tokyo University of Technology, Department of Mechanical Engineering, Tokyo, Japan; Inoue, S., Tokyo University of Technology, Department of Mechanical Engineering, Tokyo, Japan; Ohyama, Y., Tokyo University of Technology, Department of Mechanical Engineering, Tokyo, Japan; Kubota, N., Graduate School of Systems Design, Tokyo Metropolitan University, Tokyo, Japan","In recent years, the number of elderly people over the age of 65 who live alone has increased year by year. In particular, the frequency of conversation for the elderly tends to decrease to less than once in two weeks, and the frequency of going out from home tends also to decrease at a rate of less than once in 2-3 days. As a result, it has led to the social isolation of the elderly, and has caused social problems such as progression of dementia and lonely death [1]. For that reason, it is necessary to provide support to increase the frequency of conversation among isolated elderly people. Accordingly, researches on human robot interaction are attracting attention. The interaction with robot partners could create a good stimulating environment so that the elderly could communicate with their family and others. Therefore, we focus on the interaction between humans and robots, and develop a robot system that focuses on increasing the frequency of conversations caused by touching. In this paper, we explain the robot partner system that used touch sensory information. Then, we show experimental results of the effectiveness of robot partner system, and discuss the applicability of the proposed system. © 2020 IEEE.","Human robot interaction; Physical contact interaction; Robot partners; Touch sensory information","Elderly people; Physical contacts; Robot Partners; Robot system; Sensory information; Social isolation; Social problems; Social robots",,,,,,,,,,,,,,,,"Fujita, K., Fujiwara, Y., Kumagai, S., Watanabe, S., Yoshida, Y., Motohashi, Y., Shinkai, S., The frequency of going outdoors, and physical, psychological and social functioning among community-dwelling older adults [nihon koshu eisei zasshi] (2004) Japanese Journal of Public Health, 51 (3), pp. 168-180; Oku, A., Ichimura, E., Tsukamoto, M., (2017) Aging Population in Asian Countries-lessons from Japanese Experiences-, , Tech. Rep; Wada, K., Shibata, T., Saito, T., Tanie, K., Analysis of factors that bring mental effects to elderly people in robot assisted activity (2002) IEEE/RSJ International Conference on Intelligent Robots and Systems, 2, pp. 1152-1157. , Ieee; Tanaka, M., Ishii, A., Yamano, E., Ogikubo, H., Okazaki, M., Kamimura, K., Konishi, Y., Watanabe, Y., Effect of a human-type communication robot on cognitive function in elderly women living alone (2012) Medical Science Monitor: International Medical Journal of Experimental and Clinical Research, 18 (9), p. CR550; Ono, S., Woo, J., Matsuo, Y., Kusaka, J., Wada, K., Kubota, N., A health promotion support system for increasing motivation using a robot partner (2015) Transactions of the Institute of Systems. Control and Information Engineers, 284, pp. 161-171; Tang, D., Yusuf, B., Botzheim, J., Kubota, N., Chan, C.S., A novel multimodal communication framework using robot partner for aging population (2015) Expert Systems with Applications, 42 (9), pp. 4540-4555; Lemaignan, S., Warnier, M., Sisbot, E.A., Clodic, A., Alami, R., Artificial cognition for social human-robot interaction: An implementation (2017) Artificial Intelligence, 247, pp. 45-69; Sheridan, T.B., Human-robot interaction: Status and challenges (2016) Human Factors, 58 (4), pp. 525-532; Turkle, S., Sociable technologies: Enhancing human performance when (2002) Converging Technologies for Improving Human Performance, p. 150; Woo, J., Botzheim, J., Kubota, N., System integration for cognitive model of a robot partner (2017) Intelligent Automation & Soft Computing, pp. 1-14; Woo, J., Botzheim, J., Kubota, N., A modular cognitive model of socially embedded robot partners for information support (2017) ROBOMECH Journal, 4 (1), p. 10; Woo, J., Kubota, N., Conversation system based on computational intelligence for robot partner using smart phone (2013) 2013 IEEE International Conference on Systems, Man, and Cybernetics. IEEE, pp. 2927-2932; Buck, R., VanLear, C.A., Verbal and nonverbal communication: Distinguishing symbolic, spontaneous, and pseudo-spontaneous nonverbal behavior (2002) Journal of Communication, 52 (3), pp. 522-541; Bystrova, K., Ivanova, V., Edhborg, M., Matthiesen, A.-S., Ransjö-Arvidson, A.-B., Mukhamedrakhimov, R., Uvnäs-Moberg, K., Widström, A.-M., Early contact versus separation: Effects on mother-infant interaction one year later (2009) Birth, 36 (2), pp. 97-109; Hoshi, T., Shinoda, H., Robot skin based on touch-area-sensitive tactile element (2006) Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006. IEEE, pp. 3463-3468; Breazeal, C., Kidd, C.D., Thomaz, A.L., Hoffman, G., Berlin, M., Effects of nonverbal communication on efficiency and robustness in human-robot teamwork (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 708-713; Woo, J., Wada, K., Kubota, N., Robot partner system for elderly people care by using sensor network (2012) 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob). IEEE, pp. 1329-1334; Woo, J., Botzheim, J., Kubota, N., Verbal conversation system for a socially embedded robot partner using emotional model (2015) 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 37-42; Woo, J., Kubota, N., Interaction content design for information support based on robot partner (2017) 2017 10th International Conference on Human System Interactions (HSI). IEEE, pp. 155-160; Yamamoto, S., Woo, J., Chin, W.H., Matsumura, K., Kubota, N., Interactive information support by robot partners based on informationally structured space (2020) Journal of Robotics and Mechatronics, 32 (1), pp. 236-243; Woo, J., Kubota, N., A modular structured architecture using smart devices for socially-embedded robot partners (2020) Handbook of Research on Advanced Mechatronic Systems and Intelligent Robotics. IGI Global, pp. 288-309; Tanaka, R., Woo, J., Kubota, N., Action acquisition method for constructing cognitive development system through instructed learning (2019) 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-6; Woo, J., Botzheim, J., Kubota, N., Emotional empathy model for robot partners using recurrent spiking neural network model with hebbian-lms learning (2017) Malaysian Journal of Computer Science, 30 (4), pp. 258-285; Woo, J., Botzheim, J., Kubota, N., A socially interactive robot partner using content-based conversation system for information support (2018) Journal of Advanced Computational Intelligence and Intelligent Informatics, 22 (6), pp. 989-997",,,"Accesibility Study Group (SIGAAC);IEEE IES Technical Committee on Human Factors;Support Center for Advanced Telecommunications Technology Research (SCAT);The Institute of Electrical and Electronics Engineers (IEEE)","IEEE Computer Society","13th International Conference on Human System Interaction, HSI 2020","6 June 2020 through 8 June 2020",,161924,21582246,9781728173924,,,"English","Int. Conf. Hum. Syst. Interact., HSI",Conference Paper,"Final","",Scopus,2-s2.0-85091401027
"Hinaut X., Twiefel J.","53063834600;55995850300;","Teach your robot your language! trainable neural parser for modeling human sentence processing: Examples for 15 languages",2020,"IEEE Transactions on Cognitive and Developmental Systems","12","2","8918292","179","188",,2,"10.1109/TCDS.2019.2957006","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086800381&doi=10.1109%2fTCDS.2019.2957006&partnerID=40&md5=93a38438fdfb60c56036edbd50843f76","Inria Bordeaux Sud-Ouest, Talence, 33400, France; LaBRI, UMR 5800, CNRS, Bordeaux INP, Université de Bordeaux, Talence, France; Institut des Maladies Neurodégénératives, UMR 5293, CNRS, Université de Bordeaux, Bordeaux, 33076, France; Department of Informatics, Knowledge Technology Group, Universität Hamburg, Hamburg, 22527, Germany","Hinaut, X., Inria Bordeaux Sud-Ouest, Talence, 33400, France, LaBRI, UMR 5800, CNRS, Bordeaux INP, Université de Bordeaux, Talence, France, Institut des Maladies Neurodégénératives, UMR 5293, CNRS, Université de Bordeaux, Bordeaux, 33076, France; Twiefel, J., Department of Informatics, Knowledge Technology Group, Universität Hamburg, Hamburg, 22527, Germany","We present a recurrent neural network (RNN) that performs thematic role assignment and can be used for human-robot interaction (HRI). The RNN is trained to map sentence structures to meanings (e.g., predicates). Previously, we have shown that the model is able to generalize on English and French corpora. In this article, we investigate its ability to adapt to various languages originating from Asia or Europe. We show that it can successfully learn to parse sentences related to home scenarios in 15 languages, namely English, German, French, Spanish, Catalan, Basque, Portuguese, Italian, Bulgarian, Turkish, Persian, Hindi, Marathi, Malay, and Mandarin Chinese. Moreover, in the corpora, we have deliberately included variable complex sentences in order to explore the flexibility of the predicate-like output representations. This demonstrates that: 1) the learning principle of our model is not limited to a particular language (or particular sentence structures), but more generic in nature and 2) it can deal with various kind of representations (not only predicates), which enables users to adapt it to their own needs. As the model is inspired from neuroscience and language acquisition theories, this generic and language-independent aspect makes it a good candidate for modeling human sentence processing. Finally, we discuss the potential implementation of the model in a grounded robotic architecture. © 2016 IEEE.","Artificial intelligence; Artificial neural networks; Cognitive robotics; Human-robot interaction; Learning systems; Machine learning; Morphology; Natural language processing; Neural networks; Recurrent neural networks; Robot learning; Supervised learning","Computational neuroscience; Human robot interaction; Recurrent neural networks; Syntactics; Human robot Interaction (HRI); Human sentence processing; Language acquisition; Language independents; Recurrent neural network (RNN); Robotic architectures; Sentence structures; Variable complexes; Modeling languages",,,,,"37857TF; Deutscher Akademischer Austauschdienst, DAAD: 57317821; Campus France","Manuscript received November 1, 2017; revised October 11, 2018; accepted October 23, 2018. Date of publication December 2, 2019; date of current version June 10, 2020. This work was supported in part by the Campus France PHC Procope Project through the “LingoRob—Learning Language in Developmental Robots” under Grant 37857TF and in part by the Förderprogramm “Projektbezogener Personenaustausch Frankreich” through the DAAD Project under Grant 57317821. (Corresponding author: Xavier Hinaut.) X. Hinaut is with Inria Bordeaux Sud-Ouest, 33400 Talence, France, with the LaBRI, UMR 5800, CNRS, Bordeaux INP, Université de Bordeaux, Talence, France, and also with the Institut des Maladies Neurodégénératives, UMR 5293, CNRS, Université de Bordeaux, 33076 Bordeaux, France (e-mail: xavier.hinaut@inria.fr).","This work was supported in part by the Campus France PHC Procope Project through the ""LingoRob-Learning Language in Developmental Robots"" under Grant 37857TF",,,,,,,,,"Cruz, F., Twiefel, J., Magg, S., Weber, C., Wermter, S., Interactive reinforcement learning through speech guidance in a domestic scenario (2015) Proc. Int. Joint Conf. Neural Netw. (IJCNN), pp. 1-8; Hinaut, X., Petit, M., Pointeau, G., Dominey, P.F., Exploring the acquisition and production of grammatical constructions through human-robot interaction with echo state networks (2014) Front. Neurorobot, 8 (16); Hinaut, X., Dominey, P.F., Real-Time parallel processing of grammatical structure in the fronto-striatal system: A recurrent network simulation study using reservoir computing (2013) PLoS ONE, 8 (2). , Art. no. e52946; Dominey, P.F., Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning (1995) Biol. 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Neural Netw, pp. 505-514; Tong, M.H., Bickett, A.D., Christiansen, E.M., Cottrell, G.W., Learning grammatical structure with echo state networks (2007) Neural Netw, 20 (3), pp. 424-432; Marcus, G.F., Vijayan, S., Rao, S.B., Vishton, P.M., Rule learning by seven-month-old infants (1999) Science, 283 (5398), pp. 77-80; Goldberg, A., (1995) Constructions: A Construction Grammar Approach to Argument Structure, , Chicago, IL, USA: Univ. Chicago Press; Goldberg, A.E., Constructions: A new theoretical approach to language (2003) Trends Cogn. Sci, 7 (5), pp. 219-224; Tomasello, M., (2003) Constructing A Language: A Usage Based Approach to Language Acquisition, , Cambridge, MA, USA: Harvard Univ. Press; Bates, E., Macwhinney, B., Competition, variation, and language learning (1987) Mechanisms of Language Acquisition, pp. 157-193. , Hillsdale, NJ, USA Lawrence Erlbaum; Dominey, P.F., Hoen, M., Inui, T., A neurolinguistic model of grammatical construction processing (2006) J. Cogn. 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(ICDL-EpiRob), pp. 281-286; Croft, W., Logical and typological arguments for radical construction grammar (2005) Construction Grammars: Cognitive Grounding and Theoretical Extensions, pp. 273-314. , Amsterdam, The Netherlands: Benjamins; Roth, M., Lapata, M., Neural semantic role labeling with dependency path embeddings (2016) ArXiv Preprint arXiv:1605.07515; Connor, M., Gertner, Y., Fisher, C., Roth, D., Baby srl: Modeling early language acquisition (2008) Proc. 12th Conf. Comput. Nat. Lang. Learn, pp. 81-88; Twiefel, J., Hinaut, X., Wermter, S., Syntactic reanalysis in language models for speech recognition (2017) Proc. Joint IEEE Int. Conf. Develop. Learn. Epigenet. Robot. (ICDL-EpiRob), pp. 215-220; Dukes, K., SemEval-2014 task 6: Supervised semantic parsing of robotic spatial commands (2014) Proc. 8th Int. Workshop Semantic Eval. (SemEval), pp. 45-53; Lebani, G.E., Lenci, A., Modelling the meaning of argument constructions with distributional semantics (2017) AAAI, Menlo Park, CA, USA, Rep. SS-17-02, pp. 197-204; Enel, P., Procyk, E., Quilodran, R., Dominey, P.F., Reservoir computing properties of neural dynamics in prefrontal cortex (2016) PLoS Comput. Biol, 12 (6). , Art. no. e1004967; Chang, F., Symbolically speaking: A connectionist model of sentence production (2002) Cogn. Sci, 26 (5), pp. 609-651; Omaki, A., Lidz, J., Linking parser development to acquisition of syntactic knowledge (2015) Lang. Acquisition, 22 (2), pp. 158-192","Hinaut, X.; Inria Bordeaux Sud-OuestFrance; email: xavier.hinaut@inria.fr",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23798920,,,,"English","IEEE Trans. Cogn. Dev. Syst.",Article,"Final","",Scopus,2-s2.0-85086800381
"Ayoubi Y., Laribi M.A., Arsicault M., Zeghloul S.","57120041100;36500011400;6508120934;6701766953;","Safe pHRI via the variable stiffness safety-oriented mechanism (V2SOM): Simulation and experimental validations",2020,"Applied Sciences (Switzerland)","10","11","3810","","",,3,"10.3390/app10113810","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086131438&doi=10.3390%2fapp10113810&partnerID=40&md5=970258af5834400f2ba25579d96ba022","Dept. GMSC, Pprime Institute, CNRS, University of Poitiers, ENSMA, UPR, Poitiers, 3346, France","Ayoubi, Y., Dept. GMSC, Pprime Institute, CNRS, University of Poitiers, ENSMA, UPR, Poitiers, 3346, France; Laribi, M.A., Dept. GMSC, Pprime Institute, CNRS, University of Poitiers, ENSMA, UPR, Poitiers, 3346, France; Arsicault, M., Dept. GMSC, Pprime Institute, CNRS, University of Poitiers, ENSMA, UPR, Poitiers, 3346, France; Zeghloul, S., Dept. GMSC, Pprime Institute, CNRS, University of Poitiers, ENSMA, UPR, Poitiers, 3346, France","Robots are gaining a foothold day-by-day in different areas of people's lives. Collaborative robots (cobots) need to display human-like dynamic performance. Thus, the question of safety during physical human-robot interaction (pHRI) arises. Herein, we propose making serial cobots intrinsically compliant to guarantee safe pHRI via our novel designed device, V2SOM (variable stiffness safety-oriented mechanism). Integrating this new device at each rotary joint of the serial cobot ensures a safe pHRI and reduces the drawbacks of making robots compliant. Thanks to its two continuously linked functional modes-high and low stiffness-V2SOM presents a high inertia decoupling capacity, which is a necessary condition for safe pHRI. The high stiffness mode eases the control without disturbing the safety aspect. Once a human-robot (HR) collision occurs, a spontaneous and smooth shift to low stiffness mode is passively triggered to safely absorb the impact. To highlight V2SOM's effect in safety terms, we consider two complementary safety criteria: impact force (ImpF) criterion and head injury criterion (HIC) for external and internal damage evaluation of blunt shocks, respectively. A pre-established HR collision model is built in Matlab/Simulink (v2018, MathWorks, France) in order to evaluate the latter criterion. This paper presents the first V2SOM prototype, with quasi-static and dynamic experimental evaluations. © 2020 by the authors.","Friendly cobots; Human-robot collisions; PHRI; Safety criteria; V2SOM; Variable stiffness actuator",,,,,,"ANR-10-EQPX-44; Agence Nationale de la Recherche, ANR: ANR-14-CE27-0016","Acknowledgments: This work was supported by the French National Research Agency, convention ANR-14-CE27-0016. This work was sponsored by the French government research program “Investissements d’avenir” through the Robotex Equipment of Excellence (ANR-10-EQPX-44).",,,,,,,,,,"Lu, Y., Industry 4.0: A survey on technologies, applications and open research issues (2017) J. Ind. Inf. Integr, 6, pp. 1-10; Tobe, F., (2016) Why Co-Bots Will Be a Huge Innovation and Growth Driver for Robotics Industry, , https://www.aitrends.com/robotics/why-co-bots-will-be-a-huge-innovation-and-growth-driver-forrobotics-industry/, Available online: (accessed on 1 January); Grioli, G., Wolf, S., Garabini, M., Catalano, M.G., Burdet, E., Caldwell, D., Carloni, R., Laffranchi, M., Variable stiffness actuators: The user's point of view (2015) Int. J. Robot. Res, 34, pp. 727-743; Haddadin, S., de Luca, A., Albu-Schaffer, A., Robot Collisions: A Survey on Detection, Isolation, and Identification (2017) IEEE Trans. Robot, 33, pp. 1292-1312; Zinn, M., Khatib, O., Roth, B., Salisbury, J., Playing it safe (2004) IEEE Robot. 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Res, 22, pp. 479-504; Haddadin, S., Albu-Schaffer, A., Frommberger, M., Hirzinger, G., The role of the robot mass and velocity in physical human-robot interaction-Part II: Constrained blunt impacts (2008) Proceedings of the IEEE International Conference on Robotics and Automation, , Nice, France, 19 May; Pratt, G.A., Williamson, M.M., Series elastic actuators (1995) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, , Human Robot Interaction and Cooperative Robots, Pittsburgh, PA, USA, 5-9 August; Corral, E., García, M., Castejon, C., Meneses, J., Gismeros, R., Dynamic Modeling of the Dissipative Contact and Friction Forces of a Passive Biped-Walking Robot (2020) Appl. Sci, 10, p. 2342; Mathijssen, G., Cherelle, P., Lefeber, D., VanderBorght, B., Concept of a Series-Parallel Elastic Actuator for a Powered Transtibial Prosthesis (2013) Actuators, 2, pp. 59-73; Zinn, M., Roth, B., Khatib, O., Salisbury, J.K., A New Actuation Approach for Human Friendly Robot Design (2004) Int. J. Rob. Res, 23, pp. 379-398; Wolf, S., Grioli, G., Eiberger, O., Friedl, W., Grebenstein, M., Hoppner, H., Burdet, E., Catalano, M.G., Variable Stiffness Actuators: Review on Design and Components (2015) IEEE/ASME Trans. Mechatron, 21, pp. 2418-2430; López-Martínez, J., García-Vallejo, D., Giménez-Fernández, A., Torres-Moreno, J.L., A Flexible Multibody Model of a Safety Robot Arm for Experimental Validation and Analysis of Design Parameters (2013) J. Comput. Nonlinear Dyn, 9; Hyun, D., Yang, H.S., Park, J., Shim, Y., Variable stiffness mechanism for human-friendly robots (2010) Mech. Mach. Theory, 45, pp. 880-897; Tagliamonte, N.L., Sergi, F., Accoto, D., Carpino, G., Guglielmelli, E., Double actuation architectures for rendering variable impedance in compliant robots: A review (2012) Mechatronics, 22, pp. 1187-1203; VanderBorght, B., Albu-Schaeffer, A., Bicchi, A., Burdet, E., Caldwell, D., Carloni, R., Catalano, M.G., Ganesh, G., Variable impedance actuators: A review (2013) Robot. Auton. 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Theory, 108, pp. 110-122; Ayoubi, Y., Laribi, M.A., Arsicault, M., Zeghloul, S., Courreges, F., (2019) Mechanical Device with Variable Compliance for Rotary Motion Transmission, , WO/043068 A1, 7 March 2019; Ayoubi, Y., Laribi, M.A., Zeghloul, S., Arsicault, M., Design of V2SOM: The safety mechanism for cobot's rotary joints (2018) IFToMM Symposium on Mechanism Design for Robotics, pp. 147-157. , Springer: Cham, Switzerland; Payne, D.A.R., Patel, S., (2001) Levels Of Consciousness In Relation To Head Injury Criteria, , http://www.eurailsafe.net/subsites/operas/HTML/appendix/Table13.htm, Available online: (accessed on 1 January 2017); (2015) HIC Tolerance Levels Correlated To Brain Injury, , http://www.eurailsafe.net/subsites/operas/HTML/appendix/Table14.htm, Available online: (accessed on 3 June); Newman, J.A., A generalized acceleration model for brain injury threshold (GAMBIT) (1986) Proceedings of the International IRCOBI Conference, , Zurich, Switzerland, 2-4 September; Alén-Cordero, C., Carbone, G., Ceccarelli, M., Echávarri, J., Muñoz, J.L., Experimental tests in human-robot collision evaluation and characterization of a new safety index for robot operation (2014) Mech. Mach. Theory, 80, pp. 184-199; Gao, D., Wampler, C.W., Assessing the Danger of Robot Impact (2009) IEEE Robot. Autom. Mag, 16, pp. 71-74","Laribi, M.A.; Dept. GMSC, France; email: med.amine.laribi@univ-poitiers.fr",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85086131438
"Jiang C., Ni Z., Guo Y., He H.","56422994300;38862477100;35205046400;24802077000;","Pedestrian Flow Optimization to Reduce the Risk of Crowd Disasters Through Human-Robot Interaction",2020,"IEEE Transactions on Emerging Topics in Computational Intelligence","4","3","8805280","298","311",,1,"10.1109/TETCI.2019.2930249","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085726199&doi=10.1109%2fTETCI.2019.2930249&partnerID=40&md5=5df700c002bce7eb317ce95c62c7bb67","Stevens Institute of Technology, Hoboken, NJ  07030, United States; South Dakota State University, Brookings, SD, United States; University of Rhode Island, Kingstown, RI, United States","Jiang, C., Stevens Institute of Technology, Hoboken, NJ  07030, United States; Ni, Z., South Dakota State University, Brookings, SD, United States; Guo, Y., Stevens Institute of Technology, Hoboken, NJ  07030, United States; He, H., University of Rhode Island, Kingstown, RI, United States","Pedestrian flow in densely populated or congested areas usually presents irregular or turbulent motion state due to competitive behaviors of individual pedestrians, which reduces flow efficiency and raises the risk of crowd accidents. Effective pedestrian flow regulation strategies are highly valuable for flow optimization. Existing studies seek for optimal design of indoor architectural features and spatial placement of pedestrian facilities for the purpose of flow optimization. However, once placed, the stationary facilities are not adaptive to real-time flow changes. In this paper, we investigate the problem of regulating two merging pedestrian flows in a bottleneck area using a mobile robot moving among the pedestrian flows. The pedestrian flows are regulated through dynamic human-robot interaction (HRI) during their collective motion. We adopt an adaptive dynamic programming (ADP) method to learn the optimal motion parameters of the robot in real time, and the resulting outflow through the bottleneck is maximized with the crowd pressure reduced to avoid potential crowd disasters. The proposed algorithm is a data-driven approach that only uses camera observation of pedestrian flows without explicit models of pedestrian dynamics and HRI. Extensive simulation studies are performed in both MATLAB and a robotic simulator to verify the proposed approach and evaluate the performances. © 2017 IEEE.","and pedestrian crowd pressure; human-robot interaction; learning-based optimal control; Pedestrian flow regulation","Disaster prevention; Disasters; Dynamic programming; Man machine systems; MATLAB; Robot programming; Adaptive dynamic programming; Architectural features; Collective motions; Competitive behavior; Data-driven approach; Extensive simulations; Human robot Interaction (HRI); Pedestrian dynamics; Human robot interaction",,,,,"CMMI-1526835, CMMI-1527016, CMMI-1825709, IIS-1838799, OIA-1833005; National Science Foundation, NSF: 1527016, 1825709, 1838799","Manuscript received February 7, 2019; revised May 5, 2019 and June 24, 2019; accepted July 13, 2019. Date of publication August 19, 2019; date of current version May 23, 2020. The work of C. Jiang and Y. Guo was supported by the U.S. National Science Foundation under Grant CMMI-1527016, Grant CMMI-1825709, and Grant IIS-1838799. The work of Z. Ni was supported by the U.S. National Science Foundation under Grant OIA-1833005. The work of H. He was supported by the U.S. National Science Foundation under Grant CMMI-1526835. (Corresponding author: Yi Guo.) C. Jiang and Y. Guo are with the Stevens Institute of Technology, Hoboken, NJ 07030 USA (e-mail: cjiang6@stevens.edu; yi.guo@stevens.edu).",,,,,,,,,,"Luh, P.B., Wilkie, C.T., Chang, S.-C., Marsh, K.L., Olderman, N., Modeling and optimization of building emergency evacuation considering blocking effects on crowd movement (2012) IEEE Trans. Autom. Sci. 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"Washburn A., Adeleye A., An T., Riek L.D.","56125332600;57219251609;57219259143;27868000800;","Robot errors in proximate hri how functionality framing affects perceived reliability and trust",2020,"ACM Transactions on Human-Robot Interaction","9","3","19","","",,6,"10.1145/3380783","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091901668&doi=10.1145%2f3380783&partnerID=40&md5=a2c3ecc427055ac90ef02fdf1cf65455","Uc San Diego, San Diego, CA, United States","Washburn, A., Uc San Diego, San Diego, CA, United States; Adeleye, A., Uc San Diego, San Diego, CA, United States; An, T., Uc San Diego, San Diego, CA, United States; Riek, L.D., Uc San Diego, San Diego, CA, United States","Advancements within human-robot interaction generate increasing opportunities for proximate, goal-directed joint action (GDJA). However, robot errors are common and researchers must determine how to mitigate them. In this article, we examine how expectations for robot functionality affect people's perceptions of robot reliability and trust for a robot that makes errors. Here 35 participants (n = 35) performed a collaborative banner-hanging task with an autonomous mobile manipulator (Toyota HSR). Each participant received either a low-or high-functionality framing for the robot. We then measured how participants perceived the robot's reliability and trust prior to, during, and after interaction. Functionality framing changed how robot errors affected participant experiences of robot behavior. People with low expectations experienced positive changes in reliability and trust after interacting with the robot, while those with high expectations experienced a negative change in reliability and no change in trust. The low-expectation group also showed greater trust recovery following the robot's first error compared to the high group. Our findings inform human-robot teaming through: (1) identifying robot presentation factors that can be employed to facilitate trust calibration and (2) establishing the effects of framing, functionality, and the interactions between them to improve dynamic models of human-robot teaming. © 2020 ACM.","Human robot teaming; joint action; proximate interaction; robot reliability; trust","Agricultural robots; Errors; Manipulators; Reliability; Autonomous mobile manipulator; Goal directed; Human robots; Joint actions; Positive changes; Robot behavior; Social robots",,,,,"National Science Foundation, NSF: DGE-1650112; Air Force Office of Scientific Research, AFOSR: FA9550-18-1-0125","This material is based upon work supported by the Air Force Office of Scientific Research (AFOSR) through the award FA9550-18-1-0125, and National Science Foundation (NSF) Graduate Research Fellowship Program under Grant No. DGE-1650112. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF or AFOSR. Authors’ addresses: A. Washburn, A. Adeleye, T. An, and L. D. Riek, UC San Diego, San Diego, California; emails: awashburn@ucdavis.edu, {akadeley, tan, lriek}@eng.ucsd.edu. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2019 Copyright held by the owner/author(s). 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Ieee, pp. 75-84; Wickens, C.D., Dixon, S.R., 2007 the benefits of imperfect diagnostic automation: A synthesis of the literature (2007) Theoretical Issues in Ergonomics Science, 8 (3), pp. 201-212",,,,"Association for Computing Machinery",,,,,25739522,,,,"English","ACM Trans. Hum. Robot Interact",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85091901668
"Duan A., Camoriano R., Ferigo D., Huang Y., Calandriello D., Rosasco L., Pucci D.","57207208557;56085267200;57194087376;54389113100;55737371500;8423888500;55201907900;","Learning to Avoid Obstacles With Minimal Intervention Control",2020,"Frontiers in Robotics and AI","7",,"60","","",,,"10.3389/frobt.2020.00060","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086407531&doi=10.3389%2ffrobt.2020.00060&partnerID=40&md5=c65294beb4e989d01f3c0ef8c06a82e7","Dynamic Interaction Control Lab, Italian Institute of Technology, Genoa, Italy; DIBRIS, Università degli Studi di Genova, Genoa, Italy; Laboratory for Computational and Statistical Learning (IIT@MIT), Istituto Italiano di Tecnologia and Massachusetts Institute of Technology, Cambridge, MA, United States; Machine Learning and Optimization Group, School of Computer Science, University of Manchester, Manchester, United Kingdom; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy; Machine Learning Genoa (MaLGa) Center, Università di Genova, Genoa, Italy","Duan, A., Dynamic Interaction Control Lab, Italian Institute of Technology, Genoa, Italy, DIBRIS, Università degli Studi di Genova, Genoa, Italy; Camoriano, R., Laboratory for Computational and Statistical Learning (IIT@MIT), Istituto Italiano di Tecnologia and Massachusetts Institute of Technology, Cambridge, MA, United States; Ferigo, D., Dynamic Interaction Control Lab, Italian Institute of Technology, Genoa, Italy, Machine Learning and Optimization Group, School of Computer Science, University of Manchester, Manchester, United Kingdom; Huang, Y., Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genova, Italy; Calandriello, D., Laboratory for Computational and Statistical Learning (IIT@MIT), Istituto Italiano di Tecnologia and Massachusetts Institute of Technology, Cambridge, MA, United States; Rosasco, L., DIBRIS, Università degli Studi di Genova, Genoa, Italy, Laboratory for Computational and Statistical Learning (IIT@MIT), Istituto Italiano di Tecnologia and Massachusetts Institute of Technology, Cambridge, MA, United States, Machine Learning Genoa (MaLGa) Center, Università di Genova, Genoa, Italy; Pucci, D., Dynamic Interaction Control Lab, Italian Institute of Technology, Genoa, Italy","Programming by demonstration has received much attention as it offers a general framework which allows robots to efficiently acquire novel motor skills from a human teacher. While traditional imitation learning that only focuses on either Cartesian or joint space might become inappropriate in situations where both spaces are equally important (e.g., writing or striking task), hybrid imitation learning of skills in both Cartesian and joint spaces simultaneously has been studied recently. However, an important issue which often arises in dynamical or unstructured environments is overlooked, namely how can a robot avoid obstacles? In this paper, we aim to address the problem of avoiding obstacles in the context of hybrid imitation learning. Specifically, we propose to tackle three subproblems: (i) designing a proper potential field so as to bypass obstacles, (ii) guaranteeing joint limits are respected when adjusting trajectories in the process of avoiding obstacles, and (iii) determining proper control commands for robots such that potential human-robot interaction is safe. By solving the aforementioned subproblems, the robot is capable of generalizing observed skills to new situations featuring obstacles in a feasible and safe manner. The effectiveness of the proposed method is validated through a toy example as well as a real transportation experiment on the iCub humanoid robot. © Copyright © 2020 Duan, Camoriano, Ferigo, Huang, Calandriello, Rosasco and Pucci.","humanoid robots; minimal intervention control; obstacle avoidance; programming by demonstration; reinforcement learning",,,,,,"DLV-777826; National Science Foundation, NSF: CCF-1231216; European Office of Aerospace Research and Development, EOARD; European Research Council, ERC: BAA-AFRL-AFOSR-2016-0007, FA9550-17-1-0390, SLING 819789; Companhia Brasileira de Metalurgia e Mineração, CBMM","Funding. Part of this work has been carried out at the Machine Learning Genoa (MaLGa) Center, Universit? di Genova (IT). LR acknowledges the financial support of the European Research Council (grant SLING 819789), the AFOSR projects FA9550-17-1-0390 and BAA-AFRL-AFOSR-2016-0007 (European Office of Aerospace Research and Development), the EU H2020-MSCA-RISE project NoMADS - DLV-777826, and the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216.",,,,,,,,,,"Abu-Dakka, F.J., Nemec, B., Kramberger, A., Buch, A.G., Krüger, N., Ude, A., Solving peg-in-hole tasks by human demonstration and exception strategies (2014) Ind. Robot Int J, 41, pp. 575-584; Abu-Dakka, F.J., Rozo, L., Caldwell, D.G., Force-based variable impedance learning for robotic manipulation (2018) Robot. Auton. Syst, 109, pp. 156-167; Billard, A., Calinon, S., Dillmann, R., Schaal, S., “Robot programming by demonstration,” (2008) Springer Handbook of Robotics, pp. 1371-1394. , Berlin; Heidelberg, Springer, 17416157, eds B. Siciliano and O. 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Duan, A.; DIBRIS, Italy; email: anqing.duan@iit.it",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85086407531
"Zeng Z., Chen P.-J., Lew A.A.","57200529718;15519443700;7101848186;","From high-touch to high-tech: COVID-19 drives robotics adoption",2020,"Tourism Geographies","22","3",,"724","734",,155,"10.1080/14616688.2020.1762118","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084841656&doi=10.1080%2f14616688.2020.1762118&partnerID=40&md5=e0b780f2f608360f21857f749fc0d7a4","School of Geography and Ocean Science, Nanjing University, Nanjing, China; The W. A. Franke College of Business, Northern Arizona University, Flagstaff, United States; Department of Geography, Planning and Recreation, Northern Arizona University, Flagstaff, United States; PhD Institute of Socio-Economic Research (ISER) Duy Tan University 3 Quang Trung Danang, Viet Nam","Zeng, Z., School of Geography and Ocean Science, Nanjing University, Nanjing, China; Chen, P.-J., The W. A. Franke College of Business, Northern Arizona University, Flagstaff, United States, PhD Institute of Socio-Economic Research (ISER) Duy Tan University 3 Quang Trung Danang, Viet Nam; Lew, A.A., Department of Geography, Planning and Recreation, Northern Arizona University, Flagstaff, United States","Global economic and social life has been severely challenged since the World Health Organization (WHO) declared the COVID-19 disease a pandemic. Travel, tourism and hospitality, in particular, has been massively impacted by the lockdowns used to maintain social distance to manage the disease. Robotics, artificial intelligence, and human-robot interactions have gained an increased presence to help manage the spread of COVID-19 in hospitals, airports, transportation systems, recreation and scenic areas, hotels, restaurants, and communities in general. Humanoid robots, autonomous vehicles, drones, and other intelligent robots are used in many different ways to reduce human contact and the potential spread of the SARS-CoV-2 virus, including delivering materials, disinfecting and sterilizing public spaces, detecting or measuring body temperature, providing safety or security, and comforting and entertaining patients. While controversial in the past due to concerns over job losses and data privacy, the adoption of robotics and artificial intelligence in travel and tourism will likely continue after the COVID-19 pandemic becomes less serious. Tourism scholars should seize this opportunity to develop robotic applications that enhance tourist experiences, the protection of natural and cultural resources, citizen participation in tourism development decision making, and the emergence of new ‘high-touch’ employment opportunities for travel, tourism and hospitality workers. © 2020 Informa UK Limited, trading as Taylor & Francis Group.","artificial Intelligence; COVID-19 pandemic; drones; high-tech; high-touch; human-robot interaction; Robotics","COVID-19; disease spread; employment; epidemic; hospitality industry; robotics; technology adoption; tourism; viral disease; SARS coronavirus",,,,,"National Natural Science Foundation of China, NSFC: 41971173","National Natural Science Foundation of China (41971173).",,,,,,,,,,"Alexis, P., R-Tourism: Introducing the potential impact of robotics and service automation in tourism (2017) Ovidius University Annals, Series Economic Sciences, 17 (1), pp. 211-216; Bhardwaj, P., (2018), https://www.businessinsider.com/alibaba-shanghai-restaurant-robothe-robots-waiters-photos-2018-7, July 02)., Robots are replacing waiters and delivering fresh seafood right to people’s tables at Alibaba’s high-tech restaurant Shanghai; Bhimasta, R.A., Kuo, P.Y., What causes the adoption failure of service robots? 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Coronavirus disease 2019 (COVID-19) Situation Report; Woosnam, K.M., Kim, H., Hurricane impacts on southeastern United States coastal national park visitation (2014) Tourism Geographies, 16 (3), pp. 364-381. , https://doi.org/10.1080/14616688.2013.823235; Yang, G.Z., J. Nelson, B., Murphy, R.R., Choset, H., Christensen, H., H. Collins, S., Dario, P., McNutt, M., Combating COVID-19 - The role of robotics in managing public health and infectious diseases (2020) Science Robotics, 5 (40), p. eabb5589. , https://doi.org/10.1126/scirobotics.abb5589; Yang, X., Yu, Y., Xu, J., Shu, H., Xia, J., Liu, H., Wu, Y., Shang, Y., Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: A single-centered, retrospective, observational study (2020) The Lancet Respiratory Medicine, , https://doi.org/10.1016/S2213-2600(20)30079-5; Yu, C.E., Humanlike robots as employees in the hotel industry: Thematic content analysis of online reviews (2020) Journal of Hospitality Marketing & Management, 29 (1), pp. 22-38. , https://doi.org/10.1080/19368623.2019.1592733; Zhang, J., Big data and tourism geographies - An emerging paradigm for future study? (2018) Tourism Geographies, 20 (5), pp. 899-904. , https://doi.org/10.1080/14616688.2018.1519719","Chen, P.-J.; The W. A. Franke College of Business, United States; email: chen.poju@yahoo.com",,,"Routledge",,,,,14616688,,,,"English","Tour. Geogr.",Article,"Final","",Scopus,2-s2.0-85084841656
"Gao Q., Liu J., Ju Z.","56391647100;7410106559;25655056800;","Robust real-time hand detection and localization for space human–robot interaction based on deep learning",2020,"Neurocomputing","390",,,"198","206",,19,"10.1016/j.neucom.2019.02.066","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075445505&doi=10.1016%2fj.neucom.2019.02.066&partnerID=40&md5=bd79ab3d5ef4fc381934e9a1ceea9f00","State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China; University of Chinese Academy of Sciences, Beijing, China; School of Computing, University of Portsmouth, Portsmouth, United Kingdom","Gao, Q., State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China, University of Chinese Academy of Sciences, Beijing, China; Liu, J., State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China; Ju, Z., State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China, School of Computing, University of Portsmouth, Portsmouth, United Kingdom","Hand gestures are quite suitable for space human–robot interaction (SHRI) because of their natural and convenient features. While the detection and localization of hands are the premise and foundation for SHRI based on hand gestures. But hand gestures are very complicated and hand sizes are very small in some images. These problems make the robust real-time hand detection and localization very difficult. In this paper, a feature-map-fused single shot multibox detector (FF-SSD) which is a deep learning network is designed to deal with the problems of hand detection and localization in SHRI. First, the background of the method is introduced in this paper, including an astronaut assistant robot platform, the difficulties of hand detection and localization, and introduction of the state-of-the-art deep learning networks for object detection and localization. Then, the FF-SSD is proposed for detecting and localizing hands especially pony-size hands. This network takes into consideration both accuracy and speed with balanced performance. And in the experiment part, the FF-SSD is trained and tested on hand databases which include a homemade database and two public databases. At last, the superiority of the proposed method is demonstrated compared with the state-of-the-art methods. © 2019 Elsevier B.V.","Astronaut assistant robot; Deep learning; Hand detection and localization; SSD","Database systems; Human robot interaction; Object detection; Palmprint recognition; Assistant robot; Detection and localization; Hand detection; Learning network; Object detection and localizations; Robot interactions; State of the art; State-of-the-art methods; Deep learning; article; controlled study; cosmonaut; deep learning; human; nonhuman; pony; robotics; velocity",,,,,"JCTD-2018-11; 611391; 2018YFB1304600; National Natural Science Foundation of China, NSFC: 51575338, 5157540, 51575412","The authors would like to acknowledge the support from National Key R&D Program of China (Grant No. 2018YFB1304600), CAS Interdisciplinary Innovation Team (Grant No. JCTD-2018-11), DREAM project of EU FP7-ICT (grant no. 611391) and National Natural Science Foundation of China (grant no. 51575412 , 51575338 and 5157540 ). Qing Gao was born in Tangshan, China. He received the B.S. degree in automation from Electrical Engineering and Automation School, Liaoning Technology University, China in 2013. And is currently working toward the Ph.D. degree in the State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang, China. His main research interests include space robot, artificial intelligence, machine vision and human robot interaction. Jinguo Liu received the Ph.D. degree in mechatronics from the Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), in 2007, where he has been a Full Professor, since January 2011, has also been the Assistant Director of the State Key Laboratory of Robotics, since 2008, and has also been the Associate Director of the Center for Space Automation Technologies and Systems, since 2015. His research interests include bio-inspired robotics and space robot. He has authored or coauthored three books, over 100 articles and holds 50 patents in above areas. Prof. Liu has authored and coauthored over 100 papers and thirty patents in above areas. He is the Senior Member of IEEE, the Senior Member of China Mechanical Engineering Society, the lead guest editor of international journal of Advances in Mechanical Engineering. Zhaojie Ju (M’08SM’16) received the B.S. in automatic control and the M.S. in intelligent robotics both from Huazhong University of Science and Technology, China, and the Ph.D. degree in intelligent robotics at the University of Portsmouth, UK. He held a research appointment at the University College London, London, U.K., before he started his independent academic position at the University of Portsmouth, U.K., in 2012. His research interests include machine intelligence, pattern recognition, and their applications on human motion analysis, multi-fingered robotic hand control, human-robot interaction and collaboration, and robot skill learning. He has authored or co-authored over 170 publications in journals, book chapters, and conference proceedings and received four best paper awards and one Best AE Award in ICRA2018. Dr. Ju is an Associate Editor of the IEEE TRANSACTIONS ON CYBERNETICS, Journal of Intelligent & Fuzzy Systems, International Journal of Fuzzy Systems, and Chinese Journal of Mechanical Engineering.",,,,,,,,,,"Dorais, G.A., Gawdiak, Y., The personal satellite assistant: an internal spacecraft autonomous mobile monitor (2003) Proceedings of the 2003 IEEE Aerospace Conference, 1, pp. 1-348. , IEEE; Fong, T., Micire, M.J., Morse, T., Park, E., Provencher, C., To, V., Wheeler, D., Smith, E., (2013), Smart Spheres: A Telerobotic Free-flyer for Intravehicular Activities in Space; Bualat, M., Barlow, J., Fong, T., Provencher, C., Smith, T., Astrobee: developing a free-flying robot for the international space station (2015) Proceedings of AIAA SPACE 2015 Conference and Exposition, p. 4643; Smith, T., Barlow, J., Bualat, M., Fong, T., Provencher, C., Sanchez, H., Smith, E., (2016), Astrobee: A New Platform for Free-flying Robotics on the International Space Station; Murphy, T.I., (2018), http://www.airbus.com/newsroom/press-releases/en/2018/02/hello–i-am-cimon-.html, Hello, I Am Cimon!, (). 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Big Data, 3 (1), p. 9; Jones, M.J., Rehg, J.M., Statistical color models with application to skin detection (2002) Int. J. Comput. Vis., 46 (1), pp. 81-96","Liu, J.; State Key Laboratory of Robotics, China; email: liujinguo@sia.cn",,,"Elsevier B.V.",,,,,09252312,,NRCGE,,"English","Neurocomputing",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85075445505
"Alsulaimawi Z.","57207856911;","A privacy filter framework for internet of robotic things applications",2020,"Proceedings - 2020 IEEE Symposium on Security and Privacy Workshops, SPW 2020",,,"9283864","262","267",,,"10.1109/SPW50608.2020.00059","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099733620&doi=10.1109%2fSPW50608.2020.00059&partnerID=40&md5=a2f759df461ad8ee7dbafc0c0fcb7672","School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR  97331, United States","Alsulaimawi, Z., School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR  97331, United States","Traditionally robots have been stand-alone systems. In recent years, however, they have increasingly been connected to external knowledge resources through the Internet of Things (IoT). These robots are thus becoming part of IoT and can realistically allocate Internet of Robotic Things (IoRT) technologies. IoRT can facilitate Human-Robot Interaction (HRI) at functional (commanding and programming) and social levels, as well as a means for remote-interaction. IoRT-HRI can cause privacy issues for humans, in part because robots can collect data using IoT and move in the real world, partly because robots can learn to read human social cues and adapt or correct their behavior accordingly. In this paper, we address the topic of privacy-preserving for IoRT- Hri applications. The objective is to design a data release framework called a Privacy Filter (PF) that can prevent an adversary from private mining information from the released data while keeping utility data. In the experiments, we test our framework on two accessible datasets: MNIST (hand-written digits) and UCI-HAR (activity recognition from motion). Our experimental results on these datasets show that PF is highly effective in removing private information from the dataset while allowing utility data to be mined effectively. © 2020 IEEE.","Deep learning; IoRT; Privacy-preserving; Probabilistic model; Variational mutual information","Filtration; Functional programming; Privacy by design; Robot programming; Robotics; Social robots; Activity recognition; External knowledge; Human robot Interaction (HRI); Internet of thing (IOT); Privacy preserving; Private information; Remote interactions; Standalone systems; Internet of things",,,,,,,,,,,,,,,,"Pratim Ray, P., Internet of robotic things: Concept, technologies, and challenges (2016) IEEE Access, 4, pp. 9489-9500; Li, S., Da Xu, L., Zhao, S., The internet of things: A survey (2015) Information Systems Frontiers, 17 (2), pp. 243-259; Rueben, M., Smart, W.D., Privacy in human-robot interaction: Survey and future work (2016) We Robot 2016: The Fifth Annual Conference on Legal and Policy Issues Relating to Robotics, , University of Miami School of Law 2016 Discussant: Ashkan Soltani, Independent Researcher; Katz, J., Menezes, A.J., Oorschot Van, P.C., Vanstone, S.A., (1996) Handbook of Applied Cryptography, , CRC press; Brassard, G., (1988) Modern Cryptology: A Tutorial, 325. , Springer; Sadeghi, A., Wachsmann, C., Waidner, M., Security and privacy challenges in industrial internet of things (2015) 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC). IEEE, pp. 1-6; Makhdoumi, A., Salamatian, S., Fawaz, N., Médard, M., From the information bottleneck to the privacy funnel (2014) 2014 IEEE Information Theory Workshop (ITW 2014). IEEE, pp. 501-505; Yang, Y., Wu, L., Yin, G., Li, L., Zhao, H., A survey on security and privacy issues in internet-of-things (2017) IEEE Internet of Things Journal, 4 (5), pp. 1250-1258; Zhou, B., Pei, J., Luk, W., A brief survey on anonymization techniques for privacy preserving publishing of social network data (2008) ACM Sigkdd Explorations Newsletter, 10 (2), pp. 12-22; Kupwade Patil, H., Seshadri, R., Big data security and privacy issues in healthcare (2014) 2014 IEEE International Congress on Big Data. 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IEEE, pp. 474-482; Psychoula, I., Merdivan, E., Singh, D., Chen, L., Chen, F., Hanke, S., Kropf, J., Geist, M., (2018) A Deep Learning Approach for Privacy Preservation in Assisted Living; Zhao, J., Mortier, R., Crowcroft, J., Wang, L., (2018) Privacy-preserving Machine Learning Based Data Analytics on Edge Devices; Sarwate, A.D., Chaudhuri, K., Signal processing and machine learning with differential privacy: Algorithms and challenges for continuous data (2013) IEEE Signal Processing Magazine, 30 (5), pp. 86-94; Rubinstein, B.I.P., Bartlett, P.L., Huang, L., Taft, N., (2009) Learning in A Large Function Space: Privacy-preserving Mechanisms for SVM Learning; Chaudhuri, K., Sarwate, A.D., Sinha, K., A nearoptimal algorithm for differentially-private principal components (2013) The Journal of Machine Learning Research, 14 (1), pp. 2905-2943; Abadi, M., Chu, A., Goodfellow, I., Brendan McMahan, H., Mironov, I., Talwar, K., Zhang, L., Deep learning with differential privacy (2016) Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, pp. 308-318; Wagner, I., Eckhoff, D., Technical privacy metrics: A systematic survey (2018) ACM Computing Surveys (CSUR), 51 (3), p. 57; Dwork, C., Roth, A., The algorithmic foundations of differential privacy (2014) Foundations and Trends R in Theoretical Computer Science, 9 (3-4), pp. 211-407; Samarati, P., Sweeney, L., Generalizing data to provide anonymity when disclosing information (1998) PODS., 98, p. 188. , Citeseer; Banerjee, M., Chakravarty, S., Privacy preserving feature selection for distributed data using virtual dimension (2011) Proceedings of the 20th ACM International Conference on Information and Knowledge Management. ACM, pp. 2281-2284; Jafer, Y., Matwin, S., Sokolova, M., A framework for a privacy-aware feature selection evaluation measure (2015) 2015 13th Annual Conference on Privacy, Security and Trust (PST). IEEE, pp. 62-69; Xu, K., Cao, T., Shah, S., Maung, C., Schweitzer, H., Cleaning the null space: A privacy mechanism for predictors (2017) Thirty-First AAAI Conference on Artificial Intelligence; Enev, M., Jung, J., Bo, L., Ren, X., Kohno, T., Sensorsift: Balancing sensor data privacy and utility in automated face understanding (2012) Proceedings of the 28th Annual Computer Security Applications Conference. ACM, pp. 149-158; Ozan Basciftci, Y., Wang, Y., Ishwar, P., On privacyutility tradeoffs for constrained data release mechanisms (2016) 2016 Information Theory and Applications Workshop (ITA). IEEE, pp. 1-6; Wang, Y., Ozan Basciftci, Y., Ishwar, P., (2017) Privacy-utility Tradeoffs under Constrained Data Release Mechanisms; Du Pin Calmon, F., Fawaz, N., Privacy against statistical inference (2012) 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, pp. 1401-1408; Tishby, N., Pereira, F.C., Bialek, W., (2000) The Information Bottleneck Method, , arXiv preprint physics/0004057; Cover, T.M., Thomas, J.A., (2012) Elements of Information Theory, , John Wiley & Sons; Lutz, C., Tamò, A., Privacy and healthcare robots-an ant analysis (2016) We Robot 2016: The Fifth Annual Conference on Legal and Policy Issues Relating to Robotics, , University of Miami School of Law 2016 Discussant: Matt Beane, University of California Santa Barbara; Ryan Calo, M., 12 robots and privacy (2011) Robot Ethics: The Ethical and Social Implications of Robotics, p. 187; Sverre Syrdal, D., Walters, M.L., Otero, N., Lee Koay, K., Dautenhahn, K., He knows when you are sleeping-privacy and the personal robot companion (2007) Proc. Workshop Human Implications of Human-Robot Interaction, Association for the Advancement of Artificial Intelligence (AAAI'07), pp. 28-33; Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., Generative adversarial nets (2014) Advances in Neural Information Processing Systems, pp. 2672-2680; LeCun, Y., (1998) The Mnist Database of Handwritten Digits, , http://yann.lecun.com/exdb/mnist/; Martin, O., Kotsia, I., Macq, B., Pitas, I., The enterface'05 audio-visual emotion database (2006) 22nd International Conference on Data Engineering Workshops (ICDEW'06). IEEE, pp. 8-8","Alsulaimawi, Z.; School of Electrical Engineering and Computer Science, United States; email: alsulaiz@oregonstate.edu",,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE Symposium on Security and Privacy Workshops, SPW 2020","21 May 2020",,165937,,9781728193465,,,"English","Proc. - IEEE Symp. Secur. Priv. Workshops, SPW",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85099733620
"Sirintuna D., Aydin Y., Caldiran O., Tokatli O., Patoglu V., Basdogan C.","57218282723;56084865200;35090008300;36667722400;19640643500;6603706568;","A Variable-Fractional Order Admittance Controller for pHRI",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9197288","10162","10168",,2,"10.1109/ICRA40945.2020.9197288","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092731626&doi=10.1109%2fICRA40945.2020.9197288&partnerID=40&md5=7b36a14295e3ff63360fea12dcb6af4f","Koc University, College of Engineering, Istanbul, 34450, Turkey; Race Ukaea, Abingdon, OX14 3DB, United Kingdom; Sabanci University, Faculty of Engineering and Natural Sciences, Istanbul, 34956, Turkey","Sirintuna, D., Koc University, College of Engineering, Istanbul, 34450, Turkey; Aydin, Y., Koc University, College of Engineering, Istanbul, 34450, Turkey; Caldiran, O., Koc University, College of Engineering, Istanbul, 34450, Turkey; Tokatli, O., Race Ukaea, Abingdon, OX14 3DB, United Kingdom; Patoglu, V., Sabanci University, Faculty of Engineering and Natural Sciences, Istanbul, 34956, Turkey; Basdogan, C., Koc University, College of Engineering, Istanbul, 34450, Turkey","In today's automation driven manufacturing environments, emerging technologies like cobots (collaborative robots) and augmented reality interfaces can help integrating humans into the production workflow to benefit from their adaptability and cognitive skills. In such settings, humans are expected to work with robots side by side and physically interact with them. However, the trade-off between stability and transparency is a core challenge in the presence of physical human robot interaction (pHRI). While stability is of utmost importance for safety, transparency is required for fully exploiting the precision and ability of robots in handling labor intensive tasks. In this work, we propose a new variable admittance controller based on fractional order control to handle this trade-off more effectively. We compared the performance of fractional order variable admittance controller with a classical admittance controller with fixed parameters as a baseline and an integer order variable admittance controller during a realistic drilling task. Our comparisons indicate that the proposed controller led to a more transparent interaction compared to the other controllers without sacrificing the stability. We also demonstrate a use case for an augmented reality (AR) headset which can augment human sensory capabilities for reaching a certain drilling depth otherwise not possible without changing the role of the robot as the decision maker. © 2020 IEEE.",,"Agricultural robots; Augmented reality; Controllers; Decision making; Economic and social effects; Infill drilling; Manufacture; Robotics; Transparency; Collaborative robots; Emerging technologies; Fractional order; Fractional order control; Manufacturing environments; Physical humanrobot interaction (phri); Production workflows; Reality interface; Human robot interaction",,,,,"EEEAG-117E645, EEEAG-117E645, EEEAG-117E645, EEEAG-117E645; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK; EEEAG-117E645, EEEAG-117E645, EEEAG-117E645, EEEAG-117E645; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK","The Scientific and Technological Research Council of Turkey (TUBITAK) supported this work under contract EEEAG-117E645. ∗The first two authors contributed equally to this work. 1Doganay Sirintuna, Yusuf Aydin, Ozan Caldiran, and Cagatay Basdogan are with College of Engineering, Koc University, 34450, Istanbul, Turkey 2Ozan Tokatli is with RACE UKAEA, Abingdon, OX14 3DB, UK 3Volkan Patoglu is with Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, 34956, Turkey #Corresponding author: cbasdogan@ku.edu.tr","The Scientific and Technological Research Council of Turkey (TUBITAK) supported this work under contract EEEAG-117E645.",,,,,,,,,"Collaborative Robot Market, , https://www.marketsandmarkets.com/Market-Reports/collaborative-robot-market-194541294.html, , accessed:2019-0910; Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P., Collaborative manufacturing with physical human-robot interaction (2016) Robotics and Computer-Integrated Manufacturing, 40, pp. 1-13; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human-robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266; Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human-robot collaboration (2018) Autonomous Robots, 42 (5), pp. 957-975. , Jun; Aydin, Y., Tokatli, O., Patoglu, V., Basdogan, C., Stable physical human-robot interaction using fractional order admittance control (2018) IEEE Transactions on Haptics, 11 (3), pp. 464-475. , July; Tsumugiwa, T., Yokogawa, R., Yoshida, K., Stability analysis for impedance control of robot for human-robot cooperative task system (2004) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3883-3888; Dimeas, F., Aspragathos, N., Online stability in human-robot cooperation with admittance control (2016) IEEE Transactions on Haptics, 9 (2), pp. 267-278; Gallagher, W., Gao, D., Ueda, J., Improved stability of haptic human-robot interfaces using measurement of human arm stiffness (2014) Advanced Robotics, 28 (13), pp. 869-882; Lamy, X., Colledani, F., Geffard, F., Measson, Y., Morel, G., Achieving efficient and stable comanipulation through adaptation to changes in human arm impedance (2009) IEEE International Conference on Robotics and Automation, pp. 265-271. , May; Colgate, J.E., Hogan, N., Robust control of dynamically interacting systems (1988) International Journal of Control, 48 (1), pp. 65-88; Colgate, J.E., Schenkel, G.G., Passivity of a class of sampled-data systems: Application to haptic interfaces (1997) Journal of Robotic Systems, 14 (1), pp. 37-47; Hulin, T., Albu-Schäffer, A., Hirzinger, G., Passivity and stability boundaries for haptic systems with time delay (2014) IEEE Transactions on Control Systems Technology, 22 (4), pp. 1297-1309. , July; Buerger, S.P., Krebs, H.I., Hogan, N., Characterization and control of a screw-driven robot for neurorehabilitation (2001) IEEE International Conference on Control Applications, pp. 388-394; Buerger, S.P., Hogan, N., Complementary stability and loop shaping for improved human-robot interaction (2007) IEEE Transactions on Robotics, 23 (2), pp. 232-244. , April; Ryu, D., Song, J., Choi, J., Kang, S., Kim, M., Frequency domain stability observer and active damping control for stable haptic interaction (2007) IEEE International Conference on Robotics and Automation, pp. 105-110. , April; Okunev, V., Nierhoff, T., Hirche, S., Human-preference-based control design: Adaptive robot admittance control for physical humanrobot interaction (2012) IEEE RO-MAN: The 21st International Symposium on Robot and Human Interactive Communication, pp. 443-448. , Sept; Ferraguti, F., Landi, C.T., Sabattini, L., Bonfè, M., Fantuzzi, C., Secchi, C., A variable admittance control strategy for stable physical human-robot interaction (2019) The International Journal of Robotics Research, 38 (6), pp. 747-765; Hannaford, B., Ryu, J.-H., Time-domain passivity control of haptic interfaces (2002) IEEE Transactions on Robotics and Automation, 18 (1), pp. 1-10. , Feb; Aydin, Y., Tokatli, O., Patoglu, V., Basdogan, C., Fractional order admittance control for physical human-robot interaction (2017) IEEE World Haptics Conference (WHC, pp. 257-262. , June; Tokatli, O., Patoglu, V., Stability of haptic systems with fractional order controllers (2015) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1172-1177. , Sept; Tokatli, O., Patoglu, V., (2018) Using Fractional Order Elements for Haptic Rendering, pp. 373-388. , Cham: Springer International Publishing; Faria, C., Ferreira, F., Erlhagen, W., Monteiro, S., Bicho, E., Position-based kinematics for 7-dof serial manipulators with global configuration control, joint limit and singularity avoidance (2018) Mechanism and Machine Theory, 121, pp. 317-334. , http://www.sciencedirect.com/science/article/pii/S0094114X17306559; Holm, S., A simple sequentially rejective multiple test procedure (1979) Scandinavian Journal of Statistics, 6 (2), pp. 65-70; Groppe, D., (2020) Bonferroni-holm Correction for Multiple Comparisons, , https://www.mathworks.com/matlabcentral/fileexchange/28303-bonferroni-holm-correctionfor-multiple-comparisons, (Accessed February 24, 2020",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85092731626
"Kim S.K., Andrea Kirchner E., Kirchner F.","42761618100;57219437863;7006011550;","Flexible online adaptation of learning strategy using EEG-based reinforcement signals in real-world robotic applications",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9197538","4885","4891",,2,"10.1109/ICRA40945.2020.9197538","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092715733&doi=10.1109%2fICRA40945.2020.9197538&partnerID=40&md5=b34d0ab89588e3e9a50cf38913b0d84a","German Research Center for Artificial Intelligence (DFKI), Robotics Innovation Center (RIC), Bremen, Germany","Kim, S.K., German Research Center for Artificial Intelligence (DFKI), Robotics Innovation Center (RIC), Bremen, Germany; Andrea Kirchner, E., German Research Center for Artificial Intelligence (DFKI), Robotics Innovation Center (RIC), Bremen, Germany; Kirchner, F., German Research Center for Artificial Intelligence (DFKI), Robotics Innovation Center (RIC), Bremen, Germany","Flexible adaptation of learning strategy depending on online changes of the user's current intents have a high relevance in human-robot collaboration. In our previous study, we proposed an intrinsic interactive reinforcement learning approach for human-robot interaction, in which a robot learns his/her action strategy based on intrinsic human feedback that is generated in the human's brain as neural signature of the human's implicit evaluation of the robot's actions. Our approach has an inherent property that allows robots to adapt their behavior depending on online changes of the human's current intents. Such flexible adaptation is possible, since robot learning is updated in real time by human's online feedback. In this paper, the adaptivity of robot learning is tested on eight subjects who change their current control strategy by adding a new gesture to the previous used gestures. This paper evaluates the learning progress by analyzing learning phases (before and after adding a new gesture for control). The results show that the robot can adapt the previously learned policy depending on online changes of the user's intents. Especially, learning progress is interrelated with the classification performance of electroencephalograms (EEGs), which are used to measure the human's implicit evaluation of the robot's actions. © 2020 IEEE.",,"Agricultural robots; Bioelectric phenomena; Biofeedback; E-learning; Educational robots; Electroencephalography; Feedback; Reinforcement learning; Robot learning; Robotics; Classification performance; Current control strategy; Human-robot collaboration; Interactive Reinforcement Learning; Learning progress; On-line adaptation; Reinforcement signal; Robotic applications; Social robots",,,,,"Bundesministerium für Bildung und Forschung, BMBF: FKZ 01IW18003, FKZ 50RA1701, FKZ 50RA1703; Bundesministerium für Wirtschaft und Technologie, BMWi; Deutsches Zentrum für Luft- und Raumfahrt, DLR","1 Robotics Innovation Center (RIC), German Research Center for Artificial Intelligence (DFKI), Bremen, Germany. 2 Robotics group, University of Bremen, Germany. This work has been supported by the BMWi, DLR, and BMBF (grant number: FKZ 50RA1701, FKZ 50RA1703, and FKZ 01IW18003).",,,,,,,,,,"Thomaz, A.L., Hoffman, G., Breazeal, C., Real-time interactive reinforcement learning for robots (2005) Proceedings of Aaai Workshop on Human Comprehensible Machine Learning; Stahlhut, C., Navarro-Guerrero, N., Weber, C., Wermter, S., Interaction in reinforcement learning reduces the need for finely tuned hyperparameters in complex tasks (2015) Kognitive Systeme, 2; Raza, S.A., Johnston, B., Williams, M.-A., Reward from demonstration in interactive reinforcement learning (2016) The Twenty-Ninth International Flairs Conference. Aaai; Daniel, C., Viering, M., Metz, J., Kroemer, O., Peters, J., Active reward learning (2014) Proceedings of Robotics: Science and Systems; Hadfield-Menell, D., Russell, S.J., Abbeel, P., Dragan, A., Cooperative inverse reinforcement learning (2016) Proceedings of Advances in Neural Information Processing Systems (NIPS), pp. 3909-3917; Iturrate, I., Chavarriaga, R., Montesano, L., Minguez And J D R Millán, J., Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control (2015) Scientific Reports, 5, p. 13893; Kim, S.K., Kirchner, E.A., Stefes, A., Kirchner, F., Intrinsic interactive reinforcement learning-using error-related potentials for real world human-robot interaction (2017) Scientific Reports, 7, p. 17562; Leap Motion Developer Portal, , https://developer.leapmotion.com/; Ferrez, P.W., Millán, J.D.R., You are wrong!-automatic detection of interaction errors from brain waves (2005) International Joint Conferences on Artificial Intelligence (IJCAI), pp. 1413-1418; Iturrate, I., Montesano, L., Minguez, J., Single trial recognition of error-related potentials during observation of robot operation (2010) Proceedings of the 32th Annual International Conference of the Ieee Engineering in Medicine and Biology Society, pp. 4181-4184; Kim, S.K., Kirchner, E.A., Classifier transferability in the detection of error related potentials from observation to interaction (2013) Proceedings of the Ieee International Conference on Systems, Man, and Cybernetics, (SMC), pp. 3360-3365; Chavarriaga, R., Sobolewski, A., Millán, J.D.R., Errare machinale est: The use of error-related potentials in brain-machine interfaces (2014) Front. Neurosci, 8; Kim, S.K., Kirchner, E.A., Handling few training data: Classifier transfer between different types of error-related potentials (2016) Ieee Transactions on Neural Systems and Rehabilitation Engineering, 24 (3), pp. 320-332; Krell, M.M., Straube, S., Seeland, A., Wöhrle, H., Teiwes, J., Metzen, J.H., Kirchner, E.A., Kirchner, F., Pyspace-a signal processing and classification environment in python (2013) Frontiers in Neuroinformatics, 7 (40); Rivet, B., Souloumiac, A., Attina, V., Gibert, G., Xdawn algorithm to enhance evoked potentials: Application to brain-computer interface (2009) Ieee Transaction on Biomedical Engineering, 56 (8), pp. 2035-2043; Chang, C.-C., Lin, C.-J., Libsvm: A library for support vector machines (2011) Acm Transactions on Intelligent Systems and Technology (TIST), 2 (3), pp. 1-27; Schölkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L., New support vector algorithms (2000) Neural Computation, 12 (5), pp. 1207-1245; Bargsten, V., Ferandez, J.D.G., Compi: Development of a 6-dof compliant robot arm for human-robot cooperation (2015) Proceedings of the 8th International Workshop on Human-Friendly Robotics (HFR); Li, L., Chu, W., Langford, J., Schapire, R.E., A contextualbandit approach to personalized news article recommendation (2010) Proceedings of the 19th International Conference on World Wide Web, pp. 661-670. , ACM",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","",Scopus,2-s2.0-85092715733
"Parent D., Colome A., Torras C.","57219440735;55556840700;7003421114;","Variable Impedance Control in Cartesian Latent Space while Avoiding Obstacles in Null Space",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9197192","9888","9894",,1,"10.1109/ICRA40945.2020.9197192","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092697695&doi=10.1109%2fICRA40945.2020.9197192&partnerID=40&md5=bf717065932e6d17233d583944111ca7","Institut de Robòtica i Informàtica Industrial (IRI), CSIC-UPC, Barcelona, Spain","Parent, D., Institut de Robòtica i Informàtica Industrial (IRI), CSIC-UPC, Barcelona, Spain; Colome, A., Institut de Robòtica i Informàtica Industrial (IRI), CSIC-UPC, Barcelona, Spain; Torras, C., Institut de Robòtica i Informàtica Industrial (IRI), CSIC-UPC, Barcelona, Spain","Human-robot interaction is one of the keys of assistive robots. Robots are expected to be compliant with people but at the same time correctly perform the tasks. In such applications, Cartesian impedance control is preferred over joint control, as the desired interaction and environmental feedback can be described more naturally, and the force to be exerted by the robot can be readily adjusted.This paper addresses the problem of controlling a robot arm in the operational space with variable stiffness so as to continuously adapt the force exerted in each phase of motion according to the precision requirements. Moreover, performing dimensionality reduction we can separate the degrees of freedom (DoF) relevant for the task from the redundant ones. The stiffness of the former can be adjusted constantly to achieve the required accuracy, while task-redundant DoF can be used to achieve other goals such as avoiding obstacles by moving in the directions where accuracy is not critical. The designed method is tested teaching the robot to give water to drink to a model of human head. Our empirical results demonstrate that the robot can learn precision requirements from demonstration. Furthermore, dimensionality reduction is proved to be useful to avoid obstacles. © 2020 IEEE.",,"Agricultural robots; Biofeedback; Degrees of freedom (mechanics); Dimensionality reduction; Robotics; Stiffness; Assistive robots; Avoid obstacles; Avoiding obstacle; Cartesian impedance control; Joint control; Operational space; Variable impedance control; Variable stiffness; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020: 741930; European Research Council, ERC; Agencia Estatal de Investigación, AEI","This work was partially developed in the context of the project CLOTHILDE (""CLOTH manIpulation Learning from DEmonstrations""), which has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Advanced Grant agreement No 741930). This work is also supported by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI MdM-IP-2018-05.",,,,,,,,,,"(2015) People in the EU: Who Are We and How Do We Live?, , Eurostat (European Comission)., EU publications; Tellaeche, A., Maurtua, I., Ibarguren, A., Human robot interaction in industrial robotics. Examples from research centers to industry (2015) Ieee 20th Conference on Emerging Technologies & Factory Automation (ETFA), pp. 1-6. , Luxembourg; Canal, G., Alenyà, G., Torras, C., Adapting robot task planning to user preferences: An assistive shoe dressing example (2018) Autonomous Robots, pp. 1343-1356; Vila, M., Canal, G., Alenyà, G., Towards safety in physically assistive robots: Eating assistance (2018) Proceedings of the 2018 Iros Workshop on Robots for Assisted Living., pp. 1-4; Robla-Gómez, S., Becerra, V.M., Llata, J.R., González-Sarabia, E., Torre-Ferrero, C., Pérez-Oria, J., Working together: A review on safe human-robot collaboration in industrial environments (2017) Ieee Access, (5), pp. 26754-26773; Colomé, A., Planells, A., Torras, C., A friction-model-based framework for reinforcement learning of robotic tasks in non-rigid environments (2015) 2015 Ieee International Conference on Robotics and Automation (ICRA), pp. 5649-5654. , Seattle, WA; Albu-Schäeffer, A., Eiberger, O., Grebenstein, M., Haddadini, S., Ott, C., Wimböck, T., Wolf, S., Hirzinger, G., Soft robotics: From torque feedback controlled lightweight robots to intrinsically compliant systems (2008) Ieee Robotics and Automation Magazine., 15, pp. 20-30; Hogan, N., Impedance control: An approach to manipulation: Part i-Theory (1985) Asme J. 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Sys., Meas., Control., 107 (1), pp. 1-7. , March; Paraschos, A., Neumann, G., Daniel, C., Peters, J., Probabilistic movement primitives (2013) Advances in Nips, pp. 2616-2624; Colomé, A., Neumann, G., Peters, J., Torras, C., Dimensionality reduction for probabilistic movement primitives (2014) IEEE-RAS Humanoid Robots, pp. 794-800; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) Ieee Transactions on Robotics, 31 (4), pp. 850-863. , Aug; Colomé, A., Torras, C., Demonstration-free contextualized probabilistic movement primitives, further enhanced with obstacle avoidance (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3190-3195. , Vancouver, BC; Ge, S.S., Cui, Y.J., New potential functions for mobile robot path planning (2000) Ieee Transactions on Robotics and Automation, 16 (5), pp. 615-620. , Oct; Bishop, C.M., (2006) Pattern Recognition and Machine Learning (Information Science and Statistics)., , Springer-Verlag, Berlin, Heidelberg; Craig, J.J., (1989) Introduction to Robotics: Mechanics and Control., , Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 2nd edition; Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., (2008) Robotics: Modelling, Planning and Control., , Springer Publishing Company, Incorporated, 1st edition; Khatib, O., A unified approach for motion and force control of robot manipulators: The operational space formulation (1987) Ieee Journal on Robotics and Automation, 3 (1), pp. 43-53. , February; Calinon, S., Sardellitti, I., Caldwell, D.G., Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems., pp. 249-254. , Taipei; Arena, P., Fortuna, L., Muscato, G., Xibilia, M.G., Applications of quaternions in robotics (1998) Neural Networks in Multidimensional Domains. Lecture Notes in Control and Information Sciences, 234. , Springer, London; Sarabandi, S., Thomas, F., Accurate computation of quaternions from rotation matrices (2019) Advances in Robot Kinematics., pp. 39-46. , Springer International Publishing; Diebel, J., (2006) Representing Attitude: Euler Angles, Unit Quaternions, and Rotation Vectors, , Matrix 58",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85092697695
"Lee C., Kim D.-H., Singh H., Ryu J.-H.","57210979371;57215966761;57207838437;7401867592;","Successive Stiffness Increment and Time Domain Passivity Approach for Stable and High Bandwidth Control of Series Elastic Actuator",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9196995","4717","4723",,1,"10.1109/ICRA40945.2020.9196995","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090775731&doi=10.1109%2fICRA40945.2020.9196995&partnerID=40&md5=283831ab617890049b5f7e0c56f448f8","Korea Advanced Institute of Science and Technology, Department of Civil and Environmental Engineering, Eoeun-dong, Yuseong-gu, Daejeon, South Korea; Institute of Robotics and Mechatronics in the German Aerospace Center (DLR), Wessling, 82234, Germany","Lee, C., Korea Advanced Institute of Science and Technology, Department of Civil and Environmental Engineering, Eoeun-dong, Yuseong-gu, Daejeon, South Korea; Kim, D.-H., Korea Advanced Institute of Science and Technology, Department of Civil and Environmental Engineering, Eoeun-dong, Yuseong-gu, Daejeon, South Korea; Singh, H., Institute of Robotics and Mechatronics in the German Aerospace Center (DLR), Wessling, 82234, Germany; Ryu, J.-H., Korea Advanced Institute of Science and Technology, Department of Civil and Environmental Engineering, Eoeun-dong, Yuseong-gu, Daejeon, South Korea","For safe human-robot interaction, various type of flexible manipulators have been developed. Especially series elastic actuator (SEA) based manipulators have been getting huge attention since the elastic element of SEA prevents people from injury when undesirable collision happens. Moreover, it improves system durability by absorbing impact force, which could damage actuators. However, the elastic element inside SEA manipulator causes low system bandwidth which limits the speed performance of conventional impedance control approaches. To alleviate the low bandwidth issue of impedance controlled SEA while guaranteeing system stability, we implement Time Domain Passivity Approach (TDPA) and Successive Stiffness Increment (SSI) approach, which was invented in haptic and teleoperation domain. Impedance controlled SEA is reformulated as a two-port electrical circuit network for implementing TDPA. In addition, a pair of input and output power conjugate variable, dominating the system passivity is identified for implementing SSI approach. Experimental results showed that TDPA and SSI approach can render the stiffness of the impedance controller, which decides the bandwidth, upto 350 kN/m without any stability issue, while normal impedance controller only render upto 120 kN/m. Although both of the approaches significantly increased the bandwidth of the impedance controlled SEA, TDPA slightly outperformed in stability, and SSI outperformed in tracking. © 2020 IEEE.",,"Actuators; Agricultural robots; Bandwidth; Controllers; Elasticity; Manipulators; Robotics; Stiffness; System stability; Conjugate variables; Electrical circuit; Haptic and tele operations; Impedance control; Impedance controllers; Safe human-robot interaction; Series elastic actuators; Time domain passivity; Human robot interaction",,,,,"NRF-2019K2A9A1A06100174","*This research was supported by the Industrial Strategic Technology Development Program (10052967) and by NRF Korea (NRF-2019K2A9A1A06100174).",,,,,,,,,,"Pratt, G.A., Williamson, M.M., Series elastic actuators (1995) IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots; Paine, N., Mehling, J.S., Holley, J., Radford, N.A., Johnson, G., Fok, C.-L., Sentis, L., Actuator control for the nasa-jsc valkyrie humanoid robot: A decoupled dynamics approach for torque control of series elastic robots (2015) Journal of Field Robotics; Hopkins, M.A., Ressler, S.A., Lahr, D.F., Leonessa, A., Hong, D.W., Embedded joint-space control of a series elastic humanoid (2015) IROS; Ajoudani, A., Lee, J., Rocchi, A., Ferrati, M., Hoffman, E.M., Settimi, A., Caldwell, D.G., Tsagarakis, N.G., A manipulation framework for compliant humanoid coman: Application to a valve turning task (2014) 2014 IEEE-RAS International Conference on Humanoid Robots; Wang, S., Wang, L., Meijneke, C., Van Asseldonk, E., Hoellinger, T., Cheron, G., Ivanenko, Y., Kooij Der Van, H., Design and control of the mindwalker exoskeleton (2015) IEEE Transactions on Neural Systems and Rehabilitation Engineering; Kai Kwa, H., Noorden, J.H., Missel, M., Craig, T., Pratt, J.E., Neuhaus, P.D., Development of the ihmc mobility assist exoskeleton (2009) 2009 IEEE International Conference on Robotics and Automation; Vallery, H., Veneman, J., Van Asseldonk, E., Ekkelenkamp, R., Buss, M., Kooij Der Van, H., Compliant actuation of rehabilitation robots (2008) IEEE Robotics Automation Magazine; Vallery, H., Ekkelenkamp, R., Kooij Der H.Van, Buss, M., Passive and accurate torque control of series elastic actuators (2007) 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 3534-3538; Keppler, M., Lakatos, D., Ott, C., Albu-Schaffer, A., Elastic structure preserving impedance (escontrol for compliantly actuated robots (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Lee, H., Lee, J., Ryu, J., Oh, S., Relaxing the conservatism of passivity condition for impedance controlled series elastic actuators (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , will be presented on Nov; Ryu, J., Kwon, D., Hannaford, B., Stable teleoperation with time-domain passivity control (2004) IEEE Transactions on Robotics and Automation; Ryu, J.-H., Artigas, J., Preusche, C., A passive bilateral control scheme for a teleoperator with time-varying communication delay (2010) Mechatronics; Ryu, J.-H., Kwon, D.-S., Hannaford, B., Control of a flexible manipulator with noncollocated feedback: Time-domain passivity approach (2004) IEEE Transactions on Robotics; Hannaford, B., Ryu, J.-H., Time-domain passivity control of haptic interfaces (2002) IEEE Transactions on Robotics and Automation; Singh, H., Jafari, A., Peer, A., Ryu, J.-H., Enhancing the commandfollowing bandwidth for transparent bilateral teleoperation (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 4972-4979; Singh, H., Jafari, A., Ryu, J.-H., Successive stiffness increment approach for high stiffness haptic interaction (2016) International Conference on Human Haptic Sensing and Touch Enabled Computer Applications; Adams, R.J., Hannaford, B., Stable haptic interaction with virtual environments (1999) IEEE Transactions on Robotics and Automation, 15 (3), pp. 465-474; Desoer, C.A., Vidyasagar, M., Willson, A.N., (1975) Feedback Systems: Input-output Properties; Jan, A., Schaft Der Van, A., (2000) L2-gain and Passivity Techniques in Nonlinear Control, 2. , Springer; Willems, J.C., Dissipative dynamical systems part i: General theory (1972) Archive for Rational Mechanics and Analysis",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","",Scopus,2-s2.0-85090775731
"Postoaca A.-V., Negru C., Pop F.","57203220166;55437458300;22836395600;","Deadline-aware Scheduling in Cloud-Fog-Edge Systems",2020,"Proceedings - 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020",,,"9139708","691","698",,,"10.1109/CCGrid49817.2020.00-21","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089090349&doi=10.1109%2fCCGrid49817.2020.00-21&partnerID=40&md5=3009def7c44ea365e90377a35101e194","University Politehnica of Bucharest, Computer Science and Engineering Department, Romania","Postoaca, A.-V., University Politehnica of Bucharest, Computer Science and Engineering Department, Romania; Negru, C., University Politehnica of Bucharest, Computer Science and Engineering Department, Romania; Pop, F., University Politehnica of Bucharest, Computer Science and Engineering Department, Romania","Nowadays robots are one of the most important technologies and face recognition is crucial for human-robot interaction. Face recognition has direct benefits in the commercial and law enforcement fields and it enables robots to perform a wide variety of roles such as assistance, search and rescue, military and so on. In this paper we propose a Cloud-Edge architecture for robots that enables the use case of face recognition in deadline constrained environments. We formulate a mathematical model for a Data Capsule which represents structured units of data in a time series. We design the components on each layer of the architecture. We propose a deadline aware scheduler in the Fog that acts as a proxy for the processing platforms in the Cloud and we design two face recognition applications, one in the Edge for robots that is implemented with eigenfaces and one in the Cloud for the processing platforms with deep neural networks (DNN). We evaluate the performance of the face recognition applications by running a workload that consists of a well-known labelled image data set. We test the ability of the Fog scheduler to launch jobs on time when strict deadlines are in place and it runs a heavy workload of jobs. © 2020 IEEE.","Cloud Computing; Edge Computing; Face recognition; Fog Computing; Resource Management; Scheduling","Cluster computing; Deep neural networks; Fog; Machine design; Network architecture; Scheduling; Social robots; Deadline-aware; Deadline-aware scheduling; EDGE architectures; Eigenfaces; Heavy workloads; Image data; Processing platform; Search and rescue; Face recognition",,,,,"Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii, UEFISCDI: PN-III-P2-2.1-SOL-2016-03-0046","VII. ACKNOWLEDGEMENTS The research presented in this paper is supported by the following projects: NETIO ForestMon (53/05.09.2016, SMIS2014+ 105976) and ROBIN (PN-III-P1-1.2-PCCDI-2017-0734). This publication was supported by a grant of the Ministry of Innovation and Research, UEFISCDI, project number PN-III-P2-2.1-SOL-2016-03-0046 (SPERO) within PNCDI III.",,,,,,,,,,"Alexandru, A., Coardoş, D., Utilizarea tehnologiilor big data şi iot in domeniul sanatatii (2018) Rev. Româna Inform. Autom, 28, pp. 61-84; Florian, V., Neagu, G., Towards an iot platform with edge intelligence capabilities (2018) Studies in Informatics and Control, 27 (1), pp. 65-72; Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., A view of cloud computing (2010) Communications of the Acm, 53 (4); Iordache, G., Paschke, A., Mocanu, M., Negru, C., Service level agreement characteristics of monitoring wireless sensor networks for water resource management (slas4water) (2017) Studies in Informatics and Control, 26 (4), pp. 379-386; Vasiliu, L., Pop, F., Negru, C., Mocanu, M., Cristea, V., Kolodziej, J., A hybrid scheduler for many task computing in big data systems (2017) International Journal of Applied Mathematics and Computer Science, 27 (2), pp. 385-399; Shen, B., Chilamkurti, N., Wang, R., Zhou, X., Wang, S., Ji, W., Deadline-Aware rate allocation for iot services in data center network (2018) J. of Parallel and Distributed Computing, 118, pp. 296-306; Fan, J., Wei, X., Wang, T., Lan, T., Subramaniam, S., Deadline-Aware task scheduling in a tiered iot infrastructure (2017) Globecom 2017-2017 Ieee Global Communications Conference, pp. 1-7. , ieee; Dimopoulos, S., Krintz, C., Wolski, R., Justice: A deadline-Aware, fair-share resource allocator for implementing multi-Analytics (2017) 2017 Ieee International Conference on Cluster Computing (CLUSTER, pp. 233-244. , ieee; Filip, I.-D., Postoaca, A.V., Stochitoiu, R.-D., Neatu, D.-F., Negru, C., Pop, F., Data capsule: Representation of heterogeneous data in cloud-edge computing (2019) Ieee Access, 7, pp. 49558-49567; Neatu, D.-F., Stochitoiu, R.-D., Postoaca, A.-V., Filip, I.-D., Pop, F., My cloudy time machine: A scalable microservice-based platform for data processing in cloud-edge systems: A proof of concept for the robin-cloud project (2019) Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, pp. 1451-1458; Turk, M., Pentland, A., Eigenfaces for recognition (1991) Journal of Cognitive Neuroscience, 3 (1), pp. 71-86; Suykens, J., Vandewalle, J., Least squares support vector machine classifiers (1999) Neural Processing Letters, 9 (3), pp. 293-300; Hunkeler, U., Truong, H.L., Stanford-Clark, A., Mqtts a publish/subscribe protocol for wireless sensor networks (2008) 2008 3rd Int. Conf. on Communication Systems Software and Middleware and Workshops (COMSWARE'08, pp. 791-798. , IEEE; Panwalkar, S.S., Iskander, W., A survey of scheduling rules (1977) Operations Research, 25 (1), pp. 45-61; Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E., Labeled faces in the wild: A database for studying face recognition in unconstrained environments (2008) Workshop on Faces in 'Real-life'Images: Detection, Alignment, and Recognition; Nowak, E., Jurie, F., Learning visual similarity measures for comparing never seen objects (2007) 2007 Ieee Conference on Computer Vision and Pattern Recognition, pp. 1-8; Bradski, G., Kaehler, A., (2008) Learning OpenCV: Computer Vision with the OpenCV Library, , O'Reilly Media Inc",,"Lefevre L.Varela C.A.Pallis G.Toosi A.N.Rana O.Buyya R.","Association for Computing Machinery (ACM);IEEE Computer Society;IEEE Technical Committee on Scalable Computing (TCSC)","Institute of Electrical and Electronics Engineers Inc.","20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGRID 2020","11 May 2020 through 14 May 2020",,161853,,9781728160955,,,"English","Proc. - IEEE/ACM Int. Symp. Clust., Cloud Internet Comput., CCGRID",Conference Paper,"Final","",Scopus,2-s2.0-85089090349
"Qiu Y., Satoh Y., Suzuki R., Iwata K., Kataoka H.","57214161518;7403207948;57198803844;16444459300;36720714800;","Multi-view visual question answering with active viewpoint selection",2020,"Sensors (Switzerland)","20","8","2281","","",,1,"10.3390/s20082281","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083706510&doi=10.3390%2fs20082281&partnerID=40&md5=874e33a9c79831e80fd1334c37c31033","Graduate School of Science and Technology, University of Tsukuba, Tsukuba, 305-8577, Japan; National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-8560, Japan","Qiu, Y., Graduate School of Science and Technology, University of Tsukuba, Tsukuba, 305-8577, Japan, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-8560, Japan; Satoh, Y., Graduate School of Science and Technology, University of Tsukuba, Tsukuba, 305-8577, Japan, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-8560, Japan; Suzuki, R., National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-8560, Japan; Iwata, K., National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-8560, Japan; Kataoka, H., National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-8560, Japan","This paper proposes a framework that allows the observation of a scene iteratively to answer a given question about the scene. Conventional visual question answering (VQA) methods are designed to answer given questions based on single-view images. However, in real-world applications, such as human–robot interaction (HRI), in which camera angles and occluded scenes must be considered, answering questions based on single-view images might be difficult. Since HRI applications make it possible to observe a scene from multiple viewpoints, it is reasonable to discuss the VQA task in multi-view settings. In addition, because it is usually challenging to observe a scene from arbitrary viewpoints, we designed a framework that allows the observation of a scene actively until the necessary scene information to answer a given question is obtained. The proposed framework achieves comparable performance to a state-of-the-art method in question answering and simultaneously decreases the number of required observation viewpoints by a significant margin. Additionally, we found our framework plausibly learned to choose better viewpoints for answering questions, lowering the required number of camera movements. Moreover, we built a multi-view VQA dataset based on real images. The proposed framework shows high accuracy (94.01%) for the unseen real image dataset. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Deep learning; Human; Reinforcement learning; Robot interaction; Three-dimensional (3D) vision; Visual question answering","Cameras; Human robot interaction; Camera angles; Camera movement; High-accuracy; Multiple viewpoints; Question Answering; Robot interactions; State-of-the-art methods; Viewpoint selection; Iterative methods; article; deep learning; human; human experiment; reinforcement; robotics; vision",,,,,,,,,,,,,,,,"Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C.L., Parikh, D.V., Visual question answering Proceedings of the IEEE International Conference on Computer Vision, Las Condes, pp. 2425-2433. , Chile, 11–18 December 2015; Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., Parikh, D., Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering (2017) In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21-26, pp. 6904-6913. , July; Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., Zhang, L., Bottom-up and top-down attention for image captioning and visual question answering Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6077-6086. , Salt Lake City, UT, USA,18–24 June 2018; Fukui, A., Park, D.H., Yang, D., Rohrbach, A., Darrell, T., Rohrbach, M., (2016) Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding; Hudson, D.A., Manning, C.D., (2018) Compositional Attention Networks for Machine Reasoning.; Kim, J.H., Jun, J., Zhang, B.T., (2018) Bilinear Attention Networks; Das, A., Kottur, S., Gupta, K., Singh, A., Yadav, D., Moura, J.M.F., Parikh, D., Batra, D., Visual dialog (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 326-335. , Honolulu, HI, USA, 21–26 July; Das, A., Kottur, S., Moura, J.M.F., Lee, S., Batra, D., Learning cooperative visual dialog agents with deep reinforcement learning (2017) Proceedings of the IEEE International Conference on Computer Vision, pp. 2951-2960. , Venice, Italy, 22–29 October; Qiu, Y., Satoh, Y., Suzuki, R., Kataoka, H., Incorporating 3D Information into Visual Question Answering (2019) Proceedings of the IEEE International Conference on 3D Vision (3DV), pp. 756-765. , Quebec City, QB, Canada, 16–19 September; Perez, E., Strub, F., Vries, H., Dumoulin, V., Courville, A., Film: Visual reasoning with a general conditioning layer (2018) Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pp. 2-7. , New Orleans, LA, USA, February; Lin, T.Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Perona, P., Dollar, P., Microsoft Coco: Common Objects in Context (2014) Proceedings of the European Conference on Computer Vision, pp. 740-755. , Zurich, Switzerland, 6–12 September 2014; Springer: Cham, Swityerland; Johnson, J., Hariharan, B., van der Maaten, L., Li, F.-F.C., Zitnick, L., Girshick, R., Clevr: A diagnostic dataset for compositional language and elementary visual reasoning (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2901-2910. , Honolulu, HI, USA, 21–26 July; Su, H., Jampani, V., Sun, D., Maji, S., Kalogerakis, E., Yang, M.-H., Kautz, J., Splatnet: Sparse lattice networks for point cloud processing. 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"Perez-Osorio J., Wykowska A.","55978227200;15924284200;","Adopting the intentional stance toward natural and artificial agents",2020,"Philosophical Psychology","33","3",,"369","395",,14,"10.1080/09515089.2019.1688778","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076421665&doi=10.1080%2f09515089.2019.1688778&partnerID=40&md5=dfe870238c8dea528d02d003f05b34b9","Istituto Italiano di Tecnologia, Social Cognition in Human Robot Interaction, Genova, Italy","Perez-Osorio, J., Istituto Italiano di Tecnologia, Social Cognition in Human Robot Interaction, Genova, Italy; Wykowska, A., Istituto Italiano di Tecnologia, Social Cognition in Human Robot Interaction, Genova, Italy","In our daily lives, we need to predict and understand others’ behavior in order to navigate through our social environment. Predictions concerning other humans’ behavior usually refer to their mental states, such as beliefs or intentions. Such a predictive strategy is called ‘adoption of the intentional stance.’ In this paper, we review literature related to the concept of intentional stance from the perspectives of philosophy, psychology, human development, culture, and human-robot interaction. We propose that adopting the intentional stance might be a pivotal factor in facilitating social attunement with artificial agents. The paper first reviews the theoretical considerations regarding the intentional stance and examines literature related to the development of the intentional stance across life span. Subsequently, we discuss cultural norms as grounded in the intentional stance, and finally, we focus on the issue of adopting the intentional stance toward artificial agents, such as humanoid robots. At the dawn of the artificial intelligence era, the question of how–and also when–we predict and explain robots’ behavior by referring to mental states is of high interest. The paper concludes with a discussion on ethical consequences of adopting the intentional stance toward robots, and sketches future directions in research on this topic. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.","human-robot interaction; Intentional stance; mental states; mentalizing; social cognition; social robotics",,,,,,"Horizon 2020 Framework Programme, H2020: 715058; European Research Council, ERC","This project has received funding from the European Research Council (ERC) under the European Union?s Horizon 2020 Research and Innovation Program [grant awarded to AW, titled ?InStance: Intentional Stance for Social Attunement.? ERC starting grant, Grant Agreement No. 715058].",,,,,,,,,,"Allison, T., Puce, A., McCarthy, G., Social perception from visual cues: Role of the STS region (2000) Trends in Cognitive Sciences, 4, pp. 267-278; Apperly, I., (2011) Mindreaders: The cognitive basis of “theory of mind.” Mindreaders: The cognitive basis of “theory of mind.”, , New York, NY: Psychology Press; Axelrod, L., Hone, K., E-motional advantage: Performance and satisfaction gains with affective computing (2005) Proceedings of ACM CHI 2005 Conference on Human Factors in Computing Systems, Portland, OR; Baird, J.A., Moses, L.J., Do preschoolers appreciate that identical actions may be motivated by different intentions? (2001) Journal of Cognition and Development, 2, pp. 413-448; Baldwin, D.A., Baird, J.A., Saylor, M.M., Clark, M.A., Infants parse dynamic action (2001) Child Development, 72, pp. 708-717; Baron Cohen, S., (1995) Mindblindness. Learning, development, and conceptual change, , MIT Press; Bartneck, C., Forlizzi, J., (2004), ACM, &,). Shaping human-robot interaction: Understanding the social aspects of intelligent robotic products. CHI’04 Extended Abstracts on Human Factors Computing, Systems, (1731–1732; Bartsch, K., Wellman, H.M., (1995) Children talk about the mind. Children talk about the mind, , New York, NY, US: Oxford University Press; Behne, T., Carpenter, M., Call, J., Tomasello, M., Unwilling versus unable: Infants’ understanding of intentional action (2005) Developmental Psychology, 41, pp. 328-337; Broekens, J., Heerink, M., Rosendal, H., Assistive social robots in elderly care: A review (2009) Gerontechnology, 8 (2), pp. 94-103; Brunet, E., Sarfati, Y., Hardy-Baylé, M.C., Decety, J., A PET investigation of the attribution of intentions with a nonverbal task (2000) NeuroImage, 11, pp. 157-166; Cabibihan, J.J., Javed, H., Ang, M., Aljunied, S.M., Why robots? 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"Filippini C., Perpetuini D., Cardone D., Chiarelli A.M., Merla A.","57205533040;57196390915;54788843200;55501906000;24169413000;","Thermal infrared imaging-based affective computing and its application to facilitate human robot interaction: A review",2020,"Applied Sciences (Switzerland)","10","8","2924","","",,18,"10.3390/APP10082924","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085551754&doi=10.3390%2fAPP10082924&partnerID=40&md5=60ff26a38ae1c9e534a5249309859208","Department of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University g. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, Chieti, 66100, Italy; Next2U s.r.l., Via dei Peligni 137, Pescara, 65127, Italy","Filippini, C., Department of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University g. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, Chieti, 66100, Italy, Next2U s.r.l., Via dei Peligni 137, Pescara, 65127, Italy; Perpetuini, D., Department of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University g. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, Chieti, 66100, Italy; Cardone, D., Department of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University g. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, Chieti, 66100, Italy; Chiarelli, A.M., Department of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University g. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, Chieti, 66100, Italy; Merla, A., Department of Neuroscience and Imaging, Institute for Advanced Biomedical Technologies, University g. D'Annunzio of Chieti-Pescara, Via Luigi Polacchi 13, Chieti, 66100, Italy, Next2U s.r.l., Via dei Peligni 137, Pescara, 65127, Italy","Over recent years, robots are increasingly being employed in several aspects of modern society. Among others, social robots have the potential to benefit education, healthcare, and tourism. To achieve this purpose, robots should be able to engag+e humans, recognize users' emotions, and to some extent properly react and ""behave"" in a natural interaction. Most robotics applications primarily use visual information for emotion recognition, which is often based on facial expressions. However, the display of emotional states through facial expression is inherently a voluntary controlled process that is typical of human-human interaction. In fact, humans have not yet learned to use this channel when communicating with a robotic technology. Hence, there is an urgent need to exploit emotion information channels not directly controlled by humans, such as those that can be ascribed to physiological modulations. Thermal infrared imaging-based affective computing has the potential to be the solution to such an issue. It is a validated technology that allows the non-obtrusive monitoring of physiological parameters and from which it might be possible to infer affective states. This review is aimed to outline the advantages and the current research challenges of thermal imaging-based affective computing for human-robot interaction. © 2020 by the authors.","Affective computing; Emotion recognition; Human-robot interaction; Social robots; Thermal IR imaging",,,,,,"Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR: ARS01_01120","This research was funded by PON FESR MIUR R&I 2014-2020-Asse II-ADAS, ARS01_00459 and PON MIUR SI-ROBOTICS, ARS01_01120.",,,,,,,,,,"Sung, J.-Y., Guo, L., Grinter, R.E., Christensen, H.I., ""My Roomba is Rambo"": Intimate home appliances (2007) Proceedings of the International Conference on Ubiquitous Computing, Innsbruck, Austria, pp. 145-162. , 16-19, September 2007; Springer: Berlin/Heidelberg, Germany; Dautenhahn, K., Methodology & themes of human-robot interaction: A growing research field (2007) Int. J. Adv. Robot. 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"Podpora M., Gardecki A., Beniak R., Klin B., Vicario J.L., Kawala-Sterniuk A.","23019670600;25651679200;23018119100;57203919049;24345524800;57215665281;","Human interaction smart subsystem—extending speech-based human-robot interaction systems with an implementation of external smart sensors",2020,"Sensors (Switzerland)","20","8","2376","","",,11,"10.3390/s20082376","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083844056&doi=10.3390%2fs20082376&partnerID=40&md5=327a24d05881af58f0507a0f8528aeb1","Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, 45-758, Poland; Weegree Sp. z o.o. S.K, Opole, 45-018, Poland; Wireless Information Networking Group, Escola d’Enginyeria—Universitat Autonoma de Barcelona, Bellaterra08193, Spain","Podpora, M., Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, 45-758, Poland, Weegree Sp. z o.o. S.K, Opole, 45-018, Poland; Gardecki, A., Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, 45-758, Poland, Weegree Sp. z o.o. S.K, Opole, 45-018, Poland; Beniak, R., Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, 45-758, Poland, Weegree Sp. z o.o. S.K, Opole, 45-018, Poland; Klin, B., Weegree Sp. z o.o. S.K, Opole, 45-018, Poland; Vicario, J.L., Wireless Information Networking Group, Escola d’Enginyeria—Universitat Autonoma de Barcelona, Bellaterra08193, Spain; Kawala-Sterniuk, A., Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Opole, 45-758, Poland","This paper presents a more detailed concept of Human-Robot Interaction systems architecture. One of the main differences between the proposed architecture and other ones is the methodology of information acquisition regarding the robot’s interlocutor. In order to obtain as much information as possible before the actual interaction took place, a custom Internet-of-Things-based sensor subsystems connected to Smart Infrastructure was designed and implemented, in order to support the interlocutor identification and acquisition of initial interaction parameters. The Artificial Intelligence interaction framework of the developed robotic system (including humanoid Pepper with its sensors and actuators, additional local, remote and cloud computing services) is being extended with the use of custom external subsystems for additional knowledge acquisition: device-based human identification, visual identification and audio-based interlocutor localization subsystems. These subsystems were deeply introduced and evaluated in this paper, presenting the benefits of integrating them into the robotic interaction system. In this paper a more detailed analysis of one of the external subsystems—Bluetooth Human Identification Smart Subsystem—was also included. The idea, use case, and a prototype, integration of elements of Smart Infrastructure systems and the prototype implementation were performed in a small front office of the Weegree company as a decent test-bed application area. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Cloud services; COVID-19; Humanoid robots; Pepper robot; Sensor networks; Smart infrastructure; Thermal imaging","Intelligent robots; Man machine systems; Mergers and acquisitions; Robotics; Cloud computing services; Information acquisitions; Interaction framework; Interaction parameters; Proposed architectures; Prototype implementations; Sensors and actuators; Smart infrastructures; Human robot interaction",,,,,"European Commission, EC; European Regional Development Fund, FEDER","Funding: Project Title: Industrial research and development works in Weegree Sp. z o.o.(LLC) Sp.K.(LLP) on the system of hybrid intelligence supporting smart specializations of the Opole region. Agreement number: RPOP.01.01.00-16-0072/16-00. Project co-financed by the European Union from the European Regional Development Fund within the framework of the Regional Operational Programme of the Opolskie region for the years 2014–2020: Priority Axis 01 Innovation in the Economy, Measure 1.1 Innovation in Enterprises.",,,,,,,,,,"Podpora, M., Gardecki, A., Kawala-Sterniuk, A., Humanoid Receptionist Connected to IoT Subsystems and Smart Infrastructure is Smarter than Expected (2019) Ifac-Papersonline, 52, pp. 347-352; Gardecki, A., Podpora, M., Kawala-Janik, A., Innovative Internet of Things-reinforced Human Recognition for Human-Machine Interaction Purposes (2018) Ifac-Papersonline, 51, pp. 138-142; Redondo, M.E.L., Comfortability Detection for Adaptive Human-Robot Interactions (2019) Proceedings of the 8Th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), pp. 35-39. , Cambridge, UK, 3–6 Setember; Belanche, D., Casalo, L.V., Flavian, C., Customer’s acceptance of humanoid robots in services: The moderating role of risk aversion (2020) Marketing and Smart Technologies, pp. 449-458. , Springer: Singapore; Cambridge Centre for Smart Infrastructure and Construction What is Smart Infrastructure?, , https://iotevent.eu/what-is-smart-infrastructure, accessed on 1 April 2020); Sahu, S.N., Moharana, M., Prusti, P.C., Chakrabarty, S., Khan, F., Pattanayak, S.K., Real-time data analytics in healthcare using the Internet of Things (2020) Real-Time Data Analytics for Large Scale Sensor Data, pp. 37-50. , Academic Press: Cambridge, MA, USA; Greco, A., Roberto, A., Saggese, A., Vento, M., Vigilante, V., Emotion analysis from faces for social robotics (2019) Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 358-364. , In, Bari, Italy, 6–9 October; Gobillot, N., Lesire, C., Doose, D., A design and analysis methodology for component-based real-time architectures of autonomous systems (2019) J. 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Manag., 29, pp. 247-268; Rozanska, A., Podpora, M., Making eye contact with robot—Exploring user experience in interacting with Pepper Proceedings of the Advances in Intelligent Systems and Computing Book Series (AISC), pp. 172-183. , Opole, Poland, 13–14 March 2018; Robinette, P., Li, W., Allen, R., Howard, A.M., Wagner, A.R., Overtrust of Robots in Emergency Evacuation Scenarios Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 101-108. , Christchurch, New Zealand, 7–10 March 2016; Chung, M.J.-Y., Cakmak, M., Exploring the Use of Robots for Gathering Customer Feedback in the Hospitality Industry Proceedings of the HRI Workshop on Social Robots in the Wild, pp. 1-5. , Chicago, IL, USA, 5–8 March 2018; Yahaya, S.W., Lotfi, A., Mahmud, M., A consensus novelty detection ensemble approach for anomaly detection in activities of daily living (2019) Appl. Soft Comput., 83; Yahaya, S.W., Lotfi, A., Mahmud, M., Machado, P., Kubota, N., Gesture recognition intermediary robot for abnormality detection in human activities Proceedings of the IEEE Symposium Series on Computational Intelligence, pp. 1-7. , Xiamen, China, 6–9 December 2019","Podpora, M.; Faculty of Electrical Engineering, Poland; email: m.podpora@po.edu.pl",,,"MDPI AG",,,,,14248220,,,"32331291","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85083844056
"Zhang Y., Lin Z., Huang X., You X., Ye J., Wu H.","57202629460;57205178543;57211661733;57211665070;56447569000;57214357856;","A Large-Area, Stretchable, Textile-Based Tactile Sensor",2020,"Advanced Materials Technologies","5","4","1901060","","",,7,"10.1002/admt.201901060","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083531160&doi=10.1002%2fadmt.201901060&partnerID=40&md5=c998a4ac373a5026dc279e985c2f4b43","School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China","Zhang, Y., School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China; Lin, Z., School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China; Huang, X., School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China; You, X., School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China; Ye, J., School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China; Wu, H., School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China","A tactile sensor is a necessary means for intelligent equipment to acquire external environment information and improve the performance of human–robot interaction. Although high-performance tactile sensor array is widely studied, the large number of wires required to transmit data from numerous arrays is still a major obstacle in large-area application. In this study, a large-area, low-cost, stretchable, textile-based tactile sensor, which is sensitive for contact position, is proposed. The sensor has a simple three-layer structure and four external wires due to the use of a novel double-faced effect functional knitted textile with “self-uniformity” characteristic. The porous polyurethane foam with a large pore size is first reported as touch switch material. It not only has excellent touch switch function, but also makes the sensor have a soft and elastic touch and good impact buffering. In addition, the application of radial basis function neural network makes the sensor have self-learning “intelligence,” which makes the sensor flexibly and quickly arrange even on the surface of a complex 3D object. Finally, the potential applications of the sensor are demonstrated. This study shows that the sensor has great potential in the fields of wearable devices, robot interaction control, and human–computer interface. © 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim","large-area tactile sensing; stretchable tactile sensor; textile-based wearable devices","Human computer interaction; Human robot interaction; Intelligent robots; Pore size; Radial basis function networks; Textiles; External environments; Intelligent equipment; Radial basis function neural networks; Robot interaction control; Tactile sensing; Tactile sensors; Three-layer structures; Wearable devices; Wearable sensors",,,,,"National Natural Science Foundation of China, NSFC: 51175084, 51575111, 51605093","This work was supported by National Natural Science Foundation of China (Grant Nos. 51175084, 51575111, and 51605093).",,,,,,,,,,"Yang, T., Xie, D., Li, Z., Zhu, H., (2017) Mater. Sci. 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Mater., 29; Honda, S., Zhu, Q., Satoh, S., Arie, T., Akita, S., Takei, K., (2019) Adv. Funct. Mater., 29; Vu, C., Kim, J., (2017) Fibers Polym., 18, p. 10; Wang, C., Li, X., Gao, E., Jian, M., Xia, K., Wang, Q., Xu, Z., Zhang, Y., (2016) Adv. Mater., 28, p. 6640; Lee, J., Kwon, H., Seo, J., Shin, S., Koo, J.H., Pang, C., Son, S., Lee, T., (2015) Adv. Mater., 27, p. 2433; Ahn, Y., Song, S., Yun, K., (2015) Smart Mater. Struct., 24; Ning, C., Tian, L., Zhao, X., Xiang, S., Tang, Y., Liang, E., Mao, Y., (2018) J. Mater. Chem. A, 6","Wu, H.; School of Mechanical Engineering and Automation, China; email: wuhb@fzu.edu.cn",,,"Wiley-Blackwell",,,,,2365709X,,,,"English","Adv. Mater. Technol.",Article,"Final","",Scopus,2-s2.0-85083531160
"Coşar S., Bellotto N.","24477228600;23059304100;","Human Re-Identification with a Robot Thermal Camera Using Entropy-Based Sampling",2020,"Journal of Intelligent and Robotic Systems: Theory and Applications","98","1",,"85","102",,1,"10.1007/s10846-019-01026-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066040573&doi=10.1007%2fs10846-019-01026-w&partnerID=40&md5=2f43014c12c7184930e7b4d311264974","Lincoln Centre for Autonomous Systems (L-CAS), School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, United Kingdom","Coşar, S., Lincoln Centre for Autonomous Systems (L-CAS), School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, United Kingdom; Bellotto, N., Lincoln Centre for Autonomous Systems (L-CAS), School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, United Kingdom","Human re-identification is an important feature of domestic service robots, in particular for elderly monitoring and assistance, because it allows them to perform personalized tasks and human-robot interactions. However vision-based re- identification systems are subject to limitations due to human pose and poor lighting conditions. This paper presents a new re-identification method for service robots using thermal images. In robotic applications, as the number and size of thermal datasets is limited, it is hard to use approaches that require huge amount of training samples. We propose a re-identification system that can work using only a small amount of data. During training, we perform entropy-based sampling to obtain a thermal dictionary for each person. Then, a symbolic representation is produced by converting each video into sequences of dictionary elements. Finally, we train a classifier using this symbolic representation and geometric distribution within the new representation domain. The experiments are performed on a new thermal dataset for human re-identification, which includes various situations of human motion, poses and occlusion, and which is made publicly available for research purposes. The proposed approach has been tested on this dataset and its improvements over standard approaches have been demonstrated. © 2019, The Author(s).","Body motion; Elderly care; Occlusion; Re-identification; Service robots; Thermal camera","Cameras; Entropy; Infrared devices; Mobile robots; Probability distributions; Body motions; Elderly care; Occlusion; Re identifications; Service robots; Thermal camera; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020: 643691","This work was supported by the EU H2020 project ?ENRICHME? (grant agreement nr. 643691).",,,,,,,,,,"Barbosa, I.B., Cristani, M., Del Bue, A., Bazzani, L., Murino, V., Re-Identification with Rgb-D Sensors (2012) European Conference on Computer Vision, pp. 433-442. , Springer; Bedagkar-Gala, A., Shah, S.K., A survey of approaches and trends in person re-identification (2014) Image Vis. Comput., 32 (4), pp. 270-286. , https://doi.org/10.1016/j.imavis.2014.02.001; Bellotto, N., Hu, H., A bank of unscented kalman filters for multimodal human perception with mobile service robots (2010) Int. J. Soc. Robot., 2 (2), pp. 121-136. , https://doi.org/10.1007/s12369-010-0047-x; Chang, C.C., Lin, C.J., LIBSVM: A library for support vector machines (2011) ACM Trans. Intell. Syst. 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Learn., 20 (3), pp. 273-297. , https://doi.org/10.1023/A:1022627411411; Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M., Person Re-Identification by Symmetry-Driven Accumulation of Local Features (2010) IEEE CVPR, pp. 2360-2367. , https://doi.org/10.1109/CVPR.2010.5539926; Ghiass, R.S., Arandjelović, O., Bendada, H., Maldague, X., Infrared Face Recognition: A Literature Review (2013) The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1-10. , https://doi.org/10.1109/IJCNN.2013.6707096; Hermosilla, G., del Solar, J.R., Verschae, R., Correa, M., A comparative study of thermal face recognition methods in unconstrained environments (2012) Pattern Recogn., 45 (7), pp. 2445-2459. , https://doi.org/10.1016/j.patcog.2012.01.001; Joachims, T., Nédellec, C., Rouveirol, C., (1998) Text Categorization with Support Vector Machines: Learning with Many Relevant Features, , Springer, Berlin; Koide, K., Miura, J., Identification of a specific person using color, height, and gait features for a person following robot (2016) Robot. Auton. Syst., 84, pp. 76-87. , https://doi.org/10.1016/j.robot.2016.07.004; Kullback, S., Leibler, R.A., On information and sufficiency (1951) Ann. Math. Statist., 22 (1), pp. 79-86. , https://doi.org/10.1214/aoms/1177729694; Kviatkovsky, I., Adam, A., Rivlin, E., Color invariants for person reidentification (2013) IEEE Trans. Pattern Anal. Mach. Intell., 35 (7), pp. 1622-1634. , https://doi.org/10.1109/TPAMI.2012.246; Li, W., Zhao, R., Xiao, T., Wang, X., Deepreid: Deep Filter Pairing Neural Network for Person Re-Identification (2014) IEEE CVPR; Mensink, T., Verbeek, J., Perronnin, F., Csurka, G., Distance-based image classification: Generalizing to new classes at near-zero cost (2013) IEEE Trans. Pattern Anal. Mach. 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Rev., 2, pp. 3-37; Paisitkriangkrai, S., Shen, C., van Den Hengel, A., Learning to Rank in Person Re-Identification with Metric Ensembles (2015) IEEE CVPR; Pala, F., Satta, R., Fumera, G., Roli, F., Multimodal person reidentification using rgb-d cameras (2016) IEEE Trans. Circ. Syst. Video Technol., 26 (4), pp. 788-799; Vezzani, R., Baltieri, D., Cucchiara, R., People reidentification in surveillance and forensics: A survey (2013) ACM Comput. Surv., 46 (2), pp. 29:1-29:37; The temperature of the skin surface (1936) J. Am. Med. Assoc, 106 (14), pp. 1158-1162. , https://doi.org/10.1001/jama.1936.02770140020007x, W, B; Wang, X., Doretto, G., Sebastian, T., Rittscher, J., Tu, P., Shape and Appearance Context Modeling (2007) IN: PROC. ICCV; Weinrich, C., Volkhardt, M., Gross, H.M., Appearance-Based 3D Upper-Body Pose Estimation and Person Re-Identification on Mobile Robots (2013) 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 4384-4390. , https://doi.org/10.1109/SMC.2013.748; Wengefeld, T., Eisenbach, M., Trinh, T.Q., Gross, H.M., May i be your personal coach? Bringing together person tracking and visual re-identification on a mobile robot (2016) ISR 2016; Wu, Z., Peng, M., Chen, T., Thermal Face Recognition Using Convolutional Neural Network (2016) 2016 International Conference on Optoelectronics and Image Processing (ICOIP), pp. 6-9. , https://doi.org/10.1109/OPTIP.2016.7528489; Yan, Z., Duckett, T., Bellotto, N., Online learning for human classification in 3d lidar-based tracking (2017) Proceedings of the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS; Zhao, Y., Zhao, X., Xiang, Z., Liu, Y., Online learning of dynamic multi-view gallery for person re-identification (2017) Multimed. Tools Appl., 76 (1), pp. 217-241. , https://doi.org/10.1007/s11042-015-3015-5","Coşar, S.; Lincoln Centre for Autonomous Systems (L-CAS), United Kingdom; email: scosar@lincoln.ac.uk",,,"Springer",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85066040573
"Wolfe H., Peljhan M., Visell Y.","57197829801;38261756200;23981251400;","Singing Robots: How Embodiment Affects Emotional Responses to Non-Linguistic Utterances",2020,"IEEE Transactions on Affective Computing","11","2","8114206","284","295",,2,"10.1109/TAFFC.2017.2774815","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035132724&doi=10.1109%2fTAFFC.2017.2774815&partnerID=40&md5=7a3b85945cedd54fcb7f061489f12ce0","Media Arts and Technology Program, Department of Electrical and Computer Engineering, University of California, Santa Barbara, United States","Wolfe, H., Media Arts and Technology Program, Department of Electrical and Computer Engineering, University of California, Santa Barbara, United States; Peljhan, M., Media Arts and Technology Program, Department of Electrical and Computer Engineering, University of California, Santa Barbara, United States; Visell, Y., Media Arts and Technology Program, Department of Electrical and Computer Engineering, University of California, Santa Barbara, United States","Robots are often envisaged as embodied agents that might be able to intelligently and expressively communicate with humans. This could be due to their physical embodiment, their animated nature, or to other factors, such as cultural associations. In this study, we investigated emotional responses of humans to affective non-linguistic utterances produced by an embodied agent, with special attention to the way that these responses depended on the nature of the embodiment and the extent to which the robot actively moved in proximity to the human. To this end, we developed a new singing robot platform, ROVER, that could interact with humans in its surroundings. We used affective sound design methods to endow ROVER with the ability to communicate through song, via musical, non-linguistic utterances that could, as we demonstrate, evoke emotional responses in humans. We indeed found that the embodiment of the computational agent had an affect on emotional responses. However, contrary to our expectations, we found singing computers to be more emotionally arousing than singing robots. Whether the robot moved or not did not affect arousal. The results may have implications for the design of affective non-speech audio displays for human-computer or human-robot interaction. © 2010-2012 IEEE.","emotive sound; Human robot interaction; iterative prototyping design; lab study; non-linguistic utterances","Computers; Flow visualization; Human computer interaction; Iterative methods; Linguistics; Machine design; Man machine systems; Speech; Iterative prototyping; Lab Study; Music; Non-linguistic utterances; Psychology; Robot sensing system; Human robot interaction",,,,,"National Science Foundation, NSF: 1446752, 1527709; University of California, Santa Barbara, UCSB","The authors thank the AlloSphere Research Group, UCSB Systemics Lab and the Mosher Foundation for financial support, and JoAnn Kuchera-Morin, Michael Mangus, Tobias Hollerer, Marcos Novak, and Pamela Wolfe for valuable input. Photo credit of Fig. 1 to Mohit Hingorani. This work was also supported by NSF awards No. 1446752 and 1527709.",,,,,,,,,,"Saerbeck, M., Bartneck, C., Perception of affect elicited by robot motion (2010) Proc. 5th ACM/IEEE Int. Conf. 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Advertising Res., 35 (6), pp. 63-68; Kallinen, K., Ravaja, N., Emotion perceived and emotion felt: Same and different (2006) Musicae Scientiae, 10 (2), pp. 191-213; Hall, E.T., (1966) The Hidden Dimension, , New York, NY, USA: Doubleday & Co","Wolfe, H.; Media Arts and Technology Program, United States; email: wolfe@mat.ucsb.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,19493045,,,,"English","IEEE Trans. Affective Comput.",Article,"Final","",Scopus,2-s2.0-85035132724
"Suddrey G., Robinson N.","57031893500;23767534700;","A software system for human-robot interaction to collect research data: A HTML/javascript service on the pepper robot",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"459","461",,2,"10.1145/3371382.3378287","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083183771&doi=10.1145%2f3371382.3378287&partnerID=40&md5=1e7d88b9a9a2658f5e823a0252900ea9","ARC Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, QLD, Australia","Suddrey, G., ARC Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, QLD, Australia; Robinson, N., ARC Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, QLD, Australia","A key area in human-robot interaction research is the use of a robot to collect participant research data. However, traditional website-based data collection methods do not intergrate with the robot providing the interaction. This leaves a clear disconnect between the static delivery of a digital questionnaire and a lack context-relevant behaviours from the robot. In this paper, we present a HTML/Javascript software system to create a direct link between digital data collection and the robot that delivers it. In doing so, this system can be used to create more dynamic data collection sessions using the robot's speech, movement or presence to support the delivery of questionnaires. This system can also be used to create interactive sessions to support experimentation. We present two proposed use-case scenarios built with the system with the Pepper humanoid robot in mental health and well-being services. We present this software system to help speed up development time for user studies, lower the entry barrier for non-technical researchers who want to use social robots for data collection, and to create more systematic data collection methods for robots. Future work of the software includes increasing the repertoire of questionnaire items available to allow for more sophisticated data collection. © 2020 ACM.","Human-robot interaction; Interface design; Robotics; Survey toolkit","Agricultural robots; Anthropomorphic robots; Computer software; HTML; Man machine systems; Social robots; Surveys; Data collection; Data collection method; Development time; Entry barriers; Humanoid robot; Interactive session; Software systems; Use case scenario; Data acquisition",,,,,"Department of Science, Information Technology and Innovation, Queensland Government, DSITI","These authors were supported by a Research Grant from the State of Queensland acting through the Department of Science, Information Technology and Innovation. Professor Daniel Johnson and Dr Jo Mitchell advised on the content in the ’Peppers Well-being Technique’ application.",,,,,,,,,,"Boumans, R., Van Meulen, F., Hindriks, K., Neerincx, M., Rikkert, O.M.G.M., Robot for health data acquisition among older adults: A pilot randomised controlled cross-over trial (2019) BMJ Quality & Safety, , 2019 bmjqs-2018; Lovibond, S.H., Lovibond, P.F., Manual for the depression anxiety stress scales (1996) Psychology Foundation of Australia; Mavridis, N., A review of verbal and non-verbal human-robot interactive communication (2015) Robotics and Autonomous Systems, 63, pp. 22-35. , https://doi.org/10.1016/j.robot.2014.09.031, 2015; Niemelä, M., Heikkilä, P., Lammi, H., A Social service robot in a shopping mall: Expectations of the management, retailers and consumers (2017) Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction (HRI '17), pp. 227-228. , https://doi.org/10.1145/3029798.3038301, ACM, New York, NY, USA; Pot, E., Monceaux, J., Gelin, R., Maisonnier, B., Choregraphe: A graphical tool for humanoid robot programming (2009) RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 46-51. , https://doi.org/10.1109/ROMAN.2009.5326209; Saunderson, S., Nejat, G., How robots influence humans: A survey of nonverbal communication in social human-robot interaction (2019) International Journal of Social Robotics, 11 (4), pp. 575-608. , https://doi.org/10.1007/s12369-019-00523-0, 01 Aug 2019; Tanaka, F., Isshiki, K., Takahashi, F., Uekusa, M., Sei, R., Hayashi, K., Pepper learns together with children: Development of an educational application (2015) 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids), pp. 270-275. , https://doi.org/10.1109/HUMANOIDS.2015.7363546",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083183771
"Bryant D., Borenstein J., Howard A.","57193758497;25635367200;26643170000;","Why should we gender? the effect of robot gendering and occupational stereotypes on human trust and perceived competency",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"13","21",,10,"10.1145/3319502.3374778","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082003997&doi=10.1145%2f3319502.3374778&partnerID=40&md5=229e41daccc6930e8b2f30a8641492af","School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States; School of Public Policy, Georgia Institute of Technology, Atlanta, GA, United States","Bryant, D., School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States; Borenstein, J., School of Public Policy, Georgia Institute of Technology, Atlanta, GA, United States; Howard, A., School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, United States","The attribution of human-like characteristics onto humanoid robots has become a common practice in Human-Robot Interaction by designers and users alike. Robot gendering, the attribution of gender onto a robotic platform via voice, name, physique, or other features is a prevalent technique used to increase aspects of user acceptance of robots. One important factor relating to acceptance is user trust. As robots continue to integrate themselves into common societal roles, it will be critical to evaluate user trust in the robot's ability to perform its job. This paper examines the relationship among occupational gender-roles, user trust and gendered design features of humanoid robots. Results from the study indicate that there was no significant difference in the perception of trust in the robot's competency when considering the gender of the robot. This expands the findings found in prior efforts that suggest performance-based factors have larger influences on user trust than the robot's gender characteristics. In fact, our study suggests that perceived occupational competency is a better predictor for human trust than robot gender or participant gender. As such, gendering in robot design should be considered critically in the context of the application by designers. Such precautions would reduce the potential for robotic technologies to perpetuate societal gender stereotypes. © 2020 Association for Computing Machinery.","Gender stereotypes; Gendering; Human-Robot Interaction; Human-Robot Trust; Occupational Competency; Pepper robot; Robot gender","Anthropomorphic robots; Machine design; Man machine systems; Robotics; Gender stereotypes; Gendering; Human robots; Occupational Competency; Performance based; Robotic platforms; Robotic technologies; User acceptance; Human robot interaction",,,,,"National Science Foundation, NSF: 1849101, DGE-1650044; Alfred P. Sloan Foundation: G-2019-11435","This research is based upon work partially supported by funding from the Linda J. & Mark C. Smith Endowed Chair in Bioengineering at Georgia Tech, the NSF GRFP under Grant No. DGE-1650044, the Alfred P. Sloan Foundation MPHD Program under Grant No. G-2019-11435, and NSF Award No. 1849101. Special acknowledgement also goes to Harshal Mahajan for assistance with the initial design of the study and survey tools & Katelyn Fry for assistance with the statistical analyses.",,,,,,,,,,"Savela, N., Turja, T., Oksanen, A., Social acceptance of robots in different occupational fields: A systematic literature review (2018) International Journal of Social Robotics, 10 (4), pp. 493-502; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Bernotat, J., Eyssel, F., Sachse, J., November. 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"Bankins S., Formosa P.","54889093600;16645668200;","When AI meets PC: exploring the implications of workplace social robots and a human-robot psychological contract",2020,"European Journal of Work and Organizational Psychology","29","2",,"215","229",,14,"10.1080/1359432X.2019.1620328","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067678890&doi=10.1080%2f1359432X.2019.1620328&partnerID=40&md5=d26a44621abec8b2a2466b3826dd53c6","Department of Management, Macquarie Business School, Macquarie University, Sydney, Australia; Department of Philosophy, Faculty of Arts, Macquarie University, Sydney, Australia","Bankins, S., Department of Management, Macquarie Business School, Macquarie University, Sydney, Australia; Formosa, P., Department of Philosophy, Faculty of Arts, Macquarie University, Sydney, Australia","The psychological contract refers to the implicit and subjective beliefs regarding a reciprocal exchange agreement, predominantly examined between employees and employers. While contemporary contract research is investigating a wider range of exchanges employees may hold, such as with team members and clients, it remains silent on a rapidly emerging form of workplace relationship: employees’ increasing engagement with technically, socially, and emotionally sophisticated forms of artificially intelligent (AI) technologies. In this paper we examine social robots (also termed humanoid robots) as likely future psychological contract partners for human employees, given these entities transform notions of workplace technology from being a tool to being an active partner. We first overview the increasing role of robots in the workplace, particularly through the advent of sociable AI, and synthesize the literature on human–robot interaction. We then develop an account of a human-social robot psychological contract and zoom in on the implications of this exchange for the enactment of reciprocity. Given the future-focused nature of our work we utilize a thought experiment, a commonly used form of conceptual and mental model reasoning, to expand on our theorizing. We then outline potential implications of human-social robot psychological contracts and offer a range of pathways for future research. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.","artificial intelligence; human–robot interaction; Psychological contract; reciprocity; social exchange; social robots and humanoid robots",,,,,,,,,,,,,,,,,"Alcover, C., Rico, R., Turnley, W.H., Bolino, M.C., Understanding the changing nature of psychological contracts in 21st century organizations (2017) Organizational Psychology Review, 7, pp. 4-35; Bankins, S., A process perspective on psychological contract change: Making sense of, and repairing, psychological contract breach and violation through employee coping actions (2015) Journal of Organizational Behaviour, 36, pp. 1071-1095; Barley, S.R., Bechky, B.A., Milliken, F.J., The changing nature of work (2017) Academy of Management Discoveries, 3, pp. 111-115; Bartneck, C., Reichenbach, J., Carpenter, J., The carrot and the stick (2008) Interaction Studies, 9, pp. 179-203; Bekey, G.A., Current trends in robotics (2012) Robot ethics, pp. 17-34. , Cambridge, Mass: MIT Press,. 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(. Tokyo, Japan","Bankins, S.; Department of Management, Australia; email: sarah.bankins@mq.edu.au",,,"Routledge",,,,,1359432X,,,,"English","Eur. J. Work Organ. Psychol.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85067678890
"Shastha T.K., Kyrarini M., Gräser A.","57210647031;56786567700;6603445190;","Application of reinforcement learning to a robotic drinking assistant",2020,"Robotics","9","1","1","","",,5,"10.3390/ROBOTICS9010001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079825461&doi=10.3390%2fROBOTICS9010001&partnerID=40&md5=261d92b4cc73d18c0b9c7fe463817154","Friedrich Wilhelm Bessel Institut Forschungsgesellschaft m.b.H., Bremen, 28359, Germany","Shastha, T.K., Friedrich Wilhelm Bessel Institut Forschungsgesellschaft m.b.H., Bremen, 28359, Germany; Kyrarini, M., Friedrich Wilhelm Bessel Institut Forschungsgesellschaft m.b.H., Bremen, 28359, Germany; Gräser, A., Friedrich Wilhelm Bessel Institut Forschungsgesellschaft m.b.H., Bremen, 28359, Germany","Meal assistant robots form a very important part of the assistive robotics sector since self-feeding is a priority activity of daily living (ADL) for people suffering from physical disabilities like tetraplegia. A quick survey of the current trends in this domain reveals that, while tremendous progress has been made in the development of assistive robots for the feeding of solid foods, the task of feeding liquids from a cup remains largely underdeveloped. Therefore, this paper describes an assistive robot that focuses specifically on the feeding of liquids from a cup using tactile feedback through force sensors with direct human-robot interaction (HRI). The main focus of this paper is the application of reinforcement learning (RL) to learn what the best robotic actions are, based on the force applied by the user. A model of the application environment is developed based on the Markov decision process and a software training procedure is designed for quick development and testing. Five of the commonly used RL algorithms are investigated, with the intention of finding the best fit for training, and the system is tested in an experimental study. The preliminary results show a high degree of acceptance by the participants. Feedback from the users indicates that the assistive robot functions intuitively and effectively. © 2019 by the authors.","Assistive robotics; Drinking assistant; Human-in-the-loop control; Human-robot interaction; Reinforcement learning",,,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV7867","An experimental study was conducted, to test the feasibility of the robotic drinking assistant developed with reinforcement learning. The test involved sixteen able-bodied subjects and one tetraplegic user. Of them, seven were male and nine were female (one of whom was the user with tetraplegia). The average age was 25.0 ± 2.8. All participants gave their informed, signed consent to participate in this study. This study is part of the German national funded project MobiLe and the ethical contract for the studies within the project has been approved by the German Society of Social Work (in German: DGSA-Deutsche Gesellschaft für Soziale Arbeit). The aim of the experiments was to test two types of controllers—first a simple force controller with a hardcoded state machine (“if–else” table, same user control schema) and second, the RL based PDC. This was done in two phases for each-first without any liquid in the cup to get acclimatized with the control strategy, and","Funding: The research was supported by the German Federal Ministry of Education and Research (BMBF) as part of the project MobiLe (Physical Human–Robot-Interaction for Independent Living), having the grant number 16SV7867.",,,,,,,,,"Ritschel, H., Seiderer, A., Janowksi, K., Wagner, S., Andre, E., Adaptive linguistic style for an assistive robotic health companion based on explicit human feedback (2019) Proceedings of the 12th ACM International Conference on Pervasive Technologies Related to Assistive Environments, pp. 247-255. , Rhodes, Greece, 5-7 June; Ritschel, H., Socially-aware reinforcement learning for personalized human-robot interaction (2018) Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1775-1777. , Stockholm, Sweden, 10-15 July;; Tsiakas, K., Dagioglou, M., Karkaletsis, V., Makedon, F., Adaptive robot assisted therapy using interactive reinforcement learning (2016) Proceedings of the International Conference on Social Robotics, pp. 11-21. , Kansas City, MO, USA, 1-3 November; Naotunna, I., Perera, C.J., Sandaruwan, C., Gopura, R.A.R.C., Lalitharatne, T.D., Meal assistance robots: A review on current status, challenges and future directions (2015) Proceedings of the 2015 IEEE/SICE International Symposium on System Integration (SII), pp. 211-216. , Nagoya, Japan, 12-13 December;; Hall, A.K., Backonja, U., Painter, I., Cakmak, M., Sung, M., Lau, T., Thompson, H.J., Demiris, G., Acceptance and perceived usefulness of robots to assist with activities of daily living and healthcare tasks. Assist (2019) Technol, 31, pp. 133-140. , [CrossRef] [PubMed]; Chung, C.S., Wang, H., Cooper, R.A., Functional assessment and performance evaluation for assistive robotic manipulators: Literature review (2013) J. Spinal Cord Med, 36, pp. 273-289. , [CrossRef] [PubMed]; (2017) Brochure, , http://www.neater.co.uk/neater-eater/, Available online: (accessed on 8 March 2019); Zhang, X., Wang, X., Wang, B., Sugi, T., Real-time control strategy for EMG-drive meal assistance robot-My Spoon (2008) Proceedings of the 2008 International Conference on Control, pp. 800-803. , Automation and Systems, Seoul, Korea, 14-17 April; Ohshima, Y., Kobayashi, Y., Kaneko, T., Meal support system with spoon using laser range finder and manipulator (2013) Proceedings of the 2013 IEEE Workshop on Robot Vision (WORV), pp. 82-87. , Clearwater Beach, FL, USA, 15-17 January;; Topping, M.J., Smith, J.K., The development of handy 1. A robotic system to assist the severely disabled (1999) Technol. 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No. 99CH37028), Tokyo, Japan, 12-15 October;; Lopes, P., Lavoie, R., Faldu, R., Aquino, N., Barron, J., Kante, M., Magfory, B., (2018) Icraft-Eye-Controlled Robotic Feeding Arm Technology, , https://ece.northeastern.edu/personal/meleis/icraft.pdf, (accessed on 1 September); (2019) Hands-Free eating and Drinking Products for Individuals with Disabilities, , http://www.mealtimepartners.com/, Available online: (accessed on 8 March); Schröer, S., Killmann, I., Frank, B., Voelker, M., Fiederer, L., Ball, T., Burgard, W., An autonomous robotic assistant for drinking (2015) Proceedings of the International Conference on Robotics and Automation (ICRA), pp. 6482-6487. , Seattle, WA, USA, 25-30 May;; Bien, Z., Chung, M.J., Chang, P.H., Kwon, D.S., Kim, D.J., Han, J.S., Kim, J.H., Kang, S.H., Integration of a rehabilitation robotic system (KARES II) with human-friendly man-machine interaction units (2004) Auton. Robot, 16, pp. 165-191. , [CrossRef]; Goldau, F.F., Shastha, T.K., Kyrarini, M., Gräser, A., Autonomous multi-sensory robotic assistant for a drinking task (2019) Proceedings of the 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), pp. 210-216. , Toronto, ON, Canada, 24-28 June; (2019) Jaco User Guide, , https://www.robotshop.com/media/files/PDF/jaco-arm-user-guide-jaco-academique.pdf, (accessed on 25 February); Tekscan, F.D., (2018), https://www.tekscan.com/products-solutions/electronics/flexiforce-quickstart-board, (accessed on 1 September); Koller, T.L., Kyrarini, M., Gräser, A., Towards robotic drinking assistance: Low cost multi-sensor system to limit forces in human-robot-interaction (2019) Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments, pp. 243-246. , Rhodes, Greece, 5-7 June; (2019), https://training.seer.cancer.gov/anatomy/body/terminology.html, (accessed on 26 February); Sutton, R.S., Barto, A.G., (2018) Reinforcement Learning: An Introduction;, , MIT Press: Cambridge, MA, USA; Alpaydin, E., (2014) Introduction to Machine Learning;, , MIT Press: Cambridge, MA, USA; Bertsekas, D.P., (2011) Dynamic Programming and Optimal Control, , 3rd ed.; MIT Press: Cambridge, MA, USA; Rummery, G.A., Niranjan, M., (1994) On-Line Q-Learning Using Connectionist Systems, 37. , University of Cambridge, Department of Engineering: Cambridge, UK; Watkins, C.J., Dayan, P., Q-learning (1992) Mach. Learn, 8, pp. 279-292. , [CrossRef]; (2019), https://keras.io/layers/core/, (accessed on 3 March); Schmidhuber, J., Deep learning in neural networks: An overview (2015) Neural Netw, 61, pp. 85-117. , [CrossRef] [PubMed]; Arulkumaran, K., Deisenroth, M.P., Brundage, M., Bharath, A.A., Deep reinforcement learning: A brief survey (2017) IEEE Signal. Process. Mag, 34, pp. 26-38. , [CrossRef]; Brooke, J., (2019), https://www.usability.gov/how-to-and-tools/methods/system-usability-scale.html, (accessed on 15 November)","Shastha, T.K.; Friedrich Wilhelm Bessel Institut Forschungsgesellschaft m.b.H.Germany; email: tejas@uni-bremen.de",,,"MDPI AG",,,,,22186581,,,,"English","Robotics",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85079825461
"Yang J., Zeng Y., Yin Y.","56549263100;57192945846;7403273829;","Adaptive robust control with extended state observer for human-robot impedance",2020,"International Journal of Robotics and Automation","35","1",,"1","12",,,"10.2316/J.2020.206-0022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080934116&doi=10.2316%2fJ.2020.206-0022&partnerID=40&md5=81b11907e032ef996f3f4fe7b373b910","State Key Laboratory of Mechanical System and Vibration, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China","Yang, J., State Key Laboratory of Mechanical System and Vibration, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China; Zeng, Y., State Key Laboratory of Mechanical System and Vibration, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China; Yin, Y., State Key Laboratory of Mechanical System and Vibration, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China","Human-robot systems have tremendous potentials in engineering applications. To be ubiquitous, the robot should show particular impedance according to the planned action. A novel adaptive robust controller (ARC) with extended state observer (ESO) based on output feedback in the Cartesian coordinates is proposed aiming at guaranteeing the target human-robot interaction impedance, which needs to be regulated at hand. The adaptive robust control is designed to cope with the parametric uncertainties and unknown nonlinearities. An extended state observer is incorporated to address the challenging issue that the human-robot system is subjected to various external disturbances. The integration of the two algorithms is derived to weaken the control chattering and improve the tracking performances. Tracking differentiator (TD) is performed to achieve output feedback when some states are unavailable. The proof of the control framework stability is provided by the Lyapunov stability theorem. Furthermore, the effectiveness of the proposed controller is demonstrated by simulations and experiments. The results show that the adaptive robust control with extended state observer (ARC with ESO) offers superior convergence and satisfactory tracking performances. The tracking error is decreased and the control chattering is weakened due to the disturbance estimation and compensation by the extended state observer. The proposed controller has significant application potentials in the fields of man-machine interaction. © 2020 Acta Press. All rights reserved.","Adaptive robust control; Extended state observer; Human-robot impedance; Output feedback","Control nonlinearities; Controllers; Error compensation; Human robot interaction; Robust control; State estimation; Adaptive robust control; Adaptive robust controllers; Extended state observer; Human robots; Lyapunov stability theorem; Output feedback; Parametric uncertainties; Tracking differentiators; Feedback",,,,,"National Natural Science Foundation of China, NSFC: 61375098","This work was supported by the National Natural Science Foundation of China (61375098).",,,,,,,,,,"Banerjee, H., Tse, Z.T.H., Ren, H., Soft robotics with compliance and adaptation for biomedical applications and forthcoming challenges (2018) International Journal of Robotics and Automation, 33 (1), pp. 69-79; Keyi, W., Force configuration of a rigid-flexible gait rehabilitation robot (2018) International Journal of Robotics and Automation, 33 (60); Chen, X., Zeng, Y., Yin, Y., Improving the transparency of an exoskeleton knee joint based on the understanding of motor intent using energy kernel method of EMG (2017) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (6), pp. 577-588; Sharifi, M., Behzadipour, S., Vossoughi, G., Nonlinear model reference adaptive impedance control for human-robot interactions (2014) Control Engineering Practice, 32, pp. 9-27; Yuanjie, F., Yin, Y., Active and progressive exoskeleton rehabilitation using multisource information fusion from EMG and force-position EPP (2013) IEEE Transactions on Biomedical Engineering, 12 (60), pp. 3314-3321; Aguirre-Ollinger, G., Edward Colgate, J., Peshkin, M.A., Goswami, A., Active-impedance control of a lower-limb assistive exoskeleton (2007) 2007 IEEE 10th Int. 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University of California, Berkeley","Yin, Y.; State Key Laboratory of Mechanical System and Vibration, China; email: yhyin@sjtu.edu.cn",,,"Acta Press",,,,,08268185,,IJAUE,,"English","Int J Rob Autom",Article,"Final","",Scopus,2-s2.0-85080934116
"Erol B.A., Majumdar A., Benavidez P., Rad P., Choo K.-K.R., Jamshidi M.","56825414300;57195508759;26040508900;56748978800;57208540261;57189020955;","Toward Artificial Emotional Intelligence for Cooperative Social Human-Machine Interaction",2020,"IEEE Transactions on Computational Social Systems","7","1","8744387","234","246",,17,"10.1109/TCSS.2019.2922593","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068161401&doi=10.1109%2fTCSS.2019.2922593&partnerID=40&md5=0129c07d9d5f6579f74a69fb64c0b1d4","Department of Electrical and Computer Engineering, Autonomous Control Engineering Laboratories, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX  78249, United States; Department of Information Systems and Cyber Security, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX  78249, United States","Erol, B.A., Department of Electrical and Computer Engineering, Autonomous Control Engineering Laboratories, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX  78249, United States; Majumdar, A., Department of Electrical and Computer Engineering, Autonomous Control Engineering Laboratories, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX  78249, United States; Benavidez, P., Department of Electrical and Computer Engineering, Autonomous Control Engineering Laboratories, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX  78249, United States; Rad, P., Department of Information Systems and Cyber Security, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX  78249, United States; Choo, K.-K.R., Department of Information Systems and Cyber Security, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX  78249, United States; Jamshidi, M., Department of Electrical and Computer Engineering, Autonomous Control Engineering Laboratories, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX  78249, United States","The aptitude to identify the emotional states of others and response to exposed emotions is an important aspect of human social intelligence. Robots are expected to be prevalent in society to assist humans in various tasks. Human-robot interaction (HRI) is of critical importance in the assistive robotics sector. Smart digital assistants and assistive robots fail quite often when a request is not well defined verbally. When the assistant fails to provide services as desired, the person may exhibit an emotional response such as anger or frustration through expressions in their face and voice. It is critical that robots understand not only the language, but also human psychology. A novel affection-based perception architecture for cooperative HRIs is studied in this paper, where the agent is expected to recognize human emotional states, thus encourages a natural bonding between the human and the robotic artifact. We propose a method to close the loop using measured emotions to grade HRIs. This metric will be used as a reward mechanism to adjust the assistant's behavior adaptively. Emotion levels from users are detected through vision and speech inputs processed by deep neural networks (NNs). Negative emotions exhibit a change in performance until the user is satisfied. © 2014 IEEE.","Assistive robotics; human-machine interactions; humanoid robot; Internet of robotic things; smart home; supervisory control","Anthropomorphic robots; Deep neural networks; Emotional intelligence; Man machine systems; Robotics; Assistive robotics; Human machine interaction; Humanoid robot; Smart homes; Supervisory control; Human robot interaction",,,,,"Office of the Secretary of Defense, OSD: FA8750-15-2-0116; Air Force Research Laboratory, AFRL","Manuscript received December 31, 2018; revised May 11, 2019; accepted June 10, 2019. Date of publication June 24, 2019; date of current version February 24, 2020. This work was supported in part by the Air Force Research Laboratory and the OSD under Grant FA8750-15-2-0116. (Berat A. Erol and Abhijit Majumdar contributed equally to this work.) (Corresponding author: Kim-Kwang Raymond Choo.) B. A. Erol, A. Majumdar, P. Benavidez, and M. Jamshidi are with the Autonomous Control Engineering Laboratories, Department of Electrical and Computer Engineering, The University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249 USA (e-mail: baerol@gmail.com; abhijit.g.majumdar@gmail.com; patrick.benavidez@utsa.edu; mo.jamshidi@ utsa.edu).",,,,,,,,,,"Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks (2012) Proc. Adv. Neural Inf. Process. Syst., pp. 1097-1105; Szegedy, C., Going deeper with convolutions (2015) Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 1-9. , Jun; Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., Rethinking the inception architecture for computer vision (2016) Proc. IEEE Conf. Comput. Vis. 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Accessed: Dec. 1; Lapeyre, M., Rouanet, P., Oudeyer, P.-Y., The poppy humanoid robot: Leg design for biped locomotion (2013) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 349-356. , Nov; Erol, B.A., Majumdar, A., Lwowski, J., Benavidez, P., Rad, P., Jamshidi, M., Improved deep neural network object tracking system for applications in home robotics (2018) Computational Intelligence for Pattern Recognition, pp. 369-395. , Springer; Kozhirbayev, Z., Erol, B.A., Sharipbay, A., Jamshidi, M., Speaker recognition for robotic control via an IoT device (2018) Proc. World Autom. Congr. (WAC), pp. 1-5; Buolamwini, J., Gebru, T., Gender shades: Intersectional accuracy disparities in commercial gender classification (2018) Proc. 1st Conf. Fairness, Accountability Transparency, pp. 77-91; Ranganathan, H., Chakraborty, S., Panchanathan, S., Multimodal emotion recognition using deep learning architectures (2016) Proc. IEEE Winter Conf. Appl. Comput. Vis. (WACV), pp. 1-9. , Mar; Dehghan, A., Ortiz, E.G., Shu, G., Masood, S.Z., (2017) DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network, , https://arxiv.org/abs/1702.04280, Feb; Levi, G., Hassner, T., Emotion recognition in the wild via convolutional neural networks and mapped binary patterns (2015) Proc. ACM Int. Conf. Multimodal Interact, pp. 503-510. , New York, NY, USA: ACM; Ranjan, R., Sankaranarayanan, S., Castillo, C.D., Chellappa, R., (2016) An All-in-one Convolutional Neural Network for Face Analysis, , https://arxiv.org/abs/1611.00851, Nov; Hochreiter, S., Schmidhuber, J., Long short-term memory (1997) Neural Comput., 9 (8), pp. 1735-1780. , Nov; Gers, F.A., Schmidhuber, J.A., Cummins, F.A., Learning to forget: Continual prediction with LSTM (1999) Neural Comput., 12 (10), pp. 2451-2471; Gers, F.A., Schraudolph, N.N., Schmidhuber, J., Learning precise timing with LSTM recurrent networks (2003) J. Mach. Learn. Res., 3 (1), pp. 115-143","Choo, K.-K.R.; Department of Information Systems and Cyber Security, One UTSA Circle, United States; email: raymond.choo@fulbrightmail.org",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,2329924X,,,,"English","IEEE Trans. Computat. Soc. Syst.",Article,"Final","",Scopus,2-s2.0-85068161401
"Baltanas S.-F., Ruiz-Sarmiento J.-R., Gonzalez-Jimenez J.","57215534255;50861829000;6603553203;","A Face Recognition System for Assistive Robots",2020,"PervasiveHealth: Pervasive Computing Technologies for Healthcare",,,"3378225","","",,1,"10.1145/3378184.3378225","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081089739&doi=10.1145%2f3378184.3378225&partnerID=40&md5=dbdf0253b3e8b214d4ff71b05bc840ef","Machine Perception and Intelligent Robotics Group (MAPIR), Dept. of System Engineering and Automation, Biomedical Research Institute of Málaga (IBIMA), University of Málaga, Spain","Baltanas, S.-F., Machine Perception and Intelligent Robotics Group (MAPIR), Dept. of System Engineering and Automation, Biomedical Research Institute of Málaga (IBIMA), University of Málaga, Spain; Ruiz-Sarmiento, J.-R., Machine Perception and Intelligent Robotics Group (MAPIR), Dept. of System Engineering and Automation, Biomedical Research Institute of Málaga (IBIMA), University of Málaga, Spain; Gonzalez-Jimenez, J., Machine Perception and Intelligent Robotics Group (MAPIR), Dept. of System Engineering and Automation, Biomedical Research Institute of Málaga (IBIMA), University of Málaga, Spain","Assistive robots collaborating with people demand strong Human-Robot interaction capabilities. In this way, recognizing the person the robot has to interact with is paramount to provide a personalized service and reach a satisfactory end-user experience. To this end, face recognition: a non-intrusive, automatic mechanism of identification using biometric identifiers from an user's face, has gained relevance in the recent years, as the advances in machine learning and the creation of huge public datasets have considerably improved the state-of-the-art performance. In this work we study different open-source implementations of the typical components of state-of-the-art face recognition pipelines, including face detection, feature extraction and classification, and propose a recognition system integrating the most suitable methods for their utilization in assistant robots. Concretely, for face detection we have considered MTCNN, OpenCV's DNN, and OpenPose, while for feature extraction we have analyzed InsightFace and Facenet. We have made public an implementation of the proposed recognition framework, ready to be used by any robot running the Robot Operating System (ROS). The methods in the spotlight have been compared in terms of accuracy and performance in common benchmark datasets, namely FDDB and LFW, to aid the choice of the final system implementation, which has been tested in a real robotic platform. © 2020 ACM.","Assistive Robots; CNNs; Face Detection; Face Recognition; Feature Extraction; Mobile Robots; ROS","Benchmarking; Extraction; Feature extraction; Human robot interaction; Intelligent systems; Mobile robots; Open systems; User experience; Assistive robots; Biometric identifiers; CNNs; Face recognition systems; Feature extraction and classification; Open source implementation; Robot operating systems (ROS); State-of-the-art performance; Face recognition",,,,,,,,,,,,,,,,"Face alignment in-the-wild: A Survey (2017) Computer Vision and Image Understanding, 162, pp. 1-22. , (2017); Bradski, G., The opencv library (2000) Dr. Dobb's Journal of Software Tools, , (2000); Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A., Vggface2: A dataset for recognising faces across pose and age (2018) 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 67-74; Cao, Z., Hidalgo, G., Simon, T., Wei, S., Sheikh, Y., Openpose: Realtime multi-person 2d pose estimation using part affinity fields (2018) CoRR, , abs/1812. 08008 (2018); Chaves, D., Ruiz-Sarmiento, J.R., Petkov, N., Gonzalez-Jimenez, J., Integration of cnn into a robotic architecture to build semantic maps of indoor environments (2019) International Work-Conference on Artificial Neural Networks, pp. 313-324. , Springer; Deng, J., Guo, J., Xue, N., Zafeiriou, S., Arcface: Additive angular margin loss for deep face recognition (2019) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4690-4699; Population Structure and Ageing, , https://ec.europa.eu/eurostat/statistics-explained/index.php/Population, Eurostat. [n. d. ]. Accessed: 2019-09-04; Garcia-Salguero, M., Monroy, J., Solano, A., Gonzalez-Jimenez, J., Socially acceptable approach to humans by a mobile robot (2019) International Conference on Applications of Intelligent Systems (APPIS); González-Jiménez, J., Galindo, C., Ruiz-Sarmiento, J.R., Technical improvements of the Giraff telepresence robot based on users' evaluation (2012) 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication, pp. 827-832. , Paris, France; Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J., MS-CELEB-1m: A dataset and benchmark for large-scale face recognition (2016) European Conference on Computer Vision, pp. 87-102. , Springer; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2016) The IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E., Labeled faces in the wild: A database for studying face recognition in unconstrained environments (2007) Technical Report 07-49, , University of Massachusetts, Amherst; Jain, V., Learned-Miller, E., Fddb: A benchmark for face detection in unconstrained settings (2010) Technical Report UM-CS-2010-009, , University of Massachusetts, Amherst; Kemelmacher-Shlizerman, I., Seitz, S.M., Miller, D., Brossard, E., The megaface benchmark: 1 million faces for recognition at scale (2016) The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), , Las Vegas; Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks (2012) Advances in Neural Information Processing Systems, 25, pp. 1097-1105; Lin, T., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Lawrence Zitnick, C., Microsoft COCO: Common objects in context (2014) European Conference on Computer Vision, pp. 740-755. , Springer; Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L., Sphereface: Deep hypersphere embedding for face recognition (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 212-220; Luperto, M., Monroy, J., Ruiz-Sarmiento, J.R., Moreno, F., Basilico, N., Gonzalez-Jimenez, J., Borghese, N.A., Towards long-term deployment of a mobile robot for at-home ambient assisted living of the elderly (2019) 2019 European Conference on Mobile Robots (ECMR), pp. 1-6; Navarrete, P., Ruiz-Del Solar, J., Comparative study between different eigenspace-based approaches for face recognition (2002) AFSS International Conference on Fuzzy Systems, pp. 178-184. , Springer; Orlandini, A., Kristoffersson, A., Almquist, L., Björkman, P., Cesta, A., Cortellessa, G., Galindo, C., Gonzalez-Jimenez, J., ExCITE Project: A review of forty-two months of robotic telepresence technology evolution (2016) Presence: Teleoperators and Virtual Environments, 25 (3), pp. 204-221. , (2016); Pulli, K., Baksheev, A., Kornyakov, K., Eruhimov, V., Real-time computer vision with opencv (2012) Commun. ACM, 55, pp. 61-69. , (06 2012); Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A., ROS: An open-source robot operating system (2009) ICRA Workshop on Open Source Software, 3. , Kobe, Japan; Ruiz-Sarmiento, J.R., Galindo, C., Gonzalez, J., Improving human face detection through ToF cameras for ambient intelligence applications (2011) Ambient Intelligence-Software and Applications, pp. 125-132. , Springer; Ruiz-Sarmiento, J., Galindo, C., Monroy, J., Moreno, F., Gonzalez-Jimenez, J., Ontology-based conditional random fields for object recognition (2019) Knowledge-Based Systems, 168, pp. 100-108. , (2019); Schroff, F., Kalenichenko, D., Philbin, J., Facenet: A unified embedding for face recognition and clustering (2015) The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), , Boston, MA; Simonyan, K., Zisserman, A., (2014) Very Deep Convolutional Networks for Large-Scale Image Recognition, , (Sep 2014); Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Rabinovich, A., Going deeper with convolutions (2015) The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-9; Taigman, Y., Yang, M., Ranzato, M., Wolf, L., Deepface: Closing the gap to human-level performance in face verification (2014) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701-1708; Viola, P., Jones, M.J., Robust real-time face detection (2004) International Journal of Computer Vision, 57 (2), pp. 137-154. , (2004); Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., Liu, W., Cosface: Large margin cosine loss for deep face recognition (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5265-5274; Yan, S., Xiong, Y., Lin, D., Spatial temporal graph convolutional networks for skeleton-based action recognition (2018) Thirty-Second AAAI Conference on Artificial Intelligence; Zhang, K., Zhang, Z., Li, Z., Qiao, Y., Joint face detection and alignment using multitask cascaded convolutional networks (2016) IEEE Signal Processing Letters, 23 (10), pp. 1499-1503. , (Oct 2016); Zuñiga-Noël, D., Ruiz-Sarmiento, J., Gonzalez-Jimenez, J., Intrinsic calibration of depth cameras for mobile robots using a radial laser scanner (2019) International Conference on Computer Analysis of Images and Patterns, pp. 659-671. , Springer",,"Petkov N.Strisciuglio N.Travieso-Gonzalez C.M.","University of Groningen;University of Las Palmas de Gran Canaria;University of Twente;Wolfram Research","ICST","3rd International Conference on Applications of Intelligent Systems, APPIS 2020","7 January 2020 through 9 January 2020",,157657,21531633,9781450376303,,,"English","PervasiveHealth: Pervasive Computing Technol Healthc.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85081089739
"La Cruz M.D., Casañ G.A., Sanz P.J., Marín R.","57345064200;6507827566;7102001752;7101784476;","A New Virtual Reality Interface for Underwater Intervention Missions",2020,"IFAC-PapersOnLine","53","2",,"14600","14607",,,"10.1016/j.ifacol.2020.12.1468","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119380541&doi=10.1016%2fj.ifacol.2020.12.1468&partnerID=40&md5=3f1987bd2b99e073b3516b921a00de7c","IRS-Lab, Computer Science and Engineering Department, Jaume i University, Avd. Sos Baynat s/n, Castellón de la Plana, 12071, Spain","La Cruz, M.D., IRS-Lab, Computer Science and Engineering Department, Jaume i University, Avd. Sos Baynat s/n, Castellón de la Plana, 12071, Spain; Casañ, G.A., IRS-Lab, Computer Science and Engineering Department, Jaume i University, Avd. Sos Baynat s/n, Castellón de la Plana, 12071, Spain; Sanz, P.J., IRS-Lab, Computer Science and Engineering Department, Jaume i University, Avd. Sos Baynat s/n, Castellón de la Plana, 12071, Spain; Marín, R., IRS-Lab, Computer Science and Engineering Department, Jaume i University, Avd. Sos Baynat s/n, Castellón de la Plana, 12071, Spain","Nowadays, most underwater intervention missions are developed through the well-known work-class ROVs (Remote Operated Vehicles), equipped with teleoperated arms under human supervision. Thus, despite the appearance on the market of the first prototypes of the so-called I-AUV (Autonomous Underwater Vehicles for Intervention), the most mature technology associated with ROVs continues to be trusted. In order to fill the gap between ROVs and incipient I-AUVs technology, new research is under progress in our laboratory. In particular, new HRI (Human Robot Interaction) capabilities are being tested inside a three-year Spanish coordinated project focused on cooperative underwater intervention missions. In this work new results are presented concerning a new user interface which includes immersion capabilities through Virtual Reality (VR) technology. It is worth noting that a new HRI module has been demonstrated, through a pilot study, in which the users had to solve some specific tasks, with minimum guidance and instructions, following simple Problem Based Learning (PBL) scheme. Finally, it is noticeable that, although this is only a work in progress, the obtained results are promising concerning friendly and intuitive characteristics of the developed HRI module. Thus, some critical aspects, like complexity fall, training time and cognitive fatigue of the ROV pilot, seem more affordable now. © 2020 Elsevier B.V.. All rights reserved.","HRI; Marine Robotics; Problem Based Learning; Underwater Intervention; Virtual Reality","Autonomous underwater vehicles; E-learning; Human robot interaction; Remotely operated vehicles; User interfaces; Autonomous underwater vehicles]; Human supervision; Humans-robot interactions; Interaction modules; Marine robotics; New results; Problem based learning; Teleoperated; Underwater intervention; Virtual reality interfaces; Virtual reality",,,,,"UJI-B2018-34; Generalitat Valenciana, GVA: IDIFEDER/2018/013; Ministerio de Economía, Industria y Competitividad, Gobierno de España, MINECO: DPI2017 - 86372","This work has been partly funded by Spanish Ministry of Economy, Industry and Competitiveness under DPI2017 - 86372 -C3 grant (TWINBOT), by Valencian Government under IDIFEDER/2018/013 grant (CIRTESU ) and by UJI under UJI-B2018-34 grant (NEP TUNO).",,,,,,,,,,"Almeida, L., Menezes, P., Dias, J., Improving robot teleoperation experience via immersive interfaces (2017) Proceeding of 4th Experiment International Conference (exp.at'17), Faro, Portugal, 2017, pp. 87-92. , DOI: 10.1109/EXPAT.2017.7984414; Anthes, C., García Hernandez, R., Wiedemann, M., Kranzlmüller, D., State of the Art of Virtual Reality Technologies (2016) Proceedings of IEEE Aerospace Conference, Big Sky, USA, pp. 5-12. , March 2016. DOI: 10.1109/AERO.2016.7500674; Azuma, R.T., A Survey of Augmented Reality (1997) Presence: Teleoperators and Virtual Environments, 6 (4), pp. 355-385. , DOI: 10.1162/pres.1997.6.4.355; Boud, D., Feletti, G., (2013) The Challenge of Problem-based Learning, , Routledge; Brantner, G., Khatib, O., Controlling Ocean One (2018) Field and Service Robotics, pp. 3-17. , Springer International Publishing Switzerland DOI:10.1007/978-3-319-67361-5_1; Chen, J.Y.C., Haas, E.C., Barnes, M.J., Human Performance Issues and User Interface Design for Teleoperated Robots (2007) IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, 37 (6). , November 2007; Dicianno, B.E., Sibenaller, S., Kimmich, C., Cooper, R.A., Pyo, J., Joystick Use for Virtual Power Wheelchair Driving in Individuals with Tremor: Pilot Study (2009) J Rehabil Res Dev. 2009, 46 (2), pp. 269-275; Aquas Unity Plugin, , https://assetstore.unity.com/packages/tools/particles-effects/aquas-water-river-set-52103, Dogmatic Games, (n.d.), Retrieved from on March 10th 2018; Fernández, J.J., Prats, M., Sanz, P.J., García, J.C., Marín, R., Robinson, M., Ribas, D., Ridao, P., Grasping for the Seabed: Developing a New Underwater Robot Arm for Shallow-Water Intervention (2013) IEEE Robotics & Automation Magazine, 20 (4), pp. 121-130; Gandhi, R.D., Patel, D.S., Virtual Reality - Opportunities and Challenges (2018) International Journal Res Engineering Technologies, 5 (1); Gancet, J., Weiss, P., Antonelli, G., Folkert Pfingsthorn, M., Dexterous Undersea Interventions with Far Distance Onshore Supervision: The DexROV Project (2016) IFAC-PapersOnLine, 49 (23), pp. 414-419; García, J.C., Patrão, B., Almeida, L., Pérez, J., Menezes, P., Dias, J., Sanz, P.J., A Natural Interface for Remote Operation of Underwater Robots (2015) IEEE Computer Graphics, 37 (1), pp. 34-43. , DOI: 10.1109/MCG.2015.118; Haydar, M., Maidi, M., Roussel, D., Mallem, M., Drap Bale P, K., Chapman, P., Virtual Exploration of Underwater Archaeological Sites: Visualization and Interaction in Mixed Reality Environments (2008) Proceeding of 9th International Symposium on Virtual Reality, Archaeology and Cultural Heritage, , DOI:10.2312/VAST/VAST08/141-148; Huang, C.M., Mutlu, B., Anticipatory robot control for efficient human-robot collaboration (2016) Proceedings of 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), , Christchurch, New Zealand, 7-10 March 2016; (2018) ISO 9241-11:2018(en) Ergonomics of Human-system Interaction - Part 11: Usability: Definitions and Concepts, , https://www.iso.org/obp/ui/#iso:std:iso:9241:-11:ed-2:v1:en, ISO/TC 159 Ergonomics, Subcommittee SC 4,Ergonomics of human-system interaction, Retrieved from on February 25th 2020; Khatib, O., Yeh, X., Brantner, G., Soe, B., Kim, B., Ganguly, S., Stuart, H., Creuze, V., Ocean One: A Robotic Avatar for Oceanic Discovery (2016) IEEE Robotics & Automation Magazine, 23, pp. 20-29. , DOI:10.1109/MRA.2016.2613281; Kot, T., Novak, P., Utilization of the Oculus Rift HMD in Mobile Robot Teleoperation (2014) Applied Mechanics and Materials., 555, pp. 199-208. , DOI: 10.4028/www.scientific.net/AMM.555.199; Kot, T., Novak, P., Application of virtual reality in teleoperation of the military mobile robotic system TAROS (2018) International Journal of Advanced Robotic Systems, 15 (1). , DOI: 10.1177/1729881417751545; Marsh, T., (1999) Evaluation of Virtual Reality Systems for Usability, pp. 61-62. , DOI: 10.1145/632716.632756; Miller, G., The magical number seven, plus or minus two: Some limits on our capabilities for processing information (1956) Phychological Review, Pp., 63, pp. 81-97; Peshkova, E., Hitz, M., Kaufmann, B., Survey on Natural Interaction Techniques for an Unmanned Aerial Vehicle System (2017) IEEE Perv Comp, 6 (1). , January-March, DOI: 10.1109/MPRV.2017.3; Prats, M., Pérez, J., Fernández, J.J., Sanz, P.J., An Open Source Tool for Simulation and Supervision of Underwater Intervention Missions (2012) Proceedings of the IEEE/RSJ Int. Conf. On Intelligent Robots and Systems, pp. 2577-2582. , 7-12 October 2012,Vilamoura, Algarve, Portugal, DOI:10.1109/IROS.2012.6385788; Preece, J., Rogers, Y., Sharp, H., Benyon, D., Holland, S., Carey, T., (1994) Human-Computer Interaction, , Addison-Wesley Longman Ltd. Essex, UK; Ridao, P., Carreras, M., Ribas, D., Sanz, P.J., Oliver, G., Intervention AUVs: The next challenge (2015) Annual Reviews in Control 2015, 40, pp. 227-241; Rheingold, H., (1991) Virtual Reality, , Summit Books; Sanz, P.J., Ridao, P., Oliver, G., Melchiorri, C., Casalino, G., Silvestre, C., Petillot, Y., Turetta, A., TRIDENT: A Framework for Autonomous Underwater Intervention Missions with Dexterous Manipulation Capabilities (2010) IFAC Proceedings, 43 (16), pp. 187-192; Sanz, P.J., De La Cruz, M., Lunghi, G., Veiga, C., Marín, R., Di Castro, M., The Role of HRI within COMOMUIS Research Project (2019) Proceeding of the Jornadas Nacionales de Robótica, pp. 141-147. , Alicante, Spain, 13-14 June 2019,Alicante, Spain, 2019; Sheridan, T.B., Verplank, W.L., (1978) Human and Computer Control of Undersea Teleoperators, , Massachusetts Institute of Technology Cambridge, USA Technical rept. 15 Mar 1977-14 Jun 1978; Sheridan, T.B., (1992) Telerobotics, Automation, and Human Supervisory Control, , MIT press; Sutherland, I.E., The ultimate display (1965) Proceedings of the International Federation of Information Processing Congress, pp. 506-508. , 2,New York, NY, USA; Unity User Manual 2018, , https://docs.unity3d.com/Manual/, Unity (n.d.), Retrieved from on January 2nd 2018; Yuh, J., Choi, S.K., Ikehara, C., Kim, G.H., Me Murty, G., Ghasemi-Nejhad, M., Sarlear, N., Sugihara, K., Design of a semi-autonomous underwater vehicle for intervention missions (SAUVIM) (1998) Underwater Technology, Proceedings of the 1998 International Symposium on, pp. 63-68. , 15-17 April",,"Findeisen R.Hirche S.Janschek K.Martin M.",,"Elsevier B.V.","21st IFAC World Congress 2020","12 July 2020 through 17 July 2020",,145388,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85119380541
"Hollerer S., Fischer C., Brenner B., Papa M., Schlund S., Kastner W., Fabini J., Zseby T.","57221101250;57235463700;57211499981;57200739097;9436044000;8390971800;15753340900;24282038100;","Cobot attack: A security assessment exemplified by a specific collaborative robot",2020,"Procedia Manufacturing","54",,,"191","196",,,"10.1016/j.promfg.2021.07.029","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113717022&doi=10.1016%2fj.promfg.2021.07.029&partnerID=40&md5=1dbc3f08c16884edfd886626f7f7ec29","Tu Wien, Institute of Computer Engineering, Treitlstrae 1-3, Vienna, 1040, Austria; Tu Wien, Institute of Management Science, Theresianumgasse 27, Vienna, 1040, Austria; Tu Wien, Institute of Telecommunications, Guhausstrae 25, Vienna, 1040, Austria","Hollerer, S., Tu Wien, Institute of Computer Engineering, Treitlstrae 1-3, Vienna, 1040, Austria; Fischer, C., Tu Wien, Institute of Management Science, Theresianumgasse 27, Vienna, 1040, Austria; Brenner, B., Tu Wien, Institute of Telecommunications, Guhausstrae 25, Vienna, 1040, Austria; Papa, M., Tu Wien, Institute of Management Science, Theresianumgasse 27, Vienna, 1040, Austria; Schlund, S., Tu Wien, Institute of Management Science, Theresianumgasse 27, Vienna, 1040, Austria; Kastner, W., Tu Wien, Institute of Computer Engineering, Treitlstrae 1-3, Vienna, 1040, Austria; Fabini, J., Tu Wien, Institute of Telecommunications, Guhausstrae 25, Vienna, 1040, Austria; Zseby, T., Tu Wien, Institute of Telecommunications, Guhausstrae 25, Vienna, 1040, Austria","The market for cyber-physical systems, such as collaborative robots (cobots), continues to grow fast. Primarily, this is driven by megatrends, such as smart factories, which include interconnected machines and human-robot interaction. On the one hand, these new technologies lead to flexible manufacturing and a higher level of automation. On the other hand, however, it drives the demand for the safety and security of the systems, as the potential of harm to human beings is increased in such collaborative scenarios. Therefore, in the case of a cobot, increased awareness of cyber security aspects and their relations to the system's safety is advisable. In this paper, a security assessment for the Franka Emika Panda has been performed. For this specific c obot, potential a ttack s urfaces and t heir possible i mpacts on s afety-relevant parameters a re a nalyzed. Furthermore, the defined attack surfaces of Panda are tested in a systematic penetration test based on the Open Source Security Testing Methodology (OSSTM) and the OWASP testing guide. Different vulnerabilities are found, of which some could have a safety impact on the human worker. Finally, for each of the findings, countermeasures are provided to prevent a possible cyber-attack. © 2020 Elsevier B.V.. All rights reserved.","Collaborative Robots (Cobots); Factory; Human-Robot Interaction; OT Security; Production Systems; Safety","Competition; Embedded systems; Open systems; Security of data; Sustainable development; Collaborative robots; Cyber security; Flexible manufacturing; Level of automations; Penetration test; Safety and securities; Security assessment; Security testing; Human robot interaction",,,,,,,,,,,,,,,,"Bartnes, M., Safety vs. Security? (PSAM-0148). (2010) Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM), pp. 1202-1210; Cerrudo, C., (2017) Hacking Robots before Skynet., , https://ioactive.com/pdfs/Hacking-Robots-Before-Skynet.pdf, URL:. accessed: 2020-12-22; A. PANDA-DATASHEET.; B. User HANDBOOK.; Herzog, P., Osstmm 3-The Open Source Security Testing Methodology Manual., , https://www.isecom.org/OSSTMM.3.pdf, URL:. accessed: 2021-01-05; Hollerer, S., Brenner, B., Bhosale, P., Fischer, C., Hosseini, A.M., Maragkou, S., Papa, M., Kastner, W., (2021) Challenges in OT-Security and Their Impacts on Safety-related Cyber-Physical Production Systems, , (In press); (2019) Iec 62443-4-2: Technical Security Requirements for IACS.; (2020) World Robotics Report 2020., , https://ifr.org/ifr-press-releases/news/record-2.7-million-robots-work-in-factories-around-the-globe; Ionescu, T.B., Schlund, S., A Participatory Programming Model for Democratizing Cobot Technology in Public and Industrial Fablabs (2019) Procedia Cirp, 81, pp. 93-98; Jones, M., Bradley, J., Sakimura, N., Rfc 7519: Json web token (jwt) (2015) Date Retr, 5. , 2017; Khrueangsakun, S., Nuratch, S., Boonpramuk, P., (2020) Design and Development of Cyber Physical System for Real-Time Web-based Visualization and Control of Robot Arm, pp. 11-14; Maggi, F., Quarta, D., Pogliani, M., Polino, M., Zanchettin, A.M., Zanero, S., (2017) Rogue Robots: Testing the Limits of An Industrial Robot's Security., , https://documents.trendmicro.com/assets/wp/wp-industrial-robot-security.pdf, Technical Report. URL; Mayoral-Vilches, V., Juan, L.U.S., Carbajo, U.A., Campo, R., De Cámara, X.S., Urzelai, O., García, N., Gil-Uriarte, E., (2019) Industrial Robot Ransomware: Akerbeltz, pp. 1-4; (2021) Glossary-CSRC., , https://csrc.nist.gov/glossary/, URL:. accessed: 2021-01-13; (2018) Security., , https://www.pilz.com/mam/pilz/content/uploads/wp_security_en_2018_10.pdf, URL:. accessed: 2021-01-12; Saad, E., Mitchell, R., (2020) Owasp Web Security Testing Guide Version 4.2., , https://github.com/OWASP/wstg/releases/download/v4.2/wstg-v4.2.pdf, URL:. accessed: 2020-12-28; Sattinger, V., Papa, M., Stuja, K., Kubinger, W., (2019) Methodik Zur Entwicklung Sicherer Kollaborativer Produktionssysteme im Rahmen von Industrie 4.0. e & i Elektrotechnik und Informationstechnik 136.; Schmidbauer, C., Komenda, T., Schlund, S., Teaching Cobots in Learning Factories (2020) Procedia Manufacturing, 45, pp. 398-404; Schulz, T., (2020) Cybersicherheit Fr Vernetzte Anwendungen in der Industrie 4.0., 1. , Vogel Fachbuch; Tang, G., Webb, P., (2019) Human-Robot Shared Workspace in Aerospace Factories, , Human-Robot Interaction; (2017) Owasp Top Ten 2017., , https://owasp.org/www-project-top-ten/2017/A2_2017-Broken_Authentication, URL:. accessed: 2020-12-29; Vilches, V.M., Gil-Uriarte, E., Ugarte, I.Z., Mendia, G.O., Pisón, R.I., Cordero, A.H., Apa, L., Cerrudo, C., (2018) Towards An Open Standard for Assessing the Severity of Robot Security Vulnerabilities, pp. 1-24; Vogel-Heuser, B., Bauernhansl, T., Ten Hompel, M., Handbuch Industrie 4.0 Bd. 2: Automatisierung (2017) Springer Reference Technik, , 2 ed. Springer Berlin doi:10.1007/978-3-662-53248-5; Xiao, M., (2020) The Collaborative Robot Market 2019., , https://www.interactanalysis.com/the-collaborative-robot-market-2019-infographic/, Technical Report. URL","Hollerer, S.; Tu Wien, Treitlstrae 1-3, Austria; email: siegfried.hollerer@tuwien.ac.at",,,"Elsevier B.V.","10th CIRP Sponsored Conference on Digital Enterprise Technologies - Digital Technologies as Enablers of Industrial Competitiveness and Sustainability, DET 2020","12 October 2020 through 14 October 2020",,146288,23519789,,,,"English","Procedia Manuf.",Conference Paper,"Final","All Open Access, Gold",Scopus,2-s2.0-85113717022
"Fang S., Nirjon S.","57195135791;24341633800;","Superrf: Enhanced 3d rf representation using stationary low-cost mmwave radar",2020,"International Conference on Embedded Wireless Systems and Networks",,,,"120","131",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113509108&partnerID=40&md5=f1fcb3853e6e5e99b44d4a7681ce621b","Department of Computer Science, University of North Carolina at Chapel Hill, United States","Fang, S., Department of Computer Science, University of North Carolina at Chapel Hill, United States; Nirjon, S., Department of Computer Science, University of North Carolina at Chapel Hill, United States","This paper introduces SuperRF– which takes radio frequency (RF) signals from an off-the-shelf, low-cost, 77GHz mmWave radar and produces an enhanced 3D RF representation of a scene. SuperRF is useful in scenarios where camera and other types of sensors do not work, or not allowed due to privacy concerns, or their performance is impacted due to bad lighting conditions and occlusions, or an alternate RF sensing system like synthetic aperture radar (SAR) is too large, inconvenient, and costly. Applications of SuperRF includes navigation and planning of autonomous and semi-autonomous systems, human-robot interactions and social robotics, and elderly and/or patient monitoring in-home healthcare scenarios. We use low-cost, off-the-shelf parts to capture RF signals and to train SuperRF. The novelty of SuperRF lies in its use of deep learning algorithm, followed by a compressed sensing-based iterative algorithm that further enhances the output, to generate a fine-grained 3D representation of an RF scene from its sparse RF representation – which a mmWave radar of the same class cannot achieve without instrumenting the system with large sized multiple antennas or physically moving the antenna over a longer period in time. We demonstrate the feasibility and effectiveness through an in-depth evaluation. © 2020 the authors.",,,,,,,,,,,,,,,,,,"http://www.ti.com/lit/an/swra554a/swra554a.pdf, [Online; accessed 30-Nov-2018]; http://www.ti.com/product/awr1443, [Online; accessed 24-Aug-2018]; A 100 ghz fmcw mimo radar system for 3d image reconstruction (2016) Radar Conference (EuRAD), 2016 European, pp. 37-40. , IEEE; Abdelnasser, H., Harras, K. 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J., A point set generation network for 3d object reconstruction from a single image (2017) CVPR, 2, p. 6; Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., Generative adversarial nets (2014) Advances in neural information processing systems, pp. 2672-2680; Griffin, D., Lim, J., Signal estimation from modified short-time fourier transform (1984) IEEE Transactions on Acoustics, Speech, and Signal Processing, 32 (2), pp. 236-243; Hu, H., Gu, J., Zhang, Z., Dai, J., Wei, Y., Relation networks for object detection (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3588-3597; Huang, D., Nandakumar, R., Gollakota, S., Feasibility and limits of wi-fi imaging (2014) Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems, pp. 266-279. , ACM; Iovescu, C., Rao, S., The fundamentals of millimeter wave sensors; Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Davison, A., Kinectfusion: real-time 3d reconstruction and interaction using a moving depth camera (2011) Proceedings of the 24th annual ACM symposium on User interface software and technology, pp. 559-568. , ACM; Izquierdo, A., Villacorta, J. J., del Val Puente, L., Suárez, L., Design and evaluation of a scalable and reconfigurable multi-platform system for acoustic imaging (2016) Sensors, 16 (10), p. 1671; Karanam, C. R., Mostofi, Y., 3d through-wall imaging with unmanned aerial vehicles using wifi (2017) Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, pp. 131-142. , ACM; Kingma, D. P., Ba, J., (2014) Adam: A method for stochastic optimization, , arXiv preprint arXiv:1412.6980; Kotaru, M., Joshi, K., Bharadia, D., Katti, S., Spotfi: Decimeter level localization using wifi (2015) SIGCOMM Comput. Commun. Rev, 45 (4), pp. 269-282. , Aug; Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A. 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E., Rectified linear units improve restricted boltzmann machines (2010) Proceedings of the 27th international conference on machine learning (ICML-10), pp. 807-814; Nasrollahi, K., Moeslund, T. B., Super-resolution: a comprehensive survey (2014) Machine vision and applications, 25 (6), pp. 1423-1468; Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Lerer, A., Automatic differentiation in pytorch (2017) NIPS-W; Paulraj, A., Roy, R., Kailath, T., Estimation of signal parameters via rotational invariance techniques-esprit (1985) Nineteeth Asilomar Conference on Circuits, Systems and Computers, 1985, pp. 83-89. , pages IEEE; Peng, Z., Muñoz-Ferreras, J. M., Tang, Y., Liu, C., Gómez-García, R., Ran, L., Li, C., A portable fmcw interferometry radar with programmable low-if architecture for localization, isar imaging, and vital sign tracking (2017) IEEE Transactions on Microwave Theory and Techniques, 65 (4), pp. 1334-1344; Penner, J. F., Long, D. G., Ground-based 3d radar imaging of trees using a 2d synthetic aperture (2017) Electronics, 6 (1), p. 11; Pohl, N., Jaeschke, T., Vogt, M., An sige-chip-based 80 ghz fmcw-radar system with 25 ghz bandwidth for high resolution imaging (2013) Radar Symposium (IRS), 2013 14th International, 1, pp. 239-244. , pages IEEE; Pontes, J. K., Kong, C., Sridharan, S., Lucey, S., Eriksson, A., Fookes, C., (2017) Image2mesh: A learning framework for single image 3d reconstruction, , arXiv preprint arXiv:1711.10669; Pu, Q., Gupta, S., Gollakota, S., Patel, S., Whole-home gesture recognition using wireless signals (2013) Proceedings of the 19th annual international conference on Mobile computing & networking, pp. 27-38. , ACM; Ronneberger, O., Fischer, P., Brox, T., U-net: Convolutional networks for biomedical image segmentation (2015) International Conference on Medical image computing and computer-assisted intervention, pp. 234-241. , Springer; Schmidt, R., Multiple emitter location and signal parameter estimation (1986) IEEE transactions on antennas and propagation, 34 (3), pp. 276-280; Schwarz, B., Lidar: Mapping the world in 3d (2010) Nature Photonics, 4 (7), p. 429; Su, F., Joslin, C., Acoustic imaging using a 64-node microphone array and beamformer system (2015) Signal Processing and Information Technology (ISSPIT), 2015 IEEE International Symposium on, pp. 168-173. , IEEE; Sun, Y., Wang, Y., Liu, Z., Siegel, J. E., Sarma, S. E., (2018) Pointgrow: Autoregressively learned point cloud generation with self-attention, , arXiv preprint arXiv:1810.05591; Ting, J.-W., Oloumi, D., Rambabu, K., Fmcw sar system for near-distance imaging applicationspractical considerations and calibrations (2018) IEEE Transactions on Microwave Theory and Techniques, 66 (1), pp. 450-461; Vasisht, D., Kumar, S., Katabi, D., Decimeter-level localization with a single wifi access point (2016) NSDI, 16, pp. 165-178; Virmani, A., Shahzad, M., Position and orientation agnostic gesture recognition using wifi (2017) Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys ’17, pp. 252-264. , New York, NY, USA, ACM; Wang, G., Zou, Y., Zhou, Z., Wu, K., Ni, L. M., We can hear you with wi-fi! (2014) Proceedings of the 20th Annual International Conference on Mobile Computing and Networking, MobiCom ’14, pp. 593-604. , New York, NY, USA, ACM; Wang, X., Gao, L., Mao, S., Csi phase fingerprinting for indoor localization with a deep learning approach (2016) IEEE Internet of Things Journal, 3 (6), pp. 1113-1123; Wang, Y., Wu, K., Ni, L. M., Wifall: Device-free fall detection by wireless networks (2017) IEEE Transactions on Mobile Computing, 16 (2), pp. 581-594; Wang, Z., Liu, D., Yang, J., Han, W., Huang, T., Deep networks for image super-resolution with sparse prior (2015) Proceedings of the IEEE International Conference on Computer Vision, pp. 370-378; Xie, J., Xu, L., Chen, E., Image denoising and inpainting with deep neural networks (2012) Advances in neural information processing systems, pp. 341-349; Yang, J., Wright, J., Huang, T., Ma, Y., Image super-resolution as sparse representation of raw image patches (2008) 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8. , June; Yifan, W., Wu, S., Huang, H., Cohen-Or, D., Sorkine-Hornung, O., (2018) Patch-based progressive 3d point set upsampling, , arXiv preprint arXiv:1811.11286; Yu, L., Li, X., Fu, C.-W., Cohen-Or, D., Heng, P.-A., Pu-net: Point cloud upsampling network (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2790-2799; Yu, X., Porikli, F., Ultra-resolving face images by discriminative generative networks (2016) European conference on computer vision, pp. 318-333. , Springer; Zhao, M., Adib, F., Katabi, D., Emotion recognition using wireless signals (2016) Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking, pp. 95-108. , ACM; Zhao, M., Li, T., Abu Alsheikh, M., Tian, Y., Zhao, H., Torralba, A., Katabi, D., Through-wall human pose estimation using radio signals (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7356-7365; Zhao, M., Tian, Y., Zhao, H., Alsheikh, M. A., Li, T., Hristov, R., Kabelac, Z., Torralba, A., Rf-based 3d skeletons (2018) Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication, pp. 267-281. , ACM; Zhu, Y., Yao, Y., Zhao, B. Y., Zheng, H., Object recognition and navigation using a single networking device (2017) Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, pp. 265-277. , ACM; Zhu, Y., Zhu, Y., Zhao, B. Y., Zheng, H., Reusing 60ghz radios for mobile radar imaging (2015) Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, pp. 103-116. , ACM",,"Julien C.Valois F.",,"Junction Publishing","International Conference on Embedded Wireless Systems and Networks, EWSN 2020","17 February 2020 through 19 February 2020",,268419,25622331,9780994988645,,,"English","Int. Conf. Embed. Wireless Sys. Netw.",Conference Paper,"Final","",Scopus,2-s2.0-85113509108
"Malayjerdi M., Kuts V., Sell R., Otto T., Baykara B.C.","55746688600;57198728611;25123432200;25122691600;57222066267;","Virtual simulations environment development for autonomous vehicles interaction",2020,"ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)","2B-2020",,,"","",,,"10.1115/IMECE2020-23362","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101207830&doi=10.1115%2fIMECE2020-23362&partnerID=40&md5=ba5cb0371bdaf8b251001c170dac5ea2","Mechanical and Industrial Engineering Department, Tallinn University of Technology, Tallinn, Harju, Estonia","Malayjerdi, M., Mechanical and Industrial Engineering Department, Tallinn University of Technology, Tallinn, Harju, Estonia; Kuts, V., Mechanical and Industrial Engineering Department, Tallinn University of Technology, Tallinn, Harju, Estonia; Sell, R., Mechanical and Industrial Engineering Department, Tallinn University of Technology, Tallinn, Harju, Estonia; Otto, T., Mechanical and Industrial Engineering Department, Tallinn University of Technology, Tallinn, Harju, Estonia; Baykara, B.C., Mechanical and Industrial Engineering Department, Tallinn University of Technology, Tallinn, Harju, Estonia","One of the primary verification criteria of the autonomous vehicle is safe interaction with other road users. Based on studies, real-road testing is not practical for safety validation due to its time and cost consuming. Therefore, simulating miles driven is the only feasible way to overcome this limitation. The primary goal of the related research project is to develop advanced techniques in the human-robot interaction (HRI) safety validation area by usage of immersive simulation technologies. Developing methods for the creation of the simulation environment will enable us to do experiments in a digital environment rather than real. The main aim of the paper is to develop an effective method of creating a virtual environment for performing simulations on industrial robots, mobile robots, and autonomous vehicles (AGV-s) from the safety perspective for humans. A mid-size drone was used for aerial imagery as the first step in creating a virtual environment. Then all the photos were processed in several steps to build the final 3D map. Next, this mapping method was used to create a high detail simulation environment for testing an autonomous shuttle. Developing efficient methods for mapping real environments and simulating their variables is crucial for the testing and development of control algorithms of autonomous vehicles. © 2020 The Author(s). This is an Open Access article under the CC BY license.","Autonomous vehicle; Interaction; Safety; Simulations; Virtual environment","Accident prevention; Aerial photography; Antennas; Autonomous vehicles; Industrial robots; Mapping; Roads and streets; Safety testing; Digital environment; Human robot Interaction (HRI); Mapping method; Real environments; Safety validations; Simulation environment; Simulation technologies; Virtual simulations; Human robot interaction",,,,,"Eesti Teadusagentuur, ETAg: PSG453","The research has been supported by the ""Smart specialization"" project LEP18082EM “Applied research on the system of sensors and software algorithms for safety and driver assistance on remotely operated ground vehicles for off-road applications” and by the Estonian Research Council under grant PSG453 “Digital twin for propulsion drive of an autonomous electric vehicle”.",,,,,,,,,,"Heipke, C., Woodsford, P. A., Gerke, M., Updating geospatial databases from images (2008) Advances in photogrammetry, remote sensing and spatial information sciences: 2008 ISPRS congress book, pp. 355-362. , Boca Raton: CRC Press; Alexandrov, A., Application of QuickBird satellite imagery for updating cadastral information (2004) The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, B2, pp. 386-391. , XXXVPart; Ali, Z., Tuladhar, A., Zevenbergen, J., An integrated approach for updating cadastral maps in Pakistan using satellite remote sensing data (2012) International Journal of Applied Earth Observation and Geoinformation, 18, pp. 386-398; Herold, M., Goldstein, N.C., Clarke, K.C., The spatiotemporal form of urban growth: measurement, analysis and modeling (2003) Remote Sensing of Environment, 86 (3), pp. 286-302; Ottichilo, W., Khamala, E., Map updating using high resolution satellite imagery - A case of the kingdom of Swaziland (2002) International Archives of Photogrammetry and Remote Sensing, 34 (6/W6), pp. 89-92; Mourafetis, G., Enhancing cadastral surveys by facilitating the participation of owners (2015) Survey Review, 47 (344), pp. 316-324; Babawuro, U., Beiji, Z., Satellite imagery cadastral features extractions using image processing algorithms: a viable option for cadastral science (2012) International Journal of Computer Science Issues, 9 (2), pp. 30-38. , (4); Gruen, A., Baltsavias, E., Henricsson, O., (2012) Automatic extraction of man-made objects from aerial and space images (II), , eds., Boston: Birkhäuser; Awad, M.M., A morphological model for extracting road networks from high-resolution satellite images (2013) Journal of Engineering, 2013, pp. 1-9; Turner, D., Lucieer, A., Watson, C., An automated technique for generating georectified mosaics from ultra-high resolution unmanned aerial vehicle (UAV) imagery, based on structure from motion (SFM) point clouds (2012) Remote Sensing, 4 (5), pp. 1392-1410; Van der Molen, P., Rapid urbanisation and slum upgrading: what can land surveyors do? (2015) Survey Review, 47 (343), pp. 231-240; Nex, F., Remondino, F., UAV for 3D mapping applications: a review (2014) Applied Geomatics, 6 (1), pp. 1-15; Kuts, V., Sarkans, M., Otto, T., Tähemaa, T., Bondarenko, Y., Digital Twin: Concept of Hybrid Programming for Industrial Robots - Use Case (2019) ASME 2019 International Mechanical Engineering Congress and Exposition, 2B: ASME 2019 International Mechanical Engineering Congress and Exposition, IMECE2019, , November 8-14, Salt Lake City, Utah, USA. ASME; (2016) Driving to Safety: How Many Miles of Driving Would it Take to Demonstrate Autonomous Vehicle Reliability?, , Corporation, Kalra and Paddock; Rassõlkin, A., Sell, R., Leier, M., Development case study of the first estonian self-driving car, iseauto (2018) Electrical, Control and Communication",,,"American Society of Mechanical Engineers (ASME)","American Society of Mechanical Engineers (ASME)","ASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020","16 November 2020 through 19 November 2020",,167181,,9780791884492,,,"English","ASME Int Mech Eng Congress Expos Proc",Conference Paper,"Final","",Scopus,2-s2.0-85101207830
"Parra P.S., Calleros O.L., Ramirez-Serrano A.","57221946161;55797178100;6601979152;","Human-robot collaboration systems: Components and applications",2020,"International Conference of Control, Dynamic Systems, and Robotics",,,"150","1","9",,2,"10.11159/cdsr20.150","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100802211&doi=10.11159%2fcdsr20.150&partnerID=40&md5=84b4e1577c1e465dd4c9b21d4ea1e9d7","Universidad Iberoamericana Ciudad de México, Prolongación Paseo de la Reforma 880, Mexico City, Mexico; University of Calgary, 2500 University Dr NW, Calgary, AB  T2N 1N4, Canada","Parra, P.S., Universidad Iberoamericana Ciudad de México, Prolongación Paseo de la Reforma 880, Mexico City, Mexico; Calleros, O.L., Universidad Iberoamericana Ciudad de México, Prolongación Paseo de la Reforma 880, Mexico City, Mexico; Ramirez-Serrano, A., University of Calgary, 2500 University Dr NW, Calgary, AB  T2N 1N4, Canada","Collaborative robots (cobots) have emerged as a technological solution for enhanced manipulation of objects while allowing safe interaction with a human counterpart. Although substantial developments in Human-Robot Collaboration (HRC) systems have taken place in the last decade, no conceptual framework of their composition is available. The literature provides an unclear process of how to integrate human-robot interaction levels and their roles with safety and communication technologies into HRC systems. To design an ergonomic HRC system (in its physical and cognitive dimensions) a clear identification and categorization of its components is required. This paper presents a literature review analysis that identifies the tendencies of HRC in the manufacturing sector. An initial distinction between application by type of industry and task is carried out. Afterward, interaction levels in HRC systems are examined, both individually and collectively, depending on the application area. Work roles of humans and robots, safety settings, and communication interfaces are also analyzed as part of the interaction levels in the primary composition of HRC systems. Their presence and distribution along 50 selected cases are also explored. The analyzed data, results, and observations presented in this paper demonstrate clear tendencies for specific components that were identified as necessary for improving future designs of human-centered HRC systems. © 2020, Avestia Publishing. All rights reserved.","Cobots; Cognitive workload; Human Robot Cooperation; Safe Human Robot Interaction (HRI)",,,,,,,,,,,,,,,,,"Malik, A., Bilberg, A., Framework to Implement Collaborative Robots in Manual Assembly: A Lean Automotion Approach (2017) DAAAM Intl. Symp. on Intelligent Manufacturing and Automotion, pp. 1151-1160. , Vienna, Austria; Realyvásquez-Vargas, A., Introduction and configuration of a collaborative robot in an assembly task as a means to decrease occupational risks and increase efficiency in a manufacturing company (2019) Robotics and Computer-Integrated Manufacturing, 57, pp. 315-328. , et. al; Aaltonen, I., Salmi, T., Marstio, I., Refining levels of collaboration to support the design and evaluation of human-robot interaction in the manufacturing industry (2018) Procedia CIRP, 72, pp. 93-98; Kadir, B. A., Broberg, O., Conceição, C. S. d., Current research and future perspectives on human factors and ergonomics in Industry 4.0 (2019) Computers & Industrial Engineering, 137, p. 106004; O’Neill, T.A., Salas, E., Creating high performance teamwork in organizations (2018) Human Resource Management Review, 28 (4), pp. 325-331; El Zaatari, S., Marei, M., Li, W., Usman, Z., Cobot programming for collaborative industrial tasks: An overview (2019) Robotics and Autonomous Systems, 116, pp. 162-180; Antonelli, D., Stadnicka, D., Predicting and preventing mistakes in human-robot collaborative assembly (2019) IFAC-PapersOnLine, 52 (13), pp. 743-748; Berg, J., Lottermoser, A., Richter, C., Reinhart, G., Human-Robot-Interaction for mobile industrial robot teams (2019) Procedia CIRP, 79, pp. 614-619",,,,"Avestia Publishing","7th International Conference on Control, Dynamic Systems, and Robotics, CDSR 2020","9 November 2020 through 11 November 2020",,253669,23685433,,,,"English","Int. Conf. Control Dyn. Syst. Robot.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85100802211
[No author name available],[No author id available],"7th International Conference on Control, Dynamic Systems, and Robotics, CDSR 2020",2020,"International Conference of Control, Dynamic Systems, and Robotics",,,,"","",132,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100798710&partnerID=40&md5=bc4ac85d8d977a03b7a2facfffb67c82",,"","The proceedings contain 20 papers. The special focus in this conference is on Control, Dynamic Systems, and Robotics. The topics include: Trajectory planning for a human-robot interaction rehabilitation system using direct-collocation optimization; wireless sensor network security for smart home iot systems; position control and force allocation algorithms for hybrid pneumatic-electric linear actuators; adaptive force-field control of a 2-dof upper-extremity rehabilitation robot; a comparison of three stator resistance estimation methods for a permanent magnet motor; accelerated detection method for sensor and actuator intrusions in cyber-physical systems using multiple model estimation algorithm; control-oriented muscle torque (Comt) model for emg-based control of assistive robots; activation torque estimation of muscles by forward neural networks (Forward-musclenet) for semg-based control of assistive robots; non-linear parameter identification for humanoid robot components; moving object detection for humanoid navigation in cluttered dynamic indoor environments; human-robot collaboration systems: Components and applications; explore on voice parameters of social robots applied in education industry; design and modelling of a pick and place robotic manipulator; roundabout situational awareness for automated vehicles with hybrid machine learning approach; injury risk and comfort assessment applied to ambulance transportation; research on slam and path planning based on ros robot; adaptive state tracking control with actuator nonlinearities and failures.",,,,,,,,,,,,,,,,,,,,,,"Avestia Publishing","7th International Conference on Control, Dynamic Systems, and Robotics, CDSR 2020","9 November 2020 through 11 November 2020",,253669,23685433,,,,"English","Int. Conf. Control Dyn. Syst. Robot.",Conference Review,"Final","",Scopus,2-s2.0-85100798710
"Ramirez-Serrano A., Moghaddasi S.","6601979152;57221955396;","Real-time collision detection for position-controlled humanoid robots",2020,"International Conference of Control, Dynamic Systems, and Robotics",,,"125","1","9",,,"10.11159/cdsr20.125","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100793375&doi=10.11159%2fcdsr20.125&partnerID=40&md5=aeba249a677cf0c9dec12e45a9469b3e","University of Calgary, 2500 University Dr NW, Calgary, Canada","Ramirez-Serrano, A., University of Calgary, 2500 University Dr NW, Calgary, Canada; Moghaddasi, S., University of Calgary, 2500 University Dr NW, Calgary, Canada","Humanoid robots (humanoids), at least in theory, can assist humans and work with them in cluttered and confined environments. However, further developments are needed to fully enable them to work in close proximity (even in physical interactions) with humans while not risking the safety of themselves and the objects and people around them. Current methods have not been fully successful in preparing humanoids for 100% safe physical Human Robot Interaction (HRI) due partially to the unresolved challenges of detecting the characteristics of the surrounding environment. Furthermore, current humanoids employ expensive and fragile equipment making them costly, thus limiting humanity of using them. This paper presents a novel real-time and hardware inexpensive collision detection methodology that employs signals from the robots’ motor joints and data processing capabilities from the computers running the robot. The approach enables the safe close-proximity HRI for position-controlled humanoids that minimizes any negative effects caused by the detected collision. Using the proposed algorithm, humanoids can speedily identify the joint(s) responsible for the collision and the affected joints from which effective path planning movement control can be determined. Experimental results using a life-size humanoid robot having 29 degrees of freedom are presented that demonstrates the applicability of the proposed approach. © 2020, Avestia Publishing. All rights reserved.","Collision Detection; Humanoids; Path Planning; Safe Human Robot Interaction (HRI); Signal Analysis",,,,,,,,,,,,,,,,,"Zanchettin, A. M., Ceriani, N. M., Member, S., Rocco, P., Ding, H., Matthias, B., (2016) Safety in Human-Robot Collaborative Manufacturing Environments: Metrics and Control, 13 (2), pp. 882-893; Mathematics, A., Engineering, C., (2018) Human body motion controll for humanoids and bipedal robots, 119 (15), pp. 705-713; Li, L., Jiang, S., Dai, F., Gao, X., Dynamic model and balance control of two-wheeled robot with non-holonomic constraints (2015) Proc. World Congr. Intell. Control Autom, pp. 503-508. , 2015-March March; Niiyama, R., Kuniyoshi, Y., A Pneumatic Biped with an Artificial Musculoskeletal System (1998) 4th Int. Symp. Adapt. Motion Anim. Mach, pp. 80-81; Huang, Qiang, Kajita, S., Koyachi, N., Yokoi, K., Arai, H., Komoriya, K., Tanie, K., A high stability, smooth walking pattern for a biped robot (1999) IEEE Int. Conf. Robot. Autom, 1 (May), pp. 65-71; Miura, H., Shimoyama, I., Dynamic Walk of a Biped (1984) Int. J. Robot Research, 3 (2), pp. 60-74; Hildebrandt, A. C., Wittmann, R., Sygulla, F., Wahrmann, D., Rixen, D., Buschmann, T., Versatile and robust bipedal walking in unknown environments: real-time collision avoidance and disturbance rejection (2019) Auton. Robots, , 0123456789; Sabe, K., Fukuchi, M., Gutmann, J.-S., Ohashi, T., Kawamoto, K., Yoshigahara, T., (2004) Obstacle avoidance and path planning for humanoid robots using stereo vision, 1, pp. 592-597; Michel, P., Chestnutt, J., Kagami, S., Nishiwaki, K., Kuffner, J., Kanade, T., GPU-accelerated real-time 3D tracking for humanoid locomotion and stair climbing (2007) IEEE Int. Conf. Intell. Robot. Syst, pp. 463-469; Goan, Y., Yokoi, K., Sian, Neo Ee, Tanie, K., (2005) Feasibility of humanoid robots stepping over obstacles, 22 (5), pp. 130-135; Abi-Farraj, F., Henze, B., Ott, C., Giordano, P. R., Roa, M. A., Torque-Based Balancing for a Humanoid Robot Performing High-Force Interaction Tasks (2019) IEEE Robot. Autom. Lett, 4 (2), pp. 2023-2030; Duchaine, V., Lauzier, N., Baril, M., Lacasse, M., Gosselin, C., (2009) A Flexible Robot Skin for Safe Physical Human Robot Interaction, pp. 3676-3681; Dean-leon, E., Guadarrama-olvera, J. R., Bergner, F., Cheng, G., (2019) Whole-Body Active Compliance Control for Humanoid Robots with Robot Skin ICS-Hl, pp. 5404-5410; Strohmayr, M.W., (2013) The DLR Artificial Skin Step I: Uniting Sensitivity & Collision Tolerance, pp. 1012-1018; Lumelsky, V. J., Lumelsky, V. J., Member, S., Cheung, E., (1993) Whole-Sensitive Robot Arm Manipulators Real-Time Collision Avoidance in Teleoperated Whole-Sensitive Robot Arm Manipulators, 23 (1); Yamaguchi, A., Atkeson, C. G., (2019) Recent progress in tactile sensing and sensors for robotic manipulation: can we turn tactile sensing into vision ? ABSTRACT, 1864",,,,"Avestia Publishing","7th International Conference on Control, Dynamic Systems, and Robotics, CDSR 2020","9 November 2020 through 11 November 2020",,253669,23685433,,,,"English","Int. Conf. Control Dyn. Syst. Robot.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85100793375
"Ajibo C.A., Ishi C.T., Mikata R., Liu C., Ishiguro H.","57220578619;6506878555;57203008632;35956240500;26643316500;","Analysis of body gestures in anger expression and evaluation in android robot",2020,"Advanced Robotics","34","24",,"1581","1590",,2,"10.1080/01691864.2020.1855244","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097421750&doi=10.1080%2f01691864.2020.1855244&partnerID=40&md5=0877357668f2eb95c59e9a4ea717f7bf","Hiroshi Ishiguro Laboratories, ATR, Kyoto, Japan; Intelligent Robotics Laboratory, Osaka University, Osaka, Japan; Robotics Project, RIKENKyoto, Japan","Ajibo, C.A., Hiroshi Ishiguro Laboratories, ATR, Kyoto, Japan, Intelligent Robotics Laboratory, Osaka University, Osaka, Japan; Ishi, C.T., Hiroshi Ishiguro Laboratories, ATR, Kyoto, Japan, Robotics Project, RIKENKyoto, Japan; Mikata, R., Hiroshi Ishiguro Laboratories, ATR, Kyoto, Japan; Liu, C., Hiroshi Ishiguro Laboratories, ATR, Kyoto, Japan; Ishiguro, H., Hiroshi Ishiguro Laboratories, ATR, Kyoto, Japan, Intelligent Robotics Laboratory, Osaka University, Osaka, Japan","Recent studies in human–human interaction (HHI) have revealed the propensity of negative emotional expression to initiate affiliating functions which are beneficial to the expresser and also help fostering cordiality and closeness amongst interlocutors during conversation. Effort in human–robot interaction has also been devoted to furnish robots with the expression of both positive and negative emotions. However, only a few have considered body gestures in context with the dialogue act functions conveyed by the emotional utterances. This study aims on furnishing robots with humanlike negative emotional expression, specifically anger-based body gestures roused by the utterance context. In this regard, we adopted a multimodal HHI corpus for the study, and then analyzed and established predominant gestures types and dialogue acts associated with anger-based utterances in HHI. Based on the analysis results, we implemented these gesture types in an android robot, and carried out a subjective evaluation to investigate their effects on the perception of anger expression in utterances with different dialogue act functions. Results showed significant effects of the presence of gesture on the anger degree perception. Findings from this study also revealed that the functional content of anger-based utterances plays a significant role in the choice of the gesture accompanying such utterances. © 2020 Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.","android robot; Anger emotion; gesture and speech; human–robot interaction","Agricultural robots; Function evaluation; Body gesture; Dialogue acts; Emotional expressions; Human interactions; Multi-modal; Positive and negative emotions; Robot interactions; Subjective evaluations; Human robot interaction",,,,,"Japan Science and Technology Agency, JST; Exploratory Research for Advanced Technology, ERATO: JPMJER1401","This work was partly supported by Grant-in-Aid for Scientific Research on Innovative Areas JP20H05576, and Japan Science and Technology Agency (JST), ERATO [grant number JPMJER1401]. We thank Yuka Nakayama and Megumi Taniguchi for contributions in the data analysis, and Takashi Minato for assistance in the android hardware settings.",,,,,,,,,,"Lasota, P.A., Fong, T., Shah, J.A., A survey of methods for safe human–robot interaction (2017) Found Trends Robot, 5 (3), pp. 261-349; Sheridan, T.B., Human–robot interaction: status and challenges (2016) Hum Factors, 58 (4), pp. 525-532; Van Kleef, G.A., The social effects of emotions are functionally equivalent across expressive modalities (2017) Psychol Inq, 28 (2-3), pp. 211-216; Algoe, S.B., Fredrickson, B.L., Gable, S.L., The social functions of the emotion of gratitude via expression (2013) Emotion, 13 (4), pp. 605-609; Van Kleef, G.A., Van Doorn, E.A., Heerdink, M.W., Emotion is for influence (2011) Eur Rev Soc Psychol, 22 (1), pp. 114-163. , et al; Ekman, P., Are there basic emotions? (1992) Psychol Rev, 99 (3), pp. 5509-5553; Levenson, R., Human emotions: a functional view. In: Ekman P, Davidson R, editors. The nature of emotion; New York, NY: Oxford University Press 1994. p. 123–126; Wong, E., Tschan, F., Messerli, L., Expressing and amplifying positive emotions facilitate goal attainment in workplace interactions (2013) Front Psychol, 4 (188), pp. 1-15. , et al; Van Kleef, G.A., Understanding the positive and negative effects of emotional expressions in organizations: EASI does it (2014) Hum Relat, 67 (9), pp. 1145-1164; Campellone, T.R., Kring, A.M., Who do you trust? The impact of facial emotion and behaviour on decision making (2013) Cogn Emot, 27 (4), pp. 603-620; Angie, A.D., Connelly, S., Waples, E.P., The influence of discrete emotions on judgement and decision-making: a meta-analytic review (2011) Cogn Emot, 25 (8), pp. 1393-1422. , et al; Rime, B., Emotion elicits the social sharing of emotion: theory and empirical review (2009) Emot Rev, 1 (1), pp. 60-85; Zeelenberg, M., Van der Pligt, J., Manstead, A.S., Undoing regret on Dutch television: apologizing for interpersonal regrets involving actions or inactions (1998) Pers Soc Psychol B, 24 (10), pp. 1113-1119; Shahsavarani, A.M., Noohi, S., Explaining the bases and fundamentals of anger: a literature review (2015) Int J Med Rev, 1 (4), pp. 143-149; Leite, I., McCoy, M., Ullman, D., et al. Comparing models of disengagement individual and group interactions. In: 10th ACM/IEEE International Conference on Human–Robot Interaction; Portland USA, March 2015. p. 99–105; Reyes, M.E., Meza, I.V., Pineda, L.A., Robotics facial expression of anger in collaborative human–robot interaction (2019) Int J Adv Robot Syst, 16 (1), pp. 1-13; Ghazali, A.S., Ham, J., Barakova, E., The influence of social cues in persuasive social robots on psychological reactance and compliance (2018) Comput Hum Behav, 87, pp. 58-65. , et al; Ghazali, A.S., Ham, J., Barakova, E., Effects of robot facial characteristics and gender in persuasive human–robot interaction (2018) Front Robot AI, 5 (73), pp. 1-16. , et al; De Melo, C.M., Carnevale, P., Gratch, J., The effect of expression of anger and happiness computer agents on negotiations with humans. In: Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems; Taipei Taiwan, May 2011. p. 937–944; Endo, N., Momoki, S., Zecca, M., et al. Development of whole-body emotion expression humanoid robot. In: Proceedings of the IEEE International Conference on Robotics and Automation; California, USA,  May 2008. p. 2140–2145; Canamero, L.D., Fredslund, J., How does it feel? Emotional interaction with a humanoid LEGO robot. In: Proceedings of the American Association for Artificial Intelligence Fall Symposium, FS-00-04; Menlo Park, California, 2000. p. 7–16; Breazeal, C., Emotion and sociable humanoid robots (2003) Int J Hum Comput Stud, 59 (1-2), pp. 119-155; Kühnlenz, K., Sosnowski, S., Buss, M., Impact of animal-like features on emotion expression of robot head Eddie (2010) Adv Robot, 24 (8-9), pp. 1239-1255; Xu, J., Broekens, J., Hindriks, K., et al. Robot mood is contagious: effects of robot body language the imitation game. In: Proceedings of the International Conference on Autonomous Agents and Multi-agent Systems; Paris France, May 2014. p. 973–980; Tsiourti, C., Weiss, A., Wac, K., Multimodal integration of emotional signals from voice, body, and context: effects of (in) congruence on emotion recognition and attitudes towards robots (2019) Int J Soc Robot, 11 (4), pp. 555-573. , et al; Viet Tuyen, N.T., Jeong, S., Chong, N.Y., Emotional bodily expressions for culturally competent robots through long term human–robot interaction. In: Proceedings of the IEEE International Conference on Intelligent Robots and Systems; Madrid, Spain, October 2018. p. 2008–2013; Guo, F., Li, M., Qu, Q., The effect of a humanoid robot's emotional behaviors on users' emotional responses: evidence from pupillometry and electroencephalography measures (2019) Int J Hum-Comput Interact, 35 (20), pp. 1947-1959. , et al; Itoh, K., Miwa, H., Nukariya, Y., et al. Mechanisms and functions for a humanoid robot to express human-like emotions. In: Proceedings of the IEEE International Conference on Robotics and Automation; Orlando, FL, USA, May 2006. p. 4390–4392; Zecca, M., Endo, N., Momoki, S., et al. Design of the humanoid robot KOBIAN-preliminary analysis of facial and whole-body emotion expression capabilities. In: Proceedings of the 8th IEEE-RAS International Conference on Humanoid Robots; Daejeon, Korea, August 2008. p. 487–492; Liu, C., Ishi, C., Ishiguro, H., Probabilistic nod generation model based on speech and estimated utterance categories (2019) Adv Robot, 33 (15-16), pp. 731-741; Liu, C., Ishi, C., Ishiguro, H., Generation of nodding, head tilting and gazing for human–robot speech interaction (2013) Int J Hum Robot, 10 (1). , et al; Ishi, C.T., Liu, C., Ishiguro, H., et al. Evaluation of formant-based lip motion generation tele-operated humanoid robots. 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In: Proceedings IEEE International Conference on Robotics and Automation; MN, USA, May 2012. p. 3897–3902; Ishi, C.T., Machiyashiki, D., Mikata, R., A speech-driven hand gesture generation method and evaluation in android robots (2018) IEEE Robot Autom Lett, 3 (4), pp. 3757-3764. , et al; Ishi, C.T., Minato, T., Ishiguro, H., Analysis and generation of laughter motions, and evaluation in an android robot (2019) APSIPA Trans Signal Inf Process, 8 (6), pp. 1-10; Ishi, C.T., Minato, T., Ishiguro, H., Motion analysis in vocalized surprise expressions and motion generation in android robots (2017) IEEE Robot Autom Lett, 2 (3), pp. 1748-1754; Poria, S., Hazarika, D., Majumder, N., et al. MELD: a multimodal multi-party dataset for emotion recognition conversations; 2018. Preprint; Indrianingsih, E., Ariatmi, S.Z., Hum, M., A pragmalinguistics analysis of anger expressions the crime movies [PhD thesis]. Universitas Muhammadiyah Surakarta; 2017; Scarantino, A., How to do things with emotional expressions: the theory of affective pragmatics (2017) Psychol Inq, 28 (2-3), pp. 165-185; Glas, D.F., Minato, T., Ishi, C.T., et al. Erica: the ERATO intelligent conversational android. In: Proceedings of the 25th IEEE International Symposium on Robot and Human Interactive Communication; Columbia University, New York City, USA, August 2016. p. 22–29; Rains, S.A., Turner, M.M., Psychological reactance and persuasive health communication: a test and extension of the intertwined model (2007) Hum Commun Res, 33 (2), pp. 241-269; Bartneck, C., Croft, E., Kulic, D., Measuring the anthropomorphism, animacy, likeability, perceived intelligence and perceived safety of robots. In: Proceedings of the Metrics for Human–Robot Interaction Workshop Affiliation with the 3rd ACM/IEEE International Conference on Human–Robot Interaction; New York United States, March 2008. p. 37–44; Mileounis, A., Cuijpers, R.H., Barakova, E.I., Creating robots with personality: the effect of personality on social intelligence. In: International Work-Conference on the Interplay Between Natural and Artificial Computation; Elche, Spain, June 2015. p. 119–132","Ajibo, C.A.; Hiroshi Ishiguro Laboratories, Japan; email: ajibo.augustine@irl.sys.es.osaka-u.ac.jp",,,"Robotics Society of Japan",,,,,01691864,,ADROE,,"English","Adv .Rob.",Article,"Final","",Scopus,2-s2.0-85097421750
"Louie W.-Y.G., Korneder J., Hijaz A., Sochanski M.","55515613600;57219842565;57215284694;57220182820;","Investigating Therapist Vocal Nonverbal Behavior for Applications in Robot-Mediated Therapies for Individuals Diagnosed with Autism",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"416","427",,1,"10.1007/978-3-030-62056-1_35","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097207338&doi=10.1007%2f978-3-030-62056-1_35&partnerID=40&md5=3cac3cc6cdc5aa82023c9e525c0b269d","Intelligent Robotics Laboratory at Oakland University, Rochester, MI  48307, United States; Applied Behavior Analysis Clinic at Oakland University, Rochester, MI  48307, United States","Louie, W.-Y.G., Intelligent Robotics Laboratory at Oakland University, Rochester, MI  48307, United States; Korneder, J., Applied Behavior Analysis Clinic at Oakland University, Rochester, MI  48307, United States; Hijaz, A., Intelligent Robotics Laboratory at Oakland University, Rochester, MI  48307, United States; Sochanski, M., Intelligent Robotics Laboratory at Oakland University, Rochester, MI  48307, United States","Socially assistive robots (SARs) are being utilized for delivering a variety of healthcare services to patients. The design of these human-robot interactions (HRIs) for healthcare applications have primarily focused on the interaction flow and verbal behaviors of a SAR. To date, there has been minimal focus on investigating how SAR nonverbal behaviors should be designed according to the context of the SAR’s communication goals during a HRI. In this paper, we present a methodology to investigate nonverbal behavior during specific human-human healthcare interactions so that they can be applied to a SAR. We apply this methodology to study the context-dependent vocal nonverbal behaviors of therapists during discrete trial training (DTT) therapies delivered to children with autism. We chose DTT because it is a therapy commonly being delivered by SARs and modeled after human-human interactions. Results from our study led to the following recommendations for the design of the vocal nonverbal behavior of SARs during a DTT therapy: 1) the consequential error correction should have a lower pitch and intensity than the discriminative stimulus but maintain a similar speaking rate; and 2) the consequential reinforcement should have a higher pitch and intensity than the discriminative stimulus but a slower speaking rate. © 2020, Springer Nature Switzerland AG.","Autism; Nonverbal behavior; Socially Assistive Robots","Behavioral research; Diseases; Error correction; Health care; Machine design; Robotics; Speech; Children with autisms; Communication goals; Context dependent; Health care application; Healthcare services; Human-human interactions; Nonverbal behavior; Socially assistive robots; Human robot interaction",,,,,"National Science Foundation, NSF: 1948224","This work was supported by the National Science Foundation CRII Award (#1948224). We would like to thank all the participants from the Applied Behavior Analysis Clinic.","Acknowledgements. This work was supported by the National Science Foundation CRII Award (#1948224). We would like to thank all the participants from the Applied Behavior Analysis Clinic.",,,,,,,,,"Papadopoulos, I., Enablers and barriers to the implementation of socially assistive humanoid robots in health and social care: A systematic review (2020) BMJ Open, 10 (1), pp. 1-13; Rabbitt, S.M., Kazdin, A.E., Scassellati, B., Integrating socially assistive robotics into mental healthcare interventions: Applications and recommendations for expanded use (2015) Clin. Psychol. Rev., 35 (35-46); Martinez-Martin, E., Cazorla, M., A socially assistive robot for elderly exercise promotion (2019) IEEE Access, 7, pp. 75515-75529; Blanch-Hartigan, D., Measuring nonverbal behavior in clinical interactions: A pragmatic guide (2018) Patient Educ. Couns., 101, pp. 2209-2218; Brown, A.B., Elder, J.H., Communication in autism spectrum disorder: A guide for pediatric nurses (2014) Pediatr. Nurs., 40 (5), pp. 219-225; Ambady, N., Physical therapists’ nonverbal communication predicts geriatric patients’ health outcomes (2002) Psychol. Aging, 17 (3), pp. 443-452; Gable, R.A., Back to basics: Rules, praise, ignoring, and reprimands revisited (2009) Interv. Sch. Clin., 44 (4), pp. 195-205; Begum, M., Measuring the efficacy of robots in autism therapy: How informative are standard HRI metrics (2015) ACM/IEEE International Conference on Human-Robot Interaction, pp. 335-342; Feng, Y., A control architecture of robot-assisted intervention for children with autism spectrum disorders (2018) J. Robot. Pp., pp. 1-12; Salvador, M., Marsh, A.S., Gutierrez, A., Mahoor, M.H., Development of an ABA autism intervention delivered by a humanoid robot (2016) ICSR 2016. LNCS (LNAI), 9979, pp. 551-560. , https://doi.org/10.1007/978-3-319-47437-3_54, Agah, A., C, J.-J., H, Ayanna M., S, Miguel A., He, Hongsheng (eds.), Springer, Cham; (2020) Data & Statistics on Autism Spectrum Disorder, , https://www.cdc.gov/, Accessed Jun 2020; Hurt, A.A., Personality traits associated with occupational “burnout” in ABA therapists (2013) J. Appl. Res. Intellect. Disabil., 26 (4), pp. 299-308; Louie, W.-Y.G., Korneder, J.A., Abbas, I., A pilot study for a robot-mediated listening comprehension intervention for children with ASD (2020) IEEE International Symposium on Robot and Human Interactive Communication, pp. 1-4; Google Cloud (2020) Cloud Text-To-Speech-Speech Synthesis, , https://cloud.google.com/, Accessed Jun 2020; Burroughs, N.F., A reinvestigation of the relationship of teacher nonverbal immediacy and student compliance-resistance with learning (2007) Commun. Educ., 56 (4), pp. 453-475; Gorawara-Bhat, R., Dethmers, D.L., Cook, M.A., Physician eye contact and elder patient perceptions of understanding and adherence (2013) Patient Educ. Couns., 92, pp. 375-380; Cevasco, A.M., Effects of the therapist’s nonverbal behavior on participation and affect of individuals with Alzheimer’s disease during group music therapy sessions (2010) J. Music Ther., 47 (3), pp. 282-299; Yarczower, M., Kilbride, J.E., Beck, A.T., Changes in nonverbal behavior of therapists and depressed patients during cognitive therapy (1991) Psychol. Rep., 69 (3), pp. 915-919; Mutlu, B., Conversational gaze mechanisms for humanlike robots (2012) ACM Trans. Interact. Intell. Syst., 1 (2), pp. 1-33; Yoon, Y., Robots learn social skills: End-to-end learning of co-speech gesture generation for humanoid robots (2019) 19Th IEEE International Conference in Robotics and Automation, pp. 4303-4309; Feng, W., Learn2Smile: Learning non-verbal interaction through observation (2017) IEEE International Conference on Intelligent Robots and Systems, pp. 4131-4138; Randall, A., (2016) Nonverbal Behaviour as Communication, , Owen, H. (ed.) The Handbook of Communication Skills, 73–119. Routledge, New York; Woodall, W.G., Burgoon, J.K., The effects of nonverbal synchrony on message comprehension and persuasiveness (1981) J. Nonverbal Behav., 5 (4), pp. 07-223; Eikeseth, S., Intensive behavioral treatment at school for 4-to-7-year-old children with autism (2002) Behav. Modif., 26, pp. 49-68; Lovaas, O.I., Behavioral treatment and normal educational and intellectual functioning in young autistic children (1987) J. Consult. Clin. Psychol., 55, pp. 3-9; (2009), National Autism Center: National standards project: Findings and conclusions. NAC, Randolph; Claude, S.E., A mathematical theory of communication (1948) Bell Syst. Tech. J., 27 (3), pp. 379-423; Boersma, P., Weenink, D., (2020) Praat: Doing Phonetics by Computer, , http://www.praat.org/, Amsterdam; Quené, H., van Den Bergh, H., On multi-level modeling of data from repeated measures designs: A tutorial (2004) Speech Commun, 43 (1-2), pp. 103-121; Barr, D.J., Random effects structure for confirmatory hypothesis testing: Keep it maximal (2013) J. Mem. Lang., 68 (3), pp. 255-278; Vogeley, K., Bente, G., Artificial humans’: Psychology and neuroscience perspectives on embodiment and nonverbal communication (2010) Neural Netw., 23, pp. 1077-1090; Sigler, E.A., Aamidor, S., From positive reinforcement to positive behaviors: An everyday guide for the practitioner (2005) Early Child. Educ. J., 32 (4), pp. 249-253; Quam, C., Swingley, D., Development in children’s interpretation of pitch cues to emotions (2012) Child Dev, 83 (1), pp. 236-250; Fosler-Lussier, E., Morgan, N., Effects of speaking rate and word frequency on pronunciations in convertional speech (1999) Speech Commun, 29 (2-4), pp. 137-158","Louie, W.-Y.G.; Intelligent Robotics Laboratory at Oakland UniversityUnited States; email: louie@oakland.edu","Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097207338
"Ab Aziz A., Ghanimi H.M.A.","35100067700;57191921310;","Reading with robots: A personalized robot-based learning companion for solving cognitively demanding tasks",2020,"International Journal on Advanced Science, Engineering and Information Technology","10","4",,"1489","1496",,1,"10.18517/ijaseit.10.4.7077","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090860493&doi=10.18517%2fijaseit.10.4.7077&partnerID=40&md5=56f5170cbe21f6a6227ab4f0778e18a4","Human-Centred Computing Research Lab, School of Computing, Universiti Utara Malaysia, Sintok, Kedah, 06010, Malaysia","Ab Aziz, A., Human-Centred Computing Research Lab, School of Computing, Universiti Utara Malaysia, Sintok, Kedah, 06010, Malaysia; Ghanimi, H.M.A., Human-Centred Computing Research Lab, School of Computing, Universiti Utara Malaysia, Sintok, Kedah, 06010, Malaysia","Soon, sociable companion robots will become indispensable for providing related support in our daily living and tasks. This paper provides process, design perspectives, and deployment of a reading companion robot (IQRA') that monitors the cognitive load level of a reader during demanding reading tasks and to provide support for readers to complete tasks. Current technological solutions only cover external design aspects of the application and have no adaptive mechanisms to deal with dynamics with the reader's perspectives and environment. Inspired by several theories from cognitive psychology domains, a computational model of the cognitive load was developed as a basis for reasoning and analytical purposes. This analytical ability provides the robot with a computational mechanism to reason in human-like manners and analyses of the functioning of observed conditions. This is essential in providing better-informed actions and intelligent analysis. Besides, the physical and software design of the robot and essential concepts in human-robot interaction are covered. Also, five evaluation constructs were chosen to evaluate the capability of our robotbased platform. These constructs are; 1) likeability, 2) perceived intelligence, 3) sociability, 4) social presence, and 5) cognitive load. The overall results from the pilot study support the practical usage of our proposed robotic solution. © 2020, Insight Society.","Cognitive load modelling; Companion robot; Personalized reading companion; Personalized user system",,,,,,"Universiti Utara Malaysia, UUM; Ministry of Higher Education, Malaysia, MOHE","This work was funded by the Universiti Utara Malaysia Postgraduate Research Grant and RACE grant (by the Ministry of Higher Education, Malaysia). The authors would like to express thanks to Associate Profesor Dr. Faudziah Ahmad (a former member in this research group) for her guidance and assistance.",,,,,,,,,,"Sweller, J., Ayres, P., Kalyuga, S., (2011) Cognitive load theory, , New York: Springer; Paas, F., Renkl, A., Sweller, J., (2016) Cognitive Load Theory: A Special Issue of educational Psychologist, , Taylor & Francis; Antonijević, R., Cognitive Activities in Solving Mathematical Tasks: The role of a Cognitive Obstacle (2016) Eurasia J. Math. Sci. Technol. Educ, 12 (9); Choi, H.-H., van Merriënboer, J.J.G., Paas, F., Effects of the physical environment on cognitive load and learning: Towards a new model of cognitive load (2014) Educ. Psychol. Rev, 26 (2), pp. 225-244; Jalani, N.H., Sern, L.C., The Example-Problem-Based Learning Model: Applying Cognitive Load Theory (2015) Procedia-Social Behav. Sci, 195, pp. 872-880; Mohammed, H., Aziz, A.A., Ahmad, R., Exploring the need of an assistive robot to support reading process: A pilot study (2015) 2015 International Symposium on Agents, Multi-Agent Systems and Robotics (ISAMSR), pp. 35-40; Ghanimi, H.M.A., Aziz, A.A., Ahmad, F., An Ambient Agent Model for a Reading Companion Robot (2017) Information Systems: Proceedings of the Computational Intelligence in Information Systems Conference (CIIS 2016), pp. 94-106; Ghanimi, H.M.A., Ab Aziz, A., Ahmad, F., Designing an Intelligent Support Model of a Reading Companion Robot (2017) Intelligent Environments 2017: Workshop Proceedings of the 13th International Conference on Intelligent Environments, 22, p. 131; Kendon, A., (1990) Conducting Interaction: Patterns of Behavior in Focused Encounters, , Cambridge University Press; Treur, J., (2016) Network-Oriented Modeling: Addressing Complexity of Cognitive, Affective and Social Interactions, , Springer International Publishing; Treur, J., Dynamic modeling based on a temporal-causal network modeling approach (2016) Biol. Inspired Cogn. Archit, 16, pp. 131-168; Ghanimi, H.M.A., Aziz, A.A., Ahmad, F., An Agent-Based Model for Refined Cognitive Load and Reading Performance in Reading Companion Robot (2017) J. Telecommun. Electron. Comput. Eng, 9 (3-5), pp. 55-59; Brooke, J., SUS-A quick and dirty usability scale (1996) Usability Eval. Ind, 189 (194), pp. 4-7; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot, 1 (1), pp. 71-81; Schmeck, A., Opfermann, M., van Gog, T., Paas, F., Leutner, D., Measuring cognitive load with subjective rating scales during problem solving: Differences between immediate and delayed ratings (2015) Instr. Sci, 43 (1), pp. 93-114",,,,"Insight Society",,,,,20885334,,,,"English","Int. J. Adv. Sci. Eng. Inf. Technol.",Article,"Final","",Scopus,2-s2.0-85090860493
"Ferreira F., Wojtak W., Sousa E., Louro L., Bicho E., Erlhagen W.","56352828600;55255719200;36840435600;56717507200;6602434613;6602126228;","Rapid learning of complex sequences with time constraints: A dynamic neural field model",2020,"IEEE Transactions on Cognitive and Developmental Systems",,,,"","",,3,"10.1109/TCDS.2020.2991789","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085089901&doi=10.1109%2fTCDS.2020.2991789&partnerID=40&md5=5f0deab0c159500e4df3e7e8542a8118","Center of Mathematics, University of Minho, Portugal. (e-mail: flora.ferreira@gmail.com); Center of Mathematics, University of Minho, Portugal, and also with Center Algoritmi, University of Minho, Portugal.; Center Algoritmi, University of Minho, Portugal.; Center of Mathematics, University of Minho, Portugal.","Ferreira, F., Center of Mathematics, University of Minho, Portugal. (e-mail: flora.ferreira@gmail.com); Wojtak, W., Center of Mathematics, University of Minho, Portugal, and also with Center Algoritmi, University of Minho, Portugal.; Sousa, E., Center Algoritmi, University of Minho, Portugal.; Louro, L., Center Algoritmi, University of Minho, Portugal.; Bicho, E., Center Algoritmi, University of Minho, Portugal.; Erlhagen, W., Center of Mathematics, University of Minho, Portugal.","Many of our sequential activities require that behaviors must be both precisely timed and put in the proper order. This paper presents a neuro-computational model based on the theoretical framework of Dynamic Neural Fields that supports the rapid learning and flexible adaptation of coupled order-timing representations of sequential events. A key assumption is that elapsed time is encoded in the monotonic buildup of self-stabilized neural population activity representing event memory. A stable activation gradient over subpopulations carries the information of an entire sequence. With robotics applications in mind, we test the model in simulations of a learning by observation paradigm, in which the cognitive agent first memorizes the order and relative timing of observed events and, subsequently, recalls the information from memory taking potential speed constraints into account. Model robustness is tested by systematically varying sequence complexity along the temporal and the ordinal dimension. Furthermore, an adaptation rule is proposed that allows the agent to adjust in a single trial a learned timing pattern to a changing temporal context. The simulation results are discussed with respect to our goal to endow autonomous robots with the capacity to efficiently learn complex sequences with time constraints, supporting more natural human-robot interactions. IEEE",".; Adaptation models; Color; Computational modeling; Robots; Sociology; Statistics; Timing","Human robot interaction; Neural networks; Computational model; Dynamic neural field model; Dynamic neural fields; Learning by observation; Neural populations; Robotics applications; Sequential activities; Theoretical framework; Learning systems",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23798920,,,,"English","IEEE Trans. Cogn. Dev. Syst.",Article,"Article in Press","All Open Access, Green",Scopus,2-s2.0-85085089901
"Liu D., Ji Y., Ye M., Gan Y., Zhang J.","14016130600;57199146563;35241431500;57203150814;56039580400;","An Improved Attention-Based Spatiotemporal-Stream Model for Action Recognition in Videos",2020,"IEEE Access","8",,"9046775","61462","61470",,2,"10.1109/ACCESS.2020.2983355","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083465833&doi=10.1109%2fACCESS.2020.2983355&partnerID=40&md5=fc40c21da12711de5891f515b873ffd4","Key Laboratory for NeuroInformation of Ministry of Education, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai, 200093, China; Department of Informatics, Institute of Technical Aspects of Multimodal Systems (TAMS), Universität Hamburg, Hamburg, D-22527, Germany","Liu, D., Key Laboratory for NeuroInformation of Ministry of Education, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Ji, Y., Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai, 200093, China; Ye, M., Key Laboratory for NeuroInformation of Ministry of Education, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Gan, Y., Key Laboratory for NeuroInformation of Ministry of Education, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Zhang, J., Department of Informatics, Institute of Technical Aspects of Multimodal Systems (TAMS), Universität Hamburg, Hamburg, D-22527, Germany","Action recognition is an important yet challenging task in computer vision. Attention mechanism not only tells where to focus but when to focus. It plays a key role in extracting discriminative spatial and temporal features for solving the task. In this paper, we propose an improved spatiotemporal attention model based on the two-stream structure to recognize the different actions in videos. Specifically, we first extract the intra-frame spatial features and inter-frame optical flow features for each video data. Then we implement an effective attention module, which sequentially infers attention maps along three separate dimensions: channel, spatial and temporal. After adaptive feature refinement based on the attention maps, we perform a temporal pooling process to squeeze the temporal dimension. Then, these achieved spatial and temporal features are fed into the spatial LSTM and temporal LSTM, respectively. Finally, we fuse the spatial feature, temporal feature and two-stream fusion feature to classify the actions in videos. Additionally, we also collect and construct a new Ping-Pong action dataset for subsequent human-robot interaction task from YouTube. It contains 2400 labeled videos for 4 categories. We compare with other action recognition algorithms and validate the feasibility and effectiveness of the proposed method on Ping-Pong action dataset and HMDB51 dataset. © 2013 IEEE.","Action recognition; attention module; Ping-Pong action dataset; spatiotemporal-stream","Human robot interaction; Action recognition; Action recognition algorithms; Adaptive features; Attention mechanisms; Spatial features; Temporal dimensions; Temporal features; Temporal pooling; Long short-term memory",,,,,"2018-YF08-00039-GX; Deutsche Forschungsgemeinschaft, DFG; National Natural Science Foundation of China, NSFC: 61773093; Department of Science and Technology of Sichuan Province, SPDST: 17ZDYF3184; National Key Research and Development Program of China, NKRDPC: 2018YFC0831800","This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC0831800, in part by the National Natural Science Foundation of China under Grant 61773093, in part by the Important Science and Technology Innovation Projects in Chengdu under Grant 2018-YF08-00039-GX, in part by the Research Programs of Sichuan Science and Technology Department under Grant 17ZDYF3184, and in part by the German Science Foundation (DFG) and National Science Foundation of China (NSFC) in Project Crossmodal Learning under Contract Sonderforschungsbereich Transregio 169.",,,,,,,,,,"Krizhevsky, A., Sutskever, I., Hinton, G.E., ImageNet classification with deep convolutional neural networks (2012) Proc. 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Vis., pp. 20-36. , Berlin, Germany: Springer; Yang, K., Qiao, P., Li, D., Dou, Y., (2019) IF-TTN: Information Fused Temporal Transformation Network for Video Action Recognition, , http://arxiv.org/abs/1902.09928; Zang, J., Wang, L., Liu, Z., Zhang, Q., Hua, G., Zheng, N., Attentionbased temporal weighted convolutional neural network for action recognition (2018) Proc. IFIP Int. Conf. Artif. Intell. Appl. 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Conf., pp. 1-12; Gao, L., Guo, Z., Zhang, H., Xu, X., Shen, H.T., Video captioning with attention-based LSTM and semantic consistency (2017) IEEE Trans. Multime-dia, 19 (9), pp. 2045-2055. , Sep; Song, J., Guo, Y., Gao, L., Li, X., Hanjalic, A., Shen, H.T., From deterministic to generative: Multimodal stochastic RNNs for video captioning (2019) IEEE Trans. Neural Netw. Learn. Syst., 30 (10), pp. 3047-3058. , Oct; Gao, L., Li, X., Song, J., Shen, H.T., Hierarchical LSTMs with adaptive attention for visual captioning (2019) IEEE Trans. Pattern Anal. Mach. Intelligence, , early access, Jan. 21; Moore, T., Zirnsak, M., Neural mechanisms of selective visual attention (2017) Annu. Rev. Psychol., 68 (1), pp. 47-72. , Jan; Stollenga, M.F., Masci, J., Gomez, F., Schmidhuber, J., Deep networks with internal selective attention through feedback connections (2014) Proc. Adv. Neural Inf. Process. Syst., pp. 3545-3553; Chorowski, J.K., Bahdanau, D., Serdyuk, D., Cho, K., Bengio, Y., Attention-based models for speech recognition (2015) Proc. Adv. Neural Inf. Process. Syst., pp. 577-585; Hu, J., Shen, L., Sun, G., Squeeze-and-excitation networks (2018) Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 7132-7141. , Jun; Woo, S., Park, J., Lee, J.-Y., Kweon, I.S., CBAM: Convolutional block attention module (2018) Proc. Eur. Conf. Comput. Vis., pp. 3-19; Klaeser, A., Marszalek, M., Schmid, C., A spatio-temporal descriptor based on 3D-gradients (2008) Proc. Brit. Mach. Vis. Conf., pp. 1-275; Al Ghamdi, M., Zhang, L., Gotoh, Y., Spatio-temporal SIFT and its application to human action classification (2012) Proc. Eur. Conf. Comput. Vis., pp. 301-310. , Berlin, Germany: Springer; Wang, H., Schmid, C., Action recognition with improved trajectories (2013) Proc. IEEE Int. Conf. Comput. 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(CVPR), pp. 4694-4702. , Jun; Blakemore, S.-J., Decety, J., From the perception of action to the understanding of intention (2001) Nature Rev. Neurosci., 2 (8), pp. 561-567. , Aug; Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T., HMDB: A large video database for human motion recognition (2011) Proc. IEEE Int. Conf. Comput. Vis., pp. 2556-2563. , Nov; Kotsiantis, S.B., Kanellopoulos, D., Pintelas, P.E., Data preprocessing for supervised leaning (2006) Int. J. Comput. Sci., 1 (2), pp. 111-117","Ye, M.; Key Laboratory for NeuroInformation of Ministry of Education, China; email: cvlab.uestc@gmail.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85083465833
"Li Z., Zhang D., Zhao X., Wang F., Zhang B., Ye D., Han J.","57192167617;46061681400;16551339800;57215872619;57218441703;15047169800;55648254200;","A temporally smoothed MLP regression scheme for continuous knee/ankle angles estimation by using multi-channel sEMG",2020,"IEEE Access","8",,"9026881","47433","47444",,8,"10.1109/ACCESS.2020.2979008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082170777&doi=10.1109%2fACCESS.2020.2979008&partnerID=40&md5=b494c92e65004cc907ed8bb53064194e","State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China; University of Chinese Academy of Sciences, Beijing, 100049, China; College of Information Science and Engineering, Northeastern University, Shenyang, 110189, China; College of Computer and Control Engineering, Nankai University, Tianjin, 300350, China","Li, Z., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China, University of Chinese Academy of Sciences, Beijing, 100049, China; Zhang, D., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China; Zhao, X., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China; Wang, F., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China, University of Chinese Academy of Sciences, Beijing, 100049, China; Zhang, B., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China; Ye, D., College of Information Science and Engineering, Northeastern University, Shenyang, 110189, China; Han, J., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China, College of Computer and Control Engineering, Nankai University, Tianjin, 300350, China","With coexisting-cooperative-cognitive robots gradually appearing in daily life, an instinct and efficient human-robot interaction (HRI) is becoming more and more challenging and necessary. Surface electromyography (sEMG) signals, as one of mainstream manners of the interactions, are employed to predict human intentions. In this paper, to provide natural assistance for standing up and sitting down, sEMG signals acquired from active muscles of one's lower limb are utilized to predict continuous movements. A temporally smoothed multilayer perceptron (MLP) regression scheme is proposed for continuous knee/ankle angles estimation by multi-channel sEMG signals. After correlation analyses of sEMG signals and movements, a series of linear and nonlinear regression models are trained to decode human intentions from pre-processed sEMG. Furthermore, to remove out local fluctuations of direct mappings, temporally smoothed techniques are further implemented as post-processings. In the experiments of standing up and sitting down, extensive results of ten healthy subjects show that a three-layer MLP with the Savitzky-Golay filter achieves the best performance on the mean squared error (MSE, testing: 59.58) and the R2 score (R2, testing: 0.948). The proposed regression scheme is compared with other methods and is also verified by measurements of a high-precision visual motion capture system. © 2013 IEEE.","Continuous joint estimation; human-robot interaction (HRI); multilayer perceptron (MLP); surface electromyography (sEMG); temporally smoothed techniques","Man machine systems; Mean square error; Motion capture; Multilayers; Regression analysis; Signal analysis; Human robot Interaction (HRI); Joint estimation; Multi layer perceptron; Surface electromyography; temporally smoothed techniques; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 61773369, 61821005, 61903360, U1813214; China Postdoctoral Science Foundation: 2019M661155; State Key Laboratory of Robotics: 2020-Z12","This work was supported in part by the National Natural Science Foundation of China under Grant 61903360, Grant 61773369, Grant U1813214, and Grant 61821005, in part by the Self-planned Project of the State Key Laboratory of Robotics under Grant 2020-Z12, and in part by the China Postdoctoral Science Foundation funded project under Grant 2019M661155.",,,,,,,,,,"Gabriel, D., Kamen, G., (2010) Essentials of Electromyography, , Champaign, IL, USA: Human Kinetics; Farina, D., Merletti, R., Enoka, R.M., The extraction of neural strategies from the surface EMG (2004) J. 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Res., 12, pp. 2825-2830. , Oct; Nielsen, J.L.G., Holmgaard, S., Jiang, N., Englehart, K.B., Farina, D., Parker, P.A., Simultaneous and proportional force estimation for multifunction myoelectric prostheses using mirrored bilateral training (2011) IEEE Trans. Biomed. Eng., 58 (3), pp. 681-688. , Mar; Muceli, S., Farina, D., Simultaneous and proportional estimation of hand kinematics from EMG during mirrored movements at multiple Degrees-of-Freedom (2012) IEEE Trans. Neural Syst. Rehabil. Eng., 20 (3), pp. 371-378. , May; Masood, T., Bojsen-Møller, J., Kalliokoski, K.K., Kirjavainen, A., Äärimaa, V., Magnusson, S.P., Finni, T., Differential contributions of ankle plantar flexors during submaximal isometric muscle action: A PET and EMG study (2014) J. Electromyogr. Kinesiol., 24 (3), pp. 367-374. , Jun; Zhang, F., Li, P., Hou, Z.-G., Lu, Z., Chen, Y., Li, Q., Tan, M., SEMGbased continuous estimation of joint angles of human legs by using BP neural network (2012) Neurocomputing, 78 (1), pp. 139-148. , Feb; Savitzky, A., A historic collaboration (1989) Anal. Chem., 61 (15), pp. 921A-923A. , Aug","Zhao, X.; State Key Laboratory of Robotics, China; email: zhaoxingang@sia.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85082170777
"Hwang C.-L., Wang D.-S., Weng F.-C., Lai S.-L.","7403387272;57215829805;57215830580;57215828145;","Interactions between Specific Human and Omnidirectional Mobile Robot Using Deep Learning Approach: SSD-FN-KCF",2020,"IEEE Access","8",,"9016263","41186","41200",,3,"10.1109/ACCESS.2020.2976712","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082023753&doi=10.1109%2fACCESS.2020.2976712&partnerID=40&md5=f8c1e3708809ee393985caa87047e910","Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan","Hwang, C.-L., Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan; Wang, D.-S., Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan; Weng, F.-C., Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan; Lai, S.-L., Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan","To fulfill the tasks of human-robot interaction (HRI), how to detect the specific human (SH) becomes paramount. In this paper, the deep learning approach by the integration of Single-Shot Detection, FaceNet, and Kernelized Correlation Filter (SSD-FN-KCF) is developed. From the outset, the SSD is employed to detect the human up to 8m using the RGB-D camera with 320× 240 resolution. Afterward the omnidirectional mobile robot (ODMR) is driven to the neighborhood of 2.5∼ 3.0m such that the depth image can accurately estimate the detected human's pose. Subsequently, the ODMR is commanded to the vicinity of 1.0m and the orientation inside-6060° with respect to the optical axis to identify whether he/she is the SH by the FaceNet. To reduce the computation time of the FaceNet and extend the SH's tracking, the KCF is employed to achieve the task of HRI (e.g., human following). Based on the image processing result, the required pose for searching or tracking (specific) human is accomplished by the image-based adaptive finite-time hierarchical constraint control. Finally, the experiment with the SH, who is far from and on the backside of the ODMR, validates the effectiveness and robustness of the proposed approach. © 2013 IEEE.","adaptive finite-time hierarchical constraint control; Deep learning; face recognition; human detection; human following; omnidirectional mobile robot; visual tracking","Deep learning; Face recognition; Mobile robots; Optical data processing; Constraint control; Human detection; Human following; Omnidirectional mobile robot; Visual Tracking; Human robot interaction",,,,,"Ministry of Science and Technology, Taiwan, MOST: MOST-108-2221-E-011-156","This work was supported by the Project from the Ministry of Science and Technology at Taiwan under Grant MOST-108-2221-E-011-156.",,,,,,,,,,"Wang, W., Li, R., Chen, Y., Diekel, Z.M., Jia, Y., Facilitating human-robot collaborative tasks by Teaching-Learning-Collaboration from human demonstrations (2019) IEEE Trans. Autom. 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Syst., 28 (3), pp. 690-703. , Mar; Hwang, C.-L., Chen, Y.-H., Fuzzy fixed-time learning control with saturated input nonlinear switching surface and switching gain to achieve null tracking error IEEE Trans Fuzzy Syst, , to be published; Hwang, C.-L., Chen, B.-S., Adaptive finite-time saturated tracking control for a class of partially known robots IEEE Trans. Syst., Man, Cybern. Syst, , to be published; Lee, C.-K., Hwang, C.-L., Wu, F., Very deep CNN stereo camera based dynamic face emotion recognition and its application Proc. IEEE WCCI, 2020, pp. 1-6. , Glasgow, Scotland; Qian, Y., Bi, M., Tan, T., Yu, K., Very deep convolutional neural networks for noise robust speech recognition (2016) IEEE/ACM Trans. Audio, Speech, Lang. Process., 24 (12), pp. 2263-2276. , Dec","Hwang, C.-L.; Department of Electrical Engineering, Taiwan; email: clhwang@mail.ntust.edu.tw",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85082023753
"Zheng X., Shiomi M., Minato T., Ishiguro H.","57211989348;8397042200;7005390640;26643316500;","What Kinds of Robot's Touch Will Match Expressed Emotions?",2020,"IEEE Robotics and Automation Letters","5","1","8865356","127","134",,17,"10.1109/LRA.2019.2947010","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075600280&doi=10.1109%2fLRA.2019.2947010&partnerID=40&md5=6888ea7bdb22ac74f39409802bd10dd0","ATR, Kyoto, 619-0288, Japan; Osaka University, Osaka, 5608531, Japan","Zheng, X., ATR, Kyoto, 619-0288, Japan; Shiomi, M., ATR, Kyoto, 619-0288, Japan; Minato, T., ATR, Kyoto, 619-0288, Japan; Ishiguro, H., ATR, Kyoto, 619-0288, Japan, Osaka University, Osaka, 5608531, Japan","This study investigated the effects of touch characteristics that change the strength and the naturalness of the emotions perceived by people in human-robot touch interaction with an android robot that has a feminine, human-like appearance. Past studies on human-robot touch interaction focused on understanding what kinds of human touches conveyed emotion to robots, i.e., the robot's touch characteristics that can affect people's perceived emotions received less focus. In this study, we concentrated on three touch characteristics (length, type, and part) based on arousal/valence perspectives, and their effects on the perceived strength/naturalness of a commonly used emotion in human-robot interaction, i.e., happiness, and its counterpart emotion, (i.e., sadness), borrowing Ekman's definitions. Our results showed that the touch length and its type are useful to change the perceived strengths and the naturalness of the expressed emotions based on the arousal/valence perspective, although the touch part did not fit such perspective assumptions. Finally, our results suggest that a brief pat and a longer contact by the fingers are better combinations to express happy and sad emotions with our robot. Since we only used a female android, we discussed future works with a male humanoid robot and/or a robot whose appearance is less humanoid. © 2016 IEEE.","Human-robot interaction; touch interaction","Anthropomorphic robots; Behavioral research; Human computer interaction; Man machine systems; Human like; Human robots; Human touches; Humanoid robot; Touch interaction; Human robot interaction",,,,,"JPMJCR18A1; JPMJER1401; Japan Society for the Promotion of Science, KAKEN: 17K00293","Manuscript received June 2, 2019; accepted September 17, 2019. Date of publication October 11, 2019; date of current version November 20, 2019. This letter was recommended for publication by Associate Editor S. Rossi and Editor D. Lee upon evaluation of the reviewers’ comments. This work was supported in part by the JST ERATO Ishiguro Symbiotic Human Robot Interaction Project under Grant JPMJER1401, in part by the JST CREST Grant Number JPMJCR18A1, Japan, and in part by JSPS KAKENHI under Grant JP17K00293. (Corresponding author: Xiqian Zheng.) X. Zheng and H. Ishiguro are with the ATR, Kyoto 619-0288, Japan, and also with Osaka University, Osaka 5608531, Japan (e-mail: zheng.xiqian@ irl.sys.es.osaka-u.ac.jp; ishiguro@sys.es.osaka-u.ac.jp).",,,,,,,,,,"Fisher, J.D., Rytting, M., Heslin, R., Hands touching hands: Affective and evaluative effects of an interpersonal touch (1976) Sociometry, 39 (4), pp. 416-421; Deethardt, J.F., Hines, D.G., Tactile communication and personality differences (1983) J. Nonverbal Behav., 8 (2), pp. 143-156; Willis, F.N., Jr., Dodds, R.A., Age, relationship, and touch initiation (1998) J. Social Psychol., 138 (1), pp. 115-123; Lee, J.W., Guerrero, L.K., Types of touch in cross-sex relationships between coworkers: Perceptions of relational and emotional messages, inappropriateness, and sexual harassment (2001) J. Appl. Commun. Res., 29 (3), pp. 197-220; Field, T., Touch for socioemotional and physical well-being: A review (2010) Develop. 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Autom., 2, pp. 1848-1855; Safdar, S., Variations of emotional display rules within and across cultures: A comparison between Canada, USA, and Japan (2009) Can. J. Behav. Sci., 41 (1), pp. 1-10","Zheng, X.; ATRJapan; email: zheng.xiqian@irl.sys.es.osaka-u.ac.jp",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85075600280
"Pino O., Palestra G., Trevino R., De Carolis B.","8433960200;55859663600;57215608111;57213789426;","The Humanoid Robot NAO as Trainer in a Memory Program for Elderly People with Mild Cognitive Impairment",2020,"International Journal of Social Robotics","12","1",,"21","33",,28,"10.1007/s12369-019-00533-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074073473&doi=10.1007%2fs12369-019-00533-y&partnerID=40&md5=20efc0ecf49d11ff4d28162fcd051104","Department of Medicine and Surgery, University of Parma, Parma, Italy; Institut des Systmes Intelligents et de Robotique, CNRS UMR7222, Sorbonne Université, Paris, France; Department of Computer Science, Universiy of Bari, Bari, Italy","Pino, O., Department of Medicine and Surgery, University of Parma, Parma, Italy; Palestra, G., Institut des Systmes Intelligents et de Robotique, CNRS UMR7222, Sorbonne Université, Paris, France; Trevino, R., Department of Medicine and Surgery, University of Parma, Parma, Italy; De Carolis, B., Department of Computer Science, Universiy of Bari, Bari, Italy","Many studies on social interaction have used the humanoid robot NAO. In the present paper, we described our project designed to address the growing unmet need for alternative approaches to slowing the progression of cognitive decline in Mild Cognitive Impairment patients. NAO is the experimental platform used in an ecological setting: a center for the treatment of cognitive disorders and dementia of the Italian health service. This paper describes the study addressed to evaluate the effectiveness of human–robot interaction to reinforce therapeutic behavior and treatments adherence and presents the latest findings of functional tests and users investigation recently conducted. The robot was programmed to implement some tasks from the usual memory-training program protocol. In different training conditions, subjects participated in sessions with the support of NAO or only from the psychologist while the interaction was recorded for subsequent exploration. Data indicated that memory training with NAO resulted in an increase of visual gaze from patients and reinforce of therapeutic behavior reducing, in some cases, depressive symptoms. Unexpectedly, significant changes in prose memory and verbal fluency measures were detected. These findings suggest that further research on robotics in ecological settings is necessary to determine the extent to which they can effectively support clinical practice. © 2019, Springer Nature B.V.","Elderly people; Mild Cognitive Impairment; Social robot","Agricultural robots; Anthropomorphic robots; Clinical research; Ecology; Educational robots; Clinical practices; Ecological settings; Elderly people; Experimental platform; Humanoid robot NAO; Mild cognitive impairments; Social interactions; Training conditions; Social robots",,,,,,"Authors would like to thank the participants who gave their time generously to the study, the clinical teams who supported recruitment to the project, in particular Beatrice Pagliara from the Center of Cognitive Impairments and Dementia of Parma, Italy.",,,,,,,,,,"Bäck, I., Makela, K., Kallio, J., Robot-guided exercise program for the rehabilitation of older nursing home residents (2013) Annal Long Term Care, 21 (6), pp. 38-41; Boissy, P., Corriveau, H., Michaud, F., Labonté, D., Royer, M.P., A qualitative study of in-home robotic telepresence for home care of community-living elderly subjects (2007) J Telemed Telecare, 13 (2), pp. 79-84; Brooke, J., Sus: A quick and dirty usability scale (1996) Jordanp. 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J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85074073473
"Shen Z., Zhuang Y., Zhou J., Gao J., Song R.","57201269115;57207016963;55565881800;57051712400;26431599600;","Design and Test of Admittance Control with Inner Adaptive Robust Position Control for a Lower Limb Rehabilitation Robot",2020,"International Journal of Control, Automation and Systems","18","1",,"134","142",,9,"10.1007/s12555-018-0477-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070805065&doi=10.1007%2fs12555-018-0477-z&partnerID=40&md5=c5d1115d0667c6777106ed4cc241c2e8","School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China; Department of Control Science and Engineering, Jilin University, Changchun, China","Shen, Z., School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China; Zhuang, Y., School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China; Zhou, J., School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China; Gao, J., Department of Control Science and Engineering, Jilin University, Changchun, China; Song, R., School of Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China","Although admittance control has been used in rehabilitation robots in many studies as it can realize compliant human-robot interaction, the inner proportional-integral-derivative (PID) controller of conventional admittance schemes is simple and not robust enough. This study presents an admittance control scheme with inner adaptive robust position control (ARC) for a hip-knee-ankle rehabilitation robot. The ARC is capable of eliminating uncertainties and external disturbances. A healthy male subject was required to perform three experiments, including passive exercise (PE) using a position controller, patient-cooperative exercise using an admittance controller (ACE) with fixed and different parameters. The PE experiment results show that the average normalized root mean square deviation (NRMSD) of trajectory tracking of each joint using the ARC is nearly 60% less than that using the PID controller. And the ACE experiment results show that the average NRMSD using the ARC is roughly 45% less than that using the PID controller while the interaction torque using the two controllers are comparable. It demonstrates that the robot becomes not only compliant but also robust based on the proposed control scheme. Moreover, the last experiment results indicate that admittance control with smaller admittance parameters allows the robot to be more compliant. © 2019, ICROS, KIEE and Springer.","Admittance control; adptive robust control; compliant; lower limb rehabilitation robot","Electric control equipment; Human robot interaction; Machine design; Neuromuscular rehabilitation; Position control; Proportional control systems; Robust control; Three term control systems; Two term control systems; Admittance control; Ankle rehabilitation robots; compliant; External disturbances; Lower limb; Proportional integral derivative controllers; Robust position control; Root mean square deviations; Controllers",,,,,"2015B020214003, 2017B010110015, 2017B020210011; JCYJ20170818155028942; Guangzhou Science and Technology Program key projects: 201604020108","Recommended by Editor Doo Yong Lee. This work was supported by the Shenzhen Science and Technology Plan Project under Grant JCYJ20170818155028942, Guangdong Science and Technology Plan Project under Grant 2015B020214003, Grant 2017B010110015, and Grant 2017B020210011, Science and Technology Program of Guangzhou under Grant 201604020108.",,,,,,,,,,"Mukherjee, D., Patil, C.G., Epidemiology and the global burden of stroke (2011) World Neurosurg, 76 (6), pp. S85-S90; Dam, M., Tonin, P., Casson, S., Ermani, M., Pizzolato, G., Iaia, V., Battistin, L., The effects of long-term rehabilitation therapy on poststroke hemiplegic patients (1993) Stroke, 24 (8), pp. 1186-1191; Barreca, S., Wolf, S.L., Fasoli, S., Bohannon, R., Treatment interventions for the paretic upper limb of stroke survivors: a critical review (2003) Neurorehabilitation and Neural Repair, 17 (4), pp. 220-226; Riener, R., Lunenburger, L., Jezernik, S., Anderschitz, M., Colombo, G., Dietz, V., Patient-cooperative strategies for robot-aided treadmill training: first experimental results (2005) IEEE Trans on Neural Systems and Rehabilitation Engineering, 13 (3), pp. 380-394; Luu, T.P., Low, K.H., Qu, X., Lim, H.B., Hoon, K.H., Hardware development and locomotion control strategy for an over-ground gait trainer: NaTUre-Gaits (2014) IEEE Journal of Translational Engineering in Health and Medicine, 2, p. 2100209; Akdoğan, E., Adli, M.A., The design and control of a therapeutic exercise robot for lower limb rehabilitation: Physiotherabot (2011) Mechatronics, 21 (3), pp. 509-522; Tran, H.T., Cheng, H., Rui, H., Lin, X., Duong, M.K., Chen, Q., Evaluation of a fuzzy-based impedance control strategy on a powered lower exoskeleton (2015) International Journal of Social Robotics, 8 (1), pp. 103-123; Zhang, M., Xie, S.Q., Li, X., Zhu, G., Meng, W., Huang, X., Veale, A.J., Adaptive patient-cooperative control of a compliant ankle rehabilitation robot (CARR) with enhanced training safety (2018) IEEE Trans on Industrial Electronics, 65 (2), pp. 1398-1407; Saglia, J.A., Tsagarakis, N.G., Dai, J.S., Caldwell, D.G., Control strategies for patient-assisted training using the ankle rehabilitation robot (ARBOT) (2013) IEEE/ASME Trans on Mechatronics, 18 (6), pp. 1799-1808; Ayas, M.S., Altas, I.H., Fuzzy logic based adaptive admittance control of a redundantly actuated ankle rehabilitation robot (2017) Control Engineering Practice, 59, pp. 44-54; Liu, Q., Liu, A., Meng, W., Ai, Q., Xie, S.Q., Hierarchical compliance control of a soft ankle rehabilitation robot actuated by pneumatic muscles (2017) Frontiers in Neurorobotics, 11, p. 64; Wu, J., Gao, J., Song, R., Li, R., Li, Y., Jiang, L., The design and control of a 3DOF lower limb rehabilitation robot (2016) Mechatronics, 33, pp. 13-22; Veneman, J.F., Kruidhof, R., Hekman, E.E., Ekkelenkamp, R., Van Asseldonk, E.H., van der Kooij, H., Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation (2007) IEEE Trans on Neural Systems and Rehabilitation Engineering, 15 (3), pp. 379-386; Craig, J.J., (1986) Introduction to Robotics: Mechanics and Control; Kim, C.-S., Mo, E.-J., Han, S.-M., Jie, M.-S., Lee, K.-W., Robust visual servo control of robot manipulators with uncertain dynamics and camera parameters (2010) International Journal of Control, Automation and Systems, 8 (2), pp. 308-313; Markiewitcz, B.R., Analysis of computed torque drive method and comparison with conventional position servo for a computer controlled manipulator (1973) Techincal Memorandum 33-601; Kang, H.-B., Wang, J.-H., Adaptive robust control of 5 DOF Upper-limb exoskeleton robot (2015) International Journal of Control, Automation and Systems, 13 (3), pp. 733-741; Pan, L., Gao, T., Xu, F., Zhang, L., Enhanced robust motion tracking control for 6 degree-of-freedom industrial assembly robot with disturbance adaption (2018) International Journal of Control, Automation and Systems, 16 (2), pp. 921-928; Lee, B.-H., Choo, J.-H., Na, S., Marzocca, P., Librescu, L., Sliding mode robust control of supersonic three degrees-of-freedom airfoils (2010) International Journal of Control, Automation and Systems, 8 (2), pp. 279-288; Wang, R., Li, J., Zhang, S., Gao, D., Sun, H., Robust adaptive control for a class of uncertain nonlinear systems with time-varying delay (2013) Scientific World Journal, 2013, p. 963986; Seul, J., Hsia, T.C., Neural network impedance force control of robot manipulator (1998) IEEE Trans on Industrial Electronics, 45 (3), pp. 451-461; Khoshdel, V., Akbarzadeh, A., Naghavi, N., Sharifnezhad, A., Souzanchi-Kashani, M., sEMG-based impedance control for lower-limb rehabilitation robot (2018) Intelligent Service Robotics, 11 (1), pp. 97-108; Piltan, F., Nabaee, A., Ebrahimi, M., Bazregar, M., Design robust fuzzy sliding mode control technique for robot manipulator systems with modeling uncertainties (2013) International Journal of Information Technology and Computer Science, 5 (8), pp. 123-135; Ozkul, F., Barkana, D.E., Design of an admittance control with inner robust position control for a robot-assisted rehabilitation system RehabRoby (2011) Proc. of IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 104-109; Jamwal, P.K., Hussain, S., Ghayesh, M.H., Rogozina, S.V., Impedance control of an intrinsically compliant parallel ankle rehabilitation robot (2016) IEEE Trans on Industrial Electronics, 63 (6), pp. 3638-3647","Song, R.; School of Engineering, China; email: songrong@mail.sysu.edu.cn",,,"Institute of Control, Robotics and Systems",,,,,15986446,,,,"English","Int. J. Control Autom. Syst.",Article,"Final","",Scopus,2-s2.0-85070805065
"Knežević N., Lukić B., Jovanović K.","57212697678;57192677119;36137004200;","Feedforward Control Approaches to Bidirectional Antagonistic Actuators Based on Learning",2020,"Advances in Intelligent Systems and Computing","980",,,"337","345",,,"10.1007/978-3-030-19648-6_39","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065977133&doi=10.1007%2f978-3-030-19648-6_39&partnerID=40&md5=816f8cf6516f1e52fe8002539f64bf9b","School of Electrical Engineering, University of Belgrade, Belgrade, 11120, Serbia","Knežević, N., School of Electrical Engineering, University of Belgrade, Belgrade, 11120, Serbia; Lukić, B., School of Electrical Engineering, University of Belgrade, Belgrade, 11120, Serbia; Jovanović, K., School of Electrical Engineering, University of Belgrade, Belgrade, 11120, Serbia","Safe physical human-robot interaction is a decisive feature in wider adaptation of robots in homes and factories. To that end, a lot of researchers consider new actuation mechanisms and particularly Variable Stiffness Actuators (VSAs) which contribute to robot safety, but also to increase energy efficiency and outperforming rigid actuators in repetitive tasks. However, advantages of VSAs come with their price – issues in design and control of such multivariable non-linear systems. Novel approaches and methods in soft computing methods such as machine learning and neural networks are opening new horizons in VSA control. In this paper, a comparative analysis is carried out between the neural network feedforward control and locally weighted projection regression as a technique for model learning of bidirectional antagonistic VSA – qb move maker pro. Set of measurement is used to create mapping between two motor positions as inputs and measured actuator position and estimated stiffness as outputs. Comparative analysis of the two different approaches for feedforward control observing performances in open loop control, followed by closed loop testing with a simple feedback regulator for fine tuning. Learning techniques result in robust and generalized models that can predict required inputs in ordered to achieve good output tracking. © Springer Nature Switzerland AG 2020.","Locally Weighted Projection Regression; Machine learning; Model-free feedforward control; Neural network; Variable Stiffness Actuator","Actuators; Energy efficiency; Human robot interaction; Learning systems; Linear systems; Machine learning; Neural networks; Robotics; Soft computing; Stiffness; Actuation mechanism; Closed-loop testing; Comparative analysis; Locally weighted projection regressions; Model free; Physical human-robot interactions; Soft computing methods; Variable stiffness; Feedforward control",,,,,"Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja, MPNTR: TR-35003","This paper was partially funded by the Ministry of Education, Science and Technological development of the Republic of Serbia, under contracts TR-35003.",,,,,,,,,,"Vanderborght, B., Variable impedance actuators: A review (2013) Robot. Auton. Syst., 61 (12), pp. 1601-1614; Melo, K., Open source VSA-CubeBots for rapid soft robot prototyping (2014) Robot Makers-Workshop in Conjunction with 2014 Robotics Science and Systems Conference; Hogan, N., Impedance control: An approach to manipulation (1984) Proceedings of the American Control Conference, pp. 304-313. , pp; Palli, G., Melchiorri, C., de Luca, A., On the feedback linearization of robots with variable joint stiffness (2008) 2008 IEEE International Conference on Robotics and Automation, pp. 1753-1759. , pp; Tonietti, G., Schiavi, R., Bicchi, A., Design and control of a variable stiffness actuator for safe and fast physical human/robot interaction (2005) Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 526-531. , pp; Visser, L.C., Carloni, R., Stramigioli, S., Energy efficient control of robots with variable stiffness actuators (2010) IFAC Proc. Volumes, 43 (14), pp. 1199-1204; Giorgio, G., Variable stiffness actuators: The user’s point of view (2015) I. J. Robot. Res., 34, pp. 727-743; Natural Motion Machine Initiative (NMMI) (Qbmove-Maker Pro: Agonistic/Antagonistic Servo-Vsa Datasheet, , https://qbrobotics.com/wp-content/uploads/2016/03/qbmove-Advanced-datasheet.pdf., Jan 2019; Lukić, B., Jovanović, K., Rakić, A., Realization and comparative analysis of coupled and decoupled control methods for bidirectional antagonistic drives: QBmove maker pro (2016) 3Rd International Conference on Electrical, Electronic and Computing Engineering; Lukić, B.Z., Jovanović, K.M., Kvaščev, G.S., Feedforward neural network for controlling qbmove maker pro variable stiffness actuator (2016) 2016 13Th Symposium on Neural Networks and Applications (NEUREL), pp. 1-4. , pp; Mitrovic, D., Learning impedance control of antagonistic systems based on stochastic optimization principles (2011) Int. J. Robot. Res., 30 (5), pp. 556-573; Vijayakumar, S., D’Souza, A., Schaal, S., Incremental online learning in high dimensions (2005) Neural Comput, 17 (12), pp. 2602-2634","Knežević, N.; School of Electrical Engineering, Serbia; email: knezevic@etf.bg.ac.rs","Gorges D.Berns K.","Deutsche Forschungsgemeinschaft;Deutsches Forschungszentrum für Künstliche Intelligenz;Fraunhofer Zentrum Kaiserslautern;Technische Universität Kaiserslautern","Springer Verlag","28th International Conference on Robotics in Alpe-Adria-Danube Region, RAAD 2019","19 June 2019 through 21 June 2019",,225939,21945357,9783030196479,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85065977133
"Coppola C., Cosar S., Faria D.R., Bellotto N.","57192433484;24477228600;26031469100;23059304100;","Social Activity Recognition on Continuous RGB-D Video Sequences",2020,"International Journal of Social Robotics","12","1",,"201","215",,4,"10.1007/s12369-019-00541-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065185036&doi=10.1007%2fs12369-019-00541-y&partnerID=40&md5=232dcfd3074f5a713c6b30bcaa4effff","Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom; University of Lincoln, Brayford Pool, Lincoln, LN6 7TS, United Kingdom; Aston University, Aston Express Way, Birmingham, B4 7ET, United Kingdom","Coppola, C., Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom; Cosar, S., University of Lincoln, Brayford Pool, Lincoln, LN6 7TS, United Kingdom; Faria, D.R., Aston University, Aston Express Way, Birmingham, B4 7ET, United Kingdom; Bellotto, N., University of Lincoln, Brayford Pool, Lincoln, LN6 7TS, United Kingdom","Modern service robots are provided with one or more sensors, often including RGB-D cameras, to perceive objects and humans in the environment. This paper proposes a new system for the recognition of human social activities from a continuous stream of RGB-D data. Many of the works until now have succeeded in recognising activities from clipped videos in datasets, but for robotic applications it is important to be able to move to more realistic scenarios in which such activities are not manually selected. For this reason, it is useful to detect the time intervals when humans are performing social activities, the recognition of which can contribute to trigger human-robot interactions or to detect situations of potential danger. The main contributions of this research work include a novel system for the recognition of social activities from continuous RGB-D data, combining temporal segmentation and classification, as well as a model for learning the proximity-based priors of the social activities. A new public dataset with RGB-D videos of social and individual activities is also provided and used for evaluating the proposed solutions. The results show the good performance of the system in recognising social activities from continuous RGB-D data. © 2019, The Author(s).","Activity recognition; Activity temporal segmentation; Machine learning; Social activity recognition","Agricultural robots; Data streams; Learning systems; Pattern recognition; Activity recognition; Modern service; Public dataset; Realistic scenario; Robotic applications; Social activities; Temporal segmentations; Video sequences; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 643691","This work has been partially supported by the European project: ENRICHME, EC H2020 Grant Agreement No. 643691.",,,,,,,,,,"Bandla, S., Grauman, K., Active learning of an action detector from untrimmed videos (2013) Proceedings of the IEEE International Conference on Computer Vision, pp. 1833-1840; Bazzani, L., Cristani, M., Tosato, D., Farenzena, M., Paggetti, G., Menegaz, G., Murino, V., Social interactions by visual focus of attention in a three-dimensional environment (2013) Expert Syst, 30 (2), pp. 115-127; 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Faria, D.R., Premebida, C., Nunes, U., A probalistic approach for human everyday activities recognition using body motion from RGB-D images (2014) IEEE RO-MAN’14; Faria, D.R., Vieira, M., Premebida, C., Nunes, U., Probabilistic human daily activity recognition towards robot-assisted living (2015) IEEE RO-MAN’15: IEEE International Symposium on Robot and Human Interactive Communication, , Kobe, Japan; Gori, I., Aggarwal, J.K., Matthies, L., Ryoo, M.S., Multitype activity recognition in robot-centric scenarios (2016) IEEE Robot Autom Lett, 1 (1), pp. 593-600; Guo, K., (2012) Action recognition using log-covariance matrices of silhouette and optical-flow features, , Boston University, Boston; Hall, E.T., (1963) A system for the notation of proxemic behavior, , American Anthropologist, Arlington; Jalal, A., Kim, Y.H., Kim, Y.J., Kamal, S., Kim, D., Robust human activity recognition from depth video using spatiotemporal multi-fused features (2017) Pattern Recognit, 61, pp. 295-308; Kendon, A., Movement coordination in social interaction: some examples described (1970) Acta Psychol, 32, pp. 101-125; 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Wang, J., Liu, Z., Wu, Y., Yuan, J., Learning actionlet ensemble for 3d human action recognition (2014) IEEE Trans Pattern Anal Mach Intell, 36 (5), pp. 914-927; Yun, K., Honorio, J., Chattopadhyay, D., Berg, T.L., Samaras, D., Two-person interaction detection using body-pose features and multiple instance learning (2012) 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), , IEEE; Zhang, L., Hung, H., Beyond f-formations: Determining social involvement in free standing conversing groups from static images (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1086-1095","Coppola, C.; Queen Mary University of London, Mile End Road, United Kingdom; email: c.ccoppola@qmul.ac.uk",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85065185036
"Buyukgoz S., Pandey A.K., Chamoux M., Chetouani M.","57218465603;56260098500;57201364987;8964513700;","Exploring Behavioral Creativity of a Proactive Robot",2021,"Frontiers in Robotics and AI","8",,"694177","","",,,"10.3389/frobt.2021.694177","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121009988&doi=10.3389%2ffrobt.2021.694177&partnerID=40&md5=ad437dccd009da268a5b1522194066f7","SoftBank Robotics Europe, Paris, France; Institute for Intelligent Systems and Robotics, CNRS, UMR 7222, Sorbonne University, Paris, France; Socients AI and Robotics, Paris, France; BeingAI Limited, Hong Kong","Buyukgoz, S., SoftBank Robotics Europe, Paris, France, Institute for Intelligent Systems and Robotics, CNRS, UMR 7222, Sorbonne University, Paris, France; Pandey, A.K., Socients AI and Robotics, Paris, France, BeingAI Limited, Hong Kong; Chamoux, M., SoftBank Robotics Europe, Paris, France; Chetouani, M., Institute for Intelligent Systems and Robotics, CNRS, UMR 7222, Sorbonne University, Paris, France","Creativity, in one sense, can be seen as an effort or action to bring novelty. Following this, we explore how a robot can be creative by bringing novelty in a human–robot interaction (HRI) scenario. Studies suggest that proactivity is closely linked with creativity. Proactivity can be defined as acting or interacting by anticipating future needs or actions. This study aims to explore the effect of proactive behavior and the relation of such behaviors to the two aspects of creativity: 1) the perceived creativity observed by the user in the robot’s proactive behavior and 2) creativity of the user by assessing how creativity in HRI can be shaped or influenced by proactivity. We do so by conducting an experimental study, where the robot tries to support the user on the completion of the task regardless of the end result being novel or not and does so by exhibiting anticipatory proactive behaviors. In our study, the robot instantiates a set of verbal communications as proactive robot behavior. To our knowledge, the study is among the first to establish and investigate the relationship between creativity and proactivity in the HRI context, based on user studies. The initial results have indicated a relationship between observed proactivity, creativity, and task achievement. It also provides valuable pointers for further investigation in this domain. Copyright © 2021 Buyukgoz, Pandey, Chamoux and Chetouani.","creative behavior; human–robot interaction; proactive robot; self-initiated behavior; social robot",,,,,,"Horizon 2020 Framework Programme, H2020: 765955","This project has received funding from the European Union?s Horizon 2020 research and innovation programme under grant agreement No 765955 (ANIMATAS).",,,,,,,,,,"Ali, S., Moroso, T., Breazeal, C., Can Children Learn Creativity from A Social Robot? (2019) C&C '19: Proceedings of the 2019 on Creativity and Cognition, pp. 359-368. , San Diego, CA, June 23–26, 2019, New York, NY, ACM–Association for Computing Machinery; Alves-Oliveira, P., Tulli, S., Wilken, P., Merhej, R., Gandum, J., Paiva, A., Sparking Creativity with Robots: A Design Perspective (2019) Robots for Social Good: Exploring Critical Design for HRI Workshop at 14th ACM/IEEE International Conference on Human-Robot Interaction, HRI (2019), , Daegu, South Korea, in; Apiola, M., Lattu, M., Pasanen, T.A., Creativity and Intrinsic Motivation in Computer Science Education (2010) ITiCSE ’10: Proceedings of the Fifteenth Annual Conference on Innovation and Technology in Computer Science Education, pp. 199-203. , New York, NY, USA, Association for Computing Machinery; Awais, M., Henrich, D., Proactive Premature Intention Estimation for Intuitive Human-Robot Collaboration (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, , 7-12 Oct. 2012, Vilamoura-Algarve, Portugal, IEEE; Baas, M., De Dreu, C.K.W., Nijstad, B.A., A Meta-Analysis of 25 Years of Mood-Creativity Research: Hedonic Tone, Activation, or Regulatory Focus? 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"Horii T., Nagai Y.","55842337200;57226523020;","Active Inference Through Energy Minimization in Multimodal Affective Human–Robot Interaction",2021,"Frontiers in Robotics and AI","8",,"684401","","",,,"10.3389/frobt.2021.684401","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120980016&doi=10.3389%2ffrobt.2021.684401&partnerID=40&md5=7af3d2571d515abdfb2a5edbfcdf60e6","Graduate School of Engineering Science, Osaka University, Osaka, Japan; International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo, Japan; Institute for AI and Beyond, The University of Tokyo, Tokyo, Japan","Horii, T., Graduate School of Engineering Science, Osaka University, Osaka, Japan, International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo, Japan; Nagai, Y., International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo, Japan, Institute for AI and Beyond, The University of Tokyo, Tokyo, Japan","During communication, humans express their emotional states using various modalities (e.g., facial expressions and gestures), and they estimate the emotional states of others by paying attention to multimodal signals. To ensure that a communication robot with limited resources can pay attention to such multimodal signals, the main challenge involves selecting the most effective modalities among those expressed. In this study, we propose an active perception method that involves selecting the most informative modalities using a criterion based on energy minimization. This energy-based model can learn the probability of the network state using energy values, whereby a lower energy value represents a higher probability of the state. A multimodal deep belief network, which is an energy-based model, was employed to represent the relationships between the emotional states and multimodal sensory signals. Compared to other active perception methods, the proposed approach demonstrated improved accuracy using limited information in several contexts associated with affective human–robot interaction. We present the differences and advantages of our method compared to other methods through mathematical formulations using, for example, information gain as a criterion. Further, we evaluate performance of our method, as pertains to active inference, which is based on the free energy principle. Consequently, we establish that our method demonstrated superior performance in tasks associated with mutually correlated multimodal information. Copyright © 2021 Horii and Nagai.","active inference; emotion; energy based models; human-robot interaction; multimodal perception",,,,,,"Ministry of Education, Culture, Sports, Science and Technology, MEXT; Japan Science and Technology Agency, JST: JPMJCR16E2; University of Tokyo","This research was supported by JST CREST ?Cognitive Mirroring? (Grant Number: JPMJCR16E2), Institute for AI and Beyond, the University of Tokyo, and World Premier International Research Centre Initiative (WPI), MEXT, Japan.",,,,,,,,,,"Barrett, L.F., The Theory of Constructed Emotion: an Active Inference Account of Interoception and Categorization (2017) Soc. Cogn. Affect Neurosci, 12, pp. 1-23; Barros, P., Weber, C., Wermter, S., Emotional Expression Recognition with a Cross-Channel Convolutional Neural Network for Human-Robot Interaction (2015) 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids) (IEEE), pp. 582-587. , IEEE; Barros, P., Wermter, S., Developing Crossmodal Expression Recognition Based on a Deep Neural Model (2016) Adaptive Behav, 24, pp. 373-396; Breazeal, C., Aryananda, L., Recognition of Affective Communicative Intent in Robot-Directed Speech (2002) Autonomous Robots, 12, pp. 83-104; Breazeal, C., Emotion and Sociable Humanoid Robots (2003) Int. J. 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Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85120980016
"Yang W., Xu L., Yu L., Chen Y., Yan Z., Yang C.","57190577943;57202356369;57226379592;57221644237;57226385087;55540258800;","Hybrid oscillator-based no-delay hip exoskeleton control for free walking assistance",2021,"Industrial Robot","48","6",,"906","914",,,"10.1108/IR-02-2021-0038","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111347713&doi=10.1108%2fIR-02-2021-0038&partnerID=40&md5=d635679151d8026c7bae884f753035ec","Ningbo Research Institute, Zhejiang University, Ningbo, China; School of Mechanical Engineering, Zhejiang University, Hangzhou, China; Ningbo Research Institute, Zhejiang University, Ningbo, China; School of Mechanical Engineering, Yanshan University, Qinhuangdao, China; Ningbo Research Institute, Zhejiang University, Ningbo, China; Ningbo Research Institute, Zhejiang University, Ningbo, China; School of Mechanical Engineering, Zhejiang University, Hangzhou, China","Yang, W., Ningbo Research Institute, Zhejiang University, Ningbo, China, School of Mechanical Engineering, Zhejiang University, Hangzhou, China; Xu, L., School of Mechanical Engineering, Zhejiang University, Hangzhou, China, Ningbo Research Institute, Zhejiang University, Ningbo, China; Yu, L., Ningbo Research Institute, Zhejiang University, Ningbo, China; Chen, Y., School of Mechanical Engineering, Yanshan University, Qinhuangdao, China, Ningbo Research Institute, Zhejiang University, Ningbo, China; Yan, Z., Ningbo Research Institute, Zhejiang University, Ningbo, China; Yang, C., School of Mechanical Engineering, Zhejiang University, Hangzhou, China, Ningbo Research Institute, Zhejiang University, Ningbo, China","Purpose: Walking-aid exoskeletons can assist and protect effectively the group with lower limb muscle strength decline, workers, first responders and military personnel. However, there is almost no united control strategy that can effectively assist daily walking. This paper aims to propose a hybrid oscillators’ (HOs) model to adapt to irregular gait (IG) patterns (frequent alternation between walking and standing or rapid changing of walking speed, etc.) and generate compliant and no-delay assistive torque. Design/methodology/approach: The proposed algorithm, HOs, combines adaptive oscillators (AOs) with phase oscillator through switching assistive mode depending on whether or not the AOs' predicting error of hip joint degree is exceeded our expectation. HOs can compensate for delay by predicting gait phase when in AOs mode. Several treadmill and free walking experiments are designed to test the adaptability and effectiveness of HOs model under IG. Findings: The experimental results show that the assistive strategy based on the HOs is effective under IG patterns, and delay is compensated totally under quasiperiodic gait conditions where a smoother human–robot interaction (HRI) force and the reduction of HRI force peak are observed. Delay compensation is found very effective at improving the performance of the assistive exoskeleton. Originality/value: A novel algorithm is proposed to improve the adaptability of a walking assist hip exoskeleton in daily walking as well as generate compliant, no-delay assistive torque when converging. © 2021, Emerald Publishing Limited.","Hip exoskeleton; Hybrid oscillators; Motion prediction; No-delay assistive control; Walking assist robot","Agricultural robots; Exoskeleton (Robotics); Human robot interaction; Control strategies; Delay compensation; Design/methodology/approach; First responders; Military personnels; Phase oscillators; Robot interactions; Walking assistance; Walking aids",,,,,"National Natural Science Foundation of China, NSFC: 51805469; Basic Public Welfare Research Program of Zhejiang Province: LY21E050020; Ningbo Municipal Science and Technology Innovative Research Team: 2020Z022","This research is supported by National Natural Science Foundation of China (Grant No. 51805469), Zhejiang Basic Public Welfare Research Plan (Grant No. LY21E050020) and Ningbo Science and Technology Innovation 2025 Project (Grant No. 2020Z022).",,,,,,,,,,"Bae, J., Siviy, C., Rouleau, M., A lightweight and efficient portable soft exosuit for paretic ankle assistance in walking after stroke (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 2820-2827. , https://doi.org/10.1109/ICRA.2018.8461046; De la Fuente, J., Sugar, T.G., Redkar, S., Nonlinear, phase-based oscillator to generate and assist periodic motions (2017) Journal of Mechanisms Robotics, 9, p. 024502. , https://doi.org/10.1115/1.4036023; Ding, Y., Galiana, I., Siviy, C., Panizzolo, F.A., Walsh, C., IMU-based iterative control for hip extension assistance with a soft exosuit (2016) 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 3501-3508. , https://doi.org/10.1109/ICRA.2016.7487530, IEEE; Ding, Y., Myunghee, K., Kuindersma, S., Walsh, C., Human-in-the-loop optimization of hip assistance with a soft exosuit during walking (2018) Science Robotics, 3 (15), p. eaar5438. , https://doi.org/10.1126/scirobotics.aar5438; Joudzadeh, P., Hadi, A., Tarvirdizadeh, B., Borooghani, D., Alipour, K., Design and fabrication of a lower limb exoskeleton to assist in stair ascending (2019) Industrial Robot: The International Journal of Robotics Research and Application, 46 (2), pp. 290-299. , https://doi.org/10.1108/IR-09-2018-0199; Kang, I., Kunapuli, P., Young, A.J., Real-time neural network-based gait phase estimation using a robotic hip exoskeleton (2020) IEEE Transactions on Medical Robotics and Bionics, 2 (1), pp. 28-37. , https://doi.org/10.1109/TMRB.2019.2961749; Kerestes, J., Sugar, T.G., Holgate, M., Adding and subtracting energy to body motion: phase oscillator (2015) ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers Digital Collection, , https://doi.org/10.1115/DETC2014-34405; Khazoom, C., Veronneau, C., Bigue, J.-P.L., Grenier, J., Girard, A., Plante, J.-S., Design and control of a multifunctional ankle exoskeleton powered by magnetorheological actuators to assist walking, jumping, and landing (2019) IEEE Robotics and Automation Letters, 4 (3), pp. 3083-3090. , https://doi.org/10.1109/LRA.2019.2924852; Lee, S., Kim, J., Baker, L., Long, A., Karavas, N., Menard, N., Galiana, I., Walsh, C.J., Autonomous multi-joint soft exosuit with augmentation-power-based control parameter tuning reduces energy cost of loaded walking (2018) J. 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Syst, p. 1. , https://doi.org/10.1109/TCDS.2019.2933648; Xue, T., Wang, Z., Zhang, T., Zhang, M., Adaptive oscillator-based robust control for flexible hip assistive exoskeleton (2019) IEEE Robotics and Automation Letters, 4 (4), pp. 3318-3323. , https://doi.org/10.1109/LRA.2019.2926678; Yan, T., Parri, A., Ruiz Garate, V., Cempini, M., Ronsse, R., Vitiello, N., An oscillator-based smooth real-time estimate of gait phase for wearable robotics (2017) Autonomous Robots, 41 (3), pp. 759-774. , https://doi.org/10.1007/s10514-016-9566-0; Zhang, J., Fiers, P., Witte, K.A., Jackson, R.W., Poggensee, K.L., Atkeson, C.G., Collins, S.H., Human-in-the-loop optimization of exoskeleton assistance during walking (2017) Science, 356 (6344), pp. 1280-1284. , https://doi.org/10.1126/science.aal5054; Zheng, E., Manca, S., Yan, T., Parri, A., Vitiello, N., Wang, Q., Gait phase estimation based on noncontact capacitive sensing and adaptive oscillators (2017) IEEE Transactions on Biomedical Engineering, 64 (10), pp. 2419-2430. , https://doi.org/10.1109/TBME.2017.2672720","Xu, L.; School of Mechanical Engineering, China; email: linghui10@foxmail.com",,,"Emerald Group Holdings Ltd.",,,,,0143991X,,IDRBA,,"English","Ind. Robot",Article,"Final","",Scopus,2-s2.0-85111347713
"Aubin L., Mostafaoui G., Amiel C., Serré H., Capdevielle D., de Menibus M.H., Boiché J., Schmidt R., Raffard S., Marin L.","57224949182;25641665800;57221971241;57221960775;57194656562;57221966429;17345030600;9532733300;23568550800;7102404415;","Study of Coordination Between Patients with Schizophrenia and Socially Assistive Robot During Physical Activity",2021,"International Journal of Social Robotics","13","7",,"1625","1640",,1,"10.1007/s12369-021-00750-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100825402&doi=10.1007%2fs12369-021-00750-4&partnerID=40&md5=6c846f1843e7deced3d5d9b82d179c02","EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Montpellier, France; ETIS UMR8051, CY University, ENSEA, CNRS, Cergy, 95000, France; GIPSA-lab, Université Grenoble Alpes, Grenoble, 38185, France; INSERM U-1061, Montpellier, 34090, France; Service Universitaire de Psychiatrie Adulte, CHU Montpellier, Montpellier, 34090, France; Laboratoire Epsylon Universite Paul Valery Montpellier, Montpellier, 34090, France; College of the Holy Cross, Worcester, MA  01610, United States; Laboratoire Epsylon Université Paul Valery Montpellier & Service Universitaire de Psychiatrie Adulte, CHU Montpellier, Montpellier, 34090, France","Aubin, L., EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Montpellier, France; Mostafaoui, G., ETIS UMR8051, CY University, ENSEA, CNRS, Cergy, 95000, France; Amiel, C., EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Montpellier, France; Serré, H., GIPSA-lab, Université Grenoble Alpes, Grenoble, 38185, France; Capdevielle, D., INSERM U-1061, Montpellier, 34090, France; de Menibus, M.H., Service Universitaire de Psychiatrie Adulte, CHU Montpellier, Montpellier, 34090, France; Boiché, J., Laboratoire Epsylon Universite Paul Valery Montpellier, Montpellier, 34090, France; Schmidt, R., College of the Holy Cross, Worcester, MA  01610, United States; Raffard, S., Laboratoire Epsylon Université Paul Valery Montpellier & Service Universitaire de Psychiatrie Adulte, CHU Montpellier, Montpellier, 34090, France; Marin, L., EuroMov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Montpellier, France","The goal of this study was to analyze the interpersonal coordination of patients suffering from schizophrenia (SZ) using a humanoid robot NAO. NAO was controlled by a neurally-inspired model that enables motions synchronization between robot and human. Using a robot has the advantage in that it can be modified and controlled without being influenced by the human with whom they are interacting. More specifically, we were interested in non-intentional interpersonal coordination which has been found in previous studies to be preserved for SZ. We performed an experiment in which we asked patients and control participants to jointly perform fitness movements with the robot which was programmed to either synchronize with the human (a bidirectional condition) or move at a fixed frequency with them (a unidirectional condition). We showed that non-intentional interpersonal coordination was preserved for SZ during Human/Robot interaction (as for human/human interaction). Also, the best synchronization, stability and the best performance in term of speed were obtained with the bidirectional condition. Our results showed that, in this specific context of an interaction at preferential frequency of subject, there were only a few differences between SZ and controls in terms of interpersonal synchronization skills. Nevertheless, we observed more variability for SZ which could partly be explained by the weaker intrapersonal coordination that they exhibited. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.","Human–robot interaction; Physical activity; Robotic; Schizophrenia","Agricultural robots; Anthropomorphic robots; Diseases; Synchronization; Fixed frequency; Humanoid robot NAO; Interpersonal coordinations; Physical activity; Socially assistive robots; Social robots",,,,,"Centre National de la Recherche Scientifique, CNRS; Centre Hospitalier Régional Universitaire de Montpellier; Direction Générale de l’Armement, DGA","This work was supported by local grant (Clinical research funding from CHU Montpellier), CNRS RHIAPS Project and DGA (Direction G?n?rale des Arm?es)","This work was funding by local grant (Clinical research funding from CHU Montpellier), CNRS RHIAPS Project and DGA (Direction Générale des Armées).","This work was supported by local grant (Clinical research funding from CHU Montpellier), CNRS RHIAPS Project and DGA (Direction Générale des Armées)",,,,,,,,"Alahbabi, M., Almazroei, F., Almarzoqi, M., Almeheri, A., Alkabi, M., Nuaimi, A.A., Cappuccio, M., Alnajjar, F., Avatar based interaction therapy: A potential therapeutic approach for children with autism (2017) 2017 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 480-484. , https://doi.org/10.1109/ICMA.2017.8015864; Amazeen, P., Schmidt, R., Turvey, M.T., Frequency detuning of the phase entrainment dynamics of visually coupled rhythmic movements (1995) Biol Cybern; Anzalone, S., Tanet, A., Pallanca, O., Cohen, D., Chetouani, M., A humanoid robot controlled by neurofeedback to reinforce attention in autism spectrum disorder (2016) Proceedings of the 3Rd Italian Workshop on Artificial Intelligence and Robotics, , https://hal.sorbonne-universite.fr/hal-02422951; Argyle, M., Salter, V., Nicholson, H., Williams, M., Burgess, P., The communication of inferior and superior attitudes by verbal and non-verbal signals (1970) Br J Soc Clin Psychol; Barakova, E.I., Bajracharya, P., Willemsen, M., Lourens, T., Huskens, B., Long-term lego therapy with humanoid robot for children with asd (2014) Expert Syst; Chartrand, L.T., Bargh, J.A., The chameleon effect: the perception-behavior link and social interaction (1999) J Pers Soc Psychol; Ciolacu, M.V., Facial expressions and non verbal communication (2014) Procedia Soc Behav Sci; Cohen, L., Khoramshahi, M., Salesse, R.N., Bortolon, C., Słowiński, P., Krasimira Tsaneva-Atanasova, C.Z., Bernardo, M.D., Raffard, S., Influence of facial feedback during a cooperative human-robot task in schizophrenia (2017) Sci Rep; Davalos, D.B., Kisley, M.A., Ross, R.G., Effects of interval duration on temporal processing in schizophrenia (2003) Brain Cogn; Degnan, A., Berry, K., Sweet, D., Abel, K., Crossley, N., Edge, D., Social networks and symptomatic and functional outcomes in schizophrenia: a systematic review and meta-analysis (2018) Soc Psychiatry Psychiatr Epidemiol; Del-Monte, J., Capdevielle, D., Varlet, M., Marin, L., Schmidt, R., Salesse, R., Bardy, B., Raffard, S., Social motor coordination in unaffected relatives of schizophrenia patients: a potential intermediate phenotype (2013) Front Behav Neurosci; Del-Monte, J., Raffard, S., Salesse, R.N., Marin, L., Schmidt, R.C., Varlet, M., Bardy, B.G., Capdevielle, D., Nonverbal expressive behaviour in schizophrenia and social phobia (2013) Psychiatry Res; de Rugy, A., Salesse, R., Oullier, O., Temprado, J.J., A neuro-mechanical model for interpersonal coordination (2006) Biol Cybern; Dodell-Feder, D., Tully, L.M., Hooker, C.I., Social impairment in schizophrenia: new approaches for treating a persistent problem (2015) Curr Opin Psychiatry; Elvevag, B., McCormack, T., Gilbert, A., Brown, G.D.A., Weinberger, D.R., Goldberg, T.E., Duration judgements in patients with schizophrenia (2003) Psychol Med; Freitas, H., Costa, P., Silva, V., da Silva Pereira, A.P., Soares, F., Esteves, J.S., Using a humanoid robot as the promoter of the interaction with children in the context of educational games (2017) Int J Mechatron Appl Mech; Green, M.F., Horan, W.P., Lee, J., Social cognition in schizophrenia (2015) Nat Rev Neurosci; Green, M.F., Kern, R.S., Braff, D.L., Mintz, J., Neurocognitive deficits and functional outcome in schizophrenia: are we measuring the “right stuff”? 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J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85100825402
"Duarte L., Safeea M., Neto P.","57282354300;57028531100;35243406700;","Event-based tracking of human hands",2021,"Sensor Review","41","4",,"382","389",,,"10.1108/SR-03-2021-0095","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116247464&doi=10.1108%2fSR-03-2021-0095&partnerID=40&md5=8fa705ce8368c676c3040f1ef8a31381","Mechanical Engineering, University of Coimbra, Coimbra, Portugal","Duarte, L., Mechanical Engineering, University of Coimbra, Coimbra, Portugal; Safeea, M., Mechanical Engineering, University of Coimbra, Coimbra, Portugal; Neto, P., Mechanical Engineering, University of Coimbra, Coimbra, Portugal","Purpose: This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting three-dimensional (3D) hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human–robot interactions and in obstacle avoidance for human–robot safety applications. Design/methodology/approach: Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception. Findings: Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured. Originality/value: Tracking of human hands in 3 D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space). © 2021, Emerald Publishing Limited.","Dynamic vision sensor; Event camera; Human hands tracking","Depth perception; Human robot interaction; Palmprint recognition; Dynamic vision sensors; Event camera; Event-based; Human hand tracking; Human hands; Low latency; Novel methods; Region-of-interest; Regions of interest; Single event; Cameras",,,,,"Fundação para a Ciência e a Tecnologia, FCT: 2021.06508","This research was partially supported by Fundação para a Ciência e a Tecnologia 2021.06508.BD, COBOTIS (PTDC/EME-EME/32595/2017), UIDB/00285/2020, UE/FEDER through the program COMPETE 2020 PRODUTECH4S&C (46102).",,,,,,,,,,"Barranco, F., Fermuller, C., Ros, E., Real-time clustering and multi-target tracking using event-based sensors (2018) IEEE International Conference on Intelligent Robots and Systems, pp. 5764-5769; Barranco, F., Fermuller, C., Aloimonos, Y., Contour motion estimation for asynchronous event-driven cameras (2014) Proceedings of the IEEE, 102 (10), pp. 1537-1556; Borer, D., Delbruck, T., Rösgen, T., Three-dimensional particle tracking velocimetry using dynamic vision sensors (2017) Experiments in Fluids, 58 (12), pp. 1-7; Chen, N.F.Y., Pseudo-labels for supervised learning on dynamic vision sensor data, applied to object detection under ego-motion (2018) IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2018-June(118225), pp. 757-766; Conradt, J., (2009) A pencil balancing robot using a pair of AER dynamic vision sensors, pp. 781-784. , doi:, IEEE; DardeletIeng, L., Benosman, R., (2018) Event-based features selection and tracking from intertwined estimation of velocity and generative contours, , http://arxiv.org/abs/1811.07839, arXiv; Falanga, D., Kim, S., Scaramuzza, D., How fast is too fast? The role of perception latency in high-speed sense and avoid (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 1884-1891; Gallego, G., Event-based vision: a survey (2019) IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-30; Innocenti, S.U., (2020) Temporal binary representation for event-based action recognition, , http://arxiv.org/abs/2010.08946; Lichtsteiner, P., Posch, C., Delbruck, T., A 128 × 128 120 dB 15 μs latency asynchronous temporal contrast vision sensor (2008) IEEE Journal of Solid-State Circuits, 43 (2), pp. 566-576; Litzenberger, M., Embedded vision system for real-time object tracking using an asynchronous transient vision sensor (2006) 2006 IEEE 12th Digital Signal Processing Workshop and 4th IEEE Signal Processing Education Workshop, pp. 173-178; Mitrokhin, A., Event-based moving object detection and tracking (2018) IEEE International Conference on Intelligent Robots and Systems, (March), pp. 6895-6902; Mokhtari, G., Non-wearable human identification sensors for smart home environment: a review (2018) Sensor Review, 38 (3), pp. 391-404; Mueggler, E., Huber, B., Scaramuzza, D., Event-based, 6-DOF pose tracking for high-speed maneuvers (2014) IEEE International Conference on Intelligent Robots and Systems, pp. 2761-2768; Pia̧Tkowska, E., Spatiotemporal multiple persons tracking using dynamic vision sensor (2012) in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 35-40. , doi:, IEEE; Saner, D., High-speed object tracking using an asynchronous temporal contrast sensor (2014) 19th International Workshop on Vision, Modeling and Visualization, VMV 2014, pp. 87-94; Wu, J., (2019) High-Speed Object Tracking with Dynamic Vision Sensor, Lecture Notes in Electrical Engineering, , doi:, Springer, Singapore; Yahya, M., Motion capture sensing techniques used in human upper limb motion: a review (2019) Sensor Review, 39 (4), pp. 504-511; Zhu, A.Z., Atanasov, N., Daniilidis, K., Event-based feature tracking with probabilistic data association (2017) Proceedings – IEEE International Conference on Robotics and Automation, pp. 4465-4470","Neto, P.; Mechanical Engineering, Portugal; email: pedro.neto@dem.uc.pt",,,"Emerald Group Holdings Ltd.",,,,,02602288,,SNRVD,,"English","Sens. Rev.",Article,"Final","",Scopus,2-s2.0-85116247464
"She Y., Song S., Su H.-J., Wang J.","56545216500;57200117296;8379272100;7701333092;","A Parametric Study of Compliant Link Design for Safe Physical Human-Robot Interaction",2021,"Robotica","39","10",,"1739","1759",,,"10.1017/S0263574720001472","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114724896&doi=10.1017%2fS0263574720001472&partnerID=40&md5=d1236e9929eab8e916abb647e4bce203","Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA  02139, United States; Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH  43210, United States; Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States","She, Y., Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA  02139, United States, Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH  43210, United States; Song, S., Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States; Su, H.-J., Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, OH  43210, United States; Wang, J., Walker Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States","Robots of next-generation physically interact with the world rather than be caged in a controlled area, and they need to make contact with the open-ended environment to perform their task. Compliant robot links offer intrinsic mechanical compliance for addressing the safety issue for physical human-robot interactions (pHRI). However, many important research questions are yet to be answered. For instance, how do system parameters, for example, mechanical compliance, motor torque, impact velocities, and so on, affect the impact force? how to formulate system impact dynamics of compliant robots, and how to size their geometric dimensions to maximize impact force reduction. In this paper, we present a parametric study of compliant link (CL) design for safe pHRI. We first present a theoretical model of the pHRI system that is comprised of robot dynamics, an impact contact model, and dummy head dynamics. After experimentally validating the theoretical model, we then systematically study the effects of CL parameters on the impact force in more detail. Specifically, we explore how the design and actuation parameters affect the impact force of pHRI system. Based on the parametric studies of the CL design, we propose a step-by-step process and a list of concrete guidelines for designing CL with safety constraints in pHRI. We further conduct a simulation case study to validate this design process and design guidelines. © The Author(s) 2021.","Compliant links; Design; Modeling; Physical human-robot interaction; Variable stiffness links","Agricultural robots; Dynamics; Machine design; Man machine systems; Geometric dimensions; Impact velocities; Mechanical compliance; Physical human-robot interactions; Physical humanrobot interaction (phri); Research questions; Safety constraint; Theoretical modeling; Human robot interaction",,,,,"National Science Foundation, NSF: CMMI-1637656","This material is based on work supported by the National Science Foundation under Grant No: CMMI-1637656.",,,,,,,,,,"Versace, J., (1971) A Review of the Severity Index, , SAE Technical Paper 710881; Gao, D., Wampler, C.W., Head injury criterion (2009) Ieee Robot. 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Syst., 14 (9), pp. 649-658; Cai, G.P., Lim, C.W., Dynamics studies of a flexible hub-beam system with significant damping effect (2008) J. Sound Vib., 318 (1-2), pp. 1-17; Reddy, P., Shihabudheen, K.V., Jacob, J., Precise non linear modeling of flexible link flexible joint manipulator (2012) Int. Review Model. Sim., 5 (3 B), pp. 1368-1374; Song, S., She, Y., Wang, J., Su, H., Toward tradeoff between impact force reduction and maximum safe speed: Dynamic parameter optimization of variable stiffness robots (2020) J. Mech. Robot., 12 (5), pp. 054503-054511","She, Y.; Computer Science and Artificial Intelligence Laboratory, 32 Vassar St, United States; email: yushe@mit.edu",,,"Cambridge University Press",,,,,02635747,,ROBOD,,"English","Robotica",Article,"Final","",Scopus,2-s2.0-85114724896
"Zahedi F., Arnold J., Phillips C., Lee H.","57219441506;57210791266;57222348277;35102589200;","Variable damping control for pHRI: Considering stability, agility, and human effort in controlling human interactive robots",2021,"IEEE Transactions on Human-Machine Systems","51","5",,"504","513",,,"10.1109/THMS.2021.3090064","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115221431&doi=10.1109%2fTHMS.2021.3090064&partnerID=40&md5=8f2a7b5591ef328a2eb54c2186cab104","School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ  85287, United States","Zahedi, F., School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ  85287, United States; Arnold, J., School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ  85287, United States; Phillips, C., School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ  85287, United States; Lee, H., School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ  85287, United States","This article presents a multi-degree-of-freedom variable damping controller to manage the trade-off between stability and agility and to reduce user effort in physical human-robot interaction. The controller accounts for the human body's inherent impedance properties and applies a range of robotic damping from negative (energy injection) to positive (energy dissipation) values based on the user's intent of motion. To evaluate the effectiveness of the proposed controller in balancing the trade-off between stability/agility and reducing user effort, two studies are performed on both the human upper-extremity and lower-extremity to represent both industrial and rehabilitation applications of the proposed controller. These studies required subjects to perform a series of multidimensional target reaching tasks while the human user interacted with either the end-effector of a robotic arm for the upper-extremity study or a wearable ankle robot for the lower-extremity study. Stability, agility, and user effort are quantified by a variety of performance metrics. Stability is quantified by both overshoot and stabilization time. Mean and maximum speed are used to quantify agility. To quantify the user effort, both overall and maximum muscle activation, and mean and maximum root-mean-squared interaction force are calculated. The results of both the upper- and lower-extremity studies demonstrate that the controller is able to reduce user effort while increasing agility at a negligible cost to stability. © 2013 IEEE.","Agility; human effort; performance; physical human robot interaction; stability; variable impedance control","Controllers; Damping; Degrees of freedom (mechanics); Economic and social effects; End effectors; Energy dissipation; Robotics; Stabilization; Human interactive robots; Impedance properties; Interaction forces; Multi degree-of-freedom; Muscle activation; Performance metrics; Physical human-robot interactions; Stabilization time; Human robot interaction",,,,,"National Science Foundation, NSF: 1846885, 1925110","Manuscript received January 17, 2021; revised March 24, 2021; accepted May 30, 2021. Date of publication August 11, 2021; date of current version September 15, 2021. This work was supported by the National Science Foundation under Grant 1846885 and Grant 1925110. This article was recommended by Associate Editor J. YC Chen. (Fatemeh Zahedi and James Arnold contributed equally to this work.) (Corresponding author: Hyunglae Lee.) Fatemeh Zahedi, James Arnold, and Connor Phillips are with the School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85287 USA (e-mail: fzahedi1@asu.edu; jmarnol6@asu.edu; cmphil13@asu.edu).",,,,,,,,,,"Krebs, H., Volpe, B., Rehabilitation robotics (2013) Handbook of Clinical Neurology, 110, pp. 283-294; Morasso, P., Casadio, M., Sanguineti, V., Squeri, V., Vergaro, E., Robot therapy: The importance of haptic interaction (2007) Proc. Virtual Rehabil., pp. 70-77; Heyer, C., Human-robot interaction and future industrial robotics applications (2010) Proc. IEEE/RSJ Int. Conf. Intell. 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Automat., 18 (1), pp. 1-10. , Feb; Ott, C., Albu-Schaffer, A., Kugi, A., Hirzinger, G., On the passivitybased impedance control of flexible joint robots (2008) IEEE Trans. Robot., 24 (2), pp. 416-429. , Apr; Lee, H., Hogan, N., Essential considerations for design and control of human-interactive robots (2016) Proc. IEEE Int. Conf. Robot. Automat., pp. 3069-3074; Lee, K.H., Baek, S.G., Lee, H.J., Choi, H.R., Moon, H., Koo, J.C., Improving transparency in physical human-robot interaction using an impedance compensator (2017) Proc. IEEE/RSJ Int.Conf. Intell. Robots Syst., pp. 3591-3596; Ferraguti, F., Talignani Landi, C., Sabattini, L., Bonf'E, M., Fantuzzi, C., Secchi, C., A variable admittance control strategy for stable physical human-robot interaction (2019) Int. J. Robot. Res., 38 (6), pp. 747-765; Ikeura, R., Inooka, H., Variable impedance control of a robot for cooperation with a human (1995) Proc. IEEE Int. Conf. Robot. 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Centre Occupat.Health Safety, Jan. 2013, Accessed on: Jan. 14, 2021; Chang, J.L., Lin, R.Y., Saul, M., Koch, P.J., Krebs, H., Volpe, B.T., Intensive seated robotic training of the ankle in patients with chronic stroke differentially improves gait (2017) NeuroRehabilation, 4, pp. 61-68; Tokuda, K., Muscle activation patterns in acceleration-based phases during reach-to-grasp movement (2016) J. Phys. Therapy Sci., 28 (11), pp. 3105-3111","Lee, H.; School for Engineering of Matter, United States; email: hyunglae.lee@asu.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21682291,,,,"English","IEEE Trans. Human Mach. Syst.",Article,"Final","",Scopus,2-s2.0-85115221431
"Dai W., Liu Y., Lu H., Zheng Z., Zhou Z.","57188988094;57193067554;55729801700;8424484900;8974315300;","A Shared Control Framework for Human-Multirobot Foraging with Brain-Computer Interface",2021,"IEEE Robotics and Automation Letters","6","4","9464651","6305","6312",,,"10.1109/LRA.2021.3092290","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111099356&doi=10.1109%2fLRA.2021.3092290&partnerID=40&md5=c7dc6ae1ad89ea658a88de1b0e7d2319","College of Intelligence Science and Technology, National University of Defense Technology, Hunan, Changsha, China","Dai, W., College of Intelligence Science and Technology, National University of Defense Technology, Hunan, Changsha, China; Liu, Y., College of Intelligence Science and Technology, National University of Defense Technology, Hunan, Changsha, China; Lu, H., College of Intelligence Science and Technology, National University of Defense Technology, Hunan, Changsha, China; Zheng, Z., College of Intelligence Science and Technology, National University of Defense Technology, Hunan, Changsha, China; Zhou, Z., College of Intelligence Science and Technology, National University of Defense Technology, Hunan, Changsha, China","With the rapid development of multi-robot systems (MRSs), they can be widely used to perform various tasks in typical environments. However, the inevitable disadvantages of onboard sensor errors, communication delays, and underspecified environmental factors seriously affect the operation of MRSs. Therefore, this letter considers a shared control framework suitable for human-multirobot foraging with a brain-computer interface (BCI) as a means of allowing a human operator to express opinions, permitting the robots to rely on human experience and knowledge to improve cooperation. An opinion dynamics model is used to find the consensus opinion of the MRS, which, however, is likely not accurate due to the biased nature of the available environmental information. When the human operator learns the opinion of the robots, he/she can then either accept it or reject it and express his/her own opinion via the BCI. Of course, this human judgment may also be incorrect, or the BCI may suffer from false detections. Thus, the MRS does not directly follow the human operator's opinion; instead, it is added to the opinion dynamics model as a new node to generate the final consensus opinion. Extensive simulation results show that the proposed framework can markedly improve the efficiency of foraging compared with robot-only or human-only performance and traditional human-robot interaction methods. © 2016 IEEE.","brain-machine interfaces; human-robot collaboration; Multi-robot systems","Agricultural robots; Industrial robots; Multipurpose robots; Personnel; Social robots; Communication delays; Environmental factors; Environmental information; Extensive simulations; False detections; Multi-robot systems; On-board sensors; Shared control framework; Brain computer interface",,,,,"CX2018B010; National Natural Science Foundation of China, NSFC: U1813205, U1913202","Manuscript received April 7, 2021; accepted June 11, 2021. Date of publication June 24, 2021; date of current version July 15, 2021. This letter was recommended for publication by Associate Editor J. Fu and Editor M. Ani Hsieh upon evaluation of the reviewers’ comments. This work was supported in part by the National Natural Science Foundation of China under Grants U1913202 and U1813205, and in part by the Hunan Provincial Innovation Foundation For Postgraduate under Grant CX2018B010. (Corresponding author: Huimin Lu.) The authors are with the College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China (e-mail: weidai_nudt@foxmail.com; yrliubci@foxmail.com; lhm-new@nudt.edu.cn; zqzheng@nudt.edu.cn; narcz@nudt.edu.cn). Digital Object Identifier 10.1109/LRA.2021.3092290",,,,,,,,,,"Parker, L.E., Rus, D., Sukhatme, G.S., Multiple mobile robot systems (2016) Springer Handbook of Robotics, pp. 1335-1384. , Cham: Springer International Publishing; Tsankova, D., Georgieva, V., From local actions to global tasks: Simulation of stigmergy based foraging behavior (2004) Proc. 2nd Int. IEEE C. Intell. Syst., 1, pp. 353-358; Butler, Z., Rizzi, A., Hollis, R., Cooperative coverage of rectilinear environments (2000) Proc. IEEE Int. Conf. Robot. Automat., 3, pp. 2722-2727; Dai, W., Lu, H., Xiao, J., Zeng, Z., Zheng, Z., Multi-robot dynamic task allocation for exploration and destruction (2020) J. Intell. Robot. Syst., 98 (2), pp. 455-479; Johannsmeier, L., Haddadin, S., A hierarchical human-robot interaction-planning framework for task allocation in collaborative industrial assembly processes (2017) IEEE Robot. Automat. 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Rev., 63 (3), pp. 181-194; Harary, F., Acriterion for unanimity in French's theory of social power (1959) Studies in Social Power, pp. 168-182. , England: Univer. Michigan; Chen, Y., Yang, C., Chen, X., Wang, Y., Gao, X., A novel trainingfree recognition method for ssvep-based BCIs using dynamic window strategy (2021) J. Neural Eng., 18 (3)","Dai, W.; College of Intelligence Science and Technology, Hunan, China; email: weidai_nudt@foxmail.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85111099356
"Li Y., Li Y., Zhu M., Xu Z., Mu D.","57225985760;55912100600;16064959700;25958491600;57225941452;","A nonlinear momentum observer for sensorless robot collision detection under model uncertainties",2021,"Mechatronics","78",,"102603","","",,,"10.1016/j.mechatronics.2021.102603","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109941746&doi=10.1016%2fj.mechatronics.2021.102603&partnerID=40&md5=d17f4efc5254f612c757f425a8e7d688","CAS Key Laboratory of On-orbit Manufacturing and Integration for Space Optics System, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Department of Mechanical Engineering, Changchun University of Technology, Changchun, 130012, China","Li, Y., CAS Key Laboratory of On-orbit Manufacturing and Integration for Space Optics System, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China, University of Chinese Academy of Sciences, Beijing, 100049, China; Li, Y., CAS Key Laboratory of On-orbit Manufacturing and Integration for Space Optics System, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China; Zhu, M., CAS Key Laboratory of On-orbit Manufacturing and Integration for Space Optics System, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China; Xu, Z., CAS Key Laboratory of On-orbit Manufacturing and Integration for Space Optics System, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, 130033, China; Mu, D., Department of Mechanical Engineering, Changchun University of Technology, Changchun, 130012, China","Collision detection methods could reduce collision forces and improve safety during physical human-robot interaction without additional sensing devices. However, current collision detection methods result in an unavoidable trade-off between sensitivity to collisions, peaking value reduction near the initial time, and immunity to measurement noise. In this paper, a novel nonlinear extended state momentum observer (NESMO) is proposed for detecting collisions between a robot body and human under model uncertainties based on only position and current measurements. The collision detection method is divided into three steps. The first step is to identify the robot dynamic model. Then, we can deduce the generalized momentum-based state-space equations from the identified base dynamic parameters. The second step is to construct a NESMO. Benefiting from the fractional power function and the time-varying damping ratio, the NESMO achieves the required monitoring bandwidth with noise immunity. The last step is to design a novel time-varying threshold (TVT) to distinguish the collision signal from the estimated lumped disturbance. As with the dynamic model parameters, the coefficients of TVT could be obtained by offline identification. Combined with NESMO, the method can provide timely and reliable collision detection and estimation under model uncertainties. Simulation and experimental results obtained using a 6-DOF robot manipulator illustrate the effectiveness of the proposed method. © 2021","Collision detection; Nonlinear momentum observer; Robot dynamics","Dynamic models; Economic and social effects; Equations of state; Human robot interaction; Industrial manipulators; Manipulators; Momentum; Robot applications; Collision detection; Dynamic parameters; Fractional power functions; Measurement Noise; Model parameters; Model uncertainties; Physical human-robot interactions; State space equation; Uncertainty analysis",,,,,,,,,,,,,,,,"Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human–robot collaboration in industrial settings: safety, intuitive interfaces and applications (2018) Mechatron, 55, pp. 248-266; Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human–robot collaboration (2018) Auton Robo, 42, pp. 957-975; Heo, Y.J., Kim, D., Lee, W., Kim, H., Park, J., Chung, W.K., Collision detection for industrial collaborative robots: a deep learning approach (2019) IEEE Robot Autom Lett, 4, pp. 740-746; Kouris, A., Dimeas, F., Aspragathos, N., A frequency domain approach for contact type distinction in human-robot collaboration (2018) IEEE Robot Autom Lett, 3, pp. 720-727; Cao, P., Gan, Y., Dai, X., Model-based sensorless robot collision detection under model uncertainties with a fast dynamics identification (2019) Int J Adv Robot Syst, 16, pp. 1-15; Li, W., Han, Y., Wu, J., Xiong, Z., Collision detection of robots based on a force/torque sensor at the bedplate (2020) IEEE/ASME Trans Mechatron, 25, pp. 2565-2573; Haddadin, S., De Luca, A., Albu-Schäffer, A., Robot collisions: a survey on detection, isolation, and identification (2017) IEEE Trans Robot, 33, pp. 1292-1312; Garofalo, G., Mansfeld, N., Jankowski, J., Ott, C., Sliding mode momentum observers for estimation of external torques and joint acceleration (2019) Proc - IEEE Int Conf Robot Autom, pp. 6117-6123. , 2019-May; Lee, S.D., Ahn, K.H., Song, J.B., Subspace projection-based collision detection for physical interaction tasks of collaborative robots (2019) Int J Precis Eng Manuf, 20, pp. 1119-1126; Lee, S.D., Song, J.B., Sensorless collision detection based on friction model for a robot manipulator (2016) Int J Precis Eng Manuf, 17, pp. 11-17; Hu, J., Xiong, R., Contact force estimation for robot manipulator using semiparametric model and disturbance kalman filter (2018) IEEE Trans Ind Electron, 65, pp. 3365-3375; Ren, T., Dong, Y., Wu, D., Chen, K., Collision detection and identification for robot manipulators based on extended state observer (2018) Contr Eng Pract, 79, pp. 144-153; Zhao, Z.L., Guo, B.Z., A nonlinear extended state observer based on fractional power functions (2017) Automatica, 81, pp. 286-296; Birjandi, S.A.B., Kuhn, J., Haddadin, S., Observer-Extended direct method for collision monitoring in robot manipulators using proprioception and imu sensing (2020) IEEE Robot Autom Lett, 5, pp. 954-961; Sotoudehnejad, V., Kermani, M.R., Velocity-based variable thresholds for improving collision detection in manipulators (2014) Proc - IEEE Int Conf Robot Autom, pp. 3364-3369; Birjandi, S.A.B., Haddadin, S., Model-Adaptive high-speed collision detection for serial-chain robot manipulators (2020) IEEE Robot Autom Lett, 5, pp. 6544-6551; Wu, J., Wang, J., You, Z., An overview of dynamic parameter identification of robots (2010) Robot Comput Integr Manuf, 26, pp. 414-419; Gautier, M., Numerical calculation of the base inertial parameters of robots (1991) J Robot Syst, 8, pp. 485-506; Swevers, J., Ganseman, C., Bilgin Tükel, D., De Schutter, J., Van Brüssel, H., Optimal robot excitation and identification (1997) IEEE Trans Robot Autom, 13, pp. 730-740; Naghdi, M., Sadrnia, M.A., A novel fuzzy extended state observer (2020) ISA Trans, 102, pp. 1-11; Zhang, J., Yu, S., Yan, Y., Fixed-time extended state observer-based trajectory tracking and point stabilization control for marine surface vessels with uncertainties and disturbances (2019) Ocean Eng, p. 186; Sotoudehnejad, V., Takhmar, A., Kermani, M.R., Polushin, I.G., Counteracting modeling errors for sensitive observer-based manipulator collision detection (2012) IEEE Int Conf Intell Robot Syst, pp. 4315-4320; Haddadin, S., Albu-Schäffer, A., De Luca, A., Hirzinger, G., Collision detection and reaction: a contribution to safe physical human-robot interaction (2008) 2008 IEEE/RSJ Int Conf Intell Robot Syst IROS, pp. 3356-3363; Liu, X., Zhen, S., Sun, H., Zhao, H., A novel model-based robust control for position tracking of permanent magnet linear motor (2020) IEEE Trans Ind Electron, 67, pp. 7767-7777; Haddadin, S., Albu-SchäCurrency Signffer, A., Hirzinger, G., Requirements for safe robots: measurements, analysis and new insights (2009) Int J Rob Res, 28, pp. 1507-1527; Rosenstrauch, M.J., Kruger, J., Safe human-robot-collaboration-introduction and experiment using ISO/TS 15066 (2017) 2017 3rd Int Conf Control Autom Robot ICCAR 2017, pp. 740-744; Zhang, L., Wei, C., Wu, R., Cui, N., Fixed-time extended state observer based non-singular fast terminal sliding mode control for a vtvl reusable launch vehicle (2018) Aerosp Sci Technol, , 82–83:70–9; Basin, M., Yu, P., Shtessel, Y., Finite- and fixed-time differentiators utilising hosm techniques (2017) IET Contr Theo Appl, 11, pp. 1144-1152; Tian, B., Zuo, Z., Yan, X., Wang, H., A fixed-time output feedback control scheme for double integrator systems (2017) Automatica, 80, pp. 17-24","Zhu, M.; Changchun Institute of Optics, 3888 Southeast Lake Road, China; email: mingchaozhu@gmail.com",,,"Elsevier Ltd",,,,,09574158,,MECHE,,"English","Mechatronics",Article,"Final","",Scopus,2-s2.0-85109941746
"Lee Y., Do W., Yoon H., Heo J., Lee W., Lee D.","56742516600;57295567300;57295458300;57210786111;57295681300;6505601306;","Visual-inertial hand motion tracking with robustness against occlusion, interference, and contact",2021,"Science Robotics","6","58","eabe1315","","",,,"10.1126/scirobotics.abe1315","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117039252&doi=10.1126%2fscirobotics.abe1315&partnerID=40&md5=b941becec81f3ebf31595f356bd923cf","Department of Mechanical Engineering, Seoul National University, 1 Gwanak-Ro, Gwanak-Gu, Seoul, 08826, South Korea; Department of Mechanical Engineering, Stanford University, 438 Panama Street, Building 570, Stanford, CA  94305, United States; Memory Business, Samsung Electronics, 114 Samsung-Ro, Gyeonggi-Do, 17786, South Korea","Lee, Y., Department of Mechanical Engineering, Seoul National University, 1 Gwanak-Ro, Gwanak-Gu, Seoul, 08826, South Korea; Do, W., Department of Mechanical Engineering, Stanford University, 438 Panama Street, Building 570, Stanford, CA  94305, United States; Yoon, H., Department of Mechanical Engineering, Seoul National University, 1 Gwanak-Ro, Gwanak-Gu, Seoul, 08826, South Korea; Heo, J., Department of Mechanical Engineering, Seoul National University, 1 Gwanak-Ro, Gwanak-Gu, Seoul, 08826, South Korea; Lee, W., Memory Business, Samsung Electronics, 114 Samsung-Ro, Gyeonggi-Do, 17786, South Korea; Lee, D., Department of Mechanical Engineering, Seoul National University, 1 Gwanak-Ro, Gwanak-Gu, Seoul, 08826, South Korea","State-of-the-art technologies for hand (and finger) motion tracking do not always provide accurate and robust tracking. For example, severe occlusions can affect tracking with vision sensors, electromagnetic interference affects tracking with inertial measurement units (IMUs) and compasses, and ambiguous mechanical contact can affect tracking with soft sensors (i.e., the inability to distinguish motion-induced deformation). Here, we report a visual-inertial skeleton tracking (VIST) framework that provides robust and accurate hand tracking in a variety of real-world scenarios. Our proposed VIST framework comprises a sensor glove with multiple IMUs and passive visual markers as well as a head-mounted stereo camera. VIST also uses a tightly coupled filtering-based visual-inertial fusion algorithm to estimate the hand/finger motion and autocalibrates hand/glove-related kinematic parameters simultaneously while taking into account the hand anatomical constraints. Our VIST framework exhibits good tracking accuracy and robustness, affordable material cost, lightweight hardware and software, and durability to permit washing. We validate our VIST framework through quantitative and qualitative experiments in real-world conditions. Our approach to hand tracking has the potential to enrich not only human-robot interaction applications (e.g., direct humanoid hand teleoperation, hand-based collaborative robot programming, and drone swarm control) but also the user experience in many virtual reality and augmented reality applications. © 2021 American Association for the Advancement of Science. All rights reserved.",,"Augmented reality; Electromagnetic pulse; Human robot interaction; Motion analysis; Palmprint recognition; Robot programming; Stereo image processing; Virtual reality; Accurate tracking; Hand motion tracking; Hand-tracking; Inertial measurements units; Mechanical contact; Robust tracking; Severe occlusions; Soft sensors; State-of-the-art technology; Vision sensors; Collaborative robots",,,,,,,,,,,,,,,,"(2019) Video Friday: Amazon CEO Jeff Bezos Tries Dexterous Robot Hands, , https://spectrum.ieee.org/automaton/robotics/robotics-hardware/video-friday-amazonceo-jeff-bezos-dexterous-robot-hands/; Bimbo, J., Pacchierotti, C., Aggravi, M., Tsagarakis, N., Prattichizzo, D., Teleoperation in cluttered environments using wearable haptic feedback (2017) Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3401-3408. , (IEEE); Tobergte, A., Helmer, P., Hagn, U., Rouiller, P., Thielmann, S., Grange, S., Albu-Schäffer, A., Hirzinger, G., The sigma. 7 haptic interface for mirosurge: Añew bi-manual surgical console (2011) Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3023-3030. , (IEEE); (2018), https://www.kuka.com/en-de/products/robot-systems/robot-controllers/smartpad/, KUKA smartPAD teach pendant; Ackernam, E., (2015) Your finger on a tablet can control entire swarms of robots, , https://spectrum.ieee.org/automaton/robotics/robotics-software/georgia-tech-robotswarm-control/; www.oculus.com/quest-2/features/, Oculus Quest 2 & Touch; www.vive.com/us/accessory/controller/, VIVE controller; www.ultraleap.com/product/leap-motion-controller/, Leap Motion; Zhang, F., Bazarevsky, V., Vakunov, A., Tkachenka, A., Sung, G., Chang, C.-L., Grundmann, M., Mediapipe hands: On-device real-time hand tracking (2019) Proceedings of IEEE International Conference on Computer Vision (Workshops), , (IEEE); Mueller, F., Bernard, F., Sotnychenko, O., Mehta, D., Sridhar, S., Casas, D., Theobalt, C., Ganerated hands for real-time 3d hand tracking from monocular rgb (2018) Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 49-59. , (IEEE); Spurr, A., Iqbal, U., Molchanov, P., Hilliges, O., Kautz, J., Weakly supervised 3d hand pose estimation via biomechanical constraints (2020) Proceedings of European Conference on Computer Vision, pp. 211-228. , (Springer); Moon, G., Ju, Y., Lee, K., V2v-posenet: Voxel-to-voxel predictionñetwork for accurate 3d hand and human pose estimation from a single depth map (2018) Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5079-5088. , (IEEE); Zimmermann, C., Brox, T., Learning to estimate 3d hand pose from single rgb images (2017) Proceedings of IEEE International Conference on Computer Vision, pp. 4903-4911. , (IEEE); Qian, C., Sun, X., Wei, Y., Tang, X., Sun, J., Realtime and robust hand tracking from depth (2014) Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1106-1113. , (IEEE); Sharp, T., Keskin, C., Robertson, D., Taylor, J., Shotton, J., Kim, D., Rhemann, C., Izadi, S., Accurate, robust, and flexible real-time hand tracking (2015) Proceedings of ACM Conference on Human Factors in Computing Systems, pp. 3633-3642. , (Association for Computing Machinery); Hampali, S., Rad, M., Oberweger, M., Lepetit, V., Honnotate: A method for 3d annotation of hand and object poses (2020) Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3196-3206. , (IEEE); Sridhar, S., Mueller, F., Zollhöfer, M., Casas, D., Oulasvirta, A., Theobalt, C., Real-time joint tracking of a hand manipulating an object from rgb-d input (2016) Proceedings of European Conference on Computer Vision, pp. 294-310. , (Springer); Yuan, S., Ye, Q., Stenger, B., Jain, S., Kim, T.-K., Bighand2. 2m benchmark: Hand pose dataset and state of the art analysis (2017) Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4866-4874. , (IEEE); Armagan, A., Garcia-Hernando, G., Baek, S., Hampali, S., Rad, M., Zhang, Z., Xie, S., Kim, T.-K., Measuring generalisation to unseen viewpoints, articulations, shapes and objects for 3D hand pose estimation under hand-object interaction (2020) Proceedings of European Conference on Computer Vision, pp. 85-101. , (Springer); Perception Neuron, , https://neuronmocap.com/; Manus, VR, https://manus-vr.com/; Lee, Y., Kim, M., Lee, Y., Kwon, J., Park, Y., Lee, D. 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"Tan Y., Sun Z., Duan F., Solé-Casals J., Caiafa C.F.","57226466572;57209204655;24537140100;14018739300;8660998700;","A multimodal emotion recognition method based on facial expressions and electroencephalography",2021,"Biomedical Signal Processing and Control","70",,"103029","","",,,"10.1016/j.bspc.2021.103029","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112795611&doi=10.1016%2fj.bspc.2021.103029&partnerID=40&md5=fe5207c860cf61910b3f2062b642cbf4","College of Artificial Intelligence, Nankai University, Tianjin, China; Computational Engineering Applications Unit, Head Office for Information Systems and Cybersecurity, RIKEN, Wako-Shi, Japan; Department of Psychiatry, University of Cambridge, Cambridge, UK, United Kingdom; Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic, Catalonia  08500, Spain; Instituto Argentino de Radioastronomía - CCT La Plata, CONICET / CIC-PBA / UNLP, V. Elisa 1894, Argentina","Tan, Y., College of Artificial Intelligence, Nankai University, Tianjin, China; Sun, Z., Computational Engineering Applications Unit, Head Office for Information Systems and Cybersecurity, RIKEN, Wako-Shi, Japan; Duan, F., College of Artificial Intelligence, Nankai University, Tianjin, China; Solé-Casals, J., College of Artificial Intelligence, Nankai University, Tianjin, China, Department of Psychiatry, University of Cambridge, Cambridge, UK, United Kingdom, Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic, Catalonia  08500, Spain; Caiafa, C.F., College of Artificial Intelligence, Nankai University, Tianjin, China, Instituto Argentino de Radioastronomía - CCT La Plata, CONICET / CIC-PBA / UNLP, V. Elisa 1894, Argentina","Human-robot interaction (HRI) systems play a critical role in society. However, most HRI systems nowadays still face the challenge of disharmony, resulting in an inefficient communication between the human and the robot. In this paper, a multimodal emotion recognition method is proposed to establish an HRI system with a low sense of disharmony. This method is based on facial expressions and electroencephalography (EEG). The image classification method of facial expressions and the suitable feature extraction method of EEG were investigated based on the public datasets. And then these methods were applied to both images and EEG data acquired by ourselves. In addition, the Monte Carlo method was used to merge the results and solve the problem of having a small dataset. The multimodal emotion recognition method was combined with the HRI system, where it achieved a recognition rate of 83.33%. Furthermore, in order to evaluate the HRI system from the user's point of view, a perceptual assessment method was proposed to evaluate our system, in which the system was scored by the participants based on their experience, achieving an average score of 7 (the scores were ranged from 0 to 10). Experimental results demonstrate the effectiveness and feasibility of the multimodal emotion recognition method, which can be useful to reduce the sense of disharmony of HRI systems. © 2021 Elsevier Ltd","Electroencephalography; Emotion recognition; Facial expressions; Human-robot interaction system","Classification (of information); Electrophysiology; Face recognition; Human robot interaction; Man machine systems; Monte Carlo methods; Speech recognition; Classification methods; Emotion recognition; Facial Expressions; Feature extraction methods; Human-robot interaction system; Humans-robot interactions; Images classification; Interaction systems; Multimodal emotion recognition; Recognition methods; Electroencephalography; adult; Article; convolutional neural network; electroencephalography; emotion; facial expression; feasibility study; feature extraction; female; human; human experiment; male; measurement accuracy; Monte Carlo method; normal human; perception; recognition; young adult",,,,,"18JCJQJC46100; National Natural Science Foundation of China, NSFC: 11932013; Secretaría de Ciencia y Técnica, Universidad de Buenos Aires, UBACyT: 20020170100192BA; National Key Research and Development Program of China, NKRDPC: 2017YFE0129700; Tianjin Science and Technology Program: 18ZXJMTG00260, PICT 2017-3208","This work was supported by the National Key R&D Program of China (No. 2017YFE0129700), the National Natural Science Foundation of China (Key Program) (No. 11932013), the Tianjin Natural Science Foundation for Distinguished Young Scholars (No. 18JCJQJC46100), and the Tianjin Science and Technology Plan Project (No. 18ZXJMTG00260). Cesar F. 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"Freire-Obregón D., Rosales-Santana K., Marín-Reyes P.A., Penate-Sanchez A., Lorenzo-Navarro J., Castrillón-Santana M.","23396618800;57225990265;57191274555;26421312300;15042453800;57218418238;","Improving user verification in human-robot interaction from audio or image inputs through sample quality assessment",2021,"Pattern Recognition Letters","149",,,"179","184",,,"10.1016/j.patrec.2021.06.014","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110097878&doi=10.1016%2fj.patrec.2021.06.014&partnerID=40&md5=3b45362b9809aa098d6e2d1dd14f6f90","SIANI, Universidad de Las Palmas de Gran Canaria, Spain","Freire-Obregón, D., SIANI, Universidad de Las Palmas de Gran Canaria, Spain; Rosales-Santana, K., SIANI, Universidad de Las Palmas de Gran Canaria, Spain; Marín-Reyes, P.A., SIANI, Universidad de Las Palmas de Gran Canaria, Spain; Penate-Sanchez, A., SIANI, Universidad de Las Palmas de Gran Canaria, Spain; Lorenzo-Navarro, J., SIANI, Universidad de Las Palmas de Gran Canaria, Spain; Castrillón-Santana, M., SIANI, Universidad de Las Palmas de Gran Canaria, Spain","In this paper, we tackle the task of improving biometric verification in the context of Human-Robot Interaction (HRI). A robot that wants to identify a specific person to provide a service can do so by either image verification or, if light conditions are not favourable, through voice verification. In our approach, we will take advantage of the possibility a robot has of recovering further data until it is sure of the identity of the person. The key contribution is that we select from both image and audio signals the parts that are of higher confidence. For images we use a system that looks at the face of each person and selects frames in which the confidence is high while keeping those frames separate in time to avoid using very similar facial appearance. For audio our approach tries to find the parts of the signal that contain a person talking, avoiding those in which noise is present by segmenting the signal. Once the parts of interest are found, each input is described with an independent deep learning architecture that obtains a descriptor for each kind of input (face/voice). We also present in this paper fusion methods that improve performance by combining the features from both face and voice, results to validate this are shown for each independent input and for the fusion methods. © 2021","Audiovisual verification; Biometric verification; Human robot interaction","Biometrics; Deep learning; Image enhancement; Man machine systems; Audio input; Audiovisual verification; Biometric verification; Fusion methods; Humans-robot interactions; Image inputs; Image verification; Quality assessment; Sample quality; User verification; Human robot interaction",,,,,"Ministerio de Economía y Competitividad, MINECO: RTI2018-093337-B-I00; Agencia Canaria de Investigación, Innovación y Sociedad de la Información, ACIISI: CEI-2018-4; European Regional Development Fund, ERDF: ProID2020010024","This work is partially funded by the ULPGC under project ULPGC2018-08, by ACIISI and ULPGC under project CEI-2018-4, the Spanish Ministry of Economy and Competitiveness (MINECO) under project RTI2018-093337-B-I00, and by FEDER funds under project ProID2020010024. Adrian Penate-Sanchez is funded by a Beatriz Galindo grant from the Ministy of Education of Spain.",,,,,,,,,,"McInnes, L., Healy, J., Melville, J., (2018), UMAP: uniform manifold approximation and projection for dimension reduction arXiv:; Halko, N., Martinsson, P.-G., Tropp, J.A., (2009), Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions arXiv:; Al-Qaderi, M.K., Rad, A.B., A multi-modal person recognition system for social robots (2018) Appl. 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"Ta Q.M., Cheah C.C.","56743170700;7007112857;","Human–machine interaction control for stochastic cell manipulation systems",2021,"Automatica","131",,"109721","","",,,"10.1016/j.automatica.2021.109721","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107441721&doi=10.1016%2fj.automatica.2021.109721&partnerID=40&md5=ff446284d67659e07331b54a9033554a","School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","Ta, Q.M., School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore; Cheah, C.C., School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","While many physical robot systems are able to work cooperatively with humans to perform numerous tasks, existing optical cell manipulation systems can only perform either manual or automated tasks at a time. As optical cell manipulation is essentially different from robotic manipulation in the physical world due to its stochastic nature, existing human–robot interaction control techniques cannot thus be directly reproduced into optical micro-manipulation of cells. This paper proposes a human–machine interaction control approach for stochastic optical cell manipulation systems. A theoretical foundation is then developed, and thus enabling human to interpose or interact, when applicable or needed, with an automated cell manipulation system without interrupting the manipulation process. Therefore, it offers an opportunity for human to get involved with an automated cell manipulation system so as to deal with unexpected events or complex tasks, rather than to stop or restart the whole manipulation process. Both the capacity of an automated control system – which provides precise and productive cell manipulation, and human ability – in terms of decision making, if needed, are then consolidated into a single optical cell manipulation system. Rigorous mathematical formulation is developed with the consideration of the stochastic perturbations, and the stability of the human–machine interaction control system is analyzed from a stochastic perspective. Experiments are then performed to illustrate the effectiveness of the proposed approach. © 2021 Elsevier Ltd","Adaptive control; Human collaboration with automation systems; Microsystems; Motion control; Nano- and micro-technologies; Optical manipulation","Automation; Cells; Cytology; Decision making; Man machine systems; Motion control; Stochastic systems; Adaptive Control; Cell manipulation; Human collaboration with automation system; Human machine interaction; Interaction controls; Manipulation system; Nano-technology and micro-technology; Optical cells; Optical manipulation; Stochastics; Adaptive control systems",,,,,"Agency for Science, Technology and Research, A*STAR: A1883c0008","This work was supported by the Agency For Science, Technology and Research of Singapore (A*STAR) , under the AME Individual Research Grants 2017 (Ref. A1883c0008 ). The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Ciro Natale under the direction of Editor Thomas Parisini.",,,,,,,,,,"Arai, F., Yoshikawa, K., Sakami, T., Fukuda, T., Synchronized laser micro-manipulation of multiple targets along each trajectory by single laser (2004) Applied Physics Letters, 85, pp. 4301-4304; Ashkin, A., Dziedzic, J.M., Bjorkholm, J.E., Chu, S., Observation of a single-beam gradient force optical trap for dielectric particles (1986) Optics Letters, 11 (5), pp. 288-290; Banerjee, A.S., Pomerance, A., Losert, W., Gupta, S.K., Developing a stochastic dynamic programming framework for optical tweezer-based automated particle transport operations (2010) IEEE Transactions on Automation Science and Engineering, 7 (2), pp. 218-227; Butaite, U.G., Gibson, G.M., Ho, Y.L.D., Indirect optical trapping using light driven micro-rotors for reconfigurable hydrodynamic manipulation (2019) Nature Communication, 10 (1215); Cheah, C.C., Hou, S.P., Slotine, J.J.E., Region based shape control for a swarm of robots (2009) Automatica, 45, pp. 2406-2411; Cheah, C.C., Ta, Q.M., Haghighi, R., Grasping and manipulation of a micro-particle using multiple optical traps (2016) Automatica, 68, pp. 216-227; Chen, H., Lou, Y., Flocking multiple micro-particles with automatically controlled optical tweezers: Solutions and experiments (2013) IEEE Transactions on Biomedical Engineering, 60 (6), pp. 1518-1527; Chen, H., Wang, C., Li, X.J., Sun, D., Transportation of multiple biological cells through saturation-controlled optical tweezers in crowded micro-environments (2016) IEEE/ASME Transactions on Mechatronics, 21 (2), pp. 888-899; Chowdhury, S., Svec, P., Wang, C., Losert, W., Gupta, S.K., Gripper synthesis for indirect manipulation of cells using holographic optical tweezers (2012) IEEE Int. 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Conf. on medical image computing and computer-assisted intervention, pp. 819-826","Ta, Q.M.; School of Electrical and Electronic Engineering, Singapore; email: quangmin001@e.ntu.edu.sg",,,"Elsevier Ltd",,,,,00051098,,ATCAA,,"English","Automatica",Article,"Final","",Scopus,2-s2.0-85107441721
"Huang T.-R., Liu Y.-W., Hsu S.-M., Goh J.O.S., Chang Y.-L., Yeh S.-L., Fu L.-C.","35205222500;57189050292;57219980238;7102139513;14059697600;7402085802;7401812723;","Asynchronously Embedding Psychological Test Questions into Human–Robot Conversations for User Profiling",2021,"International Journal of Social Robotics","13","6",,"1359","1368",,1,"10.1007/s12369-020-00716-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096391154&doi=10.1007%2fs12369-020-00716-y&partnerID=40&md5=874638904d84cdebbf3082075b5a430b","Department of Psychology, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd, Taipei, 10617, Taiwan; Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taipei, Taiwan; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan; Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan; MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei, Taiwan; MOST AI Biomedical Research Center, Tainan, Taiwan","Huang, T.-R., Department of Psychology, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd, Taipei, 10617, Taiwan, MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei, Taiwan, MOST AI Biomedical Research Center, Tainan, Taiwan; Liu, Y.-W., Department of Psychology, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd, Taipei, 10617, Taiwan, MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei, Taiwan; Hsu, S.-M., Department of Psychology, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd, Taipei, 10617, Taiwan, MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei, Taiwan; Goh, J.O.S., Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taipei, Taiwan, MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei, Taiwan; Chang, Y.-L., Department of Psychology, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd, Taipei, 10617, Taiwan, MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei, Taiwan; Yeh, S.-L., Department of Psychology, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd, Taipei, 10617, Taiwan, MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei, Taiwan; Fu, L.-C., Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan, MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei, Taiwan","Psychological variables of a person (e.g., cognitive abilities, personality traits, emotional states, and preferences) are valuable information that can be utilized by social robots to offer personalized human–robot interaction. These variables are often latent and inferred indirectly from a third-person perspective based on an individual’s behavioral manifestations (e.g., facial emotion expressions), and hence the true values of inferred psychological variables remain unknown to a robot observer. Although earlier studies have employed robot-administered psychological tests to infer psychological variables based on an individual’s first-person responses, these tests were formally presented and could be tedious to some users. To leverage the validity and reliability of well-established psychological tests for user profiling with ease, the present study examined the possibility of asynchronously embedding psychological test questions into casual human–robot conversations. In our experiment using a big-five personality inventory, the verbal responses from users to these asynchronous test questions were then compared with the written responses to the same personality test. The personality measures estimated from the two approaches correlated strongly in a young adult population but only moderately in an older population. These findings demonstrate the validity of the proposed asynchronous method for psychological testing in human–agent interactions and suggest some caveats when this testing method is applied to older adults or other special populations. © 2020, The Author(s).","Human-centered robotics; Human–robot interaction; Psychological testing","Agricultural robots; Embeddings; Agent interaction; Asynchronous methods; Cognitive ability; Personality tests; Personality traits; Psychological tests; Psychological variables; Robot interactions; Social robots",,,,,"109-2634-F-002-023; Ministry of Science and Technology, Taiwan, MOST","This study was funded by the grants 109-2634-F-002-027 (under the MOST Joint Research Center for AI Technology and All Vista Healthcare) and 109-2634-F-002-023 (under the MOST AI Biomedical Research Center) from the Ministry of Science and Technology in Taiwan.",,,,,,,,,,"Dautenhahn, K., Socially intelligent robots: dimensions of human–robot interaction (2007) Philos Trans R Soc B, 362 (1480), pp. 679-704; Hegel, F., Muhl, C., Wrede, B., Hielscher-Fastabend, M., Sagerer, G., Understanding social robots (2009) Proceedings of the Second International Conferences on Advances in computer–human Interactions (ACHI), Cancun, Mexico, 2009, pp. 169-174. , https://doi.org/10.1109/ACHI.2009.51, IEEE; Korn, O., Social robots: technological, societal and ethical aspects of human–robot interaction (2019) Hum Comput Interact Ser; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Robot Auton Syst, 42 (3-4), pp. 143-166; Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., Hagita, N., A communication robot in a shopping mall (2010) IEEE Trans Rob, 26 (5), pp. 897-913; Murphy, A., (2017) Social and entertainment: Robotics outlook 2025, , https://loupventures.com/social-and-entertainment-robotics-outlook-2025/; Moerman, C.J., van der Heide, L., Heerink, M., Social robots to support children’s well-being under medical treatment: a systematic state-of-the-art review (2018) J Child Health Care, 23 (4), pp. 596-612; Scoglio, A.A., Reilly, E.D., Gorman, J.A., Drebing, C.E., Use of social robots in mental health and well-being research: systematic review (2019) J Med Internet Res, 21 (7); Abdi, J., Al-Hindawi, A., Ng, T., Vizcaychipi, M.P., Scoping review on the use of socially assistive robot technology in elderly care (2018) BMJ Open, 8 (2); Gockley, R., Bruce, A., Forlizzi, J., Michalowski, M., Mundell, A., Rosenthal, S., Sellner, B., Schultz, A.C., Designing robots for long-term social interaction (2006) Int Conf Intell Robot Syst (IRO2006), pp. 1338-1343. , https://doi.org/10.1109/IROS.2005.1545303; Leite, I., Martinho, C., Paiva, A., Social robots for long-term interaction: a survey (2013) Int J Soc Robot, 5 (2), pp. 291-308; Walters, M.L., Dautenhahn, K., Te Boekhorst, R., Koay, K.L., Kaouri, C., Woods, S., Nehaniv, C., Werry, I., The influence of subjects’ personality traits on personal spatial zones in a human–robot interaction experiment (2005) In: ROMAN 2005. 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J. Soc. Rob.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85096391154
"Tuan H.M., Sanfilippo F., Hao N.V.","57247838000;55388355200;57248055100;","Modelling and Control of a 2-DOF Robot Arm with Elastic Joints for Safe Human-Robot Interaction",2021,"Frontiers in Robotics and AI","8",,"679304","","",,,"10.3389/frobt.2021.679304","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114341093&doi=10.3389%2ffrobt.2021.679304&partnerID=40&md5=bde8869688311f77ca07bda9245d38d3","Department of Control Engineering and Automation, Faculty of Electrical and Electronic Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Viet Nam; Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Viet Nam; Department of Engineering Sciences, University of Agder (UiA), Grimstad, Norway","Tuan, H.M., Department of Control Engineering and Automation, Faculty of Electrical and Electronic Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Viet Nam, Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Viet Nam; Sanfilippo, F., Department of Engineering Sciences, University of Agder (UiA), Grimstad, Norway; Hao, N.V., Department of Control Engineering and Automation, Faculty of Electrical and Electronic Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Viet Nam, Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Viet Nam","Collaborative robots (or cobots) are robots that can safely work together or interact with humans in a common space. They gradually become noticeable nowadays. Compliant actuators are very relevant for the design of cobots. This type of actuation scheme mitigates the damage caused by unexpected collision. Therefore, elastic joints are considered to outperform rigid joints when operating in a dynamic environment. However, most of the available elastic robots are relatively costly or difficult to construct. To give researchers a solution that is inexpensive, easily customisable, and fast to fabricate, a newly-designed low-cost, and open-source design of an elastic joint is presented in this work. Based on the newly design elastic joint, a highly-compliant multi-purpose 2-DOF robot arm for safe human-robot interaction is also introduced. The mechanical design of the robot and a position control algorithm are presented. The mechanical prototype is 3D-printed. The control algorithm is a two loops control scheme. In particular, the inner control loop is designed as a model reference adaptive controller (MRAC) to deal with uncertainties in the system parameters, while the outer control loop utilises a fuzzy proportional-integral controller to reduce the effect of external disturbances on the load. The control algorithm is first validated in simulation. Then the effectiveness of the controller is also proven by experiments on the mechanical prototype. © Copyright © 2021 Tuan, Sanfilippo and Hao.","collaborative robot; human-robot interaction; robot arm; robotics; series elastic actuator",,,,,,"Viet Nam National University Ho Chi Minh City, VNU-HCM; Universitetet i Agder, UiA","We acknowledge the support of time and facilities from Ho Chi Minh City University of Technology (HCMUT), VNU-HCM and from the Top Research Centre Mechatronics (TRCM), University of Agder, Norway, for this study.",,,,,,,,,,"Al-Odienat, A.I., Al-Lawama, A.A., The Advantages of Pid fuzzy controllers over the conventional types (2008) Am. J. Appl. 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"Röder F., Özdemir O., Nguyen P.D.H., Wermter S., Eppe M.","57219604827;57212379605;57192431670;7003826680;55734653100;","The Embodied Crossmodal Self Forms Language and Interaction: A Computational Cognitive Review",2021,"Frontiers in Psychology","12",,"716671","","",,,"10.3389/fpsyg.2021.716671","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114290309&doi=10.3389%2ffpsyg.2021.716671&partnerID=40&md5=9ee74805e30c990214450ec6aad7e2d2","Knowledge Technology, Department of Informatics, University of Hamburg, Hamburg, Germany","Röder, F., Knowledge Technology, Department of Informatics, University of Hamburg, Hamburg, Germany; Özdemir, O., Knowledge Technology, Department of Informatics, University of Hamburg, Hamburg, Germany; Nguyen, P.D.H., Knowledge Technology, Department of Informatics, University of Hamburg, Hamburg, Germany; Wermter, S., Knowledge Technology, Department of Informatics, University of Hamburg, Hamburg, Germany; Eppe, M., Knowledge Technology, Department of Informatics, University of Hamburg, Hamburg, Germany","Human language is inherently embodied and grounded in sensorimotor representations of the self and the world around it. This suggests that the body schema and ideomotor action-effect associations play an important role in language understanding, language generation, and verbal/physical interaction with others. There are computational models that focus purely on non-verbal interaction between humans and robots, and there are computational models for dialog systems that focus only on verbal interaction. However, there is a lack of research that integrates these approaches. We hypothesize that the development of computational models of the self is very appropriate for considering joint verbal and physical interaction. Therefore, they provide the substantial potential to foster the psychological and cognitive understanding of language grounding, and they have significant potential to improve human-robot interaction methods and applications. This review is a first step toward developing models of the self that integrate verbal and non-verbal communication. To this end, we first analyze the relevant findings and mechanisms for language grounding in the psychological and cognitive literature on ideomotor theory. Second, we identify the existing computational methods that implement physical decision-making and verbal interaction. As a result, we outline how the current computational methods can be used to create advanced computational interaction models that integrate language grounding with body schemas and self-representations. © Copyright © 2021 Röder, Özdemir, Nguyen, Wermter and Eppe.","developmental psychology; developmental robotics; dialog; embodiment cognition; grounding language; minimal self; reinforcement learning",,,,,,"Deutsche Forschungsgemeinschaft, DFG: TRR 169; Institute of Development and Economic Alternatives, IDEAS: 402776968, 433323019","FR, PN, SW, and ME acknowledge funding by the DFG through the IDEAS (402776968) and LeCAREbot (433323019) projects. 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"Kothig A., Munoz J., Akgun S.A., Aroyo A.M., Dautenhahn K.","57210748983;56645651500;57209982972;57192384790;7003305185;","Connecting humans and robots using physiological signals - Closing-the-loop in HRI",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"735","742",,,"10.1109/RO-MAN50785.2021.9515383","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115097531&doi=10.1109%2fRO-MAN50785.2021.9515383&partnerID=40&md5=6958dcfbe862ac697e33b964c194c6e3",,"Kothig, A.; Munoz, J.; Akgun, S.A.; Aroyo, A.M.; Dautenhahn, K.","Technological advancements in creating and commercializing novel unobtrusive and wearable physiological sensors generate new opportunities to develop adaptive human-robot interaction (HRI) scenarios. Detecting complex human states such as engagement and stress when interacting with social agents could bring numerous advantages to create meaningful interactive experiences. Despite being widely used to explain human behaviors in post-interaction analysis with social agents, using bodily signals to create more adaptive and responsive systems remains an open challenge. This paper presents the development of an open-source, integrative, and modular library created to facilitate the design of physiologically adaptive HRI scenarios. The HRI Physio Lib streamlines the acquisition, analysis, and translation of human body signals to additional dimensions of perception in HRI applications using social robots. The software framework has four main components: signal acquisition, processing and analysis, social robot and communication, and scenario and adaptation. Information gathered from the sensors is synchronized and processed to allow designers to create adaptive systems that can respond to detected human states. This paper describes the library and presents a use case that uses a humanoid robot as a cardio-aware exercise coach that uses heartbeats to adapt the exercise intensity to maximize cardiovascular performance. The main challenges, lessons learned, scalability of the library, and implications of the physio-adaptive coach are discussed. © 2021 IEEE.","Affective computing; Human-robot interaction; Physiological sensors; Software framework","Adaptive systems; Agricultural robots; Anthropomorphic robots; Behavioral research; Computer programming; Open source software; Physiology; Signal processing; Wearable sensors; Exercise intensity; Human robot Interaction (HRI); Interaction analysis; Physiological sensors; Physiological signals; Signal acquisitions; Software frameworks; Technological advancement; Social robots",,,,,,"Researchers of the SIRRL lab at University of Waterloo are supported, in part, thanks to funding from the Canada 150 Research Chairs Program. 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On Neural Systems and Rehabilitation Engineering, 25 (8), pp. 1153-1163; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., Ros: An open-source robot operating system (2009) ICRA Workshop on Open Source Software, 3 (2-3), p. 5. , Kobe, Japan; Metta, G., Fitzpatrick, P., Natale, L., Yarp: Yet another robot platform (2006) Int. J. Of Advanced Robotic Systems, 3 (1), p. 8; Kothig, A., Muñoz, J., Mahdi, H., Aroyo, A.M., Dautenhahn, K., Hri physio lib: A software framework to support the integration of physiological adaptation in hri (2020) Int. Conf. On Social Robotics, pp. 36-47. , Springer; Loncar-Turukalo, T., Zdravevski, E., Da Silva, J.M., Chouvarda, I., Trajkovik, V., Literature on wearable technology for connected health: Scoping review of research trends, advances, and barriers (2019) Journal of Medical Internet Research, 21 (9), p. 14017; Gilgen-Ammann, R., Schweizer, T., Wyss, T., Rr interval signal quality of a heart rate monitor and an ECG holter at rest and during exercise (2019) European Journal of Applied Physiology, 119 (7), pp. 1525-1532; Hänsel, K., Poguntke, R., Haddadi, H., Alomainy, A., Schmidt, A., What to put on the user: Sensing technologies for studies and physiology aware systems (2018) Proc. Of the 2018 CHI Conf. Of Human Factors in Computing Systems, pp. 1-14; Makowski, D., Pham, T., Lau, Z.J., Brammer, J.C., Lespinasse, F., Pham, H., Schölzel, C., Chen, S.A., Neurokit2: A python toolbox for neurophysiological signal processing (2021) Behavior Research Methods, pp. 1-8; Rainville, P., Bechara, A., Naqvi, N., Damasio, A.R., Basic emotions are associated with distinct patterns of cardiorespiratory activity (2006) Int. Journal of Psychophysiology, 61 (1), pp. 5-18; Fairclough, S., Gilleade, K., Construction of the biocybernetic loop: A case study (2012) Proc. Of the 14th ACM Int. Conf. Of Multimodal Interaction, pp. 571-578; Gibson, A.L., Wagner, D., Heyward, V., Advanced fitness assessment and exercise prescription, 8e (2018) Human Kinetics; Cormier, D., Newman, G., Nakane, M., Young, J.E., Durocher, S., Would you do as a robot commands? An obedience study for humanrobot interaction (2013) Int. Conf. On Human-Agent Interaction; Aroyo, A.M., Kyohei, T., Koyama, T., Takahashi, H., Rea, F., Sciutti, A., Yoshikawa, Y., Sandini, G., Will people morally crack under the authority of a famous wicked robot? (2018) 2018 27th IEEE Int. Symp. On Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 35-42; Schneeberger, T., Ehrhardt, S., Anglet, M.S., Gebhard, P., Would you follow my instructions if i was not human? examining obedience towards virtual agents (2019) 2019 8th Int. Conf. On Affective Computing and Intelligent Interaction (ACII). IEEE, pp. 1-7; Schneider, S., Kummert, F., Comparing the effects of social robots and virtual agents on exercising motivation (2018) Int. Conf. On Social Robotics, pp. 451-461. , Springer; Schneider, S., Kummert, F., Comparing robot and human guided personalization: Adaptive exercise robots are perceived as more competent and trustworthy (2020) Int. Journal of Social Robotics, pp. 1-17; Tanaka, H., Monahan, K.D., Seals, D.R., Age-predicted maximal heart rate revisited (2001) Journal of the American College of Cardiology, 37 (1), pp. 153-156; Muñoz, J.E., Cameirão, M., Bermúdez Badiai, S., Gouveia, E.R., Closing the loop in exergaming-health benefits of biocybernetic adaptation in senior adults (2018) Proc. Of the 2018 Annu. Symp. Of Computer-Human Interaction in Play, pp. 329-339",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115097531
"Fortineau V., Makarov M., Rodriguez-Ayerbe P., Siegler I.A.","57219598993;53164158100;23569685800;7005825089;","Towards a seamless experimental protocol for human arm impedance estimation in an interactive dynamic task",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"31","36",,,"10.1109/RO-MAN50785.2021.9515486","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115084270&doi=10.1109%2fRO-MAN50785.2021.9515486&partnerID=40&md5=51afa8777c026d7866331237ce9ff939","Université Paris-Saclay, CNRS, CentraleSupélec, !!!Laboratoire des Signaux et Systèmes, Gif-sur-Yvette, 91190, France; Université Paris-Saclay, CIAMS, Orsay, 91405, France","Fortineau, V., Université Paris-Saclay, CNRS, CentraleSupélec, !!!Laboratoire des Signaux et Systèmes, Gif-sur-Yvette, 91190, France, Université Paris-Saclay, CIAMS, Orsay, 91405, France; Makarov, M., Université Paris-Saclay, CNRS, CentraleSupélec, !!!Laboratoire des Signaux et Systèmes, Gif-sur-Yvette, 91190, France; Rodriguez-Ayerbe, P., Université Paris-Saclay, CNRS, CentraleSupélec, !!!Laboratoire des Signaux et Systèmes, Gif-sur-Yvette, 91190, France; Siegler, I.A., Université Paris-Saclay, CIAMS, Orsay, 91405, France","The estimation of the human endpoint impedance interacting with a physical environment provides modelling insights both for the field of human movement science and for the design of innovative controllers for collaborative robotics based on physical human-robot interaction. Most of the existing human impedance estimation methods described in the literature rely on controlled environments closer to a laboratory than to an industrial setting. In this paper, a force perturbation method is proposed without any specific requirements on neither the force nor the position trajectories and without additional sensors placed on the human. The method is illustrated through an experimental study on a benchmark interaction task. The task was selected to be sufficiently variable for preventing the use of average trajectories and the perturbations used for the estimations are sufficiently low amplitude and short to not deteriorate the human performance in the task. The obtained impedance model parameters are in adequacy with the ones found in the literature, suggesting the validity of the proposed approach. The low constraints on the experimental settings make it applicable even out of the lab, for example, in human-robot collaboration in manufacturing environments, to adjust the robot behaviour to changes in the state of the operator (fatigue, stress) or to adjust to a variety of human operators with different interaction strategies. © 2021 IEEE.",,"Agricultural robots; Machine design; Perturbation techniques; Controlled environment; Experimental protocols; Force perturbation method; Human-robot collaboration; Impedance estimation; Manufacturing environments; Physical environments; Physical human-robot interactions; Social robots",,,,,,,,,,,,,,,,"Tsuji, T., Tanaka, Y., Bio-mimetic impedance control of robotic manipulator for dynamic contact tasks (2008) Rob Auton Syst., 56, pp. 306-316. , Apr; Burdet, E., Osu, R., Franklin, D.W., Milner, T.E., Kawato, M., The central nervous system stabilizes unstable dynamics by learning optimal impedance (2001) Nature, 414, pp. 446-449. , Nov; Mussa-Ivaldi, F.A., Hogan, N., Bizzi, E., Neural, mechanical, and geometric factors subserving arm posture in humans (1985) J. Neurosci., 5, pp. 2732-2743. , Oct; Fang, C., Ajoudani, A., Bicchi, A., Tsagarakis, N.G., Online model based estimation of complete joint stiffness of human arm (2018) IEEE Robot. Autom. Lett., 3, pp. 84-91. , Jan; Phan, G.-H., Hansen, C., Tommasino, P., Estimating human wrist stiffness during a tooling task (2020) Sensors, 20, p. 3260. , Jan. Number: 11 Publisher: Multidisciplinary Digital Publishing Institute; Dolan, J.M., Friedman, M.B., Nagurka, M.L., Dynamic and loaded impedance components in the maintenance of human arm posture (1993) IEEE Trans. Syst. Man Cybern. Syst., 23, pp. 698-709. , May; Burdet, E., Osu, R., Franklin, D.W., Yoshioka, T., Milner, T.E., Kawato, M., A method for measuring endpoint stiffness during multijoint arm movements (2000) J. Biomech., 33, pp. 1705-1709. , Dec; Erden, M.S., Billard, A., Hand impedance measurements during interactive manual welding with a robot (2015) IEEE Trans. Robot., 31, pp. 168-179. , Feb; Bennett, D.J., Hollerbach, J.M., Xu, Y., Hunter, I.W., Time-varying stiffness of human elbow joint during cyclic voluntary movement (1992) Exp Brain Res, 88, pp. 433-442. , Feb; Abe, M.O., Yamada, N., Modulation of elbow joint stiffness in a vertical plane during cyclic movement at lower or higher frequencies than natural frequency (2003) Exp Brain Res, 153, pp. 394-399. , Dec; Wang, C., Jiang, L., Guo, C., Huang, Q., Yang, B., Liu, H., Semgbased estimation of human arm force using regression model (2017) 2017 IEEE Int. Conf. Rob. And Biomim. (ROBIO), pp. 1044-1049. , Dec; Castellini, C., Arquer, A., Artigas, J., Semg-based estimation of human stiffness: Towards impedance-controlled rehabilitation (2014) Proc. IEEE RAS EMBS Int. Conf. Biomed. Robot. Biomechatronics, pp. 604-609. , Aug; Avrin, G., Siegler, I.A., Makarov, M., Rodriguez-Ayerbe, P., The self-organization of ball bouncing (2018) Biol Cybern, 112, pp. 509-522. , Dec; Fortineau, V., Makarov, M., Rodriguez-Ayerbe, P., Siegler, I.A., Interactive robotics for human impedance estimation in a rhythmic task (2020) 2020 IEEE 16th Int. Conf. On Autom. Science and Engineering (CASE), pp. 1043-1048. , Aug; Kawazoe, Y., Dynamics of the ball-racket impact in tennis: Contact force, contact time, coefficient of restitution, and deformation (2002) Asian Conf. On Multibody Dyn., 2002, pp. 286-293; Guarin, D.L., Kearney, R.E., Unbiased estimation of human joint intrinsic mechanical properties during movement (2018) IEEE Trans. Neural Syst. Rehabilitation Eng., 26, pp. 1975-1984. , Oct; Libet, B., Unconscious cerebral initiative and the role of conscious will in voluntary action (1985) Behavioral and Brain Sciences, 8, pp. 529-539. , Dec. Publisher: Cambridge University Press",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85115084270
"Zielinski K., Walas K., Heredia J., Kjargaard M.B.","57311495900;25652478200;57203274636;56463016500;","A study of cobot practitioners needs for augmented reality interfaces in the context of current technologies",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"292","298",,,"10.1109/RO-MAN50785.2021.9515406","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115077814&doi=10.1109%2fRO-MAN50785.2021.9515406&partnerID=40&md5=a92633b48ecef533b34ea51184c6e18e","Poznan University of Technology, Institute of Robotics and Machine Intelligence, Poland; University of Southern Denmark, Faculty of Engineering, Maersk Mc Kinney Moller Institute, Denmark","Zielinski, K., Poznan University of Technology, Institute of Robotics and Machine Intelligence, Poland; Walas, K., Poznan University of Technology, Institute of Robotics and Machine Intelligence, Poland; Heredia, J., University of Southern Denmark, Faculty of Engineering, Maersk Mc Kinney Moller Institute, Denmark; Kjargaard, M.B., University of Southern Denmark, Faculty of Engineering, Maersk Mc Kinney Moller Institute, Denmark","Human-Robot Interaction (HRI) for collaborative robots has not changed since the introduction of the first cobot. The main interface to communicate with the robot remains a wired display - teach pendant (TP). While attempts are made to make the programming experience better - more intuitive touch-screen displays, it generally remains the same. With the recent rapid development of Augmented Reality (AR), the HRI of the cobot could drastically change. This paper explores AR-based implementations in robotics and categorizes them based on the type of the used device, with the main focus on the least explored category - mobile AR. Furthermore, two experiments are conducted to determine the user's experience in robot programming using TP with a mobile-based AR interface. For this reason, an AR application prototype is developed as a co-interface to a TP. The results of the experiments are presented: the first examines the user's needs that are missing in current solutions, while the second one analyses the user's experience in using the robot with the AR interface. The obtained results suggest that users could benefit from mobile-based AR solutions in the commissioning and troubleshooting phase of the lifetime of the robot. However, at the same time, this solution is not advanced and accurate enough (yet) to encourage users to switch to the new platform and abandon the classical TP, while programming the robot. © 2021 IEEE.",,"Agricultural robots; Augmented reality; Robot programming; Software prototyping; Touch screens; User experience; AR application; Collaborative robots; Current technology; Human robot Interaction (HRI); Programming experience; Reality interface; Touch screen displays; User's needs; Social robots",,,,,,"ACKNOWLEDGMENT This research was conducted in collaboration with Universal Robots, as part of Krzysztof Zielinski’s master’s thesis. This work is part of the project “Energy-efficient Programming of Collaborative Robots” funded by ELFORSK.",,,,,,,,,,"Schmalstieg, D., Hollerer, T., (2016) Augmented Reality: Principles and Practice, Ser. Usability, , Pearson Education; Sutherland, I., The ultimate display (2001) Proceedings of the IFIPS Congress, 65 (2), pp. 506-508. , New York: IFIP 2, 01; Caudell, T., Mizell, D., Augmented reality: An application of heads-up display technology to manual manufacturing processes (1992) Proceedings of the Twenty-Fifth HICSS, 2 (2), pp. 659-669; Wagner, D., Reitmayr, G., Mulloni, A., Drummond, T., Schmalstieg, D., Pose tracking from natural features on mobile phones (2008) Proceedings-7th IEEE ISMAR 2008, 9, pp. 125-134; Makhataeva, Z., Varol, H., Augmented reality for robotics: A review (2020) Robotics, 9 (2), p. 21. , Apr; Bogue, R., The role of augmented reality in robotics (2020) Industrial Robot, , ahead-of-print ahead-of-print; Quintero, C.P., Li, S., Pan, M.K., Chan, W.P., Van Der Loos Machiel, H.F., Croft, E., Robot programming through augmented trajectories in augmented reality (2018) 2018 IEEE/RSJ IROS, pp. 1838-1844; Clemente, F., Dosen, S., Lonini, L., Markovic, M., Farina, D., Cipriani, C., Humans can integrate augmented reality feedback in their sensorimotor control of a robotic hand (2017) IEEE Transactions on Human-Machine Systems, 47 (4), pp. 583-589; Blankemeyer, S., Wiemann, R., Posniak, L., Pregizer, C., Raatz, A., Intuitive robot programming using augmented reality (2018) Procedia CIRP, 76, pp. 155-160; Puljiz, D., Stöhr, E., Riesterer, K.S., Hein, B., Kröger, T., Sensorless hand guidance using microsoft hololens (2019) Proceedings of the 14th ACM/IEEE HRI, Ser. HRI '19, pp. 632-633. , IEEE Press; Gong, L.L., Ong, S.K., Nee, A.Y.C., Projection-based augmented reality interface for robot grasping tasks (2019) Proceedings of the 2019 4th ICRCA, Ser. ICRCA 2019, pp. 100-104. , New York, NY, USA: Association for Computing Machinery; Hietanen, A., Pieters, R., Lanz, M., Latokartano, J., Kämäräinen, J.-K., Ar-based interaction for human-robot collaborative manufacturing (2020) Robotics and Computer-Integrated Manufacturing, 63, p. 101891; Stadler, S., Kain, K., Giuliani, M., Mirnig, N., Stollnberger, G., Tscheligi, M., Augmented reality for industrial robot programmers: Workload analysis for task-based, augmented reality-supported robot control (2016) 2016 25th IEEE RO-MAN, pp. 179-184; ABB RobotStudio®AR Viever, , https://new.abb.com/news/detail/66541/ar-smartphone-robot-installationson2021-03-08, accessed; Native robotics: Omnifit Native Robotics, , https://native-robotics.com, accessed on 2021-03-08; Norman, D.A., (2013) The Design of Everyday Things, Revis and Expand Ed, , London: The MIT Press; Minichiello, V., (1990) In-depth Interviewing: Researching People, , Longman Cheshire; Bernard, H.R., (2011) Research Methods in Anthropology: Qualitative and Quantitative Approaches, , 5th ed. Lanham, MD: AltaMira",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115077814
"Cooper S., Di Fava A., Villacanas O., Silva T., Fernandez-Carbajales V., Unzueta L., Serras M., Marchionni L., Ferro F.","57219563175;55420347200;57225154006;57225131401;15062667400;13607585100;57021986700;24780954300;56243244300;","Social robotic application to support active and healthy ageing",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"1074","1080",,,"10.1109/RO-MAN50785.2021.9515432","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115063578&doi=10.1109%2fRO-MAN50785.2021.9515432&partnerID=40&md5=03eec17b106f1243a897d0b463e74bdf","PAL Robotics, Barcelona, Spain; Clínica Humana, Mallorca, Spain; Tree Technology SA (TREE), Madrid, Spain; VICOMTECH, Donostia, Spain","Cooper, S., PAL Robotics, Barcelona, Spain; Di Fava, A., PAL Robotics, Barcelona, Spain; Villacanas, O., Clínica Humana, Mallorca, Spain; Silva, T., Tree Technology SA (TREE), Madrid, Spain; Fernandez-Carbajales, V., Tree Technology SA (TREE), Madrid, Spain; Unzueta, L., VICOMTECH, Donostia, Spain; Serras, M., VICOMTECH, Donostia, Spain; Marchionni, L., PAL Robotics, Barcelona, Spain; Ferro, F., PAL Robotics, Barcelona, Spain","With the world population ageing and with the number of healthcare users needing assistance and support increasing, healthcare is becoming more costly and, as such, the need to optimise and support independent living for older people is of paramount importance. This paper reviews the user requirements and challenges that are relevant to older adults who prefer to age healthily at home, and how socially assistive robots (SAR) can be used to help them. The main focus is placed on the social robotic application developed for the H2020 SHAPES project to promote Smart Living Environment for healthy ageing. The solution is based on the newest PAL Robotics' robot ARI, a high-performance social robot and companion designed for a wide range of multi-modal expressive gestures, gaze and personalised behaviour, which is integrated via several Digital Solutions developed within the SHAPES project to improve human-robot interaction and user acceptability for independent living support tasks. The validation process will take place over the coming months at Clinica Humana (Mallorca, Spain), which is a private clinic that provides hospital care to retirement homes, communities and home-bound patients. A description of the scenario definition is presented in the paper, together with the validation plan that will be executed during the pilot assessments and the measures that will be taken to improve user engagement. © 2021 IEEE.",,"Agricultural robots; Hospitals; Independent living systems; Intelligent buildings; Robotics; Digital solutions; Expressive gestures; Independent living; Scenario definitions; Socially assistive robots; User requirements; Validation process; World population; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 857159; Horizon 2020","ACKNOWLEDGMENT This work was supported by the SHAPES project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 857159","This work was supported by the SHAPES project, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 857159",,,,,,,,,"Abdi, J., Al-Hindawi, A., Ng, T., Vizcaychipi, M.P., Scoping review on the use of socially assistive robot technology in elderly care (2018) BMJ Open, , British Medical Journal Publishing Group; 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Interspeech 2017; Ferro, F., Nardi, F., Cooper, S., Marchionni, L., Robot control and navigation: Ari's autonomous system (2020) 29th IEEE International Conference on Robot and Human Interactive Communication (ROMAN), , IEEE, Crowdbot workshop; Spargo, M., Goodfellow, N., Scullin, C., Grigoleit, S., Andreou, A., Mavromouustakis, C., Guerra, B., Scott, M., Shaping the future of digitally enabled health (2021) Pharmacy",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115063578
"Spatola N., Wudarczyk O.A.","57193889944;57253733800;","Implicit Attitudes Towards Robots Predict Explicit Attitudes, Semantic Distance Between Robots and Humans, Anthropomorphism, and Prosocial Behavior: From Attitudes to Human–Robot Interaction",2021,"International Journal of Social Robotics","13","5",,"1149","1159",,5,"10.1007/s12369-020-00701-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108882852&doi=10.1007%2fs12369-020-00701-5&partnerID=40&md5=e0f761658f15992869f51a96a229fba1","Center for Human Technologies, Istituto Italiano di Tecnologia, Via Morego, 30, Genoa, 16163, Italy; Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany","Spatola, N., Center for Human Technologies, Istituto Italiano di Tecnologia, Via Morego, 30, Genoa, 16163, Italy; Wudarczyk, O.A., Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany","How people behave towards others relies, to a large extent, on the prior attitudes that they hold towards them. In Human–Robot Interactions, individual attitudes towards robots have mostly been investigated via explicit reports that can be biased by various conscious processes. In the present study, we introduce an implicit measure of attitudes towards robots. The task utilizes the measure of semantic priming to evaluate whether participants consider humans and robots as similar or different. Our results demonstrate a link between implicit semantic distance between humans and robots and explicit attitudes towards robots, explicit semantic distance between robots and humans, perceived robot anthropomorphism, and pro/anti-social behavior towards a robot in a real life, interactive scenario. Specifically, attenuated semantic distance between humans and robots in the implicit task predicted more positive explicit attitudes towards robots, attenuated explicit semantic distance between humans and robots, attribution of an anthropomorphic characteristic, and consequently a future prosocial behavior towards a robot. Crucially, the implicit measure of attitudes towards robots (implicit semantic distance) was a better predictor of a future behavior towards the robot than explicit measure of attitudes towards robots (self-reported attitudes). Cumulatively, the current results emphasize a new approach to measure implicit attitudes towards robots, and offer a starting point for further investigations of implicit processing of robots. © 2020, The Author(s).","Anthropomorphism; Human–robot interaction; Implicit attitude; Prosocial behaviour; Semantic priming","Agricultural robots; Semantics; Social robots; Conscious process; Explicit semantics; Implicit attitudes; Implicit measures; Implicit semantics; Robot interactions; Semantic distance; Semantic primings; Anthropomorphic robots",,,,,"Deutsche Forschungsgemeinschaft, DFG: 390523135, EXC 2002/1; Istituto Italiano di Tecnologia, IIT","Open access funding provided by Istituto Italiano di Tecnologia within the CRUI-CARE Agreement. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2002/1 “Science of Intelligence” - project number 390523135.",,,,,,,,,,"Ajzen, I., Fishbein, M., Attitude-behavior relations: a theoretical analysis and review of empirical research (1977) Psychol Bull, 84, pp. 888-918; MacDorman, K.F., Vasudevan, S.K., Ho, C.C., Does Japan really have robot mania? 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J. Soc. Rob.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85108882852
"Lachner J., Allmendinger F., Hobert E., Hogan N., Stramigioli S.","57212339974;54790643800;57223371231;35572189800;35572162100;","Energy budgets for coordinate invariant robot control in physical human–robot interaction",2021,"International Journal of Robotics Research","40","8-9",,"968","985",,,"10.1177/02783649211011639","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105721487&doi=10.1177%2f02783649211011639&partnerID=40&md5=d03ff4b3586678987f9181694fd517b5","Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, Netherlands; KUKA Deutschland GmbH, Zugspitzstraße 140, Augsburg, Germany; Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States; International Laboratory of Biomechatronics and Energy-efficient Robotics, ITMO University, St. Petersburg, Russian Federation","Lachner, J., Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, Netherlands, KUKA Deutschland GmbH, Zugspitzstraße 140, Augsburg, Germany; Allmendinger, F., KUKA Deutschland GmbH, Zugspitzstraße 140, Augsburg, Germany; Hobert, E., Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, Netherlands; Hogan, N., Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States; Stramigioli, S., Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, Netherlands, International Laboratory of Biomechatronics and Energy-efficient Robotics, ITMO University, St. Petersburg, Russian Federation","In this work we consider the current certification process of applications with physical human–robot interaction (pHRI). Two major hazards are collisions and clamping scenarios. The implementation of safety measures in pHRI applications typically depends strongly on coordinates, e.g., to monitor the robot velocity or to predict external forces. We show that the current certification process does not, in general, guarantee a safe robot behavior. In particular, in unstructured environments it is not possible to predict all risks in advance. We therefore propose to control the energy of the robot, which is a coordinate invariant entity. For an impedance controlled robot, the total energy consists of potential energy and kinetic energy. The energy flow from task description to physical interaction follows a strict causality. We assign a safe energy budget for the robot. With this energy budget, the presented controller auto-tunes its parameters to limit the exchanged kinetic energy during a collision and the potential energy during clamping scenarios. In contact, the robot behaves compliantly and therefore eliminates clamping danger. After contact, the robot automatically continues to follow the desired trajectory. With this approach the number of safety-related parameters to be determined can be reduced to one energy value, which has the potential to significantly speed up the commissioning of pHRI applications. The proposed technique is validated by experiments. © The Author(s) 2021.","coordinate invariance; energy-aware control; impedance control; Physical human–robot interaction","Agricultural robots; Budget control; Kinetic energy; Kinetics; Molecular physics; Potential energy; Certification process; Desired trajectories; Physical interactions; Robot controls; Robot interactions; Safety measures; Task description; Unstructured environments; Human robot interaction",,,,,"688188; Massachusetts Institute of Technology, MIT; Centers for Mechanical Engineering Research and Education, Massachusetts Institute of Technology","This work was partially funded by the MURAB project (grant agreement number 688188). N. Hogan was supported in part by the Centers for Mechanical Engineering Research and Education at MIT and SUSTech.","We would like to thank KUKA Systems GmbH for providing us the measurement equipment. A special thanks goes to Florian Groschup and Nicolas Brueckmann. This work was partially funded by the MURAB project (grant agreement number 688188). N. Hogan was supported in part by the Centers for Mechanical Engineering Research and Education at MIT and SUSTech.",,,,,,,,,"Albu-Schaffer, A., Ott, C., Frese, U., Hirzinger, G., (2003) Cartesian impedance control of redundant robots: Recent results with the DLR-light-weight-arms, pp. 3704-3709. , IEEE International Conference on Robotics and Automation, In; Beetz, M., Bartels, G., Albu-Schaffer, A., (2015) Robotic agents capable of natural and safe physical interaction with human co-workers, pp. 6528-6535. , 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),. 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In; Raiola, G., Cardenas, C.A., Tadele, T.S., de Vries, T., Stramigioli, S., Development of a safety- and energy-aware impedance controller for collaborative robots (2018) IEEE Robotics and Automation Letters, 3 (2), pp. 1237-1244; Rosenstrauch, M.J., Kruger, J., (2017) Safe human–robot collaboration - introduction and experiment using ISO/TS 15066, pp. 740-744. , 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), In; Rozo, L., Calinon, S., Caldwell, D., Jimenez, P., Torras, C., (2013) Learning collaborative impedance-based robot behaviors, 27 (1). , Proceedings of the AAAI Conference on Artificial Intelligence, In; Sadrfaridpour, B., Wang, Y., Collaborative assembly in hybrid manufacturing cells: An integrated framework for human–“robot interaction (2018) IEEE Transactions on Automation Science and Engineering, 15 (3), pp. 1178-1192; Schaal, S., Dynamic movement primitives - A framework for motor control in humans and humanoid robotics (2006) Adaptive Motion of Animals and Machines, pp. 261-280. , Kimura H., Tsuchiya K., Ishiguro A., Witte H., (eds), Tokyo, Springer, In:, (eds; Schindlbeck, C., Haddadin, S., (2015) Unified passivity-based Cartesian force/impedance control for rigid and flexible joint robots via task-energy tanks, pp. 440-447. , 2015 IEEE International Conference on Robotics and Automation (ICRA), In; Shahriari, E., Johannsmeier, L., Jensen, E., Haddadin, S., Power flow regulation, adaptation, and learning for intrinsically robust virtual energy tanks (2020) IEEE Robotics and Automation Letters, 5 (1), pp. 211-218; Shahriari, E., Kramberger, A., Gams, A., Ude, A., Haddadin, S., (2017) Adapting to contacts: Energy tanks and task energy for passivity-based dynamic movement primitives, pp. 136-142. , 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), In; Siciliano, B., Khatib, O., (2008) Springer Handbook of Robotics, , Berlin, Springer Science+Business Media, (eds; Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., (2009) Robotics: Modelling, Planning and Control, , London, Springer; Stramigioli, S., (2001) Modeling and IPC Control of Interactive Mechanical Systems - A Coordinate-Free Approach (Lecture Notes in Control and Information Sciences, 266. , London, Springer; Stramigioli, S., (2015) Energy-Aware Robotics (Mathematical Control Theory I, 461. , Cham, Springer, (ed; Tadele, T.S., de Vries, T.J.A., Stramigioli, S., (2014) Combining energy and power based safety metrics in controller design for domestic robots, pp. 1209-1214. , 2014 IEEE International Conference on Robotics and Automation (ICRA), In; Takegaki, M., Arimoto, S., A new feedback method for dynamic control of manipulators (1981) Journal of Dynamic Systems, Measurement, and Control, 103 (2), pp. 119-125; Wassink, M., Stramigioli, S., (2007) Towards a novel safety norm for domestic robotics, pp. 3354-3359. , IEEE/RSJ International Conference on Intelligent Robots and Systems, In","Lachner, J.; Faculty of Electrical Engineering, Netherlands; email: j.lachner@utwente.nl",,,"SAGE Publications Inc.",,,,,02783649,,IJRRE,,"English","Int J Rob Res",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85105721487
"Peng Z., Dang J., Unoki M., Akagi M.","57197828642;7006574053;6604048482;16318490800;","Multi-resolution modulation-filtered cochleagram feature for LSTM-based dimensional emotion recognition from speech",2021,"Neural Networks","140",,,"261","273",,1,"10.1016/j.neunet.2021.03.027","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103685707&doi=10.1016%2fj.neunet.2021.03.027&partnerID=40&md5=4be5802415c0e5333b69c645f703d0c2","Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, 300050, China; Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa, 923–1292, Japan; Pengcheng Laboratory, Shenzhen, 518055, China","Peng, Z., Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, 300050, China, Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa, 923–1292, Japan; Dang, J., Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin, 300050, China, Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa, 923–1292, Japan, Pengcheng Laboratory, Shenzhen, 518055, China; Unoki, M., Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa, 923–1292, Japan; Akagi, M., Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa, 923–1292, Japan","Continuous dimensional emotion recognition from speech helps robots or virtual agents capture the temporal dynamics of a speaker's emotional state in natural human–robot interactions. Temporal modulation cues obtained directly from the time-domain model of auditory perception can better reflect temporal dynamics than the acoustic features usually processed in the frequency domain. Feature extraction, which can reflect temporal dynamics of emotion from temporal modulation cues, is challenging because of the complexity and diversity of the auditory perception model. A recent neuroscientific study suggests that human brains derive multi-resolution representations through temporal modulation analysis. This study investigates multi-resolution representations of an auditory perception model and proposes a novel feature called multi-resolution modulation-filtered cochleagram (MMCG) for predicting valence and arousal values of emotional primitives. The MMCG is constructed by combining four modulation-filtered cochleagrams at different resolutions to capture various temporal and contextual modulation information. In addition, to model the multi-temporal dependencies of the MMCG, we designed a parallel long short-term memory (LSTM) architecture. The results of extensive experiments on the RECOLA and SEWA datasets demonstrate that MMCG provides the best recognition performance in both datasets among all evaluated features. The results also show that the parallel LSTM can build multi-temporal dependencies from the MMCG features, and the performance on valence and arousal prediction is better than that of a plain LSTM method. © 2021","Dimensional emotion; Multi-resolution modulation-filtered cochleagram; Parallel long short-term memory network; Temporal modulation","Behavioral research; Brain; Dynamics; Frequency domain analysis; Human robot interaction; Information filtering; Long short-term memory; Neurophysiology; Speech recognition; Time domain analysis; Auditory perception; Cochleagram; Dimensional emotion; Emotion recognition from speech; Multi-resolution modulation; Multi-resolution modulation-filtered cochleagram; Parallel long short-term memory network; Short term memory; Temporal dynamics; Temporal modulations; Modulation; arousal; article; brain; emotion; hearing; human; human experiment; long short term memory network; prediction; speech; association; automatic speech recognition; biological model; cochlea; machine learning; physiology; speech perception; Cochlea; Cues; Emotions; Humans; Machine Learning; Models, Neurological; Speech Perception; Speech Recognition Software",,,,,"18A414; Ministry of Education, Culture, Sports, Science and Technology, Monbusho","This work was supported by a Grant in Aid for Innovative Areas (No. 18H05004 ) from MEXT, Japan , and was partially supported by the Research Foundation of Education Bureau of Hunan Province, China (Grant No. 18A414 ).",,,,,,,,,,"Atlas, L., Shamma, S.A., Joint acoustic and modulation frequency (2003) EURASIP Journal on Applied Signal Processing, 2003 (7), pp. 668-675; Avila, A.R., Momin, Z.A., Santos, J.F., OShaughnessy, D., Falk, T.H., Feature pooling of modulation spectrum features for improved speech emotion recognition in the wild (2018) IEEE Transactions on Affective Computing, 3045 (100), p. 1; Aytar, Y., Vondrick, C., Torralba, A., SoundNet: Learning sound representations from unlabeled video (2016), no. 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"Peng Y., Liu J., Xie B., Shan H., He M., Hou G., Jin Y.","57216489101;23974840200;57202419971;57226549440;57226546186;57226758929;57225884560;","Research Progress of Urban Dual-arm Humanoid Grape Harvesting Robot",2021,"2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2021",,,,"879","885",,,"10.1109/CYBER53097.2021.9588266","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119369678&doi=10.1109%2fCYBER53097.2021.9588266&partnerID=40&md5=5a1c4ddd3cb85d05213f964a7e350fe5","School of Agricultural Engineering, Jiangsu University, Zhenjiang, 2012013, China","Peng, Y., School of Agricultural Engineering, Jiangsu University, Zhenjiang, 2012013, China; Liu, J., School of Agricultural Engineering, Jiangsu University, Zhenjiang, 2012013, China; Xie, B., School of Agricultural Engineering, Jiangsu University, Zhenjiang, 2012013, China; Shan, H., School of Agricultural Engineering, Jiangsu University, Zhenjiang, 2012013, China; He, M., School of Agricultural Engineering, Jiangsu University, Zhenjiang, 2012013, China; Hou, G., School of Agricultural Engineering, Jiangsu University, Zhenjiang, 2012013, China; Jin, Y., School of Agricultural Engineering, Jiangsu University, Zhenjiang, 2012013, China","Robotics has a very broad application prospect in the field of agriculture, and agricultural robots are attracting the attention of the whole society. Facing the new demand of the new urban production-leisure integrated industry, and in view of the shortcomings of the poor user friendliness and human-robot interaction of existing agricultural robots, a dual-arm humanoid 'little expert' grape harvesting robot was developed. Base on the 'one master-dual slaves' control architecture, the modular system of navigation-interaction-control-execution was constructed. The structural innovation such as fast electromechanical switching of different end-effectors, wiring-inside manipulator, and humanoid design were realized. The researches were focused on electronic compass-Lidar-RGBD fused navigation technology in trellis, the hand-arm collaborative control for non-destructive picking, voice-visual cross-media interaction, and the combination of artificial intelligence with robot. This dual-arm humanized robot has completed a series of testing. Based on the environment perception and the guidance of an electronic compass, the automatic pose diagnosis-adjusting and navigation of crawler chassis in trellis environment were realized. Besides, identification and positioning of grape cluster, the harvesting manner of gripping-cutting and flexible net holding, and the human-robot dialogue and voice manipulation were also achieved. It is expected to achieve major breakthroughs in various technologies in the near future and the commercialization of the dual-arm humanoid grape harvesting robot. © 2021 IEEE.",,"Harvesting; Human robot interaction; Intelligent robots; Leisure; Agricultural robot; Application prospect; Broad application; Control architecture; Dual arm; Electronic compass; Harvesting robot; Humans-robot interactions; Modular system; User friendliness; Navigation",,,,,"4111680002; National Natural Science Foundation of China, NSFC: 31971795; Jiangsu University, JSU; Priority Academic Program Development of Jiangsu Higher Education Institutions, PAPD","*Resrach supported by Project of Faculty of Agricultural Equipment of Jiangsu University (4111680002), National Science Foundation of China (Grant No. 31971795) and Jiangsu Higher Education Institutions (No.PAPD-2018-87) All authors are with School of Agricultural Engineering, Jiangsu University, Zhenjiang 2012013 First author: Yun Peng(e-mail: 215497226@qq.com). Corresponding author: Jizhan Liu(e-mail:liujizhan@163.com).",,,,,,,,,,"Jizhan, L., Research progress analysis of robotic harvesting technologies in greenhouse[J] (2017) Transactions of the Chinese Society for Agricultural Machinery, 48 (12), pp. 1-17. , Chinese with English abstract; Hayashi, S., Shigematsu, K., Yamamoto, S., Evaluation of a strawberry-harvesting robot in a field test[J] (2010) Biosystems Engineering, 105 (2), pp. 160-171; Jian, S., Tiezhong, Z., Liming, X., Research actuality and prospect of picking robot for fruits and vegetables[J] (2006) Transactions of the Chinese Society for Agricultural Machinery, 37 (5), pp. 158-162. , Chinese with English abstract; Williams, H.A.M., Jones, M.H., Nejati, M., Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms[J] (2019) Biosystems Engineering, 181, pp. 140-156; Fu, L., Tola, E., Al-Mallahi, A., A novel image processing algorithm to separate linearly clustered kiwifruits[J] (2019) Biosystems Engineering, 183, pp. 184-195; 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Henten, E.J.V., Hemming, J., Tuijl, B.A.J.V., An Autonomous Robot for Harvesting Cucumbers in Greenhouses[J] (2002) Autonomous Robots, 13 (3), pp. 241-258; Henten, E.J.V., Tuijl, J.V., Hemming, J., Field Test of an Autonomous Cucumber Picking Robot[J] (2003) Biosystems Engineering, 86 (3), pp. 305-313; Feng, Q., Wang, X., Zheng, W., New strawberry harvesting robot for elevated-Trough culture[J] (2012) International Journal of Agricultural and Biological Engineering, 5 (2), pp. 1-8; Feng, G., Qixin, C., Yongjie, C., Fruit Location and Stem Detection Method for Strawbery Harvesting Robot[J] (2008) International Journal of Advanced Robotic Systems, Page, pp. 89-94; Lehnert, C., Sa, I., McCool, C., Sweet pepper pose detection and grasping for automated crop harvesting[C] (2016) IEEE International Conference on Robotics and Automation (ICRA). IEEE 2016, pp. 2428-2434; Vitzrabin, E., Edan, Y., Changing task objectives for improved sweet pepper detection for robotic harvesting[J] (2016) IEEE Robotics and Automation Letters, 1 (1), pp. 578-584; Hemming, J., Bac, C.W., Van Tuijl, B.A.J., (2014) A Robot for Harvesting Sweet-pepper in Greenhouses[J]; Zhao, Y., Gong, L., Liu, C., Dual-Arm robot design and testing for harvesting tomato in greenhouse[J] (2016) IFAC-PapersOnLine, 49 (16), pp. 161-165; Jizhan, L., Research Progress Analysis of Robotic Harvesting Technologies in Greenhouse[J] (2017) Transactions of the Chinese Society for Agricultural Machinery, 48 (12), pp. 1-18",,,"IEEE Robotics and Automation Society;Shenyang Institute of Automation CAS;Shenzhen Academy of Robotics","Institute of Electrical and Electronics Engineers Inc.","11th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2021","27 July 2021 through 31 July 2021",,173761,,9781665425278,,,"English","IEEE Annu. Int. Conf. CYBER Technol. Autom., Control, Intell. Syst., CYBER",Conference Paper,"Final","",Scopus,2-s2.0-85119369678
"Li M., Chen L., Wu M., Pedrycz W., Hirota K.","57204930290;55774663200;57218664297;56854903200;7402829702;","Multimodal Information-Based Broad and Deep Learning Model for Emotion Understanding",2021,"Chinese Control Conference, CCC","2021-July",,,"7410","7414",,,"10.23919/CCC52363.2021.9549897","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117348568&doi=10.23919%2fCCC52363.2021.9549897&partnerID=40&md5=aeea86bc7ba71c44e43c79d33ca11aa8","China University of Geosciences, School of Automation, Wuhan, 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, 430074, China; University of Alberta, Department of Electrical and Computer Engineering, Edmonton, T6R 2G7, Canada; Tokyo Institute of Technology, Yokohama, 226-8502, Japan","Li, M., China University of Geosciences, School of Automation, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China, Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, 430074, China; Chen, L., China University of Geosciences, School of Automation, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China, Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, 430074, China; Wu, M., China University of Geosciences, School of Automation, Wuhan, 430074, China, Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China, Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, 430074, China; Pedrycz, W., University of Alberta, Department of Electrical and Computer Engineering, Edmonton, T6R 2G7, Canada; Hirota, K., Tokyo Institute of Technology, Yokohama, 226-8502, Japan","Multimodal information-based broad and deep learning model (MIBDL) for emotion understanding is proposed, in which facial expression and body gesture are used to achieve emotional states recognition for emotion understanding. It aims to understand coexistence multimodal information in human-robot interaction by using different processing methods of deep network and broad network, which obtains the features of depth and width dimensions. Moreover, random mapping in the initial broad learning network could cause information loss and its shallow layer network is difficult to cope with complex tasks. To address this problem, we use principal component analysis to generate the nodes of the broad learning, and the stacked broad learning network is adapted to make it easier for the existing broad learning networks to cope with complex tasks by creating deep variations of the existing network. To verify the effectiveness of the proposal, experiments completed on benchmark database of spontaneous emotion expressions are developed, and experimental results show that the proposal outperforms the state-of-the-art methods. According to the simulation experiments on the FABO database, by using the proposed method, the multimodal recognition rate is 17, 54%, 1.24%, and 0.23% higher than those of the temporal normalized motion and appearance features(TN), the multi-channel CNN (MCCNN), and the hierarchical classification fusion strategy (HCFS), respectively. © 2021 Technical Committee on Control Theory, Chinese Association of Automation.","body gesture; broad learning; Convolution neural network; emotion understanding; facial expression","Classification (of information); Complex networks; Deep learning; Gesture recognition; Human robot interaction; Network layers; Body gesture; Broad learning; Complex task; Convolution neural network; Emotion understanding; Emotional state; Facial Expressions; Learning models; Learning network; Multi-modal information; Principal component analysis",,,,,"National Natural Science Foundation of China, NSFC: 61603356, 61733016, 61973286; China University of Geosciences, Wuhan, CUG: 201839; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project: B17040","This work is supported by National Natural Science Foundation (NNS-F) of China under Grants 61973286, 61603356, and 61733016, the 111 project under Grant B17040, and the Fundamental Research Funds for the Central Universities, China University of Geosciences (No. 201839). † Corresponding author: Luefeng Chen (chenluefeng@cug.edu.cn)",,,,,,,,,,"Kazemifard, M., Ghasem-Aghaee, N., Koenig, B.L., An emotion understanding framework for intelligent agents based on episodic and semantic memories (2014) Autonomous Agents and Multi-Agent Systems, 28 (1), pp. 126-153; Chen, L.F., Wu, M., Zhou, M.T., Liu, Z., She, J.H., Hirota, K., Dynamic emotion understanding in human-robot interaction based on two-layer fuzzy SVR-TS model (2020) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50 (2), pp. 490-501; Picard, R.W., (1997) Affective Computing, , Cambridge: MIT Press; Zhang, S., Zhang, S., Huang, T., Gao, W., Tian, Q., Learning affective features with a hybrid deep model for audio-visual emotion recognition (2018) IEEE Transactions on Circuits and Systems for Video Technology, 28 (10), pp. 3030-3043; Tran, Q., Su, S., Sun, W., Tran, M., Adaptive pulsatile plane for robust noncontact heart rate monitoring (2019) IEEE Transactions on Systems, Man, and Cybernetics: Systems; Schuller, B., Batliner, A., Steidl, S., Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge (2011) Speech Communication, 53 (9), pp. 1062-1087; Koolagudi, S.G., Rao, K.S., Emotion recognition from speech using source, system, and prosodic features (2012) International Journal of Speech Technology, 15 (2), pp. 265-289; Chen, S., Tian, Y., Liu, Q., Recognizing expressions from face and body gesture by temporal normalized motion and appearance features (2013) Image and Vision Computing, 31 (2), pp. 175-185; Yin, Z., Wang, Y., Zhang, W., Liu, L., Physiological feature based emotion recognition via an ensemble deep autoencoder with parsimonious structure (2017) Robotics & IFAC-Papers On-Line, 50 (1), pp. 6940-6945; Barros, P., Jirak, D., Weber, C., Multimodal emotional state recognition using sequence-dependent deep hierarchical features (2016) Neural Networks, 72, pp. 140-151; Wang, J., Xiong, R., Chu, J., Facial feature points detecting based on Gaussian mixture models (2015) Pattern Recognition Letters, 53, pp. 62-68; Chen, L.F., Feng, Y., Maram, M.A., Wang, Y.W., Wu, M., Hirota, K., Pedrycz, W., Multi-SVM based dempster-shafer theory for gesture intention understanding using sparse coding feature (2019) Applied Soft Computing; Shojaeilangari, S., Yau, W.Y., Nandakumar, K., Li, J., Teoh, E.K., Robust representation and recognition of facial emotions using extreme sparse learning (2014) IEEE Transactions on Image Processing, 24 (7), pp. 2140-2152; Bengio, Y., Platt, J., Hofmann, T., Greedy layer-wise training of deep networks (2007) Advances in Neural Information Processing Systems, 19, pp. 153-160; Hinton, G., Osindero, S., Teh, Y., A fast learning algorithm for deep belief nets (2006) Neural Computation, 18, pp. 1527-1554; Lecun, Y., Bottou, L., Bengio, Y., Haffner, P., Gradientbased learning applied to document recognition (1998) Proceedings of the IEEE, 86 (11), pp. 2278-2324; Hochreiter, S., Schmidhuber, J., Long short-Term memory (1997) Neural Computation, 9 (8), pp. 1735-1780; Zhang, T., Zheng, W., Cui, Z., Zong, Y., Li, Y., Spatialctemporal recurrent neural network for emotion recognition (2019) IEEE Transactions on Cybernetics, 49 (3), pp. 839-847; Wu, M., Su, W., Chen, L., Liu, Z., Cao, W., Hirota, K., Weight-Adapted Convolution Neural Network for Facial Expression Recognition in Human-Robot Interaction (2019) IEEE Transactions on Systems, Man, and Cybernetics: Systems; Yu, Z.B., Liu, G.C., Liu, Q.S., Deng, J.K., Spatiotemporal convolutional features with nested lstm for facial expression recognition (2018) Neurocomputing, 317, pp. 50-57; Zhang, L., Suganthan, P.N., Visual tracking with convolutional random vector functional link network (2017) IEEE Transactions on Cybernetics, 47 (10), pp. 3243-3253; Chen, C.L.P., Liu, Z., An effective and efficient incremental learning system without the need for deep architecture (2017) IEEE Transactions on Neural Networks and Learning Systems, 29 (1), pp. 10-24; Chen, L.F., Li, M., Lai, X.Z., Hirota, K., Pedrycz, W., (2020) CNN-based Broad Learning with Efficient Incremental Reconstruction Model for Facial Emotion Recognition, , IFACPapersOnLine; Liu, Z., Chen, C.L.P., Feng, S., Feng, Q., Zhang, T., Stacked broad learning system: From incremental flatted structure to deep model (2021) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51 (1), pp. 209-222; (2006) A Bimodal Face and Body Gesture Database, , http://mmv.eecs.qmul.ac.uk/fabo/; Sun, B., Cao, S.M., He, J., Yu, L.J., Affect recognition from facial movements and body gestures by hierarchical deep spatio-Temporal features and fusion strategy (2018) Neural Networks, 105, pp. 36-51",,"Peng C.Sun J.","Chinese Association of Automation (CAA);Shanghai University;Systems Engineering Society of China;Technical Committee on Control Theory, Chinese Association of Automation (TCCT)","IEEE Computer Society","40th Chinese Control Conference, CCC 2021","26 July 2021 through 28 July 2021",,172297,19341768,9789881563804,,,"English","Chinese Control Conf., CCC",Conference Paper,"Final","",Scopus,2-s2.0-85117348568
"Vincze D., Gacsi M., Kovacs S., Korondi P., Miklosi A., Niitsuma M.","26221966300;6603300873;22034981100;55809786600;7003466993;9332897100;","Towards the automatic observation and evaluation of ethologically inspired Human-Robot Interaction",2021,"IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM","2021-July",,,"586","591",,,"10.1109/AIM46487.2021.9517561","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114963347&doi=10.1109%2fAIM46487.2021.9517561&partnerID=40&md5=a2677b58f11aa331fdb44f340e09ca28","Chuo University, Department of Precision Mechanics, Tokyo, Japan; Eötvös Loránd Research Network, MTA-ELTE Comparative Ethology Res. Grp., Budapest, Hungary; University of Miskolc, Department of Information Sciences, Miskolc, Hungary; Budapest Univ. of Technology and Economics, Department of MOGI, Budapest, Hungary; Eötvös Loránd Research Network, Department of Ethology, Budapest, Hungary","Vincze, D., Chuo University, Department of Precision Mechanics, Tokyo, Japan; Gacsi, M., Eötvös Loránd Research Network, MTA-ELTE Comparative Ethology Res. Grp., Budapest, Hungary; Kovacs, S., University of Miskolc, Department of Information Sciences, Miskolc, Hungary; Korondi, P., Budapest Univ. of Technology and Economics, Department of MOGI, Budapest, Hungary; Miklosi, A., Eötvös Loránd Research Network, Department of Ethology, Budapest, Hungary; Niitsuma, M., Chuo University, Department of Precision Mechanics, Tokyo, Japan","Human-Robot Interaction (HRI) evaluation is rapidly gaining more interest as the field of social robotics advances, but determining the effectiveness of the various HRI models can be a challenging task. Recently, ethology has been successfully incorporated in HRI models, and has been suggested to be used in HRI evaluation methods also. In ethological experiments, trained observers manually identify and record the exhibited behaviours of the participating subjects, a procedure called as coding. However, precise observation and coding of the behaviour of subjects is a resource demanding process and prone to human mistakes. Therefore, the goal of the paper is twofold. First, to introduce automatical coding as a possible method to provide accurate, unbiased, objective evaluation of HRI experiments. Second, to abolish the need for manual human work in the coding process. Accordingly, we propose a possible method which is able to automatically code simple behaviour elements in HRI experiments. The task of the proposed system is to automatically evaluate the behaviour of a mobile robot by distinguishing the cardinal behaviour elements without any information on the inner states of the model controlling the mobile robot, based solely on data gathered from the environment of the interaction. Various test scenarios were evaluated, while the mobile robot agent was exhibiting pre-programmed behaviours, where the proposed system could properly recognize and code the behaviour elements shown by the mobile robot. © 2021 IEEE.","Behaviour measurement; Ethology; HRI evaluation; Human-Robot Interaction; Observation","Biology; Intelligent mechatronics; Man machine systems; Mobile robots; Coding process; Evaluation methods; Human robot Interaction (HRI); Objective evaluation; Social robotics; Test scenario; Social robots",,,,,,"Dávid Vincze is an International Research Fellow of the Japan Society for the Promotion of Science (Postdoctoral Fellowships for Research in Japan (Standard)). This work was supported by the MTA-ELTE Comparative Ethology Research Group, grant number 01 031.",,,,,,,,,,"Blumstein, D.T., Daniel, J.C., (2007) Quantifying Behavior the JWatcher Way, , Sinauer Associates, Inc. Sunderland, MA; Deni, R., Szijarto, K., Eisler, A., Fantauzzo, C., BASIC programs for observational research using the TRS-80 Model 100 portable and Model 4 computers (1983) Behavior Research Methods & Instrumentation, 15, p. 616; Durrett, H.J., Jr., Inexpensive microcomputer systems for Research and Instruction: A dream or reality? (1978) Behavior Research Methods & Instrumentation, 10, pp. 345-351; Friard, O., Gamba, M., BORIS: A free, versatile open-source eventlogging software for video/audio coding and live observations (2016) Methods in Ecology and Evolution, 7 (11), pp. 1325-1330; Gacsi, M., Topál, J., Miklósi, Á., Dóka, A., Csányi, V., Attachment behavior of adult dogs (Canis familiaris) living at rescue centers: Forming new bonds (2001) Journal of Comparative Psychology, 115 (4), pp. 423-431; Grandgeorge, M., Evaluating human-robot interaction with ethology (2020) Human-Robot Interaction. Springer Series on Bio and Neurosystems, 12; Griffin, W.A., Pyobs: Open-source software for computer-assisted behavioral observation coding (2018) Journal of Open Research Software, 6 (1), p. 5; Ichikawa, N., Niitsuma, M., Nonverbal human-robot communication for ambient assisted living applications based on ethologically inspired social behavior model (2018) Proc. IECON 2018-44th Annual Conf. Of the IEEE Industrial Electronics Society, pp. 6045-6050. , Washington, DC; Jost, C., Le Pevedic, B., Belpaeme, T., Grandgeorge, M., Evaluating human-robot interaction with ethology (2016) The 25th IEEE RO-MAN, , New York, United States; Kerepesi, A., Kubinyi, E., Jonsson, G.K., Magnusson, M.S., Miklosi, A., Behavioural comparison of human-animal (dog) and human-robot (AIBO) interactions (2006) Behav. Proc., 73 (1), pp. 92-99; Kovács, B., Szayer, G., Tajti, F., Burdelis, M., Korondi, P., A novel potential field method for path planning of mobile robots by adapting animal motion attributes (2016) Robotics and Autonomous Systems, 82, pp. 24-34; Kovacs, Sz., Vincze, D., Gacsi, M., Miklósi, Á., Korondi, P., Ethologically inspired robot behavior implementation (2011) Proc. 4th Int. Conf. On Human System Interaction (HSI 2011), pp. 64-69. , Keio University, Yokohama, Japan; Kovacs, Sz., Vincze, D., Gacsi, M., Miklósi, Á., Korondi, P., Interpolation based fuzzy automaton for Human-Robot (2009) IFAC Proceedings Volumes, 42 (16), pp. 317-322; Kovacs, Sz., Vincze, D., Gacsi, M., Miklósi, Á., Korondi, P., Interpolation based fuzzy automaton for human-robot interaction (2009) Preprints of the 9th Intl. Symp. On Robot Control (SYROCO'09), the Intl. Federation of Automatic Control (IFAC), pp. 451-456. , Nagaragawa Convention Center, Gifu, Japan, September 9-12; Lorenz, K.Z., (1981) The Foundations of Ethology, , Springer-Verlag, Vienna; (2021) Solutions for Your Research, , https://www.mangold-international.com/, product brochure; Martin, N., Oliver, C., Hall, S., ObsWin: Observational data collection and analysis for Windows (1999) CTI Psychology Software News, 9, pp. 14-16; Miklósi, Á., Korondi, P., Matellán, V., Gacsi, M., Ethorobotics: A new approach to human-robot relationship (2017) Frontiers in Psychology, 8, p. 958; Murphy, R.R., Schreckenghost, D., Survey of metrics for humanrobot interaction (2013) 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 197-198. , Tokyo, Japan; Noldus, L.P.J.J., The Observer: A software system for collection and analysis of observational data (1991) Behavior Research Methods, Instruments, & Computers, 23, pp. 415-429; Noldus, L.P.J.J., Trienes, R.J.H., Hendriksen, A.H.M., Jansen, H., Jansen, R.G., The Observer Video-Pro: New software for the collection, management, and presentation of time-structured data from videotapes and digital media files (2000) Behavior Research Methods, Instruments, & Computers, 32, pp. 197-206; (2021) Noldus Information Technology: Official Company Website, , https://www.noldus.com/; Péter, A., (2011) An Introduction to Solomon Coder, , https://solomon.andraspeter.com/SolomonIntro.pdf, presentation, 11-12 October, Milano, Italy; Piller, I., Vincze, D., Kovács, Sz., Declarative language for behaviour description (2015) Emergent Trends in Robotics and Intelligent Systems: Advances in Intelligent Systems and Computing, 316, pp. 103-112. , Springer, Cham; Piller, I., Kovács, Sz., Fuzzy behavior description language: A declarative language for interpolative behavior modeling (2019) Acta Polytechnica Hungarica, 16 (9), pp. 47-72; Sabanovic, S., Michalowski, M.P., Simmons, R., Robots in the wild: Observing human-robot social interaction outside the lab (2006) The Advanced Motion Control, 9th IEEEcongress, , Turkey; Tapp, J., Wehby, J., Ellis, D., A multiple option observation system for experimental studies: MOOSES (1995) Behavior Research Methods, Instruments, & Computers, 27, pp. 25-31; Vincze, D., Gacsi, M., Kovacs, S., Niitsuma, M., Korondi, P., Miklósi, Á., Towards the automatic observation and coding of simple behaviours in ethological experiments (2021) Proc. Of IEEE/SICE Intl. Symp. Of System Integration (SII), pp. 841-842; Vincze, D., Kovács, Sz., Niitsuma, M., Hashimoto, H., Korondi, P., Gacsi, M., Miklósi, Á., Ethologically inspired human-robot interaction interfaces (2012) Proc. Of the 2012 Joint Intl. Conf. Of Human-Centered Computer Environments, pp. 51-57",,,,"Institute of Electrical and Electronics Engineers Inc.","2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2021","12 July 2021 through 16 July 2021",,171450,,9781665441391,,,"English","IEEE ASME Int Conf Adv Intellig Mechatron AIM",Conference Paper,"Final","",Scopus,2-s2.0-85114963347
"Naeem M.A., Sarwar A., Humayun A., Waseem M.","57213518794;57346254800;57346739200;57346254900;","The Development of Robot Control via Virtual Reality for Safe Human-Robot Interaction",2021,"Pakistan Journal of Engineering and Applied Sciences","29",,,"72","80",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119440330&partnerID=40&md5=a0aa16544d907f6aa781aa2d8e36cc10","Department of Mechatronics & Control Engineering, University of Engineering and Technology, Lahore, Pakistan","Naeem, M.A., Department of Mechatronics & Control Engineering, University of Engineering and Technology, Lahore, Pakistan; Sarwar, A., Department of Mechatronics & Control Engineering, University of Engineering and Technology, Lahore, Pakistan; Humayun, A., Department of Mechatronics & Control Engineering, University of Engineering and Technology, Lahore, Pakistan; Waseem, M., Department of Mechatronics & Control Engineering, University of Engineering and Technology, Lahore, Pakistan","In Human-Robot Interaction (HRI) domain the prime focus is laid on the safety of the involved humans. High inertias, velocities and extensive work envelop, particularly of most of the currently used industrial robots, are reasons for the hazards associated with the interaction of humans with robots. Several strategies are being investigated for ensuring safety which includes lightweight robots (LWR), force-limiting control, start-stop monitoring, speed limiting mode, etc. These approaches often require a range of hardware peripherals to be incorporated within and around the robotic system. Alternatively, the use of Virtual Reality (VR) systems to interact with otherwise dangerous robots allows for contactless interaction. In this paper we present a Virtual Reality based arrangement that allows the interaction with the robot from distance, in a virtually created environment, to ensure human safety. Keeping in view the frequent requirements of changes in robot’s movement plan, the proposed scheme demonstrates the capability of programming these plans in the virtual environment as well. We have shown the results of controlling a real robot in VR and the root mean square error (RMSE) between the two joint angles of the real and the virtual robot is 2.61 degrees and 3.08 degrees. This leads to remote/tele-control of the robot which is one of the future demands considering the social impact of COVID-19. © 2021. Pakistan Journal of Engineering and Applied Sciences. All Rights Reserved.","Human-Robot Interaction; Robot Programming; Virtual Reality",,,,,,,,,,,,,,,,,"Jerčić, P., Hagelbäck, J., Lindley, C., An affective serious game for collaboration between humans and robots (2019) Entertainment Computing, 32, p. 100319. , [1]; Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications (2018) Mechatronics, 55, pp. 248-266. , [2]; Oyekan, J. O., Hutabarat, W., Tiwari, A., Grech, R., Aung, M. H., Mariani, M. P., Dupuis, C., The effectiveness of virtual environments in developing collaborative strategies between industrial robots and humans (2019) Robotics and Computer-Integrated Manufacturing, 55, pp. 41-54. , [3]; El Beheiry, M., Doutreligne, S., Caporal, C., Ostertag, C., Dahan, M., Masson, J. B., Virtual reality: beyond visualization (2019) Journal of molecular biology, 431 (7), pp. 1315-1321. , [4]; Ohtani, H., Tamura, Y., Kageyama, A., Ishiguro, S., Scientific visualization of plasma simulation results and device data in virtual-reality space (2011) IEEE Transactions on plasma Science, 39 (11), pp. 2472-2473. , [5]; Li, J., Liu, Q., Su, H., Virtual reality method of portal slewing crane based on WPF (2013) Advances in Mechanical Engineering, 5, p. 320757. , [6]; Xu, X., Wang, L., Li, Z., Yao, S., Fang, X., Modeling of submarine initial pipe-laying process and its real-time semi-physical virtual reality system (2018) Advances in Mechanical Engineering, 10 (1), p. 1687814017747734. , [7]; Usher, W., Klacansky, P., Federer, F., Bremer, P. T., Knoll, A., Yarch, J., Pascucci, V., A virtual reality visualization tool for neuron tracing (2017) IEEE transactions on visualization and computer graphics, 24 (1), pp. 994-1003. , [8]; Das, M. T., Dülger, L. C., Mathematical modelling, simulation and experimental verification of a scara robot (2005) Simulation Modelling Practice and Theory, 13 (3), pp. 257-271. , [9]; Wolfartsberger, J., Analyzing the potential of Virtual Reality for engineering design review (2019) Automation in Construction, 104, pp. 27-37. , [10]","Naeem, M.A.; Department of Mechatronics & Control Engineering, Pakistan; email: mahsan.naeem@uet.edu.pk",,,"University of Engineering and Technology, Lahore",,,,,19951302,,,,"English","Pak. J. Eng. Appl. Sci.",Article,"Final","",Scopus,2-s2.0-85119440330
"Pollmann K.","57128090300;","The modality card deck: Co-creating multi-modal behavioral expressions for social robots with older adults",2021,"Multimodal Technologies and Interaction","5","7","33","","",,1,"10.3390/mti5070033","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109365577&doi=10.3390%2fmti5070033&partnerID=40&md5=9fd66f78c27772e2283c8fcfc33e50f1","Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, Stuttgart, 70569, Germany","Pollmann, K., Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, Stuttgart, 70569, Germany","Robots have been proposed as intelligent technology that can support the independent living and health of older adults. While significant advances are being made regarding hardware and intelligent software to support autonomous actions of robots, less emphasis has been put on designing robot behavior that is comprehensible and pleasant for older adults. However, good usability and user experience are crucial factors for acceptance and long-term use. One way to actively engage older adults in behavioral design for social robots is participatory design. The Modality Card Deck is proposed, a tool that helps to engage older adults in human-robot interaction design process and participate in design decision for robot behavior. The cards guide the users towards creating ideas for design solutions which are detailed enough to be implemented by interaction designers and software developers. This paper provides a detailed description of the Modality Card Deck and presents an evaluation of the tool in the scope of a case study. In the case study, the card deck was used in participatory design workshops with older adults to develop multi-modal robot behaviors for the Pepper robot and a quiz game application. After describing the procedure of the case study, the workshop results and learnings about working with the Modality Card Deck and older adults are presented. © 2021 by the author. Licensee MDPI, Basel, Switzerland.","Aging and design; Human-robot interaction; Multi-modal robot behavior; Older users; Participatory design; Social robots",,,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV7941","Funding: This research was conducted as part of the NIKA project and funded by the the German Federal Ministry of Education and Research (BMBF 16SV7941).",,,,,,,,,,"Forlizzi, J., How robotic products become social products: An ethnographic study of cleaning in the home Proceedings of the 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 129-136. , Arlington, VA, USA, 10–12 March 2007; Tanaka, F., Ghosh, M., The implementation of care-receiving robot at an English learning school for children Proceedings of the 6th International Conference on Human-Robot Interaction, pp. 265-266. , Lausanne, Switzerland, 8–11 March 2011; Wada, K., Shibata, T., Living with seal robots—its sociopsychological and physiological influences on the elderly at a care house (2007) IEEE Trans. Robot, 23, pp. 972-980. , [CrossRef]; McArthur, L.Z., Baron, R.M., Toward an ecological theory of social perception (1983) Psychol. Rev, 90, p. 215. , [CrossRef]; Frederiks, A.D., Octavia, J.R., Vandevelde, C., Saldien, J., Towards Participatory Design of Social Robots (2019) Proceedings of the IFIP Conference on Human-Computer Interaction, pp. 527-535. , Paphos, Cyprus, 2–6 September Springer: Cham, Switzerland, 2019; Chen, K., Chan, A., Use or non-use of gerontechnology—A qualitative study (2013) Int. J. Environ. Res. Public Health, 10, pp. 4645-4666. , [CrossRef] [PubMed]; Künemund, H., Chancen und Herausforderungen assistiver Technik. Nutzerbedarfe und Technikakzeptanz im Alter (2015) Tatup-Z. FÜR Tech. Theor. Und Prax, 24, pp. 28-35. , [CrossRef]; Syrdal, D.S., Koay, K.L., Walters, M.L., Dautenhahn, K., A personalized robot companion?-The role of individual differences on spatial preferences in HRI scenarios Proceedings of the RO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication, pp. 1143-1148. , Jeju, Korea, 26–29 August 2007; Sanders, E.B.N., Stappers, P.J., Co-creation and the new landscapes of design (2008) Co-Design, 4, pp. 5-18. , [CrossRef]; Beimborn, M., Kadi, S., Köberer, N., Mühleck, M., Spindler, M., Focusing on the human: Interdisciplinary reflections on ageing and technology (2016) Ageing and Technology, p. 311. , Transcript Verlag: Bielefeld, Germany; Merkel, S., Kucharski, A., Participatory Design in Gerontechnology: A systematic literature review (2018) Gerontologist, 59, pp. e16-e25. , [CrossRef] [PubMed]; Östlund, B., Olander, E., Jonsson, O., Frennert, S., STS-inspired design to meet the challenges of modern ageing. Welfare technology as a tool to promote user driven innovations or another way to keep older users hostage? (2015) Technol. Forecast. Soc. 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Proceedings of the 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1-6. , New Delhi, India, 14–18 October 2019; Tian, L., Carreno-Medrano, P., Allen, A., Sumartojo, S., Mintrom, M., Coronado Zuniga, E., Venture, G., Kulic, D., Redesigning Human-Robot Interaction in Response to Robot Failures: A Participatory Design Methodology Proceedings of the Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, , Yokohama, Japan, 8–13 May 2021; Association for Computing Machinery: New York, NY, USA, 2021","Pollmann, K.; Fraunhofer Institute for Industrial Engineering IAO, Nobelstr. 12, Germany; email: kathrin.pollmann@iao.fraunhofer.de",,,"MDPI AG",,,,,24144088,,,,"English","Multimodal Tech. Inter.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85109365577
"Onnasch L., Roesler E.","35111634600;57217215081;","A Taxonomy to Structure and Analyze Human–Robot Interaction",2021,"International Journal of Social Robotics","13","4",,"833","849",,6,"10.1007/s12369-020-00666-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087380676&doi=10.1007%2fs12369-020-00666-5&partnerID=40&md5=6a11d6a7f8cf4039bb6480612266a6c6","Humboldt-Universität zu Berlin, Rudower Chaussee 18, Berlin, 12489, Germany; Technische Universität Berlin, Marchstr. 12, Berlin, 10587, Germany","Onnasch, L., Humboldt-Universität zu Berlin, Rudower Chaussee 18, Berlin, 12489, Germany; Roesler, E., Technische Universität Berlin, Marchstr. 12, Berlin, 10587, Germany","Robotic systems are one of the core technologies that will shape our future. Robots already change our private and professional life by working together with humans in various domains. Evoked by this increasing trend, great variability exists in terms of robots and interaction scenarios. This has boosted research regarding shaping factors of human–robot interaction (HRI). Nevertheless, this variety hinders the comparability and the generalizability of insights. What is needed for efficient research is a structured approach that allows the analysis of superordinate attributes, making previous HRI research comparable, revealing research gaps and thus guiding future research activities. Based on the review of previous HRI frameworks we developed a new HRI taxonomy that (1) takes into account the human, the robot, the interaction and the context of the HRI, (2) is applicable to various HRI scenarios and (3) provides predefined categories to enable structured comparisons of different HRI scenarios. A graphical representation of the taxonomy, including all possible classifications, eases the application to specific HRI scenarios. To demonstrate the use and value of this taxonomy, it is applied to different studies in HRI in order to identify possible reasons for contrasting results. The exemplified applications of the taxonomy underline its value as a basis for reviews and meta-analyses. Moreover, the taxonomy offers a framework for future HRI research as it offers guidance for systematic variations of distinctive variables in HRI. © 2020, The Author(s).","Collaboration; Collaborative robots; HRI framework; Human–robot interaction; Robot morphology; Taxonomy","Agricultural robots; Taxonomies; Core technology; Graphical representations; Professional life; Research activities; Robot interactions; Robotic systems; Structured approach; Systematic variation; Human robot interaction",,,,,"Bundesanstalt für Arbeitsschutz und Arbeitsmedizin, BAuA: F2369","Open Access funding provided by Projekt DEAL. This article is based on an earlier published short essay in baua:Fokus, an online publication platform of the Federal Institute for Occupational Safety and Health (Germany) []. The HRI taxonomy was developed as part of the project “Mensch-Roboter-Teams (F2369)”, funded by the Federal Institute for Occupational Safety and Health (BAuA), and together with HFC Human-Factors-Consult GmbH.",,,,,,,,,,"Rosheim, M., (2006) Leonardo’s lost robots, , Springer, Berlin; Čapek, K., (1920) Rossum’s universal robots, p. 1. , Prague, London; Miller, R.K., Robots in industry (1989) Industrial robot handbook. VNR Competitive Manufacturing Series, pp. 1-9. , Springer, Boston; Hinds, P.J., Roberts, T.L., Jones, H., Whose job is it anyway? 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Baua, , https://doi.org/10.21934/baua:fokus20160630, Berlin","Onnasch, L.; Humboldt-Universität zu Berlin, Rudower Chaussee 18, Germany; email: linda.onnasch@hu-berlin.de",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85087380676
"Fujiwara E., Rodrigues M.D.S., Gomes M.K., Wu Y.T., Suzuki C.K.","25636959900;57226645674;57205297181;55319555800;7102649763;","Identification of Hand Gestures Using the Inertial Measurement Unit of a Smartphone: A Proof-of-Concept Study",2021,"IEEE Sensors Journal","21","12","9398661","13916","13923",,,"10.1109/JSEN.2021.3071669","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103874556&doi=10.1109%2fJSEN.2021.3071669&partnerID=40&md5=fc3064ec2a3ea20a5ab1d1715facbda4","Laboratory of Photonic Materials and Devices, School of Mechanical Engineering, University of Campinas, Campinas, 13083-860, Brazil","Fujiwara, E., Laboratory of Photonic Materials and Devices, School of Mechanical Engineering, University of Campinas, Campinas, 13083-860, Brazil; Rodrigues, M.D.S., Laboratory of Photonic Materials and Devices, School of Mechanical Engineering, University of Campinas, Campinas, 13083-860, Brazil; Gomes, M.K., Laboratory of Photonic Materials and Devices, School of Mechanical Engineering, University of Campinas, Campinas, 13083-860, Brazil; Wu, Y.T., Laboratory of Photonic Materials and Devices, School of Mechanical Engineering, University of Campinas, Campinas, 13083-860, Brazil; Suzuki, C.K., Laboratory of Photonic Materials and Devices, School of Mechanical Engineering, University of Campinas, Campinas, 13083-860, Brazil","Assessing the hand postures is crucial to implement gesture-based user-computer interfaces for controlling robots and assistive devices. Apart from data gloves and optical tracking, techniques such as surface electromyography and force myography provide a straightforward, non-invasive way to estimate poses and intentions through the forearm muscles assessment. However, most of the myography systems rely on bulky, dedicated hardware with arrays of electrodes or force probes. Therefore, this work introduces the smartphone as an alternative for identifying gestures: with the mobile device attached to the forearm, the embedded inertial measurement unit detects muscular contractions produced during the transitions between postures, yielding signatures in acquired waveforms. After computing the correlation of measured and template patterns, a competitive layer votes the class with greater probability and identifies the gesture. Prior characterization studies evaluated the effect of smartphone placement and forearm orientation in the sensor response, revealing that the IMU signatures are repeatable and robust to positioning deviations. Next, using 10-fold cross-validation, the system discerned four gestures (fist, open hand, wave in, and wave out) performed by six volunteers in ten repetitions, providing 96.6% and 94.1% average accuracies for self-calibration and inter-participant analyses, respectively. The smartphone figures as a ubiquitous and straightforward alternative for assessing gestures, with further applications in human-robot interaction and assistive technologies. © 2001-2012 IEEE.","Biomedical measurement; force myography; human-computer interaction; smartphone; user interfaces","Electromyography; Human robot interaction; Muscle; Smartphones; User interfaces; 10-fold cross-validation; Assistive technology; Characterization studies; Dedicated hardware; Inertial measurement unit; Muscular contraction; Surface electromyography; User-computer interfaces; Gesture recognition",,,,,"Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP: 2017/25666-2; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES; Conselho Nacional de Desenvolvimento Científico e Tecnológico, CNPq","Manuscript received February 8, 2021; revised March 16, 2021; accepted April 4, 2021. Date of publication April 7, 2021; date of current version June 14, 2021. This work was supported in part by the São Paulo Research Foundation (FAPESP) under Grant 2017/25666-2, in part by the Conselho Nacional de Desenvolvimento Cientifico e Tecnológico (CNPq), and in part by the Coordenação de Aperfeiçoamento de Pesoal de Nível Superior (CAPES) under Finance Code 001. The associate editor coordinating the review of this article and approving it for publication was Dr. Edward Sazonov. (Corresponding author: Eric Fujiwara.) The authors are with the Laboratory of Photonic Materials and Devices, School of Mechanical Engineering, University of Campinas, Campinas 13083-860, Brazil (e-mail: fujiwara@fem.unicamp.br; suzuki@fem.unicamp.br). Digital Object Identifier 10.1109/JSEN.2021.3071669",,,,,,,,,,"Meattini, R., Benatti, S., Scarcia, U., De Gregorio, D., Benini, L., Melchiorri, C., An sEMG-based human-robot interface for robotic hands using machine learning and synergies (2018) IEEE Trans. Compon., Packag., Manuf. 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"Wu B.-X., Yang C.-G., Zhong J.-P.","57222328292;57204881979;57222322264;","Research on Transfer Learning of Vision-based Gesture Recognition",2021,"International Journal of Automation and Computing","18","3",,"422","431",,,"10.1007/s11633-020-1273-9","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102309061&doi=10.1007%2fs11633-020-1273-9&partnerID=40&md5=285fb2a2b047cc1fb573fce6f78b13ca","College of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China; Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442, China; Foshan Newthinking Intelligent Technology Company Ltd., Foshan, 528231, China","Wu, B.-X., College of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China; Yang, C.-G., College of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China, Foshan Newthinking Intelligent Technology Company Ltd., Foshan, 528231, China; Zhong, J.-P., Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442, China","Gesture recognition has been widely used for human-robot interaction. At present, a problem in gesture recognition is that the researchers did not use the learned knowledge in existing domains to discover and recognize gestures in new domains. For each new domain, it is required to collect and annotate a large amount of data, and the training of the algorithm does not benefit from prior knowledge, leading to redundant calculation workload and excessive time investment. To address this problem, the paper proposes a method that could transfer gesture data in different domains. We use a red-green-blue (RGB) Camera to collect images of the gestures, and use Leap Motion to collect the coordinates of 21 joint points of the human hand. Then, we extract a set of novel feature descriptors from two different distributions of data for the study of transfer learning. This paper compares the effects of three classification algorithms, i.e., support vector machine (SVM), broad learning system (BLS) and deep learning (DL). We also compare learning performances with and without using the joint distribution adaptation (JDA) algorithm. The experimental results show that the proposed method could effectively solve the transfer problem between RGB Camera and Leap Motion. In addition, we found that when using DL to classify the data, excessive training on the source domain may reduce the accuracy of recognition in the target domain. © 2021, The Author(s).","gesture recognition; joint distribution adaptation (JDA); Leap Motion; red-green-blue (RGB) Camera; Transfer learning","Cameras; Deep learning; Learning algorithms; Learning systems; Social robots; Support vector machines; Transfer learning; Classification algorithm; Different distributions; Different domains; Feature descriptors; Joint distributions; Learning performance; Transfer problems; Vision-Based Gesture Recognition; Gesture recognition",,,,,"2018IT100322; 2019B1515120076; National Natural Science Foundation of China, NSFC: 61811 530281, 61861136009, U20A20200; Fundamental Research Funds for the Central Universities","This work was supported by National Nature Science Foundation of China (NSFC) (Nos. U20A20200, 61811 530281, and 61861136009), Guangdong Regional Joint Foundation (No. 2019B1515120076), Fundamental Research for the Central Universities, and in part by the Foshan Science and Technology Innovation Team Special Project (No. 2018IT100322).",,,,,,,,,,"Rautaray, S.S., Agrawal, A., Vision based hand gesture recognition for human computer interaction: A survey (2015) Artificial Intelligence Review, 43 (1), pp. 1-54; Wachs, J.P., Kölsch, M., Stern, H., Edan, Y., Vision-based hand-gesture applications (2011) Communications of the ACM, 54 (2), pp. 60-71; Weichert, F., Bachmann, D., Rudak, B., Fisseler, D., Analysis of the accuracy and robustness of the Leap Motion controller (2013) Sensors, 13 (5), pp. 6380-6393; Chen, C.L.P., Liu, Z.L., Broad learning system: An effective and efficient incremental learning system without the need for deep architecture (2018) IEEE Transactions on Neural Networks and Learning Systems, 29 (1), pp. 10-24; Yang, L., Song, S.J., Chen, C.L.P., Transductive transfer learning based on broad learning system (2018) In Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Miyazaki, Japan, pp. 912-917. , https://doi.org/10.1109/SMC.2018.00162; 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Marin, G., Dominio, F., Zanuttigh, P., Hand gesture recognition with jointly calibrated Leap Motion and depth sensor (2016) Multimedia Tools and Applications, 75 (22), pp. 14991-15015; Long, M.S., Wang, J.M., Ding, G.G., Sun, J.G., Yu, P.S., Transfer feature learning with joint distribution adaptation (2013) In Proceedings of IEEE International Conference on Computer Vision, Sydney, Australia, pp. 2200-2207. , https://doi.org/10.1109/ICCV.2013.274; Sun, Q., Chattopadhyay, R., Panchanathan, S., Ye, J.P., A two-stage weighting framework for multi-source domain adaptation (2011) Proceedings of the 24Th International Conference on Neural Information Processing Systems, pp. 505-513. , https://doi.org/10.5555/2986459.2986516, Granada, Spain; Jang, Y.H., Lee, H., Hwang, S.J., Shin, J., (2019) Learning What and Where to Transfer, , https://arxiv.org/abs/1905.05901, [Online], Available","Yang, C.-G.; College of Automation Science and Engineering, China; email: cyang@ieee.org",,,"Chinese Academy of Sciences",,,,,14768186,,,,"English","Int. J. Autom. Comput.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85102309061
"Cheng Y., Yi P., Liu R., Dong J., Zhou D., Zhang Q.","57224515634;23993138800;56706689400;55278050700;13105639000;56383216300;","Human-robot Interaction Method Combining Human Pose Estimation and Motion Intention Recognition",2021,"Proceedings of the 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021",,,"9437772","958","963",,,"10.1109/CSCWD49262.2021.9437772","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107743916&doi=10.1109%2fCSCWD49262.2021.9437772&partnerID=40&md5=b15cd0462058761a0922d24b25ca5b84","Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian, China; Dalian University and Dalian University of Technology, Dalian, China","Cheng, Y., Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian, China; Yi, P., Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian, China; Liu, R., Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian, China; Dong, J., Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian, China; Zhou, D., Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian, China; Zhang, Q., Dalian University and Dalian University of Technology, Dalian, China","Although human pose estimation technology based on RGB images is becoming more and more mature, most of the current mainstream methods rely on depth camera to obtain human joints information. These interaction frameworks are affected by the infrared detection distance so that they cannot well adapt to the interaction scene of different distance. Therefore, the purpose of this paper is to build a modular interactive framework based on RGB images, which aims to alleviate the problem of high dependence on depth camera and low adaptability to distance in the current human-robot interaction (HRI) framework based on human body by using advanced human pose estimation technology. To enhance the adaptability of the HRI framework to different distances, we adopt optical cameras instead of depth cameras as acquisition equipment. Firstly, the human joints information is extracted by a human pose estimation network. Then, a joints sequence filter is designed in the intermediate stage to reduce the influence of unreasonable skeletons on the interaction results. Finally, a human intention recognition model is built to recognize the human intention from reasonable joints information, and drive the robot to respond according to the predicted intention. The experimental results show that our interactive framework is more robust in the distance than the framework based on depth camera and is able to achieve effective interaction under different distances, illuminations, costumes, customers, and scenes. © 2021 IEEE.","human pose estimation; human-robot interaction; intention recognition","Cameras; Interactive computer systems; Man machine systems; Motion estimation; Acquisition equipments; Effective interactions; Human pose estimations; Human robot Interaction (HRI); Infrared detection; Interaction framework; Intermediate stage; Motion intention recognition; Social robots",,,,,"National Natural Science Foundation of China, NSFC; Dalian Science and Technology Innovation Fund: 2020JJ25CY001; Special Project of Central Government for Local Science and Technology Development of Hubei Province: 2021JH6/10500140","This work was supported in part by the Key Program of NSFC (Grant No.U1908214), Special Project of Central Government Guiding Local Science and Technology Development (Grant No. 2021JH6/10500140), Program for the Liaoning Distinguished Professor, Program for Innovative Research Team in University of Liaoning Province and Dalian University, and in part by the Science and Technology Innovation Fund of Dalian (Grant No. 2020JJ25CY001).",,,,,,,,,,"Mandal, F.B., Nonverbal communication in humans (2014) Journal of Human Behaviour in the Social Environment, 24 (4), pp. 417-421; Wang, L., Gao, R.X., Váncza, J., Krüger, J., Wang, X.V., Makris, S., Chryssolouris, G., Symbiotic human-robot collaborative assembly (2019) Cirp Annals-Manufacturing Technology, 68 (2), pp. 701-726; Fankhauser, P., Michael, B., Diego, R., Ralf, K., Marco, H., Roland, Y.S., Kinect v2 for mobile robot navigation: Evaluation and modelling (2015) 2015 International Conference on Advanced Robotics (ICAR). Istanbul, pp. 388-394; De Carli, D., Hohert, E., Parker, C.A., Zoghbi, S., Leonard, S., Croft, E., Bicchi, A measuring intent in human-robot cooperative manipulation (2009) 2009 Ieee International Workshop on Haptic Audio Visual Environments and Games, pp. 159-163. , Lecco; Zhu, C., Sun, W., Sheng, W., Wearable sensors based human intention recognition in smart assisted living systems (2008) 2008 International Conference on Information and Automation. Changsha, pp. 954-959; Wang, Z., Mülling, K., Deisenroth, M.P., Ben Amor, H., Vogt, D., Scholkopf, B., Probabilistic movement modeling for intention inference in human-robot interaction (2013) The International Journal of Robotics Research, 32 (7), pp. 841-858; Elfring, J., Molengraft De, R.Van., Steinbuch, M., Learning intentions for improved human motion prediction (2014) Robotics and Autonomous Systems, 62 (4), pp. 591-602; Petković, T., Marković, I., Petrović, I., Human intention recognition in flexible robotized warehouses based on markov decision processes (2017) Iberian Robotics Conference, pp. 629-640. , Seville; Liu, T., Wang, J., Meng, M.Q.H., Human robot cooperation based on human intention inference (2017) Ieee International Conference on Robotics & Biomimetics, , Zhuhai; Yu, X., He, W., Li, Y., Xue, C., Li, J., Zou, J., Yang, C., Bayesian estimation of human impedance and motion intention for human-robot collaboration (2019) Ieee Transactions on Cybernetics; Sigalas, M., Baltzakis, H., Trahanias, P., Gesture recognition based on arm tracking for human-robot interaction (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5424-5429. , Piscataway; Li, X., Human-robot interaction based on gesture and movement recognition (2020) Signal Processing: Image Communication, 81, p. 115686; Luo, X., Amighetti, A., Zhang, D., A human-robot interaction for a mecanum wheeled mobile robot with real-time 3d two-hand gesture recognition (2019) Journal of Physics: Conference Series, 1267, p. 012056; Koppula, H.S., Saxena, A., Anticipating human activities using object affordances for reactive robotic response (2015) Ieee Transactions on Pattern Analysis and Machine Intelligence, 38 (1), pp. 14-29; Canal, G., Escalera, S., Angulo, C., A real-time human-robot interaction system based on gestures for assistive scenarios (2017) Computer Vision and Image Understanding, 149, pp. 65-77; Li, K., Wu, J., Zhao, X., Tan, M., Real-time human-robot interaction for a service robot based on 3d human activity recognition and human-mimicking decision mechanism (2018) 2018 Ieee 8th Annual International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 498-503. , Tianjin; Mazhar, O., Navarro, B., Ramdani, S., Passama, R., Cherubini, A., A real-time human-robot interaction framework with robust background invariant hand gesture detection (2019) Robotics and Computer-Integrated Manufacturing, 60, pp. 34-48; (1988) GB10000-88 Human Dimensions of Chinese Adults[S], , Chinese Academy of Standardization and Information Classified Coding, Beijing: Standard Press of China; Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., Long-term recurrent convolutional networks for visual recognition and description (2017) Proceedings of the Ieee Conference on Computer Vision and Pattern Recognition. Boston, pp. 2625-2634; Zhou, X., Huang, Q., Sun, X., Xue, X., Wei, Y., Towards 3d human pose estimation in the wild: A weakly-supervised approach (2017) Proceedings of the Ieee International Conference on Computer Vision, pp. 398-407. , Honolulu","Yi, P.; Key Laboratory of Advanced Design and Intelligent Computing, China; email: istrrr@163.com
Zhang, Q.; Dalian University and Dalian University of TechnologyChina; email: zhangq@dlut.edu.cn","Shen W.Barthes J.-P.Luo J.Shi Y.Zhang J.",,"Institute of Electrical and Electronics Engineers Inc.","24th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021","5 May 2021 through 7 May 2021",,169216,,9781728165974,,,"English","Proc. IEEE Int. Conf. Comput. Support. Coop. Work Des., CSCWD",Conference Paper,"Final","",Scopus,2-s2.0-85107743916
"Huang J., Pham D.T., Li R., Qu M., Wang Y., Kerin M., Su S., Ji C., Mahomed O., Khalil R., Stockton D., Xu W., Liu Q., Zhou Z.","57221062957;7203009688;57008468700;57211002643;57190976906;57210210205;7402029829;25625875000;57222365528;8889105300;7004670741;57221159460;55577742400;55538054700;","An experimental human-robot collaborative disassembly cell",2021,"Computers and Industrial Engineering","155",,"107189","","",,4,"10.1016/j.cie.2021.107189","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102481783&doi=10.1016%2fj.cie.2021.107189&partnerID=40&md5=06c21b0780e68722f9979fdf3a6cfe5b","Department of Mechanical Engineering, School of Engineering, the University of Birmingham, Birmingham, B15 2TT, United Kingdom; School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China; Recoturbo Limited, Doncaster, DN5 8PT, United Kingdom; Homeodynamic Autonomy Limited, Leicestershire, LE17 4AD, United Kingdom; School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, 430070, China","Huang, J., Department of Mechanical Engineering, School of Engineering, the University of Birmingham, Birmingham, B15 2TT, United Kingdom; Pham, D.T., Department of Mechanical Engineering, School of Engineering, the University of Birmingham, Birmingham, B15 2TT, United Kingdom; Li, R., School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, 430070, China; Qu, M., Department of Mechanical Engineering, School of Engineering, the University of Birmingham, Birmingham, B15 2TT, United Kingdom; Wang, Y., Department of Mechanical Engineering, School of Engineering, the University of Birmingham, Birmingham, B15 2TT, United Kingdom; Kerin, M., Department of Mechanical Engineering, School of Engineering, the University of Birmingham, Birmingham, B15 2TT, United Kingdom; Su, S., Department of Mechanical Engineering, School of Engineering, the University of Birmingham, Birmingham, B15 2TT, United Kingdom; Ji, C., Department of Mechanical Engineering, School of Engineering, the University of Birmingham, Birmingham, B15 2TT, United Kingdom; Mahomed, O., Recoturbo Limited, Doncaster, DN5 8PT, United Kingdom; Khalil, R., Homeodynamic Autonomy Limited, Leicestershire, LE17 4AD, United Kingdom; Stockton, D., Homeodynamic Autonomy Limited, Leicestershire, LE17 4AD, United Kingdom; Xu, W., School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China; Liu, Q., School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China; Zhou, Z., School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China","Disassembly is the first operation in the remanufacture, repair and recycling of products that have reached the end of their service life. Both productivity and flexibility should be considered when using robots to carry out disassembly due to the complexities associated with the products returned for remanufacturing. Human-robot collaboration is a flexible semi-automated approach to mitigate against the effects of uncertainties in the frequency, quantity and quality of those End-of-Life (EoL) products. This paper presents a new experimental robotic disassembly cell comprising two collaborative robots and a human operator. The robots and the operator can work safely in tandem for individual, parallel or common disassembly tasks in a shared workspace. Active compliance control is employed by the collaborative robots to achieve complex disassembly tasks and safe human-robot interaction. The human operator communicates with the robots using a new method based on touch sensing combined with position control. The paper first provides a short literature review of robotic disassembly and human-robot collaboration focusing on disassembly. It then describes the collaborative disassembly demonstration cell. Finally, the paper details a case study highlighting the capabilities of the cell. The case study shows that the automation of complex disassembly tasks is feasible and has the potential to release people from tedious and repetitive work. © 2021 Elsevier Ltd","Disassembly; Human-robot collaboration; Remanufacturing; Robotic cell; Robotic disassembly","Compliance control; Cytology; Human robot interaction; Personnel; Position control; Robotics; Automated approach; Case-studies; Collaborative robots; Disassembly; Human operator; Human robots; Human-robot collaboration; Remanufacturing; Robotic cell; Robotic disassembly; Cells",,,,,"Innovate UK: 103667; Engineering and Physical Sciences Research Council, EPSRC: EP/N018524/1; Engineering and Physical Sciences Research Council, EPSRC","This work was supported by Innovate UK (Contract Ref. 103667), United Kingdom and the Engineering and Physical Sciences Research Council ( EPSRC) (Grant No. EP/N018524/1 ), United Kingdom.",,,,,,,,,,"(2020), https://new.abb.com/products/robotics/indus trial-robots/yumi, ABB. YuMi-IRB 14000; Alshibli, M., ElSayed, A., Kongar, E., Sobh, T., Gupta, S.M., A robust robotic disassembly sequence design using orthogonal arrays and task allocation (2019) Robotics, 8 (20), pp. 1-13; (2020), http://autoreman.altervista.org/index.html, AUTOREMAN project; Bänziger, T., Kunz, A., Wegener, K., Optimizing human–robot task allocation using a simulation tool based on standardized work descriptions (2018) Journal of Intelligent Manufacturing, pp. 1-14; Battini, D., Faccio, M., Persona, A., Sgarbossa, F., New methodological framework to improve productivity and ergonomics in assembly system design (2011) International Journal of industrial ergonomics, 41 (1), pp. 30-42; Battini, D., Delorme, X., Dolgui, A., Sgarbossa, F., Ergonomics in assembly line balancing based on energy expenditure: A multi-objective model (2015) International Journal of Production Research, 54 (3), pp. 824-845; (2020), https://www.press.bmwgroup.com/global/article/detail/T0209722EN/innovative-human-robot-cooperation-in-bmw-group-production?language=en, BMW Group. 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LBR iiwa. ^; Kwade, A., Diekmann, J., Recycling of lithium-ion batteries (2018), Springer; Lambert, A.F., Gupta, S.M., Disassembly modeling for assembly, maintenance, reuse and recycling (2016), CRC Press; Leggett, D.G., (2020), https://www.just-auto.com/news/audi-adds-human-robot-cooperation-in-manufacturing_id155721.aspx, Audi adds human-robot cooperation in manufacturing; Li, J., Barwood, M., Rahimifard, S., An automated approach for disassembly and recycling of electric vehicle components (2014) IEEE International Electric Vehicle Conference (IEVC), 2014, pp. 1-6; Li, J., Barwood, M., Rahimifard, S., Robotic disassembly for increased recovery of strategically important materials from electrical vehicles (2018) Robotics and Computer-Integrated Manufacturing, 50, pp. 203-212; Li, K., Liu, Q., Xu, W., Liu, J., Zhou, Z., Feng, H., Sequence planning considering human fatigue for human-robot collaboration in disassembly (2019) Procedia CIRP, 83, pp. 95-104; Li, R., Ji, C., Liu, Q., Zhou, Z., Pham, D., Huang, J., Su, S., Unfastening of hexagonal headed screws by a collaborative robot (2020) IEEE Transactions on Automation Science and Engineering, pp. 1-14; Liu, J., Zhou, Z., Pham, D.T., Xu, W., Ji, C., Liu, Q., Robotic disassembly sequence planning using enhanced discrete bees algorithm in remanufacturing (2017) International Journal of Production Research, 56 (9), pp. 3134-3151; Liu, Q., Liu, Z., Xu, W., Tang, Q., Zhou, Z., Pham, D.T., Human-robot collaboration in disassembly for sustainable manufacturing (2019) International Journal of Production Research, pp. 1-18; Matsas, E., Vosniakos, G.C., Batras, D., Effectiveness and acceptability of a virtual environment for assessing human-robot collaboration in manufacturing (2017) International Journal of Advanced Manufacturing Technology, 92, pp. 3903-3917; Maurtua, I., Fernandez, I., Tellaeche, A., Kildal, J., Susperregi, L., Ibarguren, A., Sierra, B., Natural multimodal communication for human-robot collaboration (2017) International Journal of Advanced Robotic Systems, 14 (4), pp. 1-12; Maurtua, I., Ibarguren, A., Kildal, J., Susperregi, L., Sierra, B., Human-robot collaboration in industrial applications: Safety, interaction and trust (2017) International Journal of Advanced Robotic Systems, 14 (4), pp. 1-10; Merdan, M., Lepuschitz, W., Meurer, T., (2010), pp. 1392-1397. , Vincze, M. 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"Aydin Y., Sirintuna D., Basdogan C.","56084865200;57218282723;6603706568;","Towards collaborative drilling with a cobot using admittance controller",2021,"Transactions of the Institute of Measurement and Control","43","8",,"1760","1773",,1,"10.1177/0142331220934643","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088586859&doi=10.1177%2f0142331220934643&partnerID=40&md5=5580853716fa56c222603a230ab9aed3","College of Engineering, Koc University, Istanbul, Turkey","Aydin, Y., College of Engineering, Koc University, Istanbul, Turkey; Sirintuna, D., College of Engineering, Koc University, Istanbul, Turkey; Basdogan, C., College of Engineering, Koc University, Istanbul, Turkey","In the near future, collaborative robots (cobots) are expected to play a vital role in the manufacturing and automation sectors. It is predicted that workers will work side by side in collaboration with cobots to surpass fully automated factories. In this regard, physical human-robot interaction (pHRI) aims to develop natural communication between the partners to bring speed, flexibility, and ergonomics to the execution of complex manufacturing tasks. One challenge in pHRI is to design an optimal interaction controller to balance the limitations introduced by the contradicting nature of transparency and stability requirements. In this paper, a general methodology to design an admittance controller for a pHRI system is developed by considering the stability and transparency objectives. In our approach, collaborative robot constrains the movement of human operator to help with a pHRI task while an augmented reality (AR) interface informs the operator about its phases. To this end, dynamical characterization of the collaborative robot (LBR IIWA 7 R800, KUKA Inc.) is presented first. Then, the stability and transparency analyses for our pHRI task involving collaborative drilling with this robot are reported. A range of allowable parameters for the admittance controller is determined by superimposing the stability and transparency graphs. Finally, three different sets of parameters are selected from the allowable range and the effect of admittance controllers utilizing these parameter sets on the task performance is investigated. © The Author(s) 2020.","admittance control; augmented reality; cobot; collaborative drilling; dynamic characterization; Physical human-robot interaction; stability; transparency","Augmented reality; Controllers; Ergonomics; Infill drilling; Manufacture; Stability; Transparency; Collaborative robots; Complex manufacturing; Dynamical characterization; General methodologies; Natural communication; Optimal interactions; Physical humanrobot interaction (phri); Stability requirements; Social robots",,,,,"EEEAG-117E645; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK","The authors thank to Professor V. Patoglu, Dr. O. Tokatli, and Dr. O. Caldiran for fruitful discussions made during the course of this study. The Scientific and Technological Research Council of Turkey (TUBITAK) supported this work under contract EEEAG-117E645. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Contract EEEAG-117E645.",,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human–robot collaboration (2018) Autonomous Robots, 42 (5), pp. 957-975; Akgun, G., Cetin, A.E., Kaplanoglu, E., Exoskeleton design and adaptive compliance control for hand rehabilitation (2020) Transactions of the Institute of Measurement and Control, 42 (3), pp. 493-502; Aydin, Y., Tokatli, O., Patoglu, V., Basdogan, C., (2017) Fractional order admittance control for physical human-robot interaction, pp. 257-262. , IEEE World Haptics Conference (WHC), Munich, In; Aydin, Y., Tokatli, O., Patoglu, V., Basdogan, C., Stable physical human-robot interaction using fractional order admittance control (2018) IEEE Transactions on Haptics, 11 (3), pp. 464-475; 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In; Stürz, Y.R., Affolter, L.M., Smith, R.S., Parameter identification of the kuka LBR IIWA robot including constraints on physical feasibility (2017) IFAC-PapersOnLine, 50 (1), pp. 6863-6868; Tabatabaei, S.H., Zaeri, A.H., Vahedi, M., Design an impedance control strategy for a teleoperation system to perform drilling process during spinal surgery (2019) Transactions of the Institute of Measurement and Control, 41 (10), pp. 2947-2956; Tavakoli, M., Patel, R.V., Moallem, M., A haptic interface for computer-integrated endoscopic surgery and training (2006) Virtual Reality, 9 (2), pp. 160-176; Tokatli, O., Patoglu, V., Using fractional order elements for haptic rendering (2018) Robotics Research: Volume 1, pp. 373-388. , Bicchi A., Burgard W., (eds), Cham, Springer International Publishing, In:, (eds; Tosun, F.E., Patoglu, V., Necessary and sufficient conditions for the passivity of impedance rendering with velocity-sourced series elastic actuation (2020) IEEE Transactions on Robotics, 36 (3), pp. 757-772; Tsuji, T., Morasso, P.G., Goto, K., Ito, K., Human hand impedance characteristics during maintained posture (1995) Biological Cybernetics, 72 (6), pp. 475-485; Tsumugiwa, T., Yokogawa, R., Yoshida, K., (2004) Stability analysis for impedance control of robot for human-robot cooperative task system, pp. 3883-3888. , IEEE/RSJ International Conference on Intelligent Robots and Systems, In; Weir, D.W., Colgate, J.E., Stability of haptic displays (2008) Haptic Rendering: Foundations, Algorithms and Applications, pp. 123-156. , Lin M.C., Otaduy M.A., (eds), CRC Press, In:, (eds; Willaert, B., Franken, M., Brussel, H.V., Poorten, E.B.V., (2011) On the use of shunt impedances versus bounded environment passivity for teleoperation systems, pp. 2111-2117. , IEEE International Conference on Robotics and Automation, In; Wojtara, T., Uchihara, M., Murayama, H., Human-robot collaboration in precise positioning of a three-dimensional object (2009) Automatica, 45 (2), pp. 333-342","Aydin, Y.; College of Engineering, Turkey; email: yaydin@ku.edu.tr",,,"SAGE Publications Ltd",,,,,01423312,,TICOD,,"English","Trans Inst Meas Control",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85088586859
"Badr A.A., Abdul-Hassan A.K.","57223101011;55293743800;","Estimating age in short utterances based on multi-class classification approach",2021,"Computers, Materials and Continua","68","2",,"1713","1729",,,"10.32604/cmc.2021.016732","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104850306&doi=10.32604%2fcmc.2021.016732&partnerID=40&md5=7f36d5b69db9f4af9b7ea4fe52ed6efb","College of Managerial and Financial Sciences, Imam Ja'afar Al-Sadiq University, Salahaddin, Iraq; Department of Computer Science, University of Technology, Baghdad, Iraq","Badr, A.A., College of Managerial and Financial Sciences, Imam Ja'afar Al-Sadiq University, Salahaddin, Iraq, Department of Computer Science, University of Technology, Baghdad, Iraq; Abdul-Hassan, A.K., Department of Computer Science, University of Technology, Baghdad, Iraq","Age estimation in short speech utterances finds many applications in daily life like human-robot interaction, custom call routing, targeted marketing, user-profiling, etc. Despite the comprehensive studies carried out to extract descriptive features, the estimation errors (i.e. years) are still high. In this study, an automatic system is proposed to estimate age in short speech utterances without depending on the text as well as the speaker. Firstly, four groups of features are extracted from each utterance frame using hybrid techniques and methods. After that, 10 statistical functionals are measured for each extracted feature dimension. Then, the extracted feature dimensions are normalized and reduced using the Quantile method and the Linear Discriminant Analysis (LDA) method, respectively. Finally, the speaker's age is estimated based on a multi-class classification approach by using the Extreme Gradient Boosting (XGBoost) classifier. Experiments have been carried out on the TIMIT dataset to measure the performance of the proposed system. The Mean Absolute Error (MAE) of the suggested system is 4.68 years, and 4.98 years, the RootMean Square Error (RMSE) is 8.05 and 6.97, respectively, for female andmale speakers. The results show a clear relative improvement in terms of MAE up to 28% and 10% for female and male speakers, respectively, in comparison to related works that utilized the TIMIT dataset. © 2021 Tech Science Press. All rights reserved.","LDA; Quantile normalization; Speaker age estimation; Statistical functionals; TIMIT dataset; XGBoost","Classifiers; Discriminant analysis; Errors; Learning systems; Estimation errors; Feature dimensions; Hybrid techniques; Linear discriminant analysis; Mean absolute error; Multi-class classification; Root mean square errors; Targeted marketing; Human robot interaction",,,,,,,,,,,,,,,,"Shivakumar, P. G., Li, M., Dhandhania, V., Narayanan, S. S., Simplified and supervised i-vector modeling for speaker age regression (2014) Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, pp. 4833-4837. , Florence, Italy; Li, M., Jung, C. S., Han, K. J., Combining five acoustic level modeling methods for automatic speaker age and gender recognition (2010) Proc. 11th Annual Conf. of the Int. Speech Communication Association, Makuhari, Chiba, Japan, pp. 2826-2829; Bahari, M. H., McLaren, M., Van-Hamme, H., Van-Leeuwen, D. A., Speaker age estimation using i-vectors (2014) Engineering Applications of Artificial Intelligence, 34, pp. 99-108. , C; Bahari, M. H., Van-Hamme, H., Speaker age estimation using hidden Markov model weight supervectors (2012) Proc. 11th Int. Conf. on Information Science, Signal Processing and Their Applications, pp. 517-521. , Montreal, QC, Canada; Singh, R., Raj, B., Baker, J., Short-term analysis for estimating physical parameters of speakers (2016) Proc. 4th Int. Conf. on Biometrics and Forensics, pp. 1-6. , Limassol, Cyprus; Kalluri, S. B., Vijayasenan, D., Ganapathy, S., A deep neural network based end to end model for joint height and age estimation from short duration speech (2019) Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pp. 6580-6584. , Brighton, United Kingdom; Kalluri, S. B., Vijayasenan, D., Ganapathy, S., Automatic speaker profiling from short duration speech data (2020) Speech Communication, 121 (May), pp. 16-28; Badr, A. A., Abdul-Hassan, A. K., A review on voice-based interface for human-robot interaction (2020) Iraqi Journal for Electrical and Electronic Engineering, 16 (2), pp. 91-102; Abdul-Hassan, A. K., Hadi, I. H., A proposed authentication approach based on voice and fuzzy logic (2020) Recent Trends in Intelligent Computing, Communication and Devices-Advances in Intelligent Systems and Computing, 1006, pp. 489-502. , Jain, S. Patnaik, F. P. Vl?adicescu and I. Sethi (Eds) Singapore: Springer; Sharma, G., Umapathy, K., Krishnan, S., Trends in audio signal feature extraction methods (2020) Applied Acoustics, 158, p. 107020; Barkana, B. D., Zhou, J., A new pitch-range based feature set for a speaker's age and gender classification (2015) Applied Acoustics, 98, pp. 52-61; Rao, K. S., Manjunath, K. E., (2017) Speech recognition using articulatory and excitation source features, , https://link.springer.com/book/10.1007/978-3-319-49220-9, Cham, Switzerland: Springer International Publishing AG, [Online]. Available; Paliwal, K. K., Spectral subband centroid features for speech recognition (1998) Proc. of the 1998 IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 2, pp. 617-620. , Seattle, Washington, USA; Chougule, S. V., Chavan, M. S., Speaker recognition in mismatch conditions: A feature level approach (2017) International Journal of Image, Graphics and Signal Processing, 9 (4), pp. 37-43; Sueur, J., Mel-frequency cepstral and linear predictive coefficients (2018) Sound Analysis and Synthesis with R, pp. 381-398. , 1st ed. Cham, Switzerland: Springer International Publishing AG; Mada-Sanjaya, W. S., Anggraeni, D., Santika, I. P., Speech recognition using linear predictive coding (LPC) and adaptive neuro-fuzzy (ANFIS) to control 5 DoF arm robot (2018) Journal of Physics: Conference Series, 1090, p. 12046; Kim, J. C., Rao, H., Clements, M. A., Investigating the use of formant based features for detection of affective dimensions in speech (2011) Proc. 4th Int. Conf., Affective Computing and Intelligent Interaction, pp. 369-377. , Memphis, TN, USA; Khulageand, A. A., Analysis of speech under stress using linear techniques and non-linear techniques for emotion recognition system (2012) Computer Science & Information Technology, 2, pp. 285-294; Bolstad, B. M., Irizarry, R. A., Åstrand, M., Speed, T. P., A comparison of normalization methods for high density oligonucleotide array data based on variance and bias (2003) Bioinformatics, 19 (2), pp. 185-193; Pan, M., Zhang, J., Quantile normalization for combining geneexpression datasets (2018) Biotechnology & Biotechnological Equipment, 32 (3), pp. 751-785; Tharwat, A., Gaber, T., Ibrahim, A., Hassanien, A. E., Linear discriminant analysis: A detailed tutorial (2017) Ai Communications, 30, pp. 169-190; Chen, T., Guestrin, C., XGBoost: A scalable tree boosting system (2016) Proc. of the ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 785-794. , San Francisco California USA; Fan, J., Wang, X., Wu, L., Zhou, H., Zhang, F., Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China (2018) Energy Conversion and Management, 164 (January), pp. 102-111; Abdurrahman, G., Sintawati, M., Implementation of xgboost for classification of parkinson's disease (2020) Journal of Physics: Conference Series, 1538 (1), p. 12024; Ghahremani, P., Nidadavolu, P. S., Chen, N., Villalba, J., Povey, D., End-to-end deep neural network age estimation (2018) Proc. of the Annual Conf. of the Int. Speech Communication Association, pp. 277-281. , Hyderabad, India; Grzybowska, J., Kacprzak, S., Speaker age classification and regression using i-vectors (2016) Proc. of the Annual Conf. of the Int. Speech Communication Association, pp. 1402-1406. , San Francisco, California, USA; Garofolo, J. S., Lamel, L. F., Fisher, W. M., Fiscus, J. G., Pallett, D. S., (1993) TIMIT acoustic-phonetic continuous speech corpus LDC93S1, , https://nvlpubs.nist.gov/nistpubs/Legacy/IR/nistir4930.pdf, USA, Philadelphia: Linguistic Data Consortium, [Online]. Available","Badr, A.A.; College of Managerial and Financial Sciences, Iraq; email: cs.19.53@grad.uotechnology.edu.iq",,,"Tech Science Press",,,,,15462218,,,,"English","Comput. Mater. Continua",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85104850306
"Denis J., Plante J.-S., Girard A.","57210786289;8618063200;57225722923;","Low-level force-control of mr-hydrostatic actuators",2021,"IEEE Robotics and Automation Letters","6","2","9369880","3849","3856",,,"10.1109/LRA.2021.3063972","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102296678&doi=10.1109%2fLRA.2021.3063972&partnerID=40&md5=4cf7f608f3b367d8a31eadf015634580","Department of Mechanical Engineering, Université de Sherbrooke, Sherbrooke, QC  J1L 1V7, Canada","Denis, J., Department of Mechanical Engineering, Université de Sherbrooke, Sherbrooke, QC  J1L 1V7, Canada; Plante, J.-S., Department of Mechanical Engineering, Université de Sherbrooke, Sherbrooke, QC  J1L 1V7, Canada; Girard, A., Department of Mechanical Engineering, Université de Sherbrooke, Sherbrooke, QC  J1L 1V7, Canada","Precise and high-fidelity force control is critical for new generations of robots that interact with humans and unknown environments. Mobile robots, such as wearable devices and legged robots, must also be lightweight to accomplish their function. Hydrostatic transmissions have been proposed as a promising strategy for meeting these two challenging requirements. In previous publications, it was shown that using magnetorheological (MR) actuators coupled with hydrostatic transmissions provides high power density and great open-loop human-robot interactions. Still, the open-loop force fidelity at low and high frequencies are decreased by the transmission's dynamics and by nonlinear friction. This letter compares control strategies for MR-hydrostatic actuator systems to increase its torque fidelity, defined as the bandwidth (measured vs desired torque reference) and transparency (minimizing the undesired forces reflected to the end effector when backdriving the robot). Four control approaches are developed and compared experimentally: (1) Open-loop control with friction compensation; (2) non-collocated pressure feedback; (3) collocated pressure feedback; (4) LQGI state feedback. A dither strategy is also implemented to smoothen ball screw friction. Results show that approaches (1), (2) and (3) can increase the performances but are facing compromises, while approach (4) can simultaneously improve all metrics. These results show the potential of using control schemes for improving the force control performance of robots using tethered architectures, addressing issues such as transmission dynamics and friction. © 2016 IEEE.","backdrivability; ball screw; force control; high bandwidth; hydrostatic transmission; magnetorheological clutch; Wearable robotics","Agricultural robots; Ball screws; Biofeedback; End effectors; Force control; Friction; Hydraulics; Hydrostatic pressure; State feedback; Transmissions; Control performance; Control strategies; Friction compensation; Hydrostatic actuators; Hydrostatic transmission; Low and high frequencies; Magneto-rheological; Transmission dynamics; Human robot interaction",,,,,"Ministère de l'Économie, de la Science et de l'Innovation - Québec, MESI; Natural Sciences and Engineering Research Council of Canada, NSERC; Fonds Québécois de la Recherche sur la Nature et les Technologies, FQRNT; Fonds de recherche du Québec – Nature et technologies, FRQNT","Manuscript received October 6, 2020; accepted February 14, 2021. Date of publication March 4, 2021; date of current version March 31, 2021. This letter was recommended for publication by Associate Editor J. D. Brown and Editor J.-H. Ryu upon evaluation of the reviewers’ comments. This work was supported in part by the Fonds québécois de la recherche sur la nature et les technologies (FRQNT), in part by the Ministère de l’Économie, de la Science et de l’Innovation (MESI), Exonetik Inc., and in part by the Natural Sciences and Engineering Research Council of Canada (NSERC). (Corresponding author: Jeff Denis.) The authors are with the Department of Mechanical Engineering, Université de Sherbrooke, Sherbrooke, QC J1L 1V7, Canada (e-mail: jeff.denis@usherbrooke.ca; jean-sebastien.plante@usherbrooke.ca; alx87grd@gmail.com).",,,,,,,,,,"Carignan, C., Cleary, K., Closed-loop force control for haptic simulation of virtual environments (2000) Haptics-e: Electron. J. Haptics Res, 1, pp. 1-14. , Jan; Buerger, S.P., Hogan, N., Novel actuation methods for high force haptics (2010) In Adv. Haptics, , https://www.intechopen.com/books/advancesin-haptics/novel-actuation-methods-for-high-force-haptics, M. H. Zadeh, Ed IntechOpen; Zinn, M., Roth, B., Khatib, O., Salisbury, J.K., A new actuation approach for human friendly robot design (2004) Int. J. Robot. Res., 23 (4-5), pp. 379-398. , Apr; Whitney, J.P., ChenMars, T.J., K.Hodgins, J., A hybrid hydrostatic transmission and human-safe haptic telepresence robot (2016) In Proc. Ieee Int. Conf. Robot. Automat, pp. 690-695. , Stockholm, Sweden: IEEE, May; Burkhard Et Al., N., A rolling-diaphragm hydrostatic transmission for remote MR-guided needle insertion (2017) In Proc. Ieee Int. Conf. Robot. Automat, pp. 1148-1153. , Singapore, Singapore: IEEE, May; Schwarm, E., Gravesmill, K.M., Whitney, J.P., A floating-piston hydrostatic linear actuator and remote-direct-drive 2-DOF gripper (2019) In Proc. Int. Conf. Robot. Automat, pp. 7562-7568. , May; Bolignari, M., Fontana, M., Design and experimental characterization of a high performance hydrostatic transmission for robot actuation (2020) Meccanica, 55, pp. 1169-1179. , Mar; Khazoom, C., Caillouette, P., Girard, A., Plante, J.-S., A supernumerary robotic leg powered by magnetorheological actuators to assist human locomotion (2020) Ieee Robot. Automat. Lett., 5 (4), pp. 5143-5150. , Oct; Khazoom, C., Véronneau, C., Bigué, J.-P.L., Grenier, J., Girard, A., Plante, J.-S., Design and control of a multifunctional ankle exoskeleton powered by magnetorheological actuators to assist walking, jumping, and landing (2019) Ieee Robot. Automat. Lett., 4 (3), pp. 3083-3090. , Jul; Viau, J., Chouinard, P., Bigue, J.-P.L., Julio, G., F.Michaud, Plante, J.-S., Tendon-driven manipulator actuated by magnetorheological clutches exhibiting both high-power and soft motion capabilities (2017) IEEE/ASME Trans. Mechatronics, 22 (1), pp. 561-571. , Feb; Véronneau Et Al., C., Multifunctional remotely actuated 3-DOF supernumerary robotic arm based on magnetorheological clutches and hydrostatic transmission lines (2020) Ieee Robot. Automat. Lett., 5 (2), pp. 2546-2553. , Apr; Veronneau, C., Lucking Bigue, J.-P., Lussier-Desbiens, A., Plante, J.-S., A high-bandwidth back-drivable hydrostatic power distribution system for exoskeletons based on magnetorheological clutches (2018) Ieee Robot. Automat. Lett., 3 (3), pp. 2592-2599. , Jul; Juvinall, R.C., Marshek, K.M., (2017) Fundamentals of Machine Component Design, 6th Ed., , Hoboken, NJ: John Wiley & Sons; Wang, C., Whitney, J.P., (2020) Series Elastic Force Control for Soft Robotic Fluid Actuators, , http://arxiv.org/abs/2004.01269; Seok, S., Wang, A., Otten, D., Kim, S., Actuator design for high force proprioceptive control in fast legged locomotion (2012) In Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst, pp. 1970-1975. , Vilamoura-Algarve, Portugal: IEEE, Oct; Olivier, J., Ortlieb, A., Bouri, M., Clavel, R., Bleuler, H., A ballscrew driven motorized hip orthosis (2014) Trans. Control Mech. Syst., 3, pp. 100-108. , Jul; Chen, J.S., Dwang, I.C., A ballscrew drive mechanism with piezoelectric nut for preload and motion control (2000) Int. J. Mach. Tools Manufacture, 40 (4), pp. 513-526. , Mar; Eppinger, S., Seering, W., Three dynamic problems in robot force control (1992) Ieee Trans. Robot. Automat., 8 (6), pp. 751-758. , Dec; Veronneau, C., Denis, J., Lebel, L.-P., Denninger, M., Girard, A., Plante, S., A lightweight force-controllable wearable arm based on magnetorheological-hydrostatic actuators (2019) In Proc. Int. Conf. Robot. Automat, pp. 4018-4024. , Jul; Gordon, D.J., Erkorkmaz, K., Accurate control of ball screw drives using pole-placement vibration damping and a novel trajectory prefilter (2013) Precis. Eng., 37 (2), pp. 308-322. , Apr; Dumanli, A., Sencer, B., Optimal high-bandwidth control of ballscrew drives with acceleration and jerk feedback (2018) Precis. Eng., 54, pp. 254-268. , Oct; Yadmellat, P., Kermani, M.R., Adaptive control of a hysteretic magnetorheological robot actuator (2016) IEEE/ASME Trans. Mechatronics, 21 (3), pp. 1336-1344. , Jun; Aström, K.J., Murray, R.M., (2008) Feedback Systems: An Introduction for Scientists and Engineers, , Princeton: Princeton Univ. Press","Denis, J.; Department of Mechanical Engineering, Canada; email: jeff.denis@usherbrooke.ca",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85102296678
"Vicentini F.","23394397200;","Collaborative Robotics: A Survey",2021,"Journal of Mechanical Design, Transactions of the ASME","143","4","040802","","",,12,"10.1115/1.4046238","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100338716&doi=10.1115%2f1.4046238&partnerID=40&md5=fc6a9282b991b03c22c0c142b20c01dc","Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing, National Research Council of Italy, Milan, 20133, Italy","Vicentini, F., Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing, National Research Council of Italy, Milan, 20133, Italy","Collaborative robotics is an umbrella term that conveys the general idea of proximity between machines and humans for some useful tasks in a shared space, with a range of options for timing (continuously, synchronously, alternately, etc.). While this type of human robot interaction is fairly common in many fields of robotics, this survey is dedicated to the technology and situations that are typical of industrial applications. The current state-of-the-art in collaborative robotics for industrial applications is the result of a long legacy of research and development in actuation principles and mechanisms from 2000s onward, together with an abundant literature about improving control performances, intuitive interaction modes, and perception with sensors. Safety technology and standardization is discussed according to the principles of risk estimation and the typical strategies for risk reduction that are suitable for different classes of robot systems are used in collaborative applications. The organization of applications (planning, role assignment, optimization of resources) has a major impact on both the quality and the safety of collaborative robotics solutions, with most of the literature focused on investigating those performance metrics that combine human factors and production figures. The survey is intended to delineate an interpretation key for the design of collaborative robotics solution that explains the relationship among all relevant factors: actuation, control, safety, physical interaction, usability, and productivity. © 2020 by ASME.","collaborative robotics; Robotic systems","Industrial research; Industrial robots; Research and development management; Risk perception; Robot programming; Robotics; Safety engineering; Surveys; Actuation principles; Collaborative application; Control performance; Intuitive interaction; Performance metrics; Physical interactions; Production figures; Research and development; Human robot interaction",,,,,,,,,,,,,,,,"Wang, L., Gao, R., Váncza, J., Krüger, J., Wang, X., Makris, S., Chryssolouris, G., Symbiotic Human-Robot Collaborative Assembly (2019) CIRP Ann, 68 (2), pp. 701-726; Krüger, J., Lien, T. K., Verl, A., Cooperation of Human and Machines in Assembly Lines (2009) CIRP Ann, 58 (2), pp. 628-646; An, C. H., Hollerbach, J. 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"Prati E., Peruzzini M., Pellicciari M., Raffaeli R.","57219292991;34971929300;26428392600;10739418500;","How to include User eXperience in the design of Human-Robot Interaction",2021,"Robotics and Computer-Integrated Manufacturing","68",,"102072","","",,9,"10.1016/j.rcim.2020.102072","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092106872&doi=10.1016%2fj.rcim.2020.102072&partnerID=40&md5=962685b5312f5cb31b927082029abfc4","Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Modena, Italy; Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, Italy","Prati, E., Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Modena, Italy; Peruzzini, M., Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Modena, Italy; Pellicciari, M., Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, Italy; Raffaeli, R., Department of Sciences and Methods for Engineering, University of Modena and Reggio Emilia, Reggio Emilia, Italy","In recent years Human-Robot Collaboration (HRC) has become a strategic research field, considering the emergent need for common collaborative execution of manufacturing tasks, shared between humans and robots within the modern factories. However, the majority of the research focuses on the technological aspects and enabling technologies, mainly directing to the robotic side, and usually neglecting the human factors. This work deals with including the needs of the humans interacting with robots in the design in human-robot interaction (HRI). In particular, the paper proposes a user experience (UX)-oriented structured method to investigate the human-robot dialogue to map the interaction with robots during the execution of shared tasks, and to finally elicit the requirements for the design of valuable HRI. The research adopted the proposed method to an industrial case focused on assembly operations supported by collaborative robots and AGVs (Automated Guided Vehicles). A multidisciplinary team was created to map the HRI for the specific case with the final aim to define the requirements for the design of the system interfaces. The novelty of the proposed approach is the inclusion of typically interaction design tools focusing in the analysis of the UX into the design of the system components, without merely focusing on the technological issues. Experimental results highlighted the validity of the proposed method to identify the interaction needs and to drive the interface design. © 2020 Elsevier Ltd","Human Factors; Human-Robot Collaboration; Human-Robot Interaction; Industrial Ergonomics; User eXperience","Agricultural robots; Automatic guided vehicles; Industrial research; Machine design; Man machine systems; User experience; User interfaces; Automated guided vehicles; Collaborative execution; Enabling technologies; Human robot Interaction (HRI); Human-robot collaboration; Multi-disciplinary teams; Technological aspects; User experiences (ux); Social robots",,,,,,,,,,,,,,,,"Rüßmann, M., Industry 4.0: The future of productivity and growth in manufacturing industries (2015) Bost. Consult. Gr., 9 (1), pp. 54-89; Goodrich, M.A., Schultz, A.C., Human–robot interaction: a survey (2008) Found. Trends® Human–Computer Interact., 1 (3), pp. 203-275; Dautenhahn, K., (2013), Soegaard R.F. Dam M. Ed. The Encyclopedia of Human-Computer Interaction Aarhus, Denmark; Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., Sabanovic, S., (2019), Human-Robot Interaction – An Introduction. 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Ergon., 12 (3), pp. 83-95; Bauer, A., Wollherr, D., Buss, M., Human-robot collaboration: A survey (2008) International Journal of Humanoid Robotics, 5 (1), pp. 47-66; Wang, L., Symbiotic human-robot collaborative assembly (2019) CIRP Ann, 68 (2), pp. 701-726; (2012), International Organization for Standardization, “UNI EN ISO 10218-1:2012 - Robots and robotic devices — Safety requirements for industrial robots — Part 1: Robots,”; Goodrich, M.A., Schultz, A.C., Human-Robot Interaction: A Survey (2007) Found. Trends® Human–Computer Interact. Human-Computer Interact., 1 (3), pp. 203-275; Mendes, N., Ferrer, J., Vitorino, J., Safeea, M., Neto, P., Human behavior and hand gesture classification for smart human-robot interaction (2017) Procedia Manuf, 11, pp. 91-98; Neto, P., Simão, M., Mendes, N., Safeea, M., Gesture-based human-robot interaction for human assistance in manufacturing (2019) Int. J. Adv. Manuf. Technol., 101 (1-4), pp. 119-135; Villani, V., Capelli, B., Secchi, C., Fantuzzi, C., Sabattini, L., Humans interacting with multi-robot systems: a natural affect-based approach (2020) Auton. Robots, 44 (3), pp. 601-616; Peruzzini, M., Grandi, F., Pellicciari, M., Exploring the potential of Operator 4.0 interface and monitoring (2020) Comput. Ind. Eng., 139; Villani, V., Sabattini, L., Secchi, C., Fantuzzi, C., A framework for affect-based natural human-robot interaction (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 1038-1044; Matsas, E., Vosniakos, G.-C., Batras, D., Prototyping proactive and adaptive techniques for human-robot collaboration in manufacturing using virtual reality (2018) Robot. Comput. Integr. Manuf., 50, pp. 168-180; Oyekan, J.O., The effectiveness of virtual environments in developing collaborative strategies between industrial robots and humans (2019) Robot. Comput. Integr. 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Humans, 35 (4), pp. 438-449; Olsen, D.R., Goodrich, M.A., “Metrics for Evaluating Human-Robot Interactions.”; Peruzzini, M., Grandi, F., Pellicciari, M., Benchmarking of tools for user experience analysis in industry 4.0 (2017) Procedia Manuf, 11, pp. 806-813; (1999), International Organization for Standardization, “ISO 13407,”; Still, B., Crane, K., Fundamentals of user-centered design: A practical approach (2017), CRC Press; Yu, M., (2017), “Understanding User Interactions Through Link Analysis in Social Networks,”; Kalbach, J., Mapping experiences: A complete guide to creating value through journeys, blueprints, and diagrams (2016), O'Reilly Media, Inc; Hackos, J.T., Redish, J., (1998) User and task analysis for interface design, 1. , Wiley New York; Bartneck, C., Belpaeme, T., Eyssel, F., Kanda, T., Keijsers, M., Šabanović, S., (2020) Human-Robot Interaction – An Introduction, , Cambridge University Press","Peruzzini, M.; Department of Engineering “Enzo Ferrari”, Italy; email: margherita.peruzzini@unimore.it",,,"Elsevier Ltd",,,,,07365845,,RCIME,,"English","Rob Comput Integr Manuf",Article,"Final","",Scopus,2-s2.0-85092106872
"Liu S., Fang Z., Liu J., Tang K., Luo J., Yi J., Hu X., Wang Z.","56084745200;57215664141;57222735916;57194323322;55968039400;56660205300;55345595400;35216891400;","A Compact Soft Robotic Wrist Brace With Origami Actuators",2021,"Frontiers in Robotics and AI","8",,"614623","","",,2,"10.3389/frobt.2021.614623","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103882505&doi=10.3389%2ffrobt.2021.614623&partnerID=40&md5=d592b79254101b7e8a0243ed3e823b9d","Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China; Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, China; Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China; Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China","Liu, S., Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China, Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, China, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China; Fang, Z., Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China, Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, China, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China; Liu, J., Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China, Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, China, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China; Tang, K., Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China, Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, China, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China; Luo, J., Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China, Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, China, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China; Yi, J., Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China, Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, China, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China; Hu, X., Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China; Wang, Z., Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China, Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, China, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China","Wrist disability caused by a series of diseases or injuries hinders the patient’s capability to perform activities of daily living (ADL). Rehabilitation devices for the wrist motor function have gained popularity among clinics and researchers due to the convenience of self-rehabilitation. The inherent compliance of soft robots enabled safe human-robot interaction and light-weight characteristics, providing new possibilities to develop wearable devices. Compared with the conventional apparatus, soft robotic wearable rehabilitation devices showed advantages in flexibility, cost, and comfort. In this work, a compact and low-profile soft robotic wrist brace was proposed by directly integrating eight soft origami-patterned actuators on the commercially available wrist brace. The linear motion of the actuators was defined by their origami pattern. The extensions of the actuators were constrained by the brace fabrics, deriving the motions of the wrist joint, i.e., extension/flexion, ulnar/radial deviation. The soft actuators were made of ethylene-vinyl acetate by blow molding, achieving mass-production capability, low cost, and high repeatability. The design and fabrication of the soft robotic wrist brace are presented in this work. The experiments on the range of motion, output force, wearing position adaptivity, and performance under disturbance have been carried out with results analyzed. The modular soft actuator approach of design and fabrication of the soft robotic wrist brace has a wide application potential in wearable devices. © Copyright © 2021 Liu, Fang, Liu, Tang, Luo, Yi, Hu and Wang.","origami; recovery device; soft actuator; soft robot; wearable",,,,,,"National Natural Science Foundation of China, NSFC: 51975268; Science, Technology and Innovation Commission of Shenzhen Municipality: ZDSYS20200811143601004","This work was supported in part by the Science, Technology and Innovation Commission of Shenzhen Municipality under grant no. ZDSYS20200811143601004, NSFC Grant 51975268, Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern Marine Science and Engineering Guangdong Laboratory (Shenzhen), SUSTECH-AISONO Joint Lab, and SUSTECH-Taizhou Innovation Fund.",,,,,,,,,,"Al-Fahaam, H., Davis, S., Nefti-Meziani, S., Wrist rehabilitation exoskeleton robot based on pneumatic soft actuators (2016) International conference for students on applied engineering, , Newcastle, United Kingdom, October 20–21, 2016, in; Andrikopoulos, G., Nikolakopoulos, G., Manesis, S., Design and development of an exoskeletal wrist prototype via pneumatic artificial muscles (2015) Meccanica, 50, pp. 2709-2730; Ang, B.W.K., Yeow, C., (2019) Design and characterization of a 3D printed soft robotic wrist sleeve with 2 DoF for stroke rehabilitation, pp. 577-582. , Seoul, South Korea, RoboSoft; Baiden, D., Ivlev, O., Human-robot-interaction control for orthoses with pneumatic soft-actuators — concept and initial trails (2013) Proceedings of the 13th international conference on rehabilitation robotics, pp. 1-6. , Seattle, WA, June 24–26, 2013; Bartlett, N.W., Lyau, V., Raiford, W.A., Holland, D., Gafford, J.B., Ellis, T.D., A soft robotic orthosis for wrist rehabilitation, ASME (2015) J. 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Neuroengineering Rehabil, 11, pp. 1-7; Gull, M.A., Bai, S., Bak, T., A review on design of upper limb exoskeletons (2020) Robotics, 9, p. 16; Guo, Y., Chen, X., Wang, Z., Environmental insulation of 3D printable origamic soft actuators (2019) IEEE-CYBER 2019, , Suzhou, China, July 29–August 2, 2019, IEEE, in; Hendricks, H.T., van Limbeek, J., Geurts, A.C., Zwarts, M.J., Motor recovery after stroke: a systematic review of the literature (2002) Arch. Phys. Med. Rehabil, 83 (11), pp. 1629-1637; Krakauer, J.W., Arm function after stroke: from physiology to recovery (2005) Semin. Neurol, 25 (4), pp. 384-395; Kwon, J., Park, J.-H., Ku, S., Jeong, Y., Paik, N.-J., Park, Y.-L., A soft wearable robotic ankle-foot-orthosis for post-stroke patients (2019) IEEE Robot. Autom. Lett, 4 (3), pp. 2547-2552; Li, S., Vogt, D.M., Rus, D., Wood, R.J., Fluid-Driven origami-inspired artificial muscles (2017) Proc. Natl. Acad. Sci. 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Soc, 2015, pp. 3889-3892; O'Neill, C.T., Phipps, N.S., Cappello, L., Paganoni, S., Walsh, C.J., A soft wearable robot for the shoulder: design, characterization, and preliminary testing (2017) Proceedings of the 2017 International Conference on Rehabilitation Robotics, pp. 1672-1678. , London, United Kingdom, July 17–20, 2017; Ombregt, L., (2013) Applied anatomy of the wrist, thumb and hand, A system of orthopaedic medicine, pp. e102-e111. , 3rd Edn, London, United Kingdom, Churchill Livingstone; Paez, L., Agarwal, G., Paik, J., Design and analysis of a soft pneumatic actuator with origami shell reinforcement (2016) Soft Robotics, 3 (3), pp. 109-119; Palmer, A.K., Werner, F.W., Murphy, D., Glisson, R., Functional wrist motion: a biomechanical study (1985) J. 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Res, 126, pp. 153-159; Sasaki, D., Noritsugu, T., Takaiwa, M., Development of active support splint driven by pneumatic soft actuator (ASSIST) (2005) Proceedings of the 2005 IEEE international conference on robotics and automation, pp. 520-525. , Barcelona, Spain, April 18–22, 2005; Su, Y., Fang, Z., Zhu, W., Sun, X., Zhu, Y., Wang, H., A hybrid robotic gripper with high payload soft origamic actuators and proprioception (2020) Proceedings of the IEEE International Conference on Robotics and Automation, , Paris, France, May 31–June 4, 2020, in; Thalman, C., Artemiadis, P., A review of soft wearable robots that provide active assistance: trends, common actuation methods, fabrication, and applications (2020) Wearable Tech, 1 (E3); Tondu, B., Lopez, P., The McKibben muscle and its use in actuating robot‐arms showing similarities with human arm behaviour (1997) Ind. Robot, 24 (6), pp. 432-439; Xiloyannis, M., Cappello, L., Binh, K.D., Antuvan, C.W., Masia, L., Preliminary design and control of a soft exosuit for assisting elbow movements and hand grasping in activities of daily living (2017) J. Rehabil. Assistive Tech. Eng; Yao, J., Li, H., Cao, K., Chen, X., Zhou, P., Zhao, Y., Design and analysis of flexible wearable wrist power glove (2018) Jme, 54 (19), pp. 1-9; Yap, H.K., Lim, J.H., Nasrallah, F., Goh, J.C.H., Yeow, R.C.H., (2015) A soft exoskeleton for hand assistive and rehabilitation application using pneumatic actuators with variable stiffness, pp. 4967-4972. , Seattle, WA, ICRA; Yi, J., Chen, X., Song, C., Zhou, J., Liu, Y., Liu, S., Customizable three-dimensional-printed origami soft robotic joint with effective behavior shaping for safe interactions (2018) IEEE Trans. Robotics, 35 (1), pp. 114-123; Zhang, X., Chen, Y., Vertex-splitting on a diamond origami pattern (2019) ASME. J. Mech. Robotics. June, 11 (3), p. 031014; Zhou, J., Chen, X., Chang, U., Liu, Y., Chen, Y., Wang, Z., A grasping component mapping approach for soft robotic end-effector control (2019) IEEE Int. Conf. on Soft Robotics (RoboSoft), , Seoul, South Korea, April 14–18, 2019; Zhu, M., Adams, W., Polygerinos, P., Carpal tunnel syndrome soft relief device for typing applications (2017) Proceedings of the 2017 Design of Medical Devices Conference, , Minneapolis, MIC, October 31, 2017, V001T03A003, in","Wang, Z.; Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, China; email: Wangz@sustech.edu.cn
Wang, Z.; Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, China; email: Wangz@sustech.edu.cn
Wang, Z.; Department of Mechanical and Energy Engineering, China; email: Wangz@sustech.edu.cn",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85103882505
"Pasquali D., Gonzalez-Billandon J., Rea F., Sandini G., Sciutti A.","57216357588;57207198944;54386065400;35280694500;36957816700;","Magic iCub: A humanoid robot autonomously catching your lies in a card game",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444682","293","302",,2,"10.1145/3434073.3444682","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102773567&doi=10.1145%2f3434073.3444682&partnerID=40&md5=72b11abb03281b66a095ce77bb17266b","RBCS and ICT, Istituto Italiano di Tecnologia and DIBRIS, Università di Genova, Genova, Italy; RBCS, Istituto Italiano di Tecnologia and DIBRIS, Università di Genova, Genova, Italy; RBCS, Istituto Italiano di Tecnologia, Genova, Italy; CONTACT, Istituto Italiano di Tecnologia, Genova, Italy","Pasquali, D., RBCS and ICT, Istituto Italiano di Tecnologia and DIBRIS, Università di Genova, Genova, Italy; Gonzalez-Billandon, J., RBCS, Istituto Italiano di Tecnologia and DIBRIS, Università di Genova, Genova, Italy; Rea, F., RBCS, Istituto Italiano di Tecnologia, Genova, Italy; Sandini, G., RBCS, Istituto Italiano di Tecnologia, Genova, Italy; Sciutti, A., CONTACT, Istituto Italiano di Tecnologia, Genova, Italy","Games are often used to foster human partners' engagement and natural behavior, even when they are played with or against robots. Therefore, beyond their entertainment value, games represent ideal interaction paradigms where to investigate natural human-robot interaction and to foster robots' diffusion in the society. However, most of the state-of-the-art games involving robots, are driven with a Wizard of Oz approach. To address this limitation, we present an end-to-end (E2E) architecture to enable the iCub robotic platform to autonomously lead an entertaining magic card trick with human partners. We demonstrate that with this architecture a robot is capable of autonomously directing the game from beginning to end. In particular, the robot could detect in real-time when the players lied in the description of one card in their hands (the secret card). In a validation experiment the robot achieved an accuracy of 88.2% (against a chance level of 16.6%) in detecting the secret card while the social interaction naturally unfolded. The results demonstrate the feasibility of our approach and its effectiveness in entertaining the players and maintaining their engagement. Additionally, we provide evidence on the possibility to detect important measures of the human partner's inner state such as cognitive load related to lie creation with pupillometry in a short and ecological game-like interaction with a robot. © 2021 IEEE Computer Society. All rights reserved.","Cognitive load; Entertainment; Human-robot interaction; Magic; Pupillometry","Agricultural robots; Anthropomorphic robots; Entertainment; Man machine systems; Cognitive loads; Humanoid robot; Interaction paradigm; Pupillometry; Robotic platforms; Social interactions; State of the art; Wizard of Oz; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020: 804388",,,,,,,,,,,"The Musical Boat for A Drinking Party-The Book of Knowledge of Ingenious Mechanical Devices, , https://aljazaribook.com/en/2019/08/07/the-musical-boat-en/, [Accessed: 01-Oct-2020]; Kitano, H., Development of an Autonomous Quadruped Robot (1998) Science (80-. )., 18, pp. 7-18; Sabe, K., Development of entertainment robot and its future (2005) IEEE Symp. VLSI Circuits, Dig. Tech. Pap., 2005, pp. 2-5; Fujita, M., Digital creatures for future entertainment robotics (2000) Proc.-IEEE Int. Conf. Robot. Autom., 1, pp. 801-806. , April; Fujita, M., Kuroki, Y., Ishida, T., Doi, T.T., A small humanoid robot SDR-4X for entertainment applications (2003) IEEE/ASME Int. Conf. Adv. Intell. Mechatronics, AIM, 2, pp. 938-943. , Aim; Billard, A., Robota: Clever toy and educational tool (2003) Rob. Auton. Syst., 42 (3-4), pp. 259-269; Lin, C.Y., Tseng, C.K., Jo, P.C., A multi-functional entertaining and educational robot (2008) J. Intell. Robot. Syst. Theory Appl., 53 (4), pp. 299-330; Tim Laue, U.F., Birbach, O., An entertainment robot for playing interactive ball games (2014) Lect. Notes Comput. Sci., 2014, pp. 592-599. , January; Kozima, H., Michalowski, M.P., Nakagawa, C., Keepon: A playful robot for research, therapy, and entertainment (2009) Int. J. Soc. Robot., 1 (1), pp. 3-18; Fujita, M., Kageyama, K., Open architecture for robot entertainment (1997) Proc. Int. Conf. Auton. 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A machine learning system to detect lies during interactions (2019) Front Robot Al, 6. , Jul; DePaulo, B.M., Lindsay, J.J., Malone, B.E., Muhlenbruck, L., Charlton, K., Cooper, H., Cues to deception (2003) Psychol Bull, 129 (1), pp. 74-118; Dionisio, D.P., Granholm, E., Hillix, W.A., Perrine, W.F., Differentiation of deception using pupillary responses as an index of cognitive processing (2001) Psychophysiology, 38 (2), pp. 205-211. , Mar; Beatty, J., (1982) Task-Evoked Pupillary Responses, Processing Load, and the Structure of Processing Resources; Nakayama, M., Shimizu, Y., Frequency analysis of task evoked pupillary response and eye-movement (2004) Proceedings of the Eye Tracking Research & Applications Symposium on Eye Tracking Research & Applications-ETRA'2004, pp. 71-76; Tobii Pro Glasses 2; Beatty, J., Lucero-Wagoner, B., The pupillary system (2000) Handb, Psychophysiol, 2; Sweller, J., Ayres, P., Kalyuga, S., (2011) Cognitive Load Theory. Psychology of Learning and Motivation, 55. , Elsevier; Leppink, J., Cognitive load theory: Practical implications and an important challenge (2017) J. Taibah Univ. Med. Set, 12 (5), pp. 385-391; Mathot, S., Pupillometry: Psychology, physiology, and function (2018) J, Cogn, 1 (1). , Feb; Webb, A.K., Honts, C.R., Kircher, J.C., Bernhardt, P., Cook, A.E., Effectiveness of pupil diameter in a probable-lie comparison question test for deception (2009) Leg. Criminol Psychol, 14 (2), pp. 279-292. , Sep; Kassin, S.M., On the psychology of confessions: Does innocence put innocents at risk? (2005) Am. Psychol, 60 (3), pp. 215-228. , Apr; Mathot, S., Fabius, J., Van Heusden, E., Vander Stigcliel, S., Safe and sensible preprocessing and baseline correction of pupil-size data (2018) Bchav. Res. 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Rebus, ""Versione italiana dei big five markers di goldberg, "" 2006; Ferguson, C.J., Negy, C., Development of a brief screening questionnaire for histrionic personality symptoms (2014) Pcrs. Individ. Dif, 66, pp. 124-127; Jones, D.N., Paulhus, D.L., Introducing the Short Dark Triad (SD3): A Brief Measure of Dark Personality Traits (2014) Assessment, 21 (1), pp. 28-41; Bracco, F., Chiorri, C., Versione Italiana Del NASA-TLX; Shapiro, S., Wilk, M., An analysis of variance test for normality (complete samples) (2005) Z Bibliothckswcs. Bibliogr., 52 (6), pp. 311-319; D'Agostino, R.B., Belanger, A., D'Agostino, R.B., A suggestion for using powerful and informative tests of normality (1990) Am. Stat, 44 (4), p. 316. , Nov; One-Class Support Vector Machines; Wangwiwattana, C., Ding, X., Larson, E.C., PupilNet, Measuring task evoked pupillary response using commodity RGB tablet cameras (2018) Proc. ACM Interactive, Mobile, Wearable Ubiquitous Technol, 1 (4), pp. 1-26. , Jan; Klingner, J., (2010) Measuring Cognitive Load during Visual Task by Combining Pupillometry and Eye Tracking, , PhD. Diss., no., May; Aroyo, A.M., Can a humanoid robot spot a liar? (2018) IEEE-PAS 18th Int. Conf. Humanoid Robot",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS)","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167644,21672148,9781450382892,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85102773567
"McHenry N., Spencer J., Zhong P., Cox J., Amiscaray M., Wong K.C., Chamitoff G.","57193901455;57226382749;57221814866;57211267016;57226375555;15133563900;6507052058;","Predictive XR Telepresence for Robotic Operations in Space",2021,"IEEE Aerospace Conference Proceedings","2021-March",,"9438161","","",,2,"10.1109/AERO50100.2021.9438161","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100310534&doi=10.1109%2fAERO50100.2021.9438161&partnerID=40&md5=1d5028bc6838efda99446b45fe2c41ea","Texas Am University, Department of Aerospace Engineering, College Stationy, TX  77845, United States","McHenry, N., Texas Am University, Department of Aerospace Engineering, College Stationy, TX  77845, United States; Spencer, J., Texas Am University, Department of Aerospace Engineering, College Stationy, TX  77845, United States; Zhong, P., Texas Am University, Department of Aerospace Engineering, College Stationy, TX  77845, United States; Cox, J., Texas Am University, Department of Aerospace Engineering, College Stationy, TX  77845, United States; Amiscaray, M., Texas Am University, Department of Aerospace Engineering, College Stationy, TX  77845, United States; Wong, K.C., Texas Am University, Department of Aerospace Engineering, College Stationy, TX  77845, United States; Chamitoff, G., Texas Am University, Department of Aerospace Engineering, College Stationy, TX  77845, United States","This paper proposes a new method for robotic teleoperation scenarios with significant time delay utilizing Extended Reality (XR) technologies, specifically for Lunar base assembly and maintenance. Traditional teleoperation interfaces interpret commands sent through a desktop computer screen and are limited in providing spatial awareness cues. Additionally, the software often has difficulty in accounting for a time delay or providing intuitive techniques for human-robot interaction (HRI). This paper presents a new XR interface for robotic teleoperation using a predictive 3D simulated virtual environment developed using the SpaceCRAFT platform. SpaceCRAFT is a multiuser Virtual Reality (VR) and Augmented Reality (AR) systems engineering toolbox software that allows for worldwide collaboration on design and test of space systems. SpaceCRAFT provides the operator a virtual interface to the robot represented through three different states, a Virtual Command State (VCS), Predicted Current State (PCS), and Time Delayed State (TDS). To improve situational awareness and the PCS representation of the robot, SpaceCRAFT processes LIDAR and RGB camera data to create a constantly updating 3D point cloud representation of the remote environment. An experiment involving teleoperation of a 3D printed robotic manipulator was run, with a simulated time delay similar to that between the Earth and the Moon. Results demonstrate that the predictive approach provides an effective and flexible method of robot teleoperation that helps achieve successful task completion, even with a large time delay. © 2021 IEEE.",,"3D printers; Augmented reality; Human robot interaction; Industrial manipulators; Interface states; Manipulators; Moon; Moon bases; Personal computers; Remote control; Robotic assembly; Robotics; Software testing; Spacecraft; Time delay; Visual communication; Augmented reality systems; Human robot Interaction (HRI); Robot teleoperation; Robotic manipulators; Robotic teleoperation; Situational awareness; Spacecraft platforms; Teleoperation interface; Virtual reality",,,,,,,,,,,,,,,,"Van Keuren, D., Moon communication relay at the naval research laboratory (2020) History. Nasa. Gov, , https://history.nasa.gov/SP-4217/ch2.htm, [Accessed: 12-Oct-2020]; Keyes, B., Micire, M., Drury, J., Yanco, H.A., (2010) Improving Human-Robot Interaction through Interface Evolution, , 02; Hogan, R., McHenry, N., Coen, M., Morrell, B., Chamitoff, G., Spacecraft: A virtual reality sandbox environment (2017) Proceedings of the International Astronautical Congress, IAC, 2; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A., (2009) ROS: An Open-source Robot Operating System, 3. , 01; Bettens, A.M., Morrell, B., Coen, M., McHenry, N., Wu, X., Gibbens, P., Chamitoff, G., (2020) UnrealNavigation: Simulation Software for Testing SLAM in Virtual Reality; Minsky, M., Telepresence (1980) Omni Magazine, pp. 1-6. , June 1980, June; De, T., Mateo Sanguino, J., 50 years of rovers for planetary exploration: A retrospective review for future directions (2017) Robotics and Autonomous Systems, 94, pp. 172-185. , http://dx.doi.org/10.1016/j.robot.2017.04.020; Sheridan, T.B., Teleoperation, telerobotics and telepresence: A progress report (1995) Control Engineering Practice, 3 (2), pp. 205-214. , https://doi.org/10.1016/0967-0661(94)00078-U; Biesiadecki, J.J., Leger, P.C., Maimone, M.W., Tradeoffs between directed and autonomous driving on the mars exploration rovers (2007) International Journal of Robotics Research, 26 (1), pp. 91-104; Goswami, A., Vadakkepat, P., (2019) Humanoid Robotics: A Reference, , Dordrecht, The Netherlands: Springer; Martin-Barrio, A., Roldan, J.J., Terrile, S., Del Cerro, J., Barrientos, A., Application of immersive technologies and natural language to hyperredundant robot teleoperation (2020) Virtual Reality, 24 (3), pp. 541-555. , https://doi.org/10.1007/s10055-019-00414-9; Siciliano, B., Khatib, O., (2016) Springer Handbook of Robotics, , Berlin: Springer International Publishing; Milgram, P., Kishino, F., A taxonomy of mixed reality visual displays (1994) IEICE Trans. Information Systems, E77-D (12), pp. 1321-1329. , 12; Azuma, R.T., A survey of augmented reality (1997) Presence: Teleoperators and Virtual Environments, 6 (4), pp. 355-385; Azuma, R., Recent advances in proteomics recent advances in proteomics (2011) IEEE Computer Graphics and Applications, pp. 34-47. , December; Billinghurst, M., Clark, A., Lee, G., A survey of augmented reality (2014) Foundations and Trends in Human-Computer Interaction, 8 (2-3), pp. 73-272; Kim, K., Billinghurst, M., Bruder, G., Duh, H.B.L., Welch, G.F., Revisiting trends in augmented reality research: A review of the 2nd decade of ismar (2008-2017) (2018) IEEE Transactions on Visualization and Computer Graphics, 24 (11), pp. 2947-2962; Van Krevelen, D., Poelman, R., A survey of augmented reality technologies, applications and limitations (2010) International Journal of Virtual Reality, 9 (2), pp. 1-20; Zhang, T., McCarthy, Z., Jowl, O., Lee, D., Chen, X., Goldberg, K., Abbeel, P., Deep imitation learning for complex manipulation tasks from virtual reality teleoperation (2018) Proceedings-IEEE International Conference on Robotics and Automation, pp. 5628-5635; Burridge, R.R., Hambuchen, K.A., Using prediction to enhance remote robot supervision across time delay (2009) 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems; Wilde, M., Chan, M., Kish, B., (2020) Predictive Human-Machine Interface for Teleoperation of Air and Space Vehicles over Time Delay, pp. 1-14; Artigas, J., Balachandran, R., Riecke, C., Stelzer, M., Weber, B., Ryu, J.H., Albu-Schaeer, A., Kontur-2: Force-feedback teleoperation from the international space station (2016) Proceedings-IEEE International Conference on Robotics and Automation, pp. 1166-1173. , 2016-June; Yang, C., Wang, X., Li, Z., Li, Y., Su, C.Y., Teleoperation control based on combination ofwave variable and neural networks (2017) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47 (8), pp. 2125-2136; Balachandran, R., Ryu, J.-H., Jorda, M., Ott, C., Albu-Schaeer, A., (2020) Closing the Force Loop to Enhance Transparency in Time-delayed Teleoperation, pp. 10198-10204; Handa, A., Van Wyk, K., Yang, W., Liang, J., Chao, Y.-W., Wan, Q., Bircheld, S., Fox, D., (2020) DexPilot: Vision-Based Teleoperation of Dexterous Robotic Hand-Arm System, pp. 9164-9170; Beik-Mohammadi, H., Kerzel, M., Pleintinger, B., Hulin, T., Reisich, P., Schmidt, A., Pereira, A., Lii, N.Y., Model mediated teleoperation with a hand-arm exoskeleton in long time delays using reinforcement learning (2020) 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN); Bowman, D.A., Hodges, L.F., An evaluation of techniques for grabbing and manipulating remote objects in immersive virtual environments (1997) Proceedings of the 1997 Symposium on Interactive 3D graphics-SI3D '97; Wingrave, C.A., Haciahmetoglu, Y., Bowman, D.A., Overcoming world in miniature limitations by a scaled and scrolling wim (2006) 3D User Interfaces (3DUI'06), pp. 11-16. , Alexandria, VA, USA; Belousov, I.R., Chellali, R., Clapworthy, G.J., Virtual reality tools for internet robotics (2001) Proceedings-IEEE International Conference on Robotics and Automation, 2, pp. 1878-1883. , February; Chitta, S., Sucan, I., Cousins, S., Moveit [ros topics] (2012) IEEE Robotics Automation Magazine, 19 (1), pp. 18-19; Thomas, G., Howard, A.M., Williams, A.B., Moore-Alston, A., Multirobot task allocation in lunar mission construction scenarios (2005) 2005 IEEE International Conference on Systems, Man and Cybernetics, 1, pp. 518-523. , Waikoloa, HI; Diy-robotics-the largest knowledge base for industrial robotics (2020) DIY-Robotics, , https://diy-robotics.com/, [Accessed: 14-Aug-2020]",,,,"IEEE Computer Society","2021 IEEE Aerospace Conference, AERO 2021","6 March 2021 through 13 March 2021",,170491,1095323X,9781728174365,,,"English","IEEE Aerosp. Conf. Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85100310534
"Klimaszewski J., Władziński M.","55443956800;56246901600;","Human body parts proximity measurement using distributed tactile robotic skin",2021,"Sensors","21","6","2138","1","17",,2,"10.3390/s21062138","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102601216&doi=10.3390%2fs21062138&partnerID=40&md5=7e8d8779ce3ed7d2628dc5d925f27e0e","Warsaw University of Technology, Faculty of Mechatronics, Institute of Automatic Control and Robotics, A. Boboli 8 St, Warsaw, 02-525, Poland; Warsaw University of Technology, Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, A. Boboli 8 St, Warsaw, 02-525, Poland","Klimaszewski, J., Warsaw University of Technology, Faculty of Mechatronics, Institute of Automatic Control and Robotics, A. Boboli 8 St, Warsaw, 02-525, Poland; Władziński, M., Warsaw University of Technology, Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, A. Boboli 8 St, Warsaw, 02-525, Poland","Safety in human–machine cooperation is the current challenge in robotics. Safe human– robot interaction requires the development of sensors that detect human presence in the robot’s workspace. Detection of this presence should occur before the physical collision of the robot with the human. Human to robot proximity detection should be very fast, allowing machine elements deceleration to velocities safe for human–machine collision. The paper presents a new, low-cost design of distributed robotic skin, which allows real-time measurements of the human body parts proximity. The main advantages of the proposed solution are low cost of its implementation based on comb electrodes matrix and real-time operation due to fast and simple electronic design. The main contribution is the new idea of measuring the distance to human body parts by measuring the operating frequency of a rectangular signal generator, which depends on the capacity of the open capacitor. This capacitor is formed between the comb electrodes matrix and a reference plate located next to the matrix. The capacitance of the open capacitor changes if a human body part is in vicinity. The application of the developed device can be very wide. For example, in the field of cooperative robots, it can lead to the improvement of human–machine interfaces and increased safety of human–machine cooperation. The proposed construction can help to meet the increasing requirements for cooperative robots. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Distributed sensor; Electronic skin; Flexible electronics; Graphene nanoplatelets; Proximity sensor; Real-time; Robotic skin; Tactile sensor","Costs; Electrodes; Robotics; Cooperative robots; Distributed robotics; Machine interfaces; Operating frequency; Proximity detection; Real time measurements; Real-time operation; Rectangular signals; Social robots; human; robotics; touch; Human Body; Humans; Robotics; Touch",,,,,,,,,,,,,,,,"Klimaszewski, J., Janczak, D., Piorun, P., Tactile Robotic Skin with Pressure Direction Detection (2019) Sensors, 19, p. 4697. , [CrossRef]; Gerratt, A.P., Sommer, N., Lacour, S.P., Billard, A., Stretchable capacitive tactile skin on humanoid robot fingers—First experiments and results Proceedings of the 2014 IEEE-RAS International Conference on Humanoid Robots, pp. 238-245. , Madrid, Spain, 18–20 November 2014; [CrossRef]; Ulmen, J., Cutkosky, M., A robust, low-cost and low-noise artificial skin for human-friendly robots Proceedings of the 2010 IEEE International Conference on Robotics and Automation, pp. 4836-4841. , Anchorage, AK, USA, 3–7 May 2010; [CrossRef]; Gong, D., He, R., Yu, J., Zuo, G., A Pneumatic Tactile Sensor for Co-Operative Robots (2017) Sensors, 17, p. 2592. , [CrossRef]; Jamone, L., Natale, L., Metta, G., Sandini, G., Highly Sensitive Soft Tactile Sensors for an Anthropomorphic Robotic Hand (2015) IEEE Sens. J, 15, pp. 4226-4233. , [CrossRef]; Huang, Y., Cai, X., Kan, W., Qiu, S., Guo, X., Liu, C., Liu, P., A flexible dual-mode proximity sensor based on cooperative sensing for robot skin applications (2017) Rev. Sci. Instrum, 88, p. 085005. , [CrossRef] [PubMed]; Rim, Y.S., Bae, S.H., Chen, H., De Marco, N., Yang, Y., Recent Progress in Materials and Devices toward Printable and Flexible Sensors (2016) Adv. Mater, 28, pp. 4415-4440. , [CrossRef] [PubMed]; Wang, X., Dong, L., Zhang, H., Yu, R., Pan, C., Wang, Z.L., Recent Progress in Electronic Skin (2015) Adv. Sci, 2, p. 1500169. , [CrossRef]; Beckerle, P., Koiva, R., Kirchner, E.A., Bekrater-Bodmann, R., Dosen, S., Christ, O., Abbink, D.A., Lenggenhager, B., Feel-Good Robotics: Requirements on Touch for Embodiment in Assistive Robotics (2018) Front. Neurorobot, 12, p. 84. , [CrossRef]; Bartolozzi, C., Natale, L., Nori, F., Metta, G., Robots with a sense of touch (2016) Nat. Mater, 15, pp. 921-925. , [CrossRef] [PubMed]; Hughes, J., Culha, U., Giardina, F., Guenther, F., Rosendo, A., Iida, F., Soft Manipulators and Grippers: A Review (2016) Front. Robot. AI, 3, p. 69. , [CrossRef]; Fraleoni-Morgera, A., Cesini, I., Kumar, P., Oddo, C.M., Hydrothermally Grown ZnO Nanorods as Promising Materials for Low Cost Electronic Skin (2020) ChemNanoMat, 6, pp. 15-31. , [CrossRef]; Hoshi, T., Shinoda, H., Robot skin based on touch-area-sensitive tactile element Proceedings of the 2006 IEEE International Conference on Robotics and Automation (ICRA 2006), pp. 3463-3468. , Orlando, FL, USA, 15–19 May 2006; [CrossRef]; Salim, A., Lim, S., Review of Recent Inkjet-Printed Capacitive Tactile Sensors (2017) Sensors, 17, p. 2593. , [CrossRef]; Hirai, Y., Suzuki, Y., Tsuji, T., Watanabe, T., Tough, bendable and stretchable tactile sensors array for covering robot surfaces Proceedings of the 2018 IEEE International Conference on Soft Robotics (RoboSoft), pp. 276-281. , Livorno, Italy, 24–28 April 2018; [CrossRef]; Fritzsche, M., Elkmann, N., Schulenburg, E., Tactile sensing: A key technology for safe physical human robot interaction Proceedings of the 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 139-140. , Lausanne, Switzerland, 8–11 March 2011; [CrossRef]; Elkmann, N., Fritzsche, M., Schulenburg, E., Tactile Sensing for Safe Physical Human-Robot Interaction Proceedings of the International Conference on Advances in Computer-Human Interactions (ACHI), pp. 212-217. , Gosier, Guadeloupe, France, 23–28 February 2011; O’Neill, J., Lu, J., Dockter, R., Kowalewski, T., Practical, stretchable smart skin sensors for contact-aware robots in safe and collaborative interactions Proceedings of the 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 624-629. , Seattle, WA, USA, 26–30 May 2015; [CrossRef]; O’Neill, J., Lu, J., Dockter, R., Kowalewski, T., Stretchable, Flexible, Scalable Smart Skin Sensors for Robotic Position and Force Estimation (2018) Sensors, 18, p. 953. , [CrossRef] [PubMed]; Viry, L., Levi, A., Totaro, M., Mondini, A., Mattoli, V., Mazzolai, B., Beccai, L., Flexible Three-Axial Force Sensor for Soft and Highly Sensitive Artificial Touch (2014) Adv. 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J, 20, pp. 5519-5526. , [CrossRef]; Lee, H., Chang, S., Yoon, E., Dual-Mode Capacitive Proximity Sensor for Robot Application: Implementation of Tactile and Proximity Sensing Capability on a Single Polymer Platform Using Shared Electrodes (2009) IEEE Sens. J, 9, pp. 1748-1755. , [CrossRef]; Purcaru, D., Gordan, I.M., Purcaru, A., Study, testing and application of proximity sensors for experimental training on measurement systems Proceedings of the 2017 18th International Carpathian Control Conference (ICCC), pp. 263-266. , Sinaia, Romania, 28–31 May 2017; Tsuji, S., Kohama, T., Tactile and proximity sensor using self-capacitance measurement on curved surface Proceedings of the 2017 IEEE International Conference on Industrial Technology (ICIT), pp. 934-937. , Toronto, ON, Canada, 22–25 March 2017; Farrow, N., McIntire, L., Correll, N., Functionalized textiles for interactive soft robotics Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 5525-5531. , Singapore, 29 May–3 June 2017; [CrossRef]; Janczak, D., Słoma, M., Wróblewski, G., Mlozniak, A., Jakubowska, M., Screen-Printed Resistive Pressure Sensors Containing Graphene Nanoplatelets and Carbon Nanotubes (2014) Sensors, 14, pp. 17304-17312. , [CrossRef]","Klimaszewski, J.; Warsaw University of Technology, A. Boboli 8 St, Poland; email: jan.klimaszewski@pw.edu.pl",,,"MDPI AG",,,,,14248220,,,"33803791","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85102601216
"Egorova A.I., Klowait N.","57222711004;57193892067;","How to say good-bye to a robot? The matter of conversational closing [КАК ПОПРОЩАТЬСЯ С РОБОТОМ? ИССЛЕДОВАНИЕ ДОСТИЖЕНИЯ РАЗГОВОРНОГО ЗАКРЫТИЯ]",2021,"Monitoring Obshchestvennogo Mneniya: Ekonomicheskie i Sotsial'nye Peremeny",,"1",,"241","270",,,"10.14515/MONITORING.2021.1.1810","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103757793&doi=10.14515%2fMONITORING.2021.1.1810&partnerID=40&md5=a690e7967d1dcc3803be7c56333f18bb","The Department of Data Collection, Surveys in Moscow and Moscow Oblast Division; The Center for Innovative Social Research; The International Center for Contemporary Sociological Theory; Public Opinion Foundation, Moscow, Russian Federation; Russian Presidential Academy of National Economy and Public Administration, Moscow, Russian Federation; Moscow School of Social and Economic Sciences, Moscow, Russian Federation; Sber Gamification Lab, Moscow, Russian Federation","Egorova, A.I., The Department of Data Collection, Surveys in Moscow and Moscow Oblast Division, Public Opinion Foundation, Moscow, Russian Federation; Klowait, N., The Center for Innovative Social Research, The International Center for Contemporary Sociological Theory, Russian Presidential Academy of National Economy and Public Administration, Moscow, Russian Federation, Moscow School of Social and Economic Sciences, Moscow, Russian Federation, Sber Gamification Lab, Moscow, Russian Federation","Artificial conversational agents are becoming increasingly popular in various spheres of life. Contemporary AI is able to talk to humans using sophisticated conversational techniques and human-like conversational patterns. For instance, Google Duplex, a cutting-edge voice interface, is capable of autonomously making customer service calls that barely register as robotic. With the frequency of human-computer interactions on the rise, there is a growing need to study their features: how misunderstandings are resolved, how conversational aims are achieved. This article is an empirical investigation of the interaction between an advanced conversational agent and human interactant. Using the framework of ethnomethodology and conversation analysis, the authors analyze the procedurally unfolding and naturally emerging conversational practices, as well as the normative structures that emerge as a result. The research is based on a recording of a call between Google Duplex and a human operator. We explore how to reach conversational closing - a microstructure that requires cooperation between the speakers. Despite interactional tensions caused by the robot's incongruous prosody, conversational closing is produced by the gradual achievement of epistemic balance. The authors emphasize the relationship between the institutional context of the interaction and the non-human agent's robotic nature. The results serve as a foundation for future studies in human-robot interaction and provide a deeper understanding of how conversational closings are achieved in liminal cases. © 2021 Russian Public Opinion Research Center, VCIOM. All rights reserved.","Conversation analysis; Conversation сlosing; Epistemics; Ethnomethodology; Human-computer interaction; Prosodic contour",,,,,,"Ministry of Education and Science of the Russian Federation, Minobrnauka: 075-15-2020-908","Acknowledgments. The article is made in collaboration with the Sber (ex. — Sber-bank’s) Gamification Laboratory, under a grant from the Ministry of Science and Higher Education of the Russian Federation (contract No. 075-15-2020-908).",,,,,,,,,,"Sacks, H., Schegloff, E., A Simplest Systematics for the Organization of Turn-Taking for Conversation (2015) Sociological Review, 14 (1), pp. 142-202. , https://sociologica.hse.ru/2015-14-1/147275640.html, Сакс Х., Щеглофф Э., Джефферсон Г. Простейшая систематика организации очередности в разговоре Социологическое обозрение. 2015. Т. 14. 1. С. 142-202. https://sociologica.hse.ru/2015-14-1/147275640.html (дата обращения: 22.02.2021) Jefferson G. P. (accessed: 22.02.2021). (in Russ); Broth, M., Mondada, L., Walking Away: The Embodied Achievement of Activity Closings in Mobile Interaction (2013) Journal of Pragmatics, 47 (1), pp. 41-58. , https://doi.org/10.1016/j.pragma.2012.11.016; Button, G., Going Up a Blind Alley: Conflating Conversation Analysis and Computational Modelling (1990) Computers and Conversation, pp. 67-90. , https://doi.org/10.1016/b9780-08-050264-9.50009-9, Luff P., Gilbert N., Fronlich D. London: Academic Press; Button, G., Lee, J. R. E., Talk and Social Organisation (1987) Intercommunication, 1. , Clevedon: Multilingual Matters; Couper-Kuhlen, E., Prosody and Sequence Organization in English Conversation (2004) Sound Patterns in Interaction. Typological Studies in Language, 62, pp. 335-376. , https://doi.org/10.1075/tsl.62.17cou, Couper-Kuhlen E., Ford C. E. (eds) Amsterdam: John Benjamins Publishing Company; Couper-Kuhlen, E., Selting, M., Towards an Interactional Perspective on Prosody and a Prosodic Perspective on Interaction (2009) Prosody in Conversation, pp. 11-56. , https://doi.org/10.1017/cbo9780511597862.003, Couper-Kuhlen E., Selting, M. (eds) Cambridge: Cambridge University Press; Drew, P., Heritage, J., Talk at Work: Interaction in Institutional Settings (1992) Studies in Interactional Sociolinguistics, 8. , Cambridge: Cambridge University Press; Haddington, P., Leave-Taking as Multiactivity: Coordinating Conversational Closings With Driving in Cars (2019) Language & Communication, 65, pp. 58-78. , https://doi.org/10.1016/j.langcom.2018.04.003; Heritage, J., Current Developments in Conversation Analysis (1989) Conversation: An Interdisciplinary Perspective, pp. 21-47. , Roger D., Bull P. 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M., Lapses: How People Arrive at, and Deal With, Discontinuities in Talk (2015) Research on Language and Social Interaction, 48 (4), pp. 430-453. , https://doi.org/10.1080/08351813.2015.1090116; Jefferson, G., Notes on a Systematic Deployment of the Acknowledgement Tokens «Yeah»; and «Mm Hm» (1984) Paper in Linguistics, 17 (2), pp. 197-216. , https://doi.org/10.1080/08351818409389201; Jefferson, G. H., Glossary of Transcript Symbols With an Introduction (2004) Conversation Analysis: Studies From the First Generation. Pragmatics & Beyond New Series, 125, pp. 13-31. , https://doi.org/10.1075/pbns.125.02jef, Lerner G. H. (ed), Amsterdam: John Benjamins Publishing; Klowait, N., A Conceptual Framework for Researching Emergent Social Orderings in Encounters With Automated Computer-Telephone Interviewing Agents (2017) The International Journal of Communication and Linguistic Studies, 15 (1), pp. 19-37. , https://cgscholar.com/bookstore/works/a-conceptual-framework-for-researching-emergent-social-orderings-in-encounters-with-automated-computertelephone-interviewing-ag?category_id=cgrn&path=cgrn%2F228%2F230, P. (accessed: 18.02.2021); Klowait, N., Reflexive Anthropomorphism: Ontological Ignorance, or Ignorant Ontology? (2018) Sociological Journal, 24 (1), pp. 8-33. , https://doi.org/10.19181/socjour.2018.24.1.5711; Klowait, N., Erofeeva, M. A., The Rise of Interactional Multimodality in Human-Computer Interaction (2021) Monitoring of Public Opinion: Economic and Social Changes, (1), pp. 46-70; Markman, K. M., (2006) Computer-Mediated Conversation: The Organization of Talk in Chat-Based Virtual Team Meetings, , http://hdl.handle.net/2152/2582, Ph.D. diss Department of Communication Studies. Austin, TX: The University of Texas at Austin. (accessed: 18.02.2021); Mondada, L., Pointing, Talk, and the Bodies (2014) From Gesture in Conversation to Visible Action as Utterance: Essays in Honor of Adam Kendon, pp. 95-124. , https://doi.org/10.1075/z.188.06mon, Seyfeddinipur M., Gullberg M., Kendon A. (eds) Amsterdam; Philadelphia, PA: John Benjamins Publishing Company; Moore, R. J., Arar, R., (2019) Conversational UX Design: A Practitioner's Guide to the Natural Conversation Framework, , https://doi.org/10.1145/3304087, New York, NY: ACM Books; Müller, F. E., Affiliating and Disaffiliating With Continuers: Prosodic Aspects of Recipiency (2009) Prosody in Conversation: Interactional Studies, pp. 131-176. , Couper-Kuhlen E., Selting M. (eds) Cambridge: Cambridge University Press; O'Leary, D.E., GOOGLE'S Duplex: Pretending to be Human (2019) IntelligentSystemsin Accounting,FinanceandManagement, 26 (1), pp. 46-53. , https://doi.org/10.1002/isaf.1443, Vol..No; Pojanapunya, P., Jaroenkitboworn, K., How to Say «Good-Bye» in Second Life (2011) Journal of Pragmatics, 43 (14), pp. 3591-3602. , https://doi.org/10.1016/j.pragma.2011.08.010; Raymond, G., Prompting Action: The Stand-Alone «So» in Ordinary Conversation (2004) Research on Language & Social Interaction, 37 (2), pp. 185-218. , https://doi.org/10.1207/s15327973rlsi3702_4; Sacks, H., Schegloff, E. A., Jefferson, G., A Simplest Systematics for the Organization of Turn-Taking for Conversation (1974) Language, 50 (4), pp. 696-735. , https://doi.org/10.1353/lan.1974.0010; Schegloff, E.A., A Practice for (Re-)Exiting a Sequence: And/But/So + Uh(m) + Silence (2009) LanguageinLife,andaLifeinLanguage:JacobMey,aFestschrift. Studies in Pragmatics, pp. 365-374. , https://doi.org/10.1163/9789004253209_047, Turner,K.(ed). Bingley: Emerald; Schegloff, E. A., Sacks, H., Opening up Closings (1973) Semiotica, 8 (4), pp. 289-327. , https://doi.org/10.1515/semi.1973.8.4.289; Selting, M., On the Interplay of Syntax and Prosody in the Constitution of Turn-Constructional Units and Turns in Conversation (1996) Pragmatics, 6 (3), pp. 371-388. , https://doi.org/10.1075/prag.6.3.06sel; Wooffitt, R., (1997) Humans, Computers and Wizards: Analysing Human (Simulated) Computer Interactions, , London: Routledge; Zellers, M., Prosodic Variation for Topic Shift and Other Functions in Local Contrasts in Conversation (2012) Phonetica, 69 (4), pp. 231-253. , https://doi.org/10.1159/000353445",,,,"Russian Public Opinion Research Center, VCIOM",,,,,22195467,,,,"Russian","Monit. Obsestven. Mnenia: Ekon. Soc. Perem.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85103757793
"Kimoto M., Iio T., Shiomi M., Shimohara K.","57050144300;24724391700;8397042200;57203054726;","Coordinating entrainment phenomena: Robot conversation strategy for object recognition",2021,"Applied Sciences (Switzerland)","11","5","2358","1","29",,,"10.3390/app11052358","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102679763&doi=10.3390%2fapp11052358&partnerID=40&md5=5107cacfaeda590a758e0f4eb39c47e3","Faculty of Science and Technology, Keio University, Kanagawa223-8522, Japan; Interaction Science Laboratories, ATR, Kyoto, 619-0288, Japan; Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba, 305-8573, Japan; Faculty of Science and Engineering, Doshisha University, Kyoto, 610-0321, Japan","Kimoto, M., Faculty of Science and Technology, Keio University, Kanagawa223-8522, Japan, Interaction Science Laboratories, ATR, Kyoto, 619-0288, Japan; Iio, T., Interaction Science Laboratories, ATR, Kyoto, 619-0288, Japan, Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba, 305-8573, Japan; Shiomi, M., Interaction Science Laboratories, ATR, Kyoto, 619-0288, Japan; Shimohara, K., Faculty of Science and Engineering, Doshisha University, Kyoto, 610-0321, Japan","This study proposes a robot conversation strategy involving speech and gestures to improve a robot’s indicated object recognition, i.e., the recognition of an object indicated by a human. Research conducted to improve the performance of indicated object recognition is divided into two main approaches: development and interactive. The development approach addresses the development of new devices or algorithms. Through human–robot interaction, the interactive approach improves the performance by decreasing the variability and the ambiguity of the references. Inspired by the findings of entrainment and entrainment inhibition, this study proposes a robot conversation strategy that utilizes the interactive approach. While entrainment is a phenomenon in which people unconsciously tend to mimic words and/or gestures of their interlocutor, entrainment inhibition is the opposite phenomenon in which people decrease the amount of information contained in their words and gestures when their interlocutor provides excess information. Based on these phenomena, we designed a robot conversation strategy that elicits clear references. We experimentally compared this strategy with the other interactive strategy in which a robot explicitly requests clarifications when a human refers to an object. We obtained the following findings: (1) The proposed strategy clarifies human references and improves indicated object recognition performance, and (2) the proposed strategy forms better impressions than the other interactive strategy that explicitly requests clarifications when people refer to objects. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Alignment; Entrainment; Human–robot interaction; Indicated object recognition",,,,,,"Japan Society for the Promotion of Science, KAKEN: JP19J01290, JP20K19897","This research work was supported in part by JSPS KAKENHI Grant Numbers JP19J01290 and JP20K19897.",,,,,,,,,,"Hatori, J., Kikuchi, Y., Kobayashi, S., Takahashi, K., Tsuboi, Y., Unno, Y., Ko, W., Tan, J., Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 3774-3781. , Brisbane, Australia, 21–25 May 2018; Nickel, K., Stiefelhagen, R., Visual recognition of pointing gestures for human-robot interaction (2007) Image Vis. Comput, 25, pp. 1875-1884; Schauerte, B., Fink, G.A., Focusing computational visual attention in multi-modal human-robot interaction (2010) Proceedings of the International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction on ICMI-MLMI ’10 Association for Computing Machinery (ACM), p. 6. , Beijing, China, 8–12 November; Iwahashi, N., A method for the coupling of belief systems through human-robot language interaction Proceedings of the The 12th IEEE International Workshop on Robot and Human Interactive Communication, ROMAN 2003, pp. 385-390. , Millbrae, CA, USA, 2 November 2003; Furnas, G.W., Landauer, T.K., Gomez, L.M., Dumais, S.T., The vocabulary problem in human-system communication (1987) Commun. ACM, 30, pp. 964-971; Shinozawa, K., Miyashita, T., Kakio, M., Hagita, N., User specification method and humanoid confirmation behavior Proceedings of the 2007 7th IEEE-RAS International Conference on Humanoid Robots, pp. 366-370. , Pittsburgh, PA, USA, 29 November–1 December 2007; Wu, E., Han, Y., Whitney, D., Oberlin, J., MacGlashan, J., Tellex, S., Robotic Social Feedback for Object Specification (2015) Proceedings of the AAAI Fall Symposia, , Providence, RI, USA, 12–14 November; Kuno, Y., Sakata, K., Kobayashi, Y., Object recognition in service robots: Conducting verbal interaction on color and spatial relationship Proceedings of the 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, pp. 2025-2031. , Kyoto, Japan, 27 September–4 October 2009; Whitney, D., Rosen, E., MacGlashan, J., Wong, L.L.S., Tellex, S., Reducing errors in object-fetching interactions through social feedback Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 1006-1013. , Singapore, 29 May–3 June 2017; Garrod, S., Anderson, A., Saying what you mean in dialogue: A study in conceptual and semantic coordination (1987) Cognition, 27, pp. 181-218; Brennan, S.E., Lexical Entrainment in Spontaneous Dialog (1996) Proc. ISSD, 96, pp. 41-44; Brennan, S.E., Clark, H.H., Conceptual pacts and lexical choice in conversation (1996) J. Exp. Psychol. Learn. Mem. Cogn, 22, pp. 1482-1493; Branigan, H.P., Pickering, M.J., Cleland, A.A., Syntactic co-ordination in dialogue (2000) Cognition, 75, pp. B13-B25; Scheflen, A.E., The Significance of Posture in Communication Systems† (1964) Psychiatry, 27, pp. 316-331; Kendon, A., Movement coordination in social interaction: Some examples described (1970) Acta Psychol, 32, pp. 101-125; Bergmann, K., Kopp, S., Gestural Alignment in Natural Dialogue (2012) Proc. Ann. Meet. Cogn. Sci. Soc, 34, pp. 1326-1331; Levitan, R., Entrainment in Spoken Dialogue Systems: Adopting, Predicting and Influencing User Behavior (2013) Proceedings of the 2013 NAACL HLT Student Research Workshop, pp. 84-90. , Atlanta, GA, USA, 13 June; Lopes, J., Eskenazi, M., Trancoso, I., From rule-based to data-driven lexical entrainment models in spoken dialog systems (2015) Comput. Speech Lang, 31, pp. 87-112; Fandrianto, A., Eskenazi, M., Prosodic Entrainment in an Information-Driven Dialog System (2012) Proceedings of the 13th Annual Conference of the International Speech Communication Association, , Portland, OR, USA, 9–13 September; Lopes, J., Eskenazi, M., Trancoso, I., Automated two-way entrainment to improve spoken dialog system performance Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8372-8376. , Vancouver, BC, Canada, 26–31 May 2013; Kimoto, M., Iio, T., Shiomi, M., Tanev, I., Shimohara, K., Hagita, N., Improvement of object reference recognition through human robot alignment Proceedings of the 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 337-342. , Kobe, Japan, 31 August–4 September 2015; Kimoto, M., Iio, T., Shiomi, M., Tanev, I., Shimohara, K., Hagita, N., Alignment Approach Comparison between Implicit and Explicit Suggestions in Object Reference Conversations Proceedings of the 4th International Conference on Human Agent Interaction, Association for Computing Machinery (ACM), pp. 193-200. , 4–7 October 2016; Kimoto, M., Iio, T., Shiomi, M., Tanev, I., Shimohara, K., Hagita, N., Robot Confirmation Behavior to Improve Object Reference Recognition (2017) J. Robot. Soc. Jpn, 35, pp. 681-692; Kimoto, M., Iio, T., Shiomi, M., Tanev, I., Shimohara, K., Hagita, N., Conversation Strategy Comparison between Explicit Request and Implicit Alignment in Object Reference Conversation (2018) J. Robot. Soc. Jpn, 36, pp. 441-452; Branigan, H.P., Pickering, M.J., Pearson, J., McLean, J.F., Linguistic alignment between people and computers (2010) J. Pragmat, 42, pp. 2355-2368; Brennan, S.E., Conversation with and through computers (1991) User Model. User-Adapt. Interact, 1, pp. 67-86; Iio, T., Shiomi, M., Shinozawa, K., Shimohara, K., Miki, M., Hagita, N., Lexical Entrainment in Human Robot Interaction (2014) Int. J. Soc. Robot, 7, pp. 253-263; Brandstetter, J., Beckner, C., Sandoval, E.B., Bartneck, C., Persistent Lexical Entrainment in HRI Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, Association for Computing Machinery (ACM), pp. 63-72. , Vienna, Austria, 6–9 March 2017; Clark, H.H., Brennan, S.E., (1991) Grounding in Communication, pp. 127-149. , American Psychological Association: Washington, DC, USA; Garrod, S., Pickering, M.J., Joint Action, Interactive Alignment and Dialog (2009) Top. Cogn. Sci, 1, pp. 292-304; Charny, J.E., Psychosomatic Manifestations of Rapport in Psychotherapy (1966) Psychosom. Med, 28, pp. 305-315; Breazeal, C., Regulation and Entrainment in Human—Robot Interaction (2002) Int. J. Robot. Res, 21, pp. 883-902; Ono, T., Imai, M., Ishiguro, H., A Model of Embodied Communications with Gestures between Human and Robots (2001) Proc. 23rd Ann. Meet. Cogn. Sci. Soc, pp. 732-737; Iio, T., Shiomi, M., Shinozawa, K., Akimoto, T., Shimohara, K., Hagita, N., Investigating Entrainment of People’s Pointing Gestures by Robot’s Gestures Using a WOZ Method (2011) Int. J. Soc. Robot, 3, pp. 405-414; Holler, J., Wilkin, K., Co-Speech Gesture Mimicry in the Process of Collaborative Referring During Face-to-Face Dialogue (2011) J. Nonverbal Behav, 35, pp. 133-153; Kawai, H., Toda, T., Ni, J., Tsuzaki, M., Tokuda, K., XIMERA: A New TTS from ATR Based on Corpus-Based Technologies Proceedings of the 5th ISCA ITRW on Speech Synthesis, pp. 179-184. , Pittsburgh, PA, USA, 14–16 June 2004; Lee, A., Kawahara, T., Recent Development of Open-Source Speech Recognition Engine Julius Proceedings of the APSIPA ASC 2009: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference, pp. 131-137. , Sapporo, Japan, 4–7 October 2009; Howard, I.P., Rogers, B.J., (1995) Binocular Vision and Stereopsis, , Oxford University: Oxford, MS, USA, ISBN 195084764; Sugiyama, O., Kanda, T., Imai, M., Ishiguro, H., Hagita, N., Anzai, Y., Humanlike conversation with gestures and verbal cues based on a three-layer attention-drawing model (2006) Connect. Sci, 18, pp. 379-402; Ball, K.K., Wadley, V.G., Edwards, J.D., Advances in Technology Used to Assess and Retrain Older Drivers (2002) Gerontechnology, 1, pp. 251-261; Sanders, A.F., Some Aspects of the Selective Process in the Functional Visual Field (1970) Ergonomics, 13, pp. 101-117; Seya, Y., Watanabe, K., Objective and Subjective Sizes of the Effective Visual Field during Game Playing Measured by the Gazecontingent Window Method (2013) Int. J. Affect. Eng, 12, pp. 11-19; Iio, T., Shiomi, M., Shinozawa, K., Miyashita, T., Akimoto, T., Hagita, N., Lexical entrainment in human-robot interaction: Can robots entrain human vocabulary? Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3727-3734. , St. Louis, MO, USA, 10–15 October 2009; Dogan, F.I., Kalkan, S., Leite, I., Learning to Generate Unambiguous Spatial Referring Expressions for Real-World Environments Proceedings of the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4992-4999. , Macau, China, 3–8 November 2019; Gorniak, P.J., (2005) The Affordance-Based Concept, , Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA; Mavridis, N., Roy, D., Grounded Situation Models for Robots: Where words and percepts meet Proceedings of the 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4690-4697. , Beijing, China, 9–15 October 2006; Mavridis, N., (2007) Grounded Situation Models for Situated Conversational Assistants, , Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA; Mavridis, N., Dong, H., To ask or to sense? Planning to integrate speech and sensorimotor acts (2012) Proceedings of the 2012 IV International Congress on Ultra Modern Telecommunications and Control Systems, pp. 227-233. , Sankt Petersburg, Russia,3–5 October; Hashimoto, M., Yamano, M., Usui, T., Effects of emotional synchronization in human-robot KANSEI communications Proceedings of the RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 52-57. , Toyama, Japan, 27 September–2 October 2009; Costa, S., Brunete, A., Bae, B.-C., Mavridis, N., Emotional Storytelling Using Virtual and Robotic Agents (2018) Int. J. Human. Robot, 15","Kimoto, M.; Faculty of Science and Technology, Kanagawa, Japan; email: kimoto@ailab.ics.keio.ac.jp
Kimoto, M.; Interaction Science Laboratories, Japan; email: kimoto@ailab.ics.keio.ac.jp",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85102679763
"Teo Y.","56023029800;","Recognition, collaboration and community: Science fiction representations of robot carers in Robot & Frank, Big Hero 6 and Humans",2021,"Medical Humanities","47","1",,"95","102",,,"10.1136/medhum-2019-011744","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083768093&doi=10.1136%2fmedhum-2019-011744&partnerID=40&md5=814940856aedbe3f2086c5c89bd6d2d8","Faculty of Media and Communication, Bournemouth University, Poole, United Kingdom","Teo, Y., Faculty of Media and Communication, Bournemouth University, Poole, United Kingdom","In the 2010s, a small number of science fiction films and television series exploring the theme of the robot carer and how humans respond to them were released. This paper explores three works in this regard: the films Robot & Frank (dir. Jake Schreier, USA 2012), Big Hero 6 (dir. Don Hall/Chris Williams, USA 2014) and the television series Humans (UK/USA, Channel 4/AMC, 2015-2018). Examining these works with some of the ethical issues currently being discussed in the use of robot technology in care work, this paper demonstrates how they align themselves with, but also challenge some of these ideas, and ultimately direct viewers to consider their own expectations of personalised healthcare. The essay begins by examining the fears of the care industry deploying robots to replace the work of human carers, followed by a discussion of the effectiveness of robots as carers as depicted in these fictional representations, and the final section considers the social environment that these robot carers are situated in, and how the robots become a reflection of human lives and a repository of memories of affective relations. These texts suggest alternate ways of thinking about human-robot interactions and care work, advocating for a more mutually dependent and reciprocal working relationship that might lead to a better quality of care. © 2021 BMJ Publishing Group. All rights reserved.","care of the elderly; dementia; film; medical humanities; television","caregiver; human; robotics; television; Caregivers; Humans; Robotics; Television",,,,,,,,,,,,,,,,"Isaac, A., (2013) I, Robot, , London: Harper Voyager; Christoph, B., Robots in the Theatre and the Media (2013) Proceedings of the Design & Semantics of Form & Movement (DeSForM2013), pp. 64-70. , https://pdfs.semanticscholar.org/32a6/be0e8b19ec1a20438a38a83b5d158b8f85ac.Pdf, Wuxi; Mads, B., Brown, N., Konrad, K., Van Lente, H., The sociology of expectations in science and technology (2006) Technology Analysis & Strategic Management, 18 (3-4), pp. 285-298; Mark, C., Artificial agents, good care, and modernity (2015) Theoretical Medicine and Bioethics, 36 (4), pp. 265-277; Mark, C., Pop, C., Simut, R., Peca, A., Pintea, S., David, D., Vanderborght, B., A survey of expectations about the role of robots in robot-assisted therapy for children with ASD: Ethical acceptability, trust, sociability, appearance, and attachment (2016) Science and Engineering Ethics, 22 (1), pp. 47-65; Istvan, C., (2008) The Seven Beauties of Science Fiction, , Middletown, Connecticut: Wesleyan University Press; Amelia, D., Beyond prosthetic memory: Posthumanism, embodiment, and caregiving robots (2016) Age, Culture, Humanities, 3; Maurice, H., (1980) The Collective Memory, , (translated by F J. Ditter, Jr and V. Y. Ditter). New York: Harper and Row; Helmut, H., The Real Soft Robots That Inspired Baymax, with Chris Atkeson (2015) Robohub, , https://robohub.org/the-real-soft-robots-that-inspired-baymax-withchris-atkeson/; Metzler Theodore, A., Lewis, L.M., Pope, L.C., Could robots become authentic companions in nursing care? (2016) Nursing Philosophy, 17 (1), pp. 36-48; Gavin, M., McFarlane, A., Science fiction and the medical humanities (2016) Medical Humanities, 42 (4), pp. 213-218; Orr David, M.R., Teo, Y., Carers' responses to shifting identity in dementia in Iris and Away from Her: Cultivating stability or embracing change? (2015) Medical Humanities, 41 (2), pp. 81-85; Dean, P., Waters, E., Schönbrodt, F., Strange carers: Robots as attachment figures and aids to parenting (2010) Interaction Studies, 11 (2), pp. 246-252; Paul, R., (2005) The Course of Recognition (Translated by David Pellauer), , Cambridge: Harvard University Press; Amanda, S., Sharkey, N., Granny and the robots: Ethical issues in robot care for the elderly (2012) Ethics and Information Technology, 14 (1), pp. 27-40; Sovacool Benjamin, K., Hess, D.J., Ordering theories: Typologies and conceptual frameworks for sociotechnical change (2017) Social Studies of Science, 47 (5), pp. 703-750; Sovacool Benjamin, K., Ramana, M.V., Back to the future: Small modular reactors, nuclear fantasies, and symbolic convergence (2015) Science, Technology & Human Values, 40 (1), pp. 96-125; Robert, S., Robots in aged care: A dystopian future? (2016) AI and Society, 31 (4), pp. 445-454; Carme, T., Robbie, the pioneer robot nanny: Science fiction helps develop ethical social opinion (2010) Interaction Studies, 11 (2), pp. 269-273","Teo, Y.; Faculty of Media and Communication, United Kingdom; email: yteo@bournemouth.ac.uk",,,"BMJ Publishing Group",,,,,1468215X,,MHEUB,"32317298","English","Med. Humanit.",Review,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85083768093
"Wolniakowski A., Valsamos C., Miatliuk K., Moulianitis V., Aspragathos N.","55643073100;54929874600;56006796300;6506258012;7003803146;","Optimization of dynamic task location within a manipulator’s workspace for the utilization of the minimum required joint torques",2021,"Electronics (Switzerland)","10","3","288","1","18",,,"10.3390/electronics10030288","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099959228&doi=10.3390%2felectronics10030288&partnerID=40&md5=7677751f021c6d1986c5c738b318621f","Faculty of Electrical Engineering, Bialystok University of Technology, Bialystok, 15-351, Poland; Department of Mechanical Engineering and Aeronautics, Polytechnic School, University of Partas, Patras, 26500, Greece; Department of Mechatronics and Robotics, Faculty of Mechanical Engineering, Bialystok University of Technology, Bialystok, 15-351, Poland; Department of Product and System Design Engineering, School of Engineering, University of the Aegean, Syros, 84100, Greece","Wolniakowski, A., Faculty of Electrical Engineering, Bialystok University of Technology, Bialystok, 15-351, Poland; Valsamos, C., Department of Mechanical Engineering and Aeronautics, Polytechnic School, University of Partas, Patras, 26500, Greece; Miatliuk, K., Department of Mechatronics and Robotics, Faculty of Mechanical Engineering, Bialystok University of Technology, Bialystok, 15-351, Poland; Moulianitis, V., Department of Product and System Design Engineering, School of Engineering, University of the Aegean, Syros, 84100, Greece; Aspragathos, N., Department of Mechanical Engineering and Aeronautics, Polytechnic School, University of Partas, Patras, 26500, Greece","The determination of the optimal position of a robotic task within a manipulator’s workspace is crucial for the manipulator to achieve high performance regarding selected aspects of its operation. In this paper, a method for determining the optimal task placement for a serial manipulator is presented, so that the required joint torques are minimized. The task considered comprises the exercise of a given force in a given direction along a 3D path followed by the end effector. Given that many such tasks are usually conducted by human workers and as such the utilized trajectories are quite complex to model, a Human Robot Interaction (HRI) approach was chosen to define the task, where the robot is taught the task trajectory by a human operator. Furthermore, the presented method considers the singular free paths of the manipulator’s end-effector motion in the configuration space. Simulation results are utilized to set up a physical execution of the task in the optimal derived position within a UR-3 manipulator’s workspace. For reference the task is also placed at an arbitrary “bad” location in order to validate the simulation results. Experimental results verify that the positioning of the task at the optimal location derived by the presented method allows for the task execution with minimum joint torques as opposed to the arbitrary position. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Dynamic task; Human-robot interaction; Optimization; Simulation; Universal robot",,,,,,"General Secretariat for Research and Technology, GSRT; Hellenic Foundation for Research and Innovation, ΕΛ.ΙΔ.Ε.Κ; Narodowa Agencja Wymiany Akademickiej, NAWA: PPI/APM/2018/1/00033/U/001, WZ/WE-IA/4/2020, WZ/WM-IIM/1/2019","Funding: The work presented was partially financed by the Polish National Agency for Academic Exchange as part of the Academic International Partnerships (PPI/APM/2018/1/00033/U/001 project) and by the original research works WZ/WM-IIM/1/2019 and WZ/WE-IA/4/2020 funded by the Polish Ministry of Science and Higher Education. This research has been also financially supported by General Secretariat for Research and Technology (GSRT) and the Hellenic Foundation for Research and Innovation (HFRI) (Code: 1184).",,,,,,,,,,"Mitsunori, U., Goya, H., Kawamura, S., Motion control with stiffness adaptation for torque minimization in multijoint robots (2013) IEEE Trans. Robot, 30, pp. 352-364; Woolfrey, J., Lu, W., Liu, D., A control method for joint torque minimization of redundant manipulators handling large external forces (2019) J. Intell. Robot. Syst, 96, pp. 3-16; Zhang, Y., Guo, D., Ma, S., Different-level simultaneous minimization of joint-velocity and joint-torque for redundant robot manipulators (2013) J. Intell. Robot. Syst, 72, pp. 301-323; Mohammed, A., Schmidt, B., Wang, L., Gao, L., Minimizing energy consumption for robot arm movement (2014) Procedia CIRP, 25, pp. 400-405; Pellicciari, M., Berselli, G., Leali, F., Vergnano, A., A method for reducing the energy consumption of pick-and-place industrial robots (2013) Mechatronics, 23, pp. 326-334; Santos, R., Steffen, V., Saramago, S., Optimal task placement of a serial robot manipulator for manipulability and mechanical power optimization (2010) Intell. Inf. Manag, 2, pp. 512-525; Kang, S.B., Ikeuch, K., Toward automatic robot instruction from perception-mapping human grasps to manipulator grasps (1997) IEEE Trans. Robot. Autom, 13, pp. 81-95; Wei, Q., Yang, C., Fan, W., Zhao, Y., Design of Demonstration-Driven Assembling Manipulator (2018) Appl. Sci, 8, p. 797; Moe, S., Schjølberg, I., Real-time hand guiding of industrial manipulator in 5 DOF using Microsoft Kinect and accelerometer (2013) Proceedings of the 2013 IEEE RO-MAN, pp. 644-649. , Gyeongju, South Korea, 26-29 August; Dubey, R., Luh, J., Redundant robot control using task based performance measures (1988) Robot. Syst, 5, pp. 409-432; Valsamos, C., Wolniakowski, A., Miatliuk, K., Moulianitis, V.C., Minimization of joint velocities during the execution of a robotic task by 6 dof articulated manipulator (2018) Mech. Mach. Sci, 67, pp. 368-375; Burdick, J.W., A classification of 3R regional manipulator singularities and geometries (1995) Mech. Mach. Theory, 30, pp. 71-89; Staffetti, E., Bruyninckx, H., Schutter, J., On the invariance of manipulability indices (2002) Advances in Robot Kinematics, pp. 57-66. , Lenarčič, J., Thomas, F., Eds.; Springer: Dordrecht, The Netherlands; Ellekilde, L.-P., Jorgensen, J., RobWork: A flexible toolbox for robotics research and education (2010) Proceedings of the ISR Symposium (41st International Symposium on Robotics) and ROBOTIK 2010 (6th German Conference on Robotics), pp. 1-7. , Munich, Germany, 7-9 June; http://dept.me.umn.edu/courses/me5286/robotlab/Resources/URScript_manual_en_3.1.pdf, URScript API Reference. (accessed on 21 June 2020)","Miatliuk, K.; Department of Mechatronics and Robotics, Poland; email: k.miatliuk@pb.edu.pl",,,"MDPI AG",,,,,20799292,,,,"English","Electronics (Switzerland)",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85099959228
"Shamonsky D.","57363866100;","User experience best practices for human-robot interaction",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12763 LNCS",,,"318","328",,,"10.1007/978-3-030-78465-2_24","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120711089&doi=10.1007%2f978-3-030-78465-2_24&partnerID=40&md5=2585d7f8de05ce6015bb2051a4c7030c","Brandeis University, 415 South Street, Waltham, MA  02453, United States","Shamonsky, D., Brandeis University, 415 South Street, Waltham, MA  02453, United States","User experience (UX) design of human-robot interaction (HRI) is an emerging practice [1]. Best practices for this discipline are actively evolving as robotics expands into commercial markets. As is typical of emerging technologies, the technology itself takes center stage, continuing to present challenges to proper functioning as it is tested in real world applications. Also, deployment comes at a high price until the market is competitive enough to drive hardware and development prices down. All these aspects preclude an emphasis on UX design, until the technology and associated market reaches a tipping point and good user experience is in demand. If robots continue to be deployed at rates the industry currently predicts, the need for user experience design knowledge and best practices for HRI is eminent. Best practices are a collection of methods, specifically principles, heuristic evaluators and taxonomies [2]. Principles embody high level guidance for design direction and design processes [3]. Heuristics ensure measurable “must have” base-functionality [4–6]. Taxonomies provide a conceptual understanding of possible categories of interactivity [7–9]. This paper focuses on two aspects of best practices, 1.) proposing a robustly user-centric set of emerging technology principles for HRI, which is the area of best practices least explored by the literature, and 2.) proposing a design matrix as a beginning step in addressing the complexity of HRI. © Springer Nature Switzerland AG 2021.","Emerging technology principles; HRI best practices; HRI heuristics; Human-robot interaction","Commerce; Heuristic methods; Machine design; Man machine systems; Best practices; Emerging technologies; Emerging technology principle; Human-robot interaction best practice; Human-robot interaction heuristic; Humans-robot interactions; Technology principles; Users' experiences; Human robot interaction",,,,,,,,,,,,,,,,"Lindblom, J., Andreasson, R., Current challenges for UX evaluation of human-robot interaction (2016) Advances in Ergonomics of Manufacturing: Managing the Enterprise of the Future. Advances in Intelligent Systems and Computing, 490, pp. 267-277. , https://doi.org/10.1007/978-3-319-41697-7_24, Schlick, C., Trzcieliński, S. (eds.), vol., pp. , Springer, Cham; Best Practice: https://en.wikipedia.org/wiki/Best_practice. Accessed 12 Feb 2021; Tognazzini, B.: First Principles, HCI Design, Human Computer Interaction (HCI), Principles of HCI Design, Usability Testing. https://asktog.com/atc/principles-of-interaction-design/. Accessed 12 Feb 2021; Clarkson, E., Arkin, R.: Applying heuristic evaluation to human-robot interaction systems. In: GVU Tech Report No. GIT-GVU-06–08 (2006). ftp://ftp.cc.gatech.edu/pub/gvu/tr/2006/ 06-08.pdf; Powers, A., FEATURE, what robotics can learn from HCI (2008) Interactions, 15 (2), pp. 67-69. , https://doi.org/10.1145/1340961.1340978; Weiss, A., Wurhofer, D., Bernhaupt, R., Altmaninger, M., Tscheligi, M., A methodological adaptation for heuristic evaluation of HRI (2010) 19Th International Symposium in Robot and Human Interactive Communication, pp. 1-6. , https://doi.org/10.1109/ROMAN.2010.5598735, Viareggio, pp; Malik, A.A., Bilberg, A., Developing a reference model for human–robot interaction (2019) Int. J. Interact. Des. Manufact. (Ijidem), 13 (4), pp. 1541-1547. , https://doi.org/10.1007/s12008-019-00591-6; Yanco, H.A., Drury, J.L., Classifying human-robot interaction: An updated taxonomy (2004) 2004 IEEE International Conference on Systems, Man and Cybernetics, the Hague, Vol. 3, P. 2841, , https://doi.org/10.1109/ICSMC.2004.1400763; Yanco, H.A., Drury, J.L., A taxonomy for human-robot interaction (2002) Proceedings of the AAAI Fall Symposium on Human-Robot Interaction, pp. 111-119. , , pp; Rosenzweig, E., (2015) Successful User Experience: Strategies and Roadmaps, , https://doi.org/10.1016/C2013-0-19353-1, Morgan Kaufmann Publishers Inc., San Francisco; Wigdor, D., Wixon, D., Brave NUI World (2011) Designing Natural User Interfaces for Touch and Gesture. Morgan Kaufman, Boston; Shneiderman, B., (1987) Designing the User Interface: Strategies for Effective Human-Computer Interaction, , Addison-Wesley Publishing Co., Reading; Scholtz, J., Evaluation methods for human-system performance of intelligent systems (2002) Proceedings of PERMIS’02; Nielsen, J., Finding usability problems through heuristic evaluation (1992) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 373-380. , https://doi.org/10.1145/142750.142834, , pp. , Association for Computing Machinery, Monterey; Nielsen, J., Enhancing the explanatory power of usability heuristics (1994) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 152-158. , https://doi.org/10.1145/191666.191729, , pp. , Association for Computing Machinery, Boston; Mankoff, J., Dey, A.K., Hsieh, G., Kientz, J., Ames, M., Lederer, S., Heuristic evaluation of ambient displays (2003) Proceedings of CHI’03, pp. 169-176. , , pp; Onnasch, L., Roesler, E., A taxonomy to structure and analyze human–robot interaction (2020) Int. J. Soc. Robot., pp. 1-17. , https://doi.org/10.1007/s12369-020-00666-5; Weiss, A., Bernhaupt, R., Lankes, M., Tscheligi, M., The USUS evaluation framework for human-robot interaction. Proc (2009) AISB, 9 (4), pp. 11-26; Nielsen, J.: 10 Usability Heuristics for Interface Design. https://www.nngroup.com/articles/ten-usability-heuristics/. Accessed 12 Feb 2021; Liu, H., Wang, L., Gesture recognition for human-robot collaboration: A review (2018) Int. J. Ind. Ergon., 68, pp. 355-367. , https://doi.org/10.1016/j.ergon.2017.02.004; Sheikholeslami, S., Moon, A., Croft, E.A., Cooperative gestures for industry: Exploring the efficacy of robot hand configurations in expression of instructional gestures for human–robot interaction (2017) Int. J. Robot. Res., 36, pp. 699-720. , https://doi.org/10.1177/027836491770 9941; Getting Started with the Guide | Alexa Skills Kit: https://developer.amazon.com/en-US/docs/alexa/alexa-design/get-started.html. Accessed 12 Feb 2021; (2021) Principles of Leap Motion Interaction Design, , https://blog.leapmotion.com/6-principles-of-interaction-design/. Accessed 12, Feb; Our Principles – Google AI: https://ai.google/principles/. Accessed 12 Feb 2021; Campana, J.R., Quaresma, M., The importance of specific usability guidelines for robot user interfaces (2017) DUXU 2017. LNCS, Vol. 10289, pp. 471-483. , https://doi.org/10.1007/978-3-319-58637-3_37, Marcus, A., Wang, W. (eds.), pp. , Springer, Cham; Shamonsky, D., (2021) User Experience Design Principles for a Natural User Interface (NUI, , https://www.ics.com/blog/user-experience-design-principles-natural-user-interf ace-nui. Accessed 12, Feb","Shamonsky, D.; Brandeis University, 415 South Street, United States; email: dshamonsky@ics.com","Kurosu M.",,"Springer Science and Business Media Deutschland GmbH","Human Computer Interaction thematic area of the 23rd International Conference on Human-Computer Interaction, HCII 2021","24 July 2021 through 29 July 2021",,262079,03029743,9783030784645,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85120711089
"Aroyo A.M., De Bruyne J., Dheu O., Fosch-Villaronga E., Gudkov A., Hoch H., Jones S., Lutz C., Sætra H., Solberg M., Tamò-Larrieux A.","57192384790;57303662000;57222085293;57189046483;57217132927;57226180297;7405933532;56201845000;57202855771;57226600463;57208304288;","Overtrusting robots: Setting a research agenda to mitigate overtrust in automation",2021,"Paladyn","12","1",,"423","436",,,"10.1515/pjbr-2021-0029","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117563397&doi=10.1515%2fpjbr-2021-0029&partnerID=40&md5=a7688a510ae419805250eee437d7640e","Sirrl, University of Waterloo, Waterloo, Canada; Ku Leuven Centre for It and Ip Law (CiTiP), Ku Leuven, Leuven, Belgium; ELaw Center for Law and Digital Technologies, Leiden University, Leiden, Netherlands; Faculty of Law: School for Theory of Law and Cross-sectoral Legal Disciplines, National Research University Higher School of Economics, Moscow, Russian Federation; FAA-HSG, University of St. Gallen, St. Gallen, Switzerland; Department of Communication, University of Illinois Chicago, Chicago, United States; Nordic Centre for Internet and Society, Bi Norwegian Business School, Nydalsveien 37, Oslo, 0484, Norway; Department of Computer Science and Communication, Østfold University College, Østfold, Norway; Department of Health Sciences in Ålesund, Norwegian University of Science and Technology, Trondheim, Norway","Aroyo, A.M., Sirrl, University of Waterloo, Waterloo, Canada; De Bruyne, J., Ku Leuven Centre for It and Ip Law (CiTiP), Ku Leuven, Leuven, Belgium; Dheu, O., Ku Leuven Centre for It and Ip Law (CiTiP), Ku Leuven, Leuven, Belgium; Fosch-Villaronga, E., ELaw Center for Law and Digital Technologies, Leiden University, Leiden, Netherlands; Gudkov, A., Faculty of Law: School for Theory of Law and Cross-sectoral Legal Disciplines, National Research University Higher School of Economics, Moscow, Russian Federation; Hoch, H., FAA-HSG, University of St. Gallen, St. Gallen, Switzerland; Jones, S., Department of Communication, University of Illinois Chicago, Chicago, United States; Lutz, C., Nordic Centre for Internet and Society, Bi Norwegian Business School, Nydalsveien 37, Oslo, 0484, Norway; Sætra, H., Department of Computer Science and Communication, Østfold University College, Østfold, Norway; Solberg, M., Department of Health Sciences in Ålesund, Norwegian University of Science and Technology, Trondheim, Norway; Tamò-Larrieux, A., FAA-HSG, University of St. Gallen, St. Gallen, Switzerland","There is increasing attention given to the concept of trustworthiness for artificial intelligence and robotics. However, trust is highly context-dependent, varies among cultures, and requires reflection on others' trustworthiness, appraising whether there is enough evidence to conclude that these agents deserve to be trusted. Moreover, little research exists on what happens when too much trust is placed in robots and autonomous systems. Conceptual clarity and a shared framework for approaching overtrust are missing. In this contribution, we offer an overview of pressing topics in the context of overtrust and robots and autonomous systems. Our review mobilizes insights solicited from in-depth conversations from a multidisciplinary workshop on the subject of trust in human-robot interaction (HRI), held at a leading robotics conference in 2020. A broad range of participants brought in their expertise, allowing the formulation of a forward-looking research agenda on overtrust and automation biases in robotics and autonomous systems. Key points include the need for multidisciplinary understandings that are situated in an eco-system perspective, the consideration of adjacent concepts such as deception and anthropomorphization, a connection to ongoing legal discussions through the topic of liability, and a socially embedded understanding of overtrust in education and literacy matters. The article integrates diverse literature and provides a ground for common understanding for overtrust in the context of HRI. © 2021 Alexander M. Aroyo et al., published by De Gruyter.","anthropomorphization liability; deception; education; overtrust; robots; social robots; trust",,,,,,"707404; 812.788; Universität St. Gallen, HSG: 1031564; Horizon 2020 Framework Programme, H2020; Norges Forskningsråd: 275347, 285216","Funding information: Orian Dheu received funding from the European Union’s Horizon 2020 Research and Innovation – Marie Skłodowska-Curie actions program under the Safer Autonomous Systems (SAS) project, grant agreement no. 812.788: https://etn-sas.eu/ . This publication reflects only the author’s view, exempting the European Union from any liability. Eduard Fosch-Villaronga received funding from the LEaDing Fellows Marie Curie COFUND fellowship, a project funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement No. 707404. Aurelia Tamò-Larrieux received funding from the International Postdoctoral Fellowship grant, University of St. Gallen, Switzerland: project number 1031564. Christoph Lutz received funding from the Research Council of Norway under grant agreement 275347 “Future Ways of Working in the Digital Economy.” Mads Solberg’s contribution was supported by grant no. 285216 from the Regional Research Fund of Central-Norway. Researchers of the SIRRL Laboratory at University of Waterloo are supported in part thanks to funding from the Canada 150 Research Chairs Program.",,,,,,,,,,"""on artificial intelligence - A European approach to excellence and trust,"" European Commission, , https://ec.europa.eu/info/publications/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en, European Commission; Lee, J.D., See, K.A., Trust in automation: Designing for appropriate reliance (2004) Hum. Factors J. Hum. Factors Ergonom Soc., 46 (NO. 1), pp. 50-80; Meyerson, D., Weick, K.E., Kramer, R.M., Tyler, T., Swift trust and temporary groups (1996) Trust Organizations: Frontiers of Theory and Research, pp. 166-195. , in, Ed., California, US SAGE Publications, Inc; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Hum. Factors, 53 (NO. 5), pp. 517-527; Sætra, H.S., Social robot deception and the culture of trust (2021) Paladyn J. Behav. Robot., 12 (NO. 1), pp. 276-286; Levine, E.E., Schweitzer, M.E., Prosocial lies: When deception breeds trust (2015) Organ. Behav. Hum. Decis. Process., 126, pp. 88-106; Robinson, S.C., Trust, transparency, and openness: How inclusion of cultural values shapes Nordic national public policy strategies for artificial intelligence (AI) (2020) Technol. Soc., 63, p. 101421. , art; Felzmann, H., Fosch-Villaronga, E., Lutz, C., Tamò-Larrieux, A., Transparency you can trust: Transparency requirements for artificial intelligence between legal norms and contextual concerns (2019) Big Data Soc., 6 (NO. 1), pp. 1-14; Parasuraman, R., Riley, V., Humans and automation: Use, misuse, disuse, abuse (1997) Hum. Factors, 39 (NO. 2), pp. 230-253; (2020) ""ethics Guidelines for Trustworthy AI,"" European Commission, , https://op.europa.eu/en/publication-detail/-/publication/d3988569-0434-11ea-8c1f-01aa75ed71a1, High Level Expert Group on AI; Robinette, P., Li, W., Allen, R., Howard, A.M., Wagner, A.R., ""overtrust of robots in emergency evacuation scenarios,"" (2016) 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), , in; Wagner, A.R., Borenstein, J., Howard, A., Overtrust in the robotic age (2018) Commun. 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"Guajo J., Anzola C.A., Betancur D., Castaño-Londoño L., Marquez-Viloria D.","57290234500;57290757700;57215965325;57212006555;57192269433;","Improvement of Visual Perception in Humanoid Robots Using Heterogeneous Architectures for Autonomous Applications",2021,"Communications in Computer and Information Science","1431 CCIS",,,"447","458",,,"10.1007/978-3-030-86702-7_38","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116824029&doi=10.1007%2f978-3-030-86702-7_38&partnerID=40&md5=ba0b4460c7c5efa457ef1bad36c153fb","Department of Electronics and Telecommunication Engineering, Instituto Tecnológico Metropolitano ITM, Medellín, Colombia; Systems and Computer Science Research Group, Institución Universtaria de Envigado, Medellín, Colombia","Guajo, J., Department of Electronics and Telecommunication Engineering, Instituto Tecnológico Metropolitano ITM, Medellín, Colombia; Anzola, C.A., Department of Electronics and Telecommunication Engineering, Instituto Tecnológico Metropolitano ITM, Medellín, Colombia; Betancur, D., Systems and Computer Science Research Group, Institución Universtaria de Envigado, Medellín, Colombia; Castaño-Londoño, L., Department of Electronics and Telecommunication Engineering, Instituto Tecnológico Metropolitano ITM, Medellín, Colombia; Marquez-Viloria, D., Department of Electronics and Telecommunication Engineering, Instituto Tecnológico Metropolitano ITM, Medellín, Colombia","Humanoid robots find application in a variety of tasks such as emotional recognition for human-robot interaction (HRI). Despite their capabilities, these robots have a sequential computing system that limits the execution of high computational cost algorithms such as Convolutional Neural Networks (CNNs), which have shown good performance in recognition tasks. This limitation reduces their performance in HRI applications. As an alternative to sequential computing units are Field-programmable gate arrays (FPGAs) and Graphics Processing Units (GPUs), which have a high degree of parallelism, high performance, and low power consumption. In this paper, we propose a visual perception enhancement system for humanoid robots using FPGA or GPU based embedded systems running a CNN, while maintaining autonomy through an external computational system added to the robot structure. Our work has as a case study the humanoid robot NAO, however, the work can be replicated on other robots such as Pepper and Robotis OP3. The development boards used were the Xilinx Ultra96 FPGA, Intel Cyclone V SoC FPGA and Nvidia Jetson TX2 GPU. Nevertheless, our design allows the integration of other heterogeneous architectures with high parallelism and low power consumption. The Tinier-Yolo, Alexnet and Inception-V1 CNNs are executed and real-time results were obtained for the FPGA and GPU cards, while in Alexnet, the expected results were presented in the Jetson TX2. © 2021, Springer Nature Switzerland AG.","CNN; Field programmable gate array (FPGA); High-level synthesis (HLS); Humanoid robot; System-on-a-Chip (SoC)","Anthropomorphic robots; Computer graphics; Electric power utilization; Embedded systems; Graphics processing unit; High level synthesis; Human robot interaction; Logic gates; Network architecture; Neural networks; Program processors; System-on-chip; Vision; Convolutional neural network; Field programmable gate array; Graphics processing; High-level synthesis; Humanoid robot; Processing units; System on a chip; System-on-a-chip; Systems-on-a-chip; Field programmable gate arrays (FPGA)",,,,,"COL0053581; P17224","This study were supported by the AE&CC research Group COL0053581, at the Sistemas de Control y Rob?tica Laboratory, attached to the Instituto Tecnol?gico Metropolitano. This work is part of the project ?Improvement of visual perception in humanoid robots for objects recognition in natural environments using Deep Learning? with ID P17224.","Acknowledgements. This study were supported by the AE&CC research Group COL0053581, at the Sistemas de Control y Robótica Laboratory, attached to the Insti-tuto Tecnológico Metropolitano. This work is part of the project “Improvement of visual perception in humanoid robots for objects recognition in natural environments using Deep Learning” with ID P17224.",,,,,,,,,"Fanello, S.R., Visual recognition for humanoid robots (2017) Robot. Auton. Syst., 91, pp. 151-168; Cha, E., Matarić, M., Fong, T., Nonverbal signaling for non-humanoid robots during human-robot collaboration (2016) 2016 11Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Pp. 601–602. 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IEEE; Ni, L., An energy-efficient digital ReRAM-crossbar-based CNN with bitwise parallelism (2017) IEEE Jo. Explor. Solid-State Comput. Devices Circuits, 3, pp. 37-46; Michel, O., Cyberbotics Ltd., Webots™: Professional mobile robot simulation (2004) International Journal of Advanced Robotic Systems, 1 (1), p. 5. , vol., no, p; Franklin, D.: NVIDIA Jetson TX2 Delivers Twice the Intelligence to the Edge (2017). https://devblogs.nvidia.com/jetson-tx2-delivers-twiceintelligence-edge/. Accessed 02 Nov 2019","Guajo, J.; Department of Electronics and Telecommunication Engineering, Colombia; email: joseguajo152012@correo.itm.edu.co","Figueroa-Garcia J.C.Diaz-Gutierrez Y.Gaona-Garcia E.E.Orjuela-Canon A.D.",,"Springer Science and Business Media Deutschland GmbH","8th Workshop on Engineering Applications, WEA 2021","6 October 2021 through 8 October 2021",,266199,18650929,9783030867010,,,"English","Commun. Comput. Info. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85116824029
"Amelia A., Mathies C., Patterson P.G.","57261040800;36241971300;57209929037;","Customer acceptance of frontline service robots in retail banking: A qualitative approach",2021,"Journal of Service Management",,,,"","",,,"10.1108/JOSM-10-2020-0374","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113860468&doi=10.1108%2fJOSM-10-2020-0374&partnerID=40&md5=044d301acabced28bc45816e44185764","School of Marketing, UNSW Sydney, Sydney, Australia; Faculty of Economics, Muhammadiyah University of Aceh, Banda Aceh, Indonesia; Faculty of Business Administration, Chiang Mai University, Chiang Mai, Thailand","Amelia, A., School of Marketing, UNSW Sydney, Sydney, Australia, Faculty of Economics, Muhammadiyah University of Aceh, Banda Aceh, Indonesia; Mathies, C., School of Marketing, UNSW Sydney, Sydney, Australia; Patterson, P.G., School of Marketing, UNSW Sydney, Sydney, Australia, Faculty of Business Administration, Chiang Mai University, Chiang Mai, Thailand","Purpose: The purpose of this paper is to explore what drives customer acceptance of frontline service robots (FSR), as a result of their interaction experiences with FSR in the context of retail banking services. Design/methodology/approach: Applications of the unified theory of acceptance and use of technology and service robot acceptance model frame the exploration of customers’ interaction experiences with physical FSR to explain acceptance. A thematic analysis of information obtained through observations, focus groups and participant interviews was applied to identify themes. Findings: This study identifies 16 dimensions that group into five main themes that influence customer acceptance of FSR in retail banking services: (1) utilitarian aspect, (2) social interaction, (3) customer responses toward FSR, (4) customer perspectives of the company brand and (5) individual and task heterogeneity. Themes 1 and 2 are labeled confirmed themes based on existing theoretical frameworks used; themes 3–5 are additional themes. Practical implications: This study provides actionable suggestions to allow managers to reflect on their strategy and consider ways to design and improve the delivery of services that involve FSR. Originality/value: This study adds to our limited knowledge of how human-robot interaction research in robotics translates to a relatively new research area in frontline services and provides a step toward a comprehensive FSR acceptance model. © 2021, Emerald Publishing Limited.","Frontline service robot; Human–robot interaction; Retail banking; sRAM; Thematic analysis; UTAUT",,,,,,,,,,,,,,,,,"Ajzen, I., The theory of planned behavior (1991) Organizational Behavior and Human Decision Processes, 50 (2), pp. 179-211; Altricher, H., Feldman, A., Posch, P., Somekh, B., (2005) Teachers Investigate their Work: An Introduction to Action Research across the Professions, , Taylor & Francis Group, Routledge; Archibald, M.M., Investigator triangulation: a collaborative strategy with potential for mixed methods research (2016) Journal of Mixed Methods Research, 10 (3), pp. 228-250; Bartneck, C., Kanda, T., Mubin, O., Al Mahmud, A., Does the design of a robot influence its animacy and perceived intelligence? 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"Pan Z., Zhang M., Wu J., Wang J., Li H.","57204645835;56122449900;57204645027;57224744872;8615868400;","Multi-Tone Phase Coding of Interaural Time Difference for Sound Source Localization with Spiking Neural Networks",2021,"IEEE/ACM Transactions on Audio Speech and Language Processing","29",,"9502013","2656","2670",,,"10.1109/TASLP.2021.3100684","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112628160&doi=10.1109%2fTASLP.2021.3100684&partnerID=40&md5=5f331de51cc24a1f843c580be514dea0","Agency of Science, Technology and Research (A-STAR), Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore","Pan, Z., Agency of Science, Technology and Research (A-STAR), Singapore, Singapore; Zhang, M., Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Wu, J., Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Wang, J., Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Li, H., Agency of Science, Technology and Research (A-STAR), Singapore, Singapore","Mammals exhibit remarkable capability of detecting and localizing sound sources in complex acoustic environments by using binaural cues in the spiking manner. Emulating the auditory process for sound source localization (SSL) by mammals, we propose a computational model for accurate and robust SSL under the neuromorphic spiking neural network (SNN) framework. The center of this model is a Multi-Tone Phase Coding (MTPC) scheme, which encodes the interaural time difference (ITD) between binaural pure tones into discriminative spike patterns that can be directly classified by SNNs. As such, SSL can be implemented as an event-driven task on highly efficient, neuromorphic parallel processors. We evaluate the proposed computational model on a directional audio dataset recorded from a microphone array in a realistic acoustic environment with background noise, obstruction, reflection, and other interferences. We report superior localization capability with a mean absolute error (MAE) of 1.02circ or 100% classification accuracy with an angle resolution of 5circ, which surpasses other SNN-based biologically plausible neuromorphic approaches by a relatively large margin and on par with human performance in similar tasks. This study opens up many application opportunities in human-robot interaction where energy efficiency is crucial. As a case study, we successfully deploy the proposed SSL system in a robotic platform to track the speaker and orient the robot's attention. © 2014 IEEE.","Neural phase coding; sound source localization; spiking neural network","Acoustic generators; Audio acoustics; Computation theory; Computational methods; Energy efficiency; Human robot interaction; Mammals; Network coding; Underwater acoustic communication; Acoustic environment; Classification accuracy; Computational model; Interaural time differences; Neuromorphic approach; Sound source localization; Spiking neural network(SNN); Spiking neural networks; Neural networks",,,,,"192 25 00054; 2019KC0AB02; China Postdoctoral Science Foundation: 495 2020M680148; Science and Engineering Research Council, SERC; National Key Research and Development Program of China, NKRDPC: 2018AAA0100202","Manuscript received October 22, 2020; revised April 24, 2021 and June 7, 2021; accepted July 20, 2021. Date of publication July 29, 2021; date of current version August 18, 2021. This research work is supported by Programmatic under Grant A1687b0033 and Programmatic under Grant I2001E0053 from the Singapore Government’s Research, Innovation and Enterprise 2020 plan (Advanced Manufacturing and Engineering domain), and in part by the Science and Engineering Research Council, Agency of Science, Technology and Research, Singapore, through the National Robotics Program under Grant 192 25 00054. The work of J. Wu was also partially supported by the Zhejiang Lab under Grant 2019KC0AB02. The work of M. Zhang was also partially supported by the China Postdoctoral Science Foundation under Grant 495 2020M680148, Zhejiang Lab’s International Talent Found for Young Professionals, and National Key R&D Program of China under Grant 2018AAA0100202. (Corresponding author: Jibin Wu.) 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Circuits Syst., pp. 1-5; Varga, A., Steeneken, H.J., Assessment for automatic speech recognition: Ii noisex-92: A database and an experiment to study the effect of additive noise on speech recognition systems (1993) Speech Commun., 12 (3), pp. 247-251","Wu, J.; Department of Electrical and Computer Engineering, Singapore; email: jibin.wu@u.nus.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23299290,,,,"English","IEEE ACM Trans. Audio Speech Lang. Process.",Article,"Final","",Scopus,2-s2.0-85112628160
"Moreno G.P., De la Cruz N.D., Ortiz J.S., Andaluz V.H.","57224319193;57224305427;56425030300;36163031900;","Human-Robot Collaborative Control for Handling and Transfer Objects",2021,"Communications in Computer and Information Science","1388 CCIS",,,"96","110",,,"10.1007/978-3-030-71503-8_8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107334282&doi=10.1007%2f978-3-030-71503-8_8&partnerID=40&md5=fe80e9b6d468b9e6941f05acc73ab8f3","Universidad de Las Fuerzas Armadas ESPE, Sangolquí, Ecuador","Moreno, G.P., Universidad de Las Fuerzas Armadas ESPE, Sangolquí, Ecuador; De la Cruz, N.D., Universidad de Las Fuerzas Armadas ESPE, Sangolquí, Ecuador; Ortiz, J.S., Universidad de Las Fuerzas Armadas ESPE, Sangolquí, Ecuador; Andaluz, V.H., Universidad de Las Fuerzas Armadas ESPE, Sangolquí, Ecuador","The document proposes the development of a 3D virtual environment, oriented to the common work activities between a unicycle type mobile manipulator robot and a human operator, in collaborative tasks. This strategy is focuses on the incorporation of virtual reality (VR), in which the operator will have access to visualize in an immersive way the behavior of the mobile manipulator robot in common tasks where the human being and the mobile manipulator robot interact. For the interaction between the human operator and the mobile manipulator robot, the graphic engine Unity 3D is used, which exchanges information with the mathematical software Matlab, in order to execute the control algorithm through the use of shared memories. The Novint Falcon haptic device allows human-robot interaction, which provides the operator with force feedback on what is happening in the virtual environment generated by the Unity 3D software and the interaction it has with the mobile manipulator. The HTC Vive immersion device allows the operator to visualize the virtual environment created for the execution of the task. In this work, the design and simulation of the locomotion system of a mobile manipulator robot is carried out for manipulation and object transfer tasks together with the human operator. Finally, the simulation results that validate the proposed control strategy are presented and discussed. © 2021, Springer Nature Switzerland AG.","Collaborative control; Matlab; Mobile manipulator; Unity 3D; Virtual reality","Biofeedback; Industrial manipulators; Machine design; Mathematical operators; MATLAB; Personnel; Social robots; Virtual reality; 3-D virtual environment; Collaborative control; Collaborative tasks; Control strategies; Design and simulation; Mathematical software; Mobile manipulator; Mobile manipulator robot; Manipulators",,,,,"CEPRA-XIV-2020–08-RVA; Universidad de las Fuerzas Armadas ESPE, ESPE","The authors would like to thank the Corporaci?n Ecuatoriana para el Desarrollo de la Investigaci?n y Academia-CEDIA for their contribution in innovation, through the CEPRA projects, especially the project CEPRA-XIV-2020?08-RVA ?Tecnolog?as Inmersivas Multi-Usuario Orientadas a Sistemas Sin?rgicos de Ense?anza y Aprendizaje?; also the Universidad de las Fuerzas Armadas ESPE and the Research Group ARSI, for the support for the development of this work.","Acknowledgements. The authors would like to thank the Corporación Ecuatoriana para el Desarrollo de la Investigación y Academia - CEDIA for their contribution in innovation, through the CEPRA projects, especially the project CEPRA-XIV-2020–08-RVA “Tecnologías Inmersi-vas Multi-Usuario Orientadas a Sistemas Sinérgicos de Enseñanza y Aprendizaje”; also the Universidad de las Fuerzas Armadas ESPE and the Research Group ARSI, for the support for the development of this work.",,,,,,,,,"Beck, S., Kunert, A., Kulik, A., Froehlich, B., Immersive group-to-group telepresence (2013) IEEE Trans. Visual. Comput. Graph., 19 (4), pp. 616-625; Selzer, M., Gazcón, N., Nagel, J.T., Larrea, M., Castro, S., Bjerg, E., (2018) Tecnologías Inmersivas Aplicadas: Realidad Virtual Y Aumentada, pp. 366-370. , XX Workshop de Investigadores en Ciencias de la Computación, pp; Pilatásig, M., Tobar, E., Paredes, L., Silva, F.M., Acurio, A., Pruna, E., Escobar, I., Sánchez, Z., Virtual system for teaching-learning of initial education using a haptic device. Springer International Publishing AG, vol (2018) AVR, 2018, pp. 118-132. , , pp; Pruna, E., Rosero, M., Pogo, R., Escobar, I., Acosta, J., Virtual reality as a tool for the cascade control learning. Springer International Publishing AG., vol. AVR 2018, pp (2018) 243–, p. 251; Gutiérrez-Maldonado, J., Alsina-Jurnet, I., Carvallo-Becíu, C., Letosa-Porta, A., Magallón-Neri, E., Aplicaciones clínicas de la realidad virtual en el ámbito escolar (2007) Medicina Psicosomatica Y Psiquiatrica En Enlace, nº 82, Pp. 32–51; Hilera, J.R., Otón, S., Martínez, J., (1999) Aplicación De La Realidad Virtual En La enseñanza a través De Internet, 8, pp. 25-35. , , vol., pp; Jiménez, S.M.S., Macías, J.L.H., Lumbreras, M.A.M., La Revolución Industrial a través de Realidad Virtual (2018) Revista Iztatl Computación, 13 (7), pp. 56-64; Garcia, R., Realidad virtual como herramienta en fisioterapia, ficción o realiad (2018) Sciencedirect, 40, pp. 1-3; Escobar, I., Gálvez, C., Corrales, G., Pruna, E., Pilatasig, M., Montaluisa, J., Virtual System using haptic devicefor real-time tele-rehabilitationof upper limbs. 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Lett., 4 (2), pp. 1447-1453; Andaluz, V.H., Leica, P., Roberti, F., Toibero, M., Carelli, R., (2012) Adaptive Coordinated Cooperative Controlof Multi-Mobile Manipulators. Frontiers in Advanced Control Systems, pp. 163-190. , , pp; Chheang, V., Saalfeld, P., Huber, T., Huettl, F., Kneist, W., Preim, B., Hansen, C., Collaborative virtual reality for laparoscopic liver surgery training (2019) Conference on Artificial Intelligence and Virtual Reality (AIVR) 2019, pp. 1-9. , , pp; Carvajal, C.P., Méndez, M.G., Torres, D.C., Terán, C., Arteaga, O.B., Andaluz, V.H., Autonomous and tele-operated navigation of aerial manipulator robots in digitalized virtual environments. Springer International Publishing AF, vol (2018) AVR, 2018, pp. 496-515. , , pp; Andaluz, V., Flavio, R., Toibero, J.M., Carel, R., Adaptive unified motion control of mobile manipulators (2012) Control Eng. Practice, 20 (12), pp. 1337-1352","Moreno, G.P.; Universidad de Las Fuerzas Armadas ESPEEcuador; email: gpmoreno1@espe.edu.ec","Botto-Tobar M.Montes Leon S.Camacho O.Chavez D.Torres-Carrion P.Zambrano Vizuete M.",,"Springer Science and Business Media Deutschland GmbH","2nd International Conference on Applied Technologies, ICAT 2020","2 December 2020 through 4 December 2020",,257279,18650929,9783030715021,,,"English","Commun. Comput. Info. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85107334282
"Pashevich E.","57223269877;","Can communication with social robots influence how children develop empathy? Best-evidence synthesis",2021,"AI and Society",,,,"","",,2,"10.1007/s00146-021-01214-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105417711&doi=10.1007%2fs00146-021-01214-z&partnerID=40&md5=3450c898fc3bbd8515987bb1735a8566","Department of Media and Communication, University of Oslo, Oslo, Norway","Pashevich, E., Department of Media and Communication, University of Oslo, Oslo, Norway","Social robots are gradually entering children’s lives in a period when children learn about social relationships and exercise prosocial behaviors with parents, peers, and teachers. Designed for long-term emotional engagement and to take the roles of friends, teachers, and babysitters, such robots have the potential to influence how children develop empathy. This article presents a review of the literature (2010–2020) in the fields of human–robot interaction (HRI), psychology, neuropsychology, and roboethics, discussing the potential impact of communication with social robots on children’s social and emotional development. The critical analysis of evidence behind these discussions shows that, although robots theoretically have high chances of influencing the development of empathy in children, depending on their design, intensity, and context of use, there is no certainty about the kind of effect they might have. Most of the analyzed studies, which showed the ability of robots to improve empathy levels in children, were not longitudinal, while the studies observing and arguing for the negative effect of robots on children’s empathy were either purely theoretical or dependent on the specific design of the robot and the situation. Therefore, there is a need for studies investigating the effects on children’s social and emotional development of long-term regular and consistent communication with robots of various designs and in different situations. © 2021, The Author(s).","Children; Cognitive empathy; Communication; Early childhood; Empathy development; Social robots","Machine design; Social robots; CAN communications; Context of use; Critical analysis; Emotional engagements; Neuropsychology; Potential impacts; Robot interactions; Social relationships; Economic and social effects",,,,,"Universitetet i Oslo, UiO","Open access funding provided by University of Oslo (incl Oslo University Hospital). This research was conducted as part of a PhD scholarship funded by the Faculty of Humanities at the University of Oslo.",,,,,,,,,,"EmotoTent: Reducing school violence through embodied empathy games (2019) Proceedings of the 18Th ACM International Conference on Interaction Design and Children, June, 12 (2019), pp. 755-760. , https://doi.org/10.1145/3311927.3326596; Asada, M., Towards artificial empathy. How can artificial empathy follow the developmental pathway of natural empathy? 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In Proceedings of the Ninth International Conference on Tangible (2015) Embedded, and Embodied Interaction (TEI’15), pp. 593-598. , https://doi.org/10.1145/2677199.2688805","Pashevich, E.; Department of Media and Communication, Norway; email: Ekaterina.pashevich@media.uio.no",,,"Springer Science and Business Media Deutschland GmbH",,,,,09515666,,,,"English","AI Soc.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85105417711
"Ferracuti F., Freddi A., Iarlori S., Longhi S., Monteriù A., Porcaro C.","54381628300;36682153900;56027864000;56911180600;16638623100;6603111837;","Augmenting robot intelligence via EEG signals to avoid trajectory planning mistakes of a smart wheelchair",2021,"Journal of Ambient Intelligence and Humanized Computing",,,,"","",,,"10.1007/s12652-021-03286-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105375113&doi=10.1007%2fs12652-021-03286-7&partnerID=40&md5=662b5812c4711ace60b80783b1259413","Department of Information Engineering, Università Politecnica delle Marche, Ancona, 60131, Italy; Institute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR), Rome, Italy; Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium; S. Anna Institute and Research in Advanced Neurorehabilitation (RAN), Crotone, Italy; Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom; Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, United Kingdom","Ferracuti, F., Department of Information Engineering, Università Politecnica delle Marche, Ancona, 60131, Italy; Freddi, A., Department of Information Engineering, Università Politecnica delle Marche, Ancona, 60131, Italy; Iarlori, S., Department of Information Engineering, Università Politecnica delle Marche, Ancona, 60131, Italy; Longhi, S., Department of Information Engineering, Università Politecnica delle Marche, Ancona, 60131, Italy; Monteriù, A., Department of Information Engineering, Università Politecnica delle Marche, Ancona, 60131, Italy; Porcaro, C., Department of Information Engineering, Università Politecnica delle Marche, Ancona, 60131, Italy, Institute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR), Rome, Italy, Research Center for Motor Control and Neuroplasticity, KU Leuven, Leuven, Belgium, S. Anna Institute and Research in Advanced Neurorehabilitation (RAN), Crotone, Italy, Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom, Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, United Kingdom","Assistive robots operate in complex environments and in presence of human beings, but the interaction between them can be affected by several factors, which may lead to undesired outcomes: wrong sensor readings, unexpected environmental conditions, or algorithmic errors represent just a few examples of the possible scenarios. When the safety of the user is not only an option but must be guaranteed, a feasible solution is to rely on a human-in-the-loop approach, e.g., to monitor if the robot performs a wrong action during a task execution or environmental conditions affect safety during the human-robot interaction, and provide a feedback accordingly. The present paper proposes a human-in-the-loop framework to enable safe autonomous navigation of an electric powered and sensorized (smart) wheelchair. During the wheelchair navigation towards a desired destination in an indoor scenario, possible problems (e.g. obstacles) along the trajectory cause the generation of electroencephalography (EEG) potentials when noticed by the user. These potentials can be used as additional inputs to the navigation algorithm in order to modify the trajectory planning and preserve safety. The framework has been preliminarily tested by using a wheelchair simulator implemented in ROS and Gazebo environments: EEG signals from a benchmark known in the literature were classified, passed to a custom simulation node, and made available to the navigation stack to perform obstacle avoidance. © 2021, The Author(s).",,"Biofeedback; Electroencephalography; Electrophysiology; Intelligent robots; Navigation; Robot programming; Trajectories; Wheelchairs; Algorithmic errors; Autonomous navigation; Complex environments; Environmental conditions; Navigation algorithms; Robot intelligences; Trajectory Planning; Wheelchair navigation; Human robot interaction",,,,,,,,,,,,,,,,"Alzahab, N.A., Apollonio, L., Di Iorio, A., Alshalak, M., Iarlori, S., Ferracuti, F., Monteriù, A., Porcaro, C., Hybrid deep learning (hDL)-based brain-computer interface (BCI) systems: a systematic review (2021) Brain Sci, 11 (1); Behncke, J., Schirrmeister, R.T., Burgard, W., Ball, T., The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks (2018) 2018 6Th International Conference on Brain-Computer Interface (BCI), pp. 1-6; Bonci, A., Longhi, S., Monteriù, A., Vaccarini, M., Navigation system for a smart wheelchair (2005) J Zhejiang Univ Sci A, 6, pp. 110-117; Cavanini, L., Cimini, G., Ferracuti, F., Freddi, A., Ippoliti, G., Monteriù, A., Verdini, F., A QR-code localization system for mobile robots: Application to smart wheelchairs. In (2017) 2017 European Conference on Mobile Robots (ECMR); Chavarriaga, R., Millan, J.D.R., Learning from EEG error-related potentials in noninvasive brain-computer interfaces (2010) IEEE Trans Neural Syst Rehabil Eng, 18 (4), pp. 381-388; Chavarriaga, R., Ferrez, P.W., Millán, J.R., To err is human: learning from error potentials in brain-computer interfaces (2008) Advances in cognitive neurodynamics ICCN 2007, pp. 777-782. , Wang R, Shen E, Gu F, (eds), Springer, Dordrecht; Ciabattoni, L., Ferracuti, F., Freddi, A., Iarlori, S., Longhi, S., Monteriù, A., Human-in-the-loop approach to safe navigation of a smart wheelchair via brain computer interface (2021) Ambient assisted living, pp. 197-209. , Monteriù A, Freddi A, Longhi S, (eds), Springer, Cham; Falkenstein, M., Hoormann, J., Christ, S., Hohnsbein, J., ERP components on reaction errors and their functional significance: a tutorial (2000) Biol Psychol, 51, pp. 87-107; Ferracuti, F., Freddi, A., Iarlori, S., Longhi, S., Peretti, P., Auditory paradigm for a P300 BCI system using spatial hearing (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 871-876; Ferracuti, F., Casadei, V., Marcantoni, I., Iarlori, S., Burattini, L., Monteriù, A., Porcaro, C., A functional source separation algorithm to enhance error-related potentials monitoring in noninvasive brain-computer interface (2020) Comput Methods Progr Biomed, 191, p. 105419; Foresi, G., Freddi, A., Iarlori, S., Monteriù, A., Ortenzi, D., Proietti Pagnotta, D., Human-robot cooperation via brain computer interface in assistive scenario. Ser Lecture notes in electrical engineering, ambient assisted living (2019) Italian Forum, 540, pp. 115-131; Gougeh, R.A., Rezaii, T.Y., Farzamnia, A., An automatic driver assistant based on intention detecting using EEG signal (2021) Proceedings of the 11Th National Technical Seminar on Unmanned System Technology, 2019, pp. 617-627. , Md Zain Z, Ahmad H, Pebrianti D, Mustafa M, Abdullah NRH, Samad R, Mat Noh M, Springer Singapore, Singapore; Guo, Y., Hastie, T., Tibshirani, R., Regularized linear discriminant analysis and its application in microarrays (2006) Biostatistics, 8 (1), pp. 86-100; Haghani, M., Bliemer, M.C.J., Farooq, B., Kim, I., Li, Z., Oh, C., Shahhoseini, Z., Macdougall, H., (2020) Applications of Brain Imaging Methods in Driving Behaviour Research, 2007, p. 09341; Haufe, S., Treder, M.S., Gugler, M.F., Sagebaum, M., Curio, G., Blankertz, B., EEG potentials predict upcoming emergency brakings during simulated driving (2011) J Neural Eng, 8 (5), p. 056001; Haufe, S., Kim, J., Kim, I.H., Sonnleitner, A., Schrauf, M., Curio, G., Blankertz, B., Electrophysiology-based detection of emergency braking intention in real-world driving (2014) J Neural Eng, 11 (5), p. 056011; Hernández, L.G., Mozos, O.M., Ferrández, J.M., Antelis, J.M., EEG-based detection of braking intention under different car driving conditions (2018) Front Neuroinform, 12, p. 29; Ippoliti, G., Longhi, S., Model-based sensor fault detection system for a smart wheelchair (2005) IFAC Proceedings, 38 (1), pp. 269-274. , 16th IFAC world congress; Iturrate, I., Antelis, J.M., Kubler, A., Minguez, J., A noninvasive brain-actuated wheelchair based on a p300 neurophysiological protocol and automated navigation (2009) IEEE Trans Rob, 25 (3), pp. 614-627; Iturrate, I., Montesano, L., Minguez J (2010) Single trial recognition of error-related potentials during observation of robot operation. In (2010) Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 4181-4184; Iturrate, I., Chavarriaga, R., Montesano, L., Minguez, J., Millan JdR (2012) Latency correction of error potentials between different experiments reduces calibration time for single-trial classification (2012) Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3288-3291; Joch, M., Hegele, M., Maurer, H., Müller, H., Maurer, L.K., Brain negativity as an indicator of predictive error processing: the contribution of visual action effect monitoring (2017) J Neurophysiol, 118 (1), pp. 486-495; Khaliliardali, Z., Chavarriaga, R., Gheorghe, L.A., del Millán, J.R., Action prediction based on anticipatory brain potentials during simulated driving (2015) J Neural Eng, 12 (6); Khaliliardali, Z., Chavarriaga, R., Zhang, H., Gheorghe, L.A., Perdikis, S., Millán, J.D.R., (2019) Real-time detection of driver’s movement intention in response to traffic lights, , bioRxiv; Kim, I.-H., Kim, J.-W., Haufe, S., Lee, S.-W., Detection of braking intention in diverse situations during simulated driving based on EEG feature combination (2014) J Neural Eng, 12 (1), p. 016001; Kumar, A., Gao, L., Pirogova, E., Fang, Q., A review of error-related potential-based brain-computer interfaces for motor impaired people (2019) IEEE Access, 7, pp. 142451-142466; Lee, S., Kim, J., Lee, S., Detecting driver’s braking intention using recurrent convolutional neural networks based EEG analysis (2017) 2017 4Th IAPR Asian Conference on Pattern Recognition (ACPR), pp. 840-845; MacKay, D.J., Bayesian interpolation (1991) Neural Comput, 4, pp. 415-447; Mao, X., Li, M., Li, W., Niu, L., Xian, B., Zeng, M., Chen, G., Review article progress in EEG-based brain robot interaction systems (2017) Comput Intell Neurosci, 2017, pp. 1-25; Nguyen, T.H., Chung, W.Y., Detection of driver braking intention using EEG signals during simulated driving (2019) Sensors, 19 (13); Nordlund, P., Gustafsson, F., Sequential Monte Carlo filtering techniques applied to integrated navigation systems (2001) Proceedings of the 2001 American Control Conference. 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No.01Ch37148), 6, pp. 4375-4380; Ogren, P., Leonard, N.E., A convergent dynamic window approach to obstacle avoidance (2005) IEEE Trans Rob, 21 (2), pp. 188-195; Omedes, J., Iturrate, I., Chavarriaga, R., Montesano, L., (2015); Perrin, X., Chavarriaga, R., Colas, F., Siegwart, R., Millan, J.R., Brain-coupled interaction for semi-autonomous navigation of an assistive robot (2010) Robot Autonom Syst, 58, pp. 1246-1255; Pires, G., Nunes, U., Castelo-Branco, M., Statistical spatial filtering for a P300-based BCI: tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis (2011) J Neurosci Methods, 195 (2), pp. 270-281; Salazar-Gomez, A.F., Delpreto, J., Gil, S., Guenther, F.H., Rus, D., Correcting robot mistakes in real time using EEG signals (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 6570-6577; Siciliano, B., Khatib, O., (2016) Springer handbook of robotics, , Springer, Berlin; Spüler, M., Niethammer, C., Error-related potentials during continuous feedback: Using EEG to detect errors of different type and severity (2015) Front Hum Neurosci, 9; Teng, T., Bi, L., A novel EEG-based detection method of emergency situations for assistive vehicles (2017) 2017 Seventh International Conference on Information Science and Technology (ICIST), pp. 335-339; Teng, T., Bi, L., Fan, X., Using EEG to recognize emergency situations for brain-controlled vehicles (2015) 2015 IEEE Intelligent Vehicles Symposium (IV), pp. 1305-1309; Teng, T., Bi, L., Liu, Y., EEG-based detection of driver emergency braking intention for brain-controlled vehicles (2018) IEEE Trans Intell Transp Syst, 19 (6), pp. 1766-1773; Vecchiato, G., Vecchio, M.D., Ascari, L., Antopolskiy, S., Deon, F., Kubin, L., Ambeck-Madsen, J., Avanzini, P., Electroencephalographic time-frequency patterns of braking and acceleration movement preparation in car driving simulation (2019) Brain Res, 1716, pp. 16-26; Wan, E.A., van Der Merwe, R., The unscented Kalman filter for nonlinear estimation. In (2000) Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No. 00EX373), Ieee, pp. 153-158; Zhang, H., Chavarriaga, R., Gheorghe, L., Millán, J., Inferring driver’s turning direction through detection of error related brain activity (2013) 2013 35Th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2196-2199; Zhang, H., Chavarriaga, R., Khaliliardali, Z., Gheorghe, L., Iturrate, I., Millán, J.R., EEG-based decoding of error-related brain activity in a real-world driving task (2015) J Neural Eng, 12 (6), p. 066028","Ferracuti, F.; Department of Information Engineering, Italy; email: f.ferracuti@univpm.it",,,"Springer Science and Business Media Deutschland GmbH",,,,,18685137,,,,"English","J. Ambient Intell. Humanized Comput.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85105375113
"van Wynsberghe A.","25655523600;","Social robots and the risks to reciprocity",2021,"AI and Society",,,,"","",,4,"10.1007/s00146-021-01207-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105182584&doi=10.1007%2fs00146-021-01207-y&partnerID=40&md5=2fed1e307aa9dd7141c6573745a252ea","Alexander Von Humboldt-Professor for Applied Ethics of Artificial Intelligence, University of Bonn, Bonn, Germany","van Wynsberghe, A., Alexander Von Humboldt-Professor for Applied Ethics of Artificial Intelligence, University of Bonn, Bonn, Germany","A growing body of research can be found in which roboticists are designing for reciprocity as a key construct for successful human–robot interaction (HRI). Given the centrality of reciprocity as a component for our moral lives (for moral development and maintaining the just society), this paper confronts the possibility of what things would look like if the benchmark to achieve perceived reciprocity were accomplished. Through an analysis of the value of reciprocity from the care ethics tradition the richness of reciprocity as an inherent value is revealed: on the micro-level, as mutual care for immediate care givers, and on the macro-level, as foundational for a just society. Taking this understanding of reciprocity into consideration, it becomes clear that HRI cannot achieve this bidirectional value of reciprocity; a robot must deceive users into believing it is capable of reciprocating to humans or is deserving of reciprocation from humans. Moreover, on the macro-level, designing social robots for reciprocity threatens the ability and willingness to reciprocate to human care workers across society. Because of these concerns, I suggest re-thinking the goals of reciprocity in social robotics. Designing for reciprocity in social robotics should be dedicated to the design of robots to enhance the ability to mutually care for those that provide us with care, as opposed to designing for reciprocity between human and robot. © 2021, The Author(s).","Care ethics; Human–robot interaction; Reciprocity; Robot ethics; Social robots","Machine design; Robotics; Growing bodies; Inherent value; Micro level; Moral development; Robot interactions; Social robotics; Social robots",,,,,"Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO: 275-20-054","This research is supported by the Netherlands Organization for Scientific Research (NWO), project number 275-20-054. Many thanks to Johanna Seibt and Marco Nǿrskov for inviting me to present an earlier version of this work at the 2020 RoboPhil conference.",,,,,,,,,,"Arnold, T., Scheutz, M., The tactile ethics of soft robotics: designing wisely for human-robot interaction (2017) Soft Robot, 4 (2), p. 2; Breazeal, C.L., (2004) Designing sociable robots, , MIT Press; Calo, R., Robots and privacy (2011) Robot ethics: the ethical and social implications of robotics, , Lin P, Abney K, Bekey GA, (eds), MIT Press; de Graaf, M.M.A., An ethical evaluation of human-robot relationships (2016) Int J Soc Robot, 8 (4), pp. 589-598; Decker, M., Caregiving robots and ethical reflection: the perspective of interdisciplinary technology assessment (2008) AI Society, 22 (3), pp. 315-330; Duska, R., Whelan, M., (1975) Moral development: a guide to Piaget and Kohlberg, , Paulist Press; Fischinger, D., Einramhof, P., Papoutsakis, K., Wohlkinger, W., Mayer, P., Panek, P., Hofmann, S., Vincze, M., Hobbit, a care robot supporting independent living at home: first prototype and lessons learned (2016) Robot Auton Syst, 75, pp. 60-78; Fogg, B., Nass, C., How users reciprocate to computers | CHI ’97 extended abstracts on human factors in computing systems (1997) Extended Abst Hum Factors Comput Syst NY.; Frennert, S., Östlund, B., Review: seven matters of concern of social robots and older people (2014) Int J Soc Robot, 6 (2), pp. 299-310; Gouldner, A.W., The norm of reciprocity: a preliminary statement (1960) Am Sociol Rev, 25 (2), pp. 161-178; Kahn, P.H., Jr., Friedman, B., Perez-Granados, D.R., Freier, N.G., Robotic pets in the lives of preschool children (2004) Extended Abstracts Hum Factors Comput Syst, 5, pp. 1449-1452; Kahn, P.H., Ishiguro, H., Friedman, B., Kanda, T., What is a Human? - Toward psychological benchmarks in the field of human-robot interaction (2006) ROMAN 2006 - the 15Th IEEE International Symposium on Robot and Human Interactive Communication, pp. 364-371. , https://doi.org/10.1109/ROMAN.2006.314461; Lammer, L., Huber, A., Weiss, A., Vincze, M., Mutual Care: How older adults react when they should help their care robot (2014) Proceedings of the 3Rd International Symposium on New Frontiers in Human–Robot Interaction, pp. 1-4; Lorenz, T., Weiss, A., Hirche, S., Synchrony and reciprocity: key mechanisms for social companion robots in therapy and care (2016) Int J Soc Robot, 8 (1), pp. 125-143; Oost, E., Reed, D., Towards a sociological understanding of robots as companions (2011) Human-robot personal relationships, , Lamers MH, Verbeek FJ, (eds), Springer; Paiva, A., Santos, F.P., Santos, F.C., Engineering Pro-Sociality With Autonomous Agents (2018) Thirty-Second Aaai Conference on Artificial Intelligence. Thirty-Second AAAI Conference on Artificial Intelligence, , https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16799; Sander-Staudt, M., Caring reciprocity as a relational and political ideal in Confucianism and care ethics (2015) Care ethics and political theory, , Engelens D, Hamington M, (eds), Oxford University Press; Sandoval, E.B., Brandstetter, J., Obaid, M., Bartneck, C., Reciprocity in human-robot interaction: a quantitative approach through the prisoner’s dilemma and the ultimatum game (2016) Int J Soc Robot, 8 (2), pp. 303-317; Scheutz, M., The inherent dangers of unidirectional emotional bonds between humans and social robots (2012) Robot ethics: the ethical and social implications of robotics, , Lin P, Abney K, Bekey G, (eds), MIT Press; Sharkey, A., Robots and human dignity: a consideration of the effects of robot care on the dignity of older people (2014) Ethics Inf Technol, 16 (1), pp. 63-75; Sharkey, A., Sharkey, N., Granny and the robots: ethical issues in robot care for the elderly (2012) Ethics Inf Technol, 14 (1), pp. 27-40; Sullins, J.P., Robots, love, and sex: the ethics of building a love machine (2012) IEEE Trans Affect Comput, 3 (4), pp. 398-409; Tronto, J., (1993) Moral boundaries: a political argument for an ethic of care, , Routledge; van Wynsberghe, A., A method for integrating ethics into the design of robots (2013) Industrial Robot An Internat J, 40 (5), pp. 433-440; van Wynsberghe, A., Designing robots for care: care centered value-sensitive design (2013) Sci Eng Ethics, 19 (2), pp. 407-433; van Wynsberghe, A., Service robots, care ethics, and design (2016) Ethics Inf Technol, 18 (4), pp. 311-321; van Wynsberghe, A., Li, S., A paradigm shift for robot ethics: from HRI to human–robot–system interaction (HRSI) (2019) Medicol Bioethics, 9, pp. 11-20; Vanlaere, L., Gastmans, C., A personalist approach to care ethics (2011) Nurs Ethics Nursing Ethics, 18 (2), pp. 161-173; Verkerk, M., The care perspective and autonomy (2001) Med Health Care Philos, 4 (3), pp. 289-294","van Wynsberghe, A.; Alexander Von Humboldt-Professor for Applied Ethics of Artificial Intelligence, Germany; email: aimee@uni-bonn.de",,,"Springer Science and Business Media Deutschland GmbH",,,,,09515666,,,,"English","AI Soc.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85105182584
"Prasad V., Stock-Homburg R., Peters J.","57201912364;56364282100;35248912800;","Human-Robot Handshaking: A Review",2021,"International Journal of Social Robotics",,,,"","",,2,"10.1007/s12369-021-00763-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102899157&doi=10.1007%2fs12369-021-00763-z&partnerID=40&md5=e0a109886012b485696c836c059df065","Technical University of Darmstadt, Darmstadt, Germany; Max Planck Institute for Intelligent Systems, Tübingen, Germany","Prasad, V., Technical University of Darmstadt, Darmstadt, Germany; Stock-Homburg, R., Technical University of Darmstadt, Darmstadt, Germany; Peters, J., Technical University of Darmstadt, Darmstadt, Germany, Max Planck Institute for Intelligent Systems, Tübingen, Germany","For some years now, the use of social, anthropomorphic robots in various situations has been on the rise. These are robots developed to interact with humans and are equipped with corresponding extremities. They already support human users in various industries, such as retail, gastronomy, hotels, education and healthcare. During such Human-Robot Interaction (HRI) scenarios, physical touch plays a central role in the various applications of social robots as interactive non-verbal behaviour is a key factor in making the interaction more natural. Shaking hands is a simple, natural interaction used commonly in many social contexts and is seen as a symbol of greeting, farewell and congratulations. In this paper, we take a look at the existing state of Human-Robot Handshaking research, categorise the works based on their focus areas, draw out the major findings of these areas while analysing their pitfalls. We mainly see that some form of synchronisation exists during the different phases of the interaction. In addition to this, we also find that additional factors like gaze, voice facial expressions etc. can affect the perception of a robotic handshake and that internal factors like personality and mood can affect the way in which handshaking behaviours are executed by humans. Based on the findings and insights, we finally discuss possible ways forward for research on such physically interactive behaviours. © 2021, The Author(s).","Handshaking; Physical HRI; Social robotics","Agricultural robots; Anthropomorphic robots; End effectors; Facial Expressions; Human robot Interaction (HRI); Human robots; Internal factors; Key factors; Natural interactions; Non-verbal behaviours; Social context; Social robots",,,,,,"This research was funded by the Interdisciplinary Research Forum (Forum Interdisziplinäre Forschung - FiF) at the Technical University of Darmstadt, the Association of Supporters of Market-Oriented Management, Marketing, and Human Resource Management (Förderverein für Marktorientierte Unternehmensfuḧrung, Marketing und Personal management e.V.), the Leap in Time Foundation (Leap in Time Stiftung), and the Center for responsible digitization (Zentrum Verantwortungsbewusste Digitalisierung - ZEVEDI).",,,,,,,,,,"Ammi, M., Demulier, V., Caillou, S., Gaffary, Y., Tsalamlal, Y., Martin, J.C., Tapus, A., Haptic human-robot affective interaction in a handshaking social protocol (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction; Arns, M., Laliberté, T., Gosselin, C., Design, control and experimental validation of a haptic robotic hand performing human-robot handshake with human-like agility (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , IEEE; Artem, M.A., Viacheslav, K.M., Volodymyr, B.P., Patrick, H., Physical human–robot interaction in the handshaking case: Learning of rhythmicity using oscillators neurons (2013) IFAC Proceedings Volumes; Åström, J., Introductory greeting behaviour: A laboratory investigation of approaching and closing salutation phases (1994) Perceptual and Motor Skills; Åström, J., Thorell, L.H., Greeting behaviour and psychogenic need: Interviews on experiences of therapists, clergymen, and car salesmen (1996) Perceptual and Motor Skills; Avelino, J., Correia, F., Catarino, J., Ribeiro, P., Moreno, P., Bernardino, A., Paiva, A., The power of a hand-shake in human-robot interactions (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 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"Liu C., Hirota K., Ma J., Jia Z., Dai Y.","57235167200;7402829702;57192114657;56669163700;7401512972;","Facial Expression Recognition Using Hybrid Features of Pixel and Geometry",2021,"IEEE Access","9",,"9335586","18876","18889",,1,"10.1109/ACCESS.2021.3054332","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100517904&doi=10.1109%2fACCESS.2021.3054332&partnerID=40&md5=b0b67b0947ea42939860ea844d7da633","School of Automation, Beijing Institute of Technology, Beijing, 100081, China; State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, 100081, China; Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China","Liu, C., School of Automation, Beijing Institute of Technology, Beijing, 100081, China, State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, 100081, China; Hirota, K., School of Automation, Beijing Institute of Technology, Beijing, 100081, China, State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, 100081, China; Ma, J., Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China; Jia, Z., School of Automation, Beijing Institute of Technology, Beijing, 100081, China, State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, 100081, China; Dai, Y., School of Automation, Beijing Institute of Technology, Beijing, 100081, China, State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, 100081, China","Facial Expression Recognition (FER) has long been a challenging task in the field of computer vision. Most of the existing FER methods extract facial features on the basis of face pixels, ignoring the relative geometric position dependencies of facial landmark points. This article presents a hybrid feature extraction network to enhance the discriminative power of emotional features. The proposed network consists of a Spatial Attention Convolutional Neural Network (SACNN) and a series of Long Short-term Memory networks with Attention mechanism (ALSTMs). The SACNN is employed to extract the expressional features from static face images and the ALSTMs is designed to explore the potentials of facial landmarks for expression recognition. A deep geometric feature descriptor is proposed to characterize the relative geometric position correlation of facial landmarks. The landmarks are divided into seven groups to extract deep geometric features, and the attention module in ALSTMs can adaptively estimate the importance of different landmark regions. By jointly combining SACNN and ALSTMs, the hybrid features are obtained for expression recognition. Experiments conducted on three public databases, FER2013, CK+, and JAFFE, demonstrate that the proposed method outperforms the previous methods, with the accuracies of 74.31%, 95.15%, and 98.57%, respectively. The preliminary results of Emotion Understanding Robot System (EURS) indicate that the proposed method has the potential to improve the performance of human-robot interaction. © 2013 IEEE.","attention mechanism; Facial expression recognition; hybrid feature; long short-term memory network; relative geometric position dependency","Convolutional neural networks; Geometry; Pixels; Social robots; Attention mechanisms; Discriminative power; Emotion understanding; Expression recognition; Facial expression recognition; Geometric position; Hybrid-feature extraction; Spatial attention; Face recognition",,,,,"WQ20141100198; Beijing Municipal Natural Science Foundation: 3192028, L191020","This work was supported in part by the National Talents Foundation of China under Grant No. WQ20141100198, in part by the Beijing Municipal Natural Science Foundation under Grant No. 3192028, and in part by the Beijing Municipal Natural Science Foundation under Grant No. L191020.",,,,,,,,,,"Chen, J., Chen, Z., Chi, Z., Fu, H., Facial expression recognition in video with multiple feature fusion (2018) Ieee Trans. 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Cybern., 49 (3), pp. 839-847. , Jan; Cai, J., Meng, Z., Khan, A.S., Li, Z., O'Reilly, J., Tong, Y., Island loss for learning discriminative features in facial expression recognition (2018) Proc. 13th Ieee Int. Conf. Autom. Face Gesture Recognit., pp. 302-309. , May; Li, K., Jin, Y., Akram, M.W., Han, R., Chen, J., Facial expression recognition with convolutional neural networks via a new face cropping and rotation strategy (2020) Vis. Comput., 36 (2), pp. 391-404. , Feb; Greco, A., Roberto, A., Saggese, A., Vento, M., Vigilante, V., Emotion analysis from faces for social robotics (2019) Proc. Ieee Int. Conf. Syst., pp. 358-364. , Man Cybern. (SMC), Oct","Dai, Y.; School of Automation, China; email: daiyaping@bit.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85100517904
"Takanokura M., Kurashima R., Ohhira T., Kawahara Y., Ogiya M.","12783114100;57221233775;57221228077;57221233948;57205127888;","Implementation and user acceptance of social service robot for an elderly care program in a daycare facility",2021,"Journal of Ambient Intelligence and Humanized Computing",,,,"","",,,"10.1007/s12652-020-02871-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098535252&doi=10.1007%2fs12652-020-02871-6&partnerID=40&md5=33f7541ee316dc60ca4d9cfc8c6a0421","Department of Industrial Engineering and Management, Faculty of Engineering, Kanagawa University, 3-27-1 Rokkakubashi, Kanagawa-ku, Yokohama, 221-8686, Japan","Takanokura, M., Department of Industrial Engineering and Management, Faculty of Engineering, Kanagawa University, 3-27-1 Rokkakubashi, Kanagawa-ku, Yokohama, 221-8686, Japan; Kurashima, R., Department of Industrial Engineering and Management, Faculty of Engineering, Kanagawa University, 3-27-1 Rokkakubashi, Kanagawa-ku, Yokohama, 221-8686, Japan; Ohhira, T., Department of Industrial Engineering and Management, Faculty of Engineering, Kanagawa University, 3-27-1 Rokkakubashi, Kanagawa-ku, Yokohama, 221-8686, Japan; Kawahara, Y., Department of Industrial Engineering and Management, Faculty of Engineering, Kanagawa University, 3-27-1 Rokkakubashi, Kanagawa-ku, Yokohama, 221-8686, Japan; Ogiya, M., Department of Industrial Engineering and Management, Faculty of Engineering, Kanagawa University, 3-27-1 Rokkakubashi, Kanagawa-ku, Yokohama, 221-8686, Japan","A social service robot in elderly care is capable of improving the quality of life of the elderly and simultaneously reducing the workload of healthcare workers. It can have positive and negative effects in terms of the social and technical contexts of healthcare services. This study aims to implement a social service robot in elderly care programs and investigate user acceptance at daycare facilities. A robot was installed at a facility for use in two types of care programs: exercise with cognitive tasks and brain training with arithmetic operations. A questionnaire survey was conducted to estimate the overall impressions and attitudes of elderly users and healthcare workers toward the installation of the robot in elderly care. The results indicated that the care program using the robot was effective, but its efficiency was not relatively high at this facility. The effectiveness of the care program depended considerably on the different capabilities of the elderly users. This issue arose from the diverse capabilities and varying attitudes of acceptance of the elderly users toward the technology. Technical improvements can solve the former issue so that the healthcare worker can more easily adjust robot functions, such as voice control. The latter issue can be improved by establishing better human–robot interaction in the context of daily programs at daycare facilities. Adoption of such robotic services should be considered in the social contexts of elderly care. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.","Acceptability; Brain training with arithmetic operations; Diversity; Elderly care; Exercise with cognitive tasks; Human factors","Health care; Mobile robots; Social robots; Surveys; Arithmetic operations; Healthcare services; Healthcare workers; Its efficiencies; Positive and negative effect; Questionnaire surveys; Robot interactions; Technical improvement; Educational robots",,,,,"Japan Society for the Promotion of Science, KAKEN: 19K04915, 19K04915, 19K04915, 19K04915; Japan Society for the Promotion of Science, KAKEN: 19K04915, 19K04915, 19K04915, 19K04915","This research was supported by JSPS KAKENHI Grant Number 19K04915. The authors would like to express their sincere appreciation to the daycare facility for their cooperative support.","JSPS KAKENHI Grant No. 19K04915.",,,,,,,,,"Bogue, R., Europe leads the way in assistive robots for the elderly (2017) Ind Robot, 44 (3), pp. 253-258; Broekens, J., Heerink, M., Rosendal, H., Assistive social robots in elderly care: a review (2009) Gerontechnology, 8 (2), pp. 94-103; (2019) Annual Report on the Ageing Society [Summary] FY 2019, , https://www8.cao.go.jp/kourei/english/annualreport/2019/pdf/2019.pdf, Accessed 19 Mar 2020; Chohen-Mansfield, J., Biddison, J., The scope and future trends of gerontechnology: consumer’s opinions and literature survey (2007) J Tech Hum Serv, 25 (3), pp. 1-19; Coco, K., Kangasniemi, M., Rantanen, T., Care personnel’s attitude and fears toward care robots in elderly care: a comparison of data from the care personnel in Finland and Japan (2018) J Nurs Scholarsh, 50 (6), pp. 634-644; De Graaf, M.M.A., Allouch, S.B., Exploring influencing variables for the acceptance of social robots (2013) Robot Auton Syst, 61 (12), pp. 1476-1486; Jaschinski, C., Ben Allouch, S., Listening to the ones who care: exploring the perceptions of informal caregivers towards ambient assisted living applications (2019) J Ambient Intell Hum Comput, 10 (2), pp. 761-778; Justo, R., Ben Letaifa, L., Palmero, C., Gonzalez-Fraile, E., Johansen, A.T., Vázquez, A., Cordasco, G., Torres, M.I., Analysis of the interaction between elderly people and a simulated virtual coach (2020) J Ambient Intell Hum Comput; Kachouie, R., Sedighadeli, S., Khosla, R., Chu, M.-T., Socially assistive robots in elderly care: a mixed-method systematic literature review (2014) Int J Hum-Comput Int, 30 (5), pp. 369-393; Kamide, H., Kawabe, K., Shigemi, S., Arai, T., Anshin as a concept of subjective well-being between humans and robots in Japan (2015) Adv Rob, 29 (24), pp. 624-1636; Khosla, R., Nhuyen, K., Chu, M.-T., Human robot engagement and acceptability in residential aged care (2017) Int J Hum-Comput Int, 33 (6), pp. 510-522; Lee, J.-Y., Song, Y.A., Jung, J.Y., Kim, H.J., Kim, B.R., Do, H.-K., Lim, J.-Y., Nurses’ needs for care robots in integrated nursing care services (2018) J Adv Nurs, 74 (9), pp. 2094-2105; Louie, W.-Y., Mccoll, D., Nejat, G., Acceptance and attitudes toward a human-like socially assistive robot by older adults (2014) Assist Technol, 26 (3), pp. 140-150; Melkas, H., Hennala, L., Pekkarinen, S., Kyrki, V., Impacts of robot implementation on care personnel and clients in elderly care institutions (2020) Int J Med Inform, 134, p. 104041; Obayashi, K., Maruyama, S., The effects of introducing communicative robots with infra-red radiation monitoring system on night shift duties of nursing facility care workers (2018) J Robot Soc Jpn, 36 (8), pp. 537-542. , (inJapanese; Parong, J., Mayer, R.E., Cognitive sequences of playing brain-training games in immersive virtual reality (2020) Appl Cogn Psychol, 34 (1), pp. 29-38; Pedersen, I., Reid, S., Aspevig, K., Developing social robots for aging populations: a literature review of recent academic sources (2018) Sociol Compass, 12 (6); Rantanen, T., Letho, P., Coco, K., The adoption of care robots in home care—a survey on the attitudes of Finnish home care personnel (2018) J Clin Nurs, 27 (9-10), pp. 1846-1859; Sabanivic, S., Robots in society, society in robots—mutual shaping of society and technology as a framework for social robot design (2010) Int J Soc Robot, 2 (4), pp. 439-450; Sparrow, R., Sparrow, L., In the hands of machines? The future of aged care (2006) Mind Mach, 16 (2), pp. 141-161; Suzuki, T., Makizono, H., Doi, T., Park, H., Lee, S., Tsutsumimoto, K., Umemura, K., Shimada, H., Community-based intervention for prevention of dementia in Japan (2015) J Prev Alz Dis, 2 (1), pp. 71-76; Takanokura, M., Yamada, T., Kawakami, M., Taki, S., Kakehi, M., Visualization of a healthcare service by physical activity of workers’ tasks at a long-term care nursing home (2016) Innov Supply Chain Manag, 10 (2), pp. 71-77; Takanokura, M., Yagi, A., Ogiya, M., Ishiguro, K., Impression of training programs for elderly care using communication robot at a daycare facility (2019) J Jpn Soc Welf Eng, 21 (2), pp. 60-68. , (in Japanese; Tanaka, M., Ishii, A., Yamano, E., Ogikubo, H., Okazaki, M., Kamimura, K., Konishi, Y., Watanabe, Y., Effect of a human-type communication robot on cognitive function in elderly women living alone (2012) Med Sci Monitor, 18 (9), pp. CR550-CR557; Tang, L.-L., Chen, J.-S., Tsai, C.-T., The use intention and perceptions of smart robots for the elderly (2019) Proceedings of the 20Th Asia-Pacific Industrial Engineering and Management Systems Conference, APIEMS, pp. 876-880; Tuisku, O., Pekkarinen, S., Hennala, L., Melkas, H., Robots do not replace a nurse with berating heart: the publicity around a robotic innovation in elderly care (2020) Inform Technol Peopl, 32 (1), pp. 47-67; Vichitvanichphong, S., Talaei-Khoei, A., Kerr, D., Ghapanchi, A.H., Assistive technologies for aged care—comparative literature survey on the effectiveness of theories for supportive and empowering technologies (2018) Inform Technol Peopl, 31 (2), pp. 405-427; Wada, K., Shibata, T., Living with seal robots—its sociopsychological and physiological influences on the elderly at a care house (2007) IEEE T Robot, 23 (5), pp. 972-980; Wang, J.-Y., Designing brain training games and evaluating the usability between young and elderly (2016) Proceedings of 2016 3Rd International Conference on Information Science and Control Engineering, IEEE, pp. 308-312; Wu, Y.-H., Fassert, C., Rigaud, A.-S., Designing robots for the elderly: appearance issue and beyond (2012) Arch Gerontol Geriat, 54, pp. 121-126","Takanokura, M.; Department of Industrial Engineering and Management, 3-27-1 Rokkakubashi, Kanagawa-ku, Japan; email: takanokura@kanagawa-u.ac.jp",,,"Springer Science and Business Media Deutschland GmbH",,,,,18685137,,,,"English","J. Ambient Intell. Humanized Comput.",Article,"Article in Press","",Scopus,2-s2.0-85098535252
"Hirota K., Dong F., Garcia Sanchez J.A.","7402829702;56577237700;55336446600;","Multiagent Smart Communication Based on CI Technology",2021,"Studies in Computational Intelligence","899",,,"3","9",,,"10.1007/978-3-030-49536-7_1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088467680&doi=10.1007%2f978-3-030-49536-7_1&partnerID=40&md5=5df5eb481e623241d51ac482c5b10d5b","School of Automation, Beijing Institute of Technology, Beijing, China; Tokyo Institute of Technology, Tokyo, Japan; Ningbo Institute of Intelligent Manufacturing Industry, Yuyao, China; Customer Solutions Development Co., Ltd., Kawasaki, Japan","Hirota, K., School of Automation, Beijing Institute of Technology, Beijing, China, Tokyo Institute of Technology, Tokyo, Japan; Dong, F., Ningbo Institute of Intelligent Manufacturing Industry, Yuyao, China; Garcia Sanchez, J.A., Customer Solutions Development Co., Ltd., Kawasaki, Japan","The presenters’ group has been studying on humans-robots interaction based on Computational Intelligence in the frame work of multiagent smart society, where a concept of Fuzzy Atmosfield (FA) is proposed to express the atmosphere in humans-robots communication. The FA is characterized by a 3D fuzzy cubic space with “friendly-hostile”, “lively-calm”, and “casual-formal” based on a cognitive science experiment and PCA. To understand easily such movement of the atmosphere, a graphical representation method is also proposed. To illustrate the FA and its visualization method, a demonstration scenario “enjoying home party by five eye robots and four humans” is introduced/demonstrated. Then, a visualization method of users’ emotion information is proposed for long distance interaction such as telecommuting and distance learning, where 3D emotion vectors in Affinity Pleasure-Arousal space are illustrated by using shape-brightness-size (SBS) figure. It gives users easily understandable emotional profile information, and provides administrator strategic suggestion to improve the interaction between the users and the system. In the matching experiment between 7 basic emotions and 7 SBS figures for 8 subjects, 83.93% matching is achieved, and the administrator finds contents improvement hint in the questionnaire of emotions for 5 reading-text-tasks by 8 subjects. It is planning to be implemented in a language learning application to provide a more comfortable learning experience by contents selection based on user’s emotion. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.",,,,,,,,,,,,,,,,,,"Liu, Z.T., Wu, M., Li, D., Chen, L.F., Dong, F.Y., Yamazaki, Y., Hirota, K., Concept of fuzzy atmosfield for representing communication atmosphere and its application to humans-robots interaction (2013) J. Adv. Comput. Intell. Intell. Inf, 17 (1), pp. 3-17; Dong, F.Y., Hirota, K., Concept of fuzzy atmosfield and its visualization (2013) On Fuzziness. A Homage to Lotfi A. Zadeh Vol. 1. Studies in Fuzziness and Soft Computing, 216, pp. 257-263. , Seising, R., Trillas, E., Moraga, C., Termini, S. (eds) Springer, Heidelberg; Garcia-Sanchez, J.A., Ohnishi, K., Shibata, A., Dong, F.Y., Hirota, K., Visualization method of emotion information for long distance interaction (2014) 7th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (IEEE-HNICEM 2014) joint with 6th International Conference on Computational Intelligence and Intelligent Informatics (ISCIII 2014), , Palawan, Philippine, DSP-09, November","Hirota, K.; School of Automation, China; email: hirota@bit.edu.cn",,,"Springer",,,,,1860949X,,,,"English","Stud. Comput. Intell.",Book Chapter,"Final","",Scopus,2-s2.0-85088467680
"Yang K., Yang G., Zhang C., Chen C., Zheng T., Cui Y., Chen T.","57188988931;7405752170;56428199200;55597280800;55324533100;27172240400;55363543900;","Cable tension analysis oriented the enhanced stiffness of a 3-DOF joint module of a modular cable-driven human-like robotic arm",2020,"Applied Sciences (Switzerland)","10","24","8871","1","17",,2,"10.3390/app10248871","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098489046&doi=10.3390%2fapp10248871&partnerID=40&md5=eed2c352ac2b8f5cd32b81abbdb5f344","School of Mechanical Engineering and Mechanics, Ningbo University, Ningbo, 315211, China; Zhejiang Key Lab of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences (CAS), Ningbo, 315201, China; Zhejiang Marine Development Research Institute, Zhoushan, 316021, China; Ningbo Artificial Intelligence Institute, Shanghai Jiao Tong University, Ningbo, 315000, China","Yang, K., School of Mechanical Engineering and Mechanics, Ningbo University, Ningbo, 315211, China, Zhejiang Key Lab of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences (CAS), Ningbo, 315201, China, Zhejiang Marine Development Research Institute, Zhoushan, 316021, China; Yang, G., Zhejiang Key Lab of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences (CAS), Ningbo, 315201, China; Zhang, C., Zhejiang Key Lab of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences (CAS), Ningbo, 315201, China; Chen, C., Zhejiang Key Lab of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences (CAS), Ningbo, 315201, China; Zheng, T., Zhejiang Key Lab of Robotics and Intelligent Manufacturing Equipment Technology, Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences (CAS), Ningbo, 315201, China; Cui, Y., School of Mechanical Engineering and Mechanics, Ningbo University, Ningbo, 315211, China; Chen, T., School of Mechanical Engineering and Mechanics, Ningbo University, Ningbo, 315211, China, Ningbo Artificial Intelligence Institute, Shanghai Jiao Tong University, Ningbo, 315000, China","Inspired by the structure of human arms, a modular cable-driven human-like robotic arm (CHRA) is developed for safe human–robot interaction. Due to the unilateral driving properties of the cables, the CHRA is redundantly actuated and its stiffness can be adjusted by regulating the cable tensions. Since the trajectory of the 3-DOF joint module (3DJM) of the CHRA is a curve on Lie group SO(3), an enhanced stiffness model of the 3DJM is established by the covariant derivative of the load to the displacement on SO(3). In this paper, we focus on analyzing the how cable tension distribution problem oriented the enhanced stiffness of the 3DJM of the CHRA for stiffness adjustment. Due to the complexity of the enhanced stiffness model, it is difficult to solve the cable tensions from the desired stiffness analytically. The problem of stiffness-oriented cable tension distribution (SCTD) is formulated as a nonlinear optimization model. The optimization model is simplified using the symmetry of the enhanced stiffness model, the rank of the Jacobian matrix and the equilibrium equation of the 3DJM. Since the objective function is too complicated to compute the gradient, a method based on the genetic algorithm is proposed for solving this optimization problem, which only utilizes the objective function values. A comprehensive simulation is carried out to validate the effectiveness of the proposed method. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Cable tension analysis; Cable-driven robots; Human-like robotic arms; Human–robot interactions; Stiffness adjustment",,,,,,"C2018005; U1909215; 2019C31067; Natural Science Foundation of Ningbo: 2019A610114; National Natural Science Foundation of China, NSFC: :51705510; Zhejiang University, ZJU: ICT20048; State Key Laboratory of Industrial Control Technology; Basic Public Welfare Research Program of Zhejiang Province: LGF19E050001","Funding: This research is funded by the Public Welfare Technology Research Program of Zhejiang Province, China (Grant number: LGF19E050001), National Natural Science Foundation of China (Grant number:51705510), National-Zhejiang Joint Natural Science Foundation of China (Grant number: U1909215), Institute of robotics and intelligent manufacturing innovation, Chinese Academy of Science (Grant number: C2018005), Ningbo Natural Science Foundation (Grant number: 2019A610114), Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China (Grant number: ICT20048) and Public Welfare Technology Research Program of Zhoushan, China (Grant number: 2019C31067).",,,,,,,,,,"Giorelli, M., Renda, F., Calisti, M., Arienti, A., Laschi, C., Learning the inverse kinetics of an octopus-like manipulator in three-dimensional space (2015) BioInspir. 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Robot, 21, pp. 457-465; Bruckmann, T., Pott, A., Hiller, M., Calculating force distributions for redundantly actuated tendon-based Stewart platforms (2006) Advances in Robot Kinematics, pp. 403-412. , Springer: Berlin/Heidelberg, Germany; Hassan, M., Khajepour, A., Optimization of Actuator Forces in Cable-Based Parallel Manipulators Using Convex Analysis (2008) IEEE Trans. Robot, 24, pp. 736-740; Borgstrom, P.H., Jordan, B.L., Borgstrom, B.J., Stealey, M.J., Sukhatme, G.S., Batalin, M.A., Kaiser, W.J., NIMS-PL: A Cable-driven Robot with Self-calibration Capabilities (2009) IEEE Trans. Robot, 25, pp. 1005-1015; You, X., Bing, L., Chen, W., Yu, S., Tension distribution algorithm of a 7-DOF cable-driven robotic arm based on dynamic minimum pre-tightening force Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 715-720. , Karon Beach, Thailand, 7–11 December 2011; Cote, A.F., Cardou, P., Gosselin, C., A tension distribution algorithm for cable-driven parallel robots operating beyond their wrench-feasible workspace Proceedings of the International Conference on Control, Automation and Systems, pp. 68-73. , Jeju, Korea, 18–21 October 2017; Fang, S., Franitza, D., Torlo, M., Bekes, F., Hiller, M., Motion Control of a Tendon-BasedParallel Manipulator Using Optimal Tension Distribution (2004) IEEE/ASME Trans. Mechatron, 9, pp. 561-568; Gosselin, C., On the Determination of the Force Distribution in Overconstrained Cable-driven Parallel Mechanisms (2011) Meccanica, 46, pp. 3-15; Lim, W.B., Song, H.Y., Yang, G., Optimization of Tension Distribution for Cable-Driven Manipulators Using Tension-Level Index (2014) IEEE/ASME Trans. Mechatron, 19, pp. 676-683; Yang, K., Yang, G., Chen, S.L., Wang, Y., Zhang, C., Fang, Z., Zheng, T., Wang, C., Study on Stiffness-Oriented Cable Tension Distribution for a Symmetrical Cable-Driven Mechanism (2019) Symmetry, 11, p. 1158; Yang, K., Yang, G., Chen, S.L., Wang, Y., Zheng, T., Fang, Z., Wang, C., Enhanced Stiffness Modeling and Identification Method for a Cable-driven Spherical Joint Module (2019) IEEE Access, 7, pp. 137875-137886; Murray, R.N., Li, Z., Sastry, S., (1994) A Mathematical Introduction to Robotics Manipulation, , CRC Press: Boca Raton, FL, USA; Lee, J.M., (2018) Introduction to Riemannian Manifolds, , Springer: Berlin/Heidelberg, Germany; Zefran, M., Kumar, V., A geometric approach to the study of the Cartesian stiffness matrix (2002) J. Mech. Des, 124, pp. 30-38; Golub, G.H., Charles, F.V.L., (1985) Matrix Computations, , The Johns Hopkins University Press: Baltimore, MD, USA; Nelder, J.A., Mead, R., A simplex method for function minimization (1965) Comput. J, 7, pp. 308-303; Affenzeller, M., Winkler, S., Wagner, S., Beham, A., (2009) Genetic Algorithms and Genetic Programming-Modern Concepts and Practical Applications, , CRC Press: Boca Raton, FL, USA","Yang, K.; School of Mechanical Engineering and Mechanics, China; email: yangkaisheng@nbu.edu.cn
Yang, K.; Zhejiang Key Lab of Robotics and Intelligent Manufacturing Equipment Technology, China; email: yangkaisheng@nbu.edu.cn
Yang, K.; Zhejiang Marine Development Research InstituteChina; email: yangkaisheng@nbu.edu.cn
Yang, G.; Zhejiang Key Lab of Robotics and Intelligent Manufacturing Equipment Technology, China; email: glyang@nimte.ac.cn",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85098489046
"Shao S., Wang T., Wang Y., Su Y., Song C., Yao C.","54967359700;56222883600;57220187996;57210559914;23111298700;55234317100;","Research of hrv as a measure of mental workload in human and dual-arm robot interaction",2020,"Electronics (Switzerland)","9","12","2174","1","17",,3,"10.3390/electronics9122174","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097881956&doi=10.3390%2felectronics9122174&partnerID=40&md5=8860214d485b3d17e3fa6e5ad5160441","State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; University of Chinese Academy of Sciences, Beijing, 100049, China","Shao, S., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; Wang, T., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; Wang, Y., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China, University of Chinese Academy of Sciences, Beijing, 100049, China; Su, Y., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China, University of Chinese Academy of Sciences, Beijing, 100049, China; Song, C., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China; Yao, C., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China","Robots instead of humans work in unstructured environments, expanding the scope of human work. The interactions between humans and robots are indirect through operating terminals. The mental workloads of human increase with the lack of direct perception to the real scenes. Thus, mental workload assessment is important, which could effectively avoid serious accidents caused by mental overloading. In this paper, the operating object is a dual-arm robot. The classification of operator’s mental workload is studied by using the heart rate variability (HRV) signal. First, two kinds of electrocardiogram (ECG) signals are collected from six subjects who performed tasks or maintained a relaxed state. Then, HRV data is obtained from ECG signals and 20 kinds of HRV features are extracted. Last, six different classifications are used for mental workload classification. Using each subject’s HRV signal to train the model, the subject’s mental workload is classified. Average classification accuracy of 98.77% is obtained using the K-Nearest Neighbor (KNN) method. By using the HRV signal of five subjects for training and that of one subject for testing with the Gentle Boost (GB) method, the highest average classification accuracy (80.56%) is obtained. This study has implications for the analysis of HRV signals characteristic of mental workload in different subjects, which could improve operators’ well-being and safety in the human-robot interaction process. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Heart rate variability; Human-robot interaction; Machine learning; Mental workload",,,,,,"National Natural Science Foundation of China, NSFC: U20A20201; Doctoral Start-up Foundation of Liaoning Province: 2020-BS-025; National Key Research and Development Program of China, NKRDPC: 2016YFE0206200; Liaoning Revitalization Talents Program: XLYC1807018","Funding: This research is funded by the National Natural Science Foundation of China (grant number U20A20201), the Doctoral Scientific Research Foundation of Liaoning Province (grant number 2020-BS-025), the LiaoNing Revitalization Talents Program (grant number XLYC1807018), and the National key research and development program of China (grant number 2016YFE0206200).",,,,,,,,,,"Spiers, A.J., Morgan, A.S., Srinivasan, K., Calli, B., Dollar, A.M., Using a variable-friction robot hand to determine proprioceptive features for object classification during within-hand-manipulation (2020) IEEE Trans. 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Disord, 19, p. 211; Alessandro, T., Alessandro, D., Andrea, D., Lorenzo, B., Francesco, S., Raffffaele, C., Lucia, B., Can machine learning predict stress reduction based on wearable sensors’ data following relaxation at workplace? A Pilot Study (2020) Processes, 8, p. 448; Seungji, L., Taejun, L., Taeyang, Y., Changrak, Y., Sung, P.K., Detection of drivers’ anxiety invoked by driving situations using multimodal biosignals (2020) Processes, 8, p. 155; Zhang, J.H., Wang, Y.C., Li, S.A., Cross-subject mental workload classification using kernel spectral regression and transfer learning techniques (2017) Cogn. Technol. Work, 21, pp. 145-157. , [CrossRef]; Villani, V., Righi, M., Sabattini, L., Secchi, C., Wearable devices for the assessment of cognitive effort for human-robot interaction (2020) IEEE Sens. J, 20, pp. 13047-13056. , [CrossRef]","Shao, S.; State Key Laboratory of Robotics, China; email: shaoshiliang@sia.cn
Shao, S.; Institutes for Robotics and Intelligent Manufacturing, China; email: shaoshiliang@sia.cn",,,"MDPI AG",,,,,20799292,,,,"English","Electronics (Switzerland)",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85097881956
"Vasconez J.P., Viscaino M., Guevara L., Auat Cheein F.","57203006271;57213833597;57191890770;23049317700;","A fuzzy-based driver assistance system using human cognitive parameters and driving style information",2020,"Cognitive Systems Research","64",,,"174","190",,2,"10.1016/j.cogsys.2020.08.007","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090916358&doi=10.1016%2fj.cogsys.2020.08.007&partnerID=40&md5=7791072d832019797aec49a9ffd3e0f4","Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile","Vasconez, J.P., Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile; Viscaino, M., Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile; Guevara, L., Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile; Auat Cheein, F., Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile","Reducing the number of traffic accidents due to human errors is an urgent need in several countries around the world. In this scenario, the use of human-robot interaction (HRI) strategies has recently shown to be a feasible solution to compensate human limitations while driving. In this work we propose a HRI system which uses the driver's cognitive factors and driving style information to improve safety. To achieve this, deep neural networks based approaches are used to detect human cognitive parameters such as sleepiness, driver's age and head posture. Additionally, driving style information is also obtained through speed analysis and external traffic information. Finally, a fuzzy-based decision-making stage is proposed to manage both human cognitive information and driving style, and then limit the maximum allowed speed of a vehicle. The results showed that we were able to detect human cognitive parameters such as sleepiness –63% to 88% accuracy–, driver's age –80% accuracy– and head posture –90.42% to 97.86% accuracy– as well as driving style –87.8% average accuracy. Based on such results, the fuzzy-based architecture was able to limit the maximum allowed speed for different scenarios, reducing it from 50 km/h to 17 km/h. Moreover, the fuzzy-based method showed to be more sensitive with respect to inputs changes than a previous published weighted-based inference method. © 2020 Elsevier B.V.","Driver assistance system; Fuzzy logic; Human cognition; Human robot interaction","Automobile drivers; Decision making; Deep neural networks; Cognitive information; Cognitive parameters; Driver assistance system; Feasible solution; Human limitations; Human robot Interaction (HRI); Inference methods; Traffic information; Human robot interaction; accuracy; adult; age; aged; Article; cognition; controlled study; decision making; deep neural network; driver; driving ability; female; fuzzy based driver assistance system; fuzzy system; head position; human; human cognitive parameters; human experiment; human robot interaction; intermethod comparison; male; methodology; middle aged; motor vehicle; priority journal; robotics; somnolence; traffic; traffic safety; velocity; weighted based inference method; young adult",,,,,"21180470, 21181420, CHILE/2018-21180513, DGIIP-PIIC-26/2020, FB0008","The authors acknowledge the support provided by Universidad Técnica Federico Santa María . This work was supported in part by the Advanced Center of Electrical and Electronic Engineering , AC3E, Basal Project FB0008, DGIIP-PIIC-26/2020 UTFSM Chile, Fondecyt 1201319 , ANID-PFCHA/DOCTORADO BECAS CHILE/2018-21180513, 21181420, and 21180470.","The authors acknowledge the support provided by Universidad T?cnica Federico Santa Mar?a. This work was supported in part by the Advanced Center of Electrical and Electronic Engineering, AC3E, Basal Project FB0008, DGIIP-PIIC-26/2020 UTFSM Chile, Fondecyt 1201319, ANID-PFCHA/DOCTORADO BECAS CHILE/2018-21180513, 21181420, and 21180470.",,,,,,,,,"Agarwal, S., Terrail, J.O.D., Jurie, F., (2018), Recent advances in object detection in the age of deep convolutional neural networks. arXiv preprint arXiv:; Benedetto, S., Pedrotti, M., Minin, L., Baccino, T., Re, A., Montanari, R., Driver workload and eye blink duration (2011) Transportation Research Part F: Traffic Psychology and Behaviour, 14, pp. 199-208. , http://www.sciencedirect.com/science/article/pii/S136984781000094X; Borowsky, A., Shinar, D., Oron-Gilad, T., Age, skill, and hazard perception in driving (2010) Accident Analysis & Prevention, 42, pp. 1240-1249; Brombacher, P., Masino, J., Frey, M., Gauterin, F., Driving event detection and driving style classification using artificial neural networks (2017) 2017 IEEE International Conference on Industrial Technology (ICIT), pp. 997-1002. , IEEE; Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Varoquaux, G., API design for machine learning software: Experiences from the scikit-learn project (2013) ECML PKDD workshop: Languages for data mining and machine learning, pp. 108-122; Cao, Z., Simon, T., Wei, S.E., Sheikh, Y., (2017), Realtime multi-person 2d pose estimation using part affinity fields. 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"Reinhardt D., Haeusler M.H., London K., Loke L., Feng Y., De Oliveira Barata E., Firth C., Dunn K., Khean N., Fabbri A., Wozniak-O’Connor D., Masuda R.","23025957400;57207538867;55900158700;22938119300;55936446400;56038797300;57218667019;57213170134;57204545881;57194695800;57218673722;57218673546;","CoBuilt 4.0: Investigating the potential of collaborative robotics for subject matter experts",2020,"International Journal of Architectural Computing","18","4",,"353","370",,,"10.1177/1478077120948742","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089976214&doi=10.1177%2f1478077120948742&partnerID=40&md5=86ce71c435b1ad0a63c40fb7c49d8994","The University of Sydney, Sydney, NSW, Australia; University of New South Wales, Randwick, NSW, Australia; Western Sydney University, Penrith South, NSW, Australia","Reinhardt, D., The University of Sydney, Sydney, NSW, Australia; Haeusler, M.H., University of New South Wales, Randwick, NSW, Australia; London, K., Western Sydney University, Penrith South, NSW, Australia; Loke, L., The University of Sydney, Sydney, NSW, Australia; Feng, Y., Western Sydney University, Penrith South, NSW, Australia; De Oliveira Barata, E., The University of Sydney, Sydney, NSW, Australia; Firth, C., University of New South Wales, Randwick, NSW, Australia; Dunn, K., University of New South Wales, Randwick, NSW, Australia; Khean, N., University of New South Wales, Randwick, NSW, Australia; Fabbri, A., University of New South Wales, Randwick, NSW, Australia; Wozniak-O’Connor, D., The University of Sydney, Sydney, NSW, Australia; Masuda, R., The University of Sydney, Sydney, NSW, Australia","Human-robot interactions can offer alternatives and new pathways for construction industries, industrial growth and skilled labour, particularly in a context of industry 4.0. This research investigates the potential of collaborative robots (CoBots) for the construction industry and subject matter experts; by surveying industry requirements and assessments of CoBot acceptance; by investing processes and sequences of work protocols for standard architecture robots; and by exploring motion capture and tracking systems for a collaborative framework between human and robot co-workers. The research investigates CoBots as a labour and collaborative resource for construction processes that require precision, adaptability and variability. Thus, this paper reports on a joint industry, government and academic research investigation in an Australian construction context. In section 1, we introduce background data to architecture robotics in the context of construction industries and reports on three sections. Section 2 reports on current industry applications and survey results from industry and trade feedback for the adoption of robots specifically to task complexity, perceived safety, and risk awareness. Section 3, as a result of research conducted in Section 2, introduces a pilot study for carpentry task sequences with capture of computable actions. Section 4 provides a discussion of results and preliminary findings. Section 5 concludes with an outlook on how the capture of computable actions provide the foundation to future research for capturing motion and machine learning. © The Author(s) 2020.","collaborative robotics; Industry 4.0; industry research; machine learning; on-site robotic fabrication","Accident prevention; Biofeedback; Construction industry; Motion tracking; Network architecture; Risk perception; Robotics; Surveys; Collaborative framework; Collaborative resources; Collaborative robots; Construction process; Industry applications; Standard architecture; Subject matter experts; Surveying industry; Social robots",,,,,,,,,,,,,,,,"(2014) Plan for growing Sydney, , https://www.planning.nsw.gov.au/Plans-for-your-area/A-Metropolis-of-Three-Cities/A-Metropolis-of-Three-Cities, Australia, NSW Government, accessed 14 December 2018; McKinsey, , https://www.mckinsey.com/~/media/McKinsey/Featured%20Insights/Asia%20Pacific/Digital%20Australia%20Seizing%20the%20opportunity%20from%20the%20Fourth%20Industrial%20Revolution/Digital-Australia-Seizing-the-opportunity-from-the-fourth-industrial-revolution-vf.ashx, accessed 14 February 2020, (2017; Rosenberg, E., Haeusler, M.H., Araullo, R., Smart architecture-bots and industry 4.0 principles for architecture (2015) 33rd eCAADe, pp. 251-259. , Vienna, eCAADe and Faculty of Architecture and Urban Planning, TU Wien, In; Østergaard, E., White paper – the role of cobots in industry 4.0 (2015) Universal Robots; Bogue, R., Rehabilitation robots (2018) Ind Rob, 45 (3), pp. 301-306; (2016) A roadmap for US robotics from internet to robotics, , https://cra.org/ccc/wp-content/uploads/sites/2/2016/11/roadmap3-final-rs-1.pdf, 2016 edition, accessed 12 March 2020; Takayama, L., Ju, W., Nass, C., Beyond dirty, dangerous and dull: what everyday people think robots should do, , Proceedings of the 3rd ACM/IEEE international conference on human robot interaction, HRI 2008, Amsterdam, The Netherlands, 12–15 March 2008, In; Colgate, J.E.C., Cobots: robots for collaboration with human operators, pp. 433-439. , Proceedings of the international mechanical engineering congress and exhibition, Atlanta, GA, 1996, In; Peshkin, M.A., Colgate, C., (1999) Ind Rob, 26 (5), pp. 335-341; Powell, J.K., Future of robotics: manufacturing gets a makeover with Baxter (2017) Forbes, , https://www.forbes.com/sites/jenniferhicks/2012/09/18/part-1-future-ofrobotics-manufacturing-gets-a-makeover-with-baxter/#2e6de5774c8f, accessed 21 January 2019; Reinhardt, D., Haeusler, M.H., Loke, L., Cobuilt: towards a novel methodology for workflow capture and analysis of carpentry tasks for human-robot collaboration, 2, pp. 207-216. , Proceedings of the 37th eCAADe and 23rd SIGraDi Conference, University of Porto, Porto, Portugal, 11–13 September 2019, In; Dubor, A., Camprodom, G., Bello Diaz, G., Sensors and workflow evolutions: developing a framework for instant, robotic toolpath revision (2016) Robotic fabrication in architecture, art and design 2016, pp. 411-425. , Reinhardt D., Burry J., Saunders R., (eds), Switzerland, Springer International Publishing, In:, (eds; Andraos, S., DMR: a semantic robotic control language, 2, pp. 261-268. , Martens B., Wurzer G., Grasl T., (eds), Real time – Proceedings of the 33rd eCAADe conference, Vienna University of Technology, Vienna, Austria, 16–18 September 2015, In:, (eds; Braumann, J., Brell-Cokcan, S., Adaptive robot control – new parametric workflows Directly from design to KUKA robots, 2, pp. 243-250. , Martens B., Wurzer G., Grasl T., (eds), Real time – proceedings of the 33rd eCAADe conference, Vienna University of Technology, Vienna, Austria, 16–18 September 2015, In:, (eds; Rossi, G., Nicholas, P., Haptic learning – towards neural-network-based adaptive Cobot path-planning for unstructured spaces, 2, pp. 201-210. , Sousa J.P., Xavier J.P., Castro Henriques G., (eds), Proceedings of the 37th eCAADe and 23rd SIGraDi conference, University of Porto, Porto, Portugal, 11–13 September 2019, In:, (eds), In; Schwartz, M., Use of a low-cost humanoid for tiling as a study in on-site fabrication: techniques and methods ACADIA // 2016: POSTHUMAN FRONTIERS: Data, Designers, and Cognitive Machines, pp. 214-223. , Proceedings of the 36th Annual Conference of the Association for Computer Aided Design Architecture (ACADIA) 978-0-692-77095-5, Ann Arbor, 27–29 October 2016, In:, [,]; Wibranek, B., Belousov, B., Sadybakasov, A., Interactive structure – robotic repositioning of vertical elements in man-machine collaborative assembly through vision-based tactile sensing, 2, pp. 705-713. , Sousa J.P., Xavier J.P., Castro Henriques G., (eds), Architecture the age of the 4th industrial reution. Proceedings of the 37th eCAADe and 23rd SIGraDi conference, 11–13 September 2019, University of Porto, Porto, Portugal, In:, (eds; Brynjolfsson, E., McAfee, A., (2014) The second machine age: work, progress, and prosperity in a time of brilliant technologies, , New York, W. W. Norton & Company; Shalev-Shwartz, S., Ben-David, S., (2014) Understanding machine learning: from theory to algorithms, , Cambridge, Cambridge University Press; Michalski, R.S., Carbonell, J.G., Mitchell, T.M., (1983) Machine learning: an artificial intelligence approach, , New York Vienna, Springer; Stanton, C., Bogdanovych, A., Ratanasena, E., Teleoperation of a humanoid robot using full-body motion capture, example movements, and machine learning, pp. 260-269. , Proceedings of Australasian Conference on Robotics and Automation (ACRA), 3–5 December 2012, Victoria University Of Wellington, New Zealand, In; Das, A., Worre Foged, I., Jensen, M., Collaborative robotic masonry and early stage fatigue prediction, 3, pp. 171-178. , Sousa J.P., Xavier J.P., Castro Henriques G., (eds), Proceedings of the 37th eCAADe and 23rd SIGraDi conference, 11–13 September 2019, University of Porto, Porto, Portugal, In:, (eds), In; Firth, C., Dunn, K., Haeusler, M.H., Review of motion capture techniques and role in developing complex robotic systems, , Real / Material / Etheral - The 2nd annual design research conference, 3–4 October 2019, Monash University Melbourne, In; Firth, C., Dunn, K., King, M., Development of an anthropomorphic end effector for collaborative use on construction sites, , Proceedings of the 25th CAADRIA conference, 5–8 August 2020, Chulalongkorn University Bangkok, Bangkok, Thailand, In","Reinhardt, D.; The University of SydneyAustralia; email: dagmar.reinhardt@sydney.edu.au",,,"SAGE Publications Inc.",,,,,14780771,,,,"English","Int. J. Archit. Comput.",Article,"Final","",Scopus,2-s2.0-85089976214
"Tigard D.W., Conradie N.H., Nagel S.K.","56653501200;57216693247;55156218200;","Socially responsive technologies: toward a co-developmental path",2020,"AI and Society","35","4",,"885","893",,3,"10.1007/s00146-020-00982-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084324517&doi=10.1007%2fs00146-020-00982-4&partnerID=40&md5=45c8d4cadcce570846b8bf80c6ddd1f4","Applied Ethics with a Focus on Ethics of Technology, Human-Technology Centre, RWTH Aachen University, Theaterplatz 14, Aachen, 52062, Germany","Tigard, D.W., Applied Ethics with a Focus on Ethics of Technology, Human-Technology Centre, RWTH Aachen University, Theaterplatz 14, Aachen, 52062, Germany; Conradie, N.H., Applied Ethics with a Focus on Ethics of Technology, Human-Technology Centre, RWTH Aachen University, Theaterplatz 14, Aachen, 52062, Germany; Nagel, S.K., Applied Ethics with a Focus on Ethics of Technology, Human-Technology Centre, RWTH Aachen University, Theaterplatz 14, Aachen, 52062, Germany","Robotic and artificially intelligent (AI) systems are becoming prevalent in our day-to-day lives. As human interaction is increasingly replaced by human–computer and human–robot interaction (HCI and HRI), we occasionally speak and act as though we are blaming or praising various technological devices. While such responses may arise naturally, they are still unusual. Indeed, for some authors, it is the programmers or users—and not the system itself—that we properly hold responsible in these cases. Furthermore, some argue that since directing blame or praise at technology itself is unfitting, designing systems in ways that encourage such practices can only exacerbate the problem. On the other hand, there may be good moral reasons to continue engaging in our natural practices, even in cases involving AI systems or robots. In particular, daily interactions with technology may stand to impact the development of our moral practices in human-to-human interactions. In this paper, we put forward an empirically grounded argument in favor of some technologies being designed for social responsiveness. Although our usual practices will likely undergo adjustments in response to innovative technologies, some systems which we encounter can be designed to accommodate our natural moral responses. In short, fostering HCI and HRI that sustains and promotes our natural moral practices calls for a co-developmental process with some AI and robotic technologies. © 2020, The Author(s).","Artificial intelligence; Blame; Human–computer interaction; Human–robot interaction; Moral responsibility; Social responsiveness","Human computer interaction; Intelligent robots; Robotics; AI systems; Designing systems; Human interactions; Human-to-human interactions; Innovative technology; Robot interactions; Robotic technologies; Human robot interaction",,,,,,,,,,,,,,,,"Alderson, N., Defining agency after implicit bias (2017) Philos Psychol, 30 (5), pp. 645-656; Alicke, M.D., Culpable control and the psychology of blame (2000) Psychol Bull, 126 (4), pp. 556-574; Allen, C., Wallach, W., (2009) Moral machines: teaching robots right from wrong, , Oxford University Press, Oxford; Bartneck, C., Forlizzi, J., Shaping human–robot interaction: Understanding the social aspects of intelligent robotic products (2004) Proceedings of the CHI2004 Workshop, pp. 1731-1732; Bastone, N., (2018) Google Assistant Now has a ‘pretty please’ Feature to Help Everybody Be More Polite, , https://www.businessinsider.co.za/google-assistant-pretty-please-now-available-2018-11, Business Insider; Belman, J., Flanagan, M., Designing games to foster empathy (2010) Cogn Technol, 14 (2), pp. 5-15; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: a review (2018) Sci Robot, 3, pp. 1-9; Birks, M., Bodak, M., Barlas, J., Harwood, J., Pether, M., Robotic seals as therapeutic tools in an aged care facility: A qualitative study (2016) J Aging Res; Boden, M., Bryson, J.J., Caldwell, D., Dautenhahn, K., Edwards, L., Kember, S., Newman, P., Winfield, A., (2011) Principles of Robotics. 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"Woo J., Ohyama Y., Kubota N.","36603728900;8358930000;7202158579;","Robot partner development platform for human-robot interaction based on a user-centered design approach",2020,"Applied Sciences (Switzerland)","10","22","7992","1","20",,4,"10.3390/app10227992","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095945700&doi=10.3390%2fapp10227992&partnerID=40&md5=e4edb1e5bdcdfd30b13a109db1652ddc","Department of Mechanical Engineering, School of Engineering, Tokyo University of Technology, Hachioji, 192-0982, Japan; Graduate School of Systems Design, Tokyo Metropolitan University, Hino, 191-0065, Japan","Woo, J., Department of Mechanical Engineering, School of Engineering, Tokyo University of Technology, Hachioji, 192-0982, Japan; Ohyama, Y., Department of Mechanical Engineering, School of Engineering, Tokyo University of Technology, Hachioji, 192-0982, Japan; Kubota, N., Graduate School of Systems Design, Tokyo Metropolitan University, Hino, 191-0065, Japan","This paper presents a robot partner development platform based on smart devices. Humans communicate with others based on the basic motivations of human cooperation and have communicative motives based on social attributes. Understanding and applying these communicative motives become important in the development of socially-embedded robot partners. Therefore, it is becoming more important to develop robots that can be applied according to needs while taking these human communication elements into consideration. The role of a robot partner is more important in not only on the industrial sector but also in households. However, it seems that it will take time to disseminate robots. In the field of service robots, the development of robots according to various needs is important and the system integration of hardware and software becomes crucial. Therefore, in this paper, we propose a robot partner development platform for human-robot interaction. Firstly, we propose a modularized architecture of robot partners using a smart device to realize a flexible update based on the re-usability of hardware and software modules. In addition, we show examples of implementing a robot system using the proposed architecture. Next, we focus on the development of various robots using the modular robot partner system. Finally, we discuss the effectiveness of the proposed robot partner system through social implementation and experiments. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Human-robot interaction; Information support; Robot partner; User-centered design",,,,,,,,,,,,,,,,,"Kim, S.H., Kim, D.H., Kim, W.S., Long-term care needs of the elderly in Korea and elderly long-term care insurance (2010) Soc.Work Public Health, 25, pp. 176-184. , [CrossRef] [PubMed]; Gardiner, C., Geldenhuys, G., Gott, M., Interventions to reduce social isolation and loneliness among older people: An integrative review (2018) Health Soc. 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Inform, 22, pp. 989-997. , [CrossRef]; Jacquemont, M., Woo, J., Botzheim, J., Kubota, N., Sartori, N., Benoit, E., Human-centric point of view for a robot partner: A cooperative project between France and Japan Proceedings of the 2016 11th France-Japan & 9th Europe-Asia Congress on Mechatronics (MECATRONICS)/17th International Conference on Research and Education in Mechatronics (REM), pp. 164-169. , Compiegne, France, 15–17 June 2016; Yamamoto, S., Woo, J., Chin, W.H., Matsumura, K., Kubota, N., Interactive Information Support by Robot Partners Based on Informationally Structured Space (2020) J. Robot. Mechatron, 32, pp. 236-243. , [CrossRef]; Kahn, P.H., Ishiguro, H., Friedman, B., Kanda, T., Freier, N.G., Severson, R.L., Miller, J., What is a human?: Toward psychological benchmarks in the field of human–robot interaction (2007) Interact. Stud, 8, pp. 363-390. , [CrossRef]; Yamasaki, N., Anzai, Y., Active interface for human-robot interaction (1995) Proceedings of the 1995 IEEE International Conference on Robotics and Automation, 3, pp. 3103-3109. , Nagoya, Japan, 21–27 May; Nielsen, L., Personas, M.S., Dam, R.F., (2013) The Encyclopedia of Human-Computer Interaction, , http://www.interaction-design.org/encyclopedia/personas.html, 2nd ed.; TheInteraction Design Foundationn: Aarhus, Denmark, (accessed on 3 October 2020); Okamoto, S., Smith, J.S.S., (2004) Japanese Language, Gender, and Ideology: Cultural Models and Real People, , Oxford University Press: Oxford, UK; Preston, D.R., (1999) Handbook of Perceptual Dialectology, 1. , John Benjamins Publishing: Amsterdam, The Netherlands; Stern, D.N., (2018) The Interpersonal World of the Infant: A View from Psychoanalysis and Developmental Psychology, , Routledge: Abingdon, UK","Woo, J.; Department of Mechanical Engineering, Japan; email: woojsk@stf.teu.ac.jp",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85095945700
"Tahir Y., Dauwels J., Thalmann D., Magnenat Thalmann N.","43561765100;6507548054;7005885082;57203082366;","A User Study of a Humanoid Robot as a Social Mediator for Two-Person Conversations",2020,"International Journal of Social Robotics","12","5",,"1031","1044",,4,"10.1007/s12369-018-0478-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081337599&doi=10.1007%2fs12369-018-0478-3&partnerID=40&md5=47dce4b28697af4bff809eb0c15fac3f","Institute for Media Innovation, Nanyang Technological University, Singapore, Singapore; School for Electronics and Electrical Engineering, Nanyang Technological University, Singapore, Singapore","Tahir, Y., Institute for Media Innovation, Nanyang Technological University, Singapore, Singapore; Dauwels, J., School for Electronics and Electrical Engineering, Nanyang Technological University, Singapore, Singapore; Thalmann, D., Institute for Media Innovation, Nanyang Technological University, Singapore, Singapore; Magnenat Thalmann, N., Institute for Media Innovation, Nanyang Technological University, Singapore, Singapore","In this work we have enhanced the perception of a humanoid robot by integrating it with a social state estimation system. We present a user study of the humanoid Nao robot as a social mediator, comprising two sets of experiments. In the first sets of experiments, the participants rate their understanding of feedback messages delivered via the Nao robot. They also assess two modalities to deliver the feedback: audio only and audio combined with gestures. In almost all cases there is an improvement of 10% or more when audio and gesture modalities are combined to deliver feedback messages. For the second sets of experiments the sociofeedback system was integrated with the Nao robot. The participants engage in two-person scenario-based conversations while the Nao robot acts as a mediator. The sociofeedback system analyzes the conversations and provides feedback via Nao. Subsequently, the participants assess the received sociofeedback with respect to various aspects, including its content, appropriateness, and timing. Participants also evaluate their overall perception of Nao as social mediator via the Godspeed questionnaire. The results indicate that the social feedback system is able to detect the social scenario with 93.8% accuracy and that Nao can be effectively used to provide sociofeedback in discussions. The results of this paper pave the way to natural human–robot interactions for social mediators in multi-party dialog systems. © 2018, Springer Science+Business Media B.V., part of Springer Nature.","Audio-Visual; Dialog; Human behavior; Sociometrics","Agricultural robots; Anthropomorphic robots; Behavioral research; Feedback control; Dialog; Estimation systems; Feedback messages; Human behaviors; Robot interactions; Social feedbacks; Social scenarios; Sociometrics; Social robots",,,,,"University of North Carolina, UNC; University of North Carolina Wilmington, UNCW; Australian Prime Ministers Centre, APMC; National Research Foundation Singapore, NRF; Nanyang Technological University, NTU","This research is supported by the BeingTogether Centre, a collaboration between Nanyang Technological University (NTU) Singapore and University of North Carolina (UNC) at Chapel Hill. The BeingTogether Centre is supported by the National Research Foundation, Prime Ministers Office, Singapore under its International Research Centres in Singapore Funding Initiative.","This research is supported by the BeingTogether Centre, a collaboration between Nanyang Technological University (NTU) Singapore and University of North Carolina (UNC) at Chapel Hill. The BeingTogether Centre is supported by the National Research Foundation, Prime Ministers Office, Singapore under its International Research Centres in Singapore Funding Initiative.",,,,,,,,,"Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Robot Auton Syst, 42 (3), pp. 143-166; Li, H., Cabibihan, J.-J., Tan, Y.K., Towards an effective design of social robots (2011) Int J Soc Robot, 3 (4), pp. 333-335; Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., Hagita, N., A communication robot in a shopping mall (2010) IEEE Trans Robot, 26 (5), pp. 897-913; Thrun, S., Bennewitz, M., Burgard, W., Cremers, A.B., Dellaert, F., Fox, D., Hahnel, D., Schulz, D., Minerva: A second-generation museum tour-guide robot (1999) Proceedings of IEEE International Conference on Robotics and Automation, , ICRA 99; Tanaka, F., Cicourel, A., Movellan, J.R., Socialization between toddlers and robots at an early childhood education center (2007) Proc Ntl Acad Sci, 104, pp. 17954-17958; Graf, B., Parlitz, C., Hägele, M., Robotic home assistant care-o-bot ® 3 product vision and innovation platform (2009) HCI, 2, pp. 312-320; Williams, K., Breazeal, C., A reasoning architecture for human-robot joint tasks using physics-, social-, and capability-based logic (2012) IROS, pp. 664-671; François, D., Polani, D., Dautenhahn, K., Towards socially adaptive robots: A novel method for real time recognition of human-robot interaction styles (2008) In Humanoids, pp. 353-359; Papadopoulos, F., Dautenhahn, K., Ho, W.C., Exploring the use of robots as social mediators in a remote human–human collaborative communication experiment (2012) Paladyn, 3 (1), pp. 1-10; Dautenhahn, K., Socially intelligent robots: dimensions of human–robot interaction (2007) Phil Trans R Soc B Biol Sci, 362 (1480), pp. 679-704; Bohus, D., Horvitz, E., Dialog in the open world: Platform and applications (2009) Proceedings of the 2009 International Conference on Multimodal Interfaces, pp. 31-38. , ACM; Salah, A.A., Gevers, T., Sebe, N., Vinciarelli, A., Human behavior understanding (2010) First International Workshop, HBU 2010, 6219. , Istanbul, Turkey, August 22, 2010, Proceedings, Springer; Salah, A.A., Ruiz-Del Solar, J., Meriçli, Ç., Oudeyer, P.-Y., Human behavior understanding for robotics (2012) Human Behavior Understanding, pp. 1-16. , Springer; Rasheed, U., Tahir, Y., Dauwels, S., Dauwels, J., Real-time comprehensive sociometrics for two-person dialogs (2013) Human Behavior Understanding. Springer, pp. 196-208; Pentland, A.S., (2010) Honest signals, , MIT press, Cambridge; Tahir, Y., Rasheed, U., Dauwels, S., Dauwels, J., Thalmann, N., Thalmann, D., Nao as social mediator: A user study (2013) Robots in Public Spaces:Towards Multi-Party, , short-term, dynamic human–robot interaction, Springer; Bartneck, C., Croft, E., Kulic, D., Measuring the anthropomorphism, animacy, likeability, perceived intelligence and perceived safety of robots (2008) Metrics for HRI Workshop, Technical Report, 471, pp. 37-44. , Citeseer; Breazeal, C., Scassellati, B., (1999) A context-dependent attention system for a social robot. rn, 255, p. 3; Sidner, C.L., Dzikovska, M., A first experiment in engagement for human–robot interaction in hosting activities (2005) Advances in Natural Multimodal Dialogue Systems, pp. 55-76. , Springer; Pollack, M.E., Brown, L., Colbry, D., Orosz, C., Peintner, B., Ramakrishnan, S., Engberg, S., McCarthy, C.E., Pearl: A mobile robotic assistant for the elderly (2002) AAAI Workshop on Automation as Eldercare, pp. 85-91; Ishiguro, H., Ono, T., Imai, M., Maeda, T., Kanda, T., Nakatsu, R., Robovie: an interactive humanoid robot (2001) Ind Robot Int J, 28 (6), pp. 498-504; Billard, A., Robota: clever toy and educational tool (2003) Robot Autonom Syst, 42 (3), pp. 259-269; Kidd, C.D., Taggart, W., Turkle, S., A sociable robot to encourage social interaction among the elderly (2006) Robotics and Automation, 2006. 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A survey on the roles and benefits of social robots in the therapy of children with autism (2013) Int J Soc Robot, 5, pp. 1-26; Powers, A., Kiesler, S., The advisor robot: Tracing people’s mental model from a robot’s physical attributes (2006) Proceedings of the 1St ACM SIGCHI/SIGART Conference on Human-Robot Interaction, pp. 218-225. , ACM; Foster, M.E., Gaschler, A., Giuliani, M., Isard, A., Pateraki, M., Petrick, R., Two people walk into a bar: Dynamic multi-party social interaction with a robot agent (2012) Proceedings of the 14Th ACM International Conference on Multimodal Interaction. ACM, pp. 3-10; Darpa cognitive agent that learns and organizes (calo) project; Tur, G., Stolcke, A., Voss, L., Peters, S., Hakkani-Tur, D., Dowding, J., Favre, B., Frandsen, M., The calo meeting assistant system (2010) IEEE Trans Audio Speech Lang Process, 18 (6), pp. 1601-1611; Al Moubayed, S., Beskow, J., Skantze, G., Granström, B., Furhat: A back-projected human-like robot head for multiparty human–machine interaction (2012) Cognitive Behavioural Systems, pp. 114-130. , Springer; Harris, J., Sharlin, E., Exploring the affect of abstract motion in social human–robot interaction (2011) Robot–Man IEEE, pp. 441-448; Saerbeck, M., Bartneck, C., Perception of affect elicited by robot motion (2010) Proceedings of the 5Th ACM/IEEE International Conference on Human–robot Interaction, pp. 53-60. , IEEE Press; Beck, A., Hiolle, A., Mazel, A., Cañamero, L., Interpretation of emotional body language displayed by robots (2010) Proceedings of the 3Rd International Workshop on Affective Interaction in Natural Environments, pp. 37-42. , ACM; Ham, J., Bokhorst, R., Cabibihan, J., The influence of gazing and gestures of a storytelling robot on its persuasive power (2011) International Conference on Social Robotics; Delaborde, A., Devillers, L., Use of nonverbal speech cues in social interaction between human and robot: Emotional and interactional markers (2010) Proceedings of the 3Rd International Workshop on Affective Interaction in Natural Environments, pp. 75-80. , ACM; Yu, L., Liu, H., Feature selection for high-dimensional data: A fast correlation-based filter solution (2003) Proceedings of the 20Th International Conference on Machine Learning (ICML-03), pp. 856-863; Hall, M.A., (1999) Correlation-based feature selection for machine learning, , University of Waikato, Hamilton; Werner, K., Oberzaucher, J., Werner, F., Evaluation of human robot interaction factors of a socially assistive robot together with older people (2012) Complex, Intelligent and Software Intensive Systems (CISIS), Conference on 2012 Sixth International, pp. 455-460. , IEEE; (2015), Nadine robot; Tahir, Y., Chakraborty, D., Maszczyk, T., Dauwels, S., Dauwels, J., Thalmann, N., Thalmann, D., Real-time sociometrics from audio-visual features for two-person dialogs (2015) 2015 IEEE International Conference on Digital Signal Processing (DSP). IEEE, pp. 823-827","Dauwels, J.; School for Electronics and Electrical Engineering, Singapore; email: JDAUWELS@ntu.edu.sg",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85081337599
"Lamon E., Fusaro F., Balatti P., Kim W., Ajoudani A.","57207201842;57218643530;57215558904;55978740100;23388667500;","A visuo-haptic guidance interface for mobile collaborative robotic assistant (MOCA)",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341357","11253","11260",,1,"10.1109/IROS45743.2020.9341357","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102414487&doi=10.1109%2fIROS45743.2020.9341357&partnerID=40&md5=9458683b5032b9446e21dfa3510e7e89","Istituto Italiano di Tecnologia, HRI Lab, Dept. of Advanced Robotics, Genova, 16163, Italy; Robotics and Automation, Dept. of Information Engineering, Universitá Degli Studi di Pisa, Pisa, Italy; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy","Lamon, E., Istituto Italiano di Tecnologia, HRI Lab, Dept. of Advanced Robotics, Genova, 16163, Italy, Robotics and Automation, Dept. of Information Engineering, Universitá Degli Studi di Pisa, Pisa, Italy; Fusaro, F., Istituto Italiano di Tecnologia, HRI Lab, Dept. of Advanced Robotics, Genova, 16163, Italy, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy; Balatti, P., Istituto Italiano di Tecnologia, HRI Lab, Dept. of Advanced Robotics, Genova, 16163, Italy, Robotics and Automation, Dept. of Information Engineering, Universitá Degli Studi di Pisa, Pisa, Italy; Kim, W., Istituto Italiano di Tecnologia, HRI Lab, Dept. of Advanced Robotics, Genova, 16163, Italy; Ajoudani, A., Istituto Italiano di Tecnologia, HRI Lab, Dept. of Advanced Robotics, Genova, 16163, Italy","In this work, we propose a novel visuo-haptic guidance interface to enable mobile collaborative robots to follow human instructions in a way understandable by non-experts. The interface is composed of a haptic admittance module and a human visual tracking module. The haptic guidance enables an individual to guide the robot end-effector in the workspace to reach and grasp arbitrary items. The visual interface, on the other hand, uses a real-time human tracking system and enables autonomous and continuous navigation of the mobile robot towards the human, with the ability to avoid static and dynamic obstacles along its path. To ensure a safer human-robot interaction, the visual tracking goal is set outside of a certain area around the human body, entering which will switch robot behaviour to the haptic mode. The execution of the two modes is achieved by two different controllers, the mobile base admittance controller for the haptic guidance and the robot's whole-body impedance controller, that enables physically coupled and controllable locomotion and manipulation. The proposed interface is validated experimentally, where a human-guided robot performs the loading and transportation of a heavy object in a cluttered workspace, illustrating the potential of the proposed Follow-Me interface in removing the external loading from the human body in this type of repetitive industrial tasks. © 2020 IEEE.",,"Agricultural robots; Controllers; End effectors; Haptic interfaces; Industrial robots; Intelligent robots; Mobile robots; Robotics; Visual servoing; Collaborative robots; External loading; Human tracking systems; Industrial tasks; Robot end effector; Robotic assistants; Static and dynamic obstacles; Visual Interface; Social robots",,,,,,,,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human-robot collaboration (2018) Autonomous Robots, 42, pp. 957-975; Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P., Collaborative manufacturing with physical human-robot interaction (2016) Robotics and Computer-Integrated Manufacturing, 40; Kim, W., Adaptable workstations for human-robot collaboration: A reconfigurable framework for improving worker ergonomics and productivity (2019) IEEE Robotics Automation Magazine, , Sep; Lorenzini, M., Kim, W., Momi, E.D., Ajoudani, A., A new overloading fatigue model for ergonomic risk assessment with application to human-robot collaboration (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 1962-1968. , May; Kormushev, P., Calinon, S., Caldwell, D.G., Imitation learning of positional and force skills demonstrated via kinesthetic teaching and haptic input (2011) Advanced Robotics, 25 (5), pp. 581-603; Muxfeldt, A., Kluth, J.-H., Kubus, D., Kinesthetic teaching in assembly operations-A user study (2014) Simulation, Modeling, and Programming for Autonomous Robots, , Springer; Schou, C., Damgaard, J.S., Bøgh, S., Madsen, O., Human-robot interface for instructing industrial tasks using kinesthetic teaching (2013) IEEE ISR 2013, pp. 1-6. , Oct; Kim, W., Lorenzini, M., Balatti, P., Wu, Y., Ajoudani, A., Towards ergonomic control of collaborative effort in multi-human mobile-robot teams (2019) The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Lorenzini, M., Fusaro, F., Balatti, P., De Momi, E., Mastrogiovanni, F., Kim, W., Ajoudani, A., Toward a synergistic framework for human-robot coexistence and collaboration (hrc2) (2019) Italian Conference on Robotics and Intelligent Machines (I-RIM); Lamon, E., Leonori, A., Kim, W., Ajoudani, A., Towards an intelligent collaborative robotic system for mixed case palletizing (2020) 2019 International Conference on Robotics and Automation (ICRA), , May; Kim, W., Balatti, P., Lamon, E., Ajoudani, A., Moca-man: A mobile and reconfigurable collaborative robot assistant for conjoined humanrobot actions (2020) 2019 International Conference on Robotics and Automation (ICRA), , May; Wu, Y., Balatti, P., Lorenzini, M., Zhao, F., Kim, W., Ajoudani, A., A teleoperation interface for loco-manipulation control of mobile collaborative robotic assistant (2019) IEEE Robotics and Automation Letters, 4 (4), pp. 3593-3600. , Oct; Bevan, S., Fit for work (2009) Musculoskeletal Disorders in the European Workforce, , London: The work foundation; Mörtl, A., The role of roles: Physical cooperation between humans and robots (2012) The International Journal of Robotics Research, 31 (13), pp. 1656-1674; Agravante, D.J., Cherubini, A., Bussy, A., Gergondet, P., Kheddar, A., Collaborative human-humanoid carrying using vision and haptic sensing (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 607-612. , May; Stückler, J., Behnke, S., Following human guidance to cooperatively carry a large object (2011) 2011 11th IEEE-RAS International Conference on Humanoid Robots, pp. 218-223. , Oct; Rozo, L., Bruno, D., Calinon, S., Caldwell, D.G., Learning optimal controllers in human-robot cooperative transportation tasks with position and force constraints (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , Sep; Alonso-Mora, J., Knepper, R., Siegwart, R., Rus, D., Local motion planning for collaborative multi-robot manipulation of deformable objects (2015) 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 5495-5502. , May; Simetti, E., Casalino, G., Wanderlingh, F., Aicardi, M., A task priority approach to cooperative mobile manipulation: Theory and experiments (2019) Robotics and Autonomous Systems, 122; Prautzsch, H., Boehm, W., Paluszny, M., (2013) Bézier and B-spline Techniques, , Springer Science & Business Media; Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., Sheikh, Y., (2018) OpenPose: Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields; Saenz, J., Vogel, C., Penzlin, F., Elkmann, N., Safeguarding collaborative mobile manipulators-evaluation of the valeri workspace monitoring system (2017) Procedia Manufacturing, 11, pp. 47-54. , 27th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2017; Alexopoulos, K., Mavrikios, D., Chryssolouris, G., Ergotoolkit: An ergonomic analysis tool in a virtual manufacturing environment (2013) International Journal of Computer Integrated Manufacturing; Moschonas, P., Kaklanis, N., Tzovaras, D., Novel human factors for ergonomy evaluation in virtual environments using virtual user models (2011) Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102414487
"Rosen E., Whitney D., Fishman M., Ullman D., Tellex S.","57195424425;57211723755;57210797500;55638484600;7801643708;","Mixed reality as a bidirectional communication interface for human-robot interaction",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340822","11431","11438",,3,"10.1109/IROS45743.2020.9340822","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102409135&doi=10.1109%2fIROS45743.2020.9340822&partnerID=40&md5=01539773722733ffe7bedbfebb2760a5","Brown University, Department of Computer Science","Rosen, E., Brown University, Department of Computer Science; Whitney, D., Brown University, Department of Computer Science; Fishman, M., Brown University, Department of Computer Science; Ullman, D., Brown University, Department of Computer Science; Tellex, S., Brown University, Department of Computer Science","We present a decision-theoretic model and robot system that interprets multimodal human communication to disambiguate item references by asking questions via a mixed reality (MR) interface. Existing approaches have either chosen to use physical behaviors, like pointing and eye gaze, or virtual behaviors, like mixed reality. However, there is a gap of research on how MR compares to physical actions for reducing robot uncertainty. We test the hypothesis that virtual deictic gestures are better for human-robot interaction (HRI) than physical behaviors. To test this hypothesis, we propose the Physio-Virtual Deixis Partially Observable Markov Decision Process (PVD-POMDP), which interprets multimodal observations (speech, eye gaze, and pointing gestures) from the human and decides when and how to ask questions (either via physical or virtual deictic gestures) in order to recover from failure states and cope with sensor noise. We conducted a between-subjects user study with 80 participants distributed across three conditions of robot communication: no feedback control, physical feedback, and MR feedback. We tested performance of each condition with objective measures (accuracy, time), as well as evaluated user experience with subjective measures (usability, trust, workload). We found the MR feedback condition was 10% more accurate than the physical condition and a speedup of 160%. We also found that the feedback conditions significantly outperformed the no feedback condition in all subjective metrics. © 2020 IEEE.",,"Agricultural robots; Biofeedback; Feedback; Intelligent robots; Man machine systems; Markov processes; Mixed reality; User experience; Bi-directional communication; Decision-theoretic; Human communications; Human robot Interaction (HRI); Partially observable Markov decision process; Physical behaviors; Physical conditions; Robot communication; Social robots",,,,,,,,,,,,,,,,"Admoni, H., Weng, T., Scassellati, B., Modeling communicative behaviors for object references in human-robot interaction (2016) Robotics and Automation (ICRA), 2016 IEEE International Conference on, pp. 3352-3359; Bangerter, A., Using pointing and describing to achieve joint focus of attention in dialogue (2004) Psychological Science, 15 (6), pp. 415-419; Bellman, R., A markovian decision process (1957) Journal of Mathematics and Mechanics, pp. 679-684; Brooke, J., SUS-a quick and dirty usability scale (1996) Usability Evaluation in Industry, 189 (194), pp. 4-7; Chai, J.Y., She, L., Fang, R., Ottarson, S., Littley, C., Liu, C., Hanson, K., Collaborative effort towards common ground in situated human-robot dialogue (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction, pp. 33-40; Goodman, N.D., Stuhlmüller, A., Knowledge and implicature: Modeling language understanding as social cognition (2013) Topics in Cognitive Science, 5; Gross, S., Krenn, B., Scheutz, M., The reliability of non-verbal cues for situated reference resolution and their interplay with language: Implications for human robot interaction (2017) Proceedings of the 19th ACM International Conference on Multimodal Interaction, pp. 189-196; Hakkani-Tür, D., Slaney, M., Celikyilmaz, A., Larry, Heck., Eye gaze for spoken language understanding in multi-modal conversational interactions (2014) Proceedings of the 16th International Conference on Multimodal Interaction, pp. 263-266; Sandra, G., Hart, NASA-task load index (NASA-TLX); 20 years later (2006) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 50, pp. 904-908. , Sage Publications Sage CA: Los Angeles, CA; Hart, S.G., Staveland, L.E., Development of NASA-TLX (task load index): Results of empirical and theoretical research (1988) Advances in Psychology, 52, pp. 139-183. , Elsevier; Pack Kaelbling, L., Littman, M.L., Cassandra, A.R., Planning and acting in partially observable stochastic domains (1998) Artificial Intelligence, 101 (1-2), pp. 99-134; Mehlmann, G., Häring, M., Janowski, K., Baur, T., Gebhard, P., André, E., Exploring a model of gaze for grounding in multimodal HRI (2014) Proceedings of the 16th International Conference on Multimodal Interaction, pp. 247-254; Perlmutter, L., Kernfeld, E., Cakmak, M., Situated language understanding with human-like and visualization-based transparency (2016) Robotics: Science and Systems; Prasov, Z., Chai, J.Y., What's in a gaze?: The role of eye-gaze in reference resolution in multimodal conversational interfaces (2008) Proceedings of the 13th International Conference on Intelligent User Interfaces, pp. 20-29; Qvarfordt, P., Gaze-informed multimodal interaction (2017) The Handbook of Multimodal-Multisensor Interfaces, pp. 365-402. , Association for Computing Machinery and Morgan & Claypool; Rosen, E., Whitney, D., Phillips, E., Chien, G., Tompkin, J., Konidaris, G., Tellex, S., (2017) Communicating Robot Arm Motion Intent through Mixed Reality Head-mounted Displays; Shridhar, M., Hsu, D., Interactive visual grounding of referring expressions for human-robot interaction (2018) Proceedings of Robotics: Science and Systems; Sibirtseva, E., Kontogiorgos, D., Nykvist, O., Karaoguz, H., Leite, I., Gustafson, J., Kragic, D., A comparison of visualisation methods for disambiguating verbal requests in human-robot interaction (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 43-50; Spaan, J.M.T., Vlassis, N.A., (2011) Perseus: Randomized Point-based Value Iteration for POMDPs, , http://arxiv.org/abs/1109.2145, CoRR, abs/1109. 2145; Ullman, D., Malle, B.F., What does it mean to trust a robot?: Steps toward a multidimensional measure of trust (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 263-264; Whitney, D., Rosen, E., MacGlashan, J., Wong, L.L.S., Tellex, S., Reducing errors in object-fetching interactions through social feedback (2017) International Conference on Robotics and Automation, , Singapore, May; Williams, T., Tran, N., Rands, J., Dantam, N.T., Augmented, mixed, and virtual reality enabling of robot deixis (2018) International Conference on Virtual, Augmented and Mixed Reality, pp. 257-275. , Springer; Williams, T., Bussing, M., Cabroll, S., Boyle, E., Tran, N., Mixed reality deictic gesture for multi-modal robot communication (2019) ACM/IEEE International Conference on Human-Robot Interaction (HRI), , 03",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102409135
"Pramanick P., Barua H.B., Sarkar C.","57201672453;57204511717;55420753600;","DeComplex: Task planning from complex natural instructions by a collocating robot",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341289","6894","6901",,,"10.1109/IROS45743.2020.9341289","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102396532&doi=10.1109%2fIROS45743.2020.9341289&partnerID=40&md5=784cf749459592238e01e2ff83d4d58c","TCS Research Innovation, India","Pramanick, P., TCS Research Innovation, India; Barua, H.B., TCS Research Innovation, India; Sarkar, C., TCS Research Innovation, India","As the number of robots in our daily surroundings like home, office, restaurants, factory floors, etc. are increasing rapidly, the development of natural human-robot interaction mechanism becomes more vital as it dictates the usability and acceptability of the robots. One of the valued features of such a cohabitant robot is that it performs tasks that are instructed in natural language. However, it is not trivial to execute the human intended tasks as natural language expressions can have large linguistic variations. Existing works assume either single task instruction is given to the robot at a time or there are multiple independent tasks in an instruction. However, complex task instructions composed of multiple inter-dependent tasks are not handled efficiently in the literature. There can be ordering dependency among the tasks, i.e., the tasks have to be executed in a certain order or there can be execution dependency, i.e., input parameter or execution of a task depends on the outcome of another task. Understanding such dependencies in a complex instruction is not trivial if an unconstrained natural language is allowed. In this work, we propose a method to find the intended order of execution of multiple inter-dependent tasks given in natural language instruction. Based on our experiment, we show that our system is very accurate in generating a viable execution plan from a complex instruction. © 2020 IEEE.",,"Agricultural robots; Intelligent robots; Robot programming; Execution plans; Factory floors; Independent tasks; Input parameter; Inter-dependent tasks; Natural language expressions; Natural languages; Task planning; Human robot interaction",,,,,,,,,,,,,,,,"Pramanick, P., Sarkar, C., Defatigue: Online non-intrusive fatigue detection by a robot co-worker (2018) 2018 27th Ieee International Symposium on Robot and Human Interactive Communication (ROMAN), pp. 1129-1136; Martinez-Martin, E., Costa, A., Cazorla, M., Pharos 2.0-A physical assistant robot system improved (2019) Sensors, 19 (20), p. 4531; Shah, P., Fiser, M., Faust, A., Kew, C., Hakkani-Tur, D., Follownet: Robot navigation by following natural language directions with deep reinforcement learning (2018) Third Machine Learning in Planning and Control of Robot Motion Workshop at Icra; Nicolescu, M., Arnold, N., Blankenburg, J., Feil-Seifer, D., Banisetty, S.B., Nicolescu, M., Palmer, A., Monteverde, T., Learning of complex-structured tasks from verbal instruction (2019) International Conference on Humanoid Robots, , (Toronto, Canada), October; Lu, D., Zhou, Y., Wu, F., Zhang, Z., Chen, X., Integrating answer set programming with semantic dictionaries for robot task planning (2017) Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 4361-4367. , AAAI Press; Paul, R., Barbu, A., Felshin, S., Katz, B., Roy, N., Temporal grounding graphs for language understanding with accrued visuallinguistic context (2017) Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 4506-4514. , AAAI Press; Matuszek, C., Herbst, E., Zettlemoyer, L., Fox, D., Learning to parse natural language commands to a robot control system (2013) Experimental Robotics, pp. 403-415. , Springer; Tellex, S., Kollar, T., Dickerson, S., Walter, M.R., Banerjee, A.G., Teller, S., Roy, N., Understanding natural language commands for robotic navigation and mobile manipulation (2011) Twenty-Fifth Aaai Conference on Artificial Intelligence; Pramanick, P., Sarkar, C., Balamuralidhar, P., Kattepur, A., Bhattacharya, I., Pal, A., Enabling human-like task identification from natural conversation (2019) 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Thomas, B.J., Jenkins, O.C., Roboframenet: Verb-centric semantics for actions in robot middleware (2012) 2012 Ieee International Conference on Robotics and Automation, pp. 4750-4755; Misra, D.K., Tao, K., Liang, P., Saxena, A., Environment-driven lexicon induction for high-level instructions (2015) Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 992-1002; Antunes, A., Jamone, L., Saponaro, G., Bernardino, A., Ventura, R., From human instructions to robot actions: Formulation of goals, affordances and probabilistic planning (2016) Robotics and Automation (ICRA), 2016 Ieee International Conference on, pp. 5449-5454; Chen, X.-P., Ji, J.-M., Sui, Z.-Q., Xie, J.-K., Handling open knowledge for service robots (2013) Twenty-Third International Joint Conference on Artificial Intelligence; Nyga, D., Roy, S., Paul, R., Park, D., Pomarlan, M., Beetz, M., Roy, N., Grounding robot plans from natural language instructions with incomplete world knowledge (2018) Proceedings of the 2nd Conference on Robot Learning 87 of Proceedings of Machine Learning Research, pp. 714-723; Thomason, J., Zhang, S., Mooney, R.J., Stone, P., Learning to interpret natural language commands through human-robot dialog (2015) Ijcai, pp. 1923-1929; Thomason, J., Padmakumar, A., Sinapov, J., Walker, N., Jiang, Y., Yedidsion, H., Hart, J., Mooney, R.J., Improving grounded natural language understanding through human-robot dialog (2019) International Conference on Robotics and Automation (ICRA), Ieee; Pramanick, P., Sarkar, C., Bhattacharya, I., Your instruction may be crisp, but not clear to me! (2019) 2019 Ieee International Conference on Robot and Human Interactive Communication (RO-MAN), Ieee; Kollar, T., Tellex, S., Roy, D., Roy, N., Grounding verbs of motion in natural language commands to robots (2014) Experimental Robotics, pp. 31-47. , Springer; Kollar, T., Tellex, S., Roy, D., Roy, N., Toward understanding natural language directions (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 259-266; Kress-Gazit, H., Fainekos, G.E., Pappas, G.J., Translating structured english to robot controllers (2008) Advanced Robotics, 22 (12), pp. 1343-1359; Meriçli, C., Klee, S.D., Paparian, J., Veloso, M., An interactive approach for situated task specification through verbal instructions (2014) Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, pp. 1069-1076. , International Foundation for Autonomous Agents and Multiagent Systems; Dzifcak, J., Scheutz, M., Baral, C., Schermerhorn, P., What to do and how to do it: Translating natural language directives into temporal and dynamic logic representation for goal management and action execution (2009) 2009 Ieee International Conference on Robotics and Automation, pp. 4163-4168; Finucane, C., Jing, G., Kress-Gazit, H., Ltlmop: Experimenting with language, temporal logic and robot control (2010) 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1988-1993; Chai, J.Y., Cakmak, M., Sidner, C., Teaching robots new tasks through natural interaction (2017) Interactive Task Learning: Agents, Robots, and Humans Acquiring New Tasks through Natural Interactions, 26. , Str¨ungmann Forum Reports, J. Lupp, series editor; Cantrell, R., Talamadupula, K., Schermerhorn, P., Benton, J., Kambhampati, S., Scheutz, M., Tell me when and why to do it!: Runtime planner model updates via natural language instruction (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 471-478; Boteanu, A., Howard, T., Arkin, J., Kress-Gazit, H., A model for verifiable grounding and execution of complex natural language instructions (2016) 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2649-2654; Pomarlan, M., Koralewski, S., Beetz, M., From natural language instructions to structured robot plans (2017) Joint German/Austrian Conference on Artificial Intelligence (K¨unstliche Intelligenz), pp. 344-351. , Springer; Artzi, Y., Zettlemoyer, L., Weakly supervised learning of semantic parsers for mapping instructions to actions (2013) Transactions of the Association for Computational Linguistics, 1, pp. 49-62; Misra, D.K., Sung, J., Lee, K., Saxena, A., Tell me dave: Contextsensitive grounding of natural language to manipulation instructions (2016) The International Journal of Robotics Research, 35 (1-3), pp. 281-300; Ghallab, M., Howe, A., Knoblock, C., McDermott, D., Ram, A., Veloso, M., Weld, D., Wilkins, D., Pddl\-The planning domain definition language (1998) AIPS-98 Planning Committee, 3, p. 14; Bastianelli, E., Castellucci, G., Croce, D., Iocchi, L., Basili, R., Nardi, D., Huric: A human robot interaction corpus (2014) Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC-2014)",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102396532
"Lestingi L., Askarpour M., Bersani M.M., Rossi M.","57219181705;57191286316;35730853100;7403708768;","A Model-driven Approach for the Formal Analysis of Human-Robot Interaction Scenarios",2020,"Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics","2020-October",,"9283204","1907","1914",,3,"10.1109/SMC42975.2020.9283204","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098874643&doi=10.1109%2fSMC42975.2020.9283204&partnerID=40&md5=5ca34bd2c27f9ca2a5e4161f181217ab","Politecnico di Milano, Milan, Italy; McMaster University, Hamilton, Canada","Lestingi, L., Politecnico di Milano, Milan, Italy; Askarpour, M., McMaster University, Hamilton, Canada; Bersani, M.M., Politecnico di Milano, Milan, Italy; Rossi, M., Politecnico di Milano, Milan, Italy","Robots are currently mostly found in industrial settings. In the future, a wider range of environments will benefit from their inclusion. This calls for the development of tools that allow professionals to set up dependable robotic applications in which people productively interact with robots aware of their needs. Given the co-existence of humans and robots, the precise analysis - e.g., through formal verification techniques - of properties related to aspects such as human needs and physiology is of paramount importance. In this paper, we present a formally-based, model-driven approach to design and verify scenarios involving human-robot interactions. Some of the features of our approach are tailored to the healthcare domain, from which our case studies are derived. In our approach, the designer specifies the main parameters of the mission to generate the model of the application, which includes mobile robots, the humans to be served, including some of their physiological features, and the decision-maker that orchestrates the execution. All components are modeled through hybrid automata to capture variables with complex dynamics. The model is verified through Statistical Model Checking (SMC), using the Uppaal tool, to determine the probability of success of the mission. The results are examined by the developer, who iteratively refines the design until the probability of success is satisfactory. © 2020 IEEE.","human-robot interaction; model-driven development; service robots; statistical model checking","Decision making; End effectors; Industrial robots; Iterative methods; Machine design; Man machine systems; Model checking; Physiological models; Physiology; Healthcare domains; Industrial settings; Model driven approach; Physiological features; Probability of success; Robotic applications; Statistical model checking; Verification techniques; Human robot interaction",,,,,,,,,,,,,,,,"(2010) Safety of Machinery-General Principles for Design-Risk Assessment and Risk Reduction, , ISO 12100, ISO; ISO 13849-1Safety of machinery-Safety-related parts of control systems-Part 1: General principles for design. ISO 2006; ISO 13482 Robots and robotic devices-Safety requirements for personal care robots. ISO, 2014; Jacobs, T., Virk, G.S., ISO 13482-The new safety standard for personal care robots (2014) Intl. Symp. on Robotics. VDE, pp. 1-6; Alur, R., Courcoubetis, C., Halbwachs, N., Henzinger, T.A., Ho, P.-H., Nicollin, X., Olivero, A., Yovine, S., The algorithmic analysis of hybrid systems (1995) Theoretical Computer Science, 138 (1), pp. 3-34; Agha, G., Palmskog, K., A survey of statistical model checking (2018) ACM Transactions on Modeling and Computer Simulation (TOMACS), 28 (1), pp. 1-39; Larsen, K.G., Pettersson, P., Yi, W., (1997) Uppaal in a Nutshell, 1 (1-2), pp. 134-152. , Springer-Verlag; David, A., Larsen, K.G., Legay, A., Mikučionis, M., Poulsen, D.B., Uppaal SMC tutorial (2015) STTT, 17 (4), pp. 397-415; (2020) HRI Toolchain, , https://github.com/LesLivia/hritoolchain; Yagoda, R.E., Coovert, M.D., How to work and play with robots: An approach to modeling human-robot interaction (2012) Computers in Human Behavior, 28 (1), pp. 60-68; Webster, M., Dixon, C., Fisher, M., Salem, M., Saunders, J., Koay, K.L., Dautenhahn, K., Formal verification of an autonomous personal robotic assistant (2014) AAAI Spring Symposium Series; Vicentini, F., Askarpour, M., Rossi, M.G., Mandrioli, D., Safety assessment of collaborative robotics through automated formal verification (2019) IEEE Transactions on Robotics; Amor, H.B., Neumann, G., Kamthe, S., Kroemer, O., Peters, J., Interaction primitives for human-robot cooperation tasks (2014) Intl. Conf. on Robotics and Automation (ICRA), pp. 2831-2837. , IEEE; Nikolaidis, S., Ramakrishnan, R., Gu, K., Shah, J., Efficient model learning from joint-action demonstrations for human-robot collaborative tasks (2015) Proc. of HRI, pp. 189-196. , IEEE; Lemaignan, S., Warnier, M., Sisbot, E.A., Clodic, A., Alami, R., Artificial cognition for social human-robot interaction: An implementation (2017) Artificial Intelligence, 247, pp. 45-69; Araiza-Illan, D., Pipe, T., Eder, K., (2016) Model-based Testing, Using Belief-desire-intentions Agents, of Control Code for Robots in Collaborative Human-robot Interactions, , arXiv preprint arXiv:1603.00656; Arai, R., Schlingloff, H., Model-based performance prediction by statistical model checking an industrial case study of autonomous transport robots (2017) Concurrency, Specification and Programming; Foughali, M., Ingrand, F., Seceleanu, C., Statistical model checking of complex robotic systems (2019) Proc. of SPIN, pp. 114-134. , Springer; Herd, B., Miles, S., McBurney, P., Luck, M., Quantitative analysis of multiagent systems through statistical model checking (2015) Int. Workshop on Engineering Multi-Agent Systems, pp. 109-130. , Springer; Askarpour, M., Mandrioli, D., Rossi, M., Vicentini, F., Formal model of human erroneous behavior for safety analysis in collaborative robotics (2019) Robotics and Computer-Integrated Manufacturing, 57, pp. 465-476; Alur, R., Dill, D.L., A theory of timed automata (1994) Theoretical Computer Science, 126 (2), pp. 183-235; Jaber, M.Y., Givi, Z., Neumann, W.P., Incorporating human fatigue and recovery into the learning-forgetting process (2013) Applied Mathematical Modelling, 37 (12-13), pp. 7287-7299; Chettibi, T., Haddad, M., Lehtihet, H., Khalil, W., Suboptimal trajectory generation for industrial robots using trapezoidal velocity profiles (2006) IROS, pp. 729-735. , IEEE; Tremblay, O., Dessaint, L.-A., Dekkiche, A.-I., A generic battery model for the dynamic simulation of hybrid electric vehicles (2007) Vehicle Power and Propulsion Conference, pp. 284-289. , IEEE; Payne, P., Lopetegui, M., Yu, S., A review of clinical workflow studies and methods (2019) Cognitive Informatics, pp. 47-61. , Springer; Kaemarungsi, K., Krishnamurthy, P., Modeling of indoor positioning systems based on location fingerprinting (2004) Proc. of Infocom, 2, pp. 1012-1022. , IEEE; Dong, H., Ugaldey, I., El Saddik, A., Development of a fatiguetracking system for monitoring human body movement (2014) Int. Instrumentation and Measurement Tech. Conf., pp. 786-791. , IEEE; Hadley, M., A deterministic model of the free will phenomenon (2018) Journal of Consciousness Exploration & Research, 9 (1)",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020","11 October 2020 through 14 October 2020",,165855,1062922X,9781728185262,PICYE,,"English","Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85098874643
"Hu Y., Benallegue M., Venture G., Yoshida E.","56576497600;36996384200;15623574000;35276803100;","Interact with Me: An Exploratory Study on Interaction Factors for Active Physical Human-Robot Interaction",2020,"IEEE Robotics and Automation Letters","5","4","9170777","6764","6771",,2,"10.1109/LRA.2020.3017475","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090841136&doi=10.1109%2fLRA.2020.3017475&partnerID=40&md5=e1cdd2f836e70bea105d70351d820297","CNRS-AIST JRL (Joint Robotics Laboratory), IRL, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-0046, Japan; Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, 184-8588, Japan","Hu, Y., CNRS-AIST JRL (Joint Robotics Laboratory), IRL, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-0046, Japan; Benallegue, M., CNRS-AIST JRL (Joint Robotics Laboratory), IRL, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-0046, Japan; Venture, G., CNRS-AIST JRL (Joint Robotics Laboratory), IRL, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-0046, Japan, Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, 184-8588, Japan; Yoshida, E., CNRS-AIST JRL (Joint Robotics Laboratory), IRL, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, 305-0046, Japan","In future robotic applications in environments such as nursing houses, construction sites, private homes, etc, robots might need to take unpredicted physical actions according to the state of the users to overcome possible human errors. Referring to these actions as active physical human-robot interactions (active pHRI), in this letter, the goal is to verify the possibility of identifying measurable interaction factors that could be used in future active pHRI controllers, by exploring and analyzing the state of the users during active pHRI. We hypothesize that active physical robot actions can cause measurable alterations in the physical and physiological data of the users, and that these measurements could be interpreted with users' personality and perceptions. We design an experiment where the participant uses the robot to play a visual puzzle game, during which, the robot takes unanticipated physical actions. We collect physiological and physical data, as well as outcomes of two state-of-the-art questionnaires on the perceptions of robots, CH-33 and Godspeed Series Questionnaires (GSQ), and a pre-experiment personality questionnaire, to relate the collected data with the users' perceptions and personality. The experiment outcomes show that we can extract a few factors related to personality, perception, physiological, and physical measurements. Even though we could not draw very clear correlations, these outcomes give fundamental insights for the design of novel pHRI experiments. © 2016 IEEE.","human factors and human-in-the-loop; human-centered robotics; Physical human-robot interaction","Agricultural robots; Machine design; Man machine systems; Physiology; Surveys; Construction sites; Exploratory studies; Interaction factor; Physical human-robot interactions; Physical measurement; Physiological data; Robotic applications; Users' perception; Human robot interaction",,,,,"Japan Society for the Promotion of Science, KAKEN: 17H01804, 18F18754","Manuscript received February 24, 2020; accepted July 9, 2020. Date of publication August 18, 2020; date of current version August 31, 2020. This letter was recommended for publication by Associate Editor M. J. Johnson and Editor A. M. Okamura upon evaluation of the Reviewers’ comments. This work was supported in part by the Grant-in-Aid for JSPS Research Fellow under Grant 18F18754 and in part by JSPS Grant-in-Aid for Research under Grant 17H01804. (Corresponding author: Yue Hu.) Yue Hu, Mehdi Benallegue, and Eiichi Yoshida are with the CNRS-AIST JRL (Joint Robotics Laboratory), IRL, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-0046, Japan (e-mail: yuehu@ieee.org; mehdi.benallegue@aist.go.jp; e.yoshida@aist.go.jp).",,,,,,,,,,"(2016) TS (Technical Specification) 15066: 2016: Robots and Robotic Devices-Collaborative Robots., , ISO, Geneva, Switzerland: International Organization for Standardization; Haddadin, S., On making robots understand safety: Embedding injury knowledge into control (2012) Int. J. Robot. Res., 31 (13), pp. 1578-1602; Bicchi, A., Peshkin, M.A., Colgate, J.E., Safety for physical human-robot interaction (2008) Springer Handbook of Robotics. 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Robots Syst., pp. 1354-1360; Erden, M.S., Tomiyama, T., Human-intent detection and physically interactive control of a robot without force sensors (2010) IEEE Trans. Robot., 26 (2), pp. 370-382. , Apr; Li, Y., Ge, S.S., Human-robot collaboration based on motion intention estimation (2014) IEEE/ASME Trans. Mechatronics, 19 (3), pp. 1007-1014. , Jun; Tirupachuri, Y., Towards partner-aware humanoid robot control under physical interactions (2019) Proc. SAI Intell. Syst. Conf., pp. 1073-1092; Babaiasl, M., Mahdioun, S.H., Jaryani, P., Yazdani, M., A review of technological and clinical aspects of robot-aided rehabilitation of upperextremity after stroke (2016) Disability Rehabil.: Assistive Technol., 11 (4), pp. 263-280; Lee, K.W., Kim, S.B., Lee, J.H., Lee, S.J., Kim, J.W., Effect of robotassisted game training on upper extremity function in stroke patients (2017) Ann. Rehabil. 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Robots, 40 (3), pp. 561-580; Endo, Y., Tada, M., Mochimaru, M., Dhaiba: Development of virtual ergonomic assessment system with human models (2014) Proc. 3rd Int. Digit. Human Symp., p. 58; Benallegue, M., Escande, A., Miossec, S., Kheddar, A., Fast c1 proximity queries using support mapping of sphere-torus-patches bounding volumes (2009) Proc. IEEE Int. Conf. Robot. Autom., pp. 483-488. , May; Carpinella, C.M., Wyman, A.B., Perez, M.A., Stroessner, S.J., The robotic social attributes scale (rosas) development and validation (2017) Proc. ACM/IEEE Int. Conf. Human-Robot Interact., pp. 254-262","Hu, Y.; CNRS-AIST JRL (Joint Robotics Laboratory), Japan; email: yuehu@ieee.org",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85090841136
"Khazoom C., Caillouette P., Girard A., Plante J.-S.","57189971587;57211933070;57225722923;8618063200;","A Supernumerary Robotic Leg Powered by Magnetorheological Actuators to Assist Human Locomotion",2020,"IEEE Robotics and Automation Letters","5","4","9128040","5143","5150",,5,"10.1109/LRA.2020.3005629","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089201240&doi=10.1109%2fLRA.2020.3005629&partnerID=40&md5=595b75f2dd076a807672d2c82c50257e","Interdisciplinary Institute for Technological Innovation (3IT), Faculty of Engineering, Université de Sherbrooke, Sherbrooke, QC, Canada; Exonetik, Sherbrooke, QC  J1L 1V8, Canada","Khazoom, C., Interdisciplinary Institute for Technological Innovation (3IT), Faculty of Engineering, Université de Sherbrooke, Sherbrooke, QC, Canada; Caillouette, P., Interdisciplinary Institute for Technological Innovation (3IT), Faculty of Engineering, Université de Sherbrooke, Sherbrooke, QC, Canada; Girard, A., Interdisciplinary Institute for Technological Innovation (3IT), Faculty of Engineering, Université de Sherbrooke, Sherbrooke, QC, Canada; Plante, J.-S., Interdisciplinary Institute for Technological Innovation (3IT), Faculty of Engineering, Université de Sherbrooke, Sherbrooke, QC, Canada, Exonetik, Sherbrooke, QC  J1L 1V8, Canada","Supernumerary robotic limbs are emerging to augment human function. Unlike exoskeletons, these robots provide additional kinematic structures to the user that enable novel human-robot interactions. To assist walking, a supernumerary leg should be compliant to impacts, minimize efforts on users, move quickly when swinging and exert large assistive forces on the ground. Here, we study the potential of a supernumerary leg powered by delocalized magnetorheological clutches (MR leg) to assist walking with three different gaits. Simulations show that the MR leg's low actuation inertia reduces the impact impulse by a factor 4 compared to geared motors and that delocalizing the clutches reduces by half the inertial forces transmitted to the user during swing. An impedance controller receives a reference trajectory based on each ankle's position to move the MR leg in synchrony with the gait cycle. Experiments show that the MR leg can comfortably contact the ground and swing at 3.9 m/s for a 1.4 m/s walk. The MR leg tracks the ankle within 5% of the gait cycle for the leader-follower gait, alternately tracks both ankles for the double gait and contacts the ground in between each step for the three-legged gait. A theoretical upper limit suggests that the average transmitted power in a gait cycle could be 84 W for the leader-follower gait, which is 4 times higher than autonomous ankle exoskeletons. © 2016 IEEE.","Compliance and impedance control; magnetorheological actuators; physically assistive devices; wearable robots","Agricultural robots; Exoskeleton (Robotics); Human robot interaction; Average transmitted power; Human locomotions; Impedance controllers; Inertial forces; Kinematic structures; Magneto-rheological; Magnetorheological actuator; Reference trajectories; Walking aids",,,,,"Ministère de l'Économie, de la Science et de l'Innovation - Québec, MESI; Canadian Space Agency, CSA; Natural Sciences and Engineering Research Council of Canada, NSERC; Canada Research Chairs; Fonds de recherche du Québec – Nature et technologies, FRQNT","Manuscript received February 21, 2020; accepted June 18, 2020. Date of publication June 29, 2020; date of current version July 10, 2020. This letter was recommended for publication by Associate Editor Y. Gu and Editor A. Kheddar upon evaluation of the reviewers’ comments. This work was supported in part by Canada Research Chairs, in part by the Canadian Space Agency, in part by Exonetik, in part by FRQNT, in part by NSERC, and in part by MESI. (Corresponding author: Charles Khazoom.) Charles Khazoom, Pierre Caillouette, and Alexandre Girard are with the Faculty of Engineering, Interdisciplinary Institute for Technological Innovation (3IT), Université de Sherbrooke, Sherbrooke, QC J1K 0A5, Canada (e-mail: charles.khazoom@usherbrooke.ca; pierre.caillouette@usherbrooke.ca; Alexandre.Girard2@USherbrooke.ca).",,,,,,,,,,"Veronneau, C., Multifunctional remotely actuated 3-DOF supernumerary robotic arm based on magnetorheological clutches and hydrostatic transmission lines (2020) Ieee Robot.Autom. Lett., 5 (2), pp. 2546-2553. , Apr; Parietti, F., Asada, H.H., Supernumerary robotic limbs for aircraft fuselage assembly: Body stabilization and guidance by bracing (2014) Proc. Ieee Int. Conf. Robot. Autom., pp. 1176-1183. , May; Bonilla, B.L., Asada, H.H., A robot on the shoulder: Coordinated human-wearable robot control using Coloured Petri Nets and Partial Least Squares predictions (2014) Proc. Ieee Int. Conf. Robot. Autom., pp. 119-125. , May; Ort, T., Wu, F., Hensel, N.C., Asada, H.H., Supernumerary robotic fingers as a therapeutic device for hemiparetic patients (2016) Proc. Asme Dyn. Syst. Control Conf., pp. 1-7. , Jan; Gonzalez, D.J., Extra robotic legs for augmenting human payload and positioning capabilities (2019) Ph.D. Dissertation, , Massachusetts Institute Technol., Cambridge, MA, USA; Mooney, L.M., Rouse, E.J., Herr, H.M., Autonomous exoskeleton reduces metabolic cost of human walking (2014) J. NeuroEng. 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Mag., 38 (6), pp. 88-113. , Dec; Elery, T., Rezazadeh, S., Nesler, C., Doan, J., Zhu, H., Gregg, R.D., Design and benchtop validation of a powered knee-ankle prosthesis with high-torque, low-impedance actuators (2018) Proc. Ieee Int. Conf. Robot. Autom., pp. 2788-2795. , May; Khazoom, C., Véronneau, C., Bigué, J.L., Grenier, J., Girard, A., Plante, J., Design and control of a multifunctional ankle exoskeleton powered by magnetorheological actuators to assist walking, jumping, and landing (2019) Ieee Robot. Autom. Lett., 4 (3), pp. 3083-3090. , Jul; Viau, J., Chouinard, P., Bigue, J.-P.L., Julio, G., Michaud, F., Plante, J.-S., Tendon-driven manipulator actuated by magneto-rheological clutches exhibiting both high-power and soft motion capabilities (2017) IEEE/ASME Trans. Mechatronics, 22 (1), pp. 561-571. , Feb; Véronneau, C., Denis, J., Lebel, L.-P., Denninger, M., Plante, J.-S., Girard, A., A lightweight force-controllable wearable arm based on magnetorheological-hydrostatic actuators (2019) Proc. Int. Conf. Robot. Autom., pp. 4018-4024. , May; Weir, D.W., Colgate, J.E., Peshkin, M.A., Measuring and increasing Z-width with active electrical damping (2008) Proc. Symp. Haptic Interfaces Virtual Environ. Teleoperator Syst., pp. 169-175. , Mar; Bégin, M.-A., Conception et validation d'un contrôleur optimisant la durée de vie et la consommation énergétique d'un actionneur magnétorhéologique (2018) Ph.D. Dissertation, , Univ. de Sherbrooke, Sherbrooke, QC, Canada; Veronneau, C., Lucking Bigue, J.-P., Lussier-Desbiens, A., Plante, J.-S., A high-bandwidth back-drivable hydrostatic power distribution system for exoskeletons based on magnetorheological clutches (2018) Ieee Robot. Autom. 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Rehabil., 13, pp. 4-15. , Jan; Ding, Y., Biomechanical and physiological evaluation of multi-joint assistance with soft exosuits (2017) Ieee Trans. Neural Syst. Rehabil. Eng., 25 (2), pp. 119-130. , Feb; Zhang, J., Human-in-The-loop optimization of exoskeleton assistance during walking (2017) Science (New York, N.Y.), 356, pp. 1280-1284. , Jun","Khazoom, C.; Interdisciplinary Institute for Technological Innovation (3IT), Canada; email: charles.khazoom@usherbrooke.ca",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85089201240
"Koskinopoulou M., Maniadakis M., Trahanias P.","57189036916;8913044900;7003421942;","Speed Adaptation in Learning from Demonstration through Latent Space Formulation",2020,"Robotica","38","10",,"1867","1879",,2,"10.1017/S0263574719001449","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074482877&doi=10.1017%2fS0263574719001449&partnerID=40&md5=e26f4c714338c10a5b525a88e34b279f","Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece; Department of Computer Science, University of Crete, Heraklion, Crete, Greece","Koskinopoulou, M., Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece, Department of Computer Science, University of Crete, Heraklion, Crete, Greece; Maniadakis, M., Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece; Trahanias, P., Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece, Department of Computer Science, University of Crete, Heraklion, Crete, Greece","Performing actions in a timely manner is an indispensable aspect in everyday human activities. Accordingly, it has to be present in robotic systems if they are going to seamlessly interact with humans. The current work addresses the problem of learning both the spatial and temporal characteristics of human motions from observation. We formulate learning as a mapping between two worlds (the observed and the action ones). This mapping is realized via an abstract intermediate representation termed Latent Space. Learned actions can be subsequently invoked in the context of more complex human-robot interaction (HRI) scenarios. Unlike previous learning from demonstration (LfD) methods that cope only with the spatial features of an action, the formulated scheme effectively encompasses spatial and temporal aspects. Learned actions are reproduced under the high-level control of a time-informed task planner. During the implementation of the studied scenarios, temporal and physical constraints may impose speed adaptations in the reproduced actions. The employed latent space representation readily supports such variations, giving rise to novel actions in the temporal domain. Experimental results demonstrate the effectiveness of the proposed scheme in the implementation of HRI scenarios. Finally, a set of well-defined evaluation metrics are introduced to assess the validity of the proposed approach considering the temporal and spatial consistency of the reproduced behaviors. © 2020 Cambridge University Press.","Artificial temporal cognition; Human-robot interaction; Latent representation; Learning from demonstration; Temporal planning","Agricultural robots; Demonstrations; Human robot interaction; Man machine systems; Mapping; Petroleum reservoir evaluation; Robot programming; Artificial temporal cognition; KEYWORDS; Latent representation; Learning from demonstration; Temporal planning; Social robots",,,,,,,,,,,,,,,,"Li, M., Bekiroglu, Y., Kragic, D., Billard, A., Learning of Grasp Adaptation through Experience and Tactile Sensing (2014) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems Iros, , Chicago, IL; Schaal, S., Peters, J., Nakanishi, J., Ijspeert, A., Control, Planning, Learning Andimitation with Dynamic Movement Primitives (2003) Iros 2003, Las Vegas, Nv, pp. 1-21; Koskinopoulou, M., Trahanias, P.E., A Methodological Framework for Robotic Reproduction of Observed Human Actions: Formulation Using Latent Space Representation (2016) 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), Cancun, Mexico (IEEE Press, pp. 565-572; Shon, A., Grimes, D.B., Baker, C., Rao, R.P.N., A Probabilistic Framework for Model-Based Imitation Learning (2004) Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society, Berkeley, Ca, pp. 1237-1242; Calinon, S., Guenterlorent, F., Billard, A., On learning, representing and generalizing a task in a humanoid robot (2007) Ieee Trans. 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An Approach to Computerized Processing of Uncertainty, , Plenum Press, New York; Khadar, B., Rajesh, A., Madhusudhan, R., Ramanaiah, M.V., Karthikeyan, K., Statistical optimization for generalised fuzzy number (2013) Int. J. Modern Eng. Res., 3 (2), pp. 647-651","Koskinopoulou, M.; Institute of Computer Science, Greece; email: mkosk@ics.forth.gr",,,"Cambridge University Press",,,,,02635747,,ROBOD,,"English","Robotica",Article,"Final","",Scopus,2-s2.0-85074482877
"Del Duchetto F., Baxter P., Hanheide M.","57207943151;35316890100;6507632075;","Are You Still With Me? Continuous Engagement Assessment From a Robot's Point of View",2020,"Frontiers in Robotics and AI","7",,"116","","",,1,"10.3389/frobt.2020.00116","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091878799&doi=10.3389%2ffrobt.2020.00116&partnerID=40&md5=243624c34a613d2eb6f7fe6d75a73700","Lincoln Centre for Autonomous Systems, School of Computer Science, University of Lincoln, Lincoln, United Kingdom","Del Duchetto, F., Lincoln Centre for Autonomous Systems, School of Computer Science, University of Lincoln, Lincoln, United Kingdom; Baxter, P., Lincoln Centre for Autonomous Systems, School of Computer Science, University of Lincoln, Lincoln, United Kingdom; Hanheide, M., Lincoln Centre for Autonomous Systems, School of Computer Science, University of Lincoln, Lincoln, United Kingdom","Continuously measuring the engagement of users with a robot in a Human-Robot Interaction (HRI) setting paves the way toward in-situ reinforcement learning, improve metrics of interaction quality, and can guide interaction design and behavior optimization. However, engagement is often considered very multi-faceted and difficult to capture in a workable and generic computational model that can serve as an overall measure of engagement. Building upon the intuitive ways humans successfully can assess situation for a degree of engagement when they see it, we propose a novel regression model (utilizing CNN and LSTM networks) enabling robots to compute a single scalar engagement during interactions with humans from standard video streams, obtained from the point of view of an interacting robot. The model is based on a long-term dataset from an autonomous tour guide robot deployed in a public museum, with continuous annotation of a numeric engagement assessment by three independent coders. We show that this model not only can predict engagement very well in our own application domain but show its successful transfer to an entirely different dataset (with different tasks, environment, camera, robot and people). The trained model and the software is available to the HRI community, at https://github.com/LCAS/engagement_detector, as a tool to measure engagement in a variety of settings. © Copyright © 2020 Del Duchetto, Baxter and Hanheide.","machine learning; robot autonomy; service robotics; tools for HRI; user engagement",,,,,,"NIST Center for Neutron Research, NCNR; Engineering and Physical Sciences Research Council, EPSRC: EP/R02572X/1","We thank the annotators and the museum's staff for supporting this research. This manuscript has been released as a pre-print at http://export.arxiv.org/pdf/2001.03515 (Del Duchetto et al., 2020). Funding. This work was jointly funded by EPSRC grant (NCNR, EP/R02572X/1) and the Lincolnshire County Council.","This work was jointly funded by EPSRC grant EP/R02572X/1) and the Lincolnshire County Council.",,,,,,,,,"Baur, T., Mehlmann, G., Damian, I., Lingenfelser, F., Wagner, J., Lugrin, B., Context-aware automated analysis and annotation of social human-agent interactions (2015) ACM Trans. Interact. Intell. 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Intell, 166, pp. 140-164; Tanaka, F., Cicourel, A., Movellan, J.R., Socialization between toddlers and robots at an early childhood education center (2007) Proc. Natl. Acad. Sci. U.S.A, 104, pp. 17954-17958. , 17984068; Won Park, H., Busche, J., Schuller, B., Breazeal, C., Picard, R.W., Personalized estimation of engagement from videos using active learning with deep reinforcement learning (2019) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, , Long Beach, CA, in; Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K., Aggregated residual transformations for deep neural networks (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492-1500. , Honolulu, HI","Del Duchetto, F.; Lincoln Centre for Autonomous Systems, United Kingdom; email: fdelduchetto@lincoln.ac.uk",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85091878799
"Cantucci F., Falcone R.","57205735711;57213828373;","Towards trustworthiness and transparency in social human-robot interaction",2020,"Proceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020",,,"9209397","","",,4,"10.1109/ICHMS49158.2020.9209397","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093931780&doi=10.1109%2fICHMS49158.2020.9209397&partnerID=40&md5=eb12d433498d06e2ef573dea15c15318","Institute of Cognitive Science and Technology, National Research Council of Italy, (ISTC-CNR), Rome, Italy","Cantucci, F., Institute of Cognitive Science and Technology, National Research Council of Italy, (ISTC-CNR), Rome, Italy; Falcone, R., Institute of Cognitive Science and Technology, National Research Council of Italy, (ISTC-CNR), Rome, Italy","Cooperation between autonomous robots and humans is becoming more and more demanding. Robots have to be able to capable of possessing and expose a wide range of cognitive functions, once humans require their help. This paper describes a cognitive architecture for human-robot interaction that allows a robot to dynamically modulate its own level of social autonomy every time a human user delegates to it a task to accomplish in her/his place. The task adoption process leverages on multiple robot's cognitive capabilities (i.e.The ability to have a theory of mind of the user, to build a model of the world, to profile the user and to make an evaluation about its own skill trustworthiness for building the user's profile). On the basis of these capabilities the robot is able to adapt its own level of intelligent collaboration by adopting the task at the different levels of help defined in the theory of delegation and adoption conceived by Castelfranchi and Falcone. Besides that, the architecture enhances robot's behavior transparency because gives to it the ability to provide a comprehensive explanation of the strategy it has adopted for accomplishing the delegated task. We propose an implementation of the cognitive architecture based on JaCaMo framework, which provides support for implementing multi-Agent systems and integrates different multi-Agent programming dimensions. © 2020 IEEE.","Explainable AI; Human-Robot Interaction; Robot Adjustable Autonomy; Theory of Mind","Architecture; Cognitive systems; Intelligent agents; Man machine systems; Multi agent systems; Transparency; Adoption process; Cognitive architectures; Cognitive capability; Cognitive functions; Multi-agent programming; Social human-robot interactions; Theory of minds; User's profiles; Social robots",,,,,,,,,,,,,,,,"Kuipers, B., How can we trust a robot? Communications of the ACM, 61 (3), p. 2018; Khan, A., Anwar, Y., Robots in healthcare: A survey Science and Information Conference, 2019, pp. 280-292. , Springer; Veloso, M.M., Biswas, J., Coltin, B., Rosenthal, S., Cobots: Robust symbiotic autonomous mobile service robots (2015) IJCAI, p. 4423; Ivanov, S.H., Webster, C., Berezina, K., Adoption of robots and service automation by tourism and hospitality companies (2017) Revista Turismo & Desenvolvimento, 27 (28), pp. 1501-1517; Falcone, R., Castelfranchi, C., The human in the loop of a delegated agent: The theory of adjustable social autonomy (2001) IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 31 (5), pp. 406-418; Wortham, R.H., Theodorou, A., Bryson, J.J., What does the robot think? transparency as a fundamental design requirement for intelligent systems Ijcai-2016 Ethics for Artificial Intelligence Workshop, 2016; Boissier, O., Bordini, R.H., Hübner, J.F., Ricci, A., Santi, A., Multi-Agent oriented programming with jacamo (2013) Science of Computer Programming, 78 (6), pp. 747-761; Castelfranchi, C., Falcone, R., Towards a theory of delegation for agent-based systems (1998) Robotics and Autonomous Systems, 24 (3-4), pp. 141-157; Scassellati, B.M., Foundations for a theory of mind for a humanoid robot (2001), Ph.D. dissertation Massachusetts Institute of Technology; Langley, P., Explainable agency in human-robot interaction AAAI Fall Symposium Series, 2016; Goodrich, M.A., Olsen, D.R., Crandall, J.W., Palmer, T.J., Experiments in adjustable autonomy (2001) Proceedings of IJCAI Workshop on Autonomy, Delegation and Control: Interacting with Intelligent Agents. Seattle, WA: American Association for Artificial Intelligence Press, pp. 1624-1629; Mostafa, S.A., Ahmad, M.S., Mustapha, A., Adjustable autonomy: A systematic literature review (2019) Artificial Intelligence Review, 51 (2), pp. 149-186; Birk, A., Pfingsthorn, M., A hmi supporting adjustable autonomy of rescue robots (2005) Robot Soccer World Cup, pp. 255-266. , Springer; Roehr, T.M., Shi, Y., Using a self-confidence measure for a system-initiated switch between autonomy modes (2010) Proceedings of the 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space, Sapporo, Japan, pp. 507-514; Petersen, K., Von Stryk, O., Towards a general communication concept for human supervision of autonomous robot teams (2011) Proceedings of the Fourth International Conference on Advances in Computer-Human Interactions (ACHI). Citeseer, pp. 228-235; Anjomshoae, S., Najjar, A., Calvaresi, D., Främling, K., Explainable agents and robots: Results from a systematic literature review Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems, 2019, pp. 1078-1088; Sridharan, M., Meadows, B., Colaco, Z., A tale of many explanations: Towards an explanation generation system for robots (2016) Proceedings of the 31st Annual ACM Symposium on Applied Computing. ACM, pp. 260-267; Georgeff, M.P., Lansky, A.L., Reactive reasoning and planning (1987) AAAI, 87, pp. 677-682; Rao, A.S., Georgeff, M.P., Bdi agents: From theory to practice (1995) ICMAS, 95, pp. 312-319; Bordini, R.H., Hübner, J.F., Bdi agent programming in agentspeak using jason (2005) International Workshop on Computational Logic in Multi-Agent Systems, pp. 143-164. , Springer; Rao, A.S., Agentspeak (l): Bdi agents speak out in a logical computable language (1996) European Workshop on Modelling Autonomous Agents in A Multi-Agent World, pp. 42-55. , Springer; Ricci, A., Piunti, M., Viroli, M., Omicini, A., Environment programming in cartago (2009) Multi-Agent Programming, pp. 259-288. , Springer; Hübner, J.F., Sichman, J.S., Boissier, O., Developing organised multiagent systems using the moise+ model: Programming issues at the system and agent levels (2007) International Journal of Agent-Oriented Software Engineering, 1 (3-4), pp. 370-395",,"Fortino G.Wang F.-Y.Nurnberger A.Kaber D.Falcone R.Mendonca D.Yu Z.Guerrieri A.",,"Institute of Electrical and Electronics Engineers Inc.","1st IEEE International Conference on Human-Machine Systems, ICHMS 2020","7 September 2020 through 9 September 2020",,163564,,9781728158716,,,"English","Proc. IEEE Int. Conf. Hum.-Mach. Syst., ICHMS",Conference Paper,"Final","",Scopus,2-s2.0-85093931780
"Liu Y., Yang W., Yan Y., Wu X., Wang X., Zhou Y., Hu Y., Chen H., Guo T.","57207962222;57206587020;57205646015;55857025600;57193560203;57216916622;55622899800;55174475100;7201788865;","Self-powered high-sensitivity sensory memory actuated by triboelectric sensory receptor for real-time neuromorphic computing",2020,"Nano Energy","75",,"104930","","",,28,"10.1016/j.nanoen.2020.104930","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085272547&doi=10.1016%2fj.nanoen.2020.104930&partnerID=40&md5=0071ba5dc3d75f51b369db60cd34518e","Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China; Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China; Key Laboratory for Micro-Nano Optoelectronic Devices of Ministry of Education, School of Physics and Electronics, Hunan University, Changsha, 410082, China","Liu, Y., Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China, Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China; Yang, W., Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China; Yan, Y., Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China; Wu, X., Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China; Wang, X., Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China; Zhou, Y., Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China; Hu, Y., Key Laboratory for Micro-Nano Optoelectronic Devices of Ministry of Education, School of Physics and Electronics, Hunan University, Changsha, 410082, China; Chen, H., Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China, Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China; Guo, T., Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, Fuzhou, 350002, China, Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, 350100, China","Artificial sensory memory, which is expected to collect, integrate, and refine massive sensory data timely for dynamically training the bioinspired neural network, is a promising candidate to achieve novel architectures of hardware artificial intelligence to mimic neural network. Unfortunately, the reports about artificial sensory memory are very limited and more importantly, there are still many unsolved problems in previously reported artificial sensory memory devices, such as the low sensitivity of perception receptors, high power consumption, and realization of instantaneous neuromorphic computing. Here, we propose a rapid-response, high-sensitivity, and self-powered artificial sensory memory, which is integrated with a triboelectric nanogenerator (TENG) and a field effect synaptic transistor, and is able to achieve real-time neuromorphic computing with a TENG matrix for the first time. Typical properties of sensory memory are successfully demonstrated, such as, excitatory post-synaptic current and paired pulse facilitation, followed with hierarchical memorial processes from sensory memory to short-term memory and to long-term memory. Finally, 28 × 28 matrix triboelectric sensory receptors are fabricated to connect the real-time handwritten image with large-scale data processing. This work proposed a remarkable self-powered artificial afferent nerve to realize rapid and high-sensitivity response, which would show a widespread potential in low consumption artificial neuromorphic interface such as human-robot interaction, edge computing and neurorobotics. © 2020 Elsevier Ltd","High-sensitivity; Organic filed effect transistor; Real-time neuromorphic computing; Self-powered; Sensory memory; Synaptic transistor","Data handling; Green computing; Human robot interaction; Matrix algebra; Triboelectricity; Bio-inspired neural network; Handwritten images; High power consumption; Large-scale data processing; Neuromorphic computing; Novel architecture; Short term memory; Typical properties; Sensory perception",,,,,"National Natural Science Foundation of China, NSFC: 61974029; National Key Research and Development Program of China, NKRDPC: 2016YFB0401103","The authors are grateful for financial support from National Natural Science Foundation of China ( 61974029 ) and National Key Research and Development Program of China ( 2016YFB0401103 ).",,,,,,,,,,"Wan, C., Cai, P., Wang, M., Qian, Y., Huang, W., Chen, X., (2019) Adv. 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Interfaces, 12, pp. 15446-15455","Chen, H.; Institute of Optoelectronic Display, China; email: hpchen@fzu.edu.cn",,,"Elsevier Ltd",,,,,22112855,,,,"English","Nano Energy",Article,"Final","",Scopus,2-s2.0-85085272547
"Vinanzi S., Cangelosi A., Goerick C.","57195222288;6701387796;8589247000;","The Role of Social Cues for Goal Disambiguation in Human-Robot Cooperation",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223546","971","977",,,"10.1109/RO-MAN47096.2020.9223546","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095751795&doi=10.1109%2fRO-MAN47096.2020.9223546&partnerID=40&md5=cc96b61873aec5712a5f2bd62d0e4839","University of Manchester, Cognitive Robotics Lab, Manchester, United Kingdom; Honda Research Institute Europe GmbH, Offenbach am Main, Germany","Vinanzi, S., University of Manchester, Cognitive Robotics Lab, Manchester, United Kingdom; Cangelosi, A., University of Manchester, Cognitive Robotics Lab, Manchester, United Kingdom; Goerick, C., Honda Research Institute Europe GmbH, Offenbach am Main, Germany","Social interaction is the new frontier in contemporary robotics: we want to build robots that blend with ease into our daily social environments, following their norms and rules. The cognitive skill that bootstraps social awareness in humans is known as intention reading and it allows us to interpret other agents' actions and assign them meaning. Given its centrality for humans, it is likely that intention reading will foster the development of robotic social understanding. In this paper, we present an artificial cognitive architecture for intention reading in human-robot interaction (HRI) that makes use of social cues to disambiguate goals. This is accomplished by performing a low-level action encoding paired with a high-level probabilistic goal inference. We introduce a new clustering algorithm that has been developed to differentiate multi-sensory human social cues by performing several levels of clustering on different feature-spaces, paired with a Bayesian network that infers the underlying intention. The model has been validated through an interactive HRI experiment involving a joint manipulation game performed by a human and a robotic arm in a toy block scenario. The results show that the artificial agent was capable of reading the intention of its partner and cooperate in mutual interaction, thus validating the novel methodology and the use of social cues to disambiguate goals, other than demonstrating the advantages of intention reading in social HRI. © 2020 IEEE.","clustering; cognitive robotics; human-robot interaction; intention reading; social cues","Agricultural robots; Bayesian networks; Clustering algorithms; Economic and social effects; Robotics; Cognitive architectures; Human robot Interaction (HRI); Human-robot cooperation; Intention readings; New clustering algorithms; Probabilistic goals; Social environment; Social interactions; Social robots",,,,,,,,,,,,,,,,"Breazeal, C., Social interactions in hri: The robot view (2004) IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 34 (2), pp. 181-186; Cangelosi, A., Schlesinger, M., Smith, L.B., (2015) Developmental Robotics: From Babies to Robots, , MIT Press; Malle, B.F., Moses, L.J., Baldwin, D.A., (2001) Intentions and Intentionality: Foundations of Social Cognition, , MIT press; Baldwin, D.A., Baird, J.A., Discerning intentions in dynamic human action (2001) Trends in Cognitive Sciences, 5 (4), pp. 171-178; Woodward, A.L., Sommerville, J.A., Gerson, S., Henderson Me, A., Buresh, J., The emergence of intention attribution in infancy (2009) Psychology of Learning and Motivation, 51, pp. 187-222; Tomasello, M., Carpenter, M., Call, J., Behne, T., Moll, H., Understanding and sharing intentions: The origins of cultural cognition (2005) Behavioral and Brain Sciences, 28 (5), pp. 675-691; Gallese, V., Goldman, A., Mirror neurons and the simulation theory of mind-reading (1998) Trends in Cognitive Sciences, 2 (12), pp. 493-501; Vanderbilt, K.E., Liu, D., Heyman, G.D., The development of distrust (2011) Child Development, 82 (5), pp. 1372-1380; Dindo, H., Donnarumma, F., Chersi, F., Pezzulo, G., The intentional stance as structure learning: A computational perspective on mindreading (2015) Biological Cybernetics, 109 (4-5), pp. 453-467; Kelley, R., King, C., Tavakkoli, A., Nicolescu, M., Nicolescu, M., Bebis, G., An architecture for understanding intent using a novel hidden markov formulation (2008) International Journal of Humanoid Robotics, 5 (2), pp. 203-224; Singh, B., Marks, T.K., Jones, M., Tuzel, O., Shao, M., A multi-stream bi-directional recurrent neural network for finegrained action detection (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1961-1970; Manzi, A., Dario, P., Cavallo, F., A human activity recognition system based on dynamic clustering of skeleton data (2017) Sensors, 17 (5), p. 1100; Granada, R., Fraga Pereira, R., Monteiro, J., Barros, R., Ruiz, D., Meneguzzi, F., Hybrid activity and plan recognition for video streams (1995) The 2017 AAAI Workshop on Plan, Activity, and Intent Recognition, pp. 54-59. , Greer, JE and Koehn, GM; Sciutti, A., Ansuini, C., Becchio, C., Sandini, G., Investigating the ability to read others' intentions using humanoid robots (2015) Frontiers in Psychology, 6; Ford Dominey, P., Warneken, F., The basis of shared intentions in human and robot cognition (2011) New Ideas in Psychology, 29 (3), pp. 260-274; Jansen, B., Belpaeme, T., A computational model of intention reading in imitation (2006) Robotics and Autonomous Systems, 54 (5), pp. 394-402; Ferreira Duarte, N., Rakovíc, M., Tasevski, J., Ignazio Coco, M., Billard, A., Santos-Victor, J., Action anticipation: Reading the intentions of humans and robots (2018) IEEE Robotics and Automation Letters, 3 (4), pp. 4132-4139; Câmpan, A., Erban, G.S., Adaptive clustering algorithms Advances in Artificial Intelligence, pp. 407-418. , Luc Lamontagne and Mario Marchand, editors, Berlin, Heidelberg, 2006. Springer Berlin Heidelberg; Young, S., Arel, I., Karnowski, T.P., Rose, D., A fast and stable incremental clustering algorithm (2010) 2010 Seventh International Conference on Information Technology: New Generations, pp. 204-209. , IEEE; Vidal, R., Subspace clustering (2011) IEEE Signal Processing Magazine, 28 (2), pp. 52-68; Vinanzi, S., Goerick, C., Cangelosi, A., Mindreading for robots: Predicting intentions via dynamical clustering of human postures (2019) 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pp. 272-277. , Aug; Cao, Z., Simon, T., Wei, S., Sheikh, Y., (2016) Realtime Multi-person 2d Pose Estimation Using Part Affinity Fields; Patacchiola, M., Cangelosi, A., Head pose estimation in the wild using convolutional neural networks and adaptive gradient methods (2017) Pattern Recognition, 71, pp. 132-143; Jha, S., Busso, C., Probabilistic estimation of the driver's gaze from head orientation and position (2017) 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 1-6. , Oct; Bellman, R., Dynamic programming (2013) Courier Corporation; Pelleg, D., Moore, A.W., X-means: Extending k-means with efficient estimation of the number of clusters (2000) ICML, 1, pp. 727-734; Aldrich, J., Ra fisher and the making of maximum likelihood 1912-1922 (1997) Statistical Science, 12 (3), pp. 162-176; Lauritzen, S.L., Spiegelhalter, D.J., Local computations with probabilities on graphical structures and their application to expert systems (1988) Journal of the Royal Statistical Society. Series B (Methodological), pp. 157-224; Rousseeuw, P.J., Silhouettes: A graphical aid to the interpretation and validation of cluster analysis (1987) Journal of Computational and Applied Mathematics, 20, pp. 53-65; Vinanzi, S., Patacchiola, M., Chella, A., Cangelosi, A., Would a robot trust you? Developmental robotics model of trust and theory of mind (2019) Philosophical Transactions of the Royal Society of London B","Vinanzi, S.; University of Manchester, United Kingdom; email: samuele:vinanzi@manchester:ac:uk",,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095751795
"Mou W., Ruocco M., Zanatto D., Cangelosi A.","56684766800;57201548087;55463260500;6701387796;","When Would You Trust a Robot? A Study on Trust and Theory of Mind in Human-Robot Interactions",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223551","956","962",,2,"10.1109/RO-MAN47096.2020.9223551","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095744596&doi=10.1109%2fRO-MAN47096.2020.9223551&partnerID=40&md5=7f363e382006cd13e8d88687a302976d","University of Manchester, Department of Computer Science, United Kingdom; University of Bristol, Department of Computer Science, United Kingdom","Mou, W., University of Manchester, Department of Computer Science, United Kingdom; Ruocco, M., University of Manchester, Department of Computer Science, United Kingdom; Zanatto, D., University of Bristol, Department of Computer Science, United Kingdom; Cangelosi, A., University of Manchester, Department of Computer Science, United Kingdom","Trust is a critical issue in human-robot interactions (HRI) as it is the core of human desire to accept and use a non-human agent. Theory of Mind (ToM) has been defined as the ability to understand the beliefs and intentions of others that may differ from one's own. Evidences in psychology and HRI suggest that trust and ToM are interconnected and interdependent concepts, as the decision to trust another agent must depend on our own representation of this entity's actions, beliefs and intentions. However, very few works take ToM of the robot into consideration while studying trust in HRI. In this paper, we investigated whether the exposure to the ToM abilities of a robot could affect humans' trust towards the robot. To this end, participants played a Price Game with a humanoid robot (Pepper) that was presented having either low-level ToM or high-level ToM. Specifically, the participants were asked to accept the price evaluations on common objects presented by the robot. The willingness of the participants to change their own price judgement of the objects (i.e., accept the price the robot suggested) was used as the main measurement of the trust towards the robot. Our experimental results showed that robots possessing a high-level of ToM abilities were trusted more than the robots presented with low-level ToM skills. © 2020 IEEE.",,"Agricultural robots; Anthropomorphic robots; Man machine systems; Critical issues; Human agent; Human robot Interaction (HRI); Humanoid robot; Price games; Theory of minds; Social robots",,,,,"Air Force Office of Scientific Research, AFOSR; U.S. Air Force, USAF: FA9550-19-1-7002; Engineering and Physical Sciences Research Council, EPSRC: EP/R004757/1; University of Bristol; Thales Group, Thales","ACKNOWLEDGEMENT This material is based upon work supported by the Air Force Office of Scientific Research, USAF under Award No. FA9550-19-1-7002. The work of Debora Zanatto was funded and delivered in partnership between the Thales Group and the University of Bristol, and with the support of the UK Engineering and Physical Sciences Research Council Grant Award EP/R004757/1 entitled ‘Thales-Bristol Partnership in Hybrid Autonomous Systems Engineering (T-B PHASE)’.",,,,,,,,,,"Vinanzi, S., Patacchiola, M., Chella, A., Cangelosi, A., Would a robot trust you? Developmental robotics model of trust and theory of mind (2019) Philosophical Transactions of the Royal Society B, 374 (1771), p. 20180032; Zanatto, D., Patacchiola, M., Goslin, J., Cangelosi, A., Investigating cooperation with robotic peers (2019) PloS One, 14 (11); Romeo, M., Hernández Garca, D., Jones, R., Cangelosi, A., Deploying a deep learning agent for hri with potential (2019) Proceedings of the 7th International Conference on Human-Agent Interaction, pp. 81-88; Hancock, P.A., Billings, D.R., Schaefer, K.E., Can you trust your robot? (2011) Ergonomics in Design, 19 (3), pp. 24-29; Breazeal, C., Kidd, C.D., Thomaz, A.L., Hoffman, G., Berlin, M., Effects of nonverbal communication on efficiency and robustness in human-robot teamwork (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 708-713; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K., Would you trust a (faulty) robot? Effects of error, task type and personality on human-robot cooperation and trust (2015) ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 1-8; Shu, P., Min, C., Bodala, I., Nikolaidis, S., Hsu, D., Soh, H., Human trust in robot capabilities across tasks (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 241-242; Zanatto, D., Phd thesis: When do we cooperate with robots ? Investigations in human-robot interaction and trust (2019) University of Plymouth; Ham, J., Bokhorst, R., Cuijpers, R., Van, P.D.D., Cabibihan, J.-J., Making robots persuasive: The influence of combining persuasive strategies (gazing and gestures) by a storytelling robot on its persuasive power (2011) International Conference on Social Robotics, pp. 71-83; Huang, C.-M., Thomaz, A.L., Joint attention in human-robot interaction (2010) 2010 AAAI Fall Symposium Series; Leslie, A.M., Pretense and representation: The origins of theory of mind (1987) Psychological Review, 94 (4), p. 412; Vanderbilt, K.E., Liu, D., Heyman, G.D., The development of distrust (2011) Child Development, 82 (5), pp. 1372-1380; Scassellati, B., Theory of mind for a humanoid robot (2002) Autonomous Robots, 12 (1), pp. 13-24; Rau, P.P., Li, Y., Li, D., Effects of communication style and culture on ability to accept recommendations from robots (2009) Computers in Human Behavior, 25 (2), pp. 587-595; Mayer, R.C., Davis, J.H., Schoorman, F.D., An integrative model of organizational trust (1995) Academy of Management Review, 20 (3), pp. 709-734; Schaefer, K., (2013) The Perception and Measurement of Human-robot Trust; Lee, J.D., See, K.A., Trust in automation: Designing for appropriate reliance (2004) Human Factors, 46 (1), pp. 50-80; Desai, M., Medvedev, M., Vázquez, M., McSheehy, S., Gadea-Omelchenko, S., Bruggeman, C., Steinfeld, A., Yanco, H., Effects of changing reliability on trust of robot systems (2012) ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 73-80; Lohani, M., Stokes, C., McCoy, M., Bailey, C.A., Rivers, S.E., Social interaction moderates human-robot trust-reliance relationship and improves stress coping (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 471-472; Bellas, F., Duro, R.J., Faiña, A., Souto, D., Multilevel darwinist brain (mdb): Artificial evolution in a cognitive architecture for real robots (2010) IEEE Transactions on Autonomous Mental Development, 2 (4), pp. 340-354; Blum, C., Winfield, A.F., Hafner, V.V., Simulation-based internal models for safer robots (2018) Frontiers in Robotics and AI, 4, p. 74; Chella, A., Dindo, H., Infantino, I., A cognitive framework for imitation learning (2006) Robotics and Autonomous Systems, 54 (5), pp. 403-408; Augello, A., Cipolla, E., Infantino, I., Manfré, A., Pilato, G., Vella, F., Social signs processing in a cognitive architecture for an humanoid robot (2018) Procedia Computer Science, 123, pp. 63-68; Lazzeri, N., Mazzei, D., Cominelli, L., Cisternino, A., De Rossi, D.E., Designing the mind of a social robot (2018) Applied Sciences, 8 (2), p. 302; Baron-Cohen, S., Leslie, A.M., Frith, U., Does the autistic child have a theory of mind (1985) Cognition, 21 (1), pp. 37-46; Sturgeon, S., Palmer, A., Blankenburg, J., Feil-Seifer, D., Perception of social intelligence in robots performing false-belief tasks (2019) IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1-7; Faul, F., Erdfelder, E., Buchner, A., Lang, A.-G., Statistical power analyses using g power 3. 1: Tests for correlation and regression analyses (2009) Behavior Research Methods, 41 (4), pp. 1149-1160; Zanatto, D., Patacchiola, M., Cangelosi, A., Goslin, J., Generalisation of anthropomorphic stereotype (2019) International Journal of Social Robotics, pp. 1-10; Zanatto, D., Phd thesis: When do we cooperate with robots? (2019) University of Plymouth; Reysen, S., Construction of a new scale: The reysen likability scale (2005) Social Behavior and Personality: An International Journal, 33 (2), pp. 201-208; McCroskey, J.C., Young, T.J., Ethos and credibility: The construct and its measurement after three decades (1981) Communication Studies, 32 (1), pp. 24-34; Bartneck, C., Kulíc, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81; Zanatto, D., Patacchiola, M., Goslin, J., Cangelosi, A., Priming anthropomorphism: Can the credibility of humanlike robots be transferred to non-humanlike robots? (2016) ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 543-544; Hofmann, W., Gawronski, B., Gschwendner, T., Le, H., Schmitt, M., A meta-analysis on the correlation between the implicit association test and explicit self-report measures (2005) Personality and Social Psychology Bulletin, 31 (10), pp. 1369-1385",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85095744596
"Xu J., Howard A.","57201781642;26643170000;","Would you Take Advice from a Robot? Developing a Framework for Inferring Human-Robot Trust in Time-Sensitive Scenarios",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223544","814","820",,,"10.1109/RO-MAN47096.2020.9223544","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095734560&doi=10.1109%2fRO-MAN47096.2020.9223544&partnerID=40&md5=163bbe2f3bae14ee1bb6be5cbe6abf6f","Georgia Institute of Technology, Atlanta, GA  30332, United States","Xu, J., Georgia Institute of Technology, Atlanta, GA  30332, United States; Howard, A., Georgia Institute of Technology, Atlanta, GA  30332, United States","Trust is a key element for successful human-robot interaction. One challenging problem in this domain is the issue of how to construct a formulation that optimally models this trust phenomenon. This paper presents a framework for modeling human-robot trust based on representing the human decision-making process as a formulation based on trust states. Using this formulation, we then discuss a generalized model of human-robot trust based on Hidden Markov Models and Logistic Regression. The proposed approach is validated on datasets collected from two different human subject studies in which the human is provided the ability to take advice from a robot. Both experimental scenarios were time-sensitive, in that a decision had to be made by the human in a limited time period, but each scenario featured different levels of cognitive load. The experimental results demonstrate that the proposed formulation can be utilized to model trust, in which the system can predict whether the human will decide to take advice (or not) from the robot. It was found that our prediction performance degrades after the robot made a mistake. The validation of this approach on two scenarios implies that this model can be applied to other interactive scenarios as long as the interaction dynamics fits into the proposed formulation. Directions for future improvements are discussed. © 2020 IEEE.",,"Agricultural robots; Decision making; Hidden Markov models; Logistic regression; Cognitive loads; Future improvements; Generalized models; Human decision making; Human subjects; Interaction dynamics; Prediction performance; Time-periods; Social robots",,,,,"Air Force Office of Scientific Research, AFOSR","*Research supported by the Air Force Office of Sponsored Research. Jin Xu and Ayanna Howard are with the Georgia Institute of Technology, Atlanta, GA",,,,,,,,,,"Xu, J., De'Aira, G.B., Chen, Y.-P., Howard, A., Robot therapist versus human therapist: Evaluating the effect of corrective feedback on human motor performance (2018) 2018 International Symposium on Medical Robotics (ISMR), pp. 1-6. , IEEE; Fitzpatrick, K.K., Darcy, A., Vierhile, M., Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (woebot): A randomized controlled trial (2017) JMIR Mental Health, 4 (2), p. e19; Brown, L., Howard, A., Engaging children in math education using a socially interactive humanoid robot (2013) IEEE-RAS International Conference on Humanoid Robots, pp. 183-188. , Atlanta, GA, Oct; Robinette, P., Li, W., Allen, R., Howard, A.M., Wagner, A.R., Overtrust of robots in emergency evacuation scenarios (2016) The Eleventh ACM/IEEE International Conference on Human Robot Interaction, pp. 101-108. , IEEE Press; Borenstein, J., Wagner, A.R., Howard, A., Overtrust of pediatric health-care robots: A preliminary survey of parent perspectives (2018) IEEE Robotics & Automation Magazine, 25 (1), pp. 46-54; Lee, J., Moray, N., Trust, control strategies and allocation of function in human-machine systems (1992) Ergonomics, 35 (10), pp. 1243-1270; Muir, B.M., Trust between humans and machines, and the design of decision AIDS (1987) International Journal of Man-machine Studies, 27 (5-6), pp. 527-539; Desai, M., Kaniarasu, P., Medvedev, M., Steinfeld, A., Yanco, H., Impact of robot failures and feedback on real-time trust (2013) Proceedings of the 8th ACM/IEEE International Conference on Humanrobot Interaction, pp. 251-258. , IEEE Press; Lee, J.J., Knox, B., Baumann, J., Breazeal, C., DeSteno, D., Computationally modeling interpersonal trust (2013) Frontiers in Psychology, 4, p. 893; Xu, A., Dudek, G., Optimo: Online probabilistic trust inference model for asymmetric human-robot collaborations (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 221-228. , ACM; Chen, M., Nikolaidis, S., Soh, H., Hsu, D., Srinivasa, S., Planning with trust for human-robot collaboration (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 307-315. , ACM; Soh, H., Shu, P., Chen, M., Hsu, D., Trust dynamics and transfer across human-robot interaction tasks: Bayesian and neural computational models (2019) Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, pp. 6226-6230; Robinette, P., Howard, A.M., Wagner, A.R., Effect of robot performance on human-robot trust in time-critical situations (2017) IEEE Transactions on Human-Machine Systems; Rabiner, L.R., A tutorial on hidden markov models and selected applications in speech recognition (1989) Proceedings of the IEEE, 77 (2), pp. 257-286; Xu, J., Howard, A., The impact of first impressions on human-robot trust during problem-solving scenarios (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 435-441. , IEEE",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095734560
"Zhong H., Li X., Gao L.","57208718405;56021323400;56406738100;","Adaptive Delay Compensation for Admittance Control of Hydraulic Series Elastic Actuator",2020,"IEEE International Conference on Automation Science and Engineering","2020-August",,"9216780","384","389",,,"10.1109/CASE48305.2020.9216780","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094114872&doi=10.1109%2fCASE48305.2020.9216780&partnerID=40&md5=ee8349bad06b515098c8aed8b1bee74c","Huazhong University of Science and Technology, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Wuhan, 430074, China","Zhong, H., Huazhong University of Science and Technology, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Wuhan, 430074, China; Li, X., Huazhong University of Science and Technology, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Wuhan, 430074, China; Gao, L., Huazhong University of Science and Technology, State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Wuhan, 430074, China","Recently, series elastic actuators (SEAs) are considered to be promising devices for achieving safe human-robot interaction. Various Impedance/admittance control methods have been proposed to realize active compliance. Although lots of research have been conducted to improve the control accuracy, however, time-delay issue in SEA control is not well investigated. To resolve this problem, this paper proposes a novel parallel adaptive time delay (P-ATS) compensator for admittance control of a hydraulic SEA. An admittance controller is first developed and the P-ATS compensator is then adopted to reduce the time delay and tracking error. It is derived from the conventional adaptive time delay (ATS) compensator in which the coefficients are mechanically updated after sampling. This paper designs a special parallel mechanism that allows the coefficient determination procedure to be conducted independently. The whale swarm algorithm with iterative counter (WSA-IC) is adopted for coefficient determination. A criterion is also proposed to evaluate the quality of identified coefficients before updating. This unique mechanism can not only provide more computational resources on coefficient determination, but also increase the quality of the output coefficients. The simulation experiments confirm the superiorities of the P-ATS compensator over conventional methods. The results also verify that the P-ATS compensator can maintain a good performance with a lower sampling rate. © 2020 IEEE.",,"Compliance control; Delay control systems; Human robot interaction; Hydraulic actuators; Iterative methods; Mechanisms; Time delay; Active compliance; Admittance control; Computational resources; Control accuracy; Conventional methods; Parallel mechanisms; Safe human-robot interaction; Series elastic actuators; Quality control",,,,,"2017QYTD04; National Natural Science Foundation of China, NSFC: 51721092","*Research supported by the National Natural Science Foundation of China (grant number 51721092) and the program for the HUST Academic Frontier Youth Team (grant number 2017QYTD04).",,,,,,,,,,"Zhao, Y., Paine, N., Sentis, L., Feedback parameter selection for impedance control of series elastic actuators (2014) 2014 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids, pp. 999-1006; Calanca, A., Muradore, R., Fiorini, P., A review of algorithms for compliant control of stiff and fixed-compliance robots (2016) IEEE/ASME Transactions on Mechatronics, 21 (2), pp. 613-624; Park, Y., Paine, N., Oh, S., Development of force observer in series elastic actuator for dynamic control (2018) Ieee Transactions on Industrial Electronics, 65 (3), pp. 2398-2407. , Mar; Calanca, A., Muradore, R., Fiorini, P., Impedance control of series elastic actuators: Passivity and acceleration-based control (2017) Mechatronics, 47, pp. 37-48. , Nov; Chawda, V., Niemeyer, G., Toward torque control of a kuka lbr iiwa for physical human-robot interaction (2017) 2017 Ieee/Rsj International Conference on Intelligent Robots and Systems (Iros, pp. 6387-6392; Li, X., Liu, Y.H., Yu, H.Y., Iterative learning impedance control for rehabilitation robots driven by series elastic actuators (2018) Automatica, 90, pp. 1-7. , Apr; Pratt, G.A., Willisson, P., Bolton, C., Hofman, A., Late motor processing in low-impedance robots: Impedance control of series-elastic actuators (2004) Proceedings of the 2004 American Control Conference, 1-6, pp. 3245-3251; Yu, H.Y., Huang, S.N., Chen, G., Pan, Y.P., Guo, Z., Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) Ieee Transactions on Robotics, 31 (5), pp. 1089-1100. , Oct; Sariyildiz, E., Chen, G., Yu, H.Y., An acceleration-based robust motion controller design for a novel series elastic actuator (2016) Ieee Transactions on Industrial Electronics, 63 (3), pp. 1900-1910. , Mar; Souzanchi-K, M., Arab, A., Akbarzadeh-T, M.R., Fateh, M.M., Robust impedance control of uncertain mobile manipulators using time-delay compensation (2018) Ieee Transactions on Control Systems Technology, 26 (6), pp. 1942-1953. , Nov; Zhao, Y., Sentis, L., Passivity of time-delayed whole-body operational space control with series elastic actuation (2016) 2016 Ieee-Ras 16th International Conference on Humanoid Robots (Humanoids, pp. 1290-1297; Kim, S., Bae, J., Force-mode control of rotary series elastic actuators in a lower extremity exoskeleton using model-inverse time delay control (2017) Ieee-Asme Transactions on Mechatronics, 22 (3), pp. 1392-1400. , Jun; Budolak, D., Ben-Tzvi, P., Series elastic actuation for improved transparency in time delayed haptic teleoperation (2019) Mechatronics, 63, p. 102278; Natori, K., Ohnishi, K., A design method of communication disturbance observer for time-delay compensation, taking the dynamic property of network disturbance into account (2008) Ieee Transactions on Industrial Electronics, 55 (5), pp. 2152-2168. , May; Chae, Y., Kazemibidokhti, K., Ricles, J.M., Adaptive time series compensator for delay compensation of servo-hydraulic actuator systems for real-time hybrid simulation (2013) Earthquake Engineering & Structural Dynamics, 42 (11), pp. 1697-1715. , Sep; Chae, Y., Rabiee, R., Dursun, A., Kim, C.Y., Real-time force control for servo-hydraulic actuator systems using adaptive time series compensator and compliance springs (2018) Earthquake Engineering & Structural Dynamics, 47 (4), pp. 854-871. , Apr 10; Zhong, H., Li, X., Gao, L., Dong, H., Position control of hydraulic series elastic actuator with parameter self-optimization 2019 Ieee 4th International Conference on Advanced Robotics and Mechatronics (ICARM, pp. 42-46; Zeng, B., Li, X., Gao, L., Zhang, Y., Dong, H., Whale swarm algorithm with the mechanism of identifying and escaping from extreme points for multimodal function optimization (2018) Neural Computing & Applications, pp. 1-21","Gao, L.; Huazhong University of Science and Technology, China; email: gaoliang@mail.hust.edu.cn",,,"IEEE Computer Society","16th IEEE International Conference on Automation Science and Engineering, CASE 2020","20 August 2020 through 21 August 2020",,163854,21618070,9781728169040,,,"English","IEEE Int. Conf. Autom. Sci. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85094114872
"Yuan Y., Lin K.-C., Wu C.-F., Niu J.","57219488492;56621850700;7501666794;57219489420;","Can the Movements of Robots Convey Emotions to People? The Robots Communicate with Human and Express Emotions through Different Actions",2020,"IRCE 2020 - 2020 3rd International Conference on Intelligent Robotics and Control Engineering",,,"9199242","92","96",,,"10.1109/IRCE50905.2020.9199242","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093076752&doi=10.1109%2fIRCE50905.2020.9199242&partnerID=40&md5=8c19ade588fff4dfe16b6ae217d43a54","Graduate Institute of Design Science, Tatung University, No.40, Sec.3, Zhongshan N.Rd, Taipei City, 104, Taiwan; Tatung University, Department of Industrial Design, No.40, Sec.3, Zhongshan N.Rd, Taipei City, 104, Taiwan","Yuan, Y., Graduate Institute of Design Science, Tatung University, No.40, Sec.3, Zhongshan N.Rd, Taipei City, 104, Taiwan; Lin, K.-C., Tatung University, Department of Industrial Design, No.40, Sec.3, Zhongshan N.Rd, Taipei City, 104, Taiwan; Wu, C.-F., Tatung University, Department of Industrial Design, No.40, Sec.3, Zhongshan N.Rd, Taipei City, 104, Taiwan; Niu, J., Graduate Institute of Design Science, Tatung University, No.40, Sec.3, Zhongshan N.Rd, Taipei City, 104, Taiwan","The service robots as a fast-growing industry in today's society, have gained the attention of all the countries. The research of service robots has gradually shifted from functional to perceptual. During the human robot interaction, in addition to the voice interaction, movements are also an important factor in expressing information and conveying emotions. Through observation, this study collected the actions from three industry fields which have a total of 22 experts and staffs, and also analyzed and classified these movements. Finally, it established the robotic actions of animation for testing and using the three kinds of vocabulary of the robot personality traits (F1. 'active factor', F2. 'helpful factor', F3. 'dominant interaction factor') as the evaluation criteria of perception, explored the user cognition to the different personality traits of robots. The results show that the robot movements have the greatest impact on the first type of F1 active factor , while the industry category only affects the third type of F3 dominant interaction factor . In the case of different industry categories, the impact of F1 active factor is also different. For overall preference, every action will increase users' preference. However, in terms of acceptance, 'actions with information transfer functions' can increase users' acceptance, but' auxiliary Action' will not improve them. The results of this study can propose action design guidelines for personalized service robots for different industries. © 2020 IEEE.","emotion; industry category; personality traits; robot action; service robot","Agricultural robots; Industrial robots; Intelligent robots; Machine design; Mobile robots; Robotics; Service industry; Evaluation criteria; Information transfers; Interaction factor; Personality traits; Personalized service; Robot personalities; Users' acceptance; Voice interaction; Human robot interaction",,,,,,,,,,,,,,,,"Nass, C., Steuer, J., Tauber, E.R., Computers are social actors (1994) Proceedings of the Sigchi Conference on Human Factors in Computing Systems, pp. 72-78; Kim, H., Kwak, S.S., Kim, M., Personality design of sociable robots by control of gesture design factors RO-MAN 2008-The 17th IEEE International Symposium on Robot and Human Interactive Communication(IEEE2008), pp. 494-499; Riek, L.D., Rabinowitch, T.-C., Bremner, P., Pipe, A.G., Fraser, M., Robinson, P., Cooperative gestures: Effective signaling for humanoid robots (2010) 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 61-68; Cheng, Y.-W., Sun, P.-C., Chen, N.-S., The essential applications of educational robot: Requirement analysis from the perspectives of experts, researchers and instructors (2018) Computers & Education, 126, pp. 399-416; Breazeal, C., Kidd, C.D., Thomaz, A.L., Hoffman, G., Berlin, M., Effects of nonverbal communication on efficiency and robustness in human-robot teamwork (2005) 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 708-713; Chidambaram, V., Chiang, Y.-H., Mutlu, B., Designing persuasive robots: How robots might persuade people using vocal and nonverbal cues (2012) Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 293-300; Lohse, M., Rothuis, R., Gallego-Pérez, J., Karreman, D.E., Evers, V., Robot gestures make difficult tasks easier: The impact of gestures on perceived workload and task performance (2014) Proceedings of the Sigchi Conference on Human Factors in Computing Systems, pp. 1459-1466; Krauss, R.M., Chen, Y., Gottesman, R.F., 13 Lexical gestures and lexical access: A process (2000) Language and Gesture, 2 (261), pp. 261-283; Huang, C.-M., Mutlu, B., Modeling and evaluating narrative gestures for humanlike robots (2013) Robotics: Science and Systems, pp. 57-64; Nehaniv, C.L., Dautenhahn, K., Kubacki, J., Haegele, M., Parlitz, C., Alami, R., A methodological approach relating the classification of gesture to identification of human intent in the context of human-robot interaction (2005) Roman 2005. IEEE International Workshop on Robot and Human Interactive Communication, pp. 371-377; Cuddy, A.J., Fiske, S.T., Glick, P., Warmth and competence as universal dimensions of social perception: The stereotype content model and the BIAS map (2008) Advances in Experimental Social Psychology, 40, pp. 61-149; Hwang, J., Park, T., Hwang, W., The effects of overall robot shape on the emotions invoked in users and the perceived personalities of robot (2013) Applied Ergonomics, 44, pp. 459-471",,,,"Institute of Electrical and Electronics Engineers Inc.","3rd International Conference on Intelligent Robotics and Control Engineering, IRCE 2020","10 August 2020 through 12 August 2020",,163225,,9781728189727,,,"English","IRCE - Int. Conf. Intell. Robot. Control Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85093076752
"Rojas R.A., Wehrle E., Vidoni R.","57195804043;36702255400;26039525900;","A multicriteria motion planning approach for combining smoothness and speed in collaborative assembly systems",2020,"Applied Sciences (Switzerland)","10","15","5086","","",,4,"10.3390/app10155086","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088818473&doi=10.3390%2fapp10155086&partnerID=40&md5=fbbf48183223d91d8417322f9a83ac4b","Faculty of Science and Technology, Free University of Bozen-Bolzano (I), Bolzano, 39100, Italy","Rojas, R.A., Faculty of Science and Technology, Free University of Bozen-Bolzano (I), Bolzano, 39100, Italy; Wehrle, E., Faculty of Science and Technology, Free University of Bozen-Bolzano (I), Bolzano, 39100, Italy; Vidoni, R., Faculty of Science and Technology, Free University of Bozen-Bolzano (I), Bolzano, 39100, Italy","Human-robot interaction is an important aspect of Industry 4.0, and the extended use of robotics in industrial environments will not be possible without enabling them to safely interact with humans. This imposes relevant constraints in the qualitative characterization of the motions of robots when sharing their workspace with humans. In this paper, we address the trade-off between two such constraints, namely the smoothness, which is related to the cognitive stress that a person undergoes when interacting with a robot, and the speed, which is related to normative safety requirements. Given an execution time, such an approach will allow us to plan safe trajectories without neglecting cognitive ergonomics and production efficiency aspects. We first present the methodology able to express the balance between these qualities in the form of a composite objective function. Thanks to the variational formalism, we identify the related set of optimal trajectories with respect to the given criterion and give a suitable parametrization to them. Then, we are able to formulate the safety requirements in terms of a reparametrization of the motion. Finally, numerical and experimental results are provided. This allows the identification of the preferable sets of the possible motions that satisfy the operator's psychological well-being and the assembly process performance by complying with the safety requirements in terms of mechanical risk prevention. © 2020 by the authors.","Collaborative robotics; Motion planning; Safety; Trajectory optimization",,,,,,,,,,,,,,,,,"Rojas, R.A., Rauch, E., Vidoni, R., Matt, D.T., Enabling Connectivity of Cyber-Physical Production Systems: A Conceptual Framework (2017) Procedia Manuf., 11, pp. 822-829; (2011) Robots and Robotic Devices-Safety Requirements for Industrial Robots-Part 1: Robots, , Standard; International Organization for Standardization: Geneva, Switzerland; (2011) Robots and Robotic Devices-Safety Requirements for Industrial Robots-Part 2: Robot Systems and Integration, , Standard; International Organization for Standardization: Geneva, Switzerland; Robots, U., (2017) User Manual UR3/CB3 Original Instructions, , Technical Report; Universal Robots: Odense, Denmark; (2018) Operatingmanual IRB 14000, , Technical Report; ABB: Zürich, Switzerland; Gualtieri, L., Rauch, E., Vidoni, R., Matt, D.T., An evaluation methodology for the conversion of manual assembly systems into human-robot collaborative workcells (2019) Procedia Manuf., 38, pp. 358-366; Gualtieri, L., Palomba, I., Merati, F.A., Rauch, E., Vidoni, R., Design of Human-Centered Collaborative Assembly Workstations for the Improvement of Operators' Physical Ergonomics and Production Efficiency: A Case Study (2020) Sustainability, 12, p. 3606; Gualtieri, L., Rauch, E., Vidoni, R., Emerging Research Fields in Safety and Ergonomics in Industrial Collaborative Robotics: A Systematic Literature Review (2020) Robot. Comput.-Integr. Manuf., 67, p. 101998; Or, C.K., Duffy, V.G., Cheung, C.C., Perception of safe robot idle time in virtual reality and real industrial environments (2009) Int. J. Ind. Ergon., 39, pp. 807-812; Arai, T., Kato, R., Fujita, M., Assessment of operator stress induced by robot collaboration in assembly (2010) CIRP Ann., 59, pp. 5-8; Lasota, P.A., Shah, J.A., Analyzing the effects of human-aware motion planning on close-proximity human-robot collaboration (2015) Hum. Factors, 57, pp. 21-33; Kokabe, M., Shibata, S., Yamamoto, T., Modeling of handling motion reflecting emotional state and its application to robots (2008) Proceedings of the SICE Annual Conference, Tokyo, Japan, 20-22 August 2008, pp. 495-501. , IEEE: Piscataway, NJ, USA; (2016) Robots and Robotic Devices-Collaborative Robots, , Technical Specification; International Organization for Standardization: Geneva, Switzerland; Gasparetto, A., Boscariol, P., Lanzutti, A., Vidoni, R., Trajectory planning in robotics (2012) Math. Comput. Sci., 6, pp. 269-279; Flash, T., Hogan, N., The coordination of arm movements: An experimentally confirmed mathematical model (1985) J. Neurosci., 5, pp. 1688-1703; Meirovitch, Y., Bennequin, D., Flash, T., Geometrical invariance and smoothness maximization for task-space movement generation (2016) IEEE Trans. Robot., 32, pp. 837-853; Oguz, O.S., Zhou, Z., Glasauer, S., Wollherr, D., An Inverse Optimal Control Approach to Explain Human Arm Reaching Control Based on Multiple Internal Models (2018) Sci. Rep., 8, p. 5583; Kühnlenz, B., Kühnlenz, K., Reduction of Heart Rate by Robot Trajectory Profiles in Cooperative HRI (2016) Proceedings of the ISR 2016: 47st International Symposium on Robotics, Munich, Germany, 21-22 June 2016, pp. 1-6. , VDE: Frankfurt, Germany; Gasparetto, A., Zanotto, V., A new method for smooth trajectory planning of robot manipulators (2007) Mech. Mach. Theory, 42, pp. 455-471; Gasparetto, A., Lanzutti, A., Vidoni, R., Zanotto, V., Validation of minimum time-jerk algorithms for trajectory planning of industrial robots (2011) J. Mech. Robot., p. 3; Kyriakopoulos, K.J., Saridis, G.N., Minimum jerk path generation (1988) Proceedings of the International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA, 24-29 April 1988, pp. 364-369. , IEEE: Piscataway, NJ, USA; Boscariol, P., Gasparetto, A., Vidoni, R., Planning continuous-jerk trajectories for industrial manipulators (2012) Engineering Systems Design and Analysis, 3, pp. 127-136. , American Society of Mechanical Engineers: New York, NY, USA; Piazzi, A., Visioli, A., Global minimum jerk trajectory planning of robot manipulators (2000) IEEE Trans. Ind. Electron., 47, pp. 140-149; Bianco, C.G.L., Minimum-jerk velocity planning for mobile robot applications (2013) IEEE Trans. Robot., 29, pp. 1317-1326; Rojas, R.A., Garcia, M.A.R., Wehrle, E., Vidoni, R., A Variational Approach to Minimum-Jerk Trajectories for Psychological Safety in Collaborative Assembly Stations (2019) IEEE Robot. Autom. Lett., 4, pp. 823-829; Ehrgott, M., (2005) Multicriteria Optimization, , Springer: Berlin/Heidelberg, Germany; Giaquinta, M., Hildebrandt, S., (2013) Calculus of Variations II, 311. , Springer Science & Business Media: Berlin, Germany; Van Brunt, B., (2006) The Calculus of Variations, , Universitext, Springer: New York, NY, USA; Boyce, W.E., DiPrima, R.C., Villagómez Velázquez, H., (2004) Elementary Differential Equations and Boundary Value Problems. Ecuaciones Diferenciales y Problemas con Valores en la Frontera, , Wiley: Hoboken, NJ, USA; Dai, Y.H., Schittkowski, K., A sequential quadratic programming algorithm with non-monotone line search (2008) Pac. J. Optim., 4, pp. 335-351; Lacevic, B., Rocco, P., Kinetostatic danger field-a novel safety assessment for human-robot interaction (2010) Proceedings of the International Conference on Intelligent Robots and Systems (IROS), Taipei, Taiwan, 18-22 October 2010, pp. 2169-2174. , IEEE: Piscataway, NJ, USA; Gualtieri, L., Rojas, R., Carabin, G., Palomba, I., Rauch, E., Vidoni, R., Matt, D.T., Advanced automation for SMEs in the I4. 0 revolution: Engineering education and employees training in the smart mini factory laboratory (2018) Proceedings of the 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bangkok, Thailand, 16-19 December 2018, pp. 1111-1115. , IEEE: Piscataway, NJ, USA; Rojas, R.A., Rauch, E., Dallasega, P., Matt, D.T., Safe Human-Machine Centered Design of an Assembly Station in a Learning Factory Environment (2018) Proceedings of the 8th International Conference on Industrial Engineering and Operations Management, , Bandung, Indonesia, 6-8 March","Vidoni, R.; Faculty of Science and Technology, Italy; email: renato.vidoni@unibz.it",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85088818473
"Strathearn C., Ma M.","57204127919;57197837250;","Modelling user preference for embodied artificial intelligence and appearance in realistic humanoid robots",2020,"Informatics","7","3","28","","",,2,"10.3390/INFORMATICS7030028","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091504309&doi=10.3390%2fINFORMATICS7030028&partnerID=40&md5=30c327d89ba641cd73ddcbbe532f1cc1","School of Computing and Digital Technologies, Staffordshire University, Staffordshire, ST4 2DE, United Kingdom; Penryn Campus, Falmouth University, Penryn, TR10 9FE, United Kingdom","Strathearn, C., School of Computing and Digital Technologies, Staffordshire University, Staffordshire, ST4 2DE, United Kingdom; Ma, M., Penryn Campus, Falmouth University, Penryn, TR10 9FE, United Kingdom","Realistic humanoid robots (RHRs) with embodied artificial intelligence (EAI) have numerous applications in society as the human face is the most natural interface for communication and the human body the most effective form for traversing the manmade areas of the planet. Thus, developing RHRs with high degrees of human-likeness provides a life-like vessel for humans to physically and naturally interact with technology in a manner insurmountable to any other form of non-biological human emulation. This study outlines a human–robot interaction (HRI) experiment employing two automated RHRs with a contrasting appearance and personality. The selective sample group employed in this study is composed of 20 individuals, categorised by age and gender for a diverse statistical analysis. Galvanic skin response, facial expression analysis, and AI analytics permitted cross-analysis of biometric and AI data with participant testimonies to reify the results. This study concludes that younger test subjects preferred HRI with a younger-looking RHR and the more senior age group with an older looking RHR. Moreover, the female test group preferred HRI with an RHR with a younger appearance and male subjects with an older looking RHR. This research is useful for modelling the appearance and personality of RHRs with EAI for specific jobs such as care for the elderly and social companions for the young, isolated, and vulnerable. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Embodied artificial intelligence; Human–computer interaction; Human–robot interaction; Realistic humanoid robots",,,,,,,,,,,,,,,,,"Hanson, D., Olney, A., Prilliman, S., Mathews, E., Zielke, M., Hammons, D., Fernandez, R., Stephanou, E., Upending the Uncanny Valley (2005) AAAI, 4, pp. 1728-1729; Nishio, T., Yoshikawa, Y., Ogawa, K., Ishiguro, H., Development of an Effective Information Media Using Two Android Robots (2019) Appl. Sci, 9, p. 3442. , [CrossRef]; Ishi, T., Minato, T., Ishiguro, H., Motion Analysis in Vocalized Surprise Expressions and Motion Generation in Android Robots (2017) IEEE Robot. Autom. 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Ma, M.; Penryn Campus, United Kingdom; email: m.ma@falmouth.ac.uk",,,"MDPI AG",,,,,22279709,,,,"English","Informatics",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85091504309
"Moualeu A., Pluckter K., Ueda J.","56423780800;56386099700;35594285800;","Role of operator muscle coactivation towards intuitive interaction with haptic assist devices",2020,"IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM","2020-July",,"9158918","1464","1470",,,"10.1109/AIM43001.2020.9158918","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090395013&doi=10.1109%2fAIM43001.2020.9158918&partnerID=40&md5=5fe13f3a8fc212e9afcffeff66fa7014","Georgia Institute of Technology, Bio-Robotics Human Modeling Lab, School of Mechanical Engineering, Atlanta, GA  30332, United States; Carnegie Mellon University, Bio-Robotics Human Modeling Lab, School of Mechanical Engineering, Georgia Institute of Technology, Intuitive Surgical Inc, Atlanta, GA  30332, United States","Moualeu, A., Georgia Institute of Technology, Bio-Robotics Human Modeling Lab, School of Mechanical Engineering, Atlanta, GA  30332, United States; Pluckter, K., Carnegie Mellon University, Bio-Robotics Human Modeling Lab, School of Mechanical Engineering, Georgia Institute of Technology, Intuitive Surgical Inc, Atlanta, GA  30332, United States; Ueda, J., Georgia Institute of Technology, Bio-Robotics Human Modeling Lab, School of Mechanical Engineering, Atlanta, GA  30332, United States","The current paper addresses a central question at the basis of our research into the control of haptic assist devices: is kinetic sensor information sufficient for the intuitive control of haptic assist devices during physical human-robot interaction (pHRI) Our study proposes the addition of operator physiological data in the form of muscle cocontraction (or coactivation) information, obtained from processing surface electromyographic (sEMG) sensor data from the operator's arm muscles. Muscle coactivation has shown in previous research to correlate to the operator's endpoint stiffness, an important system variable not directly observable. Results from target tracking experiments conducted on 10 subjects using a one-degree-of-freedom (1-DoF) haptic device show a muscle cocontraction variable as the best feature to predict the motor intent of the human operator. In addition, using muscle cocontraction data in tandem with kinetic data to train a Support Vector Machine (SVM) classifier offline results in an average cross-validation accuracy of 99%, compared to an accuracy of 89% with kinetic data only. Furthermore, statistically significant trends in cocontraction levels were observed in different stability conditions (trajectory tracking experiments, 10 subjects, p < 0.001) and across operator motor intent (target following experiments, 10 subjects, p < 0.001). These results strongly underline the importance of adding operator physiological information to the sensor data set in order to enhance the control of haptic assist devices. © 2020 IEEE.",,"Degrees of freedom (mechanics); Human robot interaction; Intelligent mechatronics; Kinetics; Support vector machines; Target tracking; Intuitive interaction; Muscle co-contraction; Muscle coactivation; One degree of freedom (1-DOF); Physical humanrobot interaction (phri); Physiological informations; Sensor informations; Stability condition; Muscle",,,,,"National Science Foundation, NSF: 1317718","The authors would like to acknowledge Florian Ginner and Gregory Forbes for their contribution to building the haptic device used in this study. Funding for this research was provided by the National Science Foundation (National Robotics Initiative Grant Number 1317718). Opinions expressed in this document can be attributed to the authors alone and do not necessarily reflect the views of any affiliated institution.",,,,,,,,,,"Villani, V., Pini, F., Leali, F., Secchi, C., Survey on human robot collaboration in industrial settings: Safety (2018) Intuitive Interfaces and Applications, Mechatronics, 55, pp. 248-266; Buerger, S.P., Hogan, N., Complementary stability and loop shaping for improved human robot interaction (2007) IEEE Transactions on Robotics, 23 (2), pp. 232-244; Gallagher, W., Gao, D., Ueda, J., Improved stability of haptic human robot interfaces using measurement of human arm stiffness (2014) Advanced Robotics, 28 (13), pp. 869-882; Colgate, E., Hogan, N., An analysis of contact instability in terms of passive physical equivalents (1989) Robotics and Automation, 1989. Proceedings, 1989 IEEE International Conference on, pp. 404-409. , IEEE; Duchaine, V., Gosselin, C.M., Investigation of human-robot interaction stability using lyapunov theory (2008) Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on, pp. 2189-2194. , IEEE; Tsumugiwa, T., Fuchikami, Y., Kamiyoshi, A., Yokogawa, R., Yoshida, K., Stability analysis for impedance control of robot in human-robot cooperative task system (2007) Journal of Advanced Mechan-ical Design, Systems, and Manufacturing, 1 (1), pp. 113-121; Corbett, E.A., Sachs, N.A., Perreault, E.J., EMG control of robotic reaching by people with tetraplegia improved through proprioceptive and force feedback (2013) 2013 6th International IEEE/EMBS Confer-ence on Neural Engineering (NER), pp. 1178-1181. , IEEE; Lenzi, T., De Rossi, S.M.M., Vitiello, N., Carrozza, M.C., Intention-based EMG control for powered exoskeletons (2012) IEEE Trans-actions on Biomedical Engineering, 59 (8), pp. 2180-2190; Shenoy, P., Miller, K.J., Crawford, B., Rao, R.P., Online electromyographic control of a robotic prosthesis (2008) IEEE Transactions on Biomedical Engineering, 55 (3), pp. 1128-1135; Lee, S., Sankai, Y., Power assist control for walking aid with hal-3 based on EMG and impedance adjustment around knee joint (2002) IEEE/RSJ International Conference on Intelligent Robots and Systems, 2, pp. 1499-1504. , IEEE; Mulas, M., Folgheraiter, M., Gini, G., An emg-controlled exoskeleton for hand rehabilitation (2005) 9th International Conference on Rehabilitation Robotics, 2005. ICORR, pp. 371-374. , IEEE, 2005; Bi, L., Guan, C., A review on emg-based motor intention prediction of continuous human upper limb motion for human-robot collaboration (2019) Biomedical Signal Processing and Control, 51, pp. 113-127; Artemiadis, P., Emg-based robot control interfaces: Past (2012) Present and Future, Advances in Robotics & Automation, 1 (2), pp. 10-12; Varol, H.A., Sup, F., Goldfarb, M., Multiclass real-time intent recognition of a powered lower limb prosthesis (2009) IEEE Transactions on Biomedical Engineering, 57 (3), pp. 542-551; Zhang, F., Huang, H., Source selection for real-time user intent recognition toward volitional control of artificial legs (2012) IEEE Journal of Biomedical and Health Informatics, 17 (5), pp. 907-914; Farrell, M.T., Herr, H., A method to determine the optimal features for control of a powered lower-limb prostheses (2011) 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6041-6046. , IEEE; Huang, H., Zhang, F., Hargrove, L.J., Dou, Z., Rogers, D.R., Englehart, K.B., Continuous locomotion-mode identification for prosthetic legs based on neuromuscular mechanical fusion (2011) IEEE Transactions on Biomedical Engineering, 58 (10), pp. 2867-2875; Young, A., Kuiken, T., Hargrove, L., Analysis of using EMG and mechanical sensors to enhance intent recognition in powered lower limb prostheses (2014) Journal of Neural Engineering, 11 (5), p. 056021; Li, Z., Wang, B., Sun, F., Yang, C., Xie, Q., Zhang, W., Semg-based joint force control for an upper-limb power-assist exoskeleton robot (2013) IEEE Journal of Biomedical and Health Informatics, 18 (3), pp. 1043-1050; Osu, R., Franklin, D.W., Kato, H., Gomi, H., Domen, K., Yoshioka, T., Kawato, M., Short-and long-term changes in joint co-contraction associated with motor learning as revealed from surface emg (2002) Journal of Neurophysiology, 88 (2), pp. 991-1004; Osu, R., Gomi, H., Multijoint muscle regulation mechanisms examined by measured human arm stiffness and EMG signals (1999) Journal of Neurophysiology, 81 (4), pp. 1458-1468; Thoroughman, K.A., Shadmehr, R., Electromyographic correlates of learning an internal model of reaching movements (1999) Journal of Neuroscience, 19 (19), pp. 8573-8588; Franklin, D.W., Liaw, G., Milner, T.E., Osu, R., Burdet, E., Kawato, M., Endpoint stiffness of the arm is directionally tuned to instability in the environment (2007) Journal of Neuroscience, 27 (29), pp. 7705-7716; Qiu, Y., Murali, K., Ueda, J., Okabe, A., Gao, D., Variability in muscle recruitment strategy between operators during assisted assembly tasks (2018) ASME 2018 Dynamic Systems and Control Conference, American Society of Mechanical Engineers Digital Collection; Gribble, P.L., Mullin, L.I., Cothros, N., Mattar, A., Role of cocontraction in arm movement accuracy (2003) Journal of Neurophysiology, 89 (5), pp. 2396-2405; Shrirao, N.A., Reddy, N.P., Kosuri, D.R., Neural network committees for finger joint angle estimation from surface EMG signals (2009) Biomedical Engineering Online, 8 (1), p. 2; Loconsole, C., Leonardis, D., Barsotti, M., Solazzi, M., Frisoli, A., Bergamasco, M., Troncossi, M., Castelli, V.P., An emg-based robotic hand exoskeleton for bilateral training of grasp (2013) World Haptics Conference (WHC), pp. 537-542. , IEEE; Chan, F.H., Yang, Y.-S., Lam, F., Zhang, Y.-T., Parker, P.A., Fuzzy EMG classification for prosthesis control (2000) IEEE Transactions on Rehabilitation Engineering, 8 (3), pp. 305-311; Huang, Y., Englehart, K.B., Hudgins, B., Chan, A.D., A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses (2005) IEEE Transactions on Biomedical Engineering, 52 (11), pp. 1801-1811; Gallagher, W., Gao, D., Ueda, J., Measurement of muscle stiffness to improve stability of haptic human-robot interfaces (2012) ASME 2012 5th Annual Dynamic Systems and Control Conference Joint with the JSME 2012 11th Motion and Vibration Conference, pp. 493-502. , American Society of Mechanical Engineers; Ueda, J., Gallagher, W., Moualeu, A., Shinohara, M., Feigh, K., Adaptive human-robot physical interaction for robot coworkers (2017) Human Modelling for Bio-Inspired Robotics, pp. 297-333. , Elsevier; Moualeu, A., Gallagher, W., Ueda, J., Support vector machine classification of muscle cocontraction to improve physical humanrobot interaction (2014) Intelligent Robots and Systems (IROS 2014) 2014 IEEE/RSJ International Conference on, pp. 2154-2159. , IEEE; Moualeu, A., Ueda, J., Haptic control in physical human-robot interaction based on support vector machine classification of muscle activity: A preliminary study (2014) ASME 2014 Dynamic Systems and Control Conference, pp. V003T42A005. , American Society of Mechanical Engineers; Gallagher, W., Ueda, J., A haptic human-robot interface accounting for human parameter stochasticity (2014) 2014 IEEE International Con-ference on Robotics and Automation (ICRA), pp. 4256-4261. , IEEE; Kosuge, K., Fujisawa, Y., Fukuda, T., Control of mechanical system with man-machine interaction (1992) Intelligent Robots and Systems, 1992, Proceedings of the 1992 LEEE/RSJ International Conference on, 1, pp. 87-92. , IEEE; Chang, C.-C., Lin, C.-J., Libsvm: A library for support vector machines (2011) ACM Transactions on Intelligent Systems and Technology (TIST), 2 (3), p. 27; Gallagher, W., McPherson, T., Huggins, J., Shinohara, M., Gao, D., Menassa, R., Ueda, J., An improved human-robot interface by measurement of muscle stiffness (2012) Biomedical Robotics and Biomechatronics (BioRob) 2012 4th IEEE RAS & EMBS International Conference on, pp. 177-182. , IEEE; Podobnik, J., Munih, M., Haptic interaction stability with respect to grasp force (2007) IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37 (6), pp. 1214-1222",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020","6 July 2020 through 9 July 2020",,162314,,9781728167947,,,"English","IEEE ASME Int Conf Adv Intellig Mechatron AIM",Conference Paper,"Final","",Scopus,2-s2.0-85090395013
"Qiu Y., Satoh Y., Suzuki R., Iwata K., Kataoka H.","57214161518;7403207948;57198803844;16444459300;36720714800;","3D-Aware Scene Change Captioning from Multiview Images",2020,"IEEE Robotics and Automation Letters","5","3","9120195","4743","4750",,4,"10.1109/LRA.2020.3003290","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089798744&doi=10.1109%2fLRA.2020.3003290&partnerID=40&md5=e113bb3498f7a9b764c3331fdbc3c588","National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan; Hirokatsu Kataoka Are with the Natl. Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan","Qiu, Y., National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan; Satoh, Y., Hirokatsu Kataoka Are with the Natl. Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan; Suzuki, R., National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan; Iwata, K., National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan; Kataoka, H., National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan","In this letter, we propose a framework that recognizes and describes changes that occur in a scene observed from multiple viewpoints in natural language text. The ability to recognize and describe changes that occurred in a 3D scene plays an essential role in a variety of human-robot interaction applications. However, most current 3D vision studies have focused on understanding the static 3D scene. Existing scene change captioning approaches recognize and generate change captions from single-view images. Those methods have limited ability to deal with camera movement, object occlusion, which are common in real-world settings. To resolve these problems, we propose a framework that observes every scene from multiple viewpoints and describes the scene change based on an understanding of the underlying 3D structure of scenes. We build three synthetic datasets consisting of primitive 3D object and scanned real object models for evaluation. The results indicate that our method outperforms the previous state-of-the-art 2D-based method by a large margin in terms of sentence generation and change understanding correctness. In addition, our method is more robust to camera movements compared to the previous method and also performs better for scenes with occlusions. Moreover, our method also shows encouraging results in a realistic scene-setting, which indicates the possibility of adapting our framework to a more complicated and extensive scene-settings. © 2016 IEEE.","computer vision for other robotic applications; deep learning for visual perception; Multi-modal perception","Agricultural robots; Cameras; Human robot interaction; Three dimensional computer graphics; Camera movement; Multi-view image; Multiple viewpoints; Natural language text; Object occlusion; Real world setting; State of the art; Synthetic datasets; Character recognition",,,,,,,,,,,,,,,,"Zhou, Y., Tuzel, O., Voxelnet: End-To-end learning for point cloud based 3d object detection (2018) Proc Ieee Conf. Comput. Vision Pattern Recognit, pp. 4490-4499; Graham, B., Engelcke, M., Maaten Der, L.Van, 3d semantic segmentation with submanifold sparse convolutional networks (2018) Proc. Proc Ieee Conf. Comput. Vision Pattern Recognit, pp. 9224-9232; Dai, A., Ruizhongtai Qi, C., Niebner, M., Shape completion using 3dencoder-predictor cnns and shape syn thesis (2017) Proc. Ieee Conf. Comput. Vision Pattern Recognit, pp. 6545-6554; Park, D.H., Darrell, T., Rohrbach, A., Robust change captioning (2019) Proc Ieee Int. Conf. Comput. Vision, pp. 4623-4632; Jhamtani, H., Berg-Kirkpatrick, T., Learning to describe differences between pairs of similar images (2018) Proc. Conf. EmpiricalMethods Natural Lang. Process; Panda, S., Abdul Hafez, A.H., Jawahar, C.V., Single and multiple view support order prediction in clutter formanipulation (2016) J. Intell. Robot. Syst, 83; Grotz, M., Sippel, D., Asfour, T., Active vision for extraction of physically plausible support relations (2019) Proc IEEE-RAS 19th Int. Conf. Humanoid Robots, pp. 439-445; Eslami, S.A., Neural scene representation and rendering[J] (2018) Science, 360 (6394), pp. 1204-1210; Sitzmann, V., Zollhofer, M., Wetzstein, G., Scene representation networks: Continuous 3D-structure-Aware neural scene representations (2019) Proc. Advances Neural Inf. Process. Syst; Kingma, D.P., Mohamed, S., Rezende, D.J., Welling, M., Semisupervised learning with deep generative models (2014) Proc. Advances Neural Inf. Process. Syst; Qi, C.R., Su, H., Mo, K., Guibas, L.J., Pointnet: Deep learning on point sets for 3d classification and segmentation (2017) Proc Ieee Conf. Comput. Vision Pattern Recognit, pp. 77-85; Qi, C.R., Yi, L., Su, H., Guibas, L.J., Pointnet++: Deep hierarchical feature learning on point sets in a metric space (2017) Proc. Advances Neural Inf. Process. Syst; Bajcsy, R., Active perception (1988) Proc Ieee, 76, p. 8; Lepora, N.F., Martinez-Hernandez, U., Prescott, T.J., Active touch for robust perception under position uncertainty (2013) Proc Ieee Int. Conf. Robot. Autom, pp. 3020-3025; Das, A., Embodied question answering (2018) Proc Ieee Conf. Comput. Vision Pattern Recognit. Workshops, pp. 1-10; Sakurada, K., Okatani, T., Change detection from a street image pair using cnn features and superpixel segmentation (2015) Brit. Mach. Vision Assoc; Sakurada, K., Wang, W., Kawaguchi, N., Nakamura, R., Dense Optical Flow Based Change Detection Network Robust to Difference of Camera Viewpoints, , arXiv 1712 02941; Herbst, E., Henry, P., Ren, X., Fox, D., Toward object discovery and modeling via 3-d scene comparison (2011) Proc Ieee Int. Conf. Robot. Autom, pp. 2623-2629; Ambrus, R., Bore, N., Folkesson, J., Jensfelt, P., Meta-rooms: Building and maintaining long term spatial models in a dynamic world (2014) Proc Ieee Int. Conf. Intell. Robots Syst, pp. 1854-1861; Langer, E., Ridder, B., Cashmore, M., Magazzeni, D., Zillich, M., Vincze, M., On-The-fly detection of novel objects in indoor environments (2017) Proc Ieee Int. Conf. Robot. Biomimetics, pp. 900-907; Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Show, attend and tell: Neural image caption generation with visual attention (2015) Int. Conf. Mach. Learn; Anderson, P., Bottom-up and top-down attention for image captioning and visual question answering (2018) Proc Ieee Conf. Comput. Vision Pattern Recognit, pp. 6077-6086; Johnson, J., Hariharan, B., Maaten Der, L.Van, Fei-Fei, L., Lawrence Zitnick, C., Girshick, R., Clevr: A diagnostic dataset for compositional language and elementary visual reasoning (2017) Proc Ieee Conf. Comput. Vision Pattern Recognit, pp. 1988-1997; Araki, R., Yamashita, T., Fujiyoshi, H., Arc2017 rgb-d dataset for object detection and segmentation (2018) Proc. Late Breaking Results Poster Int. Conf. Robot. Autom; Calli, B., Singh, A., Walsman, A., Srinivasa, S., Abbeel, P., Dollar, A.M., The ycb object and model set: Towards common benchmarks for manipulation research (2015) Proc. Int. Conf. Adv. Robot, pp. 510-517; Singh, A., Sha, J., Narayan, K.S., Achim, T., Abbeel, P., Bigbird: A large-scale 3d database of object instances (2014) Proc Ieee Int. Conf. Robot. Autom, pp. 509-516; Johnson, J., Image retrieval using scene graphs (2015) Proc. Conf. Comput. Vision Pattern Recognit; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2016) Proc Ieee Conf. Comput. Vision Pattern Recognit, pp. 770-778; Lin, C.-Y., ROUGE: A Package for Automatic Evaluation of Summaries (2004) Assoc. Comput. Linguistics; Anderson, P., Fernando, B., Johnson, M., Gould, S., Spice: Semantic propositional image caption evaluation (2016) Proc. Eur. Conf. Comput. Vision; Papineni, K., Roukos, S., Ward, T., Zhu, W.J., BLEU: A method for automatic evaluation ofmachine translation (2002) Proc. 40th Annu. Meeting Assoc. Comput. Linguistics; Denkowski, M., Lavie, A., Meteor universal: Language specific translation evaluation for any target language (2014) Proc. 9thWorkshop Statistical Mach. Translation",,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85089798744
"Guo J.","56707811200;","Conceptual mechanical design of antagonistic variable stiffness joint based on equivalent quadratic torsion spring",2020,"Science Progress","103","3",,"","",,,"10.1177/0036850420941295","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088107626&doi=10.1177%2f0036850420941295&partnerID=40&md5=94f029400c6c57c76a6378ad3c854157","School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China","Guo, J., School of Electrical Engineering and Automation, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China","The variable stiffness joint is a kind of flexible actuator with variable stiffness characteristics suitable for physical human–robot interaction applications. In the existing variable stiffness joints, the antagonistic variable stiffness joint has the advantages of simple implementation of variable stiffness mechanism and easy modular design of the nonlinear elastic element. The variable stiffness characteristics of antagonistic variable stiffness joints are realized by the antagonistic actuation of two nonlinear springs. A novel design scheme of the equivalent nonlinear torsion spring with compact structure, large angular displacement range, and desired stiffness characteristics is presented in this article. The design calculation for the equivalent quadratic torsion spring is given as an example, and the actuation characteristics of the antagonistic variable stiffness joint based on the equivalent quadratic torsion spring are illustrated. Based on the design idea of constructing the antagonistic variable stiffness joint with compact structure and high compliance, as well as the different design requirements of the joints at different positions of the multi–degrees of freedom robot arm, nine types of mechanical schemes of antagonistic variable stiffness joint with the open design concept are proposed in this article. Finally, the conceptual joint configuration schemes of the robot arm based on the antagonistic variable stiffness joint show the application scheme of the designed antagonistic variable stiffness joint in the multi–degrees of freedom robot. © The Author(s) 2020.","Antagonistic variable stiffness joint; cam-roller mechanism; conceptual mechanical design; equivalent quadratic torsion spring; multi–degrees of freedom robot arm",,,,,,,,,,,,,,,,,"Vanderborght, B., Albu-Schaeffer, A., Bicchi, A., Variable impedance actuators: a review (2013) Rob Auton Syst, 61, pp. 1601-1614; Grioli, G., Wolf, S., Garabini, M., Variable stiffness actuators: the user’s point of view (2015) Int J Rob Res, 34 (6), pp. 727-743; Li, Z., Bai, S., A novel revolute joint of variable stiffness with reconfigurability (2019) Mech Mach Theory, 133, pp. 720-736; Bacek, T., Moltedo, M., Rodriguez-Guerrero, C., Design and evaluation of a torque-controllable knee joint actuator with adjustable series compliance and parallel elasticity (2018) Mech Mach Theory, 130, pp. 71-85; Moltedo, M., Cavallo, G., Bacek, T., Variable stiffness ankle actuator for use in robotic-assisted walking: control strategy and experimental characterization (2019) Mech Mach Theory, 134, pp. 604-624; Migliore, S.A., Brown, E.A., DeWeerth, S.P., Novel nonlinear elastic actuators for passively controlling robotic joint compliance (2007) J Mech Des, 129 (4), pp. 406-412; Masahiko, O., Nobuyuki, I., Yuto, N., Stiffness readout in musculo-skeletal humanoid robot by using rotary potentiometer, pp. 2329-2333. , Proceedings of the 2010 IEEE international conference on sensors, Kona, HI, 1–4 November 2010, New York, IEEE, In; Catalano, M.G., Grioli, G., Bonomo, F., VSA-HD: from the enumeration analysis to the prototypical implementation, pp. 3676-3681. , Proceedings of the 2010 IEEE/RSJ international conference on intelligent robots and systems, Taipei, Taiwan, 18–22 October 2010, New York, IEEE, In; Wang, R.J., Huang, H.P., ADEA-active variable stiffness differential elastic actuator: design and application for safe robotics, pp. 2768-2773. , Proceedings of the 2011 IEEE international conference on robotics and biomimetics, Phuket, Thailand, 7–11 December 2011, New York, IEEE, In; Sen, S., Biologically inspired elastic transmission for stiffness variability in actuation: design and implementation, pp. 1973-1978. , Proceedings of the 2011 IEEE international conference on robotics and biomimetics, Phuket, Thailand, 7–11 December 2011, New York, IEEE, In; Palli, G., Berselli, G., Melchiorri, C., Design of a variable stiffness actuator based on flexures (2011) J Mech Rob, 3, p. 034501; Kilic, M., Yazicioglu, Y., Kurtulus, D.F., Synthesis of a torsional spring mechanism with mechanically adjustable stiffness using wrapping cams (2012) Mech Mach Theory, 57, pp. 27-39; Schmit, N., Okada, M., Design and realization of a non-circular cable spool to synthesize a nonlinear rotational spring (2012) Adv Rob, 26, pp. 235-252; Malosio, M., Spagnuolo, G., Prini, A., Principle of operation of RotWWC-VSA, a multi-turn rotational variable stiffness actuator (2017) Mech Mach Theory, 116, pp. 34-39; Petit, F., Friedl, W., Hoppner, H., Analysis and synthesis of the bidirectional antagonistic variable stiffness mechanism (2015) IEEE-ASME T Mech, 2 (2), pp. 684-695; Spagnuolo, G., Malosio, M., Dinon, T., Analysis and synthesis of LinWWC-VSA, a variable stiffness actuator for linear motion (2017) Mech Mach Theory, 110, pp. 85-95; Nam, K.H., Kim, B.S., Song, J.B., Compliant actuation of parallel-type variable stiffness actuator based on antagonistic actuation (2010) J Mech Sci Technol, 24 (11), pp. 2315-2321; Laffranchi, M., Tsagarakis, N.G., Caldwell, D.G., Analysis and development of a semiactive damper for compliant actuation systems (2013) IEEE-ASME T Mech, 18 (2), pp. 744-753; Laffranchi, M., Tsagarakis, N.G., Caldwell, D.G., Development of a hybrid actuator with controllable mechanical damping, pp. 1078-1083. , Proceedings of the 2014 IEEE international conference on robotics & automation (ICRA), Hong Kong, China, 31 May–7 June 2014, New York, IEEE, In; Buondonno, G., Alessandro, D.L., Efficient computation of inverse dynamics and feedback linearization for VSA-based robots (2016) IEEE Rob Autom Lett, 1 (2), pp. 908-915","Guo, J.; School of Electrical Engineering and Automation, China; email: guojishu_2005@163.com",,,"SAGE Publications Ltd",,,,,00368504,,,"32672104","English","Sci. Prog.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85088107626
"Kim S., Chung W.K., Kim K.","57203917002;57204958534;16550039200;","sEMG-based Static Force Estimation for Human-Robot Interaction using Deep Learning",2020,"2020 17th International Conference on Ubiquitous Robots, UR 2020",,,"9144869","81","86",,,"10.1109/UR49135.2020.9144869","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094321697&doi=10.1109%2fUR49135.2020.9144869&partnerID=40&md5=459f7e2a466635779a10ae4b0dab3734","POSTECH, Robotics Laboratory, School of Mechanical Engineering, South Korea; POSTECH, Faculty of Mechanical Engineering, South Korea","Kim, S., POSTECH, Robotics Laboratory, School of Mechanical Engineering, South Korea; Chung, W.K., POSTECH, Faculty of Mechanical Engineering, South Korea; Kim, K., POSTECH, Faculty of Mechanical Engineering, South Korea","Human-robot interaction (HRI) is a rapidly growing research area and it occurs in many applications including human-robot collaboration, human power augmentation, and rehabilitation robotics. As it is hard to exactly calculate intended motion trajectory, generally, interaction control is applied in HRI instead of pure motion control. To implement the interaction control, force information is necessary and force sensor is widely used in force feedback. However, force sensor has some limitations as 1) it is subject to breakdown, 2) it imposes additional volume and weight to the system, and 3) its applicable places are constrained. In this situation, force estimation can be a good solution. However, if force in static situation should be measured, using position and velocity is not sufficient because they are not influenced by the exerted force anymore. Therefore, we proposed sEMG-based static force estimation using deep learning. sEMG provides a useful information about human-exerting force because it reflects the human intention. Also, to extract the complex relationship between sEMG and force, deep learning approach is used. Experimental results show that when force with maximal value of 63.2 N is exerted, average force estimation error was 3.67 N. Also, the proposed method shows that force onset timing of estimated force is faster than force sensor signal. This result would be advantageous for faster human intention recognition. © 2020 IEEE.",,"Agricultural robots; Biofeedback; Man machine systems; Social robots; Complex relationships; Force sensor signals; Human robot Interaction (HRI); Human-robot collaboration; Interaction controls; Learning approach; Motion trajectories; Rehabilitation robotics; Deep learning",,,,,"2014M3C1B2048632; NRF-2017M3A9F6029736; National Research Foundation of Korea, NRF","This research was supported by the convergence technology development program for bionic arm through the National Research Foundation of Korea(NRF) funded by the Ministry of Science & ICT (No. 2014M3C1B2048632).","This research was supported by the Bio Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (NRF-2017M3A9F6029736)",,,,,,,,,"Tsarouchi, P., Makris, S., Chryssolouris, G., Human-robot interaction review and challenges on task planning and programming (2016) International Journal of Computer Integrated Manufacturing, 29 (8), pp. 916-931; 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"Garello L., Grella F., Castagnetta S., Bruno B., Recchiuto C.T., Sgorbissa A.","57219626207;57219626827;57219625009;36922823500;36452602300;6602687901;","Robot Agreeableness and User Engagement in Verbal Human-Robot Interaction",2020,"2020 17th International Conference on Ubiquitous Robots, UR 2020",,,"9144864","256","263",,1,"10.1109/UR49135.2020.9144864","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094314877&doi=10.1109%2fUR49135.2020.9144864&partnerID=40&md5=ce373e80f22e2c9cb0a82e436580a384","University of Genova, DIBRIS, Genova, Italy; Swiss Federal Institute of Technology in Lausanne (EPFL), CHILI Lab BioRob Lab, Switzerland","Garello, L., University of Genova, DIBRIS, Genova, Italy; Grella, F., University of Genova, DIBRIS, Genova, Italy; Castagnetta, S., University of Genova, DIBRIS, Genova, Italy; Bruno, B., University of Genova, DIBRIS, Genova, Italy, Swiss Federal Institute of Technology in Lausanne (EPFL), CHILI Lab BioRob Lab, Switzerland; Recchiuto, C.T., University of Genova, DIBRIS, Genova, Italy; Sgorbissa, A., University of Genova, DIBRIS, Genova, Italy","It is a well-established fact in the field of Human-Robot Interaction that the personality a social robot is endowed with (i.e., the one it expresses by its gestures and words) plays a key role towards the engagement experienced by any person interacting with it. In this article, we address the problem of designing the conversational style of a social robot to convey different degrees of Agreeableness, which is one of the Big Five personality traits, and investigate the relation between the person's engagement and the robot's degree of agreeableness.We propose to use subjective perception of time elapsed as an indicator for engagement and design two robot behaviours, one making the robot always agree with the person's opinion on topics of discussion (the compliant behaviour) and one making the robot always disagree (the contrasting behaviour). In an experiment involving 14 participants, we assess whether the robot adopting the contrasting behaviour is perceived as more engaging than the one adopting the compliant behaviour, and use participants' previous interactions with vocal assistants as a reference for estimating the naturalness, enjoyability and politeness of the conversation with the robot. Results suggest that designing social robots that are able to disagree with the person they're interacting with might be key to make them more engaging and entertaining. © 2020 IEEE.",,"Agricultural robots; Machine design; Man machine systems; Big five; Established facts; Personality traits; Subjective perceptions; User engagement; Verbal human-robot interactions; Social robots",,,,,"737858","This work has been partially supported by the European Commission Horizon2020 Research and Innovation Programme under grant agreement No. 737858.",,,,,,,,,,"Sidner, C., Dzikovska, M., Human-robot interaction: Engagement between humans and robots for hosting activities (2002) Proceedings. Fourth Ieee Int. Conf. On Multimodal Interfaces, pp. 123-128. , no. May. IEEE Computer Society; Aly, A., Tapus, A., Towards an intelligent system for generating an adapted verbal and nonverbal combined behavior in human-robot interaction (2016) Autonomous Robots, 40 (2), pp. 193-209; Rich, C., Ponsler, B., Holroyd, A., Sidner, C.L., Recognizing engagement in human-robot interaction (2010) Proceeding of the 5th ACM/IEEE Int. Conf. On Human-robot Interaction -HRI '10, pp. 375-382. , New York, New York, USA: ACM Press; Ivaldi, S., Lefort, S., Peters, J., Chetouani, M., Provasi, J., Zibetti, E., Towards engagement models that consider individual factors in hri: On the relation of extroversion and negative attitude towards robots to gaze and speech during a human-robot assembly task (2017) International Journal of Social Robotics, 9 (1), pp. 63-86; Anzalone, S.M., Boucenna, S., Ivaldi, S., Chetouani, M., Evaluating the engagement with social robots (2015) International Journal of Social Robotics, 7 (4), pp. 465-478; Foster, M.E., Gaschler, A., Giuliani, M., Automatically classifying user engagement for dynamic multi-party human-robot interaction (2017) International Journal of Social Robotics, 9 (5), pp. 659-674; Baldauf, D., Burgard, E., Wittmann, M., Time perception as a workload measure in simulated car driving (2009) Applied Ergonomics, 40 (5), pp. 929-935; Jennett, C., Cox, A.L., Cairns, P., Dhoparee, S., Epps, A., Tijs, T., Walton, A., Measuring and defining the experience of immersion in games (2008) International Journal of Human-Computer Studies, 66 (9), pp. 641-661; Attfield, S., Kazai, G., Lalmas, M., Piwowarski, B., Towards a science of user engagement (position paper) (2011) WSDM'11., , ACM Press; Sackett, A.M., Meyvis, T., Nelson, L.D., Converse, B.A., Sackett, A.L., You're having fun when time flies: The hedonic consequences of subjective time progression (2010) Psychological Science, 21 (1), pp. 111-117; Feil-Seifer, D., Mataric, M., Socially assistive robotics (2011) Ieee Robotics and Automation Magazine, 18 (1), pp. 24-31; Bruno, B., Chong, N.Y., Kamide, H., Kanoria, S., Lee, J., Lim, Y., Pandey, A.K., Sgorbissa, A., Paving the way for culturally competent robots: A position paper (2017) RO-MAN 2017-26th Ieee International Symposium on Robot and Human Interactive Communication., pp. 553-560; Salam, H., Celiktutan, O., Hupont, I., Gunes, H., Chetouani, M., Fully automatic analysis of engagement and its relationship to personality in human-robot interactions (2017) Ieee Access, 5, pp. 705-721; Cruz-Maya, A., Tapus, A., Teaching nutrition and healthy eating by using multimedia with a kompai robot: Effects of stress and user's personality (2016) 2016 IEEE-RAS 16th Int. 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On Humanoid Robots (Humanoids)., pp. 644-649. , IEEE, nov; Hwang, J., Park, T., Hwang, W., The effects of overall robot shape on the emotions invoked in users and the perceived personalities of robot (2013) Applied Ergonomics, 44 (3), pp. 459-471; Lee, K.M., Peng, W., Jin, S.-A., Yan, C., Can robots manifest personality?: An empirical test of personality recognition, social responses, and social presence in human-robot interaction (2006) Journal of Communication, 56 (4), pp. 754-772; Robert, L.P., Jr., Personality in the human robot interaction literature: A review and brief critique (2018) Twenty-fourth Americas Conference on Information Systems; Goldberg, L.R., An alternative ""description of personality"": The big-five factor structure (1990) Journal of Personality and Social Psychology, 59 (6), pp. 1216-1229; Celiktutan, O., Gunes, H., Computational analysis of human-robot interactions through first-person vision: Personality and interaction experience (2015) 2015 24th Ieee International Symposium on Robot and Human Interactive Communication (RO-MAN)., pp. 815-820. , IEEE, aug; Evers, V., Maldonado, H., Brodecki, T., Hinds, P., Relational vs. Group self-construal: Untangling the role of national culture in hri (2008) Human-Robot Interaction (HRI), 2008 3rd ACM/IEEE Int. Conf. On., pp. 255-262; Rau, P.P., Li, Y., Li, D., Effects of communication style and culture on ability to accept recommendations from robots (2009) Computers in Human Behavior, 25 (2), pp. 587-595; Wang, L., Rau, P.-L.P., Evers, V., Robinson, B.K., Hinds, P., When in Rome: The role of culture & context in adherence to robot recommendations (2010) Proceedings of the 5th ACM/IEEE Int. Conf. On Human-robot Interaction., pp. 359-366. , IEEE Press; Bruno, B., Recchiuto, C.T., Papadopoulos, I., Saffiotti, A., Koulouglioti, C., Menicatti, R., Mastrogiovanni, F., Sgorbissa, A., Knowledge representation for culturally competent personal robots: Requirements, design principles, implementation, and assessment (2019) International Journal of Social Robotics; Madden, D., (1975) Harlequin's Stick, Charlie's Cane: A Comparative Study of Commedia dell'Arte and Silent Slapstick Comedy, , Popular Press; Guarino, N., Formal ontology and information systems (1998) Proceedings of FOIS'98., pp. 3-15. , Trento, Italy: IOS Press","Sgorbissa, A.; University of Genova, Italy; email: antonio.sgorbissa@unige.it",,,"Institute of Electrical and Electronics Engineers Inc.","17th International Conference on Ubiquitous Robots, UR 2020","22 June 2020 through 26 June 2020",,161946,,9781728157153,,,"English",,Conference Paper,"Final","",Scopus,2-s2.0-85094314877
"Makhataeva Z., Varol H.A.","57195153856;24484134500;","Augmented reality for robotics: A review",2020,"Robotics","9","2","21","","",,35,"10.3390/ROBOTICS9020021","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084679354&doi=10.3390%2fROBOTICS9020021&partnerID=40&md5=97582eac06160543468dfc4d342a4aec","Department of Robotics, Nazarbayev University, Nur-Sultan City, Z05H0P9, Kazakhstan","Makhataeva, Z., Department of Robotics, Nazarbayev University, Nur-Sultan City, Z05H0P9, Kazakhstan; Varol, H.A., Department of Robotics, Nazarbayev University, Nur-Sultan City, Z05H0P9, Kazakhstan","Augmented reality (AR) is used to enhance the perception of the real world by integrating virtual objects to an image sequence acquired from various camera technologies. Numerous AR applications in robotics have been developed in recent years. The aim of this paper is to provide an overview of AR research in robotics during the five year period from 2015 to 2019. We classified these works in terms of application areas into four categories: (1) Medical robotics: Robot-Assisted surgery (RAS), prosthetics, rehabilitation, and training systems; (2) Motion planning and control: trajectory generation, robot programming, simulation, and manipulation; (3) Human-robot interaction (HRI): teleoperation, collaborative interfaces, wearable robots, haptic interfaces, brain-computer interfaces (BCIs), and gaming; (4) Multi-agent systems: use of visual feedback to remotely control drones, robot swarms, and robots with shared workspace. Recent developments in AR technology are discussed followed by the challenges met in AR due to issues of camera localization, environment mapping, and registration. We explore AR applications in terms of how AR was integrated and which improvements it introduced to corresponding fields of robotics. In addition, we summarize the major limitations of the presented applications in each category. Finally, we conclude our review with future directions of AR research in robotics. The survey covers over 100 research works published over the last five years. © 2020 by the authors.","Augmented reality; Human-robot interaction; Remote control; Robot assisted surgery; Robot programming; Robot swarms; Robotics; Teleoperation",,,,,,"Ministry of Education and Science of the Republic of Kazakhstan; Nazarbayev University, NU","Funding: This work was supported by the grant “Methods for Safe Human Robot Interaction with VIA Robots” from the Ministry of Education and Science of the Republic of Kazakhstan and by the Nazarbayev University Faculty Development Program grant “Hardware and Software Based Methods for Safe Human-Robot Interaction with Variable Impedance Robots”.",,,,,,,,,,"Gorecky, D., Schmitt, M., Loskyll, M., Zühlke, D., Human-machine-interaction in the Industry 4.0 era (2014) In Proceedings of the 12th IEEE International Conference on Industrial Informatics (INDIN), pp. 289-294. , Porto Alegre, Brazil, 27-30 July; Hockstein, N.G., Gourin, C., Faust, R., Terris, D.J., A history of robots: From science fiction to surgical robotics (2007) J. 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"Mi J., Liang H., Katsakis N., Tang S., Li Q., Zhang C., Zhang J.","56352257300;57194724149;57216948439;55366202900;8561667000;7405490589;56039580400;","Intention-Related Natural Language Grounding via Object Affordance Detection and Intention Semantic Extraction",2020,"Frontiers in Neurorobotics","14",,"26","","",,1,"10.3389/fnbot.2020.00026","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085488934&doi=10.3389%2ffnbot.2020.00026&partnerID=40&md5=22ebbfb50190228c82612d2a7d7a8f09","Institute of Machine Intelligence (IMI), University of Shanghai for Science and Technology, Shanghai, China; Technical Aspects of Multimodal Systems, Department of Informatics, University of Hamburg, Hamburg, Germany; Human-Computer Interaction, Department of Informatics, University of Hamburg, Hamburg, Germany; Department of Automation, State Key Lab of Intelligent Technologies and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China","Mi, J., Institute of Machine Intelligence (IMI), University of Shanghai for Science and Technology, Shanghai, China, Technical Aspects of Multimodal Systems, Department of Informatics, University of Hamburg, Hamburg, Germany; Liang, H., Technical Aspects of Multimodal Systems, Department of Informatics, University of Hamburg, Hamburg, Germany; Katsakis, N., Human-Computer Interaction, Department of Informatics, University of Hamburg, Hamburg, Germany; Tang, S., Institute of Machine Intelligence (IMI), University of Shanghai for Science and Technology, Shanghai, China, Technical Aspects of Multimodal Systems, Department of Informatics, University of Hamburg, Hamburg, Germany; Li, Q., Institute of Machine Intelligence (IMI), University of Shanghai for Science and Technology, Shanghai, China; Zhang, C., Department of Automation, State Key Lab of Intelligent Technologies and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University, Beijing, China; Zhang, J., Technical Aspects of Multimodal Systems, Department of Informatics, University of Hamburg, Hamburg, Germany","Similar to specific natural language instructions, intention-related natural language queries also play an essential role in our daily life communication. Inspired by the psychology term “affordance” and its applications in Human-Robot interaction, we propose an object affordance-based natural language visual grounding architecture to ground intention-related natural language queries. Formally, we first present an attention-based multi-visual features fusion network to detect object affordances from RGB images. While fusing deep visual features extracted from a pre-trained CNN model with deep texture features encoded by a deep texture encoding network, the presented object affordance detection network takes into account the interaction of the multi-visual features, and reserves the complementary nature of the different features by integrating attention weights learned from sparse representations of the multi-visual features. We train and validate the attention-based object affordance recognition network on a self-built dataset in which a large number of images originate from MSCOCO and ImageNet. Moreover, we introduce an intention semantic extraction module to extract intention semantics from intention-related natural language queries. Finally, we ground intention-related natural language queries by integrating the detected object affordances with the extracted intention semantics. We conduct extensive experiments to validate the performance of the object affordance detection network and the intention-related natural language queries grounding architecture. © Copyright © 2020 Mi, Liang, Katsakis, Tang, Li, Zhang and Zhang.","attention-based dynamic fusion; intention semantic extraction; intention-related natural language grounding; multi-visual features; object affordance detection","Extraction; Feature extraction; Human robot interaction; Large dataset; Natural language processing systems; Network architecture; Object detection; Semantics; Textures; Detection networks; ITS applications; Multi-visual features; Natural language queries; Natural languages; Semantic extraction; Sparse representation; Texture encoding; Visual languages; article; attention; extraction; human; human experiment; language; semantics",,,,,"National Science Foundation, NSF; Deutscher Akademischer Austauschdienst, DAAD; Deutsche Forschungsgemeinschaft, DFG; National Natural Science Foundation of China, NSFC: 61773083","This work was partly funded by the German Research Foundation (DFG) and National Science Foundation (NSFC) in project Crossmodal Learning under contract Sonderforschungsbereich Transregio 169, the DAAD German Academic Exchange Service under CASY project, and the National Natural Science Foundation of China (61773083).",,,,,,,,,,"Ahn, H., Choi, S., Kim, N., Cha, G., Oh, S., Interactive text2pickup networks for natural language-based human-robot collaboration (2018) IEEE Robot. 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Syst, 62, pp. 487-496; Thermos, S., Papadopoulos, G.T., Daras, P., Potamianos, G., “Deep affordance-grounded sensorimotor object recognition,” (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 49-57. , in; Thomason, J., Padmakumar, A., Sinapov, J., Hart, J., Stone, P., Mooney, R.J., “Opportunistic active learning for grounding natural language descriptions,” (2017) Conference on Robot Learning, pp. 67-76. , Mountain View, CA; Thomason, J., Padmakumar, A., Sinapov, J., Walker, N., Jiang, Y., Yedidsion, H., “Improving grounded natural language understanding through human-robot dialog,” (2019) IEEE International Conference on Robotics and Automation (ICRA), pp. 6934-6941. , Montreal, QC; Yu, L., Poirson, P., Yang, S., Berg, A.C., Berg, T.L., “Modeling context in referring expressions,” (2016) European Conference on Computer Vision (ECCV), pp. 69-85. , Amsterdam; Yu, Z., Sangwook, K., Mallipeddi, R., Lee, M., “Human intention understanding based on object affordance and action classification,” (2015) International Joint Conference on Neural Networks (IJCNN), , Killarney, IEEE; Zhang, H., Xue, J., Dana, K., “Deep ten: texture encoding network,” (2017) Proceedings 1of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2896-2905. , in; Zhu, Y., Fathi, A., Fei-Fei, L., “Reasoning about object affordances in a knowledge base representation,” (2014) European Conference on Computer Vision (ECCV), pp. 408-424. , Zurich","Tang, S.; Institute of Machine Intelligence (IMI), China; email: tang@informatik.uni-hamburg.de",,,"Frontiers Media S.A.",,,,,16625218,,,,"English","Front. Neurorobotics",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85085488934
"Bingol M.C., Aydogmus O.","57195613088;23088079600;","Practical application of a safe human-robot interaction software",2020,"Industrial Robot","47","3",,"359","368",,2,"10.1108/IR-09-2019-0180","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078963315&doi=10.1108%2fIR-09-2019-0180&partnerID=40&md5=043a205a0b8cc2d81b0a423f58e43b20","Department of Mechatronics Engineering, Faculty of Technology, Firat University, Elazig, Turkey","Bingol, M.C., Department of Mechatronics Engineering, Faculty of Technology, Firat University, Elazig, Turkey; Aydogmus, O., Department of Mechatronics Engineering, Faculty of Technology, Firat University, Elazig, Turkey","Purpose: Because of the increased use of robots in the industry, it has become inevitable for humans and robots to be able to work together. Therefore, human security has become the primary noncompromising factor of joint human and robot operations. For this reason, the purpose of this study was to develop a safe human-robot interaction software based on vision and touch. Design/methodology/approach: The software consists of three modules. Firstly, the vision module has two tasks: to determine whether there is a human presence and to measure the distance between the robot and the human within the robot’s working space using convolutional neural networks (CNNs) and depth sensors. Secondly, the touch detection module perceives whether or not a human physically touches the robot within the same work environment using robot axis torques, wavelet packet decomposition algorithm and CNN. Lastly, the robot’s operating speed is adjusted according to hazard levels came from vision and touch module using the robot’s control module. Findings: The developed software was tested with an industrial robot manipulator and successful results were obtained with minimal error. Practical implications: The success of the developed algorithm was demonstrated in the current study and the algorithm can be used in other industrial robots for safety. Originality/value: In this study, a new and practical safety algorithm is proposed and the health of people working with industrial robots is guaranteed. © 2020, Emerald Publishing Limited.","Artificial intelligence; Autonomous robots; Convolutional neural network; Human-robot interaction; Safety","Accident prevention; Agricultural robots; Application programs; Convolutional neural networks; Industrial manipulators; Industrial robots; Man machine systems; Manipulators; Robot applications; Software testing; Wavelet decomposition; Design/methodology/approach; Detection modules; Human securities; Robot manipulator; Robot operations; Safe human-robot interaction; Wavelet Packet Decomposition; Work environments; Human robot interaction",,,,,"Firat Üniversitesi, FU","Information note : This study was performed at Firat University within the scope of the doctoral thesis titled ?The Development of an Artificial Intelligence-Based Self-Programmable Robot Software Using Human-Robot Interaction.?",,,,,,,,,,"Avanzini, G.B., Ceriani, N.M., Zanchettin, A.M., Rocco, P., Bascetta, L., Safety control of industrial robots based on a distributed distance sensor (2014) IEEE Transactions on Control Systems Technology, 22 (6), pp. 2127-2140; Bingol, M.C., Aydogmus, O., Implementation of speech recognition for robot control using support vector machine (2018) International Eurasian Conference on Science, Engineering and Technology (EurasianSciEnTech), pp. 814-819; Bingol, M.C., Aydoğmuş, Ö., İnsan-robot etkileşiminde insan güvenliği için çok kanallı iletişim kullanarak evrişimli sinir ağı tabanlı bir yazılımının geliştirilmesi ve uygulaması (2019) Fırat Üniversitesi Müh. Bil. 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Najmaei, N., Kermani, M.R., Applications of artificial intelligence in safe human-robot interactions (2011) IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41 (2), pp. 448-459; Parekh, V.S., Jacobs, M.A., Deep learning and radiomics in precision medicine (2019) Expert Review of Precision Medicine and Drug Development, 4 (2), pp. 59-72; Petkovic, D., Issa, M., Pavlović, N.D., Zentner, L., Design of compliant robotic joint with embedded-sensing elements of conductive silicone rubber (2013) Industrial Robot: An International Journal, 2, pp. 143-157; Popov, D., Klimchik, A., Mavridis, N., Collision detection, localization & classification for industrial robots with joint torque sensors (2017) 2017-26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 838-843; Rajnathsing, H., Li, C., A neural network based monitoring system for safety in shared work-space human-robot collaboration (2018) Industrial Robot: The International Journal of Robotics Research and Application, 4, pp. 481-491; 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Yatsuda, A., Haramaki, T., Nishino, H., A robot motion design scheme for watching the elderly based on human gesture sensing (2019) Internet of Things, 5, pp. 168-179; Zurada, J., Wright, A.L., Graham, J.H., A neuro-fuzzy approach for robot system safety (2001) IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 31 (1), pp. 49-64","Bingol, M.C.; Department of Mechatronics Engineering, Turkey; email: mustafacanbingol@gmail.com",,,"Emerald Group Holdings Ltd.",,,,,0143991X,,IDRBA,,"English","Ind. Robot",Article,"Final","",Scopus,2-s2.0-85078963315
"Mujica M., Benoussaad M., Fourquet J.-Y.","57208247021;34167551000;7006652015;","Evaluation of Human-Robot Object Co-manipulation under Robot Impedance Control",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9197329","9143","9149",,,"10.1109/ICRA40945.2020.9197329","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092732090&doi=10.1109%2fICRA40945.2020.9197329&partnerID=40&md5=7a57f232ba167b43cc5f549d56054380","University of Toulouse, LGP-ENIT, Tarbes, France","Mujica, M., University of Toulouse, LGP-ENIT, Tarbes, France; Benoussaad, M., University of Toulouse, LGP-ENIT, Tarbes, France; Fourquet, J.-Y., University of Toulouse, LGP-ENIT, Tarbes, France","The human-robot collaboration is a promising and challenging field of robotics research. One of the main collaboration tasks is the object co-manipulation where the human and robot are in a continuous physical interaction and forces exerted must be handled. This involves some issues known in robotics as physical Human-Robot Interaction (pHRI), where human safety and interaction comfort are required. Moreover, a definition of interaction quality metrics would be relevant.In the current work, the assessment of Human-Robot object co-manipulation task was explored through the proposed metrics of interaction quality, based on human forces throughout the movement. This analysis is based on co-manipulation of objects with different dynamical properties (weight and inertia), with and without including these properties knowledge in the robot control law. Here, the human is a leader of task and the robot the follower without any information of the human trajectory and movement profile. For the robot control law, a well-known impedance control was applied on a 7-dof Kuka LBR iiwa 14 R820 robot.Results show that the consideration of object dynamical properties in the robot control law is crucial for a good and more comfortable interaction. Besides, human efforts are more significant with a higher no-considered weight, whereas it remains stable when these weights were considered. © 2020 IEEE.",,"Agricultural robots; Control theory; Robotics; Collaboration task; Dynamical properties; Human-robot collaboration; Interaction quality; Manipulation task; Physical humanrobot interaction (phri); Physical interactions; Robot impedance control; Social robots",,,,,,,,,,,,,,,,"De Luca, A., Flacco, F., Integrated control for phri: Collision avoidance, detection, reaction and collaboration (2012) Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE Ras & Embs Inter-national Conference On. IEEE, pp. 288-295; Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human.robot collaboration (2018) Autonomous Robots, pp. 1-19; Hogan, N., Buerger, S.P., Impedance and interaction control (2004) Robotics and Automation Handbook, pp. 375-398. , CRC Press; Kosuge, K., Kazamura, N., Control of a robot handling an object in cooperation with a human (1997) Robot and Human Communication, 1997. RO-MAN'97. Proceedings., 6th IEEE International Workshop On. 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IEEE, pp. 2873-2878; Roveda, L., Haghshenas, S., Prini, A., Dinon, T., Pedrocchi, N., Braghin, F., Tosatti, L.M., Fuzzy impedance control for enhancing capabilities of humans in onerous tasks execution (2018) 2018 15th International Conference on Ubiquitous Robots (UR, pp. 406-411. , IEEE; Hogan, N., Impedance control: An approach to manipulation (1984) American Control Conference, pp. 304-313. , IEEE, 1984; Impedance control: An approach to manipulation: Part II.implementation (1985) Journal of Dynamic Systems, Measurement, and Control, 107 (1), pp. 8-16; Campa, R., De La Torre, H., Pose control of robot manipulators using different orientation representations: A comparative review 2009 American Control Conference, 2009, pp. 2855-2860. , IEEE; Caccavale, F., Natale, C., Siciliano, B., Villani, L., Resolvedacceleration control of robot manipulators: A critical review with experiments (1998) Robotica, 16 (5), pp. 565-573; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human.robot physical interaction (2015) IEEE Transactions on Robotics, 31 (4), pp. 850-863; Ferraguti, F., Talignani Landi, C., Sabattini, L., Bonffie, M., Fantuzzi, C., Secchi, C., A variable admittance control strategy for stable physical human.robot interaction (2019) The International Journal of Robotics Research, 38 (6), pp. 747-765; Khalil, W., Dombre, E., Chapter 13-trajectory generation (2002) Modeling, Identiffcation and Control of Robots, 345, p. 313. , http://www.sciencedirect.com/science/article/pii/B9781903996669500130, W. Khalil and E. Dombre, Eds. Oxford: Butterworth-Heinemann",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85092732090
"Tamburella F., Tagliamonte N.L., Pisotta I., Masciullo M., Arquilla M., Van Asseldonk E.H.F., Van Der Kooij H., Wu A.R., Dzeladini F., Ijspeert A.J., Molinari M.","23767491200;35100967100;55388747400;16316975700;57204244605;14424653100;6603038110;57190841722;56215817200;7003779012;7103134641;","Neuromuscular Controller Embedded in a Powered Ankle Exoskeleton: Effects on Gait, Clinical Features and Subjective Perspective of Incomplete Spinal Cord Injured Subjects",2020,"IEEE Transactions on Neural Systems and Rehabilitation Engineering","28","5","9052712","1157","1167",,9,"10.1109/TNSRE.2020.2984790","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084696214&doi=10.1109%2fTNSRE.2020.2984790&partnerID=40&md5=c607fcdc24539e7a08ec734a9b7be24f","Neurorehabilitation 1 Department, Spinal Rehabilitation Laboratory, Laboratory of Robotic Neurorehabilitation, Santa Lucia Foundation (FSL), Rome, 00179, Italy; Advanced Robotics and Human-Centered Technologies Research Unit, Università Campus Bio-Medico di Roma, Rome, 00128, Italy; Department of Engineering Technology, University of Twente, NB Enschede, 7522, Netherlands; Department of Mechanical and Materials Engineering, Ingenuity Labs Research Institute, Queen's University, Kingston, ON  K7L 3N6, Canada; BioRob, School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland","Tamburella, F., Neurorehabilitation 1 Department, Spinal Rehabilitation Laboratory, Laboratory of Robotic Neurorehabilitation, Santa Lucia Foundation (FSL), Rome, 00179, Italy; Tagliamonte, N.L., Advanced Robotics and Human-Centered Technologies Research Unit, Università Campus Bio-Medico di Roma, Rome, 00128, Italy; Pisotta, I., Neurorehabilitation 1 Department, Spinal Rehabilitation Laboratory, Laboratory of Robotic Neurorehabilitation, Santa Lucia Foundation (FSL), Rome, 00179, Italy; Masciullo, M., Neurorehabilitation 1 Department, Spinal Rehabilitation Laboratory, Laboratory of Robotic Neurorehabilitation, Santa Lucia Foundation (FSL), Rome, 00179, Italy; Arquilla, M., Neurorehabilitation 1 Department, Spinal Rehabilitation Laboratory, Laboratory of Robotic Neurorehabilitation, Santa Lucia Foundation (FSL), Rome, 00179, Italy; Van Asseldonk, E.H.F., Department of Engineering Technology, University of Twente, NB Enschede, 7522, Netherlands; Van Der Kooij, H., Department of Engineering Technology, University of Twente, NB Enschede, 7522, Netherlands; Wu, A.R., Department of Mechanical and Materials Engineering, Ingenuity Labs Research Institute, Queen's University, Kingston, ON  K7L 3N6, Canada; Dzeladini, F., BioRob, School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland; Ijspeert, A.J., BioRob, School of Engineering, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, 1015, Switzerland; Molinari, M., Neurorehabilitation 1 Department, Spinal Rehabilitation Laboratory, Laboratory of Robotic Neurorehabilitation, Santa Lucia Foundation (FSL), Rome, 00179, Italy","Powered exoskeletons are among the emerging technologies claiming to assist functional ambulation. The potential to adapt robotic assistance based on specific motor abilities of incomplete spinal cord injury (iSCI) subjects, is crucial to optimize Human-Robot Interaction (HRI). Achilles, an autonomous wearable robot able to assist ankle during walking, was developed for iSCI subjects and utilizes a NeuroMuscular Controller (NMC). NMC can be used to adapt robotic assistance based on specific residual functional abilities of subjects. The main aim of this pilot study was to analyze the effects of the NMC-controlled Achilles, used as an assistive device, on chronic iSCI participants' performance, by assessing gait speed during 10-session training of robot-aided walking. Secondary aims were to assess training impact on participants' motion, clinical and functional features and to evaluate subjective perspective in terms of attitude towards technology, workload, usability and satisfaction. Results showed that 5 training sessions were necessary to significantly improve robot-aided gait speed on short paths and consequently to optimize HRI. Moreover, the training allowed participants who initially were not able to walk for 6 minutes, to improve gait endurance during Achilles-aided walking and to reduce perceived fatigue. Improvements were obtained also in gait speed during free walking, thus suggesting a potential rehabilitative impact, even if Achilles-aided walking was not faster than free walking. Participants' subjective evaluations indicated a positive experience. © 2001-2011 IEEE.","Achilles; ankle exoskeleton; assistance as needed; robot-aided walking; SCI","Exoskeleton (Robotics); Walking aids; Attitude towards technologies; Emerging technologies; Functional abilities; Functional features; Human robot Interaction (HRI); Incomplete spinal cord injury; Positive experiences; Subjective evaluations; Human robot interaction; 10 metere walking test; adult; aged; American Spinal Injury Association impairment scale; Article; clinical article; clinical assessment; endurance training; experience; functional residual capacity; gait; ground reaction force; human; human computer interaction; kinematics; kinetics; male; manual muscle test; motor dysfunction; motor dysfunction assessment; motor performance; muscle length; patient satisfaction; perception; program acceptability; range of motion; six minute walk test; spasticity; spatiotemporal analysis; spinal cord injury; walk test; walking speed; workload; ankle; exoskeleton (rehabilitation); gait; pilot study; spinal cord; walking; Ankle; Exoskeleton Device; Gait; Humans; Pilot Projects; Spinal Cord; Spinal Cord Injuries; Walking",,,,,"ICT-2013-10-611626","Manuscript received June 14, 2019; revised November 25, 2019 and February 20, 2020; accepted February 26, 2020. Date of publication April 1, 2020; date of current version May 8, 2020. This work was supported in part by the FP7 EU Research Program (SYMBITRON - Symbiotic Man-Machine Interactions in Wearable Exoskeletons to Enhance Mobility for Paraplegics) under Project ICT-2013-10-611626 and in part by the Italian Ministry of Health (Ricerca Corrente). (Corresponding author: F. Tamburella.) F. Tamburella, I. Pisotta, M. Masciullo, M. Arquilla, and M. Molinari are with the Spinal Rehabilitation Laboratory and the Laboratory of Robotic Neurorehabilitation, Neurorehabilitation 1 Department, Santa Lucia Foundation (FSL), 00179 Rome, Italy (e-mail: f.tamburella@ hsantalucia.it; i.pisotta@hsantalucia.it; m.masciullo@hsantalucia.it; m.arquilla@hsantalucia.it; m.molinari@hsantalucia.it).",,,,,,,,,,"Haisma, J.A., Woude Der Van, V.L.H., Stam, H.J., Bergen, M.P., Sluis, T.A.R., Bussmann, J.B.J., Physical capacity in wheelchairdependent persons with a spinal cord injury: A critical review of the literature (2006) Spinal Cord, 44 (11), pp. 642-652. , Nov; Palermo, A.E., Maher, J.L., Baunsgaard, C.B., Nash, M.S., Clinician-focused overview of bionic exoskeleton use after spinal cord injury (2017) Topics Spinal Cord Injury Rehabil., 23 (3), pp. 234-244. , Jun; Scivoletto, G., Clinical factors that affect walking level and performance in chronic spinal cord lesion patients (2008) Spine, 33 (3), pp. 259-264. , Feb; Kozlowski, A., Bryce, T., Dijkers, M., Time and effort required by persons with spinal cord injury to learn to use a powered exoskeleton for assisted walking (2015) Topics Spinal Cord Injury Rehabil., 21 (2), pp. 110-121. , Mar; Stampacchia, G., Rustici, A., Bigazzi, S., Gerini, A., Tombini, T., Mazzoleni, S., Walking with a powered robotic exoskeleton: Subjective experience, spasticity and pain in spinal cord injured persons (2016) NeuroRehabilitation, 39 (2), pp. 277-283. , Aug; Morawietz, C., Moffat, F., Effects of locomotor training after incomplete spinal cord injury: A systematic review (2013) Arch. Phys. Med. Rehabil., 94 (11), pp. 2297-2308. , Nov; Aach, M., Voluntary driven exoskeleton as a new tool for rehabilitation in chronic spinal cord injury: A pilot study (2014) Spine J., 14 (12), pp. 2847-2853. , Dec; Veneman, J.F., Kruidhof, R., Hekman, E.E.G., Ekkelenkamp, R., Van Asseldonk, E.H.F., Van Kooij Der, H., Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation (2007) IEEE Trans. Neural Syst. Rehabil. Eng., 15 (3), pp. 379-386. , Sep; Esquenazi, A., Talaty, M., Packel, A., Saulino, M., The ReWalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury (2012) Amer. J. Phys. Med. Rehabil., 91 (11), pp. 911-921. , Nov; Wang, S., Design and control of the MINDWALKER exoskeleton (2015) IEEE Trans. Neural Syst. Rehabil. Eng., 23 (2), pp. 277-286. , Mar; Gorgey, A.S., Robotic exoskeletons: The current pros and cons (2018) World J. 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Biomechatronics, pp. 918-923. , São Paulo, Brazil, Aug; Calanca, A., Muradore, R., Fiorini, P., A review of algorithms for compliant control of stiff and fixed-compliance robots (2016) IEEE/ASME Trans. Mechatronics, 21 (2), pp. 613-624. , Apr; Fritz, S., Lusardi, M., White paper:'Walking speed: The sixth vital sign (2009) J. Geriatric Phys. Therapy, 32 (2), pp. 9-46; Sylos-Labini, F., EMG patterns during assisted walking in the exoskeleton (2014) Frontiers Human Neurosci., 8, p. 423. , Jun; (2000) International Standard for Neurological Classification of Spinal Cord Injury (Rev), pp. 1-23. , Amer. 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Develop., 48 (4), pp. 387-416; (1998) Ergonomic Requirements for Office Work with Visual Display Terminals (VDTs)-Part 11: Guidance on Usability, , Standard ISO 9241-11:1998; Song, S., Geyer, H., A neural circuitry that emphasizes spinal feedback generates diverse behaviours of human locomotion (2015) J. Physiol., 593 (16), pp. 3493-3511. , Aug; Calanca, A., Capisani, L., Fiorini, P., Robust force control of series elastic actuators (2014) Actuators, 3 (3), pp. 182-204","Tamburella, F.; Neurorehabilitation 1 Department, Italy; email: f.tamburella@hsantalucia.it",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15344320,,ITNSB,"32248116","English","IEEE Trans. Neural Syst. Rehabil. Eng.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85084696214
"Oliveira R., Arriaga P., Correia F., Paiva A.","57201501871;12793958200;57189046328;7102683774;","Looking Beyond Collaboration: Socioemotional Positive, Negative and Task-Oriented Behaviors in Human–Robot Group Interactions",2020,"International Journal of Social Robotics","12","2",,"505","518",,2,"10.1007/s12369-019-00582-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072042104&doi=10.1007%2fs12369-019-00582-3&partnerID=40&md5=c5dc94f9a826532a3be14d1c05173262","ISCTE-Instituto Universitário de Lisboa (ISCTE-IUL), CIS-IUL and INESC-ID, Lisbon, Portugal; ISCTE-Instituto Universitário de Lisboa (ISCTE-IUL) and CIS-IUL, Lisbon, Portugal; Instituto Superior Técnico-Universidade de Lisboa and INESC-ID, Lisbon, Portugal","Oliveira, R., ISCTE-Instituto Universitário de Lisboa (ISCTE-IUL), CIS-IUL and INESC-ID, Lisbon, Portugal; Arriaga, P., ISCTE-Instituto Universitário de Lisboa (ISCTE-IUL) and CIS-IUL, Lisbon, Portugal; Correia, F., Instituto Superior Técnico-Universidade de Lisboa and INESC-ID, Lisbon, Portugal; Paiva, A., Instituto Superior Técnico-Universidade de Lisboa and INESC-ID, Lisbon, Portugal","In this paper, we aim to increase the understanding of human–robot interaction by considering the goal orientation displayed by the robot (i.e., competitive vs. cooperative) and the role displayed by each player (partner vs. opponent) in an entertainment group scenario. Sixty participants engaged in a card game called Sueca (two robots and two humans). Each participant played with each of the other players, and the goal orientation was manipulated by the set of verbal utterances displayed by the robot. Using a coding scheme based on Bales Interaction Process Analysis, the video-recorded interactions were analysed in terms of socioemotional positive, negative and task oriented behaviours. A marginal multilevel modelling analysis yielded significant interactions between the robotic addressee and the role the robot displayed in the socioemotional and task-oriented behaviours. Overall, our main results demonstrated the following: (1) Participants directed more behaviours towards partners than opponents, although most of these behaviours occurred between humans when they were partners. (2) When comparing players in the role of opponents, participants directed more socioemotional behaviours towards robots than towards the other human player. (3) No difference in task-oriented behaviours was observed among any of the players in this condition. These results suggest the occurrence of different behavioural patterns in competitive and collaborative interactions with robots that might be useful to inform the future development of more socially effective robots. © 2019, Springer Nature B.V.","Autonomous robots; Collaboration; Competition; Games; Groups; Human and robot interaction","Agricultural robots; Behavioral research; Economic and social effects; Coding scheme; Collaborative interaction; Goal orientations; Human players; Interaction process; Robot group; Robot interactions; Task-oriented; Social robots",,,,,"Fundação para a Ciência e a Tecnologia, FCT: FCTUID/CEC/500 21/2013, PTDC/EEISII/7174/2014, SFRH/BD/118031/2016",,,,,,,,,,,"Ahn, H.S., Sa, I.-K., Lee, D.-W., Choi, D., A playmate robot system for playing the rock–paper–scissors game with humans (2011) Artif Life Robot, 16 (2), p. 142; Anderson, C.A., Morrow, M., Competitive aggression without interaction: effects of competitive versus cooperative instructions on aggressive behavior in video games (1995) Pers Soc Psychol Bull, 21 (10), pp. 1020-1030; Arriaga, P., Oliveira, R.A., Paiva, A., Petisca, S., Correia, F., Alves-Oliveira, P., (2017) Description of the “Sueca” Card Game, , https://osf.io/6jc9w/, Retrieved from; Arriaga, P., Oliveira, R.A., Paiva, A., Petisca, S., Alves-Oliveira, P., Correia, F., (2018) Robot Utterances and Gaze, , https://osf.io/q9gu5/, Retrieved from; Axelrod, R.M., (1997) The complexity of cooperation: agent-based models of competition and collaboration, , Princeton University Press, Princeton; Aylett, R., Games robots play: once more, with feeling (2016) Emotion in games, pp. 289-302. , D’Mello S, Graesser A, Schuller B, Martin JC, (eds), Springer, Berlin; Bales, R.F., (1950) Interaction process analysis: a method for the study of small groups, , Addison-Wesley, Cambridge; Bales, R.F., Borgatta, E.F., Size of group as a factor in the interaction profile (1955) Small groups: studies in social interaction, pp. 396-413. , Hare AD, Borgatta EF, Bales RF, (eds), Knopf, New York; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int J Soc Robot, 1 (1), pp. 71-81; Bartneck, C., Suzuki, T., Kanda, T., Nomura, T., The influence of people’s culture and prior experiences with aibo on their attitude towards robots (2007) AI Soc, 21 (1-2), pp. 217-230; Bollmann, M., Hoischen, R., Jesikiewicz, M., Justkowski, C., Mertsching, B., Playing domino: A case study for an active vision system (1999) International Conference on Computer Vision Systems. 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J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85072042104
"Li J., Zuo S., Xu C., Zhang L., Dong M., Tao C., Ji R.","8322805400;57205027114;57188969826;55344237500;56081285600;23480909700;55903189500;","Influence of a Compatible Design on Physical Human-Robot Interaction Force: a Case Study of a Self-Adapting Lower-Limb Exoskeleton Mechanism",2020,"Journal of Intelligent and Robotic Systems: Theory and Applications","98","2",,"525","538",,5,"10.1007/s10846-019-01063-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069839130&doi=10.1007%2fs10846-019-01063-5&partnerID=40&md5=ac4d5e7666ddde97a26d9b861120a071","College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China; National Research Center for Rehabilitation Technical Aids, Beijing, 100176, China","Li, J., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China; Zuo, S., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China; Xu, C., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China; Zhang, L., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China; Dong, M., College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing, 100124, China; Tao, C., National Research Center for Rehabilitation Technical Aids, Beijing, 100176, China; Ji, R., National Research Center for Rehabilitation Technical Aids, Beijing, 100176, China","In the kinematic design of wearable exoskeletons, the issue of axis misalignments between the human and the exoskeleton joints should be well dealt with. Otherwise, large physical human-robot interaction (p-HRI) forces may occur at the human-robot interfaces, which makes the p-HRI uncomfortable or even unsafe. To cope with this issue, a kinematically compatible design approach of wearable exoskeletons has been investigated by researchers, and great development has been made in recent years. Moreover, the influence of such a design on the exoskeleton’s p-HRI performance should be evaluated to determine if the design is feasible. In this paper, a self-adapting lower-limb exoskeleton mechanism for three degrees of freedom gait training is proposed, and the mechanical structure of the exoskeleton mechanism is designed in detail. Then, based on the presented exoskeleton mechanism and the use of suitable force/torque sensors, a p-HRI force measurement system is developed. Subsequently, the p-HRI forces of the human-robot closed chain under the static and motion modes are detected, and the influence of the self-adapting design on the lower-limb exoskeleton mechanism’s p-HRI force feature is evaluated. The results indicate that additional human-robot connective joints could reduce the p-HRI force significantly, the compatible design of the exoskeleton mechanism is effective, and is thus applied to human lower-limb gait training. © 2019, Springer Nature B.V.","Axis misalignment; Compatible design; Lower-limb exoskeleton mechanism; Physical human-robot interaction force; Self-adapting","Alignment; Degrees of freedom (mechanics); Exoskeleton (Robotics); Joints (anatomy); Machine design; Man machine systems; Mechanisms; Wearable technology; Axis misalignments; Compatible design; Exoskeleton mechanism; Physical human-robot interactions; Self adapting; Human robot interaction",,,,,"KM201810005015; National Natural Science Foundation of China, NSFC: 51675008, 51705007; China Postdoctoral Science Foundation: 2018 T110017; Natural Science Foundation of Beijing Municipality: 17 L20019, 3171001","This work was supported by the National Natural Science Foundation of China under Grants No. 51675008 and No. 51705007, the Beijing Natural Science Foundation under Grants No. 3171001 and No. 17 L20019. Natural Science Foundation of Beijing Education Committee (No. KM201810005015) and China Postdoctoral Science Foundation (2018 T110017).",,,,,,,,,,"Hidler, J., Wisman, W., Neckel, N., Kinematic trajectories while walking within the Lokomat robotic gait-orthosis (2008) Clin. Biomech., 23 (10), pp. 1251-1259; Zanotto, D., Stegall, P., Agrawal, S.K., ALEX III: A novel robotic platform with 12 DOFs for human gait training (2013) IEEE International Conference on Robotics and Automation. Proceedings, pp. 3914-3919. , IEEE, Germany; Veneman, J.F., Kruidhof, R., Hekman, E.E.G., Ekkelenkamp, R., Asseldonk, E.H.F.V., Kooij, H.V.D., Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation (2007) IEEE Trans. Neural Syst. Rehabil. Eng., 15 (3), pp. 379-386; Prange, G.B., Jannink, M.J.A., Groothuis-Oudshoorn, C.G.M., Hermens, H.J., Ijzerman, M.J., Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke (2006) J. Rehabil. 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Eng., 18 (4), pp. 389-397; Wang, H., (2008) Anatomy of the human system, , Fudan University Press, Shanghai, China; Cenciarini, M., Dollar, A.M., Biomechanical considerations in the design of lower limb exoskeletons (2011) 2011 IEEE International Conference on Rehabilitation Robotics, , IEEE. Zurich; Cai, V.A.D., Bidaud, P., Hayward, V., Self-adjusting, isostatic exoskeleton for the human knee joint (2011) Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 612-618. , IEEE, Boston; Celebi, B., Yalcin, M., Patoglu, V., AssistON- Knee: a self-aligning knee exoskeleton (2013) IEEE/RSJ International Conference on Intelligent Robots and Systems; Radcliffe, C.W., Four-bar linkage prosthetic knee mechanisms: kinematics, alignment and prescription criteria (1994) Prosthetics Orthot. Int., 8 (18), pp. 159-173; Yamaguchi, G.T., Zajac, F.E., A planar model of the knee joint to characterize the knee extensor mechanism (1989) J. Biomech., 22 (1), pp. 1-10; Bapat, G., Sujatha, S., A method for optimal synthesis of a biomimetic four-bar linkage knee joint for a knee-ankle-foot orthosis, J (2017) Biomech, 32, pp. 20-28; Chen, G., Qi, P., Guo, Z., Yu, H., Mechanical design and evaluation of a compact portable knee–ankle–foot robot for gait rehabilitation (2016) Mech. & Mach. Theory, 103, pp. 51-64; Xu, L., Wang, D.H., Fu, Q., Yuan, G., Hu, L.Z., A novel four-bar linkage prosthetic knee based on magnetorheological effect: principle, structure, simulation and control (2016) Smart Mater. Struct., 25 (11), p. 115007","Zhang, L.; College of Mechanical Engineering and Applied Electronics Technology, China; email: zhangleiyu1988@126.com",,,"Springer",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","",Scopus,2-s2.0-85069839130
"Ferrari F., Divan S., Guerrero C., Zenatti F., Guidolin R., Palopoli L., Fontanelli D.","57188842302;57192431452;57209571111;57192423373;57209575434;35318200500;23488529800;","Human–Robot Interaction Analysis for a Smart Walker for Elderly: The ACANTO Interactive Guidance System",2020,"International Journal of Social Robotics","12","2",,"479","492",,9,"10.1007/s12369-019-00572-5","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068156286&doi=10.1007%2fs12369-019-00572-5&partnerID=40&md5=0d0c1ec69f38001b3710736da6988fbd","Department of Engineering and Computer Science, University of Trento, Trento, Italy; Department of Industrial Engineering, University of Trento, Trento, Italy","Ferrari, F., Department of Engineering and Computer Science, University of Trento, Trento, Italy; Divan, S., Department of Engineering and Computer Science, University of Trento, Trento, Italy; Guerrero, C., Department of Engineering and Computer Science, University of Trento, Trento, Italy; Zenatti, F., Department of Engineering and Computer Science, University of Trento, Trento, Italy; Guidolin, R., Department of Engineering and Computer Science, University of Trento, Trento, Italy; Palopoli, L., Department of Engineering and Computer Science, University of Trento, Trento, Italy; Fontanelli, D., Department of Industrial Engineering, University of Trento, Trento, Italy","The present study aims to investigate the interaction between older adults and a robotic walker named FriWalk, which has the capability to act as a navigation support and to guide the user through indoor environments along a planned path. To this purpose, we developed a guidance system named Simulated Passivity, which leaves the responsibility of the locomotion to the user, both to increase the mobility of elder users and to enhance their perception of control over the robot. Moreover, the robotic walker can be integrated with a tablet and graphical user interface (GUI) which provides visual indications to the user on the path to follow. Since the FriWalk and Simulated Passivity were developed to suit the needs of users with different deficits, we conducted a human–robot interaction experiment, complemented with direct interviews of the participants. The goals of the present work were to observe the relation between elders (with and without visual impairments) and the robot in completing a path (with and without the support of the GUI), and to collect the impressions about of the older adult participants about the interaction. Our results show an overall positive impression of the FriWalk and an evident flexibility and adaptability of its guidance system across different categories of users (e.g., with or without visual impairments). In the paper, we discuss the implications of these findings on service social robotics. © 2019, Springer Nature B.V.","Assistive robotics; Human–robot interaction; Perception of robots; Robotic walker; Social service robotics","Agricultural robots; Graphical user interfaces; Remote control; Robotics; Visual servoing; Graphical user interfaces (GUI); Guidance system; Indoor environment; Navigation support; Robot interactions; Robotic walkers; Social robotics; Visual impairment; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 643644","We want to thank the referents of the following elder centres of Trento and province for their support to the ACANTO Project: Civica di Trento, APSP Levico Terme San Valentino, Casa Di Riposo A.P.S.P. S. Spirito?Fondazione Montel, APSP Clementino Vannetti (Rovereto), Centro Diurno ?Sempreverde??Mattarello?Cooperativa Sad, APSP Beato De Tschiderer (Trento), and Casa Santa Maria?Kaleidoscopio Scs (Vigolo Vattaro).",,,,,,,,,,"(2015) A Cyberphysical Social Network Using Robot Friends, , http://www.ict-acanto.eu/acanto, EU Project; Andreetto, M., Divan, S., Ferrari, F., Fontanelli, D., Palopoli, L., Zenatti, F., Simulating passivity for robotic walkers via authority-sharing (2018) IEEE Robot Autom Lett, 3 (2), pp. 1306-1313; Andreetto, M., Divan, S., Fontanelli, D., Palopoli, L., Path following with authority sharing between humans and passive robotic walkers equipped with low-cost actuators (2017) IEEE Robot Autom Lett, 2 (4), pp. 2271-2278; Bennett, C., Doub, T., Bragg, A., Luellen, J., van Regenmorter, C., Lockman, J., Reiserer, R., Data mining session-based patient reported outcomes (PROs) in a mental health setting: Toward data-driven clinical decision support and personalized treatment (2011) 2011 IEEE 1St International Conference on Healthcare Informatics, Imaging and Systems Biology. 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In: Designing for - and with - Vulnerable People, , Paris; Montemerlo, M., Pineau, J., Roy, N., Thrun, S., Verma, V., Experiences with a mobile robotic guide for the elderly (2002) AAAI/IAAI, 2002, pp. 587-592; (2015) World Population Ageing, , http://www.un.org/en/development/desa/population/publications/pdf/ageing/WPA2015_Report.pdf, Accessed 12 July 2018; Nazemzadeh, P., Fontanelli, D., Macii, D., Palopoli, L., Indoor localization of mobile robots through QR code detection and dead reckoning data fusion (2017) IEEE/ASME Trans Mechatron, 22 (6), pp. 2588-2599; Nazemzadeh, P., Moro, F., Fontanelli, D., Macii, D., Palopoli, L., Indoor positioning of a robotic walking assistant for large public environments (2015) IEEE Trans Instrum Meas, 64 (11), pp. 2965-2976; Ni, D., Song, A., Tian, L., Xu, X., Chen, D., A walking assistant robotic system for the visually impaired based on computer vision and tactile perception (2015) Int J Soc Robot, 7 (5), pp. 617-628; Nielsen, J., (2013) Seniors as web users, 28. , Nielsen Norman Group, Fremont; Palopoli, L., Argyros, A., Birchbauer, J., Colombo, A., Fontanelli, D., Navigation assistance and guidance of older adults across complex public spaces: the DALi approach (2015) Intell Serv Robot, 8 (2), pp. 77-92; Peng, H., Song, G., You, J., Zhang, Y., Lian, J., An indoor navigation service robot system based on vibration tactile feedback (2017) Int J Soc Robot, 9 (3), pp. 331-341; Pinto, J.M., Wroblewski, K.E., Huisingh-Scheetz, M., Correia, C., Lopez, K.J., Chen, R.C., Kern, D.W., McClintock, M.K., Global sensory impairment predicts morbidity and mortality in older us adults (2017) J Am Geriatr Soc, 65 (12), pp. 2587-2595; Robinson, H., MacDonald, B., Broadbent, E., The role of healthcare robots for older people at home: a review (2014) Int J Soc Robot, 6 (4), pp. 575-591; Soetanto, D., Lapierre, L., Pascoal, A., Adaptive, non-singular path-following control of dynamic wheeled robots (2003) IEEE Conference on Decision and Control, 2, pp. 1765-1770. , IEEE, New York; Vaportzis, E., Giatsi Clausen, M., Gow, A.J., Older adults perceptions of technology and barriers to interacting with tablet computers: a focus group study (2017) Front Psychol, 8, p. 1687; Wachaja, A., Agarwal, P., Zink, M., Adame, M.R., Möller, K., Burgard, W., Navigating blind people with a smart walker (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6014-6019; Walsh, S.A., Conducting research with the elderly: ethical concerns for a vulnerable population (2009) South Online J Nurs Res, 9 (4), pp. 1-13; Wang, Y., Wang, S., A new directional-intent recognition method for walking training using an omnidirectional robot (2017) J Intell Robot Syst, 87 (2), pp. 231-246; Whelan, S., Murphy, K., Barrett, E., Krusche, C., Santorelli, A., Casey, D., Factors affecting the acceptability of social robots by older adults including people with dementia or cognitive impairment: a literature review (2018) Int J Soc Robot, 10, pp. 1-26","Fontanelli, D.; Department of Industrial Engineering, Italy; email: daniele.fontanelli@unitn.it",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85068156286
"Cao Y., Xiang K., Tang B., Ju Z., Pang M.","57216689721;9639042200;57209959590;25655056800;55341237100;","Design of Muscle Reflex Control for Upright Standing Push-Recovery Based on a Series Elastic Robot Ankle Joint",2020,"Frontiers in Neurorobotics","14",,"20","","",,,"10.3389/fnbot.2020.00020","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084652698&doi=10.3389%2ffnbot.2020.00020&partnerID=40&md5=eff347ee1f31cc4bbf706e13e871e099","School of Automation, Intelligent System Research Institute, Wuhan University of Technology, Wuhan, China; Intelligent System Biomedical Robotics Group, School of Computing, University of Portsmouth, Portsmouth, United Kingdom","Cao, Y., School of Automation, Intelligent System Research Institute, Wuhan University of Technology, Wuhan, China, Intelligent System Biomedical Robotics Group, School of Computing, University of Portsmouth, Portsmouth, United Kingdom; Xiang, K., School of Automation, Intelligent System Research Institute, Wuhan University of Technology, Wuhan, China; Tang, B., School of Automation, Intelligent System Research Institute, Wuhan University of Technology, Wuhan, China; Ju, Z., Intelligent System Biomedical Robotics Group, School of Computing, University of Portsmouth, Portsmouth, United Kingdom; Pang, M., School of Automation, Intelligent System Research Institute, Wuhan University of Technology, Wuhan, China","In physical human–robot interaction environment, ankle joint muscle reflex control remains significant and promising in human bipedal stance. The reflex control mechanism contains rich information of human joint dynamic behavior, which is valuable in the application of real-time decoding motion intention. Thus, investigating the human muscle reflex mechanism is not only meaningful in human physiology study but also useful for the robotic system design in the field of human–robot physical interaction. In this paper, a specialized ankle joint muscle reflex control algorithm for human upright standing push-recovery is proposed. The proposed control algorithm is composed of a proportional-derivative (PD)-like controller and a positive force controller, which are employed to mimic the human muscle stretch reflex and muscle tendon force reflex, respectively. Reflex gains are regulated by muscle activation levels of contralateral ankle muscles. The proposed method was implemented on a self-designed series elastic robot ankle joint (SERAJ), where the series elastic actuator (SEA) has the potential to mimic human muscle–tendon unit (MTU). During the push-recovery experimental study, the surface electromyography (sEMG), ankle torque, body sway angle, and velocity of each subject were recorded in the case where the SERAJ was unilaterally kneed on each subject. The experimental results indicate that the proposed muscle reflex control method can easily realize upright standing push-recovery behavior, which is analogous to the original human behavior. © Copyright © 2020 Cao, Xiang, Tang, Ju and Pang.","ankle joint; muscle reflex; push-recovery; series elastic actuator; upright standing","Behavioral research; Controllers; Machine design; Muscle; Recovery; Tendons; Human physiology studies; Muscle activation; Physical interactions; Proportional derivatives; Robot interactions; Robotic system design; Series elastic actuators; Surface electromyography; Human robot interaction; algorithm; ankle; article; body equilibrium; controlled study; electromyography; experimental study; human; muscle contraction; myotatic reflex; remission; robotics; tendon; torque",,,,,"National Natural Science Foundation of China, NSFC: 51575412, 61603284, 61903286","The authors would like to extend their gratitude to X. Xu, G. Xu, H. Xu, L. Qi, D. Peng, and H. Li from the Intelligent System Research Institute, School of Automation, Wuhan University of Technology for assisting in the experimental process. Funding. This work was supported in part by the National Natural Science Foundation of China under Grant 61603284, 61903286, and 51575412.","This work was supported in part by the National Natural Science Foundation of China under Grant 61603284, 61903286, and 51575412.",,,,,,,,,"Alexander, R.M., Elastic energy stores in running vertebrates (1984) Am. Zool, 24, pp. 85-94; Alexander, R.M., Bennet-Clark, H.C., Storage of elastic strain energy in muscle and other tissues (1977) Nature, 265, pp. 114-117. , 834252; Amiri, P., Kearney, R.E., Ankle intrinsic stiffness changes with postural sway (2019) J. 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"Val-Calvo M., Álvarez-Sánchez J.R., Ferrández-Vicente J.M., Díaz-Morcillo A., Fernández-Jover E.","57195318553;56786654900;56268451400;55940590600;57203722548;","Real-Time Multi-Modal Estimation of Dynamically Evoked Emotions Using EEG, Heart Rate and Galvanic Skin Response",2020,"International Journal of Neural Systems","30","4","2050015","","",,12,"10.1142/S0129065720500136","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081555898&doi=10.1142%2fS0129065720500136&partnerID=40&md5=2caca9c0fa1af00c8618f2a7c2757ef9","Departamento de Inteligencia Artificial, UNED, Juan del Rosal, 16, Madrid, E-28040, Spain; Departamento de Tecnologías de la Información y Las Comunicaciones, Univ. Politécnica de Cartagena, Edif. Antigones Pza Del Hospital, Cartagena, E-30202, Spain; Instituto de Bioingeniería, Univ. Miguel Hernández, Spain and CIBER-BBN, Av. de la Universidad s/n. E-03202, Elche, Spain","Val-Calvo, M., Departamento de Inteligencia Artificial, UNED, Juan del Rosal, 16, Madrid, E-28040, Spain, Departamento de Tecnologías de la Información y Las Comunicaciones, Univ. Politécnica de Cartagena, Edif. Antigones Pza Del Hospital, Cartagena, E-30202, Spain; Álvarez-Sánchez, J.R., Departamento de Inteligencia Artificial, UNED, Juan del Rosal, 16, Madrid, E-28040, Spain; Ferrández-Vicente, J.M., Departamento de Tecnologías de la Información y Las Comunicaciones, Univ. Politécnica de Cartagena, Edif. Antigones Pza Del Hospital, Cartagena, E-30202, Spain; Díaz-Morcillo, A., Departamento de Tecnologías de la Información y Las Comunicaciones, Univ. Politécnica de Cartagena, Edif. Antigones Pza Del Hospital, Cartagena, E-30202, Spain; Fernández-Jover, E., Instituto de Bioingeniería, Univ. Miguel Hernández, Spain and CIBER-BBN, Av. de la Universidad s/n. E-03202, Elche, Spain","Emotion estimation systems based on brain and physiological signals such as electro encephalography (EEG), blood-volume pressure (BVP), and galvanic skin response (GSR) are gaining special attention in recent years due to the possibilities they offer. The field of human-robot interactions (HRIs) could benefit from a broadened understanding of the brain and physiological emotion encoding, together with the use of lightweight software and cheap wearable devices, and thus improve the capabilities of robots to fully engage with the users emotional reactions. In this paper, a previously developed methodology for real-time emotion estimation aimed for its use in the field of HRI is tested under realistic circumstances using a self-generated database created using dynamically evoked emotions. Other state-of-the-art, real-time approaches address emotion estimation using constant stimuli to facilitate the analysis of the evoked responses, remaining far from real scenarios since emotions are dynamically evoked. The proposed approach studies the feasibility of the emotion estimation methodology previously developed, under an experimentation paradigm that imitates a more realistic scenario involving dynamically evoked emotions by using a dramatic film as the experimental paradigm. The emotion estimation methodology has proved to perform on real-time constraints while maintaining high accuracy on emotion estimation when using the self-produced dynamically evoked emotions multi-signal database. © 2020 World Scientific Publishing Company.","artifact removal; BVP; EEG; emotion estimation; GSR; HRI; Real-time","Electroencephalography; Electrophysiology; Human robot interaction; Artifact removal; Blood volume pressures; Emotion estimation; Emotional reactions; Galvanic skin response; Physiological signals; Real time; Real time constraints; Physiological models; adult; brain cortex; electrodermal response; electroencephalography; emotion; evoked response; factual database; heart rate; human; physiology; procedures; signal processing; Adult; Cerebral Cortex; Databases, Factual; Electroencephalography; Emotions; Evoked Potentials; Galvanic Skin Response; Heart Rate; Humans; Signal Processing, Computer-Assisted",,,,,,,,,,,,,,,,"Roseman, I.J., Spindel, M.S., Jose, P.E., Appraisals of emotion-eliciting events: Testing a theory of discrete emotions (1990) J. 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"Gandarias J.M., Wang Y., Stilli A., Garcia-Cerezo A.J., Gomez-De-Gabriel J.M., Wurdemann H.A.","57200697817;57215075241;56423623500;6701640053;7801476081;35119695800;","Open-loop position control in collaborative, modular variable-stiffness-link (VSL) robots",2020,"IEEE Robotics and Automation Letters","5","2","8972614","1772","1779",,9,"10.1109/LRA.2020.2969943","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079792157&doi=10.1109%2fLRA.2020.2969943&partnerID=40&md5=063addef0f7988bfc6c28ad282dbd02e","Systems Engineering and Automation Department, University of Málaga, Málaga, 29016, Spain; Department of Computer Science, University College London, London, WC1E 6BT, United Kingdom; Department of Mechanical Engineering, University College London, London, WC1E 6BT, United Kingdom","Gandarias, J.M., Systems Engineering and Automation Department, University of Málaga, Málaga, 29016, Spain; Wang, Y., Department of Computer Science, University College London, London, WC1E 6BT, United Kingdom; Stilli, A., Department of Computer Science, University College London, London, WC1E 6BT, United Kingdom; Garcia-Cerezo, A.J., Systems Engineering and Automation Department, University of Málaga, Málaga, 29016, Spain; Gomez-De-Gabriel, J.M., Systems Engineering and Automation Department, University of Málaga, Málaga, 29016, Spain; Wurdemann, H.A., Department of Mechanical Engineering, University College London, London, WC1E 6BT, United Kingdom","Collaborative robots open up new avenues in the field of industrial robotics and physical Human-Robot Interaction (pHRI) as they are suitable to work in close approximation with humans. The integration and control of variable stiffness elements allow inherently safe interaction: Apart from notable work on Variable Stiffness Actuators, the concept of Variable-Stiffness-Link (VSL) manipulators promises safety improvements in cases of unintentional physical collision. However, position control of these type of robotic manipulators is challenging for critical task-oriented motions. In this letter, we propose a hybrid, learning based kinematic modelling approach to improve the performance of traditional open-loop position controllers for a modular, collaborative VSL robot. We show that our approach improves the performance of traditional open-loop position controllers for robots with VSL and compensates for position errors, in particular, for lower stiffness values inside the links: Using our upgraded and modular robot, two experiments have been carried out to evaluate the behaviour of the robot during task-oriented motions. Results show that traditional model-based kinematics are not able to accurately control the position of the end-effector: the position error increases with higher loads and lower pressures inside the VSLs. On the other hand, we demonstrate that, using our approach, the VSL robot can outperform the position control compared to a robotic manipulator with 3D printed rigid links. © 2016 IEEE.","deep learning in robotics and automation; Modeling, control, and learning for soft robots; soft robot materials and design","3D printers; Controllers; Deep learning; Flexible manipulators; Human robot interaction; Kinematics; Learning systems; Machine design; Robotics; Stiffness; Close approximation; Collaborative robots; Industrial robotics; Kinematic modelling; Physical humanrobot interaction (phri); Position controller; Robotic manipulators; Soft robot; Position control",,,,,"Universidad de Málaga, UMA; Engineering and Physical Sciences Research Council, EPSRC: EP/N509577/1, EP/R037795/1, EP/S014039/1; Academy of Medical Sciences: SBF003-1109; European Commission, EC: BES-2016-078237","Manuscript received September 10, 2019; accepted January 9, 2020. Date of publication January 28, 2020; date of current version February 7, 2020. This letter was recommended for publication by Associate Editor A. Andrew Stokes and Editor K.-J. Cho upon evaluation of the reviewers’ comments. This work was supported in part by the Spanish Projects DPI2015-65186-R and RTI2018-093421-B-I00, in part by the University of Málaga, in part by the European Commission under Grant BES-2016-078237 as well as by the Springboard Award of the Academy of Medical Sciences under Grant SBF003-1109, and in part by the Engineering and Physical Sciences Research Council under Grants EP/N509577/1, EP/R037795/1, and EP/S014039/1. (Corresponding author: Juan M. Gandarias.) J. M. Gandarias, A. J. García-Cerezo, and J. M. Gómez-de-Gabriel are with the Systems Engineering and Automation Department, University of Málaga, 29016 Málaga, Spain (e-mail: jmgandarias@uma.es; gcerezo@ctima.uma.es; jesus.gomez@uma.es).",,,,,,,,,,"De Santis, A., Siciliano, B., De Luca, A., Bicchi, A., An atlas of physical human-robot interaction (2008) Mechanism Mach. Theory, 43 (3), pp. 253-270; Yanco, H., Drury, J.L., A taxonomy for human-robot interaction (2002) Proc. AAAI Fall Symp. Human-Robot. Interact., pp. 111-119; Magrini, E., Flacco, F., De Luca, A., Control of generalized contact motion and force in physical human-robot interaction (2015) Proc. IEEE Int. Conf. Robot. Autom., pp. 2298-2304; Gandarias, J.M., Gómez-De-Gabriel, J.M., García-Cerezo, A.J., Enhancing perception with tactile object recognition in adaptive grippers for human-robot interaction (2019) Sensors, 18 (3); Kronander, K., Billard, A., Learning compliant manipulation through kinesthetic and tactile human-robot interaction (2013) IEEE Trans. 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Lett., 3 (2), pp. 1292-1298. , Apr; Thuruthel, T.G., Ansari, Y., Falotico, E., Laschi, C., Control strategies for soft robotic manipulators: A survey (2018) Soft Robot., 5 (2), pp. 149-163; Ho, J.D., Localized online learning-based control of a soft redundant manipulator under variable loading (2018) Adv. Robot., 32 (21), pp. 1168-1183; Calinon, S., Sardellitti, I., Caldwell, D.G., Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies (2010) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 249-254; Thuruthel, T.G., Falotico, E., Renda, F., Laschi, C., Learning dynamic models for open loop predictive control of soft robotic manipulators (2017) Bioinspiration Biomimetics, 12 (6); Lee, C.S.G., Ziegler, M., Geometric approach in solving inverse kinematics ofPUMArobots (1984) IEEE Trans. Aerosp. Electron. Syst., AES-6 (6), pp. 695-706. , Nov; Paul, R.P., Kinematic control equations for simple manipulators (1981) IEEE Trans. Syst., Man Cybern., 11 (AES-6), pp. 449-455. , Jun; Rocha, C.R., Tonetto, C.P., Dias, A., A comparison between the Denavit-Hartenberg and the screw-basedmethods used in kinematic modeling of robot manipulators (2011) Robot. Comput.-Integr. Manuf., 27 (4), pp. 723-728","Gandarias, J.M.; Systems Engineering and Automation Department, Spain; email: jmgandarias@uma.es",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85079792157
"Spatola N.","57193889944;","Would you turn off a robot because it confronts you with your own mortality?",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"61","68",,,"10.1145/3371382.3380736","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083233791&doi=10.1145%2f3371382.3380736&partnerID=40&md5=8d5cc4e834b02c3823a9b6e3308a061f","Social Cognition in Human-Robot Interaction, Italian Institute of Technology, Genova, Italy","Spatola, N., Social Cognition in Human-Robot Interaction, Italian Institute of Technology, Genova, Italy","A future world populated by robots is a projection that has long inspired and still inspires science-fiction stories. As their complexity increase people can help but compare to these artificial entities and why not question their own ""human nature"". While it is easy to assess the superiority of humans on many dimensions, there is one, central to all humans that is not in favor of our kind: the mortality. In this study, we investigate how individuals react toward a robot that makes this comparison dimension stand out and how it may define attitudes towards robots and pro/antisocial behaviors towards them. Also, we evaluate the role of robot anthropomorphism and the spiritual thoughts activation as mediators of the above-mentioned process. Our study's results demonstrate that facing a robot that confronts people with their own mortality results in less positive attitudes towards robots, and a higher likelihood of acting negatively toward them. Also, this particular robot tends to energize more spiritual thoughts among participants and less human traits attribution. Finally, we show that spirituality may be paradoxically associated with more anthropomorphism and more negative attitude showing the ambiguous multi-component stance of this dimension. These results are discussed in terms of attitudes towards robots, Terror Management Theory, social comparison process and implications for future human-robot interaction (HRI). © 2020 ACM.","Attitude; Human robot comparison; Human robot interaction; Prosocial behaviour; Social agents","Agricultural robots; Behavioral research; Man machine systems; Human nature; Human robot Interaction (HRI); Human traits; Implications for futures; Management theory; Multicomponents; Positive attitude; Science fictions; Social robots",,,,,,,,,,,,,,,,"Spatola, N., Not as bad as it seems: When the presence of a threatening humanoid robot improves human performance (2018) Sci. Robot, 3 (21), p. eaat5843; Spatola, N., Normand, A., Human vs. Machine: The psychological and behavioral consequences of being compared to an outperforming artificial agent Psychol. Res; Gerber, J.P., Wheeler, L., Suls, J., A social comparison theory meta-analysis 60+ years on (2018) Psychol. Bull, 144 (2), pp. 177-197; Books, P., Flesh and machines. How robots will change us (2002) Nelson, pp. 1-5; Rauchbauer, B., Nazarian, B., Bourhis, M., Ochs, M., Prévot, L., Chaminade, T., Brain activity during reciprocal social interaction investigated using conversational robots as control condition (2019) Philos. Trans. R. Soc. B Biol. Sci, 374, p. 1771; Saygin, A.P., Chaminade, T., Ishiguro, H., Driver, J., Frith, C., The thing that should not be: Predictive coding and the uncanny valley in perceiving human and humanoid robot actions (2012) Soc. Cogn. Affect. Neurosci, 7 (4), pp. 413-422; Nomura, T., Suzuki, T., Kanda, T., Kato, K., Measurement of negative attitudes toward robots (2006) Interact. Stud. Stud. Soc. Behav. Commun. Biol. Artif. Syst, 7 (3), pp. 437-454; Jonas, E., Schimel, J., Greenberg, J., Pyszczynski, T., The scrooge effect: Evidence that mortality salience increases prosocial attitudes and behavior (2002) Personality and Social Psychology Bulletin, 28 (10), pp. 1342-1353; Jonas, E., Sullivan, D., Greenberg, J., Generosity, greed, norms, and death-Differential effects of mortality salience on charitable behavior (2013) J. Econ. Psychol, 35, pp. 47-57; Vail, K.E., Rothschild, Z.K., Weise, D.R., Solomon, S., Pyszczynski, T., Greenberg, J., A terror management analysis of the psychological functions of religion (2010) Personal. Soc. Psychol. Rev, 14 (1), pp. 84-94; Bartneck, C., Nomura, T., Kanda, T., Suzuki, T., Kato, K., Cultural differences in attitudes towards robots (2005) AISB'05 Conv. Soc. Intell. Interact. Anim. Robot. Agents-Proc. Symp. Robot Companions Hard Probl. Open Challenges Robot. Interact, pp. 1-4; Bartneck, C., Nomura, T., Kanda, T., Suzuki, T., Kato, K., A cross-cultural study on attitudes towards robots (2005) Proc. HCI Int, pp. 1981-1983; Bartneck, C., Suzuki, T., Kanda, T., Nomura, T., The influence of people's culture and prior experiences with Aibo on their attitude towards robots (2007) AI Soc, 21 (1), pp. 217-230; Yang, G.Z., The grand challenges of science robotics (2018) Sci. Robot, 3 (14), p. eaar7650; Kasser, T., Sheldon, K.M., Of wealth and death: Materialism, Mortality Salience, and Consumption Behavior (2000) Psychol. Sci, 11 (4), pp. 348-351; Hirschberger, G., Ein-Dor, T., Almakias, S., The self-protective altruist: Terror management and the ambivalent nature of prosocial behavior (2008) Personal. Soc. Psychol. Bull, 34 (5), pp. 666-678; Hirschberger, G., Compassionate callousness: A terror management perspective on prosocial behavior (2010) Prosocial Motives, Emotions, and Behavior: The Better Angels of Our Nature, pp. 201-219; Greenberg, J., Pyszczynski, T., Solomon, S., The Causes and consequences of a need for self-esteem: A terror management theory (1986) Public Self and Private Self, pp. 189-212; Spatola, N., Switch off a robot, switch off a mind (2019) HAI '19 Proceedings of the 7th International Conference on Human-Agent Interaction, pp. 194-199; Schwingharnmer, S.A., Stapel, D.A., Blanton, H., Different selves have different effects: Self-activation and defensive social comparison (2006) Personality and Social Psychology Bulletin, 32 (1), pp. 27-39; Epley, N., Waytz, A., Cacioppo, J.T., On seeing human: A three-factor theory of anthropomorphism (2007) Psychol. Rev, 114 (4), pp. 864-886; Haslam, N., Dehumanization: An integrative review (2006) Personal. Soc. Psychol. 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Neurosci, 7 (4), pp. 309-316; Oztop, E., Chaminade, T., Franklin, D.W., Human-humanoid interaction: Is a humanoid robot perceived as a human (2004) 2004 4th IEEE-RAS Int. Conf. Humanoid Robot, 2 (4), pp. 830-841; Chaminade, T., Franklin, D.W., Oztop, E., Cheng, G., Motor interference between humans and humanoid robots: Effect of biological and artificial motion (2005) Proceedings of 2005 4th IEEE International Conference on Development and Learning, 2005, pp. 96-101; Chaminade, T., Brain response to a humanoid robot in areas implicated in the perception of human emotional gestures (2010) PLoS One, 5 (7); Chaminade, T., How do we think machines think An fMRI study of alleged competition with an artificial intelligence (2012) Front. Hum. Neurosci, 6. , MAY 2012; Spatola, N., Monceau, S., Ferrand, L., Cognitive impact of Social Robots:How anthropomorphism boosts performance IEEE Robot. Autom. Mag; Spatola, N., Improved cognitive control in presence of anthropomorphized robots (2019) Int. 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Res, 110 (1), pp. 71-87; Musick, M.A., Wilson, J., (2008) Volunteers: A Social Profile, , Indiana University Press; McCutcheon, R.T., Guthrie, S.E., Faces in the clouds: A new theory of religion (1994) J. Sci. Study Relig, 33 (1), p. 81; Yonan, E.A., Religion as anthropomorphism: A new theory that invites definitional and epistemic scrutiny (1995) Religion, 25 (1), pp. 31-34; Carpinella, C.M., Wyman, A.B., Perez, M.A., Stroessner, S.J., The robotic social attributes scale (rosas): Development and validation (2017) ACM/IEEE International Conference on Human-Robot Interaction, pp. 254-262; Hanington, B., Martin, B., Universal methods of design: 100 ways to research complex problems, develop innovative ideas, and design effective solutions (2012) Rockport Publishers, 49 (10); King, M., Measuring spiritual belief: Development and standardization of a beliefs and values scale (2006) Psychol. 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Bull, 132 (5), pp. 778-822; Hahn, A., Gawronski, B., Implicit social cognition (2015) International Encyclopedia of the Social & Behavioral Sciences: Second Edition, pp. 714-720; Saroglou, V., Pichon, I., Trompette, L., Verschueren, M., Dernelle, R., Prosocial behavior and religion: New evidence based on projective measures and peer ratings (2005) J. Sci. Study Relig, 44 (3), pp. 323-348; Miller, L.J., (2012) The Oxford Handbook of Psychology and Spirituality, , Oxford University Press; Fisher, J.A., Disambiguating anthropomorphism: An interdisciplinary review (1991) Perspect. Ethol, 9, pp. 49-85","Spatola, N.; Social Cognition in Human-Robot Interaction, Italy; email: Nicolas.Spatola@uca.fr",,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85083233791
"Urgen B.A., Guneysu I., Yilmaz S., Cerrrahoglu B., DInçer E.","56295243200;57216331311;57216353277;57216335213;57216339315;","Do robots distract us as much as humans? the effect of human-like appearance and perceptual load",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"493","495",,2,"10.1145/3371382.3378274","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083178795&doi=10.1145%2f3371382.3378274&partnerID=40&md5=7133613cb1317dff29eff7b7075e5751","Department of Psychology, Interdisciplinary Neuroscience Program, National Magnetic Resonance Research Center (UMRAM), Aysel Sabuncu Brain Research Center, Bilkent University, Turkey; Department of Psychology, Bilkent University, Turkey; Userspots, Inc., Turkey","Urgen, B.A., Department of Psychology, Interdisciplinary Neuroscience Program, National Magnetic Resonance Research Center (UMRAM), Aysel Sabuncu Brain Research Center, Bilkent University, Turkey; Guneysu, I., Department of Psychology, Bilkent University, Turkey; Yilmaz, S., Department of Psychology, Bilkent University, Turkey; Cerrrahoglu, B., Department of Psychology, Bilkent University, Turkey; DInçer, E., Userspots, Inc., Turkey","Attention is an important mechanism for solving certain tasks, but our environment can distract us via irrelevant information. As robots increasingly become part of our lives, one important question is whether they could distract us as much as humans do, and if so to what extent. To address this question, we conducted a study in which subjects were engaged in a central letter detection task. The task irrelevant distractors were pictures of three agents; a mechanical robot, a human-like robot, and a real human. We also manipulated the perceptual load to investigate whether the demands of the task influence how much these agents distract us. Our results show that robots distract people as much as humans, as demonstrated by significant increase in reaction times and decrease in task accuracy in the presence of agent distractors as compared to the situation when there was no distractor. However, we found that the task difficulty interacted with the human-likeness of the distractor agent. When the task was less demanding, the agent that distracted most was the most human-like agent, whereas when the task was more demanding, the least human-like agent distracted the most. These results not only provide insights about how to design humanoid robots but also sets as a great example of a fruitful collaboration between human-robot interaction and cognitive sciences. © 2020 ACM.","Attention; Cognitive psychology; Human perception; Human-like appearance; Humanoid robots; Perceptual load; Robot design","Agricultural robots; Anthropomorphic robots; Human reaction time; Machine design; Man machine systems; Cognitive science; Detection tasks; Human like; Human like robots; Human likeness; Human-like agents; Humanoid robot; Task difficulty; Human robot interaction",,,,,,,,,,,,,,,,"Lavie, N., Cox, S., On the efficiency of attentional selection: Efficient visual search results in inefficient rejection of distraction (1997) Psychological Science, 8, pp. 395-398; Lavie, N., Selective attention and cognitive control: Dissociating attentional functions through different types of load (2000) Attention and Performance, XVIII, pp. 175-194. , (S. Monsell, J. Driver (eds.)) MIT Press; Lavie, N., Ro, T., Russell, C., The role of perceptual load in processing distractor faces (2003) Psychological Science, 14 (5), pp. 510-515; Lavie, N., Distracted and confused Selective attention under load (2005) Trends in Cognitive Sciences, 9 (2), pp. 75-82; Forster, S., Lavie, N., Attentional capture by entirely irrelevant distractors (2008) Visual Cognition, 16 (2-3), pp. 200-214; Lavie, N., Attention, distraction, and cognitive control under load (2010) Current Directions in Psychological Science, 19 (3), pp. 143-148; Mori, M., The uncanny valley (1970) Energy, 7 (4), pp. 33-35; Saygin, A.P., Chaminade, T., Ishiguro, H., Driver, J., Frith, C., The thing that should not be: Predictive coding and the uncanny valley in perceiving human and humanoid robot actions (2012) Social Cognitive Affective Neuroscience, 7 (4), pp. 413-422; Urgen, B.A., Kutas, M., Saygin, A.P., Uncanny valley as a window into predictive processing in the social brain (2018) Neuropsychologia, 114, pp. 181-185; Li, A.X., Florendo, M., Miller, L.E., Ishiguro, H., Saygin, A.P., Robot form and motion influences social attention (2015) 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI). Portland, USA; Urgen, B.A., Pehlivan, S., Saygin, A.P., Distinct representations in occipito-temporal, parietal, and premotor cortex during action perception revealed by fMRI and computational modeling (2019) Neuropsychologia, 127, pp. 35-47",,,"ARM;Cambridge Consultants;et al.;FN Robotics;Furhat Robotics;Halodi","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158851,21672148,9781450370578,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85083178795
"Kwon M., Biyik E., Talati A., Bhasin K., Losey D.P., Sadigh D.","57189042806;57192162692;57215833513;57215828423;55755379000;55052871500;","When humans aren't optimal: Robots that collaborate with risk-aware humans",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"43","52",,11,"10.1145/3319502.3374832","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082002826&doi=10.1145%2f3319502.3374832&partnerID=40&md5=e1be74881a4ddb2058b66f7331a655f0","Stanford University, United States; American High School, United States; Harker School, United States","Kwon, M., Stanford University, United States; Biyik, E., Stanford University, United States; Talati, A., American High School, United States; Bhasin, K., Harker School, United States; Losey, D.P., Stanford University, United States; Sadigh, D., Stanford University, United States","In order to collaborate safely and efficiently, robots need to anticipate how their human partners will behave. Some of today's robots model humans as if they were also robots, and assume users are always optimal. Other robots account for human limitations, and relax this assumption so that the human is noisily rational. Both of these models make sense when the human receives deterministic rewards: I.e., gaining either $100 or $130 with certainty. But in realworld scenarios, rewards are rarely deterministic. Instead, we must make choices subject to risk and uncertainty-and in these settings, humans exhibit a cognitive bias towards suboptimal behavior. For example, when deciding between gaining $100 with certainty or $130 only 80% of the time, people tend to make the risk-averse choice-even though it leads to a lower expected gain! In this paper, we adopt a well-known Risk-Aware human model from behavioral economics called Cumulative Prospect Theory and enable robots to leverage this model during human-robot interaction (HRI). In our user studies, we offer supporting evidence that the Risk-Aware model more accurately predicts suboptimal human behavior. We find that this increased modeling accuracy results in safer and more efficient human-robot collaboration. Overall, we extend existing rational human models so that collaborative robots can anticipate and plan around suboptimal human behavior during HRI. © 2020 Association for Computing Machinery.","Cognitive HRI; Cumulative prospect theory; Human prediction","Behavioral research; Economics; Man machine systems; Behavioral economics; Cognitive HRI; Collaborative robots; Cumulative prospect theory; Human robot Interaction (HRI); Human-robot collaboration; Real-world scenario; Risk and uncertainty; Human robot interaction",,,,,"National Science Foundation, NSF: 1941722",,,,,,,,,,,"Abbeel, P., Ng, A.Y., Apprenticeship learning via inverse reinforcement learning (2004) International Conference on Machine Learning (ICML), , ACM; Awais, M., Henrich, D., Human-robot collaboration by intention recognition using probabilistic state machines (2010) 19th International Workshop on Robotics in Alpe-Adria-Danube Region (RAAD 2010), pp. 75-80. , IEEE; Bai, H., Cai, S., Ye, N., Hsu, D., Sun Lee, W., Intentionaware online pomdp planning for autonomous driving in a crowd (2015) 2015 Ieee International Conference on Robotics and Automation (Icra), pp. 454-460. , IEEE; Baker, C.L., Saxe, R., Tenenbaum, J.B., Action understanding as inverse planning (2009) Cognition, 113 (3), pp. 329-349. , 2009; Basu, C., Biyik, E., He, Z., Singhal, M., Sadigh, D., Active learning of reward dynamics from hierarchical queries (2019) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Biyik, E., Lazar, D.A., Sadigh, D., Pedarsani, R., The green choice: Learning and influencing human decisions on shared roads (2019) Proceedings of the 58th IEEE Conference on Decision and Control (CDC); Biyik, E., Palan, M., Landolfi, N.C., Losey, D.P., Sadigh, D., Asking easy questions: A user-friendly approach to active reward learning (2019) Proceedings of the 3rd Conference on Robot Learning (CoRL); Bloem, M., Bambos, N., Infinite time horizon maximum causal entropy inverse reinforcement learning (2014) 53rd IEEE Conference on Decision and Control, pp. 4911-4916. , IEEE; Brown, D.S., Goo, W., Niekum, S., (2019) Ranking-Based Reward Extrapolation Without Rankings, , 2019); Chib, S., Greenberg, E., Understanding the metropolishastings algorithm (1995) The American Statistician, 49 (4), pp. 327-335. , 1995; Devin, S., Alami, R., An implemented theory of mind to improve human-robot shared plans execution (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 319-326. , IEEE; Diederich, A., Dynamic stochastic models for decision making under time constraints (1997) Journal of Mathematical Psychology, 41 (3), pp. 260-274. , 1997; Dragan, A.D., Lee, K.C., Srinivasa, S.S., Legibility and predictability of robot motion (2013) Proceedings of the 8th ACM/IEEE International Conference on Human-robot Interaction, pp. 301-308. , IEEE Press; Dragan, A.D., Srinivasa, S.S., (2012) Formalizing Assistive Teleoperation, , MIT Press, July; Moen Eilertsen, E., (2014) Cumulative Prospect Theory and Decision Making under Time Pressure, , Master's thesis; Evans, O., Stuhlmüller, A., Goodman, N., Learning the preferences of ignorant, inconsistent agents (2016) Thirtieth AAAI Conference on Artificial Intelligence; Finn, C., Levine, S., Abbeel, P., Guided cost learning: Deep inverse optimal control via policy optimization (2016) International Conference on Machine Learning, pp. 49-58; Gray, A., Gao, Y., Karl Hedrick, J., Borrelli, F., Robust predictive control for semi-autonomous vehicles with an uncertain driver model (2013) 2013 IEEE Intelligent Vehicles Symposium (IV), pp. 208-213. , IEEE; Halpern, J.Y., Pass, R., Seeman, L., Decision theory with resource-bounded agents (2014) Topics in Cognitive Science, 6 (2), pp. 245-257. , 2014; Kahneman, D., Tversky, A., Prospect theory: An analysis of decision under risk (2013) Handbook of the Fundamentals of Financial Decision Making: Part I. 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AAAI, pp. 1433-1438; Ziebart, B.D., Ratliff, N., Gallagher, G., Mertz, C., Peterson, K., Andrew Bagnell, J., Hebert, M., Srinivasa, S., Planning-based prediction for pedestrians (2009) 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3931-3936. , IEEE",,,"ACM SIGAI;ACM SIGCHI;IEEE Robotics and Automation Society( IEEE RAS)","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158224,21672148,9781450367462,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85082002826
"Palipana S., Malm N., Sigg S.","55840892500;56367199300;17435716100;","Beamsteering for Training-free Counting of Multiple Humans Performing Distinct Activities",2020,"18th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2020",,,"9127374","","",,1,"10.1109/PerCom45495.2020.9127374","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088706318&doi=10.1109%2fPerCom45495.2020.9127374&partnerID=40&md5=eae02e7d0e0f17fc739a0c8b76a0db0a","Aalto University, Department of Communications and Networking, Espoo, Finland","Palipana, S., Aalto University, Department of Communications and Networking, Espoo, Finland; Malm, N., Aalto University, Department of Communications and Networking, Espoo, Finland; Sigg, S., Aalto University, Department of Communications and Networking, Espoo, Finland","Recognition of the context of humans plays an important role in pervasive applications such as intrusion detection, human density estimation for heating, ventilation and air-conditioning in smart buildings, as well as safety guarantee for workers during human-robot interaction. Radio vision is able to provide these sensing capabilities with low privacy intrusion. A common challenge though, for current radio sensing solutions is to distinguish simultaneous movement from multiple subjects. We present an approach that exploits antenna installations, for instance, found in upcoming 5G technology, to detect and extract activities from spatially scattered human targets in an ad-hoc manner in arbitrary environments and without prior training of the multi-subject detection. We perform receiver-side beamforming and beam-sweeping over different azimuth angles to detect human presence in those regions separately. We characterize the resultant fluctuations in the spatial streams due to human influence using a case study and make the traces publicly available. We demonstrate the potential of this approach through two applications: 1) By feeding the similarities of the resulting spatial streams into a clustering algorithm, we count the humans in a given area without prior training. (up to 6 people in a 22.4 m2 area with an accuracy that significantly exceeds the related work). 2) We demonstrate that simultaneously conducted activities and gestures can be extracted from the spatial streams through blind source separation. © 2020 IEEE.","Beamsteering; Multi-subject recognition; Radio sensing; Training-free crowd counting","5G mobile communication systems; Air conditioning; Antennas; Blind source separation; Clustering algorithms; Intrusion detection; Ubiquitous computing; Antenna installation; Arbitrary environment; Azimuth angles; Human influences; Pervasive applications; Privacy intrusion; Radio sensing; Safety guarantees; Social robots",,,,,,"The authors would like to thank Sanaz Kianoush at IEIIT,Italy for helping with the experiments. This work has been funded by CHIST-ERA III, Radiosense project.",,,,,,,,,,"Wang, J., E-HIPA: An energy-efficient framework for highprecision multi-target-adaptive device-free localization (2016) IEEE Trans. on Mobile Comput, 16 (3), pp. 716-729; Adib, F., Smart homes that monitor breathing and heart rate (2015) Proc. of CHI; Zou, H., Freecount: Device-free crowd counting with commodity wifi (2017) Proc. of GLOBECOMM. IEEE; Sigg, S., Rf-based device-free recognition of simultaneously conducted activities (2013) Proc. of Percom (Adjunct). ACM; Bondi, E., Real-time people counting from depth imagery of crowded environments (2014) Proc. of AVSS. IEEE; Wang, T., (2012) Learning to Do More with Less: Adapting Campus Security for Lean Times, , https://www.asmag.com/showpost/12628.aspx; Cetinkaya, H.H., Akcay, M., People counting at campuses (2015) Procedia-Social and Behavioral Sciences, 182, pp. 732-736; (2016) Int. 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Areas Commun, 33 (7), pp. 1381-1393; Batista, G., A complexity-invariant distance measure for time series (2011) Proc. of the Int. Conf. on Data Mining. SIAM; Paparrizos, J., Gravano, L., K-shape: Efficient and accurate clustering of time series (2015) Proc. of SIGMOD. ACM; Keogh, E.J., Pazzani, M.J., Derivative dynamic time warping (2001) Proc. of the Int. Conf. on Data Mining. SIAM; Sakoe, H., Chiba, S., Dynamic programming algorithm optimization for spoken word recognition (1990) Readings in Speech Recognition, 159, p. 224; Sharma, N., Comparison the various clustering algorithms of weka tools (2012) Facilities, 4 (7), pp. 78-80; Sokal, R.R., A statistical method for evaluating systematic relationship (1958) University of Kansas Science Bulletin, 28, pp. 1409-1438; Salvador, S., Chan, P., Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms (2004) Proc. of the Int. Conf. on Tools with Artificial Intelligence. IEEE; Hyvarinen, A., Oja, E., Independent component analysis: Algorithms and applications (2000) Neural Networks, 13 (4-5), pp. 411-430; Cardoso, J.-F., Souloumiac, A., Blind beamforming for non-Gaussian signals (1993) Proc. Radar and Signal Process, 140 (6), pp. 362-370. , IET; Bro, R., Smilde, A.K., Principal component analysis (2014) Analytical Methods, 6 (9), pp. 2812-2831; Malm, N., User localization enabled ultra-dense network testbed (2018) Proc. of 5GWF. IEEE",,,,"Institute of Electrical and Electronics Engineers Inc.","18th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2020","23 March 2020 through 27 March 2020",,161543,,9781728146577,,,"English","Annu. IEEE Int. Conf. Pervasive Comput. Commun., PerCom",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85088706318
"Fiorini L., Mancioppi G., Semeraro F., Fujita H., Cavallo F.","56902334800;57205157293;57196939609;35611951900;14628324200;","Unsupervised emotional state classification through physiological parameters for social robotics applications",2020,"Knowledge-Based Systems","190",,"105217","","",,18,"10.1016/j.knosys.2019.105217","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075366797&doi=10.1016%2fj.knosys.2019.105217&partnerID=40&md5=732dded336e8b20236b49ef2d94fd810","The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, Pontedera (PI), Italy; Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Viet Nam; Faculty of Software and Information Science, Iwate Prefectural University (IPU), Iwate, Japan","Fiorini, L., The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, Pontedera (PI), Italy; Mancioppi, G., The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, Pontedera (PI), Italy; Semeraro, F., The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, Pontedera (PI), Italy; Fujita, H., Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Viet Nam, Faculty of Software and Information Science, Iwate Prefectural University (IPU), Iwate, Japan; Cavallo, F., The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, Pontedera (PI), Italy","Future social robots should have personalized behaviors based on user emotional state to fit more in ordinary users’ activities and to improve the human–robot interaction. Several, literature works use cameras to record emotions. However, these approaches may not be effective in everyday life, due to camera obstructions and different types of stimulation, which can be related also with the interaction with other human beings. Therefore, in this work, it is investigated the electrocardiogram, the electrodermal activity, and the electric brain activity physiological signals as main informative channels. The aforementioned signals have been acquired through the use of a wireless wearable sensor network. An experimental methodology was proposed to induce three different emotional states by means of social interaction. Two different combinations of sensors were analyzed using three different time-window frames (180s, 150s, and 120s) and classified with three unsupervised machine learning approaches (K-Means, K-medoids and Self-organizing maps). Finally, their classification performances were compared to the ones obtained by four commonly used supervised techniques (i.e. Support Vector Machine, Decision Tree and k-nearest neighbor) to discuss the optimal combination of sensors, time-window length, and unsupervised classifier. Fifteen healthy young participants were recruited in the study and more than 100 instances were analyzed. The proposed approaches achieve an accuracy of 77% in the best-unsupervised case and 85% with the best-supervised ones. © 2019 Elsevier B.V.","Cluster analysis; Emotional state detection; Physiological signals; Pleasure; Social robotics","Behavioral research; Biomedical signal processing; Brain; Cameras; Cluster analysis; Conformal mapping; Decision trees; Nearest neighbor search; Physiological models; Robotics; Self organizing maps; Support vector machines; Wearable sensors; Wireless sensor networks; Classification performance; Emotional state; Experimental methodology; Physiological parameters; Physiological signals; Pleasure; Social robotics; Unsupervised machine learning; Human robot interaction",,,,,"Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR: ARS01_01120","Authors would like to thank the people involved in the experiment for their precious support. This work was founded by“SocIal ROBOTics for active and healthy ageing” (SI-ROBOTICS) project founded by the Italian “Ministero dell’Istruzione, dell’Università e della Ricerca” under the framework “PON - Ricerca e Innovazione 2014–2020” , Grant Agreement ARS01_01120 .","Authors would like to thank the people involved in the experiment for their precious support. This work was founded by?SocIal ROBOTics for active and healthy ageing? (SI-ROBOTICS) project founded by the Italian ?Ministero dell'Istruzione, dell'Universit? e della Ricerca? under the framework ?PON - Ricerca e Innovazione 2014?2020?, Grant Agreement ARS01_01120.",,,,,,,,,"Wykowska, A., Chaminade, T., Cheng, G., Embodied artificial agents for understanding human social cognition (2016) Philos. Trans. R. Soc. B Biol. Sci., 371; Horstmann, A.C., Krämer, N.C., Great expectations? 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Eng., 9294; Patel, S., Sherrill, D., Hughes, R., Hester, T., Huggins, N., Lie-Nemeth, T., (2006), pp. 123-126. , Analysis of the severity of dyskinesia in Patients with Parkinson's disease via wearable sensors, in: Proc - BSN 2006 Int Work Wearable Implant Body Sens Networks 2006; Bullinaria, J.A., Self organizing maps: fundamentals (2004) Introd to Neural; Jain, A.K., Dubes, R.C., Algorithms for Clustering Data (1988); Li, F., Dustdar, S., Incorporating unsupervised learning in activity recognition (2011) Act Context Represent; Allen, J., Photoplethysmography and its application in clinical physiological measurement (2007) Physiol. Meas., 28, pp. 1-39; Rhee, S., Yang, B.H., Asada, H.H., Artifact-resistant power-efficient design of finger-ring plethysmographic sensors (2001) IEEE Trans. Biomed. Eng., 48, pp. 795-805; Wood, L.B., Asada, H.H., Noise cancellation model validation for reduced motion artifact wearable PPG sensors using MEMS accelerometers (2006), pp. 3525-3528. , Annu Int Conf IEEE Eng Med Biol - Proc; Clabaugh, C., Matarić, M., Robots for the people, by the people: Personalizing human-machine interaction (2018) Sci. Robot, 3; Rincon, J.A., Costa, A., Novais, P., Julian, V., Carrascosa, C., A new emotional robot assistant that facilitates human interaction and persuasion (2018) Knowl. Inf. Syst., 60, pp. 1-21","Fiorini, L.; The BioRobotics Institute, Viale Rinaldo Piaggio 34, Italy; email: laura.fiorini@santannapisa.it",,,"Elsevier B.V.",,,,,09507051,,KNSYE,,"English","Knowl Based Syst",Article,"Final","",Scopus,2-s2.0-85075366797
"Hagens M., Thill S.","57215547747;36624312800;","How much information does a robot need? Exploring the benefits of increased sensory range in a simulated crowd navigation task",2020,"Information (Switzerland)","11","2","112","","",,,"10.3390/info11020112","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081115088&doi=10.3390%2finfo11020112&partnerID=40&md5=1ccd8950b35a2abfebf5fc51e8252b65","Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Nijmegen, 6525 XZ, Netherlands","Hagens, M., Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Nijmegen, 6525 XZ, Netherlands; Thill, S., Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Nijmegen, 6525 XZ, Netherlands","Perfect information about an environment allows a robot to plan its actions optimally, but often requires significant investments into sensors and possibly infrastructure. In applications relevant to human-robot interaction, the environment is by definition dynamic and events close to the robot may be more relevant than distal ones. This suggests a non-trivial relationship between sensory sophistication on one hand, and task performance on the other. In this paper, we investigate this relationship in a simulated crowd navigation task. We use three different environments with unique characteristics that a crowd navigating robot might encounter and explore how the robot's sensor range correlates with performance in the navigation task. We find diminishing returns of increased range in our particular case, suggesting that task performance and sensory sophistication might follow non-trivial relationships and that increased sophistication on the sensor side does not necessarily equal a corresponding increase in performance. Although this result is a simple proof of concept, it illustrates the benefit of exploring the consequences of different hardware designs-rather than merely algorithmic choices-in simulation first. We also find surprisingly good performance in the navigation task, including a low number of collisions with simulated human agents, using a relatively simple A/NavMesh-based navigation strategy, which suggests that navigation strategies for robots in crowds need not always be sophisticated. © 2020 by the authors.","Crowd navigation; Sensors; Simulation","Investments; Navigation; Sensors; Hardware design; Human agent; Navigation strategies; Navigation tasks; Perfect informations; Proof of concept; Simulation; Task performance; Human robot interaction",,,,,,,,,,,,,,,,"Cabibihan, J.J., Javed, H., Ang, M., Aljunied, S.M., Why Robots? A Survey on the Roles and Benefits of Social Robots in the Therapy of Children with Autism (2013) Int. J. Soc. 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Med Syst, 40, p. 121; Shiomi, M., Iio, T., Kamei, K., Sharma, C., Hagita, N., Effectiveness of Social Behaviors for Autonomous Wheelchair Robot to Support Elderly People in Japan (2015) PLOS ONE, 10, pp. 1-15; Joosse, M., Evers, V., A Guide Robot at the Airport: First Impressions (2017) In Proceedings of the Companion of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 149-150. , Vienna, Austria, 6-9 March; Mavrogiannis, C., Hutchinson, A.M., Macdonald, J., Alves-Oliveira, P., Knepper, R.A., Effects of Distinct Robot Navigation Strategies on Human Behavior in a Crowded Environment (2019) In Proceeding of the 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Daegu, Korea, pp. 421-430. , 11-14 March; Pourmehr, S., Thomas, J., Bruce, J., Wawerla, J., Vaughan, R., Robust sensor fusion for finding HRI partners in a crowd (2017) IEEE International Conference on Robotics and Automation (ICRA), pp. 3272-3278. , Singapore, Singapore, 29 May-3 June 2017; Petrillo, A., Salvi, A., Santini, S., Valente, A.S., Adaptive multi-agents synchronization for collaborative driving of autonomous vehicles with multiple communication delays (2018) Transp. 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Technol, 4, pp. 141-149; Mainprice, J., Berenson, D., Human-robot collaborative manipulation planning using early prediction of human motion (2013) In Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots andSystems, pp. 299-306. , Tokyo, Japan, 3-7 Novermber; Brscic, D., Kidokoro, H., Suehiro, Y., Kanda, T., Escaping from Children's Abuse of Social Robots (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 59-66. , Portland, OR, USA, 2-5 March; Ha, D., Schmidhuber, J., (2018), http://xxx.lanl.gov/abs/1803.10122, (accessed on 17 Feburary 2020); Zhu, Y., Mottaghi, R., Kolve, E., Lim, J.J., Gupta, A., Fei-Fei, L., Farhadi, A., Target-driven visual navigation in indoor scenes using deep reinforcement learning (2017) Proceeeding of the 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 3357-3364. , 29 May-3 June; Bousmalis, K., Irpan, A., Wohlhart, P., Bai, Y., Kelcey, M., Kalakrishnan, M., Downs, L., Vanhoucke, V., Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping (2018) Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 4243-4250. , Brisbane, Australia, 21-25 May","Thill, S.; Donders Institute for Brain, Netherlands; email: s.thill@donders.ru.nl",,,"MDPI AG",,,,,20782489,,,,"English","Information",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85081115088
"Cremer S., Das S.K., Wijayasinghe I.B., Popa D.O., Lewis F.L.","56383673500;56383202800;36679469200;7102861346;24729085600;","Model-Free Online Neuroadaptive Controller with Intent Estimation for Physical Human-Robot Interaction",2020,"IEEE Transactions on Robotics","36","1","8884133","240","253",,5,"10.1109/TRO.2019.2946721","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079664872&doi=10.1109%2fTRO.2019.2946721&partnerID=40&md5=d54fda20330cb8392fd17c0cee575bc5","Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX  76019, United States; Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY  40292, United States","Cremer, S., Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX  76019, United States; Das, S.K., Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY  40292, United States; Wijayasinghe, I.B., Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY  40292, United States; Popa, D.O., Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY  40292, United States; Lewis, F.L., Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX  76019, United States","With the rise of collaborative robots, the need for safe, reliable, and efficient physical human-robot interaction (pHRI) has grown. High-performance pHRI requires robust and stable controllers suitable for multiple degrees of freedom (DoF) and highly nonlinear robots. In this article, we describe a cascade-loop pHRI controller, which relies on human force and pose measurements and can adapt to varying robot dynamics online. It can also adapt to different users and simplifies the interaction by making the robot behave according to a prescribed dynamic model. In our controller formulation, two neural networks (NNs) in the 'outer-loop' predict human motion intent and estimate a reference trajectory for the robot that the 'inner-loop' controller follows. The inner-loop imposes a prescribed error dynamics (PED) with the help of a model-free neuroadaptive controller (NAC), which uses a NN to feedback linearize the robot dynamics. Lyapunov stability analysis gives weight tuning laws that guarantee that the error signals are bounded and the desired reference trajectory is achieved. Our control scheme was implemented on a Personal Robot 2 robot and validated through an exploratory experimental study in point-to-point collaborative motion. Results indicate fast convergence of our controller, and the resulting tracking error, motion jerk, and human control effort are comparable with other methods that require prior training, knowledge, and calibration. © 2004-2012 IEEE.","Human intent estimation; neuroadaptive control; physical human-robot interaction (pHRI)","Controllers; Degrees of freedom (mechanics); Dynamics; Errors; Man machine systems; Motion tracking; Navigation; Lyapunov stability analysis; Multiple degrees of freedom; Neural networks (NNS); Neuro-adaptive control; Neuro-adaptive controllers; Physical human-robot interactions; Physical humanrobot interaction (phri); Reference trajectories; Human robot interaction",,,,,"1643989; National Science Foundation, NSF: 1208623","Manuscript received September 22, 2018; accepted October 5, 2019. Date of publication October 28, 2019; date of current version February 4, 2020. This work was supported by U.S. National Science Foundation Grant 1208623 and Grant 1643989. This article was recommended for publication by Associate Editor Prof. R. Carloni and Editor Prof. A. Billard upon evaluation of the reviewers’ comments. (Corresponding author: Sven Cremer.) S. Cremer and F. L. Lewis are with the Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX 76019 USA (e-mail: sven.cremer@mavs.uta.edu; lewis@uta.edu).",,,,,,,,,,"Erden, M.S., Marić, B., Assisting manual welding with robot (2011) Robot. And Comput.-Integr. Manuf., 27 (4), pp. 818-828; Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P., Collaborative manufacturing with physical human-robot interaction (2016) Robot. Comput.-Integr. Manuf., 40, pp. 1-13; Safavi, A., Zadeh, M.H., Model-based haptic guidance in surgical skill improvement (2015) Proc. IEEE Int. Conf. Syst., Man, Cybern., pp. 1104-1109. , Oct; Argall, B.D., Chernova, S., Veloso, M., Browning, B., A survey of robot learning from demonstration (2009) Robot. Auton. Syst., 57 (5), pp. 469-483; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Trans. Robot., 31 (5), pp. 1089-1100. , Oct; De Santis, A., Siciliano, B., De Luca, A., Bicchi, A., An atlas of physical human-robot interaction (2008) Mechanism Mach. Theory, 43 (3), pp. 253-270. , Mar; Argall, B.D., Billard, A.G., A survey of tactile human-robot interactions (2010) Robot. Auton. Syst., 58 (10), pp. 1159-1176; Shin, D., Sardellitti, I., Park, Y.-L., Khatib, O., Cutkosky, M., Design and control of a bio-inspired human-friendly robot (2009) Experimental Robotics Springer Tracts in Advanced Robotics, 54, pp. 43-52. , O. Khatib, V. Kumar, and G. Pappas, Eds. Berlin, Germany: Springer; Pervez, A., Ryu, J., Safe physical human robot interaction-past, present and future (2008) J. Mech. Sci. Technol., 22 (3), pp. 469-483. , May; Lewis, F.L., Dawson, D.M., Abdallah, C.T., (2004) Robot Manipulator Control: Theory and Practice, p. 638. , Boca Raton, FL, USA: CRC Press; Gribovskaya, E., Kheddar, A., Billard, A., Motion learning and adaptive impedance for robot control during physical interaction with humans (2011) Proc. IEEE Int. Conf. Robot. Autom., pp. 4326-4332. , May; Suzuki, S., Furuta, K., Adaptive impedance control to enhance human skill on a haptic interface system (2012) J. Control Sci. Eng., 2012, pp. 1-10; Li, Y., Sam Ge, S., Impedance learning for robots interacting with unknownenvironments (2014) IEEE Trans.Control Syst. Technol., 22 (4), pp. 1422-1432. , Jul; Wang, Z., Peer, A., Buss, M., An HMM approach to realistic haptic human-robot interaction (2009) Proc. 3rd Joint EuroHaptics Conf. Symp. Haptic Interfaces Virtual Environ. Teleoperator Syst., pp. 374-379. , World Haptics, Mar; Futamure, S., Bonnet, V., Dumas, R., Kulic, D., Venture, G., Dynamically consistent inverse kinematics framework using optimizations for human motion analysis (2016) Proc. IEEE-RAS 16th Int. Conf. Humanoid Robots, pp. 436-441. , Nov; Medina, J.R., Endo, S., Hirche, S., Impedance-based Gaussian processes for predicting human behavior during physical interaction (2016) Proc. IEEE Int. Conf. Robot. Autom., pp. 3055-3061. , May; Franklin, D.W., CNS learns stable, accurate, and efficient movements using a simple algorithm (2008) J. Neurosci., 28 (44), pp. 11165-11173; Ragazzini, J., Engineering aspects of the human being as a servomechanism (1948) Proc.Meet. Am. 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Conf. Autom. Sci. Eng., pp. 322-327; Ranatunga, I., Cremer, S., Popa, D.O., Lewis, F.L., Intent aware adaptive admittance control for physical Human-Robot Interaction (2015) Proc. IEEE Int. Conf. Robot. Autom., pp. 5635-5640; Ranatunga, I., Lewis, F.L., Popa, D.O., Tousif, S.M., Adaptive admittance control for human-robot interaction using model reference design and adaptive inverse filtering (2017) IEEE Trans. Control Syst. Technol., 25 (1), pp. 278-285. , Jan; Hogan, N., Sternad, D., Sensitivity of smoothness measures to movement duration, amplitude, and arrests (2009) J. Motor Behav., 41 (6), pp. 529-534. , Nov; McRuer, D.T., Krendel, E.S., (1974) Mathematical Models of Human Pilot Behavior, , Advisory Group for Aerospace Research and Development, Tech. Rep. AGARD-AG-188, Neuilly-sure-Seine, France; ANSI/RIA R15.06-2012 American National Standard for industrial robots and robot systems-safety requirements (2012) Robot. Ind. Assoc., , R. I. Association et al., Ann Arbor, MI, USA; Li, Y., Sam Ge, S., Yang, C., Learning impedance control for physical robot-environment interaction (2012) Int. J. Control, 85 (2), pp. 182-193. , Feb; Quigley, M., ROS: An open-source robot operating system (2009) Proc. ICRA Workshop Open Source Softw., pp. 1-5; Guennebaud, G., (2010) Eigen v3, , http://eigen.tuxfamily.org, [Online]; Cremer, S., Ranatunga, I., Popa, D.O., (2017) SkinLearn, , https://bitbucket.org/nextgensystems/skinlearn, [Online]; Breazeal, C., Scassellati, B., Challenges in building robots that imitate people (2002) Imitation in Animals and Artifacts., pp. 363-390. , Cambridge, MA, USA: MIT Press","Cremer, S.; Department of Electrical Engineering, United States; email: sven.cremer@mavs.uta.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15523098,,,,"English","IEEE Trans. Rob.",Article,"Final","",Scopus,2-s2.0-85079664872
"Gong L., Sun T., Li X., Lin K., Diáz-Rodríguez N., Filliat D., Zhang Z., Zhang J.","36637488200;57219691816;57195481734;57203803472;55904010200;55976331500;57216298888;57193875086;","Demonstration Guided Actor-Critic Deep Reinforcement Learning for Fast Teaching of Robots in Dynamic Environments",2020,"IFAC-PapersOnLine","53","5",,"271","278",,,"10.1016/j.ifacol.2021.04.227","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107866542&doi=10.1016%2fj.ifacol.2021.04.227&partnerID=40&md5=a8d8b14265f8f0381aa9098c8b21d147","School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Computer Science and System Engineering Department and Inria Flowers Team, Ensta, Institut Polytechnique Paris, Palaiseau, France; Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China","Gong, L., School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Sun, T., School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Li, X., School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Lin, K., School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Diáz-Rodríguez, N., Computer Science and System Engineering Department and Inria Flowers Team, Ensta, Institut Polytechnique Paris, Palaiseau, France; Filliat, D., Computer Science and System Engineering Department and Inria Flowers Team, Ensta, Institut Polytechnique Paris, Palaiseau, France; Zhang, Z., Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China; Zhang, J., Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China","Using direct reinforcement learning (RL) to accomplish a task can be very inefficient, especially in robotic configurations where interactions with the environment are lengthy and costly. Instead, learning from expert demonstration (LfD) is an alternative approach to gain better performance in an RL setting, which also greatly improves sample efficiency. We propose a novel demonstration learning framework for actor-critic based algorithms. Firstly, we put forward an environment pre-training paradigm to initialize the model parameters without interacting with the target environment, which effectively avoids the cold start problem in deep RL scenarios. Secondly, we design a general-purpose LfD framework for most of the mainstream actor-critic RL algorithms that include a policy network and a value function like PPO, SAC, TRPO, A3C. Thirdly, we build a dedicated model training platform to perform the human-robot interaction and numerical experimentation. We evaluate the method in six Mujoco simulated locomotion environments and our robot control simulation platform. Results show that several epochs of pre-training can improve the agent's performance over the early stage of training. Also, the final converged performance of the RL algorithm is also boosted by external demonstration. In general the sample efficiency is improved by 30% with the proposed method. Our demonstration pipeline makes full use of the exploration property of the RL algorithm, which is feasible for fast teaching robots in dynamic environments. ©2020 The Authors.This is an open access article under the CC BY-NC-ND license.","actor-critic framework; deep learning; deep reinforcement learning; learning from demonstration (LfD); robotics","Demonstrations; Educational robots; Efficiency; Human robot interaction; Reinforcement learning; Cold start problems; Demonstration learning; Dynamic environments; Model parameters; Numerical experimentations; Policy networks; Teaching robots; Value functions; Deep learning",,,,,"2018YFB1305104; 18391901000; National Natural Science Foundation of China, NSFC: 51775333","This work was supportefl by National Natural Science Founflation of China (Grant No.51775333), the Science Founflation of Shanghai Municipal Coııission of Science anfl Technology (Grant No.18391901000) as well as the National Key Research anfl Developıent Project (2018YFB1305104).",,,,,,,,,,"Argall, B.D., Chernova, S., Veloso, M., Browning, B., A survey of robot learning from demonstration (2009) Robotics and Autonomous Systems, 57 (5), pp. 469-483; Arulkumaran, K., Deisenroth, M.P., Brundage, M., Bharath, A.A., Deep reinforcement learning: A brief survey (2017) Ieee Signal Processing Magazine, 34 (6), pp. 26-38; Bellman, R., Dynamic programming (1966) Science, 153 (3731), pp. 34-37; Bennetot, A., Charisi, V., Diacuteaz-Rodriacuteguez, N., Should artificial agents ask for help in human-robot collaborative problem-solving? 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URL; Gong, L., Gong, C., Ma, Z., Zhao, L., Wang, Z., Li, X., Jing, X., Liu, C., Real-time human-in-the-loop remote control for a life-size traffic police robot with multiple augmented reality aided display terminals (2017) 2017 2nd International Conference on Advanced Robotics and Mechatronics (ICARM), pp. 420-425; Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Levine, S., (2018) Soft Actor-critic Algorithms and Applications, , http://arxiv.org/abs/1812.05905, CoRR, abs/1812.05905. URL; Hester, T., Veceri, A., Pietquin, O., Lanctot, M., Schaul, T., Piot, B., Sendonaris, A., Gruslys, A., (2017) Learning from Demonstrations for Real World Reinforcement Learning, , http://arxiv.org/abs/1704.03732, CoRR, abs/1704.03732. URL; Konda, V.R., Tsitsiklis, J.N., Actor-critic algorithms (2000) Advances in Neural Information Processing Systems, pp. 1008-1014; Konidaris, G., Kuindersma, S., Grupen, R., Barto, A., Robot learning from demonstration by constructing skill trees (2012) The International Journal of Robotics Research, 31 (3), pp. 360-375; Langevin, G., (2014) Inmoov. Open Source 3d Printed Robot Platform, , www.inmoov.fr/project; Lesort, T., Diáz-Rodríguez, N., Goudou, J.F., Filliat, D., State representation learning for control: An overview (2018) Neural Networks, 108, pp. 379-392. , https://doi.org/10.1016/j.neunet.2018.07.006; Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., Wierstra, D., Continuous control with deep reinforcement learning (2015) Computer Science, 8 (6), p. A187; Lin, K., Gong, L., Li, X., Sun, T., Chen, B., Liu, C., Zhang, Z., Zhang, J., (2020) Exploration-efficient Deep Reinforcement Learning with Demonstration Guidance for Robot Control, , arXiv preprint arXiv:2002.12089; Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lilli-Crap, T.P., Harley, T., Silver, D., Kavukcuoglu, K., (2016) Asynchronous Methods for Deep Reinforcement Learning, , http://arxiv.org/abs/1602.01783, CoRR, abs/1602.01783. URL; Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M., (2013) Playing Atari with Deep Reinforcement Learning; Nair, A., McGrew, B., Andrychowicz, M., Zaremba, W., Abbeel, P., (2017) Overcoming Exploration in Reinforcement Learning with Demonstrations, , http://arxiv.org/abs/1709.10089, CoRR, abs/1709.10089. URL; Nguyen, S.M., Baranes, A., Oudeyer, P.Y., (2011) Bootstrapping Intrinsically Motivated Learning with Human Demonstrations, , arXiv preprint arXiv:1112.1937; Parisi, S., Tateo, D., Hensel, M., D'Eramo, C., Peters, J., Pajarinen, J., (2020) Long-term Visitation Value for Deep Exploration in Sparse Reward Reinforcement Learning, , ArXiv, abs/2001.00119; Raffin, A., Hill, A., Traoreacute, R., Lesort, T., Diacuteaz-Rodriacuteguez, N., Filliat, D., (2018) S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning, , arXiv preprint arXiv:1809.09369; Safavi, A., Zadeh, M.H., Teaching the user by learning from the user: Personalizing movement control in physical human-robot interaction (2017) IEEE/CAA Journal of Automatica Sinica, 4 (4), pp. 704-713; Schaal, S., Learning from demonstration (1997) Advances in Neural Information Processing, Systems, pp. 1040-1046; Schulman, J., Moritz, P., Levine, S., Jordan, M., Abbeel, P., (2015) High-dimensional Continuous Control Using Generalized Advantage Estimation, , arXiv preprint arXiv:1506.02438; Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O., (2017) Proximal Policy Optimization Algorithms, , http://arxiv.org/abs/1707.06347, CoRR, abs/1707.06347. URL; Sutton, R.S., Barto, A.G., (1998) Introduction to Reinforcement Learning, 135. , MIT press Cambridge; Todorov, E., Erez, T., Tassa, Y., Mu-joco: A physics engine for model-based control (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5026-5033; Traoreacute, R., Caselles-Dupreacute, H., Lesort, T., Sun, T., Cai, G., Diacuteaz-Rodriacuteguez, N., Filliat, D., (2019) DISCORL: Continual Reinforcement Learning Via Policy Distillation, , arXiv preprint arXiv:1907.05855; Van Hasselt, H., Guez, A., Silver, D., (2015) Deep Reinforcement Learning with Double Q-learning, , http://arxiv.org/abs/1509.06461, CoRR, abs/1509.06461. URL; Vasan, G., Pilarski, P., Learning from demonstration: Teaching a myoelectric prosthesis with an intact limb via reinforcement learning (2017) IEEE, International Conference on Rehabilitation Robotics, pp. 1457-1464. , 2017; Veceriacutek, M., Hester, T., Scholz, J., Wang, F., Pietquin, O., Piot, B., Heess, N., Riedmiller, M.A., (2017) Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards, , http://arxiv.org/abs/1707.08817, CoRR, abs/1707.08817. URL; Watkins, C.J.C.H., Dayan, P., Q-learning (1992) Machine Learning, 8 (3-4), pp. 279-292; Xiang, G., Su, J., Task-oriented deep reinforcement learning for robotic skill acquisition and control (2019) Ieee Transactions on Cybernetics, PP, pp. 1-14. , 101109/TCYB.2019.2949596; Zhao, D., Xia, Z., Zhang, Q., Model-free optimal control based intelligent cruise control with hardware-in-the-loop demonstration [research frontier] (2017) Ieee Computational Intelligence Magazine, 12 (2), pp. 56-69; Zuo, S., Wang, Z., Zhu, X., Ou, Y., (2017) Continuous Reinforcement Learning from Human Demonstrations with Integrated Experience Replay for Autonomous Driving","Gong, L.; School of Mechanical Engineering, China; email: gongliang_mi@sjtu.edu.cn",,,"Elsevier B.V.","3rd IFAC Workshop on Cyber-Physical and Human Systems, CPHS 2020","3 December 2020 through 5 December 2020",,145888,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85107866542
"Castellini A., Marchesini E., Mazzi G., Farinelli A.","23476375900;57216753456;57221908245;8856607700;","Explaining the Influence of Prior Knowledge on POMCP Policies",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12520 LNAI",,,"261","276",,4,"10.1007/978-3-030-66412-1_17","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101205003&doi=10.1007%2f978-3-030-66412-1_17&partnerID=40&md5=134df3f9b3fcaa3502c40bd26336e89e","Department of Computer Science, Verona University, Strada Le Grazie 15, Verona, 37134, Italy","Castellini, A., Department of Computer Science, Verona University, Strada Le Grazie 15, Verona, 37134, Italy; Marchesini, E., Department of Computer Science, Verona University, Strada Le Grazie 15, Verona, 37134, Italy; Mazzi, G., Department of Computer Science, Verona University, Strada Le Grazie 15, Verona, 37134, Italy; Farinelli, A., Department of Computer Science, Verona University, Strada Le Grazie 15, Verona, 37134, Italy","Partially Observable Monte Carlo Planning is a recently proposed online planning algorithm which makes use of Monte Carlo Tree Search to solve Partially Observable Monte Carlo Decision Processes. This solver is very successful because of its capability to scale to large uncertain environments, a very important property for current real-world planning problems. In this work we propose three main contributions related to POMCP usage and interpretability. First, we introduce a new planning problem related to mobile robot collision avoidance in paths with uncertain segment difficulties, and we show how POMCP performance in this context can take advantage of prior knowledge about segment difficulty relationships. This problem has direct real-world applications, such as, safety management in industrial environments where human-robot interaction is a crucial issue. Then, we present an experimental analysis about the relationships between prior knowledge provided to the algorithm and performance improvement, showing that in our case study prior knowledge affects two main properties, namely, the distance between the belief and the real state, and the mutual information between segment difficulty and action taken in the segment. This analysis aims to improve POMCP explainability, following the line of recently proposed eXplainable AI and, in particular, eXplainable planning. Finally, we analyze results on a synthetic case study and show how the proposed measures can improve the understanding about internal planning mechanisms. © 2020, Springer Nature Switzerland AG.","Explainable artificial intelligence; eXplainable planning; Planning under uncertainty; POMCP; POMDP; XAI","Accident prevention; Human robot interaction; Industrial robots; Monte Carlo methods; Robot programming; Trees (mathematics); Decision process; Experimental analysis; Industrial environments; Monte-Carlo tree searches; Mutual informations; Real-world planning problem; Safety management; Uncertain environments; Multi agent systems",,,,,"Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR; Regione del Veneto","Acknowledgments. The research has been partially supported by the projects “Dipartimenti di Eccellenza 2018–2022, funded by the Italian Ministry of Education, Universities and Research (MIUR), and “GHOTEM/CORE-WOOD, POR-FESR 2014–2020”, funded by Regione del Veneto.",,,,,,,,,,"Amato, C., Oliehoek, F.A., Scalable planning and learning for multi-agent POMDPs (2015) Proceedings of AAAI 2015, pp. 1995-2002. , , pp. , AAAI Press; Bishop, C.M., (2006) Pattern Recognition and Machine Learning, , Springer-Verlag, New York; Boutilier, C., Dean, T., Hanks, S., Decision-theoretic Planning: Structural assumptions and computational leverage (1999) JAIR, 11, pp. 1-94; Browne, C., A survey of Monte Carlo tree search methods (2012) IEEE Trans. Comp. Intell. 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CoRR, abs/1908.05059; Castellini, A., Activity recognition for autonomous water drones based on unsupervised learning methods (2018) Proceedings of 4Th Italian Workshop on Artificial Intelligence and Robotics (AI*IA 2017), 2054, pp. 16-21. , , Vol., pp; Castellini, A., Subspace clustering for situation assessment in aquatic drones: A sensitivity analysis for state-model improvement (2019) Cybern. Syst., 50 (8), pp. 658-671; Castellini, A., Bicego, M., Masillo, F., Zuccotto, M., Farinelli, A., Time series segmentation for state-model generation of autonomous aquatic drones: A systematic framework (2020) Eng. Appl. Artif. Intel., 90; Castellini, A., Blum, J., Bloisi, D., Farinelli, A., Intelligent battery management for aquatic drones based on task difficulty driven POMDPs (2018) Proceedings of the 5Th Italian Workshop on Artificial Intelligence and Robotics, AIRO@AI*IA 2018, pp. 24-28. , , pp. , Trento, Italy; Castellini, A., Chalkiadakis, G., Farinelli, A., Influence of state-variable constraints on partially observable monte carlo planning (2019) Proceedings of 28Th International Joint Conference on Artificial Intelligence (IJCAI 2019), pp. 5540-5546. , , pp; Castellini, A., Masillo, F., Bicego, M., Bloisi, D., Blum, J., Farinelli, A., Peigner, S., Subspace clustering for situation assessment in aquatic drones (2019) Proceedings of Symposium on Applied Computing, SAC 2019, Pp. 930–937. ACM; Castellini, A., Masillo, F., Sartea, R., Farinelli, A., Data Analysis for Intelligent Agents (2019) Proceedings of the 18Th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), pp. 2342-2344. , eXplainable Modeling (XM), pp. , IFAAMAS; Chakraborti, T., Visualizations for an Explainable Planning Agent (2018) Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, Vol. 7, Pp. 5820–5822. International Joint Conferences on Artificial Intelligence Organization; Chakraborti, T., Sreedharan, S., Zhang, Y., Kambhampati, S., Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy (2017) Proc. 26Th Int. Joint Conference on Artificial Intelligence, IJCAI 2017, Pp. 156–163; Coulom, R.: Efficient selectivity and backup operators in Monte-Carlo tree search. In: van den Herik, H.J., Ciancarini, P., Donkers, H.H.L.M.J. (eds.) CG 2006. LNCS, vol. 4630, pp. 72–83. 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Intell., 101 (1-2), pp. 99-134; Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots (1985) Proceedings of 1985 IEEE International Conference on Robotics and Automation, 2, pp. 500-505. , , Vol., pp; Kocsis, L., Szepesvári, C.: Bandit based Monte-Carlo planning. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 282–293. Springer, Heidelberg (2006). https://doi.org/10.1007/11871842 29; Lee, J., Kim, G.-H., Poupart, P., Kim, K.-E., Monte-Carlo tree search for constrained POMDPs (2018) NIPS 2018, pp. 1-17. , , pp; Mahajan, D., Tan, C., Sharma, A., Preserving Causal Constraints in Counterfac-tual Explanations for (2019) Machine Learning Classifiers; Papadimitriou, C., Tsitsiklis, J., The complexity of Markov decision processes (1987) Math. Oper. Res., 12 (3), pp. 441-450; Potena, C., Nardi, D., Pretto, A., Joint vision-based navigation, control and obstacle avoidance for UAVs in dynamic environments (2019) European Conference on Mobile Robots (ECMR), pp. 1-7. , , pp. , 2019; Ross, S., Pineau, J., Paquet, S., Chaib-Draa, B., Online planning algorithms for POMDPs (2008) JAIR, 32, pp. 663-704; Russell, S., Norvig, P., (2003) Artificial Intelligence: a Modern Approach, , 2nd edn. Pearson Education, London; Silver, D., Huang, A., Maddison, C.J., Mastering the game of go with deep neural networks and tree search (2016) Nature, 529 (7587), pp. 484-489; Silver, D., Schrittwieser, J., Simonyan, K., Mastering the game of go without human knowledge (2017) Nature, 550, pp. 354-359; Silver, D., Veness, J., Monte-Carlo planning in large POMDPs (2010) NIPS 2010, pp. 2164-2172. , , pp; Smith, D.E., Planning as an iterative process (2012) Proceedings of Twenty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2012, Pp. 2180–2185. AAAI Press; Sreedharan, S., Chakraborti, T., Kambhampati, S., Handling model uncertainty and multiplicity in explanations via model reconciliation (2018) Proceedings of International Conference on Automated Planning and Scheduling, ICAPS 2018, Pp. 518–526; Steccanella, L., Bloisi, D.D., Castellini, A., Farinelli, A., Waterline and obstacle detection in images from low-cost autonomous boats for environmental monitoring (2020) Robot. Autonom. Syst., 124; Lorenz, U.: Leitbilder in der Künstlichen Intelligenz. Reinforcement Learning, pp. 161–170. Springer, Heidelberg (2020). https://doi.org/10.1007/978-3-662-61651-2 6; Vanzo, A., Croce, D., Bastianelli, E., Basili, R., Nardi, D., Grounded language interpretation of robotic commands through structured learning (2020) Artif. Intell., 278; Zhang, Y., Sreedharan, S., Kulkarni, A., Chakraborti, T., Zhuo, H.H., Kambhampati, S., Plan explicability and predictability for robot task planning (2017) IEEE International Conferencce on Robotics and Automation (ICRA 2017), pp. 1313-1320. , , pp","Castellini, A.; Department of Computer Science, Strada Le Grazie 15, Italy; email: alberto.castellini@univr.it","Bassiliades N.Chalkiadakis G.de Jonge D.",,"Springer Science and Business Media Deutschland GmbH","17th European Conference on Multi-Agent Systems, EUMAS 2020, and 7th International Conference on Agreement Technologies, AT 2020","14 September 2020 through 15 September 2020",,254429,03029743,9783030664114,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85101205003
"Johansen J.J., Jensen L.G., Bemman B.","57218491860;57218491987;56989782300;","Evaluating interactions with a cognitively biased robot in a creative collaborative task",2020,"Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST","328 LNICST",,,"138","157",,1,"10.1007/978-3-030-53294-9_10","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089316488&doi=10.1007%2f978-3-030-53294-9_10&partnerID=40&md5=a6a0e25f0caf13a242dc6929117fb2dc","Aalborg University, Aalborg, 9000, Denmark","Johansen, J.J., Aalborg University, Aalborg, 9000, Denmark; Jensen, L.G., Aalborg University, Aalborg, 9000, Denmark; Bemman, B., Aalborg University, Aalborg, 9000, Denmark","Within the field of human-robot interaction (HRI), robots designed for social interactions are not only evaluated in terms of efficiency and accuracy. Factors related to the “personality” or “cognitive” ability of the robot such as perceived likability and intelligence are important considerations because they must engage with their human counterparts in deeper, more authentic and sometimes creative ways. Interactive art allows for the exploration of such interactions, however, the study of robots in interactive art remains relatively less commonplace and evaluations of these robots in creative contexts are similarly lacking. In this paper, we present an interactive robot inspired by Norman White’s The Helpless Robot (1987), which has been endowed with a cognitive bias known as the Dunning-Kruger effect and the ability to collaborate with participants in a creative drawing task. We evaluate the participants’ interactions with both biased and unbiased versions of this robot using the Godspeed Questionnaire Series (GQS), which has been modified to include measures of creativity, and relate these findings to analyses of their collaborative drawings. Our results indicate a significant difference between the versions of the robot for several measures in the GQS, with the unbiased version rated more positively than the biased robot in all cases. Analysis of the drawings suggests that participants interacting with the biased robot were less inclined to collaborate in a cooperative manner. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020.","Cognitive bias; Creative collaboration; Godspeed Questionnaire Series; Helpless Robot; Human-robot interaction; Interactive art","Intelligent robots; Cognitive bias; Collaborative drawing; Collaborative tasks; Human robot Interaction (HRI); Interactive arts; Interactive robot; Measures of creativities; Social interactions; Social robots",,,,,,,,,,,,,,,,"https://www.arduino.cc/en/main/documentation, Arduino Documentation. Accessed 13 July 2019; Bartneck, C., Kanda, T., Ishiguro, H., Hagita, N., Is the uncanny valley an uncanny cliff? (2007) RO-MAN 2007-The 16th IEEE International Symposium on Robot and Human Interactive Communication, Jeju, Korea, pp. 368-373; Bartneck, C., Kulic, D., Croft, E., Measuring the anthropomorhism, animacy, like-ability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot, 1, pp. 71-81; Biswas, M., Murray, J., Towards an imperfect robot for long-term companionship: case studies using cognitive biases (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, pp. 5978-5983. , IEEE; Biswas, M., Murray, J., The effects of cognitive biases and imperfectness in long-term robot-human interactions: case studies using five cognitive biases on three robots (2017) Cogn. Syst. Res, 43, pp. 266-290; Chang, S., Sung, H., The effectiveness of seal-like robot therapy on mood and social interactions of older adults: a systematic review protocol (2013) JBI Database Syst. Rev. Implement. Rep, 10, pp. 68-75; Demers, L.P., The Blind Robot, , http://www.robotsandavatars.net/exhibition/jurysselection/commissions/the-blind-robot/, Accessed 9 July 2019; Glynn, R., Motive Colloquies, , http://www.ruairiglynn.co.uk/portfolio/motive-colloquies-2011/, Accessed 9 July 2019; Ham, J., Midden, C.J.H., A persuasive robot to stimulate energy conservation: the influence of positive and negative social feedback and task similarity on energy-consumption behavior (2014) Int. J. Soc. Robot, 6 (2), pp. 163-171; Ham, J., Cuijpers, R.H., Cabibihan, J., Combining robotic persuasive strategies: the persuasive power of a storytelling robot that uses gazing and gestures (2015) Int. J. Soc. Robot, 7 (4), pp. 479-487; Hayes, B., Ullman, D., Alexander, E., Bank, C., Scassellati, B., People help robots who help others, not robots who help themselves (2014) The 23rd IEEE International Symposium on Robot and Human Interactive Communication, Edinburgh, Scotland, UK, pp. 255-260. , IEEE; Hinwood, D., Ireland, J., Jochum, E.A., Herath, D., A proposed wizard of OZ architecture for a human-robot collaborative drawing task (2018) ICSR 2018. LNCS (LNAI), 11357, pp. 35-44. , https://doi.org/10.1007/978-3-030-05204-14, Ge, S.S., et al. (eds) Springer, Cham; Konok, V., Korcsok, B., Miklósi, Á., Gácsi, M., Should we love robots?-the most liked qualities of companion dogs and how they can be implemented in social robots (2018) Comput. Hum. Behav, 80, pp. 132-142; Leite, I., Pereira, A., Mascarenhas, S., Martinho, C., Prada, R., Paiva, A., The influence of empathy in human-robot relations (2013) Int. J. Hum. Comput. Stud, 71, pp. 250-260; Milthers, A.D.B., Bjerre Hammer, A., Jung Johansen, J., Jensen, L.G., Jochum, E.A., Löchtefeld, M., The helpless soft robot-stimulating human collaboration through robotic movement (2019) Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, CHI EA 2019, , LBW2421:1 LBW2421:6, Association for Computing Machinery, New York; Obo, T., Kasuya, C., Sun, S., Kubota, N., Human-robot interaction based on cognitive bias to increase motivation for daily exercise (2017) 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, pp. 2945-2950. , IEEE; https://processing.org/reference/, Processing Language Reference (API). Accessed 13 July 2019; Vorn, B., DSM-VI, , https://billvorn.concordia.ca/robography/DSM.html, Accessed 9 July 2019; Vorn, B., Hysterical machine, , http://billvorn.concordia.ca/robography/Hysterical.html, Accessed 9 July 2019; Weiss, A., Bartneck, C., Meta analysis of the usage of the Godspeed Questionnaire Series (2015) 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), Kobe, Japan, pp. 381-388. , IEEE; White, N., The Helpless Robot, , http://dada.compart-bremen.de/item/artwork/609, Accessed 9 July 2019","Bemman, B.; Aalborg UniversityDenmark; email: bb@create.aau.dk","Brooks A.Brooks E.I.",,"Springer","8th EAI International Conference on ArtsIT, Interactivity and Game Creation, ArtsIT 2019, and the 4th EAI International Conference on Design, Learning, and Innovation, DLI 2019","6 November 2019 through 8 November 2019",,243029,18678211,9783030532932,,,"English","Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85089316488
"Diogo A., Ayanoglu H., Teles J., Duarte E.","57225326202;57190121545;43261757200;43261022200;","Trust on Service Robots: A Pilot Study on the Influence of Eyes in Humanoid Robots During a VR Emergency Egress",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12182 LNCS",,,"580","591",,,"10.1007/978-3-030-49062-1_39","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088744188&doi=10.1007%2f978-3-030-49062-1_39&partnerID=40&md5=2216754386ce6019890f39b6cdc098ad","IADE, Universidade Europeia, Av. D. Carlos I, 4, Lisbon, 1200-649, Portugal; UNIDCOM/IADE, Av. D. Carlos I, 4, Lisbon, 1200-649, Portugal; Mathematics Unit, Faculdade de Motricidade Humana, Universidade de Lisboa, Estrada da Costa, Cruz Quebrada - Dafundo, 1499-002, Portugal; CIPER – Centro Interdisciplinar para o Estudo da Performance Humana, Estrada da Costa, Cruz Quebrada - Dafundo, 1499-002, Portugal","Diogo, A., IADE, Universidade Europeia, Av. D. Carlos I, 4, Lisbon, 1200-649, Portugal; Ayanoglu, H., IADE, Universidade Europeia, Av. D. Carlos I, 4, Lisbon, 1200-649, Portugal, UNIDCOM/IADE, Av. D. Carlos I, 4, Lisbon, 1200-649, Portugal; Teles, J., Mathematics Unit, Faculdade de Motricidade Humana, Universidade de Lisboa, Estrada da Costa, Cruz Quebrada - Dafundo, 1499-002, Portugal, CIPER – Centro Interdisciplinar para o Estudo da Performance Humana, Estrada da Costa, Cruz Quebrada - Dafundo, 1499-002, Portugal; Duarte, E., IADE, Universidade Europeia, Av. D. Carlos I, 4, Lisbon, 1200-649, Portugal, UNIDCOM/IADE, Av. D. Carlos I, 4, Lisbon, 1200-649, Portugal","Robots are found to be good, capable and trustworthy companions in various areas, including high-risk situations or emergencies, but some limitations regarding their acceptance have been reported. Amongst other aspects of the Human-Robot Interaction, trust in the robot has been considered as a main indicator of acceptance. Thus, to investigate the dynamics of human-robot acceptance, this study used a virtual reality simulation of an emergency egress to assess the influence of the robot’s appearance on trust. In particular, we were interested in examining the influence of the eyes in the robot on the participants’ decision to follow it to the exit. Since the type of interaction scenario is also a factor with an impact on trust, two environmental affordance conditions (favourable vs. unfavourable) were tested because of their well-established impact on wayfinding decisions. The results show the participants trusted the robot and followed it to the exit but, although the results favour the robot with eyes, no statistically significant differences were found in either environmental affordance. Moreover, despite perceiving the robot as machinelike and artificial, the majority of the participants felt compelled to follow it, also considering it friendly, kind, pleasant, nice, competent, knowledgeable, responsible, intelligent and sensible. Regardless of the existence of eyes, the service robot tested seems to be a promising solution for emergency egress situations in complex buildings. © 2020, Springer Nature Switzerland AG.","Eyes; Human-like robot; Human-Robot Interaction; Service robots; Trust; Virtual reality","Anthropomorphic robots; Emergency services; Human computer interaction; Intelligent robots; Mobile robots; Complex buildings; Emergency egress; High-risk situations; Humanoid robot; Pilot studies; Service robots; Statistically significant difference; Virtual reality simulations; Social robots",,,,,"Fundação para a Ciência e a Tecnologia, FCT: UID/DES/00711/2019, UIDB/00447/2020","Acknowledgments. This study was conducted at the UNIDCOM UX.Lab, supported by the Fun-dação para a Ciência e Tecnologia, (FCT), under Grant No. UID/DES/00711/2019 attributed to UNIDCOM – Unidade de Investigação em Design e Comunicação, Lisbon, Portugal. Júlia Teles was partly supported by the FCT, under Grant UIDB/00447/2020 to CIPER - Centro Interdis-ciplinar para o Estudo da Performance Humana (unit 447). The authors would like to thank the support and kind assistance provided by José Graça, from IADE’s Games and Apps Lab for his help with programming the VR simulation.","This study was conducted at the UNIDCOM UX.Lab, supported by the Fun-da??o para a Ci?ncia e Tecnologia, (FCT), under Grant No. UID/DES/00711/2019 attributed to UNIDCOM ? Unidade de Investiga??o em Design e Comunica??o, Lisbon, Portugal. J?lia Teles was partly supported by the FCT, under Grant UIDB/00447/2020 to CIPER-Centro Interdis-ciplinar para o Estudo da Performance Humana (unit 447). The authors would like to thank the support and kind assistance provided by Jos? Gra?a, from IADE?s Games and Apps Lab for his help with programming the VR simulation.",,,,,,,,,"Litzenberger, G., Hägele, M., Why service robots boom worldwide (2017) Presentation, IFR Press Conference, Brussels, Belgium; Hancock, P., Billings, D., Schaefer, K., Can You Trust Your Robot? (2011) Ergono. Design Q. Hum. Factors Appl, 19, pp. 24-29; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y.C., De Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human-robot interaction (2011) Hum. Factors, 53 (5), pp. 517-527; Amano, H., Present status and problems of firefighting robots (2002) Proceedings of the 41st SICE Annual Conference. SICE 2002, Osaka, Japan, pp. 880-885. , IEEE; Nourbakhsh, I.R., Sycara, K., Koes, M., Yong, M., Lewis, M., Burion, S., Human-robot teaming for search and rescue (2005) Second International Workshop, E4MAS 2005. IEEE Pervasive Computing, Utrecht, The Netherlands, 4 (1), pp. 72-77. , Weyns, D., Parunak H. Michel F. (eds); Kinateder, M., Comunale, B., Warren, W.H., Exit choice in an emergency evacuation scenario is influenced by exit familiarity and neighbor behaviour (2018) Saf. Sci, 106, pp. 170-175; Gaudiello, I., Zibetti, E., Lefort, S., Chetouani, M., Ivaldi, S., Trust as indicator of robot functional and social acceptance. An experimental study on user conformation to iCub answers (2016) Comput. Hum. Behav, 61, pp. 633-655; Robinette, P., Vela, P.A., Howard, A.M., Information propagation applied to robot-assisted evacuation (2012) Proceedings-IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA, pp. 856-861. , IEEE; Cramer, H., Evers, V., Van Slooten, T., Ghijsen, M., Wielinga, B., Trying too hard? Effects of mobile agents’ (Inappropriate) social expressiveness on trust, affect and compliance (2010) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2010), pp. 1471-1474. , Association for Computing Machinery, New York; Fan, L., Scheutz, M., Lohani, M., McCoy, M., Stokes, C., Do we need emotionally intelligent artificial agents? First results of human perceptions of emotional intelligence in humans compared to robots (2017) IVA 2017. LNCS (LNAI), 10498, pp. 129-141. , https://doi.org/10.1007/978-3-319-67401-8_15, Springer, Cham; DiSalvo, C., Gemperle, F., From seduction to fulfillment (2003) Proceedings of the 2003 International Conference on Designing Pleasurable Products and Interfaces (DPPI 2003), pp. 67-72. , Association for Computing Machinery, New York; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots: concepts, design, and applications (2002) Robot. Auton. Syst, 42 (3), pp. 143-166; Choi, J., Kim, M., The usage and evaluation of anthropomorphic form in robot design (2009) Undisciplined! Design Research Society Conference 2008, Sheffield Hallam University, Sheffield, UK, pp. 1-14; Jaguar land rover’s virtual eyes look at trust in self-driving cars, , https://www.jaguarlandrover.com/news/2018/08/jaguar-land-rovers-virtual-eyes-look-trust-self-driving-cars, Accessed 30 Jan 2020; Vilar, E., Rebelo, F., Noriega, P., Duarte, E., Mayhorn, C.B., Effects of competing environmental variables and signage on route-choices in simulated everyday and emergency wayfinding situations (2014) Ergonomics, 57 (4), pp. 511-524; Vilar, E., Rebelo, F., Noriega, P., Teles, J., Mayhorn, C., Signage versus environmental affordances: is the explicit information strong enough to guide human behavior during a wayfinding task? (2015) Hum. Factors Ergon. Manuf, 25, pp. 439-452; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Social Robot, 1 (1), pp. 71-81. , https://doi.org/10.1007/s12369-008-0001-3; Vilar, E., Noriega, P., Rebelo, F., Galrão, I., Semedo, D., Graça, N., Exploratory study to investigate the influence of a third person on an individual emergency wayfinding decision (2020) AHFE 2019. AISC, 955, pp. 452-461. , https://doi.org/10.1007/978-3-030-20227-9_42, Rebelo, F., Soares, M.M. (eds) Springer, Cham; Diogo, A., Duarte, E., Ayanoglu, H., Design of service robots as safety officers: a survey on the influence of morphology and appearance Proceedings of Senses & Sensibility 2019, the 10th UNIDCOM International Conference, , 27–29 November 2019, Lisbon, Portugal (in print); Bozgeyikli, E., Raij, A., Katkoori, S., Dubey, R., Point & teleport locomotion technique for virtual reality (2016) CHI PLAY 2016: Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play, pp. 205-216. , Austin, Texas, USA; Stanton, C., Stevens, Catherine J., Robot Pressure: The Impact of Robot Eye Gaze and Lifelike Bodily Movements upon Decision-Making and Trust (2014) ICSR 2014. LNCS (LNAI), 8755, pp. 330-339. , https://doi.org/10.1007/978-3-319-11973-1_34, Beetz, M., Johnston, B., Williams, M.-A. (eds) Springer, Cham; Robinette, P., Li, W., Allen, R., Howard, A.M., Wagner, A.R., Overtrust of robots in emergency evacuation scenarios (2016) 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI) 2016, Christchurch, New Zealand, pp. 101-108. , IEEE; Shibata, T., Wada, K., Ikeda, Y., Sabanovic, S., Cross-cultural studies on subjective evaluation of a seal robot (2009) Adv. Robot, 23, pp. 443-458; De Graaf, M., Allouch, S., Exploring influencing variables for the acceptance of social robots (2013) Robot. Auton. Syst, 61, pp. 1476-1486","Ayanoglu, H.; IADE, Av. D. Carlos I, 4, Portugal; email: hande.ayanoglu@universidadeeuropeia.pt","Kurosu M.",,"Springer","Thematic Area on Human Computer Interaction, HCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242229,03029743,9783030490614,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85088744188
"Lukić B., Jovanović K., Knežević N., Žlajpah L., Petrič T.","57192677119;36137004200;57212697678;6603769815;36447813900;","Maximizing the End-Effector Cartesian Stiffness Range for Kinematic Redundant Robot with Compliance",2020,"Mechanisms and Machine Science","84",,,"208","217",,,"10.1007/978-3-030-48989-2_23","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087028678&doi=10.1007%2f978-3-030-48989-2_23&partnerID=40&md5=a82923a15ec1bb075d5a9a50cb768aeb","School of Electrical Engineering, University of Belgrade, Belgrade, Serbia; Jožef Stefan Institute, Ljubljana, Slovenia","Lukić, B., School of Electrical Engineering, University of Belgrade, Belgrade, Serbia; Jovanović, K., School of Electrical Engineering, University of Belgrade, Belgrade, Serbia; Knežević, N., School of Electrical Engineering, University of Belgrade, Belgrade, Serbia; Žlajpah, L., Jožef Stefan Institute, Ljubljana, Slovenia; Petrič, T., Jožef Stefan Institute, Ljubljana, Slovenia","Compliant robots with constant joint stiffness (Serial Elastic Actuators - SEA), on the contrary to ones with variable joint stiffness (Variable Stiffness Actuators – VSA), have limited capabilities for modulating robot mechanical impedance in the interaction task. However, in the case of kinematic redundancy in specific tasks, robots can exploit the null space to adjust End-Effector (EE) Cartesian stiffness. Thus, prior knowledge of the task path or the operational workspace can be used to pre-compute joint stiffness that can enable maximal ratio between maximal and minimal stiffness of the robot’s EE during the task execution, and therefore shape achievable EE stiffness to best fit the task execution. In that light, this paper elaborates on the preselection of joint stiffnesses which influences the achievable robot’s Cartesian stiffness in a specific task. Besides optimizing the available operational EE stiffness, by pre-computed joint stiffness values, the robot will be able to adapt better to specific tasks and provide a better framework for safe and efficient physical human-robot interaction. The paper presents an approach to the selection of predefined joint stiffness values of the 7-DOFs KUKA LWR, where joint stiffness is achieved/emulated with torque feedback. In the simulation experiments, the approach is depicted in the preselection of two joint stiffness values within the prescribed range, while other joint stiffness is set constant. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.","Cartesian stiffness control; Joint stiffness; Kinematic redundancy; Physical human-robot interaction","End effectors; Human robot interaction; Kinematics; Mechanical actuators; Redundant manipulators; Cartesian stiffness; Elastic actuators; Kinematic redundancy; Mechanical impedances; Physical human-robot interactions; Prior knowledge; Redundant robot; Variable stiffness; Stiffness",,,,,,,,,,,,,,,,"Pedrocchi, N., Vicentini, F., Malosio, M., Tosatti, L.M., Safe human-robot cooperation in an industrial environment (2013) Int. J. Adv. Robot. Syst, 10 (1), p. 27; Edsinger, A., Kemp, C.C., Human-robot interaction for cooperative manipulation: handing objects to one another (2007) Proceedings-IEEE International Workshop on Robot and Human Interactive Communication, pp. 1167-1172; Ferro, F., Marchionni, L., REEM: a humanoid service robot (2014) ROBOT 2013: First Iberian Robotics Conference, , Springer, Cham; Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z., Human-robot interaction control of rehabilitation robots with series elastic actuators (2015) IEEE Trans. Robot, 31 (5), pp. 1089-1100; De Santis, A., Siciliano, B., De Luca, A., Bicchi, A., An atlas of physical human–robot interaction (2008) Mech. Mach. Theory, 43 (3), pp. 253-270; Hogan, N., Impedance control: an approach to manipulation: Part II—implementation (1985) J. Dyn. Syst. Measur. Control, 107 (1), pp. 8-16; Robinson, D.W., Pratt, J.E., Paluska, D.J., Pratt, G.A., Series elastic actuator development for a biomimetic walking robot (1999) Proceedings of 1999 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 561-568; Pratt, G.A., Williamson, M.M., Series elastic actuators (1995) Proceedings of 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems 95. Human Robot Interaction and Cooperative Robots, 1, pp. 399-406; Vanderborght, B., Albu-Schaeffer, A., Bicchi, A., Burdet, E., Caldwell, D.G., Carloni, R., Catalano, M., Wolf, S., Variable impedance actuators: a review (2013) Robot. Auton. Syst, 61 (12), pp. 1601-1614; Grioli, G., Wolf, S., Garabini, M., Catalano, M., Burdet, E., Caldwell, D., Carloni, R., Bicchi, A., Variable stiffness actuators: the user’s point of view (2015) Int. J. Robot. Res, 34 (6), pp. 727-743; Lakatos, D., Petit, F., Albu-Schäffer, A., Nonlinear oscillations for cyclic movements in human and robotic arms (2014) IEEE Trans. Robot, 30, pp. 865-879; Lukić, B., Petrič, T., Žlajpah, L., Jovanović, K., KUKA LWR robot Cartesian stiffness control based on kinematic redundancy (2020) Advances in Service and Industrial Robotics, RAAD 2019. Advances in Intelligent Systems and Computing, 980. , Berns, K., Görges, D. (eds) Springer, Cham; Knežević, N., Lukić, B., Jovanović, K., Petrič, T., Žlajpah, L., End-effector Cartesian stiffness optimization: sequential quadratic programming approach (2019) The 6th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN 2019), p. 736. , ETRAN Society; Chiaverini, S., Oriolo, G., Maciejewski, A.A., Redundant robots (2016) Springer Handbook of Robotics, pp. 221-242. , 2nd edn., Springer, Cham; Petit, F., Albu-Schäffer, A., Cartesian impedance control for a variable stiffness robot arm (2011) Proceedings of International Conference on Intelligent Robots and Systems 2011 (IROS 2011), pp. 4180-4186; Albu-Schaffer, A., Fischer, M., Schreiber, G., Schoeppe, F., Hirzinger, G., Soft robotics: what Cartesian stiffness can obtain with passively compliant, uncoupled joints? (2004) Proceedings of International Conference on Intelligent Robots and Systems, 2004 (IROS 2004), 4, pp. 3295-3301; Nemec, B., Žlajpah, L., Ude, A., Door opening by joining reinforcement learning and intelligent control (2017) 2017 18th International Conference on Advanced Robotics (ICAR), pp. 222-228. , IEEE; Todorov, E., Mujoco advanced physics simulation, , http://www.mujoco.org/","Lukić, B.; School of Electrical Engineering, Serbia; email: branko@etf.bg.ac.rs",,,"Springer Science and Business Media B.V.",,,,,22110984,,,,"English","Mech. Mach. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85087028678
"Daglarli E.","22957259400;","Computational modeling of prefrontal cortex for meta-cognition of a humanoid robot",2020,"IEEE Access","8",,"9103513","98491","98507",,1,"10.1109/ACCESS.2020.2998396","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086075046&doi=10.1109%2fACCESS.2020.2998396&partnerID=40&md5=27438cd2defe5a9ed81a37b725e6cddd","Faculty of Computer and Informatics Engineering, Istanbul Technical University, Istanbul, 34469, Turkey","Daglarli, E., Faculty of Computer and Informatics Engineering, Istanbul Technical University, Istanbul, 34469, Turkey","For robot intelligence and human-robot interaction (HRI), complex decision-making, interpretation, and adaptive planning processes are great challenges. These require recursive task processing and meta-cognitive reasoning mechanism. Naturally, the human brain realizes these cognitive skills by prefrontal cortex which is a part of the neocortex. Previous studies about neurocognitive robotics would not meet these requirements. Thus, it is aimed at developing a brain-inspired robot control architecture that performs spatial-temporal and emotional reasoning. In this study, we present a novel solution that covers a computational model of the prefrontal cortex for humanoid robots. Computational mechanisms are mainly placed on the bio-physical plausible neural structures embodied in different dynamics. The main components of the system are composed of several computational modules including dorsolateral, ventrolateral, anterior, and medial prefrontal regions. Also, it is responsible for organizing the working memory. A reinforcement meta-learning based explainable artificial intelligence (xAI) procedure is applied to the working memory regions of the computational prefrontal cortex model. Experimental evaluation and verification tests are processed by the developed software framework embodied in the humanoid robot platform. The humanoid robots' perceptual states and cognitive processes including emotion, attention, and intention-based reasoning skills can be observed and controlled via the developed software. Several interaction scenarios are implemented to monitor and evaluate the model's performance. © 2013 IEEE.","Artificial intelligence; brain modeling; cognitive robotics; human-robot interaction","Anthropomorphic robots; Brain; Computation theory; Decision making; Intelligent robots; Reinforcement learning; Robot programming; Software testing; Verification; Computational model; Experimental evaluation; Human robot Interaction (HRI); Neural structures; Robot control architecture; Robot intelligences; Software frameworks; Verification tests; Human robot interaction",,,,,"Nvidia","This work was supported by the NVIDIA Corporation through the GPU Grant Program.",,,,,,,,,,"Chaminade, T., Cheng, G., Social cognitive neuroscience and humanoid robotics (2009) J. Physiol.-Paris, 103 (3-5), pp. 286-295. , May; Asada, M., MacDorman, K.F., Ishiguro, H., Kuniyoshi, Y., Cognitive developmental robotics as a new paradigm for the design of humanoid robots (2001) Robot. Auto. Syst., 37 (2-3), pp. 185-193. , Nov; Reggia, J.A., Katz, G.E., Davis, G.P., Humanoid cognitive robots that learn by imitating: Implications for consciousness studies (2018) Frontiers Robot. AI, 5, p. 1. , Jan; Wykowska, A., Chaminade, T., Cheng, G., Embodied artificial agents for understanding human social cognition (2016) Philos. Trans. Roy. Soc. B, Biol. Sci., 371 (1693). , May; Wykowska, A., Metta, G., Becchio, C., Hortensius, R., Cross, E., Cognitive and social neuroscience methods for HRI (2018) Proc. Companion ACM/IEEE Int. Conf. Hum.-Robot Interact., pp. 405-406. , Mar; Kralik, J.D., Muldrew, D.B.C., Gunasekaran, D., Lange, R.D., Cognitive and action control for goal-directed reaching in a humanoid robot (2017) Proc. IEEE Int. Conf. Robot. 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K.:Wiley; Khamassi, M., Quilodran, R., Enel, P., Dominey, P.F., Procyk, E., Behavioral regulation and the modulation of information coding in the lateral prefrontal and cingulate cortex (2015) Cerebral Cortex, 25 (9), pp. 3197-3218. , Sep; Khamassi, M., Enel, P., Dominey, P.F., Procy, E., Medial prefrontal cortex and the adaptive regulation of reinforcement learning parameters (2013) Progress in Brain Research, 202, pp. 441-464. , Amsterdam, The Netherlands: Elsevier; Khamassi, M., Wilson, C., Rothé, R., Quilodran, R., Dominey, P.F., Procyk, E., Meta-learning, cognitive control, and physiological interactions between medial and lateral prefrontal cortex (2011) Proc. Neural Basis Motivational Cogn. 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Syst., Early Access, , Jan. 1; Ramirez-Pedraza, R., Vargas, N., Sandoval, C., Del Valle-Padilla, J.L., Ramos, F., A bio-inspired model of behavior considering decisionmaking and planning, spatial attention and basic motor commands processes (2020) Cognit. Syst. Res., 59, pp. 293-303. , Jan","Daglarli, E.; Faculty of Computer and Informatics Engineering, Turkey; email: evren.daglarli@itu.edu.tr",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85086075046
"Ramadevi O., Siva Supraja G., Swathi G., Vijaya J., Vara Prasad B.","57216362780;57216370843;57216363552;57216366867;57216375370;","Emotion sensing facial recognition",2020,"Journal of Advanced Research in Dynamical and Control Systems","12","2",,"916","922",,,"10.5373/JARDCS/V12I2/S20201115","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083292708&doi=10.5373%2fJARDCS%2fV12I2%2fS20201115&partnerID=40&md5=5662ca1731ee6dbea6b6966f87688453","Department of Computer Science and EngineeringAP, India; Lakireddy Bali Reddy College of EngineeringAP, India","Ramadevi, O., Department of Computer Science and EngineeringAP, India, Lakireddy Bali Reddy College of EngineeringAP, India; Siva Supraja, G., Department of Computer Science and EngineeringAP, India, Lakireddy Bali Reddy College of EngineeringAP, India; Swathi, G., Department of Computer Science and EngineeringAP, India, Lakireddy Bali Reddy College of EngineeringAP, India; Vijaya, J., Department of Computer Science and EngineeringAP, India, Lakireddy Bali Reddy College of EngineeringAP, India; Vara Prasad, B., Department of Computer Science and EngineeringAP, India, Lakireddy Bali Reddy College of EngineeringAP, India","Facial expressions are an important form of communication as they help us determine the person’s mood and intentions. Facial emotions are helpful for communicating with the other person without using words. There are a few terms which are highly common and often used, they are: Happiness, sad, angry, hatred, scare, surprise also neutral. It is vital that we detect and catch these features in the person’s face as they play a key role in Computer vision and Artificial Intelligence. The main objective of human emotion recognition is to evaluate the human emotions. The features of this technology are myriad, they can be of use in various fields in security systems, or while taking feedback of an interview or human robot interaction. In view of this the emotion exposure has been handled in both actual and fixed images. We have used the Cohn-Kannade Catalog (CK) which has some fixed images 640 x 400 pixels and for the actual-time, footage has been used. Extremely, for emotion perception, we essential detect the faces by via HAAR mesh from Open CV in the fixed images and in the actual videos. Once we identify the face, it can be treated and scratch spread through detection of facial landmarks. Using Support Vector Machine, we get exactness of around 93.7%. These facemask standards can be improved for better efficiency. © Institute of Advanced Scientific Research, Inc.. All rights reserved.","Classification; Expression recognitions; Face detection; Facial expressions; HAAR; Open CV; Support vector machine",,,,,,,,,,,,,,,,,"Sirovich, L., Kirby, M., Low-Dimensional procedure for the characterisation of human faces (1987) J. Optical Soc. of Am, 4, pp. 519-524. , , vol., pp; Sun, X., Jia, Z., A brief review of biomarkers for preventing and treating cardiovascular diseases (2012) Journal of Cardiovascular Disease Research, 3 (4), pp. 251-254. , ), pp; Ramji lal sahu (2016) fracture union with closed interlocking nail in segmental tibial shaft fracture Journal of Critical Reviews, 3 (2), pp. 60-64; Kanade, T., Cohn, J.F., Tian, Y., Comprehensive database for facial expression analysis (2000) In Proceedings of Fourth IEEE International Conference on Automatic Face and Gesture Recognition, Pages, pp. 46-53; Kotsia, I., Buciu, I., Pitas, I., An analysis of facial expression recognition under partial facial image occlusion (2008) Image and Vision Computing, 26 (7), pp. 1052-1106; Kirby, M., Sirovich, L., Application of the Karhunen-Loève procedure for the characterisation of human faces (1990) IEEE Trans. Pattern Analysis and Machine Intelligence, 12, pp. 831-835. , vol., pp., Dec; Turk, M., Pentland, A., Eigenfaces for recognition (1991) J. Cognitive Neuroscience, 3, pp. 71-86. , vol., pp; Bruneli, R., Poggio, T., Face recognition: Features versus templates (1993) IEEE Trans. Pattern Analysis and Machine Intelligence, 15, pp. 1042-1052. , vol., pp; Baron, R.J., Mechanism of human facial recognition (1981) Int'l J. Man Machine Studies, 15, pp. 137-178. , vol., pp; Bichsel, M., (1991) Strategies of Robust Object Recognition for Identification of Human Faces, , Ph.D. thesis, Zurich","Ramadevi, O.; Department of Computer Science and EngineeringIndia; email: odugu.rama@gmail.com",,,"Institute of Advanced Scientific Research, Inc.",,,,,1943023X,,,,"English","J. Adv. Res. Dyn. Control. Syst.",Article,"Final","",Scopus,2-s2.0-85083292708
"Wu Z., Payandeh S.","57216186585;7006688369;","Toward Design of a Drip-Stand Patient Follower Robot",2020,"Journal of Robotics","2020",,"9080642","","",,1,"10.1155/2020/9080642","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082745126&doi=10.1155%2f2020%2f9080642&partnerID=40&md5=1b1465430bbf1cd3892f558727ea0171","Network Robotics and Sensing Laboratory, School of Engineering Science, Simon Fraser University, Burnaby, BC  V5A 1S6, Canada","Wu, Z., Network Robotics and Sensing Laboratory, School of Engineering Science, Simon Fraser University, Burnaby, BC  V5A 1S6, Canada; Payandeh, S., Network Robotics and Sensing Laboratory, School of Engineering Science, Simon Fraser University, Burnaby, BC  V5A 1S6, Canada","A person following robot is an application of service robotics that primarily focuses on human-robot interaction, for example, in security and health care. This paper explores some of the design and development challenges of a patient follower robot. Our motivation stemmed from common mobility challenges associated with patients holding on and pulling the medical drip stand. Unlike other designs for person following robots, the proposed design objectives need to preserve as much as patient privacy and operational challenges in the hospital environment. We placed a single camera closer to the ground, which can result in a narrower field of view to preserve patient privacy. Through a unique design of artificial markers placed on various hospital clothing, we have shown how the visual tracking algorithm can determine the spatial location of the patient with respect to the robot. The robot control algorithm is implemented in three parts: (a) patient detection; (b) distance estimation; and (c) trajectory controller. For patient detection, the proposed algorithm utilizes two complementary tools for target detection, namely, template matching and colour histogram comparison. We applied a pinhole camera model for the estimation of distance from the robot to the patient. We proposed a novel movement trajectory planner to maintain the dynamic tipping stability of the robot by adjusting the peak acceleration. The paper further demonstrates the practicality of the proposed design through several experimental case studies. © 2020 Zewen Wu and Shahram Payandeh.",,"Agricultural robots; Hospitals; Human robot interaction; Pinhole cameras; Template matching; Design and Development; Hospital environment; Movement trajectories; Operational challenges; Pin-hole camera models; Robot control algorithm; Trajectory controllers; Visual tracking algorithm; Machine design",,,,,,,,,,,,,,,,"Najib, M., Fahmi, M., A study on techniques of person following robot (2015) International Journal of Computer Applications, 125 (13), pp. 27-30; Chen, B.X., Sahdev, R., Tsotsos, J., Person following Robot Using Selected Online Ada-boosting with Stereo Camera, pp. 48-55. , Proceedings of the 14th Conference on Computer and Robot Vision May 2017 Edmonton, Canada 2-s2.0-85031785444; Ilias, B., Abdul Shukor, S.A., Yaacob, S., Adom, A.H., Mohd Razali, M.H., A nurse following robot with high speed kinect sensor (2014) Journal of Engineering and Applied Sciences, 9 (12), pp. 2454-2459; (2020) Aethon Automates Intralogistics, TUG Smart Autonomous Mobile Robot, , http://www.aethon.com/tug; Lucas, B.D., Kanade, T., An Iterative Image Registration Technique with An Application to Stereo Vision, 2, pp. 674-679. , Proceedings of the 7th International Joint Conference on Artificial Intelligence 1981 Vancouver, Canada; Shen, L., Huang, X., Yan, Y., Bai, S., An Improved Mean-shift Tracking Algorithm with Spatial-colour Feature and New Similarity Measure, pp. 184-188. , Proceedings of IEEE International Conference on Multimedia Technology July 2011 Hangzhou, China 2-s2.0-80052953581; Susperregi, L., Martínez-Otzeta, J.M., Ansuategui, A., Ibarguren, A., Sierra, B., RGB-D, laser and thermal sensor fusion for people following in a mobile robot (2013) International Journal of Advanced Robotic Systems, 10 (6), p. 9. , 2-s2.0-84879196855; Choi, W., Pantofaru, C., Savarese, S., Detecting and Tracking People Using An RGB-D Camera Via Multiple Detector Fusion, pp. 1076-1083. , Proceedings of IEEE International Conference on Computer Vision Workshops November 2011 Barcelona, Spain 2-s2.0-84856630785; Payandeh, S., (2016) Visual Tracking in Conventional Minimally Invasive Surgery, , Boca Raton, FL, USA CRC Press; Tanimoto, S., (2012) An Interdisciplinary Introduction to Image Processing, , Cambridge, MA, USA MIT Press; (2020) Trinamic Motion Control, , https://www.trinamic.com/fileadmin/assets/Support/Appnotes/AN013Motion_control_profile_parametrization.pdf; (2020), https://www.parkermotion.com/manuals/a-ax/AX_Chp4p1.pdf; Baldor Motion Products, , http://www.multiprojekt.pl/ftp/abb_serwonapedy/archiwum%20baldor/application_note/an00118-000_motion_profiles_EN.pdf; Endo, G., Allan, B., Lemura, Y., Mobile follower robot as an assistive device for home oxygen therapy evaluation of tether control algorithms (2015) ROBOMECH Journal, 2 (1). , 2-s2.0-84966575235","Payandeh, S.; Network Robotics and Sensing Laboratory, Canada; email: payandeh@sfu.ca",,,"Hindawi Limited",,,,,16879600,,,,"English","J. Rob.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85082745126
"Khan A., Li J.P., Haq A.U., Nazir S., Ahmad N., Varish N., Malik A., Patel S.H.","56602074400;56003087700;57205168216;56028678500;7202343939;57053259800;57204061560;7403903727;","Partial Observer Decision Process Model for Crane-Robot Action",2020,"Scientific Programming","2020",,"6349342","","",,4,"10.1155/2020/6349342","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081160735&doi=10.1155%2f2020%2f6349342&partnerID=40&md5=25da848f4727d76d79ba10908a10484f","School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China; Department of Computer Science, University of Sawabi, Swabi, KPK, Pakistan; School of Computer Applications, Madanapalle Institute of Technology and Science, Madanapalle, India; Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, KL University, Guntur, India; School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China; Interdisciplinary Robotics, Intelligent Sensing and Control (RISC) Lab, Department of Computer Science and Engineering, School of Engineering University of Bridgeport, Bridgeport, CT, United States","Khan, A., School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China; Li, J.P., School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China; Haq, A.U., School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, 611731, China; Nazir, S., Department of Computer Science, University of Sawabi, Swabi, KPK, Pakistan; Ahmad, N., School of Computer Applications, Madanapalle Institute of Technology and Science, Madanapalle, India; Varish, N., Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, KL University, Guntur, India; Malik, A., School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China; Patel, S.H., Interdisciplinary Robotics, Intelligent Sensing and Control (RISC) Lab, Department of Computer Science and Engineering, School of Engineering University of Bridgeport, Bridgeport, CT, United States","The most common use of robots is to effectively decrease the human's effort with desirable output. In the human-robot interaction, it is essential for both parties to predict subsequent actions based on their present actions so as to well complete the cooperative work. A lot of effort has been devoted in order to attain cooperative work between human and robot precisely. In case of decision making, it is observed from the previous studies that short-term or midterm forecasting have long time horizon to adjust and react. To address this problem, we suggested a new vision-based interaction model. The suggested model reduces the error amplification problem by applying the prior inputs through their features, which are repossessed by a deep belief network (DBN) though Boltzmann machine (BM) mechanism. Additionally, we present a mechanism to decide the possible outcome (accept or reject). The said mechanism evaluates the model on several datasets. Hence, the systems would be able to capture the related information using the motion of the objects. And it updates this information for verification, tracking, acquisition, and extractions of images in order to adapt the situation. Furthermore, we have suggested an intelligent purifier filter (IPF) and learning algorithm based on vision theories in order to make the proposed approach stronger. Experiments show the higher performance of the proposed model compared to the state-of-the-art methods. © 2020 Asif Khan et al.",,"Decision making; Motion capture; Boltzmann machines; Cooperative works; Decision process; Deep belief network (DBN); Desirable outputs; Error amplification; State-of-the-art methods; Vision-based interaction; Human robot interaction",,,,,"2007AA01Z423; 611731; National Natural Science Foundation of China, NSFC: 61370073; Department of Science and Technology of Sichuan Province, SPDST; University of Electronic Science and Technology of China, UESTC: 2006","This work was supported by the National Natural Science Foundation of China (grant no. 61370073), the National High Technology Research and Development Program of China (grant no. 2007AA01Z423), and the project of the Science and Technology Department of Sichuan Province. All resources used for experiment support were given by Key Laboratory of Wavelet Active Media Technology School of Computer Science, University of Electronic Science and Technology of China (UESTC), No. 2006, Xiyuan Avenue, West Hi-Tech Zone, Chengdu, Sichuan, 611731, P. R. 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"Werner C., Dometios A.C., Tzafestas C.S., Maragos P., Bauer J.M., Hauer K.","57188639923;56043770000;6701475189;35243026700;57192890201;7005208898;","Evaluating the task effectiveness and user satisfaction with different operation modes of an assistive bathing robot in older adults",2020,"Assistive Technology",,,,"","",,1,"10.1080/10400435.2020.1755744","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079902011&doi=10.1080%2f10400435.2020.1755744&partnerID=40&md5=f0a19d117ef1db3d30e24e3c6d5eb96f","Center for Geriatric Medicine, Heidelberg University, Heidelberg, Germany; Agaplesion Bethanien Hospital Heidelberg, Geriatric Center at the Heidelberg University, Heidelberg, Germany; School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece","Werner, C., Center for Geriatric Medicine, Heidelberg University, Heidelberg, Germany, Agaplesion Bethanien Hospital Heidelberg, Geriatric Center at the Heidelberg University, Heidelberg, Germany; Dometios, A.C., School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece; Tzafestas, C.S., School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece; Maragos, P., School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece; Bauer, J.M., Center for Geriatric Medicine, Heidelberg University, Heidelberg, Germany, Agaplesion Bethanien Hospital Heidelberg, Geriatric Center at the Heidelberg University, Heidelberg, Germany; Hauer, K., Agaplesion Bethanien Hospital Heidelberg, Geriatric Center at the Heidelberg University, Heidelberg, Germany","Bathing robots have the potential to foster the independence of older adults who require assistance with bathing. Making human-robot interaction (HRI) for older persons as easy, effective, and user-satisfying as possible is, however, a major challenge in the development of such robots. The study aimed to evaluate the effectiveness (coverage, step effectiveness) and user satisfaction (After-Scenario Questionnaire, ASQ) with three operation modes (autonomous operation, shared control, tele-manipulation) for the HRI with a bathing robot in potential users. Twenty-five older adults who require bathing assistance tested these operation modes in a water rinsing task for the upper back. Autonomous operation led to maximum effectiveness (100%), which was significantly worse in the shared control (51.6–79.4%, p ≤ 0.001) and tele-manipulation mode (43.9–64.4%, p < .001). In the user-controlled modes, effectiveness decreased with decreasing robot assistance (shared control: 51.6–79.4% vs. tele-manipulation: 43.9–64.4%, p = 0.009–0.016). User satisfaction with the autonomous operation (ASQ: 2.0 ± 1.0pt.) was higher than with the tele-manipulation mode (ASQ: 3.0 ± 1.4pt., p = 0.003) and in trend also than with the shared control mode (ASQ: 2.5 ± 1.5pt., p = 0.071). Our study suggests that for an effective and highly satisfying HRI with a bathing robot in older users, operation modes with high robot autonomy requiring a minimum of user input seem to be necessary. © 2020, © 2020 RESNA.","Activities of daily living; aged; baths; human-robot interaction; robotics","Human rehabilitation equipment; Autonomous operations; Human robot Interaction (HRI); Potential users; Robot assistance; Robot autonomy; Shared control; Telemanipulation; User satisfaction; Human robot interaction",,,,,,,,,,,,,,,,"Abbink, D.A., Mulder, M., Neuromuscular analysis as a guideline in designing shared control (2010) Advances in haptics, pp. 499-516. , https://doi.org/10.5772/8696 , Zadeh M.H., (ed), IntechOpe, &,. 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Findings of the international MOBILATE survey: ‘Enhancing mobility in later life (2005) Gerontechnology, 3 (3), pp. 126-137. , https://doi.org/10.4017/gt.2005.03.03.002.00; Techentin, C., Voyer, D., Voyer, S.D., Spatial abilities and aging: A meta-analysis (2014) Experimental Aging Research, 40 (4), pp. 395-425. , https://doi.org/10.1080/0361073x.2014.926773; Toglia, J., Fitzgerald, K.A., O’Dell, M.W., Mastrogiovanni, A.R., Lin, C.D., The Mini-Mental State Examination and Montreal Cognitive Assessment in persons with mild subacute stroke: Relationship to functional outcome (2011) Archives of Physical Medicine and Rehabilitation, 92 (5), pp. 792-798. , https://doi.org/10.1016/j.apmr.2010.12.034; Vasunilashorn, S., Coppin, A.K., Patel, K.V., Lauretani, F., Ferrucci, L., Bandinelli, S., Guralnik, J.M., Use of the Short Physical Performance Battery Score to predict loss of ability to walk 400 meters: Analysis from the InCHIANTI study (2009) The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 64 (2), pp. 223-229. , https://doi.org/10.1093/gerona/gln022; Veronese, N., Bolzetta, F., Toffanello, E.D., Zambon, S., De Rui, M., Perissinotto, E., Manzato, E., Association between Short Physical Performance Battery and falls in older people: The Progetto Veneto Anziani Study (2014) Rejuvenation Research, 17 (3), pp. 276-284. , https://doi.org/10.1089/rej.2013.1491, …; Vogel, J., Haddadin, S., Jarosiewicz, B., Simeral, J.D., Bacher, D., Hochberg, L.R., van der Smagt, P., An assistive decision-and-control architecture for force-sensitive hand-arm systems driven by human–machine interfaces (2015) The International Journal of Robotics Research, 34 (6), pp. 763-780. , https://doi.org/10.1177/0278364914561535, …; Vollman, K.M., Interventional patient hygiene: Discussion of the issues and a proposed model for implementation of the nursing care basics (2013) Intensive Critical Care Nursing, 29 (5), pp. 250-255. , https://doi.org/10.1016/j.iccn.2013.04.004; Werner, C., Ullrich, P., Geravand, M., Peer, A., Bauer, J.M., Hauer, K., A systematic review of study results reported for the evaluation of robotic rollators from the perspective of users (2018) Disability and Rehabilitation. Assistive Technology, 13 (1), pp. 31-39. , https://doi.org/10.1080/17483107.2016.1278470; Wiener, J.M., Hanley, R.J., Clark, R., Van Nostrand, J.F., Measuring the activities of daily living: Comparisons across national surveys (1990) Journal of Gerontology, 45 (6), pp. S229-S237. , https://doi.org/10.1093/geronj/45.6.S229; Yamamoto, A., Matsuyama, M., Kamoi, H., Yamamoto, R., Kuwahara, N., Kida, N., Physical and mental burden of caregivers in providing bathing assistance (2012) Advances in Humans Aspects of Healthcare, pp. 269-275. , Duffy V.G., (ed), Taylor & Francis Group, &,. (Ed; Yanco, H.A., Drury, J., Classifying human-robot interaction: An updated taxonomy (2004) Paper presented at the 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), The Hague, Netherlands; Yang, Y., Lee, L.C., Dynamics and heterogeneity in the process of human frailty and aging: Evidence from the U.S. older adult population (2010) The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 65B (2), pp. 246-255. , https://doi.org/10.1093/geronb/gbp102; Yu, H., Spenko, M., Dubowsky, S., An adaptive shared control system for an intelligent mobility aid for the elderly (2003) Autonomous Robots, 15 (1), pp. 53-66. , https://doi.org/10.1023/a:1024488717009; Zafrani, O., Nimrod, G., Towards a holistic approach to studying human-robot interaction in later life (2018) The Gerontologist, 59 (1), pp. e26-e36. , https://doi.org/10.1093/geront/gny077; Zlatintsi, A., Dometios, A.C., Kardaris, N., Rodomagoulakis, I., Koutras, P., Papageorgiou, X., López, R., I-Support: A robotic platform of an assistive bathing robot for the elderly population (2020) Robotics and Autonomous Systems, 126, p. 103451. , https://doi.org/10.1016/j.robot.2020.103451, …","Werner, C.Rohrbacher Str. 149, Germany; email: christian.werner@bethanien-heidelberg.de",,,"Taylor and Francis Inc.",,,,,10400435,,ASTEF,,"English","Assistive Technol.",Article,"Article in Press","",Scopus,2-s2.0-85079902011
"Mizanoor Rahman S.M.","37039570500;","Grasp Rehabilitation of Stroke Patients Through Object Manipulation with an Intelligent Power Assist Robotic System",2020,"Advances in Intelligent Systems and Computing","1070",,,"244","259",,,"10.1007/978-3-030-32523-7_16","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075648958&doi=10.1007%2f978-3-030-32523-7_16&partnerID=40&md5=b74dfbda37bde75a8179d35847921601","The University of West Florida, Pensacola, FL  32514, United States","Mizanoor Rahman, S.M., The University of West Florida, Pensacola, FL  32514, United States","A 1-DOF experimental power assist robotic system was developed for grasping and lifting objects tied with it by stroke patients as a part of their upper arm grasp rehabilitation practices. A human-in-the-loop model of the targeted dynamics for lifting objects with the system was developed that considered prospective patient’s weight perception. A position control scheme based on the weight-perception-based dynamics model was developed and implemented. The control parameters were the virtual mass values used for the grasped and manipulated object. The virtual mass values that produced satisfactory human-robot interactions (HRI) in terms of maneuverability, motion, stability, health and safety, etc. were determined. The results showed that a few sets of mass values could produce satisfactory HRI. Then, stroke patients grasped and manipulated objects with the system separately for the most satisfactory virtual mass values that provided optimum kinesthetic perception. In separate experiments, the virtual mass value was exponentially reduced when the patients manipulated the objects with the system, which provided variation in kinesthetic perception. The results showed that the power-assisted manipulation with optimum kinesthetic perception significantly contributed to stroke rehabilitation. The results also showed that the rehabilitation performance varied with the variation in the kinesthetic perception. In addition, effectiveness of a proposed game-like visual interface and smart tele-rehabilitation for connected health was investigated. © 2020, Springer Nature Switzerland AG.","Connected health; Free motion grasp rehabilitation; Kinesthetic perception; Object manipulation; Power assist robot; Stroke patient","Flow measurement; Health; Human robot interaction; Intelligent robots; Position control; Robotics; Virtual reality; Visual servoing; Free motion; Kinesthetic perceptions; Object manipulation; Power assist robots; Stroke patients; Patient rehabilitation",,,,,,,,,,,,,,,,"Barsotti, M., A full upper limb robotic exoskeleton for reaching and grasping rehabilitation triggered by MI-BCI (2015) Proceedings of the 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), Singapore, pp. 49-54; Loureiro, R.C.V., Harwin, W.S., Reach & grasp therapy: Design and control of a 9-DOF robotic neuro-rehabilitation system (2007) Proceedings of the 2007 IEEE 10Th International Conference on Rehabilitation Robotics, pp. 757-763. , Noordwijk; Loureiro, R.C.V., Lamperd, B., Collin, C., Harwin, W.S., Reach & grasp therapy: Effects of the Gentle/G system assessing sub-acute stroke whole-arm rehabilitation (2009) Proceedings of the 2009 IEEE International Conference on Rehabilitation Robotics, Kyoto, pp. 755-760; Podobnik, J., Mihelj, M., Munih, M., Upper limb and grasp rehabilitation and evaluation of stroke patients using HenRiE device (2009) Proceedings of the 2009 Virtual Rehabilitation International Conference, pp. 173-178. , Haifa; Lambercy, O., Rehabilitation of grasping and forearm pronation/supination with the Haptic Knob (2009) Proceedings of the 2009 IEEE International Conference on Rehabilitation Robotics, Kyoto, pp. 22-27; Ma, S., Varley, M., Shark, L.K., Richards, J., EMG biofeedback based VR system for hand rotation and grasping rehabilitation (2010) Proceedings of the 2010 14Th International Conference on Information Visualisation, pp. 479-484. , London; Crema, A., A hybrid tool for reaching and grasping rehabilitation: The ArmeoFES (2011) Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3047-3050. , Boston, MA; Kim, H., Kinematic data analysis for post-stroke patients following bilateral versus unilateral rehabilitation with an upper limb wearable robotic system (2013) IEEE Trans. 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Eng., 24 (12), pp. 1323-1332; Hussain, I., Meli, L., Pacchierotti, C., Prattichizzo, D., A soft robotic supernumerary finger and a wearable cutaneous finger interface to compensate the missing grasping capabilities in chronic stroke patients (2017) Proceedings of the 2017 IEEE World Haptics Conference (WHC), pp. 183-188. , Munich; Igarashi, K., Katsura, S., Position based free-motion data connecting by using minimum force-differential model (2015) Proceedings of the IEEE International Conference on Mechatronics, pp. 535-540; Kazerooni, H., Extender: A case study for human-robot interaction via transfer of power and information signals (1993) Proceedings of the IEEE International Workshop on Robot and Human Communication, pp. 10-20; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., Design and control of a 1DOF power assist robot for lifting objects based on human operator’s unimanual and bimanual weight discrimination (2009) Proceedings of the 2009 IEEE International Conference on Mechatronics and Automation (ICMA 2009), Changchun, pp. 3637-3644. , China, 9–12 August 2009; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., Study on optimum maneuverability in horizontal manipulation of objects with power-assist based on weight perception (2010) Proceedings of SPIE, 7500; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., Displacement-load force-perceived weight relationships in lifting objects with power-assist (2010) Proceedings of SPIE, 7500; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., Lifting objects with a power assist system: Effects of friction between human’s hand and object on perceived weight and load force (2009) Proceedings of the 2009 IEEE/SICE International Symposium on System Integration, Tokyo, Japan, pp. 77-82. , 29 November 2009; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., Human operator’s load force characteristics in lifting objects with a power assist robot in worst-cases conditions (2009) Proceedings of the 2009 IEEE Workshop on Advanced Robotics and Its Social Impacts, pp. 126-131. , Tokyo, Japan, 23–25 November 2009, pp; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., Human’s weight perception and load force characteristics in lifting objects with a power assist robot (2009) Proceedings of the 2009 IEEE International Symposium on Micro-Nanomechatronics and Human Science (MHS 2009), Nagoya, Japan, 8–11 November 2009, pp. 535-540; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., Control of a power assist robot for lifting objects based on human operator’s perception of object weight (2009) Proceedings of the 18Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2009), Toyama, Japan, 27 September–2 October 2009, pp. 84-90; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., Unimanual and bimanual weight discrimination in lifting objects with a power assist system (2009) Proceedings of the 2009 IEEE/ICROS-SICE International Joint Conference (ICCAS-SICE 2009), pp. 4787-4792. , Fukuoka, Japan, 18–21 August 2009, pp; Gordon, A., Forssberg, H., Johansson, R., Westling, G., Visual size cues in the programming of manipulative forces during precision grip (1991) Exp. Brain Res., 83 (3), pp. 477-482; Rahman, S.M.M., Ikeura, R., Cognition-based variable admittance control for active compliance in flexible manipulation of heavy objects with a power assist robotic system (2018) Robot. Biomim., 5 (7), pp. 1-25; Rahman, S.M.M., Ikeura, R., Weight-perception-based fixed and variable admittance control algorithms for unimanual and bimanual lifting of objects with a power assist robotic system (2018) Int. J. Adv. Robot. Syst, 15 (4), pp. 1-15; Rahman, S.M.M., Ikeura, R., MPC to optimise performance in power-assisted manipulation of industrial objects (2017) IET Electr. Power Appl., 11 (7), pp. 1235-1244; Rahman, S.M.M., Ikeura, R., Hayakawa, S., Yu, H., Lifting objects with power-assist: Weight-perception-based force control concepts to improve maneuverability (2012) Adv. Eng. Forum-Special Theme Mechatron. Inf. Technol., (2-3), pp. 277-280; Rahman, S.M.M., Ikeura, R., Cognition-based control and optimization algorithms for optimizing human-robot interactions in power assisted object manipulation (2016) J. Inf. Sci. Eng., 32 (5), pp. 1325-1344; Rahman, S.M.M., Ikeura, R., Weight-perception-based novel control of a power-assist robot for the cooperative lifting of light-weight objects (2012) Int. J. Adv. Robot. Syst., 9 (118), pp. 1-13; Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H., A psychophysical model of the power assist system for lifting objects (2009) Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, USA, pp. 4125-4130; Rahman, S.M.M., Ikeura, R., Weight-prediction-based predictive optimal position and force controls of a power assist robotic system for object manipulation (2016) IEEE Trans. Ind. Electron., 63 (9), pp. 5964-5975; Valdés, B.A., Usability testing of gaming and social media applications for stroke and cerebral palsy upper limb rehabilitation (2014) Proceedings of the 36Th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3602-3605; Kawai, Y., Honda, K., Kawai, H., Miyoshi, T., Fujita, M., Tele-rehabilitation system for human lower limb using electrical stimulation based on bilateral teleoperation (2017) Proceedings of the IEEE Conference on Control Technology and Applications, HI, pp. 1446-1451; Rahman, S.M.M., A novel variable impedance compact compliant series elastic actuator for human-friendly soft robotics applications (2012) Proceedings of the 21St IEEE International Symposium on Robot and Human Interactive Communication, Paris, France, 9–13 September 2012, pp. 19-24; Rahman, S.M.M., A novel variable impedance compact compliant series elastic actuator: Analysis of design, dynamics, materials and manufacturing (2013) Appl. Mech. Mater., 245 (99-106); Rahman, S.M.M., Ikeura, R., A novel variable impedance compact compliant ankle robot for overground gait rehabilitation and assistance (2012) Procedia Eng, 41, pp. 522-531; Rahman, S.M.M., Design of a modular knee-ankle-foot-orthosis using soft actuator for gait rehabilitation (2014) Proceedings of the 14Th Annual Conference on Towards Autonomous Robotic Systems (TAROS 2013). Lecture Notes in Computer Science, 8069, pp. 195-209. , vol., Oxford University, U.K., 28–30 August 2013, Springer, Heidelberg, July; Yu, H., Rahman, S.M.M., Zhu, C., Preliminary design analysis of a novel variable impedance compact compliant actuator (2011) Proceedings of 2011 IEEE International Conference on Robotics and Biomimetics, Phuket, Thailand, 7–11 December 2011, Pp. 2553–2558; Rahman, S.M.M., Ikeura, R., Hayakawa, S., Novel human-centric force control methods of power assist robots for object manipulation (2013) Proceedings of 2013 IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO 2013), Shenzhen, China, 12–14 December 2013, Pp. 340–345","Mizanoor Rahman, S.M.; The University of West FloridaUnited States; email: smrahman@uwf.edu","Arai K.Bhatia R.Kapoor S.",,"Springer","4th Future Technologies Conference, FTC 2019","24 October 2019 through 25 October 2019",,233129,21945357,9783030325220,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85075648958
"Wang W., Coutras C., Zhu M.","57201554862;6506637247;57226840540;","Empowering computing students with proficiency in robotics via situated learning",2021,"Smart Learning Environments","8","1","24","","",,,"10.1186/s40561-021-00167-6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117613318&doi=10.1186%2fs40561-021-00167-6&partnerID=40&md5=9e8862fca7f05e0e8c23f2ff366a5179","Department of Computer Science, Montclair State University, Montclair, NJ  07043, United States","Wang, W., Department of Computer Science, Montclair State University, Montclair, NJ  07043, United States; Coutras, C., Department of Computer Science, Montclair State University, Montclair, NJ  07043, United States; Zhu, M., Department of Computer Science, Montclair State University, Montclair, NJ  07043, United States","With the increasing employment of robots in multiple areas such as smart manufacturing and intelligent transportation, both undergraduate and graduate students from computing related majors (e.g., computer science and information technology) demonstrated strong interests in learning robotics technology to broaden their career opportunities. However, instilling computing students with robotics knowledge remains a challenge since most of them have limited pre-training in engineering subjects such as electronics and mechatronics. Therefore, robotics education for computing students demands an immersive real-world learning environment by considering both theories and intensive hands-on projects. Different from traditional textbook-directed robotics learning, in this study, a situated learning-based robotics education pedagogy is proposed for computing students to equip them with robotics expertise and foster their problem-solving skills in real-world human–robot interaction contexts. To create a realistic human–robot collaboration situation, a multi-modal collaborative robot is employed in the classroom-based learning community for the whole semester. Mini-project-based homework and team projects are designed for students to practice their critical thinking and hands-on experiences. The bidirectional-evaluation approach is utilized by the instructor and students to assess the quality of the proposed pedagogy. Practice results and student evaluations suggested that the proposed situated learning-based pedagogy and robotics curriculum provided computing students to learn robotics in an effective way, which was well recognized and accepted by students even most of them were beginners. Future work of this study is also discussed. © 2021, The Author(s).","Collaborative learning; Computer science; Hands-on experience; Human–robot interaction; Situated learning; Teamwork",,,,,,"National Science Foundation, NSF: 1742125; Center for Selective C-H Functionalization, National Science Foundation; Center for Hierarchical Manufacturing, National Science Foundation, CHM, NSF","This research is partially supported through a STEM + Computing grant from the Division of Research on Learning of the NSF (# 1742125).",,,,,,,,,,"Bahrin, M.A.K., Othman, M.F., Azli, N.H.N., Talib, M.F., Industry 4.0: A review on industrial automation and robotic (2016) Jurnal Teknologi, 78, pp. 6-13; Chitta, S., Sucan, I., Cousins, S., Moveit! (2012) IEEE Robotics & Automation Magazine, 19 (1), pp. 18-19; Corke, P., Greener, E., Philip, R., An innovative educational change: Massive open online courses in robotics and robotic vision (2016) IEEE Robotics & Automation Magazine, 23 (2), pp. 81-89; (2019), Davcev, K., Koceska, N., Koceski, S, Teaching basics of serial Robot Chains’s kinematics to computer science students; Djuric, A.M., Urbanic, R., Rickli, J., A framework for collaborative robot (CoBot) integration in advanced manufacturing systems (2016) SAE International Journal of Materials and Manufacturing, 9 (2), pp. 457-464; Freaks, E., (2018) Ultrasonic Ranging Module HC-SR04 Datasheet, , Accessed; Gaz, C., Cognetti, M., Oliva, A., Giordano, P.R., De Luca, A., Dynamic identification of the franka emika panda robot with retrieval of feasible parameters using penalty-based optimization (2019) IEEE Robotics and Automation Letters, 4 (4), pp. 4147-4154; Greeno, J.G., Engeström, Y., Learning in activity (2006) The Cambridge Handbook of the Learning Sciences (2Nd Ed., pp. 79-96. , Cambridge, England, Cambridge University Press; Hussin, A.A., Education 4.0 made simple: Ideas for teaching (2018) International Journal of Education and Literacy Studies, 6 (3), pp. 92-98; Jung, S., Experiences in developing an experimental robotics course program for undergraduate education (2012) IEEE Transactions on Education, 56 (1), pp. 129-136; Kirk, D., Macdonald, D., Situated learning in physical education (1998) Journal of Teaching in Physical Education, 17 (3), pp. 376-387; Krajcik, J.S., Blumenfeld, P.C., Project-based learning (2006) The Cambridge Handbook of the Learning Sciences (2Nd Ed., pp. 317-334. , Cambridge, England, Cambridge University Press; Kumar, D., Blank, D.S., Balch, T.R., O'hara, K.J., Guzdial, M., Tansley, S., (2008) Engaging Computing Students with AI and Robotics, pp. 55-60. , In AAAI spring symposium, Using AI to motivate greater participation in computer science; Lave, J., Wenger, E., (1991) Situated learning: Legitimate peripheral participation, , Cambridge University Press; Mataric, M.J., Robotics education for all ages (2004) In Proceedings of AAAI Spring Symposium on Accessible, Hands-On AI and Robotics Education.; Miner, A., (2018) The use and perceived effectiveness of situated learning in American Sign Language-English interpreter education, , Gallaudet University; Nourbakhsh, I.R., Crowley, K., Bhave, A., Hamner, E., Hsiu, T., Perez-Bergquist, A., The robotic autonomy mobile robotics course: Robot design, curriculum design and educational assessment (2005) Autonomous Robots, 18 (1), pp. 103-127; O’Brien, B.C., Battista, A., Situated learning theory in health professions education research: a scoping review (2020) Advances in Health Sciences Education, 25 (2), pp. 483-509; Paramasivam, V., Huang, J., Elliott, S., Cakmak, M., Computer science outreach with end-user robot-programming tools (2017) In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education, pp. 447-452; Puncreobutr, V., Education 4.0: New challenge of learning (2016) St. Theresa Journal of Humanities and Social Sciences, 2 (2), pp. 92-97; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., ROS: An open-source Robot Operating System In ICRA Workshop on Open Source Software, 2009, p. 5; Shamlian, S.V., Killfoile, K., Kellogg, R., Duvallet, F., Fun with robots: A student-taught undergraduate robotics course (2006) In Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006, pp. 369-374; Stein, D., (1998) Situated Learning in Adult Education: ERIC Clearinghouse on Adult, Career, and Vocational Education.; Thomas, J.W., (2000) A Review of Research on Project-Based Learning, , California; Wang, W., Li, R., Chen, Y., Diekel, Z.M., Jia, Y., Facilitating human–robot collaborative tasks by teaching-learning-collaboration from human demonstrations (2018) IEEE Transactions on Automation Science and Engineering, 16 (2), pp. 640-653; Wang, W., Li, R., Diekel, Z.M., Chen, Y., Zhang, Z., Jia, Y., Controlling object hand-over in human-robot collaboration via natural wearable sensing (2019) IEEE Transactions on Human-Machine Systems, 49 (1), pp. 59-71","Wang, W.; Department of Computer Science, United States; email: wangw@montclair.edu",,,"Springer",,,,,21967091,,,,"English","Smart Learn. Environ.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85117613318
"Strathearn C.","57204127919;","Artificial eyes with emotion and light responsive pupils for realistic humanoid robots",2021,"Informatics","8","4","64","","",,,"10.3390/informatics8040064","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116904508&doi=10.3390%2finformatics8040064&partnerID=40&md5=f59bc628adba90adb86705777af86e4f","School of Computing, Edinburgh Napier University, 9 Sighthill Ct, Edinburgh, EH11 4BN, United Kingdom","Strathearn, C., School of Computing, Edinburgh Napier University, 9 Sighthill Ct, Edinburgh, EH11 4BN, United Kingdom","This study employs a novel 3D engineered robotic eye system with dielectric elastomer actuator (DEA) pupils and a 3D sculpted and colourised gelatin iris membrane to replicate the appearance and materiality of the human eye. A camera system for facial expression analysis (FEA) was installed in the left eye, and a photo-resistor for measuring light frequencies in the right. Unlike previous prototypes, this configuration permits the robotic eyes to respond to both light and emotion proximal to a human eye. A series of experiments were undertaken using a pupil tracking headset to monitor test subjects when observing positive and negative video stimuli. A second test measured pupil dilation ranges to high and low light frequencies using a high-powered artificial light. This data was converted into a series of algorithms for servomotor triangulation to control the photosensitive and emotive pupil dilation sequences. The robotic eyes were evaluated against the pupillometric data and video feeds of the human eyes to determine operational accuracy. Finally, the dilating robotic eye system was installed in a realistic humanoid robot (RHR) and comparatively evaluated in a human-robot interaction (HRI) experiment. The results of this study show that the robotic eyes can emulate the average pupil reflex of the human eye under typical light conditions and to positive and negative emotive stimuli. However, the results of the HRI experiment indicate that replicating natural eye contact behaviour was more significant than emulating pupil dilation. © 2021 by the author. Licensee MDPI, Basel, Switzerland.","Dielectric elastomer actuation; Human-robot interaction; Pupil interfacing; Soft robotics; Uncanny valley",,,,,,,,,,,,,,,,,"Strathearn, C., Ma, M., Biomimetic pupils for augmenting eye emulation in humanoid robots (2018) Artif. 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"Soltani Zarrin R., Zeiaee A., Langari R., Buchanan J.J., Robson N.","56423791300;52564547700;7006275578;7202798067;12645452700;","Towards autonomous ergonomic upper-limb exoskeletons: A computational approach for planning a human-like path",2021,"Robotics and Autonomous Systems","145",,"103843","","",,,"10.1016/j.robot.2021.103843","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112133415&doi=10.1016%2fj.robot.2021.103843&partnerID=40&md5=8cfcc11d5c3a75a37c9e6b96eefa4637","Department of Mechanical Engineering, Texas A&M University, College StationTX, United States; Department of Engineering Technology and Industrial Distribution, Texas A&M University, College StationTX, United States; Department of Health and Kinesiology, Texas A&M University, College StationTX, United States; Department of Mechanical Engineering, California State University, Fullerton, CA, United States","Soltani Zarrin, R., Department of Mechanical Engineering, Texas A&M University, College StationTX, United States; Zeiaee, A., Department of Mechanical Engineering, Texas A&M University, College StationTX, United States; Langari, R., Department of Mechanical Engineering, Texas A&M University, College StationTX, United States, Department of Engineering Technology and Industrial Distribution, Texas A&M University, College StationTX, United States; Buchanan, J.J., Department of Health and Kinesiology, Texas A&M University, College StationTX, United States; Robson, N., Department of Mechanical Engineering, California State University, Fullerton, CA, United States","Computational path planning approaches can enable development of autonomous rehabilitation and assistive exoskeletons. Using a human-like reference behavior for such wearable systems can ensure safe, effective, and intuitive human–robot interaction. This is of significant importance since the quality of interaction and ergonomic considerations have a substantial effect on technology usability and acceptance by the users. This paper proposes a novel framework for generating human-like paths for wearable exoskeletons in the shoulder-elbow level. The introduced method is a two-stage process where a human-like reference path is planned in the configuration space of the human arm, followed by an analytical transformation that directly maps the derived path to the configuration space of the exoskeleton. The analytical mapping presented is a function of the kinematic parameters of the system and can be adapted for other upper-limb exoskeletons. As a case study, the proposed method is used for generating human-like reference motions for a six-degree-of-freedom exoskeleton supporting scapulohumeral rhythm, glenohumeral rotations, and elbow flexion/extension. Firstly, it is shown that reaching motions associated with activities of daily living can be predicted with high accuracy in the human joint space. This is demonstrated by analyzing the experimental data collected from healthy subjects. Subsequently, it is verified through kinematic analysis that the transformation of generated paths to the exoskeleton configuration space does not alter their spatial profile in the task space. © 2021 Elsevier B.V.","Computational models; Human-like motion; Path planning; Scapulohumeral rhythm; Upper-limb exoskeletons","Agricultural robots; Behavioral research; Degrees of freedom (mechanics); Ergonomics; Kinematics; Social robots; Wearable technology; Activities of Daily Living; Computational approach; Configuration space; Kinematic parameters; Quality of interaction; Reference behaviors; Scapulohumeral rhythms; Six degree-of-freedom; Exoskeleton (Robotics)",,,,,"Qatar National Research Fund, QNRF: 7–1685–2 –626","This work was supported in part by the Qatar National Research Fund under Grant NPRP No.: 7–1685–2 –626 .",,,,,,,,,,"Gopura, R., Bandara, D., Kiguchi, K., Mann, G.K., Developments in hardware systems of active upper-limb exoskeleton robots: A review (2016) Robot. Auton. 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"Friedl W.A., Roa M.A.","24479559400;24476753400;","Experimental Evaluation of Tactile Sensors for Compliant Robotic Hands",2021,"Frontiers in Robotics and AI","8",,"704416","","",,,"10.3389/frobt.2021.704416","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117892015&doi=10.3389%2ffrobt.2021.704416&partnerID=40&md5=2051846843f8e76f10f500994f85a4ee","German Aerospace Center—DLR, Institute of Robotics and Mechatronics, Wessling, Germany","Friedl, W.A., German Aerospace Center—DLR, Institute of Robotics and Mechatronics, Wessling, Germany; Roa, M.A., German Aerospace Center—DLR, Institute of Robotics and Mechatronics, Wessling, Germany","The sense of touch is a key aspect in the human capability to robustly grasp and manipulate a wide variety of objects. Despite many years of development, there is still no preferred solution for tactile sensing in robotic hands: multiple technologies are available, each one with different benefits depending on the application. This study compares the performance of different tactile sensors mounted on the variable stiffness gripper CLASH 2F, including three commercial sensors: a single taxel sensor from the companies Tacterion and Kinfinity, the Robotic Finger Sensor v2 from Sparkfun, plus a self-built resistive 3 × 3 sensor array, and two self-built magnetic 3-DoF touch sensors, one with four taxels and one with one taxel. We verify the minimal force detectable by the sensors, test if slip detection is possible with the available taxels on each sensor, and use the sensors for edge detection to obtain the orientation of the grasped object. To evaluate the benefits obtained with each technology and to assess which sensor fits better the control loop in a variable stiffness hand, we use the CLASH gripper to grasp fruits and vegetables following a published benchmark for pick and place operations. To facilitate the repetition of tests, the CLASH hand is endowed with tactile buttons that ease human–robot interactions, including execution of a predefined program, resetting errors, or commanding the full robot to move in gravity compensation mode. © Copyright © 2021 Friedl and Roa.","grasp benchmarking; grasp stiffness; hand design; slippage detection; tactile sensors",,,,,,,"The authors want to thank Florian Schmidt for the support on the software development for controlling the hand, Ashok Meenakshi Sundaram for helping with the integration of planner and vision for the execution of demonstrations, Nikolaus Seitz for the development of electronic boards, and all the people working at the DLR workshop for their great support during the construction process.",,,,,,,,,,"Ajoudani, A., Hocaoglu, E., Altobelli, A., Rossi, M., Battaglia, E., Tsagarakis, N., Reflex Control of the Pisa/IIT SoftHand during Object Slippage (2016) Proc. IEEE Int. Conf. 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Lett, 3, pp. 124-131; Wall, V., Brock, O., Multi-task Sensorization of Soft Actuators Using Prior Knowledge (2019) Proc. IEEE Int. Conf. Robotics and Automation (ICRA), pp. 9416-9421. , IEEE; Weiner, P., Neef, C., Shibata, Y., Nakamura, Y., Asfour, T., An Embedded, Multi-Modal Sensor System for Scalable Robotic and Prosthetic Hand Fingers (2019) Sensors, 20, p. 101; Wettels, N., Parnandi, A.R., Ji-Hyun Moon, J.-H., Loeb, G.E., Sukhatme, G.S., Grip Control Using Biomimetic Tactile Sensing Systems (2009) IEEE/ASME Trans. Mechatron, 14, pp. 718-723","Friedl, W.A.; German Aerospace Center—DLR, Germany; email: werner.friedl@dlr.de",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85117892015
"Fiedler M.-A., Werner P., Khalifa A., Al-Hamadi A.","57218453987;54685435400;57242588900;6603014610;","Sfpd: Simultaneous face and person detection in real-time for human–robot interaction",2021,"Sensors","21","17","5918","","",,,"10.3390/s21175918","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114114244&doi=10.3390%2fs21175918&partnerID=40&md5=03e656ae3f20e33b5d12ff4325b28be8","Neuro-Information Technology Group, Otto von Guericke University Magdeburg, Magdeburg, 39106, Germany","Fiedler, M.-A., Neuro-Information Technology Group, Otto von Guericke University Magdeburg, Magdeburg, 39106, Germany; Werner, P., Neuro-Information Technology Group, Otto von Guericke University Magdeburg, Magdeburg, 39106, Germany; Khalifa, A., Neuro-Information Technology Group, Otto von Guericke University Magdeburg, Magdeburg, 39106, Germany; Al-Hamadi, A., Neuro-Information Technology Group, Otto von Guericke University Magdeburg, Magdeburg, 39106, Germany","Face and person detection are important tasks in computer vision, as they represent the first component in many recognition systems, such as face recognition, facial expression analysis, body pose estimation, face attribute detection, or human action recognition. Thereby, their detection rate and runtime are crucial for the performance of the overall system. In this paper, we combine both face and person detection in one framework with the goal of reaching a detection performance that is competitive to the state of the art of lightweight object-specific networks while maintaining real-time processing speed for both detection tasks together. In order to combine face and person detection in one network, we applied multi-task learning. The difficulty lies in the fact that no datasets are available that contain both face as well as person annotations. Since we did not have the resources to manually annotate the datasets, as it is very time-consuming and automatic generation of ground truths results in annotations of poor quality, we solve this issue algorithmically by applying a special training procedure and network architecture without the need of creating new labels. Our newly developed method called Simultaneous Face and Person Detection (SFPD) is able to detect persons and faces with 40 frames per second. Because of this good trade-off between detection performance and inference time, SFPD represents a useful and valuable real-time framework especially for a multitude of real-world applications such as, e.g., human–robot interaction. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Face detection; Multi-task learning; Person detection; Real-time detection","Economic and social effects; Human robot interaction; Multi-task learning; Network architecture; Attribute detections; Automatic Generation; Body pose estimation; Detection performance; Facial expression analysis; Human-action recognition; Real-time processing speed; Training procedures; Face recognition; facial expression; facial recognition; human; image processing; robotics; Facial Expression; Facial Recognition; Humans; Image Processing, Computer-Assisted; Robotics",,,,,"03ZZ04X02B; Bundesministerium für Bildung und Forschung, BMBF: 03ZZ0448L, 03ZZ0470","Funding: This work was funded by the German Federal Ministry of Education and Research (BMBF) under Grant Nos. 03ZZ0448L (RoboAssist), 03ZZ0470 (HuBA), and 03ZZ04X02B (RoboLab) within the Zwanzig20 Alliance 3Dsensation. We acknowledge support for the Article Processing Charge by the Open Access Publication Fund of Magdeburg University. 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Intell, 41, pp. 121-135; Ranjan, R., Sankaranarayanan, S., Castillo, C.D., Chellappa, R., An All-In-One convolutional neural network for face analysis (2017) Proceedings of the IEEE International Conference on Automatic Face & Gesture Recognition (FG), pp. 17-24. , Washington, DC, USA, 30 May–3 June; Levi, G., Hassner, T., Age and gender classification using convolutional neural networks Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 34-42. , Boston, MA, USA, 7–12 June 2015; Zhang, Z., Luo, P., Loy, C.C., Tang, X., Facial landmark detection by deep multi-task learning (2014) Proceedings of the European Conference on Computer Vision (ECCV), , Zurich, Switzerland, 6–12 September; Gkioxari, G., Hariharan, B., Girshick, R.B., Malik, J., (2014) R-CNNs for pose estimation and action detection, , arXiv arXiv:1406.5212; Chen, D., Ren, S., Wei, Y., Cao, X., Sun, J., Joint cascade face detection and alignment (2014) Proceedings of the European Conference on Computer Vision (ECCV), , Zurich, Switzerland, 6–12 September; Saxen, F., Handrich, S., Werner, P., Othman, E., Al-Hamadi, A., Detecting arbitrarily rotated faces for face analysis Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 3945-3949. , Taipei, Taiwan, 22–25 September 2019; He, K., Fu, Y., Xue, X., (2017) A jointly learned deep architecture for facial attribute analysis and face detection in the wild, , arXiv arXiv:1707.08705; Wu, H., Zhang, K., Tian, G., Simultaneous face detection and pose estimation using convolutional neural network cascade (2018) IEEE Access, 6, pp. 49563-49575; Cipolla, R., Gal, Y., Kendall, A., Multi-task learning using uncertainty to weigh losses for scene geometry and semantics Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7482-7491. , Salt Lake City, UT, USA, 18–23 June 2018; Simonyan, K., Zisserman, A., (2015) Very deep convolutional networks for large-scale image recognition, , arXiv arXiv:1409.1556; Glorot, X., Bengio, Y., Understanding the difficulty of training deep feedforward neural networks (2010) Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), , Sardinia, Italy, 13–15 May; Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S., Generalized intersection over union: A metric and a loss for bounding box regression Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 658-666. , Long Beach, CA, USA, 15–20 June 2019; Lin, T.Y., Maire, M., Belongie, S.J., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L., Microsoft COCO: Common objects in context (2014) Proceedings of the European Conference on Computer Vision (ECCV), , Zurich, Switzerland, 6–12 September; Yang, B., Yan, J., Lei, Z., Li, S.Z., Aggregate channel features for multi-view face detection Proceedings of the IEEE International Joint Conference on Biometrics, pp. 1-8. , Clearwater, FL, USA, 29 September–2 October 2014; Yang, S., Luo, P., Loy, C.C., Tang, X., From facial parts responses to face detection: A deep learning approach Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 3676-3684. , Santiago, Chile, 7–13 December 2015; Ohn-Bar, E., Trivedi, M.M., To boost or not to boost? On the limits of boosted trees for object detection Proceedings of the International Conference on Pattern Recognition (ICPR), pp. 3350-3355. , Cancun, Mexico, 4–8 December 2016; Zhang, C., Xu, X., Tu, D., (2018) Face detection using improved Faster RCNN, , arXiv arXiv:1802.02142","Fiedler, M.-A.; Neuro-Information Technology Group, Germany; email: marc-andre.fiedler@ovgu.de",,,"MDPI",,,,,14248220,,,"34502809","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85114114244
"Kumar Ashe A., Krishna K.M.","57295499400;7102445320;","Maneuvering Intersections Occlusions Using MPC-Based Prioritized Tracking for Differential Drive Person following Robot",2021,"IEEE International Conference on Automation Science and Engineering","2021-August",,,"1352","1357",,,"10.1109/CASE49439.2021.9551618","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116960791&doi=10.1109%2fCASE49439.2021.9551618&partnerID=40&md5=58aa9c89c8fa01fc406ffa2c09e9fdc1","Robotics Research Center (RRC), International Institue of Information Technology (IIIT-H), Telangana, Hyderabad, 500032, India","Kumar Ashe, A., Robotics Research Center (RRC), International Institue of Information Technology (IIIT-H), Telangana, Hyderabad, 500032, India; Krishna, K.M., Robotics Research Center (RRC), International Institue of Information Technology (IIIT-H), Telangana, Hyderabad, 500032, India","Human-robot interaction, particularly in wheeled mobile robots that can autonomously assist humans to traverse dynamically changing environments is a field of active research. Integrated motion planning and obstacle-avoidance pose a considerable challenge for an autonomous person-following robot (PFR). And, scenarios with intersections and occlusions along the path only increase the complexity in sustained tracking. In this paper, we use model predictive control (MPC) with early-relocation (ER) strategy to formulate a prioritized tracking scheme and implement it for a differential-drive system. Our approach ensures that the target person stays within the field of view (FOV) of the PFR consistently, even while it maneuvers intersections or crowded spots, by adding new locations to its updated path. As trajectory generation in such cases must be incremental to accommodate new information, the use of efficient representations is key. To that end, we build this social representation of following a person directly into the controller itself. MPC can naturally handle such state and input limitations as constraints to solve an on-line optimization at each time step. A non-linear MPC with ER is thus devised and tested with increasing levels of complexity arising from occlusions due to the map and its dynamic actors. By using 2D simulations, we show that for slow and medium walking speeds of the target person, the controller can plan maneuvers with an adequate margin of over 20 Hz apt for achieving a near real-time person-following behaviour. © 2021 IEEE.",,"Collision avoidance; Controllers; Human robot interaction; Mobile robots; Motion planning; Navigation; Predictive control systems; Robot programming; Changing environment; Differential drive; Drive systems; Humans-robot interactions; Integrated motion planning; Model-predictive control; Obstacles avoidance; Person following; Tracking scheme; Use-model; Model predictive control",,,,,,,,,,,,,,,,"Piezzo, C., Suzuki, K., Feasibility study of a socially assistive humanoid robot for guiding elderly individuals during walking (2017) Future Internet, 9 (3), p. 30; Nishimura, S., Itou, K., Kikuchi, T., Takemura, H., Mizoguchi, H., A study of robotizing daily items for an autonomous carrying system-development of person following shopping cart robot (2006) 2006 9th International Conference on Control, Automation, Robotics and Vision, pp. 1-6; Lynn, G., Olsen, C., Interview with greg lynn: Forward-thinking land drones (2018) Technology- Architecture+ Design, 2 (2), pp. 143-145; Jing, X.-J., Behavior dynamics based motion planning of mobile robots in uncertain dynamic environments (2005) Robotics and Autonomous Systems, 53 (2), pp. 99-123; Chan, W.P., Radmard, S., Quan Hew, Z., Morris, J., Croft, E., Van Der, L., Machiel, H.F., (2020) Autonomous Person-Specific following Robot.; Ren, Q., Zhao, Q., Qi, H., Li, L., Real-time target tracking system for person-following robot (2016) 2016 35th Chinese Control Conference (CCC), pp. 6160-6165; Alonso, J., Milans, V., Prez, J., Onieva, E., Gonzlez, C., De Pedro, T., Autonomous vehicle control systems for safe crossroads (2011) Transportation Research Part C: Emerging Technologies, 19 (6), pp. 1095-1110; Shu, K., Yu, H., Chen, X., Chen, L., Wang, Q., Li, L., Cao, D., (2020) Autonomous Driving at Intersections: A Critical-Turning-Point Approach for Left Turns; Hubmann, C., Becker, M., Althoff, D., Lenz, D., Stiller, C., Decision making for autonomous driving considering interaction and uncertain prediction of surrounding vehicles (2017) 2017 IEEE Intelligent Vehicles Symposium (IV), pp. 1671-1678; Sezer, V., Bandyopadhyay, T., Rus, D., Frazzoli, E., Hsu, D., Towards autonomous navigation of unsignalized intersections under uncertainty of human driver intent (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3578-3585; Kuhne, F., Fetter Lages, W., Gomes Da Silva, J., Jr., Model predictive control of a mobile robot using linearization (2004) Proceedings of Mechatronics and Robotics, pp. 525-530; Md Jahidul, I., Hong, J., Sattar, J., Personfollowing by autonomous robots: A categorical overview (2019) The International Journal of Robotics Research, 38 (14), pp. 1581-1618; Ma, L., Xue, J., Kawabata, K., Zhu, J., Ma, C., Zheng, N., Efficient sampling-based motion planning for onroad autonomous driving (2015) IEEE Transactions on Intelligent Transportation Systems, 16 (4), pp. 1961-1976; Gao, Y., Lin, T., Borrelli, F., Tseng, E., Hrovat, D., Predictive control of autonomous ground vehicles with obstacle avoidance on slippery roads (2010) Dynamic Systems and Control Conference, pp. 265-272. , 44175; Dudek, G., Jenkin, M., (2010) Computational Principles of Mobile Robotics, , Cambridge university press; Gu, D., Hu, H., Receding horizon tracking control of wheeled mobile robots (2006) IEEE Transactions on Control Systems Technology, 14 (4), pp. 743-749; Cheng, H., Chen, H., Liu, Y., Topological indoor localization and navigation for autonomous mobile robot (2015) IEEE Transactions on Automation Science and Engineering, 12 (2), pp. 729-738. , April",,,,"IEEE Computer Society","17th IEEE International Conference on Automation Science and Engineering, CASE 2021","23 August 2021 through 27 August 2021",,172370,21618070,9781665418737,,,"English","IEEE Int. Conf. Autom. Sci. Eng.",Conference Paper,"Final","",Scopus,2-s2.0-85116960791
"Hajjaj S.S.H., Karim N.K.A.","55812832600;57194869887;","Adoption of robotics in the TV film industry: The IoT-enabled robotic camera dolly",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"1256","1261",,,"10.1109/RO-MAN50785.2021.9515414","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115103216&doi=10.1109%2fRO-MAN50785.2021.9515414&partnerID=40&md5=b7034b4a66776aad1b7b743f48703cc4","Universiti Tenaga Nasional, National Energy University, Centre for Advanced Mechatronics and Robotics (CaMaRo), !!!Institute of Informatics and Computing in Energy (IICE), Malaysia; Universiti Sains Islam Malaysia (USIM), Muslims in Creative Media Industries (MiCMI), Faculty of Leadership and Management, Malaysia","Hajjaj, S.S.H., Universiti Tenaga Nasional, National Energy University, Centre for Advanced Mechatronics and Robotics (CaMaRo), !!!Institute of Informatics and Computing in Energy (IICE), Malaysia; Karim, N.K.A., Universiti Sains Islam Malaysia (USIM), Muslims in Creative Media Industries (MiCMI), Faculty of Leadership and Management, Malaysia","The adoption of Robotics in TV Film is not on par with other industries. In this work, we investigate the possible causes. If we think of TV robots (robotic cameras, dollys) as social robots, their level of adoption in the industry would depend on how Social they are, which we can improve through positive perception and an effective Human-Robot Interaction framework. To validate this, we interviewed a crew of camera operators about TV robots. 60% reported negative feelings; fears of loss of jobs and relevance, but 60% wanted to learn more. Based on that, we developed an Internet of Things (IoT)- enabled Robotic Camera Dolly (IRCD) for the operators, with a companion interview/guidance protocol; the Pre and Post Interview Protocol, to monitor their reactions. Next, we deployed the IRCD at their TV station, where they completed a set of filming tasks using the IRCD. There was a jump in perception, with 60% positive feelings, 95% wanting to learn more, and 95% successfully completing their robot-powered filming tasks. Also, the IRCD registered a 0.15s response time and 5s video delay time, well within industry limits. These results show that positive perception and effective HRI could stimulate the adoption of Robotics in the TV Film industry. An additional unplanned result; the IRCD, with its remote operations, enabled creators to continue making content while observing social distancing, thus reducing the financial impact of Covid-19 Lockdowns on TV Film Production. © 2021 IEEE.",,"Agricultural robots; Cameras; Entertainment industry; Industrial robots; Internet of things; Robotics; Film production; Financial impacts; Internet of Things (IOT); Interview protocols; Positive feelings; Remote operation; Robotic cameras; TV stations; Social robots",,,,,"Universiti Sains Islam Malaysia, USIM: J510050002/2021097","ACKNOWLEDGMENT We would like to thank the following for their support; TV Al-Hijrah, the Innovative & Research Management Centre, UNITEN, and the Muslims in Creative Media Industries (MiCMI), USIM. This work was funded by the UNITEN BOLD refresh fund. Grant Code: J510050002/2021097.","We would like to thank the following for their support; TV Al-Hijrah, the Innovative and Research Management Centre, UNITEN, and the Muslims in Creative Media Industries (MiCMI), USIM. This work was funded by the UNITEN BOLD refresh fund. Grant Code: J510050002/2021097",,,,,,,,,"(2019) The Contribution of the UK-based Film, Tv and Tv Related Industries to the UK Economy, and Growth Prospects to 2025, , Oliver and Ohlbaum Associates Ltd, Tech. Rep; (2017) Economic Contribution of the Film and Television Industry in India, , Motion Picture Assoc. (MPA) India, Ltd., Tech. Rep. 2018; Salichs, M.A., Encinar, I.P., Salichs, E., Castro-González, L., Malfaz, M., Study of scenarios and technical requirements of a social assistive robot for Alzheimer's disease patients and their caregivers (2016) International Journal of Social Robotics, 8 (1), pp. 85-102. , https://doi.org/10.1007/s12369-015-0319-6, Jan; Broadbent, E., Feerst, D.A., Lee, S.H., Robinson, H., Albo-Canals, J., Ahn, H.S., MacDonald, B.A., How could companion robots be useful in rural schools? (2018) International Journal of Social Robotics, 10 (3), pp. 295-307. , https://doi.org/10.1007/s12369-017-0460-5, Jun; Johal, W., Castellano, G., Tanaka, F., Okita, S., Robots for learning (2018) Int. J. Of Social Robotics, 10 (3), pp. 293-294. , https://doi.org/10.1007/s12369-018-0481-8, Jun; Heerink, M., Vanderborght, B., Broekens, J., Albó-Canals, J., New friends: Social robots in therapy and education (2016) International Journal of Social Robotics, 8 (4), pp. 443-444. , https://doi.org/10.1007/s12369-016-0374-7, Aug; Vermesan, O., Bröring, A., Tragos, E., Serrano, M., Bacciu, D., Chessa, S., Gallicchio, C., Bahr, R., Internet of robotic things-converging sensing/actuating, hyperconnectivity, artificial intelligence and iot platforms, ser (2017) Cognitive Hyperconnected Digital Transformation: Internet of Things Intelligence Evolution, pp. 97-155. , www.scopus.com, cited By: 7; Nejkovic, V., Petrovic, N., Tosic, M., Milosevic, N., Semantic approach to riot autonomous robots mission coordination (2020) Robotics and Autonomous Systems, 126, p. 103438. , shorturl. At/efnwN; Verner, I.M., Cuperman, D., Romm, T., Reitman, M., Chong, S.K., Gong, Z., Intelligent robotics in high school: An educational paradigm for the industry 4. 0 era. ser (2018) Computer Science; Deuze, M., (2007) Media Work (Digital Media and Society Series), , Polity Press; Hesmondhalgh, D., (2010) Creative Labour: Media Work in Three Cultural Industries, , Routledge; Hajjaj, S.S.H., Sahari, K.S.M., Review of agriculture robotics: Practicality and feasibility (2016) 2016 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), pp. 194-198. , Dec; Yun, S.-S., Kim, M., Choi, M.-T., Easy interface and control of tele-education robots (2013) International Journal of Social Robotics, 5 (3), pp. 335-343. , https://doi.org/10.1007/s12369-013-0192-0, Aug; Tom, C., Automation, job characteristics and job insecurity (2020) Int. J. 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Toward attitudes on education robots, predictors of attitudes, and application potentials for education robots (2015) Int. J. Of Social Robotics, 7 (5), pp. 875-888. , https://doi.org/10.1007/s12369-015-0308-9, Nov; Dautenhahn, K., Methodology & themes of human-robot interaction: A growing research field (2007) International Journal of Advanced Robotic Systems, 4 (1), p. 15. , https://doi.org/10.5772/5702; Caldwell, J.T., (2008) Production Culture: Industrial Reflexivity and Critical Practice in Film and Television, , Duke University Press; Grindstaff, L., (2002) The Money Shot: Trash, Class, and the Making of TV Talk Shows, , University of Chicago Press; Born, G., (2005) Uncertain Vision: Birt, Dyke and the Reinvention of the BBC, , Random House UK; Pennington, A., (2018) The Rise of the Robotic Camera, , Oct.accessed=2/14/2020. : shorturl. At/pMWZ3; Beacham, F., (2014) Cost-cutting Boosts the Use of Robots in Television Studios, , Oct. Accessed=2/14/2020. : shorturl. 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Of Qualitative Methods, 14, pp. 53-64; Collins, D., Pretesting survey instruments: An overview of cognitive methods (2003) Quality of Life Research, (12), p. 229. , https://link.springer.com/article/10.1023/A:1023254226592; Drennan, J., Cognitive interviewing: Verbal data in the design and pretesting of questionnaires (2003) Journal of Advanced Nursing, 42, pp. 57-63. , Apr; Foddy, W., An empirical evaluation of in-depth probes used to pretest survey questions (2020) Sociological Methods & Research, 27 (1), pp. 103-133. , https://doi.org/10.1177/0049124198027001003, Oct; Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D., User acceptance of information technology: Toward a unified view (2020) MIS Quarterly, 27 (3), pp. 425-478. , http://www.jstor.org/stable/30036540, Oct",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115103216
"Hartz A., Guth B., Jording M., Vogeley K., Schulte-Rüther M.","57200858069;57226861028;55613252200;7004117310;18234240900;","Temporal Behavioral Parameters of On-Going Gaze Encounters in a Virtual Environment",2021,"Frontiers in Psychology","12",,"673982","","",,,"10.3389/fpsyg.2021.673982","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113167115&doi=10.3389%2ffpsyg.2021.673982&partnerID=40&md5=554746d7a279dcb8a8a618e421a4374b","Molecular Neuroscience and Neuroimaging (INM-11), Institute of Neuroscience and Medicine, Jülich Research Center, Jülich, Germany; Translational Brain Research, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital RWTH Aachen, Aachen, Germany; Department of Psychiatry and Psychotherapy, University Hospital Cologne, Cologne, Germany; Cognitive Neuroscience (INM-3), Institute of Neuroscience and Medicine, Jülich Research Center, Jülich, Germany; Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany","Hartz, A., Molecular Neuroscience and Neuroimaging (INM-11), Institute of Neuroscience and Medicine, Jülich Research Center, Jülich, Germany, Translational Brain Research, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital RWTH Aachen, Aachen, Germany; Guth, B., Translational Brain Research, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital RWTH Aachen, Aachen, Germany; Jording, M., Department of Psychiatry and Psychotherapy, University Hospital Cologne, Cologne, Germany, Cognitive Neuroscience (INM-3), Institute of Neuroscience and Medicine, Jülich Research Center, Jülich, Germany; Vogeley, K., Department of Psychiatry and Psychotherapy, University Hospital Cologne, Cologne, Germany, Cognitive Neuroscience (INM-3), Institute of Neuroscience and Medicine, Jülich Research Center, Jülich, Germany; Schulte-Rüther, M., Molecular Neuroscience and Neuroimaging (INM-11), Institute of Neuroscience and Medicine, Jülich Research Center, Jülich, Germany, Translational Brain Research, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital RWTH Aachen, Aachen, Germany, Cognitive Neuroscience (INM-3), Institute of Neuroscience and Medicine, Jülich Research Center, Jülich, Germany, Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany","To navigate the social world, humans heavily rely on gaze for non-verbal communication as it conveys information in a highly dynamic and complex, yet concise manner: For instance, humans utilize gaze effortlessly to direct and infer the attention of a possible interaction partner. Many traditional paradigms in social gaze research though rely on static ways of assessing gaze interaction, e.g., by using images or prerecorded videos as stimulus material. Emerging gaze contingent paradigms, in which algorithmically controlled virtual characters can respond flexibly to the gaze behavior of humans, provide high ecological validity. Ideally, these are based on models of human behavior which allow for precise, parameterized characterization of behavior, and should include variable interactive settings and different communicative states of the interacting agents. The present study provides a complete definition and empirical description of a behavioral parameter space of human gaze behavior in extended gaze encounters. To this end, we (i) modeled a shared 2D virtual environment on a computer screen in which a human could interact via gaze with an agent and simultaneously presented objects to create instances of joint attention and (ii) determined quantitatively the free model parameters (temporal and probabilistic) of behavior within this environment to provide a first complete, detailed description of the behavioral parameter space governing joint attention. This knowledge is essential to enable the modeling of interacting agents with a high degree of ecological validity, be it for cognitive studies or applications in human-robot interaction. © Copyright © 2021 Hartz, Guth, Jording, Vogeley and Schulte-Rüther.","ecological validity; eye tracking; gaze contingency; human-agent interaction; joint attention; social gaze",,,,,,"Horizon 2020 Framework Programme, H2020: 824128; European Commission, EC; Deutsche Forschungsgemeinschaft, DFG: SCHU 2493/2-1; Bundesministerium für Bildung und Forschung, BMBF: 01GP1822, 16SV7244","This work is a part of the project EMOTISK from the Bringing technology to the people research program on human-machine interaction, funded by the Federal German Ministry of Education and Research (grant number for MS-R: 16SV7242/for KV: 16SV7244). MS-R received funding by the Excellence Initiative of the German federal and state governments and was supported by the DFG (German Research Foundation, Grant Number: SCHU 2493/2-1). KV was supported by the EC, Horizon 2020 Framework Programme, FET Proactive [Grant agreement ID: 824128] and by the EC, with funds from BMBF, Germany [Grant 01GP1822] within the funding framework Network of European Funding for Neuroscience Research (NEURON) under the ERA-NET scheme.",,,,,,,,,,"Argyle, M., Cook, M., (1976) Gaze and Mutual Gaze, , Cambridge, Cambridge University Press; Bayliss, A.P., Murphy, E., Naughtin, C.K., Kritikos, A., Schilbach, L., Becker, S.I., “Gaze leading”: initiating simulated joint attention influences eye movements and choice behavior (2013) J. Exp. Psychol, 142, p. 76. , 22800442; Bayliss, A.P., Paul, M.A., Cannon, P.R., Tipper, S.P., Gaze cuing and affective judgments of objects: i like what you look at (2006) Psychon. Bull. 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"Ragni F., Archetti L., Roby-Brami A., Amici C., Saint-Bauzel L.","57215306336;57226475106;55903756400;34167509800;24484179300;","Intention prediction and human health condition detection in reaching tasks with machine learning techniques†",2021,"Sensors","21","16","5253","","",,,"10.3390/s21165253","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111701228&doi=10.3390%2fs21165253&partnerID=40&md5=ee5fd63ff8068efe2bc8dd9e8ad2769d","Department of Mechanical and Industrial Engineering, University of Brescia, via Branze, 38, Brescia, 25123, Italy; ISIR (Institute of Intelligent Systems and Robotics), UMR CNRS 7222, AGATHE Team INSERM U 1150, Sorbonne Université, Paris, 75005, France","Ragni, F., Department of Mechanical and Industrial Engineering, University of Brescia, via Branze, 38, Brescia, 25123, Italy; Archetti, L., Department of Mechanical and Industrial Engineering, University of Brescia, via Branze, 38, Brescia, 25123, Italy; Roby-Brami, A., ISIR (Institute of Intelligent Systems and Robotics), UMR CNRS 7222, AGATHE Team INSERM U 1150, Sorbonne Université, Paris, 75005, France; Amici, C., Department of Mechanical and Industrial Engineering, University of Brescia, via Branze, 38, Brescia, 25123, Italy; Saint-Bauzel, L., ISIR (Institute of Intelligent Systems and Robotics), UMR CNRS 7222, AGATHE Team INSERM U 1150, Sorbonne Université, Paris, 75005, France","Detecting human motion and predicting human intentions by analyzing body signals are challenging but fundamental steps for the implementation of applications presenting human– robot interaction in different contexts, such as robotic rehabilitation in clinical environments, or collaborative robots in industrial fields. Machine learning techniques (MLT) can face the limit of small data amounts, typical of this kind of applications. This paper studies the illustrative case of the reaching movement in 10 healthy subjects and 21 post-stroke patients, comparing the performance of linear discriminant analysis (LDA) and random forest (RF) in: (i) predicting the subject’s intention of moving towards a specific direction among a set of possible choices, (ii) detecting if the subject is moving according to a healthy or pathological pattern, and in the case of discriminating the damage location (left or right hemisphere). Data were captured with wearable electromagnetic sensors, and a sub-section of the acquired signals was required for the analyses. The possibility of detecting with which arm (left or right hand) the motion was performed, and the sensitivity of the MLT to variations in the length of the signal sub-section were also evaluated. LDA and RF prediction accuracies were compared: Accuracy improves when only healthy subjects or longer signals portions are considered up to 11% and at least 10%, respectively. RF reveals better estimation performance both as intention predictor (on average 59.91% versus the 62.19% of LDA), and health condition detector (over 90% in all the tests). © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Human intention prediction; Machine learning; Reaching movement; Wearable sensors","Damage detection; Decision trees; Discriminant analysis; Forecasting; Industrial robots; Machine learning; Predictive analytics; Sensitivity analysis; Clinical environments; Electromagnetic sensors; Estimation performance; Intention predictions; Linear discriminant analysis; Machine learning techniques; Post stroke patients; Robotic rehabilitation; Human robot interaction; behavior; electromyography; hand; human; machine learning; movement (physiology); Electromyography; Hand; Humans; Intention; Machine Learning; Movement",,,,,,,,,,,,,,,,"Preatoni, E., Nodari, S., Lopomo, N.F., Supervised Machine Learning Applied to Wearable Sensor Data Can Accurately Classify Functional Fitness Exercises Within a Continuous Workout (2020) Front. Bioeng. 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"van Zoelen E.M., van den Bosch K., Rauterberg M., Barakova E., Neerincx M.","57211354974;35103709000;6701479494;35614959800;9133405200;","Identifying Interaction Patterns of Tangible Co-Adaptations in Human-Robot Team Behaviors",2021,"Frontiers in Psychology","12",,"645545","","",,2,"10.3389/fpsyg.2021.645545","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111068138&doi=10.3389%2ffpsyg.2021.645545&partnerID=40&md5=3b8ade9797c8b49bf212631f54abdd75","Interactive Intelligence, Intelligent Systems Department, Delft University of Technology, Delft, Netherlands; Human-Machine Teaming, Netherlands Organization for Applied Scientific Research (TNO), Soesterberg, Netherlands; Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands","van Zoelen, E.M., Interactive Intelligence, Intelligent Systems Department, Delft University of Technology, Delft, Netherlands, Human-Machine Teaming, Netherlands Organization for Applied Scientific Research (TNO), Soesterberg, Netherlands; van den Bosch, K., Human-Machine Teaming, Netherlands Organization for Applied Scientific Research (TNO), Soesterberg, Netherlands; Rauterberg, M., Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands; Barakova, E., Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands; Neerincx, M., Interactive Intelligence, Intelligent Systems Department, Delft University of Technology, Delft, Netherlands, Human-Machine Teaming, Netherlands Organization for Applied Scientific Research (TNO), Soesterberg, Netherlands","As robots become more ubiquitous, they will increasingly need to behave as our team partners and smoothly adapt to the (adaptive) human team behaviors to establish successful patterns of collaboration over time. A substantial amount of adaptations present themselves through subtle and unconscious interactions, which are difficult to observe. Our research aims to bring about awareness of co-adaptation that enables team learning. This paper presents an experimental paradigm that uses a physical human-robot collaborative task environment to explore emergent human-robot co-adaptions and derive the interaction patterns (i.e., the targeted awareness of co-adaptation). The paradigm provides a tangible human-robot interaction (i.e., a leash) that facilitates the expression of unconscious adaptations, such as “leading” (e.g., pulling the leash) and “following” (e.g., letting go of the leash) in a search-and-navigation task. The task was executed by 18 participants, after which we systematically annotated videos of their behavior. We discovered that their interactions could be described by four types of adaptive interactions: stable situations, sudden adaptations, gradual adaptations and active negotiations. From these types of interactions we have created a language of interaction patterns that can be used to describe tacit co-adaptation in human-robot collaborative contexts. This language can be used to enable communication between collaborating humans and robots in future studies, to let them share what they learned and support them in becoming aware of their implicit adaptations. © Copyright © 2021 van Zoelen, van den Bosch, Rauterberg, Barakova and Neerincx.","co-adaptation; embodiment; emergent interactions; human-robot collaboration; human-robot team; interaction patterns",,,,,,"Technische Universiteit Delft, TU Delft; African Institute for Mathematical Sciences, AIMS: V1801","This research was conducted partly as a component in the Final Master Project of EZ toward achieving her MSc in Industrial Design. The data analysis and writing was conducted as part of her Ph.D. research, which was a collaboration between the Netherlands Organization of Applied Scientific Research (TNO) and Delft University of Technology, funded by TNO under contract ‘4099 DO AIO Fonds’, and partially carried out within the APPL.AI FATE project and the AIMS projects (V1801).",,,,,,,,,,"Azevedo, C.R.B., Raizer, K., Souza, R., A vision for human-machine mutual understanding, trust establishment, and collaboration (2017) Proceedings of the 2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), pp. 1-3. , Savannah, GA, IEEE; Bach-y-Rita, P., Kercel, S.W., Sensory substitution and the human–machine interface (2003) Trends Cogn. 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"Rietz F., Sutherland A., Bensch S., Wermter S., Hellström T.","57226406078;57204639879;16315104000;7003826680;55926741800;","WoZ4U: An Open-Source Wizard-of-Oz Interface for Easy, Efficient and Robust HRI Experiments",2021,"Frontiers in Robotics and AI","8",,"668057","","",,,"10.3389/frobt.2021.668057","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111468951&doi=10.3389%2ffrobt.2021.668057&partnerID=40&md5=a2ddeb73fc1fb9a4ff81b7c29406926b","Department of Informatics, University of Hamburg, Hamburg, Germany; Department of Computing Science, Umeå University, Umeå, Sweden","Rietz, F., Department of Informatics, University of Hamburg, Hamburg, Germany; Sutherland, A., Department of Informatics, University of Hamburg, Hamburg, Germany; Bensch, S., Department of Computing Science, Umeå University, Umeå, Sweden; Wermter, S., Department of Informatics, University of Hamburg, Hamburg, Germany; Hellström, T., Department of Computing Science, Umeå University, Umeå, Sweden","Wizard-of-Oz experiments play a vital role in Human-Robot Interaction (HRI), as they allow for quick and simple hypothesis testing. Still, a publicly available general tool to conduct such experiments is currently not available in the research community, and researchers often develop and implement their own tools, customized for each individual experiment. Besides being inefficient in terms of programming efforts, this also makes it harder for non-technical researchers to conduct Wizard-of-Oz experiments. In this paper, we present a general and easy-to-use tool for the Pepper robot, one of the most commonly used robots in this context. While we provide the concrete interface for Pepper robots only, the system architecture is independent of the type of robot and can be adapted for other robots. A configuration file, which saves experiment-specific parameters, enables a quick setup for reproducible and repeatable Wizard-of-Oz experiments. A central server provides a graphical interface via a browser while handling the mapping of user input to actions on the robot. In our interface, keyboard shortcuts may be assigned to phrases, gestures, and composite behaviors to simplify and speed up control of the robot. The interface is lightweight and independent of the operating system. Our initial tests confirm that the system is functional, flexible, and easy to use. The interface, including source code, is made commonly available, and we hope that it will be useful for researchers with any background who want to conduct HRI experiments. © Copyright © 2021 Rietz, Sutherland, Bensch, Wermter and Hellström.","HRI experiments; Human-Robot interaction; open-source software; pepper robot; tele-operation interface; wizard-of-oz",,,,,,,,,,,,,,,,,"Aaltonen, I., Arvola, A., Heikkilä, P., Lammi, H., Hello Pepper, May I Tickle You? 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"Yadav S.K., Tiwari K., Pandey H.M., Akbar S.A.","57208898150;57197026328;36700129200;57201538097;","A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions",2021,"Knowledge-Based Systems","223",,"106970","","",,5,"10.1016/j.knosys.2021.106970","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104796886&doi=10.1016%2fj.knosys.2021.106970&partnerID=40&md5=eb909f5f36a68a2a5f0ea510b4381dff","Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, UP  201002, India; Cyber Physical System, CSIR-Central Electronics Engineering Research Institute (CEERI), Pilani, 333031, India; Department of CSIS, Birla Institute of Technology and Science Pilani, Pilani CampusRajasthan  333031, India; Department of Computer Science, Edge Hill University, Lancashire, United Kingdom","Yadav, S.K., Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, UP  201002, India, Cyber Physical System, CSIR-Central Electronics Engineering Research Institute (CEERI), Pilani, 333031, India; Tiwari, K., Department of CSIS, Birla Institute of Technology and Science Pilani, Pilani CampusRajasthan  333031, India; Pandey, H.M., Department of Computer Science, Edge Hill University, Lancashire, United Kingdom; Akbar, S.A., Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, UP  201002, India, Cyber Physical System, CSIR-Central Electronics Engineering Research Institute (CEERI), Pilani, 333031, India","Human activity recognition (HAR) is one of the most important and challenging problems in the computer vision. It has critical application in wide variety of tasks including gaming, human–robot interaction, rehabilitation, sports, health monitoring, video surveillance, and robotics. HAR is challenging due to the complex posture made by the human and multiple people interaction. Various artifacts that commonly appears in the scene such as illuminations variations, clutter, occlusions, background diversity further adds the complexity to HAR. Sensors for multiple modalities could be used to overcome some of these inherent challenges. Such sensors could include an RGB-D camera, infrared sensors, thermal cameras, inertial sensors, etc. This article introduces a comprehensive review of different multimodal human activity recognition methods where different types of sensors being used along with their analytical approaches and fusion methods. Further, this article presents classification and discussion of existing work within seven rational aspects: (a) what are the applications of HAR; (b) what are the single and multi-modality sensing for HAR; (c) what are different vision based approaches for HAR; (d) what and how wearable sensors based system contributes to the HAR; (e) what are different multimodal HAR methods; (f) how a combination of vision and wearable inertial sensors based system contributes to the HAR; and (g) challenges and future directions in HAR. With a more and comprehensive understanding of multimodal human activity recognition, more research in this direction can be motivated and refined. © 2021 Elsevier B.V.","Activity recognition; Computer vision; Fusion of vision and inertial sensors; Multimodality; Smart-shoes; Wearable sensors","Cameras; Computer vision; Human robot interaction; Inertial navigation systems; Infrared detectors; Security systems; Activity recognition; Critical applications; Fusion of vision and inertial sensor; Human activity recognition; Inertial sensor; Multi-modal; Multi-modality; Recognition methods; Sensor based systems; Smart-shoe; Wearable sensors",,,,,,,,,,,,,,,,"Escalera, S., Gonzalez, J., Baró, X., Shotton, J., Guest editors’ introduction to the special issue on multimodal human pose recovery and behavior analysis (2016) IEEE Trans. Pattern Anal. Mach. 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"Banisetty S.B., Forer S., Yliniemi L., Nicolescu M., Feil-Seifer D.","57195150511;57195228438;56120350400;7003316754;14033850500;","Socially Aware Navigation: A Non-linear Multi-objective Optimization Approach",2021,"ACM Transactions on Interactive Intelligent Systems","11","2","15","","",,1,"10.1145/3453445","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111423681&doi=10.1145%2f3453445&partnerID=40&md5=1ed3a3c1720e5e445c75a8e6f00a6b4f","Dept. of Computer Science and Engineering, University of Nevada Reno, 1664 N. Virginia Street, Reno, NV  89557, United States; Dept. of Mechanical Engineering, University of Nevada Reno, 1664 N. Virginia Street, Reno, NV  89557, United States","Banisetty, S.B., Dept. of Computer Science and Engineering, University of Nevada Reno, 1664 N. Virginia Street, Reno, NV  89557, United States; Forer, S., Dept. of Mechanical Engineering, University of Nevada Reno, 1664 N. Virginia Street, Reno, NV  89557, United States; Yliniemi, L., Dept. of Mechanical Engineering, University of Nevada Reno, 1664 N. Virginia Street, Reno, NV  89557, United States; Nicolescu, M., Dept. of Computer Science and Engineering, University of Nevada Reno, 1664 N. Virginia Street, Reno, NV  89557, United States; Feil-Seifer, D., Dept. of Computer Science and Engineering, University of Nevada Reno, 1664 N. Virginia Street, Reno, NV  89557, United States","Mobile robots are increasingly populating homes, hospitals, shopping malls, factory floors, and other human environments. Human society has social norms that people mutually accept; obeying these norms is an essential signal that someone is participating socially with respect to the rest of the population. For robots to be socially compatible with humans, it is crucial for robots to obey these social norms. In prior work, we demonstrated a Socially-Aware Navigation (SAN) planner, based on Pareto Concavity Elimination Transformation (PaCcET), in a hallway scenario, optimizing two objectives so the robot does not invade the personal space of people. This article extends our PaCcET-based SAN planner to multiple scenarios with more than two objectives. We modified the Robot Operating System's (ROS) navigation stack to include PaCcET in the local planning task. We show that our approach can accommodate multiple Human-Robot Interaction (HRI) scenarios. Using the proposed approach, we achieved successful HRI in multiple scenarios such as hallway interactions, an art gallery, waiting in a queue, and interacting with a group. We implemented our method on a simulated PR2 robot in a 2D simulator (Stage) and a pioneer-3DX mobile robot in the real-world to validate all the scenarios. A comprehensive set of experiments shows that our approach can handle multiple interaction scenarios on both holonomic and non-holonomic robots; hence, it can be a viable option for a Unified Socially-Aware Navigation (USAN). © 2021 Association for Computing Machinery.","Human-robot interaction; socially-assistive robotics; socially-aware navigation","Air navigation; Automata theory; Mobile robots; Multiobjective optimization; Navigation; Robot programming; Elimination transformations; Factory floors; Human environment; Human robot Interaction (HRI); Multiple interactions; Non-holonomic robot; Personal spaces; Robot operating system; Social robots",,,,,"15AI02H; National Science Foundation, NSF: #IIS- 1719027, -1719027; Office of Naval Research, ONR: 00014-14-1-0776, 00014-16-1-2312","L. Yliniemi is currently with Amazon Robotics. This work was completed prior to joining Amazon. The authors would like to acknowledge the financial support of this work by the National Science Foundation (NSF, #IIS-1719027), Nevada NASA EPSCoR (#NNX15AI02H), and the Office of Naval Research (ONR, #N00014-16-1-2312, #N00014-14-1-0776). Authors’ addresses: S. B. Banisetty, M. Nicolescu, and D. Feil-Seifer, Dept. of Computer Science and Engineering, University of Nevada Reno, 1664 N. Virginia Street, Reno, NV, 89557; emails: santoshbanisetty@nevada.unr.edu, monica@unr.edu, dave@cse.unr.edu; S. Forer and L. Yliniemi, Dept. of Mechanical Engineering, University of Nevada Reno, 1664 N. Virginia Street, Reno, NV, 89557; emails: sforer@nevada.unr.edu, logan@unr.edu. The reviewing of this article was managed by associate editor Joseph LaViola. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM. 2160-6455/2021/06-ART15 $15.00 https://doi.org/10.1145/3453445","The authors would like to acknowledge the financial support of this work by the National Science Foundation (NSF, #IIS- 1719027), Nevada NASA EPSCoR (#NNX15AI02H), and the Office of Naval Research (ONR, #N00014-16-1-2312, #N00014- 14-1-0776).",,,,,,,,,"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S., Social LSTM: Human trajectory prediction in crowded spaces (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 961-971; Alonso-Mora, J., Beardsley, P., Siegwart, R., Cooperative collision avoidance for nonholonomic robots (2018) IEEE Trans. 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Intelligent Syst.",Article,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85111423681
"Inamura T., Mizuchi Y.","7102940240;36809967300;","SIGVerse: A Cloud-Based VR Platform for Research on Multimodal Human-Robot Interaction",2021,"Frontiers in Robotics and AI","8",,"549360","","",,2,"10.3389/frobt.2021.549360","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107991005&doi=10.3389%2ffrobt.2021.549360&partnerID=40&md5=a6a06d71cf4ac5704022570c0bd401c3","National Institute of Informatics, Tokyo, Japan; Department of Informatics, The Graduate University for Advanced Studies (SOKENDAI), Tokyo, Japan","Inamura, T., National Institute of Informatics, Tokyo, Japan, Department of Informatics, The Graduate University for Advanced Studies (SOKENDAI), Tokyo, Japan; Mizuchi, Y., National Institute of Informatics, Tokyo, Japan","Research on Human-Robot Interaction (HRI) requires the substantial consideration of an experimental design, as well as a significant amount of time to practice the subject experiment. Recent technology in virtual reality (VR) can potentially address these time and effort challenges. The significant advantages of VR systems for HRI are: 1) cost reduction, as experimental facilities are not required in a real environment; 2) provision of the same environmental and embodied interaction conditions to test subjects; 3) visualization of arbitrary information and situations that cannot occur in reality, such as playback of past experiences, and 4) ease of access to an immersive and natural interface for robot/avatar teleoperations. Although VR tools with their features have been applied and developed in previous HRI research, all-encompassing tools or frameworks remain unavailable. In particular, the benefits of integration with cloud computing have not been comprehensively considered. Hence, the purpose of this study is to propose a research platform that can comprehensively provide the elements required for HRI research by integrating VR and cloud technologies. To realize a flexible and reusable system, we developed a real-time bridging mechanism between the robot operating system (ROS) and Unity. To confirm the feasibility of the system in a practical HRI scenario, we applied the proposed system to three case studies, including a robot competition named RoboCup@Home. via these case studies, we validated the system’s usefulness and its potential for the development and evaluation of social intelligence via multimodal HRI. © Copyright © 2021 Inamura and Mizuchi.","dataset of interaction; robot competition; social and embodied intelligence; software platform; virtual reality",,,,,,"Japan Society for the Promotion of Science, KAKEN: 19K20379; New Energy and Industrial Technology Development Organization, NEDO; Core Research for Evolutional Science and Technology, CREST","This work was supported by the New Energy and Industrial Technology Development Organization (NEDO) and JST CREST (Core Research for Evolutional Science and Technology) JSPS (KAKENHI) under Grant Number 19K20379.",,,,,,,,,,"Amershi, S., Cakmak, M., Knox, W.B., Kulesza, T., Power to the People: The Role of Humans in Interactive Machine Learning (2014) AIMag, 35, pp. 105-120; Bates, T., Ramirez-Amaro, K., Inamura, T., Cheng, G., On-line Simultaneous Learning and Recognition of Everyday Activities from Virtual Reality Performances (2017) 2017IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3510-3515; Bazzano, F., Gentilini, F., Lamberti, F., Sanna, A., Paravati, G., Gatteschi, V., Immersive Virtual Reality-Based Simulation to Support the Design of Natural Human-Robot Interfaces for Service Robotic Applications (2016) International Conference on Augmented Reality, Virtual Reality and Computer Graphics, pp. 33-51. , Lecce, Italy, Springer; Beattie, C., Leibo, J.Z., Teplyashin, D., Ward, T., Wainwright, M., Lefrancq, A., (2016) DeepMind Lab. arXiv, pp. 1-11; Bilberg, A., Malik, A.A., Digital Twin Driven Human-Robot Collaborative Assembly (2019) CIRP Ann, 68, pp. 499-502; Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., (2016) OpenAI Gym; Clarke, S., Schillhuber, G., Zaeh, M.F., Ulbrich, H., Prediction-based Methods for Teleoperation across Delayed Networks (2007) Multimedia Syst, 13, pp. 253-261; Codd-Downey, R., Forooshani, P.M., Speers, A., Wang, H., Jenkin, M., From ROS to unity: Leveraging Robot and Virtual Environment Middleware for Immersive Teleoperation (2014) 2014IEEE International Conference on Information and Automation (ICIA), pp. 932-936. , Hailar, China, IEEE; Coolican, H., (2017) Research Methods and Statistics in Psychology, , Psychology Press; Das, A., Datta, S., Gkioxari, G., Lee, S., Parikh, D., Batra, D., Embodied Question Answering (2018) 2018IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1-10. , Salt Lake City, IEEE; Diankov, R., Kuffner, J., (2008) OpenRAVE: A Planning Architecture for Autonomous Robotics, , Pittsburgh, PA, Tec, Robotics Institute; Duchowski, A.T., House, D.H., Gestring, J., Congdon, R., Świrski, L., Dodgson, N.A., Comparing Estimated Gaze Depth in Virtual and Physical Environments (2014) Eye Tracking Research and Applications Symposium (ETRA), pp. 103-110. , NewYork, ACM; El Saddik, A., Digital Twins: The Convergence of Multimedia Technologies (2018) IEEE Multimedia, 25, pp. 87-92; Escalera, S., Gonzàlez, J., Baró, X., Reyes, M., Lopes, O., Guyon, I., Multi-modal Gesture Recognition challenge 2013: Dataset and Results (2013) Proceedings Of the ACM International Conference On Multimodal Interaction (ICMI), pp. 445-452. , Sydney Australia, ACM; Fang, B., Wei, X., Sun, F., Huang, H., Yu, Y., Liu, H., Skill Learning for Human-Robot Interaction Using Wearable Device (2019) Tinshhua Sci. 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"Gomez-Monroy C.G.","57223406245;","Correlation of Qualitative User Experience Feedback and Interface-Generated Quantitative Data during Human-Robot Interaction",2021,"Conference on Human Factors in Computing Systems - Proceedings",,,,"","",,,"10.1145/3411763.3443430","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105783709&doi=10.1145%2f3411763.3443430&partnerID=40&md5=b06529fcb1394a16f2aff213f76a5f1f","Posgraduate Engineering School, Universidad Nacional Autónoma de México, Mexico","Gomez-Monroy, C.G., Posgraduate Engineering School, Universidad Nacional Autónoma de México, Mexico","My research is on Human-Robot Interaction (HRI) from a User Centered Design (UCD) perspective and values the user's qualitative assessment of their interaction under the concept of User Experience (UX). Traditionally, UX is retrieved in a written questionnaire as a satisfaction survey, nevertheless, with the hike in electronic devices equipped with a diversity of sensors, actuators and processing capabilities (turning everyday appliances into robots) comes an intention to migrate from qualitative to quantitative assessment of the UX; for example, visual recognition of emotional facial expressions. In this regard, what I present as a novelty in the research area is the study of interaction data generated through the robot's interface during its intended use (everyday use), while providing an anonymous experience (no cameras, no mics) to the user. For the experimental setup, I designed and built a robotic Desk-Lamp with 5 degrees of freedom (height, brightness, projection angle, sensitivity of the interface and ambient lighting) as test bench, equipped with: an interface instrumented to sense the force delivered by the user while ""pushing buttons""; 18 variable data-logging system at 20 SPS (Sample Per Second); high precision and high repeatability electronics and mechanisms; real-time sensing of the user and low-latency operation due to parallel processing. The test bench is designed to be used in multiple experiments with users, varying the robot behavior and interaction procedures to compare the interaction data generated in the interface with a written UX questionnaire. © 2021 Owner/Author.","Design of experiments (DoE); User centered design (UCD); User experience (UX)","Biofeedback; Degrees of freedom (mechanics); Electronic assessment; Feedback; Machine design; Man machine systems; Surveys; User centered design; User experience; Experience feedback; Human robot Interaction (HRI); Parallel processing; Processing capability; Qualitative assessments; Quantitative assessments; User Centered Design(UCD); User experiences (ux); Human robot interaction",,,,,"Consejo Nacional de Ciencia y Tecnología, CONACYT; Universidad Nacional Autónoma de México, UNAM: DGAPA-PAPIIT IT-103320, IT-101718","This research is possible thanks to the fully-funded fellowship program for Ph.D. students by Consejo Nacional de Ciencia y Tecnología (CONACYT), Mexico.","This research is funded by Universidad Nacional Autónoma de México through the projects: DGAPA-PAPIIT IT-103320 and IT-101718.",,,,,,,,,"Ghosh, D., Olewnik, A., Lewis, K., Kim, J., Lakshmanan, A., CYBER-empathic design-a data driven frame-work for product design (2017) Journal of Mechanical Design, 139. , https://doi.org/10.1115/1.4036780, 05 2017; (2010) Ergonomics of Human-system Interaction: Human-centered Design for Interactive Systems, , ISO 9241:210(E) Standard. 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Springer Berlin Heidelberg, Berlin, Heidelberg; Shen, W., Al Khemeiri, A., Almehrzi, A., Al Enezi, W., Rahwan, I., Crandall, J.W., ReguLating highly automated robot ecologies: Insights from three user studies (2017) Proceedings of the 5th International Conference on Human Agent Interaction (Bielefeld, Germany) (HAI '17), pp. 111-120. , https://doi.org/10.1145/3125739.3125758, Association for Computing Machinery, New York, NY, USA; Vitale, J., Tonkin, M., Herse, S., Ojha, S., Clark, J., Williams, M., Wang, X., Judge, W., Be more transparent and users will like you: A robot privacy and user experience design experiment (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction(Chicago, IL, USA) (HRI '18), pp. 379-387. , https://doi.org/10.1145/3171221.3171269, Association for Computing Machinery, New York, NY, USA; Xu, Q., Ng, J., Tan, O., Huang, Z., Tay, B., Park, T., Methodological issues in scenario-based evaluation of human-robot interaction (2015) International Journal of Social Robotics, 7 (2), pp. 279-291. , https://doi.org/10.1007/s12369-014-0248-9, 01 Apr 2015","Gomez-Monroy, C.G.; Posgraduate Engineering School, Mexico; email: gilbertogomo@gmail.com",,"ACM Special Interest Group on Computer-Human Interaction (SIGCHI)","Association for Computing Machinery","2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI EA 2021","8 May 2021 through 13 May 2021",,168787,,9781450380959,,,"English","Conf Hum Fact Comput Syst Proc",Conference Paper,"Final","",Scopus,2-s2.0-85105783709
"Tian L., Oviatt S.","56151869000;7003592124;","A taxonomy of social errors in human-robot interaction",2021,"ACM Transactions on Human-Robot Interaction","10","2","13","","",,6,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106723180&partnerID=40&md5=e52b402e4c33ab39db7f35d977027f38","Faculty of Engineering, Monash University, 14 Alliance Lane, Clayton, VIC  3800, Australia","Tian, L., Faculty of Engineering, Monash University, 14 Alliance Lane, Clayton, VIC  3800, Australia; Oviatt, S., Faculty of Engineering, Monash University, 14 Alliance Lane, Clayton, VIC  3800, Australia","Robotic applications have entered various aspects of our lives, such as health care and educational services. In such Human-robot Interaction (HRI), trust and mutual adaption are established and maintained through a positive social relationship between a user and a robot. This social relationship relies on the perceived competence of a robot on the social-emotional dimension. However, because of technical limitations and user heterogeneity, current HRI is far from error-free, especially when a system leaves controlled lab environments and is applied to in-the-wild conditions. Errors in HRI may either degrade a user's perception of a robot's capability in achieving a task (defined as performance errors in this work) or degrade a user's perception of a robot's socio-affective competence (defined as social errors in this work). The impact of these errors and effective strategies to handle such an impact remains an open question. We focus on social errors in HRI in this work. In particular, we identify the major attributes of perceived socio-affective competence by reviewing human social interaction studies and HRI error studies. This motivates us to propose a taxonomy of social errors in HRI. We then discuss the impact of social errors situated in three representative HRI scenarios. This article provides foundations for a systematic analysis of the social-emotional dimension of HRI. The proposed taxonomy of social errors encourages the development of user-centered HRI systems, designed to offer positive and adaptive interaction experiences and improved interaction outcomes. © 2021 Copyright held by the owner/author(s).","Affective computing; human-robot interaction; social norms; social robotics; socio-affective competence","Agricultural robots; Behavioral research; Educational robots; Errors; Man machine systems; Social aspects; Taxonomies; Educational services; Emotional dimensions; Human robot Interaction (HRI); Human social interactions; Perceived competence; Robotic applications; Social relationships; Technical limitations; Social robots",,,,,,,,,,,,,,,,"Adam, C., Gaudou, B., BDI agents in social simulations: A survey (2016) Knowl. Eng. 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Robot Interact",Article,"Final","",Scopus,2-s2.0-85106723180
"Manzi F., Massaro D., Di Lernia D., Maggioni M.A., Riva G., Marchetti A.","57201195835;36495047000;57189076325;8744772600;56962750600;36927793400;","Robots Are Not All the Same: Young Adults' Expectations, Attitudes, and Mental Attribution to Two Humanoid Social Robots",2021,"Cyberpsychology, Behavior, and Social Networking","24","5",,"307","314",,2,"10.1089/cyber.2020.0162","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106192360&doi=10.1089%2fcyber.2020.0162&partnerID=40&md5=978a9850759b745e2784079edafa4c5b","UniToM, Research Unit on Theory of Mind, Department of Psychology, Università Cattolica Del Sacro Cuore, Milan, Italy; Humane Technology Lab, Università Cattolica Del Sacro Cuore, Milan, Italy; Department of Psychology, Università Cattolica Del Sacro Cuore, Milan, Italy; DISEIS, Department of International Economics, Institutions and Development, Universitá Cattolica Del Sacro Cuore, Milano, Italy; CSCC, Cognitive Science and Communication Research Center, Universitá Cattolica Del Sacro Cuore, Milano, Italy; Applied Technology for NeuroPsychology Laboratory, Istituto Auxologico Italiano, Milan, Italy","Manzi, F., UniToM, Research Unit on Theory of Mind, Department of Psychology, Università Cattolica Del Sacro Cuore, Milan, Italy, Humane Technology Lab, Università Cattolica Del Sacro Cuore, Milan, Italy, Department of Psychology, Università Cattolica Del Sacro Cuore, Milan, Italy; Massaro, D., UniToM, Research Unit on Theory of Mind, Department of Psychology, Università Cattolica Del Sacro Cuore, Milan, Italy, Humane Technology Lab, Università Cattolica Del Sacro Cuore, Milan, Italy, Department of Psychology, Università Cattolica Del Sacro Cuore, Milan, Italy; Di Lernia, D., Humane Technology Lab, Università Cattolica Del Sacro Cuore, Milan, Italy, Department of Psychology, Università Cattolica Del Sacro Cuore, Milan, Italy; Maggioni, M.A., Humane Technology Lab, Università Cattolica Del Sacro Cuore, Milan, Italy, DISEIS, Department of International Economics, Institutions and Development, Universitá Cattolica Del Sacro Cuore, Milano, Italy, CSCC, Cognitive Science and Communication Research Center, Universitá Cattolica Del Sacro Cuore, Milano, Italy; Riva, G., Humane Technology Lab, Università Cattolica Del Sacro Cuore, Milan, Italy, Department of Psychology, Università Cattolica Del Sacro Cuore, Milan, Italy, Applied Technology for NeuroPsychology Laboratory, Istituto Auxologico Italiano, Milan, Italy; Marchetti, A., UniToM, Research Unit on Theory of Mind, Department of Psychology, Università Cattolica Del Sacro Cuore, Milan, Italy, Humane Technology Lab, Università Cattolica Del Sacro Cuore, Milan, Italy, Department of Psychology, Università Cattolica Del Sacro Cuore, Milan, Italy","The human physical resemblance of humanoid social robots (HRSs) has proven to be particularly effective in interactions with humans in different contexts. In particular, two main factors affect the quality of human-robot interaction, the physical appearance and the behaviors performed by the robot. In this study, we examined the psychological effect of two HRSs, NAO and Pepper. Although some studies have shown that these two robots are very similar in terms of the human likeness, other evidence has shown some differences in their design affecting different psychological elements of the human partner. The present study aims to analyze the variability of the attributions of mental states (AMS), expectations of robotic development and negative attitudes as a function of the physical appearance of two HRSs after observing a real interaction with a human (an experimenter). For this purpose, two groups of young adults were recruited, one for the NAO (N = 100, M = 20.22) and the other for the Pepper (N = 74, M = 21.76). The results showed that both the observation of interaction and the type of robot affect the AMS, with a greater AMS to Pepper robot compared to NAO. People's expectations, instead, are influenced by the interaction and are independent of the type of robot. Finally, negative attitudes are independent of both the interaction and the type of robot. The study showed that also subtle differences in the physical appearance of HSRs have significant effects on how humans perceived robots. © Federico Manzi et al. 2021; Published by Mary Ann Liebert, Inc. 2021.","comparison of robots; expectations; human-robot interaction; mind attribution; negative attitudes; social robots","attitude; human; Italy; motivation; perception; procedures; psychology; robotics; social interaction; student; young adult; Attitude; Humans; Italy; Motivation; Robotics; Social Interaction; Social Perception; Students; Young Adult",,,,,"Università Cattolica del Sacro Cuore, UCSC","This research was funded by Università Cattolica del Sacro Cuore (Human-Robot Confluence project).",,,,,,,,,,"Marchetti, A, Manzi, F, Itakura, S, Theory of mind and humanoid robots from a lifespan perspective (2018) Zeitschrift für Psychologie, 226, pp. 98-109; Belpaeme, T, Kennedy, J, Ramachandran, A, Social robots for education: a review (2018) Science Robotics, 3, p. eaat5954; Marchetti, A, Di Dio, C, Manzi, F, Robotics in clinical and developmental psychology (2020) Reference module in neuroscience and biobehavioral psychology, pp. 00005-4. , B978-0-12-818697-8; Marchetti, A, Di Dio, C, Massaro, D, The psychosocial fuzziness of fear in the coronavirus (COVID-19) era and the role of robots (2020) Frontiers in Psychology, 11, p. 2245; Dario, P, Guglielmelli, E, Laschi, C., Humanoids and personal robots: design and experiments (2001) Journal of Robotic Systems, 18, pp. 673-690; Robins, B, Dautenhahn, K, Dubowski, J., Does appearance matter in the interaction of children with autism with a humanoid robot? (2006) Interaction Studies, 7, pp. 479-512; Wu, Y-H, Fassert, C, Rigaud, A-S., Designing robots for the elderly: appearance issue and beyond (2012) Archives of Gerontology and Geriatrics, 54, pp. 121-126; DiSalvo, CF, Gemperle, F, Forlizzi, J, All robots are not created equal: the design and perception of humanoid robot heads (2002) Proceedings of the conference on Designing interactive systems processes, practices, methods, and techniques-DIS'02, p. 321. , London, England: ACM Press; Duffy, BR., Anthropomorphism and the social robot (2003) Robotics and Autonomous Systems, 42, pp. 177-190; Manzi, F, Peretti, G, DiDioC, Arobot is notworth another: exploring children's mental state attribution to different humanoid robots (2020) Frontiers in Psychology, 11, p. 2011; Prakash, A, Rogers, WA., Why some humanoid faces are perceived more positively than others: effects of humanlikeness and task (2015) International Journal of Social Robotics, 7, pp. 309-331; Mori, M., The Uncanny Valley Energy, 7, pp. 33-35; Mori, M, MacDorman, K, Kageki, N., The Uncanny Valley [From the Field] (2012) IEEE Robotics & Automation Magazine, 19, pp. 98-100; MacDorman, KF, Ishiguro, H., The uncanny advantage of using androids in cognitive and social science research (2006) Interaction Studies, 7, pp. 297-337; Dio Cinzia, Di, Federico, Manzi, Giulia, Peretti, How children think about the robot's mind. The role of attachment and Theory of Mind in the attribution of mental states to a robotic agent Sistemi Intelligenti, 2020, pp. 41-56. , [in Italian]; Di Dio, C, Manzi, F, Itakura, S, It does not matter who you are: fairness in pre-schoolers interacting with human and robotic partners (2020) International Journal of Social Robotics, 12, pp. 1045-1059; Di Dio, C, Manzi, F, Peretti, G, Shall I Trust You? From child-robot interaction to trusting relationships (2020) Frontiers in Psychology, 11, p. 469; Manzi, F, Ishikawa, M, Di Dio, C, The understanding of congruent and incongruent referential gaze in 17-month-old infants: an eye-tracking study comparing human and robot (2020) Scientific Reports, 10, p. 11918; Phillips, E, Zhao, X, Ullman, D, What ishuman-like?: Decomposing Robots' Human-like Appearance Using the Anthropomorphic roBOT(ABOT) Database (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 105-113. , Chicago, IL: ACM; Pandey, AK, Gelin, R., A mass-produced sociable humanoid robot: pepper: the first machine of its kind (2018) IEEE Robotics & Automation Magazine, 25, pp. 40-48; Thunberg, S, Thellman, S, Ziemke, T., Don't judge a book by its cover: a study of the social acceptance of NAO vs. Pepper (2017) Proceedings of the 5th International Conference on Human Agent Interaction, pp. 443-446. , Bielefeld, Germany: ACM; Ainsworth, MDS., Object relations, dependency, and attachment: a theoretical review of the infant-mother relationship (1969) Child Development, 40, p. 969; Ainsworth, MDS, Bell, S, Stayton, DJ., Individual differences in Strange Situation behavior of one year olds (1971) The origins of human social relations, pp. 17-58. , New York, NY: Academic Press; Bowlby, J., (1969) Attachment and loss, I. , Attachment. New York, NY: Basic Books; Bowlby, J., (1973) Attachment and loss, 2. , Separation. New York, NY: Basic Books; Bowlby, J., (1980) Attachment and loss, 3. , Loss, sadness and depression. New York, NY: Basic Books; Beuscher, LM, Fan, J, Sarkar, N, Socially assistive robots: measuring older adults' perceptions (2017) Journal of Gerontological Nursing, 43, pp. 35-43; Zanatto, D, Patacchiola, M, Cangelosi, A, Generalisation of anthropomorphic stereotype (2020) International Journal of Social Robotics, 12, pp. 163-172; Sinnema, L, Alimardani, M., The attitude of elderly and young adults towards a humanoid robot as a facilitator for social interaction (2019) Social Robotics. Vol. 11876. Lecture Notes in Computer Science, pp. 24-33. , Salichs MA, Ge SS, Barakova EI, et al., eds. Cham: Springer International Publishing; Edwards, A, Edwards, C, Westerman, D, Initial expectations, interactions, and beyond with social robots (2019) Computers in Human Behavior, 90, pp. 308-314; Nomura, T, Suzuki, T, Kanda, T, Measurement of negative attitudes toward robots (2006) Interaction Studies, 7, pp. 437-454; Inghilleri, P, Riva, G, Riva, E, Introduction: Positive change in global world: Creative individuals and complex societies (2015) Enabling Positive Change Flow and Complexity in Daily Experience, pp. 1-5. , eds. Berlin, DE: De Gruyter Open. 2015","Manzi, F.; UniToM, Largo Agostino Gemelli, 1, Italy; email: federico.manzi@unicatt.it",,,"Mary Ann Liebert Inc.",,,,,21522715,,,"33181030","English","Cyberpsychol. Behav. Soc. Networking",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85106192360
"Fox J., Gambino A.","7404282916;57190125239;","Relationship Development with Humanoid Social Robots: Applying Interpersonal Theories to Human-Robot Interaction",2021,"Cyberpsychology, Behavior, and Social Networking","24","5",,"294","299",,5,"10.1089/cyber.2020.0181","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104996747&doi=10.1089%2fcyber.2020.0181&partnerID=40&md5=2c9b2e4f1fe28cbe4b393ec956dad64d","School of Communication, The Ohio State University, 3016 Derby Hall, 154 North Oval Mall, Columbus, Ohio  43210-1339, United States; Donald P. Bellisario College of Communications, The Pennsylvania State University, University Park, Pennsylvania, United States","Fox, J., School of Communication, The Ohio State University, 3016 Derby Hall, 154 North Oval Mall, Columbus, Ohio  43210-1339, United States; Gambino, A., Donald P. Bellisario College of Communications, The Pennsylvania State University, University Park, Pennsylvania, United States","Humanoid social robots (HSRs) are human-made technologies that can take physical or digital form, resemble people in form or behavior to some degree, and are designed to interact with people. A common assumption is that social robots can and should mimic humans, such that human-robot interaction (HRI) closely resembles human-human (i.e., interpersonal) interaction. Research is often framed from the assumption that rules and theories that apply to interpersonal interaction should apply to HRI (e.g., the computers are social actors framework). Here, we challenge these assumptions and consider more deeply the relevance and applicability of our knowledge about personal relationships to relationships with social robots. First, we describe the typical characteristics of HSRs available to consumers currently, elaborating characteristics relevant to understanding social interactions with robots such as form anthropomorphism and behavioral anthropomorphism. We also consider common social affordances of modern HSRs (persistence, personalization, responsiveness, contingency, and conversational control) and how these align with human capacities and expectations. Next, we present predominant interpersonal theories whose primary claims are foundational to our understanding of human relationship development (social exchange theories, including resource theory, interdependence theory, equity theory, and social penetration theory). We consider whether interpersonal theories are viable frameworks for studying HRI and human-robot relationships given their theoretical assumptions and claims. We conclude by providing suggestions for researchers and designers, including alternatives to equating human-robot relationships to human-human relationships. © Jesse Fox and Andrew Gambino 2021; Published by Mary Ann Liebert, Inc. 2021.","anthropomorphism; computers are social actors; human-robot interaction; relationship development; social affordances; social robots","human; human relation; interpersonal communication; man machine interaction; procedures; robotics; social interaction; Communication; Humans; Interpersonal Relations; Man-Machine Systems; Robotics; Social Interaction",,,,,,,,,,,,,,,,"Bickmore, TW, Picard, RW., Establishing and maintaining long-term human-computer relationships (2005) ACM Transactions on Computer-Human Interaction, 12, pp. 293-327; De Melo, CM, Gratch, J., Beyond believability: quantifying the differences between real and virtual humans (2015) 15th International Conference on Intelligent Virtual Agents, pp. 109-118. , Delft, Netherlands: Springer; Krämer, NC, Von Der Pütten, AM, Eimler, SC., Humanagent and human-robot interaction theory: similarities to and differences from human-human interaction (2012) Human-computer interaction: the agency perspective, pp. 215-240. , Zacarias M, De Oliveira JV, eds. Berlin, Germany: Springer; Nass, C, Brave, S., (2005) Wired for speech: how voice activates and advances the human-computer relationship, , Cambridge, MA: MIT Press; Breazeal, C., (2002) Designing sociable robots, , Cambridge, MA: MIT Press; Hegel, F, Muhl, C, Wrede, B, Understanding social robots (2009) 2nd International Conference on Advances in Computer-Human Interactions, pp. 169-184. , Cancun, Mexico: IEEE; Zhao, S., Humanoid social robots as a medium of communication (2006) New Media and Society, 8, pp. 401-419; Liu, B, Sundar, SS., Should machines express sympathy and empathy? Experiments with a health advice chatbot (2018) Cyberpsychology, Behavior, and Social Networking, 21, pp. 625-636; Cassell, J, Sullivan, J, Churchill, E, (2000) Embodied conversational agents, , Cambridge, MA: MIT Press; Di Dio, C, Manzi, F, Peretti, G, Shall I trust you? From child human-robot interaction to trusting relationships (2020) Frontiers in Psychology, 11, p. 469; Kahn, PH, Gary, HE, Shen, S., Children's social relationships with current and near-future robots (2013) Child Development Perspectives, 7, pp. 32-37; Nass, C, Moon, Y., Machines and mindlessness: social responses to computers (2000) Journal of Social Issues, 56, pp. 81-103; Eyssel, F, Hegel, F., (S)he's got the look: gender stereotyping of robots (2012) Journal of Applied Social Psychology, 42, pp. 2213-2230; Edwards, A, Edwards, C, Westerman, D, Initial expectations, interactions, and beyond with social robots (2019) Computers in Human Behavior, 90, pp. 308-314; Gambino, A, Fox, J, Ratan, RA., Building a stronger CASA: extending the computers are social actors paradigm (2020) Human-Machine Communication, 1, pp. 71-86; Reeves, B, Nass, C., (1996) The media equation: how people treat computers, television, and new media like real people and places, , New York, NY: Cambridge; Beer, JM, Fisk, AD, Rogers, WA., Toward a framework for levels of robot autonomy in human-robot interaction (2014) Journal of Human-Robot Interaction, 3, pp. 74-99; Bigman, YE, Waytz, A, Alterovitz, R, Holding robots responsible: the elements of machine morality (2019) Trends in Cognitive Sciences, 23, pp. 365-368; Fox, J, McEwan, B., Distinguishing technologies for social interaction: the perceived social affordances of communication channels scale (2017) Communication Monographs, 84, pp. 298-318; Rodríguez-Hidalgo, C., Me and my robot smiled at one another: the process of socially enacted communicative affordance in human-machine communication (2020) Human Machine Communication, 1, pp. 55-69; Epley, N, Waytz, A, Cacioppo, JT., On seeing human: a threefactor theory of anthropomorphism (2007) Psychological Review, 114, pp. 864-886; Leite, I, Martinho, C, Paiva, A., Social robots for long-term interaction: a survey (2013) International Journal of Social Robotics, 5, pp. 291-308; Ruijten, PA, Haans, A, Ham, J, Perceived humanlikeness of social robots: testing the Rasch model as a method for measuring anthropomorphism (2019) International Journal of Social Robotics, 11, pp. 477-494; Van Straten, CL, Peter, J, Kühne, R., Child-robot relationship formation: a narrative review of empirical research (2020) International Journal of Social Robotics, 12, pp. 325-344; Nowak, KL, Fox, J., Avatars and computer-mediated communication: a review of the definitions, uses, and effects of digital representations (2018) Review of Communication Research, 6, pp. 30-53; Strohkorb, S, Huang, CM, Ramachandran, A, Establishing sustained, supportive human-robot relationships: building blocks and open challenges (2016) AAAI Spring Symposium Series, pp. 179-182. , Stanford, CA: AAAI; Aylett, R, Kriegel, M, Wallace, I, Do I remember you? Memory and identity in multiple embodi-ments (2013) 2013 22nd IEEE International Symposium on Robot and Human Interactive Communication, pp. 143-148. , Seoul, South Korea: IEEE; Altman, I, Taylor, DA., (1973) Social penetration: the development of interpersonal relationships, , New York, NY: Holt, Rinehart & Winston; Dautenhahn, K., Robots we like to live with?! A developmental perspective on a personalized, life-long robot companion (2004) 2004 13th IEEE International Workshop on Robot and Human Interactive Communication, pp. 17-22. , Kurakshiki, Japan: IEEE; Hinde, RA., (1979) Towards understanding relationships, , London, UK: Academic Press; Roloff, ME., (1981) Interpersonal communication: the social exchange approach, , Thousand Oaks, CA: Sage; Foa, UG, Foa, EB., (1974) Societal structures of the mind, , Oxford, England: Charles C. Thomas; Thibaut, JW, Kelley, HH., (1959) The social psychology of groups, , New York, NY: Wiley; Walster, E, Walster, GW, Berscheid, E., (1978) Equity, , Boston, MA: Allyn & Bacon; Kelley, HH, Thibaut, JW., (1978) Interpersonal relations: a theory of interdependence, , New York, NY: Wiley; Dautenhahn, K., Methodology & themes of human-robot interaction: a growing research field (2007) International Journal of Advanced Robotic Systems, 4, pp. 103-108; Fong, T, Nourbakhsh, I, Dautenhahn, K., A survey of socially interactive robots: concepts, design and applications (2002) Robotics and Autonomous Systems, 42, pp. 142-166; De Graaf, MMA., An ethical review on human-robot relationships (2016) International Journal of Social Robotics, 8, pp. 589-598; Scheutz, M., The inherent dangers of unidirectional emotional bonds between humans and social robots (2012) Robot ethics: the ethical and social implications of robotics, pp. 205-222. , Lin P, Abney J, Bekey JA, eds. Cambridge, MA: MIT Press; Kang, SH, Gratch, J., Virtual humans elicit socially anxious interactants' verbal self-disclosure (2010) Computer Animation and Virtual Worlds, 21, pp. 473-482; Lucas, GM, Rizzo, A, Gratch, J, Reporting mental health symptoms: breaking down barriers to care with virtual human interviewers (2017) Frontiers Robotics AI, 4, p. 51. , article; Pickard, MD, Roster, CA, Chen, Y., Revealing sensitive information in personal interviews: is self-disclosure easier with humans or avatars and under what conditions? (2016) Computers in Human Behavior, 65, pp. 23-30; De Graaf, MMA, Allouch, SB., The influence of prior expectations of a robot's lifelikeness on users' intentions to treat a zoomorphic robot as a companion (2017) International Journal of Social Robotics, 9, pp. 17-32; Krämer, NC, Eimler, S, Von Der Pütten, A, Theory of companions: what can theoretical models contribute to applications and understanding of human-robot interaction? (2011) Applied Artificial Intelligence, 25, pp. 474-502; Riva, G, Banos, RM, Botella, C, Positive technology: using interactive technologies to promote positive functioning (2012) Cyberpsychology, Behavior, and Social Networking, 15, pp. 69-77","Fox, J.; School of Communication, 3016 Derby Hall, 154 North Oval Mall, United States; email: fox.775@osu.edu",,,"Mary Ann Liebert Inc.",,,,,21522715,,,"33434097","English","Cyberpsychol. Behav. Soc. Networking",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85104996747
"Jafarzadeh M., Brooks S., Yu S., Prabhakaran B., Tadesse Y.","57191518404;57222228039;57222226595;6603723328;15726557200;","A wearable sensor vest for social humanoid robots with GPGPU, IoT, and modular software architecture",2021,"Robotics and Autonomous Systems","139",,"103536","","",,1,"10.1016/j.robot.2020.103536","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101922805&doi=10.1016%2fj.robot.2020.103536&partnerID=40&md5=5746807e7a9fb51b4a1bece4604a926c","Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX  75080, United States; Humanoid, Biorobotics and Smart Systems (HBS) Laboratory, Department of Mechanical Engineering, United States; Department of Computer Science, The University of Texas at Dallas, Richardson, TX  75080, United States","Jafarzadeh, M., Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX  75080, United States, Humanoid, Biorobotics and Smart Systems (HBS) Laboratory, Department of Mechanical Engineering, United States; Brooks, S., Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX  75080, United States, Humanoid, Biorobotics and Smart Systems (HBS) Laboratory, Department of Mechanical Engineering, United States; Yu, S., Department of Computer Science, The University of Texas at Dallas, Richardson, TX  75080, United States; Prabhakaran, B., Department of Computer Science, The University of Texas at Dallas, Richardson, TX  75080, United States; Tadesse, Y., Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX  75080, United States, Humanoid, Biorobotics and Smart Systems (HBS) Laboratory, Department of Mechanical Engineering, United States","Currently, most social robots interact with their surroundings and humans through sensors that are integral parts of the robots, which limits the usability of the sensors, human–robot interaction, and interchangeability. A wearable sensor garment that fits many robots is needed in many applications. This article presents an affordable wearable sensor vest, and an open-source software architecture with the Internet of Things (IoT) for social humanoid robots. The vest consists of touch, temperature, gesture, distance, vision sensors, and a wireless communication module. The IoT feature allows the robot to interact with humans locally and over the Internet. The designed architecture works for any social robot that has a general-purpose graphics processing unit (GPGPU), I2C/SPI buses, Internet connection, and the Robotics Operating System (ROS). The modular design of this architecture enables developers to easily add/remove/update complex behaviors. The proposed software architecture provides IoT technology, GPGPU nodes, I2C and SPI bus mangers, audio-visual interaction nodes (speech to text, text to speech, and image understanding), and isolation between behavior nodes and other nodes. The proposed IoT solution consists of related nodes in the robot, a RESTful web service, and user interfaces. We used the HTTP protocol as a means of two-way communication with the social robot over the Internet. Developers can easily edit or add nodes in C, C++, and Python programming languages. Our architecture can be used for designing more sophisticated behaviors for social humanoid robots. © 2020 Elsevier B.V.","GPGPU; Human–robot interaction; Internet of Things; Senor vest; Social robot; Software architecture","Agricultural robots; Anthropomorphic robots; C++ (programming language); Computer graphics; Graphics processing unit; HTTP; Hypertext systems; Interchanges; Internet of things; Open source software; Open systems; Program processors; Software architecture; User interfaces; Wearable sensors; Web services; Audio-visual interactions; General purpose graphics processing unit (GPGPU); Internet of thing (IOT); Modular software architectures; Python programming language; RESTful Web services; Two way communications; Wireless communications; Social robots",,,,,,,,,,,,,,,,"Eyssel, F., An experimental psychological perspective on social robotics (2017) Robot. Auton. 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"Skantze G.","6505943187;","Turn-taking in Conversational Systems and Human-Robot Interaction: A Review",2021,"Computer Speech and Language","67",,"101178","","",,8,"10.1016/j.csl.2020.101178","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098741102&doi=10.1016%2fj.csl.2020.101178&partnerID=40&md5=0d720eaafb5bf8ab86077d074141cb43","Department of Speech Music and HearingKTH, Sweden","Skantze, G., Department of Speech Music and HearingKTH, Sweden","The taking of turns is a fundamental aspect of dialogue. Since it is difficult to speak and listen at the same time, the participants need to coordinate who is currently speaking and when the next person can start to speak. Humans are very good at this coordination, and typically achieve fluent turn-taking with very small gaps and little overlap. Conversational systems (including voice assistants and social robots), on the other hand, typically have problems with frequent interruptions and long response delays, which has called for a substantial body of research on how to improve turn-taking in conversational systems. In this review article, we provide an overview of this research and give directions for future research. First, we provide a theoretical background of the linguistic research tradition on turn-taking and some of the fundamental concepts in theories of turn-taking. We also provide an extensive review of multi-modal cues (including verbal cues, prosody, breathing, gaze and gestures) that have been found to facilitate the coordination of turn-taking in human-human interaction, and which can be utilised for turn-taking in conversational systems. After this, we review work that has been done on modelling turn-taking, including end-of-turn detection, handling of user interruptions, generation of turn-taking cues, and multi-party human-robot interaction. Finally, we identify key areas where more research is needed to achieve fluent turn-taking in spoken interaction between man and machine. © 2020 The Author","Dialogue systems; Gaze; Prosody; Social robotics; Turn-taking","Man machine systems; Conversational systems; Fundamental concepts; Human-human interactions; Multi-modal; Research traditions; Response delays; Spoken interaction; Turn-taking; Social robots",,,,,"2013-1403; Vetenskapsrådet, VR","This work is supported by the Swedish research council (VR) project Coordination of Attention and Turn-taking in Situated Interaction (#2013-1403). Thanks to Nigel Ward for initial discussions on this review, and to Erik Ekstedt, Martin Johansson and Raveesh Meena for their contributions to our work on modelling turn-taking at KTH. 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"Çakar K., Aykol Ş.","56719640900;57220031060;","Understanding travellers’ reactions to robotic services: a multiple case study approach of robotic hotels",2021,"Journal of Hospitality and Tourism Technology","12","1",,"155","174",,4,"10.1108/JHTT-01-2020-0015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097997693&doi=10.1108%2fJHTT-01-2020-0015&partnerID=40&md5=454226ef0b0f227004f81b68777e8f7f","Faculty of Tourism, Mardin Artuklu University, Mardin, Turkey","Çakar, K., Faculty of Tourism, Mardin Artuklu University, Mardin, Turkey; Aykol, Ş., Faculty of Tourism, Mardin Artuklu University, Mardin, Turkey","Purpose: The purpose of this paper is to investigate travellers’ behaviour and examine their reactions to high-tech hotels offering robotic services to customers. Design/methodology/approach: Data were gathered from user-generated content within the context of a qualitative research method by analysing the online narratives of travellers at TripAdvisor who visited robotic hotels. Data analysis was realised through content analysis, which revealed various themes and categories of traveller behaviour and reactions to technology. Findings: Results show that robotic services significantly improve the quality of service offered to travellers, while positively affecting travellers’ intention to revisit robotic hotels within the context of customer engagement behaviours. Research limitations/implications: The results of the present research reveal that the introduction of new technologies in the service industry, such as the robotic butler, can have considerable effects on guest behaviour and attitudes. This field has emerged as a new sub-dimension of customer engagement. Practical implications: The use of robots will most likely enhance experiences through interaction between customers and robots. Additionally, in cases where social distancing is required, the use of robots in the hospitality and tourism industry may increase the mobility of people wishing to travel by applying social distancing through use of robots in services. Originality/value: The study contributes to the extant literature by identifying the concept of adoption as a sub-dimension deriving from human–robot interaction, thus generating the novelty of the research. © 2020, Emerald Publishing Limited.","Customer behaviour; Customer engagement; Robotic services; Service robots",,,,,,,,,,,,,,,,,"Ajzen, I., The theory of planned behavior (1991) Organizational Behavior and Human Decision Processes, 50 (2), pp. 179-211; Amaral, F., Tiago, T., Tiago, F., User-generated content: tourists’ profiles on tripadvisor (2014) International Journal of Strategic Innovative Marketing, 1 (3), pp. 137-145; Belanche, D., Casaló, L.V., Flavián, C., Schepers, J., Service robot implementation: a theoretical framework and research agenda (2020) The Service Industries Journal, 40 (3-4), pp. 203-225; Berezina, K., Ciftci, O., Cobanoglu, C., Robots, artificial intelligence, and service automation in restaurants (2019) Robots, Artificial Intelligence, and Service Automation in Travel, Tourism and Hospitality, pp. 185-219. , Ivanov, S. and Webster, C., and,(Eds), Emerald, London; Berman, J., (2017) Top ten high-tech hotels around the world, , www.smartmeetings.com/technology/96753/top-10-high-tech-hotels-world, (accessed: 5 March 2018; Bhaumik, A., (2018) From AI to Robotics: Mobile, Social, and Sentient Robots, , CRC Press, Boca Raton, FL; Bitner, M.J., Brown, S.W., Meuter, M.L., Technology infusion in service encounters (2000) Journal of the Academy of Marketing Science, 28 (1), pp. 138-149; Blocker, C.P., Barrios, A., The transformative value of a service experience (2015) Journal of Service Research, 18 (3), pp. 265-283; Bowen, J., Morosan, C., Beware hospitality industry: the robots are coming (2018) Worldwide Hospitality and Tourism Themes, 10 (6), pp. 726-733; Brady, S., (2019) Hate flying but love travel? 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Hosp. Tour. Technol.",Article,"Final","",Scopus,2-s2.0-85097997693
"Liu K., Hacker F., Daraio C.","57188640840;57223217174;6602972223;","Robotic surfaces with reversible, spatiotemporal control for shape morphing and object manipulation",2021,"Science Robotics","6","53","eabf5116","","",,3,"10.1126/SCIROBOTICS.ABF5116","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105360366&doi=10.1126%2fSCIROBOTICS.ABF5116&partnerID=40&md5=ee13f6834ada57bcc2a03b816a2805bc","Division of Engineering and Applied Science, California Institute of Technology, 1200 E California Blvd, Pasadena, CA  91105, United States; Department of Mechanical and Process Engineering, ETH-Zürich, Rämistrasse 101, Zürich, 8092, Switzerland","Liu, K., Division of Engineering and Applied Science, California Institute of Technology, 1200 E California Blvd, Pasadena, CA  91105, United States; Hacker, F., Division of Engineering and Applied Science, California Institute of Technology, 1200 E California Blvd, Pasadena, CA  91105, United States, Department of Mechanical and Process Engineering, ETH-Zürich, Rämistrasse 101, Zürich, 8092, Switzerland; Daraio, C., Division of Engineering and Applied Science, California Institute of Technology, 1200 E California Blvd, Pasadena, CA  91105, United States","Continuous and controlled shape morphing is essential for soft machines to conform, grasp, and move while interacting safely with their surroundings. Shape morphing can be achieved with two-dimensional (2D) sheets that reconfigure into target 3D geometries, for example, using stimuli-responsive materials. However, most existing solutions lack the ability to reprogram their shape, face limitations on attainable geometries, or have insufficient mechanical stiffness to manipulate objects. Here, we develop a soft, robotic surface that allows for large, reprogrammable, and pliable shape morphing into smooth 3D geometries. The robotic surface consists of a layered design composed of two active networks serving as artificial muscles, one passive network serving as a skeleton, and cover scales serving as an artificial skin. The active network consists of a grid of strips made of heat-responsive liquid crystal elastomers (LCEs) containing stretchable heating coils. The magnitude and speed of contraction of the LCEs can be controlled by varying the input electric currents. The 1D contraction of the LCE strips activates in-plane and out-of-plane deformations; these deformations are both necessary to transform a flat surface into arbitrary 3D geometries. We characterize the fundamental deformation response of the layers and derive a control scheme for actuation. We demonstrate that the robotic surface provides sufficient mechanical stiffness and stability to manipulate other objects. This approach has potential to address the needs of a range of applications beyond shape changes, such as human-robot interactions and reconfigurable electronics. © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works",,"Active networks; Agricultural robots; Artificial organs; Deformation; Geometry; Human robot interaction; Liquid crystals; Stiffness; Deformation response; Liquid crystal elastomers; Mechanical stiffness; Object manipulation; Out-of-plane deformations; Spatiotemporal control; Stimuli-responsive materials; Two Dimensional (2 D); Robotics",,,,,,,,,,,,,,,,"Oliver, K., Seddon, A., Trask, R. S., Morphing in nature and beyond: A review of natural and synthetic shape-changing materials and mechanisms (2016) J. Mater. Sci, 15, pp. 10663-10689; Hanlon, R. T., Conroy, L.-A., Forsythe, J. W., Mimicry and foraging behaviour of two tropical sand-flat octopus species off North Sulawesi, Indonesia (2008) Biol. J. Linn. Soc, 93, pp. 23-38; Guayasamin, J. M., Krynak, T., Krynak, K., Culebras, J., Hutter, C. 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Robotics",Article,"Final","",Scopus,2-s2.0-85105360366
"Bevins A., Duncan B.A.","57200625900;36025235500;","Aerial flight paths for communication: How participants perceive and intend to respond to drone movements",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444645","16","23",,2,"10.1145/3434073.3444645","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102759086&doi=10.1145%2f3434073.3444645&partnerID=40&md5=4072f2108017ec579a9877ff4e880431","University of Nebraska-Lincoln, Lincoln, NE, United States","Bevins, A., University of Nebraska-Lincoln, Lincoln, NE, United States; Duncan, B.A., University of Nebraska-Lincoln, Lincoln, NE, United States","This work has developed an iteratively refined understanding of participants' natural perceptions and responses to unmanned aerial vehicle (UAV) flight paths, or gestures. This includes both what they believe the UAV is trying to communicate to them, in addition to how they expect to respond through physical action. Previous work in this area has focused on eliciting gestures from participants to communicate specific states, or leveraging gestures that are observed in the world rather than on understanding what the participants believe is being communicated and how they would respond. This work investigates previous gestures either created or categorized by participants to understand the perceived content of their communication or expected response, through categories created by participant free responses and confirmed through forced choice testing. The human-robot interaction community can leverage this work to better understand how people perceive UAV flight paths, inform future designs for non-anthropomorphic robot communications, and apply lessons learned to elicit informative labels from people who may or may not be operating the vehicle. We found that the Negative Attitudes towards Robots Scale (NARS) can be a good indicator of how we can expect a person to react to a robot. Recommendations are also provided to use motion approaching/retreating from a person to encourage following, perpendicular to their field of view for blocking, and to use either no motion or large altitude changes to encourage viewing. © 2021 IEEE Computer Society. All rights reserved.","Drones; Gestures; Motion; UAV","Agricultural robots; Antennas; Anthropomorphic robots; Drones; Flight paths; Machine design; Man machine systems; Field of views; Free response; Future designs; Physical action; Specific state; Human robot interaction",,,,,"National Science Foundation, NSF: 1638099, 1750750, 1757908, 1925368, CNS-1757908, IIS-1638099, IIS-1750750, IIS-1925368","This work was supported by the National Science Foundation under IIS-1638099, IIS-1750750, CNS-1757908, and IIS-1925368.",,,,,,,,,,"Acharya, U., Bevins, A., Duncan, B.A., Investigation of human-robot comfort with a small unmanned aerial vehicle compared to a ground robot (2017) 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 2758-2765; Baytas, M.A., Çay, D., Zhang, Y., Obaid, M., Yantaç, A.E., Fjeld, M., The design of social drones: A review of studies on autonomous flyers in inhabited environments (2019) Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems., pp. 1-13; Bevins, A., McPhaul, N., Duncan, B.A., Content is king: Impact of task design for eliciting participant agreement in crowdsourcing for HRI (2020) International Conference on Social Robotics., pp. 640-651. , Springer; Cauchard, J.R., Jane, L.E., Zhai, K.Y., Landay, J.A., Drone & me: An exploration into natural human-drone interaction (2015) Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing., pp. 361-365; Cauchard, J.R., Zhai, K.Y., Spadafora, M., Landay, J.A., Emotion encoding in human-drone interaction (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 263-270; Duncan, B.A., Beachly, E., Bevins, A., Elbaum, S., Detweiler, C., Investigation of communicative flight paths for small unmanned aerial systems (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 602-609; Duncan, B.A., Murphy, R.R., Comfortable approach distance with small unmanned aerial vehicles (2013) 2013 IEEE RO-MAN. IEEE, pp. 786-792; Firestone, J.W., Quiñones, R., Duncan, B.A., Learning from users: An elicitation study and taxonomy for communicating small unmanned aerial system states through gestures (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 163-171; Funk, M., Human-drone interaction: Let's get ready for flying user interfaces (2018) Interactions, 25 (3), pp. 78-81; Richard Landis, J., Koch, G.G., (1977) The Measurement of Observer Agreement for Categorical Data. Biometrics, pp. 159-174; Matrosov, M., Volkova, O., Tsetserukou, D., LightAir: A novel system for tangible communication with quadcopters using foot gestures and projected image (2016) ACM SIGGRAPH 2016 Emerging Technologies., pp. 1-2; Nozaki, H., Flying display: A movable display pairing projector and screen in the air (2014) CHI'14 Extended Abstracts on Human Factors in Computing Systems., pp. 909-914; Obaid, M., Kistler, F., Kasparavičiute, G., Evren Yantaç, A., Fjeld, M., How would you gesture navigate a drone? A user-centered approach to control a drone (2016) Proceedings of the 20th International Academic Mindtrek Conference., pp. 113-121; Riek, L.D., Rabinowitch, T.-C., Bremner, P., Pipe, A.G., Fraser, M., Robinson, P., Cooperative gestures: Effective signaling for humanoid robots (2010) Proceedings of the 5th ACM/IEEE International Conference on Human-robot Interaction. IEEE Press, pp. 61-68; Scheible, J., Hoth, A., Saal, J., Su, H., Displaydrone: A flying robot based interactive display (2013) Proceedings of the 2ndACM International Symposium on Pervasive Displays., pp. 49-54; Sharma, M., Hildebrandt, D., Newman, G., Young, J.E., Eskicioglu, R., Communicating affect via flight path Exploring use of the Laban Effort System for designing affective locomotion paths (2013) 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 293-300; Sverre Syrdal, D., Dautenhahn, K., Lee Koay, K., Walters, M.L., The negative attitudes towards robots scale and reactions to robot behaviour in a live human-robot interaction study (2009) Adaptive and Emergent Behaviour and Complex Systems; Szafir, D., Mutlu, B., Fong, T., Communication of intent in assistive free flyers (2014) Proceedings of the 2014 ACM/IEEE International Conference on Human-robot Interaction., pp. 358-365; Szafir, D., Mutlu, B., Fong, T., Communicating directionality in flying robots (2015) 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 19-26; Wojciechowska, A., Frey, J., Sass, S., Shafir, R., Cauchard, J.R., Collocated human-drone interaction: Methodology and approach strategy (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 172-181; Yamazaki, Y., Tamaki, M., Premachandra, C., Perera, C.J., Sumathipala, S., Sudantha, B.H., Victim detection using UAV with onboard voice recognition system (2019) 2019 Third IEEE International Conference on Robotic Computing (IRC). IEEE, pp. 555-559",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS)","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167644,21672148,9781450382892,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102759086
"Chita-Tegmark M., Law T., Rabb N., Scheutz M.","56700303500;57212389268;57222470528;6603548841;","Can you trust your trust measure?",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444677","92","100",,3,"10.1145/3434073.3444677","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102749657&doi=10.1145%2f3434073.3444677&partnerID=40&md5=688a535317758b5864203387730debff","Tufts University, Medford, MA, United States","Chita-Tegmark, M., Tufts University, Medford, MA, United States; Law, T., Tufts University, Medford, MA, United States; Rabb, N., Tufts University, Medford, MA, United States; Scheutz, M., Tufts University, Medford, MA, United States","Trust in human-robot interactions (HRI) is measured in two main ways: through subjective questionnaires and through behavioral tasks. To optimize measurements of trust through questionnaires, the field of HRI faces two challenges: the development of standardized measures that apply to a variety of robots with different capabilities, and the exploration of social and relational dimensions of trust in robots (e.g., benevolence). In this paper we look at how different trust questionnaires [18,30,35) fare given these challenges that pull in different directions (being general vs. being exploratory) by studying whether people think the items in these questionnaires are applicable to different kinds of robots and interactions. In Study 1 we show that after being presented with a robot (non-humanoid) and an interaction scenario (fire evacuation), participants rated multiple questionnaire items such as ""This robot is principled"" as ""Non-applicable to robots in general"" or ""Non-applicable to this robot' In Study 2 we show that the frequency of these ratings change (indeed, even for items rated as N/A to robots in general) when a new scenario is presented (game playing with a humanoid robot). Finally, while overall trust scores remained robust to N/A ratings, our results revealed potential fallacies in the way these scores are commonly interpreted. We conclude with recommendations for the development, use and results-reporting of trust questionnaires for future studies, as well as theoretical implications for the field of HRI. © 2021 IEEE Computer Society. All rights reserved.","Human-robot interaction; Human-robot trust; Subjective trust; Trust measure","Agricultural robots; Anthropomorphic robots; Behavioral research; Man machine systems; Surveys; Fire evacuation; Game playing; Human robot Interaction (HRI); Humanoid robot; Measurements of; Pull-in; Results reporting; Trust scores; Social robots",,,,,"Air Force Office of Scientific Research, AFOSR: FA9550-18-1-0465","This work was partly funded by AFOSR grant FA9550-18-1-0465.",,,,,,,,,,"Allen, K., Bergin, R., Pickar, K., Exploring trust, group satisfaction, and performance in geographically dispersed and co-located university technology commercialization teams (2004) VentureWetl. Proceedings of Open, the Annual Conference. 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IEEE, pp. 181-188; Mayer, R.C., Davis, J.H., The effect of the performance appraisal system on trust for management: A field quasi-experiment (1999) Journal of Applied Psychology, 84 (1), p. 123; Mayer, R.C., Davis, J.H., Schoorman, F.D., An integrative model of organizational trust (1995) Academy of Management Review, 20 (3), pp. 709-734; McCroskey, J.C., Teven, J.J., Goodwill: A reexamination of the construct and its measurement (1999) Communications Monographs, 66 (1), pp. 90-103; Novitzky, M., Robinette, P., Benjamin, M.R., Gleason, D.K., Fitzgerald, C., Schmidt, H., Preliminary interactions of human-robot trust, cognitive load, and robot intelligence levels in a competitive game (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction., pp. 203-204; Robinette, P., Li, W., Allen, R., Howard, A.M., Wagner, A.R., Overtrust of robots in emergency evacuation scenarios (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 101-108; Rossi, A., Dautenhahn, K., Koay, K.L., Walters, M.L., The impact of peoples' personal dispositions and personalities on their trust of robots in an emergency scenario (2018) Paladyn, Journal of Behavioral Robotics, 9 (1), pp. 137-154; Rossi, A., Holthaus, P., Dautenhahn, K., Koay, K.L., Walters, M.L., Getting to know Pepper: Effects of people's awareness of a robot's capabilities on their trust in the robot (2018) Proceedings of the 6th International Conference on Human-Agent Interaction., pp. 246-252; Sanders, T., Kaplan, A., Koch, R., Schwartz, M., Hancock, P.A., The relationship between trust and use choice in human-robot interaction (2019) Human Factors, 61 (4), pp. 614-626; Schaefer, K., (2013) The Perception and Measurement of Human-robot Trust, 2013; Schaefer, K.E., Measuring trust in human robot interactions: Development of the trust perception scale-HRl (2016) Robust Intelligence and Trust in Autonomous Systems., pp. 191-218. , Springer; Strohkorb Sebo, S., Krishnamurthi, P., Scassellati, B., I don't believe you"": Investigating the effects of robot trust violation and repair (2019) 201914th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 57-65; Shu, P., Min, C., Bodala, I., Nikolaidis, S., Hsu, D., Soh, H., Human trust in robot capabilities across tasks (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction., pp. 241-242; Sung, J.-Y., Guo, L., Grinter, R.E., Christensen, H.I., My Roomba is Rambo"": Intimate home appliances (2007) International Conference on Ubiquitous Computing., pp. 145-162. , Springer; Tay, B., Jung, Y., Park, T., When stereotypes meet robots: The double-edge sword of robot gender and personality in human-robot interaction (2014) Computers in Human Behavior, 38, pp. 75-84; Ullman, D., Malle, B.F., What does it mean to trust a robot? Steps toward a multidimensional measure of trust (2018) Companion of the 2018 ACM/IEEE International Conference on Human-robot Interaction., pp. 263-264; Ullman, D., Malle, B.F., Measuring gains and losses in human-robot trust: Evidence for differentiable components of trust (2019) 201914th ACM/IEEE International Conference on Human-Robot Interaction (HJU). IEEE, pp. 618-619; Volante, W.G., Sosna, J., Kessler, T., Sanders, T., Hancock, P.A., Social conformity effects on trust in simulation-based human-robot interaction (2019) Human Factors, 61 (5), pp. 805-815; Wang, N., Pynadath, D.V., Rovira, E., Barnes, M.J., Hill, S.G., Is it my looks? or something i said? The impact of explanations, embodiment, and expectations on trust and performance in human-robot teams (2018) International Conference on Persuasive Technology., pp. 56-69. , Springer; Wheeless, L.R., Grotz, J., The measurement of trust and its relationship to self-disclosure (1977) Human Communication Research, 3 (3), pp. 250-257; Xie, Y., Bodala, I.P., Ong, D.C., Hsu, D., Soh, H., Robot capability and intention in trust-based decisions across tasks (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 39-47; Yagoda, R.E., Gillan, D.J., You want me to trust a ROBOT? The development of a human-robot interaction trust scale (2012) International Journal of Social Robotics, 4 (3), pp. 235-248; You, S., Robert, L.P., Jr., Human-robot similarity and willingness to work with a robotic co-worker (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction., pp. 251-260; Zolin, R., Hinds, P.J., Fruchter, R., Levitt, R.E., Interpersonal trust in cross-functional, geographically distributed work: A longitudinal study (2004) Information and Organization, 14 (1), pp. 1-26",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS)","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167644,21672148,9781450382892,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85102749657
"Ullman D., Aladia S., Malle B.F.","55638484600;57222466938;6701615560;","Challenges and opportunities for replication science in HRI: A case study in human-robot trust",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444652","110","118",,2,"10.1145/3434073.3444652","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102742013&doi=10.1145%2f3434073.3444652&partnerID=40&md5=7b346388c59a7129d95a4f99610fa6dc","Brown University, Providence, RI, United States","Ullman, D., Brown University, Providence, RI, United States; Aladia, S., Brown University, Providence, RI, United States; Malle, B.F., Brown University, Providence, RI, United States","As human-robot interaction (HRI) researchers, like all scientists, we must demonstrate the reproducibility of fndings-especially across robots. We present a three-study replication efort that illustrates the challenges and opportunities for replication science in HRI. A recent human-robot trust study (Ullman & Malle, 2017) sug-gested that people ""in the loop"" with a robot via a simple button press trusted the robot (a Thymio) more than those who observed the robot complete the task autonomously. This intriguing fnding was based on a small sample ( = 40) and was therefore greatly underpowered (observed power 1-β= .35), prompting replication. To test whether the in-the-loop efect generalizes to similar robots, we conducted a conceptual replication (Study 1) using the Create robot. The efect did not replicate, despite a large sample ( = 140) and expected power of .86. This result called for a direct replication (Study 2) using the original Thymio robot. The efect again did not replicate, despite a large sample ( = 200) and ex-pected power of .96. We then conducted an online study (Study 3) with videos of both robots to examine whether diferent expecta-tions for each robot drove the divergent results, but the hypothesis was disconfrmed ( = 400). However, one fnding held across all studies: Participants consistently trusted imagined future robots far less in social than nonsocial use contexts (efect sizes of s=-0.71,-0.78, and-0.79 in the lab studies; =-0.38 in the online study). © 2021 IEEE Computer Society. All rights reserved.","Hu-man in the loop; Human-robot interaction; Human-robot trust; Replication; Reproducibility; Social robotics; Trust","Agricultural robots; Man machine systems; Expected power; Human robot Interaction (HRI); Human robots; Online studies; Reproducibilities; Small samples; Study replications; Use context; Social robots",,,,,"National Aeronautics and Space Administration, NASA: NNX15AI06H; National Science Foundation, NSF: 1717701; Office of Naval Research, ONR: N00014-16-1-2278","The authors thank Seo Hyun Lim, Ariel Rotter-Aboyoun, McKenna Cisler, Maya Cratsley, Maya Omori, Jenny Fu, and Liora Morhayim for their contributions to Study 1, and thank Maya Cratsley, Dylan Davis, Oliver Quick, Isabel Riches, Kaitlyn Mi, and Vincent Rice for their contributions to Study 2. This work was supported by Ofce of Naval Research grant N00014-16-1-2278 and National Science Foundation grant 1717701. Daniel Ullman was supported by the National Space Grant College and Fellowship Program, Space Grant Opportunities in NASA STEM (NNX15AI06H).",,,,,,,,,,"Bainbridge, W.A., Hart, J.W., Kim, E.S., Scassellati, B., The benefts of interactions with physically present robots over video-displayed agents (2011) International Journal of Social Robotics, 3 (1), pp. 41-52; Baxter, P., Kennedy, J., Senft, E., Lemaignan, S., Belpaeme, T., From characterising three years of HRI to methodology and reporting recommendations (2016) Proceedings of the International Conference on Human-Robot Interaction., pp. 391-398; Cohen, J., (1988) Statistical Power Analysis for the Behavioral Sciences (2nd Ed.)., , Routledge, Hillsdale, NJ; Estimating the reproducibility of psychological science (2015) Science, 349 (6251), p. aac4716. , Open Science Collaboration; Ullman, D., Malle, B.F., (2019) Multi-Dimensional Measure of Trust (MDMT), , http://research.clps.brown.edu/SocCogSci/Measures/; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y.C., De Visser, E.J., Parasuraman, R., A meta-analysis of factors afecting trust in human-robot interaction (2011) Human Factors, 53 (5), pp. 517-527; Henrich, J., Heine, S.J., Norenzayan, A., The weirdest people in the world? (2010) Behavioral and Brain Sciences, 33 (2-3), pp. 61-83; Hirano, T., Shiomi, M., Iio, T., Kimoto, M., Tanev, I., Shimohara, K., Hagita, N., How do communication cues change impressions of human-robot touch interaction? (2018) International Journal of Social Robotics, 10 (1), pp. 21-31; Anthony Hof, K., Bashir, M., Trust in automation: Integrating empirical evidence on factors that infuence trust (2015) Human Factors, 57 (3), pp. 407-434; Hofman, G., Zhao, X., A primer for conducting experiments in human-robot interaction (2020) ACM Transactions on Human-Robot Interaction, 10 (1), p. 31; Irfan, B., Kennedy, J., Lemaignan, S., Papadopoulos, F., Senft, E., Belpaeme, T., Social psychology and human-robot interaction: An uneasy marriage (2018) Companion of the International Conference on Human-Robot Interaction., pp. 13-20; (2007) IRobot Create Robot, , https://www.irobot.com/, iRobot; Koole, S.L., Lakens, D., Rewarding replications: A sure and simple way to improve psychological science (2012) Perspectives on Psychological Science, 7 (6), pp. 608-614; Law, T., Malle, B.F., Scheutz, M., A touching connec-tion: How observing robotic touch can afect human trust in a robot (2020) International Journal of Social Robotics; Law, T., Scheutz, M., Trust: Recent concepts and evaluations in human-robot interaction (2021) Trust in Human-Robot Interaction, pp. 27-57. , http://www.sciencedirect.com/science/article/pii/B9780128194720000022, Chang S. Nam and Joseph B. Lyons (Eds.)., Academic Press; Lee, J.D., See, K.A., Trust in automation: Designing for appro-priate reliance (2004) Human Factors, 46 (1), pp. 50-80; Leyzberg, D., Spaulding, S., Toneva, M., Scassellati, B., The physical presence of a robot tutor increases cognitive learning gains (2012) Proceedings of the Annual Meeting of the Cognitive Science Society, 34; Jue Li, J., Ju, W., Reeves, B., Touching a mechanical body: Tactile contact with body parts of a humanoid robot is physiologically arousing (2017) Journal of Human-Robot Interaction, 6 (3), pp. 118-130; Litman, L., Robinson, J., Abberbock, T., TurkPrime.Com: A versatile crowdsourcing data acquisition platform for the behavioral sciences (2017) Behavior Research Methods, 49 (2), pp. 433-442; Malle, B.F., Ullman, D., A multidimensional conception and measure of human-robot trust (2021) Trust in Human-Robot Interaction, pp. 3-25. , http://www.sciencedirect.com/science/article/pii/B9780128194720000010, Chang S. Nam and Joseph B. Lyons (Eds.). Academic Press; Park, E., Lee, J., I am a warm robot: The efects of temperature in physical human-robot interaction (2014) Robotica, 32 (1), pp. 133-142; Pashler, H., Wagenmakers, E., Editors' introduction to the special section on replicability in psychological science: A crisis of confdence? (2012) Perspectives on Psychological Science, 7 (6), pp. 528-530; Phillips, E., Zhao, X., Ullman, D., Malle, B.F., What is human-like?: Decomposing robots' human-like appearance using the anthro-pomorphic robot (ABOT) database (2018) Proceedings of the International Conference on Human-Robot Interaction., pp. 105-113; Robinette, P., Li, W., Allen, R., Howard, A.M., Wagner, A.R., Overtrust of robots in emergency evacuation scenarios (2016) Proceedings of the International Conference on Human-Robot Interaction., pp. 101-108; Sanders, T., Oleson, K.E., Billings, D.R., Chen, J.Y.C., Hancock, P.A., A model of human-robot trust: Theoretical model development (2011) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 55, pp. 1432-1436. , SAGE Publications Sage CA: Los Angeles, CA; Schaefer, K.E., (2013) The Perception and Measurement of Human-robot Trust, , PhD Thesis, STARS Electronic Theses and Dissertations; Schooler, J.W., Metascience could rescue the 'replication crisis' (2014) Nature News, 515 (7525), p. 9; Schönbrodt, F.D., Wagenmakers, E.-J., Zehetleitner, M., Perugini, M., Sequential hypothesis testing with Bayes factors: Efciently testing mean diferences (2017) Psychological Methods, 22 (2), pp. 322-339. , June; (2011) Thymio Robot, , https://www.thymio.org/, Thymio; Ullman, D., Malle, B.F., Human-robot trust: Just a button press away (2017) Proceedings of the International Conference on Human-Robot Interaction., pp. 309-310; Ullman, D., Malle, B.F., Measuring gains and losses in human-robot trust: Evidence for diferentiable components of trust (2019) Proceedings of the International Conference on Human-Robot Interaction., pp. 618-619",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS)","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167644,21672148,9781450382892,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102742013
"Rajaei N., Fujikawa T., Yamada Y.","36925996600;7102532161;35410760400;","Experimental investigation of human soft tissue behavior for constructing human-robot contact force-displacement measuring system",2021,"ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics",,,"9419555","15","19",,,"10.1109/ISR50024.2021.9419555","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106531221&doi=10.1109%2fISR50024.2021.9419555&partnerID=40&md5=a4e92f7e4c0ff5f33aa1aa171e27f919","Graduate School of Engineering, Nagoya University, Department of Mechanical Systems Engineering, Furo-cho Chikusa-ku, Nagoya, 464-8603, Japan","Rajaei, N., Graduate School of Engineering, Nagoya University, Department of Mechanical Systems Engineering, Furo-cho Chikusa-ku, Nagoya, 464-8603, Japan; Fujikawa, T., Graduate School of Engineering, Nagoya University, Department of Mechanical Systems Engineering, Furo-cho Chikusa-ku, Nagoya, 464-8603, Japan; Yamada, Y., Graduate School of Engineering, Nagoya University, Department of Mechanical Systems Engineering, Furo-cho Chikusa-ku, Nagoya, 464-8603, Japan","Lack of knowledge in the human soft tissue deformation during contact between humans and robots is an important obstacle to the development of a standard safety testing device in human-robot interaction (HRI). Human dummies and commercial devices are used to test safety criteria during contact in HRI. However, the responses of these dummies still do not correctly reflect the soft tissue deformation for different human body parts. In this study, we investigate the net behavior of the human upper arm soft tissue deformation during constrained impacts. The net behavior included the soft tissue deformation when is directly and indirectly subjected to impact forces. Using a pendulum device, dynamic and quasi-static (Q.S) impacts were applied to human upper arms, and the differences between soft tissue deformation at the impact location and at the opposite side of the impact for two impactor shapes were measured. Impact force-displacement curves showed a nonlinear behavior for dynamic and Q.S impacts, with greater nonlinearity during dynamic than during Q.S impacts. Our results also demonstrated that soft tissue deformation at the impact location is lower than that at the opposite side of the impact. We also found that the radius of the impactor shape affects the upper arm soft tissue deformation, demonstrating that a larger radius impactor led to a greater soft tissue deformation than a smaller radius impactor. These findings are important for developing testing devices for HRI. © 2021 IEEE.",,"Agricultural robots; Behavioral research; Deformation; Robotics; Safety testing; Tissue; Commercial Devices; Contact forces; Experimental investigations; Human robot Interaction (HRI); Impact locations; Nonlinear behavior; Soft tissue deformation; Testing device; Social robots",,,,,"Ministry of Economy, Trade and Industry, METI","This work was supported by METI (Ministry of Economy, Trade and Industry of Japan) as a part of the project of ”The International Standardization on Measurement Methods of Skin Injury Tolerance for Next-generation Safety of Machinery in Human-machine Interaction”.",,,,,,,,,,"Robla-Gomez, S., Becerra, V.M., Llata, J.R., Gonzalez-Sarabia, E., Torre-Ferrero, C., Perez-Oria, J., Working together: A review on safe human-robot collaboration in industrial environments (2017) IEEE Access, 5, pp. 26754-26773; (2016) Ts 15066: 2016: Robots and Robotic Devices-collaborative Robots, , I. ISO Geneva, Switzerland: International Organization for Standardization; Suita, K., Yamada, Y., Tsuchida, N., Imai, K., Ikeda, H., Sugimoto, N., A failure-to-safety"" kyozon"" system with simple contact detection and stop capabilities for safe human-autonomous robot coexistence (1995) Proceedings of 1995 IEEE International Conference on Robotics and Automation, 3, pp. 3089-3096; Yamada, Y., Hirasawa, Y., Huang, S., Umetani, Y., Fail-safe human/robot contact in the safety space (1996) Proceedings 5th IEEE International Workshop on Robot and Human Communication. ROMAN' 96 TSUKUBA, pp. 59-64; Melia, M., Geissler, B., Konig, J., Ottersbach, H.J., Umbreit, M., Letzel, S., Muttray, A., Pressure pain thresholds: Subject factors and the meaning of peak pressures (2019) European Journal of Pain, 23 (1), pp. 167-182; Fujikawa, T., Sugiura, R., Nishikata, R., Nishimoto, T., Critical contact pressure and transferred energy for soft tissue injury by blunt impact in human-robot interaction (2017) 2017 17th International Conference on Control, Automation and Systems (ICCAS). IEEE, pp. 867-872; Sugiura, R., Fujikawa, T., Nishikata, R., Nishimoto, T., Soft tissue bruise injury by blunt impact in human-robot interaction-difference of tolerance between chest and extremities (2019) 2019 19th International Conference on Control, Automation and Systems (ICCAS). IEEE, pp. 792-797; Desmoulin, G.T., Anderson, G.S., Method to investigate contusion mechanics in living humans (2011) Journal of Forensic Biomechanics, 2; Behrens, R., Elkmann, N., Study on meaningful and verified thresholds for minimizing the consequences of human-robot collisions (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 3378-3383; Fujikawa, T., Kubota, M., Yamada, Y., Ikeda, H., Estimating child collision injury based on automotive accident data for risk assessment of mobile robots (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, pp. 2902-2907; Oberer, S., Schraft, R.D., Robot-dummy crash tests for robot safety assessment (2007) Proceedings 2007 IEEE International Conference on Robotics and Automation. IEEE, pp. 2934-2939; Haddadin, S., Albu-Schaffer, A., Hirzinger, G., Safety evaluation of physical human-robot interaction via crash-testing (2007) Robotics: Science and Systems, 3, pp. 217-224. , Citeseer; Haddadin, S., Albu-Schaffer, A., Hirzinger, G., The role of the robot mass and velocity in physical human-robot interaction-part i: Non-constrained blunt impacts (2008) 2008 IEEE International Conference on Robotics and Automation. IEEE, pp. 1331-1338; Pungrasmi, T., Shimaoka, Y., Okamoto, T., Watanabe, R., Contact area effects on superficial and deep pain threshold for service robot safety design using a pain-sensing system-Development of a humaninspired pain-sensing system (2019) Panasonic Technical Journal, 65 (1), pp. 21-26; Haddadin, S., Albu-Schaffer, A., Hirzinger, G., Requirements for safe robots: Measurements, analysis and new insights (2009) The International Journal of Robotics Research, 28 (11-12), pp. 1507-1527; Haddadin, S., Haddadin, S., Khoury, A., Rokahr, T., Parusel, S., Burgkart, R., Bicchi, A., Albu-Schaffer, A., On making robots understand safety: Embedding injury knowledge into control (2012) The International Journal of Robotics Research, 31 (13), pp. 1578-1602; Basafa, E., Farahmand, F., Real-time simulation of the nonlinear visco-elastic deformations of soft tissues (2011) International Journal of Computer Assisted Radiology and Surgery, 6 (3), pp. 297-307; Lim, Y.-J., Deo, D., Singh, T.P., Jones, D.B., De, S., In situ measurement and modeling of biomechanical response of human cadaveric soft tissues for physics-based surgical simulation (2009) Surgical Endoscopy, 23 (6), p. 1298; Egorov, V., Tsyuryupa, S., Kanilo, S., Kogit, M., Sarvazyan, A., Soft tissue elastometer (2008) Medical Engineering & Physics, 30 (2), pp. 206-212; Samur, E., Sedef, M., Basdogan, C., Avtan, L., Duzgun, O., A robotic indenter for minimally invasive measurement and characterization of soft tissue response (2007) Medical Image Analysis, 11 (4), pp. 361-373; Schwartz, J.-M., Denninger, M., Rancourt, D., Moisan, C., Laurendeau, D., Modelling liver tissue properties using a non-linear viscoelastic model for surgery simulation (2005) Medical Image Analysis, 9 (2), pp. 103-112; Fung, Y.-C., Mechanical properties and active remodeling of blood vessels (1993) Biomechanics, pp. 321-391. , Springer; Hayes, W., Keer, L.M., Herrmann, G., Mockros, L., A mathematical analysis for indentation tests of articular cartilage (1972) Journal of Biomechanics, 5 (5), pp. 541-551; Sneddon, I.N., The relation between load and penetration in the axisymmetric boussinesq problem for a punch of arbitrary profile (1965) International Journal of Engineering Science, 3 (1), pp. 47-57","Rajaei, N.; Graduate School of Engineering, Japan; email: nader.rajaei@mae.nagoya-u.ac.jp",,"IEEE Robotics and Automation Society;Nagoya University","Institute of Electrical and Electronics Engineers Inc.","2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021","4 March 2021 through 6 March 2021",,168860,,9781665438629,,,"English","ISR - IEEE Int. Conf. Intell. Saf. Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85106531221
"Zhao D., Zhang T., Yang J., Wang S., Yokoi H.","57195492133;57223939003;56176391300;9639840600;7201964047;","A novel safety evaluation approach of transfer interaction based on optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals",2021,"ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics",,,"9419492","173","176",,,"10.1109/ISR50024.2021.9419492","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106509922&doi=10.1109%2fISR50024.2021.9419492&partnerID=40&md5=69bf26fd326e2dc196f7813bfa5a6711","School of Electrical Engineering, Shenyang University of Technology, China; Shenyang University of Technology, China; Kochi University of Technology, Kochi, CO, 7828502, Japan; University of Electro-Communications, Tokyo, CO, 1828585, Japan","Zhao, D., School of Electrical Engineering, Shenyang University of Technology, China; Zhang, T., Shenyang University of Technology, China; Yang, J., Shenyang University of Technology, China; Wang, S., Kochi University of Technology, Kochi, CO, 7828502, Japan; Yokoi, H., University of Electro-Communications, Tokyo, CO, 1828585, Japan","Monitoring human-robot interaction (HRI) process of people with mobility inconvenience when to employ multi-robots is an effective approach to mitigate human error and enhance safety. However, the interaction process of transfer that occurs frequently in daily life has not been studied. In this study, we proposed a comprehensive transfer monitoring method according to the mental states monitoring by functional Near-InfraRed Spectroscopy (fNIRS) and subjective workload questionnaire. 7 subjects were to perform transfer behavior under two contrasted levels (self-rising transfer vs. assisted-rising transfer by welfare-robots). After removing physiological noises, six oxygenated and deoxygenated hemoglobin (HbO and HbR) features-mean, slope, variance, peak, skewness, and kurtosis-were calculated. All possible 2-and 3-feature combinations of the calculated features were then used to classify self-rising transfer vs. assisted-rising transfer by linear discriminant analysis (LDA). Then, we established the Subj ective Workload Assessment System (SWAS) for 2 subj ects, which was used to make an evaluation for HRI. The experiment results demonstrated that the optimal feature-combination selection by mean and peak values, which provided a sound theoretical basis for distinguishing mental states of transfer tasks. The SWAS effectively quantify the mental states and physical load of users when to implement multiply welfare-robots. It was contributed to guide the motion planning of multiply welfare-robots for the ultimate goal of safe transfer, and also could be applied in similar scenarios. © 2021 IEEE.",,"Agricultural robots; Behavioral research; Discriminant analysis; Higher order statistics; Infrared devices; Near infrared spectroscopy; Robot programming; Robotics; Effective approaches; Feature combination; Functional near infrared spectroscopy; Functional near-infrared spectroscopy (fnirs); Human robot Interaction (HRI); Interaction process; Linear discriminant analysis; Subjective workload; Human robot interaction",,,,,,,,,,,,,,,,"Fairclough, S.H., Fundamentals of physiological computing (2008) Interact. Comput., 21, pp. 133-145. , Oct; Zander, T.O., Kothe, C., Jatzev, S., Gaertner, M., Enhancing human-computer interaction with input from active and passive brain-computer interfaces (2010) Brain-computer Interfaces, pp. 181-199. , Cham: Springer; Khan, M.J., Hong, K.S., Passive BCI based on drowsiness detection: An fNIRS study (2015) Biomed. Opt. Express, 6, pp. 4063-4078. , Sept; Thongpang, S., A micro-electrocorticography platform and deployment strategies for chronic BCI applications (2011) Clin. EEG Neurosci., 42, pp. 259-265. , Oct; Zhao, D.H., Yang, J.Y., Bai, D.C., Okoye, M.O., Hiroshi, Y., Quantitative estimation of differentiated mental fatigue between self-rising transfer and multiple welfare robots-assisted rising transfer (2020) Electronics, 9 (4), p. 594. , Mar; Naseer, N., Noori, F.M., Qureshi, N.K., Hong, K.S., Determining optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals in brain-computer interface application (2016) Front. Hum Neurosci, 25, p. 10. , May","Zhao, D.; School of Electrical Engineering, China; email: zhaodonghui@sut.edu.cn",,"IEEE Robotics and Automation Society;Nagoya University","Institute of Electrical and Electronics Engineers Inc.","2nd IEEE International Conference on Intelligence and Safety for Robotics, ISR 2021","4 March 2021 through 6 March 2021",,168860,,9781665438629,,,"English","ISR - IEEE Int. Conf. Intell. Saf. Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85106509922
"Mazhar O., Ramdani S., Cherubini A.","56103590300;55910112100;56155388200;","A deep learning framework for recognizing both static and dynamic gestures",2021,"Sensors","21","6","2227","","",,1,"10.3390/s21062227","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102799819&doi=10.3390%2fs21062227&partnerID=40&md5=bee9aae38c3b33e71f24826b57735388","LIRMM, Université de Montpellier, CNRS, Montpellier, 34392, France; Cognitive Robotics Department, Delft University of Technology, Delft, 2628 CD, Netherlands","Mazhar, O., LIRMM, Université de Montpellier, CNRS, Montpellier, 34392, France, Cognitive Robotics Department, Delft University of Technology, Delft, 2628 CD, Netherlands; Ramdani, S., LIRMM, Université de Montpellier, CNRS, Montpellier, 34392, France; Cherubini, A., LIRMM, Université de Montpellier, CNRS, Montpellier, 34392, France","Intuitive user interfaces are indispensable to interact with the human centric smart environments. In this paper, we propose a unified framework that recognizes both static and dynamic gestures, using simple RGB vision (without depth sensing). This feature makes it suitable for inex-pensive human-robot interaction in social or industrial settings. We employ a pose-driven spatial attention strategy, which guides our proposed Static and Dynamic gestures Network—StaDNet. From the image of the human upper body, we estimate his/her depth, along with the region-of-interest around his/her hands. The Convolutional Neural Network (CNN) in StaDNet is fine-tuned on a background-substituted hand gestures dataset. It is utilized to detect 10 static gestures for each hand as well as to obtain the hand image-embeddings. These are subsequently fused with the augmented pose vector and then passed to the stacked Long Short-Term Memory blocks. Thus, human-centred frame-wise information from the augmented pose vector and from the left/right hands image-embeddings are aggregated in time to predict the dynamic gestures of the performing person. In a number of experiments, we show that the proposed approach surpasses the state-of-the-art results on the large-scale Chalearn 2016 dataset. Moreover, we transfer the knowledge learned through the proposed methodology to the Praxis gestures dataset, and the obtained results also outscore the state-of-the-art on this dataset. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Commercial robots and applications; Cyber-physical systems; Gestures recognition; Human activity recognition; Operator interfaces","Convolutional neural networks; Embeddings; Image segmentation; Industrial robots; Large dataset; Social robots; User interfaces; Industrial settings; Intuitive user interface; Learning frameworks; Region of interest; Short term memory; Smart environment; Spatial attention; Unified framework; Deep learning; automated pattern recognition; female; gesture; hand; human; male; Deep Learning; Female; Gestures; Hand; Humans; Male; Neural Networks, Computer; Pattern Recognition, Automated",,,,,"Horizon 2020 Framework Programme, H2020: 871237, 871449","The research presented in this article was carried out as parts of the SOPHIA and the OpenDR projects, which have received funding from the European Union?s Horizon 2020 research and innovation programme under Grant Agreement No. 871237 and 871449 respectively.",,,,,,,,,,"Li, G., Wu, H., Jiang, G., Xu, S., Liu, H., Dynamic gesture recognition in the internet of things (2018) IEEE Access, 7, pp. 23713-23724. , [CrossRef]; Kofman, J., Wu, X., Luu, T.J., Verma, S., Teleoperation of a robot manipulator using a vision-based human-robot interface (2005) IEEE Trans. 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Mazhar, O.; Cognitive Robotics Department, Netherlands; email: o.mazhar@tudelft.nl",,,"MDPI AG",,,,,14248220,,,"33806741","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85102799819
"Bonci A., Cheng P.D.C., Indri M., Nabissi G., Sibona F.","7004656573;57211499233;6603713226;57211501339;57204801691;","Human-robot perception in industrial environments: A survey",2021,"Sensors","21","5","1571","1","29",,7,"10.3390/s21051571","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101219547&doi=10.3390%2fs21051571&partnerID=40&md5=d2cdd5666357ae03fb0ec4b381334a3a","Dipartimento di Ingegneria dell’Informazione (DII), Università Politecnica delle Marche, Ancona, 60131, Italy; Dipartimento di Elettronica e Telecomunicazioni (DET), Politecnico di Torino, Torino, 10129, Italy","Bonci, A., Dipartimento di Ingegneria dell’Informazione (DII), Università Politecnica delle Marche, Ancona, 60131, Italy; Cheng, P.D.C., Dipartimento di Elettronica e Telecomunicazioni (DET), Politecnico di Torino, Torino, 10129, Italy; Indri, M., Dipartimento di Elettronica e Telecomunicazioni (DET), Politecnico di Torino, Torino, 10129, Italy; Nabissi, G., Dipartimento di Ingegneria dell’Informazione (DII), Università Politecnica delle Marche, Ancona, 60131, Italy; Sibona, F., Dipartimento di Elettronica e Telecomunicazioni (DET), Politecnico di Torino, Torino, 10129, Italy","Perception capability assumes significant importance for human–robot interaction. The forthcoming industrial environments will require a high level of automation to be flexible and adaptive enough to comply with the increasingly faster and low-cost market demands. Autonomous and collaborative robots able to adapt to varying and dynamic conditions of the environment, including the presence of human beings, will have an ever-greater role in this context. However, if the robot is not aware of the human position and intention, a shared workspace between robots and humans may decrease productivity and lead to human safety issues. This paper presents a survey on sensory equipment useful for human detection and action recognition in industrial environments. An overview of different sensors and perception techniques is presented. Various types of robotic systems commonly used in industry, such as fixed-base manipulators, collaborative robots, mobile robots and mobile manipulators, are considered, analyzing the most useful sensors and methods to perceive and react to the presence of human operators in industrial cooperative and collaborative applications. The paper also introduces two proofs of concept, developed by the authors for future collaborative robotic applications that benefit from enhanced capabilities of human perception and interaction. The first one concerns fixed-base collaborative robots, and proposes a solution for human safety in tasks requiring human collision avoidance or moving obstacles detection. The second one proposes a collaborative behavior implementable upon autonomous mobile robots, pursuing assigned tasks within an industrial space shared with human operators. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","3D sensors; Collision avoidance; Collision detection; Human action recognition; Human-robot collaboration; Human-robot perception; Machine vision; Robot guidance","Costs; Industrial manipulators; Industrial robots; Manipulators; Mobile robots; Navigation; Robotics; Surveys; Autonomous Mobile Robot; Collaborative application; Collaborative behavior; Collaborative robots; Industrial environments; Level of automations; Perception capability; Robotic applications; Social robots; human; industry; perception; robotics; Humans; Industry; Perception; Robotics",,,,,"Horizon 2020 Framework Programme, H2020: 820434; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR: ARS01-01031; European Regional Development Fund, FEDER","Funding: This work was supported in part by the EU H2020 ENCORE Project “Energy aware BIM Cloud Platform in a COst-effective Building Renovation Context”, European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 820434, in part by the REACT Project “Methods and innovative instruments for REACTive Product Design and Manufacturing”, Project from the Italian Ministry of University and Research (MIUR)—in National Operative Plan (PON) for Research and Innovation 2014–2020, Project No. ARS01-01031, Grant Agreement D.D. 13 July 2018, n. 1825—Code ARS01-01031, and in part by the HD3Flab Project “Human Digital Flexible Factory of the Future Laboratory” EU ERDF (European Regional Development Fund), Regional Operative Plan (POR) MARCHE Region FESR (Fondo Europeo di Sviluppo Regionale) 2014/2020, AXIS 1, Specific Objective 2, ACTION 2.1.",,,,,,,,,,"Wu, X., Goepp, V., Siadat, A., Cyber Physical Production Systems: A Review of Design and Implementation Approaches Proceedings of the 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 1588-1592. , Macao, China, 15–18 December 2019; Bonci, A., Pirani, M., Longhi, S., An embedded database technology perspective in cyber-physical production systems (2017) Procedia Manuf, 11, pp. 830-837; Bonci, A., Pirani, M., Dragoni, A.F., Cucchiarelli, A., Longhi, S., The relational model: In search for lean and mean CPS technology Proceedings of the 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), pp. 127-132. , Emden, Germany, 24–26 July 2017); Bonci, A., Pirani, M., Cucchiarelli, A., Carbonari, A., Naticchia, B., Longhi, S., A review of recursive holarchies for viable systems in CPSs Proceedings of the 2018 IEEE 16th International Conference on Industrial Informatics (INDIN), pp. 37-42. , Porto, Portugal, 18–20 July 2018; Yildiz, Y., Cyberphysical Human Systems: An Introduction to the Special Issue (2020) IEEE Control Syst. 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"Rahman S.M.M.","35087645600;","Machine learning-based cognitive position and force controls for power-assisted human-robot collaborative manipulation",2021,"Machines","9","2","28","1","20",,5,"10.3390/machines9020028","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100792291&doi=10.3390%2fmachines9020028&partnerID=40&md5=94f13e4ca45a78c7c9016e125297c4c9","Department of Intelligent Systems and Robotics, University of West Florida, Pensacola, FL  32514, United States","Rahman, S.M.M., Department of Intelligent Systems and Robotics, University of West Florida, Pensacola, FL  32514, United States","Manipulation of heavy objects in industries is very necessary, but manual manipulation is tedious, adversely affects a worker’s health and safety, and reduces efficiency. On the contrary, autonomous robots are not flexible to manipulate heavy objects. Hence, we proposed human-robot systems, such as power assist systems, to manipulate heavy objects in industries. Again, the selection of appropriate control methods as well as inclusion of human factors in the controls is important to make the systems human friendly. However, existing power assist systems do not address these issues properly. Hence, we present a 1-DoF (degree of freedom) testbed power assist robotic system for lifting different objects. We also included a human factor, such as weight perception (a cognitive cue), in the robotic system dynamics and derived several position and force control strategies/methods for the system based on the human-centric dynamics. We developed a reinforcement learning method to predict the control parameters producing the best/optimal control performance. We also derived a novel adaptive control algorithm based on human characteristics. We experimentally evaluated those control methods and compared the system performance between the control methods. Results showed that both position and force controls produced satisfactory performance, but the position control produced significantly better performance than the force controls. We then proposed using the results to design control methods for power assist robotic systems for handling large and heavy materials and objects in various industries, which may improve human-robot interactions (HRIs) and system performance. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Adaptive control; Force control; Human cognition; Human-robot collaboration; Machine learning; Object manipulation; Position control; Power assist robot; Weight perception",,,,,,,,,,,,,,,,,"Ayoub, M.M., Problems and solutions in manual materials handling: The state of the art (1992) Ergonomics, 35, pp. 713-728; Okamura, A., Smaby, N., Cutkosky, M., An overview of dexterous manipulation (2000) Proceedings of the IEEE International Conference on Robotics and Automation, pp. 255-262. , San Francisco, CA, USA, 24-28 April; Kazerooni, H., Extender: A case study for human-robot interaction via transfer of power and information signals (1993) Proceedings of the 1993 2nd IEEE International Workshop on Robot and Human Communication, pp. 10-20. , Tokyo, Japan, 3-5 November; Peter, N., Kazerooni, H., Industrial-strength human-assisted walking robots (2001) IEEE Robot. Autom. 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"Recupero D.R.","57206674454;","Technology enhanced learning using humanoid robots",2021,"Future Internet","13","2","32","1","17",,2,"10.3390/fi13020032","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100411257&doi=10.3390%2ffi13020032&partnerID=40&md5=02a80da24cd2dc05a0f71ecc32815011","Department of Mathematics and Computer Science, University of Cagliari, Cagliari, 09124, Italy","Recupero, D.R., Department of Mathematics and Computer Science, University of Cagliari, Cagliari, 09124, Italy","In this paper we present a mixture of technologies tailored for e-learning related to the Deep Learning, Sentiment Analysis, and Semantic Web domains, which we have employed to show four different use cases that we have validated in the field of Human-Robot Interaction. The approach has been designed using Zora, a humanoid robot that can be easily extended with new software behaviors. The goal is to make the robot able to engage users through natural language for different tasks. Using our software the robot can (i) talk to the user and understand their sentiments through a dedicated Semantic Sentiment Analysis engine; (ii) answer to open-dialog natural language utterances by means of a Generative Conversational Agent; (iii) perform action commands leveraging a defined Robot Action ontology and open-dialog natural language utterances; and (iv) detect which objects the user is handing by using convolutional neural networks trained on a huge collection of annotated objects. Each module can be extended with more data and information and the overall architectural design is general, flexible, and scalable and can be expanded with other components, thus enriching the interaction with the human. Different applications within the e-learning domains are foreseen: The robot can either be a trainer and autonomously perform physical actions (e.g., in rehabilitation centers) or it can interact with the users (performing simple tests or even identifying emotions) according to the program developed by the teachers. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Human-robot interaction; Robotic operating system; Virtual reality; Voice assistant technology","Anthropomorphic robots; Application programs; Convolutional neural networks; Deep learning; E-learning; Object detection; Sentiment analysis; Software agents; Software testing; Conversational agents; Data and information; Humanoid robot; Natural languages; Physical action; Robot actions; Software behavior; Technology enhanced learning; Human robot interaction",,,,,,,,,,,,,,,,"Grace, K., Salvatier, J., Dafoe, A., Zhang, B., Evans, O., When Will AI Exceed Human Performance? Evidence from AI Experts (2018) J. Artif. 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"Amorim A., Guimares D., Mendona T., Neto P., Costa P., Moreira A.P.","57217043675;57218477710;57224295035;35243406700;57210535795;55971638300;","Robust human position estimation in cooperative robotic cells",2021,"Robotics and Computer-Integrated Manufacturing","67",,"102035","","",,6,"10.1016/j.rcim.2020.102035","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089262510&doi=10.1016%2fj.rcim.2020.102035&partnerID=40&md5=a71ddb2d360217e403772c3c9ae485d3","INESC TEC, Faculty of Engineering, University of Porto, Porto, Portugal; Univ Coimbra, centre for Mechanical Engineering, Materials and Processes, Department of Mechanical Engineering, Coimbra, Portugal","Amorim, A., INESC TEC, Faculty of Engineering, University of Porto, Porto, Portugal; Guimares, D., INESC TEC, Faculty of Engineering, University of Porto, Porto, Portugal; Mendona, T., INESC TEC, Faculty of Engineering, University of Porto, Porto, Portugal; Neto, P., Univ Coimbra, centre for Mechanical Engineering, Materials and Processes, Department of Mechanical Engineering, Coimbra, Portugal; Costa, P., INESC TEC, Faculty of Engineering, University of Porto, Porto, Portugal; Moreira, A.P., INESC TEC, Faculty of Engineering, University of Porto, Porto, Portugal","Robots are increasingly present in our lives, sharing the workspace and tasks with human co-workers. However, existing interfaces for human-robot interaction / cooperation (HRI/C) have limited levels of intuitiveness to use and safety is a major concern when humans and robots share the same workspace. Many times, this is due to the lack of a reliable estimation of the human pose in space which is the primary input to calculate the human-robot minimum distance (required for safety and collision avoidance) and HRI/C featuring machine learning algorithms classifying human behaviours / gestures. Each sensor type has its own characteristics resulting in problems such as occlusions (vision) and drift (inertial) when used in an isolated fashion. In this paper, it is proposed a combined system that merges the human tracking provided by a 3D vision sensor with the pose estimation provided by a set of inertial measurement units (IMUs) placed in human body limbs. The IMUs compensate the gaps in occluded areas to have tracking continuity. To mitigate the lingering effects of the IMU offset we propose a continuous online calculation of the offset value. Experimental tests were designed to simulate human motion in a human-robot collaborative environment where the robot moves away to avoid unexpected collisions with de human. Results indicate that our approach is able to capture the human's position, for example the forearm, with a precision in the millimetre range and robustness to occlusions. © 2020 Elsevier Ltd","Human tracking; Human-robot interaction; Occlusions; Sensor fusion","Agricultural robots; Behavioral research; Learning algorithms; Machine learning; Robotics; 3D vision sensor; Collaborative environments; Cooperative robotics; Experimental test; Inertial measurement unit; Minimum distance; Online calculations; Position estimation; Human robot interaction",,,,,"UIDB/00285/2020; Fundação para a Ciência e a Tecnologia, FCT: PTDC/EME- EME/32595/2017","This research was partially supported by Portugal 2020 project DM4Manufacturing POCI-01-0145-FEDER-016418 by UE/FEDER through the program COMPETE 2020, and the Portuguese Foundation for Science and Technology COBOTIS project ( PTDC/EME- EME/32595/2017 ). This research is also sponsored by FEDER funds through the program COMPETE Programa Operacional Factores de Competitividade, and by national funds through FCT Fundação para a Ciência e a Tecnologia under the project UIDB/00285/2020 .","This research was partially supported by Portugal 2020 project DM4Manufacturing POCI-01-0145-FEDER-016418 by UE/FEDER through the program COMPETE 2020, and the Portuguese Foundation for Science and Technology COBOTIS project (PTDC/EME- EME/32595/2017). This research is also sponsored by FEDER funds through the program COMPETE Programa Operacional Factores de Competitividade, and by national funds through FCT Funda??o para a Ci?ncia e a Tecnologia under the project UIDB/00285/2020.",,,,,,,,,"Saenz, J., Behrens, R., Schulenburg, E., Petersen, H., Gibaru, O., Neto, P., Elkmann, N., Methods for considering safety in design of robotics applications featuring human-robot collaboration (2020) Int. J. Adv. Manuf. 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A, 32, pp. 922-923","Amorim, A.; INESC TEC, Portugal; email: antonio.m.amorim@inesctec.pt",,,"Elsevier Ltd",,,,,07365845,,RCIME,,"English","Rob Comput Integr Manuf",Article,"Final","",Scopus,2-s2.0-85089262510
"Chen X., Zhang K., Liu H., Leng Y., Fu C.","57211101760;57193994064;57205643389;55376585000;57208278421;","A Probability Distribution Model-based Approach for Foot Placement Prediction in the Early Swing Phase with a Wearable IMU Sensor",2021,"IEEE Transactions on Neural Systems and Rehabilitation Engineering",,,,"","",,,"10.1109/TNSRE.2021.3133656","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121352104&doi=10.1109%2fTNSRE.2021.3133656&partnerID=40&md5=2bfe4d2f79469514c7323c5bff7d0023","Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, 518055, China, and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, 518055, China.; Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, 518055, China, and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, 518055, China and Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada.; Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, 518055, China, and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, 518055, China. (e-mail: fucl@sustech.edu.cn)","Chen, X., Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, 518055, China, and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, 518055, China.; Zhang, K., Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, 518055, China, and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, 518055, China and Department of Mechanical Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada.; Liu, H., Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, 518055, China, and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, 518055, China.; Leng, Y., Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, 518055, China, and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, 518055, China.; Fu, C., Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, 518055, China, and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, 518055, China. (e-mail: fucl@sustech.edu.cn)","Predicting the next foot placement of humans during walking can help improve compliant interactions between humans and walking aid robots. Previous studies have focused on foot placement estimation with wearable inertial sensors after heel-strike, but few have predicted foot placements in advance during the early swing phase. In this study, a Bayesian inference-based foot placement prediction approach was proposed. Possible foot placements were modeled as a probability distribution grid map. With selected foot motion feature events detected sequentially in the early swing phase, the foot placement probability map could be updated iteratively using the feature models we built. The weighted center of the probability distribution was regarded as the predicted foot placement. Prediction errors were evaluated with collected walking data sets. When testing with the data from inertial measurement units, the prediction errors were (5.46 cm &#x00B1; 10.89 cm, -0.83 cm &#x00B1; 10.56 cm) for cross-velocity walking data and (-4.99 cm &#x00B1; 12.31 cm, -11.27 cm &#x00B1; 7.74 cm) for cross-subject&#x2013;cross-velocity walking data. The results were comparable to previous works yet the prediction could be made earlier. For the subject who walked with more stable gaits, the prediction error can be further decreased. The proposed foot placement prediction approach can be utilized to help walking aid robots adjust their pose before each heel-strike event during walking, which will make human&#x2013;robot interactions more compliant. This study is also expected to inspire additional probabilistic gait analysis works. Author","Bayes methods; Bayesian inference; Estimation; Feature extraction; Foot; foot placement; gait prediction; Human walking; inertial sensors; Legged locomotion; Robots; Trajectory",,,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15344320,,ITNSB,,"English","IEEE Trans. Neural Syst. Rehabil. Eng.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85121352104
"Longo F., Padovano A., Aiello G., Fusto C., Certa A.","57201515306;56429595900;36785734900;57192253390;23972451400;","How 5G-based industrial IoT is transforming human-centered smart factories: A Quality of Experience model for Operator 4.0 applications",2021,"IFAC-PapersOnLine","54","1",,"255","262",,,"10.1016/j.ifacol.2021.08.030","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120668847&doi=10.1016%2fj.ifacol.2021.08.030&partnerID=40&md5=746a60a9a2de1ac24322e6f9b9833f4e","Department of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria, Rende, 87036, Italy; CAL-TEK S.r.l., Rende, 87036, Italy; University of Palermo, Palermo, 90133, Italy","Longo, F., Department of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria, Rende, 87036, Italy, CAL-TEK S.r.l., Rende, 87036, Italy; Padovano, A., Department of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria, Rende, 87036, Italy; Aiello, G., University of Palermo, Palermo, 90133, Italy; Fusto, C., Department of Mechanical, Energy and Management Engineering (DIMEG), University of Calabria, Rende, 87036, Italy; Certa, A., University of Palermo, Palermo, 90133, Italy","As factories move towards Industry 4.0, the replacement of control communications based on Ethernet and other wired technologies with wireless becomes an imperative to fulfill the envisioned flexibility and easy reconfigurability of the production facilities. While wired connections are often unwanted for their limited flexibility and high maintenance costs, common wireless technologies in manufacturing such as Bluetooth, Wi-Fi, ZigBee, or 4G cannot fulfil the requirements of timeliness, reliability, data rates, scalability and availability of cyber-physical production systems. Today, the fifth-generation (5G) broadband cellular network technology holds the promise for an enhanced Quality of Service (QoS) and Quality of Experience (QoE) that unlock a huge amount of value and opportunity. However, at present, application of 5G wireless communication technology in the manufacturing industry is still at its infancy. In this paper, 5G is presented as a game changer for human-machine and human-robot interaction in three manufacturing use cases (i.e. uRLLC-based human-robot collaboration, eMBB-based AR-assisted operations and mMTC-based interaction with the digital twin). A QoS/QoE model is presented to drive the implementation of 5G-aided solutions for the Operator 4.0, thus maximizing the network quality and acceptance rate of digital technologies in the shop floor (that have never been widely accepted and used in practice by the industrial workforce). This paper explores how a 5G could finally provide the necessary network infrastructure for the human-machine symbiosis in the factory of the future. © 2021 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0)","5G; Cloud manufacturing; Cyber-Physical Production System; Industrial internet of things (IIoT); Mobile wireless network; Operator 4.0","Cyber Physical System; Digital storage; Human robot interaction; Industry 4.0; Internet of things; Man machine systems; Quality of service; Wi-Fi; Wireless networks; Cloud Manufacturing; Control communications; Cybe-physical production system; Cyber physicals; Industrial internet of thing; Mobile wireless network; Operator 4.0; Production system; Quality-of-service; Reconfigurability; 5G mobile communication systems",,,,,,"hiT s orw k was spu ported in part by the ruE opean project 5G RAE : 5G ANHE NCD RE OOB T AOTU NOYM (Grant agreement ID: 101016681 ) ifnanced nu der the programme H2020-ICT -2020 U.E 2.1.1. - IIASRTDUN L LADE HIPSRE - Leadership in enabling and industrial technologies - Information and Communication eT chnologies (IC)T .",,,,,,,,,,"Empowering Vertical Industries through 5G Networks - Current Status and Future Trends, , 5GPPP 2020. 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Padovano, A.; Department of Mechanical, Italy; email: antonio.padovano@unical.it
Fusto, C.; Department of Mechanical, Italy",,,"Elsevier B.V.","17th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2021","7 June 2021 through 9 June 2021",,146678,24058963,,,,"English","IFAC-PapersOnLine",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85120668847
"Dehio N., Smith J., Wigand D.L., Mohammadi P., Mistry M., Steil J.J.","57118138400;57207881813;57194450654;56423507600;12646109500;6701553064;","Enabling impedance-based physical human–multi–robot collaboration: Experiments with four torque-controlled manipulators",2021,"International Journal of Robotics Research",,,,"","",,,"10.1177/02783649211053650","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120626585&doi=10.1177%2f02783649211053650&partnerID=40&md5=e8b74da10e5dfbe2e7415c2cfce3ec1f","Institute for Anthropomatics and Robotics - Intelligent Process Automation and Robotics Lab, Karlsruhe Institute of Technology, Germany; Institute of Robotics and Process Control, Technical University of Braunschweig, Germany; Institute for Perception, Action and Behaviour, University of Edinburgh, United Kingdom; 4Research Institute for Cognition and Robotics, Bielefeld University, United Kingdom","Dehio, N., Institute for Anthropomatics and Robotics - Intelligent Process Automation and Robotics Lab, Karlsruhe Institute of Technology, Germany, Institute of Robotics and Process Control, Technical University of Braunschweig, Germany, Institute for Perception, Action and Behaviour, University of Edinburgh, United Kingdom; Smith, J., Institute for Perception, Action and Behaviour, University of Edinburgh, United Kingdom; Wigand, D.L., 4Research Institute for Cognition and Robotics, Bielefeld University, United Kingdom; Mohammadi, P., Institute of Robotics and Process Control, Technical University of Braunschweig, Germany; Mistry, M., Institute for Perception, Action and Behaviour, University of Edinburgh, United Kingdom; Steil, J.J., Institute of Robotics and Process Control, Technical University of Braunschweig, Germany","Robotics research into multi-robot systems so far has concentrated on implementing intelligent swarm behavior and contact-less human interaction. Studies of haptic or physical human-robot interaction, by contrast, have primarily focused on the assistance offered by a single robot. Consequently, our understanding of the physical interaction and the implicit communication through contact forces between a human and a team of multiple collaborative robots is limited. We here introduce the term Physical Human Multi-Robot Collaboration (PHMRC) to describe this more complex situation, which we consider highly relevant in future service robotics. The scenario discussed in this article covers multiple manipulators in close proximity and coupled through physical contacts. We represent this set of robots as fingers of an up-scaled agile robot hand. This perspective enables us to employ model-based grasping theory to deal with multi-contact situations. Our torque-control approach integrates dexterous multi-manipulator grasping skills, optimization of contact forces, compensation of object dynamics, and advanced impedance regulation into a coherent compliant control scheme. For this to achieve, we contribute fundamental theoretical improvements. Finally, experiments with up to four collaborative KUKA LWR IV+ manipulators performed both in simulation and real world validate the model-based control approach. As a side effect, we notice that our multi-manipulator control framework applies identically to multi-legged systems, and we execute it also on the quadruped ANYmal subject to non-coplanar contacts and human interaction. © The Author(s) 2021.","model-based grasping; multi-objective torque-control; multi-robot manipulation; Physical human-robot interaction","Collaborative robots; Industrial robots; Intelligent robots; Man machine systems; Manipulators; Robot learning; Robotics; Torque control; Humaninteraction; Model-based grasping; Model-based OPC; Multi objective; Multi-objective torque-control; Multi-robot collaboration; Multi-robot manipulation; Multirobots; Physical humanrobot interaction (phri); Robot manipulation; Multipurpose robots",,,,,"ICT-23-2014 644727","The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was partially funded by the European Community’s H2020 robotics program: CogIMon ICT-23-2014 644727 and by the state Baden-Württemberg: Roboshield.",,,,,,,,,,"Aghili, F., A unified approach for inverse and direct dynamics of constrained multibody systems based on linear projection operator: Applications to control and simulation (2005) IEEE Transactions on Robotics, 21 (5), pp. 834-849; 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I—Theory. II—Implementation. III - Applications (1985) Journal of Dynamic Systems, Measurement, and Control, 107, pp. 1-7; Hutter, M., Gehring, C., Jud, D., (2016) Anymal—a highly mobile and dynamic quadrupedal robot, pp. 38-44. , 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea, 9-14 October 2016, In; Johnson, A.M., Haynes, G.C., Koditschek, D.E., (2012) Standing self-manipulation for a legged robot, pp. 272-279. , 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura-Algarve, Portugal, 7-12 Oct. 2012, In; Johnson, A.M., Koditschek, D.E., Legged self-manipulation (2013) IEEE Access, 1, pp. 310-334; Karayiannidis, Y., Smith, C., Kragic, D., (2014) Mapping human intentions to robot motions via physical interaction through a jointly-held object, pp. 391-397. , The 23rd IEEE International Symposium on Robot and Human Interactive Communication, Edinburgh, UK, 25–29 August 2014, In; Kolling, A., Walker, P., Chakraborty, N., Human interaction with robot swarms: A survey (2016) IEEE Transactions on Human-Machine Systems, 46 (1), pp. 9-26; Kosuge, K., Sato, B., Kazamura, N., Mobile robot helper (2000) The International Journal of Robotics Research, 1, pp. 583-588; Leveroni, S., Salisbury, K., Reorienting objects with a robot hand using grasp gaits (1996) Robotics Research, pp. 39-51. , G Giralt, G Hirzinger, (eds), London, Springer London, In:, (eds; Lin, H.C., Smith, J., Kouhkiloui Babarahmati, K., (2018) A projected inverse dynamics approach for multi-arm Cartesian impedance control, pp. 1-5. , IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, 21–25 May 2018, In; Marino, A., Pierri, F., A two stage approach for distributed cooperative manipulation of an unknown object without explicit communication and unknown number of robots (2018) Robotics and Autonomous Systems, 103, pp. 122-133; Mistry, M., Righetti, L., (2011) Operational space control of constrained and underactuated systems, , Cambridge, MA, MIT Press; Mohammadi, P., Mingo Hoffman, E., Dehio, N., Compliant humanoids moving toward rehabilitation applications: Transparent integration of real-time control, whole-body motion generation, and virtual reality (2019) IEEE Robotics & Automation Magazine, 26 (4), pp. 83-93. , (, a; Mohammadi, P., Mingo Hoffman, E., Muratore, L., (2019) Reactive walking based on upper-body manipulability: An application to intention detection and reaction, , 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, 20–24 May 2019, (, b), In; Moura, J., Ivan, V., Erden, M.S., Equivalence of the projected forward dynamics and the dynamically consistent inverse solution (2019) Proceedings of Robotics: Science and Systems, 15, pp. 1-10; Murray, R.M., Li, Z., Sastry, S.S., (1994) A Mathematical Introduction to Robotic Manipulation, , Boca Raton, FL, CRC Press; Nakanishi, J., Cory, R., Mistry, M., Operational space control: A theoretical and empirical comparison (2008) The International Journal of Robotics Research, 27 (6), pp. 737-757; Nakashima, A., Hayakawa, Y., Stability analysis of multi-fingered grasp under destabilizing gravity effect (2011) IFAC Proceedings Volumes, 44 (1), pp. 14667-14674; Parra-Vega, V., Sanchez, A., Izaguirre, C., Toward aerial grasping and manipulation with multiple uavs (2013) Journal of Intelligent & Robotic Systems, 70 (1), pp. 575-593; Peng, Y.C., Carabis, D.S., Wen, J.T., Collaborative manipulation with multiple dual-arm robots under human guidance (2018) International Journal of Intelligent Robotics and Applications, 2 (2), pp. 252-266; Peternel, L., Tsagarakis, N., Caldwell, D., Robot adaptation to human physical fatigue in human-robot co-manipulation (2018) Autonomous Robots, 42 (5), pp. 1011-1021; Righetti, L., Buchli, J., Mistry, M., Optimal distribution of contact forces with inverse-dynamics control (2013) The International Journal of Robotics Research, 32 (3), pp. 280-298; Righetti, L., Buchli, J., Mistry, M., (2011) Inverse dynamics control of floating-base robots with external constraints: a unified view, pp. 1085-1090. , IEEE International Conference on Robotics and Automation, Shanghai, China, 9-13 May 2011, In; Schneider, S.A., Cannon, R.H., Object impedance control for cooperative manipulation: Theory and experimental results (1992) IEEE Transactions on Robotics and Automation, 8 (3), pp. 383-394; Sentis, L., Khatib, O., (2006) A whole-body control framework for humanoids operating in human environments, pp. 2641-2648. , Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006, Orlando, FL, 15-19 May 2006, In; Sentis, L., Park, J., Khatib, O., Compliant control of multicontact and center-of-mass behaviors in humanoid robots (2010) IEEE Transactions on Robotics, 26 (3), pp. 483-501; Sieber, D., Hirche, S., Human-guided multirobot cooperative manipulation (2019) IEEE Transactions on Control Systems Technology, 27 (4), pp. 1492-1509; Silvério, J., Rozo, L., Calinon, S., (2015) Learning bimanual end-effector poses from demonstrations using task-parameterized dynamical systems, pp. 464-470. , 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, 28 September–2 October 2015, In; Simetti, E., Casalino, G., Manipulation and transportation with cooperative underwater vehicle manipulator systems (2017) IEEE Journal of Oceanic Engineering, 42 (4), pp. 782-799; Stewart, D.E., Trinkle, J.C., An implicit time-stepping scheme for rigid body dynamics with inelastic collisions and coulomb friction (1996) The International Journal for Numerical Methods in Engineering, 39 (15), pp. 2673-2691; Trinkle, J.C., Pang, J.S., Sudarsky, S., On dynamic multi-rigid-body contact problems with coulomb friction (1997) Journal of Applied Mathematics and Mechanics, 77 (4), pp. 267-279; Tsiamis, A., Verginis, C.K., Bechlioulis, C.P., (2015) Cooperative manipulation exploiting only implicit communication, pp. 864-869. , 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, 28 September–2 October 2015, In; Vaillant, J., Bouyarmane, K., Kheddar, A., Multi-character physical and behavioral interactions controller (2017) IEEE Transactions on Visualization and Computer Graphics, 23 (6), pp. 1650-1662; Wang, Y., Dehio, N., Tanguy, A., Impact-aware task-space quadratic-programming control (2020) Robotics, , https://arxiv.org/abs/2006.01987; Wigand, D.L., Dehio, N., Wrede, S., (2020) Model-based specification of control architectures for compliant interaction with the environment, , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, 24 October–24 January 2021, In; Wigand, D.L., Nordmann, A., Dehio, N., Domain-specific language modularization scheme applied to a multi-arm robotics use-case (2017) Journal of Software Engineering for Robotics, 8 (1), pp. 45-64; Wimböck, T., Ott, C., Albu-Schäffer, A., Comparison of object-level grasp controllers for dynamic dexterous manipulation (2012) The International Journal of Robotics Research, 31 (1), pp. 3-23","Dehio, N.; Institute for Anthropomatics and Robotics - Intelligent Process Automation and Robotics Lab, Germany; email: niels.dehio@kit.edu",,,"SAGE Publications Inc.",,,,,02783649,,IJRRE,,"English","Int J Rob Res",Article,"Article in Press","",Scopus,2-s2.0-85120626585
"Krzysiak R., Butail S.","57355576200;35408719100;","Information-Based Control of Robots in Search-and-Rescue Missions With Human Prior Knowledge",2021,"IEEE Transactions on Human-Machine Systems",,,,"","",,,"10.1109/THMS.2021.3113642","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120067350&doi=10.1109%2fTHMS.2021.3113642&partnerID=40&md5=a9d1ce273dad3e846d85e1bb295210c1","Department of Mechanical Engineering, Northern Illinois University, DeKalb, IL 60115 USA (e-mail: z1824735@students.niu.edu).; Department of Mechanical Engineering, Northern Illinois University, DeKalb, IL 60115 USA (e-mail: sbutail@niu.edu).","Krzysiak, R., Department of Mechanical Engineering, Northern Illinois University, DeKalb, IL 60115 USA (e-mail: z1824735@students.niu.edu).; Butail, S., Department of Mechanical Engineering, Northern Illinois University, DeKalb, IL 60115 USA (e-mail: sbutail@niu.edu).","Multirobot systems provide a scalable and robust solution for monitoring tasks. In time-intensive missions such as search and rescue, the inclusion of a human has the potential advantage of incorporating prior knowledge about the target location or dynamics. In this article, we develop a general information-theoretic framework to control multiple autonomous robots in search and rescue missions that include a human teleoperator. Human prior knowledge is modeled to capture the target location and dynamics, and mutual-information-based control is formulated to let autonomous robots weigh between two strategies: independent search or assisting the human by staying in proximity. The control actions optimize a weighted sum of normalized mutual information calculated using particle-filtered estimates of the target and the reference robot. We implement the framework to simulate two widely different scenarios designed after search-and-rescue missions from literature, and incorporate varying levels of accuracy in human prior knowledge. Our results indicate that the mission performance depends on how robots weigh between the two strategies, with the amount of the optimal control effort shared between strategies affected by prior knowledge and the number of robots. Comparison with existing strategies points to the benefits of an information-based control in situations where human prior knowledge is inaccurate. The proposed information-theoretic abstraction of the human&#x2013;robot interaction can be implemented on a wide variety of scenarios and the results highlight the role of human prior knowledge toward effective robotic assistance in time-intensive missions. IEEE","Atmospheric measurements; Human--robot interaction (HRI); Mutual information; mutual information; particle filtering; Particle measurements; Probability density function; Robot sensing systems; Robots; search and rescue (SAR); teleoperation; Uncertainty","Human robot interaction; Information filtering; Information theory; Remote control; Robotics; Search engines; Uncertainty analysis; Atmospheric measurement; Human--robot interaction; Humans-robot interactions; Mutual informations; Particle Filtering; Particle measurement; Robot sensing system; Search and rescue; Uncertainty; Probability density function",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21682291,,,,"English","IEEE Trans. Human Mach. Syst.",Article,"Article in Press","",Scopus,2-s2.0-85120067350
"Rossi A., Perugia G., Rossi S.","57189499587;57193757433;55619320887;","Investigating Customers’ Perceived Sensitivity of Information Shared with a Robot Bartender",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"119","129",,,"10.1007/978-3-030-90525-5_11","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119858917&doi=10.1007%2f978-3-030-90525-5_11&partnerID=40&md5=d83b21b7a66143fc52d0999c32d6c185","ICAROS Center, University of Naples Federico II, Naples, Italy; Department of Information Technology, Uppsala University, Uppsala, Sweden; Human Technology Interaction Group, Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, Netherlands; Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Naples, Italy","Rossi, A., ICAROS Center, University of Naples Federico II, Naples, Italy; Perugia, G., Department of Information Technology, Uppsala University, Uppsala, Sweden, Human Technology Interaction Group, Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, Netherlands; Rossi, S., ICAROS Center, University of Naples Federico II, Naples, Italy, Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Naples, Italy","Personalised experiences with service robots positively affect people’s perception of the robot and, consequently, foster the success of the interaction. This implies that people need to share their personal information with the robot, which could let people feel uneasy when such interactions happen in public spaces or in the presence of strangers. Therefore, it is difficult for a service robot to personalise a human-robot interaction (HRI) when this can lead to a breach of privacy. As a first step, the current study investigated people’s perception of the sensitivity of various categories of potentially private personal information that are likely to be used by a service robot in a public business, such as a bar. We conducted a questionnaire-based study, where participants rated 15 personal information that they could share with either a human or robot bartender. The potentially private information was rated by participants according to their level of sensitivity. We analysed responses from 76 participants. We clearly identified information that are perceived as highly sensitive, such as those related to a person’s identity (e.g. sexual orientation, political beliefs), and as low in sensitivity, such as those related to personal interests (e.g. sports, TV shows). Our findings also showed that older people consider sharing their preference of drinks more sensitive than younger people, especially when the bartender is a robot. We did not find significant differences in users’ ratings due to their gender. © 2021, Springer Nature Switzerland AG.","Human-robot interaction; Privacy; Social robotics","Beverages; Information dissemination; Man machine systems; Mobile robots; Multipurpose robots; Robotics; Surveys; 'current; Humans-robot interactions; Information shared; Personal information; Privacy; Private information; Public space; Service robots; Sexual orientations; Social robotics; Human robot interaction",,,,,,"Acknowledgements. This work has been supported by Italian PON I&C 2014-2020 within the BRILLO research project “Bartending Robot for Interactive Long-Lasting Operations”, no. F/190066/01-02/X44.",,,,,,,,,,"Basharat, I., Azam, F., Muzaffar, A.W., Database security and encryption: A survey study (2012) Int. J. Comput. Appl., 47, pp. 28-34; Berezina, K., Ciftci, O., Cobanoglu, C., Robots, Artificial Intelligence, and Service Automation in Restaurants. Ivanov, S., Webster, C. (Ed.), pp (2019) 185–219; Bissonette, R., The mental health gatekeeper role: A paradigm for conceptual pretest (1977) Int. J. Soc. Psychiatry, 23 (1), pp. 31-34; Blake, L., (2015) Last Call: The New Orleans Bartender as Conversational Healing Tonic; Chatzimichali, A., Harrison, R., Chrysostomou, D., Toward privacy-sensitive human-robot interaction: Privacy terms and human-data interaction in the personal robot era (2021) Paladyn J. Behav. Robot., 12 (1), pp. 160-174; Dautenhahn, K., Robots we like to live with?!-a developmental perspective on a personalized, life-long robot companion (2004) RO-MAN 2004. 13Th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No.04Th8759), pp. 17-22. , https://doi.org/10.1109/ROMAN.2004.1374720, pp; Fung, B.C.M., Wang, K., Chen, R., Yu, P.S., Privacy-preserving data publishing: A survey of recent developments (2010) ACM Comput. Surv., 42 (4), pp. 1-53; Gosling, S.D., Rentfrow, P.J., Swann, W.B., A very brief measure of the big-five personality domains (2003) J. Res. Personal., 37 (6), pp. 504-528. , https://doi.org/10. 1016/S0092-6566(03)00046-1; Hegel, F., Gieselmann, S., Peters, A., Holthaus, P., Wrede, P., Towards a typology of meaningful signals and cues in social robotics (2011) RO-MAN, pp. 72-78. , https://doi.org/10.1109/ROMAN.2011.6005246, pp., 2011; Hidayanti, I., Herman, L.E., Farida, N., Engaging customers through social media to improve industrial product development: The role of customer co-creation value (2018) J. Relat. Market., 17 (1), pp. 17-28; Kuhn, S., Mostert, P., Relationship intention and relationship quality as predictors of clothing retail customers’ loyalty (2018) Int. Rev. Retail Distrib. Consumer Res., 28 (2), pp. 206-230; Lutz, C., Tamò, A., Robocode-ethicists: Privacy-friendly robots, an ethical responsibility of engineers? (2015) Proceedings of the ACM Web Science Conference. Websci ’15, , Association for Computing Machinery, New York, NY, USA; Masuda, T., Misaki, D., Development of japanese green tea serving robot “t-bartender” (2005) IEEE International Conference Mechatronics and Automation, 2, pp. 1069-1074. , vol., pp., 2005; Rios-Martinez, J., Spalanzani, A., Laugier, C., From proxemics theory to socially-aware navigation: A survey (2015) Int. J. Soc. Robot., 7 (2), pp. 137-153; Room, R., Smoking and drinking as complementary behaviours (2004) Biomed. Pharma-Cotherapy, 58 (2), pp. 111-115; Rossi, A., Dautenhahn, K., Koay, K.L., Walters, M.L.: How the timing and magnitude of robot errors influence peoples’ trust of robots in an emergency scenario. In: Kheddar, A., Yoshida, E., Ge, S.S., Suzuki, K., Cabibihan, J.J., Eyssel, F., He, H. (eds.) Social Robotics, pp. 42–52. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-70022-9 5; Rossi, A., Dautenhahn, K., Koay, K.L., Walters, M.L.: Human perceptions of the severity of domestic robot errors. In: Kheddar, A., Yoshida, E., Ge, S.S., Suzuki, K., Cabibihan, J.J., Eyssel, F., He, H. (eds.) Social Robotics, pp. 647–656. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-70022-9 64; Rossi, A., Dautenhahn, K., Koay, K.L., Walters, M.L., Holthaus, P.: Evaluating people’s perceptions of trust in a robot in a repeated interactions study. In: Wagner, A.R., Feil-Seifer, D., Haring, K.S., Rossi, S., Williams, T., He, H., Sam Ge, S. (eds.) Social Robotics, pp. 453–465. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-62056-1 38; Rossi, A., Giura, V., Leva, C.D., Rossi, S., (2021) I Know What You Would like to Drink: Benefits and Detriments of Sharing Personal Info with a Bartender Robot; Rossi, A., Staffa, M., Origlia, A., Di Maro, M., Rossi, S., Brillo: A robotic architecture for personalised long-lasting interactions in a bartending domain (2021) Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, Pp. 426–429. HRI ’21 Companion, Association for Computing Machinery, New York, NY, USA; Rueben, M., Themes and research directions in privacy-sensitive robotics (2018) IEEE Workshop on Advanced Robotics and Its Social Impacts (ARSO), pp. 77-84. , pp., 2018; Sannon, S., Stoll, B., Difranzo, D., Jung, M.F., Bazarova, N.N., “i just shared your responses”: Extending communication privacy management theory to interactions with conversational agents (2020) Proc. ACM Hum.-Comput. Interact., 4, pp. 1-18; Schulz, T., Herstad, J., Walking away from the robot: Negotiating privacy with a robot (2017) Proceedings of the 31St British Computer Society Human Computer Interaction Conference. HCI ’17, BCS Learning & Development Ltd., Swindon, GBR; Stubbs, A.G.P., (2001) Learning to Pour: An Exploration into the Socialization of the Male Night Shift Bartender, , https://scholar.uwindsor.ca/etd/2591; Syrdal, D.S., Walters, M., Otero, N., Koay, K., Dautenhahn, K., He knows when you are sleeping”-privacy and the personal robot companion (2007) In Proceedings of Workshop on Human Implications of Human-Robot Interaction. AAAI, pp. 28-33. , pp; Warta, S.F., Newton, O.B., Song, J., Best, A., Fiore, S.M., Effects of social cues on social signals in human-robot interaction during a hallway navigation task (2018) Proc. Hum. Factors Ergon. Soc. Ann. Meet., 62 (1), pp. 1128-1132. , https://doi.org/10.1177/1541931218621258","Rossi, A.; ICAROS Center, Italy; email: alessandra.rossi@unina.it","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119858917
"Yuan Q., Leong I.S.W.","54793877000;57352567800;","Human-Robot Coordination in Agile Robot Manipulation",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"532","540",,,"10.1007/978-3-030-90525-5_46","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119847548&doi=10.1007%2f978-3-030-90525-5_46&partnerID=40&md5=e2bb27013ec5915f5cc50616b13fc4ea","Institute for Infocomm Research (I2R), A*STAR, Singapore, Singapore","Yuan, Q., Institute for Infocomm Research (I2R), A*STAR, Singapore, Singapore; Leong, I.S.W., Institute for Infocomm Research (I2R), A*STAR, Singapore, Singapore","With the practical demands in flexible and adaptive robot manipulation skills in various environment settings, there are more challenges to be tackled to enable the robot with valid responsive behaviors in task handling. This paper discuss on the methods to achieve agile robot manipulations tasks with the advantages from human-robot teleoperation, robot perception, knowledge-based robot programming, robot motion planning and robot skill learning. Teleoperation serves as a typical Human-Robot Interaction (HRI) manner to allow the human user to guide the robot behavior in a direct manner. Robot automation, including the sensor perception (object detection, pose estimation etc.), motion planning and motion control that can handle well defined problems, but is also lack of general sense of understanding capability and not good at solving not fully defined task challenges. An agile robotic system should have a knowledge database which defines the skill sets required for the robot to handle various robot tasks. Meanwhile, the system should be able to take human assistance inputs through HRI when the robot is stuck. Moreover, the system should be able to pick up new skill set with each human knowledge input. Methodology discussion is the main scope of the paper, and preliminary experiments on telemanipulation with UR5 robot are demonstrated to show the flexible robot guidance through HRI inputs. Future work will aim to add in perception, motion planning, and picking up skill modules to come up with a more agile solution in handling variation of tasks. © 2021, Springer Nature Switzerland AG.","Knowledge based programming; Motion planning; Telemanipulation","Human robot interaction; Intelligent robots; Manipulators; Motion planning; Object detection; Remote control; Robot programming; Robotics; Agile robots; Human robots; Humans-robot interactions; Knowledge based; Knowledge based programming; Motion-planning; Robot coordination; Robot manipulation; Skill sets; Telemanipulation; Knowledge based systems",,,,,"A19E4a0101; Agency for Science, Technology and Research, A*STAR","This research was supported by Agency for Science, Technology and Research (A*STAR) under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund—Pre-Positioning Programme (IAF-PP) grant number A19E4a0101. All authors are with Institute for Infocomm Research (I2R), A*STAR, Singapore.",,,,,,,,,,"Stenmark, M., Malec, J., Knowledge-based instruction of manipulation tasks for industrial robotics (2015) Robot. Comput.-Integr. Manuf., 33, pp. 56-67; Stenmark, M., Malec, J., Knowledge-based industrial robotics (2013) The 12Th Scandinavian Conference on Artificial Intelligence (SCAI), , IOS Press; Miller, N., Motion capture from inertial sensing for untethered humanoid teleoperation (2004) 4Th IEEE/RAS International Conference on Humanoid Robots, 2. , vol., IEEE; Yuan, Q., Task-orientated robot teleoperation using wearable IMUs (2017) 2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). IEEE; Tee, K.P., Wu, Y., Experimental evaluation of divisible human-robot shared control for teleoperation assistance (2018) TENCON 2018–2018 IEEE Region 10 Conference. IEEE; Weng, C., A telemanipulation-based human-robot collaboration method to teach aerospace masking skills (2019) IEEE Trans. Ind. Inform., 16 (5), pp. 3076-3084; Yuan, Q., Flexible telemanipulation based handy robot teaching on tape masking with complex geometry (2020) Robot. Comput.-Integr. Manuf., 66; Quintero, C.P., Flexible virtual fixture interface for path specification in telemanipulation (2017) 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE; Pan, J., Real-time collision detection and distance computation on point cloud sensor data (2013) 2013 IEEE International Conference on Robotics and Automation. IEEE; Diankov, R., Kuffner, J., Openrave: A planning architecture for autonomous robotics (2008) Technical Report CMU-RI-TR-08-34, 79. , Robotics Institute, Pittsburgh, PA; Chitta, S.: MoveIt!: an introduction. In: Koubaa, A. (ed.) Robot Operating System (ROS). SCI, vol. 625, pp. 3–27. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-26054-9 1; Sucan, I.A., Moll, M., Kavraki, L.E., The open motion planning library (2012) IEEE Robot. Autom. Mag., 19 (4), pp. 72-82; Lavalle, S.M., (2006) Planning Algorithms, , Cambridge University Press, Cambridge; Kuffner, J.J., Lavalle, S.M., RRT-connect: An efficient approach to single-query path planning (2000) Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), 2. , vol., IEEE; Suárez-Ruiz, F., Zhou, X., Pham, Q.C., Can robots assemble an IKEA chair? (2018) Sci. Robot., 3 (17), p. eaat6385; Yuan, Q., A mirrored motion remapping method in telemanipulation-based face-to-face dual-arm robot teaching (2019) 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE; Supplementary Video. https://www.youtube.com/watch?v=crnbbfZPVbg","Yuan, Q.; Institute for Infocomm Research (I2R), Singapore; email: yu0017ng@e.ntu.edu.sg","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119847548
"Gruber M.E., Hancock P.A.","57226408001;7102723856;","The Self-Evaluation Maintenance Model in Human-Robot Interaction: A Conceptual Replication",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"268","280",,,"10.1007/978-3-030-90525-5_23","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119845566&doi=10.1007%2f978-3-030-90525-5_23&partnerID=40&md5=593f205074ce4d33ce128cf74462f43a","University of Central Florida, Orlando, United States","Gruber, M.E., University of Central Florida, Orlando, United States; Hancock, P.A., University of Central Florida, Orlando, United States","Understanding human-robot social comparison is critical for creating psychologically safe robots (i.e., robots that do not cause psychological discomfort). However, there has been limited research examining social comparison processes in human-robot interaction (HRI). We aimed to conceptually replicate prior research suggesting that the Self-Evaluation Maintenance (SEM) model of social comparison applies to HRI. In short, the SEM model describes the mechanisms in which others can impact one’s self-evaluation. We applied the model to an online presentation of a humanoid robot, RUDY. We predicted that task relevance would moderate the relationship between the robot’s performance level and participant evaluations of the robot. When RUDY engaged in a low-relevance task (guessing someone’s age), participants would evaluate RUDY accurately (i.e., they would rate RUDY more positively when it performed well than when it performed poorly). However, when RUDY engaged in a high-relevance task (understanding how people feel), participants would evaluate RUDY inaccurately (i.e., they would rate RUDY negatively regardless of its actual performance). Contrary to our hypothesis, we found that participants in both the high- and low-relevance conditions evaluated RUDY accurately. Our results suggest that SEM effects may not generalize to all types of tasks and robots. A “highly relevant” task might mean something different depending on the exact nature of the human-robot relationship. Given the inconsistency between these findings and past research, discerning the boundary conditions for SEM effects may be crucial for developing psychologically safe robots. © 2021, Springer Nature Switzerland AG.","Human-robot interaction; Self-evaluation; Social comparison","Anthropomorphic robots; Man machine systems; Human robots; Humanoid robot; Humans-robot interactions; Maintenance effect; Maintenance models; Performance; Performance:level; Safe robots; Self evaluation; Social comparison; Human robot interaction",,,,,,,,,,,,,,,,"Darling, K., (2012) Extending Legal Protection to Social Robots: The Effects of Anthropomorphism, Empathy, and Violent Behavior Towards Robotic Objects, pp. 213-234. , https://doi.org/10.2139/ssrn.2044797, In: Calo, R., Froomkin, M., Kerr, I. (eds.), Robot Law, pp., Edward Elgar; Leite, I., Pereira, A., Mascarenhas, S., Martinho, C., Prada, R., Paiva, A., The influence of empathy in human-robot relations (2013) Int. J. Hum Comput Stud., 71 (3), pp. 250-260. , https://doi.org/10.1016/j.ijhcs.2012.09.005; Kim, B., How early task success affects attitudes toward social robots (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 287-289. , https://doi.org/10.1145/3371382.3378241; Jhangiani, R., Tarry, H., Principles of social psychology – 1st international edition (2014) Bccampus Open Education, , https://opentextbc.ca/socialpsychology/; Tesser, A., Toward a self-evaluation maintenance model of social behavior (1988) Adv. Exp. Soc. Psychol., 21 (1), pp. 181-227. , https://doi.org/10.1016/S0065-2601(08)60227-0; Gino, F., Galinsky, A.D., Vicarious dishonesty: When psychological closeness creates distance from one’s moral compass (2012) Organ. Behav. Hum. Decis. Process., 119 (1), pp. 15-26. , https://doi.org/10.1016/j.obhdp.2012.03.011; Cialdini, R.B., Borden, R.J., Thorne, A., Walker, M.R., Freeman, S., Sloan, L.R., Basking in reflected glory: Three (football) field studies (1976) J. Pers. Soc. Psychol., 34 (3), pp. 366-375. , https://doi.org/10.1037/0022-3514.34.3.366; Tesser, A., Smith, J., Some effects of task relevance and friendship on helping: You don’t always help the one you like (1980) J. Exp. Soc. Psychol., 16 (6), pp. 582-590. , https://doi.org/10.1016/0022-1031(80)90060-8; Crawford, M.T., The renegotiation of social identities in response to a threat to self-evaluation maintenance (2007) J. Exp. Soc. Psychol., 43 (1), pp. 39-47. , https://doi.org/10.1016/j.jesp.2005. 12.011; Nicholls, E., Stukas, A.A., Narcissism and the self-evaluation maintenance model: Effects of social comparison threats on relationship closeness (2011) J. Soc. Psychol., 151 (2), pp. 201-212. , https://doi.org/10.1080/00224540903510852; Pleban, R., Tesser, A., The effects of relevance and quality of another’s performance on interpersonal closeness (1981) Soc. Psychol. Quart., 44 (3), pp. 278-285. , https://doi.org/10.2307/3033841; Kamide, H., Kawabe, K., Shigemi, S., Arai, T., Social comparison between the self and a humanoid (2013) ICSR 2013. LNCS (LNAI), Vol. 8239, pp. 190-198. , https://doi.org/10.1007/978-3-319-02675-6_19, Herrmann, G., Pearson, M.J., Lenz, A., Bremner, P., Spiers, A., Leonards, U. (eds.), pp., Springer, Cham; (2016) Families Caring for an Aging America, , https://doi.org/10.17226/23606, Schulz, R., Eden, J. (eds.), National Academies Press, Washington, D.C; Nomura, T., Suzuki, T., Kanda, T., Kato, K., Measurement of negative attitudes toward robots (2006) Interac. Stud. Soc. Behav. Commun. Biol. Artif. Syst., 7 (3), pp. 437-454. , https://doi.org/10.1075/is.7.3.14nom; Bender, L., Walia, G., Kambhampaty, K., Nygard, K., Nygard, T.E., Social sensitivity and classroom team projects: An empirical investigation (2012) SIGCSE ’12, pp. 403-408. , https://doi.org/10.1145/2157136.2157258, pp; Ma, D.S., Kantner, J., Wittenbrink, B., Chicago face database: Multiracial expansion (2020) Behav. Res. Methods, 53 (3), pp. 1289-1300. , https://doi.org/10.3758/s13428-020-01482-5; Tesser, A., Campbell, J., A self-evaluation maintenance approach to school behavior (1982) Educ. Psychol., 17 (1), pp. 1-12. , https://doi.org/10.1080/00461528209529240; INF Robotics [Photograph of RUDY]. INF Robotics Inc (2020); Bartneck, C., Croft, E., Kulic, D., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot., 1 (1), pp. 71-81. , https://doi.org/10.1007/s12369-008-0001-3; Corp, I.B.M., (2020) IBM SPSS Statistics for Macintosh. (Version, 10 (Software), p. 27. , https://www.ibm.com/analytics/spss-statistics-software; Olejnik, S.F., Algina, J., Parametric ANCOVA and the rank transform ANCOVA when the data are conditionally non-normal and heteroscedastic (1984) J. Educ. Stat., 9 (2), pp. 129-149. , https://doi.org/10.3102/10769986009002129; Damholdt, M.F., Christina, V., Kryvous, A., Smedegaard, C.V., Seibt, J., What is in three words? Exploring a three-word methodology for assessing impressions of a social robot encounter online and in real life (2019) Paladyn, J. Behav. Robot., 10 (1), pp. 438-453. , https://doi.org/10.1515/pjbr-2019-0034; Eyssel, F., Kuchenbrandt, D.: Social categorization of social robots: anthropomorphism as a function of robot group membership. Br. J. Soc. Psychol. 51(4), 724–731 (2012). https://doi. org/10.1111/j.2044-8309.2011.02082.x","Gruber, M.E.; University of Central FloridaUnited States; email: miraeg@knights.ucf.edu","Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85119845566
"Mazare M., Tolu S., Taghizadeh M.","57193602017;23394070700;22942267300;","Adaptive variable impedance control for a modular soft robot manipulator in configuration space",2021,"Meccanica",,,,"","",,,"10.1007/s11012-021-01436-x","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118541272&doi=10.1007%2fs11012-021-01436-x&partnerID=40&md5=e7817c92d5dbe5ad0947964a5a9adf7e","Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, P.O.B, Tehran, 1743524155, Iran; Department of Electrical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark","Mazare, M., Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, P.O.B, Tehran, 1743524155, Iran; Tolu, S., Department of Electrical Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark; Taghizadeh, M., Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, P.O.B, Tehran, 1743524155, Iran","Compliance is a strong requirement for human–robot interactions. Soft-robots provide an opportunity to cover the lack of compliance in conventional actuation mechanisms, however, the control of them is very challenging given their intrinsic complex motions. Therefore, soft-robots require new approaches to e.g., modeling, control, dynamics, and planning. One of the control strategies that ensures compliance is the impedance control. During the task execution in the presence of coupling force and position constraints, a dynamic behavior increases the flexibility of the impedance control. This imposes some additional constraints on the stability of the control system. To tackle them, we propose a variable impedance control in configuration space for a modular soft robot manipulator (MSRM) in the presence of model uncertainties and external forces. The external loads are estimated in configuration space using a momentum-based approach in order to reduce the calculation complexity, and the adaptive back-stepping sliding mode (ABSM) controller is designed to guard against uncertainties. Stability analysis is performed using Lyapunov theory which guarantees not only the exponential stability of each state under the designed control law, but also the global stability of the closed-loop system. The system performance is benchmarked against other conventional control methods, such as the sliding mode (SM) and inverse dynamics PD controllers. The results show the effectiveness of the proposed variable impedance control in stabilizing the position error and diminishing the impact of the external load compared to SM and PD controllers. © 2021, Springer Nature B.V.","Back-stepping sliding mode; Configuration space; Robust adaptive control; Soft robot; Variable impedance control","Closed loop systems; Compliance control; Control theory; Controllers; Flexible manipulators; Industrial robots; Inverse problems; Modular robots; Robot applications; Robot programming; System stability; Uncertainty analysis; Wave functions; Back stepping; Back-stepping sliding mode; Configuration space; Modulars; Robots manipulators; Robust-adaptive control; Sliding modes; Soft robot; Variable impedance control; Human robot interaction",,,,,,"This study was not funded by anyone.",,,,,,,,,,"Burgner-Kahrs, J., Rucker, D.C., Choset, H., Continuum robots for medical applications: a survey (2015) IEEE Trans Rob, 31, pp. 1261-1280; Ehsani-Seresht, A., Hashemi-Pour Moosavi, S., Dynamic modeling of the cable-driven continuum robots in hybrid position-force actuation mode (2020) J Mech Robot; Ji, D., Kang, T.H., Shim, S., Hong, J., Analysis of twist deformation in wire-driven continuum surgical robot (2020) Int J Control Autom Syst, 18, pp. 10-20; Liu, H., Farvardin, A., Grupp, R., Murphy, R.J., Taylor, R.H., Iordachita, I., Armand, M., Shape tracking of a dexterous continuum manipulator utilizing two large deflection shape sensors (2015) IEEE Sens J, 15, pp. 5494-5503; Siciliano, B., Villani, L., (1999) Robot force control, , Springer, US; Raibert, M.H., Craig, J.J., Hybrid position/force control of manipulators (1981) J Dyn Syst Meas Contr, 103, pp. 126-133; Yip, M.C., Camarillo, D.B., Model-less hybrid position/force control: a minimalist approach for continuum manipulators in unknown, constrained environments (2016) IEEE Robot Autom Lett, 1, pp. 844-851; Bajo, A., Simaan, N., Hybrid motion/force control of multi-backbone continuum robots (2015) Int J Robot Res, 35, pp. 422-434; Mahvash, M., Dupont, P.E., Stiffness control of surgical continuum manipulators (2011) IEEE Trans Rob, 27, pp. 334-345; Wei, Y., Chen, Y., Yang, Y., Li, Y., A soft robotic spine with tunable stiffness based on integrated ball joint and particle jamming (2016) Mechatronics, 33, pp. 84-92; Hogan, N., impedance control: an approach to manipulation: part i—theory (1985) j Dyn Syst Meas Contr, 107, pp. 1-7; Hogan, N., impedance control: an approach to manipulation: part II—implementation (1985) J Dyn Syst Meas Contr, 107, pp. 8-16; Hogan, N., Impedance control: an approach to manipulation: part III—applications (1985) J Dyn Syst Meas Contr, 107, pp. 17-24; Khan, S., Herrmann, G., Grafi, M., Pipe, T., Melhuish, C., Compliance control and human-robot interaction: part 1—survey (2014) Int J Human Robot; Ba, K., Ma, G., Yu, B., Jin, Z., Huang, Z., Zhang, J., Kong, X., A nonlinear model-based variable impedance parameters control for position-based impedance control system of hydraulic drive unit (2020) Int J Control Autom Syst, 18, pp. 1806-1817; Ficuciello, F., Villani, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human-robot physical interaction (2015) IEEE Trans Rob, 31, pp. 850-863; Kronander, K., Billard, A., Stability considerations for variable impedance control (2016) IEEE Trans Rob, 32, pp. 1298-1305; Dong, J., Xu, J., Zhou, Q., Hu, S., Physical human–robot interaction force control method based on adaptive variable impedance (2020) J Franklin Inst, 357, pp. 7864-7878; Duan, J., Gan, Y., Chen, M., Dai, X., Adaptive variable impedance control for dynamic contact force tracking in uncertain environment (2018) Robot Auton Syst, 102, pp. 54-65; Liang, L., Chen, Y., Liao, L., Sun, H., Liu, Y., a novel impedance control method of rubber unstacking robot dealing with unpredictable and time-variable adhesion force (2021) Robot Comput-Integr Manuf, 67, p. 102038; Toscano, L., Falkenhahn, V., Hildebrandt, A., Braghin, F., Sawodny, O., Configuration space impedance control for continuum manipulators (2015) 2015 6Th International Conference on Automation, Robotics and Applications (ICARA), pp. 597-602. , https://doi.org/10.1109/ICARA.2015.7081215; He, G., Fan, Y., Su, T., Zhao, L., Zhao, Q., variable impedance control of cable actuated continuum manipulators (2020) Int J Control Autom Syst, 18, pp. 1839-1852; Cheng, H., Yin, Y.K., Li, Z., Dynamics and control of redundantly actuated parallel manipulators (2003) IEEE/ASME Trans Mechatron, 8 (4), pp. 83-91; Mazare, M., Taghizadeh, M., Adaptive back-stepping robust control of a 3-[P2 (US)] parallel robot on optimal trajectory (2019) Int Robot AutomJ, 5 (3), pp. 101-110; Ficuciell, F., Villan, L., Siciliano, B., Variable impedance control of redundant manipulators for intuitive human–robot physical interaction (2015) IEEE Trans Rob, 31 (4), pp. 850-863; Luca, D., Albu-Schaffer, A., Haddadin, S., Hirzinger, G., Collision detection and safe reaction with the DLR-III lightweight robot arm (2006) IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China., pp. 1623-1630. , https://doi.org/10.1109/IROS.2006.282053","Taghizadeh, M.; Faculty of Mechanical and Energy Engineering, P.O.B, Iran; email: mo_taghizadeh@sbu.ac.ir",,,"Springer Science and Business Media B.V.",,,,,00256455,,MECCB,,"English","Meccanica",Article,"Article in Press","All Open Access, Green",Scopus,2-s2.0-85118541272
"Diab M., Pomarlan M., Borgo S., Besler D., Rossel J., Bateman J., Beetz M.","57196709757;35410087600;22333522400;57295231000;57295342600;7202065408;36818071500;","Failrecont - An ontology-based framework for failure interpretation and recovery in planning and execution",2021,"CEUR Workshop Proceedings","2969",,,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116983741&partnerID=40&md5=463e9da524e41337cd1b2a97820cce79","Institute of Industrial and Control Engineering, Universitat Politecnica de Catalunya, Spain; Faculty of Linguistics and Literature, University of Bremen, Germany; Laboratory for Applied Ontology (LOA), Istc Cnr, Italy; Institute for Artificial Intelligence, University of Bremen, Germany","Diab, M., Institute of Industrial and Control Engineering, Universitat Politecnica de Catalunya, Spain; Pomarlan, M., Faculty of Linguistics and Literature, University of Bremen, Germany; Borgo, S., Laboratory for Applied Ontology (LOA), Istc Cnr, Italy; Besler, D., Institute for Artificial Intelligence, University of Bremen, Germany; Rossel, J., Institute of Industrial and Control Engineering, Universitat Politecnica de Catalunya, Spain; Bateman, J., Faculty of Linguistics and Literature, University of Bremen, Germany; Beetz, M., Institute for Artificial Intelligence, University of Bremen, Germany","Autonomous mobile robot manipulators have the potential to act as robot helpers at home to improve quality of life for various user populations, such as elderly or handicapped people, or to act as robot co-workers on factory floors, helping in assembly applications where collaborating with other operators may be required. However, robotic systems do not show robust performance when placed in environments that are not tightly controlled. An important cause of this is that failure handling often consists of scripted responses to foreseen complications, which leaves the robot vulnerable to new situations and ill-equipped to reason about failure and recovery strategies. Instead of libraries of hard-coded reactions that are expensive to develop and maintain, more sophisticated reasoning mechanisms are needed to handle failure. This requires an ontological characterization of what failure is, what concepts are useful to formulate causal explanations of failure, and integration with knowledge of available resources including the capabilities of the robot as well as those of other potential cooperative agents in the environment, e.g. a human user. We propose the FailRecOnt framework as a step in this direction. We have integrated an ontology for failure interpretation and recovery with a contingency-based task and motion planning framework such that a robot can deal with uncertainty, recover from failures, and deal with human-robot interactions. A motivating example has been introduced to justify this proposal. The proposal has been tested with a challenging scenario. © 2021 CEUR-WS. All rights reserved.",,"Failure (mechanical); Human robot interaction; Manipulators; Motion planning; Recovery; Robot applications; Robot programming; Factory floors; Failure and recoveries; Failure handling; Ontology-based; Planning and execution; Quality of life; Recovery strategies; Robotic systems; Robust performance; Workers'; Ontology",,,,,"FPI 2017, GA 958371; PID2020-114819GB-I00; Deutsche Forschungsgemeinschaft, DFG","This work was partially funded by the Spanish Government through the project PID2020-114819GB-I00, by Deutsche Forschungsgemeinschaft (DFG) through the Collaborative Research Center 1320, EASE, and by European CSA project OntoCommons (GA 958371). M. Diab is supported by the Spanish Government through the grants FPI 2017.",,,,,,,,,,"Akbari, A., Diab, M., Rosell, J., Contingent task and motion planning under uncertainty for human-robot interactions (2020) Applied Sciences, 10, p. 1665; Laprie, J., Dependable computing and fault tolerance : Concepts and terminology (1995) Twenty-Fifth International Symposium on Fault-Tolerant Computing, 1995, Highlights from Twenty-Five Years, p. 2; Carlson, J., Murphy, R., How ugvs physically fail in the field (2005) Robotics, IEEE Transactions on, 21, pp. 423-437; Honig, S., Oron-Gilad, T., Understanding and resolving failures in human-robot interaction: Literature review and model development (2018) Frontiers in Psychology, 9, p. 861. , https://www.frontiersin.org/article/10.3389/fpsyg.2018.00861; Ross, R., Collier, R., Hare, G. O, Demonstrating social error recovery with agentfactory (2004) International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1424-1425; Hornung, R., Urbanek, H., Klodmann, J., Osendorfer, C., van der Smagt, P., Model-free robot anomaly detection (2014) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3676-3683; Azzalini, D., Castellini, A., Luperto, M., Farinelli, A., Amigoni, F., Hmms for anomaly detection in autonomous robots (2020) International Conference on Autonomous Agents and Multiagent Systems (AAMAS); Pastor, P., Kalakrishnan, M., Chitta, S., Theodorou, E., Schaal, S., Skill learning and task outcome prediction for manipulation (2011) 2011 IEEE International Conference on Robotics and Automation, pp. 3828-3834; Haidu, A., Kohlsdorf, D., Beetz, M., Learning action failure models from interactive physicsbased simulations (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5370-5375; Bauer, A. S., Schmaus, P., Stulp, F., Leidner, D., Probabilistic effect prediction through semantic augmentation and physical simulation (2020) IEEE International Conference on Robotics and Automation (ICRA), pp. 9278-9284. , https://elib.dlr.de/134290/; Jensen, R. M., Veloso, M., Bryant, R. E., Fault tolerant planning: Toward probabilistic uncertainty models in symbolicñon-deterministic planning (2004) ICAPS; Diab, M., Pomarlan, M., Besler, D., Akbari, A., Rosell, J., Bateman, J., Beetz, M., An ontology for failure interpretation in automated planning and execution (2019) Iberian Robotics conference, pp. 381-390. , Springer; Hoffmann, J., Brafman, R., Contingent planning via heuristic forward search with implicit belief states (2005) Proc. ICAPS, p. 2005; Masolo, C., Borgo, S., Gangemi, A., Guarino, N., Oltramari, A., (2003) WonderWeb Deliverable D18 Ontology Library, , Technical Report, IST Project 2001-33052 WonderWeb: Ontology Infrastructure for the Semantic Web; Niles, I., Pease, A., Towards a standard upper ontology (2001) Proceedings of the international conference on Formal Ontology in Information Systems, pp. 2-9. , Volume 2001, ACM; Hanheide, M., Gobelbecker, M., Horn, G. S., Pronobis, A., Sjoo, K., Aydemir, A., Jensfelt, P., Wyatt, J. L., Robot task planning and explanation in open and uncertain worlds (2017) Artificial Intelligence, 247, pp. 119-150. , https://www.sciencedirect.com/science/article/pii/S000437021500123X, https://doi.org special on AI and Robotics; Galton, A., States, processes and events, and the ontology of causal relations (2012) Formal Ontology in Information Systems - Proceedings of the Seventh International Conference, FOIS 2012, pp. 279-292. , Gray, Austria, July 24-27, 2012; Uschold, M., Gruninger, M., Ontologies: Principles, methods and applications (1996) The Knowledge Engineering Review, 11, pp. 93-136; Diab, M., Akbari, A., Ud Din, M., Rosell, J., PMK - a knowledge processing framework for autonomous robotics perception and manipulation (2019) Sensors, 19. , http://www.mdpi.com/1424-8220/19/5/1166; Diab, M., Pomarlan, M., Besler, D., Akbari, A., Rosell, J., Bateman, J., Beetz, M., Skillman - a skill-based robotic manipulation framework based on perception and reasoning (2020) Robotics and Autonomous Systems, p. 103653. , http://www.sciencedirect.com/science/article/pii/S0921889020304930, https://doi.org; Rosell, J., Perez, A., Aliakbar, A., Palomo, L., Garcia, N., The kautham project: A teaching and research tool for robot motion planning (2014) Proceedings of the IEEE Emerging Technology and Factory Automation (ETFA), pp. 1-8; Antoniou, G., van Harmelen, F., (2004) Web Ontology Language: OWL, pp. 67-92",,"Sanfilippo E.M.Kutz O.Troquard N.Hahmann T.Masolo C.Hoehndorf R.Vita R.Hedblom M.M.Righetti G.Sormaz D.Terkaj W.Sales T.P.de Cesare S.Gailly F.Guizzardi G.Guizzardi G.Lycett M.Partridge C.Partridge C.Pastor O.Besser D.Borgo S.Diab M.Gangemi A.Gangemi A.Olivares-Alarcos A.Pomarlan M.Porzel R.Jansen L.Brochhausen M.Porello D.Garbacz P.Seppala S.Gruninger M.Vizedom A.Dooley D.Warren R.McGinty H.K.Lange M.Algergawy A.Karam N.Karam N.Klan F.Michel F.Rosati I.Rosati I.",,"CEUR-WS","Joint Ontology Workshops 2021 Episode VII: The Bolzano Summer of Knowledge, JOWO 2021","11 September 2021 through 18 September 2021",,172257,16130073,,,,"English","CEUR Workshop Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85116983741
"Wang C., Zhang S., Hu J., Huang Z., Shi C.","57283939400;57202710733;57283493100;57283493200;55448007200;","Upper-Limb Muscle Synergy Features in Human-Robot Interaction with Circle-Drawing Movements",2021,"Applied Bionics and Biomechanics","2021",,"8850785","","",,,"10.1155/2021/8850785","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116351654&doi=10.1155%2f2021%2f8850785&partnerID=40&md5=c4e8c149442d74108ef8fd67c0bfe9ed","Emergency Trauma Surgical Department, Ningbo First Hospital, Zhejiang, Ningbo, 315010, China; Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Zhejiang, Ningbo, 315300, China; Rehabilitation Department, Ningbo Yinzhou No. 2 Hospital, Zhejiang, Ningbo, 315192, China","Wang, C., Emergency Trauma Surgical Department, Ningbo First Hospital, Zhejiang, Ningbo, 315010, China; Zhang, S., Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Zhejiang, Ningbo, 315300, China; Hu, J., Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Zhejiang, Ningbo, 315300, China; Huang, Z., Rehabilitation Department, Ningbo Yinzhou No. 2 Hospital, Zhejiang, Ningbo, 315192, China; Shi, C., Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Zhejiang, Ningbo, 315300, China","The upper-limb rehabilitation robots can be developed as an efficient tool for motor function assessments. Circle-drawing has been used as a specific task for robot-based motor function measurement. The upper-limb movement-related kinematic and kinetic parameters measured by motion and force sensors embedded in the rehabilitation robots have been widely studied. However, the muscle synergies characterized by multiple surface electromyographic (sEMG) signals in upper limbs during human-robot interaction (HRI) with circle-drawing movements are rarely investigated. In this research, the robot-assisted and constrained circle-drawing movements for upper limb were used to increase the consistency of muscle synergy features. Both clockwise and counterclockwise circle-drawing tasks were implemented by all healthy subjects using right hands. The sEMG signals were recorded from six muscles in upper limb, and nonnegative matrix factorization (NMF) analysis was utilized to obtain muscle synergy information. Both synergy pattern and activation coefficient were calculated to represent the spatial and temporal features of muscle synergies, respectively. The results obtained from the experimental study confirmed that high structural similarity of muscle synergies was found among the subjects during HRI with circle-drawing movement by healthy subjects, which indicates healthy people may share a common underlying muscle control mechanism during constrained upper-limb circle-drawing movement. This study indicates the muscle synergy analysis during the HRI with constrained circle-drawing movement could be considered as a task for upper-limb motor function assessment. © 2021 Cheng Wang et al.",,"Factorization; Man machine systems; Matrix algebra; Muscle; Circle drawings; Electromyographic signal; Healthy subjects; Humans-robot interactions; Motor function; Muscle synergies; Rehabilitation robot; Specific tasks; Upper limbs; Upper-limb rehabilitation; Human robot interaction",,,,,,,,,,,,,,,,"Duret, C., Grosmaire, A.G., Krebs, H.I., Robot-assisted therapy in upper extremity hemiparesis: Overview of an evidence-based approach (2019) Frontiers in Neurology, 10, p. 412. , 2-s2.0-85067868341; Aisen, M.L., Krebs, H.I., Hogan, N., The effect of robot-assisted therapy and rehabilitative training on motor recovery following stroke (1997) Archives of Neurology, 54 (4), pp. 443-446. , 2-s2.0-0030945986 9109746; Winstein, C.J., Stein, J., Arena, R., Bates, B., Cherney, L.R., Cramer, S.C., Deruyter, F., Zorowitz, R.D., Guidelines for adult stroke rehabilitation and recovery: A guideline for healthcare professionals from the American Heart Association/American Stroke Association (2016) Stroke, 47 (6), pp. 98-169; 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Yang, J.F., Winter, D., Electromyographic amplitude normalization methods: Improving their sensitivity as diagnostic tools in gait analysis (1984) Archives of Physical Medicine and Rehabilitation, 65 (9), pp. 517-521; Tresch, M.C., Cheung, V.C., D'Avella, A., Matrix factorization algorithms for the identification of muscle synergies: Evaluation on simulated and experimental data sets (2006) Journal of Neurophysiology, 95 (4), pp. 2199-2212. , 2-s2.0-33646196851 16394079; Li, F., Wang, Q., Cao, S., Wu, D., Wang, Q., Chen, X., Lower-limb Muscle Synergies in Children with Cerebral Palsy, pp. 1226-1229. , 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER) 2013 San Diego, CA, USA 2-s2.0-84897694762; Clark, D.J., Ting, L.H., Zajac, F.E., Neptune, R.R., Kautz, S.A., Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke (2010) Journal of Neurophysiology, 103 (2), pp. 844-857. , 2-s2.0-76649138271 20007501; Torres-Oviedo, G., Ting, L.H., Subject-specific muscle synergies in human balance control are consistent across different biomechanical contexts (2010) Journal of Neurophysiology, 103 (6), pp. 3084-3098. , 2-s2.0-77953509203 20393070; 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"Yin J., Wang S., Guo W., Shao M.","55242885300;57254734900;57216525919;57196121077;","More than appearance: the uncanny valley effect changes with a robot’s mental capacity",2021,"Current Psychology",,,,"","",,,"10.1007/s12144-021-02298-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114677867&doi=10.1007%2fs12144-021-02298-y&partnerID=40&md5=6cd0079f2eb611f64c4d84bf549243ef","Department of Psychology, Ningbo University, No. 616 Fenghua Rd, Ningbo, 315211, China; Center of Group Behavior and Social Psychological Service, Ningbo University, Ningbo, China","Yin, J., Department of Psychology, Ningbo University, No. 616 Fenghua Rd, Ningbo, 315211, China, Center of Group Behavior and Social Psychological Service, Ningbo University, Ningbo, China; Wang, S., Department of Psychology, Ningbo University, No. 616 Fenghua Rd, Ningbo, 315211, China, Center of Group Behavior and Social Psychological Service, Ningbo University, Ningbo, China; Guo, W., Department of Psychology, Ningbo University, No. 616 Fenghua Rd, Ningbo, 315211, China, Center of Group Behavior and Social Psychological Service, Ningbo University, Ningbo, China; Shao, M., Department of Psychology, Ningbo University, No. 616 Fenghua Rd, Ningbo, 315211, China, Center of Group Behavior and Social Psychological Service, Ningbo University, Ningbo, China","Robots are being used to socially interact with humans. To enhance the quality of human-robot interaction, engineers aim to build robots with both a humanlike appearance and high mental capacity, but there is a lack of empirical evidence regarding how these two characteristics jointly affect people’s emotional response to robots. The current two experiments (each N = 80) presented robots with either a mechanical or humanlike appearance, with mental capacities operationalized as low or high, and with either self-oriented mentalization to mainly concentrate on the robot itself or other-oriented mentalization to read others’ minds. It was found that when the robots had a humanlike appearance, they were more dislikeable than when they had a mechanical appearance, replicating the uncanny valley effect for appearance. Importantly, given a humanlike appearance, robots with high mental ability elicited stronger dislike than those with low mental ability, showing an uncanny valley effect for mind, but this difference was absent for robots with a mechanical appearance. In addition, this effect was limited to robots with self-oriented mentalization ability and did not extend to robots with other-oriented mentalization ability. Hence, the exterior appearance and interior mental capacity of robots interact to influence people’s emotional reaction to them, and the uncanny valley as it pertains to the mind depends on the robot’s appearance in addition to its mental ability. This implies that social robots with humanlike appearances should be designed with obvious other-directed social abilities to make them more likeable. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.","Humanlike appearance; Mental capacity; Mind; Social robot; Uncanny valley",,,,,,"SJWZ2020001","This work was supported by the Fundamental Research Funds for the Provincial Universities of Zhejiang (Grant no. SJWZ2020001).",,,,,,,,,,"Appel, M., Weber, S., Krause, S., Mara, M., On the eeriness of service robots with emotional capabilities (2016) In 2016 11Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 411-412. , IEEE; Appel, M., Izydorczyk, D., Weber, S., Mara, M., Lischetzke, T., The uncanny of mind in a machine: Humanoid robots as tools, agents, and experiencers (2020) Computers in Human Behavior, 102, pp. 274-286; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81; Bradford, E.E.F., Jentzsch, I., Gomez, J.-C., From self to social cognition: Theory of mind mechanisms and their relation to executive functioning (2015) Cognition, 138, pp. 21-34. , PID: 25704580; Bradford, E.E.F., Jentzsch, I., Gomez, J.-C., Chen, Y., Zhang, D., Su, Y., Cross-cultural differences in adult theory of mind abilities: A comparison of native-English speakers and native-Chinese speakers on the self/other differentiation task (2018) Quarterly Journal of Experimental Psychology, 71 (12), pp. 2665-2676; Bradford, E.E.F., Gomez, J.C., Jentzsch, I., Exploring the role of self/other perspective-shifting in theory of mind with behavioural and EEG measures (2019) Social Neuroscience, 14 (5), pp. 530-544. , PID: 30122110; Broadbent, E., Kumar, V., Li, X., Sollers, J., Stafford, R.Q., MacDonald, B.A., Wegner, D.M., Robots with display screens: A robot with a more humanlike face display is perceived to have more mind and a better personality (2013) PLoS One, 8 (8). , PID: 24015263; Broadbent, E., Interactions with robots: The truths we reveal about ourselves (2017) Annual Review of Psychology, 68 (1), pp. 627-652. , PID: 27648986; Burleigh, T.J., Schoenherr, J.R., Lacroix, G.L., Does the uncanny valley exist? 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Psychol.",Article,"Article in Press","",Scopus,2-s2.0-85114677867
"Rogers W.A., Kadylak T., Bayles M.A.","57265321400;57190182209;57212064724;","Maximizing the Benefits of Participatory Design for Human–Robot Interaction Research With Older Adults",2021,"Human Factors",,,,"","",,,"10.1177/00187208211037465","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114481103&doi=10.1177%2f00187208211037465&partnerID=40&md5=acc56e98026162d94b7f9783f5d1ae3e","University of Illinois Urbana-Champaign, United States","Rogers, W.A., University of Illinois Urbana-Champaign, United States; Kadylak, T., University of Illinois Urbana-Champaign, United States; Bayles, M.A., University of Illinois Urbana-Champaign, United States","Objective: We reviewed human–robot interaction (HRI) participatory design (PD) research with older adults. The goal was to identify methods used, determine their value for design of robots with older adults, and provide guidance for best practices. Background: Assistive robots may promote aging-in-place and quality of life for older adults. However, the robots must be designed to meet older adults’ specific needs and preferences. PD and other user-centered methods may be used to engage older adults in the robot development process to accommodate their needs and preferences and to assure usability of emergent assistive robots. Method: This targeted review of HRI PD studies with older adults draws on a detailed review of 26 articles. Our assessment focused on the HRI methods and their utility for use with older adults who have a range of needs and capabilities. Results: Our review highlighted the importance of using mixed methods and including multiple stakeholders throughout the design process. These approaches can encourage mutual learning (to improve design by developers and to increase acceptance by users). We identified key phases used in HRI PD workshops (e.g., initial interview phase, series of focus groups phase, and presentation phase). These approaches can provide inspiration for future efforts. Conclusion: HRI PD strategies can support designers in developing assistive robots that meet older adults’ needs, capabilities, and preferences to promote acceptance. More HRI research is needed to understand potential implications for aging-in-place. PD methods provide a promising approach. © Copyright 2021, Human Factors and Ergonomics Society.","aging-in-place; assistive robots; human–robot interaction; older adults; participatory design","Machine design; Assistive robots; Multiple stakeholders; Mutual learning; Participatory design; Provide guidances; Quality of life; Robot development; Robot interactions; Social robots",,,,,"National Institutes of Health, NIH; National Institute on Aging, NIA: P01 AG17211; National Institute on Disability, Independent Living, and Rehabilitation Research, NIDILRR: 90REGE0006-01-00","The authors were supported in part by grants from the National Institutes of Health (National Institute on Aging) Grant P01 AG17211 under the auspices of the Center for Research and Education on Aging and Technology Enhancement (CREATE; www.create-center.org), as well as the National Institute on Disability, Independent Living, and Rehabilitation Research (90REGE0006-01-00) under the auspices of the Rehabilitation and Engineering Research Center on Technologies to Support Aging-in-Place for People with Long-Term Disabilities (TechSAge; www.techsagererc.org). We thank Max Chan, Leonardo Galoso, Carson Smith, and Siyao Zhang for their support on this review.",,,,,,,,,,"Bedaf, S., Marti, P., De Witte, L., What are the preferred characteristics of a service robot for the elderly? 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(2015) World report on ageing and health, , https://www.who.int/ageing/events/world-report-2015-launch/en/","Rogers, W.A.; University of Illinois Urbana-ChampaignUnited States; email: wendyr@illinois.edu",,,"SAGE Publications Inc.",,,,,00187208,,HUFAA,,"English","Hum. Factors",Review,"Article in Press","",Scopus,2-s2.0-85114481103
"Cornagliotto V., Digo E., Pastorelli S.","57217010142;57203841907;57192185226;","Using a robot calibration approach toward fitting a human arm model",2021,"Mechanisms and Machine Science","102",,,"199","207",,,"10.1007/978-3-030-75259-0_22","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105513054&doi=10.1007%2f978-3-030-75259-0_22&partnerID=40&md5=5ef771b16dc02e7af153d3c90b012d32","Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy","Cornagliotto, V., Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy; Digo, E., Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy; Pastorelli, S., Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy","In the context of Industry 4.0, the human-robot interaction (HRI) can be improved by tracking the human arm in the workspace shared with the robot. This goal takes advantage of a customized human arm modeling and it should be conveniently achieved with a limited number of sensors and a reduced computational time. In this paper, considering the analogy between human and robotic arms, a new method for the identification of a custom-made human arm model was inspired by a robot calibration process. The Denavit-Hartenberg (DH) parameters of the arm model were estimated recording a suitable number of hand poses. Hence, a robotic arm was exploited to test the new method. To simplify the fitting procedure of a reliable robot model, the minimum number of the necessary end-effector (EE) poses was investigated. Through an optoelectronic system, the EE pose trajectory of a UR3 robot was recorded. The optimization of the DH parameters was repeatedly run decreasing the downsampling frequency of the acquired data and then the trajectory error was evaluated. A new reference dataset of robot configurations was acquired permutating the joints degrees of freedom among values of 0, +90, or −90°. Hence, the method to fit the model considering few EE poses was tested on six robot configurations randomly selected from the dataset. Overall, trajectory errors highlighted the applicability of this method in the context of HRI. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.","Denavit-Hartenberg parameters; Human-robot interaction; Industry 4.0; Optimization algorithm; Optoelectronic system; Robotic model","Calibration; Degrees of freedom (mechanics); End effectors; Industrial robots; Optoelectronic devices; Robotic arms; Robotics; Trajectories; Computational time; Downsampling; Fitting procedure; Human robot Interaction (HRI); Optoelectronic systems; Robot calibration; Robot configurations; Trajectory errors; Human robot interaction",,,,,,,,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human–robot collaboration (2018) Auton. Robots, 42, pp. 957-975. , https://doi.org/10.1007/s10514-017-9677-2; Bauer, A., Wollherr, D., Buss, M., Human–robot collaboration: A survey (2008) Int. J. Humanoid Robot., 5, pp. 47-66; Lasota, P.A., Fong, T., Shah, J.A., A survey of methods for safe human-robot interaction (2017) Found. Trends Robot., 5, pp. 261-349. , https://doi.org/10.1561/2300000052; Melchiorre, M., Scimmi, L.S., Mauro, S., Pastorelli, S.P., Vision-based control architecture for human–robot hand-over applications (2020) Asian J. Control, pp. 105-117. , https://doi.org/10.1002/asjc.2480; Scimmi, L.S., Melchiorre, M., Mauro, S., Pastorelli, S.P., Implementing a vision-based collision avoidance algorithm on a UR3 robot (2019) 2019 23Rd International Conference Mechatronics Technology ICMT 2019, , https://doi.org/10.1109/icmect.2019.8932105; Digo, E., Antonelli, M., Pastorelli, S., Gastaldi, L., Upper limbs motion tracking for collaborative robotic applications (2020) International Conference on Human Interaction and Emerging Technologies; Digo, E., Antonelli, M., Cornagliotto, V., Pastorelli, S., Gastaldi, L., Collection and analysis of human upper limbs motion features for collaborative robotic applications (2020) Robotics, , https://doi.org/10.3390/robotics9020033; Laitenberger, M., Raison, M., Périé, D., Begon, M., Refinement of the upper limb joint kinematics and dynamics using a subject-specific closed-loop forearm model (2015) Multibody Syst. Dyn., 33, pp. 413-438. , https://doi.org/10.1007/s11044-014-9421-z; Reinbolt, J.A., Schutte, J.F., Fregly, B.J., Koh, B.I., Haftka, R.T., George, A.D., Mitchell, K.H., Determination of patient-specific multi-joint kinematic models through two-level optimization (2005) J. Biomech., 38, pp. 621-626. , https://doi.org/10.1016/j.jbiomech.2004.03.031; Ayusawa, K., Ikegami, Y., Nakamura, Y., Simultaneous global inverse kinematics and geometric parameter identification of human skeletal model from motion capture data (2014) Mech. Mach. Theory, 74, pp. 274-284. , https://doi.org/10.1016/j.mechmachtheory.2013.12.015; Begon, M., Wieber, P.B., Yeadon, M.R., Kinematics estimation of straddled movements on high bar from a limited number of skin markers using a chain model (2008) J. Biomech., 41, pp. 581-586. , https://doi.org/10.1016/j.jbiomech.2007.10.005; Prokopenko, R.A., Frolov, A.A., Biryukova, E.V., Roby-Brami, A., Assessment of the accuracy of a human arm model with seven degrees of freedom (2001) J. Biomech., 34, pp. 177-185. , https://doi.org/10.1016/S0021-9290(00)00179-2; Nubiola, A., Bonev, I.A., Absolute calibration of an ABB IRB 1600 robot using a laser tracker (2013) Robot. Comput. Integr. Manuf., 29, pp. 236-245. , https://doi.org/10.1016/j.rcim.2012.06.004; Zhang, X., Song, Y., Yang, Y., Pan, H., Stereo vision based autonomous robot calibration (2017) Rob. Auton. Syst., 93, pp. 43-51. , https://doi.org/10.1016/j.robot.2017.04.001; Meng, Y., Zhuang, H., Autonomous robot calibration using vision technology (2007) Robot. Comput. Integr. Manuf., 23, pp. 436-446. , https://doi.org/10.1016/j.rcim.2006.05.002; Motta, J.M.S.T., de Carvalho, G.C., McMaster, R.S., Robot calibration using a 3D vision-based measurement system with a single camera (2001) Robot. Comput. Integr. Manuf., 17, pp. 487-497. , https://doi.org/10.1016/S0736-5845(01)00024-2; Zhang, T., Song, Y., Wu, H., Wang, Q., A novel method to identify DH parameters of the rigid serial-link robot based on a geometry model (2020) Ind. Rob., , https://doi.org/10.1108/IR-05-2020-0103; Gao, G., Sun, G., Na, J., Guo, Y., Wu, X., Structural parameter identification for 6 DOF industrial robots (2018) Mech. Syst. Signal Process., 113, pp. 145-155. , https://doi.org/10.1016/j.ymssp.2017.08.011; Gates, D.H., Walters, L.S., Cowley, J., Wilken, J.M., Resnik, L., Range of motion requirements for upper-limb activities of daily living (2016) Am. J. Occup. Ther., 70","Cornagliotto, V.; Department of Mechanical and Aerospace Engineering, Italy; email: valerio.cornagliotto@polito.it",,,"Springer Science and Business Media B.V.",,,,,22110984,,,,"English","Mech. Mach. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85105513054
"Al-Mashhadani M.I.A., Aldhyani T.H.H., Hmoud Al-Adhaileh M., Bamhdi A.M., Alzahrani M.Y., Alsaade F.W., Alkahtani H.","57222965261;57191275820;24464269700;55375576200;56125509600;22937047900;57221086789;","Human-animal affective robot touch classification using deep neural network",2021,"Computer Systems Science and Engineering","38","1",,"25","37",,3,"10.32604/CSSE.2021.014992","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104946316&doi=10.32604%2fCSSE.2021.014992&partnerID=40&md5=e1f0617aac1cc5e9c40ec78def59d057","AL-Iraqia University, College of Education, Computer Department, Baghdad, Iraq; Community College of Abqaiq, King Faisal University, Al-Ahsa, Saudi Arabia; Deanship of E-learning and Distance Education, King Faisal University, Al-Ahsa, Saudi Arabia; UMM Al-Qura University, College of Computing, Makkah, Saudi Arabia; Department of Computer Sciences and Information Technology, Albaha University, Al Baha, Saudi Arabia; College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia","Al-Mashhadani, M.I.A., AL-Iraqia University, College of Education, Computer Department, Baghdad, Iraq; Aldhyani, T.H.H., Community College of Abqaiq, King Faisal University, Al-Ahsa, Saudi Arabia; Hmoud Al-Adhaileh, M., Deanship of E-learning and Distance Education, King Faisal University, Al-Ahsa, Saudi Arabia; Bamhdi, A.M., UMM Al-Qura University, College of Computing, Makkah, Saudi Arabia; Alzahrani, M.Y., Department of Computer Sciences and Information Technology, Albaha University, Al Baha, Saudi Arabia; Alsaade, F.W., College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia; Alkahtani, H., AL-Iraqia University, College of Education, Computer Department, Baghdad, Iraq, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia","Touch gesture recognition is an important aspect in human-robot interaction, as it makes such interaction effective and realistic. The novelty of this study is the development of a system that recognizes human-animal affective robot touch (HAART) using a deep learning algorithm. The proposed system was used for touch gesture recognition based on a dataset provided by the Recognition of the Touch Gestures Challenge 2015. The dataset was tested with numerous subjects performing different HAART gestures; each touch was performed on a robotic animal covered by a pressure sensor skin. A convolutional neural network algorithm is proposed to implement the touch recognition system from row inputs of the sensor devices. The leave-one-subject-out cross-validation method was used to validate and evaluate the proposed system. A comparative analysis between the results of the proposed system and the state-of-the-art performance is presented. Findings show that the proposed system could recognize the gestures in almost real time (after acquiring the minimum number of frames). According to the results of the leave-one-subject-out cross-validation method, the proposed algorithm could achieve a classification accuracy of 83.2%. It was also superior compared with existing systems in terms of classification ratio, touch recognition time, and data preprocessing on the same dataset. Therefore, the proposed system can be used in a wide range of real applications, such as image recognition, natural language recognition, and video clip classification. © 2021 CRL Publishing. All rights reserved.","Deep learning; Touch gesture classification; Touch gesture recognition","Animals; Classification (of information); Convolutional neural networks; Deep learning; Deep neural networks; Gesture recognition; Image recognition; Learning algorithms; Classification accuracy; Comparative analysis; Cross-validation methods; Data preprocessing; Natural languages; Real applications; Recognition systems; State-of-the-art performance; Social robots",,,,,,,,,,,,,,,,"Flagg, A., MacLean, K., Affective touch gesture recognition for a furry zoomorphic machine (2019) Proc. of the 7th Int. Conf. on Tangible, Embedded and Embodied Interaction, pp. 25-32. , New York, NY, USA; Ortega, F. R., Rishe, N., Barreto, A., Abyarjoo, F., Adjouadi, M., Multi-touch gesture recognition using feature extraction (2015) Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering, pp. 291-296. , Switzerland; Chang, J., MacLean, K., Yohanan, S., Gesture recognition in the haptic creature (2010) Haptics: Generating and Perceiving Tangible Sensations, pp. 385-391. , Berlin, Heidelberg: Verlag; Aldhyani, T. H. H., Al-Yaari, M., Alkahtani, H., Maashi, M., Water quality prediction using artificial intelligence algorithms Applied Bionics and Biomechanics, 2020, p. 12. , 2020; Alkahtani, H., Aldhyani, T. H. H., Al-Yaari, M., Adaptive anomaly detection framework model objects in cyberspace Applied Bionics and Biomechanics, 2020, p. 14. , 2020; Jung, M. M., Leij, L. V. D., Kelders, S. 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Conf. on Multimodal Interaction, pp. 407-413. , Seattle Washington, USA; Gaus, Y. F. A., Olugbade, T., Jan, A., Qin, R., Liu, J., Social touch gesture recognition using random forest and boosting on distinct feature sets (2015) Proc. of the ACM on Int. Conf. on Multimodal Interaction, pp. 399-406. , Seattle Washington, USA; Hughes, D., Farrow, N., Profita, H., Correll, N., Detecting and identifying tactile gestures using deep autoencoders, geometric moments and gesture level features (2015) Proc. of the 2015 ACM on Int. Conf. on Multimodal Interaction, pp. 415-422. , Seattle, Washington, USA; Ta, V. C., Johal, W., Portaz, M., Castelli, E., Vaufreydaz, D., The Grenoble system for the social touch challenge at ICMI 2015 (2015) Proc. of the 2015 ACM on Int. Conf. on Multimodal Interaction, pp. 391-398. , Seattle Washington, USA; Hughes, D., Krauthammer, A., Correll, N., Recognizing social touch gestures using recurrent and convolutional neural networks (2017) Int. Conf. on Robotics and Automation (ICRA), pp. 2315-2321. , Singapore","Aldhyani, T.H.H.; Community College of Abqaiq, Saudi Arabia; email: taldhyani@kfu.edu.sa",,,"Tech Science Press",,,,,02676192,,CSSEE,,"English","Comput Syst Sci Eng",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85104946316
"Islam M.S., Rahman M.M., Muhammad G., Shamim Hossain M.","57222186351;55568450200;56605566900;57219458636;","Design of A Social Robot Interact with Artificial Intelligence by Versatile Control Systems",2021,"IEEE Sensors Journal",,,,"","",,1,"10.1109/JSEN.2021.3062682","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101796484&doi=10.1109%2fJSEN.2021.3062682&partnerID=40&md5=6ed5c6c8a2a501f896a9dc17235fa560","Hajee Mohammad Danesh Science and Technology University (HSTU), Dinajpur-5200 Bangladesh.; Chair of Pervasive and Mobile Computing and Dept. of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. (e-mail: ghulam@ksu.edu.sa); Chair of Pervasive and Mobile Computing and with the Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.","Islam, M.S., Hajee Mohammad Danesh Science and Technology University (HSTU), Dinajpur-5200 Bangladesh.; Rahman, M.M., Hajee Mohammad Danesh Science and Technology University (HSTU), Dinajpur-5200 Bangladesh.; Muhammad, G., Chair of Pervasive and Mobile Computing and Dept. of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. (e-mail: ghulam@ksu.edu.sa); Shamim Hossain, M., Chair of Pervasive and Mobile Computing and with the Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.","Robots are a combination of mechatronics, computer science, and artificial intelligence. Robotics is a branch of engineering that involves the conception, design, manufacture, and operation of actions. Whenever a robot has to interact with the human-society, it has to adopt a special skill called Human-Robot Interaction and thus the term Social-Robot comes into account. A social robot has to be able to express emotions, communicate with high-level dialogue, use natural cues, and learn to recognize models of other agents. An autonomous social robot cannot follow orders instead of doing something on its own. To make the robot more interactive and communicative, lots of sensors and modules have to be used along with its moving mechanism. Therefore, a social robot becomes complex and expensive. To overcome the issue of the complexity and costliness, in this paper, a design of social robot using a combination of embedded systems, the Internet of Robotic Things (IORT) and Android operating system has been introduced to be interactive and communicative to human, be intelligent enough to solve complex mathematics and be able to follow the operator&#x2019;s command simultaneously. By using the Internet as the robot&#x2019;s source of information and the Android phone as the robot&#x2019;s sensory and control system partially, and adding them all to the robot&#x2019;s embedded system wirelessly, we have not only become able to make the robot more advanced and intelligent, but also reduce the cost of construction by a significant amount. IEEE","Artificial intelligence; dynamic control system; micro-controller; social robot","Android (operating system); Control systems; Embedded systems; Intelligent robots; Machine design; Man machine systems; Mathematical operators; Robotics; Complex mathematics; Express emotions; Human society; Moving mechanisms; Social robots",,,,,,,,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,1530437X,,,,"English","IEEE Sensors J.",Article,"Article in Press","",Scopus,2-s2.0-85101796484
"Tang Z.Q., Heung H.L., Tong K.Y., Li Z.","57210643773;57193806931;35772793800;55617655400;","Model-based online learning and adaptive control for a “human-wearable soft robot” integrated system",2021,"International Journal of Robotics Research","40","1",,"256","276",,12,"10.1177/0278364919873379","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072047010&doi=10.1177%2f0278364919873379&partnerID=40&md5=8e72d1bf684fea94c95dd530f74e6277","Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong; Department of Surgery and the Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong","Tang, Z.Q., Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong; Heung, H.L., Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong; Tong, K.Y., Department of Biomedical Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong; Li, Z., Department of Surgery and the Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong","Soft robots are considered intrinsically safe with regard to human–robot interaction. This has motivated the development and investigation of soft medical robots, such as soft robotic gloves for stroke rehabilitation. However, the output force of conventional purely soft actuators is usually limited. This restricts their application in stroke rehabilitation, which requires a large force and bidirectional movement. In addition, accurate control of soft actuators is difficult owing to the nonlinearity of purely soft actuators. In this study, a soft robotic glove is designed based on a soft-elastic composite actuator (SECA) that integrates an elastic torque compensating layer to increase the output force as well as achieving bidirectional movement. Such a hybrid design also significantly reduces the degree of nonlinearity compared with a purely soft actuator. A model-based online learning and adaptive control algorithm is proposed for the wearable soft robotic glove, taking its interaction environment into account, namely, the human hand/finger. The designed hybrid controller enables the soft robotic glove to adapt to different hand conditions for reference tracking. Experimental results show that satisfactory tracking performance can be achieved on both healthy subjects and stroke subjects (with the tracking root mean square error (RMSE) < 0.05 rad). Meanwhile, the controller can output an actuator–finger model for each individual subject (with the learning error RMSE < 0.06 rad), which provides information on the condition of the finger and, thus, has further potential clinical application. © The Author(s) 2019.","human–robot interaction; Model-based learning control; soft robot","Actuators; Adaptive control systems; Agricultural robots; Controllers; E-learning; Learning systems; Mean square error; Medical robotics; Neuromuscular rehabilitation; Robotics; Wearable technology; Adaptive control algorithms; Degree of non-linearity; Interaction environment; Modeling-based learning; Robot interactions; Root mean square errors; Soft robot; Stroke rehabilitation; Social robots",,,,,"C4930759, KPF18HLF12; ITS/065/18FP; Chinese University of Hong Kong, CUHK","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the CUHK T Stone Robotics Institute (grant number C4930759), the Knowledge Transfer Project Fund (grant number KPF18HLF12) of the Chinese University of Hong Kong, and the Innovation and Technology Fund of Hong Kong (grant number ITS/065/18FP).",,,,,,,,,,"Amirabdollahian, F., Loureiro, R., Driessen, B., Harwin, W., Error correction movement for machine assisted stroke rehabilitation (2001) Integration of Assistive Technology in the Information Age, 9, pp. 60-65; Amirabdollahian, F., Loureiro, R., Harwin, W., (2002) Minimum jerk trajectory control for rehabilitation and haptic applications, 4, pp. 3380-3385. , Proceedings IEEE International Conference on Robotics and Automation, 2002 (ICRA’02), IEEE, In; Ansari, N.N., Naghdi, S., Arab, T.K., Jalaie, S., The interrater and intrarater reliability of the modified ashworth scale in the assessment of muscle spasticity: Limb and muscle group effect (2008) NeuroRehabilitation, 23 (3), pp. 231-237; Ao, D., Song, R., Tong, K., Sensorimotor control of tracking movements at various speeds for stroke patients as well as age-matched and young healthy subjects (2015) PLoS One, 10 (6), p. e0128328; Arimoto, S., Kawamura, S., Miyazaki, F., (1984) Bettering operation of dynamic systems by learning: A new control theory for servomechanism or mechatronics systems, pp. 1064-1069. , The 23rd IEEE Conference on Decision and Control, IEEE, In; Awad, L.N., Bae, J., O’Donnell, K., A soft robotic exosuit improves walking in patients after stroke (2017) Science Translational Medicine, 9 (400). , (,): eaai9084; Balasubramanian, S., Klein, J., Burdet, E., Robot-assisted rehabilitation of hand function (2010) Current Opinion in Neurology, 23 (6), pp. 661-670; Balasubramanian, S., Wei, R., Perez, M., Rupert: An exoskeleton robot for assisting rehabilitation of arm functions (2008) Virtual Rehabilitation, 2008, pp. 163-167. , IEEE,. 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"Priyanayana K.S., Jayasekara A.G.B.P., Gopura R.A.R.C.","57195409302;35090187000;24779435600;","Multimodal Behavior Analysis of Human-Robot Navigational Commands",2020,"2020 3rd International Conference on Control and Robots, ICCR 2020",,,"9344419","79","84",,,"10.1109/ICCR51572.2020.9344419","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101627681&doi=10.1109%2fICCR51572.2020.9344419&partnerID=40&md5=675eda6c1be54388d25353216826e02e","University of Moratuwa (of Aff.), Dept. of Electrical Engineering, Sri Lanka","Priyanayana, K.S., University of Moratuwa (of Aff.), Dept. of Electrical Engineering, Sri Lanka; Jayasekara, A.G.B.P., University of Moratuwa (of Aff.), Dept. of Electrical Engineering, Sri Lanka; Gopura, R.A.R.C., University of Moratuwa (of Aff.), Dept. of Electrical Engineering, Sri Lanka","Human-Robot Interactions have to be more human like and human-human communications are multimodal interactions. In general communication, humans tend to use multiple modalities at a time to convey a message through. Multimodal interactions could be in many modalities such as gestures, speech, gaze and etc. Major multimodal human-human combination is the speech-hand gesture interaction. Hand gestures are used in a diverse range in these interactions. They add different meanings and enhance the understanding of the complete interaction in multiple dimensions. Purpose of this paper is to conduct a comprehensive analysis on multimodal relationship of speech-hand gesture interaction and its effect on the true meaning of interactions. Therefore this paper will focus on different aspects of each modality with regards to multimodal interactions such as vocal uncertainties, static and dynamic hand gestures, deictic, redundant and unintentional gestures, their timeline parameters, hand features and etc. Furthermore this paper discuss the effect of each speech-gesture parameter on understanding of the vocal ambiguities. Complete analysis of these aspects was conducted through detailed human study and results are interpreted through above multimodal aspects. Further vocal commands are analyzed using different vocal categories and different types of uncertainties. Hand gestures are analyzed though timeline parameters and hand feature analysis. For the timeline analysis, parameters were decided from the feedback of the participants on effectiveness of each parameter. Lag time, speed of the gesture movements and range of the gesture were considered for the timeline analysis. © 2020 IEEE.","Human robot interaction; Multimodal interaction; Non verbal communication; Social robotics","Agricultural robots; Behavioral research; Biofeedback; Interactive computer systems; Modal analysis; Uncertainty analysis; Comprehensive analysis; Feature analysis; Human-human communication; Multi-Modal Interactions; Multimodal behavior analysis; Multiple dimensions; Multiple modalities; Timeline analysis; Human robot interaction",,,,,,,,,,,,,,,,"Barber, D.J., Julian Abich, I.V., Phillips, E., Talone, A.B., Jentsch, F., Hill, S.G., Field assessment of multimodal communication for dismounted human-robot teams. 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Control Robot., ICCR",Conference Paper,"Final","",Scopus,2-s2.0-85101627681
"Anvaripour M., Khoshnam M., Menon C., Saif M.","37080076100;55324740600;23989261600;7103101428;","FMG- and RNN-Based Estimation of Motor Intention of Upper-Limb Motion in Human-Robot Collaboration",2020,"Frontiers in Robotics and AI","7",,"573096","","",,1,"10.3389/frobt.2020.573096","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097787430&doi=10.3389%2ffrobt.2020.573096&partnerID=40&md5=6477fbe2450d3c47e6f2ba9dab07c4e6","Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada; Menrva Research Group, Schools of Mechatronic System and Engineering Science, Simon Fraser University, Vancouver, BC, Canada","Anvaripour, M., Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada; Khoshnam, M., Menrva Research Group, Schools of Mechatronic System and Engineering Science, Simon Fraser University, Vancouver, BC, Canada; Menon, C., Menrva Research Group, Schools of Mechatronic System and Engineering Science, Simon Fraser University, Vancouver, BC, Canada; Saif, M., Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada","Research on human-robot interactions has been driven by the increasing employment of robotic manipulators in manufacturing and production. Toward developing more effective human-robot collaboration during shared tasks, this paper proposes an interaction scheme by employing machine learning algorithms to interpret biosignals acquired from the human user and accordingly planning the robot reaction. More specifically, a force myography (FMG) band was wrapped around the user's forearm and was used to collect information about muscle contractions during a set of collaborative tasks between the user and an industrial robot. A recurrent neural network model was trained to estimate the user's hand movement pattern based on the collected FMG data to determine whether the performed motion was random or intended as part of the predefined collaborative tasks. Experimental evaluation during two practical collaboration scenarios demonstrated that the trained model could successfully estimate the category of hand motion, i.e., intended or random, such that the robot either assisted with performing the task or changed its course of action to avoid collision. Furthermore, proximity sensors were mounted on the robotic arm to investigate if monitoring the distance between the user and the robot had an effect on the outcome of the collaborative effort. While further investigation is required to rigorously establish the safety of the human worker, this study demonstrates the potential of FMG-based wearable technologies to enhance human-robot collaboration in industrial settings. © Copyright © 2020 Anvaripour, Khoshnam, Menon and Saif.","collision avoidance; force myography; human-robot collaboration; industrial robot; recurrent neural network",,,,,,,,,,,,,,,,,"Anvaripour, M., Khoshnam, M., Menon, C., Saif, M., Safe human robot cooperation in task performed on the shared load (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 3761-3767. , Montreal, QC, IEEE; Anvaripour, M., Saif, M., Controlling robot gripper force by transferring human forearm stiffness using force myography (2018) 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 672-675. , a, Windsor, ON, IEEE; Anvaripour, M., Saif, M., Hand gesture recognition using force myography of the forearm activities and optimized features (2018) 2018 IEEE International Conference on Industrial Technology (ICIT), pp. 187-192. , b, Lyon, IEEE; Assad, C., Wolf, M., Stoica, A., Theodoridis, T., Glette, K., BioSleeve: a natural EMG-based interface for HRI (2013) 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 69-70. , Tokyo, IEEE; Avanzini, G.B., Ceriani, N.M., Zanchettin, A.M., Rocco, P., Bascetta, L., Safety control of industrial robots based on a distributed distance sensor (2014) IEEE Trans. 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Neuroeng. Rehabil, 11, p. 2. , 24397984; Yao, K., Cohn, T., Vylomova, K., Duh, K., Dyer, C., Depth-gated lstm (2015) arXiv, , 1508.03790; Zakia, U., Menon, C., Estimating exerted hand force via force myography to interact with a biaxial stage in real-time by learning human intentions: a preliminary investigation (2020) Sensors, 20, p. 2104. , 32276456; Zhao, Q., Yuan, X., Tu, D., Lu, J., Multi-initialized states referred work parameter calibration for gaze tracking human-robot interaction (2012) Int. J. Adv. Robot. Syst, 9, p. 75","Menon, C.; Menrva Research Group, Canada; email: cmenon@sfu.ca",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85097787430
"Mura D., Knoop E., Catalano M.G., Grioli G., Bächer M., Bicchi A.","57204466353;55922286800;35589596200;24780378400;25627224600;7004326697;","On the role of stiffness and synchronization in human–robot handshaking",2020,"International Journal of Robotics Research","39","14",,"1796","1811",,6,"10.1177/0278364920903792","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079198536&doi=10.1177%2f0278364920903792&partnerID=40&md5=f2e2bb768547e77dc6b836dc62b8171f","Centro di Ricerca “E. Piaggio”, Univ. di Pisa, Pisa, Italy; Disney Research, Zurich, Switzerland; Department of Soft Robotics for Human Cooperation and Rehabilitation, Istituto Italiano di Tecnologia, Genoa, Italy","Mura, D., Centro di Ricerca “E. Piaggio”, Univ. di Pisa, Pisa, Italy; Knoop, E., Disney Research, Zurich, Switzerland; Catalano, M.G., Department of Soft Robotics for Human Cooperation and Rehabilitation, Istituto Italiano di Tecnologia, Genoa, Italy; Grioli, G., Department of Soft Robotics for Human Cooperation and Rehabilitation, Istituto Italiano di Tecnologia, Genoa, Italy; Bächer, M., Disney Research, Zurich, Switzerland; Bicchi, A., Centro di Ricerca “E. Piaggio”, Univ. di Pisa, Pisa, Italy, Department of Soft Robotics for Human Cooperation and Rehabilitation, Istituto Italiano di Tecnologia, Genoa, Italy","This article presents a system for soft human–robot handshaking, using a soft robot hand in conjunction with a lightweight and impedance-controlled robot arm. Using this system, we study how different factors influence the perceived naturalness, and give the robot different personality traits. Capitalizing on recent findings regarding handshake grasp force regulation, and on studies of the impedance control of the human arm, we investigate the role of arm stiffness as well as the kinesthetic synchronization of human and robot arm motions during the handshake. The system is implemented using a lightweight anthropomorphic arm, with a Pisa/IIT Softhand wearing a sensorized silicone glove as the end-effector. The robotic arm is impedance-controlled, and its stiffness changes according to different laws under investigation. An internal observer is employed to synchronize the human and robot arm motions. Thus, we simulate both active and passive behavior of the robotic arm during the interaction. Using the system, studies are conducted where 20 participants are asked to interact with the robot, and then rate the perceived quality of the interaction using Likert scales. Our results show that the control of the robotic arm kinesthetic behavior does have an effect on the interaction with the robot, in term of its perceived personality traits, responsiveness, and human-likeness. Our results pave the way towards robotic systems that are capable of performing human–robot interactions in a more human-like manner, and with personality. © The Author(s) 2020.","human–robot handshaking; pHRI; Soft manipulation","Agricultural robots; Interactive computer systems; Robotic arms; Robotics; Silicones; Stiffness; Anthropomorphic arm; Impedance control; Perceived quality; Personality traits; pHRI; Robot interactions; Soft manipulation; Stiffness changes; Social robots",,,,,"Horizon 2020 Framework Programme, H2020: 262533, 645599, 688857; European Commission, EC","This project has received funding from the European Union’s Horizon 2020 research and innovation programme (grant agreement number 645599 (Soma) and number 688857 (SoftPro)) with: (grant agreement number 645599 (Soma), number 688857 (SoftPro), and number 262533 (SOPHIA)). The content of this publication is the sole responsibility of the authors. The European Commission or its services cannot be held responsible for any use that may be made of the information it contains.","We thank Pamela David and Nicola Fauceglia for their valuable work in the EKF observer implementation, and Gaspare Santaera, Mattia Poggiani, Michele Maimeri, Mathew J Pollayil, and George J Pollayil for their help with the experimental framework setting. We also thank Federica Felici for help in the design and implementation of the experimental protocol, and Patricia Capsi Morales for advice on the statistical analysis. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project has received funding from the European Union?s Horizon 2020 research and innovation programme (grant agreement number 645599 (Soma) and number 688857 (SoftPro)) with: (grant agreement number 645599 (Soma), number 688857 (SoftPro), and number 262533 (SOPHIA)). The content of this publication is the sole responsibility of the authors. The European Commission or its services cannot be held responsible for any use that may be made of the information it contains.",,,,,,,,,"Ajoudani, A., Tsagarakis, N.G., Bicchi, A., (2012) Tele-impedance: Towards transferring human impedance regulation skills to robots, pp. 382-388. , 2012 IEEE International Conference on Robotics and Automation, IEEE, In; Aldrich, J., Skelton, R., (2006) Backlash-free motion control of robotic manipulators driven by tensegrity motor networks, pp. 2300-2306. , Proceedings of the 45th IEEE Conference on Decision and Control, IEEE, In; Arns, M., Laliberté, T., Gosselin, C., (2017) Design, control and experimental validation of a haptic robotic hand performing human-robot handshake with human-like agility, pp. 4626-4633. , 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, In; Artemiadis, P.K., Katsiaris, P.T., Liarokapis, M.V., Kyriakopoulos, K.J., (2010) Human arm impedance: Characterization and modeling in 3D space, pp. 3103-3108. , 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, In; Avelino, J., Moreno, P., Bernardino, A., (2018) The power of a hand-shake in human–robot interactions, pp. 1864-1869. , 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, (, a), In; Avelino, J., Paulino, T., Cardoso, C., Nunes, R., Moreno, P., Bernardino, A., Towards natural handshakes for social robots: human-aware hand grasps using tactile sensors (2018) Paladyn, Journal of Behavioral Robotics, 9 (1), pp. 221-234. , (, b; Avraham, G., Nisky, I., Fernandes, H.L., Toward perceiving robots as humans: Three handshake models face the turing-like handshake test (2012) IEEE Transactions on Haptics, 5 (3), pp. 196-207; Baltaxe-Admony, L.B., Robbins, A.S., Jung, E.A., (2016) Simulating the human shoulder through active tensegrity structures, , ASME 2016 International Design Engineering Technical Conferences and Computers and Information Engineering Conference, American Society of Mechanical Engineers,. 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In; Orefice, P.H., Ammi, M., Hafez, M., Tapus, A., (2018) Pressure variation study in human–human and human–robot handshakes: Impact of the mood, pp. 247-254. , 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), IEEE, In; Papageorgiou, D., Doulgeri, Z., (2015) A kinematic controller for human–robot handshaking using internal motion adaptation, pp. 5622-5627. , 2015 IEEE International Conference on Robotics and Automation (ICRA), IEEE, In; Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., (2010) Robotics: Modelling, Planning and Control, , Springer Science & Business Media; Tsalamlal, M.Y., Martin, J.C., Ammi, M., Tapus, A., Amorim, M.A., (2015) Affective handshake with a humanoid robot: How do participants perceive and combine its facial and haptic expressions?, pp. 334-340. , 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), IEEE, In; Tsuji, T., Morasso, P.G., Goto, K., Ito, K., Human hand impedance characteristics during maintained posture (1995) Biological cybernetics, 72 (6), pp. 475-485; Vigni, F., Knoop, E., Prattichizzo, D., Malvezzi, M., The role of closed-loop hand control in handshaking interactions (2019) IEEE Robotics and Automation Letters, 4 (2), pp. 878-885; Wang, Z., Peer, A., Buss, M., (2009) An HMM approach to realistic haptic human–robot interaction, pp. 374-379. , World Haptics 2009-Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, IEEE, In","Mura, D.; Centro di Ricerca “E. Piaggio”, Italy; email: domenico.mura@phd.unipi.it",,,"SAGE Publications Inc.",,,,,02783649,,IJRRE,,"English","Int J Rob Res",Article,"Final","",Scopus,2-s2.0-85079198536
"Zhang D., Lo F.P.-W., Zheng J.-Q., Bai W., Yang G.-Z., Lo B.","57207943701;57202887297;57210807859;55570917300;57211728432;15834859900;","Data-Driven Microscopic Pose and Depth Estimation for Optical Microrobot Manipulation",2020,"ACS Photonics","7","11",,"3003","3014",,,"10.1021/acsphotonics.0c00997","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097583707&doi=10.1021%2facsphotonics.0c00997&partnerID=40&md5=bdc4d834c0e963fa7a03267903b994d9","Imperial College London, London, United Kingdom; Oxford University, Oxford, United Kingdom; Shanghai Jiao Tong University, Shanghai, China","Zhang, D., Imperial College London, London, United Kingdom; Lo, F.P.-W., Imperial College London, London, United Kingdom; Zheng, J.-Q., Oxford University, Oxford, United Kingdom; Bai, W., Imperial College London, London, United Kingdom; Yang, G.-Z., Shanghai Jiao Tong University, Shanghai, China; Lo, B., Imperial College London, London, United Kingdom","Optical microrobots have a wide range of applications in biomedical research for both in vitro and in vivo studies. In most microrobotic systems, the video captured by a monocular camera is the only way for visualizing the movements of microrobots, and only planar motion, in general, can be captured by a monocular camera system. Accurate depth estimation is essential for 3D reconstruction or autofocusing of microplatforms, while the pose and depth estimation are necessary to enhance the 3D perception of the microrobotic systems to enable dexterous micromanipulation and other tasks. In this paper, we propose a data-driven method for pose and depth estimation in an optically manipulated microrobotic system. Focus measurement is used to obtain features for Gaussian Process Regression (GPR), which enables precise depth estimation. For mobile microrobots with varying poses, a novel method is developed based on a deep residual neural network with the incorporation of prior domain knowledge about the optical microrobots encoded via GPR. The method can simultaneously track microrobots with complex shapes and estimate the pose and depth values of the optical microrobots. Cross-validation has been conducted to demonstrate the submicron accuracy of the proposed method and precise pose and depth perception for microrobots. We further demonstrate the generalizability of the method by adapting it to microrobots of different shapes using transfer learning with few-shot calibration. Intuitive visualization is provided to facilitate effective human-robot interaction during micromanipulation based on pose and depth estimation results. © 2020 American Chemical Society. All rights reserved.","deep learning; depth estimation; image processing; optical microrobot; pose estimation","Cameras; Deep neural networks; Depth perception; Human robot interaction; Micromanipulators; Transfer learning; 3D reconstruction; Biomedical research; Data-driven methods; Focus measurement; Gaussian process regression; Micro manipulation; Mobile microrobots; Monocular cameras; Microrobots",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/P012779/1","The authors acknowledge funding from the UK Engineering and Physical Sciences Research Council (EPSRC) Program Grant EP/P012779/1 (Microrobotics for Surgery). The authors would like to acknowledge Drs. M. Power and F. Seichepine for the cleanroom training and Drs. M. Grammatikopoulou and A. Barbot for the optical tweezer system training.",,,,,,,,,,"Li, J., De Ávila, B.E.-F., Gao, W., Zhang, L., Wang, J., Micro/nanorobots for biomedicine: Delivery, surgery, sensing, and detoxification (2017) Science Robotics, 2, p. eaam6431; Crocker, J.C., Grier, D.G., Methods of digital video microscopy for colloidal studies (1996) J. Colloid Interface Sci., 179, pp. 298-310; Jones, P.H., Maragò, O.M., Volpe, G., (2015) Optical Tweezers: Principles and Applications, , Cambridge University Press; Neuman, K.C., Nagy, A., Single-molecule force spectroscopy: Optical tweezers, magnetic tweezers and atomic force microscopy (2008) Nat. Methods, 5, p. 491; Waigh, T.A., Advances in the microrheology of complex fluids (2016) Rep. Prog. Phys., 79, p. 074601; Sahoo, S.K., Misra, R., Parveen, S., (2017) Nanomedicine in Cancer, pp. 73-124. , Pan Stanford; Thompson, R.E., Larson, D.R., Webb, W.W., Precise nanometer localization analysis for individual fluorescent probes (2002) Biophys. 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Tech., 65, pp. 139-149; Pech-Pacheco, J.L., Cristóbal, G., Chamorro-Martinez, J., Fernández-Valdivia, J., (2000) Diatom Autofocusing in Brightfield Microscopy: A Comparative Study, pp. 314-317. , Proceedings 15th International Conference on Pattern Recognition; ICPR-2000, Barcelona, Spain, Sept 3-7, 2000, IEEE; Nayar, S.K., Nakagawa, Y., Shape from focus (1994) IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, pp. 824-831; Williams, C.K.I., Rasmussen, C.E., Gaussian Processes for Regression (1996) Advances in Neural Information Processing Systems, 8; Rasmussen, C.E., (2004) Gaussian Processes in Machine Learning. Summer School on Machine Learning, 3176, pp. 63-71; Schulz, E., Speekenbrink, M., Krause, A., A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions (2018) Journal of Mathematical Psychology, 85, pp. 1-16; Chang, J., Wetzstein, G., Deep optics for monocular depth estimation and 3d object detection. (2019) Proceedings of the IEEE International Conference on Computer Vision, pp. 10193-10202. , Seoul, South Korea, Oct 27- Nov 3, 2019, IEEE; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778; Gidaris, S., Singh, P., Komodakis, N., Unsupervised representation learning by predicting image rotations. (2018) ArXiv Preprint arXiv:1803.07728; Kawata, S., Sun, H.-B., Tanaka, T., Takada, K., Finer features for functional microdevices (2001) Nature, 412, p. 697; Zhang, D., Barbot, A., Lo, B., Yang, G.Z., Distributed Force Control for Microrobot Manipulation via Planar Multi-Spot Optical Tweezer (2020) Adv. Opt. Mater., p. 2000543","Zhang, D.; Imperial College LondonUnited Kingdom; email: d.zhang17@imperial.ac.uk
Yang, G.-Z.; Shanghai Jiao Tong UniversityChina; email: gzyang@sjtu.edu.cn",,,"American Chemical Society",,,,,23304022,,,,"English","ACS Photonics",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85097583707
"Ramis S., Buades J.M., Perales F.J.","57006625200;36838410800;35845246500;","Using a social robot to evaluate facial expressions in the wild",2020,"Sensors (Switzerland)","20","23","6716","1","24",,4,"10.3390/s20236716","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096558864&doi=10.3390%2fs20236716&partnerID=40&md5=bbf3c1aadf576b7462b5fed7342f5416","Departament de Matemàtiques i Informàtica, Universitat Illes Balears, Palma de Mallorca, 07122, Spain","Ramis, S., Departament de Matemàtiques i Informàtica, Universitat Illes Balears, Palma de Mallorca, 07122, Spain; Buades, J.M., Departament de Matemàtiques i Informàtica, Universitat Illes Balears, Palma de Mallorca, 07122, Spain; Perales, F.J., Departament de Matemàtiques i Informàtica, Universitat Illes Balears, Palma de Mallorca, 07122, Spain","In this work an affective computing approach is used to study the human-robot interaction using a social robot to validate facial expressions in the wild. Our global goal is to evaluate that a social robot can be used to interact in a convincing manner with human users to recognize their potential emotions through facial expressions, contextual cues and bio-signals. In particular, this work is focused on analyzing facial expression. A social robot is used to validate a pre-trained convolutional neural network (CNN) which recognizes facial expressions. Facial expression recognition plays an important role in recognizing and understanding human emotion by robots. Robots equipped with expression recognition capabilities can also be a useful tool to get feedback from the users. The designed experiment allows evaluating a trained neural network in facial expressions using a social robot in a real environment. In this paper a comparison between the CNN accuracy and human experts is performed, in addition to analyze the interaction, attention and difficulty to perform a particular expression by 29 non-expert users. In the experiment, the robot leads the users to perform different facial expressions in motivating and entertaining way. At the end of the experiment, the users are quizzed about their experience with the robot. Finally, a set of experts and the CNN classify the expressions. The obtained results allow affirming that the use of social robot is an adequate interaction paradigm for the evaluation on facial expression. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Affective computing; Convolutional neural network (CNN); Facial expression recognition; Human-robot interaction; Social robots","Biofeedback; Convolutional neural networks; End effectors; Face recognition; Affective Computing; Designed experiments; Expression recognition; Facial expression recognition; Facial Expressions; Interaction paradigm; Real environments; Trained neural networks; Social robots; emotion; facial expression; facial recognition; human; robotics; social interaction; Emotions; Facial Expression; Facial Recognition; Humans; Robotics; Social Interaction",,,,,"European Commission, EC: PERGAMEX RTI2018-096986-B-C31, PID2019-104829RA-I00/AEI/10.13039/501100011033; Ministerio de Economía y Competitividad, MINECO; European Regional Development Fund, ERDF; Ministerio de Economía, Industria y Competitividad, Gobierno de España, MINECO","This work is supported by the Ministerio de Econom?a, Industria y Competitividad (MINECO), the AgenciaEstatal de Investigaci?n (AEI), and the European Regional Development Funds (ERDF, EU) under projects PERGAMEX RTI2018-096986-B-C31 (MINECO/AEI/ERDF, EU) and Project EXPLainable Artificial INtelligence systems for health and well-beING (EXPLAINING) (PID2019-104829RA-I00/AEI/10.13039/501100011033).","Funding: This work is supported by the Ministerio de Economía, Industria y Competitividad (MINECO), the AgenciaEstatal de Investigación (AEI), and the European Regional Development Funds (ERDF, EU) under projects PERGAMEX RTI2018-096986-B-C31 (MINECO/AEI/ERDF, EU) and Project EXPLainable Artificial INtelligence systems for health and well-beING (EXPLAINING) (PID2019-104829RA-I00/AEI/10.13039/501100011033).",,,,,,,,,"Fernández-Caballero, A., González, P., López, M.T., Navarro, E., Special Issue on Socio-Cognitive and Affective Computing (2018) Appl. 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"Awde A., Boudaoud M., Regnier S., Clevy C.","57222351193;36801114100;57191056413;6506507449;","Observer-based disturbance control for small-scale collaborative robotics",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340748","2872","2878",,,"10.1109/IROS45743.2020.9340748","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102409702&doi=10.1109%2fIROS45743.2020.9340748&partnerID=40&md5=7979d881e770eb7e5c1e84b9ad711c66","Univ. Bourgogne Franche-Comté, FEMTO-ST, UFC/CNRS/ENSMM, 24 rue Savary, Besancon, 25000, France; Sorbonne Université, Institut des Systèmes Intelligents et de Robotique, UMR 7222, ISIR, Paris, F-75005, France","Awde, A., Univ. Bourgogne Franche-Comté, FEMTO-ST, UFC/CNRS/ENSMM, 24 rue Savary, Besancon, 25000, France, Sorbonne Université, Institut des Systèmes Intelligents et de Robotique, UMR 7222, ISIR, Paris, F-75005, France; Boudaoud, M., Sorbonne Université, Institut des Systèmes Intelligents et de Robotique, UMR 7222, ISIR, Paris, F-75005, France; Regnier, S., Sorbonne Université, Institut des Systèmes Intelligents et de Robotique, UMR 7222, ISIR, Paris, F-75005, France; Clevy, C., Univ. Bourgogne Franche-Comté, FEMTO-ST, UFC/CNRS/ENSMM, 24 rue Savary, Besancon, 25000, France","Collaborative robotics allows merging the best capabilities of humans and robots to perform complex tasks. This allows the user to interact with remote and directly inaccessible environments such as the micro-scale world. This interaction is made possible by the bidirectional exchange of information (displacement - force) between the user and the environment through a haptic interface. The effectiveness of the human/robot interaction is highly dependent on how the human feels the forces. This is a key point to enable humans to make the right decisions in a collaborative task. This paper discusses the design of a dynamic observer to estimate the forces applied by a human operator on a class of parallel pantograph-type haptic interfaces used to control small-scale robotic systems. The objective is to reject disturbances in order to improve the human force perception capability over a wide frequency range. A dynamic pantograph model is proposed and experimentally validated. The observer is designed on the basis of the proposed dynamic model and its efficiency in estimating the applied human force is demonstrated for the first time with pantograph-type interfaces. Experimental validation first shows the effectiveness of the perturbation observer for external human force estimation with a response time of less than 0.2 s and a mean error of less than 7 mN and then the effectiveness of the controller in improving the quality of human sensation of forces down to 10 mN. © 2020 IEEE.",,"Agricultural robots; Haptic interfaces; Intelligent robots; Mice (computer peripherals); Pantographs; Robotics; Collaborative tasks; Displacement force; Disturbance control; Exchange of information; Experimental validations; Perturbation observers; Reject disturbances; Wide frequency range; Social robots",,,,,,,,,,,,,,,,"Boudaoud, M., Lu, T., Liang, S., Oubellil, R., Régnier, S., A voltage/frequency modeling for a multi-dofs serial nanorobotic system based on piezoelectric inertial actuators (2018) IEEE/ASME Transactions on Mechatronics, 23 (6), pp. 2814-2824; Agnus, J., Chaillet, N., Clévy, C., Dembélé, S., Gauthier, M., Haddab, Y., Laurent, G., Tamadazte, B., Robotic microassembly and micromanipulation at femto-st (2013) Journal of Micro-Bio Robotics, 8 (2), pp. 91-106; Boudaoud, M., Faria, M.G.D., Gorrec, Y.L., Haddab, Y., Lutz, P., An output feedback lpv control strategy of a nonlinear electrostatic microgripper through a singular implicit modeling (2014) Control Engineering Practice, 28, pp. 97-111; Oubellil, R., Voda, A., Boudaoud, M., Régnier, S., Mixed stepping/ scanning mode control of stick-slip sem-integrated nano-robotic systems (2019) Sensors and Actuators A: Physical, 285, pp. 258-268; Oubellil, R., Voda, A., Boudaoud, M., Régnier, S., A 2-dof h control strategy for a 3 axes robotic system operating at the nanometer scale (2016) International Conference on System Theory, Control and Computing (ICSTCC), pp. 355-362; Michalos, G., Makris, S., Papakostas, N., Mourtzis, D., Chryssolouris, G., Automotive assembly technologies review: Challenges and outlook for a flexible and adaptive approach (2010) CIRP Journal of Manufacturing Science and Technology, 2 (2), pp. 81-91; Francoise, V., Sahbani, A., Morel, G., A comanipulation device for orthopedic surgery that generates geometrical constraints with realtime registration on moving bones (2011) International Conference on Robotics and Biomimetics, pp. 38-43; Torterotot, C.P., Vitrani, M., Mozer, P., Morel, G., Ultrasound image-based comanipulation for enhanced perception of the contacts with a distal soft organ (2011) International Conference on Robotics and Biomimetics, pp. 1140-1146; Lamy, X., Colledani, F., Geffard, F., Measson, Y., Morel, G., Achieving efficient and stable comanipulation through adaptation to changes in human arm impedance (2009) International Conference on Robotics and Automation, pp. 265-271; Florez, J.M., Szewczyk, J., Morel, G., An impedance control strategy for a hand-held instrument to compensate for physiological motion (2012) International Conference on Robotics and Automation, pp. 1952-1957; Carlson, T., Demiris, Y., Collaborative control for a robotic wheelchair: Evaluation of performance, attention, and workload (2012) Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42 (3), pp. 876-888; Rehbein, J., Wrütz, T., Hotze, D., Biesenbach, R., Collaborative control with industrial robots (2017) International Conference on Research and Education in Mechatronics (REM), pp. 1-4; Bolopion, A., Régnier, S., A review of haptic feedback teleoperation systems for micromanipulation and microassembly (2013) Transactions on Automation Science and Engineering, 10 (3), pp. 496-502; Bolopion, A., Xie, H., Haliyo, D.S., Régnier, S., Haptic teleoperation for 3-d microassembly of spherical objects (2012) IEEE/ASME Transactions on Mechatronics, 17 (1), pp. 116-127; Komati, B., Kudryavtsev, A., Clévy, C., Laurent, G., Tamadazte, B., Agnus, J., Lutz, P., Automated robotic microassembly of flexible optical components (2016) International Symposium on Assembly and Manufacturing (ISAM), pp. 93-98; Bargiel, S., Rabenorosoa, K., Clévy, C., Gorecki, C., Lutz, P., Towards micro-assembly of hybrid moems components on a reconfigurable silicon free-space micro-optical bench (2010) Journal of Micromechanics and Microengineering (JMM), 20; Sitti, M., Hashimoto, H., Two-dimensional fine particle positioning under an optical microscope using a piezoresistive cantilever as a manipulator (2000) Journal of Micromechatronics, 1 (1), pp. 25-48; Fan, X., Sun, M., Lin, Z., Song, J., He, Q., Sun, L., Xie, H., Automated noncontact micromanipulation using magnetic swimming microrobots (2018) Transactions on Nanotechnology, 17 (4), pp. 666-669; Kuncová-Kallio, J., Kallio, P., Lab automation in cultivation of adherent cells (2006) Transactions on Automation Science and Engineering, 3 (2), pp. 177-186; Lu, T., Pacoret, C., Hériban, D., Mohand-Ousaid, A., Régnier, S., Hayward, V., Kilohertz bandwidth, dual-stage haptic device lets you touch brownian motion (2017) IEEE Transactions on Haptics, 10 (3), pp. 382-390; Mohand-Ousaid, A., Millet, G., Régnier, S., Haliyo, S., Hayward, V., Haptic interface transparency achieved through viscous coupling (2012) The International Journal of Robotics Research, 31 (3), pp. 319-329; Hayward, V., Choksi, J., Lanvin, G., Ramstein, C., Design and multi-objective optimization of a linkage for a haptic interface (1994) Advances in Robot Kinematics and Computational Geometry, pp. 359-368; Dufresne, A., Martial, O., Ramstein, C., Multimodal user interface system for blind and visually occupied users: ergonomic evaluation of the haptic and auditive dimensions (1995) Human-Computer Interaction, pp. 163-168; Chen, W., Yang, J., Guo, L., Li, S., Disturbance-observer-based control and related methods-an overview (2015) Transactions on Industrial Electronics, 63 (2), pp. 1083-1095; Khatib, O., A unified approach for motion and force control of robot manipulators: The operational space formulation (1987) Journal on Robotics and Automation, 3 (1), pp. 43-53; Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., (2010) Robotics: Modelling, Planning and Control, , Springer Science & Business Media; Millet, G., Haliyo, S., Régnier, S., Hayward, V., The ultimate haptic device: First step (2009) World Haptics EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, pp. 273-278",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102409702
"Anvari B., Wurdemann H.A.","56442218700;35119695800;","Modelling social interaction between humans and service robots in large public spaces",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341133","11189","11196",,,"10.1109/IROS45743.2020.9341133","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102399215&doi=10.1109%2fIROS45743.2020.9341133&partnerID=40&md5=c267effe5cce80c11aee1c80e2d748d5","University of Southampton, Transportation Research Group, Civil, Maritime and Environmental Engineering, Science Boldrewood Innovation Campus, United Kingdom; University College London, Department of Mechanical Engineering, United Kingdom","Anvari, B., University of Southampton, Transportation Research Group, Civil, Maritime and Environmental Engineering, Science Boldrewood Innovation Campus, United Kingdom, University College London, Department of Mechanical Engineering, United Kingdom; Wurdemann, H.A., University College London, Department of Mechanical Engineering, United Kingdom","With the advent of service robots in public places (e.g., in airports and shopping malls), understanding socio-psychological interactions between humans and robots is of paramount importance. On the one hand, traditional robotic navigation systems consider humans and robots as moving obstacles and focus on the problem of real-time collision avoidance in Human-Robot Interaction (HRI) using mathematical models. On the other hand, the behavior of a robot has been determined with respect to a human. Parameters for human-human interaction have been assumed and applied to interactions involving robots. One major limitation is the lack of sufficient data for calibration and validation procedures. This paper models, calibrates and validates the socio-psychological interaction of the human in HRIs among crowds. The mathematical model is an extension of the Social Force Model for crowd modelling. The proposed model is calibrated and validated using open source datasets (including uninstructed human trajectories) from the Asia and Pacific Trade Center shopping mall in Osaka (Japan).In summary, the results of the calibration and validation on the multiple HRIs encountered in the datasets show that humans react to a service robot to a higher extend within a larger distance compared to the interaction range towards another human. This microscopic model, calibration and validation framework can be used to simulate HRI between service robots and humans, predict humans' behavior, conduct comparative studies, and gain insights into safe and comfortable human-robot relationships from the human's perspective. © 2020 IEEE.",,"Agricultural robots; Aircraft accidents; Calibration; Intelligent robots; Mobile robots; Navigation systems; Real time systems; Shopping centers; Calibration and validations; Comparative studies; Human robot Interaction (HRI); Human-human interactions; Microscopic modeling; Robotic navigation system; Social force models; Social interactions; Social robots",,,,,,,,,,,,,,,,"Murai, R., Sakai, T., Kawano, H., Matsukawa, Y., Kitano, Y., Honda, Y., Campbell, K.C., A novel visible light communication system for enhanced control of autonomous delivery robots in a hospital (2012) IEEE/SICE International Symposium System Integration, pp. 510-516; Ozkil, A.G., Fan, Z., Dawids, S., Aanes, H., Kristensen, J.K., Christensen, K.H., Service robots for hospitals: A case study of transportation tasks in a hospital. In Automation and Logistics (2009) Ieee International Conference, pp. 289-294; Jeon, S., Lee, J., Kim, J., Multi-robot task allocation for real-time hospital logistics (2017) Ieee International Conference on Systems, Man, and Cybernetics, pp. 2465-2470; Veloso, M.M., Biswas, J., Coltin, B., Rosenthal, S., CoBots: Robust symbiotic autonomous mobile service robots (2015) International Conference on Artificial Intelligence, pp. 4423-4429; Kulyukin, V., Gharpure, C., Nicholson, J., Robocart: Toward Robot-Assisted navigation of grocery stores by the visually impaired (2005) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2845-2850; Kulic, D., Croft, E., Safe motion planning for human-robot interaction: Design and experiments (2006) Mobile Robots: Towards New Applications, InTech, pp. 149-170; Thrun, S., Bennewitz, M., Burgard, W., Cremers, A.B., Dellaert, F., Fox, D., Hahnel, D., Schulz, D., Minerva: A second-generation museum tour-guide robot (1999) Ieee International Conference on Robotics and Automation, pp. 1999-2005; Shiomi, M., Kanda, T., Ishiguro, H., Hagita, N., Interactive humanoid robots for a science museum (2007) Ieee Intelligent Systems, 22 (2), pp. 25-32; Siegwart, R., Arras, K.O., Bouabdallah, S., Burnier, D., Froidevaux, G., Greppin, X., Jensen, B., Philippsen, R., Robox at Expo. 02: A large-scale installation of personal robots (2003) Robotics and Autonomous Systems, 42 (3-4), pp. 203-222; Burgard, W., Cremers, A.B., Fox, D., Hahnel, D., Lakemeyer, G., Schulz, D., Steiner, W., Thrun, S., The interactive museum Tour-Guide robot (1998) National Conference on Artificial Intelligence, pp. 11-18; Gross, H.-M., Boehme, H.-J., Schroeter, C., Mueller, S., Koenig, A., Martin, Ch., Merten, M., Bley, A., Shopbot: Progress in developing an interactive mobile shopping assistant for everyday use (2008) Ieee International Conference on Systems, Man, and Cybernetics, pp. 3471-3478; Kanda, T., Enabling Harmonized Human-Robot Interaction in a public space (2017) Human-Harmonized Information Technology, 2, pp. 115-137. , Tokyo; Walters, M.L., Dautenhahn, K., Boekhorst, R.T., Koay, K.L., Kaouri, C., Woods, S., Nehaniv, C., Werry, I., The influence of subjects' personality traits on personal spatial zones in a human-robot interaction experiment (2005) Ieee International Workshop on Robot and Human Interactive Communication, pp. 347-352; Kessler, J., Schroeter, C., Gross, H.-M., Approaching a person in a socially acceptable manner using a fast marching planner (2011) Intelligent Robotics and Applications, pp. 368-377; Hüttenrauch, H., Eklundh, K.S., Green, A., Topp, E.A., Investigating spatial relationships in human-robot interaction (2006) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5052-5059; Feil-Seifer, D., Mataríc, M.J., Distance-based computational models for facilitating robot interaction with children (2012) Journal of Human-Robot Interaction, 1 (1), pp. 55-77; Dautenhahn, K., Walters, M., Woods, S., Koay, K.L., Nehaniv, C.L., Sisbot, A., Alami, R., Simeón, T., How may I serve you?: A robot companion approaching a seated person in a helping context (2006) Conference on Human-Robot Interaction, pp. 172-179; Shi, C., Shiomi, M., Smith, C., Kanda, T., Ishiguro, H., A Model of distributional handing interaction for a mobile robot (2013) Robotics: Science and Systems Conference; Shi, C., Shimada, M., Kanda, T., Ishiguro, H., Hagita, N., Spatial formation model for initiating conversation (2011) Robotics: Science and Systems Conference; Butler, J.T., Agah, A., Psychological effects of behavior patterns of a mobile personal robot (2001) Autonomous Robots, 10, pp. 185-202; Shi, D., Collins, E.G., Jr., Goldiez, B., Donate, A., Liu, X., Dunlap, D., Human-aware robot motion planning with velocity constraints (2008) International Symposium on Collaborative Technologies and Systems, pp. 490-497; Trulls, E., Murtra, A.C., Perez-Ibarz, J., Ferrer, G., Vasquez, D., Mirats-Tur, J.M., Sanfeliu, A., Autonomous navigation for mobile service robots in urban pedestrian environments (2011) Journal of Field Robotics, 28 (3), pp. 329-354; Trautman, P., Probabilistic tools for human-robot cooperation (2012) Human Agent Robot Teamwork Workshop Hri; 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Anvari, B., A mathematical model for driver and pedestrian interaction in shared space environments (2012) The Universities Transport Study Group (UTSG); Anvari, B., Daamen, W., Knoop, V.L., Hoogendoorn, S.P., Bell, M.G.H., Shared space modeling based on social forces and distance potential field (2014) Pedestrian and Evacuation Dynamics, pp. 907-916; Anvari, B., Bell, M.G., Sivakumar, A., Ochieng, W.Y., Modelling shared space users via rule-based social force model (2015) Transportation Research Part C: Emerging Technologies, 51, pp. 83-103; Anvari, B., Bell, M.G., Angeloudis, P., Ochieng, W.Y., Calibration and validation of a shared space model: Case study (2016) Transportation Research Record: Journal of the Transportation Research Board, (2588), pp. 43-52; Anvari, B., Nip, C.K., Majumdar, A., Toward an accurate microscopic passenger train evacuation model using MassMotion (2017) 95th Annual Meeting of the Transportation Research Board, , 2017. The National Academies Press; Brscic, D., Drazen, K.T., Ikeda, T., Miyashita, T., Person tracking in large public spaces using 3D range sensors (2013) Ieee Transactions on Human-Machine Systems, 43 (6), pp. 522-534; Kato, Y., Kanda, T., Ishiguro, H., May I help you?: Design of human-like polite approaching behavior (2015) ACM/IEEE International Conference on Human-Robot Interaction, pp. 35-42; Kidokoro, H., Kanda, T., Brscic, D., Shiomi, M., Simulation-based behavior planning to prevent congestion of pedestrians around a robot (2013) Ieee Transactions on Robotics, 31 (6), pp. 1419-1431; Chen, X., Treiber, M., Kanagaraj, V., Li, H., Social force models for pedestrian traffic-state of the art (2017) Transport Reviews, pp. 1-29; Lewin, K., (1951) Field Theory in Social Science: Selected Theoretical Papers, , New York:Harper &Row: D. Cartwright (Ed.); Zhang, X., Wang, Y., Yu, H., Extended social force model-based mean shift for pedestrian tracking under obstacle avoidance (2016) Iet Computer Vision, 11 (1), pp. 1-9; Helbing, D., Molnar, P., Social force model for pedestrian dynamics (1995) Physical Review E, 51 (5), p. 4282; Johansson, A., Helbing, D., Shukla, P.K., Specification of a microscopic pedestrian model by evolutionary adjustment to video tracking data (2008) Advances in Complex System at World Scientific Publishing Company, 25; Walters, M.L., The Design Space for Robot Appearance and behavior for Social Robot Companions (2008) University of Hertfordshire, Dissertation; Takayama, L., Pantofaru, C., Influences on proxemic behaviors in human-robot interaction (2009) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5495-5502; Molnár, P., Modellierung und Simulation der Dynamik von Fußgängerströmen (1995) PhD Thesis; Ferrer, G., Sanfeliu, A., Proactive kinodynamic planning using the extended social force model and human motion prediction in urban environments (2014) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1730-1735; Seer, S., Rudloff, C., Matyus, T., Brändle, N., Validating social force based models with comprehensive real world motion data (2014) Transportation Research Procedia., 2, pp. 724-732; Zanlungo, F., Ikeda, T., Kanda, T., Social force model with explicit collision prediction (2011) Epl (Europhysics Letters), 93 (6), p. 68005; Rios-Martinez, J., Spalanzani, A., Laugier, E.C., From Proxemics theory to socially-aware navigation: A survey (2015) International Journal of Social Robotics, 7 (2), pp. 137-153; Dragan, A.D., Srinivasa, S., Generating legible motion (2013) Proc.Robotics: Science and Systems; Khambhaita, H., Alami, R., Assessing the social criteria for humanrobot collaborative navigation: A comparison of human-aware navigation planners 26th Ieee International Symposium on Robot and Human Interactive Communication, p. 11401145. , 2017. ublisher= Springer","Anvari, B.; University of Southampton, United Kingdom; email: b.anvari@ucl.ac.uk",,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102399215
"Chacko S.M., Granado A., Rajkumar A., Kapila V.","57195962656;57219370960;57211426446;7005709613;","An augmented reality spatial referencing system for mobile robots",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340742","4446","4452",,,"10.1109/IROS45743.2020.9340742","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102397396&doi=10.1109%2fIROS45743.2020.9340742&partnerID=40&md5=2ff5e75f0e9641a0f8bbfb2e91173c14","NYU Tandon School of Engineering, Mechatronics, Controls, and Robotics Laboratory (MCRL), Mechanical and Aerospace Engineering, Brooklyn, NY  11201, United States","Chacko, S.M., NYU Tandon School of Engineering, Mechatronics, Controls, and Robotics Laboratory (MCRL), Mechanical and Aerospace Engineering, Brooklyn, NY  11201, United States; Granado, A., NYU Tandon School of Engineering, Mechatronics, Controls, and Robotics Laboratory (MCRL), Mechanical and Aerospace Engineering, Brooklyn, NY  11201, United States; Rajkumar, A., NYU Tandon School of Engineering, Mechatronics, Controls, and Robotics Laboratory (MCRL), Mechanical and Aerospace Engineering, Brooklyn, NY  11201, United States; Kapila, V., NYU Tandon School of Engineering, Mechatronics, Controls, and Robotics Laboratory (MCRL), Mechanical and Aerospace Engineering, Brooklyn, NY  11201, United States","The deployment of a mobile service robot in domestic settings is a challenging task due to the dynamic and unstructured nature of such environments. Successful operation of the robot requires continuous human supervision to update its spatial knowledge about the dynamic environment. Thus, it is essential to develop a human-robot interaction (HRI) strategy that is suitable for novice end users to effortlessly provide task-specific spatial information to the robot. Although several approaches have been developed for this purpose, most of them are not feasible or convenient for use in domestic environments. In response, we have developed an augmented reality (AR) spatial referencing system (SRS), which allows a non-expert user to tag any specific locations on a physical surface to allocate tasks to be performed by the robot at those locations. Specifically, in the AR-SRS, the user provides a spatial reference by creating an AR virtual object with a semantic label. The real-world location of the user-created virtual object is estimated and stored as spatial data along with the user-specified semantic label. We present three different approaches to establish the correspondence between the user-created virtual object locations and the real-world coordinates on an a priori static map of the service area available to the robot. The performance of each approach is evaluated and reported. We also present use-case scenarios to demonstrate potential applications of the AR-SRS for mobile service robots. © 2020 IEEE.",,"Agricultural robots; Augmented reality; Intelligent robots; Interactive computer systems; Location; Mobile robots; Mobile telecommunication systems; Semantics; Domestic environments; Dynamic environments; Human robot Interaction (HRI); Mobile service robots; Spatial informations; Spatial reference; Spatial referencing; Specific location; Human robot interaction",,,,,"National Science Foundation, NSF: DRL-1417769; EEC-1542286, DRL-1614085, 76156-10488","Work supported in part by the National Science Foundation under a DRK-12 grant DRL-1417769, an ITEST grant DRL-1614085, and an RET Site grant EEC-1542286, and a NY Space Grant Consortium grant 76156-10488.",,,,,,,,,,"Kim, H., User-centered approach to path planning of cleaning robots: Analyzing user's cleaning behavior (2007) ACM/IEEE Int. Conf. On Human Robot Interaction (HRI), pp. 373-380; Zhao, C., Mei, W., Pan, W., Building a grid-semantic map for the navigation of service robots through human-robot interaction (2015) Digital Communications and Networks, 1 (4), pp. 253-266; Kemp, C.C., A point-and-click interface for the real world: Laser designation of objects for mobile manipulation (2008) ACM/IEEE Int. Conf. On Human Robot Interaction (HRI), pp. 241-248; Ishii, K., Designing laser gesture interface for robot control (2009) IFIP Conf. On Human-Computer Interaction (INTERACT), pp. 479-492; Abidi, S., Williams, M., Johnston, B., Human pointing as a robot directive (2013) ACM/IEEE Int. Conf. On Human Robot Interaction (HRI), pp. 67-68; Tolgyessy, M., Foundations of visual linear human-robot interaction via pointing gesture navigation (2017) Int. J. of Social Robotics, 9 (4), pp. 509-523; Rasch, R., Tidy up my room: Multi-agent cooperation for service tasks in smart environments (2019) J. of Ambient Intelligence and Smart Environments, 11 (3), pp. 261-275; Sakamoto, D., Sketch and run: A stroke-based interface for home robots (2009) SIGCHI Conf. On Human Factors in Computing Systems (CHI), pp. 197-200; Frank, J.A., Sahasrabudhe, Y., Kapila, V., An augmented reality approach for reliable autonomous path navigation of mobile robots (2015) Indian Control Conf. (ICC), pp. 437-443; Frank, J.A., Kapila, V., Path bending: Interactive human-robot interfaces with collision-free correction of user-drawn paths (2015) Int. Conf. On Intelligent User Interfaces (IUI), pp. 186-190; Milgram, P., Applications of augmented reality for human-robot communication (1993) Int. Conf. On Intelligent Robots and Systems (IROS), 3, pp. 1467-1472; Green, S.A., Human-robot collaboration: A literature review and augmented reality approach in design (2008) Int. J. of Advanced Robotic Systems, 5 (1), pp. 1-18; Sunao, H., TouchMe: An augmented reality based remote robot manipulation (2011) Int. Conf. On Artificial Reality and Telexistence (ICAT); Sprute, D., A framework for interactive teaching of virtual borders to mobile robots (2007) IEEE Int. Symp. Robot and Human Interactive Communication (RO-MAN), pp. 1175-1181; Kasahara, S., ExTouch: Spatially-aware embodied manipulation of actuated objects mediated by augmented reality (2013) ACM Int. Conf. On Tangible, Embedded, and Embodied Interaction (TEI), pp. 223-228; Frank, J.A., Krishnamoorthy, S.P., Kapila, V., Toward mobile mixed-reality interaction with multi-robot systems (2017) IEEE Robotics and Automation Letter, 2, pp. 1901-1908; Chen, L., PinpointFly: An egocentric position-pointing drone interface using mobile AR (2019) SIGGRAPH Asia Emerging Technologies, pp. 34-35; Sprute, D., Tönnies, K., König, M., Virtual borders: Accurate definition of a mobile robot's workspace using augmented reality (2018) Int. Conf. On Intelligent Robots and Systems (IROS), pp. 8574-8581; Cao, Y., V. Ra: An in-situ visual authoring system for robot-IoT task planning with augmented reality (2019) Designing Interactive Systems Conference (DIS), pp. 1059-1070; Chacko, S.M., Kapila, V., An augmented reality interface for human-robot interaction in unconstrained environments (2019) Int. Conf. On Intelligent Robots and Systems (IROS), pp. 3222-3228; Zollmann, S., Flyar: Augmented reality supported micro aerial vehicle navigation (2014) IEEE Trans. On Visualization and Computer Graphics (TVCG), 20 (4), pp. 560-568; Chacko, S.M., Kapila, V., Augmented reality as a medium for human-robot collaborative tasks (2019) IEEE Int. Symp. Robot and Human Interactive Communication (RO-MAN); Grisetti, G., Stachniss, C., Burgard, W., Improved techniques for grid mapping with Rao-Blackwellized particle filter (2007) IEEE Tran. On Robotics, 23 (1), pp. 34-46; Fox, D., Adapting the sample size in particle filters through KLDsampling (2003) The Int. J. of Robotics Research, 22 (12), pp. 985-1003; Iso 9283, manipulating industrial robots (1998) Performance Criteria and Related Test Methods, Int. Organization for Standardization; Roewekaemper, J., On the position accuracy of mobile robot localization based on particle filters combined with scan matching (2012) Int. Conf. On Intelligent Robots and Systems (IROS), pp. 3158-3164",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102397396
"Schlosser P., Ledermann C.","57216366892;8872955700;","Using diverse neural networks for safer human pose estimation: Towards making neural networks know when they don't know",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341634","10305","10312",,,"10.1109/IROS45743.2020.9341634","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102394821&doi=10.1109%2fIROS45743.2020.9341634&partnerID=40&md5=a54b3b58e8dcb0bc7c47d9f6fc3d25b1","Karlsruhe Institute of Technology (KIT), Institute of Anthropomatics and Robotics (IAR-IPR), Intelligent Process Automation and Robotics Lab, Karlsruhe, 76131, Germany","Schlosser, P., Karlsruhe Institute of Technology (KIT), Institute of Anthropomatics and Robotics (IAR-IPR), Intelligent Process Automation and Robotics Lab, Karlsruhe, 76131, Germany; Ledermann, C., Karlsruhe Institute of Technology (KIT), Institute of Anthropomatics and Robotics (IAR-IPR), Intelligent Process Automation and Robotics Lab, Karlsruhe, 76131, Germany","In recent years, human pose estimation has seen great improvements by the use of neural networks. However, these approaches are unsuitable for safety-critical applications such as human-robot interaction (HRI), as no guarantees are given whether a produced detection is correct or not and false detections with high confidence scores are produced on a regular basis.In this work, we propose a method to identify and eliminate false detections by comparing keypoint detections from different neural networks and assigning a 'Don't know' label in the case of a mismatch. Our approach is driven by the principle of software diversity, a technique recommended by the safety standard IEC 61508-7 [1] for dealing with software implementation faults. We evaluate our general concept on the MPII human pose dataset [2] using available ground truth data to calculate a suitable threshold for our keypoint comparison, reducing the number of false detections by approx. 61%. For the application at runtime, where no ground truth data is available, we introduce a method to calculate the needed threshold directly from keypoint detections. In further experiments, it was possible to reduce the number of false detections by approx. 75%. Eliminating keypoints by comparison also lowers the correct detection rate, which we maintained above 75% in all experiments. As this effect is limited and non-critical regarding safety we believe that the proposed approach can lead the way to a safe use of neural networks for human pose estimation in the future. © 2020 IEEE.",,"Agricultural robots; Human robot interaction; Intelligent robots; Safety engineering; False detections; Ground truth data; Human pose estimations; Human robot Interaction (HRI); Keypoint detection; Safety critical applications; Software diversity; Software implementation; Neural networks",,,,,"Karlsruhe Institute of Technology, KIT","This work was funded by the Ministry of Economics, Work and Housing of the State of Baden-Württemberg in the research project ’RoboShield’ 1Intelligent Process Automation and Robotics Lab, Institute of An-thropomatics and Robotics (IAR-IPR), Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany. Corresponding author: Patrick Schlosser (patrick.schlosser@kit.edu)",,,,,,,,,,"(2010) Functional Safety of Electrical/electronic/programmable Electronic Safety-related Systems-Part 7: Overview of Techniques and Measures, International Electrotechnical Commission (IEC), , Std. IEC 61 508-7: 2010; Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B., 2D human pose estimation: New benchmark and state of the art analysis (2014) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3686-3693; Nguyen, D.H.P., Hoffmann, M., Roncone, A., Pattacini, U., Metta, G., Compact real-time avoidance on a humanoid robot for human-robot interaction (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 416-424; (2019) Mpii Human Pose Dataset Results, , http://human-pose.mpi-inf.mpg.de/#results, Acessed: 2019-12-11; Pishchulin, L., Andriluka, M., Gehler, P., Schiele, B., Strong appearance and expressive spatial models for human pose estimation (2013) Proceedings of the IEEE International Conference on Computer Vision, pp. 3487-3494; Tompson, J.J., Jain, A., LeCun, Y., Bregler, C., Joint training of a convolutional network and a graphical model for human pose estimation (2014) Advances in Neural Information Processing Systems, pp. 1799-1807; Su, Z., Ye, M., Zhang, G., Dai, L., Sheng, J., (2019) Cascade Feature Aggregation for Human Pose Estimation; Ruggero Ronchi, M., Perona, P., Benchmarking and error diagnosis in multi-instance pose estimation (2017) Proceedings of the IEEE International Conference on Computer Vision, pp. 369-378; Hein, M., Andriushchenko, M., Bitterwolf, J., Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem (2019) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 41-50; Johnson, S., Everingham, M., Learning effective human pose estimation from inaccurate annotation (2011) CVPR 2011. IEEE, pp. 1465-1472; Yang, Y., Ramanan, D., Articulated human detection with flexible mixtures of parts (2012) IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (12), pp. 2878-2890; Toshev, A., Szegedy, C., Deeppose: Human pose estimation via deep neural networks (2014) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1653-1660; Tompson, J., Goroshin, R., Jain, A., LeCun, Y., Bregler, C., Efficient object localization using convolutional networks (2015) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 648-656; Newell, A., Yang, K., Deng, J., Stacked hourglass networks for human pose estimation (2016) European Conference on Computer Vision, pp. 483-499. , Springer; Cao, Z., Hidalgo, G., Simon, T., Wei, S., Sheikh, Y., Openpose: Realtime multi-person 2D pose estimation using part affinity fields (2019) IEEE Transactions on Pattern Analysis and Machine Intelligence; Zhang, F., Zhu, X., Ye, M., Fast human pose estimation (2019) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3517-3526; Carreira, J., Agrawal, P., Fragkiadaki, K., Malik, J., Human pose estimation with iterative error feedback (2016) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4733-4742; Ferrari, V., Marin-Jimenez, M., Zisserman, A., Progressive search space reduction for human pose estimation (2008) 2008 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp. 1-8; (2020) Coco Keypoints Evaluation, , http://cocodataset.org/#keypoints-eval, Accessed: 2020-02-25; Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L., Microsoft coco: Common objects in context (2014) European Conference on Computer Vision, pp. 740-755. , Springer; Moosavi-Dezfooli, S.-M., Fawzi, A., Fawzi, O., Frossard, P., Universal adversarial perturbations (2017) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1765-1773; Antônio C, D.A., Martinez, A.S., Schirru, R., A neural model for transient identification in dynamic processes with ""don't know"" response (2003) Annals of Nuclear Energy, 30 (13), pp. 1365-1381; Pinheiro, V.H.C., Dos Santos, M.C., Do Desterro, F.S.M., Schirru, R., Pereira, C.M.D.N.A., Nuclear power plant accident identification system with"" don't know"" response capability: Novel deep learning-based approaches (2020) Annals of Nuclear Energy, 137, p. 107111; Simonyan, K., Zisserman, A., Two-stream convolutional networks for action recognition in videos (2014) Advances in Neural Information Processing Systems, pp. 568-576; Kawana, Y., Ukita, N., Huang, J.-B., Yang, M.-H., Ensemble convolutional neural networks for pose estimation (2018) Computer Vision and Image Understanding, 169, pp. 62-74; Winter, D.A., (2009) Biomechanics and Motor Control of Human Movement, , John Wiley & Sons","Schlosser, P.; Karlsruhe Institute of Technology (KIT), Germany; email: patrick.schlosser@kit.edu",,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102394821
"Cullinan M.F., Bourke E., Kelly K., McGinn C.","56377589000;57192948569;55734347726;36651232500;","Dynamic characterization and phenomenological modeling of sleeve pneumatic artificial muscles",2020,"Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME","142","10","101005","","",,,"10.1115/1.4047446","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096903604&doi=10.1115%2f1.4047446&partnerID=40&md5=093ff83ce7ee50b295be4d86058c2c0c","Department of Mechanical and Manufacturing Engineering, University of Dublin, Trinity College, Dublin 2, Ireland","Cullinan, M.F., Department of Mechanical and Manufacturing Engineering, University of Dublin, Trinity College, Dublin 2, Ireland; Bourke, E., Department of Mechanical and Manufacturing Engineering, University of Dublin, Trinity College, Dublin 2, Ireland; Kelly, K., Department of Mechanical and Manufacturing Engineering, University of Dublin, Trinity College, Dublin 2, Ireland; McGinn, C., Department of Mechanical and Manufacturing Engineering, University of Dublin, Trinity College, Dublin 2, Ireland","Pneumatic artificial muscles (PAMs) are a compelling actuator for physical Human-robot interaction (pHRI) due to their low mass, high-force capacity, and musclelike characteristics. However, their low efficiency and bandwidth have forced mobile robotics researchers to examine alternative actuators for performing dynamic tasks like walking and grasping. Recently, the sleeve PAM, has been proposed and shown to improve the efficiency and force capacity when compared with traditional PAM designs. However, the increase in the dynamic performance of sleeve PAMs has not yet been studied. The aim of this research is to compare the dynamic performance of sleeve and traditional PAMS, and to develop a phenomenological model of their dynamic performance. Testing found that the isometric bandwidth of sleeve muscles can be 100% greater than that of traditional muscles at rest length if pressure response is considered, although this improvement decreases with contraction. If force is instead considered, the increase in bandwidth is even greater (up to 120% greater than that of a traditional PAM). The volume of both PAMs was determined using an experimental method, and a phenomenological model was fitted. When these models were used to simulate the performance of a PAM-actuated system, it was shown that both approximate the behavior of the measured system with good accuracy. Finally, a proposed implementation is given which illustrates how the benefits of the sleeved PAM actuator design could be realized in a practical robotics application. © 2020 by ASME.",,"Bandwidth; Efficiency; Human robot interaction; Mechanisms; Muscle; Pneumatic drives; Pneumatics; Robotics; Dynamic characterization; Dynamic performance; Experimental methods; Phenomenological modeling; Physical humanrobot interaction (phri); Pneumatic artificial muscle; Pressure response; Robotics applications; Pneumatic actuators",,,,,,,,,,,,,,,,"Vanderborght, B., Verrelst, B., Van Ham, R., Naudet, J., Vermeulen, J., Lefeber, D., Daerden, F., LUCY, a Bipedal Walking Robot With Pneumatic Artificial Muscles (2004) IEEE Conference on Mechatronics and Robotics, pp. 106-114. , Aachen, Germany; Eichhorn, M., Ament, C., Nguyen, T. T., Modelling of the 4-Axis Kinematic Manipulator AirArm Driven by Pneumatic Muscle Actuators (2009) International Conference on Control and Automation, 100565, pp. 1301-1307. , Christchurch, New Zealand, Dec. 9-11; Costa, N., Bezdicek, M., Brown, M., Gray, J., Caldwell, D., Hutchins, S., Joint Motion Control of a Powered Lower Limb Orthosis for Rehabilitation (2006) IEEE Int. J. Autom. Comput, 3 (3), pp. 271-281; Andrikopoulos, G., Nikolakopoulos, G., Manesis, S., A Survey on Applications of Pneumatic Artificial Muscles (2011) 19th Mediterranean Conference on Control and Automation, pp. 1439-1446. , Corfu, Greece, June 20-23; (2016) DMSP Fluidic Muscle DMSP/MAS, , Festo, Germany; Bicchi, A., Tonietti, G., Fast and 'Soft-Arm' Tactics: Dependability in Human-Friendly Robots (2004) IEEE Robot. Autom. Mag, 11 (2), pp. 22-33; Leidner, D., Borst, C., Dietrich, A., Beetz, M., Albu-Schaffer, A., Classifying Compliant Manipulation Tasks for Automated Planning in Robotics (2015) IEEE International Conference on Intelligent Robots and System, pp. 1769-1776. , Hamburg, Germany, Sept. 28-Oct. 2; Daerden, F., Lefeber, D., Pneumatic Artificial Muscles: Actuators for Robotics and Automation (2002) Eur. J. Mech. Environ. Eng, 47 (1), pp. 11-21; Sakthivelu, V., Chong, S. H., Tan, M. H., Ghazaly, M. M., Phenomenological Modeling and Classic Control of a Pneumatic Muscle Actuator System (2016) Int. J. Control Autom, 9 (4), pp. 301-312; Zang, X., Liu, Y., Heng, S., Lin, Z., Zhao, J., Position Control of a Single Pneumatic Artificial Muscle With Hysteresis Compensation Based on Modified Prandtl-Ishlinskii Model (2017) Biomed. Mater. Eng, 28 (2), pp. 131-140; Kingsley, D. A., Quinn, R. D., Ritzmann, R. E., A Cockroach Inspired Robot With Artificial Muscles (2006) International Conference on Intelligent Robots and Systems, pp. 659-666. , Beijing, China, Oct. 9-15; Davis, S., Caldwell, D., Pneumatic Muscle Actuators for Humanoid applications-Sensor and Valve Integration (2006) Sixth IEEE-RAS International Conference on Humanoid Robot, pp. 456-461. , Genova, Italy, Dec. 4-6; Chou, C.-P., Hannaford, B., Measurement and Modelling of McKibben Pneumatic Artificial Muscles (1996) Robot. Autom. IEEE Trans, 12 (1), pp. 90-102; Meller, M. A., Bryant, M., Garcia, E., Reconsidering the McKibben Muscle: Energetics, Operating Fluid, and Bladder Material (2014) J. Intell. Mater. Syst. Struct, 25 (18), pp. 2276-2293; McGinn, C., Cullinan, M. F., Otubela, M., Kelly, K., Design of a Terrain Adaptive Wheeled Robot for Human-Orientated Environments (2018) Auton. Robots, 43, pp. 63-78; Davis, S., Canderle, J., Artrit, P., Tsagarakis, N., Caldwell, D. G., Enhanced Dynamic Performance in Pneumatic Muscle Actuators (2002) Proceedings of 2002 IEEE International Conference on Robotics Automation, 3, pp. 2836-2841. , Washington, DC, May 11-15; Davis, S., Tsagarakis, N., Canderle, J., Caldwell, D. G., Enhanced Modelling and Performance in Braided Pneumatic Muscle Actuators (2003) Int. J. Rob. Res, 22 (3-4), pp. 213-227; Driver, T., Shen, X., Sleeve Muscle Actuator: Concept and Prototype Demonstration (2013) J. Bionic Eng, 10 (2), pp. 222-230; Driver, T., Shen, X., Design and Control of a Sleeve Muscle-Actuated Robotic Elbow (2014) ASME J. Dyn. Syst. Meas. Control, 136 (4), p. 041023; Zheng, H., Shen, X., Sleeve Muscle Actuator and Its Application in Transtibial Prostheses (2013) IEEE Int. Conf. Rehabil. Rob, 18 (11), pp. 1492-1501; Zheng, H., Shen, X., Double-Acting Sleeve Muscle Actuator for Bio-Robotic Systems (2013) Actuators, 2 (4), pp. 129-144; Cullinan, M. F., Bourke, E., Kelly, K., McGinn, C., A Mckibben Type Sleeve Pneumatic Muscle and Integrated Mechanism for Improved Stroke Length (2017) ASME J. Mech. Rob, 9 (1), p. 011013; Reynolds, D. B., Repperger, D. W., Phillips, C. A., Bandry, G., Modeling the Dynamic Characteristics of Pneumatic Muscle (2003) Ann. Biomed. Eng, 31 (3), pp. 310-317; (2015) STA-1 Aluminium S Type Tension & Compression Load Cell, , LCM Systems Ltd., LCM Systems Ltd., New Port, UK; (2011) DSC Digital Strain Gauge to Data Converter, , Mantracourt Electronics Ltd., Mantracourt Electronics Ltd., Exeter, UK; (2014) TruStability Board Mount Pressure Sensors HSC Series- High Accuracy, Compensated/Amplified, , Honeywell, Charlotte, NC; (2014) Position Transducer Potentiometric Up to 150mm Series T/TS, , Novotechnik U.S, Inc., Southborough, MA; Matrix Sp, A, (2017) Solenoid Valves 720 Series 2/2 3/2, , Italy; Sirotti, M., (2016) Tubo in Lattice 'DRELAX' (TDS 1416), , ALGAM I.A.I, Novate Milanese MI, Italy; Klute, G. K., Hannaford, B., Fatigue Characteristics of McKibben Artificial Muscle Actuators (1998) IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications, pp. 1776-1781. , Victoria, BC, Canada, Oct. 17; Woods, B. K., Gentry, M. F., Kothera, C. S., Wereley, N. M., Fatigue Life Testing of Swaged Pneumatic Artificial Muscles as Actuators for Aerospace Applications (2012) J. Intell. Mater. Syst. Struct, 23 (3), pp. 327-343; Bubert, E. A., (2009) Highly Extensible Skin for a Variable Wing-Span Morphing Aircraft Utilizing Pneumatic Artificial Muscle, , Masters dissertation, University of Maryland, College Park, MD; Serres, J. L., Reynolds, D. B., Phillips, C. A., Gerschutz, M. J., Repperger, D. W., Characterisation of a Phenomenological Model for Commercial Pneumatic Muscle Actuators (2009) Comput. Methods Biomech. Biomed. Eng, 12 (4), pp. 423-430; Serres, J. L., (2008) Dynamic Characterization of a Pneumatic Muscle Actuator and Its Application to a Resistive Training Device, , Ph.D. dissertation, Wright State University, Dayton, OH; Serres, J. L., Reynolds, D. B., Phillips, C. A., Rogers, D. B., Repperger, D.W., Characterisation of a Pneumatic Muscle Test Station With Two Dynamic Plants in Cascade (2010) Comput. Methods Biomech. Biomed. Eng, 13 (1), pp. 11-18; (2014) ISO6358-Pneumatic Fluid power-Determination of Flow-Rate Characteristics of Components Using Compressible Fluids, , International Standards Organisation (ISO), Geneva, Switzerland; Matrix Sp, A, CURVE di Porta BX721 Serie (2018) Italy, p. 7; Richer, E., Hurmuzlu, Y., A High Performance Pneumatic Force Actuator System-Part II: Nonlinear Controller Design (2000) ASME J. Dyn. Syst. Meas. Control, 122 (3), pp. 426-434; Woods, B. K. S., Kothera, C. S., Wang, G., Wereley, N. M., Dynamics of a Pneumatic Artificial Muscle Actuation System Driving a Trailing Edge Flap (2014) Smart Mater. Struct, 23 (9), p. 095014; Kim, Y. J., Design of Low Inertia Manipulator With High Stiffness and Strength Using Tension Amplifying Mechanisms (2015) IEEE International Conference on Intelligence and Robotic System, pp. 5850-5856. , Hamburg, Germany, Sept. 28-Oct. 2; Lee, J., Kim, Y., Roh, S., Kim, J., Lee, Y., Kim, J., Choi, B., Roh, K., Tension Propagation Analysis of Novel Robotized Surgical Platform for Transumbilical Single-Port Access Surgery (2013) 2013 IEEE/RSJ International Conference on Intelligence and Robotic System, pp. 3083-3089. , Tokyo, Japan, Nov. 3-7","Cullinan, M.F.; Department of Mechanical and Manufacturing Engineering, Ireland; email: cullinmf@tcd.ie",,,"American Society of Mechanical Engineers (ASME)",,,,,00220434,,JDSMA,,"English","J Dyn Syst Meas Control Trans ASME",Article,"Final","",Scopus,2-s2.0-85096903604
"Qi J., Ding X., Li W., Han Z., Xu K.","57195515733;7401929247;57207125144;57219180553;54584606300;","Fusing hand postures and speech recognition for tasks performed by an integrated leg–arm hexapod robot",2020,"Applied Sciences (Switzerland)","10","19","6995","1","26",,1,"10.3390/app10196995","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092764160&doi=10.3390%2fapp10196995&partnerID=40&md5=5b304a19e1f5b60a325444888e958e3d","Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China; Beijing Institute of Computer and Electronics Application, Beijing, 100191, China; First Research Institute of the Ministry of Public Security of People’s Republic of China, Beijing, 100191, China","Qi, J., Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China; Ding, X., Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China; Li, W., Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China, Beijing Institute of Computer and Electronics Application, Beijing, 100191, China; Han, Z., First Research Institute of the Ministry of Public Security of People’s Republic of China, Beijing, 100191, China; Xu, K., Robotics Institute, School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China","Hand postures and speech are convenient means of communication for humans and can be used in human–robot interaction. Based on structural and functional characteristics of our integrated leg-arm hexapod robot, to perform reconnaissance and rescue tasks in public security application, a method of linkage of movement and manipulation of robots is proposed based on the visual and auditory channels, and a system based on hand postures and speech recognition is described. The developed system contains: a speech module, hand posture module, fusion module, mechanical structure module, control module, path planning module and a 3D SLAM (Simultaneous Localization and Mapping) module. In this system, three modes, i.e., the hand posture mode, speech mode, and a combination of the hand posture and speech modes, are used in different situations. The hand posture mode is used for reconnaissance tasks, and the speech mode is used to query the path and control the movement and manipulation of the robot. The combination of the two modes can be used to avoid ambiguity during interaction. A semantic understanding-based task slot structure is developed by using the visual and auditory channels. In addition, a method of task planning based on answer-set programming is developed, and a system of network-based data interaction is designed to control movements of the robot using Chinese instructions remotely based on a wide area network. Experiments were carried out to verify the performance of the proposed system. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Hand postures recognition; Hexapod robots; Human–robot interaction (HRI); Manipulation; Speech recognition",,,,,,"National Natural Science Foundation of China, NSFC: 51775011, 91748201; Natural Science Foundation of Beijing Municipality: 3192017","Funding: This work was funded by the Natural Science Foundation of Beijing Municipality (3192017) and the National Natural Science Foundation of China (Grant No. 51775011 and Grant No. 91748201).",,,,,,,,,,"Pavlovic, V., Sharma, R., Huang, T., Visual interpretation of hand gestures for human-computer interaction: A review (1997) IEEE Trans. Pattern Anal. Mach. Intell, 19, pp. 677-695. , [CrossRef]; Badi, H.S., Hussein, S., Hand posture and gesture recognition technology (2014) Neural Comput. Appl, 25, pp. 871-878. , [CrossRef]; Chang, C.C., Chen, J.J., Tai, W.K., Han, C.C., New approach for static gesture recognition (2006) J. Inf. Sci. Eng, 22, pp. 1047-1057; Stiefelhagen, R., Fogen, C., Gieselmann, P., Holzapfel, H., Nickel, K., Waibel, A., Natural human-robot interaction using speech, headNatural human-robot interaction using speech, head pose and gestures (2004) Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), , Sendai, Japan, 28 September–2 October; Stiefelhagen, R., Ekenel, H.K., Fügen, C., Gieselmann, P., Holzapfel, H., Kraft, F., Nickel, K., Ekenel, H.K., Enabling multimodal human–robot interaction for the karlsruhe humanoid robot (2007) IEEE Trans. Robot, 23, pp. 840-851. , [CrossRef]; Nickel, K., Stiefelhagen, R., Visual recognition of pointing gestures for human–robot interaction (2007) Image Vis. Comput, 25, pp. 1875-1884. , [CrossRef]; Nickel, K., Stiefelhagen, R., Real-time person tracking and pointing gesture recognition for human-robot interaction (2004) Comput. Vision Hum. Comput. Interact, 3058, pp. 28-38; Nickel, K., Stiefelhagen, R., Pointing gesture recognition based on 3D-tracking of face, hands and head orientation (2003) Proceedings of the 5th International Conference on Multimodal Interfaces, , Vancouver, BC, Canada, 5–7 November; Seemann, E., Nickel, K., Stiefelhagen, R., Head pose estimation using stereo vision for human-robot interaction (2004) Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, , Seoul, Korea, 19 May; Burger, B., Ferrané, I., Lerasle, F., Infantes, G., Two-handed gesture recognition and fusion with speech to command a robot (2011) Auton. Robot, 32, pp. 129-147. , [CrossRef]; Liu, H., Fang, T., Zhou, T., Wang, Y., Wang, L., Deep learning-based multimodal control interface for human-robot collaboration (2018) Procedia CIRP, 72, pp. 3-8. , [CrossRef]; Ghidary, S.S., Nakata, Y., Saito, H., Hattori, M., Takamori, T., Multi-modal interaction of human and home robot in the context of room map generation (2002) Auton. Robot, 13, pp. 169-184. , [CrossRef]; Rogalla, O., Ehrenmann, M., Zollner, R., Becher, R., Dillmann, R., Using gesture and speech control for commanding a robot assistant (2002) Proceedings of the 11th IEEE International Workshop on Robot and Human Interactive Communication, , Berlin, Germany, 27 September; Van Delden, S., Umrysh, M.A., Rosario, C., Hess, G., Pick-and-place application development using voice and visual commands (2012) Ind. Robot. Int. J, 39, pp. 592-600. , [CrossRef]; Van Delden, S., Umrysh, M.A., Visual detection of objects in a robotic work area using hand gestures (2011) Proceedings of the 2011 IEEE International Symposium on Robotic and Sensors Environments (ROSE), , Montreal, QC, Canada, 17–18 September; Chen, X., Qin, S., Approach to high efficient hierarchical pathfinding of indoor mobile service robots based on grid map and Floyd-Warshall algorithm (2017) Proceedings of the 2017 Chinese Automation Congress (CAC), , Jinan, China, 20–22 October; Chen, X., Qin, S., An effective approach to SLAM toward autonomous operation for a Leg/Arm composite mobile robot in unknown environment based on RGB-D images (2017) Proceedings of the 2017 Chinese Automation Congress (CAC), , Jinan, China, 20–22 October; Li, W., Xu, K., Qi, J., Ding, X., A Natural Language Processing Method of Chinese Instruction for Multi-legged Manipulating Robot (2018) Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), , Kuala Lumpur, Malaysia, 12–15 December; Law, H., Teng, Y., Russakovsky, O., Deng, J., (2019) CornerNet-Lite: Efficient Keypoint Based Object Detection, , arXiv arXiv:1904.08900; Lafferty, J., McCallum, A., Pereira, F.C., Conditional random fields: Probabilistic models for segmenting and labeling sequence data (2001) Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), , Williams College, Williamstown, MA, USA, 28 June–1 July","Xu, K.; Robotics Institute, China; email: xk007@buaa.edu.cn",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85092764160
"Atashzar S.F., Huang H.-Y., Duca F.D., Burdet E., Farina D.","12040259900;57207276734;57218488959;7004358183;7004908025;","Energetic Passivity Decoding of Human Hip Joint for Physical Human-Robot Interaction",2020,"IEEE Robotics and Automation Letters","5","4","9144407","5953","5960",,2,"10.1109/LRA.2020.3010459","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089308232&doi=10.1109%2fLRA.2020.3010459&partnerID=40&md5=7fd0601f146bc21e504a080e7c12ae41","Department of Mechanical and Aerospace Engineering and Electrical and Computer Engineering, New York University (NYU), New York, NY, United States; Department of Bioengineering, Imperial College London, London, United Kingdom; Department of Electrical and Computer Engineering, Technical University of Munich, München, Germany","Atashzar, S.F., Department of Mechanical and Aerospace Engineering and Electrical and Computer Engineering, New York University (NYU), New York, NY, United States; Huang, H.-Y., Department of Bioengineering, Imperial College London, London, United Kingdom; Duca, F.D., Department of Electrical and Computer Engineering, Technical University of Munich, München, Germany; Burdet, E., Department of Mechanical and Aerospace Engineering and Electrical and Computer Engineering, New York University (NYU), New York, NY, United States; Farina, D., Department of Mechanical and Aerospace Engineering and Electrical and Computer Engineering, New York University (NYU), New York, NY, United States","The capacity of the biomechanics of human limbs to absorb energy during physical human-robot interaction (pHRI) can play an imperative role in controlling the performance of human-centered robotics systems. Using the concept of 'excess of passivity,' we have recently designed passivity signature maps for elbow and wrist joints. We have also shown that this knowledge can be exploited and extrapolated during the interaction with a robotic system by transparency-maximized algorithms. A major application is in robotic rehabilitation systems and assistive technologies. Here, for the first time, the nonlinear energy capacitance of the hip joint and the affecting factors are decoded. This can be critical for maximizing the performance of wearable exoskeletons. Knowledge regarding energy absorption behavior can significantly help to reduce the conservatism of control algorithms. In this work, the energetic behavior is studied for three different hip angles, while perturbations were provided at three different interaction speeds. The results show that the increase in agonist and antagonist muscle contractions can consistently expand the margins of the passivity map. Additionally, by separating the effects of agonist and antagonist contractions, it was identified that the passivity margins have a correlation with the subject's posture during interaction with the robot and the correlation depends on the type of muscle contraction. A preliminary design of a stabilizer is also formulated that takes into account variable passivity behavior of the joint, in the energy domain, to enhance the performance while guaranteeing pHRI stability. © 2016 IEEE.","Biomechanics; human-robot interaction; rehabilitation robotics; robot control","Agricultural robots; Decoding; Exoskeleton (Robotics); Hip prostheses; Joints (anatomy); Man machine systems; Muscle; Absorption behaviors; Assistive technology; Human centered robotics; Muscle contractions; Physical human-robot interactions; Physical humanrobot interaction (phri); Preliminary design; Robotic rehabilitation; Social robots",,,,,"National Science Foundation, NSF: 2031594; Horizon 2020 Framework Programme, H2020: 779982, 871767; European Commission, EC","Manuscript received February 24, 2020; accepted June 27, 2020. Date of publication July 20, 2020; date of current version July 31, 2020. This letter was recommended for publication by Associate Editor M. Malvezzi and Editor Allison M. Okamura upon evaluation of the reviewers’ comments. This work was supported in part by the European Commission under Grant H2020-ICT 779982 EXTEND and Grant 871767 REHYB, and in part by the USA National Science Foundation Award 2031594. (Atashzar and Huang are equally contributed to this work.) (Corresponding author: S. Farokh Atashzar.) S. Farokh Atashzar is with the Department of Mechanical and Aerospace Engineering and Electrical and Computer Engineering, New York University (NYU), New York, NY 10003 USA, and also with the NYU Wireless, Brooklyn, NY 11201 USA (e-mail: f.atashzar@nyu.edu).",,,,,,,,,,"Atashzar, S.F., Shahbazi, M., Patel, R.V., Haptics-enabled interactive neurorehabilitation mechatronics: Classification, functionality, challenges and ongoing research (2019) Mechatronics, 57, pp. 1-19; Avraham, C., Nisky, I., The effect of tactile augmentation on manipulation and grip force control during force-field adaptation (2020) J. NeuroEng. 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World Haptics Conf., pp. 695-700. , Apr; Khalil, H.K., Grizzle, J., (2002) Nonlinear Systems, 3. , Englewood Cliffs, NJ, USA: Prentice-Hall; Lawn, C.A., Hannaford, B., Performance testing of passive communication and control in teleoperation with time delay (1993) Proc. IEEE Int. Conf. Robot. Autom., pp. 776-783; Shahbazi, M., Atashzar, S.F., Tavakoli, M., Patel, R.V., Position-force domain passivity of the human arm in telerobotic systems (2018) IEEE/ASME Trans. Mechatronics, 23 (2), pp. 552-562. , Apr; Yang, C., Wang, X., Li, Z., Li, Y., Su, C.-Y., Teleoperation control based on combination of wave variable and neural networks (2017) IEEE Trans. Syst., Man, Cybern.: Syst., 47 (8), pp. 2125-2136. , Aug; Hashtrudi-Zaad, K., Salcudean, S., Transparency in time-delayed systems and the effect of local force feedback for transparent teleoperation (2002) IEEE Trans. Robot. Autom., 18 (1), pp. 108-114. , Feb; Niemeyer, G., Slotine, J.-J.E., Telemanipulation with time delays (2004) Int. J. 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Autom., 20 (2), pp. 365-373. , Apr; Atashzar, S.F., Shahbazi, M., Tavakoli, M., Patel, R.V., A grasp-based passivity signature for haptics-enabled human-robot interaction: Application to design of a new safety mechanism for robotic rehabilitation (2017) Int. J. Robot. Res., 36 (5-7), pp. 778-799; Atashzar, S.F., Polushin, I.G., Patel, R.V., A small-gain approach for nonpassive bilateral telerobotic rehabilitation: Stability analysis and controller synthesis (2017) IEEE Trans. Robot., 33 (1), pp. 49-66. , Feb; Atashzar, S.F., Shahbazi, M., Tavakoli, M., Patel, R.V., A passivity-based approach for stable patient-robot interaction in hapticsenabled rehabilitation systems: Modulated time-domain passivity control (2017) IEEE Trans. Control Syst. Technol., 25 (3), pp. 991-1006. , May; Tanner, N.A., Niemeyer, G., High-frequency acceleration feedback in wave variable telerobotics (2006) IEEE/ASME Trans. 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Eng., 28 (5), pp. 1138-1145. , May","Atashzar, S.F.; Department of Mechanical and Aerospace Engineering and Electrical and Computer Engineering, United States; email: f.atashzar@nyu.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85089308232
"Sakurai E., Kurashige K., Tsuruta S., Sakurai Y., Knauf R., Damiani E., Kutics A., Frati F.","57201291229;7003427849;7006779373;16231381600;6701888422;57195375517;6602966535;14819178000;","Embodiment matters: toward culture-specific robotized counselling",2020,"Journal of Reliable Intelligent Environments","6","3",,"129","139",,1,"10.1007/s40860-020-00109-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087487620&doi=10.1007%2fs40860-020-00109-y&partnerID=40&md5=bbf9e8b747887c35e9498c0749840555","School of Management, Sanno University, Tokyo, Japan; Department of Information and Electronic Engineering, Muroran Institute of Technology, Muroran, Japan; Tokyo Denki University, Inzai, Japan; School of Interdisciplinary Mathematical Sciences, Meiji University, Tokyo, Japan; Fakultät für Informatik und Automatisierung, Technische Universität Ilmenau, Ilmenau, Germany; Cyber-Physical Systems Center (C2PS), Khalifa University, Abu Dhabi, United Arab Emirates; Computer Science Department, Università degli Studi di Milano, Milan, Italy; College of Liberal Arts, International Christian University, Tokyo, Japan","Sakurai, E., School of Management, Sanno University, Tokyo, Japan; Kurashige, K., Department of Information and Electronic Engineering, Muroran Institute of Technology, Muroran, Japan; Tsuruta, S., Tokyo Denki University, Inzai, Japan; Sakurai, Y., School of Interdisciplinary Mathematical Sciences, Meiji University, Tokyo, Japan; Knauf, R., Fakultät für Informatik und Automatisierung, Technische Universität Ilmenau, Ilmenau, Germany; Damiani, E., Cyber-Physical Systems Center (C2PS), Khalifa University, Abu Dhabi, United Arab Emirates, Computer Science Department, Università degli Studi di Milano, Milan, Italy; Kutics, A., College of Liberal Arts, International Christian University, Tokyo, Japan; Frati, F., Computer Science Department, Università degli Studi di Milano, Milan, Italy","In this paper, we propose adding the traditional Japanese nodding behavior to the repertoire of social movements to be used in the context of human–robot interaction. Our approach is motivated by the notion that in many cultures, trust-building can be boosted by small body gestures. We discuss the integration of a robot capable of such movements within CRECA, our context-respectful counseling agent. The frequent nodding called “unazuki” in Japan, often accompanying the “un-un” sound (meaning “I agree”) of Japanese onomatopoeia, underlines empathy and embodies unconditioned approval. We argue that “unazuki” creates more empathy and promotes longer conversation between the robotic counsellor and people. We set up an experiment involving ten subjects to verify these effects. Our quantitative evaluation is based on the classic metrics of utterance, adapted to support the Japanese language. Interactions featuring “unazuki” showed higher value of this metrics. Moreover, subjects assessed the counselling robot’s trustworthiness and kindness as “very high” (Likert scale: 5.5 versus 3—4.5) showing the effect of social gestures in promoting empathetic dialogue to general people including the younger generation. Our findings support the importance of social movements when using robotized agents as a therapeutic tool aimed at improving emotional state and social interactions, with unambiguous evidence that embodiment can have a positive impact that warrants further exploration. The 3D printable design of our robot supports creating culture-specific libraries of social movements, adapting the gestural repertoire to different human cultures. © 2020, Springer Nature Switzerland AG.","Counselling robot; Dialog promotion; Evaluation; Implementation; Nodding (Unazuki); Social movements","3D printers; End effectors; Machine design; Social robots; Emotional state; Human cultures; Quantitative evaluation; Robot interactions; Social interactions; Social movements; Therapeutic tools; Younger generations; Economic and social effects",,,,,"Artificial Intelligence Research Promotion Foundation; Japan Society for the Promotion of Science, KAKEN: JP15K00349, JP15K00382","This work was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Numbers JP15K00349 and JP15K00382 as well as by Artificial Intelligence Research Promotion Foundation.","We thank students of Cognitive Robotics Lab, Muroran Institute of Technology, and of Distributed Intelligent Systems Lab, Tokyo Denki University, for their dedicated help with developing our system.",,,,,,,,,"Asay, T.P., Lambert, M.J., The empirical case for the common factors in therapy: Quantitative findings (1966) The Heart and Soul of Change: What Works in Therapy, , https://doi.org/10.1037/11132-001, Duncan BL, Miller SD, American Psychological Association; Chita-Tegmark, M., Scheutz, M., Assistive robots for the social management of health: A framework for robot design and human—robot interaction research (2020) Int J Soc Robot, , https://doi.org/10.1007/s12369-020-00634-z; Dix, A., Finlay, J., Abowd, G., Beale, R., (2004) Human-computer interaction, , Prentice Hall, Harlow, UK; Floridi, L., Taddeo, M., Turilli, M., Turing’s imitation game: Still an impossible challenge for all machines and some judges - an evaluation of the 2008 loebner contest (2009) Mind Mach, 19 (1), pp. 145-150; Gelso, C., Williams, E., Fretz, B., (2014) Counseling psychology, , American Psychological Association, Washington, DC; Han, S., Lee, K., Lee, D., Lee, G.G., Counseling dialog system with 5w1h extraction (2013) Proceeding of the SIGDIAL 2013 Conference, pp. 349-353. , http://www.aclweb.org/anthology/W13-4054; Hoenen, M., Lübke, K.T., Pause, B.M., Non-anthropomorphic robots as social entities on a neurophysiological level (2016) Comput Hum Behav, 57, pp. 182-186; (2018) Ibm Watson Products and Services, , https://www.ibm.com/watson/products-services/; (2017) Humanitarian future for messaging apps, , https://www.icrc.org/en/publication/humanitarian-futures-messaging-apps, Tech. rep; Ivey, A., Packard, N., Ivey, M., (2006) Basic Attending Skills, , 4, Microtraning Associates, Ann Arbor; Kang, S.H., Feng, A.W., Leuski, A., Casas, D., Shapiro, A., The effect of an animated virtual character on mobile chat interactions (2015) Proceedings of the 3Rd International Conference on Human-Agent Interaction, HAI ’15, pp. 105-112. , https://doi.org/10.1145/2814940.2814957; Kemp, J., Zhang, T., Inglis, F., Wiljer, D., Sockalingam, S., Crawford, A., Lo, B., Strudwick, G., Delivery of compassionate mental health care in a digital technology-driven age: scoping review (2020) J Med Internet Res, 22 (3); Likert, R., A technique for the measurement of attitudes (1932) Arch Psychol, 22, pp. 5-55. , https://legacy.voteview.com/pdf/Likert_1932.pdf; Lytras, M.D., Raghavan, V., Damiani, E., Big data and data analytics research: From metaphors to value space for collective wisdom in human decision making and smart machines (2017) Int J Semant Web Inf Syst, 13 (1), pp. 1-10; Mabuchi, T., Sato, J., Takeda, T., Kubota, N., Rehabilitation support system using multimodal interface device for aphasia patients (2016) Intelligent Robotics and Applications, pp. 395-404. , Springer International Publishing, Cham; Megaro, V., Thomaszewski, B., Nitti, M., Hilliges, O., Gross, M., Coros, S., Interactive design of 3d-printable robotic creatures (2015) ACM Trans Graph, 34 (6), pp. 216:1-216:9; Microsoft conversational ai tools enable developers to build (2018) Connect and Manage Intelligent Bots, , https://tinyurl.com/ybgewjmf; (2018) What are Azure Cognitive Services?, , https://docs.microsoft.com/en-us/azure/cognitive-services/welcome; Obayashi, K., Kodate, N., Masuyama, S., Measuring the impact of age, gender and dementia on communication-robot interventions in residential care homes (2020) Geriatr Gerontol Int, , https://doi.org/10.1111/ggi.13890; Osugi, T., Kawahara, J.I., Effects of head nodding and shaking motions on perceptions of likeability and approachability (2018) Perception, 47 (1), pp. 16-29; Pearce, M.J., Pargament, K.I., Oxhandler, H.K., Vieten, C., Wong, S., Novel online training program improves spiritual competencies in mental health care (2020) Spirit Clin Pract, , https://doi.org/10.1037/scp0000208; Poggi, I., D’Errico, F., Vincze, L., Types of nods. 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H. Freeman & Co, New York; Yamamoto, Y., Shinozaki, T., Tsuruta, S., Kurashige, K., Knauf, R., Nodding behavioral context respectful counseling agent (2016) 2016 World Automation Congress (WAC), pp. 1-6. , https://doi.org/10.1109/WAC.2016.7583027; Zaga, C., de Vries, R.A., Li, J., Truong, K.P., Evers, V., A simple nod of the head: The effect of minimal robot movements on children’s perception of a low-anthropomorphic robot (2017) Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI ’17, pp. 336-341. , https://doi.org/10.1145/3025453.3025995","Frati, F.; Computer Science Department, Italy; email: fulvio.frati@unimi.it",,,"Springer Science and Business Media Deutschland GmbH",,,,,21994668,,,,"English","J. Reli. Intell. Environ.",Article,"Final","",Scopus,2-s2.0-85087487620
"Barjuei E.S., Ardakani M.M.G., Caldwell D.G., Sanguineti M., Ortiz J.","56514082800;55211556600;7202685497;7003404992;16310887500;","Optimal Selection of Motors and Transmissions in Back-Support Exoskeleton Applications",2020,"IEEE Transactions on Medical Robotics and Bionics","2","3","9144503","320","330",,5,"10.1109/TMRB.2020.3010611","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097127651&doi=10.1109%2fTMRB.2020.3010611&partnerID=40&md5=c5eec5aeeca4657a63923c1984249aad","Advanced Robotic Department, Istituto Italiano di Tecnologia, Genova, Italy; Department of Automatic Control, Faculty of Engineering, Lund University, Lund, Sweden; Department of Computer Science, Bioengineering Robotics, and Systems Engineering (DIBRIS), University of Genova, Genova, Italy","Barjuei, E.S., Advanced Robotic Department, Istituto Italiano di Tecnologia, Genova, Italy; Ardakani, M.M.G., Department of Automatic Control, Faculty of Engineering, Lund University, Lund, Sweden; Caldwell, D.G., Advanced Robotic Department, Istituto Italiano di Tecnologia, Genova, Italy; Sanguineti, M., Department of Computer Science, Bioengineering Robotics, and Systems Engineering (DIBRIS), University of Genova, Genova, Italy; Ortiz, J., Advanced Robotic Department, Istituto Italiano di Tecnologia, Genova, Italy","Because of the central role the actuator unit (electrical motors and transmission parts) has in wearable robots, improving the performance of the torque/force control system is vital, particularly for exoskeletons. This paper proposes an optimal approach to the selection of the main components of an actuation system (brushless DC motor and gearbox transmission) to be used in a back-support exoskeleton, but the principles can be extended and applied to other types of exoskeletons. To perform the optimization, an analytical model based on the dynamics of human-robot interaction has been developed. Moreover, to incorporate the weight of the actuator in the optimization framework, a mathematical relation between the weight and technical characteristics of the components, based on the polynomial regression technique using the low-discrepancy sequences method are developed. Consequently, the optimization criteria in terms of the closed-loop system frequency bandwidth, system power consumption and the weight of the components are formulated by imposing technical constraints on simulation parameters. The optimization results demonstrate two possible actuator combinations. Subsequently, the selected actuator components are evaluated in a lifting scenario by means of a linear quadratic regulator (LQR) controller with double integral action. Extensive simulation results in terms of the control frequency bandwidth, torque tracking control, current produced by motors and system robustness with respect to external disturbances are presented and discussed to make comparisons between the possible combinations of the components and their feasibility in the back-support exoskeleton applications. © 2018 IEEE.","control frequency bandwidth; dynamic modeling; exoskeletons; LQR; optimization; Wearable robots","Actuators; Agricultural robots; Bandwidth; Brushless DC motors; Closed loop systems; Polynomial regression; Robots; Transmissions; Control frequency bandwidths; Extensive simulations; Linear quadratic regulator controllers; Low-discrepancy sequences; Optimization criteria; Optimization framework; Simulation parameters; Torque tracking control; Exoskeleton (Robotics)",,,,,"Consiglio Nazionale delle Ricerche, CNR: PDGP 2018/20 DIT.AD016.001; Istituto Nazionale per l'Assicurazione Contro Gli Infortuni sul Lavoro, INAIL","This work was supported by the Italian Workers? Compensation Authority (INAIL). The work of Marcello Sanguineti was supported in part by CNR (National Research Council of Italy) under Project PDGP 2018/20 DIT.AD016.001 ?Technologies for Smart Communities.?",,,,,,,,,,"Bai, S., Virk, G.S., Sugar, T., (2018) Wearable Exoskeleton Systems: Design, Control and Applications, , London, U.K.: Inst. Eng. Technol; Mikolajewska, E., Mikolajewski, D., Exoskeletons in neurological diseases-current and potential future applications (2011) Adv. Clin. Exp. Med., 20 (2), pp. 227-233; Crowell, H.P., Park, J.-H., Haynes, C.A., Neugebauer, J.M., Boynton, A.C., Design, evaluation, and research challenges relevant to exoskeletons and exosuits: A 26-year perspective from the U.S. army research laboratory (2019) Iise Trans. Occup. Ergonom. Hum. Factors, 7 (3-4), pp. 199-212; De Looze, M.P., Bosch, T., Krause, F., Stadler, K.S., O'Sullivan, L.W., Exoskeletons for industrial application and their potential effects on physical work load (2016) Ergonomics, 59 (5), pp. 671-681; Barjuei, E.S., Toxiri, S., Medrano-Cerda, G.A., Caldwell, D.G., Ortiz, J., Bond graph modeling of an exoskeleton actuator (2018) Proc. Ieee 10th Comput. Sci. Electron. Eng. (CEEC), pp. 101-106. , Colchester, U.K; Kumar, V., Hote, Y.V., Jain, S., Review of exoskeleton: History, design and control (2019) Proc. Ieee 3rd Int. Conf. Recent Develop. Control Autom. Power Eng. (RDCAPE), pp. 677-682. , Noida, India; Solis, J., Sansanayuth, T., Shojaei, E., Velocity control improvement for the human-friendly assist robot vehicle (2016) Proc. IEEE/SICE Int. Symp. Syst. Integr. (SII), pp. 331-336. , Sapporo, Japan; Li, J., Magnetically-driven medical robots: An analytical magnetic model for endoscopic capsules design (2018) J. Magn. Mater., 452, pp. 278-287. , Apr; Poliero, T., Toxiri, S., Caldwell, D.G., Ortiz, J., Actuator optimization for a back-support exoskeleton: The influence of the objective function (2018) Proc. Int. Symp. Wearable Robot., pp. 530-534; Malcolm, P., Galle, S., De Clercq, D., Fast exoskeleton optimization (2017) Science, 356 (6344), pp. 1230-1231; Mombaur, K., Optimizing design characteristics of passive and active spinal exoskeletons for challenging work tasks (2018) Proc. Wearable Robot. Challenges Trends Proc. 4th Int. Symp. Wearable Robot. (WeRob), 22, pp. 249-253. , Pisa, Italy, Oct; Zeiaee, A., Soltani-Zarrin, R., Langari, R., Tafreshi, R., Kinematic design optimization of an eight degree-of-freedom upper-limb exoskeleton (2019) Robotica, 37 (12), pp. 2073-2086; Yin, P., Yang, L., Liu, B., Design and optimization of a wearable upper limb exoskeleton based on adams (2020) Proc. Iop Conf. Mater. Sci. Eng., 717; Karavas, N., Ajoudani, A., Tsagarakis, N., Saglia, J., Bicchi, A., Caldwell, D., Tele-impedance based assistive control for a compliant knee exoskeleton (2015) Robot. Auton. Syst., 73, pp. 78-90. , Nov; Koopman, A.S., The effect of control strategies for an active backsupport exoskeleton on spine loading and kinematics during lifting (2019) J. Biomech., 91, pp. 14-22. , Jun; Zibafar, A., Ghaffari, S., Vossoughi, G., Achieving transparency in series elastic actuator of sharif lower limb exoskeleton using LLNFNARX model (2016) Proc. Ieee 4th Int. Conf. Robot. Mechatron. (ICROM), pp. 398-403. , Tehran, Iran; Dresscher, D., De Vries, T.J., Stramigioli, S., Motor-gearbox selection for energy efficiency (2016) Proc. Ieee Int. Conf. Adv. Intell. Mechatron. (AIM), pp. 669-675. , Banff, AB, Canada; Rocon, E., Pons, J.L., (2011) Exoskeletons in Rehabilitation Robotics: Tremor Suppression, 69. , Berlin, Germany: Springer-Verlag; Laschowski, B., McPhee, J., Andrysek, J., Lower-limb prostheses and exoskeletons with energy regeneration: Mechatronic design and optimization review (2019) Asme J. Mech. Robot., 11 (4). , https://doi.org/10.1115/1.4043460, Aug; Laschowski, B., McPhee, J., Andrysek, J., Lower-limb prostheses and exoskeletons with energy regeneration: Mechatronic design and optimization review (2019) J. Mech. Robot., 11 (4); Klein, J., Spencer, S., Allington, J., Bobrow, J.E., Reinkensmeyer, D.J., Optimization of a parallel shoulder mechanism to achieve a high-force, low-mass, robotic-arm exoskeleton (2010) Ieee Trans. Robot., 26 (4), pp. 710-715. , Aug; Jatsun, S., Savin, S., Yatsun, A., Improvement of energy consumption for a lower limb exoskeleton through verticalization time optimization (2016) Proc. Ieee 24th Mediterr. Conf. Control Autom. (MED), pp. 322-326. , Athens, Greece; Rezazadeh, S., Hurst, J.W., On the optimal selection of motors and transmissions for electromechanical and robotic systems (2014) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., pp. 4605-4611. , Chicago, IL, USA, Sep; Zhou, K., Doyle, J.C., Glover, K., (1996) Robust and Optimal Control, 40. , Upper Saddle River, NJ, USA: Prentice-Hall; Alessandri, A., Cervellera, C., Sanguineti, M., Design of asymptotic estimators: An approach based on neural networks and nonlinear programming (2007) Ieee Trans. Neural Netw., 18 (1), pp. 86-96. , Jan; Barjuei, E.S., Ardakani, M.M.G., Caldwell, D.G., Sanguineti, M., Ortiz, J., On the optimal selection of motors and transmissions for a back-support exoskeleton (2019) Proc. Ieee Int. Conf. Cyborg Bionic Syst. (CBS), pp. 42-47. , Munich, Germany; Barjuei, E.S., Ortiz, J., A comprehensive performance comparison of linear quadratic regulator (LQR) controller, model predictive controller (MPC), H∞ loop shaping and μ-synthesis on spatial compliant link-manipulators (2020) Int. J. Dyn. Control, , https://doi.org/10.1007/s40435-020-00640-z; Nikooyan, A.A., Zadpoor, A.A., Mass-spring-damper modelling of the human body to study running and hopping-An overview (2011) Proc. Inst. Mech. Eng. H, J. Eng. Med., 225 (12), pp. 1121-1135; Giberti, H., Cinquemani, S., Legnani, G., A practical approach to the selection of the motor-reducer unit in electric drive systems (2011) Mech. Based Design Struct. Mach., 39 (3), pp. 303-319; Cusimano, G., Optimization of the choice of the system electric drive-device-Transmission for mechatronic applications (2007) Mech. Mach. Theory, 42 (1), pp. 48-65; Barjuei, E.S., Boscariol, P., Vidoni, R., Gasparetto, A., Robust control of three-dimensional compliant mechanisms (2016) J. Dyn. Syst. Meas. Control, 138 (10); Paice, A., Moore, J., On the Youla-Kucera parametrization for nonlinear systems (1990) Syst. Control Lett., 14 (2), pp. 121-129; Pommaret, J., Quadrat, A., Generalized bezout identity (1998) Appl. Algebra Eng. Commun. Comput., 9 (2), pp. 91-116; Chang, Y.-W., Hsieh, C.-J., Chang, K.-W., Ringgaard, M., Lin, C.-J., Training and testing low-degree polynomial data mappings via linear SVM (2010) J. Mach. Learn. Res., 11, pp. 1471-1490. , Aug; Alessandri, A., Cervellera, C., Maccio, D., Sanguineti, M., Optimization based on quasi-monte carlo sampling to design state estimators for non-linear systems (2010) Optimization, 59 (7), pp. 963-984; Zoppoli, R., Sanguineti, M., Gnecco, G., Parisini, T., (2020) Neural Approximations for Optimal Control and Decision, , Cham, Switzerland: Springer; Catalá, M.M., Schroll, A., Laube, G., Arampatzis, A., Muscle strength and neuromuscular control in low-back pain: Elite athletes versus general population (2018) Front. Neurosci., 12, p. 436. , Jul; Barjuei, E.S., Boscariol, P., Gasparetto, A., Giovagnoni, M., Vidoni, R., Control design for 3D flexible link mechanisms using linearized models (2014) Advances on Theory and Practice of Robots and Manipulators, pp. 181-188. , https://doi.org/10.1007/978-3-319-07058-221, Cham, Switzerland: Springer; Bianchi, F., Localization strategies for robotic endoscopic capsules: A review (2019) Exp. Rev. Med. Devices, 16 (5), pp. 381-403","Barjuei, E.S.; Advanced Robotic Department, Italy; email: erfan.shojaei@iit.it",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,25763202,,,,"English","IEEE Trans. Med. Rob. Bion.",Article,"Final","",Scopus,2-s2.0-85097127651
"Belgiovine G., Rea F., Zenzeri J., Sciutti A.","57194709146;54386065400;35333418000;36957816700;","A Humanoid Social Agent Embodying Physical Assistance Enhances Motor Training Experience",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223335","553","560",,2,"10.1109/RO-MAN47096.2020.9223335","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095764550&doi=10.1109%2fRO-MAN47096.2020.9223335&partnerID=40&md5=764df432c70c8b12d606199da868b984","Istituto Italiano di Tecnologia, Robotics Brain and Cognitive Sciences Unit, Genova, Italy; Istituto Italiano di Tecnologia, Contact Unit, Genova, Italy","Belgiovine, G., Istituto Italiano di Tecnologia, Robotics Brain and Cognitive Sciences Unit, Genova, Italy; Rea, F., Istituto Italiano di Tecnologia, Robotics Brain and Cognitive Sciences Unit, Genova, Italy; Zenzeri, J., Istituto Italiano di Tecnologia, Robotics Brain and Cognitive Sciences Unit, Genova, Italy; Sciutti, A., Istituto Italiano di Tecnologia, Contact Unit, Genova, Italy","Skilled motor behavior is critical in many human daily life activities and professions. The design of robots that can effectively teach motor skills is an important challenge in the robotics field. In particular, it is important to understand whether the involvement in the training of a robot exhibiting social behaviors impacts on the learning and the experience of the human pupils. In this study, we addressed this question and we asked participants to learn a complex task - stabilizing an inverted pendulum - by training with physical assistance provided by a robotic manipulandum, the Wristbot. One group of participants performed the training only using the Wristbot, whereas for another group the same physical assistance was attributed to the humanoid robot iCub, who played the role of an expert trainer and exhibited also some social behaviors. The results obtained show that participants of both groups effectively acquired the skill by leveraging the physical assistance, as they significantly improved their stabilization performance even when the assistance was removed. Moreover, learning in a context of interaction with a humanoid robot assistant led subjects to increased motivation and more enjoyable training experience, without negative effects on attention and perceived effort. With the experimental approach presented in this study, it is possible to investigate the relative contribution of haptic and social signals in the context of motor learning mediated by human-robot interaction, with the aim of developing effective robot trainers. © 2020 IEEE.",,"Agricultural robots; Anthropomorphic robots; Educational robots; Machine design; Robotics; Social robots; Daily life activities; Experimental approaches; Humanoid robot; Inverted pendulum; Motor trainings; Relative contribution; Social behavior; Training experiences; Economic and social effects",,,,,"Horizon 2020 Framework Programme, H2020: 804388; European Research Council, ERC","*This work was supported by a Starting Grant from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme. G.A. No 804388, wHiSPER 1GB (giulia.belgiovine@iit.it), FR and JZ are with the Robotics, Brain and Cognitive Sciences Unit, Istituto Italiano di Tecnologia, Genova, Italy 2GB is with Department of Informatics, Bioengineering, Robotics,and System Engineering, University of Genoa, Genoa, Italy 3AS is with the CONTACT unit, Istituto Italiano di Tecnologia, Genova, Italy",,,,,,,,,,"Avila Mireles, E.J., Zenzeri, J., Squeri, V., Morasso, P., De Santis, D., Skill learning and skill transfer mediated by cooperative haptic interaction (2017) IEEE Trans. Neural Syst. Rehabil. Eng., 25 (7), pp. 832-843; Ganesh, G., Takagi, A., Osu, R., Yoshioka, T., Kawato, M., Burdet, E., Two is better than one: Physical interactions improve motor performance in humans (2014) Sci. Rep., 4, pp. 1-7; Sciutti, A., Sandini, G., Interacting with robots to investigate the bases of social interaction (2017) IEEE Trans. Neural Syst. Rehabil. Eng., 25 (12), pp. 2295-2304; Galofaro, E., Morasso, P., Zenzeri, J., Improving motor skill transfer during dyadic robot training through the modulation of the expert role (2017) IEEE Int. Conf. Rehabil. Robot., pp. 78-83; Zenzeri, J., De Santis, D., Morasso, P., Strategy switching in the stabilization of unstable dynamics (2014) PLoS One, 9 (6); Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Sci. Robot., 3 (21), pp. 1-10; Gorsic, M., Cikajlo, I., Novak, D., Competitive and cooperative arm rehabilitation games played by a patient and unimpaired person: Effects on motivation and exercise intensity (2017) J. Neuroeng. Rehabil., 14 (1), pp. 1-18; Paez Granados, D.F., Yamamoto, B.A., Kamide, H., Kinugawa, J., Kosuge, K., Dance teaching by a robot: Combining cognitive and physical human-robot interaction for supporting the skill learning process (2017) IEEE Robot. Autom. Lett., 2 (3), pp. 1452-1459; Wulf, G., Lewthwaite, R., Optimizing performance through intrinsic motivation and attention for learning: The optimal theory of motor learning (2016) Psychon. Bull. Rev., 23 (5), pp. 1382-1414; Wolpert, D.M., Miall, R.C., Kawato, M., Internal models in the cerebellum (1998) Trends Cogn. Sci., 2 (9), pp. 338-347; Fasola, J., Mataríc, M.J., Robot motivator: Increasing user enjoyment and performance on a physical/cognitive task (2010) 2010 IEEE 9th Int. Conf. Dev. Learn. ICDL-2010-Conf. Progr., pp. 274-279; Vasalya, A., Ganesh, G., Kheddar, A., More than just co-workers: Presence of humanoid robot co-worker influences human performance (2018) PLoS One, 13 (11), pp. 1-19; Li, J., The benefit of being physically present: A survey of experimental works comparing copresent robots, telepresent robots and virtual agents (2015) Int. J. Hum. Comput. Stud., 77, pp. 23-37; Masia, L., Casadio, M., Sandini, G., Morasso, P., Eye-hand coordination during dynamic visuomotor rotations (2009) PLoS One, 4 (9); Iandolo, R., Perspectives and challenges in robotic neurorehabilitation (2019) Appl. Sci., 9 (15); Metta, G., The icub humanoid robot : An open-systems platform for research in cognitive development (2010) Neural Networks; Rea, F., Muratore, P., Sciutti, A., 13-year-olds approach human-robot interaction like adults (2017) 2016 Jt. IEEE Int. Conf. Dev. Learn. Epigenetic Robot. ICDL-EpiRob 2016, pp. 138-143; Chiorri, C., Bracco, F., Piccinno, T., Modafferi, C., Battini, V., Psychometric properties of a revised version of the ten item personality inventory (2015) Eur. J. Psychol. Assess., 31 (2), pp. 109-119; Aron, A., Aron, E.N., Smollan, D., Inclusion of other in the self scale and the structure of interpersonal closeness (1992) J. Pers. Soc. Psychol., 63 (4), pp. 596-612; Bartneck, C., Kulíc, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. J. Soc. Robot., 1 (1), pp. 71-81; Ferrari, F., Paladino, M.P., Jetten, J., Blurring human-machine distinctions: Anthropomorphic appearance in social robots as a threat to human distinctiveness (2016) Int. J. Soc. Robot., 8 (2), pp. 287-302; Gray, H.M., Gray, K., Wegner, D.M., Dimensions of mind perception (2014) Exp. Philos., 2, p. 88; Wang, N., Pynadath, D.V., Hill, S.G., Trust calibration within a human-robot team: Comparing automatically generated explanations (2016) ACM/IEEE Int. Conf. Human-Robot Interact., 2016, pp. 109-116. , April; Bracco, F., Chiorri, C., Versione Italiana Del NASA-TLX.; Adams, N., Little, T.D., Ryan, R.M., Self-determination theory (2017) Dev. Self-Determination Through Life-Course, 55 (1), pp. 47-54; Mazzola, C., Aroyo, A.M., Rea, F., Sciutti, A., Interacting with a social robot affects visual perception of space (2020) ACM/IEEE Int. Conf. Human-Robot Interact., pp. 549-557",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85095764550
"Song Z.-B., Hu X.-Q.","23061778500;57204363133;","Design and performance evaluation of compliant actuator based on given nonlinear stiffness",2020,"Chinese Journal of Engineering Design","27","4",,"416","424",,,"10.3785/j.issn.1006-754X.2020.00.051","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091763919&doi=10.3785%2fj.issn.1006-754X.2020.00.051&partnerID=40&md5=4948c1e1a12958eed0057165e08e608f","Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, School of Mechanical Engineering, Tianjin University, Tianjin, 300072, China","Song, Z.-B., Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, School of Mechanical Engineering, Tianjin University, Tianjin, 300072, China; Hu, X.-Q., Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, School of Mechanical Engineering, Tianjin University, Tianjin, 300072, China","Both safe human-robot interaction and environment-adapted bionic motion need compliance in robot joints. In order to solve the problems of the existing passive compliant actuators, such as weak adaptability to different external loads, the need for additional stiffness adjustment motor, and the mismatch between stiffness and load, a new nonlinear stiffness compliant actuator was designed, which adopted ""cam+torsion spring"" mechanism combined with gear transmission and could achieve the given bidirectional nonlinear stiffness. Firstly, the working principle of the nonlinear stiffness mechanism, the calculation method of the effective contour curve of the cam and the working principle of the nonlinear stiffness compliant actuator were introduced. Then, based on the proposed bidirectional nonlinear stiffness mechanism, an actuator prototype was designed. At last, the performances of the proposed compliant actuator were evaluated. Through simulation and experimental analysis of the stiffness characteristics of the prototype, it was verified that the designed compliant actuator could adjust the stiffness well according to the given nonlinear stiffness characteristic curve. Through comparing with the similar actuators, it was shown that the proposed actuator had good comprehensive characteristics. Through the experiments of tracking sinusoidal torque signals with different peak torques and frequencies, it was preliminarily proved that the designed compliant actuator had good torque control performance. The research results show the effectiveness of the proposed design method, which provide a new idea for the design of actuator of robot joints. © 2020, Zhejiang University Press.","Compliant actuator; Given nonlinear stiffness; Performance evaluation; Structural design",,,,,,,,,,,,,,,,,"Yu, H., Huang, S., Chen, G., Human-robot interaction control of rehabilitation robots with series elastic actuators[J] (2015) IEEE Transactions on Robotics, 31 (5), pp. 1089-1100; Lens, T., Kunz, J., Von Stryk, O., (2010) Biorob-arm:a quickly deployable and intrinsically safe, light-weight robot arm for service robotics applications [C], pp. 1-6. , Proceedings of the Joint Conference of ISR 2010 (41st International Symposium on Robotics) and ROBOTIK 2010 (6th German Conference on Robotics). 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J. Eng. Design",Article,"Final","",Scopus,2-s2.0-85091763919
"Fitzsimons K., Kalinowska A., Dewald J.P., Murphey T.D.","57190114009;57210805200;7005428181;6701823092;","Task-based hybrid shared control for training through forceful interaction",2020,"International Journal of Robotics Research","39","9",,"1138","1154",,,"10.1177/0278364920933654","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086517439&doi=10.1177%2f0278364920933654&partnerID=40&md5=bff4bc7249c73a9ce06ca5d9afeb8b91","Department of Mechanical Engineering, Northwestern University, Evanston, IL, United States; Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL, United States","Fitzsimons, K., Department of Mechanical Engineering, Northwestern University, Evanston, IL, United States; Kalinowska, A., Department of Mechanical Engineering, Northwestern University, Evanston, IL, United States; Dewald, J.P., Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL, United States; Murphey, T.D., Department of Mechanical Engineering, Northwestern University, Evanston, IL, United States","Despite the fact that robotic platforms can provide both consistent practice and objective assessments of users over the course of their training, there are relatively few instances where physical human–robot interaction has been significantly more effective than unassisted practice or human-mediated training. This article describes a hybrid shared control robot, which enhances task learning through kinesthetic feedback. The assistance assesses user actions using a task-specific evaluation criterion and selectively accepts or rejects them at each time instant. Through two human subject studies (total (Formula presented.)), we show that this hybrid approach of switching between full transparency and full rejection of user inputs leads to increased skill acquisition and short-term retention compared with unassisted practice. Moreover, we show that the shared control paradigm exhibits features previously shown to promote successful training. It avoids user passivity by only rejecting user actions and allowing failure at the task. It improves performance during assistance, providing meaningful task-specific feedback. It is sensitive to initial skill of the user and behaves as an “assist-as-needed” control scheme, adapting its engagement in real time based on the performance and needs of the user. Unlike other successful algorithms, it does not require explicit modulation of the level of impedance or error amplification during training and it is permissive to a range of strategies because of its evaluation criterion. We demonstrate that the proposed hybrid shared control paradigm with a task-based minimal intervention criterion significantly enhances task-specific training. © The Author(s) 2020.","human performance augmentation; Physical human–robot interaction; rehabilitation robotics","Agricultural robots; Feedback; Human robot interaction; Control schemes; Error amplification; Evaluation criteria; Kinesthetic feedback; Objective assessment; Robot interactions; Robotic platforms; Skill acquisition; Educational robots",,,,,"National Science Foundation, NSF: 1637764; U.S. Department of Defense, DOD; National Defense Science and Engineering Graduate, NDSEG","This work was supported by the National Science Foundation (grant number 1637764) and by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program. 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"Li C., Fahmy A., Li S., Sienz J.","57191520275;56180466300;16679228400;6603625906;","An Enhanced Robot Massage System in Smart Homes Using Force Sensing and a Dynamic Movement Primitive",2020,"Frontiers in Neurorobotics","14",,"30","","",,2,"10.3389/fnbot.2020.00030","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087836398&doi=10.3389%2ffnbot.2020.00030&partnerID=40&md5=43fdf121c66808089cde0201623993ce","Centre for Robotics and Neural Systems, University of Plymouth, Plymouth, United Kingdom; Shandong Marine Corrosion and Safety Protection Engineering Research Center, Qingdao University of Science and Technology, Qingdao, China; ASTUTE 2020 in Future Manufacturing Research Institute, College of Engineering, Swansea University, Swansea, United Kingdom; Department of Electrical Power and Machines, Helwan University, Helwan, Egypt","Li, C., Centre for Robotics and Neural Systems, University of Plymouth, Plymouth, United Kingdom, Shandong Marine Corrosion and Safety Protection Engineering Research Center, Qingdao University of Science and Technology, Qingdao, China; Fahmy, A., ASTUTE 2020 in Future Manufacturing Research Institute, College of Engineering, Swansea University, Swansea, United Kingdom, Department of Electrical Power and Machines, Helwan University, Helwan, Egypt; Li, S., Shandong Marine Corrosion and Safety Protection Engineering Research Center, Qingdao University of Science and Technology, Qingdao, China; Sienz, J., ASTUTE 2020 in Future Manufacturing Research Institute, College of Engineering, Swansea University, Swansea, United Kingdom","With requirements to improve life quality, smart homes, and healthcare have gradually become a future lifestyle. In particular, service robots with human behavioral sensing for private or personal use in the home have attracted a lot of research attention thanks to their advantages in relieving high labor costs and the fatigue of human assistance. In this paper, a novel force-sensing- and robotic learning algorithm-based teaching interface for robot massaging has been proposed. For the teaching purposes, a human operator physically holds the end-effector of the robot to perform the demonstration. At this stage, the end position data are outputted and sent to be segmented via the Finite Difference (FD) method. A Dynamic Movement Primitive (DMP) is utilized to model and generalize the human-like movements. In order to learn from multiple demonstrations, Dynamic Time Warping (DTW) is used for the preprocessing of the data recorded on the robot platform, and a Gaussian Mixture Model (GMM) is employed for the evaluation of DMP to generate multiple patterns after the completion of the teaching process. After that, a Gaussian Mixture Regression (GMR) algorithm is applied to generate a synthesized trajectory to minimize position errors. Then a hybrid position/force controller is integrated to track the desired trajectory in the task space while considering the safety of human-robot interaction. The validation of our proposed method has been performed and proved by conducting massage tasks on a KUKA LBR iiwa robot platform. © Copyright © 2020 Li, Fahmy, Li and Sienz.","dynamic motion primitive; dynamic time warping; gaussian mixture regression; hybrid force/position; teaching by demonstration","Automation; Behavioral research; Educational robots; End effectors; Gaussian distribution; Intelligent buildings; Learning algorithms; Physical therapy; Wages; Desired trajectories; Dynamic movement primitives; Dynamic time warping; Gaussian Mixture Model; Gaussian mixtures; Human assistance; Multiple patterns; Teaching interfaces; Human robot interaction; article; dynamic time warping; human; human experiment; learning algorithm; massage; motion; robotics; teaching; validation process",,,,,,,,,,,,,,,,"Bernhardt, M., Frey, M., Colombo, G., Riener, R., Hybrid force-position control yields cooperative behaviour of the rehabilitation robot lokomat (2005) 9th International Conference on Rehabilitation Robotics, 2005, pp. 536-539. , Chicago, IL; Calinon, S., Billard, A., Incremental learning of gestures by imitation in a humanoid robot (2007) Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, pp. 255-262. , Washington, DC, ACM; Chelikowsky, J.R., Troullier, N., Saad, Y., Finite-difference-pseudopotential method: electronic structure calculations without a basis (1994) Phys. 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EECS, 1, pp. 1-7; Tong, K.-F., Huang, J., New proximity coupled feeding method for reconfigurable circularly polarized microstrip ring antennas (2008) IEEE Trans. Antenn. Propag, 56, pp. 1860-1866; Zhang, Y., Zhang, W., Recent patents on Chinese massage robot (2017) Recent Patents Eng, 11, pp. 156-161","Li, S.; Shandong Marine Corrosion and Safety Protection Engineering Research Center, China; email: leeshaoxiang@126.com",,,"Frontiers Media S.A.",,,,,16625218,,,,"English","Front. Neurorobotics",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85087836398
"Zhang Y., Huang Y., Sun X., Zhao Y., Guo X., Liu P., Liu C., Zhang Y.","57188851472;56924408700;57211062581;57194640049;56668993200;57187388600;57225012472;14620003500;","Static and Dynamic Human Arm/Hand Gesture Capturing and Recognition via Multiinformation Fusion of Flexible Strain Sensors",2020,"IEEE Sensors Journal","20","12","8955861","6450","6459",,13,"10.1109/JSEN.2020.2965580","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080999690&doi=10.1109%2fJSEN.2020.2965580&partnerID=40&md5=df47ebed00749a8615cadb182e289d6e","School of Electronic Science and Applied Physics, Hefei University of Technology, Hefei, 230009, China","Zhang, Y., School of Electronic Science and Applied Physics, Hefei University of Technology, Hefei, 230009, China; Huang, Y., School of Electronic Science and Applied Physics, Hefei University of Technology, Hefei, 230009, China; Sun, X., School of Electronic Science and Applied Physics, Hefei University of Technology, Hefei, 230009, China; Zhao, Y., School of Electronic Science and Applied Physics, Hefei University of Technology, Hefei, 230009, China; Guo, X., School of Electronic Science and Applied Physics, Hefei University of Technology, Hefei, 230009, China; Liu, P., School of Electronic Science and Applied Physics, Hefei University of Technology, Hefei, 230009, China; Liu, C., School of Electronic Science and Applied Physics, Hefei University of Technology, Hefei, 230009, China; Zhang, Y., School of Electronic Science and Applied Physics, Hefei University of Technology, Hefei, 230009, China","Human arm/hand gestures capturing and recognition provide an intelligent and convenient way for use in application from human-machine interface (HMI) and human-computer interaction (HCI) to human-robot interaction (HRI). As human gestures constitute a powerful inter-human communication modality, they can be considered as well an intuitive and convenient mean for the communication between human and machines. This paper presents a novel real-time static and dynamic human gesture capturing and recognition method designed using flexible wearable data band/data glove, which are designed based on three/ten stretchable strain sensors respectively. The wearable data bands and data gloves are worn by human arm/hand to ensure that the sensors are accurately attached to the human joints for accurate measurement of joints movements of shoulders joints, elbows joints, wrists joints, metacarpal and proximal joints of fingers. In this work, a new idea of real-time static and dynamic human gesture capturing and recognition is introduced and developed based on the radial basis function neural network (RBFNN). Dynamic time warping (DTW) is used to select dynamic behavior candidates and also to recognize gestures by comparing the observed records with a series of pre-recorded reference data patterns. The solution deals simultaneously with static and dynamic gestures as well as with multiple joints within the interest space. The experimental results of human arm/hand static gestures and dynamic gestures capturing and recognition verify the effectiveness of the proposed methods. © 2001-2012 IEEE.","data band; data glove; gesture capturing and recognition; Strain sensor","Human computer interaction; Intelligent robots; Radial basis function networks; Wearable sensors; Accurate measurement; Dynamic time warping; Human communications; Human computer interaction (HCI); Human Machine Interface; Human robot Interaction (HRI); Multi-information fusion; Radial basis function neural networks; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 61673369, 61901005, 91648206; Natural Science Foundation of Anhui Province: 1908085QF261","Manuscript received December 8, 2019; accepted January 7, 2020. Date of publication January 10, 2020; date of current version May 15, 2020. This work was supported by the National Natural Science Foundation of China (No. 91648206, 61673369, 61901005), and the Anhui Provincial Natural Science Foundation (1908085QF261). The associate editor coordinating the review of this article and approving it for publication was Dr. Marco J. Da Silva. (Corresponding author: Ying Huang.) The authors are with the School of Electronic Science and Applied Physics, Hefei University of Technology, Hefei 230009, China (e-mail: 1320696987@qq.com; hf.hy@163.com; 1225388504@qq.com; 824816621@qq.com; ahgxh@mail.hfut.edu.cn; 147213929@163.com; hgdliucaixia@163.com; 52352442@qq.com).",,,,,,,,,,"Tsarouchi, P., Athanasatos, A., Makris, S., Chatzigeorgiou, X., Chryssolouris, G., High level robot programming using body and hand gestures (2016) Procedia Cirp, 55, pp. 1-5. , Jan; Makris, S., Tsarouchi, P., Surdilovic, D., Krüger, J., Intuitive dual arm robot programming for assembly operations (2014) Cirp Ann., 63 (1), pp. 13-16; Makris, S., Dual arm robot in cooperation with humans for flexible assembly (2017) Cirp Ann.-Manuf. Technol., 66 (1), pp. 13-16; Lu, Z., Chen, X., Li, Q., Zhang, X., Zhou, P., A hand gesture recognition framework and wearable gesture-based interaction prototype for mobile devices (2014) Ieee Trans. Human-Mach. 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Med., 57, pp. 123-129. , Feb","Huang, Y.; School of Electronic Science and Applied Physics, China; email: hf.hy@163.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,1530437X,,,,"English","IEEE Sensors J.",Article,"Final","",Scopus,2-s2.0-85080999690
"Zahedi E., Khosravian F., Wang W., Armand M., Dargahi J., Zadeh M.","57213036379;57211668159;57211670337;35236429300;6603763357;15520363400;","Towards Skill Transfer via Learning-Based Guidance in Human-Robot Interaction: An Application to Orthopaedic Surgical Drilling Skill",2020,"Journal of Intelligent and Robotic Systems: Theory and Applications","98","3-4",,"667","678",,3,"10.1007/s10846-019-01082-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074756992&doi=10.1007%2fs10846-019-01082-2&partnerID=40&md5=54eb9229bd5fd84057bb67122a06a943","Department of Mechanical, Industrial, Aerospace Engineering, Concordia University, Montreal, QC  H3G 1M8, Canada; Johns Hopkins University, Baltimore, MD  21218, United States","Zahedi, E., Department of Mechanical, Industrial, Aerospace Engineering, Concordia University, Montreal, QC  H3G 1M8, Canada; Khosravian, F., Johns Hopkins University, Baltimore, MD  21218, United States; Wang, W., Johns Hopkins University, Baltimore, MD  21218, United States; Armand, M., Johns Hopkins University, Baltimore, MD  21218, United States; Dargahi, J., Department of Mechanical, Industrial, Aerospace Engineering, Concordia University, Montreal, QC  H3G 1M8, Canada; Zadeh, M., Johns Hopkins University, Baltimore, MD  21218, United States","This paper presents a machine learning-based guidance (LbG) approach for kinesthetic human-robot interaction (HRI) that can be used in virtual training simulations. Demonstrated positional and force skills are learned to both discriminate the skill levels of users and produce LbG forces. Force information is obtained from virtual forces, which developed based on real computed tomography (CT) data, rather than force sensors. A femur bone drilling simulation is developed to provide a practice environment for orthopaedic residents. The residents are provided with haptic feedback that enable them to feel the variable stiffness of bone layers. The X-ray views of the bone are also presented to them for better tracking of a pre-defined path inside the bone. The simulation is capable of planning a drill path, generating X-rays based on user defined orientation, and recording motion data for user assessment and skill modeling. The knowledge of expert surgeons is also incorporated into the simulation to provide LbG forces for improving the unpredictable motions of the residents. To discriminate the skill level of users, machine learning tools are used to develop surgical expert and resident models. In addition, to improve residents performance, the expert HCRF is used to generate adaptive LbG forces regarding the similarities between residents motions and the expert model. Experimental results show that the learning-based approach is able to assess the skill of users and improve residents performance. © 2019, Springer Nature B.V.","Human-robot interaction; Machine learning-based guidance; Virtual surgical simulation","Air navigation; Bone; Computerized tomography; Infill drilling; Learning algorithms; Machine learning; Man machine systems; Robotic surgery; Surgery; Virtual reality; X rays; Haptic feedbacks; Human robot Interaction (HRI); Learning-based approach; Skill transfer; Surgical drilling; Surgical simulation; Variable stiffness; Virtual training; Human robot interaction",,,,,"Johns Hopkins University, JHU","We thank Russell H. Taylor for facilitating this research in the LCSR lab at Johns Hopkins University.",,,,,,,,,,"Schapira, D., Schapira, C., Osteoporosis: the evolution of a scientific term (1992) Osteoporos. Int., 2 (4), p. 164; Wade, S., Strader, C., Fitzpatrick, L., Anthony, M., O’Malley, C., Estimating prevalence of osteoporosis: examples from industrialized countries (2014) Arch. Osteoporos., 9 (1), p. 182; Goldacre, M.J., Roberts, S.E., Yeates, D., Mortality after admission to hospital with fractured neck of femur: database study (2002) Bmj, 325 (7369), p. 868; Thorngren, K.G., (2008) National Registration of Hip Fractures; Coles, T., Meglan, D., John, N., The role of haptics in medical training simulators: a survey of the state of the art (2011) IEEE Trans. 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Eng., 55 (4), p. 1255; Reiley, C.E., Plaku, E., Hager, G.D., Motion generation of robotic surgical tasks: Learning from expert demonstrations (2010) Engineering in Medicine and Biology Society, pp. 967-970; Chmarra, M.K., Klein, S., de Winter, J.C., Jansen, F.W., Dankelman, J., Objective classification of residents based on their psychomotor laparoscopic skills (2010) Surg. Endosc., 24 (5), p. 1031","Zadeh, M.; Johns Hopkins UniversityUnited States; email: mzadeh2@jhu.edu",,,"Springer",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","",Scopus,2-s2.0-85074756992
"Nguyen V.Q., LaPre A.K., Price M.A., Umberger B.R., Sup F.C., IV","57197792797;54787761900;57189264177;6602216087;16417770800;","Inclusion of actuator dynamics in simulations of assisted human movement",2020,"International Journal for Numerical Methods in Biomedical Engineering","36","5","e3334","","",,2,"10.1002/cnm.3334","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082711287&doi=10.1002%2fcnm.3334&partnerID=40&md5=003e6c2cd702cd6883ac549ca309603b","Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA, United States; FTL Labs Corp., Amherst, MA, United States; Department of Kinesiology, University of Massachusetts, Amherst, MA, United States","Nguyen, V.Q., Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA, United States; LaPre, A.K., FTL Labs Corp., Amherst, MA, United States; Price, M.A., Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA, United States; Umberger, B.R., Department of Kinesiology, University of Massachusetts, Amherst, MA, United States; Sup, F.C., IV, Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA, United States","Simulation of musculoskeletal systems using dynamic optimization is a powerful approach for studying the biomechanics of human movements and can be applied to human-robot interactions. The simulation results of human movements augmented by robotic devices may be used to evaluate and optimize the device design and controller. However, simulations are limited by the accuracy of the models which are usually simplified for computation efficiency. Typically, the powered robotic devices are often modeled as massless, ideal torque actuators that is without mass and internal dynamics, which may have significant impacts on the simulation results. This article investigates the effects of including the mass and internal dynamics of the device in simulations of assisted human movement. The device actuator was modeled in various ways with different detail levels. Dynamic optimization was used to find the muscle activations and actuator commands in motion tracking and predictive simulations. The results showed that while the effects of device mass and inertia can be small, the electrical dynamics of the motor can significantly impact the results. This outcome suggests the importance of using an accurate actuator model in simulations of human movement augmented by assistive devices. Novelty: Demonstrating the effects of including mass and internal dynamics of the actuator in simulations of assisted human movement A new OpenSim electric motor actuator class to capture the electromechanical dynamics for use in simulation of human movement assisted by powered robotic devices. © 2020 John Wiley & Sons, Ltd.","actuator dynamics; DC motor; dynamic optimization; exoskeletons; predictive simulation; prostheses","Actuators; DC motors; Dynamics; Electromechanical devices; Exoskeleton (Robotics); Motion tracking; Musculoskeletal system; Prosthetics; Actuator dynamics; Assistive devices; Computation efficiency; Dynamic optimization; Electro mechanical dynamics; Internal dynamics; Muscle activation; Predictive simulations; Human robot interaction; animal; animal shell; biomechanics; computer simulation; human; movement (physiology); physiology; skeletal muscle; Animal Shells; Animals; Biomechanical Phenomena; Computer Simulation; Humans; Movement; Muscle, Skeletal",,,,,"National Science Foundation, NSF: IIS‐1526986","This work was supported in part by a grant from the National Science Foundation (IIS‐1526986).","National Science Foundation, Grant/Award Number: IIS‐1526986 Funding information",,,,,,,,,"Handford, M.L., Srinivasan, M., Robotic lower limb prosthesis design through simultaneous computer optimizations of human and prosthesis costs (2016) Sci Rep, 6 (February). , https://doi.org/10.1038/srep19983; Uchida, T.K., Seth, A., Pouya, S., Dembia, C.L., Hicks, J.L., Delp, S.L., Simulating ideal assistive devices to reduce the metabolic cost of running (2016) PLoS One, 11 (9). , https://doi.org/10.1371/journal.pone.0163417; Zhou, L., Li, Y., Bai, S., A human-centered design optimization approach for robotic exoskeletons through biomechanical simulation (2017) Robot Auton Syst, 91, pp. 337-347. , https://doi.org/10.1016/j.robot.2016.12.012; Lee, L.-F., Umberger, B.R., Generating optimal control simulations of musculoskeletal movement using OpenSim and MATLAB (2016) PeerJ, 4. , https://doi.org/10.7717/peerj.1638; Mansouri, M., Reinbolt, J.A., A platform for dynamic simulation and control of movement based on OpenSim and MATLAB (2012) J Biomech, 45 (8), pp. 1517-1521. , https://doi.org/10.1016/j.jbiomech.2012.03.016; Sartori, M., Reggiani, M., Farina, D., Lloyd, D.G., EMG-driven forward-dynamic estimation of muscle force and joint moment about multiple degrees of freedom in the human lower extremity (2012) PLoS One, 7 (12). , https://doi.org/10.1371/journal.pone.0052618; Anderson, F.C., Pandy, M.G., Dynamic optimization of human walking (2001) J Biomech Eng, 123 (5), pp. 381-390. , https://doi.org/10.1115/1.1392310; Ackermann, M., van den Bogert, A.J., Optimality principles for model-based prediction of human gait (2010) J Biomech, 43 (6), pp. 1055-1060. , https://doi.org/10.1016/j.jbiomech.2009.12.012; Xiang, Y., Arora, J.S., Abdel-Malek, K., Optimization-based prediction of asymmetric human gait (2011) J Biomech, 44 (4), pp. 683-693. , https://doi.org/10.1016/j.jbiomech.2010.10.045; Fey, N.P., Klute, G.K., Neptune, R.R., Optimization of prosthetic foot stiffness to reduce metabolic cost and intact knee loading during below-knee amputee walking: a theoretical study (2012) J Biomech Eng, 134 (11). , https://doi.org/10.1115/1.4007824; Herr, H.M., Grabowski, A.M., Bionic ankle-foot prosthesis normalizes walking gait for persons with leg amputation (2012) Proc Biol Sci, 279 (1728), pp. 457-464. , https://doi.org/10.1098/rspb.2011.1194; Quesada, R.E., Caputo, J.M., Collins, S.H., Increasing ankle push-off work with a powered prosthesis does not necessarily reduce metabolic rate for transtibial amputees (2016) J Biomech, 49 (14), pp. 3452-3459. , https://doi.org/10.1016/j.jbiomech.2016.09.015; Lawson, B.E., Mitchell, J., Truex, D., Shultz, A., Ledoux, E., Goldfarb, M., A robotic leg prosthesis: design, control, and implementation (2014) IEEE Robot Automat Mag, 21 (4), pp. 70-81. , https://doi.org/10.1109/MRA.2014.2360303; Delp, S.L., Anderson, F.C., Arnold, A.S., OpenSim: open-source software to create and analyze dynamic simulations of movement (2007) IEEE Trans Biomed Eng, 54 (11), pp. 1940-1950; Thelen, D.G., Anderson, F.C., Delp, S.L., Generating dynamic simulations of movement using computed muscle control (2003) J Biomech, 36 (3), pp. 321-328. , https://doi.org/10.1016/S0021-9290(02)00432-3; Sugar, T.G., Bates, A., Holgate, M., Limit cycles to enhance human performance based on phase oscillators (2015) J Mech Robot, 7 (1). , https://doi.org/10.1115/1.4029336; Cherry, M.S., Kota, S., Young, A., Ferris, D.P., Running with an elastic lower limb exoskeleton (2016) J Appl Biomech, 32 (3), pp. 269-277. , https://doi.org/10.1123/jab.2015-0155; Seth, A., Sherman, M., Reinbolt, J.A., Delp, S.L., OpenSim: a musculoskeletal modeling and simulation framework for in silico investigations and exchange (2011) Procedia Iutam, 2, pp. 212-232. , https://doi.org/10.1007/s10549-015-3663-1.Progestin; 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Nguyen, V.Q., Johnson, R.T., Sup, F.C., IV, Umberger, B.R., Bilevel optimization for cost function determination in dynamic simulation of human gait (2019) IEE Trans Neural Syst Rehabil Eng, 27 (7), pp. 1426-1435; Davy, D.T., Audu, M.L., A dynamic optimization technique for predicting muscle forces in the swing phase of gait (1987) J Biomech, 20 (2), pp. 187-201. , https://doi.org/10.1016/0021-9290(87)90310-1; Kaplan, M.L., Heegaard, H., Predictive algorithms for neuromuscular control of human locomotion (2001) J Biomech, 34 (8), pp. 1077-1083. , https://doi.org/10.1016/S0021-9290(01)00057-4; Wächter, A., Biegler, L.T., On the implementation of primal-dual interior point filter line search algorithm for large-scale nonlinear programming (2006) Math Prog, 106 (1), pp. 25-57. , https://doi.org/10.1007/s10107-004-0559-y; Mehrabi, N., Sharif Razavian, R., Ghannadi, B., McPhee, J., Predictive simulation of reaching moving targets using nonlinear model predictive control (2017) Front Comput Neurosci, 10 (January), p. 143. , https://doi.org/10.3389/fncom.2016.00143; Berret, B., Chiovetto, E., Nori, F., Pozzo, T., Evidence for composite cost functions in arm movement planning: an inverse optimal control approach (2011) PLoS Comput Biol, 7 (10). , https://doi.org/10.1371/journal.pcbi.1002183; Albrecht, S., Ramírez-Amaro, K., Ruiz-Ugalde, F., Imitating human reaching motions using physically inspired optimization principles (2011) IEEE-RAS Int Conf Human Robot, pp. 602-607. , https://doi.org/10.1109/Humanoids.2011.6100856; Koelewijn, A.D., Dorschky, E., van den Bogert, A.J., A metabolic energy expenditure model with a continuous first derivative and its application to predictive simulations of gait (2018) Comput Methods Biomech Biomed Engin, 21 (8), pp. 521-531. , https://doi.org/10.1080/10255842.2018.1490954","Sup, F.C.; Department of Mechanical and Industrial Engineering, United States; email: sup@umass.edu",,,"Wiley-Blackwell",,,,,20407939,,,"32170995","English","Int. J. Numer. Methods Biomed. Eng.",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85082711287
"Albini A., Cannata G.","57200625396;7005466764;","Pressure distribution classification and segmentation of human hands in contact with the robot body",2020,"International Journal of Robotics Research","39","6",,"668","687",,2,"10.1177/0278364920907688","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081979112&doi=10.1177%2f0278364920907688&partnerID=40&md5=7f6fc79b704de74f2b9ba7c2ce8d79a4","Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy","Albini, A., Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy; Cannata, G., Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy","This article deals with the problem of the recognition of human hand touch by a robot equipped with large area tactile sensors covering its body. This problem is relevant in the domain of physical human–robot interaction for discriminating between human and non-human contacts and to trigger and to drive cooperative tasks or robot motions, or to ensure a safe interaction. The underlying assumption used in this article is that voluntary physical interaction tasks involve hand touch over the robot body, and therefore the capability to recognize hand contacts is a key element to discriminate a purposive human touch from other types of interaction. The proposed approach is based on a geometric transformation of the tactile data, formed by pressure measurements associated to a non-uniform cloud of 3D points (taxels) spread over a non-linear manifold corresponding to the robot body, into tactile images representing the contact pressure distribution in two dimensions. Tactile images can be processed using deep learning algorithms to recognize human hands and to compute the pressure distribution applied by the various hand segments: palm and single fingers. Experimental results, performed on a real robot covered with robot skin, show the effectiveness of the proposed methodology. Moreover, to evaluate its robustness, various types of failures have been simulated. A further analysis concerning the transferability of the system has been performed, considering contacts occurring on a different sensorized robot part. © The Author(s) 2020.","human–robot interaction; robot skin; Tactile sensing","Deep learning; Digital storage; Linear transformations; Metadata; Pressure distribution; Contact pressure distribution; Cooperative tasks; Geometric transformations; Nonlinear manifolds; Physical interactions; Robot interactions; Robot skin; Tactile sensing; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020: 820767","The research leading to these results has received funding from the European Community’s Framework Programme Horizon 2020 (grant agreement number 820767, project CoLLaboratE).","The research leading to these results has received funding from the European Community?s Framework Programme Horizon 2020 (grant agreement number 820767, project CoLLaboratE).",,,,,,,,,"Abadi, M., Agarwal, A., Barham, P., (2015) TensorFlow: Large-scale machine learning on heterogeneous systems, , https://www.tensorflow.org/; Albini, A., Cannata, G., (2018) Tactile images generation from contacts involving adjacent robot links, pp. 306-312. , 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), In; Albini, A., Denei, S., Cannata, G., (2017) Enabling natural human–robot physical interaction using a robotic skin feedback and a prioritized tasks robot control architecture, pp. 99-106. , 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), (, a), In; Albini, A., Denei, S., Cannata, G., (2017) Human hand recognition from robotic skin measurements in human–robot physical interactions, pp. 4348-4353. , 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (, b), In; Badrinarayanan, V., Kendall, A., Cipolla, R., Segnet: A deep convolutional encoder–decoder architecture for image segmentation (2017) IEEE Transactions on Pattern Analysis and Machine Intelligence, 39 (12), pp. 2481-2495; Beyerer, J., Puente León, F., Frese, C., (2016) Morphological Image Processing, pp. 607-647. , Berlin, Springer; Bicchi, A., Salisbury, J.K., Brock, D.L., Contact sensing from force measurements (1993) The International Journal of Robotics Research, 12 (3), pp. 249-262; Billard, A., Calinon, S., Dillmann, R., Schaal, S., Robot programming by demonstration (2008) Springer Handbook of Robotics, pp. 1371-1394. , Berlin, Springer, In; Cannata, G., Denei, S., Mastrogiovanni, F., (2010) Tactile sensing: Steps to artificial somatosensory maps, pp. 576-581. , 19th International Symposium Robot and Human Interactive Communication, In; Cannata, G., Maggiali, M., Metta, G., Sandini, G., (2008) An embedded artificial skin for humanoid robots, pp. 434-438. , 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, In; Cao, L., Kotagiri, R., Sun, F., Li, H., Huang, W., Aye, Z.M.M., (2016) Efficient spatio-temporal tactile object recognition with randomized tiling convolutional networks in a hierarchical fusion strategy, pp. 3337-3345. , Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI’16), AAAI Press, In; Cheung, E., Lumelsky, V., (1989) Development of sensitive skin for a 3D robot arm operating in an uncertain environment, 2, pp. 1056-1061. , Proceedings, 1989 International Conference on Robotics and Automation, In; Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L., ImageNet: A large-scale hierarchical image database (2009) CVPR09, , In; Desbrun, M., Meyer, M., Alliez, P., Intrinsic parameterizations of surface meshes (2002) Computer Graphics Forum, 21 (3), pp. 209-218; Dhanachandra, N., Manglem, K., Chanu, Y.J., Image segmentation using k-means clustering algorithm and subtractive clustering algorithm (2015) Procedia Computer Science, 54, pp. 764-771; Duchaine, V., Gosselin, C.M., (2007) General model of human–robot cooperation using a novel velocity based variable impedance control, pp. 446-451. , Second Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (WHC’07), In; Ebert, D.M., Henrich, D.D., (2002) Safe human–robot-cooperation: Image-based collision detection for industrial robots, 2, pp. 1826-1831. , IEEE/RSJ International Conference on Intelligent Robots and Systems, In; Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P., Why does unsupervised pre-training help deep learning? 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(2014) Advances in Neural Information Processing Systems, 27, pp. 3320-3328. , Ghahramani Z., Welling M., Cortes C., Lawrence., Weinberger K.Q., (eds), Curran Associates, Inc., In:, (eds","Albini, A.; Department of Informatics, Italy; email: alessandro.albini@dibris.unige.it",,,"SAGE Publications Inc.",,,,,02783649,,IJRRE,,"English","Int J Rob Res",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85081979112
"Xiong X., Yu Z., Ma T., Wang H., Lu X., Fan H.","57217427202;7404347135;55490554800;55878609000;55687439900;36647626600;","Classifying action intention understanding EEG signals based on weighted brain network metric features",2020,"Biomedical Signal Processing and Control","59",,"101893","","",,4,"10.1016/j.bspc.2020.101893","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080089076&doi=10.1016%2fj.bspc.2020.101893&partnerID=40&md5=5e5ea45232e7b2b658cccc6a3ddc6495","key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu  210096, China; College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an, Shanxi  710054, China; Department of Rehabilitation, Zhongda Hospital, Southeast University, Nanjing, Jiangsu  210009, China; Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Shandong Technology and Business University, Yantai, Shandong  264005, China","Xiong, X., key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu  210096, China; Yu, Z., College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an, Shanxi  710054, China; Ma, T., College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an, Shanxi  710054, China; Wang, H., key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing, Jiangsu  210096, China; Lu, X., Department of Rehabilitation, Zhongda Hospital, Southeast University, Nanjing, Jiangsu  210009, China; Fan, H., Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Shandong Technology and Business University, Yantai, Shandong  264005, China","Classification of action intention understanding is important for intelligent human-robot interaction research, and feature extraction is one of the key factors. In recent years, many feature extraction methods were proposed for the classification task. Although these methods make some achievements, it is still necessary to design new methods that can complete the classification task more efficiently. Based on three kinds of action intention understanding EEG signals, we first used synchronization likelihood (SL) to construct functional connectivity matrices in multiple frequency bands, then calculated eleven kinds of weighted brain network metrics in the functional connectivity matrices, subsequently adopted a statistical threshold to determine which kind of metric is the most useful, and finally used the metrics that were selected by the threshold as classification features to carry out the action intention understanding classification task. In experimental results, both eight metrics come from delta band and five metrics come from theta band shown their statistical values (p < 0.05), almost each classification accuracy with the single significant metric feature was higher than random level, the classification accuracy with significant metrics fusion was even close to 80%, and all permutation tests of the real classification accuracies with SVM classifier were less than 0.05. The experimental results suggest that the novel feature extraction method is extremely effective for the classification of action intention understanding EEG signals. Meanwhile, the combination of different features and classifiers given in this paper is useful to the classification tasks. © 2020 Elsevier Ltd","Action intention understanding; Classification accuracy; Synchronization likelihood; Weighted brain network metric","Classification (of information); Extraction; Feature extraction; Human robot interaction; Intelligent robots; Support vector machines; Action intentions; Brain networks; Classification accuracy; Classification features; Feature extraction methods; Functional connectivity; Statistical threshold; Synchronization likelihoods; Biomedical signal processing; article; classifier; electroencephalogram; feature extraction; functional connectivity; human; nerve cell network",,,,,"H2018042; BE2017007-3; National Natural Science Foundation of China, NSFC: 61773114","The authors would like to thank the anonymous referees for constructive recommendations, which improve the paper substantially. This work was supported in part by the National Natural Science Foundation of China under Grants 61773114 , the Foundation of Hygiene and Health of Jiangsu Province under Grant H2018042 , and the Key Research and Development Plan (Industry Foresight and Common Key Technology) of Jiangsu Province under Grant BE2017007-3 .",,,,,,,,,,"Satpute, A.B., Fenker, D.B., Waldmann, M.R., An f-MRI study of causal judgements (2005) Eur. J. Neurosci., 22 (5), pp. 1233-1238; Ortigue, S., Sinigaglia, C., Rizzolatti, G., Grafton, S.T., Understanding actions of others: the electrodynamics of the left and right hemispheres. A high-density EEG neuroimaging study (2010) PLoS One, 5; Cacippo, J.T., Berntson, G.G., Decety, J., Social neuroscience and its relationship to social psychology (2010) Soc. Cognit., 28, pp. 675-685; Catmur, C., Understanding intentions from actions: direct perception, inference, and the roles of mirror and mentalizing systems (2015) Conscious. 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"Zeller F., Smith D.H., Au Duong J., Mager A.","57099059200;57000688100;57209828231;57209822188;","Social Media in Human–Robot Interaction",2020,"International Journal of Social Robotics","12","2",,"389","402",,1,"10.1007/s12369-019-00573-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068872653&doi=10.1007%2fs12369-019-00573-4&partnerID=40&md5=a850e062f567fdc0dcdf18f8a2ad0af2","School of Professional Communication, Ryerson University, 350 Victoria Street, Toronto, ON, Canada; McMaster University, 1280 Main Street West, Hamilton, ON, Canada; Toronto, ON, Canada","Zeller, F., School of Professional Communication, Ryerson University, 350 Victoria Street, Toronto, ON, Canada; Smith, D.H., McMaster University, 1280 Main Street West, Hamilton, ON, Canada; Au Duong, J., School of Professional Communication, Ryerson University, 350 Victoria Street, Toronto, ON, Canada; Mager, A., Toronto, ON, Canada","The objective of this paper is to discuss and demonstrate in a case study the application of social media as a tool for acceptance, sociability and interaction in human–robot interaction (HRI). By adopting the concept of mediatization as discussed in media and communication studies, we aim to propose an extended usage of social media in the design of robots but also in collecting data on their acceptance and sociability. We aim to provide a starting point for ongoing studies into the extension of HRI evaluation frameworks, which provide additional ways to measure HRI based on a media and communication studies approach. Our discussion is supported by results of an HRI public engagement project, which included a social media strategy. The project was designed around a hitchhiking robot, called hitchBOT, which was dependent on the public’s engagement and support in its hitchhiking objectives. The robot’s social media strategies included accounts and profiles on Facebook, Twitter, Instagram, and a website. The aim was to use social media in order to engage not only with those people finding hitchBOT on the side of the road, but also a wider international audience. The robot’s social media accounts provided a tool to communicate hitchBOT’s origins, personality, goals, and travels. Our use of social media to communicate hitchBOT’s personality and to instill trust, interest and emotional engagement constituted a novel contribution to the design of social robots and to the scope and scale of evaluation in HRI. © 2019, Springer Nature B.V.","Human–robot interaction; Mediatization; Public engagement; Public understanding; Social media","Agricultural robots; Machine design; Social networking (online); Emotional engagements; Evaluation framework; Facebook; Public engagement; Robot interactions; Social media; Social robots",,,,,,,,,,,,,,,,"Hjarvard, S., Mediatisation of society. 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Pennebaker Conglomerates, , Austin.www.LIWC.net; Bae, Y., Lee, H., Sentiment analysis of Twitter audiences: measuring the positive or negative influence of popular twitterers (2012) J Am Soc Inf Sci Technol, 63, pp. 2521-2535; Grimmer, J., Stewart, B.M., Text as data: the promise and pitfalls of automatic content analysis methods for political texts (2013) Polit Anal, 21, pp. 267-297; Kennedy, H., Perspectives on sentiment analysis (2012) J Broadcast Electron Media, 56, pp. 435-450; Young, L., Soroka, S., Affective news: the automated coding of sentiment in political texts (2012) Polit Commun, 29, pp. 205-231; Tausczik, Y.R., Pennebaker, J.W., The psychological meaning of words: LIWC and computerized text analysis methods (2010) J Lang Soc Psychol, 29, pp. 24-54; Bednarek, M., Caple, H., (2012) News discourse, , Continuum International Publishing Group, London, New York","Zeller, F.; School of Professional Communication, 350 Victoria Street, Canada; email: fzeller@ryerson.ca",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85068872653
"Xu Y., Liu J., Zhai Y., Gan J., Zeng J., Cao H., Scotti F., Piuri V., Labati R.D.","57222004368;57200213596;57198524573;35902507700;55695155000;57203500008;6602632600;35426171300;57201496557;","Weakly supervised facial expression recognition via transferred DAL-CNN and active incremental learning",2020,"Soft Computing","24","8",,"5971","5985",,3,"10.1007/s00500-019-04530-1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076203205&doi=10.1007%2fs00500-019-04530-1&partnerID=40&md5=2a8e3e83f6c78496516fc583deb7a440","Department of Intelligent Manufacturing, Wuyi University, Jiangmen, 529020, China; Dipartimento di Informatica, Universita’ Degli Studi di Milano, Via Celoria 18, Milan, MI  20133, Italy","Xu, Y., Department of Intelligent Manufacturing, Wuyi University, Jiangmen, 529020, China; Liu, J., Department of Intelligent Manufacturing, Wuyi University, Jiangmen, 529020, China; Zhai, Y., Department of Intelligent Manufacturing, Wuyi University, Jiangmen, 529020, China; Gan, J., Department of Intelligent Manufacturing, Wuyi University, Jiangmen, 529020, China; Zeng, J., Department of Intelligent Manufacturing, Wuyi University, Jiangmen, 529020, China; Cao, H., Department of Intelligent Manufacturing, Wuyi University, Jiangmen, 529020, China; Scotti, F., Dipartimento di Informatica, Universita’ Degli Studi di Milano, Via Celoria 18, Milan, MI  20133, Italy; Piuri, V., Dipartimento di Informatica, Universita’ Degli Studi di Milano, Via Celoria 18, Milan, MI  20133, Italy; Labati, R.D., Dipartimento di Informatica, Universita’ Degli Studi di Milano, Via Celoria 18, Milan, MI  20133, Italy","In recent years, facial expression recognition (FER) has becoming a growing topic in computer vision with promising applications on virtual reality and human–robot interaction. Due to the influence of illumination, individual differences, attitude variation, etc., facial expression recognition with robust accuracy in complex environment is still an unsolved problem. Meanwhile, with the wide use of social communication, massive data are uploaded to the Internet; the effective utilization of those data is still a challenge due to noisy label phenomenon in the study of FER. To resolve the above-mentioned problems, firstly, a double active layer-based CNN is established to recognize the facial expression with high accuracy by learning robust and discriminative features from the data, which could enhance the robustness of network. Secondly, an active incremental learning method was utilized to tackle the problem of using Internet data. During the training phase, a two-stage transfer learning method is explored to transfer the relative information from face recognition to FER task to alleviate the inadequate training data in deep convolution network. Besides, in order to make better use of facial expression data from Web site and further improve the FER accuracy, Unconstrained Facial Expression Database from Web site database is built in this paper. Extensive experiments performed on two public facial expression recognition databases FER 2013 and SFEW 2.0 have demonstrated that the proposed scheme outperforms the state-of-the-art methods, which could achieve 67.08% and 51.90%, respectively. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.","Deep convolutional neural network; Facial expression recognition; Two-stage transfer learning; Weakly supervised active incremental learning","Convolution; Database systems; Deep neural networks; Human computer interaction; Human robot interaction; Neural networks; Virtual reality; Websites; Convolutional neural network; Discriminative features; Facial expression recognition; Incremental learning; Individual Differences; State-of-the-art methods; Transfer learning; Transfer learning methods; Face recognition",,,,,"2017B030314131; 2015zk11; National Natural Science Foundation of China, NSFC: 61771347; Bureau of Science and Technology of Jiangmen Municipality: 268; Science and Technology Planning Project of Guangdong Province: 2017A010101019; Colleges Innovation Project of Guangdong: 2017KTSCX181; Leading Talents Program of Guangdong Province: 2017KQNCX206","This work is supported by National Natural Science Foundation of China (No. 61771347); Characteristic Innovation Project of Guangdong Province (No. 2017KTSCX181); Young Innovative Talents Project of Guangdong Province (2017KQNCX206); Jiangmen Science and Technology Project ([2017] No. 268); 2017 Guangdong Science and Technology Plan Project (No. 2017A010101019); Youth Foundation of Wuyi University (No. 2015zk11); the Opening Project of GuangDong Province Key Laboratory of Information Security Technology (Grant No. 2017B030314131); the 2018 Opening Project of GuangDong Province Key Laboratory of Digital Signal and Image Processing.","This work is supported by National Natural Science Foundation of China (No. 61771347); Characteristic Innovation Project of Guangdong Province (No. 2017KTSCX181); Young Innovative Talents Project of Guangdong Province (2017KQNCX206); Jiangmen Science and Technology Project ([2017] No. 268); 2017 Guangdong Science and Technology Plan Project (No. 2017A010101019); Youth Foundation of Wuyi University (No. 2015zk11); the Opening Project of GuangDong Province Key Laboratory of Information Security Technology (Grant No. 2017B030314131); the 2018 Opening Project of GuangDong Province Key Laboratory of Digital Signal and Image Processing.",,,,,,,,,"Ahmed, A., (2016) Facial Expression Recognition Using Gabor Wavelet and Artificial Neural Networks, , Dissertation Sudan University of Science and Technology; Alsamirat, M.A., Al-Alem, F., Al-Ayoub, M., Jararweh, Y., Gupta, B., Impact of digital fingerprint image quality on the fingerprint recognition accuracy (2019) Multimed Tools Appl, 78 (3), pp. 3649-3688; Burkert, P., Trier, F., Afzal, M., (2015) Dexpression: Deep Convolutional Neural Network for Expression Recognition; Chen, Y.S., Performance identification in large-scale class data from advanced facets of computational intelligence and soft computing techniques (2019) Int J High Perform Comput Netw, 13 (3), pp. 283-293; Chen, J., Liu, X., Tu, P., Person-specific expression recognition with transfer learning (2012) Proceedings of IEEE International Conference on Image Processing, pp. 2621-2624; Ding, H., Zhou, S., Chellappa, R., Facenet2expnet: Regularizing a deep face recognition net for expression recognition (2017) Proceedings of the 12Th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 118-126; 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Zhou, Y., Xu, T., Zheng, W., Classification and recognition approaches of tomato main organs based on DCNN (2017) Trans Chin Soc Agric Eng, 33 (15), pp. 219-226; Zhou, Z., Shin, J., Gurudu, S., (2018) AFT*: Integrating Active Learning and Transfer Learning to Reduce Annotation Efforts; Zhu, R., Zhang, T., Zhao, Q., A transfer learning approach to cross-database facial expression recognition (2015) International Conference on Biometrics, pp. 293-298","Zhai, Y.; Department of Intelligent Manufacturing, China; email: yikuizhai@163.com",,,"Springer",,,,,14327643,,,,"English","Soft Comput.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85076203205
"Lee S., Naguib A.M.","7601402085;55808267500;","Toward a Sociable and Dependable Elderly Care Robot: Design, Implementation and User Study",2020,"Journal of Intelligent and Robotic Systems: Theory and Applications","98","1",,"5","17",,8,"10.1007/s10846-019-01028-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065710025&doi=10.1007%2fs10846-019-01028-8&partnerID=40&md5=d9b1f2ebfee6ca9db6eb1ba0fbe818d3","Intelligent Systems Research Institute, School of Information and Communication Engineering, Sungkyunkwan University, Suwon, South Korea","Lee, S., Intelligent Systems Research Institute, School of Information and Communication Engineering, Sungkyunkwan University, Suwon, South Korea; Naguib, A.M., Intelligent Systems Research Institute, School of Information and Communication Engineering, Sungkyunkwan University, Suwon, South Korea","A critical demand for innovation in elderly care, especially in delivering the quality of service to elderlies, arises as many parts of the world are rapidly in transition to an elderly society. The advancement of robotic technologies, especially, in cognitive robotics and sociable human-robot interaction offers a great opportunity for meeting such demand. This paper presents the design and implementation of a next generation of elderly care robot, named as “HomeMate,” based on an innovative undertaking in its sociability and dependability with extensive user studies. The elderlies taken care of by Senior Welfare Centers are chosen as the target sector, for which the following five service scenarios are designed: infotainment, video chatting, game playing, medicine alarm and, in particular, errand services. Unlike conventional approaches, the scenarios are designed here to ensure the overall quality of service by maximizing the synergy under an elderly-caregiver-service robot ecosystem. The unique features implemented in HomeMate include 1) the principle of affordance in appearance design by matching functionality and anthropomorphism indices, 2) the sociability implemented by balancing between autonomy and user controllability as well as by integrating multimodal interactions into HomeMate avatar, and 3) the emphasis on dependability to inspire confidence on HomeMate as a trusted assistant, for which the principle of dependability is proposed and implemented with a cognitive framework. Experiments and user studies strongly support the proposed design principles and verify the dependability in performance. © 2019, Springer Nature B.V.","Affordance; Dependability; Elderly care robot; Emotional design; HomeMate; Interaction design; Service robot; Sociability","Interactive computer graphics; Machine design; Mobile robots; Quality of service; Robotics; Telecommunication services; Affordances; Dependability; Elderly care; Emotional design; HomeMate; Interaction design; Service robots; Sociability; Human robot interaction",,,,,"Sungkyunkwan University, SKKU; Ministry of Science, ICT and Future Planning, MSIP: KEIT P0006886; Korea Evaluation Institute of Industrial Technology, KEIT; Institute for Information and Communications Technology Promotion, IITP: 2015-1 0060160","The major part of this paper is based on the results from the research project, the KORUS-Tech Program (KT-2010-SW-AP-FSO-0004), funded by Korean Ministry of Industry and Trade, for which the first author served as the principal investigator. The project was carried out by the partnership with Yujin Robot Co. and Bonavision Co. in Korea as well as Georgia-Tech and Penn State in US. Yujin Robot contributed to the design and manufacturing of HomeMate platforms while Bonavision contributed to the development of a dialog manager. Georgia-Tech contributed to sociable interaction with human following and joint attention in the 1st phase of the project, while Penn State contributed to the US user study in the 2nd phase. The contributions of the project team members at Sungkyunkwan University are also very much appreciated: In particular, Angie Lorena Mar?n Mej?a for Avatar and Korean user study, Joo Yun Han for Korean user study, Zhao Xinshuang for calling gesture recognition, Ahmed M. Naguib for Adaptive Bayesian Evidence Network and Chen Xi for Evidence Collection. Finally, our appreciation goes to the support, in part, by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant (No.2019-0-00421, AI Graduate School Support Program) , in part, by the ?3D Visual Recognition Project? (KEIT: 2015-1 0060160) sponsored by the Korea Ministry of Science, ICT and Planning (MSIP), and, in part, by the ?Robocarechair: A Smart Transformable Robot for Multi-Functional Assistive Personal Care Project,? KEIT P0006886, of the Korea Evaluation Institute of Industrial Technology (KEIT).",,,,,,,,,,"(2015) “World Population Prospects, , The, Revision”; Mlinac, M.E., Feng, M.C., Assessment of Activities of Daily Living, Self-Care, and Independence (2016) Arch. Clin. 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Robot., 1, p. 4; Kittmann, R., Let me introduce myself: I am Care-O-bot 4, a gentleman robot (2015) Mensch Und Computer 2015–proceedings, pp. 223-232; http://www.riken.jp/en/pr/press/2015/20150223_2/, Robear, The strong robot with the gentle touch; Sung, J., (2011) Towards the Human-Centered Design of Everyday Robots, , http://hdl.handle.net/1853/39539, PhD dissertation at Georgia-Tech, URI; http://en.yujinrobot.com, the website of Yujin Robots Co. to refer to; Marin, A.L., Jo, D., Lee, S., Designing robotic avatars: Are user's impression affected by avatar's age? (2013) Proceedings of the 8Th ACM/IEEE International Conference on Human-Robot Interaction, pp. 195-196. , IEEE Press; http://aibrain.asia, the website of Bonavision Co. to refer to; Zhao, X., Naguib, A.M., Lee, S., Kinect based calling gesture recognition for taking order service of elderly care robot (2014) The 23Rd IEEE International Symposium on Robot and Human Interactive Communication. IEEE; Xinshuang, Z., Naguib, A.M., Lee, S., Octree segmentation based calling gesture recognition for elderly care robot (2014) Proceedings of the 8Th International Conference on Ubiquitous Information Management and Communication, , ACM; Lee, S., Caller Identification Based on Cognitive Robotic Engine (2006) ROMAN 2006 - the 15Th IEEE International Symposium on Robot and Human Interactive Communication, , IEEE; Lee, S., Wei, L., Naguib, A.M., Adaptive Bayesian recognition and pose estimation of 3D industrial objects with optimal feature selection (2016) 20 16 IEEE International Symposium on Assembly and Manufacturing (ISAM, pp. 50-55. , IEEE; Naguib, A.M., Lee, S., An adaptive evidence structure for Bayesian recognition of 3D objects (2015) Proceedings of the 9Th International Conference on Ubiquitous Information Management and Communication, p. 75. , ACM; Chen, X., Lee, S., Visual search of an object in cluttered environments for robotic errand service (2013) 2013 IEEE International Conference on Systems, Man, and Cybernetics, pp. 4060-4065. , IEEE; http://isrc.skku.ac.kr/DB3D/db.php; https://youtu.be/4ENFcHP1GaI, Errand Service Scenario Videos, https://youtu.be/DAh4IMUiRZ0, https://youtu.be/D3u5CY_soLk; https://youtu.be/8AHDN8LHgME, https://youtu.be/EozwRwDhyBw, https://youtu.be/XPoH6hu_tSc; Lee, H., Sung, J., Šabanović, S., Han, J., Cultural design of domestic robots: A study of user expectations in Korea and the United States (2012) 2012 IEEE ROMAN: The 21St IEEE International Symposium on Robot and Human Interactive Communication, pp. 803-808. , Paris; Naguib, A.M., Chen, X., Lee, S., Cognitive Re cognition Under Occlusion for Visually Guided Robotic Errand Service (2016) Intelligent Autonomous Systems, 13, pp. 81-94. , Springer, Cham","Lee, S.; Intelligent Systems Research Institute, South Korea; email: lsh1@skku.edu",,,"Springer",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","",Scopus,2-s2.0-85065710025
"Song H., Zhang Z., Barakova E.I., Ham J., Markopoulos P.","57215834135;57215831429;35614959800;23485318400;57050756600;","Robot role design for implementing social facilitation theory in musical instruments practicing",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"253","260",,2,"10.1145/3319502.3374787","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082014705&doi=10.1145%2f3319502.3374787&partnerID=40&md5=151620f96d4e32d8ec28e6621cad1f8f","Eindhoven University of Technology, Eindhoven, Netherlands","Song, H., Eindhoven University of Technology, Eindhoven, Netherlands; Zhang, Z., Eindhoven University of Technology, Eindhoven, Netherlands; Barakova, E.I., Eindhoven University of Technology, Eindhoven, Netherlands; Ham, J., Eindhoven University of Technology, Eindhoven, Netherlands; Markopoulos, P., Eindhoven University of Technology, Eindhoven, Netherlands","The application of social robots has recently been explored in various types of educational settings including music learning. Earlier research presented evidence that simply the presence of a robot can influence a person's task performance, confirming social facilitation theory and findings in human-robot interaction. Confirming the evaluation apprehension theory, earlier studies showed that next to a person's presence, also that person's social role could influence a user's performance: The presence of a (non-) evaluative other can influence the user's motivation and performance differently. To be able to investigate that, researchers need the roles for the robot which is missing now. In the current research, we describe the design of two social roles (i.e., evaluative role and non-evaluative role) of a robot that can have different appearances. For this, we used the SocibotMini: A robot with a projected face, allowing diversity and great flexibility of human-like social cue presentation. An empirical study at a real practice room including 20 participants confirmed that users (i.e., children) evaluated the robot roles as intended. Thereby, the current research provided the robot roles allowing to study whether the presence of social robots in certain social roles can stimulate practicing behavior and suggestions of how such roles can be designed and improved. Future studies can investigate how the presence of a social robot in a certain social role can stimulate children to practice. © 2020 Association for Computing Machinery.","Child-robot interaction; Evaluation apprehension theory; Musical instrument practicing; Robot role design; Social facilitation theory","Machine design; Man machine systems; Musical instruments; Social robots; Child-robot interactions; Educational settings; Empirical studies; Evaluation apprehension theory; Motivation and performance; Social facilitations; Social roles; Task performance; Economic and social effects",,,,,"China Scholarship Council, CSC","This research is funded by China Scholarship Council (CSC). Thanks to Centrum voor de Kunsten Eindhoven (CKE) for participant recruitment and hosting the experiment.",,,,,,,,,,"Barakova, E.I., Bajracharya, P., Willemsen, M., Lourens, T., Huskens, B., Long-term lego therapy with humanoid robot for children with asd (2015) Expert Systems, 32 (6), pp. 698-709. , http://doi.org/10.1111/exsy12098, (Dec. 2015); Bray, R.M., Sugarman, R., Social facilitation among interacting groups: Evidence for the evaluation-apprehension hypothesis (1980) Personality and Social Psychology Bulletin, 6 (1), pp. 137-142. , http://doi.org/10.1177/014616728061021, March 1980; Brown, L.N., Howard, A.M., The positive effects of verbal encouragement in mathematics education using a social robot (2014) 2014 IEEE Integrated STEM Education Conference, pp. 1-5. , http://doi.org/10.1109/ISECon.2014.6891009, IEEE, Princeton, NJ, USA; Nicholas Carleton, R., McCreary, D.R., Norton, P.J., Asmundson, G.G.J., Brief fear of negative evaluation scale-revised (2006) Depression and Anxiety, 23 (5), pp. 297-303. , http://doi.org/10.1002/da.20142, (2006); Colon-Leon, V., (2018) A Model of Parental Involvement in the Music Education of Students with Special Education Needs, , http://scholarlyrepository.miami.edu/oa_dissertations/2103, Dissertation. 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Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85082014705
"Tien M., Park Y.Y., Jung K.-H., Kim S.-Y., Kye J.-E.","57209821275;7405371556;56494024900;57195605531;55616283600;","Performance evaluation on the accuracy of the semantic map of an autonomous robot equipped with P2P communication module",2020,"Peer-to-Peer Networking and Applications","13","2",,"704","716",,1,"10.1007/s12083-019-00851-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077062140&doi=10.1007%2fs12083-019-00851-y&partnerID=40&md5=93509f2522e62c52c66dd0ed7c2337a9","Department of Sun Moon University, Asan, South Korea; Office of Strategic R&D Planning, Ministry of Trade Industry and Energy, Sejong-si, South Korea","Tien, M., Department of Sun Moon University, Asan, South Korea; Park, Y.Y., Department of Sun Moon University, Asan, South Korea; Jung, K.-H., Department of Sun Moon University, Asan, South Korea; Kim, S.-Y., Department of Sun Moon University, Asan, South Korea; Kye, J.-E., Office of Strategic R&D Planning, Ministry of Trade Industry and Energy, Sejong-si, South Korea","Semantic mapping plays an important role in the mobile robotic area, helping robots to perform numerous complicated tasks in both industry and daily life. According to the semantic map, the robot can automatically operate some challenging tasks such as path-planning, place localization, and human-robot interaction. Also, the terminologies of a group of homogenous or heterogeneous robots that perform a general purpose to solve a heavy task with resource limitation become more popular in robot society. To build a multi-robot system, the communication protocol is an essential component to cooperate with a team of robots. Therefore, we propose a multi-robot mapping system, which adopts the P2P communication protocol to solve the mapping problem and map segmentation method from the given mapping or floor plan. The P2P network is constructed based on a centralized overlay network to be scalable and broadcast the control command from the base station to all robots. For generating a semantic map, there have been many works to cope with this task; however, they are still lack of performance. Thus, we perform both feature extraction and feature selection from Voronoi node to enhance the performance of map segmentation. Then to support vector machine (SVM) algorithm and artificial neural network (ANN) are adopted for classifying the segmented areas from Voronoi Graph. The experiment of SVM and ANN shows that they accomplish in map segmentation; however, the SVM algorithm has slightly higher accuracy. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.","Artificial neural network; Multi-robot mapping; P2P networking; Semantic map; Suport vector machine; Voronoi diagram","Feature extraction; Industrial robots; Mapping; Motion planning; Multipurpose robots; Neural networks; Peer to peer networks; Robot programming; Semantics; Support vector machines; Multirobots; P2P networking; Semantic map; Vector machines; Voronoi diagrams; Human robot interaction",,,,,"National Research Foundation of Korea, NRF: NRF2016R1A2B4014223","This work was supported by National Research Foundation of Korea Grant Funded by the Korean Government (NRF2016R1A2B4014223). Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.",,,,,,,,,,"Bormann, R., Hampp, J., Hägele, M., New brooms sweep clean - an autonomous robotic cleaning assistant for professional office cleaning (2015) In IEEE International Conference on Robotics and Automation (ICRA), pp. 4470-4477; Blodow, N., Goron, L.C., Marton, Z., Pangercic, D., Rühr, T., Tenorth, M., Beetz, M., Autonomous semantic mapping for robots performing everyday manipulation tasks in kitchen environments (2011) In IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4263-4270; Howard, A., Multi-robot simultaneous localization and mapping using particle filters (2005) In The International Journal of Robotics Research, 25, pp. 1243-1256; León, R., Barea, L.M., Bergasa, E., Lopez, M., Ocaña, D.S., SLAM and map merging (2009) Journal of Physical Agents; Dinnissen, P., Givigi, S.N., Schwartz, H.M., Map merging of Multi-Robot SLAM using Reinforcement Learning (2012) In IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 53-60; 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Althaus, P., Christensen, H.I., Behavior coordination in structured environments (2003) In Advanced Robotics, 17 (7), pp. 657-674; Tapussiegwart, R., Incremental robot mapping with fingerprints of places (2005) In IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2429-2434; Torralbamurphyfreemanrubin, Context-based vision system for place and object recognition (2003) In Proc. of Ninth IEEE International Conference on Computer Vision, 1, pp. 273-280; Friedman, S., Pasula, H.M., Fox, D., Voronoi random fields: Extracting topological structure of indoor environments via place labeling (2007) In IJCAI; Beeson, P., Jong, N.K., Kuipers, B., Towards autonomous topological place detection using the extended Voronoi graph (2005) In Proc of the IEEE International Conference on Robotics and Automation, pp. 4373-4379; Fabrizi, E., Saffiotti, A., Augmenting topology-based maps with geometric information (2002) In Robotics and Autonomous Systems, 40, pp. 91-97; Quigley, M., Conley, K., Gerkey, P., Brianfaust, J., Foote, T., Leibs, J., Wheelerng Andrew, R., ROS: An open-source robot operating system (2009) In ICRA Workshop on Open Source Software; 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Blöchliger, F., Fehr, M., Dymczyk, M., Schneider, T., Siegwart, R., Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps (2018) In IEEE International Conference on Robotics and Automation (ICRA), pp. 1-9; de las Heras, L.-P., Ahmed, S., Liwicki, M., Valveny, E., Sánchez, G., Statistical segmentation and structural recognition for floor plan interpretation (2013) International Journal on Document Analysis and Recognition (IJDAR), 17 (3), pp. 221-237; Ahmed, S., Liwicki, M., Weber, M., Dengel, A., Automatic room detection and room labeling from architectural floor plans (2012) 10Th IAPR International Workshop on Document Analysis Systems, pp. 339-343","Park, Y.Y.; Department of Sun Moon UniversitySouth Korea; email: yypark215@gmail.com",,,"Springer",,,,,19366442,,,,"English","Peer-to-Peer Netw. Appl.",Article,"Final","",Scopus,2-s2.0-85077062140
"Oliveira J., Ferreira A., Reis J.C.P.","49864144600;57190866650;24345131900;","Design and experiments on an inflatable link robot with a built-in vision sensor",2020,"Mechatronics","65",,"102305","","",,8,"10.1016/j.mechatronics.2019.102305","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086845167&doi=10.1016%2fj.mechatronics.2019.102305&partnerID=40&md5=50563ba82aaef7c9b5e3ea461c5b20ed","IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal","Oliveira, J., IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal; Ferreira, A., IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal; Reis, J.C.P., IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal","Soft robots and manipulators with inflatable links possess inherent safety characteristics that make them a very interesting choice for tasks requiring human-robot interaction. However, they may face important challenges in their performance due to low structural stiffness. Accurate positioning of the end effector may prove a difficult task due to limitations in gravity compensation, or due to the occurrence of oscillations during motion, especially in the absence of precise information about the payload that is being manipulated. In this paper a novel approach to the design and control of a robot with an inflatable link is proposed, where the link assumes the triple role of compliant structural element, touch sensor, and active mechanism to adjust the performance of vibration control algorithms. For this, a vision sensor is placed inside the link to provide information about the structural deformation. The sensor is used to measure the tip displacement, and also to detect contact of the link surface with the surrounding environment. The concept was implemented on a two-joint rigid manipulator with a single inflatable link. Experiments on vibration control and contact detection of the inflatable link are reported where the control system was able to significantly reduce tip oscillations, including when a payload was added to the tip. The robot was also able to make binary detection of contact between the inflatable link and the user, and change its operation mode accordingly. © 2019","Flexible robots; Inflatable robots; Physical human-robot interaction; Soft material robotics","Computer vision; Human robot interaction; Man machine systems; Manipulators; Vibration control; Flexible robots; Inflatable robot; Inherent safety; Performance; Physical humanrobot interaction (phri); Soft manipulator; Soft material robotic; Soft robot; Softer materials; Vision sensors; Machine design",,,,,"Fundação para a Ciência e a Tecnologia, FCT: SFRH/BSAB/142990/2018, UID/EMS/50022/2019","This work was partially supported by FCT , through IDMEC, under LAETA, project UID/EMS/50022/2019 . João C.P. Reis acknowledges the support by FCT through grant SFRH/BSAB/142990/2018 .",,,,,,,,,,"Kim, S., Laschi, C., Trimmer, B., Soft robotics: a bioinspired evolution in robotics (2013) TrendsBiotechnol, 31 (5), pp. 287-294; Rus, D., Tolley, M.T., Design, fabrication and control of soft robots (2015) Nature, 521 (7553), pp. 467-475; Benosman, M., Le Vey, G., Control of flexible manipulators: a survey (2004) Robotica, 22 (5), pp. 533-545; Dwivedy, S., PEberhard, Dynamic analysis of flexible manipulators, a literature review (2006) Mech Mach Theory, 41 (7), pp. 749-777; Kiang, C.T., Spowage, A., Yoong, C.K., Review of control and sensor system of flexible manipulator (2015) J Intell Robot Syst, 77 (1), pp. 187-213; Sayahkarajy, M., Mohamed, Z., Mohd Faudzi, A.A., Review of modelling and control of flexible-link manipulators (2016) Proc Inst MechEng Part I, 230 (8), pp. 861-873; Reis, J.C., da Costa, J.S., Motion planning and actuator specialization in the control of active-flexible link robots (2012) J Sound Vib, 331 (14), pp. 3255-3270; Reis, J.C., da Costa, J.S., Design and experimental control of an active-flexible tubular manipulator link (2012) J Intell Mater SystStruct, 23 (15), pp. 1741-1757; Baldur, R., Blach, W., Inflatable manipulator (1985) Robots 9 conf. proc.; Koren, Y., Weinstein, Y., Inflatable structure. 1991., U.S. Patent 5,065,640; Salomonski, N., Shoham, M., Grossman, G., Light robot arm based on inflatable structure (1995) CIRP Ann Manuf Technol, 44 (1), pp. 87-90; Rybski, M., Shoham, M., Grossman, G., Robotic manipulators based on inflatable structures (1996) Robot Comput-Integr Manuf, 12 (1), pp. 111-120; Sanan, S., Moidel, J.B., Atkeson, C.G., Robots with inflatable links (2009) 2009 IEEE/RSJ international conference on intelligent robots and systems, pp. 4331-4336. , IEEE; Sanan, S., Ornstein, M.H., Atkeson, C.G., Physical human interaction for an inflatable manipulator (2011) 2011 annual international conference of the IEEE engineering in medicine and biology society, pp. 7401-7404. , IEEE; Voisembert, S., Riwan, A., Mechbal, N., Barraco, A., A novel inflatable robot with constant and continuous volume (2011) Robotics and automation (ICRA), 2011 IEEE international conference on, pp. 5843-5848. , IEEE; Voisembert, S., Riwan, A., Mechbal, N., Numerical evaluation of a new robotic manipulator based on inflatable joints (2012) 2012 IEEE international conference on automation science and engineering (CASE), pp. 544-549. , IEEE; Voisembert, S., Mechbal, N., Riwan, A., Aoussat, A., Design of a novel long-range inflatable robotic arm: manufacturing and numerical evaluation of the joints and actuation (2013) J Mech Robot, 5 (4), p. 45001; Qi, R., Lam, T.L., Xu, Y., Mechanical design and implementation of a soft inflatable robot arm for safe human-robot interaction (2014) 2014 IEEE international conference on robotics and automation (ICRA), pp. 3490-3495. , IEEE; Qi, R., Lam, T.L., Xu, Y., Kinematic modeling and control of a multi-joint soft inflatable robot arm with cable-driven mechanism (2014) 2014 IEEE international conference on robotics and automation (ICRA), pp. 4819-4824. , IEEE; Kim, H.-J., Tanaka, Y., Kawamura, A., Kawamura, S., Nishioka, Y., Development of an inflatable robotic arm system controlled by a joystick (2015) Robot and jhuman interactive communication (RO-MAN), 2015 24th IEEE international symposium on, pp. 664-669. , IEEE; Kim, H.-J., Tanaka, Y., Kawamura, A., Kawamura, S., Nishioka, Y., Improvement of position accuracy for inflatable robotic arm using visual feedback control method (2015) 2015 IEEE international conference on advanced intelligent mechatronics (AIM), pp. 767-772. , IEEE; Best, C.M., Wilson, J.P., Killpack, M.D., Control of a pneumatically actuated, fully inflatable, fabric-based, humanoid robot (2015) Humanoid robots (Humanoids), 2015 IEEE-RAS 15th international conference on, pp. 1133-1140. , IEEE; Gillespie, M.T., Best, C.M., Killpack, M.D., Simultaneous position and stiffness control for an inflatable soft robot (2016) 2016 IEEE international conference on robotics and automation (ICRA), pp. 1095-1101. , IEEE; Hyatt, P., Kraus, D., Sherrod, V., Rupert, L., Day, N., Killpack, M.D., Configuration estimation for accurate position control of large-scale soft robots (2019) IEEE/ASME Trans Mechatron, 24 (1), pp. 88-99; Immega, G., Antonelli, K., The ksi tentacle manipulator (1995) Proceedings of 1995 IEEE international conference on robotics and automation, 3, pp. 3149-3154. , IEEE; Pritts, M.B., Rahn, C.D., Design of an artificial muscle continuum robot (2004) IEEE international conference on robotics and automation, 2004. proceedings. 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(2019) Adv Robot, pp. 1-13; Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G., Robotics: modelling, planning and control (2010), Springer Science & Business Media; Singhose, W.E., (1997) Command generation for flexible systems, , Ph.D. thesis Massachusetts Institute of Technology; Singhose, W., Command shaping for flexible systems: a review of the first 50 years (2009) Int J Precis EngManuf, 10 (4), pp. 153-168","Reis, J.C.P.; IDMEC, Portugal; email: joao.c.p.reis@tecnico.ulisboa.pt",,,"Elsevier Ltd",,,,,09574158,,MECHE,,"English","Mechatronics",Article,"Final","",Scopus,2-s2.0-85086845167
"Zhao H., Wang M., Liu J., Wang P., Guo S.","55496714400;57215321364;37034409100;57213430555;7403650479;","Mechanism study of flexible tactile sensor based on electrical double-layer capacitor [基于双电层电容的柔性触觉传感器机理研究]",2020,"Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument","41","2",,"25","32",,4,"10.19650/j.cnki.cjsi.J1905671","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085765697&doi=10.19650%2fj.cnki.cjsi.J1905671&partnerID=40&md5=9cf3e7df4fff8cee9429946bf9d9ae36","School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300132, China; Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300132, China; State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, 300132, China","Zhao, H., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300132, China, Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300132, China; Wang, M., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300132, China, Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300132, China; Liu, J., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300132, China, Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300132, China, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, 300132, China; Wang, P., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300132, China, Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300132, China; Guo, S., School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300132, China, Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction, Hebei University of Technology, Tianjin, 300132, China, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, 300132, China","Tactile information is one of the important ways for robots to perceive the working environment and a key factor of ensuring safety and comfortability in human-robot collaboration. However, compared with the development of visual, auditory and olfactory sensors, the application and industrialization of robotic tactile sensors still relatively lag behind. This paper proposes a robot flexible tactile sensor based on the principle of the electrical double-layer capacitor. The pressure sensor has the advantages of simple structure, high sensitivity, large measurement range, high flexibility, high signal to noise ratio, low fabrication and use cost, etc. The sensor consists of upper and lower two layers of electrodes and a fiber layer of intermediate ion gel. When external pressure is applied to the sensor, the ion fiber layer is compressed, and the contact area between the electrodes and the ion fiber layer is increased, which results in the increasing of the sensor capacitance. The experiment data shows that the pressure and capacitance have a good linear relationship, and the sensitivity of the sensor is high, which can reach 3.97 nF/kPa, the pressure measurement range is wide, which is up to 500 kPa, the maximum hysteresis error is 5.97%, and the dynamic response can properly follow the external pressure stimulus. The proposed tactile sensor could be widely applied to the situations such as robotic sensing, human-robot interaction, wearable equipment, and so on, and has great academic and industrial value. © 2020, Science Press. All right reserved.","Electrical double-layer capacitors; Pressure graphing; Robotic tactile sensing; Tactile sensor","Capacitance; Electrodes; Human robot interaction; Ions; Robotics; Safety factor; Signal to noise ratio; Electrical double layer capacitor; Flexible tactile sensors; High signal-to-noise ratio; Human-robot collaboration; Linear relationships; Sensor capacitance; Tactile information; Working environment; Wearable sensors",,,,,,,,,,,,,,,,"Hammock, M.L., Chortos, A., Tee, B.C., 25th anniversary article: The evolution of electronic skin (e-skin): a brief history, design considerations, and recent progress (2013) Advanced Materials, 25 (42), pp. 5997-6038; Liang, C.H., Wang, P., Cao, B.B., Wearable intelligent detection system of sleep quality (2018) Journal of Electronic Measurement and Instrumentation, 32 (5), pp. 159-167. , 梁超, 王鹏, 曹贝贝, 等. 可穿戴智能睡眠质量检测系统[J]. 电子测量与仪器学报, 2018, 32(5): 159-167; Koh, A., Kang, D., Xue, Y., A soft, wearable microfluidic device for the capture, storage, and colorimetric sensing of sweat (2016) Science Translational Medicine, 8 (366); Yao, J.T., Chen, J.T., Chen, X.B., Embedded flexible multi-dimensional displacement and force sensor (2018) Chinese Journal of Scientific Instrument, 39 (10), pp. 184-192. , 姚建涛, 陈俊涛, 陈新博, 等. 可嵌入式多维柔性力/位传感器[J]. 仪器仪表学报, 2018, 39(10): 184-192; Kim, D.H., Ghaffari, R., Lu, N., Flexible and stretchable electronics for biointegrated devices (2015) Annual Review of Biomedical Engineering, 14 (1), pp. 113-128; Wang, Y.B., Zhang, X.D., Mu, X.Q., Research on the prediction method of elderly fall-down for elderly-assistant and walking-assistant robot (2018) Journal of Electronic Measurement and Instrumentation, 32 (7), pp. 1-7. , 王亚宾, 张小栋, 穆小奇, 等. 用于助老伴行机器人的老年人摔倒预测方法研究[J]. 电子测量与仪器学报, 2018, 32(7): 1-7; Sawada, H., Kitani, M., Hayashi, Y., Wearable, human-interactive, health-monitoring, wireless devices fabricated by macroscale printing techniques (2014) Advanced Functional Materials, 24 (22), pp. 3299-3304; Chou, T.L., Chu, C.H., Lin, C.T., Sensitivity analysis of packaging effect of silicon-based piezoresistive pressure sensor (2009) Sensors and Actuators: A Physical, 152 (1), pp. 29-38; Castellanos-Ramos, J., Navas-González, R., Macicior, H., Tactile sensors based on conductive polymers (2010) Microsystem Technologies, 16 (5), pp. 765-776; Xu, F., Ge, Y.J., Shuang, F., Research of a three-dimensional force flexible tactile sensor based on novel conductive rubber (2011) Chinese Journal of Scientific Instrument, 32 (6), pp. 1350-1356. , 徐菲, 葛运建, 双丰, 等. 基于新型导电橡胶的三维力柔性阵列触觉传感器研究[J]. 仪器仪表学报, 2011, 32(6): 1350-1356; Hu, X.H., Xu, Z., Ming, L., A flexible capacitive tactile sensor array with micro structure for robotic application (2014) Science China Information Sciences, 57 (12), pp. 1-6; Tiwana, M.I., Shashank, A., Redmond, S.J., Characterization of a capacitive tactile shear sensor for application in robotic and upper limb prostheses (2011) Sensors & Actuators: A Physical, 165 (2), pp. 164-172; Yan, Y., Huang, Y., Chen, M., Research on photovoltaic STC curves fitting based on capacitors array I-V tracer (2017) Journal of Electronic Measurement and Instrumentation, 31 (2), pp. 272-279. , 颜奕, 黄宇, 陈鸣, 等. 基于电容阵列I-V测试仪的光伏STC曲线拟合研究[J]. 电子测量与仪器学报, 2017, 31(2): 272-279; Guo, X.P., Hou, X.J., Yang, Y.H., Flexible self-powered sensor based on PVDF-TrFE film (2018) Chinese Journal of Scientific Instrument, 39 (7), pp. 42-48. , 郭雪培, 侯晓娟, 杨玉华, 等. 基于PVDF-TrFE薄膜的柔性自供电传感器[J]. 仪器仪表学报, 2018, 39(7): 42-48; Kim, M.S., Ahn, H.R., Lee, S., A dome-shaped piezoelectric tactile sensor arrays fabricated by an air inflation technique (2014) Sensors and Actuators A: Physical, 212 (6), pp. 151-158; Li, C., Cordovilla, F., Jagdheesh, R., Design optimization and fabrication of a novel structural SOI piezoresistive pressure sensor with high accuracy (2018) Sensors, 18 (2), p. 439; Pramanik, C., Saha, H., Gangopadhyay, U., Design optimization of a high performance silicon MEMS piezoresistive pressure sensor for biomedical applicati-ons (2006) Journal of Micromechanics and Microengin-eering, 16 (10), p. 2060; Zhao, X., Gao, H., Zhang, G., Active health monitoring of an aircraft wing with embedded piezoelectric sensor/actuator network: I. Defect Detection, Localization and Growth monitoring (2007) Smart Materials and Structures, 16 (4), p. 1208; Guo, S.J., Xiao, J., Liu, J.Y., Capacitor array flexible pressure sensor design and analysis (2018) Chinese Journal of Scientific Instrument, 39 (7), pp. 49-55. , 郭士杰, 肖杰, 刘今越, 等. 电容阵列柔性压力传感器设计与分析[J]. 仪器仪表学报, 2018, 39(7): 49-55; Dobrzynska, J.A., Gijs, M.A.M., Flexible polyimide-based force sensor (2012) Sensors and Actuators A: Physical, 173 (1), pp. 127-135; Yu, L.L., Zhu, J.J., Zhao, J.T., The present situation and development trend of supercapacitors (2015) Chinese Journal of Nature, 37 (3), pp. 188-196. , 余丽丽, 朱俊杰, 赵景泰. 超级电容器的现状及发展趋势[J]. 自然杂志, 2015, 37(3): 188-196; Xiang, Y., Cao, G.P., A review on the mechanism of the energy storage about the electrochemical double-layer capacitors (2016) Energy Storage Science and Technology, 5 (6), pp. 816-827. , 向宇, 曹高萍. 双电层电容器储能机理研究概述[J]. 储能科学与技术, 2016, 5(6): 816-827; Liu, Z.X., Dong, G.J., Zhang, M.L., Preparation and performance of electric double layer capacitor (2001) Chinese Journal of Power Sources, 25 (6), pp. 413-415 and 435. , 刘志祥, 董国君, 张密林. 双电层电容器的制备及性能[J]. 电源技术, 2001, 25(6): 413-415, 435","Liu, J.; School of Mechanical Engineering, China; email: liujixiao@hebut.edu.cn",,,"Science Press",,,,,02543087,,YYXUD,,"Chinese","Yi Qi Yi Biao Xue Bao",Article,"Final","",Scopus,2-s2.0-85085765697
"Niu J., Wang X., Wang D., Ran L.","7202340841;57203137650;57205423348;35103113700;","A novel method of human joint prediction in an occlusion scene by using low‐cost motion capture technique",2020,"Sensors (Switzerland)","20","4","1119","","",,5,"10.3390/s20041119","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079735541&doi=10.3390%2fs20041119&partnerID=40&md5=602d1a4253b77d884a0b0cc8e8ce9f24","School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; China National Institute of Standardization, Beijing, 100191, China","Niu, J., School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Wang, X., School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Wang, D., School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China; Ran, L., China National Institute of Standardization, Beijing, 100191, China","Microsoft Kinect, a low‐cost motion capture device, has huge potential in applications that require machine vision, such as human‐robot interactions, home‐based rehabilitation and clinical assessments. The Kinect sensor can track 25 key three‐dimensional (3D) “skeleton” joints on the human body at 30 frames per second, and the skeleton data often have acceptable accuracy. However, the skeleton data obtained from the sensor sometimes exhibit a high level of jitter due to noise and estimation error. This jitter is worse when there is occlusion or a subject moves slightly out of the field of view of the sensor for a short period of time. Therefore, this paper proposed a novel approach to simultaneously handle the noise and error in the skeleton data derived from Kinect. Initially, we adopted classification processing to divide the skeleton data into noise data and erroneous data. Furthermore, we used a Kalman filter to smooth the noise data and correct erroneous data. We performed an occlusion experiment to prove the effectiveness of our algorithm. The proposed method outperforms existing techniques, such as the moving mean filter and traditional Kalman filter. The experimental results show an improvement of accuracy of at least 58.7%, 47.5% and 22.5% compared to the original Kinect data, moving mean filter and traditional Kalman filter, respectively. Our method provides a new perspective for Kinect data processing and a solid data foundation for subsequent research that utilizes Kinect. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Kalman filter; Kinect; Occlusion; Uman joint prediction","Data handling; Human robot interaction; Jitter; Motion capture; Musculoskeletal system; Clinical assessments; Estimation errors; Frames per seconds; Kinect; Microsoft kinect; Occlusion; Robot interactions; Traditional Kalman filters; Kalman filters; algorithm; biomechanics; human; joint; joint characteristics and functions; movement (physiology); physiology; software; three-dimensional imaging; Algorithms; Biomechanical Phenomena; Humans; Imaging, Three-Dimensional; Joints; Movement; Range of Motion, Articular; Software",,,,,"National Key Research and Development Program of China, NKRDPC: 2017YFF0206602","This work was supported by the National Key R&D Program of China (2017YFF0206602).","Data Availability：The data used to support the findings of this study are included within this paper. It is also available from the corresponding author upon request. Author Contributions: Conceptualization: J.N.; Formal analysis: J.N.; Funding acquisition: L.R.; Investigation: X.W. and D.W.; Methodology: J.N., X.W.; D.W. and L.R.; Validation: X.W.; Writing – original draft: X.W.; Writing – review & editing: J.N. and X.W. All authors have read and agreed to the published version of the manuscript Funding: This work was supported by the National Key R&D Program of China (2017YFF0206602). Conflicts of Interest: The authors declare that there are no conflicts of interest regarding the publication of this paper.",,,,,,,,,"Lasota, P.A., Shah, J.A., A Multiple‐Predictor Approach to Human Motion Prediction (2017) Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA, , ), Singapore, Singapore,29 May–3 June; Haidegger, T., Barreto, M., Gonçalves, P., Habib, M.K., Ragavan, S.K.V., Li, H., Vaccarella, A., Prestes, E., Applied ontologies and standards for service robots (2013) Rob. Autom. Syst., 61, pp. 1215-1223; Habib, M.K., Baudoin, Y., Nagata, F., Robotics for rescue and risky intervention (2011) Proceedings of the 37Th Annual Conference of the IEEE Industrial Electronics Society (IECON 2011, , ), Melbourne, VIC, Australia, 7–10 November; Michalos, G., Makris, S., Papakostas, N., Mourtzis, D., Chryssolouris, G., Automotive assembly technologies review: Challenges and outlook for a flexible and adaptive approach (2010) CIRP J. Manuf. Sci. Technol., 2, pp. 81-91; Prakash, P., Dhanasekaran, C., Surya, K., Pius, V.K., Vishal, A.S., Kumar, S.V., Gesture controlled dual six axis robotic arms with rover using MPU (2019) Mater. Today: Proc., 21, pp. 547-556; Kuhner, D., Fiederer, L.D.J., Aldinger, J., Burget, F., Völker, M., Schirrmeister, R.T., Do, C., Burgard, W., A service assistant combining autonomous robotics, flexible goal formulation, and deep‐learning‐based brain–computer interfacing (2019) Rob. Autom. 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Graphics, 22, pp. 2437-2450; Plantard, P., Shum, H.P.H., Multon, F., Filtered Pose Graph for Efficient Kinect Pose Reconstruction (2017) Multimedia Tools Appl, 76, pp. 4291-4312; Morasso, P., Spatial Control of Arm Movements (1981) Exp. Brain Res., 42, pp. 223-227; Huber, M.E., Seitz, A.L., Leeser, M., Sternad, D., Validity and Reliability of Kinect Skeleton for Measuring Shoulder Joint Angles: A Feasibility Study Chartered Society of Physiotherapy (2015) Physiotherapy, 101, pp. 389-393; Shotton, J., Sharp, T., Kipman, A., Girshick, R., Fitzgibbon, A., Cook, M., Finocchio, M., Blake, A., Difficient Human Pose Estimation from Single depth Images (2013) IEEE Trans. Pattern Anal. Mach. Intell., 35, pp. 2821-2840","Niu, J.; School of Mechanical Engineering, China; email: niujw@ustb.edu.cn",,,"MDPI AG",,,,,14248220,,,"32085653","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85079735541
"Muthugala M.A.V.J., Vengadesh A., Wu X., Rajesh Elara M., Iwase M., Sun L., Hao J.","56046158700;56938885100;57212173479;36456051800;7202400925;55492960600;57211207108;","Expressing attention requirement of a floor cleaning robot through interactive lights",2020,"Automation in Construction","110",,"103015","","",,8,"10.1016/j.autcon.2019.103015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076089136&doi=10.1016%2fj.autcon.2019.103015&partnerID=40&md5=995fb2baa27a6fa0d994014ed1c348c1","Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore, 487372, Singapore; Department of Industrial Design, Zhejiang University310027, China; Department of Robotics and Mechatronics, Tokyo Denky University, Tokyo, 120-8551, Japan; College of Computer Science and Technology, Zhejiang University310027, China","Muthugala, M.A.V.J., Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore, 487372, Singapore; Vengadesh, A., Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore, 487372, Singapore; Wu, X., Department of Industrial Design, Zhejiang University310027, China; Rajesh Elara, M., Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore, 487372, Singapore; Iwase, M., Department of Robotics and Mechatronics, Tokyo Denky University, Tokyo, 120-8551, Japan; Sun, L., College of Computer Science and Technology, Zhejiang University310027, China; Hao, J., College of Computer Science and Technology, Zhejiang University310027, China","Cleaning robots are used to cater to the demands in both domestic and industrial premises. Much of research work is being conducted to improve functionalities and efficiency of cleaning robots. Users of these robots prefer human-friendly interactive features in these functional robots. Therefore, the focus of the research work in the area of cleaning robots has been drifted from improving core cleaning related functionalities to implementing human-friendly interactive features in recent years. The existing floor cleaning robots do not possess the ability to express their state to users. Lack of expressiveness is one of the major factors that degrade human-robot interaction. Therefore, this paper proposes a method to express a floor cleaning robot's attention requirement to its users. The attention requirement of the robot in a situation is determined by a fuzzy inference system that analyzes bumping level and experience of attention level requirement. The requirement of attention is expressed to users by varying the color of lights attached to the robot. Based on the color variation, users can identify the robot's situation and users could take corrective measures. Experimental results confirm that the robot can express its attention requirement to a user based on the state. A user study has also been conducted to evaluate the performance improvement of the proposed method. According to the outcomes, the proposed method can remarkably improve human-friendliness of a floor cleaning robot. © 2019 Elsevier B.V.","Cleaning robots; Expressiveness; Human-friendly robotics; Human-robot interaction; Service robotics","Cleaning; Floors; Fuzzy inference; Man machine systems; Robotics; Cleaning robot; Color variations; Corrective measures; Expressiveness; Fuzzy inference systems; Human-friendly; Interactive features; Service robotics; Human robot interaction",,,,,"RGAST1702; Singapore University of Technology and Design, SUTD: ZJUIDEA1702","The authors acknowledge the commitment of the volunteers who participated in the user study. This research was supported by National Robotics R&D Programme Office (grant: RGAST1702) and Singapore University of Technology and Design-Zhejiang University IDEA SEED Grant (grant: ZJUIDEA1702).","The authors acknowledge the commitment of the volunteers who participated in the user study. This research was supported by National Robotics R&D Programme Office (grant: RGAST1702 ) and Singapore University of Technology and Design - Zhejiang University IDEA SEED Grant (grant: ZJUIDEA1702 ). Appendix A",,,,,,,,,"Strengers, Y., Robots and Roomba Riders: Non-human Performers in Theories of Social Practice (2019), pp. 215-234. , Springer International Publishing Cham; Bordoloi, A.K., Islam, M.F., Zaman, J., Phukan, N., Kakoty, N.M., A floor cleaning robot for domestic environments (2017) Proceedings of the Advances in Robotics, pp. 191-195. , ACM New York, USA ISBN 978-1-4503-5294-9; Zhang, L., Yang, Y., Gu, Y., Sun, X., Yao, X., Shuai, L., A new compact stair-cleaning robot (2016) J. Mech. 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"Magrini E., Ferraguti F., Ronga A.J., Pini F., De Luca A., Leali F.","56423508000;55903887600;57210259631;54409111900;7201948195;12345355900;","Human-robot coexistence and interaction in open industrial cells",2020,"Robotics and Computer-Integrated Manufacturing","61",,"101846","","",,42,"10.1016/j.rcim.2019.101846","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070094714&doi=10.1016%2fj.rcim.2019.101846&partnerID=40&md5=e09e28d1d23d4f6ae9c399c47ece77ac","Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Sapienza Università di Roma, Via Ariosto 25, Roma, 00185, Italy; Dipartimento di Scienze e Metodi dell'Ingegneria, Università di Modena e Reggio Emilia, Via Amendola 2, Reggio Emilia, 42122, Italy; Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Via P. Vivarelli 10, Modena, 41125, Italy","Magrini, E., Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Sapienza Università di Roma, Via Ariosto 25, Roma, 00185, Italy; Ferraguti, F., Dipartimento di Scienze e Metodi dell'Ingegneria, Università di Modena e Reggio Emilia, Via Amendola 2, Reggio Emilia, 42122, Italy; Ronga, A.J., Dipartimento di Scienze e Metodi dell'Ingegneria, Università di Modena e Reggio Emilia, Via Amendola 2, Reggio Emilia, 42122, Italy; Pini, F., Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Via P. Vivarelli 10, Modena, 41125, Italy; De Luca, A., Dipartimento di Ingegneria Informatica, Automatica e Gestionale, Sapienza Università di Roma, Via Ariosto 25, Roma, 00185, Italy; Leali, F., Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Via P. Vivarelli 10, Modena, 41125, Italy","Recent research results on human–robot interaction and collaborative robotics are leaving behind the traditional paradigm of robots living in a separated space inside safety cages, allowing humans and robot to work together for completing an increasing number of complex industrial tasks. In this context, safety of the human operator is a main concern. In this paper, we present a framework for ensuring human safety in a robotic cell that allows human–robot coexistence and dependable interaction. The framework is based on a layered control architecture that exploits an effective algorithm for online monitoring of relative human–robot distance using depth sensors. This method allows to modify in real time the robot behavior depending on the user position, without limiting the operative robot workspace in a too conservative way. In order to guarantee redundancy and diversity at the safety level, additional certified laser scanners monitor human–robot proximity in the cell and safe communication protocols and logical units are used for the smooth integration with an industrial software for safe low-level robot control. The implemented concept includes a smart human-machine interface to support in-process collaborative activities and for a contactless interaction with gesture recognition of operator commands. Coexistence and interaction are illustrated and tested in an industrial cell, in which a robot moves a tool that measures the quality of a polished metallic part while the operator performs a close evaluation of the same workpiece. © 2019 Elsevier Ltd","Collaborative robotics; Depth sensing; Human-machine interface; Industrial cell; Polishing; Safe human–robot interaction","Cells; Cytology; Industrial research; Laser safety; Man machine systems; Polishing; Quality control; Robotics; Collaborative activities; Depth sensing; Human Machine Interface; Industrial cells; Interaction with gestures; Layered control architecture; Low level robot control; Robot interactions; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020: 637080; European Commission, EC","This work is partly supported by the European Commission, within the H2020-FoF-2014 SYMPLEXITY project (www.symplexity.eu).",,,,,,,,,,"Hägele, M., Nilsson, K., Pires, J., Bischoff, R., Industrial robotics (2016) Handbook of Robotics (2nd Ed.), pp. 1385-1421. , B. Siciliano O. Khatib Springer; Ong, S., Yew, A., Thanigaivel, N., Nee, A., Augmented reality-assisted robot programming system for industrial applications Robot. Comput. Integr. Manuf., 61, p. 101820. , 2020; Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., Fraisse, P., Collaborative manufacturing with physical human–robot interaction (2016) Robot. Comput. Integr. Manuf., 40, pp. 1-13; Ferraguti, F., Pini, F., Gale, T., Messmer, F., Storchi, C., Leali, F., Fantuzzi, C., Augmented reality based approach for on-line quality assessment of polished surfaces (2019) Robot. Comput. Integr. Manuf., 59, pp. 158-167; Lu, Y., Industry 4.0: a survey on technologies, applications and open research issues (2017) J. Indus. Inf. 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"Keller I., Lohan K.S.","57192279328;35194251000;","On the Illumination Influence for Object Learning on Robot Companions",2020,"Frontiers in Robotics and AI","6",,"154","","",,,"10.3389/frobt.2019.00154","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079059009&doi=10.3389%2ffrobt.2019.00154&partnerID=40&md5=cbebf7c3ea79d224898085faed8ff80e","Department of Mathematical and Computer Science, Heriot-Watt University, Edinburgh, United Kingdom; EMS Institute for Development of Mechatronic Systems, NTB University of Applied Sciences in Technology, Buchs, Switzerland","Keller, I., Department of Mathematical and Computer Science, Heriot-Watt University, Edinburgh, United Kingdom; Lohan, K.S., Department of Mathematical and Computer Science, Heriot-Watt University, Edinburgh, United Kingdom, EMS Institute for Development of Mechatronic Systems, NTB University of Applied Sciences in Technology, Buchs, Switzerland","Most collaborative tasks require interaction with everyday objects (e.g., utensils while cooking). Thus, robots must perceive everyday objects in an effective and efficient way. This highlights the necessity of understanding environmental factors and their impact on visual perception, such as illumination changes throughout the day on robotic systems in the real world. In object recognition, two of these factors are changes due to illumination of the scene and differences in the sensors capturing it. In this paper, we will present data augmentations for object recognition that enhance a deep learning architecture. We will show how simple linear and non-linear illumination models and feature concatenation can be used to improve deep learning-based approaches. The aim of this work is to allow for more realistic Human-Robot Interaction scenarios with a small amount of training data in combination with incremental interactive object learning. This will benefit the interaction with the robot to maximize object learning for long-term and location-independent learning in unshaped environments. With our model-based analysis, we showed that changes in illumination affect recognition approaches that use Deep Convolutional Neural Network to encode features for object recognition. Using data augmentation, we were able to show that such a system can be modified toward a more robust recognition without retraining the network. Additionally, we have shown that using simple brightness change models can help to improve the recognition across all training set sizes. © Copyright © 2020 Keller and Lohan.","data augmentation; human-robot interaction; long-term engagement; object learning; object recognition; visual perception",,,,,,"EP/R026173/1; Engineering and Physical Sciences Research Council, EPSRC: EP/J015040/1","This work was supported by ORCA Hub EPSRC under Grant EP/R026173/1 and consortium partners.",,,,,,,,,,"Ahmad, J., Muhammad, K., Baik, S.W., Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search (2017) PLoS ONE, 12, p. e0183838. , 28859140; Bhattacharyya, S., A brief survey of color image preprocessing and segmentation techniques (2011) J. Pattern Recogn. Res, 6, pp. 120-129; Bradski, G., The opencv library (2000) Dobbs J. Softw. 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Probl. Eng, 2014, p. 138927; Zou, X., Kittler, J., Messer, K., (2007) Illumination Invariant Face Recognition: A Survey, pp. 1-8. , Crystal City, VA, IEEE","Keller, I.; Department of Mathematical and Computer Science, United Kingdom; email: i.keller@hw.ac.uk",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85079059009
"Wang X., Liang C.J., Menassa C.C., Kamat V.R.","57208692778;55787565100;15923411800;7004477339;","Real-time process-level digital twin for collaborative human-robot constructionwork",2020,"Proceedings of the 37th International Symposium on Automation and Robotics in Construction, ISARC 2020: From Demonstration to Practical Use - To New Stage of Construction Robot",,,,"1528","1535",,3,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109390676&partnerID=40&md5=94130128825b89c6da479bdebc7dd0de","Department of Civil and Environmental Engineering, University of Michigan, United States; Robotics Institute, University of Michigan, United States","Wang, X., Department of Civil and Environmental Engineering, University of Michigan, United States; Liang, C.J., Department of Civil and Environmental Engineering, University of Michigan, United States; Menassa, C.C., Department of Civil and Environmental Engineering, University of Michigan, United States, Robotics Institute, University of Michigan, United States; Kamat, V.R., Department of Civil and Environmental Engineering, University of Michigan, United States, Robotics Institute, University of Michigan, United States","Widespread use of autonomous robots in on-site construction has been limited because it is impractical to preprogram robots to perform quasi-repetitive tasks due to the relatively loose work tolerances and deviations of as-built work from the project design. Robotization of field construction work must thus be conceived as a collaborative human-robot endeavor capable of planning and improvising during the performance of construction tasks. Although humans can control robot motion through teleoperation, it is often impractical due to the range of a robot's motion and associated safety issues arising from heavy or large construction materials. An intuitive and safe bi-directional interface is thus needed to enable construction robots to seamlessly interact with and partner with human co-workers. This paper proposes a framework that allows human-robot interaction and collaboration within a real-time, process-level, immersive virtual reality (VR) digital twin that is created by combining the as-designed BIM model and the evolving as-built workspace geometry obtained from on-site sensors. Humans can use the digital twin to remotely demonstrate a task plan to the robot. The robot understands the communicated objectives and plans its motion to complete the task, which is communicated back through the system for human evaluation and approval before the robot executes the task. A case study involving imperfect rough carpentry (i.e., stud framing) and a 6DOF KUKA drywall-installing robot arm is conducted to demonstrate and evaluate the digital twin system. © 2020 Proceedings of the 37th International Symposium on Automation and Robotics in Construction, ISARC 2020: From Demonstration to Practical Use - To New Stage of Construction Robot. All rights reserved.","Digital twin; Human-robot interaction; Improvisation; Virtual reality","Agricultural robots; Architectural design; Digital twin; Industrial robots; Machine design; Robotics; Social robots; Virtual reality; Construction robots; Construction works; Human evaluation; Immersive virtual reality; On-site construction; Project designs; Real-time process; Repetitive task; Robot programming",,,,,"National Science Foundation, NSF: CBET 1638186, CBET 1804321","The authors would like to acknowledge the financial support received from the U.S. National Science Foundation (NSF) CBET 1638186 and CBET 1804321. 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[ros topics] (2012) IEEE Robotics & Automation Magazine, 19 (1), pp. 18-19; Hornung, A, Wurm, KM, Bennewitz, M, Stachniss, C, Burgard, W, An efficient probabilistic 3d mapping framework based on octrees armin hornung (2013) Autonomous Robots Journal, , Springer; Sucan, Ioan A, Moll, Mark, Kavraki, Lydia E, The open motion planning library (2012) IEEE Robotics & Automation Magazine, 19 (4), pp. 72-82; Pan, Jia, Chitta, Sachin, Manocha, Dinesh, Fcl: A general purpose library for collision and proximity queries (2012) 2012 IEEE International Conference on Robotics and Automation, pp. 3859-3866. , IEEE; Smits, Ruben, Bruyninckx, H, Aertbeliën, E, (2011) Kdl: Kinematics and dynamics library; Chitta, Sachin, Marder-Eppstein, Eitan, Meeussen, Wim, Pradeep, Vijay, Tsouroukdissian, Adolfo Rodríguez, Bohren, Jonathan, Coleman, David, Perdomo, Enrique Fernández, ros-control: A generic and simple control framework for ros (2017) The Journal of Open Source Software",,,"Advanced Construction Technology Center;Architectural Institute of Japan;Council for Construction Robot Research;et al.;Japan Robot Association;The International Association for Automation and Robotics in Construction (IAARC)","International Association on Automation and Robotics in Construction (IAARC)","37th International Symposium on Automation and Robotics in Construction: From Demonstration to Practical Use - To New Stage of Construction Robot, ISARC 2020","27 October 2020 through 28 October 2020",,169727,,9789529436347,,,"English","Proc. Int. Symp. Autom. Robot. Constr., ISARC: From Demonstr. Pract. Use - New Stage Constr. Robot",Conference Paper,"Final","",Scopus,2-s2.0-85109390676
"Chinchali S.P., Livingston S.C., Pavone M.","36460799600;55325370300;12240587200;","Multi-objective Optimal Control for Proactive Decision Making with Temporal Logic Models",2020,"Springer Proceedings in Advanced Robotics","10",,,"127","144",,,"10.1007/978-3-030-28619-4_16","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107039425&doi=10.1007%2f978-3-030-28619-4_16&partnerID=40&md5=105efc5eef0e857f1f5cac1c1c73fabd","Stanford University, Stanford, CA, United States; California Institute of Technology, Pasadena, CA, United States","Chinchali, S.P., Stanford University, Stanford, CA, United States; Livingston, S.C., California Institute of Technology, Pasadena, CA, United States; Pavone, M., Stanford University, Stanford, CA, United States","The operation of today’s robots entails interactions with humans, in settings ranging from autonomous driving amidst human-driven vehicles to collaborative manufacturing. To effectively do so, robots must proactively decode the intent or plan of humans and concurrently leverage such a knowledge for safe, cooperative task satisfaction—a problem we refer to as proactive decision making. However, the problem of proactive intent decoding coupled with robotic control is computationally intractable as a robot must reason over several possible human behavioral models and resulting high-dimensional state trajectories. In this paper, we address the proactive decision making problem using a novel combination of algorithmic and data mining techniques. First, we distill high-dimensional state trajectories of human-robot interaction into concise, symbolic behavioral summaries that can be learned from data. Second, we leverage formal methods to model high-level agent goals, safe interaction, and information-seeking behavior with temporal logic formulae. Finally, we design a novel decision-making scheme that simply maintains a belief distribution over high-level, symbolic models of human behavior, and proactively plans informative control actions. Leveraging a rich dataset of real human driving data in crowded merging scenarios, we generate temporal logic models and use them to synthesize control strategies using tree-based value iteration and reinforcement learning (RL). Results from cooperative and adversarial simulated self-driving car scenarios demonstrate that our data-driven control strategies enable safe interaction, correct model identification, and significant dimensionality reduction. © 2020, Springer Nature Switzerland AG.","Data-mining; Decision-making; Formal methods; Human-robot interaction",,,,,,"Office of Naval Research, ONR: N00014-17-1-2433","The authors were partially supported by the Office of Naval Research, ONR YIP Program, under Contract N00014-17-1-2433.",,,,,,,,,,"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Isard, M., Tensorflow: A system for large-scale machine learning (2016) Proceedings of the OSDI; Braziunas, D., (2003) POMDP Solution Methods, , Technical report; Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., Zaremba, W., (2016), https://arxiv.org/abs/1606.01540, OpenAI gym; Chinchali, S.P., Livingston, S.C., Pavone, M., Burdick, J.W., Simultaneous model identification and task satisfaction in the presence of temporal logic constraints (2016) Proceedings of the IEEE Conference on Robotics and Automation; Gmytrasiewicz, P.J., Doshi, P., A framework for sequential planning in multi-agent settings (2005) J. 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Res., 24, pp. 49-79; Javdani, S., Srinivasa, S.S., Bagnell, J.A., Shared autonomy via hindsight optimization (2015) Robotics, Science and Systems; Jones, A., Schwager, M., Belta, C., Information-guided persistent monitoring under temporal logic constraints (2015) American Control Conference, pp. 1911-1916. , , pp; Knight, W., New self-driving car tells pedestrians when it’s safe to cross the street (2016) MIT Technology Review; Konda, V., Tsitsiklis, J., Actor-critic algorithms (2000) Advances in Neural Information Processing Systems; Madani, O., Hanks, S., Condon, A., On the undecidability of probabilistic planning and infinite-horizon partially observable markov decision problems (1999) Proceedings of the AAAI Conference on Artificial Intelligence; Nguyen, T.-H.D., Hsu, D., Lee, W.-S., Leong, T.-Y., Kaelbling, L.P., Lozano-Perez, T., Grant, A.H., Capir: Collaborative action planning with intention recognition (2011) Seventh Artificial Intelligence and Interactive Digital Entertainment Conference; Papadimitriou, C.H., Tsitsiklis, J.N., The complexity of markov decision processes (1987) Math. Oper. Res., 12 (3), pp. 441-450; Raman, V., Donzé, A., Sadigh, D., Murray, R.M., Seshia, S.A., Reactive synthesis from signal temporal logic specifications (2015) Hybrid Systems, Computation and Control; Robicquet, A., Sadeghian, A., Alahi, A., Savarese, S., Learning social etiquette: Human trajectory understanding in crowded scenes (2016) European Control Conference; Sadigh, D., Sastry, S.S., Seshia, S.A., Dragan, A., Information gathering actions over human internal state (2016) IEEE/RSJ International Conference on Intelligent Robots & Systems; Trautman, P., Krause, A., Unfreezing the robot: Navigation in dense, interacting crowds (2010) IEEE/RSJ International Conference on Intelligent Robots & Systems; Wongpiromsarn, T., Frazzoli, E., Control of probabilistic systems under dynamic, partially known environments with temporal logic specifications (2012) Proceedings of the IEEE Conference on Decision and Control","Chinchali, S.P.; Stanford UniversityUnited States; email: csandeep@stanford.edu",,,"Springer Science and Business Media B.V.",,,,,25111256,,,,"English","Springer. Proc. Adv. Robot.",Book Chapter,"Final","",Scopus,2-s2.0-85107039425
"Roy R.N., Drougard N., Gateau T., Dehais F., Chanel C.P.C.","55904560000;55936524900;56032393200;13005898500;36681830500;","How can physiological computing benefit human-robot interaction?",2020,"Robotics","9","4","100","1","24",,6,"10.3390/robotics9040099","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096654806&doi=10.3390%2frobotics9040099&partnerID=40&md5=ac1b319bd6cb0a71aef56a70373377c8","ISAE-SUPAERO, Université de Toulouse, France; ANITI—Artificial and Natural Intelligence Toulouse Institute, Université de Toulouse, France","Roy, R.N., ISAE-SUPAERO, Université de Toulouse, France, ANITI—Artificial and Natural Intelligence Toulouse Institute, Université de Toulouse, France; Drougard, N., ISAE-SUPAERO, Université de Toulouse, France, ANITI—Artificial and Natural Intelligence Toulouse Institute, Université de Toulouse, France; Gateau, T., ISAE-SUPAERO, Université de Toulouse, France; Dehais, F., ISAE-SUPAERO, Université de Toulouse, France, ANITI—Artificial and Natural Intelligence Toulouse Institute, Université de Toulouse, France; Chanel, C.P.C., ISAE-SUPAERO, Université de Toulouse, France, ANITI—Artificial and Natural Intelligence Toulouse Institute, Université de Toulouse, France","As systems grow more automatized, the human operator is all too often overlooked. Although human-robot interaction (HRI) can be quite demanding in terms of cognitive resources, the mental states (MS) of the operators are not yet taken into account by existing systems. As humans are no providential agents, this lack can lead to hazardous situations. The growing number of neurophysiology and machine learning tools now allows for efficient operators’ MS monitoring. Sending feedback on MS in a closed-loop solution is therefore at hand. Involving a consistent automated planning technique to handle such a process could be a significant asset. This perspective article was meant to provide the reader with a synthesis of the significant literature with a view to implementing systems that adapt to the operator’s MS to improve human-robot operations’ safety and performance. First of all, the need for this approach is detailed regarding remote operation, an example of HRI. Then, several MS identified as crucial for this type of HRI are defined, along with relevant electrophysiological markers. A focus is made on prime degraded MS linked to time-on-task and task demands, as well as collateral MS linked to system outputs (i.e., feedback and alarms). Lastly, the principle of symbiotic HRI is detailed and one solution is proposed to include the operator state vector into the system using a mixed-initiative decisional framework to drive such an interaction. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Automated planning; Human-robot interaction; Mental state monitoring; Mixed-initiative; Passive BCI; Physiological computing; Teleoperation; Telerobotics",,,,,,,,,,,,,,,,,"Van Erp, J., Lotte, F., Tangermann, M., Brain-computer interfaces: Beyond medical applications (2012) Computer, 45, pp. 26-34. , [CrossRef]; Cinel, C., Valeriani, D., Poli, R., Neurotechnologies for human cognitive augmentation: Current state of the art and future prospects (2019) Front. Hum. Neurosci, 13, p. 13. , [CrossRef] [PubMed]; Vasic, M., Billard, A., Safety issues in human-robot interactions Proceedings of the 2013 IEEE International Conference on Robotics and Automation, pp. 197-204. , Karlsruhe, Germany, 6–10 May 2013; Zacharaki, A., Kostavelis, I., Gasteratos, A., Dokas, I., Safety bounds in human robot interaction: A survey (2020) Saf. Sci, 127, p. 104667. , [CrossRef]; Goodrich, M.A., Schultz, A.C., Human-robot Interaction: A Survey (2007) Found. Trends Hum.-Comput. 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Neurosci, 10, p. 539; Chanel, C.P., Roy, R.N., Dehais, F., Drougard, N., Towards Mixed-Initiative Human–Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction (2020) Sensors, 20, p. 296. , [CrossRef]; Appriou, A., Cichocki, A., Lotte, F., Modern Machine-Learning Algorithms: For Classifying Cognitive and Affective States From Electroencephalography Signals (2020) IEEE Syst. Man Cybern. Mag, 6, pp. 29-38. , [CrossRef]; Pongsakornsathien, N., Lim, Y., Gardi, A., Hilton, S., Planke, L., Sabatini, R., Kistan, T., Ezer, N., Sensor networks for aerospace human-machine systems (2019) Sensors, 19, p. 3465. , [CrossRef]; Cavallo, F., Limosani, R., Fiorini, L., Esposito, R., Furferi, R., Governi, L., Carfagni, M., Design impact of acceptability and dependability in assisted living robotic applications (2018) Int. J. Interact. Des. Manuf, 12, pp. 1167-1178. , [CrossRef]; Gimhae, G.N., Six human factors to acceptability of wearable computers (2013) Int. J. Multimed. Ubiquitous Eng, 8, pp. 103-114; Dias, R.D., Ngo-Howard, M.C., Boskovski, M.T., Zenati, M.A., Yule, S.J., Systematic review of measurement tools to assess surgeons’ intraoperative cognitive workload (2018) Br. J. Surg, 105, pp. 491-501. , [CrossRef] [PubMed]; Omurtag, A., Roy, R.N., Dehais, F., Chatty, L., Garbey, M., Tracking mental workload by multimodal measurements in the operating room (2019) Neuroergonomics, pp. 99-103. , Elsevier: Amsterdam, The Netherlands; Memar, A.H., Esfahani, E.T., Objective Assessment of Human Workload in Physical Human-robot Cooperation Using Brain Monitoring (2019) ACM Trans. Hum. Robot Interact, 9, pp. 1-21. , [CrossRef]","Roy, R.N.; ISAE-SUPAERO, France; email: raphaelle.roy@isae-supaero.fr
Roy, R.N.; ANITI—Artificial and Natural Intelligence Toulouse Institute, France; email: raphaelle.roy@isae-supaero.fr",,,"MDPI AG",,,,,22186581,,,,"English","Robotics",Review,"Final","All Open Access, Gold",Scopus,2-s2.0-85096654806
"Zeng W., Ouyang W., Luo P., Liu W., Wang X.","57219670060;27068028000;57203040853;57204425655;55736875200;","3D human mesh regression with dense correspondence",2020,"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",,,"9157190","7052","7061",,3,"10.1109/CVPR42600.2020.00708","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094681073&doi=10.1109%2fCVPR42600.2020.00708&partnerID=40&md5=4fb38cc0c1081f207f42c73a70ff2520","Chinese University of Hong Kong, Hong Kong; University of Sydney, Australia; University of Hong Kong, Hong Kong; SenseTime Research","Zeng, W., Chinese University of Hong Kong, Hong Kong; Ouyang, W., University of Sydney, Australia; Luo, P., University of Hong Kong, Hong Kong; Liu, W., SenseTime Research; Wang, X., Chinese University of Hong Kong, Hong Kong, SenseTime Research","Estimating 3D mesh of the human body from a single 2D image is an important task with many applications such as augmented reality and Human-Robot interaction. However, prior works reconstructed 3D mesh from global image feature extracted by using convolutional neural network (CNN), where the dense correspondences between the mesh surface and the image pixels are missing, leading to suboptimal solution. This paper proposes a model-free 3D human mesh estimation framework, named DecoMR, which explicitly establishes the dense correspondence between the mesh and the local image features in the UV space (i.e. a 2D space used for texture mapping of 3D mesh). DecoMR first predicts pixel-to-surface dense correspondence map (i.e., IUV image), with which we transfer local features from the image space to the UV space. Then the transferred local image features are processed in the UV space to regress a location map, which is well aligned with transferred features. Finally we reconstruct 3D human mesh from the regressed location map with a predefined mapping function. We also observe that the existing discontinuous UV map are unfriendly to the learning of network. Therefore, we propose a novel UV map that maintains most of the neighboring relations on the original mesh surface. Experiments demonstrate that our proposed local feature alignment and continuous UV map outperforms existing 3D mesh based methods on multiple public benchmarks. Code will be made available at https://github.com/zengwang430521/DecoMR. © 2020 IEEE",,"3D modeling; Augmented reality; Convolutional neural networks; Human robot interaction; Mapping; Mesh generation; Pattern recognition; Pixels; Textures; Dense correspondences; Image features; Local feature; Local image features; Mapping functions; Original meshes; Suboptimal solution; Texture mapping; Image reconstruction",,,,,"Australian Research Council, ARC: DP200103223","We thank reviewers for helpful discussions and comments. 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Garnett, editors, Curran Associates, Inc; Varol, G., Ceylan, D., Russell, B., Yang, J., Yumer, E., Laptev, I., Schmid, C., BodyNet: Volumetric inference of 3d human body shapes (2018) Proceedings of the European Conference on Computer Vision (ECCV), pp. 20-36; Varol, G., Romero, J., Martin, X., Mahmood, N., Black, M.J., Laptev, I., Schmid, C., Learning from synthetic humans (2017) CVPR; Xia, L., Chen, C.-C., Aggarwal, J.K., View invariant human action recognition using histograms of 3d joints (2012) 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 20-27. , IEEE; Xu, Y., Zhu, S.-C., Tung, T., Denserac: Joint 3d pose and shape estimation by dense render-and-compare (2019) Proceedings of the IEEE International Conference on Computer Vision, pp. 7760-7770; Yao, P., Fang, Z., Wu, F., Feng, Y., Li, J., (2019) Densebody: Directly Regressing Dense 3d Human Pose and Shape from a Single Color Image, , arXiv preprint; Zanfir, A., Marinoiu, E., Sminchisescu, C., Monocular 3d pose and shape estimation of multiple people in natural scenes-the importance of multiple scene constraints (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2148-2157; Zhu, H., Zuo, X., Wang, S., Cao, X., Yang, R., Detailed human shape estimation from a single image by hierarchical mesh deformation (2019) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4491-4500",,,,"IEEE Computer Society","2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020","14 June 2020 through 19 June 2020",,162261,10636919,,PIVRE,,"English","Proc IEEE Comput Soc Conf Comput Vision Pattern Recognit",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85094681073
"Börner H., Endo S., Hirche S.","57210807820;53881190400;15725278100;","Estimation of involuntary components of human arm impedance in multi-joint movements via feedback jerk isolation",2020,"Frontiers in Neuroscience","14",,"459","1","23",,1,"10.3389/fnins.2020.00459","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086368392&doi=10.3389%2ffnins.2020.00459&partnerID=40&md5=c47093996d920085c296d8a96984731b","Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany","Börner, H., Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany; Endo, S., Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany; Hirche, S., Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany","Stable and efficient coordination in physical human-robot interaction requires consideration of human feedback behavior. In unpredictable tasks, where voluntary cognitive feedback is too slow to guarantee desired task execution, the human must rely on involuntary intrinsic and reflexive feedback. The combined effects of these two feedback mechanisms and the inertial characteristics can be summarized in the involuntary impedance components. In this work, we present a method for the estimation of the involuntary impedance components of the human arm in multi-joint movements. We apply force perturbations to evoke feedback jerks that can be isolated using a high pass filter and limit the duration of the estimation interval to guarantee exclusion of voluntary cognitive feedback. Dynamic regressor representation of the rigid body dynamics of the arm and first order Taylor series expansion of the feedback behavior yield a model that is linear in the involuntary impedance components. The constant values of the inertial parameters are estimated in a static posture maintenance task and subsequently inserted to estimate the remaining components in a dynamic movement task. The method is validated with simulated data of a neuromechanical model of the human arm and its performance is compared to established methods from the literature. The results of the validation demonstrate superior estimation performance for moderate movement velocities, and less influence of the variability of the movements. The applicability to real data and the plausibility of the limited estimation interval are successfully demonstrated in an experiment with human participants. © 2020 Börner, Endo and Hirche.","Dynamic regressor representation; Feedback jerk isolation; Human motor control; Human-robot interaction; Impedance estimation; Involuntary impedance components","adult; analytic method; analytical parameters; arm movement; Article; biological model; biomechanics; body movement; body position; cognition; controlled study; dynamics; feedback system; female; human; human experiment; impedance; intermethod comparison; involuntary movement; joint function; male; motor control; musculoskeletal system parameters; normal human; rigidity; robotics; simulation; validation study; velocity",,,,,"Horizon 2020 Framework Programme, H2020: 87176; Seventh Framework Programme, FP7: 337654; European Research Council, ERC","This research was supported by the European Union's Horizon 2020 research and innovation programme REHYB under grant agreement n? 87176 and the ERC Starting Grant Control based on Human Models (con-humo) under grant agreement n? 337654.",,,,,,,,,,"Ajoudani, A., Fang, C., Tsagarakis, N.G., Bicchi, A., A reduced-complexity description of arm endpoint stiffness with applications to teleimpedance control (2015) 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1017-1023. , Hamburg: IEEE; Artemiadis, P.K., Katsiaris, P.T., Liarokapis, M.V., Kyriakopoulos, K.J., (2010) Human Arm Impedance: Characterization and Modeling in 3D Space, in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3103-3108. , Taipei: IEEE; Asao, T., Suzuki, S., Kotani, K., Estimation of driver's steering intention by using mechanical impedance (2013) International Conference on Human Interface and the Management of Information, pp. 3-11. , (Las Vegas: Springer); Bennett, D., Hollerbach, J., Xu, Y., Hunter, I., Time-varying stiffness of human elbow joint during cyclic voluntary movement (1992) Exp. 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"Li W., Gu J., Chen B., Han J.","57216172193;7403129439;57216750122;14522692900;","Incremental Instance-Oriented 3D Semantic Mapping via RGB-D Cameras for Unknown Indoor Scene",2020,"Discrete Dynamics in Nature and Society","2020",,"2528954","","",,4,"10.1155/2020/2528954","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084497442&doi=10.1155%2f2020%2f2528954&partnerID=40&md5=d90a1408f442ac8ee6350fb11fe62dd6","School of Electrical Engineering, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin, 300401, China; School of Artificial Intelligence, Key Laboratory of Big Data Computing, Hebei University of Technology, Tianjin, 300401, China; Wmg Data Science, University of Warwick, Coventry, CV4 7AL, United Kingdom","Li, W., School of Electrical Engineering, State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin, 300401, China; Gu, J., School of Artificial Intelligence, Key Laboratory of Big Data Computing, Hebei University of Technology, Tianjin, 300401, China; Chen, B., School of Artificial Intelligence, Key Laboratory of Big Data Computing, Hebei University of Technology, Tianjin, 300401, China; Han, J., Wmg Data Science, University of Warwick, Coventry, CV4 7AL, United Kingdom","Scene parsing plays a crucial role when accomplishing human-robot interaction tasks. As the ""eye"" of the robot, RGB-D camera is one of the most important components for collecting multiview images to construct instance-oriented 3D environment semantic maps, especially in unknown indoor scenes. Although there are plenty of studies developing accurate object-level mapping systems with different types of cameras, these methods either process the instance segmentation problem in completed mapping or suffer from a critical real-time issue due to heavy computation processing required. In this paper, we propose a novel method to incrementally build instance-oriented 3D semantic maps directly from images acquired by the RGB-D camera. To ensure an efficient reconstruction of 3D objects with semantic and instance IDs, the input RGB images are operated by a real-time deep-learned object detector. To obtain accurate point cloud cluster, we adopt the Gaussian mixture model as an optimizer after processing 2D to 3D projection. Next, we present a data association strategy to update class probabilities across the frames. Finally, a map integration strategy fuses information about their 3D shapes, locations, and instance IDs in a faster way. We evaluate our system on different indoor scenes including offices, bedrooms, and living rooms from the SceneNN dataset, and the results show that our method not only builds the instance-oriented semantic map efficiently but also enhances the accuracy of the individual instance in the scene. © 2020 Wei Li et al.",,,,,,,,,,,,,,,,,,"Kaiming, H., Georgia, G., Piotr, D., Ross, G., Mask R-CNN, , Proceedings of the International Conference on Computer Vision (ICCV) October 2017 Venice, Italy IEEE Transactions on Pattern Analysis and Machine Intelligence 2-s2.0-85040313738; He, K., Zhang, X., Ren, S., Sun, J., Deep Residual Learning for Image Recognition, , Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) June 2016 Las Vegas, NV, USA IEEE Computer Society 2-s2.0-84986274465; Ren, S., He, K., Girshick, R., Sun, J., Faster R-CNN: Towards real-time object detection with region proposal networks (2017) Ieee Transactions on Pattern Analysis and Machine Intelligence, 39 (6), pp. 1137-1149. , 2-s2.0-85019258369; 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Yan, Y., Ren, J., Sun, G., Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement (2018) Pattern Recognition, 79, pp. 65-78. , 2-s2.0-85044678901; Yan, Y., Ren, J., Zhao, H., Cognitive fusion of thermal and visible imagery for effective detection and tracking of pedestrians in videos (2018) Cognitive Computation, 10 (1), pp. 94-104. , 2-s2.0-85036527547; Wang, Z., Ren, J., Zhang, D., Sun, M., Jiang, J., A deep-learning based feature hybrid framework for spatiotemporal saliency detection inside videos (2018) Neurocomputing, 287, pp. 68-83. , 2-s2.0-85042360312","Gu, J.; School of Artificial Intelligence, China; email: jhgu@hebut.edu.cn",,,"Hindawi Limited",,,,,10260226,,,,"English","Discrete Dyn. Nat. Soc.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85084497442
"Chen H., Su L., Zhang B., Sun F., Yuan J., Liu J.","57208696658;57216502095;55721040900;56470132200;55709566900;56336428900;","Overall Understanding of Indoor Scenes by Fusing Multiframe Local RGB-D Data Based on Conditional Random Fields",2020,"IEEE Access","8",,"9055402","65035","65045",,,"10.1109/ACCESS.2020.2985227","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083699519&doi=10.1109%2fACCESS.2020.2985227&partnerID=40&md5=631cd431ff3010aa77d675e6dcfcefee","College of Computer Science, Nankai University, Tianjin, 300350, China; College of Software, Nankai University, Tianjin, 300350, China; College of Artificial Intelligence, Nankai University, Tianjin, China","Chen, H., College of Computer Science, Nankai University, Tianjin, 300350, China; Su, L., College of Software, Nankai University, Tianjin, 300350, China; Zhang, B., College of Computer Science, Nankai University, Tianjin, 300350, China; Sun, F., College of Software, Nankai University, Tianjin, 300350, China; Yuan, J., College of Artificial Intelligence, Nankai University, Tianjin, China; Liu, J., College of Artificial Intelligence, Nankai University, Tianjin, China","Indoor mobile robots normally cannot capture the whole information of a scene by a single frame of perceptive data due to the limited sensor scope. The category of the current scene may be misjudged by robotics due to incomplete scene information, which leads to operation error. To address this problem, we propose an approach that leverages conditional random fields (CRFs) to fuse multiframe RGB and depth (RGB-D) visual data corresponding to the same scene. This method takes full advantage of prior knowledge that object categories significantly relate to the scene attributes. As a new image arrives, we incrementally integrate the current object detection results to update scene understanding by identifying duplicate objects between images, ranking available objects in terms of their relevance to the scene, and fusing new information with the existing CRF. With this approach, scene classification can be solved with higher precision based on multiview images than on single image frames sampled in the same places. Additionally, a configuration map of a scene is incrementally built into the above framework. The map includes identities of the recognized objects and various relations between them. This kind of map would not only benefit common robotic tasks but also offer a novel channel for human-robot interaction. We test the efficiency of our method on image sequences extracted from the NYU v2 dataset. The results show that our approach achieves the best performance against state-of-the-art baselines. © 2013 IEEE.","Conditional random fields; multiframe image fusion; scene configuration map; scene understanding","Object detection; Random processes; Robotics; Statistical tests; Conditional random field; Conditional Random Fields(CRFs); Indoor mobile robots; Multi-view image; Object categories; Operation errors; Scene classification; Scene understanding; Human robot interaction",,,,,"2018YFB0204304; National Natural Science Foundation of China, NSFC: 61873327","This work was supported in part by the National Natural Science Foundation of China under Grant 61873327, and in part by the National Key Research and Development Plan under Grant 2018YFB0204304.",,,,,,,,,,"Dalal, N., Triggs, B., Histograms of oriented gradients for human detection (2005) Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 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(CVPR), pp. 770-778. , Jun; Song, X., Herranz, L., Jiang, S., Depth CNNs for RGB-D scene recognition: Learning from scratch better than transferring from RGBCNNs (2017) Proc. AAAI Conf. Artif. Intell., pp. 4271-4277; Song, X., Jiang, S., Herranz, L., Combining models from multiple sources for RGB-D scene recognition (2017) Proc. Twenty-Sixth Int. Joint Conf. Artif. Intell., pp. 4523-4529. , Aug; Li, Y., Zhang, Z., Cheng, Y., Wang, L., Tan, T., MAPNet: Multimodal attentive pooling network for RGB-D indoor scene classification (2019) Pattern Recognit., 90, pp. 436-449. , Jun; Song, X., Jiang, S., Herranz, L., Chen, C., Learning effective RGBD representations for scene recognition (2019) IEEE Trans. Image Process., 28 (2), pp. 980-993. , Feb; Ayub, A., Wagner, A., (2019) CBCL: Brain-inspired Model for RGB-D Indoor Scene Classification, , http://arxiv.org/abs/1911.00155","Sun, F.; College of Software, China; email: fengchisun@nankai.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85083699519
"Ye Y., Liu Y., Yin W., Deng J., Zhu X.","57188725742;57189309885;57330602700;7402612791;57330611700;","Improving the measurement accuracy of distance and positioning for capacitive proximity detection in human-robot interaction",2021,"Microsystem Technologies","27","12",,"4305","4317",,,"10.1007/s00542-021-05223-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118913067&doi=10.1007%2fs00542-021-05223-2&partnerID=40&md5=258bfaf09938f2f74077660fdb73028b","School of Electronics and Information Engineering, Anhui University, No. 111 Jiulong Road, Hefei, Anhui Province  230601, China; School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China; Institute of High Energy Physics Chinese Academy of Sciences, Beijing, 100049, China; Shenzhen Nuoan Environmental and Safety Inc., Shenzhen, 518055, China; School of Intelligent Manufacturing Engineering, Ma’anshan University, No. 8 Huangchi Road, Dangtu, Ma’anshan City, Anhui Province  243100, China","Ye, Y., School of Electronics and Information Engineering, Anhui University, No. 111 Jiulong Road, Hefei, Anhui Province  230601, China; Liu, Y., School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China, Institute of High Energy Physics Chinese Academy of Sciences, Beijing, 100049, China; Yin, W., Shenzhen Nuoan Environmental and Safety Inc., Shenzhen, 518055, China; Deng, J., School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China; Zhu, X., School of Intelligent Manufacturing Engineering, Ma’anshan University, No. 8 Huangchi Road, Dangtu, Ma’anshan City, Anhui Province  243100, China","Capacitive proximity sensors have been widely used in human-robot interaction, but suffer from the problem of the imperfect measurement accuracy in the nonlinear detection range. Meanwhile, sensor calibration makes the measurement tedious, time consuming, and is difficult to be used in 3D positioning. In this paper, we use a hand as the sensing target and propose a new distance estimation and positioning method, which combines with capacitive sensing configurations of a sensor array and machine learning. Fourteen sensing configurations are designed to generate 14 capacitive sensors to detect the target, and the capacitances are used as the input vector of the machine learning models. We compare the four machine learning algorithms (i.e. fitting method, support vector regression (SVR), radial basis function (RBF) network and BP neural network (BPNN)) for hand distance estimation. The experimental results show the RBF offers the best performance that the root mean squared error (RMSE), mean absolute error (MAE) and mean relative error (MRE) are 0.59 cm, 0.45 cm and 5.32%. Due to the lack of a proper 3D positioning device to verify the target positions, all tests for positioning are grounded on the simulation data using COMSOL, which can mimic the real world applications with high accuracy. The testing results are compared regarding the use of a Y-shaped sensor arrangement, and show that our proposed method performs better than the method with a Y-shaped sensor arrangement, the accuracy is improved by more than 98.8% and the BPNN model achieves the highest accuracy. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.",,"Backpropagation; Capacitive sensors; Errors; Learning systems; Man machine systems; Mean square error; Radial basis function networks; Support vector regression; Capacitive proximity sensors; Machine learning models; Mean relative error; Measurement accuracy; Nonlinear detection; Root mean squared errors; Sensing configuration; Support vector regression (SVR); Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 61902252; Natural Science Foundation of Guangdong Province: 2018 A030310509","This work was supported by the National Natural Science Foundation of China under grant 61902252, the Natural Science Funding of Guangdong Province No. 2018 A030310509.",,,,,,,,,,"Braun, A., Wichert, R., Kuijper, A., Fellner, D.W., Capacitive proximity sensing in smart environments (2015) J Ambient Intell Smart Environ, 7 (4), pp. 483-510; 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"Matarese M., Sciutti A., Rea F., Rossi S.","57215283247;36957816700;54386065400;55619320887;","Toward Robots' Behavioral Transparency of Temporal Difference Reinforcement Learning with a Human Teacher",2021,"IEEE Transactions on Human-Machine Systems","51","6",,"578","589",,,"10.1109/THMS.2021.3116119","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118249923&doi=10.1109%2fTHMS.2021.3116119&partnerID=40&md5=6cd4c9c9757c1563c4782127f2410ecd","University of Genova, Italian Institute of Technology, Genova, Italy; Italian Institute of Technology, Genova, Italy; University of Napoli Federico II, Napoli, Italy","Matarese, M., University of Genova, Italian Institute of Technology, Genova, Italy; Sciutti, A., Italian Institute of Technology, Genova, Italy; Rea, F., Italian Institute of Technology, Genova, Italy; Rossi, S., University of Napoli Federico II, Napoli, Italy","The high request for autonomous human-robot interaction (HRI), combined with the potential of machine learning (ML) techniques, allow us to deploy ML mechanisms in robot control. However, the use of ML can make robots' behavior unclear to the observer during the learning phase. Recently, transparency in HRI has been investigated to make such interactions more comprehensible. In this work, we propose a model to improve the transparency during reinforcement learning (RL) tasks for HRI scenarios: the model supports transparency by having the robot show nonverbal emotional-behavioral cues. Our model considered human feedback as the reward of the RL algorithm and it presents emotional-behavioral responses based on the progress of the robot learning. The model is managed only by the temporal-difference error. We tested the architecture in a teaching scenario with the iCub humanoid robot. The results highlight that when the robot expresses its emotional-behavioral response, the human teacher is able to understand its learning process better. Furthermore, people prefer to interact with an expressive robot as compared to a mechanical one. Movement-based signals proved to be more effective in revealing the internal state of the robot than facial expressions. In particular, gaze movements were effective in showing the robot's next intentions. In contrast, communicating uncertainty through robot movements sometimes led to action misinterpretation, highlighting the importance of balancing transparency and the legibility of the robot goal. We also found a reliable temporal window in which to register teachers' feedback that can be used by the robot as a reward. © 2013 IEEE.","Human-robot interaction; humanoid robot; reinforcement learning (RL); social robotics; transparency","Anthropomorphic robots; Behavioral research; Feedback; Human robot interaction; Job analysis; Personnel training; Robotics; Uncertainty analysis; Behavioral response; Grasping; Human teachers; Humanoid robot; Humans-robot interactions; Reinforcement learning; Social robotics; Task analysis; Uncertainty; Reinforcement learning",,,,,"Horizon 2020 Framework Programme, H2020: 804388; European Research Council, ERC","This work was supported by the Starting Grant from the European Research Council under the European Union?s Horizon 2020 research and innovation programme G.A. No 804388, wHiSPER.",,,,,,,,,,"Wortham, R., Theodorou, A., Bryson, J., What does the robot think? transparency as a fundamental design requirement for intelligent systems (2016) Proc. IJCAI Workshop Ethics Artif. Intell., , Jun; Iden, J., Belief, judgment, transparency, trust: Reasoning about potential pitfalls in interacting with artificial autonomous entities (2017) Proc. Robot. Sci. Syst. 2017 Workshop/Morality Soc. Trust Auton. Robots, , Eds., N. Amato, S. Srinivasa, and N. Ayanian, Cambridge, MA, Aug; Raggioli, L., Rossi, S., Areinforcement-learning approach for adaptive and comfortable assistive robot monitoring behavior (2019) Proc. 28th IEEE Int. Conf. Robot Hum. Interactive Commun., pp. 1-6; Kober, J., Bagnell, J.A., Peters, J., Reinforcement learning in robotics: A survey (2013) Int. J. Robot. Res., 32 (11), pp. 1238-1274; Broekens, J., Chetouani, M., Towards transparent robot learning through TDRL-based emotional expressions (2021) IEEE Trans. Affect. 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Neurosci., 21 (17), pp. 6917-6932","Matarese, M.; University of Genova, Italy; email: marco.matarese@iit.it",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21682291,,,,"English","IEEE Trans. Human Mach. Syst.",Article,"Final","",Scopus,2-s2.0-85118249923
"Sharp A., Pryor M.","56397743200;7004159251;","Virtual Fixture Generation for Task Planning with Complex Geometries",2021,"Journal of Computing and Information Science in Engineering","21","6","061001","","",,,"10.1115/1.4049993","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106018141&doi=10.1115%2f1.4049993&partnerID=40&md5=faff5081080fabda4a1892999e577fb6","Nuclear and Applied Robotics Laboratory, Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States","Sharp, A., Nuclear and Applied Robotics Laboratory, Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States; Pryor, M., Nuclear and Applied Robotics Laboratory, Department of Mechanical Engineering, University of Texas at Austin, Austin, TX  78712, United States","Many robotic processes require the system to maintain a tool's orientation and distance from a surface. To do so, researchers often use virtual fixtures (VFs) to either guide the robot along a path or forbid it from leaving the workspace. Previous efforts relied on volumetric primitives (planes, cylinders, etc.) or raw sensor data to define VFs. However, those approaches only work for a small subset of real-world objects. Extending this approach is complicated not only by VF generation but also by generalizing user traversal of the VF to command a robot trajectory remotely. In this study, we present the concept of task VFs, which convert layers of point cloud-based Guidance VF into a bidirectional graph structure and pair it with a Forbidden Region VF. These VFs are hardware-agnostic and can be generated from virtually any source data, including from parametric objects (superellipsoids, supertoroids, etc.), meshes (including from computer-aided design (CAD)), and real-time sensor data for open-world scenarios. We address surface convexity and concavity since these and distance to the task surface determine the size and resolution of VF layers. This article then presents the manipulator-to-task transform tool for task VF visualization and to limit human-robot interaction ambiguities. Testing confirmed generation success and users performed spatially discrete experiments to evaluate task VF usability complex geometries, which showed their interpretability. The manipulator-to-task transform tool applies many robotic applications, including collision avoidance, process design, training, task definition, etc. for virtually any geometry. © 2016 John Benjamins Publishing Company. All rights reserved.",,"Computer aided design; Computer hardware; Educational robots; End effectors; Fixtures (tooling); Geometry; Graph structures; Human robot interaction; Industrial manipulators; Robotics; Complex geometries; Forbidden region; Interpretability; Real time sensors; Real-world objects; Robot trajectory; Robotic applications; Virtual fixture; Manipulators",,,,,"University of Texas at Austin, UT; Los Alamos National Laboratory, LANL","This material is based upon work supported by Los Alamos National Laboratory and the University of Texas at Austin.",,,,,,,,,,"(2018) Code of Federal Regulations, , https://www.nrc.gov/reading-rm/doc-collections/cfr/part020/part020-1003.html, United States Nuclear Regulatory Commission, Accessed January 12, 2018; Rosenberg, L., Virtual Fixtures: Perceptual Tools for Telerobotic Manipulation (1993) Virtual Reality Annual International Symposium, pp. 76-82. , Seattle, WA, Sept. 18 22, IEEE; Abbott, J. J., Marayong, P., Okamuray, A. 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A., Chitta, S., (2017) MoveIt!, , http://moveit.ros.org/, Accessed February 14, 2017; (2018) Bowl, , https://3dwarehouse.sketchup.com/model/6dc5e034-c223-4b84-9c13-96511f451665/bowl, Men in Black, Accessed October 3, 2018; Pryor, M., Landsberger, S., Mobile Manipulation and Survey System for H-Canyon and Other Applications Across the DOE Complex (2017) WM2017 Conference, pp. 1-15. , Phoenix, AZ, Mar. 5 9; Photonics, I., (2018) FLC 30 Cutting Head Brochure, , http://www.ipgphotonics.com/en/162/Widget/FLC+30+Cutting+Head+Brochure.pdf, Accessed January 10, 2018; Cindy Passmore, M., Michsael Parchman, M., James Tysinger, P., Guidelines for Constructing a Survey (2002) Family Med, 34 (4), pp. 281-286","Sharp, A.; Nuclear and Applied Robotics Laboratory, United States; email: asharp@utexas.edu",,,"American Society of Mechanical Engineers (ASME)",,,,,15309827,,,,"English","J. Comput. Inf. Sci. Eng.",Article,"Final","",Scopus,2-s2.0-85106018141
"Ince G., Yorganci R., Ozkul A., Duman T.B., Köse H.","56040248000;54929920000;56577886900;57208839640;25031883600;","An audiovisual interface-based drumming system for multimodal human–robot interaction",2021,"Journal on Multimodal User Interfaces","15","4",,"413","428",,,"10.1007/s12193-020-00352-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096018839&doi=10.1007%2fs12193-020-00352-w&partnerID=40&md5=f3819f1c7a14cdf317b67e5c5e1bfe3b","Faculty of Computer and Informatics Engineering, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey; Artificial Intelligence and Data Science Application and Research Center, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey","Ince, G., Faculty of Computer and Informatics Engineering, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey, Artificial Intelligence and Data Science Application and Research Center, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey; Yorganci, R., Faculty of Computer and Informatics Engineering, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey; Ozkul, A., Faculty of Computer and Informatics Engineering, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey; Duman, T.B., Faculty of Computer and Informatics Engineering, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey; Köse, H., Faculty of Computer and Informatics Engineering, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey, Artificial Intelligence and Data Science Application and Research Center, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey","This paper presents a study of an audiovisual interface-based drumming system for multimodal human–robot interaction. The interactive multimodal drumming game is used in conjunction with humanoid robots to establish an audiovisual interactive interface. This study is part of a project to design robot and avatar assistants for education and therapy, especially for children with special needs. It specifically focuses on evaluating robot/virtual avatar tutors, tangible interaction devices, and mobile multimedia devices within a simple drumming-based interactive music tutoring game scenario. Several parameters, including the effect of the embodiment of the tutor/interface and the presence of feedback and training mechanisms, were the focus of interest. For that purpose, we created an interactive drumming game relying on turn-taking and imitation principles, in which a human user is able to play drums with a humanoid robot (Robotic Drum Mate). Three interactive scenarios with different experimental setups for humanoid robots and mobile devices were developed and tested. As a part of those scenarios, a system that enables drum strokes to be automatically detected and recognized using auditory cues was implemented and incorporated into the experimental framework. We verified the applicability and effectiveness of the proposed system in a drum-playing game with adult human test subjects by evaluating it both objectively and subjectively. The results showed that the physical robot tutor, the feedback and training mechanisms had a positive effect on the subjects’ performance and, as expected, although the physical medium is preferred, the virtual medium for drumming caused less failure. © 2020, Springer Nature Switzerland AG.","Audio processing; Audiovisual interaction; Drum game; Human–computer-interaction (HCI); Human–robot-interaction (HRI); Multimodality; Non-verbal communication","Anthropomorphic robots; Audiovisual; Educational robots; Machine design; Audiovisual interfaces; Interactive interfaces; Interactive music; Mobile multimedia device; Physical medium; Physical robots; Robot interactions; Tangible interaction; Social robots",,,,,"Istanbul Teknik Üniversitesi, IT: MOA-2019-42321","This work is supported by the Scientific Research Project Unit (BAP) of Istanbul Technical University, Project Number: MOA-2019-42321.",,,,,,,,,,"Walus, B.P., Pauletto, S., Mason-Jones, A., Sonification and music as support to the communication of alcohol-related health risks to young people (2016) J Multimodal User Interfaces, 10, p. 235246; Kose-Bagci, H., Ferrari, E., Dautenhahn, K., Syrdal, D., Nehaniv, C.L., Effects of embodiment and gestures on social interaction in drumming games with a humanoid robot (2009) Adv Robot, 24 (14), pp. 1951-1996; Robins, B., Dickerson, P., Stribling, P., Dautenhahn, K., Robot-mediated joint attention in children with autism: a case study in robot–human interaction (2004) Interact Stud, 5 (2), pp. 161-198; Kose-Bagci, H., Dautenhahn, K., Nehaniv, C.L., Emergent dynamics of turn-taking interaction in drumming games with a humanoid robot (2008) Proceedings of IEEE International Symposium on Robot and Human Interactive Communication, pp. 346-353. , RO-MAN; Dautenhahn, K., Nehaniv, C.L., Walters, M.L., Robins, B., Kose-Bagci, H., Mirza, N.A., Blow, M., KASPAR: a minimally expressive humanoid robot for human–robot interaction research (2009) Appl Bionics Biomech, 6 (3), pp. 369-397; Ince, G., Duman, T.B., Yorganci, R., Kose, H., Towards a robust drum stroke recognition system for human–robot interaction (2015) Proceedings of the IEEE/SICE International Symposium on System Integration (SII), pp. 744-749; Gurpinar, C., Uluer, P., Akalin, N., Kose, H., Sign recognition system for an assistive robot sign tutor for children (2020) Int J Soc Robot, 12, p. 355369; Towards an affective robot companion for audiology rehabilitation: How does pepper feel today?. 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Multimodal User Interfaces",Article,"Final","",Scopus,2-s2.0-85096018839
"Robinson N.L., Kavanagh D.J.","23767534700;56605573400;","A social robot to deliver a psychotherapeutic treatment: Qualitative responses by participants in a randomized controlled trial and future design recommendations",2021,"International Journal of Human Computer Studies","155",,"102700","","",,,"10.1016/j.ijhcs.2021.102700","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112520099&doi=10.1016%2fj.ijhcs.2021.102700&partnerID=40&md5=c83246b06fd02f8e00a39b7573ebd266","Centre for Children's Health Research, Institute of Health and Biomedical Innovation, School of Psychology and Counselling; Australian Research Council Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, Australia, Queensland University of Technology, Brisbane, Australia; Monash University, Department of Electrical and Computer Systems Engineering, Turner Institute for Brain and Mental Health, Victoria, Australia","Robinson, N.L., Centre for Children's Health Research, Institute of Health and Biomedical Innovation, School of Psychology and Counselling, Australian Research Council Centre of Excellence for Robotic Vision, Queensland University of Technology, Brisbane, Australia, Queensland University of Technology, Brisbane, Australia, Monash University, Department of Electrical and Computer Systems Engineering, Turner Institute for Brain and Mental Health, Victoria, Australia; Kavanagh, D.J., Centre for Children's Health Research, Institute of Health and Biomedical Innovation, School of Psychology and Counselling","This paper reports the design and qualitative evaluation of a social robot programmed to deliver a talk-based treatment program to improve health behaviour change for food intake and weight loss. A qualitative study was conducted to investigate factors that influenced human-robot interaction and its relationship to health treatment outcomes. Semi-structured interviews were undertaken on completion of a randomised controlled trial that used an autonomous robot to deliver a 4-week behavioral intervention to help coach people to decrease the consumption of high calorie foods. Questions focused on individuals’ preferences, learnings and outcomes from their participation in the trial. Twenty participants completed the treatment, and 18 conducted an interview. Content analysis found that a social robot to deliver a psychotherapeutic treatment was effective and feasible. Participants did make changes to their health behaviour change with a >50% reduction in high calorie intake and average reduction of 4.4 kilograms in weight loss. The robot received positive evaluations on its interactive nature and sociable persona. Most participants made improvements that were aligned with their chosen health goal after completing the robot-delivered sessions, and reported that the robot sessions helped them to achieve their behaviour change goals, such as consuming fewer high calorie foods. Detailed recommendations are provided for the future design of healthcare interventions by robots, including key considerations for robot behaviour, treatment content, and presentation of the program. Future recommendations are presented for the development of robot personalization to more closely resemble techniques and skills from client-centred counselling. © 2021","Healthcare; Humanoid Robot; Imagery; Intervention; Motivation; Randomised Controlled Trial","Behavioral research; Health; Social robots; Behavioral interventions; Behaviour changes; Qualitative evaluations; Qualitative response; Qualitative study; Randomized controlled trial; Semi structured interviews; Treatment outcomes; Machine design",,,,,"Department of Education and Training; Department of Science, Information Technology and Innovation, Queensland Government, DSITI","We would like to thank the research assistants who provided support for the initial coding of transcripts and interrater reliability score. Funding: This work was supported by the Australian Postgraduate Award on behalf of the Department of Education and Training and by a Research Grant from the State of Queensland acting through the Department of Science, Information Technology and Innovation. The funding bodies had no role in the study design, extraction, analysis or interpretation of the paper, writing of the manuscript, or the decision to submit the paper for publication. NLR is a director and shareholder in a robotics company, but that company has no financial interest in, or conflict with, the subject matter or materials discussed in this manuscript.",,,,,,,,,,"Abbott, R., Orr, N., McGill, P., Whear, R., Bethel, A., Garside, R., Stein, K., Thompson-Coon, J., How do “robopets” impact the health and well-being of residents in care homes? A systematic review of qualitative and quantitative evidence (2019) Int. J. 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Hum.-Robot Interact., 10, p. 13; Trost, M.J., Ford, A.R., Kysh, L., Gold, J.I., Mataric, M., (2019), pp. 451-458. , Socially Assistive Robots for Helping Pediatric Distress and Pain: A Review of Current Evidence and Recommendations for Future Research and Practice. 35; Tung, F.-W., Child Perception of Humanoid Robot Appearance and Behavior (2016) International Journal of Human–Computer Interaction, 32, pp. 493-502; Vaismoradi, M., Turunen, H., Bondas, T., (2013), pp. 398-405. , Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study; Vandemeulebroucke, T., de Casterlé, B.D., Gastmans, C., How do older adults experience and perceive socially assistive robots in aged care: a systematic review of qualitative evidence (2018) Aging Ment. Health, 22, pp. 149-167; Weiss, B., Wechsung, I., Hillmann, S., Möller, S., Multimodal HCI: exploratory studies on effects of first impression and single modality ratings in retrospective evaluation (2017) Journal on Multimodal User Interfaces, 11, pp. 115-131; Xu, J., Broekens, J., Hindriks, K., Neerincx, M.A., Mood contagion of robot body language in human robot interaction (2015) Autonomous Agents and Multi-Agent Systems, 29, pp. 1216-1248; Zhou, M.X., Mark, G., Li, J., Yang, H., Trusting Virtual Agents: The Effect of Personality (2019) ACM Trans. Interact. Intell. Syst., 9, p. 10","Robinson, N.L.18 Alliance Lane, Clayton Campus, Australia; email: nicole.robinson@monash.edu",,,"Academic Press",,,,,10715819,,IHSTE,,"English","Int J Hum Comput Stud",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85112520099
"he Z., Liu T.","57195555620;56352343300;","Design of a three-dimensional capacitor-based six-axis force sensor for human-robot interaction",2021,"Sensors and Actuators, A: Physical","331",,"112939","","",,,"10.1016/j.sna.2021.112939","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111040221&doi=10.1016%2fj.sna.2021.112939&partnerID=40&md5=1a9ac8f28d3d441e709748cf4a9ae1ef","The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China","he, Z., The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China; Liu, T., The State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China","Multi-dimensional force sensing capability of the robot plays a critical role in its interaction with the human and environment. The six-axis force sensor as a typical representative of the multi-axis force sensor, however, has not been extensively used in the field of human-robot interaction due to its high price. A low cost, a good sensing accuracy, and a high integration level six-axis force sensor is highly desirable to create. In this paper, to weaken the crosstalk phenomenon between the six-axis and optimize the sensitivity of overall system, a 3D capacitor structure with a cross-shape configuration of the shear force sensing cell was proposed. Eight electrodes were arranged on three perpendicular planes of the Cartesian coordinate system independently. The cross-shape differential capacitor was designed to achieve the improvement of sensitivity along the shear force direction. With Polyoxymethylene (POM) selected as the substrate material, a monolithic spatial structure was manufactured using CNC technology, and then conductive copper paint was sprayed on the specific surfaces of this 3D structure. The cost-effective prototype sensor has been manufactured without the manual work and experimentally validated by comparing it with a commercial six-axis force sensor. The characteristics of the prototype were analyzed in terms of its linearity, interference error, hysteresis, time domain response, SNR, offset repeatability and time drift. Finally, to verify the instrumented six-axis force sensor further, a controlled lab test was performed to measure the interaction forces with the Baxter Research Robot in a simulated scenario. © 2021 Elsevier B.V.","Human-robot interaction; Manufacturing process; Six-axis force sensor; Three-dimensional capacitor structure","Cost effectiveness; Costs; Man machine systems; Substrates; Force sensing; Humans-robot interactions; Manufacturing process; Multi dimensional; Multi-Axis; Multi-Axis-; Shear force; Six axis force sensor; Three dimensional capacitors; Three-dimensional capacitor structure; Human robot interaction",,,,,"National Natural Science Foundation of China, NSFC: 51775485; Natural Science Foundation of Zhejiang Province, ZJNSF: LZ20E050002","This work was supported in part by the NSFC Grant No. 51775485 , and the Zhejiang Provincial Natural Science Foundation of China under Grant No, LZ20E050002 .",,,,,,,,,,"Azocar, A.F., Mooney, L.M., Duval, J.-F., Simon, A.M., Hargrove, L.J., Rouse, E.J., Design and clinical implementation of an open-source bionic leg (2020) Nat. 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Mechatron., 23, pp. 1662-1672; Oh, H.S., Kim, U., Kang, G., Seo, J.K., Choi, H.R., Multi-axial force/torque sensor calibration method based on deep-learning (2018) IEEE Sens. J., 18, pp. 5485-5496; WACOH-TECH Inc, DynPick: WEF-6A1000-30 Series https://wacoh-tech.com/products/dynpick/; Bicchi, A., A criterion for optimal design of multi-axis force sensors (1992) Rob. Auton. Syst., 10, pp. 269-286; Uchiyama, M., Nakamura, Y., Hakomori, K., Evaluation of the robot force sensor structure using singular value decomposition (1990) Adv. Robot., 5, pp. 39-52; Kim, D., Lee, C.H., Kim, B.C., Lee, D.H., Lee, H.S., Canh Toan, N., Six-axis capacitive force/torque sensor based on dielectric elastomer (2013) Electroactive Polymer Actuators and Devices, , Y. BarCohen","Liu, T.; The State Key Laboratory of Fluid Power and Mechatronic Systems, China; email: liutao@zju.edu.cn",,,"Elsevier B.V.",,,,,09244247,,SAAPE,,"English","Sens Actuators A Phys",Article,"Final","",Scopus,2-s2.0-85111040221
"Spatola N., Wudarczyk O.A.","57193889944;57253733800;","Ascribing emotions to robots: Explicit and implicit attribution of emotions and perceived robot anthropomorphism",2021,"Computers in Human Behavior","124",,"106934","","",,,"10.1016/j.chb.2021.106934","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108868886&doi=10.1016%2fj.chb.2021.106934&partnerID=40&md5=dd30606cd85938a3ccd7d40d35a59781","Istituto Italiano di Tecnologia, Center for Human Technologies, Genova, Italy; Department of Psychology, Humboldt-Universität zu Berlin, Germany","Spatola, N., Istituto Italiano di Tecnologia, Center for Human Technologies, Genova, Italy; Wudarczyk, O.A., Department of Psychology, Humboldt-Universität zu Berlin, Germany","Despite growing literature on various aspects of robot perception by humans, little is still known about how robots' anthropomorphism is perceived at various degrees of conscious perception. In the current study, in two experiments, we assessed whether explicit and implicit attributions of primary and secondary emotions towards robots predict participants' perception of robots' anthropomorphism. In Experiment 1, participants explicitly evaluated whether robots were likely to experience different primary and secondary emotions, followed by the assessment of the level of attributed anthropomorphism towards robots. In Experiment 2, participants completed an adapted version of the Implicit Association Test (IAT), which aimed at implicit evaluation of participants' attribution of different primary and secondary emotions to robots, followed by the assessment of the level of attributed robot anthropomorphism. At the explicit level, attribution of secondary emotions to robots was linked to higher perception of robots' warmth and competence. At the implicit level, participants’ perception of conceptual similarity between robots and humans (i.e. lower difference between attribution of primary and secondary emotions to humans and robots), predicted more anthropomorphic warmth perception and less discomfort towards robots. The current results reveal that implicit and explicit attribution of primary/secondary emotions towards robots predict specific aspects of robot anthropomorphism, with the warmth dimension consistently being predicted across both levels of evaluation. These results shed light on a novel approach to predict various aspects of robot anthropomorphism, which in the future might be useful for evaluations of social robots for their suitability in human-robot interactions. © 2021","Anthropomorphism; Human-robot interaction; Implicit measure; Primary emotion; Secondary emotion","Anthropomorphic robots; Behavioral research; Forecasting; Man machine systems; 'current; Anthropomorphism; Humans-robot interactions; Implicit measures; Primary emotion; Robot perception; Robot warmths; Secondary emotion; Social robots; Human robot interaction",,,,,"Deutsche Forschungsgemeinschaft, DFG: 390523135","This work was funded by the Deutsche Forschungsgemeinschaft ( DFG , German Research Foundation) under Germany's Excellence Strategy - EXC 2002/1 ″ Science of Intelligence ” - project number 390523135 .",,,,,,,,,,"Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International journal of social robotics, 1 (1), pp. 71-81; Carpinella, C.M., Wyman, A.B., Perez, M.A., Stroessner, S.J., The robotic social attributes scale (RoSAS): Development and validation (2017) ACM/IEEE International Conference on human-robot interaction, pp. 254-262. , Part F1271; Cohen, J., Statistical power analysis for the behavioral sciences (2013), Academic Press; De Houwer, J., The Implicit Association Test as a tool for studying dysfunctional associations in psychopathology: Strengths and limitations (2002) Journal of Behavior Therapy and Experimental Psychiatry, 33 (2), pp. 115-133; De Houwer, J., Teige-Mocigemba, S., Spruyt, A., Moors, A., Implicit measures: A normative analysis and review (2009) Psychological Bulletin, 135 (3), pp. 347-368; Dehaene, S., Naccache, L., Le Clec'H, G., Koechlin, E., Mueller, M., Dehaene-Lambertz, G., Van De Moortele, P.F., Le Bihan, D., Imaging unconscious semantic priming (1998) Nature, 395 (6702), pp. 597-600; Demoulin, S., Leyens, J.P., Paladino, M.P., Rodriguez-Torres, R., Rodriguez-Perez, A., Dovidio, J.F., Dimensions of “uniquely” and “non-uniquely” human emotions (2004) Cognition & Emotion, 18 (Issue 1), pp. 71-96; Dupree, C.H., Fiske, S.T., Universal dimensions of social signals: Warmth and competence (2017) Social signal processing, pp. 23-33. , J.K. 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(1992) Psychological Review, 99 (3), pp. 566-571; Urquiza-Haas, E.G., Kotrschal, K., The mind behind anthropomorphic thinking: Attribution of mental states to other species (2015) Animal Behaviour, 109, pp. 167-176; Viki, C.T., Winchester, L., Titshall, L., Chisango, T., Pina, A., Russell, R., Beyond secondary emotions: The infrahumanization of outgroups using human-related and animal-related words (2006) Social Cognition, 24 (6), pp. 753-775; Wilson, T.D., Lindsey, S., Schooler, T.Y., A model of dual attitudes (2000) Psychological Review, 107 (1), pp. 101-126; Złotowski, J., Sumioka, H., Eyssel, F., Nishio, S., Bartneck, C., Ishiguro, H., Model of dual anthropomorphism: The relationship between the media equation effect and implicit anthropomorphism (2018) International journal of social robotics, 10 (5), pp. 701-714","Spatola, N.; Istituto Italiano di Tecnologia, Italy; email: nicolas.spatola@iit.it",,,"Elsevier Ltd",,,,,07475632,,CHBEE,,"English","Comput. Hum. Behav.",Article,"Final","",Scopus,2-s2.0-85108868886
"Hancock P.A., Kessler T.T., Kaplan A.D., Brill J.C., Szalma J.L.","7102723856;56704593400;57203577853;24330733300;6603405857;","Evolving Trust in Robots: Specification Through Sequential and Comparative Meta-Analyses",2021,"Human Factors","63","7",,"1196","1229",,11,"10.1177/0018720820922080","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086255999&doi=10.1177%2f0018720820922080&partnerID=40&md5=c870dd4b53b5bc8414ffc9e885a908d3","University of Central Florida, Orlando, United States; Institute for Simulation and Training, University of Central Florida, Orlando, United States; United States Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH, United States","Hancock, P.A., University of Central Florida, Orlando, United States, Institute for Simulation and Training, University of Central Florida, Orlando, United States; Kessler, T.T., University of Central Florida, Orlando, United States; Kaplan, A.D., University of Central Florida, Orlando, United States; Brill, J.C., United States Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH, United States; Szalma, J.L., University of Central Florida, Orlando, United States","Objective: The objectives of this meta-analysis are to explore the presently available empirical findings on the antecedents of trust in robots and use this information to expand upon a previous meta-analytic review of the area. Background: Human–robot interaction (HRI) represents an increasingly important dimension of our everyday existence. Currently, the most important element of these interactions is proposed to be whether the human trusts the robot or not. We have identified three overarching categories that exert effects on the expression of trust. These consist of factors associated with (a) the human, (b) the robot, and (c) the context in which any specific HRI event occurs. Method: The current body of literature was examined and all qualifying articles pertaining to trust in robots were included in the meta-analysis. A previous meta-analysis on HRI trust was used as the basis for this extended, updated, and evolving analysis. Results: Multiple additional factors, which have now been demonstrated to significantly influence trust, were identified. The present results, expressed as points of difference and points of commonality between the current and previous analyses, are identified, explained, and cast in the setting of the emerging wave of HRI. Conclusion: The present meta-analysis expands upon previous work and validates the overarching categories of trust antecedent (human-related, robot-related, and contextual), as well as identifying the significant individual precursors to trust within each category. A new and updated model of these complex interactions is offered. Application: The identified trust factors can be used in order to promote appropriate levels of trust in robots. © Copyright 2020, Human Factors and Ergonomics Society.","human–robot interaction; meta-analysis; robotics; trust","Ergonomics; Human engineering; Empirical findings; Meta analysis; Robot interactions; Trust factor; Updated model; Social robots; article; human; meta analysis; robotics; trust",,,,,"U.S. Department of Energy, USDOE: 88ABW-2020-1129; Oak Ridge Institute for Science and Education, ORISE; Air Force Research Laboratory, AFRL","This research was supported, in part, by an appointment to the Student Research Participation Program at the U.S. Air Force Research Laboratory (AFRL), 711th Human Performance Wing, Human Effectiveness Directorate, Human Centered Intelligence, Surveillance, and Reconnaissance Division, Human Trust and Interaction Branch administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy and AFRL. 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"Senft E., Hagenow M., Radwin R., Zinn M., Gleicher M., Mutlu B.","56032346700;57222274180;35609269200;7004128002;6701816786;15060220700;","Situated Live Programming for Human-Robot Collaboration",2021,"UIST 2021 - Proceedings of the 34th Annual ACM Symposium on User Interface Software and Technology",,,,"613","625",,,"10.1145/3472749.3474773","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118198228&doi=10.1145%2f3472749.3474773&partnerID=40&md5=41b48986e267277308332fa028f915da","Department of Computer Sciences, University of Wisconsin-Madison, United States; Department of Mechanical Engineering, University of Wisconsin-Madison, United States; Department of Industrial and Systems Engineering, University of Wisconsin-Madison, United States","Senft, E., Department of Computer Sciences, University of Wisconsin-Madison, United States; Hagenow, M., Department of Mechanical Engineering, University of Wisconsin-Madison, United States; Radwin, R., Department of Industrial and Systems Engineering, University of Wisconsin-Madison, United States; Zinn, M., Department of Mechanical Engineering, University of Wisconsin-Madison, United States; Gleicher, M., Department of Computer Sciences, University of Wisconsin-Madison, United States; Mutlu, B., Department of Computer Sciences, University of Wisconsin-Madison, United States","We present situated live programming for human-robot collaboration, an approach that enables users with limited programming experience to program collaborative applications for human-robot interaction. Allowing end users, such as shop floor workers, to program collaborative robots themselves would make it easy to ""retask""robots from one process to another, facilitating their adoption by small and medium enterprises. Our approach builds on the paradigm of trigger-action programming (TAP) by allowing end users to create rich interactions through simple trigger-action pairings. It enables end users to iteratively create, edit, and refine a reactive robot program while executing partial programs. This live programming approach enables the user to utilize the task space and objects by incrementally specifying situated trigger-action pairs, substantially lowering the barrier to entry for programming or reprogramming robots for collaboration. We instantiate situated live programming in an authoring system where users can create trigger-action programs by annotating an augmented video feed from the robot's perspective and assign robot actions to trigger conditions. We evaluated this system in a study where participants (n = 10) developed robot programs for solving collaborative light-manufacturing tasks. Results showed that users with little programming experience were able to program HRC tasks in an interactive fashion and our situated live programming approach further supported individualized strategies and workflows. We conclude by discussing opportunities and limitations of the proposed approach, our system implementation, and our study and discuss a roadmap for expanding this approach to a broader range of tasks and applications. © 2021 ACM.","end-user programming; human-robot collaboration; human-robot interaction; trigger-action programming","Agricultural robots; Application programs; Man machine systems; Robot programming; Collaborative application; Collaborative robots; End-users; Human-robot collaboration; Humans-robot interactions; Programming experience; Robot programs; Shopfloors; Trigger-action programming; Workers'; Human robot interaction",,,,,"80NSSC19M0124","This work was funded by a NASA University Leadership Initiative (ULI) grant awarded to the University of Wisconsin–Madison and The Boeing Company (Cooperative Agreement #80NSSC19M0124). We would also like to extend our gratitude to Titus Smith who developed the interface.",,,,,,,,,,"Bambusek, D., Materna, Z., Kapinus, M., Beran, V., Smrz, P., Combining interactive spatial augmented reality with head-mounted display for end-user collaborative robot programming (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 1-8; Bauer, A., Wollherr, D., Buss, M., Human-robot collaboration: A survey (2008) International Journal of Humanoid Robotics, 5 (1), pp. 47-66. , 2008; Billard, A., Calinon, S., Dillmann, R., Schaal, S., (2008) Survey: Robot Programming by Demonstration, , Technical Report. 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IEEE, pp. 3296-3302; Wu, Y., Kirillov, A., Massa, F., Lo, W., Girshick, R., (2019) detectron2, , https://github.com/facebookresearch/detectron2; Zhang, L., He, W., Martinez, J., Brackenbury, N., Lu, S., Ur, B., AutoTap: Synthesizing and repairing trigger-action programs using lTL properties (2019) 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE). IEEE, pp. 281-291; Zhang, L., He, W., Morkved, O., Zhao, V., Littman, M.L., Lu, S., Ur, B., Trace2TAP: Synthesizing trigger-Action programs from traces of behavior (2020) Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4 (3), pp. 1-26. , 2020",,,"ACM SIGCHI;ACM SIGGRAPH","Association for Computing Machinery, Inc","34th Annual ACM Symposium on User Interface Software and Technology, UIST 2021","10 October 2021 through 14 October 2021",,172553,,9781450386357,,,"English","UIST - Proc. Annu. ACM Symp. User Interface Softw. Technol.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85118198228
"Sharifi M., Mehr J.K., Mushahwar V.K., Tavakoli M.","55224489900;57221662636;6603751651;8668966400;","Adaptive CPG-Based Gait Planning with Learning-Based Torque Estimation and Control for Exoskeletons",2021,"IEEE Robotics and Automation Letters","6","4","9520273","8261","8268",,,"10.1109/LRA.2021.3105996","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113331974&doi=10.1109%2fLRA.2021.3105996&partnerID=40&md5=762cb216da3463a69abc312b06e452d7","Department of Electrical and Computer Engineering, Department of Medicine and Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, Alberta, Canada; Department of Medicine, Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, Alberta, Canada; Department of Electrical and Computer Engineering, Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, Alberta, Canada","Sharifi, M., Department of Electrical and Computer Engineering, Department of Medicine and Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, Alberta, Canada; Mehr, J.K., Department of Electrical and Computer Engineering, Department of Medicine and Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, Alberta, Canada; Mushahwar, V.K., Department of Medicine, Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, Alberta, Canada; Tavakoli, M., Department of Electrical and Computer Engineering, Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, Alberta, Canada","In this letter, a new adaptable gait trajectory shaping method is proposed for lower-limb exoskeletons by defining central pattern generators (CPGs). These CPGs are synchronized across different joints and updated online in response to the human users' physical behavior to enhance their safety and comfort. In this CPG structure of a high-level control scheme, an overall locomotion frequency is defined for all joint motions that can be modulated as a function of the human-robot interaction (HRI) energy. The amplitude and equilibrium position of oscillation for each joint can be adjusted in real-time based on the HRI torque. Logarithmic barrier functions are also formulated for these connected CPGs to avoid exceeding safe bounds of the joints' motion. A supervised learning algorithm is employed to identify the exoskeleton-limb dynamics and estimate the active HRI torque on different joints based on an autoregressive network with exogenous inputs (NARX) model. In order to track the reference trajectories generated by CPGs, a proportional derivative (PD) controller with torque compensation is designed. In the experimental evaluation of this intelligent control strategy, an able-bodied person wearing the Indego exoskeleton could amend and personalize the gait features considerably over a short period of time by applying active torques on different joints. In these experiments, the user increased the motion amplitude of the hip and knee joints up to 14% and made more than 200% variation in the gait frequency, which implies a considerable level of flexibility in locomotion planning for further user studies. © 2016 IEEE.","autonomous human-robot interaction; central pattern generator (CPG); Locomotion control; lower-limb exoskeleton; NARX model; supervised learning","Agricultural robots; Exoskeleton (Robotics); Joints (anatomy); Learning algorithms; Torque; Torque control; Central pattern generator; Equilibrium positions; Experimental evaluation; Human robot Interaction (HRI); Intelligent control strategies; Logarithmic barrier function; Proportional-derivative controllers; Reference trajectories; Human robot interaction",,,,,"Canadian Institutes of Health Research, CIHR; Natural Sciences and Engineering Research Council of Canada, NSERC; Canada Foundation for Innovation, CFI","Manuscript received May 31, 2021; accepted August 1, 2021. Date of publication August 20, 2021; date of current version September 1, 2021. This letter was recommended for publication by Associate Editor J. J. Cabibihan and Editor P. Valdastri upon evaluation of the reviewers’ comments. This work was supported in part by the Natural Sciences and Engineering Research Council (NSERC), Canadian Institutes of Health Research (CIHR), Canada Foundation for Innovation (CFI), and in part by the Alberta Jobs, Economy and Innovation Ministry’s Major Initiatives Fund to the Center for Autonomous Systems in Strengthening Future Communities. (Corresponding author: Mojtaba Sharifi.) Mojtaba Sharifi and Javad K. Mehr are with the Department of Electrical and Computer Engineering, Department of Medicine and Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, Alberta, Canada and also with the Department of Medicine, and Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, Alberta, Canada (e-mail: sharifi3@ualberta.ca; khodaeim@ualberta.ca).",,,,,,,,,,"Sharifi, M., Patient-robot-Therapist collaboration using resistive impedance controlled tele-robotic systems subjected to time delays (2018) J. Mechanisms Robot., 10; Murray, S.A., Farris, R.J., Golfarb, M., Hartigan, C., Kandilakis, C., Truex, D., FES coupled with a powered exoskeleton for cooperative muscle contribution in persons with paraplegia (2018) Proc. 40th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., pp. 2788-2792; Zeilig, G., Safety and tolerance of the rewalk exoskeleton suit for ambulation by people with complete spinal cord injury: A pilot study (2012) J. Spinal Cord Med., 35, pp. 96-101; Jansen, O., Hybrid assistive limb exoskeleton hal in the rehabilitation of chronic spinal cord injury: Proof of concept; the results in 21 patients (2018) World Neurosurgery, 110, pp. 73-78; Evans, R.W., Robotic locomotor training leads to cardiovascular changes in individuals with incomplete spinal cord injury over a 24-week rehabilitation period: A randomized controlled pilot study (2021) Arch. Phys. Med. Rehabil., 102 (8), pp. 1447-1456; Inkol, K.A., McPhee, J., Assessing control of fixed-support balance recovery in wearable lower-limb exoskeletons using multibody dynamic modelling (2020) Proc. 8th IEEE RAS/EMBS Int. Conf. Biomed. Robot. Biomechatronics, pp. 54-60; Esquenazi, A., Powered exoskeletons for walking assistance in persons with central nervous system injuries: A narrative review (2017) PM&R, 9 (1), pp. 46-62; Azimi, V., Nguyen, T.T., Sharifi, M., Fakoorian, S.A., Simon, D., Robust ground reaction force estimation and control of lower-limb prostheses: Theory and simulation (2020) IEEE Trans. Syst., Man, Cybern. Syst., 50, pp. 3024-3035. , Aug; Blank, A.A., Current trends in robot-assisted upper-limb stroke rehabilitation: Promoting patient engagement in therapy (2014) Curr.Phys. Med. Rehabil. Rep., 2, pp. 184-195; Sharifi, M., Zakerimanesh, A., Mehr, J.K., Torabi, A., Mushahwar, V.K., Tavakoli, M., Impedance variation and learning strategies in human-robot interaction (2021) IEEE Trans. Cybern., pp. 1-14; Meng, W., Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation (2015) Mechatronics, 31, pp. 132-145; Shahbazi, M., Atashzar, S.F., Tavakoli, M., Patel, R.V., Roboticsassisted mirror rehabilitation therapy: A therapist-in-the-loop assistas-needed architecture (2016) IEEE/ASME Trans. Mechatronics, 21, pp. 1954-1965. , Aug; Sharifi, M., Impedance learning-based adaptive control for humanrobot interaction (2021) IEEE Trans. Control Syst. Technol., pp. 1-14; Vantilt, J., Model-based control for exoskeletons with series elastic actuators evaluated on sit-to-stand movements (2019) J. NeuroEngineering Rehabil., 16 (1), pp. 1-21; Riani, A., Adaptive integral terminal sliding mode control for upper-limb rehabilitation exoskeleton (2018) Control Eng. Pract., 75, pp. 108-117; Lu, R., Li, Z., Su, C., Xue, A., Development and learning control of a human limbwith a rehabilitation exoskeleton (2014) IEEE Trans. Ind. Electron., 61 (7), pp. 3776-3785. , Jul; Sharifi, M., Assist-as-needed policy for movement therapy using telerobotics-mediated therapist supervision (2020) ControlEng.Pract., 101; Ijspeert, A., From swimming to walking with a salamander robot driven by a spinal cord model (2007) Science, 315, pp. 1416-1420; Schrade, S., Bio-inspired control of joint torque and knee stiffness in a robotic lower limb exoskeleton using a central pattern generator (2017) Proc. Int. Conf. Rehabil. Robot., pp. 1387-1394; Gui, K., Ageneralized framework to achieve coordinated admittance control for multi-joint lower limb robotic exoskeleton (2017) Proc. Int. Conf. Rehabil. Robot., pp. 228-233; Zhang, D., Cooperative control for a hybrid rehabilitation system combining functional electrical stimulation and robotic exoskeleton (2017) Front. Neurosci., 11, p. 725; Mehr, J.K., Sharifi, M., Mushahwar, V.K., Tavakoli, M., Intelligent locomotion planning with enhanced postural stability for lower-limb exoskeletons (2021) IEEE Robot. Automat. Lett., 6 (4), pp. 7588-7595. , Oct; Gui, K., Liu, H., Zhang, D., A practical and adaptivemethod to achieve EMG-based torque estimation for a robotic exoskeleton (2019) IEEE/ASME Trans. Mechatronics, 24, pp. 483-494. , Apr; Liu, X., Wang, Q., Real-time locomotion mode recognition and assistive torque control for unilateral knee exoskeleton on different terrains (2020) IEEE/ASME Trans. Mechatronics, 25 (6), pp. 2722-2732. , Dec; Wu, X., Liu, D., Liu, M., Chen, C., Guo, H., Individualized gait pattern generation for sharing lower limb exoskeleton robot (2018) IEEE Trans. Automat. Sci. Eng., 15 (4), pp. 1459-1470. , Oct; Kang, I., Kunapuli, P., Young, A.J., Real-time neural network-based gait phase estimation using a robotic hip exoskeleton (2020) IEEE Trans. Med. Robot. Bionics, 2 (1), pp. 28-37. , Feb; Gupta, R., Continuous angular position estimation of human ankle during unconstrained locomotion (2020) Biomed. Signal Process. Control, 60; Kouzbary, H.A., Kouzbary, M.A., Tham, L.K., Liu, J., Shasmin, H.N., Osman, N.A.A., Generating an adaptive and robust walking pattern for a prosthetic ankle-foot by utilizing a nonlinear autoregressive network with exogenous inputs (2021) IEEE Trans. Neural Netw. Learn. Syst., pp. 1-9; Diaconescu, E., The use of narx neural networks to predict chaotic time series (2008) Wseas Trans. Comput. Res., 3, pp. 182-191; Xin, L., Omnidirectionalmobile robot dynamicmodel identification by narx neural network and stability analysis using the aplf method (2020) Symmetry, 12; Xie, H., Time series prediction based on narx neural networks: An advanced approach (2009) Proc. Int. Conf. Mach. Learn. Cybern., pp. 1275-1279; Du, L., Response characteristics prediction of surge protective device based on narx neural network (2020) IEEE Trans. Electromagn. Compat., 62 (1), pp. 74-82. , Feb; Azimi, V., Stable robust adaptive impedance control of a prosthetic leg (2015) ASME Dyn. Syst. Cont. Conf., pp. 1-10","Sharifi, M.; Department of Electrical and Computer Engineering, Canada; email: sharifi3@ualberta.ca",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85113331974
"Prendergast J.M., Balvert S., Driessen T., Seth A., Peternel L.","56727884600;57226186629;57226181452;35957454200;56943287700;","Biomechanics Aware Collaborative Robot System for Delivery of Safe Physical Therapy in Shoulder Rehabilitation",2021,"IEEE Robotics and Automation Letters","6","4","9488306","7177","7184",,,"10.1109/LRA.2021.3097375","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110837600&doi=10.1109%2fLRA.2021.3097375&partnerID=40&md5=97179a705484e9811e60c5d5479fb00c","Biomechanical Engineering and Cognitive Robotics Departments, Delft University of Technology, CD, Delft, Netherlands","Prendergast, J.M., Biomechanical Engineering and Cognitive Robotics Departments, Delft University of Technology, CD, Delft, Netherlands; Balvert, S., Biomechanical Engineering and Cognitive Robotics Departments, Delft University of Technology, CD, Delft, Netherlands; Driessen, T., Biomechanical Engineering and Cognitive Robotics Departments, Delft University of Technology, CD, Delft, Netherlands; Seth, A., Biomechanical Engineering and Cognitive Robotics Departments, Delft University of Technology, CD, Delft, Netherlands; Peternel, L., Biomechanical Engineering and Cognitive Robotics Departments, Delft University of Technology, CD, Delft, Netherlands","In this work, we explore using computational musculoskeletal modeling to equip an industrial collaborative robot with awareness of the internal state of a patient to safely deliver physical therapy. A major concern of robot-mediated physical therapy is that robots may unwittingly injure patients. For patients with shoulder injuries this typically means the risk of tearing a rotator-cuff muscle tendon. Risk of reinjury hampers both human and robot therapists and it is the main reason for conservative physical therapy. Advances in human musculoskeletal modeling, however, can equip robots with additional perception of potential reinjury risks. While the ultimate goal is to improve the safety, range-of-motion and activity that patients receive through robot-mediated therapy, the aim of this letter is to develop and test a framework that enables the robot to understand the state of the patient and to execute physical therapy movements that demonstrate low injury risk and achieve a large range-of-motion in human subjects. We build on prior work in human-robot interaction via impedance control, but take robot awareness of the human to the next level by including and manipulating a musculoskeletal model in parallel to the patient. Taking the most common shoulder impairments (i.e., rotator-cuff tears) as an example, we demonstrate planned, model-based trajectories that minimize strain in these muscles and corresponding robot-mediated movements on healthy subjects. Our experiments suggest that musculoskeletal awareness is a promising approach to plan and deliver therapeutic movements that are safe and effective via an industrial robot. © 2016 IEEE.","Human-robot interaction; model-based awareness; musculoskeletal modeling; OpenSim; physical therapy robot","Agricultural robots; Industrial manipulators; Industrial robots; Muscle; Patient treatment; Physical therapy; Risk perception; Collaborative robots; Healthy subjects; Impedance control; Musculoskeletal model; Range of motions; Shoulder impairments; Shoulder injury; Shoulder rehabilitations; Human robot interaction",,,,,"Technische Universiteit Delft, TU Delft","Manuscript received February 24, 2021; accepted June 24, 2021. Date of publication July 16, 2021; date of current version July 28, 2021. The work of J. Micah Prendergast was supported by the 3mE Cohesion Grant from Delft University of Technology. (Corresponding author: J. Micah Prendergast.) The authors are with the Biomechanical Engineering and Cognitive Robotics Departments, Delft University of Technology, 2628, CD Delft, Netherlands (e-mail: j.m.prendergast@tudelft.nl; s.balvert@student.tudelft.nl; t.driessen-1@student.tudelft.nl; a.seth@tudelft.nl; l.peternel@tudelft.nl). Digital Object Identifier 10.1109/LRA.2021.3097375 Fig. 1. Physical system and biomechanical simulation setup. (a) A Kuka LBR iiwa robotic manipulator delivers motion to the shoulder of a test subject through an elbow brace and load cell attachment. (b) An OpenSim biomechanical model of the shoulder matches the movement of the subject and strains in the rotator-cuff muscles (highlighted) are estimated. (c) The estimated strains of the individual rotator-cuff muscles presented as 2D heatmaps over the shoulder range of motion (axes).",,,,,,,,,,"Minagawa, H., Prevalence of symptomatic and asymptomatic rotator cuff tears in the general population: From mass-screening in one village (2013) J. 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Eng., 14 (4), pp. 456-469. , Dec","Prendergast, J.M.; Biomechanical Engineering and Cognitive Robotics Departments, CD, Netherlands; email: j.m.prendergast@tudelft.nl",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85110837600
"Saunderson S.P., Nejat G.","16029308800;12789569200;","Persuasive robots should avoid authority: The effects of formal and real authority on persuasion in human-robot interaction",2021,"Science Robotics","6","58","eabd5186","","",,1,"10.1126/scirobotics.abd5186","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116816421&doi=10.1126%2fscirobotics.abd5186&partnerID=40&md5=78cf72ef88c295eb4fa20c9926d683e9","Autonomous Systems and Biomechatronics Lab, Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON  M5S 3G8, Canada","Saunderson, S.P., Autonomous Systems and Biomechatronics Lab, Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON  M5S 3G8, Canada; Nejat, G., Autonomous Systems and Biomechatronics Lab, Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Road, Toronto, ON  M5S 3G8, Canada","Social robots must take on many roles when interacting with people in everyday settings, some of which may be authoritative, such as a nurse, teacher, or guard. It is important to investigate whether and how authoritative robots can influence people in applications ranging from health care and education to security and in the home. Here, we present a human-robot interaction study that directly investigates the effect of a robot's peer or authority role (formal authority) and control of monetary rewards and penalties (real authority) on its persuasive influence. The study consisted of a social robot attempting to persuade people to change their answers to the robot's suggestion in a series of challenging attention and memory tasks. Our results show that the robot in a peer role was more persuasive than when in an authority role, contrary to expectations from human-human interactions. The robot was also more persuasive when it offered rewards over penalties, suggesting that participants perceived the robot's suggestions as a less risky option than their own estimates, in line with prospect theory. In general, the results show an aversion to the persuasive influence of authoritative robots, potentially due to the robot's legitimacy as an authority figure, its behavior being perceived as dominant, or participant feelings of threatened autonomy. This paper explores the importance of persuasion for robots in different social roles while providing critical insight into the perception of robots in these roles, people's behavior around these robots, and the development of human-robot relationships. © 2021 American Association for the Advancement of Science. All rights reserved.",,"Home health care; Man machine systems; Human-human interactions; Humans-robot interactions; Interaction studies; Monetary rewards; Peer roles; Persuasive robots; Prospect theory; Reward and penalty; Social robots; Teachers'; Human robot interaction",,,,,,,,,,,,,,,,"Olson, J., Maio, G., Attitudes in social behavior (2003) Handbook of Social Psychology, Personality and Social Psychology, 5, pp. 299-325. , T. 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H., Ward, T., Ridgeway, V., Nimmo-Smith, I., The structure ofñormal human attention: The test of everyday attention (1996) J. Int. Neuropsychol. Soc, 2, pp. 525-534; Brickenkamp, R., (1962) Test d2: Aufmerksamkeits-Belastungs-Test, , Verlag Psychol; Wilkins, A. J., Shallice, T., McCarthy, R., Frontal lesions and sustained attention (1987) Neuropsychologia, 25, pp. 359-365; Nachar, N., The Mann-Whitney U: A test for assessing whether two independent samples come from the same distribution (2008) Tutor. Quant. Methods Psychol, 4, pp. 13-20","Saunderson, S.P.; Autonomous Systems and Biomechatronics Lab, 5 King's College Road, Canada; email: shane.saunderson@mail.utoronto.ca",,,"American Association for the Advancement of Science",,,,,24709476,,,"34550717","English","Sci. Robotics",Article,"Final","",Scopus,2-s2.0-85116816421
"Zhou L., Chen W., Chen W., Bai S., Zhao Z., Wang J., Yu H.","57195431282;35776127700;55715940500;23092693700;57190885478;56075130700;7405853761;","Hysteresis modeling and compensation of a rotary series elastic actuator with nonlinear stiffness",2021,"Review of Scientific Instruments","92","9","095005","","",,,"10.1063/5.0053074","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114731532&doi=10.1063%2f5.0053074&partnerID=40&md5=f2b1b43ba9cf9031c3fe24dbed63aa99","School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China; College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China; School of Electrical and Automation, Anhui University, Hefei, 230601, China; Department of Materials and Production, Aalborg University, Aalborg, 9220, Denmark; Hangzhou Innovation Institute, Beihang University, Hangzhou, 310053, China; Department of Biomedical Engineering, National University of Singapore, Singapore, 117575, Singapore","Zhou, L., School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China, College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China; Chen, W., School of Electrical and Automation, Anhui University, Hefei, 230601, China; Chen, W., School of Electrical and Automation, Anhui University, Hefei, 230601, China; Bai, S., Department of Materials and Production, Aalborg University, Aalborg, 9220, Denmark; Zhao, Z., Hangzhou Innovation Institute, Beihang University, Hangzhou, 310053, China; Wang, J., School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China, Hangzhou Innovation Institute, Beihang University, Hangzhou, 310053, China; Yu, H., Department of Biomedical Engineering, National University of Singapore, Singapore, 117575, Singapore","Series elastic actuators (SEAs) have widely been adapted in robots where safe human-robot interaction is required for accurate and robust force control. Recent research on the SEAs has shown that the SEA with a user-defined variable stiffness possesses several advantages over the constant stiffness SEA, such as large force range and bandwidth while keeping low output impedance and high force fidelity. However, a limitation of this type of SEA is that an obvious hysteresis effect exists and the associated torque curves are nonlinear and vary with amplitudes. Conventional mathematical hysteresis models are usually developed with some kind of black-box modeling, and the model parameters are adjusted through parameter identification methods. It is challenging to tune the model parameters to match the experimental data well among inputs with different amplitudes, let alone the inverse model of the hysteresis, which is necessary to compensate the hysteresis effect in control. In this paper, a rotary SEA (rSEA) with nonlinear stiffness is proposed. A concept called “virtual deformation” is introduced to mathematically transform the nonlinear curve into a polyline hysteresis model. This eases torque estimation with respect to the deformation of the rSEA. A hysteresis compensation torque controller is implemented for precise torque control. A prototype of the rSEA was fabricated, and the experimental results verified modeling accuracy of the proposed model. Our results showed that, with the new model, the computation cost was greatly reduced while keeping the modeling accuracy almost the same compared with the nonlinear backlash model. © 2021 Author(s).",,"Actuators; Deformation; Human robot interaction; Hysteresis loops; Inverse problems; Mathematical transformations; Stiffness; Torque; Torque control; Hysteresis compensation; Hysteresis modeling; Low output impedance; Non-linear stiffness; Parameter identification methods; Robust force control; Safe human-robot interaction; Series elastic actuators; Hysteresis; article; compensation; human; hysteresis; rigidity; torque",,,,,"2020ZDSJ0605, 2020ZDSJ0881; 2021C03050; National Natural Science Foundation of China, NNSFC: 51975002, 61773042; Natural Science Foundation of Zhejiang Province, ZJNSF: LQ20F030005; Fundo para o Desenvolvimento das Ciências e da Tecnologia, FDCT: 0022/2019/AKP","This work was supported by the National Natural Science Foundation of China (Grant Nos. 61773042 and 51975002), the Zhejiang Provincial Key R & D Program (Grant No. 2021C03050), the Scientific Research Project of Agriculture and Social Development of Hangzhou (Grant Nos. 2020ZDSJ0881 and 2020ZDSJ0605), the Zhejiang Provincial Natural Science Foundation of China (Grant No. LQ20F030005), and the Macao Science and Technology Development Fund under Project No. 0022/2019/AKP.",,,,,,,,,,"Pratt, G., Williamson, M., (1995) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 399-406. , and in (IEEE Pittsburgh 1995) pp; Cao, L., Dolovich, A., Zhang, W.J., (2013) Mech. 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Mechatron., 24, p. 36; Gu, G.-Y., Gupta, U., Zhu, J., Zhu, L.-M., Zhu, X., (2017) IEEE Trans. Rob., 33, p. 1263; Kim, D.-H., Oh, J.-H., (2019) IEEE/ASME Trans. Mechatron., 24, p. 1316; Ikhouane, F., Manosa, V., Rodellar, J., (2007) Syst. Control Lett., 56, p. 197; Ismail, M., Ikhouane, F., Rodellar, J., (2009) Arch. Comput. Methods Eng., 16, p. 161; Lapshin, R.V., (2020) Rev. Sci. Instrum., 91, p. 065106; Li, Z., Wang, R., Fang, J., Dai, X., Li, Z., (2017) Rev. Sci. Instrum., 88, p. 075003; Choi, W., Won, J., Lee, J., Park, J., (2017) Auton. Rob., 41, p. 1221; Bashash, S., Jalili, N., (2008) J. Dyn. Syst., Meas., Control, 130, p. 031008; Li, Z., Shan, J., Gabbert, U., (2018) IEEE/ASME Trans. Mechatron., 23, p. 966; Zhou, L., Chen, W., Chen, W., Bai, S., Wang, J., Zhang, J., (2019) Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 68-73. , and in (IEEE Hong Kong China 2019) pp; Holnicki-Szulc, J., (2008) Smart Technologies for Safety Engineering, pp. 11-35. , (John Wiley & Sons Ltd. Chichester 2008) pp; Li, X., Pan, Y., Chen, G., Yu, H., (2017) Int. J. Rob. Res., 36, p. 759","Chen, W.; School of Electrical and Automation, China; email: whchen@buaa.edu.cn
Chen, W.; School of Electrical and Automation, China",,,"American Institute of Physics Inc.",,,,,00346748,,RSINA,"34598513","English","Rev. Sci. Instrum.",Article,"Final","",Scopus,2-s2.0-85114731532
"Martinez-Peon D., Olguín-Díaz E., Muñoz-Vázquez A.J., Francisco P.C., Méndez D.S.","56596745900;25925277800;57204699024;53982953600;57226806959;","Modeling and control of exoskeleton for wrist and forearm rehabilitation",2021,"Biomedical Signal Processing and Control","70",,"103022","","",,,"10.1016/j.bspc.2021.103022","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112766219&doi=10.1016%2fj.bspc.2021.103022&partnerID=40&md5=61d9318c6793478bc61bdd338a3dab09","Department of Electrical and Electronic Engineering, National Technological Institute of Mexico/IT Nuevo Leon, Mexico; Robotics and Advanced Manufacturing, Research Center for Advanced Studies (Cinvestav), Saltillo, Mexico; Department of Multidisciplinary Engineering, Texas A&M University, 6200 Tres Lagos Blvd, Higher Education Center at Mcallen, Texas, 78504, United States; Department of Electromechanical Engineering, National Technological Institute of Mexico/ IT Superior de Poza Rica, Veracruz, Mexico","Martinez-Peon, D., Department of Electrical and Electronic Engineering, National Technological Institute of Mexico/IT Nuevo Leon, Mexico; Olguín-Díaz, E., Robotics and Advanced Manufacturing, Research Center for Advanced Studies (Cinvestav), Saltillo, Mexico; Muñoz-Vázquez, A.J., Department of Multidisciplinary Engineering, Texas A&M University, 6200 Tres Lagos Blvd, Higher Education Center at Mcallen, Texas, 78504, United States; Francisco, P.C., Department of Electromechanical Engineering, National Technological Institute of Mexico/ IT Superior de Poza Rica, Veracruz, Mexico; Méndez, D.S., Department of Electromechanical Engineering, National Technological Institute of Mexico/ IT Superior de Poza Rica, Veracruz, Mexico","This paper presents the modeling of the user's dynamics synchronized with a wrist and forearm exoskeleton of 3 degrees of freedom (DoF). The biomechanical model includes components that are presented in post-stroke patients, such as viscosity and elasticity parameters. The dynamic model of the uncoupled exoskeleton is considered, so that the FE (flexion/extension), AA (adduction/abduction) and PS (pronation/supination) exercises are carried out one at a time, and not simultaneously. This work shows a comparison of the implementation of two control schemes, a PD control and an adaptive nonlinear PID-like controller. These schemes are applied to the motion control of the upper limb, which stands for wrist and forearm rehabilitation, with 3 DoF. To demonstrate the feasibility of our proposal, the user motion is included in the simulations, assuming human–robot interaction, and emulating a therapy session for patients with low and high spasticity. The results show that the adaptive nonlinear PID-like controller presents a lower mean square error than the PD control, for both low and high spasticity. The most important implications of the proposed study are, the possibility to track the smooth references, similar to a realistic therapy session and with an exoskeleton system; and the inference that adaptive controllers provide a good degree of robustness with respect to gain variations and uncertain dynamic effects. Our research is also important for designing high-end wrist and forearm exoskeletons for stroke patients, whose dynamics change over rehabilitation sessions; in this sense, typical controls, like PD, are not completely adequate to compensate these changes. © 2021","Adaptive nonlinear PID; Exoskeleton; Human–robot interaction; Motor rehabilitation; PD control; Spasticity; Wrist and forearm","Adaptive control systems; Controllers; Degrees of freedom (mechanics); Exoskeleton (Robotics); Mean square error; Patient rehabilitation; Patient treatment; 3 degrees of freedom; Adaptive nonlinear PID; Exoskeleton; Humans-robot interactions; Motor rehabilitation; Nonlinear PID; PD control; PID like controllers; Spasticity; Wrist and forearm; Human robot interaction; abduction; adduction; Article; biomechanics; data analysis software; degree of freedom; dynamics; elasticity; feasibility study; forearm; human; kinematics; model; motion; passive movement; physical model; pronation; rehabilitation care; simulation; spasticity; stroke patient; supination; upper limb; viscosity; wrist",,,,,"Tecnológico Nacional de México, TNM: 6771.18-P; Consejo Nacional de Ciencia y Tecnología, CONACYT","We thank CONACYT for the scholarship provided and we thank TecNM, Mexico for financial support (project number 6771.18-P ).",,,,,,,,,,"Huysamen, K., Power, V., O'Sullivan, L., Elongation of the surface of the spine during lifting and lowering, and implications for design of an upper body industrial exoskeleton (2018) Applied Ergon., 72, pp. 10-16. , http://www.sciencedirect.com/science/article/pii/S0003687018300978, URL:; Molteni, F., Gasperini, G., Cannaviello, G., Guanziroli, E., Exoskeleton and end-effector robots for upper and lower limbs rehabilitation: Narrative review (2018) Phys. 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Appl., 3, pp. 45-58; Lin, C.-H., Su, Y.-Y., Lai, Y.-H., Lan, C.-C., A spatial-motion assist-as-needed controller for the passive, active, and resistive robot-aided rehabilitation of the wrist (2020) IEEE Access, 8, pp. 133951-133960; Yaxi, W., Xu, Q., Design and testing of a soft parallel robot based on pneumatic artificial muscles for wrist rehabilitation (2021) Sci. Rep., 11; Hussain, S., Jamwal, P., van Vliet, P., Ghayesh, M., State-of-the-art robotic devices for wrist rehabilitation: Design and control aspects (2020) IEEE Trans. Hum.-Mach. Syst., PP, pp. 1-12; Lv, G., Zhu, H., Gregg, R.D., On the design and control of highly backdrivable lower-limb exoskeletons: A discussion of past and ongoing work (2018) IEEE Control Syst. Mag., 38 (6), pp. 88-113; Li, Z., Deng, C., Zhao, K., Human-cooperative control of a wearable walking exoskeleton for enhancing climbing stair activities (2020) IEEE Trans. Ind. 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Lett., PP, p. 1","Martinez-Peon, D.; Department of Electrical and Electronic Engineering, Mexico; email: dulce.martinez@itnl.edu.mx",,,"Elsevier Ltd",,,,,17468094,,,,"English","Biomed. Signal Process. Control",Article,"Final","",Scopus,2-s2.0-85112766219
"Thepsoonthorn C., Ogawa K.-I., Miyake Y.","57188841724;55885908800;35734660000;","The Exploration of the Uncanny Valley from the Viewpoint of the Robot’s Nonverbal Behaviour",2021,"International Journal of Social Robotics","13","6",,"1443","1455",,2,"10.1007/s12369-020-00726-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098576255&doi=10.1007%2fs12369-020-00726-w&partnerID=40&md5=60683bc56c6d2b27bdeda342b0e852e1","Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan","Thepsoonthorn, C., Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan; Ogawa, K.-I., Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan; Miyake, Y., Department of Computer Science, Tokyo Institute of Technology, Yokohama, Japan","Many studies have been conducted to find approaches to overcome the Uncanny Valley. However, the focus on the influence of the robot’s appearance leaves a big missing part: the influence of the robot’s nonverbal behaviour. This impedes the complete exploration of the Uncanny Valley. In this study, we explored the Uncanny Valley from the viewpoint of the robot’s nonverbal behaviour in regard to the Uncanny Valley hypothesis. We observed a relationship between the participants’ ratings on human-likeness of the robot’s nonverbal behavior and affinity toward the robot’s nonverbal behavior, and define the point where the affinity toward the robot’s nonverbal behavior significantly drops down as the Uncanny Valley. In this study, an experiment of human–robot interaction was conducted. The participants were asked to interact with a robot with different nonverbal behaviours, ranging from 0 (no nonverbal behavior, speaking only) to 3 (gaze, head nodding, and gestures) combinations and to rate the perceived human-likeness and affinity toward the robot’s nonverbal behavior by using a questionnaire. Additionally, the participants’ fixation duration was measured during the experiment. The result showed a biphasic relationship between human-likeness and affinity rating results. A curve resembling the Uncanny Valley is found. The result was also supported by participants’ fixation duration. It showed that the participants had the longest fixation at the robot when the robot expressed the nonverbal behaviours that fall into the Uncanny Valley. This exploratory study provides evidence suggesting the existence of the Uncanny Valley from the viewpoint of the robot’s nonverbal behaviour. © 2021, The Author(s).","Affinity; Fixation duration; Human-likeness; Robot’s nonverbal behaviours; Uncanny Valley","Agricultural robots; Social robots; Complete explorations; Exploratory studies; Fixation duration; Human likeness; Non-verbal behaviours; Nonverbal behavior; Robot interactions; Uncanny valley; Landforms",,,,,"Japan Society for the Promotion of Science, KAKEN: 15H01771, 18H11500","This study was supported by Japan Society for the Promotion of Science (KAKENHI) (Grant Numbers 15H01771 and 18H11500).",,,,,,,,,,"Mori, M., The uncanny valley (1970) Energy, 7 (4), pp. 33-35; Macdorman, K.F., Subjective ratings of robot video clips for human like- ness, familiarity, and eeriness: An exploration of the uncanny valley (2006) Iccs/Cogsci 2006 Long Symposium: Toward Social Mechanisms of Android Science, pp. 26-29; MacDorman, K.F., Ishiguro, H., The uncanny advantage of using androids in cognitive and social science research (2006) Interact Stud, 7 (3), pp. 297-337; MacDorman, K.F., Entezari, S.O., Individual differences predict sensitivity to the uncanny valley (2015) Interact Stud, 16 (2), pp. 141-172; Mathur, M.B., Reichling, D.B., Navigating a social world with robot partners: a quantitative cartography of the uncanny valley (2016) Cognition, 146, pp. 22-32; Seyama, J., Nagayama, R.S., The uncanny valley: effect of realism on the impression of artificial human faces (2007) Presence Teleoperators Virtual Environ, 16 (4), pp. 337-351; Bartneck, C., Kanda, T., Ishiguro, H., Hagita, N., Is the uncanny valley an uncanny cliff? (2007) In: The 16Th IEEE International Symposium on Robot and Human Interactive Communication, pp. 368-373. , RO-MAN, IEEE; Bartneck, C., Kanda, T., Ishiguro, H., Hagita, N., My robotic doppelgänger-a critical look at the uncanny valley (2009) In: The 18Th IEEE International Symposium on Robot and Human Interactive Communication, 2009, pp. 269-276. , RO-MAN, IEEE; Bartneck, C., Croft, E., Kulic, D., Measuring the anthropomorphism, animacy, likeability, perceived intelligence and perceived safety of robots (2008) Metrics HRI Workshop Tech Rep, 471, pp. 37-44; Burleigh, T.J., Schoenherr, J.R., Lacroix, G.L., Does the uncanny valley exist? 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Comparing attitudes by implicit and explicit measures (2009) AI Soc, 23 (4), pp. 485-510; DeVito, J.A., (2011) Human communication: the basic course, , Pearson Higher Ed, New York; Axtell, R.E., Axtell, R.E., (1993) Do’s and taboos around the world, , Wiley, New York; Turkle, S., Taggart, W., Kidd, C.D., Dasté, O., Relational artifacts with children and elders: the complexities of cybercompanionship (2006) Connect Sci, 18 (4), pp. 347-361; Jokinen, K., (2010) ) Non-verbal signals for turn-taking and feedback.; Novick, D.G., Hansen, B., Ward, K., Coordinating turn-taking with gaze (1996) In: Proceedings, Fourth International Conference on Spoken Language, 96, pp. 1888-1891","Thepsoonthorn, C.; Department of Computer Science, Japan; email: chidchanok.thepsoonthorn@gmail.com",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85098576255
"Swaminathan J., Akintoye J., Fraune M.R., Knight H.","57261916500;57262211400;56085661200;26321722700;","Robots that run their own human experiments: Exploring relational humor with multi-robot comedy",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"1262","1268",,,"10.1109/RO-MAN50785.2021.9515324","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115060802&doi=10.1109%2fRO-MAN50785.2021.9515324&partnerID=40&md5=4eeeab12ed1599ef175349c208f4cd75","Oregon State University, Collaborative Robots and Intelligent Systems (CoRIS) Institute, Corvallis, OR, United States; University of Maryland Baltimore County, Department of Mechanical Engineering, Baltimore, MD, United States; New Mexico State University, Department of Psychnology, Las Cruces, NM, United States","Swaminathan, J., Oregon State University, Collaborative Robots and Intelligent Systems (CoRIS) Institute, Corvallis, OR, United States; Akintoye, J., University of Maryland Baltimore County, Department of Mechanical Engineering, Baltimore, MD, United States; Fraune, M.R., New Mexico State University, Department of Psychnology, Las Cruces, NM, United States; Knight, H., Oregon State University, Collaborative Robots and Intelligent Systems (CoRIS) Institute, Corvallis, OR, United States","This paper proposes a street-style study method to conduct human-robot interaction studies in-the-wild where the robots conduct their own experiments by recruiting their audience, conducting the study and gathering data. This street-style study method was implemented using multi-robot comedy as the platform deployed at an arts and technology festival to validate the concept. Twelve robot comedy shows occurred over seven hours with two robots on stage, who queried the audience during and at the end of each show. The multi-robot aspect enabled the robots to act out interactions relative each other, oneself and the audience. The final street-style study method evolved from pilots at a local farmer's market, with hardware designed for portability and easy replication. The robots conducted their own human experiments in that they queried the audience after displaying experimentally balanced episodes of relational humor, with permutations of who was the 'butt of the joke.' Our study results explore the relational humor of the two robots and the audience, asking the audience to agree or disagree with particular perspectives. Delivered as part of the show, the robots invite the audience to vote via a show of hands. ANOVA analyses of the percent-agreement results find that (1) audiences were generally positive about all aspects of the show unless both robots were being negative, and (2) audiences were more ready to protect the robot comedian's ego than their own, strongly supporting the statement that the robot was doing a good job.New Abstract © 2021 IEEE.",,"Agricultural robots; Industrial robots; Multipurpose robots; ANOVA analysis; Multirobots; Study methods; Social robots",,,,,,,,,,,,,,,,"Fallatah, A., Urann, H., Knight, J., The robot show must go on: Effective responses to robot failures (2019) International Conference on on Intelligent Robots and Systems. IEEE; Herath, D.C., Jochum, E., Vlachos, E., An experimental study of embodied interaction and human perception of social presence for interactive robots in public settings (2017) IEEE Transactions on Cognitive and Developmental Systems, 10 (4), pp. 1096-1105; Jochum, E., Vlachos, E., Christoffersen, A., Nielsen, S.G., Hameed, I.A., Tan, Z.-H., Using theatre to study interaction with care robots (2016) International Journal of Social Robotics, 8 (4), pp. 457-470; Knight, H., Eight lessons learned about non-verbal interactions through robot theater (2011) International Conference on Social Robotics, pp. 42-51. , Springer; Knight, H., Satkin, S., Ramakrishna, V., Divvala, S., A savvy robot standup comic: Online learning through audience tracking (2011) Workshop Paper (TEI'10); Lu, D.V., Smart, W.D., Human-robot interactions as theatre (2011) 2011 RO-MAN. IEEE, pp. 473-478; Breazeal, C., Brooks, A., Gray, J., Hancher, M., Kidd, C., McBean, J., Stiehl, D., Strickon, J., Interactive robot theatre (2003) Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No. 03CH37453), 4, pp. 3648-3655; Vilk, J., Fitter, N.T., Comedians in cafes getting data: Evaluating timing and adaptivity in real-world robot comedy performance (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 223-231; Sirkin, D., Mok, B., Yang, S., Ju, W., Mechanical ottoman: How robotic furniture offers and withdraws support (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction. ACM, pp. 11-18; Hoffman, G., Weinberg, G., Gesture-based human-robot jazz improvisation (2010) 2010 IEEE International Conference on Robotics and Automation. IEEE, pp. 582-587; Strabala, K., Lee, M.K., Dragan, A., Forlizzi, J., Srinivasa, S.S., Cakmak, M., Micelli, V., Toward seamless human-robot handovers (2013) Journal of Human-Robot Interaction, 2 (1), pp. 112-132; Feil-Seifer, D., Mataric, M.J., Defining socially assistive robotics (2005) 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005. IEEE, pp. 465-468; Katevas, K., Healey, P.G., Harris, M.T., Robot comedy lab: Experimenting with the social dynamics of live performance (2015) Frontiers in Psychology, 6, p. 1253; Sjöbergh, J., Araki, K., A complete and modestly funny system for generating and performing Japanese stand-up comedy (2008) Coling 2008: Companion Volume: Posters, pp. 111-114; Weber, K., Ritschel, H., Lingenfelser, F., André, E., Real-time adaptation of a robotic joke teller based on human social signals (2018) Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems. 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ACM, pp. 383-383; Natural Readers for Text to Speech, , https://www.naturalreaders.com/online/, accessed: 2019-07-19; Goffman, E., (1978) The Presentation of Self in Everyday Life, , Harmondsworth London; Estrada, E., Vargas-Estrada, E., How peer pressure shapes consensus, leadership, and innovations in social groups (2013) Scientific Reports, 3, p. 2905",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115060802
"Humblot-Renaux G., Li C., Chrysostomou D.","57221629253;57192578646;24477398900;","Why talk to people when you can talk to robots? Far-field speaker identification in the wild",2021,"2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021",,,,"272","278",,,"10.1109/RO-MAN50785.2021.9515482","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115047504&doi=10.1109%2fRO-MAN50785.2021.9515482&partnerID=40&md5=4ad2b87eb4caf0768e0a3a671632e006","Aalborg University, Robotics and Automation Group, Department of Materials and Production, Fibigerstraede 16, Aalborg East, DK-9220, Denmark","Humblot-Renaux, G., Aalborg University, Robotics and Automation Group, Department of Materials and Production, Fibigerstraede 16, Aalborg East, DK-9220, Denmark; Li, C., Aalborg University, Robotics and Automation Group, Department of Materials and Production, Fibigerstraede 16, Aalborg East, DK-9220, Denmark; Chrysostomou, D., Aalborg University, Robotics and Automation Group, Department of Materials and Production, Fibigerstraede 16, Aalborg East, DK-9220, Denmark","Equipping robots with the ability to identify who is talking to them is an important step towards natural and effective verbal interaction. However, speaker identification for voice control remains largely unexplored compared to recent progress in natural language instruction and speech recognition. This motivates us to tackle text-independent speaker identification for human-robot interaction applications in industrial environments. By representing audio segments as time-frequency spectrograms, this can be formulated as an image classification task, allowing us to apply state-of-the-art convolutional neural network (CNN) architectures. To achieve robust prediction in unconstrained, challenging acoustic conditions, we take a data-driven approach and collect a custom dataset with a far-field microphone array, featuring over 3 hours of ""in the wild""audio recordings from six speakers, which are then encoded into spectral images for CNN-based classification. We propose a shallow 3-layer CNN, which we compare with the widely used ResNet-18 architecture: in addition to benchmarking these models in terms of accuracy, we visualize the features used by these two models to discriminate between classes, and investigate their reliability in unseen acoustic scenes. Although ResNet-18 reaches the highest raw accuracy, we are able to achieve remarkable online speaker recognition performance with a much more lightweight model which learns lower-level vocal features and produces more reliable confidence scores. The proposed method is successfully integrated into a robotic dialogue system and showcased in a mock user localization and authentication scenario in a realistic industrial environment: https://youtu.be/IVtZ8LKJZ7A. © 2021 IEEE.",,"Agricultural robots; Audio acoustics; Audio recordings; Authentication; Classification (of information); Convolutional neural networks; Image classification; Industrial robots; Loudspeakers; Network architecture; Social robots; Spectroscopy; Speech processing; Acoustic conditions; Data-driven approach; Industrial environments; Robust predictions; Speaker identification; Speaker recognition; Text-independent speaker identification; Verbal interaction; Speech recognition",,,,,"857061","ACKNOWLEDGEMENTS We would like to thank the research assistants Martin Bieber, Jinha Park and Hahyeon Kim for their significant help with robotic integration and lending their voices to this experiment. We would also like to thank Letizia Marchegiani for her valuable technical input. Lastly, we would like to acknowledge support by the H2020-WIDESPREAD project no. 857061 “Networking for Research and Development of Human Interactive and Sensitive Robotics Taking Advantage of Additive Manufacturing – R2P2” and EU’s SMART EUREKA programme under grant agreement S0218-chARmER.",,,,,,,,,,"Marchegiani, L., Posner, I., Leveraging the urban soundscape: Auditory perception for smart vehicles (2017) IEEE International Conference on Robotics and Automation (ICRA), pp. 6547-6554; Gandhi, D., Gupta, A., Pinto, L., (2020) Swoosh! Rattle! 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Lecture Notes in Computer Science, pp. 176-185. , Springer 11876; Jalil, A.M., Hasan, F.S., Alabbasi, H.A., Speaker identification using convolutional neural network for clean and noisy speech samples (2019) First International Conference of Computer and Applied Sciences (CAS), pp. 57-62; Andersen, R.E., Hansen, E.B., Cerny, D., Madsen, S., Pulendralingam, B., Bøgh, S., Chrysostomou, D., Integration of a skillbased collaborative mobile robot in a smart cyber-physical environment (2017) Procedia Manufacturing, 11, pp. 114-123; Schou, C., Andersen, R.S., Chrysostomou, D., Bøgh, S., Madsen, O., Skill-based instruction of collaborative robots in industrial settings (2018) Robotics and Computer-Integrated Manufacturing, 53, pp. 72-80; An, N.N., Thanh, N.Q., Liu, Y., Deep cnns with self-attention for speaker identification (2019) IEEE Access, 7, pp. 85327-85337; Cai, D., Qin, X., Cai, W., Li, M., The dku system for the speaker recognition task of the 2019 voices from a distance challenge (2019) INTERSPEECH, pp. 2493-2497; Lieto, A., Moro, D., Devoti, F., Parera, C., Lipari, V., Bestagini, P., Tubaro, S., Hello? who am i talking to? 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Association for Computing Machinery, pp. 114-115; Ding, I.-J., Shi, J.-Y., Kinect microphone array-based speech and speaker recognition for the exhibition control of humanoid robots (2017) Computers & Electrical Engineering, 62, pp. 719-729; Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A., Learning deep features for discriminative localization (2016) IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921-2929; Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L., Imagenet: A large-scale hierarchical image database (2009) IEEE Conference on Computer Vision and Pattern Recognition, pp. 248-255; Zheng, T.F., Li, L., (2017) Robustness-Related Issues in Speaker Recognition, pp. 27-37. , Springer; Loshchilov, I., Hutter, F., Sgdr: Stochastic gradient descent with warm restarts (2017) 5th International Conference on Learning Representations, ICLR",,,,"Institute of Electrical and Electronics Engineers Inc.","30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021","8 August 2021 through 12 August 2021",,171425,,9781665404921,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85115047504
"Taheri A., Meghdari A., Mahoor M.H.","56513500500;7003828407;8423254700;","A Close Look at the Imitation Performance of Children with Autism and Typically Developing Children Using a Robotic System",2021,"International Journal of Social Robotics","13","5",,"1125","1147",,4,"10.1007/s12369-020-00704-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092561144&doi=10.1007%2fs12369-020-00704-2&partnerID=40&md5=a6c19332aba57dfd0960dc3ac6808620","Social and Cognitive Robotics Laboratory, Center of Excellence in Design, Robotics and Automation (CEDRA), Sharif University of Technology, Tehran, Iran; Ritchie School of Engineering and Computer Science, Computer Vision and Social Robotics Lab, University of Denver, Denver, CO, United States","Taheri, A., Social and Cognitive Robotics Laboratory, Center of Excellence in Design, Robotics and Automation (CEDRA), Sharif University of Technology, Tehran, Iran; Meghdari, A., Social and Cognitive Robotics Laboratory, Center of Excellence in Design, Robotics and Automation (CEDRA), Sharif University of Technology, Tehran, Iran; Mahoor, M.H., Ritchie School of Engineering and Computer Science, Computer Vision and Social Robotics Lab, University of Denver, Denver, CO, United States","Deficit in imitation skills is one of the core symptoms of children with Autism Spectrum Disorder (ASD). In this study, we have tried to look closer at the body gesture imitation performance of 20 participants with autism, i.e. ASD group, and 20 typically developing subjects, i.e. TD group, in a set of robot-child and human-child gross imitation tasks. The results of manual scoring by two specialists indicated that while the TD group showed a significantly better imitation performance than the ASD group during the tasks, both ASD and TD groups performed better in the human-child mode than the robot-child mode in our experimental setup. Next, to introduce an automated imitation assessment system, we present different mathematical models of the children’s imitation performance using some State-Image based algorithms including Acceptable Bound, Mahalanobis Distance, and Signals’ Cross-Correlations as well as Hidden Markov Models based on the time-dependent kinematics data of the participants’ joints. Among the different studied models, we observed that the “State-Image Acceptable Bound method with position, velocity, and acceleration features” is the best one. This method has a mean Pearson correlation of ~ 45%, which is fairly comparable to the related works (out of autism field) in assessing the quality of dynamic actions. Finally, for a treatment application of using artificial intelligence algorithms in automated evaluation of children’s behaviors as an unbiased and quantifiable measurement in HRI, we propose a reciprocal gross imitation human–robot interaction platform with the potential to aid in the cognitive rehabilitation of children with autism. © 2020, Springer Nature B.V.","Autism Spectrum Disorder; Automated assessment; Hidden Markov model; Human–robot-interaction; Imitation; Social robots","Agricultural robots; Correlation methods; Diseases; Hidden Markov models; Intelligent robots; Patient rehabilitation; Robotics; Artificial intelligence algorithms; Automated evaluation; Children with autisms; Cognitive rehabilitation; Cross correlations; Mahalanobis distances; Pearson correlation; Robot interactions; Social robots",,,,,"Cognitive Sciences and Technologies Council, CSTC: 95p22; Sharif University of Technology: G980517","Authors Alireza Taheri and Ali Meghdari has received research grants from “Sharif University of Technology” (Grant No. G980517) and the “Cognitive Sciences and Technology Council” (CSTC) of Iran (Grant No. 95p22), respectively. The author Mohammad H. Mahoor declares that he has no conflict of interest.","Our profound gratitude goes to the “Center for the Treatment of Autistic Disorders (CTAD)” and its psychologists for their contributions to the clinical trials with the children with autism. We sincerely appreciate Prof. Minoo Alemi and Prof. Hamidreza Pouretemad for their consults during the study. This research was funded by Sharif University of Techology (Grant No. G980517) and the “Cognitive Sciences and Technology Council” (CSTC) of Iran ( http://www.cogc.ir/ ) (Grant No. 95p22). We also appreciate “Dr. AliAkbar Siasi Memorial Grant Award” for the complementary support of the Social & Cognitive Robotics Laboratory.","Our profound gratitude goes to the ?Center for the Treatment of Autistic Disorders (CTAD)? and its psychologists for their contributions to the clinical trials with the children with autism. We sincerely appreciate Prof. Minoo Alemi and Prof. Hamidreza Pouretemad for their consults during the study. This research was funded by Sharif University of Techology (Grant No. G980517) and the ?Cognitive Sciences and Technology Council? (CSTC) of Iran (http://www.cogc.ir/) (Grant No. 95p22). We also appreciate ?Dr. AliAkbar Siasi Memorial Grant Award? for the complementary support of the Social & Cognitive Robotics Laboratory.",,,,,,,,"Scassellati, B., Admoni, H., Matarić, M., Robots for use in autism research (2012) Annu Rev Biomed Eng, 14, pp. 275-294; Taheri, A.R., Alemi, M., Meghdari, A., Pouretemad, H.R., Basiri, N.M., Social robots as assistants for autism therapy in Iran: Research in progress (2014) 2014 Second RSI/ISM International Conference on Robotics and Mechatronics (Icrom). IEEE, pp. 760-766; Ingersoll, B., The social role of imitation in autism: implications for the treatment of imitation deficits (2008) Infants Young Child, 21 (2), pp. 107-119; Carpenter, M., Pennington, B.F., Rogers, S.J., Interrelations among social-cognitive skills in young children with autism (2002) J Autism Dev Disord, 32 (2), pp. 91-106; Uzgiris, I., Imitation as activity: Its developmental aspects (1999) Imitation in Infancy, Ch. 6. Cambridge University, Cambridge, England, pp. 186-206. , https://eric.ed.gov/?id=ED433967, Nadel J, Butterworth G; Cabibihan, J.J., Javed, H., Ang, M., Aljunied, S.M., Why robots? A survey on the roles and benefits of social robots in the therapy of children with autism (2013) Int J Soc Robot, 5 (4), pp. 593-618; Taheri, A., Meghdari, A., Alemi, M., Pouretemad, H., Human–robot interaction in autism treatment: a case study on three pairs of autistic children as twins, siblings, and classmates (2018) Int J Social Robot, 10 (1), pp. 93-113; Kajopoulos, J., Wong, A.H.Y., Yuen, A.W.C., Dung, T.A., Kee, T.Y., Wykowska, A., Robot-assisted training of joint attention skills in children diagnosed with autism (2015) International Conference on Social Robotics, pp. 296-305. , Springer, Cham; Shukla, J., Cristiano, J., Oliver, J., Puig, D., Robot assisted interventions for individuals with intellectual disabilities: impact on users and caregivers (2019) Int J Soc Robot, 11 (4), pp. 631-649; Ismail, L.I., Verhoeven, T., Dambre, J., Wyffels, F., Leveraging robotics research for children with autism: a review (2019) Int J Soc Robot, 11 (3), pp. 389-410; Diehl, J.J., Schmitt, L.M., Villano, M., Crowell, C.R., The clinical use of robots for individuals with autism spectrum disorders: a critical review (2012) Res Autism Spectr Disord, 6 (1), pp. 249-262; Pennisi, P., Tonacci, A., Tartarisco, G., Billeci, L., Ruta, L., Gangemi, S., Pioggia, G., Autism and social robotics: a systematic review (2016) Autism Res, 9 (2), pp. 165-183; Pour, A.G., Taheri, A., Alemi, M., Meghdari, A., Human–robot facial expression reciprocal interaction platform: case studies on children with autism (2018) Int J Soc Robot, 10 (2), pp. 179-198; Costa, A.P., Charpiot, L., Lera, F.R., Ziafati, P., Nazarikhorram, A., van Der Torre, L., Steffgen, G., A comparison between a person and a robot in the attention, imitation, and repetitive and stereotypical behaviors of children with autism spectrum disorder (2018) Proceedings Workshop on Social human–robot Interaction of Human-Care Service Robots at HRI2018; So, W.C., Wong, M.Y., Cabibihan, J.J., Lam, C.Y., Chan, R.Y., Qian, H.H., Using robot animation to promote gestural skills in children with autism spectrum disorders (2016) J Comput Assist Learn, 32 (6), pp. 632-646; Ali, S., Mehmood, F., Dancey, D., Ayaz, Y., Khan, M.J., Naseer, N., An adaptive multi-robot therapy for improving joint attention and imitation of ASD children (2019) IEEE Access, 7, pp. 81808-81825; Aly, A., (2020) Human Posture Recognition and Gesture Imitation with a Humanoid Robot.; Fujimoto, I., Matsumoto, T., De Silva, P.R.S., Kobayashi, M., Higashi, M., Mimicking and evaluating human motion to improve the imitation skill of children with autism through a robot (2011) Int J Soc Robot, 3 (4), pp. 349-357; Pirsiavash, H., Vondrick, C., Torralba, A., Assessing the quality of actions (2014) European Conference on Computer Vision, pp. 556-571. , Springer, Cham; Zariffa, J., Kapadia, N., Kramer, J.L., Taylor, P., Alizadeh-Meghrazi, M., Zivanovic, V., Relationship between clinical assessments of function and measurements from an upper-limb robotic rehabilitation device in cervical spinal cord injury (2011) IEEE Trans Neural Syst Rehabil Eng, 20 (3), pp. 341-350; Daunoravičienė, K., Žižienė, J., Pauk, J., Idzkowski, A., Raudonytė, I., Juocevičius, A., Stroke-affected upper extremity movement assessment via continuous relative phase analysis (2017) Measurement, 110, pp. 84-89; Bosecker, C., Dipietro, L., Volpe, B., Igo Krebs, H., Kinematic robot-based evaluation scales and clinical counterparts to measure upper limb motor performance in patients with chronic stroke (2010) Neurorehabil Neural Repair, 24 (1), pp. 62-69; Butler, E.E., Ladd, A.L., LaMont, L.E., Rose, J., Temporal–spatial parameters of the upper limb during a reach & grasp cycle for children (2010) Gait Posture, 32 (3), pp. 301-306; Zhou, L., Bai, S., Hansen, M.R., Rasmussen, J., Modeling of human arm energy expenditure for predicting energy optimal trajectories (2011) Model Identif Control, 31 (3), pp. 91-101. , https://doi.org/10.4173/mic.2011.3.1; Balasubramanian, S., Melendez-Calderon, A., Roby-Brami, A., Burdet, E., On the analysis of movement smoothness (2015) J Neuroeng Rehabil, 12 (1), p. 112; Feng, Y., Jia, Q., Wei, W., A control architecture of robot-assisted intervention for children with autism spectrum disorders (2018) J Robot, 2018. , https://doi.org/10.1155/2018/3246708; Gordon, A.S., Automated video assessment of human performance (1995) Proceedings of AI-ED, pp. 16-19; Perše, M., Kristan, M., Perš, J., Kovačič, S., (2007) Automatic evaluation of organized basketball activity using bayesian networks, , na; (2020), https://www.softbankrobotics.com/emea/en/nao; Dong, Y., An application of Deep Neural Networks to the in-flight parameter identification for detection and characterization of aircraft icing (2018) Aerosp Sci Technol, 77, pp. 34-49; Dong, Y., Implementing deep learning for comprehensive aircraft icing and actuator/sensor fault detection/identification (2019) Eng Appl Artif Intell, 83, pp. 28-44; Rozo, L., Silverio, J., Calinon, S., Caldwell, D.G., Learning controllers for reactive and proactive behaviors in human–robot collaboration (2016) Front Robot AI, 3, p. 30; Calinon, S., Guenter, F., Billard, A., On learning, representing, and generalizing a task in a humanoid robot (2007) IEEE Trans Syst Man Cybern Part B (Cybern), 37 (2), pp. 286-298; Rabiner, L.R., A tutorial on hidden Markov models and selected applications in speech recognition (1989) Proc IEEE, 77 (2), pp. 257-286; Duquette, A., Michaud, F., Mercier, H., Exploring the use of a mobile robot as an imitation agent with children with low-functioning autism (2008) Auton Robots, 24 (2), pp. 147-157; Giannopulu, I., Montreynaud, V., Watanabe, T., PEKOPPA: A minimalistic toy robot to analyse a listener-speaker situation in neurotypical and autistic children aged 6 years (2014) Proceedings of the Second International Conference on Human-Agent Interaction, pp. 9-16; Ricks, D.J., Colton, M.B., Trends and considerations in robot-assisted autism therapy (2010) 2010 IEEE International Conference on Robotics and Automation. IEEE, pp. 4354-4359; Pioggia, G., Igliozzi, R., Sica, M.L., Ferro, M., Muratori, F., Ahluwalia, A., De Rossi, D., Exploring emotional and imitational android-based interactions in autistic spectrum disorders (2008) J Cyber Ther Rehabil, 1 (1), pp. 49-61; Ingersoll, B., Brief report: pilot randomized controlled trial of reciprocal imitation training for teaching elicited and spontaneous imitation to children with autism (2010) J Autism Dev Disord, 40 (9), pp. 1154-1160; Taheri, A., Meghdari, A., Alemi, M., Pouretemad, H., Human–robot interaction in autism treatment: a case study on three pairs of autistic children as twins, siblings, and classmates (2018) Int J Soc Robot, 10 (1), pp. 93-113; Taheri, A., Meghdari, A., Alemi, M., Pouretemad, H., Teaching music to children with autism: a social robotics challenge (2019) Sci Iran, 26 (Special Issue on: Socio-Cognitive Engineering), pp. 40-58","Taheri, A.; Social and Cognitive Robotics Laboratory, Iran; email: artaheri@sharif.edu",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85092561144
"Mizanoor Rahman S.M.","37039570500;","Performance metrics for human-robot collaboration: An automotive manufacturing case",2021,"2021 IEEE International Workshop on Metrology for Automotive, MetroAutomotive 2021 - Proceedings",,,,"260","265",,2,"10.1109/MetroAutomotive50197.2021.9502881","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114962212&doi=10.1109%2fMetroAutomotive50197.2021.9502881&partnerID=40&md5=16e40d2e9ab1c981055c632726623dd2","University of West Florida, Hal Marcus College of Science and Engineering, Dept. of Intelligent Systems and Robotics, Pensacola, FL, United States","Mizanoor Rahman, S.M., University of West Florida, Hal Marcus College of Science and Engineering, Dept. of Intelligent Systems and Robotics, Pensacola, FL, United States","A human-robot collaborative system in the form of a power and skill assist robotic system was developed where a human and a robot could collaborate to perform object manipulation for targeted assembly tasks in automotive manufacturing. We assumed such assembly tasks as the representative assembly tasks in automotive manufacturing. We reflected human's weight perception in the dynamics and control of the power and skill assist system following a psychophysical method using a reinforcement learning scheme. We recruited 20 human subjects who separately performed assembly tasks with the system in human-robot collaboration (HRC). We then observed the collaborative assembly tasks, conducted extensive literature reviews, reviewed our previous and ongoing related works and brainstormed with the subjects and other relevant researchers, and then proposed HRC performance assessment metrics and methods for collaborative automotive manufacturing. The proposed metrics comprised of assessment criteria and methods related to both human-robot interaction (HRI) and manufacturing performance. We then verified the proposed performance metrics in pilot studies in the laboratory environment using the same collaborative system and subjects. The verification results proved the effectiveness of the assessment metrics and methods in terms of usability, practicability and reliability. We then proposed to apply classification and regression type machine learning approaches under supervised and reinforcement learning setups to learn different classes and decision-making rules respectively regarding HRC performance. The proposed performance metrics and methods can serve as the preliminary efforts towards developing comprehensive assessment metrics for HRC in general and for human-robot collaborative automotive manufacturing in particular. © 2021 IEEE.","Assembly tasks; Automotive manufacturing; Human-robot collaboration; Machine learning; Performance metrics","Decision making; Learning systems; Manufacture; Reinforcement learning; Robotic assembly; Automotive manufacturing; Comprehensive assessment; Human robot Interaction (HRI); Human-robot collaboration; Machine learning approaches; Manufacturing performance; Performance assessment; Psychophysical methods; Social robots",,,,,,,,,,,,,,,,"Kothiyal, K.P., Kayis, B., Workplace design for manual assembly tasks: Effect of spatial arrangement on work-cycle time (1995) International Journal of Occupational Safety and Ergonomics, 1 (2), pp. 136-143; Dömel, A., Kriegel, S., Kabecker, M., Brucker, M., Bodenmüller, T., Suppa, M., Toward fully autonomous mobile manipulation for industrial environments (2017) International Journal of Advanced Robotic Systems, 14 (4), pp. 1-19. , July; Sadrfaridpour, B., Wang, Y., Collaborative assembly in hybrid manufacturing cells: An integrated framework for human-robot interaction (2018) IEEE Transactions on Automation Science and Engineering, 15 (3), pp. 1178-1192. , July; Pearce, M., Mutlu, B., Shah, J., Radwin, R., Optimizing makespan and ergonomics in integrating collaborative robots into manufacturing processes (2018) IEEE Trans. On Automation Science and Engineering, 15 (4), pp. 1772-1784. , Oct; Takubo, T., Arai, H., Tanie, K., Arai, T., Human-robot cooperative handling using variable virtual nonholonomic constraint (2009) Int. J. Of Automation Technology, 3 (6), pp. 653-662; Kusaka, T., Tanaka, T., Kaneko, S., Kajiwara, H., Analysis of power assist effect during skill assist for periodic motions under use of semiactive assist mechanisms (2012) Proc. Of 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 758-763; Kotronis, C., Routis, I., Tsadimas, A., Nikolaidou, M., Anagnostopoulos, D., A model-based approach for the design of cyberphysical human systems emphasizing human concerns (2019) Proc. Of 2019 IEEE International Congress on Internet of Things (ICIOT), Milan, Italy, pp. 100-107; Yagi, E., Harada, D., Kobayashi, M., Upper-limb power-assist control for agriculture load lifting (2009) International Journal of Automation Technology, 3 (6), pp. 716-722. , Nov; Yamada, Y., Konosu, H., Morizono, T., Umetani, Y., Proposal of skill-assist: A system of assisting human workers by reflecting their skills in positioning tasks (1999) Proc. Of 1999 IEEE International Conference on Systems, Man, and Cybernetics, 4, pp. 11-16; Kazerooni, H., Exoskeletons for human power augmentation (2005) Proc. Of 2005 IEEE/RSJ Int. Conf. Of Intelligent Robots and Systems, pp. 3459-3464; Kusaka, T., Tanaka, T., Simultaneous realization of power assist and skill assist for sinusoidal motions by using semi-active assist mechanism (2014) Proc. Of 2014 SICE Annual Conference (SICE), pp. 1762-1767; Kazerooni, H., Extender: A case study for human-robot interaction via transfer of power and information signals (1993) Proc. Of 1993 IEEE Int. Workshop on Robot and Human Communication, pp. 10-20; Rahman, S.M.M., Ikeura, R., Cognition-based control and optimization algorithms for optimizing human-robot interactions in power assisted object manipulation (2016) Journal of Information Science and Engineering, 32 (5), pp. 1325-1344; Rahman, S.M.M., Ikeura, R., Calibrating intuitive and natural humanrobot interaction and performance for power-assisted heavy object manipulation using cognition-based intelligent admittance control schemes (2018) International Journal of Advanced Robotic Systems, 15 (4), pp. 1-18. , July-August; Campeau-Lecours, A., Coté-Allard, U., Vu, D., Routhier, F., Gosselin, B., Gosselin, C., Intuitive adaptive orientation control for enhanced human-robot interaction (2019) IEEE Transactions on Robotics, 35 (2), pp. 509-520. , April; Terashima, K., Watanabe, K., Ueno, Y., Masui, Y., Auto-tuning control of power assist system based on the estimation of operator's skill level for forward and backward driving of omni-directional wheelchair (2010) Proc. Of 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 6046-6051; Rahman, S.M.M., Ikeura, R., MPC to optimise performance in powerassisted manipulation of industrial objects (2017) IET Electric Power Applications, 11 (7), pp. 1235-1244. , August; Rahman, S.M.M., Machine learning-based cognitive position and force controls for power-assisted human-robot collaborative manipulation (2021) Machines, 9 (2-28), pp. 1-21; Zimmerman, T.A., (2017) Metrics and Key Performance Indicators for Robotic Cybersecurity Performance Analysis, , https://doi.org/10.6028/NIST.IR.8177, March; Murphy, R.R., Schreckenghost, D., Survey of metrics for humanrobot interaction (2013) Proc. Of 2013 8th ACM/IEEE Int. Conference on Human-Robot Interaction (HRI), pp. 197-198. , Tokyo, Japan; Rahman, S.M.M., Ikeura, R., Cognition-based variable admittance control for active compliance in flexible manipulation of heavy objects with a power assist robotic system (2018) Robotics and Biomimetics, 5 (7), pp. 1-25. , November; Rahman, S.M.M., Wang, Y., Mutual trust-based subtask allocation for human-robot collaboration in flexible lightweight assembly in manufacturing (2018) Mechatronics, 54, pp. 94-109. , October; Rahman, S.M.M., Wang, Y., Walker, I.D., Mears, L., Pak, R., Remy, S., Trust-based compliant robot-human handovers of payloads in collaborative assembly in flexible manufacturing (2016) Proc. Of the 12th IEEE Int. Conf. Of Automation Science and Engineering, pp. 355-360. , August 21- 24; Rahman, S.M.M., Sadr, B., Wang, Y., Trust-based optimal subtask allocation and model predictive control for human-robot collaborative assembly in manufacturing (2015) Proc. Of ASME Dynamic Systems and Controls Conference, pp. V002T32A004. , October 28-30, , Paper No. DSCC2015- 9850; Marvel, J.A., Bagchi, S., Zimmerman, M., Aksu, M., Antonishek, B., Wang, Y., Mead, R., Amor, H.B., Test methods and metrics for effective HRI in collaborative human-robot teams (2019) Proc. Of 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 696-697. , Daegu, Korea (South); Rahman, S.M.M., Mixed-initiative collaboration between a humanoid robot and a virtual human through a common platform for a real-world common task: Evaluation and benchmarking (2019) Journal of Ambient Intelligence and Smart Environments, 11 (5), pp. 429-452. , IOS Press, September; Hoffman, G., Evaluating fluency in human-robot collaboration (2019) IEEE Transactions on Human-Machine Systems, 49 (3), pp. 209-218. , June; Rahman, S.M.M., Kapila, V., A systems approach to analyzing design-based research in robotics-focused middle school STEM lessons through cognitive apprenticeship (2017) Proc. Of 2017 ASEE Annual Conference & Exposition, pp. 1-25. , June 25 - 28, , Columbus, Ohio, USA, Paper ID #18442; Rahman, S.M.M., Cyber-physical-social system between a humanoid robot and a virtual human through a shared platform for adaptive agent ecology (2018) IEEE/CAA Journal of Automatica Sinica, SI on IoTbased Smart and Complex Systems, 5 (1), pp. 190-203. , January; Rahman, S.M.M., Cognitive cyber-physical system (C-CPS) for human-robot collaborative manufacturing (2019) Proc. Of 2019 14th IEEE Int. Conference on System of Systems Engineering (SoSE 2019), pp. 125-130. , May 19-22, , Anchorage, Alaska, USA; Rahman, S.M.M., Trustworthy power assistance in object manipulation with a power assist robotic system (2019) Proc. Of IEEE SoutheastCon 2019, pp. 1-6. , April 11-14, , USA; Wheeless, S., Rahman, S.M.M., Sensor fusion-based supervised learning approach to developing collaborative manipulation system with variable autonomy (2021) Proc. Of International Conference on Intelligent Human Systems Integration, pp. 24-30. , February 22-24, , Palermo, Italy; Rahman, S.M.M., Psychophysics-based cognitive reinforcement learning to optimize human-robot interaction in power-assisted object manipulation (2021) Proc. Of International Conference on Intelligent Human Systems Integration, pp. 56-62. , February 22-24, , Palermo, Italy; Rahman, S.M.M., Image processing-based supervised learning to predict robot intention for multimodal interactions between a virtual human and a social robot (2021) Proc. Of International Conference on Intelligent Human Systems Integration, pp. 533-539. , February 22-24, , Palermo, Italy","Mizanoor Rahman, S.M.; University of West Florida, United States; email: smrahman@uwf.edu",,,"Institute of Electrical and Electronics Engineers Inc.","1st IEEE International Workshop on Metrology for Automotive, MetroAutomotive 2021","1 July 2021 through 2 July 2021",,171247,,9781665439060,,,"English","IEEE Int. Workshop Metrol. Automot., MetroAutomotive - Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85114962212
"Strauch C., Hirzle T., van der Stigchel S., Bulling A.","56767914200;57203517985;55887364000;6505807414;","Decoding binary decisions under differential target probabilities from pupil dilation: A random forest approach",2021,"Journal of Vision","21","7",,"1","13",,,"10.1167/jov.21.7.6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85110586106&doi=10.1167%2fjov.21.7.6&partnerID=40&md5=613af5637e26d8587f3bf3ed5271bac4","Experimental Psychology, Helmholtz Institute, Utrecht University, Netherlands; Institute of Media Informatics, Ulm University, Germany; Institute for Visualisation and Interactive Systems, University of Stuttgart, Germany","Strauch, C., Experimental Psychology, Helmholtz Institute, Utrecht University, Netherlands; Hirzle, T., Institute of Media Informatics, Ulm University, Germany; van der Stigchel, S., Experimental Psychology, Helmholtz Institute, Utrecht University, Netherlands; Bulling, A., Institute for Visualisation and Interactive Systems, University of Stuttgart, Germany","Although our pupils slightly dilate when we look at an intended target, they do not when we look at irrelevant distractors. This finding suggests that it may be possible to decode the intention of an observer, understood as the outcome of implicit covert binary decisions, from the pupillary dynamics over time. However, few previous works have investigated the feasibility of this approach and the few that did, did not control for possible confounds such as motor-execution, changes in brightness, or target and distractor probability. We report on our efforts to decode intentions from pupil dilation obtained under strict experimental control on a single trial basis using a machine learning approach. The basis for our analyses are data of 69 participants who looked at letters that needed to be selected with stimulus probabilities that varied systematically in a blockwise manner (n = 19,417 trials). We confirm earlier findings that pupil dilation is indicative of intentions and show that these can be decoded with a classification performance of up to 76% area under the curve for receiver operating characteristic curves if targets are rarer than distractors. To better understand which characteristics of the pupillary signal are most informative, we finally compare relative feature importances. The first derivative of pupil size changes was found to be most relevant, allowing us to decode intention within only about 800 ms of trial onset. Taken together, our results provide credible insights into the potential of decoding intentions from pupil dilation and may soon form the basis for new applications in visual search, gaze-based interaction, or human-robot interaction. © ISSN 1534-7362 Copyright 2021 The Authors This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.","binary decision-making; classification; machine-learning; oddball effect; pupil dilation; pupillometry; random forest",,,,,,,,,,,,,,,,,"Bednarik, R., Vrzakova, H., Hradis, M., What do you want to do next: A novel approach for intent prediction in gaze-based interaction (2012) Proceedings of the 2012 Symposium on Eye Tracking Research and Applications, pp. 83-90; de Gee, J. W., Knapen, T., Donner, T. H., Decision-related pupil dilation reflects upcoming choice and individual bias (2014) Proceedings of the National Academy of Sciences of the United States of America, 111 (5), pp. E618-E625; Donders, F. C., Over de snelheid van psychische processen (1868) Onderzoekingen gedaan in het Physiologisch Laboratorium der Utrechtsche Hoogeschool (1868-1869), 2, pp. 92-120; Eberhardt, L. V., Strauch, C., Hartmann, T. S., Huckauf, A., Increasing pupil size is associated with improved detection performance in the periphery, , (submitted); Ehlers, J., Meinecke, A., Voluntary pupil control in noisy environments (2020) Proceedings of the 2020 Symposium on Eye-Tracking Research and Applications, pp. 1-5; Einhäuser, W., Koch, C., Carter, O. L., Pupil dilation betrays the timing of decisions (2010) Frontiers in Human Neuroscience, 4, pp. 1-9; Einhäuser, W., Stout, J., Koch, C., Carter, O., Pupil dilation reflects perceptual selection and predicts subsequent stability in perceptual rivalry (2008) Proceedings of the National Academy of Sciences, 105 (5), pp. 1704-1709; Hakerem, G., Sutton, S., Pupillary response at visual threshold (1966) Nature, 212 (5061), pp. 485-486; Hayes, T. R., Petrov, A. A., Mapping and correcting the influence of gaze position on pupil size measurements (2016) Behavior Research Methods, 48 (2), pp. 510-527; Huang, C.-M., Mutlu, B., Anticipatory robot control for efficient human-robot collaboration (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 83-90; Jackson, I., Sirois, S., Infant cognition: Going full factorial with pupil dilation (2009) Developmental Science, 12 (4), pp. 670-679; Jang, Y.-M., Mallipeddi, R., Lee, S., Kwak, H.-W., Lee, M., Human intention recognition based on eyeball movement pattern and pupil size variation (2014) Neurocomputing, 128, pp. 421-432; Jangraw, D. C., Wang, J., Lance, B. J., Chang, S.-F., Sajda, P., Neurally and ocularly informed graph-based models for searching 3D environments (2014) Journal of Neural Engineering, 11 (4), p. 046003; Klingner, J., Fixation-aligned pupillary response averaging (2010) Proceedings of the 2010 Symposium on Eye-Tracking Research and Applications, pp. 275-282; Koelewijn, T., Zekveld, A. A., Festen, J. M., Kramer, S. E., Pupil dilation uncovers extra listening effort in the presence of a single-talker masker (2012) Ear and Hearing, 33 (2), pp. 291-300; Kootstra, T., Teuwen, J., Goudsmit, J., Nijboer, T., Dodd, M., Van der Stigchel, S., Machine learning-based classification of viewing behavior using a wide range of statistical oculomotor features (2020) Journal of Vision, 20 (9), pp. 1-1; Martin, J. T., Whittaker, A. H., Johnston, S. J., Component processes in free-viewing visual search: Insights from fixation-aligned pupillary response averaging (2020) Journal of Vision, 20 (7), pp. 5-5; Medathati, N. V. K., Desai, R., Hillis, J., Towards inferring cognitive state changes from pupil size variations in real world conditions (2020) ACM Symposium on Eye Tracking Research and Applications, pp. 1-10; Murphy, P. R., O’connell, R. G., O’sullivan, M., Robertson, I. H., Balsters, J. H., Pupil diameter covaries with bold activity in human locus coeruleus (2014) Human Brain Mapping, 35 (8), pp. 4140-4154; Naber, M., Murphy, P., Pupillometric investigation into the speed-accuracy trade-off in a visuo-motor aiming task (2020) Psychophysiology, 57 (3), p. e13499; Naber, M., Stoll, J., Einhauser, W., Carter, O., How to become a mentalist: reading decisions from a competitoraAZs pupil can be achieved without training but requires instruction (2013) PLoS One, 8 (8), p. e73302; Pasquali, D., Aroyo, A. M., Gonzalez-Billandon, J., Rea, F., Sandini, G., Sciutti, A., Your eyes never lie: A robot magician can tell if you are lying (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 392-394; Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Duchesnay, E., Scikit-learn: Machine learning in python (2011) Journal of Machine Learning Research, 12, pp. 2825-2830; Pfleging, B., Fekety, D. K., Schmidt, A., Kun, A. L., A model relating pupil diameter to mental workload and lighting conditions (2016) Proceedings of the 2016 Chi Conference on Human Factors in Computing Systems, pp. 5776-5788; Privitera, C. M., Renninger, L. W, Carney, T., Klein, 5, Aguilar, M., Pupil dilation during visual target detection (2010) Journal ofVision, 10 (10), p. 3; Quesque, F., Behrens, F., Kret, M. E., Pupils say more than a thousand words: Pupil size reflects how observed actions are interpreted (2019) Cognition, 190, pp. 93-98; Richer, F., Beatty, J., Pupillary dilations in movement preparation and execution (1985) Psychophysiology, 22 (2), pp. 204-207; Spiller, M., Ying-Hsang, L., Zakir, H., Gedeon, T., Geissler, J., Nurnberger, A., Predicting visual search task success from eye gaze data as a basis for user-adaptive information visualization systems (2021) The ACM Transactions on Interactive Intelligent Systems; Stoll, J., Chatelle, C., Carter, O., Koch, C., Laureys, 5, Einhauser, W., Pupil responses allow communication in locked-in syndrome patients (2013) Current Biology, 23 (15), pp. R647-R648; Strauch, C., Ehlers, J., Huckauf, A., Pupil-assisted target selection (pats) (2017) Ifip Conference on Human-Computer Interaction, pp. 297-312; Strauch, C., Greiter, L., Huckauf, A., Towards pupil-assisted target selection in natural settings: Introducing an on-screen keyboard (2017) Ifip Conference on Human-Computer Interaction, pp. 534-543; Strauch, C., Greiter, L., Huckauf, A., Pupil dilation but not microsaccade rate robustly reveals decision formation (2018) Scientific Reports, 5 (1), pp. 1-9; Strauch, C., Huckauf, A., Krejtz, K., Duchowski, A. T., Towards a selection mechanism integrating focal fixations, pupil size, and microsaccade dynamics (2018) Eye Tracking for Spatial Research, Proceedings of the 3rd International Workshop, pp. 9-15; Strauch, C., Koniakowsky, I., Huckauf, A., Decision making and oddball effects on pupil size: Evidence for a sequential process (2020) Journal of Cognition, 3 (1), pp. 1-17; Van der Stoep, N., Van der Smagt, M., Notaro, C., Spock, Z., Naber, M., The additive nature of the human multisensory evoked pupil response (2021) Scientific Reports, 11 (1), pp. 1-12; van Kempen, J., Loughnane, G. M., Newman, D.P., Kelly, S. P., Thiele, A., O’Connell, R. G., Bellgrove, M. A., Behavioural and neural signatures of perceptual decision-making are modulated by pupil-linked arousal (2019) Elife, 8, p. e42541; Wetzel, N., Einhauser, W., Widmann, A., Picture-evoked changes in pupil size predict learning success in children (2020) Journal of Experimental Child Psychology, 192, p. 104787; Wierda, S. M., van Rijn, H., Taatgen, N. A., Martens, S., Pupil dilation deconvolution reveals the dynamics of attention at high temporal resolution (2012) Proceedings of the National Academy of Sciences, 109 (22), pp. 8456-8460","Strauch, C.; Experimental Psychology, Netherlands; email: c.strauch@uu.nl",,,"Association for Research in Vision and Ophthalmology Inc.",,,,,15347362,,,"34259827","English","J. Vis.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85110586106
"Lyons J.B., Vo T., Wynne K.T., Mahoney S., Nam C.S., Gallimore D.","15623605700;57208575959;57195673495;57208580201;8421659200;57223948422;","Trusting Autonomous Security Robots: The Role of Reliability and Stated Social Intent",2021,"Human Factors","63","4",,"603","618",,1,"10.1177/0018720820901629","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079129504&doi=10.1177%2f0018720820901629&partnerID=40&md5=a59c648d4c59f7f99415cf716526d8ac","Air Force Research Laboratory, United States; Ball Aerospace, United States; University of Baltimore, United States; North Carolina State University, United States; Naval Medical Research Unit – Dayton, United States","Lyons, J.B., Air Force Research Laboratory, United States; Vo, T., Ball Aerospace, United States; Wynne, K.T., University of Baltimore, United States; Mahoney, S., Air Force Research Laboratory, United States; Nam, C.S., North Carolina State University, United States; Gallimore, D., Naval Medical Research Unit – Dayton, United States","Objective: This research examined the effects of reliability and stated social intent on trust, trustworthiness, and one’s willingness to endorse use of an autonomous security robot (ASR). Background: Human–robot interactions in the domain of security is plausible, yet we know very little about what drives acceptance of ASRs. Past research has used static images and game-based simulations to depict the robots versus actual humans interacting with actual robots. Method: A video depicted an ASR interacting with a human. The ASR reviewed access credentials and allowed entrance once verified. If the ASR could not verify one’s credentials it instructed the visitor to return to the security checkpoint. The ASR was equipped with a nonlethal device and the robot used this device on one of the three visitors (a research confederate). Manipulations of reliability and stated social intent of the ASR were used in a 2 × 4 between subjects design (N = 320). Results: Reliability influenced trust and trustworthiness. Stated social intent influenced trustworthiness. Participants reported being more favorable toward use of the ASR in military contexts versus public contexts. Conclusion: The study demonstrated that reliability of the ASR and statements regarding the ASR’s stated social intent are important considerations influencing the trust process (inclusive of intentions to be vulnerable and trustworthiness perceptions). Application: If robotic systems are authorized to use force against a human, public acceptance may be increased with availability of the intent-based programming of the robot and whether or not the robot’s decision was reliable. © 2019, Human Factors and Ergonomics Society.","autonomous robots; benevolence; transparency; trust in human–robot interaction; trustworthiness","Computer systems programming; Economic and social effects; Network security; Reliability; Robot programming; Robots; Transparency; Autonomous security; benevolence; Public acceptance; Robot interactions; Robotic systems; Security checkpoint; Trust and trustworthiness; trustworthiness; Social robots; adult; army; article; female; human; human experiment; major clinical study; male; perception; reliability; robotics; simulation; trust; videorecording",,,,,"Air Force Research Laboratory, AFRL: FA8650-14-C-6585, FA8650-16-D-6616-0001","This research was supported by Air Force Research Laboratory (AFRL) contracts: FA8650-14-C-6585 and FA8650-16-D-6616-0001. 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Factors",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85079129504
"Rueben M., Klow J., Duer M., Zimmerman E., Piacentini J., Browning M., Bernieri F.J., Grimm C.M., Smart W.D.","36814315700;57193761549;57224011970;57223994750;57224009947;57223994572;6602539256;7101846420;7003593473;","Mental Models of a Mobile Shoe Rack",2021,"ACM Transactions on Human-Robot Interaction","10","2","16","","",,1,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106687454&partnerID=40&md5=deb2e2c8d52c5a90ba2261bac57c8369","University of Southern California, Los Angeles, CA  90089, United States; Oregon State University, Corvallis, OR  97330, United States; Collaborative Robotics and Intelligent Systems (CoRIS) Institute; School of Psychological Science; Interaction Lab in the Department of Computer Science","Rueben, M., University of Southern California, Los Angeles, CA  90089, United States, Interaction Lab in the Department of Computer Science; Klow, J., Oregon State University, Corvallis, OR  97330, United States, Collaborative Robotics and Intelligent Systems (CoRIS) Institute; Duer, M., Oregon State University, Corvallis, OR  97330, United States; Zimmerman, E., Oregon State University, Corvallis, OR  97330, United States; Piacentini, J., Oregon State University, Corvallis, OR  97330, United States; Browning, M., Oregon State University, Corvallis, OR  97330, United States; Bernieri, F.J., Oregon State University, Corvallis, OR  97330, United States, School of Psychological Science; Grimm, C.M., Oregon State University, Corvallis, OR  97330, United States, Collaborative Robotics and Intelligent Systems (CoRIS) Institute; Smart, W.D., Oregon State University, Corvallis, OR  97330, United States, Collaborative Robotics and Intelligent Systems (CoRIS) Institute","Most people do not have direct access to knowledge about the inner workings of robots. Instead, they must develop mental models of the robot, a process that is not well understood. This article presents findings from a long-term, in-the-wild, qualitative, hypothesis-generating study of the mental model formation process. The focus was on how (qualitatively) users form mental models of the robot-specifically its perceptual capabilities, rules of behavior, and communication with other humans. Participants of diverse ages had multiple interactions with the robot over six weeks in a non-laboratory setting. The robot's rules of behavior were changed every two weeks. A novel, non-anthropomorphic robot was created for the study with a realistic use case: Storing people's shoes during a yoga class. This article reports findings from a case study analysis of 28 interviews conducted over six weeks with six participants. These findings are organized into six topics: (1) variability in the rate at which mental models are updated to be more predictive, (2) types of reasoning and hypothesizing about the robot, (3) borrowing from existing mental models and use of imagination, (4) attributing sensing capabilities where there are no visible sensors, (5) judgments about whether the robot is autonomous or teleoperated, and (6) experimenting with the robot. Specific suggestions for future research are given throughout, culminating in a set of study design recommendations. This work demonstrates the fruitfulness of long-term, in-the-wild studies of human-robot interaction, of which mental model formation is a foundational aspect. © 2021 Copyright held by the owner/author(s).","in-the-wild studies; long-term studies; Mental models; natural settings; non-anthropomorphic robots; privacy; robotic furniture; transparency","Agricultural robots; Anthropomorphic robots; Cognitive systems; Case study analysis; Foundational aspects; Mental model; Multiple interactions; Study design; Teleoperated; Visible sensors; Social robots",,,,,"National Science Foundation, NSF: IIS-1528121","M. Rueben is located at the University of Southern California, Los Angeles, CA, 90089, USA. He is with the Interaction Lab in the Department of Computer Science. All other authors are located at Oregon State University, Corvallis, OR, 97330, USA. Klow, Grimm, and Smart are with the Collaborative Robotics and Intelligent Systems (CoRIS) Institute. Bernieri is with the School of Psychological Science. Duer, Zimmerman, Piacentini, and Browning are undergraduate students. M. Rueben’s work was partially supported by an NSF NRI grant for “Socially Aware, Expressive, and Personalized Mobile Remote Presence: Co-Robots as Gateways to Access to K-12 In-School Education,” Grant No. NSF IIS-1528121. Authors’ addresses: M. Rueben, University of Southern California, Ronald Tutor Hall, Room 423, 3710 McClintock Ave, Los Angeles, CA 90089; email: mrueben@usc.edu; J. Klow, M. Duer, E. Zimmerman, J. Piacentini, M. Browning, F. J. Bernieri, C. M. Grimm, and W. D. Smart, Oregon State University, 101 Covell Hall, Corvallis, OR 97331; emails: {klowj, duerma, zimmerer, piacentj, brownim2, frank.bernieri, cindy.grimm, smartw}@oregonstate.edu.",,,,,,,,,,"Borgman, C.L., The user's mental model of an information retrieval system: An experiment on a prototype online catalog (1999) Int. J. Hum.-Comput. 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J. Soc. Robot, 7 (3), pp. 347-360. , 2015",,,,"Association for Computing Machinery",,,,,25739522,,,,"English","ACM Trans. Hum. Robot Interact",Article,"Final","",Scopus,2-s2.0-85106687454
"Kivrak H., Cakmak F., Kose H., Yavuz S.","55292948300;56982829900;25031883600;8669253700;","Social navigation framework for assistive robots in human inhabited unknown environments",2021,"Engineering Science and Technology, an International Journal","24","2",,"284","298",,2,"10.1016/j.jestch.2020.08.008","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090113797&doi=10.1016%2fj.jestch.2020.08.008&partnerID=40&md5=164d66287e4c26963051cac0d9c8b994","Department of Computer Engineering, Karabuk University, Karabuk, Turkey; Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey; Department of Computer Engineering, Yildiz Technical University, Istanbul, Turkey","Kivrak, H., Department of Computer Engineering, Karabuk University, Karabuk, Turkey, Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey; Cakmak, F., Department of Computer Engineering, Yildiz Technical University, Istanbul, Turkey; Kose, H., Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey; Yavuz, S., Department of Computer Engineering, Yildiz Technical University, Istanbul, Turkey","In human-populated environments, robot navigation requires more than mere obstacle avoidance for safe and comfortable human-robot interaction. Socially aware navigation approaches become vital for deploying mobile service robots in human interactive environments, where the robot operates in interaction with human implicitly or explicitly. These approaches aim to generate human-friendly paths in human-robot interactive environments considering social cues and human behaviour patterns. This paper proposes a social navigation framework for mobile service robots, maintaining humans’ safety and comfort while navigating towards the goal location in human interactive environments. Our main contribution is that the presented social navigation framework is designed to be used in human interactive unknown environments. To achieve this goal, we use a variant of a pedestrian model called Collision Prediction based Social Force model (CP-SFM). This model is particularly developed for low or average density environments and takes the motion of the people tracked in the environment into account during the navigation. The model is employed as a local planner to generate human-friendly plausible routes for our service robot in corridor like indoor environment scenarios. A variety of different extensions and improvements of the conventional social force model are employed in the implementation stage. A novel improvement in producing multi-level mapping, identifying obstacle repulsion points and adopting CP-SFM for application in motion planning as local task solver is presented. The whole framework has been implemented as ROS nodes, and tested both in real world and simulation environments and successfully verified based on the obtained results. © 2020 Karabuk University","Human-aware navigation; Human-robot interaction; Mobile robots; ROS; Social navigation; Social robotics",,,,,,"118E214, 118E215; Consejo Nacional de Investigaciones CientÃ­ficas y TÃ©cnicas, CONICET","This research is supported by the Turkish Scientific and Technical Research Council (TUBITAK), with Project No: 118E214 and Project No: 118E215.",,,,,,,,,,"Uluer, P., Akalin, N., Köse, H., A new robotic platform for sign language tutoring (2015) Int. J. Soc. Robot., 7 (5), pp. 571-585; Gürpinar, C., Uluer, P., Akalin, N., Köse, H., Sign recognition system for an assistive robot sign tutor for children (2019) Int. J. Soc. 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Robot., 6 (3), pp. 443-455; Kivrak, H., Çakmak, F., Köse, H., Yavuz, S., Socially aware robot navigation using the collision prediction based pedestrian model (2018), International Conference on Intelligent Robots and Systems (IROS 2018): Workshop on Robotic Co-workers 4.0: Human Safety and Comfort in Human-Robot Interactive Social Environments, IEEE; Kivrak, H., Uluer, P., Kose, H., Gumuslu, E., Barkana, D.E., Cakmak, F., Yavuz, S., Physiological data-based evaluation of a social robot navigation system, in (2020) RO-MAN 2020-The 29th IEEE International Symposium on Robot and Human Interactive Communication, , IEEE; Stentz, A., (1995), 95, pp. 1652-1659. , The focussed D∗ algorithm for real-time replanning, in: IJCAI; Koenig, S., Likhachev, M., (2002), pp. 476-483. , http://dl.acm.org/citation.cfm?id=777092.777167, D∗ lite, in: Eighteenth National Conference on Artificial Intelligence, American Association for Artificial Intelligence, Menlo Park, CA, USA; Otte, M., Frazzoli, E., (2015), pp. 461-478. , RRTx: Real-time motion planning/replanning for environments with unpredictable obstacles, in: Algorithmic Foundations of Robotics XI, Springer; Forer, S., Banisetty, S.B., Yliniemi, L., Nicolescu, M., Feil-Seifer, D., Socially-aware navigation using non-linear multi-objective optimization (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1-9. , IEEE; Teso-Fz-Betoño, D., Zulueta, E., Fernandez-Gamiz, U., Saenz-Aguirre, A., Martinez, R., Predictive dynamic window approach development with artificial neural fuzzy inference improvement (2019) Electronics, 8 (9), p. 935; Lu, D.V., Hershberger, D., Smart, W.D., Layered costmaps for context-sensitive navigation (2014), pp. 709-715. , Intelligent Robots and Systems (IROS 2014), 2014 IEEE/RSJ International Conference on, IEEE; Sisbot, E.A., Marin-Urias, L.F., Alami, R., Simeon, T., A human aware mobile robot motion planner (2007) IEEE Trans. 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E, 51 (5), p. 4282; Herrera, D., Gimenez, J., Monllor, M., Roberti, F., Carelli, R., Cognitive social zones for improving the pedestrian collision avoidance with mobile robots (2019) Revista Politécnica, 42 (2), pp. 7-14; Ondřej, J., Pettré, J., Olivier, A.-H., Donikian, S., A synthetic-vision based steering approach for crowd simulation (2010), 29, p. 123. , ACM Transactions on Graphics (TOG), ACM; Linder, T., Breuers, S., Leibe, B., Arras, K.O., On multi-modal people tracking from mobile platforms in very crowded and dynamic environments (2016) 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 5512-5519. , IEEE; Jafari, O.H., Mitzel, D., Leibe, B., Real-time rgb-d based people detection and tracking for mobile robots and head-worn cameras (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 5636-5643. , IEEE; Linder, T., Girrbach, F., Arras, K.O., Towards a robust people tracking framework for service robots in crowded, dynamic environments (2015), Assistance and Service Robotics Workshop (ASROB-15) at the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS); Hall, E.T., (1966) The hidden dimension, 609. , Doubleday Garden City, NY; Jones, C.M., Healy, S.D., Differences in cue use and spatial memory in men and women (2006) Proc. R. Soc. B: Biol. Sci., 273 (1598), pp. 2241-2247; Ge, S.S., Cui, Y.J., Dynamic motion planning for mobile robots using potential field method (2002) Autonomous Robots, 13 (3), pp. 207-222","Kivrak, H.; Department of Computer Engineering, Turkey; email: kivrakh@karabuk.edu.tr",,,"Elsevier B.V.",,,,,22150986,,,,"English","Eng. Sci. Technol. Int. J.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85090113797
"Schneider S., Kummert F.","36148533200;6602460088;","Comparing Robot and Human guided Personalization: Adaptive Exercise Robots are Perceived as more Competent and Trustworthy",2021,"International Journal of Social Robotics","13","2",,"169","185",,1,"10.1007/s12369-020-00629-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079458170&doi=10.1007%2fs12369-020-00629-w&partnerID=40&md5=6ed4e721cec6d5477771871c3cf47880","Applied Informatics, Faculty of Technology, Bielefeld University, Bielefeld, Germany; HRI Lab, Department of Social Informatics, Kyoto University, Kyoto, Japan","Schneider, S., Applied Informatics, Faculty of Technology, Bielefeld University, Bielefeld, Germany, HRI Lab, Department of Social Informatics, Kyoto University, Kyoto, Japan; Kummert, F., Applied Informatics, Faculty of Technology, Bielefeld University, Bielefeld, Germany","Learning and matching a user’s preference is an essential aspect of achieving a productive collaboration in long-term Human–Robot Interaction (HRI). However, there are different techniques on how to match the behavior of a robot to a user’s preference. The robot can be adaptable so that a user can change the robot’s behavior to one’s need, or the robot can be adaptive and autonomously tries to match its behavior to the user’s preference. Both types might decrease the gap between a user’s preference and the actual system behavior. However, the Level of Automation (LoA) of the robot is different between both methods. Either the user controls the interaction, or the robot is in control. We present a study on the effects of different LoAs of a Socially Assistive Robot (SAR) on a user’s evaluation of the system in an exercising scenario. We implemented an online preference learning system and a user-adaptable system. We conducted a between-subject design study (adaptable robot vs. adaptive robot) with 40 subjects and report our quantitative and qualitative results. The results show that users evaluate the adaptive robots as more competent, warm, and report a higher alliance. Moreover, this increased alliance is significantly mediated by the perceived competence of the system. This result provides empirical evidence for the relation between the LoA of a system, the user’s perceived competence of the system, and the perceived alliance with it. Additionally, we provide evidence for a proof-of-concept that the chosen preference learning method (i.e., Double Thompson Sampling (DTS)) is suitable for online HRI. © 2020, The Author(s).","Adaptive robots; Human–robot interaction; Preference learning; User experience","Agricultural robots; Behavioral research; Intelligent robots; Learning systems; Machine design; User experience; Adaptable system; Level of automations; Perceived competence; Personalizations; Preference learning; Robot interactions; Socially assistive robots; Thompson samplings; Social robots",,,,,"EXC 277; Deutsche Forschungsgemeinschaft, DFG; Universität Bielefeld","Open Access funding provided by Projekt DEAL. This research/work was supported by the Cluster of Excellence Cognitive Interaction Technology ?CITEC? (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG).","Open Access funding provided by Projekt DEAL. This research/work was supported by the Cluster of Excellence Cognitive Interaction Technology ‘CITEC’ (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG).",,,,,,,,,"Andrist, S., Mutlu, B., Tapus, A., Look like me: Matching robot personality via gaze to increase motivation (2015) Proceedings of the 33Rd Annual ACM Conference on Human Factors in Computing Systems, pp. 3603-3612. , ACM; Beer, J.M., Fisk, A.D., Rogers, W.A., Toward a framework for levels of robot autonomy in human–robot interaction (2014) J Hum Robot Interact, 3 (2), pp. 74-99; Bickmore, T.W., Picard, R.W., Establishing and maintaining long-term human–computer relationships (2005) ACM Trans Comput Hum Interact (TOCHI), 12 (2), pp. 293-327; Brooke, J., Sus—a quick and dirty usability scale (1996) Usability Eval Ind, 189 (194), pp. 4-7; Burger, J.M., (2013) Desire for control: personality, social and clinical perspectives, , Springer, Berlin; Cakmak, M., Chao, C., Thomaz, A.L., Designing interactions for robot active learners (2010) IEEE Trans Autono Ment Dev, 2 (2), pp. 108-118; Carpinella, C.M., Wyman, A.B., Perez, M.A., Stroessner, S.J., The robotic social attributes scale (Rosas): Development and validation (2017) Proceedings of The, 2017, pp. 254-262. , ACM/IEEE international conference on human–robot interaction, ACM; Cronbach, L.J., Coefficient alpha and the internal structure of tests (1951) Psychometrika, 16 (3), pp. 297-334; Endsley, M.R., Kiris, E.O., The out-of-the-loop performance problem and level of control in automation (1995) Hum Factors, 37 (2), pp. 381-394; Epley, N., Waytz, A., Cacioppo, J.T., On seeing human: a three-factor theory of anthropomorphism (2007) Psychol Rev, 114 (4), p. 864; Eriksson, J., Mataric, M.J., Winstein, C.J., Hands-off assistive robotics for post-stroke arm rehabilitation (2005) 9Th International Conference on Rehabilitation Robotics, 2005, pp. 21-24. , ICORR 2005. 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"Henschel A., Bargel H., Cross E.S.","57214603084;57223102911;56497637800;","Faces do not attract more attention than non-social distractors in the Stroop task",2021,"Collabra: Psychology","7","1","18521","","",,,"10.1525/collabra.18521","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104897696&doi=10.1525%2fcollabra.18521&partnerID=40&md5=e144cb60bf671ee39886399d44b02283","Universität zu Lübeck, Lübeck, Germany; Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom; Department of Cognitive Science, Macquarie University, Sydney, Australia","Henschel, A., Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom; Bargel, H., Universität zu Lübeck, Lübeck, Germany; Cross, E.S., Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom, Department of Cognitive Science, Macquarie University, Sydney, Australia","As robots begin to receive citizenship, are treated as beloved pets, and given a place at Japanese family tables, it is becoming clear that these machines are taking on increasingly social roles. While human-robot interaction research relies heavily on self-report measures for assessing people's perception of robots, a distinct lack of robust cognitive and behavioural measures to gauge the scope and limits of social motivation towards artificial agents exists. Here we adapted Conty and colleagues' (2010) social version of the classic Stroop paradigm, in which we showed four kinds of distractor images above incongruent and neutral words: human faces, robot faces, object faces (for example, a cloud with facial features) and flowers (control). We predicted that social stimuli, like human faces, would be extremely salient and draw attention away from the to-be-processed words. A repeated-measures ANOVA indicated that the task worked (the Stroop effect was observed), and a distractor-dependent enhancement of Stroop interference emerged. Planned contrasts indicated that specifically human faces presented above incongruent words significantly slowed participants' reaction times. To investigate this small effect further, we conducted a second experiment (N=51) with a larger stimulus set. While the main effect of the incongruent condition slowing down participants' reaction time replicated, we did not observe an interaction effect of the social distractors (human faces) drawing more attention than the other distractor types. We question the suitability of this task as a robust measure for social motivation and discuss our findings in the light of recent conflicting results in the social attentional capture literature. © This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY-4.0). View this license's legal deed at http://creativecommons.org/licenses/by/4.0 and legal code at http://creativecommons.org/licenses/by/4.0/legalcode for more information.","Eye contact effect; Pareidolia; Robots; Salient social cues; Social motivation",,,,,,"ERC-2015-StG-677270-SOCIALROBOTS; European Research Council, ERC","This work has received funding from the European Research Council (ERC) under the European Union’s Horizon2020 research and innovation programme (grant agree-","We would like to thank Lauren Colbert for her assistance with the data collection for Experiment 2 and Prof. Guillaume Thierry and Dr. Kami Koldewyn, who helped improve the design of these studies with their valuable feedback. We also want to express our gratitude towards Dr. Andrew Milne and Eline Smit for sharing their insights into Bayesian regression analysis and to Dr. Andrew Milne and Te-Yi Hsieh for their valuable comments on the final draft of the manuscript. 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"Hurtado J.V., Londoño L., Valada A.","57219740156;57222059029;37121030000;","From Learning to Relearning: A Framework for Diminishing Bias in Social Robot Navigation",2021,"Frontiers in Robotics and AI","8",,"650325","","",,1,"10.3389/frobt.2021.650325","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103838222&doi=10.3389%2ffrobt.2021.650325&partnerID=40&md5=d110cc4998a8d03cf3bcc2f45ddf056a","Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany","Hurtado, J.V., Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany; Londoño, L., Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany; Valada, A., Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany","The exponentially increasing advances in robotics and machine learning are facilitating the transition of robots from being confined to controlled industrial spaces to performing novel everyday tasks in domestic and urban environments. In order to make the presence of robots safe as well as comfortable for humans, and to facilitate their acceptance in public environments, they are often equipped with social abilities for navigation and interaction. Socially compliant robot navigation is increasingly being learned from human observations or demonstrations. We argue that these techniques that typically aim to mimic human behavior do not guarantee fair behavior. As a consequence, social navigation models can replicate, promote, and amplify societal unfairness, such as discrimination and segregation. In this work, we investigate a framework for diminishing bias in social robot navigation models so that robots are equipped with the capability to plan as well as adapt their paths based on both physical and social demands. Our proposed framework consists of two components: learning which incorporates social context into the learning process to account for safety and comfort, and relearning to detect and correct potentially harmful outcomes before the onset. We provide both technological and societal analysis using three diverse case studies in different social scenarios of interaction. Moreover, we present ethical implications of deploying robots in social environments and propose potential solutions. Through this study, we highlight the importance and advocate for fairness in human-robot interactions in order to promote more equitable social relationships, roles, and dynamics and consequently positively influence our society. © Copyright © 2021 Hurtado, Londoño and Valada.","algorithmic fairness; ethics; fairness-aware learning; responsible innovation; robot learning; social robot navigation",,,,,,"Albert-Ludwigs-Universität Freiburg","This work was partly funded by the BrainLinks-Brain Tools center of the University of Freiburg, a scholarship from the Graduate School of Robotics of the University Freiburg (according to the Graduate Funding Law of the Ministry of Science, Research and Arts of the State of Baden-Württemberg), and a grant from the Eva Mayr-Stihl Stiftung.","Funding. This work was partly funded by the BrainLinks-Brain Tools center of the University of Freiburg, a scholarship from the Graduate School of Robotics of the University Freiburg (according to the Graduate Funding Law of the Ministry of Science, Research and Arts of the State of Baden-W?rttemberg), and a grant from the Eva Mayr-Stihl Stiftung.",,,,,,,,,"Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., Wallach, H., A reductions approach to fair classification (2018) arXiv, , 1803.02453; Aljalbout, E., Golkov, V., Siddiqui, Y., Strobel, M., Cremers, D., Clustering with deep learning: taxonomy and new methods (2018) arXiv, , 1801.07648; Anderson, M., Anderson, S.L., Robot be good (2010) Sci. Am, 303, pp. 72-77; Argyle, M., Cook, M., Cramer, D., Gaze and mutual gaze (1994) Br. J. 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Graph, 27, pp. 1470-1480. , 33048751; Watkins, C.J., Dayan, P., Q-learning (1992) Mach. Learn, 8, pp. 279-292; Wilson, B., Hoffman, J., Morgenstern, J., Predictive inequity in object detection (2019) arXiv, , 1902.11097; Winner, L., (1978) Autonomous Technology: Technics-Out-of-Control as a Theme in Political Thought, , Cambridge, MIT Press; Wittrock, M.C., Learning as a generative process (2010) Educ. Psychol, 45, pp. 40-45; Woodworth, B., Gunasekar, S., Ohannessian, M.I., Srebro, N., Learning non-discriminatory predictors (2017) arXiv, , 1702.06081; Yu, A., Direct discrimination and indirect discrimination: a distinction with a difference (2019) WJ Legal Stud, 9, p. 1; Zafar, M.B., Valera, I., Rogriguez, M.G., Gummadi, K.P., “Fairness constraints: mechanisms for fair classification,” (2017) Artificial Intelligence and Statistics, pp. 962-970. , Fort Lauderdale, FL, PMLR; Zhang, L., Wu, Y., Wu, X., A causal framework for discovering and removing direct and indirect discrimination (2016) arXiv, , 1611.07509","Hurtado, J.V.; Department of Computer Science, Germany; email: hurtadoj@cs.uni-freiburg.de",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85103838222
"Yu Y., Wei S., Sheng H., Zhang Y.","57217143590;8454271000;57219223386;57222510235;","Research on real-time joint stiffness configuration of a series parallel hybrid 7-DOF humanoid manipulator in continuous motion",2021,"Applied Sciences (Switzerland)","11","5","2433","","",,,"10.3390/app11052433","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102944537&doi=10.3390%2fapp11052433&partnerID=40&md5=2fd1a89a8467b0dfaf84e0d84bba6ec7","School of Automation, Beijing University of Posts and Telecommunications, No 10, Xitucheng Road, Haidian District, Beijing, 100876, China","Yu, Y., School of Automation, Beijing University of Posts and Telecommunications, No 10, Xitucheng Road, Haidian District, Beijing, 100876, China; Wei, S., School of Automation, Beijing University of Posts and Telecommunications, No 10, Xitucheng Road, Haidian District, Beijing, 100876, China; Sheng, H., School of Automation, Beijing University of Posts and Telecommunications, No 10, Xitucheng Road, Haidian District, Beijing, 100876, China; Zhang, Y., School of Automation, Beijing University of Posts and Telecommunications, No 10, Xitucheng Road, Haidian District, Beijing, 100876, China","In this paper, the real-time joint stiffness configuration strategy of a series parallel hybrid 7-DOF (degree of freedom) humanoid manipulator with flexible joints in continuous motion is studied. Firstly, considering the potential human robot accidental collision, combined with the manipulator safety index (MSI) and human body injury thresholds, the motion speed and joint stiffness of the robot are optimized in advance. Secondly, using hyperbolic tangent function for reference, the relationship between joint torques and passive joint deflection angles of the robot is given, which is beneficial for the real-time calculation of joint stiffness and obtain reasonable joint stiffness. Then, the structural model of the selected humanoid manipulator is described, on this basis, the relationship between the joint space stiffness and the Cartesian space stiffness of the humanoid manipulator is analyzed through Jacobian matrix, and the results show that the posture and joint space stiffness of the humanoid manipulator directly affect the Cartesian space stiffness of the humanoid manipulator. Finally, according to whether the humanoid manipulator works in the human-robot interaction environment, the real-time joint stiffness configuration of the humanoid manipulator in continuous motion is simulated and analyzed. The research shows that the humanoid manipulator with flexible joints can adjust the joint stiffness in real-time during continuous motion, and the joint stiffness configuration strategy can effectively improve the safety of human body in human-robot collision. In addition, in application, when the joint space stiffness of the robot is lower, the position accuracy can be improved by trajectory compensation. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Human-robot collision; Hyperbolic tangent function; Real-time joint stiffness configuration strategy; Serial parallel hybrid 7-DOF humanoid manipulator",,,,,,"Beijing Municipal Natural Science Foundation: L172031","Funding: This work was supported by the Beijing Municipal Natural Science Foundation under Grant L172031.",,,,,,,,,,"Chu, M., Wu, X., Modeling and Self-learning Soft-grasp Control for Free-floating Space Manipulator during Target Capturing using Variable Stiffness Method (2018) IEEE Access, 6, pp. 7044-7054. , [CrossRef]; Wolf, S., Bahls, T., Chalon, M., Friedl, W., Grebenstein, M., Höppner, H., Kühne, M., Özparpucu, M.C., Soft robotics with variable stiffness actuators: Tough robots for soft human robot interaction (2015) Soft Robotics, pp. 231-254. , Verl, A., Albu-Schäffer, A., Brock, O., Raatz, A., Eds.; Springer: Berlin/Heidelberg, Germany; Baspinar, C., Robust Position/Force Control of Constrained Flexible Joint Robots with Constraint Uncertainties (2020) J. 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February (accessed on 10 February 2021); Rosenstrauch, M.J., Kruger, J., Safe human-robot-collaboration-introduction and experiment using ISO/TS 15066 Proceedings of the 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), pp. 740-744. , Nagoya, Japan, 24–26 April 2017; Anvaripour, M., Saif, M., Collision Detection for Human-Robot Interaction in an Industrial Setting using Force Myography and a Deep Learning Approach Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 2149-2154. , Bari, Italy, 6–9 October 2019; Nascimiento, H., Mujica, M., Benoussaad, M., Collision Avoidance Interaction Between Human and a Hidden Robot Based on Kinect and Robot Data Fusion (2020) IEEE Robot. Autom. Lett, 6, pp. 88-94. , [CrossRef]; Melia, M., Schmidt, M., Geissler, B., König, J., Krahn, U., Ottersbach, H.J., Letzel, S., Muttray, A., Measuring mechanical pain: The refinement and standardization of pressure pain threshold measurements (2015) Behav. Res. Methods, 47, pp. 216-227. , [CrossRef] [PubMed]; Melia, M., Geissler, B., König, J., Ottersbach, H.J., Umbreit, M., Letzel, S., Muttray, A., Pressure pain thresholds: Subject factors and the meaning of peak pressures (2019) Eur. J. Pain, 23, pp. 167-182. , [CrossRef] [PubMed]; Bicchi, A., Tonietti, G., Fast and “soft-arm” tactics: Dealing with the safety-performance tradeoff in robot arms design and control (2004) Ieee Robot. Autom. Mag, 11, pp. 22-33. , [CrossRef]; Gao, D., Wampler, C.W., Head injury criterion (2009) Ieee Robot. Autom. Mag, 16, pp. 71-74. , [CrossRef]; Kuli, D., Croft, E., Pre-collision safety strategies for human-robot interaction (2007) Auton. 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Manuf, 55, pp. 19-28. , [CrossRef]; Singh, A.P., Padayachee, J., Bright, G., Joint Stiffness and Link Deflection Identification of Serial Robotic Manipulators and End-Effectors Proceedings of the 2019 Southern African Universities Power Engineering Conference/Robotics and Mechatron-ics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA), pp. 6-11. , Bloemfontein, South Africa, 28–30 January 2019; Doriot, N., Wang, X., Effects of age and gender on maximum voluntary range of motion of the upper body joints (2006) Ergonomics, 49, pp. 269-281. , [CrossRef] [PubMed]; Bertomeu-Motos, A., Lledó, L.D., Díez, J.A., Catalan, J.M., Ezquerro, S., Badesa, F.J., Garcia-Aracil, N., Estimation of Human Arm Joints Using Two Wireless Sensors in Robotic Rehabilitation Tasks (2015) Sensors, 15, pp. 30571-30583. , [CrossRef] [PubMed]; Suarez, A., Jimenez-Cano, A.E., Vega, V.M., Heredia, G., Rodriguez-Castaño, A., Ollero, A., Lightweight and human-size dual arm aerial manipulator Proceedings of the 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1778-1784. , Miami, FL, USA, 13–16 June 2017","Yu, Y.; School of Automation, No 10, Xitucheng Road, Haidian District, China; email: yuyangyy@bupt.edu.cn
Wei, S.; School of Automation, No 10, Xitucheng Road, Haidian District, China; email: wsmly@bupt.edu.cn",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85102944537
"Qbilat M., Iglesias A., Belpaeme T.","57202920069;8704705100;13103663500;","A proposal of accessibility guidelines for human-robot interaction",2021,"Electronics (Switzerland)","10","5","561","1","14",,3,"10.3390/electronics10050561","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101679973&doi=10.3390%2felectronics10050561&partnerID=40&md5=2204497ebe7478f7af09e3f7876a0b65","Department of Computer Science and Engineering, Universidad Carlos III de Madrid, Leganés, 28911, Spain; IDLab – imec, Ghent University, Ghent, 9052, Belgium","Qbilat, M., Department of Computer Science and Engineering, Universidad Carlos III de Madrid, Leganés, 28911, Spain; Iglesias, A., Department of Computer Science and Engineering, Universidad Carlos III de Madrid, Leganés, 28911, Spain; Belpaeme, T., IDLab – imec, Ghent University, Ghent, 9052, Belgium","We will increasingly become dependent on automation to support our manufacturing and daily living, and robots are likely to take an important place in this. Unfortunately, currently not all the robots are accessible for all users. This is due to the different characteristics of users, as users with visual, hearing, motor or cognitive disabilities were not considered during the design, imple-mentation or interaction phase, causing accessibility barriers to users who have limitations. This research presents a proposal for accessibility guidelines for human-robot interaction (HRI). The guidelines have been evaluated by seventeen HRI designers and/or developers. A questionnaire of nine five-point Likert Scale questions and 6 open-ended questions was developed to evaluate the proposed guidelines for developers and designers, in terms of four main factors: usability, social acceptance, user experience and social impact. The questions act as indicators for each factor. The majority (15 of 17 participants) agreed that the guidelines are helpful for them to design and imple-ment accessible robot interfaces and applications. Some of them had considered some ad hoc guidelines in their design practice, but none of them showed awareness of or had applied all the proposed guidelines in their design practice, 72% of the proposed guidelines have been applied by less than or equal to 8 participants for each guideline. Moreover, 16 of 17 participants would use the proposed guidelines in their future robot designs or evaluation. The participants recommended the importance of aligning the proposed guidelines with safety requirements, environment of interaction (indoor or outdoor), cost and users’ expectations. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Accessibility; Guidelines; Human-robot interaction; Inclusive design",,,,,,"European Commission, EC: AT17-5509-UMA, FP7-ICT-601116, UMA18-FEDERJA-074","This work has been partially funded by the EU ECHORD++ project (FP7-ICT-601116), and the UMA18-FEDERJA-074, the AT17-5509-UMA and the CSO2017-86747-R Spanish projects.",,,,,,,,,,"Feil-Seifer, D., Matarić, M.J., Toward Socially Assistive Robotics for Augmenting Interventions for Children with Autism Spec-trum Disorders (2009) Springer Tracts Adv. Robot, 54, pp. 201-210. , doi.org; Heerink, M., Kröse, B., Wielinga, B., Evers, V., Measuring the Influence of Social Abilities on Acceptance of an Interface Robot and a Screen Agent by Elderly Users (2009) People Comput. XXIII Celebr. People Technol. Proc. 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Care, pp. 185-203; Visual Disabilities-Introduction, , https://webaim.org/articles/visual/, WebAIM: (accessed on 6 June 2020); (2015) Blindness and Deafness, , WHO WHO: Geneva, Switzerland; Change the Definition of Blindness, , https://www.who.int/blindness/Change%20the%20Defini-tion%20of%20Blindness.pdf, (accessed on 3 February 2021); Priority eye diseases, , https://www.who.int/blindness/causes/priority/en/index4.html, WHO (accessed on 14 June 2020); Blindness: Causes, Type, Treatment & Symptoms, , https://www.medicinenet.com/blindness/article.htm, (ac-cessed on 29 November 2020); Kim, E., Paul, R., Shic, F., Scassellati, B., Bridging the Research Gap: Making HRI Useful to Individuals with Autism (2012) J. Human-Robot Interact, 1, pp. 26-54; Fernaeus, Y., Håkansson, M., Jacobsson, M., Ljungblad, S., How Do You Play with a Robotic Toy Animal? A Long-Term Study of Pleo (2010) Proc. IDC2010 9th Int. Conf. Interact. Des. Child, pp. 39-48; Deafness and Hearing Loss, , https://www.who.int/news-room/fact-sheets/detail/deafness-and-hearing-loss, (accessed on 19 June 2020); https://www.w3.org/WAI/people-use-web/abilities-barriers/#auditory, Diverse Abilities and Barriers Web Accessibility Initiative (WAI) W3C. (accessed on 20 June 2020); Gouaillier, D., Hugel, V., Blazevic, P., Kilner, C., Monceaux, J., Lafourcade, P., Marnier, B., Maisonnier, B., Mechatronic Design of NAO Humanoid (2009) Proceedings of the 2009 IEEE International Conference on Robotics and Automation, , Kobe, Japan, 12–17 May; Pot, E., Monceaux, J., Gelin, R., Maisonnier, B., Robotics, A., Choregraphe: A Graphical Tool for Humanoid Robot Programming (2009) Proc.-IEEE Int. Work. Robot Hum. Interact. Commun, pp. 46-51; Motor Disabilities-Assistive Technologies, , https://webaim.org/articles/motor/assistive, WebAIM: (accessed on 23 June 2020); Andreasen Struijk, L.N.S., Egsgaard, L.L., Lontis, R., Gaihede, M., Bentsen, B., Wireless Intraoral Tongue Control of an Assistive Robotic Arm for Individuals with Tetraplegia (2017) J. Neuroeng. Rehabil, 14, pp. 1-8; https://www.w3.org/WAI/people-use-web/abilities-barriers/#cognitive, Diverse Abilities and Barriers Web Accessibility Initiative (WAI) W3C. (accessed on 24 June 2020); Cognitive Disabilities-Design Considerations, , https://webaim.org/articles/cognitive/design, WebAIM: (ac-cessed on 4 July 2020); Law, M., Sutherland, C., Ahn, H.S., Macdonald, B.A., Peri, K., Johanson, D.L., Vajsakovic, D.-S., Broadbent, E., Developing Assistive Robots for People with Mild Cognitive Impairment and Mild Dementia: A Qualitative Study with Older Adults and Experts in Aged Care (2019) BMJ, 9; https://www.section508.gov/manage/laws-and-policies, IT Accessibility Laws and Policies Section508.gov. (accessed on 30 July 2020); https://eur-lex.europa.eu/legal-con-tent/EN/TXT/?qid=1596098310471&uri=CELEX:32016L2102, EUR-Lex-32016L2102-EN-EUR-Lex. (accessed on 30 July 2020); Internet Accessibility | Samuelson-Glushko Canadian Internet Policy and Public Interest Clinic (CIPPIC), , https://cippic.ca/fr/node/128422#canadian, (accessed on 30 July 2020); https://www.w3.org/TR/WCAG21/#wcag-2-layers-of-guidance, Web Content Accessibility Guidelines (WCAG) 2.1. (accessed on 30 July 2020); Mobile Guidelines-Funka, , https://www.funka.com/en/our-assignments/research-and-innovation/archive---research-projects/mobile-guidelines/, (accessed on 26 February 2020); https://www.bbc.co.uk/guide-lines/futuremedia/accessibility/, BBC-Future Media Standards & Guidelines-Accessibility Guidelines v2.0. (accessed on 30 July 2020); IBM Human Ability and Accessibility Center | Developer Guidelines, , https://www.ibm.com/able/guide-lines/index.html, (accessed on 30 July 2020); 2014-Robots and Robotic Devices—Safety Requirements for Personal Care Robots, , https://www.iso.org/standard/53820.html, ISO-ISO 13482:. (accessed on 1 December 2020); Guide to the Ethical Design and Application of Robots and Robotic Systems–Explore AI Ethics, , https://www.exploreaiethics.com/guidelines/bs-86112016/, British Standard BS 8611: Robots and Robotic Devices. (accessed on 20 December 2020); Qbilat, M., Iglesias, A., Accessibility Guidelines for Tactile Displays in Human-Robot Interaction (2018) A Comparative Study and Proposal, , Springer: Cham; https://www.w3.org/TR/wai-aria-practices-1.1/, WAI-ARIA Authoring Practices 1.1. (accessed on 26 November 2020); Personal User Experience (PUX) Recommendations and Lessons Learned, , https://gpii.eu/pux/guidelines/, (ac-cessed on 3 February 2021); Bandera, P., Bustos, L.V., Calderita, A., Dueñas, F., Fernandez, R., Fuentetaja, A., Garcia Olaya, F.J., Iglesias, L.J., CLARC: A Robotic Architecture for Comprehensive Geriatric Assessment Proc. XVII Workshop of Phys-ical Agents, , http://waf2016.uma.es/web/proceedings.pdf, (accessed on 29 August 2020)","Qbilat, M.; Department of Computer Science and Engineering, Spain; email: 100344402@alumnos.uc3m.es
Iglesias, A.; Department of Computer Science and Engineering, Spain; email: aiglesia@inf.uc3m.es",,,"MDPI AG",,,,,20799292,,,,"English","Electronics (Switzerland)",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85101679973
"Yang F., Gao Y., Ma R., Zojaji S., Castellano G., Peters C.","57195590226;57197868611;57222267172;57202001554;23007528900;26425154400;","A dataset of human and robot approach behaviors into small free-standing conversational groups",2021,"PLoS ONE","16","2 February","e0247364","","",,3,"10.1371/journal.pone.0247364","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102096812&doi=10.1371%2fjournal.pone.0247364&partnerID=40&md5=d673c1dd93b379542d8df932cac126a7","Department of Computational Science and Technology, KTH Royal Institute of Technology, Stockholm, Sweden; Department of Information Technology, Uppsala University, Uppsala, Sweden","Yang, F., Department of Computational Science and Technology, KTH Royal Institute of Technology, Stockholm, Sweden; Gao, Y., Department of Information Technology, Uppsala University, Uppsala, Sweden; Ma, R., Department of Computational Science and Technology, KTH Royal Institute of Technology, Stockholm, Sweden; Zojaji, S., Department of Computational Science and Technology, KTH Royal Institute of Technology, Stockholm, Sweden; Castellano, G., Department of Information Technology, Uppsala University, Uppsala, Sweden; Peters, C., Department of Computational Science and Technology, KTH Royal Institute of Technology, Stockholm, Sweden","The analysis and simulation of the interactions that occur in group situations is important when humans and artificial agents, physical or virtual, must coordinate when inhabiting similar spaces or even collaborate, as in the case of human-robot teams. Artificial systems should adapt to the natural interfaces of humans rather than the other way around. Such systems should be sensitive to human behaviors, which are often social in nature, and account for human capabilities when planning their own behaviors. A limiting factor relates to our understanding of how humans behave with respect to each other and with artificial embodiments, such as robots. To this end, we present CongreG8 (pronounced 'con-gregate'), a novel dataset containing the full-body motions of free-standing conversational groups of three humans and a newcomer that approaches the groups with the intent of joining them. The aim has been to collect an accurate and detailed set of positioning, orienting and full-body behaviors when a newcomer approaches and joins a small group. The dataset contains trials from human and robot newcomers. Additionally, it includes questionnaires about the personality of participants (BFI-10), their perception of robots (Godspeed), and custom human/robot interaction questions. An overview and analysis of the dataset is also provided, which suggests that human groups are more likely to alter their configuration to accommodate a human newcomer than a robot newcomer. We conclude by providing three use cases that the dataset has already been applied to in the domains of behavior detection and generation in real and virtual environments. © 2021 Yang et al. 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"Vallès-Peris N., Domènech M.","57202077580;8430937200;","Caring in the in-between: a proposal to introduce responsible AI and robotics to healthcare",2021,"AI and Society",,,,"","",,,"10.1007/s00146-021-01330-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121352390&doi=10.1007%2fs00146-021-01330-w&partnerID=40&md5=fe3117e3f09dc33472dcfd349fd22f65","Barcelona Science and Technology Studies Group (STS-B), Department of Social Psychology, Universitat Autònoma de Barcelona, Barcelona, Spain","Vallès-Peris, N., Barcelona Science and Technology Studies Group (STS-B), Department of Social Psychology, Universitat Autònoma de Barcelona, Barcelona, Spain; Domènech, M., Barcelona Science and Technology Studies Group (STS-B), Department of Social Psychology, Universitat Autònoma de Barcelona, Barcelona, Spain","In the scenario of growing polarization of promises and dangers that surround artificial intelligence (AI), how to introduce responsible AI and robotics in healthcare? In this paper, we develop an ethical–political approach to introduce democratic mechanisms to technological development, what we call “Caring in the In-Between”. Focusing on the multiple possibilities for action that emerge in the realm of uncertainty, we propose an ethical and responsible framework focused on care actions in between fears and hopes. Using the theoretical perspective of Science and Technology Studies and empirical research, “Caring in the In-Between” is based on three movements: the first is a change of focus from the world of promises and dangers to the world of uncertainties; the second is a conceptual shift from assuming a relationship with robotics based on a Human–Robot Interaction to another focused on the network in which the robot is embedded (the “Robot Embedded in a Network”); and the last is an ethical shift from a general normative framework to a discussion on the context of use. Based on these suggestions, “Caring in the In-Between” implies institutional challenges, as well as new practices in healthcare systems. It is articulated around three simultaneous processes, each of them related to practical actions in the “in-between” dimensions considered: monitoring relations and caring processes, through public engagement and institutional changes; including concerns and priorities of stakeholders, with the organization of participatory processes and alternative forms of representation; and making fears and hopes commensurable, through the choice of progressive and reversible actions. © 2021, The Author(s).","Care robots; Ethics of care; Measured action; Robot embedded in a network (REN); Science and technology studies","Economic and social effects; Ethical technology; Human robot interaction; Robotics; Care robot; Context of use; Empirical research; Ethic of care; Humans-robot interactions; Measured action; Robot embedded in a network; Science and technology studies; Technological development; Uncertainty; Health care",,,,,,"Open Access Funding provided by Universitat Autonoma de Barcelona. 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Ethical debates around biobanks and social robots (2021) Nanoethics, pp. 1-14. , (a); Vallès-Peris, N., Barat-Auleda, O., Domènech, M., Robots in Healthcare? What Patients Say (2021) Int J Environ Res Public Health, 18, p. 9933; Vallor, S., Bekey, G.A., Artificial intelligence and the ethics of self-learning robots (2017) Robot ethics, pp. 338-353. , Lin P, Abney K, Jenkins R, (eds), Oxford University Press, Oxford; van der Plas, A., Smits, M., Wehrmann, C., Beyond speculative robot ethics: a vision assessment study on the future of the robotic caretaker (2010) Account Res, 17 (6), pp. 299-315; van Wynsberghe, A., Designing robots for care: care centered value-sensitive design (2013) Sci Eng Ethics, 19 (2), pp. 407-433; van Wynsberghe, A., (2015) Healthcare robots. Ethics, design and implementation, , Routledge, London; Verbeek, P.-P., Materializing morality: design ethics and technological mediation (2006) Sci Technol Human Values, 31 (3), pp. 361-380; Volti, R., (2005) Society and technological change, , Macmillan; Willems, D., Pols, J., Goodness! The empirical turn in health care ethics (2010) Med Antropol, 22 (1), pp. 161-170","Vallès-Peris, N.; Barcelona Science and Technology Studies Group (STS-B), Spain; email: nuria.valles@uab.cat",,,"Springer Science and Business Media Deutschland GmbH",,,,,09515666,,,,"English","AI Soc.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85121352390
"Tung W.-F., Jara Santiago Campos J.","17436472600;57372254200;","User experience research on social robot application",2021,"Library Hi Tech",,,,"","",,,"10.1108/LHT-08-2021-0248","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121130621&doi=10.1108%2fLHT-08-2021-0248&partnerID=40&md5=3a21231714fcc5392766585a1cfdc5dc","Department of Information Management, Fu Jen Catholic University, New Taipei City, Taiwan; Master Program in Global Entrepreneurial Management, Fu Jen Catholic University, New Taipei City, Taiwan","Tung, W.-F., Department of Information Management, Fu Jen Catholic University, New Taipei City, Taiwan; Jara Santiago Campos, J., Master Program in Global Entrepreneurial Management, Fu Jen Catholic University, New Taipei City, Taiwan","Purpose: Social robot, a subtype of robots that is designed for the various interactive services for human, which must deliver superior user experience (UX) by expressing human-like social behavior or service and emotional sensitivity. This study develops a social robot app called the “Music Buddy” in ASUS Zenbo that provides a situational music based on the users' electroencephalogram (EEG) data. The research uses this app to explore its UX criteria and the prioritization of human robot interaction (HRI). Design/methodology/approach: The research methodologies include the both system development and decision analysis for the social robot. The first part is to design and develop a social robot app. The second part is to investigate the criteria of HRI through the Analytic Hierarchy Process (AHP) from UX aspects. Findings: In view of the results of the AHP, the first-layer criteria consist of personalized function, easy-to-use the system and intelligent process. In terms of prioritization of multi-criteria, the overall ranking discloses the nine criteria in order including autonomy for robot, easy-to-use EEG device, accurate music preference, simple operations for brainwave device and easy-to-use applications, active music recommendation, automatic updates of music and easy-to-use robot as well as fast detection for emotion. Originality/value: This research includes a self-developed social robot app and its UX research using AHP. This paper contributes to the improvement and innovation of the social robot design according to the results of UX research on HRI of social robot. © 2021, Emerald Publishing Limited.","AHP; HRI; Music buddy; Social robot; User experience; UX",,,,,,"Ministry of Science and Technology, Taiwan, MOST: 109-2410-H-030 -039 -MY2","Funding: This research study is funded by Ministry of Science and Technology (Taiwan) 109-2410-H-030 -039 -MY2.",,,,,,,,,,"Caic, M., Mahr, D., Oderkerken-Schröder, G., Value of social robots in services: social cognition perspective (2019) Journal of Services Marketing, 33 (4), pp. 463-478; Casas, J.A., Céspedes, N., Cifuentes, C.A., Gutierrez, L.F., Rincón-Roncancio, M., Múnera, M., Expectation Vs. reality: attitudes towards a socially assistive robot in cardiac rehabilitation (2019) Applied Sciences, 9 (4651), pp. 1-19; Castellano, G., Leite, I., Pereira, A., Martinho, C., Paiva, A., Mcowan, P.W., Affect recognition for interactive companions: challenges and design in real world scenarios (2009) Journal On Multimodal User Interfaces, 3 (1-2), pp. 89-98; Feil-Seifer, D., Kristine, S., Matarić, M.J., Benchmarks for evaluating socially assistive robotics. Interaction Studies Interaction Studies (2007) Social Behaviour and Communication in Biological and Artificial Systems Psychological Benchmarks of Human–Robot Interaction, 8 (3), pp. 423-439; Goodrich, A., Schultz, A.C., Human-robot interaction: a survey (2007) Foundations and Trends In Human-Computer Interaction, 1 (3), pp. 203-275; Graaf, M.M., Allouch, S.B., Exploring influencing variables for the acceptance of social robots (2013) Robotics and Autonomous Systems, 61 (12), pp. 1476-1486; Hirokawa, E., Suzuki, K., Design of a huggable social robot with affective expressions using projected images (2018) Applied Sciences, 8 (2298), pp. 1-27; Hogan, A., Laufer, D., Trong, D., (2016) The Six Steps for Justifying Better UX, pp. 1-45. , Forrester Research, Cambridge, MA: Rep; Khan, S., Germak, C., Reframing hri design opportunities for social robots: lessons learnt from A service robotics case study approach using UX for HRI (2018) Future Internet, 101 (10), pp. 1-22; Krishna, M., Nakirekanti, M., Ramesh, S., Brain computer interface system for mind controlled robot using Bluetooth (2014) International Journal of Computer Applications, 104 (15), pp. 20-23; Lindblom, J., Andreasson, R., Current challenges for UX evaluation of human-robot interaction (2016) Advances in Ergonomics of Manufacturing: Managing The Enterprise of the Future: Proceedings of the AHFE 2016 International Conference on Human Aspects of Advanced Manufacturing, 490, pp. 267-277; Lindblom, J., Ziemke, T., Cort, R., Alenjung, B., User experience in social human-robot interaction (2017) International Journal of Ambient Computing and Intelligence, 8, pp. 12-31; Martinez-Martin, E., Costa, A., Cazorla, M., PHAROS 2.0—a physical assistant robot system improved (2019) Sensors, 19 (20), p. 4531; Morville, P., (2016) User experience design, , Http://Semanticstudios.Com/User_Experience_Design/, October 11, Retrieved March 12, 2020; (2019), https://store.neurosky.com/pages/mindwave, May 2, Retrieved March 13, 2020; Paunovic, G., (2017) The Bottom Line: why Good UX design means better business, , https://www.forbes.com/sites/forbesagencycouncil/2017/03/23/the-bottom-linewhy-good-ux-design-means-better-business/#7c22ed152396, November 28, Retrieved March 12, 2020; Salem, M., Eyssel, F., Rohlfing, K., Kopp, S., Joublin, F., To err is human(-like): effects of robot gesture on perceived anthropomorphism and likability (2013) International Journal of Social Robotics, 5 (3), pp. 313-323; Satty, R.W., The analytic hierarchy process–what it is and how it is used (1987) Mathematical Modeling, 9 (3-5), pp. 161-176; Van Oers, R., Wesselman, E., (2016) Social Robots, 2016, pp. 1-25. , KPMG Advisory N.V, Amstelveen: Rep; Woods, S., Dautenhahn, K., Kaouri, C., Boekhorst, R., Okay, K., Walters, M.L., Are robots like people? Relationships between participant and robot personality traits in human–robot interaction studies (2007) Interaction Studies, 8 (2), pp. 281-305","Tung, W.-F.; Department of Information Management, Taiwan; email: 076144@mail.fju.edu.tw",,,"Emerald Group Holdings Ltd.",,,,,07378831,,,,"English","Libr. Hi Tech",Article,"Article in Press","",Scopus,2-s2.0-85121130621
"Jaroslavceva J., Wake N., Sasabuchi K., Ikeuchi K.","57366397600;56735885700;55758644400;57192153568;","Robot ego-noise suppression with labanotation-template subtraction",2021,"IEEJ Transactions on Electrical and Electronic Engineering",,,,"","",,,"10.1002/tee.23523","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120816015&doi=10.1002%2ftee.23523&partnerID=40&md5=0433f10a843cc1761a514e6a070cdacb","Applied Robotics Research, Microsoft, Redmond, WA  98052, United States","Jaroslavceva, J., Applied Robotics Research, Microsoft, Redmond, WA  98052, United States; Wake, N., Applied Robotics Research, Microsoft, Redmond, WA  98052, United States; Sasabuchi, K., Applied Robotics Research, Microsoft, Redmond, WA  98052, United States; Ikeuchi, K., Applied Robotics Research, Microsoft, Redmond, WA  98052, United States","In this study, we aim to improve automatic-speech-recognition (ASR) accuracy in the presence of robot ego-noise toward a better human-robot interaction. Although several noise reduction methods have been proposed to increase ASR accuracy or signal-to-noise ratio (SNR) by predicting ego-noises through a short-time motion-template subtraction or a neural network, these methods showed poor performance in some practical use cases, such as attenuating long-term motion-associated ego-noise. Based on the motion-template subtraction method, we address the problem of creating ego-noise templates associated with a wide variety of robot motions. For representing robot motions, we employ a dance notation referred to as Labanotation. The rationales behind our approach are: (i) Labanotation allows quantizing infinite motion patterns using a finite number of Labanotation combinations; (ii) Labanotation-based motion description is hardware-independent; and (iii) long-time noise templates facilitate the localization of noise templates in a speech-with-noise signal compared to short-time templates. The effectiveness of the Labanotation-template subtraction (LTS) method was tested for five commercial ASRs in terms of ASR accuracy, SNR, and source-to-distortion ratio. We show that LTS leads to a reasonable performance, comparable to the other methods. The contribution of this study is (i) to propose to use Labanotation to reasonably collect noise templates, (ii) to demonstrate the practical effectiveness of LTS as well as examples of Labanotations for household actions. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.","automatic speech recognition; ego-noise; human-robot interaction; labanotation","Man machine systems; Signal to noise ratio; Speech recognition; Automatic speech recognition; Ego-noise; Humans-robot interactions; Labanotations; Motion templates; Noise reduction methods; Noise suppression; Recognition accuracy; Robot motion; Subtraction method; Human robot interaction",,,,,"108‐0075","The authors wish to thank Prof. M. Nakamura, a Labanotation expert, and Dr. M. Sato, an expert of dance studies, for helpful discussions. This work was supported by Microsoft Japan, Tokyo, 108‐0075.",,,,,,,,,,"Wake, N., Yanokura, I., Sasabuchi, K., Ikeuchi, K., (2020) Verbal focus-of-attention system for learning-from-observation. arXiv preprint arXiv:2007.08705; Wang, J., Zhang, J., Wei, J., Lu, W., Dang, J., (2014) Automatic speech recognition under robot ego noises. 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Proc. of IEEE Workshop on Machine Learning for Signal Process, pp. 431-436; Ince, G., Nakadai, K., Rodemann, T., Hasegawa, Y., Tsujino, H., Imura, J., (2009) Ego noise suppression of a robot using template subtraction. Proc. of IEEE Int. Conf. on Intelligent Robots and Systems, pp. 199-204; Ito, A., Kanayama, T., Suzuki, M., Makino, S., (2005) Internal noise suppression for speech recognition by small robots. Ninth European Conference on Speech Communication and Technology; Schmidt, A., Löllmann, H.W., Kellermann, W., A novel ego-noise suppression algorithm for acoustic signal enhancement in autonomous systems. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, 2018, pp. 6583-6587. , https://doi.org/10.1109/ICASSP.2018.8462211; Schmidt, A., Kellermann, W., (2019) Informed ego-noise suppression using motor data-driven dictionaries. ICASSP 2019 — 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, pp. 116-120. , https://doi.org/10.1109/ICASSP.2019.8682570; Nishimura, Y., Nakano, M., Nakadai, K., Tsujino, H., Ishizuka, M., (2006) Speech recognition for a robot under its motor noises by selective application of missing feature theory and MLLR. ISCA Tutorial and Research Workshop on Statistical And Perceptual Audition; Ikeuchi, K., Yan, Z., Ma, Z., Sato, Y., Nakamura, M., Kudoh, S., Describing upper-body motions based on Labanotation for learning-from-observation robots (2018) International Journal of Computer Vision, 126 (12), pp. 1415-1429; Hutchinson-Guest, A., (1977) Labanotation: The System of Analyzing and Recording Movement, , New York, NY, Theatre Arts Book; Choensawat, W., Takahashi, S., Nakamura, M., Choi, W., Hachimura, K., Description and representation of stylized traditional dance body motion by using Labanotation (2010) Transactions of the Virtual Reality Society of Japan, 15 (3), pp. 379-388; Miller, G.A., The magical number seven, plus or minus two: Some limits on our capacity for processing information (1956) Psychological Review, 63 (2), pp. 81-97; Saudabayev, A., Rysbek, Z., Khassenova, R., Varol, H.A., Human grasping database for activities of daily living with depth, color and kinematic data streams (2018) Scientific Data, 5 (1); Ikeuchi, K., Baumert, D., Kudho, S., Takizawa, M., (2019) Design of conversational humanoid robot based on hardware independent gesture generation. arXiv preprint arXiv:1905.08702; Sasabuchi, K., Yaguchi, H., Nagahama, K., Hori, S., Mizohana, H., Inaba, M., The seed-noid robot platform: Designing a multi-purpose compact robot from continuous evaluation and lessons from competitions (2018) IEEE Robotics and Automation Letters, 3, pp. 3983-3990; (2020), https://docs.microsoft.com/en-gb/azure/Kinect-dk/hardware-specification/, Azure Kinect DK Hardware Specifications. Microsoft Docs., Accessed June 2; (2020) Festvox, , http://www.festvox.org/cmu_arctic/, Accessed June 1; (2020) Tatoeba: Collection of Sentences and Translations, , https://tatoeba.org/eng/, Accessed June 2, 2020; Schmidt, A., Brendel, A., Haubner, T., Kellermann, W., Motor data-regularized nonnegative matrix factorization for ego-noise suppression (2020) EURASIP Journal on Audio, Speech, and Music Processing, 2020 (1), pp. 1-15. , https://doi.org/10.1186/s13636-020-00178-0; Su, L., (2018) Vocal melody extraction using patch-based CNN. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE; Park, S.R., Lee, J., (2016) A fully convolutional neural network for speech enhancement. arXiv preprint arXiv:1609.07132; Lin, K.W.E., Balamurali, B.T., Koh, E., Lui, S., Herremans, D., Singing voice separation using a deep convolutional neural network trained by ideal binary mask and cross entropy (2020) Neural Computing and Applications, 32 (4), pp. 1037-1050; Shmyrev, N., (2020) Cmusphinx Open Source Speech Recognition, , https://cmusphinx.github.io/, Accessed June 10, 2020; (2020) Automatic Speech Recognition|Cloud Speech-To-Text, , https://cloud.google.com/speech-to-text, Accessed June 10, 2020; (2020) Speech To Text|Microsoft Azure, , https://azure.microsoft.com/en-us/services/cognitive-services/speech-to-text/, Accessed June 10, 2020; (2020) Watson Speech To Text — Overview, , https://www.ibm.com/cloud/watson-speech-to-text, Accessed June 10, 2020; Chen, F., Loizou, P.C., Predicting the intelligibility of vocoded and wideband mandarin Chinese (2011) The Journal of the Acoustical Society of America, 129 (5), pp. 3281-3290. , https://doi.org/10.1121/1.3570957","Jaroslavceva, J.; Applied Robotics Research, United States; email: jarosjek@fel.cvut.cz",,,"John Wiley and Sons Inc",,,,,19314973,,,,"English","IEEJ Trans. Electr. Electron. Eng.",Article,"Article in Press","",Scopus,2-s2.0-85120816015
[No author name available],[No author id available],"13th International Conference on Social Robotics, ICSR 2021",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13086 LNAI",,,"","",828,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119873613&partnerID=40&md5=b1c439db710edc0cc48ceb9b41253264",,"","The proceedings contain 79 papers. The special focus in this conference is on Social Robotics. The topics include: Investigating Customers’ Perceived Sensitivity of Information Shared with a Robot Bartender; sex Robots: Auto-erotic Devices, Fetishes or New Form of Transitional Object for Adults?; explaining Before or After Acting? How the Timing of Self-Explanations Affects User Perception of Robot Behavior; human vs Robot Lie Detector: Better Working as a Team?; appropriate Robot Reactions to Erroneous Situations in Human-Robot Collaboration; simplified Robot Programming Framework for a Gearbox Assembly Application; gaze Assisted Visual Grounding; towards a Programming-Free Robotic System for Assembly Tasks Using Intuitive Interactions; controlling Industrial Robots with High-Level Verbal Commands; cross-Cultural Timeline of the History of Thought of the Artificial; why We Need Emotional Intelligence in the Design of Autonomous Social Robots and How Confucian Moral Sentimentalism Can Help; how to Tune Your Draggin’: Can Body Language Mitigate Face Threat in Robotic Noncompliance?; Migratable AI : Investigating Users’ Affect on Identity and Information Migration of a Conversational AI Agent; the Self-Evaluation Maintenance Model in Human-Robot Interaction: A Conceptual Replication; Birds of a Feather Flock Together: A Study of Status Homophily in HRI; “Space Agency”: A “Strong Concept” for Designing Socially Interactive, Robotic Environments; early Prediction of Student Engagement-Related Events from Facial and Contextual Features; common Reality: An Interface of Human-Robot Communication and Mutual Understanding; autonomous Group Detection, Delineation, and Selection for Human-Agent Interaction; a Framework of Controlled Robot Language for Reliable Human-Robot Collaboration; people’s Perceptions of Gendered Robots Performing Gender Stereotypical Tasks.",,,,,,,,,,,,,,,,,,,,"Li H.Ge S.S.Wu Y.Wykowska A.He H.Liu X.Li D.Perez-Osorio J.",,"Springer Science and Business Media Deutschland GmbH","13th International Conference on Social Robotics, ICSR 2021","10 November 2021 through 13 November 2021",,268159,03029743,9783030905248,,,"English","Lect. Notes Comput. Sci.",Conference Review,"Final","",Scopus,2-s2.0-85119873613
"Dessì D., Diego R.R.","57192682012;57320534000;","LexTex: a framework to generate lexicons using WordNet word senses in domain specific categories",2021,"Journal of Intelligent Information Systems",,,,"","",,,"10.1007/s10844-021-00679-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118354591&doi=10.1007%2fs10844-021-00679-0&partnerID=40&md5=d107eddd7e0d7865a835c18c5823295e","FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Karlsruhe, Germany; Karlsruhe Institute of Technology, Institute AIFB, Karlsruhe, Germany; University of Cagliari, Cagliari, Italy; R2M Solution s.r.l., Pavia, Italy","Dessì, D., FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Karlsruhe, Germany, Karlsruhe Institute of Technology, Institute AIFB, Karlsruhe, Germany, University of Cagliari, Cagliari, Italy; Diego, R.R., University of Cagliari, Cagliari, Italy, R2M Solution s.r.l., Pavia, Italy","Lexicons have risen as alternative resources to common supervised methods for classification or regression in different domains (e.g., Sentiment Analysis). These resources (especially lexical) lack of important domain context and it is not possible to tune/edit/improve them depending on new domains and data. With the exponential production of data and annotations witnessed today in several domains, leveraging lexical resources to improve existing lexicons becomes a must. In this work, a novel framework to build lexicons independently from the target domain and from input categories where each text needs to be classified is provided. It employs state-of-the-art Natural Language Processing, Word Sense Disambiguation tools, and techniques to make the method as general as possible. The framework takes as input a heterogeneous collection of annotated text towards a fixed number of categories. Its output is a list of WordNet word senses with weights for each category. We prove the effectiveness of the framework taking as case study the Emotion Detection task by employing the generated lexicons within such a domain. The results prove the effectiveness of proposed framework. Additionally, the paper shows an use case on the human-robot interaction within the Emotion Detection task. Furthermore we applied our methodology in several other domains and compared our approach against common supervised methods (regressors) showing the effectiveness of the generated lexicons. By freely providing the framework we aim at encouraging and disseminating the production of context-aware and domain-specific lexicons in other domains as well. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.","Emotion detection; Lexicon generation; Word sense disambiguation","Human robot interaction; Nanowires; Ontology; Sentiment analysis; Detection tasks; Different domains; Domain specific; Emotion detection; Lexicon generation; Sentiment analysis; Supervised methods; Word sense; Word Sense Disambiguation; Wordnet; Supervised learning",,,,,"956573","This research was partially funded by the H2020 project STAR - Novel AI technology for dynamic and unpredictable manufacturing environments (grant number 956573).",,,,,,,,,,"Abedjan, Z., Boujemaa, N., Campbell, S., Data science in healthcare: Benefits, challenges and opportunities (2019) In Data Science for Healthcare, pp. 3-38. , . Springer; Abura’Ed, A., Bravo, A., Chiruzzo, L., (2018), Using regression and convolutions for cross-document semantic linking and summarization of scholarly literature. In Proceedings of the 3nd joint workshop on bibliometric-enhanced information retrieval and natural language processing for digital libraries (BIRNDL2018). 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"Losey D.P., Bajcsy A., O’Malley M.K., Dragan A.D.","55755379000;57201504784;35917120900;55193779100;","Physical interaction as communication: Learning robot objectives online from human corrections",2021,"International Journal of Robotics Research",,,,"","",,,"10.1177/02783649211050958","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118185320&doi=10.1177%2f02783649211050958&partnerID=40&md5=0f784205e03f1e8fa0f27d9dd92b8c56","Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, United States; University of California, Berkeley, Berkeley, CA, United States; Rice University, Houston, TX, United States","Losey, D.P., Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, United States; Bajcsy, A., University of California, Berkeley, Berkeley, CA, United States; O’Malley, M.K., Rice University, Houston, TX, United States; Dragan, A.D., University of California, Berkeley, Berkeley, CA, United States","When a robot performs a task next to a human, physical interaction is inevitable: the human might push, pull, twist, or guide the robot. The state of the art treats these interactions as disturbances that the robot should reject or avoid. At best, these robots respond safely while the human interacts; but after the human lets go, these robots simply return to their original behavior. We recognize that physical human–robot interaction (pHRI) is often intentional: the human intervenes on purpose because the robot is not doing the task correctly. In this article, we argue that when pHRI is intentional it is also informative: the robot can leverage interactions to learn how it should complete the rest of its current task even after the person lets go. We formalize pHRI as a dynamical system, where the human has in mind an objective function they want the robot to optimize, but the robot does not get direct access to the parameters of this objective: they are internal to the human. Within our proposed framework human interactions become observations about the true objective. We introduce approximations to learn from and respond to pHRI in real-time. We recognize that not all human corrections are perfect: often users interact with the robot noisily, and so we improve the efficiency of robot learning from pHRI by reducing unintended learning. Finally, we conduct simulations and user studies on a robotic manipulator to compare our proposed approach with the state of the art. Our results indicate that learning from pHRI leads to better task performance and improved human satisfaction. © The Author(s) 2021.","impedance control; inverse reinforcement learning; personal robots; physical human–robot interaction","E-learning; Human robot interaction; Manipulators; Reinforcement learning; 'current; Impedance control; Inverse reinforcement learning; Learn+; Objective functions; Personal robot; Physical humanrobot interaction (phri); Physical interactions; Push pull; State of the art; Dynamical systems",,,,,"National Science Foundation, NSF; Office of Naval Research, ONR; Air Force Office of Scientific Research, AFOSR","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is support by AFOSR, ONR YIP, and NSF CAREER. Dylan Losey and Andrea Bajcsy are both supported by the NSF GRFP.",,,,,,,,,,"Abbeel, P., Ng, A., (2004) Apprenticeship learning via inverse reinforcement learning, , International Conference on Machine Learning (ICML), In; Akgun, B., Cakmak, M., Jiang, K., Thomaz, A.L., Keyframe-based learning from demonstration (2012) International Journal of Social Robotics, 4 (4), pp. 343-355; Argall, B.D., Chernova, S., Veloso, M., Browning, B., A survey of robot learning from demonstration (2009) Robotics and Autonomous Systems, 57 (5), pp. 469-483; Bajcsy, A., Losey, D.P., O’Malley, M.K., Dragan, A.D., (2017) Learning robot objectives from physical human interaction, , Conference on Robot Learning (CoRL), In; Bajcsy, A., Losey, D.P., O’Malley, M.K., Dragan, A.D., (2018) Learning from physical human corrections, one feature at a time, pp. 141-149. , ACM/IEEE International Conference on Human–Robot Interaction (HRI), In; Baker, C.L., Tenenbaum, J.B., Saxe, R.R., Goal inference as inverse planning (2007) Proceedings of the Annual Meeting of the Cognitive Science Society, 29, pp. 779-784; 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Department of Mechanical Engineering, United States; email: losey@vt.edu",,,"SAGE Publications Inc.",,,,,02783649,,IJRRE,,"English","Int J Rob Res",Article,"Article in Press","All Open Access, Green",Scopus,2-s2.0-85118185320
"Cantucci F., Falcone R., Castelfranchi C.","57205735711;57213828373;57204261558;","Investigating adjustable social autonomy in human robot interaction",2021,"CEUR Workshop Proceedings","2963",,,"49","60",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116911162&partnerID=40&md5=727452a20908cdafe4c89b8ba1ba0286","Institute of Cognitive Science and Technology, National Research Council of Italy, (ISTC-CNR), Rome, Italy","Cantucci, F., Institute of Cognitive Science and Technology, National Research Council of Italy, (ISTC-CNR), Rome, Italy; Falcone, R., Institute of Cognitive Science and Technology, National Research Council of Italy, (ISTC-CNR), Rome, Italy; Castelfranchi, C., Institute of Cognitive Science and Technology, National Research Council of Italy, (ISTC-CNR), Rome, Italy","More and more often, Human Robot Interaction(HRI) applications require the design of robotics systems whose decision process implies the capability to evaluate not only the physical environment, but especially the mental states and the features of its human interlocutor, in order to adapt their social autonomy every time humans require the robot's help. Robots will be really cooperative and effective when they will expose the capability to consider not only the goals or interests explicitly required by humans, but also those one that are not declared and to provide help that go beyond the literal task execution. In order to improve the quality of this kind of smart help, a robot has to operate a meta-evaluation of its own predictive skills to build a model of the interlocutor and of her/his goals. The robot's capability to self-trust its skills to interpret the interlocutor and the context, is a fundamental requirement for producing smart and effective decisions towards humans. In this work we propose a simulated experiment, designed with the goal to test a cognitive architecture for trustworthy human robot collaboration. The experiment has been designed in order to demonstrate how the robot's capability to learn its own level of self-trust on its predictive abilities in perceiving the user and building a model of her/him, allows it to establish a trustworthy collaboration and to maintain an high level of user's satisfaction, with respect to the robot's performance, also when these abilities progressively degrade. © 2021 CEUR-WS. All rights reserved.","Cognitive modelling; Robot autonomy adaptation; Theory of mind; Transparency; Trustworthy HRI","Agricultural robots; Machine design; Man machine systems; Quality control; Robotics; Cognitive model; Decision process; Humans-robot interactions; Mental state; Physical environments; Robot autonomy; Robot autonomy adaptation; Robotic systems; Theory of minds; Trustworthy human robot interaction; Human robot interaction",,,,,,,,,,,,,,,,"Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Graepel, T., Mastering atari, go, chess and shogi by planning with a learned model (2020) Nature, 588, pp. 604-609; Fujita, H., Ai-based computer-aided diagnosis (ai-cad): the latest review to read first (2020) Radiological physics and technology, 13, pp. 6-19; Hardalov, M., Koychev, I., Nakov, P., Towards automated customer support (2018) International Conference on Artificial Intelligence: Methodology, Systems, and Applications, pp. 48-59. , Springer; Commission, E., White paper on artificial intelligence-a european approach to excellence and trust Com (2020) 65 Final, , (2020); Smuha, N. 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"Feng Y., Perugia G., Yu S., Barakova E.I., Hu J., Rauterberg G.W.M.","57196468593;57193757433;13906567800;35614959800;56442891500;57218000808;","Context-Enhanced Human-Robot Interaction: Exploring the Role of System Interactivity and Multimodal Stimuli on the Engagement of People with Dementia",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00823-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116771414&doi=10.1007%2fs12369-021-00823-4&partnerID=40&md5=03b7512f4891ce960c05e67d8ae7481c","Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands; Department of Industrial Design, Northwestern Polytechnical University, Xi’an, China; Human-Technology Interaction Group, Eindhoven University of Technology, Eindhoven, Netherlands; Department of Information Technology, Uppsala University, Uppsala, Sweden","Feng, Y., Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands, Department of Industrial Design, Northwestern Polytechnical University, Xi’an, China; Perugia, G., Human-Technology Interaction Group, Eindhoven University of Technology, Eindhoven, Netherlands, Department of Information Technology, Uppsala University, Uppsala, Sweden; Yu, S., Department of Industrial Design, Northwestern Polytechnical University, Xi’an, China; Barakova, E.I., Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands; Hu, J., Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands; Rauterberg, G.W.M., Department of Industrial Design, Eindhoven University of Technology, Eindhoven, Netherlands","Engaging people with dementia (PWD) in meaningful activities is the key to promote their quality of life. Design towards a higher level of user engagement has been extensively studied within the human-computer interaction community, however, few extend to PWD. It is generally considered that increased richness of experiences can lead to enhanced engagement. Therefore, this paper explores the effects of rich interaction in terms of the role of system interactivity and multimodal stimuli by engaging participants in context-enhanced human-robot interaction activities. The interaction with a social robot was considered context-enhanced due to the additional responsive sensory feedback from an augmented reality display. A field study was conducted in a Dutch nursing home with 16 residents. The study followed a two by two mixed factorial design with one within-subject variable - multimodal stimuli - and one between-subject variable - system interactivity. A mixed method of video coding analysis and observational rating scales was adopted to assess user engagement comprehensively. Results disclose that when additional auditory modality was included besides the visual-tactile stimuli, participants had significantly higher scores on attitude, more positive behavioral engagement during activity, and a higher percentage of communications displayed. The multimodal stimuli also promoted social interaction between participants and the facilitator. The findings provide sufficient evidence regarding the significant role of multimodal stimuli in promoting PWD’s engagement, which could be potentially used as a motivation strategy in future research to improve emotional aspects of activity-related engagement and social interaction with the human partner. © 2021, The Author(s).","Context-enhanced human-robot interaction; Engagement; Interactivity; Multimodal stimuli; People with dementia; Social robots","Augmented reality; Human computer interaction; Human robot interaction; Man machine systems; Sensory feedback; Video signal processing; Context-enhanced human-robot interaction; Engagement; Humans-robot interactions; Interactivity; Multi-modal; Multimodal stimulus; People with dementia; Social interactions; Social robots; User engagement; Neurodegenerative diseases",,,,,"China Scholarship Council, CSC","The authors would like to acknowledge all participated residents, caregivers and Trudy van de schans-Robijns from Vitalis for their involvement and contributions. We also thank Famke Boschman for her help in the user study and data analysis. This study is partly sponsored by China Scholarship Council.",,,,,,,,,,"Alenljung, B., Lindblom, J., Andreasson, R., Ziemke, T., User experience in social human-robot interaction (2019) Rapid Automation: Concepts, Methodologies, Tools, and Applications, pp. 1468-1490. , IGI Global; Anderiesen, H., Scherder, E., Goossens, R., Visch, V., Eggermont, L., Play experiences for people with alzheimers disease (2015) International Journal of Design, 9 (2), p. 2015; Bainbridge, W.A., Hart, J.W., Kim, E.S., Scassellati, B., The benefits of interactions with physically present robots over video-displayed agents (2011) Int J Soc Robot, 3 (1), pp. 41-52; Bemelmans, R., Gelderblom, G.J., Jonker, P., De Witte, L., Socially assistive robots in elderly care: A systematic review into effects and effectiveness (2012) J Am Med Dir Assoc, 13 (2), pp. 114-120; Bennett, B., McDonald, F., Beattie, E., Carney, T., Freckelton, I., White, B., Willmott, L., Assistive technologies for people with dementia: ethical considerations (2017) Bull World Health Organ, 95 (11), p. 749; 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J. Soc. Rob.",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85116771414
"Gorelova G., Melnik E., Safronenkova I.","56820155200;57170217000;57194093674;","The Problem Statement of Cognitive Modeling in Social Robotic Systems",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12998 LNAI",,,"62","75",,,"10.1007/978-3-030-87725-5_6","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116481956&doi=10.1007%2f978-3-030-87725-5_6&partnerID=40&md5=d544727682e79f5793ba3b5eecae18e3","Engineering and Technology Academy of the Southern Federal University, 44, Nekrasovsky Lane, GSP-17, Taganrog, 347922, Russian Federation; Federal Research Centre the Southern Scientific Center of the Russian Academy of Sciences, 41, Chekhov Street, Rostov-on-Don, 344006, Russian Federation","Gorelova, G., Engineering and Technology Academy of the Southern Federal University, 44, Nekrasovsky Lane, GSP-17, Taganrog, 347922, Russian Federation; Melnik, E., Federal Research Centre the Southern Scientific Center of the Russian Academy of Sciences, 41, Chekhov Street, Rostov-on-Don, 344006, Russian Federation; Safronenkova, I., Federal Research Centre the Southern Scientific Center of the Russian Academy of Sciences, 41, Chekhov Street, Rostov-on-Don, 344006, Russian Federation","In connection with the rapid development of robotics, the problems of interaction between humans and robots, considered as social interaction problems, are becoming more and more urgent. The problems of interaction of robots with people at the social level require versatile interdisciplinary research to identify and exclude their possible negative consequences. Practical information about such consequences in different areas of robot use may be considered insufficient. In this paper, it is proposed to use an approach to research based on simulation modeling to obtain additional information about the object, which is defined as the “Social Robotic System” (SRS). It is proposed to apply the methodology of cognitive simulation of complex systems as a methodology that uses the capabilities of cognitive processes in subjects in their interaction with objects. The toolkit for cognitive modeling of complex systems is also proposed to use for the development and substantiation of management decisions in the field of interaction between robots and humans. Two formulations of the problem for social robotic systems are considered. The models of these systems are in the form of cognitive maps. The results of the study of the SRS structural properties and stability properties on its cognitive maps are presented. The final stage of cognitive modeling is the development of scenarios for the possible development of situations in the system with internal and external environmental changes. All this research makes it possible to conclude the consistency of the models with possible real situations of human-robot interaction and to justify the necessary management decisions. © 2021, Springer Nature Switzerland AG.","Cognitive modeling; Complex system; Digital humanities; Imitation; Interaction; Social robotics","Decision making; Decision support systems; Human robot interaction; Large scale systems; Robotics; Stability; Cognitive maps; Cognitive model; Digital humanities; Imitation; Interaction; Management decisions; Problem statement; Robotic systems; Social interactions; Social robotics; Cognitive systems",,,,,"Russian Foundation for Basic Research, РФФИ: 18-29-22086, AAAA-A19-119011190173-6","Acknowledgement. This study is supported by the by the RFBR project 18-29-22086 and the GZ SSC RAS N GR project AAAA-A19-119011190173-6.",,,,,,,,,,"Gorelova, G.V., Cognitive modeling of complex systems in the aspect of socio-cyber-physical systems (2019) Manage. Econ. Soc. 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Society for Industrial and Applied Mathematics (1978) Philadelphia; Certificate of state registration of computer programs no (2018) 2018661506 Dated 09, , July; Abramova, N.A., Ginsberg, K.S., Novikov, D.A., (2006) The Human Factor in Management, , KomKniga, Moscow","Safronenkova, I.; Federal Research Centre the Southern Scientific Center of the Russian Academy of Sciences, 41, Chekhov Street, Russian Federation; email: safronenkova050788@yandex.ru","Ronzhin A.Rigoll G.Meshcheryakov R.",,"Springer Science and Business Media Deutschland GmbH","6th International Conference on Interactive Collaborative Robotics, ICR 2021","27 September 2021 through 30 September 2021",,266009,03029743,9783030877248,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85116481956
"Roselli C., Ciardo F., Wykowska A.","57212379782;57164931800;15924284200;","Intentions with actions: The role of intentionality attribution on the vicarious sense of agency in Human–Robot interaction",2021,"Quarterly Journal of Experimental Psychology",,,,"","",,,"10.1177/17470218211042003","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114597952&doi=10.1177%2f17470218211042003&partnerID=40&md5=3219f003e3845aef526f98deee599d58","Social Cognition in Human-Robot Interaction Unit, Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy; Dipartimento di Informatica, Bioingegneria, Robotica ed Ingegneria dei Sistemi (DIBRIS), Genoa, Italy","Roselli, C., Social Cognition in Human-Robot Interaction Unit, Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy, Dipartimento di Informatica, Bioingegneria, Robotica ed Ingegneria dei Sistemi (DIBRIS), Genoa, Italy; Ciardo, F., Social Cognition in Human-Robot Interaction Unit, Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy; Wykowska, A., Social Cognition in Human-Robot Interaction Unit, Center for Human Technologies, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy","Sense of Agency (SoA) is the feeling of control over one’s actions and their consequences. In social contexts, people experience a “vicarious” SoA over other humans’ actions; however, the phenomenon disappears when the other agent is a computer. This study aimed to investigate the factors that determine when humans experience vicarious SoA in Human–Robot Interaction (HRI). To this end, in two experiments, we disentangled two potential contributing factors: (1) the possibility of representing the robot’s actions and (2) the adoption of Intentional Stance towards robots. Participants performed an Intentional Binding (IB) task reporting the time of occurrence for self- or robot-generated actions or sensory outcomes. To assess the role of action representation, the robot either performed a physical keypress (Experiment 1) or “acted” by sending a command via Bluetooth (Experiment 2). Before the experiment, attribution of intentionality to the robot was assessed. Results showed that when participants judged the occurrence of the action, vicarious SoA was predicted by the degree of attributed intentionality, but only when the robot’s action was physical. Conversely, digital actions elicited the reversed effect of vicarious IB, suggesting that disembodied actions of robots are perceived as non-intentional. When participants judged the occurrence of the sensory outcome, vicarious SoA emerged only when the causing action was physical. Notably, intentionality attribution predicted vicarious SoA for sensory outcomes independently of the nature of the causing event, physical or digital. In conclusion, both intentionality attribution and action representation play a crucial role for vicarious SoA in HRI. © Experimental Psychology Society 2021.","Human-Robot Interaction; Intentional Binding; Intentional Stance; Vicarious Sense of Agency",,,,,,"Horizon 2020 Framework Programme, H2020: ERC – 2016-StG-715058; European Research Council, ERC","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has received support from the European Research Council under the European Union’s Horizon 2020 research and innovation programme, ERC Starting Grant, G.A. number: ERC – 2016-StG-715058, awarded to Agnieszka Wykowska. The content of this paper is the sole responsibility of the authors. 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J. Exp. Psychol.",Article,"Article in Press","All Open Access, Green",Scopus,2-s2.0-85114597952
"Pinney J., Carroll F., Newbury P.","57237208000;36241288500;57237483900;","Robots and Uncertainty: An Investigation into the Impact of the Aesthetic Visualisation on Peoples Trust of Robots",2021,"Lecture Notes in Mechanical Engineering",,,,"1","10",,,"10.1007/978-981-16-4803-8_1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113767920&doi=10.1007%2f978-981-16-4803-8_1&partnerID=40&md5=def31e876f524d740d0795dfa29723eb","Cardiff Metropolitan University, Cardiff, C5 2YB, United Kingdom; Springer Heidelberg, Tiergartenstr. 17, Heidelberg, 69121, Germany","Pinney, J., Cardiff Metropolitan University, Cardiff, C5 2YB, United Kingdom, Springer Heidelberg, Tiergartenstr. 17, Heidelberg, 69121, Germany; Carroll, F., Cardiff Metropolitan University, Cardiff, C5 2YB, United Kingdom, Springer Heidelberg, Tiergartenstr. 17, Heidelberg, 69121, Germany; Newbury, P., Cardiff Metropolitan University, Cardiff, C5 2YB, United Kingdom, Springer Heidelberg, Tiergartenstr. 17, Heidelberg, 69121, Germany","Human senses have evolved to pick-up on sensory cues. Beyond our perception, they play an integral role in our emotional processing, learning, and interpretation. They are what help us to sculpt our everyday experiences and can be triggered by aesthetics to form the foundations of our interactions with each other and our surroundings. Aesthetics, described by the ancient Greeks as sensation, is the ability to receive stimulation from one or more of our five bodily senses. In terms of Human-Robot Interaction (HRI), robots also have the ‘potential’ senses to interact with the environment and people around them. They can offer an ‘embodiment’ that has the potential to make the interaction with technology a more natural, engaging, and acceptable experience. However, for many reasons, people still do not seem to trust and accept robots. This paper explores that robots have unique opportunities to improve their facilities for empathy, emotion, and social awareness beyond their more cognitive functionalities. By applying various different design elements to design of the human robotic interaction, we have revealed that certain facial aesthetics seem to be more trustworthy than others (cartoon face versus human face etc.) and also certain visual variables (i.e. blur) afforded uncertainty more so than others. Consequentially, this paper reports uncertainties in and between the visualisations greatly influenced participants willingness to accept and trust the robot. By understanding what aesthetic elements initiate what affective processes, this paper further enriches our knowledge of how we might design for certain emotions, feelings and ultimately more socially acceptable and trusting robotic experiences. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.","Aesthetics; Human robot interaction; Robots; Trust",,,,,,,,,,,,,,,,,"Floyd, M., Drinkwater, M.W., Aha, D.W., Case-based behavior adaptation using an inverse trust metric (2014) In: AAAI Workshop-Technical Report; Barnes, M., Jentsch, F., (2010) Human-Robot Interactions in Future Military Operations, , Ashgate Publishing Company; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Robot. Auton. 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Syst., 5, pp. 1-18; Stoeva, D., Gelautz, M., Body language in affective human-robot interaction (2020) ACM/IEEE International Conference on Human-Robot Interaction, pp. 606-608. , , pp; Hancock, J.T., Landrigan, C., Silver, C., Expressing emotion in text-based communication (2007) Conference on Human Factors in Computing Systems-Proceedings, pp. 929-932. , , pp; Richert, A., Müller, S., Schrode, S., Jeschke, S., Anthropomorphism in social robotics: Empirical results on human–robot interaction in hybrid production workplaces (2018) AI Soc, 3 (3), pp. 413-424; Duffy, R., Anthropomorphism and the social robot (2003) Robot. Auton. Syst., 42 (3-4), pp. 177-190; Damiano, L., Dumouchel, P., Anthropomorphism in human-robot co-evolution (2018) Front. Psychol., 9 (1-9); Luptetti, M.L., (2020), https://interactions.acm.org/blog/view/robots-aesthetics-and-heritage-con texts. Accessed 05, May; Canning, C., Donahue, T.J., Scheutz, M., Investigating human perceptions of robot capabilities in remote human-robot team tasks based on first-person robot video feeds (2014) IEEE International Conference on Intelligent Robots and Systems, pp. 4354-4361. , , pp; Billings, D.R., Schaefer, K.E., Chen, J.Y., Hancock, P.A., Human-robot interaction: Developing trust in robots (2012) HRI 2012-Proceedings of the 7Th Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 109-110. , , pp; Cahill, V., Using trust for secure collaboration in uncertain environments (2003) IEEE Pervasive Computing, 2 (3), pp. 52-61; Paradeda, R.B., Hashemian, M., Rodrigues, R.A., Paiva, A., How facial expressions and small talk may influence trust in a robot (2016) ICSR 2016. LNCS (LNAI), Vol. 9979, pp. 169-178. , https://doi.org/10.1007/978-3-319-47437-3_17, Agah, A., Cabibihan, J.-J., Howard, A.M., Salichs, M.A., He, H. (eds.), pp. , Springer, Cham; Mayer, R.C., Davis, J.H., Schoorman, F.D., Model of trust (1995) Management, 20 (3), pp. 709-734; Gambetta, D., Can we trust trust? Trust: Making Break. Co-oper (2000) Relat, 13, pp. 213-237; Desteno, D., Detecting the trustworthiness of novel partners in economic exchange (2012) Psychol. Sci., 23 (12), pp. 1549-1556; Lee, B., Empathy, androids and ‘authentic experience’ (2006) Connect. Sci., 18 (4), pp. 419-428; Riek, L.D., Robinson, P., Real-time empathy: Facial mimicry on arobot (2008) ACM Workshop on Affective Interaction in Natural Environments (AFFINE) at the International ACM Conference on Multimodal Inter-Faces, pp. 1-5. , ICMI 2008), pp; Valdesolo, P., (2020) What Body Language Indicates ‘Trusthworthy, , https://www.scientificamerican. com/article/psychologist-uncover-hidden-signals-of-trust-using-a-robot/. Accessed 04, May; Merritt, S.M., Ilgen, D.R., Not all trust is created equal: Dispositional and history-based trust in human-automation interactions (2008) Hum. Factors, 50 (2), pp. 194-210; Lewis, M., Sycara, K., Walker, P., The role of trust in human-robot interaction (2018) Stud. Syst. Decis. Control, 117, pp. 135-159; Wakeham, J., Uncertainty: History of the Concept. 2nd edn, no. November (2015) Elsevier; Robinette, P., Li, W., Allen, R., Howard, A.M., Wagner, A.R., Overtrust of robots in emergency evacuation scenarios (2016) ACM/IEEE International Conference on Human-Robot Interaction, pp. 101-108. , , pp; Nooteboom, B., (2019) Uncertainty and the Economic Need for Trust, 42. , , vol. , Brill, Boston; Tannert, C., Elvers, H., Jandrig, B., The ethics of uncertainty (2007) EMBO Rep, 8 (10), pp. 892-896; Glaser, J.E., (2014) Conversational Intelligence: How Great Leaders Build Trust & Get Extraordinary Results. 1St Edn. Bibliomotion, , New York; Frederiksen, M., Trust in the face of uncertainty: A qualitative study of intersubjective trust and risk (2014) Int. Rev. Sociol., 24 (1), pp. 130-144; Sanders, T.L., Trust, and prior experience in human-robot interaction (2017) Proceedings of the Human Factors and Ergonomics Society, pp. 1809-1813. , , pp","Pinney, J.; Cardiff Metropolitan UniversityUnited Kingdom; email: st20102131@outlook.cardiffmet.ac.uk","Chew E.P. P. Abdul Majeed A.Liu P.Platts J.Myung H.Kim J.Kim J.",,"Springer Science and Business Media Deutschland GmbH","8th International Conference on Robot Intelligence Technology and Applications, RiTA 2020","11 December 2020 through 13 December 2020",,263529,21954356,9789811648021,,,"English","Lect. Notes Mech. Eng.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85113767920
"de Jong D., Hortensius R., Hsieh T.-Y., Cross E.S.","57230884900;35519731900;57230093800;56497637800;","Empathy and schadenfreude in human–robot teams",2021,"Journal of Cognition","4","1",,"1","19",,,"10.5334/JOC.177","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113379789&doi=10.5334%2fJOC.177&partnerID=40&md5=57b313803eb633b23d86ab4af5610ff2","Institute of Neuroscience and Psychology, School of Psychology, University of Glasgow, Scotland, United Kingdom; Istituto Italiano di Tecnologia, Center for Translational Neurophysiology of Speech and Communication, (CTNSC), Ferrara, Italy; Università di Ferrara, Dipartimento di Scienze Biomediche e Chirurgico Specialistiche, Ferrara, Italy; Department of Cognitive Science, Macquarie University, 16 University Ave, Sydney, NSW  2109, Australia; Department of Psychology, Utrecht University, Heidelberglaan 1, Utrecht, 3584 CS, Netherlands","de Jong, D., Institute of Neuroscience and Psychology, School of Psychology, University of Glasgow, Scotland, United Kingdom, Istituto Italiano di Tecnologia, Center for Translational Neurophysiology of Speech and Communication, (CTNSC), Ferrara, Italy, Università di Ferrara, Dipartimento di Scienze Biomediche e Chirurgico Specialistiche, Ferrara, Italy; Hortensius, R., Institute of Neuroscience and Psychology, School of Psychology, University of Glasgow, Scotland, United Kingdom, Department of Psychology, Utrecht University, Heidelberglaan 1, Utrecht, 3584 CS, Netherlands; Hsieh, T.-Y., Institute of Neuroscience and Psychology, School of Psychology, University of Glasgow, Scotland, United Kingdom; Cross, E.S., Institute of Neuroscience and Psychology, School of Psychology, University of Glasgow, Scotland, United Kingdom, Department of Cognitive Science, Macquarie University, 16 University Ave, Sydney, NSW  2109, Australia","Intergroup dynamics shape the ways in which we interact with other people. We feel more empathy towards ingroup members compared to outgroup members, and can even feel pleasure when an outgroup member experiences misfortune, known as schadenfreude. Here, we test the extent to which these intergroup biases emerge during interactions with robots. We measured trial-by-trial fluctuations in emotional reactivity to the outcome of a competitive reaction time game to assess both empathy and schadenfreude in arbitrary human-human and human-robot teams. Across four experiments (total n = 361), we observed a consistent empathy and schadenfreude bias driven by team membership. People felt more empathy towards ingroup members than outgroup members and more schadenfreude towards outgroup members. The existence of an intergroup bias did not depend on the nature of the agent: the same effects were observed for human-human and human–robot teams. People reported similar levels of empathy and schadenfreude towards a human and robot player. The human likeness of the robot did not consistently influence this intergroup bias. In other words, similar empathy and schadenfreude biases were observed for both humanoid and mechanoid robots. For all teams, this bias was influenced by the level of team identification; individuals who identified more with their team showed stronger intergroup empathy and schadenfreude bias. Together, we show that similar intergroup dynamics that shape our interactions with people can also shape interactions with robots. Our results highlight the importance of taking intergroup biases into account when examining social dynamics of human-robot interactions. Copyright © 2021 Korean Society for Sexual Medicine and Andrology","Collaboration; Empathy; Human-robot interactions; Intergroup bias; Schadenfreude; Social cognition",,,,,,"Horizon 2020 Framework Programme, H2020: 677270; Leverhulme Trust: PLP-2018-152; European Research Council, ERC; Fundação Bial","This study was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement number 677270 to E.S.C.), the Leverhulme Trust (PLP-2018-152 to E.S.C.), and the BIAL Foundation (to R.H.).",,,,,,,,,,"Abubshait, A., Beatty, P., McDonald, C., Hassall, C. D., Krigolson, O., Wiese, E., A win-win situation: Does familiarity with a social robot modulate feedback monitoring and learning? 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Cross, E.S.; Institute of Neuroscience and Psychology, United Kingdom; email: Emily.Cross@mq.edu.au",,,"Ubiquity Press",,,,,25144820,,,,"English","J. Cogn.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85113379789
[No author name available],[No author id available],"8th International Conference on HCI in Business, Government and Organizations, HCIBGO 2021, Held as Part of the 23rd HCI International Conference, HCII 2021",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12783 LNCS",,,"","",656,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112233382&partnerID=40&md5=e0849d82d1f88c4b8dce4e2c44318c14",,"","The proceedings contain 43 papers. The special focus in this conference is on HCI in Business, Government and Organizations. The topics include: How Blockchain Innovations Emerge: From the Perspective of Knowledge Search; the New Normal? Motivators for and Hindrances to Telework; mobile Wallet Adoption: Does Ubiquity Make a Difference?; social Commerce: The Mediating Effects of Trust and Value Co-creation on Social Sharing and Shopping Intentions; augmented Reality: Does It Encourage Customer Loyalty?; dimensions of Retail Customer Experience and Its Outcomes: A Literature Review and Directions for Future Research; human-Robot Interactions Design for Interview Process: Needs-Affordances-Features Perspective; Designing the Empathetic Research IoT Network (ERIN) Chatbot for Mental Health Resources; empirical Research as a Challenge in Day-to-Day Teaching During the Pandemic of 2020/21 - Practical Solutions; enhanced Product Presentation with Augmented Reality: The Role of Affective Reactions and Authenticity; an Experiment on the Impact of Information on the Trust in Artificial Intelligence; Privacy, Ethics, Trust, and UX Challenges as Reflected in Google’s People and AI Guidebook; leveraging Artificial Intelligence in Medicine Compliance Check; Haptic Interaction for VR: Use-Cases for Learning and UX, Using the Example of the BMBF Project SmartHands; enterprise Social Media Use in Classroom Team Project: A Mixed-Methods Exploration of the Effects of Affordances on Team Productivity and Use; The Need for New Education Platforms that Support Inclusive Social Learning: A Case Study of Online and Hybrid K-12 Learning Experience During the COVID-19 Pandemic; inside-Out: How Internal Social Media Platforms Can Accelerate Innovation and Push External Crowdsourcing Towards New Frontiers.",,,,,,,,,,,,,,,,,,,,"Nah F.F.Siau K.",,"Springer Science and Business Media Deutschland GmbH","8th International Conference on HCI in Business, Government and Organizations, HCIBGO 2021, Held as Part of the 23rd HCI International Conference, HCII 2021","24 July 2021 through 29 July 2021",,262219,03029743,9783030777494,,,"English","Lect. Notes Comput. Sci.",Conference Review,"Final","",Scopus,2-s2.0-85112233382
[No author name available],[No author id available],"AHFE Conferences on Neuroergonomics and Cognitive Engineering, Industrial Cognitive Ergonomics and Engineering Psychology, and Cognitive Computing and Internet of Things, 2021",2021,"Lecture Notes in Networks and Systems","259",,,"","",532,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112051496&partnerID=40&md5=f88b6b6174f07a581ecfd99af1e53b1b",,"","The proceedings contain 60 papers. The special focus in this conference is on Neuroergonomics and Cognitive Engineering, Industrial Cognitive Ergonomics and Engineering Psychology, and Cognitive Computing and Internet of Things. The topics include: Human-Computer Interaction (HCI) Approach for the Optimal Generation and Selection of Batches Destination Options in Steel Making Factories; advantage Design of Small Commodities Under Cultural Transfer; cognitive Living Spaces by Using IoT Devices and Ambient Biosensor Technologies; human-Centric Emergent Configurations: Supporting the User Through Self-configuring IoT Systems; playful Screening of Executive Functions Using Augmented Reality and Gaze Based Assessment; towards a Measure of Situation Awareness for Space Mission Schedulers; advanced Cyber and Physical Situation Awareness in Urban Smart Spaces; design of an IoT Architecture in Livestock Environments for the Treatment of Information for the Benefit of Cattle; requirements Analysis on Emotional Preferences for Leisure Activities in Virtual Reality for Female Nursing Home Residents – A Mixed Method Approach; virtual Reality-Based Sensory Triggers and Gaze-Based Estimation for Mental Health Care; towards Decision Support with Assessment of Neuropsychological Profiles in Alzheimer's Dementia Using Playful Tablet-Based Multimodal Activation; THERADIA: Digital Therapies Augmented by Artificial Intelligence; electrotactile Stimulation, A New Feedback Channel for First Responders; multisensory Wearable Vital Monitoring System for Military Training, Exercise and Deployment; a Database for Cognitive Workload Classification Using Electrocardiogram and Respiration Signal; Using BERT Model for Intent Classification in Human-Computer Dialogue Systems to Reduce Data Volume Requirement; monitoring Human Performance on Future Deep Space Missions; human-Machine Learning with Mental Map; preface; expectations in Human-Robot Interaction.",,,,,,,,,,,,,,,,,,,,"Ayaz H.Asgher U.Paletta L.",,"Springer Science and Business Media Deutschland GmbH","AHFE Conferences on Neuroergonomics and Cognitive Engineering, Industrial Cognitive Ergonomics and Engineering Psychology, and Cognitive Computing and Internet of Things, 2021","25 July 2021 through 29 July 2021",,262279,23673370,9783030802844,,,"English","Lect. Notes Networks Syst.",Conference Review,"Final","",Scopus,2-s2.0-85112051496
"Balle S.N.","57221329987;","Empathic responses and moral status for social robots: an argument in favor of robot patienthood based on K. E. Løgstrup",2021,"AI and Society",,,,"","",,1,"10.1007/s00146-021-01211-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104950822&doi=10.1007%2fs00146-021-01211-2&partnerID=40&md5=3787db2c2f40a90ea3212ba2a25225a5","Department of Theology, Aarhus University, Aarhus, Denmark","Balle, S.N., Department of Theology, Aarhus University, Aarhus, Denmark","Empirical research on human–robot interaction (HRI) has demonstrated how humans tend to react to social robots with empathic responses and moral behavior. How should we ethically evaluate such responses to robots? Are people wrong to treat non-sentient artefacts as moral patients since this rests on anthropomorphism and ‘over-identification’ (Bryson and Kime, Proc Twenty-Second Int Jt Conf Artif Intell Barc Catalonia Spain 16–22:1641–1646, 2011)—or correct since spontaneous moral intuition and behavior toward nonhumans is indicative for moral patienthood, such that social robots become our ‘Others’ (Gunkel, Robot rights, MIT Press, London, 2018; Coeckelbergh, Kairos J Philos Sci 20:141–158, 2018)?. In this research paper, I weave extant HRI studies that demonstrate empathic responses toward robots with the recent debate on moral status for robots, on which the ethical evaluation of moral behavior toward them is dependent. Patienthood for robots has standardly been thought to obtain on some intrinsic ground, such as being sentient, conscious, or having interest. But since these attempts neglect moral experience and are curbed by epistemic difficulties, I take inspiration from Coeckelbergh and Gunkel’s ‘relational approach’ to explore an alternative way of accounting for robot patienthood based on extrinsic premises. Based on the ethics of Danish theologian K. E. Løgstrup (1905–1981) I argue that empathic responses can be interpreted as sovereign expressions of life and that these expressions benefit human subjects—even if they emerge from social interaction afforded by robots we have anthropomorphized. I ultimately develop an argument in defense of treating robots as moral patients. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.","Empathy; Ethics; K. E. 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Emotional and neural processing differences when observing human–human vs human–robot interactions (2015) SocCogn Affect Neurosci, 10 (11), pp. 1515-1524; Wolf, J., Phenomenology in Løgstrup’s Creation Theology (2017) Reformation theology for a post-secular age løgstrup, prenter, wingren, and the future of scandinavian creation theology, , Gregersen NH, Uggla BK, Wyller T, (eds), Vandenhoeck & Ruprecht, Göttingen; Young, J.E., Sung, J., Voida, A., Sharlin, E., Igarashi, T., Christensen, H.I., Grinter, R.E., Evaluating human-robot interaction (2011) Int J Soc Robot, 3 (1), pp. 53-67","Balle, S.N.; Department of Theology, Denmark; email: snb@cas.au.dk",,,"Springer Science and Business Media Deutschland GmbH",,,,,09515666,,,,"English","AI Soc.",Article,"Article in Press","",Scopus,2-s2.0-85104950822
"Beniiche A., Rostami S., Maier M.","57219472051;57219471855;35485001300;","Robonomics in the 6G Era: Playing the Trust Game with On-Chaining Oracles and Persuasive Robots",2021,"IEEE Access","9",,"9383225","46949","46959",,1,"10.1109/ACCESS.2021.3068239","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103237706&doi=10.1109%2fACCESS.2021.3068239&partnerID=40&md5=8009d17f5d69742baffd6b95584abc0f","Optical Zeitgeist Laboratory, Institut National de la Recherche Scientifique (INRS), Montreal, QC, Canada","Beniiche, A., Optical Zeitgeist Laboratory, Institut National de la Recherche Scientifique (INRS), Montreal, QC, Canada; Rostami, S., Optical Zeitgeist Laboratory, Institut National de la Recherche Scientifique (INRS), Montreal, QC, Canada; Maier, M., Optical Zeitgeist Laboratory, Institut National de la Recherche Scientifique (INRS), Montreal, QC, Canada","6G is anticipated to become more human-centered than 5G and should not only explore more spectrum at high-frequency bands but, more importantly, converge driving technological trends such as blockchain technologies and connected robotics. This paper focuses on the emerging field of robonomics, which studies the sociotechnical impact of blockchain technologies on social human-robot interaction and behavioral economics for the social integration of robots into human society. Advanced blockchain technologies such as oracles enable the on-chaining of blockchain-external off-chain information stemming from human users. In doing so, they leverage on human intelligence rather than machine learning only. In this paper, we investigate the widely studied trust game of behavioral economics in a blockchain context, paying close attention to the importance of developing efficient cooperation and coordination technologies. After identifying open research challenges of blockchain-enabled implementations of the trust game, we first develop a smart contract that replaces the experimenter in the middle between trustor and trustee and demonstrate experimentally that a social efficiency of up to 100% can be achieved by using deposits to enhance both trust and trustworthiness. We then present an on-chaining oracle architecture for a networked $N$ -player trust game that involves a third type of human agents called observers, who track the players' investment and reciprocity. The presence of third-party reward and penalty decisions helps raise the average normalized reciprocity above 80%, even without requiring any deposit. Finally, we experimentally demonstrate that mixed logical-affective persuasive strategies for social robots improve the trustees' trustworthiness and reciprocity significantly. © 2013 IEEE.","6G; behavioral economics; blockchain on-chaining; networked N-player trust game; oracles; smart contracts; social robots","5G mobile communication systems; Artificial intelligence; Blockchain; Deposits; Economics; Behavioral economics; Cooperation and coordination; High frequency bands; Research challenges; Social human-robot interactions; Social integrations; Technological trends; Trust and trustworthiness; Social robots",,,,,"Natural Sciences and Engineering Research Council of Canada, NSERC: 2016-04521","This work was supported by NSERC Discovery under Grant 2016-04521.",,,,,,,,,,"Novo, O., Blockchain meets IoT: An architecture for scalable access management in IoT (2018) IEEE Internet Things J., 5 (2), pp. 1184-1195. , Apr; Adler, J., Berryhill, R., Veneris, A., Poulos, Z., Veira, N., Kastania, A., ASTRAEA: A decentralized blockchain oracle (2018) Proc. 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(RO-MAN), New York City, NY, USA, pp. 519-524. , Aug; Cardenas, I.S., Kim, J.-H., Robonomics: The study of robot-human peer-to-peer financial transactions and agreements (2020) Proc. Companion ACM/IEEE Int. Conf. Hum.-Robot Interact., Cambridge, U.K., pp. 8-15. , Mar; Saunderson, S., Nejat, G., It would make me happy if you used my guess: Comparing robot persuasive strategies in social human_robot interaction (2019) IEEE Robot. Autom. Lett., 4 (2), pp. 1707-1714. , Apr; Abbas, T., Khan, V.-J., Markopoulos, P., CoZ: A crowd-powered system for social robotics (2020) Elsevier SoftwareX, 11, pp. 1-7. , Jan./Jun","Maier, M.; Optical Zeitgeist Laboratory, Canada; email: maier@ieee.org",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85103237706
"Zhong H., Li X., Gao L., Li C.","57208718405;56021323400;56406738100;8583249500;","Toward Safe Human-Robot Interaction: A Fast-Response Admittance Control Method for Series Elastic Actuator",2021,"IEEE Transactions on Automation Science and Engineering",,,,"","",,1,"10.1109/TASE.2021.3057883","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101772343&doi=10.1109%2fTASE.2021.3057883&partnerID=40&md5=d2196e8af820c8c7daa61ee8788c5493","State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.; State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China (e-mail: gaoliang@mail.hust.edu.cn); State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China.","Zhong, H., State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.; Li, X., State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.; Gao, L., State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China (e-mail: gaoliang@mail.hust.edu.cn); Li, C., State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China.","Series elastic actuator (SEA) is a promising compliance device due to its lower output mechanical impedance, and it is widely applied to ensure safe human-robot interaction. Although some efforts have been made to achieve accurate stiffness tracking, the time-delay issue in SEA control has still not been well investigated. However, the time delay can cause an inaccurate response and increase the risk of injury. To overcome this problem, this article proposes a fast-response admittance control method for SEAs. First, an admittance control scheme considering the external force estimation is developed for a hydraulic SEA. Then, a parallel adaptive time-series (ATS) (P-ATS) compensator is proposed and further adopted in the admittance control scheme to compensate for the time delay and tracking error. The P-ATS compensator is a modification of the ATS compensator, which is enhanced with a unique parallel mechanism. Such a mechanism can save more computational resources on locating better parameters the for P-ATS compensator, thus improving its performance. Moreover, the parameter setting is converted to an optimization task, which is solved by the whale swarm algorithm (WSA) to achieve higher accuracy. The newly located parameters are compared to the current parameters based on a proposed evaluation criterion, thus guaranteeing the quality of the updated parameters. All the above strategies are employed to improve the SEA admittance control performance. The results obtained from both simulation and real-world experiments validate that, compared to conventional methods, the proposed method achieves a better performance in SEA stiffness tracking with lower time delay and tracking error. IEEE","Admittance; Admittance control; delay compensation; Delay effects; Delays; Force; Hydraulic systems; Impedance; manufacturing automation; Robots; series elastic actuator (SEA).",,,,,,"U.S. Naval Research Laboratory, NRL","This research was supported by SDIO/IST and managed by the Naval Research Laboratory.",,,,,,,,,,,,,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15455955,,,,"English","IEEE Trans. Autom. Sci. Eng.",Article,"Article in Press","",Scopus,2-s2.0-85101772343
"Spatola N., Chevalère J., Lazarides R.","57193889944;56036381400;56429778700;","Human vs computer: What effect does the source of information have on cognitive performance and achievement goal orientation?",2021,"Paladyn","12","1",,"175","186",,,"10.1515/pjbr-2021-0012","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101333421&doi=10.1515%2fpjbr-2021-0012&partnerID=40&md5=47707854a81e238f40eff901662b005d","Department of Education, University of Potsdam, Potsdam, Germany","Spatola, N., Department of Education, University of Potsdam, Potsdam, Germany; Chevalère, J., Department of Education, University of Potsdam, Potsdam, Germany; Lazarides, R., Department of Education, University of Potsdam, Potsdam, Germany","The increasing presence of computers in society calls for the need to better understand how differently the sociocognitive mechanisms involved in natural human relationships operate in human-robot interactions. In the present study, we investigated one fundamental aspect often neglected in the literatures on psychology and educational sciences: how the source of information, either human or computer, influences its perceived reliability and modulates cognitive and motivational processes. In Experiment 1, participants performed a reasoning task that presented cues following participants' errors, helping them to succeed in the task. Using two levels of task difficulty, we manipulated the source of the cues as either a human or a computer. In addition to task accuracy, Experiment 2 assessed the impact of the information source on socially and nonsocially related dimensions of achievement goals. In Experiment 1, participants who believed that they received cues from a human teacher performed better on difficult trials compared to those who believed that they received cues from a computer. In Experiment 2, we replicated these findings by additionally showing that the nature of the source only had an impact on the socially related dimension of achievement goals, which in turn mediated the source's effect on reasoning performance. For the first time, the present study showed modulations of cognitive and motivational processes resulting from the manipulation of the type of information source aimed at providing assistance with a reasoning task. The findings highlight the importance of considering the social and motivational aspects involved in human-computer interactions. © 2021 Nicolas Spatola et al., published by De Gruyter 2021.","achievement goal orientations; cognitive performances; human-robot interaction; logical reasoning test",,,,,,"Deutsche Forschungsgemeinschaft, DFG: EXC 2002/1","Funding: This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2002/1 “Science of Intelligence” – project number 390523135.",,,,,,,,,,"Chassignol, M., Khoroshavin, A., Klimova, A., Bilyatdinova, A., ""artificial intelligence trends in education: A narrative overview,"" (2018) Procedia Computer Science, 136, pp. 16-24; Nwana, H.S., ""intelligent tutoring systems: An overview,"" (1990) Artif. Intell. Rev., 4 (4), pp. 251-277; Nass, C., Moon, Y., ""machines and mindlessness: Social responses to computers,"" (2000) J. Soc. 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Instr., 22 (1), pp. 27-36; Gómez-Veiga, I., Vila Chaves, J.O., Duque, G., García Madruga, J.A., ""a new look to a classic issue: Reasoning and academic achievement at secondary school,"" (2018) Front. Psychol., 9, p. 400. , art; Deary, I.J., Strand, S., Smith, P., Fernandes, C., ""intelligence and educational achievement,"" (2007) Intelligence, 35 (1), pp. 13-21; Fryer, J.W., Elliot, A.J., ""stability and change in achievement goals,"" (2007) J. Educ. Psychol., 99 (4), pp. 700-714; Muis, K.R., Edwards, O., ""examining the stability of achievement goal orientation,"" (2009) Contemp. Educ. Psychol., 34 (4), pp. 265-277; Festinger, L., ""a theory of social comparison processes,"" (1954) Hum. Relations, 7 (2), pp. 117-140; Garcia, S.M., Tor, A., Gonzalez, R., ""some affective consequences of social comparison and reflection processes: The pain and pleasure of being close,"" (2006) Personal. Soc. Psychol. Bull., 32 (7), pp. 970-982; Garcia, S.M., Tor, A., ""rankings, standards, and competition: Task vs scale comparisons,"" (2007) Organ. Behav. Hum. Decis. Process., 102 (1), pp. 95-108","Spatola, N.; Department of Education, Germany; email: nicolasspatola@hotmail.fr",,,"De Gruyter Open Ltd",,,,,20814836,,,,"English","Paladyn",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85101333421
"Paillacho Chiluiza D.F., Solorzano Alcivar N.I., Paillacho Corredores J.S.","56084736200;57192008773;57215835311;","LOLY 1.0: A Proposed Human-Robot-Game Platform Architecture for the Engagement of Children with Autism in the Learning Process",2021,"Advances in Intelligent Systems and Computing","1273 AISC",,,"225","238",,3,"10.1007/978-3-030-59194-6_19","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094170515&doi=10.1007%2f978-3-030-59194-6_19&partnerID=40&md5=9590701436a8cb6312e8b9fbcf78f81d","ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador; Facultad de Ingeniería en Electricidad y Computación – FIEC, CIDIS, Guayaquil, Ecuador; Facultad de Arte Diseño y Comunicación Audiovisual - FADCOM, Guayaquil, Ecuador","Paillacho Chiluiza, D.F., ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador, Facultad de Ingeniería en Electricidad y Computación – FIEC, CIDIS, Guayaquil, Ecuador; Solorzano Alcivar, N.I., ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador, Facultad de Arte Diseño y Comunicación Audiovisual - FADCOM, Guayaquil, Ecuador; Paillacho Corredores, J.S., ESPOL Polytechnic University, Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador, Facultad de Ingeniería en Electricidad y Computación – FIEC, CIDIS, Guayaquil, Ecuador","Existing studies indicate the use of the Socially Assistive Robotic (SAR) system has enormous potential to advance therapeutic interventions in the learning process of children with Autistic Spectrum Disorder (ASD). Interactive technology, including the use of educational video games, can motivate ASD children to engage by adopting the technology as part of their communications tools. The complexity in the integration of various components of SAR linked to educational/serious games hinder the modularity and scalability of these robotic systems. This work proposes the design of an architecture for a SAR platform through the integration and reproduction of robot prototype, “Loly 1.0”. Loly 1.0 has mechanical movements, audio, and gestures connected to educational games in real-time. This will support the learning process of ASD children. The robotic platform architecture design is comprised of three main parts: robot, educational game, and cloud services. A proof of concept of the SAR prototype was per-formed with non-ASD and ASD children. Evaluation and analysis of “synchronized operational precision between robot and video game” and “degrees of attention and usability” will be conducted. The lessons learned during the testing process will help to improve the architectural designs of hardware/software integration and connectivity. The results of reproducing a SAR prototype will be to attract the attention of an ASD child through the use a video game to engage the process of “learn by playing”. This will contribute to the design of engaging and personalized Human-Robot-Interaction (HRI) and Human-Computer-Interaction (HCI) technology beneficial for inclusive education. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.","ASD; Dashboard; Educational videogames; Human-robot interaction; Learning process; Robotic platform architecture; SAR architecture; Serious games; Socially Assistive Robot","Cell proliferation; Engineering education; Human computer interaction; Integration; Integration testing; Learning systems; Machine design; Robotics; Self reproducing automata; Social robots; Autistic spectrum disorders; Educational video games; Evaluation and analysis; Hardware/software integration; Human computer interaction (HCI); Human robot Interaction (HRI); Interactive technology; Therapeutic intervention; Educational robots",,,,,,"We extend a special thanks to the undergraduate students of the Faculty of Electrical and Computer Engineering-FIEC and the Faculty of Art, Design and Audiovisual Communication-FADCOM at ESPOL who participated in the Loly project: Kevin Alvarado, Joseph Cevallos, Keyla Toapanta, Sebasti?n Fuenzalida, Andrea Le?n, Denisse Guizado, Joel Rodriguez and Fabricia Hurtado.",,,,,,,,,,"Jain, S., Thiagarajan, B., Shi, Z., Clabaugh, C., Matarić, M.J., Modeling engagement in long-term, in-home socially assistive robot interventions for children with autism spectrum disorders (2020) Sci. Robot., 5 (39). , https://robotics.sciencemag.org/content/5/39/eaaz3791; Dugger, C.E., (2012) The Effects of Early Intervention on Children with Autism Spectrum Disorders, , Research papers; Fridin, M., Yaakobi, Y., Educational Robot for Children with ADHD/ADD (2011) Architecture Design; Kozima, H., Nakagawa, C., Yasuda, Y., Interactive robots for communication-care: A case-study in autism therapy (2005) Paper Presented at the ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication; Contreras-Espinosa, R.S., Serra, A., Terrón, J.L., Games and ADHD-ADD: A systematic mapping study (2019) Acta Ludologica, 2 (2), pp. 4-26; Herrera, G., Pictogram Room: Aplicación de tecnologías de interacción natural para el desarrollo del niño con autismo (2012) Anuario De Psicología Clínica Y De La Salud, 8, pp. 41-46; Moore, D., Cheng, Y., McGrath, P., Powell, N.J., Collaborative virtual environment technology for people with Autism (2005) Focus Autism Other Dev. Disabil., 20 (4), pp. 231-243; López Raventós, C., El videojuego como herramienta educativa. Posibilidades y problemáti-cas acerca de los serious games (2016) Apertura (Guadalajara, Jal.), 8 (1), pp. 136-151; Solorzano, N.I., Sornoza, L.I., Carrera, D.A., Adoption of children’s educational video games monitored with dashboards in the cloud (2019) Risti-Revista Iberica De Sistemas E Tecnologias De Informacao, 2019 (19), pp. 146-160; Frauenberger, C., (2015) Rethinking Autism and Technology Interactions, 22 (2), pp. 57-59; González, J.L., Cabrera, M., Gutiérrez, F.L., (2007) Diseño De Videojuegos Aplicados a La Educación Especial, , http://aipoes/articulos/1/12410.pdf; González, J.L., Gutiérrez, F.L., (2014) Jugabilidad Como Medida De Calidad En El Desarrollo De Videojuegos, , Paper presented at the CoSECivi; Warren, Z., McPheeters, M.L., Sathe, N., Foss-Feig, J.H., Glasser, A., Veenstra-Vanderweele, J., A systematic review of early intensive intervention for Autism spectrum disorders (2011) Pediatrics, 127 (5), pp. e1303-e1311; Knop, F., Domínguez, A.K., (2017) El diseño Y Las características Del Videojuego En Los Juguetes lúdicos; Solorzano, N.I., Carrera, D.A., Sornoza, L.I., Mendoza, M., Developing a dashboard for monitoring usability of educational games apps for children (2019) Proceedings of the 2019 2Nd International Conference on Computers in Management and Business, , Paper presented at the, Cambridge, United Kingdom; Danev, L., Hamann, M., Fricke, N., Hollarek, T., Paillacho, D., Development of animated facial expressions to express emotions in a robot: RobotIcon (2017) Paper Presented at the 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM); Tello, J.A., Zapata, M., Paillacho, D., Kinematic optimization of the robot head movements for the evaluation of human-robot interaction in social robotics (2020) AHFE 2019. AISC, 975, pp. 108-118. , https://doi.org/10.1007/978-3-030-20216-3_11, Di Nicolantonio, M., Rossi, E., Alexander, T. (eds.) , Springer, Cham; Creswell, J.W., (2013) Qualitative Inquiry & Research Design: Choosing among Five Approaches, , Sage, Los Angeles","Paillacho Chiluiza, D.F.; ESPOL Polytechnic University, Ecuador; email: dpaillac@espol.edu.ec","Botto-Tobar M.Zamora W.Larrea Plua J.Bazurto Roldan J.Santamaria Philco A.",,"Springer Science and Business Media Deutschland GmbH","1st International Conference on Systems and Information Sciences, ICCIS 2020","27 July 2020 through 29 July 2020",,250079,21945357,9783030591939,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85094170515
"Nascimento H., Mujica M., Benoussaad M.","57219534655;57208247021;34167551000;","Collision Avoidance Interaction between Human and a Hidden Robot Based on Kinect and Robot Data Fusion",2021,"IEEE Robotics and Automation Letters","6","1","9234671","88","94",,6,"10.1109/LRA.2020.3032104","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093670662&doi=10.1109%2fLRA.2020.3032104&partnerID=40&md5=477ac5c1bfe3be82b6181bacdf85fe4a","Polytechnic School, Automation and Control Department, University of Pernambuco, Recife - PE, Brazil; LGP-ENIT, University of Toulouse, Tarbes, France","Nascimento, H., Polytechnic School, Automation and Control Department, University of Pernambuco, Recife - PE, Brazil; Mujica, M., LGP-ENIT, University of Toulouse, Tarbes, France; Benoussaad, M., LGP-ENIT, University of Toulouse, Tarbes, France","Human-Robot Interaction (HRI) is a largely addressed subject today. In order to ensure co-existence and space sharing between human and robot, collision avoidance is one of the main strategies for interaction between them without contact. It is thus usual to use a 3D depth camera sensor (Microsoft Kinect V2) which may involve issues related to occluded robot in the camera view. While several works overcame this issue by applying infinite depth principle or increasing the number of cameras, in the current work we developed and applied an original new approach that combines data of one 3D depth sensor (Kinect) and proprioceptive robot sensors. This method uses the principle of limited safety contour around the obstacle to dynamically estimate the robot-obstacle distance, and then generate the repulsive force that controls the robot. For validation, our approach is applied in real time to avoid collisions between dynamical obstacles (humans or objects) and the end-effector of a real 7-dof Kuka LBR iiwa collaborative robot. Our method is experimentally compared with existing methods based on infinite depth principle when the robot is hidden by the obstacle with respect to the camera view. Results showed smoother behavior and more stability of the robot using our method. Extensive experiments of our method, using several strategies based on distancing and its combination with dodging were done. Results have shown a reactive and efficient collision avoidance, by ensuring a minimum obstacle-robot distance (of approx text{240 mm}), even when the robot is in an occluded zone in the Kinect camera view. © 2016 IEEE.","Collision avoidance; peception-action coupling; sensor-based control","Agricultural robots; Cameras; Collision avoidance; Data fusion; End effectors; Collaborative robots; Human robot Interaction (HRI); Kinect cameras; Microsoft kinect; New approaches; Obstacle distance; Repulsive forces; Robot sensors; Human robot interaction",,,,,,"Manuscript received July 22, 2020; accepted September 20, 2020. Date of publication October 21, 2020; date of current version October 28, 2020. This letter was recommended for publication by Associate Editor A. Faust and Editor N. Amato upon evaluation of the Reviewers’ comments. This work was supported by the BRAFITEC (Brazil-France Technology) program. (Corresponding author: Mourad Benoussaad.) Hugo Nascimento is with the Polytechnic School, Automation and Control department, University of Pernambuco, Recife - PE 50720-001, Brazil (e-mail: hugonascimentoal@gmail.com).",,,,,,,,,,"Tan, J.T.C., Duan, F., Zhang, Y., Watanabe, K., Kato, R., Arai, T., Human-Robot collaboration in cellular manufacturing: Design and development Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst., IROS 2009, pp. 29-34; Krüger, J., Lien, T.K., Verl, A., Cooperation of human andmachines in assembly lines (2009) CIRP Ann.-Manuf. Technol., 58 (2), pp. 628-646; Santis, A.D., Siciliano, B., Luca, A.D., Bicchi, A., An atlas of physical human-robot interaction (2008) Mechanism Mach. Theory, 43, pp. 253-270; Alami, R., Safe and dependable physical human-robot interaction in anthropic domains: State of the art and challenges (2006) Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst., pp. 1-16. , Oct; Popov, D., Klimchik, A., Mavridis, N., Collision detection, localization & classification for industrial robots with joint torque sensors (2017) Proc. 26th IEEE Int. Symp. Robot Hum. Interactive Commun., 2017, pp. 838-843. , Janua, May 2018; De Luca, A., Flacco, F., Integrated control for pHRI: Collision avoidance, detection, reaction and collaboration (2012) Proc. IEEE RAS EMBS Int. Conf. Biomed. Robot. Biomechatronics, pp. 288-295; Flacco, F., Krö Ger, T., De Luca, A., Khatib, O., A depth space approach to human-robot collision avoidance (2012) Proc. IEEE Int. Conf. Robot. Automat., pp. 338-345; Schmidt, B., Wang, L., Depth camera based collision avoidance via active robot control (2014) J. Manuf. Syst., 33 (4), pp. 711-718; Chen, J.-H., Song, K.-T., Collision-Free Motion Planning for Human-Robot Collaborative Safety under Cartesian Constraint (2018) IEEE Int. Conf. Robot. Automat., pp. 1-7; Safeea, M., Neto, P., KUKASunriseToolbox: InterfacingCollaborative Robots with MATLAB (2019) IEEE Robot. Automat. Mag., 26 (1), pp. 91-96. , Mar; Flacco, F., Kroeger, T., De Luca, A., Khatib, O., A Depth Space Approach for EvaluatingDistance to Objects:With Application toHuman-Robot Collision Avoidance (2015) J. Intell. Robot. Syst.: Theory Appl., 80, pp. 7-22; Wasenmüller, O., Stricker, D., Comparison of kinect v1 and v2 depth images in terms of accuracy and precision (2017) Lecture Notes Comput. Sci. (Including Subseries Lecture Notes Artif. Intell. Lecture Notes Bioinf.), pp. 34-45. , 10117 LNCS; KUKA Sunrise.Connectivity FRI 1.7. Minneapolis, MN, USA: Augsburg, Germany, 1st edition (2015), pp. 1-59. , K. R. Group; Gaz, C., De Luca, A., Payload estimation based on identified coefficients of robot dynamics-With an application to collision detection (2017) Proc. IEEE Int. Conf. Intell. Robot. Syst., pp. 3033-3040; Zhang, W., Ma, X., Cui, L., Chen, Q., 3 points calibration method of part coordinates for arc welding robot (2008) Intelligent Robotics and Applications, C. Xiong, Y. Huang, Y. Xiong, and H. Liu, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 216-224; Chen, S.Y., Kalman filter for robot vision: A survey (2012) IEEE Trans. Ind. Electron., 59 (11), pp. 4409-4420. , Nov; Khatib, O., Real-time obstacle avoidance for manipulators and mobile robots (1985) Proc. IEEE Int. Conf. Robot. Automat., 2, pp. 500-505. , Mar; Albu-Schäffer, A., Haddadin, S., Ott, C., Stemmer, A., Wimböck, T., Hirzinger, G., The DLR lightweight robot: Design and control concepts for robots in human environments (2007) Ind. Robot, 34 (5), pp. 376-385","Benoussaad, M.; LGP-ENIT, France; email: mourad.benoussaad@enit.fr",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85093670662
[No author name available],[No author id available],"3rd International Conference on Innovative Computing and Communication, ICICC 2020",2021,"Advances in Intelligent Systems and Computing","1166",,,"","",2354,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089537190&partnerID=40&md5=5be34987de41d826d91683a1135bf83d",,"","The proceedings contain 196 papers. The special focus in this conference is on Innovative Computing and Communication. The topics include: Tracking of soccer players using optical flow; selection of probabilistic data structures for spv wallet filtering; hybrid bf-pso algorithm for automatic voltage regulator system; malware classification using multi-layer perceptron model; protocol random forest model to enhance the effectiveness of intrusion detection identification; detecting user’s spreading influence using community structure and label propagation; bagging- and boosting-based latent fingerprint image classification and segmentation; selecting social robot by understanding human–robot interaction; flooding and forwarding based on efficient routing protocol; evaluating user influence in social networks using k-core; hardware-based parallelism scheme for image steganography speed up; predicting group size for software issues in an open-source software development environment; wireless cnc plotter for pcb using android application; epileptic seizure detection using machine learning techniques; analysis of minimum support price prediction for indian crops using machine learning and numerical methods; a robust methodology for creating large image datasets using a universal format; a comparative empirical evaluation of topic modeling techniques; an analysis of machine learning techniques for flood mitigation; link prediction in complex network: Nature inspired gravitation force approach; hridaya kalp: A prototype for second generation chronic heart disease detection and classification; depression anatomy using combinational deep neural network; two-stream mid-level fusion network for human activity detection; content classification using active learning approach; analysis of navic multipath signal sensitivity for soil moisture in presence of vegetation; transaction privacy preservations for blockchain technology; deeper into image classification.",,,,,,,,,,,,,,,,,,,,"Gupta D.Khanna A.Bhattacharyya S.Hassanien A.E.Anand S.Jaiswal A.",,"Springer","3rd International Conference on Innovative Computing and Communication, ICICC 2020","21 February 2020 through 23 February 2020",,243279,21945357,9789811551475,,,"English","Adv. Intell. Sys. Comput.",Conference Review,"Final","",Scopus,2-s2.0-85089537190
"Díaz A., Mahu R., Novoa J., Wuth J., Datta J., Yoma N.B.","57189037645;56225650900;57140069500;26039642700;57220466147;6603542511;","Assessing the effect of visual servoing on the performance of linear microphone arrays in moving human-robot interaction scenarios",2021,"Computer Speech and Language","65",,"101136","","",,1,"10.1016/j.csl.2020.101136","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089286171&doi=10.1016%2fj.csl.2020.101136&partnerID=40&md5=21d77753322d4ca70ee66ec998de6ffe","Speech Process. and Transm. Lab., Elec. Eng. Dept., U. de Chile, Santiago, Chile","Díaz, A., Speech Process. and Transm. Lab., Elec. Eng. Dept., U. de Chile, Santiago, Chile; Mahu, R., Speech Process. and Transm. Lab., Elec. Eng. Dept., U. de Chile, Santiago, Chile; Novoa, J., Speech Process. and Transm. Lab., Elec. Eng. Dept., U. de Chile, Santiago, Chile; Wuth, J., Speech Process. and Transm. Lab., Elec. Eng. Dept., U. de Chile, Santiago, Chile; Datta, J., Speech Process. and Transm. Lab., Elec. Eng. Dept., U. de Chile, Santiago, Chile; Yoma, N.B., Speech Process. and Transm. Lab., Elec. Eng. Dept., U. de Chile, Santiago, Chile","Social robotics is becoming a reality and voice-based human-robot interaction is essential for a successful human-robot collaborative symbiosis. The main objective of this paper is to assess the effect of visual servoing in the performance of a linear microphone array regarding distant ASR in a mobile, dynamic and non-stationary robotic testbed that can be representative of real HRI scenarios. Visual servoing and image target tracking are different tasks, and this paper focuses on an effect that is rarely addressed in the literature: the dependence of the beamforming directivity on look direction. The datasets required to carry out the study reported here do not exist and had to be generated. A state-of-the-art mobile robotic testbed had to be set up with target speech and noise sources. A linear microphone array was chosen as a case of study and its response was measured. Standard beamforming methods were evaluated with respect to visual servoing: delay-and-sum combined with image tracking; weighted delay-and-sum; and, MVDR also combined with image tracking. The results presented here show that the performance of beamforming methods is dramatically degraded in moving and non-stationary conditions. In this context, visual servoing in HRI can significantly improve the performance of a linear microphone array regarding ASR accuracy. The average reduction in WER achieved when the robot head was steered toward the target speech source was as high as 28.2%. Finally, it is worth highlighting that the methodology adopted here is applicable to any microphone array, linear or not. © 2020 Elsevier Ltd","Automatic speech recognition; Beamforming; Human-robot interaction; Source tracking; Visual servoing","Beamforming; Man machine systems; Microphones; Robotics; Speech communication; Target tracking; Testbeds; Visual servoing; Beamforming methods; Linear microphonearray; Microphone arrays; Non-stationary condition; Robotic testbed; Social robotics; Standard beamforming; State of the art; Social robots",,,,,,,,,,,,,,,,"Kumatani, K., Arakawa, T., Yamamoto, K., McDonough, J., Raj, B., Singh, R., Tashev, I., Microphone array processing for distant speech recognition: towards real-world deployment (2012) Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, Hollywood, California; Araki, S., Ito, N., Delcroix, M., Ogawa, A., Kinoshita, K., Higuchi, T., Yoshioka, T., Nakatani, T., Online meeting recognition in noisy environments with time-frequency mask based MVDR beamforming (2017) 2017 Hands-Free Speech Communications and Microphone Arrays (HSCMA), , IEEE; Araki, S., Ono, N., Kinoshita, K., Delcroix, M., Meeting recognition with asynchronous distributed microphone array using block-wise refinement of mask-based MVDR beamformer (2018) 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); Araki, S., Okada, M., Higuchi, T., Ogawa, A., Nakatani, T., Spatial correlation model based observation vector clustering and MVDR beamforming for meeting recognition (2016) 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); Higuchi, T., Ito, N., Araki, S., Yoshioka, T., Delcroix, M., Nakatani, T., Online MVDR beamformer based on complex Gaussian mixture model with spatial prior for noise robust ASR (2017) IEEE/ACM Trans. Audio Speech Lang. Process., 25 (4), pp. 780-793; Tashev, I.J., Sound Capture and Processing: Practical Approaches (2009), John Wiley & Sons Chichester; Crocco, M., Trucco, A., Stochastic and analytic optimization of sparse aperiodic arrays and broadband beamformers with robust superdirective patterns (2012) IEEE Transactions on Audio, Speech, and Language Processing, , IEEE; a. H. S. Chaumette, F., Visual servoing and visual tracking (2008) Handbook of Robotics, , Springer; Goodrich, M.A., Schultz, A.C., Human-robot interaction: a survey (2008) Found. Trends Hum.-Comput. Interact., 1 (3), pp. 203-275; Lopes, L.S., Teixeira, A., Human-robot interaction through spoken language dialogue (2000) Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000), 1, pp. 528-534. , (Cat. 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Am., 109 (1), pp. 185-193; Król, D., Lorenc, A., Świcęiński, R., Detecting laterality and nasality in speech with the use of a multi-channel recorder (2015) IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); Lorenc, A., Król, D., Klessa, K., An acoustic camera approach to studying nasality in speech: the case of Polish nasalized vowels (2018) J. Acoust. Soc. Am., 144 (6), pp. 3603-3617; Haddad, K., Hald, J.R., 3D localization of acoustic sources with a spherical array (2008) J. Acoust. Soc. Am., 123 (5), p. 3311; Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y., ROS: an open-source Robot Operating System (2009) ICRA Workshop on Open Source Software","Yoma, N.B.; Speech Process. and Transm. Lab., Chile; email: nbecerra@ing.uchile.cl",,,"Academic Press",,,,,08852308,,CSPLE,,"English","Comput Speech Lang",Article,"Final","",Scopus,2-s2.0-85089286171
"Aydin Y., Tokatli O., Patoglu V., Basdogan C.","56084865200;36667722400;19640643500;6603706568;","A computational multicriteria optimization approach to controller design for physical human-robot interaction",2020,"IEEE Transactions on Robotics","36","6","9162045","1791","1804",,1,"10.1109/TRO.2020.2998606","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097751364&doi=10.1109%2fTRO.2020.2998606&partnerID=40&md5=c82c21b01c7853ee64de8da66e50de04","College of Engineering, Koc University, Istanbul, 34450, Turkey; Race Ukaea, Abingdon, OX14 3DB, United Kingdom; Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, 34956, Turkey","Aydin, Y., College of Engineering, Koc University, Istanbul, 34450, Turkey; Tokatli, O., Race Ukaea, Abingdon, OX14 3DB, United Kingdom; Patoglu, V., Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, 34956, Turkey; Basdogan, C., College of Engineering, Koc University, Istanbul, 34450, Turkey","Physical human-robot interaction (pHRI) integrates the benefits of human operator and a collaborative robot in tasks involving physical interaction, with the aim of increasing the task performance. However, the design of interaction controllers that achieve safe and transparent operations is challenging, mainly due to the contradicting nature of these objectives. Knowing that attaining perfect transparency is practically unachievable, controllers that allow better compromise between these objectives are desirable. In this article, we propose a multicriteria optimization framework, which jointly optimizes the stability robustness and transparency of a closed-loop pHRI system for a given interaction controller. In particular, we propose a Pareto optimization framework that allows the designer to make informed decisions by thoroughly studying the tradeoff between stability robustness and transparency. The proposed framework involves a search over the discretized controller parameter space to compute the Pareto front curve and a selection of controller parameters that yield maximum attainable transparency and stability robustness by studying this tradeoff curve. The proposed framework not only leads to the design of an optimal controller, but also enables a fair comparison among different interaction controllers. In order to demonstrate the practical use of the proposed approach, integer and fractional order admittance controllers are studied as a case study and compared both analytically and experimentally. The experimental results validate the proposed design framework and show that the achievable transparency under fractional order admittance controller is higher than that of integer order one, when both controllers are designed to ensure the same level of stability robustness. © 2004-2012 IEEE.","Fractional order control; interaction controllers; multicriteria optimization; physical human-robot interaction (pHRI); transparency-stability robustness tradeoff","Agricultural robots; Controllers; Machine design; Man machine systems; Multiobjective optimization; Pareto principle; System stability; Transparency; Collaborative robots; Controller parameter; Multicriteria optimization; Pareto optimization; Physical human-robot interactions; Physical humanrobot interaction (phri); Physical interactions; Stability robustness; Human robot interaction",,,,,"EEEAG-117E645; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK; EEEAG-117E645; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK","Manuscript received October 31, 2019; revised March 23, 2020; accepted May 16, 2020. Date of publication August 7, 2020; date of current version December 3, 2020. This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Contract EEEAG-117E645. This article was recommended for publication by Associate Editor G. Venture and Editor A. Billard upon evaluation of the reviewers’ comments. (Corresponding author: Cagatay Basdogan.) Yusuf Aydin and Cagatay Basdogan are with the College of Engineering, Koc University, Istanbul 34450, Turkey (e-mail: yaydin@ku.edu.tr; cbasdogan@ku.edu.tr).","The authors would like to thank to Prof. A. Kucukyilmaz for her valuable comments. Moreover, the author Y. Aydin thanks to A. Aytekin, O. Caldiran, E.S. Emgin, G. Serhat, and C. Yesil for fruitful discussions during the study. The Scientific and Technological Research Council of Turkey (TUBITAK) supported this work under contract EEEAG-117E645.",,,,,,,,,"Ikeura, R., Monden, H., Inooka, H., Cooperative motion control of a robot and a human (1994) Proc. Ieee Int.Workshop Robot Human Commun., pp. 112-117. , Jul; Mörtl, A., The role of roles: Physical cooperation between humans and robots (2012) Int. J. Robot. Res., 31 (13), pp. 1656-1674; Wojtara, T., Human-robot collaboration in precise positioning of a three-dimensional object (2009) Automatica, 45 (2), pp. 333-342; Tavakoli, M., Patel, R.V., Moallem, M., A haptic interface for computer-integrated endoscopic surgery and training (2006) Virtual Reality, 9 (2), pp. 160-176. , Mar; Pehlivan, A.U., Losey, D.P., O'Malley, M.K., Minimal assist-asneeded controller for upper limb robotic rehabilitation (2016) Ieee Trans. 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"Hossain M.P., Atikuzzaman Md., Rahim M.M., Hossain M.T., Mousome M.S., Rahaman M.A.","57219988333;37048672500;57222025273;57222011347;57222017563;56605926600;","Human robot interaction system for behavioral improvement of autistic children",2020,"2020 2nd International Conference on Advanced Information and Communication Technology, ICAICT 2020",,,"9333508","117","122",,,"10.1109/ICAICT51780.2020.9333508","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101030232&doi=10.1109%2fICAICT51780.2020.9333508&partnerID=40&md5=3ccce9f7f92cb164e65b2424b81edc58","Green University of Bangladesh, Dept of Computer Science and Engineering, Bangladesh","Hossain, M.P., Green University of Bangladesh, Dept of Computer Science and Engineering, Bangladesh; Atikuzzaman, Md., Green University of Bangladesh, Dept of Computer Science and Engineering, Bangladesh; Rahim, M.M., Green University of Bangladesh, Dept of Computer Science and Engineering, Bangladesh; Hossain, M.T., Green University of Bangladesh, Dept of Computer Science and Engineering, Bangladesh; Mousome, M.S., Green University of Bangladesh, Dept of Computer Science and Engineering, Bangladesh; Rahaman, M.A., Green University of Bangladesh, Dept of Computer Science and Engineering, Bangladesh","An estimated one-third of people with autism are nonverbal. Many Research has been done in this field and confirms that people with autism experience a lack of communication skills, anxiety, dealing with change, bullying, etc. To assist autistic children and to improve their behavior, we have developed an artificial intelligence robot that can react to their movement via an ultrasonic sensor and can talk to them via voice commands. Using this robot, children with ASD can develop both their behavior and communication skill. The robot works in two methods. The first one is the ultrasonic sensor, implemented with an AI algorithm that can detect a person within seventy centimeters. This ultrasonic sensor sends the data to the controller and the controller receives the signal and sends an activation signal to the robot. Finally, the robot moves his head to the direction of the person is located or to the moving direction. The second method is voice commands. The robot can perform each physical action it is given via voice commands like a salute, handshaking, body movement, and answering questions achieving 97.75% average accuracy with the response time of 328.75ms. This accuracy is achieved by testing the system for 10 iterations. As autistic children hesitate to communicate freely with normal people, this human-robot interaction will improve their behavior and communication skills as well. This humanoid robot will improve the quality of living of the autistic or disabled people. This humanoid robot can be used in any public sector like school, colleges, an autism center, and industry. © 2020 IEEE.","AI; Autism spectrum disorder; Human-robot interaction; Humanoid robot; Robotics","Anthropomorphic robots; Artificial intelligence; Behavioral research; Diseases; Industrial robots; Man machine systems; Ultrasonic applications; Ultrasonic sensors; Autistic children; Body movements; Communication skills; Disabled people; Humanoid robot; Moving direction; Physical action; Public sector; Social robots",,,,,,"ACKNOWLEDGMENT This work is partially supported by the Green University of Bangladesh.",,,,,,,,,,"(2019) Autistic Spectrum Disorder (Asd), , https://www.nhsinform.scot/illnesses-and-conditions/brain-nerves-and-spinal-cord/autistic-spectrum-disorder-asd, N. inform, accessed on 10/20/2019; Speaks, A., (2019) Autism Statistics and Facts, , https://www.autismspeaks.org/autism-facts-and-figures, online;Accessed on 10/20/2019; Shamsuddin, S., Yussof, H., Ismail, L.I., Mohamed, S., Hanapiah, F.A., Zahari, N.I., Humanoid robot nao interacting with autistic children of moderately impaired intelligence to augment communication skills (2012) Procedia Engineering, 41, pp. 1533-1538; Dickstein-Fischer, L., Alexander, E., Yan, X., Su, H., Harrington, K., Fischer, G.S., An affordable compact humanoid robot for autism spectrum disorder interventions in children (2011) 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5319-5322; Shamsuddin, S., Yussof, H., Hanapiah, F.A., Mohamed, S., Jamil, N.F.F., Yunus, F.W., Robot-assisted learning for communicationcare in autism intervention (2015) 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 822-827; Yang, X., Shyu, M.-L., Yu, H.-Q., Sun, S.-M., Yin, N.-S., Chen, W., Integrating image and textual information in human-robot interactions for children with autism spectrum disorder (2018) IEEE Transactions on Multimedia, 21 (3), pp. 746-759; Othman, A., Mohsin, M., How could robots improve social skills in children with autism? (2017) 2017 6th International Conference on Information and Communication Technology and Accessibility (ICTA), pp. 1-5; Yang, J.-Y., Kwon, D.-S., The effect of multiple robot interaction on human-robot interaction (2012) 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 30-33; Dautenhahn, K., Methodology & Themes of human-robot interaction: A growing research field (2007) Ternational Journal of Advanced Robotic Systems, 4 (1), p. 15; Xue, Z., Ruehl, S., Hermann, A., Kerscher, T., Dillmann, R., An autonomous ice-cream serving robot (2011) 2011 IEEE International Conference on Robotics and Automation, pp. 3451-3452; Shieh, M.-Y., Lu, C.-M., Chen, C.-C., Chuang, C.-Y., Lai, Y.-S., Design and implementation of an interactive nurse robot (2007) SICE Annual Conference 2007, pp. 2121-2125; Ban, K.-D., Kwak, K.-C., Yoon, H.-S., Chung, Y.-K., Fusion technique for user identification using camera and microphone in the intelligent service robots (2007) 2007 IEEE International Symposium on Consumer Electronics, pp. 1-6","Hossain, M.P.; Green University of Bangladesh, Bangladesh; email: parvezhossain92@gmail.com",,,"Institute of Electrical and Electronics Engineers Inc.","2nd International Conference on Advanced Information and Communication Technology, ICAICT 2020","28 November 2020 through 29 November 2020",,166883,,9780738123233,,,"English",,Conference Paper,"Final","",Scopus,2-s2.0-85101030232
"Valente C., Møller H., Johnsen F.M., Saxegård S., Brunsdon E.R., Alvseike O.A.","43462208100;7401855650;57210344043;57218476934;57218476493;6602285577;","Life cycle sustainability assessment of a novel slaughter concept",2020,"Journal of Cleaner Production","272",,"122651","","",,4,"10.1016/j.jclepro.2020.122651","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089276437&doi=10.1016%2fj.jclepro.2020.122651&partnerID=40&md5=46b2151e4a8af6e2f0aa8b3cef9b367a","NORSUS Norwegian Institute for Sustainability Research, Stadion 4, Kråkerøy1671, Norway; Fjordland AS, Brynsengveien 10, Postboks 6259 Etterstad, Oslo, 0603, Norway; Animalia, Postboks 396 Økern, Oslo, 0396, Norway","Valente, C., NORSUS Norwegian Institute for Sustainability Research, Stadion 4, Kråkerøy1671, Norway; Møller, H., NORSUS Norwegian Institute for Sustainability Research, Stadion 4, Kråkerøy1671, Norway; Johnsen, F.M., NORSUS Norwegian Institute for Sustainability Research, Stadion 4, Kråkerøy1671, Norway; Saxegård, S., NORSUS Norwegian Institute for Sustainability Research, Stadion 4, Kråkerøy1671, Norway; Brunsdon, E.R., Fjordland AS, Brynsengveien 10, Postboks 6259 Etterstad, Oslo, 0603, Norway; Alvseike, O.A., Animalia, Postboks 396 Økern, Oslo, 0396, Norway","This article presents a Life Cycle Sustainability Assessment of an innovative slaughter concept, i.e., a semi-automated version of the Meat Factory Cell (MFC). The system is characterised by division of labour with close human-robot interaction, as compared to a Conventional Slaughter and Cutting Process (CSCP). A case study is built which considers the conditions at a Norwegian slaughter facility. Several assumptions are made for the MFC as the concept is still in the development phase. A sensitivity analysis has been employed to highlight the key factors leading to changes in the environmental, economic and social aspects of the Life Cycle Sustainability Assessment framework. The results show that MFC is a viable alternative to CSCP within a life cycle sustainability assessment, even though there is a trade-off between the three dimensions of sustainability and variability among the considered impact categories. At the slaughterhouse, the climate change performance for the CSCP is 387 kg CO2eq/ton carcass. For the MFC concept, results showed no substantial changes in the environmental performance. For the Life Cycle Costing, the difference between the two concepts arises primarily from lower energy costs due to a 50% reduction in the cost of chilling. From a cost perspective, the innovative concept is relatively more profitable for small and medium-size abattoirs than for very large slaughterhouses. The social LCA indicates that a more efficient abattoir system using MFC might lead to the loss of low-skilled jobs but creates opportunities for more qualified personnel. © 2020 Elsevier Ltd","Life cycle sustainability assessment; Meat sector; Pork; Slaughterhouse","Artificial life; Climate change; Costs; Economic and social effects; Environmental management; Life cycle; Sensitivity analysis; Social aspects; Social robots; Cutting process; Development phase; Environmental performance; Life cycle costing; Life cycle sustainability assessments; Meat factories; Qualified personnel; Three dimensions; Sustainable development",,,,,"Norges ForskningsrÃ¥d","This work was supported by the grant number: 256266, project name Meat 2.0. under the Norwegian Research Council programme BION?R - Sustainable Innovation in Food and Bio-based Industries.",,,,,,,,,,"Alvseike, O., Prieto, M., Torkveen, K., Ruud, C., Nesbakken, T., Meat inspection and hygiene in a Meat Factory Cell – an alternative concept (2018) Food Contr., 90, pp. 32-39; Andersson, A.M., Sand, R., Landbruket I Trøndelag Som Energiprodusent- Og Konsument. TFoU-Rapport 2018:15 (2018), Trøndelag Forskning og Utvikling AS Norwegian; Arcese, G., Lucchetti, M.C., Massa, I., Valente, C., State of the art in S-LCA: integrating literature review and automatic text analysis (2018) Int. J. Life Cycle Assess., 23, pp. 394-405–405; Asiedu, Y., Gu, P., Product life cycle cost analysis: state of the art review (1998) Int. J. Prod. Res., 36 (4), pp. 883-908; Bamber, N., Turner, I., Arulnathan, V., Li, Y., Ershadi, S.Z., Smart, A., Pelletier, N., Comparing sources and analysis of uncertainty in consequential and attributional life cycle assessment: review of current practice and recommendations (2020) Int. J. Life Cycle Assess., 25, pp. 168-180; Basset-Mens, C., van der Werf, H.M.G., Concept-based environmental assessment of farming systems: the case of pig production in France (2005) Agric. Ecosyst. Environ., 105, pp. 127-144; Bonesmo, H., Little, S.M., Harstad, O.M., Beauchemin, K.A., Skjelvåg, A.O., Sjelmo, O., Estimating farm-scale greenhouse gas emission intensity of pig production in Norway (2012) Acta Agric. Scand. Sect. A Anim. Sci., 62, pp. 318-325; Bonou, A., Birkved, M., LCA of Pork Products & Evaluation of Alternative Super-chilling Techniques (2016), Technical University of Denmark; Ciroth, A., Eisfeld, F., PSILCA – A Product Social Impact Life Cycle Assessment Database. Database Version 1.0. Documentation (2016), GreenDelta; Costa, D., Quinteiro, P., Dias, A.C., A systematic review of life cycle sustainability assessment: current state, methodological challenges, and implementation issues (2019) Sci. Total Environ., 686, pp. 774-787; de Boer, I., Cederberg, C., Eady, S., Gollnow, S., Kristensen, T., Macleod, M., Meul, M., Zonderland-Thomassen, M., Greenhouse gas mitigation in animal production: towards an integrated life cycle sustainability assessment (2011) Curr. 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(2016) Taking Stock of Industrial Ecology, pp. 45-68. , R. Clift A. Druckman SpringerOpen. University of Surrey Guildford, UK; Heijungs, R., Sun, S., The computational structure of life cycle assessment (2002) Int. J. Life Cycle Assess., 7, p. 314; Heijungs, R., Settanni, E., Guinée, J., Toward a computational structure for life cycle sustainability analysis: unifying LCA and LCC (2013) Int. J. Life Cycle Assess., 18 (9), pp. 1722-1733; Hunkeler, D., Klöpffer, W., Pesonen, H., Ciroth, A., Swarr, T., Pagan, B., Itsubo, N., Life Cycle Costing (2008), SETAC World; Ingris, Ingris årsstatistikk 2018 (Ingris annual statistics 2018). Animalia (2019), https://www.animalia.no/contentassets/28e0db72674d496186f0570a9e606fca/arsstatistikk-2018.pdf, (Accessed 9 June 2019); ISO, NS-EN ISO 14044:2006. Environmental Management. Life Cycle Assessment. 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Life Cycle Assess., 24, pp. 1093-1104–1104; Toniolo, S., Tosato, R.C., Gambaro, F., Ren, J., Chapter 3 - life cycle thinking tools: life cycle assessment, life cycle costing and social life cycle assessment (2020) 2020. Life Cycle Sustainability Assessment for Decision-Making, , J. Ren S. Toniolo Elsevier; UNEP/SETAC, Guidelines for social life cycle assessment of Products (2009) UNEP/SETAC Life Cycle Initiative, , Benoît C. B. Mazijn United Nations Environment Programme Nairobi (Kenya); UNEP/SETAC, Towards a life cycle sustainability assessment: making informed choices on products. UNEP/SETAC life cycle initiative (2011) UNEP/SETAC Life Cycle Initiative, , S. Valdivia C.M.L. Ugaya G. Sonnemann J. Hildenbrand United Nations Environment Programme Nairobi (Kenya); UNEP/SETAC, The Methodological Sheets for Sub-categories in Social Life Cycle Assessment (S-LCA). UNEP/SETAC Life Cycle Initiative (2013), UNEP/SETAC Life Cycle Initiative, United Nations Environment Programme Nairobi (Kenya); Weidema, B.P., Wesnæs, M.S., Data quality management for life cycle inventories—an example of using data quality indicators (1996) J. Clean. Prod., 4, pp. 167-174; Weidemann, S., McGahan, E., Grist, S., Grant, T., Environmental Assessment of Two Pork Supply Chains Using Life Cycle Assessment (2010), AgriFutures Australia; Winkler, T., Schopf, K., Aschemann, R., Winiwarter, W., From farm to fork – a life cycle assessment of fresh Austrian pork (2016) J. Clean. Prod., 116, pp. 80-89; Woodward, D.G., Life cycle costing—theory, information acquisition and application (1997) Int. J. Proj. Manag., 15 (6), pp. 335-344; WRI, WBCSD, Greenhouse Gas Protocol: Product Life Cycle Accounting and Reporting Standard (2011), World Resources Institute, World Business Council for Sustainable Development Washington, DC; Geneva. Switzerland","Valente, C.; NORSUS Norwegian Institute for Sustainability Research, Stadion 4, Kråkerøy, Norway; email: clara@norsus.no",,,"Elsevier Ltd",,,,,09596526,,JCROE,,"English","J. Clean. Prod.",Article,"Final","",Scopus,2-s2.0-85089276437
"Neerincx A., Luijk A.","57189047256;57221484124;","Social robot's processing of context-sensitive emotions in child care: A dutch use case",2020,"ICMI 2020 Companion - Companion Publication of the 2020 International Conference on Multimodal Interaction",,,,"503","505",,1,"10.1145/3395035.3425181","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096669546&doi=10.1145%2f3395035.3425181&partnerID=40&md5=fb6215720f602ea8e130a0e5fee8899f","Utrecht University, Utrecht, Netherlands; Dutch Child and Family Center, Capelle aan den IJssel, Netherlands","Neerincx, A., Utrecht University, Utrecht, Netherlands; Luijk, A., Dutch Child and Family Center, Capelle aan den IJssel, Netherlands","Therapists, psychologists, family counselors and coaches in youth care show a clear need for social technology support, e.g. for education, motivation and guidance of the children. For example, the Dutch Child and Family Center explores the possibilities of social robot assistance in their regular care pathways. This robot should address the affective processes in the communication appropriately. Whereas there is an enormous amount of emotion research in human-robot interaction, there is not yet a proven set of models and methods that can be put into this practice directly. Our research aims at a model for robot's emotional recognition and expression that is effective in the Dutch youth care. Consequently, it has to take account of personal differences (e.g., child's developmental phase and mental problems) and the context (e.g., family circumstances and therapy approach). Our study distinguishes different phases that may partially run in parallel. First, possible solutions for affective computing by social robots are identified to set the general design space and understand the constraints. Second, in an exploration phase, focus group sessions are conducted to identify core features of emotional expressions that the robot should or could process, including the context-dependencies. Third, in the testing phase, via scenario-based design and child-robot interaction experiments a practical model of affect processing by a social robot in youth care is derived. This short paper provides an overview of the general approach of this research and some preliminary results of the design space and focus group. © 2020 ACM.","Affective computing; Child care; Child-robot interaction; Social robots; Trust","Educational robots; Interactive computer systems; Machine design; Affective Computing; Child-robot interactions; Context dependency; Emotional expressions; Emotional recognition; Exploration phase; Scenario-based design; Social technologies; Social robots",,,,,,,,,,,,,,,,"Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Science Robotics, 3, p. 21; Cameron, D., Fernando, S., Collins, E., Millings, A., Moore, R., Sharkey, A., Evers, V., Prescott, T., Presence of life-like robot expressions influences children's enjoyment of human-robot interactions in the field (2015) Proceedings of the Aisb Convention 2015. The Society for the Study of Artificial Intelligence and Simulation of Behaviour; Dawe, J., Sutherland, C., Barco, A., Broadbent, E., Can social robots help children in healthcare contexts? A scoping review (2019) Bmj Paediatrics Open, 3, p. 1; De Gelder, B., De Borst, A.W., Watson, R., The perception of emotion in body expressions (2015) Wiley Interdisciplinary Reviews: Cognitive Science, 6 (2), pp. 149-158; Grabowski, K., Rynkiewicz, A., Lassalle, A., Baron-Cohen, S., Schuller, B., Cummins, N., Baird, A., Ucka, I., Emotional expression in psychiatric conditions: New technology for clinicians (2019) Psychiatry and Clinical Neurosciences, 73 (2), pp. 50-62; Blanson Henkemans, O.A., Bierman, B.P.B., Janssen, J., Looije, R., Neerincx, M.A., Van Dooren, M.M.M., De Vries, J.L.E., Huisman, S.D., Design and evaluation of a personal robot playing a self-management education game with children with diabetes type 1 (2017) International Journal of Human-Computer Studies, 106, pp. 63-76; Kennedy, J., Lemaignan, S., Montassier, C., Lavalade, P., Irfan, B., Papadopoulos, F., Senft, E., Belpaeme, T., Child speech recognition in human-robot interaction: Evaluations and recommendations (2017) Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 82-90; Southam-Gerow, M.A., Kendall, P.C., Emotion regulation and understanding: Implications for child psychopathology and therapy (2002) Clinical Psychology Review, 22 (2), pp. 189-222; Tzavela, E.C., Mitskidou, P., Mertika, A., Stalikas, A., Kasvikis, Y., Treatment engagement in the early phase of cognitivebehavior therapy for panic disorder: A grounded theory analysis of patient experience (2018) Psychotherapy Research, 28 (6), pp. 842-860; Van Straten, C.L., Peter, J., Kühne, R., Child-robot relationship formation: A narrative review of empirical research (2020) International Journal of Social Robotics, 12 (2), pp. 325-344; Zhang, Y., Song, W., Tan, Z., Zhu, H., Wang, Y., Man Lam, C., Weng, Y., Siu Man Chan, B., Could social robots facilitate children with autism spectrum disorders in learning distrust and deception? (2019) Computers in Human Behavior, 98, pp. 140-149",,,"ACM SIGCHI","Association for Computing Machinery, Inc","2020 International Conference on Multimodal Interaction, ICMI 2020","25 October 2020 through 29 October 2020",,166127,,9781450380027,,,"English","ICMI Companion - Companion Publ. Int. Conf. Multimodal Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85096669546
"Zahedi F., Bitz T., Phillips C., Lee H.","57219441506;57219436637;57222348277;35102589200;","Regulation of 2D arm stability against unstable, damping-defined environments in physical human-robot interaction",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9340829","11330","11336",,1,"10.1109/IROS45743.2020.9340829","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102404254&doi=10.1109%2fIROS45743.2020.9340829&partnerID=40&md5=9e2c576c300bf89a0907fb3276358714","Arizona State University, School for Engineering of Matter, Transport, and Energy, Tempe, AZ  85287, United States","Zahedi, F., Arizona State University, School for Engineering of Matter, Transport, and Energy, Tempe, AZ  85287, United States; Bitz, T., Arizona State University, School for Engineering of Matter, Transport, and Energy, Tempe, AZ  85287, United States; Phillips, C., Arizona State University, School for Engineering of Matter, Transport, and Energy, Tempe, AZ  85287, United States; Lee, H., Arizona State University, School for Engineering of Matter, Transport, and Energy, Tempe, AZ  85287, United States","This paper presents an experimental study to investigate how humans interact with a robotic arm simulating primarily unstable, damping-defined, mechanical environments, and to quantify lower bounds of robotic damping that humans can stably interact with. Human subjects performed posture maintenance tasks while a robotic arm simulated a range of negative damping-defined environments and transiently perturbed the human arm to challenge postural stability. Analysis of 2-dimensional kinematic responses in both the time domain and phase space allowed us to evaluate stability of the coupled human-robot system in both anterior-posterior (AP) and medial-lateral (ML) directions, and to determine the lower bounds of robotic damping for stable physical human-robot interaction (pHRI). All subjects demonstrated higher capacity to stabilize their arm against negative damping-defined environments in the AP direction than the ML direction, evidenced by all 3 stability measures used in this study. Further, the lower bound of robotic damping for stable pHRI was more than 3.5 times lower in the AP direction than the ML direction: -30.0 Ns/m and -8.2 Ns/m in the AP and ML directions, respectively. Sensitivity analysis confirmed that the results in this study were relatively insensitive to varying experimental conditions. Outcomes of this study would allow us to design a less conservative robotic impedance controller that utilizes a wide range of robotic damping, including negative damping, and achieves more transparent and agile operations without compromising coupled stability and safety of the human-robot system, and thus improves the overall performance of pHRI. © 2020 IEEE.",,"Agricultural robots; Damping; Intelligent robots; Machine design; Man machine systems; Phase space methods; Robotic arms; Robotics; Sensitivity analysis; System stability; Time domain analysis; Anterior posteriors; Experimental conditions; Human-robot systems; Impedance controllers; Mechanical environment; Physical human-robot interactions; Physical humanrobot interaction (phri); Posture maintenances; Social robots",,,,,"National Science Foundation, NSF: 1925110, 1846885","Research supported by National Science Foundation Awards #1846885 and #1925110. Hyunglae Lee is with the School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ 85287, USA (e-mail: hyunglae.lee@asu.edu; *corresponding author: 480-727-7463; fax:",,,,,,,,,,"Haddadin, S., Suppa, M., Fuchs, S., Bodenmuller, T., Albu-Schaffer, A., Hirzinger, G., Towards the robotic co-worker (2011) Robotics Research, pp. 261-282. , Springer; Tsarouchi, P., Makris, S., Chryssolouris, G., Human-robot interaction review and challenges on task planning and programming (2016) International Journal of Computer Integrated Manufacturing, 29 (8), pp. 916-931; Albu-Schaffer, A., Haddadin, S., Ott, C., Stemmer, A., Wimbock, T., Hirzinger, G., The DLR lightweight robot: Design and control concepts for robots in human environments (2007) Industrial Robot-an International Journal, 34 (5), pp. 376-385; Yoshikawa, T., Force control of robot manipulators (2000) Proc. 2000 IEEE International Conference on Robotics and Automation, pp. 220-226; Chiaverini, S., Siciliano, B., Villani, L., A survey of robot interaction control schemes with experimental comparison (1999) IEEE/ASME Trans on Mechatronics, 4 (3), pp. 273-285; Hogan, N., Impedance control-an approach to manipulation. 1. theory (1985) Journal of Dynamic Systems Measurement and Control-Transactions of the Asme, 107 (1), pp. 1-7; Hogan, N., Buerger, S.P., Impedance and interaction control (2005) Robotics and Automation Handbook, , New York, CRC Press; Buerger, S.P., Hogan, N., Complementary stability and loop shaping for improved human-robot interaction (2007) IEEE Transactions on Robotics, 23 (2), pp. 232-244. , Apr; Tsumugiwa, T., Yokogawa, R., Hara, K., Variable impedance control based on estimation of human arm stiffness for human-robot cooperative calligraphic task (2002) IEEE International Conference on Robotics and Automation (ICRA), pp. 644-650. , Washington D. C., USA; Burdet, E., Osu, R., Franklin, D.W., Yoshioka, T., Milner, T.E., Kawato, M., A method for measuring endpoint stiffness during multi-joint arm movements (2000) J Biomech, 33 (12), pp. 1705-1709. , Dec; Gomi, H., Osu, R., Task-dependent viscoelasticity of human multijoint arm and its spatial characteristics for interaction with environments (1998) J Neurosci, 18 (21), pp. 8965-8978. , Nov 1; De Vlugt, E., Schouten, A.C., Van Der Helm, T.F.C., Closed-loop multivariable system identification for the characterization of the dynamic arm compliance using continuous force disturbances: A model study (2003) Journal of Neuroscience Methods, 122 (2), pp. 123-140. , Jan 30; Perreault, E.J., Kirsch, R.F., Acosta, A.M., Multiple-input, multiple-output system identification for characterization of limb stiffness dynamics (1999) Biological Cybernetics, 80 (5), pp. 327-337. , May; Franklin, D.W., Liaw, G., Milner, T.E., Osu, R., Burdet, E., Kawato, M., Endpoint stiffness of the arm is directionally tuned to instability in the environment (2007) J Neurosci, 27 (29), pp. 7705-7716. , Jul 18; Krutky, M.A., Trumbower, R.D., Perreault, E.J., Influence of environmental stability on the regulation of end-point impedance during the maintenance of arm posture (2013) Journal of Neurophysiology, 109 (4), pp. 1045-1054. , Feb; Burdet, E., Osu, R., Franklin, D.W., Milner, T.E., Kawato, M., The central nervous system stabilizes unstable dynamics by learning optimal impedance (2001) Nature, 414 (6862), pp. 446-449. , Nov 22; Perreault, E.J., Kirsch, R.F., Crago, P.E., Effects of voluntary force generation on the elastic components of endpoint stiffness (2001) Exp Brain Res, 141 (3), pp. 312-323. , Dec; Hanzlick, H., Murphy, H., Lee, H., Stability of the human ankle in relation to environmental mechanics (2017) Proc. IEEE International Conference on Robotics and Automation (ICRA 2017), , Singapore; Goswami, A., Thuilot, B., Espiau, B., Compas-like biped robot Part I: Stability and bifurcation of passive gaits (1996) INRIA, 2996; Hanzlick, H., Hyunglae, L., Gender difference of ankle stability in the sagittal and frontal planes (2017) Conf Proc IEEE Eng Med Biol Soc, 2017, pp. 1621-1624. , Jul; Lee, H., Hogan, N., Essential considerations for design and control of human-interactive robots (2016) Proc. 2016 IEEE International Conference on Robotics and Automation (ICRA 2016), pp. 3069-3074. , Stockholm; Kearney, R.E., Hunter, I.W., System-identification of human joint dynamics (1990) Critical Reviews in Biomedical Engineering, 18 (1), pp. 55-87; Lee, H., Krebs, H.I., Hogan, N., Multivariable dynamic ankle mechanical impedance with active muscles (2014) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22 (5), pp. 971-981. , Sep; Lee, H., Perreault, E.J., Stabilizing stretch reflexes are modulated independently from the rapid release of perturbation-triggered motor plans (2019) Sci Rep, 9 (1), p. 13926. , Sep 26; Arnold, J., Hanzlick, H., Lee, H., Variable damping control of the robotic ankle joint to improve trade-off between performance and stability (2019) IEEE International Conference on Robotics and Automation (ICRA), pp. 1699-1704. , Montreal, Canada; Bitz, T., Zahedi, F., Lee, H., Variable damping control of a robotic arm to improve trade-off between agility and stability and reduce user effort (2020) IEEE International Conference on Robotics and Automation (ICRA), , France","Lee, H.; Arizona State University, United States; email: hyunglae.lee@asu.edu",,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","",Scopus,2-s2.0-85102404254
"Zimmermann Y., Kucuktabak E.B., Farshidian F., Riener R., Hutter M.","57211426393;57203330918;55480761600;7003404145;35409405200;","Towards dynamic transparency: Robust interaction force tracking using multi-sensory control on an arm exoskeleton",2020,"IEEE International Conference on Intelligent Robots and Systems",,,"9341054","7417","7424",,3,"10.1109/IROS45743.2020.9341054","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102402174&doi=10.1109%2fIROS45743.2020.9341054&partnerID=40&md5=8c5de3725a3722c0330ae9f1abe2e1c4","ETH Zurich, Systems Lab, Switzerland; ETH Zurich, Sensory-Motor Systems Lab, Switzerland; Spinal Cord Injury Center, University Hospital Balgrist, Zurich, Switzerland","Zimmermann, Y., ETH Zurich, Systems Lab, Switzerland, ETH Zurich, Sensory-Motor Systems Lab, Switzerland; Kucuktabak, E.B., ETH Zurich, Systems Lab, Switzerland; Farshidian, F., ETH Zurich, Systems Lab, Switzerland; Riener, R., ETH Zurich, Sensory-Motor Systems Lab, Switzerland, Spinal Cord Injury Center, University Hospital Balgrist, Zurich, Switzerland; Hutter, M., ETH Zurich, Systems Lab, Switzerland","A high-quality free-motion rendering is one of the most vital traits to achieve an immersive human-robot interaction. Rendering free-motion is notably challenging for rehabilitation exoskeletons due to their relatively high weight and powerful actuators required for strength training and support. In the presence of dynamic human movements, accurate feedback linearization of the robot's dynamics is necessary to allow for a linear synthesis of interaction wrench controllers. Hence, we introduce a virtual model controller that uses two 6-DoF force sensors to control the interaction wrenches of a multi-DoF torque-controlled exoskeleton over the joint accelerations and inverse dynamics. Furthermore, we propose a disturbance observer for controlling the joint acceleration to diminish the influence of modeling errors on the inverse dynamics. To provide a high-bandwidth, low-bias estimation of the system's acceleration, we introduce a bias-observer which fuses the information from joint encoders and seven low priced IMUs. We have validated the performance of our proposed control structure on the shoulder and arm exoskeleton ANYexo. The experimental comparison of the controllers shows a reduction of the felt inertia and maximum reflected joint torque by a factor of more than three compared to state of the art. The controllers' robustness w.r.t. a model mismatch is validated. The experiments show that the closed-loop acceleration control improves the tracking, particularly at joints with low inertia. The proposed controllers' performance sets a new benchmark in haptic transparency for comparable devices and should be transferable to other applications. © 2020 IEEE.",,"Acceleration control; Agricultural robots; Benchmarking; Biofeedback; Dynamics; Exoskeleton (Robotics); Feedback linearization; Human robot interaction; Intelligent robots; Inverse problems; Robotic arms; Tools; Transparency; Comparable devices; Control structure; Disturbance observer; Experimental comparison; Haptic transparencies; Interaction forces; Strength trainings; Virtual modeling; Controllers",,,,,,,,,,,,,,,,"Proietti, T., Crocher, V., Roby-Brami, A., Jarrasse, N., (2016) Upper-limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies; Otten, A., Voort, C., Stienen, A., Aarts, R., Van Asseldonk, E., Kooij Der, H.Van, Limpact:A hydraulically powered self-aligning upper limb exoskeleton (2015) IEEE/ASME Transactions on Mechatronics; Gopura, R.A., Bandara, D.S., Kiguchi, K., Mann, G.K., Developments in hardware systems of active upper-limb exoskeleton robots: A review (2016) Robotics and Autonomous Systems; Klamroth-Marganska, V., Blanco, J., Campen, K., Curt, A., Dietz, V., Ettlin, T., Felder, M., Riener, R., Three-dimensional, task-specific robot therapy of the arm after stroke: A multicentre, parallel-group randomised trial (2014) The Lancet Neurology; Ferreira, M., Emi, M., Chaves, A., Ma, F., Oliveira, C., Maria, A., Bruno, C., Vimieiro, S., Effectiveness of robot therapy on body function and structure in people with limited upper limb function : A systematic review and meta-analysis (2018) PlosOne; Zimmermann, Y., Forino, A., Riener, R., Hutter, M., Anyexo: A versatile and dynamic upper-limb rehabilitation robot (2019) Ieee Robotics and Automation Letters, , oct; Nef, T., Guidali, M., Riener, R., ARMin III-arm therapy exoskeleton with an ergonomic shoulder actuation (2009) Applied Bionics and Biomechanics, 6 (2), pp. 127-142; Kim, B., Deshpande, A.D., An upper-body rehabilitation exoskeleton Harmony with an anatomical shoulder mechanism: Design, modeling, control, and performance evaluation (2017) The International Journal of Robotics Research; Vallery, H., Veneman, J., Van Asseldonk, E., Ekkelenkamp, R., Buss, M., Kooij Der, H.Van, Compliant actuation of rehabilitation robots (2008) Ieee Robotics and Automation Magazine; Just, F., Ozen, O., Bösch, P., Bobrovsky, H., Klamroth-Marganska, V., Riener, R., Rauter, G., Exoskeleton transparency: Feed-forward compensation vs. disturbance observer (2018) At-Automatisierungstechnik; Farshidian, F., Jelavic, E., Winkler, A.W., Buchli, J., Robust wholebody motion control of legged robots (2017) Ieee International Conference on Intelligent Robots and Systems; Mao, Y., Agrawal, S.K., Design of a cable-driven arm exoskeleton (CAREX) for neural rehabilitation (2012) Ieee Transactions on Robotics; Perry, J.C., Rosen, J., Burns, S., Upper-limb powered exoskeleton design (2007) IEEE/ASME Transactions on Mechatronics; Hutter, M., Gehring, C., Jud, D., Lauber, A., Bellicoso, C.D., Tsounis, V., Hwangbo, J., Bachmann, S., Anymal-A highly mobile and dynamic quadrupedal robot (2016) Submitted to IEEE/RSJ Intenational Conference on Intelligent Robots and Systems (IROS); Brooks, T.L., Telerobotic response requirements (1990) Systems, Man and Cybernetics, 1990. Conference Proceedings., , IEEE International Conference on; Bellicoso, C.D., Gehring, C., Hwangbo, J., Fankhauser, P., Hutter, M., Perception-less terrain adaptation through whole body control and hierarchical optimization (2016) IEEE-RAS International Conference on Humanoid Robots; Horibe, T.E.T.B., Gillespie, R.B., Comparing series elasticitz and admittance control for haptic rendering (2016) EuroHaptics 2016; Carignan, C., Cleary, K., Closed-loop force control for haptic simulation of virtual environments (2000) Haptics-e, , feb; Hogan, N., Impedance control: An approach to manipulation: Part I-theory (1985) Journal of Dynamic Systems, Measurement and Control, Transactions of the Asme, 107 (1), pp. 1-7. , mar; Morari, M., Robust process control (1987) Chemical Engineering Research and Design; Haddadin, S., De Luca, A., Albu-Schäffer, A., Robot collisions: A survey on detection, isolation, and identification (2017) Ieee Transactions on Robotics, 33 (6), pp. 1292-1312",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020","24 October 2020 through 24 January 2021",,167055,21530858,9781728162126,85RBA,,"English","IEEE Int Conf Intell Rob Syst",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85102402174
"Miseikis J., Caroni P., Duchamp P., Gasser A., Marko R., Miseikiene N., Zwilling F., De Castelbajac C., Eicher L., Fruh M., Fruh H.","55786158600;57218305407;57218306941;57218306940;57218307008;57218306052;56682894200;57218305681;57218305578;57218304827;6603955424;","Lio-A Personal Robot Assistant for Human-Robot Interaction and Care Applications",2020,"IEEE Robotics and Automation Letters","5","4","9134708","5339","5346",,28,"10.1109/LRA.2020.3007462","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088699394&doi=10.1109%2fLRA.2020.3007462&partnerID=40&md5=a20ec1e7e520a1fa79313d39b16f2df5","F AND P Robotics Ag, Zurich, CH-8005, Switzerland","Miseikis, J., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Caroni, P., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Duchamp, P., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Gasser, A., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Marko, R., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Miseikiene, N., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Zwilling, F., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; De Castelbajac, C., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Eicher, L., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Fruh, M., F AND P Robotics Ag, Zurich, CH-8005, Switzerland; Fruh, H., F AND P Robotics Ag, Zurich, CH-8005, Switzerland","Lio is a mobile robot platform with a multi-functional arm explicitly designed for human-robot interaction and personal care assistant tasks. The robot has already been deployed in several health care facilities, where it is functioning autonomously, assisting staff and patients on an everyday basis. Lio is intrinsically safe by having full coverage in soft artificial-leather material as well as collision detection, limited speed and forces. Furthermore, the robot has a compliant motion controller. A combination of visual, audio, laser, ultrasound and mechanical sensors are used for safe navigation and environment understanding. The ROS-enabled setup allows researchers to access raw sensor data as well as have direct control of the robot. The friendly appearance of Lio has resulted in the robot being well accepted by health care staff and patients. Fully autonomous operation is made possible by a flexible decision engine, autonomous navigation and automatic recharging. Combined with time-scheduled task triggers, this allows Lio to operate throughout the day, with a battery life of up to 8 hours and recharging during idle times. A combination of powerful computing units provides enough processing power to deploy artificial intelligence and deep learning-based solutions on-board the robot without the need to send any sensitive data to cloud services, guaranteeing compliance with privacy requirements. During the COVID-19 pandemic, Lio was rapidly adjusted to perform additional functionality like disinfection and remote elevated body temperature detection. It complies with ISO13482 - Safety requirements for personal care robots, meaning it can be directly tested and deployed in care facilities. © 2016 IEEE.","automation in life sciences: biotechnology; autonomous agents; human-centered robotics; pharmaceutical and health care; physical human-robot interaction; Service robotics","Agricultural robots; Charging (batteries); Deep learning; Health care; Intelligent robots; Man machine systems; Ultrasonic applications; Visual servoing; Autonomous navigation; Autonomous operations; Collision detection; Healthcare facility; Mechanical sensors; Mobile robot platforms; Privacy requirements; Safety requirements; Human robot interaction",,,,,,"Manuscript received February 24, 2020; accepted June 15, 2020. Date of publication July 7, 2020; date of current version July 15, 2020. This letter was recommended for publication by Associate Editor J. Son and Editor Y. Choi upon evaluation of the reviewers’ comments. This work was supported by F&P Robotics AG. (Corresponding author: Justinas Miseikis.) The authors are with the F&P Robotics AG, CH-8005 Zürich, Switzerland (e-mail: j.miseikis@gmail.com; pic@fp-robotics.com; pdu@fp-robotics.com; ags@fp-robotics.com; mra@fp-robotics.com; nmi@fp-robotics.com; frz@fp-robotics.com; cdc@fp-robotics.com; lei@fp-robotics.com; mfr@fp-robotics.com; hrf@fp-robotics.com).",,,,,,,,,,"Flandorfer, P., Population ageing and socially assistive robots for elderly persons: The importance of sociodemographic factors for user acceptance (2012) Int. J. Population Res., 2012, pp. 1-13; Oliver, D., Foot, C., Humphries, R., (2014) Making Our Health and Care Systems Fit for An Ageing Population, , London, UK: King's Fund London; Suisse, C., Die zukunft des pflegeheimmarkts (2015) Pfäffikon: Schellenberg Druck AG, pp. 1-44; (2003) WHO | Violence against Health Workers, , https://www.who.int/violence_injury_prevention/violence/workplace/en/, World Health Organisation, Accessed: May13, 2020; Heinz, R., Rolf, M., Rainer, U., Themenreport pflege 2030 (2012) Was Ist zu Erwarten-was Ist zu Tun? Bertelsmann Stiftung; Ikeuchi, T., Sakurai, R., Furuta, K., Kasahara, Y., Imamura, Y., Shinkai, S., Utilizing social robot to reduce workload of healthcare professionals in psychiatric hospital: A preliminary study (2018) Innov. 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Berlin, pp. 37-62. , Germany: Springer; Stachniss, C., Frese, U., Grisetti, G., (2007) OpenSLAM, 2. , http://www.openslam.org; Fox, D., Burgard, W., Dellaert, F., Thrun, S., Monte Carlo localization: Efficient position estimation for mobile robots (1999) Proc. 16th Nat. Conf. Artif. Intell. 11th Conf. Innovative Appl. Artif. Intell., 1999, pp. 2-2; Fischinger, D., Hobbit, a care robot supporting independent living at home: First prototype and lessons learned (2016) Robot. Auton. Syst., 75, pp. 60-78; Thermal Imaging for Detecting Elevated Body Temperature | FLIR Systems, , https://www.flir.com/discover/publicsafety/thermal-imaging-for-detecting-elevated-body-temperature/, Accessed May 5 2020; Niemueller, T., Lakemeyer, G., Ferrein, A., Incremental task-level reasoning in a competitive factory automation scenario (2013) Proc. AAAI Spring Symp.-Designing Intell. Robots: Reintegrating AI, pp. 1-6; Wielemaker, J., Schrijvers, T., Triska, M., Lager, T., SWI-prolog (2012) Theory Pract. Log. Program., 12 (1-2), pp. 67-96; (2016) Applying Human Factors and Usability Engineering to Medical Devices: Guidance for Industry and Food and Drug Administration Staff, , Washington, DC: FDA D. Administration Food et al; Wirth, L., Siebenmann, J., Gasser, A., Erfahrungen aus dem Einsatz von assistenzrobotern für Menschen im alter (2020) Mensch-Roboter-Kollaboration. Berlin, pp. 257-279. , Germany: Springer; Gasser, A., Steinemann, S., Opwis, K., (2017) A Qualitative View on Elders Interacting with A Health Care Robot with Bodily Movements, , M. S. thesis, Depart. Psychol. Univ. Basel for the degree of Master of Science in Psychology; Bendel, O., Co-Robots as care robots (2020) AAAI Spring Symp. Series; Fischinger, D., HOBBIT-The mutual care robot (2013) Proc. RSJ Int. Conf. Intell. Robots Syst., pp. 1-6; Broadbent, E., Stafford, R., MacDonald, B., Acceptance of healthcare robots for the older population: Reviewand future directions (2009) Int. J. Social Robot., 1 (4), pp. 319-330; Syrdal, D.S., Dautenhahn, K., Walters, M.L., Koay, K.L., Sharing spaces with robots in a home scenario-anthropomorphic attributions and their effect on proxemic expectations and evaluations in a live HRI trial (2008) Proc. AAAI Fall Symp. : AI Eldercare: New Solutions Old Problems, pp. 116-123; Broadbent, E., Attitudes towards health-care robots in a retirement village (2012) Australas. J. Ageing, 31 (2), pp. 115-120","Miseikis, J.; F AND P Robotics AgSwitzerland; email: j.miseikis@gmail.com",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85088699394
"Angelov D., Hristov Y., Ramamoorthy S.","57212510875;57212512068;15042865300;","From demonstrations to task-space specifications. Using causal analysis to extract rule parameterization from demonstrations",2020,"Autonomous Agents and Multi-Agent Systems","34","2","45","","",,1,"10.1007/s10458-020-09471-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086567395&doi=10.1007%2fs10458-020-09471-w&partnerID=40&md5=13c7cdf63284881d758da3cefec57b67","School of Informatics, University of Edinburgh, Edinburgh, United Kingdom","Angelov, D., School of Informatics, University of Edinburgh, Edinburgh, United Kingdom; Hristov, Y., School of Informatics, University of Edinburgh, Edinburgh, United Kingdom; Ramamoorthy, S., School of Informatics, University of Edinburgh, Edinburgh, United Kingdom","Learning models of user behaviour is an important problem that is broadly applicable across many application domains requiring human–robot interaction. In this work, we show that it is possible to learn generative models for distinct user behavioural types, extracted from human demonstrations, by enforcing clustering of preferred task solutions within the latent space. We use these models to differentiate between user types and to find cases with overlapping solutions. Moreover, we can alter an initially guessed solution to satisfy the preferences that constitute a particular user type by backpropagating through the learned differentiable models. An advantage of structuring generative models in this way is that we can extract causal relationships between symbols that might form part of the user’s specification of the task, as manifested in the demonstrations. We further parameterize these specifications through constraint optimization in order to find a safety envelope under which motion planning can be performed. We show that the proposed method is capable of correctly distinguishing between three user types, who differ in degrees of cautiousness in their motion, while performing the task of moving objects with a kinesthetically driven robot in a tabletop environment. Our method successfully identifies the correct type, within the specified time, in 99% [97.8–99.8] of the cases, which outperforms an IRL baseline. We also show that our proposed method correctly changes a default trajectory to one satisfying a particular user specification even with unseen objects. The resulting trajectory is shown to be directly implementable on a PR2 humanoid robot completing the same task. © 2020, The Author(s).","Explainability; Human–robot interaction; Robot learning","Anthropomorphic robots; Constrained optimization; Demonstrations; Human robot interaction; Motion planning; Specifications; Behavioural types; Causal analysis; Causal relationships; Constraint optimizations; Generative model; Human demonstrations; Learning models; Robot interactions; Behavioral research",,,,,"Heriot-Watt University; Engineering and Physical Sciences Research Council, EPSRC; University of Edinburgh, ED: EP/L016834/1","This research is supported by the Engineering and Physical Sciences Research Council (EPSRC), as part of the CDT in Robotics and Autonomous Systems at Heriot-Watt University and The University of Edinburgh. Grant reference EP/L016834/1.",,,,,,,,,,"Angelov, D., Hristov, Y., Burke, M., Ramamoorthy, S., Composing diverse policies for temporally extended tasks (2020) IEEE Robotics and Automation Letters, 5 (2), pp. 2658-2665; Argall, B.D., Chernova, S., Veloso, M., Browning, B., A survey of robot learning from demonstration (2009) Robotics and Autonomous Systems, 57 (5), pp. 469-483; Bertsekas, D.P., (2014) Constrained optimization and Lagrange multiplier methods, , Academic Press, Cambridge; Brown, D.S., Niekum, S., (2018) Machine teaching for inverse reinforcement learning: Algorithms and applications; Byrd, R.H., Lu, P., Nocedal, J., Zhu, C., A limited memory algorithm for bound constrained optimization (1995) SIAM Journal on Scientific Computing, 16 (5), pp. 1190-1208; Chen, T.Q., Li, X., Grosse, R., Duvenaud, D., (2018) Isolating sources of disentanglement in variational autoencoders. 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( (2018) The Limits and Potentials of Deep Learning for Robotics. Arxiv E-Prints. Arxiv; Thomaz, A., Breazeal, C., Tutelage and socially guided robot learning (2004) In 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat No04ch37566), 4, pp. 3475-3480; Vukoviundefined, N., Mitiundefined, M., Miljkoviundefined, Z., Trajectory learning and reproduction for differential drive mobile robots based on gmm/hmm and dynamic time warping using learning from demonstration framework (2015) Engineering Applications of Artificial Intelligence, 45 (C), pp. 388-404. , https://doi.org/10.1016/j.engappai.2015.07.002; Wirth, C., Akrour, R., Neumann, G., Fürnkranz, J., A survey of preference-based reinforcement learning methods (2017) The Journal of Machine Learning Research, 18 (1), pp. 4945-4990; Zhifei, S., Meng Joo, E., A survey of inverse reinforcement learning techniques (2012) International Journal of Intelligent Computing and Cybernetics, 5 (3), pp. 293-311","Angelov, D.; School of Informatics, United Kingdom; email: d.angelov@ed.ac.uk",,,"Springer",,,,,13872532,,,,"English","Auton. Agents Multi-Agent Syst.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85086567395
"Bayon C., Emmens A.R., Afschrift M., Van Wouwe T., Keemink A.Q.L., Van Der Kooij H., Van Asseldonk E.H.F.","57190131293;56593199500;56342411600;57210275410;55325320200;6603038110;14424653100;","Can Momentum-Based Control Predict Human Balance Recovery Strategies?",2020,"IEEE Transactions on Neural Systems and Rehabilitation Engineering","28","9","9127521","2015","2024",,2,"10.1109/TNSRE.2020.3005455","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090509878&doi=10.1109%2fTNSRE.2020.3005455&partnerID=40&md5=40e4c9599a63a2e5657e17a371f4649f","Department of Biomechanical Engineering, University of Twente, Enschede, AE, Netherlands","Bayon, C., Department of Biomechanical Engineering, University of Twente, Enschede, AE, Netherlands; Emmens, A.R., Department of Biomechanical Engineering, University of Twente, Enschede, AE, Netherlands; Afschrift, M., Department of Biomechanical Engineering, University of Twente, Enschede, AE, Netherlands; Van Wouwe, T., Department of Biomechanical Engineering, University of Twente, Enschede, AE, Netherlands; Keemink, A.Q.L., Department of Biomechanical Engineering, University of Twente, Enschede, AE, Netherlands; Van Der Kooij, H., Department of Biomechanical Engineering, University of Twente, Enschede, AE, Netherlands; Van Asseldonk, E.H.F., Department of Biomechanical Engineering, University of Twente, Enschede, AE, Netherlands","Human-like balance controllers are desired for wearable exoskeletons in order to enhance human-robot interaction. Momentum-based controllers (MBC) have been successfully applied in bipeds, however, it is unknown to what degree they are able to mimic human balance responses. In this paper, we investigated the ability of an MBC to generate human-like balance recovery strategies during stance, and compared the results to those obtained with a linear full-state feedback (FSF) law. We used experimental data consisting of balance recovery responses of nine healthy subjects to anteroposterior platform translations of three different amplitudes. The MBC was not able to mimic the combination of trunk, thigh and shank angle trajectories that humans generated to recover from a perturbation. Compared to the FSF, the MBC was better at tracking thigh angles and worse at tracking trunk angles, whereas both controllers performed similarly in tracking shank angles. Although the MBC predicted stable balance responses, the human-likeness of the simulated responses generally decreased with an increased perturbation magnitude. Specifically, the shifts from ankle to hip strategy generated by the MBC were not similar to the ones observed in the human data. Although the MBC was not superior to the FSF in predicting human-like balance, we consider the MBC to be more suitable for implementation in exoskeletons, because of its ability to handle constraints (e.g. ankle torque limits). Additionally, more research into the control of angular momentum and the implementation of constraints could eventually result in the generation of more human-like balance recovery strategies by the MBC. © 2001-2011 IEEE.","Balance; control; exoskeleton; human-robot interaction","Biofeedback; Controllers; Exoskeleton (Robotics); Momentum; Recovery; State feedback; Anteroposterior; Balance recoveries; Full state feedback; Healthy subjects; Hip strategies; Human likeness; Simulated response; Torque limits; Human robot interaction; adult; ankle; article; clinical article; controlled study; female; hip; human; human experiment; male; remission; robotics; simulation; standing; thigh; torque; trunk; ankle; biomechanics; body equilibrium; motion; Ankle; Biomechanical Phenomena; Humans; Motion; Postural Balance; Torque",,,,,"611626; European Cooperation in Science and Technology, COST: CA16116; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO: P16-05","Manuscript received January 29, 2020; revised April 29, 2020 and June 8, 2020; accepted June 24, 2020. Date of publication June 29, 2020; date of current version September 7, 2020. This work was supported in part by the Dutch Research Council (NWO) through the research program Wearable Robotics under Project P16-05, in part by the EU Research Program FP7-ICT-2013-10 through the SYMBITRON project under Contract 611626, and in part by COST (European Cooperation in Science and Technology) Action CA16116. (C. Bayón and A. R. Emmens are co-first authors.) (Corresponding author: C. Bayón.) C. Bayón, A. R. Emmens, A. Q. L. Keemink, and E. H. F. van Asseldonk are with the Department of Biomechanical Engineering, University of Twente, 7500 AE Enschede, The Netherlands (e-mail: c.bayoncalderon@utwente.nl).",,,,,,,,,,"Mergner, T., Lippi, V., Posture control-human-inspired approaches for humanoid robot benchmarking: Conceptualizing tests, protocols and analyses (2018) Frontiers Neurorobotics, 12 (5), p. 21. , May; Geursen, J.B., Altena, D., Massen, C.H., Verduin, M., A model of the standing man for the description of his dynamic behaviour (1976) Agressologie, 17 B, pp. 63-69. , Jan; Winter, D.A., Patla, A.E., Prince, F., Ishac, M., Gielo-Perczak, K., Stiffness control of balance in quiet standing (1998) J. Neurophysio., 80 (3), pp. 1211-1221. , Sep; Pai, Y.-C., Patton, J., Center of mass velocity-position predictions for balance control (1997) J. Biomech., 30 (4), pp. 347-354. , Apr; Peterka, R.J., Sensorimotor integration in human postural control (2002) J. 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Program., 106 (1), pp. 25-57. , http://link.springer.com/10.1007/s10107-004-0559-y, Mar; Gillis, J., (2015) Practical Methods for Approximate Robust Periodic Optimal Control of Nonlinear Mechanical Systems, , Ph. D. dissertation, KU Leuven, Leuven, Belgium; Mukaka, M.M., Statistics corner: A guide to appropriate use of correlation coefficient in medical research (2012) Malawi Med. J., 24 (3), pp. 69-71. , https://www.mmj.medcol.mw; Popovic, M., Hofmann, A., Herr, H., Zero spin angular momentum control: Definition and applicability (2004) Proc. 4th IEEE/RAS Int. Conf. Humanoid Robots, 1, pp. 478-493. , Nov; Allum, J.H.J., Carpenter, M.G., Honegger, F., Adkin, A.L., Bloem, B.R., Age-dependent variations in the directional sensitivity of balance corrections and compensatory arm movements in man (2002) J. Physiol., 542 (2), pp. 643-663. , Jul; Runge, C.F., Shupert, C.L., Horak, F.B., Zajac, F.E., Ankle and hip postural strategies defined by joint torques (1999) Gait Posture, 10 (2), pp. 161-170. , http://www.ncbi.nlm.nih.gov/pubmed/10502650, Oct; Hof, A.L., The equations of motion for a standing human reveal three mechanisms for balance (2007) J. Biomech., 40 (2), pp. 451-457. , Jan; Engelhart, D., Boonstra, T.A., Aarts, R.G.K.M., Schouten, A.C., Kooij Der, H.Van, Comparison of closed-loop system identification techniques to quantify multi-joint human balance control (2016) Annu. Rev. Control, 41, pp. 58-70. , May","Bayon, C.; Department of Biomechanical Engineering, Netherlands; email: c.bayoncalderon@utwente.nl",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15344320,,ITNSB,"32746307","English","IEEE Trans. Neural Syst. Rehabil. Eng.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85090509878
"Wu J., Wang Z., Chen W., Wang Y., Liu Y.-H.","57216927309;56650312800;57207468299;57208909059;57201932064;","Design and Validation of a Novel Leaf Spring-Based Variable Stiffness Joint with Reconfigurability",2020,"IEEE/ASME Transactions on Mechatronics","25","4","9095357","2045","2053",,3,"10.1109/TMECH.2020.2995533","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089941774&doi=10.1109%2fTMECH.2020.2995533&partnerID=40&md5=d5316b0b1286a877f6081c5ce982ded6","T Stone Robotics Institute and Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, Shatin, Hong Kong","Wu, J., T Stone Robotics Institute and Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, Shatin, Hong Kong; Wang, Z., T Stone Robotics Institute and Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, Shatin, Hong Kong; Chen, W., T Stone Robotics Institute and Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, Shatin, Hong Kong; Wang, Y., T Stone Robotics Institute and Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, Shatin, Hong Kong; Liu, Y.-H., T Stone Robotics Institute and Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, Shatin, Hong Kong","Compliant joints can provide safer physical human-robot interaction. To effectively balance the response bandwidth and output impedance, variable stiffness joints have been studied by many researchers. Leaf spring-based variable stiffness joint (LSVSJ) has the advantages of easy construction and large stiffness range. This article presents the design and validation of a novel LSVSJ with reconfigurability (LSJR). Compared with most of the existing LSVSJ, this novel LSJR ensures the contact performance between the roller and the leaf spring, uses a novel effective length adjustment mechanism, and provides multiple stiffness ranges. The compliance of the LSJR is generated by leaf springs. The spring is bent by two rows of rollers integrated on an adjustable slider, where the distance between the rollers can be changed so as to achieve a tighter contact between the roller and the spring. The effective length of the spring can be adjusted by changing the position of the slider locating at the intersection between a straight rail and an arcuate rail. A reconfigurable number of leaf springs provide six different stiffness ranges (e.g., 138.55-396.08 N·m/rad for single leaf spring, and 384.56-1222.60 N·m/rad for a combination of three leaf springs). By integrating a high-resolution angle sensor, the LSJR can also measure the joint torque. Moreover, the hollow shaft structure of the joint is beneficial for passing cables through it. Experiments were performed to measure the stiffness of the LSJR under different configurations, validate its efficacy in unexpected collisions, and investigate its potential to be a one-degree-of-freedom torque sensor. © 1996-2012 IEEE.","Compliant joint; leaf spring; variable stiffness","Degrees of freedom (mechanics); Human robot interaction; Rollers (machine components); Stiffness; Universal joints; Compliant joints; Contact performance; Degree of freedom; Length adjustment; Output impedance; Physical human-robot interactions; Reconfigurability; Variable stiffness; Leaf springs",,,,,"14202918, 415011, T42-409/18-R; Innovation and Technology Fund, ITF: ITS/112/15FP","This work was supported in part by the HK RGC under Grant T42-409/18-R, Grant 14202918, and Grant 415011, in part by the HK ITF under Grant ITS/112/15FP and Grant ITT/012/15GP.",,,,,,,,,,"Yamada, Y., Hirasawa, Y., Huang, S., Umetani, Y., Suita, K., Humanrobot contact in the safeguardng space (1997) IEEE/ASME Trans.Mechatronics, 2 (4), pp. 230-236. , Dec; De Santis, A., Siciliano, B., De Luca, A., Bicchi, A., An atlas of physical human-robot interaction (2008) Mechanism Mach. 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Autom., pp. 434-439; Liu, L., Leonhardt, S., Ngo, C., Misgeld, B.J., Impedance-controlled variable stiffness actuator for lower limb robot applications (2020) IEEE Trans. Autom. Sci., 17 (2), pp. 991-1004. , Apr; Sardellitti, I., Medrano-Cerda, G.A., Tsagarakis, N., Jafari, A., Caldwell, D.G., Gain scheduling control for a class of variable stiffness actuators based on lever mechanisms (2013) IEEE Trans. Robot., 29 (3), pp. 791-798. , Jun; Chang, H., Kim, S.J., Kim, J., Feedforward motion control with a variable stiffness actuator inspired by muscle cross-bridge kinematics (2019) IEEE Trans. Robot., 35 (3), pp. 747-760. , Jun","Wang, Z.; T Stone Robotics Institute and Department of Mechanical and Automation Engineering, Hong Kong; email: zrwang@mae.cuhk.edu.hk",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10834435,,IATEF,,"English","IEEE ASME Trans Mechatron",Article,"Final","",Scopus,2-s2.0-85089941774
"Bouteraa Y., Abdallah I.B., Elmogy A., Ibrahim A., Tariq U., Ahmad T.","57202361442;57202360704;35731342700;24174699500;14827558600;57201882800;","A fuzzy logic architecture for rehabilitation robotic systems",2020,"International Journal of Computers, Communications and Control","15","4","3814","1","17",,7,"10.15837/IJCCC.2020.4.3814","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087838411&doi=10.15837%2fIJCCC.2020.4.3814&partnerID=40&md5=b4d7bcbf10813acb0b176a922c4abb55","Department of Computer Engineering, Prince Sattam Bin Abdulaziz University, Saudi Arabia; Digital Research Center of Sfax and CEM Lab-ENIS, University of Sfax, Tunisia; Computers and Control Eng. Department, Faculty of Engineering, Tanta University, Egypt; Department of Information Systems, Prince Sattam Bin Abdulaziz University, Saudi Arabia","Bouteraa, Y., Department of Computer Engineering, Prince Sattam Bin Abdulaziz University, Saudi Arabia, Digital Research Center of Sfax and CEM Lab-ENIS, University of Sfax, Tunisia; Abdallah, I.B., Digital Research Center of Sfax and CEM Lab-ENIS, University of Sfax, Tunisia; Elmogy, A., Department of Computer Engineering, Prince Sattam Bin Abdulaziz University, Saudi Arabia, Computers and Control Eng. Department, Faculty of Engineering, Tanta University, Egypt; Ibrahim, A., Department of Computer Engineering, Prince Sattam Bin Abdulaziz University, Saudi Arabia; Tariq, U., Department of Information Systems, Prince Sattam Bin Abdulaziz University, Saudi Arabia; Ahmad, T., Department of Information Systems, Prince Sattam Bin Abdulaziz University, Saudi Arabia","Robots are highly incorporated in rehabilitation in the last decade to compensate lost functions in disabled individuals. By controlling the rehabilitation robots from far, many benefits are achieved. These benefits include but not restricted to minimum hospital stays, decreasing cost, and increasing the level of care. The main goal of this work is to have an effective solution to take care of patients from far. Tackling the problem of the remote control of rehabilitation robots is undergoing and highly challenging. In this paper, a remote wrist rehabilitation system is presented. The developed system is a sophisticated robot ensuring the two wrist movements (Flexion/extension and abduction/adduction). Additionally, the proposed system provides a software interface enabling the physiotherapists to control the rehabilitation process remotely. The patient's safety during the therapy is achieved through the integration of a fuzzy controller in the system control architecture. The fuzzy controller is employed to control the robot action according to the pain felt by the patient. By using fuzzy logic approach, the system can adapt effectively according to the patients' conditions. The Queue Telemetry Transport Protocol (MQTT) is considered to overcome the latency during the human robot interaction. Based on a Kinect camera, the control technique is made gestural. The physiotherapist gestures are detected and transmitted to the software interface to be processed and be sent to the robot. The acquired measurements are recorded in a database that can be used later to monitor patient progress during the treatment protocol. The obtained experimental results show the effectiveness of the developed remote rehabilitation system. Copyright. © 2020 by the authors.","Fuzzy logic; IoT; Rehabilitation robotics; Vision-based gesture control",,,,,,"Prince Sattam bin Abdulaziz University, PSAU: 2019/01/10761","The authors would like to acknowledge the support of the Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University under the research project # 2019/01/10761.",,,,,,,,,,"Abdallah, I.B., Bouteraa, Y., Boucetta, R., Chokri, R., Kinect-based Computed Torque Control for lynxmotion robotic arm (2015) 2015 7th International Conference on Modelling, Identification and Control (ICMIC), pp. 1-6; Abdallah, I.B., Bouteraa, Y., Chokri, R., Kinect-based sliding mode control for Lynxmotion robotic arm (2016) Advances in Human-Computer Interaction; Ali, A., Ahmed, S., Kadir, K.A., Karman, M.J., Yarooq, R.S., Fuzzy PID controller for upper limb rehabilitation robotic system (2018) 2018 IEEE International Conference on Innovative Research and Development (ICIRD), , IEEE, 2018; Ates, S., Haarman, C., Stienen, A., SCRIPT passive orthosis: Design of interactive hand and wrist exoskeleton for rehabilitation at home after stroke (2017) Autonomous Robots, 41 (3), pp. 711-723; Bouteraa, Y., Abdallah, I.B., Exoskeleton robots for upper-limb rehabilitation (2016) 2016 13th International Multi-Conference on Systems, Signals & Devices (SSD), , IEEE, 2016; Bouteraa, Y., Abdallah, I.B., A gesture-based telemanipulation control for a robotic arm with biofeedback-based grasp (2017) Industrial Robot: An International Journal, 44 (5), pp. 575-587; Bouteraa, Y., Abdallah, I.B., Chokri, R., Design of smart robot for wrist rehabilitation (2016) International journal of smart sensing and intelligent systems, 9 (2), pp. 1029-1053; Bouteraa, Y., Abdallah, I.B., Elmogy, A., Training of Hand Rehabilitation Using Low Cost Exoskeleton and Vision-Based Game Interface (2019) Journal of Intelligent & Robotic Systems, 96, pp. 31-47; Bower, K.J., Louie, J., Landesrocha, Y., Clinical feasibility of interactive motion-controlled games for stroke rehabilitation (2015) Journal of NeuroEngineering and Rehabilitation, 12 (63), p. 2015; Ding, I., Chang, C., Eigenspace-based Method with a User Adaptation Scheme for Human Gesture Recognition by Using Kinect 3D Data (2015) Applied Mathematical Modelling, Elsevier, 39 (19), pp. 5769-5777; Du, G., Zhang, P., Markerless human-robot interface for dual robot manipulators using Kinect sensor (2014) Robotics and Computer-Integrated Manufacturing, 30, pp. 150-159. , Elsevier; Dzitac, I., Filip, F.G., Manolescu, M.J., Fuzzy logic is not fuzzy: World-renowned computer scientist LotfiA. 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J. Comput. Commun. Control",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85087838411
"Andriella A., Torras C., Alenyà G.","57202547412;7003421114;6508265237;","Cognitive System Framework for Brain-Training Exercise Based on Human-Robot Interaction",2020,"Cognitive Computation","12","4",,"793","810",,7,"10.1007/s12559-019-09696-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088605524&doi=10.1007%2fs12559-019-09696-2&partnerID=40&md5=c036b6871e6e7f31c18a5ebf073e7b26","Institut de Robòtica i Informàtica Industrial, CSIC-UPC, C/Llorens i Artigas 4-6, Barcelona, 08028, Spain","Andriella, A., Institut de Robòtica i Informàtica Industrial, CSIC-UPC, C/Llorens i Artigas 4-6, Barcelona, 08028, Spain; Torras, C., Institut de Robòtica i Informàtica Industrial, CSIC-UPC, C/Llorens i Artigas 4-6, Barcelona, 08028, Spain; Alenyà, G., Institut de Robòtica i Informàtica Industrial, CSIC-UPC, C/Llorens i Artigas 4-6, Barcelona, 08028, Spain","Every 3 seconds, someone develops dementia worldwide. Brain-training exercises, preferably involving also physical activity, have shown their potential to monitor and improve the brain function of people affected by Alzheimer disease (AD) or mild cognitive impairment (MCI). This paper presents a cognitive robotic system designed to assist mild dementia patients during brain-training sessions of sorting tokens, an exercise inspired by the Syndrom KurzTest neuropsychological test (SKT). The system is able to perceive, learn and adapt to the user’s behaviour and is composed of two main modules. The adaptive module based on representing the human-robot interaction as a planning problem, that can adapt to the user performance offering different encouragement and recommendation actions using both verbal and gesture communication in order to minimize the time spent to solve the exercise. As safety is a very important issue, the cognitive system is enriched with a safety module that monitors the possibility of physical contact and reacts accordingly. The cognitive system is presented as well as its embodiment in a real robot. Simulated experiments are performed to (i) evaluate the adaptability of the system to different patient use-cases and (ii) validate the coherence of the proposed safety module. A real experiment in the lab, with able users, is used as preliminary evaluation to validate the overall approach. Results in laboratory conditions show that the two presented modules effectively provide additional and essential functionalities to the system, although further work is necessary to guarantee robustness and timely response of the robot before testing it with patients. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.","Adaptive robot; Cognitive robotic system; Cognitive training; HRI; Robot; Safety; Socially assistive robotics","Accident prevention; Brain; Cognitive systems; Intelligent robots; Man machine systems; Neurodegenerative diseases; Robotics; Robots; Social robots; Assistive robotics; Cognitive robotics; Cognitive training; Laboratory conditions; Mild cognitive impairments (MCI); Neuropsychological tests; Simulated experiments; Training exercise; Robot programming",,,,,"Horizon 2020 Framework Programme, H2020: 721619; Ministerio de Ciencia e Innovación, MICINN: TIN2017-90086-R; Agencia Estatal de Investigación, AEI: MDM-2016-0656","This project has been partially funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement SOCRATES (No 721619), by the Spanish Ministry of Science and Innovation HuMoUR (TIN2017-90086-R), and by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). Acknowledgements","We would like to thank Carla Abdelnour, Joan Hernandez and Natalia Tantinya from Fundaci? ACE for the fruitful discussions and the help in the design of the sorting tokens exercise.",,,,,,,,,"(2018) Alzheimer’s Disease International (ADI), pp. 1-48. , https://www.alz.co.uk/research/WorldAlzheimerReport2018.pdf; Abdelnour, C., Tantyna, N., Hernandez, J., Giakoumis, D., Ribes, J.C., Gerlowska, J., Skrobas, U., Boada, M., Are there differences in the opinion of patients with Alzheimer’s disease and their caregivers about having support from a service robot at home? 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IROS, 2009, pp. 35-41; Lampit, A., Valenzuela, M., Gates, N., Computerized cognitive training is beneficial for older adults (2015) J Am Geriatr Soc, 63 (12), pp. 2610-2612; Lasota, P., Fong, T., Shah, J.A., A survey of methods for safe human-robot interaction (2017) Found Trends Robot, 5 (3), pp. 261-349; Lasota, P., Rossano, G., Shah, J.A., Toward safe close-proximity human-robot interaction with standard industrial robots (2014) IEEE International Conference on Automation Science and Engineering, pp. 339-344; Lemaignan, S., Warnier, M., Sisbot, E.A., Clodic, A., Alami, R., Artificial cognition for social human–robot interaction: an implementation (2017) Artif Intell, 247, pp. 45-69; Levesque, H., Lakemeyer, G., Cognitive robotics (2008) Found Artif Intell, 3 (7), pp. 869-886; Li, K., Fu, Y., Prediction of human activity by discovering temporal sequence patterns (2014) IEEE Trans Pattern Anal Mach Intell, 36 (8), pp. 1644-1657; Mainprice, J., Sisbot, E.A., Jaillet, L., Cortės, J., Alami, R., Simėon, T., Planning human-aware motions using a sampling-based costmap planner (2011) Proceedings - IEEE International Conference on Robotics and Automation, pp. 5012-5017; Marti̇n, F., Agu̇ero, C., Caṅas, J.M., Abella, G., Beni̇tez, R., Rivero, S., Valenti, M., Marti̇nez-marti̇n, P., Robots in therapy for dementia patients (2013) Journal of Physical Agents, 7 (1), pp. 49-56; Matthias, B., Reisinger, T., Example application of ISO/TS 15066 to a collaborative assembly scenario (2016) Inproceedings of the 47Th International Symposium on Robotics, pp. 1-5; McCallum, S., Boletsis, C., Dementia games: A literature review of dementia-related serious games (2013) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2013, 8101, pp. 15-27. , LNCS; McColl, D., Nejat, G., Meal-time with a socially assistive robot and older adults at a long-term care facility (2013) J Human-Robot Interact, 2 (1), pp. 152-171; Mohan, V., Morasso, P., Sandini, G., Kasderidis, S., Inference through embodied simulation in cognitive robots (2013) Cogn Comput, 5 (3), pp. 355-382; Nikolaidis, S., Lasota, P., Rossano, G., Martinez, C., Fuhlbrigge, T., Shah, J., Human-robot collaboration in manufacturing: Quantitative evaluation of predictable, convergent joint action (2013) 2013 44Th International Symposium on Robotics, ISR 2013. IEEE, pp. 1-6; Pérula-Martínez, R., Castro-González, Á., Malfaz, M., Alonso-Martín, F., Salichs, M.A., Bioinspired decision-making for a socially interactive robot (2019) Cogn Syst Res, 54, pp. 287-301; Ragaglia, M., Bascetta, L., Rocco, P., Zanchettin, A.M., Integration of perception, control and injury knowledge for safe human-robot interaction (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 1196-1202; Salichs, E., Fernández-Rodicio, E., Castillo, J., Castro-González, Á., Malfaz, M., Salichs, M.Á., A social robot assisting in cognitive stimulation therapy (2018) Advances in Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection, pp. 344-347. , Demazeau Y, An B, Bajo J, Fernández-Caballero A, Cham, Springer International Publishing; Sheikh, Z.C., Simon, T., Wei, S.E., (2017) Yaser: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields CVPR; Sisbot, E.A., Alami, R., A human-aware manipulation planner (2012) IEEE Trans Robot, 28 (5), pp. 1045-1057; Sisbot, E.A., Marin-Urias, L.F., Broquėre, X., Sidobre, D., Alami, R., Synthesizing robot motions adapted to human presence: a planning and control framework for safe and socially acceptable robot motions (2010) Int J Soc Robot, 2 (3), pp. 329-343; Tapus, A., Mataric, M., Socially assistive robotic music therapist for maintaining attention of older adults with cognitive impairments (2008) Proceedings of AAAI Fall Symposium AI in Eldercare, pp. 124-127; Tapus, A., Mataric, M.J., Scassellati, B., Socially assistive robotics [Grand challenges of robotics] (2007) IEEE Robot Autom Mag, 14 (1), pp. 35-42","Andriella, A.; Institut de Robòtica i Informàtica Industrial, C/Llorens i Artigas 4-6, Spain; email: aandriella@iri.upc.edu",,,"Springer",,,,,18669956,,,,"English","Cognitive Comput.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85088605524
"Ding Y., Guo W., Liu X., Luo Z.","7404136191;56484963300;22993818600;12646073900;","A Human-Guided Vision-Based Measurement System for Multi-Station Robotic Motion Platform Based on V-Rep",2020,"Robotica","38","7",,"1227","1241",,,"10.1017/S0263574719001371","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072771651&doi=10.1017%2fS0263574719001371&partnerID=40&md5=abc2b4c4de6e840f05232c9e60f08842","Key Laboratory of Mechanism Theory and Equipment Design, Ministry of Education, Tianjin University, Tianjin, 300354, China; School of Engineering, University of Warwick, Coventry, United Kingdom","Ding, Y., Key Laboratory of Mechanism Theory and Equipment Design, Ministry of Education, Tianjin University, Tianjin, 300354, China; Guo, W., Key Laboratory of Mechanism Theory and Equipment Design, Ministry of Education, Tianjin University, Tianjin, 300354, China; Liu, X., School of Engineering, University of Warwick, Coventry, United Kingdom; Luo, Z., Key Laboratory of Mechanism Theory and Equipment Design, Ministry of Education, Tianjin University, Tianjin, 300354, China","In the manufacturing process of sophisticated and individualized large components, classical solutions to build large machine tools cannot meet the demand. A hybrid robot, which is made up of a 3 degree-of-freedom (3-DOF) parallel manipulator and a 2-DOF serial manipulator, has been developed as a plug-and-play robotized module that can be rapidly located in multi-stations where machining operations can be performed in situ. However, processing towards high absolute accuracy has become a huge challenge due to the movement of robot platform. In this paper, a human-guided vision system is proposed and integrated in the robot system to improve the accuracy of the end-effector of a robot. A handheld manipulator is utilized as a tool for human-robot interaction in the large-scale unstructured circumstances without intelligence. With 6-DOF, humans are able to manipulate the robot (end-effector) so as to guide the camera to see target markers mounted on the machining datum. Simulation is operated on the virtual control platform V-Rep, showing a high robust and real-time performance on mapping human manipulation to the end-effector of robot. And then, a vision-based pose estimation method on a target marker is proposed to define the position and orientation of machining datum, and a compensation method is applied to reduce pose errors on the entire machining trajectory. The algorithms are tested on V-Rep, and the results show that the absolute pose error reduces greatly with the proposed methods, and the system is immune to the motion deviation of the robot platform. © 2019 Cambridge University Press.","5-DOF hybrid robot; Human-guided manipulator; Pose estimation; V-Rep; Vision-based measurement system","Degrees of freedom (mechanics); End effectors; Human robot interaction; Industrial robots; Intelligent robots; Machine components; Machine tools; Manipulators; Simulation platform; Virtual reality; 3 degree of freedom (3-DOF); Hybrid robots; Machining trajectories; Manufacturing process; Pose estimation; Position and orientations; Real time performance; Vision-based measurements; Visual servoing",,,,,"Horizon 2020 Framework Programme, H2020: 734272; National Natural Science Foundation of China, NSFC: 51420105007, 51775376; National Key Research and Development Program of China, NKRDPC: 2017YFE0111300","This work was supported by the National Key Research and Development Program (2017YFE0111300), National Natural Science Foundation of China (51775376 and 51420105007), and EU H2020 project (734272).",,,,,,,,,,"Summers, M., Robot capability test and development of industrial robot positioning system for the aerospace industry (2005) SAE Aerospace Manufacturing and Automated Fastening Conference, , Texas, USA Paper No. 01-3336; Neumann, K.E., The key to aerospace automation (2006) SAE Aerospace Manufacturing and Automated Fastening Conference and Exhibition, , Detroit, USA Paper No. 01-3144; Neumann, K.E., Tricept application (2002) Proceedings of the 3rd Chemnitz Parallel Kinematics Seminar, pp. 547-551. , Zwickau, Germany; Huang, T., Dong, C., Liu, H., Qin, X., Mei, J., Liu, Q., Wang, M., (2018) Five-Degree-of-Freedom Hybrid Robot with Rotational Supports, , United States Patent, No.: US; Frommknecht, A., Kuehnle, J., Effenberger, I., Sergej, P., MulTi-sensor measurement system for robotic drilling (2017) Rob. Comput.-Integr. Manuf., 47, pp. 4-10; Galetto, M., Advances in large-scale metrology-review and future trends (2016) CIRP Ann. Manuf. Technol., 65 (2), pp. 643-665; Vincze, M., Prenninger, J.P., Gander, H., A laser tracking system to measure position and orientation of robot end effectors under motion (1994) Int. J. Rob. Res., 13 (4), pp. 305-314; Flynn, R., Synthesizing metrology technologies to reduce engineering time for large cnc machine compensation (2011) SAE Int. J. Mater. Manuf., 5 (1), pp. 49-59; Estler, W.T., Edmundson, K.L., Peggs, G.N., Parker, D.H., Large-scale metrology-An update (2002) CIRP Ann. Manuf. Technol., 51 (2), pp. 587-609; Zhang, X., Song, Y., Yang, Y., Pan, H., Stereo vision based autonomous robot calibration (2017) Rob. Auton. Syst., 93, pp. 43-51; Roth, Z.S., Zhuang, H., Wang, K., Simultaneous calibration of a robot and a hand-mounted camera (1995) T-R A, 11 (5), pp. 649-660; Flandin, G., Chaumette, F., Marchand, E., Eye-in-hand/eye-to-hand cooperation for visual servoing (2000) Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2741-2746. , San Francisco, CA, USA; Li, X., Cheah, C.C., Human-guided robotic manipulation: Theory and experiments (2014) Proceedings of the IEEE International Conference on Robotics and Automation, pp. 4594-4599. , Hong Kong, China; Li, X., Chi, G., Vidas, S., Cheah, C.C., Human-guided robotic comanipulation: Two illustrative scenarios (2016) IEEE Trans. Control Syst. Technol., 24 (5), pp. 1751-1763; Krüger, J., Lien, T.K., Verl, A., Cooperation of human and machines in assembly lines (2009) CIRP Ann. Manuf. Technol., 58 (2), pp. 628-646; Morioka, M., Sakakibara, S., A new cell production assembly system with human-robot cooperation (2010) CIRP Ann. 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"Fernández-Rodicio E., Castro-González Á., Alonso-Martín F., Maroto-Gómez M., Salichs M.Á.","57195252678;36655483500;55215683700;57203536791;6603829011;","Modelling multimodal dialogues for social robots using communicative acts",2020,"Sensors (Switzerland)","20","12","3440","1","30",,4,"10.3390/s20123440","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086639154&doi=10.3390%2fs20123440&partnerID=40&md5=3fac12b2299271f597860bb122f30590","Robotics Lab, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés, Madrid, 28911, Spain","Fernández-Rodicio, E., Robotics Lab, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés, Madrid, 28911, Spain; Castro-González, Á., Robotics Lab, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés, Madrid, 28911, Spain; Alonso-Martín, F., Robotics Lab, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés, Madrid, 28911, Spain; Maroto-Gómez, M., Robotics Lab, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés, Madrid, 28911, Spain; Salichs, M.Á., Robotics Lab, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés, Madrid, 28911, Spain","Social Robots need to communicate in a way that feels natural to humans if they are to effectively bond with the users and provide an engaging interaction. Inline with this natural, effective communication, robots need to perceive and manage multimodal information, both as input and output, and respond accordingly. Consequently, dialogue design is a key factor in creating an engaging multimodal interaction. These dialogues need to be flexible enough to adapt to unforeseen circumstances that arise during the conversation but should also be easy to create, so the development of new applications gets simpler. In this work, we present our approach to dialogue modelling based on basic atomic interaction units called Communicative Acts. They manage basic interactions considering who has the initiative (the robot or the user), and what is his/her intention. The two possible intentions are either ask for information or give information. In addition, because we focus on one-to-one interactions, the initiative can only be taken by the robot or the user. Communicative Acts can be parametrised and combined in a hierarchical manner to fulfil the needs of the robot’s applications, and they have been equipped with built-in functionalities that are in charge of low-level communication tasks. These tasks include communication error handling, turn-taking or user disengagement. This system has been integrated in Mini, a social robot that has been created to assist older adults with cognitive impairment. In a case of use, we demonstrate the operation of our system as well as its performance in real human–robot interactions. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Dialogue management; Dialogue modelling; Human–robot interaction; Multimodal interaction","Atomic interactions; Cognitive impairment; Communication errors; Dialogue modelling; Effective communication; Multi-modal information; Multi-Modal Interactions; Multimodal dialogue; Social robots; aged; emotion; female; human; interpersonal communication; male; robotics; social interaction; Aged; Communication; Emotions; Female; Humans; Male; Robotics; Social Interaction",,,,,"S2018/NMT-4331; Ministerio de Ciencia, Innovación y Universidades, MCIU; European Commission, EC: RTI2018-096338-B-I00; Ministerio de Economía y Competitividad, MINECO: RoboCity2030-DIH-CM; Agencia Estatal de Investigación, AEI","The research leading to these results has received funding from the projects Development of social robots to help seniors with cognitive impairment (ROBSEN), funded by the Ministerio de Economia y Competitividad; RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by ?Programas de Actividades I+D en la Comunidad de Madrid? and cofunded by Structural Funds of the EU; and Robots sociales para estimulaci?n f?sica, cognitiva y afectiva de mayores (ROSES) RTI2018-096338-B-I00 funded by Agencia Estatal de Investigaci?n (AEI), Ministerio de Ciencia, Innovaci?n y Universidades.","Funding: The research leading to these results has received funding from the projects Development of social robots to help seniors with cognitive impairment (ROBSEN), funded by the Ministerio de Economia y Competitividad; RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by “Programas de Actividades I+D en la Comunidad de Madrid” and cofunded by Structural Funds of the EU; and Robots sociales para estimulación física, cognitiva y afectiva de mayores (ROSES) RTI2018-096338-B-I00 funded by Agencia Estatal de Investigación (AEI), Ministerio de Ciencia, Innovación y Universidades.",,,,,,,,,"Bui, H., Chong, N.Y., An Integrated Approach to Human-Robot-Smart Environment Interaction Interface for Ambient Assisted Living Proceedings of the IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), pp. 32-37. , Genova, Italy, 27–28 September 2018; Barrett, E., Burke, M., Whelan, S., Santorelli, A., Oliveira, B., Cavallo, F., Dröes, R., Meiland, F., Evaluation of a Companion Robot for Individuals With Dementia: Quantitative Findings of the MARIO Project in an Irish Residential Care Setting (2019) J. 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J. Soc. Robot, 8, pp. 85-102; Gómez, M.M., Castro-González, Á., Castillo, J.C., Malfaz, M., Salichs, M.A., A Bio-inspired Motivational Decision Making System for Social Robots Based on the Perception of the User (2018) Sensors, 8, p. 2691; Goodman, T., Spence, R., The effect of System Response Time on interactive computer aided problem solving (1978) ACM SIGGRAPH Comput. Graph, 12, pp. 100-104; Guynes, J.L., Impact of system response time on state anxiety (1988) Commun. ACM, 31, pp. 342-347; Miller, R.B., Response time in man-computer conversational transactions (1968) Proceedings of the AFIPS Fall Joint Computer Conference, 33, pp. 267-277. , San Francisco, CA, USA, 9–11 December; Shiwa, T., Kanda, T., Imai, M., Ishiguro, H., Hagita, N., Anzai, Y., How Quickly Should Communication Robots Respond? (2009) J. Robot. Soc. Jpn, 27, pp. 87-95","Fernández-Rodicio, E.; Robotics Lab, Av. de la Universidad 30, Spain; email: enrifern@ing.uc3m.es",,,"MDPI AG",,,,,14248220,,,"32570807","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85086639154
"Tizzano G.R., Spezialetti M., Rossi S.","57218354794;55421242300;55619320887;","A Deep Learning Approach for Mood Recognition from Wearable Data",2020,"IEEE Medical Measurements and Applications, MeMeA 2020 - Conference Proceedings",,,"9137218","","",,,"10.1109/MeMeA49120.2020.9137218","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088901849&doi=10.1109%2fMeMeA49120.2020.9137218&partnerID=40&md5=4ba4b5d24b0e99451a27445f44b4c82e","University of Naples Federico II, Department of Electrical Engineering and Information Technologies, Naples, Italy","Tizzano, G.R., University of Naples Federico II, Department of Electrical Engineering and Information Technologies, Naples, Italy; Spezialetti, M., University of Naples Federico II, Department of Electrical Engineering and Information Technologies, Naples, Italy; Rossi, S., University of Naples Federico II, Department of Electrical Engineering and Information Technologies, Naples, Italy","Emotion and mood recognition plays a key role in human-robot interaction, especially in the context of socially assistive robotics. Mood-aware robots could be useful as companions and social assistants for elders and people affected by depression and other mood disorders. An interesting option for continuously tracking a user's mood is the use of wearable and mobile devices. However, the classification of the mood from physiological and kinematics data is still a challenge, due to intersubjects differences: on one hand, 'one-fits-all' classification approaches usually achieve lower accuracy than person-specific methods; on the other hand, personalized models require in general a large amount of data from a single subject to be trained and, therefore, becomes effective after long periods of acquisition. In this paper, we propose a deep learning approach for mood recognition from a publicly available dataset that includes a gyroscope, accelerometer, and heart-rate data. We propose the use of long-short term memory networks (LSTM), testing them both as classifiers and as features extractors in hybrid models. We compared their performances both against and in conjunction with traditional machine learning approaches, namely support vector machines (SVM) and Gaussian mixture models (GMM). We also consider transfer learning strategies to reduce the amount of personal data needed to train the model. Our results show that the use of LSTMs significantly improves the classification accuracy with respect to machine learning approaches, especially if employed as feature extractors and combined with SVM. However, we observed that transfer learning does not achieve significant results in boosting the training of a personalized model. © 2020 IEEE.","Affect sensing and analysis; Affective computing; Deep learning; Machine learning","Learning systems; Long short-term memory; mHealth; Physiological models; Social robots; Support vector machines; Transfer learning; Wearable technology; Assistive robotics; Classification accuracy; Classification approach; Feature extractor; Gaussian Mixture Model; Machine learning approaches; Personalized model; Short term memory; Deep learning",,,,,"2015KBL78T; Ministero dell’Istruzione, dell’Università e della Ricerca, MIUR","This work has been supported by MIUR (Italian Ministry of Education, Universities, and Research) within the PRIN 2015 research project “UPA4SAR - User-centered Profiling and Adaptation for Socially Assistive Robotics” (grant n. 2015KBL78T).",,,,,,,,,,"Busetta, P., Kuflik, T., Merzi, M., Rossi, S., Service delivery in smart environments by implicit organizations (2004) MOBIQUITOUS, pp. 356-363. , Aug; Rossi, S., Staffa, M., Tamburro, A., Socially assistive robot for providing recommendations: Comparing a humanoid robot with a mobile application (2018) International Journal of Social Robotics, 10, pp. 265-278. , Jan; Beedie, C., Terry, P., Lane, A., Distinctions between emotion and mood (2005) Cognition and Emotion, 19 (6), pp. 847-878; Karg, M., Samadani, A., Gorbet, R., Khnlenz, K., Hoey, J., Kuli, D., Body movements for affective expression: A survey of automatic recognition and generation (2013) IEEE Transactions on Affective Computing, 4, pp. 341-359. , Oct; Jenke, R., Peer, A., Buss, M., Feature extraction and selection for emotion recognition from EEG (2014) IEEE Transactions on Affective Computing, 5, pp. 327-339. , July; Cacioppo, J.T., Berntson, G.G., Larsen, J.T., Poehlmann, K.M., Ito, T.A., The psychophysiology of emotion (2000) Handbook of Emotions, 2, pp. 173-191; Quiroz, J.C., Yong, M.H., Geangu, E., Emotion-recognition using smart watch accelerometer data: Preliminary findings (2017) ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 805-812; Rossi, S., Bove, L., Di Martino, S., Ercolano, G., A two-step framework for novelty detection in activities of daily living (2018) Social Robotics, pp. 329-339. , (S. S. Ge, J.-J. Cabibihan, M. A. Salichs, E. Broadbent, H. He, A. R. Wagner, and á. Castro-González, eds.), (Cham) Springer International Publishing; Quiroz, J.C., Geangu, E., Yong, M.H., Emotion recognition using smart watch sensor data: Mixed-design study (2018) JMIR Ment Health, 5, p. e10153. , Aug; Zenonos, A., Khan, A., Kalogridis, G., Vatsikas, S., Lewis, T., Sooriyabandara, M., Healthyoffice: Mood recognition at work using smartphones and wearable sensors (2016) 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp. 1-6. , March; LiKamWa, R., Liu, Y., Lane, N., Zhong, L., Moodscope: Building a mood sensor from smartphone usage patterns (2013) MobiSys 2013-Proceedings of the 11th Annual International Conference on Mobile Systems, Applications, and Services, pp. 389-401; Zhang, Z., Song, Y., Cui, L., Liu, X., Zhu, T., Emotion recognition based on customized smart bracelet with built-in accelerometer (2016) PeerJ, 4, p. e2258",,,"IEEE;IEEE Instrumentation and Measurement Society;IEEE Sensors Council Italy Chapter;Politecnica di Bari;Politecnico di Torino;Societa Italiana di Analisi del Movimento in Clinica","Institute of Electrical and Electronics Engineers Inc.","15th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2020","1 June 2020 through 3 June 2020",,161821,,9781728153865,,,"English","IEEE Med. Meas. Appl., MeMeA - Conf. Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85088901849
"Benli E., Motai Y., Rogers J.","57194856620;9734468900;57207977901;","Visual Perception for Multiple Human-Robot Interaction from Motion Behavior",2020,"IEEE Systems Journal","14","2","8944061","2937","2948",,1,"10.1109/JSYST.2019.2958747","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086070002&doi=10.1109%2fJSYST.2019.2958747&partnerID=40&md5=8a8fd94956c631b00b276223eae38a71","Department of Electrical and Electronics Engineering, Gümüşhane University, Gümüşhane, 29100, Turkey; Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, United States; U.S. Army Research Laboratory, Adelphi, MD, United States","Benli, E., Department of Electrical and Electronics Engineering, Gümüşhane University, Gümüşhane, 29100, Turkey; Motai, Y., Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, United States; Rogers, J., U.S. Army Research Laboratory, Adelphi, MD, United States","Visual perception is an important component for human-robot interaction processes in robotic systems. Interaction between humans and robots depends on the reliability of the robotic vision systems. The variation of camera sensors and the capability of these sensors to detect many types of sensory inputs improve the visual perception. The analysis of activities, motions, skills, and behaviors of humans and robots have been addressed by utilizing the heat signatures of the human body. The human motion behavior is analyzed by body movement kinematics, and the trajectory of the target is used to identify the objects and the human target in the omnidirectional (O-D) thermal images. The process of human target identification and gesture recognition by traditional sensors have problem for multitarget scenarios since these sensors may not keep all targets in their narrow field of view (FOV) at the same time. O-D thermal view increases the robots' line-of-sights and ability to obtain better perception in the absence of light. The human target is informed of its position, surrounding objects and any other human targets in its proximity so that humans with limited vision or vision disability can be assisted to improve their ability in their environment. The proposed method helps to identify the human targets in a wide FOV and light independent conditions to assist the human target and improve the human-robot and robot-robot interactions. The experimental results show that the identification of the human targets is achieved with a high accuracy. © 2007-2012 IEEE.","Command cognition; human motion analysis; multiple human targets; multiple robots; omnidirectional (O-D) camera; robotic perception; target identification; thermal vision; visual perception; walking behavior","Behavioral research; Man machine systems; Robotics; Vision; Field of views; Heat signatures; Motion behavior; Robot interactions; Robotic systems; Target identification; Thermal images; Visual perception; Human robot interaction",,,,,"Army Research Laboratory, ARL; Naval Surface Warfare Center, NSWC; U.S. Navy","Manuscript received February 20, 2019; revised September 9, 2019 and November 24, 2019; accepted December 5, 2019. Date of publication December 27, 2019; date of current version June 3, 2020. This work was supported in part by the U.S. Navy, Naval Surface Warfare Center Dahlgren, in part by the U.S. Army Research Laboratory, and in part by the Ministry of National Education of Turkey. (Corresponding author: Emrah Benli.) E. Benli is with the Department of Electrical and Electronics Engineering, Gümüs¸hane University, Gümüs¸hane 29100, Turkey (e-mail: benlie@vcu.edu).",,,,,,,,,,"Benli, E., Motai, Y., Rogers, J., Human behavior-based target tracking with an omni-directional thermal camera (2019) IEEE Trans. Cogn. Develop. Syst, 11 (1), pp. 36-50. , Mar; Cavaliere, D., Senatore, S., Loia, V., Proactive UAVs for cognitive contextual awareness (2019) IEEE Syst. J, 13 (3), pp. 3568-3579. , Sep; Ju, Z., Ji, X., Li, J., Liu, H., An Integrative framework of human hand gesture segmentation for human-robot interaction (2017) IEEE Syst. J, 11 (3), pp. 1326-1336. , Sep; Hwang, C., Chen, B., Syu, H., Wang, C., Karkoub, M., Humanoid robot's visual imitation of 3-D motion of a human subject using neural-networkbased inverse kinematics (2016) IEEE Syst. J, 10 (2), pp. 685-696. , Jun; Gupta, M., Kumar, S., Behera, L., Subramanian, V.K., A novel vision-based tracking algorithm for a human-following mobile robot (2017) IEEE Trans. Syst. Man Cybern., Syst, 47 (7), pp. 1415-1427. , Jul; Gaglio, S., Re, G.L., Morana, M., Human activity recognition process using 3-D posture data (2015) IEEE Trans. Human-Mach. Syst, 45 (5), pp. 586-597. , Oct; Ye, M., Lan, X., Wang, Z., Yuen, P.C., Bi-directional center-constrained top-ranking for visible thermal person re-identification (2019) IEEE Trans. Inf. Forensics Secur, 15, pp. 407-419. , Jun; Wang, L., Tan, T., Ning, H., Hu, W., Silhouette analysis-based gait recognition for human identification (2003) IEEE Trans. Pattern Anal. Mach. 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Man Cybern., Syst, 47 (7), pp. 1169-1180. , Jul; Qu, X., Zhang, D., Lu, G., Guo, Z., Door knob hand recognition system (2017) IEEE Trans. Syst. Man Cybern., Syst, 47 (11), pp. 2870-2881. , Nov; Caron, G., Mouaddib, E.M., Marchand, E., 3D model based tracking for omnidirectional vision:Anewspherical approach (2012) Robot.Auton. Syst, 60 (8), pp. 1056-1068. , Aug; Mariottini, G.L., Scheggi, S., Morbidi, F., Prattichizzo, D., An accurate and robust visual-compass algorithm for robot-mounted omnidirectional cameras (2012) Robot. Auton. Syst, 60 (9), pp. 1179-1190. , Sep; Bauzano, E., Estebanez, B., Garcia-Morales, I., Muñoz, V.F., Collaborative human-robot system for HALS suture procedures (2016) IEEE Syst. J, 10 (3), pp. 957-966. , Sep; Tung, T., Gomez, R., Kawahara, T., Matsuyama, T., Multiparty interaction understanding using smart multimodal digital signage (2014) IEEE Trans. Human-Mach. Syst, 44 (5), pp. 625-637. , Oct; Taniguchi, A., Taniguchi, T., Tetsunari, I., Spatial concept acquisition for a mobile robot that integrates self-localization and unsupervised word discovery from spoken sentences (2016) IEEE Trans. Cogn. Develop. Syst, 8 (4), pp. 285-297. , Dec; Mohandes, M., Deriche, M., Liu, J., Image-based and sensor-based approaches to Arabic sign language recognition (2014) IEEE Trans. Human-Mach. Syst, 44 (4), pp. 551-557. , Aug; Taniguchi, T., Nagasaka, S., Nakashima, R., Nonparametric Bayesian double articulation analyzer for direct language acquisition from continuous speech signals (2016) IEEE Trans. Cogn. Develop. Syst, 8 (3), pp. 171-185. , Sep; Cai, H., Liu, B., Zhang, J., Chen, S., Liu, H., Visual focus of attention estimation using eye center localization (2017) IEEE Syst. J, 11 (3), pp. 1320-1325. , Sep; Moacdieh, N.M., Sarter, N., Using eye tracking to detect the effects of clutter on visual search in real time (2017) IEEE Trans. Human-Mach. Syst, 47 (6), pp. 896-902. , Dec; Castellini, C., Tommasi, T., Noceti, N., Odone, F., Caputo, B., Using object affordances to improve object recognition (2011) IEEE Trans. Auton. Mental Develop, 3 (3), pp. 207-215. , Sep; Sciutti, A., Patanè, L., Nori, F., Sandini, G., Understanding object weight from human and humanoid lifting actions (2014) IEEE Trans. Auton. Mental Develop, 6 (2), pp. 80-92. , Jun; Khan, M.U.S., On the correlation of sensor location and human activity recognition in body area networks (BANs) (2018) IEEE Syst. J, 12 (1), pp. 82-91. , Mar; Hu, N., Englebienne, G., Lou, Z., Krose, B., Learning to recognize human activities using soft labels (2017) IEEE Trans.PatternAnal.Mach. Intell, 39 (10), pp. 1973-1984. , Oct; Aladrén, A., López-Nicolás, G., Puig, L., Guerrero, J.J., Navigation assistance for the visually impaired using RGB-D sensor with range expansion (2016) IEEE Syst. J, 10 (3), pp. 922-932. , Sep; Wei, P., Zhao, Y., Zheng, N., Zhu, S.C., Modeling 4D human-object interactions for joint event segmentation, recognition, and object localization (2017) IEEE Trans. Pattern Anal. Mach. Intell, 39 (6), pp. 1165-1179. , 1 Jun; Zaraki, A., Design and evaluation of a unique social perception system for human-robot interaction (2017) IEEE Trans. Cogn. Develop. Syst, 9 (4), pp. 341-355. , Dec; Qiu, S., Li, Z., He, W., Zhang, L., Yang, C., Su, C.Y., Brain-machine interface and visual compressive sensing-based teleoperation control of an exoskeleton robot (2017) IEEE Trans. Fuzzy Syst, 25 (1), pp. 58-69. , Feb; Kawanishi, Y., Voting-based hand-waving gesture spotting from a low-resolution far-infrared image sequence (2018) Proc. IEEE Visual Commun. Image Process, pp. 1-4; Hussain, S., Saxena, R., Han, X., Khan, J.A., Shin, H., Hand gesture recognition using deep learning (2017) Proc. Int. SoC Design Conf, pp. 48-49; Islam, M.R., Mitu, U.K., Bhuiyan, R.A., Shin, J., Hand gesture feature extraction using deep convolutional neural network for recognizing American sign language (2018) Proc. 4th Int. Conf. Frontiers Signal Process, pp. 115-119","Benli, E.; Department of Electrical and Electronics Engineering, Turkey; email: benlie@vcu.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,19328184,,,,"English","IEEE Syst. J.",Article,"Final","",Scopus,2-s2.0-85086070002
"Keshmiri S., Shiomi M., Sumioka H., Minato T., Ishiguro H.","36154455600;8397042200;6505862269;7005390640;26643316500;","Gentle versus strong touch classification: Preliminary results, challenges, and potentials",2020,"Sensors (Switzerland)","20","11","3033","","",,,"10.3390/s20113033","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085714206&doi=10.3390%2fs20113033&partnerID=40&md5=eef343dc34dac6a5ea2e16026685a353","Advanced Telecommunications Research Institute International (ATR), Kyoto, 619-0237, Japan; Graduate School of Engineering Science, Osaka University, Osaka, 565-0871, Japan","Keshmiri, S., Advanced Telecommunications Research Institute International (ATR), Kyoto, 619-0237, Japan; Shiomi, M., Advanced Telecommunications Research Institute International (ATR), Kyoto, 619-0237, Japan; Sumioka, H., Advanced Telecommunications Research Institute International (ATR), Kyoto, 619-0237, Japan; Minato, T., Advanced Telecommunications Research Institute International (ATR), Kyoto, 619-0237, Japan; Ishiguro, H., Advanced Telecommunications Research Institute International (ATR), Kyoto, 619-0237, Japan, Graduate School of Engineering Science, Osaka University, Osaka, 565-0871, Japan","Touch plays a crucial role in humans’ nonverbal social and affective communication. It then comes as no surprise to observe a considerable effort that has been placed on devising methodologies for automated touch classification. For instance, such an ability allows for the use of smart touch sensors in such real-life application domains as socially-assistive robots and embodied telecommunication. In fact, touch classification literature represents an undeniably progressive result. However, these results are limited in two important ways. First, they are mostly based on overall (i.e., average) accuracy of different classifiers. As a result, they fall short in providing an insight on performance of these approaches as per different types of touch. Second, they do not consider the same type of touch with different level of strength (e.g., gentle versus strong touch). This is certainly an important factor that deserves investigating since the intensity of a touch can utterly transform its meaning (e.g., from an affectionate gesture to a sign of punishment). The current study provides a preliminary investigation of these shortcomings by considering the accuracy of a number of classifiers for both, within- (i.e., same type of touch with differing strengths) and between-touch (i.e., different types of touch) classifications. Our results help verify the strength and shortcoming of different machine learning algorithms for touch classification. They also highlight some of the challenges whose solution concepts can pave the path for integration of touch sensors in such application domains as human-robot interaction (HRI). © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Human-agent physical interaction; Physical interaction; Touch classification","Contact sensors; Learning algorithms; Machine learning; Affective communication; Human robot Interaction (HRI); Real-life applications; Socially assistive robots; Solution concepts; Touch sensors; Human robot interaction; algorithm; gesture; human; machine learning; touch; Algorithms; Gestures; Humans; Machine Learning; Touch; Touch Perception",,,,,"JPMJER1401; Japan Society for the Promotion of Science, KAKEN: JP17K00293; Japan Science and Technology Agency, JST; Core Research for Evolutional Science and Technology, CREST: JPMJCR18A1","Funding: This research work was supported by Japan Science and Technology Agency (JST) CREST (Grant No. JPMJCR18A1), Japan Society for the Promotion of Science (JSPS) KAKENHI (Grant Number JP17K00293), and JST ERATO Ishiguro Symbiotic Human Robot Interaction Project (Grant Number: JPMJER1401).",,,,,,,,,,"Hertenstein, M.J., Verkamp, J.M., Kerestes, A.M., Holmes, R.M., The communicative functions of touch in humans, nonhuman primates, and rats: A review and synthesis of the empirical research (2006) Genet. Soc. Gen. Psychol. Monogr., 132, pp. 5-94; Field, T., Touch for socioemotional and physical well-being: A review (2010) Dev. Rev., 30, pp. 367-383; Nie, J., Park, M., Marin, A.L., Sundar, S.S., Can you hold my hand? Physical warmth in human-robot interaction (2012) Proceedings of the 7th ACM/IEEE International Conference on Human-Robot Interaction, 66, pp. 201-202. , Boston, MA, USA, 5-8 March; Altun, K., MacLean, K.E., Recognizing affect in human touch of a robot (2015) Pattern Recognit. Lett., 66, pp. 31-40; Yang, G.Z., Bellingham, J., Dupont, P.E., Fischer, P., Floridi, L., Full, R., Jacobstein, N., Merrifield, R., The grand challenges of Science Robotics (2018) Sci. Robot., 3, pp. 31-40; Mataric, M.J., Socially assistive robotics: Human augmentation versus automation (2018) Sci. 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"Wu R., Zhou C., Chao F., Yang L., Lin C.-M., Shang C.","57196320869;57204367634;54794935400;35199129500;26643274200;25633243000;","Integration of an actor-critic model and generative adversarial networks for a Chinese calligraphy robot",2020,"Neurocomputing","388",,,"12","23",,2,"10.1016/j.neucom.2020.01.043","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078204962&doi=10.1016%2fj.neucom.2020.01.043&partnerID=40&md5=7a0cc815425c5ddb34dd907947126364","Department of Artificial Intelligence, School of Informatics, Xiamen University361005, China; Department of Computer Science, Aberystwyth University, United Kingdom; Computer Science and Digital Technologies Department, Northumbria University, United Kingdom; Department of Electrical Engineering, Yuan Ze University, Taiwan","Wu, R., Department of Artificial Intelligence, School of Informatics, Xiamen University361005, China; Zhou, C., Department of Artificial Intelligence, School of Informatics, Xiamen University361005, China; Chao, F., Department of Artificial Intelligence, School of Informatics, Xiamen University361005, China, Department of Computer Science, Aberystwyth University, United Kingdom; Yang, L., Computer Science and Digital Technologies Department, Northumbria University, United Kingdom; Lin, C.-M., Department of Electrical Engineering, Yuan Ze University, Taiwan; Shang, C., Department of Computer Science, Aberystwyth University, United Kingdom","As a combination of robotic motion planning and Chinese calligraphy culture, robotic calligraphy plays a significant role in the inheritance and education of Chinese calligraphy culture. Most existing calligraphy robots focus on enabling the robots to learn writing through human participation, such as human–robot interactions and manually designed evaluation functions. However, because of the subjectivity of art aesthetics, these existing methods require a large amount of implementation work from human engineers. In addition, the written results cannot be accurately evaluated. To overcome these limitations, in this paper, we propose a robotic calligraphy model that combines a generative adversarial network (GAN) and deep reinforcement learning to enable a calligraphy robot to learn to write Chinese character strokes directly from images captured from Chinese calligraphic textbooks. In our proposed model, to automatically establish an aesthetic evaluation system for Chinese calligraphy, a GAN is first trained to understand and reconstruct stroke images. Then, the discriminator network is independently extracted from the trained GAN and embedded into a variant of the reinforcement learning method, the “actor-critic model”, as a reward function. Thus, a calligraphy robot adopts the improved actor-critic model to learn to write multiple character strokes. The experimental results demonstrate that the proposed model successfully allows a calligraphy robot to write Chinese character strokes based on input stroke images. The performance of our model, compared with the state-of-the-art deep reinforcement learning method, shows the efficacy of the combination approach. In addition, the key technology in this work shows promise as a solution for robotic autonomous assembly. © 2020 Elsevier B.V.","Deep reinforcement learning; Generative adversarial nets; Motion planning; Robot control; Robotic calligraphy","Deep learning; Function evaluation; Human robot interaction; Machine learning; Motion planning; Reinforcement learning; Robotic assembly; Robotics; Adversarial networks; Autonomous assembly; Combination approaches; Evaluation function; Generative adversarial nets; Reinforcement learning method; Robot controls; Robotic motion planning; Robot programming; actor-critic model; Article; artificial neural network; automation; Chinese script; controlled study; deep reinforcement learning; experimental study; generative adversarial network; handwriting; image analysis; intermethod comparison; machine learning; model; performance; priority journal; reward; robotics; technology",,,,,"Horizon 2020 Framework Programme, H2020; H2020 Marie Skłodowska-Curie Actions, MSCA: 663830; National Natural Science Foundation of China, NSFC: 61673322, 61673326, 91746103; Natural Science Foundation of Fujian Province: 2017J01128, 2017J01129; Fundamental Research Funds for the Central Universities: 20720190142","The authors are very grateful to the anonymous reviewers for their constructive comments which have helped significantly in revising this work. This work was supported by the National Natural Science Foundation of China (Nos. 61673322, 61673326, and 91746103), the Fundamental Research Funds for the Central Universities (No. 20720190142), Natural Science Foundation of Fujian Province of China (Nos. 2017J01128 and 2017J01129), and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 663830.","The authors are very grateful to the anonymous reviewers for their constructive comments which have helped significantly in revising this work. This work was supported by the National Natural Science Foundation of China (Nos. 61673322 , 61673326 , and 91746103 ), the Fundamental Research Funds for the Central Universities (No. 20720190142 ), Natural Science Foundation of Fujian Province of China (Nos. 2017J01128 and 2017J01129 ), and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 663830.",,,,,,,,,"Zeng, H., Huang, Y., Chao, F., Zhou, C., Survey of robotic calligraphy research (2016) CAAI Trans. Intell. Syst., 11 (1), pp. 15-26; Chao, F., Chen, F., Shen, Y., He, W., Sun, Y., Wang, Z., Zhou, C., Jiang, M., Robotic free writing of Chinese characters via human–robot interactions (2014) Int. J. Humanoid Rob., 11 (1), p. 1450007; Potkonjak, V., Robot Handwriting: Why and How? (2011) Interdisciplinary Applications of Kinematics, pp. 19-35. , A. Kecskeméthy V. Potkonjak A. 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"Scales P., Aycard O., Auberge V.","57219437760;6603377094;56210936000;","Studying Navigation as a Form of Interaction: A Design Approach for Social Robot Navigation Methods",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9197037","6965","6972",,2,"10.1109/ICRA40945.2020.9197037","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092711508&doi=10.1109%2fICRA40945.2020.9197037&partnerID=40&md5=2ada281825cb70c6dc661f8c59a3ec77","Univ. Grenoble Alpes, Cnrs, Grenoble, 38000, France","Scales, P., Univ. Grenoble Alpes, Cnrs, Grenoble, 38000, France; Aycard, O., Univ. Grenoble Alpes, Cnrs, Grenoble, 38000, France; Auberge, V., Univ. Grenoble Alpes, Cnrs, Grenoble, 38000, France","Social Navigation methods attempt to integrate knowledge from Human Sciences fields such as the notion of Proxemics into mobile robot navigation. They are often evaluated in simulations, or lab conditions with informed participants, and studies of the impact of the robot behavior on humans are rare. Humans communicate and interact through many vectors, among which are motion and positioning, which can be related to social hierarchy and the socio-physical context. If a robot is to be deployed among humans, the methods it uses should be designed with this in mind. This work acts as the first step in an ongoing project in which we explore how to design navigation methods for mobile robots destined to be deployed among humans. We aim to consider navigation as more than just a functionality of the robot, and to study the impact of robot motion on humans. In this paper, we focus on the person-following task. We selected a state of the art person-following method as the basis for our method, which we modified and extended in order for it to be more general and adaptable. We conducted pilot experiments using this method on a real mobile robot in ecological contexts. We used results from the experiments to study the Human-Robot Interaction as a whole by analysing both the person-following method and the human behavior. Our preliminary results show that the way in which the robot followed a person had an impact on the interaction that emerged between them. © 2020 IEEE.",,"Agricultural robots; Air navigation; Behavioral research; Machine design; Mobile robots; Navigation; Robotics; Design approaches; Mobile Robot Navigation; Navigation methods; Pilot experiment; Robot navigation; Social hierarchy; Social navigation; State of the art; Social robots",,,,,"ANR-19-P3IA-0003; Centre National de la Recherche Scientifique, CNRS","1Philip Scales (philip.scales@univ-grenoble-alpes.fr), Olivier Aycard2 (olivier.aycard@imag.fr), and Véronique Aubergé3(veronique.auberge@univ-grenoble-alpes.fr) are with Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, 38000 Grenoble, France *This work has been partially supported by MIAI @ Grenoble Alpes, (ANR-19-P3IA-0003)",,,,,,,,,,"(2013) The Quiet Professional: An Investigation of U. S. Military Explosive Ordnance Disposal Personnel Interactions with Everyday Field Robots, , C. 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"Manjunatha H., Esfahani E.T.","57192710332;16424554200;","Application of reinforcement and deep learning techniques in brain-machine interfaces",2020,"Advances in Motor Neuroprostheses",,,,"1","14",,2,"10.1007/978-3-030-38740-2_1","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091051298&doi=10.1007%2f978-3-030-38740-2_1&partnerID=40&md5=05fa1a2a455a765c04dbde7f31d443e6","Human-In-the-Loop System Laboratory, University at Buffalo, The State University of New York, Buffalo, NY, United States","Manjunatha, H., Human-In-the-Loop System Laboratory, University at Buffalo, The State University of New York, Buffalo, NY, United States; Esfahani, E.T., Human-In-the-Loop System Laboratory, University at Buffalo, The State University of New York, Buffalo, NY, United States","From early adoption in rehabilitation, the brain-machine interfaces (BMIs) have dovetailed into applications empowering humans in controlling external devices such as prosthesis and wheelchairs with a high level of autonomy. The success of such brain-machine interfaces depends on the decoding algorithms that translate the brain activity into the human's intention or cognition state. Taking advantage of this decoding, a machine can have a robust perception of human's cognitive state and modify its actions accordingly. This decoding process can be viewed as a machine learning problem where features of brain activities are mapped to some labeled events or classes in a controlled environment. This mapping traditionally relies on the subject and task-specific signal processing approaches. Thus, the conventional machine learning methods fail to generalize well and transfer the learned features between different tasks and subjects, especially in out-of-thelab applications. Recently, deep learning (DL) has shown great success in learning the patterns from very large data and generalizing well on different applications. With respect to brain activity analysis, deep learning and reinforcement learning (RL) techniques can significantly simplify analysis pipelines and facilitate better generalization between subjects, tasks, and also learn intricate coupled dynamics. In this regard, this paper provides information on the state of the art and challenges in implementing deep learning and reinforcement learning algorithms in brain- machine interfaces. In order to demonstrate the use of deep learning techniques in BMIs, we also present a case study of physical human-robot interaction where the brain activity is used to classify the task difficulty while interacting with the robot. © Springer Nature Switzerland AG 2020.","Brain-machine interface; EEG; Motor control; Reinforcement learning",,,,,,,,,,,,,,,,,"Teplan, M., Fundamentals of EEG measurement (2002) Meas. Sci. Rev., 2 (2), pp. 1-11; Rao, R.P.N., (2013) Brain-Computer Interfacing: An Introduction, , Cambridge University Press, Cambridge; Fakhr-Motamedi, S., Moshrefi-Torbati, M., Hill, M., Hill, C.M., White, P.R., Signal processing techniques applied to human sleep EEG signals-a review (2014) Biomed. Signal Process. Control, 10, pp. 21-33; Guru, K.A., Esfahani, E.T., Raza, S.J., Bhat, R., Wang, K., Hammond, Y., Wilding, G., Chowriappa, A.J., Cognitive skills assessment during robot-assisted surgery: separating the wheat from the chaff (2015) BJU Int, 115 (1), pp. 166-174; Memar, A.H., Esfahani, E.T., Physiological measures for human performance analysis in human-robot teamwork: case of tele-exploration (2018) IEEE Access, 6, pp. 3694-3705; Rahman, M., Karwowski, W., Fafrowicz, M., Hancock, P.A., Neuroergonomics applications of electroencephalography in physical activities: a systematic review (2019) Front. Hum. 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Eng, 27 (3), pp. 533-542; Karavas, G.K., Larsson, D.T., Artemiadis, P., A hybrid BMI for control of robotic swarms: preliminary results 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5065-5075. , IEEE, 2017; Ang, K.K., Chua, K.S.G., Phua, K.S., Wang, C., Chin, Z.Y., Kuah, C.W.K., Low, W., Guan, C., A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke (2015) Clin. EEG Neurosci, 46 (4), pp. 310-320; Ofner, P., Schwarz, A., Pereira, J., Müller-Putz, G., Decoding movements of the upper limb from EEG, in cuttingEEG (2017); Shamlo-Bigdely, N., Mullen, T., Kothe, C., Su, K.-M., Robbins, K.A., The PREP pipeline: standardized preprocessing for large-scale EEG analysis (2015) Front. Neuroinform, 9 (16); Zhang, W., Tan, C., Sun, F., Wu, H., Zhang, B., A review of EEG-based brain-computer interface systems design (2018) Brain Sci. 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"Bu F., Le T., Du X., Vasudevan R., Johnson-Roberson M.","57216468068;57216467956;56262200200;57213570482;23008918800;","Pedestrian Planar LiDAR Pose (PPLP) Network for Oriented Pedestrian Detection Based on Planar LiDAR and Monocular Images",2020,"IEEE Robotics and Automation Letters","5","2","8943147","1626","1633",,5,"10.1109/LRA.2019.2962358","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077263371&doi=10.1109%2fLRA.2019.2962358&partnerID=40&md5=bbaabe4a8ad812b2a45328f40352f0c3","Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI  48109, United States; Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI  48109, United States","Bu, F., Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI  48109, United States; Le, T., Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI  48109, United States; Du, X., Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI  48109, United States; Vasudevan, R., Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI  48109, United States; Johnson-Roberson, M., Department of Naval Architecture and Marine Engineering, University of Michigan, Ann Arbor, MI  48109, United States","Pedestrian detection is an important task for human-robot interaction and autonomous driving applications. Most previous pedestrian detection methods rely on data collected from three-dimensional (3D) Light Detection and Ranging (LiDAR) sensors in addition to camera imagery, which can be expensive to deploy. In this letter, we propose a novel Pedestrian Planar LiDAR Pose Network (PPLP Net) based on two-dimensional (2D) LiDAR data and monocular camera imagery, which offers a far more affordable solution to the oriented pedestrian detection problem. The proposed PPLP Net consists of three sub-networks: an orientation detection network (OrientNet), a Region Proposal Network (RPN), and a PredictorNet. The OrientNet leverages state-of-the-art neural-network-based 2D pedestrian detection algorithms, including Mask R-CNN and ResNet, to detect the Bird's Eye View (BEV) orientation of each pedestrian. The RPN transfers 2D LiDAR point clouds into occupancy grid map and uses a frustum-based matching strategy for estimating non-oriented 3D pedestrian bounding boxes. Outputs from both OrientNet and RPN are passed through the PredictorNet for a final regression. The overall outputs of our proposed network are 3D bounding box locations and orientation values for all pedestrians in the scene. We present oriented pedestrian detection results on two datasets, the CMU Panoptic Dataset and a newly collected FCAV M-Air Pedestrian (FMP) Dataset, and show that our proposed PPLP network based on 2D LiDAR and monocular camera achieves similar or better performance to previous state-of-the-art 3D-LiDAR-based pedestrian detection methods in both indoor and outdoor environments. © 2016 IEEE.","computer vision for automation; Human detection and tracking; recognition","Cameras; Human robot interaction; Human detection and tracking; Light detection and ranging; Orientation detections; Outdoor environment; Pedestrian detection; Recognition; Threedimensional (3-d); Two Dimensional (2 D); Optical radar",,,,,"N022977; Office of Naval Research, ONR: N00014-18-1-2575; Ford Motor Company","Manuscript received August 15, 2019; accepted December 6, 2019. Date of publication December 25, 2019; date of current version February 6, 2020. This letter was recommended for publication by Associate Editor Tamim Asfour upon evaluation of the reviewers’ comments. This work was supported by a grant from Ford Motor Company via the Ford-UM Alliance under Award N022977 and by the Office of Naval Research under Award Number N00014-18-1-2575. (Corresponding author: Matthew Johnson-Roberson.) F. Bu and R. Vasudevan are with the Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109 USA (e-mail: fanbu@umich.edu; ramv@umich.edu).",,,,,,,,,,"Ku, J., Mozifian, M., Lee, J., Harakeh, A., Waslander, S.L., Joint 3d proposal generation and object detection from view aggregation (2018) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), pp. 1-8; Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J., Frustum pointnets for 3d object detection from rgb-d data (2018) Proc. IEEE Conf. Comput. Vis. 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Vis., pp. 1440-1448","Johnson-Roberson, M.; Department of Naval Architecture and Marine Engineering, United States; email: mattjr@umich.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23773766,,,,"English","IEEE Robot. Autom.",Article,"Final","",Scopus,2-s2.0-85077263371
"Iyer S.S., Joseph J.V., Vashista V.","57215284186;57215293521;53980678300;","Evolving Toward Subject-Specific Gait Rehabilitation Through Single-Joint Resistive Force Interventions",2020,"Frontiers in Neurorobotics","14",,"15","","",,2,"10.3389/fnbot.2020.00015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082686737&doi=10.3389%2ffnbot.2020.00015&partnerID=40&md5=9475161c9432e7199ba0786e0759ef99","Human Centered Robotics Lab, Indian Institute of Technology Gandhinagar, Gandhinagar, India","Iyer, S.S., Human Centered Robotics Lab, Indian Institute of Technology Gandhinagar, Gandhinagar, India; Joseph, J.V., Human Centered Robotics Lab, Indian Institute of Technology Gandhinagar, Gandhinagar, India; Vashista, V., Human Centered Robotics Lab, Indian Institute of Technology Gandhinagar, Gandhinagar, India","Walking is one of the most relevant tasks that a person performs in their daily routine. Despite its mechanical complexities, any change in the external conditions that applies some external perturbation, or in the human musculoskeletal system that limits an individual's movement, entails a motor response that can either be compensatory or adaptive in nature. Incidentally, with aging or due to the occurrence of a neuro-musculoskeletal disorder, a combination of such changes including reduced sensory perception, muscle weakness, spasticity, etc. has been reported, and this can significantly degrade the human walking performance. Various studies in gait rehabilitation literature have identified a need for the development of better rehabilitation paradigms and have implied that an efficient human robot interaction is critical. Understanding how humans respond to a particular gait alteration can be beneficial in designing an effective rehabilitation paradigm. In this context, the current work investigates human locomotor adaptation to resistive alteration to the hip and ankle strategies of walking. A cable-driven robotic system, which does not add mobility constraints, was used to implement resistive force interventions within the hip and ankle joints separately through two experiments with eight healthy adult participants in each. In both cases, the intervention was applied during the push-off phase of walking, i.e., from pre-swing to terminal swing. The results showed that subjects in both groups adopted a compensatory response to the applied intervention and demonstrated intralimb and interlimb adaptation. Overall, the participants demonstrated a deviant gait implying lower limb musculoskeletal adjustments as if to compensate for a hip or ankle abnormality. © Copyright © 2020 Iyer, Joseph and Vashista.","cable-driven robots; gait rehabilitation; single joint intervention; subject-specific paradigm; wearable robotics","Cables; Joints (anatomy); Patient rehabilitation; Robotics; Sensory perception; Cable driven robots; Gait rehabilitation; single joint intervention; Subject-specific; Wearable robotics; Human robot interaction; adult; ankle; article; case report; clinical article; female; gait; hip; human; human experiment; male; robotics",,,,,"DST/INSPIRE/04/2015/001077; Science and Engineering Research Board, SERB: ECR/2016/000200","The authors would like to acknowledge the contribution of Nicholas Airhart and Jacob Quick toward the hardware design during their summer internship at IIT Gandhinagar. Funding. This work is partly supported by Inspire Faculty Award, DST, India (DST/INSPIRE/04/2015/001077), and Early Career Research Award, SERB, India (ECR/2016/000200), and IIT Gandhinagar, India.","This work is partly supported by Inspire Faculty Award, DST, India (DST/INSPIRE/04/2015/001077), and Early Career Research Award, SERB, India (ECR/2016/000200), and IIT Gandhinagar, India.",,,,,,,,,"Bae, J., Siviy, C., Rouleau, M., Menard, N., O'Donnell, K., Geliana, I., A lightweight and efficient portable soft exosuit for paretic ankle assistance in walking after stroke (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 2820-2827. , Brisbane, IEEE; Banala, S.K., Agrawal, S.K., Scholz, J.P., Active leg exoskeleton (alex) for gait rehabilitation of motor-impaired patients (2007) 2007 IEEE 10th International Conference on Rehabilitation Robotics, pp. 401-407. , IEEE; Bastian, A.J., Understanding sensorimotor adaptation and learning for rehabilitation (2008) Curr. Opin. 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"Cappuccio M.L., Peeters A., McDonald W.","56524371200;57194170457;56828818700;","Sympathy for Dolores: Moral Consideration for Robots Based on Virtue and Recognition",2020,"Philosophy and Technology","33","1",,"9","31",,14,"10.1007/s13347-019-0341-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073954386&doi=10.1007%2fs13347-019-0341-y&partnerID=40&md5=a7251a43a2ea8facc7b514c719535895","School of Engineering and Information Technology, University of New South Wales, Northcott Dr, Campbell, ACT  2612, Australia; College of Humanities and Social Sciences, United Arab Emirates University, 15551, Al Ain, Abu Dhabi, United Arab Emirates; Faculty of Law, Humanities and the Arts (19.1066), University of Wollongong, Wollongong, NSW  2522, Australia","Cappuccio, M.L., School of Engineering and Information Technology, University of New South Wales, Northcott Dr, Campbell, ACT  2612, Australia, College of Humanities and Social Sciences, United Arab Emirates University, 15551, Al Ain, Abu Dhabi, United Arab Emirates; Peeters, A., Faculty of Law, Humanities and the Arts (19.1066), University of Wollongong, Wollongong, NSW  2522, Australia; McDonald, W., College of Humanities and Social Sciences, United Arab Emirates University, 15551, Al Ain, Abu Dhabi, United Arab Emirates","This paper motivates the idea that social robots should be credited as moral patients, building on an argumentative approach that combines virtue ethics and social recognition theory. Our proposal answers the call for a nuanced ethical evaluation of human-robot interaction that does justice to both the robustness of the social responses solicited in humans by robots and the fact that robots are designed to be used as instruments. On the one hand, we acknowledge that the instrumental nature of robots and their unsophisticated social capabilities prevent any attribution of rights to robots, which are devoid of intrinsic moral dignity and personal status. On the other hand, we argue that another form of moral consideration—not based on rights attribution—can and must be granted to robots. The reason is that relationships with robots offer to the human agents important opportunities to cultivate both vices and virtues, like social interaction with other human beings. Our argument appeals to social recognition to explain why social robots, unlike other technological artifacts, are capable of establishing with their human users quasi-social relationships as pseudo-persons. This recognition dynamic justifies seeing robots as worthy of moral consideration from a virtue ethical standpoint as it predicts the pre-reflective formation of persistent affective dispositions and behavioral habits that are capable of corrupting the human user’s character. We conclude by drawing attention to a potential paradox drawn forth by our analysis and by examining the main conceptual conundrums that our approach has to face. © 2019, Springer Nature B.V.","Alienation; Character; Habit formation; Moral consideration for robots; Moral patiency; Social recognition theory; Social robotics; Virtue ethics",,,,,,,,,,,,,,,,,"(1988) Ethica Nichomachea, , I. 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Nørskov; Nørskov, M., (2016) Social robots: boundaries, potential, challenges, , (ed), Routledge, London; Nørskov, M., Technological dangers and the potential of human–robot interaction: a philosophical investigation of fundamental epistemological mechanisms of discrimination (2016) Social robots: boundaries, potential, challenges, pp. 99-122. , Nørskov M, (ed), Routledge, London; Oakley, J., Cocking, D., (2008) Virtue ethics and professional roles, , Cambridge University Press, Cambridge; Peeters, A., Haselager, P., Designing Virtuous Sex Robots, , preparation; Rietveld, E., Situated normativity: the normative aspect of embodied cognition in unreflective action (2008) Mind, 117 (468), pp. 973-1001; Rodogno, R., Robots and the limits of morality (2016) Social robots: boundaries, potential, challenges, pp. 39-56. , Nørskov M, (ed), Routledge, London; Scheutz, M., The inherent dangers of unidirectional emotional bonds between humans and social robots (2012) Robot ethics: the ethical and social implications of robotics, pp. 205-222. , Lin P, Abney K, Bekey GA, (eds), MIT Press, Cambridge; Seibt, J., Hakli, R., Nørskov, M., Sociable robots and the future of social relations (2014) Frontiers in Artificial Intelligence and Applications, 273; Sparrow, R., Can machines be people? 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Reframing the question about robot rights (2018) Information, 9 (4), p. 73; Torrance, S., Ethics and consciousness in artificial agents (2008) AI & SOCIETY, 22 (4), pp. 495-521; Truong, A., (2016) Parents are worried the Amazon Echo is conditioning their kids to be rude, , Quartz, 9 June 2016; Vallor, S., Moral deskilling and upskilling in a new machine age: reflections on the ambiguous future of character (2015) Philosophy & Technology, 28 (1), pp. 107-124; Vallor, S., (2016) Technology and the virtues: a philosophical guide to a future worth wanting, , Oxford University Press, Oxford; Epley, N., Waytz, A., Cacioppo, J.T., On seeing human: A three-factor theory of anthropomorphism (2007) Psychological Review, 114 (4), pp. 864-886; Whitby, B., Sometimes it’s hard to be a robot: a call for action on the ethics of abusing artificial agents (2008) Interacting with Computers, 20 (3), pp. 326-333; Whitby, B., Oversold, unregulated, and unethical: why we need to respond to robot nannies (2010) Interaction Studies, 11 (2), pp. 290-294; Whitby, B., Do you want a robot lover? The ethics of caring technologies (2012) Robot ethics: the ethical and social implications of robotics, pp. 233-248. , Lin P, Abney K, Bekey GA, (eds), MIT Press, Cambridge; You, S., Nie, J., Suh, K., Sundar, S.S., When the robot criticizes you..: Self-serving bias in human-robot interaction (2011) In Proceedings of the 6Th International Conference on Human-Robot Interaction, pp. 295-296. , https://doi.org/10.1145/1957656.1957778; Zagzebski, L., Exemplarist virtue theory (2010) Metaphilosophy, 41 (1-2), pp. 41-57; Zwolinski, M., Schmidtz, D., Environmental virtue ethics (2013) The Cambridge companion to virtue ethics, pp. 221-239. , Russell DC, (ed), Cambridge University Press, Cambridge","Cappuccio, M.L.; School of Engineering and Information Technology, Northcott Dr, Australia; email: m.cappuccio@unsw.edu.au",,,"Springer",,,,,22105433,,,,"English","Philos. Technol.",Article,"Final","",Scopus,2-s2.0-85073954386
"van Maris A., Zook N., Caleb-Solly P., Studley M., Winfield A., Dogramadzi S.","57193757921;57224238437;16244225300;8922780300;7006158694;6507900733;","Designing Ethical Social Robots—A Longitudinal Field Study With Older Adults",2020,"Frontiers in Robotics and AI","7",,"1","","",,12,"10.3389/frobt.2020.00001","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079392950&doi=10.3389%2ffrobt.2020.00001&partnerID=40&md5=c3677620efc79af33d658b49b161318a","Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom; Department of Health and Social Sciences, University of the West of England, Bristol, United Kingdom; Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, United Kingdom","van Maris, A., Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom; Zook, N., Department of Health and Social Sciences, University of the West of England, Bristol, United Kingdom; Caleb-Solly, P., Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom; Studley, M., Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom; Winfield, A., Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom; Dogramadzi, S., Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom, Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, United Kingdom","Emotional deception and emotional attachment are regarded as ethical concerns in human-robot interaction. Considering these concerns is essential, particularly as little is known about longitudinal effects of interactions with social robots. We ran a longitudinal user study with older adults in two retirement villages, where people interacted with a robot in a didactic setting for eight sessions over a period of 4 weeks. The robot would show either non-emotive or emotive behavior during these interactions in order to investigate emotional deception. Questionnaires were given to investigate participants' acceptance of the robot, perception of the social interactions with the robot and attachment to the robot. Results show that the robot's behavior did not seem to influence participants' acceptance of the robot, perception of the interaction or attachment to the robot. Time did not appear to influence participants' level of attachment to the robot, which ranged from low to medium. The perceived ease of using the robot significantly increased over time. These findings indicate that a robot showing emotions—and perhaps resulting in users being deceived—in a didactic setting may not by default negatively influence participants' acceptance and perception of the robot, and that older adults may not become distressed if the robot would break or be taken away from them, as attachment to the robot in this didactic setting was not high. However, more research is required as there may be other factors influencing these ethical concerns, and support through other measurements than questionnaires is required to be able to draw conclusions regarding these concerns. © Copyright © 2020 van Maris, Zook, Caleb-Solly, Studley, Winfield and Dogramadzi.","attachment; deception; ethics; longitudinal study; older adults; social robots",,,,,,"Horizon 2020 Framework Programme, H2020: 721619","First and foremost we would like to thank all participants that took part in this study. We also would like to thank the care managers of the retirement villages who gave us the opportunity to run our experiments at their premises. Funding. This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No. 721619 for the SOCRATES project.","This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the",,,,,,,,,"Adar, E., Tan, D.S., Teevan, J., Benevolent deception in human computer interaction (2013) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1863-1872. , Paris, ACM; Arkin, R.C., Ulam, P., Wagner, A.R., Moral decision making in autonomous systems: enforcement, moral emotions, dignity, trust, and deception (2012) Proc. 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The 2015 Revision, New York, United Nations, , New York, NY, Population Division; Van Maris, A., Zook, N., Caleb-Solly, P., Studley, M., Winfield, A., Dogramadzi, S., Ethical considerations of (contextually) affective robot behaviour (2018) Hybrid Worlds: Societal and Ethical Challenges - Proceedings of the International Conference on Robot Ethics and Standards (ICRES 2018), pp. 13-19. , Troy, CLAWAR Association Ltd; Wagner, A.R., Arkin, R.C., Acting deceptively: providing robots with the capacity for deception (2011) Int. J. Soc. Robot, 3, pp. 5-26; Weijers, S., (2013) Exploring Human-Robot Social Relations, , Master's thesis, University of Twente, Twente, The Netherlands; Winfield, A., Ethical standards in robotics and AI (2019) Nat. Electr, 2, pp. 46-48; Winkle, K., Van Maris, A., Social influence and deception in socially assistive robotics (2019) Hybrid Worlds: Societal and Ethical Challenges - Proceedings of the International Conference on Robot Ethics and Standards (ICRES 2018), pp. 45-46. , London, CLAWAR Association Ltd","van Maris, A.; Bristol Robotics Laboratory, United Kingdom; email: anouk.vanmaris@uwe.ac.uk",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85079392950
"Wortham R.H.","57194335737;","Transparency for robots and autonomous systems: Fundamentals, technologies and applications",2020,"Transparency for Robots and Autonomous Systems",,,,"1","207",,3,"10.1049/PBCE130E","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112146844&doi=10.1049%2fPBCE130E&partnerID=40&md5=a53af85274518c9607e1337a4f23f60a","The Electronic and Electrical Engineering Department, The University of Bath, United Kingdom","Wortham, R.H., The Electronic and Electrical Engineering Department, The University of Bath, United Kingdom","Based on scientific understanding and empirical evidence of how humans understand and interact with robotic and autonomous systems, the author reviews the concerns that have been raised around the deployment of AI and robots in human society, and the potential for disruption and harm. He explains why transparency ought to be a fundamental design consideration for Human Computer Interaction (HCI) and artificial intelligent systems. Starting with a survey of global research in the field and what transparency means in the wider context of trust, control and ethics, the author then introduces a transparent robot control architecture, and the impact of transparency using real-time displays. He presents a case study of a muttering robot, and covers current and upcoming standards for transparency, as well as future perspectives for the design, manufacture and operation of autonomous robotic systems. Specifically, chapters cover transparency in the wider context of trust; a transparent robot control architecture, the impact of transparency using real-time displays, transparency using audio - the Muttering Robot, the effects of appearance on transparency, synthesis and further work, and several examples of Instinct reactive planner commands. This book provides key insights into transparency in robots and autonomous systems for industry, academic researchers and engineers working on intelligent autonomous system design, human robot interaction, AI, and machine ethics. It also offers points of interest for professionals developing governmental or organisational policies and standards for the design of intelligent autonomous and AI systems, and government and standard bodies working in the emerging applications of AI. © The Institution of Engineering and Technology 2020.","Autonomous systems; Emotional response; Ethical aspects; Human-robot interaction; Intelligent robots; Moral requirement; Naive observer; Philosophical aspects; Robot anthropomorphism; Robot intelligence; Robot transparency; Transparent robots; Unplanned naive robot encounters",,,,,,,,,,,,,,,,,"(2017) Statement on Algorithmic Transparency and Accountability, , https://www.acm.org/binaries/content/assets/public-policy/2017_usacm_statement_algorithms.pdf, Washington, DC, USA: Association for Computing Machinery, Available from; (2017) APPG AI Findings 2017, , http://www.appg-ai.org/wp-content/uploads/2017/12/appgai_2017_findings.pdf, London, UK: Big Innovation Centre, Available from:; (2017) Mechanical Turk, , https://www.mturk.com, Available from:; Ananny, M., Crawford, K., Seeing Without Knowing: Limitations of the Transparency Ideal and Its Application to Algorithmic Accountability (2016) New Media & Society, pp. 1-17. , https://doi.org/10.1177/1461444816676645, Available from:; (2016) ArduinoWebsite, , https://www.arduino.cc/, Available from:; (2017) Principles Developed in Conjunction with the 2017 Asilomar Conference [Benevolent AI 2017], , https://futureoflife.org/ai-principles, Available from:; (2016) Atmel StudioWebsite, , http://www.atmel.com/Microsite/atmel-studio/, Available from; (2016) AVR Libc Reference Manual, , http://www.atmel.com/webdoc/avrlibcreferencemanual/, Available from; Axelrod, R., Hamilton, W.D., The Evolution of Cooperation (1981) Science, 211 (4489), pp. 1390-1396; Bae, J.E., Kim, M.S., Selective Visual Attention Occurred in Change Detection Derived by Animacy of Robot’s Appearance (2011) Proceedings of the 2011 international conference on collaboration technologies and systems, CTS 2011, pp. 190-193. , https://doi.org/10.1109/CTS.2011.5928686, Available from:; Barfeh, D., Bustamante, R., Jose, E., Lansigan, F., Mendoza, E., Mariano, V., Real Time Multi Target Capturing Using Partitioning in Robot Vision (2017) ACM international conference proceeding series, pp. 89-93. , https://doi.org/10.1145/3175516.3175534, Wuhan, China: ACM NewYork, Available from:; Barsalou, L.W., Simulation, Situated Conceptualization, and Prediction (2009) Philosophical Transactions of the Royal Society B: Biological Sciences, 364 (1521), pp. 1281-1289. , https://doi.org/10.1098/rstb.2008.0319, Available from:; Bartneck, C., Kulic, D., Croft, E., Zoghbi, S., Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81. , https://doi.org/10.1007/s12369-008-0001-3, Available from:; Bastos, M.T., Mercea, D., The Brexit Botnet and User-Generated Hyperpartisan News (2017) Social Science Computer Review, , https://doi.org/10.1177/0894439317734157, Available from:; Bauman, C.W., Mcgraw, A.P., Bartels, D.M., Warren, C., Revisiting External Validity: Concerns About Trolley Problems and Other Sacrificial Dilemmas in Moral Psychology (2014) Social and Personality Psychology Compass, 8 (9), pp. 536-554. , https://doi.org/10.1111/spc3.12131, Available from:; Benoit, K., Conway, D., Lauderdale, B.E., Laver, M., Mikhaylov, S., Crowd-Sourced Text Analysis: Reproducible and Agile Production of Political Data (2016) American Political Science Review, 110 (2), pp. 278-295. , https://doi.org/10.1017/S0003055416000058, Available from:; Berinsky, A.J., Huber, G.A., Lenz, G.S., Evaluating Online Labor Markets for Experimental Research: Amazon.com’s Mechanical Turk (2012) Political Analysis, 20 (3), pp. 351-368. , https://doi.org/10.1093/pan/mpr057, Available from:; Berwick, R.C., Chomsky, N., (2015) Why Only Us: Language and Evolution, , Boston, MA, USA: MIT Press; Billington, R., (1988) Living Philosophy, , Third ed. 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"Tausch A., Kluge A.","57219250178;23397227500;","The best task allocation process is to decide on one’s own: effects of the allocation agent in human–robot interaction on perceived work characteristics and satisfaction",2020,"Cognition, Technology and Work",,,,"","",,,"10.1007/s10111-020-00656-7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097901764&doi=10.1007%2fs10111-020-00656-7&partnerID=40&md5=1e47c426d12b72c0e92c6240258a042e","Scientific Management Products and Work Systems, Federal Institute for Occupational Safety and Health, Dortmund, Germany; Chair of Work and Organisational Psychology, Ruhr University Bochum, Bochum, Germany","Tausch, A., Scientific Management Products and Work Systems, Federal Institute for Occupational Safety and Health, Dortmund, Germany; Kluge, A., Chair of Work and Organisational Psychology, Ruhr University Bochum, Bochum, Germany","New technologies are ever evolving and have the power to change human work for the better or the worse depending on the implementation. For human–robot interaction (HRI), it is decisive how humans and robots will share tasks and who will be in charge for decisions on task allocation. The aim of this online experiment was to examine the influence of different decision agents on the perception of a task allocation process in HRI. We assume that inclusion of the worker in the allocation will create more perceived work resources and will lead to more satisfaction with the allocation and the work results than a decision made by another agent. To test these hypotheses, we used a fictional production scenario where tasks were allocated to the participant and a robot. The allocation decision was either made by the robot, by an organizational unit, or by the participants themselves. We then looked for differences between those conditions. Our sample consisted of 151 people. In multiple ANOVAs, we could show that satisfaction with the allocation process, the solution, and with the result of the work process was higher in the condition where participants themselves were given agency in the allocation process compared to the other two. Those participants also experienced more task identity and autonomy. This has implications for the design of allocation processes: The inclusion of workers in task allocation can play a crucial role in leveraging the acceptance of HRI and in designing humane work systems in Industry 4.0. © 2020, The Author(s).","Autonomy; Decision-making; Human–robot interaction (HRI); Task allocation; Task identity; Trust","Computer applications; Human computer interaction; Allocation decision; Allocation process; Decision agent; On-line experiments; Organizational units; Robot interactions; Task allocation; Work process; Human robot interaction",,,,,,,,,,,,,,,,"Abdallah, S., Lesser, V., Modeling task allocation using a decision theoretic model (2005) Proceedings of 4Th International Joint Conference on Autonomous Agents and Multiagent Systems, , AAMAS; Alami, R., Clodic, A., Montreuil, V., Sisbot, E.A., Chatila, R., Task planning for human–robot interaction (2005) Proceedings of the 2005 Joint Conference on Smart Objects and Ambient Intelligence Innovative Context-Aware Services Usages and Technologies, p. 81. , https://doi.org/10.1145/1107548.1107574, Bailly G, Crowley JL, ACM, New York, NY; Alami, R., Clodic, A., Montreuil, V., Sisbot, E.A., Chatila, R., Toward human-aware robot task planning (2006) AAAI Spring Symposium: To Boldly Go Where No human–robot Team has Gone Before, pp. 39-46; Baker, M., Yanco, H.A., Autonomy mode suggestions for improving human–robot interaction (2004) 2004 IEEE International Conference on Systems, Man and Cybernetics, pp. 2948-2953. , IEEE; Bakker, A.B., Demerouti, E., Job demands-resources theory: taking stock and looking forward (2017) J Occup Health Psychol, 22, pp. 273-285; Brockner, J., Wiesenfeld, B.M., An integrative framework for explaining reactions to decisions: interactive effects of outcomes and procedures (1996) Psychol Bull, 120, p. 189; Bucknall, T., Thomas, S., Critical care nurse satisfaction with levels of involvement in clinical decisions (1996) J Adv Nurs, 23, pp. 571-577; Cheung, J.H., Sinclair, R.R., Shi, J., Wang, M., Do job demands of Chinese manufacturing employees predict positive or negative outcomes? A test of competing hypotheses (2015) Stress Health, 31, pp. 432-442; Cosenzo, K., Chen, J., Reinerman-Jones, L., Barnes, M., Nicholson, D., Adaptive automation effects on operator performance during a reconnaissance mission with an unmanned ground vehicle (2010) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 25, pp. 2135-2139. , SAGE Publications, Los Angeles, CA; Dabholkar, P.A., Sheng, X., Consumer participation in using online recommendation agents: effects on satisfaction, trust, and purchase intentions (2012) Serv Ind J, 32, pp. 1433-1449; Debernard, S., Vanderhaegen, F., Millot, P., An experimental investigation of dynamic allocation of tasks between air traffic controller and AI systems (1993) Analysis, design and evaluation of man-machine systems 1992, pp. 95-100. , Strassen HG, (ed), Elsevier, The Hague; Driscoll, J.W., Trust and participation in organizational decision making as predictors of satisfaction (1978) Acad Manag J, 21, pp. 44-56; Fantini, P., Pinzone, M., Sella, F., Taisch, M., Collaborative robots and new product introduction: Capturing and transferring human expert knowledge to the operators (2017) International Conference on Applied Human Factors and Ergonomics, pp. 259-268. , Springer; Fitts, P.M., (1951) Human Engineering for an Effective Air-Navigation and Traffic-Control System, , National Research Council, Division of Anthropology and Psychology, Committee on Aviation Psychology; Fitzsimons, G.J., Greenleaf, E.A., Lehmann, D.R., Decision and consumption satisfaction: Implications for channel relations (1997) Marketing Studies Center Working Paper Series, p. 313; Flemisch, F., Heesen, M., Hesse, T., Kelsch, J., Schieben, A., Beller, J., Towards a dynamic balance between humans and automation: authority, ability, responsibility and control in shared and cooperative control situations (2012) Cognit Technol Work, 14, pp. 3-18; Flemisch, F., Abbink, D.A., Itoh, M., Pacaux-Lemoine, M.-P., Weßel, G., (2019) Cognit Technol Work, 21, pp. 555-568; Freedy, A., Visser, E., Weltman, G., Coeyman, N., Measurement of trust in human–robot collaboration (2007) Proceedings of the 2007 International Conference on Collaborative Technologies and Systems, pp. 106-114. , IEEE, Orlando, FL; 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Hackman, J.R., Oldham, G.R., Development of the job diagnostic survey (1975) J Appl Psychol, 60, p. 159; Hackman, J.R., Oldham, G.R., (1980) Work redesign, , Addison-Wesley, Reading; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y., de Visser, E.J., Parasuraman, R., A meta-analysis of factors affecting trust in human–robot interaction (2011) Hum Factors, 53, pp. 517-527; Hervé, M., Pavie, X., O'Keeffe, M., (2000) Valuing people to create value: an innovative approach to leveraging motivation at work, , World Scientific, Singapore; Hoc, J.-M., Lemoine, M.-P., Cognitive evaluation of human-human and human–machine cooperation modes in air traffic control (1998) Int J Aviat Psychol, 8, pp. 1-32; Hughes, R., Considering the vignette technique and its application to a study of drug injecting and HIV risk and safer behaviour (1998) Sociol Health Illn, 20, pp. 381-400; Inagaki, T., Itoh, M., Human’s overtrust in and overreliance on advanced driver assistance systems: a theoretical framework (2013) Int J Veh Technol, 2013, pp. 1-8; 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(1986) Organ Dyn, 14, pp. 65-79; Michalos, G., Spiliotopoulos, J., Makris, S., Chryssolouris, G., A method for planning human robot shared tasks (2018) CIRP J Manuf Sci Technol, 22, pp. 76-90; Moray, N., Inagaki, T., Itoh, M., Adaptive automation, trust, and self-confidence in fault management of time-critical tasks (2000) J Exp Psychol Appl, 6, p. 44; Morgeson, F.P., Humphrey, S.E., The Work Design Questionnaire (WDQ): developing and validating a comprehensive measure for assessing job design and the nature of work (2006) J Appl Psychol, 91, pp. 1321-1339; Munzer, T., Mollard, Y., Lopes, M., Impact of robot initiative on human–robot collaboration (2017) Paper Presented at the HRI 2017 - ACM/IEEE International Conference on human–robot Interaction, , Vienna; Nikolakis, N., Kousi, N., Michalos, G., Makris, S., Dynamic scheduling of shared human–robot manufacturing operations (2018) Proc CIRP, 72, pp. 9-14; Older, M.T., Waterson, P.E., Clegg, C.W., A critical assessment of task allocation methods and their applicability (1997) Ergon, 40, pp. 151-171; Onnasch, L., Wickens, C.D., Li, H., Manzey, D., Human performance consequences of stages and levels of automation: an integrated meta-analysis (2014) Hum Factors, 56, pp. 476-488; Parasuraman, R., Sheridan, T.B., Wickens, C.D., A model for types and levels of human interaction with automation (2000) IEEE Trans Syst Man Cybern Part A Syst Hum, 30, pp. 286-297; Rajaonah, B., Tricot, N., Anceaux, F., Millot, P., The role of intervening variables in driver–ACC cooperation (2008) Int J Hum Comput Stud, 66, pp. 185-197; Romero, D., Noran, O., Stahre, J., Bernus, P., Fast-Berglund, Å., Towards a human-centred reference architecture for next generation balanced automation systems: Human-automation symbiosis (2015) IFIP International Conference on Advances in Production Management Systems, Springer, pp. 556-566; Rücker, D., Hornfeck, R., Paetzold, K., Investiagting ergonomics in the context of human–robot collaboration as a sociotechnical system (2018) Paper Presented at the Advances in Human Factors in Robots and Unmanned Systems, , AHFE; Ryan, J.C., Banerjee, A.G., Cummings, M.L., Roy, N., Comparing the performance of expert user heuristics and an integer linear program in aircraft carrier deck operations (2014) IEEE Trans Cybern, 44, pp. 761-773; Rystedt, L.W., Devereux, J., Sverke, M., Comparing and combining the demand-control-support model and the effort reward imbalance model to predict long-term mental strain (2007) Eur J Work Organ Psychol, 16, pp. 261-278; Schmidt, K., Cooperative work and its articulation: requirements for computer support (1994) Trav Hum, 57, pp. 345-366; Schmidtler, J., Knott, V., Hölzel, C., Bengler, K., Human centered assistance applications for the working environment of the future (2015) Occup Ergon, 12, pp. 83-95; Shaw, J.C., Wild, E., Colquitt, J.A., To justify or excuse?: a meta-analytic review of the effects of explanations (2003) J Appl Psychol, 88, p. 444; Sheridan, T.B., Supervisory control of remote manipulators, vehicles and dynamic processes: experiments in command and display aiding (1984) Advances in man-machine systems research, pp. 49-137. , Rouse WB, (ed), JAI Press, New York; Sims, H.P., Szilagyi, A.D., Keller, R.T., The measurement of job characteristics (1976) Acad Manag J, 19, pp. 195-212; Slemp, G.R., Vella-Brodrick, D.A., Optimising employee mental health: the relationship between intrinsic need satisfaction, job crafting, and employee well-being (2013) J Happiness Stud, 15, pp. 957-977; Spector, P.E., Perceived control by employees: a meta-analysis of studies concerning autonomy and participation at work (1986) Hum Relat, 39, pp. 1005-1016; Stanton, N.A., Salmon, P.M., Walker, G.H., Baber, C., Jenkins, D.P., (2005) Human factors methods. 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Technol. Work",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85097901764
[No author name available],[No author id available],"21th Annual Conference on Towards Autonomous Robotics, TAROS 20120",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12228 LNAI",,,"","",412,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097827598&partnerID=40&md5=3414d639369089870498f089f69c78cc",,"","The proceedings contain 43 papers. The special focus in this conference is on Towards Autonomous Robotics. The topics include: Resistance Tuning of Soft Strain Sensor Based on Saline Concentration and Volume Changes; magnetic Force Driven Wireless Motor; a Cable-Based Gripper for Chemistry Labs; one-Shot 3D Printed Underactuated Gripper; an Inhomogeneous Structured Eversion Actuator; prototyping Sensors and Actuators for Robot Swarms in Mixed Reality; A Novel Shape Memory Alloy (SMA) Wire-Based Clutch Design and Performance Test; towards Growing Robots: A Piecewise Morphology-Controller Co-adaptation Strategy for Legged Locomotion; openKilo: A Truly Open-Source Kilobot Design Revision that Aids Repair and Extensibility; requirements Specification and Integration Architecture for Perception in a Cooperative Team of Forestry Robots; an Incremental Learning Approach for Physical Human-Robot Collaboration; experiment Establishing Spatiotemporal Resolution of Human Thermal Perception for Developing a Prosthetic Robotics Hand Thermal Interface; shearTouch - Towards a Wearable Tactile Feedback Device to Provide Continuous Shear Force Sensation in Real Time; a Passively Compliant Idler Mechanism for Underactuated Dexterous Grippers with Dynamic Tendon Routing; tactile Feedback in a Tele-Operation Pick-and-Place Task Improves Perceived Workload; towards Safer Robot Motion: Using a Qualitative Motion Model to Classify Human-Robot Spatial Interaction; expression of Grounded Affect: How Much Emotion Can Arousal Convey?; an Experiment on Human-Robot Interaction in a Simulated Agricultural Task; towards Robust Mission Execution via Temporal and Contingent Planning; LocalSPED: A Classification Pipeline that Can Learn Local Features for Place Recognition Using a Small Training Set; real World Bayesian Optimization Using Robots to Clean Liquid Spills; preface.",,,,,,,,,,,,,,,,,,,,"Mohammad A.Dong X.Russo M.",,"Springer Science and Business Media Deutschland GmbH","21th Annual Conference on Towards Autonomous Robotics, TAROS 20120","16 September 2020 through 16 September 2020",,252749,03029743,9783030634858,,,"English","Lect. Notes Comput. Sci.",Conference Review,"Final","",Scopus,2-s2.0-85097827598
"Tanaka Y., Morie T., Tamukoh H.","57197734548;7005143434;7801453348;","An Amygdala-Inspired Classical Conditioning Model Implemented on an FPGA for Home Service Robots",2020,"IEEE Access","8",,"9260211","212066","212078",,1,"10.1109/ACCESS.2020.3038161","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097712435&doi=10.1109%2fACCESS.2020.3038161&partnerID=40&md5=5d26c046b10845840ad9cf2c6ff6deba","Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka, 808-0196, Japan; Japan Society for Promotion of Science, Tokyo, 102-0083, Japan; Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology, Fukuoka, 808-0196, Japan","Tanaka, Y., Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka, 808-0196, Japan, Japan Society for Promotion of Science, Tokyo, 102-0083, Japan; Morie, T., Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka, 808-0196, Japan, Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology, Fukuoka, 808-0196, Japan; Tamukoh, H., Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka, 808-0196, Japan, Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology, Fukuoka, 808-0196, Japan","This study develops an intelligent system for home service robots mimicking human brain function that can manage common knowledge applicable to any environment and local knowledge reflecting its specific environment. Deep learning is effective for acquiring common knowledge because the performance of deep learning relies on the amounts of training and big training data that can be accessed for such knowledge; however, deep learning is ineffective for acquiring local knowledge because no big training data for such knowledge exist. Thus, we propose a brain-inspired learning model and system for acquiring local knowledge using small training data. We focus on the amygdala because its classical fear conditioning is effective for training using small training data. We propose an amygdala-inspired classical conditioning model comprising multiple self-organizing maps (lateral nucleus) and a fully connected neural network (central nucleus), imitating the function and structure of the amygdala. The proposed model is applied to a task of a waiter robot in a restaurant, and the model can learn customers' preferences after only a few human-robot interactions. We accelerate the computation of the model and reduce its power consumption by proposing a hardware-oriented algorithm for the model and its digital hardware design and implement it in an XCZU9EG field programmable gate array. The hardware-oriented algorithm reduces the multiplication operations and exponential functions requiring huge hardware resources. The performance of the hardware operated at 150 MHz is 1,273 times faster than the software implementation on Arm Cortex-A53, and the power consumption of the chip is 5.009 W. © 2013 IEEE.","Amygdala; classical conditioning; field programmable gate array (FPGA); hardware; home service robot","Brain; Conformal mapping; Electric power utilization; Exponential functions; Field programmable gate arrays (FPGA); Intelligent systems; Mobile robots; Self organizing maps; Social robots; Classical conditioning; Digital hardware design; Fully connected neural network; Hardware resources; Home service robot; Human brain functions; Multiplication operations; Software implementation; Deep learning",,,,,"Japan Society for the Promotion of Science, KAKEN: 19J11524","This work was supported by the JSPS KAKENHI under Grant 19J11524.",,,,,,,,,,"Iocchi, L., Holz, D., Ruiz-Del-Solar, J., Sugiura, K., Zant Der, T.Van, RoboCuphome: Analysis and results of evolving competitions for domestic and service robots (2015) Artif. Intell., 229, pp. 258-281. , http://www.sciencedirect.com/science/article/pii/S0004370215001174, Dec; Hinton, G.E., Osindero, S., Teh, Y.-W., A fast learning algorithm for deep belief nets (2006) Neural Comput., 18 (7), pp. 1527-1554. , Jul; Lecun, Y., Bottou, L., Bengio, Y., Haffner, P., Gradient-based learning applied to document recognition (1998) Proc. Ieee, 86 (11), pp. 2278-2324; Krizhevsky, A., Sutskever, I., Hinton, G.E., ImageNet classification with deep convolutional neural networks (2012) Proc. Nips, 1, pp. 1097-1105; Redmon, J., Angelova, A., Real-time grasp detection using convolutional neural networks (2015) Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 1316-1322. , May; Hori, S., Ishida, Y., Kiyama, Y., Tanaka, Y., Kuroda, Y., Hisano, M., Imamura, Y., Tamukoh, H., (2017) Hibikino-MusashiHome 2017 Team Description Paper, , http://arxiv.org/abs/1711.05457; Ono, T., Tanaka, Y., Ishida, Y., Abe, Y., Kanamaru, K., Kamimura, D., Nakamura, K., Tamukoh, H., (2020) Hibikino-MusashiHome 2020 Team Description Paper, , http://arxiv.org/abs/2005.14451; Ishida, Y., Tamukoh, H., Semi-automatic dataset generation for object detection and recognition and its evaluation on domestic service robots (2020) J. Robot. Mechtron., 32 (1), pp. 245-253. , Feb; Weiskrantz, L., Behavioral changes associated with ablation of the amygdaloid complex in monkeys (1956) J. Comparative Physiol. Psychol., 49 (4), pp. 381-391; LeDoux, J.E., (1996) The Emotional Brain: The Mysterious Underpinnings of Emotional Life, , New York, NY, USA: Simon and Schuster; Aggleton, J.P., (2000) The Amygdala: A Functional Analysis, , Oxford, U.K.: Oxford Univ. Press; Rolls, E.T., (2005) Emotion Explained, , Oxford, U.K.: Oxford Univ. Press; Phelps, E.A., LeDoux, J.E., Contributions of the amygdala to emotion processing: From animal models to human behavior (2005) Neuron, 48 (2), pp. 175-187. , Oct; Nakahara, H., Yonekawa, H., Fujii, T., Shimoda, M., Sato, S., GUINNESS: AGUI based binarized deep neural network framework for software programmers (2019) Ieice Trans. Inf. Syst., E102D (5), pp. 1003-1011; Pavlov, I.P., (1927) Conditioned Reflexes: An Investigation of the Physiological Activity of the Cerebral Cortex, , Oxford, U.K.: Oxford Univ. Press; LeDoux, J., Farb, C., Ruggiero, D., Topographic organization of neurons in the acoustic thalamus that project to the amygdala (1990) J. Neurosci., 10 (4), pp. 1043-1054. , Apr; Tsvetkov, E., Carlezon, W.A., Benes, F.M., Kandel, E.R., Bolshakov, V.Y., Fear conditioning occludes LTP-induced presynaptic enhancement of synaptic transmission in the cortical pathway to the lateral amygdala (2002) Neuron, 34 (2), pp. 289-300. , Apr; Armony, J.L., Servan-Schreiber, D., Cohen, J.D., LeDoux, J.E., An anatomically constrained neural network model of fear conditioning (1995) Behav. Neurosci., 109 (2), pp. 246-257; Morén, J., Balkenius, C., A computational model of emotional learning in the amygdala (2000) Proc. Animals Animats, 6th Int. Conf. Simulation Adapt. Behaviour, pp. 115-124; Izhikevich, E.M., Solving the distal reward problem through linkage of STDP and dopamine signaling (2007) Cerebral Cortex, 17 (10), pp. 2443-2452. , Oct; Li, Y., Nakae, K., Ishii, S., Naoki, H., Uncertainty-dependent extinction of fear memory in an amygdala-mPFC neural circuit model (2016) Plos Comput. 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Accessed: Oct. 26; Yamamoto, T., Terada, K., Ochiai, A., Saito, F., Asahara, Y., Murase, K., Development of human support robot as the research platform of a domestic mobile manipulator (2019) Robomech J., 6 (1). , Dec; Shires, G., Wennborg, H., (2012) Web Speech Api Specification, , W3C Community Group, Final Report; Redmon, J., Farhadi, A., YOLO9000: Better, faster, stronger (2017) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 6517-6525. , Jul; (2020), https://www.xilinx.com/products/design-tools/software-zone/sdsoc.html, SDSoC Development Environment. Accessed: Oct. 26; (2020), https://www.xilinx.com/products/design-tools/vivado/integration/esl-design.html, Vivado High-Level Synthesis. Accessed: Oct. 26; Oliphant, T.E., (2006) A Guide to NumPy, 1. , New York, NY, USA: Trelgol Publishing; Okuta, R., Unno, Y., Nishino, D., Hido, S., Loomis, C., Cupy: A numpycompatible library for NVIDIA GPU calculations (2017) Proc. Ml Syst. Work-shop Nips, p. 151; (2020), https://www.xilinx.com/products/design-tools/vitis/vitis-ai.html, Accessed: Oct. 26","Tanaka, Y.email: tanaka.yuichiro483@mail.kyutech.jp",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85097712435
"Ngo H.Q.T., Le V.N., Thien V.D.N., Nguyen T.P., Nguyen H.","36629026200;57220814871;57220814314;57198024756;55203190700;","Develop the socially human-aware navigation system using dynamic window approach and optimize cost function for autonomous medical robot",2020,"Advances in Mechanical Engineering","12","12",,"","",,2,"10.1177/1687814020979430","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097685309&doi=10.1177%2f1687814020979430&partnerID=40&md5=316ff52cdb181c0bbed8ffba97cc6be0","Department of Mechatronics, Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), HCMC, Viet Nam; Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Viet Nam; HUTECH Institute of Engineering, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Viet Nam","Ngo, H.Q.T., Department of Mechatronics, Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), HCMC, Viet Nam, Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Viet Nam; Le, V.N., Department of Mechatronics, Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), HCMC, Viet Nam, Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Viet Nam; Thien, V.D.N., Department of Mechatronics, Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), HCMC, Viet Nam, Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Viet Nam; Nguyen, T.P., HUTECH Institute of Engineering, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Viet Nam; Nguyen, H., HUTECH Institute of Engineering, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Viet Nam","In previous works, the perceived safety and comfort are currently not the principal objectives of all industries, especially robotics system. It might lead not to take psychological safety into consideration of adjusting robot behavior, hence, the human-robot interaction lacks of ease and naturalness. In this paper, a novel framework of human’s zones to ensure safety for social interactions in human-machine system is proposed. In the context of service robot in hospital, machine should not produce any actions that may induce worry, surprise or bother. To maintain the comfortable interaction, an algorithm to update human’s state into personal space is developed. Then, a motion model of robot is demonstrated with assumption of the reference path under segmentation. Dynamic Window Approach is employed for motion planning while Optimize Cost function searches the shortest path in a graph. To validate our approach, three test cases (without human-aware framework, with basic model of human’s zone and with extended personal space) are carried out in the same context. Moreover, three interactive indicators, for instance collision index (CI), interaction index (CII) and relative velocity of robot (Vr), are analyzed in different situations. Lack of human-aware framework, robot might break all thresholds and meet the potential collisions. While robot with basic model of human’s zone in its perception maintains the physically safe thresholds but not socially, it respects whole criterions in both physical constraints and social relations. As a result, our findings are useful for robot’s navigation in presence of human while the socially comfortable interaction is guaranteed. © The Author(s) 2020.","cost function; dynamic window approach; human-aware generator; medical robot; motion constraints; motion planning; navigation system",,,,,,"University of Science Ho Chi Minh City, HCMUS: T-CK-2020-27; Viet Nam National University Ho Chi Minh City, VNU-HCM","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is funded by Ho Chi Minh City University of Technology – VNU-HCM under grant number T-CK-2020-27. We acknowledge the support of time and facilities from Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for this study.","The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is funded by Ho Chi Minh City University of Technology ? VNU-HCM under grant number T-CK-2020-27. We acknowledge the support of time and facilities from Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for this study.",,,,,,,,,"Carpin, S., Lewis, M., Wang, J., USARSim: a robot simulator for research and education, pp. 1400-1405. , Proceedings 2007 IEEE international conference on robotics and automation, Roma, Italy, 10–14 April 2007, New York, IEEE, In; Yu, Q., Yuan, C., Fu, Z., An autonomous restaurant service robot with high positioning accuracy (2012) Ind Rob, 39, pp. 271-281; Chalasani, P., Motwani, R., Rao, A., Algorithms for robot grasp and delivery, p. 347. , Proceeding 2nd international workshop on algorithmic foundations of robotics (WAFR), Toulouse, France, February 1997, In:, p; Tseng, C.C., Lin, C.L., Shih, B.Y., SIP-enabled surveillance patrol robot (2013) Robot Comput Integr Manuf, 29, pp. 394-399; Louie, W.Y.G., McColl, D., Nejat, G., Acceptance and attitudes toward a human-like socially assistive robot by older adults (2014) Assist Technol, 26, pp. 140-150; 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No. 04CH37566), Sendai, Japan, 28 September–2 October 2004, New York, IEEE, In; Quigley, M., Conley, K., Gerkey, B., ROS: an open-source robot operating system, 3 (3), p. 5. , ICRA workshop on open source software, Kobe, Japan, May 2009, In:, p; Phan, M.H., Ngo, H.Q.T., A multidisciplinary mechatronics program: from project-based learning to a community-based approach on an open platform (2020) Electronics, 9, p. 954; Ngo, H.Q.T., Tran, A.S., Using fuzzy logic scheme for automated guided vehicle to track following path under various load, pp. 312-316. , 2018 4th international conference on green technology and sustainable development (GTSD), Ho Chi Minh City, Vietnam, 23–24 November 2018, New York, IEEE, In","Nguyen, T.P.; HUTECH Institute of Engineering, Viet Nam; email: nt.phuong@hutech.edu.vn",,,"SAGE Publications Inc.",,,,,16878132,,,,"English","Adv. Mech. Eng.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85097685309
[No author name available],[No author id available],"12th International Conference on Social Robotics, ICSR 2020",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12483 LNAI",,,"","",708,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097134044&partnerID=40&md5=f7d8ffc847b8764f0203ae31d3051f4a",,"","The proceedings contain 57 papers. The special focus in this conference is on Social Robotics. The topics include: Creating MyJay: A New Design for Robot-Assisted Play for Children with Physical Special Needs; humans and Robots in Times of Quarantine Based on First-Hand Accounts; wake Up and Talk with Me! In-the-Field Study of an Autonomous Interactive Wake Up Robot; i Like the Way You Move: A Mixed-Methods Approach for Studying the Effects of Robot Motion on Collaborative Human Robot Interaction; effects of Proactive Explanations by Robots on Human-Robot Trust; language Learning with Artificial Entities: Effects of an Artificial Tutor’s Embodiment and Behavior on Users’ Alignment and Evaluation; preface; design and Evaluation of Affective Expressions of a Zoomorphic Robot; an Exploration of Simple Reactive Responses for Conveying Aliveness Using the Haru Robot; on the Role of Personality and Empathy in Human-Human, Human-Agent, and Human-Robot Mimicry; perceptions of People’s Dishonesty Towards Robots; human-Robot Collaboration and Dialogue for Fault Recovery on Hierarchical Tasks; gaze-Speech Coordination Influences the Persuasiveness of Human-Robot Dialog in the Wild; Robots Are Moral Actors: Unpacking Current Moral HRI Research Through a Moral Foundations Lens; perception of a Social Robot’s Mood Based on Different Types of Motions and Coloured Heart; let’s Learn Biodiversity with a Virtual “Robot”?; blind Trust: How Making a Device Humanoid Reduces the Impact of Functional Errors on Trust; what Am I Allowed to Do Here?: Online Learning of Context-Specific Norms by Pepper; exploring the Effect of Explanations During Robot-Guided Emergency Evacuation; human-Robot Teams: A Review.",,,,,,,,,,,,,,,,,,,,"Wagner A.R.Feil-Seifer D.Haring K.S.Rossi S.Williams T.He H.Sam Ge S.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Social Robotics, ICSR 2020","14 November 2020 through 18 November 2020",,251579,03029743,9783030620554,,,"English","Lect. Notes Comput. Sci.",Conference Review,"Final","",Scopus,2-s2.0-85097134044
"Siebert F.W., Pickl J., Klein J., Rötting M., Roesler E.","42962296200;57220156941;57217216758;6506942608;57217215081;","Let’s Not Get Too Personal – Distance Regulation for Follow Me Robots",2020,"Communications in Computer and Information Science","1293",,,"459","467",,2,"10.1007/978-3-030-60700-5_58","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097057184&doi=10.1007%2f978-3-030-60700-5_58&partnerID=40&md5=df26ac4707d43b8ffe9dde6dc6aec17e","University of Jena, Fürstengraben 1, Jena, 07743, Germany; Department of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany","Siebert, F.W., University of Jena, Fürstengraben 1, Jena, 07743, Germany; Pickl, J., Department of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany; Klein, J., Department of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany; Rötting, M., Department of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany; Roesler, E., Department of Psychology and Ergonomics, Technische Universität Berlin, Berlin, Germany","The spatial behavior of robots working alongside humans critically influences the experience of comfort and personal space of users. The spatial behavior of service robots is especially important, as they move in close proximity to their users. To identify acceptable spatial behavior of Follow Me robots, we conducted an experimental study with 24 participants. In a within-subject design, human-robot distance was varied within the personal space (0.5 and 1.0 m) and social space (1.5 and 2.0 m). In all conditions, the robot carried a personal item of the participants. After each condition, the subjective experience of users in their interaction with the robot was assessed on the dimensions of trust, likeability, human likeness, comfort, expectation conformity, safety, and unobtrusiveness. The results show that the subjective experience of participants during the interaction with the Follow Me robot was generally more positive in the social distance conditions (1.5 and 2.0 m) than in the personal distance conditions (0.5 and 1 m). Interestingly, the following behavior was not perceived as comparable to human-human following behavior in the 0.5 and 2.0 m conditions, which were rated as either closer than human following or further away. This result, in combination with the more positive user experience in the social space conditions, illustrates that an exact transfer of interaction conventions from human-human interaction to human-robot interaction may not be feasible. And while users generally rate the interaction with Follow Me robots as positive, the following-distance of robots will need to be considered to optimize robot-behavior for user acceptance. © 2020, Springer Nature Switzerland AG.","Follow Me robots; Human robot interaction; Proxemics","Behavioral research; Human computer interaction; Machine design; User experience; Close proximity; Human following; Human-human interactions; Human-robot distances; Social distance; Spatial behaviors; Subjective experiences; User acceptance; Social robots",,,,,,,,,,,,,,,,"Tapus, A., Mataric, M.J., Scassellati, B., Socially assistive robotics [grand challenges of robotics] (2007) IEEE Robot. Autom. Mag., 14 (1), pp. 35-42. , https://doi.org/10.1109/mra.2007.339605; Elara, M.R., Rojas, N., Chua, A., Design principles for robot inclusive spaces: A case study with Roomba (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA), Pp. 5593–5599. IEEE, , https://doi.org/10.1117/12.403770; Graether, E., Mueller, F., (2012) Joggobot: A Flying Robot as Jogging Companion. In: CHI 2012 Extended Abstracts on Human Factors in Computing Systems, Pp. 1063–1066 (2012); Honig, S.S., Oron-Gilar, T., Zaichyk, H., Sarne-Fleischmann, V., Olatunji, S., Edan, Y., Toward socially aware person-following robots (2018) IEEE Trans. Cogn. Dev. Syst., 10 (4), pp. 936-954. , https://doi.org/10.1109/tcds.2018.2825641; Onnasch, L., Roesler, E., A taxonomy to structure and analyze human-robot interaction Int. J. Soc. Robot., , in press; Hall, E.T., Birdwhistell, R.L., Bock, B., Bohannan, P., Diebold, A.R., Durbin, M., Proxemics [and comments and replies] (1968) Curr. Anthropol, 9, pp. 83-108. , https://doi.org/10.1086/200975; Hall, E.T., (1966) The Hidden Dimension, 609. , vol., Doubleday, Garden City; Honig, S.S., Dror, K., Tal, O.G., Yael, E., The influence of following angle on performance metrics of a human-following robot (2016) 2016 25Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 593-598. , https://doi.org/10.1109/roman.2016.7745178, pp., IEEE, New York; Siebert, F.W., Klein, J., Rötting, M., Roesler, E., The influence of distance and lateral offset of follow me robots on user perception (2020) Front. Robot. AI, 7 (74). , https://doi.org/10.3389/frobt.2020.00074; Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F.J., Marín-Jiménez, M.J., Automatic generation and detection of highly reliable fiducial markers under occlusion (2014) Pattern Recognit, 47 (6), pp. 2280-2292. , https://doi.org/10.1016/j.patcog.2014.01.005; Lauckner, M., Kobiela, F., Manzey, D.: ‘Hey robot, please step back!’-exploration of a spatial threshold of comfort for human-mechanoid spatial interaction in a hallway scenario (2014) The 23Rd IEEE International Symposium on Robot and Human Interactive Communication, pp. 780-787. , https://doi.org/10.1109/roman.2014.6926348, pp., IEEE, Edinburgh","Siebert, F.W.; University of Jena, Fürstengraben 1, Germany; email: felix.siebert@uni-jena.de","Stephanidis C.Antona M.Ntoa S.",,"Springer Science and Business Media Deutschland GmbH","22nd International Conference on Human-Computer Interaction, HCI International 2020","19 July 2020 through 24 July 2020",,251559,18650929,9783030606992,,,"English","Commun. Comput. Info. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097057184
"Sun W., Yu J., Cai Y.","57218330569;7405527003;35589577500;","Analysis and study of variable stiffness joints based on bi-material nested elastomers",2020,"Proceedings of the ASME Design Engineering Technical Conference","10",,"V010T10004","","",,,"10.1115/DETC2020-22160","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096134342&doi=10.1115%2fDETC2020-22160&partnerID=40&md5=0e50ecc3aea7e7eee351a482f79dbf7b","Robotics Institute, Beihang University, Beijing, 100191, China","Sun, W., Robotics Institute, Beihang University, Beijing, 100191, China; Yu, J., Robotics Institute, Beihang University, Beijing, 100191, China; Cai, Y., Robotics Institute, Beihang University, Beijing, 100191, China","Variable stiffness joints are a kind of compliant mechanisms which can improve the safety of physical human-robot interaction which has attracted much attention in recent years. Elastic elements are used in the internal kinematic structures of variable stiffness joints. In this paper, Elastomers with magneto-rheological fluids (MRFs) properties have great potential application for variable stiffness joints (VSJs) and implantable bioelectronics devices by allowing actuators to rapidly and reversibly changed from a “fluid” to a “solid-like” state and precisely controlled within a very short time and over a wide range under a magnetic field. A new adjustable stiffness composites (ASCs) that combine an outside liner (PDMS) with a chamber (MRFs) is introduced. When improving the magnetic field intensity, the MRFs hardens and the stiffness increases by as much as 4–5 orders of magnitude. In order to solve the bi-material nested cantilever beam subjected to a concentrated tangential force at the free end of the beam, a theoretical approach is proposed by means of the Airy stress function method together with the stress function test solution. By comparing the results obtained from the theoretical and experimental measurements, a very good agreement is found, showing the accuracy and feasibility of the theoretical results. This solution will be useful in analyzing cantilever beam with arbitrary variations of bi-material nested and it can serve as a basis for establishing stiffness and stress theories. Due to the highly deformable of the ASCs, it has great potentially for our future soft robot design and manufacture work. Copyright © 2020 ASME.","Airy stress function method; Magneto-rheological fluids; Nested; Variable stiffness joints","Cantilever beams; Elastomers; Functions; Human robot interaction; Machine design; Magnetic actuators; Magnetic fields; Mechanisms; Nanocantilevers; Stresses; Adjustable stiffness; Airy stress function method; Bioelectronics devices; Magnetic-field intensity; Magneto-rheological fluid; Orders of magnitude; Physical human-robot interactions; Theoretical approach; Stiffness",,,,,"National Natural Science Foundation of China, NSFC: U1813221; Beijing Municipal Natural Science Foundation: 3182019; Fundamental Research Funds for the Central Universities: YWF-19-BJ-J-345","This work was supported in part by the Beijing Municipal Natural Science Foundation (Grant No. 3182019), National Natural Science Foundation of China (Grant No. U1813221) and the Fundamental Research Funds Universities (No. YWF-19-BJ-J-345).",,,,,,,,,,"Migliore, S A, Brown, E A, Deweerth, S P., Novel Nonlinear Elastic Actuators for Passively Controlling Robotic Joint Compliance (2007) Journal of Mechanical Design, 129 (4), pp. 406-412. , [J]; 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Koo, H J, So, J H, Dickey, M D, Towards All Soft Matter Circuits: Prototypes of Quasi Liquid Devices with Memristor Characteristics (2011) Advanced Materials, 23 (31), pp. 3559-3564. , [J]; Isabela, A D C, Chrimes, A F, Zavabeti, A, A Gallium based Magnetocaloric Liquid Metal Ferrofluid (2017) Nano Letters, , [J]","Cai, Y.; Robotics Institute, China; email: caiyueri@buaa.edu.cn",,"Computers and Information in Engineering Division;Design Engineering Division","American Society of Mechanical Engineers (ASME)","ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2020","17 August 2020 through 19 August 2020",,164730,,9780791883990,,,"English","Proc. ASME Des. Eng. Tech. Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85096134342
"Ötting S.K., Masjutin L., Steil J.J., Maier G.W.","57192939244;57219892967;6701553064;7201409088;","Let’s Work Together: A Meta-Analysis on Robot Design Features That Enable Successful Human–Robot Interaction at Work",2020,"Human Factors",,,,"","",,3,"10.1177/0018720820966433","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095954429&doi=10.1177%2f0018720820966433&partnerID=40&md5=d1c68a9c36a9caf4da93e8c26ec90738","Bielefeld University, Germany; Technische Universität Braunschweig, Germany","Ötting, S.K., Bielefeld University, Germany; Masjutin, L., Bielefeld University, Germany; Steil, J.J., Technische Universität Braunschweig, Germany; Maier, G.W., Bielefeld University, Germany","Objective: This meta-analysis reviews robot design features of interface, controller, and appearance and statistically summarizes their effect on successful human–robot interaction (HRI) at work (that is, task performance, cooperation, satisfaction, acceptance, trust, mental workload, and situation awareness). Background: Robots are becoming an integral part of many workplaces. As interactions with employees increase, ensuring success becomes ever more vital. Even though many studies investigated robot design features, an overview on general and specific effects is missing. Method: Systematic selection of literature and structured coding led to 81 included experimental studies containing 380 effect sizes. Mean effects were calculated using a three-level meta-analysis to handle dependencies of multiple effect sizes in one study. Results: Sufficient feedback through the interface, clear visibility of affordances, and adaptability and autonomy of the controller significantly affect successful HRI, whereas appearance does not. The features of the interface and controller affect performance and satisfaction but do not affect situation awareness and trust. Specific effects of adaptability on cooperation and acceptance, as well as autonomy on mental workload, could be shown. Conclusion: Robot design at work needs to cover multiple features of interface and controller to achieve successful HRI that covers not only performance and satisfaction, but also cooperation, acceptance, and mental workload. More empirical research is needed to investigate mediating mechanisms and underrepresented design features’ effects. Application: Robot designers should carefully choose design features to balance specific effects and implementation costs with regard to tasks, work design aims, and employee needs in the specific work context. © Copyright 2020, Human Factors and Ergonomics Society.","adaptability; autonomy; design features; human likeness; interface","Controllers; Job analysis; Machine design; Personnel; Empirical research; Implementation cost; Multiple features; Robot interactions; Situation awareness; Specific effects; Structured coding; Task performance; Social robots; article; awareness; effect size; empirical research; employee; experimental study; human; human experiment; meta analysis; robotics; satisfaction; task performance; trust; visibility; workload; workplace",,,,,,,,,,,,,,,,"Adalgeirsson, S.O., Breazeal, C., MeBot: A robotic platform for socially embodied presence (2010) HRI ’10: Proceedings of the 5th ACM/IEEE International Conference on Human-Robot Interaction, pp. 15-22. , http://dl.acm.org/citation.cfm?id=1734454.1734467, In,., IEEE Press, (, –; 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Witmer, B.G., Singer, M.J., Measuring presence in virtual environments: A presence questionnaire (1998) Presence: Teleoperators and Virtual Environments, 7, pp. 225-240. , doi:10.1162/105474698565686; Wixom, B.H., Todd, P.A., A theoretical integration of user satisfaction and technology acceptance (2005) Information Systems Research, 16, pp. 85-102. , doi:10.1287/isre.1050.0042; Yanco, H.A., Keyes, B., Drury, J.L., Nielsen, C.W., Few, D.A., Bruemmer, D.J., Evolving interface design for robot search tasks (2007) Journal of Field Robotics, 24, pp. 779-799. , doi:10.1002/rob.20215; You, H.-C., Chen, K., Applications of affordance and semantics in product design (2007) Design Studies, 28, pp. 23-38. , doi:10.1016/j.destud.2006.07.002; Young, J.E., Sung, J., Voida, A., Sharlin, E., Igarashi, T., Christensen, H., Grinter, R.E., Evaluating human-robot interaction (2011) International Journal of Social Robotics, 3, pp. 53-67. , doi:10.1007/s12369-010-0081-8; Zanatto, D., Patacchiola, M., Goslin, J., Cangelosi, A., Priming anthropomorphism: Can the credibility of humanlike robots be transferred to non-humanlike robots? (2016) HRI ’16: Proceedings of the 11th Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 543-544. , https://doi.org/10.1109/HRI.2016.7451847, In,., IEEE Press, (, –; Zhang, T., Adams, J.A., Evaluation of a geospatial annotation tool for unmanned vehicle specialist interface (2012) International Journal of Human-Computer Interaction, 28, pp. 361-372. , doi:10.1080/10447318.2011.590122","Ötting, S.K.; Bielefeld UniversityGermany; email: soetting@uni-bielefeld.de",,,"SAGE Publications Inc.",,,,,00187208,,HUFAA,,"English","Hum. Factors",Article,"Article in Press","",Scopus,2-s2.0-85095954429
[No author name available],[No author id available],"5th International Conference on Interactive Collaborative Robotics, ICR 2020",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12336 LNAI",,,"","",330,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092930033&partnerID=40&md5=92824ec62c91fe31e108224bb482a66d",,"","The proceedings contain 31 papers. The special focus in this conference is on Interactive Collaborative Robotics. The topics include: A Combination of Theta*, ORCA and Push and Rotate for Multi-agent Navigation; evaluation of Image Synthesis for Automotive Purposes; collision Detection in the Work of Collaborative Robots Using an Intelligent System; method of Formation of Reference Movement Speed of Working Tool of Multilink Manipulator; icr 2020 preface; A Concept for a HRC Workspace Using Proximity Sensors; distributing Tasks in Multi-agent Robotic System for Human-Robot Interaction Applications; a*-Based Path Planning Algorithm for Swarm Robotics; modeling of Human-Machine Interaction in an Industrial Exoskeleton Control System; gesture-Based Intelligent User Interface for Control of an Assistive Mobile Information Robot; distributed Methods for Autonomous Robot Groups Fault-Tolerant Management; planning to Score a Goal in Robotic Football with Heuristic Search; q-Learning of Spatial Actions for Hierarchical Planner of Cognitive Agents; modeling of Increased Rigidity of Industrial Manipulator; Accurate Autonomous UAV Landing Using Vision-Based Detection of ArUco-Marker; Spatial Resolution-Independent CNN-Based Person Detection in Agricultural Image Data; approach to Obstacle Localization for Robot Navigation in Agricultural Territories; fast Face Features Extraction Based on Deep Neural Networks for Mobile Robotic Platforms; an Estimation of Distributed Algorithms of the Fault-Tolerant Management in the Robot Groups; Comparison of ROS-Based Monocular Visual SLAM Methods: DSO, LDSO, ORB-SLAM2 and DynaSLAM; Cooperative Guidance for Waypoint Following of Distributed Multi-UAV System; indoor vs. Outdoor Scene Classification for Mobile Robots; Mathematical Modelling of Control and Simultaneous Stabilization of 3-DOF Aerial Manipulation System.",,,,,,,,,,,,,,,,,,,,"Ronzhin A.Rigoll G.Meshcheryakov R.",,"Springer Science and Business Media Deutschland GmbH","5th International Conference on Interactive Collaborative Robotics, ICR 2020","7 October 2020 through 9 October 2020",,249569,03029743,9783030603366,,,"English","Lect. Notes Comput. Sci.",Conference Review,"Final","",Scopus,2-s2.0-85092930033
"Leipnitz A., Strutz T., Jokisch O.","56516864800;56058587500;6506242738;","Spatial Resolution-Independent CNN-Based Person Detection in Agricultural Image Data",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12336 LNAI",,,"189","199",,,"10.1007/978-3-030-60337-3_19","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092899800&doi=10.1007%2f978-3-030-60337-3_19&partnerID=40&md5=007ffc47af7da4816406d46dd7e37ecf","Institute of Communications Engineering, Leipzig University of Telecommunications (HfTL), Leipzig, Germany","Leipnitz, A., Institute of Communications Engineering, Leipzig University of Telecommunications (HfTL), Leipzig, Germany; Strutz, T., Institute of Communications Engineering, Leipzig University of Telecommunications (HfTL), Leipzig, Germany; Jokisch, O., Institute of Communications Engineering, Leipzig University of Telecommunications (HfTL), Leipzig, Germany","Advanced object detectors based on Convolutional Neural Networks (CNNs) offer high detection rates for many application scenarios but only within their respective training, validation and test data. Recent studies show that such methods provide a limited generalization ability for unknown data, even for small image modifications including a limited scale invariance. Reliable person detection with aerial robots (Unmanned Aerial Vehicles, UAVs) is an essential task to fulfill high security requirements or to support robot control, communication, and human-robot interaction. Particularly in an agricultural context, persons need to be detected from a long distance and a high altitude to allow the UAV an adequate and timely response. While UAVs are able to produce high resolution images that enable the detection of persons from a longer distance, typical CNN input layer sizes are comparably small. The inevitable scaling of images to match the input-layer size can lead to a further reduction in person sizes. We investigate the reliability of different YOLOv3 architectures for person detection in regard to those input-scaling effects. The popular VisDrone data set with its varying image resolutions and relatively small depiction of humans is used as well as high resolution UAV images from an agricultural data set. To overcome the scaling problem, an algorithm is presented for segmenting high resolution images in overlapping tiles that match the input-layer size. The number and overlap of the tiles are dynamically determined based on the image resolution. It is shown that the detection rate of very small persons in high resolution images can be improved using this tiling approach. © 2020, Springer Nature Switzerland AG.","Convolutional neural network; Image tiling; Input-layer scaling; Person detection; Resolution invariance","Agricultural robots; Agriculture; Aircraft detection; Antennas; Convolutional neural networks; Image resolution; Object detection; Robotics; Social robots; Unmanned aerial vehicles (UAV); Application scenario; Generalization ability; High detection rate; High resolution image; Image modification; Object detectors; Person detection; Spatial resolution; Image enhancement",,,,,"Bundesministerium für Bildung und Forschung, BMBF: 01DJ18011; Bundesministerium für Gesundheit, BMG","The authors acknowledge the financial support by the Federal Ministry of Education and Research of Germany (BMBF) within the framework of the EU Era.Net RUS+ project HARMONIC (national project number 01DJ18011).","Acknowledgements. The authors acknowledge the financial support by the Federal Ministry of Education and Research of Germany (BMBF) within the framework of the EU Era.Net RUS+ project HARMONIC (national project number 01DJ18011).",,,,,,,,,"Redmon, J., Farhadi, A., (2018) Yolov3: An Incremental Improvement, , arXiv preprint arXiv; Redmon, J., Darknet Framework for Object Detection, , https://github.com/pjreddie/darknet, Accessed 23 Jul 2020; Bochkovskiy, A., Improved Darknet Framework for Object Detection, , https://github.com/AlexeyAB/darknet, Accessed 23 Jul 2020; Rosenfeld, A., Zemel, R.S., Tsotsos, J.K., (2018) The Elephant in The; Azulay, A., Weiss, Y., Why do deep convolutional networks generalize so poorly to small image transformations? (2019) J. Mach. Learn. Res., 20 (184), pp. 1-25; Pinckaers, H., Litjens, G.J.S., (2018) Training Convolutional Neural Networks with Megapixel Images; Zhang, P., Zhong, Y., Li, X., Slimyolov3: Narrower, faster and better for real-time UAV applications (2019) The IEEE International Conference on Computer Vision (ICCV) Workshops; Steinmann, L., Sommer, L., Schumann, A., Beyerer, J., (2019) Fast and Lightweight Person Detector for Unmanned Aerial Vehicles, , EUSIPCO; Tayara, H., Chong, K., Object detection in very high-resolution aerial images using one-stage densely connected feature pyramid network (2018) Sensors, 18 (10), p. 3341; Huang, Z., Wang, J., (2019) DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection; Yang, F., Fan, H., Chu, P., Blasch, E., Ling, H., Clustered object detection in aerial images (2019) Proceedings of the IEEE International Conference on Computer Vision, pp. 8311-8320; Lu, Y., Javidi, T., Efficient object detection for high resolution images 2015 53Rd Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp. 1091-1098. , https://doi.org/10.1109/ALLERTON.2015.7447130, 2015; Zhang, J., Huang, J., Chen, X., Zhang, D., How to fully exploit the abilities of aerial image detectors (2019) The IEEE International Conference on Computer Vision (ICCV) Workshops; Pang, J., Li, C., Shi, J., Xu, Z., Feng, H., R2-CNN: Fast tiny object detection in large-scale remote sensing images (2019) IEEE Trans. Geosci. Remote Sens., 57 (8), pp. 5512-5524. , https://doi.org/10.1109/TGRS.2019.2899955; Růžička, V., Franchetti, F., Fast and accurate object detection in high resolution 4k and 8k video using gpus (2018) 2018 IEEE High Performance Extreme Computing Conference (HPEC), pp. 1-7. , https://doi.org/10.1109/HPEC.2018.8547574; Lin, T.-Y., Microsoft COCO: Common objects in context (2014) ECCV 2014. LNCS, 8693, pp. 740-755. , https://doi.org/10.1007/978-3-319-10602-148, Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.), Springer, Cham; Zhu, P., Wen, L., Bian, X., Ling, H., Hu, Q., (2018) Vision Meets Drones: A Challenge; Leipnitz, A., Tile Based Object Detection, , http://www1.hft-leipzig.de/leipnitz/papers/TiledDetection-resources, Accessed 23 Jul 2020","Leipnitz, A.; Institute of Communications Engineering, Germany; email: leipnitz@hft-leipzig.de","Ronzhin A.Rigoll G.Meshcheryakov R.",,"Springer Science and Business Media Deutschland GmbH","5th International Conference on Interactive Collaborative Robotics, ICR 2020","7 October 2020 through 9 October 2020",,249569,03029743,9783030603366,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85092899800
"Streitmatter G., Wiens G.","57219424505;7003372495;","Human-robot collaboration: A predictive collision detection approach for operation within dynamic environments",2020,"2020 International Symposium on Flexible Automation, ISFA 2020",,,"v001t04a005","","",,1,"10.1115/ISFA2020-9659","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092648722&doi=10.1115%2fISFA2020-9659&partnerID=40&md5=2c3d6b37bd22d43e992e1280ad1e8497","University of Florida, Gainesville, FL, United States","Streitmatter, G., University of Florida, Gainesville, FL, United States; Wiens, G., University of Florida, Gainesville, FL, United States","Robots and humans closely working together within dynamic environments must be able to continuously look ahead and identify potential collisions within their ever-changing environment. To enable the robot to act upon such situational awareness, its controller requires an iterative collision detection capability that will allow for computationally efficient Proactive Adaptive Collaboration Intelligence (PACI) to ensure safe interactions. In this paper, an algorithm is developed to evaluate a robot's trajectory, evaluate the dynamic environment that the robot operates in, and predict collisions between the robot and dynamic obstacles in its environment. This algorithm takes as input the joint motion data of predefined robot execution plans and constructs a sweep of the robot's instantaneous poses throughout time. The sweep models the trajectory as a point cloud containing all locations occupied by the robot and the time at which they will be occupied. To reduce the computational burden, Coons patches are leveraged to approximate the robot's instantaneous poses. In parallel, the algorithm creates a similar sweep to model any human(s) and other obstacles being tracked in the operating environment. Overlaying temporal mapping of the sweeps reveals anticipated collisions that will occur if the robot-human do not proactively modify their motion. The algorithm is designed to feed into a segmentation and switching logic framework and provide real-time proactive-n-reactive behavior for different levels of human-robot interactions, while maintaining safety and production efficiency. To evaluate the predictive collision detection approach, multiple test cases are presented to quantify the computational speed and accuracy in predicting collisions. Copyright © 2020 ASME","Collision Detection; Coons Patches; Human-Robot Interaction; Multiple Interference Detection; Swept Volume Interference","Collision avoidance; Computation theory; Intelligent robots; Iterative methods; Robot programming; Adaptive collaboration; Changing environment; Computationally efficient; Dynamic environments; Human-robot collaboration; Operating environment; Production efficiency; Situational awareness; Social robots",,,,,"National Science Foundation, NSF: 1830383; University of Florida, UF; Niroo Research Institute, NRI","Funding was provided by University of Florida’s Scholars Program and by the NSF/NRI: INT: COLLAB: Manufacturing USA: Intelligent Human-Robot Collaboration for Smart Factory (Award I.D. #:1830383). Any opinions, findings and conclusions or recommendations expressed are those of the researchers and do not necessarily reflect the views of the National Science Foundation.",,,,,,,,,,"Xiong, Q., Zhang, J., Wang, P., Gao, X., Transferable Two-stream Convolutional Neural Network in Human-robot Collaboration (2020) 48th SME North American Manufacturing Research Conference, , R., NAMRC 48; Nicora, M.L., Ambrosetti, R., Wiens, G.J., Fassi, I., Human-Robot Collaboration in Smart Manufacturing: Robot Reactive Behavior Intelligence (2020) ASME Manufacturing Science and Engineering Conference, , MSEC2020-10274; Jiménez, P., Thomas, F., Torras, C., 3D Collision Detection: A Survey (2001) Computers & Graphics, 25 (2), pp. 1-7; Culley, R. K., Kempf, K. G., A Collision Detection Algorithm based on Velocity and Distance bounds (1986) IEEE Int. Conf. on Robotics and Automation, 2, pp. 1064-1066. , San Francisco, CA; Abrams, S., Allen, P. K., Computing Swept Volumes (2000) Journal of Visualization and Computer Animation, 11 (2), pp. 69-82; Mamou, K., Ghorbel, F., A Simple and Efficient Approach for 3D Mesh Approximate Convex Decomposition (2009) IEEE International Conference on Image Processing, ICIP V9, pp. 3501-3504; Gaschler, A., Petrick, R. P. A., Kroger, T., Knoll, A., Khatib, O., Robot Task and Motion Planning With Sets of Convex Polyhedra (2013) RSS Workshop on Combined Robot Motion Planning and AI Planning for Practical Applications, pp. 1-5; Erdim, H. Ilieş, Classifying Points for Sweeping Solids (2008) Computer-Aided Design, 40 (9), pp. 987-998. , H. T; Ilies, H. T., Continuous Collision and Interference Detection For 3D Geometric Models (2009) J. Comput. Inf. Sci. Eng, 9 (2), pp. 1-7; Farin, G., Hansford, D., Discrete Coons Patches (1999) Comput. Aided. Gerom. Design, 16, pp. 691-700; Crane, C., Duffy, J., (1998) Kinematic Analysis of Robot Manipulators, pp. 39-43. , Cambridge University Press, New York, NY, Chapter 4",,,"Dynamic Systems and Control Division","American Society of Mechanical Engineers (ASME)","2020 International Symposium on Flexible Automation, ISFA 2020","8 July 2020 through 9 July 2020",,163396,,9780791883617,,,"English","Int. Symp. Flex. Autom. ISFA",Conference Paper,"Final","",Scopus,2-s2.0-85092648722
"Zhong Q., Zheng C., Zhang H.","25653248900;57219265400;57007699100;","Research on discriminative skeleton-based action recognition in spatiotemporal fusion and human-robot interaction",2020,"Complexity","2020",,"8717942","","",,1,"10.1155/2020/8717942","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091978497&doi=10.1155%2f2020%2f8717942&partnerID=40&md5=07ee2f80441ff4104e5f7245f30639bf","Robotics Institute, Ningbo University of Technology, Ningbo, 315211, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710054, China","Zhong, Q., Robotics Institute, Ningbo University of Technology, Ningbo, 315211, China, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710054, China; Zheng, C., Robotics Institute, Ningbo University of Technology, Ningbo, 315211, China; Zhang, H., Robotics Institute, Ningbo University of Technology, Ningbo, 315211, China","A novel posture motion-based spatiotemporal fused graph convolutional network (PM-STGCN) is presented for skeleton-based action recognition. Existing methods on skeleton-based action recognition focus on independently calculating the joint information in single frame and motion information of joints between adjacent frames from the human body skeleton structure and then combine the classification results. However, that does not take into consideration of the complicated temporal and spatial relationship of the human body action sequence, so they are not very efficient in distinguishing similar actions. In this work, we enhance the ability of distinguishing similar actions by focusing on spatiotemporal fusion and adaptive feature extraction for high discrimination information. Firstly, the local posture motion-based attention (LPM-TAM) module is proposed for the purpose of suppressing the skeleton sequence data with a low amount of motion in the temporal domain, and the representation of motion posture features is concentrated. Besides, the local posture motion-based channel attention module (LPM-CAM) is introduced to make use of the strongly discriminative representation between different action classes of similarity. Finally, the posture motion-based spatiotemporal fusion (PM-STF) module is constructed which fuses the spatiotemporal skeleton data by filtering out the low-information sequence and enhances the posture motion features adaptively with high discrimination. Extensive experiments have been conducted, and the results demonstrate that the proposed model is superior to the commonly used action recognition methods. The designed human-robot interaction system based on action recognition has competitive performance compared with the speech interaction system. © 2020 Qiubo Zhong et al.",,"Classification (of information); Convolutional neural networks; Information filtering; Man machine systems; Musculoskeletal system; Speech recognition; Adaptive feature extractions; Classification results; Competitive performance; Convolutional networks; Discrimination informations; Information sequences; Spatio-temporal fusions; Temporal and spatial; Social robots",,,,,"jg20190487; 2020SCG090; State Key Laboratory for Manufacturing Systems Engineering: sklms2019011","This material is based upon work funded by the State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University Foundation of China, under grant no. sklms2019011, ""13th Five-Year Plan"" Talent Training Project of Higher Education in Zhejiang Province under grant no. jg20190487, and Research Project of Educational Science Planning in Zhejiang Province under grant no. 2020SCG090.",,,,,,,,,,"Simonyan, K., Zisserman, A., (2014) Two-stream Convolutional Networks for Action Recognition in Videos; Bagautdinov, T., Alahi, A., Fleuret, F., Fua, P., Savarese, S., Social Scene Understanding: End-to-end Multi-person Action Localization and Collective Activity Recognition, , Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) July 2017 Honolulu, HI, USA 2-s2.0-85041892279; Wang, H., Schmid, C., Action Recognition with Improved Trajectories, pp. 3551-3558. , Proceedings of the IEEE International Conference on Computer Vision (ICCV) December 2013 Sydney, Australia 2-s2.0-84898805910; Cao, Z., Simon, T., Wei, S.-E., Sheikh, Y., Realtime Multi-person 2d Pose Estimation Using Part Affinity Fields, , Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) July 2017 Honolulu, HI, USA 2-s2.0-85042428134; Chen, Y., Wang, Z., Peng, Y., Zhang, Z., Yu, G., Sun, J., Cascaded Pyramid Network for Multi-person Pose Estimation, , Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition June 2018 Salt Lake City, UT, USA 2-s2.0-85061772116; Liu, J., Shahroudy, A., Xu, D., Wang, G., Spatio-temporal LSTM with trust gates for 3d human action recognition (2016) Computer Vision-ECCV 2016, 9907, pp. 816-833. , 2-s2.0-84990059379; Li, C., Zhong, Q., Xie, D., Pu, S., Skeleton-based Action Recognition with Convolutional Neural Networks, , Proceedings of the IEEE International Conference on Multimedia & Expo Workshops ICMEW July 2017 Hong Kong, China 2-s2.0-85031695454; Yan, S., Xiong, Y., Lin, D., (2018) Spatial Temporal Graph Convolutional Networks for Skeleton-based Action Recognition, , http://arxiv.org/abs/1801.07455v2; Yang, C., Jiang, Y., Na, J., Li, Z., Cheng, L., Su, C.-Y., Finite-time convergence adaptive fuzzy control for dual-arm robot with unknown kinematics and dynamics (2019) Ieee Transactions on Fuzzy Systems, 27 (3), pp. 574-588. , 2-s2.0-85052604492; Shi, L., Zhang, Y., Cheng, J., Lu, H., (2019) Skeleton-based Action Recognition with Multi-stream Adaptive Graph Convolutional Networks, , https://arxiv.org/abs/1912.06971; Liu, Z., Zhang, H., Chen, Z., Wang, Z., Ouyang, W., (2020) Disentangling and Unifying Graph Convolutions Fo Skeleton-based Action Recognition, , https://arxiv.org/abs/2003.14111; Yang, C., Jiang, Y., He, W., Na, J., Li, Z., Xu, B., Adaptive parameter estimation and control design for robot manipulators with finite-time convergence (2018) Ieee Transactions on Industrial Electronics, 65 (10), pp. 8112-8123. , 2-s2.0-85041508667; Yang, C., Luo, J., Pan, Y., Liu, Z., Su, C.-Y., Personalized variable gain control with tremor attenuation for robot teleoperation (2018) Ieee Transactions on Systems, Man, and Cybernetics: Systems, 48 (10), pp. 1759-1770. , 2-s2.0-85018863536; Peng, W., Hong, X., Chen, H., Zhao, G., Learning graph convolutional network for skeleton-based human action recognition by neural searching (2020) Proceedings of the Aaai Conference on Artificial Intelligence, 34 (3), pp. 2669-2676; Obinata, Y., Yamamto, T., (2020) Temporal Extension Module for Skeleton-based Action Recognition, , https://arxiv.org/abs/2003.08951; Shahroudy, A., Liu, J., Ng, T.-T., Wang, G., Ntu Rgb + D: A Large Scale Dataset for 3d Human Activity Analysis, pp. 1010-1019. , Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2016 Las Vegas, NV, USA 2-s2.0-84986252370; Kay, W., Carreira, J., Simonyan, K., (2017) The Kinetics Human Action Video Dataset, , https://arxiv.org/abs/1705.06950; Shi, L., Zhang, Y., Cheng, J., Lu, H., Two-stream Adaptive Graph Convolutional Networks for Skeleton-based Action, , Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) June 2019 Long Beach, CA, USA","Zhong, Q.; Robotics Institute, China; email: zhongqiubo@nbut.edu.cn",,,"Hindawi Limited",,,,,10762787,,,,"English","Complexity",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85091978497
"Vlachos E., Hansen A.F., Holck J.P.","55486988700;57218422983;57208081808;","A Robot in the Library",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12215 LNCS",,,"312","322",,,"10.1007/978-3-030-50267-6_24","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089166821&doi=10.1007%2f978-3-030-50267-6_24&partnerID=40&md5=f46d5d2a1400866ef432f1b6aa4a160b","University Library of Southern Denmark, University of Southern Denmark, Campusvej 55, Odense, 5230, Denmark","Vlachos, E., University Library of Southern Denmark, University of Southern Denmark, Campusvej 55, Odense, 5230, Denmark; Hansen, A.F., University Library of Southern Denmark, University of Southern Denmark, Campusvej 55, Odense, 5230, Denmark; Holck, J.P., University Library of Southern Denmark, University of Southern Denmark, Campusvej 55, Odense, 5230, Denmark","If robots are to be beneficial and appealing within an international library setting, useful to patrons and cooperative with library personnel, then library culture becomes an important issue. Human-robot interaction designers need to consider technological factors, the library culture, as well as expectations from users to develop a solution that could withstand the fall of time and not atone when enthusiasm is lost. We reviewed the recent literature and performed qualitative analyses of our findings to explore the tasks that are proven to have been robotized in a library, and to investigate current cultural and technological barriers that would decrease acceptance rates of robots in a library. Search of Scopus, Web of Science, IEEEXplore, and LISTA databases was conducted complemented with Google searches. Articles with scientific content were included if they described the use of a robot in a library setting, were written in English, and were published within 2016–2018. We identified 1037 references and after title, abstract and full-text screening according to the eligibility criteria we included 18 records in our analysis. We summarize the main roles of library robots as: robots for navigation, book location and placement; robots as information desks; and robots in education. Barriers towards robotic acceptance was found to be: anxiety and fear among librarians of being replaced by robots; the lack of resources (time, money, space) for maintaining a robot and the cost of organizational restructuring; maintaining the enthusiasm around it over time; and the patrons’ need for human contact. © 2020, Springer Nature Switzerland AG.","Cultural barriers; Library; Literature search; Robot; Technological benefits","Abstracting; Human computer interaction; Libraries; Social robots; Acceptance rate; Eligibility criterion; Library robots; Organizational restructuring; Qualitative analysis; Scientific content; Technological barriers; Technological factors; Educational robots",,,,,,,,,,,,,,,,"https://www.lib.uchicago.edu/mansueto/tech/asrs, The Joe and Rika Mansueto Library. Accessed 21 Jan 2020; Bodleian’s Book Storage Facility, , https://www.bodleian.ox.ac.uk/bodley/news/2015/oct-19, Accessed 21 Jan 2020; Breazeal, C., Social interactions in HRI: the robot view (2004) IEEE Trans. Syst. Man Cybernetics, 34 (2), pp. 181-186. , IEEE; Tan, Z.-H., iSociobot: a multimodal interactive social robot (2017) Int. J. Soc. Robot, 10 (1), pp. 5-19. , https://doi.org/10.1007/s12369-017-0426-7; Vlachos, E., Schärfe, H., Social robots as persuasive agents (2014) SCSM 2014. LNCS, 8531, pp. 277-284. , https://doi.org/10.1007/978-3-319-07632-4_26, Meiselwitz, G. (ed) Springer, Cham; Frey, C.B., Osborne, M.A., The future of employment: how susceptible are jobs to computerisation? (2017) Technol. Forecast. Soc. Chang, 114, pp. 254-280; Bartels, E.M., How to perform a systematic search (2013) Best Pract. Res. Clin. Rheumatol, 27, pp. 295-306; Pujari, T.S., Deosarkar, S.B., Design of intelligent and robotic library system (2017) 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pp. 1903-1908. , IEEE; Rashid, M.R., Uzzaman, N., Hossain, S., Shuvra, N.K.D., Development of a self-navigating algorithm for library book finder robot (2017) 2017 3rd International Conference on Electrical Information and Communication Technology (EICT), pp. 1-6. , IEEE; Angal, Y., Gade, A., Development of library management robotic system (2017) 2017 International Conference on Data Management, Analytics and Innovation (ICDMAI), pp. 254-258. , IEEE; Lin, W., Yueh, H.P., Evaluating children’s performance and perception of robotic assistance in library book locating (2018) 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 1185-1189. , IEEE; Kroski, E., (2017) The Makerspace Librarian’s Sourcebook, , (ed): Facet Publishing, London; Tanaka, M., Okada, K., Wada, M., Guiding robot at entrance hall of university library (2017) 2017 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), pp. 269-270. , IEEE; Herron, J., Intelligent agents for the library (2017) J. Electron. Res. Med. Libraries, 14 (3–4), pp. 139-144. , Taylor & Francis; Brigham, T.J., Merging technology and emotions: introduction to affective computing (2017) Med. Ref. Serv. Q, 36 (4), pp. 399-407. , Taylor & Francis; Pham, H., Giordano, A., Miller, L., Giannitti, J., Mena, M., DiNardi, A., A ubiquitous approach for automated library book location management (2018) Proceedings of the 2018 International Conference on Computing and Big Data, pp. 78-82. , ACM; Stahl, B., Mohnke, J., Seeliger, F., Roboter ante portas? about the deployment of a humanoid robot into a library (2018) Proceedings of the IATUL Conferences, , Paper 6; Animireddy, S.P., Singh, K.P., Natarajan, V., Robotic library assistant (2018) 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 1443-1447. , IEEE; Liu, J., Zhu, F., Wang, Y., Wang, X., Pan, Q., Chen, L., RF-scanner: shelf scanning with robot-assisted RFID systems (2017) IEEE INFOCOM 2017-IEEE Conference on Computer Communications, pp. 1-9. , IEEE; Guth, L., Vander Meer, P.F., Telepresence robotics in an academic library: a study of exposure and adaptation among patrons and employees (2017) Library Hi Tech, 35 (3), pp. 408-420. , Emerald; Asemi, A., Asemi, A., Artificial Intelligence (AI) application in library systems in Iran: a taxonomy study (2018) Library Philosophy and Practice; Talley, N.B., Imagining the use of intelligent agents and artificial intelligence in academic law libraries (2016) Law Libr. J, 108, pp. 383-401; Massis, B., Artificial intelligence arrives in the library (2018) Inf. Learn. Sci, 119 (7), pp. 456-459. , (/8), Emerald; Prato, S.C., Beyond the computer age: a best practices intro for implementing library coding programs (2017) Children and Libraries, 15 (1), pp. 19-21. , Association for Library Service to Children; https://sfpl.org/index.php?pg=2000795701, The TechMobile. Accessed 13 Jan 2020; Calvert, P., Robots, the quiet workers, are you ready to take over? (2017) Public Library Quarterly, 36 (2), pp. 167-172. , Routledge; Vlachos, E., Faber Hansen, A., Holck, J.P., The essence of being a librarian in disruptive times (2019) IFLA WLIC 2019-Athens, Greece-Libraries: dialogue for change, Session 113c-IFLA Poster Session","Vlachos, E.; University Library of Southern Denmark, Campusvej 55, Denmark; email: evl@bib.sdu.dk","Rauterberg M.",,"Springer","8th International Conference on Culture and Computing, C and C 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242469,03029743,9783030502669,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85089166821
[No author name available],[No author id available],"17th International Conference on Image Analysis and Recognition, ICIAR 2020",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12131 LNCS",,,"","",858,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087488656&partnerID=40&md5=4d087e234b7bf24aa3833d4432d4332b",,"","The proceedings contain 73 papers. The special focus in this conference is on Image Analysis and Recognition. The topics include: Slicing and dicing soccer: Automatic detection of complex events from spatio-temporal data; RN-vid: A feature fusion architecture for video object detection; color inference from semantic labeling for person search in videos; 2D bidirectional gated recurrent unit convolutional neural networks for end-to-end violence detection in videos; video based live tracking of fishes in tanks; using external knowledge to improve zero-shot action recognition in egocentric videos; a semantics-guided warping for semi-supervised video object instance segmentation; two-stream framework for activity recognition with 2d human pose estimation; video object segmentation using convex optimization of foreground and background distributions; flowChroma - A deep recurrent neural network for video colorization; deep learning for partial fingerprint inpainting and recognition; a visual perception framework to analyse neonatal pain in face images; combining asynchronous events and traditional frames for steering angle prediction; survey of preprocessing techniques and classification approaches in online signature verification; SSIM based signature of facial micro-expressions; learning to search for objects in images from human gaze sequences; detecting defects in materials using deep convolutional neural networks; visual perception ranking of chess players; video tampering detection for decentralized video transcoding networks; generalized subspace learning by roweis discriminant analysis; benchmark for generic product detection: A low data baseline for dense object detection; understanding public speakers’ performance: First contributions to support a computational approach; open source multipurpose multimedia annotation tool; SLAM-based multistate tracking system for mobile human-robot interaction.",,,,,,,,,,,,,,,,,,,,"Campilho A.Karray F.Wang Z.",,"Springer","17th International Conference on Image Analysis and Recognition, ICIAR 2020","24 June 2020 through 26 June 2020",,241149,03029743,9783030503468,,,"English","Lect. Notes Comput. Sci.",Conference Review,"Final","",Scopus,2-s2.0-85087488656
"Lingyun G., Lin Z., Zhaokui W.","57216566104;57216564676;57207120590;","Hierarchical Attention-Based Astronaut Gesture Recognition: A Dataset and CNN Model",2020,"IEEE Access","8",,"9060947","68787","68798",,1,"10.1109/ACCESS.2020.2986473","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083902210&doi=10.1109%2fACCESS.2020.2986473&partnerID=40&md5=c5caaf2c0dcfd438c072af97c9456939","School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China; Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati, Cincinnati, OH  45221, United States","Lingyun, G., School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China; Lin, Z., Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati, Cincinnati, OH  45221, United States; Zhaokui, W., School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China","In the space station, visual gesture recognition is an important component for gesture based human-robot interaction to control the robot, which assists astronauts to complete simple, repetitive and cooperative work. However, public gesture datasets are captured in the daily environment and existing approaches trained on these datasets achieve imprecise performance for astronaut gesture recognition. In this paper, we introduce a new astronaut gesture dataset (DSSL-Astronaut gesture dataset) and a novel hierarchical attention single-shot detector network (HA-SSD) for astronaut gesture recognition. Specifically, this dataset consists of the real and augmented images. The real images captured in the simulated space station are closed to the real images in the space station environment. Meanwhile, we utilize the Mask-RCNN model to segment the foreground image including an astronaut from real data and capture the background images of the simulated space station at different views and illuminations. Then we combine them to synthesize the augmented images. A novel HA-SSD model consists of a lightweight backbone named MobileNet and a hierarchical attention mechanism. The MobileNet is used as the feature extractor, which contains several depth-wise separable convolutions to make a trade-off between latency and accuracy. Meanwhile, the hierarchical channel-wise attention module is adopted to exploit the fine semantic information to enrich the features for improving the performance of gesture recognition. We conduct experiments to demonstrate that our dataset is suitable for the space station and our approach is able to effectively localize and recognize the gestures with strong generality. © 2013 IEEE.","Assist robot; attention; gesture dataset; gesture recognition; single-shot detector","Economic and social effects; Human robot interaction; Image segmentation; Manned space flight; Space stations; Attention mechanisms; Augmented images; Background image; Cooperative works; Detector networks; Feature extractor; Foreground images; Semantic information; Semantics",,,,,"National Natural Science Foundation of China, NSFC: 11872034","This work was supported by the National Natural Science Foundation of China under Grant 11872034.",,,,,,,,,,"Rui, Z., Zhaokui, W., Yulin, Z., A person-following nanosatellite for in-cabin astronaut assistance: System design and deep-learning-based astronaut visual tracking implementation (2019) Acta Astronautica, 162, pp. 121-134. , Sep; Rui, Z., Zhaokui, W., An intelligent nano-satellite for astronaut assistance (2018) Proc. 69th Int. Astron. Congr., pp. 1-5. , Oct; Dorais, G.A., Gawdiak, Y., The personal satellite assistant: An internal spacecraft autonomous mobile monitor (2003) Proc. IEEE Aerosp. Conf., 1, pp. 1-348. , Mar; Mohan, S., Saenz-Otero, A., Nolet, S., Miller, D.W., Sell, S., SPHERES fiight operations testing and execution (2009) Acta Astronautica, 65 (7-8), pp. 1121-1132. , Oct; Mitani, S., Goto, M., Konomura, R., Shoji, Y., Hagiwara, K., Shigeto, S., Tanishima, N., Int-ball: Crew-supportive autonomous mobile camera robot on ISS/JEM (2019) Proc. IEEE Aerosp. Conf., pp. 1-15. , Mar; Bualat, M., Barlow, J., Fong, T., Provencher, C., Smith, T., Astrobee: Developing a free-ỳing robot for the international space station (2015) Proc. AIAA SPACE Conf. Exposit., p. 4643. , Aug; He, K., Gkioxari, G., Dollar, P., Girshick, R., Mask R-CNN (2017) Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pp. 2961-2969. , Oct; Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H., (2017) MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, , http://arxiv.org/abs/1704.04861; Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C., SSD: Single shot MultiBox detector (2016) Proc. Eur. Conf. Comput. Vis., pp. 21-37. , Cham, Switzerland: Springer; Mittal, A., Zisserman, A., Torr, P., Hand detection using multiple proposals (2011) Proc. BMVC, 40, pp. 1-75. , Princeton, NJ, USA, Citeseer; Redmon, J., Farhadi, A., (2018) YOLOv3: An Incremental Improvement, , http://arxiv.org/abs/1804.02767; Plawiak, P., Sosnicki, T., Niedzwiecki, M., Tabor, Z., Rzecki, K., Hand body language gesture recognition based on signals from specialized glove and machine learning algorithms (2016) IEEE Trans. Ind. Informat., 12 (3), pp. 1104-1113. , Jun; Qi, J., Jiang, G., Li, G., Sun, Y., Tao, B., Intelligent human-computer interaction based on surface EMG gesture recognition (2019) IEEE Access, 7, pp. 61378-61387; Hu, Y., Wong, Y., Dai, Q., Kankanhalli, M., Geng, W., Li, X., SEMGbased gesture recognition with embedded virtual hand poses and adversarial learning (2019) IEEE Access, 7, pp. 104108-104120; Ren, Y., Xie, X., Li, G., Wang, Z., Hand gesture recognition with multiscale weighted histogram of contour direction normalization for wearable applications (2018) IEEE Trans. Circuits Syst. Video Technol., 28 (2), pp. 364-377. , Feb; Han, J., Awad, G., Sutherland, A., Automatic skin segmentation and tracking in sign language recognition (2009) IET Comput. Vis., 3 (1), pp. 24-35; Van den Bergh, M., Van Gool, L., Combining RGB and ToF cameras for real-time 3D hand gesture interaction (2011) Proc. IEEE Workshop Appl. Comput. Vis. 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Appl., 10 (5), pp. 1-11. , Sep; Zhang, S., Zhang, S., A novel human-3DTV interaction system based on free hand gestures and a touch-based virtual interface (2019) IEEE Access, 7, pp. 165961-165973; Krompiec, P., Park, K., Enhanced player interaction using motion controllers for _rst-person shooting games in virtual reality (2019) IEEE Access, 7, pp. 124548-124557; Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., Pietikäinen, M., Deep learning for generic object detection: A survey (2020) Int. J. Comput. Vis., 128 (2), pp. 261-318. , Feb; Wang, P., Li, W., Ogunbona, P., Wan, J., Escalera, S., RGB-D-based human motion recognition with deep learning: A survey (2018) Comput. Vis. Image Understand., 171, pp. 118-139. , Jun; Wang, H., Wang, P., Song, Z., Li, W., Large-scale multimodal gesture recognition using heterogeneous networks (2017) Proc. IEEE Int. Conf. Comput. Vis. Workshops (ICCVW), pp. 3129-3137. , Oct; Girshick, R., Donahue, J., Darrell, T., Malik, J., Rich feature hierarchies for accurate object detection and semantic segmentation (2014) Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 580-587. , Jun; Girshick, R., Fast R-CNN (2015) Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pp. 1440-1448. , Dec; Redmon, J., Divvala, S., Girshick, R., Farhadi, A., You only look once: Unified, real-time object detection (2016) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 779-788. , Jun; Gao, Q., Liu, J., Ju, Z., Zhang, L., Li, Y., Liu, Y., Hand detection and location based on improved SSD for space human-robot interaction (2018) Proc. Int. Conf. Intell. Robot. Appl., pp. 164-175. , Cham, Switzerland: Springer; Ni, Z., Chen, J., Sang, N., Gao, C., Liu, L., Light YOLO for high-speed gesture recognition (2018) Proc. 25th IEEE Int. Conf. Image Process. (ICIP), pp. 3099-3103. , Oct; Gao, Q., Liu, J., Ju, Z., Robust real-time hand detection and localization for space human_robot interaction based on deep learning (2019) Neurocomput-ing, , Oct; Bao, P., Maqueda, A.I., Del-Blanco, C.R., García, N., Tiny hand gesture recognition without localization via a deep convolutional network (2017) IEEE Trans. Consum. Electron., 63 (3), pp. 251-257. , Aug; Li, Y., Wang, X., Liu, W., Feng, B., Deep attention network for joint hand gesture localization and recognition using static RGB-D images (2018) Inf. Sci., 441, pp. 66-78. , May; Nuzzi, C., Pasinetti, S., Pagani, R., Docchio, F., Sansoni, G., Hand gesture recognition for collaborative workstations: A smart command system prototype (2019) Proc. Int. Conf. Image Anal. Process., pp. 332-342. , Cham, Switzerland: Springer; Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C., MobileNetV2: Inverted residuals and linear bottlenecks (2018) Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pp. 4510-4520. , Jun; Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.-C., Tan, M., Chu, G., Le, Q., Searching for MobileNetV3 (2019) Proc. IEEE/CVF Int. Conf. Comput. Vis. (ICCV), pp. 1314-1324. , Oct; Zhang, J., Li, W., Ogunbona, P.O., Wang, P., Tang, C., RGB-D-based action recognition datasets: A survey (2016) Pattern Recognit., 60, pp. 86-105. , Dec; Wan, J., Li, S.Z., Zhao, Y., Zhou, S., Guyon, I., Escalera, S., ChaLearn looking at people RGB-D isolated and continuous datasets for gesture recognition (2016) Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Work-shops (CVPRW), pp. 56-64. , Jun; Escalera, S., Gonzàlez, J., Baró, X., Reyes, M., Lopes, O., Guyon, I., Athitsos, V., Escalante, H., Multi-modal gesture recognition challenge 2013: Dataset and results (2013) Proc. 15th ACM Int. Conf. Multimodal Interact. (ICMI), pp. 445-452; Zhang, Y., Cao, C., Cheng, J., Lu, H., EgoGesture: A new dataset and benchmark for egocentric hand gesture recognition (2018) IEEE Trans. Multimedia, 20 (5), pp. 1038-1050. , May; Takahashi, R., Matsubara, T., Uehara, K., Data augmentation using random image cropping and patching for deep CNNs (2019) IEEE Trans. Circuits Syst. Video Technol., , early access, Aug. 13; Meng, Z., Guo, X., Pan, Z., Sun, D., Liu, S., Data segmentation and augmentation methods based on raw data using deep neural networks approach for rotating machinery fault diagnosis (2019) IEEE Access, 7, pp. 79510-79522; Ioffe, S., Szegedy, C., (2015) Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, , http://arxiv.org/abs/1502.03167; Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D., Grad-CAM: Visual explanations from deep networks via gradient-based localization (2017) Proc. IEEE Int. Conf. Comput. Vis., pp. 618-626. , Oct; Hu, J., Shen, L., Sun, G., Squeeze-and-excitation networks (2018) Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 7132-7141. , Jun; Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I., Attention is all you need (2017) Proc. Adv. Neural Inf. Process. Syst., pp. 5998-6008; Lin, M., Chen, Q., Yan, S., (2013) Network in Network, , http://arxiv.org/abs/1312.4400; Radford, A., Metz, L., Chintala, S., (2015) Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, , http://arxiv.org/abs/1511.06434","Zhaokui, W.; School of Aerospace Engineering, China; email: wangzk@tsinghua.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85083902210
"Chanel C.P.C., Roy R.N., Dehais F., Drougard N.","36681830500;55904560000;13005898500;55936524900;","Towards mixed-initiative human–robot interaction: Assessment of discriminative physiological and behavioral features for performance prediction",2020,"Sensors (Switzerland)","20","1","296","","",,7,"10.3390/s20010296","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077673780&doi=10.3390%2fs20010296&partnerID=40&md5=8fdbacf32e09fe5b4140059f3a5fe173","ISAE-SUPAERO, Université de Toulouse, Toulouse, 31400, France","Chanel, C.P.C., ISAE-SUPAERO, Université de Toulouse, Toulouse, 31400, France; Roy, R.N., ISAE-SUPAERO, Université de Toulouse, Toulouse, 31400, France; Dehais, F., ISAE-SUPAERO, Université de Toulouse, Toulouse, 31400, France; Drougard, N., ISAE-SUPAERO, Université de Toulouse, Toulouse, 31400, France","The design of human–robot interactions is a key challenge to optimize operational performance. A promising approach is to consider mixed-initiative interactions in which the tasks and authority of each human and artificial agents are dynamically defined according to their current abilities. An important issue for the implementation of mixed-initiative systems is to monitor human performance to dynamically drive task allocation between human and artificial agents (i.e., robots). We, therefore, designed an experimental scenario involving missions whereby participants had to cooperate with a robot to fight fires while facing hazards. Two levels of robot automation (manual vs autonomous) were randomly manipulated to assess their impact on the participants’ performance across missions. Cardiac activity, eye-tracking, and participants’ actions on the user interface were collected. The participants performed differently to an extent that we could identify high and low score mission groups that also exhibited different behavioral, cardiac and ocular patterns. More specifically, our findings indicated that the higher level of automation could be beneficial to low-scoring participants but detrimental to high-scoring ones, and vice versa. In addition, inter-subject single-trial classification results showed that the studied behavioral and physiological features were relevant to predict mission performance. The highest average balanced accuracy (74%) was reached using the features extracted from all input devices. These results suggest that an adaptive HRI driving system, that would aim at maximizing performance, would be capable of analyzing such physiological and behavior markers online to further change the level of automation when it is relevant for the mission purpose. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Human; Human behavior; Intelligent sensors; Performance prediction; Physiological computing; Robot interaction","Automation; Behavioral research; Eye tracking; Forecasting; Intelligent robots; Machine design; Physiology; User interfaces; Human; Human behaviors; Intelligent sensors; Performance prediction; Physiological computing; Robot interactions; Human robot interaction; adult; behavior; computer interface; female; genetic procedures; human; male; man machine interaction; physiology; robotics; Adult; Behavior; Biosensing Techniques; Female; Humans; Male; Man-Machine Systems; Robotics; User-Computer Interface",,,,,"2021","Funding: This work has been financially supported by the Dassault Aviation Chair (https://www.isae-supaero. fr/fr/isae-supaero/mecenat-relations-avec-la-fondation-isae-supaero/chaire-dassault-aviation-casac) (CASAC 2016–2021).","This work has been financially supported by the Dassault Aviation Chair (https://www.isae-supaero. fr/fr/isae-supaero/mecenat-relations-avec-la-fondation-isae-supaero/chaire-dassault-aviation-casac) (CASAC 2016?2021). Acknowledgments: Many thanks are given to Corentin Chauffaut for the constructive discussions about this work.",,,,,,,,,"Finzi, A., Orlandini, A., (2005) A Mixed-Initiative Approach to Human-Robot Interaction in Rescue Scenarios; American Association for Artificial Intelligence: Menlo Park, , CA, USA; Nagatani, K., Kiribayashi, S., Okada, Y., Otake, K., Yoshida, K., Tadokoro, S., Nishimura, T., Fukushima, M., Emergency response to the nuclear accident at the Fukushima Daiichi Nuclear Power Plants using mobile rescue robots (2013) J. Field Robot., 30, pp. 44-63; Bresina, J.L., Morris, P.H., Mixed-initiative planning in space mission operations (2007) AI Mag, 28, p. 75; Gateau, T., Chanel, C.P.C., Le, M.H., Dehais, F., Considering human’s non-deterministic behavior and his availability state when designing a collaborative human-robots system (2016) In Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4391-4397. , Daejeon, Korea, 9–14 October; Bevacqua, G., Cacace, J., Finzi, A., Lippiello, V., Mixed-Initiative Planning and Execution for Multiple Drones in Search and Rescue Missions (2015) In Proceedings of the Twenty-Fifth International Conference in Automated Planning and Scheduling (ICAPS), pp. 315-323. , Jerusalem, Israel, 7–11 June; Dehais, F., Duprès, A., Blum, S., Drougard, N., Scannella, S., Roy, R.N., Lotte, F., Monitoring Pilot’s Mental Workload Using ERPs and Spectral Power with a Six-Dry-Electrode EEG System in Real Flight Conditions (2019) Sensors, 19, p. 1324; Dehais, F., Dupres, A., Di Flumeri, G., Verdiere, K., Borghini, G., Babiloni, F., Roy, R., Monitoring Pilot’s Cognitive Fatigue with Engagement Features in Simulated and Actual Flight Conditions Using an Hybrid fNIRS-EEG Passive BCI (2018) Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 544-549. , Miyazaki, Japan, 7–10 October; Roy, R.N., Bonnet, S., Charbonnier, S., Campagne, A., Mental fatigue and working memory load estimation: Interaction and implications for EEG-based passive BCI (2013) Proceedings of the 2013 35Th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6607-6610. , Osaka, Japan, 3–7 July; Dehais, F., Rida, I., Roy, R.N., Iversen, J., Mullen, T., Callan, D., A pBCI to Predict Attentional Error Before it Happens in Real Flight Conditions (2019) In Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 4155-4160. , Bari, Italy, 6–9 October; Dehais, F., Hodgetts, H.M., Causse, M., Behrend, J., Durantin, G., Tremblay, S., Momentary lapse of control: A cognitive continuum approach to understanding and mitigating perseveration in human error (2019) Neurosci. Biobehav. Rev., 100, pp. 252-262; Crandall, J.W., Goodrich, M.A., Nielsen, C.W., Olsen, D.R., Jr., Validating human–robot interaction schemes in multitasking environments (2005) IEEE Trans. Syst. Man Cybern. Part a Syst. Hum., 35, pp. 438-449; de Souza, P.E.U., Chanel, C.P.C., Dehais, F., MOMDP-based target search mission taking into account the human operator’s cognitive state (2015) In Proceedings of the IEEE 27Th International Conference on Tools with Artificial Intelligence (ICTAI), Mare, Italy, 9–11 November, pp. 729-736; Nikolaidis, S., Ramakrishnan, R., Gu, K., Shah, J., Efficient Model Learning from Joint-Action Demonstrations for Human-Robot Collaborative Tasks (2015) In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, pp. 189-196. , Portland, OR, USA, 2–5 March 2015; ACM: New York, NY, USA; Jiang, S., Arkin, R.C., Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey (2015) Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 954-961. , Hong Kong, China, 9–12 October; de Souza, P.E.U., (2017) Towards Mixed-Initiative Human-Robot Interaction: A Cooperative Human-Drone Team Framework, , Ph.D. Thesis, Université de Toulouse, Délivré par l’Institut Supérieur de l’Aéronautique et de l’Espace (ISAE-SUPAERO), Toulouse, France; Vanderhaegen, F., Dissonance engineering: A new challenge to analyse risky knowledge when using a system (2014) Int. J. Comput. Commun. Control, 9, pp. 776-785; Scerbo, M.W., (2008) Adaptive Automation, pp. 239-252. , Oxford University Press: Oxford, UK; de Visser, E., Parasuraman, R., Adaptive aiding of human-robot teaming: Effects of imperfect automation on performance, trust, and workload (2011) J. Cogn. Eng. Decis. Mak., 5, pp. 209-231; Mower, E., Feil-Seifer, D.J., Mataric, M.J., Narayanan, S., Investigating implicit cues for user state estimation in human-robot interaction using physiological measurements (2007) Procedings of the RO-MAN 2007—The 16Th IEEE International Symposium on Robot and Human Interactive Communication, pp. 1125-1130. , Jeju Island, Korea, 26–29 August; Gateau, T., Ayaz, H., Dehais, F., In silico versus over the clouds: On-the-fly mental state estimation of aircraft pilots, using a functional near infrared spectroscopy based passive-BCI (2018) Front. Hum. Neurosci., 12, p. 187; Adams, J.A., Multiple robot/single human interaction: Effects on perceived workload (2009) Behav. Inf. Technol., 28, pp. 183-198; Harriott, C.E., Zhang, T., Adams, J.A., Evaluating the applicability of current models of workload to peer-based human-robot teams (2011) Procedings of the 2011 6Th ACM/IEEE International Conference on Human-Robot Interaction (HRI), , Ulm, Germany, 15–16, September; Rodríguez-Fernández, V., Menéndez, H.D., Camacho, D., Analysing temporal performance profiles of UAV operators using time series clustering (2017) Expert Syst. Appl., 70, pp. 103-118; Regis, N., Dehais, F., Rachelson, E., Thooris, C., Pizziol, S., Mickaël Causse, C.T., Formal Detection of Attentional Tunneling in Human Operator-Automation Interactions (2014) IEEE Trans. Hum. Mach. Syst., 44, pp. 326-336; Rani, P., Liu, C., Sarkar, N., Vanman, E., An empirical study of machine learning techniques for affect recognition in human–robot interaction (2006) Pattern Anal. Appl., 9, pp. 58-69; Singh, G., Bermùdez, I., Badia, S., Ventura, R., Silva, J.L., Physiologically Attentive User Interface for Robot Teleoperation—Real Time Emotional State Estimation and Interface Modification Using Physiology, Facial Expressions and Eye Movements (2018) In Proceedings of the 11Th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC); Heard, J., Harriott, C.E., Adams, J.A., A Survey of Workload Assessment Algorithms (2018) IEEE Trans. Hum. Mach. Syst.; Ahlstrom, U., Friedman-Berg, F.J., Using eye movement activity as a correlate of cognitive workload (2006) Int. J. Ind. Ergon., 36, pp. 623-636; Chanel, C.P.C., Wilson, M.D., Scannella, S., Online ECG-based Features for Cognitive Load Assessment (2019) In Proceedings of the IEEE International Conference on Systems, pp. 6-9. , Man and Cybernetics, Bari, Italy, October; Fallahi, M., Motamedzade, M., Heidarimoghadam, R., Soltanian, A.R., Miyake, S., Effects of mental workload on physiological and subjective responses during traffic density monitoring: A field study (2016) Appl. Ergon., 52, pp. 95-103; Tjolleng, A., Jung, K., Hong, W., Lee, W., Lee, B., You, H., Son, J., Park, S., Classification of a Driver’s cognitive workload levels using artificial neural network on ECG signals (2017) Appl. Ergon., 59, pp. 326-332; Valéry, B., Matton, N., Scannella, S., Dehais, F., Global difficulty modulates the prioritization strategy in multitasking situations (2019) Appl. Ergon., 80, pp. 1-8; Drougard, N., Chanel, C.P.C., Roy, R.N., Dehais, F., Mixed-initiative mission planning considering human operator state estimation based on physiological sensors Proceedings of the IROS-2017 Workshop on Human-Robot Interaction in Collaborative Manufacturing Environments (HRI-CME), , Vancouver, BC, Canada, September 2017; Charles, J.A., Chanel, C.P.C., Chauffaut, C., Chauvin, P., Drougard, N., Human-Agent Interaction Model Learning based on Crowdsourcing (2018) In Proceedings of the 6Th International Conference on Human-Agent Interaction, Southampton, UK, pp. 20-28. , 15–18 December; Åkerstedt, T., Gillberg, M., Subjective and objective sleepiness in the active individual (1990) Int. J. Neurosci., 52, pp. 29-37; Hart, S.G., Staveland, L.E., Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research (1988) In Advances in Psychology; Elsevier: Amsterdam, the Netherlands, 52, pp. 139-183; Malik, M., Bigger, J.T., Camm, A.J., Kleiger, R.E., Malliani, A., Moss, A.J., Schwartz, P.J., Heart rate variability: Standards of measurement, physiological interpretation, and clinical use (1996) Eur. Heart J., 17, pp. 354-381; Sörnmo, L., Laguna, P., (2005) Bioelectrical Signal Processing in Cardiac and Neurological Applications, , Elsevier Academic Press: Amsterdam, the Netherlands; Harrar, S.W., Bathke, A.C., Nonparametric methods for unbalanced multivariate data and many factor levels (2008) J. Multivar. Anal., 99, pp. 1635-1664; Wickens, C.D., Dehais, F., (2019) Expertise in Aviation, , Oxford University Press: Oxford, UK; Ko, S.M., Ji, Y.G., How we can measure the non-driving-task engagement in automated driving: Comparing flow experience and workload (2018) Appl. Ergon., 67, pp. 237-245; Durantin, G., Gagnon, J.F., Tremblay, S., Dehais, F., Using near infrared spectroscopy and heart rate variability to detect mental overload (2014) Behav. Brain Res., 259, pp. 16-23; Sarter, N.B., Woods, D.D., How in the world did we ever get into that mode? Mode error and awareness in supervisory control (1995) Hum. Factors, 37, pp. 5-19; Landi, C.T., Villani, V., Ferraguti, F., Sabattini, L., Secchi, C., Fantuzzi, C., Relieving operators’ workload: Towards affective robotics in industrial scenarios (2018) Mechatronics, 54, pp. 144-154; Huang, H.M., Messina, E., Albus, J., (2003) Toward a Generic Model for Autonomy Levels for Unmanned Systems (ALFUS); Technical Report, , National Inst of Standards and Technology: Gaithersburg, MD, USA; Tang, F., Ito, E., (2017) Human-Assisted Navigation through Sliding Autonomy, pp. 26-30. , Shanghai, China, 29–31 December; Tang, F., Mohammed, M., Longazo, J., Experiments of human-robot teaming under sliding autonomy (2016) In Proceedings of the 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pp. 113-118. , Banff, AB, Canada, 12–15 July; Wright, J., Chen, J., Barnes, M., Human-automation interaction for multiple robot control: The effect of varying automation assistance and individual differences on operator performance (2018) Ergonomics, 61, pp. 1033-1045; Parasuraman, R., Wickens, C.D., Humans: Still Vital After All These Years of Automation (2008) Hum. Factors, 50, pp. 511-520; Dehais, F., Causse, M., Vachon, F., Tremblay, S., Cognitive conflict in human–automation interactions: A psychophysiological study (2012) Appl. Ergon., 43, pp. 588-595; Dehais, F., Peysakhovich, V., Scannella, S., Fongue, J., Gateau, T., Automation surprise in aviation: Real-time solutions (2015) Proceedings of the 33Rd Annual ACM Conference on Human Factors in Computing Systems, pp. 2525-2534. , Seoul, Korea; Lyons, J.B., Being transparent about transparency: A model for human-robot interaction (2013) Proceedings of the 2013 AAAI Spring Symposium Series, pp. 25-27. , Palo Alto, CA, USA, March","Chanel, C.P.C.; ISAE-SUPAERO, France; email: caroline.chanel@isae-supaero.fr",,,"MDPI AG",,,,,14248220,,,"31948046","English","Sensors",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85077673780
"Marti P., Iacono I., Parlangeli O., Stienstra J.","57193313740;53879697500;6508237137;53865710500;","Social Coordination in Human–Robot Interaction Through Reciprocal Engagement",2020,"Smart Innovation, Systems and Technologies","158",,,"273","285",,,"10.1007/978-981-13-9652-6_25","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076116736&doi=10.1007%2f978-981-13-9652-6_25&partnerID=40&md5=edf2baa801b2201a666a334d856fd60f","University of Siena, Siena, 53100, Italy; Eindhoven University of Technology, Eindhoven, Netherlands","Marti, P., University of Siena, Siena, 53100, Italy, Eindhoven University of Technology, Eindhoven, Netherlands; Iacono, I., University of Siena, Siena, 53100, Italy; Parlangeli, O., University of Siena, Siena, 53100, Italy; Stienstra, J., Eindhoven University of Technology, Eindhoven, Netherlands","In this paper, we provide a theoretical and experimental investigation on human–robot interaction scenarios where collaborative activities are perceived more meaningful and easier to perform by the human agent in the situation of reciprocal engagement with the robot. Inspired by the perceptual-crossing paradigm (Auvray and Rohde in Front Hum Neurosci, [1]), we defined reciprocal engagement as an interaction concept that seeks to extend perceptual crossing to functional purposes. For instance, when a robot and a person face a door and they both intend to go through it, a designed behaviour negotiates who is to go first in appropriate manner, either the human or the robot. The objective of the study is to explore social coordination mechanisms that are easy and natural for the human agent in order to engage in interaction with a robot. Two experiments were conducted which explore interaction scenarios with and without reciprocal engagement between a person and a robot to appreciate their significance for the human actor. In the first one, interaction scenarios are evaluated on the basis on video clips; in the second one, participants were asked to interact directly with the robot according to the same scenarios. The results show a preference of participants towards reciprocal engagement situations which were considered more meaningful and easier to learn. With this work, we hope to inspire design thinking to shift from discrete, procedural design mechanisms to continuous and action-driven mechanisms of social coordination when addressing the interaction between humans and systems in smart environments. © 2020, Springer Nature Singapore Pte Ltd.","Human-centred design; Human–robot interaction; Interactive systems; Reciprocal engagement; Smart environments; Social coordination","Ecosystems; Machine design; Regional planning; Human centred design; Interactive system; Reciprocal engagement; Robot interactions; Smart environment; Social coordination; Human robot interaction",,,,,"National Aeronautics and Space Administration, NASA: NAG5-10243, NAG5-10708, NAG5-8107, NAG5-9982; Henry Luce Foundation, LUCE","Financial support from NASA Long Term Astrophysics Program grants NAG5-10708 (M. G. and R. M. S.), NAG5-8107 (W. N. B.), and NAG5-9982 (M. E.) is gratefully acknowledged. Funds were also provided by NASA grant NAG5-10243 (M. G. and R. M. S.) and by the Clare Boothe Luce Program of the Henry Luce Foundation (R. M. S.).",,,,,,,,,,"Auvray, M., Rohde, M., Perceptual crossing: The simplest online paradigm (2012) Front. Hum. Neurosci., 6; de Jaegher, H., Social understanding through direct perception? Yes, by interacting (2009) Conscious. Cogn., 18, pp. 535-542; Di Paolo, E., Rohde, M., Iizuka, H., Sensitivity to social contingency or stability of interaction? Modelling the dynamics of perceptual-crossing (2008) New Ideas Psychol, 26, pp. 278-294; Froese, T., Di Paolo, E., Modelling social interaction as perceptual-crossing: An investigation into the dynamics of the interaction process (2010) Connect. Sci., 22, pp. 43-68; Marti, P., Iacono, I., Tittarelli, M., Stienstra, J.T., Exploring movement qualities in a reciprocal engagement (2014) Proceedings of the Icdl-Epirob, pp. 13-16. , Genova, Oct; Merleau-Ponty, M., (1962) Phenomenology of Perception (Trans: Smith, C.), , Humanities Press, New York; Liang, R.H., Chung, W.M., Kao, H.L., Li, T.Y., InTouch: Crossing social interaction with perception (2013) Design, User Experience, and Usability. User Experience in Novel Technological Environments. Lecture Notes in Computer Science, 8014, pp. 306-315. , Marcus, A. (ed.), Springer, Berlin; Auvray, M., Lenay, C., Stewart, J., Perceptual interactions in a minimalist virtual environment (2009) New Ideas Psychol, 27 (1), pp. 32-47; Lenay, C., It’s so touching”: Emotional value in distal contact (2010) Int. J. Des., 4 (2), pp. 15-25; Deckers, E., Wensveen, S.A.G., Ahn, R., Overbeeke, K., Designing for perceptual-crossing to improve user involvement (2011) Proceedings of CHI 2011, pp. 1929-1938. , Vancouver, ACM, New York, NY, USA ©2011. ISBN: 978-1-4503-0228-9; Marti, P., Perceiving while being perceived (2010) Int. J. Des., 4 (2), pp. 27-38; Anas, S.A.B., Qiu, S., Rauterberg, G.W.M., Hu, J., Exploring social interaction with everyday object based on perceptual crossing (2016) Proceedings of HAI 2016, 24–27 Oct 2016, Biopolis, Singapore, pp. 11-18. , ACM, New York. ISBN 978-1-4503-4508-8; Parlangeli, O., Chiantini, T., Guidi, S., A mind in a disk: The attribution of mental state to technological systems (2012) Work, 41, pp. 1118-1123; Amirabdollahian, F., Op Den Akker, F.R., Bedaf, S., Bormann, R., Draper, H., Gelderblom, G.J., Gutierrez Ruiz, C., Dautenhahn, K., Assistive technology design and development for acceptable robotics companions for ageing years (2013) Paladyn J. Behav. Robot., 4 (2), pp. 94-112. , ISSN (Print) 2081-4836","Marti, P.; University of SienaItaly; email: marti@unisi.it","Rehm M.Saldien J.Manca S.",,"Springer","4th International Conference on Smart Learning Ecosystems and Regional Development, SLERD 2019","22 May 2019 through 24 May 2019",,232689,21903018,9789811396519,,,"English","Smart Innov. Syst. Technol.",Conference Paper,"Final","",Scopus,2-s2.0-85076116736
[No author name available],[No author id available],"4th International Conference on Smart Learning Ecosystems and Regional Development, SLERD 2019",2020,"Smart Innovation, Systems and Technologies","158",,,"","",285,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076105150&partnerID=40&md5=2395a155ce8dc9424596e4590aedf3a6",,"","The proceedings contain 25 papers. The special focus in this conference is on International Conference on Smart Learning Ecosystems and Regional Development. The topics include: A Multidimensional Space Approach to Innovative Learning Environments; concept Integration to Develop Next Generation of Technology-Enhanced Learning Systems; improving Writing for Romanian Language; a Prototype for the Automatic Assessment of Critical Thinking; prediction of Newcomer Integration in Online Knowledge Building Communities Using Time Series Analyses; challenge of Talent Attraction in Small and Medium Urban Areas: Case of Valmiera City, Latvia; smart City Community Engagement Through Learning; meaningful Learning by Creating Technology-Mediated Knowledge Boundary Objects Between School and the Workplace; smartwatches for Context-Aware Ubiquitous Learning; game-Based Mobile Learning with Augmented Reality: Are Teachers Ready to Adopt It?; teaching Experience Design Using Poems as Cultural Probes; services, Schools and Skills: Mobile Media and Local Development in a South African Rural Area; A Cognitive-Driven BCI-Based E-Learning Platform for Learning Disorders: A Preliminary Study; The Structured Methodological Framework “Deejay”: Foundation and Its Application to the Design of an ASD-Oriented AAC Tool; creating New Learning Experiences for Students with Dyslexia: A Design Thinking and Human-Centered Approach; social Coordination in Human–Robot Interaction Through Reciprocal Engagement; The International Summer School for Advanced Studies UniBS: CONTEMPORARY IDENTITIES. Interweaving Spaces and Relations in the Design of Living Services; Towards a DSS: A Toolkit for Processes of Co-designing; The Impact of a Research-Based Institutional Strategy for Opening up Educational Practices: The Case of the MOOC-Maker Project; engagement in a Blended University Course.",,,,,,,,,,,,,,,,,,,,"Rehm M.Saldien J.Manca S.",,"Springer","4th International Conference on Smart Learning Ecosystems and Regional Development, SLERD 2019","22 May 2019 through 24 May 2019",,232689,21903018,9789811396519,,,"English","Smart Innov. Syst. Technol.",Conference Review,"Final","",Scopus,2-s2.0-85076105150
[No author name available],[No author id available],"4th Future Technologies Conference, FTC 2019",2020,"Advances in Intelligent Systems and Computing","1070",,,"","",980,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075698620&partnerID=40&md5=ca703f0f237fcb607a6ba350fd8e994a",,"","The proceedings contain 73 papers. The special focus in this conference is on Future Technologies. The topics include: Understanding Dynamic Logic Using 2D Images; screens Affordances in Image Consumption; photofeeler-D3: A Neural Network with Voter Modeling for Dating Photo Impression Prediction; Human-Augmented Robotic Intelligence (HARI) for Human-Robot Interaction; kinematic Analysis of the Bio-inspired Design of a Wearable Compliant Gait Rehabilitation Robotic System for Smart and Connected Health; grasp Rehabilitation of Stroke Patients Through Object Manipulation with an Intelligent Power Assist Robotic System; Reasonably Optimal Utilisation Through Evolution (ROUTE) in Airspace Design; autonomous Robot Navigation with Fuzzy Logic in Uncontrolled Environments; The Future of Socially Assistive Robotics: Considering an Exploratory Governance Framework (EGF); enhancing the Linguistic Landscape with the Proper Deployment of the Internet of Things Technologies: A Case Study of Smart Malls; a Novel Power Management System for Autonomous Robots; some Cyberpsychology Techniques to Distinguish Humans and Bots for Authentication; multi Criteria Method for Determining the Failure Resistance of Information System Components; time-Invariant Cryptographic Key Generation from Cardiac Signals; Privacy Implication and Technical Requirements Toward GDPR Compliance; STRIDE-Based Threat Modeling for MySQL Databases; Efficient RSA Crypto Processor Using Montgomery Multiplier in FPGA; new Residue Signed-Digit Addition Algorithm; study and Evaluation of Unsupervised Algorithms Used in Network Anomaly Detection; Recurrent Binary Patterns and CNNs for Offline Signature Verification; design and Fabrication of Smart Bandeau Bra for Breathing Pattern Measurement; an Improvement of Compelling Graphical Confirmation Plan and Cryptography for Upgrading the Information Security and Preventing Shoulder Surfing Assault.",,,,,,,,,,,,,,,,,,,,"Arai K.Bhatia R.Kapoor S.",,"Springer","4th Future Technologies Conference, FTC 2019","24 October 2019 through 25 October 2019",,233129,21945357,9783030325220,,,"English","Adv. Intell. Sys. Comput.",Conference Review,"Final","",Scopus,2-s2.0-85075698620
[No author name available],[No author id available],"10th International Symposium on Ambient Intelligence, ISAmI 2019",2020,"Advances in Intelligent Systems and Computing","1006",,,"","",223,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068622044&partnerID=40&md5=4f76daf3ccdf106808a7ff3d8e9bd7f0",,"","The proceedings contain 26 papers. The special focus in this conference is on Ambient Intelligence. The topics include: Blockchain-based architecture: A MAS proposal for efficient agri-food supply chains; sensing as a service: An architecture proposal for big data environments in smart cities; Design of an AI-based workflow-guiding system for stratified sampling; internet data extraction and analysis for profile generation; original content verification using hash-based video analysis; Me3CA - Monitoring environment exercise and emotion by a cognitive assistant; a new conductivity sensor for monitoring the fertigation in smart irrigation systems; dynamic rules extraction in big data context for knowledge capitalization systems; real-time low-cost active and assisted living for the elderly; multi-agent system and classification algorithms applied for ehealth in order to support the referral of post-operative patients; System to detect and approach humans from an aerial view for the landing phase in a UAV delivery service; smart coach—a recommendation system for young football athletes; functional prototype for intrusion detection system oriented to intelligent iot models; continuous authentication in mobile devices using behavioral biometrics; improving motivation in wrist rehabilitation therapies; Motorized circular rail with RGB-D sensor on cart for physical rehabilitation; assisting dependent people at home through autonomous unmanned aerial vehicles; a computing framework to check real-time requirements in ambient intelligent systems; a ubiquitous computing platform for virtualizing collective human eyesight and hearing capabilities; a recurrent neural network approach to improve the air quality index prediction; experiences in context aware-services; gesture control system for industry 4.0 human-robot interaction – A usability test.",,,,,,,,,,,,,,,,,,,,"Novais P.Carneiro D.Chamoso P.Omatu S.Lloret J.Navarro E.",,"Springer Verlag","10th International Symposium on Ambient Intelligence, ISAmI 2019","26 June 2019 through 28 June 2019",,227779,21945357,9783030240967,,,"English","Adv. Intell. Sys. Comput.",Conference Review,"Final","",Scopus,2-s2.0-85068622044
[No author name available],[No author id available],"AHFE International Conference on Additive Manufacturing, Modeling Systems and 3D Prototyping, 2019",2020,"Advances in Intelligent Systems and Computing","975",,,"","",667,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067408045&partnerID=40&md5=8a84c6932d9d80933396c4135bf03066",,"","The proceedings contain 61 papers. The special focus in this conference is on Additive Manufacturing, Modeling Systems and 3D Prototyping. The topics include: Kinematic optimization of the robot head movements for the evaluation of human-robot interaction in social robotics; personalized human factor and ergonomics: Usability design of 3D printed patient-specific fracture external fixator; parametric design of applied origami with a synthetic computational approach; generative design for printable mass customization jewelry products; application of robust design techniques for 3D printing on textiles; an algorithmic approach to viewsheds analysis for cultural landscape: Manziana and the bracciano lake area; design and digital fabrication of a parametric joint for bamboo sustainable structures; visionaria. an open design approach for the regeneration of historical urban heritage; Three renaissance vaults in Milan. cultural heritage and digital workflows for BIM modelling; 3D printing to innovate the guitar design; four projects of pier luigi nervi. a methodology for the construction and 3D print of architectural models; rapid prototyping for dissemination of perspective treatises; semantic and procedural approaches in generative modeling for the representation of cultural heritage; procedural modelling as a tool for morphological analysis of the design idea; resolution analysis of image-based 3D models; generative models for experimentation and knowledge of perspective principles; using virtual reality and rapid prototyping to co-create together with hospitalized children; avoiding product abandonment through user centered design: A case study involving the development of a 3d printed customized upper limb prosthesis; evaluation of orthosis rapid prototyping during the design process: Analysis of verification models.",,,,,,,,,,,,,,,,,,,,"Rossi E.Di Nicolantonio M.Alexander T.",,"Springer Verlag","AHFE International Conference on Additive Manufacturing, Modeling Systems and 3D Prototyping, 2019","24 July 2019 through 28 July 2019",,226919,21945357,9783030202156,,,"English","Adv. Intell. Sys. Comput.",Conference Review,"Final","",Scopus,2-s2.0-85067408045
[No author name available],[No author id available],"AHFE International Conference on Human Factors in Transportation, 2019",2020,"Advances in Intelligent Systems and Computing","964",,,"","",829,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067346859&partnerID=40&md5=435afa26d3a1a9bf13846b0d7296498d",,"","The proceedings contain 73 papers. The special focus in this conference is on Human Factors in Transportation. The topics include: The Impact of Different Human-Machine Interface Feedback Modalities on Older Participants&#x2019; User Experience of CAVs in a Simulator Environment; User Experience in Immersive VR-Based Serious Game: An Application in Highly Automated Driving Training; comparison of Child and Adult Pedestrian Perspectives of External Features on Autonomous Vehicles Using Virtual Reality Experiment; an Inclusive, Fully Autonomous Vehicle Simulator for the Introduction of Human-Robot Interaction Technologies; investigating Drivers&#x2019; Behaviour During Diverging Maneuvers Using an Instrumented Vehicle; model of Driving Skills Decrease in the Context of Autonomous Vehicles; the User and the Automated Driving: A State-of-the-Art; explicit Forward Glance Duration Hidden Markov Model for Inference of Spillover Detection; proposal for Graduated Driver Licensing Program: Age vs. Experience, Abu Dhabi Case Study; a Novel Method for Designing Metaphor-Based Driver-Vehicle Interaction Concepts in Automated Vehicles; impact of Mind Wandering on Driving; assessing the Relation Between Emotional Intelligence and Driving Behavior: An Online Survey; the Effect of Tram Driver&#x2019;s Cab Design on Posture and Physical Strain; Engineering the Right Change Culture in a Complex (GB) Rail Industry; application of Cognitive Work Analysis to Explore Passenger Behaviour Change Through Provision of Information to Help Relieve Train Overcrowding; decrease Driver&#x2019;s Workload and Increase Vigilance; analysis of Driving Performance Data to Evaluate Brake Manipulation by Railway Drivers; sharing the Road: Experienced Cyclist and Motorist Knowledge and Perceptions; pedestrian Attitudes to Shared-Space Interactions with Autonomous Vehicles &#x2013; A Virtual Reality Study.",,,,,,,,,,,,,,,,,,,,"Stanton N.",,"Springer Verlag","AHFE International Conference on Human Factors in Transportation, 2019","24 July 2019 through 28 July 2019",,226949,21945357,9783030205027,,,"English","Adv. Intell. Sys. Comput.",Conference Review,"Final","",Scopus,2-s2.0-85067346859
"Zhong B., Xia L.","56954316600;57203912224;","A Systematic Review on Exploring the Potential of Educational Robotics in Mathematics Education",2020,"International Journal of Science and Mathematics Education","18","1",,"79","101",,20,"10.1007/s10763-018-09939-y","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056746910&doi=10.1007%2fs10763-018-09939-y&partnerID=40&md5=701d8ae3d890c9f790d159d5a01e67dc","School of Education Science, Nanjing Normal University, No. 122, Ninghai Road, Nanjing, Jiangsu  210097, China","Zhong, B., School of Education Science, Nanjing Normal University, No. 122, Ninghai Road, Nanjing, Jiangsu  210097, China; Xia, L., School of Education Science, Nanjing Normal University, No. 122, Ninghai Road, Nanjing, Jiangsu  210097, China","By providing students with a highly interactive and hands-on learning experience, robotics promises to inspire a new generation of mathematical learning. This paper aims to review the empirical evidence on the application of robotics in mathematics education and to define future research perspectives of robot-assisted mathematics education. After a systematic search in online database via keyword search and snowballing approach, we analyzed 20 empirical studies on how to teach and learn mathematical knowledge through robotics. The results indicate that (1) most studies were conducted with a small sample size, the largest research groups were elementary school students and secondary school students, most studies used LEGO robots, robots were primarily applied to teach and/or learn graphics, geometry, and algebra, and almost half of the studies taught mathematics by engaging students in game-like interactions with robots; (2) half of the studies adopted a non-experimental research design, and most studies evaluated student performance through observation, test/examination, questionnaires, or verbal interviews; and (3) instructional implications proposed in the 20 papers can be clustered into four themes: human-robot interaction, connections between mathematics and real life, pedagogical suggestions, and facility conditions. The 20 papers suggest that robotics generally plays an active role in mathematics education; however, there are indeed situations in which no significant improvement was found in students’ mathematical learning. In view of this, we prospect the future research perspectives of robot-assisted mathematics education and propose that more rigorous intervention studies could be conducted to further explore the integration of robotics and mathematics education. © 2018, Ministry of Science and Technology, Taiwan.","Educational robotics; Mathematics education; Teaching/learning strategies; Transdisciplinary issues",,,,,,,,,,,,,,,,,"Adams, K.D., Cook, A.M., Performing mathematics activities with non-standard units of measurement using robots controlled via speech-generating devices: Three case studies (2017) Disability and Rehabilitation: Assistive Technology, 12 (5), pp. 491-503; Astleitner, H., Wiesner, C., An integrated model of multimedia learning and motivation (2004) Journal of Educational Multimedia and Hypermedia, 13 (1), pp. 3-21; Benitti, F.B.V., Exploring the educational potential of robotics in schools: A systematic review (2012) Computers & Education, 58 (3), pp. 978-988; Bers, M.U., Flannery, L., Kazakoff, E.R., Sullivan, A., Computational thinking and tinkering: Exploration of an early childhood robotics curriculum (2014) Computers & Education, 72, pp. 145-157; Brown, L.N., Howard, A.M., The positive effects of verbal encouragement in mathematics education using a social robot (2014) Proceedings of the 4Th IEEE Integrated STEM Education Conference, pp. 1-5. , Princeton, NJ, IEEE; Ceci, S.J., Williams, W.M., Sex differences in math-intensive fields (2010) Current Directions in Psychological Science, 19 (5), pp. 275-279; Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., Eltoukhy, M., Assessing elementary students’ computational thinking in everyday reasoning and robotics programming (2017) Computers & Education, 109, pp. 162-175; Cheryan, S., Master, A., Meltzoff, A.N., Cultural stereotypes as gatekeepers: Increasing girls’ interest in computer science and engineering by diversifying stereotypes (2015) Frontiers in Psychology, 6, pp. 1-8; Clements, D.H., Meredith, J.S., Research on Logo: Effects and efficacy (1993) Journal of Computing in Childhood Education, 4 (4), pp. 263-290; Fernandes, E., Fermé, E., Oliveira, R., The robot race: Understanding proportionality as a function with robots in mathematics class (2009) Proceedings of the Sixth Congress of European Research in Mathematics Education, pp. 1211-1220. , V. Durand-Guerrier, S. Soury-Lavergne, F. Arzarello, Lyon, France, Institut National de Recherche Pedagogique; Gomoll, A., Hmelo-Silver, C.E., Šabanović, S., Francisco, M., Dragons, ladybugs, and softballs: Girls’ STEM engagement with human-centered robotics (2016) Journal of Science Education and Technology, 25 (6), pp. 899-914; Hwang, W.Y., Wu, S.Y., A case study of collaboration with multi-robots and its effect on children’s interaction (2014) Interactive Learning Environments, 22 (4), pp. 429-443; Johnson, J., Children, robotics, and education (2003) Artificial Life and Robotics, 7 (1), pp. 16-21; Julià, C., Antolí, J.Ò., Spatial ability learning through educational robotics (2016) International Journal of Technology and Design Education, 26 (2), pp. 185-203; Keren, G., Ben-David, A., Fridin, M., Kindergarten Assistive Robotics (KAR) as a tool for spatial cognition development in pre-school education (2012) Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1084-1089. , Algarve, Portugal, IEEE; Keren, G., Fridin, M., Kindergarten Social Assistive Robot (KindSAR) for children’s geometric thinking and metacognitive development in preschool education: A pilot study (2014) Computers in Human Behavior, 35, pp. 400-412; Kitchenham, B., Procedures for performing systematic reviews (2004) Keele, 33, pp. 1-26; Kitchenham, B., Brereton, O.P., Budgen, D., Turner, M., Bailey, J., Linkman, S., Systematic literature reviews in software engineering–a systematic literature review (2009) Information and Software Technology, 51 (1), pp. 7-15; Kucuk, S., Sisman, B., Behavioral patterns of elementary students and teachers in one-to-one robotics instruction (2017) Computers & Education, 111, pp. 31-43; La Paglia, F., La Cascia, C., Francomano, M.M., La Barbera, D., Educational robotics to improve mathematical and metacognitive skills (2017) Annual Review of CyberTherapy and Telemedicine, 15 (14), pp. 70-75; Leonard, J., Buss, A., Gamboa, R., Mitchell, M., Fashola, O.S., Hubert, T., Almughyirah, S., Using robotics and game design to enhance children’s self-efficacy, STEM attitudes, and computational thinking skills (2016) Journal of Science Education and Technology, 25 (6), pp. 860-876; Lindh, J., Holgersson, T., Does LEGO training stimulate pupils’ ability to solve logical problems? (2007) Computers & Education, 49 (4), pp. 1097-1111; Mandin, S., De Simone, M., Soury-Lavergne, S., Robot moves as tangible feedback in a mathematical game at primary school (2017) Adv. Intell. Syst. Comput., 457, pp. 245-257; Martin, F., Lurgio, M., Coffey, D., Robotic jewelry: Inventing locally contextualized mathematics in a fourth grade classroom (2006) Proceedings of the Second International Conference on Informatics in Secondary Schools: Evolution and Perspectives, pp. 214-225. , V. Dagiene, R. Mittermeir, Vilnius, Lithuania, ISSEP; Master, A., Cheryan, S., Meltzoff, A.N., Computing whether she belongs: Stereotypes undermine girls’ interest and sense of belonging in computer science (2016) Journal of Educational Psychology, 108 (3), pp. 424-437; Master, A., Cheryan, S., Moscatelli, A., Meltzoff, A.N., Programming experience promotes higher STEM motivation among first-grade girls (2017) Journal of Experimental Child Psychology, 160, pp. 92-106; Menekse, M., Higashi, R., Schunn, C.D., Baehr, E., The role of robotics teams’ collaboration quality on team performance in a robotics tournament (2017) Journal of Engineering Education, 106 (4), pp. 564-584; Mitnik, R., Nussbaum, M., Soto, A., An autonomous educational mobile robot mediator (2008) Autonomous Robots, 25 (4), pp. 367-382; Mubin, O., Stevens, C.J., Shahid, S., Al Mahmud, A., Dong, J.J., A review of the applicability of robots in education (2013) Technology for Education and Learning, 1, pp. 1-7; Nickels, M., Cullen, C.J., Mathematical thinking and learning through robotics play for children with critical illness: The case of Amelia (2017) Journal for Research in Mathematics Education, 48 (1), pp. 22-77; Nussbaum, M., Soto, A., Collaborative robotic instruction: A graph teaching experience (2009) Computers & Education, 53 (2), pp. 330-342; Padayachee, K., Gouws, P.M., Lemmer, M., Evaluating the effectiveness of LEGO robots in engaged scholarship (2015) Proceedings of the Annual Global Online Conference on Information and Computer Technology (GOCICT), pp. 16-20. , Louisville, KY, IEEE; Papert, S., (1980) Mindstorms: Children, computers, and powerful ideas, , New York, NY: Basic Books, Inc; Pinto, A.H., Tozadore, D.C., Romero, R.A., A question game for children aiming the geometrical figures learning by using a humanoid robot (2015) Proceedings of the 12Th Latin American Robotics Symposium and Third Brazilian Symposium on Robotics, pp. 228-233. , Uberlandia, Brazil, IEEE; Ponce, P., Molina, A., Hernández, L., Acha, E., Morales, B., Huitron, C., Teaching math in elementary schools by LabVIEW and LEGO robots (2017) Lecture Notes in Networks and Systems, 13, pp. 255-270; Rhine, D., Martin, F., Integrating mathematical analysis of sensors and motion in a mobile robotics course (2008) Lecture Notes in Computer Science, 5090, pp. 41-52; Rusk, N., Resnick, M., Berg, R., Pezalla-Granlund, M., New pathways into robotics: Strategies for broadening participation (2008) Journal of Science Education and Technology, 17 (1), pp. 59-69; Shankar, R., Ploger, D., Nemeth, A., Hecht, S.A., Robotics: Enhancing pre-college mathematics learning with real-world examples (2013) Proceedings of The120th ASEE Annual Conference and Exposition, pp. 1-17. , Atlanta, GA, ASEE; Shih, B.Y., Chang, C.J., Chen, Y.H., Chen, C.Y., Liang, Y.D., LEGO NXT information on test dimensionality using Kolb’s innovative learning cycle (2012) Natural Hazards, 64 (2), pp. 1527-1548; Toh, L.P.E., Causo, A., Tzuo, P.W., Chen, I., Yeo, S.H., A review on the use of robots in education and young children (2016) Journal of Educational Technology & Society, 19 (2), pp. 148-163; Trochim, W.M.K., Donnelly, J.P., (2006) Research Methods Knowledge Base, , http://www.socialresearchmethods.net/kb/, Mason, Thomson, Retrieved from; Walker, E., Girotto, V., Kim, Y., Muldner, K., The effects of physical form and embodied action in a teachable robot for geometry learning (2016) Proceedings of the 16Th International Conference on Advanced Learning Technologies (ICALT), pp. 381-385. , J. M. Spector et al., Austin, TX, CPS; Wei, C.W., Hung, I.C., Lee, L., Chen, N.S., A joyful classroom learning system with robot learning companion for children to learn mathematics multiplication (2011) Turkish Online Journal of Educational Technology, 10 (2), pp. 11-23; Yelland, N.J., The strategies and interactions of young children in Logo tasks (1994) Journal of Computer Assisted Learning, 10 (1), pp. 33-49","Xia, L.; School of Education Science, No. 122, Ninghai Road, China; email: lynnexia@126.com",,,"Springer",,,,,15710068,,,,"English","Int. J. Sci. Math. Educ.",Article,"Final","",Scopus,2-s2.0-85056746910
"Ezzat A., Kogkas A., Holt J., Thakkar R., Darzi A., Mylonas G.","57221503521;56624483200;57225706713;57212005836;14633357600;13905959400;","An eye-tracking based robotic scrub nurse: proof of concept",2021,"Surgical Endoscopy","35","9",,"5381","5391",,1,"10.1007/s00464-021-08569-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107629711&doi=10.1007%2fs00464-021-08569-w&partnerID=40&md5=cee59fe6d7a5141e5b54cd1a3a23729c","Hamlyn Centre for Robotic Surgery, Imperial College London, London, SW7 2AZ, United Kingdom; Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, United Kingdom; University College London, London, United Kingdom; St George’s, University of London, London, United Kingdom","Ezzat, A., Hamlyn Centre for Robotic Surgery, Imperial College London, London, SW7 2AZ, United Kingdom, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, United Kingdom; Kogkas, A., Hamlyn Centre for Robotic Surgery, Imperial College London, London, SW7 2AZ, United Kingdom, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, United Kingdom; Holt, J., University College London, London, United Kingdom; Thakkar, R., St George’s, University of London, London, United Kingdom; Darzi, A., Hamlyn Centre for Robotic Surgery, Imperial College London, London, SW7 2AZ, United Kingdom, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, United Kingdom; Mylonas, G., Hamlyn Centre for Robotic Surgery, Imperial College London, London, SW7 2AZ, United Kingdom, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, United Kingdom","Background: Within surgery, assistive robotic devices (ARD) have reported improved patient outcomes. ARD can offer the surgical team a “third hand” to perform wider tasks and more degrees of motion in comparison with conventional laparoscopy. We test an eye-tracking based robotic scrub nurse (RSN) in a simulated operating room based on a novel real-time framework for theatre-wide 3D gaze localization in a mobile fashion. Methods: Surgeons performed segmental resection of pig colon and handsewn end-to-end anastomosis while wearing eye-tracking glasses (ETG) assisted by distributed RGB-D motion sensors. To select instruments, surgeons (ST) fixed their gaze on a screen, initiating the RSN to pick up and transfer the item. Comparison was made between the task with the assistance of a human scrub nurse (HSNt) versus the task with the assistance of robotic and human scrub nurse (R&HSNt). Task load (NASA-TLX), technology acceptance (Van der Laan’s), metric data on performance and team communication were measured. Results: Overall, 10 ST participated. NASA-TLX feedback for ST on HSNt vs R&HSNt usage revealed no significant difference in mental, physical or temporal demands and no change in task performance. ST reported significantly higher frustration score with R&HSNt. Van der Laan’s scores showed positive usefulness and satisfaction scores in using the RSN. No significant difference in operating time was observed. Conclusions: We report initial findings of our eye-tracking based RSN. This enables mobile, unrestricted hands-free human–robot interaction intra-operatively. Importantly, this platform is deemed non-inferior to HSNt and accepted by ST and HSN test users. © 2021, The Author(s).","Assistive robotic devices; Eye-tracking; Gaze interactions; Robotic scrub nurse; Smart operating room; Surgery","animal experiment; Article; comparative study; end to end anastomosis; eye fixation; eye tracking; female; frustration; human; human computer interaction; male; medical student; nonhuman; nurse; operation duration; pig; proof of concept; pupil diameter; satisfaction; scrub nurse; segmentectomy; simulation training; surgical registrar; surgical training; task performance; three-dimensional imaging; usability; verbal communication; visual feedback; workflow; animal; laparoscopy; robot assisted surgery; robotics; Animals; Eye-Tracking Technology; Laparoscopy; Robotic Surgical Procedures; Robotics; Swine; Task Performance and Analysis",,,,,"National Institute for Health Research, NIHR; NIHR Imperial Biomedical Research Centre, BRC","Within laparoscopic surgery, robotic devices have been developed to improve clinical outcomes, in so consolidating the shifts towards minimally invasive surgery (MIS). The first marketed surgical robot was the voice controlled laparoscopic camera holder (AESOP). Since then a new era of robotics emerged []. The da Vinci® (Intuitive Surgical, Inc.), first emerged in 1997, is a slave robotic manipulator controlled via a computer console by a master-surgeon. To date da Vinci® reports an excess of 1.5 million laparoscopic surgeries, demonstrating reduced post-operative pain, hospital stay and improved surgical accessibility and view in confined anatomical spaces []. Such findings have encouraged research of more sophisticated assistive robotic devices (ARD) during surgery. ARD in surgery describes machinery that is controlled by the surgeon in support of surgical task delivery. Recently, the United Kingdom has seen new legislation towards artificial intelligence funding to establish wider ARD within surgery supported by strong evidence [, ]. ARD has been hypothesized as an approach to disrupt preventable healthcare human errors, described as one of the main culprits resulting in patient harm [].",,,,,,,,,,"Sackier, J.M., Wang, Y., Robotically assisted laparoscopic surgery. From concept to development (1994) Surg Endosc, 8, pp. 63-66. , COI: 1:STN:280:DyaK2c3gsV2itg%3D%3D; Alemzadeh, H., Raman, J., Leveson, N., Kalbarczyk, Z., Iyer, R.K., Adverse events in robotic surgery: a retrospective study of 14 years of FDA data (2016) PLoS One, 11 (4); Care robots could revolutionise UK care system and provide staff extra support. [online (2020) GOV.UK, , https://www.gov.uk/government/news/care-robots-could-revolutionise-uk-care-system-and-provide-staff-extra-support, Accessed 4 Oct 2020; Clark, G., (2015) Letter to David Lane and Rob Buckingham., , https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/414382/BIS-15-215-Greg-Clark-to-Professors-Buckingham-and-Lane-RAS-Strategy.pdf, Accessed 01 Nov 2020; Makary, M.A., Daniel, M., Medical error—the third leading cause of death in the US (2016) BMJ, 353, p. i2139; Mewes, A., Hensen, B., Wacker, F., Hansen, C., Touchless interaction with software in interventional radiology and surgery: A systematic literature review (2016) Int J Comput Assist Radiol Surg, pp. 1-15; Wachs, J.P., Stern, H.I., Edan, Y., A gesture-based tool for sterile browsing of radiology images [published correction appears in J Am Med Inform Assoc. 2009 May-Jun;16(3):284] (2008) J Am Med Inform Assoc, 15 (3), pp. 321-323; Palep, J.H., Robotic assisted minimally invasive surgery (2009) J Minim Access Surg, 5 (1), pp. 1-7; Hong, N., Kim, M., Lee, C., Head-mounted interface for intuitive vision control and continuous surgical operation in a surgical robot system (2019) Med Biol Eng Comput, 57, pp. 601-614; El-Shallaly, G.E., Mohammed, B., Muhtaseb, M.S., Hamouda, A.H., Nassar, A.H., Voice recognition interfaces (VRI) optimize the utilization of theatre staff and time during laparoscopic cholecystectomy (2005) Minim Invasive Ther Allied Technol, 14 (6), pp. 369-371. , COI: 1:STN:280:DC%2BD28zktFOlug%3D%3D; Jacob, M., Li, Y.T., Akingba, G., Wachs, J.P., Gestonurse: a robotic surgical nurse for handling surgical instruments in the operating room (2012) J Robot Surg, 6 (1), pp. 53-63; Ebert, L.C., Hatch, G., Ampanozi, G., Thali, M.J., Ross, S., You can’t touch this: touch-free navigation through radiological images (2012) Surg Innov, 19 (3), pp. 301-307; Kogkas, A., Ezzat, A., Thakkar, R., Darzi, A., Mylonas, G., (2019) Free-view, 3D gaze-guided robotic scrub nurse, pp. 164-172. , Springer International, Cham; Kogkas, A.A., Darzi, A., Mylonas, G.P., Gaze-contingent perceptually enabled interactions in the operating theatre (2017) Int J Comput Assist Radiol Surg, 12 (7), pp. 1131-1140; Van Der Laan, J.D., Heino, A., De Waard, D., A simple procedure for the assessment of acceptance of advanced transport telematics (1997) Transp Res Part C Emerg Technol, 5 (1), pp. 1-10; Padilla, D.A., Adriano, J.A.B., Balbin, J.R., Matala, I.G., Nicolas, J.J.R., Villadelgado, S.R.R., Implementation of eye gaze tracking technique on FPGA-based on-screen keyboard system using verilog and MATLAB (2017) IEEE Region 10 Annual International Conference. Proceedings/Tencon; Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., de Weijer, J., (2011) Eye tracking: a comprehensive guide to methods and measures, pp. 607-609. , OUP, Oxford; WHO Guidelines for Safe Surgery (2009) World Health Organization, Geneva, , https://www.ncbi.nlm.nih.gov/books/NBK143227/, Objective 7, The team will prevent inadvertent retention of instruments and sponges in surgical wounds., Accessed 8th Oct 2020; Salas, E., Burke, C.S., Bowers, C.A., Wilson, K.A., Team training in the skies: does crew resource management (CRM) training work? (2001) Hum Factors, 43, pp. 641-674. , COI: 1:STN:280:DC%2BD383mtlShtQ%3D%3D; Rivera-Rodriguez, A.J., Karsh, B.T., Interruptions and distractions in healthcare: review and reappraisal (2010) Qual Saf Health Care, 19 (4), pp. 304-312. , COI: 1:STN:280:DC%2BC3cjjtVSltQ%3D%3D; Gillie, T., Broadbent, D., What makes interruptions disruptive? A study of length, similarity, and complexity (1989) Psychol Res, 50, pp. 243-250; Velasquez, C.A., Mazhar, R., Chaikhouni, A., Zhou, T., Wachs, J.P., Taxonomy of Communications in the Operating Room (2018) Advances in Human Factors and Ergonomics in Healthcare and Medical Devices. AHFE 2017. Advances in Intelligent Systems and Computing, 590. , https://doi.org/10.1007/978-3-319-60483-1_25, Duffy V., Lightner N, Springer, Cham; Avrunin, G.S., Clarke, L.A., Conboy, H.M., Osterweil, L.J., Dias, R.D., Yule, S.J., Goldman, J.M., Zenati, M.A., Toward improving surgical outcomes by incorporating cognitive load measurement into process-driven guidance (2018) Softw Eng Healthc Syst SEHS IEEE ACM Int Workshop; Ashrafian, H., Clancy, O., Grover, V., Darzi, A., The evolution of robotic surgery: surgical and anaesthetic aspects (2017) Br J Anaesth, 119, pp. i72-i84. , COI: 1:STN:280:DC%2BC1M3jsFOisA%3D%3D; Lai, F., Entin, E., Robotic surgery and the operating room (2005) Proc Hum Factors Ergon Soc Annu Meet, 49, pp. 1070-1073; Lingard, L., Espin, S., Whyte, S., Communication failures in the operating room: an observational classification of recurrent types and effects (2004) Qual Saf Health Care, 13 (5), pp. 330-334. , COI: 1:STN:280:DC%2BD2cvpvFektQ%3D%3D; Cao, C.G.L., Taylor, H., Effects of new technology on the operating room team (2004) Work with computing systems, pp. 309-312. , Khalid HM, Helander MG, Yeo AW, (eds), Damai Sciences, Kuala Lumpur; Webster, J.L., Cao, C.G., Lowering communication barriers in operating room technology (2006) Hum Factors, 48, pp. 747-758; Tørring, B., Gittell, J.H., Laursen, M., Communication and relationship dynamics in surgical teams in the operating room: an ethnographic study (2019) BMC Health Serv Res, 19, p. 528; Yánez Benítez, C., Güemes, A., Aranda, J., Impact of personal protective equipment on surgical performance during the COVID-19 pandemic (2020) World J Surg, 44 (9), pp. 2842-2847; Gross, B., Rusin, L., Kiesewetter, J., Crew resource management training in healthcare: a systematic review of intervention design, training conditions and evaluation (2019) BMJ Open, 9 (2); Kerr, H.L., Stewart, N., Pace, A., Elsayed, S., Sharps injury reporting amongst surgeons (2009) Ann R Coll Surg Engl, 91 (5), pp. 430-432; Pear, S.M., Williamson, T.H., The RN first assistant: an expert resource for surgical site infection prevention (2009) AORN J, 89 (6), pp. 1093-1097; McClelland, G., Smith, M.B., Just a routine operation: a critical discussion (2016) J Perioper Pract, 26 (5), pp. 114-117. , COI: 1:STN:280:DC%2BC2s7psFGnsg%3D%3D; Cooper, S., Wakelam, A., Leadership of resuscitation teams: ""Lighthouse Leadership' (1999) Resuscitation, 42 (1), pp. 27-45","Ezzat, A.; Hamlyn Centre for Robotic Surgery, United Kingdom; email: ae1119@ic.ac.uk",,,"Springer",,,,,09302794,,,"34101012","English","Surg. Endosc.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85107629711
"Kirtay M., Oztop E., Asada M., Hafner V.V.","36809877600;8744870300;55843502400;55910634600;","Modeling robot trust based on emergent emotion in an interactive task",2021,"IEEE International Conference on Development and Learning, ICDL 2021",,,,"","",,1,"10.1109/ICDL49984.2021.9515645","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114555692&doi=10.1109%2fICDL49984.2021.9515645&partnerID=40&md5=cafd102682c4d38df0f6a5720488c639","Humboldt-Universität zu Berlin, Adaptive Systems Group, Department of Computer Science, Berlin, Germany; Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Symbiotic Intelligent Systems Research Center, Japan; Ozyegin University, Istanbul, Turkey","Kirtay, M., Humboldt-Universität zu Berlin, Adaptive Systems Group, Department of Computer Science, Berlin, Germany; Oztop, E., Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Symbiotic Intelligent Systems Research Center, Japan, Ozyegin University, Istanbul, Turkey; Asada, M., Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Symbiotic Intelligent Systems Research Center, Japan; Hafner, V.V., Humboldt-Universität zu Berlin, Adaptive Systems Group, Department of Computer Science, Berlin, Germany","Trust is an essential component in human-human and human-robot interactions. The factors that play potent roles in these interactions have been an attractive issue in robotics. However, the studies that aim at developing a computational model of robot trust in interaction partners remain relatively limited. In this study, we extend our emergent emotion model to propose that the robot's trust in the interaction partner (i.e., trustee) can be established by the effect of the interactions on the computational energy budget of the robot (i.e., trustor). To be concrete, we show how high-level emotions (e.g., wellbeing) of an agent can be modeled by the computational cost of perceptual processing (e.g., visual stimulus processing for visual recalling) in a decision-making framework. To realize this approach, we endow the Pepper humanoid robot with two modules: an auto-associative memory that extracts the required computational energy to perform a visual recalling, and an internal reward mechanism guiding model-free reinforcement learning to yield computational energy cost-aware behaviors. With this setup, the robot interacts with online instructors with different guiding strategies, namely reliable, less reliable, and random. Through interaction with the instructors, the robot associates the cumulative reward values based on the cost of perceptual processing to evaluate the instructors and determine which one should be trusted. Overall the results indicate that the robot can differentiate the guiding strategies of the instructors. Additionally, in the case of free choice, the robot trusts the reliable one that increases the total reward - and therefore reduces the required computational energy (cognitive load)- to perform the next task. © 2021 IEEE.","Emotions; HRI; Internal reward; Trust; Visual recalling","Anthropomorphic robots; Associative processing; Budget control; Decision making; Reinforcement learning; Auto-associative Memory; Computational costs; Computational model; Decision-making frameworks; Emotion modeling; Guiding Strategy; Online instructors; Perceptual processing; Social robots",,,,,"Deutsche Forschungsgemeinschaft, DFG: 390523135, EXC 2002/1","This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2002/1 “Science of Intelligence” - project number 390523135. Additional support is provided by the International Joint Research Promotion Program, Osaka University under the project “Developmentally and biologically realistic modeling of perspective invariant action understanding”.",,,,,,,,,,"Atoyan, H., Duquet, J.-R., Robert, J.-M., Trust in new decision aid systems (2006) Proceedings of the 18th Conference on L'Interaction Homme-Machine, pp. 115-122; Shahrdar, S., Menezes, L., Nojoumian, M., A survey on trust in autonomous systems (2018) Science and Information Conference. Springer, pp. 368-386; Khavas, Z.R., Ahmadzadeh, S.R., Robinette, P., (2020) Modeling Trust in Human-robot Interaction: A Survey, pp. 529-541; Hancock, P.A., Billings, D.R., Schaefer, K.E., Can you trust your robot? (2011) Ergonomics in Design, 19 (3), pp. 24-29; Kirtay, M., Vannucci, L., Falotico, E., Oztop, E., Laschi, C., Sequential decision making based on emergent emotion for a humanoid robot (2016) 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids), pp. 1101-1106. , http://ieeexplore.ieee.org/document/7803408/; Sanders, T., Oleson, K.E., Billings, D.R., Chen, J.Y., Hancock, P.A., A model of human-robot trust: Theoretical model development (2011) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 55 (1), pp. 1432-1436. , SAGE Publications Sage CA: Los Angeles, CA; Novitzky, M., Robinette, P., Benjamin, M.R., Gleason, D.K., Fitzgerald, C., Schmidt, H., Preliminary interactions of humanrobot trust, cognitive load, and robot intelligence levels in a competitive game (2018) Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 203-204. , https://doi.org/10.1145/3173386.3177000, ser. HRI '18. New York, NY, USA: Association for Computing Machinery; Ahmad, M.I., Bernotat, J., Lohan, K., Eyssel, F., (2019) Trust and Cognitive Load during Human-robot Interaction, , arXiv; Correia, F., Alves-Oliveira, P., Maia, N., Ribeiro, T., Petisca, S., Melo, F.S., Paiva, A., Just follow the suit! trust in human-robot interactions during card game playing (2016) 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN). IEEE, pp. 507-512; Patacchiola, M., Cangelosi, A., A developmental Bayesian model of trust in artificial cognitive systems (2016) 2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pp. 117-123; Patacchiola, M., Cangelosi, A., A developmental cognitive architecture for trust and theory of mind in humanoid robots (2020) IEEE Transactions on Cybernetics; Vinanzi, S., Patacchiola, M., Chella, A., Cangelosi, A., Would a robot trust you? developmental robotics model of trust and theory of mind (2019) Philosophical Transactions of the Royal Society B, 374 (1771), p. 20180032; Chaminade, T., Oztop, E., Cheng, G., Kawato, M., From selfobservation to imitation: Visuomotor association on a robotic hand (2008) Brain Research Bulletin, 75 (6), pp. 775-784; Kirtay, M., Oztop, E., Emergent emotion via neural computational energy conservation on a humanoid robot (2015) IEEE-RAS International Conference on Humanoid Robots, pp. 450-455. , 2015-February February; Kirtay, M., Vannucci, L., Albanese, U., Laschi, C., Oztop, E., Falotico, E., Emotion as an emergent phenomenon of the neurocomputational energy regulation mechanism of a cognitive agent in a decisionmaking task (2019) Adaptive Behavior; Hertz, J., Krogh, A., Palmer, R.G., (1991) Introduction to the Theory of Neural Computation, , USA: Addison-Wesley Longman Publishing Co., Inc; Sutton, R.S., Barto, A.G., (1998) Reinforcement Learning: An Introduction, 1 (1). , MIT press Cambridge; Kirtay, M., Wudarczyk, O.A., Pischedda, D., Kuhlen, A.K., Rahman, R.A., Haynes, J.D., Hafner, V.V., Modeling robot co-representation: State-of-The-art, open issues, and predictive learning as a possible framework (2020) 2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pp. 1-8. , Oct",,,"et al.;IEEE;IEEE Computational Intelligence Society;Microsoft;NSFC;Peking University","Institute of Electrical and Electronics Engineers Inc.","2021 IEEE International Conference on Development and Learning, ICDL 2021","23 August 2021 through 26 August 2021",,171395,,9781728162423,,,"English","IEEE Int. Conf. Dev. Learn., ICDL",Conference Paper,"Final","",Scopus,2-s2.0-85114555692
"Lacerda C.M.F., Melo L.D.P., Lahr G.J.G., Becker M., Caurin G.A.P.","57275339000;57275043900;56503631100;57210541797;6602494854;","Physical interaction analysis of the human body dorsal region with a robotic manipulator",2021,"2021 14th IEEE International Conference on Industry Applications, INDUSCON 2021 - Proceedings",,,,"1011","1016",,,"10.1109/INDUSCON51756.2021.9529638","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115837551&doi=10.1109%2fINDUSCON51756.2021.9529638&partnerID=40&md5=d739281dcd74a5be299b667fbec8cc12","Engineering School of Sao Carlos, University of Sao Paulo, Sao Carlos, Brazil; Medicine Department, Federal University of Juiz de Fora, Governador Valadares, Brazil","Lacerda, C.M.F., Engineering School of Sao Carlos, University of Sao Paulo, Sao Carlos, Brazil; Melo, L.D.P., Medicine Department, Federal University of Juiz de Fora, Governador Valadares, Brazil; Lahr, G.J.G., Engineering School of Sao Carlos, University of Sao Paulo, Sao Carlos, Brazil; Becker, M., Engineering School of Sao Carlos, University of Sao Paulo, Sao Carlos, Brazil; Caurin, G.A.P., Engineering School of Sao Carlos, University of Sao Paulo, Sao Carlos, Brazil","This article studies the development of a simulated model for the human dorsal. Safety protocols are required in order to avoid injures in humans when in contact with a robotic manipulator. One way of developing these protocols is looking for limit values for the main variables which are responsible for modeling the physics of contact, such as deformation. Physical human-robot interaction researchers have been doing it for each part of the human body. This work used the same process focusing on the dorsal. Developing an anatomical investigation of this human body region made it possible to divide and group parts with similar anatomical compositions. Points were attached to each group creating a 2D map of dorsal composed of 9 of them. Specific stiffness was evaluated mathematically for each point. To put them in the simulation environment, Multi-Joint dynamics with Contact was used. In this software, the dorsal simulated model was put in contact with the LBR iiwa model simulated. It happened under the control of an impedance controller. An iteration process was done using a reference force for contacting interface among the bodies, data of LBR iiwa end-effector displacement, and a linear equation approach relating them to set the stiffness value of each point of the dorsal model according to the one evaluated before. Results showed that the dorsal model and the manipulator stabilized in a common position showing the controller action happened satisfactorily. Geometric restrictions (higher and width) of the completed dorsal model generated some difficulties to stabilize the simulation with the exact stiffness values evaluated. However, the variation of them through the dorsal model was preserved, similarly to the real dorsal stiffness variations. © 2021 IEEE","Contact; Dorsal; MuJoCo; PHRI; Physical human robot interaction; Safety","Flexible manipulators; Industrial manipulators; Process control; Robotics; Stiffness; Impedance controllers; Iteration process; Physical human-robot interactions; Physical interactions; Robotic manipulators; Simulation environment; Specific stiffness; Stiffness variations; Human robot interaction",,,,,"Fundação de Amparo à Pesquisa do Estado de São Paulo, FAPESP: 2017/01555-7,2013/07276-1; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, CAPES; Financiadora de Estudos e Projetos, FINEP","Authors would like to thank AWS, CNPQ Grant 314936/2018-1, FAPESP Grants 2017/01555-7,2013/07276-1 CEPOF, and FINEP. This study was also partially funded by the Coordenacao de Aperfeic¸oamento de Pessoal de Nivel Superior - Brazil (CAPES) – Finance Code 001.","Authors would like to thank AWS, CNPQ Grant 314936/2018-1, FAPESP Grants 2017/01555-7,2013/07276-1 CEPOF, and FINEP. This study was also partially funded by the Coordenacao de Aperfei?oamento de Pessoal de Nivel Superior - Brazil (CAPES) - Finance Code 001.",,,,,,,,,"Whitsell, B., Artemiadis, P., Physical human-robot interaction (phri) in 6 dof with asymmetric cooperation (2017) IEEE Access, 5, pp. 10834-10845; de Luca, A., Flacco, F., Integrated control for phri: Collision avoidance, detection, reaction and collaboration (2012) 2012 4th IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 288-295; Povse, B., Koritnik, D., Bajd, T., Munih, M., Correlation between impact-energy density and pain intensity during robot-man collision (2010) 2010 3rd IEEE RAS EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 179-183; Park, J., Haddadin, S., Song, J., Albu-Schäffer, A., Designing optimally safe robot surface properties for minimizing the stress characteristics of human-robot collisions (2011) 2011 IEEE International Conference on Robotics and Automation, pp. 5413-5420; Ding, M., Ikeura, R., Mori, Y., Mukai, T., Hosoe, S., Measurement of human body stiffness for lifting-up motion generation using nursing-care assistant robot - Riba (2013) SENSORS 2013 IEEE, pp. 1-4; Lasota, P.A., Song, T., Shah, J.A., A survey of methods for safe human-robot interaction (2017) Foundations and Trends in Robotics, 5 (4), pp. 261-349; Young, M., (1989) The Technical Writer's Handbook, , Mill Valley, CA: University Science; Conkur, E.S., Buckingham, R., Clarifying the definition of redundancy as used in robotics (1997) Robotica, 15 (5), pp. 583-586; Hogan, N., Impedance control: An approach to manipulation: Part I - III (1985) Journal of Dynamic Systems, Measurements, and Control, 107; Qiao, G., Rahmatalla, S., Identification of damping and stiffness parameters of cervical and lumbar spines of supine humans under vertical whole-body vibration (2020) Journal of Low Frequency Noise, Vibration and Active Control, 39 (1), pp. 59-71; Kurfess, T., (2005) Robotics and Automation Handbook; Moore, K.L., Daley, A.F., (2018) Clinically Oriented Anatomy, , Philadelphia, PA, Wolters Kluwer; Netter, F.H., (2015) Atlas of Human Anatomy, , Philadelphia, PA, Elsevier; Erez, T., Tassa, Y., Todorov, E., Simulation tools for model-based robotics: Comparison of bullet, havok, mujoco, ode and physx (2015) 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 4397-4404; Todorov, E., Erez, T., Tassa, Y., MujoCo: A physics engine for model-based control (2012) 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5026-5033; Lo, W.L.A., Yu, Q., Mao, Y., Li, W., Hu, C., Li, L., Lumbar muscles biomechanical characteristics in young people with chronic spinal pain (2019) BMC Musculoskeletal Disorders, 20 (1); Todorov, E., (2018) MuJoCo: Modeling, Simulation and Visualization of Multi-Joint Dynamics with Contact, , http://www.mujoco.org/book/index.html, Roboti Publishing, Available: Accessed March 9, 2021",,"de Sales Guerra Tsuzuki M.de Oliveira Pessoa M.A.",,"Institute of Electrical and Electronics Engineers Inc.","14th IEEE International Conference on Industry Applications, INDUSCON 2021","15 August 2021 through 18 August 2021",,171655,,9781665441186,,,"English","IEEE Int. Conf. Ind. Appl., INDUSCON - Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85115837551
"Zakia U., Menon C.","57190259490;23989261600;","Toward Long-Term FMG Model-Based Estimation of Applied Hand Force in Dynamic Motion during Human-Robot Interactions",2021,"IEEE Transactions on Human-Machine Systems","51","4","9475880","310","323",,,"10.1109/THMS.2021.3087902","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111661338&doi=10.1109%2fTHMS.2021.3087902&partnerID=40&md5=ba38032238dfae22ef8d4435e95434bd","Faculty of Applied Science, Menrva Research Group, School of Engineering Science, Simon Fraser University, Metro Vancouver, BC  V5A 1S6, Canada; Biomedical and Mobile Health Technology Laboratory, Eth Zurich, Zürich, 8092, Switzerland; Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Metro Vancouver, BC  V5A 1S6, Canada","Zakia, U., Faculty of Applied Science, Menrva Research Group, School of Engineering Science, Simon Fraser University, Metro Vancouver, BC  V5A 1S6, Canada; Menon, C., Biomedical and Mobile Health Technology Laboratory, Eth Zurich, Zürich, 8092, Switzerland, Menrva Research Group, Schools of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Metro Vancouver, BC  V5A 1S6, Canada","Physical human-robot interaction (pHRI) is reliant on human actions and can be addressed by studying human upper-limb motions during interactions. Use of force myography (FMG) signals, which detect muscle contractions, can be useful in developing machine learning algorithms as controls. In this paper, a novel long-term calibrated FMG-based trained model is presented to estimate applied force in dynamic motion during real-time interactions between a human and a linear robot. The proposed FMG-based pHRI framework was investigated in new, unseen, real-time scenarios for the first time. Initially, a long-term reference dataset (multiple source distributions) of upper-limb FMG data was generated as five participants interacted with the robot applying force in five different dynamic motions. Ten other participants interacted with the robot in two intended motions to evaluate the out-of-distribution (OOD) target data (new, unlearned), which was different than the population data. Two practical scenarios were considered for assessment: i) a participant applied force in a new, unlearned motion (scenario 1), and ii) a new, unlearned participant applied force in an intended motion (scenario 2). In each scenario, few long-term FMG-based models were trained using a baseline dataset [reference dataset (scenario 1, 2) and/or a learnt participant dataset (scenario 1)] and a calibration dataset (collected during evaluation). Real-time evaluation showed that the proposed long-term calibrated FMG-based models (LCFMG) could achieve estimation accuracies of 80%-94% in all scenarios. These results are useful towards integrating and generalizing human activity data in a robot control scheme by avoiding extensive HRI training phase in regular applications. © 2013 IEEE.","Admittance control; calibration data; force myography (FMG); generalized zero-shot learning (GZSL); human-robot interaction (HRI); long-term calibrated FMG (LCFMG) model; multiple source adaptation; out-of-distribution (OOD) samples","Educational robots; Learning algorithms; Machine learning; Man machine systems; Muscle; Population statistics; Human activities; Model-based estimation; Muscle contractions; Physical humanrobot interaction (phri); Population data; Real time evaluation; Real time interactions; Upper limb motion; Human robot interaction",,,,,"Canadian Institutes of Health Research, CIHR; Natural Sciences and Engineering Research Council of Canada, NSERC; Canada Research Chairs","Manuscript received August 5, 2020; revised January 18, 2021 and March 19, 2021; accepted May 23, 2021. Date of publication July 7, 2021; date of current version July 19, 2021. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC), in part by the Canadian Institutes of Health Research (CIHR), and in part by the Canada Research Chair (CRC) program. This article was recommended by Associate Editor B. Hu. (Corresponding author: Umme Zakia.) Umme Zakia is with the Menrva Research Group, School of Engineering Science, Faculty of Applied Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada (e-mail: umme_zakia@sfu.ca).",,,,,,,,,,"Xiao, Z.G., Menon, C., Towards the development of a wearable feedback system for monitoring the activities of the upper-extremities (2014) J. Neuroeng. Rehabil., 11 (2), pp. 1-13. , Jan; Oskoei, M.A., Hu, H., Myoelectric control systems-A survey (2007) Biomed. Signal Process. Control, 2 (4), pp. 275-294. , Oct; Duan, F., Dai, L., Chang, W., Chen, Z., Zhu, C., Li, W., SEMG-based identification of hand motion commands using wavelet neural network combined with discrete wavelet transform (2016) IEEE Trans. Ind. Electron., 63 (3), pp. 1923-1934. , Mar; Allard, U.C., A convolutional neural network for robotic arm guidance using sEMG based frequency-features (2016) Proc. IEEE/RSJ Int. Conf. IROS, pp. 2464-2470; Meattini, R., Benatti, S., Scarcia, U., De Gregorio, D., Benini, L., Melchiorri, C., An sEMG-based human-robot interface for robotic hands using machine learning and synergies (2018) IEEE Trans. Compon., Pack. Manuf. Tech., 8 (7), pp. 1149-1158. , Jul; Radmand, A., High-density forcemyography: A possible alternative for upper-limb prosthetic control (2016) J. Rehab. Res. Develop., 53 (4), pp. 443-456; Godiyal, A.K., Verma, H.K., Khanna, N., Joshi, D., Aforcemyographybased system for gait event detection in overground and ramp walking (2018) IEEE Trans. Inst. Measure, 67 (10), pp. 2314-2323. , Oct; Godiyal, A.K., Mondal, M., Joshi, S.D., Joshi, D., Force myography based novel strategy for locomotion classification (2018) IEEE Trans. Hum.-Mach. Syst., 48 (6), pp. 648-657. , Dec; Xiao, Z.G., Menon, C., A review of force myography research and development (2019) Sensors, 19 (20). , Oct; Belyea, A., Englehart, K., Scheme, E., FMG versus EMG: A comparison of usability for real-time motion recognition based control (2019) IEEE Trans. Biomed. Eng., 66 (11), pp. 3098-3104. , Nov; Islam, M.R.U., A comparative study of motion detection with FMG and sEMGmethods for assistive applications (2020) J. Rehab. Assist. Tech. Eng., , Dec; Li, Y., Ge, S.S., Human-robot collaboration based on motion intention estimation (2014) IEEE Trans. Mechatronics, 19 (3), pp. 1007-1014. , Jun; Gaglio, S., Re, G.L., Morana, M., Human activity recognition process using 3D posture data (2015) IEEE Trans. Hum.-Mach. Syst., 45 (5), pp. 586-597. , Oct; Jiang, X., Merhi, L.-K., Menon, C., Force exertion affects grasp classification using force myography (2018) IEEE Trans. Hum.-Mach. Syst., 48 (2), pp. 219-226. , Apr; Sakr, M., Menon, C., Exploratory evaluation of the force myography (FMG) signals usage for admittance control of a linear actuator (2018) Proc. IEEE Int. Conf. BioRob, pp. 903-908. , Aug; Zakia, U., Menon, C., Estimating exerted hand force via force myography to interact with a biaxial stage in real-time by learning human intentions:Apreliminary investigation (2020) Sensors, 20 (7). , Apr; Alpaydin, E., (2010) Introduction to Machine Learning, , Cambridge, MA, USA: MIT Press; Xian, Y., Zero-shot learning-A comprehensive evaluation of the good, the bad and the ugly (2019) Trans. Pattern Anal. Mach. Intell., 41 (9), pp. 2251-2265. , Sept; Zegers, P., Sundareshan, M.K., Systematic testing of generalization level during training in regression-type learning scenarios (2014) Proc. IEEE Int. Joint Conf. Neural Netw., pp. 2807-2812. , Jul; Chao, W.L., Anempirical study and analysis of generalized zero-shot learning for object recognition in the wild (2016) Proc. Eur. Conf. Comp. Vis., pp. 52-68; Scheme, E., Englehart, K., Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use (2011) J. Rehabil. Res. Develop., 48 (6), pp. 643-659; Côté-Allard, U., Deep learning for electromyographic hand gesture signal classification using transfer learning (2019) IEEE Trans. Neural Syst. Rehabil. Eng., 27 (4), pp. 760-771. , Apr; Côté-Allard, U., Transfer learning for sEMG hand gestures recognition using convolutional neural networks (2017) Proc. IEEE Int. Conf. Syst., Man, Cybern., pp. 1663-1668; Kanoga, S., Matsuoka, M., Kanemura, A., Transfer learning over time and position inwearable myoelectric control systems (2018) Proc. 40th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., pp. 1-4; Prahm, C., Counteracting electrode shifts in upper-limb prosthesis control via transfer learning (2019) IEEE Trans. Neural Syst. Rehabil. Eng., 27 (5), pp. 956-962. , May; Du, Y., Surface EMG-based inter-session gesture recognition enhanced by deep domain adaptation (2017) Sensors, 17; Prahm, C., Transfer learning for rapid Re-calibration of a myoelectric prosthesis after electrode shift (2017) Converging Clinicical and Engineering Research on Neurorehabilitation II, 15. , https://doi.org/10.1007/978-3-319-46669-9_28, New York, NY, USA: Springer; Côté-Allard, U., Unsupervised domain adversarial self-calibration for electromyography-based gesture recognition (2020) IEEE Access, 8, pp. 177941-177955; Field, A., (2017) Discovering Statistics Using IBM SPSS, , 5th ed., Newbury Park, CA, USA: Sage; Côté-Allard, U., (2020) Virtual Reality to Study the Gap between Offline and Real-time EMG-based Gesture Recognition, , Dec","Zakia, U.; Faculty of Applied Science, Canada; email: umme_zakia@sfu.ca",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21682291,,,,"English","IEEE Trans. Human Mach. Syst.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85111661338
"Lopes J.M., Figueiredo J., Pinheiro C., Reis L.P., Santos C.P.","57208665200;57195993391;57208665955;13907511600;57211929847;","Biomechanical Assessment of Adapting Trajectory and Human-Robot Interaction Stiffness in Impedance-Controlled Ankle Orthosis",2021,"Journal of Intelligent and Robotic Systems: Theory and Applications","102","4","76","","",,,"10.1007/s10846-021-01423-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109701139&doi=10.1007%2fs10846-021-01423-0&partnerID=40&md5=54c6f871f0d2ba121c9ecd2dba635dee","Centre for MicroElectroMechanical Systems (CMEMS), University of Minho, Guimarães, 4800-058, Portugal; Artificial Intelligence and Computer Science Laboratory (LIACC), University of Porto, Porto, 4200-465, Portugal","Lopes, J.M., Centre for MicroElectroMechanical Systems (CMEMS), University of Minho, Guimarães, 4800-058, Portugal; Figueiredo, J., Centre for MicroElectroMechanical Systems (CMEMS), University of Minho, Guimarães, 4800-058, Portugal; Pinheiro, C., Centre for MicroElectroMechanical Systems (CMEMS), University of Minho, Guimarães, 4800-058, Portugal; Reis, L.P., Artificial Intelligence and Computer Science Laboratory (LIACC), University of Porto, Porto, 4200-465, Portugal; Santos, C.P., Centre for MicroElectroMechanical Systems (CMEMS), University of Minho, Guimarães, 4800-058, Portugal","Gait disabilities empowered intensive research on the field of human-robot interaction to promote effective gait rehabilitation. Assist-as-needed strategies are becoming prominent, appealing to the users’ participation in their rehabilitation therapy. This study proposes and assesses the biomechanical effects of an adaptive impedance control strategy that innovatively allows adaptability in interaction-based stiffness and gait trajectory towards a fully assist-as-needed therapy. By modulating the interaction-based stiffness per gait phase, we hypothesize that the strategy appeals to a symbiotic human-orthotic cooperation, augmenting the user’s muscular activity. The interaction stiffness was estimated by modelling the human-orthosis interaction torque vs angle curve with a linear regression model. The strategy also allows for real-time trajectory adaptations at different gait phases to fulfil the users’ needs. The biomechanical assessment of the impedance-controlled ankle orthosis involved eight healthy volunteers walking at 1.0 and 1.6 km/h. The results revealed a stronger muscular activation regarding the non-assisted leg for the gastrocnemius lateralis (increment ratio ≥ 1.0 for both gait speeds) and for the tibialis anterior muscle (increment ratio ≥ 1.0 for 1.6 km/h). The strategy guided users successfully on a healthy gait pattern while allowing deviations (median error < 5.0°) given the users’ intention weighted by interaction stiffness. Findings showed the relevance for adapting gait trajectory as users prefer higher trajectories as the speed increases. No significant temporal variations or neither knee angular compensations were observed (p value ≥0.11). Overall results support that this strategy may be applied for intensity-adapted gait training, allowing different human-robot compliant levels. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Adaptive assistive strategies; Human-orthosis interaction; Impedance control; Locomotion and actuation systems; Robotic rehabilitation","Agricultural robots; Biomechanics; Educational robots; Man machine systems; Regression analysis; Stiffness; Trajectories; Biomechanical assessment; Biomechanical effects; Impedance control strategy; Linear regression models; Muscular activation; Muscular activities; Real-time trajectories; Rehabilitation therapy; Human robot interaction",,,,,"NORTE-01-0145-FEDER-030386; Fundação para a Ciência e a Tecnologia, FCT: UIDB/04436/2020, UIDP/04436/2020","This work has been supported by the FEDER Funds through the Programa Operacional Regional do Norte and national funds from Fundação para a Ciência e Tecnologia with the SmartOs project under Grant NORTE-01-0145-FEDER-030386, and under the national support to R&D units grant through the reference project UIDB/04436/2020 and UIDP/04436/2020.",,,,,,,,,,"Mikolajczyk, T., Ciobanu, I., Badea, D.I., Iliescu, A., Pizzamiglio, S., Schauer, T., Seel, T., Berteanu, M., Advanced technology for gait rehabilitation: an overview (2018) Adv. Mech. Eng., 10, pp. 1-19; Meng, W., Liu, Q., Zhou, Z., Ai, Q., Sheng, B., Xie, S.S., Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation (2015) Mechatronics., 31, pp. 132-145; Sanz-Merodio, D., Cestari, M., Arevalo, J.C., Garcia, E., Control motion approach of a lower limb orthosis to reduce energy consumption (2012) Int. J. Adv. Robot. Syst., 9, p. 232; Sankai, Y., HAL: Hybrid assistive limb based on cybernics (2010) Springer Tracts Adv. Robot., 66, pp. 25-34; Esquenazi, A., Talaty, M., Packel, A., Saulino, M., The Rewalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury (2012) Am. J. Phys. Med. Rehabil., 91, pp. 911-921; Quintero, H.A., Farris, R.J., Hartigan, C., Clesson, I., Goldfarb, M., A powered lower limb orthosis for providing legged mobility in paraplegic individuals (2012) Top Spinal Cord Inj. Rehabiliation., 17, pp. 25-33; Neuhaus, P.D., Noorden, J.H., Craig, T.J., Torres, T., Kirschbaum, J., Pratt, J.E., Design and evaluation of Mina: A robotic orthosis for paraplegics (2011) IEEE Int. Conf. Rehabil. Robot., , https://doi.org/10.1109/ICORR.2011.5975468; Riener, R., Lünenburger, L., Jezernik, S., Anderschitz, M., Colombo, G., Dietz, V., Patient-cooperative strategies for robot-aided treadmill training: first experimental results (2005) IEEE Trans. Neural Syst. Rehabil. Eng., 13, pp. 380-394; Fleerkotte, B.M., Koopman, B., Buurke, J.H., Van Asseldonk, E.H.F., Van Der Kooij, H., Rietman, J.S., The effect of impedance-controlled robotic gait training on walking ability and quality in individuals with chronic incomplete spinal cord injury: An explorative study (2014) J. Neuroeng. Rehabil., 11, pp. 1-15; Lewis, C.L., Ferris, D.P., Invariant hip moment pattern while walking with a robotic hip exoskeleton (2011) J. 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Biomech., 43, pp. 203-209; Winter, D.A., (2009) Biomechanics and motor control of human movement, , John Wiley & Sons, Inc., Hoboken, New Jersey; Perry, J., (1992) Gait analysis: normal and pathological function, , SLACK Incorporated, Thorofare, New Jersey; Figueiredo, J., Felix, P., Santos, C.P., Moreno, J.C., Towards human-knee orthosis interaction based on adaptive impedance control through stiffness adjustment (2017) 2017 International Conference on Rehabilitation Robotics (ICORR), pp. 406-411. , IEEE, London; Cao, J., Xie, S.Q., Das, R., Zhu, G.L., (2014) Control strategies for effective robot assisted gait rehabilitation: The state of art and future prospects; Hussain, S., Xie, S.Q., Jamwal, P.K., Adaptive impedance control of a robotic orthosis for gait rehabilitation (2013) IEEE Trans. Cybern., 43, pp. 1025-1034; Dos Santos, W.M., Siqueira, A.A.G., Optimal impedance control for robot-Aided rehabilitation of walking based on estimation of patient behavior (2016) Proc. IEEE RAS EMBS Int. Conf. Biomed. Robot. Biomechatronics. 2016-July, pp. 1023-1028. , https://doi.org/10.1109/BIOROB.2016.7523765; dos Santos, W.M., Siqueira, A.A.G., Optimal impedance via model predictive control for robot-aided rehabilitation (2019) Control. Eng. Pract., 93, p. 104177; Perez-Ibarra, J.C., Siqueira, A.A.G., Krebs, H.I., Assist-As-needed ankle rehabilitation based on adaptive impedance control (2015) IEEE Int. Conf. Rehabil. Robot. 2015-Septe, pp. 723-728. , https://doi.org/10.1109/ICORR.2015.7281287; Pérez-Ibarra, J.C., Siqueira, A.A.G., Silva-Couto, M.A., De Russo, T.L., Krebs, H.I., Adaptive impedance control applied to robot-aided neuro-rehabilitation of the ankle (2019) IEEE Robot. Autom. Lett., 4, pp. 185-192; Lopes, J.M., Pinheiro, C., Figueiredo, J., Reis, L.P., Santos, C.P., Assist-as-needed impedance control strategy for a wearable ankle robotic orthosis (2020) IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 10-15. , Ponta Delgada, Portugal; Tucker, M.R., Lambercy, O., Gassert, R., Olivier, J., Bleuler, H., Bouri, M., Pagel, A., Del Millán, J.R., Control strategies for active lower extremity prosthetics and orthotics: a review (2015) J. Neuroeng. Rehabil., 12, p. 1; Hogan, N., Impedance control: An approach to manipulation (1985) J. Dyn. Syst. Meas. 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Usability Evaluation in Industry. Pp. 189–194. Taylor & Francis Ltd; Sauro, J., Lewis, J.R., (2012) Quantifying the User Experience: Practical Statistics for User Research, , Elsevier; Fernandes, P.N., Figueiredo, J., Moreno, J.C., Santos, C.P., Feedback-error learning control for powered assistive devices (2020) MEDICON 2019: XV Mediterranean Conference on Medical and Biological Engineering and Computing, pp. 1998-2013; Van Den Bogert, A.J., Geijtenbeek, T., Even-Zohar, O., Steenbrink, F., Hardin, E.C., A real-time system for biomechanical analysis of human movement and muscle function (2013) Med. Biol. Eng. Comput., 51, pp. 1069-1077; Pinheiro, C., Lopes, J.M., Figueiredo, J., Goncalves, L.M., Santos, C.P., Design and technical validation of a wearable biofeedback system for robotic gait rehabilitation (2020) 2020 IEEE Int. Conf. Auton. Robot Syst. Compet. ICARSC, 2020, pp. 16-21","Lopes, J.M.; Centre for MicroElectroMechanical Systems (CMEMS), Portugal; email: a74982@alunos.uminho.pt",,,"Springer Science and Business Media B.V.",,,,,09210296,,JIRSE,,"English","J Intell Rob Syst Theor Appl",Article,"Final","",Scopus,2-s2.0-85109701139
"Xu C., Lin L., Aung Z.M., Chen X., Sun M., Chai G., Xie L.","56505860600;56949467300;57194874781;55739098400;57215785449;7005035258;7402590591;","A Preliminary Study on Animal Experiments of Robot-Assisted Craniotomy",2021,"World Neurosurgery","149",,,"e748","e757",,,"10.1016/j.wneu.2021.01.108","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101653401&doi=10.1016%2fj.wneu.2021.01.108&partnerID=40&md5=9e3221a29b7e42ec2ac38c2d1c172795","Institute of Forming Technology & Equipment, Shanghai, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China; Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China","Xu, C., Institute of Forming Technology & Equipment, Shanghai, China; Lin, L., Institute of Forming Technology & Equipment, Shanghai, China; Aung, Z.M., Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Chen, X., Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Sun, M., Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Chai, G., Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Xie, L., Institute of Forming Technology & Equipment, Shanghai, China, Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China","Background: Traditional craniotomy relies on the surgeon's experience and can be complicated owing to excessive skull bone removal, undesirable brain tissue penetration, or severe bleeding. For craniotomy, we developed a robot system based on intraoperative cone-beam computed tomography image guidance and human-robot cooperative interaction, aiming to improve the safety and accuracy of surgery and reduce the labor-intensiveness of the procedure. Methods: Intraoperative cone-beam computed tomography image guidance was adopt to improve the accuracy in our experiment. Craniotomy was performed using an interactive method based on human-robot collaboration, which could achieve a natural interactive method in accordance with surgeons' operating habits. The frequency-based method of contact distinction and the method of torque estimation were used to improve the safety of the designed robot. Results: An animal experiment was conducted to verify the effectiveness of the robot system. During the drilling process, the position error was 0.92 ± 0.17 mm (upper surface) and 0.97 ± 0.11 mm (lower surface), and the angle error was 3.37 ± 1.43°. During the milling process, the position error was 1.06 ± 0.13 mm (upper surface) and 1.09 ± 0.09 mm (lower surface). The results showed that the system had sufficient precision and could better complete craniotomy with human-robot collaboration. In addition, with the feedback of multisensor information, the robot system could achieve a sufficient level of safety. Conclusions: The robot system can achieve accurate positioning and safe user-friendly human-robot interaction, which solves problems encountered in the drilling and milling of craniotomy, meets clinical needs, and provides a new method for robot-assisted craniotomy. © 2021 Elsevier Inc.","Computer-aided surgery; Craniotomy surgery; Human-robot collaboration; Image guidance","animal experiment; Article; cone beam computed tomography; craniotomy; error; experimental dog; female; intraoperative period; male; nonhuman; robot assisted surgery; surgeon; animal; animal experiment; computer assisted surgery; craniotomy; devices; dog; procedures; robot assisted surgery; Animal Experimentation; Animals; Cone-Beam Computed Tomography; Craniotomy; Dogs; Robotic Surgical Procedures; Surgery, Computer-Assisted",,,"MicronTracker","jiangsu firstimaging medical equipment, China; shanghai rigs medical equipment, China","YG2017ZD03, ZH2018QNB31; National Natural Science Foundation of China, NSFC: 51911540479, 61973211, M-0221","Conflict of interest statement: The work described in this article was supported by National Natural Science Foundation of China (Grant Nos. 61973211 , 51911540479 , M-0221 ), the Key Project of Medical Engineering Cross Research Fund of Shanghai Jiao Tong University (Grant Nos. YG2017ZD03 , ZH2018QNB31 ), and the Project of Scientific Research Project of Medical Robot Research Institute of Shanghai Jiao Tong University.",,,,,,,,,,"Engelhardt, M., Uhlenbruch, S., Christmann, A., Miede, C., Schmieder, K., Accidental dural tears occurring during supratentorial craniotomy—a prospective analysis of predisposing factors in 100 patients (2005) Zentralbl Neurochir, 66, pp. 70-74; Balasundaram, I., Al-Hadad, I., Parmar, S., Recent advances in reconstructive oral and maxillofacial surgery (2012) Br J Oral Maxillofac Surg, 50, pp. 695-705; Hassfeld, S., Mühling, J., Navigation in maxillofacial and craniofacial surgery (1998) Comput Aided Surg, 3, pp. 183-187; Dogangil, G., Davies, B.L., Baena, F.R.Y., A review of medical robotics for minimally invasive soft tissue surgery (2010) Proc Inst Mech Eng H, 224, p. 653; Simorov, A., Otte, R.S., Kopietz, C.M., Oleynikov, D., Review of surgical robotics user interface: What is the best way to control robotic surgery? (2012) Surg Endosc, 26, pp. 2117-2125; McBeth, P.B., Louw, D.F., Rizun, P.R., Sutherland, G.R., Robotics in neurosurgery (2004) Am J Surg, 188, pp. 68-75; Mcbeth, P.B., Louw, D.F., Rizun, P.R., Sutherland, G.R., Robotics in neurosurgery (2004) Am J Surg, 188 (4A Suppl), pp. 68-75; Varma, T.R.K., Eldridge, P., Use of the NeuroMate stereotactic robot in a frameless mode for functional neurosurgery (2006) Int J Med Robot, 2, pp. 107-113; Sutherland, G.R., Wolfsberger, S., Lama, S., Zareinia, K., The evolution of neuroArm (2013) Neurosurgery, 72, pp. 27-32; Sutherland, G.R., Lama, S., Gan, L.S., Wolfsberger, S., Zareinia, K., Merging machines with microsurgery: clinical experience with neuroArm (2013) J Neurosurg, 118, pp. 521-529; Bast, P., Popovic, A., Wu, T., Robot and computer-assisted craniotomy: resection planning, implant modelling and robot safety (2006) Int J Med Robot, 2, pp. 168-178; Bast, P., Engelhardt, M., Popovic, A., Schmieder, K., Radermacher, K., CRANIO—development of a system for computer and robot-assisted craniotomy [in German] (2002) Biomed Tech (Berl), 47, pp. 9-11; Engelhardt, M., Bast, P., Jeblink, N., Analysis of surgical management of calvarial tumours and first results of a newly designed robotic trepanation system (2006) Minim Invasive Neurosurg, 49, pp. 98-103; Eggers, G., Wirtz, C., Korb, W., Robot-assisted craniotomy (2005) Minim Invasive Neurosurg, 48, pp. 154-158; Vos, W.D., Casselman, J., Swennen, G.R.J., Cone-beam computerized tomography (CBCT) imaging of the oral and maxillofacial region: a systematic review of the literature (2009) Int J Oral Maxillofac Surg, 38, pp. 609-625; Motta, A.T., de Assis Ribeiro Carvalho, F., Cevidanes, L.H., de Oliveira Almeida, M.A., Assessment of mandibular advancement surgery with 3D CBCT models superimposition (2010) Dental Press J Orthod, 15, p. 45e1; Santis, A.D., Siciliano, B., Luca, A.D., Bicchi, A., An atlas of physical human-robot interaction (2008) Mechanism Machine Theory, 43, pp. 253-270; Albu-Schäffer, A., Haddadin, S., Ott, C., Stemmer, A., Wimböck, T., Hirzinger, G., The DLR lightweight robot: design and control concepts for robots in human environments (2007) Ind Rob, 34, pp. 376-385; Kouris, A., Dimeas, F., Aspragathos, N., Contact distinction in human-robot cooperation with admittance control (2016), pp. 001951-001956. , 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Budapest; Phong, L.D., Choi, J., Kang, S., External force estimation using joint torque sensors for a robot manipulator (2012), pp. 4507-4512. , 2012 IEEE International Conference on Robotics and Automation Saint Paul, MN; Korb, W., Engel, D., Boesecke, R., Development and first patient trial of a surgical robot for complex trajectory milling (2003) Comput Aided Surg, 8, pp. 247-256; Cunha-Cruz, V., Follmann, A., Popovic, A., Robot-and computer-assisted craniotomy (CRANIO): from active systems to synergistic man-machine interaction (2010) Proc Inst Mech Eng H, 224, pp. 441-452; Niccolini, M., Castelli, V., Diversi, C., Kang, B., Mussa, F., Sinibaldi, E., Development and preliminary assessment of a robotic platform for neuroendoscopy based on a lightweight robot (2016) Int J Med Robot, 12, pp. 4-17","Xie, L.; Institute of Forming Technology & EquipmentChina; email: lexie@sjtu.edu.cn",,,"Elsevier Inc.",,,,,18788750,,,"33540095","English","World Neurosurg.",Article,"Final","",Scopus,2-s2.0-85101653401
"Perugia G., Paetzel-Prüsmann M., Alanenpää M., Castellano G.","57193757433;57222066404;57222072366;23007528900;","I Can See It in Your Eyes: Gaze as an Implicit Cue of Uncanniness and Task Performance in Repeated Interactions With Robots",2021,"Frontiers in Robotics and AI","8",,"645956","","",,2,"10.3389/frobt.2021.645956","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104580349&doi=10.3389%2ffrobt.2021.645956&partnerID=40&md5=b615824d8990c7be3a992a3fd2801650","Uppsala Social Robotics Lab, Department of Information Technology, Uppsala University, Uppsala, Sweden; Computational Linguistics, Department of Linguistics, University of Potsdam, Potsdam, Germany","Perugia, G., Uppsala Social Robotics Lab, Department of Information Technology, Uppsala University, Uppsala, Sweden; Paetzel-Prüsmann, M., Computational Linguistics, Department of Linguistics, University of Potsdam, Potsdam, Germany; Alanenpää, M., Uppsala Social Robotics Lab, Department of Information Technology, Uppsala University, Uppsala, Sweden; Castellano, G., Uppsala Social Robotics Lab, Department of Information Technology, Uppsala University, Uppsala, Sweden","Over the past years, extensive research has been dedicated to developing robust platforms and data-driven dialog models to support long-term human-robot interactions. However, little is known about how people's perception of robots and engagement with them develop over time and how these can be accurately assessed through implicit and continuous measurement techniques. In this paper, we explore this by involving participants in three interaction sessions with multiple days of zero exposure in between. Each session consists of a joint task with a robot as well as two short social chats with it before and after the task. We measure participants' gaze patterns with a wearable eye-tracker and gauge their perception of the robot and engagement with it and the joint task using questionnaires. Results disclose that aversion of gaze in a social chat is an indicator of a robot's uncanniness and that the more people gaze at the robot in a joint task, the worse they perform. In contrast with most HRI literature, our results show that gaze toward an object of shared attention, rather than gaze toward a robotic partner, is the most meaningful predictor of engagement in a joint task. Furthermore, the analyses of gaze patterns in repeated interactions disclose that people's mutual gaze in a social chat develops congruently with their perceptions of the robot over time. These are key findings for the HRI community as they entail that gaze behavior can be used as an implicit measure of people's perception of robots in a social chat and of their engagement and task performance in a joint task. © Copyright © 2021 Perugia, Paetzel-Prüsmann, Alanenpää and Castellano.","engagement; long-term interaction; mutual gaze; perception of robots; uncanny valley",,,,,,"Stiftelsen för Strategisk Forskning, SSF: RIT15-0133","This work was supported by the Swedish Foundation for Strategic Research under the COIN project (RIT15-0133).",,,,,,,,,,"Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: a review (2017) J. Hum. 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"Shin Y., Choi M.-Y., Choi J., Na J.-H., Kim S.Y.","57213264505;57214865856;57194009468;7103258612;57204447592;","Design of an Electro-Stimulated Hydrogel Actuator System with Fast Flexible Folding Deformation under a Low Electric Field",2021,"ACS Applied Materials and Interfaces","13","13",,"15633","15646",,3,"10.1021/acsami.1c00883","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104048101&doi=10.1021%2facsami.1c00883&partnerID=40&md5=3b0f4f1a6b8122b92a0583a9c63abc15","Graduate School of Energy Science and Technology, Chungnam National University, Daejeon, 34134, South Korea; Department of Convergence System Engineering, Chungnam National University, Daejeon, 34134, South Korea; Department of Electrical, Electronics and Communication Engineering Education, Chungnam National University, Daejeon, 34134, South Korea; Department of Chemical Engineering Education, Chungnam National University, Daejeon, 34134, South Korea","Shin, Y., Graduate School of Energy Science and Technology, Chungnam National University, Daejeon, 34134, South Korea; Choi, M.-Y., Department of Convergence System Engineering, Chungnam National University, Daejeon, 34134, South Korea; Choi, J., Graduate School of Energy Science and Technology, Chungnam National University, Daejeon, 34134, South Korea; Na, J.-H., Department of Convergence System Engineering, Chungnam National University, Daejeon, 34134, South Korea, Department of Electrical, Electronics and Communication Engineering Education, Chungnam National University, Daejeon, 34134, South Korea; Kim, S.Y., Graduate School of Energy Science and Technology, Chungnam National University, Daejeon, 34134, South Korea, Department of Chemical Engineering Education, Chungnam National University, Daejeon, 34134, South Korea","Soft actuators have recently been widely studied due to their significant advantages including light weight, continuous deformability, high environment adaptability, and safe human-robot interactions. In this study, we designed electrically responsive poly(sodium 4-vinylbenzenesulfonate/2-hydroxyethylmethacrylate/acrylamide) (P(VBS/HEMA/AAm)) and poly(sodium 4-vinylbenzenesulfonate/2-hydroxyethyl methacrylate/acrylic acid) (P(VBS/HEMA/AAc)) hydrogels. A series of P(VBS/HEMA/AAm) and P(VBS/HEMA/AAc) hydrogels were prepared by adjusting the monomer composition and cross-linking density to systemically analyze various factors affecting the actuation of hydrogels under an electric field. All hydrogels exhibited more than 65% gel fraction and a high equilibrium water content (EWC) of more than 90%. The EWC of hydrogels gradually increased with decreasing cross-linker content and was also influenced by the monomer composition. The mechanical properties of hydrogels were proportional to the cross-linking density. Particularly, hydrogels showed bending deformation even at low voltages below 10 V, and the electrically responsive bending actuation of hydrogels can be modulated by cross-linking density, monomer composition, applied voltage, ion strength of the electrolyte solution, and geometrical parameters of the hydrogel. By controlling these factors, hydrogels showed a fast response with a bending of more than 100° within a minute. In addition, hydrogels did not show significant cytotoxicity in a biocompatibility test and exhibited more than 84% cell viability. These results indicate that P(VBS/HEMA/AAm) and P(VBS/HEMA/AAc) hydrogels with fast response properties even under a low electric field have the potential to be used in a wide range of soft actuator applications. © 2021 American Chemical Society.","crosslinking; electroactive; hydrogel; soft actuator; soft robotics","Actuators; Bending (deformation); Biocompatibility; Density (specific gravity); Electric fields; Electrolytes; Geometry; Human robot interaction; Monomers; Sodium compounds; Biocompatibility tests; Cross-linking density; Electrolyte solutions; Equilibrium water contents; Folding deformations; Hydrogel actuators; Monomer compositions; Safe human-robot interaction; Hydrogels",,,,,"Ministry of Science, ICT and Future Planning, MSIP: NRF-2017M2A2A6A01071261, NRF-2018R1A2B6008850; National Research Foundation of Korea, NRF","This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2018R1A2B6008850). This work was partially supported by the Basic Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2017M2A2A6A01071261).",,,,,,,,,,"Hines, L., Petersen, K., Lum, G.Z., Sitti, M., Soft Actuators for Small-Scale Robotics (2017) Adv. Mater., 29, pp. 1603483-1603535; Ilievski, F., Mazzeo, A.D., Shepherd, R.F., Chen, X., Whitesides, G.M., Soft Robotics for Chemists (2011) Angew. Chem., Int. Ed., 50, pp. 1890-1895; Rus, D., Tolley, M.T., Design, Fabrication and Control of Soft Robots (2015) Nature, 521, pp. 467-475; Xiong, J., Chen, J., Lee, P.S., Functional Fibers and Fabrics for Soft Robotics, Wearables, and Human-Robot Interface (2020) Adv. Mater., 9, p. 2002640; Wang, X., Guo, R., Liu, J., Liquid Metal based Soft Robotics: Materials, Designs, and Applications (2019) Adv. Mater. 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Commun., 7, p. 12263; Fung, Y.C., Wittrick, W.H., A boundary Layer Phenomenon in the Large Deflexion of Thin Plates (1955) Q. J. Mech. Appl. Math., 8, pp. 191-210","Kim, S.Y.; Graduate School of Energy Science and Technology, South Korea; email: kimsy@cnu.ac.kr
Na, J.-H.; Department of Convergence System Engineering, South Korea; email: junhee.na@cnu.ac.kr",,,"American Chemical Society",,,,,19448244,,,"33764732","English","ACS Appl. Mater. Interfaces",Article,"Final","",Scopus,2-s2.0-85104048101
"Jirak D., Biertimpel D., Kerzel M., Wermter S.","37031181800;57217596667;41861835400;7003826680;","Solving visual object ambiguities when pointing: an unsupervised learning approach",2021,"Neural Computing and Applications","33","7",,"2297","2319",,2,"10.1007/s00521-020-05109-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087409055&doi=10.1007%2fs00521-020-05109-w&partnerID=40&md5=ab0c2e0e3baa714006f1d1ea0802981e","Department of Informatics, Knowledge Technology, University of Hamburg, Vogt-Kölln-Str. 30, Hamburg, 22527, Germany; Science Faculty, University of Amsterdam, Science Park 904, Amsterdam, 1098 XH, Netherlands","Jirak, D., Department of Informatics, Knowledge Technology, University of Hamburg, Vogt-Kölln-Str. 30, Hamburg, 22527, Germany; Biertimpel, D., Science Faculty, University of Amsterdam, Science Park 904, Amsterdam, 1098 XH, Netherlands; Kerzel, M., Department of Informatics, Knowledge Technology, University of Hamburg, Vogt-Kölln-Str. 30, Hamburg, 22527, Germany; Wermter, S., Department of Informatics, Knowledge Technology, University of Hamburg, Vogt-Kölln-Str. 30, Hamburg, 22527, Germany","Whenever we are addressing a specific object or refer to a certain spatial location, we are using referential or deictic gestures usually accompanied by some verbal description. Particularly, pointing gestures are necessary to dissolve ambiguities in a scene and they are of crucial importance when verbal communication may fail due to environmental conditions or when two persons simply do not speak the same language. With the currently increasing advances of humanoid robots and their future integration in domestic domains, the development of gesture interfaces complementing human–robot interaction scenarios is of substantial interest. The implementation of an intuitive gesture scenario is still challenging because both the pointing intention and the corresponding object have to be correctly recognized in real time. The demand increases when considering pointing gestures in a cluttered environment, as is the case in households. Also, humans perform pointing in many different ways and those variations have to be captured. Research in this field often proposes a set of geometrical computations which do not scale well with the number of gestures and objects and use specific markers or a predefined set of pointing directions. In this paper, we propose an unsupervised learning approach to model the distribution of pointing gestures using a growing-when-required (GWR) network. We introduce an interaction scenario with a humanoid robot and define the so-called ambiguity classes. Our implementation for the hand and object detection is independent of any markers or skeleton models; thus, it can be easily reproduced. Our evaluation comparing a baseline computer vision approach with our GWR model shows that the pointing-object association is well learned even in cases of ambiguities resulting from close object proximity. © 2020, The Author(s).","Grow-when-required networks; Human–robot interaction; Object ambiguities; Pointing gestures; Pointing intention","Anthropomorphic robots; Human robot interaction; Object recognition; Petroleum reservoir evaluation; Unsupervised learning; Cluttered environments; Environmental conditions; Gesture interfaces; Object association; Pointing gestures; Robot interactions; Spatial location; Verbal communications; Object detection",,,,,"Deutsche Forschungsgemeinschaft, DFG: CML (TRR 169","Open Access funding provided by Projekt DEAL. We would like to thank German I. Parisi for the support in the GWR setup. The authors gratefully acknowledge partial support from the German Research Foundation DFG under Project CML (TRR 169).",,,,,,,,,,"Astor, K., Gredebaeck, G., Gaze following in 4.5- and 6-month-old infants: The impact of proximity on standard gaze following performance tests (2019) Infancy, 24 (1), pp. 79-89; Behne, T., Liszkowski, U., Carpenter, M., Tomasello, M., Twelve-month-olds comprehension and production of pointing (2012) Brit J Dev Psychol, 30 (3), pp. 359-375; Breazeal, C., Social interactions in HRI: the robot view (2004) Trans Syst Man Cybern Part C, 34 (2), pp. 181-186; Canal, G., Escalera, S., Angulo, C., A real-time human–robot interaction system based on gestures for assistive scenarios (2016) Comput Vis Image Underst, 149, pp. 65-77; Carpenter, M., Nagell, K., Tomasello, M., Butterworth, G., Moore, C., Social cognition, joint attention, and communicative competence from 9 to 15 months of age (1998) Monogr Soc Res Child Dev, 63 (4), p. i-174; Churamani, N., Kerzel, M., Strahl, E., Barros, P., Wermter, S., Teaching emotion expressions to a human companion robot using deep neural architectures (2017) International Joint Conference on Neural Networks (IJCNN). IEEE, Anchorage, Alaska, pp. 627-634; Cosgun, A., Trevor, J.A., Christensen, H.I., Did you mean this object? Detecting ambiguity in pointing gesture targets (2015) Towards a Framework for Joint Action Workshop, HRI, p. 2015. , Portland, OR, USA; Escalera, S., Gonzàlez, J., Escalante, H.J., Baró, X., Guyon, I., Looking at people special issue (2018) Int J Comput Vis, 126, pp. 1405-1573; Gromov, B., Gambardella, L.M., Giusti, A., Robot identification and localization with pointing gestures (2018) 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3921-3928; Gulzar, K., Kyrki, V., See what I mean-probabilistic optimization of robot pointing gestures (2015) 2015 IEEE-RAS 15Th International Conference on Humanoid Robots (Humanoids), pp. 953-958; Hafez, B., Weber, C., Kerzel, M., Wermter, S., Deep intrinsically motivated continuous actor-critic for efficient robotic visuomotor skill learning (2019) Paladyn J Behav Robot, 10 (1), pp. 14-29; Jirak, D., Wermter, S., Potentials and limitations of deep neural networks for cognitive robots (2017) Eucog Meeting Proceedings. Eucog, , Zürich, Switzerland; Kerzel, M., Strahl, E., Magg, S., Navarro-Guerrero, N., Heinrich, S., Wermter, S., Nico—neuro-inspired companion: A developmental humanoid robot platform for multimodal interaction (2017) Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 113-120; Kishimoto, T., Cross-sectional and longitudinal observations of pointing gestures by infants and their caregivers in Japan (2017) Cogn Dev, 43, pp. 235-244; Kohonen, T., The self-organizing map (1990) Proc IEEE, 78 (9), pp. 1464-1480; Kondaxakis, P., Gulzar, K., Kinauer, S., Kokkinos, I., Kyrki, V., Robot–robot gesturing for anchoring representations (2019) IEEE Trans Robot, 35 (1), pp. 216-230; Marsland, S., Shapiro, J., Nehmzow, U., A self-organising network that grows when required (2002) Neural Netw, 15 (8), pp. 1041-1058; Moore, C., Angelopoulos, M., Bennett, P., The role of movement in the development of joint visual attention (1997) Infant Behav Dev, 20 (1), pp. 83-92; Nagai, Y., Learning to comprehend deictic gestures in robots and human infants (2005) ROMAN 2005. 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Technologies and Analytics, pp. 403-412; Shukla, D., Erkent, O., Piater, J., Probabilistic detection of pointing directions for human–robot interaction (2015) 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1-8; Shukla, D., Erkent, O., Piater, J., A multi-view hand gesture RGB-D dataset for human–robot interaction scenarios (2016) 2016 25Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 1084-1091. , . In:, pp; Siqueira, H., Sutherland, A., Barros, P., Kerzel, M., Magg, S., Wermter, S., Disambiguating affective stimulus associations for robot perception and dialogue (2018) IEEE-RAS International Conference on Humanoid Robots (Humanoids), pp. 433-440; Sklansky, J., Finding the convex hull of a simple polygon (1982) Pattern Recognit Lett, 1 (2), pp. 79-83; Tomasello, M., Carpenter, M., Liszkowski, U., A new look at infant pointing (2007) Child Dev, 78 (3), pp. 705-722; Wu, Y.H., Fassert, C., Rigaud, A.S., Designing robots for the elderly: appearance issue and beyond (2012) Arch Gerontol Geriatr, 54 (1), pp. 121-126","Jirak, D.; Department of Informatics, Vogt-Kölln-Str. 30, Germany; email: jirak@informatik.uni-hamburg.de",,,"Springer Science and Business Media Deutschland GmbH",,,,,09410643,,,,"English","Neural Comput. Appl.",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85087409055
"Tolksdorf N.F., Siebert S., Zorn I., Horwath I., Rohlfing K.J.","57214683267;57214681828;55996186500;57209296053;14024566800;","Ethical Considerations of Applying Robots in Kindergarten Settings: Towards an Approach from a Macroperspective",2021,"International Journal of Social Robotics","13","2",,"129","140",,10,"10.1007/s12369-020-00622-3","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078902756&doi=10.1007%2fs12369-020-00622-3&partnerID=40&md5=5031bb5ba04e7849342827c6c7783458","Faculty of Arts and Humanities, Paderborn University, Warburger Straße 100, Paderborn, 33098, Germany; Faculty of Applied Social Sciences, TH Köln University of Applied Sciences, Ubierring 40, Cologne, 50678, Germany; Faculty of Mechanical Engineering, Technology and Diversity, Paderborn University, Warburger Straße 100, Paderborn, 33098, Germany","Tolksdorf, N.F., Faculty of Arts and Humanities, Paderborn University, Warburger Straße 100, Paderborn, 33098, Germany; Siebert, S., Faculty of Applied Social Sciences, TH Köln University of Applied Sciences, Ubierring 40, Cologne, 50678, Germany; Zorn, I., Faculty of Applied Social Sciences, TH Köln University of Applied Sciences, Ubierring 40, Cologne, 50678, Germany; Horwath, I., Faculty of Mechanical Engineering, Technology and Diversity, Paderborn University, Warburger Straße 100, Paderborn, 33098, Germany; Rohlfing, K.J., Faculty of Arts and Humanities, Paderborn University, Warburger Straße 100, Paderborn, 33098, Germany","In child–robot interaction (cHRI) research, many studies pursue the goal to develop interactive systems that can be applied in everyday settings. For early education, increasingly, the setting of a kindergarten is targeted. However, when cHRI and research are brought into a kindergarten, a range of ethical and related procedural aspects have to be considered and dealt with. While ethical models elaborated within other human–robot interaction settings, e.g., assisted living contexts, can provide some important indicators for relevant issues, we argue that it is important to start developing a systematic approach to identify and tackle those ethical issues which rise with cHRI in kindergarten settings on a more global level and address the impact of the technology from a macroperspective beyond the effects on the individual. Based on our experience in conducting studies with children in general and pedagogical considerations on the role of the institution of kindergarten in specific, in this paper, we enfold some relevant aspects that have barely been addressed in an explicit way in current cHRI research. Four areas are analyzed and key ethical issues are identified in each area: (1) the institutional setting of a kindergarten, (2) children as a vulnerable group, (3) the caregivers’ role, and (4) pedagogical concepts. With our considerations, we aim at (i) broadening the methodology of the current studies within the area of cHRI, (ii) revalidate it based on our comprehensive empirical experience with research in kindergarten settings, both laboratory and real-world contexts, and (iii) provide a framework for the development of a more systematic approach to address the ethical issues in cHRI research within kindergarten settings. © 2020, The Author(s).","Child–robot interaction; Early childhood education; Kindergarten settings; Robot ethics","Agricultural robots; Educational robots; Philosophical aspects; Professional aspects; Early childhood educations; Ethical considerations; Institutional setting; Interactive system; Procedural aspects; Robot ethics; Robot interactions; Vulnerable groups; Social robots",,,,,,"Open Access funding provided by Projekt DEAL. We thank the three anonymous reviewers for their helpful comments on earlier drafts of the manuscript. This research was supported by the Digital Society research program funded by the Ministry of Culture and Science of the German State of North Rhine-Westphalia.",,,,,,,,,,"Jensen, J.J., Skov, M.B., A review of research methods in children’s technology design (2005) Proceeding of the 2005 Conference on Interaction Design and children—IDC ’05, pp. 80-87. , In:, ACM Press, Boulder, pp; Hood, D., Lemaignan, S., Dillenbourg, P., When children teach a robot to write: An autonomous teachable humanoid which uses simulated handwriting (2015) Proceedings of the Tenth Annual ACM/IEEE International Conference on human–robot interaction—HRI ’15, pp. 83-90. , ACM Press, Portland; Leite, I., Hajishirzi, H., Andrist, S., Lehman, J., Managing chaos: Models of turn-taking in character-multichild interactions (2013) Proceedings of the 15Th ACM on International Conference on Multimodal interaction—ICMI ’13, pp. 43-50. , ACM Press, Sydney; Bethel, C.L., Burke, J.L., Murphy, R.R., Salomon, K., Psychophysiological experimental design for use in human–robot interaction studies (2007) 2007 International Symposium on Collaborative Technologies and Systems, pp. 99-105. , In:, IEEE, Orlando, pp; Hong, D., Chiu, D.K.W., Shen, V.Y., Requirements elicitation for the design of context-aware applications in a ubiquitous environment (2005) Proceedings of the 7Th International Conference on Electronic commerce—ICEC ’05, pp. 590-596. , ACM Press, Xi’an; Vogt, P., van Den Berghe, R., de Haas, M., Second language tutoring using social robots: A large-scale study (2019) 2019 14Th ACM/IEEE International Conference on human–robot Interaction (HRI). 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IEEE, pp. 955-960. , Edinburgh; Westlund, J.K., Gordon, G., Spaulding, S., Lessons from teachers on performing HRI studies with young children in schools (2016) 2016 11Th ACM/IEEE International Conference on human–robot Interaction (HRI). IEEE, Christchurch, pp. 383-390","Tolksdorf, N.F.; Faculty of Arts and Humanities, Warburger Straße 100, Germany; email: nils.tolksdorf@upb.de",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85078902756
"Huggins M., Alghowinem S., Jeong S., Colon-Hernandez P., Breazeal C., Park H.W.","57222188713;51460942700;56006453500;6506539341;7003691791;57191796621;","Practical guidelines for intent recognition: BERT with minimal training data evaluated in real-world HRI application",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3444671","341","350",,1,"10.1145/3434073.3444671","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102728534&doi=10.1145%2f3434073.3444671&partnerID=40&md5=7202c2918d2e4c21636335b794db5c31","MIT Media Lab, Cambridge, MA, United States","Huggins, M., MIT Media Lab, Cambridge, MA, United States; Alghowinem, S., MIT Media Lab, Cambridge, MA, United States; Jeong, S., MIT Media Lab, Cambridge, MA, United States; Colon-Hernandez, P., MIT Media Lab, Cambridge, MA, United States; Breazeal, C., MIT Media Lab, Cambridge, MA, United States; Park, H.W., MIT Media Lab, Cambridge, MA, United States","Intent recognition models, which match a written or spoken input's class in order to guide an interaction, are an essential part of modern voice user interfaces, chatbots, and social robots. However, getting enough data to train these models can be very expensive and challenging, especially when designing novel applications such as real-world human-robot interactions. In this work, wefi rst investigate how much training data is needed for high performance in an intent classification task. We train and evaluate BiLSTM and BERT models on various subsets of the ATIS and Snips datasets. Wefi nd that only 25 training examples per intent are required for our BERT model to achieve 94% intent accuracy compared to 98% with the entire datasets, challenging the belief that large amounts of labeled data are required for high performance in intent recognition. We apply this knowledge to train models for a real-world HRI application, character strength recognition during a positive psychology interaction with a social robot, and evaluate against the Character Strength dataset collected in our previous HRI study. Our real-world HRI application results also confirm that our model can produce 76% intent accuracy with 25 examples per intent compared to 80% with 100 examples. In a real-world scenario, the difference is only one additional error per 25 classifications. Finally, we investigate the limitations of our minimal data models and offer suggestions on developing high quality datasets. We conclude with practical guidelines for training BERT intent recognition models with minimal training data and make our code and evaluation framework available for others to replicate our results and easily develop models for their own applications. © 2021 IEEE Computer Society. All rights reserved.","BERT; Intent recognition; Natural language processing; Real-world HRI application","Agricultural robots; Large dataset; Man machine systems; Social robots; User interfaces; Classification tasks; Evaluation framework; Intent recognition; Novel applications; Positive psychology; Practical guidelines; Real-world scenario; Voice user interface; Classification (of information)",,,,,"FP7 Information and Communication Technologies, ICT; 2017-0-00162","This work was supported by the Information and Communication Technology (ICT) R&D program of the Ministry of Science and Institute for Information and Communication Technology Promotion of Republic of Korea under grant 2017-0-00162, “Development of Human-Care Robot Technology for Aging Society”.",,,,,,,,,,"https://developer.amazon.com/en-US/alexa/alexa-skills-kityear=2020, [n.d.]. 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Association for Computational Linguistics, New Orleans, Louisiana; Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M., Generalizing from a few examples: A survey on few-shot learning (2020) ACM Computing surx'Eys (CSUR), 53 (3), pp. 1-34; Williams, J.D., Kamal, E., Ashour, M., Amr, H., Miller, J., Zweig, G., Fast and easy language understanding for dialog systems with Microsoft Language Understanding Intelligent Service (LUIS) (2018) Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 159-161. , https://doi.org/10.18653/vlWl5-4622, Association for Computational Linguistics, Prague, Czech Republic; Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Dean, J., (2016) Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation, , arXiv: 1609.08144 [cs.CL]; Zhang, X., Zhao, J., LeCun, Y., (2015) Character-level Convolutional Networks for Text Classification, , arXiv: 1509.01626 [cs.LG]; Zhao, Z., Wu, Y., (2016) At Tent Ion-Based Convolutional Neural Networks for Sentence Classification; Zhou, J., Xu, W., End-to-end learning of semantic role labeling using recurrent neural networks (2015) Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 1127-1137. , https://doi.org/10.311S/vl/Pl5-1109, Association for Computational Linguistics, Beijing.China; Kaiser, L., Nachum, O., Roy, A., Bengio, S., (2017) Learning to Remember Rare Events, , arXiv: 1703.03129 [cs.LG]",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS)","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167644,21672148,9781450382892,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","All Open Access, Bronze, Green",Scopus,2-s2.0-85102728534
"Wang N., Chen C., Nuovo A.D.","55446268900;57203137144;56962754700;","A Framework of Hybrid Force/Motion Skills Learning for Robots",2021,"IEEE Transactions on Cognitive and Developmental Systems","13","1","8964480","162","170",,6,"10.1109/TCDS.2020.2968056","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102768405&doi=10.1109%2fTCDS.2020.2968056&partnerID=40&md5=1bb07da14eb1d60399c0a70cc612b017","Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom; Key Laboratory of Autonomous Systems and Networked Control, College of Automation Science and Engineering, South China University of Technology, Guangzhou, China; Centre for Automation and Robotics Research, Sheffield Hallam University, Sheffield, United Kingdom","Wang, N., Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom; Chen, C., Key Laboratory of Autonomous Systems and Networked Control, College of Automation Science and Engineering, South China University of Technology, Guangzhou, China; Nuovo, A.D., Centre for Automation and Robotics Research, Sheffield Hallam University, Sheffield, United Kingdom","Human factors and human-centered design philosophy are highly desired in today's robotics applications such as human-robot interaction (HRI). Several studies showed that endowing robots of human-like interaction skills can not only make them more likeable but also improve their performance. In particular, skill transfer by imitation learning can increase the usability and acceptability of robots by users without computer programming skills. In fact, besides positional information, muscle stiffness of the human arm and contact force with the environment also play important roles in understanding and generating human-like manipulation behaviors for robots, e.g., in physical HRI and teleoperation. To this end, we present a novel robot learning framework based on dynamic movement primitives (DMPs), taking into consideration both the positional and contact force profiles for human-robot skills transferring. Distinguished from the conventional method involving only the motion information, the proposed framework combines two sets of DMPs, which are built to model the motion trajectory and the force variation of the robot manipulator, respectively. Thus, a hybrid force/motion control approach is taken to ensure the accurate tracking and reproduction of the desired positional and force motor skills. Meanwhile, in order to simplify the control system, a momentum-based force observer is applied to estimate the contact force instead of employing force sensors. To deploy the learned motion-force robot manipulation skills to a broader variety of tasks, the generalization of these DMP models in actual situations is also considered. Comparative experiments have been conducted using a Baxter robot to verify the effectiveness of the proposed learning framework on real-world scenarios like cleaning a table. © 2016 IEEE.","Dynamic movement primitives (DMPs); force observer; generalization; hybrid force/motion control; robot learning; skill transfer","Behavioral research; Cell proliferation; Educational robots; Industrial manipulators; Machine design; Man machine systems; Manipulators; Robot applications; Robot programming; Transfer learning; Comparative experiments; Computer programming skills; Conventional methods; Human robot Interaction (HRI); Human-centered designs; Motion trajectories; Positional information; Robotics applications; Human robot interaction",,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/P030033/1; National Natural Science Foundation of China, NSFC: 61803039","Manuscript received July 31, 2019; revised November 13, 2019; accepted November 27, 2019. Date of publication January 21, 2020; date of current version March 11, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61803039 and in part by the U.K. EPSRC under Grant EP/P030033/1 (NUMBERS). (Ning Wang and Chuize Chen contributed equally to this work.) (Corresponding author: Ning Wang.) Ning Wang is with the Bristol Robotics Laboratory, University of the West of England, Bristol BS16 1QY, U.K. (e-mail: katie.wang@brl.ac.uk).",,,,,,,,,,"Mataríc, M.J., Socially assistive robotics: Human augmentation versus automation (2017) Sci. Robot, 2 (4). , Mar, Art. no. eaam5410; Wang, N., Nuovo, A.D., Cangelosi, A., Jones, R., Temporal patterns in multi-modal social interaction between elderly users and service robot (2019) Interact. Stud, 20 (1), pp. 4-24; Di Nuovo, A., Conti, D., Trubia, G., Buono, S., Di Nuovo, S., Deep learning systems for estimating visual attention in robot-Assisted therapy of children with autism and intellectual disability (2018) Robotics, 7 (2), p. 25; Liang, P., Yang, C., Wang, N., Li, Z., Li, R., Burdet, E., Implementation and test of human-operated and human-like adaptive impedance controls on Baxter robot (2014) Proc. Adv. Auton. Robot. Syst. (TAROS, pp. 109-119; Li, Y., Tee, K.P., Yan, R., Chan, W.L., Wu, Y., Limbu, D.K., Adaptive optimal control for coordination in physical human-robot interaction (2015) Proc IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS, pp. 20-25; Yang, C., Zeng, C., Cong, Y., Wang, N., Wang, M., A learning framework of adaptive manipulative skills from human to robot (2019) IEEE Trans. Ind. Informat, 15 (2), pp. 1153-1161. , Feb; Kirchner, E.A., de Gea Fernandez, J., Kampmann, P., Schröer, M., Metzen, J.H., Kirchner, F., Intuitive interaction with robots-Technical approaches and challenges (2015) Proc. Formal Model. Verification Cyber Phys. Syst, pp. 224-248; Prewett, M.S., Johnson, R.C., Saboe, K.N., Elliott, L.R., Coovert, M.D., Managing workload in human-robot interaction: A review of empirical studies (2010) Comput. Human Behav, 26 (5), pp. 840-856. , Sep; Moniz, A., Krings, B.-J., Robots working with humans or humans working with robots? Searching for social dimensions in new human-robot interaction in industry (2016) Societies, 6 (3), p. 23. , Aug; Sciutti, A., Mara, M., Tagliasco, V., Sandini, G., Humanizing human-robot interaction: On the importance of mutual understanding (2018) IEEE Technol. Soc. Mag, 37 (1), pp. 22-29. , Mar; Argall, B.D., Chernova, S., Veloso, M., Browning, B., A survey of robot learning from demonstration (2009) Robot. Auton. 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Res, 32 (3), pp. 263-279; Pastor, P., Hoffmann, H., Asfour, T., Schaal, S., Learning and generalization of motor skills by learning from demonstration (2009) Proc IEEE Int. Conf. Robot. Autom, pp. 763-768; Yang, C., Zeng, C., Fang, C., He, W., Li, Z., A DMPS-based framework for robot learning and generalization of humanlike variable impedance skills (2018) IEEE/ASME Trans. Mechatronics, 23 (3), pp. 1193-1203. , Jun; Haddadin, S., De Luca, A., Albu-Schäffer, A., Robot collisions: A survey on detection, isolation, and identification (2017) IEEE Trans. Robot, 33 (6), pp. 1292-1312. , Dec; Pook, P.K., Ballard, D.H., Recognizing teleoperated manipulations (1993) Proc IEEE Int. Conf. Robot. Autom, pp. 578-585; Sweeney, J.D., Grupen, R.A., A model of shared grasp affordances from demonstration (2007) Proc. 7th IEEE-RAS Int. Conf. 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Humanoid Robots, pp. 560-565; Kalakrishnan, M., Righetti, L., Pastor, P., Schaal, S., Learning force control policies for compliant manipulation (2011) Proc IEEE/RSJ Int. Conf. Intell. Robots Syst, pp. 4639-4644; De Luca, A., Schröder, D., Thummel, M., An acceleration-based state observer for robot manipulators with elastic joints (2007) Proc IEEE Int. Conf. Robot. Autom, pp. 3817-3823; Haddadin, S., (2013) Towards Safe Robots: Approaching Asimov?s 1st Law. Heidelberg, Germany, , Springer; De Luca, A., Mattone, R., Sensorless robot collision detection and hybrid force/motion control (2005) Proc IEEE Int. Conf. Robot. Autom, pp. 999-1004; De Luca, A., Albu-Schaffer, A., Haddadin, S., Hirzinger, G., Collision detection and safe reaction with the DLR-III lightweight manipulator arm (2006) Proc IEEE/RSJ Int. Conf. Intell. Robots Syst, pp. 1623-1630; Lewis, F.L., Xie, L., Popa, D., (2017) Optimal and Robust Estimation: With An Introduction to Stochastic Control Theory. Boca Raton, FL, USA, , CRC Press","Wang, N.; Bristol Robotics Laboratory, United Kingdom; email: katie.wang@brl.ac.uk",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23798920,,,,"English","IEEE Trans. Cogn. Dev. Syst.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85102768405
"Aliev K., Antonelli D.","57195604043;21833419400;","Proposal of a monitoring system for collaborative robots to predict outages and to assess reliability factors exploiting machine learning",2021,"Applied Sciences (Switzerland)","11","4","1621","1","20",,2,"10.3390/app11041621","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101433633&doi=10.3390%2fapp11041621&partnerID=40&md5=2a9f2db2bba39a81c3bf6005265a8aae","Department of Management and Production Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi 24, Torino, 10138, Italy","Aliev, K., Department of Management and Production Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi 24, Torino, 10138, Italy; Antonelli, D., Department of Management and Production Engineering, Polytechnic University of Turin, Corso Duca degli Abruzzi 24, Torino, 10138, Italy","Industry standards pertaining to Human-Robot Collaboration (HRC) impose strict safety requirements to protect human operators from danger. When a robot is equipped with dangerous tools, moves at a high speed or carries heavy loads, the current safety legislation requires the continuous on-line monitoring of the robot’s speed and a suitable separation distance from human workers. The present paper proposes to make a virtue out of necessity by extending the scope of on-line monitoring to predicting failures and safe stops. This has been done by implementing a platform, based on open access tools and technologies, to monitor the parameters of a robot during the execution of collaborative tasks. An automatic machine learning (ML) tool on the edge of the network can help to perform the on-line predictions of possible outages of collaborative robots, especially as a consequence of human-robot interactions. By exploiting the on-line monitoring system, it is possible to increase the reliability of collaborative work, by eliminating any unplanned downtimes during execution of the tasks, by maximising trust in safe interactions and by increasing the robot’s lifetime. The proposed framework demonstrates a data management technique in industrial robots considered as a physical cyber-system. Using an assembly case study, the parameters of a robot have been collected and fed to an automatic ML model in order to identify the most significant reliability factors and to predict the necessity of safe stops of the robot. Moreover, the data acquired from the case study have been used to monitor the manipulator’ joints; to predict cobot autonomy and to provide predictive maintenance notifications and alerts to the end-users and vendors. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Collaborative robots; Human robot collaboration; Machine learning; On-line monitoring",,,,,,,,,,,,,,,,,"Aliev, K., Antonelli, D., Analysis of Cooperative Industrial Task Execution by Mobile and Manipulator Robots (2019) Advances in Manufacturing II, pp. 248-260. , Trojanowska, J., Ciszak, O., Machado, J.M., Pavlenko, I., Eds.; Springer International Publishing: Cham, Switzerland; Grigore, L.S., Priescu, I., Joita, D., Oncioiu, I., The Integration of Collaborative Robot Systems and Their Environmental Impacts (2020) Processes, 8, p. 494; Bortolini, M., Ferrari, E., Gamberi, M., Pilati, F., Faccio, M., Assembly system design in the Industry 4.0 era: A general framework (2017) IFAC-PapersOnLine, 50, pp. 5700-5705; Michalos, G., Spiliotopoulos, J., Makris, S., Chryssolouris, G., A method for planning human robot shared tasks (2018) CIRP J. Manuf. Sci. 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Mach. Learn. Res, 18, pp. 826-830","Antonelli, D.; Department of Management and Production Engineering, Corso Duca degli Abruzzi 24, Italy; email: dario.antonelli@polito.it",,,"MDPI AG",,,,,20763417,,,,"English","Appl. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85101433633
"Akalin N., Loutfi A.","55953644800;7004383993;","Reinforcement learning approaches in social robotics",2021,"Sensors (Switzerland)","21","4","1292","1","37",,5,"10.3390/s21041292","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100651693&doi=10.3390%2fs21041292&partnerID=40&md5=e785841cb1a1d5b92ed67fdaa4d548fc","School of Science and Technology, Örebro University, Örebro, SE-701 82, Sweden","Akalin, N., School of Science and Technology, Örebro University, Örebro, SE-701 82, Sweden; Loutfi, A., School of Science and Technology, Örebro University, Örebro, SE-701 82, Sweden","This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-anderror with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Human-robot interaction; Physical embodiment; Reinforcement learning; Reward design; Social robotics","Decision making; Learning systems; Robotics; Social robots; Surveys; Algorithmic measures; Communication capabilities; Communication medium; Decision-making problem; Evaluation methods; Interactive Reinforcement Learning; Reinforcement learning approach; Reinforcement learning method; Reinforcement learning; human; interpersonal communication; learning; robotics; Communication; Humans; Learning; Robotics",,,,,"Horizon 2020 Framework Programme, H2020: 721619","This research was funded by European Union?s Horizon 2020 research and innovation program under the Marie Sk?odowska-Curie grant agreement No 721619 for the SOCRATES project.",,,,,,,,,,"Keizer, S., Ellen Foster, M., Wang, Z., Lemon, O., Machine Learning for Social Multiparty Human–Robot Interaction (2014) ACM Trans. Interact. Intell. 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Res, 48, pp. 67-113; Zhang, X., Yao, L., Huang, C., Sheng, Q.Z., Wang, X., Intent Recognition in Smart Living through Deep Recurrent Neural Networks Proceedings of the International Conference on Neural Information Processing, pp. 748-758. , Guangzhou, China, 14–18 November 2017; Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., Davidson, J., Learning Latent Dynamics for Planning from Pixels Proceedings of the 36th International Conference on Machine Learning (ICML 2019), pp. 2555-2565. , Long Beach, CA, USA, 9–15 June 2019; Kostavelis, I., Giakoumis, D., Malassiotis, S., Tzovaras, D., A POMDP Design Framework for Decision Making in Assistive Robots Proceedings of the International Conference on Human-Computer Interaction (HCI 2017), pp. 467-479. , Vancouver, BC, Canada, 9–14 July 2017; Hausknecht, M., Stone, P., Deep Recurrent Q-Learning for Partially Observable MDPs (2015) Proceedings of the 2015 AAAI Fall Symposium on Sequential Decision Making for Intelligent Agents (AAAI-SDMIA15), , Arlington, VA, USA, 12–14 November","Akalin, N.; School of Science and Technology, Sweden; email: neziha.akalin@oru.se",,,"MDPI AG",,,,,14248220,,,"33670257","English","Sensors",Review,"Final","All Open Access, Gold",Scopus,2-s2.0-85100651693
[No author name available],[No author id available],"14th International Conference on Intelligent Robotics and Applications, ICIRA 2021",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13014 LNAI",,,"","",3266,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118219718&partnerID=40&md5=e94e6e639b65db5132ffe8e0dee306ee",,"","The proceedings contain 299 papers. The special focus in this conference is on Intelligent Robotics and Applications. The topics include: Camera Pose Estimation Based on Feature Extraction and Description for Robotic Gastrointestinal Endoscopy; a Nature-Inspired Algorithm to Adaptively Safe Navigation of a Covid-19 Disinfection Robot; a Master-Slave Robotic System for Transurethral Surgery; analysis of Dynamic Friction and Elongation Characteristics of the Tendon Sheath System; a Tactile Sensor with Contact Angle Compensation for Robotic Palpation of Tissue Hardness; a Robotic Endoscopic Injection Needle with Integrated Tactile Sensor for Intraoperative Autonomous Tumor Localization; vision-Based Pointing Estimation and Evaluation in Toddlers for Autism Screening; multi-Person Absolute 3D Pose and Shape Estimation from Video; effects of Wrist Configuration and Finger Combination on Translational Range of Bimanual Precision Manipulation; research and Analysis on Energy Consumption of Underwater Hexapod Robot Based on Typical Gait; Robustness of Combined sEMG and Ultrasound Modalities Against Muscle Fatigue in Force Estimation; an Abnormal Behavior Recognition Method Based on Fusion Features; a Dynamic Head Gesture Recognition Method for Real-Time Human-Computer Interaction; research on Passive Energy-Regulated Bionic Shell for Lateral Fall Recovery Behavior of Large Quadruped Robots; research on Adaptive Control in Complex Terrain for Quadruped Robot; an Intuitive Interaction System Using Laser Spot for Daily Household Tasks Operation Autonomous; design of a Novel Wheelchair-Exoskeleton Robot for Human Multi-mobility Assist; gaze Based Implicit Intention Inference with Historical Information of Visual Attention for Human-Robot Interaction; online Object-Oriented Semantic Mapping in Triger Classification Environment; Bearing Fault Diagnosis Based on Attentional Multi-scale CNN; MF-SLAM: Multi-focal SLAM.",,,,,,,,,,,,,,,,,,,,"Liu X.Nie Z.Yu J.Xie F.Song R.",,"Springer Science and Business Media Deutschland GmbH","14th International Conference on Intelligent Robotics and Applications, ICIRA 2021","22 October 2021 through 25 October 2021",,267169,03029743,9783030890971,,,"English","Lect. Notes Comput. Sci.",Conference Review,"Final","",Scopus,2-s2.0-85118219718
[No author name available],[No author id available],"14th International Conference on Intelligent Robotics and Applications, ICIRA 2021",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13013 LNAI",,,"","",3266,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118217502&partnerID=40&md5=4429aa7fe226e14cab14f81f2f6e278b",,"","The proceedings contain 299 papers. The special focus in this conference is on Intelligent Robotics and Applications. The topics include: Camera Pose Estimation Based on Feature Extraction and Description for Robotic Gastrointestinal Endoscopy; a Nature-Inspired Algorithm to Adaptively Safe Navigation of a Covid-19 Disinfection Robot; a Master-Slave Robotic System for Transurethral Surgery; analysis of Dynamic Friction and Elongation Characteristics of the Tendon Sheath System; a Tactile Sensor with Contact Angle Compensation for Robotic Palpation of Tissue Hardness; a Robotic Endoscopic Injection Needle with Integrated Tactile Sensor for Intraoperative Autonomous Tumor Localization; vision-Based Pointing Estimation and Evaluation in Toddlers for Autism Screening; multi-Person Absolute 3D Pose and Shape Estimation from Video; effects of Wrist Configuration and Finger Combination on Translational Range of Bimanual Precision Manipulation; research and Analysis on Energy Consumption of Underwater Hexapod Robot Based on Typical Gait; Robustness of Combined sEMG and Ultrasound Modalities Against Muscle Fatigue in Force Estimation; an Abnormal Behavior Recognition Method Based on Fusion Features; a Dynamic Head Gesture Recognition Method for Real-Time Human-Computer Interaction; research on Passive Energy-Regulated Bionic Shell for Lateral Fall Recovery Behavior of Large Quadruped Robots; research on Adaptive Control in Complex Terrain for Quadruped Robot; an Intuitive Interaction System Using Laser Spot for Daily Household Tasks Operation Autonomous; design of a Novel Wheelchair-Exoskeleton Robot for Human Multi-mobility Assist; gaze Based Implicit Intention Inference with Historical Information of Visual Attention for Human-Robot Interaction; online Object-Oriented Semantic Mapping in Triger Classification Environment; Bearing Fault Diagnosis Based on Attentional Multi-scale CNN; MF-SLAM: Multi-focal SLAM.",,,,,,,,,,,,,,,,,,,,"Liu X.Nie Z.Yu J.Xie F.Song R.",,"Springer Science and Business Media Deutschland GmbH","14th International Conference on Intelligent Robotics and Applications, ICIRA 2021","22 October 2021 through 25 October 2021",,267169,03029743,9783030890940,,,"English","Lect. Notes Comput. Sci.",Conference Review,"Final","",Scopus,2-s2.0-85118217502
[No author name available],[No author id available],"14th International Conference on Intelligent Robotics and Applications, ICIRA 2021",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13015 LNAI",,,"","",3266,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118153385&partnerID=40&md5=9854be13632da9c93394e1008e354ee4",,"","The proceedings contain 299 papers. The special focus in this conference is on Intelligent Robotics and Applications. The topics include: Camera Pose Estimation Based on Feature Extraction and Description for Robotic Gastrointestinal Endoscopy; a Nature-Inspired Algorithm to Adaptively Safe Navigation of a Covid-19 Disinfection Robot; a Master-Slave Robotic System for Transurethral Surgery; analysis of Dynamic Friction and Elongation Characteristics of the Tendon Sheath System; a Tactile Sensor with Contact Angle Compensation for Robotic Palpation of Tissue Hardness; a Robotic Endoscopic Injection Needle with Integrated Tactile Sensor for Intraoperative Autonomous Tumor Localization; vision-Based Pointing Estimation and Evaluation in Toddlers for Autism Screening; multi-Person Absolute 3D Pose and Shape Estimation from Video; effects of Wrist Configuration and Finger Combination on Translational Range of Bimanual Precision Manipulation; research and Analysis on Energy Consumption of Underwater Hexapod Robot Based on Typical Gait; Robustness of Combined sEMG and Ultrasound Modalities Against Muscle Fatigue in Force Estimation; an Abnormal Behavior Recognition Method Based on Fusion Features; a Dynamic Head Gesture Recognition Method for Real-Time Human-Computer Interaction; research on Passive Energy-Regulated Bionic Shell for Lateral Fall Recovery Behavior of Large Quadruped Robots; research on Adaptive Control in Complex Terrain for Quadruped Robot; an Intuitive Interaction System Using Laser Spot for Daily Household Tasks Operation Autonomous; design of a Novel Wheelchair-Exoskeleton Robot for Human Multi-mobility Assist; gaze Based Implicit Intention Inference with Historical Information of Visual Attention for Human-Robot Interaction; online Object-Oriented Semantic Mapping in Triger Classification Environment; Bearing Fault Diagnosis Based on Attentional Multi-scale CNN; MF-SLAM: Multi-focal SLAM.",,,,,,,,,,,,,,,,,,,,"Liu X.Nie Z.Yu J.Xie F.Song R.",,"Springer Science and Business Media Deutschland GmbH","14th International Conference on Intelligent Robotics and Applications, ICIRA 2021","22 October 2021 through 25 October 2021",,267169,03029743,9783030891336,,,"English","Lect. Notes Comput. Sci.",Conference Review,"Final","",Scopus,2-s2.0-85118153385
[No author name available],[No author id available],"14th International Conference on Intelligent Robotics and Applications, ICIRA 2021",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","13016 LNAI",,,"","",3266,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118146044&partnerID=40&md5=9223f5bbb900dcb69bfa66068584d16b",,"","The proceedings contain 299 papers. The special focus in this conference is on Intelligent Robotics and Applications. The topics include: Camera Pose Estimation Based on Feature Extraction and Description for Robotic Gastrointestinal Endoscopy; a Nature-Inspired Algorithm to Adaptively Safe Navigation of a Covid-19 Disinfection Robot; a Master-Slave Robotic System for Transurethral Surgery; analysis of Dynamic Friction and Elongation Characteristics of the Tendon Sheath System; a Tactile Sensor with Contact Angle Compensation for Robotic Palpation of Tissue Hardness; a Robotic Endoscopic Injection Needle with Integrated Tactile Sensor for Intraoperative Autonomous Tumor Localization; vision-Based Pointing Estimation and Evaluation in Toddlers for Autism Screening; multi-Person Absolute 3D Pose and Shape Estimation from Video; effects of Wrist Configuration and Finger Combination on Translational Range of Bimanual Precision Manipulation; research and Analysis on Energy Consumption of Underwater Hexapod Robot Based on Typical Gait; Robustness of Combined sEMG and Ultrasound Modalities Against Muscle Fatigue in Force Estimation; an Abnormal Behavior Recognition Method Based on Fusion Features; a Dynamic Head Gesture Recognition Method for Real-Time Human-Computer Interaction; research on Passive Energy-Regulated Bionic Shell for Lateral Fall Recovery Behavior of Large Quadruped Robots; research on Adaptive Control in Complex Terrain for Quadruped Robot; an Intuitive Interaction System Using Laser Spot for Daily Household Tasks Operation Autonomous; design of a Novel Wheelchair-Exoskeleton Robot for Human Multi-mobility Assist; gaze Based Implicit Intention Inference with Historical Information of Visual Attention for Human-Robot Interaction; online Object-Oriented Semantic Mapping in Triger Classification Environment; Bearing Fault Diagnosis Based on Attentional Multi-scale CNN; MF-SLAM: Multi-focal SLAM.",,,,,,,,,,,,,,,,,,,,"Liu X.Nie Z.Yu J.Xie F.Song R.",,"Springer Science and Business Media Deutschland GmbH","14th International Conference on Intelligent Robotics and Applications, ICIRA 2021","22 October 2021 through 25 October 2021",,267169,03029743,9783030890919,,,"English","Lect. Notes Comput. Sci.",Conference Review,"Final","",Scopus,2-s2.0-85118146044
"Koay K.L., Webster M., Dixon C., Gainer P., Syrdal D., Fisher M., Dautenhahn K.","9732723100;15069696700;57225816972;57006604200;22986616600;55741961100;7003305185;","Use and usability of software verification methods to detect behaviour interference when teaching an assistive home companion robot: A proof-of-concept study",2021,"Paladyn","12","1",,"402","422",,,"10.1515/pjbr-2021-0028","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115968990&doi=10.1515%2fpjbr-2021-0028&partnerID=40&md5=2730d25ca3e0e502d3577d5af92a71e5","Department of Computer Science, School of Physics, Engineering and Computer Science, University of Hertfordshire, College Lane, Hertfordshire, Hatfield, AL10 9AB, United Kingdom; School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, L2 2QP, United Kingdom; Department of Computer Science, University of Manchester, Manchester, M13 9PL, United Kingdom; Department of Computer Science, University of Liverpool, Liverpool, United Kingdom; Department of Computer Science, School of Physics, Engineering and Computer Science, University of Hertfordshire, College Lane, Hertfordshire, Hatfield, AL10 9AB, United Kingdom; Departments of Electrical and Computer Engineering and Systems Design Engineering, University of WaterlooON, Canada","Koay, K.L., Department of Computer Science, School of Physics, Engineering and Computer Science, University of Hertfordshire, College Lane, Hertfordshire, Hatfield, AL10 9AB, United Kingdom; Webster, M., School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, L2 2QP, United Kingdom; Dixon, C., Department of Computer Science, University of Manchester, Manchester, M13 9PL, United Kingdom; Gainer, P., Department of Computer Science, University of Liverpool, Liverpool, United Kingdom; Syrdal, D., Department of Computer Science, School of Physics, Engineering and Computer Science, University of Hertfordshire, College Lane, Hertfordshire, Hatfield, AL10 9AB, United Kingdom; Fisher, M., Department of Computer Science, University of Manchester, Manchester, M13 9PL, United Kingdom; Dautenhahn, K., Departments of Electrical and Computer Engineering and Systems Design Engineering, University of WaterlooON, Canada","When studying the use of assistive robots in home environments, and especially how such robots can be personalised to meet the needs of the resident, key concerns are issues related to behaviour verification, behaviour interference and safety. Here, personalisation refers to the teaching of new robot behaviours by both technical and non-technical end users. In this article, we consider the issue of behaviour interference caused by situations where newly taught robot behaviours may affect or be affected by existing behaviours and thus, those behaviours will not or might not ever be executed. We focus in particular on how such situations can be detected and presented to the user. We describe the human-robot behaviour teaching system that we developed as well as the formal behaviour checking methods used. The online use of behaviour checking is demonstrated, based on static analysis of behaviours during the operation of the robot, and evaluated in a user study. We conducted a proof-of-concept human-robot interaction study with an autonomous, multi-purpose robot operating within a smart home environment. Twenty participants individually taught the robot behaviours according to instructions they were given, some of which caused interference with other behaviours. A mechanism for detecting behaviour interference provided feedback to participants and suggestions on how to resolve those conflicts. We assessed the participants' views on detected interference as reported by the behaviour teaching system. Results indicate that interference warnings given to participants during teaching provoked an understanding of the issue. We did not find a significant influence of participants' technical background. These results highlight a promising path towards verification and validation of assistive home companion robots that allow end-user personalisation. © 2021 Kheng Lee Koay et al., published by De Gruyter.","behaviour interference; companion robots; formal verification; human-robot interaction",,,,,,,"Funding information : This work was supported by the EPSRC-funded project “Trustworthy Robotic Assistants” (refs. EP/K006509/1 and EP/K006193/1), the EPSRC FAIR-SPACE (EP/R026092/1), RAIN (EP/R026084/1) and ORCA (EP/R026173/1) RAI Hubs, the Royal Academy of Engineering and the Canada 150 Research Chairs Program.",,,,,,,,,,"(2018) Emotech, ""olly-the First Home Robot with Personality,"", , https://www.indiegogo.com/projects/olly-the-first-home-robot-with-personality#/, [Accessed: July 7, 2021]; (2021) IRobot, ""roomba®e5 Robot Vacuum,"", , https://shop.irobot.co.uk, [Accessed: July 7, 2021]; (2021) SoftBank Robotics, ""pepper,"", , https://www.softbankrobotics.com/emea/en/pepper, [Accessed: July 7, 2021]; (2018) Mayfield Robotics, ""kuri,"", , https://www.heykuri.com/explore-kuri/, [Accessed: July 7, 2021]; Dautenhahn, K., ""socially intelligent robots: Dimensions of human-robot interaction,"" (2007) Philos. Trans. R Soc. B Biol. 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Comput., 39, pp. 78-92. , pp","Koay, K.L.; Department of Computer Science, College Lane, Hertfordshire, United Kingdom; email: K.L.Koay@herts.ac.uk",,,"De Gruyter Open Ltd",,,,,20814836,,,,"English","Paladyn",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85115968990
"Feng Y., Fan J.C., Tao B.X., Wang S.G., Mo J.Q., Wu Y.Q., Liang Q.H., Chen X.J.","57171189400;57212006683;57218294466;55849805700;57216173278;35235719200;57206922740;55739098400;","An image-guided hybrid robot system for dental implant surgery",2021,"International Journal of Computer Assisted Radiology and Surgery",,,,"","",,1,"10.1007/s11548-021-02484-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113610984&doi=10.1007%2fs11548-021-02484-0&partnerID=40&md5=76685e1bb949fb90476623746dd32e2f","School of Mechanical Engineering, Shanghai Jiao Tong University, Room 901, Dongchuan Road 800, Minhang District, Shanghai, 200240, China; Department of Second Dental Center, Shanghai Ninth People’s Hospital, College of Stomatology, Shanghai Jiao Tong University, School of Medicine, National Clinical Research Center for Oral Disease, Shanghai Key Laboratory of Stomatology and Shanghai Research Institute of Stomatology, 639, Zhizaoju Road, Shanghai, 200011, China","Feng, Y., School of Mechanical Engineering, Shanghai Jiao Tong University, Room 901, Dongchuan Road 800, Minhang District, Shanghai, 200240, China; Fan, J.C., School of Mechanical Engineering, Shanghai Jiao Tong University, Room 901, Dongchuan Road 800, Minhang District, Shanghai, 200240, China; Tao, B.X., Department of Second Dental Center, Shanghai Ninth People’s Hospital, College of Stomatology, Shanghai Jiao Tong University, School of Medicine, National Clinical Research Center for Oral Disease, Shanghai Key Laboratory of Stomatology and Shanghai Research Institute of Stomatology, 639, Zhizaoju Road, Shanghai, 200011, China; Wang, S.G., School of Mechanical Engineering, Shanghai Jiao Tong University, Room 901, Dongchuan Road 800, Minhang District, Shanghai, 200240, China; Mo, J.Q., School of Mechanical Engineering, Shanghai Jiao Tong University, Room 901, Dongchuan Road 800, Minhang District, Shanghai, 200240, China; Wu, Y.Q., Department of Second Dental Center, Shanghai Ninth People’s Hospital, College of Stomatology, Shanghai Jiao Tong University, School of Medicine, National Clinical Research Center for Oral Disease, Shanghai Key Laboratory of Stomatology and Shanghai Research Institute of Stomatology, 639, Zhizaoju Road, Shanghai, 200011, China; Liang, Q.H., School of Mechanical Engineering, Shanghai Jiao Tong University, Room 901, Dongchuan Road 800, Minhang District, Shanghai, 200240, China; Chen, X.J., School of Mechanical Engineering, Shanghai Jiao Tong University, Room 901, Dongchuan Road 800, Minhang District, Shanghai, 200240, China","Purpose: Dental implant surgery is an effective method for remediating the loss of teeth. Robot is expected to increase the accuracy of dental implant surgery. However, most of them are industrial serial robot, with low stiffness and non-unique inverse kinematic solution, which may reduce the success rate and safety of robotic surgery. Compared to serial robot, parallel robot is more stiffness and has unique inverse kinematic. However, its workspace is small, which may not meet surgical requirements. Therefore, a novel hybrid robot dedicated to dental implant is proposed. Methods: The hybrid robot is composed of three translation joints, two revolute joints, and Stewart parallel manipulator. Stewart is used for performing surgical operation, while the joints are used for enlarging the workspace of Stewart. In order to ensure the safety of robot motion, physical human–robot interaction based on a variable admittance controller is applied in the robotic system. In addition, considering the small workspace of Stewart, an optimal model is proposed to minimize the joint movement of Stewart in adjusting the orientation of drill bit. Results: Phantom experiments were carried out based on the prototype robot. In the experiments, the optimal model could be solved after 20 iterations, finding an ideal joint configuration. The proposed variable admittance controller could enhance comfort level effectively. The accuracy of robot is evaluated by angle, entry and exit deviation, which are 0.74 ± 0.25°, 0.93 ± 0.28 mm, and 0.96 ± 0.23 mm, respectively. Conclusion: The phantom experiments validate the functionality of the proposed hybrid robot. The satisfactory performance makes it more widely used in the practical dental implant surgery in the future. © 2021, CARS.","Dental implant surgery; Hybrid robot system; Physical human–robot interaction; Stewart manipulator",,,,,,"National Key Research and Development Program of China, NKRDPC: 2017YFB1302901","This work was supported by Grants from National Key R&D Program of China (2017YFB1302901).",,,,,,,,,,"Keren, S.Y., Daniel, R., An overview of the mechanical integrity of dental implants (2015) Biomed Res Int, 2015, pp. 1-11; Wu, Y., Wang, F., Fan, S., Chow, K.F., Robotics in dental implantology (2019) Oral Maxillofac Surg Clin North Am, 31 (3), pp. 513-518; Jiang, J., Zhang, Y., Application of robot to tooth-arrangement and dental implantology (2012) Robot, 34 (5), pp. 634-640; Sun, X., McKenzie, F.D., Bawab, S., Li, J., Yoon, Y., Huang, J.K., Automated dental implantation using image-guided robotics: registration results (2011) Int J Comput Assist Radiol Surg, 6, pp. 627-634; Sun, X., Yoon, Y., Li, J., McKenzie, F.D., Automated image-guided surgery for common and complex dental implants (2014) Journal of Medical Engineering & Technology, 38 (5), pp. 251-259. , (,),., (,):., https://doi.org/10.3109/03091902.2014.913079; Cao, Z., Qin, C., Fan, S., Yu, D., Wu, Y., Qin, J., Chen, X., Pilot stud-y of a surgical robot system for zygomatic implant placement (2020) Med Eng Phys, 75, pp. 72-78; Li, J., Shen, Z., Xu, W.Y.T., Lam, W.Y.H., Hsung, R.T.C., Pow, E.H.N., Kosuge, K., Wang, Z., A compact dental robotic system using soft bracing technique (2019) IEEE Robot Automation Lett, 4 (2), pp. 1271-1278; Gwangho, K., Hojin, S., Sungbeen, I., Dongwan, K., Sanghwa, J., A study on simulator of human-robot cooperative manipulator for dental implant surgery (2009) IEEE Int Symp Ind Electron, 2009, pp. 2159-2164; Rawal, S., Tillery, D.E., Jr., Brewer, P., Robotic-assisted prosthetically driven planning and immediate placement of a dental implant (2020) Compend Contin Educ Dent, 41 (1), pp. 26-31. , PID: 31895579, PMID: 31895579; Nakano, T., Sugita, N., Ueta, T., Tamaki, Y., Mitsuishi, M., A parallel robot to assist vitreoretinal surgery (2009) Int J Comput Assist Radiol Surg, 4 (6), pp. 517-526; Kuang, S., Leung, K.S., Wang, T., Hu, L., Chui, E., Liu, W.Y., Wang, Y., A novel passive/active hybrid robot for orthopaedic trauma surgery (2012) Int J Med Robot Comput Assist Surg, 8 (4), pp. 458-467; Shivesh, K., Hendrik, W., de Gea, J., Fernández, A.M., Frank, K., A survey on modularity and distributivity in series-parallel hybrid robots (2020) Mechatronics; Peng, Y., Yu, H., Du, Z., Design and kinematic analysis of a hybrid manipulator for spine surgery (2016) 2016 IEEE International Conference on Mechatronics and Automation, pp. 884-889. , https://doi.org/10.1109/ICMA.2016.7558679; Sharkawy, A.N., Koustoumpardis, P., Aspragathos, N.A., A neural network-based approach for variable admittance control in human–robot cooperation: online adjustment of the virtual inertia (2020) Intel Serv Robot, 13, pp. 495-519; Du, Z., Wang, W., Yan, Z., Dong, W., Wang, W., Variable admittance control based on fuzzy reinforcement learning for minimally invasive surgery manipulator (2017) Sensors, 17 (4), p. 844; Kang, G., Oh, H.S., Seo, J.K., Kim, U., Choi, H.R., Variable admittance control of robot manipulators based on human intention (2019) IEEE/ASME Trans Mechatron, 24 (3), pp. 1023-1032; Chen, X., Yuan, J., Wang, C., Huang, Y., Kang, L., Modular preoperative planning software for computer-aided oral implantology and the application of a novel stereolithographic template: a pilot study (2010) Clin Implant Dentistry Rel Res, 12 (3), pp. 181-193; Chen, X., Ye, M., Lin, Y., Wu, Y., Wang, C., Image guided oral implantology and its application in the placement of zygoma implants (2009) Comput Methods Programs Biomed, 93 (2), pp. 162-173; Chen, X., Wu, Y., Wang, C., Application of a surgical navigation system in the rehabilitation of maxillary defects using zygoma implants: report of one case (2011) Int J Oral Maxillofac Implants, 26 (5), pp. e29-e34. , PID: 22010098, PMID: 22010098; Qin, C., Cao, Z., Fan, S., Wu, Y., Sun, Y., Constantinus, P., Wang, C., Chen, X., An oral and maxillofacial navigation system for implant placement with automatic identification of fiducial points (2019) Int J CARS, 14, pp. 281-289; Li, S., Du, Z., Yu, H., A robot-assisted spine surgery system based on intraoperative 2D fluoroscopy navigation (2020) IEEE Access; Hansen, N., (2016) The CMA Evolution Strategy: A Tutorial, , . arXiv preprint arXiv: 604.00772; Lopez-Franco, C., Hernandez-Barragan, J., Alanis, A.Y., Arana-Daniel, N., A soft computing approach for inverse kinematics of robot manipulators (2018) Eng Appl Artif Intell, 74, pp. 104-120; Frösch, L., Mukaddam, K., Filippi, A., Zitzmann, N.U., Kühl, S., Comparison of heat generation between guided and conventional implant surgery for single and sequential drilling protocols—an in vitro study (2019) Clin Oral Implants Res, 30 (2), pp. 121-130; Li, Y., Hu, J., Tao, B., Yu, D., Shen, Y., Fan, S., Wu, Y., Chen, X., Automatic robot-world calibration in an optical-navigated surgical robot system and its application for oral implant placement (2020) Int J CARS, 15, pp. 1685-1692; Zhou, W., Fan, S., Wang, F., Huang, W., Jamjoom, F.Z., Wu, Y., A novel extraoral registration method for a dynamic navigation system guiding zygomatic implant placement in patients with maxillectomy defects (2020) Int J Oral Maxillofac Surg, 50 (1), pp. 116-120","Liang, Q.H.; School of Mechanical Engineering, Room 901, Dongchuan Road 800, Minhang District, China; email: qhliang@sjtu.edu.cn
Chen, X.J.; School of Mechanical Engineering, Room 901, Dongchuan Road 800, Minhang District, China; email: xiaojunchen@sjtu.edu.cn",,,"Springer Science and Business Media Deutschland GmbH",,,,,18616410,,,,"English","Int. J. Comput. Assisted Radiol. Surg.",Article,"Article in Press","",Scopus,2-s2.0-85113610984
"Obayashi K., Kodate N., Masuyama S.","57214471068;36174137100;16735658400;","Assessing the Impact of an Original Soft Communicative Robot in a Nursing Home in Japan: Will Softness or Conversations Bring more Smiles to Older People?",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00815-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112084042&doi=10.1007%2fs12369-021-00815-4&partnerID=40&md5=1590b19ce3593ecce1f3ecbbbbaf3738","Faculty of Healthcare Management, Nihon Fukushi University, Mihama, Japan; Social Welfare Corporation Tokyo Seishin-kai, Nishitokyo, Japan; Universal Accessibility and Ageing Research Centre, Nishitokyo, Japan; School of Social Policy, Social Work and Social Justice, University College Dublin, Hanna Sheehy Skeffington Building, Belfield, Dublin 4, D04 N9Y1, Ireland; Institute for Future Initiatives, University of Tokyo, Tokyo, Japan; Public Policy Research Center, Hokkaido University, Sapporo, Japan; La Fondation France-Japon, L’ École des hautes études en sciences sociales, Paris, France; Traveler’s Medical Center, Tokyo Medical University, Tokyo, Japan","Obayashi, K., Faculty of Healthcare Management, Nihon Fukushi University, Mihama, Japan, Social Welfare Corporation Tokyo Seishin-kai, Nishitokyo, Japan, Universal Accessibility and Ageing Research Centre, Nishitokyo, Japan; Kodate, N., Universal Accessibility and Ageing Research Centre, Nishitokyo, Japan, School of Social Policy, Social Work and Social Justice, University College Dublin, Hanna Sheehy Skeffington Building, Belfield, Dublin 4, D04 N9Y1, Ireland, Institute for Future Initiatives, University of Tokyo, Tokyo, Japan, Public Policy Research Center, Hokkaido University, Sapporo, Japan, La Fondation France-Japon, L’ École des hautes études en sciences sociales, Paris, France; Masuyama, S., Universal Accessibility and Ageing Research Centre, Nishitokyo, Japan, Traveler’s Medical Center, Tokyo Medical University, Tokyo, Japan","It has been reported that robotics-aided care can contribute to enhancing older people’s social participation and quality of life in nursing homes, while simultaneously reducing the burden on care professionals at nighttime. Due to increasing demand for social care and the relative workforce shortage, it is likely that a greater number and variety of robots will be introduced and implemented in the future. While the benefits of applying robots and assistive technologies are recognized, the current limitations and weaknesses have also been identified. One of these is the difficulty associated with a user-centered design, involving older adults with impaired cognitive and sensory abilities in nursing homes. In order to overcome this challenge, a project was carried out to develop a soft and compact bedside communication robot with an input/output device, connected to existing technologies (e.g. monitoring camera, biological sensor). Drawing on the principle of gemba (deference to frontline professionals’ experience, expertise and skills), users’ feedback was reflected in the iterative steps of robot development. The original soft and communicative robot was introduced and its effectiveness was tested by measuring older people’s reactions and changes in their behaviors and engagement levels. The article reports the development process and results of a small-scale evaluation study, comparing the impact of this original soft-type robot with and without its communicative functions. The human–robot interactions were captured on video, and the analysis revealed that while communicative robots reduced the psychosocial burden on older adults, positive emotional, verbal, visual and behavioral engagement was generated with the help of the non-verbal plush toy. © 2021, The Author(s).","Communication; Eldercare; Human–robot interaction; Socially assistive robot; Technology assessment; User-centered design","Agricultural robots; Hospitals; Nursing; Social robots; Assistive technology; Communication robot; Communicative functions; Communicative robots; Development process; Input/output devices; Small-scale evaluation; Social participation; Behavioral research",,,,,"Toyota Foundation: D18-ST-0005; JKA Foundation","This study was in part financially supported by The Japan Keirin Autorace Foundation and The Toyota Foundation (D18-ST-0005). The authors wish to thank all the participants, funding bodies, A.I.Viewlife Co. Ltd., NTT Data Corporation, Trendmaster Ltd. and Yukai Engineering Inc., and staff members who provided help. We would particularly like to thank Hiromasa Kondo, Yoshimi Okamoto, Yoko Ishii, Takahiro Nonoda and Mark Linne.",,,,,,,,,,"Taylor, M.E., Close, J.C.T., Dementia (2018) Handb Clin Neurol, 159, pp. 303-321; World report on ageing and health (2015) World Health Organization, , Geneva, Switzerland; Broadbent, E., Stafford, R., MacDonald, B., Acceptance of healthcare robots for the older population: review and future directions (2009) Int J of Soc Robotics, 1, pp. 319-330; Obayashi, K., Kodate, N., Masuyama, S., Socially assistive robots and their potential in enhancing older people’s activity and social participation (2018) J Am Med Dir Assoc, 19 (5), pp. 462-463. , (,),., (,):., https://doi.org/10.1093/geront/gnx180; Block, A., Kuchenbecker, K., Softness, warmth, and responsiveness improve robot hugs (2019) Int J of Soc Robotics, 11, pp. 49-64; Bradwell, H.L., Edwards, K.J., Winnington, R., Thill, S., Jones, R.B., Companion robots for older people: importance of user-centred design demonstrated through observations and focus groups comparing preferences of older people and roboticists in South West England (2019) BMJ Open, 9, pp. 303-321; (2015) White Paper on People with Disabilities, Heisei 27, Cabinet Office of Japan, , https://www8.cao.go.jp/shougai/whitepaper/h27hakusho/zenbun/h1_06_01_06.html, Accessed 30 April 2021; Koutentakis, D., Pilozzi, A., Huang, X., Designing socially assistive robots for Alzheimer’s disease and related dementia patients and their caregivers: where we are and where we are headed (2020) Healthcare, , (,),.,., https://doi.org/10.3390/healthcare8020073; Stahl, B., Coeckelbergh, M., Ethics of healthcare robotics: towards responsible research and innovation (2016) Robot Autonom Syst, 86 (12), pp. 152-161; Bourgault, A., Upvall, M., Graham, A., Using Gemba boards to facilitate evidence-based practice in critical care (2018) Crit Care Nurse, 38 (3), pp. e1-e7; Liebengood, S.M., Cooper, M., Nagy, P., Going to the Gemba: identifying opportunities for improvement in radiology (2013) J Am Coll Radiol, 10 (12), pp. 977-979; Pilotto, A., D’Onofrio, G., Benelli, E., Zanesco, A., Cabello, A., Margelí, M.C., Wanche-Politis, S., Kilias, D., Information and communication technology systems to improve quality of life and safety of Alzheimer’s disease patients: a multicenter international survey (2011) J Alzheimers Dis, 23, pp. 131-141. , (,),.,:., https://doi.org/10.3233/JAD-2010-101164; Takayanagi, K., Kirita, T., Shibata, T., Comparison of verbal and emotional responses of elderly people with mild/moderate dementia and those with severe dementia in responses to seal robot, PARO (2014) Front Aging Neurosci, 6, p. 257; Bemelmans, R., Gelderblom, G.J., Jonker, P., Witte, L.D., Effectiveness of robot PARO in intramural psychogeriatric care: a multicenter quasi-experimental study (2015) J Am Med Dir Assoc, 16, pp. 946-950; Moyle, W., Jones, C.J., Murfield Lukmanthalib, J.E., Beattie, E.R.A., Shum, D.K.H., O’Dwyer, S.T., Mervin, M.C., Draper, B.M., Use of a robotic seal as a therapeutic tool to improve dementia symptoms: A cluster-randomized controlled trial (2017) J am Med Dir Assoc, 18 (9), pp. 766-773. , https://doi.org/10.1016/j.jamda.2017.03.018; Nuovo, A.D., Broz, F., Wang, N., Belpaeme, T., Cangelosi, A., Jones, R., Esposito, R., Dario, P., The multi-modal interface of robot-era multi-robot services tailored for the elderly (2017) Int J Soc Robotics, 11, pp. 109-126; Wilson, J.R., Lee, N.Y., Saechao, A., Tickle-Degnen, L., Scheutz, M., Supporting human autonomy in a robot-assisted medication sorting task (2018) Int J Soc Robotics, 10, pp. 621-641; Pu, L., Moyle, W., Jones, C., The multi-modal interface of robot-era multi-robot services tailored for the elderly (2019) J Clin Nurs, 29, pp. 437-446; Moyle, W., Bramble, M., Jones, C.J., Murfield, J., She had a smile on her face as wide as the great Australian bite”: a qualitative examination of family perceptions of a therapeutic robot and a plush toy (2019) Gerontologist, 59 (1), pp. 177-185. , (,),., (,):., https://doi.org/10.1093/geront/gnx180; Obayashi, K., Kodate, N., Masuyama, S., Enhancing older people’s activity and participation with socially assistive robots: A multicentre quasi-experimental study using the ICF framework (2018) Adv Robot, 3 (22), pp. 1207-1216; Obayashi, K., Kodate, N., Masuyama, S., Can connected technologies improve sleep quality and safety of older adults and care-givers? An evaluation study of sleep monitors and communicative robots at a residential care home in Japan (2020) Technol Soc; Obayashi, K., Kodate, N., Masuyama, S., Measuring the impact of age, gender and dementia on communication-robot interventions in residential care homes (2020) Geriatr Gerontol Int, 24 (4), pp. 373-378; Masuyama, S., Obayashi, K., Ogata, T., Kondo, H., Okamoto, Y., Ishii, Y., Measuring impacts of introducing communicative robots with infrared radiation monitoring system on workload of night shifts in nursing care facilities (2017) Age Ageing, 46, pp. iii13-iii59; Whelan, S., Murphy, K., Barrett, E., Krusche, C., Santorelli, A., Casey, D., Factors affecting the acceptability of social robots by older adults including people with dementia or cognitive impairment: a literature review (2018) Int J of Soc Robot, 10, pp. 643-668; Hung, L., Liu, C., Woldum, E., Au-Yeung, A., Berndt, A., Wallsworth, C., Horne, N., Chaudhury, H., The benefits of and barriers to using a social robot PARO in care settings: A scoping review (2019) BMC Geriatr., , Accessed 26 April 2021; Technical Committee for Soft Robotics (2021) IEEE Robotics and Automation Society, , https://www.ieee-ras.org/soft-roboticsAccessed, 26 April; Carpenter, I., Hirdes, J.P., (2013) A Good Life in Old Age? Monitoring and Improving Quality in Long-Term Care, pp. 93-127. , OECD/European Commission, Ch. Using interRAI assessment systems to measure and maintain quality of long-term care; Morris, J.N., Fries, B.E., Frijters, D., Hirdes, J.P., Steel, R.K., interRAI home care quality indicators (2013) BMC Geriatr; Hirdes, J.P., Everdingen, C.V., Ferris, J., Franco-Martin, M., Fries, B.E., Heikkilä, J., Hirdes, J., van Audenhove, C., The interRAI suite of mental health assessment instruments: an integrated system for the continuum of care (2019) Front Psychiatry; Ikegami, N., Ishibashi, T., Hirdes, T.T., (2017) Interrai Hoshiki Gaido Bukku: Kea Puran Sakusei/Shitsu No Kanri/Kango Deno Katsuyo, , (interrai method guidebook: care planning, quality control and utilization in nursing, in Japanese); Pangman, V., Sloan, J., Guse, L., An examination of psychometric properties of the mini-mental status examination and the standardized mini-mental status examination: implications for clinical practice (2000) Appl Nurs Res, 13 (4), pp. 209-213; Jones, C., Sung, B., Moyle, W., Assessing engagement in people with dementia: a new approach to assessment using video analysis (2015) Arch Psychiatr Nurs, 29 (6), pp. 5-24; Wright, J., Robots vs migrants? Reconfiguring the future of Japanese institutional eldercare (2019) Crit Asian Stud, 51 (3), pp. 331-354; Vandemeulebroucke, T., de Casterlé, B.D., Gastmans, C., The use of care robots in aged care: a systematic review of argument-based ethics literature (2018) Arch Gerontol Geriatr, 74, pp. 15-25; Suwa, S., Tsujimura, M., Kodate, N., Donnelly, S., Kitinoja, H., Hallila, J., Toivonen, M., Yu, W., Exploring perceptions towards home-care robots for older people in Finland, Ireland, and Japan: a comparative questionnaire study (2020) Arch Gerontol Geriatr, 91, p. 104178; Bennett, B., McDonald, F., Beattie, E., Carney, T., Freckelton, I., White, B., Willmott, L., Assistive technologies for people with dementia: ethical considerations (2017) Bull World Health Organ, 95, pp. 749-755; Anderson, M., Anderson, S.L., Berenz, V., A value-driven eldercare robot: virtual and physical instantiations of a case-supported principle-based behavior paradigm (2019) Proc IEEE, 107, pp. 526-540; Ethically aligned design: A vision for prioritizing human well-being with autonomous and intelligent systems (2019) The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, , https://standards.ieee.org/content/ieee-standards/en/industry-connections/ec/autonomous-systems.html; Greenhalgh, T., Shaw, S., Wherton, J., Hughes, G., Lynch, J., A’Court, C., Hinder, S., Stones, R., SCALS: a fourth-generation study of assisted living technologies in their organisational, social, political and policy context (2016) BMJ Open, , (,),.,., https://doi.org/10.1136/bmjopen-2015-010208; Rantan, T., Lehto, P., Vuorinen, P., Coco, K., The adoption of care robots in home care—a survey on the attitudes of Finnish home care personnel (2018) J Clin Nurs, 27 (9-10), pp. 1846-1859","Kodate, N.; La Fondation France-Japon, France; email: naonori.kodate@ucd.ie",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Article in Press","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85112084042
"Rebola C.B., Ramirez-Loaiza S.","37011519000;57212004745;","Co-designing Technologies for Well Being: A Robot Companion for Older Adults",2021,"Advances in Intelligent Systems and Computing","1364 AISC",,,"871","882",,,"10.1007/978-3-030-73103-8_63","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105930575&doi=10.1007%2f978-3-030-73103-8_63&partnerID=40&md5=34daeab1d2f500fbe70e7fb42a7c35f4","University of Cincinnati, Cincinnati, OH  45221, United States","Rebola, C.B., University of Cincinnati, Cincinnati, OH  45221, United States; Ramirez-Loaiza, S., University of Cincinnati, Cincinnati, OH  45221, United States","Robotics is an industry that has disrupted the health field in different areas, including older adult care. Robotic companions for older adults have proved to produce many benefits, including decreasing loneliness, anxiety, stress, depression, agitation, and contributing to the improvement of social relationships, to mention a few. Currently, there are different options in the market of robotic pets for the older adult population. Among them is the Joy for All line of products. In December of 2015, Hasbro released the Joy for All - Robotic Cat to the market. While the product has been successfully adopted by the older adult population, there are opportunities to improve the nature of advanced human-robot interaction. The class, Technologies to Extend Life, taught in the Master of Design program at the University of Cincinnati, focused on designing the next generation of Joy for All robotic intelligence that could provide psycho-social support for older adults. With this project, the class had two primary objectives: 1) Understand the real challenges older adults face in activities of daily living; and 2) Develop innovative hardware and software tools that can meet some of the identified daily challenges. The class had a co-design approach in the many phases that included different participants (older adults and caregivers), utilizing a variety of tools to assess, analyze, develop, design, implement, and evaluate the interventions with older adults and communities. The significance of this project is to use a participatory approach to assess and analyze the challenges older adults face in activities of daily living that robotic companions may help alleviate, thus having an empathic perspective to the creation of assistive technologies. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.","Assistive technologies; Co-design; Robotics","Commerce; Industrial robots; Robotics; Activities of Daily Living; Assistive technology; Co-design approach; Hardware and software; Participatory approach; Primary objective; Robotic intelligence; Social relationships; Social robots",,,,,"National Science Foundation, NSF: 1717701","Acknowledgments. This project was funded by a grant from the National Science Foundation, division of Industrial Innovation and Partnership, No. 1717701. Next Generation Robotic Intelligence that Provides Psycho-Social Support for Older Adults.",,,,,,,,,,"Marsilio, J., McKittrick, S., Umbell, L., Garner, M., Maiewski, S., Wenos, J., Effects of a robotic cat on agitation and quality of life in individuals with dementia in a long-term care facility (2018) Physician Assistant Capstones, , https://commons.lib.jmu.edu/pacapstones/35; Coghlan, S., Waycott, J., Neves, B.B., Vetere, F., Using robot pets instead of companion animals for older people: A case of “reinventing the wheel”? (2018) Proceedings of the 30Th Australian Conference on Computer-Human Interaction, Pp. 172–183, , https://doi.org/10.1145/3292147.3292176; AIST: PARO Therapeutic Robot (2019). http://www.parorobots.com/. Accessed 16 Nov 2019; Consequential Robotics: Miro-b (2019). From Consequential Robotics Website. http://con sequentialrobotics.com/miro-beta. Accessed 16 Nov 2019; Ageless Innovation: Joy for All Companion Pets (2019). From Ageless Innovation LLC Website. https://joyforall.com/. Accessed 16 Nov 2019; US Census Bureau: The Older Population in the United States: 2010 to 2050 (2010). https://www.census.gov/library/publications/2010/demo/p25-1138.html; US Census Bureau: An Aging Nation: The Older Population in the United States (2014a). https://www.census.gov/library/publications/2014/demo/p25-1140.html. Accessed 1 Sept 2019; US Census Bureau: Nation’s Older Population to Nearly Double (2014b). https://www.cen sus.gov/newsroom/press-releases/2014/cb14-84.html; AARP: Loneliness and Social Connections: A National Survey of Adults 45 and Older (2018). https://www.aarp.org/research/topics/life/info-2018/loneliness-social-connec tions.html?CMP=RDRCT-PRI-HOMFAM-073118; World Health Organization: Mental Health of Older Adults (2017). https://www.who.int/news-room/fact-sheets/detail/mental-health-of-older-adults. Accessed 16 Nov 2019; Gastfriend, J., As Baby Boomers Move Into Old Age (2018) Who Will Care for Us?, , https://www.forbes.com/sites/jodygastfriend/2018/08/10/caregiver-shortage-who-will-care-for-us/#24aa2be3532d; The Gerontological Society of America: 2018 Trends in the Behavioral and Social Sciences (2018). https://www.geron.org/images/membership/BSSTrendsReport2018.pdf","Ramirez-Loaiza, S.; University of CincinnatiUnited States; email: ramirej3@mail.uc.edu","Arai K.",,"Springer Science and Business Media Deutschland GmbH","Future of Information and Communication Conference, FICC 2021","29 April 2021 through 30 April 2021",,257959,21945357,9783030731021,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85105930575
"Kim S., Srinivasan D., Nussbaum M.A., Leonessa A.","55718638500;55190345500;7102451255;7003758699;","Human Gait during Level Walking with an Occupational Whole-Body Powered Exoskeleton: Not Yet a Walk in the Park",2021,"IEEE Access","9",,"9386116","47901","47911",,2,"10.1109/ACCESS.2021.3068836","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103293783&doi=10.1109%2fACCESS.2021.3068836&partnerID=40&md5=da3cc6901e9ba56608d26df9869fa7ad","Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, United States; Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, United States","Kim, S., Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, United States; Srinivasan, D., Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, United States; Nussbaum, M.A., Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, United States; Leonessa, A., Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, United States","With rapid advancements in exoskeleton technologies, a whole-body powered exoskeleton (WB-PEXO) for augmenting human physical capacity (a 'super-operator') is generating increasing attention as an integral part of Industry 4.0. Our understanding of WB-PEXO use is lagging, however, largely due to the lack of detailed evaluations via human-subjects testing of a WB-PEXO. We examined (independently from the manufacturer of a WB-PEXO) the potential impacts of using a state-of-the-art WB-PEXO prototype (pre-alpha prototype version of the Sarcos Guardian® XO®) on users ( $n=5$ ) during a common basic activity in the workplace, level walking. With emphasis on the 'human', impacts of XO use (compared to a no EXO baseline) were assessed in terms of lower limb intersegmental coordination, muscle activity, and postural dynamic stability. A larger variance between participants was observed for intersegmental coordination with XO use, and participants appeared to rely on more hip motions. When using the XO, participants exhibited higher muscle activity levels in the lower limb muscle groups monitored. Further, there was a moderate to high similarity in muscle activity profiles between the XO and no EXO conditions ( $R_{XY}\left ({\tau }\right)=0.70-0.92)$ , yet muscle activity profiles when using the XO were generally time-lagged from those without the XO. We discuss the results within the context of developing a mental model for walking with the XO, and aspects of human-robot interaction such as transparency of the XO and understanding user state and intention. In concluding, we outline several future research topics for occupational WB-PEXO development. © 2013 IEEE.","Gait performance; human-robot interaction; occupational exoskeleton; whole body system","Human robot interaction; Muscle; Human subjects; Integral part; Inter-segmental coordination; Muscle activities; Potential impacts; Prototype versions; Research topics; State of the art; Exoskeleton (Robotics)",,,,,"National Science Foundation, NSF: 1718801, 1839946","This work was supported by the U.S. National Science Foundation under Grant 1839946.",,,,,,,,,,"Bevan, S., Economic impact of musculoskeletal disorders (MSDs) on work in Europe (2015) Best Pract. Res. Clin. Rheumatology, 29 (3), pp. 356-373. , Jun; Waters, T.R., National efforts to identify research issues related to prevention of work-related musculoskeletal disorders (2004) J. Electromyogr. 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Biomech., 15 (12), pp. 933-939. , Jan; You, J.-Y., Chou, Y.-L., Lin, C.-J., Su, F.-C., Effect of slip on movement of body center of mass relative to base of support (2001) Clin. Biomech., 16 (2), pp. 167-173. , Feb; Ackerman, E., (2020) Sarcos Demonstrates Powered Exosuit That Gives Workers Super Strength, , https://spectrum.ieee.org/automaton/robotics/industrial-robots/sarcos-guardian-xo-poweredexoskeleton, Dec. 10, 2019. IEEE Spectrum. Accessed: Aug. 10","Srinivasan, D.; Department of Industrial and Systems Engineering, United States; email: sdivya1@vt.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85103293783
[No author name available],[No author id available],"11th International Workshop on Spoken Dialogue Systems, IWSDS 2020",2021,"Lecture Notes in Electrical Engineering","704",,,"","",403,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096436877&partnerID=40&md5=ab5556b266d5a43951ab6b76ce98b754",,"","The proceedings contain 31 papers. The special focus in this conference is on Spoken Dialogue Systems. The topics include: Towards Personalization of Spoken Dialogue System Communication Strategies; authoring Negotiation Content and Programming Simulated Patients; a Spanish Corpus for Talking to the Elderly; analysis of Prosodic Features During Cognitive Load in Patients with Depression; co-creating Requirements and Assessing End-User Acceptability of a Voice-Based Chatbot to Support Mental Health: A Thematic Analysis of a Living Lab Workshop; development of a Dialogue System that Supports Recovery for Patients with Schizophrenia; caption Generation of Robot Behaviors Based on Unsupervised Learning of Action Segments; towards a Natural Human-Robot Interaction in an Industrial Environment; nudges with Conversational Agents and Social Robots: A First Experiment with Children at a Primary School; toxicBot: A Conversational Agent to Fight Online Hate Speech; exploring Boundaries Among Interactive Robots and Humans; mixOut: A Simple Yet Effective Data Augmentation Scheme for Slot-Filling; towards Similar User Utterance Augmentation for Out-of-Domain Detection; response Generation to Out-of-Database Questions for Example-Based Dialogue Systems; packing, Stacking, and Tracking: An Empirical Study of Online User Adaptation; on the Use of Phonotactic Vector Representations with FastText for Language Identification; the Influence of Syntax on the Perception of In-Vehicle Prompts and Driving Performance; learning Grammar in Confined Worlds; a Content and Knowledge Management System Supporting Emotion Detection from Speech; a Script Knowledge Based Dialogue System for Indoor Navigation; towards a Humorous Chat-Bot Companion for Senior Citizens; data Collection Design for Dialogue Systems for Low-Resource Languages; masheli: A Choctaw-English Bilingual Chatbot.",,,,,,,,,,,,,,,,,,,,"D'Haro L.F.Callejas Z.Nakamura S.",,"Springer Science and Business Media Deutschland GmbH","11th International Workshop on Spoken Dialogue Systems, IWSDS 2020","21 September 2020 through 23 September 2020",,250759,18761100,9789811583940,,,"English","Lect. Notes Electr. Eng.",Conference Review,"Final","",Scopus,2-s2.0-85096436877
"Casiddu N., Burlando F., Porfirione C., Vacanti A.","56088000200;57218200734;57194096211;57209328312;","Humanoid Robotics: Guidelines for Usability Testing",2021,"Advances in Intelligent Systems and Computing","1269 AISC",,,"102","109",,1,"10.1007/978-3-030-58282-1_17","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091114518&doi=10.1007%2f978-3-030-58282-1_17&partnerID=40&md5=bff23182202450827e858b33ce5c5910","Dipartimento Architettura e Design, Università degli Studi di Genova, Stradone di Sant‘Agostino 37, Genova, GE  16123, Italy","Casiddu, N., Dipartimento Architettura e Design, Università degli Studi di Genova, Stradone di Sant‘Agostino 37, Genova, GE  16123, Italy; Burlando, F., Dipartimento Architettura e Design, Università degli Studi di Genova, Stradone di Sant‘Agostino 37, Genova, GE  16123, Italy; Porfirione, C., Dipartimento Architettura e Design, Università degli Studi di Genova, Stradone di Sant‘Agostino 37, Genova, GE  16123, Italy; Vacanti, A., Dipartimento Architettura e Design, Università degli Studi di Genova, Stradone di Sant‘Agostino 37, Genova, GE  16123, Italy","In the macrofield of human–computer interaction, human–robot interaction (HRI) is a relatively new and unexplored field of study. As several efforts have been made in robotics to expand the range of functionalities of hardware and software, perhaps the hardest challenge is to ensure good usability and fluid interaction for users. With regard to remote operated robots, these aspects are unrelated and the interaction features may be taken into account at an advanced design stage. Nevertheless, when it comes to humanoid robots, interaction features are the keypoint, therefore the design of the latter should be driven by UX and UCD practices. The actual fact is that too little attention is paid to these issues or, at best, the latter are approached by IT’s point of view, who are in charge of the software development. In conjunction, a team of engineers take care of the mechanical parts and, in the most highly developed project, the appearance is curated by industrial designers. Given the importance of the interaction features, the design process should be rearranged in favour of the last-mentioned phase. However, the team in charge of this task should be composed of industrial designers, that we can consider as UI designers, along with UX expert. This study applies UX practices to the design process of a humanoid robot, providing practical guidance and guidelines to put in place usability testing during the various stages of development. Regarding testing, only user satisfaction is often investigated. Instead, the present approach includes an adaptation of typical elements of the web design area, such as learnability, efficiency, memorability and errors. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.","Humanoid robotics; Human–robot interaction; Usability testing; User experience","Anthropomorphic robots; Human computer interaction; Machine design; Robotics; Software design; Systems engineering; Computer interaction; Fluid interactions; Hardware and software; Industrial designers; Interaction features; Practical guidance; Robot interactions; Usability testing; Human robot interaction",,,,,,,,,,,,,,,,"Destephe, M., Walking in the uncanny valley: importance of the attractiveness on the acceptance of a robot as a working partner (2015) Front. Psychol, 6, p. 204. , https://doi.org/10.3389/fpsyg.2015.00204; Yanco, H.A., (2011) Beyond Usability Evaluation: Analysis of Human-Robot Interaction at a Major Robotics Competition, , Human–Computer Interaction, Lawrence Erlbaum Associates, Inc; Albert, W., Tullis, T., (2013) Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics, , Newnes, Oxford; Barnum, C.M., (2010) Usability Testing Essentials: Ready, Set…Test!, , Elsevier, Amsterdam; Vincent, J., (2015) Social Robots from a Human Perspective, , Springer, Heidelberg; Polak, R.F., Differences between young and older users when interacting with a humanoid robot: a qualitative usability study for rehabilitation (2018) Ann. Phys. Rehabil. Med, 61, p. e499. , https://doi.org/10.1016/j.rehab.2018.05.1163, (ScienceDirect); Hass, C., A practical guide to usability testing’. consumer informatics and digital health: solutions for health and health care (2019), pp. 107-124. , https://doi.org/10.1007/978-3-319-96906-0_6, Edmunds, M., et al. (eds), Springe, Heidelberg; Turner, C.W., Determining usability test sample size (2006) Int. Encycl. Ergon. Hum. Factors, 3, pp. 3084-3088; Schmettow, M., Sample size in usability studies (2012) Commun. ACM, 55 (4), pp. 64-70. , https://doi.org/10.1145/2133806.2133824; Nielsen, J., Landauer, T.K., A mathematical model of the finding of usability problems (1993) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems-CHI ’93, pp. 206-213. , https://doi.org/10.1145/169059.169166, ACM Press; Hwang, W., Salvendy, G., Number of people required for usability evaluation: the 10±2 rule (2010) Commun. ACM, 53 (5), pp. 130-133. , https://doi.org/10.1145/1735223.1735255; McDonald, S., Studying actions in context: a qualitative shadowing method for organizational research (2005) Qual. Res, 5 (4), pp. 455-473. , https://doi.org/10.1177/1468794105056923, (SAGE Publications); Holtzblatt, K., Beyer, H., (1997) Contextual Design: Defining Customer-Centered Systems, , Elsevier, Amsterdam; Di Pascale, M., (2019) Manuale di sopravvivenza per UX designer: Guida pratica alla progettazione, , HOEPLI EDITORE, Milan","Burlando, F.; Dipartimento Architettura e Design, Stradone di Sant‘Agostino 37, Italy; email: francesco.burlando@unige.it","Karwowski W.Ahram T.Etinger D.Tankovic N.Taiar R.",,"Springer Science and Business Media Deutschland GmbH","3rd International Conference on Human Systems Engineering and Design: Future Trends and Applications, IHSED 2020","22 September 2020 through 24 September 2020",,244539,21945357,9783030582814,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85091114518
"Sueur C., Forin-Wiart M.-A., Pelé M.","23494021200;56215319100;12808659700;","Are they really trying to save their buddy? The anthropomorphism of animal epimeletic behaviours",2020,"Animals","10","12","2323","1","21",,3,"10.3390/ani10122323","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097526674&doi=10.3390%2fani10122323&partnerID=40&md5=033bcbcc3d9d7fbca90de651045feecc","Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, F-67000, France; Centre Européen d’Enseignement et de Recherche en Éthique, Strasbourg, F-67000, France; Institut Universitaire de France, Paris, 75006, France; Anthropo-Lab, ETHICS EA7446, Lille Catholic University, Lille, 59000, France","Sueur, C., Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, F-67000, France, Centre Européen d’Enseignement et de Recherche en Éthique, Strasbourg, F-67000, France, Institut Universitaire de France, Paris, 75006, France; Forin-Wiart, M.-A., Université de Strasbourg, CNRS, IPHC UMR 7178, Strasbourg, F-67000, France; Pelé, M., Anthropo-Lab, ETHICS EA7446, Lille Catholic University, Lille, 59000, France","Anthropomorphism is a natural tendency in humans, but it is also influenced by many characteristics of the observer (the human) and the observed entity (here, the animal species). This study asked participants to complete an online questionnaire about three videos showing epimeletic behaviours in three animal species. In the videos, an individual (a sparrow, an elephant and a macaque, respectively) displayed behaviours towards an inanimate conspecific that suddenly regained consciousness at the end of the footage. A fourth video showed a robot dog being kicked by an engineer to demonstrate its stability. Each video was followed by a series of questions designed to evaluate the degree of anthropomorphism of participants, from mentaphobia (no attribution of intentions and beliefs, whatever the animal species) to full anthropomorphism (full attribution of intentions and beliefs by animals, to the same extent as in humans) and to measure how far the participants had correctly assessed each situation in terms of biological reality (current scientific knowledge of each species). There is a negative correlation (about 61%) between the mental states attributed to animals by humans and the real capability of animals. The heterogeneity of responses proved that humans display different forms of anthropomorphism, from rejecting all emotional or intentional states in animals to considering animals to show the same intentions as humans. However, the scores participants attributed to animals differed according to the species shown in the video and to human socio-demographic characteristics. Understanding the potential usefulness of these factors can lead to better relationships with animals and encourage a positive view of human-robot interactions. Indeed, reflective or critical anthropomorphism can increase our humanity. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Animal ethics; Birds; Cognitive biases; Comparative thanatology; Elephants; Empathy; Mentaphobia; Primates; Robot","adult; animal experiment; article; cognitive bias; consciousness; demography; dog; elephant; empathy; ethics; female; Macaca; male; mental health; nonhuman; questionnaire; robotics; sparrow; videorecording",,,,,,,,,,,,,,,,"Wilson, E.O., (1992) Biophilia, 1, p. 79. , Harvard University Press: Cambridge, MA, USA; Wilson, E.O., Biophilia and the conservation ethic (2017) Evolutionary Perspectives on Environmental Problems, pp. 263-272. , Routledge: Philadelphia, PA, USA; Simaika, J.P., Samways, M.J., Biophilia as a universal ethic for conserving biodiversity (2010) Conserv. Biol, 24, pp. 903-906. , [CrossRef] [PubMed]; DeLoache, J.S., Pickard, M.B., LoBue, V., How very young children think about animals (2011) Am. Psych. 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Sueur, C.; Centre Européen d’Enseignement et de Recherche en ÉthiqueFrance; email: cedric.sueur@iphc.cnrs.fr
Sueur, C.; Institut Universitaire de FranceFrance; email: cedric.sueur@iphc.cnrs.fr",,,"MDPI AG",,,,,20762615,,,,"English","Animals",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85097526674
"Lyu S., Cheah C.C.","57193015269;7007112857;","Human-Robot Interaction Control Based on a General Energy Shaping Method",2020,"IEEE Transactions on Control Systems Technology","28","6","8897113","2445","2460",,4,"10.1109/TCST.2019.2949525","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085993304&doi=10.1109%2fTCST.2019.2949525&partnerID=40&md5=8c3565b754554edba97fb14d28918b8c","School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore; Hangzhou Innovation Institute, Beihang University, Hangzhou, 310052, China","Lyu, S., School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Hangzhou Innovation Institute, Beihang University, Hangzhou, 310052, China; Cheah, C.C., School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","Coexistence of the human and robot in the same workspace requires the robot to perform robot tasks, such as trajectory tracking, and also interaction tasks, such as keeping a safe distance with the human. According to various human-robot interaction (HRI) scenarios, different interaction tasks usually require different task requirements or specifications, leading to different control strategies. Besides, due to different natures of the robot tasks and interaction tasks, different controllers may be required when the task is switched from one to another. So far, there is no theoretical framework that integrates different robot and interaction task requirements into a unified robot control strategy. In this article, a general HRI control framework is proposed for the scenario of the human and robot coexisting in the same workspace. We propose a general potential energy function that can be used to derive a stable and unified controller for various robot tasks and HRI tasks. By using the proposed method, various tasks can be specified at a user level by simply adjusting certain task parameters. Interactive weights are also defined to specify the interaction behaviors of robots according to different HRI applications. A human-dominant interaction mode is presented to demonstrate the applications of the proposed method. We show how the control framework can be applied to existing robot control systems with the velocity control or torque control mode by developing a joint velocity reference command and an adaptive controller. The stability of the adaptive control system is shown by using Lyapunov-like analysis. The control methods are implemented on two industrial manipulators and experimental results are presented to illustrate the effectiveness of the proposed control framework. © 1993-2012 IEEE.","Adaptive control; general energy shaping; human-robot interaction; potential energy function; robot control; unified control method","Adaptive control systems; Controllers; Industrial manipulators; Man machine systems; Potential energy functions; Adaptive controllers; Human robot Interaction (HRI); Human-robot interaction control; Interaction behavior; Lyapunov like analysis; Robot control systems; Theoretical framework; Trajectory tracking; Human robot interaction",,,,,"A1883c0008","Manuscript received February 20, 2019; revised August 15, 2019; accepted October 9, 2019. Date of publication November 12, 2019; date of current version October 9, 2020. Manuscript received in final form October 20, 2019. This work was supported by the Agency for Science, Technology and Research of Singapore (A∗STAR) under the Advanced Manufacturing and Engineering (AME) Individual Research Grants 2017 under Project A1883c0008. Recommended by Associate Editor E. Zergeroglu. (Corresponding author: Shangke Lyu.) S. Lyu was with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798. He is now with the Hangzhou Innovation Institute, Beihang University, Hangzhou 310052, China (e-mail: lyus0001@e.ntu.edu.sg).",,,,,,,,,,"Khatib, O., Real-Time obstacle avoidance for manipulators and mobile robots (1986) Int. J. Robot. Res, 5 (1), pp. 90-98; Haddadin, S., Albu-Schaffer, A., De Luca, A., Hirzinger, G., Collision detection and reaction: A contribution to safe physical human-robot interaction (2008) Proc IEEE/RSJ Int. Conf. Intell. Robots Syst, pp. 3356-3363. , Sep; Kazerooni, H., Guo, J., Human extenders (1993) Asme J. Dyn. Syst. Meas. 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Res, 23 (4-5), pp. 379-398; Choi, T.-Y., Choi, B.-S., Seo, K.-H., Position and compliance control of a pneumatic muscle actuated manipulator for enhanced safety (2011) Ieee Trans. Control Syst. Technol, 19 (4), pp. 832-842. , Jul; Gribovskaya, E., Kheddar, A., Billard, A., Motion learning and adaptive impedance for robot control during physical interaction with humans (2011) Proc Ieee Int. Conf. Robot. Automat, pp. 4326-4332. , May; Oh, S., Woo, H., Kong, K., Frequency-shaped impedance control for safe human-robot interaction in reference tracking application (2014) IEEE/ASME Trans. Mechatronics, 19 (6), pp. 1907-1916. , Dec; Zhang, J., Cheah, C.C., Passivity and stability of human-robot interaction control for upper-limb rehabilitation robots (2015) Ieee Trans. Robot, 31 (2), pp. 233-245. , Apr; Li, X., Chi, G., Vidas, S., Cheah, C.C., Human-guided robotic comanipulation: Two illustrative scenarios (2016) Ieee Trans. Control Syst. Technol, 24 (5), pp. 1751-1763. , Sep; Li, Y., Tee, K.P., Yan, R., Chan, W.L., Wu, Y., A framework of human-robot coordination based on game theory and policy iteration (2016) Ieee Trans. Robot, 32 (6), pp. 1408-1418. , Dec; Lyu, S., Cheah, C.C., Adaptive robot control for human-dominant interactions using a general task function (2016) Proc Ieee Int. Conf. Real-Time Comput. Robot, pp. 39-44. , Jun; Arimoto, S., (1996) Control Theory of Non-Linear Mechanical Systems: A Passivity-Based and Circuit-Theoretic Approach, , London, U.K.: Oxford Univ. Press; Slotine, J.-J.E., Li, W., On the adaptive control of robot manipulators (1987) Int. J. Robot. Res, 6 (3), pp. 49-59; Kelly, R., Regulation of manipulators in generic task space: An energy shaping plus damping injection approach (1999) Ieee Trans. Robot. Autom, 15 (2), pp. 381-386. , Apr; Rimon, E., Koditschek, D.E., Exact robot navigation using artificial potential functions (1992) Ieee Trans. Robot. Autom, 8 (5), pp. 501-518. , Oct; Cowan, N.J., Weingarten, J.D., Koditschek, D.E., Visual servoing via navigation functions (2002) Ieee Trans. Robot. Autom, 18 (4), pp. 521-533. , Aug; Cheah, C.C., Q.Wang, D., Region reaching control of robots: Theory and experiments (2005) Proc Ieee Int. Conf. Robot. Autom, pp. 974-979. , Apr; Haghighi, R., Cheah, C.C., Optical manipulation of multiple groups of microobjects using robotic tweezers (2016) Ieee Trans. Robot, 32 (2), pp. 275-285. , Apr; Cheah, C.C., Wang, D.Q., Sun, Y.C., Region-reaching control of robots (2007) Ieee Trans. Robot, 23 (6), pp. 1260-1264. , Dec; Cheah, C.C., Li, X., (2015) Task-Space Sensory Feedback Control of Robot Manipulators, , Springer; Hou, S.P., Cheah, C.C., Multiplicative potential energy function for swarm control (2009) Proc IEEE/RSJ Int. Conf. Intell. Robots Syst., St. Louis, MO, Usa, pp. 4363-4368. , Oct; Slotine, J.-J.E., Li, W., (1991) Applied Nonlinear Control. Englewood Cliffs, NJ, Usa, , Prentice-Hall","Lyu, S.; School of Electrical and Electronic Engineering, Singapore; email: lyus0001@e.ntu.edu.sg",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,10636536,,IETTE,,"English","IEEE Trans Control Syst Technol",Article,"Final","",Scopus,2-s2.0-85085993304
"Moradi M., Dang S., Alsalem Z., Desai J., Palacios A.","57221596033;57221606277;57221602324;56231471400;57221599791;","Integrating Human Hand Gestures with Vision Based Feedback Controller to Navigate a Virtual Robotic Arm",2020,"2020 23rd IEEE International Symposium on Measurement and Control in Robotics, ISMCR 2020",,,"9263771","","",,,"10.1109/ISMCR51255.2020.9263771","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099575679&doi=10.1109%2fISMCR51255.2020.9263771&partnerID=40&md5=9f263bbc84d4bc0d90adb331adf992cd","Wichita State University, Department of Biomedical Engineering, Wichita, KS  67260, United States","Moradi, M., Wichita State University, Department of Biomedical Engineering, Wichita, KS  67260, United States; Dang, S., Wichita State University, Department of Biomedical Engineering, Wichita, KS  67260, United States; Alsalem, Z., Wichita State University, Department of Biomedical Engineering, Wichita, KS  67260, United States; Desai, J., Wichita State University, Department of Biomedical Engineering, Wichita, KS  67260, United States; Palacios, A., Wichita State University, Department of Biomedical Engineering, Wichita, KS  67260, United States","This paper reports the design and development of a real-time IMU-Vision-based hybrid control algorithm to interact with a 6-DOF Kinova virtual robotic arm. Human Robot Interaction (HRI) control scheme proposed in this paper utilizes an embedded gyroscopic sensor from a Myo Gesture Control Armband's inertial measurement unit and an 800*600-pixel resolution from a Microsoft HD camera. The algorithm exploits the mathematical features of a numerical discrete time integrator and a mean filter to process the raw angular velocity data from the gyroscope. The processed data further provides the angular displacements to the end-effector of the robotic arm during clockwise or counterclockwise actions along x, y, and z axes from the user. This also facilitates the end effector (gripper) motion which was also controlled simultaneously by the roll action through threshold comparison in the algorithm. A vision based feedback system was designed using a computer vision toolbox and blob analysis technique in order to make the system more reliable and to control the distance of the end-effector while reaching to desired objects. The results demonstrated a significant control of the 6-DOF virtual robotic limb using the gyroscopic information and user inputs. The virtual robotic arm stopped the movement after reaching 320 mm from the desired object as expected. For three different objects, the maximum error between the real and the measured distance was calculated as 15.3 cm for cylindrical object. Due to its smooth control and arm gesture controller, this technology has the potential to assist people with either physical impairments or neurological disorders to perform activities of daily living using assistive robotic arm in the near future. © 2020 IEEE.","assistive robotic limb; gesture; hybrid control; IMU; vision","Agricultural robots; Biofeedback; Computer control systems; Controllers; Feedback; Human robot interaction; Robotic arms; Robotics; Visual servoing; Activities of Daily Living; Assistive robotic arms; Design and Development; Discrete-time integrators; Human robot Interaction (HRI); Inertial measurement unit; Neurological disorders; Vision-based feedbacks; End effectors",,,,,,,,,,,,,,,,"(2018) Rehabilitation Research and Training Center on Disability Statistics and Demographics, , Annual Report on People with Disabilities in America; Timmcrmans, A.A.A., Technology-assisted training of arm-hand skills in stroke: Concepts on acquisition of motor control and therapist guidelines for rehabilitation technology design (2009) Journal of Neuroengineeriug and Rehabilitation, 6 (1), p. 1; RTisic, D., Conlrol of prehension lor Ihe transradial prosthesis: Natural-hke intake recognition system (2009) Journal of Automatic Conlrol, 19 (1), pp. 27-31; Coman, M., Al, C., Application of distance measuring with Matlab Simulink (2010) 3rd International Conference on Human System Interaction; Ossmann, T., Asterics, a flexible assistive technology constnietion set (2012) Procedia Computer Science, 14, pp. 1-9; Madusanka, G.K., A review on hybrid myoelectric control systems for upper limb prosthesis (2015) 201 Moratuwa Engineering Research Conference (MERCon); Guanglong, D., Zhang, P., IMt.-based online kinematic calibration of robot manipulator (2013) The Scientific World Journal 2013; Schmid, R., Stereo vision and imu based real-time ego-motion and depth image computation on a handheld device (2013) 2013 International Conference on Robotics and Automation. IKFK; Lobo-Prat, J., Non-invasive control interfaces lor intention detection in aclive movemenl-assislive devices (2014) Journal of Neuroengineenng and Rehabilitation, 11 (1), p. 168; Bonnet, T., Inertia measurement 1 nil-based wearable computers for assisted living applications: A signal processing perspective (2016) IEEE Sitmal Processus Magazine, 33 (2), pp. 28-35; Schabron, B., Integration of forearm sfmg signals with imu sensors for trajectory planning and control of assistive robotic ami (2019) 2019 41st Annual International Conference of the [F.FF Engineering in Medicine and Biology Society (EMBC), pp. 5274-5277. , Berlin, Germany; Schabron, B., Artificial neural network to detect human hand gestures for a robotic ami control (2019) 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1662-1665. , Berlin. Gemiauy; Al-Shabi, M., Simulation and implementation of real-time vision-based control system for 2-1 )oF robotic arm using P1D with hardware-in-thc-loop (2015) Intelligent Control and Automation, 6 (2), p. 147; Minali, L., Yoshimura, X., Roike, Y., Hybrid control of a vision-guided robot ann by E(Xi. KMG. FFG biosignals and head movement acquired via a consumer-grade wearable device (2016) Icec Access, 4, pp. 9528-9541; Rios, F., Augusma, I., Alba-Flores, R., Wireless control of a robotic ann using 3d motion tracking sensors and arliiicial neural networks (2017) International Journal of Engineering Research and Huiovation, 9 (1), pp. 13-24; Taveme, L.T., Video-based prediction of hand-grasp preshaping with application to prosthesis control (2019) 2019 International Conference on Robotics and Automation (ICRA); Bandala, M., Vision-based assisted tele-operation of a aial-ann hydraulicallv actuated robot for pipe culling and grasping in nuclear environments (2019) Robotics, 8 (2), p. 42; Uang, Q.R., A virtual blind cane using a line laser-based vision system and an inertial measurement unit (2016) Sensors, 16 (1), p. 95; Schabron, B., Alashqar, Z., Fuhrman, N., Jibbe, R., Desai, J., Artificial neural xefwork lo delect human hand gestures for a robotic arm control (2019) 2019 41st Annual International Conference of the i KEF. Engineering in Medicine and Biology Society (EMBC), pp. 1662-1665. , Berlin, Germany",,"Kiss B.Harmati I.Taqvi Z.",,"Institute of Electrical and Electronics Engineers Inc.","23rd IEEE International Symposium on Measurement and Control in Robotics, ISMCR 2020","15 October 2020 through 17 October 2020",,165357,,9781665404792,,,"English",,Conference Paper,"Final","",Scopus,2-s2.0-85099575679
"Jin L., Zhang H., Shen Y., Ye C.","57208013044;57031171700;7404767808;7202201245;","Human-Robot Interaction for Assisted Object Grasping by a Wearable Robotic Object Manipulation Aid for the Blind",2020,"Proceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020",,,"9209377","","",,2,"10.1109/ICHMS49158.2020.9209377","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093963317&doi=10.1109%2fICHMS49158.2020.9209377&partnerID=40&md5=778eb73d5ac7ca7da6ed6ab87697044d","Virginia Commonwealth Univ, Richmond, VA, United States; Univ of Nevada, Reno, Reno, NV, United States","Jin, L., Virginia Commonwealth Univ, Richmond, VA, United States; Zhang, H., Virginia Commonwealth Univ, Richmond, VA, United States; Shen, Y., Univ of Nevada, Reno, Reno, NV, United States; Ye, C., Virginia Commonwealth Univ, Richmond, VA, United States","This paper presents a new hand-worn device, called wearable robotic object manipulation aid (W-ROMA), that can help a visually impaired individual locate a target object and guide the hand to take a hold of it. W-ROMA may assist the individual for navigational (e.g., grasping a chair and moving it to make path) or non-navigational purpose (e.g, grasping a mug). The device consists of a sensing unit and a guiding unit. The sensing unit uses a Structure Core sensor, comprising of an RGB-D camera and an Inertial Measurement Unit (IMU), to detect the target object and estimate the device pose. Based on the object and pose information, the guiding unit computes the Desired Hand Movement (DHM) and convey it to the user by an electro-Tactile display to guide the hand to approach the object. A speech interface is developed and used as an additional way to convey the DHM and used for human-robot interaction. A new method, called Depth Enhanced Visual-Inertial Odometry (DVIO), is proposed for 6-DOF device pose estimation. It tightly couples the camera's depth and visual data with the IMU data in a graph optimization process to produce more accurate pose estimation than the existing state-of-The-Art approach. The estimated poses are used to 'stitched' the imaging and point cloud data captured at different points to form a larger view of the scene for object detection. They can also be used to position the individual for wayfinding. Experimental results demonstrate that the DVIO method outperforms the state-of-The-Art VIO approach in 6-DOF pose estimation. © 2020 IEEE.","6-DOF pose estimation; assisted object grasping/manipulation; electro-Tactile sensation; visual-inertial odometry","Cameras; Man machine systems; Object detection; Object recognition; Robotics; Wearable sensors; 6-dof pose estimations; Electro-tactile displays; Inertial measurement unit; Object manipulation; Point cloud data; Speech interface; State-of-the-art approach; Visually impaired; Human robot interaction",,,,,"National Institutes of Health, NIH: R01EY026275; National Eye Institute, NEI","This work was supported in part by the National Eye Institute (NEI) of the National Institute of Health under Award R01EY026275. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agency. 978-1-7281-5871-6/20/$31.00 ©2020 IEEE",,,,,,,,,,"Bourne, R.R., Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: A systematic review and meta-Analysis (2017) The Lancet Global Health, 5 (9), pp. e888-e897; Agin, G.J., Real Time Control of a Robot with a Mobile Camera (1979) Technical Note, 179. , SRI International, Feb; Hutchinson, S.A., Hager, G.D., Corke, P.I., A tutorial on visual servo control (1996) IEEE Transaction on Robotics and Automation, 12 (5), pp. 651-670; Bigham, J.P., Jayant, C., Miller, A., White, B., Yeh, T., VizWiz:: LocateIt-enabling blind people to locate objects in their environment (2010) Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern RecognitionWorkshops; http://www.looktel.com/recognizer, LookTel Recognizer; Matusiak, K., Skulimowski, P., Strumiłło, P., Object recognition in a mobile phone application for visually impaired users (2013) Proc. International Conference on Human System Interactions, pp. 479-484; Nanayakkara, S., Shilkrot, R., Maes, P., EyeRing: A Finger-worn Assistant (2012) Proc. ACM SIGCHI Conference on Human Factors in Computing, pp. 1961-1966; Horvath, S., Galeotti, J., Wu, B., Klatzky, R., Siegel, M., Stetten, G., FingerSight: Fingertip Haptic Sensing of the Visual Environment (2014) IEEE Journal of Translational Engineering in Health and Medicine, 2; Schauerte, B., Martinez, M., Constantinescu, A., An Assistive Vision System for the Blind that Helps Find Lost Things (2012) Proceedings of the 13th International Conference on Computers Helping People with Special Needs, 2, pp. 566-572; Thakoor, K., Mante, N., Zhang, C., Siagian, C., Weiland, J., Itti, L., Medioni, G., A system for assisting the visually impaired in localization and grasp of desired objects (2014) Proceedings of European Conference on Computer Vision, pp. 643-657; Lowe, D.G., Distinctive image features from scale-invariant keypoints (2004) International Journal of Computer Vision, 60 (2), pp. 91-110; Liu, X., Zhang, H., Jin, L., Ye, C., A wearable robotic object manipulation aid for the visually impaired Proc IEEE International Conference on Micro/Nano Sensors for AI, Healthcare, and Robotics, 2018, pp. 5-9; Tensorflow Lite Examples, , https://www.tensorflow.org/lite/examples, Accessed on 12/03/2019; Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Guadarrama, S., Speed/accuracy trade-offs for modern convolutional object detectors Proc IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 7310-7311; Lin, T.-Y., Microsoft coco: Common objects in context (2014) Proc. 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Van Helostein, 2017; Solomonow, M., Lyman, J., Freedy, A., Electrotactile two-point discrimination as a function of frequency, body site, laterality, and stimulation codes (1977) Annals of Biomedical Engineering, 5 (1), pp. 47-60; Rahimi, M., Jiang, F., Ye, C., Shen, Y., Dynamic Spatiotemporal Pattern Identification and Analysis Using a Fingertip-based Electro-Tactile Display Array (2019) Proceedings of IEEE International Conference on Intelligent Robots and Systems, pp. 8132-8137; Dellaert, F., Kaess, M., Square root sam: Simultaneous localization and mapping via square root information smoothing (2006) The International Journal of Robotics Research, 25 (12), pp. 1181-1203; Gutierrez-Gomez, D., Mayol-Cuevas, W., Guerrero, J.J., Inverse depth for accurate photometric and geometric error minimisation in RGBD dense visual odometry (2015) Proc IEEE International Conference on Robotics and Automation, pp. 83-89; Civera, J., Davison, A.J., Montiel, J.M., Inverse depth parametrization for monocular SLAM (2008) IEEE Transactions on Robotics, 24 (5), pp. 932-945; Qin, T., Li, P., Shen, S., Vins-mono: A robust and versatile monocular visual-inertial state estimator (2018) IEEE Transactions on Robotics, 34 (4), pp. 1004-1020; Zhang, H., Jin, L., Ye, C., A depth-enhanced visual inertial odometry for a robotic navigation aid for blind people Proc. Visual-Inertial Navigation: Challenges and Applications Workshop at 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems; Leutenegger, S., Furgale, P., Rabaud, V., Chli, M., Konolige, K., Siegwart, R., Keyframe-based visual-inertial SLAM using nonlinear optimization (2013) Proceedings of Robotics Science and Systems; Qin, T., Cao, S., Pan, J., Shen, S., (2019) A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors, , arXiv preprint arXiv 1901, 03642",,"Fortino G.Wang F.-Y.Nurnberger A.Kaber D.Falcone R.Mendonca D.Yu Z.Guerrieri A.",,"Institute of Electrical and Electronics Engineers Inc.","1st IEEE International Conference on Human-Machine Systems, ICHMS 2020","7 September 2020 through 9 September 2020",,163564,,9781728158716,,,"English","Proc. IEEE Int. Conf. Hum.-Mach. Syst., ICHMS",Conference Paper,"Final","",Scopus,2-s2.0-85093963317
"Kuhnlenz B., Kuhnlenz K.","55909854300;8611373500;","Social Bonding Increases Unsolicited Helpfulness Towards A Bullied Robot",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223454","833","838",,1,"10.1109/RO-MAN47096.2020.9223454","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095756052&doi=10.1109%2fRO-MAN47096.2020.9223454&partnerID=40&md5=6a89d4c85c6d1491aeb4b4cb9c46be43","Academic Center for Sciences and Humanities; Coburg University of Applied Sciences and Arts, Department of Electrical Engineering and Computer Science, Coburg, D-96450, Germany","Kuhnlenz, B., Academic Center for Sciences and Humanities; Kuhnlenz, K., Coburg University of Applied Sciences and Arts, Department of Electrical Engineering and Computer Science, Coburg, D-96450, Germany","This paper is a first step towards the investigation of civil courage in human-robot interaction (HRI). The main research question is if human users would help a robot being bullied by other humans. Previous work showed that pro-social behavior can be induced in human users towards a robot pro-actively asking for their help in order to accomplish a specific task by applying mechanisms of social bonding. In contrast, this paper investigates unsolicited helpful behavior towards a robot being bullied by a third person subsequent to an interaction task. To this end, social bonding in terms of small talk including explicit emotional adaptation to induce a feeling of similarity is applied to a human-robot dialog scenario in a user study. As an interaction context, a cooperative object classification task is chosen, where a robot reads objects from a list needed by the robot to fulfill another task later. To induce bullying behavior, the list is took away from the robot by a disturbing third person after the completed interaction. The two experimental conditions of the study differ in whether or not social bonding is applied prior to the interaction. According to previous work, results showed increased ratings for social presence and anthropomorphism, as well as increased unsolicited helpfulness of the participants in the social bonding condition. Surprisingly, unsolicited help occurred only verbally and directed towards the robot and none of the human users took action against the bullying third person. It is discussed, that this may be due to social-psychological side-effects caused by the passive presence of the human experimental examiner and that additional channels of emotional adaptation by the robot may be required. © 2020 IEEE.",,"Agricultural robots; Economic and social effects; Bonding conditions; Experimental conditions; Human robot Interaction (HRI); Interaction context; Object classification; Research questions; Social behavior; Social presence; Social robots",,,,,"Bundesministerium für Bildung und Forschung, BMBF","This work has been supported in part by the Federal Ministry of Education and Research (BMBF).",,,,,,,,,,"Horstmann, A., Bock, N., Linhuber, E., Szczuka, J., Strassmann, C., Krmer, N., Do a robots social skills and its objection discourage interactants from switching the robot off? PLOS ONE, 13; Martins, G.S., Santos, L., Dias, J., User-adaptive interaction in social robots: A survey focusing on non-physical interaction (2019) International Journal of Social Robotics, 11, p. 185205; Youssef, K., Okada, M., Exploring the social bonding that evolves between a human and a minimally designed accompanying robot (2016) Information and Media Technologies, 11, pp. 250-263; Perez-Osorio, J., Wykowska, A., Adopting the intentional stance toward natural and artificial agents (2019) Philosophical Psychology, 33 (7), pp. 1-27; Barendregt, W., Paiva, A., Kappas, A., Vasalou, A., Serholt, S., Basedow, C., Alves-Oliveira, P., Child-robot interaction: Social bonding (2014) Learning and Ethics, In: Proceedings of Interaction Design and Children (IDC); Keijsers, M., Bartneck, C., Mindless robots get bullied (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction-HRI 18; Belpaeme, T., Baxter, P.E., Read, R., Wood, R., Cuayhuitl, H., Kiefer, B., Racioppa, S., Humbert, R., Multimodal childrobot interaction: Building social bonds (2013) Journal of Human-Robot Interaction, 1 (2), p. 3353; Salvini, P., Ciaravella, G., Yu, W., Ferri, G., Manzi, A., Mazzolai, B., Laschi, C., Dario, P., How safe are service robots in urban environments? Bullying a robot (2010) Proceedings of the International Symposium in Robot and Human Interactive Communication (HRI); Nakagawa, K., Shiomi, M., Shinozawa, K., Matsumura, R., Ishiguro, H., Hagita, N., Effect of robots active touch on peoples motivation (2011) Proceedings of IEEE International Conference on Human-Robot Interaction (HRI); Nakagawa, K., Shiomi, M., Shinozawa, K., Matsumura, R., Ishiguro, H., Hagita, N., Effect of robots whispering behavior on peoples motivation (2013) International Journal of Social Robotics, 5 (1), p. 516; Krebs, D., Empathy and altruism (1975) Journal of Personality and Social Psychology, 32, p. 11341146; Frey, D., Irle, M., Theorien der sozialpsychologie (2002) Band II: Gruppen-Interaktions-und Lerntheorien, 2. , Verlag Hans Huber; Batson, C.D., Duncan, B.D., Ackermann, P., Buckley, T., Birch, K., Is empathic emotion a source of altruistic motivation? (1981) Journal of Personality and Social Psychology, 40, p. 290302; Bickmore, T.W., Cassell, J., Small talk and conversational storytelling in embodied interface agents (1999) Proceedings of the AAAI Fall Symposium Narrative Intelligence; Gonsior, B., Buß, M., Sosnowski, S., Wollherr, D., Kühnlenz, Buss, M., Towards transferability of theories on prosocial behavior from social psychology to hri (2012) Proceedings of the IEEE International Workshop on Advanced Robotics and Its Social Impacts (ARSO); Gonsior, B., Sosnowski, S., Buß, M., Wollherr, D., Kühnlez, K., An emotional adaption approach to increase helpfulness towards a robot (2012) Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS); Kühnlenz, B., Sosnowski, S., Buß, M., Wollherr, D., Kühnlenz, K., Buss, M., Increasing helpfulness towards a robot by emotional adaption to the user (2013) International Journal of Social Robotics, 5, p. 457476; Kühnlenz, B., Busse, F., Frtsch, P., Wolf, M., Kühnlenz, K., Effect of explicit emotional adaptation on prosocial behavior of humans towards robots depends on prior robot experience (2018) Proceedings of the IEEE International Conference on Robot and Human Interactive Communication (RO-MAN); Thomas, A.P., Bull, P., Roger, D., Conversational exchange analysis (1982) Journal of Language and Social Psychology, 1 (2), p. 141156; Bartneck, C., Kulic, D., Croft, E., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), p. 7181; Heerink, M., Krose, B., Evers, V., Wielinga, B., Measuring acceptance of an assistive social robot: A suggested toolkit (2009) Proceedings of the 18th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095756052
"Saad E., Broekens J., Neerincx M.A.","57201978976;22940425000;9133405200;","An Iterative Interaction-Design Method for Multi-Modal Robot Communication",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223529","690","697",,1,"10.1109/RO-MAN47096.2020.9223529","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095752866&doi=10.1109%2fRO-MAN47096.2020.9223529&partnerID=40&md5=18377ed0bf512bbf6c0ecc37209de44b","Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft, Netherlands; Leiden University, Leiden Institute of Advanced Computer Science, Leiden, Netherlands","Saad, E., Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft, Netherlands; Broekens, J., Leiden University, Leiden Institute of Advanced Computer Science, Leiden, Netherlands; Neerincx, M.A., Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft, Netherlands","The design space of human-robot interaction is large and multi-dimensional. A sound design requires a systematic theory-driven exploration, specification and refinement of design variables. There is a need for a practical method and tool to iteratively specify the content of the dialogue (e.g., speech acts) with the accompanying expressive behavior (e.g., gesture openness) as prescribed by social science theory, e.g., task- and person-oriented communication. This paper presents an iterative interaction-design (ID) method for multi-modal robot communication. Following the ID-method, a designer first creates his/her own individual design and, subsequently, provides an iteration to the evolving iterative design. To support the design method, we developed an ID-tool (available for download). The tool support entails (a) selecting the theory-based communication style; (b) creating and linking the dialogue act components for the concerning use case; and (c) setting the associated expression parameters.We conducted a study with Industrial Design students (N = 13) who followed the ID-method and used our tool to design person- and task-oriented communications for a reception robot. Our method produced distinctive task- and person-oriented dialogue styles, i.e., provided the predicted theory-based multi-modal communicative behaviors. The task-oriented style showed a more formal, shorter and less chatty communication. Overall, there was a rather smooth design convergence process, in which the individual designs were harmonized into the iterative design. For the selected design problem, the ID-tool had a satisfactory usability. Next steps include validation of the communication styles in an empirical study and, subsequently, identification of reusable design patterns. © 2020 IEEE.","Communication Styles; Expressive Behaviors; Human-Robot Interaction; Interaction Design; Iterative Design; Multi-modal Communication; Reception Robot","Agricultural robots; End effectors; Industrial robots; Iterative methods; Product design; Social robots; Speech communication; Communication styles; Design Convergence; Iterative interaction; Multi dimensional; Robot communication; Social science theory; Specification and refinement; Systematic theories; Machine design",,,,,,,,,,,,,,,,"Kato, Y., Kanda, T., Ishiguro, H., May i help you?-design of human-like polite approaching behavior (2015) ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 35-42. , Mar; Mizumaru, K., Satake, S., Kanda, T., Ono, T., Stop doing it! approaching strategy for a robot to admonish pedestrians (2019) ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 449-457. , Mar; Neuwelt, P.M., Kearns, R.A., Cairns, I.R., The care work of general practice receptionists (2016) Journal of Primary Health Care, 8, pp. 122-129. , June; Hewitt, H., McCloughan, L., McKinstry, B., Front desk talk: Discourse analysis of receptionist-patient interaction (2009) British Journal of General Practice, 59 (565), pp. e260-e266; Ward, J., Mcmurray, R., The unspoken work of general practitioner receptionists: A re-examination of emotion management in primary care (2011) Social Science & Medicine, 72, pp. 1583-1587. , March; Arber, S., Sawyer, L., The role of the receptionist in general practice: A 'dragon behind the desk'? (1985) Social Science & Medicine, 20 (9), pp. 911-921; Alazri, M., Heywood, P., Leese, B., How do receptionists view continuity of care and access in general practice? (2007) European Journal of General Practice, 13 (2), pp. 75-82; Xu, J., Broekens, J., Hindriks, K., Neerincx, M.A., Mood expression through parameterized functional behavior of robots (2013) 2013 IEEE RO-MAN, pp. 533-540. , Aug; Pelachaud, C., Badler, N.I., Steedman, M., Generating facial expressions for speech (1996) Cognitive Science, 20, pp. 1-46; Bales, R.F., (1951) Interaction Process Analysis: A Method for the Study of Small Groups, pp. 30-84. , Cambridge, Massachusetts: Addison-Wesley Press Inc., ch. 2: Theoretical framework; Miller, K., (2005) Communication Theories: Perspectives, Processes and Contexts, , 2nd ed. New York: McGraw Hill; Froehle, C., Service personnel, technology, and their interaction in influencing customer satisfaction (2006) Decision Sciences-DECISION SCI, 37. , February; Kang, I., Han, S., Lee, J., Task-oriented and relationship-building communications between air traffic controllers and pilots (2017) Sustainability, 9 (10); Pincus, J.D., Communication satisfaction, job satisfaction and job performance (1986) Human Communication Research, 12 (3), pp. 395-419; Michalowski, M.P., Sabanovic, S., Simmons, R., A spatial model of engagement for a social robot (2006) 9th IEEE International Workshop on Advanced Motion Control, 2006, pp. 762-767. , March; Holthaus, P., Pitsch, K., Wachsmuth, S., How can i help? (2011) Int. Journal of Social Robotics, 3 (4), pp. 383-393; Saad, E., Broekens, J., Neerincx, M.A., Hindriks, K.V., Enthusiastic robots make better contact (2019) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1094-1100. , Macau, China, Nov; Hall, E.T., (1966) The Hidden Dimension, pp. 113-129. , ch. X: Distances in Man. Garden City, N. Y. : Doubleday; Mumm, J., Mutlu, B., Human-robot proxemics: Physical and psychological distancing in human-robot interaction (2011) ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 331-338. , March; Macharet, D.G., Florencio, D.A., Learning how to increase the chance of human-robot engagement (2013) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2173-2179. , Nov; Otsuka, T., Nakadai, K., Takahashi, T., Komatani, K., Ogata, T., Okuno, H.G., Voice-awareness control for a humanoid robot consistent with its body posture and movements (2010) Paladyn, 1 (1), pp. 80-88. , Mar; Schwenk, M., Arras, K.O., R2-d2 reloaded: A flexible sound synthesis system for sonic human-robot interaction design (2014) The 23rd IEEE International Symposium on Robot and Human Interactive Communication, pp. 161-167. , Aug; McGinn, C., Torre, I., Can you tell the robot by the voice? An exploratory study on the role of voice in the perception of robots (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 211-221. , March; Trovato, G., Ramos, J.G., Azevedo, H., Moroni, A., Magossi, S., Ishii, H., Simmons, R., Takanishi, A., Designing a receptionist robot: Effect of voice and appearance on anthropomorphism (2015) IEEE RO-MAN, pp. 235-240. , Aug; Niculescu, A., Van Dijk, B., Nijholt, A., Li, H., See, S.L., Making social robots more attractive: The effects of voice pitch, humor and empathy (2013) International Journal of Social Robotics, 5 (2), pp. 171-191. , Apr; Widmeyer, W.N., Loy, J., When you're hot, you're hot! warmcold effects in first impressions of persons and teaching effectiveness (1988) Journal of Educational Psychology, 80, pp. 118-121. , 03; Boyd, R.L., Bresin, K., Ode, S., Robinson, M.D., Cognitive egocentrism differentiates warm and cold people (2013) Journal of Research in Personality, 41 (7), pp. 90-96. , Feb; Peters, R., Broekens, J., Neerincx, M.A., Robots educate in style: The effect of context and non-verbal behaviour on children's perceptions of warmth and competence (2017) 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN), pp. 449-455. , Aug; Kendon, A., Cook, M., The consistency of gaze patterns in social interaction (1969) British Journal of Psychology (London, England : 1953), 60, pp. 481-494. , December; Nguyen, T.-H.D., Carstensdottir, E., Ngo, N., El-Nasr, M.S., Gray, M., Isaacowitz, D., Desteno, D., Modeling warmth and competence in virtual characters (2015) Intelligent Virtual Agents, pp. 167-180. , W.-P. Brinkman, J. Broekens, and D. Heylen, Eds. Cham: Springer International Publishing; Prajod, P., Al Owayyed, M., Rietveld, T., Der Van Steeg, J.-J., Broekens, J., The effect of virtual agent warmth on human-agent negotiation (2019) Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, Ser. AAMAS '19, pp. 71-76. , Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems; Cassell, J., Bickmore, T., Campbell, L., Vilhjalmsson, H., Yan, H., (2000) Conversation As A System Framework: Designing Embodied Conversational Agents; Granström, B., House, D., Effective interaction with talking animated agents in dialogue systems (2005) Advances in Natural Multimodal Dialogue Systems, pp. 215-243. , Springer Netherlands; Zafari, S., Schwaninger, I., Hirschmanner, M., Schmidbauer, C., Weiss, A., Koeszegi, S.T., You are doing so great! the effect of a robot's interaction style on self-efficacy in hri (2019) 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 1-7. , Oct; Goetz, J., Kiesler, S., Cooperation with a robotic assistant (2002) Extended Abstracts on Human Factors in Computing Systems, Ser. CHI EA '02, pp. 578-579. , New York, NY, USA: ACM; Sutherland, C.J., Ahn, B.K., Brown, B., Lim, J., Johanson, D.L., Broadbent, E., MacDonald, B.A., Ahn, H.S., The doctor will see you now: Could a robot be a medical receptionist? (2019) 2019 International Conference on Robotics and Automation (ICRA), pp. 4310-4316. , May; Patricia, V., Albert, M., Effects of color on emotions (1994) Journal of Experimental Psychology, 123 (4), pp. 394-409; Wang, S., Ding, R., A qualitative and quantitative study of color emotion using valence-arousal (2012) Frontiers of Computer Science, 6 (4), pp. 469-476. , Aug; Terada, K., Yamauchi, A., Ito, A., Artificial emotion expression for a robot by dynamic color change (2012) 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication, pp. 314-321. , Sep; Sauppé, A., Mutlu, B., Design patterns for exploring and prototyping human-robot interactions (2014) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, p. 14391448. , New York, NY, USA: Association for Computing Machinery; Lund, A., Measuring usability with the use questionnaire (2001) Usability and User Experience Newsletter of the STC Usability SIG, 8 (1); Lewis, J.R., IBM computer usability satisfaction questionnaires: Psychometric evaluation and instructions for use (1995) International Journal of Human-Computer Interaction, pp. 57-78",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095752866
"Alimardani M., Kemmeren L., Okumura K., Hiraki K.","55836794100;57219855085;57219849487;7102170992;","Robot-Assisted Mindfulness Practice: Analysis of Neurophysiological Responses and Affective State Change",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223428","683","689",,5,"10.1109/RO-MAN47096.2020.9223428","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092931419&doi=10.1109%2fRO-MAN47096.2020.9223428&partnerID=40&md5=07bfbf48d9fc8d0e4c476dabcbb0c144","Tilburg University, Department of Cognitive Science and Ai, Tilburg, 5037 AB, Netherlands; University of Tokyo, Graduate School of Arts and Sciences, Tokyo, 153-0041, Japan","Alimardani, M., Tilburg University, Department of Cognitive Science and Ai, Tilburg, 5037 AB, Netherlands; Kemmeren, L., Tilburg University, Department of Cognitive Science and Ai, Tilburg, 5037 AB, Netherlands; Okumura, K., University of Tokyo, Graduate School of Arts and Sciences, Tokyo, 153-0041, Japan; Hiraki, K., University of Tokyo, Graduate School of Arts and Sciences, Tokyo, 153-0041, Japan","Mindfulness is the state of paying attention to the present moment on purpose and meditation is the technique to obtain this state. This study aims to develop a robot assistant that facilitates mindfulness training by means of a Brain-Computer Interface (BCI) system. To achieve this goal, we collected EEG signals from two groups of subjects engaging in a meditative vs. non-meditative human-robot interaction (HRI) and evaluated cerebral hemispheric asymmetry, which is recognized as a well-defined indicator of emotional states. Moreover, using self-reported affective states, we strived to explain asymmetry changes based on pre- and post-experiment mood alterations. We found that unlike earlier meditation studies, the fronto-central activations in alpha and theta frequency bands were not influenced by robot-guided mindfulness practice, however there was a significantly greater right-sided activity in the occipital gamma band of Meditation group, which is attributed to increased sensory awareness and open monitoring. In addition, there was a significant main effect of Time on participant's self-reported affect, indicating an improved mood after interaction with the robot regardless of the interaction type. Our results suggest that EEG responses during robot-guided meditation hold promise in real-time detection and neurofeedback of mindful state to the user, however the experienced neurophysiological changes may differ based on the meditation practice and recruited tools. This study is the first to report EEG changes during mindfulness practice with a robot. We believe that our findings driven from an ecologically valid setting, can be used in development of future BCI systems that are integrated with social robots for health applications. © 2020 IEEE.",,"Agricultural robots; Brain computer interface; Educational robots; End effectors; Neurophysiology; Affective state; Emotional state; Hemi-spheric asymmetries; Human robot Interaction (HRI); Neurofeedback; Real-time detection; Self reported affect; Theta frequency; Social robots",,,,,"Japan Society for the Promotion of Science, KAKEN: 15F15046, 18A4; Cabinet Office, Government of Japan","This research was supported by ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan), Grant-in-Aid for JSPS Research Fellow 15F15046, andSJTRCESTg(rant#PJMJCR18A4).",,,,,,,,,,"Kabat-Zinn, J., Mindfulness-based interventions in context: Past, present, and future (2003) Clinical Psychology: Science and Practice, 10 (2), pp. 144-156; Gu, J., Strauss, C., Bond, R., Cavanagh, K., How do mindfulness-based cognitive therapy and mindfulness-based stress reduction improve mental health and wellbeing? A systematic review and meta-analysis of mediation studies (2015) Clinical Psychology Review, 37, pp. 1-12; Lee, D.J., Kulubya, E., Goldin, P., Goodarzi, A., Girgis, F., Review of the neural oscillations underlying meditation (2018) Frontiers in Neuroscience, 12, p. 178; Lomas, T., Ivtzan, I., Fu, C.H., A systematic review of the neurophysiology of mindfulness on EEG oscillations (2015) Neuroscience & Biobehavioral Reviews, 57, pp. 401-410; Sas, C., Chopra, R., MeditAid: A wearable adaptive neurofeedback-based system for training mindfulness state (2015) Personal and Ubiquitous Computing, 19 (7), pp. 1169-1182; Fingelkurts, A.A., Fingelkurts, A.A., Kallio-Tamminen, T., EEG-guided meditation: A personalized approach (2015) Journal of Physiology-Paris, 109 (4-6), pp. 180-190; Kosunen, I., Salminen, M., Järvelä, S., Ruonala, A., Ravaja, N., Jacucci, G., RelaWorld: Neuroadaptive and immersive virtual reality meditation system (2016) Proceedings of the 21st International Conference on Intelligent User Interfaces, pp. 208-217. , March; Imel, Z., Baldwin, S., Bonus, K., MacCoon, D., Beyond the individual: Group effects in mindfulness-based stress reduction (2008) Psychotherapy Research, 18 (6), pp. 735-742; Mantzios, M., Giannou, K., Group vs. single mindfulness meditation: Exploring avoidance, impulsivity, and weight management in two separate mindfulness meditation settings (2014) Applied Psychology: Health and Well-Being, 6 (2), pp. 173-191; Schaub, R., Clinical meditation teacher: A new role for health professionals (2011) Journal of Evidence-Based Complementary & Alternative Medicine, 16 (2), pp. 145-148; Li, J., The benefit of being physically present: A survey of experimental works comparing copresent robots, telepresent robots and virtual agents (2015) International Journal of Human-Computer Studies, 77, pp. 23-37; Shamekhi, A., Bickmore, T., Breathe deep: A breath-sensitive interactive meditation coach (2018) Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare, pp. 108-117. , May; Tapus, A., Tapus, C., Mataric, M., The role of physical embodiment of a therapist robot for individuals with cognitive impairments (2009) RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 103-107. , IEEE; Alimardani, M., Hiraki, K., Development of a real-time brain-computer interface for interactive robot therapy: An exploration of EEG and emg features during hypnosis (2017) Int. J. Comput. Electric. Autom. Control Inform. Eng, 11, pp. 187-195; Tjon, D., Tinga, A., Alimardani, M., Louwerse, M., Brain activity reflects sense of presence in 360 degrees video for virtual reality (2019) The 28th International Conference on Information Systems Development; Kaur, C., Singh, P., EEG derived neuronal dynamics during meditation: Progress and challenges (2015) Advances in Preventive Medicine; Braboszcz, C., Cahn, B.R., Levy, J., Fernandez, M., Delorme, A., Increased gamma brainwave amplitude compared to control in three different meditation traditions (2017) PLoS One, 12 (1); Davidson, R.J., Asymmetric brain function, affective style, and psychopathology: The role of early experience and plasticity (1994) Development and Psychopathology, 6 (4), pp. 741-758; Moyer, C.A., Donnelly, M.P., Anderson, J.C., Valek, K.C., Huckaby, S.J., Wiederholt, D.A., Rice, B.L., Frontal electroencephalographic asymmetry associated with positive emotion is produced by very brief meditation training (2011) Psychological Science, 22 (10), pp. 1277-1279; Keune, P.M., Bostanov, V., Hautzinger, M., Kotchoubey, B., Approaching dysphoric mood: State-effects of mindfulness meditation on frontal brain asymmetry (2013) Biological Psychology, 93 (1), pp. 105-113; Mennella, R., Patron, E., Palomba, D., Frontal alpha asymmetry neurofeedback for the reduction of negative affect and anxiety (2017) Behaviour Research and Therapy, 92, pp. 32-40; Tarrant, J., Cope, H., Combining frontal gamma asymmetry neurofeedback with virtual reality: A proof of concept case study (2018) NeuroRegulation, 5 (2), p. 57; Williams, M., Teasdale, J.D., Segal, Z., Kabat-Zinn, J., (2007) The Mindful Way Through Depression: Freeing Yourself from Chronic Unhappiness, , Guilford Press; Watson, D., Clark, L.A., Tellegen, A., Development and validation of brief measures of positive and negative affect: The panas scales (1988) Journal of Personality and Social Psychology, 54 (6), p. 1063; Oostenveld, R., Fries, P., Maris, E., Schoffelen, J.M., FieldTrip: Open source software for advanced analysis of meg, EEG, and invasive electrophysiological data (2011) Computational Intelligence and Neuroscience; Delorme, A., Makeig, S., EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis (2004) Journal of Neuroscience Methods, 134 (1), pp. 9-21; Johnson, S., Gur, R.M., David, Z., Currier, E., One-session mindfulness meditation: A randomized controlled study of effects on cognition and mood (2015) Mindfulness, 6 (1), pp. 88-98; Berkovich-Ohana, A., Glicksohn, J., Goldstein, A., Mindfulness-induced changes in gamma band activity-implications for the default mode network, self-reference and attention (2012) Clinical Neurophysiology, 123 (4), pp. 700-710; Cahn, B.R., Delorme, A., Polich, J., Occipital gamma activation during vipassana meditation (2010) Cognitive Processing, 11 (1), pp. 39-56; Travis, F., Shear, J., Focused attention, open monitoring and automatic self-transcending: Categories to organize meditations from vedic, buddhist and Chinese traditions (2010) Consciousness and Cognition, 19 (4), pp. 1110-1118; Leite, I., Martinho, C., Pereira, A., Paiva, A., As time goes by: Long-term evaluation of social presence in robotic companions RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, pp. 669-674. , IEEE; Lee, K.M., Peng, W., Jin, S.A., Yan, C., Can robots manifest personality?: An empirical test of personality recognition, social responses, and social presence in human-robot interaction (2006) Journal of Communication, 56 (4), pp. 754-772; Pozharliev, R., Verbeke, W.J., Van Strien, J.W., Bagozzi, R.P., Merely being with you increases my attention to luxury products: Using EEG to understand consumers' emotional experience with luxury branded products (2015) Journal of Marketing Research, 52 (4), pp. 546-558; Ambrosini, E., Vallesi, A., Asymmetry in prefrontal resting-state EEG spectral power underlies individual differences in phasic and sustained cognitive control (2016) Neuroimage, 124, pp. 843-857; Rabbitt, S.M., Kazdin, A.E., Scassellati, B., Integrating socially assistive robotics into mental healthcare interventions: Applications and recommendations for expanded use (2015) Clinical Psychology Review, 35, pp. 35-46",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","All Open Access, Green",Scopus,2-s2.0-85092931419
"Tang X., Li W., Li X., Ma W., Dang X.","23394027100;57207124728;57218467762;57208317723;57213424909;","Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network",2020,"Expert Systems with Applications","149",,"113285","","",,39,"10.1016/j.eswa.2020.113285","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079322525&doi=10.1016%2fj.eswa.2020.113285&partnerID=40&md5=09476c7f0515d0250409a52ed7dfd37c","Chongqing Key Laboratory of Complex Systems and Bionic Control, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; College of Mobile Telecommunications, Chongqing University of Posts and Telecom, Chongqing, 401520, China","Tang, X., Chongqing Key Laboratory of Complex Systems and Bionic Control, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; Li, W., Chongqing Key Laboratory of Complex Systems and Bionic Control, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; Li, X., Chongqing Key Laboratory of Complex Systems and Bionic Control, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; Ma, W., Chongqing Key Laboratory of Complex Systems and Bionic Control, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; Dang, X., College of Mobile Telecommunications, Chongqing University of Posts and Telecom, Chongqing, 401520, China","Electroencephalogram (EEG) signals classification plays a crucial role in brain computer interfaces (BCIs) system. However, the inherent complex properties of EEG signals make it challenging to get them analyzed and modeled. In this paper, a novel method based on conditional empirical mode decomposition (CEMD) and one-dimensional multi-scale convolutional neural network (1DMSCNN) is proposed to recognize motor imagery (MI) EEG signals. In the CEMD algorithm, the correlation coefficient between the original EEG signal and each intrinsic modal component (IMF) is used as the first condition to select IMFs, and the relative energy occupancy rates between the IMFs are the second condition. The CEMD algorithm is applied to remove the noise of EEG signals. Then, an EEG signals combination method is proposed to encode event-related synchronization/de-synchronization (ERS/ERD) information between the channels. Finally, a model called 1DMSCNN is built to classify the processed EEG signals. The proposed method is applied to the dataset collected in our laboratory and BCI competition IV dataset 2b. The results indicate that the proposed method can achieve higher accuracy for EEG signals classification, compared with other state-of-the-art works. In addition, the proposed algorithm is applied to the online recognition of EEG signals, a BCI system that directly interacts with brain and wheelchair is designed and implemented. This system can directly command wheelchair to turn left and right through EEG signals. The online experimental results indicate that the designed intelligent wheelchair system is a feasible BCI application. It verifies the proposed algorithm can be used in expert and intelligent systems. Our method can provide a stimulus to the development of human-robot interaction. © 2020","Convolutional neural network; Empirical mode decomposition; Feature extraction; Intelligent wheelchair; Motor imagery EEG","Brain computer interface; Convolution; Convolutional neural networks; Electroencephalography; Feature extraction; Human robot interaction; Intelligent robots; Intelligent systems; One dimensional; Wheelchairs; Brain computer interfaces (BCIs); Correlation coefficient; EEG signals classification; Electroencephalogram signals; Empirical Mode Decomposition; Event-related synchronizations; Intelligent wheelchair; Motor imagery EEG; Biomedical signal processing",,,,,"National Natural Science Foundation of China, NSFC: 61673079, 61703068; Natural Science Foundation of Chongqing: cstc2018jcyjAX0160","This work is supported by the National Natural Science Foundation of China under Project 61673079 , 61703068 and the Natural Science Foundation of Chongqing under Project cstc2018jcyjAX0160 .",,,,,,,,,,"Chu, Y., Zhao, X., Zou, Y., Xu, W., Han, J., Zhao, Y., A decoding scheme for incomplete motor imagery EEG with deep belief network (2018) Frontiers in Neuroscience, 12, p. 680; Cecotti, H., Graeser, A., Convolutional neural network with embedded Fourier transform for EEG classification (2008) 2008 19th International Conference on Pattern Recognition, pp. 1-4; Dose, H., Møller, J.S., Iversen, H.K., Puthusserypady, S., An end-to-end deep learning approach to MI-EEG signal classification for BCIs (2018) Expert Systems with Applications, 114, pp. 532-542; Gui, Q., Jin, Z., Xu, W., Ruiz-Blondet, M.V., Laszlo, S., Multichannel EEG-based biometric using improved RBF neural networks (2015) 2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), pp. 1-6; Gaur, P., Pachori, R.B., Wang, H., Prasad, G., An empirical mode decomposition based filtering method for classification of motor-imagery EEG signals for enhancing brain-computer interface (2015) 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1-7; Goh, S.K., Abbass, H.A., Tan, K.C., Al-Mamun, A., Thakor, N., Bezerianos, A., Spatio–spectral representation learning for electroencephalographic gait-pattern classification (2018) IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26 (9), pp. 1858-1867; 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Chongqing Key Laboratory of Complex Systems and Bionic Control, China; email: cqyddxliwei@foxmail.com",,,"Elsevier Ltd",,,,,09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","",Scopus,2-s2.0-85079322525
"Helps T., Taghavi M., Wang S., Rossiter J.","57201631960;55807930000;57217091869;56230431100;","Twisted Rubber Variable-Stiffness Artificial Muscles",2020,"Soft Robotics","7","3",,"386","395",,7,"10.1089/soro.2018.0129","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086161121&doi=10.1089%2fsoro.2018.0129&partnerID=40&md5=712141266f8ea2d38d60cdad6625d446","Department of Engineering Mathematics, University of Bristol, 75 Woodland Road, Bristol, BS8 1UB, United Kingdom; Bristol Robotics Laboratory, Bristol, United Kingdom","Helps, T., Department of Engineering Mathematics, University of Bristol, 75 Woodland Road, Bristol, BS8 1UB, United Kingdom, Bristol Robotics Laboratory, Bristol, United Kingdom; Taghavi, M., Department of Engineering Mathematics, University of Bristol, 75 Woodland Road, Bristol, BS8 1UB, United Kingdom, Bristol Robotics Laboratory, Bristol, United Kingdom; Wang, S., Department of Engineering Mathematics, University of Bristol, 75 Woodland Road, Bristol, BS8 1UB, United Kingdom; Rossiter, J., Department of Engineering Mathematics, University of Bristol, 75 Woodland Road, Bristol, BS8 1UB, United Kingdom, Bristol Robotics Laboratory, Bristol, United Kingdom","Variable-stiffness artificial muscles are important in many applications including running and hopping robots, human-robot interaction, and active suspension systems. Previously used technologies include pneumatic muscles, layer and granular jamming, series elastic actuators, and shape memory polymers. All these are limited in terms of cost, complexity, the need for fluid power supplies, or controllability. In this article, we present a new concept for variable-stiffness artificial muscles (the twisted rubber artificial muscle, TRAM) made from twisted rubber cord that overcomes these limitations. Rubber cord is inexpensive, readily available, and inherently compliant. When an extended piece of rubber cord is twisted, the tensile force it exerts is reduced and its stiffness is altered. This behavior makes twisted rubber ideal for use as an artificial muscle, because its output force and natural stiffness are both controllable by varying twist angle. We investigate the behavior of four types of rubber cord and evaluate which type of rubber allows for the greatest reversible reduction in average stiffness (fluoroelastomer [FKM standard] rubber, 56.42% reduction) and initial stiffness (silicone rubber, 92.62%). Tensile force and stiffness can be further altered by increasing the twist angle of the artificial muscle beyond a threshold angle, which initiates nonlinear buckling behavior. This, however, can cause plastic deformation of the artificial muscle. Using a single TRAM, we show how the equilibrium position and natural frequency of a system can be simultaneously altered by controlling twist angle. We further demonstrate independent position and stiffness control of a functional robotic arm system using an antagonistic pair of TRAMs. TRAMs are ready for immediate inclusion in a wide range of robotic systems. © Tim Helps et al. 2020; Published by Mary Ann Liebert, Inc. 2020.","artificial; muscle; rubber; twisted; variable-stiffness",,,,,,"Engineering and Physical Sciences Research Council, EPSRC: EP/ M026388/1, EP/M020460/1, EP/R02961X/1; Royal Academy of Engineering, RAENG","T.H. is supported by the Royal Academy of Engineering and the Office of the Chief Science Adviser for National Security under the UK Intelligence Community Postdoctoral Fellowship Programme and EPSRC grant EP/M026388/1. M.T. is supported by EPSRC grant EP/R02961X/1 and EP/ M026388/1. J.R. is supported by the Royal Academy of Engineering through the Chair in Emerging Technologies scheme and EPSRC grants EP/M020460/1 and EP/M026388/1.",,,,,,,,,,"Alexander, R.M., Elastic energy stores in running vertebrates (1984) Am Zool, 24, pp. 85-94; Farley, C.T., González, O., Leg stiffness and stride frequency in human running (1996) J Biomech, 29, pp. 181-186; Tonietti, G., Schiavi, R., Bicchi, A., Design and control of a variable stiffness actuator for safe and fast physical human/ robot interaction (2005) Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005, pp. 526-531. , Barcelona, Spain; IEEE; Sharp, R.S., Hassan, S.A., An evaluation of passive automotive suspension systems with variable stiffness and damping parameters (1986) Veh Syst Dyn, 15, pp. 335-350; Taghavi, M., Helps, T., Huang, B., 3D-printed ready-touse variable-stiffness structures (2018) IEEE Robot Autom Lett, 3, pp. 2402-2407; Kawamura, S., Yamamoto, T., Ishida, D., Development of passive elements with variable mechanical impedance for wearable robots (2002) Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. 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III. Some simple problems in cylindrical polar coordinates (1948) Philos Trans R Soc A Math Phys Eng Sci, 240, pp. 509-525; Suphadon, N., Busfield, J.J.C., Elastic behaviour of rubber cylinders under combined torsion and tension loading (2009) Plast Rubber Compos, 38, pp. 337-342; Kovacs, G., Düring, L., Michel, S., Stacked dielectric elastomer actuator for tensile force transmission (2009) Sens Actuators A Phys, 155, pp. 299-307; Haines, C.S., Lima, M.D., Li, N., Artificial muscles from fishing line and sewing thread (2014) Science, 343, pp. 868-872; Thompson, J.M.T., Champneys, A.R., From helix to localized writhing in the torsional post-buckling of elastic rods (1996) Proc R Soc London ser A Math Phys Eng Sci, 452, pp. 117-138; Van Der Heijden, G.H.M., Thompson, J.M.T., Helical and localized buckling in twisted rods: A unified analysis of the symmetric case (2000) Nonlinear Dyn, 21, pp. 71-99; Neukirch, S., Writhing Instabilities of twisted rods: From infinite to finite length (2002) J Mech Phys Solids, 50, pp. 1175-1191","Helps, T.; Department of Engineering Mathematics, 75 Woodland Road, United Kingdom; email: tim.helps@bristol.ac.uk",,,"Mary Ann Liebert Inc.",,,,,21695172,,,"31855114","English","Soft Robotics",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85086161121
"Poyil A.T., Steuber V., Amirabdollahian F.","55362468300;6603424105;6507523741;","Adaptive robot mediated upper limb training using electromyogram-based muscle fatigue indicators",2020,"PLoS ONE","15","5","e0233545","","",,,"10.1371/journal.pone.0233545","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085714077&doi=10.1371%2fjournal.pone.0233545&partnerID=40&md5=e2c87be15f943b5e95fa824f4ff10077","School of Computer Science, University of Hertfordshire, Hatfield, United Kingdom","Poyil, A.T., School of Computer Science, University of Hertfordshire, Hatfield, United Kingdom; Steuber, V., School of Computer Science, University of Hertfordshire, Hatfield, United Kingdom; Amirabdollahian, F., School of Computer Science, University of Hertfordshire, Hatfield, United Kingdom","Studies on improving the adaptability of upper limb rehabilitation training do not often consider the implications of muscle fatigue sufficiently. In this study, electromyogram features were used as fatigue indicators in the context of human-robot interaction. They were utilised for auto-Adaptation of the task difficulty, which resulted in a prolonged training interaction. The electromyogram data was collected from three gross-muscles of the upper limb in 30 healthy participants. The experiment followed a protocol for increasing the muscle strength by progressive strength training, that was an implementation of a known method in sports science for muscle training, in a new domain of robotic adaptation in muscle training. The study also compared how the participants in three experimental conditions perceived the change in task difficulty levels. One task benefitted from robotic adaptation (Intervention group) where the robot adjusted the task difficulty. The other two tasks were control groups 1 and 2. There was no difficulty adjustment at all in Control 1 group and the difficulty was adjusted manually in Control 2 group. The results indicated that the participants could perform a prolonged progressive strength training exercise with more repetitions with the help of a fatigue-based robotic adaptation, compared to the training interactions, which were based on manual/no adaptation. This study showed that it is possible to alter the level of the challenge using fatigue indicators, and thus, increase the interaction time. The results of the study are expected to be extended to stroke patients in the future by utilising the potential for adapting the training difficulty according to the patient's muscular state, and also to have a large number repetitions in a robot-Assisted training environment. © 2020 Public Library of Science. All rights reserved.",,"adult; Article; clinical article; comparative study; controlled study; electromyogram; female; human; intervention study; kinematics; male; muscle fatigue; muscle strength; muscle training; resistance training; robotics; signal processing; stroke patient; task performance; upper limb; electromyography; muscle fatigue; stroke rehabilitation; Electromyography; Humans; Muscle Fatigue; Robotics; Stroke Rehabilitation; Upper Extremity",,,,,,,,,,,,,,,,"Goodrich, M.A., Schultz, A.C., Goodrich, M.A., Schultz, A.C., Human-robot interaction a survey (2007) Foundations and Trends R in Human-Computer Interaction, 1 (3), pp. 203-275; David Jaffe L, M., Nelson, D., Thiemer, J., (2012) Perspectives in Assistive Technology; Laut, J., Porfiri, M., Raghavan, P., The Present and Future of Robotic Technology in Rehabilitation (2016) Current Physical Medicine and Rehabilitation Reports, 4 (4), pp. 312-319. , https://doi.org/10.1007/s40141-016-0139-0; Stein, J., Krebs, H.I., Frontera, W.R., Fasoli, S.E., Hughes, R., Hogan, N., Comparison of two techniques of robot-Aided upper limb exercise training after stroke (2004) American Journal of Physical Medicine & Rehabilitation, 83 (9), pp. 720-728; Xu, G., Gao, X., Chen, S., Wang, Q., Zhu, B., Li, J., A novel approach for robot-Assisted upper-limb rehabilitation (2017) International Journal of Advanced Robotic Systems, 14 (6); Son, J., Ryu, J., Ahn, S., Kim, E.J., Lee, J.A., Kim, Y., Effects of 4-Week Intensive Active-Resistive Training with an EMG-Based Exoskeleton Robot on Muscle Strength in Older People: A Pilot Study (2016) BioMed Research International, 2016, pp. 1-5; Han, C., Wang, Q., Pp, M., Mz, Q., Effects of intensity of arm training on hemiplegic upper extremity motor recovery in stroke patients: A randomized controlled trial (2013) Clinical Rehabilitation, 27 (1), pp. 75-81; Oujamaa, L., Relave, I., Froger, J., Mottet, D., Pelissier, J.Y., Rehabilitation of arm function after stroke (2009) Literature Review. 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"Werner C., Kardaris N., Koutras P., Zlatintsi A., Maragos P., Bauer J.M., Hauer K.","57188639923;57189596992;56242401400;34871164000;35243026700;57192890201;7005208898;","Improving gesture-based interaction between an assistive bathing robot and older adults via user training on the gestural commands",2020,"Archives of Gerontology and Geriatrics","87",,"103996","","",,3,"10.1016/j.archger.2019.103996","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076372784&doi=10.1016%2fj.archger.2019.103996&partnerID=40&md5=96908e7096f9c5e4a762ceac8d4818ae","Center for Geriatric Medicine, Heidelberg University, Heidelberg, Germany; Agaplesion Bethanien Hospital Heidelberg, Geriatric Center at the Heidelberg University, Heidelberg, Germany; Institute of Communication and Computer Systems (ICCS), National Technical University of Athens, Athens, Greece","Werner, C., Center for Geriatric Medicine, Heidelberg University, Heidelberg, Germany, Agaplesion Bethanien Hospital Heidelberg, Geriatric Center at the Heidelberg University, Heidelberg, Germany; Kardaris, N., Institute of Communication and Computer Systems (ICCS), National Technical University of Athens, Athens, Greece; Koutras, P., Institute of Communication and Computer Systems (ICCS), National Technical University of Athens, Athens, Greece; Zlatintsi, A., Institute of Communication and Computer Systems (ICCS), National Technical University of Athens, Athens, Greece; Maragos, P., Institute of Communication and Computer Systems (ICCS), National Technical University of Athens, Athens, Greece; Bauer, J.M., Center for Geriatric Medicine, Heidelberg University, Heidelberg, Germany, Agaplesion Bethanien Hospital Heidelberg, Geriatric Center at the Heidelberg University, Heidelberg, Germany; Hauer, K., Agaplesion Bethanien Hospital Heidelberg, Geriatric Center at the Heidelberg University, Heidelberg, Germany","Background: Gesture-based human-robot interaction (HRI) depends on the technical performance of the robot-integrated gesture recognition system (GRS) and on the gestural performance of the robot user, which has been shown to be rather low in older adults. Training of gestural commands (GCs) might improve the quality of older users’ input for gesture-based HRI, which in turn may lead to an overall improved HRI. Objective: To evaluate the effects of a user training on gesture-based HRI between an assistive bathing robot and potential elderly robot users. Methods: Twenty-five older adults with bathing disability participated in this quasi-experimental, single-group, pre-/post-test study and underwent a specific user training (10−15 min) on GCs for HRI with the assistive bathing robot. Outcomes measured before and after training included participants’ gestural performance assessed by a scoring method of an established test of gesture production (TULIA) and sensor-based gestural performance (SGP) scores derived from the GRS-recorded data, and robot's command recognition rate (CRR). Results: Gestural performance (TULIA = +57.1 ± 56.2 %, SGP scores = +41.1 ± 74.4 %) and CRR (+31.9 ± 51.2 %) significantly improved over training (p < .001). Improvements in gestural performance and CRR were highly associated with each other (r = 0.80–0.81, p < .001). Participants with lower initial gestural performance and higher gerontechnology anxiety benefited most from the training. Conclusions: Our study highlights that training in gesture-based HRI with an assistive bathing robot is highly beneficial for the quality of older users’ GCs, leading to higher CRRs of the robot-integrated GRS, and thus to an overall improved HRI. © 2019 Elsevier B.V.","Activities of daily living; Aged; Gestures; Human-robot interaction; Robotics; Training","aged; aging; anxiety; Article; body movement; body position; daily life activity; female; gesture; human; human computer interaction; male; physical disability; physical performance; priority journal; quasi experimental study; robotics; scoring system; bath; human computer interaction; procedures; robotics; self help device; very elderly; Activities of Daily Living; Aged; Aged, 80 and over; Baths; Computer User Training; Female; Gestures; Humans; Male; Robotics; Self-Help Devices",,,,,"Horizon 2020 Framework Programme, H2020: 643666; Horizon 2020","This study was supported by the European Commission’s Horizon 2020 research and innovation programme (H2020-EU.3.1.4. - Active ageing and self-management of health) under the project “I-SUPPORT - ICT-Supported Bath Robots” (grant agreement no. 643666 ). The funding source had no role in the design and conduct of the study; collection, analysis and interpretation of data; and preparation, review or approval of the manuscript.","This study was supported by the European Commission's Horizon 2020 research and innovation programme (H2020-EU.3.1.4. - Active ageing and self-management of health) under the project ?I-SUPPORT - ICT-Supported Bath Robots? (grant agreement no. 643666). 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"Charisi V., Gomez E., Mier G., Merino L., Gomez R.","56766259400;14015483200;57209469070;7003946345;12806763700;","Child-Robot Collaborative Problem-Solving and the Importance of Child's Voluntary Interaction: A Developmental Perspective",2020,"Frontiers in Robotics and AI","7",,"15","","",,5,"10.3389/frobt.2020.00015","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080869492&doi=10.3389%2ffrobt.2020.00015&partnerID=40&md5=435ddb57d1ffda79ba6836a8e09e55d9","Centre for Advanced Studies, Joint Research Centre, European Commission, Seville, Spain; Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain; Service Robotics Lab, School of Engineering, Universidad Pablo de Olavide, Seville, Spain; Honda Research Institute Japan Co., Ltd, Saitama, Japan","Charisi, V., Centre for Advanced Studies, Joint Research Centre, European Commission, Seville, Spain; Gomez, E., Centre for Advanced Studies, Joint Research Centre, European Commission, Seville, Spain, Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain; Mier, G., Service Robotics Lab, School of Engineering, Universidad Pablo de Olavide, Seville, Spain; Merino, L., Service Robotics Lab, School of Engineering, Universidad Pablo de Olavide, Seville, Spain; Gomez, R., Honda Research Institute Japan Co., Ltd, Saitama, Japan","The emergence and development of cognitive strategies for the transition from exploratory actions towards intentional problem-solving in children is a key question for the understanding of the development of human cognition. Researchers in developmental psychology have studied cognitive strategies and have highlighted the catalytic role of the social environment. However, it is not yet adequately understood how this capacity emerges and develops in biological systems when they perform a problem-solving task in collaboration with a robotic social agent. This paper presents an empirical study in a human-robot interaction (HRI) setting which investigates children's problem-solving from a developmental perspective. In order to theoretically conceptualize children's developmental process of problem-solving in HRI context, we use principles based on the intuitive theory and we take into consideration existing research on executive functions with a focus on inhibitory control. We considered the paradigm of the Tower of Hanoi and we conducted an HRI behavioral experiment to evaluate task performance. We designed two types of robot interventions, “voluntary” and “turn-taking”—manipulating exclusively the timing of the intervention. Our results indicate that the children who participated in the voluntary interaction setting showed a better performance in the problem solving activity during the evaluation session despite their large variability in the frequency of self-initiated interactions with the robot. Additionally, we present a detailed description of the problem-solving trajectory for a representative single case-study, which reveals specific developmental patterns in the context of the specific task. Implications and future work are discussed regarding the development of intelligent robotic systems that allow child-initiated interaction as well as targeted and not constant robot interventions. © Copyright © 2020 Charisi, Gomez, Mier, Merino and Gomez.","child-robot interaction; education; problem solving; robotics; self-initiated interaction",,,,,,"European Commission, EC","We thank the teachers and the participant children. We also thank M. Escobar-Planas, E. Moreno-Cruz, and A. Bolivar-Gonzalez for their contribution to the data annotation and analysis. This research was supported by the HUMAINT project of the Centre for Advanced Studies of the JRC, European Commission.",,,,,,,,,,"Alves-Oliveira, P., Arriaga, P., Paiva, A., Hoffman, G., Yolo, a robot for creativity: a co-design study with children (2017) Proceedings of the 2017 Conference on Interaction Design and Children, pp. 423-429. , Stanford, CA, ACM; Bandura, A., (1971) Social Learning Theory, , Morristown, NJ; Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: a review (2018) Sci. 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AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85080869492
"Martin F.A., Malfaz M., Castro-González Á., Castillo J.C., Salichs M.Á.","55215683700;8613265500;36655483500;7202601719;6603829011;","Four-features evaluation of text to speech systems for three social robots",2020,"Electronics (Switzerland)","9","2","267","","",,3,"10.3390/electronics9020267","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079181032&doi=10.3390%2felectronics9020267&partnerID=40&md5=02c410ab10c9a0ddf6514c12b962edbc","Department of Robotics, University Carlos III of Madrid, Avda de la Universidad, 30, Leganés, Madrid  28911, Spain","Martin, F.A., Department of Robotics, University Carlos III of Madrid, Avda de la Universidad, 30, Leganés, Madrid  28911, Spain; Malfaz, M., Department of Robotics, University Carlos III of Madrid, Avda de la Universidad, 30, Leganés, Madrid  28911, Spain; Castro-González, Á., Department of Robotics, University Carlos III of Madrid, Avda de la Universidad, 30, Leganés, Madrid  28911, Spain; Castillo, J.C., Department of Robotics, University Carlos III of Madrid, Avda de la Universidad, 30, Leganés, Madrid  28911, Spain; Salichs, M.Á., Department of Robotics, University Carlos III of Madrid, Avda de la Universidad, 30, Leganés, Madrid  28911, Spain","The success of social robotics is directly linked to their ability of interacting with people. Humans possess verbal and non-verbal communication skills, and, therefore, both are essential for social robots to get a natural human–robot interaction. This work focuses on the first of them since the majority of social robots implement an interaction system endowed with verbal capacities. In order to do this implementation, we must equip social robots with an artificial voice system. In robotics, a Text to Speech (TTS) system is the most common speech synthesizer technique. The performance of a speech synthesizer is mainly evaluated by its similarity to the human voice in relation to its intelligibility and expressiveness. In this paper, we present a comparative study of eight off-the-shelf TTS systems used in social robots. In order to carry out the study, 125 participants evaluated the performance of the following TTS systems: Google, Microsoft, Ivona, Loquendo, Espeak, Pico, AT&T, and Nuance. The evaluation was performed after observing videos where a social robot communicates verbally using one TTS system. The participants completed a questionnaire to rate each TTS system in relation to four features: intelligibility, expressiveness, artificiality, and suitability. In this study, four research questions were posed to determine whether it is possible to present a ranking of TTS systems in relation to each evaluated feature, or, on the contrary, there are no significant differences between them. Our study shows that participants found differences between the TTS systems evaluated in terms of intelligibility, expressiveness, and artificiality. The experiments also indicated that there was a relationship between the physical appearance of the robots (embodiment) and the suitability of TTS systems. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Accessibility technologies; Natural language generation; Speech-based; Text to speech systems; User studies",,,,,,"Comunidad de Madrid; European Commission, EC; Ministerio de EconomÃ­a y Competitividad, MINECO; Agencia Estatal de InvestigaciÃ³n, AEI","Funding: The research leading to these results has received funding from the projects: “Development of social robots to help seniors with cognitive impairment (ROBSEN)”, funded by the Ministerio de Economia y Competitividad; “RoboCity2030-DIH-CM”, funded by Comunidad de Madrid and co-funded by Structural Funds of the EU; “Robots Sociales para estimulación física, cognitiva y afectiva de mayores (ROSES)” funded by Agencia Estatal de Investigación (AEI) Acknowledgments: Give thanks to all the entities that have financed part of this research, as well as, to all the users who have wanted to participate and contribute to the development of this work.",,,,,,,,,,"van Bezooijen, R., Pols, L.C., Evaluating text-to-speech systems: Some methodological aspects (1990) Speech Commun, 9, pp. 263-270; Handley, Z., Is text-to-speech synthesis ready for use in computer-assisted language learning? 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"Kopp T., Baumgartner M., Kinkel S.","57201388519;57218326101;6701428946;","Success factors for introducing industrial human-robot interaction in practice: an empirically driven framework",2020,"International Journal of Advanced Manufacturing Technology",,,,"","",,7,"10.1007/s00170-020-06398-0","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097442312&doi=10.1007%2fs00170-020-06398-0&partnerID=40&md5=793ccebc27f39a00c7bb2f6f28b10792","Institute for Learning and Innovation in Networks, Karlsruhe University of Applied Sciences, Moltkestraße 30, Karlsruhe, 76133, Germany","Kopp, T., Institute for Learning and Innovation in Networks, Karlsruhe University of Applied Sciences, Moltkestraße 30, Karlsruhe, 76133, Germany; Baumgartner, M., Institute for Learning and Innovation in Networks, Karlsruhe University of Applied Sciences, Moltkestraße 30, Karlsruhe, 76133, Germany; Kinkel, S., Institute for Learning and Innovation in Networks, Karlsruhe University of Applied Sciences, Moltkestraße 30, Karlsruhe, 76133, Germany","Human-robot interaction (HRI) promises to be a means whereby manufacturing companies will be able to address current challenges like a higher demand for customization. However, despite comparably low costs, there are only few applications in practice. To date, it remains unclear which factors facilitate or hinder the successful introduction of industrial collaborative robots (cobots). In a three-step approach, we first developed a comprehensive two-dimensional framework covering three separate phases and four essential components for human-robot working systems. Secondly, we reviewed related literature to identify relevant success factors. Thirdly, in an online survey we asked leading representatives of German manufacturing companies (n = 81) to assess the importance of these factors from a practical point of view. The results reveal that besides technology-related factors like occupational safety and appropriate cobot configuration, employee-centered factors like the fear of job loss and ensuring an appropriate level of trust in the robot are considered important. However, company representatives seem to underestimate the impact of subtle measures to increase employee acceptance which could be incorporated into internal communication strategies prior to and during the introduction of cobots. Comparative analysis based on three distinct application scenarios suggests that most success factors’ practical importance is independent of the motivation for implementing HRI. Furthermore, answers from practitioners in free-text fields reveal that success factors which intuitively come to their mind such as financial factors are not necessarily perceived most important. 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(2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction - HRI '18, pp. 279-288. , Kanda T, Sabanovic S, Hoffman G, (eds), ACM Press, New York; Granulo, A., Fuchs, C., Puntoni, S., Psychological reactions to human versus robotic job replacement (2019) Nat Hum Behav, 3, pp. 1062-1069; Salvini, P., Laschi, C., Dario, P., Design for acceptability: improving robots’ coexistence in human society (2010) Int J Soc Robot, 2, pp. 451-460; Acemoglu, D., Restrepo, P., Robots and jobs: evidence from US labor markets (2019) J Polit Econ, 128, pp. 2188-2244; Sauppé, A., Mutlu, B., The social impact of a robot co-worker in industrial settings (2015) CHI 2015 crossings: CHI 2015 ; proceedings of the 33rd Annual CHI Conference on Human Factors in Computing Systems ; April 18 - 23, 2015, Seoul, Republic of Korea, pp. 3613-3622. , Kim J, (ed), ACM, New York; Charalambous, G., Fletcher, S., Webb, P., Identifying the key organisational human factors for introducing human-robot collaboration in industry: an exploratory study (2015) Int J Adv Manuf Technol, 81, pp. 2143-2155; Sadrfaridpour, B., Wang, Y., Collaborative assembly in hybrid manufacturing cells: an integrated framework for human–robot interaction (2018) IEEE Trans Autom Sci Eng, 15, pp. 1178-1192; Broadbent, E., Interactions With Robots: The truths we reveal about ourselves (2017) Annu Rev Psychol, 68, pp. 627-652; Charalambous, G., Fletcher, S., Webb, P., Human-automation collaboration in manufacturing: Identifying key implementation factors (2013) Proceedings of the 11Th International Conference on Manufacturing Research (ICMR2013), 2013, pp. 301-306. , Cranfield University, UK, 19–20 September; Hoff, K.A., Bashir, M., Trust in automation: integrating empirical evidence on factors that influence trust (2015) Hum Factors, 57, pp. 407-434; French, B., Duenser, A., Heathcote, A., (2018) Trust in Automation - a Literature Review Report, , CSIRO, Clayton Victoria; de Visser, E.J., Peeters, M.M.M., Jung, M.F., Kohn, S., Shaw, T.H., Pak, R., Neerincx, M.A., Towards a theory of longitudinal trust calibration in human–robot teams (2019) Int J Soc Robot, 8, pp. 459-478; Phillips, E.K., Ososky, S., Grove, J., Jentsch, F., From tools to teammates: toward the development of appropriate mental models for intelligent robots (2011) Proc Hum Factors Ergon Soc Annu Meet, 55, pp. 1491-1495; Lewis, M., Sycara, K., Walker, P., The role of trust in human-robot interaction (2018) Foundations of Trusted Autonomy, pp. 135-160. , Abbass HA, Scholz J, Reid DJ, (eds), Springer International Publishing, Cham; Wullenkord, R., Fraune, M.R., Eyssel, F., Getting in touch: How imagined, actual, and physical contact affect evaluations of robots (2016) 25Th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 26-31 August 2016, pp. 980-985. , Columbia University NY, IEEE, Piscataway; Flook, R., Shrinah, A., Wijnen, L., Eder, K., Melhuish, C., Lemaignan, S., Social Cues in Robot Interaction, Trust and Acceptance (2019) IS, 20, pp. 455-486; Desai, M., Kaniarasu, P., Medvedev, M., Impact of robot failures and feedback on real-time trust (2013) HRI 2013: Proceedings of the 8th ACM/IEEE International Conference on Human-Robot Interaction, pp. 251-258. , IEEE, Piscataway; Parasuraman, R., Riley, V., Humans and automation: use, misuse, disuse, abuse (1997) Hum Factors, 39, pp. 230-253; Gompei, T., Umemuro, H., Factors and development of cognitive and affective trust on social robots (2018) Social Robotics: 10Th International Conference, ICSR 2018, 11357, pp. 45-54. , Ge SS, Qingdao, China, 28–30 November 2018, proceedings, Springer, Cham; Turja, T., Oksanen, A., Robot acceptance at work: a multilevel analysis based on 27 EU countries (2019) Int J Soc Robot, 11, pp. 679-689; Finch, H., Comparison of the performance of nonparametric and parametric MANOVA test statistics when assumptions are violated (2005) Methodology, 1, pp. 27-38; Elkmann, N., Berndt, D., Leye, S., Richter, K., Mecke, R., Arbeitssysteme der Zukunft (2015) Produktion und Logistik mit Zukunft, pp. 49-149. , Schenk M, (ed), Springer, Berlin Heidelberg, Berlin, Heidelberg; Russwurm, S., Software: Die Zukunft der Industrie (2013) Industrie 4.0, pp. 21-36. , Sendler U, (ed), Springer Berlin Heidelberg, Berlin; Li, D., Rau, P.L.P., Li, Y., A Cross-cultural study: effect of robot appearance and task (2010) Int J Soc Robot, 2, pp. 175-186; Vanderborght, B., Berte, J., de Coppel, G., Towards an acceptable socially collaborative robot for the manufacturing industry (2017) ICRA Workshop IC3 - Industry of the Future: Collaborative, Connected, Cognitive: Novel Approaches Stemming from Factory of the Future & Industry 4.0 Initiatives, pp. 1-2. , . In:, Singapore, pp","Kopp, T.; Institute for Learning and Innovation in Networks, Moltkestraße 30, Germany; email: tobias.kopp@hs-karlsruhe.de",,,"Springer Science and Business Media Deutschland GmbH",,,,,02683768,,IJATE,,"English","Int J Adv Manuf Technol",Article,"Article in Press","All Open Access, Hybrid Gold",Scopus,2-s2.0-85097442312
"Permatasari D.A., Fakhrurroja H., Machbub C.","57209274119;55869180100;56619583100;","Human-Robot Interaction Based on Dialog Management Using Sentence Similarity Comparison Method",2020,"International Journal on Advanced Science, Engineering and Information Technology","10","5",,"1881","1888",,3,"10.18517/ijaseit.10.5.7606","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097429165&doi=10.18517%2fijaseit.10.5.7606&partnerID=40&md5=bbf3503012f556275c70f426da786790","School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jl. Ganesha 10, Bandung, 40132, Indonesia","Permatasari, D.A., School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jl. Ganesha 10, Bandung, 40132, Indonesia; Fakhrurroja, H., School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jl. Ganesha 10, Bandung, 40132, Indonesia; Machbub, C., School of Electrical Engineering and Informatics, Bandung Institute of Technology, Jl. Ganesha 10, Bandung, 40132, Indonesia","Advances in developing dialogue systems regarding speech recognition, language understanding, and speech synthesis. Dialogue systems to support human interaction with a robot efficiently by using spoken language. Facilities that provide convenience in carrying out daily activities for someone, such as older people, are necessary. The existence of Human-Robot Interaction (HRI), so that this interaction can give orders to the robot to do work that cannot be done by humans. This study presents a dialogue management system for HRI with a comparison sentence similarity method between TF-IDF (Term Frequency-Inverse Document Frequency) Cosine Similarity Algorithm and Jaccard Coefficient and using Finite State Machine (FSM). Dialogue Management is a way to find the response of the answer. When the user says something or in other words, is responsible for managing the flow of the conversation to command the robot. TF-IDF is used to give the weight of the term relationship and comparison between Cosine Similarity and Jaccard Coefficient for comparison method to determine the classification of similarity sentences from the dialogue manager to improve the intent of the dialogue, for the FSM method to set the sequence flow dialogue. We use Google Cloud Speech API as an engine for speech to text using Kinect V2 as an audio sensor. There are eight scenarios created in this system. The speech recognition process using Google Speech for an average of 2.62 seconds shows a reasonably fast response. TF-IDF Cosine Similarity method can produce enough accuracy of 97.43%, and Jaccard Coefficient indicates an accuracy level of 91.57%. The state of the FSM method can be considered as an efficient structure for building dialogue management. © xx xx","cosine similarity; dialogue manager; finite state machine; Google cloud speech; human-robot interaction; TF-IDF",,,,,,,"This work was supported by the Post Graduate Team Research 2018 from the Ministry of Research, Technology and Higher Education, Republic of Indonesia.",,,,,,,,,,"Bui, T. H., (2006) Multimodal Dialogue Management - State of the art; Maharani, D. A., Fakhrurroja, H., Riyanto, Machbub, C., Hand gesture recognition using K-means clustering and Support Vector Machine (2018) ISCAIE 2018 - 2018 IEEE Symposium on Computer Applications and Industrial Electronics, pp. 1-6; Meng, L., Huang, M., Dialogue intent classification with long short-term memory networks (2018) Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 10619, pp. 42-50. , LNAI; Hsieh, M. C., Hung, W. S., Lin, S. W., Luo, C. H., Designing an assistive dialog agent for a case of spinal cord injury (2009) Proceedings - 2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009; Sadohara, K., Sub-lexical Dialogue Act Classification in a Spoken Dialogue System Support for the Elderly with Cognitive Disabilities (2013) Proc. Fourth Work. Speech Lang. Process. Assist. Technol, pp. 93-98; Schwarzler, S., Schenk, J., Ruske, G., Wallhoff, F., A multi-agent framework for a hybrid dialog management system (2009) 2009 IEEE International Conference on Multimedia and Expo, pp. 958-961; Holzapfel, H., A dialogue manager for multimodal human-robot interaction and learning of a humanoid robot (2008) Ind. Rob, 35 (6), pp. 528-535; Lee, C., Cha, Y. S., Kuc, T. Y., Implementation of dialogue system for intelligent service robots (2008) 2008 International Conference on Control, Automation and Systems, ICCAS 2008; Raux, A., Eskenazi, M., A Finite-State Turn-Taking Model for Spoken Dialog Systems (2009) Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the ACL; Yi, S., Jung, K., A Chatbot by Combining Finite State Machine, Information Retrieval, and Bot-Initiative Strategy (2017) Alexa Price Proc, pp. 1-10; Su, B., Kuan, T., Tseng, S., Wang, J., Su, P., Improved TF-IDF weight method based on sentence similarity for spoken dialogue system (2016) 2016 International Conference on Orange Technologies (ICOT), pp. 36-39; Lee, C., Jung, S., Kim, S., Lee, G. G., Example-based dialog modeling for practical multi-domain dialog system (2009) Speech Commun; Fakhrurroja, H., Permatasari, D. A., Purwarianti, A., Machbub, C., Dialogue Management for Human Robot Interaction Using Artificial Intelligence Markup Language (2018) ICEECS 2018 International Conference on Electrical Engineering and Computer Science; Zhang, X., LeCun, Y., Character-level Convolutional Networks for Text Classificatio (2015) Advances in Neural Information Processing Systems, 28; Petcu, D., Craciun, C., Rak, M., Towards a Cross Platform Cloud API - Components for Cloud Federation (2011) CLOSER; Google Speech API (2017) Google Cloud Platform, , Google; Assefi, M., Liu, G., Wittie, M. P., Izurieta, C., An Experimental Evaluation of Apple Siri and Google Speech Recognition (2015) Proccedings 2015 ISCA SEDE; Chiu, C. C., State-of-the-Art Speech Recognition with Sequence-to-Sequence Models (2018) ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Manning, C. D., Raghavan, P., Schutze, H., (2009) An Introduction to Information Retrieval, , Cambridge University Press; Agarwal, N., Rawat, M., Vijay, M., Comparative Analysis Of Jaccard Coefficient and Cosine Similarity for Web Document Similarity Measure (2014) Int. J. Adv. Res. Eng. Technol; Gomaa, H., (2016) Real-Time Software Design For Embedded Systems, , New York: Cambridge University Press",,,,"Insight Society",,,,,20885334,,,,"English","Int. J. Adv. Sci. Eng. Inf. Technol.",Article,"Final","",Scopus,2-s2.0-85097429165
"Dong J., Jo S.H., Kwon W., Kang D., Chung Y.","35117553700;57220154785;57212541649;57283802500;57220155917;","User-Specific Interfaces of Teaching Devices for Manipulation of Collaborative Robot",2020,"Communications in Computer and Information Science","1293",,,"129","133",,,"10.1007/978-3-030-60700-5_17","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097041849&doi=10.1007%2f978-3-030-60700-5_17&partnerID=40&md5=bd2fa7797bdb2d78ddc72e0e6af7f822","Electronics and Telecommunications Research Institute (ETRI), Daegu, South Korea; Kyungbuk National University, Daegu, South Korea","Dong, J., Electronics and Telecommunications Research Institute (ETRI), Daegu, South Korea; Jo, S.H., Kyungbuk National University, Daegu, South Korea; Kwon, W., Electronics and Telecommunications Research Institute (ETRI), Daegu, South Korea; Kang, D., Electronics and Telecommunications Research Institute (ETRI), Daegu, South Korea; Chung, Y., Electronics and Telecommunications Research Institute (ETRI), Daegu, South Korea","As the use of robotics in manufacturing and industrial settings continues to advance, expand, and evolve at a speedy pace, efficient collaboration between robots and workers becomes increasingly important. Known as cobots, collaborative robots are highly designed so they can perform tasks continuously and accurately alongside human workers in a safe mode. However, it is not easy to control cobots proficiently and directly in case of scaling of industrial robots for the beginners and skilled engineers. Thus, research for human-robot interaction (HRI) still remains to be challenged. Particularly, users who work with collaborative robot currently need robust and facile methodologies for human-robot collaboration to enhance user experience (UX) and to use direct teaching affordably. Up to date, teaching devices considering more intuitive graphical user interface (GUI) have been intensively exploited, thereby eliminating the need for safety barriers. However, it is still inevitable to consider safety issues that may arise when users work with cobots. Therefore, it is important to properly assign well-adapted functions to each user. There is still a big challenge to develop a more efficient way to authorization of suitable functions to different users. This paper deals with the user-specific interface considering the user’s role for improving the usability and convenience. It provides multiple functions of teaching devices simply used by not only the robot engineers but also developers and operators. We applied the method of user experience such as user journey map and persona to develop the interfaces of teaching device that can operate appropriate tasks with ease by users. In the present study, we introduce the user-specific interfaces for developers, operators, and robot engineers including beginner and experts. © 2020, Springer Nature Switzerland AG.","Collaborative robot; Human-Robot Interaction (HRI); Teaching device; User-Specific interface","Engineers; Graphical user interfaces; Human computer interaction; Industrial robots; Social robots; Collaborative robots; Graphical user interfaces (GUI); Human robot Interaction (HRI); Human-robot collaboration; Industrial settings; Skilled engineers; Specific interface; User experiences (ux); User experience",,,,,"Electronics and Telecommunications Research Institute, ETRI: 20ZD1130","This work was supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government. [20ZD1130, Development of ICT Convergence Technology for Daegu-GyeongBuk Regional Industry].","Acknowledgements. This work was supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government. [20ZD1130, Development of ICT Convergence Technology for Daegu-GyeongBuk Regional Industry].",,,,,,,,,"Holmes, A., Intuitive interfaces in human-robot interaction (2018) Towards, Autonomous Robotic Systems. 19Th Towards Autonomous Robotic Systems (TAROS) Conference. LNCS, , Giulian, M., Assaf, T., Giannaccini, M.E. (eds.), Springer, Cham; Khan, S., Germak, C., Reframing HRI design opportunities for social robots: Lessons learnt from a service robotics case study approach using UX for HRI (2018) Future Internet, 10, p. 101; Tonkin, M., Vitale, J., Herse, S., Williams, M., Judge, W., Wang, X., Design methodology for the UX of HRI: A field study of a commercial social robot at an airport (2018) HRI 2018 Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 407-415. , pp; Yoo, S., Development and validation of block coding based human-robot teaching pendant UX (2018) J. Integr. Des. Res., 17 (4), pp. 9-18; Kraft, M., Rickert, M., How to teach your robot in 5 minutes: Applying UX paradigms to human-robot-interaction (2017) 26Th IEEE International Symposium on Robot and Human Interactive Communication, pp. 942-949. , pp; Balázs, D., Korondi, P., Sziebig, G., Thomessen, T., Evaluation of flexible graphical user interface for intuitive human robot interactions (2014) Acta Polytechnica Hungarica, 11 (1), pp. 135-151; Hartson, R., Pyla, P.S., (2012) The UX Book: Process and Guidelines for Ensuring a Quality User Experience, , Elsevier, Amsterdam; Examples of UX Personas, , https://qubstudio.com/blog/4-examples-of-ux-personas/; A beginner’s Guide to User Journey Mapping, , https://uxplanet.org/a-beginners-guide-to-user-journey-mapping-bd914f4c517c","Dong, J.; Electronics and Telecommunications Research Institute (ETRI)South Korea; email: jydong@etri.re.kr","Stephanidis C.Antona M.Ntoa S.",,"Springer Science and Business Media Deutschland GmbH","22nd International Conference on Human-Computer Interaction, HCI International 2020","19 July 2020 through 24 July 2020",,251559,18650929,9783030606992,,,"English","Commun. Comput. Info. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85097041849
[No author name available],[No author id available],"Thematic Area on Human Computer Interaction, HCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12182 LNCS",,,"","",2109,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088789887&partnerID=40&md5=82d1bcf185a9e00907febd5db28efc32",,"","The proceedings contain 145 papers. The special focus in this conference is on Human Computer Interaction. The topics include: Enhancing Drone Pilots’ Engagement Through a Brain-Computer Interface; human-Drone Interaction: Using Pointing Gesture to Define a Target Object; on Positive Effect on Humans by Poor Operability of Robot; evaluating a Mouse-Based and a Tangible Interface Used for Operator Intervention on Two Autonomous Robots; human-Robot Interaction in Health Care: Focus on Human Factors; emotion Synchronization Method for Robot Facial Expression; database Semantics for Talking Autonomous Robots; the Current Status and Challenges in Augmented-Reality Navigation System for Robot-Assisted Laparoscopic Partial Nephrectomy; multi-human Management of Robotic Swarms; modelling the Collaboration of a Patient and an Assisting Humanoid Robot During Training Tasks; a Comparative Study of Hand-Gesture Recognition Devices for Games; Trust on Service Robots: A Pilot Study on the Influence of Eyes in Humanoid Robots During a VR Emergency Egress; theses on the Future Design of Human-Robot Collaboration; sexual Robots: The Social-Relational Approach and the Concept of Subjective Reference; one-Hand Controller for Human-Drone Interaction – a Human-Centered Prototype Development; Touch Position Detection on the Front of Face Using Passive High-Functional RFID Tag with Magnetic Sensor; floating Hierarchical Menus for Swipe-Based Navigation on Touchscreen Mobile Devices; a Virtual Mouse Interface for Supporting Multi-user Interactions; fingerTalkie: Designing a Low-Cost Finger-Worn Device for Interactive Audio Labeling of Tactile Diagrams; identification Method of Digits for Expanding Touchpad Input; multimodal Analysis of Preschool Children’s Embodied Interaction with a Tangible Programming Environment; affective Haptics and Multimodal Experiments Research; foreword.",,,,,,,,,,,,,,,,,,,,"Kurosu M.",,"Springer","Thematic Area on Human Computer Interaction, HCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242229,03029743,9783030490614,,,"English","Lect. Notes Comput. Sci.",Conference Review,"Final","",Scopus,2-s2.0-85088789887
"Gao P., Reily B., Paul S., Zhang H.","57211889781;57192817969;57218264947;57192094962;","Visual reference of ambiguous objects for augmented reality-powered human-robot communication in a shared workspace",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12190 LNCS",,,"550","561",,,"10.1007/978-3-030-49695-1_37","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088514908&doi=10.1007%2f978-3-030-49695-1_37&partnerID=40&md5=d158880cc720ef81c0e5668e0f557e58","Human-Centered Robotics Lab, Colorado School of Mines, 1500 Illinois Street, Golden, CO  80401, United States","Gao, P., Human-Centered Robotics Lab, Colorado School of Mines, 1500 Illinois Street, Golden, CO  80401, United States; Reily, B., Human-Centered Robotics Lab, Colorado School of Mines, 1500 Illinois Street, Golden, CO  80401, United States; Paul, S., Human-Centered Robotics Lab, Colorado School of Mines, 1500 Illinois Street, Golden, CO  80401, United States; Zhang, H., Human-Centered Robotics Lab, Colorado School of Mines, 1500 Illinois Street, Golden, CO  80401, United States","In shared workspaces, teammates working with a common set of objects must be able to unambiguously reference individual objects in order to effectively collaborate. When teammates are autonomous robots, human teammates must be able to communicate their intended reference object without overtly interfering with their workflow. In human-robot interaction, the problem of visual reference is defined as identifying the specific object referred to by a human (e.g., through a pointing gesture recognized by an augmented reality device), and relating this object to the associated object in the robotic teammate’s field of view, thereby identifying the intended object from a set of ambiguous objects. As human and robot teammates typically observe their shared workspace from differing perspectives, achieving visual reference of objects is a challenging yet crucial problem. In this paper, we present a novel approach to visual reference of ambiguous objects that introduces a graph matching-based approach which fuses visual and spatial information of the objects in a shared workspace through augmented reality-powered human-robot communication. Our approach represents the objects in a scene with a graph where edges encoding the spatial relationships among objects and attribute vectors describing each object’s appearance associated with each node. Then, we formulate visual object reference for human-robot communication in a shared workspace as an optimization-based graph matching problem, which identifies the correspondence of nodes in graphs built from the human and robot teammates’ observations. We conduct extensive experimental evaluation on two introduced datasets, showing that our approach is able to obtain accurate visual references of ambiguous objects and outperforms existing visual reference methods. © Springer Nature Switzerland AG 2020.","Augmented reality; Collaborative perception; Graph matching; Human-robot communication; Visual reference","Augmented reality; Graph structures; Graph theory; Human computer interaction; Mixed reality; Experimental evaluation; Graph matching problems; Human-robot communication; Individual objects; Pointing gestures; Reference objects; Spatial informations; Spatial relationships; Social robots",,,,,"National Science Foundation, NSF: CNS-1823245; U.S. Department of Energy, USDOE: DE-FE0031650, IIS-1849348; Association of Research Libraries, ARL: ARO W911NF-17-1-0447, FA7000-18-2-0016, W911NF-17-2-0181","This work was partially supported by ARL W911NF-17-2-0181, ARO W911NF-17-1-0447, USAFA FA7000-18-2-0016, DOE DE-FE0031650, IIS-1849348, and NSF CNS-1823245.",,,,,,,,,,"Reardon, C., Zhang, H., Fink, J., Shaping of shared autonomous solutions with minimal interaction (2018) Front. Neurorobot, 12, p. 54; Williams, T., Szafir, D., Chakraborti, T., Phillips, E., Virtual, augmented, and mixed reality for human-robot interaction (VAM-HRI) (2019) ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 671-672; Reardon, C., Lee, K., Rogers, J.G., Fink, J., Augmented reality for human-robot teaming in field environments (2019) HCII 2019. LNCS, 11575, pp. 79-92. , https://doi.org/10.1007/978-3-030-21565-16, Chen, J.Y.C., Fragomeni, G. (eds) Springer, Cham; Nebehay, G., Pflugfelder, R., Consensus-based matching and tracking of keypoints for object tracking (2014) IEEE Winter Conference on Applications of Computer Vision, pp. 862-869. , IEEE; Zhao, R., Oyang, W., Wang, X., Person re-identification by saliency learning (2016) IEEE Trans. Pattern Anal. Mach. Intell, 39 (2), pp. 356-370; Zhao, Y., Shen, X., Jin, Z., Lu, H., Hua, X.-S., Attribute-driven feature disentangling and temporal aggregation for video person re-identification (2019) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4913-4922; Leonardos, S., Zhou, X., Daniilidis, K., Distributed consistent data association via permutation synchronization (2017) IEEE International Conference on Robotics and Automation; Hu, N., Huang, Q., Thibert, B., Guibas, L.J., Distributable consistent multi-object matching (2018) IEEE Conference on Computer Vision and Pattern Recognition; Zhou, X., Zhu, M., Daniilidis, K., Multi-image matching via fast alternating min-imization (2015) IEEE International Conference on Computer Vision; Maset, E., Arrigoni, F., Fusiello, A., Practical and efficient multi-view matching (2017) IEEE International Conference on Computer Vision; Pachauri, D., Kondor, R., Singh, V., Solving the multi-way matching problem by permutation synchronization (2013) Advances in Neural Information Processing Systems; Almohamad, H., Duffuaa, S.O., A linear programming approach for the weighted graph matching problem (1993) IEEE Trans. Pattern Anal. Mach. Intell, 15 (5), pp. 522-525; Wang, R., Yan, J., Yang, X., Learning combinatorial embedding networks for deep graph matching (2019) IEEE International Conference on Computer Vision; Cho, M., Lee, J., Lee, K.M., Reweighted random walks for graph matching (2010) ECCV 2010. LNCS, 6315, pp. 492-505. , https://doi.org/10.1007/978-3-642-15555-036, Daniilidis, K., Maragos, P., Paragios, N. (eds) Springer, Heidelberg; Leordeanu, M., Hebert, M., A spectral technique for correspondence problems using pairwise constraints (2005) Tenth IEEE International Conference on Computer Vision, ICCV 2005, 2, pp. 1482-1489. , IEEE; Sobreira, H., Map-matching algorithms for robot self-localization: a comparison between perfect match, iterative closest point and normal distributions transform (2019) J. Intell. Robot. Syst, 93 (3–4), pp. 533-546; Suh, Y., Adamczewski, K., Mu Lee, K., Subgraph matching using compactness prior for robust feature correspondence (2015) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5070-5078","Zhang, H.; Human-Centered Robotics Lab, 1500 Illinois Street, United States; email: hzhang@mines.edu","Chen J.Y.C.Fragomeni G.",,"Springer","12th International Conference on Virtual, Augmented and Mixed Reality, VAMR 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020","19 July 2020 through 24 July 2020",,242159,03029743,9783030496944,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85088514908
"Asemi A., Ko A., Nowkarizi M.","16232345800;35299650200;26422426400;","Intelligent libraries: a review on expert systems, artificial intelligence, and robot",2020,"Library Hi Tech","39","2",,"412","434",,5,"10.1108/LHT-02-2020-0038","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087172321&doi=10.1108%2fLHT-02-2020-0038&partnerID=40&md5=8e2d71ca9813ea8645f7fdf6b15b5a2c","University of Isfahan, Isfahan, Iran; Doctoral School of Business Informatics, Corvinus University of Budapest, Budapest, Hungary; Department of Knowledge and Information Science, Ferdowsi University of Mashhad, Mashhad, Iran; Corvinus University of Budapest, Budapest, Hungary","Asemi, A., University of Isfahan, Isfahan, Iran, Doctoral School of Business Informatics, Corvinus University of Budapest, Budapest, Hungary; Ko, A., Corvinus University of Budapest, Budapest, Hungary; Nowkarizi, M., Department of Knowledge and Information Science, Ferdowsi University of Mashhad, Mashhad, Iran","Purpose: This paper reviews literature on the application of intelligent systems in the libraries with a special issue on the ES/AI and Robot. Also, it introduces the potential of libraries to use intelligent systems, especially ES/AI and robots. Design/methodology/approach: Descriptive and content review methods are applied, and the researchers critically reviewed the articles related to library ESs and robots from Web of Science as a general database and Emerald as a specific database in library and information science from 2007–2017. Four scopes considered to classify the articles as technology, service, user and resource. It is found that published researches on the intelligent systems have contributed to many librarian purposes like library technical services like the organization of information resources, storage and retrieval of information resources, library public services as reference services, information desk and other purposes. Findings: A review of the previous studies shows that ESs are a useable intelligent system in library and information science that mimic librarian expert’s behaviors to support decision making and management. Also, it is shown that the current information systems have a high potential to be improved by integration with AI technologies. In this researches, librarian robots mostly designed for detection and replacing books on the shelf. Improving the technology of gripping, localizing and human-robot interaction are the main concern in recent librarian robot research. Our conclusion is that we need to develop research in the area of smart resources. Originality/value: This study has a new approach to the literature review in this area. We compared the published papers in the field of ES/AI and robot and library from two databases, general and specific. © 2020, Asefeh Asemi, Andrea Ko and Mohsen Nowkarizi.","Artificial Intelligent (AI); Expert System (ES); Intelligent library; Intelligent systems; Library system; Robot; Smart library",,,,,,,"This research has been supported by the “Project no. NKFIH-869-4/2019 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the Tématerületi Kiválósági Program 2019 funding scheme.”",,,,,,,,,,"Albayrak, E., Erensal, Y.C., Using analytic hierarchy process (AHP) to improve human performance: an application of multiple criteria decision making problem (2004) Journal of Intelligent Manufacturing, 15 (4), pp. 491-503; Amin, S.H., Razmi, J., An integrated fuzzy model for supplier management: a case study of ISP selection and evaluation (2009) Expert Systems with Applications, 36 (4), pp. 8639-8648. , http://isiarticles.com/bundles/Article/pre/pdf/19174.pdf; Asemi, A., Akbari, A., Kheradmandnia, M., Farazi, A., Investigating the level of desirability of information in management information systems in libraries in Iran (2012) The Electronic Library, 30 (6), pp. 833-843; Ataman, B.K., Requirements for information professionals in a digital environment: some thoughts (2009) Program Electronic Library and Information Systems, 43 (2), pp. 215-228; Bailey, C.W., Intelligent library systems: artificial intelligence technology and library automation systems (1991) Advances in Library Automation and Networking, , http://www.digital-scholarship.org/cwb/intlibs.pdf, Jr. (,Hewitt, J.A. 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University of IsfahanIran; email: asemi@edu.ui.ac.ir",,,"Emerald Group Holdings Ltd.",,,,,07378831,,,,"English","Libr. Hi Tech",Review,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85087172321
[No author name available],[No author id available],"4th Iberian Robotics Conference, ROBOT 2019",2020,"Advances in Intelligent Systems and Computing","1093 AISC",,,"","",692,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079182572&partnerID=40&md5=992e08b093fd22056c9d121d466e46f5",,"","The proceedings contain 57 papers. The special focus in this conference is on Iberian Robotics. The topics include: Emergency Landing Spot Detection for Unmanned Aerial Vehicle; ROS Framework for Perception and Dual-Arm Manipulation in Unstructured Environments; modular Dual-Arm Robot for Precision Harvesting; integrating Multiple Sources of Knowledge for the Intelligent Detection of Anomalous Sensory Data in a Mobile Robot; a Manipulation Control Strategy for Granular Materials Based on a Gaussian Mixture Model; visual and Tactile Fusion for Estimating the Pose of a Grasped Object; object Classification for Robotic Platforms; effects of a Social Force Model Reward in Robot Navigation Based on Deep Reinforcement Learning; a Cross-Situational Learning Based Framework for Grounding of Synonyms in Human-Robot Interactions; robot Navigation to Approach People Using G2-Spline Path Planning and Extended Social Force Model; a Novel Model for Emotion Detection from Facial Muscles Activity; producing Parameterized Value Functions Through Modulation for Cognitive Developmental Robots; benchmarking Deep and Non-deep Reinforcement Learning Algorithms for Discrete Environments; versatile and Massive Experimentation of Robot Swarms in Industrial Scenarios; edge-Enabled Autonomous Navigation and Computer Vision as a Service: A Study on Mobile Robot’s Onboard Energy Consumption and Computing Requirements; a Bio-Inspired Approach for Robot Swarm in Smart Factories; Positioning System for UAV Precision Tasks Near Walls in GPS Denied and Metallic Environments; CRAS (Climbing Robot for Autonomous InSpection): The Challenges of a High-Temperature Tank; indoor Environment Monitoring in Search of Gas Leakage by Mobile Robot; performance of New Global Appearance Description Methods in Localization of Mobile Robots; exoFlex: An Upper-Limb Cable-Driven Exosuit.",,,,,,,,,,,,,,,,,,,,"Silva M.F.Luis Lima J.Reis L.P.Sanfeliu A.Tardioli D.",,"Springer","4th Iberian Robotics Conference, ROBOT 2019","20 November 2019 through 22 November 2019",,236649,21945357,9783030361495,,,"English","Adv. Intell. Sys. Comput.",Conference Review,"Final","",Scopus,2-s2.0-85079182572
"Kazanzides P., Vagvolgyi B.P., Pryor W., Deguet A., Leonard S., Whitcomb L.L.","6603602878;55888077300;57207943258;8436717200;7102579852;7005958612;","Teleoperation and Visualization Interfaces for Remote Intervention in Space",2021,"Frontiers in Robotics and AI","8",,"747917","","",,,"10.3389/frobt.2021.747917","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121386313&doi=10.3389%2ffrobt.2021.747917&partnerID=40&md5=e5bd4508546967b17a301ced961b1195","Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States; Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, United States; Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, United States","Kazanzides, P., Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States, Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, United States; Vagvolgyi, B.P., Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, United States; Pryor, W., Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States, Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, United States; Deguet, A., Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, United States; Leonard, S., Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, United States; Whitcomb, L.L., Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States, Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, United States, Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, United States","Approaches to robotic manufacturing, assembly, and servicing of in-space assets range from autonomous operation to direct teleoperation, with many forms of semi-autonomous teleoperation in between. Because most approaches require one or more human operators at some level, it is important to explore the control and visualization interfaces available to those operators, taking into account the challenges due to significant telemetry time delay. We consider one motivating application of remote teleoperation, which is ground-based control of a robot on-orbit for satellite servicing. This paper presents a model-based architecture that: 1) improves visualization and situation awareness, 2) enables more effective human/robot interaction and control, and 3) detects task failures based on anomalous sensor feedback. We illustrate elements of the architecture by drawing on 10 years of our research in this area. The paper further reports the results of several multi-user experiments to evaluate the model-based architecture, on ground-based test platforms, for satellite servicing tasks subject to round-trip communication latencies of several seconds. The most significant performance gains were obtained by enhancing the operators’ situation awareness via improved visualization and by enabling them to precisely specify intended motion. In contrast, changes to the control interface, including model-mediated control or an immersive 3D environment, often reduced the reported task load but did not significantly improve task performance. Considering the challenges of fully autonomous intervention, we expect that some form of teleoperation will continue to be necessary for robotic in-situ servicing, assembly, and manufacturing tasks for the foreseeable future. We propose that effective teleoperation can be enabled by modeling the remote environment, providing operators with a fused view of the real environment and virtual model, and incorporating interfaces and control strategies that enable interactive planning, precise operation, and prompt detection of errors. Copyright © 2021 Kazanzides, Vagvolgyi, Pryor, Deguet, Leonard and Whitcomb.","model-mediated control; satellite servicing; scene modeling; space robotics; teleoperation",,,,,,"National Science Foundation, NSF: EEC-1460674, IIS-1208540, NRI-1637789","Some of this work was supported by NSF IIS-1208540 and NSF EEC-1460674, Research Experience for Undergraduates (REU). The da Vinci Research Kit (dVRK) was supported by NSF NRI-1637789.",,,,,,,,,,"Bejczy, A., Kim, W., Predictive Displays and Shared Compliance Control for Time-Delayed Telemanipulation (1990) IEEE Intl. Workshop on Intelligent Robots and Systems, pp. 407-412. , Ibaraki, Japan; Chen, Z., (2017) A Scalable, High-Performance, Real-Time Control Architecture with Application to Semi-autonomous Teleoperation, , Baltimore, US, Johns Hopkins University, Ph.D. thesis; Chitta, S., Sucan, I., Cousins, S., MoveIt! [ROS Topics] (2012) IEEE Robot. Automat. 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Robots and Systems, pp. 5059-5064. , Vilamoura, Portugal; Xia, T., Leonard, S., Kandaswamy, I., Blank, A., Whitcomb, L.L., Kazanzides, P., Model-based Telerobotic Control with Virtual Fixtures for Satellite Servicing Tasks (2013) IEEE Intl. Conf. on Robotics and Automation, pp. 1479-1484. , Karlsruhe, Germany; Yoshikawa, T., Sudou, A., Dynamic Hybrid Position/force Control of Robot Manipulators-On-Line Estimation of Unknown Constraint (1993) IEEE Trans. Robot. Automat, 9, pp. 220-226","Kazanzides, P.; Department of Computer Science, United States; email: pkaz@jhu.edu",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85121386313
"Palermo M., Moccia S., Migliorelli L., Frontoni E., Santos C.P.","57217044113;57192919602;57198434616;9737451300;24403024500;","Real-time human pose estimation on a smart walker using convolutional neural networks",2021,"Expert Systems with Applications","184",,"115498","","",,1,"10.1016/j.eswa.2021.115498","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109217957&doi=10.1016%2fj.eswa.2021.115498&partnerID=40&md5=c8f5cc350f6790b578d949b99b2ae43c","Centre for Microelectromechanical Systems, University of Minho, Braga, Portugal; The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy; Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy; Department of Information Engineering, Universitá Politecnica delle Marche, Ancona, Italy","Palermo, M., Centre for Microelectromechanical Systems, University of Minho, Braga, Portugal; Moccia, S., The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy, Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy; Migliorelli, L., Department of Information Engineering, Universitá Politecnica delle Marche, Ancona, Italy; Frontoni, E., Department of Information Engineering, Universitá Politecnica delle Marche, Ancona, Italy; Santos, C.P., Centre for Microelectromechanical Systems, University of Minho, Braga, Portugal","Rehabilitation is important to improve quality of life for mobility-impaired patients. Smart walkers are a commonly used solution that should embed automatic and objective tools for data-driven human-in-the-loop control and monitoring. However, present solutions focus on extracting few specific metrics from dedicated sensors with no unified full-body approach. We investigate a general, real-time, full-body pose estimation framework based on two RGB+D camera streams with non-overlapping views mounted on a smart walker equipment used in rehabilitation. Human keypoint estimation is performed using a two-stage neural network framework. The 2D-Stage implements a detection module that locates body keypoints in the 2D image frames. The 3D-Stage implements a regression module that lifts and relates the detected keypoints in both cameras to the 3D space relative to the walker. Model predictions are low-pass filtered to improve temporal consistency. A custom acquisition method was used to obtain a dataset, with 14 healthy subjects, used for training and evaluating the proposed framework offline, which was then deployed on the real walker equipment. An overall keypoint detection error of 3.73 pixels for the 2D-Stage and 44.05 mm for the 3D-Stage were reported, with an inference time of 26.6 ms when deployed on the constrained hardware of the walker. We present a novel approach to patient monitoring and data-driven human-in-the-loop control in the context of smart walkers. It is able to extract a complete and compact body representation in real-time and from inexpensive sensors, serving as a common base for downstream metrics extraction solutions, and Human-Robot interaction applications. Despite promising results, more data should be collected on users with impairments, to assess its performance as a rehabilitation tool in real-world scenarios. © 2021 Elsevier Ltd","computer vision; deep learning; human pose estimation; rehabilitation; smart walker","Cameras; Computer vision; Deep learning; Human robot interaction; Low pass filters; Neural networks; Convolutional neural network; Data driven; Deep learning; Full body; Human pose estimations; Human-in-the-loop control; Keypoints; Quality of life; Real- time; Smart walkers; Patient rehabilitation",,,,,"Fundação para a Ciência e a Tecnologia, FCT: UIDB/04436/2020, UIDP/04436/2020; Programa Operacional Temático Factores de Competitividade, POFC: POCI-01-0247-FEDER-033067","This work has been supported by the FEDER funds through the COMPETE 2020 ? Programa Operacional Competitividade e Internacionaliza??o (POCI) and P2020 with the Reference Project EML under Grant POCI-01-0247-FEDER-033067, and by FCT national funds, under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020.","This work has been supported by the FEDER funds through the COMPETE 2020 – Programa Operacional Competitividade e Internacionalização (POCI) and P2020 with the Reference Project EML under Grant POCI-01-0247-FEDER-033067, and by FCT national funds, under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020.",,,,,,,,,"Achilles, F., Ichim, A.-E., Coskun, H., Tombari, F., Noachtar, S., Navab, N., Patient mocap: Human pose estimation under blanket occlusion for hospital monitoring applications (2016) Medical image computing and computer-assisted intervention (MICCAI 2016), pp. 491-499. , S. Ourselin L. Joskowicz M.R. Sabuncu G. Unal W. 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Salakhutdinov (Eds.), Proceedings of the 36th international conference on machine learning, ICML 2019, 9–15 June 2019, Long Beach, California, USA (pp. 6105–6114). PMLR of Proceedings of Machine Learning Research; Tompson, J., Goroshin, R., Jain, A., LeCun, Y., Bregler, C., Efficient object localization using convolutional networks (2015) Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 648-656; Wei, S.-E., Ramakrishna, V., Kanade, T., Sheikh, Y., Convolutional pose machines (2016) Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4724-4732; (2011), WHO. World Report on Disability. World Health Organization; Zhao, L., Peng, X., Tian, Y., Kapadia, M., Metaxas, D.N., Semantic graph convolutional networks for 3d human pose regression (2019) Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3425-3435","Palermo, M.; Centre for Microelectromechanical Systems, Portugal; email: a76886@alunos.uminho.pt",,,"Elsevier Ltd",,,,,09574174,,ESAPE,,"English","Expert Sys Appl",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85109217957
"Almeida A., Vicente P., Bernardino A.","57222238375;57205948891;7003407125;","Where is my hand? Deep hand segmentation for visual self-recognition in humanoid robots",2021,"Robotics and Autonomous Systems","145",,"103857","","",,,"10.1016/j.robot.2021.103857","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114791888&doi=10.1016%2fj.robot.2021.103857&partnerID=40&md5=023fbed74355a1661728ad1d85b2bb13","Institute for Systems and Robotics (ISR/IST), LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Avenida Rovisco Pais 1 - Torre Norte, Lisboa, 1049-001, Portugal","Almeida, A., Institute for Systems and Robotics (ISR/IST), LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Avenida Rovisco Pais 1 - Torre Norte, Lisboa, 1049-001, Portugal; Vicente, P., Institute for Systems and Robotics (ISR/IST), LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Avenida Rovisco Pais 1 - Torre Norte, Lisboa, 1049-001, Portugal; Bernardino, A., Institute for Systems and Robotics (ISR/IST), LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Avenida Rovisco Pais 1 - Torre Norte, Lisboa, 1049-001, Portugal","The ability to distinguish between the self and the background is of paramount importance for robotic tasks. The particular case of hands, as the end effectors of a robotic system that more often enter into contact with other elements of the environment, must be perceived and tracked with precision to execute the intended tasks with dexterity and without colliding with obstacles. They are fundamental for several applications, from Human–Robot Interaction tasks to object manipulation. Modern humanoid robots are characterized by high number of degrees of freedom which makes their forward kinematics models very sensitive to uncertainty. Thus, resorting to vision sensing can be the only solution to endow these robots with a good perception of the self, being able to localize their body parts with precision. In this paper, we propose the use of a Convolution Neural Network (CNN) to segment the robot hand from an image in an egocentric view. It is known that CNNs require a huge amount of data to be trained. To overcome the challenge of labeling real-world images, we propose the use of simulated datasets exploiting domain randomization techniques. We fine-tuned the Mask-RCNN network for the specific task of segmenting the hand of the humanoid robot Vizzy. We focus our attention on developing a methodology that requires low amounts of data to achieve reasonable performance while giving detailed insight on how to properly generate variability in the training dataset. Moreover, we analyze the fine-tuning process within the complex model of Mask-RCNN, understanding which weights should be transferred to the new task of segmenting robot hands. Our final model was trained solely on synthetic images and achieves an average IoU of 82% on synthetic validation data and 56.3% on real test data. These results were achieved with only 1000 training images and 3 h of training time using a single GPU. © 2021 Elsevier B.V.","Deep learning; Domain randomization; Robotic hand; Self-recognition; Visual segmentation","Agricultural robots; Anthropomorphic robots; Degrees of freedom (mechanics); End effectors; Image segmentation; Robotic arms; Robotics; Convolution neural network; Forward kinematics; Number of degrees of freedom; Object manipulation; Randomization techniques; Robot interactions; Simulated datasets; Synthetic images; Social robots",,,,,"Fundação para a Ciência e a Tecnologia, FCT: PD/BD/135115/2017, PTDC/EEI-ROB/1155/2020","This work was supported by:, LARSyS - FCT ProjectUIDB/50009/2020, FCT Project HAVATAR: PTDC/EEI-ROB/1155/2020 and, FCT Ph.D. grant PD/BD/135115/2017.",,,,,,,,,,"Vicente, P., Jamone, L., Bernardino, A., Online body schema adaptation based on internal mental simulation and multisensory feedback (2016) Front, Robot. AI, 3, p. 7; Zenha, R., Vicente, P., Jamone, L., Bernardino, A., Incremental adaptation of a robot body schema based on touch events (2018) Joint IEEE International Conference on Development and Learning and Epigenetic Robotics, ICDL-EpiRob, pp. 119-124. , IEEE; Stepanova, K., Pajdla, T., Hoffmann, M., Robot self-calibration using multiple kinematic chains—A simulation study on the icub humanoid robot (2019) IEEE Robot. Autom. 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Weinberger K.Q. Curran Associates, Inc; Ren, S., He, K., Girshick, R., Sun, J., Faster R-CNN: Towards real-time object detection with region proposal networks (2015) IEEE Trans. Pattern Anal. Mach. Intell., 39; Moreno, P., Nunes, R., Figueiredo, R., Ferreira, R., Bernardino, A., Santos-Victor, J., Beira, R., Aragão, M., Vizzy: A humanoid on wheels for assistive robotics (2016) Robot 2015: Second Iberian Robotics Conference, pp. 17-28. , Reis L.P. Moreira A.P. Lima P.U. Montano L. Muñoz Martinez V. Springer International Publishing Cham","Vicente, P.email: pvicente@isr.tecnico.ulisboa.pt",,,"Elsevier B.V.",,,,,09218890,,RASOE,,"English","Rob Autom Syst",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85114791888
"Velentza A.-M., Fachantidis N., Pliasa S.","57204963116;57270670800;57200880755;","Which One? Choosing Favorite Robot After Different Styles of Storytelling and Robots’ Conversation",2021,"Frontiers in Robotics and AI","8",,"700005","","",,,"10.3389/frobt.2021.700005","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115604143&doi=10.3389%2ffrobt.2021.700005&partnerID=40&md5=c691d7e3e478131dbb3e6ed3c7615886","School of Educational and Social Policies, University of Macedonia, Thessaloniki, Greece; Laboratory of Informatics and Robotics in Education and Society (LIRES) Robotics Lab, University of Macedonia, Thessaloniki, Greece","Velentza, A.-M., School of Educational and Social Policies, University of Macedonia, Thessaloniki, Greece, Laboratory of Informatics and Robotics in Education and Society (LIRES) Robotics Lab, University of Macedonia, Thessaloniki, Greece; Fachantidis, N., School of Educational and Social Policies, University of Macedonia, Thessaloniki, Greece, Laboratory of Informatics and Robotics in Education and Society (LIRES) Robotics Lab, University of Macedonia, Thessaloniki, Greece; Pliasa, S., School of Educational and Social Policies, University of Macedonia, Thessaloniki, Greece, Laboratory of Informatics and Robotics in Education and Society (LIRES) Robotics Lab, University of Macedonia, Thessaloniki, Greece","The influence of human-care service robots in human–robot interaction is becoming of great importance, because of the roles that the robots are taking in today’s and future society. Thus, we need to identify how humans can interact, collaborate, and learn from social robots more efficiently. Additionally, it is important to determine the robots’ modalities that can increase the humans’ perceived likeness and knowledge acquisition and enhance human–robot collaboration. The present study aims to identify the optimal social service robots’ modalities that enhance the human learning process and level of enjoyment from the interaction and even attract the humans’ attention to choosing a robot to collaborate with it. Our target group was college students, pre-service teachers. For this purpose, we designed two experiments, each one split in two parts. Both the experiments were between groups, and human participants had the chance to watch the Nao robot performing a storytelling exercise about the history of robots in a museum-educational activity via video annotations. The robot’s modalities were manipulated on its body movements (expressive arm and head gestures) while performing the storytelling, friendly attitude expressions and storytelling, and personality traits. After the robot’s storytelling, participants filled out a knowledge acquisition questionnaire and a self-reported enjoyment level questionnaire. In the second part, we introduce the idea of participants witnessing a conversation between the robots with the different modalities, and they were asked to choose the robot with which they want to collaborate in a similar activity. Results indicated that participants prefer to collaborate with robots with a cheerful personality and expressive body movements. Especially when they were asked to choose between two robots that were cheerful and had expressive body movements, they preferred the one which originally told them the story. Moreover, participants did not prefer to collaborate with a robot with an extremely friendly attitude and storytelling style. © Copyright © 2021 Velentza, Fachantidis and Pliasa.","conversation; expressive body movement; human–robot interaction; knowledge acquisition; robot characteristics; robot personality traits; social robotics/HRI; storytelling",,,,,,"European Commission, EC; University of Crete, UoC; European Social Fund, ESF","This research was funded in the context of the project “Mapping the characteristics of social robots in order to enhance cognitive functions and familiarity in humans” (MIS 5047258) under the call for proposals “Support for researchers with an emphasis on young researchers” (ΕDBΜ-103). The project is co-financed by Greece and the European Union (European Social Fund; ESF) by the Operational Programme Human Resources Development, Education and Lifelong Learning 2014-2020.","We would like to thank Ioannis Lefkos for technical support, Elias Economou, Experimental Psychology Laboratory, University of Crete, for supporting the research with the pilot study, and Marianna Karatsiori for proofreading the manuscript.",,,,,,,,,"Aly, A., Tapus, A., Towards an Intelligent System for Generating an Adapted Verbal and Nonverbal Combined Behavior in Human-Robot Interaction (2016) Auton. 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Agent Multi-agent Syst, 29 (6), pp. 1216-1248; Xu, J., Broekens, J., Hindriks, K., Mark, A., Neerincx, Effects of Bodily Mood Expression of a Robotic Teacher on Students (2014) 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2614-2620. , Chicago, IL, USA, 14-18 Sept. 2014, IEEE; Zhang, Z., Niu, Y., Wu, S., Lin, S., Kong, L., Analysis of Influencing Factors on Humanoid Robots' Emotion Expressions by Body Language (2018) Advances In Neural Networks – ISNN 2018, Lecture Notes In Computer Science, pp. 775-785. , Huang T., Lv J., Sun C., Tuzikov A.V., (eds), Cham, Springer International Publishing, Editors; Zhou, M.X., Mark, G., Li, J., Yang, H., Trusting Virtual Agents (2019) ACM Trans. Interact. Intell. Syst, 9 (2–3), pp. 1-36","Velentza, A.-M.; School of Educational and Social Policies, Greece; email: annamarakiv@gmail.com",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85115604143
"Yagi S., Nakata Y., Nakamura Y., Ishiguro H.","57214830614;49864220300;55871593000;26643316500;","Can an android’s posture and movement discriminate against the ambiguous emotion perceived from its facial expressions?",2021,"PLoS ONE","16","8 August","e0254905","","",,,"10.1371/journal.pone.0254905","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112296867&doi=10.1371%2fjournal.pone.0254905&partnerID=40&md5=5dc8eae532f202b2cacbfa1342a3a590","Department of Engineering Science, Osaka University, Toyonaka Osaka, Japan; JST ERATO, Chiyodaku, Tokyo, Japan","Yagi, S., Department of Engineering Science, Osaka University, Toyonaka Osaka, Japan, JST ERATO, Chiyodaku, Tokyo, Japan; Nakata, Y., Department of Engineering Science, Osaka University, Toyonaka Osaka, Japan, JST ERATO, Chiyodaku, Tokyo, Japan; Nakamura, Y., Department of Engineering Science, Osaka University, Toyonaka Osaka, Japan, JST ERATO, Chiyodaku, Tokyo, Japan; Ishiguro, H., Department of Engineering Science, Osaka University, Toyonaka Osaka, Japan, JST ERATO, Chiyodaku, Tokyo, Japan","Expressing emotions through various modalities is a crucial function not only for humans but also for robots. The mapping method from facial expressions to the basic emotions is widely used in research on robot emotional expressions. This method claims that there are specific facial muscle activation patterns for each emotional expression and people can perceive these emotions by reading these patterns. However, recent research on human behavior reveals that some emotional expressions, such as the emotion “intense”, are difficult to judge as positive or negative by just looking at the facial expression alone. Nevertheless, it has not been investigated whether robots can also express ambiguous facial expressions with no clear valence and whether the addition of body expressions can make the facial valence clearer to humans. This paper shows that an ambiguous facial expression of an android can be perceived more clearly by viewers when body postures and movements are added. We conducted three experiments and online surveys among North American residents with 94, 114 and 114 participants, respectively. In Experiment 1, by calculating the entropy, we found that the facial expression “intense” was difficult to judge as positive or negative when they were only shown the facial expression. In Experiments 2 and 3, by analyzing ANOVA, we confirmed that participants were better at judging the facial valence when they were shown the whole body of the android, even though the facial expression was the same as in Experiment 1. These results suggest that facial and body expressions by robots should be designed jointly to achieve better communication with humans. In order to achieve smoother cooperative human-robot interaction, such as education by robots, emotion expressions conveyed through a combination of both the face and the body of the robot is necessary to convey the robot’s intentions or desires to humans. © 2021 Yagi et al. 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"Shi L., Copot C., Vanlanduit S.","57211018738;35301588200;7004271926;","A Bayesian Deep Neural Network for Safe Visual Servoing in Human–Robot Interaction",2021,"Frontiers in Robotics and AI","8",,"687031","","",,,"10.3389/frobt.2021.687031","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109005855&doi=10.3389%2ffrobt.2021.687031&partnerID=40&md5=ed2fcc4bfe304ec6b8e93e880b833815","InViLab, Faculty of Applied Engineering, University of Antwerp, Antwerp, Belgium","Shi, L., InViLab, Faculty of Applied Engineering, University of Antwerp, Antwerp, Belgium; Copot, C., InViLab, Faculty of Applied Engineering, University of Antwerp, Antwerp, Belgium; Vanlanduit, S., InViLab, Faculty of Applied Engineering, University of Antwerp, Antwerp, Belgium","Safety is an important issue in human–robot interaction (HRI) applications. Various research works have focused on different levels of safety in HRI. If a human/obstacle is detected, a repulsive action can be taken to avoid the collision. Common repulsive actions include distance methods, potential field methods, and safety field methods. Approaches based on machine learning are less explored regarding the selection of the repulsive action. Few research works focus on the uncertainty of the data-based approaches and consider the efficiency of the executing task during collision avoidance. In this study, we describe a system that can avoid collision with human hands while the robot is executing an image-based visual servoing (IBVS) task. We use Monte Carlo dropout (MC dropout) to transform a deep neural network (DNN) to a Bayesian DNN, and learn the repulsive position for hand avoidance. The Bayesian DNN allows IBVS to converge faster than the opposite repulsive pose. Furthermore, it allows the robot to avoid undesired poses that the DNN cannot avoid. The experimental results show that Bayesian DNN has adequate accuracy and can generalize well on unseen data. The predictive interval coverage probability (PICP) of the predictions along x, y, and z directions are 0.84, 0.94, and 0.95, respectively. In the space which is unseen in the training data, the Bayesian DNN is also more robust than a DNN. We further implement the system on a UR10 robot, and test the robustness of the Bayesian DNN and the IBVS convergence speed. Results show that the Bayesian DNN can avoid the poses out of the reach range of the robot and it lets the IBVS task converge faster than the opposite repulsive pose.1 © Copyright © 2021 Shi, Copot and Vanlanduit.","Bayesian neural network; deep learning; human–robot interaction; image-based visual servoing; safety",,,,,,,,,,,,,,,,,"Anvaripour, M., Saif, M., Collision Detection for Human-Robot Interaction in an Industrial Setting Using Force Myography and a Deep Learning Approach (2019) 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 2149-2154. , IEEE; Bateux, Q., Marchand, E., Leitner, J., Chaumette, F., Corke, P., Training Deep Neural Networks for Visual Servoing (2018) 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1-8. , IEEE; Bertoni, L., Kreiss, S., Alahi, A., Monoloco: Monocular 3d Pedestrian Localization and Uncertainty Estimation (2019) Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6861-6871. , In; Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B., Simple Online and Realtime Tracking (2016) 2016 IEEE International Conference on Image Processing (ICIP), pp. 3464-3468. , IEEE; Bjelonic, M., (2018) YOLO ROS: Real-Time Object Detection for ROS; Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D., (2015) Weight Uncertainty in Neural Networks, , arXiv preprint; Chaumette, F., Hutchinson, S., Corke, P., Visual Servoing (2016) Handbook of Robotics, pp. 841-866. , Springer, chap. 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"Boualia S.N., Amara N.E.B.","57211427430;57219480110;","Deep full-body HPE for activity recognition from RGB frames only",2021,"Informatics","8","1","2","","",,1,"10.3390/informatics8010002","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106475438&doi=10.3390%2finformatics8010002&partnerID=40&md5=b51a4f44b17df392c9c7a2296aa798f5","University of Tunis El Manar, National Engineering School of Tunis, Tunis, 1002, Tunisia; Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse, LATIS- Laboratory of Advanced Technology and Intelligent Systems, Sousse, 4023, Tunisia","Boualia, S.N., University of Tunis El Manar, National Engineering School of Tunis, Tunis, 1002, Tunisia, Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse, LATIS- Laboratory of Advanced Technology and Intelligent Systems, Sousse, 4023, Tunisia; Amara, N.E.B., Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse, LATIS- Laboratory of Advanced Technology and Intelligent Systems, Sousse, 4023, Tunisia","Human Pose Estimation (HPE) is defined as the problem of human joints’ localization (also known as keypoints: elbows, wrists, etc.) in images or videos. It is also defined as the search for a specific pose in space of all articulated joints. HPE has recently received significant attention from the scientific community. The main reason behind this trend is that pose estimation is considered as a key step for many computer vision tasks. Although many approaches have reported promising results, this domain remains largely unsolved due to several challenges such as occlusions, small and barely visible joints, and variations in clothing and lighting. In the last few years, the power of deep neural networks has been demonstrated in a wide variety of computer vision problems and especially the HPE task. In this context, we present in this paper a Deep Full-Body-HPE (DFB-HPE) approach from RGB images only. Based on ConvNets, fifteen human joint positions are predicted and can be further exploited for a large range of applications such as gesture recognition, sports performance analysis, or human-robot interaction. To evaluate the proposed deep pose estimation model, we apply it to recognize the daily activities of a person in an unconstrained environment. Therefore, the extracted features, represented by deep estimated poses, are fed to an SVM classifier. To validate the proposed architecture, our approach is tested on two publicly available benchmarks for pose estimation and activity recognition, namely the J-HMDBand CAD-60datasets. The obtained results demonstrate the efficiency of the proposed method based on ConvNets and SVM and prove how deep pose estimation can improve the recognition accuracy. By means of comparison with state-of-the-art methods, we achieve the best HPE performance, as well as the best activity recognition precision on the CAD-60 dataset. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.","Convnets; Deep learning; Human activity recognition; Human pose estimation; SVM",,,,,,,,,,,,,,,,,"Qiang, L., Zhang, W., Hongliang, L., Ngan, K.N., Hybrid human detection and recognition in surveillance (2016) Neurocomputing, 194, pp. 10-23; D’Eusanio, A., Simoni, A., Pini, S., Borghi, G., Vezzani, R., Cucchiara, R., Multimodal hand gesture classification for the human-car interaction (2020) Informatics, 7, p. 31; Unzueta, L., Goenetxea, J., Rodriguez, M., Linaza, M.T., Dependent 3D human body posing for sports legacy recovery from images and video (2014) Proceedings of the 2014 22nd European Signal Processing Conference (EUSIPCO), pp. 361-365. , Lisbon, Portugal, 1-5 September; Chen, C., Yang, Y., Nie, F., Odobez, J.M., 3D human pose recovery from image by efficient visual feature selection (2011) Comput. Vis. 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"Wu M., Su W., Chen L., Liu Z., Cao W., Hirota K.","57218664297;57196287282;55774663200;56101506800;7402082944;7402829702;","Weight-Adapted Convolution Neural Network for Facial Expression Recognition in Human-Robot Interaction",2021,"IEEE Transactions on Systems, Man, and Cybernetics: Systems","51","3","8663434","1473","1484",,13,"10.1109/TSMC.2019.2897330","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101063006&doi=10.1109%2fTSMC.2019.2897330&partnerID=40&md5=612ef7ead8da44095cd633c31ef3f435","School of Automation, China University of Geosciences, Wuhan, 430074, China; Tokyo Institute of Technology, Tokyo, 226-8502, Japan","Wu, M., School of Automation, China University of Geosciences, Wuhan, 430074, China; Su, W., School of Automation, China University of Geosciences, Wuhan, 430074, China; Chen, L., School of Automation, China University of Geosciences, Wuhan, 430074, China; Liu, Z., School of Automation, China University of Geosciences, Wuhan, 430074, China; Cao, W., School of Automation, China University of Geosciences, Wuhan, 430074, China; Hirota, K., Tokyo Institute of Technology, Tokyo, 226-8502, Japan","The weight-adapted convolution neural network (WACNN) is proposed to extract discriminative expression representations for recognizing facial expression. It aims to make good use of the convolution neural network's (CNN's) potential performance in avoiding local optima and speeding up convergence by the hybrid genetic algorithm (HGA) with optimal initial population, in such a way that it realizes deep and global emotion understanding in human-robot interaction. Moreover, the idea of novelty search is introduced to solve the deception problem in the HGA, which can expend the search space to help genetic algorithm jump out of local optimum and optimize large-scale parameters. In the proposal, the facial expression image preprocessing is conducted first, then the low-level expression features are extracted by using a principal component analysis. Finally, the high-level expression semantic features are extracted and recognized by WACNN which is optimized by HGA. In order to evaluate the effectiveness of WACNN, experiments on JAFFE, CK+, and static facial expressions in the wild 2.0 databases are carried out by using {k} -fold cross validation, and experimental results show the recognition accuracies of the proposal are superior to that of the state-of-the-art, such as local directional ternary pattern and weighted mixture deep neural network (DNN), which aim to extract discriminative and are the DNN-based methods. Moreover, recognition accuracies of the proposal are also higher than the deep CNN without HGA, which indicates that the proposal has better global optimization ability. Meanwhile, preliminary application experiments are also carried out by using the proposed algorithm on the emotional social robot system, where nine volunteers and two-wheeled robots experience the scenario of emotion understanding. Application results indicate that the wheeled robots can recognize basic expressions, such as happy, surprise, and so on. © 2013 IEEE.","Accelerated gradient descent (AGD); convolution neural network (CNN); facial expression recognition (FER); genetic algorithm (GA)","Convolution; Deep neural networks; Face recognition; Genetic algorithms; Global optimization; Man machine systems; Semantics; Social robots; Convolution neural network; Directional ternary patterns; Emotion understanding; Facial expression recognition; High level expression; Hybrid genetic algorithms; Optimization ability; Recognition accuracy; Neural networks",,,,,"National Natural Science Foundation of China, NSFC: 61603356, 61733016; China University of Geosciences, Wuhan, CUG: 201839; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project: B17040","Manuscript received August 6, 2018; revised November 17, 2018; accepted January 20, 2019. Date of publication March 8, 2019; date of current version February 17, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61603356 and Grant 61733016, in part by the 111 Project under Grant B17040, and in part by the Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan, under Grant 201839. This paper was recommended by Associate Editor S. Nahavandi. (Corresponding author: Luefeng Chen.) M. Wu, W. J. Su, L. F. Chen, Z. T. Liu, and W. H. Cao are with the School of Automation, China University of Geosciences, Wuhan 430074, China, and also with the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China (e-mail: wumin@cug.edu.cn; suwanjuan@cug.edu.cn; chenluefeng@cug.edu.cn; liuzhentao@cug.edu.cn; weihuacao@cug.edu.cn).",,,,,,,,,,"Jang, J., Cho, H., Kim, J., Lee, J., Yang, S., Facial attribute recognition by recurrent learning with visual fixation (2019) IEEE Trans. Cybern., 49 (2), pp. 616-625. , Feb; Chen, L.F., Dynamic emotion understanding in human-robot interaction based on two-layer fuzzy SVR-TS model IEEE Trans. Syst., Man, Cybern., Syst., , to be published; Poria, S., Cambria, E., Bajpai, R., Hussain, A., A review of affective computing: From unimodal analysis to multimodal fusion (2017) Inf. Fusion, 37, pp. 98-125. , Sep; Halder, A., General and interval type-2 fuzzy face-space approach to emotion recognition (2013) IEEE Trans. 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"Kelley M.S., Noah J.A., Zhang X., Scassellati B., Hirsch J.","57191975685;36789839000;57188577368;57203072853;35299240000;","Comparison of Human Social Brain Activity During Eye-Contact With Another Human and a Humanoid Robot",2021,"Frontiers in Robotics and AI","7",,"599581","","",,2,"10.3389/frobt.2020.599581","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100860968&doi=10.3389%2ffrobt.2020.599581&partnerID=40&md5=7af491fd1e5fec4a87606c8591375b40","Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States; Brain Function Laboratory, Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States; Social Robotics Laboratory, Department of Computer Science, Yale University, New Haven, CT, United States; Departments of Neuroscience and Comparative Medicine, Yale School of Medicine, New Haven, CT, United States; Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom","Kelley, M.S., Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States, Brain Function Laboratory, Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States; Noah, J.A., Brain Function Laboratory, Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States; Zhang, X., Brain Function Laboratory, Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States; Scassellati, B., Social Robotics Laboratory, Department of Computer Science, Yale University, New Haven, CT, United States; Hirsch, J., Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, United States, Brain Function Laboratory, Department of Psychiatry, Yale School of Medicine, New Haven, CT, United States, Departments of Neuroscience and Comparative Medicine, Yale School of Medicine, New Haven, CT, United States, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom","Robot design to simulate interpersonal social interaction is an active area of research with applications in therapy and companionship. Neural responses to eye-to-eye contact in humans have recently been employed to determine the neural systems that are active during social interactions. Whether eye-contact with a social robot engages the same neural system remains to be seen. Here, we employ a similar approach to compare human-human and human-robot social interactions. We assume that if human-human and human-robot eye-contact elicit similar neural activity in the human, then the perceptual and cognitive processing is also the same for human and robot. That is, the robot is processed similar to the human. However, if neural effects are different, then perceptual and cognitive processing is assumed to be different. In this study neural activity was compared for human-to-human and human-to-robot conditions using near infrared spectroscopy for neural imaging, and a robot (Maki) with eyes that blink and move right and left. Eye-contact was confirmed by eye-tracking for both conditions. Increased neural activity was observed in human social systems including the right temporal parietal junction and the dorsolateral prefrontal cortex during human-human eye contact but not human-robot eye-contact. This suggests that the type of human-robot eye-contact used here is not sufficient to engage the right temporoparietal junction in the human. This study establishes a foundation for future research into human-robot eye-contact to determine how elements of robot design and behavior impact human social processing within this type of interaction and may offer a method for capturing difficult to quantify components of human-robot interaction, such as social engagement. © Copyright © 2021 Kelley, Noah, Zhang, Scassellati and Hirsch.","dorsolateral prefontal cortex; eye-contact; fNIRS; human-robot interaction; social cognition; social engagement; tempoparietal junction; TPJ",,,,,,,"The authors are grateful for the significant contributions of members of the Yale Brain Function and Social Robotics Labs, including Jennifer Cuzzocreo, Courtney DiCocco, Raymond Cappiello, and Jake Brawer.",,,,,,,,,,"Admoni, H., Scassellati, B., Social Eye Gaze in Human-robot Interaction: A Review (2017) J. 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Social Robotics; Ye, J.C., Tak, S., Jang, K.E., Jung, J., Jang, J., NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy (2009) Neuroimage, 44 (2), pp. 428-447; Zhang, X., Noah, J.A., Dravida, S., Hirsch, J., Signal processing of functional NIRS data acquired during overt speaking (2017) Neurophotonics, 4 (4), p. 041409; Zhang, X., Noah, J.A., Hirsch, J., Separation of the global and local components in functional near-infrared spectroscopy signals using principal component spatial filtering (2016) Neurophotonics, 3 (1), p. 015004","Hirsch, J.; Interdepartmental Neuroscience Program, United States; email: Joy.Hirsch@yale.edu
Hirsch, J.; Brain Function Laboratory, United States; email: Joy.Hirsch@yale.edu
Hirsch, J.; Departments of Neuroscience and Comparative Medicine, United States; email: Joy.Hirsch@yale.edu
Hirsch, J.; Department of Medical Physics and Biomedical Engineering, United Kingdom; email: Joy.Hirsch@yale.edu",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85100860968
"Thoduka S., Hochgeschwender N.","57195423565;25645533000;","Benchmarking Robots by Inducing Failures in Competition Scenarios",2021,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12778 LNCS",,,"263","276",,,"10.1007/978-3-030-77820-0_20","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112115808&doi=10.1007%2f978-3-030-77820-0_20&partnerID=40&md5=b440af44bcf6a5b9503ef45ba2d30654","Department of Computer Science, Hochschule Bonn-Rhein-Sieg, Sankt Augustin, 53757, Germany","Thoduka, S., Department of Computer Science, Hochschule Bonn-Rhein-Sieg, Sankt Augustin, 53757, Germany; Hochgeschwender, N., Department of Computer Science, Hochschule Bonn-Rhein-Sieg, Sankt Augustin, 53757, Germany","Domestic service robots are becoming more ubiquitous and can perform various assistive tasks such as fetching items or helping with medicine intake to support humans with impairments of varying severity. However, the development of robots taking care of humans should not only be focused on developing advanced functionalities, but should also be accompanied by the definition of benchmarking protocols enabling the rigorous and reproducible evaluation of robots and their functionalities. Thereby, of particular importance is the assessment of robots’ ability to deal with failures and unexpected events which occur when they interact with humans in real-world scenarios. For example, a person might drop an object during a robot-human hand over due to its weight. However, the systematic investigation of hazardous situations remains challenging as (i) failures are difficult to reproduce; and (ii) possibly impact the health of humans. Therefore, we propose in this paper to employ the concept of scientific robotic competitions as a benchmarking protocol for assessing care robots and to collect datasets of human-robot interactions covering a large variety of failures which are present in real-world domestic environments. We demonstrate the process of defining the benchmarking procedure with the human-to-robot and robot-to-human handover functionalities, and execute a dry-run of the benchmarks while inducing several failure modes such as dropping objects, ignoring the robot, and not releasing objects. A dataset comprising colour and depth images, a wrist force-torque sensor and other internal sensors of the robot was collected during the dry-run. In addition, we discuss the relation between benchmarking protocols and standards that exist or need to be extended with regard to the test procedures required for verifying and validating conformance to standards. © 2021, Springer Nature Switzerland AG.","Assistive robots; Benchmarking; Robotics competitions","Benchmarking; Ergonomics; Health; Health risks; Large dataset; Risk management; Safety engineering; Testing; Domestic environments; Domestic services; Force-torque sensors; Internal sensors; Real-world scenario; Scientific robotics; Test procedures; Unexpected events; Human robot interaction",,,,,"Horizon 2020 Framework Programme, H2020: 871252","Acknowledgement. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 871252 (METRICS).",,,,,,,,,,"(2021) Darpa Robotics Challenge, , https://archive.darpa.mil/roboticschallenge/.Accessed, Jan; (2021) European Robotics League, , https://www.eu-robotics.net/roboticsleague/.Accessed, Jan; RoboCup. https://www.robocup.org/. Accessed Jan 2021; Robot Competitions Kick Innovation In Cognitive Systems and Robotics. http://rockinrobotchallenge.eu/. Accessed Jan 2021; (2021) Robotics Coordination Action for Europe Two, , https://www.eu-robotics.net/eurobotics/about/projects/rockeu2.html.Accessed, Jan; World Robot Challenge. https://worldrobotsummit.org/en/wrs2020/challenge/. Accessed Jan 2021; Multi-Annual Roadmap for Robotics in Europe. Technical guide (2020) Sparc-Partnership for Robotics in Europe (2016); Amigoni, F., Competitions for benchmarking: Task and functionality scoring complete performance assessment (2015) IEEE Robot. 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In, pp., IEEE","Thoduka, S.; Department of Computer Science, Germany; email: santosh.thoduka@h-brs.de","Duffy V.G.",,"Springer Science and Business Media Deutschland GmbH","12th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, DHM 2021, Held as Part of the 23rd HCI International Conference, HCII 2021","24 July 2021 through 29 July 2021",,262189,03029743,9783030778194,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85112115808
"Yang D., Chae Y.-J., Kim D., Lim Y., Kim D.H., Kim C.H., Park S.-K., Nam C.","57226499899;57226494488;57226505181;57226501875;57198637622;55697746900;7501839099;35590319700;","Effects of Social Behaviors of Robots in Privacy-Sensitive Situations",2021,"International Journal of Social Robotics",,,,"","",,,"10.1007/s12369-021-00809-2","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111768804&doi=10.1007%2fs12369-021-00809-2&partnerID=40&md5=dba0f0b973ea9b10d7b6e330c148f6b0","Koh Young Technology, Seoul, South Korea; Korea Institute of Science and Technology (KIST), Seoul, South Korea; Seoul Robotics Co., Ltd, Seoul, South Korea; Inha University, Incheon, South Korea; Department of HY-KIST Bio-convergence, Hanyang University, Seoul, South Korea","Yang, D., Koh Young Technology, Seoul, South Korea; Chae, Y.-J., Korea Institute of Science and Technology (KIST), Seoul, South Korea; Kim, D., Seoul Robotics Co., Ltd, Seoul, South Korea; Lim, Y., Korea Institute of Science and Technology (KIST), Seoul, South Korea, Department of HY-KIST Bio-convergence, Hanyang University, Seoul, South Korea; Kim, D.H., Korea Institute of Science and Technology (KIST), Seoul, South Korea; Kim, C.H., Korea Institute of Science and Technology (KIST), Seoul, South Korea; Park, S.-K., Korea Institute of Science and Technology (KIST), Seoul, South Korea; Nam, C., Inha University, Incheon, South Korea","In this paper, we investigate the effect of robot social behaviors in privacy-sensitive situations that are designed to reduce concerns of users regarding privacy invasion. As social robots ought to spend considerable time with humans, there has been research on user privacy protection. However, previous work mostly focuses on technical approaches not to disclose private information such as video/audio recordings and personally identifiable information. Although social robots could protect the privacy of users using those technologies, the users may still feel uncomfortable if they do not notice such the protection techniques being held. We design and test several behaviors of a social robot that can help users understand the internal process of the robot protecting user privacy so that users could have less concerns about their privacy. We choose three categories for the behavior design based on previous studies in psychology and human–robot interaction. They are (i) the gaze of the robot, (ii) the distance between the robot and a user, and (iii) clarity in expressing intent of the robot. In each category, we design and test three behaviors including a baseline (default) behavior. We conduct user studies with 56 participants in two scenarios to find effective behaviors. In the two scenarios, participants are asked to change clothes and write personal information in the presence of the robot in the vicinity, respectively. From the result, we find that users feel more comfortable in privacy-sensitive situations if they observe the robot perfor-ming the behaviors respecting user privacy. The most effective behavior of the robot is shown to be turning around from the user and showing robot’s back. As the robot combines more behaviors (such as telling the user that video recording is suspended, moving away from the user, and then turning around), the concern of users tends to decrease. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.","Privacy-sensitive robotics, User privacy, Human-robot interaction, Social robotics, Robot behaviors; Social human–robot interaction and User privacy","Agricultural robots; Behavioral research; Machine design; Privacy by design; Social robots; Video recording; Effective behaviors; Personal information; Personally identifiable information; Privacy invasions; Private information; Protection techniques; Robot interactions; Three categories; Economic and social effects",,,,,"Inha University, Inha; Ministry of Science, ICT and Future Planning, MSIP: CRC-15-04-KIST; National Research Council of Science and Technology, NST","This study was funded by the National Research Council of Science & Technology (NST) grant by the Korea government (MSIP) (No. CRC-15-04-KIST) and INHA UNIVERSITY Research Grant.","This work was supported in part by the National Research Council of Science & Technology (NST) grant by the Korea government (MSIP) (No. CRC-15-04-KIST) and INHA UNIVERSITY Research Grant. The IRB approval no. is KIST No. 2019-019.",,,,,,,,,"Argyle, M., Cook, M., (1976) Gaze and Mutual Gaze; Bansal, G., Gefen, D., The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online (2010) Decis Support Syst, 49 (2), pp. 138-150; Buchanan, T., Paine, C., Joinson, A.N., Reips, U.D., Development of measures of online privacy concern and protection for use on the internet (2007) J Am Soc Inf Sci Technol, 58 (2), pp. 157-165; Burgoon, J.K., Privacy and communication (1982) Ann Int Commun Assoc, 6 (1), pp. 206-249; Caine, K., Šabanovic, S., Carter, M., The effect of monitoring by cameras and robots on the privacy enhancing behaviors of older adults (2012) Proceedings of ACM/IEEE International Conference on human–robot Interaction (HRI), pp. 343-350; Chatzimichali, A., Harrison, R., Chrysostomou, D., Toward privacy-sensitive human–robot interaction: privacy terms and human-data interaction in the personal robot era (2021) Paladyn J Behav Robot, 12 (1), pp. 160-174; Cronbach, L.J., Coefficient alpha and the internal structure of tests (1951) Psychometrika, 16 (3), pp. 297-334; Culnan, M.J., Protecting privacy online: is self-regulation working? 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J. Soc. Rob.",Article,"Article in Press","",Scopus,2-s2.0-85111768804
"Papadopoulos G.Th., Antona M., Stephanidis C.","57196706610;11241082800;7004072937;","Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning",2021,"IEEE Access","9",,"9431107","73890","73909",,,"10.1109/ACCESS.2021.3080517","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105863905&doi=10.1109%2fACCESS.2021.3080517&partnerID=40&md5=f45dac025c9475bdfb165f60457fc36b","Foundation for Research and Technology - Hellas (FORTH), Institute of Computer Science (ICS)GR-70013, Greece; Computer Science Department (CSD), University of Crete (UoC), Heraklion, GR-70013, Greece","Papadopoulos, G.Th., Foundation for Research and Technology - Hellas (FORTH), Institute of Computer Science (ICS)GR-70013, Greece; Antona, M., Foundation for Research and Technology - Hellas (FORTH), Institute of Computer Science (ICS)GR-70013, Greece; Stephanidis, C., Foundation for Research and Technology - Hellas (FORTH), Institute of Computer Science (ICS)GR-70013, Greece, Computer Science Department (CSD), University of Crete (UoC), Heraklion, GR-70013, Greece","Learning from Demonstration (LfD) constitutes one of the most robust methodologies for constructing efficient cognitive robotic systems. Despite the large body of research works already reported, current key technological challenges include those of multi-agent learning and long-term autonomy. Towards this direction, a novel cognitive architecture for multi-agent LfD robotic learning is introduced in this paper, targeting to enable the reliable deployment of open, scalable and expandable robotic systems in large-scale and complex environments. In particular, the designed architecture capitalizes on the recent advances in the Artificial Intelligence (AI) (and especially the Deep Learning (DL)) field, by establishing a Federated Learning (FL)-based framework for incarnating a multi-human multi-robot collaborative learning environment. The fundamental conceptualization relies on employing multiple AI-empowered cognitive processes (implementing various robotic tasks) that operate at the edge nodes of a network of robotic platforms, while global AI models (underpinning the aforementioned robotic tasks) are collectively created and shared among the network, by elegantly combining information from a large number of human-robot interaction instances. Regarding pivotal novelties, the designed cognitive architecture a) introduces a new FL-based formalism that extends the conventional LfD learning paradigm to support large-scale multi-agent operational settings, b) elaborates previous FL-based self-learning robotic schemes so as to incorporate the human in the learning loop and c) consolidates the fundamental principles of FL with additional sophisticated AI-enabled learning methodologies for modelling the multi-level inter-dependencies among the robotic tasks. The applicability of the proposed framework is explained using an example of a real-world industrial case study (subject to ongoing research activities) for agile production-based Critical Raw Materials (CRM) recovery. © 2013 IEEE.","artificial intelligence; federated learning; human-robot interaction; Learning from demonstration","Autonomous agents; Cognitive systems; Computer aided instruction; Deep learning; Educational robots; Industrial research; Learning systems; Network architecture; Robotics; Social robots; Cognitive architectures; Collaborative learning; Collaborative learning environment; Critical raw materials; Fundamental principles; Industrial case study; Learning from demonstration; Technological challenges; Multi agent systems",,,,,,"This work was supported by the ICS-FORTH internal Research and Technological Development (RTD) Programme ’Ambient Intelligence and Smart Environments.","This work was supported by the ICS-FORTH internal Research and Technological Development (RTD) Programme 'Ambient Intelligence and Smart Environments.",,,,,,,,,"Ravichandar, H., Polydoros, A.S., Chernova, S., Billard, A., Recent advances in robot learning from demonstration (2020) Annu. 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"Park K.-B., Choi S.H., Lee J.Y., Ghasemi Y., Mohammed M., Jeong H.","57199326425;57199324903;57194376603;57222736395;57223423906;57193237154;","Hands-free human-robot interaction using multimodal gestures and deep learning in wearable mixed reality",2021,"IEEE Access","9",,"9395580","55448","55464",,4,"10.1109/ACCESS.2021.3071364","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103894294&doi=10.1109%2fACCESS.2021.3071364&partnerID=40&md5=a3e04951985aca3b3386d50d59f5768e","Department of Industrial Engineering, Chonnam National University, Gwangju, 61186, South Korea; Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, United States","Park, K.-B., Department of Industrial Engineering, Chonnam National University, Gwangju, 61186, South Korea; Choi, S.H., Department of Industrial Engineering, Chonnam National University, Gwangju, 61186, South Korea; Lee, J.Y., Department of Industrial Engineering, Chonnam National University, Gwangju, 61186, South Korea; Ghasemi, Y., Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, United States; Mohammed, M., Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, United States; Jeong, H., Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, United States","This study proposes a novel hands-free interaction method using multimodal gestures such as eye gazing and head gestures and deep learning for human-robot interaction (HRI) in mixed reality (MR) environments. Since human operators hold some objects for conducting tasks, there are many constrained situations where they cannot use their hands for HRI interactions. To provide more effective and intuitive task assistance, the proposed hands-free method supports coarse-to-fine interactions. Eye gazing-based interaction is used for coarse interactions such as searching and previewing of target objects, and head gesture interactions are used for fine interactions such as selection and 3D manipulation. In addition, deep learning-based object detection is applied to estimate the initial positioning of physical objects to be manipulated by the robot. The result of object detection is then combined with 3D spatial mapping in the MR environment for supporting accurate initial object positioning. Furthermore, virtual object-based indirect manipulation is proposed to support more intuitive and efficient control of the robot, compared with traditional direct manipulation (e.g., joint-based and end effector-based manipulations). In particular, a digital twin, the synchronized virtual robot of the real robot, is used to provide a preview and simulation of the real robot to manipulate it more effectively and accurately. Two case studies were conducted to confirm the originality and advantages of the proposed hands-free HRI: (1) performance evaluation of initial object positioning and (2) comparative analysis with traditional direct robot manipulations. The deep learning-based initial positioning reduces much effort for robot manipulation using eye gazing and head gestures. The object-based indirect manipulation also supports more effective HRI than previous direct interaction methods. © 2013 IEEE.","Deep learning; Eye gazing; Hands-free interaction; Head gestures; Human-robot interaction; Mixed reality; Object detection","Deep learning; Digital twin; End effectors; Man machine systems; Mixed reality; Object detection; Object recognition; Wearable technology; Comparative analysis; Direct interactions; Direct manipulation; Efficient control; Hands-free interactions; Human robot Interaction (HRI); Physical objects; Robot manipulation; Human robot interaction",,,,,"Ministry of Science, ICT and Future Planning, MSIP; National Research Foundation of Korea, NRF; Institute for Information and Communications Technology Promotion, IITP: 2020-0-01532; Kementerian Pendidikan Malaysia, KPM: 2019R1I1A3A01059082","This work was supported in part by the Republic of Korea’s Ministry of Science and ICT (MSIT) through the High-Potential Individuals Global Training Program supervised by the Institute of Information and Communications Technology Planning and Evaluation (IITP) under Grant 2020-0-01532, and in part by the National Research Foundation of Korea (NRF) funded by the Ministry of Education through the Basic Science Research Program under Grant 2019R1I1A3A01059082.",,,,,,,,,,"Matthias, B., Kock, S., Jerregard, H., Kallman, M., Lundberg, I., Safety of collaborative industrial robots: Certification possibilities for a collaborative assembly robot concept (2011) Proc IEEE Int. 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Eng, 26 (1), pp. 161-170. , Jan; Ruzaij, M.F., Neubert, S., Stoll, N., Thurow, K., Auto calibrated head orientation controller for robotic-wheelchair using MEMS sensors and embedded technologies (2016) Proc IEEE Sensors Appl. Symp. (SAS), pp. 1-6. , Apr; Laddi, A., Bhardwaj, V., Kapoor, N., Pankaj, D., Kumar, A., Unobtrusive head gesture based directional control system for patient mobility cart (2015) Proc. Int. Conf. Signal Process Comput. Control (ISPCC), pp. 236-240. , Sep; Ohtsuka, H., Kato, T., Shibasato, K., Kashimoto, T., Non-contact head gesture maneuvering system for electric wheelchair using a depth sensor (2015) Proc. 9th Int. Conf. Sens. Technol. (ICST), pp. 98-103. , Dec; Fall, C.L., Turgeon, P., Campeau-Lecours, A., Maheu, V., Boukadoum, M., Roy, S., Massicotte, D., Gosselin, B., Intuitive wireless control of a robotic arm for people living with an upper body disability (2015) Proc. 37th Annu. Int. Conf IEEE Eng. Med. Biol. Soc. (EMBC), pp. 4399-4402. , Aug; Kyrarini, M., Zheng, Q., Haseeb, M.A., Gräser, A., Robot learning of assistive manipulation tasks by demonstration via head gesture-based interface (2019) Proc IEEE 16th Int. Conf. Rehabil. Robot. (ICORR), pp. 1139-1146. , Jun; Sidenmark, L., Gellersen, H., Eye&head: Synergetic eye and head movement for gaze pointing and selection (2019) Proc. UIST, pp. 1161-1174. , Oct; Sidenmark, L., Mardanbegi, D., Gomez, A.R., Clarke, C., Gellersen, H., BimodalGaze: Seamlessly refined pointing with gaze and filtered gestural head movement (2020) Proc. ETRA, pp. 1-9. , Jun; Pfeuffer, K., Alexander, J., Chong, M.K., Zhang, Y., Gellersen, H., Gaze-shifting: Direct-indirect input with pen and touch modulated by gaze (2015) Proc. UIST, pp. 373-383. , Nov; Morimoto, C.H., Mimica, M.R.M., Eye gaze tracking techniques for interactive applications (2005) Comput. Vis. 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AVI, pp. 1-8. , May; Mohan, P., Goh, W.B., Fu, C.-W., Yeung, S.-K., DualGaze: Addressing the midas touch problem in gaze mediated VR interaction (2018) Proc. ISMAR-Adjunct, pp. 79-84. , Oct; Mardanbegi, D., Langlotz, T., Gellersen, H., Resolving target ambiguity in 3D gaze interaction through VOR depth estimation (2019) Proc. CHI, pp. 1-12. , May; Nukarinen, T., Kangas, J., Špakov, O., Isokoski, P., Akkil, D., Rantala, J., Raisamo, R., Evaluation of HeadTurn: An interaction technique using the gaze and head turns (2016) Proc. NordiCHI, pp. 1-8. , Oct; Špakov, O., Isokoski, P., Majaranta, P., Look and lean: Accurate headassisted eye pointing (2014) Proc. ETRA, pp. 35-42. , Mar; Huy, D.Q., Vietcheslav, I., Lee, G.S.G., See-Through and spatial augmented reality-A novel framework for human-robot interaction (2017) Proc. ICCAR, pp. 719-726. , Apr; Chadalavada, R.T., Andreasson, H., Schindler, M., Palm, R., Lilienthal, A.J., Bi-directional navigation intent communication using spatial augmented reality and eye-Tracking glasses for improved safety in human-robot interaction (2020) Robot. Comput.-Integr. Manuf, 61. , Feb, Art 101830; Mohammed, M., Jeong, H., Lee, J.Y., Human-robot collision avoidance scheme for industrial settings based on injury classification Proc. ACM/IEEE HRI, 2021, pp. 549-551. , Mar; Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P., Focal loss for dense object detection (2017) Proc IEEE Int. Conf. Comput. Vis. (ICCV), pp. 2980-2988. , Oct; (2020) Unity3D, , https://unity.com/, [Online]; (2020) Universal Robots, , https://www.universal-robots.com/, [Online]; (2017) COCO Dataset, , https://cocodataset.org/#home/, [Online]; Jiang, G., Luo, M., Bai, K., Chen, S., A precise positioning method for a puncture robot based on a PSO-optimized BP neural network algorithm (2017) Appl. Sci, 7 (10). , Sep, Art. no 969; Kebria, P.M., Al-Wais, S., Abdi, H., Nahavandi, S., Kinematic and dynamic modelling of UR5 manipulator (2016) Proc. SMC, pp. 4229-4234. , Oct; Grieves, M., Vickers, J., Digital twin: Mitigating unpredictable undesirable emergent behavior in complex systems (2017) Transdisciplinary Perspectives on Complex Systems, pp. 85-113. , Cham Switzerland Springer Aug; Zhu, M., Recall, precision and average precision (2004) Dept. Statist. Actuarial Sci., Univ. Waterloo, , Waterloo, ON, Canada Working Paper 2004-09; Hart, S.G., Staveland, L.E., Development of nasa-Tlx (task load index): Results of empirical and theoretical research (1988) Adv. Psychol, 52, pp. 139-183. , Apr","Lee, J.Y.; Department of Industrial Engineering, South Korea; email: jaeyeol@chonnam.ac.kr",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85103894294
"Casiddu N., Burlando F., Porfirione C., Vacanti A.","56088000200;57218200734;57194096211;57209328312;","Designing Synthetic Emotions of a Robotic System",2021,"Advances in Intelligent Systems and Computing","1269 AISC",,,"148","155",,2,"10.1007/978-3-030-58282-1_24","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091112979&doi=10.1007%2f978-3-030-58282-1_24&partnerID=40&md5=977411de26c23d746337763871eb4091","Dipartimento Architettura e Design, Università degli Studi di Genova, Stradone di Sant‘Agostino 37, Genova, GE  16123, Italy","Casiddu, N., Dipartimento Architettura e Design, Università degli Studi di Genova, Stradone di Sant‘Agostino 37, Genova, GE  16123, Italy; Burlando, F., Dipartimento Architettura e Design, Università degli Studi di Genova, Stradone di Sant‘Agostino 37, Genova, GE  16123, Italy; Porfirione, C., Dipartimento Architettura e Design, Università degli Studi di Genova, Stradone di Sant‘Agostino 37, Genova, GE  16123, Italy; Vacanti, A., Dipartimento Architettura e Design, Università degli Studi di Genova, Stradone di Sant‘Agostino 37, Genova, GE  16123, Italy","As massive changes in our lifestyle and society structure are coming, due to the contemporary pandemic situation, we face more than ever the challenge of employing non-human and teleassistance devices in many areas, especially those that require assisting and caring for weak users. Humanoid robots are proven to be a valuable asset in these situations but require to be carefully designed in their interaction features, in order to be accepted and valuably used by their users. In particular, this study is focused on robots that have a certain degree of human-likeness that allows to define them “humanoids”; for this kind of robots we can say that the area devoted to replicate human facial features is the most important interface for human–robot interaction. Actually, more than 60% of human–human interaction is conducted non-verbally, by using facial expressions and gestures. For a robot to be able to engage in this kind of interaction with a human and provide understandable feedbacks is a massive step towards acceptance and development of an affectional relationship by users. Being meant to reproduce human emotions, visual feedbacks are mostly developed referring to eminent researches in the psychological field: as Paul Ekman already observed in 1998, human faces have a universal coding for six basic expressions that represent as many basic emotions: fear, anger, disgust, happiness, sadness and surprise. Our research focuses on the design of an expression system to be implemented in a European-funded project for an assistive robot that will support weak users at home or in assistance facilities and their caregivers. Our main concern regarding this project are to design a dynamic, human-friendly system that is scalable and visually recalls real facial expressions without being too much human-like, in order to avoid the well-known uncanny effect described by Masairo Mori. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.","Human–robot interaction; Robotic faces; Synthetic emotions","Anthropomorphic robots; Machine design; Robotics; Systems engineering; Visual communication; Assistive robots; Expression system; Facial Expressions; Human interactions; Interaction features; Robot interactions; Synthetic emotions; Universal coding; Social robots",,,,,,,,,,,,,,,,"Vermesan, O., Bacquet, J., (2017) Cognitive Hyperconnected Digital Transformation: Internet of Things Intelligence Evolution, , (eds) River Publishers AHFE; Toshio, F., Facial expressive robotic head system for human-robot communication and its application in home environment (2004) Proc. IEEE, 92 (11), pp. 1851-1865; iSalvo, C., Gemperle, F., Forlizzi, J., Kiesler, S., All robots are not created equal: the design and perception of humanoid robot heads (2002) Proceedings of the Designing Interactive System, pp. 321-326; Pascalis, O., Scott, L.S., Kelly, D.J., Shannon, R.W., Nicholson, E., Coleman, M., Nelson, C.A., Plasticity of face processing in infancy (2005) PNAS, 102 (14), pp. 5297-5300; Blow, M., Dautenhahn, K., Appleby, A., Nehaniv, C.L., Lee, D., The art of designing robot faces: dimensions for human-robot interaction (2006) Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction, pp. 331-332; Ekamn, P., Rosenberg, E.L., (1998) What the Face Reveals, , Oxford University Press, Oxford; Ekman, P., Friesen, W.V., (1978) Manual for the Facial Action Coding System, , Consulting Psychologists Press, Palo alto; Frédéric, D., De Greeff, J., Belpaeme, T., Towards retro-projected robot faces: an alternative to mechatronic and android faces (2009) RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication. IEEE; Kalegina, A., Schroeder, G., Allchin, A., Berlin, K., Cakmak, M., Characterizing the design space of rendered robot faces (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 96-104; Todorov, A., Said, C.P., Engell, A.D., Oosterhof, N.N., Understanding evaluation of faces on social dimensions (2008) Trends Cogn. Sci, 12 (12), pp. 455-460; Wang, S., Ding, R., Analysis of relationships between color and emotion by classification based on associations (2008) 2008 International Conference on Computer Science and Software Engineering. Wuhan, Hubei, pp. 269-272; Da Pos, O, Valentini, S, (2005) Proceedings of AIC Colour 05, Granada; Da Pos, O., Green-Armytage, P., Facial expressions, colours and basic emotions (2007) Colour Des. Creativity, 1 (1), p. 2","Vacanti, A.; Dipartimento Architettura e Design, Stradone di Sant‘Agostino 37, Italy; email: annapaola.vacanti@edu.unige.it","Karwowski W.Ahram T.Etinger D.Tankovic N.Taiar R.",,"Springer Science and Business Media Deutschland GmbH","3rd International Conference on Human Systems Engineering and Design: Future Trends and Applications, IHSED 2020","22 September 2020 through 24 September 2020",,244539,21945357,9783030582814,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85091112979
"Ali S., Mehmood F., Ayaz Y., Khan M.J., Asgher U.","57209995060;57207940444;16548915200;55607228600;55902392400;","Effect of Paired Stimuli on Joint Attention of Children with ASD",2021,"Advances in Intelligent Systems and Computing","1201 AISC",,,"362","368",,,"10.1007/978-3-030-51041-1_48","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088259872&doi=10.1007%2f978-3-030-51041-1_48&partnerID=40&md5=16494fef02cbbf9a9d8a6b60974e94e1","School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan","Ali, S., School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan; Mehmood, F., School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan; Ayaz, Y., School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan; Khan, M.J., School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan; Asgher, U., School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, Pakistan","Recently, one of the prominent applications of human-robot interaction is in assistive therapy using humanoids for children with Autism Spectrum Disorder (ASD). These robotic therapies have shown promising results in improvising new communication methodologies, improvement in motor movements, joint attention and physical behavior of the children suffering from ASD. In daily life interactions, multiple types of social cues are used. The focus of this study is on the combination of most commonly used social cues i.e., visual, speech and motion used for improvement in joint attention of autistic child. The paired stimuli combination used in this research are visual plus speech (V+S), speech plus motion (S+M), and motion plus visual (M+V). The paired stimuli of V+S was presented using robot’s blinking eyes and the speech sound “Hello! I am NAO robot”. S+M cue consisted of waving hand with speech sound “Hello! Nice to meet you” and M+V cue was presented as sitting down of the robot with colored eye cue. The aim is to measure the responses of the children towards combination of different social cues in terms of Joint Attention (JA). The duration for which the eye contact is established also gives information about the response of a child towards a certain paired stimulus. The experimentation was carried on 12 subjects over a period of 2 months. The frequency of trials was one trial per week per subject. Each of the subject participated in 8 experiments. The results show that each combination of paired stimuli introduced to the children with ASD has same effect on joint attention of the child. The average accuracy for each type of paired stimuli was 66.23%, 66.40%, and 66.95% respectively. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.","Autism; Human-robot interaction; Joint attention; Paired stimuli","Ergonomics; Speech; Autistic children; Children with autisms; Communication methodology; Joint attention; Motor movements; Physical behaviors; Robotic therapies; Speech sounds; Social robots",,,,,,,,,,,,,,,,"Mavadati, S.M., Feng, H., Gutierrez, A., Mahoor, M.H., Comparing the gaze responses of children with autism and typically developed individuals in human-robot interaction (2014) 2014 IEEE-RAS International Conference on Humanoid Robots, pp. 1128-1133; Baron-Cohen, S., The cognitive neuroscience of autism (2004) J. Neurol. Neurosurg. Psychiatry, , https://jnnp.bmj.com/content/75/7/945, Accessed 18 June 2020; Werry, I., Dautenhahn, K., Harwin, W., Evaluating the response of children with autism to a robot (2001) Proceedings of the RESNA 2001 Annual Conference; Kozima, H., Nakagawa, C., Yasuda, Y., Children–robot interaction: a pilot study in autism therapy (2007) Prog. Brain Res, 164, pp. 385-400; Wada, K., Shibata, T., Robot therapy in a care house-its sociopsychological and physiological effects on the residents (2006) Proceedings of the 2006 IEEE International Conference on Robotics and Automation, ICRA 2006, pp. 3966-3971; Marti, P., Pollini, A., Rullo, A., Shibata, T., Engaging with artificial pets (2005) Proceedings of the 2005 Annual Conference on European Association of Cognitive Ergonomics, pp. 99-106; Kozima, H., Yano, H., Designing a robot for contingency-detection game (2001) Working Notes Workshop Robotic & Virtual Interactive Systems in Autism Therapy, , Hatfield, UK, 27–28 September, University of Hertfordshire; Billard, A., Robins, B., Nadel, J., Dautenhahn, K., Building Robota, a mini-humanoid robot for the rehabilitation of children with autism (2007) Assist. Technol, 19, pp. 37-49; Dautenhahn, K., Billard, A., Games children with autism can play with Robota, a humanoid robotic doll (2002) Universal Access and Assistive Technology, pp. 179-190. , Keates, S., Langdon, P., Clarkson, P.J., Robinson, P. (eds) Springer, London; Robins, B., Dautenhahn, K., Dubowski, J., Does appearance matter in the interaction of children with autism with a humanoid robot? (2006) Interact. Stud, 7, pp. 479-512; Dautenhahn, K., Nehaniv, C.L., Walters, M.L., Robins, B., Kose-Bagci, H., Mirza, N.A., Blow, M., KASPAR–a minimally expressive humanoid robot for human–robot interaction research (2009) Appl. Bionics Biomech, 6, pp. 369-397; Sial, S.B., Sial, M.B., Ayaz, Y., Shah, S.I.A., Zivanovic, A., Interaction of robot with humans by communicating simulated emotional states through expressive movements (2016) Intell. Serv. Robot, 9, pp. 231-255; Zheng, Z., Das, S., Young, E.M., Swanson, A., Warren, Z., Sarkar, N., Autonomous robot-mediated imitation learning for children with autism (2014) 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 2707-2712; Ali, S., Mehmood, F., Dancey, D., Ayaz, Y., Khan, M.J., Naseer, N., de Cassia Amadeu, R., Nawaz, R., An adaptive multi-robot therapy for improving joint attention and imitation of ASD children (2019) IEEE Access, 7, pp. 81808-81825; Ali, S., Mehmood, F., Ayaz, Y., Asgher, U., Khan, M.J., Effect of different visual stimuli on joint attention of ASD children using NAO robot (2019) International Conference on Applied Human Factors and Ergonomics, pp. 490-499; Mehmood, F., Ayaz, Y., Ali, S., de Cassia Amadeu, R., Sadia, H., Dominance in visual space of ASD children using multi-robot joint attention integrated distributed imitation system (2019) IEEE Access, 7, pp. 168815-168827","Ali, S.; School of Mechanical and Manufacturing Engineering (SMME), Pakistan; email: sarababer@smme.nust.edu.pk","Ayaz H.Asgher U.",,"Springer","AHFE Virtual Conferences on Neuroergonomics and Cognitive Engineering, and Industrial Cognitive Ergonomics and Engineering Psychology, 2020","16 July 2020 through 20 July 2020",,241619,21945357,9783030510404,,,"English","Adv. Intell. Sys. Comput.",Conference Paper,"Final","",Scopus,2-s2.0-85088259872
"Lin N., Zheng H., Li Y., Wang R., Chen X., Zhang X.","57202398822;57210979922;57221104390;57221093911;56669038100;55715219100;","Self-Sensing Pneumatic Compressing Actuator",2020,"Frontiers in Neurorobotics","14",,"572856","","",,1,"10.3389/fnbot.2020.572856","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098155206&doi=10.3389%2ffnbot.2020.572856&partnerID=40&md5=74d73ca14bb39ae1d71ad82dc12f5007","School of Data Science, University of Science and Technology of China, Hefei, China; School of Computer Science and Technology, University of Science and Technology of China, Hefei, China; School of Information Science and Technology, University of Science and Technology of China, Hefei, China","Lin, N., School of Data Science, University of Science and Technology of China, Hefei, China; Zheng, H., School of Computer Science and Technology, University of Science and Technology of China, Hefei, China; Li, Y., School of Computer Science and Technology, University of Science and Technology of China, Hefei, China; Wang, R., School of Information Science and Technology, University of Science and Technology of China, Hefei, China; Chen, X., School of Computer Science and Technology, University of Science and Technology of China, Hefei, China; Zhang, X., School of Computer Science and Technology, University of Science and Technology of China, Hefei, China","Using soft pneumatic actuator is a feasible solution in the complex unstructured environment, owing to their inherent compliance, light weight, and safety. However, due to the limitations of soft actuators' materials and structures, they fall short of motion accuracy and load capacity, or need large-size, bulky compressors. Meanwhile, in order to gain better control, it is essential for them to sense the environments as well. This leads to high-price sensors or a complicated manufacture technique. Here, a self-sensing vacuum soft actuation structure is proposed, aiming at acquiring good balance among precision, output force, and actuation pressure. The actuator mainly comprises a flexible membrane and a compression spring. When actuated, the flexible membrane outside the actuator compresses the internal spring skeleton, realizing large contractile motion in axial direction. Its built-in force sensor can indirectly measure the absolute displacement of the actuator with certain accuracy (about 5% F.S.). Besides, it does not require high actuation pressure to generate enough output force. The actuator is quite easy to manufacture with low cost, and there are a variety of materials to choose from. We established quasi-static models for actuators built of two different kinds of membrane materials, and tested their accuracy and output force. In addition, to break through the limits of vacuum actuation, a method of positive-negative pressure combined actuation has been proposed, which lowers the requirements for air source equipments, increases actuation pressure, and reduces potential safety threats at the same time. This kind of soft actuators can also effectively resist and detect impacts. The design of a two-finger dexterous robot hand and robot joint based on this soft actuator illustrates its broad application prospects in the fields of mobile robots, wearable devices, and human–robot interaction. © Copyright © 2020 Lin, Zheng, Li, Wang, Chen and Zhang.","design; pneumatic actuator; safety; self-sensing; soft robotics","Human robot interaction; Machine design; Manufacture; Pneumatics; Springs (components); Structural design; Absolute displacement; Compression springs; Dexterous robot hand; Flexible membranes; Negative pressures; Quasi static models; Robot interactions; Unstructured environments; Pneumatic actuators; polyethylene; Article; device safety; equipment design; human computer interaction; mathematical model; measurement accuracy; pressure; robotics; self sensing pneumatic compressing actuator; thickness",,"polyethylene, 9002-88-4",,,"National Natural Science Foundation of China, en:NSFC, NNSF, NNSFC",,,,,,,,,,,"Acome, E., Mitchell, S., Morrissey, T., Emmett, M., Benjamin, C., King, M., Hydraulically amplified self-healing electrostatic actuators with muscle-like performance (2018) Science, 359, pp. 61-65. , 29302008; Ahn, S.-K., Kasi, R.M., Kim, S.-C., Sharma, N., Zhou, Y., Stimuli-responsive polymer gels (2008) Soft Matter, 4, pp. 1151-1157. , 32907254; Amjadi, M., Pichitpajongkit, A., Lee, S., Ryu, S., Park, I., Highly stretchable and sensitive strain sensor based on silver nanowire–elastomer nanocomposite (2014) ACS Nano, 8, pp. 5154-5163. , 24749972; Anderson, I.A., Gisby, T.A., McKay, T.G., O'Brien, B.M., Calius, E.P., Multi-functional dielectric elastomer artificial muscles for soft and smart machines (2012) J. Appl. Phys, 112, p. 041101; Atalay, A., Sanchez, V., Atalay, O., Vogt, D.M., Haufe, F.L., Wood, R.J., Batch fabrication of customizable silicone-textile composite capacitive strain sensors for human motion tracking (2017) Adv. Mater. Technol, 2, p. 1700136; Belding, L., Baytekin, B., Baytekin, H.T., Rothemund, P., Verma, M.S., Nemiroski, A., Slit tubes for semisoft pneumatic actuators (2018) Adv. Mater, 30, p. 1704446. , 29334140; Chossat, J., Park, Y., Wood, R.J., Duchaine, V., A soft strain sensor based on ionic and metal liquids (2013) IEEE Sensors J, 13, pp. 3405-3414; Chou, C.-P., Hannaford, B., Measurement and modeling of mckibben pneumatic artificial muscles (1996) IEEE Trans. Robot. Autom, 12, pp. 90-102; Cianchetti, M., Ranzani, T., Gerboni, G., Nanayakkara, T., Althoefer, K., Dasgupta, P., Soft robotics technologies to address shortcomings in today's minimally invasive surgery: the stiff-flop approach (2014) Soft Robot, 1, pp. 122-131; Connolly, F., Polygerinos, P., Walsh, C.J., Bertoldi, K., Mechanical programming of soft actuators by varying fiber angle (2015) Soft Robot, 2, pp. 26-32; Connolly, F., Walsh, C.J., Bertoldi, K., Automatic design of fiber-reinforced soft actuators for trajectory matching (2017) Proc. Natl. Acad. Sci. 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"Meng F.-G., Zhang B., Zhao X.-G., Sui J.","57222006088;57218441703;16551339800;57222003708;","Estimation of Ankle Joint Continuous Motion Based on Electromyographic Signals",2020,"Proceedings - 2020 Chinese Automation Congress, CAC 2020",,,"9327764","6399","6406",,,"10.1109/CAC51589.2020.9327764","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100914895&doi=10.1109%2fCAC51589.2020.9327764&partnerID=40&md5=ab4cf68bed9cd5aa50689c633070d395","State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, College of Information Science and Engineering, Northeastern University, Shenyang, China; State Key Laboratory of Robotics, Shenyang Institute Automation, Chinese Academy of Sciences, Institutes of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China","Meng, F.-G., State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, College of Information Science and Engineering, Northeastern University, Shenyang, China; Zhang, B., State Key Laboratory of Robotics, Shenyang Institute Automation, Chinese Academy of Sciences, Institutes of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China; Zhao, X.-G., State Key Laboratory of Robotics, Shenyang Institute Automation, Chinese Academy of Sciences, Institutes of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China; Sui, J., State Key Laboratory of Robotics, Shenyang Institute Automation, Chinese Academy of Sciences, Institutes of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China","Human electromyogram (EMG) contains abundant body movement information, which is often used as the information medium of human-robot interaction. Precisely analyzing the muscle activity information contained in the patient's EMG and using it for the motion control of the rehabilitation robot is of great significance in the rehabilitation training process. Aiming at the problem that ankle rehabilitation robot needs to use the patient's EMG to identify the ankle motion intention and estimate the angle of continuous motion in the human-robot interactive rehabilitation training, this paper proposes a new method based on the patient's EMG to directly identify the type of ankle motion and estimate the continuous motion angle of the joint. Considering the nonlinear relationship between EMG and joint motion angles, Hammerstein model is selected to describe the relationship between them. The linear and nonlinear structure of the Hammerstein model simplifies the difficulty of model identification. First, obtain the real motion angle of the ankle joint while collecting the EMG of the muscles related to the ankle joint motion, and use the threshold judgment method to complete the recognition of the ankle joint motion intention. Then, use the EMG as the input of the motion model and the real motion angle of the ankle joint as the motion model output, identify and obtain the Hammerstein motion model of plantarflexion motion and dorsiflexion motion respectively. Finally, use EMG as the input of the recognized model to estimate the continuous motion angle of the ankle joint. The experiment shows that the motion model established by the method in this paper can realize the recognition of the motion intention of the ankle joint and the accurate estimation of the continuous motion angle, this method is worth promoting. © 2020 IEEE.","ankle joint angle; continuous estimation; EMG; Hammerstein model","Educational robots; Human robot interaction; Muscle; Accurate estimation; Ankle rehabilitation robots; Electromyographic signal; Model identification; Non-linear relationships; Nonlinear structure; Rehabilitation robot; Rehabilitation training; Patient rehabilitation",,,,,"National Natural Science Foundation of China, NSFC: 61773369","This work is supported by National Natural Science Foundation of China under Grant 61773369.",,,,,,,,,,"Leocani, L., Diserens, K., Moccia, M., Caltagirone, C., Disability through covid-19 pandemic: Neurorehabilitation cannot wait (2020) European Journal of Neurology, 27, pp. 51-57; Ahsan, M.R., Emg signal classification for human computer interaction: A review[j] (2009) European Journal of Scientific Research, 33 (3), pp. 480-501; Yin, A.H., Fan, Y.J., Xu, L.D., Emg and epp-integrated humanmachine interface between the paralyzed and rehabilitation exoskeleton (2012) Iee Trans. 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Autom. Congr., CAC",Conference Paper,"Final","",Scopus,2-s2.0-85100914895
"Sohn W.J., Nayeem R., Zuzarte I., Hogan N., Sternad D.","55700580700;57219436060;56449166200;35572189800;7003842039;","Control of Complex Objects: Challenges of Linear Internal Dynamics",2020,"Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics","2020-November",,"9224336","1229","1235",,,"10.1109/BioRob49111.2020.9224336","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095589751&doi=10.1109%2fBioRob49111.2020.9224336&partnerID=40&md5=7380f37d072072cb497fe0bca9ec7aa5","Northeastern University, Department of Electrical and Computer EngineeringMA  02115, United States; Northeastern University, Department of BioengineeringMA  02115, United States; Massachusetts Institute of Technology, Department of Mechanical Engineering and Brain and Cognitive Sciences, Cambridge, MA  02139, United States; Northeastern University, Department of Biology, Electrical and Computer Engineering, and Physics, Boston, MA  02115, United States","Sohn, W.J., Northeastern University, Department of Electrical and Computer EngineeringMA  02115, United States; Nayeem, R., Northeastern University, Department of Electrical and Computer EngineeringMA  02115, United States; Zuzarte, I., Northeastern University, Department of BioengineeringMA  02115, United States; Hogan, N., Massachusetts Institute of Technology, Department of Mechanical Engineering and Brain and Cognitive Sciences, Cambridge, MA  02139, United States; Sternad, D., Northeastern University, Department of Biology, Electrical and Computer Engineering, and Physics, Boston, MA  02115, United States","Physical interaction with an object that has internal dynamics can be challenging, both for humans and robots. An example is carrying a cup of coffee, where the nonlinear dynamics between the cup and the liquid can be chaotic and unpredictable. This study examined how nonlinearity of an object's dynamics contributed to the difficulty of a task and if linearization of the object dynamics facilitated performance. Human subjects did a task in a virtual set-up with a haptic interface using a robotic manipulandum. The task of transporting a cup of coffee was reduced to a 2D cart-and-pendulum model; subjects moved the cart and felt the dynamics of the pendulum representing sloshing coffee. Performance with the nonlinear system was compared to a linearized mass-spring version of the system. Subjects (n=16) executed continuous rhythmic, self-paced movements. In the linearized system subjects chose to move at frequencies close to the resonant frequencies and clearly avoided the anti-resonance frequency. In the nonlinear system subjects did not avoid the anti-resonance frequency. To evaluate performance, mutual information quantified predictability between the interaction force and the cup and object dynamics. Mutual information was lower in trials when the cup moved close to the anti-resonance frequency in both linear and nonlinear systems. The magnitudes of the interaction forces were higher in the linear system, especially at frequencies slightly below the anti-resonance. These results run counter to the expectation that linearization would simplify this task. These findings may be useful as design considerations for robot control and human-robot interaction: if humans interact with robots that exhibit complex dynamics in the frequency range of human actions, linearizing a nonlinear system may potentially disturb intuitive and low-effort cooperation. © 2020 IEEE.",,"Agricultural robots; Dynamics; Haptic interfaces; Linear systems; Linearization; Machine design; Natural frequencies; Nonlinear systems; Pendulums; Robotics; Design considerations; Interaction forces; Internal dynamics; Linear and nonlinear systems; Linearized systems; Mutual informations; Physical interactions; Self-paced movement; Social robots",,,,,"1723998; 1637824, 1724135, NSF-M3X 1826097; National Science Foundation, NSF: 1637854, 1825942; National Institutes of Health, NIH: R01-HD087089","*This work was supported by the following grants awarded to Dagmar Sternad: NIH R01-HD087089, NSF-CRCNS 1723998, NSF-NRI 1637854, NSF-M3X 1825942. Neville Hogan was supported in part by the Eric P. and Evelyn E. Newman Fund, NSF-NRI 1637824, NSF-CRCNS 1724135, NSF-M3X 1826097, NIH R01-HD087089.",,,,,,,,,,"Siciliano, B., Khatib, O., Springer handbook of robotics preface to the second edition (2016) Springer Handbook of Robotics, pp. Xvii-Xvii; Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Sch%ffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human&robot collaboration (2018) Autonomous Robots, 42 (5), pp. 957-975; Kim, S., Laschi, C., Trimmer, B., Soft robotics: A bioinspired evolution in robotics (2013) Trends in Biotechnology, 31 (5), pp. 23-30. , May; Lu, N.S., Kim, D.H., Flexible and stretchable electronics paving the way for soft robotics (2014) Soft Robotics, 1 (1), pp. 53-62. , Mar; Majidi, C., Soft robotics: A perspective-current trends and prospects for the future (2014) Soft Robotics, 1 (1), pp. 5-11. , Mar; Siciliano, B., Villani, L., An inverse kinematics algorithm for interaction control of a flexible arm with a compliant surface (2001) Control Engineering Practice, 9 (2), pp. 191-198. , Feb (in English); Spong, M.W., Adaptive-control of flexible joint manipulators (1989) Systems & Control Letters, 13 (1), pp. 15-21. , Jul (in English); Hogan, N., Adaptive-control of mechanical impedance by coactivation of antagonist muscles (1984) IEEE Transactions on Automatic Control, 29 (8), pp. 681-690. , in English; Nikolaidis, S., Lasota, P., Rossano, G., Martinez, C., Fuhlbrigge, T., Shah, J., Human-robot collaboration in manufacturing: Quantitative evaluation of predictable, convergent joint action (2013) IEEE International Symposium on Robotics, pp. 1-6; Mainprice, J., Berenson, D., Human-robot collaborative manipulation planning using early prediction of human motion (2013) 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 299-306. , in English; Hasson, C.J., Shen, T., Sternad, D., Energy margins in dynamic object manipulation (2012) Journal of Neurophysiology, 108 (5), pp. 1349-1365. , Sep; Sternad, D., Human control of interactions with objects-variability, stability and predictability (2017) Geometric and Numerical Foundations of Movements, 117, pp. 301-335. , in English; Sternad, D., Hasson, C.J., Predictability and robustness in the manipulation of dynamically complex objects (2016) Advances in Experimental Medicine and Biology, 957, pp. 55-77; Maurice, P., Hogan, N., Sternad, D., Predictability, force and (anti-)resonance in complex object control (2018) Journal of Neurophysiology, , Apr 18; Nasseroleslami, B., Hasson, C.J., Sternad, D., Rhythmic manipulation of objects with complex dynamics: Predictability over chaos (2014) PLoS Computational Biology, 10 (10), p. e1003900. , Oct; Bazzi, S., Sternad, D., Robustness in human manipulation of dynamically complex objects through control contraction metrics (2020) IEEE Robotics and Automation Letters, pp. 1-1; Bazzi, S., Ebert, J., Hogan, N., Sternad, D., Convergence and predictability in human control of dynamically complex objects (2018) Chaos: An Interdisciplinary Journal of Nonlinear Science, 28. , 103103; Bazzi, S., Ebert, J., Hogan, N., Sternad, D., Stability and predictability in dynamically complex physical interactions (2018) IEEE International Conference on Robotics and Automation (ICRA 2018), , Brisbane, Australia, May 21-25; Lind Der Van, R.Q., Lammertse, P., Hapticmaster&a generic force controlled robot for human interaction (2003) Industrial Robot: An International Journal, 30 (6), pp. 515-524; Linde Der Van, R.Q., HapticMaster & a generic force controlled robot for human interaction (2003) Industrial Robot: An International Journal, 30 (6), pp. 515-524; Mussa-Ivaldi, F.A., Hogan, N., Bizzi, E., Neural, mechanical, and geometric factors subserving arm posture in humans (1985) Journal of Neuroscience, 5 (10), pp. 2732-2743. , Oct; Burdet, E., Osu, R., Franklin, D.W., Yoshioka, T., Milner, T.E., Kawato, M., A method for measuring endpoint stiffness during multi-joint arm movements (2000) Journal of Biomechanics, 33 (12), pp. 1705-1709. , Dec; Silverman, B.W., (1986) Density Estimation for Statistics and Data Analysis, p. 176. , Chemical Rubber Company Press",,,,"IEEE Computer Society","8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020","29 November 2020 through 1 December 2020",,164150,21551774,9781728159072,,,"English","Proc. IEEE RAS EMBS Int. Conf. Biomed. Rob. Biomechatronics, BioRob",Conference Paper,"Final","",Scopus,2-s2.0-85095589751
"Dangol R., Alsadoon A., Prasad P.W.C., Seher I., Alsadoon O.H.","57218669999;56575128400;57215215975;24462911300;57208347593;","Speech Emotion Recognition UsingConvolutional Neural Network and Long-Short TermMemory",2020,"Multimedia Tools and Applications","79","43-44",,"32917","32934",,6,"10.1007/s11042-020-09693-w","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089995170&doi=10.1007%2fs11042-020-09693-w&partnerID=40&md5=676b8bfb464da420485622128f8eee1e","School of Computing and Mathematics, Charles Sturt University, Sydney, Australia; Department of Information Technology, Study Group Australia, Sydney Campus, Sydney, Australia; Department of Islamic Sciences, Al Iraqia University, Baghdad, Iraq","Dangol, R., School of Computing and Mathematics, Charles Sturt University, Sydney, Australia, Department of Information Technology, Study Group Australia, Sydney Campus, Sydney, Australia; Alsadoon, A., School of Computing and Mathematics, Charles Sturt University, Sydney, Australia, Department of Information Technology, Study Group Australia, Sydney Campus, Sydney, Australia; Prasad, P.W.C., School of Computing and Mathematics, Charles Sturt University, Sydney, Australia, Department of Information Technology, Study Group Australia, Sydney Campus, Sydney, Australia; Seher, I., School of Computing and Mathematics, Charles Sturt University, Sydney, Australia, Department of Information Technology, Study Group Australia, Sydney Campus, Sydney, Australia; Alsadoon, O.H., Department of Islamic Sciences, Al Iraqia University, Baghdad, Iraq","Human-Robot interactions involve human intentions and human emotion. After the evolvement of positive psychology, the psychological research has a tremendous concentration to study the factors involved in the human emotion generation. Speech emotion recognition (SER) is a challenging job due to the complexity of emotions. Human emotion recognition is gaining importance as good emotional health can lead to good social and mental health. Although there are different approaches for speech emotion recognition, the most advanced model is Convolutional Neural Network (CNN) using Long Short-term Memory (LSTM) network. But they also suffer from the lack of parallelization of the sequences and computation times. Meanwhile, attention-mechanism has way better exhibitions in learning significant feature representations for specific tasks. Based on this technique, we propose an emotion recognition system with relation aware, self-attention mechanism to memorize the discriminative features for SER, where spectrograms are utilized as input. A CNN with a relation-aware self-attention is modelled to analyse 3D log-Mel spectrograms to extract the high-level features. Different layers such as 3D convolutional layers, 3D Max-pooling layers, and LSTM networks are used in the model. Here, the attention layer is exercised to support distinct parts of emotion and assemble discriminative utterance-level representations for SER. Finally, the fully connected layer is equipped with the utterance level representations with 64 output units to achieve higher-level representations. The approach of relation-aware attention-based 3D CNN and LSTM model provided a better outcome of 80.80% on average scale in speech emotion recognition. The proposed model in this paper focuses on enhancement of the attention mechanism to gain additional benefits of sequence to sequence parallelization by improving the recognition accuracy. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.","Convolutional Neural Network (CNN); Deep Learning; Hierarchical Spectral Clustering (HSC); Long short-term memory; Long Short-term Memory (LSTM); Relation-aware self-attention mechanism; Speech Emotion Recognition (SER)","Convolution; Convolutional neural networks; Human robot interaction; Long short-term memory; Man machine systems; Spectrographs; Attention mechanisms; Discriminative features; Feature representation; High-level features; Human emotion recognition; Psychological research; Recognition accuracy; Speech emotion recognition; Speech recognition",,,,,,,,,,,,,,,,"Aldeneh, Z., Mower Provost, E., (2017) Using regional saliency for speech emotion recognition, pp. 2741-2745; Hajarolasvadi, N., Demirel, H., 3D CNN-Based Speech Emotion Recognition Using K-Means Clustering and Spectrograms (2019) Entropy, 21 (5), p. 479; Huang, K., Wu, C., Su, M., Kuo, Y., Detecting Unipolar and Bipolar Depressive Disorders from Elicited Speech Responses Using Latent Affective Structure Model (2018) IEEE Trans Affect Comput, 11, pp. 1-404; Huang, K.Y., Wu, C.H., Su, M.H., Attention-based convolutional neural network and long short-term memory for short-term detection of mood disorders based on elicited speech responses (2019) Pattern Recognition, 88, pp. 668-678; Huang, K., Wu, C., Hong, Q., Su, M., Chen, Y., Speech Emotion Recognition Using Deep Neural Network Considering Verbal and Nonverbal Speech Sounds (2019) ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5866-5870. , https://doi.org/10.1109/ICASSP.2019.8682283; Jing, S., Mao, X., Chen, L., Prominence features: Effective emotional features for speech emotion recognition (2018) Digital Signal Process, 72, pp. 216-231; Liu, Z.T., Wu, M., Cao, W.-H., Mao, J.-W., Xu, J.-P., Tan, G.-Z., Speech emotion recognition based on feature selection and extreme learning machine decision tree (2018) Neurocomputing, 273, pp. 271-280; Lorenzo-Trueba, J., Eje Henter, G., Takaki, S., Yamagishi, J., Morino, Y., Ochiai, Y., Investigating different representations for modeling and controlling multiple emotions in DNN-based speech synthesis (2018) Speech Commun, 99, pp. 135-143; Lotfian, R., Busso, C., Over-sampling Emotional Speech Data Based on Subjective Evaluations Provided by Multiple Individuals (2019) IEEE Trans Affect Comput, p. 1. , (,),.,:., https://doi.org/10.1109/TAFFC.2019.2901465; Motamed, S., Setayeshi, S., Rabiee, A., Speech emotion recognition based on a modified brain emotional learning model (2017) Biol Inspired Cognitive Architect, 19, pp. 32-38; Pérez-Benito, F.J., Villacampa-Fernández, P., Conejero, J.A., García-Gómez, J.M., Navarro-Pardo, E., A happiness degree predictor using the conceptual data structure for deep learning architectures (2019) Comput Methods Prog Biomed, 168, pp. 59-68; Poorna, S.S., Nair, G.J., Multistage classification scheme to enhance speech emotion recognition Int J Speech Technol, 22 (2), pp. 327-340. , (,):., https://doi.org/10.1007/s10772-019-09605-w; Raffel, C., Ellis, D.P.W., (2015) Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems; Shaw, P., Uszkoreit, J., Vaswani, A., (2018) Self-Attention with Relative Position Representations, , arXiv.org; Sun, L., Fu, S., Wang, F., Decision tree SVM model with Fisher feature selection for speech emotion recognition (2019) EURASIP J Audio Speech Music Process, 2019 (1), p. 1; Tokuno, S., Mitsuyoshi, S., Suzuki, G., Tsumatori, G., (2014) Stress Evaluation Using Voice Emotion Recognition Technology: A novel stress evaluation technology for disaster responders; Zeng, Y., Mao, H., Peng, D., Yi, Z., Spectrogram based multi-task audio classification (2019) Multimed Tools Appl, 78 (3), pp. 3705-3722; Zhao, J., Mao, X., Chen, L., Speech emotion recognition using deep 1D & 2D CNN LSTM networks (2019) Biomed Signal Process Control, 47, pp. 312-323","Alsadoon, A.; School of Computing and Mathematics, Australia; email: alsadoon.abeer@gmail.com",,,"Springer",,,,,13807501,,MTAPF,,"English","Multimedia Tools Appl",Article,"Final","",Scopus,2-s2.0-85089995170
"Kirtay M., Wudarczyk O.A., Pischedda D., Kuhlen A.K., Abdel Rahman R., Haynes J.-D., Hafner V.V.","36809877600;57253733800;54796974100;36602831900;7005210496;9741021000;55910634600;","Modeling robot co-representation: State-of-the-art, open issues, and predictive learning as a possible framework",2020,"ICDL-EpiRob 2020 - 10th IEEE International Conference on Development and Learning and Epigenetic Robotics",,,"9278031","","",,5,"10.1109/ICDL-EpiRob48136.2020.9278031","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100059707&doi=10.1109%2fICDL-EpiRob48136.2020.9278031&partnerID=40&md5=7cb2615a3e9d5c2ef9dec82fe2ed278a","Department of Computer Science, Adaptive Systems Group, Humboldt-Universität zu Berlin, Berlin, Germany; Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Charité-Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, And Berlin Institute of Health Berlin, Berlin, Germany","Kirtay, M., Department of Computer Science, Adaptive Systems Group, Humboldt-Universität zu Berlin, Berlin, Germany; Wudarczyk, O.A., Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany; Pischedda, D., Bernstein Center for Computational Neuroscience, Charité-Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, And Berlin Institute of Health Berlin, Berlin, Germany; Kuhlen, A.K., Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany; Abdel Rahman, R., Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany; Haynes, J.-D., Bernstein Center for Computational Neuroscience, Charité-Universitätsmedizin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, And Berlin Institute of Health Berlin, Berlin, Germany; Hafner, V.V., Department of Computer Science, Adaptive Systems Group, Humboldt-Universität zu Berlin, Berlin, Germany","Robots are getting increasingly more present in many spheres of human life, making the need for robots that can successfully engage in natural social interactions with humans paramount. Successful human-robot interaction could be achieved more effectively if robots could act predictably and could predict the humans' actions. If robots could represent human partners and generate behaviors that are in line with the partners' expectations based on human's mental models of interdependent action, human agents would be able to apply predictive and adaptive mechanisms acquired in human interactions to interact with robots effectively. How could robots be predictable and be capable of predicting human behavior? We propose that this could be achieved by having an internal representation of both oneself and the other agent, that is by equipping the robot with the ability to co-represent. Here, co-representation refers to the representation of the partner's actions alongside one's own actions. Although co-representation constitutes an essential process for successful human social interaction, as it supports understanding of others' actions, to date co-representation processes have only scarcely been integrated into robotic platforms. We highlight the state-of-the-art findings on co-representation in social robotics, discuss current research limitations and open issues for creating computational models of co-representation in robots, and put forward the idea that predictive learning might constitute a particularly promising framework to build models of co-representing robots. Overall, in this article, we offer an integrated view of the state-of-the-art findings in robotics literature on co-representation and outline directions for future research, with the aim to boost success in building robots equipped with co-representation models fit for smooth social interactions. © ICDL-EpiRob 2020. All rights reserved.","Co-representation; Predictive learning; Social and cognitive robotics","Agricultural robots; Behavioral research; Learning systems; Robotics; Adaptive mechanism; Computational model; Human interactions; Human social interactions; Internal representation; Representation model; Robotic platforms; Social interactions; Social robots",,,,,"Deutsche Forschungsgemeinschaft, DFG: 390523135, EXC 2002/1","This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2002/1 ”Science of Intelligence” - project number 390523135. *MK, OW, and DP contributed equally to this work.",,,,,,,,,,"Breazeal, C., 'Social interactions in HRI: The robot view (2004) Ieee Trans. Syst. Man Cybern. Part C Appl. Rev, 34 (2), pp. 181-186. , May; Wiese, E., Metta, G., Wykowska, A., 'Robots as intentional agents: Using neuroscientific methods to make robots appear more social (2017) Front. 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Wudarczyk, O.A.; Department of PsychologyGermany; email: wudarczo@hu-Berlin.de
Pischedda, D.; Bernstein Center for Computational Neuroscience, Germany; email: doris.pischedda@charite.de",,"Advanced Center of Electrical and Electronic Engineering;Advanced Mining Technology Center (AMTC);Synopsys;The Clover: Ingenieria 2030;Universidad Tecnica Federico Santa Maria","Institute of Electrical and Electronics Engineers Inc.","10th Joint IEEE International Conference on Development and Learning and Epigenetic Robotics, ICDL-EpiRob 2020","26 October 2020 through 30 October 2020",,165847,,9781728173061,,,"English","ICDL-EpiRob - IEEE Int. Conf. Dev. Learn. Epigenetic Robot.",Conference Paper,"Final","",Scopus,2-s2.0-85100059707
"Abubshait A., Momen A., Wiese E.","57195436971;57194714779;57191244776;","Pre-exposure to Ambiguous Faces Modulates Top-Down Control of Attentional Orienting to Counterpredictive Gaze Cues",2020,"Frontiers in Psychology","11",,"2234","","",,3,"10.3389/fpsyg.2020.02234","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091409725&doi=10.3389%2ffpsyg.2020.02234&partnerID=40&md5=667d018eab08a68c4d69089eca2cae66","Robotics Domain, Italian Institute of Technology, Genoa, Italy; Department of Psychology, George Mason University, Fairfax, VA, United States","Abubshait, A., Robotics Domain, Italian Institute of Technology, Genoa, Italy; Momen, A., Department of Psychology, George Mason University, Fairfax, VA, United States; Wiese, E., Department of Psychology, George Mason University, Fairfax, VA, United States","Understanding and reacting to others’ nonverbal social signals, such as changes in gaze direction (i.e., gaze cue), are essential for social interactions, as it is important for processes such as joint attention and mentalizing. Although attentional orienting in response to gaze cues has a strong reflexive component, accumulating evidence shows that it can be top-down controlled by context information regarding the signals’ social relevance. For example, when a gazer is believed to be an entity “with a mind” (i.e., mind perception), people exert more top-down control on attention orienting. Although increasing an agent’s physical human-likeness can enhance mind perception, it could have negative consequences on top-down control of social attention when a gazer’s physical appearance is categorically ambiguous (i.e., difficult to categorize as human or nonhuman), as resolving this ambiguity would require using cognitive resources that otherwise could be used to top-down control attention orienting. To examine this question, we used mouse-tracking to explore if categorically ambiguous agents are associated with increased processing costs (Experiment 1), whether categorically ambiguous stimuli negatively impact top-down control of social attention (Experiment 2), and if resolving the conflict related to the agent’s categorical ambiguity (using exposure) would restore top-down control to orient attention (Experiment 3). The findings suggest that categorically ambiguous stimuli are associated with cognitive conflict, which negatively impact the ability to exert top-down control on attentional orienting in a counterpredicitive gaze-cueing paradigm; this negative impact, however, is attenuated when being pre-exposed to the stimuli prior to the gaze-cueing task. Taken together, these findings suggest that manipulating physical human-likeness is a powerful way to affect mind perception in human-robot interaction (HRI) but has a diminishing returns effect on social attention when it is categorically ambiguous due to drainage of cognitive resources and impairment of top-down control. © Copyright © 2020 Abubshait, Momen and Wiese.","categorical boundary; cognitive conflict; gaze-cueing; human-robot interaction; mind perception; social-cognition",,,,,,,,,,,,,,,,,"Abubshait, A., Weis, P.P., Wiese, E., Does context matter? Effects of robot appearance and reliability on social attention differs based on lifelikeness of gaze task (2020) Int. J. Soc. Robot, , [Preprint]; Abubshait, A., Wiese, E., You look human, but act like a machine: agent appearance and behavior modulate different aspects of human-robot interaction (2017) Front. 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"Ashtari E., Basiri M.A., Nejati S.M., Zandi H., Rezaei S.H.S., Masouleh M.T., Kalhor A.","57221131510;57221131478;57221132752;57221133396;57189311516;16550342100;24483441300;","Indoor and outdoor face recognition for social robot, sanbot robot as case study",2020,"2020 28th Iranian Conference on Electrical Engineering, ICEE 2020",,,"9260698","","",,,"10.1109/ICEE50131.2020.9260698","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098324518&doi=10.1109%2fICEE50131.2020.9260698&partnerID=40&md5=5375ef0149b79a17803c67de86014aac","School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran","Ashtari, E., School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran; Basiri, M.A., School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran; Nejati, S.M., School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran; Zandi, H., School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran; Rezaei, S.H.S., School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran; Masouleh, M.T., School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran; Kalhor, A., School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran","The interaction between human and robots is of paramount importance in comforting robot and human in the context of social demand. For the purpose of human-robot interaction, the robot should have the ability to perform a variety of actions including face recognition, path planning, etc. In this paper, face recognition has been implemented on the Sanbot robot. Since the Sanbot robot is intended to work in real environment, therefore indoor and outdoor environment is taken into account in proposing the corresponding face recognition algorithm. For each case a robust pre-processing algorithm should be designed and which can circumvent a challenging problem in face recognition, namely, different lighting conditions (light intensity, angle of radiation, etc.). In case of indoor environment, faces in an captured image by the robot HD camera are found using a Haar-cascade algorithm. Afterwards, a histogram equalization is applied to face images in order to standardize them. Then commonly practiced Deep convolutional neural network structures such as Inception and ResNet are used to design a model and trained end-to-end on a customized dataset with strong augmentation. Finally, by using a voting method, proper prediction is carried out on each face. In what concerns the outdoor environment, which has more challenges, upon applying histogram Equalization on the captured image, faces are found using a MultiTask Cascaded Convolutional Neural Network. Then face images are aligned as head orientation are corrected. Finally, cropped face image is fed to Siamese Network in order to extract face features and verifying individuals. From several practical results it has been inferred that the accuracy of the indoor method is nearly 93% without voting and with voting 97%, and the outdoor method is about 95%. © 2020 IEEE.","Deep Neural Network; Face recognition; Human-robot interaction; Sanbot robot; Siamese Neural Network; Social robot","Convolution; Convolutional neural networks; Deep neural networks; Equalizers; Face recognition; Graphic methods; Robot programming; Cascade algorithm; Face recognition algorithms; Histogram equalizations; Indoor environment; Lighting conditions; Outdoor environment; Pre-processing algorithms; Real environments; Social robots",,,,,,"This project was finical supported by the Tosan Techno Co.",,,,,,,,,,"Bauer, J., Camera-based fall detection system with the service robot sanbot elf (2018) Smart Public Building 2018 Conference Proceedings, pp. 15-28. , In: Uckelmann, D., (ed.), Stuttgart, DE; Lafaye, J., Gouaillier, D., Wieber, P.B., Linear model predictive control of the locomotion of pepper a humanoid robot with omnidirectional wheels (2014) Proc. 14th IEEE-RAS Int. Conf. Humanoid Robots (Humanoids), pp. 336-341; Gouaillier, D., Hugel, V., Blazevic, P., Kilner, C., Monceaux, J., Lafourcade, P., Marnier, B., Maisonnier, B., The nao humanoid: A combination of performance and affordability (2008) IEEE Transactions on Robotics; Jain, A.K., Nandakumar, K., Ross, A., 50 years of biometric research: Accomplishments, challenges, and opportunities (2016) Pattern Recognition Letters, 79, pp. 80-105. , https://doi.org/10.1016/j.patrec.2015.12.013, August; Olszewska, I.J., Automated face recognition: Challenges and solutions (2016) Pattern Recognition-Analysis and Applications. InTech; Krizhevsky, A., Sutskever, I., Hinton, G.E., Imagenet classification with deep convolutional neural networks (2012) Advances in Neural Information Processing Systems, 2, pp. 1097-1105; Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L., Imagenet: A large-scale hierarchical image database (2010) Institute of Electrical and Electronics Engineers (IEEE), pp. 248-255; Wilson, P.I., Fernandez, J., Facial feature detection using haar classifiers (2006) Journal of Computing Sciences in Colleges, pp. 127-133; Pizer, S.M., Philip Amburn, E., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., Ter Haar Romeny, B., Zuiderveld, K., Adaptive histogram equalization and its variations (1987) Computer Vision, Graphics, and Image Processing, 39 (3), pp. 355-368; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition (2016) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770-778. , 2016-December. IEEE Computer Society; Szegedy, C., Vanhoucke, V., (2015) Rethinking the Inception Architecture for Computer Vision; Liu, W., Ssd: Single shot multibox detector (2016) Computer Vision-ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, 9905. , Leibe B., Matas J., Sebe N., Welling M. (eds). Springer, Cham; Zhang, Z., Mert Sabuncu, R., Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels.; Zuiderveld, K., Contrast limited adaptive histogram equalization (1994) Graphics Gems, pp. 474-485. , Elsevier; Zhang, K., Zhang, Z., Li, Z., Qiao, Y., Joint face detection and alignment using multitask cascaded convolutional networks (2016) IEEE Signal Processing Letters, 23 (10), pp. 1499-1503; Chopra, S., Hadsell, R., LeCun, Y., Learning a similarity metric discriminatively, with application to face verification (2005) Proceedings-2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, 1, pp. 539-546. , IEEE Computer Society; Parkhi, O.M., Vedaldi, A., Zisserman, A., Deep face recognition (2015) British Machine Vision Association and Society for Pattern Recognition, pp. 411-4112; Schroff, F., Kalenichenko, D., Philbin, J., Facenet: A unified embedding for face recognition and clustering (2015) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 815-823. , 07-12-June-2015",,,,"Institute of Electrical and Electronics Engineers Inc.","28th Iranian Conference on Electrical Engineering, ICEE 2020","4 August 2020 through 6 August 2020",,165457,,9781728172965,,,"English","Iranian Conf. Electr. Eng., ICEE",Conference Paper,"Final","",Scopus,2-s2.0-85098324518
"Robinson N.L., Hicks T.-N., Suddrey G., Kavanagh D.J.","23767534700;57219857089;57031893500;56605573400;","The Robot Self-Efficacy Scale: Robot Self-Efficacy, Likability and Willingness to Interact Increases after a Robot-Delivered Tutorial",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223535","272","277",,4,"10.1109/RO-MAN47096.2020.9223535","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095742132&doi=10.1109%2fRO-MAN47096.2020.9223535&partnerID=40&md5=38c6b05d5bbb8d7a7768c2f33e79de62","Monash University, Department of Electrical and Computer Systems Engineering, Clayton, VIC, Australia; Queensland University of Technology, Science and Engineering Faculty, Brisbane, QLD, Australia; Queensland University of Technology, Institute of Health and Biomedical Innovation, Centre for Children's Health Research, Health Faculty, Brisbane, QLD, Australia","Robinson, N.L., Monash University, Department of Electrical and Computer Systems Engineering, Clayton, VIC, Australia; Hicks, T.-N., Queensland University of Technology, Science and Engineering Faculty, Brisbane, QLD, Australia; Suddrey, G., Queensland University of Technology, Science and Engineering Faculty, Brisbane, QLD, Australia; Kavanagh, D.J., Queensland University of Technology, Institute of Health and Biomedical Innovation, Centre for Children's Health Research, Health Faculty, Brisbane, QLD, Australia","An individual's self-efficacy to interact with a robot has important implications around the content, utility and success of the interaction. Individuals need to achieve a high level of self-efficacy in human robot-interaction in a reasonable time-frame for positive effects to occur in short-term human-robot scenarios. This trial explored the impact of a 2-minute automated robot-delivered tutorial designed to teach people from the general public how to use the robot as a method to increase robot self-efficacy scores. This trial assessed scores before (T1) and after (T2) an interaction with the robot to investigate changes in self-efficacy, likability and willingness to use it. The 40 participants recruited had on average very low level of robotic experience. After the tutorial, people reported significantly higher robot self-efficacy with very large effect sizes to operate a robot and apply the robot to a task (\eta -p^2 = 0.727 and 0.660). Significant increases in likability and willingness to interact with the robot were also found (\eta -p^2 = 0.465 and 0.480). Changes in likability and self-efficacy contributed to 64% of the variance in changes to willingness to use the robot. Initial differences were found in robot self-efficacy for older people and those with less robotics and programming experience compared with other participants, but scores across these subgroups were similar after completion of the tutorial. This demonstrated that high levels of self-efficacy, likeability and willingness to use a social robot can be reached in a very short time, and on comparable levels, regardless of age or prior robotics experience. This outcome has significant implications for future trials using social robots, since these variables can strongly influence experimental outcomes. © 2020 IEEE.",,"Agricultural robots; Robot programming; Robotics; Effect size; General publics; Human robots; Implications for futures; Likeability; Older People; Programming experience; Self efficacy; Social robots",,,,,"Monash University, MU; Queensland University of Technology, QUT","*This work was supported by a Research Grant from the State of Queensland acting through the Department of Science, Information Technology and Innovation 1Nicole L. Robinson is in the Department of Electrical and Computer Systems Engineering and School of Psychological Sciences, Monash University, Clayton, VIC, Australia nicole.robinson@monash.edu 2Teah-Neal Hicks is in the Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD, Australia teahneal.hicks@connect.qut.edu.au 3Gavin Suddrey is in the Science and Engineering Faculty, Queensland University of Technology, Brisbane, QLD, Australia g.suddrey@qut.edu.au 4David J. Kavanagh is in the Health Faculty, Queensland University of Technology, Institute of Health and Biomedical Innovation, Centre for Children’s Health Research, Brisbane, QLD, Australia david.kavanagh@qut.edu.au",,,,,,,,,,"Bandura, A., Self-efficacy mechanism in human agency (1982) American Psychologist, 37 (2), pp. 122-147; Bandura, A., A social cognitive perspective on positive psychology (2011) International Journal of Social Psychology, 26 (1), pp. 7-20; Bandura, A., (1986) Social Foundations of Thought and Action: A Social Cognitive Theory, , Englewood Cliffs, N. J: Prentice-Hall; Karsten, R., Mitra, A., Schmidt, D., Computer self-efficacy: A meta-analysis (2012) Journal of Organizational and End User Computing, 24 (4), pp. 54-80; Latikka, R., Turja, T., Oksanen, A., Self-efficacy and acceptance of robots (2019) Computers in Human Behavior, 93, pp. 157-163; Turja, T., Rantanen, T., Oksanen, A., Robot use self-efficacy in healthcare work (rush): Development and validation of a new measure (2019) AI & Society, 34, pp. 137-143. , Mar; Robinson, N.L., Connolly, J., Johnson, G.M., Kim, Y., Hides, L., Kavanagh, D.J., Measures of incentives and confidence in using a social robot (2018) Science Robotics, 3 (21), p. eaat6963; De Graaf, M., Ben Allouch, S., Van Dijk, J., Why do they refuse to use my robot?: Reasons for non-use derived from a long-term home study (2017) ACM/IEEE International Conference on Human-Robot Interaction, pp. 224-233. , ACM, (New York, NY, USA); Rosenthal-Von Der Pütten, A.M., Bock, N., Brockmann, K., Not your cup of tea? How interacting with a robot can increase perceived self-efficacy in hri and evaluation (2017) ACM/IEEE International Conference on Human-Robot Interaction, pp. 483-492. , IEEE; Rosenthal-Von Der Pütten, A., Bock, N., Development and validation of the self-efficacy in human-robot-interaction scale (se-hri) (2018) ACM Transactions on Human-Robot Interaction, 7. , Dec; Zafari, S., Schwaninger, I., Hirschmanner, M., Schmidbauer, C., Weiss, A., Koeszegi, S.T., You are doing so great!-the effect of a robot's interaction style on self-efficacy in hri (2019) IEEE International Conference on Robot and Human Interactive Communication, pp. 1-7. , IEEE; Kshirsagar, A., Dreyfuss, B., Ishai, G., Heffetz, O., Hoffman, G., Monetary-incentive competition between humans and robots: Experimental results (2019) ACM/IEEE International Conference on Human-Robot Interaction, pp. 95-103; Robotics, S., Pepper Technical Overview.; Suddrey, G., Robinson, N., A software system for human-robot interaction to collect research data: A HTML/javascript service on the pepper robot (2020) ACM/IEEE International Conference on Human-Robot Interaction; Cohen, J., (2013) Statistical Power Analysis for the Behavioral Sciences, , Routledge; Leite, I., Martinho, C., Paiva, A., Social robots for long-term interaction: A survey (2013) International Journal of Social Robotics, 5 (2), pp. 291-308; Kanda, T., Hirano, T., Eaton, D., Ishiguro, H., Interactive robots as social partners and peer tutors for children: A field trial (2004) Human-Computer Interaction, 19 (1-2), pp. 61-84; Robinson, N., Cottier, T.V., Kavanagh, D., Psychosocial health interventions by social robots: Systematic review of randomized controlled trials (2019) Journal of Medical Internet Research, 21 (5); Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., Tanaka, F., Social robots for education: A review (2018) Science Robotics, 3 (21), p. eaat5954; Mubin, O., Ahmad, M.I., Kaur, S., Shi, W., Khan, A., Social robots in public spaces: A meta-review (2018) International Conference on Social Robotics, pp. 213-220. , Springer",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095742132
"Mousavi S.A., Golkar N.","57218580996;57218580843;","Management of human-robot interaction in order to control of ankle knee during walking",2020,"HORA 2020 - 2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings",,,"9152867","","",,1,"10.1109/HORA49412.2020.9152867","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089706630&doi=10.1109%2fHORA49412.2020.9152867&partnerID=40&md5=fbdc1b0e93caab6742dedbfcb12e890d","Islamic Azad University, Department of Biomedical Engineering, Mashhad, Iran","Mousavi, S.A., Islamic Azad University, Department of Biomedical Engineering, Mashhad, Iran; Golkar, N., Islamic Azad University, Department of Biomedical Engineering, Mashhad, Iran","Patients who have had a stroke or a traumatic brain injury are usually motor-impaired. Nerve damage can cause lower limb paralysis and impair walking. Survivors of stroke with varying degrees of motor dysfunction not only experience the discomfort of daily living, but also feel a great deal of stress along with the economic burden on the family and society. Many types of rehabilitation robots have been developing to aid rehabilitation in people with stroke. In this study, a new method for knee joint control using rehabilitation robots based on human-robot interaction process is present. The control process is based on quadriceps data when walking for a healthy human, using fuzzy rules designed to control the contribution of humans and robots to the walking process. The main purpose of this study was to present and simulate a smart solution to determine the amount of human and robot presence in knee motion correction in patients with stroke and cerebral palsy. Dynamic modeling using two-dimensional mapping of Hunan is use instead of humans and the robot model is a PID controller whose coefficients are determined using the Ziegler-Nigel's method. The results show that the proposed model has been able to follow the knee angle curve with fuzzy system. Moreover, our results show that using a fuzzy system can lift the pressure on a person and help him walk. The proposed PID system can also play a significant role in reducing the fuzzy system error. The proposed control system without the presence of fuzzy system has an RMS error of 181/3 and also the RMS error in the presence of fuzzy system is equal to 0.698 indicating that the proposed control system is functioning properly. © 2020 IEEE.","component; Fuzzy Control; Knee Angle Control; Rehabilitation","Agricultural robots; Brain; Errors; Fuzzy inference; Fuzzy systems; Human computer interaction; Joints (anatomy); Process control; Robotics; Three term control systems; Control process; Economic burden; Healthy humans; PID controllers; Rehabilitation robot; Smart solutions; Traumatic Brain Injuries; Two dimensional mapping; Human robot interaction",,,,,,,,,,,,,,,,"Meng, W., Design and modelling of a compliant ankle rehabilitation robot redundantly driven by pneumatic muscles (1892) 2019 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). Ieee, 2019.j. Clerk Maxwell, a Treatise on Electricity and Magnetism, 3rd Ed., 2, pp. 68-73. , Oxford: Clarendon; Shen, Z., Design and test of admittance control with inner adaptive robust position control for a lower limb rehabilitation robot (2020) International Journal of Control, Automation and Systems, 18 (1), pp. 134-142; Elissa, K., Title of Paper if Known, , unpublished; Xu, J., Adaptive human-robot interaction control of the lower extremity robotic exoskeleton with magnetorheological actuators (2019) 2019 Ieee 4th International Conference on Advanced Robotics and Mechatronics (ICARM), , ieee; Bingjing, G., Jianhai, H., Xiangpan, L., Lin, Y., Human-robot interactive control based on reinforcement learning for gait rehabilitation training robot (2019) International Journal of Advanced Robotic Systems, 16, pp. 1729881419839584; Yan, H., Human-in-the-loop optimization control for the ankle exoskeleton during walking based on iterative learning and particle swarm optimization algorithm (2019) 2019 Ieee 4th International Conference on Advanced Robotics and Mechatronics (ICARM), , ieee; Gopinath, D., Jain, S., Argall, B.D., Human-in-the-loop optimization of shared autonomy in assistive robotics (2016) Ieee Robotics and Automation Letters, 2, pp. 247-254; Miller, D.J., Stergiou, N., Kurz, M.J., An improved surrogate method for detecting the presence of chaos in gait (2016) Journal of Biomechanics, 39, pp. 2873-2876; Simpson, R.C., Levine, S.P., Bell, D.A., Jaros, L.A., Koren, Y., Borenstein, J., Navchair: An assistive wheelchair navigation system with automatic adaptation (2014) Assistive Technology and Artificial Intelligence, pp. 235-255. , ed: Springer; Li, Q., Chen, W., Wang, J., Dynamic shared control for human-wheelchair cooperation (2011) 2011 Ieee International Conference on Robotics and Automation, pp. 4278-4283; Driessen, B., Kate, T.T., Liefhebber, F., Versluis, A., Van Woerden, J., Collaborative control of the manus manipulator (2015) Universal Access in the Information Society, 4, pp. 165-173; Lerner, Z.F., A robotic exoskeleton for treatment of crouch gait in children with cerebral palsy: Design and initial application (2016) Ieee Transactions on Neural Systems and Rehabilitation Engineering, 25 (6), pp. 650-659; Li, Z., Kang, Y., Xiao, Z., Song, W., Human-robot coordination control of robotic exoskeletons by skill transfers (2016) Ieee Transactions on Industrial Electronics, 64, pp. 5171-5181; Hénon, M., A two-dimensional mapping with a strange attractor (1976) The Theory of Chaotic Attractors, pp. 94-102. , ed: Springer; Khansari-Zadeh, S.M., Billard, A., Learning Stable Nonlinear Dynamical; Liu, D.F., Tang, Z.Y., Pei, Z.C., Swing motion control of lower extremity exoskeleton based on admittance method (2015) J Beijing Univ Aero Astro; Åström, K.J., Hägglund, T., Revisiting the ziegler-nichols step response method for pid control (2004) Journal of Process Control, 14 (6), pp. 635-650; Bovi, G., A multiple-task gait analysis approach: Kinematic kinetic and emg reference data for healthy young and adult subjects (2011) Gait & Posture, 33 (1), pp. 6-13","Mousavi, S.A.; Islamic Azad University, Iran; email: mousavibme@mshdiau.ac.ir",,,"Institute of Electrical and Electronics Engineers Inc.","2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2020","26 June 2020 through 27 June 2020",,162106,,9781728193526,,,"English","HORA - Int. Congr. Human-Comput. Interact., Optim. Robot. Appl., Proc.",Conference Paper,"Final","",Scopus,2-s2.0-85089706630
"De Souza P.E.U., Chanel C.P.C., Mailliez M., Dehais F.","56896827900;36681830500;57204116438;13005898500;","Predicting Human Operator's Decisions Based on Prospect Theory",2020,"Interacting with Computers","32","3",,"221","232",,1,"10.1093/iwcomp/iwaa016","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097487057&doi=10.1093%2fiwcomp%2fiwaa016&partnerID=40&md5=7963e71f6769cf7873e601f926ed19d8","ISAE-SUPAERO, Université de Toulouse 10, Av Edouard Belin, Toulouse, 31400, France","De Souza, P.E.U., ISAE-SUPAERO, Université de Toulouse 10, Av Edouard Belin, Toulouse, 31400, France; Chanel, C.P.C., ISAE-SUPAERO, Université de Toulouse 10, Av Edouard Belin, Toulouse, 31400, France; Mailliez, M., ISAE-SUPAERO, Université de Toulouse 10, Av Edouard Belin, Toulouse, 31400, France; Dehais, F., ISAE-SUPAERO, Université de Toulouse 10, Av Edouard Belin, Toulouse, 31400, France","The aim of this work is to predict human operator's (HO) decisions in a specific operational context, such as a cooperative human-robot mission, by approximating his/her utility function based on prospect theory (PT). To this aim, a within-subject experiment was designed in which the HO has to decide with limited time and incomplete information. This experiment also involved a framing effect paradigm, a typical cognitive bias causing people to react differently depending on the context. Such an experiment allowed to acquire data concerning the HO's decisions in two different mission scenarios: search and rescue and Mars rock sampling. The framing was manipulated (e.g. positive vs. negative) and the probability of the outcomes causing people to react differently depending on the context. Statistical results observed for this experiment supported the hypothesis that the way the problem was presented (positively or negatively framed) and the emotional commitment affected the HO's decisions. Thus, based on the collected data, the present work is willed to propose: (i) a formal approximation of the HO's utility function founded on the prospect theory and (ii) a model used to predict the HO's decisions based on the economics approach of multi-dimensional consumption bundle and PT. The obtained results, in terms of utility function fit and prediction accuracy, are promising and show that similar modeling and prediction method should be taken into account when an intelligent cybernetic system drives human-robot interaction. The advantage of predicting the HO's decision, in this operational context, is to anticipate his/her decision, given the way a question is framed to the HO. Such a predictor lays the foundation for the development of a decision-making system capable of choosing how to present the information to the operator while expecting to align his/her decision with the given operational guideline. © 2020 The Author(s) 2020. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.","cognitive bias; framing effect; human-computer interaction (HCI); human-robot(s) interaction (HRI); model-based predictor design; prospect theory","Behavioral research; Decision making; Digital storage; Forecasting; Personnel; Decision-making systems; Formal approximations; Incomplete information; Multi dimensional; Operational guidelines; Prediction accuracy; Search and rescue; Subject experiment; Social robots",,,,,,,,,,,,,,,,"Agresti, A., Kateri, M., (2011) Categorical Data Analysis, , Springer; Bago, B., De Neys, W., Fast logic?: Examining the time course assumption of dual process theory (2017) Cognition, 158, pp. 90-109; Barberis, N. C., Thirty years of prospect theory in economics: A review and assessment (2013) J. Econ. Perspect, 27, pp. 173-195; Barnes, M. J., Chen, J. Y., Jentsch, F., Designing for mixedinitiative interactions between human and autonomous systems in complex environments (2015) 2015 IEEE int. conf. systems, man, and cybernetics (SMC), pp. 1386-1390. , IEEE; Baron, J., (2007) Thinking and Deciding, , Cambridge University Press; Bates, D., Machler, M., Bolker, B., Walker, S., Fitting linear mixed-effects models using lme4 (2015) J. Stat. Softw, 67, pp. 1-48; Belloni, A., Towards a framework to deal with ethical conflicts in autonomous agents and multi-agent systems (2014) CEPE 2014 well-being, flourishing, and ICTs, , paper 8; Bevacqua, G., Cacace, J., Finzi, A., Lippiello, V., Mixedinitiative planning and execution for multiple drones in search and rescue missions (2015) 25th int. conf. automated planning and scheduling (ICAPS), pp. 315-323; Biswas, M., Murray, J., The effects of cognitive biases and imperfectness in long-term robot-human interactions: Case studies using five cognitive biases on three robots (2017) Cogn. Syst. Res, 43, pp. 266-290; Cassotti, M., Habib, M., Poirel, N., Aite, A., Houde, O., Moutier, S., Positive emotional context eliminates the framing effect in decision-making (2012) Emotion, 12, p. 926; de Winter, J. C., Dodou, D., Why the Fitts list has persisted throughout the history of function allocation (2014) Cogn. Tech. Work, 16, pp. 1-11; Dehais, F., Causse, M., Vachon, F., Tremblay, S., Cognitive conflict in human-automation interactions: A psychophysiological study (2012) Appl. Ergon, 43, pp. 588-595; Dehais, F., Goudou, A., Lesire, C., Tessier, C., Towards an anticipatory agent to help pilots (2005) AAAI 2005 fall symposium ""from reactive to anticipatory cognitive embodied systems, , Arlington, Virginia; Dehais, F., Hodgetts, H. M., Causse, M., Behrend, J., Durantin, G., Tremblay, S., Momentary lapse of control: A cognitive continuum approach to understanding and mitigating perseveration in human error (2019) Neurosci. Biobehav. Rev, 100, pp. 252-262; Dehais, F., Peysakhovich, V., Scannella, S., Fongue, J., Gateau, T., Automation surprise in aviation: real-time solutions (2015) Proc. 33rd annual ACMconf. human factors in computing systems, pp. 2525-2534. , ACM; Gombolay, M., Bair, A., Huang, C., Shah, J., Computational design of mixed-initiative human-robot teaming that considers human factors: Situational awareness, workload, and workflow preferences (2017) Int. J. Robot. Res, 36, pp. 597-617; Guo, M., Andersson, S., Dimarogonas, D. V., Humanin- the-Loop Mixed-Initiative Control Under Temporal Tasks (2018) IEEE International Conference on Robotics and Automation (ICRA), pp. 6395-6400. , Brisbane, QLD, 2018; Jiang, S., Arkin, R. C., Mixed-initiative human-robot interaction: Definition, taxonomy, and survey (2015) 2015 IEEE int. conf. systems, man, and cybernetics (SMC), pp. 954-961. , IEEE; Kahneman, D., (2011) Thinking, Fast and Slow, , Macmillan; Kahneman, D., Tversky, A., Prospect theory: An analysis of decision under risk (1979) Econometrica, pp. 263-291; Kahneman, D., Tversky, A., Prospect theory: An analysis of decision under risk (1979) Econometrica, 47, pp. 263-291; Kappes, A., Nussberger, A.-M., Faber, N. S., Kahane, G., Savulescu, J., Crockett, M. J., Uncertainty about the impact of social decisions increases prosocial behaviour (2018) Nat. Hum. Behav, 2, p. 573; Klein, G., Developing expertise in decision making (1997) Think. Reason, 3, pp. 337-352; Kolling, A., Walker, P., Chakraborty, N., Sycara, K., Lewis, M., Human interaction with robot swarms: A survey (2016) IEEE T. Hum.-Mach. 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P., (2010) Prospect Theory: For Risk and Ambiguity, , Cambridge University Press; Xue, S., Zeng, J., Zhang, G., A review of autonomous robotic search (2011) 2011 int. conf. electrical and control engineering (ICECE), pp. 3792-3795. , IEEE; Zhang, G., Comparison of decision-making mechanism between emotion behavior selection and prospect theory (2016) 8th int. conf. intelligent human-machine systems and cybernetics (IHMSC), 1, pp. 538-540. , IEEE","Chanel, C.P.C.; ISAE-SUPAERO, Av Edouard Belin, France; email: caroline.chanel@isae-supaero.fr",,,"Oxford University Press",,,,,09535438,,INTCE,,"English","Interact Comput.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85097487057
"Bitz T., Zahedi F., Lee H.","57219436637;57219441506;35102589200;","Variable Damping Control of a Robotic Arm to Improve Trade-off between Agility and Stability and Reduce User Effort",2020,"Proceedings - IEEE International Conference on Robotics and Automation",,,"9196572","11259","11265",,4,"10.1109/ICRA40945.2020.9196572","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092721397&doi=10.1109%2fICRA40945.2020.9196572&partnerID=40&md5=59908da290895ff3cd0959ad0c9ef534","Arizona State University, School for Engineering of Matter, Transport, Energy, Tempe, AZ  85287, United States","Bitz, T., Arizona State University, School for Engineering of Matter, Transport, Energy, Tempe, AZ  85287, United States; Zahedi, F., Arizona State University, School for Engineering of Matter, Transport, Energy, Tempe, AZ  85287, United States; Lee, H., Arizona State University, School for Engineering of Matter, Transport, Energy, Tempe, AZ  85287, United States","This paper presents a variable damping controller to improve the trade-off between agility and stability in physical human-robot interaction (pHRI), while reducing user effort. Variable robotic damping, defined as a dual-sided logistic function, was determined in real time throughout a range of negative to positive values based on the user's intent of movement. To evaluate the effectiveness of the proposed controller, we performed a set of human experiments with subjects interacting with the end-effector of a 7 degree-of-freedom robot. Twelve subjects completed target reaching tasks under three robotic damping conditions: fixed positive, fixed negative, and variable damping. On average, the variable damping controller significantly shortened the rise time by 22.4% compared to the fixed positive damping. It is also important to note that the rise time in the variable damping condition was as fast as that in the fixed negative damping condition and there was no statistical difference between the two conditions. The variable damping controller significantly decreased the percentage overshoot by 49.6% and shortened the settling time by 29.0% compared to the fixed negative damping. Both the maximum and mean root-mean-squared (RMS) interaction forces were significantly lower in the variable damping condition than the other two fixed damping conditions, i.e., the variable damping controller reduced user effort. The maximum and mean RMS interaction forces were at least 17.3% and 20.3% lower than any of the fixed damping conditions, respectively. The results of this study demonstrate that humans can extract the benefits of the variable damping controller in the context of pHRI, as it significantly improves the trade-off between agility and stability and reduces user effort in comparison to fixed damping controllers. © 2020 IEEE.",,"Agricultural robots; Controllers; Degrees of freedom (mechanics); Economic and social effects; End effectors; Human robot interaction; Robotics; Damping controllers; Degree of freedom; Interaction forces; Logistic functions; Negative damping; Physical humanrobot interaction (phri); Root mean squared; Statistical differences; Damping",,,,,"National Science Foundation, NSF: 1846885, 1925110","Research supported by National Science Foundation Awards #1846885 and #1925110.","This work is part of the Sustainable Production Initiativeand the Production Area of Advance at the Chalmers Universityof Technology",,,,,,,,,"Newman, W.S., Stability and performance limits of interaction controllers (1992) Journal of Dynamic Systems Measurement and Control-Transactions of the Asme, 114 (4), pp. 563-570. , Dec; Hogan, N., Buerger, S.P., Impedance and interaction control (2005) Robotics and Automation Handbook, , New York, CRC Press; Colgate, J.E., Hogan, N., Robust-control of dynamically interacting systems (1988) International Journal of Control, 48 (1), pp. 65-88. , Jul; Kazerooni, H., Human robot interaction via the transfer of power and information signals (1990) Ieee Transactions on Systems Man and Cybernetics, 20 (2), pp. 450-463. , Mar-Apr; Krebs, H.I., Volpe, B., Rehabilitation robotics (2013) Handbook of Clinical Neurology, Neurological Rehabilitation, 110, pp. 283-294. , 3rd series; http://www.sarcos.com/; Zoss, A.B., Kazerooni, H., Chu, A., Biomechanical design of the Berkeley lower extremity exoskeleton (bleex) (2006) Ieee-Asme Transactions on Mechatronics, 11 (2), pp. 128-138. , Apr; Kawamoto, H., Sankai, Y., Power assist system hal-3 for gait disorder person (2002) Proc. International Conference Computers Helping People with Special Needs (Lecture Note in Computer Science), 2398, pp. 196-203; Heyer, C., Human-robot interaction and future industrial robotics applications (2010) IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4749-4754. , Taiwan; Lee, H., Hogan, N., Essential considerations for design and control of human-interactive robots (2016) Proc. 2016 IEEE International Conference on Robotics and Automation (ICRA 2016), pp. 3069-3074. , Stockholm; Arnold, J., Hanzlick, H., Lee, H., Variable damping control of the robotic ankle joint to improve trade-off between performance and stability (2019) IEEE International Conference on Robotics and Automation (ICRA), pp. 1699-1704. , Montreal, Canada",,,,"Institute of Electrical and Electronics Engineers Inc.","2020 IEEE International Conference on Robotics and Automation, ICRA 2020","31 May 2020 through 31 August 2020",,163172,10504729,9781728173955,PIIAE,,"English","Proc IEEE Int Conf Rob Autom",Conference Paper,"Final","",Scopus,2-s2.0-85092721397
"Di Dio C., Manzi F., Peretti G., Cangelosi A., Harris P.L., Massaro D., Marchetti A.","14069963000;57201195835;57216431805;6701387796;7403636494;36495047000;36927793400;","Shall I Trust You? From Child–Robot Interaction to Trusting Relationships",2020,"Frontiers in Psychology","11",,"469","","",,21,"10.3389/fpsyg.2020.00469","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083508265&doi=10.3389%2ffpsyg.2020.00469&partnerID=40&md5=18bc6bc4b9e2b8d898fa6d03474da7b0","Research Unit on Theory of Mind, Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy; School of Computer Science, The University of Manchester, Manchester, United Kingdom; Graduate School of Education, Harvard University, Cambridge, MA, United States","Di Dio, C., Research Unit on Theory of Mind, Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy; Manzi, F., Research Unit on Theory of Mind, Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy; Peretti, G., Research Unit on Theory of Mind, Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy; Cangelosi, A., School of Computer Science, The University of Manchester, Manchester, United Kingdom; Harris, P.L., Graduate School of Education, Harvard University, Cambridge, MA, United States; Massaro, D., Research Unit on Theory of Mind, Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy; Marchetti, A., Research Unit on Theory of Mind, Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy","Studying trust in the context of human–robot interaction is of great importance given the increasing relevance and presence of robotic agents in the social sphere, including educational and clinical. We investigated the acquisition, loss, and restoration of trust when preschool and school-age children played with either a human or a humanoid robot in vivo. The relationship between trust and the representation of the quality of attachment relationships, Theory of Mind, and executive function skills was also investigated. Additionally, to outline children’s beliefs about the mental competencies of the robot, we further evaluated the attribution of mental states to the interactive agent. In general, no substantial differences were found in children’s trust in the play partner as a function of agency (human or robot). Nevertheless, 3-year-olds showed a trend toward trusting the human more than the robot, as opposed to 7-year-olds, who displayed the reverse pattern. These findings align with results showing that, for 3- and 7-year-olds, the cognitive ability to switch was significantly associated with trust restoration in the human and the robot, respectively. Additionally, supporting previous findings, we found a dichotomy between attributions of mental states to the human and robot and children’s behavior: while attributing to the robot significantly lower mental states than the human, in the Trusting Game, children behaved in a similar way when they related to the human and the robot. Altogether, the results of this study highlight that similar psychological mechanisms are at play when children are to establish a novel trustful relationship with a human and robot partner. Furthermore, the findings shed light on the interplay – during development – between children’s quality of attachment relationships and the development of a Theory of Mind, which act differently on trust dynamics as a function of the children’s age as well as the interactive partner’s nature (human vs. robot). © Copyright © 2020 Di Dio, Manzi, Peretti, Cangelosi, Harris, Massaro and Marchetti.","attachment; developmental robotics; HRI; social interaction; Theory of Mind",,,,,,"American Honda Motor","We wish to thank Dr. Gabriella Fumagalli, Principal of the ?I.C. Leonardo da Vinci?, and Mrs. Fulvia Maria Frigerio for their deep involvement in the organization of and support to the study. We wish to thank Dr. Mario Uboldi, Principal of the ?I.C. Giovanni Pascoli?, and Mrs. Maura Panzone for their important contribution to the organization of the project and support to the study. We also wish to thank all the teachers, who actively helped in conducting the study and, in particular, the schools ?Scuola dell?Infanzia Premenugo,? ?Scuola dell?Infanzia Rodano,? ?Scuola dell?Infanzia Caleppio,? ?Scuola Primaria Rasori,? and ?Scuola Primaria Settala.? Funding. This work has been in part supported by the Honda grant ?DecIFER? and the US EOARD grant ?THRIVE++? to AC, and by the Universit? Cattolica del Sacro Cuore, Milan.",,,,,,,,,,"Allison, E., Fonagy, P., When is truth relevant? (2016) Psychoanal. 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Sci, 371. , 27069052; Yagoda, R.E., Gillan, D.J., You want me to trust a ROBOT? the development of a humanerobot interaction trust scale (2012) Int. J. Soc. Robot, 4, pp. 235-248; Yang, Y., Tian, Y., Fang, J., Lu, H., Wei, K., Yi, L., Trust and deception in children with autism spectrum disorders: a social learning perspective (2017) J. Autism Dev. Disord, 47, pp. 615-625. , 27981390; Zanatto, D., Patacchiola, M., Cangelosi, A., Goslin, J., Generalisation of Anthropomorphic stereotype (2019) Int. J. Soc. Robot, 2019, pp. 1-10; Zelazo, P.D., The dimensional change card sort (DCCS): a method of assessing executive function in children (2006) Nat. Protoc, 1, pp. 297-301. , 17406248","Di Dio, C.; Research Unit on Theory of Mind, Italy; email: cinzia.didio@unicatt.it",,,"Frontiers Media S.A.",,,,,16641078,,,,"English","Front. Psychol.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85083508265
"Zhao D., Yang J., Bai D., Okoye M.O., Hiroshi Y.","57195492133;56176391300;27267525100;57207307577;57216298578;","Quantitative estimation of differentiated mental fatigue between self-rising transfer and multiple welfare robots-assisted rising transfer",2020,"Electronics (Switzerland)","9","4","594","","",,1,"10.3390/electronics9040594","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083060006&doi=10.3390%2felectronics9040594&partnerID=40&md5=b05369e6f6fab8eacb14ad785a13b573","School of Electrical Engineering, Shenyang University of Technology, Shenyang, 110870, China; School of Informatics and Engineering, The University of Electro-Communications, Tokyo, 1820021, Japan","Zhao, D., School of Electrical Engineering, Shenyang University of Technology, Shenyang, 110870, China; Yang, J., School of Electrical Engineering, Shenyang University of Technology, Shenyang, 110870, China; Bai, D., School of Electrical Engineering, Shenyang University of Technology, Shenyang, 110870, China; Okoye, M.O., School of Electrical Engineering, Shenyang University of Technology, Shenyang, 110870, China; Hiroshi, Y., School of Informatics and Engineering, The University of Electro-Communications, Tokyo, 1820021, Japan","The multi-robot system (MRS) and relevant control strategy are a potential and effective approach to assist people with weak motion capability for various forms of assisted living. However, the rising transfer, a frequent and strenuous behavior, and its human-robot interaction (HRI) process with MRS, especially mental state, has never been researched, although it directly determines the user experience and security. In this paper, Functional Near-InfraRed Spectroscopy (fNIRS), a brain imaging technique to perform a continuous measure of the mental state, is introduced to monitor the user’s mental fatigue when implementing a behavior transfer in two difficulty levels assisted by multiple welfare-robots. Twenty-five subjects performed self-rising transfer and multiple welfare robots-assisted rising transfer. After removing physiological noises, six features of oxygenated and deoxygenated hemoglobin (HbO and HbR, respectively) features, which included the mean, slope, variance, peak, skewness, and kurtosis, were calculated. To maximize the distinction of fNIRS between self-rising transfer and assisted-rising transfer (multiple welfare robots assisted rising transfer), the optimal statistical feature combination for linear discriminant analysis (LDA) classification was proposed. In addition, the classification accuracy is regarded as a standard to quantify the difference of mental states between two contrasting behaviors. By fitting the index, we established the mental fatigue model that grows exponentially as the workload increases. Finally, the mental fatigue model is applied to guide the nursing mode of caregivers and the control strategy of the MRS. Our findings disclose that the combinations containing mean and peak values significantly yielded higher classification accuracies for both HbO and HbR than the entire other combinations did, across all the subjects. They effectively quantify mental fatigue to provide an evaluation with a theoretical foundation for enhancing the user experience and optimizing the control strategy of MRS. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Assisted-rising transfer; Human robot interaction; Mental fatigue model; Multiple welfare robots; Self-rising transfer",,,,,,"[2012]145; 20162536; Shenyang Science and Technology Bureau: Z17-5-068","Funding: This research was funded in part by “Nature Science Foundation of Liaoning province” under Grant 20162536, in part by “Liaoning Special Professor Foundation Subsidized Project” under Grant [2012]145, in part by “Science and Technology Bureau Project of Shenyang City” under Grant Z17-5-068.",,,,,,,,,,"Wilson, G., Pereyda, C., Raghunath, N., Cruz, G.D., Goel, S., Nesaei, S., Minor, B., Cook, D.J., Robot-enabled support of daily activities in smart home environments (2019) Cogn. Sys. 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Lett., 553, pp. 84-89; Naseer, N., Noori, F.M., Qureshi, N.K., Hong, K.S., Determining optimal feature-combination for LDA classification of functional near-infrared spectroscopy signals in brain-computer interface application (2016) Front. Hum. Neurosci, 10, pp. 1-10; Noori, F.M., Naseer, N., Qureshi, N.K., Nazeer, H., Optimal feature selection from fNIRS signals using genetic algorithms for BCI (2017) Neurosci. Lett., 647, pp. 61-66; Yassierli, M.A., Effects of age, gender, and task parameters on fatigue development during intermittent isokinetic torso extensions (2009) Int. J. Ind. Ergonomics, 1, pp. 185-191; Bu, U., Fatigue and psychological distress in the working population: Psychometrics, prevalence, and correlates (2002) J. Psychosom. Res., 52, pp. 445-452; Gadanho, S.C., Learning behavior-selection by emotions and cognition in a multi-goal robot task (2003) J. Mach. Learn. Res, 4, pp. 385-412; Fellous, J.M., Arbib, M.A., (2005) Who Needs Emotions? The Brain Meets The Robot, , Oxford University Press: New York, NY, USA; Hu, X.S., Hong, K.S., Ge, S.S., FNIRS-based online deception decoding (2012) J. Neural Eng., 9; Zimmermann, R., Marchal-Crespo, L., Edelmann, J., Lambercy, O., Fluet, M.C., Riener, R., Detection of motor execution using a hybrid fNIRS-biosignal BCI: A feasibility study (2013) J. Neuroeng. Rehabil, 10, pp. 1-15","Yang, J.; School of Electrical Engineering, China; email: junyouyang@sut.edu.cn",,,"MDPI AG",,,,,20799292,,,,"English","Electronics (Switzerland)",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85083060006
"McGinn C., Dooley D.","36651232500;57209812686;","What should robots feel like? Factors that influence the perception of robot-skin",2020,"ACM/IEEE International Conference on Human-Robot Interaction",,,,"281","288",,,"10.1145/3319502.3374835","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081992038&doi=10.1145%2f3319502.3374835&partnerID=40&md5=d85476554d59d65a12f83dc87f40fc9f","Trinity College Dublin, Dublin, Ireland","McGinn, C., Trinity College Dublin, Dublin, Ireland; Dooley, D., Trinity College Dublin, Dublin, Ireland","It's widely accepted that a robot's embodiment plays an important role during human-robot interaction (HRI). While many studies have explored the effect of robot appearance, relatively little is known about how the texture and stiffness of the surface material, or what may be referred to as 'robot-skin', influences how the robot is perceived. Gaining improved understanding in this area may have direct and actionable consequences on robot design, since at present nearly all commercially available service robots have similar exterior surfaces composed of smooth, stiff materials, usually plastic. This study is framed around systematically investigating the type of textures that may be better suited for these robots. First, experiments were undertaken to classify the textural characteristics of 27 distinct materials which could potentially be used as a robotskin. A representative subset of these materials was then selected for a second experiment that explored how the stiffness and tactile properties of the material influenced its perceived suitability for use on a service robot. The research found that people strongly preferred surface textures that were soft, rather than stiff. The most suitable material stiffness was found to be context dependent; soft options were preferred in the blind test condition, but for cases where participants were presented with the 3D image of a service robot in an immersive virtual reality environment, medium stiffness materials were preferred. In the final part of the study, we identified a range of textural properties that seem to correlate with high and low suitability for use on service robots. It is hoped that these findings are useful to help inform the design of future HRI systems, and motivate further investigation into the social roles of robot-skin. © 2020 Association for Computing Machinery.","Haptics; Human-robot interaction; Mixed-methods; Robot skin; Texture analysis; Virtual reality","Human robot interaction; Man machine systems; Mobile robots; Social robots; Stiffness; Textures; Virtual reality; Haptics; Human robot Interaction (HRI); Immersive virtual reality; Mixed method; Robot skin; Textural characteristic; Textural properties; Texture analysis; Machine design",,,,,,,,,,,,,,,,"Amadasun, M., King, R., Textural features corresponding to textural properties (1989) IEEE Transactions on Systems, Man, and Cybernetics, 19 (5), pp. 1264-1274. , 1989; Balli Altuglu, T., Altun, K., Recognizing touch gestures for social human-robot interaction (2015) Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pp. 407-413. , ACM; Bartneck, C., Yogeeswaran, K., Min Ser, Q., Robert Sparrow SihengWang, G., Eyssel, F., Robots and racism (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 196-204. , ACM; Bhushan, N., Ravishankar Rao, A., Lohse, G.L., The texture lexicon: Understanding the categorization of visual texture terms and their relationship to texture images (1997) Cognitive Science, 21 (2), pp. 219-246. , 1997; Chen, S., Ge, S., Experimental research on the tactile perception from fingertip skin friction (2017) Wear, 376, pp. 305-314. , 2017; Chuong, C., Nickoloff, B.J., Elias, P.M., Goldsmith, L.A., MacHer, E., Maderson, P.A., Sundberg, J.P., Thestrup-Pederson, K., What is the'true'function of skin (2002) Experimental Dermatology, 11 (2), pp. 159-187. , 2002; Luiza Ciesielska-Wrobel, I., Van Langenhove, L., The hand of textiles-definitions, achievements, perspectives-a review (2012) Textile Research Journal, 82 (14), pp. 1457-1468. , 2012; Robin, I.M., Dunbar, The social role of touch in humans and primates: Behavioural function and neurobiological mechanisms (2010) Neuroscience & Biobehavioral Reviews, 34 (2), pp. 260-268. , 2010; Field, T., Touch for socioemotional and physical well-being: A review (2010) Developmental Review, 30 (4), pp. 367-383. , 2010; Freinkel, R.K., Woodley, D.T., (2001) The Biology of the Skin, , CRC Press; Fritzsche, M., Elkmann, N., Schulenburg, E., Tactile sensing: A key technology for safe physical human robot interaction (2011) 2011 6th ACM/ IEEE International Conference on Human-Robot Interaction (HRI), pp. 139-140. , IEEE; Gallace, A., Spence, C., The science of interpersonal touch: An overview (2010) Neuroscience & Biobehavioral Reviews, 34 (2), pp. 246-259. , 2010; Goris, K., Saldien, J., Vanderborght, B., Lefeber, D., Mechanical design of the huggable robot probo (2011) International Journal of Humanoid Robotics, 8 (3), pp. 481-511. , 2011; Hu, Y., Hoffman, G., Using skin texture change to design emotion expression in social robots (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 2-10. , IEEE; Kidd, C.D., Taggart, W., Turkle, S., A sociable robot to encourage social interaction among the elderly (2006) Proceedings 2006 IEEE International Conference on Robotics and Automation 2006. ICRA 2006, pp. 3972-3976. , IEEE; Lowe, R., Andreasson, R., Alenljung, B., Lund, A., Billing, E., Designing for a wearable affective interface for the nao robot: A study of emotion conveyance by touch (2018) Multimodal Technologies and Interaction, 2 (1), p. 2. , 2018; Manfredi, L.R., Saal, H.P., Brown, K.J., Zielinski, M.C., John, F.D., III, Polashock, V.S., Bensmaia, S.J., Natural scenes in tactile texture (2014) Journal of Neurophysiology, 111 (9), pp. 1792-1802. , 2014; McGinn, C., Why do robots need a head the role of social interfaces on service robots (2019) International Journal of Social Robotics, pp. 1-15. , 2019; McGinn, C., Bourke, E., Murtagh, A., Donovan, C., Lynch, P., Cullinan, M.F., Kelly, K., Meet stevie: A socially assistive robot developed through application of a âdesign-thinkingâapproach (2019) Journal of Intelligent & Robotic Systems, pp. 1-20. , 2019; Nakagawa, K., Shiomi, M., Shinozawa, K., Matsumura, R., Ishiguro, H., Hagita, N., Effect of robot's active touch on people's motivation (2011) Proceedings of the 6th International Conference on Humanrobot Interaction, pp. 465-472. , ACM; Park, Y., Chen, B., Wood, R.J., Design and fabrication of soft artificial skin using embedded microchannels and liquid conductors (2012) IEEE Sensors Journal, 12 (8), pp. 2711-2718. , 2012; Ravishankar Rao, A., Lohse, G.L., Towards a texture naming system: Identifying relevant dimensions of texture (1996) Vision Research, 36 (11), pp. 1649-1669. , 1996; Saldien, J., Goris, K., Vanderborght, B., Verrelst, B., Van Ham, R., Lefeber, D., Anty: The development of an intelligent huggable robot for hospitalized children (2006) 9th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines; Schiff, W., Foulke, E., (1982) Tactual Perception: A Sourcebook, , CambridgeUniversity Press; Silvera-Tawil, D., Rye, D., Velonaki, M., Artificial Skin and Tactile Sensing for Socially Interactive Robots: A Review (2015) Robotics and Autonomous Systems, 63, pp. 230-243. , 2015; Tamura, H., Mori, S., Yamawaki, T., Textural features corresponding to visual perception (1978) IEEE Transactions on Systems, Man, and Cybernetics, 8 (6), pp. 460-473. , 1978; Terada, K., Yamauchi, A., Ito, A., Artificial emotion expression for a robot by dynamic color change (2012) 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication, pp. 314-321. , IEEE; Wouter, M., Tiest, B., Tactual perception of material properties (2010) Vision Research, 50 (24), pp. 2775-2782. , 2010; Tiest Bergmann, W.M., Kappers, A.M., Analysis of haptic perception of materials by multidimensional scaling and physical measurements of roughness and compressibility (2006) Acta Psychologica, 121 (1), pp. 1-20. , 2006; Pradhono Tomo, T., Schmitz, A., Keat Wong, W., Kristanto, H., Somlor, S., Hwang, J., Jamone, L., Sugano, S., Covering a robot fingertip with uskin: A soft electronic skin with distributed 3-axis force sensitive elements for robot hands (2017) IEEE Robotics and Automation Letters, 3 (1), pp. 124-131. , 2017; Weber, A.I., Saal, H.P., Lieber, J.D., Cheng, J., Manfredi, L.R., Dammann, J.F., Bensmaia, S.J., Spatial and temporal codes mediate the tactile perception of natural textures (2013) Proceedings of the National Academy of Sciences, 110 (42), pp. 17107-17112. , 2013; Aisling Whitaker, T., Simões-Franklin, C., Newell, F.N., Vision and touch: Independent or integrated systems for the perception of texture (2008) Brain Research, 1242, pp. 59-72. , 2008; Jam Willemse, C., Toet, A., Van Bf, E.J., Affective and behavioral responses to robot-initiated social touch: Toward understanding the opportunities and limitations of physical contact in human-robot interaction (2017) Frontiers in ICT, 4, p. 12. , 2017; Yohanan, S., MacLean, K.E., The role of affective touch in humanrobot interaction: Human intent and expectations in touching the haptic creature (2012) International Journal of Social Robotics, 4 (2), pp. 163-180. , 2012; Dimensions of tactual impressions (1) (1968) Japanese Psychological Research, 10 (3), pp. 123-137. , MASAAKI YOSHIDA, 1968",,,"ACM SIGAI;ACM SIGCHI;IEEE Robotics and Automation Society( IEEE RAS)","IEEE Computer Society","15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020","23 March 2020 through 26 March 2020",,158224,21672148,9781450367462,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85081992038
"Xiong B., Zeng N., Li Y., Du M., Huang M., Shi W., Mao G., Yang Y.","57191674846;36703553900;7502076797;36463370900;57208392389;57208387005;57225448628;55719990600;","Determining the online measurable input variables in human joint moment intelligent prediction based on the hill muscle model",2020,"Sensors (Switzerland)","20","4","1185","","",,4,"10.3390/s20041185","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079898187&doi=10.3390%2fs20041185&partnerID=40&md5=fdbdb66be0a4d085f939365715843c43","College of Physics and Information Engineering, Fuzhou University, Fuzhou City, Fujian Province  350116, China; Department of Mathematics and Physics, Fujian University of Technology, Fuzhou City, Fujian Province  350118, China; Department of Instrumental and Electrical Engineering, Xiamen University, Fujian, 361005, China; Fujian Key Laboratory of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University, Fuzhou City, Fujian Province  350116, China; Fujian provincial key laboratory of eco-industrial green technology, Wuyi University, Wuyishan City, Fujian Province  354300, China; Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL  60208, United States","Xiong, B., College of Physics and Information Engineering, Fuzhou University, Fuzhou City, Fujian Province  350116, China, Department of Mathematics and Physics, Fujian University of Technology, Fuzhou City, Fujian Province  350118, China; Zeng, N., Department of Instrumental and Electrical Engineering, Xiamen University, Fujian, 361005, China; Li, Y., Fujian Key Laboratory of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University, Fuzhou City, Fujian Province  350116, China; Du, M., College of Physics and Information Engineering, Fuzhou University, Fuzhou City, Fujian Province  350116, China, Fujian provincial key laboratory of eco-industrial green technology, Wuyi University, Wuyishan City, Fujian Province  354300, China; Huang, M., College of Physics and Information Engineering, Fuzhou University, Fuzhou City, Fujian Province  350116, China; Shi, W., College of Physics and Information Engineering, Fuzhou University, Fuzhou City, Fujian Province  350116, China; Mao, G., Department of Mathematics and Physics, Fujian University of Technology, Fuzhou City, Fujian Province  350118, China; Yang, Y., Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL  60208, United States","Introduction: Human joint moment is a critical parameter to rehabilitation assessment and human-robot interaction, which can be predicted using an artificial neural network (ANN) model. However, challenge remains as lack of an effective approach to determining the input variables for the ANN model in joint moment prediction, which determines the number of input sensors and the complexity of prediction. Methods: To address this research gap, this study develops a mathematical model based on the Hill muscle model to determining the online input variables of the ANN for the prediction of joint moments. In this method, the muscle activation, muscle-tendon moment velocity and length in the Hill muscle model and muscle-tendon moment arm are translated to the online measurable variables, i.e. muscle electromyography (EMG), joint angles and angular velocities of the muscle span. To test the predictive ability of these input variables, an ANN model is designed and trained to predict joint moments. The ANN model with the online measurable input variables is tested on the experimental data collected from ten healthy subjects running with the speeds of 2, 3, 4 and 5 m/s on a treadmill. The variance accounted for (VAF) between the predicted and inverse dynamics moment is used to evaluate the prediction accuracy. Results: The results suggested that the method can predict joint moments with a higher accuracy (mean VAF = 89.67±5.56 %) than those obtained by using other joint angles and angular velocities as inputs (mean VAF = 86.27±6.6%) evaluated by jack-knife cross-validation. Conclusions: The proposed method provides us with a powerful tool to predict joint moment based on online measurable variables, which establishes the theoretical basis for optimizing the input sensors and detection complexity of the prediction system. It may facilitate the research on exoskeleton robot control and real-time gait analysis in motor rehabilitation. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.","Artificial neural network; Extreme learning machine; Hill muscle model; Joint moment prediction; Online input variables","Angular velocity; Complex networks; Exoskeleton (Robotics); Forecasting; Human robot interaction; Inverse problems; Muscle; Neural networks; Tendons; Artificial neural network models; Detection complexity; Effective approaches; Extreme learning machine; Hill muscle models; Input variables; Intelligent prediction; Joint moment; Learning systems; adult; electromyography; human; joint; male; physiology; skeletal muscle; theoretical model; young adult; Adult; Electromyography; Humans; Joints; Male; Models, Theoretical; Muscle, Skeletal; Neural Networks, Computer; Young Adult",,,,,"JT180344/ JT180320/JAT170398; GY‐Z17151/GY‐Z17144; Northwestern Memorial Foundation; National Natural Science Foundation of China, NSFC: 61773124, 61773415; National Key Research and Development Program of China, NKRDPC: 2016YFE0122700","Acknowledgments: This work was supported in part by in part by National Nature Science Foundation of China (61773124, 61773415), in part by National Key Research and Development Program of China (2016YFE0122700), in part by UK‐China Industry Academia Partnership Programmer\276, the Science and Tecohnology Project in Fujian Province Education Department (JT180344/ JT180320/JAT170398), and in part by the Scientific Fund Projects in Fujian University of Technology (GY‐Z17151/GY‐Z17144). Y.Y. is supported by the Dixon Translational Research Grants Initiative from the Northwestern Memorial Foundation.","This work was supported in part by in part by National Nature Science Foundation of China (61773124, 61773415), in part by National Key Research and Development Program of China (2016YFE0122700), in part by UK-China Industry Academia Partnership Programmer\276, the Science and Tecohnology Project in Fujian Province Education Department (JT180344/ JT180320/JAT170398), and in part by the Scientific Fund Projects in Fujian University of Technology (GY-Z17151/GY-Z17144). Y.Y. is supported by the Dixon Translational Research Grants Initiative from the Northwestern Memorial Foundation.",,,,,,,,,"Park, H.S., Peng, Q., Zhang, L.Q., A Portable telerehabilitation system for remote evaluations of impaired elbows in neurological disorders (2008) IEEE Trans. Neural Syst. Rehabil. 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"Vo T., Lyons J.B.","57208575959;15623605700;","Producing an Immersive Experience Using Human-Robot Interaction Stimuli",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","12425 LNCS",,,"266","276",,,"10.1007/978-3-030-60128-7_21","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092934520&doi=10.1007%2f978-3-030-60128-7_21&partnerID=40&md5=639b69f99896d18f9184989ad7b73a9d","Ball Aerospace Corporation, Broomfield, United States; Air Force Research Laboratory, WPAFB, Dayton, United States","Vo, T., Ball Aerospace Corporation, Broomfield, United States; Lyons, J.B., Air Force Research Laboratory, WPAFB, Dayton, United States","Human-Robot Interaction (HRI) is an emerging topic within contemporary science and this topic spans the literatures between engineering, psychology, computer science, artificial intelligence, machine learning and robotics. The study of HRI requires scenarios and other affordances from which to contextualize the HRI to study the factors that shape human attitudes, behaviors, and biases as they relate to robots. The current paper details the rationale, software/hardware, and contextual considerations associated with the creation of HRI stimuli used in experiments, specifically the creation of a set of video stimuli. These stimuli centered on the concept of an autonomous security robot (ASR) and provided a scenario wherein humans (research confederates) would interact with the ASR in a realistic scenario. A video was created to foster a realistic visual and auditory representation of the HRI encounter from a dynamic perspective – meaning shifting perspectives between a first-person experiential vantage point to a birds-eye-view of the broader situation. The scenario used involved a security context where the confederates sought access to a secure facility. The ASR’s role was to examine visitor access credentials and determine if the visitor was authorized or not. The robot’s behaviors, while scripted, were depicted as autonomous in the video and included verbal interactions/instructions as well as physical limb motions, gestures, and instructions. Several important features of the stimuli were considered in its creation, namely: realism, immersive experience, and simulated vulnerability of the human to the robot. The current paper walks through each of these considerations with details provided in our approach. To date, the HRI stimuli have been used in multiple experiments and have demonstrated flexibility in addressing multiple research questions in the domain of HRI. © 2020, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.","Artificial intelligence; Autonomous robots; Human-Robot Interaction; Immersive experiences; Security robots","Artificial intelligence; Behavioral research; Engineering education; Human computer interaction; User experience; Auditory representation; Autonomous security; Human robot Interaction (HRI); Important features; Multiple research; Realistic scenario; Software/hardware; Verbal interaction; Social robots",,,,,,,,,,,,,,,,"(2012) Office of the under Secretary of Defense for Acquisition, , Technology and Logistics, Washington, DC; (2016) Office of the under Secretary of Defense for Acquisition, Technology, and Logistics, , Washington, DC; Hancock, P.A., Billings, D.R., Schaefer, K.E., Chen, J.Y.C., de Visser, E.J., Parasuraman, R., A metaanalysis of factors affecting trust in humanrobot interaction (2011) Hum. Factors, 53 (5), pp. 517-527; Lyons, J.B., Vo, T., Wynne, K.T., Mahoney, S., Nam, C.S., Gallimore, D., Trusting autonomous robots: The role of reliability and stated social intent Human Factors, , in press; Mayer, R.C., Davis, J.H., Schoorman, F.D., An integrated model of organizational trust (1995) Acad. Manag. Rev., 20, pp. 709-734; Robinette, P., Allen, R., Howard, A.M., Wagner, A.R., Overtrust of robots in emergency evacuation scenarios (2016) HRI 2016, 11Th ACM/IEEE International Conference on Human-Robot Interaction, Pp. 101–108; Salem, M., Lakatos, G., Amirabdollahian, F., Dautenhahn, K., Would you trust a (Faulty) robot? Effects of error, task type, personality on humanrobot cooperation and trust (2015) HRI 2015, 10Th ACM/IEEE International Conference on Humanrobot Interaction, pp. 1-8; Booth, S., Tomkin, J., Pfister, H., Waldo, J., Gajos, K., Nagpal, R., Piggybacking robots: Humanrobot overtrust in University dormitory security (2017) HRI 2017, 11Th ACM/IEEE International Conference on Humanrobot Interaction, Pp. 426–434; Wiggers, K., (2017) Meet the 400Pound Robots that Will Soon Patrol Parking Lots, Offices, and Malls, , https://www.digitaltrends.com/cooltech/knightscoperobotsinterview/; Inbar, O., Meyer, J., Manners matter: Trust in robotic peacekeepers (2015) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59 (1), pp. 185-189; Long, S.K., Karpinskymosely, N.D., Bliss, J., Trust of simulated robotic peacekeepers among resident and expatriate Americans (2017) Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61, pp. 2091-2095; Lyons, J.B., Being transparent about transparency: A model for humanrobot interaction (2013) Trust and Autonomous Systems: Papers from the AAAI Spring Symposium (Technical Report SS1307), , Sofge, D., Kruijff, G.J., Lawless, W.F. (eds.) , AAAI Press, Menlo Park; Gallimore, D., Lyons, J.B., Vo, T., Mahoney, S., Wynne, K.T., Trusting robocop: Genderbased effects on trust of an autonomous robot (2019) Front. Psychol.: Humanmedia Interact., 10, p. 482; Lyons, J.B., Nam, C.S., Jessup, S.A., Vo, T.Q., Wynne, K.T., The role of individual differences as predictors of trust in autonomous security robots (2020) Proceedings of IEEE Humanmachine Systems Conference, Rome, IT","Lyons, J.B.; Air Force Research Laboratory, United States; email: joseph.lyons.6@us.af.mil","Stephanidis C.Harris D.Li W.-C.Schmorrow D.D.Fidopiastis C.M.Zaphiris P.Ioannou A.Ioannou A.Fang X.Sottilare R.A.Schwarz J.",,"Springer Science and Business Media Deutschland GmbH","22nd International Conference on Human-Computer Interaction,HCII 2020","19 July 2020 through 24 July 2020",,249799,03029743,9783030601270,,,"English",,Conference Paper,"Final","",Scopus,2-s2.0-85092934520
"Mustaqeem, Sajjad M., Kwon S.","57212930354;57215455402;37561675600;","Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM",2020,"IEEE Access","8",,"9078789","79861","79875",,51,"10.1109/ACCESS.2020.2990405","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084848439&doi=10.1109%2fACCESS.2020.2990405&partnerID=40&md5=d0318344e7e6cfcbfca58d03cf715bac","Department of Software, Interaction Technology Laboratory, Sejong University, Seoul, 05006, South Korea; Department of Computer Science, Digital Image Processing Laboratory, Islamia College Peshawar, Peshawar, 25000, Pakistan","Mustaqeem, Department of Software, Interaction Technology Laboratory, Sejong University, Seoul, 05006, South Korea; Sajjad, M., Department of Computer Science, Digital Image Processing Laboratory, Islamia College Peshawar, Peshawar, 25000, Pakistan; Kwon, S., Department of Software, Interaction Technology Laboratory, Sejong University, Seoul, 05006, South Korea","Emotional state recognition of a speaker is a difficult task for machine learning algorithms which plays an important role in the field of speech emotion recognition (SER). SER plays a significant role in many real-time applications such as human behavior assessment, human-robot interaction, virtual reality, and emergency centers to analyze the emotional state of speakers. Previous research in this field is mostly focused on handcrafted features and traditional convolutional neural network (CNN) models used to extract high-level features from speech spectrograms to increase the recognition accuracy and overall model cost complexity. In contrast, we introduce a novel framework for SER using a key sequence segment selection based on redial based function network (RBFN) similarity measurement in clusters. The selected sequence is converted into a spectrogram by applying the STFT algorithm and passed into the CNN model to extract the discriminative and salient features from the speech spectrogram. Furthermore, we normalize the CNN features to ensure precise recognition performance and feed them to the deep bi-directional long short-term memory (BiLSTM) to learn the temporal information for recognizing the final state of emotion. In the proposed technique, we process the key segments instead of the whole utterance to reduce the computational complexity of the overall model and normalize the CNN features before their actual processing, so that it can easily recognize the Spatio-temporal information. The proposed system is evaluated over different standard dataset including IEMOCAP, EMO-DB, and RAVDESS to improve the recognition accuracy and reduce the processing time of the model, respectively. The robustness and effectiveness of the suggested SER model is proved from the experimentations when compared to state-of-the-art SER methods with an achieve up to 72.25%, 85.57%, and 77.02% accuracy over IEMOCAP, EMO-DB, and RAVDESS dataset, respectively. © 2013 IEEE.","deep bidirectional long shot term memory; key segment sequence selection; normalization of CNN features; radial-based function network (RBFN); Speech emotion recognition","Behavioral research; Complex networks; Convolutional neural networks; Human robot interaction; Learning algorithms; Machine learning; Spectrographs; High-level features; Real-time application; Recognition accuracy; Similarity measurements; Spatiotemporal information; Speech emotion recognition; Speech spectrogram; Temporal information; Speech recognition",,,,,"Sejong University; Ministry of Science, ICT and Future Planning, MSIP: 2017-0-00189; Institute for Information and Communications Technology Promotion, IITP","This work was supported in part by the Institute for Information and Communications Technology Planning and Evaluation (IITP) funded by the Korea Government through the Ministry of Science and ICT (MSIT) (Voice emotion recognition and indexing for affective multimedia service) under Grant 2017-0-00189, and in part by the 2020 Faculty Research Fund of Sejong University.",,,,,,,,,,"Liu, B., Qin, H., Gong, Y., Ge, W., Xia, M., Shi, L., EERA-ASR: An energy-efficient reconfigurable architecture for automatic speech recognition with hybrid DNN and approximate computing (2018) IEEE Access, 6, pp. 52227-52237; Cummins, N., Amiriparian, S., Hagerer, G., Batliner, A., Steidl, S., Schuller, B.W., An image-based deep spectrum feature representation for the recognition of emotional speech Proc. 25th ACM Multimedia Conf. 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Interspeech, pp. 1-5. , Sep; Tripathi, S., Kumar, A., Ramesh, A., Singh, C., Yenigalla, P., (2019) Deep Learning Based Emotion Recognition System Using Speech Features and Transcriptions, , http://arxiv.org/abs/1906.05681, [Online]; Jiang, P., Fu, H., Tao, H., Lei, P., Zhao, L., Parallelized convolutional recurrent neural network with spectral features for speech emotion recognition (2019) IEEE Access, 7, pp. 90368-90377; Zeng, Y., Mao, H., Peng, D., Yi, Z., Spectrogram based multi-task audio classification (2019) Multimedia Tools Appl., 78 (3), pp. 3705-3722. , Feb; Jalal, M.A., Loweimi, E., Moore, R.K., Hain, T., Learning temporal clusters using capsule routing for speech emotion recognition (2019) Proc. Interspeech, pp. 1-5. , Sep; Bhavan, A., Chauhan Hitkul, P., Shah, R.R., Bagged support vector machines for emotion recognition from speech (2019) Knowl.-Based Syst., 184. , Nov; Zamil, A.A.A., Hasan, S., Jannatul Baki, S.M., Adam, J.M., Zaman, I., Emotion detection from speech signals using voting mechanism on classified frames (2019) Proc. Int. Conf. Robot., Elect. Signal Process. Techn. (ICREST), pp. 281-285. , Jan","Kwon, S.; Department of Software, South Korea; email: skwon@sejong.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85084848439
"Fuse Y., Tokumaru M.","57201797804;7004698797;","Social Influence of Group Norms Developed by Human-Robot Groups",2020,"IEEE Access","8",,"9043537","56081","56091",,4,"10.1109/ACCESS.2020.2982181","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082929962&doi=10.1109%2fACCESS.2020.2982181&partnerID=40&md5=453b8222909fcd616b837a174ddcab09","Graduate School of Science and Engineering, Kansai University, Osaka, 564-8680, Japan; Department of Electrical Electronic and Information Engineering, Faculty of Engineering Science, Kansai University, Osaka, 564-8680, Japan","Fuse, Y., Graduate School of Science and Engineering, Kansai University, Osaka, 564-8680, Japan; Tokumaru, M., Department of Electrical Electronic and Information Engineering, Faculty of Engineering Science, Kansai University, Osaka, 564-8680, Japan","Several studies on how social robots respond, gesture, and display emotion in human-robot interactions have been conducted. In particular, sociality of robots implies that robots do not only exhibit human-like behaviors, but also display a tendency to adapt to a group of individuals. For robots to exhibit sociality, they need to adapt to group norms without telling them how to behave by the group members. In this study, we investigated the effect of group norms on human decision-making in human-robot groups, which comprise two robots using our proposed robotic model. Furthermore, we conducted quizzes with the robots and a human participant using unclear and vague answers. We assessed this influence by making the participant and the two robots repeat a set of actions: to answer the same quiz and recognize each answer of the group members. Additionally, we evaluated the extent to which the group norms changed the opinions of humans using a questionnaire. We analyzed the results of the questionnaire and chronological change in their answers for the quiz with the same question. The quiz experimental results showed that the human participants changed their answers after they discovered the answers of the robots for the first time due to social influence from the robots assumed that the human participants were confused about the diversity of the answers in the group and were aware of the consideration of the robots of the group norm. This is to ensure that they can adjust their answers to the group norm. Moreover, the questionnaire results revealed that the group norms gave the human participants right answers to the quiz that has no correct answers. Therefore, we concluded that robots attempt to comply with a group norm affects human's decision-making. © 2013 IEEE.","group norm; human-robot group; human-robot interaction; social influence; Social robotics","Behavioral research; Decision making; Human robot interaction; Man machine systems; Robotics; Social aspects; Social robots; Surveys; Group members; group norm; Human decision making; Human like; Human robots; Robotic modeling; Social influence; Social robotics; Economic and social effects",,,,,,,,,,,,,,,,"Kachouie, R., Sedighadeli, S., Khosla, R., Chu, M.-T., Socially assistive robots in elderly care: A mixed-method systematic literature review (2014) Int. J. Hum.-Comput. Interact., 30 (5), pp. 369-393. , May; Hegel, R., Muhl, C., Wrede, B., Hielscher-Fastabend, M., Sagerer, G., Understanding social robots (2009) Proc. 2nd Int. Conf.Adv. Comput.-Hum. Interact, pp. 169-174. , Feb; Fong, T., Nourbakhsh, I., Dautenhahn, K., A survey of socially interactive robots (2003) Robot. Auto. Syst., 42 (31), pp. 143-166. , Mar; Breazeal, L., (2004) Designing Sociable Robots, , Cambridge MA USA: MIT Press; Duffy, B.R., Anthropomorphism and the social robot (2003) Robot. Auto. Syst., 42 (31), pp. 177-190. , Mar; Feldman, D.C., The development and enforcement of group norms (1984) Acad. Manage. Rev., 9 (1), pp. 47-53. , Jan; Boyd, R., The puzzle of human sociality (2006) Science, 314 (5805), pp. 1555-1556; Fuse, Y., Takenouchi, H., Tokumaru, M., A robot model that obeys a norm of a human group by participating in the group and interacting with its members (2019) IEICE Trans. Inf. Syst, E102D (1), pp. 185-194. , Jan; Fuse, Y., Takenouchi, H., Tokumaru, M., A robot in a human-robot group learns group norms and makes decisions through indirect mutual interaction with humans (2020) Adv. Comput. Intell. Intell. Inform., 24 (1), pp. 169-178; Brandstetter, J., Racz, P., Beckner, C., Sandoval, E.B., Hay, J., Bartneck, C., A peer pressure experiment: Recreation of the Asch conformity experiment with robots (2014) Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst, pp. 1335-1340. , Sep; Salomons, N., van der Linden, M., Sebo, S.S., Scassellati, B., Humans conform to robots: Disambiguating trust, truth, and conformity (2018) Proc. ACM/IEEE Int. Conf. Hum.-Robot Interact. (HRI), pp. 187-195; Asch, S.E., Effects of group pressure upon the modification and distortion of judgment (1951) Groups, Leadership, and Men, pp. 222-236. , H. Guetzkow, Ed. Pittsburgh, PA, USA: Carnegie Press; Aminoff, E.M., Clewett, D., Freeman, S., Frithsen, A., Tipper, C., Johnson, A., Grafton, S.T., Miller, M.B., Individual differences in shifting decision criterion: A recognition memory study (2012) Memory Cognition, 40 (7), pp. 1016-1030. , Oct; Deutsch, M., Gerard, H.B., A study of normative and informational social influences upon individualjudgment (1955) Abnormal Social Psychol., 51 (3), pp. 629-636; Rashotte, L.S., Social influence (2007) The Blackwell Encyclopedia of Sociology, 9, pp. 4426-4429. , G. Ritzer, and J. M. Ryan, Eds. Oxford, U.K.: Blackwell; Cialdini, R.B., Goldstein, N.J., Social influence: Compliance and conformity (2004) Annu. Rev. Psychol., 55 (1), pp. 591-621. , Feb; Sherif, M., A study of some social factors in perception (1934) Arch. Psychol., 27 (187), pp. 5-16; Cialdini, R.B., (2009) Influence: Science and Practice, , New York, NY, USA: HarperCollins; Jetten, J., Spears, R., Manstead, A.S.R., Strength of identification and intergroup differentiation: The influence of group norms (1997) Eur. J. Social Psychol, 27 (5), pp. 603-609. , Sep; Postmes, T., Spears, R., Cihangir, S., Quality of decision making and group norms (2001) Personality Social Psychol, 80 (6), pp. 918-930; Jetten, J., Postmes, T., McAuliffe, B.J., We're all individuals': Group norms of individualism and collectivism, levels of identification and identity threat (2002) Eur. J. Social Psychol, 32 (2), pp. 189-207. , Mar; Robinette, P., Li, W., Allen, R., Howard, A.M., Wagner, A.R., Overtrust of robots in emergency evacuation scenarios (2016) Proc. 11th ACM/IEEE Int. Conf. Hum.-Robot Interact. (HRI), pp. 101-108. , Mar; Williams, R., MacHado, C.V., Druga, S., Breazeal, C., Maes, P., My doll says it's ok': A study of children's conformity to a talking doll (2018) Proc. 17th ACM Conf. Interact. Design Children (IDC), pp. 625-631; Vollmer, A.-L., Read, R., Trippas, D., Belpaeme, T., Children conform, adults resist: A robot group induced peer pressure on normative social conformity (2018) Sci. Robot., 3 (21). , Aug; Beckner, C., Rácz, P., Hay, J., Brandstetter, J., Bartneck, C., Participants conform to humans but not to humanoid robots in an English past tense formation task (2016) J. Lang. Social Psychol., 35 (2), pp. 158-179. , Mar; Kaelbling, L.P., Littman, M.L., Moore, A.W., Reinforcement learning: A survey, journal of artificial intelligence research (1996) J. Artif. Intell. Res., 4, pp. 237-285; Kober, J., Bagnell, J.A., Peters, J., Reinforcement learning in robotics: A survey (2013) Int. J. Robot. Res., 32 (11), pp. 1238-1274; Moerland, T.M., Broekens, J., Jonker, C.M., Emotion in reinforcement learning agents and robots: A survey (2018) Mach. Learn., 107 (2), pp. 443-480. , Feb; Semet, Y., (2002) Interactive Evolutionary Computation: A Survey of Existing Theory, , Univ. Illinois, Champaign, IL, USA, Tech. Rep; Takagi, H., Iba, H., Interactive evolutionary computation (2005) New Gener. Comput., 23 (2), pp. 113-114; Neubert, K., Brunner, E., A studentized permutation test for the non-parametric Behrens-Fisher problem (2007) Comput. Statist. Data Anal., 51 (10), pp. 5192-5204. , Jun; Cohen, J., (1988) Statistical Power Analysis for the Behavioral Sciences, 2nd Ed, , New York, NY, USA: Academic","Fuse, Y.; Graduate School of Science and Engineering, Japan; email: k359679@kansai-u.ac.jp",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,21693536,,,,"English","IEEE Access",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85082929962
"Marras M., Marín-Reyes P.A., Lorenzo-Navarro J., Castrillón-Santana M., Fenu G.","57193883534;57191274555;15042453800;22333278500;24469552000;","Deep Multi-biometric Fusion for Audio-Visual User Re-Identification and Verification",2020,"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)","11996 LNCS",,,"136","157",,1,"10.1007/978-3-030-40014-9_7","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079549512&doi=10.1007%2f978-3-030-40014-9_7&partnerID=40&md5=1241dd15458a9a1caec42fbb30673a24","Department of Mathematics and Computer Science, University of Cagliari, V. Ospedale 72, Cagliari, 09124, Italy; Instituto Universitario Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (SIANI), Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, Las Palmas de Gran Canaria, 35017, Spain","Marras, M., Department of Mathematics and Computer Science, University of Cagliari, V. Ospedale 72, Cagliari, 09124, Italy; Marín-Reyes, P.A., Instituto Universitario Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (SIANI), Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, Las Palmas de Gran Canaria, 35017, Spain; Lorenzo-Navarro, J., Instituto Universitario Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (SIANI), Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, Las Palmas de Gran Canaria, 35017, Spain; Castrillón-Santana, M., Instituto Universitario Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (SIANI), Universidad de Las Palmas de Gran Canaria, Campus Universitario de Tafira, Las Palmas de Gran Canaria, 35017, Spain; Fenu, G., Department of Mathematics and Computer Science, University of Cagliari, V. Ospedale 72, Cagliari, 09124, Italy","From border controls to personal devices, from online exam proctoring to human-robot interaction, biometric technologies are empowering individuals and organizations with convenient and secure authentication and identification services. However, most biometric systems leverage only a single modality, and may face challenges related to acquisition distance, environmental conditions, data quality, and computational resources. Combining evidence from multiple sources at a certain level (e.g., sensor, feature, score, or decision) of the recognition pipeline may mitigate some limitations of the common uni-biometric systems. Such a fusion has been rarely investigated at intermediate level, i.e., when uni-biometric model parameters are jointly optimized during training. In this chapter, we propose a multi-biometric model training strategy that digests face and voice traits in parallel, and we explore how it helps to improve recognition performance in re-identification and verification scenarios. To this end, we design a neural architecture for jointly embedding face and voice data, and we experiment with several training losses and audio-visual datasets. The idea is to exploit the relation between voice characteristics and facial morphology, so that face and voice uni-biometric models help each other to recognize people when trained jointly. Extensive experiments on four real-world datasets show that the biometric feature representation of a uni-biometric model jointly trained performs better than the one computed by the same uni-biometric model trained alone. Moreover, the recognition results are further improved by embedding face and voice data into a single shared representation of the two modalities. The proposed fusion strategy generalizes well on unseen and unheard users, and should be considered as a feasible solution that improves model performance. We expect that this chapter will support the biometric community to shape the research on deep audio-visual fusion in real-world contexts. © 2020, Springer Nature Switzerland AG.","Audio-visual learning; Cross-modal biometrics; Deep biometric fusion; Multi-biometric system; Re-identification; Verification","Embeddings; Human robot interaction; Speech recognition; Verification; Biometric fusion; Computational resources; Cross-modal; Environmental conditions; Multibiometric systems; Re identifications; Secure authentications; Shared representations; Biometrics",,,,,"CEI2018-4; TIN2015-64395-R; Ministerio de Ciencia, Innovación y Universidades, MCIU: RTI2018-093337-B-I00; European Social Fund, ESF; Universidad de Las Palmas de Gran Canaria, ULPGC; Regione Autonoma della Sardegna","Mirko Marras gratefully acknowledges Sardinia Regional Government for the financial support of his PhD scholarship (P.O.R. Sardegna F.S.E. Operational Programme of the Autonomous Region of Sardinia, European Social Fund 2014?2020, Axis III ?Education and Training?, Thematic Goal 10, Priority of Investment 10ii, Specific Goal 10.5). This research work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TIN2015-64395-R MINECO/FEDER) and the Spanish Ministry of Science, Innovation and Universities (RTI2018-093337-B-I00), by the Office of Economy, Industry, Commerce and Knowledge of the Canary Islands Government (CEI2018-4), and the Computer Science Department at the Universidad de Las Palmas de Gran Canaria.",,,,,,,,,,"Abozaid, A., Haggag, A., Kasban, H., Eltokhy, M., Multimodal biometric scheme for human authentication technique based on voice and face recognition fusion (2018) Multimed. Tools Appl., 78, pp. 1-17; Barra, S., Casanova, A., Fraschini, M., Nappi, M., Fusion of physiological measures for multimodal biometric systems (2017) Multimed. 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Intell., 35 (3), pp. 653-668; Zheng, Y., Pal, D.K., Savvides, M., Ring loss: Convex feature normalization for face recognition (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5089-5097. , pp; Zhong, Y., Deng, Y., A survey on keystroke dynamics biometrics: Approaches, advances, and evaluations (2015) Recent Advances in User Authentication Using Keystroke Dynamics Biometrics, pp. 1-22. , pp","Marín-Reyes, P.A.; Instituto Universitario Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (SIANI), Campus Universitario de Tafira, Spain; email: pedro.marin102@alu.ulpgc.es","De Marsico M.Sanniti di Baja G.Fred A.Fred A.",,"Springer","8th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2019","19 February 2019 through 21 February 2019",,236709,03029743,9783030400132,,,"English","Lect. Notes Comput. Sci.",Conference Paper,"Final","",Scopus,2-s2.0-85079549512
"Ciardo F., Beyer F., De Tommaso D., Wykowska A.","57164931800;55446108200;36727098400;15924284200;","Attribution of intentional agency towards robots reduces one's own sense of agency",2020,"Cognition","194",,"104109","","",,15,"10.1016/j.cognition.2019.104109","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074163224&doi=10.1016%2fj.cognition.2019.104109&partnerID=40&md5=ddd32ad98e4845d6e2c861c9baa96db1","Italian Institute of Technology, Genoa, Italy; Queen Mary University, London, United Kingdom","Ciardo, F., Italian Institute of Technology, Genoa, Italy; Beyer, F., Queen Mary University, London, United Kingdom; De Tommaso, D., Italian Institute of Technology, Genoa, Italy; Wykowska, A., Italian Institute of Technology, Genoa, Italy","In the presence of others, sense of agency (SoA), i.e. the perceived relationship between our own actions and external events, is reduced. The present study aimed at investigating whether the phenomenon of reduced SoA is observed in human-robot interaction, similarly to human-human interaction. To this end, we tested SoA when people interacted with a robot (Experiment 1), with a passive, non-agentic air pump (Experiment 2), or when they interacted with both a robot and a human being (Experiment 3). Participants were asked to rate the perceived control they felt on the outcome of their action while performing a diffusion of responsibility task. Results showed that the intentional agency attributed to the artificial entity differently affect the performance and the perceived SoA on the outcome of the task. Experiment 1 showed that, when participants successfully performed an action, they rated SoA over the outcome as lower in trials in which the robot was also able to act (but did not), compared to when they were performing the task alone. However, this did not occur in Experiment 2, where the artificial entity was an air pump, which had the same influence on the task as the robot, but in a passive manner and thus lacked intentional agency. Results of Experiment 3 showed that SoA was reduced similarly for the human and robot agents, threby indicating that attribution of intentional agency plays a crucial role in reduction of SoA. Together, our results suggest that interacting with robotic agents affects SoA, similarly to interacting with other humans, but differently from interacting with non-agentic mechanical devices. This has important implications for the applied of social robotics, where a subjective decrease in SoA could have negative consequences, such as in robot-assisted care in hospitals. © 2019 The Authors","Diffusion of responsibility; Human-robot interaction; Intentional agency; Sense of agency","adult; article; diffusion; female; human; human experiment; male; responsibility; robotics; behavior; motor activity; physiology; psychomotor performance; social interaction; young adult; Adult; Female; Humans; Intention; Male; Motor Activity; Psychomotor Performance; Robotics; Social Interaction; Young Adult",,,,,"Horizon 2020 Framework Programme, H2020: 715058; European Research Council, ERC","This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant awarded to AW, titled “InStance: Intentional Stance for Social Attunement”. Grant agreement No: 715058)","This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant awarded to AW, titled ?InStance: Intentional Stance for Social Attunement?. Grant agreement No: 715058)",,,,,,,,,"Bandura, A., Social cognitive theory of self-regulation (1991) Organizational Behavior and Human Decision Processes, 50 (2), pp. 248-287; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) International Journal of Social Robotics, 1 (1), pp. 71-81; Bates, D., Maechler, M., Bolker, B., Walker, S., lme4: Linear mixed-effects models using Eigen and S4 (2014) R package version, 1 (7), pp. 1-23; Berberian, B., Sarrazin, J.C., Le Blaye, P., Haggard, P., Automation technology and sense of control: A window on human agency (2012) PloS One, 7 (3); Beyer, F., Sidarus, N., Bonicalzi, S., Haggard, P., Beyond self-serving bias: Diffusion of responsibility reduces sense of agency and outcome monitoring (2017) Social Cognitive and Affective Neuroscience, 12 (1), pp. 138-145; Beyer, F., Sidarus, N., Fleming, S., Haggard, P., Losing control in social situations: How the presence of others affects neural processes related to sense of agency (2018) eNeuro, , ENEURO-0336; Chambon, V., Sidarus, N., Haggard, P., From action intentions to action effects: How does the sense of agency come about? (2014) Frontiers in Human Neuroscience, 8, p. 320; Chaminade, T., Rosset, D., Da Fonseca, D., Nazarian, B., Lutscher, E., Cheng, G., Deruelle, C., How do we think machines think? An fMRI study of alleged competition with an artificial intelligence (2012) Frontiers in Human Neuroscience, 6, p. 103; Chekroun, P., Brauer, M., The bystander effect and social control behavior: The effect of the presence of others on people's reactions to norm violations (2002) European Journal of Social Psychology, 32 (6), pp. 853-867; Ciardo, F., Wykowska, A., Response coordination emerges in cooperative but not competitive joint task (2018) Frontiers in Psychology, 9, p. 1919; Ciardo, F., De Tommaso, D., Beyer, F., Wykowska, A., Reduced sense of agency in human-robot interaction (2018) International conference on social robotics, pp. 441-450. , November Springer Cham; Dennett, D.C., Intentional systems (1971) The Journal of Philosophy, 68, pp. 87-106; Dennett, D.C., True believers: The intentional strategy and why it works (1981), MITpress Cambridge,MA; Duffy, B.R., Joue, G., Intelligent robots: The question of embodiment (2000) Proceedings of the Brain-Machine Workshop; Efron, B., Tibshirani, R.J., An introduction to the bootstrap (1994), CRC press; Engbert, K., Wohlschläger, A., Haggard, P., Who is causing what? The sense of agency is relational and efferent-triggered (2008) Cognition, 107 (2), pp. 693-704; Gallagher, H.L., Jack, A.I., Roepstorff, A., Frith, C.D., Imaging the intentional stance in a competitive game (2002) Neuroimage, 16 (3), pp. 814-821; Gray, H.M., Gray, K., Wegner, D.M., Dimensions of mind perception (2007) Science, 315 (5812), p. 619; Haggard, P., Sense of agency in the human brain (2017) Nature Reviews Neuroscience, 18 (4), p. 196; Heider, F., Simmel, M., An experimental study of apparent behavior (1944) The American Journal of Psychology, 57 (2), pp. 243-259; Khalighinejad, N., Bahrami, B., Caspar, E.A., Haggard, P., Social transmission of experience of agency: An experimental study (2016) Frontiers in Psychology, 7, p. 1315; Krach, S., Hegel, F., Wrede, B., Sagerer, G., Binkofski, F., Kircher, T., Can machines think? Interaction and perspective taking with robots investigated via fMRI (2008) PloS One, 3 (7), p. e2597; Kuznetsova, A., Brockhoff, P.B., Christensen, R.H.B., Package ‘lmertest’ (2015) R package version, 2; Latane, B., Darley, J.M., Group inhibition of bystander intervention in emergencies (1968) Journal of Personality and Social Psychology, 10 (3), p. 215; Marchesi, S., Ghiglino, D., Ciardo, F., Perez-Osorio, J., Baykara, E., Wykowska, A., Do we adopt the intentional stance toward humanoid robots? (2019) Frontiers in Psychology, 10, p. 450; Mathôt, S., Schreij, D., Theeuwes, J., OpenSesame: An open-source, graphical experiment builder for the social sciences (2012) Behavior Research Methods, 44 (2), pp. 314-324; Obhi, S.S., Hall, P., Sense of agency in joint action: Influence of human and computer co-actors (2011) Experimental Brain Research, 211 (3-4), pp. 663-670; Sahaï, A., Desantis, A., Grynszpan, O., Pacherie, E., Berberian, B., Action co-representation and the sense of agency during a joint Simon task: Comparing human and machine co-agents (2019) Consciousness and Cognition, 67, pp. 44-55; Sebanz, N., Knoblich, G., Prinz, W., Representing others’ actions: Just like one's own? (2003) Cognition, 88 (3), pp. B11-B21; Sidarus, N., Vuorre, M., Haggard, P., How action selection influences the sense of agency: An ERP study (2017) NeuroImage, 150, pp. 1-13; Stenzel, A., Chinellato, E., Bou, M.A.T., del Pobil, Á., Lappe, M., Liepelt, R., When humanoid robots become human-like interaction partners: Corepresentation of robotic actions (2012) Journal of Experimental Psychology Human Perception and Performance, 38 (5), p. 1073; Takahashi, H., Terada, K., Morita, T., Suzuki, S., Haji, T., Kozima, H., Naito, E., Different impressions of other agents obtained through social interaction uniquely modulate dorsal and ventral pathway activities in the social human brain (2014) Cortex, 58, pp. 289-300; Wang, Y., Quadflieg, S., In our own image? Emotional and neural processing differences when observing human–human vs human–robot interactions (2015) Social Cognitive and Affective Neuroscience, 10 (11), pp. 1515-1524; Ward, E., Ganis, G., Bach, P., Spontaneous vicarious perception of the content of another's visual perspective (2019) Current Biology; Wen, W., Does delay in feedback diminish sense of agency? A review (2019) Consciousness and Cognition, 73; Wiese, E., Wykowska, A., Zwickel, J., Müller, H.J., I see what you mean: How attentional selection is shaped by ascribing intentions to others (2012) PloS One, 7 (9); Wohlschläger, A., Haggard, P., Gesierich, B., Prinz, W., The perceived onset time of self-and other-generated actions (2003) Psychological Science, 14 (6), pp. 586-591; Wykowska, A., Wiese, E., Prosser, A., Müller, H.J., Beliefs about the minds of others influence how we process sensory information (2014) PloS One, 9 (4); Zwickel, J., Agency attribution and visuospatial perspective taking (2009) Psychonomic Bulletin & Review, 16 (6), pp. 1089-1093","Ciardo, F.; Istituto Italiano di Tecnologia, Via E. Melen, 83, Italy; email: francesca.ciardo@iit.it",,,"Elsevier B.V.",,,,,00100277,,CGTNA,"31675616","English","Cognition",Article,"Final","All Open Access, Hybrid Gold, Green",Scopus,2-s2.0-85074163224
"Mohamed S.C., Rajaratnam S., Hong S.T., Nejat G.","57189006913;57200407046;57214130184;12789569200;","Person Finding: An Autonomous Robot Search Method for Finding Multiple Dynamic Users in Human-Centered Environments",2020,"IEEE Transactions on Automation Science and Engineering","17","1","8790973","433","449",,3,"10.1109/TASE.2019.2928774","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078232798&doi=10.1109%2fTASE.2019.2928774&partnerID=40&md5=1306088b4ef28e0ebda8af94a4f278f8","Department of Mechanical and Industrial Engineering, Autonomous Systems and Biomechatronics Laboratory (ASBLab), University of Toronto, Toronto, ON  M5S3G8, Canada","Mohamed, S.C., Department of Mechanical and Industrial Engineering, Autonomous Systems and Biomechatronics Laboratory (ASBLab), University of Toronto, Toronto, ON  M5S3G8, Canada; Rajaratnam, S., Department of Mechanical and Industrial Engineering, Autonomous Systems and Biomechatronics Laboratory (ASBLab), University of Toronto, Toronto, ON  M5S3G8, Canada; Hong, S.T., Department of Mechanical and Industrial Engineering, Autonomous Systems and Biomechatronics Laboratory (ASBLab), University of Toronto, Toronto, ON  M5S3G8, Canada; Nejat, G., Department of Mechanical and Industrial Engineering, Autonomous Systems and Biomechatronics Laboratory (ASBLab), University of Toronto, Toronto, ON  M5S3G8, Canada","Robot search for multiple dynamic users within a multi-room environment is important for social robots to find and engage in various human-robot interaction scenarios with these users. In this paper, we present a novel autonomous person search technique for a robot finding a group of dynamic users before a deadline. The uniqueness of our approach is that unlike existing robot search methods, we consider activity information to predict where, when, and for how long a user will be in a specific room. This allows for the generation of search plans without any assumption on the frequency of user movements. We represent our search problem as an extension of the orienteering problem (OP), which we define herein as the robot person search OP (PSOP). User activity information is represented as spatial-temporal user activity probability density functions (APDFs). We solve the PSOP using APDFs to generate a search plan to maximize the expected number of users found before the deadline. The solution of the PSOP is obtained in two steps. First, by solving a variant of the multiperiod knapsack problem to determine which rooms should be searched and for how long these rooms should be searched. Then, we solve the traveling salesman problem to obtain the order in which to search these rooms. Experiments were conducted to validate the performance of our robot search method in finding different numbers of multiple dynamic users for varying environment sizes and search durations. We also compared our method with two coverage planners and a Markov decision process planner. On average, our planner found more users than the other planners for a variety of scenarios. Finally, we performed experiments that introduced uncertainty into both the APDFs as well as during the search to validate the robustness of our overall approach. Note to Practitioners - The majority of current social robot applications either consider users being collocated with the robot in the same region or users being static within another region in the environment. However, several applications exist where users are dynamic within their environments and for which a robot needs to find them in order to provide assistance, for example, in office buildings, airports, museums, hospitals, and long-term care facilities. In general, these users are performing activities within these regions. We uniquely consider such activity information in order to model user location probabilities. We developed a robot search planner that uses these probabilities to find users of interest in multi-room environments. The planner is novel as it reasons about when and which regions to search and for how long, as well as if the same region needs to be searched multiple times as users can perform multiple activities during the search time frame in the same region or revisit a region to perform a new activity. We have integrated the search planner within a robot system architecture. The robot travels to each region and then uses a local planner to navigate to locations within the region. At each location, a person identification technique is used to identify the target users in order to engage in human-robot interactions. Experiments were performed for two search applications: 1) a simulated Blueberry robot finding multiple residents in a virtual representation of one of our collaborating long-term care facilities and 2) the physical Blueberry robot finding multiple staff/students on a physical floor of a university building. For both experiments, plans were generated on the robot's onboard Lenovo Thinkpad X230 using the robot operating system (ROS) in Ubuntu. User activity data and maps used for the experiments in the care facility can be found on our website (http://asblab.mie.utoronto.ca/research-areas/person-search-human-centered-environments), under multi-user robot search. The physical Blueberry robot was also equipped with an ASUS Xtion IR depth camera, a Logitech pro c920 RGB camera, and a Hokuyo laser range finder for person identification and navigation in the environment. The results showed that our system was effective in finding multiple dynamic users under varying environment sizes and search durations. Our search planner also outperformed other planners and was robust to uncertainties in the user model. Future work will consider environments with multiple floors and crowded regions, planners that directly reason about environment dynamics, and local planners that reason about user location probabilities within regions. © 2004-2012 IEEE.","Human-robot interaction (HRI); multiple dynamic users; orienteering problem (OP); search plans; social robots","Cameras; College buildings; Combinatorial optimization; Dynamics; Floors; Hospitals; Location; Man machine systems; Markov processes; Office buildings; Planning; Probability density function; Range finders; Robot applications; Search engines; Traveling salesman problem; Human robot Interaction (HRI); multiple dynamic users; Orienteering problem; Search plans; Social robots; Human robot interaction",,,,,"Ontario Centres of Excellence, OCE; Consortium canadien en neurodégénérescence associée au vieillissement, CCNA; Natural Sciences and Engineering Research Council of Canada, NSERC; Canada Research Chairs","This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC), in part by Dr. Robot Inc., in part by the Ontario Centres of Excellence (OCE), in part by the Canadian Consortium on Neurodegeneration in Aging (CCNA), and in part by the Canada Research Chairs (CRC) Program.","Manuscript received October 1, 2018; revised March 8, 2019; accepted June 26, 2019. Date of publication August 7, 2019; date of current version January 9, 2020. This paper was recommended for publication by Associate Editor J. Burgner-Kahrs and Editor J. Wen upon evaluation of the reviewers’ comments. This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC), in part by Dr. Robot Inc., in part by the Ontario Centres of Excellence (OCE), in part by the Canadian Consortium on Neurodegeneration in Aging (CCNA), and in part by the Canada Research Chairs (CRC) Program. (Corresponding author: Sharaf C. Mohamed.) 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"Winkle K., Senft E., Lemaignan S.","57201504964;56032346700;18234152100;","LEADOR: A Method for End-To-End Participatory Design of Autonomous Social Robots",2021,"Frontiers in Robotics and AI","8",,"704119","","",,,"10.3389/frobt.2021.704119","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121438565&doi=10.3389%2ffrobt.2021.704119&partnerID=40&md5=c4176e97ec8a7658d4261f8bcd454dc5","Division of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden; Department of Computer Sciences, University of Wisconsin–Madison, Madison, WI, United States; Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom","Winkle, K., Division of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden; Senft, E., Department of Computer Sciences, University of Wisconsin–Madison, Madison, WI, United States; Lemaignan, S., Bristol Robotics Laboratory, University of the West of England, Bristol, United Kingdom","Participatory design (PD) has been used to good success in human-robot interaction (HRI) but typically remains limited to the early phases of development, with subsequent robot behaviours then being hardcoded by engineers or utilised in Wizard-of-Oz (WoZ) systems that rarely achieve autonomy. In this article, we present LEADOR (Led-by-Experts Automation and Design Of Robots), an end-to-end PD methodology for domain expert co-design, automation, and evaluation of social robot behaviour. This method starts with typical PD, working with the domain expert(s) to co-design the interaction specifications and state and action space of the robot. It then replaces the traditional offline programming or WoZ phase by an in situ and online teaching phase where the domain expert can live-program or teach the robot how to behave whilst being embedded in the interaction context. We point out that this live teaching phase can be best achieved by adding a learning component to a WoZ setup, which captures implicit knowledge of experts, as they intuitively respond to the dynamics of the situation. The robot then progressively learns an appropriate, expert-approved policy, ultimately leading to full autonomy, even in sensitive and/or ill-defined environments. However, LEADOR is agnostic to the exact technical approach used to facilitate this learning process. The extensive inclusion of the domain expert(s) in robot design represents established responsible innovation practice, lending credibility to the system both during the teaching phase and when operating autonomously. The combination of this expert inclusion with the focus on in situ development also means that LEADOR supports a mutual shaping approach to social robotics. We draw on two previously published, foundational works from which this (generalisable) methodology has been derived to demonstrate the feasibility and worth of this approach, provide concrete examples in its application, and identify limitations and opportunities when applying this framework in new environments. Copyright © 2021 Winkle, Senft and Lemaignan.","autonomous robots; HRI; mutual shaping of technology and society; participatory design; robot development; social robotics",,,,,,"Engineering and Physical Sciences Research Council, EPSRC; University of Bristol: EP/L015293/1; Seventh Framework Programme, FP7: 611391","This work was partially supported by the EPSRC via the Centre for Doctoral Training in Future Autonomous and Robotic Systems (FARSCOPE) at the Bristol Robotics Laboratory, University of the West of England and University of Bristol (grant number EP/L015293/1), partially funded by the EU FP7 DREAM project (grant no. 611391) and partially funded by the KTH Digital Futures Research Center.",,,,,,,,,,"Alves-Oliveira, P., Arriaga, P., Paiva, A., Hoffman, G., YOLO, a Robot for Creativity (2017) Proceedings of the 2017 Conference on Interaction Design and Children (IDC ’17), pp. 423-429. , New York, NY, United States, New York, NY, USA, Association for Computing Machinery; Amershi, S., Cakmak, M., Knox, W.B., Kulesza, T., Power to the People: The Role of Humans in Interactive Machine Learning (2014) AIMag, 35, pp. 105-120; Arkin, R.C., Fujita, M., Takagi, T., Hasegawa, R., Ethological Modeling and Architecture for an Entertainment Robot (2001) Proceedings 2001 ICRA. 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Intelligence, 172, pp. 716-737; Valencia, S., Luria, M., Pavel, A., Bigham, J.P., Admoni, H., Co-designing Socially Assistive Sidekicks for Motion-Based AAC (2021) HRI ’21:Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, pp. 24-33. , March 2021, New York, NY, USA, Association for Computing Machinery; Winkle, K., Caleb-Solly, P., Turton, A., Bremner, P., Mutual Shaping in the Design of Socially Assistive Robots: A Case Study on Social Robots for Therapy (2019) Int. J. Soc. Robotics, 12, pp. 847-866. , a; Winkle, K., Caleb-Solly, P., Turton, A., Bremner, P., Social Robots for Engagement in Rehabilitative Therapies (2018) HRI ’18:Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, pp. 289-297. , Chicago, IL, United States, March 2018, New York, NY, USA, ACM; Winkle, K., Lemaignan, S., Caleb-Solly, P., Leonards, U., Turton, A., Bremner, P., Effective Persuasion Strategies for Socially Assistive Robots (2019) 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 277-285. , b, Daegu, South Korea, March 2019; Winkle, K., Lemaignan, S., Caleb-Solly, P., Leonards, U., Turton, A., Bremner, P., In-situ Learning from a Domain Expert for Real World Socially Assistive Robot Deployment (2020) Proceedings of Robotics: Science and Systems 2020, , in; Wu, Y.-H., Fassert, C., Rigaud, A.-S., Designing Robots for the Elderly: Appearance Issue and beyond (2012) Arch. Gerontol. Geriatr, 54, pp. 121-126","Winkle, K.; Division of Robotics, Sweden; email: winkle@kth.se
Senft, E.; Department of Computer Sciences, United States; email: esenft@wisc.edu",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Review,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85121438565
"Meyer J.T., Gassert R., Lambercy O.","57210637699;8937521600;21933933900;","An analysis of usability evaluation practices and contexts of use in wearable robotics",2021,"Journal of NeuroEngineering and Rehabilitation","18","1","170","","",,,"10.1186/s12984-021-00963-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120937120&doi=10.1186%2fs12984-021-00963-8&partnerID=40&md5=3a8910c216104f313ec06193f466dfa2","Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland; Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence And Technological Enterprise (CREATE), Singapore, Singapore","Meyer, J.T., Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland; Gassert, R., Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland, Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence And Technological Enterprise (CREATE), Singapore, Singapore; Lambercy, O., Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland, Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence And Technological Enterprise (CREATE), Singapore, Singapore","Background: User-centered design approaches have gained attention over the past decade, aiming to tackle the technology acceptance issues of wearable robotic devices to assist, support or augment human capabilities. While there is a consensus that usability is key to user-centered design, dedicated usability evaluation studies are scarce and clear evaluation guidelines are missing. However, the careful consideration and integration of user needs appears to be essential to successfully develop an effective, efficient, and satisfactory human-robot interaction. It is primarily the responsibility of the developer, to ensure that this users involvement takes place throughout the design process. Methods: Through an online survey for developers of wearable robotics, we wanted to understand how the design and evaluation in actual daily practice compares to what is reported in literature. With a total of 31 questions, we analyzed the most common wearable robotic device applications and their technology maturity, and how these influence usability evaluation practices. Results: A total of 158 responses from a heterogeneous population were collected and analyzed. The dataset representing contexts of use for augmentation (16.5%), assistance (38.0%), therapy (39.8%), as well as few other specific applications (5.7%), allowed for an insightful analysis of the influence of technology maturity on user involvement and usability evaluation. We identified functionality, ease of use, and performance as the most evaluated usability attributes and could specify which measures are used to assess them. Also, we could underline the frequent use of qualitative measures alongside the expected high prevalence of performance-metrics. In conclusion of the analysis, we derived evaluation recommendations to foster user-centered design and usability evaluation. Conclusion: This analysis might serve as state-of-the-art comparison and recommendation for usability studies in wearable robotics. We believe that by motivating for more balanced, comparable and user-oriented evaluation practices, we may support the wearable robotics field in tackling the technology acceptance limitations. © 2021, The Author(s).","Exoskeletons; Usability evaluation; User-centered design; Wearable robotics",,,,,,"National Research Foundation Singapore, NRF; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, SNF; Vontobel-Stiftung; National Centre of Competence in Research Robotics; ETH Zürich Foundation","This work was supported by Vontobel Foundation, the Swiss National Science Foundation through the National Centre of Competence in Research on Robotics, the ETH Zurich Foundation in collaboration with Hocoma AG, and the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program.",,,,,,,,,,"Bao, G., Pan, L., Fang, H., Wu, X., Yu, H., Cai, S., Yu, B., Wan, Y., Academic review and perspectives on robotic exoskeletons (2019) IEEE Trans Neural Syst Rehabil Eng, 27 (11), pp. 2294-2304; Reinkensmeyer, D.J., JNER at 15 years: analysis of the state of neuroengineering and rehabilitation (2019) J NeuroEng Rehabi, 16 (1), p. 144; Xiloyannis, M., Alicea, R., Georgarakis, A.-M., Haufe, F.L., Wolf, P., Masia, L., Riener, R., Soft robotic suits: State of the art, core technologies, and open challenges (2021) IEEE Trans Robotics; Molteni, F., Gasperini, G., Cannaviello, G., Guanziroli, E., Exoskeleton and end-effector robots for upper and lower limbs rehabilitation: narrative review (2018) PM&R, 10, pp. 174-188; McFarland, T., Fischer, S., Considerations for industrial use: a systematic review of the impact of active and passive upper limb exoskeletons on physical exposures (2019) IISE Trans Occup Ergonomics Hum Factors, 7 (3-4), pp. 322-347; Usability: Definitions and concepts. 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NeuroEng. Rehabil.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85120937120
"Eldardeer O., Gonzalez-Billandon J., Grasse L., Tata M., Rea F.","57367727700;57207198944;57204041834;7005632914;54386065400;","A Biological Inspired Cognitive Framework for Memory-Based Multi-Sensory Joint Attention in Human-Robot Interactive Tasks",2021,"Frontiers in Neurorobotics","15",,"648595","","",,,"10.3389/fnbot.2021.648595","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120917179&doi=10.3389%2ffnbot.2021.648595&partnerID=40&md5=085e8e439425010c0a86cd6194f2b4bc","Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi, Università di Genova, Genova, Italy; Robotics, Brain, and Cognitive Science Department, Istituto Italiano di Tecnologia, Genova, Italy; COgNiTive Architecture for Collaborative Technologies, Istituto Italiano di Tecnologia, Genova, Italy; Neuroscience/CCBN Department, The University of Lethbridge, Lethbridge, AB, Canada","Eldardeer, O., Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi, Università di Genova, Genova, Italy, Robotics, Brain, and Cognitive Science Department, Istituto Italiano di Tecnologia, Genova, Italy; Gonzalez-Billandon, J., Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi, Università di Genova, Genova, Italy, COgNiTive Architecture for Collaborative Technologies, Istituto Italiano di Tecnologia, Genova, Italy; Grasse, L., Neuroscience/CCBN Department, The University of Lethbridge, Lethbridge, AB, Canada; Tata, M., Neuroscience/CCBN Department, The University of Lethbridge, Lethbridge, AB, Canada; Rea, F., Robotics, Brain, and Cognitive Science Department, Istituto Italiano di Tecnologia, Genova, Italy","One of the fundamental prerequisites for effective collaborations between interactive partners is the mutual sharing of the attentional focus on the same perceptual events. This is referred to as joint attention. In psychological, cognitive, and social sciences, its defining elements have been widely pinpointed. Also the field of human-robot interaction has extensively exploited joint attention which has been identified as a fundamental prerequisite for proficient human-robot collaborations. However, joint attention between robots and human partners is often encoded in prefixed robot behaviours that do not fully address the dynamics of interactive scenarios. We provide autonomous attentional behaviour for robotics based on a multi-sensory perception that robustly relocates the focus of attention on the same targets the human partner attends. Further, we investigated how such joint attention between a human and a robot partner improved with a new biologically-inspired memory-based attention component. We assessed the model with the humanoid robot iCub involved in performing a joint task with a human partner in a real-world unstructured scenario. The model showed a robust performance on capturing the stimulation, making a localisation decision in the right time frame, and then executing the right action. We then compared the attention performance of the robot against the human performance when stimulated from the same source across different modalities (audio-visual and audio only). The comparison showed that the model is behaving with temporal dynamics compatible with those of humans. This provides an effective solution for memory-based joint attention in real-world unstructured environments. Further, we analyzed the localisation performances (reaction time and accuracy), the results showed that the robot performed better in an audio-visual condition than an audio only condition. The performance of the robot in the audio-visual condition was relatively comparable with the behaviour of the human participants whereas it was less efficient in audio-only localisation. After a detailed analysis of the internal components of the architecture, we conclude that the differences in performance are due to egonoise which significantly affects the audio-only localisation performance. Copyright © 2021 Eldardeer, Gonzalez-Billandon, Grasse, Tata and Rea.","active perception; biological motion control; computational neuroscience; decision-making; human robot interaction; joint attention; memory; multisensory integration","Anthropomorphic robots; Behavioral research; Biomimetics; Decision making; Man machine systems; Sensory perception; Active perceptions; Audio-visual; Biological motion; Biological motion control; Computational neuroscience; Decisions makings; Humans-robot interactions; Joint attention; Multi-Sensory; Multisensory integration; Human robot interaction",,,,,,,,,,,,,,,,"Admoni, H., Scassellati, B., Social eye gaze in human-robot interaction: a review (2017) J. Hum. 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Boakes; Yu, C., Smith, L.B., Joint attention without gaze following: human infants and their parents coordinate visual attention to objects through eye-hand coordination (2013) PLoS ONE, 8, p. e79659. , 24236151","Eldardeer, O.; Dipartimento di Informatica, Italy; email: omar.eldardeer@iit.it",,,"Frontiers Media S.A.",,,,,16625218,,,,"English","Front. Neurorobotics",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85120917179
"Tuomi A., Tussyadiah I.P., Hanna P.","57207467461;8286650200;55171887700;","Spicing up hospitality service encounters: the case of Pepper™",2021,"International Journal of Contemporary Hospitality Management","33","11",,"3906","3925",,5,"10.1108/IJCHM-07-2020-0739","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102183109&doi=10.1108%2fIJCHM-07-2020-0739&partnerID=40&md5=a134d213044a8697f9dea4f9627e7669","School of Hospitality and Tourism Management, University of Surrey, Guildford, UK and Haaga-Helia University of Applied Sciences, Helsinki, Finland; School of Hospitality and Tourism Management, University of Surrey, Guildford, United Kingdom; School of Psychology, University of Surrey, Guildford, United Kingdom","Tuomi, A., School of Hospitality and Tourism Management, University of Surrey, Guildford, UK and Haaga-Helia University of Applied Sciences, Helsinki, Finland; Tussyadiah, I.P., School of Hospitality and Tourism Management, University of Surrey, Guildford, United Kingdom; Hanna, P., School of Psychology, University of Surrey, Guildford, United Kingdom","Purpose: This paper aims to explore the implications of integrating humanoid service robots into hospitality service encounters by evaluating two service prototypes using Softbank Robotics’ popular service robot Pepper™: to provide information (akin to a receptionist) and to facilitate order-taking (akin to a server). Drawing both studies together, the paper puts forward novel, theory-informed yet context-rooted design principles for humanoid robot adoption in hospitality service encounters. Design/methodology/approach: Adopting a multiple method qualitative approach, two service prototypes are evaluated with hospitality and tourism experts (N = 30, Prototype 1) and frontline hospitality employees (N = 18, Prototype 2) using participant observation, in situ feedback, semi-structured interviews and photo-elicitation. Findings: The adoption of humanoid service robots in hospitality is influenced by the following four layers of determinants: contextual, social, interactional and psychological factors, as well as extrinsic and intrinsic drivers of adoption. These empirical findings both confirm and extend previous conceptualizations of human-robot interaction (HRI) in hospitality service. Research limitations/implications: Despite using photo-elicitation to evoke insight regarding the use of different types of service robots in hospitality, the paper mostly focuses on anthropomorphized service robots such as Pepper™. Practical implications: Adopting humanoid service robots will transform hospitality operations, whereby the most routine, unpleasant tasks such as taking repeat orders or dealing with complaints may be delegated to service robots or human-robot teams. Social implications: Working with and receiving service from Pepper™ changes the service encounter from direct practical, technical considerations to more nuanced social and psychological implications, particularly around feelings of self-esteem, social pressure and social judgment. Originality/value: This paper presents one of the first empirical studies on HRI in hospitality service encounters using Softbank Robotics’ Pepper™. In doing so, the paper presents a novel framework for service robot adoption rooted in first-hand user interaction as opposed to previous, theory-driven conceptualizations of behavior or empirical studies exploring behavioral intention. © 2021, Emerald Publishing Limited.","Design; Hospitality management; Human-robot interaction; Pepper™; Service encounter; Service robot adoption",,,,,,,"The authors would like to thank Georgia Blanco-Litchfield for their help with developing code for PepperTM.",,,,,,,,,,"Ashcroft, E., Tuomi, A., Wang, M., Solnet, D., Resistance to the adoption of ICTs in independent restaurants: insights from China and the UK (2019) e-Review of Tourism Research, 16 (2-3), pp. 105-114; Baum, T., Human resources in tourism: still waiting for change? A 2015 reprise (2015) Tourism Management, 50, pp. 204-212; Belanche, D., Casaló, L., Flavián, C., Frontline robots in tourism and hospitality: service enhancement or cost reduction? 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J. Contemp. Hosp. Manage.",Article,"Final","",Scopus,2-s2.0-85102183109
"Nichols E., Gao L., Vasylkiv Y., Gomez R.","57196921189;57220176595;57192275802;12806763700;","Design and Analysis of a Collaborative Story Generation Game for Social Robots",2021,"Frontiers in Computer Science","3",,"674333","","",,,"10.3389/fcomp.2021.674333","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117892495&doi=10.3389%2ffcomp.2021.674333&partnerID=40&md5=65135efa36133a5b9157baf39b3a96b0","Honda Research Institute Japan Co., Ltd, Wakoshi, Japan; EleutherAI, Edmonton, AB, Canada; HCI Lab, University of Manitoba, Winnipeg, MB, Canada","Nichols, E., Honda Research Institute Japan Co., Ltd, Wakoshi, Japan; Gao, L., EleutherAI, Edmonton, AB, Canada; Vasylkiv, Y., HCI Lab, University of Manitoba, Winnipeg, MB, Canada; Gomez, R., Honda Research Institute Japan Co., Ltd, Wakoshi, Japan","Storytelling plays a central role in human socializing and entertainment, and research on conducting storytelling with robots is gaining interest. However, much of this research assumes that story content is curated. In this paper, we introduce the task of collaborative story generation, where an artificial intelligence agent, or a robot, and a person collaborate to create a unique story by taking turns adding to it. We present a collaborative story generation system which works with a human storyteller to create a story by generating new utterances based on the story so far. Our collaborative story generation system consists of a publicly-available large scale language model that was tuned on a dataset of writing prompts and short stories, and a ranker that samples from the language model and chooses the best possible output. We improve storytelling quality by optimizing the ranker’s sample size to strike a balance between quality and computational cost. Since latency can be detrimental to human-robot interaction, we examine the performance-latency trade-offs of our approach and find the optimal ranker sample size that strikes the best balance between quality and computational cost. We evaluate our system by having human participants play the collaborative story generation game and comparing the stories they create with our system to a naive baseline. Next, we conduct a detailed elicitation survey that sheds light on issues to consider when adapting our collaborative story generation system to a social robot. Finally, in a first step towards allowing human players to control the genre or mood of stories generated, we present preliminary work on steering story generation sentiment polarity with a sentiment analysis model. We find that our proposed method achieves a good balance of steering capability and text coherence. Our evaluation shows that participants have a positive view of collaborative story generation with a social robot and consider rich, emotive capabilities to be key to an enjoyable experience. © Copyright © 2021 Nichols, Gao, Vasylkiv and Gomez.","AI agents; interactivity; language models; social robotics; storytelling",,,,,,,"The work presented here includes content that was previously presented at the 13th Annual ACM SIGGRAPH Conference on Motion, Interaction and Games (Nichols et al., 2020) and work that is to appear at the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (Nichols et al., 2021) and is reproduced here with permission.",,,,,,,,,,"Barbieri, F., Camacho-Collados, J., Espinosa-Anke, L., Neves, L., TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification (2020) Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 1644-1650. , Online, Association for Computational Linguistics; Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C., A Neural Probabilistic Language Model (2003) J. machine Learn. 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Comput. Sci.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85117892495
"Fosch-Villaronga E., Mahler T.","57189046483;56436244300;","Cybersecurity, safety and robots: Strengthening the link between cybersecurity and safety in the context of care robots",2021,"Computer Law and Security Review","41",,"105528","","",,5,"10.1016/j.clsr.2021.105528","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102615126&doi=10.1016%2fj.clsr.2021.105528&partnerID=40&md5=49a9a9a79abc8800995412a11a0dd6e3","eLaw Center for Law and Digital Technologies, Leiden University, Leiden, Netherlands; Norwegian Research Center for Computers and Law, University of OsloOslo, Norway, Norway","Fosch-Villaronga, E., eLaw Center for Law and Digital Technologies, Leiden University, Leiden, Netherlands; Mahler, T., Norwegian Research Center for Computers and Law, University of OsloOslo, Norway, Norway","This paper addresses the interplay between robots, cybersecurity, and safety from a European legal perspective, a topic under-explored by current technical and legal literature. The legal framework, together with technical standards, is a necessary parameter for the production and deployment of robots. However, European law does not regulate robots as such, and there exist multiple and overlapping legal requirements focusing on specific contexts, such as product safety and medical devices. Besides, the recently enacted European Cybersecurity Act establishes a cybersecurity certification framework, which could be used to define cybersecurity requirements for robots, although concrete cyber-physical implementation requirements are not yet prescribed. In this article, we illustrate cybersecurity challenges and their subsequent safety implications with the concrete example of care robots. These robots interact in close, direct contact with children, elderly, and persons with disabilities, and a malfunctioning or cybersecurity threat may affect the health and well-being of these people. Moreover, care robots may process vast amounts of data, including health and behavioral data, which are especially sensitive in the healthcare domain. Security vulnerabilities in robots thus raise significant concerns, not only for manufacturers and programmers, but also for those who interact with them, especially in sensitive applications such as healthcare. While the latest European policymaking efforts on robot regulation acknowledge the importance of cybersecurity, many details, and their impact on user safety have not yet been addressed in depth. Our contribution aims to answer the question whether the current European legal framework is prepared to address cyber and physical risks from care robots and ensure safe human–robot interactions in such a sensitive context. Cybersecurity and physical product safety legal requirements are governed separately in a dual regulatory framework, presenting a challenge in governing uniformly and adequately cyber-physical systems such as care robots. We conceptualize and discuss the challenges of regulating cyber-physical systems’ security with the current dual framework, particularly the lack of mandatory certifications. We conclude that policymakers need to consider cybersecurity as an indissociable aspect of safety to ensure robots are truly safe to use. © 2021 The Authors","Connected products; Cybersecurity; GDPR; Healthcare; Human–robot interaction; Medical Devices; NIS Directive; Product Safety; Robots; Safety","Concretes; Cyber Physical System; Embedded systems; Health care; Health risks; Laws and legislation; Medical robotics; Security of data; Healthcare domains; Legal requirements; Persons with disabilities; Regulatory frameworks; Robot interactions; Security vulnerabilities; Sensitive application; Technical standards; Human robot interaction",,,,,"707404; Horizon 2020; 144789, 24794 7; Norges Forskningsråd","Part of this project was funded by the LEaDing Fellows Marie Curie COFUND fellowship, a project that has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement No. 707404 . This research was also financed by the project “Vulnerability in the Robot Society” ( VIROS , grant number 144789 ) and the project “Security in Internet Governance and Networks: Analysing the Law ( SIGNAL , grant number 24794 7), both financed by the Research Council of Norway. The authors wish to thank Lee Bygrave, Peter Davis, Luca Tosoni, Ida Christiane Hunsbedt, Carlos J. Calleja and Gino D'Paola Puche from the University of Oslo, as well as two anonymous reviewers for useful comments on an earlier version of this paper. The usual disclaimer applies nevertheless.",,,,,,,,,,"Alemzadeh, H., Raman, J., Leveson, N., Kalbarczyk, Z., Iyer, R.K., Adverse events in robotic surgery: a retrospective study of 14 years of FDA data (2016) PLoS One, 11 (4); Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., (2016), Mané, D. 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"Spaulding S., Shen J., Park H.W., Breazeal C.","56085336700;57224831212;57191796621;7003691791;","Lifelong Personalization via Gaussian Process Modeling for Long-Term HRI",2021,"Frontiers in Robotics and AI","8",,"683066","","",,,"10.3389/frobt.2021.683066","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108331937&doi=10.3389%2ffrobt.2021.683066&partnerID=40&md5=d063382b0327b65a6b62347d8af9d26b","Massachusetts Institute of Technology, Cambridge, MA, United States","Spaulding, S., Massachusetts Institute of Technology, Cambridge, MA, United States; Shen, J., Massachusetts Institute of Technology, Cambridge, MA, United States; Park, H.W., Massachusetts Institute of Technology, Cambridge, MA, United States; Breazeal, C., Massachusetts Institute of Technology, Cambridge, MA, United States","Across a wide variety of domains, artificial agents that can adapt and personalize to users have potential to improve and transform how social services are provided. Because of the need for personalized interaction data to drive this process, long-term (or longitudinal) interactions between users and agents, which unfold over a series of distinct interaction sessions, have attracted substantial research interest. In recognition of the expanded scope and structure of a long-term interaction, researchers are also adjusting the personalization models and algorithms used, orienting toward “continual learning” methods, which do not assume a stationary modeling target and explicitly account for the temporal context of training data. In parallel, researchers have also studied the effect of “multitask personalization,” an approach in which an agent interacts with users over multiple different tasks contexts throughout the course of a long-term interaction and learns personalized models of a user that are transferrable across these tasks. In this paper, we unite these two paradigms under the framework of “Lifelong Personalization,” analyzing the effect of multitask personalization applied to dynamic, non-stationary targets. We extend the multi-task personalization approach to the more complex and realistic scenario of modeling dynamic learners over time, focusing in particular on interactive scenarios in which the modeling agent plays an active role in teaching the student whose knowledge the agent is simultaneously attempting to model. Inspired by the way in which agents use active learning to select new training data based on domain context, we augment a Gaussian Process-based multitask personalization model with a mechanism to actively and continually manage its own training data, allowing a modeling agent to remove or reduce the weight of observed data from its training set, based on interactive context cues. We evaluate this method in a series of simulation experiments comparing different approaches to continual and multitask learning on simulated student data. We expect this method to substantially improve learning in Gaussian Process models in dynamic domains, establishing Gaussian Processes as another flexible modeling tool for Long-term Human-Robot Interaction (HRI) Studies. © Copyright © 2021 Spaulding, Shen, Park and Breazeal.","adaptive personalization; continual learning; educational games; Gaussian processes; human-robot interaction; lifelong learning; transfer learning; user modeling",,,,,,"National Science Foundation, NSF: IIP-1717362, IIS-1734443","This work was supported by NSF Grant IIS-1734443 and IIP-1717362.",,,,,,,,,,"Baker, R.S., Challenges for the Future of Educational Data Mining: The baker Learning Analytics Prizes (2019) JEDM— J. Educ. 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Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85108331937
"Bessler J., Prange-Lasonder G.B., Schaake L., Saenz J.F., Bidard C., Fassi I., Valori M., Lassen A.B., Buurke J.H.","57204240176;14045673000;24829528200;57190879394;9236211000;6506998602;39362641500;57223139063;26326698400;","Safety Assessment of Rehabilitation Robots: A Review Identifying Safety Skills and Current Knowledge Gaps",2021,"Frontiers in Robotics and AI","8",,"602878","","",,7,"10.3389/frobt.2021.602878","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104943313&doi=10.3389%2ffrobt.2021.602878&partnerID=40&md5=203155725bd8aef8a4b7df616f497d15","Roessingh Research and Development, Enschede, Netherlands; Department of Biomedical Signals and Systems, University of Twente, Enschede, Netherlands; Department of Biomechanical Engineering, University of Twente, Enschede, Netherlands; Fraunhofer Institute for Factory Operation and Automation, Magdeburg, Germany; Interactive Robotics Laboratory, CEA LIST, Palaiseau, France; National Research Council of Italy, Milan, Italy; Department of Robot Technology, Danish Technological Institute, Odense, Denmark","Bessler, J., Roessingh Research and Development, Enschede, Netherlands, Department of Biomedical Signals and Systems, University of Twente, Enschede, Netherlands; Prange-Lasonder, G.B., Roessingh Research and Development, Enschede, Netherlands, Department of Biomechanical Engineering, University of Twente, Enschede, Netherlands; Schaake, L., Roessingh Research and Development, Enschede, Netherlands; Saenz, J.F., Fraunhofer Institute for Factory Operation and Automation, Magdeburg, Germany; Bidard, C., Interactive Robotics Laboratory, CEA LIST, Palaiseau, France; Fassi, I., National Research Council of Italy, Milan, Italy; Valori, M., National Research Council of Italy, Milan, Italy; Lassen, A.B., Department of Robot Technology, Danish Technological Institute, Odense, Denmark; Buurke, J.H., Roessingh Research and Development, Enschede, Netherlands, Department of Biomedical Signals and Systems, University of Twente, Enschede, Netherlands","The assessment of rehabilitation robot safety is a vital aspect of the development process, which is often experienced as difficult. There are gaps in best practices and knowledge to ensure safe usage of rehabilitation robots. Currently, safety is commonly assessed by monitoring adverse events occurrence. The aim of this article is to explore how safety of rehabilitation robots can be assessed early in the development phase, before they are used with patients. We are suggesting a uniform approach for safety validation of robots closely interacting with humans, based on safety skills and validation protocols. Safety skills are an abstract representation of the ability of a robot to reduce a specific risk or deal with a specific hazard. They can be implemented in various ways, depending on the application requirements, which enables the use of a single safety skill across a wide range of applications and domains. Safety validation protocols have been developed that correspond to these skills and consider domain-specific conditions. This gives robot users and developers concise testing procedures to prove the mechanical safety of their robotic system, even when the applications are in domains with a lack of standards and best practices such as the healthcare domain. Based on knowledge about adverse events occurring in rehabilitation robot use, we identified multi-directional excessive forces on the soft tissue level and musculoskeletal level as most relevant hazards for rehabilitation robots and related them to four safety skills, providing a concrete starting point for safety assessment of rehabilitation robots. We further identified a number of gaps which need to be addressed in the future to pave the way for more comprehensive guidelines for rehabilitation robot safety assessments. Predominantly, besides new developments of safety by design features, there is a strong need for reliable measurement methods as well as acceptable limit values for human-robot interaction forces both on skin and joint level. © Copyright © 2021 Bessler, Prange-Lasonder, Schaake, Saenz, Bidard, Fassi, Valori, Lassen and Buurke.","development phase; hazards; mechanical safety; physical human-robot interaction; regulation; rehabilitation robots; safety assessment; standardization",,,,,,"Horizon 2020 Framework Programme, H2020: 779966; Horizon 2020","This work as part of the COVR project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 779966.",,,,,,,,,,"Akiyama, Y., Yamada, Y., Ito, K., Oda, S., Okamoto, S., Hara, S., Test method for contact safety assessment of a wearable robot -analysis of load caused by a misalignment of the knee joint (2012) 2012 Ieee ro-man: the 21st IEEE international symposium on robot and human interactive communication, pp. 539-544. , IEEE, Paris, France, September 9-13, 2012. 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Bessler, J.; Department of Biomedical Signals and Systems, Netherlands; email: j.bessler@rrd.nl",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Review,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85104943313
"Weidemann A., Rußwinkel N.","57190274631;36961405400;","The Role of Frustration in Human–Robot Interaction – What Is Needed for a Successful Collaboration?",2021,"Frontiers in Psychology","12",,"640186","","",,2,"10.3389/fpsyg.2021.640186","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103631143&doi=10.3389%2ffpsyg.2021.640186&partnerID=40&md5=e0962bbfb5ef7b121107160f9f8367d5","Cognitive Modeling in Dynamic Human-Machine Systems, Faculty of Mechanical Engineering and Transport Systems, Department of Psychology and Ergonomics, Technical University of Berlin, Berlin, Germany; Junior Research Group MTI-engAge, Control Systems Group, Department of Electrical Engineering and Computer Science, Faculty of Electrical Engineering and Computer Science, Technical University of Berlin, Berlin, Germany","Weidemann, A., Cognitive Modeling in Dynamic Human-Machine Systems, Faculty of Mechanical Engineering and Transport Systems, Department of Psychology and Ergonomics, Technical University of Berlin, Berlin, Germany, Junior Research Group MTI-engAge, Control Systems Group, Department of Electrical Engineering and Computer Science, Faculty of Electrical Engineering and Computer Science, Technical University of Berlin, Berlin, Germany; Rußwinkel, N., Cognitive Modeling in Dynamic Human-Machine Systems, Faculty of Mechanical Engineering and Transport Systems, Department of Psychology and Ergonomics, Technical University of Berlin, Berlin, Germany","To realize a successful and collaborative interaction between human and robots remains a big challenge. Emotional reactions of the user provide crucial information for a successful interaction. These reactions carry key factors to prevent errors and fatal bidirectional misunderstanding. In cases where human–machine interaction does not proceed as expected, negative emotions, like frustration, can arise. Therefore, it is important to identify frustration in a human–machine interaction and to investigate its impact on other influencing factors such as dominance, sense of control and task performance. This paper presents a study that investigates a close cooperative work situation between human and robot, and explore the influence frustration has on the interaction. The task for the participants was to hand over colored balls to two different robot systems (an anthropomorphic robot and a robotic arm). The robot systems had to throw the balls into appropriate baskets. The coordination between human and robot was controlled by various gestures and words by means of trial and error. Participants were divided into two groups, a frustration- (FRUST) and a no frustration- (NOFRUST) group. Frustration was induced by the behavior of the robotic systems which made errors during the ball handover. Subjective and objective methods were used. The sample size of participants was N = 30 and the study was conducted in a between-subject design. Results show clear differences in perceived frustration in the two condition groups and different behavioral interactions were shown by the participants. Furthermore, frustration has a negative influence on interaction factors such as dominance and sense of control. The study provides important information concerning the influence of frustration on human–robot interaction (HRI) for the requirements of a successful, natural, and social HRI. The results (qualitative and quantitative) are discussed in favor of how a successful und effortless interaction between human and robot can be realized and what relevant factors, like appearance of the robot and influence of frustration on sense of control, have to be regarded. © Copyright © 2021 Weidemann and Rußwinkel.","collaboration; frustration; human–robot interaction (HRI); influence; recommendations",,,,,,"Bundesministerium für Bildung und Forschung, BMBF","The research presented in this manuscript was primarily supported by the German Ministry of Education and Research.",,,,,,,,,,"Abd, M.A., Gonzalez, I., Nojoumian, M., Engeberg, E.D., Trust, satisfaction and frustration measurements during human-robot interaction (2017) Proceedings of the 30th Florida Conference on Recent Advances in RoboticsMay 11-12, 2107, , Boca Raton, FL, Florida Atlantic University; Amsel, A., (1992) Frustration Theory: An Analysis of Dispositional Learning and Memory, , Cambridge, Cambridge University Press; Bartneck, C., Croft, E., Kulic, D., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. 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Behav, 19, pp. 311-316. , 16187811; Yang, E., Mitigating user frustration through adaptive feedback based on human-automation etiquette strategies (2016) Graduate Theses Dissert, 2016 (15843); Yuan, J., Ding, N., Liu, Y., Yang, J., Unconscious emotion regulation Nonconscious reappraisal decreases emotion related physiological reactivity during frustration (2014) Cogn. Emot, 29, pp. 1042-1053. , 25297822","Weidemann, A.; Cognitive Modeling in Dynamic Human-Machine Systems, Germany; email: a.weidemann@tu-berlin.de
Weidemann, A.; Junior Research Group MTI-engAge, Germany; email: a.weidemann@tu-berlin.de",,,"Frontiers Media S.A.",,,,,16641078,,,,"English","Front. Psychol.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85103631143
"Ramachandran B.R.N., Lim J.C.","57194071257;57216339387;","User validation study of a social robot for use in hospital wards",2021,"ACM/IEEE International Conference on Human-Robot Interaction",,,"3447162","215","219",,,"10.1145/3434074.3447162","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102760459&doi=10.1145%2f3434074.3447162&partnerID=40&md5=14b7f7574310cccd317ac2d0559789fe","NCS Pte Ltd, Singapore, Singapore","Ramachandran, B.R.N., NCS Pte Ltd, Singapore, Singapore; Lim, J.C., NCS Pte Ltd, Singapore, Singapore","For robots to play an increased role in healthcare settings, the understanding on nurse-patient human-centric interactions in their unique scenario-based setting is crucial for Human-Robot Interaction (HRI) design. We aim to investigate HRI design for a robot to perform nursing tasks in hospitals. The robot's personality is designed with animated eyes, voice with local accent and contextual phrases with politeness to mimic nurse's behavior as they engage with patients. The study was designed with scenario-based use cases focusing on user-centric and task-centric HRI components. This paper reports on findings based on 2 online surveys conducted and performed by 60 participating nurses from a local Hospital. In the first survey, the tasks performed by the robot include greeting, patient identification, pain level check, and performing vital signs measurement. This is to identify the correlation of politeness, friendliness and strictness exhibited by the robot, and the effects on the user's perception on trustworthiness and their willingness to follow the robot's instructions for a given task. In the second survey, a comparison is made between an item delivery task performed by a nurse and the same task performed by a robot, to assess the user receptivity and the usability of the robot to assist in the task with its ability to communicate its service intent. The results imply that in the current design, if the robot communicates with the user with politeness and friendliness, it will increase the perceived trustworthiness of the user. When the robot exhibited strictness to get the patient to comply, a decrease in the user-perceived friendliness, politeness, and robot trustworthiness was also revealed in the findings. Finally, participants indicated high receptivity in interacting with the robot and the feasibility of a robot-assisted nurse workflow. These results support the progression to further development towards user validation studies in an actual hospital ward setting. © 2021 ACM.","Communication intent; Healthcare operational workflow; Healthcare robots; Hospital; Human-robot interaction; Politeness; Robot personality; Trust","Agricultural robots; Hospitals; Man machine systems; Nursing; Social robots; Surveys; Hospital wards; Human robot Interaction (HRI); Human-centric; Online surveys; Patient identification; Scenario-based; User's perceptions; Validation study; Machine design",,,,,,,,,,,,,,,,"Abella, A., Gorin, A.L., Construct algebra: Analytical dialog management (1999) Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, pp. 191-199. , Association for Computational Linguistics, College Park, Maryland, USA; (2021) WHO | A Universal Truth: No Health without A Workforce, , https://www.who.int/workforcealliance/knowledge/resources/hrhreport2013/en/, Global Health Workforce Alliance and World Health Organization. Publisher: World Health Organization; Ball, J.E., Post-operative mortality, missed care and nurse staffing in nine countries: A cross-sectional study (2018) International Journal of Nursing Studies, 78, pp. 10-15. , https://doi.org/10.1016/j.ijnurstu.2017.08.004, Feb. 2018; Global, N., (2021) Milo Drinks Brand, , https://www.nestle.com/brands/allbrands/milo, Retrieved January 12, 2021 from; Goodrich, M.A., Olsen, D.R., Seven principles of efficient human robot interaction (2003) SMC'03 Conference Proceedings, 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme-System Security and Assurance (Cat. No. 03CH37483), 4, pp. 3942-3948. , https://doi.org/10.1109/ICSMC.2003.1244504ISSN:1062-922X, 4. IEEE, Washington, DC, USA; Goren-Bar, D., Designing model-based intelligent dialogue systems (2001) Information Modeling in the New Millennium., pp. 268-284. , IGI Global, Hershey, PA, USA; Lafranca, L., Li, J., Humans and robots in times of quarantine based on first-hand accounts (2020) International Conference on Social Robotics, pp. 688-707. , https://doi.org/10.1007/978-3-030-62056-1_57, Springer, Springer Nature Switzerland AG, Cham, Switzerland; Lee, N., Kim, J., Kim, E., Kwon, O., The influence of politeness behavior on user compliance with social robots in a healthcare service setting (2017) International Journal of Social Robotics, 9 (5), pp. 727-743. , https://doi.org/10.1007/s12369-017-0420-0, Nov 2017; Lyons, J., Being transparent about transparency: A model for human-robot interaction (2013) AAAI Spring Symposium: Trust and Autonomous Systems (AAAI Technical Report Vol. SS-13-07), , http://www.aaai.org/ocs/index.php/SSS/SSS13/paper/view/5712, AAAI, Palo Alto, CA, USA; Parekh, S., Cheng Lim, J., Receptivity & interaction of social robots in hospitals (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (Cambridge, United Kingdom) (HRI '20), pp. 389-391. , https://doi.org/10.1145/3371382.3378243, Association for Computing Machinery, New York, NY, USA; Russell, J., A circumplex model of affect (1980) Journal of Personality and Social Psychology, 39, pp. 1161-1178. , https://doi.org/10.1037/h0077714, 12 1980; Severinson-Eklundh, K., Green, A., Hüttenrauch, H., Social and collaborative aspects of interaction with a service robot (2003) Robotics and Autonomous Systems, 42 (3), pp. 223-234. , https://doi.org/10.1016/S0921-8890(02)00377-9, 2003; Xu, T., Zhang, H., Yu, C., See you see me: The role of eye contact in multimodal human-robot interaction (2016) ACM Transactions on Interactive Intelligent Systems, 6 (1), pp. 21-222. , https://doi.org/10.1145/2882970, May 2016; Yang, G., Combating COVID-19-the role of robotics in managing public health and infectious diseases (2020) Science Robotics, 5 (40). , https://doi.org/10.1126/scirobotics.abb5589, March 2020 Publisher: Science Robotics Section: Editorial; Yoon, S., Blumberg, B.M., Schneider, G.E., Motivation driven learning for interactive synthetic characters (2000) Proceedings of the Fourth International Conference on Autonomous Agents (Barcelona, Spain) (AGENTS '00), pp. 365-372. , https://doi.org/10.1145/336595.337537, Association for Computing Machinery, New York, NY, USA; Zheng, L., A task-analytic framework comparing preoperative electronic health record-mediated nursingworkflow in different settings (2020) CIN: Computers, Informatics, Nursing, 38 (6), pp. 294-302. , https://doi.org/10.1097/CIN.0000000000000588, June 2020; Zhong, K., Qiao, T., Zhang, L., A study of emotional communication of emoticon based on russell's circumplex model of affect (2019) Design, User Experience, and Usability. Design Philosophy and Theory, pp. 577-596. , Aaron Marcus and Wentao Wang (Eds.) Springer International Publishing, Cham",,,"ACM SIGAI;ACM SIGCHI;IEEE;IEEE Robotics and Automation Society (IEEE RAS","IEEE Computer Society","2021 ACM/IEEE International Conference on Human-Robot Interaction, HRI 2021","8 March 2021 through 11 March 2021",,167645,21672148,9781450382908,,,"English","ACM/IEEE Int. Conf. Hum.-Rob. Interact.",Conference Paper,"Final","",Scopus,2-s2.0-85102760459
"Wang Q., Jiao W., Wang P., Zhang Y.","57209691307;57209686494;57220187855;56088829600;","Digital Twin for Human-Robot Interactive Welding and Welder Behavior Analysis",2021,"IEEE/CAA Journal of Automatica Sinica","8","2","9269521","334","343",,7,"10.1109/JAS.2020.1003518","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097130846&doi=10.1109%2fJAS.2020.1003518&partnerID=40&md5=049a99cdd207e65d3f931a60810308e4","Institute for Sustainable Manufacturing, University of Kentucky, Department of Electrical and Computer Engineering, Lexington, United States; Institute for Sustainable Manufacturing, Department of Electrical and Computer Engineering; Department of Mechanical Engineering, University of Kentucky, Lexington, United States","Wang, Q., Institute for Sustainable Manufacturing, University of Kentucky, Department of Electrical and Computer Engineering, Lexington, United States; Jiao, W., Institute for Sustainable Manufacturing, University of Kentucky, Department of Electrical and Computer Engineering, Lexington, United States; Wang, P., Institute for Sustainable Manufacturing, Department of Electrical and Computer Engineering, Department of Mechanical Engineering, University of Kentucky, Lexington, United States; Zhang, Y., Institute for Sustainable Manufacturing, University of Kentucky, Department of Electrical and Computer Engineering, Lexington, United States","This paper presents an innovative investigation on prototyping a digital twin (DT) as the platform for human-robot interactive welding and welder behavior analysis. This human-robot interaction (HRI) working style helps to enhance human users' operational productivity and comfort; while data-driven welder behavior analysis benefits to further novice welder training. This HRI system includes three modules: 1) a human user who demonstrates the welding operations offsite with her/his operations recorded by the motion-tracked handles; 2) a robot that executes the demonstrated welding operations to complete the physical welding tasks onsite; 3) a DT system that is developed based on virtual reality (VR) as a digital replica of the physical human-robot interactive welding environment. The DT system bridges a human user and robot through a bi-directional information flow: a) transmitting demonstrated welding operations in VR to the robot in the physical environment; b) displaying the physical welding scenes to human users in VR. Compared to existing DT systems reported in the literatures, the developed one provides better capability in engaging human users in interacting with welding scenes, through an augmented VR. To verify the effectiveness, six welders, skilled with certain manual welding training and unskilled without any training, tested the system by completing the same welding job; three skilled welders produce satisfied welded workpieces, while the other three unskilled do not. A data-driven approach as a combination of fast Fourier transform (FFT), principal component analysis (PCA), and support vector machine (SVM) is developed to analyze their behaviors. Given an operation sequence, i.e., motion speed sequence of the welding torch, frequency features are firstly extracted by FFT and then reduced in dimension through PCA, which are finally routed into SVM for classification. The trained model demonstrates a 94.44% classification accuracy in the testing dataset. The successful pattern recognition in skilled welder operations should benefit to accelerate novice welder training. © 2014 Chinese Association of Automation.","Digital twin (DT); human-robot interaction (HRI); machine learning; virtual reality (VR); welder behavior analysis","Classification (of information); Digital twin; Educational robots; Fast Fourier transforms; Pattern recognition; Statistical tests; Support vector machines; Welding; Behavior analysis; Classification accuracy; Data-driven approach; Frequency features; Operation sequences; Physical environments; Welder trainings; Welding operations; Social robots",,,,,,,,,,,,,,,,"Kang, H.S., Lee, J.Y., Choi, S., Kim, H., Park, J.H., Son, J.Y., Kim, B.H., Noh, S.D., Smart manufacturing: Past research, present findings, and future directions (2016) Int. J. Precis. Eng. 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Res., 12 (85), pp. 2825-2830","Zhang, Y.; Institute for Sustainable Manufacturing, United States; email: yuming.zhang@uky.edu",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,23299266,,,,"English","IEEE CAA J. Autom. Sin.",Article,"Final","",Scopus,2-s2.0-85097130846
"Coeckelbergh M.","22233758600;","How to Use Virtue Ethics for Thinking About the Moral Standing of Social Robots: A Relational Interpretation in Terms of Practices, Habits, and Performance",2021,"International Journal of Social Robotics","13","1",,"31","40",,7,"10.1007/s12369-020-00707-z","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093969450&doi=10.1007%2fs12369-020-00707-z&partnerID=40&md5=456ef0c095e1ed84a1a043f1d03c8c82","Universitat Wien, Vienna, Austria","Coeckelbergh, M., Universitat Wien, Vienna, Austria","Social robots are designed to facilitate interaction with humans through “social” behavior. As literature in the field of human–robot interaction shows, this sometimes leads to “bad” behavior towards the robot or “abuse” of the robot. Virtue ethics offers a helpful way to capture the intuition that although nobody is harmed when a robot is “mistreated”, there is still something wrong with this kind of behavior: it damages the moral character of the person engaging in that behavior, especially when it is habitual. However, one of the limitations of current applications of virtue ethics to robots and technology is its focus on the individual and individual behavior and insufficient attention to temporal and bodily aspects of virtue. After positioning its project in relation to the work of Shannon Vallor and Robert Sparrow, the present paper explores what it would mean to interpret and apply virtue ethics in a more social and relational way and a way that takes into account the link between virtue and the body. In particular, it proposes (1) to use the notion of practice as a way to conceptualize how the individual behavior, the virtue of the person, and the technology in question are related to their wider social-practical context and history, and (2) to use the notions of habit and performance conceptualize the incorporation and performance of virtue. This involves use of the work of MacIntyre, but revised by drawing on Bourdieu’s notion of habit in order to highlight the temporal, embodiment, and performative aspect of virtue. The paper then shows what this means for thinking about the moral standing of social robots, for example for the ethics of sex robots and for evaluating abusive behaviors such as kicking robots. The paper concludes that this approach does not only give us a better account of what happens when people behave “badly” towards social robots, but also suggests a more comprehensive virtue ethics of technology that is fully relational, performance-oriented, and able to not only acknowledges but also theorize the temporal and bodily dimension of virtue. © 2020, The Author(s).","Bourdieu; Embodiment; Habit; Incorporation; MacIntyre; Moral standing; Performance; Practice; Social robots; Virtue ethics","Agricultural robots; Philosophical aspects; Individual behavior; MacIntyre; Performance-oriented; Robot interactions; Shannon; Virtue ethics; Social robots",,,,,"Universität Wien; Erwin Schrödinger International Institute for Mathematics and Physics, ESI; Fakultät für Geowissenschaften, Geographie und Astronomie, Universität Wien, FGGA",,,,,,,,,,,"Ames, R.T., Rosemont, H., (1998) The analects of confucius: a philosophical translation, , Random House, New York and Toronto; Bartneck, C., van Der Hoek, M., Mubin, O., Al Mahmud, A., Daisy, Daisy, Give me your answer do!”—switching off a robot (2007) Proceedings of the 2Nd ACM/IEEE International Conference on human–robot Interaction, pp. 217-222. , Washington, DC; Bourdieu, P., (1984) Distinction: A Social Critique of the Judgement of Taste (Trans: Nice R)., , Harvard University Press, Cambridge; Bourdieu, P., (1990) The Logic of Practice (Trans: Nice R)., , Polity Press, Cambridge; Brey, P., Briggle, A., Spence, E., (2012) The good life in a technological age, , (eds), Routledge, New York; Bryson, J., Robots should be slaves (2010) Close engagements with artificial companions: key social, psychological, ethical and design issues, , Wilks Y, (ed), John Benjamins, Amsterdam; Cappuccio, M.L., Peeters, A., McDonald, W., Sympathy for dolores: moral consideration for robots based on virtue and recognition (2020) Philos Technol, 33, pp. 9-31; Coeckelbergh, M., Violent computer games, empathy, and cosmopolitanism (2007) Ethics Inf Technol, 9, pp. 219-231; Coeckelbergh, M., Robot rights? Towards a social-relational justification of moral consideration (2010) Ethics Inf Technol, 12, pp. 209-222; Coeckelbergh, M., Environmental virtue: motivation, skill, and information technology (2011) J Environ Philos, 8, pp. 141-170; Coeckelbergh, M., (2012) Growing moral relations: critique of moral status ascription, , Palgrave Macmillan, New York; Coeckelbergh, M., Care robots, virtual virtue, and the best possible life (2012) The Good life in a technological age, pp. 281-292. , Brey P, Briggle A, Spence E, (eds), Routledge, New York; Coeckelbergh, M., Is it wrong to kick a robot? Towards a relational and critical robot ethics and beyond (2016) What social robots can and should do, pp. 7-8. , Seibt J, Nørskov M, Andersen SS, (eds), IOS Press, Amsterdam; Coeckelbergh, M., Technology Games: using Wittgenstein for Understanding and Evaluating Technology (2018) Sci Eng Ethics, 24, pp. 1503-1519; Coeckelbergh, M., (2019) Moved by machines, , Routledge, New York; Coghlan, S., Vetere, F., Waycot, J., Barbosa Neves, B., Could social robots make us kinder or crueller to humans and animals? (2019) Int J Soc Robot, 11 (5), pp. 741-751; Darling, K., Extending legal protection to social robots: The effects of anthropomorphism, empathy, and violent behavior towards robotic objects (2016) Robot Law, pp. 213-232. , Calo R, Froomkin M, I Kerr, Edward Elgar Publishing, Cheltenham and Northampton; Darling, K., Who’s Johnny? 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(2018) Ethics Inf Technol, 20, pp. 87-99; Hursthouse, R., Pettigrove, G., (2016) Virtue Ethics in Stanford Encyclopedia of Philosophy, , https://plato.stanford.edu/entries/ethics-virtue/, Retrieved 25 September 2020 from; Ihde, D., (1998) Expanding hermeneutics: visualism in science, , Northwestern University Press, Evanston; Johnson, D.G., Computer systems: moral entities but not moral agent (2006) Ethics Inf Technol, 8, pp. 195-204; Kant, I., (2012) Lectures on Anthropology, , Wood AW, Louden RB, Cambridge University Press, Cambridge; Ku, H., Choi, J.J., Lee, S.J., Do, W., Shelly, a tortoise-like robot for one-to-many interaction with children (2018) Proceedings of the ACM/IEEE International Conference on human–robot Interaction, pp. 353-354; MacIntyre, A., (1999) Dependent rational animals: why human beings need the virtues, , Open Court, Chicago; MacIntyre, A., (2007) After virtue: a study in moral theory, , 3, University of Notre Dame Press, Notre Dame; McCormick, M., Is it wrong to play violent video games? (2001) Ethics Inf Technol, 3, pp. 277-287; Scheutz, M., The inherent dangers of unidirectional emotional bonds between humans and social robots (2012) Robot Ethics., pp. 205-221. , Lin P, Abney K, Bekey G, MIT Press, Cambridge; Sparrow, R., Robots, rape, and representation (2017) Int J Soc Robot, 9, pp. 465-477; Sparrow, R., Virtue and vice in our relationships with robots: is there an asymmetry and how might it be explained? (2020) Int J Soc Robot, , (,), https://doi.org/10.1007/s12369-020-00631-2; Vallor, S., (2016) Technology and the virtues, , Oxford University Press, New York; Varela, F.J., Rosch, E., Thompson, E., (1991) The embodied mind: cognitive science and human experience, , The MIT Press, Cambridge; Winograd, T., Flores, F., (1986) Understanding computers and cognition: a new foundation for design, , Ablex, Norwood","Coeckelbergh, M.; Universitat WienAustria; email: mark.coeckelbergh@univie.ac.at",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","All Open Access, Hybrid Gold",Scopus,2-s2.0-85093969450
"Noori F.M., Uddin M.Z., Torresen J.","57189591227;24482836700;6602518300;","Ultra-Wideband Radar-Based Activity Recognition Using Deep Learning",2021,"IEEE Access","9",,,"138132","138143",,,"10.1109/ACCESS.2021.3117667","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117245471&doi=10.1109%2fACCESS.2021.3117667&partnerID=40&md5=6c2af6622326b0006b5ce294193e3afe","Department of Informatics, University of Oslo, Oslo, 0373, Norway; Software and Service Innovation, SINTEF Digital, Oslo, 0373, Norway; RITMO Centre, University of Oslo, Oslo, 0315, Norway","Noori, F.M., Department of Informatics, University of Oslo, Oslo, 0373, Norway; Uddin, M.Z., Software and Service Innovation, SINTEF Digital, Oslo, 0373, Norway; Torresen, J., Department of Informatics, University of Oslo, Oslo, 0373, Norway, RITMO Centre, University of Oslo, Oslo, 0315, Norway","With recent advances in the field of sensing, it has become possible to build better assistive technologies. This enables the strengthening of eldercare with regard to daily routines and the provision of personalised care to users. For instance, it is possible to detect a person's behaviour based on wearable or ambient sensors; however, it is difficult for users to wear devices 24/7, as they would have to be recharged regularly because of their energy consumption. Similarly, although cameras have been widely used as ambient sensors, they carry the risk of breaching users' privacy. This paper presents a novel sensing approach based on deep learning for human activity recognition using a non-wearable ultra-wideband (UWB) radar sensor. UWB sensors protect privacy better than RGB cameras because they do not collect visual data. In this study, UWB sensors were mounted on a mobile robot to monitor and observe subjects from a specific distance (namely, 1.5-2.0 m). Initially, data were collected in a lab environment for five different human activities. Subsequently, the data were used to train a model using the state-of-the-art deep learning approach, namely long short-term memory (LSTM). Conventional training approaches were also tested to validate the superiority of LSTM. As a UWB sensor collects many data points in a single frame, enhanced discriminant analysis was used to reduce the dimensions of the features through application of principal component analysis to the raw dataset, followed by linear discriminant analysis. The enhanced discriminant features were fed into the LSTMs. Finally, the trained model was tested using new inputs. The proposed LSTM-based activity recognition approach performed better than conventional approaches, with an accuracy of 99.6%. We applied 5-fold cross-validation to test our approach. We also validated our approach on publically available dataset. The proposed method can be applied in many prominent fields, including human-robot interaction for various practical applications, such as mobile robots for eldercare. © 2013 IEEE.","Human activity recognition; LDA; LSTM; PCA; XeThru UWB sensor","Cameras; Discriminant analysis; Human robot interaction; Long short-term memory; Mobile robots; Pattern recognition; Principal component analysis; Ultra-wideband (UWB); Wearable sensors; Activity recognition; Ambient sensors; Assistive technology; Eldercare; Human activity recognition; LDA; PCA; Ultra wideband radars; Ultra-wideband sensors; Xethru ultra-wideband sensor; Energy utilization",,,,,"247697; 262762, 288285, 312333; Norges Forskningsråd","This work was supported in part by the Research Council of Norway (RCN) as a part of the Multimodal Elderly Care Systems (MECS) Project under Grant 247697, in part by the Vulnerability in the Robot Society (VIROS) Project under Grant 288285, in part by the Predictive and Intuitive Robot Companion (PIRC) under Grant 312333, and in part by the Centre of Excellence Scheme under Project 262762.",,,,,,,,,,"Department of Economic and Social Affairs, Population Division (2017). 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"Bukhtiyarov I.V., Denisov E.I.","6506267463;7103372530;","Hygienic aspects of robotization: Risk factors and safety principles",2021,"Gigiena i Sanitariya","100","1",,"6","12",,,"10.47470/0016-9900-2021-100-1-6-12","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101799152&doi=10.47470%2f0016-9900-2021-100-1-6-12&partnerID=40&md5=9accc8a17841ed683546fb8c2386a464","N.F. Izmerov Research Institute of Occupational Health, Moscow, 105275, Russian Federation","Bukhtiyarov, I.V., N.F. Izmerov Research Institute of Occupational Health, Moscow, 105275, Russian Federation; Denisov, E.I., N.F. Izmerov Research Institute of Occupational Health, Moscow, 105275, Russian Federation","The paper presents a review and essays on the hygienic aspects of the problem of robotics. The absence of generally accepted international terminology is noted. There is given a definition of basic terms as a robot, an artificial intelligence system (AIS), and a cyber-physical system (CFS) - according to authoritative sources. In the literature, the term robot is often used to mean CFS. The origin of robots in Russia is briefly described. The role of AIS as the basis of a technological breakthrough is considered. There is represented statistical data on the scientific, economic, and social aspects of the introduction of robotics. Robots are believed to be more likely to replace tasks rather than jobs and create new types of them. The medical and social aspects of robotization based on the experience of the European Union, the USA, and South Korea are examined, and the prospects of creating ""smart jobs"" are emphasized. The types of robots and their application in industry and medicine for diagnosis, treatment, and rehabilitation are described. It is emphasized that robots are the most advanced machines. The dangers created by robots, their causes, and possible consequences from physical (noise, vibration), chemical, electrical, ergonomic, and other perils are described. The presence of both ""traditional"" and new risk factors is noted. The systematics of the stages of human-robot interaction is proposed: Ethical aspects in the design, communication psychology, contacts with the machine during its use and safety aspects, human physiological responses, possible clinical manifestations of health disorders. The safety principles of robots and CFS are formulated, and that the ""smarter"" the robots, the greater the risks of program failures and breakdowns are noted. The role of information hygiene and the need for training and health education of workers and the population are examined. Estimates are given of the prospects for the robotization of the profession. The occupation of hygienists in the era of digitalization and robotization is noted to have a future. © 2021 Izdatel'stvo Meditsina. All rights reserved.","Artificial intelligence; Cyber physical systems; Hygienic safety; Prophylaxis; Prospects of occupation; Risk factors; Robots",,,,,,,,,,,,,,,,,"Brynjolfsson, E., McAfee, A., (2014) The second machine age: work, progress, and prosperity in a time of brilliant technologies, , W.W. Norton & Company, Inc. New York; Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., Artificial intelligence and life in 2030. 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"Kühne K., Fischer M.H., Zhou Y.","57205682827;7402920854;36443159500;","The Human Takes It All: Humanlike Synthesized Voices Are Perceived as Less Eerie and More Likable. Evidence From a Subjective Ratings Study",2020,"Frontiers in Neurorobotics","14",,"593732","","",,1,"10.3389/fnbot.2020.593732","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098480079&doi=10.3389%2ffnbot.2020.593732&partnerID=40&md5=fd7a21603dbd012f850999cb2bc52b20","Division of Cognitive Sciences, University of Potsdam, Potsdam, Germany","Kühne, K., Division of Cognitive Sciences, University of Potsdam, Potsdam, Germany; Fischer, M.H., Division of Cognitive Sciences, University of Potsdam, Potsdam, Germany; Zhou, Y., Division of Cognitive Sciences, University of Potsdam, Potsdam, Germany","Background: The increasing involvement of social robots in human lives raises the question as to how humans perceive social robots. Little is known about human perception of synthesized voices. Aim: To investigate which synthesized voice parameters predict the speaker's eeriness and voice likability; to determine if individual listener characteristics (e.g., personality, attitude toward robots, age) influence synthesized voice evaluations; and to explore which paralinguistic features subjectively distinguish humans from robots/artificial agents. Methods: 95 adults (62 females) listened to randomly presented audio-clips of three categories: synthesized (Watson, IBM), humanoid (robot Sophia, Hanson Robotics), and human voices (five clips/category). Voices were rated on intelligibility, prosody, trustworthiness, confidence, enthusiasm, pleasantness, human-likeness, likability, and naturalness. Speakers were rated on appeal, credibility, human-likeness, and eeriness. Participants' personality traits, attitudes to robots, and demographics were obtained. Results: The human voice and human speaker characteristics received reliably higher scores on all dimensions except for eeriness. Synthesized voice ratings were positively related to participants' agreeableness and neuroticism. Females rated synthesized voices more positively on most dimensions. Surprisingly, interest in social robots and attitudes toward robots played almost no role in voice evaluation. Contrary to the expectations of an uncanny valley, when the ratings of human-likeness for both the voice and the speaker characteristics were higher, they seemed less eerie to the participants. Moreover, when the speaker's voice was more humanlike, it was more liked by the participants. This latter point was only applicable to one of the synthesized voices. Finally, pleasantness and trustworthiness of the synthesized voice predicted the likability of the speaker's voice. Qualitative content analysis identified intonation, sound, emotion, and imageability/embodiment as diagnostic features. Discussion: Humans clearly prefer human voices, but manipulating diagnostic speech features might increase acceptance of synthesized voices and thereby support human-robot interaction. There is limited evidence that human-likeness of a voice is negatively linked to the perceived eeriness of the speaker. © Copyright © 2020 Kühne, Fischer and Zhou.","human-robot interaction; paralinguistic features; synthesized voice; text-to-speech; uncanny valley","Industrial manipulators; Attitudes to robots; Content analysis; Diagnostic features; Human perception; Personality traits; Speaker characteristics; Subjective rating; Three categories; Social robots; adult; Article; auditory stimulation; controlled study; emotion; exploratory research; female; human; human computer interaction; male; paralanguage; perception; personality; qualitative analysis; robotics; speech intelligibility; voice analysis; voice parameter",,,,,,,,,,,,,,,,"Antona, M., Ioannidi, D., Foukarakis, M., Gerlowska, J., Rejdak, K., Abdelnour, C., My robot is happy today: how older people with mild cognitive impairments understand assistive robots' affective output, (2019) Proceedings of the 12th ACM International Conference on Pervasive Technologies Related to Assistive Environments, pp. 416-424. , Rhodes; Aylett, M.P., Sutton, S.J., Vazquez-Alvarez, Y., The right kind of unnatural: designing a robot voice, (2019) Proceedings of the 1st International Conference on Conversational User Interfaces, pp. 1-2. , Dublin; Aylett, M.P., Vazquez-Alvarez, Y., Butkute, S., Creating robot personality: effects of mixing speech and semantic free utterances, (2020) Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 110-112. , Cambridge; Aylett, M.P., Vinciarelli, A., Wester, M., Speech synthesis for the generation of artificial personality (2017) IEEE Trans. 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"Zhou J., Chen Y., Hu Y., Wang Z., Li Y., Gu G., Liu Y.","57195419573;7601430448;55654699700;35216891400;57155456400;36494658000;57201932064;","Adaptive Variable Stiffness Particle Phalange for Robust and Durable Robotic Grasping",2020,"Soft Robotics","7","6",,"743","757",,7,"10.1089/soro.2019.0089","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098170954&doi=10.1089%2fsoro.2019.0089&partnerID=40&md5=c660ca456a73b64655f52770db4a87f6","Department of Mechanical Engineering, The University of Hong Kong, Hong Kong; Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong; Soft Robotics and Biodesign Lab, Robotics Institute, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong","Zhou, J., Department of Mechanical Engineering, The University of Hong Kong, Hong Kong; Chen, Y., Department of Mechanical Engineering, The University of Hong Kong, Hong Kong; Hu, Y., Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong; Wang, Z., Department of Mechanical Engineering, The University of Hong Kong, Hong Kong; Li, Y., Department of Mechanical Engineering, The University of Hong Kong, Hong Kong; Gu, G., Soft Robotics and Biodesign Lab, Robotics Institute, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Liu, Y., Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong","Grasping is an important characteristic of robots in interacting with humans and the environment. Due to the inherent compliance of soft grippers, they can easily adapt to novel objects and operate safely in a human-centered environment. However, soft hands suffer from poor grasping robustness and operation durability, especially for heavy objects or objects with sharp spikes, mainly due to their fragile material and low structural stiffness of the soft actuators. Thus, the widespread use of soft hands in daily applications is still limited. Existing works have shown a promising direction to enhance grasping performance by solving the contradiction between inherent compliance/adaptability and loading capacity. It is known that the stiffness of the robotic phalange is highly related to the performance of robotic hands. In this article, we propose a novel variable stiffness particle phalange, called VSPP here. The proposed VSPP exhibits variable stiffness characteristics without the need for dedicated actuation by utilizing passive particle jamming resulted from forces in interacting with the environment. The VSPP can cooperate with any kind of actuators, soft or rigid, to function as a compliant and robust robotic hand. A prototype robotic hand based on VSPP could maintain reliable grasping even when pierced by sharp objects such as a needle, a cactus, and a durian. This durability is effective both in air and underwater, thus presents new possibilities for the soft robotic hand to work in a harsh environment. The inherent multidirectional compliance of the VSPP makes safety in human/robot interaction guaranteed. The design and modeling presented in this research will provide useful guidance in VSPP applications. A prototype gripper, VSPP-3, composed of three 2-segments VSPP fingers and pneumatic joints, has been built for demonstrations in reliable and robust grasping of daily objects. The sample grasping has shown that the proposed VSPP has great potential for a robust and durable soft robotic hand or gripper design. © Copyright 2020, Mary Ann Liebert, Inc., publishers 2020.","grasping durability; grasping robustness; passive particle jamming; soft grippers; variable stiffness","Actuators; Agricultural robots; Durability; End effectors; Grippers; Robotic arms; Stiffness; Design and modeling; Fragile materials; Gripper designs; Harsh environment; Loading capacities; Robotic grasping; Structural stiffness; Variable stiffness; Robotics; article; finger; human; joint; rigidity; robotics; spike",,,,,"SGDX2019081623201196","This work was supported by Shenzhen/Hong Kong Innovation Circle Programme (Type D SGDX2019081623201196).",,,,,,,,,,"Rus, D, Tolley, MT., Design, fabrication and control of soft robots (2015) Nature, 521, pp. 467-475; Cho, KJ, Koh, JS, Kim, S, Review of manufacturing processes for soft biomimetic robots (2009) Int J Precis Eng Manuf, 10, pp. 171-181; Bicchi, A., Hands for dexterous manipulation and robust grasping: A difficult road toward simplicity (2000) IEEE Transact Robot Autom, 16, pp. 652-662; 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"Tanevska A., Rea F., Sandini G., Cañamero L., Sciutti A.","57194323352;54386065400;35280694500;6602322296;36957816700;","A Socially Adaptable Framework for Human-Robot Interaction",2020,"Frontiers in Robotics and AI","7",,"121","","",,7,"10.3389/frobt.2020.00121","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094968227&doi=10.3389%2ffrobt.2020.00121&partnerID=40&md5=9fc2fc40881948e41ac31ed10b0c7e0f","Department of Robotics, Brain and Cognitive Science, Italian Institute of Technology (IIT), Genova, Italy; EECAiA Lab, School of Computer Science, University of Hertfordshire, Hatfield, United Kingdom; Cognitive Architecture for Collaborative Technologies Unit, Italian Institute of Technology (IIT), Genova, Italy","Tanevska, A., Department of Robotics, Brain and Cognitive Science, Italian Institute of Technology (IIT), Genova, Italy, EECAiA Lab, School of Computer Science, University of Hertfordshire, Hatfield, United Kingdom, Cognitive Architecture for Collaborative Technologies Unit, Italian Institute of Technology (IIT), Genova, Italy; Rea, F., Department of Robotics, Brain and Cognitive Science, Italian Institute of Technology (IIT), Genova, Italy; Sandini, G., Department of Robotics, Brain and Cognitive Science, Italian Institute of Technology (IIT), Genova, Italy; Cañamero, L., EECAiA Lab, School of Computer Science, University of Hertfordshire, Hatfield, United Kingdom; Sciutti, A., Cognitive Architecture for Collaborative Technologies Unit, Italian Institute of Technology (IIT), Genova, Italy","In our everyday lives we regularly engage in complex, personalized, and adaptive interactions with our peers. To recreate the same kind of rich, human-like interactions, a social robot should be aware of our needs and affective states and continuously adapt its behavior to them. Our proposed solution is to have the robot learn how to select the behaviors that would maximize the pleasantness of the interaction for its peers. To make the robot autonomous in its decision making, this process could be guided by an internal motivation system. We wish to investigate how an adaptive robotic framework of this kind would function and personalize to different users. We also wish to explore whether the adaptability and personalization would bring any additional richness to the human-robot interaction (HRI), or whether it would instead bring uncertainty and unpredictability that would not be accepted by the robot's human peers. To this end, we designed a socially adaptive framework for the humanoid robot iCub. As a result, the robot perceives and reuses the affective and interactive signals from the person as input for the adaptation based on internal social motivation. We strive to investigate the value of the generated adaptation in our framework in the context of HRI. In particular, we compare how users will experience interaction with an adaptive versus a non-adaptive social robot. To address these questions, we propose a comparative interaction study with iCub whereby users act as the robot's caretaker, and iCub's social adaptation is guided by an internal comfort level that varies with the stimuli that iCub receives from its caretaker. We investigate and compare how iCub's internal dynamics would be perceived by people, both in a condition when iCub does not personalize its behavior to the person, and in a condition where it is instead adaptive. Finally, we establish the potential benefits that an adaptive framework could bring to the context of repeated interactions with a humanoid robot. © Copyright © 2020 Tanevska, Rea, Sandini, Cañamero and Sciutti.","affective interaction; emotion recognition; human-robot interaction; personalized HRI; social adaptability",,,,,,"Horizon 2020 Framework Programme, H2020: 804388; European Research Council, ERC; University of Hertfordshire, UH; Horizon 2020","This work has been supported by a Starting Grant from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme. G.A. No 804388, wHiSPER.","The authors would like to thank those who participated in the study, as well as Matthew Lewis and Imran Khan from the University of Hertfordshire for their help and availability. Funding. 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Tanevska, A.; EECAiA Lab, United Kingdom; email: ana.tanevska@iit.it
Tanevska, A.; Cognitive Architecture for Collaborative Technologies Unit, Italy; email: ana.tanevska@iit.it",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85094968227
"Pillai R., Sivathanu B.","57188833633;56938718200;","Adoption of AI-based chatbots for hospitality and tourism",2020,"International Journal of Contemporary Hospitality Management","32","10",,"3199","3226",,29,"10.1108/IJCHM-04-2020-0259","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091348347&doi=10.1108%2fIJCHM-04-2020-0259&partnerID=40&md5=a9b7a107f72f9785efb5b93e64ca5eef","Department of Management, Pune Institute of Business Management, Pune, India; Department of Management, Sri Balaji University, Pune, India","Pillai, R., Department of Management, Pune Institute of Business Management, Pune, India; Sivathanu, B., Department of Management, Sri Balaji University, Pune, India","Purpose: This study aims to investigate the customers’ behavioral intention and actual usage (AUE) of artificial intelligence (AI)-powered chatbots for hospitality and tourism in India by extending the technology adoption model (TAM) with context-specific variables. Design/methodology/approach: To understand the customers’ behavioral intention and AUE of AI-powered chatbots for tourism, the mixed-method design was used whereby qualitative and quantitative techniques were combined. A total of 36 senior managers and executives from the travel agencies were interviewed and the analysis of interview data was done using NVivo 8.0 software. A total of 1,480 customers were surveyed and the partial least squares structural equation modeling technique was used for data analysis. Findings: As per the results, the predictors of chatbot adoption intention (AIN) are perceived ease of use, perceived usefulness, perceived trust (PTR), perceived intelligence (PNT) and anthropomorphism (ANM). Technological anxiety (TXN) does not influence the chatbot AIN. Stickiness to traditional human travel agents negatively moderates the relation of AIN and AUE of chatbots in tourism and provides deeper insights into manager’s commitment to providing travel planning services using AI-based chatbots. Practical implications: This research presents unique practical insights to the practitioners, managers and executives in the tourism industry, system designers and developers of AI-based chatbot technologies to understand the antecedents of chatbot adoption by travelers. TXN is a vital concern for the customers; so, designers and developers should ensure that chatbots are easily accessible, have a user-friendly interface, be more human-like and communicate in various native languages with the customers. Originality/value: This study contributes theoretically by extending the TAM to provide better explanatory power with human–robot interaction context-specific constructs – PTR, PNT, ANM and TXN – to examine the customers’ chatbot AIN. This is the first step in the direction to empirically test and validate a theoretical model for chatbots’ adoption and usage, which is a disruptive technology in the hospitality and tourism sector in an emerging economy such as India. © 2020, Emerald Publishing Limited.","AI-based chatbots; Anthropomorphism; Mixed method; Perceived intelligence; Perceived trust; PLS-SEM; TAM",,,,,,,,,,,,,,,,,"Adam, M., Wessel, M., Benlian, A., AI-based chatbots in customer service and their effects on user compliance (2020) Electronic Markets, 30 (1), pp. 1-19; Ali, F., Park, E., Kwon, J., Chae, B.K., 30 years of contemporary hospitality management: uncovering the bibliometrics and topical trends (2019) International Journal of Contemporary Hospitality Management, 31 (7), pp. 2641-2665; Armstrong, J.S., Overton, T.S., Estimating nonresponse bias in mail surveys (1977) Journal of Marketing Research, 14 (3), pp. 396-402; Ba, S., Pavlou, A.P., Evidence of the effect of trust building technology in electronic markets: price premiums and buyer behavior (2002) MIS Quarterly, 26 (3), pp. 243-268; Bartneck, C., Kanda, T., Ishiguro, H., Hagita, N., Is the uncanny valley an uncanny cliff? 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Access to places via shared videos (2009) Annals of Tourism Research, 36 (1), pp. 24-40; Tussyadiah, I.P., Park, S., Consumer evaluation of hotel service robots (2018) Information and Communication Technologies in Tourism, pp. 308-320. , Springer, Cham; Ukpabi, D., Karjaluoto, H., Olaleye, S.A., Mogaji, E., Dual perspectives on the role of artificially intelligent robotic virtual agents in the tourism, travel and hospitality industries (2018) Proceedings of the 11th Annual Conference of the EuroMed Academy of Business (EMAB), pp. 1339-1351. , EuroMed Press, Valletta, Malta; Van Doorn, J., Mende, M., Noble, S.M., Hulland, J., Ostrom, A.L., Grewal, D., Petersen, J.A., Domo arigato Mr Roboto: emergence of automated social presence in organizational frontlines and customers’ service experiences (2017) Journal of Service Research, 20 (1), pp. 43-58; Venkatesh, V., Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model (2000) Information Systems Research, 11 (4), pp. 342-365; Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D., User acceptance of information technology: toward a unified view (2003) MIS Quarterly, 27 (3), pp. 425-478; Wang, Y.Y., Wang, Y.S., Lin, T.C., Developing and validating a technology upgrade model (2018) International Journal of Information Management, 38 (1), pp. 7-26; Warner, R.M., Sugarman, D.B., Attributions of personality based on physical appearance, speech, and handwriting (1986) Journal of Personality and Social Psychology, 50 (4), pp. 792-799; Wei, J., Vinnikova, A., Lu, L., Xu, J., Understanding and predicting the adoption of fitness mobile apps: evidence from China (2020) Health Communication, 36 (6), pp. 1-12; Weiss, A., Bartneck, C., Meta-analysis of the usage of the Godspeed questionnaire series (2015) Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication, pp. 381-388. , Kobe; Werner, K., Oberzaucher, J., Werner, F., Evaluation of human-robot interaction factors of a socially assistive robot together with older people (2012) Proceedings – 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems, pp. 455-460. , IEEE, Palermo; Willemse, C., Wykowska, A., In natural interaction with embodied robots, we prefer it when they follow our gaze: a gaze-contingent mobile eyetracking study (2019) Philosophical Transactions of the Royal Society B: Biological Sciences, 374 (1771), p. 20180036; (2017) 5 ways chatbots are changing the face of travel industry, , www.xcubelabs.com/blog/chatbots-travel-industry/; Yagoda, R.E., Gillan, D.J., You want me to trust a ROBOT? The development of a human-robot interaction trust scale (2012) International Journal of Social Robotics, 4 (3), pp. 235-248; Yeoman, I., Mars, M., Robots, men and sex tourism (2012) Futures, 44 (4), pp. 365-371; You, S., Kim, J.-H., Lee, S., Kamat, V., Robert, L.P., Enhancing perceived safety in human-robot collaborative construction using immersive virtual environments (2018) Automation in Construction, 96, pp. 161-170; Yu, C.E., Humanlike robots as employees in the hotel industry: thematic content analysis of online reviews (2020) Journal of Hospitality Marketing and Management, 29 (1), pp. 22-38; Zarouali, B., Broeck, E., Van Den, Walrave, M., Poels, K., Predicting consumer responses to a chatbot on Facebook (2018) Cyberpsychology, Behavior, and Social Networking, 21 (8), pp. 491-497; Zhang, T., Tao, D., Qu, X., Zhang, X., Lin, R., Zhang, W., The roles of initial trust and perceived risk in public’s acceptance of automated vehicles (2019) Transportation Research Part C: Emerging Technologies, 98, pp. 207-220; Zhou, T., Lu, Y., Wang, B., Integrating TTF and UTAUT to explain mobile banking user adoption (2010) Computers in Human Behavior, 26 (4), pp. 760-767","Pillai, R.; Department of Management, India; email: rajasshrie1@gmail.com",,,"Emerald Group Holdings Ltd.",,,,,09596119,,,,"English","Int. J. Contemp. Hosp. Manage.",Article,"Final","",Scopus,2-s2.0-85091348347
"Nakagawa S., Yonekura S., Kanazawa H., Nishikawa S., Kuniyoshi Y.","55182764800;7005902061;55307547600;36959913600;7005371269;","Estimation of Mental Health Quality of Life using Visual Information during Interaction with a Communication Agent",2020,"29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020",,,"9223606","1321","1327",,1,"10.1109/RO-MAN47096.2020.9223606","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095745538&doi=10.1109%2fRO-MAN47096.2020.9223606&partnerID=40&md5=1e0df9ee227043865a917b0caa0e132c","University of Tokyo, Graduate School of Information Science Technology, Tokyo, Japan","Nakagawa, S., University of Tokyo, Graduate School of Information Science Technology, Tokyo, Japan; Yonekura, S., University of Tokyo, Graduate School of Information Science Technology, Tokyo, Japan; Kanazawa, H., University of Tokyo, Graduate School of Information Science Technology, Tokyo, Japan; Nishikawa, S., University of Tokyo, Graduate School of Information Science Technology, Tokyo, Japan; Kuniyoshi, Y., University of Tokyo, Graduate School of Information Science Technology, Tokyo, Japan","It is essential for a monitoring system or a communication robot that interacts with an elderly person to accurately understand the user's state and generate actions based on their condition. To ensure elderly welfare, quality of life (QOL) is a useful indicator for determining human physical suffering and mental and social activities in a comprehensive manner. In this study, we hypothesize that visual information is useful for extracting high-dimensional information on QOL from data collected by an agent while interacting with a person. We propose a QOL estimation method to integrate facial expressions, head fluctuations, and eye movements that can be extracted as visual information during the interaction with the communication agent. Our goal is to implement a multiple feature vectors learning estimator that incorporates convolutional 3D to learn spatiotemporal features. However, there is no database required for QOL estimation. Therefore, we implement a free communication agent and construct our database based on information collected through interpersonal experiments using the agent. To verify the proposed method, we focus on the estimation of the mental health QOL scale, which is the most difficult to estimate among the eight scales that compose QOL based on a previous study. We compare the four estimation accuracies: single-modal learning using each of the three features, i.e., facial expressions, head fluctuations, and eye movements and multiple feature vectors learning integrating all the three features. The experimental results show that multiple feature vectors learning has fewer estimation errors than all the other single-modal learning, which uses each feature separately. The experimental results for evaluating the difference between the estimated QOL score by the proposed method and the actual QOL score calculated by the conventional method also show that the average error is less than 10 points and, thus, the proposed system can estimate the QOL score. Thus, it is clear that the proposed new approach for estimating human conditions can improve the quality of human-robot interactions and personalized monitoring. © 2020 IEEE.",,"Agricultural robots; Eye movements; Visual communication; Communication agents; Communication robot; Conventional methods; Estimation methods; Facial Expressions; Free communications; Spatio temporal features; Visual information; Social robots",,,,,"Ministry of Education, Culture, Sports, Science and Technology, Monbusho; KDDI Foundation","*Research supported by the KDDI Foundation Research Grant Program 2019 and Graduate Program for Social ICT Global Creative Leaders (GCL) of The University of Tokyo by the Ministry of Education, Culture, Sports, Science and Technology (MEXT).",,,,,,,,,,"(2019) World Population Ageing 2019, , The United Nations; (2017) World Population Ageing 2017, , The United Nations; (2015) New Robot Strategy: Japan's Robot Strategy-Vision, Strategy, Action Plan, , Prime Minister's Office; Li, S., Deng, W., (2018) Deep Facial Expression Recognition: A Survey; El Ayadi, M., Kamel, M., Karray, F., Survey on speech emotion recognition: Features, classification schemes, and databases (2011) Pattern Recognition, 44 (3), pp. 572-587; (2018) Annual Report on the Ageing Society 2018, , Cabinet Office; Karnofsky, D.A., The clinical evaluation of chemotherapeutic agents in cancer (1949) Evaluation of Chemotherapeutic Agents, pp. 191-205; Fukuhara, S., Bito, S., Green, J., Hsiao, A., Kurokawa, K., Translation, adaptation, and validation of the sf-36 health survey for use in Japan (1998) Journal of Clinical Epidemiology, 51 (11), pp. 1037-1044; Wallerstedt, A., Quality of life after open radical prostatectomy compared with robot-assisted radical prostatectomy (2019) European Urology Focus, 5 (3), pp. 389-398; Kårefjärd, A., Nordgren, L., Effects of dog-assisted intervention on quality of life in nursing home residents with dementia (2019) Scandinavian Journal of Occupational Therapy, 26 (6), pp. 433-440; Nakagawa, S., Enomoto, D., Yonekura, S., Kanazawa, H., Kuniyoshi, Y., A telecare system that estimates quality of life through communication (2018) 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 325-330; Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M., Learning spatiotemporal features with 3d convolutional networks (2015) Proceedings of the IEEE International Conference on Computer Vision, pp. 4489-4497; Chang, Y., Vieira, M., Turk, M., Velho, L., Automatic 3d facial expression analysis in videos (2005) International Workshop on Analysis and Modeling of Faces and Gestures, pp. 293-307. , W. Zhao, S. Gong, X. Tang (Eds.). Springer-Verlag Berlin Heidelberg, Heidelberg, Germany; Wang, J., Yin, L., Wei, X., Sun, Y., 3D facial expression recognition based on primitive surface feature distribution (2006) Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, pp. 1399-1406; Scherer, K.R., Vocal communication of emotion: A review of research paradigms (2003) Speech Commun., 40 (1-2), pp. 227-256; Kwon, O.W., Chan, K., Hao, J., Lee, T.W., Emotion recognition by speech signals (2003) Eighth European Conference on Speech Communication and Technology, pp. 125-128. , International Speech Communication Association, Baixas, France; Vidrascu, L., Devillers, L., Detection of real-life emotions in call centers (2005) Ninth European Conference on Speech Communication and Technology, pp. 1841-1844. , International Speech Communication Association, Baixas, France; Ren, F., Deng, J., Background knowledge-based multi-stream neural network for text classification (2018) Applied Sciences, 8 (12), p. 2472. , https://doi.org/10.3390/app8122472; Ren, F., Matsumoto, K., Semi-automatic creation of youth slang corpus and its application to affective computing (2016) IEEE Transactions on Affective Computing, 7 (2), pp. 176-189; Ren, F., Wu, Y., Predicting user-topic opinions in twitter with social and topical context (2013) IEEE Transactions on Affective Computing, 4 (4), pp. 412-424; Quan, C., Ren, F., Sentence emotion analysis and recognition based on emotion words using ren-cecps (2010) Int. J. Adv. Intell., 2 (1), pp. 105-117; Lin, Y.P., Wang, C.H., Jung, T.P., Wu, T.L., EEG-based emotion recognition in music listening (2010) IEEE T. Biomed, Eng., 57 (7), pp. 1798-1806; Jenke, R., Peer, A., Buss, M., Feature extraction and selection for emotion recognition from EEG (2014) IEEE T. Affect. Comput, 5 (3), pp. 327-339; Ren, F., Dong, Y., Wang, W., Emotion recognition based on physiological signals using brain asymmetry index and echo state network (2019) Neural Computing and Applications, 31, pp. 4491-4501. , doi. org/10. 1007/s00521-018-3664-1; Bänziger, T., Grandjean, D., Scherer, K.R., Emotion recognition from expressions in face, voice, and body: The multimodal emotion recognition test (mert) (2009) Emotion, 9 (5), pp. 691-704. , dx; Fan, Y., Lu, X., Li, D., Video-based emotion recognition using cnn-rnn and c3d hybrid networks (2016) Proceedings of the 18th ACM International Conference on Multimodal Interaction, pp. 445-450; Cao, Z., Simon, T., Wei, S.E., Sheikh, Y., Realtime multi-person 2d pose estimation using part affinity fields (2017) Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit, pp. 7291-7299; Zeng, Z., Pantic, M., Roisman, G.I., A survey of affect recognition methods: Audio, visual, and spontaneous expressions (2008) IEEE Transactions on Pattern Analysis and Machine Intelligence, 31 (1), pp. 39-58; Cohn, J.F., Reed, L.I., Ambadar, Z., Automatic analysis and recognition of brow actions and head motion in spontaneous facial behavior (2004) 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583), 1, pp. 610-616. , IEEE; Zhang, Y., Ji, Q., Active and dynamic information fusion for facial expression understanding from image sequences (2005) IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (5), pp. 699-714; Liu, P., Yin, L., Spontaneous facial expression analysis based on temperature changes and head motions (2015) 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 1, pp. 1-6. , IEEE; Zhao, L.M., Li, R., Zheng, W.L., Lu, B.L., Classification of five emotions from EEG and eye movement signals: Complementary representation properties (2019) 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 611-614; Lu, Y., Zheng, W.-L., Li, B., Combining eye movements and EEG to enhance emotion recognition (2015) Twenty-Fourth International Joint Conference on Artificial Intelligence; Hurukawa, S., Yoneya, M., Hsin-L, L., Mind movements read from the eyes, key technology of heart-touching-ai (2016) NTT Communication Science Laboratories, NTT Technical Journal 2016, pp. 22-25; Kocaguneli, E., Menzies, T., Software effort models should be assessed via leave-one-out validation (2013) Journal of Systems and Software, 86 (7), pp. 1879-1890",,,"et al.;Furhat Robotics;IEEE;IEEE Robotics and Automation Society (RA);Korea Robotics Society (KROS);Robotics Society of Japan (RSJ)","Institute of Electrical and Electronics Engineers Inc.","29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020","31 August 2020 through 4 September 2020",,164066,,9781728160757,,,"English","IEEE Int. Conf. Robot Hum. Interact. Commun., RO-MAN",Conference Paper,"Final","",Scopus,2-s2.0-85095745538
"Hashemi A., McPhee J.","57215457448;7005199260;","Trajectory planning for a human-robot interaction rehabilitation system using direct-collocation optimization",2020,"International Conference of Control, Dynamic Systems, and Robotics",,,"134","1","2",,,"10.11159/cdsr20.134","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100746222&doi=10.11159%2fcdsr20.134&partnerID=40&md5=3d29120aeda11cedaa58289e6d8068b4","University of Waterloo, 200 University Ave W., Waterloo, Canada","Hashemi, A., University of Waterloo, 200 University Ave W., Waterloo, Canada; McPhee, J., University of Waterloo, 200 University Ave W., Waterloo, Canada","One of the essential considerations in Human-Robot Interaction (HRI) research is the trajectory planning phase [1]. This issue becomes vital in rehabilitation robotics due to the sensitive nature of the problem and patient involvement. An appropriate trajectory is conducive to improved clinical progress whereas an unsuitable practice can lead to the aggravation of the patient status. Due to the prevalence of stroke in recent years, this study aims to find the task-space manipulation trajectory for a 2 degrees of freedom (DOF) post-stroke end-effector-based rehabilitation robot, developed by Quanser, the Toronto Rehabilitation Institute (TRI), and the University of Waterloo. This planar robot provides upper-limb repetitive practices by moving the patient’s hand in the horizontal plane. The model of the robot is coupled with a 2D upper extremity musculoskeletal model, consisting of 6 lumped muscles, to form the HRI nonlinear dynamic system [2, 3]. This model includes four states, 2 robot angles and angular velocities, and 8 inputs, 2 robot motor torques and 6 arm muscle activations. An optimization-based trajectory planning scheme is presented and a multi-variable single-phase continuous-time optimization problem is set up to acquire the end-effector trajectory. Previous research on the trajectory planning of rehabilitation robots has considered a predefined path and found the corresponding parameters [4, 5]. In this study, however, the decision variables are free at the optimization points and splines are used only after optimization, in the interpolation phase. Other studies have considered kinematic and skeletal dynamic human models in the planar frameworks without considering muscles [6,7]. The integration of the human arm model with muscle dynamics in the optimization has the merit of producing a clinically plausible solution. A direct-collocation method is used to solve the problem, with the help of an open-source optimization library in MATLAB called “OptimTraj” [8]. The decision variables are the robot states, and the HRI nonlinear dynamics is considered as the equality constraint: © 2020, Avestia Publishing. All rights reserved.",,,,,,,,,,,,,,,,,,"Meng, Q., Xie, Q., Deng, Z., Yu, H., A general kinematics model for trajectory planning of upper limb exoskeleton robots (2019) International Conference on Intelligent Robotics and Applications, 11745, pp. 63-75. , LNAI; Ghannadi, B., Mehrabi, N., McPhee, J., Development of a human-robot dynamic model to support model-based control design of an upper limb rehabilitation robot (2015) Barcelona: ECCOMAS Thematic Conference on Multibody Dynamics; Mehrabi, N., Razavian, R. S., McPhee, J., A Physics-Based Musculoskeletal Driver Model to Study Steering Tasks (2015) J. Comput. Nonlinear Dyn, 10 (2), pp. 1-8; Rosati, G., Volpe, G., Biondi, A., Trajectory planning of a two-link rehabilitation robot arm (2007) IFToMM World Congress; Kalani, H., Akbarzadeh, A., Nabavi, S. N., Moghimi, S., Dynamic modeling and CPG-based trajectory generation for a masticatory rehab robot (2018) Intell. Serv. Robot, 11 (2), pp. 187-205; Tang, Sh., Chen, L., Barsotti, M., Hu, L., Li, Y., Wu, X., Bai, L., Hou, W., Kinematic synergy of multi-DOF movement in upper limb and its application for rehabilitation exoskeleton motionplanning (2019) Frontiers in Neurorobotics, 13. , (November); Nagengast, A. J., Braun, D. A., Wolpert, D. M., Optimal control predicts human performance on objects with internal degrees of freedom,"" (2009) PLoS Comput Biol, 5 (6), p. e1000419; Kelly, M., An introduction to trajectory optimization: How to do your own direct collocation (2017) SIAM Rev, 59 (4), pp. 849-904; Berret, B., Chiovetto, E., Nori, F., Pozzo, T., Evidence for composite cost functions in arm movement planning: an inverse optimal control approach (2011) PLoS Comput Biol, 7 (10), p. e1002183",,,,"Avestia Publishing","7th International Conference on Control, Dynamic Systems, and Robotics, CDSR 2020","9 November 2020 through 11 November 2020",,253669,23685433,,,,"English","Int. Conf. Control Dyn. Syst. Robot.",Conference Paper,"Final","All Open Access, Bronze",Scopus,2-s2.0-85100746222
"Feil-Seifer D., Haring K.S., Rossi S., Wagner A.R., Williams T.","14033850500;55639550500;55619320887;7401456914;7404172627;","Where to Next the Impact of COVID-19 on Human-Robot Interaction Research",2020,"ACM Transactions on Human-Robot Interaction","10","1","3405450","","",,26,"10.1145/3405450","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097195243&doi=10.1145%2f3405450&partnerID=40&md5=4ab65f200571ff4bd776e18d0a79ddd6","University of Nevada, Reno, Italy","Feil-Seifer, D., University of Nevada, Reno, Italy; Haring, K.S., University of Nevada, Reno, Italy; Rossi, S., University of Nevada, Reno, Italy; Wagner, A.R., University of Nevada, Reno, Italy; Williams, T., University of Nevada, Reno, Italy","The COVID-19 pandemic will have a profound and long-lasting impact on the entire scientific endeavor. Scientists already are adapting research programs to adapt to changes in what is prioritized - and what is possible; educators are changing the way that the next generation of researchers are trained, and flagship conferences in many fields are being cancelled, postponed, and fundamentally transformed. These broad-reaching changes are particularly impactful to human-oriented domains such as human-robot interaction (HRI). Because in-person human-subject experiments can take a year or more to conduct, the research we will see published in the field in the immediate future may appear to be ""business as usual,""with accounts of laboratory studies with large numbers of in-person participants. The research currently being performed, however, is of course a different story entirely. Studies that were under way when the current crisis began will be truncated, resulting either in work that cannot be published or in work whose true impact is difficult to accurately assess. Yet HRI research performed in the coming years will be changed in fundamentally different ways; the inability to perform - or expect future performance of - in-person human subjects research, especially research involving tactile or multiparty interaction, will change both the dominant methodological techniques employed by HRI researchers and the very research questions that the field chooses to - and is able to - address. These challenges demand that HRI researchers identify precisely how the field can maintain research quality and impact while the ability to conduct human-subject studies is severely impaired for an undetermined amount of time. A natural inclination may be simply to wait the crisis out in the hope of a speedy return to normalcy; however, in this article, we argue that the community can also take this opportunity to reevaluate and refocus how research in this field is conducted and how students are mentored in ways that will yield benefits for years to come after the current crisis has ended. © 2020 Owner/Author.","COVID-19 impact; Human-robot interaction; research","Agricultural robots; Man machine systems; Business-as-usual; Future performance; Human robot Interaction (HRI); Human subject experiments; Laboratory studies; Multi-party interactions; Research programs; Research questions; Human robot interaction",,,,,,,,,,,,,,,,"Aust, F., Diedenhofen, B., Ullrich, S., Musch, J., Seriousness checks are useful to improve data validity in online research (2013) Behavior Research Methods, 45 (2), pp. 527-535; Carlson, Z., Lemmon, L., Higgins, M., Frank, D., Salek Shahrezaie, R., Feil-Seifer, D., Perceived mistreatment and emotional capability following aggressive treatment of robots and computers (2019) International Journal of Social Robotics, 11 (5), pp. 727-739. , https://doi.org/10.1007/s12369-019-00599-8, (Oct. 2019); Cesta, A., Cortellessa, G., Orlandini, A., Tiberio, L., Long-term evaluation of a telepresence robot for the elderly: Methodology and ecological case study (2016) International Journal of Social Robotics, 8, pp. 421-441. , https://doi.org/10.1007/s12369-016-0337-z; Visser De, E.J., Peeters, M.M.M., Jung, M.F., Kohn, S., Shaw, T.H., Pak, R., Neerincx, M.A., Towards a theory of longitudinal trust calibration in human-robot teams (2020) International Journal of Social Robotics, 12, pp. 459-478; Edwards, C., Edwards, A., Spence, P.R., Westerman, D., Initial interaction expectations with robots: Testing the human-to-human interaction script (2016) Communication Studies, 67 (2), pp. 227-238; Fitter, N.T., Raghunath, N., Cha, E., Sanchez, C.A., Takayama, L., Mataric, M.J., Are we there yet Comparing remote learning technologies in the university classroom (2020) IEEE Robotics and Automation Letters, 5 (2), pp. 2706-2713; Sophie Haring, K., Watanabe, K., Silvera-Tawil, D., Velonaki, M., Matsumoto, Y., Touching an Android robot: Would you do it and how (2015) Proceedings of the 2015 International Conference on Control, Automation, and Robotics, pp. 8-13. , IEEE, Los Alamitos, CA; Blake Jackson, R., Williams, T., Smith, N., Exploring the role of gender in perceptions of robotic noncompliance (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction., pp. 559-567; Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., Hagita, N., An affective guide robot in a shopping mall (2009) Proceedings of the 4th ACM/IEEE International Conference on Human Robot Interaction., pp. 173-180; Kwon, M., Biyik, E., Talati, A., Bhasin, K., Losey, D.P., Sadigh, D., When humans aren't optimal: Robots that collaborate with risk-aware humans (2020) Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (HRI'20). ACM, pp. 43-52. , https://doi.org/10.1145/3319502.3374832, New York, NY; Picard, R.W., (2000) Affective Computing, , MIT Press, Cambridge, MA; Pu, L., Moyle, W., Jones, C., Todorovic, M., The effectiveness of social robots for older adults: A systematic review and meta-analysis of randomized controlled studies (2018) Gerontologist, 59 (1), pp. e37-e51. , https://doi.org/10.1093/geront/gny046; Riether, N., Hegel, F., Wrede, B., Horstmann, G., Social facilitation with social robots (2012) Proceedings of the 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI'12). IEEE, pp. 41-47. , Los Alamitos, CA; Robinette, P., Howard, A.M., Wagner, A.R., Effect of robot performance on human-robot trust in time-critical situations (2017) IEEE Transactions on Human-Machine Systems, 47 (4), pp. 425-436; Rossi, S., Rossi, A., Dautenhahn, K., The secret life of robots: Perspectives and challenges for robot's behaviours during non-interactive tasks (2020) International Journal of Social Robotics, , https://doi.org/10.1007/s12369-020-00650-z, Published online. April 23 2020; Sabanovic, S., Michalowski, M.P., Simmons, R., Robots in the wild: Observing human-robot social interaction outside the lab (2006) Proceedings of the 9th IEEE International Workshop on Advanced Motion Control. IEEE, pp. 596-601. , Los Alamitos, CA; Zhi Tan, X., Vazquez, M., Carter, E.J., Morales, C.G., Steinfeld, A., Inducing bystander interventions during robot abuse with social mechanisms (2018) Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction., pp. 169-177; Vollmer, A.-L., Read, R., Trippas, D., Belpaeme, T., Children conform, adults resist: A robot group induced peer pressure on normative social conformity (2018) Science Robotics, 3 (21), p. eaat7111; Wagner, A.R., Robinette, P., Howard, A., Modeling the human-robot trust phenomenon: A conceptual framework based on risk (2018) ACM Transactions on Interactive Intelligent Systems, 8 (4), pp. 1-24; Wladis, C., Samuels, J., Do online readiness surveys do what they claim Validity, reliability, and subsequent student enrollment decisions (2016) Computers & Education, 98, pp. 39-56; Yang, G.-Z., Nelson, B.J., Murphy, R.R., Choset, H., Christensen, H., Collins, S.H., Dario, P., Combating COVID-19-The role of robotics in managing public health and infectious diseases (2020) Science Robotics, 5 (40), p. eabb5589. , https://robotics.sciencemag.org/content/5/40/eabb5589.full.pdf",,,,"Association for Computing Machinery",,,,,25739522,,,,"English","ACM Trans. Hum. Robot Interact",Article,"Final","All Open Access, Bronze",Scopus,2-s2.0-85097195243
"Mazzoleni N., Kim J.Y., Bryant M.","57195067080;57221537932;57203196529;","The effect of resistive forces in variable recruitment fluidic artificial muscle bundles: A configuration study",2020,"Proceedings of SPIE - The International Society for Optical Engineering","11374",,"113740J","","",,2,"10.1117/12.2557907","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096363573&doi=10.1117%2f12.2557907&partnerID=40&md5=505d8db2f051a739b1a2201ffdd730fe","North Carolina State University, Department of Mechanical and Aerospace Engineering, 1840 Entrepreneur Dr., Raleigh, NC  27606, United States","Mazzoleni, N., North Carolina State University, Department of Mechanical and Aerospace Engineering, 1840 Entrepreneur Dr., Raleigh, NC  27606, United States; Kim, J.Y., North Carolina State University, Department of Mechanical and Aerospace Engineering, 1840 Entrepreneur Dr., Raleigh, NC  27606, United States; Bryant, M., North Carolina State University, Department of Mechanical and Aerospace Engineering, 1840 Entrepreneur Dr., Raleigh, NC  27606, United States","The use of soft, compliant actuators has recently gained research attention as a potential approach to improve human-robot interaction compatibility. Fluidic artificial muscles, or McKibben actuators, are a popular class of soft actuator due to their low cost and high force-to-weight ratio. However, traditional McKibben actuators face efficiency problems, as in most actuation schemes, the actuator is sized for the largest possible load, resulting in energy loss when operating at lower force regimes. To address this issue, our group has developed a bio-inspired actuation strategy called variable recruitment. In variable recruitment, actuators are placed within a bundle and can be sequentially activated depending on the required load. This strategy mimics the hierarchical architecture of mammalian muscle tissue and improves system efficiency and bandwidth while allowing for variable stiffness properties. Previous variable recruitment models and controllers assume that the force output of each actuator is independent and that these forces sum to provide the total bundle force. However, our recent work has shown that there is significant interaction between actuators within a bundle, particularly at lower recruitment states. This is because at these states, inactive or partially activated actuators resist bundle motion and reduce total force production. In this paper, we study these resistive effects at low recruitment states by considering two different variable recruitment configurations: a fixed-end configuration (with resistive forces) and a tendon configuration (designed with tendons to eliminate resistive forces). We then assess the tradeoffs between the two configurations. We found that while using the tendon configuration eliminates resistive forces, if we consider both configurations with the same overall system length, the tendon configuration has less overall system free strain because its FAMs have to be shorter than those of the fixed-end configuration. However, despite this difference in free strain, our results still show that the tendon configuration can have higher maximum load capacity and efficiency than the fixed-end configuration and that the specific application and system requirements will dictate the proper configuration choice. © 2020 SPIE","Fluidic artificial muscles; Inactive/partially activated motor units; Resistive forces; Variable recruitment","Actuators; Biomimetics; Energy dissipation; Human robot interaction; Mammals; Muscle; Compliant actuator; Fluidic artificial muscles; Hierarchical architectures; Maximum load capacity; McKibben actuators; Recruitment models; System requirements; Variable stiffness; Tendons",,,,,"National Science Foundation, NSF: CMMI-1845203","The authors gratefully acknowledge funding support for this research from the National Science Foundation under Award No. CMMI-1845203 and Program Officer Irina Dolinskaya, as well as the NSF Graduate Research Fellowship Program.",,,,,,,,,,"Tondu, B., Modelling of the McKibben artificial muscle: A review Article Journal of Intelligent Material Systems and Structures, 23 (3), pp. 225-253; Tiwari, R., Meller, M.A., Wajcs, K.B., Moses, C., Reveles, I., Garcia, E., Hydraulic artificial muscles (2012) Journal of Intelligent Material Systems and Structures, 23 (3), pp. 301-312; Henneman, E., Somjen, G., Carpenter, D.O., Excitability and inhibitability of motoneurons of different sizes (1965) Journal of Neurophysiology, 28 (3), pp. 599-620; Bryant, M., Meller, M.A., Garcia, E., Variable recruitment fluidic artificial muscles: Modeling and experiments (2014) Smart Materials and Structures, 23 (7), p. 074009; DeLaHunt, S.A., Pillsbury, T.E., Wereley, N.M., Variable recruitment in bundles of miniature pneumatic artificial muscles (2016) Bioinspiration & Biomimetics, 11 (5), p. 056014; Meller, M., Chipka, J., Volkov, A., Bryant, M., Garcia, E., Improving actuation efficiency through variable recruitment hydraulic McKibben muscles: Modeling, orderly recruitment control, and experiments (2016) Bioinspiration & Biomimetics, 11 (6), p. 065004; Jenkins, T.E., Chapman, E.M., Bryant, M., Bio-inspired online variable recruitment control of fluidic artificial muscles (2016) Smart Materials and Structures, 25 (12), p. 125016; Chapman, E., Jenkins, T., Bryant, M., Parametric study of a fluidic artificial muscle actuated electrohydraulic system (2016) Volume 2: Modeling, Simulation and Control; Bio-Inspired Smart Materials and Systems, , Energy Harvesting, V002T06A003, ASME; Meller, M.A., Bryant, M., Garcia, E., Reconsidering the McKibben muscle: Energetics, operating fluid, and bladder material (2014) Special Issue Article Journal of Intelligent Material Systems and Structures, 25 (18), pp. 2276-2293; Kim, J.Y., Mazzoleni, N., Bryant, M., Investigation of resistive forces in variable recruitment fluidic artificial muscle bundles (2020) Proceedings of the 38th IMAC, A Conference and Exposition on Structural Dynamics 2020; Meller, M., Chipka, J., Volkov, A., Bryant, M., Garcia, E., Improving actuation efficiency through variable recruitment hydraulic McKibben muscles: Modeling, orderly recruitment control, and experiments (2016) Bioinspiration & Biomimetics, 11 (6), p. 065004; Robinson, R.M., Kothera, C.S., Wereley, N.M., Variable recruitment testing of pneumatic artificial muscles for robotic manipulators (2015) IEEE/ASME Transactions on Mechatronics, 20 (4), pp. 1642-1652; DeLaHunt, S.A., Pillsbury, T.E., Wereley, N.M., Variable recruitment in bundles of miniature pneumatic artificial muscles (2016) Bioinspiration & Biomimetics, 11 (5), p. 056014; Chipka, J., Meller, M.A., Volkov, A., Bryant, M., Garcia, E., Linear dynamometer testing of hydraulic artificial muscles with variable recruitment (2017) Original Article Journal of Intelligent Material Systems and Structures, 28 (15), pp. 2051-2063; Meller, M., Kogan, B., Bryant, M., Garcia, E., Model-based feedforward and cascade control of hydraulic McKibben muscles (2018) Sensors and Actuators, A: Physical; Klute, G.K., Hannaford, B., Accounting for elastic energy storage in mckibben artificial muscle actuators (2000) Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, 122 (2), pp. 386-388; Kothera, C.S., Jangid, M., Sirohi, J., Wereley, N.M., Experimental characterization and static modeling of McKibben actuators (2009) Journal of Mechanical Design, Transactions of the ASME, 131 (9), pp. 0910101-09101010; Ball, E., Garcia, E., Effects of bladder geometry in pneumatic artificial muscles (2016) Journal of Medical Devices, Transactions of the ASME, 10 (4); Yu, Z., Pillsbury, T., Wang, G., Wereley, N.M., Hyperelastic analysis of pneumatic artificial muscle with filament-wound sleeve and coated outer layer (2019) Smart Materials and Structures, 28 (10), p. 105019",,"Knez M.Lakhtakia A.Martin-Palma R.J.","The Society of Photo-Optical Instrumentation Engineers (SPIE)","SPIE","Bioinspiration, Biomimetics, and Bioreplication X 2020","27 April 2020 through 8 May 2020",,164172,0277786X,9781510635258,PSISD,,"English","Proc SPIE Int Soc Opt Eng",Conference Paper,"Final","",Scopus,2-s2.0-85096363573
"Sharma V.K., Murthy L.R.D., Singh Saluja K., Mollyn V., Sharma G., Biswas P.","57211923587;57205505055;57218826854;57217096577;57220951992;14007579800;","Webcam controlled robotic arm for persons with SSMI",2020,"Technology and Disability","32","3",,"179","197",,4,"10.3233/TAD-200264","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090357700&doi=10.3233%2fTAD-200264&partnerID=40&md5=bd409ae8ea113024f156eb2ecbd934d6","Indian Institute of Science, Bangalore, India; Indian Institute of Technology, Madras, India; Indian Institute of Information Technology, Kalyani, India","Sharma, V.K., Indian Institute of Science, Bangalore, India; Murthy, L.R.D., Indian Institute of Science, Bangalore, India; Singh Saluja, K., Indian Institute of Science, Bangalore, India; Mollyn, V., Indian Institute of Technology, Madras, India; Sharma, G., Indian Institute of Information Technology, Kalyani, India; Biswas, P., Indian Institute of Science, Bangalore, India","BACKGROUND: People with severe speech and motor impairment (SSMI) often uses a technique called eye pointing to communicate with outside world. One of their parents, caretakers or teachers hold a printed board in front of them and by analyzing their eye gaze manually, their intentions are interpreted. This technique is often error prone and time consuming and depends on a single caretaker. OBJECTIVE: We aimed to automate the eye tracking process electronically by using commercially available tablet, computer or laptop and without requiring any dedicated hardware for eye gaze tracking. The eye gaze tracker is used to develop a video see through based AR (augmented reality) display that controls a robotic device with eye gaze and deployed for a fabric printing task. METHODS: We undertook a user centred design process and separately evaluated the web cam based gaze tracker and the video see through based human robot interaction involving users with SSMI. We also reported a user study on manipulating a robotic arm with webcam based eye gaze tracker. RESULTS: Using our bespoke eye gaze controlled interface, able bodied users can select one of nine regions of screen at a median of less than 2 secs and users with SSMI can do so at a median of 4 secs. Using the eye gaze controlled human-robot AR display, users with SSMI could undertake representative pick and drop task at an average duration less than 15 secs and reach a randomly designated target within 60 secs using a COTS eye tracker and at an average time of 2 mins using the webcam based eye gaze tracker. CONCLUSION: The proposed system allows users with SSMI to manipulate physical objects without any dedicated eye gaze tracker. The novelty of the system is in terms of non-invasiveness as earlier work mostly used glass based wearable trackers or head/face tracking but no other earlier work reported use of webcam based eye tracking for controlling robotic arm by users with SSMI. © 2020 - IOS Press and the authors. All rights reserved.","assistive technology; Eye gaze tracking; human robot interaction; SSMI","acceleration; accuracy; adult; algorithm; arm movement; Article; classifier; clinical article; deep learning; electroencephalography; eye movement; eye tracking; female; gaze; human; kinematics; load carrying capacity; male; motor dysfunction; object manipulation; robotics; speech disorder; videorecording; young adult",,,"Logitech C615 camera",,"Microsoft Research","We would like to thank all participants and colleagues at Vidyasagar, Chennai for taking part in our research and also grateful to Microsoft Research, India and DST SERB for funding the research.",,,,,,,,,,"Agarwal, A., JeevithaShree, D.V., Saluja, K.S., Sahay, A., Mounika, P., Sahu, A., Biswas P0 comparing two webcam-based eye gaze trackers for users with severe speech and motor impairment (2019) Research into Design for a Connected World, pp. 641-652; Alsharif, S., Kuzmicheva, O., Gräser, A., (2016) Gaze Gesture-based Human Robot Interface, Zweite Transdisziplinäre Konferenz, , Technische Unterstützungssysteme, die Menschen wirklich wollen; Baltrusaitis, T., Zadeh, A., Lim, Y.C., Morency, L.P., Openface 2.0: Facial behavior analysis toolkit (2018) 2018 13th Ieee International Conference on Automatic Face &Gesture Recognition, pp. 59-66; Bannat, A., Gast, J., Rehrl, T., Rösel, W., Rigoll, G., Wallhoff, F., A multimodal human-robot-interaction scenario: Working together with an industrial robot (2009) International Conference on Human-Computer Interaction, pp. 303-311; Betke, M., James, G., Peter, F., The camera mouse: Visual tracking of body features to provide computer access for people with severe disabilities (2002) Ieee Transactions on Neural Systems and Rehabilitation Engineering., 10 (1), pp. 1-10; Biswas, P., Jeevithashree, D.V., Eye gaze controlled MFD for military aviation (2018) Acm International Conference on Intelligent User Interfaces (IUI; Biswas, P.R., (2009) Modelling Perception Using Image Processing Algorithms, , 23rd British Computer Society Conference on Human-Computer Interaction; Biswas, P., (2016) Using Eye Gaze Controlled Interfaces in Automotive Environments, , Springer Briefs in Computer Science; Bonneau, E., Taha, F., Gravez, P., Lamy, S., Surgicobot: Surgical gesture assistance cobot for maxillo-facial interventions (2004) Perspective in Image-Guided Surgery, pp. 353-360; Borgestig, M., Eye gaze performance for children with severe physical impairments using gaze-based assistive technology-A longitudinal study (2016) Assistive Technology., 28 (2), pp. 93-102; Bremner, P., Celiktutan, O., Gunes, H., Personality perception of robot avatar tele-operators (2016) 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 141-148; Chen, Y., Newman, W.S., A human-robot interface based on electrooculography (2004) Proceedings of the Ieee Intl Conf on Robotics and Automation; Cristanti, R.Y., Sigit, R., Harsono, T., Adelina, D.C., Nabilah, A., Anggraeni, N.P., Eye gaze tracking to operate Android-based communication helper application (2017) International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC); Cristina, S., Camilleri, K.P., Cursor control by point-of-regard estimation for a computer with integrated webcam (2014) The 8th International Conference on Advanced Engineering Computing and Applications in Sciences (ADVCOMP), pp. 126-131. , The International Academy, Research and Industry Association (IARIA); Cuong, N.H., Hoang, H.T., Eye-gaze detection with a single webcam based on geometry features extraction (2010) Control Automation Robotics &Vision (ICARCV)., pp. 2507-2512; Dalal, N., Triggs, B., Histograms of oriented gradients for human detection (2005) 2005 Ieee Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 1, pp. 886-893. , San Diego C, USA; Dautenhahn, K., Woods, S., Kaouri, C., Walters, M.L., Koay, K.L., Werry, I., What is a robot companion-friend, assistant or butler? 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Accessed on 19/3/2020; Wojciechowski, A., Fornalczyk, K., Single web camera robust interactive eye-gaze tracking method (2015) Bulletin of the Polish Academy of Sciences Technical Sciences., 63 (4), pp. 879-886; Woodworth, R.S., The accuracy of voluntary movement (1899) Psychological Review, 3; Zaira, P.R., (2014) The Wam Arm: Modelling, Control and Its Application in a Hmi Based on Gaze Tracking, , Thesis Submitted for the Degree of Doctor of Philosophy the University of Leicester; Zhang, G., Eye-Gaze-Controlled telepresence robots for people with motor disabilities (2019) Proceedings of the ACM/IEEE Human Robot Interaction","Biswas, P.; Indian Institute of ScienceIndia; email: pradipta@iisc.ac.in",,,"IOS Press",,,,,10554181,,TEDIF,,"English","Technol. Disabil.",Article,"Final","All Open Access, Green",Scopus,2-s2.0-85090357700
"Fattal C., Cossin I., Pain F., Haize E., Marissael C., Schmutz S., Ocnarescu I.","55976419500;57197868522;55362468500;57218099214;57218109055;57220410462;55330539700;","Perspectives on usability and accessibility of an autonomous humanoid robot living with elderly people",2020,"Disability and Rehabilitation: Assistive Technology",,,,"1","13",,5,"10.1080/17483107.2020.1786732","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087906458&doi=10.1080%2f17483107.2020.1786732&partnerID=40&md5=de1432d90f17508adfab08d8100f8180","PM&R Department, Rehabilitation Center La Châtaigneraie, Menucourt, France; Association APPROCHE, Ploemeur, France; Strate School of Design, Sèvres, France; Rehabilitation Center Jacques Arnaud, Bouffémont, France; Rehabilitation Center Ugecam, Coubert, France","Fattal, C., PM&R Department, Rehabilitation Center La Châtaigneraie, Menucourt, France, Association APPROCHE, Ploemeur, France; Cossin, I., Strate School of Design, Sèvres, France; Pain, F., Strate School of Design, Sèvres, France; Haize, E., Association APPROCHE, Ploemeur, France, Rehabilitation Center Jacques Arnaud, Bouffémont, France; Marissael, C., PM&R Department, Rehabilitation Center La Châtaigneraie, Menucourt, France, Association APPROCHE, Ploemeur, France; Schmutz, S., Association APPROCHE, Ploemeur, France, Rehabilitation Center Ugecam, Coubert, France; Ocnarescu, I., Strate School of Design, Sèvres, France","Purpose: The aim of this study was to assess how cohabitation with a social robot for seven days night and day is perceived by elderly potential users and to suggest technological improvements for future home integration. Methods: Fifteen participants were invited to interact with Pepper®, 24 h a day and 7 days a week. Perceived utility assessment was achieved using a dedicated questionnaire. Usability assessment was carried out by measuring satisfaction with the human-robot interaction and listing malfunctions. Results: Seventy-eight tasks out of 115 tasks were reported respectively as very high and high. On day 1, satisfaction was at its higher level with a mean of 6.8. On day 8, it maintained at 6.0 despite technical malfunctions and failures, listed with details in this paper. Acceptability was positive. Pepper was mainly considered as an assistant and a link with family and friends and never as an enemy or intruder. Pepper has proven to be potentially able to meet five needs: making the person’s environment safe by alerting contact persons, reminding the user of health-related tasks, enabling “doubt removal”, maintaining social relationships and following the user. Conclusions: Pepper is more than a “demonstration” device classified as level 6 of the nine-point Technology Readiness Level (TRL) scale for the hard and software and level 5 in behavioural terms. Once the technological locks and malfunctions have been overcome, Pepper should be applicable in the home, allowing users to test it in a more familiar environment.Implications for Rehabilitation Pepper, a humanoid robot, has been perceived as rich in potentialities that can help and secure a fragile person. Following this experience of cohabitation with a humanoid robot, the participants were unanimous in thinking that assistive robotics is the future. To be useful, the robot’s people tracking function must inevitably be improved. Pepper, the first humanoid robot ever marketed, is more than just a “demonstration”device, classified at level 6 of Technological Readiness Levels (TRL) for supporting hardware and software and level 5 in terms of behavior. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.","acceptance; elderly people; frailty; social support; Socially assistive robot",,,,,,,"This work was supported by the French Public Investment Bank BpiFrance. We are grateful to M Busy, E Lagrue, JM Montanier, A Mazel, R Gelin, the engineers who participated to the development and adaptation of the applications implemented in Pepper. We also thank Mrs Catherine Stott for her valuable translation.",,,,,,,,,,"Pu, L., Moyle, W., Jones, C., The effectiveness of social robots for older adults: a systematic review and meta-analysis of randomized controlled studies (2019) Gerontologist, 59 (1), pp. e37-e51; Blanpain, N., Buisson, G., (2016), Projections de population 2013-2070 pour la France. Résultats;,; 187 INSEE, 3 November; (2019), Highlights. 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Assistive Technol.",Article,"Final","",Scopus,2-s2.0-85087906458
"Papanagiotou D., Senteri G., Manitsaris S.","57368411400;57218659334;16230817800;","Egocentric Gesture Recognition Using 3D Convolutional Neural Networks for the Spatiotemporal Adaptation of Collaborative Robots",2021,"Frontiers in Neurorobotics","15",,"703545","","",,,"10.3389/fnbot.2021.703545","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120901611&doi=10.3389%2ffnbot.2021.703545&partnerID=40&md5=ad8481ea65b257ce67035c7f7108409e","Centre for Robotics, MINES ParisTech, PSL Université, Paris, France","Papanagiotou, D., Centre for Robotics, MINES ParisTech, PSL Université, Paris, France; Senteri, G., Centre for Robotics, MINES ParisTech, PSL Université, Paris, France; Manitsaris, S., Centre for Robotics, MINES ParisTech, PSL Université, Paris, France","Collaborative robots are currently deployed in professional environments, in collaboration with professional human operators, helping to strike the right balance between mechanization and manual intervention in manufacturing processes required by Industry 4.0. In this paper, the contribution of gesture recognition and pose estimation to the smooth introduction of cobots into an industrial assembly line is described, with a view to performing actions in parallel with the human operators and enabling interaction between them. The proposed active vision system uses two RGB-D cameras that record different points of view of gestures and poses of the operator, to build an external perception layer for the robot that facilitates spatiotemporal adaptation, in accordance with the human's behavior. The use-case of this work is concerned with LCD TV assembly of an appliance manufacturer, comprising of two parts. The first part of the above-mentioned operation is assigned to a robot, strengthening the assembly line. The second part is assigned to a human operator. Gesture recognition, pose estimation, physical interaction, and sonic notification, create a multimodal human-robot interaction system. Five experiments are performed, to test if gesture recognition and pose estimation can reduce the cycle time and range of motion of the operator, respectively. Physical interaction is achieved using the force sensor of the cobot. Pose estimation through a skeleton-tracking algorithm provides the cobot with human pose information and makes it spatially adjustable. Sonic notification is added for the case of unexpected incidents. A real-time gesture recognition module is implemented through a Deep Learning architecture consisting of Convolutional layers, trained in an egocentric view and reducing the cycle time of the routine by almost 20%. This constitutes an added value in this work, as it affords the potential of recognizing gestures independently of the anthropometric characteristics and the background. Common metrics derived from the literature are used for the evaluation of the proposed system. The percentage of spatial adaptation of the cobot is proposed as a new KPI for a collaborative system and the opinion of the human operator is measured through a questionnaire that concerns the various affective states of the operator during the collaboration. Copyright © 2021 Papanagiotou, Senteri and Manitsaris.","actions; CNN; collaborative robot; egocentric vision; gestures; human-robot collaboration; pose estimation; recognition","Assembly; Assembly machines; Behavioral research; Deep learning; Gesture recognition; Human robot interaction; Man machine systems; Manufacture; Neural networks; Action; CNN; Collaborative robots; Egocentric vision; Gesture; Gestures recognition; Human operator; Human-robot collaboration; Pose-estimation; Recognition; Convolution",,,,,"Horizon 2020: 820767","The research leading to these results has received funding from the EU Horizon 2020 Research and Innovation Programme under Grant Agreement No. 820767, CoLLaboratE project.",,,,,,,,,,"Ajoudani, A., Zanchettin, A.M., Ivaldi, S., Albu-Schoffer, A., Kosuge, K., Khatib, O., Progress and prospects of the human-robot collaboration (2018) Auton. 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"Wu M., Drnach L., Bong S.M., Song Y.S., Ting L.H.","57216796974;57204658243;57211639304;57211646767;7102389009;","Human-Human Hand Interactions Aid Balance During Walking by Haptic Communication",2021,"Frontiers in Robotics and AI","8",,"735575","","",,,"10.3389/frobt.2021.735575","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119449215&doi=10.3389%2ffrobt.2021.735575&partnerID=40&md5=f8d0751583f28ea9c1dda05f402c5572","The Wallace H. Coulter, Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States; School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States; Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO, United States; Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University School of Medicine, Atlanta, GA, United States","Wu, M., The Wallace H. Coulter, Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States; Drnach, L., School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States; Bong, S.M., The Wallace H. Coulter, Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States; Song, Y.S., Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO, United States; Ting, L.H., The Wallace H. Coulter, Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States, Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University School of Medicine, Atlanta, GA, United States","Principles from human-human physical interaction may be necessary to design more intuitive and seamless robotic devices to aid human movement. Previous studies have shown that light touch can aid balance and that haptic communication can improve performance of physical tasks, but the effects of touch between two humans on walking balance has not been previously characterized. This study examines physical interaction between two persons when one person aids another in performing a beam-walking task. 12 pairs of healthy young adults held a force sensor with one hand while one person walked on a narrow balance beam (2 cm wide x 3.7 m long) and the other person walked overground by their side. We compare balance performance during partnered vs. solo beam-walking to examine the effects of haptic interaction, and we compare hand interaction mechanics during partnered beam-walking vs. overground walking to examine how the interaction aided balance. While holding the hand of a partner, participants were able to walk further on the beam without falling, reduce lateral sway, and decrease angular momentum in the frontal plane. We measured small hand force magnitudes (mean of 2.2 N laterally and 3.4 N vertically) that created opposing torque components about the beam axis and calculated the interaction torque, the overlapping opposing torque that does not contribute to motion of the beam-walker’s body. We found higher interaction torque magnitudes during partnered beam-walking vs. partnered overground walking, and correlation between interaction torque magnitude and reductions in lateral sway. To gain insight into feasible controller designs to emulate human-human physical interactions for aiding walking balance, we modeled the relationship between each torque component and motion of the beam-walker’s body as a mass-spring-damper system. Our model results show opposite types of mechanical elements (active vs. passive) for the two torque components. Our results demonstrate that hand interactions aid balance during partnered beam-walking by creating opposing torques that primarily serve haptic communication, and our model of the torques suggest control parameters for implementing human-human balance aid in human-robot interactions. © Copyright © 2021 Wu, Drnach, Bong, Song and Ting.","balance assistance; beam-walking; haptic communication; human-human interaction; human-robot interaction; physical interaction; walking aid; walking balance",,,,,,,"The authors would like to thank the members of the Neuromechanics Laboratory for their feedback on earlier drafts and presentations of this work.",,,,,,,,,,"Aguirre-Ollinger, G., Colgate, J.E., Peshkin, M.A., Ambarish Goswami, A., A 1-DOF Assistive Exoskeleton with Virtual Negative Damping: Effects on the Kinematic Response of the Lower Limbs (2007) 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, , San Diego, CA, USA, 29 October–2 November, 2007, in; Boonsinsukh, R., Panichareon, L., Phansuwan-Pujito, P., Light Touch Cue through a Cane Improves Pelvic Stability during Walking in Stroke (2009) Arch. 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"Ullrich D., Butz A., Diefenbach S.","16680229200;55150450600;25723185400;","The Development of Overtrust: An Empirical Simulation and Psychological Analysis in the Context of Human–Robot Interaction",2021,"Frontiers in Robotics and AI","8",,"554578","","",,4,"10.3389/frobt.2021.554578","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104576644&doi=10.3389%2ffrobt.2021.554578&partnerID=40&md5=d53e4a7c45d7fb398aa0078b5828ce12","Department of Computer Science, LMU Munich, Munich, Germany; Department of Psychology, LMU Munich, Munich, Germany","Ullrich, D., Department of Computer Science, LMU Munich, Munich, Germany; Butz, A., Department of Computer Science, LMU Munich, Munich, Germany; Diefenbach, S., Department of Psychology, LMU Munich, Munich, Germany","With impressive developments in human–robot interaction it may seem that technology can do anything. Especially in the domain of social robots which suggest to be much more than programmed machines because of their anthropomorphic shape, people may overtrust the robot's actual capabilities and its reliability. This presents a serious problem, especially when personal well-being might be at stake. Hence, insights about the development and influencing factors of overtrust in robots may form an important basis for countermeasures and sensible design decisions. An empirical study [N = 110] explored the development of overtrust using the example of a pet feeding robot. A 2 × 2 experimental design and repeated measurements contrasted the effect of one's own experience, skill demonstration, and reputation through experience reports of others. The experiment was realized in a video environment where the participants had to imagine they were going on a four-week safari trip and leaving their beloved cat at home, making use of a pet feeding robot. Every day, the participants had to make a choice: go to a day safari without calling options (risk and reward) or make a boring car trip to another village to check if the feeding was successful and activate an emergency call if not (safe and no reward). In parallel to cases of overtrust in other domains (e.g., autopilot), the feeding robot performed flawlessly most of the time until in the fourth week; it performed faultily on three consecutive days, resulting in the cat's death if the participants had decided to go for the day safari on these days. As expected, with repeated positive experience about the robot's reliability on feeding the cat, trust levels rapidly increased and the number of control calls decreased. Compared to one's own experience, skill demonstration and reputation were largely neglected or only had a temporary effect. We integrate these findings in a conceptual model of (over)trust over time and connect these to related psychological concepts such as positivism, instant rewards, inappropriate generalization, wishful thinking, dissonance theory, and social concepts from human–human interaction. Limitations of the present study as well as implications for robot design and future research are discussed. © Copyright © 2021 Ullrich, Butz and Diefenbach.","demonstration; human–robot interaction; overtrust; prior experience; psychological perspective; reputation",,,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV8097","Part of this research has been funded by the German Federal Ministry of Education and Research (BMBF), project GINA (FKZ: 16SV8097).",,,,,,,,,,"Albright, J., Bell, A., Schneider, J., Nyce, C., (2015) Marketplace of change: automobile insurance in the era of autonomous vehicles, , https://assets.kpmg/content/dam/kpmg/pdf/2016/06/id-market-place-of-change-automobile-insurance-in-the-era-of-autonomous-vehicles.pdf, KPMG, : (Accessed April 2, 2021; (2018) Autonomes Fahren: crash eines Tesla Model X [Online], , https://www.autobild.de/artikel/autonomes-fahren-crash-eines-tesla-model-x-10516973.html, Available:, Accessed August 5, 2019; Bagheri, N., Jamieson, G.A., The impact of context-related reliability on automation failure detection and scanning behaviour (2004) Proceedings of the 2004 IEEE International Conference on Systems, Man and Cybernetics, pp. 212-217. , Melbourne, October 17–20, 2004, IEEE; Barber, B., (1983) The logic and limits of trust, , New Brunswick, NJ, Rutgers University Press; Bartneck, C., Kanda, T., Mubin, O., Al Mahmud, A., Does the design of a robot influence its animacy and perceived intelligence? 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AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85104576644
"Christoforakos L., Gallucci A., Surmava-Große T., Ullrich D., Diefenbach S.","57193253870;57372580600;57223053443;16680229200;25723185400;","Can Robots Earn Our Trust the Same Way Humans Do? A Systematic Exploration of Competence, Warmth, and Anthropomorphism as Determinants of Trust Development in HRI",2021,"Frontiers in Robotics and AI","8",,"640444","","",,1,"10.3389/frobt.2021.640444","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104589930&doi=10.3389%2ffrobt.2021.640444&partnerID=40&md5=839df519789405f522d1ad8192c3c47f","Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany; Department of Computer Science, Ludwig-Maximilians-Universität München, Munich, Germany","Christoforakos, L., Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany; Gallucci, A., Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany; Surmava-Große, T., Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany; Ullrich, D., Department of Computer Science, Ludwig-Maximilians-Universität München, Munich, Germany; Diefenbach, S., Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany","Robots increasingly act as our social counterparts in domains such as healthcare and retail. For these human-robot interactions (HRI) to be effective, a question arises on whether we trust robots the same way we trust humans. We investigated whether the determinants competence and warmth, known to influence interpersonal trust development, influence trust development in HRI, and what role anthropomorphism plays in this interrelation. In two online studies with 2 × 2 between-subjects design, we investigated the role of robot competence (Study 1) and robot warmth (Study 2) in trust development in HRI. Each study explored the role of robot anthropomorphism in the respective interrelation. Videos showing an HRI were used for manipulations of robot competence (through varying gameplay competence) and robot anthropomorphism (through verbal and non-verbal design cues and the robot's presentation within the study introduction) in Study 1 (n = 155) as well as robot warmth (through varying compatibility of intentions with the human player) and robot anthropomorphism (same as Study 1) in Study 2 (n = 157). Results show a positive effect of robot competence (Study 1) and robot warmth (Study 2) on trust development in robots regarding anticipated trust and attributed trustworthiness. Subjective perceptions of competence (Study 1) and warmth (Study 2) mediated the interrelations in question. Considering applied manipulations, robot anthropomorphism neither moderated interrelations of robot competence and trust (Study 1) nor robot warmth and trust (Study 2). Considering subjective perceptions, perceived anthropomorphism moderated the effect of perceived competence (Study 1) and perceived warmth (Study 2) on trust on an attributional level. Overall results support the importance of robot competence and warmth for trust development in HRI and imply transferability regarding determinants of trust development in interpersonal interaction to HRI. Results indicate a possible role of perceived anthropomorphism in these interrelations and support a combined consideration of these variables in future studies. Insights deepen the understanding of key variables and their interaction in trust dynamics in HRI and suggest possibly relevant design factors to enable appropriate trust levels and a resulting desirable HRI. Methodological and conceptual limitations underline benefits of a rather robot-specific approach for future research. © Copyright © 2021 Christoforakos, Gallucci, Surmava-Große, Ullrich and Diefenbach.","anthropomorphism; competence; human-robot interaction; social robots; trust; trust development; trustworthiness; warmth",,,,,,"Bundesministerium für Bildung und Forschung, BMBF: 16SV8097","This research was funded by the German Federal Ministry for Education and Research (BMBF), Project GINA (FKZ: 16SV8097).",,,,,,,,,,"Aly, A., Tapus, A., Towards an intelligent system for generating an adapted verbal and nonverbal combined behavior in human–robot interaction (2016) Autonomous Robots, 40, pp. 193-209; Bagheri, N., Jamieson, G.A., (2004) The impact of context-related reliability on automation failure detection and scanning behaviour. In: 2004 IEEE International Conference on Systems, Man and Cybernetics Vol. 1. (IEEE Cat. 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Psychol, 52, pp. 113-117; Welge, J., Hassenzahl, M., Better than human: about the psychological superpowers of robots (2016) International Conference on Social Robotics, pp. 993-1002. , Cham, Springer; Wu, Y.H., Wrobel, J., Cornuet, M., Kerherv,é, H., Damnée, S., Rigaud, A.S., Acceptance of an assistive robot in older adults: a mixed-method study of human–robot interaction over a 1-month period in the Living Lab setting (2014) Clin. Intervent. Aging, 9, p. 801. , 24855349","Christoforakos, L.; Department of Psychology, Germany; email: lara.christoforakos@psy.lmu.de",,,"Frontiers Media S.A.",,,,,22969144,,,,"English","Front. Robot. AI",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85104589930
"Song Z., Gao D., Zhao Y., Dai J.S.","23061778500;57195942838;57195947085;7401678857;","An Improved Bouc-Wen Model Based on Equitorque Discretization for a Load-Dependent Nonlinear Stiffness Actuator",2021,"IEEE Transactions on Automation Science and Engineering","18","2","9102432","840","849",,,"10.1109/TASE.2020.2993277","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104066790&doi=10.1109%2fTASE.2020.2993277&partnerID=40&md5=37a145a17c19606320c8f34ef44f6cb7","Key Laboratory of Mechanism Theory and Equipment Design of the Ministry of Education, China; Centre for Robotics Research, School of Physical Sciences and Engineering, King's College London, London, United Kingdom","Song, Z., Key Laboratory of Mechanism Theory and Equipment Design of the Ministry of Education, China; Gao, D., Key Laboratory of Mechanism Theory and Equipment Design of the Ministry of Education, China; Zhao, Y., Key Laboratory of Mechanism Theory and Equipment Design of the Ministry of Education, China; Dai, J.S., Centre for Robotics Research, School of Physical Sciences and Engineering, King's College London, London, United Kingdom","A novel concept of designing compliant actuators called 'small load, low stiffness; large load, high stiffness' was applied and proven effective in human-robot interaction in our previous work. This concept was proposed based on a common scenario of interactive action where humans and some animals can adjust the stiffness of limbs in the light of tasks or contacting the environment. Based on this concept, we developed a new load-dependent nonlinear stiffness actuator (LDNSA) with a compact elastic mechanical structure. However, hysteresis is brought in inevitably along with the nonideal elastic mechanism and assembly errors, which limits the dynamic performance of the LDNSA. In this article, an equitorque discretization to implement real-Time compensation is proposed to improve the classical Bouc-Wen model, commonly used to compensate for hysteresis, which is proven effective in controlling the performance of the LDNSA. Afterward, a stability analysis of the hysteresis compensation system with impedance control is also discussed as well as to guarantee the safety of the system. Finally, experiments are conducted to evaluate the performance and advantages of the LDNSA with the proposed hysteresis compensation method. The results show that the proposed model can solve the hysteresis of the LDNSA well. Note to Practitioners-Many VSAs have been developed to realize theoretically both high bandwidth and good safety due to completely decoupling stiffness and load. However, there exists some redundancy range that is rarely used in physical interaction with the environment. To avoid this redundancy, a novel load-dependent nonlinear stiffness actuator (LDNSA) was proposed in our previous work. However, hysteresis is brought in via adopting an nonideal elastic element, which is also common in many automated devices. In this article, we proposed a novel compensation algorithm to deal with the hysteresis, which can be carried out in real time. Experimental results show that compared with the Bouc-Wen model and the PD model, the proposed algorithm can solve the hysteresis of the LDNSA better. The proposed hysteresis compensation methodology can improve the performance of a rehabilitation robot for shoulder training with a modularized LDNSA. © 2004-2012 IEEE.","Equitorque discretization; hysteresis compensation; Lyapunov function; nonlinear stiffness actuator","Educational robots; Elasticity; Human robot interaction; Mechanical actuators; Redundancy; Stiffness; Compensation algorithm; Dynamic performance; Hysteresis compensation; Mechanical structures; Non-linear stiffness; Physical interactions; Real-time compensations; Rehabilitation robot; Hysteresis",,,,,"17JCZDJC30300; Engineering and Physical Sciences Research Council, EPSRC: EP/E012574/1, EP/S019790/1; National Natural Science Foundation of China, NSFC: 51535008, 51721003, 51975401","Manuscript received June 5, 2018; revised October 22, 2019; accepted January 27, 2020. Date of publication May 27, 2020; date of current version April 7, 2021. This article was recommended for publication by Associate Editor K. Suzuki and Editor K. Saitou upon evaluation of the reviewers’ comments. This work was supported in part by the Tianjin Municipal Science and Technology Department Program under Grant 17JCZDJC30300, in part by the Natural Science Foundation of China under Grant 51975401, Grant 51535008, and Grant 51721003, and in part by the Engineering and Physical Science Research Council (EPSRC) in the UK under Grant EP/E012574/1 and Grant EP/S019790/1. (Corresponding author: Zhibin Song.) Zhibin Song, Dong Gao, and Yaru Zhao are with the Key Laboratory of Mechanism Theory and Equipment Design of the Ministry of Education, China, and also with the School of Mechanical Engineering, Tianjin University, Tianjin 300072, China (e-mail: songzhibin@tju.edu.cn; gaodong@tju.edu.cn; zhaoyaru1125@tju.edu.cn).",,,,,,,,,,"Yu, H., A novel compact compliant actuator design for rehabilitation robots (2013) Proc IEEE 13th Int. Conf. Rehabil. Robot. (ICORR, pp. 1-6. , Jun; Morbi, A., Ahmadi, M., Safely rendering small impedances in admittance-controlled haptic devices (2016) IEEE/ASME Trans. Mechatronics, 21 (3), pp. 1272-1280. , Jun; Jardim, B., Siqueira, A.A.G., Development and analysis of series elastic actuators for impedance control of an active ankle-foot orthosis (2013) J. Brazilian Soc. Mech. Sci. Eng, 36 (3), pp. 501-510; Ham, R., Sugar, T., Vanderborght, B., Hollander, K., Lefeber, D., Compliant actuator designs. 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Boca Raton, FL, USA, , CRC Press; Ramli, M.H.M., Chen, X., An extended Bouc-Wen model based adaptive control for micro-positioning of smart actuators (2016) Proc. Int. Conf. Adv. Mech. Syst. ICAMechS, pp. 189-194. , Nov; Hou, C., Hu, L., Asymptotic output tracking control for a class of nonlinear systems with unknown failures of hysteretic actuators (2015) Proc. 34th Chin. Control Conf CCC, pp. 1-7. , Jul; Ikhouane, F., Mañosa, V., Rodellar, J., Dynamic properties of the hysteretic Bouc-Wen model (2007) Syst. Control Lett, 56 (3), pp. 197-205. , Mar; Laudani, A., Fulginei, F.R., Salvini, A., Bouc-Wen hysteresis model identification by the metric-Topological evolutionary optimization (2014) IEEE Trans. Magn, 50 (2), pp. 621-624. , Feb; Minh, T.V., Chen, X., Adaptive control for piezoelectric actuator using discrete-Time Bouc-Wen model (2014) Proc IEEE Int. Conf. Robot. 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Electron, 67 (4), pp. 2925-2935. , Apr","Song, Z.; Key Laboratory of Mechanism Theory and Equipment Design of the Ministry of EducationChina; email: songzhibin@tju.edu.cn",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15455955,,,,"English","IEEE Trans. Autom. Sci. Eng.",Article,"Final","",Scopus,2-s2.0-85104066790
"Xiong H., Diao X.","57195556094;9745130100;","A review of cable-driven rehabilitation devices",2020,"Disability and Rehabilitation: Assistive Technology","15","8",,"885","897",,11,"10.1080/17483107.2019.1629110","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092723773&doi=10.1080%2f17483107.2019.1629110&partnerID=40&md5=01a3ef1e580abd560ec7910322b11e14","School of Engineering Technology, Purdue University, West Lafayette, IN, United States","Xiong, H., School of Engineering Technology, Purdue University, West Lafayette, IN, United States; Diao, X., School of Engineering Technology, Purdue University, West Lafayette, IN, United States","Purpose: To conduct a survey on the research and development of cable-driven rehabilitation devices (CDRDs). Method: This review searches in the databases of PubMed, IEEE Xplore Digital Library, Science Direct, and Google Scholar using various combinations of the following keywords: cable, wire, rehabilitation, assistance, therapy, training, robot, elastic, and pneumatic. Searches in the above databases for references cited by the above-searched references are also conducted to include a larger context of CDRDs. Results: CDRDs are classified into four categories, namely, serial exoskeleton-based, parallel exoskeleton-based, serial end-effector-based, and parallel end-effector-based CDRDs. Each category of CDRDs are further grouped based on the part of the human body to be rehabilitated. All four categories of CDRDs are examined and compared and their advantages and shortcomings are discussed based on popular rehabilitation requirements on weight, adaptability, versatility, misalignment, and safety. Open issues of CDRDs are also discussed. Conclusions: Each category of CDRDs has its own advantages and shortcomings. The selection of a CDRD highly depends on the specific application. Regarding the convenience of setting up a CDRD for rehabilitation, parallel CDRDs usually have better adaptability than serial ones. However, uncertainties come with parallel CDRDs, which makes the control of parallel CDRDs more challenging. Moreover, the strategy of inherent safety has a great potential to further improve the safety of CDRDs.Implications for rehabilitation CDRDs (and general RRDs) can deliver high-intensity training while therapists usually cannot. With up-to-date human-robot interaction techniques (e.g., virtual reality), CDRDs are more interesting and motivating to trainees than conventional manual rehabilitation therapies. CDRDs also provide financial benefits in the long-run. Currently existing RRDs available for clinical practice are mainly designed for the rehabilitation of shoulders, elbows, and knees. Parallel exoskeleton-based CDRDs can also be used for the rehabilitation of many other parts of trainees. Thus, CDRDs extend the coverage of RRDs in rehabilitation. Owing to their simple structures and light weights, CDRDs can be portable and used for rehabilitation at home. In this way, CDRDs can improve the duration and intensity of rehabilitation for those with limited access to rehabilitation institutes. As well known, the higher intensity of training leads to a higher rate of recovery. © 2019 Informa UK Limited, trading as Taylor & Francis Group.","Cable-driven; review; robotic rehabilitation device","disabled person; equipment design; exoskeleton (rehabilitation); human; questionnaire; rehabilitation; robotics; self help device; Disabled Persons; Equipment Design; Exoskeleton Device; Humans; Robotics; Self-Help Devices; Surveys and Questionnaires",,,,,,,,,,,,,,,,"Babaiasl, M., Mahdioun, S.H., Jaryani, P., A review of technological and clinical aspects of robot-aided rehabilitation of upper-extremity after stroke (2016) Disabil Rehabil Assist Technol, 11, pp. 263-280; Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., A survey on robotic devices for upper limb rehabilitation (2014) J Neuroeng Rehabil, 11, p. 3; Barreca, S., Wolf, S.L., Fasoli, S., Treatment interventions for the paretic upper limb of stroke survivors: a critical review (2003) Neurorehabil Neural Repair, 17, pp. 220-226; Dobkin, B.H., Strategies for stroke rehabilitation (2004) Lancet Neurol, 3, pp. 528-536; Fazekas, G., Horvath, M., Troznai, T., Robot-mediated upper limb physiotherapy for patients with spastic hemiparesis: a preliminary study (2007) J Rehabil Med, 39, pp. 580-582; Prange, G.B., Jannink, M.J.A., Groothuis-Oudshoorn, C.G.M., Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke (2006) J Rehabil Res Dev, 43, pp. 171-184; Kwakkel, G., Kollen, B.J., Krebs, H.I., Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review (2008) Neurorehabil Neural Repair, 22, pp. 111-121; Niyetkaliyev, A.S., Hussain, S., Ghayesh, M.H., Review on design and control aspects of robotic shoulder rehabilitation orthoses (2017) IEEE Trans Human-Mach Syst, 47, pp. 1134-1145; Lo, H.S., Xie, S.Q., Exoskeleton robots for upper-limb rehabilitation: state of the art and future prospects (2012) Med Eng Phys, 34, pp. 261-268; Basteris, A., Nijenhuis, S.M., Stienen, A.H.A., Training modalities in robot-mediated upper limb rehabilitation in stroke: a framework for classification based on a systematic review (2014) J NeuroEngineering Rehabil, 11, p. 111; Loureiro, R.V., Harwin, W.S., Nagai, K., Advances in upper limb stroke rehabilitation: a technology push (2011) Med Biol Eng Comput, 49, p. 1103; Gopura, R., Bandara, D.V., Kiguchi, K., Developments in hardware systems of active upper-limb exoskeleton robots: A review (2016) Rob. 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Mach. Theory, 99, pp. 1-18; Gonçalves, R.S., Carvalho, J.C.M., Rodrigues, L.A.O., Cable-driven parallel manipulator for lower limb rehabilitation (2014) Appl Mechan Mater, 459, pp. 535-542; Barbosa, A.M., Carvalho, J.C.M., Gonçalves, R.S., Cable-driven lower limb rehabilitation robot (2018) J. 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IEEE International Conference on Rehabilitation Robotics; 2017 Jul 17–20; Surdilovic, D., Zhang, J., Bernhardt, R., STRING-MAN: wire-robot technology for safe, flexible and human-friendly gait rehabilitation (2007) International Conference on Rehabilitation Robotics, pp. 446-453. , Noordwijk, Netherlands:. 13–15 June; Meunier, G., Boulet, B., Nahon, M., Control of an overactuated cable-driven parallel mechanism for a radio telescope application (2009) IEEE Trans Contr Syst Technol, 17, pp. 1043-1054; Jamshidifar, H., Khosravani, S., Fidan, B., Vibration decoupled modeling and robust control of redundant cable-driven parallel robots (2018) IEEE/ASME Transact Mechatron, 23, pp. 690-701; Zi, B., Duan, B.Y., Du, J.L., Dynamic modeling and active control of a cable-suspended parallel robot (2008) Mechatronics, 18, pp. 1-12; Babaghasabha, R., Khosravi, M.A., Taghirad, H.D., Adaptive robust control of fully-constrained cable driven parallel robots (2015) Mechatronics, 25, pp. 27-36; Caverly, R.J., Forbes, J.R., Dynamic modeling and noncollocated control of a flexible planar cable-driven manipulator (2014) IEEE Trans Robot, 30, pp. 1386-1397; Williams, R.L., Gallina, P., Vadia, J., Planar translational cable‐direct‐driven robots (2003) J Robotic Syst, 20, pp. 107-120; Khosravi, M.A., Taghirad, H.D., Dynamic modeling and control of parallel robots with elastic cables: singular perturbation approach (2014) IEEE Trans Robot, 30, pp. 694-704; Fleerkotte, B.M., Buurke, J.H., Koopman, B., Effectiveness of the LOwer Extremity Powered ExoSkeleton (LOPES) robotic gait trainer on ability and quality of walking in SCI patients (2013) Converging clinical and engineering research on neurorehabilitation, pp. 161-165. , Pons J.L., Torricelli D., Pajaro M., (eds), Berlin, Heidelberg: Springer Berlin Heidelberg,. 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Rehabil, 28, pp. 823-830; Hu, J., Lim, Y., Ding, Y., An advanced rehabilitation robotic system for augmenting healthcare, pp. 2073-2076. , Annual International Conference of the IEEE EMBS; 2011 Aug 30–Sept 3; Boston, MA. p; Sulzer, J.S., Peshkin, M.A., Patton, J.L., MARIONET: An exotendon-driven rotary series elastic actuator for exerting joint torque (2005) International Conference Rehabilitation Robotics, pp. 103-108. , Chicago, IL, USA:. 28 June–1 July","Diao, X.; School of Engineering Technology, United States; email: diaox@purdue.edu",,,"Taylor and Francis Ltd.",,,,,17483107,,,"31287340","English","Disabil. Rehabil. Assistive Technol.",Review,"Final","",Scopus,2-s2.0-85092723773
"Caroppo A., Leone A., Siciliano P.","15845020000;7102613483;7004477925;","Comparison Between Deep Learning Models and Traditional Machine Learning Approaches for Facial Expression Recognition in Ageing Adults",2020,"Journal of Computer Science and Technology","35","5",,"1127","1146",,4,"10.1007/s11390-020-9665-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094670362&doi=10.1007%2fs11390-020-9665-4&partnerID=40&md5=a3f8f35d2349050a59921a116a86d825","Institute for Microelectronics and Microsystems, National Research Council of Italy, Lecce, 73100, Italy","Caroppo, A., Institute for Microelectronics and Microsystems, National Research Council of Italy, Lecce, 73100, Italy; Leone, A., Institute for Microelectronics and Microsystems, National Research Council of Italy, Lecce, 73100, Italy; Siciliano, P., Institute for Microelectronics and Microsystems, National Research Council of Italy, Lecce, 73100, Italy","Facial expression recognition is one of the most active areas of research in computer vision since one of the non-verbal communication methods by which one understands the mood/mental state of a person is the expression of face. Thus, it has been used in various fields such as human-robot interaction, security, computer graphics animation, and ambient assistance. Nevertheless, it remains a challenging task since existing approaches lack generalizability and almost all studies ignore the effects of facial attributes, such as age, on expression recognition even though the research indicates that facial expression manifestation varies with age. Recently, a lot of progress has been made in this topic and great improvements in classification task were achieved with the emergence of deep learning methods. Such approaches have shown how hierarchies of features can be directly learned from original data, thus avoiding classical hand designed feature extraction methods that generally rely on manual operations with labelled data. However, research papers systematically exploring the performance of existing deep architectures for the task of classifying expression of ageing adults are absent in the literature. In the present work a tentative to try this gap is done considering the performance of three recent deep convolutional neural networks models (VGG-16, AlexNet and GoogLeNet/Inception V1) and evaluating it on four different benchmark datasets (FACES, Lifespan, CIFE, and FER2013) which also contain facial expressions performed by elderly subjects. As the baseline, and with the aim of making a comparison, two traditional machine learning approaches based on handcrafted features extraction process are evaluated on the same datasets. Carrying out an exhaustive and rigorous experimentation focused on the concept of “transfer learning”, which consists of replacing the output level of the deep architectures considered with new output levels appropriate to the number of classes (facial expressions), and training three different classifiers (i.e., Random Forest, Support Vector Machine and Linear Regression), VGG-16 deep architecture in combination with Random Forest classifier was found to be the best in terms of accuracy for each dataset and for each considered age-group. Moreover, the experimentation stage showed that the deep learning approach significantly improves the baseline approaches considered, and the most noticeable improvement was obtained when considering facial expressions of ageing adults. © 2020, Institute of Computing Technology, Chinese Academy of Sciences.","ageing adult; computer vision; deep learning; facial expression; machine learning","Benchmarking; Classification (of information); Computer graphics; Convolutional neural networks; Decision trees; Deep neural networks; Extraction; Face recognition; Human computer interaction; Human robot interaction; Learning systems; Network architecture; Random forests; Support vector machines; Support vector regression; Transfer learning; Classification tasks; Expression recognition; Facial expression recognition; Feature extraction methods; Features extraction; Machine learning approaches; Non-verbal communications; Random forest classifier; Deep learning",,,,,,,,,,,,,,,,"Zeng, Z., Pantic, M., Roisman, G.I., Huang, T.S., A survey of affect recognition methods: Audio, visual, and spontaneous expressions (2009) IEEE Transactions on Pattern Analysis and Machine Intelligence, 31 (1), pp. 39-58; Pantic, M., Rothkrantz, L.J.M., Automatic analysis of facial expressions: The state of the art (2000) IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (12), pp. 1424-1445; Fasel, B., Luettin, J., Automatic facial expression analysis: A survey (2003) Pattern Recognition, 36 (1), pp. 259-275; Carroll, J.M., Russell, J.A., Do facial expressions signal specific emotions? Judging emotion from the face in context (1996) Journal of Personality and Social Psychology, 70 (2), pp. 205-218; Rolls, E.T., Ekman, P., Perrett, D.I., Facial expressions of emotion: An old controversy and new findings: Discussion RSPTB, 335, p. 69. , 1273; Shbib, R., Zhou, S., Facial expression analysis using active shape model (2015) International Journal of Signal Processing, Image Processing and Pattern Recognition, 8 (1), pp. 9-22; Cheon, Y., Kim, D., Natural facial expression recognition using differential-AAM and manifold learning (2009) Pattern Recognition, 42 (7), pp. 1340-1350; Soyel, H., Demirel, H., Facial expression recognition based on discriminative scale invariant feature transform (2010) Electronics Letters, 46 (5), pp. 343-345; Gu, W., Xiang, C., Venkatesh, Y.V., Huang, D., Lin, H., Facial expression recognition using radial encoding of local Gabor features and classifier synthesis (2012) Pattern Recognition, 45 (1), pp. 80-91; Shan, C., Gong, S., McOwan, P.W., Facial expression recognition based on local binary patterns: A comprehensive study (2009) Image and Vision Computing, 27 (6), pp. 803-816; Chen, J., Chen, Z., Chi, Z., Fu, H., Facial expression recognition based on facial components detection and HOG features (2014) In Proc. the Scientific Cooperations International Workshops on Electrical and Computer Engineering Subfields, pp. 884-888. , Aug; Guo, G., Guo, R., Li, X., Facial expression recognition influenced by human ageing (2013) IEEE Transactions on Affective Computing, 4 (3), pp. 291-298; Wang, S., Wu, S., Gao, Z., Ji, Q., Facial expression recognition through modeling age-related spatial patterns (2016) Multimedia Tools and Applications, 75 (7), pp. 3937-3954; Malatesta, C.Z., Izard, C.E., The facial expression of emotion: Young, middle-aged, and older adult expressions (1984) In Emotion in Adult Development, pp. 253-273. , Malatesta C Z, Izard C E (eds.), Sage Publications; Malatesta-Magai, C., Jonas, R., Shepard, B., Culver, L.C., Type A behavior pattern and emotion expression in younger and older adults (1992) Psychology and Aging, 7 (4), pp. 551-561; Malatesta, C.Z., Fiore, M.J., Messina, J.J., Affect, personality, and facial expressive characteristics of older people (1987) Psychology and Aging, 2 (1), pp. 64-69; Lozano-Monasor, E., López, M.T., Vigo-Bustos, F., Fernández- Caballero, A., Facial expression recognition in ageing adults: From lab to ambient assisted living (2017) Journal of Ambient Intelligence and Humanized Computing, 8 (4), pp. 567-578; LeCun, Y., Bengio, Y., Hinton, G., Deep learning (2015) Nature, 521 (7553), pp. 436-444; Yu, D., Deng, L., Deep learning and its applications to signal and information processing [Exploratory DSP (2011) IEEE Signal Processing Magazine, 28 (1), pp. 145-154. , ]; Li, S., Deng, W., (2019) Deep Facial Expression Recognition: A Survey, , https://arxiv.org/abs/1804.08348, Dec; Ginne, R., Jariwala, K., Facial expression recognition using CNN: A survey (2018) International Journal of Advances in Electronics and Computer Science, 5 (3), pp. 13-16; Goodfellow, I.J., Erhan, D., Carrier, P.L., Challenges in representation learning: A report on three machine learning contests (2013) Proc. the 20Th International Conference on Neural Information Processing, pp. 117-124. , Nov; 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Zuiderveld, K., Contrast limited adaptive histogram equalization (1994) Academic Press Professional, pp. 474-485. , Graphics Gems IV, Heckbert P S (ed.); Hubel, D.H., Wiesel, T.N., Receptive fields and functional architecture of monkey striate cortex (1968) The Journal of Physiology, 195 (1), pp. 215-243; Pan, S.J., Yang, Q., A survey on transfer learning (2010) IEEE Transactions on Knowledge and Data Engineering, 22 (10), pp. 1345-1359; Russakovsky, O., Deng, J., Su, H., ImageNet large scale visual recognition challenge (2015) International Journal of Computer Vision, 115 (3), pp. 211-252; Lin, M., Chen, Q., Yan, S., (2013) Network in Network, , https://arxiv.org/abs/1312.4400, Dec. 2019; Milborrow, S., Nicolls, F., Active shape models with SIFT descriptors and MARS (2014) Proc. the 9Th International Conference on Computer Vision Theory and Applications, pp. 380-387. , Jan; Shan, C., Gong, S., McOwan, P.W., Facial expression recognition based on local binary patterns: A comprehensive study (2009) Image and Vision Computing, 27 (6), pp. 803-816; Ebner, N.C., Riediger, M., Lindenberger, U., FACES — A database of facial expressions in young, middle-aged, and older women and men: Development and validation (2010) Behavior Research Methods, 42 (1), pp. 351-362; Minear, M., Park, D.C., A lifespan database of adult facial stimuli (2004) Behavior Research Methods, Instruments, & Computers, 36 (4), pp. 630-633; Li, W., Li, M., Su, Z., Zhu, Z., A deep-learning approach to facial expression recognition with candid images (2015) Proc. the 14Th IAPR International Conference on Machine Vision Applications, pp. 279-282. , May; Goodfellow, I.J., Erhan, D., Carrier, P.L., Challenges in representation learning: A report on three machine learning contests (2013) In Proc. the 20Th International Conference on Neural Information Processing, pp. 117-124. , Nov; Wu, T., Turaga, P., Chellappa, R., Age estimation and face verification across ageing using landmarks (2012) IEEE Transactions on Information Forensics and Security, 7 (6), pp. 1780-1788; Giannopoulos, P., Perikos, I., Hatzilygeroudis, I., Deep learning approaches for facial emotion recognition: A case study on FER-2013 (2018) In Advances in Hybridization of Intelligent Methods: Models, Systems and Applications, pp. 1-16. , Hatzilygeroudis I, Palade V (eds.), Springer; Georgescu, M.I., Ionescu, R.T., Popescu, M., (2018) Local Learning with Deep and Handcrafted Features for Facial Expression Recognition., , https://arxiv.org/pdf/1804.10892.pdf, arXiv:1804.10892, Dec. 2019; Abadi, M., Barham, P., Chen, J., TensorFlow: A system for large-scale machine learning (2016) Proc. the 12Th USENIX Symposium on Operating Systems Design and Implementation, pp. 265-283. , Nov; Caroppo, A., Leone, A., Siciliano, P., (2018) Facial Expression Recognition in Ageing Adults: A Comparative Study, pp. 349-359. , Leone A, Caroppo A, Rescio G et al. (eds.), Springer; Li, W., Tsangouri, C., Abtahi, F., Zhu, Z., A recursive framework for expression recognition: From web images to deep models to game dataset (2018) Machine Vision and Applications, 29 (3), pp. 489-502; Wang, X., Wang, X., Ni, Y., Unsupervised domain adaptation for facial expression recognition using generative adversarial networks (2018) Computational Intelligence and Neuroscience; Ionescu, R.T., Popescu, M., Grozea, C., Local learning to improve bag of visual words model for facial expression recognition Proc. the 2013 ICML Workshop on Challenges in Representation Learning, , June 2013; Benitez-Quiroz, C.F., Srinivasan, R., Feng, Q., Wang, Y., Martinez, A.M., (2017) Emotionet Challenge: Recognition of Facial Expressions of Emotion in the Wild, , https://arxiv.org/abs/1703.01210, Dec. 2019; Mollahosseini, A., Hasani, B., Mahoor, M.H., AffectNet: A database for facial expression, valence, and arousal computing in the wild (2019) IEEE Transactions on Affective Computing, 10 (1), pp. 18-31; He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778. , June 2016; Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., Rethinking the inception architecture for computer vision (2016) Proc. the 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818-2826. , June; Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A., Inception-v4, Inception-ResNet and the impact of residual connections on learning (2017) Proc. the 31St AAAI Conference on Artificial Intelligence, pp. 4278-4284. , February; Chollet, F., Xception: Deep learning with depthwise separable convolutions (2017) Proc. the 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1800-1827. , July","Caroppo, A.; Institute for Microelectronics and Microsystems, Italy; email: andrea.caroppo@cnr.it",,,"Springer",,,,,10009000,,JCTEE,,"English","J Comput Sci Technol",Article,"Final","",Scopus,2-s2.0-85094670362
"Azagra P., Civera J., Murillo A.C.","56403248100;23007888000;10040627500;","Incremental Learning of Object Models from Natural Human-Robot Interactions",2020,"IEEE Transactions on Automation Science and Engineering","17","4","9062316","1883","1900",,1,"10.1109/TASE.2020.2980246","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083453649&doi=10.1109%2fTASE.2020.2980246&partnerID=40&md5=de978be5a01c3daed9d20ade7de0f33f","Depto. de Info. e Ingenieria de Sistemas and Instituto de Investigacion en Ingenieria de Aragon, University of Zaragoza, Zaragoza, Spain","Azagra, P., Depto. de Info. e Ingenieria de Sistemas and Instituto de Investigacion en Ingenieria de Aragon, University of Zaragoza, Zaragoza, Spain; Civera, J., Depto. de Info. e Ingenieria de Sistemas and Instituto de Investigacion en Ingenieria de Aragon, University of Zaragoza, Zaragoza, Spain; Murillo, A.C., Depto. de Info. e Ingenieria de Sistemas and Instituto de Investigacion en Ingenieria de Aragon, University of Zaragoza, Zaragoza, Spain","In order to perform complex tasks in realistic human environments, robots need to be able to learn new concepts in the wild, incrementally, and through their interactions with humans. This article presents an end-To-end pipeline to learn object models incrementally during the human-robot interaction (HRI). The pipeline we propose consists of three parts: 1) recognizing the interaction type; 2) detecting the object that the interaction is targeting; and 3) learning incrementally the models from data recorded by the robot sensors. Our main contributions lie in the target object detection, guided by the recognized interaction, and in the incremental object learning. The novelty of our approach is the focus on natural, heterogeneous, and multimodal HRIs to incrementally learn new object models. Throughout the article, we highlight the main challenges associated with this problem, such as high degree of occlusion and clutter, domain change, low-resolution data, and interaction ambiguity. This article shows the benefits of using multiview approaches and combining visual and language features, and our experimental results outperform standard baselines. Note to Practitioners-This article was motivated by challenges in recognition tasks for dynamic and varying scenarios. Our approach learns to recognize new user interactions and objects. To do so, we use multimodal data from the user-robot interaction; visual data are used to learn the objects and speech is used to learn the label and help with the interaction-Type recognition. We use state-of-The-Art deep learning (DL) models to segment the user and the objects in the scene. Our algorithm for incremental learning is based on a classic incremental clustering approach. The pipeline we propose works with all sensors mounted on the robot, so it allows mobility on the system. This article uses the data recorded from a Baxter robot, which enables the use of the manipulation arms in future steps, but it would work with any robot that can have the same sensors mounted. The sensors used are two RGB-D cameras and a microphone. The pipeline currently has high computational requirements to run the two DL-based steps. We have tested it with a desktop computer, including a GTX 1060 and 32 GB of RAM. © 2004-2012 IEEE.","Human-robot interaction (HRI); incremental learning; multimodal data; object recognition","Learning systems; Man machine systems; Object detection; Pipelines; Visual languages; Human environment; Human robot Interaction (HRI); Incremental learning; Language features; Low resolution; Object learning; Robot sensors; Target object; Human robot interaction",,,,,"DGA T45 17R/FSE; Ministerio de Ciencia, Innovación y Universidades, MCIU; European Commission, EC; European Regional Development Fund, ERDF; Agencia Estatal de Investigación, AEI: PGC2018-096367-B-I00, PGC2018-098817-A-I00, RTC-2017-6421-7","Manuscript received October 17, 2019; revised December 19, 2019; accepted February 21, 2020. Date of publication April 9, 2020; date of current version October 6, 2020. This article was recommended for publication by Associate Editor Y. Pan and Editor Y. Ding upon evaluation of the reviewers’ comments. This work was supported in part by the European Union (CHISTERA IGLU), in part by the FEDER, Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación, under Grant PGC2018-096367-B-I00, Grant PGC2018-098817-A-I00, and Grant RTC-2017-6421-7, and in part by the Aragón Regional Government under Grant DGA T45 17R/FSE. (Corresponding author: Pablo Azagra.) The authors are with the Departamento de Informática e Ingeniería de Sistemas and Instituto de Investigación en Ingeniería de Aragón, University of Zaragoza, 50009 Zaragoza, Spain (e-mail: pazagra@ unizar.es).",,,,,,,,,,"Azagra, P., Golemo, F., Mollard, Y., Lopes, M., Civera, J., Murillo, A.C., A multimodal dataset for object model learning from natural human-robot interaction (2017) Proc IEEE/RSJ Int. Conf. Intell. Robots Syst. 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"Kerzel M., Pekarek-Rosin T., Strahl E., Heinrich S., Wermter S.","41861835400;57212390115;55512341500;36903266000;7003826680;","Teaching NICO How to Grasp: An Empirical Study on Crossmodal Social Interaction as a Key Factor for Robots Learning From Humans",2020,"Frontiers in Neurorobotics","14",,"28","","",,5,"10.3389/fnbot.2020.00028","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086987132&doi=10.3389%2ffnbot.2020.00028&partnerID=40&md5=94bfc3c5427b73284b71c7dc733a3e37","Knowledge Technology, Department of Informatics, University of Hamburg, Hamburg, Germany","Kerzel, M., Knowledge Technology, Department of Informatics, University of Hamburg, Hamburg, Germany; Pekarek-Rosin, T., Knowledge Technology, Department of Informatics, University of Hamburg, Hamburg, Germany; Strahl, E., Knowledge Technology, Department of Informatics, University of Hamburg, Hamburg, Germany; Heinrich, S., Knowledge Technology, Department of Informatics, University of Hamburg, Hamburg, Germany; Wermter, S., Knowledge Technology, Department of Informatics, University of Hamburg, Hamburg, Germany","To overcome novel challenges in complex domestic environments, humanoid robots can learn from human teachers. We propose that the capability for social interaction should be a key factor in this teaching process and benefits both the subjective experience of the human user and the learning process itself. To support our hypothesis, we present a Human-Robot Interaction study on human-assisted visuomotor learning with the robot NICO, the Neuro-Inspired COmpanion, a child-sized humanoid. NICO is a flexible, social platform with sensing and manipulation abilities. We give a detailed description of NICO's design and a comprehensive overview of studies that use or evaluate NICO. To engage in social interaction, NICO can express stylized facial expressions and utter speech via an Embodied Dialogue System. NICO is characterized in particular by combining these social interaction capabilities with the abilities for human-like object manipulation and crossmodal perception. In the presented study, NICO acquires visuomotor grasping skills by interacting with its environment. In contrast to methods like motor babbling, the learning process is, in part, supported by a human teacher. To begin the learning process, an object is placed into NICO's hand, and if this object is accidentally dropped, the human assistant has to recover it. The study is conducted with 24 participants with little or no prior experience with robots. In the robot-guided experimental condition, assistance is actively requested by NICO via the Embodied Dialogue System. In the human-guided condition, instructions are given by a human experimenter, while NICO remains silent. Evaluation using established questionnaires like Godspeed, Mind Perception, and Uncanny Valley Indices, along with a structured interview and video analysis of the interaction, show that the robot's active requests for assistance foster the participant's engagement and benefit the learning process. This result supports the hypothesis that the ability for social interaction is a key factor for companion robots that learn with the help of non-expert teachers, as these robots become capable of communicating active requests or questions that are vital to their learning process. We also show how the design of NICO both enables and is driven by this approach. © Copyright © 2020 Kerzel, Pekarek-Rosin, Strahl, Heinrich and Wermter.","crossmodal learning; developmental robotics; human-robot interaction; neurocognitive models; visuomotor learning","Anthropomorphic robots; Educational robots; Learning systems; Speech processing; Surveys; User experience; Domestic environments; Empirical studies; Experimental conditions; Facial Expressions; Object manipulation; Social interactions; Subjective experiences; Visuo-motor learning; Social robots; article; child; empiricism; facial expression; female; human; human experiment; learning; male; object manipulation; perception; questionnaire; robotics; skill; social interaction; speech; structured interview; systematic review; teacher; teaching; videorecording",,,,,"Deutsche Forschungsgemeinschaft, DFG: CML (TRR 169","The authors gratefully acknowledge partial support from the German Research Foundation DFG under project CML (TRR 169).",,,,,,,,,,"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Tensorflow: a system for large-scale machine learning (2016) Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation, OSDI'16, pp. 265-283. , Berkeley, CA, USENIX Association; Andrychowicz, M., Wolski, F., Ray, A., Schneider, J., Fong, R., Welinder, P., Hindsight experience replay (2017) Advances in Neural Information Processing Systems, pp. 5048-5058. , Long Beach, CA, 31180036; Barros, P., Churamani, N., Lakomkin, E., Siqueira, H., Sutherland, A., Wermter, S., The OMG-emotion behavior dataset (2018) Proceedings of the International Joint Conference on Neural Networks (IJCNN 2018), pp. 1408-1414. , Rio de Janeiro, IEEE; Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int. 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Neurorobotics",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85086987132
"Averta G., Della Santina C., Valenza G., Bicchi A., Bianchi M.","57195416332;57188680141;35773784200;7004326697;57202798446;","Exploiting upper-limb functional principal components for human-like motion generation of anthropomorphic robots",2020,"Journal of NeuroEngineering and Rehabilitation","17","1","63","","",,12,"10.1186/s12984-020-00680-8","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084626366&doi=10.1186%2fs12984-020-00680-8&partnerID=40&md5=fbe921ceebe1daaff14caec4c2e7fd4a","Research Center Enrico Piaggio, University of Pisa, Largo Lucio Lazzarino 1, Pisa, 56126, Italy; Soft Robotics for Human Cooperation and Rehabilitation, Fondazione Istituto Italiano di Tecnologia, via Morego, 30, Genova, 16163, Italy; Dipartimento di Ingegneria dell'Informazione, University of Pisa, Via G. Caruso, 16, Pisa, 56122, Italy; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar st, Cambridge, MA  02139, United States","Averta, G., Research Center Enrico Piaggio, University of Pisa, Largo Lucio Lazzarino 1, Pisa, 56126, Italy, Soft Robotics for Human Cooperation and Rehabilitation, Fondazione Istituto Italiano di Tecnologia, via Morego, 30, Genova, 16163, Italy, Dipartimento di Ingegneria dell'Informazione, University of Pisa, Via G. Caruso, 16, Pisa, 56122, Italy; Della Santina, C., Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar st, Cambridge, MA  02139, United States; Valenza, G., Research Center Enrico Piaggio, University of Pisa, Largo Lucio Lazzarino 1, Pisa, 56126, Italy, Dipartimento di Ingegneria dell'Informazione, University of Pisa, Via G. Caruso, 16, Pisa, 56122, Italy; Bicchi, A., Research Center Enrico Piaggio, University of Pisa, Largo Lucio Lazzarino 1, Pisa, 56126, Italy, Soft Robotics for Human Cooperation and Rehabilitation, Fondazione Istituto Italiano di Tecnologia, via Morego, 30, Genova, 16163, Italy, Dipartimento di Ingegneria dell'Informazione, University of Pisa, Via G. Caruso, 16, Pisa, 56122, Italy; Bianchi, M., Research Center Enrico Piaggio, University of Pisa, Largo Lucio Lazzarino 1, Pisa, 56126, Italy, Dipartimento di Ingegneria dell'Informazione, University of Pisa, Via G. Caruso, 16, Pisa, 56122, Italy","Background: Human-likeliness of robot movements is a key component to enable a safe and effective human-robot interaction, since it contributes to increase acceptance and motion predictability of robots that have to closely interact with people, e.g. for assistance and rehabilitation purposes. Several parameters have been used to quantify how much a robot behaves like a human, which encompass aspects related to both the robot appearance and motion. The latter point is fundamental to allow the operator to interpret robotic actions, and plan a meaningful reactions. While different approaches have been presented in literature, which aim at devising bio-aware control guidelines, a direct implementation of human actions for robot planning is not straightforward, still representing an open issue in robotics. Methods: We propose to embed a synergistic representation of human movements for robot motion generation. To do this, we recorded human upper-limb motions during daily living activities. We used functional Principal Component Analysis (fPCA) to extract principal motion patterns. We then formulated the planning problem by optimizing the weights of a reduced set of these components. For free-motions, our planning method results into a closed form solution which uses only one principal component. In case of obstacles, a numerical routine is proposed, incrementally enrolling principal components until the problem is solved with a suitable precision. Results: Results of fPCA show that more than 80% of the observed variance can be explained by only three functional components. The application of our method to different meaningful movements, with and without obstacles, show that our approach is able to generate complex motions with a very reduced number of functional components. We show that the first synergy alone accounts for the 96% of cost reduction and that three components are able to achieve a satisfactory motion reconstruction in all the considered cases. Conclusions: In this work we moved from the analysis of human movements via fPCA characterization to the design of a novel human-like motion generation algorithm able to generate, efficiently and with a reduced set of basis elements, several complex movements in free space, both in free motion and in case of obstacle avoidance tasks. © 2020 The Author(s).","Assistive robotics; Companion robots; Exoskeletons; Functional principal components; Human-robot interaction; Rehabilitation robotics","algorithm; anthropomorphic robot; arm movement; Article; controlled study; cost control; daily life activity; functional principal component analysis; kinematics; man machine interaction; principal component analysis; priority journal; robotics; algorithm; human; motion; movement (physiology); physiology; principal component analysis; procedures; robotics; upper limb; Algorithms; Humans; Motion; Movement; Principal Component Analysis; Robotics; Upper Extremity",,,,,"Horizon 2020 Framework Programme, H2020: 688857, 810346",,,,,,,,,,,"Bartneck, C., Kulić, D., Croft, E., Zoghbi, S., Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots (2009) Int J Soc Robot, 1 (1), pp. 71-81; Fink, J., Anthropomorphism and human likeness in the design of robots and human-robot interaction (2012) International Conference on Social Robotics, pp. 199-208. , https://doi.org/10.1007/978-3-642-34103-8_20, Springer; Von Zitzewitz, J., Boesch, P.M., Wolf, P., Riener, R., Quantifying the human likeness of a humanoid robot (2013) Int J Soc Robot, 5 (2), pp. 263-276; Morasso, P., Spatial control of arm movements (1981) Exp Brain Res, 42 (2), pp. 223-227. , 1:STN:280:DyaL3M3mtFSltg%3D%3D 7262217; Flash, T., Hogan, N., The coordination of arm movements: An experimentally confirmed mathematical model (1985) J Neurosci, 5 (7), pp. 1688-1703. , 1:STN:280:DyaL2M3mtlOltA%3D%3D 4020415 6565116; Lo, A.C., Guarino, P.D., Richards, L.G., Haselkorn, J.K., Wittenberg, G.F., Federman, D.G., Ringer, R.J., Volpe, B.T., Robot-assisted therapy for long-term upper-limb impairment after stroke (2010) N Engl J Med, 362 (19), pp. 1772-1783. , 1:CAS:528:DC%2BC3cXmtVKgtLY%3D 20400552 5592692; Frisoli, A., Salsedo, F., Bergamasco, M., Rossi, B., Carboncini, M.C., A force-feedback exoskeleton for upper-limb rehabilitation in virtual reality (2009) Appl Bionics Biomech, 6 (2), pp. 115-126; Klamroth-Marganska, V., Blanco, J., Campen, K., Curt, A., Dietz, V., Ettlin, T., Felder, M., Kollmar, A., Three-dimensional, task-specific robot therapy of the arm after stroke: A multicentre, parallel-group randomised trial (2014) Lancet Neurol, 13 (2), pp. 159-166. , 24382580; Huber, M., Rabin, B., Docan, C., Burdea, G.C., Abdelbaky, M., Golomb, M.R., Feasibility of modified remotely monitored in-home gaming technology for improving hand function in adolescents with cerebral palsy (2010) IEEE Trans Inf Technol Biomed, 14 (2), pp. 526-534. , 20071262; Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Troy, A., Leonhardt, S., A survey on robotic devices for upper limb rehabilitation (2014) J Neuroeng Rehabil, 11 (1), p. 3. , 24401110 4029785; Lo, H.S., Xie, S.Q., Exoskeleton robots for upper-limb rehabilitation: State of the art and future prospects (2012) Med Eng Phys, 34 (3), pp. 261-268. , 22051085; Merians, A.S., Fluet, G., Tunik, E., Qiu, Q., Saleh, S., Adamovich, S., Movement rehabilitation in virtual reality from then to now: How are we doing? 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NeuroEng. Rehabil.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85084626366
"Zlatintsi A., Dometios A.C., Kardaris N., Rodomagoulakis I., Koutras P., Papageorgiou X., Maragos P., Tzafestas C.S., Vartholomeos P., Hauer K., Werner C., Annicchiarico R., Lombardi M.G., Adriano F., Asfour T., Sabatini A.M., Laschi C., Cianchetti M., Güler A., Kokkinos I., Klein B., López R.","34871164000;56043770000;57189596992;55413195600;56242401400;8532478500;35243026700;6701475189;13402652600;7005208898;57188639923;6603194216;56274915300;36514208700;15724268700;7005616188;6603776567;23388237000;57192086563;9250105400;55744934900;57215819169;","I-Support: A robotic platform of an assistive bathing robot for the elderly population",2020,"Robotics and Autonomous Systems","126",,"103451","","",,10,"10.1016/j.robot.2020.103451","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079903362&doi=10.1016%2fj.robot.2020.103451&partnerID=40&md5=fd26b212add636e9ca3fea1dd338fe70","Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), Greece; OMEGA Technology, Athens, Greece; Bethanien Hospital, Germany; Fondazione Santa Lucia, Rome, Italy; Karlsruhe Institute of Technology (KIT), Germany; Scuola Superiore Sant'Anna (SSSA), Pisa, Italy; Department of Computing, Imperial College London, United Kingdom; Department of Computer Science, University College London, United Kingdom; Frankfurt University of Applied Sciences, Frankfurt am Main, Germany; Robotnik Automation, SLL, Valencia, Spain","Zlatintsi, A., Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), Greece; Dometios, A.C., Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), Greece; Kardaris, N., Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), Greece; Rodomagoulakis, I., Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), Greece; Koutras, P., Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), Greece; Papageorgiou, X., Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), Greece; Maragos, P., Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), Greece; Tzafestas, C.S., Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), Greece; Vartholomeos, P., OMEGA Technology, Athens, Greece; Hauer, K., Bethanien Hospital, Germany; Werner, C., Bethanien Hospital, Germany; Annicchiarico, R., Fondazione Santa Lucia, Rome, Italy; Lombardi, M.G., Fondazione Santa Lucia, Rome, Italy; Adriano, F., Fondazione Santa Lucia, Rome, Italy; Asfour, T., Karlsruhe Institute of Technology (KIT), Germany; Sabatini, A.M., Scuola Superiore Sant'Anna (SSSA), Pisa, Italy; Laschi, C., Scuola Superiore Sant'Anna (SSSA), Pisa, Italy; Cianchetti, M., Scuola Superiore Sant'Anna (SSSA), Pisa, Italy; Güler, A., Department of Computing, Imperial College London, United Kingdom; Kokkinos, I., Department of Computer Science, University College London, United Kingdom; Klein, B., Frankfurt University of Applied Sciences, Frankfurt am Main, Germany; López, R., Robotnik Automation, SLL, Valencia, Spain","In this paper we present a prototype integrated robotic system, the I-Support bathing robot, that aims at supporting new aspects of assisted daily-living activities on a real-life scenario. The paper focuses on describing and evaluating key novel technological features of the system, with the emphasis on cognitive human–robot interaction modules and their evaluation through a series of clinical validation studies. The I-Support project on its whole has envisioned the development of an innovative, modular, ICT-supported service robotic system that assists frail seniors to safely and independently complete an entire sequence of physically and cognitively demanding bathing tasks, such as properly washing their back and their lower limbs. A variety of innovative technologies have been researched and a set of advanced modules of sensing, cognition, actuation and control have been developed and seamlessly integrated to enable the system to adapt to the target population abilities. These technologies include: human activity monitoring and recognition, adaptation of a motorized chair for safe transfer of the elderly in and out the bathing cabin, a context awareness system that provides full environmental awareness, as well as a prototype soft robotic arm and a set of user-adaptive robot motion planning and control algorithms. This paper focuses in particular on the multimodal action recognition system, developed to monitor, analyze and predict user actions with a high level of accuracy and detail in real-time, which are then interpreted as robotic tasks. In the same framework, the analysis of human actions that have become available through the project's multimodal audio–gestural dataset, has led to the successful modeling of Human–Robot Communication, achieving an effective and natural interaction between users and the assistive robotic platform. In order to evaluate the I-Support system, two multinational validation studies were conducted under realistic operating conditions in two clinical pilot sites. Some of the findings of these studies are presented and analyzed in the paper, showing good results in terms of: (i) high acceptability regarding the system usability by this particularly challenging target group, the elderly end-users, and (ii) overall task effectiveness of the system in different operating modes. © 2020 Elsevier B.V.","Assistive human–robot interaction (HRI); Audio–gestural command recognition; Bathing robot; Human–robot communication; Multimodal dataset; Online validation with elderly users","Adaptive control systems; Environmental technology; Human robot interaction; Motion planning; Robotics; Command recognition; Elderly users; Multi-modal dataset; Robot communication; Robot interactions; Robot programming",,,,,"Horizon 2020 Framework Programme, H2020: 643666; European Commission, EC: H2020-643666; National Technical University of Athens, NTUA","This research work was supported by the EU under the project I-SUPPORT with grant H2020-643666. The authors would like to thank Dr. Vincenzo Genovese and Mariangela Manti (SSSA), Holger Ro?berg and Dr. Inga Schl?mer (FRA-AUS), Dr. Vassilis Pitsikalis (NTUA) and all students/researchers that were involved in the data collection process.","This research work was supported by the EU under the project I-SUPPORT with grant H2020-643666 .",,,,,,,,,"Organization, W.H., World Report on Ageing and Health (2015), World Health Organization; Zarit, S.H., Reever, K.E., Bach-Peterson, J., Relatives of the impaired elderly: correlates of feelings of burden (1980) Gerontologist, 20 (6), pp. 649-655; McFall, S., Miller, B.H., Caregiver burden and nursing home admission of frail elderly persons (1992) J. 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"Tashtoush T., Garcia L., Landa G., Amor F., Laborde A.N., Oliva D., Safar F.","45261616700;57225789277;57221847297;57221855437;57221843502;22835830800;57190664343;","Human-Robot Interaction and Collaboration (HRI-C) Utilizing Top-View RGB-D Camera System",2021,"International Journal of Advanced Computer Science and Applications","12","1",,"10","17",,2,"10.14569/IJACSA.2021.0120102","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100389191&doi=10.14569%2fIJACSA.2021.0120102&partnerID=40&md5=358b0393fad6ba3daf95ce4aee845b75","School of Engineering, Texas A&M International University, Laredo, TX  78041, United States; Department of Science and Technology, National University of Quilmes, Quilmes, Argentina","Tashtoush, T., School of Engineering, Texas A&M International University, Laredo, TX  78041, United States; Garcia, L., School of Engineering, Texas A&M International University, Laredo, TX  78041, United States; Landa, G., School of Engineering, Texas A&M International University, Laredo, TX  78041, United States; Amor, F., Department of Science and Technology, National University of Quilmes, Quilmes, Argentina; Laborde, A.N., Department of Science and Technology, National University of Quilmes, Quilmes, Argentina; Oliva, D., Department of Science and Technology, National University of Quilmes, Quilmes, Argentina; Safar, F., Department of Science and Technology, National University of Quilmes, Quilmes, Argentina","In this study, a smart and affordable system that utilizes an RGB-D camera to measure the exact position ofan operator with respect to an adjacent robotic manipulator was developed. This developed technology was implemented in a simulated human operation in an automated manufacturing robot to achieve two goals; enhancing the safety measures around the robot by adding an affordable smart system for human detection and robot control and developing a system that will allow the between the human-robot collaboration to finish a predefined task. The system utilized an Xbox Kinect V2 sensor/camera and Scorbot ER-V Plus to model and mimics the selected applications. To achieve these goals, a geometric model for the Scorbot and Xbox Kinect V2 was developed, a robotics joint calibration was applied, an algorithm of background segmentation was utilized to detect the operator and a dynamic binary mask for the robot was implemented, and the efficiency of both systems based on the response time and localization error was analyzed. The first application of the Add-on Safety Device aims to monitor the working-space and control the robot to avoid any collisions when an operator enters or gets closer. This application will reduced and remove physical barriers around the robots, expand the physical work area, reduce the proximity limitations, and enhance the human-robots interaction (HRI) in an industrial environment while sustaining a low cost. The system was able to respond to human intrusion to prevent any collision within 500 ms on average, and it was found that the system’s bottleneck was PC and robot inter-communication speed. The second application was developing a successful collaborative scenario between a robot and a human operator, where a robot will deposit an object on the operator’s hand, mimicking a real-life human-robot collaboration (HRC) tasks. The system was able to detect the operator’s hand and it’s location then command the robot to place an object on the hand, the system was able to place the object within a mean error of 2:4 cm, and the limitation of this system was the internal variables and data transmitting speed between the robot controller and main computer. These results are encouraging and ongoing work aims to experiment with different operations and implement gesture detection in real-time collaboration tasks while keeping the human operator safe and predicting their behavior. © 2021, International Journal of Advanced Computer Science and Applications. All Rights Reserved.","computer vision; human-robot collaboration (HRC); human-robot interaction (HRI); industrial system modeling; Kinect; manufacturing systems design; RGB camera; robot end-effector; Robotics manipulator; robotics safety; scorbot","Accident prevention; Cameras; Computer vision; Costs; End effectors; Machine design; Man machine systems; Manipulators; Manufacture; Robotics; Human-robot collaboration; Human-robot interaction; Humans-robot interactions; Industrial system modeling; Industrial systems; Kinect; Manufacturing system design; RGB cameras; Robot's end effectors; Robotic manipulators; Robotic safety; Scorbot; System models; Human robot interaction",,,,,"P120A170102; Texas A and M International University, TAMIU; Universidad Nacional de Quilmes, UNQ","This project was funded and supported by “100,000 Strong in the Americas Innovation Fund – Exxon Mobil”, and the U.S. Department of Education - Minority Science and Engineering Improvement (MSEIP) Program (Grant # P120A170102), National University of Quilmes, and Texas A&M International University. 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Appl.",Article,"Final","All Open Access, Gold",Scopus,2-s2.0-85100389191
"Lambelet C., Temiraliuly D., Siegenthaler M., Wirth M., Woolley D.G., Lambercy O., Gassert R., Wenderoth N.","57197796521;57188839417;57219341268;57219340211;11839714300;21933933900;8937521600;57206162402;","Characterization and wearability evaluation of a fully portable wrist exoskeleton for unsupervised training after stroke",2020,"Journal of NeuroEngineering and Rehabilitation","17","1","132","","",,5,"10.1186/s12984-020-00749-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092293443&doi=10.1186%2fs12984-020-00749-4&partnerID=40&md5=1e0986b835f715ec64e97fc196d38e93","Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland; Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland","Lambelet, C., Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland; Temiraliuly, D., Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland; Siegenthaler, M., Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland; Wirth, M., Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland; Woolley, D.G., Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland; Lambercy, O., Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland; Gassert, R., Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland; Wenderoth, N., Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland","Background: Chronic hand and wrist impairment are frequently present following stroke and severely limit independence in everyday life. The wrist orientates and stabilizes the hand before and during grasping, and is therefore of critical importance in activities of daily living (ADL). To improve rehabilitation outcomes, classical therapy could be supplemented by novel therapies that can be applied in unsupervised settings. This would enable more distributed practice and could potentially increase overall training dose. Robotic technology offers new possibilities to address this challenge, but it is critical that devices for independent training are easy and appealing to use. Here, we present the development, characterization and wearability evaluation of a fully portable exoskeleton for active wrist extension/flexion support in stroke rehabilitation. Methods: First we defined the requirements, and based on these, constructed the exoskeleton. We then characterized the device with standardized haptic and human-robot interaction metrics. The exoskeleton is composed of two modules placed on the forearm/hand and the upper arm. These modules weigh 238 g and 224 g, respectively. The forearm module actively supports wrist extension and flexion with a torque up to 3.7 Nm and an angular velocity up to 530 deg/s over a range of 154. The upper arm module includes the control electronics and battery, which can power the device for about 125 min in normal use. Special emphasis was put on independent donning and doffing of the device, which was tested via a wearability evaluation in 15 healthy participants and 2 stroke survivors using both qualitative and quantitative methods. Results: All participants were able to independently don and doff the device after only 4 practice trials. For healthy participants the donning and doffing process took 61 ±15 s and 24 ±6 s, respectively. The two stroke survivors donned and doffed the exoskeleton in 54 s/22 s and 113 s/32 s, respectively. Usability questionnaires revealed that despite minor difficulties, all participants were positive regarding the device. Conclusions: This study describes an actuated wrist exoskeleton which weighs less than 500 g, and which is easy and fast to don and doff with one hand. Our design has put special emphasis on the donning aspect of robotic devices which constitutes the first barrier a user will face in unsupervised settings. The proposed device is a first and intermediate step towards wearable rehabilitation technologies that can be used independently by the patient and in unsupervised settings. © 2020 The Author(s).","Admittance control; Donning/doffing; eWrist; Home-based; Robotic rehabilitation; Stroke; Unsupervised training; Wearability/usability evaluation; Wearable exoskeleton; Wrist","adult; Article; cerebrovascular accident; controlled study; female; fully portable wrist exoskeleton; human; kinematics; male; priority journal; qualitative analysis; quantitative analysis; questionnaire; standardization; stroke rehabilitation; stroke survivor; task performance; torque; velocity; aged; cerebrovascular accident; daily life activity; devices; equipment design; exoskeleton (rehabilitation); joint characteristics and functions; middle aged; robotics; stroke rehabilitation; wrist; Activities of Daily Living; Aged; Equipment Design; Exoskeleton Device; Female; Humans; Male; Middle Aged; Range of Motion, Articular; 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NeuroEng. Rehabil.",Article,"Final","All Open Access, Gold, Green",Scopus,2-s2.0-85092293443
"Hasumoto R., Nakadai K., Imai M.","57208908294;6603685669;7402062745;","Reactive Chameleon: A Method to Mimic Conversation Partner’s Body Sway for a Robot",2020,"International Journal of Social Robotics","12","1",,"239","258",,1,"10.1007/s12369-019-00557-4","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066078457&doi=10.1007%2fs12369-019-00557-4&partnerID=40&md5=0f668856af3243006927414c4e902e30","Keio University, Hiyoshi, Kohoku-ku, Yokohama-shi, Kanagawa-ken  223-8522, Japan; Honda Research Institute Japan Co., Ltd., Honcho, Wako-shi, Saitama  351-0188, Japan","Hasumoto, R., Keio University, Hiyoshi, Kohoku-ku, Yokohama-shi, Kanagawa-ken  223-8522, Japan; Nakadai, K., Honda Research Institute Japan Co., Ltd., Honcho, Wako-shi, Saitama  351-0188, Japan; Imai, M., Keio University, Hiyoshi, Kohoku-ku, Yokohama-shi, Kanagawa-ken  223-8522, Japan","Along with improvements in information processing capabilities and development of machine learning, research that robots provide services instead of people is progressing. In order to raise satisfaction with the service provided by the robot, it is important to build a robot that makes it easy for people to talk and can get a good impression from people. In human–human interaction, mimicking the conversation partner’s motion is one of the factors that influence the impression from the conversation partner. The influence coming from the mimicking in the interaction is called the “chameleon effect”. As the chameleon effect, it is known that a person unconsciously mimics interaction partner’s nonverbal behavior, in addition, it is also known that a good impression can be obtained from the interaction partner by consciously mimicking the partner’s nonverbal behavior without being noticed. Previous studies have shown that the chameleon effect occurs also even in human–robot interaction by mimicking interaction partner’s nonverbal behavior, and interaction partner has good impression on the robot. However, considering the conversation scene, there are problems with the method verified in the previous research when robots mimic interaction partner’s nonverbal behavior. The first problem is that the robot cannot perform gestures using its arms and head at the same time as mimicking nonverbal behavior during conversation. The second problem is that the conversation partner may notice that the robot mimics his/her nonverbal behavior during the conversation. In this thesis, we propose “Reactive Chameleon”, a method to generate body sway for a robot that based mimicking a conversation partner’s body sway. Reactive Chameleon designed for a robot to get a good impression from the conversation partner by chameleon effect without disturbing robot gestures. In Reactive Chameleon, the volume of the mimicking is dynamically adjusted to the conversation partner and the adjusted robot’s sway merged with robot’s own body sway for the conversation partner not to notice that robot mimics his/her body sway. We conducted an experiment to verify that the impression that the participant has toward the robot is affected by having a conversation with a robot that mimics a participant’s body sway. Results of the experiment show that the robot that imitates body sway with Reactive Chameleon got high evaluation from the conversation partner by chameleon effect. © 2019, Springer Nature B.V.","Body sway; Chameleon effect; Human–robot interaction","Agricultural robots; Body sway; Chameleon effect; Human interactions; Information processing capability; Nonverbal behavior; Robot gestures; Robot interactions; Social robots",,,,,"Japan Society for the Promotion of Science, KAKEN; Ministry of Education, Culture, Sports, Science and Technology, Monbusho: 1 6 H 0 2 8 8 4","This work was supported by JSPS?(or MEXT) KAKENHI Grant Number 1 6 H 0 2 8 8 4. The authors have no conflicts of interest directly relevant to the content of this journal.",,,,,,,,,,"Bailenson, J.N., Yee, N., Patel, K., Beall, A.C., Detecting digital chameleons (2005) Comput Hum Behav, 24, pp. 66-87; Bailenson, J.N., Yee, N., Digital chameleons: automatic assimilation of nonverbal gestures in immersive virtual environments (2005) Psychol Sci, 16 (10), pp. 814-819; Bartneck, C., Croft, E., Kulic, D., Measuring the anthropomorphism, animacy, likeability, perceived intelligence and perceived safety of robots (2008) Metrics for HRI Workshop, 471, pp. 37-44. , Technical report; Brščić, D., Ikeda, T., Kanda, T., Do you need help? 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IEEE; Stevens, C.J., Mimicry and expressiveness of an ECA in human–agent interaction: familiarity breeds content! (2016) Comput Cogn Sci, 2 (1), p. 1; Tatsukawa, K., Nakano, T., Ishiguro, H., Yoshikawa, Y., Eyeblink synchrony in multimodal human–android interaction (2016) Sci Rep, 6, p. 39718; Van Baaren, R.B., Holland, R.W., Kawakami, K., Van Knippenberg, A., Mimicry and prosocial behavior (2004) Psychol Sci, 15 (1), pp. 71-74","Hasumoto, R.; Keio University, Hiyoshi, Kohoku-ku, Japan; email: hasumoto@ailab.ics.keio.ac.jp",,,"Springer Science and Business Media B.V.",,,,,18754791,,,,"English","Int. J. Soc. Rob.",Article,"Final","",Scopus,2-s2.0-85066078457
"Istenic A., Bratko I., Rosanda V.","57222291101;7003286588;57211391986;","Are pre-service teachers disinclined to utilise embodied humanoid social robots in the classroom?",2021,"British Journal of Educational Technology","52","6",,"2340","2358",,,"10.1111/bjet.13144","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109076293&doi=10.1111%2fbjet.13144&partnerID=40&md5=da48925dcae508da926a1680f2230faa","Faculty of Education, University of Primorska, Koper, Slovenia; Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia; Institute of Psychology and Education, Federal University of Kazan, Kazan, Russian Federation; Faculty of Computer and Information Science, University of Ljubjana, Ljubljana, Slovenia; State High School with Slovenian Teaching Language Ivan Cankar, Gorizia, Italy","Istenic, A., Faculty of Education, University of Primorska, Koper, Slovenia, Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia, Institute of Psychology and Education, Federal University of Kazan, Kazan, Russian Federation; Bratko, I., Faculty of Computer and Information Science, University of Ljubjana, Ljubljana, Slovenia; Rosanda, V., State High School with Slovenian Teaching Language Ivan Cankar, Gorizia, Italy","Teachers' readiness for technology integration depends also on their beliefs about the contribution of technology to teaching and learning, which influence their motivation for its adoption. Initial pre-service teacher education is critical in reducing the attitude-behaviour divide supporting technology acceptability, acceptance and use. Acceptance of interaction between human and robot is more complicated than human-computer interaction acceptance. Social robots are radical innovations, harder for potential users to accept in human social spaces than are incremental innovations. In 2019, a survey using a convenience sample of 121 first-year students was conducted to examine pre-service teachers' beliefs about social robot educational technology. It examined the following factors derived from the Unified Theory of Acceptance and Use of Technology adopted for social robots in education: Perceived social dimension, Intention to use, Perceived usability, Anxiety. Based on our findings, it seems there is a critical disjunction between researchers' efforts to equip social robots with human manners and social intelligence and participants' rejection of this technology precisely because it mimics being human. Further, we report that ICT familiarity as assessed using PISA's Information Communication Technology—ICT familiarity factors is related to robot acceptability. These findings need further examination to inform educational robotics design and Human-Robot Interaction research and teacher education and training. Practitioners notes What is already known about this topic In the age of robotic technology, teachers face requirements to prepare students for work and life with social robots. Social robots are tested for classroom integration. Teachers' readiness to implement robot lessons depends on their beliefs about social robotic technology's contribution to teaching. Research and development in the field of social robotics still tend to focus more frequently on technology applications rather than pedagogical issues and advancing teaching and learning. What this paper adds Participants refuse to accept the idea of social robot-based instruction. The identified belief pattern is based mainly on the perceived social dimension, intention to use, perceived usability and anxiety. Participants critically perceive the robot's social dimension. Some of PISA's ICT familiarity factors are related to robots acceptability factors. Implication for policy and/or practice The policy and practice need to address how social robots could be integrated into current teaching and learning practices and more importantly how could robotic technology facilitate innovative pedagogical models for effective and efficient learning. The introduction of social robots should follow instructional design requirements and not merely technological advancement. Teacher initial education has to provide social robotic learning environments for pre-service teachers to experiment and design. © 2021 British Educational Research Association","acceptability; embodied humanoid social robots; ICT familiarity; pre-service teachers","Computer aided instruction; Economic and social effects; Education computing; Educational technology; Engineering education; Human computer interaction; Machine design; Personnel training; Robot learning; Robotics; Social robots; Students; Teaching; Incremental innovation; Information communication technology; Pre-service teacher education; Research and development; Technological advancement; Technology application; Technology Integration; Unified theory of acceptance and use of technology; Educational robots",,,,,"Javna Agencija za Raziskovalno Dejavnost RS, ARRS: P2‐0209, P2‐0210","The research of Andreja Istenic and Ivan Bratko is financially supported by the Slovene Research Agency (P2‐0210 and P2‐0209). 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"Oliff H., Liu Y., Kumar M., Williams M.","57195531830;35206193200;55389194300;57202877303;","Improving Human-Robot Interaction Utilizing Learning and Intelligence: A Human Factors-Based Approach",2020,"IEEE Transactions on Automation Science and Engineering","17","3","9007403","1597","1610",,4,"10.1109/TASE.2020.2967093","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087544170&doi=10.1109%2fTASE.2020.2967093&partnerID=40&md5=6a50e35f110f684af940ea14d20dd8ab","High-Value Manufacturing Group, School of Engineering, Cardiff University, Cardiff, CF24 3AA, United Kingdom; Cardiff Business School, Cardiff University, Cardiff, CF10 3EU, United Kingdom; Olympus Surgical Technologies Europe, Cardiff, CF3 0LT, United Kingdom","Oliff, H., High-Value Manufacturing Group, School of Engineering, Cardiff University, Cardiff, CF24 3AA, United Kingdom; Liu, Y., High-Value Manufacturing Group, School of Engineering, Cardiff University, Cardiff, CF24 3AA, United Kingdom; Kumar, M., Cardiff Business School, Cardiff University, Cardiff, CF10 3EU, United Kingdom; Williams, M., Olympus Surgical Technologies Europe, Cardiff, CF3 0LT, United Kingdom","Several decades of development in the fields of robotics and automation have resulted in human-robot interaction is commonplace, and the subject of intense study. These interactions are particularly prevalent in manufacturing, where human operators (HOs) have been employed in numerous robotics and automation tasks. The presence of HOs continues to be a source of uncertainty in such systems, despite the study of human factors, in an attempt to better understand these variations in performance. Concurrent developments in intelligent manufacturing present opportunities for adaptability within robotic control. This article examines relevant human factors and develops a framework for integrating the necessary elements of intelligent control and data processing to provide appropriate adaptability to robotic elements, consequently improving collaborative interaction with human colleagues. A neural network-based learning approach is used to predict the influence on human task performance and use these predictions to make informed changes to programed behavior, and a methodology developed to explore the application of learning techniques to this area further. This article is supported by an example case study, in which a simulation model is used to explore the application of the developed system, and its performance in a real-world production scenario. The simulation results reveal that adaptability can be realized with some relatively simple techniques and models if applied in the right manner and that such adaptability is helpful to tackle the issue of performance disparity in manufacturing operations. Note to Practitioners-This article presents research into the application of intelligent methodologies to this problem and builds a framework to describe how this information can be captured, generated, and used within manufacturing production processes. This framework helps identify which areas require further research and serves as a basis for the development of a methodology, by which a control system may enable adaptable behavior to reduce the impact of human performance variation and improve human-machine interaction (HMI). This article also presents a simulation-based case study to support the development and evaluate the presented control system on a representative real-world problem. The methodology makes use of a machine-learning approach to identify the complex influence of several identified human factors on human performance. This knowledge can be used to adjust the robotic behavior to match the predicted performance of multiple different operators over different scenarios. This adaptability reduces performance disparity by reducing idle times and enabling leaner production through workpiece-in-progress reduction. Future work will focus on expanding the intelligent capabilities of the proposed system to deal with uncertainty and improve decision-making ability. © 2004-2012 IEEE.","Collaborative robotics; human factors; human-machine interaction (HMI); intelligent manufacturing; machine learning; neural networks","Concurrency control; Control systems; Data handling; Decision making; Human engineering; Industrial research; Intelligent robots; Learning systems; Man machine systems; Manufacture; Robotics; Collaborative interaction; Concurrent development; Human-machine interaction; Intelligent Manufacturing; Machine learning approaches; Manufacturing operations; Network-based learning; Predicted performance; Social robots",,,,,,,,,,,,,,,,"Moniz, A., Krings, B.-J., Robots working with humans or humans working with robots? 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"Liu L., Leonhardt S., Ngo C., Misgeld B.J.E.","56927144200;7004971042;56394026400;6504592448;","Impedance-Controlled Variable Stiffness Actuator for Lower Limb Robot Applications",2020,"IEEE Transactions on Automation Science and Engineering","17","2","8922620","991","1004",,19,"10.1109/TASE.2019.2954769","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083212312&doi=10.1109%2fTASE.2019.2954769&partnerID=40&md5=aff68e3ff6e315c6d5f3dff9d19a9c96","Department of Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, 52074, Germany","Liu, L., Department of Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, 52074, Germany; Leonhardt, S., Department of Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, 52074, Germany; Ngo, C., Department of Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, 52074, Germany; Misgeld, B.J.E., Department of Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, Aachen, 52074, Germany","We present a novel application of the variable stiffness actuator (VSA)-based assistance/rehabilitation robot-featured impedance control using a cascaded position torque control loop. The robot follows the adaptive impedance control paradigm, thereby achieving an adaptive assistance level according to human joint torque. The feedforward human joint torque command is used to cooperatively adjust the impedance controller and the stiffness trajectory of the VSA (this functional architecture is referred to as the cooperative control framework). In this way, the task performance during movement training can be improved regarding: 1) safety-for example, when the subject intends to contribute considerable effort, low-gain impedance control is activated with a low stiffness actuator to further decrease output impedance and 2) tracking performance-for example, for the subject with less effort, high-gain impedance control is used while pursuing high stiffness to enhance the torque bandwidth. Regarding the safety aspect, we demonstrate that the torque controller designed at low stiffness can be sensitive to the disturbance for low output impedance while maintaining tracking performance. A precondition for this is to treat the input disturbance separately. This is guaranteed by our previously proposed torque control of the VSA using the linear quadratic Gaussian technique. This approach is also employed here, but with additional discussion on the observer design to serve the proposed cooperative control approach. Here, the effectiveness of the proposed control system is experimentally verified using a VSA prototype and a one-degree-of-freedom lower limb exoskeleton worn by a human test person. Note to Practitioners-Control of 'physical human-robot interaction' can be achieved by the mechanical parts of the variable stiffness actuator (VSA). However, the mechanical construction for stiffness variation may limit the capacity to achieve low output stiffness and fast stiffness variation in speed. These limitations may become more evident in the assistance/rehabilitation robot applications. To overcome these limitations, the impedance control scheme can be employed to achieve a programmable impedance range and impedance variation speed. This control scheme has been widely applied on the fixed-compliance joint but lacks a way to be implemented on the VSA joint because of its existing capacity to control the impedance with the mechanical construction. This article presents a novel application of the impedance-controlled VSA used on a lower limb robot. We describe how to adjust the actuator stiffness to cooperatively work with the adaptive impedance control scheme. Based on our approach, the robot with the impedance-controlled VSA joint can extend the capacity of bandwidth and low output impedance. This is an improvement on the impedance-controlled fixed-compliance joint. The cooperative control framework presented here was tested on an exoskeleton system with two healthy test persons and is also applicable to other actuator prototypes. Future research aims to employ this system for actual patient training. © 2004-2012 IEEE.","Impedance control; lower limb robot; physical human-robot interaction (pHRI); torque control; variable stiffness actuator (VSA)","Bandwidth; Closed loop control systems; Controllers; Degrees of freedom (mechanics); Electric impedance; Exoskeleton (Robotics); Human robot interaction; Joints (anatomy); Man machine systems; Mechanical actuators; Robot applications; Stiffness; Torque; Torque control; Controlled variables; Functional architecture; Impedance controllers; Impedance variations; Linear quadratic Gaussian; Mechanical construction; Physical human-robot interactions; Tracking performance; Compliance control",,,,,"Deutsche Forschungsgemeinschaft, DFG: LE 817/30-1","Manuscript received November 30, 2018; revised July 2, 2019 and October 23, 2019; accepted November 17, 2019. Date of publication December 4, 2019; date of current version April 7, 2020. This article was recommended for publication by Associate Editor G. Nejat and Editor D. O. Popa upon evaluation of the reviewers’ comments. This work was supported in part by the German Research Foundation (DFG) under Grant LE 817/30-1. (Corresponding author: Lin Liu.) The authors are with the Chair of Medical Information Technology, Helmholtz-Institute for Biomedical Engineering, RWTH Aachen University, 52074 Aachen, Germany (e-mail: liu@hia.rwth-aachen.de; leonhardt@ hia.rwth-aachen.de; ngo@hia.rwth-aachen.de; misgeld@hia.rwth-aachen.de).",,,,,,,,,,"Beckerle, P., A human-robot interaction perspective on assistive and rehabilitation robotics (2017) Frontiers Neurorob., 11 (24), pp. 1-6. , May; De Santis, A., Siciliano, B., De Luca, A., Bicchi, A., An atlas of physical human-robot interaction (2008) Mech. Mach. Theory, 43 (3), pp. 253-270. , Mar; Hogan, N., Impedance control: An approach to manipulation: Part II-implementation (1985) J. Dyn. Syst. Meas. Control, 107 (1), pp. 8-16. , Mar; Zhu, H., Doan, J., Stence, C., Lv, G., Elery, T., Gregg, R., Design and validation of a torque dense, highly backdrivable powered knee-ankle orthosis (2017) Proc. IEEE Int. Conf. Robot. Automat., pp. 504-510. , May/Jun; Zanotto, D., Stegall, P., Agrawal, S.K., Adaptive assist-as-needed controller to improve gait symmetry in robot-assisted gait training (2014) Proc. IEEE Int. Conf. Robot. Automat., pp. 724-729. , May/Jun; Riener, R., Lunenburger, L., Jezernik, S., Anderschitz, M., Colombo, G., Dietz, V., Patient-cooperative strategies for robot-aided treadmill training: First experimental results (2005) IEEE Trans. Neural Syst. Rehabil. Eng., 13 (3), pp. 380-394. , Sep; Vallery, H., Veneman, J., Asseldonk, E.V., Ekkelenkamp, R., Buss, M., Kooij, H.V.D., Compliant actuation of rehabilitation robots (2008) IEEE Robot. Autom. Mag., 15 (3), pp. 60-69. , Sep; Karavas, N., Ajoudani, A., Tsagarakis, N., Saglia, J., Bicchi, A., Caldwell, D., Tele-impedance based stiffness and motion augmentation for a knee exoskeleton device (2013) Proc. IEEE Int. Conf. Robot. Automat., pp. 2194-2200. , May; Kong, K., Bae, J., Tomizuka, M., A compact rotary series elastic actuator for human assistive systems (2012) IEEE/ASME Trans. Mechatronics, 17 (2), pp. 288-297. , Apr; Mehling, J.S., Holley, J., O'Malley, M.K., Leveraging disturbance observer based torque control for improved impedance rendering with series elastic actuators (2015) Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst., pp. 1646-1651. , Sep./Oct; Malzahn, J., Kashiri, N., Roozing, W., Tsagarakis, N., Caldwell, D., What is the torque bandwidth of this actuator? (2017) Proc. IEEE/RSJ Int. Conf. Intell. Robot. Syst., pp. 4762-4768. , Sep; Calanca, A., Fiorini, P., On the role of compliance in force control (2014) Proc. Int. Conf. Intell. Auton. Syst., pp. 1243-1255. , Jul; Li, X., Pan, Y., Chen, G., Yu, H., Continuous tracking control for a compliant actuator with two-stage stiffness (2018) IEEE Trans. Autom. Sci. Eng., 15 (1), pp. 57-66. , Jan; Bolivar, E., Allen, D., Ellson, G., Cossio, J., Voit, W., Gregg, R., Towards a series elastic actuator with electrically modulated stiffness for Powered Ankle-Foot Orthoses (2016) Proc. IEEE Int. Conf. Automat. Sci. Eng., pp. 1086-1093. , Aug; Vanderborght, B., Variable impedance actuators: A review (2013) Robot. Auto. Syst., 61 (12), pp. 1601-1614; Zhakatayev, A., Rubagotti, M., Varol, H.A., Time-optimal control of variable-stiffness-actuated systems (2017) IEEE/ASME Trans. Mechatronics, 22 (3), pp. 1247-1258. , Jun; Ménard, T., Grioli, G., Bicchi, A., A stiffness estimator for agonistic-antagonistic variable-stiffness-actuator devices (2014) IEEE Trans. Robot., 30 (5), pp. 1269-1278. , Oct; Beyl, P., Safe and compliant guidance by a powered knee exoskeleton for robot-assisted rehabilitation of gait (2012) Adv. Robot., 25 (5), pp. 513-535. , Apr; Hussain, S., Xie, S.Q., Jamwal, P.K., Adaptive impedance control of a robotic orthosis for gait rehabilitation (2013) IEEE Trans. Cybern., 43 (3), pp. 1025-1034. , Jun; Jafari, A., Tsagarakis, N.G., Sardellitti, I., Caldwell, D.G., A new actuator with adjustable stiffness based on a variable ratio lever mechanism (2014) IEEE/ASME Trans. Mechatronics, 19 (1), pp. 55-63. , Feb; Visser, L.C., Carloni, R., Stramigioli, S., Energy-efficient variable stiffness actuators (2011) IEEE Trans. Robot., 27 (5), pp. 865-875. , Oct; Cestari, M., Sanz-Merodio, D., Arevalo, J.C., Garcia, E., An adjustable compliant joint for lower-limb exoskeletons (2015) IEEE/ASME Trans. Mechatronics, 20 (2), pp. 889-898. , Apr; Sardellitti, I., Medrano-Cerda, G.A., Tsagarakis, N., Jafari, A., Caldwell, D.G., Gain scheduling control for a class of variable stiffness actuators based on lever mechanisms (2013) IEEE Trans. Robot., 29 (3), pp. 791-798. , Jun; Braun, D.J., Robots driven by compliant actuators: Optimal control under actuation constraints (2013) IEEE Trans. Robot., 29 (5), pp. 1085-1101. , Oct; Petit, F., Dietrich, A., Albu-Schäffer, A., Generalizing torque control concepts: Using well-established torque control methods on variable stiffness robots (2015) IEEE Robot. Autom. Mag., 22 (4), pp. 37-51. , Dec; Grosu, V., Rodriguez-Guerrero, C., Grosu, S., Vanderborght, B., Lefeber, D., Design of smart modular variable stiffness actuators for robotic-assistive devices (2017) IEEE/ASME Trans. Mechatronics, 22 (4), pp. 1777-1785. , Aug; Jimenez-Fabian, R.E., Weckx, M., Rodriguez-Cianca, D., Lefeber, D., Vanderborght, B., Online reconfiguration of a variable-stiffness actuator (2018) IEEE/ASME Trans. Mechatronics, 23 (4), pp. 1866-1876. , Aug; Haddadin, S., Huber, F., Albu-Schäffer, A., Optimal control for exploiting the natural dynamics of Variable Stiffness robots (2012) Proc. IEEE Int. Conf. Robot. Automat., pp. 3347-3354. , May; Li, X., Chen, W., Lin, W., Low, K.H., A variable stiffness robotic gripper based on structure-controlled principle (2018) IEEE Trans. Autom. Sci. Eng., 15 (3), pp. 1104-1113. , Jul; Skogestad, S., Postlethwaite, I., (2005) Multivariable Feedback Control: Analysis and Design, , 2nd ed. Hoboken, NJ, USA: Wiley; Mefoued, S., Mohammed, S., Amirat, Y., Toward movement restoration of knee joint using robust control of powered orthosis (2013) IEEE Trans. Control Syst. Technol., 21 (6), pp. 2156-2168. , Nov; Liu, L., Leonhardt, S., Misgeld, B.J.E., Experimental validation of a torque-controlled variable stiffness actuator tuned by gain scheduling (2018) IEEE/ASME Trans. Mechatronics, 23 (5), pp. 2109-2120. , Oct; Chalvet, V., Braun, D.J., Algorithmic design of low-power variable-stiffness mechanisms (2017) IEEE Trans. Robot., 33 (6), pp. 1508-1515. , Dec; Calanca, A., Muradore, R., Fiorini, P., A review of algorithms for compliant control of stiff and fixed-compliance robots (2016) IEEE/ASME Trans. Mechatronics, 21 (2), pp. 613-624. , Apr; Doyle, J., Stein, G., Multivariable feedback design: Concepts for a classical/modern synthesis (1981) IEEE Trans. Autom. Control, Vol. AC-26, (1), pp. 4-16. , Feb; Stilwell, D.J., State-space interpolation for a gain-scheduled autopilot (2001) J. Guid. Control Dyn., 24 (3), pp. 460-465. , May/Jun; Liu, L., Lüken, M., Leonhardt, S., Misgeld, B.J.E., EMG-driven model-based knee torque estimation on a variable impedance actuator orthosis (2017) Proc. IEEE Int. Conf. Cyborg Bionic Syst., pp. 262-267. , Oct; Riener, R., Edrich, T., Identification of passive elastic joint moments in the lower extremities (1999) J. Biomech., 32 (5), pp. 539-544; Stein, R.B., Zehr, E.P., Lebiedowska, M.K., Popovic, D.B., Scheiner, A., Chizeck, H.J., Estimating mechanical parameters of leg segments in individuals with and without physical disabilities (1996) IEEE Trans. Rehabil. Eng., 4 (3), pp. 201-211. , Sep; Lin, D.C., Rymer, W.Z., A quantitative analysis of pendular motion of the lower leg in spastic human subjects (1991) IEEE Trans. Biomed. Eng., 38 (9), pp. 906-918. , Sep","Liu, L.; Department of Medical Information Technology, Germany; email: liu@hia.rwth-aachen.de",,,"Institute of Electrical and Electronics Engineers Inc.",,,,,15455955,,,,"English","IEEE Trans. Autom. Sci. Eng.",Article,"Final","",Scopus,2-s2.0-85083212312
"Saibaba C.H.M.H., Waris S.F., Raju S.H., Sarma V., Jadala V.C., Prasad C.","57190764432;57215217330;57191645958;57269955100;56460347100;56594432300;","Intelligent Voice Assistant by Using OpenCV Approach",2021,"Proceedings of the 2nd International Conference on Electronics and Sustainable Communication Systems, ICESC 2021",,,,"1586","1593",,,"10.1109/ICESC51422.2021.9532956","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116648885&doi=10.1109%2fICESC51422.2021.9532956&partnerID=40&md5=c41d75d87e5d97db3c9f37d42d735d67","Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Vaddeswaram, 522502, India; Gandhi Institute of Technology and Management, Department of Computer Science and Engineering, Andhra Pradesh, Gandhi Nagar, 530045, India","Saibaba, C.H.M.H., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Vaddeswaram, 522502, India; Waris, S.F., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Vaddeswaram, 522502, India; Raju, S.H., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Vaddeswaram, 522502, India; Sarma, V., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Vaddeswaram, 522502, India; Jadala, V.C., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Vaddeswaram, 522502, India; Prasad, C., Gandhi Institute of Technology and Management, Department of Computer Science and Engineering, Andhra Pradesh, Gandhi Nagar, 530045, India","The basic plan of our work is to design and implement Artificial Intelligence based Voice Recognition System Software and to create a bundle that consists integration of several tasks, where we can process and execute using Clients Individual voice command. An Artificial Intelligent based Virtual or Personal Assistant were indicated as IVA or IPA could be a package of Intelligent mechanism which execute different tasks and services based on the queries and command for operating intelligent based system. It is identified as square measure at some areas of disciplines utilized for voice detection, where voice recognition is identified as communication channel between persons will be notified by machine. where machines can identify voice of a person wherever the voice is only approach of double communication, and a lot of typically, it permits to free-up each hands and vision doubtless for performing an additional action in comparison or facilitates conjointly inactivated individuals. In this paper we concentrated on the different artificial intelligent technologies that support the voice recognition and natural language processing. Generally, to try and do such quite tasks we would like a voice assistant to prefer and to get gadgets like Alexa. However identical practicality will be done by several the powerful packages like pywhatkit, Wikipedia, pyttsx3, pygame, speech recognition, OpenCV etc., we majorly focused on the practical implementation of voice recognition module based on the OpenCV. In modern technology speech recognition, object identification, digital image processing, language processing plays a major role in security and other factors, the most common mechanisms in artificial intelligence in association with machine and deep learning is that the mechanisms for identifying the natural language of a personalities. Latest terminology during present issue will cause artificial intelligence-based human-robot interaction that is based on multi-disciplinary technologies such as natural language processing, deep and machine learning. We even concentrated on the different tools that support voice recognition and the software companies such as google, Microsoft and amazon. Where new trends and methodologies are implemented at rapid growth. The major factors in this module are natural language processing, speech recognition and the voice management. One is every of such tools is voice assistant, which can be integrated into several alternative intelligent systems. Voice may be a ton of economical than writing on a keyboard. Here we tend to square measure desegregation varied options and place along as one practicable file. Hence, we will create several things among less quantity of your time with the simplest performance. In this research we verified different models, mechanisms and procedures adopted by various researchers in this field to recognize the voice. The basic set up of our work is to integrate as many tasks as achievable and build it execute by our voice command. The design and implementation of the OpenCV approach is given along with the experimental analysis and finally the result analysis is given in the graphical form. © 2021 IEEE.","Computer Vision; OpenCV; Pygame; Pyttsx3; Pywhatkit; Speech Recognition; Wikipedia","Computer vision; Deep learning; Human robot interaction; Intelligent robots; Intelligent systems; Learning algorithms; Natural language processing systems; Speech; Artificial intelligent; Design and implements; Opencv; Pygame; Pyttsx3; Pywhatkit; System softwares; Voice command; Voice-recognition systems; Wikipedia; Speech recognition",,,,,,,,,,,,,,,,"Polyakov, E.V., Mazhanov, M.S., Rolich, A.Y., Voskov, L.S., Kachalova, M.V., Polyakov, S.V., Investigation and development of the intelligent voice assistant for the internet of things using machine learning (2018) 2018 Moscow Workshop on Electronic and Networking Technologies (MWENT), pp. 1-5. , March), IEEE; Accurate and compact large vocabulary speech recognition on mobile devices (2013) Interspeech, pp. 662-665; Ai based voice assistant using python International Journal of Emerging Technologies and Innovative Research, 6 (2), pp. 506-509. , http://www.jetir.org/papers/JETIR1902381.pdf, www.jetir.org UGC and issn Approved), ISSN: 2349-5162, February-2019; Bohouta, G., Kepuska, V.Z., Comparing speech recognition systems (microsoft api google api and cmu sphinx)"", int (2017) Journal of Engineering Research and Application 2017; Artificial Intelligence (AI), Sometimes Called Machineintelligence, , https://en.wikipedia.org/wiki/Artificial-intelligence; Huang, J., Zhou, M., Yang, D., Extracting chatbot knowledge from online discussion forums (2007) Ijcai, 7, pp. 423-428. , January; Mohasi, L., Mashao, D., Text-to-speech technology in human-computer interaction (2006) 5th Conference on Human Computer Interaction in Southern Africa, South Africa (CHISA 2006, Acm SIGHI), pp. 79-84; Jiang, J., Hassan, A.A., Jones, R., Ozertem, U., Zitouni, I., Gurunath, K.R., Khan, O.Z., Automatic online evaluation of intelligent assistants (2015) Proceedings of the 24th International Conference on World Wide Web, pp. 506-516. , May; Fryer, L.K., Carpenter, R., Bots as language learning tools (2006) Language Learning & Technology; (2019) Microsoft. Cortana, , https://www.microsoft.com/windows/cortana/, 21 May, accessed on 28 October 2019; Saibaba, C.M.H., Uday Kumar, V., Praveenkumar, K., Bhattacharyya, D., A new privacy approach using graph-emd (2016) International Journal of Software Engineering and Its Applications, 10 (7), pp. 11-16. , August; Tumuluru, P., Lakshmi Ramani, B., Saibaba, C.M.H., Venkateswarlu, B., Ravindar, N., Opencv algorithms for facial recognition (2019) International Journal of Innovative Technology and Exploring Engineering, 8 (8), pp. 927-933. , June; Lakshmi Maturi, V., Reddy Boya, N., Polisetti, J., Adavi, S., Saibaba, C.M.H., Twitter sentimental analysis using machine learning techniques (2019) International Journal of Innovative Technology and Exploring Engineering, 8 (6), pp. 1592-1594. , April; Prasuna, K., Rama Rao, K.V.S.N., Saibaba, C.M.H., Application of machine learning techniques in predicting breast cancer-a survey (2019) International Journal of Innovative Technology and Exploring Engineering, 8 (8), pp. 826-832. , June; Lakshmi Ramani, B., Poosapati, P., Tumuluru, P., Saibaba, C.M.H., Radha, M., Prasuna, K., Deep learning and fuzzy rule-based hybrid fusion model for data classification (2019) International Journal of Recent Technology and Engineering, 8 (2), pp. 3205-3213. , July; Madhusudanan, J., Geetha, S., Prasanna, V.V., Vignesh, U., Iyappan, P., Hybrid aspect of context-aware middleware for pervasive smart environment: A review (2018) Mobile Information Systems; Maguluri, L.P., Srinivasarao, T., Ragupathy, R., Syamala, M., Nalini, N.J., Efficient smart emergency response system for fire hazards using iot (2018) International Journal of Advanced Computer Science and Applications, , January; Mekala, M.S., Viswanathan, P., (2020) Sensor Stipulation with Tham Index for Smart Agriculture DecisionMaking IoT System, , April; Kumar, K.G., Avinash, B.S., Rahimi-Gorji, M., Majdoubi, J., (2020) Photocatalytic Activity and Smartness of TiO2 Nanotube Arrays for Room Temperature Acetone Sensing, , February; Sai, T.V.R., Haaris, S., Sridevi, S., Website evaluation using opinion mining (2018) International Journal of Engineering and Technology(UAE), 7 (2), pp. 51-53; Sailaja, V.N., Pavani, R., A study on understanding the relationship between personal attributes and investor perception towards etf funds investment (2018) Journal of Advanced Research in Dynamical and Control Systems, 10 (8), pp. 204-209; Satish Babu, J., Ravi Kumar, T., Bano, S., Optimizing webpage relevancy using page ranking and content based ranking (2018) International Journal of Engineering and Technology(UAE), 7 (7), pp. 1025-1029. , 2.7; Smys, S., Basar, A., Wang, H., Artificial neural network based power management for smart street lighting systems (2020) Journal of Artificial Intelligence, 2 (1), pp. 42-52; Chen, J.I.Z., Smys, S., Social multimedia security and suspicious activity detection in sdn using hybrid deep learning technique (2020) Journal of Information Technology, 2 (2), pp. 108-115",,,,"Institute of Electrical and Electronics Engineers Inc.","2nd International Conference on Electronics and Sustainable Communication Systems, ICESC 2021","4 August 2021 through 6 August 2021",,171934,,9781665428675,,,"English","Proc. Int. Conf. Electron. Sustain. Commun. Syst., ICESC",Conference Paper,"Final","",Scopus,2-s2.0-85116648885
"Nath A.","57191073753;","Artemis robot: Building artificially intelligent robots with child-like curiosity",2020,"Proceedings of the International Conference on Industrial Engineering and Operations Management",,"August",,"","",,,,"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096556932&partnerID=40&md5=b92e31e273026ef2edbed245356a0e75","Grade 8 Student École secondaire Toronto Ouest, Toronto, ON, Canada","Nath, A., Grade 8 Student École secondaire Toronto Ouest, Toronto, ON, Canada","Intelligent robots and their algorithms are usually task specific. A robot that plays chess can train itself to become a better chess player. The same robot cannot train itself to sort out garbage or learn how to do diagnostic tests for COVID19. This is different from how humans learn. Human start developing multiple capabilities from birth, moving their limbs, tracking movements with their eyes, turning head towards source of sound, and identifying faces, emotions and body language. They can also specialize in many tasks, such as driving, cooking, and playing sports without losing their multiple capabilities. Humans learn by observing, practicing, getting positive or negative reinforcements, and asking questions. These have multimodal ways-vision, speech, hearing, smell, taste, touch, and emotions to gather data. The learning is continuous and happens over years. As a result, they can combine their vast repository of knowledge and skill sets, transfer their learnings, apply logic to handle a diverse range of tasks. For robots, we expect them to develop superhuman capabilities. In return, we train them on singular stream such as text or still images, provide no contextual data of their environment, complete absence of multi-modal inputs such as sound or touch to triangulate information they have received, and the learning is non-continuous and non-immersive. This goes against how humans learn. So, how can we build robots that learn just like humans and become more intelligent gradually every day? The ARTEMIS (Artificially intelligent Real-time Training by Environment, Mapping, Immersion, and Sounds) Robot is a curious robot. It learns just like humans do by interacting with other humans and acquiring knowledge through multiple modes about its environment. In this process, it becomes a little more intelligent every day and starts developing multiple capabilities. ARTEMIS has stereo vision, hearing capabilities and is embedded with multiple sensors including GPS, accelerometer, temperature, sound, and brightness sensor. Using machine learning it can identify faces and objects, and their depth and position. It is also able to identify different sounds and classify them as familiar sounds, white noise or those requiring attention. It uses embedded logic and probability ranges to deduce the environment it is in-home, street, office, or school. By combining and comparing global data with data from its sensor it can make appropriate conversations. Just like a child learns through encouragement and repetition, ARTEMIS looks forward to having its logical deductions confirmed or reinforced by humans. If that does not happen or if its logic does not match its observations, then it asks a question. The response to that question allows ARTEMIS to reconfigure its logic, the probability estimates, and make a more accurate deduction next time. This constant-human robot interaction helps ARTEMIS improve its learning and develop multi-capabilities. As learnings of each ARTEMIS robot is based on its interaction with humans and environment, each ARTEMIS is unique and will have different logical, conversational and skills capabilities. © IEOM Society International.","Logic; Machine Learning; Probability; Robot; Stereo Vision",,,,,,,,,,,,,,,,,,"Nath, A.; Grade 8 Student École secondaire Toronto OuestCanada; email: Artash.Nath@gmail.com",,"CINTAS;EATON Powering Business worldwide;EFR Certification;SIEMENS;ToolingTechGroup","IEOM Society","Proceedings of the 5th NA International Conference on Industrial Engineering and Operations Management, IOEM 2020","10 August 2020 through 14 August 2020",,144118,21698767,,,,"English","Proc. Int. Conf. Ind. Eng. Oper. Manage.",Conference Paper,"Final","",Scopus,2-s2.0-85096556932
"Oprea M.","57216896843;","Integration of robotics projects in the pre-university educational environment",2020,"eLearning and Software for Education Conference",,,,"190","198",,,"10.12753/2066-026X-20-109","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096480712&doi=10.12753%2f2066-026X-20-109&partnerID=40&md5=7085df50e543329c2f73bbec42ada1ab","Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, Bucharest, Romania","Oprea, M., Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, Bucharest, Romania","Our students must be prepared for the jobs of the future: smart home developer, specialist in human-robot interaction, augmented reality architect etc. They need to be encouraged to explore more, discover and understand how things work around them. Their training must be supported by Internet of Things (IoT), Robotics and Artificial Intelligence (AI) courses. These courses are part of the STEM educational model aimed to the integrated approach of Science, Technology, Engineering and Mathematics. The integration of these courses into school curriculum of pre-university education becomes a necessity. In this study I presented how to make two projects of Educational Robotics using the Arduino development platform and I nalysed, from the point of view of methodological efficiency, the results obtained by implementing in class such projects. In the first project I showed how a line follower robot can be made. For this purpose I used the Arduino Uno development board, two DC motors with gearbox, a line tracking module with IR proximity sensors and a L298N dual H-Bridge motor driver module. The sensors emit IR beams to the surface on which the robot is located. The radiation reflected by it is detected by sensors that produce signals proportional to the degree of reflectance of the respective surface. Specifically, white surfaces generate strong reflection signals while dark surfaces strongly absorb IR radiation, producing a very weak reflection phenomenon. The IR signal detected by the sensors is sent for analysis to the Arduino board. If its value exceeds a set threshold, Arduino sends a command to the motor driver that the robot path is changed to the minimum IR reflectance area. In the second project I presented how to make a obstacle avoiding robot. As in the first project, I used the Arduino Uno development board, mounted on a chassis fitted with two DC motors with gearbox, controlled by a L298N driver module. To avoid obstacles I used an ultrasonic HC-SR04 sensor. Additionally, for controlling the robot with an Android smartphone, I added a HC-06 Bluetooth module. The ultrasonic sensor emits impulses in the environment in which the robot moves. When ultrasonic waves encounter an obstacle they are reflected on it and received by the sensor. The captured signal is sent by the sensor to the Arduino board where it is processed, the distance between the sensor and the obstacle being calculated. If the value of this distance is below a set threshold level, Arduino sends a command to the motor driver to change the path of the robot, thus avoiding collision with the detected obstacle. In the final part of the article I performed a comparative analysis of the results obtained in the STEM type preparation by the classes of students who developed projects of Educational Robotics in relation to the classes that have gone in a traditional direction of preparation. © 2020, National Defence University - Carol I Printing House. All rights reserved.","Arduino; IR sensors; Line follower robot; Obstacle avoiding robot; Ultrasonic sensor",,,,,,,,,,,,,,,,,"Junior, L., Guerra, F., Bravo, L., Hernandez, M., Neto, O., Martins, P., A Low-Cost and Simple Arduino-Based Educational Robotics Kit (2013) Journal of Selected Areas in Robotics and Control (JSRC). 3, pp. 1-12; Eguchi, A., (2013) Educational Robotics Theories and Practice: Tips for How to Do It Right; Araújo, A., Portugal, D., Couceiro, M., Rocha, R., Integrating Arduino-based Educational Mobile Robots in ROS (2013) J Intell Robot Syst., p. 77; Eguchi, A., Educational Robotics for Promoting 21st Century Skills (2014) Journal of Automation, Mobile Robotics & Intelligent Systems, 8; Eguchi, A., Robotics as a Learning Tool for Educational Transformation (2014) Proceedings of 4Th International Workshop Teaching Robotics, Teaching with Robotics & 5Th International Conference Robotics in Education Padova (Italy) July 18, pp. 27-34. , 2014, ISBN 978-88-95872-06-3 pp; Eguchi, A., (2015) Educational Robotics as a Learning Tool for Promoting Rich Environments for Active Learning (Reals); Khine, M., (2017) Robotics in STEM Education: Redesigning the Learning Experience; Eguchi, A., (2017) Learner-Centered Approach with Educational Robotics; Lepuschitz, W., Merdan, M., Koppensteiner, G., Balogh, R., Obdrzalek, D., (2018) Robotics in Education: Methods and Applications for Teaching and Learning; Aparício, J., Pereira, S., Aparicio, M., Costa, C., (2019) Learning Programming Using Educational Robotics, pp. 1-6","Oprea, M.; Faculty of Automatic Control and Computers, Romania; email: opreamarin2007@yahoo.com","ROCEANU I.",,"National Defence University - Carol I Printing House","16th International Scientific Conference on eLearning and Software for Education, eLSE 2020","30 April 2020 through 1 May 2020",,243719,2066026X,,,,"English","eLearning Softw. Educ. Conf.",Conference Paper,"Final","",Scopus,2-s2.0-85096480712
"Hou J.-Y., Qi Y.-Y., Wu X.-X., Jia Y.-D.","57191162750;57221034193;24175007000;7401503740;","Cross-Lingual Knowledge Distillation for Chinese Video Captioning [跨语言知识蒸馏的视频中文字幕生成]",2021,"Jisuanji Xuebao/Chinese Journal of Computers","44","9",,"1907","1921",,,"10.11897/SP.J.1016.2021.01907","https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115208509&doi=10.11897%2fSP.J.1016.2021.01907&partnerID=40&md5=13eedb2536cc516c89fc9dfbf67254d3","Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, 100081, China","Hou, J.-Y., Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, 100081, China; Qi, Y.-Y., Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, 100081, China; Wu, X.-X., Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, 100081, China; Jia, Y.-D., Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, 100081, China","Video captioning aims to automatically generate the natural language descriptions of a video, which requires understanding the visual content and describing it with grammatically accurate sentences. Video captioning has wide applications in video recommendation, vision assistance, human-robot interaction and many other fields, and has attracted growing attention in the fields of computer vision and natural language processing. Although remarkable progress has been made on English video captioning, using other languages such as Chinese to describe a video remains under-explored. In this paper, we investigate Chinese video captioning. However, the insufficiency of paired videos and Chinese captions makes it difficult to train a powerful model for Chinese video captioning. Since there exist many English video captioning methods and training data, it is a feasible method to perform Chinese video captioning by translating the English captions via machine translation. However, the difference between Chinese and Western cultures and the performance of machine translation algorithms will both affect the quality of generated Chinese captions. To this end, we propose a cross-lingual knowledge distillation method for Chinese video captioning. Based on a two-branches structure, our method does not only directly generate Chinese captions according to the video content, but also takes full advantage of the easily accessible English video captions as the privileged information to guide the generation of Chinese video captions. Since the Chinese and English captions are semantically correlated with respect to the video content, our method learns cross-lingual knowledge from them and utilizes knowledge distillation to integrate the high-level semantic information in English captions into Chinese captions generation. Meanwhile, the consistency between the training target and the captioning target is guaranteed by the end-to-end training strategy, thus effectively improving the performance of Chinese video captioning. Benefit from the mechanism of knowledge distillation, our method only utilizes English captions data during the training stage, and after training it can directly generate Chinese captions from the input video. To verify the universality and flexibility of our cross-lingual knowledge distillation method, we use four mainstream visual captioning models for evaluation, covering the CNN-RNN structure, RNN-RNN structure, CNN-CNN structure and model based on Top-Down attention mechanism. These models are widely used as the backbone models in a large number of visual captioning methods. Moreover, we extend the English video captioning dataset MSVD into a cross-lingual video captioning dataset with Chinese captions, called MSVD-CN. MSVD-CN contains 1970 video clips collected from the Internet and 11758 Chinese captions besides the original 41 English captions per video in MSVD. In order to reduce the annotation mistakes caused by annotators' typos or misunderstandings of the video contents, we propose two automatic inspection methods to perform semantic and syntactic checks, respectively, on the collected manual annotations in the data collection stage. Extensive experiments are carried out on the MSVD-CN dataset, via four widely used evaluation metrics for video captioning including BLEU, METEOR, ROUGE-L, and CIDEr. The results demonstrate that the superiority of proposed cross-lingual knowledge distillation on Chinese video captioning. Furthermore, we also report some qualitative experiment results to show the effectiveness of our method. © 2021, Science Press. All right reserved.","Chinese video captioning; Cross-lingual video captioning dataset; Knowledge distillation; Privileged information; Video understanding","Computational linguistics; Computer aided language translation; Distillation; Human computer interaction; Human robot interaction; Natural language processing systems; Semantics; Video recording; Visual languages; Attention mechanisms; Automatic inspection; Distillation method; Evaluation metrics; High level semantics; Machine translations; NAtural language processing; Qualitative experiments; Distilleries",,,,,"National Natural Science Foundation of China, NSFC: 62072041","This work is supported by the National Nature Science Foundation of China (No. 62072041).",,,,,,,,,,"Boroditsky, L., Does language shape thought?: Mandarin and English speakers' conceptions of time (2001) Cognitive Psychology, 43 (1), pp. 1-22; Hinton, G, Vinyals, O, Dean, J., (2015) Distilling the knowledge in a neural network, , arXiv preprint arXiv: 1503.02531; 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